{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "316164ad-9e27-492f-aac4-c54c6c9c7649",
   "metadata": {},
   "source": [
    "# Example-01: Derivative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "958fa649-25fb-42cd-84cf-e22834e90ba4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Given an input function, its higher order (partial) derivatives with respect to one or sevaral tensor arguments can be computed using forward or reverse mode automatic differentiation\n",
    "# Derivative orders can be different for each tensor argument\n",
    "# Input function is expected to return a tensor or a (nested) list of tensors\n",
    "\n",
    "# Derivatives are computed by nesting torch jacobian functions\n",
    "# For higher order derivatives, nesting results in exponentially growing redundant computations\n",
    "# Note, forward mode is more memory efficient in this case\n",
    "\n",
    "# If the input function returns a tensor, the output is referred as derivative table representation\n",
    "# This representation can be evaluated near given evaluation point (at a given deviation) if the input function returns a scalar or a vector\n",
    "# Table representation is a (nested) list of tensors, it can be used as a redundant function representation near given evaluation point (taylor series)\n",
    "# Table structure for f(x), f(x, y) and f(x, y, z) is shown bellow (similar structure holds for a function with more aruments)\n",
    "\n",
    "# f(x)\n",
    "# t(f, x)\n",
    "# [f, Dx f, Dxx f, ...]\n",
    "\n",
    "# f(x, y)\n",
    "# t(f, x, y)\n",
    "# [\n",
    "#     [    f,     Dy f,     Dyy f, ...],\n",
    "#     [ Dx f,  Dx Dy f,  Dx Dyy f, ...],\n",
    "#     [Dxx f, Dxx Dy f, Dxx Dyy f, ...],\n",
    "#     ...\n",
    "# ]\n",
    "\n",
    "# f(x, y, z)\n",
    "# t(f, x, y, z)\n",
    "# [\n",
    "#     [\n",
    "#         [         f,          Dz f,          Dzz f, ...],\n",
    "#         [      Dy f,       Dy Dz f,       Dy Dzz f, ...],\n",
    "#         [     Dyy f,      Dyy Dz f,      Dyy Dzz f, ...],\n",
    "#         ...\n",
    "#     ],\n",
    "#     [\n",
    "#         [      Dx f,       Dx Dz f,       Dx Dzz f, ...],\n",
    "#         [   Dx Dy f,    Dx Dy Dz f,    Dx Dy Dzz f, ...],\n",
    "#         [  Dx Dyy f,   Dx Dyy Dz f,   Dx Dyy Dzz f, ...],\n",
    "#         ...\n",
    "#     ],\n",
    "#     [\n",
    "#         [    Dxx f,     Dxx Dz f,     Dxx Dzz f, ...],\n",
    "#         [ Dxx Dy f,  Dxx Dy Dz f,  Dxx Dy Dzz f, ...],\n",
    "#         [Dxx Dyy f, Dxx Dyy Dz f, Dxx Dyy Dzz f, ...],\n",
    "#         ...\n",
    "#     ],\n",
    "#     ...\n",
    "# ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8923ac82-cc4e-4bc0-a196-b30ce2474913",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.series import series\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49f1ae45-fcc0-4663-9f66-f3b01d9b1c96",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c85724c-981d-4a2e-b61b-51e24259a442",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Basic derivative interface\n",
    "\n",
    "# derivative(\n",
    "#     order:int,                             # derivative order\n",
    "#     function:Callable,                     # input function\n",
    "#     *args,                                 # function(*args) = function(x:Tensor, ...)\n",
    "#     intermediate:bool = True,              # flag to return all intermediate derivatives\n",
    "#     jacobian:Callable = torch.func.jacfwd  # torch.func.jacfwd or torch.func.jacfrev\n",
    "# )\n",
    "\n",
    "# derivative(\n",
    "#     order:tuple[int, ...],                 # derivative orders\n",
    "#     function:Callable,                     # input function\n",
    "#     *args,                                 # function(*args) = function(x:Tensor, y:Tensor, z:Tensor, ...)\n",
    "#     intermediate:bool = True,              # flag to return all intermediate derivatives\n",
    "#     jacobian:Callable = torch.func.jacfwd  # torch.func.jacfwd or torch.func.jacfrev\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd7b6d08-4672-4de0-82ad-c68ad2e129df",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120.0\n",
      "1.0, 1.0, 2.0, 6.0, 24.0, 120.0\n",
      "6.0\n",
      "6.0\n"
     ]
    }
   ],
   "source": [
    "# Derivative\n",
    "\n",
    "# Input:  scalar\n",
    "# Output: scalar\n",
    "\n",
    "# Set test function\n",
    "\n",
    "# Note, the first function argument is a scalar tensor\n",
    "# Input function can have other additional arguments\n",
    "# Other arguments are not used in computation of derivatives\n",
    "\n",
    "def fn(x, a, b, c, d, e, f):\n",
    "    return a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5\n",
    "\n",
    "# Set derivative order\n",
    "\n",
    "n = 5\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor(0.0, dtype=dtype, device=device)\n",
    "\n",
    "# Set fixed parameters\n",
    "\n",
    "a, b, c, d, e, f = torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute n'th derivative\n",
    "\n",
    "value = derivative(n, fn, x, a, b, c, d, e, f, intermediate=False, jacobian=torch.func.jacfwd)\n",
    "print(value.cpu().numpy().tolist())\n",
    "\n",
    "# Compute all derivatives upto given order\n",
    "\n",
    "# Note, function value itself is referred as zero order derivative\n",
    "# Since function returns a tensor, output is a list of tensors\n",
    "\n",
    "values = derivative(n, fn, x, a, b, c, d, e, f, intermediate=True, jacobian=torch.func.jacfwd)\n",
    "print(*[value.cpu().numpy().tolist() for value in values], sep=', ')\n",
    "\n",
    "# Note, intermediate flag (default=True) can be used to return all derivatives\n",
    "# For jacobian parameter, torch.func.jacfwd or torch.func.jacrev functions can be passed\n",
    "\n",
    "# Evaluate derivative table representation for a given deviation from the evaluation point\n",
    "\n",
    "dx = torch.tensor(1.0, dtype=dtype, device=device)\n",
    "print(evaluate(derivative(n, fn, x, a, b, c, d, e, f) , [dx]).cpu().numpy().tolist())\n",
    "print(fn(x + dx, a, b, c, d, e, f).cpu().numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae6bddd2-2ed5-4656-aac8-3aa520f69fd2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.0, 0.0], [0.0, 2.0]]\n",
      "3.0, [-2.0, 2.0], [[2.0, 0.0], [0.0, 2.0]]\n",
      "3.0, [-2.0, 2.0], [[2.0, 0.0], [0.0, 2.0]]\n",
      "1.0\n",
      "1.0\n",
      "[1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "[[-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0], [-2.0, 2.0]]\n"
     ]
    }
   ],
   "source": [
    "# Derivative\n",
    "\n",
    "# Input:  vector\n",
    "# Output: scalar\n",
    "\n",
    "# Set test function\n",
    "\n",
    "# Note, the first function argument is a vector tensor\n",
    "# Input function can have other additional arguments\n",
    "# Other arguments are not used in computation of derivatives\n",
    "\n",
    "def fn(x, a, b, c):\n",
    "    x1, x2 = x\n",
    "    return a + b*(x1 - 1)**2 + c*(x2 + 1)**2\n",
    "\n",
    "# Set derivative order\n",
    "\n",
    "n = 2\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Set fixed parameters\n",
    "\n",
    "a, b, c = torch.tensor([1.0, 1.0, 1.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute only n'th derivative\n",
    "\n",
    "# Note, for given input & output the result is a hessian\n",
    "\n",
    "value = derivative(n, fn, x, a, b, c, intermediate=False, jacobian=torch.func.jacfwd)\n",
    "print(value.cpu().numpy().tolist())\n",
    "\n",
    "# Compute all derivatives upto given order\n",
    "\n",
    "# Note, fuction value itself is referred as zero order derivative\n",
    "# Output is a list of tensors (value, jacobian, hessian, ...)\n",
    "\n",
    "values = derivative(n, fn, x, a, b, c, intermediate=True, jacobian=torch.func.jacfwd)\n",
    "print(*[value.cpu().numpy().tolist() for value in values], sep=', ')\n",
    "\n",
    "# Compute jacobian and hessian with torch\n",
    "\n",
    "print(fn(x, a, b, c).cpu().numpy().tolist(), \n",
    "      torch.func.jacfwd(lambda x: fn(x, a, b, c))(x).cpu().numpy().tolist(), \n",
    "      torch.func.hessian(lambda x: fn(x, a, b, c))(x).cpu().numpy().tolist(), \n",
    "      sep=', ')\n",
    "\n",
    "# Evaluate derivative table representation for a given deviation from the evaluation point\n",
    "\n",
    "dx = torch.tensor([+1.0, -1.0], dtype=dtype, device=device)\n",
    "print(evaluate(values, [dx]).cpu().numpy())\n",
    "print(fn(x + dx, a, b, c).cpu().numpy())\n",
    "\n",
    "# Evaluate can be mapped over a set of deviation values\n",
    "\n",
    "print(torch.func.vmap(lambda x: evaluate(values, [x]))(torch.stack(5*[dx])).cpu().numpy().tolist())\n",
    "\n",
    "# Derivative can be mapped over a set of evaluation points\n",
    "\n",
    "# Note, the inputt function is expeted to return a tensor\n",
    "\n",
    "print(torch.func.vmap(lambda x: derivative(1, fn, x, a, b, c, intermediate=False))(torch.stack(5*[x])).cpu().numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd190cb3-ed62-435b-8fea-4a5cb5ee4c76",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.0, 0.0], [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]\n",
      "\n",
      "[-1.0, -1.0, -1.0]\n",
      "[-1.0, -1.0, -1.0]\n"
     ]
    }
   ],
   "source": [
    "# Derivative\n",
    "\n",
    "# Input:  vector\n",
    "# Output: vector\n",
    "\n",
    "# Set test function\n",
    "\n",
    "# Note, the first function argument is a vector tensor\n",
    "# Input function can have other additional arguments\n",
    "# Other arguments (if any) are not used in computation of derivatives\n",
    "\n",
    "def fn(x):\n",
    "    x1, x2 = x\n",
    "    X1 = 1.0*x1 + 2.0*x2\n",
    "    X2 = 3.0*x1 + 4.0*x2\n",
    "    X3 = 5.0*x1 + 6.0*x2\n",
    "    return torch.stack([X1, X2, X3])\n",
    "\n",
    "# Set derivative order\n",
    "\n",
    "n = 1\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute derivatives\n",
    "\n",
    "values = derivative(n, fn, x)\n",
    "print(*[value.cpu().numpy().tolist() for value in values], sep=', ')\n",
    "print()\n",
    "\n",
    "# Evaluate derivative table representation for a given deviation from the evaluation point\n",
    "\n",
    "dx = torch.tensor([+1, -1], dtype=dtype, device=device)\n",
    "print(evaluate(values, [dx]).cpu().numpy().tolist())\n",
    "print(fn(x + dx).cpu().numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8701357-d23a-4daf-abe5-8333c4b2c2ca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n",
      "[1, 2, 3, 1, 2, 3]\n",
      "[1, 2, 3, 1, 2, 3, 1, 2, 3]\n",
      "[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]\n",
      "[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]\n",
      "[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]\n",
      "[[[4.0, 4.0, 4.0], [4.0, 4.0, 4.0]]]\n",
      "[[[4.0, 4.0, 4.0], [4.0, 4.0, 4.0]]]\n",
      "[[[4.0, 4.0, 4.0], [4.0, 4.0, 4.0]]]\n"
     ]
    }
   ],
   "source": [
    "# Derivative\n",
    "\n",
    "# Input:  tensor\n",
    "# Output: tensor\n",
    "\n",
    "# Set test function\n",
    "\n",
    "def fn(x):\n",
    "    return 1 + x + x**2 + x**3\n",
    "\n",
    "# Set derivative order\n",
    "\n",
    "n = 3\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.zeros((1, 2, 3), dtype=dtype, device=device)\n",
    "\n",
    "# Compute derivatives\n",
    "\n",
    "# Note, output is a list of tensors\n",
    "\n",
    "values = derivative(n, fn, x)\n",
    "print(*[list(value.shape) for value in values], sep='\\n')\n",
    "\n",
    "# Evaluate derivative table representation for a given deviation from the evaluation point\n",
    "\n",
    "# Note, evaluate function works with scalar or vector tensor input\n",
    "# One should compute derivatives of a wrapped function and reshape the result of evaluate\n",
    "\n",
    "# Set wrapped function\n",
    "\n",
    "def gn(x, shape):\n",
    "    return fn(x.reshape(shape)).flatten()\n",
    "\n",
    "print(fn(x).cpu().numpy().tolist())\n",
    "print(gn(x.flatten(), x.shape).reshape(x.shape).cpu().numpy().tolist())\n",
    "\n",
    "# Compute derivatives\n",
    "\n",
    "values = derivative(n, gn, x.flatten(), x.shape)\n",
    "\n",
    "# Set deviation value\n",
    "\n",
    "dx = torch.ones_like(x)\n",
    "\n",
    "# Evaluate\n",
    "\n",
    "print(evaluate(values, [dx.flatten()]).reshape(x.shape).cpu().numpy().tolist())\n",
    "print(gn((x + dx).flatten(), x.shape).reshape(x.shape).cpu().numpy().tolist())\n",
    "print(fn(x + dx).cpu().numpy().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1fb30895-12e5-432b-a145-e4bca8f5932e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Derivative\n",
    "\n",
    "# Input:  vector\n",
    "# Output: nested list of tensors\n",
    "\n",
    "# Set test function\n",
    "\n",
    "def fn(x):\n",
    "    x1, x2, x3, x4, x5, x6 = x\n",
    "    X1 = 1.0*x1 + 2.0*x2 + 3.0*x3\n",
    "    X2 = 4.0*x4 + 5.0*x5 + 6.0*x6\n",
    "    return [torch.stack([X1]), [torch.stack([X2])]]\n",
    "\n",
    "# Set derivative order\n",
    "\n",
    "n = 1\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute derivatives\n",
    "\n",
    "values = derivative(n, fn, x, intermediate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bbcd01b-ce9f-49fc-99e1-7ab7b3191488",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[1.0, 1.0], [1.0, 1.0]], [[1.0, 1.0], [1.0, 1.0]]]]\n",
      "[8.0]\n",
      "[8.0]\n",
      "(0, 0, 0, 0, 0, 0): [0.0]\n",
      "(0, 0, 0, 0, 1, 0): [0.0]\n",
      "(0, 0, 0, 0, 0, 1): [0.0]\n",
      "(0, 0, 1, 0, 0, 0): [0.0]\n",
      "(0, 0, 0, 1, 0, 0): [0.0]\n",
      "(0, 0, 1, 0, 1, 0): [0.0]\n",
      "(0, 0, 1, 0, 0, 1): [0.0]\n",
      "(0, 0, 0, 1, 1, 0): [0.0]\n",
      "(0, 0, 0, 1, 0, 1): [0.0]\n",
      "(1, 0, 0, 0, 0, 0): [0.0]\n",
      "(0, 1, 0, 0, 0, 0): [0.0]\n",
      "(1, 0, 0, 0, 1, 0): [0.0]\n",
      "(1, 0, 0, 0, 0, 1): [0.0]\n",
      "(0, 1, 0, 0, 1, 0): [0.0]\n",
      "(0, 1, 0, 0, 0, 1): [0.0]\n",
      "(1, 0, 1, 0, 0, 0): [0.0]\n",
      "(1, 0, 0, 1, 0, 0): [0.0]\n",
      "(0, 1, 1, 0, 0, 0): [0.0]\n",
      "(0, 1, 0, 1, 0, 0): [0.0]\n",
      "(1, 0, 1, 0, 1, 0): [1.0]\n",
      "(1, 0, 1, 0, 0, 1): [1.0]\n",
      "(1, 0, 0, 1, 1, 0): [1.0]\n",
      "(1, 0, 0, 1, 0, 1): [1.0]\n",
      "(0, 1, 1, 0, 1, 0): [1.0]\n",
      "(0, 1, 1, 0, 0, 1): [1.0]\n",
      "(0, 1, 0, 1, 1, 0): [1.0]\n",
      "(0, 1, 0, 1, 0, 1): [1.0]\n"
     ]
    }
   ],
   "source": [
    "# Derivative\n",
    "\n",
    "# Input:  vector, vector, vector\n",
    "# Output: vector\n",
    "\n",
    "# Set test function\n",
    "\n",
    "def fn(x, y, z):\n",
    "    x1, x2 = x\n",
    "    y1, y2 = y\n",
    "    z1, z2 = z\n",
    "    return torch.stack([(x1 + x2)*(y1 + y2)*(z1 + z2)])\n",
    "\n",
    "# Set derivative orders for x, y and z\n",
    "\n",
    "nx, ny, nz = 1, 1, 1\n",
    "\n",
    "# Set evaluation point\n",
    "# Note, evaluation point is a list of tensors\n",
    "\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "y = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "z = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute n'th derivativ\n",
    "\n",
    "value = derivative((nx, ny, nz), fn, x, y, z, intermediate=False)\n",
    "print(value.cpu().numpy().tolist())\n",
    "\n",
    "# Compute all derivatives upto given order\n",
    "\n",
    "values = derivative((nx, ny, nz), fn, x, y, z, intermediate=True)\n",
    "\n",
    "# Evaluate derivative table representation for a given deviation from the evaluation point\n",
    "\n",
    "dx = torch.tensor([1.0, 1.0], dtype=dtype, device=device)\n",
    "dy = torch.tensor([1.0, 1.0], dtype=dtype, device=device)\n",
    "dz = torch.tensor([1.0, 1.0], dtype=dtype, device=device)\n",
    "print(evaluate(values, [dx, dy, dz]).cpu().numpy().tolist())\n",
    "print(fn(x + dx, y + dy, z + dz).cpu().numpy().tolist())\n",
    "\n",
    "# Note, if the input function has vector arguments and returns a tensor, it can be repsented with series\n",
    "\n",
    "for key, value in series(tuple(map(len, (x, y, z))), (nx, ny, nz), values).items():\n",
    "    print(f'{key}: {value.cpu().numpy().tolist()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b4d1b6e-7fa1-4879-a91a-9d6ab165fe73",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[6.0, 4.0], [4.0, 10.0]]]\n",
      "[6.0]\n",
      "[4.0]\n",
      "[10.0]\n"
     ]
    }
   ],
   "source": [
    "# Redundancy free computation\n",
    "\n",
    "# Set test function\n",
    "\n",
    "def fn(x):\n",
    "    x1, x2 = x\n",
    "    return torch.stack([1.0*x1 + 2.0*x2 + 3.0*x1**2 + 4.0*x1*x2 + 5.0*x2**2])\n",
    "\n",
    "# Set derivative order\n",
    "\n",
    "n = 2\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute n'th derivative\n",
    "\n",
    "value = derivative(n, fn, x, intermediate=False)\n",
    "print(value.cpu().numpy().tolist())\n",
    "\n",
    "# Since derivatives are computed by nesting of jacobian function, redundant computations appear starting from the second order\n",
    "# Redundant computations can be avoided if all input arguments are scalar tensors\n",
    "\n",
    "def gn(x1, x2):\n",
    "    return fn(torch.stack([x1, x2]))\n",
    "\n",
    "print(derivative((2, 0), gn, *x, intermediate=False).cpu().numpy().tolist())\n",
    "print(derivative((1, 1), gn, *x, intermediate=False).cpu().numpy().tolist())\n",
    "print(derivative((0, 2), gn, *x, intermediate=False).cpu().numpy().tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9d9fc1-9478-47be-9696-d8e30056d734",
   "metadata": {},
   "source": [
    "# Example-02: Derivative table representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9194ea1a-5a1b-402e-93b2-fdc55a9eca34",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Input function f: R^n x R^m x ... -> R^n is referred as a mapping\n",
    "# The first function argument is state, other arguments (used in computation of derivatives) and knobs\n",
    "# State and all knobs are vector-like tensors\n",
    "# Note, functions of this form can be used to model tranformations throught accelerator magnets\n",
    "\n",
    "# In this case, derivatives can be used to generate a (parametric) model of the input function\n",
    "# Function model can be represented as a derivative table or coefficients of monomials (series representation)\n",
    "\n",
    "# In this example, table representation is used to model transformation throught a sextupole accelerator magnet\n",
    "# Table is computed with respect to state variables (phase space variables) and knobs (magnet strength and length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87b75205-6af6-4775-bcd4-4b856b1c47d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import signature\n",
    "from ndmap.signature import get\n",
    "from ndmap.index import index\n",
    "from ndmap.index import reduce\n",
    "from ndmap.index import build\n",
    "from ndmap.series import series\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b23e7f22-280b-4f1b-8821-317be9523f2e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6aec6e68-a848-4713-8579-5a2aa56ec962",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Mapping (sextupole accelerator magnet transformatijet)\n",
    "# Given initial state, magnet strength and length, state is propagated using explicit symplectic integration\n",
    "# Number of integration steps is set by count parameter, integration step length is length/count\n",
    "\n",
    "def mapping(x, k, l, count=10):\n",
    "    (qx, px, qy, py), (k, ), (l, ) = x, k, l/(2.0*count)\n",
    "    for _ in range(count):\n",
    "        qx, qy = qx + l*px, qy + l*py\n",
    "        px, py = px - 2.0*l*k*(qx**2 - qy**2), py + 2.0*l*k*qx*qy\n",
    "        qx, qy = qx + l*px, qy + l*py\n",
    "    return torch.stack([qx, px, qy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b784d45e-2d4f-4e32-a409-7a9c53c5256f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4])\n",
      "torch.Size([4, 4])\n",
      "torch.Size([4, 4, 4])\n",
      "torch.Size([4, 4, 4, 4])\n",
      "torch.Size([4, 4, 4, 4, 4])\n",
      "torch.Size([4, 4, 4, 4, 4, 4])\n",
      "torch.Size([4, 4, 4, 4, 4, 4, 4])\n"
     ]
    }
   ],
   "source": [
    "# Table representation (state)\n",
    "\n",
    "# Set evaluation point & parameters\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor([10.0], dtype=dtype, device=device)\n",
    "l = torch.tensor([0.1], dtype=dtype, device=device)\n",
    "\n",
    "# Compute derivatives (table representation)\n",
    "# Since derivatives are computed only with respect to the state, output table is a list of tensors\n",
    "\n",
    "t = derivative(6, mapping, x, k, l)\n",
    "\n",
    "print(*[element.shape for element in t], sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a4107f58-8cfe-45e4-9061-0d21839db12d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00010000041624970626, 0.0010000066749862018, 0.00010000016750044096, 5.000018166514047e-09]\n",
      "[0.0001000004162497062, 0.0010000066749862018, 0.00010000016750044096, 5.000018166514046e-09]\n"
     ]
    }
   ],
   "source": [
    "# Compare table and exact mapping near the evaluation point (change order to observe convergence)\n",
    "# Note, table transformation is not symplectic\n",
    "\n",
    "dx = torch.tensor([0.0, 0.001, 0.0001, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(evaluate(t, [dx]).cpu().tolist())\n",
    "print(mapping(x + dx, k, l).cpu().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7fbc3b8a-ddb4-47a9-90a0-3b4e4799636d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,), (1,), (2,), (3,), (4,), (5,), (6,)]\n"
     ]
    }
   ],
   "source": [
    "# Each bottom element (tensor) in the (flattend) derivative table is assosiated with a signature\n",
    "# Signature is a tuple of derivative orders\n",
    "\n",
    "print(signature(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee668111-a0aa-4ba9-870d-e2409880ad47",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.  0.1 0.  0. ]\n",
      " [0.  1.  0.  0. ]\n",
      " [0.  0.  1.  0.1]\n",
      " [0.  0.  0.  1. ]]\n"
     ]
    }
   ],
   "source": [
    "# For a given signature, corresponding element can be extracted or changed with get/set functions\n",
    "\n",
    "print(get(t, (1, )).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c688731-6776-4824-a275-b9ed00d111d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 0 0 0]\n",
      " [1 1 0 0]\n",
      " [1 0 1 0]\n",
      " [1 0 0 1]\n",
      " [1 1 0 0]\n",
      " [0 2 0 0]\n",
      " [0 1 1 0]\n",
      " [0 1 0 1]\n",
      " [1 0 1 0]\n",
      " [0 1 1 0]\n",
      " [0 0 2 0]\n",
      " [0 0 1 1]\n",
      " [1 0 0 1]\n",
      " [0 1 0 1]\n",
      " [0 0 1 1]\n",
      " [0 0 0 2]]\n"
     ]
    }
   ],
   "source": [
    "# Each bottom element is related to monomials\n",
    "# For given order, monomial indices with repetitions can be computed\n",
    "# These repetitions account for evaluation of the same partial derivatives with diffenent orders, e.g. df/dxdy vs df/dydx\n",
    "\n",
    "print(index(4, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1f332f5-f3a6-4d68-91a1-611b09d08385",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000416e-04 1.00000667e-03 1.00000168e-04 5.00001817e-09]\n",
      "[1.00000416e-04 1.00000667e-03 1.00000168e-04 5.00001817e-09]\n",
      "[1.00000416e-04 1.00000667e-03 1.00000168e-04 5.00001817e-09]\n"
     ]
    }
   ],
   "source": [
    "# Explicit evaluation\n",
    "\n",
    "print(evaluate(t, [dx]).cpu().numpy())\n",
    "print((t[0] + t[1] @ dx + 1/2 * t[2] @ dx @ dx + 1/2 * 1/3 * t[3] @ dx @ dx @ dx + 1/2 * 1/3 * 1/4 * t[4] @ dx @ dx @ dx @ dx + 1/2 * 1/3 * 1/4 * 1/5 * t[5] @ dx @ dx @ dx @ dx @ dx + 1/2 * 1/3 * 1/4 * 1/5 * 1/6 * t[6] @ dx @ dx @ dx @ dx @ dx @ dx).cpu().numpy())\n",
    "print((t[0] + (t[1] + 1/2 * (t[2] + 1/3 * (t[3] + 1/4 * (t[4] + 1/5 * (t[5] + 1/6 * t[6] @ dx) @ dx) @ dx) @ dx) @ dx) @ dx).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94bfcd0d-1ca0-4f05-98d3-7b889a5cbc69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.  0.  0.  0. ]\n",
      " [0.1 1.  0.  0. ]\n",
      " [0.  0.  1.  0. ]\n",
      " [0.  0.  0.1 1. ]]\n"
     ]
    }
   ],
   "source": [
    "# Series representation can be generated from a given table\n",
    "# This representation stores monomial powers and corresponding coefficients\n",
    "\n",
    "s = series((4, ), (6, ), t)\n",
    "print(torch.stack([s[(1, 0, 0, 0)], s[(0, 1, 0, 0)], s[(0, 0, 1, 0)], s[(0, 0, 0, 1)]]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9325c49-1a2d-4d37-bef0-f59b5d2cfe19",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000416e-04 1.00000667e-03 1.00000168e-04 5.00001817e-09]\n",
      "[1.00000416e-04 1.00000667e-03 1.00000168e-04 5.00001817e-09]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate series\n",
    "\n",
    "print(evaluate(t, [dx]).cpu().numpy())\n",
    "print(evaluate(s, [dx]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7b07492b-5f44-4925-a18f-310ffdbbd8e2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Table representation (state & knobs)\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor([10.0], dtype=dtype, device=device)\n",
    "l = torch.tensor([0.1], dtype=dtype, device=device)\n",
    "\n",
    "# Compute derivatives (table representation)\n",
    "# Since derivatives are computed with respect to state and knobs, output table is a nested list of tensors\n",
    "\n",
    "t = derivative((6, 1, 1), mapping, x, k, l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f40ca1b-ec32-4988-8cf2-bb282ce2081d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.  0.1 0.  0. ]\n",
      " [0.  1.  0.  0. ]\n",
      " [0.  0.  1.  0.1]\n",
      " [0.  0.  0.  1. ]]\n"
     ]
    }
   ],
   "source": [
    "# In this case, bottom table element signature is a tuple with several integers\n",
    "\n",
    "print(get(t, (1, 0, 0)).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c1324ac-7134-487d-8d01-bdc27f5c2bd3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00010000041624970626, 0.0010000066749862018, 0.00010000016750044096, 5.000018166514047e-09]\n",
      "[0.0001010004271286862, 0.001000006741987918, 0.00010000017425071835, 5.1510191197368185e-09]\n",
      "[0.00010100042712809422, 0.0010000067409770773, 0.00010000017430164039, 5.151524128394736e-09]\n"
     ]
    }
   ],
   "source": [
    "# Compare table and exact mapping near evaluation point (change order to observe convergence)\n",
    "# Note, table transofrmation is not symplectic\n",
    "\n",
    "dx = torch.tensor([0.0, 0.001, 0.0001, 0.0], dtype=dtype, device=device)\n",
    "dk = torch.tensor([0.1], dtype=dtype, device=device)\n",
    "dl = torch.tensor([0.001], dtype=dtype, device=device)\n",
    "\n",
    "print(evaluate(t, [dx, 0.0*dk, 0.0*dl]).cpu().tolist())\n",
    "print(evaluate(t, [dx, 1.0*dk, 1.0*dl]).cpu().tolist())\n",
    "print(mapping(x + dx, k + dk, l + dl).cpu().tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e031c18b-8836-45ee-acc5-0d05d56d58ce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0, 0)\n",
      "(0, 0, 1)\n",
      "(0, 1, 0)\n",
      "(0, 1, 1)\n",
      "(1, 0, 0)\n",
      "(1, 0, 1)\n",
      "(1, 1, 0)\n",
      "(1, 1, 1)\n",
      "(2, 0, 0)\n",
      "(2, 0, 1)\n",
      "(2, 1, 0)\n",
      "(2, 1, 1)\n",
      "(3, 0, 0)\n",
      "(3, 0, 1)\n",
      "(3, 1, 0)\n",
      "(3, 1, 1)\n",
      "(4, 0, 0)\n",
      "(4, 0, 1)\n",
      "(4, 1, 0)\n",
      "(4, 1, 1)\n",
      "(5, 0, 0)\n",
      "(5, 0, 1)\n",
      "(5, 1, 0)\n",
      "(5, 1, 1)\n",
      "(6, 0, 0)\n",
      "(6, 0, 1)\n",
      "(6, 1, 0)\n",
      "(6, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "# Each bottom element (tensor) in the (flattend) derivative table is assosiated with a signature\n",
    "# Signature is a tuple of derivative orders\n",
    "\n",
    "print(*[index for index in signature(t)], sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c9dde6d2-0942-4a95-94c1-f7422d64e33b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.447578e-06 9.756747e-05 0.000000e+00 0.000000e+00]\n",
      "\n",
      "[1.01000427e-04 1.00000674e-03 1.00000174e-04 5.15101912e-09]\n",
      "[1.01000427e-04 1.00000674e-03 1.00000174e-04 5.15101912e-09]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute series\n",
    "\n",
    "s = series((4, 1, 1), (6, 1, 1), t)\n",
    "\n",
    "# Keys are generalized monomials\n",
    "\n",
    "print(s[(1, 1, 1, 1, 1, 1)].cpu().numpy())\n",
    "print()\n",
    "\n",
    "# Evaluate series\n",
    "\n",
    "print(evaluate(t, [dx, dk, dl]).cpu().numpy())\n",
    "print(evaluate(s, [dx, dk, dl]).cpu().numpy())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d26886a-3cdf-462c-b130-08a3fba097d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduced table representation\n",
    "\n",
    "sequence, shape, unique = reduce((4, 1, 1), t)\n",
    "out = derivative((6, 1, 1), lambda x, k, l: x, x, k, l)\n",
    "build(out, sequence, shape, unique)\n",
    "compare(t, out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d4b0416-12e3-4228-b744-fe0eec64e36b",
   "metadata": {},
   "source": [
    "# Example-03: Derivative table propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46789618-1dec-48fb-8885-97e0e4f57324",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Given a mapping f(state, *knobs, ...) and a derivative table t, derivatives of f(t, *knobs, ...) are computed\n",
    "# This can be used to propagate derivative table throught a given mapping (computation of parametric fixed points and other applications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4f6d188-c552-4405-9b11-8be51d029564",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f4fab75-e802-4919-bb27-586094c20595",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ad4878b-ca35-4d99-8a28-b59ba7326861",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define mappings\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=100):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def ring(x, w):\n",
    "    x = quad(x, w, +0.25, 0.5)\n",
    "    x = drif(x, w, 5.0)\n",
    "    x = quad(x, w, -0.20, 0.5)\n",
    "    x = quad(x, w, -0.20, 0.5)\n",
    "    x = drif(x, w, 5.0)\n",
    "    x = quad(x, w, +0.25, 0.5)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dacb1099-6df9-46c2-ae79-ce6cbf4a149c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-8.88568650e-05 -5.43957672e-05  4.97569694e-04 -1.40349102e-04]\n",
      "[-8.88568650e-05 -5.43957672e-05  4.97569694e-04 -1.40349102e-04]\n"
     ]
    }
   ],
   "source": [
    "# Direct\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute derivatives\n",
    "\n",
    "t = derivative((1, 4), ring, x, w)\n",
    "\n",
    "# Evaluate for a given deviation\n",
    "\n",
    "dx = torch.tensor([0.001, 0.0, 0.001, 0.0], dtype=dtype, device=device)\n",
    "dw = torch.tensor([0.001], dtype=dtype, device=device)\n",
    "\n",
    "print(ring(x + dx, w + dw).cpu().numpy())\n",
    "print(evaluate(t, [dx, dw]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4187fa06-cd7f-46dd-bcde-67f3877aa543",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-8.88568650e-05 -5.43957672e-05  4.97569694e-04 -1.40349102e-04]\n",
      "[-8.88568650e-05 -5.43957672e-05  4.97569694e-04 -1.40349102e-04]\n"
     ]
    }
   ],
   "source": [
    "# Propagation\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Set identity table\n",
    "\n",
    "t = identity((1, 4), [x, w])\n",
    "\n",
    "# Propagate table\n",
    "\n",
    "t = propagate((4, 1), (1, 4), t, [w], quad, +0.25, 0.5)\n",
    "t = propagate((4, 1), (1, 4), t, [w], drif, 5.0)\n",
    "t = propagate((4, 1), (1, 4), t, [w], quad, -0.20, 0.5)\n",
    "t = propagate((4, 1), (1, 4), t, [w], quad, -0.20, 0.5)\n",
    "t = propagate((4, 1), (1, 4), t, [w], drif, 5.0)\n",
    "t = propagate((4, 1), (1, 4), t, [w], quad, +0.25, 0.5)\n",
    "\n",
    "# Evaluate for a given deviation\n",
    "\n",
    "dx = torch.tensor([0.001, 0.0, 0.001, 0.0], dtype=dtype, device=device)\n",
    "dw = torch.tensor([0.001], dtype=dtype, device=device)\n",
    "\n",
    "print(ring(x + dx, w + dw).cpu().numpy())\n",
    "print(evaluate(t, [dx, dw]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7dbd793-9fb9-45e6-9d96-11f684e798f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 0, 0, 0, 0): [-0.09072843 -0.05430498  0.          0.        ]\n",
      "(0, 1, 0, 0, 0): [18.26293553 -0.09072843  0.          0.        ]\n",
      "(0, 0, 1, 0, 0): [ 0.          0.          0.49625858 -0.14063034]\n",
      "(0, 0, 0, 1, 0): [0.         0.         5.35963583 0.49625858]\n",
      "(1, 0, 0, 0, 1): [ 1.87420951 -0.09097396  0.          0.        ]\n",
      "(0, 1, 0, 0, 1): [-24.33227046   1.87420951   0.           0.        ]\n",
      "(0, 0, 1, 0, 1): [0.         0.         1.31324305 0.28161167]\n",
      "(0, 0, 0, 1, 1): [0.         0.         1.46426265 1.31324305]\n",
      "(1, 0, 0, 0, 2): [-2.65007558  0.18727838  0.          0.        ]\n",
      "(0, 1, 0, 0, 2): [30.20570906 -2.65007558  0.          0.        ]\n",
      "(0, 0, 1, 0, 2): [ 0.          0.         -2.12812904 -0.37146989]\n",
      "(0, 0, 0, 1, 2): [ 0.          0.         -8.46895909 -2.12812904]\n",
      "(1, 0, 0, 0, 3): [ 3.41796043 -0.28459189  0.          0.        ]\n",
      "(0, 1, 0, 0, 3): [-35.88102956   3.41796043   0.           0.        ]\n",
      "(0, 0, 1, 0, 3): [0.         0.         2.94802229 0.46018886]\n",
      "(0, 0, 0, 1, 3): [ 0.          0.         15.6516443   2.94802229]\n",
      "(1, 0, 0, 0, 4): [-4.17749922  0.38289808  0.          0.        ]\n",
      "(0, 1, 0, 0, 4): [41.3560843  -4.17749922  0.          0.        ]\n",
      "(0, 0, 1, 0, 4): [ 0.          0.         -3.77254434 -0.54775243]\n",
      "(0, 0, 0, 1, 4): [  0.           0.         -23.00943634  -3.77254434]\n"
     ]
    }
   ],
   "source": [
    "# Series representation\n",
    "\n",
    "s = clean(series((4, 1), (1, 4), t))\n",
    "for key, value in s.items():\n",
    "    print(f'{key}: {value.cpu().numpy()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebf5728f-f437-4087-8a02-a2694bb51270",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.72649397e-08 8.09919549e-08 0.00000000e+00 0.00000000e+00]\n",
      "[2.72649397e-08 8.09919549e-08 0.00000000e+00 0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "# Check invariant\n",
    "# Note, ring has two quadratic invariants (actions), zeros are padded to match state length\n",
    "\n",
    "# Define invariant\n",
    "\n",
    "matrix = torch.tensor([[4.282355639365032, 0.0, 0.0, 0.0], [0.0, 0.23351633638449415, 0.0, 0.0], [0.0, 0.0, 2.484643367729646, 0.0], [0.0, 0.0, 0.0, 0.40247224732044934]], dtype=dtype, device=device)\n",
    "def invariant(x):\n",
    "    qx, px, qy, py = matrix.inverse() @ x\n",
    "    return torch.stack([0.5*(qx**2 + px**2), 0.5*(qy**2 + py**2), *torch.tensor(2*[0.0], dtype=dtype, device=device)])\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.001, 0.0, 0.001, 0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Evaluate invarint for a given state and transformed state\n",
    "\n",
    "print(invariant(x).cpu().numpy())\n",
    "print(invariant(ring(x, w)).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "447bb0d5-8ab8-423e-82ce-e77ba2402997",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 0, 0, 0): [0.02726494 0.         0.         0.        ]\n",
      "(0, 2, 0, 0): [9.16928491 0.         0.         0.        ]\n",
      "(0, 0, 2, 0): [0.         0.08099195 0.         0.        ]\n",
      "(0, 0, 0, 2): [0.         3.08672633 0.         0.        ]\n",
      "\n",
      "(2, 0, 0, 0): [0.02726494 0.         0.         0.        ]\n",
      "(0, 2, 0, 0): [9.16928491 0.         0.         0.        ]\n",
      "(0, 0, 2, 0): [0.         0.08099195 0.         0.        ]\n",
      "(0, 0, 0, 2): [0.         3.08672633 0.         0.        ]\n",
      "\n",
      "(2, 0, 0, 0): [0.02726494 0.         0.         0.        ]\n",
      "(0, 2, 0, 0): [9.16928491 0.         0.         0.        ]\n",
      "(0, 0, 2, 0): [0.         0.08099195 0.         0.        ]\n",
      "(0, 0, 0, 2): [0.         3.08672633 0.         0.        ]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Invariant propagation\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute table and series representations of invariant\n",
    "\n",
    "t = derivative((2, ), invariant, x)\n",
    "s = series((4, ), (2, ), t)\n",
    "\n",
    "print(*[f'{key}: {value.cpu().numpy()}' for key, value in clean(s, epsilon=1.0E-14).items()], sep='\\n')\n",
    "print()\n",
    "\n",
    "# Compute table and series representations of transformed invariant\n",
    "\n",
    "t = derivative((2, ), lambda x: invariant(ring(x, w)), x)\n",
    "s = series((4, ), (2, ), t)\n",
    "\n",
    "print(*[f'{key}: {value.cpu().numpy()}' for key, value in clean(s, epsilon=1.0E-14).items()], sep='\\n')\n",
    "print()\n",
    "\n",
    "# Propagate invariant\n",
    "\n",
    "t = derivative((2, ), ring, x, w)\n",
    "t = propagate((4, ), (2, ), t, [], invariant)\n",
    "s = series((4, ), (2, ), t)\n",
    "\n",
    "print(*[f'{key}: {value.cpu().numpy()}' for key, value in clean(s, epsilon=1.0E-14).items()], sep='\\n')\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1c67d7-5359-4c7b-b621-98a8ea1b67b7",
   "metadata": {},
   "source": [
    "# Example-04: Jet class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1fefbd8-1aa1-46c1-a3ba-4168cb3fc272",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Jet is a convenience class to work with jets (evaluation point & derivative table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1916260-e836-453d-9f44-f1879bf9e331",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.jet import Jet\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38e18c76-f154-4190-802c-9bfb89dddc6a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39ea012c-27f8-42b6-9624-fbed0d88b26e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define mappings\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=100):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute table representation\n",
    "\n",
    "t = derivative((1, 4), lambda x, w: quad(drif(x, w, 1.0), w, 1.0, 1.0, 1), x, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3301ddf-2a75-4e39-8c86-75326f643405",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set jet\n",
    "\n",
    "j = Jet((4, 1), (1, 4), point=[x, w], dtype=dtype, device=device)\n",
    "j = j.propagate(drif, 1.0)\n",
    "j = j.propagate(quad, 1.0, 1.0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62207495-21df-49dc-a5df-8cc56afb3ada",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.0005005 -0.001      0.0014995  0.001    ]\n",
      "[ 0.0005005 -0.001      0.0014995  0.001    ]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate at given deviation\n",
    "\n",
    "dx = torch.tensor([0.001, 0.0, 0.001, 0.0], dtype=dtype, device=device)\n",
    "dw = torch.tensor([0.001], dtype=dtype, device=device)\n",
    "\n",
    "print(evaluate(t, [dx, dw]).cpu().numpy())\n",
    "print(j([dx, dw]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9707a1e-2153-4e6c-9893-f0d156b01ee6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.0005005 -0.001      0.0014995  0.001    ]\n"
     ]
    }
   ],
   "source": [
    "# Composition\n",
    "\n",
    "j1 = Jet.from_mapping((4, 1), (1, 4), [x, w], drif, 1.0, dtype=dtype, device=device)\n",
    "j2 = Jet.from_mapping((4, 1), (1, 4), [x, w], quad, 1.0, 1.0, 1, dtype=dtype, device=device)\n",
    "\n",
    "print((j1 @ j2)([dx, dw]).cpu().numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b6bb65-afc3-4470-a675-0e14f9adba77",
   "metadata": {},
   "source": [
    "# Example-05: Nonlinear mapping approximation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f401d399-6e2e-45b0-839a-ff4bc8cb9eb5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Composition of several nonlinear mappings can be approximated by its table representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a439c47-5b52-4dbd-9039-8ce89d8606e7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06f83b07-6a12-4301-bc1b-8b941f23db52",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "760a411c-6d91-4ae0-81a7-0e9ab3d330a8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set test mapping\n",
    "# Rotation with two sextupoles separated by negative identity linear transformation\n",
    "# Note, result is expected to have zero degree two coefficients due to negative identity linear transformation between sextupoles\n",
    "\n",
    "def spin(x, mux, muy):\n",
    "    (qx, px, qy, py), mux, muy = x, mux, muy\n",
    "    return torch.stack([qx*mux.cos() + px*mux.sin(), px*mux.cos() - qx*mux.sin(), qy*muy.cos() + py*muy.sin(), py*muy.cos() - qy*muy.sin()])\n",
    "\n",
    "def drif(x, l):\n",
    "    (qx, px, qy, py), l = x, l\n",
    "    return torch.stack([qx + l*px, px, qy + l*py, py])\n",
    "\n",
    "def sext(x, ks, l, n=1):\n",
    "    (qx, px, qy, py), ks, l = x, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px, qy + l*py\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px, qy + l*py\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def ring(x):\n",
    "    mux, muy = 2.0*numpy.pi*torch.tensor([1/3 + 0.01, 1/4 + 0.01], dtype=dtype, device=device)\n",
    "    x = spin(x, mux, muy)\n",
    "    x = drif(x, -0.05)\n",
    "    x = sext(x, 10.0, 0.1, 100)\n",
    "    x = drif(x, -0.05)\n",
    "    mux, muy = 2.0*numpy.pi*torch.tensor([0.50, 0.50], dtype=dtype, device=device)\n",
    "    x = spin(x, mux, muy)\n",
    "    x = drif(x, -0.05)\n",
    "    x = sext(x, 10.0, 0.1, 100)\n",
    "    x = drif(x, -0.05)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "662c2dce-69fe-4334-a710-b5fa29964bc6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 0, 0, 0): [0.55339155 0.83292124 0.         0.        ]\n",
      "(0, 1, 0, 0): [-0.83292124  0.55339155  0.          0.        ]\n",
      "(0, 0, 1, 0): [0.         0.         0.06279052 0.99802673]\n",
      "(0, 0, 0, 1): [ 0.          0.         -0.99802673  0.06279052]\n",
      "(3, 0, 0, 0): [-7.53257307e-09  2.82424677e-03  0.00000000e+00  0.00000000e+00]\n",
      "(2, 1, 0, 0): [-1.96250238e-08 -1.27525063e-02  0.00000000e+00  0.00000000e+00]\n",
      "(2, 0, 1, 0): [ 0.00000000e+00 -0.00000000e+00  9.21186111e-06  3.34331441e-04]\n",
      "(2, 0, 0, 1): [ 0.00000000e+00 -0.00000000e+00  1.59704766e-05 -5.06941449e-03]\n",
      "(1, 2, 0, 0): [-1.11004920e-08  1.91940679e-02  0.00000000e+00  0.00000000e+00]\n",
      "(1, 1, 1, 0): [ 0.00000000e+00 -0.00000000e+00 -2.98671134e-05 -9.79459005e-04]\n",
      "(1, 1, 0, 1): [ 0.00000000e+00 -0.00000000e+00 -1.48185697e-05  1.53623878e-02]\n",
      "(1, 0, 2, 0): [-1.05857397e-06  1.97282603e-05  0.00000000e+00  0.00000000e+00]\n",
      "(1, 0, 1, 1): [ 1.48154798e-05 -1.18570682e-03  0.00000000e+00  0.00000000e+00]\n",
      "(1, 0, 0, 2): [2.88067554e-05 9.18409783e-03 0.00000000e+00 0.00000000e+00]\n",
      "(0, 3, 0, 0): [-9.21338044e-10 -9.62979589e-03  0.00000000e+00  0.00000000e+00]\n",
      "(0, 2, 1, 0): [ 0.00000000e+00 -0.00000000e+00  2.40366928e-05  7.09979811e-04]\n",
      "(0, 2, 0, 1): [ 0.00000000e+00 -0.00000000e+00 -1.38786549e-05 -1.15294375e-02]\n",
      "(0, 1, 2, 0): [ 1.80396305e-06 -2.59196622e-05  0.00000000e+00  0.00000000e+00]\n",
      "(0, 1, 1, 1): [-2.98509155e-05  1.72488752e-03  0.00000000e+00  0.00000000e+00]\n",
      "(0, 1, 0, 2): [ 1.66318846e-05 -1.38269522e-02  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 3, 0): [ 0.00000000e+00 -0.00000000e+00 -1.47704279e-08  4.12534350e-06]\n",
      "(0, 0, 2, 1): [ 0.00000000e+00 -0.00000000e+00 -3.31103168e-09 -1.96719688e-04]\n",
      "(0, 0, 1, 2): [ 0.00000000e+00 -0.00000000e+00  3.93325786e-09  3.12683573e-03]\n",
      "(0, 0, 0, 3): [ 0.00000000e+00 -0.00000000e+00  2.56887204e-10 -1.65665408e-02]\n",
      "(4, 0, 0, 0): [ 6.48869023e-07 -3.91844462e-06  0.00000000e+00  0.00000000e+00]\n",
      "(3, 1, 0, 0): [-3.91685700e-06  1.51001133e-05  0.00000000e+00  0.00000000e+00]\n",
      "(3, 0, 1, 0): [ 0.00000000e+00 -0.00000000e+00 -4.36165465e-07  5.76316391e-06]\n",
      "(3, 0, 0, 1): [ 0.00000000e+00 -0.00000000e+00  6.56410859e-06  1.31668166e-05]\n",
      "(2, 2, 0, 0): [ 8.85501662e-06 -1.48880894e-05  0.00000000e+00  0.00000000e+00]\n",
      "(2, 1, 1, 0): [ 0.00000000e+00 -0.00000000e+00  1.92232731e-06 -2.78183189e-05]\n",
      "(2, 1, 0, 1): [ 0.00000000e+00 -0.00000000e+00 -2.96894787e-05 -3.16635607e-05]\n",
      "(2, 0, 2, 0): [1.81838643e-08 2.18816315e-06 0.00000000e+00 0.00000000e+00]\n",
      "(2, 0, 1, 1): [-9.93314202e-07 -3.25277215e-05  0.00000000e+00  0.00000000e+00]\n",
      "(2, 0, 0, 2): [ 7.67316422e-06 -2.51871510e-05  0.00000000e+00  0.00000000e+00]\n",
      "(1, 3, 0, 0): [-8.88618620e-06 -4.33921249e-06  0.00000000e+00  0.00000000e+00]\n",
      "(1, 2, 1, 0): [ 0.00000000e+00 -0.00000000e+00 -2.81910856e-06  4.44963121e-05]\n",
      "(1, 2, 0, 1): [ 0.00000000e+00 -0.00000000e+00  4.47107608e-05  6.22767407e-06]\n",
      "(1, 1, 2, 0): [-5.02653030e-08 -6.69892371e-06  0.00000000e+00  0.00000000e+00]\n",
      "(1, 1, 1, 1): [2.90625131e-06 1.04277202e-04 0.00000000e+00 0.00000000e+00]\n",
      "(1, 1, 0, 2): [-2.28808176e-05  2.60346923e-05  0.00000000e+00  0.00000000e+00]\n",
      "(1, 0, 3, 0): [ 0.00000000e+00 -0.00000000e+00  2.09236592e-09  3.51323891e-08]\n",
      "(1, 0, 2, 1): [ 0.00000000e+00 -0.00000000e+00 -6.41657941e-09 -1.78350571e-06]\n",
      "(1, 0, 1, 2): [ 0.00000000e+00 -0.00000000e+00 -6.10954936e-07  1.86435774e-05]\n",
      "(1, 0, 0, 3): [ 0.00000000e+00 -0.00000000e+00  3.05503037e-06 -5.00150901e-05]\n",
      "(0, 4, 0, 0): [3.33982092e-06 8.88114110e-06 0.00000000e+00 0.00000000e+00]\n",
      "(0, 3, 1, 0): [ 0.00000000e+00 -0.00000000e+00  1.37553970e-06 -2.36217768e-05]\n",
      "(0, 3, 0, 1): [ 0.00000000e+00 -0.00000000e+00 -2.24184174e-05  1.77513110e-05]\n",
      "(0, 2, 2, 0): [3.54703897e-08 5.11986525e-06 0.00000000e+00 0.00000000e+00]\n",
      "(0, 2, 1, 1): [-2.15414781e-06 -8.31702815e-05  0.00000000e+00  0.00000000e+00]\n",
      "(0, 2, 0, 2): [1.72952174e-05 1.78791226e-05 0.00000000e+00 0.00000000e+00]\n",
      "(0, 1, 3, 0): [ 0.00000000e+00 -0.00000000e+00 -3.32576455e-09 -2.16775859e-08]\n",
      "(0, 1, 2, 1): [ 0.00000000e+00 -0.00000000e+00  1.73891518e-08  1.32003603e-06]\n",
      "(0, 1, 1, 2): [ 0.00000000e+00 -0.00000000e+00  8.58583303e-07 -7.35197022e-06]\n",
      "(0, 1, 0, 3): [ 0.00000000e+00 -0.00000000e+00 -4.60205741e-06 -3.44817289e-05]\n",
      "(0, 0, 4, 0): [-2.95410616e-10 -3.91436795e-10  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 3, 1): [ 1.72261161e-08 -3.76703368e-08  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 2, 2): [-3.76632244e-07  1.04173914e-06  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 1, 3): [ 3.81179356e-06 -5.44741177e-06  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 0, 4): [-1.51552013e-05 -3.46854944e-07  0.00000000e+00  0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute derivative table\n",
    "\n",
    "n = 4\n",
    "t = derivative(n, ring, x)\n",
    "\n",
    "# Compute and print series\n",
    "\n",
    "s = clean(series((4, ), (n, ), t), epsilon=1.0E-12)\n",
    "print(*[f'{key}: {value.cpu().numpy()}' for key, value in clean(s, epsilon=1.0E-14).items()], sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "158a2a1c-205a-4b9a-ba3f-f65384aa11dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare phase space trajectories\n",
    "# Note, change order to observe convergence\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Direct tracking\n",
    "\n",
    "x = torch.linspace(0.0, 5.0, 10, dtype=dtype, device=device)\n",
    "x = torch.stack([x, *3*[torch.zeros_like(x)]]).T\n",
    "\n",
    "count = 512\n",
    "table = []\n",
    "\n",
    "for _ in range(count):\n",
    "    table.append(x)\n",
    "    x = torch.func.vmap(lambda x: ring(x))(x)\n",
    "\n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qx, px, *_ = table\n",
    "\n",
    "for q, p in zip(qx.cpu().numpy(), px.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "\n",
    "# Table tracking\n",
    "# Note, table representation is not symplectic\n",
    "    \n",
    "x = torch.linspace(0.0, 5.0, 10, dtype=dtype, device=device)\n",
    "x = torch.stack([x, *3*[torch.zeros_like(x)]]).T\n",
    "\n",
    "count = 512\n",
    "table = []\n",
    "\n",
    "for _ in range(count):\n",
    "    table.append(x)\n",
    "    x = torch.func.vmap(lambda x: evaluate(t, [x]))(x)\n",
    "\n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qx, px, *_ = table\n",
    "\n",
    "for q, p in zip(qx.cpu().numpy(), px.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='red', marker='x', s=1)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1df68d-5e62-4ea7-956a-1651dd0da0ff",
   "metadata": {},
   "source": [
    "# Example-06: Fixed point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b89ca78-cefd-4b1e-b905-af489b00080f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# In this example fixed points are computed for a simple symplectic nonlinear transformation\n",
    "# Fixed point are computed with Newton root search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac55d6e7-54ef-415b-85cf-e1cd99de4049",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import clean_point\n",
    "from ndmap.pfp import chain_point\n",
    "from ndmap.pfp import matrix\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51a2cb6c-c22d-43ab-9efc-86965d9ac6ef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a53b360-e546-40f5-bc71-73d83fd235d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set forward & inverse mappings\n",
    "\n",
    "mu = 2.0*numpy.pi*torch.tensor(1/3 - 0.01, dtype=dtype)\n",
    "kq, ks, ko = torch.tensor([0.0, 0.25, -0.25], dtype=dtype)\n",
    "\n",
    "def forward(x):\n",
    "    q, p = x\n",
    "    q, p = q*mu.cos() + p*mu.sin(), p*mu.cos() - q*mu.sin()\n",
    "    q, p = q, p + (kq*q + ks*q**2 + ko*q**3)\n",
    "    return torch.stack([q, p])\n",
    "\n",
    "def inverse(x):\n",
    "    q, p = x\n",
    "    q, p = q, p - (kq*q + ks*q**2 + ko*q**3)\n",
    "    q, p = q*mu.cos() - p*mu.sin(), p*mu.cos() + q*mu.sin()\n",
    "    return torch.stack([q, p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "152f6d49-70fb-47f5-a9f3-ca806ce88259",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute period three fixed points\n",
    "\n",
    "# Set fixed point period\n",
    "\n",
    "period = 3\n",
    "\n",
    "# Set tolerance epsilon\n",
    "\n",
    "epsilon = 1.0E-12\n",
    "\n",
    "# Set random initial points\n",
    "\n",
    "points = 4.0*torch.rand((128, 2), dtype=dtype, device=device) - 2.0\n",
    "\n",
    "# Perform 512 root search iterations for each initial point\n",
    "\n",
    "points = torch.func.vmap(lambda point: fixed_point(512, forward, point, power=period))(points)\n",
    "\n",
    "# Clean points (remove nans, duplicates, points from the same chain)\n",
    "\n",
    "points = clean_point(period, forward, points, epsilon=epsilon)\n",
    "\n",
    "# Generate fixed point chains\n",
    "\n",
    "chains = torch.func.vmap(lambda point: chain_point(period, forward, point))(points)\n",
    "\n",
    "# Classify fixed point chains (elliptic vs hyperbolic)\n",
    "# Generate initials for hyperbolic fixed points using corresponding eigenvectors\n",
    "\n",
    "kinds = []\n",
    "for chain in chains:\n",
    "    point, *_ = chain\n",
    "    values, vectors = torch.linalg.eig(matrix(period, forward, point))\n",
    "    kind = all(values.log().real < epsilon)\n",
    "    kinds.append(kind)\n",
    "    if not kind:\n",
    "        lines = [point + vector*torch.linspace(-epsilon, +epsilon, 1024, dtype=dtype).reshape(-1, 1) for vector in vectors.real.T]\n",
    "        lines = torch.stack(lines)\n",
    "        \n",
    "# Plot phase space\n",
    "\n",
    "x = torch.linspace(0.0, 1.5, 21, dtype=dtype, device=device)\n",
    "x = torch.stack([x, torch.zeros_like(x)]).T\n",
    "\n",
    "count = 1024\n",
    "table = []\n",
    "\n",
    "for _ in range(count):\n",
    "    table.append(x)\n",
    "    x = torch.func.vmap(lambda x: forward(x))(x)\n",
    "    \n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qs, ps = table\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.xlim(-2.0, 2.0)\n",
    "plt.ylim(-2.0, 2.0)\n",
    "\n",
    "for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "    \n",
    "# Plot (approximated) stable and unstable  manifolds of hyperbolic fixed points\n",
    "\n",
    "count = 310\n",
    "\n",
    "for line in lines:\n",
    "    \n",
    "    x = torch.clone(line)\n",
    "    table = []\n",
    "    for _ in range(count):\n",
    "        table.append(x)\n",
    "        x = torch.func.vmap(lambda x: forward(x))(x)\n",
    "    table = torch.stack(table).swapaxes(0, -1)\n",
    "    qs, ps = table\n",
    "    for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "        plt.scatter(q, p, color='gray', marker='o', s=1)\n",
    "        \n",
    "    x = torch.clone(line)\n",
    "    table = []\n",
    "    for _ in range(count):\n",
    "        table.append(x)\n",
    "        x = torch.func.vmap(lambda x: inverse(x))(x)\n",
    "    table = torch.stack(table).swapaxes(0, -1)\n",
    "    qs, ps = table\n",
    "    for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "        plt.scatter(q, p, color='gray', marker='o', s=1)\n",
    "        \n",
    "# Plot chains\n",
    "\n",
    "for chain, kind in zip(chains, kinds):\n",
    "    plt.scatter(*chain.T, color = {True:'blue', False:'red'}[kind], marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97ea376a-dab1-489e-a7b1-35de11625bee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0.], dtype=torch.float64)\n",
      "tensor([-1.110223024625e-16,  0.000000000000e+00], dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set mapping around elliptic fixed point\n",
    "\n",
    "point, *_ = chains[kinds].squeeze()\n",
    "\n",
    "def mapping(x):\n",
    "    x = x + point\n",
    "    for _ in range(period):\n",
    "        x = forward(x)\n",
    "    x = x - point\n",
    "    return x\n",
    "\n",
    "# Test mapping\n",
    "\n",
    "x = torch.zeros_like(point)\n",
    "print(x)\n",
    "print(mapping(x))\n",
    "\n",
    "# Plot phase space\n",
    "\n",
    "x = torch.linspace(0.0, 1.5, 21, dtype=dtype, device=device)\n",
    "x = torch.stack([x, torch.zeros_like(x)]).T\n",
    "\n",
    "count = 1024\n",
    "table = []\n",
    "\n",
    "for _ in range(count):\n",
    "    table.append(x)\n",
    "    x = torch.func.vmap(lambda x: forward(x))(x)\n",
    "    \n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qs, ps = table\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.xlim(-2.0, 2.0)\n",
    "plt.ylim(-2.0, 2.0)\n",
    "\n",
    "for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "    \n",
    "x = torch.linspace(0.0, 0.5, 11, dtype=dtype, device=device)\n",
    "x = torch.stack([x, torch.zeros_like(x)]).T\n",
    "\n",
    "count = 1024\n",
    "table = []\n",
    "\n",
    "for _ in range(count):\n",
    "    table.append(x)\n",
    "    x = torch.func.vmap(lambda x: mapping(x))(x)\n",
    "    \n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qs, ps = table + point.reshape(2, 1, 1)\n",
    "\n",
    "for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='red', marker='o', s=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a424d95-66a0-4e48-8ca3-5c8764ed167e",
   "metadata": {},
   "source": [
    "# Example-07: Parametric fixed point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d8fa436-fe42-4982-9ec9-e0c55af3ac19",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Given a mapping depending on a set of knobs (parameters), parametric fixed points can be computed (position of a fixed point as function of parameters)\n",
    "# Parametric fixed points can be used to construct responce matrices, e.g. closed orbit responce\n",
    "# In this case only first order derivatives of the fixed point(s) with respect to parameters are computed\n",
    "# Or higher order expansions can be computed\n",
    "# In this example parametric fixed points of a symplectic mapping are computed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ef16bef-a11b-4aa6-99e4-9531794f432c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import flatten\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import clean_point\n",
    "from ndmap.pfp import chain_point\n",
    "from ndmap.pfp import matrix\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f4fb2a4-b3f7-4bbd-8807-cb89395a82b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3bf7f13-dd2a-4dfc-b980-22310373508e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set mapping\n",
    "\n",
    "def mapping(x, k):\n",
    "    q, p = x\n",
    "    a, b = k\n",
    "    q, p = q*mu.cos() + p*mu.sin(), p*mu.cos() - q*mu.sin()\n",
    "    return torch.stack([q, p + a*q**2 + b*q**3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73d6a75f-56fc-4444-b0cf-ab7c16ccaec6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute dynamical fixed points\n",
    "# Note, fixed point might fail due to escape to large values\n",
    "\n",
    "# Set parameters\n",
    "\n",
    "mu = 2.0*numpy.pi*torch.tensor(1/5 - 0.01, dtype=dtype, device=device)\n",
    "k = torch.tensor([0.25, -0.25], dtype=dtype, device=device)\n",
    "\n",
    "# Compute and plot phase space trajectories\n",
    "\n",
    "x = torch.linspace(0.0, 1.5, 21, dtype=dtype)\n",
    "x = torch.stack([x, torch.zeros_like(x)]).T\n",
    "\n",
    "count = 1024\n",
    "table = []\n",
    "for _ in range(count):\n",
    "    table.append(x)\n",
    "    x = torch.func.vmap(lambda x: mapping(x, k))(x)\n",
    "    \n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qs, ps = table\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.xlim(-2.0, 2.0)\n",
    "plt.ylim(-2.0, 2.0)\n",
    "for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "    \n",
    "# Set tolerance epsilon\n",
    "    \n",
    "epsilon = 1.0E-12\n",
    "\n",
    "# Compute chains\n",
    "    \n",
    "period = 5\n",
    "points = torch.rand((32, 2), dtype=dtype, device=device)\n",
    "points = torch.func.vmap(lambda point: fixed_point(16, mapping, point, k, power=period))(points)\n",
    "points = clean_point(period, mapping, points, k, epsilon=epsilon)\n",
    "chains = torch.func.vmap(lambda point: chain_point(period, mapping, point, k))(points)\n",
    "\n",
    "# Plot chains\n",
    "\n",
    "for chain in chains:\n",
    "    point, *_ = chain\n",
    "    value, vector = torch.linalg.eig(matrix(period, mapping, point, k))\n",
    "    color = 'blue' if all(value.log().real < epsilon) else 'red'\n",
    "    plt.scatter(*chain.T, color=color, marker='o')    \n",
    "    if color == 'blue':\n",
    "        ep, *_ = chain\n",
    "    else:\n",
    "        hp, *_ = chain\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19a14798-4063-48ac-82cc-4bf4cd06ec1d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute hyperbolic fixed point for a set of knobs\n",
    "\n",
    "dks = torch.stack(2*[torch.linspace(0.0, 0.01, 101, dtype=dtype, device=device)]).T\n",
    "\n",
    "fps = [hp]\n",
    "for dk in dks:\n",
    "    *_, initial = fps\n",
    "    fps.append(fixed_point(16, mapping, initial, k + dk, power=period))\n",
    "    \n",
    "fps = torch.stack(fps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20e11212-9d78-4682-ad89-232da39c51cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "# Set computation order\n",
    "# Note, change order to observe convergence\n",
    "\n",
    "order = 4\n",
    "pfp = parametric_fixed_point((order, ), hp, [k], mapping, power=period)\n",
    "\n",
    "# Set period mapping and check fixed point propagation\n",
    "\n",
    "def function(x, k):\n",
    "    for _ in range(period):\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "out = propagate((2, 2), (0, order), pfp, [k], function)\n",
    "for x, y in zip(flatten(pfp, target=list), flatten(out, target=list)):\n",
    "    print(torch.allclose(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d2404c9f-d996-4469-86c3-5244d1189320",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABI0AAAEvCAYAAADIJDF2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAu3ElEQVR4nO3db5Bl910e+OfbEmLDX8mxDUbqmdGyzvYIcBncK8wmyzqmrOmhdmUK54WdLEgBI5as4pkmpJCXStyRofizhh5RcZHSOmJNqtYyuNhiHBgpqhglW2XkqEVsC3natnBJI4lUMVgoCaHWRvRvX9zTozvdrb7nztyevt39+VR1uc+555w+t+zrbv30fJ9TrbUAAAAAwLCZ3b4BAAAAAKaPRSMAAAAANrFoBAAAAMAmFo0AAAAA2MSiEQAAAACbWDQCAAAAYJOrd/sGxvHKV76yHTlyZLdvAwAAAGDfeOyxx/6ktfaqjfv31KLRkSNHsrKystu3AQAAALBvVNXTW+03ngYAAADAJhaNAAAAANjEohEAAAAAm1g0AgAAAGATi0YAAAAAbGLRCAAAAIBNLBoBAAAA9NXa9tv7iEUjAAAAgD6WlrJ6fDFHDrfMzCRHDresHl9MlpZ2+852hEUjAAAAgFFay+ojL2TuwXty8txiWms5eW4xcw/ek9VHXtiXiaOrd/sGAAAAAHZVa0nVy28nSVUWzi7nZJKTuScnc0+S5FRO5NTZ5Ty18fh9QNIIAAAAOLjGGDk790xlMcsX7VvMcs49s/8WjBKLRgAAAMBBNebI2aHZluUsXrRvOYs5NLv/RtMSi0YAAADAfjXqSWfdyNmpnMjJ3JOWmZzMPTmVE1k4u7xpZO2Bo4sXXq+sXTjvgaOL+7LTqNeiUVUtVNVnq+rJqrpri9dvr6rzVfXJ7uudQ689UFUvVNW/3HDOjVX1ie6aH66qay7/7QAAAACk99hZ75Gzqsy98dqsHjuRU4eWU1U5dWg5q8dOZO6N127uQNoHRhZhV9VVSd6f5C1Jnk3yaFWdbq19ZsOhH26t3bnFJf6PJF+V5Ec37P/5JMuttfur6p8l+eEkvzLuGwAAAAAOkD6l1cNjZxksAp08t5i5c/dkNScyN3TOodnBSNqw5Szm1Oxykg3XXVrKXGtDpdeVtOV9uWCU9Esa3ZzkydbaF1prX05yf5K39v0BrbV/neQ/D++rqkry5iQf6XZ9MMn39b0mAAAAcAD1La3uO3Z2KSNnWzxVbb/qs2h0fZJnhraf7fZt9Laq+nRVfaSqZkdc868meaG19uKIawIAAACMXVrda+zsAI6cjWPkeFpPH03yodbal6rqRzNIDr15EheuqjuS3JEkhw4dmsQlAQAAgGkzauysSw+dTHIy9+Rk7kmSnMqJnDq7PDQyNtB77OyAjZyNo0/S6Lkkw8mhG7p9F7TWvtha+1K3+YEkbxhxzS8mubaq1hetNl1z6Nr3ttbmW2vzr3rVq3rcLgAAALCnTLq0etyxswM0cjaOPotGjyZ5bfe0s2uSvD3J6eEDquo1Q5u3Jjm73QVbay3J7yb5W92u25L8Vt+bBgAAAPaAUY+87/b1HTs7NNuynM3poUOzmxeBjJ1dvmpb/Re28aCq701yKslVSe5rrf1MVd2dZKW1drqqfjaDxaIXkzyf5Mdaa6vduf9vkrkkX5NBwuiHW2sPVtV/nUGp9iuS/Psk/8tQWmlL8/PzbWVl5dLeKQAAAHDlLC1l9ZEXsnB2kAQ6NDtI/8y98dpNCaIjhwcLResjZ0k3dnZoOU89/VJp9erxwWLSqZzIYpaznME5q8dOZO7MFiNlfZ60Rqrqsdba/Kb9fRaNpoVFIwAAANgDxlzgmZlJWmtpQwNRlbVUVdbWhq47xkIU/b3cotGkirABAACAg6BPekdp9b7Qp9MIAAAAoHdhdaK0ej+waAQAAACMLq0eo7A6UVq9HxhPAwAAgIOuT1fQOCNnXXpo7tzmTqOFo9k8UmbsbCpJGgEAAMBBNkaCqPfI2aWkh4ydTR1JIwAAANivJlxa3buwOpEe2gckjQAAAGA/mnRp9biF1Yn00B5n0QgAAAD2m50orVZYfeAYTwMAAIC9ZtTY2U6VVhs5O1AkjQAAAGAv6Tl2tmOl1UbODgxJIwAAAJgGfUqrh8fOMlgEOnlukBJazYnMDZ2jtJrLJWkEAAAAu61vaXU3drZeQN0yc6GYeuHs0CKP0momwKIRAAAA7KYxS6t7jZ0prWYCjKcBAADATppkaXXGGDszcsZlkjQCAACAnTLp0upxx86MnHEZLBoBAADAuDYuzmzVETTG2Nmh2ZblbE4PHZrdvAhk7IwrxXgaAAAAjGNpKauPvJCFs4Mk0KHZQfpn7o3XXpwg6jt21qWH5s4N0kOLWc5yBmmihaPZPFJm7IwrRNIIAAAA+pqW0mpjZ1wBkkYAAACwTmk1XCBpBAAAAInSatjAohEAAAD7m9JquCTG0wAAANi/lFbDJZM0AgAAYH9SWg2XpdeiUVUtVNVnq+rJqrpri9dvr6rzVfXJ7uudQ6/dVlWf775uG9r/jqp6vKo+XVUPVNUrJ/OWAAAAOBBGjZ116aH1HqGWmQv9QgtnN6d9eo+dLS1l7sxynnq6sraWPPV0Ze7M8qbuI9jrRi4aVdVVSd6f5HiSm5K8o6pu2uLQD7fWXt99faA79xVJ3pPkO5PcnOQ9VXVdVV2d5J4kf7O19rokn05y50TeEQAAAPuf0mrYcX2SRjcnebK19oXW2peT3J/krT2vfyzJQ62151trf5rkoSQLGTxXsJJ8dVVVkq9L8kdj3z0AAAD7i9JqmBp9irCvT/LM0PazGSSHNnpbVX13ks8lWWytPfMy517fWvuLqvqxJI8n+S9JPp/kf7uE+wcAAGC/UFoNU2VSRdgfTXKkGzV7KMkHtzu4qr4iyY8l+fYk35TBeNq7X+bYO6pqpapWzp8/P6HbBQAAYKoorYap0ydp9FyS2aHtG7p9F7TWvji0+YEkvzB07ps2nPtwktd35/1hklTVryfZVLDdHXNvknuTZH5+fotcIgAAAFOvtYsXYDZu900PdQ7NDhaVhi1nMadmlzNoQ+lID8El65M0ejTJa6vqxqq6Jsnbk5wePqCqXjO0eWuSs933Dya5pSu/vi7JLd2+55LcVFWv6o57y9A5AAAA7CdKq2FPGpk0aq29WFV3ZrDYc1WS+1prT1TV3UlWWmunk7yrqm5N8mKS55Pc3p37fFW9N4OFpyS5u7X2fJJU1T9J8m+r6i+SPL1+DgAAAPvI8NhZBotAJ88NeoZWcyJzQ4mj3umh9bGzLoVUz1ROzS5n4WiUVsMEVduqiX5Kzc/Pt5WVld2+DQAAAJLRI2edI4cHi0HrI2dJN3Z2aDlPPf1SafXq8UGH0cbS6tVjJzJ3ZouRsp4/H9heVT3WWpvfuH9SRdgAAAAcJD1HzhKl1bBX9SnCBgAA4CAZleAZY+QsUVoNe5WkEQAAAC/pkyDqnnS2Xj7dMnOhlHrh7PKmBSal1bA3WTQCAABgYDhBdG4xrQ0SQnMP3pPVR164aIGn95POLmXsDJgKxtMAAAAOgj6l0V2C6GSSk7nnQnH1qe4pZU9dyshZYuwM9ihJIwAAgP1u0qXV446cJcbOYA+yaAQAALCXbVyg2WK778hZMkgQLWdzgujQ7NBxRs7gQDCeBgAAsFctLWX1kReycHaQBDo0O0gAzb3x2pdSRGOMnK0niObODRJEi1nOcgaJooWjuXikzMgZ7HuSRgAAAHvRNJRWGzmDfU3SCAAAYBqNKq5WWg3sMEkjAACAadOzuFppNbCTLBoBAABcKaNKq7t9fcfOlFYDO8l4GgAAwJXQp7Q66T92prQa2GGSRgAAADttzMfe9xo7U1oN7DBJIwAAgMs1wdLqZIziagkiYAdJGgEAAFyOSZZWJ+MXV0sQATvEohEAAMBWdqO0OlFcDUwN42kAAAAb7WZpdffzjZ0Bu03SCAAAYNg0lFZ352y7DbDDJI0AAICDRWk1QC+SRgAAwMGhtBqgN4tGAADA/jCquFppNcBYei0aVdVCVX22qp6sqru2eP32qjpfVZ/svt459NptVfX57uu2of3XVNW9VfW5qlqtqrdN5i0BAAAHTp8EUTd2tp4Eapm5kBBaODs0JjZuemhpKXNnlvPU05W1teSppytzZ5Y3pZcA9pqRnUZVdVWS9yd5S5JnkzxaVadba5/ZcOiHW2t3bjj3FUnek2Q+SUvyWHfunyb5qSR/3Fr7a1U1k+QVl/92AACAA2c4QZTBGNnJc4Mnla3mROaGOovWx87We4rSHV9blVZ3HUb1TOXU7HIWjkZpNXCg9CnCvjnJk621LyRJVd2f5K1JNi4abeVYkodaa8935z6UZCHJh5L8UJK5JGmtrSX5k7HvHgAA2N9GlVYnYxVXK60G6K/PeNr1SZ4Z2n6227fR26rq01X1kaqa3e7cqrq2235vVf1+Vf1GVX3DmPcOAADsZz1Lq5OexdVKqwHGMqki7I8mOdJae12Sh5J8cMTxVye5IcnHW2vfkeT3krxvqwOr6o6qWqmqlfPnz0/odgEAgF01wdLqpGdxtdJqgLH0GU97Lsns0PYN3b4LWmtfHNr8QJJfGDr3TRvOfTjJF5P8eZLf7Pb/RpIf3uqHt9buTXJvkszPz7etjgEAAPaQpaWsPvJCFs4OkkCHZgcJoLk3XvtSimiMkbP1BNHcuUGCaDHLWc4gUbRwNBePlRk7A+itT9Lo0SSvraobq+qaJG9Pcnr4gKp6zdDmrUnOdt8/mOSWqrquqq5LckuSB1trLYN00pu6474n/TqSAACAvWyMBFGvkbNk/ASRsTOAXkYmjVprL1bVnRksAF2V5L7W2hNVdXeSldba6STvqqpbk7yY5Pkkt3fnPl9V781g4SlJ7l4vxU7yk0n+RVWdSnI+yd+d3NsCAAB2xaji6p0orU4kiAB2QLWNs8NTbH5+vq2srOz2bQAAAFvpM3aWZGYmaa2lDQ0+VNZSVVlb63a0Qen13IObR85Wj53I3BkLQgCTUlWPtdbmN+6fVBE2AACwX40qre729R07U1oNsDf0KcIGAAAOqp7pod5jZ0qrAfYMSSMAAGBrYz72vldxtdJqgD1D0ggAAA6qCZZWJ2MUV0sQAewJkkYAAHAQLS1l9fhijhxumZlJjhweFE9fNHKWMR57342dncxg7KyyllM5kZO5Jw8cXdzcgyRBBDD1LBoBAMB+M6q4etKl1YniaoB9yHgaAADsJ32Kq3eitLr72cbOAPYPSSMAANgvxkgQ7UhpdXfOttsA7BmSRgAAsBeMKq1OxiquVloNwCiSRgAAMO16llYnPRNESqsB6MGiEQAA7KYJllYnPYurlVYD0IPxNAAA2C2TLK1OxiuuNnYGwAiSRgAAsBsmXVqdjJ8gMnYGwDYkjQAAYCeMKq7eidLqRIIIgImRNAIAgEnrWVy9I6XViQQRABNh0QgAAPoaVVrd7es7dqa0GoBpZjwNAAD66FNanfQfO1NaDcCUkzQCAIBRxnzsfa+xM6XVAEw5SSMAAJhgaXUyRnG1BBEAU0zSCACAg22SpdXJ+MXVEkQATCmLRgAA7F+jiqsnXVqdKK4GYN8wngYAwP7Up7h6J0qru59t7AyAvU7SCACA/WeMBNGOlFZ352y7DQBTrteiUVUtVNVnq+rJqrpri9dvr6rzVfXJ7uudQ6/dVlWf775u2+Lc01X1B5f3NgAAOFBGjZ11CaL1LqGWmQsdQwtnL0789B47W1rK3JnlPPV0ZW0teerpytyZ5U3dRwCwX4xcNKqqq5K8P8nxJDcleUdV3bTFoR9urb2++/pAd+4rkrwnyXcmuTnJe6rquqFrf3+SP7v8twEAwIExyeJqpdUA8LL6JI1uTvJka+0LrbUvJ7k/yVt7Xv9Ykodaa8+31v40yUNJFpKkqr4myY8n+enxbxsAgH1nVHqo2zfR4mql1QDwsvoUYV+f5Jmh7WczSA5t9Laq+u4kn0uy2Fp75mXOvb77/r1JfjHJn4970wAA7DN9SquTnSmuVloNAFuaVBH2R5Mcaa29LoM00Qe3O7iqXp/km1tr/8+oC1fVHVW1UlUr58+fn8jNAgAwRcZIDyU7VFxt7AwANumTNHouyezQ9g3dvgtaa18c2vxAkl8YOvdNG859OMl3JZmvqqe6e3h1VT3cWhs+dv3a9ya5N0nm5+e3yCgDADDVWrt4EWbjdt/0UOfQ7GBRadhyFnNqdjmJx94DwKT0SRo9muS1VXVjVV2T5O1JTg8fUFWvGdq8NcnZ7vsHk9xSVdd1Bdi3JHmwtfYrrbVvaq0dSfI3knxuqwUjAAD2uEmWVieKqwHgChq5aNRaezHJnRksAJ1N8uuttSeq6u6qurU77F1V9URVfSrJu5Lc3p37fAbdRY92X3d3+wAA2OtGFVdPurQ6UVwNAFdQta2eSjGl5ufn28rKym7fBgAAPYurjxweLBStj5wl3djZoeU89fRLpdWrxweLSRtLq1ePncjcmS1GykaNvAEAvVXVY621+Y37J1WEDQDAQTFGgmhHSqu7c7bdBgAuW58ibAAADpIJFlcrrQaAvUvSCACAl0yyuFppNQDsaRaNAAAOiitdXK20GgD2NONpAAAHQZ/i6r5jZ12CaO7c5uLqhaO5eKzM2BkA7FmSRgAA+91uF1cbOwOAPUnSCABgL+vz6HnF1QDAJZA0AgDYq3qWVieKqwGA8Vk0AgCYRhMsrU4UVwMA4zOeBgAwbSZZWp0orgYALomkEQDANJl0aXWiuBoAuCSSRgAAV9Ko4uqdKK1OJIgAgLFJGgEAXCk9i6t3pLQ6kSACAMZi0QgAYBImWFyttBoAmAbG0wAALtcki6uVVgMAU0LSCADgcky6uFppNQAwJSSNAAC2sxvF1RJEAMAUkDQCAHg5u1lcLUEEAOwyi0YAwMGkuBoAYFvG0wCAg0dxNQDASJJGAMDBorgaAKAXSSMAYH9RXA0AMBG9kkZVtVBVn62qJ6vqri1ev72qzlfVJ7uvdw69dltVfb77uq3b91VV9dtVtVpVT1TVz03uLQEAB5biagCAiRm5aFRVVyV5f5LjSW5K8o6qummLQz/cWnt99/WB7txXJHlPku9McnOS91TVdd3x72utzSX59iR/vaqOX/7bAQD2pVGl1d0+xdUAAJPTZzzt5iRPtta+kCRVdX+Styb5TI9zjyV5qLX2fHfuQ0kWWmsfSvK7SdJa+3JV/X6SGy7h/gGA/a5PaXWiuBoAYML6jKddn+SZoe1nu30bva2qPl1VH6mq2b7nVtW1Sf7nJP+6700DAAfEGOmhRHE1AMAkTaoI+6NJPtRa+1JV/WiSDyZ586iTqurqJB9K8svrSaYtjrkjyR1JcujQoQndLgAwFSZYWp0orgYAmKQ+SaPnkswObd/Q7bugtfbF1tqXus0PJHlDz3PvTfL51tqpl/vhrbV7W2vzrbX5V73qVT1uFwDYEyZZWp0orgYAmLA+i0aPJnltVd1YVdckeXuS08MHVNVrhjZvTXK2+/7BJLdU1XVdAfYt3b5U1U8n+fokJy/rHQAA02dUcfWkS6sTxdUAABM2cjyttfZiVd2ZwWLPVUnua609UVV3J1lprZ1O8q6qujXJi0meT3J7d+7zVfXeDBaekuTubt8NSX4qyWqS36/BH3H/dP2pawDAHtanuHonSqu7n23sDABgMqpt9cjaKTU/P99WVlZ2+zYAgJfTBiNmcw9uXuRZPXYic2deWsCZmUlaa2lDwefKWqoqa2tD1+z79DQAAC5JVT3WWpvfuH9SRdgAwEEwweJqpdUAANOtT6cRAMBki6uVVgMATD2LRgDAlS+uVloNADD1jKcBwEG3W8XVxs4AAKaapBEA7GcTTBD1GjsbN0Fk7AwAYGpJGgHAfjXJBFEUVwMAHDSSRgCwH006QaS4GgDgwLFoBAB70aixsy5BtL6w0zJzYcFn4ezFqR/F1QAAbKXaxj8yp9j8/HxbWVnZ7dsAgN3VZ+wsycxM0lpLG/p3RJW1VFXW1rodrWX1+CCBtLG4evXYicyd2TBW1tr22wAA7DlV9VhrbX7jfkkjAJgmEyyu3pEEkbEzAIADQxE2AEyLSRZXdx1Ec+c2J4gWjubiYmrF1QAAbEHSCACmwaSLqyWIAAC4TJJGAHAljOoC6psgymDs7OS5zWNnp2aXkwxdU4IIAIDLIGkEADttaSmrxxdz5HDLzExy5PCgfHq4tDrpmSDqxs7Wn4RWWbvwhLQHji5u+RS1bbcBAOBlWDQCgMsx7cXVAABwiYynAcClUlwNAMA+JmkEAJdCcTUAAPucpBEAbEVxNQAAB5ykEQBspLgaAAAsGgFwwCiuBgCAXoynAXBwKK4GAIDeJI0AOBgUVwMAwFgkjQDYHxRXAwDARPVKGlXVQlV9tqqerKq7tnj99qo6X1Wf7L7eOfTabVX1+e7rtqH9b6iqx7tr/nKVv7ABuESKqwEAYOJGLhpV1VVJ3p/keJKbkryjqm7a4tAPt9Ze3319oDv3FUnek+Q7k9yc5D1VdV13/K8k+ZEkr+2+Fi73zQCwDymuBgCAXdFnPO3mJE+21r6QJFV1f5K3JvlMj3OPJXmotfZ8d+5DSRaq6uEkX9dae6Tb/2tJvi/JmXHfAAD7mOJqAADYNX3G065P8szQ9rPdvo3eVlWfrqqPVNXsiHOv774fdc1U1R1VtVJVK+fPn+9xuwDsCRNMECmuBgCAyZtUEfZHk3yotfalqvrRJB9M8uZJXLi1dm+Se5Nkfn6+jTgcgL1gkgmiKK4GAICd0Cdp9FyS2aHtG7p9F7TWvtha+1K3+YEkbxhx7nPd9y97TQD2qUkniBRXAwDAjuizaPRoktdW1Y1VdU2Styc5PXxAVb1maPPWJGe77x9McktVXdcVYN+S5MHW2n9I8p+q6o3dU9N+MMlvXeZ7AWAajBo76xJE6ws7LTMXFnwWzl6c+lFcDQAAu6faxj/mtzqo6nuTnEpyVZL7Wms/U1V3J1lprZ2uqp/NYLHoxSTPJ/mx1tpqd+4PJfnfu0v9TGvtV7v980n+ryR/JYMC7L/fRtzM/Px8W1lZGftNAnCF9Bk7SzIzk7TW0ob+3UVlLVWVtbVuR2tZPT5IIG0srl49diJzZzaMlbW2/TYAALClqnqstTa/cX+vTqPW2u8k+Z0N+/7x0PfvTvLulzn3viT3bbF/Jcm39vn5AEyBUYsyw2NnGYyRnTw3eFrZak5kbuj4Xh1E6wmirsOonqmcml3OwtEorgYAgCtgUkXYAOxnkyyu7jqI5s5tThAtHM3FxdSKqwEAYNf06TQC4CCbdHH1uB1EEkQAALArJI0ADrpRY2d9E0TpOXaWSBABAMAeIGkEcJAtLWX1+GKOHG6ZmUmOHB6UTw+XVic9E0Td2Nn6k9AqaxeekPbA0cUtn6K27TYAALCrLBoB7FcbF2m22O47dnZotmU5mxNEh2aHrjnu2BkAADDVjKcB7EeKqwEAgMskaQSw3yiuBgAAJkDSCGCvUVwNAABcAZJGAHuJ4moAAOAKsWgEMC0UVwMAAFPEeBrANFBcDQAATBlJI4CdNsEEkeJqAADgSpE0AthJk0wQRXE1AABw5UgaAeyUSSeIFFcDAABXkEUjgEs1auysSxCtL+y0zFxY8Fk4e3HqR3E1AAAwbapt/IecKTY/P99WVlZ2+zYA+o2dJZmZSVpraUNr9JW1VFXW1rodrWX1+CCBtLG4evXYicyd2TBW1tr22wAAAGOoqsdaa/Mb90saAWw0weLqHUkQGTsDAACuAEXYAMMmWVzddRDNnducIFo4mouLqRVXAwAAU0bSCGDdpIurJYgAAIA9TNIIODhGdQH1TRBlMHZ28tzmsbNTs8tJhq4pQQQAAOxRkkbAwbC0lNXjizlyuGVmJjlyeFA+PVxanfRMEHVjZ+tPQqusXXhC2gNHF7d8itq22wAAAFPIohGwP2xXXr3bxdUAAAB7UK9Fo6paqKrPVtWTVXXXNse9rapaVc1329dU1a9W1eNV9amqetPQse/o9n+6qh6oqlde7psBDqhRKaJu7Gw9DdQycyEltHB2aFRsnATR0lLmziznqacra2vJU09X5s4sb0ouAQAA7FUjF42q6qok709yPMlNSd5RVTdtcdzXJjmR5BNDu38kSVpr35bkLUl+sapmqurqJPck+Zuttdcl+XSSOy/zvQD70XYJom67T4pIcTUAAMB4+hRh35zkydbaF5Kkqu5P8tYkn9lw3HuT/HySfzi076YkH0uS1tofV9ULSeaT/PsMmmK/uqq+mOTrkjx56W8D2JeWlrL6yAtZODtY3Dk02z3C/o3XbkoRncz25dWKqwEAAMbTZzzt+iTPDG0/2+27oKq+I8lsa+23N5z7qSS3VtXVVXVjkjd0x/1Fkh9L8niSP8pgcemfX9pbAPalMXqIRqaIFFcDAACM7bKLsKtqJskvJfkHW7x8XwaLTCtJTiX5eJK/rKqvyGDR6NuTfFMG42nvfpnr31FVK1W1cv78+cu9XWBajBo769tDlB7l1YqrAQAAxlZt4z+obTyg6ruSLLXWjnXb706S1trPdttfn+QPk/xZd8o3Jnk+ya2ttZUN1/p4kncm+eokP9da+55u/3cnuau19r3b3cv8/HxbWVnZ7hBgL+gzdpZkZiZpraUNrW9X1lI1KJ9OMkgkHR8kkE7lRBaznOUMUkWrx04MyqmHiq4vWiDauA0AAHAAVdVjrbX5jfv7JI0eTfLaqrqxqq5J8vYkp9dfbK39x9baK1trR1prR5I8km7BqKq+qqq+uruBtyR5sbX2mSTPJbmpql7VXeYtSc5ezhsEpsSEiquTHgmiZLwUkbEzAACA3kYWYbfWXqyqO5M8mOSqJPe11p6oqruTrLTWTm9z+quTPFhVaxksFP1Ad80/qqp/kuTfVtVfJHk6ye2X91aAXTfB4ur1HqK5c5sTRAtHc3E5tfJqAACAiRs5njZNjKfBFBtjTKzX2FnSe4wNAACAS/dy42kjk0YASUb3AfVNEGUwdnby3Oaxs1Ozy0mGrilBBAAAsGsu++lpwAGwtJTV44s5crhlZiY5cniQKtqY9jn3TGUxyxftW8wgJXRBN3a2/iS0ytqFJ6Q9cHRxy6eobbsNAADAjrBoBAfdNBdXAwAAsGuMp8FBprgaAACAlyFpBPvVBBNEvcbOxk0QGTsDAACYapJGsB9NMkEUxdUAAAAHkaQR7DeTThAprgYAADiQLBrBXrTd6FmXIFpf2GmZubDgs3D24tSP4moAAABeTrWN//A5xebn59vKyspu3wbsrh6jZzMzSWstbWhduLKWqsraWrejtaweHySQNhZXrx47kbkzG8bKWtt+GwAAgD2pqh5rrc1v3C9pBNNkQuXVO5IgMnYGAABwoCjChmkxqfLqroNo7tzmBNHC0VxcTK24GgAAgJchaQTTYJLl1RJEAAAATICkEVwJo/qA+iSIOodmBwtKw5azmFOzy0kkiAAAAJgMSSPYaUtLWT2+mCOHW2ZmkiOHBwXUF0bOOiMTRMmF0bP1p6FV1i48Je2Bo4ubnqJ2EQtGAAAAjMGiEVyOCRVXJztUXg0AAACXyHgaXKpJFVcnyqsBAACYOpJGsJUJJoh6jZ0prwYAAGDKSBrBRpNMEKVncXX3cyWIAAAAmBaSRjBs0gmicYqrEwkiAAAApoZFIw6e7UbPugTR+sJOy8yFBZ+FsxenfhRXAwAAsJ9V2/gP0FNsfn6+rays7PZtsJf1GD2bmUlaa2lDa6qVtVRV1ta6Ha1l9fgggbSxuHr12InMndkwVtba9tsAAACwS6rqsdba/Mb9kkbsHxMqr96RBJGxMwAAAPaYXotGVbVQVZ+tqier6q5tjntbVbWqmu+2r6mqX62qx6vqU1X1pqFjr6mqe6vqc1W1WlVvu9w3wwG2tJTV44s5crhlZiY5cniQBLpQXJ30Gz0bp4NoaSlzZ5bz1NODBNJTT9cgYTT8MwEAAGCPGrloVFVXJXl/kuNJbkryjqq6aYvjvjbJiSSfGNr9I0nSWvu2JG9J8otVtf4zfyrJH7fW/lp33X9zGe+Dg2yS5dUSRAAAAJAkubrHMTcnebK19oUkqar7k7w1yWc2HPfeJD+f5B8O7bspyceSpLX2x1X1QpL5JP8uyQ8lmeteW0vyJ5f8LtjfRvUBdQmik0lO5p6czD1JklM5kVNnl4ceYT8YPTt5bvPo2anZ5STdcUtLmWtt6LxK2rIFIQAAAA6UPuNp1yd5Zmj72W7fBVX1HUlmW2u/veHcTyW5taqurqobk7whyWxVXdu9/t6q+v2q+o2q+oZLegfsb33GztIjQZSMN3omQQQAAMABd9lF2N242S8l+QdbvHxfBotMK0lOJfl4kr/MIOF0Q5KPt9a+I8nvJXnfy1z/jqpaqaqV8+fPX+7tMk0mVFyd7FB5NQAAABxg1Tb+g/rGA6q+K8lSa+1Yt/3uJGmt/Wy3/fVJ/jDJn3WnfGOS55Pc2lpb2XCtjyd5Z5Kz3fFf21pbq6rZJA+01r5lu3uZn59vKysr2x3CXrG0lNVHXsjC2UEa6NDsIAU098ZrL0oRHTk8WChaHzlLurGzQ4MC6iSDxaXjg8WkUzmRxSxnOYNzVo+dGJRTbzfetnEbAAAADpCqeqy1Nr9xf5+k0aNJXltVN1bVNUnenuT0+outtf/YWntla+1Ia+1IkkfSLRhV1VdV1Vd3N/CWJC+21j7TBitVH03ypu4y35PNHUnsZduliCZZXJ0orwYAAIAdMLIIu7X2YlXdmeTBJFclua+19kRV3Z1kpbV2epvTX53kwapaS/Jckh8Yeu0nk/yLqjqV5HySv3uJ74FpMypFNOni6u5nKq8GAACAyRk5njZNjKftAT1HxWZmktZa2lDYrbKWqsra2njXAgAAAC7d5YynwUtGlVd3KaL1p5K1zFx4WtnC2ZcWeRRXAwAAwHSTNKK/nuXVI1NEiqsBAABgakgasb1RCaIxyqtHpogUVwMAAMDUG1mEzQHQJ0HUt7y6deee25wiWjial8qpFVcDAADAVJM0Ogi2SxGNkSA690xlMcsXXWoxg4WmC8ZJEUkQAQAAwNSSNNrvRqWI+iaIMhg7O3lu89jZqdnlJEMLPlJEAAAAsOdJGu1nPVNEvRJE3djZ+pPQKmsXnpD2wNHFLZ+itu02AAAAMNUsGu1lo8qruxTR+uJOy8yFRZ+Fsy8lf0YWV3fXGqu8GgAAANjTqm1caJhi8/PzbWVlZbdvYzr0Ka9OMjOTtNbShtYHK2upqqytZZBGOj5IH20srl49diJzZ5a3f9z9xm0AAABgT6mqx1pr8xv3SxpNo1EJojHKq0emiMZNEBk7AwAAgANBEfa06ZMg6lte3fUQzZ3bnCJaOJqXyqkVVwMAAAAbSBpdSRNMEPUqrx4nRSRBBAAAAAyRNLpSlpbysd98Ibc8sZy/XKtcNdPyr75lMW/+/mvHTxBlMHZ28tzmsbNTs8tJhhZ8pIgAAACASyBpdCW0lo/95gt58+P35H1ri0la3re2mDc/fk8+9psvjJ8g6sbO1p+EVlm78IS0B44ubvkUtW23AQAAADawaHQlVOWWJ5YvLOy0zFxY8LnliYtTPyOLq7vrjVVeDQAAADCmahtTKVNsfn6+rays7PZtXJLBOk5LG1qnq6wlqZeCQa1l9figw2hjcfXqsROZO7O8/ePuN24DAAAAjFBVj7XW5jfulzS6Qq6a2TpBdNXMZSSIjJ0BAAAAO0QR9pXQutLrxzcniF73Lbm4mFpxNQAAADAFLBpdCVV58/dfm4/lRH7iieVkrfITM8t53bdk8PQ0CSIAAABgyug0upJ0EAEAAABTRqfRNJAgAgAAAPYIi0YAAAAAbGLRCAAAAIBNei0aVdVCVX22qp6sqru2Oe5tVdWqar7bvqaqfrWqHq+qT1XVm7Y453RV/cGlvgEAAAAAJm/k09Oq6qok70/yliTPJnm0qk631j6z4bivTXIiySeGdv9IkrTWvq2qXp3kTFX9d621te6c70/yZxN5JwAAAABMTJ+k0c1JnmytfaG19uUk9yd56xbHvTfJzyf5/4b23ZTkY0nSWvvjJC8kWU8hfU2SH0/y05d68wAAAADsjD6LRtcneWZo+9lu3wVV9R1JZltrv73h3E8lubWqrq6qG5O8Icls99p7k/xikj+/lBsHAAAAYOeMHE8bpapmkvxSktu3ePm+JEeTrCR5OsnHk/xlVb0+yTe31har6siI69+R5I4kOXTo0OXeLgAAAAA99Fk0ei4vpYOS5IZu37qvTfKtSR6uqiT5xiSnq+rW1tpKksX1A6vq40k+l+R/TDJfVU919/Dqqnq4tfamjT+8tXZvknu7889X1dO93x0H3SuT/Mlu3wQcED5vcGX4rMGV4bMGV4bP2vQ4vNXOaq1te1ZVXZ3BQs/3ZLBY9GiSv91ae+Jljn84yU+01laq6qu6n/FfquotSf5Ra+27Nxx/JMm/bK1963jvB7ZXVSuttfndvg84CHze4MrwWYMrw2cNrgyftek3MmnUWnuxqu5M8mCSq5Lc11p7oqruTrLSWju9zemvTvJgVa1lsOD0A5O4aQAAAAB2Vq9Oo9ba7yT5nQ37/vHLHPumoe+fSvLfjrj2UxmMtwEAAAAwJfo8PQ32qnt3+wbgAPF5gyvDZw2uDJ81uDJ81qbcyE4jAAAAAA4eSSMAAAAANrFoxJ5RVQtV9dmqerKq7tri9eWq+mT39bmqeqHbf7iqfr/b/0RV/a9D57yhqh7vrvnLVVVX8C3BVNqhz9rD3TXXz3v1FXxLMJUu9bM29PrXVdWzVfVPh/b5vQYb7NBnze812OByPmtV9ZdDr50e2n9jVX2iu+aHq+qaK/R26BhPY0+oqquSfC7JW5I8m+TRJO9orX3mZY7/+0m+vbX2Q93/sVRr7UtV9TVJ/iDJf99a+6Oq+ndJ3pXkExmUvf9ya+3MFXhLMJV28LP2cJKfaK2tXJE3AlPucj5rQ/vuSfKqJM+31u7s9vm9BkN28LP2cPxegwsu97NWVX/WWvuaLY779SS/2Vq7v6r+WZJPtdZ+ZafeB5tJGrFX3JzkydbaF1prX05yf5K3bnP8O5J8KElaa19urX2p2/+V6f53X1WvSfJ1rbVH2mD19NeSfN8O3T/sFRP/rAFbuuTPWjJIFCX5hiT/amif32uw2cQ/a8CWLuuztpUuLfvmJB/pdn0wfq9dcf6gZ6+4PskzQ9vPdvs2qarDSW5M8rGhfbNV9enuGj/fWvuj7vxn+1wTDpCd+Kyt+9UucvyPjMzApX/WqmomyS8m+Yktrun3GlxsJz5r6/xeg5dc1t+QSf6rqlqpqkeq6vu6fX81yQuttRdHXZOdY9GI/ejtST7SWvvL9R2ttWdaa69L8t8kua2qvmHX7g72j3E+a3+ntfZtSf6H7usHrvjdwt618bP295L8Tmvt2W3OAcY3zmfN7zW4dJv+hkxyuLU2n+RvJzlVVd+8O7fGRhaN2CueSzI7tH1Dt28rb8/LRB271MMfZPDL/bnuOn2uCQfFTnzW0lp7rvvP/5zk/84gwgwH2eV81r4ryZ1V9VSS9yX5war6ufi9BlvZic+a32uw2WX9DTn0mfpCkoeTfHuSLya5tqqu7nFNdohFI/aKR5O8tmvPvyaD/6M5vfGgqppLcl2S3xvad0NV/ZXu++uS/I0kn22t/Yck/6mq3thFin8wyW/t/FuBqTbxz1pVXV1Vr+z2f0WS/ymDBSU4yC75s9Za+zuttUOttSMZjM38WmvtLr/XYEsT/6z5vQZbupy/Ia+rqq/svn9lkr+e5DNdP9/vJvlb3aG3xe+1K86iEXtCN8d6Z5IHk5xN8uuttSeq6u6qunXo0Lcnub9d/FjAo0k+UVWfSvJvkryvtfZ499rfS/KBJE8m+cMknjDDgbZDn7WvTPJg13X0yQz+DdH/ufPvBqbXZX7WtuP3GgzZoc+a32uwwQT+hlzp/ob83SQ/N/TUtZ9M8uNV9WQGHUf/fKffCxer/n+DAAAAAHBQSBoBAAAAsIlFIwAAAAA2sWgEAAAAwCYWjQAAAADYxKIRAAAAAJtYNAIAAABgE4tGAAAAAGxi0QgAAACATf5/+z+GUCed7I4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot parametric fixed point position for a given set of knobs\n",
    "\n",
    "out = torch.func.vmap(lambda dk: evaluate(pfp, [hp, dk]))(dks)\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.scatter(*fps.T.cpu().numpy(), color='blue', marker='o')\n",
    "plt.scatter(*out.T.cpu().numpy(), color='red', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9052ca2b-6372-457f-a603-78ea26d417d3",
   "metadata": {},
   "source": [
    "# Example-08: Fixed point manipulation (collision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a92f384-e597-4d5f-873a-c3679be85164",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example the distance between a pair of hyperbolic and elliptic fixed points is minimized\n",
    "# First, using a set of initial guesses within a region, a pair is obtained\n",
    "# For a given pair, first order parametric dependence of fixed point positions is computed\n",
    "# Gradient of the distance function between the points is computed (GD minimization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8278e1e4-1256-43c7-9685-3460c25f08a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import clean_point\n",
    "from ndmap.pfp import chain_point\n",
    "from ndmap.pfp import matrix\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b4bebc2-ab8b-448b-9e2c-be35f383a563",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "909fcaae-d7f0-4a63-8557-e742dd4f0208",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set mapping\n",
    "\n",
    "limit = 8\n",
    "phase = 2.0*numpy.pi*(1/4 + 0.005)\n",
    "phase = torch.tensor(phase/(limit + 1), dtype=dtype, device=device)\n",
    "\n",
    "def mapping(state, knobs):\n",
    "    q, p = state\n",
    "    for index in range(limit):\n",
    "        q, p = q*phase.cos() + p*phase.sin(), p*phase.cos() - q*phase.sin()\n",
    "        q, p = q, p + knobs[index]*q**2\n",
    "    q, p = q*phase.cos() + p*phase.sin(), p*phase.cos() - q*phase.sin()\n",
    "    q, p = q, p + q**2\n",
    "    return torch.stack([q, p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1b33c53-ac28-400a-9879-42da06680629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Locate fixed points and select a pair\n",
    "\n",
    "# Set initial knobs\n",
    "\n",
    "knobs = torch.tensor(limit*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute and plot phase space trajectories\n",
    "\n",
    "state = torch.linspace(0.0, 1.5, 21, dtype=dtype)\n",
    "state = torch.stack([state, torch.zeros_like(state)]).T\n",
    "\n",
    "count = 1024\n",
    "table = []\n",
    "for _ in range(count):\n",
    "    table.append(state)\n",
    "    state = torch.func.vmap(lambda state: mapping(state, knobs))(state)\n",
    "    \n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qs, ps = table\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.xlim(-1., 1.)\n",
    "plt.ylim(-1., 1.)\n",
    "for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "    \n",
    "# Set tolerance epsilon\n",
    "    \n",
    "epsilon = 1.0E-12\n",
    "\n",
    "# Compute chains\n",
    "\n",
    "period = 4\n",
    "points = 4.0*torch.rand((512, 2), dtype=dtype, device=device) - 2.0\n",
    "points = torch.func.vmap(lambda point: fixed_point(64, mapping, point, knobs, power=period))(points)\n",
    "points = clean_point(period, mapping, points, knobs, epsilon=epsilon)\n",
    "chains = torch.func.vmap(lambda point: chain_point(period, mapping, point, knobs))(points)\n",
    "\n",
    "# Plot chains\n",
    "\n",
    "for chain in chains:\n",
    "    point, *_ = chain\n",
    "    value, vector = torch.linalg.eig(matrix(period, mapping, point, knobs))\n",
    "    color = 'blue' if all(value.log().real < epsilon) else 'red'\n",
    "    plt.scatter(*chain.T, color=color, marker='o')    \n",
    "    if color == 'blue':\n",
    "        ep, *_ = chain\n",
    "    else:\n",
    "        hp, *_ = chain\n",
    "        \n",
    "ep_chain, *_ = [chain for chain in chains if ep in chain]\n",
    "hp_chain, *_ = [chain for chain in chains if hp in chain]\n",
    "\n",
    "ep, *_ = ep_chain\n",
    "hp, *_ = hp_chain[(ep - hp_chain).norm(dim=-1) == (ep - hp_chain).norm(dim=-1).min()]\n",
    "\n",
    "plt.scatter(*ep.cpu().numpy(), color='black', marker='x')\n",
    "plt.scatter(*hp.cpu().numpy(), color='black', marker='x')\n",
    "plt.plot(*torch.stack([ep, hp]).T.cpu().numpy(), color='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "698d8a79-0f07-42b5-afbd-6b970e6c487e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute first order parametric fixed points\n",
    "\n",
    "order = 1\n",
    "\n",
    "php = parametric_fixed_point((order, ), hp, [knobs], mapping, power=period)\n",
    "pep = parametric_fixed_point((order, ), ep, [knobs], mapping, power=period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15a39caf-c777-4304-a51a-fb4a2cd7e54b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set objective function\n",
    "\n",
    "def objective(knobs, php, pep):\n",
    "    dhp = evaluate(php, [torch.zeros_like(knobs), knobs])\n",
    "    dep = evaluate(pep, [torch.zeros_like(knobs), knobs])\n",
    "    return (dep - dhp).norm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1183bdcb-6143-4ca6-916a-c4227f5cf8fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set learning rate and update knobs\n",
    "\n",
    "lr = 0.0025\n",
    "gradient = derivative(1, objective, knobs, php, pep, intermediate=False)\n",
    "knobs -= lr*gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ddf228a-7f2e-43ae-92aa-45145580565b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAHWCAYAAAB9p1B9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAB+E0lEQVR4nO39e3Rb9Z0v/L+/vlvOzbFMMDEONu7YkHBIik3t0NhQRBPclFt4ShLaMnY64czYfVjNOXbb1eFnpzw9T+ss6DnPmHWGnIIHBk5MDy2UAZuLW0jSIANhDOW2RU2AkBTKVgJlageSkM/vD2lvtmTJ1mXrZr1fa2lJlrb2/m5J3p/v/atEBERERDT35aQ6AURERJQcDPpERERZgkGfiIgoSzDoExERZQkGfSIioizBoE9ERJQlbAn6Sqm7lFIfKKVeCfO6Ukr9f0qpCaXUH5RSX7S8doNS6o/+2w12pIeIiIims6uk/y8A1s3w+uUAvuC/bQXwPwFAKbUYQC+ALwG4EECvUqrUpjQRERGRhS1BX0T2ADg6wyZXArhHfMYALFJKVQBYC+BJETkqIh8CeBIzZx6IiIgoRslq018K4F3L34f8z4V7noiIiGyWl+oEREoptRW+pgGUlJRcUF9fn+IUERERJccLL7zgFZHyePeTrKB/GMCZlr8r/c8dBnBx0PNPh9qBiOwEsBMAGhoaZP/+/YlIJxERUdpRSr1jx36SVb3/MIBv+3vxNwH4i4i8B+BxAF9VSpX6O/B91f8cERER2cyWkr5Sahd8JXanUuoQfD3y8wFARP4ZwDCANgATAKYAtPtfO6qUugXA8/5d/VhEZuoQSERERDGyJeiLyKZZXhcAnWFeuwvAXXakg4iIiMLjjHxERERZgkGfiIgoSzDoExERZQkGfSIioizBoE9ERJQlGPSJiIiyBIM+ERFRlmDQJyIiyhIM+kRERFmCQZ+IiChLMOgTERFlCQZ9IqIs4fV6sWPHDni93lQnhVKEQZ+IKEsMDg6ip6cHg4ODqU4KpYgtq+ylmtfrxeDgINrb2+F0OlOdHCKitNTe3h5wT9lnTpT0mXslIpqd0+lEd3c3C0dZbE6U9Jl7JSIimt2cCPpG7pWIiIjCmxPV+0RERDQ7Bn0iIqIswaBPRESUJRj0iYiIsgSDPhERUZZg0CciIsoSDPpERERZgkGfiIgoSzDoExERZQkGfSIioizBoE9ERJQlGPSJiIiyRNYFfa/Xix07dsDr9aY6KUREREmVdUF/cHAQPT09GBwcTHVSiIiIkmpOLK0bjfb29oB7IiKibJF1Qd/pdKK7uzvVySAiIkq6rKveJyIiylYM+kRERFmCQZ+IiChLMOgTEVFcOBQ6czDoExFRXDgUOnNkXe99IiKyF4dCZw4GfSIiiguHQmcOVu8TEWUAtpuTHRj0iYgyANvNyQ6s3iciygBsNyc7MOgTEWUAtpuTHVi9T0RElCUY9GfAjjNERDSX2BL0lVLrlFIepdSEUuoHIV7/uVLqRf/tDaXUR5bXPrO89rAd6bELO84QEdFcEnebvlIqF8DtAC4DcAjA80qph0XkNWMbEfmeZfvvAlhl2cUxEVkZbzoSgR1niIhoLrGjpH8hgAkROSAixwEMAbhyhu03Adhlw3ETzug443Q6U50UIiKiuNkR9JcCeNfy9yH/c9MopZYBqAbwO8vTRUqp/UqpMaXUVeEOopTa6t9uv67rNiSbiCjzsK8RxSPZHfk2AnhARD6zPLdMRBoAbAbw35VSZ4d6o4jsFJEGEWkoLy9PRlqJiNIO+xpRPOwYp38YwJmWvyv9z4WyEUCn9QkROey/P6CUehq+9v43bUgXEdGcw75GFA87SvrPA/iCUqpaKVUAX2Cf1gtfKVUPoBSA2/JcqVKq0P/YCeAiAK8Fv5eIiHzY14jiEXdJX0ROKqW6ADwOIBfAXSLyqlLqxwD2i4iRAdgIYEhExPL2cwDcoZQ6BV8G5KfWXv9ERERkHxUYgzNDQ0OD7N+/P9XJICIiSgql1Av+/m9x4Yx8REREWYJBn4iIKEsw6BMREWUJBn0iIqIswaBPRESUJRj0iYiIsgSDPhERUZZg0LcJF8EgomC8LlC6YdC3CRfBIKJgvC5QurFjwR0CF8Egoul4XaB0w2l4iYgyhNfrxeDgINrb27ngTpbhNLxERBnCrrZ9NhdQvFi9T0SUYEawBoDu7u6Y98PmAooXgz4RUYLZFaydTmdcmQYiBn0iogRjsKZ0wTZ9IspaHEdP2YZBn4iyFjvGUbZh9T4RZS12jKNsw6BPRFmLbe2UbVi9T0RElCUY9ImIiLIEg34GYA9jIiKyA4N+BmAPYyIisgM78mWAaHoYc0EOIiIKhyX9FImmyt7oYRxJEGetABERhcOSforYtQBHMI47JiKicBj0UyRRwZnjjonmPjbjUaxYvZ8i0VTZExFZsRmPYsWSPhFRhmEzHsWKQZ+IKMOwGY9ixep9SmucmIiIyD4M+pTW2HZJRNnMKPjAppp5Vu9TWmPbJdnB4/Ggq6sLVVVVePPNN3H22WfjzTffxIoVK+BwOMztjhw5gqeeegqrV6+Gx+OBrutYv349+vr62OmWUsIyvLvMjv0pEbFjP0nV0NAg+/fvT3UyiCjNeL1eDAwMAAA2bdqEXbt2AQD27duH0dHRmPfb3NyMr371q+jq6mLwp6Qyhmf29PS8JCIr490fgz4RZTSjFL9q1So4HA5s374dANDW1obh4WEAvgmwxsfHoy7pv/LKK/jkk0/M18vKyrBx40aW/CnplFIviEhD3Pth0CeiTGMN9OPj42Ypvre319zGWtKPtYRuHKeurg5DQ0M4cuQIAKCzs9OsUSAKx85JlBj0GfSJbJXus7yFC/RGKX7VqlXo6elJWNrHxsbQ0tKCEydOID8/H3v27EFTU1NCjkVzw44dO9DT04P+/v64h1jaFfTZkY+IACRuPYh4eL1e9Pf3Y3x8HCdOnMDu3bsxOjpqpi/Rgd6qqakJe/bsMQP/+vXroWlaWmaQKD2kY0dklvSJCED6lPStnfEAmG30LS0tKCgoSGqgD6Wrqwu33347AF/mqL+/PyXpMKTL90aJxep9Bn2iOSNcZ7ze3l5MTU1hfHwcAwMDqKurS3FKfUG2ubkZExMTKCsrw759+1KaLjurkCl9MehnWdBnbp7mmlBV90BgZ7x0HSLn8Xhw0UUX4ciRI3C5XHjyySdTlhZeG7ID2/SzTDq2t1L6yKQLv1Gqtwb6lpYWuFwurFq1Km0DvVVdXR06OjqwY8cOvPnmm/B4PCkr7XMefooGg36GSMcOIRS5RAflTMgUjo2Nob29HaWlpXC73QCA1tZW5Ofnp03VfTR6enpw//3346233kJ7ezueeeaZVCeJaFYM+hmCufnMluignK6ZQmunvPvuuw8TExOorq42S/Wp7JAXL6fTCaUUAGB8fDylpX27ZFKNEcWGQZ8oCRIdlNMtU+jxeLBt2zYsX77cWCwEnZ2dyMvLw+Dg4JwZ375+/Xrcfvvt+OSTT7B161azuSJTZUKNEcXHlo58Sql1AP4HgFwAvxCRnwa9/rcAdgA47H9qQER+4X/tBgD/6H/+/xGRu2c7XjZ25CPKBEZJ8YknnsDo6ChcLhcuuugiAOnbKS8eXq8XF1xwAQ4ePIjm5uaMr+JnST99pU1HPqVULoDbAVwG4BCA55VSD4vIa0Gb3i8iXUHvXQygF0ADAAHwgv+9H8abLiJKDiNQrFmzBu3t7dA0Dd3d3SgoKMBtt92W8VXeM3E6nfj6179ujtv3er0ZHSzTrcaI7Jdjwz4uBDAhIgdE5DiAIQBXRvjetQCeFJGj/kD/JIB1NqSJKK0Za2R7vd5UJyUuXq8XmzZtQk9PD771rW9B0zTU19ejp6cHjz766JwO+AYjyLvdbvT19aU2MUSzsKNNfymAdy1/HwLwpRDbbVBKtQB4A8D3ROTdMO9dakOaiNJapredhhp2t3btWvzN3/wNbrvttoSWdo3OgVNTU+ZzU1NTeOWVV8xV8xwOR9KaE7q6uvDzn/8cH3/8MX7zm99wIR5Ka8nqyPdvAHaJyKdKqRsB3A3gK9HsQCm1FcBWAKiqqrI/hURJlK697WdjdNCbnJw0g73Rbp+oIGsc8+abb8Zjjz2Gffv2mYvtBLN2pHviiSdQUlKS8OGATqcTpaWl+Pjjj+H1eudEL36au+LuyKeUagbQJyJr/X//EABE5P8Ns30ugKMislAptQnAxSJyo/+1OwA8LSK7ZjomO/IRJY+1c9cNN9yA4eHhhM+Dbw30Rj+B+vp6aJoGAOaQP4O1pP/yyy9jz5495mtlZWXo6OhI+Ap8X/nKV3Ds2DG0tLRkfC9+Sj9p05EPwPMAvqCUqoavd/5GAJutGyilKkTkPf+fVwB43f/4cQD/TSlV6v/7qwB+aEOaiMgmAwMD2L59OyYnJ3HbbbcBgO0d9KydAW+55RazJuHAgQNmwB8cHMRjjz0GYOaRAEb1v9frxdDQEI4cOYIdO3ZgfHwcu3btSkjgb2pqwooVK/D888/j2LFjtu+fyC5xB30ROamU6oIvgOcCuEtEXlVK/RjAfhF5GMD/rZS6AsBJAEcB/K3/vUeVUrfAl3EAgB+LyNF400RE8bEugGNVV1eHRx991LbjGAHaqLI3SvMtLS1oa2vDzTffjL1795pDyCIZ3+90Os0Odd/97nexfv16TExMYHR0FGvWrMFDDz2UkOr34uJiAMDExASr+Cl9iUjG3S644AIhIvvpui69vb1SW1sr8A2jld7eXunv7xdd1207Rn9/v2iaJm1tbeZx2traxO12S1tbm2iaFvb9xvtm2ibUOVVXVwsAKSsri/i90dA0TUpLSwWAtLS02L5/OxnfgV3fKSUefIXouOMnZ+QjIrN6Xdd1cwa92tpaXH311bZ10As+RvAEPsZxjJoEY/srrrgCDz/8cMiq/4ceegi7dvm6AIVLp1Hyn5qawo4dO3DkyBF0dXXZvjJeXV0dzjnnHDzzzDM4efKkrfu2W6aPHqHYMegTZTljrP3o6ChaW1sB+DrK2dX+HVyF73K5AACrVq3CV7/61WmzvxnBfnJyEtu3b8fTTz+N4eHhgKp/4/G2bdswPDxsvrekpCTsbHI9PT149tlnsWfPnoStjGcE/XPOOcfW/dotU0ePkA3sqC5I9o3V+5FhFR7NRNd16e7uDqjK7+7utr0qP7gKX9M08xhGVb3b7Taf6+/vD2hWMLa3Vv1bmwh6e3vNGwCpra2V7u7ukOeg67pZzZ+IKniXyyUAxOFwiNvttn3/lL1gU/V+ygN4LDcG/cgYF8/+/v5UJ4XSiNHGbQQoI1D29vbaGuytx3C5XAH7N4J9a2urAJD6+nrztxprZlXXdXM/xjFD7cM4ZnV1te0ZYk3TpLi42Nw/kV0Y9GlWLOmTVahg39raKi6Xy9aObaFK98G/QeP1lpaWaSX9eGiaFlBzESrDq2maWdrv7u6O63ihNDY2CgBpbGy0fd+UHlJxbbUr6LNNfw7j4hkEfN6m/tRTT5mT1iRiFj3rcrrDw8Mhj2G01998880AAsf727Hcbl1dHdxuN/r7+zE+Po4rrrgi5DZVVVV466238Nxzz8V9zGAXXnghnn/+eeTl5WX8AjwUWkZ3hLQj55DsG0v6RJEJLvlihmrvWAUP83O5XAGlIGupaLYmJ2NfHR0dcsYZZ8i8efOkvLxcysrKpKCgQE4//fSI2sqN44SqZRAR6ejoEADS0dER38mHYPQtQIJqEij1Mrmkn/IAHsuNQZ/sMJebPzRNE5fLJTU1NWYAamlpsbXdXmR6VX59fb3ZVGB8vkYQDNde73a7pbq6WlasWCHz588PyKCEuimlpKKiQjo7O8Oei67rZjNGb2/vtNeN12pra23//nVdD8gAEdmBQZ8oTnOxo2OoyXVm6s0e73FCddQLDvahJvfRdV06OzulsrJScnNzpwX2BQsWTCvp5+TkTNtuppK6tTd/cJ+FRLfrDw0NSUlJiQwNDdm+b8pODPpEcZpLJX3jXLq7uxMa7I1jzdRRL3jIXfDxrQHXuOXl5cmKFSukoqJCqqqqQlbh67ouHR0dAZkEpVTY6n5rb/62trZprxu9+FtbW+P7QEIwMl0OhyMhs/9R9mHQJ5K5FbhjERzsjUBmd7u9cayZhuFZx87PVLIvKioyg3ZRUVHYIB+OpmlSWFho7qO4uDhsYB0ZGZHy8nIZGRmZ9loi2/U7OzvN9DU3N9u+/0TK9v+pdMWgTyRzs4o+UrquS0tLiwCQ1atXJ2RyHeuxwpXujTH3RsYj+LsI1ZmwuLhYWltbYy4Fu91uKSgoMPcXbnicNYMSLJElfV3XZcGCBQJAKisrbd9/ImXz/1Q6syvoc8geZbRsnE7U6/Wiv78fDz74ICYmJgAA+fn56O/vDzsFbTw8Hg+uuuoqaJoWMAwPQMAc+sePHzfTYKTzpptuwtDQEE6dOgUAWLhwIRYuXIj7778/riF6TU1NaG5uNtetf/3110Nut2rVKoyOjk5bLRAAVqxYgd27d2PFihUxpyMcp9OJuro6PP+8bwHRTBq6l43/U1nFjpxDsm8s6VO2svZKh3/WN7sn1zGOY1TVG+3i9fX1Idvuu7u7p616F9xur5SyvX+BpmmilDLbzsNtE25FPqMKvrOz07Y0WVn7V3DoHsULrN6nVItnulS2GUbPGoARple6XYye70YGwzoUT+Tz9v1QQwA1TZPy8nIznTk5OQnrxe50OgWA5Ofnh/wsrCMIgiVy2J6I7zOqqqoSAFJVVcXfO8XFrqCfk8xaBZpbjFmpBgcHk/K+bOXxePC1r30NN954IzRNQ21tLXp7e+F2u21fJc7r9aKvrw9PPfUUAF/1eH9/P/bu3Yu6ujp4vV7s2LEDAwMD2L59O0pKSgKqre+//36ce+650HUdCxcuRGtrK1577TVcd911tqbTcOLECfP+xhtvnPb61NRUwL3VwMAAqqurMTExgf7+ftvT5nQ6cdZZZwEADh48iL6+PtuPQRQ1O3IOyb6xpJ8eWNJPLLfbLfX19WYnPWOe+kSV7oObDqyd9WYbe2+s2GcdS5+MKu1bb73VPF5DQ8O012cq6YsktjOfSOACPDONMiCaDVi9TzQ3GbPplZaWmu32iQ72wQvxBA/5m2nsva7r0tDQEFCdv3nz5qRk6qwzDoYK+kbGKdyQwEQO2zNYh+8lKnORLMywpw6DPiUE/6lTxxjL7nA4zCBRXl6e0HXZg0v3wWPvjW3Ctd+LfB44MUPbeqJYMxuhgr4xpLGlpSXk+xNd0hfxfX5Gp8bS0tKMLu1zOF/qMOhTQvCfOvmMqnHrWHaHw5GQ2fSCj2sdex9cup9t/L2xTX5+vtlDP9QkOKHce6/IsmUiSvnu7703tnO47LLLZixFNzc3zzhBTjJK+iK+z8mouQmXAckELBSkDoM+JQT/qZPHCKrW6t+qqiqpra1NaOleJLCEH2ohHusUtsGr5hlpb2hokLy8PLOEH03Adzh8Vx/j5nDEFvitU/KGKkEbGZZw/QtmyxTYyahVKC4uTvj3S3MPgz5RhjJK9tY2eyR4CF7w8a0l/FCd3Iwan+ChegYjWMI/b3406V62LDDgG7dly6I/D6OTXGlpachtZhqnLyLS2Ng444x+dtI0TUpKSsxMEgM/RcOuoM8he0RJ4vV60dXVhfr6euzYsQMffvghAKCtrQ39/f0JGYIXKg2bNm3C8PAwWlpa0Nvba86uZ91mcnISvb295lA9q8ceewzPPvus+fcNN9wQVboPHozu+XD6+vpw7NgxAEBRUVHIbXbt2oXh4WHs2rUr5Ou+a+nn94lUV1eH0dFR5Obm4sSJEwkbxkg0E07DS5RgHo8HXV1dmJychNvtBgCUlpbivPPOw5e+9CX09PQkZYpWI+CPjo4CAC655JJpY8e9Xi9uuOEGDA8Po7+/f1q6xsbG0NbW5isx5OTg7//+76Mef15VBbzzTujno/HII4+YjysrK0NuM9M4fQBQSgXcJ1pTUxMqKyvxzjvv4IMPPoDH40l4Ro/IikGfKEG8Xi8GBgZw3333mXPkA0BZWRkeeeSRuOaej5Z1/nwAcLlcASV8r9eLwcFBTE5OYnh4GG1tbdPmXvd6vWbAB4ALLrgAAwMDUaflJz8Btm4FrHHY4fA9HymjNsJw8cUXh9zO4XAE3AfLz88PuE+GoaEhuFwuTE5OYs2aNSFrU8g+xm87EetSZCQ72giSfWObPqUro72+ubk5YO752tpa6ezsDDvsLZGs0+LW1taG7LRntPGHGodv2Lx5s3k+ixYtiqv/Qby9941JdwBIYWFh2M80Hcbph6JpmixcuFDgXyGREmeujEgCO/IRpQ8j2FsDfbggm0zWgF9eXj5jD/eWlpaw6dR13VzcBmF6yieTNQOydOnSsNvNNk4/mb33gxm/ldzcXOns7OSImQSZKyOS7Ar6rN4nipFRfe/1evH4448HVOFXVVXhrLPOws6dO1NWdevxeLBmzRrouo7y8vKQ1cherxcPPvggAKCgoCBs9eeNN95oVusvWLAgpdXRHo8H999/PwBfW/wDDzwQcjuv14t3333X3C6UkydPBtwn0//+3/8bLS0tOHHiBG6//XaMj4/jN7/5Daugw4i1mt7pdKK7uzuBKcswduQckn1jSZ9Sze12B6wkB0BqamqkpaUl4ZPqRCKSEr7I7EPzRHwlJet5RjoeP1GMtQgASEVFRdjtjCaAmYZCJnPIXihut9uc3Aiz1LZku7lSTR8rsKRPlFxjY2P41re+hS996Ut48MEHMTU1hbKyMmzcuBEejwcDAwNp0SErkhI+EDg0r6urK2zp6aabbjIfL1myBOvWrUtY2mczNjYWMFzwmmuuCbut0WP/6quvDvu9iCRvyF4oTU1N2LNnDy699FJMTU1hz5492LBhA371q1+xxB/E6Fga3MGUomRHziHZN5b0KVmMeeeD58QHICUlJWk1wYrRr6CsrGzWEr61895sJSdj1j0AKT1fXddl8eLFZlqKiopmLBXPNhufSOpL+gZN08zvDf6OkmznJyuwIx9R4oTrmOdwOOT6668Xl8uV8s5sVsEL50Qa8K3L54bidrvNfSqlEpX8iGzYsMFMS25u7qzNDMb0xp2dnWG3MabGXbx4ccq/z+DAD1b3kwWDPpGNrCX6hoaGgBIl/L270y3QWxmlWkQwna/R1h28wE4oVVVV5n7PPPNMu5MdMU3TAr6PmUrvxvZGAHW5XDNut2jRopT14A+VntWrV0tRUVFA4E/lCBBKDwz6lFYycViMsW59Z2dnwFzyxq20tDRtOuaFEzyPf21t7awldyMYhppzP9iSJUvMz2PJkiU2pjxy1jHtRm3LbN+HUetRVlY2a0Zt6dKlsw79SzZN0wJWXTTOJZ2akyi5GPQprWRCz1ojyG/evFlqa2sDeoEbt6qqKmloaJCampqMuMBaS/gzVemL+DII1h79kWRkrEHf4XDYmfSIDA0NBcwPEGm/gkiq9g0rVqwQALJixQo7kmwbo/bJmGsA8C1u1NzcnLY1TukgEwsgkWDQp7SSrv9oRqDfsmXLtPbS6upqs6Tf2dmZ1tX3objdbrNzYSQlWqNaP5oSY2Vlpfl5LVy40IZUR07TtGkBP5Lhgtb+DZHUZqRjSd9K13Xp7OwMGNoXyfedrTKhABILBn1KiXQN7lbWavtQVaRGST8TSvKhGFX6RsAvKSmZNQBEGwgNRonZuO3cuTPO1M9O13XZsmWL5ObmxjQ/QDR9FkREVq5cKQBk5cqV8SY9odxut9mMY/yW07npKVUy4RoVCwZ9Sol0zUUbgXD16tUBF0ajnXvLli0ZV5IPRdf1gOreSIcNRjLVbrjjBTeBDA0NxXMKM9I0Tc4444y4jmlM0bt58+aItjeC/hlnnJH2gSJ4WKbRJNXS0pLxv22aGYM+pUS65aKNUn1DQ8O0En2qFrhJlOCAH2kVr67rUlNTIwCktbU16uNa57lPVIlf0zSzbd16KywsjKpGRtf1gCaPSFiHZUbSByAdhOroN3/+/Izpi0LRY9CnrGWsnDY0NDRtKtxFixZJa2vrnCv1aJoWEJyiadO1rkg321C3UHRdlwULFthe4ne73bJs2bJpNTPGbeXKlVF/j8a5OhyOiJsD3G632ZRQWVkZy6mkhFHqtw6rNH4bHR0dGd2ERdMx6FNWCTUzXklJScBFbi5U34cSPGlLtJ24jHb51atXx1zrETxHvHHLzc2V/Px82bx586z7drvdUltbK+vXrze/u1C3/Pz8mOb313XdnGwn2syNMUohVcMS42EE/1DzSzgcjozvwxKvdKudjBWDPmWFcKWZkpISGRoakra2tjkZ6A3BAb+6ujqq89V13awhCLe8bKTcbnfIEr9xU0qJUkry8vKkuLhYFi9eLHl5eVJYWBj2PdZbXV1dXB3TOjo6zH1F01lR5PNRCoWFhRn9ezIyxx0dHdOmjXY4HHM6cxxOuvZDihaDPs15mqZJfX19wIWrqqoqay5a1iF5sXTCEwkcxx9L1X4wY/hYcLNKrDeHwyGNjY22fJ9GM0FxcXHUn5N1lEI6zMxnB+u8FMEZgJaWFmlpaZmTTWHBWNJn0Kc0p2matLW1mVW1NTU10tzcnBUXKMPIyIjk5OTEFfBFPl+GtqqqyvaLntvtlqqqKikvL5fi4mLJyckxS/vBTQDGvcPhkBUrVtj+XWqaZpbWOzo6on6/rusyb948ASDz5s3L+AARzMgAGCV943/L+G1UV1dzgZ80x6BPc45xYbJWR8/16vtgRknaCPg5OTlxXYyNUQ0NDQ02pzQ8TdOkublZqqqqkhJIrE0gs01DPJNly5aZgTCWjEMm0TRNWlpapi0ole5rTGSztAr6ANYB8ACYAPCDEK9vA/AagD8A+C2AZZbXPgPwov/2cCTHY9Cfe9xud0Dnrvr6+qy78GiaFtB3IScnJ6YObVbnn3++AJDzzz/fnkSmGWufhXibMNxut1lLMduyvXOFkckMHkFRVlY2Z+a2mCvSJugDyAXwJoAaAAUAXgJwbtA2lwBw+B//PYD7La/9NdpjMujPHUbp3rjoOByOrJtlzLjwFhcXB/Rgt6O3tZGJKCoqmnMXb2tvffg7Ocb7uzGm5AUin9xnLrCOjgmerrq4uJjz/aeBdAr6zQAet/z9QwA/nGH7VQD2Wf5m0M9SwaX78vLyrBtWNDIyEhDsAd/qfnZ9DtYOaqtXr7Zln+lA1/WAlRHtmoveWtoHEjv7YLqyrldh7QBorDrJ4J8a6RT0rwXwC8vf3wIwMMP2AwD+0fL3SQD7AYwBuCqSYzLoZz7rim/ZWLo3JqaxBpji4mLb28CtE+ssWLBgTnzGwSV8h8NhayAaGhoKyITF28SSydxut1RXV8uiRYsCMudDQ0NSX1+fdZn0VMrIoA/gm/7gXmh5bqn/vgbA2wDODvPerf7Mwf6qqir7P1FKqlhWfMt0RjV+ZWVlwGIyOTk5tg1bC8W6hHBDQ0NGB/6RkZGA0qfD4UjI7yd46uFsDvwivtJ/a2ur2Qxn1NBlY4Y9VdIp6EdUvQ/ABeB1AKfNsK9/AXDtbMdkST+zud1us90w2klUMpFRqjeGhBm3vLw8qaqqSnimR9O0gNn0WltbM+4iHTyqAf6e+onKKOm6Pm09h8rKyqzJoIZjDKcdGhoKaJqrrq5m8E+wdAr6eQAOAKjG5x35lgdtswq+zn5fCHq+1Cj1A3AC+COCOgGGujHoZy5N08yLRXl5+Zy8SFhL9BUVFVJUVBQQPAoLC5MS7K1GRkYCAv+iRYvSPoAZszE2NzebY/CNmpGOjo6E/3aMJX6t311OTo40NDSwXVtCL/qTiRnKTJE2Qd+XFrQBeMMf2H/kf+7HAK7wPx4F8GcEDc0DsBrAy/6MwssAtkRyPAb9zGWs6Z6oatlUcLvdUlNTIytXrpSVK1fKwoULAy6E8Peer6ioSGkvaE3TAqrGi4qKkjaWPhqapkljY+O0zFKqfjcjIyPT0lJQUCDLli2bM7/hWBkZM+uQv5aWFg71SwC7gr7y7SuzNDQ0yP79+1OdDIrS2NgY2tra8OGHH6K7uxv9/f2pTlLEPB4Ptm7dimPHjk177ZVXXgn5/MKFC+FwOFBdXY277roLdXV1yUjqjMbGxnDdddfhgw8+wCeffGI+v3TpUiilcOaZZ2JwcDBpafV6vRgYGMC7776LJ554AiKC3NxcHDx40NymqqoKS5YsQXFxMXbu3JmSz9Hr9eL73/8+7rnnHpw8edJ8Pi8vD+Xl5Vi6dCkuueQS9PT0wOl0Jj19qebxeNDV1YUTJ05g9+7dAIDa2lq43e6s/DwSQSn1gog0xL0fBn1KBq/Xi3PPPRe6rqO8vByvvfZa2l0MQgX2/Px81NfX4ze/+Q2OHDkS9r3FxcVmMDp69Ci+/vWvo6+vL+3O0eDxeNDe3o7XX38dH330UcBry5YtQ05ODlavXo233noLx48fh1IK+fn5WLVqFRwOBxwOB7q6umY8PyMQ1NXV4dixYxgdHcXixYvhcDhw1lln4fe//z1OnTqFQ4cOTXtvZWUl/vrXv+Kaa67Bz372s7T5HI3P7e2334au6wEZAACoqKhAbm4unE4nFi5ciDvuuCMtMnvJ4vV6ce2115qBv7OzEwMDAylO1dzAoM+gn1F6enqwY8cOlJaWYnh4GE1NTSlJh9frRX9/P5577jmcffbZePPNN837gwcP4q233gr73tLSUtTW1k57PpUl0HgZn8dTTz2F999/Hzk5OQGl7Jk0NDTA4XDgvPPOw7Fjx/D000+jqakJhw4dQm1t7awZpeB9vf/++xARXHXVVWmdYTKMjY1h48aNOH78OCYnJ/Hxxx9P22bRokVYuHAhLrroIvO39eUvf3lO1wh4vV7U1dXh6NGjKC4uxvj4eEb+b6QbBn0G/Yzh9XrR3NyMiYkJuFwuPPnkk0k5rlHSrKqqMoP7nj17MDExEfY91dXVARdjo6R/8OBBDAwMzPmLl9frRV9fH4aHh8OW9IeHh2fMHFmVlZVh48aNYUv6p5122pyoFvd4PLjxxhvxl7/8BV6vF06nE++88w4+/PDDkNsvW7YMS5cuRX19fUDG88ILL8z4zwIAurq6cPvttwNAUv/n5zIGfQb9jNHX14ft27ejtrYWjzzySMICp7UUv2LFCjz++OMhA3x1dTWqqqqmlfTnygU30ayZqYmJibAl/WzJKIVjNAX86U9/Mkv6hw8fnrUmpba2Fi0tLdA0DSKCc845J+M+S6/Xi8svvxz79+9HR0cH7rzzzlQnKeMx6DPoZwSPx4P169djYmICvb296OvrS8gxurq6cPz4cezZsyfgNeMCagT3jLp43ncf8KMfAQcPAlVVwE9+Alx/fapTlTnS8PMzMqa///3voZSaVtKfqYmptrYWa9euxSuvvIIVK1bA4XAAQET9K1Lhsssuw+joKMrKyrBv377M+J9LYwz6DPoZ4Wtf+xqGh4dRX1+PvXv32nZhspbqrRfKlpYWKKWwYsUKOJ3OtLwYRuS++4CtW4Gpqc+fcziAnTtTHrgyQoZ+ftZaFGtJf7ZmqebmZhQUFKTV797j8eBLX/oS/vKXv2D16tXYt29fStOT6Rj0GfQzwtjYGNrb2zE4OGhb5z2v14tNmzZhdHTUfK62thZXX3313KmeP+ss4J13pj+/bBnw9tvJTk3mmWOfnzG00ev1BpT0n3322Wm1WwDQ2toKAClvsqqsrMThw4exdOnSkKM0KHJ2Bf08OxJDc5fX68Xg4CDa29tjunD8+te/hqZp+PWvfx130DdK9w8++KBZ6qmursa11147d4K9IVy7b4Q967PeHPv8nE5nyKaxUJkBj8djZoh3796NBx98EGvXrk1JDUB5eTkOHz6M8vLypB2TZsagTzMaHBxET08PAKC7uzuq93q9Xjz44IMAgPHx8bjS4fF4cNVVV0HTNAC+kv31118f9UXMGIuvlMJPf/pTPPbYY5jyVwE7HA6sW7cO3//+96GUSu0Y66qq0CXVqqrkpyUTZcnnFyozENz0NTExYWaSn3jiCZSUlCSlX4vH48E7/u9g4cKFCT0WRY7V+zSjeEr6xtj86upqjIyMxHyR8Xq9WLNmDTRNQ3V1Nc4+++xZL1pGuq+44grs2rULALBp06aAjEN9fb352GB9zmgyAICpqSmMj49DRJIzzjpD26TTRpp/fkYJfWpqCg6HA5s2bcKdd96J8fFx3HLLLXjssccAIO6SudFHYGpqCs8884z5fKyZ5misXr0abrcbCxcuxLPPPsuOfHFimz6Dftozeu/GM07XWsKvr6/HQw89FPbi4fF4sG3bNnz3u9/F9773PWiahra2NgwPDwOA+dgYsjdTSf/dd9+dcSx6S0sLzjvvPHg8nsSVmtKw93lGScHnZ52F0Ol0YtOmTdi1axempqYwNTWFV155BRdeeCEcDge2b99uvs/6O7VmPHt7e81tNm3ahIcffhhr1qzBzTffjFWrVkWc+bQ2A1iHsiZyGO3ZZ5+NAwcOoKamBm+++abt+882DPoM+mnvsccew7e//W3cc889WLduXdTv93g8WLNmDXRdn7H3v3FBu++++zAxMYHy8nLzPQ899FBASf/hhx+OqNbCWhIDPi/pHz582KyyNCxduhTnnHNO5gwFJFtYA7zD4cDU1BSGhoYCZiG0BnMrI5jPVtIHYGYOjH2FyxREWmoP7gibqDny77//fmzZsgV33nknrrvuOlv3nY0Y9Bn00168Jf3ZxvkaVfiTk5PmhbG+vh4///nP8U//9E+47bbbbA/C1szAL3/5y4AMQFFREU477TTcf//9KZtmmBLLWmIODvAGYxbCmUr60ZbQgdAlfWuNQTQZAK/Xiw0bNpg9/10uF3bt2mXrkFqjSa6trQ2PPvqoLfvNZnYFfVuW1k32jUvrZobu7m4BIN3d3VG/V9d1aWlpCft+Xdelra1NAEhvb695S+YSsZqmicvlko6OjoAlax0Oh3R3d6fVcrUUH03TpKWlRaqqqgKW2C0rK5POzk7p7u6Wzs7OpC8pq+t6wO/fSFd3d7e4XK4Zf4e6rptLXcf6fxqO8b9fU1PDJXZtApuW1k15AI/lxqCf/oyAGEvw0zRN6uvrBYC0tbVNe/9sr6eC2+2WZcuWBay73tjYmBZpo9gZ68WXlZUFBPvm5ua0WzPemgGwBvOZ/kd0XZfa2loBILW1tbb8Xq37dLlcce+PfBj0Ka0ZpfC2trao39va2ioApLq6OuAipGmatLW1ma/X19enXVDVNE3y8/PNC25ra2vapZEio2maVFdXm99laWmpNDc3Z0QtjpHpNmrLZgr81vO0o7RvlPKrq6vTKlOU6Rj0Ka0ZATraf3pd180LUEtLS8DzRum+paUlpn0ny8jISEDgz7QSv67r0tHRISUlJZKfny8Oh0PKysrE4XBIfn6+FBcXi8PhMG8lJSVSVlYmxcXFkpOTI8XFxbJkyRJpbm5O2+9oJqFK92VlZSmrto/nt2NtBpsp8BsZ6dbW1piPZTAyGtb/X4ofgz6ltViDfn9/v1mKt7433PPhjt3Y2Cjz5s0Th8MhixcvlpKSEikqKpK8vDzJz8+XM844Q3Jzc6W4uFgaGhpsv6AHl/jtbC+1y8jIiMyfP1/y8vIkLy9PFi5cKHl5eaKUCqjKjvdWVFQkRUVFopSSvLw8qaioELfbnerTDylU6b61tdX234eRsWhoaJClS5fKF77wBSksLJSysjIpLy+XgoICMw0FBQVSUlIiZ599tsybN09KSkrk/PPPj7jGIZLA39HRIQCko6MjrvPSNE1qamrS9jefyRj0Ka3FWr3vdrulvr4+IChEWuoxglisQWv58uW2Vt2OjIxIXl6eAJBly5alvLTvdrulsrJSiouLI/6MYi3pWzM84W65ubmSn58vK1euTIsaAWtbdCJK95qmyfnnny8FBQURfT6Rfj+NjY2zpnO2jrF2lfSN//t0bHrLdAz6lNZiKelbq/CtmQWjV3Jvb2/YYzU3N4cMZDk5OTOW9MNdSDs6Omy5aBkXUztKUbHQNE1WrlwZ9lwBTCvp5+fnx10a1zRNVqxYITk5OQEl/ZkyG0uWLElZDYCu67J69WozLXa2R7vdbikvL58xeJeVlQWU9OfNmydLliyR+vp6s3RvlPTDZRiGhoZmTIfRuS9U5zo7Svput1tqampsadbRdV36+/uZcbBg0Ke0FkvQN4J7bW1twPtmCvq6rsvpp58+rQR56623RnR8t9stZ5xxRsiL6MKFC+MOQpqmmaX9RYsWxbWvaI97/vnnhw0ySikpKCiYNVDYTdd12bBhg+Tk5ITNiKxYsSLpJX+j85kRgOMNNrquS2dnZ9hgn5+fL/X19bJs2TLp7OyM6ni6rsuWLVtk/vz5kpOTE7DfkZGRsO8bGRmR8vLykNsYGZ7Vq1fHdL4iYmbY6+vrY96HwWjO6+/vj3tfcwWDPqW1mUoV4YQa1z9T1b6u69Lc3Bxw0Yv2AmrQNE1WrVoV8gI904U0EitWrBAAUlJSkpRgtnPnzpDnkZOTI8uXL0+LqnTD0NBQzCVXu1g7jxYXF9uS0TvttNNCZrS2b99uawlW07RpmdZwv9eZqt6N/6Pm5uaY0mFkcmpra22prWFJfzoGfUprsUzME+o94XL8uq4HlGSVUrYECbfbPW0ClngDkLWK347e0eHoui5f+9rXpqW9sLAw6SX6aI2MjMi8efOmpX379u0JP3ZnZ6d5vHg6nxmjHoJL30op2blzp40pnn7cBQsWmMcLV6Nknd8i+P8pnom0RGZvgqP4MehTWoulej/UhSdUxz7rtsZF1e624KGhoWntz7EeQ9M086Lc0NBgazoNbrdbSkpKAtJbUFAQdy1FsoWqpbj11lsTdjxd16W0tFQAX0/9WEuWbrc7IPAatw0bNiSltOp2u83fa25ubsj/u3C1ZvFMpGVg0E88Bn1Ka3aV9MONAjCqzO0q4YfidrsD2p3z8/NjrhpvaGhIWNDXNC1giBcAWb9+fcZWjWqaNm0GvESVlK2Zx87Ozpj2EerzLyoqSnqnxIULF5rHD9U2Hy4wx9IUZzAy9263m9XxCWZX0M8BUQKMj48H3MfC4/Hgr3/9K1pbW3HbbbeZz3u9Xrz22msAgIKCgoSt4NXU1ITf//735t8nTpxAS0sLvF5v1Ps6efJkwL1dPB4PLrjgAhw/ftx8bufOnfi3f/u3hK2Tnmh1dXXQNA2nnXaa+dzWrVvh8XhsPY7X68X9998PAKiqqkJfX19M+7jkkksCPv/Kykq8+OKLSV906dJLLzUfHzp0KKL3eL1eM+2rVq2K+pjbtm3D8PAwbrnlFnR3d2fsby6bMOhTQgwMDMDlcmHVqlURB0mHwxFwv23bNuzZswclJSUBq+X19fXh1KlTAIBzzz3X5pQHampqwoYNG8y/P/jgg5iDg/XeLtdeey0mJyfNv4eGhvB3f/d3th4jFZxOp7kCnGH9+vW2HqO/vx8HDx4EAFRXV0cdsDweD1atWoX33nvPfG7z5s0YHx9PyRLL7777rvn4y1/+csBrxu+ut7cXXV1d5vMDAwPYs2cPXC4Xenp6ojqe1+vF8uXL4XK5AjLllObsqC5I9o3V+5kh2ir+4Pb7cO35lZWVZhVqMnqi67oeMPSquLg46mrM5cuXC+CbAMgut956a1KqwFNpaGgo4Bzt/L6NyWpKS0tj2m9jY2NA2latWmVb2mJh7YAa3GE0XIfYeDrwxdv5j6IDVu9Tuou2iv+WW26Bpmm45ZZbAAB79+6FpmnYu3dvwHaffvopAGD+/PlJKVE5nU787d/+rfn3sWPH0N/fH9U+Pvroo4D7eI2NjeG//Jf/Yv69c+fOOVHCD3bdddeZNT8AAmpd4lVZWQkAuPzyy6P+HY2NjeH55583/y4sLMSuXbtsS1ssGho+X2r9wgsvDHhtzZo1qK+vx5o1a8znPB4PHnzwQQAI+Iwj4fV6zffG04RHKWBHziHZN5b0M8NMk4GEElyyDzcCwBjaNW/ePNvTHI6u67J48WKzJLVy5cqo3m+Mg160aJEtpVXrOPDy8vK495fORkZGzHPNycmxZZ/WXvvV1dVRv9/6+efm5qZ8LQFN06SwsFAAyIIFCwJqokLNdGl9Ltopc61D/yJZC4PsAfbep3QXba/g4N7F4aokjclIzjjjDLuTPCOjOhjwTV0b7YXS6JEe7xrjbrfblqGEmcQ6fNKO0RrGbw0x9NoP/vxj7fVvuPdekWXLRJTy3d97b/T7sE5SVVVVFfBaqJkuo1nAKhjn108Nu4I+q/cpYYzewJH2Cp6amgq4v+KKK9DW1oYrrrgirv3aZefOnVBKAfD1wo+mir+urg5r164FgIBe6bG45pprzMfnnHNO0nuJp4L1nL/97W/HvT+jY1tzc3PUHTOto0WKi4tj6thpuO8+YOtW4J13ABHf/datvucjZR3Nkpuba45IMF7bt28fAOD6669HXV0dPB4PnnjiCXR3d2Pv3r1RNW2MjY1B0zQ0NzfjoYceYm/9TGRHziHZN5b0M0O0a4IHdwwKV9I3JkFZsGBBQtI9E+v48cbGxqjeu2zZMgF8K+7FStf1gFJvoqtWw03pa72tXr064SU+XdfN4yml4t5frPMmaJoWMHdDrNPWGpYt89W3Bt+i+Yls2bIlbHqM/yFjSd1wi1pFys759Sk6YEmf0p1RCti+fTsGBgZm3d7oTDQ+Pg6v14v29nb09vZicnIyYKhbcXFxwH0k7rsPOOssICfHdx9NScqqpKTEfPz2229H9d7S0tKA+1h8//vfh+//3zdHgd0dGcfGxrBw4UIopaCUwtatW2d9zzPPPIPy8nIopVBUVITHHnvM1jQBvt9Sbm4uACAnJ/7L1gcffBBwH6n29nZ89tln5t+Dg4NxpcM/YjDi54ONjY3hrrvuAgDk5eUFpMfr9WJychK9vb24++674XQ6MTAwAE3TUFtbG/UwO4/Hg9LSUlRXV8d93pQ6DPqUNrq6uuByuTA6OoqBgQE4nU6UlJRg+/btAReZioqKgPvZ2FGFarBWnR49ejSq98aSWZnp+Pfcc0/M+wk2NjaGBQsWoLm5GR9//HHM+/n0009x+eWXIy8vLyCtdigqKgIAfPbZZ3Ht2+v1YsmSJQAAl8sV1XutEwSdc845cWe6qqqiez7Yt771LTMT+O1vfzsgPYODg9i+fTtKSkrgdDpDVvVHyuv14qqrroLb7c6aJqU5y47qgmTfWL2fOaKdgz+4M1+o1baMqtkFCxZEtF87qlCtrAuqRFO9vnnzZgEgmzdvju3AIuYyvXl5eTHvI1hXV9eM1fdLly4Ne549PT0zvrerq8u2dBprvgO+dQViZe3EF+1c8dbvvqWlJeY0GO69V8ThCPxdOhyRdeYbGRmRoqIis1rf+B8x/mc0TTP/d3RdD5jSOtqlfK2d99hbPzXA3vuUCeKdoCdUvwBrL/pI2lSVCh30Y20ats5xHs2FP942fetENfEEPYOmabJkyZKQwTqWiX6CJwsybhs2bIg7rSKB7frxZHqMzENjY2PUIzCs52VX8Iul9/7IyIjZt6OkpCTgPEL1hTEyOi6XK+r+F/G8l+zDoE8ZIdphe8HDgYwLmPUiZl3gpKKiYtZ92l3Stwb9aMbrr1y5Mur3WBnjsIH4h62FWiQGsGcp21DB/6tf/Wrc+xX5/DMoLCyMeR81NTUCQGpqaqJ63+rVq83zKSkpifn48dI0TfLz881OjdZ5MEJlknVdN/8Po63ZiOe9ZC8GfcoImqZJa2urtLS0RFQyCl7zW9d16ezslNra2oDx6MaUo5FMxRtPFWool112mXnxr6ysjPh9xljqWHt8G1W5RUVFMb3fyjq5DABZsWKFraU4t9ttBiY7q/qN0m08PfhjbWapqKgwz6Wuri7m48cjeAnljo6OgNeDS/nW/6dYqvWtmXaW8lOLQZ8yRrSl/eD1vUMtr+t2uwPaM2djxwQohkWLFgUEy0h1dnYKEPtkLkYQzc/Pj+n9hg0bNgQE4+uvvz6u/YWj67r5HRm3SGdnDMeOjI/RPBRtm7y1KWTJkiUxHz9Wbrfb7NNRVFRk/n+IhF7iNp5Z90Ti6/tA9mPQp4xhtOu3trZGdOEJrtIPzgQYoint2+nss8+OqaRvtCUHl84iVVxcLIBvwZ9YBVe929XeHk5wO3i8fRHsCPqxlvStQT8V1ftGs0SojG6ojHE8s+5pmia1tbUs5acRBn3KGLquS2trq0TaoU/Xdenu7haXyzVt2lBr5ySj5BxLqS0esZb0Y21LFvFdhK0dt2JlDcDx1hhEavv27QHHjac/gtF7Pp45+GP9HqxBP5bvMFZGE5lRrR+8fkOoJrRwGeVIxFtDELyv4NE3FBsGfcoo0Vbxh5pJLFQHJWOZ3UiH79nByMAks6R//vnnm8eMdiZAQ/AytfFWtUfDetx4Ana6lPTj6UgYDbfbbdbwAL7FlYzfuRFQjf+tUIvpxDLrnlEzV11dHff/VLhZNSl6DPqUUdxut9TW1kpnZ2dEuf5QvYZDXUCsw/eqq6uTUqKwXoST1aZv7a0d64XYOn0sgJj2Eavg6Xxj5XA4BIA4HI6Y9xFrm74x5BJI/GJPRm2X9bdWWloa8N0b/w/d3d3mXBjxjqnXdT2gWt+O82BJ3x4M+pRRrO30kXYKCh4fHKq0r2mauURqPO3l0bAO3YqmpB9rsBGxpz3fGnRjGYcfLzuC/vz58+OuqYi1pJ+s1Q11XQ9YNQ/wrZxnDeDh1rWIZ0y9NaNtXZGP0oNdQZ/T8FJStLe3Rz3laVdXF9ra2jA6OorBwcGQ0/LW1dXB7XYjLy8PALBr166AefoTYXx83Hy8aNGiiN935plnBtxHw1jdz7iP19/93d/Zsp9Yffe7343pfd/4xjfMx9bV7qIxNjYWcB+ppqYmcypgANi4cWNMx5+J1+tFW1sb3G43AN+Uzd3d3XjhhRdmnGLXeK8xze5FF10U9Qp4g4ODGB0dBRD9NL2UQezIOQBYB8ADYALAD0K8Xgjgfv/rzwI4y/LaD/3PewCsjeR4LOlnplg6FwW/J9QUoyKBU7SWlZUldI35xsZG81jRzK5XXV1tNkNEy45qbdhQ0o5H8HS/sdB13WzqiPWziGc6ZOsY+bKyspiOH4qmadLc3BzQSTQnJ2fa7zjcFLu9vb0BbfvRlvKN/7POzs6IV8Wk5EK6VO8DyAXwJoAaAAUAXgJwbtA2/wDgn/2PNwK43//4XP/2hQCq/fvJne2YDPqZKdTsetG8x9osENy+b62aBHzTtCYq8FuXto2mfT6ejnzxVu9bl19NVdAXsSfjEW8GKJ5mFmuGD3GORBD5vJp+6dKlAft1OBzTmi+s7fXW373xXKwBXyT0kD9KL3YFfTuq9y8EMCEiB0TkOIAhAFcGbXMlgLv9jx8AcKny1VNeCWBIRD4VkbfgK/FfaEOaKA21t7eju7sbra2t0HU9omr4cM0CwcvuOp1O7Nq1C1X+5clOnjyJ9evX217Vb92fUgp9fX0RvzeeVfZOnjwZcB+tO++803zc1dUV0z6M5XbtamKIle/69/l9tIxq72irvwHgX//1XwP+3rhxY8wr/j322GM488wzsX37dhw+fNh8vrm5Ge+88w7WrVsXsP3g4CCGh4fR1taG9vZ2eL1e3HDDDRgeHobL5QpYQjdSHo8Hl112GSoqKuByuaJebhfw/U/s2LEjrv+1ePeRDmlIp33MKN5cA4BrAfzC8ve3AAwEbfMKgErL328CcAIYAPBNy/N3Arg2zHG2AtgPYH9VVZWtOShKnlhK+6HG7Vv3Zd2PpmnmpD3wd7Szs0OStRkhNzc3qvcaHQBXr14d9XHjLenDUoqMtQMcbCilx7uPkZER8/2xTvRjdPwsLS2N6f3BQx8RRYlf13XZvHmzFBUVBdQYGekJNbol1Gx7IvYshGOtJYh1WJ0dw/Li3Uc6pCHR+0AaVe8nJehbb6zez1xGdWZ3d3dUbYfBC/EY+wqVGdB13awCBuwdymftPR5t9bJRNRzLOPt4q7StwcWOfcQq3n1Y27xjnT7YmIY4ntkIr7nmmmmBv76+Pmx/FbfbLaeffnrA0rzGbf78+TMGe6M5wlr1bp0xL9Ypct1ut9TU1EhDQ0NMk/gY7BiWF+8+0iENid5HOgX9ZgCPW/7+IYAfBm3zOIBm/+M8AF4AKnhb63Yz3Rj0M1+0S+6GWzgkXFvkyMiIOU+5EejinYwmeErZaIbricS34I7RgSw/Pz+mC0rw9LupYC2lx5qGsrIy8/uM9cJq9P1wOBwx9/vQdT2gRin45nQ6zZJ8bm5uyGC/ZMmSsMHWOsFOa2urOQ4/+LV4Zsyz7oPSXzoF/TwAB+DriGd05FsetE0nAjvy/dL/eDkCO/IdADvyZYVoZ+gTCT1hT7iqT+M1ozQETF+GNFp1dXUBF+1oA0Y8k/NYO3pt2bIl6veL2FNSj0fw4juxsGO+Ak3TzP3EMpLCuh9jueRIb0opWb58+YzBvr+/36y6D55gx5r5jWXyHeMY3d3d0tjYKNXV1Qkd6UL2SZug70sL2gC8AV+1/Y/8z/0YwBX+x0UA/g98HfWeA1Bjee+P/O/zALg8kuMx6Ge+aJfcNYRrxwyetteg6/q0ntHz58+XLVu2RFVCCi6lzps3L+L3GuLpvW+dGCaWY4ukPuhbjx/r5EB2LK0rImawXrlyZVz7EfF9N0uXLpXi4mIzfcEl/fLyclm9evWsv3Xjd9zb2zstE2tXCd/ar4a99TNHWgX9ZN8Y9OeGWEv71ip9a/t+8HAmg6Zp0tDQMK3j1KJFiyKaFjhUx61YagziWXBHxN7OfNu3b49pH/GwI9Nhx9z7IiINDQ0CQBoaGuLaj53CzbJnvBbP9LoGYzrszZs3T+sLQ+mNQZ8yXrRL7hpCVfMbz4e7aIr4SuuFhYXTAnhZWZksXrx4WiDXNG3auGwgtt73IvFNCiPy+fz7sa6OZ507Ptml/eCV9mJlR/W+SHoF/eAqfWum1Y6Jd6zYjp+5GPQp40W75K6VcYFsbW0NCPKzDZkx2jOtvfCD21yt98G3efPmxVw6imdSGJH4A15wR8Rbb701pv3EwnrcaDtAWhmdM/Py8uJKj5GZq6ysTOnsc9Y2+lBV+sbvPN6Ab2QsRkZGpL6+nu34GYhBn+aEWKr4RcLPRBZumt5gRpV/8MpzM91OP/30uKpD4y3pz5s3z8yQxHrRXrBgQcA5JSPgBZfy4/kM7Qr61tUZo81w2kXTNCkvLw/ZRm+U8I1McTxj8UU+zzzEOryPUo9Bn+YEo/f9yMhIwLCkSBgXRuMCHmqa3tlKR0aHwuXLlwdMmGLthFVSUiIdHR1xB0hjiFesk0tZmxpiXdo1eKU4OzqyzUTX9YDjlZSUxLU/o3km3vXsNU0zM0CxzJsQK2sTlJHhLS8vn/a7t6uEbxwzVHMYZRYGfZpT4pn7O1SPfmtNgHW98VSKtx3ZWj0fT0k3eB7+rq6umPc1G6Pq2rjFU61szbBEOxtiKJWVlQJAFixYkJQaj+DaqVC/S7tL+OE6vlLmYdCnOSWWFfgMM/Xo7+/vN0s58Qxzipd1jfRYxukbjJn5Yp2kx1BRUZHwwL9+/fqAY8Q6v4DhjDPOMPd1zTXXxJ0+Y1rkeL+TSFh/oy6XK2wPfeuiUfEG6XATWlFmYtCnOSeedseZhjTN1lkqGYyJeeKtYrXOORDrNLQivs8kuLNiRUWFLbUhbrc7YAlaALJ+/fq492vtf2HH96dpWsAQQLtrgqxzURgjVcIFX+tv1I4Svsjn/WVqa2uT+nu3Yypamo5Bn+aceDsbzTR5SfCwqGSXfIz2/EWLFsV1XGsVd6xD9wwjIyMhRynE06s/uNOeXQFfxL6JeayM2hcgtumRQzGq6K2zQbpcrrCB0Pq7ra2ttWU9e03TpLq6OiUdFe1YdCaV0jXTwqBPc47xz+Z2u2Nug59tmtJUtXHaOQOcMV4/3p7wIr7P6/TTT58WqAFIT09PxPvp6ekJuY94aiOsrDMixttz38o6Ja9SKuLV8kIJHlMP+Kb5nWnWSWuVfm1trS21DXbN3BfP8dMxaEYqXTMtDPo0Z8U6jM8w20XPGviTVd1vZ9C3zve+fPlyG1In0tXVFTJoW2/z588Xt9stmqZNm+gn1M3OfgLW5oJYhzyG43a7A2oRou1s6Ha7pbq6OuAzCddubxVcpW9Hz3q32x0wDDDVnVczUbpmWhj0ac4y2j+bm5tjruq0LrYTqn10toVN7Gb0FI9nYhpD8CQ7dqU73IyF0d5KSkpsn/zF2gyRiIuxMYeCEfhXrlw5Y+m8u7tbWlpapLOz01z5L5rqeesYfbuq9HVdN/dZXl6edkGL4sOgT3NW8NCmWEtAkYx1TkZVqK7rZtC3a0z4kiVLzHNbsWKFLfs06Loe0LM90tvFF1+ckM8vUVX7VrquS2dnZ0DmoqioSCoqKmT58uVSUVEhFRUV0tjYaA69tN5KS0sjWlBHJDDghxqjHysjs1xaWsoZ9+YgBn2a06xtnbH2ZI50zLO1mjWSatloGavr2dmpKniSnURf5Hfu3Dmt01+ypvFNZNV+sKGhISkuLpaCgoJZMzmlpaXS2dkZ8e/FGJZqdLCzM+Druh5Qs0VzD4M+zXl2BH6RwBJ/uGr8ULULMy3eE805WIeF2ZmZsJb2KyoqbNtvOgle0jhZVdZut1uqqqpClvQ7OjqiXqHOWrq3uznJrhX4KL0x6FNWsAbseIbyWYdQhavGt06Raj1ubW1tVCU6K2sp3+4V3YJL+8lcQCdZrKX8TF0Zzhrwy8rKYpqAaqZ9z9R3heYOBn1Ka3b1gLVzSFM01fihxlobaYjkoq3rekDnsPz8/ISUwIaGhgLSF8+Qs3Rz6623JqTDYjJZq93LyspsPQdrf5R4MsWUGRj0Ka3ZOdbVzs52wdX4s5WOjHbYzs7OgAxAbW2tdHR0SEtLi2zevFmqqqpk5cqVsnLlSqmoqJi2mt3IyEjMaZ5N8HS3iTxWsuzcuTPjazGsw+fs7M9hMDruVVVV2Vp7QOmJQZ/Smt1jXe2cRzx4jvNI20GDO2JFcsvPz0946VvXdZk/f755TKVURgf+4Hb8TKvWN4b0GesklJWV2d45VNM0c6ggO+7NLF3H3UeLQZ+yjp2z6QVX30fTk9oI/jOV9JcuXSqdnZ1J7XhmnZs+U0v8wc0VBQUFGVWtH5yhTMScBSKfT2Bld5PBXJSuM+xFi0GfspKu69LS0mJbdWkiJklJlVCBP5Pa+IOr9HNzczNqvLm1U50dfVDCcbvdUlpampAmg2BzoZQ8F85BhEHfjs+QMpRRyqmpqbFtcRJrh6hEXayTIVTgnz9/flqX+oPbvgFITk5OxgV868x8iepJn+xZ9+ZKKXkuYNCnrJWIOcuDq2UzudTvdrsD2viN2+bNm9PufIJ76AOQJUuWZFTAD15KuKWlJWGfszGUtKysLCmf0VwpJc8FDPqU1RK1Oll3d/e0JVEz8YIXPGTQWoJOdfA30mZdLTBTe+m73W7zPBwOR0J70VubDzg8L/sw6FPWS9TUo6FK/Zk6JMrtdktFRcW04KqUSnq1/9DQkBQWFk5rfjCqqu0uuZ46dWrGv+NlDfj5+fkJLXmnerlcSj0GfSL5fKxyY2Nj1FOjziTU5DyZ3NY/NDQUdj55pZQ4HA5pbGy09fzcbrdUVlaKw+GQvLy8kMcuLCxMSGfD3t5euWntWjlVVSWilJyqqpKb1q61rYSczIAv8vnvvKamJmN/gxQfBn0imT7Zjt2loOAe2XYukpIKIyMjMn/+/GmL51gzAPn5+VJcXCzFxcWSn58vDodDKisrpwU2Xdfl+uuvl4KCgmnbG73LQ92KiooSFuxFfCX6m9auFQByEyCn/PcAfBmBOEv8IyMjkpOTk7SAzzH5JMKgb8dnSHOE0RZvTJpjd3unsX/jwmv3/OmpoOu6dHR0SElJiSilwmYCgm8Oh8O8hWqTD3XLycmRvLw8KSgoSNoQwlNVVXITIMuWLZMVK1aIUsqXAaiqimu/brfbDPjJGGFgrdbP9AwnxYdBnyiI0bO5tbU1IT3vg1dKy+Tq/mCapkljY6MZzK0l93BV89ZbqJJ+YWGhrFy5MjWfkVLymVJy4403yk033SS5ublyChBRKuZdWkv4OTk5Ce8PYZ2Torq6es781ig2DPpEQYKr+hMxtji4ur+0tFRaW1vn9AVZ13XZsmWLzJs3L6CkX1JSIkuXLk3L4XWnqqrkv65YIX19fXLeeed9XtUfY0k/FSX84DUiKLvZFfRzQDRHOJ1O3H333ejt7UVnZyceeeQR9PT0wOv12naMuro6uN1udHd3o6ysDB9++CF2796Nyy+/HH19fbYeC/fdB5x1FpCT47u/7z779h0Fp9OJX/ziF/iP//gPTE5Omre//vWvOHToEJqamlKSrnBEBN8791ycuPRSnHz/fbz4yiu4CcD/APC9c87xlXai4PF4cOmll+LUqVPIycnBo48+mvBzHhgYwPDwMFavXg2Xy4WBgYGEHo+yiB05h2TfWNKn2VhLSfHO0x+OpmnTFt+xrXf1vfeKOBy+yjjj5nD4nqdZ9fb2Sl9fn7zR3BxX731N0wI6JXZ2diYmwUHH5Hh8CgZW79NckKgZv4xFcRobGwVI3BzlxtA+a/C3ZZGVZcsCA75xW7bMjmTPaZ988on09/fLv/zLvwT01I+2176u61JVVWV+r9XV1QnvvMnx+BSOXUGf1fuUUoODg+jp6cHg4KCt+62rq8OTTz6JhQsXAgDuuusueDweW48B+Kq++/r6MDIygpaWFhQXF2NychKXXnppfE0LBw9G9zyZ3G43pqam4HK5oJQyn7c+jkRfXx8O+j/v4uJijIyMwOl02prWYP39/dA0DTU1NXjooYcSfjzKPgz6lFLt7e3o7+9He3t7QvY/MDCA8vJyHDlyBFdddZW9be4WdXV12L17N373u9+hpKQEU1NT2LFjBy688MLYMhtVVdE9TwCAv/71r3jmmWdw7rnnYunSpTHvx+Px4K677gIA5OXl4Xe/+x3q6ursSmbYY/7iF78AAFRWVib8eJSdGPQppZxOJ7q7uxNWoqmrq8PevXtRX18PTdOwZs2ahJT4DU1NTXjhhRdQVlYGAHjrrbewevXq6Ev9P/kJ4HAEPudw+J6nsPbs2YOTJ0/iK1/5Slz7aW9vx7FjxwAAN954Y1I6K27duhUffvghAOBLX/pSwo9H2YlBn+a84MDf1dWFHTt2JLTUv2/fPlRXVwMAjh49ih07dmDDhg2RH/P664GdO4FlywClfPc7d/qep5COHj2KF154AV/84hfNTFcsxsbG8PzzzwMAFi5ciL6+PptSGJ7X68WhQ4cAADU1Nejp6Un4MSk7MehTVnA6nXjooYfQ1taGuro69PT0JHQYVF1dHZ577jl0d3dj8eLFAHyl0Kiq+6+/Hnj7beDUKd89A/6MnnrqKeTm5qK1tTWu/WzcuBEnT54EAHzzm99MSrt6X18fDhw4gKqqKgwPD7MtnxKGQZ+yRl1dHR599FHzgvr000/bP7bewul0or+/H88880xAdf8Xv/hFjI2NJeSY2epPf/oTXnnlFTQ1NWH+/Pkx78fj8eD9998HACxYsCAppXyPx2N2ZC0oKGBbPiUUgz5lna6uLrS1tWH37t3Yvn17wic+Ca7un5qaQktLCwO/jX7729+iuLgYF110UVz7aW9vx6effgoA+Na3vpWUEve2bdswNTWFkpIS/Ou//mvCj0fZjUGfso4xc5/L5QIA3HPPPbbP3BfMqO7v7OxEfn4+Tpw4gZaWFnR1dSX0uNngzTffxIEDB9DS0oLCwsKY9+P1evHqq68CSF4pf2xsDK+//jpWr16NF154IeEdBr1eb0L7s1D6U74x/5mloaFB9u/fn+pkUIbzer1Ys2YNNE0DANTX1+Ohhx5KePXq2NgYWlpacOLECQBAdXU1RkZG5my17tjYGL71rW/hwgsvxKFDh3DmmWfimWeewSWXXILi4mI8++yz+OCDD/DlL3/ZfH3v3r1YsmQJLr74YvT09IQtcYsI/tf/+l+YmppCV1cX8vLyYk5nV1cXbr/9dgBAc3MznnnmmZj3FQmPx4MvfvGLmJqaQm1tLf74xz8m9HgAsGPHDvT09KC/vx/d3d0JPx7ZRyn1gog0xL0fBn3KZh6PB11dXXjjjTdw8OBB1NbWwu12J7xad2xsDJdeeimmpqYAAGVlZdi3b1/aBn6Px4OtW7fi448/xtGjR+FyuVBcXIzx8XGcOHECeXl5+OIXv4hjx47h6aefRlNTEw4dOoTzzjsPQ0NDOHLkSMzHXrRoEc466ywAvh76X//619HX1wen04lXXnkFv/rVr3D11VfjP/2n/xTXOVZWVuLw4cMoLCzESy+9lPDv4rLLLsPo6CgAoLOzMynz63u9XgwODqK9vZ2dBTMMgz6DPtmop6cHO3bsAAC4XC7s2rUr4RdFj8eDyy+/HG+99RaA1AZ+j8eD9vZ2HDx4EOXl5WaJ+eTJkzh69CiUUnjnnXdi3n9ZWRnWrl0bVUn/1KlT5jC2YEuXLkVRURG2bt2KBQsW4MYbb4x6xr1gp59+Ov785z9jyZIlZme+RPrOd76DO++8E42NjeyxT7OyK+jHXhdGNIf09PRgfHwco6OjGB0dxZo1axJe1W+082/YsAF79uzBkSNHcNFFFyUl8Hu9XvT39+Ppp5/GoUOH8OGHH+KTTz4BABw+fDjke6qrq7Fw4cKoS/pOpxNdXV1RBzWv14u+vj7827/9mzns8e2338ZHH32Ew4cP48ILL8SxY8fw/vvv48iRI3EFzbGxMbOdO55+AdEcb9euXQB8cwEw4FOysKRP5Of1ejEwMID77rsPExMTqK+vx969exN+QfZ6vWbgB4DS0lK43e6EBH6Px4NvfvObePnll81e6oaioiKUlZWFLOlbq9RTycis7NmzB2vWrIGu67j77rvj7hdRU1ODt956C3l5edi7d2/CO9R94QtfwMTEBBwOB/793/89bZt1KH3YVdKPb4k+YDGAJwH80X9fGmKblQDcAF4F8AcA11le+xcAbwF40X9bGclxucoeJZKmaQErndmyVO4sdF0PWKmvrKzM9uOOjIxIXl5ewFLACxculIqKCqmqqop/ZcAkeuqpp6Svr09qamrMc6mtrY15VboVK1YIAFmxYoXNKZ1O13VZvXp10pbqpbkB6bC0LoB+AD/wP/4BgJ+F2OZvAHzB//gMAO8BWCSfB/1roz0ugz4lmnWJU1uWyo2ApmkBQaympsa2pVV37twZEOxzc3OlsbExKRkau/3Hf/yH/Lf/9t/kl7/85bTPrLm5OabPrKKiQgBIRUVFAlIcqLu7WwBIS0sLl86liNkV9OMdp38lgLv9j+8GcFXwBiLyhoj80f/4TwA+AFAe53GJEsqYtrekpASTk5NYv359QmfvA3xt/M8++6w5ic+BAwdw7bXXxn1Mj8eDG2+80fy7sbERr776Kp577rmMrFbes2cPTpw4ga985SvmZ2bMeOh2u9Hf35/iFIbn9Xrx4IMPAvDNvpfq5hLKPvEG/SUi8p7/8fsAlsy0sVLqQgAFAN60PP0TpdQflFI/V0olvgcNUYTq6uowOjpqLs27fft2bNq0KaGB3+l0YmRkxAxiu3fvjjuIdXR0GLVu2LBhQ8YGeyD0ojpOpxOPPPIIHP5VCe+6666oVlIcGxszV7czhgYmSn9/PyYmJlBTU5OUIXpE08xWFQBgFMArIW5XAvgoaNsPZ9hPBQAPgKag5xSAQvhqCv5/M7x/K4D9APZXVVXZX3dCFIau6+Jyucwq5O7u7oQfU9M0KSsrEwDicDjial4wqq7nz5+f8dXJDzzwgPzkJz+Rjz/+eNprmqZJaWmpWXUeqdraWvNzTmRzh67rUlVVZTZDEEUDyareFxGXiKwIcfsNgD8rpSoAwH//Qah9KKUWAHgUwI9EZMyy7/f85/MpgEEAF86Qjp0i0iAiDeXlbB3IdsmcTtTpdGLXrl2ora0FADzwwANJqerft28fSkpK4p6r/9SpUwAAh8OR0dXJ77333oyL6tTV1ZkT9Lz88ssRl/aN96xbty6hNSD9/f04ePAgAF/VPlEqxFu9/zCAG/yPbwDwm+ANlFIFAB4EcI+IPBD0mpFhUPD1B3glzvRQlhgcHERPT4+5OlmiGVXI9fX1eOutt7B9+3bccMMNCQ/8o6OjAXP1xxL4c3JyAu4z1ejoKIqLi7F69eqw29xxxx0oKyvDhx9+iK6uroj2awz/TfQw4Oeeew4AsHjxYtxxxx0JPRZROPFeBX4K4DKl1B8BuPx/QynVoJT6hX+bbwBoAfC3SqkX/beV/tfuU0q9DOBlAE4A/0+c6aEs0d7ejv7+frS3tyftmHV1ddi7dy96e3vR2tqK4eHhhLfLNjU1Yc+ePWbgX79+fdQZDaPt27jPRAcOHMCBAwewZs0aFBUVhd2urq4OGzduBABMTk5G9FkZE/8Y94ng8XjMUv6WLVuSMvkSF9ahUOIK+iJyREQuFZEv+JsBjvqf3y8i3/E/vldE8kVkpeX2ov+1r4jIef7mgm+KyF/jPiPKCk6nE93d3UmvrnY6nejr68PFF18MAHjyySdx2WWXRdVxLFpNTU3YunUrAODIkSPYsGFDVBdzo5OacZ9MHo8HF198MVauXIkzzjgDZ5xxBi666KKoPi8RwejoKBYuXIjGxsZZtzd+E5H25M/Pzw+4T4Suri689dZbqK2tRU9PT8KOY0h2TRhljsyu7yNKka6uLrS1teGZZ57B6OgorrrqqoSWqvr6+sw+BXv27ImqR/8ZZ5wRcJ8IY2NjOOuss3DaaafB4XAgPz8fSinU19dj9+7deOmll/Dee+/hvffewzPPPIP6+noopZCTk4Pi4mKcfvrpaGxsDLnE8auvvor33nsPl1xySUSr6HV1dZnDHo0q9VQzSvZr165NSkY1FTVhlBkY9CljpFOVpdPpxN13343u7m4sW7YMmqYldHy40afAqKK//fbbI27fP/PMMwPu7eD1etHV1YXTTz8d+fn5aG5uxjvvvANd13Hs2DGcPHkyov2ICD755BP8+c9/xv79+7Fjxw4sWbIEZ555JsbGxvDZZ5/hd7/7HZYsWYLzzjsvon06nU5ce+21AICDBw8mtBYmEmNjYxgaGgKApNVMpaomjDKAHUMAkn3jjHzZqb+/XwBIf39/qpMSwBjOt2zZMnG5XAkd9qVpmpSUlAgAKS8vj2gInnXonx1D9kZGRqSwsDBghr/gm1JKAEhOTo6ceeaZsnDhQjn77LNl0aJFsnr1asnNzQ3YLtztsssuk76+PnnjjTeiSqOu6+ZQPJfLNeO2jY2NAkAaGxvj+VjCMobpLVy4MOOHTFLqIE1m5CNKmnStshwYGEB9fT3eeeedhFf1Gz36y8rKoOs6mpubZy3J3nvvvXA4HJiamoqr46HX68X69etx+eWXT1usB/C1iZ9zzjloaWnB66+/DhHBZ599hoMHD+Kjjz7CxMQEPvzwQ+zbtw8nT56EiODUqVMYGhqCw+HAypUrA/ZXUFCA888/H2+//Ta+8Y1vRFVidzqdWLt2LQCkdCIir9eLjz/+GACwaNEilrwp5Rj0KWOka5Wl0au/u7sb1dXV0DQtob36m5qazOFoExMTWLNmzYwBcd26dejs7AQAPPXUUzFlSMbGxnD22Wfj0UcfDXg+Pz8fjY2N0DQNx48fx2uvvYbdu3dHFWivu+46TE5OYnx8HJqmobGx0WwymDdvHp588km8+OKLWLlyZcxzFaRKf38/PvroI5SWlppV/ESpxKBPZAOn04n+/n58+9vfBuAr4X3ta19LWHtyV1cXuru7sWjRIui6HjC3fijGFLXRdgI0XHXVVWaJ1dDR0YE//elPtk7rW1dXh+eeew4TExNobW3Fa6+9hsOHDwMAPvnkEzQ3N+Oxxx6LaF/GZ//444/PmNFJZO/9p556CgBw9tlnJ3y5XqJIMOgT2airqwv9/f3weDwYHh6etRQeKyOTYcwm99JLL4Xs+W5N17JlywAAv//976M61tjYGP785z+bfyulMDIygjvvvDNhtS6vvvoqcnNzsXXrVjPdhssvvzyiwD8wMIDa2lpMTEyErXnxeDx47z3f8iEXXXRR/Am38Hq9OHDgAADfmgFEacGOjgHJvrEjH6U7TdOkvLzcXOe9t7c3IZ24rMcBIL29vWG3bWlpEQBSXV0dVVoqKysDOteNjIzYkPLwjh49Kj/+8Y/l4YcfNp8bGhqatjRwJB0mjWVsw62X0Nraavsyxobe3l4BIGVlZUlZmpnmNrAjH1H6Mtr56+vrMTExge3btydkSJ9xHGMM/3333Re2ZmHnzp2ora3FW2+9FVWfgyNHjpiPS0pKsG7duvgSPYunnnoKOTk55gRIgK/df2hoCL4Zu4HPPvsMF1xwway1KEazhnEfbGpqCoBvtkK7ay3effddAMCVV16ZlVX76TTElj7HoE+UIMEB+Ze//GVCZu+rq6uD2+02q7LDTdVbV1eH66+/HgCwb9++iC/Gx44dMx8bU9wmynvvvYeXX3455KI61113HZ555hnz78nJSVx33XUz7m/Tpk1oa2vDpk2bEpLemRjt+cZ9tuGsgOmJQZ8ogawL9RhD+i644ALbe6E7nU5cffXVAHw9+jdt2hQyqBszCY6Ojka8YJBRugaAn/70p/YlOoTf/va3KC4uDtu+3tTUZGZcAOAPf/jDjJmoXbt2YXh4GLt27Qr5eqI68Xm9XrPm4Ctf+Yqt+84U6TrENtsx6BMlmHVIn8PhwOTkJK644grbqz17enrgcrkA+FakCxX4jZkEW1paMDw8HFGTg6858fP3J8qBAwfw5ptvzrqozn//7//drAUQkYhqH8I1e9TX1wfc26Wvrw/PP/88AKCystLWfWeKdB1im+0Y9ImSwOht/9vf/hbl5eXQdd32pXmdTid27doVEPhDtd07nU5zPfe77rpr1uYGayk40uFy0RKJfFEdp9OJJ554wvz7xRdfDFtz0tXVZTZ7hFpq9/XXXw+4t8vw8DAAoLS0NOIlfomSgUGfKImamprw2muvoa2tDcPDw2hubp5xqF20ggN/uBLuwMAAysvLceTIkVlnEKypqTEfb9682ZZ0BnvttdeiWlSnqakJFRUV5t/GXPvBrM0eq1atmva60XRhbcKww+rVqwEAbW1tLOlSWmHQJ0oyo4rd6Nm/Y8eOsG3wse5/165d5v5DzRVgHV2gadqMna1OP/1083FOTo7tzRLGojqnnXZaxIvqAMCvf/1r8/FM4+C3bNmCtrY2bNmyZdpriareN3ruG/dE6YJBnygFnE4nHnroIbNnf7iq+Hj3bzQlzBT4e3t7MTk5GTaY33HHHcjNzQXgG77X19dnWzoB4N///d9x9OhRXHrppcjJifyS1NTUZG5/7NixsM0UM3Xme/PNNwPu7eD1es2agy996Uu27ZfIDgz6RCliDLUzquLvueceW6v6jaA+U+B3Op0oKSnB9u3bw/YxqKurC6gav/fee+NOo9E58Pjx49i9ezeqqqrwhS98Ier9/MM//IP5ONxUxMZYfOPe6uyzzw64t0N/fz92796NlpYW9PT02LZfIjsw6BOlkLUq/q233sKOHTsiWjkvUsGBP1T7fXt7u9nHIFzgv/fee1FYWAgA+Mtf/oKbbrop5jT19fXh8su/h2XLBF/9qhuTk5N49NFXsH379qj3ZS29f/TRRyG3mWmCnkSU9MfHxwH4Vglkez6lGwZ9ohQLruqfmJjAtm3bbNu/dZIgTdOm9R8w+hgYgT9cU8B3vvMd8+9f/vKXMZX2RQRjYx/h8cf/B7zebVi9+hm89tpx/OpXt2Fs7KOA4YGRuPLKK83H1vUBrGaaoGfFihUB93b4zne+g5KSkoDPiyhdMOgTpQGjqr+7uxutra2orq5GX1+frVX9xqQ2o6Oj0wK7tXOhpmkhMx19fX04//zzAQAnT57EJZdcEnX6lFJ4/fWfA7gJa9a8jvz8T/C73+0EcBNef/3nUfei/9nPfmaW4E+ePBlymzvvvBPDw8O48847o9p3LLxeLzo7OzE5OYl//Md/TPjxiKJmxwT+yb5xwR2ay/r7+82FZerr6yNaWCYSuq5Lb2+v1NbWmvsOXmRG0zRpbW2VlpaWkMfVdV1ycnLM9HV2dkadDqVEgFOyYMECOf/88/37OiVKxXZeBQUFAkAKCgpCvm4sqtPa2hrVa7EwFtmJ9bMhCgdccIdobmpvb0dvb69ZHW9XVb/T6URfX585LbCmadNK/HV1dSgpKcGePXtCtv87nU788z//s/n3HXfcEfWUwmeeKQC+h48//hgvvfSS/9nv+Z+PXklJScB9sAsvvDDg3uD1es3mhODXYmUM0WtoaLB9lAORLezIOST7xpI+ZQNN06StrU2Ghoakvr7e1uVZdV2X+vr6kCV+TdPM19ra2kIuOdvZ2WmWaPPy8iJO26lTp2Tt2pv8771JgFP+e8jatTfJqVOnoj6X4uJiASDFxcUhXzc+x+CaC2PZ3ZaWFtuW1a2urjaXLyayE1jSJ5rb6urq8Oijj6Kvrw+apmH9+vW2tfMbnQdDlfiNjn9Gx75QE/f09fWhqqoKgK8t/dJLL41oxIFSCk1Ni7B27U2oqvK14VdV/Rxr196EpqZFUbfpe71eHD9+HIBvkp9Qr2/btg3Dw8N4+OGHA1577rnnzDTZ1cv+kksuCbgnSjt25BySfWNJn9KBruvS399vWykxHLfbLeXl5WbJure317Z9W0v8tbW10tvba56PcX6apoU8T03TpKqqykxXfn6+jIyMRHTc4BJ9LCV8kcAah/r6+mmvG/0jQtVYdHR0CADp6OiI6djBNE0z+0vY+R0RidhX0k95AI/lxqBP6cAIKP39/Qk/lq7r4nK5BIA0NzeLy+WyrYOftTo/VMCaKXDqui4tLS3me3NycmxthphNRUWFeeyWlpZpr4er2hexvxOf8f3U1tYmPCNI2YdBnyjFklXStx6vra0toGRr17GtmYrW1tZpJX7juN3d3dOCqK7r0tDQENDG39nZmfDPRdM0yc3NFQCilAoZ2I3e9MEZGU3TzPb37u5uW9KzZcsWASBbtmyxZX9EVgz6RFlI13Xp7u42q5HDdbSLdd/WTIV130YGx8gYtLW1TXtvc3Oz+V4AUlVVZVttRCiNjY3msc4///yQ24QL+naXyt1utzgcDgEgLpcr7v0RBWPQJ8pi1gCdiLH8RlAM3vdM4/h1XZfOzk7Jy8sLCPx2NkVYj2WMzw9XWtc0TVwul3R3dye8Pd+oNSguLk5oRoeyF4M+UZazdsJrbW0N23Yd776DmxGMzIbL5QrZvOF2u6W0tDSg1F9YWGhblf/IyIgUFRWZ+54/f37I/RoZl+CSt67rZk2JXaVyo9ahsbHRlv1FI9nNTJQadgV9DtkjylDGsLu2tjYcP34cw8PD2Lp1K3bs2BH3sL7gIX2bNm0yhwvedtttaGtrw6pVq9DT04OBgYGAYzY1NeGNN95Ab28vFi1aBAD49NNPcfvtt2PVqlU4++yzo57Qx+DxePD1r38dn3zyCQAgLy8PTzzxRMghd8bKgNYVAgFgYGAAExMTqK2ttWU5Y+skP+edd17c+4vW4OAgenp6Qg6tJJrGjpxDsm8s6RMFMiaaMUqcdnVOi6Sd39puHlzidLvdUlVVJQsWLAgo+efl5UlDQ0PIqvdQNE2TlStXBkwBXFhYGHakgNFMYe2QaDCG+dk1Ta512uRUtOezpJ8dwOp9IjIYF35j+NyyZctsa0sPbud3uVwhx/MbwT/UEEajA2JlZWVA8DeC95IlS2TFihVSUVEhS5YskTPOOENWrFgh5eXlUlhYKCUlJQHvqa6uDntu1oxKcFoSUbXvdrulurpampub0649nxmCuYNBn4imCR5zX15ebms7f/CQweChezNN5mNs09nZKVVVVbJ06dJpGYDZbjk5ObJ58+awQcyaxuCRDdZhibW1tbZliOzORNgpmXNJUGIx6BNRSEapuqysLCHj+Wdbqc8INKHG9Afvq6OjQxYsWDBrSb++vj7syn/W/YUL+CKfN4EYTRF2sK6qZ1eTip1Y0p87GPSJaEbWUn9wlbyd+w4O7sFj+u0eWRAsuPkh3MyBRkbFzhnzjKF/DQ0NDKyUUAz6RDSr4Cr52traiDvPRbJva3APtVpfW1ub2c/AOsTPrhJocHNGuIBvd7W+gavqUbLYFfTzYu72T0Qx83q9GBwcRHt7u20rvIXidDpx9913Y2BgAPfddx8mJiawY8cOvPrqq7j77rvjOrbT6UR3dzeuuOIKXHXVVdA0Df39/Xj11Vdx2223masEejwebNu2DcuXL0dPT4/5/p6eHkxOTqKkpARXXHEFHn744Yg+D6/Xaw6127dvHzRNQ21tLa6//np0dXUFvN/r9WLTpk0YHR0FAFx//fWoq6uL+ZytPB4PTp06BQBoa2uzZZ9EicagT5QCxthqAOju7k7osZxOJ/r6+rBp0yZ0dXVhcnISw8PD2LRpE3bt2hV3psNYindwcBBPPPEERkdHcfz4cVx00UWYmpqCw+HA3XffDQAoLy9He3u7+d7JyUn09PTg6aefxvDwMHRdNzMNALBt2zbcfPPN2Lt3r5kxmJycxPbt2wH4PruCggIzk2Hl9Xpxww03mAHf5XKhq6srrnO12rp1K9555x1UV1ejr6/Ptv0SJZQd1QXJvrF6nzJdKjtYWTuf2VndL/J5lb610xxmWCMguMe/tV3eOs2w8Rz8nfDCjcE39tnb2xuwip6d/RkMxloDzc3Ntu6XKBSwep8ocxlV46lglHat1f3j4+O46KKLplWPR8uo0vd6vXA4HJiamsL4+LhZs7Bq1So4HA7zONbPwWgq2LZtm1nSBxBQ0r/44otnbALweDxmU4Ph4osvtr0k7vF48P777wMAvvzlL9u6b6KEsiPnkOwbS/pE8TMWpDFKxLBxKJtVcGdCWHr8j4yMxNWz36gpcLvd4nK5zI51Rg1GIkr4M61LQJQoYEmfiOJRV1eHJ598MqCzm9frxde+9rWQbeSxsnYmNNr49+3bh9HRUTz//PPQdT2gDwAAOBwOrFu3Drfccgtuvvlm/PrXv8b4+DhuueUWPPbYY+Z2zz77LPbs2YPa2lpMTEwAAOrr6/HQQw/Zlv5gAwMDZufBhx56KKEdMYnsxqBPlOWcTid27doVsiMegLir/I1jWKvYjR793/3ud/FP//RPWL58udk5z3D//fdD0zQcOHDArK4/dOhQQNW9Ye3atTjrrLPMRYASFYjHxsbMkQN2jgSIRLJGfNDcxqBPRAHD74zhdUYQLikpsb3/gdH2DwDr1q0L6AMARF7SN7a1I2MyG4/HA5fLhcnJSZSXl9s6EiASyRzxQXOX8jUVxPhmpRYDuB/AWQDeBvANEfkwxHafAXjZ/+dBEbnC/3w1gCEAZQBeAPAtETk+23EbGhpk//79MaebiGZmHQu/bt063HzzzQkvRaczr9eL5uZmTExMwOFw4Le//S2ampqSngaW9LOXUuoFEWmIez9xBv1+AEdF5KdKqR8AKBWR74fY7q8iMi/E878E8GsRGVJK/TOAl0Tkf852XAZ9ouT52te+huHhYQBAa2sr8vPzMTAwkNSq7VTr6enBjh07APhK2f39/SlOEWUbu4J+vNX7VwK42P/4bgBPA5gW9ENRSikAXwGw2fL+PgCzBn0iSp7bbrsNx48fx4kTJ7B7924AwI033oiSkhJbO/ylI6PG44EHHgAA1NbWBswqSJRp4g36S0TkPf/j9wEsCbNdkVJqP4CTAH4qIg/BV6X/kYic9G9zCMDSONNDRDaz9vLv7+/H+Pg4jh8/juHhYbzxxhu4+uqrk9aunkzBY/6NUQFz6Rwp+8wa9JVSowBOD/HSj6x/iIgopcK1FSwTkcNKqRoAv1NKvQzgL9EkVCm1FcBWAKiqqormrURkA6fTaVZrWwOiUe09NTVlTrObyYHRyNzcddddOHLkSNh5/Yky0axBX0Rc4V5TSv1ZKVUhIu8ppSoAfBBmH4f99weUUk8DWAXgVwAWKaXy/KX9SgCHZ0jHTgA7AV+b/mzpJqLEMebbHxgYwFNPPYU9e/bgueeew+7du6HresYG/7GxMVxxxRXQdR2Ab62ARx55ZE43YVB2yYnz/Q8DuMH/+AYAvwneQClVqpQq9D92ArgIwGv+GYaeAnDtTO8nShWv14sdO3bA6/WmOilpyRh7/6tf/Qr9/f248MILAQDj4+Po6enBmjVr8Nhjj+Gyyy5DT09P2n6OXq8XfX196OrqwqWXXgpd11FaWgqXy4W9e/cy4NPcEs90fvC1y/8WwB8BjAJY7H++AcAv/I9Xwzdc7yX//RbL+2sAPAdgAsD/AVAYyXE5DS8lQ39/vwCQ/v7+VCclI1gXzzGmqS0vLzen3m1tbRWXy2XrevbxCF6Yx7iVlJSI2+1OabpStRgTpS/YNA1vXEP2UoVD9igZOC46dtYZ92699daAnv/GsL9Vq1bhmmuuwS233JK0UQAejwddXV2oq6uDx+Mxl90FgObmZpSUlKR8OOKOHTvQ09OD/v5+TsJDprQYp58qDPpEoaVrRiW45/+ePXsA+HrEa5oGl8tlTvtr12RA1gBv/G09NgC4XK5pK/+lWrp+h5RaDPoM+kTTZEIp0QjG1pL+8uXLzVEARkYA8E2EMz4+HhC4jZkCrUvwWh8b+x4fHw8oyQOf1zLU1dXB6XSmTaAnmk26TM5DRGmkvb094D4dGeP+DY8++qg59z4QWNI3Arc1eG/btg0AzFkCgx8b2xuZnuAMAzvmUTZj0CeaQ4yFczJN8Cp8RqYgVBW9UaIHEPLx8ePHs3qdAKKZsHqfiIgozdlVvR/vOH0iIiLKEAz6REREWYJBn4iIKEsw6BMREWUJBn0iihvXKSDKDAz6RBS3wcFB9PT0YHBwMNVJIaIZcJw+EcUtEyYFIiIGfSKyQaZOCkSUbVi9T0RElCUY9InIlOgOeezwR5RaDPpEZEp0h7xkd/hjJoMoENv0iciU6A55ye7wZ2QyALDPARG44A4RzWFerxeDg4Nob2/ninuU0exacIclfSKasziqgCgQ2/SJiIiyBIM+ERFRlmDQJyIiyhIM+kRERFmCQZ+IbMWx8UTpi0GfiGzFFfeI0heH7BGRrbjiHlH6YtAnIltxbDxR+mL1PhERUZZg0CciIsoSDPpERERZgkGfiIgoSzDoExERZQkGfSIioizBoE9ERJQlGPSJiIiyBIM+ERFRlmDQJyIiyhIM+kSUMFxxjyi9MOgTUcJwxT2i9MIFd4goYbjiHlF6YdAnooThintE6YXV+0RERFmCQZ+IiChLMOgTERFlCQZ9oizEoXRE2YlBnygLcSgdUXaKq/e+UmoxgPsBnAXgbQDfEJEPg7a5BMDPLU/VA9goIg8ppf4FQCuAv/hf+1sReTGeNBHR7DiUjig7KRGJ/c1K9QM4KiI/VUr9AECpiHx/hu0XA5gAUCkiU/6g/4iIPBDNcRsaGmT//v0xp5uIiCiTKKVeEJGGePcTb/X+lQDu9j++G8BVs2x/LYAREZmK87hEREQUpXiD/hIRec//+H0AS2bZfiOAXUHP/UQp9Qel1M+VUoXh3qiU2qqU2q+U2q/rehxJJiIiyk6zBn2l1KhS6pUQtyut24mvnSBsW4FSqgLAeQAetzz9Q/ja+BsBLAYQtmlARHaKSIOINJSXl8+WbCJKMxwxQJR6s3bkExFXuNeUUn9WSlWIyHv+oP7BDLv6BoAHReSEZd9GLcGnSqlBAP81wnQTUYYxRgwA4NS8RCkS79z7DwO4AcBP/fe/mWHbTfCV7E2WDIOCrz/AK3Gmh4jSFEcMEKVevL33ywD8EkAVgHfgG7J3VCnVAOA/i8h3/NudBWAfgDNF5JTl/b8DUA5AAXjR/56/znZc9t4nIqJsYlfv/bhK+iJyBMClIZ7fD+A7lr/fBrA0xHZfief4REREFDnOyEdEEWNnPKLMxqBPRBHj9L1EmS3ejnxElEXYGY8oszHoE1HEnE4nh9sRZTBW7xMREWUJBn0iIqIswaBPRESUJRj0iYiIsgSDPhGlBMf8EyUfgz4RpQTH/BMlH4fsEVFKcMw/UfIx6BNRSnDMP1HysXqfiIgoSzDoExERZQkGfSIioizBoE9ERJQlGPSJiIiyBIM+ERFRlmDQJyIiyhIM+kRERFmCQZ+IiChLMOgTUVrgAjxEicegT0RpgQvwECUe594norTABXiIEo9Bn4jSAhfgIUo8Vu8TERFlCQZ9IiKiLMGgT0RElCUY9ImIiLIEgz4REVGWYNAnIiLKEgz6REREWYJBn4iIKEsw6BNR2uJ8/ET2YtAnorTF+fiJ7MVpeIkobXE+fiJ7MegTUdrifPxE9mL1PhERUZZg0CciIsoSDPpERERZgkGfiIgoSzDoExERZQkGfSIioizBoE9ERJQlGPSJKKNwal6i2MUV9JVS/5dS6lWl1CmlVMMM261TSnmUUhNKqR9Ynq9WSj3rf/5+pVRBPOkhormPU/MSxS7eGfleAXANgDvCbaCUygVwO4DLABwC8LxS6mEReQ3AzwD8XESGlFL/DGALgP8ZZ5qIaA7j1LxEsYurpC8ir4uIZ5bNLgQwISIHROQ4gCEAVyqlFICvAHjAv93dAK6KJz1ENPcZU/M6nc5UJ4Uo4ySjTX8pgHctfx/yP1cG4CMRORn0PBERESXArNX7SqlRAKeHeOlHIvIb+5MUNh1bAWz1//mpUuqVZB07BZwA5movpbl8bgDPL9Px/DLXXD43AKizYyezBn0RccV5jMMAzrT8Xel/7giARUqpPH9p33g+XDp2AtgJAEqp/SIStuNgppvL5zeXzw3g+WU6nl/mmsvnBvjOz479JKN6/3kAX/D31C8AsBHAwyIiAJ4CcK1/uxsAJK3mgIiIKNvEO2TvaqXUIQDNAB5VSj3uf/4MpdQwAPhL8V0AHgfwOoBfisir/l18H8A2pdQEfG38d8aTHiIiIgovriF7IvIggAdDPP8nAG2Wv4cBDIfY7gB8vfujtTOG92SSuXx+c/ncAJ5fpuP5Za65fG6ATeenfLXsRERENNdxGl4iIqIskbZBfy5P8auUWqyUelIp9Uf/fWmIbS5RSr1ouX2ilLrK/9q/KKXesry2MtnnMJNIzs+/3WeWc3jY8nzafndAxN/fSqWU2/8b/oNS6jrLa2n5/YX7X7K8Xuj/Pib8389Zltd+6H/eo5Ram9SERyCCc9umlHrN/139Vim1zPJayN9pOong/P5WKaVbzuM7ltdu8P+W/6iUuiG5KY9MBOf3c8u5vaGU+sjyWlp/f0qpu5RSH6gww9CVz//nP/c/KKW+aHkt+u9ORNLyBuAc+MYlPg2gIcw2uQDeBFADoADASwDO9b/2SwAb/Y//GcDfp/qcLOnuB/AD/+MfAPjZLNsvBnAUgMP/978AuDbV5xHv+QH4a5jn0/a7i/T8APwNgC/4H58B4D0Ai9L1+5vpf8myzT8A+Gf/440A7vc/Pte/fSGAav9+clN9TlGe2yWW/6+/N85tpt9putwiPL+/BTAQ4r2LARzw35f6H5em+pyiPb+g7b8L4K4M+v5aAHwRwCthXm8DMAJAAWgC8Gw8313alvRlbk/xeyV8aQIiS9u1AEZEZCqRibJRtOdnyoDvDojg/ETkDRH5o//xnwB8AKA8WQmMQcj/paBtrOf9AIBL/d/XlQCGRORTEXkLwARi66CbKLOem4g8Zfn/GoNv3pBMEcl3F85aAE+KyFER+RDAkwDWJSidsYr2/DYB2JWUlNlARPbAV6gL50oA94jPGHzz21Qgxu8ubYN+hDJ1it8lIvKe//H7AJbMsv1GTP8R/8Rf1fNzpVSh7SmMT6TnV6SU2q+UGjOaLpD+3x0Q5fenlLoQvhLKm5an0+37C/e/FHIb//fzF/i+r0jem0rRpm8LfCUrQ6jfaTqJ9Pw2+H9zDyiljAnT0v27A6JIo79ZphrA7yxPp/v3N5tw5x/TdxfvKntxUWkyxW8izHRu1j9ERJRSYYdQ+HN058E3z4Hhh/AFmwL4hnF8H8CP401zNGw6v2UiclgpVQPgd0qpl+ELJCln8/f3rwBuEJFT/qdT/v1RaEqpbwJoANBqeXra71RE3gy9h7T1bwB2icinSqkb4aux+UqK05QIGwE8ICKfWZ6bC9+fbVIa9CVNpvhNhJnOTSn1Z6VUhYi85w8KH8ywq28AeFBETlj2bZQyP1VKDQL4r7YkOgp2nJ+IHPbfH1BKPQ1gFYBfIcXfnT9NcZ+fUmoBgEfhy8SOWfad8u8vhHD/S6G2OaSUygOwEL7/tUjem0oRpU8p5YIvU9cqIp8az4f5naZT0Jj1/ETkiOXPX8DXL8V478VB733a9hTGJ5rf10YAndYnMuD7m02484/pu8v06v1MneL3YfjSBMyetmntU/5AY7R/XwUg3RYfmvX8lFKlRrW2UsoJ4CIAr2XAdwdEdn4F8E1cdY+IPBD0Wjp+fyH/l4K2sZ73tQB+5/++HgawUfl691cD+AKA55KU7kjMem5KqVUA7gBwhYh8YHk+5O80aSmPTCTnV2H58wr4ZkcFfDWIX/WfZymAryKwVjEdRPLbhFKqHr4ObW7Lc5nw/c3mYQDf9vfibwLwF3/BIbbvLlk9FKO9AbgavjaKTwH8GcDj/ufPADBs2a4NwBvw5dx+ZHm+Br4LzwSA/wOgMNXnZElbGYDfAvgjgFEAi/3PNwD4hWW7s+DLzeUEvf93AF6GL1jcC2Beqs8p2vMDsNp/Di/577dkwncXxfl9E8AJAC9abivT+fsL9b8EX7PDFf7HRf7vY8L//dRY3vsj//s8AC5P9bnEcG6j/uuM8V09PNvvNJ1uEZzf/wvgVf95PAWg3vLeDv93OgGgPdXnEsv5+f/uA/DToPel/fcHX6HuPf/14hB8fUr+M4D/7H9dAbjdf+4vwzKaLZbvjjPyERERZYlMr94nIiKiCDHoExERZQkGfSIioizBoE9ERJQlGPSJiIiyBIM+ERFRlmDQJyIiyhIM+kRERFni/w+PAN0LVsnRlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Iterate\n",
    "\n",
    "# Set number of iterations\n",
    "\n",
    "nitr = 5\n",
    "\n",
    "# Loop\n",
    "\n",
    "for intr in range(nitr):\n",
    "    \n",
    "    # Compute and plot phase space trajectories\n",
    "\n",
    "    state = torch.linspace(0.0, 1.5, 21, dtype=dtype)\n",
    "    state = torch.stack([state, torch.zeros_like(state)]).T\n",
    "\n",
    "    table = []\n",
    "    for _ in range(count):\n",
    "        table.append(state)\n",
    "        state = torch.func.vmap(lambda state: mapping(state, knobs))(state)\n",
    "\n",
    "    table = torch.stack(table).swapaxes(0, -1)\n",
    "    qs, ps = table\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.xlim(-1., 1.)\n",
    "    plt.ylim(-1., 1.)\n",
    "    for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "        plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "\n",
    "    # Find fixed points near previous values\n",
    "\n",
    "    points = torch.stack([hp, ep])\n",
    "    points = torch.func.vmap(lambda point: fixed_point(64, mapping, point, knobs, power=period))(points)\n",
    "    points = clean_point(period, mapping, points, knobs, epsilon=epsilon)\n",
    "    chains = torch.func.vmap(lambda point: chain_point(period, mapping, point, knobs))(points)\n",
    "\n",
    "    # Plot chains and selected pair\n",
    "\n",
    "    for chain in chains:\n",
    "        point, *_ = chain\n",
    "        value, vector = torch.linalg.eig(matrix(period, mapping, point, knobs))\n",
    "        color = 'blue' if all(value.log().real < epsilon) else 'red'\n",
    "        plt.scatter(*chain.T, color=color, marker='o')    \n",
    "        if color == 'blue':\n",
    "            ep, *_ = chain\n",
    "        else:\n",
    "            hp, *_ = chain\n",
    "\n",
    "    ep_chain, *_ = [chain for chain in chains if ep in chain]\n",
    "    hp_chain, *_ = [chain for chain in chains if hp in chain]\n",
    "\n",
    "    ep, *_ = ep_chain\n",
    "    hp, *_ = hp_chain[(ep - hp_chain).norm(dim=-1) == (ep - hp_chain).norm(dim=-1).min()]\n",
    "\n",
    "    plt.scatter(*ep.cpu().numpy(), color='black', marker='x')\n",
    "    plt.scatter(*hp.cpu().numpy(), color='black', marker='x')\n",
    "    plt.plot(*torch.stack([ep, hp]).T.cpu().numpy(), color='gray')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    # Recompute parametric fixed points\n",
    "    # Note,  not strictly necessary to do at each iteration\n",
    "\n",
    "    php = parametric_fixed_point((order, ), hp, [knobs], mapping, power=period)\n",
    "    pep = parametric_fixed_point((order, ), ep, [knobs], mapping, power=period)\n",
    "\n",
    "    # Update\n",
    "\n",
    "    lr *= 2.0\n",
    "    gradient = derivative(1, objective, knobs, php, pep, intermediate=False)\n",
    "    knobs -= lr*gradient"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb20443-9c78-42ea-bf02-50eeb5036859",
   "metadata": {},
   "source": [
    "# Example-09: Fixed point manipulation (change point type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b079f14-529a-4a06-b5d8-3fa6647edc80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example real parts of the eigenvalues of a hyperbolic fixed point are minimized\n",
    "# First, using a set of initial guesses within a region, a hyperbolic point is located\n",
    "# Parametric fixed point is computed and propagated\n",
    "# Propagated table is used as a surrogate model to generate differentible objective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4448001c-cda4-4a15-b1d1-7c06012d5ef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import nest\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import clean_point\n",
    "from ndmap.pfp import chain_point\n",
    "from ndmap.pfp import matrix\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1551340-9be8-436f-a573-c5db0e165072",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45a302dc-8062-41dd-be20-839e42dd6214",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set mapping\n",
    "\n",
    "limit = 8\n",
    "phase = 2.0*numpy.pi*(1/4 + 0.005)\n",
    "phase = torch.tensor(phase/(limit + 1), dtype=dtype, device=device)\n",
    "\n",
    "def mapping(state, knobs):\n",
    "    q, p = state\n",
    "    for index in range(limit):\n",
    "        q, p = q*phase.cos() + p*phase.sin(), p*phase.cos() - q*phase.sin()\n",
    "        q, p = q, p + knobs[index]*q**2\n",
    "    q, p = q*phase.cos() + p*phase.sin(), p*phase.cos() - q*phase.sin()\n",
    "    q, p = q, p + q**2\n",
    "    return torch.stack([q, p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb19177b-a44a-4057-9d2a-dd4a21fe5415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Locate fixed points and select a pair\n",
    "\n",
    "# Set initial knobs\n",
    "\n",
    "knobs = torch.tensor(limit*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute and plot phase space trajectories\n",
    "\n",
    "state = torch.linspace(0.0, 1.5, 21, dtype=dtype)\n",
    "state = torch.stack([state, torch.zeros_like(state)]).T\n",
    "\n",
    "count = 1024\n",
    "table = []\n",
    "for _ in range(count):\n",
    "    table.append(state)\n",
    "    state = torch.func.vmap(lambda state: mapping(state, knobs))(state)\n",
    "    \n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qs, ps = table\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.xlim(-1., 1.)\n",
    "plt.ylim(-1., 1.)\n",
    "for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "    \n",
    "# Set tolerance epsilon\n",
    "    \n",
    "epsilon = 1.0E-12\n",
    "\n",
    "# Compute chains\n",
    "\n",
    "period = 4\n",
    "points = 4.0*torch.rand((512, 2), dtype=dtype, device=device) - 2.0\n",
    "points = torch.func.vmap(lambda point: fixed_point(64, mapping, point, knobs, power=period))(points)\n",
    "points = clean_point(period, mapping, points, knobs, epsilon=epsilon)\n",
    "chains = torch.func.vmap(lambda point: chain_point(period, mapping, point, knobs))(points)\n",
    "\n",
    "# Plot chains\n",
    "\n",
    "for chain in chains:\n",
    "    point, *_ = chain\n",
    "    value, vector = torch.linalg.eig(matrix(period, mapping, point, knobs))\n",
    "    color = 'blue' if all(value.log().real < epsilon) else 'red'\n",
    "    plt.scatter(*chain.T, color=color, marker='o')    \n",
    "    if color == 'blue':\n",
    "        ep, *_ = chain\n",
    "    else:\n",
    "        hp, *_ = chain\n",
    "        \n",
    "ep_chain, *_ = [chain for chain in chains if ep in chain]\n",
    "hp_chain, *_ = [chain for chain in chains if hp in chain]\n",
    "\n",
    "ep, *_ = ep_chain\n",
    "hp, *_ = hp_chain[(ep - hp_chain).norm(dim=-1) == (ep - hp_chain).norm(dim=-1).min()]\n",
    "\n",
    "plt.scatter(*ep.cpu().numpy(), color='black', marker='x')\n",
    "plt.scatter(*hp.cpu().numpy(), color='black', marker='x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4530497d-a868-40de-afb0-f381d1f8a905",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[2.739351528140e-01, -1.149307994931e+00],\n",
      "        [5.971467208895e-01, 1.145141455240e+00]], dtype=torch.float64)\n",
      "tensor([-1.784726318725e-15, -1.784726318725e-15], dtype=torch.float64)\n",
      "\n",
      "tensor([[1.251182357765e+00, 1.288812994113e-01],\n",
      "        [1.428760927086e-01, 8.139613303868e-01]], dtype=torch.float64)\n",
      "tensor([2.545448611841e-01, -2.545448611841e-01], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Matrix around (dynamical) fixed points\n",
    "# Note, eigenvalues of a hyperbolic fixed point are not on the unit circle\n",
    "\n",
    "em = matrix(period, mapping, ep, knobs)\n",
    "print(em)\n",
    "print(torch.linalg.eigvals(em).log().real)\n",
    "print()\n",
    "\n",
    "hm = matrix(period, mapping, hp, knobs)\n",
    "print(hm)\n",
    "print(torch.linalg.eigvals(hm).log().real)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38a502cd-5c99-4f37-8930-19ff1acb4a4e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute first order parametric fixed points\n",
    "\n",
    "order = 1\n",
    "\n",
    "php = parametric_fixed_point((order, ), hp, [knobs], mapping, power=period)\n",
    "pep = parametric_fixed_point((order, ), ep, [knobs], mapping, power=period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6bcf637e-9728-4a02-8576-e95d223f229e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Propagate parametric identity table\n",
    "# Note, propagated table can be used as a surrogate model around (parametric) fixed point\n",
    "# Here it is used to compute parametric matrix around fixed point and its egenvalues\n",
    "\n",
    "t = identity((1, 1), [hp, knobs], parametric=php)\n",
    "t = propagate((2, limit), (1, 1), t, [knobs], nest(period, mapping, knobs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1f3dbdf-a82e-471f-b9bd-d2d4936f2322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set objective function\n",
    "\n",
    "def objective(knobs):\n",
    "    hm = derivative(1, lambda x, k: evaluate(t, [x, k]), hp, knobs, intermediate=False)\n",
    "    return torch.linalg.eigvals(hm).log().real.abs().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62111448-0df6-47f1-aa16-36aef2e125b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5.090897223682e-01, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Initial objective value\n",
    "\n",
    "print(objective(knobs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa8d62df-3655-451d-898a-e11f884a95cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.974190541982e-01, -4.137846718749e-01, -5.970747283789e-01, -7.018818743275e-01,\n",
      "        -7.018818743275e-01, -5.970747283789e-01, -4.137846718749e-01, -1.974190541982e-01],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Objective gradient\n",
    "\n",
    "print(derivative(1, objective, knobs, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02353143-4095-4641-8d5b-3ee4ced18609",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set learning rate and update knobs\n",
    "\n",
    "lr = 0.01\n",
    "gradient = derivative(1, objective, knobs, intermediate=False)\n",
    "knobs -= lr*gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79a03c77-351e-4f1c-9bd3-95d8edcabe01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4875424149522949\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.44253915640171704\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3947019262885917\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.34869831355705994\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAHWCAYAAAB9p1B9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAACOYElEQVR4nO29e3Qc133n+b0gwEc3RQLshmmKECBAUACZ9CE5AWw1aKFpb4uiYR5aFrW7AumEaSDHmgng8RnONiIfrwMgOj7rAY7onQx0EnMiIRzJJpzxJg5WQuuB2BRluqWQDmXLshoSLY0caZ24mlacCSBblPnbP7pv8Xah+lmPfv0+59Tpd9Wt6qr63d9bEBEYhmEYhql+6ko9AIZhGIZh3IGFPsMwDMPUCCz0GYZhGKZGYKHPMAzDMDUCC32GYRiGqRFY6DMMwzBMjWCL0BdCPCyE+LkQ4kcZPhdCiD8RQlwSQvxQCPFvlM+OCSFeTS3H7BgPwzAMwzCrsUvT/wsAB7J8/nEAN6eWzwD4UwAQQmwBMA7gwwA+BGBcCNFk05gYhmEYhlGwRegT0VkAv8jylU8C+G+U5DkAjUKIbQDuAPA0Ef2CiN4G8DSyTx4YhmEYhikSt3z62wH8g/L6zdR7md5nGIZhGMZm6ks9gHwRQnwGSdcAvF7vb3d3d5d4RAzDMEyhvPfee0gkEvD7/aivrxgRVHK+//3vJ4io2ep63DribwG4QXndknrvLQD7DO+fMVsBEZ0EcBIAenp66MKFC06Mk2EYhmHKDiHEG3asxy3z/jyA301F8d8K4JdE9DMATwLYL4RoSgXw7U+9xzAMwzCMzdii6QshTiOpsfuFEG8iGZHfAABE9GcAFgAMALgEYAVAOPXZL4QQ9wM4n1rVHxNRtoBAhmEYhmGKxBahT0SDOT4nACMZPnsYwMN2jINhGIZhmMxUdEW+RCKB6elpJBKJUg+FYRiGYcqeihb6s7OzGBsbw+zsbKmHwjAMwzBlT0XnS4TD4bRHhmEYhmEyU9FC3+/3IxKJlHoYDMMwDFMRVLR5n2EYhmGY/GGhzzAMwzA1Agt9hmEYhqkRWOgzDMMwTI3AQp9hGIZhagQW+gzDMAxTI7DQZxiGYZgagYU+wzAMw9QILPQZhmFqCO5ZUtuw0GcYhqkhuGdJbVPRZXgZhmGYwuCeJbUNC32GYZgagnuW1DZs3mcYhmGYGoGFPsMwDMPUCCz0GYZhGKZGqBmhz2kqDMMwTK1TM0Kf01QYhmGYWqdmovc5TYVhmFoikUhgZmYGADA6Ogq/31/iETHlQM0IfU5TYRimmkgkEpidnUU4HDYV6LOzs5icnAQAeL1evv8xAGpI6DMMw1QT0mUJwFSgh8NhLC8v688ZBmChzzAMU5Hkcln6/X5MTEy4OCKmEqiZQD6GYZhqIZdpn2EywUKfYRimwuBsJKZY2LzPMAxTYXA2ElMsLPQZhmEqDM5GYoqFzfsMwzAMUyOw0GcYhmGYGoGFPsMwDMPUCCz0GYZhGKZGYKHPMAzDMDUCC32GYRiGqRFY6DMMwzBMjcBCn2EYhmFqBBb6JiQSCUxPTyORSJR6KAzDMAxjGyz0TeC61gzDMEw1wmV4TeC61gzDMEw1wpq+CbKuNbesZBimXGC3I2MHLPQZhmEqAHY7MnbA5n2GYWqSRCKB2dlZhMPhirDqsduRsQPW9BmGqUkqTXNmtyNjB6zpMwxTk7DmzNQiLPQZhqlJpObMMLWELeZ9IcQBIcSSEOKSEOI+k8+/IoR4IbW8IoT4Z+Wz3yifzdsxHoZhGIZhVmNZ0xdCrAHwIIDbAbwJ4LwQYp6Ifiy/Q0T/Qfn+ZwHsUVbxDhHttjoOhmEYhmGyY4em/yEAl4joNSJ6F8AcgE9m+f4ggNM2bJdhGIZhmAKwQ+hvB/APyus3U++tQgjRBqAdwLeVt9cLIS4IIZ4TQtyZaSNCiM+kvndB0zQbhs0wDMMwtYXbKXv3APgmEf1Gea+NiHoAHAHwfwshbjL7IRGdJKIeIuppbm52Y6wMwzAMU1XYIfTfAnCD8rol9Z4Z98Bg2ieit1KPrwE4g3R/P8MwDMMwNmGH0D8P4GYhRLsQYi2Sgn1VFL4QohtAE4CY8l6TEGJd6rkfwF4APzb+lmEYhmEY61iO3iei94QQowCeBLAGwMNE9JIQ4o8BXCAiOQG4B8AcEZHy81sAfFUIcRXJCciX1ah/hmEYhmHsQ6TL4Mqgp6eHLly4UOphMAzDMIwrCCG+n4p/swTX3mcYhmGYGoGFvg1wn2uGYRjGYWwpm89C3wYqrVsXwzAMU3H47FgJN9yxAe7WxTCMmyQSCczOziIcDnOr3drhsh0rYU3fBrjPNcMwbsLWxZrkPTtWwpo+wzCMS9ilobN1kSkW1vQZhmFcIJFI4O6778bY2BimpqZMP883IJiti0yxsNBnGIaxCVVwG4X47OwsnnnmGQDAxYsXV/2WTfaMG7B5n2EYxiKJRAIzMzM4d+4cFhcXsby8jPPnz2NhYQEAEIlEEA6HoWkaLl68iJmZmVXrkKb6Q4cOYXp6uiAXAAf2MfnCQp9hGKZApJA9dOgQ5ufnsby8jMnJSQDAwMAAAGBhYQEDAwO6MPf7/WlmfTlRAIDR0VHdZD89PY2xsTEAyclCrjGEw2HMzMxgcnISy8vLmJiYcGKXmSqBhT7DMAzMtWVVMA8ODmJ+fh7hcFg3xZ85cwYLCwsYHx/H+Pg4gKQABwCv15tR804kEhgcHMTi4qL+XSngDx06hDNnzuDQoUNZxyvH8NRTT+Hdd9+15yAw1Q8RVdzy27/928QwDGMnU1NTBICmpqZWvQeABgYGCACNj4/rSzwep6mpKdI0jYiINE1Ley1R39c0jUKhkL7ezs5OisVi+udm4zAjHo9Td3d32viM22WqByQb2FmWn6zpMwzDIOlTX15exvLyMhKJBPx+v/7eysoKAGDHjh263358fFzX/KU2L7Xv5eVleL1eU/M/AF3D7+zsxKVLlxAOhxGPx/VxaJqGp556CocOHUJXV9eqsSYSCRw/fhzxeBzBYBANDQ04ceIE+/OZnLDQZ5gKg4O2nMHv98Pr9WJsbEw3t/v9foyOjuLYsWNYWFhAd3c34vG47reXvndp8pcm+eXl5VXm/6mpKd2/v7y8DCDpMjh+/DgWFhYQCoX091966SUsLi7i+PHjePzxx1f957Ozs/pvAOjfPXXqFJ8TTFZY6DNMhSG1SSB7oBdTGIlEAsvLyxgfH9eF89LSEu68807E4/E0gX/ixAmcPn1a/67xP0kkErqmv2/fvlUTNDXY7tSpU5idndWtAV6vFydOnAAA/dG4fmmBkFaH7u5uLCwsYGZmJmssAcOU3D9fzMI+faaWyeQ3ZopH0zTdZ6/60uV73d3daf57o9/djv8k2zrMPpNjGBgY0Mc2Pj6eVzwAU3mAffoMU5vI1C7GPqS5XE2xA5CmcXd1denH3VgG14n/RGYOrKyswOPx4MCBAzh27Jg+Fun7f/755/HQQw/plgCp6TOMKXbMHNxeWNNnGMaIFW27HKwnRuuBmjmAlLUBKc1eIi0RSGUVMNULWNNnmOolV7De0tISjh8/rmt9tYw8VmqEfKFadzlYT4wV+Q4dOqRr8h/84AcBAC0tLbr1AUhaIF555RVcunSpJGNmKg+uvV8CCmmswdQmueqwy4jv48ePuzyy8kMNclMj5CsNOfGYn5/H2NgY5ufn4fF4cPbsWSwtLeHBBx/E/v370yZ5XV1diMVimJqa0osCMUw2WNMvAZUYfc1pYu6Sq3WqMbq7FjD6uGXpWjWXfmVlBTMzM/pnlXjeqv+9rAa4vLyMUChkWqWvHKwUTAVhh4/A7aXSffrl4D8slHyrhDGMUxh93OPj46ui6eVi9ItX6nmrZhVU8n7kSyXeG90C7NOvXCpxZp5L82QYu1HjFnw+H5aXl9OuG5mjDiTPy6eeegqLi4sIhUKrztdKOm+NzXxOnDiB3t5eAJn3oxItGmZUohW04rBj5uD2UumaPsMwuZEa7sDAQFpOuqrZq/Xmq0VLVPcNeWr3lWjRMPu/quU/dALYpOmXXIAXs7DQZ5jqQd7ojc1rotEoNTc3UzQaXVU8p5qFQzwep4GBgbQmPLmoxONRiROVUmKX0GfzPsMwJcGYaifr1D/11FPYu3cvzp07B03T8MADD2D//v04ceJEWknbajX/zs/PY2FhAfv27cu4j0ZzfiUej0p2vVSyG4WFPsMwrqHeNKX/VjajkZHpCwsLWFxcRCQSwdq1a7Fjx46a8vPmIwzlsTtz5kzFNtmpxIlKNcQcsNBnGMYRZIodAD2FTr1pqsJNCq1Tp06t+k0ikUBzc3NFaYRWUIWh2TEEkgV8Hn74YSwsLGB2drZiBVClUYnWiVXY4SNwe2GfPsOUH0a/sppGJ98fHx+n8fHxqgu+cwrjMTS+rwYyVhpm5wOTGbBPn2GYUqOa62dmZjA5OYnl5WVMTEzoRXOAa/3mJycnMTU1pWus1WAudRK18NDy8rJexVO2AFa1/0pDng9AskkQ///uwEKfYZiiUYW2ZGVlBdPT0wiHw2l9481Mo1VhLnWQy5cv4/z582hvb8f09DSApIA0Tp4qEeOkkHEHkbQaVBY9PT104cKFUg+DYWoetZDM6dOn9felUCon7S2RSGBqagp/93d/h507d8Lj8WBlZQUvvvgibr75Zrz88su4cuUKAKChoQF79+7F2NhYSQXrJz7xCSwsLKCzsxOXLl3C+Pg4BgcHudlSDSKE+D4R9VheDwv9yqUa0keY6mB6ehpjY2MYGBjAiRMnMD8/X/LzUq3VDwAXL17UK/jlS1NTE44cOYKJiYmS7Mtzzz2H3/md38GHPvQh/PznP8fMzAxOnz6NyclJhEIhnD59mq/9GsEuoc/m/QqG/aGMG+QzuQyHw3qefbb8cqfHKC0OKysrpkK+v78fQoicmv7Pf/5zvPHGG3j77bfx4IMP4sknn8SnPvUp1zX/Z599FpcuXdJb587Pz+ufLS4u4tixYxWbsseUBhb6FQz7Qxk3kAF6mqbpqXNGIeP3+3Hq1Cl9cuA0RiEv6/DLiYckFAphz549AJDWmS+f9U9MTGBhYQFXr17FpUuXMD09jb/7u7/DN7/5TdeEbDgchqZpeP755/HhD38Y4XAYly9fxrlz5/Duu+9yyh5TOHakALi9cMoew6ym2PS3XL8bHx8nABQKhUpaNlUt1yvHIh+RSl+Lx+M0Pj5OkUjEtlSweDxOPp9P304kErFhb7Kj/ifGcrXydSgU4nS3GgKcsscwjEqh7h5jGdzl5WV4vV69u5vU6EdHR1e97ybSN69q89Jsv2fPHuzduxfAteI1asaAHXR1deHcuXPYu3cvLl++jIcffhjDw8OOBtFlKmIkH6VFY+3atY6NgalS7Jg5uL3UoqbPRUyYXGRqXGNGPB6n7u5uAkAjIyPU3d1NIyMjBXd3cwK1aItsPgMTbd5tLTcej1NzczMBIK/XS7FYzLFtGa93s9fyuIyPj/O9oQYAd9mrLbgjFZMv+ZwrUmB0dnZSZ2enbi7Od9JgN1LQRyKRVSZ7lJEpOx6Pk9fr1QV/PB53dHtS2EsXi/qfGj+r5Op8TG5Y6NcYrOnXJlLTlcIlH20+17miaZouXCORCAGg7u5uxwWYGdI/HwwGdUGvCvlSTEByEYvFyOPx6ON0knz897FYTLdAsFJQvbDQZ5gaQGq63d3dujYshUCum7yZ8C+HvvSqVi+tDHKf7AzAcxI5WWpqanLczJ/L3WI8R5jqhIU+w1QxUgMeGhrSBePAwIAubCKRiKlAN2t4o/p8S9moRVoYVEEvXQyRSKSiBJamaXpEv8/nc3TsuRrTGK1BTOlxYjJtl9Dn6H2GKUNGR0f1CPVQKIQbb7wRCwsL6O3t1XvPy0ItMgpf0zRMT0+nNbwBks1ZsrWzdRpj9D0AdHZ24lOf+lRBufPlhN/vxz333IMHH3wQly9fxsTEhN4C14lteb1ejI2NmTam6erqwuOPP+7ItpniKOvCaXbMHNxeWNNnqg2jNic1+vb29jRtXUauS/O+qv1Lv/j4+PiqdZdLYF4wGKRQKFQVWqmmadTU1EQAaMOGDY7uk1n0vprhkC3Sn3Gfctb0Sy7Ai1lY6DOVSqabgfTVw+BrN97Q1f7qMuVOClUzk7/bSGGkCno5xkrw1ReKTHMEQIFAwLXtqueBOjFUP+OgvuqirIQ+gAMAlgBcAnCfyee/B0AD8EJq+X3ls2MAXk0tx/LZHgt9plLJdENWA/Ry5dcHg0Hq7++nWCxWshQ7I2b++koKzCsWTdOosbGRAFBjY6Nr+6lp2qqJlRT6rOlXJ2Uj9AGsAfATAB0A1gL4AYAPGL7zewBmTH67BcBrqcem1POmXNtkoc9UKpluyPneqFUNr1w0OTXCHKnAvGoW9EZUbX9kZMS17aqC3zhZZMFffdgl9C231hVCBABMENEdqdefBwAi+r+U7/wegB4iGjX8dhDAPiK6N/X6qwDOENFpZIFb6zK1igyKA1DSADi14c3x48exsLCA/v5+rF27FjMzMzXV5z2RSKCzsxO//OUv0draijfeeMPVbcv2wWpQpGx1PDU1VX6BZExRlFNr3e0A/kF5/SaAD5t877AQoh/AKwD+AxH9Q4bfbrdhTAxT1uTTrtYMWVu+2N/bwdLSEu68807E43G9BvzAwIDjLV6NnfUSiQSWlpbwH//jf8R/+S//BZ/97Gfx5S9/GVeuXMGePXv0vgFOHx+/34/29na88MIL2LJli6PbMtu21+vF5OQkAMDr9SIcDmN5eRnj4+N590ko5fnEuItbKXv/L4DTRPRrIcS9AE4B+FghKxBCfAbAZwCgtbXV/hEyjEskEgkcO3ZMbwFrpokZNXoAaa/dTglSBa4U+N3d3Thx4gT27dvniLBYWlrC8ePH8cUvfhHPPvus3hjI2D73Bz/4ATRNw/nz56FpGgDge9/7HgDg/PnzrvSb93g8AIC3334biUTCVcEp2+9evHgRhw4dwuzsLCYnJzE1NZX3OMo6xYyxF6v+AQABAE8qrz8P4PNZvr8GwC9TzwcBfFX57KsABnNtk336TCVh9K+qKXaZfPvGaH6jL99Nn63qs1ervzmVoiaLzfT396dlKRjTFkdGRigUClE0GqWBgQGKRqMUDAapr69P/wyp6Pb+/n5HU+rkfwqXWu8aUQNE1TLL+e4zxwCUPyijQL56JAPw2nEtkG+H4TvblOefAvAcXQvkex3JIL6m1PMtubbJQp+pJKQAl9HVavAVUfoNV35XCi1ZqS5XVTYnkFUBpfCVVd/sFg5SyEvhLbcXDAZpYGBAz1IoNFBN0zR9wgCAmpubHRP8mqbpqXPBYNCRbWTDWJVPnaQx1YFdQt+yeZ+I3hNCjAJ4MqXFP0xELwkh/jg1yHkA/14IcQjAewB+gWQ0P4joF0KI+wGcT63uj4noF1bHxDDlSiKRwJ49ewAA999/P6anp9Mq6UlefPFFnD17Fnv37tVNtHb3ic81TmnGB5Dms7fL/CvN9//6r/+Ks2fP6ub5YDCIgYEBnDhxAl1dXUgkEnjiiScwNTWlB6tJc/RTTz2FPXv2mFb28/v9+Na3voV7770XP/zhD6FpGm677TY8++yztgca+v1+fPSjH8Xrr7+Om266ydZ158P8/DwWFhawb98+RCIRnDhxAgD0RyC33579+rWBLT59IloAsGB474+U559H0uxv9tuHATxsxzgYppyQN9EDBw7g/PnzGBwcxOzsLKanpzE1NYVnn30WY2NjaG9vBwCsrKzopVY1TcPZs2ddH69aSjYej+vlcsfGxmwRBOo2ZFleKeQ/+9nP4oEHHkBXVxf8fj98Ph8A6D5qiQxWk759WdpXokayd3V14cyZM1haWkIgEICmabj33ntx5swZy/ti5NKlS2mPbqKWVwaSpXlPnTqVJsRz+e3NPpcTMzkBYyofrr1fxfDMvTTI4y4Dz7q7uxGPx/WAN+DazVkNSvN4PLomnUgk0NzcnHf0tdXxGmvjj4+PY2pqyrZzx3hMgKRgWbt2rR6o197ejrVr1+LBBx8EAL3OvIxGl8JcjunUqVNp6WoAVk0OpPDq6urCBz/4QZw9exY//OEPsbS0ZLsQ+/CHP4yzZ89CCOF6MJ963kxPTyMcDutC/MyZMzhx4kTWiP5EIgFN0xAKhXDbbbfpViX1nOD6/lWCHT4Ctxf26ecHl+N0HjNfu9rdLlPxFPX3hQZd2T12tbKb3eVyjYGJ8liZHS/phy52DGq9f7mo64nH43rfeSf8+5qm6f0PShHMR7Q6oE/+t7JSoho7YvYfyOMvn1dTr4RKB+USyFeKhYV+fnBErj2ox9F4wzSrkGcWmGdsgqPi9uRMjksGzDkh7CVm7X2NxzBTnwE7tms89vF4XG+JKwMp7UQK/VIE8xGtDuhTs0Dk+2r1RPWclRMmuQ9qS2e+h5Qeu4Q+m/erGDuDrmoZ1dcJwLQQysrKCpaXl5FIJNJ+Ozo6qn8vE0aTv5OohXUkoVAIp0+fttUcrebYSzcBgFUmfmmCl+epXedrOBzGU089hcXFRZw7d043t3d1dWFoaAjT09N6QKWd3HTTTXjmmWewsrLiuokfuBbQ19vbC6/Xi8HBQQDJOAfgWsvmUCiEvXv36v+LLPo0PT2NZ555Bp2dnbjjjjv0ls6zs7P6f8NuwwrHjpmD2wtr+sVRiZp/OYw5m6YvUbV11VxdLsdapt/JtLLOzk5HmuEYc+zVlDFV68+WfqhpGo2MjFBraysFAgGKxWI0NDREPp+PTp48mabJZkOtL6BaUWKxGHV3d1MsFrNnpxVU03g2644TqOem1PDlfquWj2znpbGPgpl5n92GpQFs3mcKJR9Tc7nh9A0m33xvo/nZrL+5+pmZsCkFMmZAmrThUGEdo7A3y7HP1mwoEolQIBCgYDCY1sAGAG3atCntNQBqaGigaDSa17jUegdE13LYnfDrx+Nxam1tdb35DtFqf77x/My3zoNZrId6HpfDRLwWYaHPFEwlCv1cN5hib0DGG1umm5pZNTpVm88k1Et9YzRrdevz+dKEnx2YCftQKEQjIyOmGqfZb9VqdgBow4YNq4S82eL1esnr9dLc3FzG8Zn59uPxODU1NTnmew8EAgSAWltbXf3/7T7nSlEQiskMC32mYEotiMzIdGMxCt5M4y7WEmCMVlY1IinAIpFImqk+m6ZfTqhR2wCoo6PD9ghsuf9yO6pmr5qH1QA+STwep/7+fr0P/ebNm2nz5s1pAr29vZ0CgQAFAgEaHh6mQCCga9Bmwj+fY6FOdvv6+ggA9fX12XZMJDIQTp5DpaZQLT/TeV3O53wtwEKfqQqMmpgxxUvVBM0sFPneiMzMnaowyJS+Vkk3OjPtvrOz05F0K7VcsOpjl/9ntmwA9RirS2dnJw0PD2ecoEhf/5YtW9J+d8MNN1BHR0dGH70xop2IdBeCEyb4eDyuT2gCgYDt6y8E4wQw2+TYaNUy/nfsyy8tLPSZqsCY2ywF/NDQkF433UxTM67DrDa7WWqdmRnfmMbU399fcbnJsVhMz0GXAtQJs6w8VlJoqsc3kwVEava9vb3U29tLLS0t+jhbWlqov7+/ILeDpmm6pm6cNJhhFlgpJy1OTYqktWjDhg2OBAzmi9GiJZsVmVnW1GZKZsK9kibA1QgLfabikTcRKUBkkRp5MzYzqxt/qwpsNedYFeIySj2TEMxHOy1X5ORGBuo1NTU5NmHRNC1tIpbp+BtR/wvVfG9lnJqmUUdHR9o6e3t7M5qljRNH9fdO+PXj8TitX79e9+2XChnIaIyxMB4LVcN3oqkSYx0W+kxFkI8/Xq1aJ33CZp3VzH6rVnFThbs6cTAL1st3jOWKmTuiubnZEa3S6HKR0f/ZzMFyMhCLxWhkZEQXgJs2baJgMGjLpCQej68K+stkrpcWJNXH7qRfn4ho+/btBIDWrVtXkmqLRjeZvBakZU0KfeOkl4V+ecJCn6kIsvkBs2nruTCm0Rk1GPWzaruJGf20TlsozKrqqe+bCXwZHS992+pkwU52795NAEgIkdXEb5a5YjYRsBM19dBt377xP1OvMeP/KK8leTzkuVVOdSYYFvp2HEPGBQoJtLOSHpTP7ytRo1fJFmzoBKq2buazz3Q8zQL1nBpnb2+vHhtQV1dHDzzwQNZ9UScdZu/ZiaZpeo2BlpYWR7aRbdvG/ynXJFhOjGTaJQv+8oKFPpMR40VeCRdtMX5FVdDn8vur1oBKiz42i8p3WrtXm9PIqnrqf2QW5BWJRKi/v1/XvqWG6+Q4d+3alTa5aGpqMv1eKTR9oms5++vXry9ZQJ/R2mZ2LIjS/9/x8fGyKTDFJGGhz2TE6O8ux4vWqCUa/fD5RBCrkcny+6pmYjwOZjnj5Y7RlO9UVL7EWGxHxgmoFgYz7c9YYMdpK4REZgI0NDTowXxmmAl4KZCdNL2rcQft7e2ObScbma41s/8nmyXHGBRYSddRNcBCn8mIFU3fLRO4UfuQN2WZ920mXIyaSCQSob6+Pr2evDpRyGUFKHfkDVbthOdUepm6Tandy2I78tjJMWQy90oB6qbAJ7rm029paaGGhgY6efKk6ffMtFunA/kkqguiHM7BbBabbBizMMpRmahmWOgzjuBWAQ7jxEQNIsokpFXNXQ3cM0bvZ8rLrwTkZMVtU75Ru5fR+fI/ypTbLU36119/vS7Y3NQCe3p60gL5fD6f6ffMmuw4WaBHRbWClEOFPqLCylvL57FYjDX9EsJCn3EEt4PdpPCORCI5zfDGG5HU9tU0PTVXv1K0e7kvIyMjq6rp2V0r30gm7T4fbdDMpJ+vUHv0UaK2NiIhko+PPlrc+KUWLav0HT161PR7alqoxC2hr9YE6OjoqIhzUo3+l+eBrKOR65yspGuvkmChz1QUmaLrVbOrMTq9kMjhSrzRSE1Zuifc8turmHWcy5Z/Lz+fmprStexCTfqPPkrk8STvPnLxeIoT/HIM9fX1WQW4mYB3y7xPlD5BMgbQlSPGPP9QKJQ2Ic22D5VqZSt3WOgzFYXqF1ZvBsa0Kfm9TIF81UIsFktrdysDytws/ysnHZn6pWcS+NJ/f9111xGQbJhTiEWirS1d4Mulra3wfZCavjrxMMPMpy8nDD09PYVvuEA07Vojp/b29oop8WwU/vKcVd1oxv/drJ0xYx0W+kxZY9TsM2n6Rq2gEtMNC0HWoVcrybW3t7t+gzQz3+dz7NXJW7F+aiHMhb4Qhe1DPB5P687X2tqaUZia5eS7qekTXZt4SFdKJWF2bmRK/cs0wWeswUKfKWuyXfj5FHipNqRWrWr3Ho+HRkZGXN13Yw97qc2rqYGZTLdSg7v99tvTXBGFjt8uTV/NbABAR44cyfhdMwHllk9fomma7sopdSMeO8hU50Cd4Muy2k4WQKqF+wcRC307jiFjI8aLTzUdG+vo15LPz8xv39TUZFv9+UJQg/Zkwxwp8NV0rExCX5r16+rqCEj2sS9mH+zy6UvTfq7IfSJzAeW20CdK/gcej0ef9FWKmd8M40TKTACrsSFOUEv3Ehb6TFlhZqZXL/hKjawvFqkVG/PXnc61zzYeKfDVoD2i/LsMqhMXq5qq1ej9WCymu0iEELR582aKRqOm381kxZCNZ9w2tas1+T0eT8Vq/Mbr2EwAG/37max8xd4TauFeImGhz5QVmW4Atdaq08yMXyq/vSSTwDemPWYaWywWo/b29jShX2qftBpJLq0nmchUga4Umj5R8rjLCQeQrCZYqYJfYnYuGYMA1foa6nP1O2oWT64Mllx1JKoNu4R+PRjGBvx+P8LhMGZmZrCysgIAGB8fx+joKPx+PyKRSIlH6CxLS0sYHR3Fu+++i7NnzwIAfD4f7rnnHvj9fv04lGJct912GzRNQ3NzM5599ll0dXUBAGZmZjA5OQkAmJqayji+cDiM119/XX+9fv16fPWrX3V+8BlYWlrCr371KwCAx+PBysoKOjs7M34/kUgAALq6ukryHxjx+/345je/iUOHDiEWi+HKlSvYu3cvHn/8cRw4cKDUwyuK2dlZTE5OYmBgAAsLC/B6vQCAsbExjI+P6+/39vbqz3fs2IGBgQEcOnQIDz30EABgZWVFXxcAeL3ejPeO2dlZjI2N4cyZM1hYWNDfHx0d1e9DHo8Ho6OjAJLnu/y8HM6DUsFCn7GFRCKBY8eOpV182QRJtZBIJDA1NYWHH34Yly9fBgAEg0E0NDRgZmZGF7ClGtvBgwehaRp8Pl+awFcJhUIIh8Om63juueewsrICv9+Py5cvg4iwdevWku7XvffeizfffBNAUki0tLTgkUceyfj9paWltEfJ888/n/boJn6/H/Pz85iYmMCf/umf4urVq7owrETBL8+fQ4cOYd++fWnnk3w+OzuLcDisXycAsLCwgH379sHj8QBITuLC4TCWl5fTfptrm++++y4WFxdx7tw5ANAnDQBw/vx59Pb25jWRqAnsMBe4vbB5v/yQ5rlAIED9/f1Vm6OrVs8LBoNpEeQ+n6+s9ltNEVMD2Mxa5mZCVpJbu3YtAaXtFkeU7stXj3uu3xhL8BIRbdu2jQDQtm3bnBxyTqLRqB6MKITIGJtQ6UhzvLHPhlryudhrR40hGh8fT6uJgJQ7qtLvS2CfPlNOyAtZXmzVFE2r1pg3BubJG4qbRXXyQQ1eM/qyzUrSZmLHjh0EgNasWUMA6Prrr3dy2FnRNG1VrMT69euzCslsqYjbt28nALR9+3aHR54bVfDX1dVVvI+fKHN3P3mPkHEZsuy21QZh6nOz8t6Z7kuVEgzIQp8pGWaV3NSLrBIuoFxomkYjIyPU2tqqt29Vl0AgQMFgsOw0h3g8TsFgUNdwzDoVyptuPkV1jPu+detWJ4efEWPwm1w6Ozuz/k6tF2EU+kNDQwSAhoaGHBx5/kSjUcvpkOWEMZrfTBB3d3frCkM+VTiNQj5bR05jn45MgX6VkvbHQp8pGcZWq0TmFc8qESk029raVgmY9vb2su4wpkbpy5uo8UaYraa+kVgsRuvXr0/T9Jubm53ejVWopX+lCdzn81Fra2tOjThbSdhSRe9nIxaLkdfrLUt3UaGY1e6QwlfeL+RkLJNVyrg+9fyVk4h8S3Zn0uhZ06+AhYV+6VB92qqmXymzZTOkYDhy5IheOEUumzdvpt7e3oq4+UoNftOmTdTf3582Actm7s+E9Odv2LBBPy4bN250chdMMevml++5lqlqHNG1PP1y63xnnLz5fL6qMPcTpd8n1JS7XNUg1d9Kt4CMCYhGo3kpHJV8jyJioW/HMWSKINOFUymzZYk033d0dFBra2uaMNmwYQP19fVVhKCXxONxampq0mMMjKhBffl2ebvpppsIAN100026Lz1X0JydyP/IGLi3Zs0aGhoayuu/kROh/v7+VZ+VY597STweT6tF4PF4Kup8zITZfSLfmh7GQEB5fPK1XlXaPcoIC32mJJhdOJVwMck4hGAwqFspjL7hI0eOUGdnZ8VpVapJ2FhtjyhdgOSr5RORLmzr6ur09QshXDk+sVhMn8QYl46OjrzWEY/H9dgGM6GuaZoewb979267d8EyctKjWp9KWeTJKdT7Rz7auNHMr7oKKlWLzwcW+ozrZBLu5Wo2U6uEmQWB9fX1UUdHh+tNb+xE0zTdFJwp+EtOcAptjjM3N6cHlqmCZ9OmTY4dLzk5M2r3xZi689nvTZs26ftUrhi1fiCZcdDe3l5xE9Rc5KtAGGOIKlUZKQQW+ozrlLtpX/oGh4aGKBgMmgr6cg/GK4RoNJqm4ZsJADXqvRgT9vve9z4CkqlxMpgPALW0tNgatCm1WrVVrrrU19fnbdKX5BOot2vXLgJAu3btsmEvnENOhmScRSW7ouxA3otkYKDxelZjWKolo4iFPuM6+cyu3UQNKgwEAqbmYBm5nau+fKURj8epoaFB1/Az7Zf0fwaDwaL2vbGxUT+WwWBQzyWXgthqIZl4PE6BQICuu+66tP9NFgMq1ueey7QvOXLkCAHZ2/KWE1L49/b2rrKGbN++vSTdG0uBsa6/eq3LSYD6fjlaIguFhT7jOkZNv1Rm/Xg8Tn19fWkCSTX/Sk2/WrWfWCymm9uzCd54PK4H4OVTiMeMaDSadnxvv/32NMEvhCCv10vXX399Xm4SKeTf//7309atW/WUQHUiob6uq6sryv2ST6tgItKDOFtbWwtafzkg00uN18GmTZuq0vRvhpz4G2N01AlAtTT8YqHPuIqa7ywvIjcvJqnhmGn0fX19evnfatdyYrGYruHn0mKlWX/Lli2Wjsvc3NwqLdzj8aQJf7l4vV7aunXrqmXbtm20c+fOVUJeCvZNmzaRECKtHK3P5yvakjA3N0cej4eOHDmS9fyUOd7d3d3FHp6SI4W/rDAoF4/HUzWurFyoVj8Z/1Dpmr0RFvqMq6jRsmqKjRs3E7NAps2bN1Nra2tFB+EVihq019DQkHPfZUGbQCBgedvRaHRVDYOWlhYSQqzy9+ezrFu3joBrRX+ksF+7di01NDTQyZMnix6repxyCfONGzcSUJr6A3YjJ8Y9PT2rTP/BYLAmhL8068ssnGrQ8CUs9BnXMJbdVVNmnJxNx2Ix6uzsTMujb2trqwmN3ojqo25qasrLdDs8PEwAaHh42JYxaJqmr1NdhBDU29tLQgjdPC+FjnyUEwYp7GVWgBT2zc3N5PV6aW5uzvI4ZQDf5s2bcx6nahL6KtIyZ+wVUU2BrGaovnyze1Sp45CswEKfcQ0z372TF4801anNVTo7O6vWR58LY2e5fPzzqt+/WH9+JqLRKPl8Prr99tsL0u7VZePGjeT1emlyctK0A16xaNq1pjy56vITXau/n88EoRKR11K1pqwaMXNDmhUCqkTTPwt9xjXUqH2nhX0kEkkz5ft8vqq6KRWK0Yff1NSUl5VDWgU2bNjgqFVEnQBs2bKFgsEgeTweGh0dJa/Xu+pxbGyMmpubHWsfK7MV8rWGaJqmpwm2tLQ4MqZyIFdxqmAwSIFAgAKBQEVPrrOlFcuaHZVq5WChz7iGeiE5NVNW82qlllapF6ddGAV+a2tr3gJ89+7dBJRnpTmniEaj+vEqxLohgwvXr1/v4OjKC7UMdV9f3yorQCAQoL6+vopLAczUUU9tElaJWj6RfUK/HjYghDgA4D8DWAPgz4noy4bPjwP4fQDvAdAADBHRG6nPfgPgxdRXf0pEh+wYE2Mfhw4dwpkzZ3Do0CH4fD4sLy9jeXkZiUQCfr/f0roTiQRmZmZw7tw5LC4uAgA6Ozvx2GOPoaury47hVyRPPPEEPvGJT+Dq1auor6/Hvffei4mJibyP95UrV9Ieq52lpSUcOnQIV65cQUNDA2ZmZvL+7bvvvgsA+NWvfoWlpaWaOO/8fr9+jBKJBKampvDd734Xb731Fn76058iFovp392zZw927tyJffv2YWxszPI17wSJRAKzs7MIh8Pwer0YGxuD1+tFJBJBIpHA8vIyIpEIPB4PwuFwqYdbWqzOGpAU9D8B0AFgLYAfAPiA4TsfBeBJPf93AL6hfPavhW6TNX13kbPkUCiUVvjC6oxZ0671dlfXX8vaPVFSY1XT4YqJvpdpjXV1dVXpq1ZR6xE0NDQU7Do4efKkfqx7e3sdGmVloKa+9fX1rcoCKNfa/9kq9Mn7Vb6NpsoVlIt5H0AAwJPK688D+HyW7+8BcE55zUK/zFGj9eXFY9WvH4/H9RxpoPC68NVILBajjo4OPQJeRsUXY15VJw6VWHgmXzRN0+MXgOK75anlf52KN6hEYrEYtbe3U0tLS5rw7+joKCvhb6zQpyokLPTtF/p3I2nSl69/B8BMlu/PAPg/ldfvAbgA4DkAd+azTRb67qH2vFbT9qyg9gtvb2+3ZZ2VztzcXJp2X4zGakTeqNesWVOV2r7RUtTR0VG0EOrt7dXXs379+rIRZuWCDARUJ1hysl5O165Z0DFX5Cuh0Afw6ZRwX6e8tz312AHgfwC4KcNvP5OaHFyoZs2l3FAL8dhh1tc0TdfwzdrA1hqxWIza2trSBH5jY6Mtx0XmqyOVklbpNz2JFEBq/Qafz2fpmMXjcb2GAAC66667bBxx9WDW+KecJu5uBB27hTFTqpyEfl7mfQAhAC8DeF+Wdf0FgLtzbZM1ffews1uVuq5y0xDcJhqNruooJ4SgXbt22XZcNE1Lq8seCAQqXvDHYrFVZZitCnx13ep677rrroo/Xk5hll7b3t5e8pgcNTWvktPziFanH5aT0K8H8BqAdlwL5Nth+M4eJIP9bja83yS1fgB+AK/CEARotrDQdx419UXmPlsR1MaUvEr3rxVDNBqlxsZGam5u1ivSSWHf2trqiAnemPbX2NhYcXUP1L4LamBZY2Oj7dUZ1aA+aepnH39m1NbNcgmFQiU9v9SqfJWq5ROVsaafHAsGALySEuxfSL33xwAOpZ4vAvgnAC+klvnU+31Ipuv9IPU4nM/2WOg7j5rXOj4+rpvkBwYGilqfnDiUw03BLWQjlB07dtDmzZvTBL0U9tddd53jQkWtzieX6667jtra2srW1y8rqw0PD6eZ8YFrPeSdshQZBT9SUf21bJnKhlQQ1PiK/v5+x6/xTIXCpNCvtvtMWQl9txcW+s5jLHKhBsgUippSVW1R+tFolJqammjXrl00MjJCR44cocbGRurs7FzVJhZIto5tbm52TLPPRKZ2xBs2bKCenh7q6emh3t7ekvU1kH0W7rrrLtNocac0+2zjUX38cpLm8/kcnXBUMpqmpZn7nRa6mXz2Vu5V5YxdQl8k11VZ9PT00IULF0o9DCZPgsEgzp49i6amJsRisbIufqIWKjEWtlm7di327NkDAPj7v/97XLlyBS+88ALee++9jOurr69HV1cX3nzzTfzWb/0WHnnkkZLufyKRwMTEBL71rW/h8uXL+NWvfrXqO01NTeju7kZHRwe+973voa+vD//wD/+AG264Ac8//zzGxsZw4sQJzM7O4tZbb824nZmZGSQSCbz44otoaWnBc889h1tvvRVvvfUWbrrpJrz88st6MZ14PI6333571Xp6e3tRX1+PtWvX4qtf/aqrx25paQm/8zu/g6WlJfzLv/xL2mcbN27EmjVrcMMNN2DLli04efJkWZ/XbrG0tISDBw/i0qVLAJLX/je/+U1HCvosLS1hdHQUe/bswdjYGABgdnYWy8vLmJycxNTUFCKRiO3bLRVCiO8TUY/l9bDQZ5xkaWkJgUAAb7/9NoLBIM6cOVOScciKXbfddhvuu+8+LC8vAwA8Hg8+//nP48tf/jKuXLmC+vp6nD17tqB119fXY8eOHfjIRz6Ct99+GwsLC/D7/bh8+TK+/vWv48CBA07skmWWlpZw77336sdCCIFLly6ZCl+VhoYGXLlyBU1NTfjgBz+Izs5O/OQnP9EFuUSt6pYPPp8PwWAQFy9exMc+9jG0tLRgdHS0LCrA/df/+l/xB3/wB2hoaMA777yz6vPGxkYMDAzgu9/9LrZs2YL6+mSxU4/HU3MTgkQigUAgoAv+kZGRgiok5sv09LQu7EOhEPbu3YvJyUlEIhG89NJLOHHiRFUddxb6LPQdRS1rCUB/XugN+Pbbb8fi4iJ8Ph/OnTvn2kUotYCuri54PB5cvHgRi4uL6O7uRjweT/tuc3MzNE3TX/f39+PKlSt5afrVdlOXE4F33303q6b/hS98Ie2YZSIQCKChoSGnpv+Rj3ykbEu8qkhL0NNPP43XX38d7733nj5pyoTP58M999wD4Np5o1Jt5xCQPI/27NmDd955B01NTXjllVds/28TiQQOHz6sT9LHx8fh9XpZ08+FHT4Ctxf26TuLWoFPrXJVaMS9pml6P++RkRFnBptCBs319/dTNBrVi/+oy8DAAMViMQoGg7ofW34/GAxSX19f+VQZe/RRorY2IiGSj48+WuoRpSEj6vv7+2loaIiCwSANDQ3pndrKpme7w8dRnnd9fX105MgRam1tpd27d+vnlzG9MNMiG0yp56d6bpYq1sIKap0Iu65/Y/CevDcFAgG9VoCTnUBLCTiQj3EKtSCPDOgrRuirqTNOpOjJizsWi6UJeflctuWtuHzdRx8l8niSl6dcPJ6yE/xlTxkcR5mFMDIyQiMjIxQIBPQJgVzUKndqaWr1XJbPY7GYHmArJwjlOiFQ60S0tbXZsk61xr5abU+mAxebXVQJsNBnHMM4Uy525izT9ILBoO0CV63dLx+bmpp07aiio3fb2tIFlVxsunHWDBVyHNVMmUyavrQYqJMC9XkwGEyzdJXL+b9r1y4CQLt27bJlfTIyX95b5H3JrhLh5QwLfaasUYt2FNsExYiqNcnUoO7uborFYllvcupNNR6Pr6rYFYvFKBQKUSQSSXteMsuAEObCSojSjKdSqaLjKIVdJk1fzZGX1gFpGSilqXv37t0EgHbv3m3L+owdP6VCIicA1QwLfaasMRb3sYrapEfVdIyCXi3DKYW82iFQfW6mManP5QQjFApRNBql/v5+CgaDzmsTFaKhlj01dBwzxbTI87m/v78kmrAsrGRXvxRjvJGxnkg1Y5fQry8m+I9hcnHbbbehs7MTd9xxB0ZHRy2ta2lpCbfddhs0TdMjof1+v57OpWYazMzMYHJyUv/t+fPnsbCwoKf0DA4Oore3FysrKwCSkdMHDhzAF7/4RezZswd33XUXvvjFL+LKlStYXFzE4uIiAOAHP/iBHq1+7733oqGhAVu3bsXzzz+PRx55JGO+elF86UvAZz4DpMaYGmjyfSZ/yuA4GrNIrg3Dg8HBQTz00EN4/vnn8eEPfxjDw8M4ffo0VlZW9M/la/kbeS3NzMzo35PrV9Nhn332WRw/fhxf/OIXcf/992NhYQFA8twFksqeGxkDH/nIR/D1r38dH/nIRyyvS9Z+2LFjB3p7exEOhzE7O6tH6pd75kfZYMfMwe2FNf3yRwbWhEIhS+tRNXyzrnyqb1/NNOjv79c1/WLMm1KDMNP0VVMqlFgCW10CZR69XzG4cBzV3gDBYJCi0ajuIlL7TRgXo9Up12t5jqtWNKQsaap1y3gOqj5voxvASc1frcRp9T5AlG49lKb8ao3UNwNckY/z9MuZ0dFRPPjgg5YKc6gafnNzM5599ll0dXWlafbHjh3DwsICuru78eyzzwIovqZAIeMaHR3F1q1b8cQTT+Dy5cv6Z1u2bMHg4CAmJiZY86hSZK7+888/j5tvvhnPPPOMXogGSK/7EIlEcPHiRUc1fQBp1q2BgQGcOHEC8/Pzq64DWYfhhz/8Id5+++2068pu7KjRsbS0hOPHj+PEiRPw+Xz6vaRcija5Cefp1zCVMLuV0bVWgvikpqNqJGq3PtVnX6qoXalFqWlXQLLNaDVHEtca0vIzPDysa6/q0tHRYarpu3GNqnEsauoaDAFvKrksaHYg8/St5OjL/ajmVLx8AQfy1S6VEK1qVeibpeGoQTxS6JcLmqbRyMhIWuvXhoYGbsta4cjz0NjpT7p0hoeHyypVTCoExgBWmdeuCn9V8Hd3d9s6QTFOzoul1JP6coKFfg1TCk2/0G0WW9CHaLWfXm5bbZlZrtG6MopadtgTQrDgr1Bisdgqrb6lpYU6OzvLtiWxihrZLifhxs538XhcT3+1cxLtZI2OWoWFPuMqhVgXrKTRaJqWVnRHrQgYiUTK3q0hiUajJIQgAFRXV1cRQoK5RiwWo4aGBl3Yt7a2lm3lu3xQq2PKSpvGzzo7O23ZP7XFbjEBfJXgviwFLPQZVynkQrRi2jfegAoxE6ougZMnT1JTUxP19vbSyZMnyefz0eHDh2nXrl20ZcsWeuCBB6i9vZ0CgYBjN/JoNEp1dXUEgDweT8UKjFpCummkwK+vr6eRkRFXBFAsFqPu7m46efKkfm7GYjEaGhoin89Hc3NzNDIyQu3t7QWPSU6eVZ+/WnFTxqQEg0HL+2F1ElEJ7stSwEKfKVtkJb5CbyBmpkZjH4BMvxsYGKChoSFdm1mzZk3GVClpdpfPN23aRFu2bHHEDK82Henv77d9/U6zf/9+AkD79++nubk53W0xOjpKR48eJQDk9XopGo3SzTffrH+3UlH/r4aGBkctNLFYTBfu0WiUvF6vvl05hvXr1+vP5QRSLjKocHh4OO8JQKZJdE9PDwGgnp4ey/tlRzwPa/qrYaHPlC3FXvQy8Eia9fNxE8Tjcb0u+dq1a3WB7/F4sgr96667jurq6tKE/7p168jn89kq/DVNo+uvv54A0J49e2xbr52ogn1sbEw/HidPnkw7ZlIomS3GaonDw8P689HRUX2yMDw8XOrdzYhq0ndK4Guapmvu27dvN52ktrW1ZT1316xZQ36/P20S0Nvbm/e5a3Z9yvx9qxNTuwL4mNWw0Gdco9CZd7ERt7J8aDQaXVVuM9N2zFroyqWxsZG2b99Ou3fvpt7eXurt7dW7fmXS+uVN1U7NX5pOyyWoT9M0uuWWWwgAHTx4MOPxa2hoKFrTzya01q1bRwDo6NGjpT4UOpqm6UF7Tgj8aDRKPp+Pent70zR3o/auLlu2bKFAIEDDw8MUCARWZRCYWQC2bNmS89ozC7JVrz0rqMG2dmcD1Lr2z0KfcRSz1J9sJnaVYiP31e3kunnEYjFTzbOxsZF6e3szBl3F43Hq7++n3bt3U0tLS5r51MwlUIjpNBOxWEy/MTc1NVlaV7Gogl4+yiWbpl8smTR99XirywMPPGDj3haO1H4B+3q/ExHNzc2R1+tNO1fNBH1rayv19vZST09PxvNXrfwn2/JKK5dcbrjhBgKS/eUzYWZBK9YlZ1yvU1o++/lZ6NtxDJksqL501Xybz0VXjNBXG4bE4/GsLgJVK5PLhg0bioquln5VVRgZNX87ugTu2LGDANCOHTssr6sQpECXWrhcVE3fTaLRqK7pmy2liAeIRqO6Wb+zs9PyJE814Wfb15aWFurr67NUxCcej1NfX58+qZLL+vXrC9L27TDvW9XyVW3ervbe1QQLfcZRrGj6xZj3jbX6M00czHKnvV6vZXNsLBajLVu2mGpjdhTZsbvFaDYmJydNhUypBH0mMo3TbeEvteX6+nrLGRZzc3NpgXhGYdzY2GhZ0JthTDHMdd4aJ9VWzft25Pur1zxr9qthoc84SrZZdy6K0fSNJTszbVPeWOTS1NRkWyqcTNeSAl8Ikab1z83NFb3ulpYWXbtzGqOlolQadCGYxQG4UYktHo/T5s2bCbAeua5pmi54hRBpE8f6+npH00OJkoLfaFlobm42/a5R6FtpkGVWW6MY1PsGa/arsUvo14FhTJidncXY2BhmZ2cL/q1sELKitjTNgWyeoT5GIpFVTTXuuOOOtNe///u/b1uzEL/fj5mZGTz++OMQQoCIIITQPz9y5AiWlpaKWvf73//+tEcnuOOOOyCEwI033qi/98ADD4CI8OSTTzq2XTt48sknQUTYv38/AKChoQELCwsIBoOObjccDuOXv/wlGhsb8eijjxa9nueeew7d3d24cuUK6urq0NDQgKtXrwJINsp59tln8b3vfc/RVra33nortm7dCgBYv349hBC47777TL8rG/XIxz179qQ9FsLs7Czi8Ti6u7vxrW99q+hGOKOjo4hEIjh37hwuX75sev0zNmDHzMHthTV951Fn2oWa2orx7akugUyzfOl/v+666whINrVxShNQC+vIVEBY0AalZtXU1GR7ZLj6H8ml0lH3pa6uzpFAP03TdC2/tbW16PXMzc2lWVfU5243XlLrDMjtGzEG8sXjcUsNguysj88NdjIDNu8zTqP69Qspq5tPup0RdWKRyT3Q0dFhe4BdNtQbqIzsL7akrhp82NnZaes4pWlVBiOWuyk/Hw4fPpz2XwshbN+GWprWSsS+WRZJXV2da5X8VNS6FYB5FL/x+rIiaIu51rPBDXYyw0KfcRw1gl/eKPK9sAu9eNXvZ4rcDwQCaRqM0zdUVRNUg6TMtKd8kILs8OHDto6zmjR8I+q+2V3jQFZw7OnpKfpcmpubo7Vr11J9fb2u4ZeyHoOaelhfX78qDsWsNXV/fz8Fg8GCBa0q8PMN8lV/KxsBlWvzrHKDhT7jOOpFHYlEChLihQbz5aPpq+leblV2U7V9WeVv3bp1RWn7skhPsZMGI7I4jlzKJSrfTtQI//Xr19u6bln5rq2trajfx2KxVUGTdXV1JS3ApE6MzaxK6kRe7WZZjJYvJxj9/f0FC22jO6rQSUMtwkKfcRx1Ni61g3w1/UJL8cpmI7FYLKNPXxYQyWS2dAJN0/QqfuoNvhgT/V133UUA6K677rJlbOpNs7u725Z1liNOWDI0TdPL4BYbp2F0NwGgvr4+28ZYDEahb3RbqNeWlcY4dnTSk/cUuR5Oz8uOXUKfo/eZjMzOzmJychIvvfQSFhcXMTAwgHA47Mi27r//fsTjcdx///0Zv1NINoBd+P1+HD16FEByglxXl7xk2tvbC17XCy+8kPZohT/8wz/Un3u93qKyLCqF4eFh/bkxe6NYpqam8NZbbwEAPvrRjxb8+yeeeAJvvPEGAOgZHuvXr8fDDz9c1Hi+9jXgxhuBurrk49e+Vvg6EokErly5AgDYtm0bIpEIJiYm0j6fnZ1FOBzG5cuX8bXURo4ePVpwVsHMzAwuXbqEzs5OzMzMFDxWv9+P06dPY2pqCo899himpqYcu7cwBuyYObi9sKbvDmogn/roRCle1bcorQTGmb+sGgaAhoaGitmlotA0jTZu3JimQXk8noLXc+TIEQJAR44csTwmOKD9ljN276/UiFtbW4syKxsLRMFCMOCjjxJ5PEm7q1w8nuT7haD6843WC2PAnbG5VSFYKberZgoUcj9h7NP0Sy7Ai1lY6LtPoVG6xUThyvWHQiHTbIFYLEYbNmzQ/Yhusm3bNt2v3NDQQOvWrSu4WI8UNHa4JtSgsVpAFu/xeDy2pDxa+S/m5uZW9WnYsGFD0cKrrS1d4MulkFCDeDyeNhFRUxCNAXexWMxS8J5ayKfQfZbjkGPJ937CsNC34xgyeSB9f9Kfnu+FrgYM5XtjUEuBmlkK1KC6fAP5Hn00efMUIvlYqPYk6evr07ctb/her7egddjVs1xNZ3O6UU0sFjPtTKguo6Ojjo5BsmnTJgJAmzZtsrwu2RComIBQY9tmq9H6QpgL/ULmc1IQy7RN1epgvBatpOipaY7FlNuVmv7IyAhH7hcIC33GFYxRtvle6MWYANWbkVkgoIx+z1fTt8tsSpS8WcnMAZmXXaiws0PTj8Vijpv2R0dHswr5bEt9fb1j0et27nex/4Xa3VEWbdq6daulsVjV9OPxuG6JOnz48CqTuWp1s5qiV4yWbxaYW2wnzlrGLqHPgXxMVsLhMMbHxxGJRDAyMoJz587lVYrW7/frJT3zDcA7ceIEQqEQduzYob8ny4QCwMDAgP5cCIFEIpF1fV/4AmDc9MpK8v1C6erqwpYtWwAAy8vLAFBw8Nwtt9yS9lgMarDT4cOHi16PkUQigb1790IIUVRgluS9997Dxz/+cXz2s5+1bWwSGVC5Zs0afOMb37C0rvfeey/tMV+OHDmi///yt/X19ZbG8qUvAcppDiD5+ktfyv3bRCKBj3/84/jZz34GADh//vyq8rXz8/NYWFjA/Pw8jh8/jrNnz8Lr9RYUvJdIJDA4OIjFxUWEQiGcPn067xK5sqT3sWPHcl6zjAvYMXNwe2FNvzQUahYsNG2P6JoGEIlEVmkHsp94a2trXuu1w2yqsnPnTgKgd+MrVNO3o2f5wYMHCbA3Jz8fzf7kyZOmvzXWCjAuY2Njto2TiPSudfX19UWvQ61aV2hsiDwH1O55Vqr5SYp1Q6nmdmSwXEhN24ov34pZX3UpyA56HMRXOGDzPuMW6k2jmAI9hZgCc5n91AjlXDdsOwKkVKQJVS6F+vStRu/H43HbTftqrIJVYT02Nma6LjsnKHbsvzRR+3y+goWfzO2XgZTXXXddyQRXPB7XawW0tLRQf39/2v4YzerF+vLVtrnFBO/JsYyPjxdc78MK1dapj4U+4xrF9rbWNE3Xbu0q0qOm7bW0tGS9advp0yci2rp1KwHQMwgK1fTlDbqjo6Oo7auRz5s3by5qHZJ4PE433njjKgE9OTlpab1E5pYDuyooSsvC0aNHi16HLL9bTNqn1PSl0M/UutZp4vF4WoClcZJspl0XOmmXqEV0rAjQYoJ7C8F4vyj2vlWusNBnXMPYlasQCjVpq9qI2U1C1fSl5pENu6L3ia4J/VJp+rIkrc/ns5y2JjsVymXTpk22NjmZm5tL6ydvl+CX/4GV4DkrAZXve9/70vZp48aNRY+jWOLxODU3N6dNAI1uMDX91Yp2HY/H9QBaqw2unNa8jUKeNX0W+owF1AuqkItJCulgMJjX99XoYqmZGC/kSCSim1n7+vpcu6itavrSSlFsjQG7TPtGTdxuv7uKUeO3Olmx4xhYSZ2UDZjk4ramrwp8s/Q8onSNWl5/xZjlNU3Ta/MXUsTHSlEvK1SbkDfCQp9xFVXbL6TjntXUvUxFfmSedTGBRcVi1advJTecyB6BZ0z5c7pW/AMPPJC2PatuCTs6FVrR9G+//XYCrnVdtJquVwjxeFwPIt2wYQNFo1FTIafG4EhffDHBd6pZvxArkDrpkBMON6/TasUuoc8pe0xe+P1+eL1eTE5OAkDetbL9fr+eGpRvuo6aunf69Gk93Ujl29/+tv786aefdiUVyOfzAQCampoAoOBa4a+++mraY6GMjo6mPRbD/v37016fO3eu6HXlw/Hjx3Hy5En99S9/+Us899xzRa/vf/7P/5n2WAxWUie///3vA4Be4/7q1atFj6MQlpaWsHfvXvziF78AALzzzjt48cUX09LzEokEpqenAQCRSAT3338/Ll26hO7u7oLPmZmZGSwuLgIovDZ/OBzG1NQUTpw4gampKT11lykT7Jg5uL2wpl8aijWfFRMIJLUFGYRk/J1anQ8WoooLoZSavixDu3///oJ/K5mbm0sbv9PV/FTU7Vrxg6vrKRYrmr6MUZGBfIWeA4UiLWxSY5dLR0dHRj++bIMdjUaLCtyzUmrX7B5R7WZ3twCb9xm3KfbiVVN+CqnopwYPGrctfftqe1OnzYdWffpWhI0dwk5dh5X1FIMxna9Y7Jj8WPkfpB9dLg0NDbYGQKqo1w2QzFYZGhqiUCi0KjVPdYlJgV1MmV3jugq91qs9mK6UsNBnXCeX9p2NYgr1qDeQTOk+8XicWlpaCAD19vY6enOxGr1vRdjcfPPNBIBuvvnmgn8rUcduR2qele1bYXJykoQQRe+DFYuL/B9kSWYUGRCYC2OEvrSUGbGzmQ5RcbU1jOOp5rS5UmKX0GefPpM30lcHAGNjYwWVoZXldC9evJi3/z0cDiMSieCpp57CbbfdhoGBASwsLKRtd35+Hm+++SaAZAnSwcFBx/37GzZs0MdXCFZ8yffffz+8Xi/uv//+gn9rxh/90R/Zsp5isVKmd3x8HESE8fHxon7/4x//OO2xEGQswW9+8xsIIQAAL7/8clHjMCORSGBsbAx9fX3QNE0v8evxePDII4+s+v7s7CwWFhbQ39+Pd999F/fdd19RZXbltmWMx969e/Musyt/q8YT+P1+JBIJLC8vY3x8vOBrhXEQO2YOAA4AWAJwCcB9Jp+vA/CN1OfPA7hR+ezzqfeXANyRz/ZY0y8txZjsiu3BbTQ1GusFaJqml+WVi1P+fauavpUyvDJavKGhoeDfEjlTza9Q1IwLK2Owuo7e3l7dMlQo6rmmtte1o8mQ0ZwPgO66665Vfnn1+pPP5bXV399flB8/Ho/r6XlWzfqlqL5XC6BczPsA1gD4CYAOAGsB/ADABwzf+QMAf5Z6fg+Ab6SefyD1/XUA2lPrWZNrmyz0S4cVH500HRaSAmTsCmZWple269y9e7djgl/TNL27WrE+fSuV4KwKura2tpILfSJ7TPwPPPAA1dXVFR2IaMXNEovFaOPGjQQgrYTx+vXriz7f5Dku0/GME1gV1ZwvhamVznkSuc5C8vGN41JreMjxO1V9rxYpJ6EfAPCk8vrzAD5v+M6TAAKp5/UAEgCE8bvq97ItLPRLh5WWmGqxj0KCjFRtP9v21RK9dgt+Y7aAFP6FYKU4j505/sUW4ykHLZ2IaN++fQSA9u3bV9TvrZby9fl8+j6opXA3b95cUPEhM2G/Zs0aOnjwILW1tVEgEMgasCeLV0kLUrGBe+Pj4zQyMrIqQLCQdRiDbAut4Gk14M+OgMFyX0c5Cf27Afy58vp3AMwYvvMjAC3K658A8AOYAfBp5f2HANydYTufAXABwIXW1taiDyhjDat9sNXa+vkiNfmRkRGKRCIZbybGEr2FWhWycf311+s3Zfn8pptuKmgdVgS3rKJXqHVBUk1C3+o6ZFnZ9vb2on4fjUbTxqCa+desWUMjIyMZb/qxWIza29tp9+7deqc/45JJeBuDWeUEoFiTPlF697xizfB2BOsVsg4zoej2GEqxjpoT+urCmn7pMPMnFjKrlSd0d3d3UVW+5MWQKR84EolQf3+/3hhFCn4rM+94PK7Xkfd4PHpb1UIb51jR9K0KOqkNWllHOQhsotJr+kREJ0+eTNsXGXMhl40bN9LWrVtp69atdP3119Pu3bupp6dHdw0Zl/e///3U0dGxSrsnWl3WVl57kUikaO1crrfYfHyz8bmhIZu5N9weQ6nWUU5Cn837NUoxKXxW6nmrZkip1WfyGZoVM4lEIkVdjLJWu3pzr6+vL7iOvBVhY7WPvKZptghcK8iGQVbGYNXiQXRt8tXe3m7p5iz/T7ncfPPNaVp/tqWhoYHWrl2rP890LhmFnBQIhZTCzrVeK353t/Pwne7UV86Uk9CvB/AakoF4MpBvh+E7I0gP5PvL1PMdSA/kew0cyFf2GDWPQm9AxRTrkaj1vLPdtGKxGHk8nlU320LN/aopd82aNfo6d+7cWdC4iayZlefm5sjr9dLc3FzBv5WUWuir229ra7O8jmJR3UBWOsdpmqZnAshFZnjISZrU7I059wCotbU1q6vLTDDLay0SiVgStlbz8Y3rcauufi0X+ykboZ8cCwYAvIKk2f4Lqff+GMCh1PP1AP47kql5fwegQ/ntF1K/WwLw8Xy2x0K/tKizbRlRX2jb3WJvOrm68Bm/GwqFVqXabdiwwdSMaiQajerlVgGkBXAVI7itmpWtVqMrJ6FfrEnajn1Q0zyLieA3rku2TM53aWlpofb29qyWIjOBb2WybFy3XU1wiim6xRRHWQl9txcW+qXFeEMqxsxoxbyophfl075TjepXe7xv2LCBdu7cSa2trXrwVTwep97eXj09Ty5GTa2YFrGlbq2r/t7Jdrr5bL9YZDlfq+O3krZnRjQapS1bttCNN95I69evp76+PvJ4PDQ6Okper5fGxsbI5/PR0NBQXue60YytVugrNq2OyHo+voqdkwcmNyz0mZJiFlxUzDqKuWmYaTzZfH1S41eD2VQNXi4bN25MK6+qCnz1+8WmzVlNu7Oq6Ut/eCm0fbu2LXPkrTTtISJdOz9y5Iil9diJmupmnMzK66S5udlS4F4x8TSZkFp+MBisSXO727DQZ0qKXb61Ys2DRt9mPB7PaTnQNE33q6uavNkEQAhBGzduXBUQ2NraWvQ+W9UurdacJ6KSafvqdq0E4dk1aZGNmgrNwHAK1fKlWs7keT4yMlJ0Wp5x/YVmzpita2pqKs1yVat+djdhoc+UFLsaaRTr2zeLYs7HZSAnB7I6nrpI0//69eupsbFRD8aSS2Njo6WbpZXyr0TpAq/YcZRC27erwx4R0eHDhwkAHT582NJ67rrrLgKSZW5LifE8DoVCenyMXT58IvsC94iuXfuyha+cuFdKud1KDQZkoc+UFLsuHKupQ8bqX5nyeM1+F4lEKBAIpFVVMy719fW0efNm6u3ttVzkx6qmr+aCF1N9TaLun1XhmQtjIRsrQn9ubs6yi0UihanH43GsNW4u1HPVmPZqlw9fbsdO37tZBb5KEqKV2vmPhT5TNVgV/EbNIxaLFRRvIH3+hw8fps2bN9Mtt9xCfr+f1q9fbylFzojV6P25uTlav349XX/99UUFEkqM2r6TZn5jjISV42kMrrRCLBbT0+mKaYBklUwBdVKA2uHDz7adYqk0AW9Gpe4DC32m7LByMeWroWfbrrxRSi243Ap5WC3/SkR6pcGtW7da2idjHIMVP7sZmqbR9u3b07ZhJRaByF5Nn4ho165dBIB27dpleV35YjxXVS1evQbkBNaqFUJux2pVSonbefnMNVjoM2WHVbOZ9LcXe6Mzdhuzq+qYXdjhj1aFqBXz5MmTJ9PSF4HiswKMaJqmB8rZOamwWn7XiBxjMdUVC0W6oaQQNgp1uzVyuU450bQrj96u4kBM4bDQZ8oOaSYvttxtsXX5VaSQl2bRbH5Tt7FD05cTh507d9qyH2YpilYEtDFoz0rgohG7AxBjsZgerGnlP8mFMTLfaM4fHx/XYwzsSKWT67UzPU9drx1lgJnCYaHPlB3GpjiFYseNSg2Akt3HpI9f3qhKofVrmka7d+8mwHpueCwW0/3bVprGECUD7YwafzHC3xgn4ES8gN2aPhHp/8n1119v+zkhJ8EytU2NzJefy/Pd6mTXuF05ibCry6SxLoeV+hxMcbDQZ8oOYyR9Mdhh5pRuAmOfcVXjMt6AnUat9x4KhSytSxUUdmm9Zhq6cVm3bh1Fo1EiyizknRL4VgsTZUKt1jgyMmLLOo3nn9m5rE5OOzs7bTsX1Ymznb53NT6GNfzSwEKfqVrsSjEy+vjlus2KoDiNTNfbvHmzZc3LTk3fSD7CPNeyZs0aW7MeiMj2SY4kHo/rTZTq6ur0SU2hyAlvJBLRtez+/n5Td5cq8K1G5xuRk8vW1tai3Wxm2FGBk7EGC32mrLGaFmNXMRGjj1+OTVokYrGYpTiEfJFm5N27d9u2Tqe0X6LihL9T5Vij0aheo8CJfY3FYrqLQwiRd318omvnkqrVZzPVOynw4/G43hTKqjXJSKWmuVUTLPSZssZqJL9dkffqTTYUCq26cbml9W/bto0A0LZt22xbp1ParxknT55clebnVhnf9evXE5CslOgUxgqNmzZtor6+voyCOxgMUk9Pz6oshf7+/oymetX07oTAt3syoQr6Si1oU02w0GfKGqvpd0T2Cn61XKi6LjXjIB6PUyQSoVAoZHuVNtned+vWrbatU2r6O3fuJK/Xa7tJvVxwY3KjaRoNDQ2tKr28bt06amlpocOHD1Nrayv19vbqWRhy6ezspEgkklXYq+l6dgXsqeuXLgWfz2fbuqWgl1YxN2NgmNWw0GfKGrs0AztLiOZqOqJmH9gZXKWmhrW2tlpenxGpgQshbF93KZFdCaXp3Y6CPLmIx+MUCARo06ZNWV0ZHR0d1NPTkxYvkml9atMmJzJH1CBRO/vac3peecFCnylrpHYdjUYta/zypmNH+pFqYpUpfaqvXw3Eki4Bq8K/paVFD3BzogiMm2Z+N1H3q7m52dVta5pGIyMjtH379lWafr7xH6rJ3Y74lEzbkH58u6ruGWF/fnnAQp8pa9RCO/Kx2JuG3YVGjClVRh+o0Rwrg9T6+/sLDvhTG85s3rzZ0rgzIYPuZNlbJ4Ld3Ea6LqQvv9io+lIgs0aampp0k7sTgaJOxAjkK+B5IuA+LPSZskbeFKLRqK7tWDEPOlWmNFvwk5nwlxrVyMhIVj9uLBajlpaWNNO704KrmjT+St0X1b/uRMCeuh21rr7dfvxc1yoH9rkPC32mIrCzj7d6o7PLVJpP1LM0+/f3968K4gKSZWYDgQD19vbSrl27aOvWrWlV7twQ+ETXtGPZKrgSNf5K3gf1/ASSpX2datsrrys7Yl1U8i2wxZq++7DQZyoCGWQkzZtWo4DVm51dQUuFpDvJaP+RkRHTCYC6CCGosbHRddN0pWrJRJU5dmnOV88HJ/z36vakNcGJ7XBTnfKEhT5TEaitOK3W5idKN5+2t7fbFmFvzOfPZ3IiJwDDw8OrNP1SCHuJ1JZVM7PVtrZOcvLkSX2cra2tFafhq4V57Mz6MMOJRjpGrV21zrEJv3xgoc9UBOoNxQ5Nn2h1oxK7zJvGfH55Y3XKROs0RheDHf3Z7eSBBx4wbfZTKUgNX6b3NTU1OXp8c6WcFrM+NSVP1qgYGRmh8fFxLrlbZrDQZyoaqz5BTdN0Dcvu8q/GdqdNTU0587HLkQceeEAXpLK+/Lp168pmP4wV/gD3qvxZRdO0NHO+UwF76vbsjmeRlrdIJEIDAwNpzYdYuy8/WOgzFY0d0b+qf9+pHGg1z9rpG7uTqIJVVgfcuHGjI3UDsqH2C6hE7Z4omZmhnhdOa/hEzgTumWn6MjW1Us/zaoaFPlPR2BH9a4yWdkrwqznXlar1y+p2slCQXLZs2aKb/x944AFHtp1J0E9OTpIQwtF4g6tXr2Z9XSjRaFR3STQ1NTlSstmIE1q+cf2yQ6DU9uXEgqP0ywcW+kzFYueNxCj4nShzSlRdWv/hw4d1TV8VwnV1dXp1t4MHD+oThfr6+px1/Y8ePUpAslHNyZMn01r/qttwsjOgkfHxcfrcHXfQ1dZWIiHoamsrfe6OO4rSlOPxOPX29uouibq6OlesJE4LfIlqSVAj97kEb/nAQp+pWOwu7GFsb2pnsRIVtyqtucnY2Jiu6Xd1da3yscvF6/XSvn37CADt27eP5ubmdAFobMMr2+CWQtBLrl69Sp+74w4CQJ8D6GrqEUByIlCAxm8suONW3QUi5/LxM21HdglUI/i50U55wEKfqVicMhmqN0i70pnMMGr97e3tFS/8idLruJtp+sZJgPo6m6ZfKq62tuqCXi6fA5Kaf57E4/G0gL0NGza4JvCdzsdXMV6TaqBsIBBgwV8GsNBnKg4zYW+3qT8Sieg9zp3UjIzd02SEfCXViC+UbJp+WSIEXTUI/asAUZ7dCOPxuF4ZUFqQ3BJ8TuTjF4qx/DSb+EsLC32m4jAz68v37PTFS40/GAw6XiglEomkaYIej8eebT76KFFbW1JAtbUlXzMFYUXT1zRNLxQkNXw3YzhGRkYIALW1tdm+3VwTbbVDppq3z5p+aWGhz1QUmQrzqIFKdqYiySImUlNyOod6aGhIz4WX/v6iA70efZTI40lennLxeFjwF4BVn74UutK94WZqo6Zpae1y7UatkmmGWgCIBX35YJfQrwPDuMDs7CwmJyfh9Xrh9/v19/1+P/bu3QsAOHfuHBKJhOVt+f1+nDp1CuPj4+js7EQ8Hsedd95py7ozbe+hhx7CG2+8gVAoBAC4fPky+vv7MTo6Wvh2v/AFYGUl/b2VleT7TF4IIdB466343B134CutrRBC4CutrfjcHXeg8dZbIYTI+NsnnngCf/qnfwoAaGhowLPPPotbb73VlXEnEgkMDg7i8uXL8Pl8eOSRR2xb78TEBCYmJrBiPLcM39uxY4d+3czOztqyfaaMsGPm4PbCmn7lkc2kqGrmdqfcOdGSNxuaptHIyEhaBHtTU1NhmqIQ6Vq+XPL0RTPXKDRPPxqNup6Wp6KWgLYzJkXteyH7YBivBfU65Kj98gNs3meqCfWGI33+dgX5OeFCyEUsFksz9xdUyKWtzVzot7U5PeyaR6ZjAsmWyW6iaZoehNre3m7LeT81NUXxeJzGx8dpZGQk6zkoJwZyksyBe+WFXUKfzftM2dDb24vx8XGEw2EASZfA2NiYZROj6kL4zne+g4mJCcdM/ZJbb70Vf//3f4++vj40NTXh7bffxuLiInbu3Iknnngi+4+/9CXA40l/z+NJvs84xhNPPIF/+Zd/AQBs3rzZNtN6vkxNTeG1114DANx9991pbrBikNfP6OgoJicnsbS0hMXFRczPz6/6biKRgKZpCIVCmJ2dxdTUlH4dMlWGHTMHtxfW9KsDVZOXWoYM9rO7y5cxuM/pvGfjttUI/7yKu3D0vuts3ryZANCaNWtcr7aoniN2aPlynVNTU7rLYGRkJGOnRTvaXjPOAtb0mUpHaiIzMzNYXl7G+Pg4VlZWMDk5icnJSZw+fRqRSAR+vx+JRALT09NFa+gyuE8G2i0uLuLYsWOOa/xy29FoFL29vRBCgIhw6NAhLC0tZf7R0aPA//gfwNWrycejRx0fZy3zxBNP4Je//CUAwOPxoKury9XtT0xM4PXXXwdgj5YPJM87qa2HQiF4PB4sLCyYavrhcBjj4+OIRCJYXl525bpgSoQdMwe3F9b0qwOZxif97Wqtbxj877nSjArdprGxiFtEo1E9yK++vp56e3srtoZ/NSF9+W6W2JXE43E9/sPn89nmy1ctaPJcl+9nipexu0Q2Yx+wSdOvL81Ug6lUEokEZmdnEQ6HLWsjfr8fXq8Xi4uLCIVCWF5exuDgoP756Oio1eGabnNiYgIAcPbsWXznO9/Rt2WHdpWLAwcO4MUXX8QHP/hBXLlyBefPn0cgEEAsFnNdu2SSLC0t4d133wUAfOADH8CBAwdc3f5nPvMZrKysYMOGDXjsscds8+UDSQ1+eXkZADA4OIjTp09jZmYGADA5OQkAiEQi+m+lZYD9+VWMHTMHtxfW9EuHXRq3xOh3zLReu+v1G338bqTzqUSjUaqvr9e3b5cflymc3t5e/X8IBoOubjsej+tWBru2nanctTGmhVPyKguwT59xC6v+9GxIv+Pzzz+fdVt+v1/XSOwYi1rAJxgMYmFhAYODg675Mg8cOIAf/ehHaGpqAgC8/vrraGtryx3ZX6EkEgmMjo7ipptuwtGjR7Fv3z4cPXo04+sPfehDaGtrw913340bb7wRu3fvRl9fH8bGxmz/j15++WUAwJo1a/DVr37V1nVnI5FI4ODBg3j77bfh8/ls27a8pmZnZ/VjNTs7i4WFBYRCIYRCISwuLq4qlFXuOHkfqinsmDm4vbCm7y6qn8+JDnmq3zGXr11t+WlXdL+xO5+bPnZj455S+JRzIeMgRkZGKBAIUG9vL/X09FAgEKChoSEKBoM0NDRE/f39NDw8rD8GAgH9O8bmRFaW7du30549e6i9vd2W4jmyI6DX67XhaOWPWognEonYsk55fcpzWl4faglsNX/fiW6XTlHr8Qbg4jyMWzjVClddv7wpyZrnmdp5qgLaWMzH6valYHK75ris3S8rwbkp+KPRKPl8PrrrrrsoEAhQT08P9fb2UiAQoKNHj1J7e3ua+dvK0traSh0dHXTkyBEKBoN05MiRjK97e3uptbWVDh8+TG1tbbRr1660jndyqauro5aWlqKFfzwe11P13CzGo7YxtrN7n5r6agzmM14nlSZEnb4PlTt2CX2RXFdl0dPTQxcuXCj1MBgHmJiY0AOMgGTBEjXQKJFIYGZmBisrK1hZWcHS0hJmZmZsCYJbWlrCwYMHcenSJXR2duKxxx5zNbjuiSeewMDAAIgIHo8Hb7zxhq3m10QigampKTz99NN47bXX0NHRgaWlJbzzzjt5/T4QCAAA3nvvPRARGhoacMstt+AnP/kJbrrpJly6dAk333wzXn31Vdx888348Y9/DAC45ZZb8NOf/tTy/7S0tIR7770Xy8vL+M1vfoMXXngB8v5VX1+Pe++9FxMTEwUdsw996EM4f/48Nm/ejOeff961//v222/H4uIifD4fzp07Z9t2jYG28j+/ePHiquNvZ1Au4zxCiO8TUY/lFdkxc3B7YU2/epFadyQSyRpo5FQxEdWS0Nzc7Ho63dDQUFpwnx3bj8fj1NfXR5s2bTLVwD0eT1ZNf3h4uCyDvqLRKDU2NtKaNWvSAvEKGac8Jps2bXJwpOnE43G93K5dZn0ic01YPZ8rRaNnzEE5mPcBbAHwNIBXU49NJt/ZDSAG4CUAPwTwvyuf/QWA1wG8kFp257NdFvq1QyaTnuoSkLXF7ajip2kaRSIR3fTqtuDXNC3N/93U1GRp+7FYjDZs2JAm5K+77jratGkT7d69myKRSNkJ80Ix9jkIBAJ575M81k60sM2ErEthp1mfaLW5Xu054WYFSsYZykXoTwG4L/X8PgD/yeQ7vwXg5tTz6wH8DEAjXRP6dxe6XRb65YPTfjY1cC9Xhz75PTu0GtXn6vP5XA/uUxu/FJvOF4vF0tIC161bR4FAoCqLAakFboBkydl8kP78zZs3OzvAFJqm6cGqdmr5ct3qtSgnAW6nozLOUC5CfwnAttTzbQCW8vjND5RJAAv9CsfpYKBc5km5fVm3fGRkxLZJiBpd7bbGbxT8xQiItrY2/feNjY1VKexV1EmOx+PJa38PHjxIAOjgwYMujJD0QNW+vj7HBXE8Hs9Ya5+pPMpF6P+z8lyorzN8/0MAXgZQR9eE/lLK7P8VAOvy2S4L/fLBzcj+TJp+PsV9it12KU39qrXB4/EUFKEej8dp3bp1uoZfKzd+1Z3R39+f8/utra16ZoHTaJqWFrHvNJUWnc9kxzWhD2ARwI9Mlk8ahTyAt7OsZ1tKwN9qeE8AWAfgFIA/yvL7zwC4AOCCGxcoU/4Y+4U7FWwWj8epublZN/W76Qcvti57IBAo2NRdLQSDQQJAW7ZsyTnZ2b17NwGg3bt3Oz4uabXy+Xy21BfIRa2nuFUb5aLp52XeB7AJwN9nM+UD2AfgsXy2y5o+Q3RNk5HBWE42z1EFv9PbMiJNwgBoaGgor99s375dj0qvtZu+aiEJhUIZv6dpmn6cenp6HB2Tk758pjawS+hbLcM7D+BY6vkxAH9j/IIQYi2Avwbw34jom4bPtqUeBYA7kbQgMExehMNhDAwM4NKlSwCAlZUVx8p0dnV14dlnn0VnZycA4Gtf+1r21rg2MjExAY/HAwB45JFH8tpuY2MjAOCGG26ouRzsrq4u3HPPPQCAV155JePxmpiYwFtvvQUA+vF1iqmpKZw9e9axbRlL1HLJWiYjVmYMAHwA/hbJlL1FAFtS7/cA+PPU808DuIJraXkvIJWaB+DbAF5EUtg/CmBjPttlTb92MZosVZ+/WnrUye13d3frFga38tfVBj35ZBO46asuR9QA0EzavsyV37Bhg+MxD9Ld0tbW5sj5YiyVbVe1SqZ8QDmY90u1sNCvXbIFJ+UK+rOLeDyuC34pVNwQ/Go2Qa5SwbLIz/bt22smiE9F0zS9fPDw8LDpd+QxytdlYmUsMrskn+DCYojH4xQKhSgSiejnSWdnZ03+99WKXUKfu+wxFUU4HMbU1JRpv29pxp6cnMTU1BQmJiYwMTGBpaUlW02d0tQfCoUAAIuLi7jtttscN/ePjY0hEomgtbUV8XgcU1NTGb97ww03AADeeustjI6OOjouSSKRwKc//Wl4vV589rOfxcaNG/N+/MY3vmHrWPx+PzZv3gwAeOaZZ0z/+w0bNqQ9OsXExITeRfHkyZOObGN+fh6Li4uYnp7GxYsXAQCXLl3C/Py8I9tjKhg7Zg5uL6zpM5lQi/kA9jbmMVKqRj1y3xoaGjI25tE0jfbs2UMA6OjRo46MIxaLUUtLC1133XV0++23pxXHKXRZu3Yteb1eGh0dJa/XS3Nzc5bHp3YwNAu8lCb3QCBgeVvZcNrVIlNLA4EABYNBisVirli8GHcBm/cZZjVqGl8kEqFQKESxWMzR1D7V3O9Ga954PE4NDQ264M+0PadKzM7NzdGGDRv0OgDqIsclJwAyZ16+zvS+/J1c1qxZQ42NjdTT02PpeMrMB7O0xZ6eHscj993IEFD7UDgxuWXKAxb6DJMDY5tRJ5uPqAF+bmj8amBfJj9xLBaj1tZWamtrs5wXrmkajYyM0Pbt2/UWwEgV/lmzZg3V1dURgLSyv4UsxsmCujQ2NlJ/f39Rwl+myZkdIzeEvhqH4VSqHtfYrw3sEvr1BfsDGKZCkH7/5eVljI2NYXx8HOPj42mf2YXf78e3vvUt3HnnnYjH47jtttvwrW99y7FWrQcOHMDevXvxzDPP4MUXX8TS0tKqbd16663weDyIx+MIh8N4+eWXi9rW0tISQqEQ3nzzzbT3N2zYgPfeew+/+c1v9Pfee+89AMDGjRvh9Xr19+vq6uDz+XD58mVcvXo17f23334bv/rVrwAk0y6N/PM//zPOnj2L/fv34/vf/35BKYgf/vCHcfbsWQghkEgkXE9f/O53vwsAaGtrw9jYmGPb2bt3L/bu3YvR0dGaS9FkCsSOmYPbC2v6TCG4WZnMTY1fLRiUaVuxWIza29stNdqRUfBy2bJlS5q2j5Q5vrm5mbZv304jIyMF7XcsFqO2tjbaunUrXXfddWnrNW7H6/VmjGMwQ9M0vUKfUdN2Q9N3Yxtcbrc2AJv3GaZ4nJwIuOnjzxWsRnStLG0wGCx4/XNzc2lCd//+/auEvVW/u4qmaTQ0NESbNm2iNWvWmLoAABQU6KeavlX6+voISDa/cYJC+wAUCzfWqQ1Y6DNMEUhh73QhHzc1frkv7e3tpn0Bsvm1c7F27dqMAn/Xrl2OChpVaBq1/rq6ury3HYvFqLu7e1VcQ7YgPztwo/gPF+KpHewS+pynz9QUs7OzGBsbw7lz5zA+Pm67b18iffzd3d26j9+pPP7R0VF0d3fj9ddfx/T0NAKBQNq2Tp48if7+fgghChrD0tIS3n33Xf314uIiAEAIgWg0ihdeeMGxmAUgGZNw8eJFBAIBCCGSWkqKq1ev4rd/+7fx3HPP5VzPX/3VXyEej+Ov/uqvHBurkUQigV//+tcAgB07djh2nGZnZ7GwsICBgQEcOnSIS+8yubFj5uD2wpo+UyyZNCOnzP1ule1Vzfww0erlPhdidZB57EIIamxs1NftdAU7M6LRqJ4hgJRbASmTf67MhEyWDifz9NVMESei9tXUVHnesm+/ugGb9xmmOMwEvJM3TLfK9qrlXpuamtJMyuoYBgYG8tr++973PgJA73vf+2jjxo26kC1VSlgsFtP9+mvWrNHN/e3t7Vl/J9Pm3Azkk66DQCDgeK19CbfSrW5Y6DOMDZhpTE5tR60S6FTOthrR39zcnCb41THk0xp406ZNBCTb86rrLCWxWEyvBSC1/fXr12fV9mXFulAolHY8rMQ65MLJdROlC3i3ek4wpYWFPsPYgDTDSiHopLakaZpugm9vb3fU1C+F9MDAQNpnUuvt7+/Pue1du3YRkCyRu2XLFgJAW7dutX28hSKj7lXBn0vbN4vgj0aj1NzcXFAKYD5kSxN0ArUiH5v2qxe7hD4H8jGMggz0m52dtX3dfr8fjz32mB50Nzk5ibvvvtv2wCu1IdCOHTvS1i97uZ89ezbnPu7fvx8A8O677+Ltt9+2dYxWePjhh/UmObIw0G9+85usx3HPnj1pjwDw5S9/GZqm4ctf/rKt45uamsIzzzyDYDDoSEGeRCKRFrAXDof1wlNOBaYyVYQdMwe3F9b0GbswavZumEpVjR8O+vilBqj68OX+RSKRnPuoBiHK1L1t27bZPs5ikD5zdWzZAgzNctmd8ulbqYuQD6o/n/34tQNY02cY6/j9fkQiEfj9fiQSCV37nZycxMzMjCMpUFLj7+zsBJBMhRscHLR9O+FwGAMDA1hYWNBTBv1+PyYmJuDxeDA5OZl1u36/H//4j/8IAHrq3vve9z5bx1gsExMT2LZtG4BrpX//5m/+JuP3T58+jYWFBZw+fVp/b+3atWmPdpBIJPRjtXPnTtvWK9c9MTEBTdN0rX5mZgZjY2OYmZmxdVtM9cJCn2FSSNM+AL1XvVOm/q6uLsRiMYRCIQDOCH6/349Tp05lrRWwuLiYdf/kxKSuLnmreOWVV/LKjXcav9+P9evXA7g2tn//7/99xu/Lmv5qbf+9e/emPdrBzMwMYrGYPkY7mZ2dxeTkJKanp+H1ernGPlMU3HCHYVJIf2g4HNY1f6/Xi3A4rFsB5Gd24Pf7cfr0aQwODmJxcRGLi4sIBAJ47LHHbCvmIosE3XbbbdA0DbfddhueffZZjI6OAkgKweXl5YzNaB599FEEAgHdp//OO+/gyJEjeO211yyNi4gghMj4Oh++/vWv42Mf+xjeeecdAMCf/Mmf4I/+6I/y/v3w8DBeeuklDA8PF7TdbMhJWyAQ0I+xXYTDYSwvLwOAXohncHBQP0cZJi/s8BG4vbBPn3GTQlPdrKwfJql2dpAplc/M729Epp/JJVeKXC7Gx8fpjjs+R62tV0kIotbWq3THHZ8r6tju3r1bH9fw8HDWbRr/v0z5+1Zw2p/vVhlppvwAp+wxjDuoKVGytr3dwVMyl9zn8zlWr9+sK59aoTBTcyBjtT8A5PP5ihrf1atJAZ9cz+cIuJp6BN1xx+fo6tWrBa1v+/btBIA8Hg/5fL6M6XdmgXxOVOQbGhpytGqhPBfHx8c5gK/GsEvos0+fYXIQDod137vH43EkeMrv92Nqagrnzp3TffCBQABjY2O2+fllKl9nZyfi8ThmZmZW+f2PHz9u+rtYLKan+wHA5cuX9biHQhBC4OWXvwLgcwD+M5JhRf8ZwOfw8stfKdjE39zcDAD41a9+hcuXL+PTn/606fceeughLCws4KGHHtLfa2hoSHu0SiKRwNmzZwEAP/3pT21ZpxGZnifdMgxTMHbMHNxeWNNn3EbV7qVpNRKJOF6vHwWUzc0XOf7Ozk5d882nPavUYmUp3MbGxqLcEEJQSsOHsiRN/YXS29urjwUAHT582PR7bhTnke6C9vZ2R7sPqnX92bxfO4DN+wxTGtzwq8bjcQqFQroJ2s5Ygmxtf7PVKVBdAXLp7OwseELS2nrNpH9tSfr4C0UKfY/HkzXn3jipyVa1sFic9udLzCZtTPVjl9Bn8z7DFEAikcDMzAw0TcPKyopjVdC6urrw9NNP61XxvvOd72BsbAwTExOWzf3Gtr9qPQKZFjY5OYljx46lbUu6ApqamvT3Ll26VFCqIRHhllv+A6RJH7gKaeq/5Zb/kNREiuDKlSsAgF/84hemn/t8Puzbtw8+nw9Ash2xpmnw+Xw4ceJEUds0sn379rRHqxgr70kGBwfR3d2NS5cu4fTp09xOlykMO2YObi+s6TOlQg3qgwvmVTPt2i6t38xiITV9aQ43279YLEadnZ10yy236GMKBoN5a/x2Ru9LTb+zszOjqV7tMCj3R1b0GxkZKXibmZAdDnP1AciXTJkV6vscxV87wCZNn/P0GaYAZK60LPKSLcfdDqR2PTMzgzNnzuCZZ57BuXPnbNmmrEaYSCSwsrKCp556CocOHdKtCbOzs3o+uFqf4NZbb8Wrr76Kjo4OfV3PPPMMBgcHcfr06ZzjmpiYAJGaly9AVHgQH3BNw3/ttddw9epVPPfcczhw4EDad0ZHRxGPx9HZ2elYPvvS0hKuXr0KABgYGLBlneFwGE899RQWFhZw+PBhfPSjH8Xo6GhaPQkAnKfPFIYdMwe3F9b0mXLArKe5it2pfarWb3dwn1yvsUZAtjx+tf69XJzqI2CGpml6AB+yWEHM8vHtbn0r/fkdHR2OBF2q+yfjE2KxGKft1RDgQD6GKS25hHquSUGx25QCOhQK2dYYSA1s6+/v14Pesk00ZG2B/v5+2rlzZ1GmfitIYS6Xu+66y3S7Zv+T3YV5pJuht7fXlvVJpLtFTirGx8fT6irYfX4x5QsLfYYpM8w69k1NTVE8HndM47dT65capBQwUuvPpyKhsXhPf3+/o50Kia4V15FLd3e36ffc6KRot9A3njvqOSQzO0ZGRhw/xkz5wEKfYcqMTC1PndL41YC7TNX0isEsnc2sjK1KLBajpqamVeZ+p3LWY7EYbdiwQd+O1+s1LQ1sFsRnt5Yfj8f1fbfLXSDPGTn2TMF8rOXXDiz0GabMyCTondL45TYz5dxbQWr90Wh0lf84k1tDTkRkFLsq+O3USGOxmF4gCADt3Lkz48RCNYXL7ZsV6rGCtIw0NTXZNsFRrTnSiqJOuLL9B+znr05Y6DNMGWMWbJXJEmDHtqTgt9PPT2Qe4Cf3I9O2VM1XXXp7eykUChUtGGUMgSzEIxefz5fxN2ZFefr7+ykYDNomoHt6erIWBioWM39+LtgCUL2w0GeYMkYVjPKG7aTJ3+jntyuKXjX1h0Ih3WKhbstsH1S/c1tb2yrNv7+/X29elO84Ojo6Vk0iAPPuepn89nZXzXPCtG92nuQbt8GafvXCQp9hyhh581X9x2ZBfnab+tUWvXb5+aWQl/sihWkkEtGFarb9UaPsVbO8FNz9/f00PDyc9jg0NESBQIACgQANDw/r3QeNGn6mbZrVp3dCQDth2lcFfS6XEAv52oGFPsNUAMaqd2pLVKcE//j4uO4HNubdW123OpFRzc3ZNFJN02hkZIQ6OzspGo2u8vnnuwghaOvWrQQka+1napSjTn6kxUONfbDrmGiapu+LXZMIuV5pScllCVID/rgOf3XDQp9hKgizkrfqRMDuCYBqlu/s7LTVzy/HPTIyYprPLy0BmbZpNNUb/f/G1+vWrdOfd3Z25jxOZhMQJ5rUONX4ppCUQnUyY1fjIKY8YaHPMBWIekNXTf9OVNpTA/zs9PPLCYrUpmUxHxm0mE/rV/U4xONxGhkZofb2dhoZGdFft7W1USAQoGg0SoFAgNrb203T8ozrVN0O8jiYRcBbxYn6/UT5B+PJ/yEWi+Vsi8xUPiz0GaYCUU3P0s8vhb8UTHbn86t+/mAwaGsVP7WYjyr8VU3VyZRFiTGQUR5Dp1IaNU3T/y+7Uv/UdedTTEhODlSXEVO9sNBnmApE7dIntU6pGff399PIyIiltDYzjILfTq2fKLPwN6b4OWHNMLpN1DRCdSJgt8/bKdO+Geo5o+6HzJCQlQnttGIw5QcLfYapIFRtVzVrG1PgnPLPGnO+ZUBbNnN5oWTT/I37qWrihZbEVc34akqkqu2qrg27mxOpMQl2VfXLtb1QKKRvU54bxjbPLPSrGxb6DFNBmPlpzYr1OO2flQJTpsD5fD7b67dn0/yNpmtVcBlT1DL56I3CzijU1SBGO036EtVq4rSgVa0VIyMj1N3drU/U4vE4BYNB6uvr4zr8NQALfYapIMyi87NF7BejARdCLBbTBaMT2jDRauEvi/uoExu5n1KQqpYAo3A3WgeMdQKk5m9sGGQnmqalteV1Wsga3RbqJNFYjIm1/eqGhT7DVDGqwHOqpKpR4HZ2dhZUJS9fjMV9jCWDx8fH9YmAsca/maZvtm4ZSW8WU2AnqhB2WuCrsRjGtE55fgSDQb3yIQv96oaFPsNUMU5r+sZtqal9Tmj9cjuqpq9W6pPCTeb+y0Y/xkc5KZDrkFq9jKR3MrAuFovpbhE3hGu2wjvGCRtH8Fc/LPQZhrENGSwmTdf5lIC1ilnNArWSoNmjmn5nFizo5FjVsTi5HdXSka0yX6F1+ZnKpiyEPoAtAJ4G8GrqsSnD934D4IXUMq+83w7geQCXAHwDwNp8tstCn6km3NTq8xmLmlrnRLpbpu2qGny+mr5bBWmkWd/n89ma8WDEGNyZ7bzguvu1RbkI/SkA96We3wfgP2X43r9meP8vAdyTev5nAP5dPttloc9UA8Yc80LNtE7d9NX0QlWzdlrzL1fcNOub+e25TS5DVD5CfwnAttTzbQCWMnxvldAHIAAkANSnXgcAPJnPdlnoM9WAWlFNXTIJf2NlO7PGN3ZjzHevNXNyPB4nr9fruFmfyLwSXyEZH0x1Uy5C/5+V50J9bfjeewAuAHgOwJ2p9/wALinfuQHAj/LZLgt9phowu4GbtbE1an5q9Duwum2vU+NUi+uEQqGsUfXVgJrv7/V6HTXrE1HahC8TRu2fJwG1g2tCH8AigB+ZLJ80CnkAb2dYx/bUYweA/wHgpkKFPoDPpCYOF1pbWx06rAxTWozav4xOl8JVbRkrq/vJ99wwAxvzw6tR85e+dNk21+fzuRLTkE+ufabiRuwCqH7sEvr1yAERhTJ9JoT4JyHENiL6mRBiG4CfZ1jHW6nH14QQZwDsAfD/AGgUQtQT0XsAWgC8lWUcJwGcBICenh7KNW6GqUTC4bD+ODs7i2eeeUb/zO/3Y+/evVhcXMSePXswPz+PRCKBxcVFBINB/bdO4vf7cerUKczMzGBlZQUXL17EwsICBgcHsXfvXoyOjsLv9zs+DqdIJBK4++6704770NAQurq6HN3uzMwMFhcXEQqFMDg4iImJCQBYdTz9fj8ikYj+Wj1fGCYfcgr9HMwDOAbgy6nHvzF+QQjRBGCFiH4thPAD2AtgiohICPEdAHcDmMv0e4apJdSbejgcxvLyMoDkzX9paQnnzp1DJBKBx+PB2NgYOjs7AQANDQ0AgOnpaV0AzM7OIhwO2y6E/X6/LpQSiQSOHTuGhYUFLC4u4ty5c9izZw88Hk/FTQCWlpZw8OBBXLp0CQDQ3t6O3/3d38Xo6Kir43jooYcwPT0NAPB6vWlC3ohxEsAwObFiJgDgA/C3SKbsLQLYknq/B8Cfp573AXgRwA9Sj8PK7zsA/B2SKXv/HcC6fLbLPn2mlpAlZmUEuTSnG1PX1LxttWSrG+NT3QxyqRS/v/H4wuEiP5m2L2sUuFnml6kcUA6BfKVaWOgztYRaklf6lzMFAcogv1JUaDPrfqdOAJxoG2x1rCMjI7qwlcfXiVLE2TA2HXIjK4OpPOwS+lbN+wzDOEw4HMbjjz+OZ555RvcvT09PY2xsDAB08+78/Dzi8TgGBgZMTeuJRMIxkz+w2uyv+v0XFxexuLgIADh48CA+9alPYWVlBUtLS5iZmXHcZ66OB4A+Jkl7eztuuukmV8ZixOjGAYDm5mb20zOOIJITiMqip6eHLly4UOphMIxrGAW2mQDPJdTlRGFqaspVP7AUuIlEAk8++aTuM5d0dnbiU5/6VNp7VmICjAIeWC3kASAUCqGrq8u1iYc6vtnZWRw6dAinT58GsDpgj2GMCCG+T0Q9ltfDQp9hqh8pCIHSChhVIK+srJhOAiShUAh79uxZ9b7H48Hg4CBOnz6dJtjl+8ePH8fCwkLW9ZUy0FBOvgYGBvRxuj0RYyoPu4Q+m/cZpgaYnZ3F5OQkBgYG9PecNveboboA5BiMWjmAVS4BI+fPnzcV7PJ944ShnLIJpNn+0KFD6O3tTXuPYZyGNX2GqQHU1DqpVZbK3J8PmSYDQG5Nf35+3tWJTDZUU345jasYSjFJZK5hl6Zf8kj8YhaO3meYwjGr7W7s4sZlXe1Fpk66WTXRKbj6X2kBR+8zDJMNo2ZmLOTi9/vh9XoxNjYGr9eLcDisWwMAlJ32X8ns2bMH+/fvRzgcrljtn6v/VQcs9BmmSpmdnV2V1mdE9S9LgT8wMJAmnCpFKJUjo6Oj+oRKHsOJiQlMTk7iqaee0mMWynGCZRb8WY7jZAqDhT7DVCn5aGbyRj49Pa0L/FOnTsHv9+s+f03TcPHiRezZswdjY2M8AVDIJ3UyHA6nCU+Jqv2XIzL4E8hdDpipHFjoM0yVUohmpk4QpPCS70mNdHFxURf+MhoecK7GfyVgZk2R7505cwanTp1aJTzNtP9yRC0aVK4TE6ZwWOgzDJN1gnD//fcDAK5cuZKWRuf1egEgTehVo0vArDCS1NwHBwcBXBOKiUQCy8vLCIVCWFhY0H+nCs9KMZMb0yuZ6oCFPsMwpkiNdWpqCk8//XRaGp3H40nT/tTOftkmAZU4KVD3KRwOY3BwMG3iIwW4mhY5Pj6um+6rUXhW4v/IpLAjBcDthVP2GMZ5iknfM/7GmOYlU9iMzWRKmSpotm31vXg8ntYxEEoTITXVcWBgIK0LYjXD6XvuA07ZYxjGSQo1Q6tmb0m+aV4zMzOYnJzE8vKyY1pxpqA7qZ0vLy/rvnZ1PF6vV3drjI+PY3x8XC8KNDMzg9HRUczOzq4KhKxmOH2vcmGhzzCMLagBawB0YagK2cHBQZw/f173hWfCLF3Mav+ATEF3UlivrKzogl4lHA7jjTfewJNPPokDBw7g1ltvTetyKCcK8rvVLvCBwieETPnAQp9hGFsIh8Npuedm/n3ZDGffvn1pPv/BwcE04WmcQExMTOjat/qeJJ8JgZl2qr6nWilkhP2hQ4cwOzuLpaUlXLp0Cffffz9OnTqF5eVlRCIRPbaBhSBTMdjhI3B7YZ8+w5Qnqi88k39f9Xkb35OlgSORCAUCAQJAkUiEiIgikYjuTzfGBMj1wIKfWY43Ho/r45brjUQiNDAwoH9mZTsMUwxgnz7DMOWGUeNVn2eqBXDmzBk9vQ2Ars2HQiEAyUY66mMoFNJrBCwtLeH48eP44he/qPval5eXkUgkCjazq4WK1Gh9szGrjwxTSXCXPYZhSooaYAcgLQderU1vFoj3iU98QvfJP/7447Z0DuR0NKYcsavLHgt9hmEqFqnpnzhxAl1dXasmECy8mWrBLqHP5n2GYSqWrq4uPP744/pr1b2gmuk5yI5hkrDQZximKmHfO8OshoU+wzBVCafRMcxq6ko9AIZhGIZh3IGFPsMwlkkkEpienkYikSj1UBiGyQILfYZhLCOr78lce4ZhyhP26TNMCai2XHAOmmOYyoA1fYYpAdWmGcuguWqYwDBMNcOaPsOUANaMGYYpBSz0GaYEcDoZwzClgM37DMMwDFMjsNBnGIZhmBqBhT7DMAzD1Ajs02cYxjKJREJviTs6OspR/AxTprDQZxjGMrOzs5icnAQAeL1eDlJkmDKFhT7DMJYJh8NYXl7WnzMMU54IIir1GAqmp6eHLly4UOphMAxjA9VWnZBhnEAI8X0i6rG6Hg7kYximpFRbdUKGKWdY6DMMYyuFdtwLh8OYmppCOBzmbn0M4zAs9BmGsZVCNXe1bj9r/QzjLBzIxzCMrVjpK1BrPQk4noFxGxb6DMPYipW+ArXWk0BaNgDU1H4zpYOFPsMwTImoNcsGU3pY6DMMw5SIWrNsMKWHA/kYhmEYpkZgoc8wDMMwNYIloS+E2CKEeFoI8WrqscnkOx8VQrygLL8SQtyZ+uwvhBCvK5/ttjIehmEYhmEyY1XTvw/A3xLRzQD+NvU6DSL6DhHtJqLdAD4GYAXAU8pXIvJzInrB4ngYhmEYhsmAVaH/SQCnUs9PAbgzx/fvBhAlohWL22UYpgRwxTyGqWysCv2tRPSz1PN/BLA1x/fvAXDa8N6XhBA/FEJ8RQixLtMPhRCfEUJcEEJc0DTNwpAZhikWrpjHMJVNzpQ9IcQigPebfPQF9QURkRAiY8s+IcQ2AB8E8KTy9ueRnCysBXASwB8C+GOz3xPRydR30NPTU3mtARmmCuC8coapbHIKfSIKZfpMCPFPQohtRPSzlFD/eZZV/W8A/pqIrijrllaCXwshZgH8H3mOm2GYElBIXjmXmGWY8sOqeX8ewLHU82MA/ibLdwdhMO2nJgoQQggk4wF+ZHE8DMOUCewKYJjyw2pFvi8D+EshxDCAN5DU5iGE6AHwb4no91OvbwRwA4BnDL//mhCiGYAA8AKAf2txPAzDlAnsCmCY8kMQVZ57vKenhy5cuFDqYTAMwzCMKwghvk9EPVbXwxX5GIZhGKZGYKHPMAzDMDUCC32GYRiGqRFY6DMMwzBMjcBCn2EYhmFqBBb6DMMwDFMjsNBnGIZhmBqBhT7DMAzD1Ags9BmGYRimRmChzzAMwzA1Agt9hmEcJ5FIYHp6GolEotRDYZiahoU+wzCOwx33GKY8sNplj2EYJifccY9hygMW+gzDOI7f70ckEin1MBim5mHzPsMwDMPUCCz0GYZhGKZGYKHPMAzDMDUCC32GYRiGqRFY6DMMwzBMjcBCn2EYhmFqBBb6DMMwDFMjsNBnGIZhmBqBhT7DMAzD1Ags9BmGcRVuvsMwpYOFPsMwrsLNdximdHDtfYZhXIWb7zBM6WChzzCMq3DzHYYpHWzeZxiGYZgagYU+wzAMw9QILPQZhmEYpkZgoc8wDMMwNQILfYZhGIapEVjoMwzDMEyNwEKfYRiGYWoEFvoMwzAMUyOw0GcYhmGYGoGFPsMwJYUb8DCMe7DQZximpHADHoZxD669zzBMSeEGPAzjHiz0GYYpKdyAh2Hcg837DMMwDFMjsNBnGIZhmBqBhT7DMAzD1Ags9BmGYRimRmChzzAMwzA1giWhL4T4X4UQLwkhrgoherJ874AQYkkIcUkIcZ/yfrsQ4vnU+98QQqy1Mh6GYRiGYTJjVdP/EYC7AJzN9AUhxBoADwL4OIAPABgUQnwg9fF/AvAVIuoE8DaAYYvjYRiGYRgmA5aEPhG9TERLOb72IQCXiOg1InoXwByATwohBICPAfhm6nunANxpZTwMwzAMw2TGDZ/+dgD/oLx+M/WeD8A/E9F7hvcZhqlhuBY/wzhHzop8QohFAO83+egLRPQ39g8p4zg+A+AzqZe/FkL8yK1tlwA/gGq941XzvgG8f3awFUDL2NjYmwD+yeFtGeH/r3Kp5n0DgC47VpJT6BNRyOI23gJwg/K6JfXeZQCNQoj6lLYv3880jpMATgKAEOICEWUMHKx0qnn/qnnfAN6/Sof3r3Kp5n0Dkvtnx3rcMO+fB3BzKlJ/LYB7AMwTEQH4DoC7U987BsA1ywHDMAzD1BpWU/Y+JYR4E0AAwONCiCdT718vhFgAgJQWPwrgSQAvA/hLInoptYo/BHBcCHEJSR//Q1bGwzAMwzBMZix12SOivwbw1ybv/38ABpTXCwAWTL73GpLR/YVysojfVBLVvH/VvG8A71+lw/tXuVTzvgE27Z9IWtkZhmEYhql2uAwvwzAMw9QIZSv0q7nErxBiixDiaSHEq6nHJpPvfFQI8YKy/EoIcWfqs78QQryufLbb7X3IRj77l/reb5R9mFfeL9v/Dsj7/9sthIilzuEfCiH+d+Wzsvz/Ml1LyufrUv/HpdT/c6Py2edT7y8JIe5wdeB5kMe+HRdC/Dj1X/2tEKJN+cz0PC0n8ti/3xNCaMp+/L7y2bHUufyqEOKYuyPPjzz27yvKvr0ihPhn5bOy/v+EEA8LIX4uMqShiyR/ktr3Hwoh/o3yWeH/HRGV5QLgFiTzEs8A6MnwnTUAfgKgA8BaAD8A8IHUZ38J4J7U8z8D8O9KvU/KuKcA3Jd6fh+A/5Tj+1sA/AKAJ/X6LwDcXer9sLp/AP41w/tl+9/lu38AfgvAzann1wP4GYDGcv3/sl1Lynf+AMCfpZ7fA+AbqecfSH1/HYD21HrWlHqfCty3jyrX17+T+5btPC2XJc/9+z0AMya/3QLgtdRjU+p5U6n3qdD9M3z/swAerqD/rx/AvwHwowyfDwCIAhAAbgXwvJX/rmw1faruEr+fRHJMQH5juxtAlIhWnByUjRS6fzoV8N8BeewfEb1CRK+mnv9/AH4OoNmtARaB6bVk+I66398E8L+k/q9PApgjol8T0esALqG4AF2nyLlvRPQd5fp6Dsm6IZVCPv9dJu4A8DQR/YKI3gbwNIADDo2zWArdv0EAp10ZmQ0Q0VkklbpMfBLAf6MkzyFZ32Ybivzvylbo50mllvjdSkQ/Sz3/RyQrkGXjHqw+ib+UMvV8RQixzvYRWiPf/VsvhLgghHhOui5Q/v8dUOD/J4T4EJIayk+Ut8vt/8t0LZl+J/X//BLJ/yuf35aSQsc3jKRmJTE7T8uJfPfvcOqc+6YQQhZMK/f/DihgjCm3TDuAbytvl/v/l4tM+1/Uf2cpZc8qokxK/DpBtn1TXxARCSEyplCkZnQfRLLOgeTzSAqbtUimcfwhgD+2OuZCsGn/2ojoLSFEB4BvCyFeRFKQlByb/79HABwjoqupt0v+/zHmCCE+DaAHQFB5e9V5SkQ/MV9D2fL/AjhNRL8WQtyLpMXmYyUekxPcA+CbRPQb5b1q+P9so6RCn8qkxK8TZNs3IcQ/CSG2EdHPUkLh51lW9b8B+GsiuqKsW2qZvxZCzAL4P2wZdAHYsX9E9Fbq8TUhxBkAewD8Pyjxf5cak+X9E0JsAvA4kpPY55R1l/z/MyHTtWT2nTeFEPUANiN5reXz21KS1/iEECEkJ3VBIvq1fD/DeVpOQiPn/hHRZeXlnyMZlyJ/u8/w2zO2j9AahZxf9wAYUd+ogP8vF5n2v6j/rtLN+5Va4nceyTEBuce2yj+VEjTS/30ngHJrPpRz/4QQTdKsLYTwA9gL4McV8N8B+e3fWiQLV/03Ivqm4bNy/P9MryXDd9T9vhvAt1P/1zyAe0Qyur8dwM0A/s6lcedDzn0TQuwB8FUAh4jo58r7puepayPPj3z2b5vy8hCS1VGBpAVxf2o/mwDsR7pVsRzI59yEEKIbyYC2mPJeJfx/uZgH8LupKP5bAfwypTgU99+5FaFY6ALgU0j6KH6NZKetJ1PvXw9gQfneAIBXkJy5fUF5vwPJG88lAP8dwLpS75MyNh+AvwXwKoBFAFtS7/cA+HPlezciOZurM/z+2wBeRFJYPApgY6n3qdD9A9CX2ocfpB6HK+G/K2D/Pg3gCoAXlGV3Of9/ZtcSkm6HQ6nn61P/x6XU/9Oh/PYLqd8tAfh4qfeliH1bTN1n5H81n+s8Laclj/37vwC8lNqP7wDoVn47lPpPLwEIl3pfitm/1OsJAF82/K7s/z8klbqfpe4XbyIZU/JvAfzb1OcCwIOpfX8RSjZbMf8dV+RjGIZhmBqh0s37DMMwDMPkCQt9hmEYhqkRWOgzDMMwTI3AQp9hGIZhagQW+gzDMAxTI7DQZxiGYZgagYU+wzAMw9QILPQZhmEYpkb4/wEJIhRBrX0JBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3054166976781556\n"
     ]
    }
   ],
   "source": [
    "# Iterate\n",
    "\n",
    "# Set number of iterations\n",
    "\n",
    "nitr = 5\n",
    "\n",
    "# Loop\n",
    "\n",
    "for intr in range(nitr):\n",
    "\n",
    "    state = torch.linspace(0.0, 1.5, 21, dtype=dtype)\n",
    "    state = torch.stack([state, torch.zeros_like(state)]).T\n",
    "\n",
    "    count = 1024\n",
    "    table = []\n",
    "    for _ in range(count):\n",
    "        table.append(state)\n",
    "        state = torch.func.vmap(lambda state: mapping(state, knobs))(state)\n",
    "\n",
    "    table = torch.stack(table).swapaxes(0, -1)\n",
    "    qs, ps = table\n",
    "\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.xlim(-1., 1.)\n",
    "    plt.ylim(-1., 1.)\n",
    "    for q, p in zip(qs.cpu().numpy(), ps.cpu().numpy()):\n",
    "        plt.scatter(q, p, color='black', marker='o', s=1)\n",
    "\n",
    "    # Set tolerance epsilon\n",
    "\n",
    "    epsilon = 1.0E-12\n",
    "\n",
    "    # Compute chains\n",
    "\n",
    "    period = 4\n",
    "    points = torch.stack([hp, ep])\n",
    "    points = torch.func.vmap(lambda point: fixed_point(64, mapping, point, knobs, power=period))(points)\n",
    "    points = clean_point(period, mapping, points, knobs, epsilon=epsilon)\n",
    "    chains = torch.func.vmap(lambda point: chain_point(period, mapping, point, knobs))(points)\n",
    "\n",
    "    # Plot chains\n",
    "\n",
    "    for chain in chains:\n",
    "        point, *_ = chain\n",
    "        value, vector = torch.linalg.eig(matrix(period, mapping, point, knobs))\n",
    "        color = 'blue' if all(value.log().real < epsilon) else 'red'\n",
    "        plt.scatter(*chain.T, color=color, marker='o')    \n",
    "        if color == 'blue':\n",
    "            ep, *_ = chain\n",
    "        else:\n",
    "            hp, *_ = chain\n",
    "\n",
    "    ep_chain, *_ = [chain for chain in chains if ep in chain]\n",
    "    hp_chain, *_ = [chain for chain in chains if hp in chain]\n",
    "\n",
    "    ep, *_ = ep_chain\n",
    "    hp, *_ = hp_chain[(ep - hp_chain).norm(dim=-1) == (ep - hp_chain).norm(dim=-1).min()]\n",
    "\n",
    "    plt.scatter(*ep.cpu().numpy(), color='black', marker='x')\n",
    "    plt.scatter(*hp.cpu().numpy(), color='black', marker='x')\n",
    "\n",
    "    plt.show()\n",
    "    print(objective(knobs).item())\n",
    "    \n",
    "    # Compute parametric fixed points\n",
    "\n",
    "    php = parametric_fixed_point((order, ), hp, [knobs], mapping, power=period)\n",
    "    pep = parametric_fixed_point((order, ), ep, [knobs], mapping, power=period)\n",
    "    \n",
    "    # Propagate parametric fixed points\n",
    "\n",
    "    t = identity((1, 1), [hp, knobs], parametric=php)\n",
    "    t = propagate((2, limit), (1, 1), t, [knobs], nest(period, mapping, knobs))\n",
    "    \n",
    "    # Update\n",
    "\n",
    "    lr += 0.005\n",
    "    gradient = derivative(1, objective, knobs, intermediate=False)\n",
    "    knobs = knobs - lr*gradient"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7056772b-ad88-465f-ab99-5de6f5e9a464",
   "metadata": {},
   "source": [
    "# Example-10: Alignment indices chaos indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5b8e139-1eb3-497e-909f-c6351dd3c8c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "\n",
    "torch.set_printoptions(precision=8, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ac5ba1c-91af-46bd-bdfb-05e0a896f110",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a883b669-2946-4b45-8101-a2c32ab2c92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set fixed parameters\n",
    "\n",
    "a1, b1 = 0, 1\n",
    "a2, b2 = 0, 1\n",
    "\n",
    "f1 = torch.tensor(2.0*numpy.pi*0.38, dtype=dtype, device=device)\n",
    "f2 = torch.tensor(2.0*numpy.pi*0.41, dtype=dtype, device=device)\n",
    "\n",
    "cf1, sf1 = f1.cos(), f1.sin()\n",
    "cf2, sf2 = f2.cos(), f2.sin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e2e4e7d-76d1-4f16-9c39-640fbcb0919a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set 4D symplectic mapping\n",
    "\n",
    "def mapping(x):\n",
    "    q1, p1, q2, p2 = x\n",
    "    return torch.stack([\n",
    "        b1*(p1 + (q1**2 - q2**2))*sf1 + q1*(cf1 + a1*sf1),\n",
    "        -((q1*(1 + a1**2)*sf1)/b1) + (p1 + (q1**2 - q2**2))*(cf1 - a1*sf1),\n",
    "        q2*cf2 + (p2*b2 + q2*(a2 - 2*q1*b2))*sf2,\n",
    "        -((q2*(1 + a2**2)*sf2)/b2) + (p2 - 2*q1*q2)*(cf2 - a2*sf2)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c52b6cdc-defc-4401-9438-3ef068ddb250",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set 4D symplectic mapping with tangent dynamics\n",
    "\n",
    "def tangent(x, vs):\n",
    "    x, m = derivative(1, mapping, x)\n",
    "    vs = torch.func.vmap(lambda v: m @ v)(vs)\n",
    "    return x, vs/vs.norm(dim=-1, keepdim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "041045e3-e9e6-4975-a9a7-74e82bd54c43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set generalized alignment indices computation\n",
    "\n",
    "# Note, if number if vectors is equal to two, the index tends towards zero for regular orbits\n",
    "# And tends towards a constant value for chaotic motion\n",
    "# If the number of vectors is greater than two, index tends towards zero for all cases\n",
    "# But for chaotic orbits, zero is reached (exponentialy) faster\n",
    "\n",
    "def gali(vs, threshold=1.0E-12):\n",
    "    return (threshold + torch.linalg.svdvals(vs).prod()).log10()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9a0791b-873c-4915-bf36-4f15096d063a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First, consider two initial conditions (regular and chaotic)\n",
    "\n",
    "count = 1024\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "x = torch.tensor([0.50000, 0.0, 0.05, 0.0], dtype=dtype)\n",
    "orbit = []\n",
    "for _ in range(count):\n",
    "    x = mapping(x)\n",
    "    orbit.append(x)\n",
    "q, p, *_ = torch.stack(orbit).T\n",
    "plt.scatter(q, p, s =1, color='blue')\n",
    "\n",
    "x = torch.tensor([0.68925, 0.0, 0.10, 0.0], dtype=dtype)\n",
    "orbit = []\n",
    "for _ in range(count):\n",
    "    x = mapping(x)\n",
    "    orbit.append(x)\n",
    "q, p, *_ = torch.stack(orbit).T\n",
    "plt.scatter(q, p, s =1, color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3abcd814-9305-4e11-82a8-449c2ffae2db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute and plot the last gali index at each iteration\n",
    "# Note, running minimum is appended at each iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "x = torch.tensor([0.50000, 0.0, 0.05, 0.0], dtype=dtype, device=device)\n",
    "vs = torch.eye(4, dtype=dtype, device=device)\n",
    "out = []\n",
    "for _ in range(count):\n",
    "    x, vs = tangent(x, vs)\n",
    "    res = gali(vs)\n",
    "    out.append(res if not out else min(res, out[-1]))\n",
    "out = torch.stack(out)\n",
    "plt.scatter(range(count), out, color='blue', marker='o')\n",
    "    \n",
    "x = torch.tensor([0.68925, 0.0, 0.10, 0.0], dtype=dtype, device=device)\n",
    "vs = torch.eye(4, dtype=dtype, device=device)\n",
    "out = []\n",
    "for _ in range(count):\n",
    "    x, vs = tangent(x, vs)\n",
    "    res = gali(vs)\n",
    "    out.append(res if not out else min(res, out[-1]))\n",
    "out = torch.stack(out)\n",
    "plt.scatter(range(count), out, color='red', marker='o')\n",
    "               \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ce599a3d-c656-408d-8d7d-37f02241decf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbsAAAHLCAYAAABRSzbfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydeXhdVb3+PzuHpGlOk54mTRs70FJaCqW1gGGUqYggsyDiRRQUBBy4gAKKFn7CBRRBFBBEUUGQ4QoIMggyXArIKAHpbSkUSmkp7e0Y0qQnpAnJ/v3xrm/WOqcpKk1LWtb7POc5J3tYe+19Tta73u+0kjRNiYiIiIiI2JRR8mF3ICIiIiIiYn0jkl1ERERExCaPSHYREREREZs8ItlFRERERGzyiGQXEREREbHJI5JdRERERMQmj0h2ERERERF9DkmSXJokyatJkvxvkiR3JUmSW5f2ItlFRERERPRFPAxMTNP048BrwPfXpbFIdhERERERfQ5pmj6Upul77s9ngRHr0l4ku4iIiIiIvo7jgQfWpYHNeqkjEREREREbMT7zmc+ky5cv79U2X3jhhZeBtmDTtWmaXmt/JEnyCFDXw6lT0zS92x0zFXgPuHld+hLJLiIiIiKC5cuX09DQ0KttJknSlqZp/dr2p2m67z85/yvAwcCn0nUs5BzJLiIiIiICSJGA6htIkuQzwHeBvdI0bV3X9iLZRUREREQ49B2yA64C+gEPJ0kC8Gyapl//oI1FsouIiIiI6HNI03Rsb7YXyS4iIiIigr5mxuxtxNSDiIiIiIhNHlHZRURERESwqSu7SHYREREREWzqZBfNmBERERERmzyisouIiIiIICq7iIiIiIiIjRxR2UVEREREEJVdRERERETERo6o7CIiIiIiHDZdZRfJLiIiIiICmTE7P+xOrDdEM2ZERERExCaPqOwiIiIiIogBKhERERERERs5orKLiIiIiGBTV3aR7CIiIiIiHDZdsotmzIiIiIiITR5R2UVEREREsKmbMaOyi4iIiIjY5BGVXUREREQEm7qyi2QXEREREcGmTnbRjBkRERERsckjKruIiIiICKKyi4iIiIiI2MgRlV1EREREhMOmq+wi2UVEREREEM2YERERERERGzmisouIiIiIICq7iIiIiIiIjRxR2UVEREREEJVdRERERETERo6o7CIiIiIi2NSVXSS7iIiIiAiHTZfsohkzIiIiImKTR1R2ERERERFs6mbMqOwiIiIiIjZ5RGUXEREREcGmruwi2UVEREREILLr/LA7sd4QzZgREREREZs8orKLiIiIiGBTN2NGZRcRERERsckjKruIiIiICIdNV9lFsouIiIiIIJoxIyIiIiIiNnJEZRcRERERQVR2ERERERERGzmisouIiIiIoK8puyRJLgAOA7qApcBX0jRd9EHbi8ouIiIiIqIv4tI0TT+epul2wH3A/1uXxqKyi4iIiIigrym7NE2bgz+zqIMfGJHsIiIiIiIcep3sBidJ0hD8fW2aptf+qycnSXIRcCywEpiyLh2JZBcRERERsb6wPE3T+rXtTJLkEaCuh11T0zS9O03TqcDUJEm+D5wC/PCDdiSSXUREREQEH4YZM03Tff/FQ28G7mcdyC4GqERERERE9DkkSTIu+PMw4NV1aS8qu4iIiIgI+lqACnBxkiTjUerBfODr69JYJLuIiIiICPoa2aVp+rnebC+aMSMiIiIiNnlEZRcRERERQV9Tdr2NqOwiIiIiIjZ5RGUXEREREeGw6Sq7SHYREREREUQzZkRERERExEaOqOwiIiIiIojKLiIiIiIiYiNHVHYREREREURlFxERERERsZEjKruIiIiICIfOD7sD6w2R7CIiIiIiiGbMiIiIiIiIjRxR2UVEREREEJVdRERERETERo6o7CIiIiIi2NSVXSS7iIiIiAg2dbKLZsyIiIiIiE0eUdlFRERERBCVXURERERExEaOqOw2ELJJkubTNPmw+xERERGxdmy6yi6S3QZCJLqIiIi+jWjGjNjIkE2S9MPuQ0RERERfQlR2myCiioyIiPj3EZVdRERERETERo2o7CIiIiIiiMouIiIiIiJiI0dUdhERERERbOrKLpJdRERERITDpkt20YwZEREREbHJIyq7iIiIiAg2dTNmVHYfUWSTJI3J5xERER8VRGX3EUVMPI+IiCjEpq3sItlFRERERLCpk100Y0ZEREREbPKIyi4iIiIigqjsIiIiIiIiNnJEZRcRERER4dD5YXdgvSGSXUREREQE0YwZ0Sfx7+bIvd/xtq/43T7HfLyIiIiNHVHZbaT4Z3ly2SRJw2PyaZrYtnBfSGS2rfi8sM3itnrvjiIiIj5cRGUXsRGimMyySZLmR6yp2orPC48PX9ZmPk2TK5Ik3Xct50dERET0RUSy2wRQTDo9qTWAl9+GLJC/DO5MkjT/FXgMqAZ+mSTpy0mSHgj8KEnSo4HfFLW52BHfD4C7eyDTnvoSERGxscCUXW+++g6iGXMTQKjiTH31ZLK8C81uXjoDjugH3/49fA9YMB4YCtkn1NbIJEkbgVuBHwE/cNfZMrimkVpz0bZo2oyIiOiLiMpuI8e/oqhecvvOSdOkBfgkkF0NhwHnAFWzRXR7AYclSdoBZJDqO20E5IdCfnfIbwFPuTYz7lUCfH8tptGo8iIiNjZsusoukt1GgH+FNEzRhduOc+dt57aPTJK0zu37E1AOPAJcAPwvsBi4exTcCzSXwo4noT8Og3lPAo2w3RzID4PmnWAJev0O+AJQisykxRGc72dmjYiI6Cvom2bMJEnOSJIkTZJk8Lq0E8luI0BPpsEwaKSnffk0TW7oIeJyhXvfC1gGzLtcZsoTgQsBHoAd9wOGAQvh9u3hj9fC6KPcSVtBzSLI/h0GI3/f0nvgb0DTLDjAtV9a1M+IiIiIfxdJkowE9gPeWue20jSORZsMLkxSzpGf7nzgbuBFIJ9WwZHNsCMwHbK36vC3gSeBkcBkYABSaH8MmtwNeMjtGwm0AH8AZiFybOmhG9XAgp2AcvjiE+oHyOwZ1mcoTmuI/r6IiA8P9fUlaUNDWa+2mSSrX0jTtP6Dn5/cgYxPdwP1aZou/6BtRWW3EWFtKql7+zlp8pj7fOb28LdRkL8S+EIzPA4Xng3jboX85SK6QaNETE8C33Ft7YD8c68Ay4GHr4RkMuQHwM+AryHRd8pkWLyv8+f1g/zZcBKQPxQakfLLBkSXv7aQ6H5U1P9IdBERESGSJDkMWJim6fReaS8qu40XBRGXB8EX/wK3HAXtt0HZNMhNgQ537HHADcA4YB5++x+AKcBc9w7QvB+wM7AH8Hngr8APYLdpSkfY9my4/WL4/ADgCOBwZLfMBJ1rBK6AF/4OlyEz54I9RYC45ucjP2FPsPuyzx/0GUVERPxrqK9P0oaGzD8/8N9AknTOR/Nmw7Vpml7r9yePAHVrnAhTkYdlvzRNVyZJMo91VHaR7DYCFFc8CYngcGAa0AT8A3gOmA5c484tB76NiGy/oM1SRHjfRVGZTwI/ARZMgdOmwW/dcflngE8D/w/9/CbAjOkw6XPwtz/JB1gGHHgXYtIS13ATcB9yDN6B7KHLIPcPT7SG0Yj02ogkFxHxYaG+Pkkbnu/dNpMSPpAZM0mSScD/AK1u0whgEbBTmqZrmyO/f5uR7Po2/pmJL5sk6ZvAkLOAO2DrN2EBLnncHWPEZij2na1tG8CZapbT0S/th1+BH/1exHr7ocCpwDHw6yWKX9l6kWssj1i3FRgKdAFvQPM34IdoKve9CtilFWYAS4FvumvB2kkvmjwjItYP+hLZrdFOVHYfPYSD/Y5Jkj5/OWRPF9E8vpZzXgA+sZZ9te59XgdwCYpUKQd+B1W3wv8BvwbOXcv5GWACMAqZJr9TD/c3SKkdfyxy8A1zbVYj0yjAz4EF0HkzZAYCIyE7U4e1Ad9wh11DISLRRUSsH9TXJ2nDc73bZrJZJLuIdUA2SdJfAMdvAdk3xU/mwc3XQ7ZB/LLIbfsV8PV1uN4rwDZIjVWjSMyekL8Dtj4S/g5UVcAVrXDaZMSGI5DcLEfMeAQyeV7kXtXQuQQyc6FmjAivoO1YfDoiYr2iL5NdbyBGY/ZR9JSInU2S9K9u+38iotsLT3Qgovs+cHuwLSS6UuAN5Os7GEVlLgfyN8J4d8xkYHfgOiBfApufCvmxcr81Bu0A5EdJ3f0PkD1SJtSPAdlWeZc/PR24Aw68FF47D7gJ+fJ+CpwN3A/8Hli8OZnbgHNhRT+1VwrkW/39v4DqdobPJ+bxRUT0ElLky+jNVx9CVHYbCUzVPJUkaRhociTKdXsQyO8H+YdEEoN6aCN/KtxwJRx3KNTcI/VkpNV0HrA/cBWyR44BZkH+uyopVgZMRMQ5GgW0jMCbHeH9FWS+Ve09Wi9SHf45YDvkz7sM5TzcCGy2Jyx8Aj4Nb70idbof0DwYss6AMRKRaujXg2jijIhYF9TvkKQNT/3z4/4dJBV9R9lFstsIsG2SpPNQCYHNWTOY5GeI9GqmQHbamufnjwbuRP6yS2Hr7UUW+wD3Tge+BQ8/qZSE6xHP3YWiNO8ubutYyN4oM+lfEU+1A7ehVIKZwGfcsT0FveS/Arnfw8OI3zIHIcZsQFJzNyRXh+8Ef/07+QPU9UbgtAPgbw+o/XuBQyg0b1YlSdocCS8i4gMhkl3EBkNx9OGWSVIQY3swsgAaMigq8uNILc0O9uW/Ait+DzW3wlVHwynHQPZmyN8KTIPstZAfCNmV8sW9cRCyfV6J0gZqEaNNcp9LgJVIRr6OJNfjKBJlL3jqYvgPZEI9i0Kiq6TnSitPIMU4A8dvw5BMHIqYeCRwFLDFbvDe06pF9ij8pkuk3IZqczbZPb+P0ov+voiI90f9Dkna8LfebTMZEMku4p/gn/miSoGmfrBitdQeeII5Dvjl0cBXgTGQHav9/4uIcSTitUklqCTKCkQu3zwP0c6dyJu3EJiDCrrWAcOBepRv8Cfgu7C0Xc6854AZ0Hw5PAocA/wC+RYNtcA8R7ohHnJX2wsFwLQANfXujx1Q/l498PEd4Dcvwrnw2BLFtTxNYZoFrLnk0fs9x4iICCGSXcQGQfHAvDayy5+HRngL3HhS72+g9eby+wFbw95XwmNnQfZS+BKKC3kLqBkIPIPslIcjaTgM2S4rUKjl1Ui9NSG/2qtu/9bIcTfGdaYKybLpSPmNA06sQM6/0fDwz+F54Mfw8ir4LBKEpSjtrpNCBfgFZI6dC5xSAs93wY4DEMcOBA5E5HdIFfAJeG0afBKal8u02UFhpOja1vXr6XlHRHzUUb99kjasLX/pAyIZGMkuogeEA7OlDhSb5t5B/PKJsyF7sbaFSeOnAD+5DfY4SkWgDfkBqDZYA5JBBwOf2gHKXmSHDqhBGQH7oATy8RSeb9gHBU8+iqqsnAIcsh8izhzKCi9D6QZTgWQnePPvsr82wC03aoUFkNJb1sM1vuT6UoYiQ3PAjtWIaMehAJqR7r0c+C9YeLHMuN9BPF2MfJomX0qS9C5iIEtERE+IZBexQVBcEizcl0/TZJ8kSf8bGHIQ8A94eBF8mUJf2LPApGmQnUIB8ichybMCOcoGD6EmWcqKfnD8arhuGHAsDL9YJsW5iITKUZDKQmSl7EAi8EWUf95UATWtrszXvnDgI3D/nsge2YUKYtYge+M2Lp7z9y/CffDUnwrLlwHkT4Ds7/T5+64PI4Ez+8H9q+HAAUhNHo0cgdVICn5sT/jDE/BDeONN+DHi3OKyZBOA59M02S1J0qeLCC8qvYiPOuq3T9KGHgLc1gXJoEh2EUUornkZ4lfAl7dAS/SMcxsXAndAdlXhsTsgMjoJpQzciXx4r6MVypvGw+jZPvbk1zsB0+GHq6WmKpEPrMK9jwFqJsNr0318yjIUgTkXRXXuhtTYJxFB1iJD5neQmXIv4N5jkVR9HQWa/ASoqgI2g5pG3mlUKkNP2BfV//wfZPJscdfM1iO1Nxw41N38ZrvBfz8Nt8JP71GsS1grNMTaIjgj8UV8FFG/XZI2/E/vtpkMjmQX0QN6Irr/RlGOIKvdozcCX5eiWvF1yP5qzXZywF9Q2sAStNoByD13DCKpt5A5cXdESK8jLq1FrroSRHaV+ELNTUiw2QKwGUR8K/ALHjwJXA5sWQF7t8ocegdSf18DrtjJHTgHOBYx8XZlwGYwppXn35Rvr4meUxdqUarFDu4+9pmMWHpHRHw7Ax/bDZqfhi/Ca39RWsTd9Fz5JVZmiYgQItlFrHesTdXtj5LFQQP/N1DON3gFBzLP2UD+P8CngjZGIxHYdAL88Hfy71kSeCVSSJ8BnkKicQYitpzbPwYYNBBogcVdIra5ro3JwFYWMVkBz98D/4Usiy8i9diEzImLkEn0P11/21BbHSi4JjMRSdjJiGW/DDytKjFrw47I9VgNHD/UdXoiMnPm3AFVE+CdWbAnzJsp4n+Qwqoz4KJb0zR5PknSHSPxRXwEUb9dkjY81LttJkMj2UUEKM4NyyZJ+how3OXB/avIL4HsUC3rey7ydzUhc+VeiLwWI1L7T1Tz8h63fShenXUgM2YlshAOBZIKWNGq9hbj190od+8L3OurwJDDgaNg76N1/L54ldaE/H817vPuKGcug0yVw4G6i9DaQx0wr0K811OwDEjltbl+7A5sW4oUo1WnrkVRpCCJ+XM4fw48gjj6t0Fbpu6OS5L0hkh4ER8xRLKL2ODoTi6vgL+2wmcmIifcAuDangkwf7lWPzCcCvz4ctjldCmsg5FfbRCe5MajqildiBPaENGNRJH+Wbe9pgSog85FUmONKKBzESI9KznWhkirn9u+APjeQOAf8NoY2B4vusx99z/ArogQy5GP8fXJiAkfwi/ruC1k315zuSLcNb/t2pgLHFfh/viC62gWLej3sWp4pREOgvybWgniAPwagC8AW/egsGPKQsRHAfWTk7ThwX9+3L+D5GOR7CICFJsxj0Mmy0mjYMV8qJmIRuUu4Dn49ZMK/ihGKSqjtcdt8O2jpJ4WubZuAsYiH94oFORRigimAymdbyFBNKgE2ru0vRzIWK7bAkhbtd0IrhX57e537YxCZGgqMe8+N7lzHkG+uyOu1I7st5TOMBCZYl8cDLstF3HOBvKHokotRyIz5yxYMVmrMISJ5N3PEonCcuSfHLQnukDGXWQMMm9WIpvw1fDufNgK+CXeP/ossAvvn6sXEbEpoX5ykjbc37ttJiMi2UUEKFYSU4EfXADN50LVvihM8W2gE8ZNE1FZysHR+DD7h1Bk/nXAauCXo+DM+RrbF6LaleMQSXQi4vkzUmCzEUEMdO12IH4YikizZjDkl/uAFCNCW5R8kTt3F0RUs9w1w0LRq911G901K4CrLgBK4NNTFRSTQ4T82OmQu9z3JT8M0kWQzHI3uRheq5da7AlHI3/lBOCIAcD5+FDOA92N5VD+3yJoPlsTiNEodQH3PD/pyC2fJOknkRqNSi9iU0Qku4gNgpDwHkJCavQxSA6tRmkGxfY7h3JgxZXw1Kkau388Fs6ZI5LZGRFLGaq4tQyZ7Ia6Zp2Fkjwa6Cvxqi2Dgj8qgf6j1FBzl/fXNbs28ojwytznDnwpzUoU9NKBLIuv4wkyi/por9nAndOA+2Dvy0RUlchf9/ABkH1A180fiuyVP0M209tg5NV++aEQ+bNg8aXyzZ0B9D8GNbwMmTnHIjafp2fM5fDOIvk0l6BiNd1txdXTIzZh1H88SRvu++fH/TtIRkWyi6Cw8HNBkEoFsvV10u0I++G1WgKuJ+TPgu9dKkvnWYig9kJ8UIPC7h9CRNiKyLEEWUUnIlVmSq0U+fgs164OyPZDzrsl8G6HeKINr9RA2zoR4Vm3ce9dKC+vC3FKI4rMnIVf9qoVEWUrIsdH7wPuUpL5Pq6tV4HXt4HTXhF5nQT8/C638z7d8JBpRXUyR8Hx88VvRujHTUUkNxdFqYwGtquC9mbJulnA43DyEllOB+ADbIrrbsYlhiI2FUSyi1ivKDZhfhu48FuIdeYiU1sbPH8b7L2WNg5DBDcTjeEZd9pIlLv9Bfc3aMAfTmFdymH4CMwyRIRlSFWNdecmAyC/SoqskcKVxNuC8031tbttJkZL8Cow4/5+BinM0cg02oX37bWiNIHFl6AIku1gt3N1L6fdBVWHq3TaQkTYzY+7BzAbaq7sYaXzY6DuZhXAHgMMP8F9mI9soXvga5S9gdbWuwYWrtT9fsr1/XLgxIDUJiVJOiOSXMQmgPqPJ2nDPb3bZrJFJLsIByO70cDLxyJT2tcQ09wAr76t5O+eQu+fRYEnw5B62w25pHIoPWAUIhgjoTa3f3c0cJdTqLpK3WXL8Qnl44GqfrB0tVdpefcOIi0jry7XfrvbZlGbtr/cbbP+WdpCI7JGdiE/4u6ur014slwG3DAKyblFUHW1zy9sPklLFuGu1TzdnfBfkH1izeeWQy7Q2cDW5yL5+hx+ifY6NHvoQhOO/4DO2TJpfoY1l1rqSe319YCWvty3iA8H9ZOStOHPvdtmMrbvkF3Jh92BjzJCVTcNeO1GFEHSD2iHn7yt8PgZazn/OqR+7kfj8usoZ20EIsAlSLnNR/618W77bDSGv4hMg/MR+WTw5swKFHBS1Q/S1SKkJcj/Z0Eqi93nZW7fCnWbMtdWLVKXFgtiEaAWINPprlPhrplH9TLzqF61lb8sdW19bT78cCqwAzQfBc8epHv63rWQv0+BOM3jYchkFIqZg/zjkC/6lTe59v8IfPEC99DqEXM2uPf56L9jInAjZE6APYZJeT8StPVn927fZUgifZlM+nLfIiLWB6Ky+xBR4O/ZApVMCSTWkIt7Dq8H+eRaUMJ4P/fehEjnYZRHXY0U2mJ37Bi3bTZSJh14n1s1hSkDl6GAjt+jes5z3PahiI9tbdda96pDaqzRXc+CRTrxS/qAn11l8UnmLe66i5GPsRyv6tpRoMtkPNHa9kXAH80EmZd/bzek+B50xwPkt0FMeDacP0FFrIsxHnhxFMrTWOQ6tz+Sx3V4O+wM4Ofw6kPiyGsoLGn2fmvpRTUV0ZdRPylJG+7s3TaTraKyi0ADYz5NkyNB4fBdaIR2DPF+RPckGoeHIlV4KyKYfRH52NJ0LyFSakED80XIUrpigI6tRiuWn4J8fzujmI1ngOZSKcd/APeera496I553bVzKzKl3o3S4VqQmtsXiaVJeFMpiKjaEGk1uX0jkYqrQT/IFe6YakR81YgIO909j0TkOAb4/j/gm1ciFXcMPPw59WMv90gBql7RBe+c4BTkxWs+09nAOfMR0R3uOveie7Xgs+dHAT+DrevhJxVaENeiV3OI0IzUcu+TnB4REbFhEcnuQ0Y2SdJloNH9DuQYepI1Fnob5t7zN8uUBlJvj+PLfV2P1pnbDbmgbGWDK93fbyFCy0+EF1bBnYNhwRZANXxvMtxyNDx8Bjx2FFy3K1ABZyKxc87FWnZnwe+kHn98hojjOeDT7v051/3HESnOxvsI90DkF6o8C2CxaFAzd1YiwjYzaTXevNqIr/Zi6rEE2OcyqLoZGAMvHS6yHe/2H4vSFr4MfO8A2OVsWN7DdzEGqLsSOAgfLjoNqbkmNPvI6bnwG+ByGD1YKXy4QwDyZ7u/17KKxT9bhT4i4kNBivcv9NarDyGaMT8EhOasvyVJuscUtKL31mjEHA1MhOzlhWYxkPlv6bGw3Y0K4/8iIoN5iBAm4ZOiLwdOHAovLJFS2x2N1bVAdnu8HTKDWCiHGMWiVMx5V4pCONsR072IrwZ9MErcmwzcB9veI7PqU+ia81xf7BIW8VntLg0irWVB86X4aFFLSi9H5Fnp2mhCueVN7jaedG2CVOcs4PWjcUwIh52g88fjfW43o8LVL94I2WP992PXzJegBW8vdN/N7si0abkbxrhNwB3w0u/lKjRxfhLw8/cxa0ZE9CXUT0zShjt6t81km75jxoxk9yEimyTpvsDdJfBaF2z1OUR0TwPjIHtjUSLzzVB1jMx4toL3MuSjG4OvRwlSb6/NlNoagysDNhidPNJdB/zCdRV4u2ElYiTwSXVt+NBLk1pLEEOVIwnXgMjvVWA2bHcP/NU1ZxVVLP2gHF9HM4tPjWhz+xe4Yw2hqdAS0y3AxcqX7QN8HZkwO1BQzk1TkTLbH/b/lrY3oriTI5EqXYAqh11f9P3kh8I3l8Av70GOvgpUy20MYmqzi1g0zVmQzlQR7p+7Xd8GLoyEF7ERoH7bJG24rXfbTCZGsvvIwwa+hc6M2YLMdp/fCfJ/h+ypwBUB0e0Ko59RknMDPrjjZkQOpmrmofqY44Bt+7kPpuAm4zPHzZY4BjGILWBX4jqz0l1gQdA5yyRfjKvh5drpRJJpkmu7DjnetkYOwE/CyfPl27sXEW/dYLW7dLWIsBVxZAafqlCK1Jst6trgumDWkSp3TAsSmiNc90yY3o8PnpkL3H4NcjIeDZ//hvYfCNx+B/zoSCnhq4GvFH1X33CP6JxrUGTPP4BL3bMbSOFifm3uol+GF1bDnkVt5dM0Ge2+82LVHokw4sNE/bZJ2vDfvdtm8vFIdh95dA9w4yE7W2kENwG/wNeU/ESRz6cSHfdzJFbaEJeNR+PtTxA3DQOG1yMFV44PnRyGL3PShgbq0RSyi4VFNrrXXDzZWVSJZZWb06wa+bj2wptDjZEaUPRKJ7ADDP+ddh+Jr9Q1ERHSMtdkM/L7gQ9OLUexIeUoMrTZdcuEaBvi6gXu71Z3Sycj9ba/u5VO4MFd1dfjL9Yj+B2F7oUDgdvvgeyhflt3BRXz4d2Fd0aaQ7UEOSCfdNt+AD9aruvvjep13gYUr4weEdEXEMkuYr3hhiRJfwjMS9Pk2SRJS4FPlMDPuiSKPh8cmwGab4bcMRqMm5BoyiNBdRjio21LEfvtg4/7N/VmDYFGeYvzB2+SyyBWsAS61xGrzEBqrw3ebRSZVAD9R6Cw/ovdcXch/2MD3NuosX8xPiJzDBr8X0XxOHPwCed1rlvjXPcrEOnNxyvZLL7QSYnbZ1Grtfigl1fd3zVIZD6EuLjT3crT33LP5I+w4xxZJ6tRAv+f0Wrp49zth3gI+OSTwJXQeRtkjkbrKVUEzy+PWG1nePcoPYOPB21EBRfRF1E/IUkbbundNpPtI9l9pNGT2WpSkqRHorHyBuD1wMRVDjyG/ErPo6VoPoc47SfAjhWIRcYjBszhE9JG4Eul1OJlUo7CiCmTUHORz3AJYqnnoLNV5NGESGZHt77evFO1/tzjKEbFyoZNRkSxm+vWHoPxfr1WSDsULPIi8pM9hyImr7ra9ecZ2OFWH5BiKzzYKj0/c9eZi0yglYgoxyNl+Ko7vhxfCm0WXoChbrACv6wQp8DI8ySGGxFBHYWsnvlRUDdfC9Ne6c7PD0aysQLemQqDjkZrJFkCodlXr0GhoYvghamqX2rk/CPgtEh8EX0EkewiehU9rUpuf/8/ZIazQImfASdfD9mvOmX3DByxq8L6/x9KB9tqMEp8G4fYZoI7eRliplFueykyWRrZgY/yMMKbi6TWc6g+5NvwRofPgtgRyHwFbv+9TK7zkfKpReP5gcARI1xfciiXoMPdlDEh7nomwUq1r/MV+eAsYIcfuf7cCWwNX3hS69SZD8zMnj9yTdzvbqsGpTiY4uvEc/vTbv9I98gs8TyHzMfZoyB7m5Ty2cgfGq54cgqacBgyQPOewF6w8AIYfi5ektoBy5DaXYFqdzbBLUukIA3FpcViIEvEh4H6CUna8IfebTOpj2T3kUZxxfwxaFx8KU2Tw5IkfQSn+o5M0uyf4E1gC3fupe79m8MQsQ3D2/UG0m1qpBO/Pk8ZIp5avD2xFU9yrUgyPgDMhuc75DdsBI7fD157SASCa/JmxGez8RVQ7FIjEXlUlSC74QJI5+gSVg3F+HW12zYc8UOVFefcFY74i4jo+ZMojPi8D65bqVvcCy1AXo+IaAZKuzgZ72asdo/Jqn914ONnqlw/JiPX4kvufQ4w6Fw4/gL1oQS54XZ0j+lStLqEYQfgL0DVSfDqtbD1TngWBm86vtpd+EU9iOEden67AQ9Hcov4kFG/TZI23Ni7bSY7RbL7SCOcuS9MknSrYN+fkb8IfFBEBgVz3IAG268CyeH44JOh+AHVZJhFRdqCdRacYiVMbHmCRUjJzYC3ZurPBkQkk74Fo6/WIL8MueUyaKzO412BOby/rf9gJJ9s1ddl+OjNNkiX+MVn21wXLLMhi0/tGzJZH/76DHzmcEToS9yDeESdnNGh+pYTgC9+Dpb+CYaMhayrbXYSPsZmEl7kzsfn+b3u+r4/3Sv70ImCLvOXwf5niMDvc33+ElK1xTgO+OVEYCS88AB84iwUvFIZHGRFP8tdp+ZC1iX9PYFU6w74ot9R3UVsSESyi+gVFJumuv/OJCmdXuVNAp5N0+SUJEmbELkcCByPlre5zgphWt0sy5FrQoPoMvyyBUPdxSa4v60Oly1EV4pCO2fD4pWqvrIIOHM/+P5DcjfVoRqZXfiVCibglxAa6D4nW7hrmMSrRYRqgS7GaGY6dVWnOxulnmxJIKvMVYty8LZy5JUf7y68ApkKG4FZ0P6ISOom5LM7sQLmtao9W90BpJ7yeBPmdNfVGnetufj8P0s+/+nndI2qV8SxXwR+GHynf0WrIBhuRypx+DHw8s2w7aFIcoZZ8p2o8OgYPdwbzpWp9D4UYVrFmmbNiIgNgfptkrShONl0HZHsGskuAmh2+VZb9rBEzFcpTHL+DfDFoWg0rUMENxJfa6sdX1LEUg1yiI2sJMgSvJ/sWshPV0L622jsXQicjgb63yACqXWXmeAuW4lfqNVcf+Yry5TizaUW6bECT3ZdkLaKOJvxSeO2vE8lUFYB77aKADuQ8jKOuAZ4cCgi+053jVFILTW56y2A+935Jw+luxr2Lg0yc2bwZctq3GOyspeWgtiECHQBUrU5FHC59UWw/1RFvoZmzK+hxWRDvAOU7QvvPAKDTkUMayVjSvAVAF5Xv+fdCNu63a8A27jPRnjfTpL055EEI9YjNnWyi7UxNwDC5V9s26QkSau2kcIoxu0oNc0wEviikVsdvoCklaoy+dIWnFSNiC6LHGWN+MXmHofXpst6WQ6cchnsiohuEiK2q92l9kLjdBkam2ejcTqDr3ySAzID8OkMueD61tcqoAKSCpFiVXDIate1JuCtVnV1q34iIlORXUhZMQmxVQUKu2xBjrty12A9HDgRTi6Be5fAw68Ac+HZXVXC7GAUJ2LlxR5AsTid7jFajM9u7jHesqf2XQVkp8KD1+rZnATkXbRMMdEBDAbyj8CgaphxJcqzsIgZm3CMoTu9Y/QwBRyBiC5fXZh0bmXHItFFrDek6B+tN199CFHZbWB0px3sDjRBduaalTQs0tBiTBaMxUdaWFTFInz5LhChdCIGGoYUYCsaYOe6Y+6A5ld06tYD4N5V8B9A/lr40knije3QoGs+NVM8Zi0tB4bbdfuhgTvM/DZf3RjEjmHhS4vGzOCXQcion50dfhHZfHD9LneZLVF1mGVIyFmwyWljXef2Q9Gm0/E10yzyZx4sdjU7B03Uc1rxihK956Ji11e40yfg3Wwd+JifL6HFcpcghfjoZZA9Q0sgfQefXw/6/ha7z8+6r6NmCrwzDQadjcg6XBI+j76Ik4FZ8PIjsFPQXiS4iA2B+q2TtOE3vdtmsmdUdh9JmAlqH5CdbFnhQGafFwPNFeK3P6NoRmoR0S3E+7+68PW1nA+MHBpdrcjkStf4EmCOBvKtK+CUVSqLdTtw+0mKQPwScCg+p7wU8Yglcte65pd2wdIOeHeVFnYFxISVwau8aJtFnzgJ1bkK2ldB50oRXQnQv0Kn5PACtsTd5gz39yRge9dcEy4YpR9+0dXxQUcX0519X3cWDNpetSspF/k8gdI77gdOQxZFi9dpcd236NE/InPv/Uj1Zc9QoejPXw9HoJJiuK9hiv/K2QXYHOicBoOmwOKL3XeRx5ufy1HB6cX6e9t6yJ/h29g2rpIQEbHO2OzD7sBHEbeB6kJ2wJCiffk0TaqSJM22akmd4UCyPVJL89AAaUWaLcikDT9tGYb35ZltMIPYokTmuytbNWjn6+GIBp12FD6nvAa/2EGdO73JXaYMv/JAfysxVo53eJXha3hlkY2yuEpLJ2QykOkUWVpaQpmLEC0Nrm8ZB7hbakYRiyNdX5cAjHbVTF5HTDTeHWxVom37VEgaUaXmuTBoXzg5D0c/owjYm9GqBeArrZnVcZi7Xhdw4fYw/R+wSxdc/lUlnn8LnwB/K2uiCmie5mqCXoSifmrdSV34GmoLEGnf4M+dR6yfGbEBYEv8bKKIZLcB0R1ltz3c8A9Zs7YIglLyaZq8kSRpJyr8/O4z0L8fYpxZiCxq8PbFMI8rgwbMMXiSW6lt75wO9wDHjYfPzFZC+lhgjwaYii9zmcMLMctMsPqT5n4rJSA58PU0Qf8obe6kFe7vdvcK/4kCs2dSCdkOyHbqou1dOrTFndKFLKNl7u9liHTMenop8NZtcC0w6m1YchucMxBFkZS7A22iMMttu9Rd4HXgEeUEPnqwtl03Te61kxDx5RF3mnV2d+Dlf+jve0fAIW/rWBPVX0OJ8Qe5WzTLLYjwWA7584Cb4I1/wJa/Q6Tc6R68MfnTkG+BR51yPgTvI4yroEdE/PuIZswPAfv8Qy61FvxM3d4/jkLa25+B/pPRCLs4ONnC2EuDbf0Q4dTioxIb3XE3SchcD1TNVjDGGEQYX8KH+1sdyXLEkR2uSSM/E2xdOEJa7UyYlkoAftE6U3hmAy3DE6LB8v0s6W41UAFl/USmtvKQLcKwGM8HFow6FPgbIuZjXZMzgHdXwss3ugMq8OkYde59gbvhMUhhfQPlXXTCF6bAje6aS0dBfoSCcjpQDl4bcJ67ndPeVoxMpXuevwEuvEtpBAAvB7e7b/B53nnATHEalyAbsk0U2lyD5wMTYJ8tfNxRzRN6L668Y9voRcQFZj+iiAEqEb2F65IkPQsRzRU9DFDfT5I0g8bgQbuikXYo3iyYcwdW4DOxRyIHX45uX+BbNyp1YJZrYpY77SQUjXipDqMSz1dmiczhic6UnRVrzgDZkuAAszWany7Ms6tE5sTQR2URoVbBpQs/0HdAp/sHaaPQjGilJk315dz2CvyCDI2ossuraIHxb7ntn5yCdwCOxKvfDHJGluIJ8SzXyF7AA9A+XWXFDhkLn58j8+9oZHFc4W6txG0rR0n/bcCjD8Ff99ME4hakPEuD2zXkT4WlV8KQw5Gzz3InKxEpN8FLJ8F2xwKPQ3a+/KyfJ+bjRfQu6scnacM1vdtm8qkYoPKRxXeAFWmaWDpL8Qz6SkSEg7ZBpT6MYTJ41gEfnNKJiKUWDdjzgLzKMf5gP50yC1iKcsUed+2H2QotQfNZvHhc4T6X4HmtFLxTDfy6dqbQWt2rrWh/W/Bu7GVBK/gOZPpBpsT3wSy2dtlyxFvWXBMyD5pQW4wiKqeiAMf9gCumIVskiPlX4qNX5yBCrnH7z3SN3q2Ty85WusK9c+C/3XXmoZKdjWgeYoLsOnd6HZDdDz6zk/ry81ESah2IQ0PcciUM2QZW3IW+/JloMtBCd47gdteiqjGX65wD0zQ5n57VV1RkER8Y5rPrzVcfQiS7DQQbhMyH09OaZr9MkvRUNKg3v4I3aZl5sBiWC1CJlzc5ePVPcNrhsOVDCpZYjgJh7kErZ09BVkPjHRNj9tu0PDoLqDRFUgaUlTrzZQuFP+aSolcXnszC4JWQrPMUVmu289x1jNwsB84+V+CjNi2gMYuvijYXEfwtiOCrgacudtetdTuX4CNtWvFFM0eiyKAd0CKtQHICHDJZ1543Xvl6p6KyXo/jF4c9ApHgLVPkMtz775C9Qw/6YfcIdkA+U8OJwJ2vQM3p8M4caD4Xb4K277YD2BmWHg75nfRbskoul7JmcXEiIj4oNmGyi2bMDYy1DUy2/TrkN5oxDSaNQia1PD46xKIecvgKInPRAFkL714EJ+CT0p9FPHgW8FIFLHSRmMPROF+DhIxZIY3gTJjl3OWMc0uApB9emVmfyvHFqCvdifujOP65eFVnqQpGckZ6XcFFoVvSda52kZpus1lAw1laBuf/pFAIv+rubSRSunY/Z56KnHtj8JVmrC+j3EOxaBhbfXyMu88X3cVehHeXa+IwAym2nDvE+OnXJ8EXrlUzp7pHsQ+K/MwPgOwqfw/VSP0dfwG8cy4MGoHWcjIFfQayy54Od7bCEdtD9h/+fIvibU7jygkRHwz1WyVpw5X//Lh/B8kB0Yz5kUFPgQT2Kj52Km51gQUusTl0llnUYxj5OMJ9bkWDYl6Dbw4FwLwA/BSVvzwMaG4VCdbiTZbN+CRqu0xo1rTtBURnxGYONTOp4j534BkyVKRhkIqdawomVIAlhef1d79S8+NVFjVlPrwavGV3BZonjEbzgK+hSMr7gB9diV/FdTbyjZnqXIRPSq9FkSVtSA22oXIyI5GcA54doT//gMyb5ajE2JHA96/V4geT0LP+8UFqYh9gO0d0p7hLNQL/CRx3Lgw6Ce59G0XfNLnntB+sOEkNPw3sHRAd6HdW4AvsIYglIuJ9sYlXUIlktwERDj49DUQH4sb4ThhdilRSF96WaGVDzO64Eg3CK+D+y+GwyzV+34Cq8J+Fxuv7h8L5o6RuJrttJfhymsYzZnkwlReaMjuBxH4tYSWUkHVKg1cGSZo6ChPL++Erh4QkHlZUgW42zrh7z5QqMMb4sTPoXzVeKHbiiztbcI0t+FCO1qnbDdjjCfj1393GJ5Hssjqjy9CDXIzY6WB3Uivyo44DToL+ZwH7KOqyZrwIa2fgmyioZRw+0vUY4M6/6NbuvU9K8EeoDNmfkdIDcdsN18IhZ+tabx0J/JeuX3MX3H8j/PRseOxwyA9zEZ0B3m+9xIiIjQlJkpyXJMnCJElecq8D16W9SHbrGT1VSAk/Z5MkzSZJ+mc0zrbiPtTiV73OIHKwmVKZ278AmAuP3azNDShAIl+hWIeRuCjM8fDqfOXWdblrlOMrj9mPIEyZA5+m1ua2p6bgSvDpDz29TCW1IMI232I/vB8vPLbTvYdEadEpJcG5QFmJJ7lSdy+W6G7W1GX4tMOQTye5x9aBEsgnAofdiCTZq3hzq6lVy8Foc/cwDJ95n0GmzQmQOVX3+uo24sLXkIK+BvHUg0i1H/E5kdntB8MVB3gL5TeRSfNXKA7lm6CUhANg86/DH5+hW1keOAxeuJjuNYpenUoBdiv8M6YkRPx76Hs+u5+nabqde92/Lg1FstuAKFZ2YVWMW/ECrjsSL4xUbMOXlirFJ0q3wN4l8GU0kB4DMEopBguR+ZJnlEZmQSmhxdCCT0Jfl5FGO96amCmBxPyFITHZAW34KEyLfGnDZVLjzZu2z+7LfH/mBwzJvTQ4Z7V/QKGL0PyLdorFnFhagtXCbnePazQ+L64Dibmly5F/cS5+Rdq8O6mJQsavdN/NXHe/1e5CRwJL4Pz9dMqdiJ8mIX9dJfDLP2lbC7DPAyLj/Dbw+j16HOfhke2CW76qg79wEUqUnAlcBp8ohYd/5677tDvenfc08MY/sSCsC6IfcBNGNGNG9BbWpvJA5bpm49LoSvGDqKmcFnfg7u6gF4EsLJ4Gf+zSZlDB5L1fkbK7uwJOPgl+3SGTWj+8yDIY8YXqzkyaJizLbEdoToXCdfHKKFR1GbzJtYrCmpkh25oJ01RjWXAtOy6DXwfI/QMl/ZSmUIbMm9YNCwI1PyR4lTcaH8Qyzr1fjvOBXQz7z3TP+RGUqLeIwlUlSvCJ8pVIxr3o7nEsYrXdtI7dpNMVHHT+NrLkPh48gtnAo4P1eM5/BVimZH/r86UoUOlEIHszfHsqXDcHmi9CbPk7+PTlcM4zwP5KfF861t/vx/GTqUhOERs5TkmS5H+TJLkuSZJB69JQJLsPCcW1DltRzlYOCp1mpnYsWnCZe7XAa7fBp9DCro+gwsRVaNWCy0Dy7mm1m2dN95p9+e1F2y0Nrg1X/MRSAszmaVIwDJoxZYbbb7liyyj8lZkatBIpxq7GuEaUdj3bZ+bOfi5IBnU0467Zv8RbWKFQ7fUraq4Wv5CrxZpMRROF6x5A8ngFvvimSeIuCtWsRfoswhfcziDH3TSYdBLc/wrssbsOuwl9PwcDf1gun+r+wDdPgAuRGTM/UNuPDx7Ztaiqywkg86arAFMH5M92z3mRgjf/GaIZMuJ90ftmzMFJkjQEr5PCyyVJ8kiSJDN7eB2GPAFbosVY/g83rH1QxNSDDYSeVioHT3Z/TZL0M6Uo9H00Ugvg61zm0MjchgbhGZC1ARbIXwKcC3usFlk9ewdwOZzypAJfrLkM3ldkPEPwHm7LoPJd3TZBI19jzQq8kivOqbPIkJGIWZYF92LpBha1aSkH9m5qz3xnxsZm/gyjQB27da52xNfpfItBcx14tWrWVrtXy4WvQ/Eos5EJ8jogezQqiTIJPxOpRZORpqAPeddIZdC3Ye4+XnTn/FbtZJ9R8JC5AGvROn03IBIccjPcfoxKvFlQqMGU3y+Azw9ESrIFHr0c9rkHFh4KWyECfTA4L6q7iH8F9WOTtOGS3m0z+VzvpB4kSTIauC9N04kftI2o7DYAwsVbe6pev3+SpMPBV1s2f50N7mYOHIPWa2uCe1dqsJ4E5CcDMyG7WgPdAQCt8IcnlcRcSWGiOHiLI/ial+H+DAoG6WYKU5odRQ2EUsr8XKYAw8jKsA1TqZ1BW+XB8WbWDINYrL5madCmydCMI7pSCsqN2Wmh6y9L4Bt1j7UOpWAMQ3ONfRDH/fRWlLPwPL7SSpN7hYE2VqqtBe+nmI8nvSXA8dD5DOS3F7GNxKdKPIsKvLQC5x8Dn58Irw5WM2ECeguaB30FuH2l68MSFYkeeSh8D8j3i0QX8QHRxyqoJEnyseDPw5HX+gMjkt0GRDjw1AXmpD0QzxVEjVhugGFrNNjm4dWZKjvWgSLkOQk4XAruDuCHxwKXyO00ksLASfDEZjzSRqFZ03inPSSc1qAhk4ZNaAQ2QsziE94s/t9FDRasYF5Dwerla5CoRVuY8ivBm0lDBrNITeiOEM2U6v76l+jxZUoKSd0Iz1RSq3v2ZSh1YAxKC1jgbnfS2YhhXkQRm8vcgZbwZ8+iGm/7xZ08F2XvT9Szypyh9+XAD5D77Yslamo0MHqwVB+z4dfLtcDv4e6xHeeanQG84i7FhcAIyF+mLt0FsA/kt6Ab2SRJv+YmWT1NuiIiCtC3AlQuSZJkRpIk/4tSiL+9Lo3FJX42AN5vOZZskqRfADa3ApTmA1uNBtYORHQvoWi/ORrUFlnb+wKnw/MdUnS3XwOMg+NuhKPxxVZM3ZhrrDjFIIzMBJFEhuDADkhbIRmAyMqUjSW+gQ+kMRlV4t4nug6HEZtW58sIDUQY1pYxU0tRhzsRwRhj5/GM7fprh/YvV58tR700aLYLXyjFyH4YMmWWo8Rw69YpT+oZXrgDevij8Al0I/HhnuPwEwDcRV5y+ye4Z/AF6N8A+RV6FtvOhguAZ4Chy6XE/9DhFpdfoFN+gaJtDdu494NXwb6XwonHQL4UTXry0PwAPISCnpqA36Zpcmskt4iNDGmafvmfH/WvIyq7DYieksrzaZqMB590babDdnxgxHg0dW/RoDsXDcgTAA6HIzrgxzhf1ARgjFTeSsQL1qSZ70zpGClk8ZxjQim1IJQOaF8JDIRkCzTAg69raerTGCUM7TTSq8VHV9oxlnMX2hVbdY80UlivzMJCy/G5BnZdu561TfDeoWCWpJ8ne4JD24Jmy/H8ZfFBo4CvoniTycCnf+X6MwflMbg8x4IK2rX4ZSPMv5l3x1e7e6xHxDgBfodiYW4APj9ZK6KPQcEoX5ovvlyALyA9CY/7kF3ntJuB/SB/NWR/Dx9DE5//dset7XcX1V1EAfqYGbO3EcluA2Ft649dmCTpaJCssNj0JnwJsFo0vX8a7p8u89cFaKB+/nrgGo2ph+HTD14e45WKWQRDd1k73oxn+eE2wIPnqNTlkZVVBzvCZL0wXy70udlAb6bGDuQYs8ovIfH1C7YZEVbiCSsMVDFfXyXelGmw8+29Em/67FKOYFk/VYFJSt1SRaW+GxX4RcNL8FVjOlAK3R6IQH44G65oRBVXHkE5BS+6Z5JzjViYZw5vwu1CsrESsep4YAfY5QQ4cbzj9Ok67Tl3Szdto0dWjdIj/hsRo313oPS7uUDdX3RM3j2TTmCPgf7vkayJSHgRHyVEsttAyKdpkmHNyikTcArNBvgyfNTiaKQAZina8CzcoqHjHa88DtmZOuy/gO0eAubI5GXk1YnnHrMGmikvjPo3d5wd338gJIPxdbjA+8/M9mcXMftnaBsFb7dvYs3anvTw2RRSlsJqMabcQkebKckyd3xbcHwpzmHntlmJsqCPFrkJIkDLbLCMCLu1OqSQ24HTkEX2cBD7tSHZNQ9JLBcs063wBuIf8kC3f4Xbtsw1WgfsBQ/vLhV5JJrbPAMsfgX2xJtdD5nsCnsfLfWH6/OjiCx/gMzZ57vLcziwM+THrhnZGRVexBqIyi6it9BcNLDk0zQZhavnbGawEnwQxBi68+q+jAbC5olw+2xovh64Q+a1iWiVbOYBP1Qou6sL3Z0z14kPaLSylPZbNE4wV1OmGl8JpTw4CTTq5vAhnIbwl1T8g2/F56RlKCS94r8NJrdMzdm1S/EL2K4Obs78nUZydoNhFKepvoD5kxKZbDOlCmoxsdq/xGcVmOB+A7+CwoF3wcNL3MEL6F5otZt0K/B1QY3E7ZllXb+WIRIcCtTDVVMUSXkNKuB9L8q9OwK4GKAf1LQCh/v0CcMilDbxaeDMw9V09vfAePjrHPgZa0eM2IzoRt8KUOlVxACVDYS1zZ4zwKChyFZl5rq5aJBciGxanaq1eDOw90x47DztP3CV+LAB+PKtQCsc/7bSD8zaZ2RnKP7cgs+962/EYTljJimKCKIgcc0SwU2BhZEgxbJxGF7lQWHpMDvX/JVhSRfLFjcbYxOFdtlyfN5bGIWSDdq2PhD0291bONKXuWPS1e4S/aB9tedYC/8fiZ5953zIDEM2xBJEcGPcAeUouKgJX5U6T2E051y8uXMH+OxkyF8DX1qtXS34RWrpgoUVuu0paK1CUD3Mp4Nby96lKiyvA9nf+XvLlwInQPZX+jtWWIn4KCGS3QZCaC4KP5eDiC6IKOzsgEw5MB/mLVHQAijYrtsy8IT+ngX84HS67ZJHUVgBpQpvXQv5yTipCkVetnc58WYqKI9Xm5ngwkaIbfhMdUOo0Lrwla0t6MRIJzzeAmFMARpx2vMw02MJXs5k8YEfdqM5fH5ePuiz9SlUoqZSzR9ozkpz0lWIADMZ+S3LXAmWdzugqhSqOuHgLrnqFgGXPKOE8JMX4ZWkycHa4GHbF2NpHO3u+CZEjhn3eV+4aS489gochNT7OODCBr1btsNvgB+iqNvng1tpQwFLte7c6fa8W+WnfD9EAvwIw8yYmygi2W0gGLm9GXzOJkmaN/9OPzTYtTh/Uj28e4/Ml3kUhffsfsjBl4GfXOldQ+yMRrobffCjCRwb2yrwdaSzyHW0eYmSsNu7nKIJ1VNIAEZKFkFpKimUjabGejJntiGym4AiGbsolJg9wVjZ5KmZKs1MaIwNhT67EhQQ0o4nFYNViM7gs86r8LUv7d7s/jKQDKSbiPtX0+3DLBsIu7RCe4cSvycCX3gG/jgLXl0JW5+KvogufEGALF6VNuGXn+hCNkgrKrALsCPs/Tpcd7MWgN3jJB2yF7J61qHamRmk+k0gt6HJVE2SpD91277s9mdLtRzRtsEjKS5yYBOxSHgRmxqiz24DwQaPLYp3WFkpw2K6Q9SXoWCDGuDZG+GWh5DN6tdyEZUj/46Rx7cf0bGrKawcYvEjRnSLEdHlu5xbq5/UJFAYpgk+cmMgniQI+hwQA13BfvCpAmG1EVM84TYjGDOTWnvWfni9UCWZarM+mC/M0hIq3MMz4g7bM3I3v2Ropg0rttgzCGcLlXSveFA2GHYsUXzKL1AJt0eAd69EcqsJEf1KRH51ri2LTA370lL0fHPwhdPh2ZOkIp8dKK5dgFT9kSh1YQ/g7+60HRGBtaHKKocA/4snwdHn+czckOCCJxSDVj7K2IR9dpHsNhDWlnpANRrYGvELh3bq/VtosHoa4DkXUbcYuEB+IxvQ6ARqtAZpGDZvYgy8WFvoLvlOl6uvXKpIz4yF6duAbpGXJUhthMqJYL99NsdfFZ5pzSyYx9ePDH9xYeBIFr+SbEmw33yBIenZTVbilafBzKDhNiPCNgrNmxapEwbgGPGEfQRvOrVzLd/PhW7uOECP6X9R2a7+l8BL99Bd3q07W93Ivgpvvjazp5lmcddxE6FdvgX/ALZcqaTzPRChHk53MKcWZzhB/Dos6P6lwOZA83kuAvg8rVwPUJUk6T7EiioRHw3EQtAbCDaYhO4vcHUta1EkZQaYCws7tETPVWgsrUWlv4bcB/MOVqReF3AL0HQb0AT7n6QyjsPw47cFAuKuaSH1LfgazhljQ+tcCYXJeTa4lxa998Ob4IxJnRoBPImVI3U10HXO8s2W4CM+83hVaCbLYt/BajwR2A1ZsAx4n15IimEkZ3F7TRQuR2T5gNam+QKtLfATAbtnm00Ef+fnQ7Yf/HS1/Gn5A+C1B2CrLVB5LzNZ466zAF91xnx29mXl8apzLnAHnLlcat4KBe6P1sHrPxm4EV6eDNsOhuzywjSX8NbzaZqcnyTpZegrainaR8RHEvWjkrThB73bZvL13ikE3RuIym4DIAxGKSC6NE26y3g0ocHNmaomo4n/0ony0VwLMFP+liywPfBn6A7SsDHYOMeElaUfWHRmd3oB7ssvNjWUBZ/NPFhS9G4REh3BOaFJMozQ7L4QhUWlw5cFwoSvsuAcU5HVFK7eTnCt4rSEMIrT2uwMPtdR+GVYX63/YfSmvVvb5rs0+7A5SKshOwLyq2Uq/A2w3QOw1dfhtTfRGj/P4Vedz+CTzq2vdg27RwvSqQGOhO+ipXyGI0V3PdB/ADw2HfgcbNsPyX4Cf9wA/f0KfvtXkeqzmJ9IchFAzLOL6B20oag9Q1WSpKzAs1EdME6q7kRgwdkwaaZKf51zKlCt6inzkAto71uBMjjiqzrG4iFCHsngc5uX4a1opbiVxw02YBtCf1WYH2cKpIrClAQ730ySVgbMzu1CI+sYfP5ZJb7qSI41/Xn2Cn2C4InGfHSmrKzPdh92fxkKHwj4CM6cnmu3arSHY9cNc/M6g3Y6gz5ng7azkN1G3ZkA/AU45VeulukUeOdcJPleDe5lDEq2zCIirQ2ubUE5lcA4GHI6HHco/KRaUZgjgKdWuUc+B36yGg5pUPrCSJyJcpXI7BKUlL4DsHmaJt9Blcug55U5KNoXEbExI5LdBoDNmkejeoaGavC1uUqBYTDvGTgFDV7HXwwz7nHxFpOB2zSA7oOiKc0MmMdbx8zdZtxgFb7q8KKhDRFdGpb7CkuIhKRRnCpgDsFmtz9HYfkuC/1vQuy6wr2a8Llw4H14TcHLzJAlwc2ExBMGl5jqCdUQFAbMgK+CbUrK9lXiTZcd7gFZ4IvdS0jy9lysuosRqpV0s+Ably+ZbAHblcDweimoK4FtvwuDdkU26WtRBrl9/8Pxifdmf7ZAGXvmzjfLeOBgHfYUWk3oUXzllAuAzm/Aq/XeIp1NkvR6YFIJnAHkHIE9xz8PjI2q7yOClBigEtE7eHlA4d9W3JcWNCi3wugB8An0xQxDHyYA1MJrjyjPqhbNzq1iyDfwKW/9kJIr0S5ybtsKfBZAFkVfJqHZ0Hx1rRSaKsEP/rbaQCk+ytGCUJrxGdC4tobh1q5BMmOYO34vlEsxHqma4YhsjKlbkHRtwZONKTlLHwBPOG3BvtAMG/r0SilUph34wtvgl4dwz7T7n9WeTxc+UMcUrJFjHild8/FVu/4Pg2cbtOnOainybZ9x99+KcgbMzuwmO9S6ezAyDn2o4FMrRsK2O8n1eSIq0VmLFH4l8EngzgbtDzG8Cz67jX5H1mwH/xxR3UVs7Ih5dhsANlAc4iqezEWz5Z8lSdq8Eqo60I6/wR9X+1zkC6+B4w9WtQy+p0U7D3NtVoIG5uVetJiYMdeTBTa24IP9jNcSU0qV+MCLNtZcJsFmZ6berMEmRFCWG2YOwceB12HxKsWhVABbWXDKcGBXRHTT0QHL0CJty3QOro81o9w5413HW92xBDdmN2QEG5pe7R4I3sNgHDN9lrntYS4fruMLEImF1zQzp23rCPbl8P7XoXoeOwKZCni5EfIHwUt/geGP6Pv9f8ARN7nnfjJeHZahyUPY9zr8ckZWyXt/GH0gvHWeaqM+77r9tHbxPbSawnXAf7rLNAHZV7Tm3bZvukDZtQSyhIjq7iOCPuZn603EaMwNhDBIxQTAvWgZmS37ASPhb3Pgc4jcDkZjbQZVSfk+8DfEF4uB489DZLAMnprqx/1aNJsfgw/qs3G/FNV87OxyhGfEFobXgw/AaMEvz2Nh8V3qKwNdZ/Lq6MI/ibMmAJsPRYQ2xfVxAlIj1cCgHXSN9EWd20b38kXMc51fBNwPzITXOsQhQwYjRVQG3X5OY/RS/GoM4NVaaEYxRdYebOtizWAXq7fZ4fq7GHWgo+h8M/uaz67dnWsPey7djtJ2t6r808Ant4E9XoG/bQM1ryjHftBRKH9ylOuLI0pa3b2awjQVCYXRmy9C/h4R2v2IN88fC9+co7nHy1vAhW+qqkqIvIvafD9EkvvooH5kkjZ8p3fbTL7Td6IxI9mtZ4Sz5VXIvGTlm15AhXtfQOPaq0i9XYFm5PugsfTk6+GQr8KpqIrGD4EVt6mhqosUBBGmGRg3DXPH2xhq42UpWuOtO+rRWNHC6C1a0aJaShF5jUSD+fXw2Nsi1M0noyiHHdxxLRSmEIT+rwp3XB4xeGtwnPnFLEhlHH5F83ZkFnTL6bxzNbyNK0pykLvple7drpWnMC/OIjrN12cI0wpCeUywvRYRsD2vyqDdMO0CvC+yw93fPH/tFat0+KCBOueqJXDKUNhtiSY+NcciJTsO7wtsxReYtr53UFghxp7j6/DT2/T7OBWpyjxwI3A5WjXjaQrNlvu75m1ZoZNwkb92O7GiykcGmzrZRTPmekZYjSI5FY680pPd40jUVKLB5kQ0Tn4PVQBrQqlZJ6PIu1HIVLViKN3mxEspjK4391IW8dRICgVOWRiCb6Oemers71HucxNSNaPc60Vo/4uS1/cZhlYYtVSAGRTmpVkYKO7dfGqlSBGamssDy/HKzsivyj2YCSjkcKR7CEfBoMNh0HQ9wBn3iNRrDnDXsry9LD74w6IxLYw/i1+51dIWqoJtoXkS1zfL1LaoHyPh0EfYgifERrctR7c6q0acOX0l7N0BpwyDCxcpX7LmIvjDVPjyRLTEQSuFyygtxJOrKUjccZXufTycWQ3DGkVsde6Rfxst//Mz9Nuajn43X0ExMiHmBJ8jwX0EsQmbMSPZbUgMg+9sAee+qYGkypHgHMQVFo+xGzAVBe01XQPMEu9cgktd+A3dJbzCCP1lukS3a8nMoNX4gM+0w6k6G/TN/2QkWOM6YibM3dXIwgtUkPpgYJ9T0Si6DEnHtqCtHF5ahmkD4ENELbfO/rEsCTCHVy3L3A28io9Q3AMpnz2Q7P0aTLoDeARm3KpdZdujUb7JXSOHD6Kx2YD5JsN1cqy2JxTm2tm9mOPTiNQImeC8cnwiYymeiALz43BHrn9rhT3a/MIH205VZOWZM6XO+BJerVbhyTtMq7Dnav7VEmB/+OKDUN2o30ulexz7o5KbTQPhzyvhGCD/FeD6wtJgjwaPpLhuZkTExoxoxtwAyCZJmgGa5wKnQ/aeQvPQ/kmSPgncDhxYAce3KlAvB7xxLbx1ksTC88gkdfvNQD/IHqlzhrImt5jVELwJs8wiGS1Qw8LbLeAh6w7eFamvefCj32k9ta1Pcsc1UkgkliJgCsouFqYMWNsVKDJzGJI4TfhglzYKzZqhSgRfIaXFnWvhql9CaRmDh0H7IngJOAHemAlb1rtjjIDKg3ZCmy4Urrpu5s4w1aE8OMY+D0REbcRpStWqxFTgy8CBFN5qtbvUye1aINkJzvy7AlaagM13hx89CT+4DCnqcvfcSpBP09Sd+e8scCck7x/CaytllnzRbfoqmj/sjCZAp7t3E6E91cQsXq2DiE0W9SOStOE/e7fN5Oy+Y8aMqQcbCM0jgC1KuqP9wkFlJeKC0wBqZXb6NvDG54BFsA0qEbUMuP0o1MZqrW83FD/emrAyK6dxV7YEykypgM8lM39dDp97Ngapuz/Cdb+DH0yErY9F8mMOGo1r8VGJrWi0NFupEUXo/zJzovmfmtx2yx2zc41Usng/mKkiI6wcknB17roXAWcDf1+k83caA9Ngy7sgbYA37sFL3Lzrg0WZlgfXtuuF/xHhzMFUlaVb1PawDzxJGgGar9ACSirg3S5fNOUugJkq6vwg+q6zT8p0PeMMd0ArnkRtgmITCJtM2Lv1+xuw1WD5c3d3mye4x9bmPh8MLAgIzCZlayO1mH7wEUDMs4tYV2TfBn7VVVilFw0gM9znGuCx+fBZ5KLiMFh8nkLHt8edehT61hb4AEQb40xsLcBb3EqgsJIIwQnm7OtAI+JuOvnZi3TM8Ye7/a9SWKnf/GKdRe12BO3aqwyv6sIompFo5K117eYoXH3AiCUkTzO1ms8xh4gvC1wG7A3sOFdLAYyBJN2BLdMxsg82w7wnkMzpCtoI8wnDRHa7rpkx7R7LKUwit37mKUxEBx+t2UWBOuxfqpqkFci8uKIVdimBzx8E/wf8CKg5T0vzMBruPAq4AznbSt3zqkDKsk73ymg0yzHiGwV8F7JnqazcU4hQb8LXWz0V+EMRgQ0FvhdsC1VdVHYRGzMi2W1ItFIQARAusbIzGruuR0LlbwBNUHeALHPgSk5ZeawFmp3bZN+Ey0I0/mUIcpHNXGfh9KZEbAA+DNgZOi+D7zXALoe6k401c/iizV1B46HZLMwRK1Z2FpbfFrzAqzojsNCEGKq8SvwqAx3BftvW5O5rgnv9EcXh/8+LkM6FITvBUzD6HkTqf6MwctTI3yJHi0uk2bWM/ELzp6k2m10Q7DNpberPjuunlSb69/OZDH/tgqV/gaoTFLi04jy4B8hepBUO5l2KmGoukvj2HZS692p8eof1vQyohbIRmhNsDjRPVCHxme4r+XTwNeGavgMF39ot5NM0OT6ujLDpwxZv7c1XH0Ikuw2EDGiguq9we+gTyaCBbj6Kv6ABfviA4jFa3at7Dba5PljSeMLiI2wMt2j61EwKpspAKqYRlc/fHnhIg9xPtkGsupDC6h1GcuArTJtvzoggDOywwd06VxqcawEVVRSWxQrREbQXhvxXBm104YnJfGalKOa+Ds0avgz86e+w2Z5wyAFwXzUcAu88hCfTlfiyYmZ2tGdthGxOUCMsO9fuqw2RTah4S4PPZmd2zz9TAe2rvTDcDWVjvPY7VVv5JjJrPobSUEafDgsfQBOQWbiZD4WVA0xJVuOjYcuA46BsG/gDkJ0JiyvEm/+FIoCb91RTFgOzGAVMgVyE2SRJ/0hExMaNSHYbCJ1A/mrY2pn6MhQW3S0B7i9VXcxxaNB540ZZqKwAyVDQ4LUAjY4UFuivQ+NtBcHkvgSSErwKspn/lsgx+BCc+VV49y74wk74wbyWwuhCu5CZGHN45RP63uwVzuqMnMLoS/MRGgmY6dCqn4TRhsW2f1Mydn6oUu165ShwpRJ4AJjwBOz/AFzXCLccwKD0RNkLy+HdR4I+hoE6pirDgJ5QOdn3ERKemSyLy9oY6VfSTf5lAxxnlmj3OPQdvtYIfzxdVstXrSuXK7qS55CfcgHwOpqwGPHad1WLzMSV6EczDDgYjtgX3gHYX6snPA/ceygc8YQO+RZrIgmsD3FR100cUdlFrAtscPgasq4tdtubA/9HDlcGrFZjaRMKVvm4O97I7yugb2wRZM8tLNCPO88Ek1n7ui/Q4d4tYnA/nXzadPjpAOi/Jz7pz8yFZu60SEsjgQy+CLORXHH0onXMXiEDG0ypVeB9ehUUrmJu90zR51A1Wq5FMdl14BXPXu7YnwLXPQDv/Qa2+zbcshP9n4P225Catfw2U3GhegxVnpGzyecyPFl2ILKxdIaQxO2LcX1PSlTRxni1xX0N8y6Hqw6FBsTJ+QHwW+DA2yCdj6TZAvQDMRU3DB/hCvq+s+66w4FRUDYFmu+CT6GV1a+6B+68NXA97gf5s3R6Pk2TR4OE8phcHrExI6YebABkkyS1sbd4slOKxuEa4LrDYfRdcBxw/pVww6kac0eiQe+US1Agws/hJ88omi6Dt1qBr2hVBWRKZMJM+qGBr8ldbH9Iz9Cin+fvhExiOQoLPYO3jfYL/ibYXx4cY0qsouhcM4UaAdp1TMYsxhULxUcvmo8vVIwED7At2G6EZ8ExUJhwbWQE/uGbPxI0Czlkgh4Kd8MOc+W42je4Rwt3zQbbjLg68QnyNkmwAJ6V+IV5zUQKnoA7/DlpV2FBFOPbISeprzc8IFfjb/eE7BOqIDNoG7TUAUjB1aLv0tSe+SPt2VqOQRM8f55u8xIkgMejyik3Azu5w4rTDmIKwqaN+mFJ2nBi77aZ/FdMPfjIwcZngw0YTYN9AjiVqoE5AqBUY2Kd21cNvhoHhZHv5XgXjlkZS5BiSEwxdSBH4P5Ao4IXzzf/nAVo2GDcGbz3K9peTISmpkzpdBSdH5o0M8E2eyBGhBbYUhK8G0qK2jDlZyZQO8bOMVOqydyQnMvQ4D/SbfsO8MVZqGjbYfBitRLeHsGT20B33fbgmkawRnDmnzMTLPhi0mPwStmIN5w4lGtCYuLXVRNjCdB8rc6/GwWT0Ab5nRRsufcrsOIo9LtYgthrAoX+xyb8BMP6Xgk7TvEVeX6L/HcvTVE5u/wx6lq2h6jM4s8RmxCiGTNiXdDTwBDOjLPLNbCNcX+3oGRfW5vF3E7loIGqERY/I2EEfmwdSmHec1IiZQdABtJGuonu3YvgzLH4KipQGEhipGQRC+EAXZxvZgRoJj+K2isJ/m6nkNBAZGqqrxihz8tu1tqy/WZiLC06rgS/soGRoz2cLCKp0e6ZNAOnPQHLfw6cACceDW+gYCIL6DE/ZdiW+ebCQJTweZWiWcksZGIsdc/AjrdnXQrpahFeKRJhll0wG2i/Gu7cTykot/8d+DQ076d5T80FKJ0ih8iuEZgYPFvzI5qd1CYJe8G2U6TkQGkIL02Dpi1Ya36U+e2i7y5iY0Qkuw2ETNHf5v94wv3968nw7I2aiOevhs7zpPCeQ+PVRJCt8xG4051jKq6RQvHS35kvQ7Nici7wA/jpRdB/C7xfx+o7GnmBNzeGJkTzVXXnMwQw0jPmteNMqoQvU3FmjsxRuEp58cvMhxbYEebdhUqzLGjH1GJxgri1GZJjKfJn9UOrnu5zKTxxK5SNgXknwvWby2ZoJsHids0cac/HnoUp1U5EeCvdccMpJHFHfIkzISclIrpOxI9jcAVSnoQHz4CrgUcvgtMeghmnw/BzYdLZcN1+qCDm60hN1iMyH+4aCAOPal3jh8Nnd4X8KJHqdvfAu28Ci2XW/LPr4pvu3QKqvvcv+O4iGW6kiEnlER8UNih8N9gWDgTnoWoZtMm0uDNAh18P1MbKrW5G39bcwnrDFmDZhc/Z6jQ/XS06+FhgGPywFc7cFUmHWrx6s1+BhcZbw932UApXMLdB3NIFbKAPyczdRwG6KDT9Fe8PUawAwROfKaIw3cFMqIYwV66zh+ND061VOylFZsCzgG/MRRRwmBimBJk2wed1tFAYrGPPzEy5oQLFXfN1vJINlWkQgZqUaMJifrty4OFW4Nfw2E5aqudwgN1h4Xlyed4N8j+aD7QF2SlHuL6aM7AUP3koRY7fWrihGrKHwkFA3TTx+3eAJ4AtgseaT9PkKtZOZtGvF9FXEcluA+Ah4JwennQ2SdJSnC9mBYzFjaePK//JYj0sroBy6Jyv8cnMXLX4Is/lKNUg4wa0dxehJL25cP434PxdUaSLpSGYvy1DYTqAwUxyoakzVH+hYgujMduDz3ZeMQG04ZMCTY2ZT63YpGnkYDNFM8VVBOfaeSXBqxRPkKGvzcinAq8s7dxK5NusA770BHztFzBoCFzyZbj7EPHftcHzMaIDT+bWjk0CLPUDpKjsueaC8yxas5/2pV0++LUErYz0zipgPjx8New9Ah49Er5/HuQvdi7GqXDFN4AzUEb6kyjQZwd3nRIKVd5I159vABfBX1Gt1UnA0nQKVwCfuByeRZbe/L+QhhBNnBsxos8uYl2xH8gp4lCJBoWH0FhzJsBIqbpxAE+L0O7Aj+U2xf+Ua8NiPGYG1ylHqi7tkI+u//bA4XDDn5RSR4O7YEhgpkyMCEwimqkxzH8zhWQEZ/6nEKauwvw78wUaQYZEGebbWbkuI65QjZn/raSoXfAkEbYZml/tV26kF/4TmiLM4VWWTQJ2RHbE7y+F1/4ALIErttLicNPwUaqWdhC2bWbPjmC/kb49szxeupmKdf1NSv1tmvjuAv66BIVQ/kSuuk+jg/LnqclHgR0WIQU5G2WH5xCx5VyDTcHzscbrYI96mcifBrLJNB4Hhpzuvoo0TXhCJPbPyCwS3kaMSHYRHxTZJEmHgYIUHFrc9ovQGNQGUAPPoLHv1SU65gvI1WPRmaDMgUY0Ztbhc79DJIMhGegaaJL7pmoEfnC28P4wX8EGXBt0O/HEER5vbXQF+2BNk521G5od7dqmiCxE3yI2w8hNI8Qw7y5suzh4pauH8wjux6Ipw2jOkCzBV2exezf1m0OmzV/8HRgPBx0gp9b1FBa2bkW2ZwtaseAWu4aZd0O/Xws+dyQ0byJTpj0SE347A3+dD1woAdcM/GaqupU/SwpvEaiAdB6/VJKF7xoB59317TvKAPvCieMhP1S7xgNLD4KtJ8O4JEmzewF7/Wu5dpHwIvoaItmtZ+TTNFkE3t/jtp2CZtALcHwxygecvIp3qVWjEmJkgUYRVy0as6bjxy6z8mUGQH45yhi+Gy6cCtsOxJvrzPRnSs2iIcEri7AqiaGcQhNn8cwtDAyBwIEYnFtDYeFnu86QCm9Ss5cdZ2qsgsKAF1OCYcqCyR+7l8rg/EzwMr+i7bdtnfggjvBaI9HCsSOBCffCaQ/Ab6fAsk/DT9B3a2bZMAWC4LMRnfnpTL0aKVYHzxn/HCtQ4WgLKi1DZsYVr0DyOfjsPZoAzTga3roU8ifIdZddBA9fAD89A82OnrT+I7XaiCJ+S4Nr1wNToXmJcj2/B2T/AnXT1e29gC88AW8ElX/CKkDFiIS3kSElBqhErDsOc1+8jWVXAU3ba9ypBejQb+PryEJm1kXcZ0qAhTJzmngZixcw/d03ma6C7BbAk3DVM3BOPzTILcIzYieFlT/AR0eGYfV2nJGY+ZpCE6IRmak0s7vV4AnWFGAer4SakGxtBHjPLwHUErys0LStnFCJJ0kriN3trKRQ6YWpBxaVWlG03YJVLJneyND+SfvhycvSMM7U98At04AXYPjhKt81DZH0EteX1cEzCwOAbIIQ9rkTfT9GvPhjkhKZpTNBUzn3KJ/6E3CnFnkY6r4CdocLvy5Vdh8KZuHX6Ee2zF1zgvs77172vbucxKphWjz4eKQWW/BC/Nv869apWHEloi8hkt0GQhjIB35M2wFNqGmCn4yH5guUZhAKi0bch+d8wXv74jpwlem7IDMQkoOAc+CWa+GUse6gxfjB1cxpFo0X+r8qKfxFWH5WsV8uSGnoHgUtpL0y2G6k1R6cF/rrzM/GYBhcJoVhqs9Mjbjzw/bM/xUGmFgmdqgAw7w8u8esOzYkljK3zRSgKS4jbfNrWdDLbiji8dONUHcXfH0IrDhEZPese95tQTuWkmDndwbtlbvnXIs3CdcGx5XLJJ0BsqWeh4ej8qZv/R5+chEMOVeRlO98FbgeXjxdFoIVI1Bt0EqUQb4AJejthUjPCL7NPYPRwGUw/FA4fyz87FLFrzQCj34LrkTBvSDlthdrRl5GNbcRI/rsInoLNjAsAWb8Aw5FkXVkYP/ZQN4pPbwF8L9AA+Es71aqwYu0ElRMOF2JIlsehC9Wo+UTaqF9OZ6wLAAlzAsLgzLAK5LK4LMNxKb6Wtw51boGrUjNrA7aCANOQn9gGHTSDhppB/h9RgohWZj5tbiCSZOeWXd0Y01wjWLfXHGwS2XwDGy/qT9rw5Sr/W1BJW0o4/9I4LClsPBe+PEEuAWRXS1Srhl8rkhoRjUl2Ykntn7ueSwKrumCWjJO4Zm7z9ISssA7U4E6eP0YkVL7ap374Nkw5G3IdsGBu8Jbp6JZ11x38o74IgGd7js0H+WOwA7i9Qluc/ZqpcrYPCebJOnjrGnKLCiaEJcG2ngQozEjehP2z98FfAYFaR7/tvb9DGCRF141aJxsHoEGpQYdV4qC7YyjwsBDGuGx29wG55MqcwEH3SRiJ7ThTZmdRQ1ZZKHZzowwStGAbx20wTxUW2FUZAhj5zCopQu6IzzC62QoJB3bZoorDFyxJprx/q8c/tdthBv+84X3Y2rUnk/WvfoFxxtZlSNSCJXxAmArdPFDpsD/IBtiRXCumYDpoc3w+WSRQlyGJzx0P0kJlJUWdrsc5cRxOnCwyG4B8PBlOmDpVMjXy+97CSio5kkK/XXmp8zjI2zHqh+79JOp3cT59siU+RSQb4V/8M8RTZkRfQGR7D4ElKPVyNvwBfJpU13C9hs1Rj6DOOR10Lo/84BhGpOGo3xha6sToBYyl8OPpsLe1XhfVFgTEXfBgXhFYVIxHPyNHEIYUeTwkX3NFAaghIN7OLMzJRm2ZWqxHXjXyUwzmZp8CQnPEG4rxZNeaG5soruGaPf6bgMpVIadFLadw6djWB9y7jybINh92PmmJndGy1YMnwvl0xR52z5B/r0ZFPoTw2hXgmcWmk/N75nHL7jbTveX3b+f8intkY5yz/TZo2HHx5V+MAl4bCq8cRFwlNay2xcFrrAY+fF+DGyNFOont9K9NLh7rHHbJ8PeR8N/I4J7AfgPnGl9FuwJ5BuI2FQQA1QiehsdyPexL85sOR2ajoKyLTTujcK7fagA3oasU4CvI3Fh1rQ20GDb7qqxmIkuhwbeMPesAm+KLA0uYmY0ixK0AJaMu5gpDvArEoSkGCpDUzGm5DLBMVBQE9IT2XuFqrCsaH+o8IoRmkjDYJYufLV/M8+5IIzu/WG7RnDWN/B1NEPCDoN47JhqlFC5Fyw+GGifBZ/eU4lwf3P3bqZfI2kjfJtw2H3a91QD9IP0bQqT1d21jf+yQHOXq77zOzjxIf2mdkQijjPhiIvhs64UyujfQ+elyGH8LErWbH/N5wY2owlDHZpZlcJn3FIIWz8EvwL2ORey9e5nVq8liIpRnHxebM6M5s2IDYlIdhsAoTssn6bJihJfSONBNLbssUgH3vCmxrk6t78RdPCDEni1KPKuBO9iqTpLH375XagxgujCB44Y6YRRhxagYcea+S5UZJ34Shvg/WOVwfmmkKBwdQIorF4SVjsxoi0gsPdgs+o1a2LmkLoK3818GZKh+eGM9MyfmHPvbXhTp5lgq1lznT1TazkKfXVGgO3Bgw+DT0oRkQ6DuoOA7eCt5Am4ZRi0jdFyFovxVVMo6n8YjGP2STdpSKw4c5GPtX8/uot92+Hv3Agr9oPMqQoa3RV4qQQOOxvYDe4F5p0qDht9OIpymYEqGExw/ZuDLAnzECHOBbaG7faDF/aDW4HsBbruSKBpFHxtFdz/PikI4Xvx9og+guizi1gXZJMkXbFF4d+7dPkASXPnzAJ4XRF0Jm6qUS4z5cAMRW02uXbK0DjdAhpxrlKAXbdKqIV35+BNe8VmstBvZkSXp9DcSfC3heyHkYWdRcdasEMYmAKFhBT+A9g/RAfAKp0Uqr7iKFCDkYyRXhh5aaRnKiz02xnZtiAzZw6fymDXDYNjKoP7NJ9kaIIM/XBd6PmVoZnKONj8UOCIRbB8rtbMm4RmN9ZeeI8hqbYH13H5lZQikrU+WhsZEZ6dOsj8jPvCVqNgq7GwXSncXQrcNIV9ToVnr9QjWAYyBdgEYxGaSTW5ay3HT5ImArXwiWq4v1oWicloYdnsfBHgyby/eotKLuLDRCS79Yx8miYvvFn49+EUrlSQxZkyX4Qj8HxSCzQPBTIwY6XMUnPdOeUoYKES4En4WZdLHjeScTP/7kGxnMJISWNZIwwrEdaBN23WuvPzrJlsbjb50PxmiqTY32ekUELPtvxOIHVGuc1K1m6uhDV/scXEV0yQoamyOLqyEb/ygqlJixwtxZtSrb8uyrE7eCeDz1EzsrTnbXl5dyNl/tgsMcJX8c/KAnvCewsJz34IWURCIBKyHEPrRylkHMm9uxpqBgIXAr+Bn86BFe4aWybT4Iph7DJdqSqAaqc+iSoULHDbavH28Q6U3DnG9eskuKIRfuNOOdRtPtU9zuJE8rjK+UaG6LOL+KDIJkm6Z9G2amAKGkdq8Mm6j60WmT2Oxt8ZoJpQ1dq2GJ8a1QbKwboAfnMbfGcoBWqs/Un8Inmm9izwo7zw2G5/mw3Uw10H2vA/WCOIUOWADx21QItOfOhee3BcO4UDOxSSEACDgRFQVVJIXqH/LlRs4a/X7stUSjVeZRabWs1/Z4uyrsDXxxyBlJn56lqDa4WqOAykCYNNQqKuBg5AkvsS5KT9vnu1otQEe8ZGxARtWAa5qTpTsKWQtrrzcv6ZZPq54gLOVLB0PzjzMh/A9AvgjWQRP5wMXCPf258vg+xlcPK5qMD1bciEMA5verbnMA4YBqdtD0P6wVvB4z3OdXnSWvxzsZrKRoBoxoxYF/SUcHs6IrlOZIGchyb8B6FJ9f7u2CWggb7Fr/1pRUXKcQe2qTxUdxCGG3zLBlJYlNkIznIVQnXXGeyvxg/oRoAWpQiebMy85ar0dw/YBPtDWJHlnkp/lQKJHZDTK/TzhT68MLLU+lJ8XSNlU3yVRceG/jm7ZAl+yZ5qvJ8yfBYUfe7Eq6zQzGt9NDJyKq7zAbReziFVKnuyMz4yNjS32vMqw6+OjjtmtNpKBuMjlIpTJDpUSWdIKbSfAUOmALvCgUM1/zl/rK755X3hs29DvgRuAp56ANnRZ+KLrq5w7b6OfoTzgSa4fzVcA1zhdn/CvZvloTjXLtwWSS/iw0Aku/WMYvNNPk2TfKmPwmxHE/yyiSqzaFbDgkjMNqVtmZ+uG/XA/a7ocwmFofF1rEloxXlfRja4c3MUqr4SfEBJsa8tXIqnPdhupGfHh2kJFggSRmqa4kutTEo5sFnhQywJrhcSWJhG8H4w9Vnew7H2H5Bz7TXinFn47G1LkbBnGJo27R7D+wnbtS9yKGQOQnW8Lm2W/+5XiDzAz2LC+zHfnYXemgodhS+tVlz1xV07cRORToCp8PyfgDwk4xGZ/xyZCe4FbtPSPmcAL/9FxzELr+jKUTk0M72Wwe7AOefq0HLgL+7S70dyERsB+piyS5LkP5MkeTVJkpeTJLlkXdqKZLcB0W3K2UvJuquRm+R+gCaNPcZDlhVANdAichyFD6RsB7gJHvsHXn1ZMMpwd0HzlZUGrwx+ORpcQ6EqbKVQFYXKxn4tVfhE7FDRhVGdtRQSjBFue/DZyNF4juV0h2CG6QchwpQFI9JqJJVNBfYEI8ocvrRYpod9w9z92UBvxLoa78+EQrNt2Eb4TMxMiruXKiStpsHiEmBb4DVUlmSFa6MFb4K1PppqtOe5LPhsJGvHmvnXmWv7D4TF+8KOZyD/nE2EVitQhVdgnyNV9LkC2Al497soD28mIuccUnwLXLunQ9U2wCT48k4ywc9C1omnisyYMUAl4oMiSZIpKKlqcpqm2wI/XZf2ItmtZxSHXefTNHn2Ecit1rgzHJVkOu1tBcGNdud1AP8J3cEeh6EBpRQd139PuP0V2NvMiGFww+uukZDcLPjEmNQG5pWImMxUablcYWqA+atgzQhF8xGZT9DIJoz2NITmv9CB3WXHtyFWKffXhTUDVkJVlSn6XEZhgnhxH+z40BxanF5gZFOBL249lEJFZ8QeqsVQCdvtGAm1u7ayQBPU7efauB/59PZC5DIQr+RsgpEJrmOTEPCm1iy+qgtBv9x3XTcK3rkM2RgPR2Q31P3Wrijh0WOVNP7oFAnOXYF730SRo0tcu3sgx/EYlIi+wD2T0QpO+eYJKs7yn4i/4f0XeTVEAuxD6HurHnwDuDhN09UAaZouXZfGItmtZ/y6h9qA+6HiGseiMenho1X1YiQw2g2U1UD+SjT41WgyPwYRXU09sDN83r49m82Dggia8KbGTjRYmUnMBvFOpCLG4oMQTJWBH8Dt3cyQsGZagqmiimCfwc438urJd1eJglLIIQfUlpBsDgOq/OoJVgHFfHxh9GOoOg3WJ8u1gzXJswIRfY5CdWcwv1kH+gLa0ABfjg8sMVVtkjycTBQrR9x+m1zsAPwO/lYLnAP8H1qDEHxh5tDHaJVYjJCb8GRoRGfnhKbbDhg0EanWI+Hda4HdoO5zwF5d/OxG2PZKWDoNnj1Azf4HUNOI1qGahX58D+o87sJXh/4aVO0HLJLCm40qp+XHr2nG7In4oomzj6H3zZiDkyRpCF4n/Ru92QrYI0mS55IkeTxJkh3X5dYi2a1nTHDv4T/5AWgWvARN9l+4VWMIKEzcAhDpQEJnsa8c1QVSAfdBauoiVBbz0MBkpiwjGVMGJUG7pXjzXHHKgKmRkADDfUZqRlxQqNzsbxt4S4NjwyhLI6z3ulCuXejsK/fEFpJkJT5YJUyFWBuMR+2ZhPdhyOH9Xx345W+MxOw+luGjMEP1GpKS9SVUfUZIoT+uAmiCPQ4A9gaeQ4yxDL/CQzjJsPOsj2bGrcVPPFbi1bdNCEpRKO9zwF3Qf3e9MxI4EL5zFHCtYmd+8gDMa1V96zbg3mmI7F5ERDzDtVWFlkAYilTeRLhwVJDOOFtqsSczZiS4jxSWp2laH7yuDXcmSfJIkiQze3gdhpz31cAuKOX4tiRJPvBvJ5LdesZn3Hv4Dz4ODQr3u7/Pxuc5TydYAq0NDW5zZeEqBerG6sPDryjPrniAbZ7j/raBztSW+XyMdJopLJbcRqG5z0yfRhA9BZ6EwSvFzugwLy8kpXB/6LfrAngPEd576HfuAlWKzZihH9L8dGaWfL9fdDkapDNFL0stMJ+n3b/10fpvpNaICGZg0X1Yn0JVnKWQ/CuDdsw8ukx/v7wfcAMqZJlBqnZl0XOzCE37fkE/nBq3bSB+lQXwpJdD/rcrkY+wBHgAXj4bfffjIT8FvncW0P/j/AgVmD4BGNeAN1XbQrUl8PI0fLXyWmCcAldec7f5H0G3iwO1ovmyD+JDSD1I03TfNE0n9vC6G/0E70yFv6P/oMEf9PYi2a1nmN/C/s4mSXoHEmeL0Djx8B1KHyhD41YtrlLKSDTI3KGZdg7gYHjhYvi0DWDhwJ+FKqvW0u0HC/bbANiEykSZX8cG9BCWVkBwjJlGw0CQUFFZOzk0HzNFF5JacaBK+DdNKEhlpWtkM8forJlXFyJMIcjhiai8h+NK3L5qPEFaEEolsFmZJ4g88oWFQSlG2i2um/3w9Sqt/TDfzszIIYnmgv2W6jABtt0VuBnlATzj+rkCH6wy0J0TKm0zYbfhS7hVU6i87TodKIL3BOR3Gw/b7oQU33D48zTgYHg5+V9eBAY9B0tnwsNA7km45WZkp5yk9saAJk0/QiQ4EK5DIvDgsIs9EF3Mu4v4F/BnlJJMkiRboRFl+QdtLJLdekY2SdKTi/6p70Hj5a1oPKVDY0YFKsFkHGC+tPaZshw1Asx0kfFGIKa6jMyqKPRrdeL9SF34EHcjGBuMjTShMCXB1I+ZOW317jCxvPtmKSwMHaq2UO10FR2zxgywDV9zbLM1Vz4IsTbzpZkNc6xphjWYzc3uN6mAd9v9F2CqzCJGw3ZNua7GlwizY+z7sGdhRGjBRBa4Yv5AI1sXffTsjSjp8nzEGq+6dizBPXxebfhnY6Tc5fpjk4scXt21IPn1Lcjeg8+puw4+uyv8ci/Y9lA4ZBicvDOcORFG3yd+OxdYONNdfwX0H4US0fdBwSvqPo2IS292u9ZWEzOaM/sg+laAynXAmCRJZiKr+HFpmn7gCdJm//yQiHVBT6abe9Bk+AzcCua1fjx6BDgQNz67Qe1BZPrcenu4/xE4MFRhFghRigIQlrjtYXpBFk901fiAFNCFQzMbrJnrZf4x8AN0qFzMj2bHh6rFCNn25fAm0H7B58QYI+c2jAUGAJvBZqtgszbov0odf7fL575ZqsTazCb2bCqDbW3Bdrv+ZsN0rfxbajMf3Lv13VIPTL3ZNU0B1uKrd5st2vJE7Hnbs23HK0gjP6ecd6lXf1+th61vBE5Da0JVuzbMNN2Gz30L1ehINCMyJRqmMxhRz4L8JcBDKAilFNJnFHfCkcAi+HU1PD8TmAt/uxI4A7IdkL/PfVUT6K6p+rfVsMdobd5yV+BFeHS13H1bJ0n6ag/KLvyaYjmxPgAzY/YRpGnaDnypt9qLym49ozjfKJ+myQFoQevNq5258knvItoR/d6qoTtv6pCdnOjaQVaozlYK0woM8/EqKDSl2UBo72HAhZkmLSDFyNNmZuHfUKjQMoibyiicyYX/MHacmRktuMRUZLe59T18QcbQMRj47gzWXhYfbRlGP67tH7Y4tSD02VkYpj1T65cl6od9NlVr203lLXGfa4N7s+cbmlQr8M/MVHg++FwJNMHWk+HPx6KKAv8PEWoOH1xk5kvrV6fbtiC4X/B+PCO/Ue6cBqh5hO7fTNIPkqPhsWPpNj9UACyD004FLoL8Gbiq5e68Zbrf8agvWXu2XSoQcygSp/8sOCUSXcT6RiS79QzzTYS+uweR5/XTja7W8nl+rBuLxqgqG8xKYZ+/w5aDgU6VgMqYOcwIz5nhmmfjB+mQ3GxAr8UPgKEZ0syZ5Uhthf6eTNAWwTlmzns/grE2qylcBNVMq6F5M+2ikPDeY02nm0Pxr9ZI19II1mbyDI8Pg1o2K6M7OMbIxsyFZsI00i4mPPCq10VX0uTuN6wsYyRqZmdTtRZVmUFkZt9XVm18dk+45VyUpnAe8uGZj9CuGapuM4cOo7D02DAKVyWfr36tuBzYEfKN0L4aXroV9i6VGZOvKf7kzxeIb7/3XTj5Msh2wf6PwNK/IGJdBEPGI0U7FGhRVPHJyMr506GQd77kuKZdH0bfy7PrVUSy2wAoNmU2oPFvX+QHyQz2wXQr0biUt8Hsbjeu7Q/P/95tCyMD7Rushaph7rOptzAAJPTVmZ8uNO0ZMRksCtPeQwVp5GXngSdZUys2qFYW7SuuoBIGq+DMlN2MUY5Mmabw/gWYcqvFB6qY7yzMsysFBlRA/2HIw7Qc2hv1BbS446rwBB3m0GWD7SZEbXJiz7CRwlSDTrwKBk98Zsa078OUmhFYBr64E7y1PQq+/pm7v5AYwacoGuEtc32pxQcN1SATZxvdJMUlwLfkcisbDNuVADvDnwCugroBsB2yKPzkSqXNfBXFtGwDtD8Af5gInbPRMkDz4d0GXf6mUlVEG70Esm9CVWCqzLr806joIjYUItmtZ/SURHvLFhqPLsHVFNzLB/iVAGUlzhKVB2bB0QCLXDBLMAgWvM9F5ikbQMPB13x1ZrYLg0ig0My2Gq94yoJtpmjMbFccdAI+ny0XHG/7w+MN4eyvBLzJ0ohtM3xW+WBEfAPWVG7FQRvhfotiNBVa8NyMTHN6L0HEa+Rr/sfQh1mcXximfpj52NThalQiJ1TToRk5TM8IidSUr5mZs25tvEb0I/g2Yp9w0mOTlTZ8FRnrlylHMx3MxZPqMOBcOHAKNC8HRsKMJ6GsHppfAd6C0RMlLM85FT77OFw1TCsItQEfA75c4VySTlX2L4GtS6GzQ5ddhn73412XjOAOIyq7PofOXn71IUSyW8/oMbdonHz7E1DlivyfxEVj3XHvdDmya4WlT2hQuH+aKw1mM/oKPLGNhHfm4AMdwA+oNrsP/Wnht26Dtqm8EtaMqCzBr+4d+ofC65iSCiM1Q2Vpx1uwSzUyeQ0pgQFDYLPNUQGrOrdjsHuNRXkSo9znrSGZAGVbQf/NVWVlQJlXWzbwh4RnZstqd72qKiirQuv5DEeKcrH3wRmhm0U17DcUBpyYmrJnYdcrdftW4H2h4FWcPcdwjUFTiXYvYTJ8GwrVvRrun4Kcu9shJQrefBsGL9l3WufamowkWRNSdV1ohpUDHoeqo+CN+fpdNjeo/uUb1fDsTAVNHQ3cshfULVKzO6OA0WyrW/1gJiLPw3UfmVK19QvgqiRJbTUPw6vB50h6EesbkezWM8xcE/5NAxxSAacAXKzJuvGRBTvWAWRcCtQIjSHtprpCuEF10Ci8uij122nBk435h8LkaQuUCBVL6JczZWDJzAT7TZlUBn/bflNyRjx2XCVrBpTQhgjHzJir3eflrGm+fA/v2wMps8FQVg39K/xaeD39sjO49sp1TnfF7OXQ3u5XOCC4p3CWavcbKjXwkw87xq5lBGfKzdRl+EwsoKg0OD6ccJgC6/LnH/g5uOV0lC93KIVmTeuPfTY/4jik6Jrc9epQPgtIetXBhbfBltvIJ7wMePkVWT0nA5O20Dq004HFDfrNnuy6egqK4vzStYhELUCmn37XP0aFpi9M0+R7QaDKbDyiObMPIK5nF9FbsCCVtxqBI2WJWrgIhuwkC1UFvuTiBIBS5fCytV+0tUCx2CA4Hx9hZyhHnGF+N/PvWdRlV9BWmMdm6sgGZTOzGbeAH6yNvMLglWKntEVh5pAytEANC51/zyRUSGLhy8ipmPQ2C855L7iYI74BFWsuppqUBO0NwE0pdL5dLjRh5ij02fVEeKE/0ggvfEYuMtFy6LonE+GEwdRwGGFpt2N5eRX4CUQjfPErwI3AFxGJWdqDnWftmnJfhsjN1JzNsIwMa+Gc0xFRZfwCENPdqfw3/OAaibbT6uF7DUo2vwnx7QgkNJ9fCW/9HZikNfUybjtoojeXwsjMSHJ9DDFAJaI3YP/km+8Hf7gRtjwKhp8KTNI41H+yjisF/giQg6uAFY/Ij5cxJWBmNlehpHkmPgDFov3sh1Y8eIapCcXJ1sVqJlQnhg4UuFGN9z+FgRo2iFcg6+PwoE8teFIJTZ1pO17Vmcprci/3IBiApGpIevb5veA9iOTcbIhMnf23gs3G4PP4RrjOzaM7MbGqxD+rFvQ8W90zGEihL80tzNBtjrQgG3uuYR6c+e9CRW3RlpYLaUrMyMq+F4sytWMtgrPcdbsJnt8elRfb3fW7jsIgnQpkHT4AP9GwSVFLcK+Lgd/q2BWtUFPiY2g23xWadwZug10mO3NmvZa4+xLwyRKZ2psQ+W1eAk9N9z+J37vLDQNuT9PEMjMiIjYkItltQHTPYjvgdIBhkL0SaHET/uk+Iv5EgFL5O56HwsjLIr9blUtLALyKsIHWtdM9yJoSsfY6i7aF/rpwBWw7zgb5kBBDVKABtw5fTDk0aXYWtQdF5g5TXvbaDLFNjsLIzJ5Ir/gdPAHmUDbYZMTAOcQYi4EmJaqDJyQjMFOhIJK3/hrxGVGZEiZ4TsVKN/y7mcI6meAVdSY4377H0CxpBLsYdjwGhfTug5jHfH7Wjyr8JGYFepSNePO2JcPnke93JtRcCW91wZBqPaVbntEpz04DHlLk5izgjZmyWp7ZJSJbhvjyi12aD2QQ596EfvuvuzScVuI6d30Sm7gZM1ZQ2QAIc+0APj/NhXabGqvUePRrFNbdfwTkLwce1Wz5YPAmruIozE5kJ2rCqzAzeYUmzI7geBtQbeDtR2HBYhulQl+eHZPDK0WLXjQFYhGfHa4/ZgIkON6QA8osbLAOEdlwPMnl3AVHu31jka12FYrKMBJb7rYtoVAZGiEWk2M5IryFdCvIVV0a/I3YyoO+5/G2ZfAE1Yj3YRoZtVK4tl8NYoDiRP+Q8G1ykcWbl40Uc/j19EqDY+x4U6GdaNWEkShS8258YnuFu90n8SbvnkyoWWA2DBoM3AWbXwIvfFe3sA9QtwXwJrwxFL63PXz7H3DFRAnKfYCvu2bzu0L2GXXhZuCzA2DfVfBYkqR7B+vbhebLWE0lYkMgkt0GwBpVVAbD0uX4gavaBwICGhDnA7P95HyNdeIsErMSDYgl+EE1jNg0UrQB3I6xAdRIDPyAnmNNkjL+MduUwUh4qPvbTH9hewTn1Lh+bbY5Xq0ZLCClnEJFZljlbtZMnrY6Qg4FnNiqCSuCY6w9U4d1wBtI0bkC6tm3fImvkHDsmS2ikKDABwq1Bdsr8OXLzLxbjStqSmHKhk047LsykjU1WYIv7mzquD3YZibQNvwirrNRjkAlimyy73qRO74FT8zVdK+20E3qddC8CDqmwVbTYMWhsPQedevRN3WJUQA3w88fgitOd9b0q2HYt2QpZWfIL4Lt5sP+wPOrFKl5JB5hEegw7y7WzOwD6GNqrDcRzZjrGeE/cLXb9sflzlPUAa8AVGl82gHHacvQwPSiwruXQeGAaAEIFlVnOVkhuVieXKjMOoPzoDD8M/yRlwQvOy6HS/QLrmN+qFpkt2oJ2rBB3WDHVQ2BzbZCJORC/lmOpGDeNdST786IDQoDU8LjTNGNR09uWzQ8D3UPaqzb/wZSdm/rvKTCB9rY5Y34iqMoTf2ZUgvNlWa+tMlDqNBMRUFhsrkFtRhxZdB3Zd+TqS5T7VX4+pzg/YzDUFL3QfgFfC27Yhl+ctOKvnNTq6bOy3VclSsWsGIysAMMqdDXOgHY53A1+8UJwItSdE3AT7+l06cB+1+ufryOlvjZcZSqr8wGRrrI5LiuXR/FJl5BJSq7DYhG9A/+wyRJS4BJXVoOZfN2DfMTcDzXCsMr4fk5QWR/6LcB77tZRuHgGYaxm2+vMjivHJ+sHLZn+yy4IrxODm9yNT9VJbIwGjHYQA3ePFfpzh00BE9gy90rNC+G/jnDKveeD7bl3LspNpNVYUSmnbsZ3jxag0b9l4E5KMOrSSschD5Ly7FbgSyloYq1Z9NE4bI/lguXD54R+JJebcHfNhmwa5W4c2zyYqRnx5uis+uU4QOUQCRlxNWGfkB1KM7/OnfsEsQ2TXj/XxM+v2+J638z3WRY0w9WTIea17WQ61DXzefvgh1L4Ftd8OqNMOlUWHGlvvYvDFWllB2A7F8gfzgwCrKXw09cl6DnvNN8mibFcVAREb2NqOzWM3pyvo/FDdu1yg0mo8Gk2e3vRAe8hB8LC4JHQswo+tuCFzJF76ZIbMC0wTs0f5rZ016leH4Jnc6ViD/MnxXuM7VYgcxrg6rgvaXQ3qxctve6gjqYIUmF7yFxgU8VGExhoMraXq7SCjk0VI9GCnIzfOJ6ufpuKxy0ufcWRCZDi56bfbbkctsemjyLnfMWKGTPz543QXsGIzHzwZnaMkVvAUVm4gzN0Vn3zN9Gk58JqALaxyr0Y7M+FP8mSpEazFMQmNO+GmpGwLOtMPo5qTITky93aR6wAGCer8V9wxKY94D09J8BxsCBl+uzEZ2hp/+JtuINER8ONuEAlWQdlgeK+BcQ+iJ+kyTpJcCZyOqUBfb5HCrEe7bGnCEV8GorbH0HZI+Uwa2uHz4R22b4pWj1zPtQlAB4wjJCg8LVxKvwg101PatBKFRmIXla+yMp9G/hrleFrIaDSqC9S+wdBtSEydJlxrgWfDLQvde4mx3tto3Ak5f591YFr+XoyTXhSdL8d/+DN5OGASxN7rgB2reqVT7SYnMu7v4XUGj2s/sJZygE2y3Fws4HX+kEfJSltZehUMDaDMeUG3h/W+iXrXS3EhYaMJ9ozv19B1ox4bfuvCY8oa4O+v823d9z2uHmRRXwWits9ThwELy2Su6/vXeFF55RF7edBVUTFIzyHPo5fgmtGnTnFPj2NF3mBqT6Xix6XKHgjYEqHy7qK5O0YbvebTN5khfSNK3v3VY/GKIZcwOiDnHPSDSuLIRuE2QexzUVMM6Zw75NkQkzJDrwgYVmlrRgg9CfY2QVbg9VhZ1XTiGp9QuuE26rxXNGqBRyaKCtRPXObH8XhebS7gT20YiUhiJ5UYP3qVnnwZNYG4WmT1N3LsikIJDlWaRHliOyW4FGcxfUkra6Z9Ko/gwogdouHdZI4SSgDQWJLkPEYM/NzLk5t8/QhQ9UsfzCEsTby/DLKVk0pX0X5nvL4E2Ytq2saBvou7D4nTYK00EsmGgo+hFlgCMQ8dmkphH/e2hBX8EsSJ15s6xE+XYZgCdhxSqdtjVKRZiMqoP95wRongI3TNPv+6XL4KdneMvFTDyPG9GFQSmLg2CVYl9eJLoNDPPZbaKIZLeBEJpubAwDNOPv0N81AK2QOUAHLQKyptQMoeG5EXFGE16d4Y7PU+jLs1D64kAUi9o0mJkyR6FtqRxfAcWm4kZk5Uiq5tCAbkRg17I+l6JEb+qATyDVVoMfwee599CXNxgpuBxe3RnbWtBKGNGJO6cNBaoMcO2+rXbau3w4f/c/dpcuNRpv1gzz6yzysR9eAdn9m0BtoTDn0PLZQrK3kmuhSjbFZwRozGBkaNssSMX8bra/3fXBjulCv4fR+ACXI1Bmd/h9luLjgTKIlYDETao6V6t7i4HOqVJqh1TDG42upms/aF2tdRnbp+mSI4HjzoAbRsHJrqrPw2fD9y9WN+ZbN4tWP4BIbH0Gfcz02JuIZLeeEf4TZ9CYMxSNgwdOpns2vrkNiObbqVPpw4IBrshvt/QRGHIQ3kQYtlFJoeILy03lgg6GhBhGE7YF2yrwUQpm6sugAb0Wr3zmB30wwssCVZujiiWjEcGZKXEeChgxX9tgd4KV8hqItERd0VM1clyMClpZWyvxpk0zc1pnd9Z5ZW1Q9jisalZ/QwIwhZtz77PwxGgEZZVjFlBoqrQUA1NqmaJt1nYFheqxEhGWBYyEii/Mqyv2tZkazOO/z/AaJfjv5IvIvnike0z3BX2sRIrWpY7k3y4UpKNROM8hj8Pte8Hnh8ILS2DGahU7aEeK7bSxcMoc5YReMR9+fSq8cSlsebo09mTXNVN34eSvp9y7iIjeRgxQ2UDIp2lSAXwZjU0rgLemu502sjiVNONPQEZlmLrX/TFTZqDUcqBB0gZC8DyQoXBR1o5gv5ksO4JjjRCN/Ez1WORlsXIpRRxU627GSCEMza/FLbK3P7ALIjGLhnRKC/A2ty2RPe2TqADVdmjkn0d3mkA3ic0Lts9D3s057vWGe81GkuUFtGTuX4CngNEwYAf5F3Ou3y34LIfFyMY8BhGWRVWGQSzmz+wIXrV4NWVpCzk8SdlzrXS3bZOP0MdqKjL8Pi0AJVR0Zg6uwqvHEnyULohh3qB7orPwBCSk98ebRS0Qpg1YBtkt/KXMlbitU6+fvxpeWuIt2d+s9xZWvgp34auVff5KFYC2CUEr0tm341Gs7iI+ZGziFVRigMoGgM1aD0uSdAKaaHcC25WiyrrjofkCtzp5J5zSBVc1QFrv0q1sPbYcXrVl8Yt9GnmBD0u3ARV3nkXxWdSdKYOwqsdwCqMHM+pbd3URI9saZLOyAT38Udeg0TCZAOyFVFYooYa6Do3FrzrQhE8ON1+cmTdzeF/dAHpe8w58rl4TCpWYjwhuDrzTKiUWRj+CL31m1WIWIB62ezUSGo78dcuQeAzTACrxwS1WXi3jjg1JyIJDbL+9r8BXubHJQvg8zWdnE5/KomONlWwyVOPex+PV50P438oiePlJ2PZoFO7bhp8wORNq+yrP4224tAPgkGt1ry9fpJ/RXGCfsUqR6XTHLcRbuk/+OmR/Bflj4Gs3q2s/Qvwc1VzfQ302SRsm9G6bSUMMUPnIwWavj6DlUFpAS5W7ga8ToATaO+AqR0ZLgDobxIzQyvGJ4DbLDwNF7LM1WsaaRGiqzUybNigXE535oiwwwtoZhgb2ZgorglSggop8ApHZy4iE3sP720bj15dpwpcJyyET50B8lKaZO4vTE0LCC+tlDgjaaULSZg4Mmg2D/gLLmyUE33DNGImUIRU3yj3XhYjUbNWIeYgstkSpHi14Nd6FV7eri7ZZMrc9c/O/hSbJYjN1GP1qit6qrJjKNFOmfY9h8EqJO64JX+TZ9uX1eLfdCTne2hDhEezv0orli5f7xegr0Vd+yklw1QWw7UB4YaVu8fY5amoRvgiNxfV86Vf6tlkAz6B53cnAJNZUc5H4+ghigErEuqDYXLN5P3jLVv925qsqkEupAziW7gjNgvXOwJsnwQc7EOyj6O/QxGkDMfS8WKvtt0E4TE8w5PCBFZZnZu3VgWxk7+ETuHE7ahAJmQKzNIM6vHnzWr2/u1QEszq4R4t8DNWoEQLAZiX4oJY65P+bjCfY78LgN2DwyzD075Il5kAqRcRUgRIfh6H9M/C+M+PdkYinLZqxI7jk63gzJu75LQmenZmEwzSELIUm5+LvwkyT9nuxlANT6jZJyeGrveRc/8zsug+qxjwJkXIOFp8LdVPdM7B1D911O5dD3Qh4523/E/pEKcztAJ6GP6/U7a50lxvjHlcncPyecP4TIsItgZvcqgvz3LZZKDXBcu+M5IqTzSPxRfQ2ohlzPWPrJEkXFG1bhSxMm2+BuCGHHB4L0WB7BpCFd6a4wrw5PNnl8OY3U3Wh+rMBz7Yb0ZUWnWPTdhtcq/HKrhrxUEuwrRaRQDVSR+DXfMsCw6cggnnc3aExwHA0Ulse3UvwxDR+uRc8ANxbAVyNkrA+Xo1fp845ydq7vLm0DV/jsdivOA6R0GL3cFfjVRRo8H8Q3n0C+vdTtGHmWuQarERkMAsftFGJSKKfu6WWYJ/d2kJ8OoHVGM247QTfQQc+FcG2mWk4TA63Z23fiSk++2zkXubus47CEmRZ9z2NxFckWIRPTZhO4eTnaeA44G/4BVc7IF3plv7LQXOjTlmJhO+NwHE3w2+OgU8jq2+Zu+U6tDTVb/eFkx/RVKMROGeqnuE+T6rQ+TMoirMDqcJZeBSbNyPxbTjUVyRpw1a922YyPZoxPzJYgMaZxWmajEuSdBEyZY4Bvvgm3GID21BIZ0MyEo0s5c7qaAElbfjBNIMP87fBsBxvjgxNXGF1FDN/heZOOz4MVqnBh8DbMSMR0S2mMJKwEvhYlevAC3jTpJkXVwCfAubA7T/n5KPg1wfAN9Mqvsnn0JC4NfopWqfeo7u+VVmTIii7ZQoULtY6FJHsYth8CWzeBDstxjuj7Pj34Afv0d+1nWEV/KGRzqEatB+9DeWkXYlG3wpESKPQBOR1vK3OgoBGus8LgmdiJtHZ+PqWRpKmnsCrtZDgMsHfphqhMEjIvu9hFCp+O7Y2+Lsl+AryiOwmun3L0KO/CtnVH6eb8JISFbnpaFQTFWiqMtc9be7UT3Qhnl/3GQz3L1fBhHmPwK3ISp8F+Q/nQgNwoOt60wDgFPjixS7o9X0ILRLeBoIFqGyiiMpuPSObJOlofAYZaEy9sB+K39gNHwVwg/v7KGR5+zw+ws9m9TnXiJkazRxp/qeQzGxfGIRC0IZFENps3wbV0FRahgb1SjRogk9H+Pgw5J9bCO+9qPOSjyNlNxje+TtXVMNprUD/36JR7w38iB7m04XvRnwG63gbhQu0wpoFotsoTCRvQiP9crqLRqfNXila1FgW6F8BL7VyzvYS14OmItUz0j2nHVwX51KY+zbB3dZypPAySOY0u22h8m6isHKKKbowaCUkPPCkZWZM881VIgIeg//exrtts/AKuNGdNxBVax6KV5u18MYjsOUwdy1Tw62uqpu7XQsEzQObH4pMoj+Hv7X6+dAnh8F1i3x50Tp3meeBn14L3AWnPaDH+CDKgKgHfgNsGcnuQ0d9/yRtGNu7bSYz+46yi6kHGwAvu39UE0jfAB+yFiqs0WictsGsDm+6C/1pHUg5mH8oTDo39RcGlIT5dpXBeSE5gg9+DIMqrGJXU7DNzHh8Ao3yL2lfUuU6vC0wD74Fp6Wfhv5XoZDF21Ck5Mvu77fxgSpNFJYByyPps5o162WGxAhrEuAANKKPcA91lHuNBsZCsjmUDdHq5BbWvxh4pRX6wYWLYFA6jNxFKKJiFr4WVilK2etCI3oTsuWZorIi0cvwCrAteNkEJAw0MfMz+EkMeLVn53RR+P3gbsmCjMyXF05eWoPrLAGmuHvBbXsDttwTnlqEys5l6CbUpEQft6z2XWwFnroH+CP8ulU/oZFu++2L1J0KpN4WIUHcAVKOR+qzXb75WrW7PSK0cEWEEJHoNiC6evnVhxDNmOsZoQ9iUpKkjcDwsbB0Dgyx2XoXfpmWV9GgZX4xVzqsu8qJUwXtS1x5yZ5MWWGCuSFUDlZ1o4zCIsGVrJkfM5JCH1mn6+eQYcDL8N5ctTHA/G2fgtHXaFB+6ffItHk3hdVObDR/L7hYqOpCIiv+iZpP7z0KUxBClLv9Zv9djVd27wWfV0DZKr3Kl4pf56MReuYimqYDHx8Cry/VyP2g6+52yAQ4G18qrI1ucx15/CRmBH49Ody2fq5LoZmyOAozjNI0pimhMNm8g8LJkrXRQmHJOFNr7a6/+yLi60CRUQvgkzshM+fOiI1apOw6gcWN3qo7FPHWJ3eAeXPg5FJ4ocMbCJYhQbsaOGUq/PIiba+6GZrH+K5WAsedJJf108gXeEzRtxgVXURvIpLdeob9w9p7LklSRkKNFQ6pRCPEXKQULGz9ZrxPzgitjO5pc5n58ELYmnXgfT6hSbKGQrUXBqXY4NiEV2+1yDljAyuI/IbsCbwK78x1WQIVwGEw4XqY9SmYtxipt++4kwYjkhuIJygjrDZ8gpoRXTk+BSEkvmL0RIhh26b0LJRydfDZ3vP6XLZYpDfI7JGLVeL/4aV65uOBkxAh/Nw9ux3QiqWPI0JrQd/JBJTLbt+BRUjOxSeND8RLpdX4NBAjyjAwxZLTbV8//MoL5fiqLtaeWQ06il7ldKdNpA2QbI9fpcIt9XPFA3DarkADJP2gqgMyXZAdCItXqpm9gAtvgx9fBl87A36NCCsLfGYo/HGJfsr7XASPXg9//aoiMJmjANcxKFJzIXAu0LwfVD3U8/I/9jmS3gbAJu6zi2S3nhGuypxNkjQD0AiZKYhMjGSWIVOamRlnUbhYa1hlowyNFp3By/x0htCEEObahebMUG2Ef5eiwXgoIjtTdCOBzSuAmQrTKwf6lwF11CXXszgdAvwAX3TZEsVzFObPlVNIZOXBvgFo2Mzh7ao9JZGzlr97QhDw0r3Qq1N23fbFGvc+GJHdCti2AUY6p9TrKGJ2P8jOhHwpCuiYjezSs/CJ620o8HQuhcWecxSuJWfmZ1PNlvRvcTj98GvemaIz8jNTtX3Ooe9wQXB+qBLNeuBM2Mnu6PdmxNquWz5trNs2AVJX4acEEZ1Z1RcjRcZN8Ntr4YaTJHTzwFVLtH8H4FKAcWpuV2DcrfC/yJJa415HAts95BYxPjBJud8XgQ7rZkbC20DoY6bH3kQku/WM4kK32SRJeQMNZKOQGakDVjRATQXd66Wl0yEZig9MAF9p40V8XHcYfWnHmY8H1vTxhDCizLlzLDzeogznqy+0o2pfZTsAb8PSpTq/ait45zXYby6L0zEyabYudeRcgUL/svhcutCMaTdnDsAssvnlWLOwc4hi1dZTonlPhDj4fdozP6GpvVWI5edA1Qr4zOPwmVdheSNMg/y56Nln3G2ciL6P45AJ0ML9rTLLCnxdraGIS13Ebbfp2Py3YS6emakH4gkyh1fa9nsxQitzx1keXzuFPt/QT9uBgkxsUpV1t96o/t/fCgdOgXSaz6xoQ0tQ0aqfBvsC98FxN8L9x6rbo9yxs9xtPLW7LKN/RFke/afCjIs0F/g+cAnw0vawxz/g9w/AlgcmafaBNf10kegi1hWR7NYzejLNdK6CjIWqOQve88BnzCdTpoGlvxFZKX7g60B+vQkU+uYsgCVcwcBcY2HQQ2giAz8IhxVBqtGADV5dlk0A3obmpWrvYyXwymtytjx/Frx7qQbsamCzanwiuVU1MZKzEMY6fJJeDm9+bMLn6RX/PEOzZPh36L8zQl2bP68Yofq0toz8hiKmygLbwuDZsPsTmgzchL6HgcAoePch6D8ZEcBzSKm3obSF0uDvTtesLa9n0ZvmoyvBLwlk/lrw379FYYaBSVl8cnkn+g204QNcwM/YrcqLEaF953l8kEsl7DUfXp6m9MVMcPriVvnkKoA/XqrA4eRLfvpS4po9sQJuaFWzn9wG7ntFPMpEGbivRyUEJgGL/6GfzccBAqKLBLeBsYmbMWM05gZGBrdepgUtOBKaBr4cZF6uoZ5WOqBEZNk92JUX7Q+PL6cwUMF+yKGvbyDezGnqcCVe5ZXiSlguF9G14SIxxyph8McnAleLo0qBzYYhhWb5bzn3boarumB/FpHLQqQV5uGLOy+mcPUCU17v9178CiM4/1UY+Y1AgfE7Iy/V/sAR8LE9YZcylfffF/nmroP+ZyHivwEJ2hb0TGa55obhlw6yYCQzQ9pkxP42ZgkHHlOAtt/Uu1l6y/G+2XJEru34oBWbSIUTnWXw7isU+oXb1PfsWPfTmuw50eZdy/AW2fkAj/vqKKvRt3xVq8+UefQVFYA+FqAVRo+QcSKP5gyfcpd/G/06tl5LVGZExLogKrsNiBzeUjRoGZrOukHrWPCJwm0ukt3MXBY52QGMgsx++NEnJLAwIs+IrivYZ6rNBk1TFM2ujXK1313TsRw5XzabAG/N8u0nw+C41+CGMvi/3+jcj5UhgtgSr5SG41WcmSaNmN6m0BxpJGMK0FRhsUp7by2vEOYvbKPQP/jvwsyfuwfb9gcWwmdulnnz/+ZqxJ4N/AK4A946GjY/Az27uxFv55Aan4svKG0KrBUfFFSJL0VWiydH++7Mf9fm9tt5IKZpCvY14QNWOvC/Afvuy6D/ftD5EGTq8QFReaAFtuwHN0yH46pdn9yuCe529wWuAQ69Gj75Oxh7guY/GRSE8rrr8jJ33pPAibNQNehj1bUDXFd/hzdiv1EB5KOq+1AQlV1Eb6DJvbcBI+fjZ+QlsG01fnaeUT55t2nLTIzg8/HMf0fRZ4Jtdp59Dmf1mWC7/e1MWAXLAW22FfCqD4AZPAQyi+CG78PS9kCdjsbXpLRXzr2X49eZW4yIbol7rcCvQ2fBIoZ/xR8XHmuvYjXYtpZzPgi2RkrvGOCb8LFDVILlcBRaaIrrtygvbxj64le413h8Ll4rfk2cDrwvrh8+T6kCr8qN8DLBK+eu10rhwrChirPflU2AwqCUJZo8tTdQ+L27yM/jhsEVjZDt52NkWoBdKsTbU4BPHgDsr2+6Em9J/5q7VC1wOXDDCZC9TPcwEk0hGpHK2x7I7wRPANnWnvPuotpbz7CVyjfRPLtIdhsA9k9aSrD2pwUiGElNxC+fU6HFpbuL+7bjB765eELK4F1g4YzMBjxTdvYtm6ozNTC06LxheF/SGODjJQpAea3L5dZN4apkKXQOg6U/VrubV8CgrdAoPh6FHmyJV3VtqMjzbJSA/rL7PA9vrlyFZEmYY1eOV3bhK0chmRarNktnCMl1MV7y9BY+gSJSboTtboKvHK2VeSfD5kcBf0KRkdMQN26NbNMNyDpaishvkfvcD79CQRl63m3osZTjTYyBX607DaEEv8KBmSstMKUS778zVWewCdASKNsPmbS7KCTMFXBaKdy+Gqq20GlZ4K1Wv6zPhQ8AV0A+LenOrihHaYm7o296BHDO71Q9iHvgbGT2PBIpv5umwBF/l98u/xVdp7hAdBihGRHx7yKS3QaA/ZPW4oP4uhWAkY/lSJnpCjTwhWqsHPGEJXnbwFbsizHYYBYGnxi5Wp6XHVfu2m13fasF2Np3ONkcfjWNU9JqeGeR+jZYaQeeeHL4FAPwlVFWuPflFFZKaaMwuCQ0OYYE937EF6Yy9JTDtz5UXqguVyE2+w7sMkHFHw8GvgKMgVenA5fhc90Wo+9wHD7bYTV63jaJ6UTPN/x+izk9/L7DhPMwqMV+V0Z+9t8eBrvYaxmFCfLmx8tA2iHP5fNvQnaU78YeukW+D0rLeKmLMXieBsXqtCLT5xgUlDLjWpktW4CfAhcCPA53fgXyu8KQ36sN8ArPbikGraxndPbyqw8hkt0GxHbI2tUBGvPL8eYnGwwrtG8c+GVdzOzUiY/qKzZblhYdC4WDmeXK2Q+wODjCSLYL8dcgl3Fu5i8GuJD2T/rk6W6iG4gnntA3t9K9lgevMM/NzJTlrkE7v7i9Yn9bqPxM5dlxoamzE59IHhJeb8CuBb749X/BlofrSz4OmKHVumfMR6P6Xuj7WYwiOUfRndNOI/oOcnifqpUlse/Oinmb8qrAV8FpC46z79G++/A3Ybl69tlSGrpg4XL06MMJUgckA2Dzwa6aynzdbSX6KTYht+Szc4AXRWYz8bExIIPCLNeNpRdoHfrz91Q3tgTOAbJdsOPv4bpnZOQwzi5WdxERHxSxEPQGQMHMdBg8tkhpWYNuRlPY+4BH8bEcB8C7J7lQdjNZ5ej2sTCWwnXPwlwrGwTNlmSfzSdn5BaaQke64yxC9NNVOmjpUrU7eCfU4wdh+VuuaPIQZMozVWehpKARvCl4raSQ3DZzxxtRbenet8UrxHkUkpM9HCO2nmDHL8YTnI3mm+EJdQQ+8KU38DaeTO1aJ8ArL8qP9yDwaWi/C8o+h1/BoAZlWy/BF3seSKEJ0szUTfhJiSn+0eh7exsFGXUgBmrEq7wm1yUjRJuorEDffR7PLLXQPA2qBru/O+guG5aUwLtd0N89+vwqPeUWPOeOLgUWwVO13fEsdCLr7Wj0s70D+PmuwG4w5DL5/PZAyvEsNB/YFfnx3gZ+WUR0Mbl8/aG+NEkbanq3zWRJLAT9kUM+TZMc8O4iPwkvqGloJscmYAn03yLYZ99SBo0YljpgUZrgc6o6g2OtBFUYsVecZzcQDapNQX8YAO+5WpGDd0AyZB60v6UBsH8VheZLI473ELE14X1mFnwSmi2h0OxZh2ROua7Dg+71P6gW11PuNR3phjn/v733jpPrKu//33d2ZjTa2V2NV22tjixZiiyQY+QGBmNjU4LBdHDMlxYCCTgYAkkgDuBQ8qPX0AzEQCCmNwOOscE2zbgRV+EqS1ZhZUnLStpdj7bN74/nPHOee+bOqo0WST6f12v23rl97r17PufztOOu6W48sbnrrvv0Ku54WopMlWa/ub5WQX+L3oMB4CPwF88Tk+bzgV9AcTbs/C7SolcQwrkH8Z0OUo+CZD4+gnIYn1KgyryCV/ZqwlQBqyRpq+HoM9eITEt0thLLVug6A3m/jOpPcjA2DlNz8MgAfGIAyt1yqJnAsTm4Cvhvl5z+NsS1XEFqJrzKXdp64NlQH9n9J272VmT/gpu2Af96hmRxhIhEF7G/iMpuEqEK78tIT3bBJYhg+g4SyFBC2u4VSHBDmXSE5EykIVRTZic+0q5Mo29Hw+K0pqY2gD34BORV5nhFvGrUPLGvAB88Awau8SWvkpMQWbIcX+9S1ZvNjdNoF6vO5rl9VyJBLEvwjqzrqAeT1FyVlqSIN1eebM4BPjWgggTH9LhjaqqABqYM4EuDgTeTLsKHM7YCer5+/BDl64B3wv/uhL8DVsK6n8CiU4ELgO8ht+M0t/tW9zNK+LJfmnevJsl25Nl3mdOOuJ/YZ+bVnNmPJ0Lw+X4l0sM27ZLZu++B5fPcNv1yzrEh3xdKCvC7EThlCtyxWy7lO0gGxq0/Bk6A8hz4HH4M4NPdNibbhme/Dcrvl6jN7YiSa0diePT13hQHcZ00rC4ktZuPau0xk62HjrKLeXaTgFlJUhs037WT3VCkF6Rl2Iw0MpVgBy1PoaMWWBVn0xDs1Ko6jR23frtd+EawG5jRDg8PyTmOy8HacWAlVK+RxjWZhZCFNQOqmqniiyvbxG4NQlHfXAUxXR6D9Pe/DjwAO+/1o6PX+XEYSn0wrQ868njfH6TJTlOS57rzLXHr9Lx6wCp+GAC9wTo9UOj5wKvIecAl8Ix3wsvuhe/BovPggcvgmBEkMnMrwvXLkOeluXHz8aMTjCEi1UZTaoRm1e2jxQFG3Lzm5JXwvr5hs98u0hGa7r1YrsFLJsFdjQtVYMgR3Z92S9DJdsRNWQCJSDkRBl8L117ixek38a/bWsSXOfhZSblbhzyBrz0NXv0zuQ3q51NEopsExAoqEQeKpwXf6wFzGiVXxfvXxhCyG8KHnKuJcwRf1qmEz2MpmKklMkiXFLN+uxw+qRl3rDnAsLu6EYBz4TsXA9dJ41nsIh04kseHDiq5haZCNRdqAIk1Wf4M+Do8cgU8eK84au5wn9uQ77cird79wB/XwB8ehlvG4dZxuGsY/rgZdq5BzJzXIc7PnyFm0H7SaQrqAFN/nkaH9tMaKPn24JPi+xHy/SQ8B3iJ/L5jXgz33oxInh632X0Iwe3Ek9Fs/HPVAgH63PW/10Zc6jykR6zXvDo1W5ZIl0WxkZ+r4KYtyLvirMCJaSnaEaL7JN5NrO5Efuyu4y3C01vxr9Yyd/iFwOBb5Le+C2+BfeRncgs6kcd/3yv3cLsjIvYB0Yw5CVDz5auQUolzkAHJyx9BWoAfIWbLMeBmGT+spx0pUVHB24+U5NpJKzn1zYG3EdnUhgrpwsHaqK1AWiQ1g+oYbTOBWWfTk1wlBZ53roWuWYjZbzbezgY+xH8jQnrr8IpOWXwRQgILkeJQN8DAZ6WLvx4hs+1uvg3p1p+LEMEvqI+vxkKkKskTT4LRG2X/e8xp1Dq5HCh2A89yN3ilO2A/QnDrzTVq9OdyWmvSVHPrLXjT7jzgu/CBy4QUNsEDD8Ixq9xv7UXu/RnudykpdZrfOAf/PNXUuYF0VK+aMpUA+0nXv8zhE9v7SY+Yrp2fHrjpejix2x13SAJVxvEuxDaEnO7D1xK/DPjwRcB727kpkY7T3Xh34ZMQv9w48LovAa+Hj+4WT+zdiFK8HyHJHNLvi6pucrA6n9Ru7tzzdvuCpP/QMWNGZTcJUGF1KfAPwNlPlooUgA8w0YZmUOii3uMGT2Ta+GlP3/bOIZ1HlSNtwtKoTSU6bUBVBcxEWpYxYFY38HN6NwO1tW6DGfhoRiU6vegB0hGYA1AbwhdlriBMdRxi0PqSiLBfIxWwf4U4ak4Avg5f+DRCgL931/k84C1Ipa6ZwDduhKOhvMx9f5G7iZsRkrgD2NSHqLufIaEQv3bXPA8ht2l48+t2vMprFUrIPZuNV8MbgXOlevJzgLlwzGpYdxvis9NqK39AOiJqSqzSmIagJOSCQurmSyVBVXm2koWaOdW0UMAHO9lqK+PAZjjxMdTfj9q4+Oo0i0VTBle47yvcoW4A8fP+cYinuO8j7nKLyFCA47gRrf4GPrMbrnenXo4fSOJO4L+Bd9JYOSUmlkfsD6KymwTYAVxfA3xiCdK4vAKJs74MV/kZWAvXboGnPAaRfwrt1WvDpR58S4pF0qTWhk8eV8WkXfPl+MCF+cCCOXDjZrG+zXTH63oRPPJtmNqOOJc0XUBbU1Usg9SroQy7fn8OyC9GVOCLEYXzE/hGn3Tdf4ookJUIsZ2OEF7FnX+lm64wv3stYuLbgo9c1OjDM+GhU0UkX3AzIjfKbt/5wKzHIdL1ZOAN7trvxJtgVeEdz8RDDO0rRvEl0u5y5+oBboCHPgYvld/Reyf0nAG8EUnQnuMudYP7jXPwg+9q0JIL9acPn6+n96SfdN7dLnz0JfjCBFX8WHfqYKtSz9gY3gzFDr9NbTw9Lqz2pz6PBJrMmgbv3QH/9gbgNPif87yLcATR2fORYJUR4F8uEx/mue6nvB94N/CVVwJdUP6kXEdUdwcfq9uS2s3lPW+3L0h2HTrKLgaoHGTYgVsBPnEq9F4PPavcBlrGCeoN2fIteD5Rp0iBdACCDUDR44TJ5Bq4osEtdluNmFE3Gttku8UwNwebal8EPurOo6ouNF1qsEe/7J8iunZ34GOAK2H0Cunm/xqfYXweddMtNyCNew9CckvxZFd0Ce6dQ9LSbsU33hsQN+H3YMH74IJlUF4NgzcgpHoHwjVPuh26NiKkswhJbe7BB7uob1G/t4rw8oiaBB+huU3OvwA492PwPeg5H7Z/HaZ3ut+9FWn9F7nfqLdb/bqq2kpmHrzs0ojaYbOdxuXoO6HzVdIRKFpdZVysDMdOB3qF6BS5YJe/QnT03B3wb89B1N3bxSx5hzt9BXn0qrELwPzzhKu73eU9C6mu0vNlufwvI8VoIEZkTgoOoQCVJEm+ifSPwNXRr9Vqx+/v8aIZ8yDD/nO+EWCzNAr11qIQ7FCWusKpQAQ1N6nZMWs/+z2MzCyYeY3q02P3AMVZMDDs4sqfzKb1IEqsF5JuvJrTvlGG6XJ0yDtz8lpG7BjgONh2BVyBpFdcjaiuEtK9/zQiGk9HlOxpbv4s4Pg5UHwe0u9/Fsw6AY5ZIObOv0SE2io3vwhpwK+HwUsQt9wShDS3IibRB/tkNFK+6z5L8KkQefebevGKtZWYh0SKasb2NvmhL3Y/rw+mPxkGf4QMBQA+k2Ehvm5mD97HttNNNRfPlgdTBagdJAt9B2yUpppLdXv3vhy7DIbXy3xSgGRKOt5J55fhLexf+BH8YEC+nHK+94ZqH20XfqzgTyGjvqtb9mQkoOuNwGC7ZGtAmuiiGfPRgVqt9pJarXa8I7jvIok6+41oxpwk6D/ovUhswutOAl6GeOw/i3jnq8By+N1X4ZQzkO7udPyAnWtlfT1XWv011nQJ3g/TiS/3pQlPIO3ubrfupDmwc7O4rR6zAmldX4u8V+vcCafjk6bBV0VZR91Ht3NcWrqpsxASWQb8Bu69V2pD3oT40/4ecdychRDRUqR1WwRMXUBjqoKSUQdCEMbGBm67bfDIGpEI2xH5sMVd3mLkul6GmAer7tyLgOSfkICZpyImzX53AGvS1F5Cq2CDVqruQr4E77xMzNmvQIpFvtVdiiq95QhpL8EHlq7FmyY34Z/pOD5/DuReuNJfdTP2CMI2O0mTnSabq39w0J2vF2/aHPEWUHX/VZHb/GOk//EcYPtXgdXw5hXyKxcjr6B+tKf9j38DXV8SoluB1Np8NnDTa+G9l7hxcmMllYOO1bmkdvOUPW+3L0iqB27GTJIkAR4CzqzVavft73GispsE2H/MfowrTn0qWuprHGgXs089YVwrYIAfb8ymGtj1ukyPCT7dQHPzpuCTkbUdVxLkdH6XbEZGJ+gnXZwZGosr98t0eFyOPTWHJ7p5cPu9vhjKBrhwCMY+grSGOh7qM4G/6HKjAD0kxLvzYRjdjJgct+EDSLYgrfo6xEZ5v5sfhaknwdzF8LiiV36L5bzcgwS5fA1pTa9HlB7fNB+bl1fF1/TUii+tggatzMAT+OulysoLEedXARki6ATk2fQhz7AbT1hqotR0FO3saLSlzZPrJJ2uoApwCr5jZDnd5vLp+rnU3y0Vg5oRo59x5JZfj/hO6QbmS7bFKrePvmoa9FcEOEdO/1rkcfXMg38GZl0ifcANGXcxEt2jCk8CthwI0UFUdgcdocllsBsG+6D8F0gv/gxkhOvbqOdj/+5BOOWNMl8PRCghvq2z8K1NIZiCr7bSTWNBYZBGS6MF5rn9+t269wOfP8OduBdpjOfiQ/LVcbRFdqo95M8xtRvpvz8f+C+4da1U+L0aeBX86XNw1BKkRTsR6cb344em0WsvIX6po3L4PAJVlr3u2tTM6GLn1ZmUaK+hR6599Hbxe61z9249MA1+8B547vVu+SpEXeYvRuyJFTzBTnHfj6f12IavHDPqzvNG+Pm98HKYvxk2nCqL2IJPUCuRNk9qAQLFZnzyuCaVazpBn9lO0xDUd2sjP23givP33rTRRWduhrHd6RR9FYT9eC59F/DNhYiqvwxe813p5I24X7oasWz3IsP+tAMXI3f9GoQ0lyKZJzNxI5ubIX4i2bUeq3NJ7eYWR3EkI6zHO8IBLqnVapfU1yfJ1biogQAX1Wq1H7ptPgvcX6vVPnIg1xKV3UFGwz/lNNdT1Wg4JSlHGpsedMOb5Ej73kqIIGgzH0VW6oFuY315ZXxSOUi1FG21FsHDl4A0+FXSY8uBV3W7qRONKomCOwDLgP8RotOo/9c4olsFnIlEMpyMmOA2IGpVq3poGaxO8MngWq1FS4ZpRGgP9fqcSbcQ3ei4+A5ZJ5/842DBYmllT3DnLcJzPw1/OhUhg+tw8fL/g/Q6FiG9AB3sT5PPW40ZpE20eeDd8NRuOAs2LJNAJr6G3NY+RGwuwmdh9+PVnnY61J/nrASp9APrbCviA1msL1glW5GUheDEeTD2IHXVV8K7jrU2QQFfAOclwA80b3KRvFUr8PXM+xAL6mJkQIip7b7P1o0E4y5xl3uHuWt2FITou2sxtIJKKz+wrVarrTafS8wZqdVqZ9VqtZUZHyW6PNKD/uaB/rxIdpMA/ef8HECXxGfQR3qUcBcB92NgQQHvzdeGSctHjflt61MlQ009gOyoKlsxpR3qHaoSkDyOWevBV/zQg1qy048zZSqZ5ruQYJTHw11rhOiuQ7rqV8JRpyJ+uXOQlmwtvmS+DaXXT74dX9C5gzTZKeFZ0nPL810SHDM6DqM7ETNnVUjvCYicOBlYB0e9Bf7rbxD5cDOw6V6kbNkP3TGXIMpulNbn4Ckq7lya8zcXeLeYd1dCz4th7CcI4ZXwY+rMxisxzQPYjdxLTTVRwqvQmGepJu12N+3CR3XaFkGDpFw0568A5vsUTj2NQt+YMeR2X6crXg7Pfo6IfM0Y+Q4iWHvdaXit9H3WIY9oN3XrN/3TYPCZaXKLPrtHDc4C7q7VahsP9EDRjDlJ0H/UnYg7ZiXwxIsRArgMZ6eByo3Q/zSkDZyDj8RU1aP+mxH82CrWP6fz6pcDXxB6Pt6EeVwRasPS2hx9LEIYr0bsqTfgh8RRZ44dvaAXn5vWQ92+9sfLJXzuSuADwD8iau59+ALWLrKvPlhpyc23A/k5pJPQdapkBz6/TxPL+vH+w93Bsm1CfOAI1JHitt+L9fBKpOXVJK+d7hYUn4yESb4BP5p6Hl88utXox0coAfwPPPQFycH7Z2SYoPMQ8+925L3oQgJxqtTHQKwTXwFv+tTgk368P7dZIMtWPOkpk1XNfCf84kE4syADug6Z1eDLtqpl+mqkf/Hsq4HvwBM+J0niWrTnb1fCB+6Ut2wx8CJEERbcfuPA62bDMVvggbOAq8SMGUnu4GB1ktRubvExEw4sQCVJki8Dv6vVap870GuJym4SYHuk65Am83rwpkgNKCg7sacKDnxXeZy0+TJHmujA5+LZYIMpZj8beMA8769hGXzjt3g/WNntmCd71G+9MFVay2HgciGPXyDV/N+L9MnOQchsPUIoICSnQwt1Al05yHfjCzlriH4JT2BV0kP46HY98lvoQSRPYObMdzu1N4QQ1zoZn++J7SI/lgLfh8eeD5yCyJGdv0SGIe11x664C6/S+oAV8EMEqTnz5bDgmWLyvRhp+X+Mzy/c5S5tCsIa/Qj56TNWq4ANgNIOD/gsb62VqdBAFmvutJ+tcKZTgxoHo4HBetghfEzMObg0m/uAM+G3bxRRr8bwd90pP+M+JJEc97N6gIuQvtIdW+Rnl6/ey1sZcUShVqu9shVEBzGpfFKgjvWZwDFvgB9+Wtqut6qvqkS9JNQXQMhuLWl/21Y8QYBvpLRxsmYqVX5qoho2+6HzG+UYR58ArJfzsQkhs4rbUMtpjeFNeWrirCDlv1YCX5LW6YfAY2H4I1A8A/i4O8xt7hBl/ECxc4COHEJMajIcdJ/d+NFKXbAJFTzxbscTj6qhCn6UciVmDWipQl5D/fsRu2UHPOMJch/++BB3LAMuhV98Es78OjB8O7xyFXA+8FF3jm3uY5Vmq7AcX26tF/gP+LcbYHWfLzfyj8CNyP00vlbuwI9qD8I4BaST0euWqW9uN76DpKykilBN4bvMsQZJl6cDaIOcy4/XRSOIMK4gr9sO5G2ajzve8bJiFWKc6HeHfBXijJmGmPnf5I7x8BvhJZ+Ev0ZSMa8H/jdQdVHltR5Z3o8jBVHZHWSUk6Smym4rwKAInHbwak1tQTkRQ/UGxlZX2Y43UUE6KEWdJeFyrbsJabNmNzA67AhzGXzqdvjXWaSVi+0HjZp1o/jGfiXwE7h9syiiQdh5PRRPwydGb8QTXSfeHNvRjqimeQgxbSI9uvlu0iqqjca+Wag6dV/wASBz8cWrVfFpAvn9QAWOfrIks58GZ14MN52PRFPc/jBSz+NWc0xVuwO0XuVV8Ip2FHidvBBnIqkTL0SIT+P4t+KjJgfxpdM0JaGKj8pVJ5uGSxbNcstY6tPTVk9N43qecfjNFkimpfPV2/Apn6r6luJGFiwhnam3ymuyGf9aL+qW7+cC/28abH+M/Dx+5etZ/zWSt/cCoGL+nyIi9gWR7A4ybM+zAFCS6cngWwpTvWL6Y5CecIm0squa7TWxKWfmNTzOKj5txEp4M1YB6OgyF9TDB94I8DekE7oV6rhRKZHHD2FzM2z6vbRgG+B/t0HXSUiAxdkI0e0kPQhtBVd7cx7SsKsC6ydNIDYaVKd5hGS1/IddnpUD2IEn1DCKs4KotHVyoY87VnL/uuHENyEt+W+B3+1EBrNRb4YlPL3mVkI7EnngaZB/laSnaMDPdYiyW4GwwQZEFmmHqYAPShk2h7UBT8PI+6CmTkgrf8UYPoFOSRTpPtDtre/KmbbfVkIE5hoQWbYOOOZYbkY4ers79IV9Qm7rgcEdUHnQnbvPV41rywnRDdZqSX9UcgcNBycY89BBJLtJxAjAt6Sdegn4xqQfabSGET/NDjzR6TYVfGPVjhCIfjDbd+LjwdvdfA++IewBWC7HSdrh2o/xL30ggSBKdGG6gRKdmhWXyTF+foXEjb8Dan3wjIXIaJznIa3Zdnwx5h5cQeY5rkB0L1K15H48yVXw6ma6+a7pB7pO/XPqs1PVNgNv7lQS7XXXvwSph/l4xPy6Cl/IypknZx0L/7BCgkK2yqJFpwKXXwrDJyIaA9Jm1V6E1VtJekvwfsh3wtEfFjPxx+GmHyEjn/4Qb+4edz+l6q4bfCDKEMIa2hHSPkIVb560LdMYcuu106TbOJ8yw7BoHtziSKlkDgv+9VScCLz5MqQD95l7+d5z/OtbQN66NW5afoGc+sdAeb1Ef84HjnOE3JCzGomv5Rhv8edQQiS7SUDqn7IqomE2eDPmiBszdQQ3gCqNtS+n482SOrXJ5JqeoMvKpFMTdDq1CxhwCdiLeOQM4KgX4QlHic4SHm5nDf9fCFwnYudK4Ali1qoHfGxACFzVXBkxpx2lr9s2qO2UaNDUsXU6xXy36k4/FbNeGd+mKOh6+xtU7ZUQR5cSppKlphjMgKOfDWfnYA6sexvSO/kp8MhlwM/9M0mZRLfRWlTwCvKJUkPiVDjxq0g9rV8j97uCd4LNwVsJpuMH/FWiUlJTtTaesY0Gtaj/zrZYymp98Pg57vXNpS3rJXxqXzvCcdeBPP8+4GYReiP4YJYTkeBYlkrh548ifb4NZ0j24ypc2g4xty5i/xHJbrJRkiDzEqQycXeBNFhLcaOC40tTjCD8ombLKj7CroBXc514wtPutY22m9WFSIBehAyeztTvgBhVt5OdSK7LepBcupOhdjH85HZ4F5zwBxlonLcjIfK/d4cv4oXXMUU4apYcZ3gzPLJTDp/YfLoZ+HHm9LslJLt+Nt48qes0EjMj6Tyl9pSU5iHhl1pN+niEBO9Halcuh1c/U2LlfyzPiFcCA2ch+Qka+bEIr/DuprWjns+j3sFIvgjPeoJcx6/gvT9CVPVKd+p1SEx/GbEMDCKKbgrp9AFVaQWkU1WkMXjJJtAp8el7pNOt0DYNHhn3fKn5d+3IU9I78XbgWlXL74Nvv9j3g9qB/0K48Dfvhyc9Uzh7JXKA17ifokbkqOYOHqIZM6K1eLrw2Qj4XvaY4ybrq9O3RZ+QtigKk4ieChXPTbCOeXiT5BJgLrygi3RUYxgEompqutu/JIR2D/AB+H03TF2C+Lts2Lu2ZN0gDXa/DOiqYi7RHAlVY3ZeP5rnZ9WmbqfEV8EruYrZL1R7HeZYVXztz4X4EdjtKOxO6U79e0mILyBKag0wfCkSuHK/u65FeIW3kdapPKtsHw/8lfByF/zb1xHJdD1yj3cg/tF5+Mop/fjhBpS0VHapclNnW4l066Sqz6a62CCXAly7A6ZOEyOBvnY2JkpdtePA3+Ku64Xwp2/52JoScOU84eXvuG2ORUiOLeLL69R1NEZgRqUXsbeIZDfZONmUqhyhrtymTiFNdlq2yVZZsT1u29O2LYwqOz2GJcB6YcwORKXdgBTotLDEksdXC57h9vmdhLrfjJDeMGKH6kbC6nKkie6oHNAvY90NYhw8oZlS61Dqpw2vxjT/TxPJlZz1ZpSCfS1ZZhGf/sZ+d8xpeJU4HR/Ash64G/7iyRIVuRJ5Rv0go0J8Uu4HHUjnQRPve2ldpKbtCJwi93o1kpz2ViRYZRU+hWAr3kc7hq+POYJ/T2wUpjVt6jqtrKJsFOZ3OofbcuS8w+N+/Fftkw3hDQ5b9dTLgHvgqJPk1eh361gk8zcArJD/j7+DunXibGR8l2+7y7BjREal11pEn11E6/BhcbqPgG+vx5EGqx/fQmhPWv13VXxIOWa9JTcbmanTbkw0pqYN5OWEH/o20gKpbygffDrwpbNWAd+CW66RqlqfQxraC4G3IQS4Cz/46lxgRrcUaR4Y98JxatGMkacfVVequrbhI1wGzGcUn2Ruo0TVH6fbKPkpgak51Kq3Ge6CBhD73zQkcMWaNhdSH9dmxqvgqRfDY3EjJjwVbvwUjJ6KZIpV3D6jSBrFnXhT54Egj48AXQjFi+EZcyQIaBo8/2rgUuS9WYzwcy8+HHIQP8K55luW8PkB2omq4k3fGn2pFoYwz84t65kGv9kIxYXeNauEp6evAqciuXInvApRo7+TS56JvJ6f+bUUhX4+MPweWPcsufPPvl/WX4iMCjXfnVoJzhaGjjhwRDNmREsxttEXzE11fXrc1PpHdBuN6VZozLfO20i7YjDtyEHefeotVj9wC3/6Z0ibLUNVp6bBucBt8KfbhdRuRfxzf4kkUqkK1XiRTpz5st/nZ2mATErRqTkSPGmpcrPKyEaI6vesvDvMvnocJb7QPKpqT5XloDv/XETBKlHOdcddB6yHWS+S1IqHPiSRkFeDhFR8nno1Gcr45PBWwPpSVwNPFVt4H3xvLcLVv3Y/s4q8XDNJd5LCyBFVe9Zfp++jJsuporP+O43+dfLtidP84K4qDkv417KMxCvdjAtCWQ+slTt2g9u+D/hVhxlp6hS5/F2yKe91u52ScWf+j2jKjNg7RLKbZLSdJRW1+sGb9Qq4hCJ82z9kdtIGTKG+MUuKNtVA1VxXER95eIo7+IB8//b3OepqaCQ4S3IzkMa+Ax66TAYFuA7pYl+HdLm7kbiMbnzcyGOKouB2jntTWEfRFYy2xy3j89W2kU5qLzX5WHOnBrPod0teeuNsFGaFdOCKVXsarqj+toVIqoKqvCXueD2QfA4WvAhe2i3P8PIb4Za/Q6JZ8kgPoIQo0ztpTdCK/r7jgFfIwLNnATdA180IaX3EXeogwjCLEVWn1VVG8NaBTrw4ruBJrWy2U7K0JejG8ObQTllfxQ+rp6IRc8oeJATqByB2y5/6p7HWTb8wIMd5KcBfyrJu5JW+FZev56CFGspJUrts325ixASIyi6iJajz0rD5x1Vnv7bj6n+z/jntZYfBA01PgBnaR011FaQVrCABFAvF4/9U+/itSlKJpk3SejFZ3ud2v88dTh0y43hFV3HHGh32ZtZijnRwSYW0gtNqKTbFQP14Om/VnN3OJpjr8e0xFKPmY4NZdKqKT1VhFV/KTE2f89z69cDrgdfBC3JSBLIPpPr1u91NWomPSewlLc33B/qbQDoKx0k/ZoWMd8t8fLHnuQjzaKkeS3DjeKuAkpYmmet7ps9NozarpIeLypn9StC10OfO5fBPRF9TfZPWghDwFnjsDLEG62u7EbnTLwS4wQ8HtMhNn4mUFrMYrNWSDxIjNCP2DpHsJgl1rtogVqci+EZHfWxqaczhq9JrxQvwrYc2SGqWsuSXQ8LjEq3yX0JUykJ88vMiHvkWSJGmLGWneW5lROnc6X1y78AHSWxHFKiqyjKi3kaHfNuuZWPqJFRxP2q3+dhsZ0tmIbE1W14mTXjhR3+jJbw8PlM/jNpUH2a/m19EWgXmkdHcnwa8W1x2y4DLh2H0Y7KMDiSdIe+Os5EDh5Kd898lJ8ilfR/h03ciI58uQ57LLryJWaNkVW6N4dMM9D2zpcRs5K+2Espg6phTsuyF5DGeC23AZzvymmxyp6TkrutlYsbsQfgPt89lwJ/eI3dQ47XWImmGL8Pn28WIzIODGKAS0Tq8RJq+mSCh4m36BVEHm/H+FavmrNrT5dogWfJrww1rY5VOD9Kw34Iqlam/BinCBH44HyWQCl4V/hQeuArug6u+iox88xok63cL0mBOR5KjFuRgeKc0tKokphbxZkZNfbBRlWo2zVJZNmXAEmaHOabuM410OoI1b86gkciqeMLryfjMwBNeBQn9fyKi2I5z9+Y62ealH4cF3Y4bj4Uvf8HdqFsR83GPO99GDkzhWYJfJOfoOkNSIk4GXgNj30KiP2Yj79N9yCPXsXdK7hZpnI/1wan/TgOdIC3Zqu67WiC0hmZJhv5JumWxC9akHR+VORchrM+8w+33UfgDYmD4rbukDUgJ0HfIT+FkxD3aDfwL8rb+g7usSHCtRzRjRrQc92FSD8APuKmOjzAoADM/HnwH341S81N9G23UB4BNQkQcA9wi6qxepQO8SVBV3SjCZreKqjsBzj4fabdX4YsbqqqruGOoCm1DhtZJkZO9Jm24wzqXqtRsw67bWdj1HcG2ZXPcjoxtVBlapaclycJPya1Xj9RcPJH24MdJ+g8xJX7qXsk53PZt/MA1i9w+2/BjAe4vrFJdBKwUabQM+C9oOx95wZbiCUpLhik5KcGNudukloQRfPCKMpa+V/qOWXMm1DthK5Dlw+6OjOEHX1BB+QAiPDXeacFpMsrBYoToupGozC3ILZyJxEDtAt7svlvBqakH0YwZsTeIZDfZuEN6v30grUEJUUW7kKfRTzqRvIpXepD2p4BvwKr4hqkK0iBq4zoKXOl8ZyvhrsuheB4iySxhdOAVzW3ALXDtkMSNu2LPnIe0ZlvwaQbzkWjPR4a8/aorR1phjeKjLVVJZiV+22VWhWm0pB7DKsOQ+JRcp7njWdU3Ha/4rHlTW/65+GosqvI63Hb97vesQtTdcYj+uAv4DTzla/APJ8Au6JoJfOFyZMiCn+N9fv1IRM/dSITnvhKfJfVVwOlSfftsJBD0JYi97zK3uoQ8N+0AqXlzmlu2G28F1s6TdrY05UB9dyWzXKuvuO89HfCLLXJXO/HiUC2lmgExBxh8B9KB+qp/C04E/vF5QnIFd3d+iLx2P0b+X1YCfzR3IaL1iMou4oCQMrmMSEyH6gHakBZChYOaALUXbWETya0pM/MtU3JR5bINaWx7XYRMhca8OiWLTUhDvE6iCDqR6XJ8YrWWK9MAy+Fx72/MERwv9JVl+d9sQApm+6qZDzEafAi2C9Whnk+Vn1V7Sng2gKUcfEp4pTwPn793jFt3A/B6eNyT2fl+uPC1wKZrkKHaf4LPKxyQ51AfH08jQPcG9p51uPMvg+JiUXdVKL8fH++/Ap9v1+V+4jDyDk3Bk1mZdCCUlVCagqDkpx0a9dtVoTYAZ+bkVLYvpn0zjV86B3gdyD9Bp3QVHnCXJ+kqQojPeKXc6fOBwSVi8b8NMSaD6/44RRdNmhF7g0h2k4CUmWWLD2Kv3/1OhEAq+HHJrEnSNkJKeONmfRtpcxPgG+VRpLLHNsTsdZtLeFJSCRvPCuJb2iTTXwPf65L2fAW+ur5aC6chzhoNqCnizJehGXA0OJ+d6geyCcwiJOgw4tIeI9wvNJdOMZ8wiEXvR6gYdZsB0qOkL0QI7BbgxfBi+MTHkdL9m65B8gJw24OXx6q+t7H3/jz72yvIwzkGji7CBhjUSiob8HW5hhAyUVKr4slPP/Z9BE+CYS6nRhAroxUgca7hcods1ol/ZafgefIm96vpBTZD1zNFtW0FXvYlueRVAP8nau80gDnilizgiDJANGO2BjWO7ACV/J43iWgVCsCm2yS2owRi/lNbz2akvdyBb/O0tdCk4GHSlVO0h25747rvqEvUywPcLf664jFw+6VuLFIll9lmpw7ZlutgdK2EwX1vDrxus3TJz0Ha6E6kjZ0DTG2H4T7ZPQcUNSBFSUKVmZ7TpjRYAtIfrOoqVJyhv80SXeiDq98EPMJtIVsR2qn+Dv1u/12UpPJI4IrLrGaL3L/HfBwuvAu+8QWY2w6/vAae/DSkPNtLkPy7TUjPI4/vQZTwUbTNjHV6H0bd/HK//JzL4VL49g540WZkVIoXIrJoHWJ2bkfYxQWX1G+X5sJrBRVV8GpxUNPlLnxLpj7aIfnsNC1c0axWY8WJuvJ+xFrwZeieLSbO05F/iQ0AV8POmc4XuBQu+6VYai3UZxfLhkXsDaKymyQM1mrJCDC321sGmYmkCaiZqRNfCUttQRpkAI3dpfGMaQPxudD3KsCotCRn6QG0sdRGfABpxDfWx9hbmmwW/8oKfHidtsNTnQ3LjtRT97V1MHGSuCWxZibILFNnSIJZpkq7TwhLeqrYmu2rZFghTeDWPKs+t4X4kdF7ED/ecfDSHPzvEDy5HQauAr7ijr0cX6u0ild5/eaTpWzD+6Pqboacdz6wGF70HCSzZD3yXsxH3okdCLPou1Q2h1SrgSo2W0lFlXuVxgAVqA8GezPQNU020aqqu90dc5tIsaBh6uM2fgfpNrQBz9ji/NnnwyW4Wgqb4f0IV691pwvJLZoyW4Pos4s4YOg/4+/6fAlJSTnoEKLbijRCu/ABAdod3oW3A2lACniSGcYP+6MEqeZQOmCnbvh1cSv9nab+qqNQK6vcD9wGDw3L7JVw31mIDWk1QpRVfOzG6LDUvRzHlSVTB16P+eXqAwuDT1S1KCGqcppCOmBFySUrl84SVrNPBU/AIfnp/hXSaQq6j1V1HRnbzcMXjO5FFN5qxBM1AyG8T8IzLpbvReCPV7ntPo8Pbc27e3+P+zyAmJI3IgSo5ugQeg9mIybq42DqEyRe/2XI+/U3iLrTwKV+hDU0cmQMeZ7KuWp/VBPmMOmamtaEqWkIihE4cwb07pCv2oebaTap6i5zkajR38K1iJGgH3jJbFdn82cweJaE93C3ZDT2yOZAYzHoqOwOHEe6GTOS3STjZCSgsQCuFcink35V2WGmWYrOKrjwrVJV2OaOr43Vg+Ouq7zSbWiJporkE2zxETRzkQZtPmkS1m66Xlc9cTw0XVpzZFj9JKvxttvkJ9i3GXE1M3tmmUBDZG1vTYnh9VqSbXM3rB9ReHPN5wGE+F7hzMfAH36LjOL2FUThLcQXwNbPdtIqb4Bsn57+Hi2dNhfyi+VZPQ/KH0I6Kfcg75uS2jSkXzGCN5Orz9cWOLCW1Bw+kdx2rEr4cfFG5GdO7fbFocEbGiqIz3rTJW5Bn1hY1yI1tp8EfBaXaH6H1Kn59wdh8CQzDmRExH4gkt0k4xrgP3Fc9TWoN7xV/DhkSl7WFqCmTfANUbhNGL1ZABiV5cks8QtOB2lc9aQahTiAqJNeaVurUH4Tkq+1EF+bU8Pq8u3pnLoUWVhiKNFY0qtZ8MiezJXWBBkiKwAlJLk9mTibKceJzqeKbwq+DqcGrfS47dYD0+GkdomELQI33o4Y6tYhhKdm0e14wusnTXQh2dl7pNfRAywUqbQOBnVEhK1Ip0VN5SNucyW4dnNYVW4adakomH1L+GjMIr5OwC7p0IF/Lat4k6ZaQ9+nG/TJ9te5QwwigyZVAF4lqQY3uGtaji8dq4gRma1FNGNGtAw7gU8h4Qmv+wNA3pNXJ2kzpcKOcgDpKExLdGqW0gT1qTlgm+teLxHf2zngzYz9+ECLTcAN8Mha6IFfnQODb0BaotOQVqiCqIOuLhmIFaSRrAelVPAmN/UlhWbFLKLroNF82WGWh0Q1EfR4zcyZoYnUEmiWuTLcLjxXBSG3Re77DsT8qLl4xyAP4B7gDfDUv5fWuw+4dy1i0nwn8CxEcU9B1OBdbqofNWv2k84xVMJ1qo5lcpyn5Hxxya3uMn+P+F5numVDeD+cEtgYQmIVPBF24a0PnfgkOtspUytzGxw1BX7RJ7upIaALT9VVJIeOpcDdULwEfjtPAn9HkE7W05Gf+nL3E1jh+oYOkeQi9hWR7CYZJyJJsr2k/3nrZGUrVahSCwdnDc2cWtfQmjYVw+Mumbws6mwx+Ia7ik/W7per2gosgye9yS2ab65FRRp5OVcRZOigULFZxWGLOWcRnZops5Qdwbw1j+4N9qT2QgLNusa9QUjOY/gE9Ir5bJTjn4Wo7DXAVTuRUc8fQMhxET7wxSq8AdLj9lnCs8q1gsj3JfKsR+D5W4AzkBiY9fjBXdW8Dd58qZ0lSNdl1e/2HdNBXhVVGBuB4d3iudyJH7xDOVQt4C8Gahch7+Q58L2N8r9RAS78uPDxyz4nP2GTOf1rENGqiGPatQ6xXFhESzF3pXSw5yA8AiXfq+4nHQmnJKMmJn2D1FeipGgr8GoDVUIGTt0J4iXZ7brYC/AN4wD1cdrohW19sAb+t+Cu5bGIQFFloGUn6Xc5dVbRQWN+2jSaE51uY4NQwuAV3U9NePtCdBYh0YVKr0KabPcVJerRkPXRztUsvAzvlysB62DWs+HVz5T72w9cNQyPnIUovApCeiW8qtOglfUIYTpzcz1i05KtXsMyOHoOrITv1XJ84CK3+a1IYEgbvlam+vLA1+TSkRCU4CwpqgK0xOhSaNraodgumx81wwenaGK59t/mA4/T/frh+aeKT27MXcJS97nJTfkRvAUZJ3g6jYouBqi0BjFAJeKAYP8xx+70w6G+VhdWkX96O4adNjI50qHeIawCVL+e9tLr/r8KsNHZgyp4wtCgCqca+mSfZ1yKNGhz8IUO6yKoKCSagzSBKBGFvq5mPjqdtplj5DO2CffbX0LKQlZQy4Ec25JpHq/wpuGLS89AiKsKxzxZIjP6kUEOd34bUXk6Snoer+ZU4e2gedCKKmkl8Hly2j+O8y8/RszRfUjnZbbbZRBvllTzt20VtN+h76KWKNX3TPMJdF9nEh1Bpvr62BRRtYaWQMyqW4FzvUV1JnCVO8XJOAvIoIhhEFIcrNUSG+UZEbEnRLKbZPwCaX+m4R35dbLThsdCAwBs5RTMd0hXTtEWResXjoD0qTc6E2YJX5+yim88B2UyGxESmi6mfh2NMFAVlzJfdpiLCc2XzcybahNtRmDNVFwrya5VUNVZCj4apbmDdF1OVW3IQG1rkJb8OuBP30eCVB6PT7yvko7O3IE3a6qPFBqDZqZDskCe4bO65fl1I4Q3E59+UHXLlbCKeKuBDVYpB/MFGs3qjjiL7XD3Dm94KJlDlhG1tggkRqcPeLpw8RxEsy5281cghdbO3SGHfxtChOUkqW0lnYIwwQOK2AtEM2ZES6Bmlr9F/omLCJ/AqJiTyohPZXbGzlnKTnva9s2yQSptmE7/Drh3GI7rwufU4TZYh5jG7ocSDJ7s9l2FsLGtmJLMkn2SHOlADoU2tBqtoMpETZs2eCQclcDCklyWr+1QgyX0SvABIahNiDlZVVsPcD887iR412LpYBSQDOud5wCfBJ6PkF6PO8Z62Yf73fwmvDlTn2kFX69zuew/E/hTH/d+xC2uILb0uXjuVEWnsquCPPthfAikDaIC3+EqkQ5aqUr80mygPM0bH3J4RTeE+PWedD+ibqfBv7pfdbI7TAW49TkyrM9md/ifAS9yp7Wmy2jGjNgTItlNErTn2Y93q/UDMColJgoI6XUFO2Z1j9qCaajqdL+6SarXnaxC2uQ4Sr21q/XBY8VExBykpeoqCisXkFSDejRlSD42MrDNzCsBWB9c1sciDLrY0/aHCqx/MTTjqjobQO7hPETlzUDicmdI+OEPEUnzY+CRyxAyezzCUAo1Pfe74+0wx7bnrlB3snbloA2OvQ1efxl+3JxevI1RFR14hWbzNbWlsFHAavqs4q0LrhBmUoCjpsG6HT6fvc2cTgM41+CuZasovTH3i94BvBe5H3+FWDuvRzygBeDb+P+pGJnZGkRlF9FSnIC4ObYgodaA/PcXkMZHxxcD3zqoWtNkXw1OgfQbpSNMj+DNjyVg52ZXfdeaHNX05tRCP/Tk4PGfRSLgjwceGTZj1fXgTWY2qEORFe5vicsGoGgllVDRYbZXu2loGsza51CAkrBGUirh2KoxGliyENEvy2Sb2o1w9knw2ZyY9L4I/BQkZvEz7lhalWWUevJ/PR2hF+/TA59vt8idazl0tcPj2vnMzXDBhxA/2Xp8Wc1BxJTZjbw7VXcYLWCg75UGP+m7qapOg1ag/m6O7ZBX1T5FLRK9yP2i94PItvUywsFSd+qH3wHPBgY/Dh/El+b8LdIvUHUHkeRaiRigEnFAsCaWG5B/erUQwah/K3YhDYz1x43TGP4dvkXayGSRoCrG+rA7ebOBU3a1ndAGvS9A2swexOlik4f1WlMBGArrg8vy06m6URILFVq4rd3+cFF2kL62qllm0y9UjeWRJr/HmYfvBFaLD28O/OKFwE+GgW8i9b5Oxkd06vEH8NVWqqTTEcqk8wRLYlt8/JM5D7xlVG2LGh1iS56MuUtXH10b6QoqbWZar6RDPZClbQosaPdXq+pOLez9iFED5CecgtSU2YW7HUD5HtGnZ+CV4HRMcJdDLAYdsSdEsptklPBFLKRdGa0/hdoQzmtP2ie3N12k0Gag+2h1i+m6QglDzZgD0rB15eDv3TlnIldYxUdgNgSZhAorLPUFExOWPV54bRPteyjD+hTVpKkdA0viGhhUpm7OHB4C7oSOx8GZcOb5SM/olzuREI0bEH/fdLw615oj/fh8yTBYxqR4JDngTp54KaLeC/iqOiV82iV4O5QqNs3B0+jgMt43rOv1lFpZxR2n3CEGAnXrqXCs4jJjZsrt6PlLX0/z+d8Xa/pNy2DDKyUw5ffIED+fAD52T/YTiCpv/xHNmBEtQw9ilFqMcNEKAEbr9p0+kM67+kK0x6xD+agPBXzjY6up2IABbZiKXWKyaoc0kWykbpYsFoHjJf9qBXBUER5x6QVT2/FjsGmASYV04SZrurREGubQNYu+tI1yqIQO9eCUEHofID0qQhmfZA7S1K9HWOc4KC6QZQO3w6uPhbcDm6H3dODNDyPhG/+BKLzj0PAmIbqNSKDRJrw5s4KvlzkdeftWyjW9cjH/8yEk/HGL23wxftw7jSIZR0hL36dOvKncEp0SX5iM7kyfwwNpg4Naxmciao6vyW/lo/AhJP1whTvtM4Dyl8Vfp/UxXwdMXybHC9VcVHcRzRDJbhLxKiTgXJvueuClI7sqSCi2xZ66R2EKgva8tVdOyVezB7yZS1UAwBKY/ns5VrfbZgzXO7eNd0g8+rGqzsKaMDWOPQv2WJboDgc1lwWrfJX4daAbS+ogz0FJsOJ2WwfHzYJF0PMmt9mmh4GfI2WTe9w+en9U1dmAFfusOhDCm42WdPspCMH0I72sQXzHSnPntHuunS/wLcYIjakH2uFSZedU3hp8lTHbV6u4w12+3l3yU9rpRfLeFwOveI8bQvHT8CaAsvzytXgzZlRyrUX02UW0BP8f4o84zg3s3aMrKtQ5qfwRtyy0AxSC76r8FNZvor1vJbiZuHItJaR1yyN+nu3AEnhgjbQei5HAv+FxH0mQalStQgPvG7KqDhoVmRIYpAksNLXZlIV8sM/hBHuvrFlT1e50fDkaDVpZBSyH/AIZkmngYfhnZIwbfa4/2Yks/DrytszF26e1EvM2XDQSPhhonhyb49HqLF/7GRz/dSRGphdhkNnIcx9CCE/t7dpqaQumvjlrTSjjWxM1h+agtltOUZziRw0q40e1GgI+BpKVce8Qz10tv+73ALfCV86HC98AFwGcJspuMUL5WRk5kfz2H9GMGXHAsP+AWwAK8k8u7UReGpYpQoRt4BuQcKrINZm3247hfG390h7mu/D+IvUnVYFj+NUSpOhgBW+dbAMS7eLb1AKrWLL8dAqr6kJFF6YXEEyzlh9uyPI16j0PCV2DTeYCM3yyd7FbTI2nAr9CFNgfhpAchVvwCs+ZBurnUJWnz0sT2jVoJQ9n57h1Ga4WF/JCVpB3UXMFOvEtllV6Cq2oooEqBbOte+RJu1jC1+32m+kdUD5di/u9Lrl8HZJh+Pzvwl99HT7xN/Bcd+gTEaWowaGKQWd8iGbMiGaIZDcJsP+AawDabTQm0gYVZdkPwp21DWv2pLQF0fW2101JBlhtd/PkEXOarU6ySK5JQ8872k3DZUnKNtDQqN4wyy3RTclYF35Cs+XhSnAWWb/N5uFZf6bthMyFxFU7GeiDo2aJm+4KvJvvD32ISfN+fOHnUEU6f2w9BaJCgwn1fYiqm4+3aHfhmUStB+qvA2/W1PEMdZn691Ttlaiz0fCQG5vYvQr6qqql8+kg8q4fOEtM/YPA6W7d67/kMiS2SAWiu/FJFooLB4hoAaKyizggWGW3AmCX78gC9dJNQ8CZbyEdwq1+EUiHeofQRkmDA9Rf14+LiVBzoja4ABU+kHyM193jLmYR8idfdOHwM8wJOhCFUMY33mq+tObG0HxpiSuLKNUsGm4b4nAiwFAJ672wOY4gdsMZ7lPFpxAsh+Ji2WTbw0JGrwF2wdJnIs/py2uB7yE1xiqyD/Pw+Y0DCJOV3A5LSI3YXhuHFzyTd30OsQvORwKUNNBkyH1sKoL+FBu0orZJHdHcwr2TxRycWJDREDSWSi2kY4iZ89xtiA9xtbzyfUis1heRXLw7AG4Q8+U5NDakX0T+z6IZM6IZItlNAqyy6wYo2RzcfD0uuw18pfksRafLNRAlDE7Rbep+lVFpjBI1YSrUkDRdetUabJDMcutNuHp9P5tDl6XMyFhukWWmbKbiskybhxNUqel8aNK1pmRLiG2I8p4GdPjctl2IS++xcN9FcEw7Up2g9ltE3a1DIjLBP7sxPOmVSXcmRp2JelSyTL6PEJ7G/Ws0iYZOavSlXqIqOdsR01QDuyxn9pkiVg1XYKXOnbjp1bhzb4bpjxGa3opEZ67R2zobBnPwf+Z0Ea1DjRigEtFCbAWoSkdaQgtG6+OgTNMNlLi0CLQiLNmkCElvDD8UzxCIb8eS1CCiKp7F8bWTRP31gB9LrQc/VI020BXSZrDQhJkVxJJFirreHiuL/MJ9Qr/foY5mJkubnlHFpwf04CsKDMp8skDSQApu07PbYSU88CYkguNjwL1XITl4NyB+vA5EEylx7sBHe9p7uAgoccGP4M33IAKxiq/PakmuQjoyE7wVAdJmdDu0gXt3x0ZgbEDcgxqsqcWAOhFN+hqQd3UD8HZ5JZcDnwZOOU+4/cSfActEv3ZiAowdykSfXURzRLKbZCwFGA+aHjfTCdKLV7KzjchE0G3GzLSARFUOQ5pItIWqAG8EpssimzbX0DBn+evC5VnbEGxPxjGaYW+3O1Rh/Wdq1rSjrut6VYCW1AfcckdSHTkhj4EhSUJ7LCJv3og4sdhEunSYRn22mfnQ/1qRa3gmfGwGPPRrJIvbmiZ13vrn9PWx/jyC9SU8mwFtBWjLQbndB3eOm82mIWk55Kj77U52614M9FwGv1rtzP7HCyduJU12g7VaUiBGYx4oos8uomXoBij4oLd6UvmYqwG9y2xszZnNMEZzc2Z9iB9oVE4L4a1DwF1yMV12Ow1Z14hLawJrZoK0y8KSYPtiupzoHIczbASqVao23NEuH8Wrvor8p+5CHFwr3PQFSAFJ7sYTni0fZhGS3TS5lvwJ8PcyxkJ9VPp+Gsepy4rz12W6vmSW67xehqsR1kk6cHMcIbE7QFYOyXWsBW5GRjx4lVu3GuAvRQMvRTRrPfMhSWqbClHZHQhi6kFEy1BGgulYLPP9usL52XqmIP/5NsotrD8IaZORJUTtMrchpaHqrYE2sBqdVwb+gx98BNj5kPhrigvcQTQQZQbpIBTrm6uQ9t+VSJNklg+vFOxjf3yzAJdw28MJoZpSaKCKVqIBGzCUzv/YjjyHJTCj6MlgNfA8xI93KfDLYdi5BonQvAUJ47gNiWscQN4060OcjZgxO4Bt8E748CsRMl2BvDc78ORXdZelIxyoDVFNnOqnU3K0LV3Bf8bGYWpBrJtj5tDqpuYPyPu/RgZ+2AT0XwSfBV52jURn0iN3RavajSGjIgBcOxKVXURzRLKbRAwCZwGMyY2Xgcnz3lxUId0TboZw3VjGfG3cBxXoeYA6ibx3mOe+CVF/HUU8uan5kvT2qWPo8lC5hZVUJvLHhdeVte3hSnSK8PpHzXK9f23BOuvTtOXGOmTVIPLiHJ0TYtqOvFTDwM5xhOBU4WnpsAEaUxFMSbP8YklsuxIxPVTxOeo2pUVNk22kTefjwXbgyc+8q20F2D4ii7Xf1o50j3pAqkBXgV3C4/MBVsF/A58HLgPolXSENiT9ENwAEcCziDhQxACViJahCvAk36sFfD3f+fhCvHYEAx1TDtJKLjRzag9bw92qSKxC3bQI0sgt59XvAD6m0S/HI5U1NPlYA1qsArEqLiwhpsfPIsAwqKWZuTNr28MtKCVEVgCO5jna36vx/UpsFdLVmatISsgcUVSDwMPj0uo/D/Hd/RPQ1Q0P34govLsQ4ltvPgPumD3IszYpEs8+lv+5H7gGP8xPP16tVd1ym+Ki756Smq2uMuaO4Uz0mmewC2jrkLN34u0AVeDCHQj79cKb5Rfz5heLeH0c8ASAb9WHv0sllc+f4ClEHJ5IkuT4JEl+lyTJrUmS3JwkyUkHcrxIdpMAa1rpAxgJqn0N4ct72e7QREbvvcm1GwFfOcWaGo/jv84HmOeOo/leSmQh4ewpanIiRUcwby92T/6/IwHNVKrNxbP3SdWXve9KeBVhB1v79BiEhI6BTyR9roDKQ6QHeNV5GwwTPpuFnAwyYFwPnqA0imSENJmBV3XgzZfgk8utedPNzwFqA/4QKiJzuOCtRcCQENpNbtc3IiL2A2+S87wbeJnb/jfuON8wlxVNmfuHQ9Bn90Hg32u12vHAO933/UYku0lAQ54dfrxW2YB0LUtIk5k1HYVPLOuNasN0eytmRZ56PcY3u3UlkGBuNW9VaCRHS3ZWyYVEty8mzLDxP5LMlxah39H+vrBsWrjcBgZVZT5p9/l3g4hEWiqrL7wUWLDA1aTbRHOys6ZTr6CPOQNu0rJhZXwynPrnxkiPZl4k7T/WQ2uAih2ZoyTbFKfI5e0m/eqWkawDeuUyj8tJjvuZSETmdcDbPw7M8QMgbweejJR1fSLyf/ZRYpDKgeAQM2PW8KFz05CyA/uNSHaTjCsBBqXnKul1eXGxVPGVoZXctIGxId5twTYK7XWPIW2kFvOt++JAWrHj4OG3uu/TYWoRCe2bgpDeXDfvEpvrVVMqeOWnVVSyhuIJTZ0TKUO7bSmYP1IQVlPRh6YqTu/TNLMdZp0+B6gnRE7tlpdnCAkkOR0Z3XQX8NGHJB1hYA1iyrwfIb71NEZpjuKZbRQ+BCd2I6yzBHmf+vGdMPXNqaLTupi4Q2iIsbUwQCo9obZbOFqN6sqlFTf/Jx3O4O8l8GQz8PAHRbm9E3jvFWKH+C0SsfldXEI6ouj+kYgjCG8CPpQkyQbgw8jAV/uNSHaTiHNw3pn10la064qdiNTTuGzIfjKhv87CNCh1M2YnpJVFB7BO6ggPglduGhyhIxhYUrOBFHaq++8psGRvTZhHqrJTNFNvdl1I8tacidmvJApPOzcFxGm1CGiDTa/FvR+ahtCPV3lVcxzwz3hMbIXnISVLSojbUC2omnc3TtqH7BQb0EhuGtSiy6dIkPAc/KtcwFtmzwF+BPLengx/ixv9YEjmn4j46lYgvH46EsiifUS9hGjG3D8cJDPmDOdv009qkPkkSa5OkuTOjM+5yHDSb67VavMRW9SXDuT3HYmtyiGLHwO/BDgNhq+GqTmAknRRR5BWQP0dChv51owI24LlGu42o520AjsOHnkNd70WjrsOpD/dgzSCu5GaFS4cvX4gVSB2WiIdPagqRM1iBPvbRt0GpYSBK0eSolPofbP3ZrebKpNA+p4OmP06zLZqknTLpw9JR6kPsY+fAGyFue+Bm9rhxNpqJAdP762mlNgycBU33QFTV8Bb1rB9MUxfgrwON7jVOi4PCBnZfFDw5KfmT636YgnQMdzUNmgb8Sl9u9ymWhTmFYuAUxbw8MsfojwPfncxbPoL+MEf5PDfR4Ttj4F/PAuWXS1KT08TcUhhW61WW91sZa1WO6vZuiRJvgpc6L5+GymBut+Iym6SoH6EXZBu38iLKUql3p6eSJaPxCJFfBX3RRu7frgBjnuOOXc9nh28mqvQaJYMVcZEiqzZsj0pOsz0SEJ4XyA7h7AUbG9JMrz3VamwrMpKRxifL9MT3wC8/beI3Ot3H80nUOLUY+v5ZsBjclLS7h63uXacSvigUWis2Tpmltu8UFWeUO/uj41AsZA+RAF561aBjFzORsoFuGWjjGf8lD/A+cDZp0mM6ZDb78NXw38uTN/F6LPbfxxiASqbcemViPv2vgM5WCS7ScYNAGXXt9e2S0uEaeABNKq1PSEkv/oo4+AJ6+fSBT7dnave+FbxDesY3k80Uf3K0O8WklWWOfLRYrK0yLofbRnL9HtYfYaMbfVZudw7Hax3GLEOzANOg9+8H3jw96SDVNSUOeCOZQm0DCxieQFpVrbgfXQ5vN3d1L1sSDewhQ1s7p26K0tu8ZjfVVdNw5HY95HUinkyex7wOXeo1/1aAlDXuZ/61iU0qkyiKXN/cAgWgv5b4CNJktwG/Ad+gPr9QiS7ScYVABe56ikFgLyrDk199INU1ZTwg5m3dZfCPKdOEJNVG0JaM+CBK2S49HNw0TFqztqGL0zc7/abi4QCVPBJyDrNMj9mKUBtoO0QPs1MmEcq8WUp1xKNo7dDo4pT06Y6zcJqNnnId4sJsx1p9GcjtTN3wRM/jYQxcrrbpx9hsI34GpqWgEvAMngnbN+GxP73uMW7zCWBJ0Hd1Vb3UV+dNcmbdzYpCJep6CvhDervBz6/2V3eC4VX7wCWn+QFZC9i2xoCjrufhsHtyklSi+ru8EetVvt1rVZ7fK1WW1Wr1U6u1Wq3HMjxItlNErSnuQrgapcZMAaQlx659dhbZPnnmgWvQDoHq97IdgDT4VYkTH2OXQ+No5drlF6FxsY3JKcs06bOh5nHezJhHqkI1ZtOm/3+0NepCFWfs4dbebQb6choLa0S8NffRiJudZ8diI1SfYT2nBVYDdNnIFGZnfiR0zUVQRWcfRdLNBY1AF8cYSw93+YOW0Re/6q7og24+PIqcILkzC8CeJI3ftyEvMIzEYV3bYaEiMpu/3CImTFbikh2kwTtaa52f8bAj95axfeI1TwUKrlc8F1JMfThFXARc934xnGGfD4IvAfomAVdGrxizZiq7KpI67UIUXjWrJmVJmBJzyoXzPeQHJuZP480TGTqVTWFWR+mKpiyXkpGqQo2QL7dk9IwInmWImGL/cBzYFZyFaKdRvGlxKrmk/fHPAt4qzvOLoRZRhA2sp0xJdkc8rjD4ahy+FfBDldVkF+RFNI1EIrudJtw535pO+vdz+DDRQZXwidqc9hwsZQGu8Md7ikvICJij4hk9+fAdTLZOQ71hs8qu7D+oK6H7Cdmt68HC1TcyjwSQ36bBB0s0p0q/tz1Hv6o+QyQblitKTLL9Gi/Z41lR5NtCeaPVGSR+kQqN1ymzyW8/+7Z5XO+szSMrzjWI/MP/xPwpxtJ5/GNBcd1y/NdEtm5DLEZgu9EjZlD2A4ZZjtdPm7W225+m9TJHB5J55xrkMpmcO95np3A8sfAvckwbIZyshl+LPS8VA//fSJagEOwgkpLEcnuz4GC/GOrsEsRVVZ6wUS5d1mok502jDOAW4XousGbKacEO1qy0yCGGaTVmEZUNjNL7k0OXZZZ70hG+BsnIjxoDGAJVWFo5nS+PmUM9d1OQ2x9W5E6mqeCsJf6AvU5W7IDqPihhPoQlaXmcT229Slbf13YKTMpBxa1EV96UzlafXcn4L4M7OQ64KoHZeQDToPBWjcU4FzkEscAzpUgloiIiRDJbpJQTpLae3Cd4mctBsTfAHkxP7XhxxCzASnjwfew4WgjbT7C7VMfZbwDuAv+dI0MDHZ0Dmlm5pqTKQaQhm+3m9eGsYd0gErZfaYEH+2jh6ZKzHxotns0qDpoVG32PmQF+EDjvQT/vKwp0xFXvl027cQrsEXIe7UV+DScmzwMv7zd7NsfXKN73guKUoByrftUcAMC499B9cNZpafd+SJ+7B59twvU3/Fkiuw2teCDPNUieg7I69snxZ97EXJ7wo9gadIHj/UDqbcBj3zfjYhgEANU9g+HWDRmSxHJbhLxDtTlka8Xkgc851h/iCW18Cnt6Xv9oNp43SnVohYDdMDoOD6qzxKNDVhQX47bJzNApURzlRJ+b2a2fDQQHUxstt1XE6ciNGfm08Eqmtw9HembrIIfPg0GT4dsZa/ndZGfi/Fu3JJbrCXD9F0NUw8swqCrwELRlROFp9ZRdTf2gpQ8GxPL+z1ISbD7cSbO2cLdBUS4Tn0WDJ6RPm3EviOaMSNaAu1p/h/A7+6tW3cA+Y8t4MPT1Lmv5iJdpg1HlsKzGAFPUBWoXQFX4UJBcRzWQ6OSsJU9BvG5WFAPckmlIWhjm0WGzebtp8SjC2HkqrJSqPRAiEgrqNh7NxZsWyGl8KbmvOwBeZRz3Oc64HIoXwxc+0vSHR77mQHME6fYKoR9lFls9KUqORs4pZemrZ1ur9dju/ydQm5WLFZwhopPy7FeiFhgT8aXPmA+nOYOuQLgBqhcQx1jxGjMiEZEspsk6D/fIMCwt+wA3owZmiND7KleJpjulDagZWms1BRVR+j3gXSgivpyBsz2YRTlnhTbRCrv0YqJfJdZijcMSoFGn56uH/WrtbOk8frtSF9mHTJmzvnAny4n/VxHzfE6IOkSdVdGXgdVjdY+pedSG2Sz1Jisyj8uQEVLbWpfrhukPmcZnvQ+SUdYg3TPBt8j+92D/Nto/20+cJGbP6BqwY9iHIJJ5S1FJLtJwmCtllRwHd+58k+tY1fUGwpVcoqJnk5Wo5JSetpoVSSPeC2OXW1NS7udrrPzSnq6/Z7UWnjMicxwj0bCyyL6vbkvdhuVU1n3HBmhPsn5xG6FpgD0yfzxmxH1lFKO9nxTqJsy2/H1ubQgtHaqbGK5vn/KXtYKUSSdm+dqZ5bNpvpGdQN3j7jz/lbI7oUIoZXfAQxKgZciQoB3b5M8+g8iIyP8KuMORkREsptEvAo3cPiYiKw5uqITH1gQNhphBQqdz4reTJGdmrhGJZl8HAlgYNQpyOnBttZfZ0uHDZA2Z1ZozLULTW2hAgzXPdrMl4pmnQM74E0YoBImfus6NyxP/ThqkjSr1ayo78gx1KuG3foC+MI7gIf/O+Pa9HtFKhKejB8eT02kI+6yrSTDTe3QP6G/TkMv3X5tzryhr/KYmedSuYTNSJHoa89yo5UPSdzNTsQE+gJ8vvq7ga8Qsb+IPruIA0Y5SWofw7ku1khTIv/nee//sA1TiNA/Z/12lvA0gKAeaLBJqkPNhjqJaZmyVEMKjYSn02qwXZZi25NpM8tE92hFM/Vm5/N4f95oxvJwW3f/k5yoO303rNmxE5+aUIa//Sfgm+gf0s/fHXMmwiwjiLqzeXOaL6BhkVmRwnZbWwqv/p76IE8d7LwKnARiu5wvrsP7gLuvdqMbdIrKq7hL+jlwCi7+CnFPR+w7YoBKRMvwAxzPrBVTzVTt9WpY9tRcWs3ZxiE0b4bLrM+vAF7Z/Ub8NKdBvTFLiogSUBKz/hqzXSrfzqJCY1BKs09o8ny0qjrFRJ0CzHyz+9Zh5q0Kc342DVZRU2EJL5e6cY4vJL6/D17/RuBVw/DgZ/HP2xx3JlL2Zww/cIKmFFjT+5g5F3iiVdVXMNu2+cOTE34umkVtiBWEa4AThLyWIwGaAPxQjBVPR0jwGER4akn8O4FPERGRRiS7ScR8XDPV5qZ69+uBKa4Rs0rNOvSb+egye9TaYPVKAzTX7qiNaajsFFkVVUJkmS0nUnURHlkk12xZW7AMGk2OgfJLcul3A9I1U8eQjOzN8Jlu2PRl4ErweZamE5S0C0GWERk1Qnp0jnD8RU08t++wnjsrAb2U3ky5cRyEveZL2sEq4J/cri/6JXzt5b50J0gRle+6+Q+sgn8gYn8QA1QiDhgF4D/xEdg+diBvRnMuZZcIgzTpZaUcWGKsmykHgF5pnLoR8xbuPE3VXDif9d1c+x5NmVmN+aMZzToCeyK9rI5DOG8UX2JCe22BAt1E7YD/DN8B+CrAT0h3bhwbVRCFp7bGrOAXe65m1VOyTJxFeS3VIqqHXgpscmP0PQE/ONHgasfLa2TwVhdvzCBwsR57NRERDYhkN0kYwafTcR9MnYLr+uTFLDTFzas5MjRlYqYhIYb+kvogm3fBw0MSFVOPhrEBDbv9cTKVQqjwQuhxskyXIQFGeGSRmA75Y++bKwPWNNBnSnA8Y+qsDfvATc1x0ULObcBRi6Xg81vgwovgF9cD3xiCP2rAih63A4pdktBWwSeYq71xjLS53UZgQiPhlYLpsBeCNvzmHGDuDKAbrvq4BKgkX4LyzRIvQ0X8d73uZ/UgKu9lQPlLROwHos8uomWod4C1d6xvQ53c8s0TxSfqHYfzSRFpKHulceokPcp0Stm589anWYqvGdllHbOZColoRDP1Oxosy8qrm0jtBeZwm9JS95lVxJRZBZbAmS9GfGR3goyIEJxvpuxSt03Z99cSnf1ezFin12CSzdtyMgKCBqhodaE3b3P7LXPjs776mXwXuOo9QDc8313OK/D1fr5GxIEgkl3EAWOwVkvOxRV/HgbKMDYOMMXbb6DRkW8/4XJID5ZZb0QqSIN1jzg2FiFmzKRIo3JQZDWaE5k2myES3Z5h1a41P4ajRYTrS8H+qu407aDqt0lykHemTCWdVADTdLEPXgk8CTgXfnoJ8Hng4WuQtO0O/1mGfArp0zR02NQPZ22SkFZ2GiFaMNubzQr42tUMAffJKe9NruBsYOk75FpUtN6NcPEIMHgSERGZiGQ3SSgnSW35HNc27ApW1nvbTQglDFTJMmdi1tf9dduksegE8nZjNZuFyi4Le0tyEfuGvekMNPPPhduE2xmFGEZNapoAg5AskCQ2FxX5V+3AFUgBgkfuBR6gzk4VhFFsKbKwwzVG2sKQy9gG0mM2ZrzDypNr3GVSkP7apcBzgWuhnn83gojRDcB2qKePriCWDNtXxAoqES3BYK2WsMwXokiX9cI1CgOe2GzieJbPrtkyS3aPDEuXuBMaI/igcWgXaN7ARtJrHbLueRiU0mx9s+2bBK9YZaUKqgBi354nEkqjNJ8HtzolxQaQAH+nIDsRIqngbVTN3k3NowvN7DZwBRpaH/sKl3D/K7uAslRIKQFfB+bW3gwlz7kzEZF6AvDQFbLs34mISCOS3SShnCQ1VrlYgSJQ1f/5vK86sW2osQGx5GcbDNtbt/vUc+y2iM20irQGdZOUNWNqiLkSoW2dwmCVcD7iwBAG7mQFqlizpTVx2v31uYVFnd36pN2bM/Vw7SDGv4Uij3qRd+RlcPzfIZGZvwBuHEcqEowKyS10nyF8J0pNkuqjg0Y1qSjS+P7iv0/N+WILmt7HPcB8eD3io/sFUE4+BjmJr+lGvAKb3abPcYf7CnGYn/1B9NlFtAYlU4t5xCxXstpl5sNAlSYmn4ZcO6vstEx83SeSFQgR8edDMxXdTK1lrdPvbcGyDHOmfa8GnJFJo0GUZZYjoyPcg6i72kNAr/h7S8gLbCugNMsDzYoStts2MWXqodtxqaGulufv3TozuIGIT4RzPwSsxAeeXkBERBqR7CYJg7VawlaTTD6ibcCot9vof69Vc2FEm21A7Lqc2YYS9UhMbaAa0gBsgIpdHpamgsYGN+LA0SwSM0OdZaq+cJtmaR9IIputtjyO8xv3Sr7dVoQxxhFn1yok+e7XiOPs4T45TjuSwqKZ3Preqr9Ok+Uwy8IgFVsrU7/rWMAFiasZw5j775bJO91lnqDLZwoBFhHrajtS73yHW/1DIvYV0WcX0Tr0O7FVkkjM+s3Xf/w+Jq6YYtEsRSHRnQZ8ikMqCtM2sCGxZZFbjKw8eMiKXJ3oWTRbvxfPTd8LE/IPg0JealEA4dNVCNvcgzBMPz5vr0xalVkCU2RV+gl9e+PBOifpxlxZT01DoE9mLkDI7BVaRuV44dx293kbwrPL3OpLiNgfRDNmRGsw4tqYgrwI0v6M+gag301Dn53Ohyah0J9XN2HmYXTYBx40hKyPmulECBvaaPZsDfZkvtT5ve10WFNmuCzvXrR84yYMiA9O5ZC+a8uQ5erP60d6afo+tZP9XmYllRMsh7SvOfDrjeFf6zHceatQfrHzMH+wWzb8vSg6FZe7gGmyGJDRi2I0ZoRFJLtJwuuTpEbJ5XaXHelZk08OWE92EIqNzGwgt2CZHlTtQBV7Iqvo8qQLQYe5X80a4YjWIct0STBfytg2a1m4PmsUcvy7VgBYJ+VI1rjDqaQ6HjgPif64DbgfkVVD7tAzzSXYQJPQXGlz72yCewk/CgL4yM2C362Es5aOI+rytS7v7tY+BlfB5S+WwJQcIk5vQ151VXb/71Qi9hGxgkpES7DTTXPgvei2dww+/86SGsG83U+3zTJpDrtpOzQnsazUA4hE9+eGBpvAngNRLOw+o9nbWXU1OgQzctLnsTlyU5D6W+rP2468m2pbVFPmmDlWmGPXrACC9THb63HLkymyqozj0xXI9T3Jdc2+CeXbJOfuMnP4N5DOdhi8PkZjRqQRyW6S8LVaLaHg/hmXunYiVdECSea1qi2rd5w1n2pAXJd7N0FwSuiryyM2Il0WVu/IIrxoxmwd7P0NlVozNRcGrWQFpIRBSHnqw/4kOUlFUN/bLoDVvuZlwRxiDnA6Ysq8HzFnbkcYpxuxGUK6EooGTdnhCyyxWVOm/dm6jQt2SVxRoenArz6C+O2Kx1IFrn2/7LIOPxiDpgDeh/wLAcwiYn8QA1QiWoOy47Ue9z30e2xvsl/ouwuX2WNYX5yNjkPXhcouXE+wTdb6iNZhonvdTN3tadusY5v1SkRjANPFLKlBKiX8ejVXDrr1VXzQU4m0ncqa3sNglHBev4fvr5m2uXXPBTGfDt9LDk9mFyA5dVMQwrvgVCG9JxCxv4hmzIiWoJwkNdod2S2zL0LePwWNxsyKyGxQcA7NnmAq7FsbQ21RbICKLt9TVGBE6zFRRGVbxjrMumYEZ4+d5QskMIvP8BGZqq70XWtHVFybW6/J5JYUFWHenK2wktVBa5Zc7v4xxsbN1ypiTkX6iQ+4RX9tLuW/rvc/QxEDVCIsItlNEgZrtYSKaxNucgEqqvO1odhAYzi3NQs1++RwJiqNuhv1jVUZ0o2dNZENBFcZ+oYiDj6s9J4oYEW31eWacxdWW7Hm6CyT5yjkjTmTHglSuRPPHEqGnUhiWwkhm17SpGgjKW3+nDVjZs1bYiwiZtRCerl2Bm/Vn90rq1cAj0fGu1t0p2zTjgzWeh9ijY1v7v4hKruI1kEbhHuaLN8VLLeO/dBM1GA2yujl24i3zN5/GMTQTF002z+iNWgWbNLMlJmlwKH5c8s6Vsmr/jnUlVODabGCsEkVr+y0ekoh2DYrHSE0V4bm+DAtIdi+CqIuR3zjuekGeB/AcV119+Ogc0uegBvvjhigEpFGJLtJQjlJanS6f97bnKgbA8jXR5Xero2INf3YhiBHWumltrUN2qivt5m0m+WqBHT7Ko2N4USNayS81qPZPZ5oWbMqKuEygvU2YGXUKakpcNQCCRcOc+HaEL/dQsQUsQuRTiOkhxbX7Yv4gCodV9ZWAtKPqrwx0orQbKubbAB4/QlQ9TEyXSfDCwEe3Fn/lZUBOMVtf9VrZVk5SWrRlLlviAEqEa1BwVVQCYdIcf+yI7osVG+h76PZsnqjOOqJsWG0g2ZTaGx4s44dcfCwt743u+1EnRRoLCNm9i2AME5PYAUgTXoV6iXufGkT0uZ1JauwmkqWnxnS7++4mXf7JwVvVeULkio+js+SuOA9wC5/6I8iZswqQDt8yp0mqru9RzRjRrQEg7VaUh+WZIO98a4RyvkU7wYzZdY84bKgoaw3HGGyuEVWHUyLqOwmB1lkFa7P+h56p+zzmijoyNVFTXLIWzfdJ3pbItNpPekNT3h6er0E9bdBdgEEm4YQps2M0NjJK0hEZifAv8qyAkYt/FsuVUz9EuAOvcxd4sODGKQS4RHJbpJQTpKaDnnwm5vDZkocDv2QnUOXtcw6/OsHM42bhofXK2ko6U0x247SGMTQjBwj0R0cTGRCbstY32zbrDw8+7zDZzuKvBv9wBIpPdLpVum7pYEo0xC/WSfCjSqvtHSYrcoSBlPp8Wy+qKpIDYbRGmFFs607dw9wzDbqGRLdwM+By5NxaPeneDpwxwzXYTzX392o7PYN0YwZ0Rq0yf/3WtyNrysy6TZX68uY2PQTpifUozCNz66NJgWgg+3qaEZmE5k2Iw4ulPB0PpyG60MSbEaWihISkVuB2Xj1pp0pS05lPG+qosqqg5ml5Noy5iHdgYNG5eeU3XaAbu/iO6X2PF4K9fGyxoF/fxv0bnOHevaz65cTlV2EIpLdZKIk/7DX4aw+9UaiIxVuXW9gwkYnNAHV/SwZDV1d1ZWQVko9IGGdzLBySlY4eyS6g4s9qbYs0pqoespE6s8+0w5gGzBNwhiLzhZp1VUOP5qqmjOtqdOquRLpd9SquTZzTN1W95sSbGP2KRec/+3o8+qnryTfl7oMR7+IHE6Q3gE9X3L/DideXr+zUdntPaLPLqJ1KEgvdDPunzJlfnRfm4VrNwnNtvv7+dEMtZflE2pSP7HpsSMOLiYyGzfzm2Z9z3ruYT6fTgdl3fSsY+HfRyUn9eVlJY3bfXLBJzweNL7TocJrk/Oc6K61He/i6wVgeb1aGb+mPprI8M2NPyMiIpLdZCInN1yLUIRmzLDNyDQR5YL5+vh1WSaq0P/WjNTCRraZ6Szi4CPsmJAxn7XdRH49aDymTl36SZdTdVlkpL47m2qAWafTLP+yjfJUv15o0rSpNKSPUxuXNEDoTRVtkeknAGdVPREYdHE2TyNiP3EkK7vYik0SdrrpTDdtSxXFLQPOHKMNgTX52AryGvWWSibPIjVtnezo1iEBEqzbU4MZcfDQTG3r8t0Zy3Sa5bdzEZeAN1VX3XKdggSo5JFxfe70i20OHJgxd/DDR1n/3JhZplNt8aw5XqXYSMa8fdc1BSEHhXHgo1fRhh8wZA7ABTvp1FOPAV2y2/yfEbEf0JHKj1REZTdJaHMjGnTiLEaplIF8PXgl1ZsOe7shGkyVmGmWD8euh0YyyzJ1RrKbXGR1MPak3Pb0nQnWVRFSnC7zWakBVoGpX06RFSUc7os5RmiZsPuRsQ+uAb7O828nUgia38vljINUgHmvdBttgx0DVCIUsSWbLGwG2qALWA1GpfmGZ6ZdbmsKqtM+bIgmbARLwYdgOWZZljqENFlGHFxkdUKsOguDVAi+jyIKzqq38Bh2W30HRt22i+CRcWGLMbdICUhHOtBx7DT1wFoe9LtCv1vVB94yobl14Kv9DJpzltw2JcgNwR0/koDRTuAW4C+A51wvm+ZABOou2WW++3oB8J9E7AsONdNjKxGV3SRh8DTqDvTFEDju85DzJhog22nfEBDQzNQYmiXJmJKxXA8eCW7yMdFzscuyvjd7XlmkGG6vZFfxBGeVVajAcsE2sHfqLquzZiM0szp0br4NSRhXxTY3JwOp3+++jwPsgkU7hAxnu+X/SYzGjPCIZDdJKK8E2qQ9qSu4IBozaad5lRRr0gxNoE3NVnvrg2vW0Gatizh4aGZyzlJze9o/a/tmnZlRoCyyKF9sHHxVoaSkxGQPaYNPLEFm1XHV5YWM7UKzZlG+3gQM62mXwm9xdTMdhndIJ3IMIb/BZ8Wx7fYVR3rqQWzJJgsvATphPXAOmMg21/C04ccOy/rn36Oysw2aqroyzQNPdP8wQAUaG8r4mhx8qHkx6xnY5eE6fXZqthw0+9jldvvdGfssFNvfrArk+6Ft2BOOdYKN4E0QGnyiVVB0fZvZL8uUORYsU1+gzeFzJkyGJJirPOJf/XX3SGGGdnP4dYjXseJ+1V//BK5aTcQ+IgaoRBw4XFmlQaCsCUMpvxvCTaGCg0bSq49dZ/efSMWFDWVWtEvoE9qTGoxoPfZWye3t897bd8MRYxXqnZ7QdImZt4pMTZETmSx1n2bbhecIO3dTpB8I0JZzZfWA5W5aQgZkWOl2mwn8ELgg5ttFGESymyS84i340O2FiEPeKjv9Lw2rT4R5SA2BKVlpBXnSKQe6fgo+DJ1g/0h0f37s6TloR2Wi9WG1nKzap2E6ygDQ4VikIvNFoJijntymQ/oUzWFsmoA99BSznZKcBqHo9oXgeJh9bWWWKUAZzjZ3aTEw+HJJ52lD/q3uRwZ2HcILz/+8OEZj7gsONTNmkiSrkiS5PkmSO5IkuTxJkq4DOV4ku0nCV1YDJfd/rQrOmiPbAKv4mvntmio6c6ymDWIz7K0qjJg8ZKk0ne7NPMHyid4Rp+yGIKXsdBqOWBD627JM7GHQSZaqswqQJuudSX+OO2VtXBaVvypqTjcZBLbgie4EN40BKoc1vgi8rVarPRb4PvBPB3KwSHaThNrNQMH9M6YqRphGqEz6iWSZkSYkuSzFl9Ug7ou5LGJysS/qzm7fbH8ytg3fE+cTrJpt9V1rRkBZZkhrhbCkFwasZJlCrZ/aTp0C7HZXmjgz5hfM4QH63LSIuPu+DHBZVHb7ikNs1INjgV+6+auAFxzIwWJrNkk4CujPd9NDn5DaEE7muQZHA1TsSM5ZJs2m5su2YF2zwsC2EWy23ublRUw+VG1N1EkZC77rM7M2wfD52eosHUigCtQDVfp0v5IfMUNLmGgwitqnbJAKwbx1CeeQVm8Mn6+n27Thc/Y0IEVHVNBBYt2rncyBrq2yf+8Q/PU8+N1GnxbYyuue0wAACkVJREFUCzzWTbuBNcDX7onKbl+gZswWY0aSJNZ7ekmtVrtkL/e9Cxmw6QfAi5AUyv1GbM0mCS8DoIMyfWnnvSKHr07RzNEfBrTsde99T8rOfkbNNOLPgz2p7Dy+fJh+z3peWeZse6y29PwwZL4bhWFPVAV8t323OYxC19lSY/oOj+GTypUA1cRpozhtgnqb27bdfcbcZjdCeY4sGkI4cxDxOC4E3oeYOd+VcVciJhXbarXa6mYrkyS5Ghm2MMRFwKuBTyZJ8g7gR7g3dH8RyW6S8FgAepjGQ0FhE9egFPHKrlnibao8WBiMEqqyULWFyo8m67Iax4jJR9gBAZ8uAH5wOVspBTPVfVQe2aopmG1NB2e3nte+oKOQHxV1p8Em4+bQqsSyojftabJsWrbDp0l0NvVA1Z493hDMHACOLtLJMJ1usyFE5Y0gqm4a8ELEjBnV3d5jsnPjarXaWXvY5GkASZIcCzzrQM4VfXaThE8CUJbmxpopLSzR2YAAaOKv21sF19ZkeSljnUUkuz8vmin4UOWF3+3yPUVvmn1z9hhBZ8n6mcNkcOuryypzZ5eH89qZC0vk2eOakcuZomO2zmjg13bEnzeOkN4HM+5oxOGDJElmuWkO+DfgcwdyvNiaTRLumg3QIdSSilxzjyArxcA6+TPHp8ubjaCxUZxoGm4/0bKIyUdIWqF5OTRd7s1zb9YZanZes23bsLyDxXFvprSkZ0dJ0O/6TqvxSUc4CEdUGKbR1KkK0ppK22T/8hTgoc31Q2oWQwFReJqTN5jx6yKa4xAc9eC8JEne4Oa/B1x6IAeLrdokobwFBqlI5zmsz0ybL+du85LUn5HPIrqs/Klm24TrsoIYsogy4s+LLHIKSUoLP4+SXTWlRHpIn1EkOAU3NYRZxuwTrEvcdRR2yndVgSqj1P6ly5XYqmZd0ayrku7QqQ+vLTieBsToTxhDEupe2ZhNOI7464bwQ2lFHL6o1WqfQActbAFiqzapKMn/ts1VUjNTaMJs8H80M1eG03BZlvLbk3ksvhaHBkIllw+W6fIxmo+Dl3UcMuYxaks7REqg5phJEXLDXnlZkrPlv/S7dt7AFLekMYoz3Md+gjQEysBZwDUiNkvjnvBmIgOMWOEYsfc41OpZthKxVZtUVHxSeT31AOo+ES1ladMP2qAxzSBc1kzdTQm2CZVdBxM2fhF/ZljSqpJNZNVgH6v0COYh3ZyVzD64Cj/2vdFjB52g/IA75rjf3ao3/a4qTWFrbRYQE6WaMHHbFknH3OXcckuU84Gvuatvh9KAmC67gVcCFyNDaUXsGw5S6sEhgxigMkmQiLC8/L/bXqs2INbZ31CRopkis8ElIZr56MJpVHOHB5optlDBZ5k87ce+WMExbXRwQ/Ru2FkqpS0Ue/spmqlWLbOlxQrmu/3YDmAFWOoutwRtOS/4Bt0m0YwZESK2cJOKKSThPz9QV3Y2Si1VIDeL6JoRYDMzZxaphcnj8XU4NKFmyPB5WhOj3bbZNHy+wTpraaibR0Ofn6m4kndqs+CkmKqyukUCv1wxhcYoiALphHQ1Z5bMMps10QmcDOUfUifD8pBEYz4dIb1uIvYVh2CASksRW7dJguT7fFgSgLoQE461tWjXVHu42pOtR2GGUS17CimfaH1oCiVjGnHowBKVIZsUEamZU/131kdXxQev2KhO+/xxDJHHB7BAmmTDa3Hf8wNCfJqLZyMu1ZwJPigF0tup6dNWU7HLwqoqM5Hil/OpF1efPySX/70z4Npr5DRvBt4bc+wiHGLLNqnIC6HZyu7aeNj4ae3dZqYbQONjC7+HtRNDTGT+jDg0kfXMQ7UXBrOEQSvhsWynCGPG1Kn1B44G87bj5Nblq9S1QRiV2QxakUVhq6zocXYjsk3TENqBuxFz5hQ5/jS9/FdB6RohvicQk8r3FUeyzy62dpOKEsyGetmHaelV9WkBJN0gy1fSzI+yN2bMcHlQQSO+Doc47DNqNqBr6LuzKtBGV47iA1Fcnc2ZkFZ/HaRVoj1PSLZOXebdpzYu77iNCrC1MVWM4pa1u+8a4dmJ1Psadss7kYxx9dltBpa4Y5Zh8T0wtRv4fycx5+U3UkQSs6w1NWJiRDNmRAuRl39Ua7JUFMzUJptPqOomenzN1k3k39nTMSMOHVjSs8uyyGiid0HVHRk+uyzzpTV/hurOlCRLRsWXp4WkQ4QspCZOzasbMctthKdaQDqRocmrsuyoAs4MezfT3SZfBHZGVRfhEFu2ScLg+6Deey5i7C4OYfFn3PYTmiGhkRRD7A8hRhx6CJVUmDcXmjHttNl88M5MVbP5ngJf1BEXkmLgQ1SaKWbU79V33NrNbG3MqtlOC0HrabUAw0xE/ZWAeUg54dpOys5i8tMd0Yy5r4hmzIgDxg8uguf+K7AUsR7NJu0SaaiXuS/EtbePcSI/XsShjz09t5CcJiJEu52urwTLsogvXJYV9BLk+hUM2VlCS0Uk44lN/wdKSC6B7tOGj/bsRghuhzvdq926EeB02b5nC2AHl4l4VCOp1eLYhhERERGPdnQlSe3kFh/zarhloiF+JhOxix8RERERARzZASqxgkpERERExBGPqOwiIiIiImJtzIiIiIiIiMMdUdlFRERERABR2UVERERERBzWiMouIiIiIiKWC4uIiIiIeHQgmjEjIiIiIiIOY0RlFxERERFxxJsxo7KLiIiIiDjiEZVdRERERARwZPvsItlFRERERMQKKhEREREREYc7orKLiIiIiABigEpERERERMRhjajsIiIiIiKOeJ9dJLuIiIiIiCOe7KIZMyIiIiLiiEdUdhERERERQAxQiYiIiIiIOKwRlV1ERERERPTZRUREREREHO6Iyi4iIiIiAjiyfXaR7CIiIiIiohkzIiIiIiLicEdUdhERERERQFR2ERERERERhzWisouIiIiIoEYMUImIiIiIeBQgmjEjIiIiIiIOY0RlFxERERERUw8iIiIiIiIOd0RlFxEREREBHNkBKlHZRURERETUzZit/BwIkiR5UZIkdyVJMp4kyepg3duTJLk/SZJ7kiR5+t4cLyq7iIiIiIhDEXcCzwc+bxcmSbICeClwHDAHuDpJkmNrtdqE/BrJLiIiIiICOLTMmLVa7Q8ASZKEq84FvlGr1XYDDyZJcj9wEnD9RMeLZsyIiIiIiMMJc4EN5vtGt2xCRGUXEREREcE4XDkIM1p82FKSJDeb75fUarVL9EuSJFcDPRn7XVSr1X7YyguJZBcRERERQa1We8af4Zxn7cdum4D55vs8t2xCRDNmRERERMThhB8BL02SZEqSJI8BlgI37mmnSHYREREREYcckiR5XpIkG4FTgZ8kSXIlQK1Wuwv4FrAG+F/gDXuKxARIarXawbzeiIiIiIiIPzuisouIiIiIOOIRyS4iIiIi4ohHJLuIiIiIiCMekewiIiIiIo54RLKLiIiIiDjiEckuIiIiIuKIRyS7iIiIiIgjHpHsIiIiIiKOePz/1fweHee7lD4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute indicator using all avaliable vectors for a grid of initial conditions\n",
    "\n",
    "def gali(vs):\n",
    "    return torch.linalg.svdvals(vs.nan_to_num()).prod()\n",
    "\n",
    "# Set grid\n",
    "\n",
    "n1 = 501\n",
    "n2 = 501\n",
    "\n",
    "q1 = torch.linspace(-1.0, +1.0, n1, dtype=dtype, device=device)\n",
    "q2 = torch.linspace(+0.0, +1.0, n2, dtype=dtype, device=device)\n",
    "\n",
    "qs = torch.stack(torch.meshgrid(q1, q2, indexing='ij')).swapaxes(-1, 0).reshape(n1*n2, -1)\n",
    "ps = torch.full_like(qs, 1.0E-10)\n",
    "\n",
    "q1, q2, p1, p2 = torch.hstack([qs, ps]).T\n",
    "\n",
    "vs = torch.tensor(n1*n2*[torch.eye(4).tolist()], dtype=dtype, device=device)\n",
    "qs = torch.stack([q1, p1, q2, p2]).T\n",
    "\n",
    "# Set tast\n",
    "# Perform 512 iterations, compute min of indicator value over the next 64 iterations\n",
    "\n",
    "def task(qs, vs, count=512, total=64, level=1.0E-10):\n",
    "    for _ in range(count):\n",
    "        qs, vs = tangent(qs, vs)\n",
    "    out = []\n",
    "    for _ in range(total):\n",
    "        qs, vs = tangent(qs, vs)\n",
    "        out.append(gali(vs))\n",
    "    return (torch.stack(out).min() + level*torch.sign(qs.norm())).log10()\n",
    "\n",
    "# Compute and clean data\n",
    "\n",
    "out = torch.vmap(task)(qs, vs)\n",
    "out = out.nan_to_num(neginf=0.0)\n",
    "out[(out >= -2.0)*(out != 0.0)] = -2.0\n",
    "out[out == 0.0] = torch.nan\n",
    "out = out.reshape(n1, n2)\n",
    "\n",
    "# Plot\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.imshow(\n",
    "    out.cpu().numpy(),\n",
    "    vmin=-10.0,\n",
    "    vmax=-2.0,\n",
    "    aspect='equal',\n",
    "    origin='lower',\n",
    "    cmap='hot', \n",
    "    interpolation='nearest')\n",
    "plt.colorbar()\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1b42a98-fe47-4ae7-9613-114ed5b49e66",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Example-11: Closed orbit (dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70f1f56c-1877-4291-8a2f-9d75c8d5d249",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example derivatives of closed orbit with respect to momentum deviation are computed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43e44d11-1a03-4f8c-a3a7-82df2e4bb4a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.series import series\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=8, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0d70fa1d-1104-4f6c-85e1-a623639ce449",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be0696f0-5b8b-4028-9f3e-4d473175c1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb26687c-9b4f-436b-ba87-b2848af36127",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set transport maps between observation points \n",
    "# Note, here observation poins are locations between elements, lattice start and lattice end\n",
    "# An observable (closed orbit) is computed at observation points\n",
    "\n",
    "# All maps are expected to have identical signature of differentible parameters\n",
    "# State and momentum deviation in this example\n",
    "# But each map can have any number of additional args and kwargs after required parameters\n",
    "\n",
    "def map_01_02(x, w): return quad(x, w, 0.19, 0.50)\n",
    "def map_02_03(x, w): return drif(x, w, 0.45)\n",
    "def map_03_04(x, w): return sext(x, w, 0.00, 0.10)\n",
    "def map_04_05(x, w): return drif(x, w, 0.45)\n",
    "def map_05_06(x, w): return bend(x, w, 22.92, 0.015, 0.00, 3.0)\n",
    "def map_06_07(x, w): return drif(x, w, 0.45)\n",
    "def map_07_08(x, w): return sext(x, w, 0.00, 0.10)\n",
    "def map_08_09(x, w): return drif(x, w, 0.45)\n",
    "def map_09_10(x, w): return quad(x, w, -0.21, 0.50)\n",
    "def map_10_11(x, w): return quad(x, w, -0.21, 0.50)\n",
    "def map_11_12(x, w): return drif(x, w, 0.45)\n",
    "def map_12_13(x, w): return sext(x, w, 0.00, 0.10)\n",
    "def map_13_14(x, w): return drif(x, w, 0.45)\n",
    "def map_14_15(x, w): return bend(x, w, 22.92, 0.015, 0.00, 3.0)\n",
    "def map_15_16(x, w): return drif(x, w, 0.45)\n",
    "def map_16_17(x, w): return sext(x, w, 0.00, 0.10)\n",
    "def map_17_18(x, w): return drif(x, w, 0.45)\n",
    "def map_18_19(x, w): return quad(x, w, 0.19, 0.50)\n",
    "\n",
    "transport = [\n",
    "    map_01_02,\n",
    "    map_02_03,\n",
    "    map_03_04,\n",
    "    map_04_05,\n",
    "    map_05_06,\n",
    "    map_06_07,\n",
    "    map_07_08,\n",
    "    map_08_09,\n",
    "    map_09_10,\n",
    "    map_10_11,\n",
    "    map_11_12,\n",
    "    map_12_13,\n",
    "    map_13_14,\n",
    "    map_14_15,\n",
    "    map_15_16,\n",
    "    map_16_17,\n",
    "    map_17_18,\n",
    "    map_18_19    \n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, w):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, w)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4bda838-f9e7-40aa-8cbf-568852ae112b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# The first step is to compute dynamical fixed point\n",
    "\n",
    "# Set initial guess\n",
    "# Note, in this example zero is a fixed point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Set knobs\n",
    "\n",
    "w = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Find fixed point\n",
    "\n",
    "fp = fixed_point(16, fodo, x, w, power=1)\n",
    "\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c95878f6-0ab7-45ea-b7f2-971c6f682986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0, 0, 0, 0): [0. 0. 0. 0.]\n",
      "(0, 0, 0, 0, 1): [1.81613351 0.         0.         0.        ]\n",
      "(0, 0, 0, 0, 2): [0.56855511 0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "# Compute parametric closed orbit\n",
    "\n",
    "pfp = parametric_fixed_point((2, ), fp, [w], fodo)\n",
    "chop(pfp)\n",
    "\n",
    "# Print series representation\n",
    "\n",
    "for key, value in series((4, 1), (0, 2), pfp).items():\n",
    "    print(f'{key}: {value.cpu().numpy()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "81c0b250-6e0c-455f-8884-6686a06ba96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n",
      "tensor([1.81613351e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], dtype=torch.float64)\n",
      "tensor([1.81670206e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], dtype=torch.float64)\n",
      "\n",
      "tensor([  1.81670185e-03,   1.13882757e-19, -5.50391130e-235,   0.00000000e+00], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check convergence\n",
    "\n",
    "print(evaluate(series((4, 1), (0, 0), pfp), [x, w + 1.0E-3]))\n",
    "print(evaluate(series((4, 1), (0, 1), pfp), [x, w + 1.0E-3]))\n",
    "print(evaluate(series((4, 1), (0, 2), pfp), [x, w + 1.0E-3]))\n",
    "print()\n",
    "\n",
    "out = fixed_point(16, fodo, x, w + 1.0E-3, power=1)\n",
    "chop([out])\n",
    "\n",
    "print(out)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "022f1ef1-96de-4139-bf64-b6517788184b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Propagate closed orbit\n",
    "\n",
    "out = []\n",
    "\n",
    "jet = identity((0, 1), fp, parametric=pfp)\n",
    "out.append(jet)\n",
    "\n",
    "for mapping in transport:\n",
    "    jet = propagate((4, 1), (0, 2), jet, [w], mapping)\n",
    "    out.append(jet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cff7e5c8-d2d5-4903-901a-40811262f87e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Check periodicity\n",
    "\n",
    "print(compare(pfp, jet))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cae68275-4659-450a-bc95-c309fda3cfad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 1st order dispersion\n",
    "\n",
    "pos = [0.00, 0.50, 0.95, 1.05, 1.50, 4.50, 4.95, 5.05, 5.50, 6.00, 6.50, 6.95, 7.05, 7.50, 10.50, 10.95, 11.05, 11.50, 12.00]\n",
    "res = torch.stack([series((4, 1), (0, 2), jet)[(0, 0, 0, 0, 1)][0] for jet in out])\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(pos, res.cpu().numpy(), marker='x', color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ebb02ef-1954-4d15-98d5-9a409ad718d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 2nd order dispersion\n",
    "\n",
    "pos = [0.00, 0.50, 0.95, 1.05, 1.50, 4.50, 4.95, 5.05, 5.50, 6.00, 6.50, 6.95, 7.05, 7.50, 10.50, 10.95, 11.05, 11.50, 12.00]\n",
    "res = torch.stack([series((4, 1), (0, 2), jet)[(0, 0, 0, 0, 2)][0] for jet in out])\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(pos, res.cpu().numpy(), marker='x', color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85452e8-6cfb-47ef-a434-366de354b268",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Example-12: Closed orbit (quadrupole shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad32fff3-f3da-4bd0-80e5-6ae33b617a50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02473f42-5b53-48d1-a478-9e3e1e4d04e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5af703e0-68ed-4dec-a1d4-496aaea2c8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d3cb9e4-34ec-46f6-8215-e21743c55323",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, d): \n",
    "    dxf, dyf, dxd, dyd = d\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    x = slip(x, -dxf, -dyf)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxd, +dyd)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = slip(x, -dxd, -dyd)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxf, +dyf)\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, d):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, d)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b421fbd5-6d20-4531-b0ed-54fa3270fb49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "fp = fixed_point(16, fodo, x, d, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb5b15aa-cf71-4b90-aee5-41b63c29f504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[-4.621551e-01, 0.000000e+00, 1.165780e+00, 0.000000e+00],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
       "          [0.000000e+00, 3.344042e+00, 0.000000e+00, -4.891066e+00],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [d], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4ec9581a-17f5-46eb-8d17-9d500befb801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[-4.621551e-01, 0.000000e+00, 1.165780e+00, 0.000000e+00],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
       "          [0.000000e+00, 3.344042e+00, 0.000000e+00, -4.891066e+00],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 4), (0, 1), pfp, [d], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b83a5e1-dbcc-4468-a52b-94aa952c1ef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([8.482363e-04, 8.125977e-21, 3.353913e-03, 6.171311e-20], dtype=torch.float64)\n",
      "tensor([8.482363e-04, 0.000000e+00, 3.353913e-03, 0.000000e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test single random shift\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = 1.0E-3*torch.randn_like(x)\n",
    "\n",
    "fp = fixed_point(64, fodo, x, d, power=1, epsilon=1.0E-9)\n",
    "chop([fp])\n",
    "\n",
    "print(fp)\n",
    "print(evaluate(pfp, [x, d]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f0ebe1d-d86c-454f-ad59-86802a0f58d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-9.932218e-06,  4.372133e-22, -1.483549e-06,  1.693071e-21], dtype=torch.float64)\n",
      "tensor([1.253079e-03, 9.554150e-20, 5.881071e-03, 3.289974e-19], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Estimate center & spread (tracking)\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = 1.0E-3*torch.randn(8192, 4, dtype=dtype, device=device)\n",
    "\n",
    "fp = torch.func.vmap(lambda d: fixed_point(64, fodo, x, d, power=1))(d)\n",
    "chop([fp])\n",
    "\n",
    "print(fp.T.mean(-1))\n",
    "print(fp.T.std(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3838c50a-21c7-4a2c-8220-641697a0a905",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 3.969686e-06,  0.000000e+00, -2.718688e-05,  0.000000e+00], dtype=torch.float64)\n",
      "tensor([1.240640e-03, 0.000000e+00, 5.905480e-03, 0.000000e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Estimate center & spread (surrogate)\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = 1.0E-3*torch.randn(8192, 4, dtype=dtype, device=device)\n",
    "\n",
    "fp = torch.func.vmap(lambda d: evaluate(pfp, [x, d]))(d)\n",
    "chop([fp])\n",
    "\n",
    "print(fp.T.mean(-1))\n",
    "print(fp.T.std(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d4d5f58-b78d-4632-af6d-0955ec51b2c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.254045e-03, 0.000000e+00, 5.924960e-03, 0.000000e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Estimate spread (error propagation)\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "s = derivative(1, lambda d: evaluate(pfp, [x, d]), d, intermediate=False)\n",
    "\n",
    "print((s @ (1.0E-3*torch.eye(4,  dtype=dtype, device=device))**2 @ s.T).diag().sqrt())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eed591e-97f4-4c13-a53b-fc4fb0736995",
   "metadata": {},
   "source": [
    "# Example-13: Closed orbit (sextupole shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8c85315-a67e-4f13-ad35-8e2136f6b6d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6b913f4-e7ee-4410-8a97-cf1ac9f52a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ae4b140-8589-4973-b6f8-d588ecbc8316",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f22acc90-000b-4e94-baca-a613df54181e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, d):\n",
    "    dxsf1, dysf1, dxsd1, dysd1, dxsf2, dysf2, dxsd2, dysd2 = d\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxsf1, +dysf1)\n",
    "    x = sext(x, [0.0], 0.50, 0.10)\n",
    "    x = slip(x, -dxsf1, -dysf1)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxsd1, +dysd1)\n",
    "    x = sext(x, [0.0], -0.50, 0.10)\n",
    "    x = slip(x, -dxsd1, -dysd1)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxsd2, +dysd2)\n",
    "    x = sext(x, [0.0], -0.50, 0.10)\n",
    "    x = slip(x, -dxsd2, -dysd2)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxsf2, +dysf2)\n",
    "    x = sext(x, [0.0], 0.50, 0.10)\n",
    "    x = slip(x, -dxsf2, -dysf2)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, d):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, d)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56b58c71-f4ae-44e4-b818-6ba412efb93a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "fp = fixed_point(16, fodo, x, d, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6fb2e38-d269-4a4f-bf38-31c9de7a887e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Compute & check parametric fixed point\n",
    "# Note, there is no first order contribution from sextupole shifts\n",
    "\n",
    "pfp = parametric_fixed_point((2, ), fp, [d], fodo)\n",
    "out = propagate((4, 8), (0, 2), pfp, [d], fodo)\n",
    "print(compare(pfp, out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4411bab-734c-45a2-8bbc-0dfaee9d8407",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 2.446138e-07, -1.144473e-08, -8.221335e-07,  6.156318e-09], dtype=torch.float64)\n",
      "tensor([ 2.442916e-07, -1.142839e-08, -8.240374e-07,  6.170032e-09], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test for a random shift\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = 1.0E-3*torch.randn(8, dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(64, fodo, x, d, power=1, epsilon=1.0E-9)\n",
    "\n",
    "print(fp)\n",
    "print(evaluate(pfp, [x, d]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3cac879f-551a-4ec5-9bf5-d7922d316f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.018030e-09, -2.975896e-10, -1.314171e-08, -2.126598e-11], dtype=torch.float64)\n",
      "tensor([6.895581e-07, 3.187716e-08, 1.813973e-06, 2.939394e-08], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Estimate center & spread (tracking)\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = 1.0E-3*torch.randn(8192, 8, dtype=dtype, device=device)\n",
    "\n",
    "fp = torch.func.vmap(lambda d: fixed_point(64, fodo, x, d, power=1))(d)\n",
    "chop([fp])\n",
    "\n",
    "print(fp.T.mean(-1))\n",
    "print(fp.T.std(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "757522a2-5e28-4141-9e33-29f34fa63c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-4.887936e-09, -1.410492e-10, -3.069499e-09, -4.740614e-10], dtype=torch.float64)\n",
      "tensor([6.769223e-07, 3.104300e-08, 1.819394e-06, 2.833964e-08], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Estimate center & spread (surrogate)\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "d = 1.0E-3*torch.randn(8192, 8, dtype=dtype, device=device)\n",
    "\n",
    "fp = torch.func.vmap(lambda d: evaluate(pfp, [x, d]))(d)\n",
    "chop([fp])\n",
    "\n",
    "print(fp.T.mean(-1))\n",
    "print(fp.T.std(-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d75dce-4460-47cb-aa13-7f87d0fe0d05",
   "metadata": {},
   "source": [
    "# Example-14: Closed orbit (responce matrix & correction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2583e9ab-5501-4f49-b395-f59e9f4c73e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example orbit responce matrix is computed\n",
    "# Quadrupole shifts are introduced and responce matrix is used to correct the orbit at observation locations\n",
    "\n",
    "# Correctors are at sextupole centers\n",
    "# Observation points are at bend centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0a8970a-837c-4af3-8417-4615d2071ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6a506da-4f29-4794-ba37-0e5392536963",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da6570f1-8968-455a-9938-b797b1821a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=25):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b17fe3a9-6c67-43f0-bf2b-42ac0a8eb7ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, c, d): \n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = c\n",
    "    dxf, dyf, dxd, dyd = d\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    x = slip(x, -dxf, -dyf)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf1, cysf1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def map_02_03(x, c, d):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = c\n",
    "    dxf, dyf, dxd, dyd = d\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd1, cysd1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxd, +dyd)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = slip(x, -dxd, -dyd)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd2, cysd2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def map_03_04(x, c, d):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = c\n",
    "    dxf, dyf, dxd, dyd = d\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf2, cysf2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = slip(x, +dxf, +dyf)\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02,\n",
    "    map_02_03,\n",
    "    map_03_04\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, c, d):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, c, d)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "c = torch.tensor(8*[0.0], dtype=dtype, device=device)\n",
    "d = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "print(fodo(x, c, d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e69337a-c507-4471-962e-5f69dfb2b8c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "c = torch.tensor(8*[0.0], dtype=dtype, device=device)\n",
    "d = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, c, d, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02f19aad-3b9c-4497-a4d3-935bbb6b89da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[7.583253e+00, 5.939607e+00, 7.583253e+00, 5.939607e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
       "          [-4.334732e-01, -9.086786e-02, 4.334732e-01, 9.086786e-02, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.349234e+01, 2.165892e+01, 1.349234e+01, 2.165892e+01],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, -4.019148e-01, -7.725758e-02, 4.019148e-01, 7.725758e-02]],\n",
       "         dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [c], fodo, d)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "63760982-d712-4ce6-ad69-055c1ce8678b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[7.583253e+00, 5.939607e+00, 7.583253e+00, 5.939607e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
       "          [-4.334732e-01, -9.086786e-02, 4.334732e-01, 9.086786e-02, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.349234e+01, 2.165892e+01, 1.349234e+01, 2.165892e+01],\n",
       "          [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, -4.019148e-01, -7.725758e-02, 4.019148e-01, 7.725758e-02]],\n",
       "         dtype=torch.float64)]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 8), (0, 1), pfp, [c], fodo, d)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4dd52aa-5eda-4910-962e-d84e18589062",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Orbit derivatives at observation locations\n",
    "\n",
    "bag = []\n",
    "\n",
    "pfp = propagate((4, 8), (0, 1), pfp, [c], map_01_02, d)\n",
    "chop(pfp)\n",
    "bag.append(pfp)\n",
    "\n",
    "pfp = propagate((4, 8), (0, 1), pfp, [c], map_02_03, d)\n",
    "chop(pfp)\n",
    "bag.append(pfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "61afd3e6-b21c-4fcb-aceb-d5ea5552fe66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[6.221115e+00, 4.042085e+00, 6.753998e+00, 4.569069e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
      "        [6.753998e+00, 4.569069e+00, 6.221115e+00, 4.042085e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
      "        [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.808222e+01, 2.754871e+01, 1.855563e+01, 2.802741e+01],\n",
      "        [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.855563e+01, 2.802741e+01, 1.808222e+01, 2.754871e+01]],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute responce matrix\n",
    "\n",
    "def orbit(c, pfp):\n",
    "    qx, _, qy, _ = evaluate(pfp, [x, c]) \n",
    "    return torch.stack([qx, qy])\n",
    "\n",
    "pfp1, pfp2 = bag\n",
    "\n",
    "rx1, ry1 = derivative(1, orbit, c, pfp1, intermediate=False)\n",
    "rx2, ry2 = derivative(1, orbit, c, pfp2, intermediate=False)\n",
    "\n",
    "rm = torch.stack([rx1, rx2, ry1, ry2])\n",
    "print(rm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99e550ce-9345-4582-9ee2-f6d39bd4e237",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-2.161122e-03, -2.161122e-03, -3.001730e-03, -3.001730e-03], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Generate perturbed orbit\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "c = torch.tensor(8*[0.0], dtype=dtype, device=device)\n",
    "d = torch.tensor([0.001, 0.001, -0.001, 0.001], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, c, d, power=1)\n",
    "\n",
    "qx1, _, qy1, _ = map_01_02(fp, c, d)\n",
    "qx2, _, qy2, _ = map_02_03(map_01_02(fp, c, d), c, d)\n",
    "\n",
    "o = torch.stack([qx1, qx2, qy1, qy2])\n",
    "chop([o])\n",
    "print(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1394e6ef-4c84-435a-bbce-82b035466e8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.070014e-18,  3.763685e-18, -1.733072e-17, -1.661201e-17], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Correct orbit\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "c = - (torch.linalg.pinv(rm) @ o)\n",
    "d = torch.tensor([0.001, 0.001, -0.001, 0.001], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, c, d, power=1)\n",
    "\n",
    "qx1, _, qy1, _ = map_01_02(fp, c, d)\n",
    "qx2, _, qy2, _ = map_02_03(map_01_02(fp, c, d), c, d)\n",
    "\n",
    "o = torch.stack([qx1, qx2, qy1, qy2])\n",
    "chop([o])\n",
    "print(o)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e23c026-350b-4d62-82e1-5ddbb49f4573",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Example-15: Tune (chromaticity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d6f923d-601e-4736-b214-8e2df57d5918",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8ef0143-3b64-4487-8a4d-73ef78b0c491",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1942acce-37c9-4b5c-80e6-9fbd0573816e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a69d84a9-7376-4ed6-b669-cd34e56abe50",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, w, k):\n",
    "    ksf, ksd, ksb = k\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, ksf, 0.10)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, ksb, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, ksd, 0.10)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, ksd, 0.10)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, ksb, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, ksf, 0.10)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, d, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, d, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor(1*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, w, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c148092-c7ba-41b3-b2de-4f0027d02a0e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor(1*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, w, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5ac8f74-78ac-437d-b5d0-35f7ac1b16a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "   tensor([[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]], dtype=torch.float64)],\n",
       "  [tensor([[1.816134e+00],\n",
       "           [0.000000e+00],\n",
       "           [0.000000e+00],\n",
       "           [0.000000e+00]], dtype=torch.float64),\n",
       "   tensor([[[0.],\n",
       "            [0.],\n",
       "            [0.]],\n",
       "   \n",
       "           [[0.],\n",
       "            [0.],\n",
       "            [0.]],\n",
       "   \n",
       "           [[0.],\n",
       "            [0.],\n",
       "            [0.]],\n",
       "   \n",
       "           [[0.],\n",
       "            [0.],\n",
       "            [0.]]], dtype=torch.float64)]]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, 1), fp, [w, k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e47fbd61-c250-4f37-8c2c-92d4fe55e6bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "   tensor([[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]], dtype=torch.float64)],\n",
       "  [tensor([[1.816134e+00],\n",
       "           [0.000000e+00],\n",
       "           [0.000000e+00],\n",
       "           [0.000000e+00]], dtype=torch.float64),\n",
       "   tensor([[[0.],\n",
       "            [0.],\n",
       "            [0.]],\n",
       "   \n",
       "           [[0.],\n",
       "            [0.],\n",
       "            [0.]],\n",
       "   \n",
       "           [[0.],\n",
       "            [0.],\n",
       "            [0.]],\n",
       "   \n",
       "           [[0.],\n",
       "            [0.],\n",
       "            [0.]]], dtype=torch.float64)]]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 1, 3), (0, 1, 1), pfp, [w, k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4d080e8-ec1e-41fa-ad29-6c8bddfba185",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 1, 1), fp, parametric=pfp)\n",
    "jet = propagate((4, 1, 3), (1, 1, 1), jet, [w, k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d64e48f0-a136-478f-99cb-2a0dc8face2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute tune\n",
    "\n",
    "def tune(w, k):\n",
    "    m = derivative(1, lambda x: evaluate(jet, [x, w, k]), fp, intermediate=False)\n",
    "    t, *_ = twiss(m)\n",
    "    return t\n",
    "\n",
    "print(tune(w, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cc1098c-ef11-4ecb-9f21-7f5f71634db2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64), tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.]], dtype=torch.float64)], [tensor([[-2.343079e-01],\n",
      "        [-2.416176e-01]], dtype=torch.float64), tensor([[[3.618782e-01],\n",
      "         [8.705079e-02],\n",
      "         [5.873074e+00]],\n",
      "\n",
      "        [[-2.986097e-01],\n",
      "         [-4.727344e-01],\n",
      "         [-1.106560e+01]]], dtype=torch.float64)]]\n"
     ]
    }
   ],
   "source": [
    "# Compute parametric tune\n",
    "\n",
    "t = derivative((1, 1), tune, w, k)\n",
    "\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02dd6a3f-856e-44a1-a736-a51279aa6721",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n",
      "tensor([2.525452e-01, 1.196688e-01], dtype=torch.float64)\n",
      "\n",
      "tensor([2.525452e-01, 1.196687e-01], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check convergence\n",
    "\n",
    "print(evaluate(t, [w, k]))\n",
    "print(evaluate(t, [w + 1.0E-3, k]))\n",
    "print()\n",
    "\n",
    "print(tune(w + 1.0E-3, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44d0a4ad-d7d7-46fe-9d34-00343d8bcabf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n",
      "tensor([2.525452e-01, 1.196688e-01], dtype=torch.float64)\n",
      "tensor([2.526084e-01, 1.195504e-01], dtype=torch.float64)\n",
      "\n",
      "tensor([2.526084e-01, 1.195502e-01], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check convergence\n",
    "\n",
    "print(evaluate(t, [w, k]))\n",
    "print(evaluate(t, [w + 1.0E-3, k]))\n",
    "print(evaluate(t, [w + 1.0E-3, k + 1.0E-2]))\n",
    "print()\n",
    "\n",
    "print(tune(w + 1.0E-3, k + 1.0E-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1075e4f-a003-4096-a0d8-5f0c8c39b374",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0, 0, 0): tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n",
      "(1, 0, 0, 0): tensor([-2.343079e-01, -2.416176e-01], dtype=torch.float64)\n",
      "(1, 1, 0, 0): tensor([3.618782e-01, -2.986097e-01], dtype=torch.float64)\n",
      "(1, 0, 1, 0): tensor([8.705079e-02, -4.727344e-01], dtype=torch.float64)\n",
      "(1, 0, 0, 1): tensor([5.873074e+00, -1.106560e+01], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Series representation\n",
    "\n",
    "for key, value in clean(series((1, 3), (1, 1), t)).items():\n",
    "    print(f'{key}: {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b53e320f-97ec-4a6d-b224-a6a9d71c4516",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-2.343079e-01, -2.416176e-01], dtype=torch.float64)\n",
      "tensor([-2.914335e-15,  1.165734e-15], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set chromaticity to zero\n",
    "\n",
    "A = derivative((1, 1), lambda w, k: evaluate(t, [w, k]), w, k, intermediate=False)\n",
    "b = derivative(1, lambda w, k: evaluate(t, [w, k]), w, k, intermediate=False).flatten()\n",
    "\n",
    "print(derivative(1, lambda w: evaluate(t, [w, k]), w, intermediate=False).flatten())\n",
    "print(derivative(1, lambda w: evaluate(t, [w, - (torch.linalg.pinv(A.squeeze()) @ b)]), w, intermediate=False).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9dd20a0e-5a86-4d33-bb2f-852583a72b9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-2.343079e-01, -2.416176e-01], dtype=torch.float64)\n",
      "tensor([1.000000e+00, 1.000000e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set chromaticity to one\n",
    "\n",
    "A = derivative((1, 1), lambda w, k: evaluate(t, [w, k]), w, k, intermediate=False)\n",
    "b = -1.0 + derivative(1, lambda w, k: evaluate(t, [w, k]), w, k, intermediate=False).flatten()\n",
    "\n",
    "print(derivative(1, lambda w: evaluate(t, [w, k]), w, intermediate=False).flatten())\n",
    "print(derivative(1, lambda w: evaluate(t, [w, - (torch.linalg.pinv(A.squeeze()) @ b)]), w, intermediate=False).flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5bec9a-8ca0-4ee4-8daf-3fc4ae4ced3e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Example-16: Tune (responce matrix & correction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34e2bb90-1151-46c8-af48-3547ce2e1ec8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fd86cee-b7a5-4313-ac9e-79fd196ca36e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65117f39-b23e-4d8f-ad71-b27367bfef3c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f417785-0148-48e4-a207-9944d1018918",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, k):\n",
    "    kqf, kqd, kqb = k\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.0, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.0, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2ec343b-0827-4df0-91bb-6dab993fadcf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "835bfd5f-0466-422c-9632-0a53ecaba004",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa3bf81b-3c99-41d7-9e72-656020d24e69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 3), (0, 1), pfp, [k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12e6f7b1-98ea-46aa-af37-68d064b7706d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 1), fp, parametric=pfp)\n",
    "jet = propagate((4, 3), (1, 1), jet, [k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d1d9d72-ac01-4016-b716-3e877d58e4aa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute tune\n",
    "\n",
    "def tune(k):\n",
    "    m = derivative(1, lambda x: evaluate(jet, [x, k]), fp, intermediate=False)\n",
    "    t, *_ = twiss(m)\n",
    "    return t\n",
    "\n",
    "print(tune(k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4929b534-0a35-4db5-a7fd-3241bbba02b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute parametric tune\n",
    "\n",
    "t = derivative((1, ), tune, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7730bec7-4b61-4158-b54b-b19afdd38580",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n",
      "tensor([2.646746e-01, 9.564416e-02], dtype=torch.float64)\n",
      "\n",
      "tensor([2.646564e-01, 9.339156e-02], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check convergence\n",
    "\n",
    "print(evaluate(t, [k]))\n",
    "print(evaluate(t, [k + torch.tensor([5.0E-3, 5.0E-3, 1.0E-3], dtype=dtype, device=device)]))\n",
    "print()\n",
    "\n",
    "print(tune(k + torch.tensor([5.0E-3, 5.0E-3, 1.0E-3], dtype=dtype, device=device)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c14a8261-a260-46a7-99cd-781ff07d1f6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.217003e+00, 3.230152e-01, 4.195000e+00],\n",
      "        [-7.757018e-01, -2.437956e+00, -8.197983e+00]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Responce matrix\n",
    "\n",
    "print(derivative(1, lambda k: evaluate(t, [k]), k, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ae0da0a6-9871-4d10-bde8-09710dc97160",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n",
      "tensor([2.627666e-01, 1.199040e-01], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Correct tunes (increase horizontal by 0.01)\n",
    "\n",
    "A = derivative(1, lambda k: evaluate(t, [k]), k, intermediate=False)\n",
    "b = -torch.tensor([0.01, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(tune(k))\n",
    "print(tune(-(torch.linalg.pinv(A) @ b)))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e45a0bb3-6dbc-4c4f-9aee-3e65e1e011a7",
   "metadata": {},
   "source": [
    "# Example-17: Tune (spread)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64adfd8a-c67e-4096-b71c-432890488fa9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f59699d6-947f-4fac-9327-f9a33c587476",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "043ba457-21bc-485c-ae26-398d51b84e88",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efb457a6-9b0c-49e5-9c7f-23e7b71185d3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, k):\n",
    "    kqf, kqd, kqb = k\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.0, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.0, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a674c73-751e-431e-b177-a4ed852e815b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88be5819-14c8-44b5-a6eb-60f31490f5ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64),\n",
       "  tensor([[[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]],\n",
       "  \n",
       "          [[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]],\n",
       "  \n",
       "          [[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]],\n",
       "  \n",
       "          [[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((2, ), fp, [k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21e78d86-e754-4a96-ab7e-ca82055202fd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64),\n",
       "  tensor([[[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]],\n",
       "  \n",
       "          [[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]],\n",
       "  \n",
       "          [[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]],\n",
       "  \n",
       "          [[0., 0., 0.],\n",
       "           [0., 0., 0.],\n",
       "           [0., 0., 0.]]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 3), (0, 2), pfp, [k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f8ca945d-51a8-4a33-a353-957373ea9d45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 2), fp, parametric=pfp)\n",
    "jet = propagate((4, 3), (1, 2), jet, [k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c7a76f9-198e-444d-9cec-2772b562fcea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527795e-01, 1.199104e-01], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute tune\n",
    "\n",
    "def tune(k):\n",
    "    m = derivative(1, lambda x: evaluate(jet, [x, k]), fp, intermediate=False)\n",
    "    t, *_ = twiss(m)\n",
    "    return t\n",
    "\n",
    "print(tune(k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75a24168-d78c-4344-8465-99db88121365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.527650e-01, 1.198656e-01], dtype=torch.float64)\n",
      "tensor([1.958515e-03, 4.040516e-03], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute tune spread (direct)\n",
    "\n",
    "sf = 1.0E-3\n",
    "sd = 1.0E-3\n",
    "sb = 1.0E-4\n",
    "\n",
    "def wrapper(k):\n",
    "    t, *_ = twiss(derivative(1, fodo, x, k, intermediate=False))\n",
    "    return t\n",
    "\n",
    "err = torch.tensor([sf, sd, sb])*torch.randn(8192).unsqueeze(1).to(dtype).to(device)\n",
    "out = torch.func.vmap(wrapper)(err).T\n",
    "\n",
    "print(out.mean(-1))\n",
    "print(out.std(-1))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd14777c-8b0c-4171-834f-4b6bcd3b1ad7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute parametric tune\n",
    "\n",
    "t = derivative((1, ), tune, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "825b1694-45c2-4156-b403-f836e3d04b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.528075e-01, 1.198527e-01], dtype=torch.float64)\n",
      "tensor([1.973939e-03, 4.063140e-03], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute tune spread (surrogate)\n",
    "\n",
    "sf = 1.0E-3\n",
    "sd = 1.0E-3\n",
    "sb = 1.0E-4\n",
    "\n",
    "err = torch.tensor([sf, sd, sb])*torch.randn(8192).unsqueeze(1).to(dtype).to(device)\n",
    "out = torch.func.vmap(lambda k: evaluate(t, [k]))(err).T\n",
    "\n",
    "print(out.mean(-1))\n",
    "print(out.std(-1))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29cd7b36-7df2-4145-b057-d1c708871dfc",
   "metadata": {},
   "source": [
    "# Example-18: Parametric twiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e4c3903-481b-4de1-9f3b-54ade0534303",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "from twiss.wolski import propagate as propagate_twiss\n",
    "from twiss.convert import wolski_to_cs\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2fd7482-8a02-4433-9865-48e50fa167ec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5a8c2a4-e707-48e6-b611-32283e6810b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0e309fd-0d4a-4a08-a2a3-8cf4ea352c03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, k): \n",
    "    kqf, kqd, kqb = k\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def map_02_03(x, k):\n",
    "    kqf, kqd, kqb = k\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.1)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def map_03_04(x, k):\n",
    "    kqf, kqd, kqb = k\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.1)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02,\n",
    "    map_02_03,\n",
    "    map_03_04\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "608c0359-293a-43b8-8d38-6f122c118f8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "566da1db-4fe5-48fd-bde2-c266e5082f7c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "648fb483-852d-405f-956f-347ab3240d55",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 3), (0, 1), pfp, [k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86ba29c9-e517-45d2-8b77-fd729789c1cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 1), fp, parametric=pfp)\n",
    "jet = propagate((4, 3), (1, 1), jet, [k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7e8a435-158a-4f96-adff-cfcad2f44253",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.528e-01, 1.199e-01, 8.703e+00, 8.703e+00, 1.553e+01, 1.678e+01, 1.678e+01, 9.586e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute twiss\n",
    "\n",
    "# Note, exact or jet one-turn transport can be used\n",
    "# Other maps can be replaced with jets too\n",
    "\n",
    "def fn(k, fodo):\n",
    "    \n",
    "    bxs = []\n",
    "    bys = []\n",
    "    \n",
    "    m = derivative(1, fodo, fp, intermediate=False)\n",
    "    \n",
    "    t, _, w = twiss(m)\n",
    "    _, bx, _, by = wolski_to_cs(w)\n",
    "    \n",
    "    for mapping in transport:\n",
    "        w = propagate_twiss(w, derivative(1, mapping, x, k, intermediate=False))\n",
    "        _, bx, _, by = wolski_to_cs(w)\n",
    "        bxs.append(bx)\n",
    "        bys.append(by)\n",
    "    \n",
    "    return torch.stack([*t, *bxs, *bys])\n",
    "\n",
    "print(fn(k, fodo=lambda x: evaluate(jet, [x, k])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9098db2-d0e7-459d-92ae-eda18d9f3b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor([2.528e-01, 1.199e-01, 8.703e+00, 8.703e+00, 1.553e+01, 1.678e+01, 1.678e+01, 9.586e+00], dtype=torch.float64), tensor([[ 1.217e+00,  3.230e-01,  4.195e+00],\n",
      "        [-7.757e-01, -2.438e+00, -8.198e+00],\n",
      "        [-3.111e+01, -1.531e+01, -1.101e+02],\n",
      "        [-3.111e+01, -1.531e+01, -1.101e+02],\n",
      "        [-1.749e+00, -3.120e+01, -1.799e+02],\n",
      "        [ 1.153e+02,  3.308e+02,  1.106e+03],\n",
      "        [ 1.153e+02,  3.308e+02,  1.106e+03],\n",
      "        [ 5.215e+01,  2.146e+02,  6.957e+02]], dtype=torch.float64)]\n"
     ]
    }
   ],
   "source": [
    "# Compute parametric derivative using exact map (tune & beta)\n",
    "\n",
    "d = derivative((1, ), lambda k: fn(k, fodo=lambda x: fodo(x, k)), k)\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62ac9c73-5b26-4651-8e24-9eb3d676f577",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor([2.528e-01, 1.199e-01, 8.703e+00, 8.703e+00, 1.553e+01, 1.678e+01, 1.678e+01, 9.586e+00], dtype=torch.float64), tensor([[ 1.217e+00,  3.230e-01,  4.195e+00],\n",
      "        [-7.757e-01, -2.438e+00, -8.198e+00],\n",
      "        [-3.111e+01, -1.531e+01, -1.101e+02],\n",
      "        [-3.111e+01, -1.531e+01, -1.101e+02],\n",
      "        [-1.749e+00, -3.120e+01, -1.799e+02],\n",
      "        [ 1.153e+02,  3.308e+02,  1.106e+03],\n",
      "        [ 1.153e+02,  3.308e+02,  1.106e+03],\n",
      "        [ 5.215e+01,  2.146e+02,  6.957e+02]], dtype=torch.float64)]\n"
     ]
    }
   ],
   "source": [
    "# Compute parametric derivative using jet map (tune & beta)\n",
    "\n",
    "d = derivative((1, ), lambda k: fn(k, fodo=lambda x: evaluate(jet, [x, k])), k)\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2255334e-8ccf-49e8-8888-b2b685dd0fa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.253 0.120 8.703 8.703 15.532 16.780 16.780 9.586\n",
      "0.255 0.116 8.645 8.645 15.481 17.337 17.337 9.922\n",
      "0.255 0.116 8.646 8.646 15.482 17.366 17.366 9.940\n"
     ]
    }
   ],
   "source": [
    "# Check covergence\n",
    "\n",
    "dk = torch.tensor([1.0E-3, 1.0E-3, 1.0E-4], dtype=dtype, device=device)\n",
    "\n",
    "values = fn(k, fodo=lambda x: fodo(x, k))\n",
    "print(' '.join([f'{value:.3f}' for value in values.cpu().tolist()]))\n",
    "\n",
    "values = evaluate(d, [dk])\n",
    "print(' '.join([f'{value:.3f}' for value in values.cpu().tolist()]))\n",
    "\n",
    "values = fn(k + dk, fodo=lambda x: fodo(x, k + dk))\n",
    "print(' '.join([f'{value:.3f}' for value in values.cpu().tolist()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "654152a0-e66b-4ec1-8716-102b72a9aeac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.217e+00,  3.230e-01,  4.195e+00],\n",
      "        [-7.757e-01, -2.438e+00, -8.198e+00],\n",
      "        [-3.111e+01, -1.531e+01, -1.101e+02],\n",
      "        [-3.111e+01, -1.531e+01, -1.101e+02],\n",
      "        [-1.749e+00, -3.120e+01, -1.799e+02],\n",
      "        [ 1.153e+02,  3.308e+02,  1.106e+03],\n",
      "        [ 1.153e+02,  3.308e+02,  1.106e+03],\n",
      "        [ 5.215e+01,  2.146e+02,  6.957e+02]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Responce matrix\n",
    "\n",
    "m = derivative((1, ), lambda k: fn(k, fodo=lambda x: evaluate(jet, [x, k])), k, intermediate=False)\n",
    "\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b423bb2-1173-42e9-a7ee-779e77d7761f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.064e-03, -1.258e-03, -4.094e-05], dtype=torch.float64)\n",
      "tensor(9.036e-01, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.000e-03, -1.000e-03, -9.990e-05], dtype=torch.float64)\n",
      "tensor(4.251e-02, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor(8.589e-05, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor(3.559e-10, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Correction\n",
    "\n",
    "# The target values (tunes and beta functions) are associated with model response matrix\n",
    "# Given measured values, the goal is to alter knobs to get target values\n",
    "\n",
    "# Set target values\n",
    "\n",
    "vf = fn(k, fodo=lambda x: fodo(x, k))\n",
    "\n",
    "# Set initial solution\n",
    "\n",
    "sol = torch.zeros_like(dk)\n",
    "\n",
    "# Iterate\n",
    "\n",
    "for _ in range(4):\n",
    "\n",
    "    # Compute current values and set difference\n",
    "\n",
    "    vi = fn(k + dk + sol, fodo=lambda x: evaluate(jet, [x, k + dk + sol]))\n",
    "\n",
    "    # Set difference\n",
    "\n",
    "    dv = vf - vi\n",
    "\n",
    "    # Update solution\n",
    "\n",
    "    sol += torch.linalg.pinv(m) @ dv\n",
    "\n",
    "    # Verbose\n",
    "\n",
    "    print(-dk)\n",
    "    print(sol)\n",
    "    print(dv.norm())\n",
    "    print()\n",
    "    \n",
    "    # Continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "961c67f7-3585-49d5-b32b-3b8741235952",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note, similar to tune spread example, it is possible to compute twiss spread\n",
    "# First order computation can be performed using error propagation\n",
    "# Or higher order jets can be sampled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28236397-30e5-434b-91c1-b7fdd80d0020",
   "metadata": {},
   "source": [
    "# Example-19: Parametric phase advance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2383d32c-d2f3-4a53-86fd-f5cad0f49333",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "from twiss.wolski import propagate as propagate_twiss\n",
    "from twiss.wolski import advance\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5eefd972-305b-4283-aa5f-26aec1b400f3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b93d925-59de-4f3d-aa1b-39f123af6fc4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb119a41-088f-4c08-8e3e-9f18bd4d3963",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points \n",
    "\n",
    "def map_01_02(x, k): \n",
    "    kqf, kqd, kqb = k\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def map_02_03(x, k):\n",
    "    kqf, kqd, kqb = k\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = quad(x, [0.0], -0.21 + kqd, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.1)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def map_03_04(x, k):\n",
    "    kqf, kqd, kqb = k\n",
    "    x = bend(x, [0.0], 22.92, 0.015 + kqb, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.1)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19 + kqf, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02,\n",
    "    map_02_03,\n",
    "    map_03_04\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4562e010-2f28-49ac-b3c2-f3276f64bab9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d08f85d3-8e4c-43c0-a101-ba77d2f89b5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91a8fa30-fbab-4869-ba91-6eeb563ae1a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.],\n",
       "          [0., 0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 3), (0, 1), pfp, [k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c1b99c3-e3b6-4f3c-a1ca-72e68d2fb9f2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 1), fp, parametric=pfp)\n",
    "jet = propagate((4, 3), (1, 1), jet, [k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c291b54-e05e-481d-b730-efb83291a148",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.528e-01, 1.199e-01, 2.521e-01, 1.084e+00, 2.521e-01, 2.468e-01, 2.599e-01, 2.468e-01], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute phase advance\n",
    "\n",
    "# Note, exact or jet one-turn transport can be used\n",
    "# Other maps can be replaced with jets too\n",
    "\n",
    "def fn(k, fodo):\n",
    "    \n",
    "    mus = []\n",
    "    \n",
    "    m = derivative(1, fodo, fp, intermediate=False)\n",
    "    \n",
    "    t, n, _ = twiss(m)\n",
    "    \n",
    "    for mapping in transport:\n",
    "        mu, n = advance(n, derivative(1, mapping, x, k, intermediate=False))\n",
    "        mus.append(mu)\n",
    "    \n",
    "    mus = torch.stack(mus).T\n",
    "\n",
    "    return torch.stack([*t, *mus.flatten()])\n",
    "\n",
    "print(fn(k, fodo=lambda x: evaluate(jet, [x, k])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65210434-411d-464a-a126-50ea5dad6160",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor([2.528e-01, 1.199e-01, 2.521e-01, 1.084e+00, 2.521e-01, 2.468e-01, 2.599e-01, 2.468e-01], dtype=torch.float64), tensor([[1.217e+00, 3.230e-01, 4.195e+00],\n",
      "        [-7.757e-01, -2.438e+00, -8.198e+00],\n",
      "        [4.458e-01, 4.852e-01, 2.926e+00],\n",
      "        [6.755e+00, 1.059e+00, 2.051e+01],\n",
      "        [4.458e-01, 4.852e-01, 2.926e+00],\n",
      "        [-1.523e+00, -5.303e+00, -1.726e+01],\n",
      "        [-1.827e+00, -4.712e+00, -1.699e+01],\n",
      "        [-1.523e+00, -5.303e+00, -1.726e+01]], dtype=torch.float64)]\n"
     ]
    }
   ],
   "source": [
    "# Compute parametric derivative using exact map (tune & advance)\n",
    "\n",
    "d = derivative((1, ), lambda k: fn(k, fodo=lambda x: fodo(x, k)), k)\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2a73415-1cb4-4f43-a6bc-d99693fcadab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor([2.528e-01, 1.199e-01, 2.521e-01, 1.084e+00, 2.521e-01, 2.468e-01, 2.599e-01, 2.468e-01], dtype=torch.float64), tensor([[1.217e+00, 3.230e-01, 4.195e+00],\n",
      "        [-7.757e-01, -2.438e+00, -8.198e+00],\n",
      "        [4.458e-01, 4.852e-01, 2.926e+00],\n",
      "        [6.755e+00, 1.059e+00, 2.051e+01],\n",
      "        [4.458e-01, 4.852e-01, 2.926e+00],\n",
      "        [-1.523e+00, -5.303e+00, -1.726e+01],\n",
      "        [-1.827e+00, -4.712e+00, -1.699e+01],\n",
      "        [-1.523e+00, -5.303e+00, -1.726e+01]], dtype=torch.float64)]\n"
     ]
    }
   ],
   "source": [
    "# Compute parametric derivative using jet map (tune & advance)\n",
    "\n",
    "d = derivative((1, ), lambda k: fn(k, fodo=lambda x: evaluate(jet, [x, k])), k)\n",
    "\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38100b72-0f3c-4704-937a-58644a215e86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.253 0.120 0.252 1.084 0.252 0.247 0.260 0.247\n",
      "0.255 0.116 0.253 1.094 0.253 0.238 0.252 0.238\n",
      "0.255 0.116 0.253 1.094 0.253 0.238 0.251 0.238\n"
     ]
    }
   ],
   "source": [
    "# Check covergence\n",
    "\n",
    "dk = torch.tensor([1.0E-3, 1.0E-3, 1.0E-4], dtype=dtype, device=device)\n",
    "\n",
    "values = fn(k, fodo=lambda x: fodo(x, k))\n",
    "print(' '.join([f'{value:.3f}' for value in values.cpu().tolist()]))\n",
    "\n",
    "values = evaluate(d, [dk])\n",
    "print(' '.join([f'{value:.3f}' for value in values.cpu().tolist()]))\n",
    "\n",
    "values = fn(k + dk, fodo=lambda x: fodo(x, k + dk))\n",
    "print(' '.join([f'{value:.3f}' for value in values.cpu().tolist()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e36040dd-367b-47a9-884a-a0ccab42d509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.217e+00, 3.230e-01, 4.195e+00],\n",
      "        [-7.757e-01, -2.438e+00, -8.198e+00],\n",
      "        [4.458e-01, 4.852e-01, 2.926e+00],\n",
      "        [6.755e+00, 1.059e+00, 2.051e+01],\n",
      "        [4.458e-01, 4.852e-01, 2.926e+00],\n",
      "        [-1.523e+00, -5.303e+00, -1.726e+01],\n",
      "        [-1.827e+00, -4.712e+00, -1.699e+01],\n",
      "        [-1.523e+00, -5.303e+00, -1.726e+01]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Responce matrix\n",
    "\n",
    "m = derivative((1, ), lambda k: fn(k, fodo=lambda x: evaluate(jet, [x, k])), k, intermediate=False)\n",
    "\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9328e331-744a-4b7d-9e1c-0edb99240c7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.043e-03, -1.068e-03, -8.224e-05], dtype=torch.float64)\n",
      "tensor(1.846e-02, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor(2.001e-04, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor(2.786e-08, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor([-1.000e-03, -1.000e-03, -1.000e-04], dtype=torch.float64)\n",
      "tensor(2.344e-15, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Correction\n",
    "\n",
    "# The target values (tunes and advance functions) are associated with model response matrix\n",
    "# Given measured values, the goal is to alter knobs to get target values\n",
    "\n",
    "# Set target values\n",
    "\n",
    "vf = fn(k, fodo=lambda x: fodo(x, k))\n",
    "\n",
    "# Set initial solution\n",
    "\n",
    "sol = torch.zeros_like(dk)\n",
    "\n",
    "# Iterate\n",
    "\n",
    "for _ in range(4):\n",
    "\n",
    "    # Compute current values and set difference\n",
    "\n",
    "    vi = fn(k + dk + sol, fodo=lambda x: evaluate(jet, [x, k + dk + sol]))\n",
    "\n",
    "    # Set difference\n",
    "\n",
    "    dv = vf - vi\n",
    "\n",
    "    # Update solution\n",
    "\n",
    "    sol += torch.linalg.pinv(m) @ dv\n",
    "\n",
    "    # Verbose\n",
    "\n",
    "    print(-dk)\n",
    "    print(sol)\n",
    "    print(dv.norm())\n",
    "    print()\n",
    "    \n",
    "    # Continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c172e35-8002-43ab-9d28-4c6b9610c820",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note, similar to tune spread example, it is possible to compute advance spread\n",
    "# First order computation can be performed using error propagation\n",
    "# Or higher order jets can be sampled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cce1f7f-7db6-4d66-8b27-25bdd3ea1771",
   "metadata": {},
   "source": [
    "# Example-20: Poisson bracket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa45886b-a6da-4c80-b1a8-dc5a4e239c22",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.bracket import bracket\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3689983-c2a9-4a65-8a89-29f6e214c211",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac456e1a-8722-4b5a-a653-d70122b42fa8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.000e+00, dtype=torch.float64)\n",
      "tensor([1.000e+00, 0.000e+00], dtype=torch.float64)\n",
      "tensor([0.000e+00, 1.000e+00], dtype=torch.float64)\n",
      "tensor([0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Poisson bracket between observable/mapping or mapping/observable or mapping/mapping\n",
    "\n",
    "# [f, g]                   -> [f, g]\n",
    "# [[f1, f2], g]            -> [[f1, g], [f2, g]]\n",
    "# [f, [g1, g2]]            -> [[f, g1], [f, g2]]\n",
    "# [[f1, f2], [g1, g2]]     -> [[f1, g1], [f2, g2]]\n",
    "\n",
    "\n",
    "def f(x): q, p = x ; return q\n",
    "def g(x): q, p = x ; return p\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "print(bracket(f, g)(x))\n",
    "\n",
    "def f(x): q, p = x ; return torch.stack([q, p])\n",
    "def g(x): q, p = x ; return p\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "print(bracket(f, g)(x))\n",
    "\n",
    "def f(x): q, p = x ; return q\n",
    "def g(x): q, p = x ; return torch.stack([q, p])\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "print(bracket(f, g)(x))\n",
    "\n",
    "def f(x): q, p = x ; return torch.stack([q, p])\n",
    "def g(x): q, p = x ; return torch.stack([q, p])\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "print(bracket(f, g)(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81f37f6c-d4d1-4614-aebb-8c6056472be7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.000e+00, 0.000e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Returns a function that can be differentiated\n",
    "\n",
    "def f(x): q, p = x ; return q**2\n",
    "def g(x): q, p = x ; return p\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "print(derivative(1, bracket(f, g), x, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f736bff-d6d6-4580-a7d2-94ca712b3f95",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.000e+00, 0.000e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Accepts Table input\n",
    "# Note, evaluation is at deviation point\n",
    "\n",
    "tf = propagate((2, ), (2, ), identity((2, ), [x]), [], f)\n",
    "tg = propagate((2, ), (2, ), identity((2, ), [x]), [], g)\n",
    "print(derivative(1, bracket(tf, tg), x, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8049d1ea-443b-4bad-8d01-fc0bd0cd52f8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([2.000e+00, 0.000e+00], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Accepts Series input\n",
    "# Note, evaluation is at deviation point\n",
    "\n",
    "sf = propagate((2, ), (2, ), identity((2, ), [x], flag=True), [], f)\n",
    "sg = propagate((2, ), (2, ), identity((2, ), [x], flag=True), [], g)\n",
    "print(derivative(1, bracket(tf, tg), x, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "885b01fd-7567-4523-b2e8-6c20c5db9da0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor(0., dtype=torch.float64), tensor([2.000e+00, 0.000e+00], dtype=torch.float64)]\n"
     ]
    }
   ],
   "source": [
    "# Propagate identity\n",
    "\n",
    "t = propagate((2, ), (1, ), identity((1, ), [x]), [], bracket(f, g))\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cfbdf42-f926-42c3-8137-21f6d710de31",
   "metadata": {},
   "source": [
    "# Example-21: Taylor integrator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe00852d-8513-4d2f-9ca3-438b79f37205",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Given an autonomous hamiltonian function H\n",
    "# The exact solution is x(t) = exp([-t H]) x with Poisson bracket operator [f] g := [f, g]\n",
    "# Truncated solution is x(t) = exp([-t H]) x = x + t [-H] x + 1/2! t**2 [-H]**2 x + ...\n",
    "# Such series solution doesn't preserve symplectic structure in general\n",
    "# Taylor integration is differentiable with respect to time step, initial value and parameters\n",
    "\n",
    "# Note, generation or derivatives can be extremely slow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "935f3a26-1591-4ccc-ab90-462f1ea31573",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.taylor import taylor\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22cf0927-7122-46e1-8034-095a0873bd20",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float32\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6a7ce9f-726f-4fcf-8152-be139a86a71e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Integrate for a given time step and ititial condition\n",
    "# Note, hamiltonian is not preserved for all cases, but the hamiltonian drift time scale is different\n",
    "\n",
    "# Set nonlinear oscillator hamiltonian function\n",
    "\n",
    "def h(x):\n",
    "    q, p = x\n",
    "    return p**2/2 + q**2/2 + q**3/3\n",
    "\n",
    "# Set time step\n",
    "\n",
    "dt = torch.tensor(0.15, dtype=dtype, device=device)\n",
    "\n",
    "# Set initial condition\n",
    "\n",
    "xi = torch.tensor([0.4, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Integrate and plot orbits for several values of truncation order\n",
    "\n",
    "count = 1024\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.xlim(-1.0, 1.0)\n",
    "plt.ylim(-1.0, 1.0)\n",
    "\n",
    "for order, color in zip([1, 2, 4], ['black', 'red', 'blue']):\n",
    "    orbit = []\n",
    "    x = torch.clone(xi)\n",
    "    for _ in range(count):\n",
    "        x = taylor(order, dt, h, x)\n",
    "        orbit.append(x)\n",
    "    orbit = torch.stack(orbit)\n",
    "    plt.scatter(*orbit.T.cpu().numpy(), color=color, s=1)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b06e560f-e946-454b-b5fd-4f5f9490e239",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0): tensor([0., 0.])\n",
      "(1, 0): tensor([9.987502e-01, -4.997917e-02])\n",
      "(0, 1): tensor([4.997917e-02, 9.987502e-01])\n",
      "(2, 0): tensor([-1.249219e-03, -4.993753e-02])\n",
      "(1, 1): tensor([-4.164063e-05, -2.497397e-03])\n",
      "(0, 2): tensor([-5.206163e-07, -4.164063e-05])\n",
      "(3, 0): tensor([5.203993e-07, 4.161458e-05])\n",
      "(2, 1): tensor([2.604167e-08, 2.601563e-06])\n",
      "(1, 2): tensor([4.340278e-10, 5.208334e-08])\n",
      "(0, 3): tensor([0.000000e+00, 4.340278e-10])\n",
      "(4, 0): tensor([-2.170139e-10, -2.604167e-08])\n",
      "(3, 1): tensor([ 0.000000e+00, -1.736111e-09])\n",
      "(2, 2): tensor([0., 0.])\n",
      "(1, 3): tensor([0., 0.])\n",
      "(0, 4): tensor([0., 0.])\n"
     ]
    }
   ],
   "source": [
    "# Generate derivative table representation at zero\n",
    "# Note, here a smaller time step is used\n",
    "\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "t = derivative(4, lambda x: taylor(6, 0.05, h, x), x)\n",
    "\n",
    "# Compute series representation\n",
    "\n",
    "s = series((2, ), (4, ), t)\n",
    "\n",
    "# Print series\n",
    "\n",
    "for key, value in s.items():\n",
    "    print(f'{key}: {value.cpu()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92fe87b3-666e-45a4-b026-89483597006c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Integrate using derivative table and a batch of initials\n",
    "\n",
    "q = torch.linspace(0.1, 0.6, 21)\n",
    "p = torch.zeros_like(q)\n",
    "x = torch.stack([q, p]).T\n",
    "\n",
    "orbits = []\n",
    "\n",
    "for _ in range(count):\n",
    "    x = torch.func.vmap(lambda x: evaluate(t, [x]))(x)\n",
    "    orbits.append(x)\n",
    "\n",
    "orbits = torch.stack(orbits).swapaxes(0, 1)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.xlim(-1.5, 1)\n",
    "plt.ylim(-1, 1)\n",
    "\n",
    "for orbit in orbits:\n",
    "    plt.scatter(*orbit.T.cpu().numpy(), color='black', s=1)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b71127a-a282-4cd8-9859-e71a8eb46d3d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Derivatives with respect to time step and other parameters\n",
    "\n",
    "def h(x, kq, ks):\n",
    "    q, p = x\n",
    "    return p**2/2 + kq**2/2 + ks*q**3/3\n",
    "\n",
    "dt = torch.tensor(0.01, dtype=dtype, device=device)\n",
    "xi = torch.tensor([0.4, 0.0], dtype=dtype, device=device)\n",
    "kq = torch.tensor(1.0, dtype=dtype, device=device)\n",
    "ks = torch.tensor(1.0, dtype=dtype, device=device)\n",
    "\n",
    "t = derivative((2, 1, 1, 1), lambda xi, dt, kq, ks: taylor(4, dt, h, xi, kq, ks), xi, dt, kq, ks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10828357-0894-47b1-ada3-c49d2c43aef5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([4.999716e-01, -3.787356e-03])\n",
      "tensor([4.999748e-01, -3.787395e-03])\n"
     ]
    }
   ],
   "source": [
    "# Check table\n",
    "\n",
    "dxi = torch.tensor([0.1, 0.0], dtype=dtype, device=device)\n",
    "ddt = torch.tensor(0.005, dtype=dtype, device=device)\n",
    "dkq = torch.tensor(0.01, dtype=dtype, device=device)\n",
    "dks = torch.tensor(0.01, dtype=dtype, device=device)\n",
    "\n",
    "print(taylor(4, dt + ddt, h, xi + dxi, kq + dkq, ks + dks))\n",
    "print(evaluate(t, [dxi, ddt, dkq, dks]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64cfb1f0-90b6-49e3-9418-d7ca87bdaa61",
   "metadata": {},
   "source": [
    "# Example-22: Yoshida integrator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3774465d-b739-409e-8e28-ac4942f801a9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Given a time-reversible integration step of difference order 2n\n",
    "# Yoshida coefficients can be used to construct integration step of difference order 2(n+1)\n",
    "# s(2(n+1))(dt) = s(2n)(x1 dt) o s(2n)(x2 dt) o s(2n)(x1 dt)\n",
    "\n",
    "# If a hamiltonian vector field can be splitted into several sovable parts\n",
    "# Second order time-reversible symmetric integrator can be easily constructed\n",
    "# s1(dt/2) o s2(dt/2) o ... o sn(dt/2) o sn(dt/2) o ... o s2(dt/2) o s1(dt/2)\n",
    "# Yoshida procedure can be then applied repeatedly to obtain higher order integration steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "618f11a7-1012-4d1d-af53-30caf4003cbe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import first\n",
    "from ndmap.util import nest\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.yoshida import coefficients\n",
    "from ndmap.yoshida import yoshida\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3611d0f2-0c57-4a84-b5d0-a232f429b9cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.3512071919596578, -1.7024143839193153, 1.3512071919596578]\n",
      "[1.1746717580893635, -1.349343516178727, 1.1746717580893635]\n",
      "[1.1161829393253857, -1.2323658786507714, 1.1161829393253857]\n",
      "[1.0870271062991708, -1.1740542125983413, 1.0870271062991708]\n"
     ]
    }
   ],
   "source": [
    "# Given integration step on Yoshida order n\n",
    "# Yoshida coefficent for the next order can be computed\n",
    "\n",
    "# Note, sum of coefficients is equal to one\n",
    "\n",
    "print(coefficients(1)) # 2 -> 4\n",
    "print(coefficients(2)) # 4 -> 6\n",
    "print(coefficients(3)) # 6 -> 8\n",
    "print(coefficients(4)) # 8 -> 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d566742-d9c3-40c4-8a91-7a7365e6ff01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1.351', '-1.702', '1.351']\n",
      "['1.587', '-2.000', '1.587', '-1.823', '2.297', '-1.823', '1.587', '-2.000', '1.587']\n",
      "['1.175', '-1.349', '1.175']\n"
     ]
    }
   ],
   "source": [
    "# Given integration step on Yoshida order n\n",
    "# Yoshida coefficent for m order can be computed\n",
    "\n",
    "# Note, sum of coefficients is equal to one\n",
    "\n",
    "print([f'{coefficient:.3f}' for coefficient in coefficients(1, 1)]) # 2 -> 4\n",
    "print([f'{coefficient:.3f}' for coefficient in coefficients(1, 2)]) # 2 -> 6\n",
    "print([f'{coefficient:.3f}' for coefficient in coefficients(2, 2)]) # 4 -> 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e144009-cc86-4905-b095-dfea152a60a8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0], ['1.351', '-1.702', '1.351']]\n",
      "[[0, 0, 0, 0, 0, 0, 0, 0, 0], ['1.587', '-2.000', '1.587', '-1.823', '2.297', '-1.823', '1.587', '-2.000', '1.587']]\n",
      "[[0, 0, 0], ['1.175', '-1.349', '1.175']]\n",
      "\n",
      "[[0, 1, 0], ['0.500', '1.000', '0.500']]\n",
      "[[0, 1, 0, 1, 0, 1, 0], ['0.676', '1.351', '-0.176', '-1.702', '-0.176', '1.351', '0.676']]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Given a set of mappings and (start, final) Yoshida orders\n",
    "# Corresponding Yoshida coefficients can be computed\n",
    "# Note, mapping can be an integation step\n",
    "\n",
    "# Mapping is a step (last argument should be False)\n",
    "\n",
    "ns, cs = coefficients(1, 1, 1, False) ; print([ns, [f'{c:.3f}' for c in cs]]) # 2 -> 4\n",
    "ns, cs = coefficients(1, 1, 2, False) ; print([ns, [f'{c:.3f}' for c in cs]]) # 2 -> 6\n",
    "ns, cs = coefficients(1, 2, 2, False) ; print([ns, [f'{c:.3f}' for c in cs]]) # 4 -> 6\n",
    "print()\n",
    "\n",
    "# Two mappings (merge edge mappings)\n",
    "# Note, number of mappings can be arbitrary\n",
    "\n",
    "ns, cs = coefficients(2, 0, 0, True)  ; print([ns, [f'{c:.3f}' for c in cs]]) # 2 -> 2\n",
    "ns, cs = coefficients(2, 0, 1, True)  ; print([ns, [f'{c:.3f}' for c in cs]]) # 2 -> 4\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ab39eba-2e5a-4d14-bd19-3854bc3c61bb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.375000e-01, 4.062500e-02], dtype=torch.float64) 3\n",
      "tensor([1.354787e-01, 3.997254e-02], dtype=torch.float64) 9\n",
      "tensor([1.357446e-01, 3.982467e-02], dtype=torch.float64) 27\n",
      "tensor([1.357004e-01, 3.981894e-02], dtype=torch.float64) 81\n",
      "tensor([1.356984e-01, 3.981700e-02], dtype=torch.float64) 243\n",
      "tensor([1.357016e-01, 3.981564e-02], dtype=torch.float64) 729\n",
      "tensor([1.357009e-01, 3.981567e-02], dtype=torch.float64) 2187\n",
      "tensor([1.357007e-01, 3.981572e-02], dtype=torch.float64) 6561\n",
      "tensor([1.357009e-01, 3.981567e-02], dtype=torch.float64) 19683\n",
      "tensor([1.357007e-01, 3.981573e-02], dtype=torch.float64) 59049\n",
      "\n",
      "tensor([1.375000e-01, 4.062500e-02], dtype=torch.float64) 3\n",
      "tensor([1.354787e-01, 3.997254e-02], dtype=torch.float64) 7\n",
      "tensor([1.357446e-01, 3.982467e-02], dtype=torch.float64) 19\n",
      "tensor([1.357004e-01, 3.981894e-02], dtype=torch.float64) 55\n",
      "tensor([1.356984e-01, 3.981700e-02], dtype=torch.float64) 163\n",
      "tensor([1.357016e-01, 3.981564e-02], dtype=torch.float64) 487\n",
      "tensor([1.357009e-01, 3.981567e-02], dtype=torch.float64) 1459\n",
      "tensor([1.357007e-01, 3.981572e-02], dtype=torch.float64) 4375\n",
      "tensor([1.357009e-01, 3.981567e-02], dtype=torch.float64) 13123\n",
      "tensor([1.357007e-01, 3.981573e-02], dtype=torch.float64) 39367\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Integrate rotation\n",
    "\n",
    "# h = h1 + h2 \n",
    "# h1 = 1/2 q**2 -> [q, p] -> [q, p - t*q]\n",
    "# h2 = 1/2 p**2 -> [q, p] -> [q + t*q, p]\n",
    "\n",
    "# Set mappings\n",
    "\n",
    "def fn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - t*q])\n",
    "\n",
    "def gn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "# Set time step\n",
    "\n",
    "t = torch.tensor(0.5, dtype=torch.float64)\n",
    "\n",
    "# Set initial condition\n",
    "\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "\n",
    "# Without merging\n",
    "\n",
    "for i in range(10):\n",
    "    print(yoshida(0, i, False, [fn, gn])(x, t), len(first(yoshida(0, i, False, [fn, gn]).table)))\n",
    "print()\n",
    "\n",
    "# With merging\n",
    "    \n",
    "for i in range(10):\n",
    "    print(yoshida(0, i, True, [fn, gn])(x, t), len(first(yoshida(0, i, True, [fn, gn]).table)))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a6706cf-b2b7-450e-a9f6-af59bedbe8e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.357008e-01, 3.981570e-02], dtype=torch.float64)\n",
      "tensor([1.357008e-01, 3.981570e-02], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Several steps\n",
    "\n",
    "count = 100\n",
    "t = torch.tensor(0.5, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "for _ in range(count):\n",
    "    x = yoshida(0, 1, True, [fn, gn])(x, t/count)\n",
    "print(x)\n",
    "\n",
    "count = 100\n",
    "t = torch.tensor(0.5, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "x = nest(count, yoshida(0, 1, True, [fn, gn]))(x,  t/count)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d55fd88-33f0-4120-8241-5d36e5af6f93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64)\n",
      "\n",
      "tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64)\n",
      "\n",
      "[4, 6, 3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Multistep\n",
    "\n",
    "# h = h1 + h2 \n",
    "# h1 = 1/2 q**2 + 1/3 q**3 -> [q, p] -> [q, p - t*q - t*q**2]\n",
    "# h2 = 1/2 p**2            -> [q, p] -> [q + t*q, p]\n",
    "\n",
    "def fn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - t*q - t*q**2])\n",
    "\n",
    "def gn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor(0.1, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "\n",
    "print(yoshida(0, 1, True, [fn, gn])(x, t))\n",
    "print()\n",
    "\n",
    "# h = h1 + h2 + h3\n",
    "# h1 = 1/2 q**2 -> [q, p] -> [q, p - t*q]\n",
    "# h2 = 1/3 q**3 -> [q, p] -> [q, p - t*q**2]\n",
    "# h3 = 1/2 p**2 -> [q, p] -> [q + t*q, p]\n",
    "\n",
    "def fn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - t*q])\n",
    "\n",
    "def gn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - t*q**2])\n",
    "\n",
    "def hn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor(0.1, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "\n",
    "print(yoshida(0, 1, True, [fn, gn, hn])(x, t))\n",
    "print()\n",
    "\n",
    "# Note, the last mapping has the smallest number of evaluations\n",
    "\n",
    "sequence, _ = yoshida(0, 1, True, [fn, gn, hn]).table\n",
    "\n",
    "print([*map(sequence.count, sorted(set(sequence)))])\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0977910c-bc83-4c7a-b169-9eecc2a7f760",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Increase order\n",
    "\n",
    "def fn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - t*q - t*q**2])\n",
    "\n",
    "def gn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor(0.1, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "\n",
    "s2 = yoshida(0, 0, True, [fn, gn])\n",
    "print(torch.allclose(s2(x, t), yoshida(0, 0, True, [fn, gn])(x, t)))\n",
    "\n",
    "s4 = yoshida(1, 1, False, [s2])\n",
    "print(torch.allclose(s4(x, t), yoshida(0, 1, True, [fn, gn])(x, t)))\n",
    "\n",
    "s6 = yoshida(1, 2, False, [s2])\n",
    "print(torch.allclose(s6(x, t), yoshida(0, 2, True, [fn, gn])(x, t)))\n",
    "\n",
    "s6 = yoshida(2, 2, False, [s4])\n",
    "print(torch.allclose(s6(x, t), yoshida(0, 2, True, [fn, gn])(x, t)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c20d1a8-0082-4b3c-b518-dd2024f81974",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Step with parameters (matched signatures)\n",
    "\n",
    "def fn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - a*t*q - b*t*q**2])\n",
    "\n",
    "def gn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor(0.1, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "a = torch.tensor(1.0, dtype=torch.float64)\n",
    "b = torch.tensor(1.0, dtype=torch.float64)\n",
    "\n",
    "print(yoshida(0, 1, True, [fn, gn])(x, t, a, b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "01bb072d-6ed9-4d3f-8ff4-4dc326aac1c2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Step with parameters (pass fixed parameters)\n",
    "\n",
    "def fn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - a*t*q - b*t*q**2])\n",
    "\n",
    "def gn(x, t):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor(0.1, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "a = torch.tensor(1.0, dtype=torch.float64)\n",
    "b = torch.tensor(1.0, dtype=torch.float64)\n",
    "\n",
    "print(yoshida(0, 1, True, [fn, gn], parameters=[[a, b], []])(x, t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63908574-3fd3-4e53-99e5-e7320b40279a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64), tensor([[9.939735e-01, 9.979712e-02],\n",
      "        [-1.207179e-01, 9.939427e-01]], dtype=torch.float64)]\n",
      "\n",
      "[tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64), tensor([8.841321e-02, -1.214017e-01], dtype=torch.float64)]\n",
      "[[tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64), tensor([8.841321e-02, -1.214017e-01], dtype=torch.float64)]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Step can be differentiated with respect to initials, time step and/or parametes\n",
    "\n",
    "def fn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - a*t*q - b*t*q**2])\n",
    "\n",
    "def gn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor(0.1, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "a = torch.tensor(1.0, dtype=torch.float64)\n",
    "b = torch.tensor(1.0, dtype=torch.float64)\n",
    "\n",
    "# Derivative with respect to initial\n",
    "\n",
    "print(derivative(1, yoshida(0, 1, True, [fn, gn]), x, t, a, b))\n",
    "print()\n",
    "\n",
    "# Derivative with respect to time step\n",
    "\n",
    "print(derivative(1, lambda t, x, a, b: yoshida(0, 1, True, [fn, gn])(x, t, a, b), t, x, a, b))\n",
    "print(derivative((0, 1), yoshida(0, 1, True, [fn, gn]), x, t, a, b))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d97eaf2b-9792-4d71-8575-408c771721c0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64)\n",
      "tensor([1.094304e-01, 8.841961e-02], dtype=torch.float64)\n",
      "\n",
      "tensor([1.139844e-01, 8.272697e-02], dtype=torch.float64)\n",
      "tensor([1.139846e-01, 8.272929e-02], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# For derivative table propagation all knobs should be vectors\n",
    "\n",
    "def fn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - a*t*q - b*t*q**2])\n",
    "\n",
    "def gn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor([0.1], dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "a = torch.tensor([1.0], dtype=torch.float64)\n",
    "b = torch.tensor([1.0], dtype=torch.float64)\n",
    "\n",
    "step = yoshida(0, 1, True, [fn, gn])\n",
    "\n",
    "def wrapper(x, t, a, b):\n",
    "    (t, ), (a, ), (b, ) = t, a, b\n",
    "    return step(x, t, a, b)\n",
    "\n",
    "print(step(x, *t, *a, *b))\n",
    "print(wrapper(x, t, a, b))\n",
    "print()\n",
    "\n",
    "out = propagate((2, 1, 1, 1),\n",
    "                4*(1, ),\n",
    "                identity(4*(1, ), [x, t, a, b]),\n",
    "                [t, a, b],\n",
    "                wrapper)\n",
    "\n",
    "dt = torch.tensor([+0.001], dtype=torch.float64)\n",
    "dx = torch.tensor([+0.005, -0.005], dtype=torch.float64)\n",
    "da = torch.tensor([-0.001], dtype=torch.float64)\n",
    "db = torch.tensor([+0.001], dtype=torch.float64)\n",
    "\n",
    "print(wrapper(x + dx, t + dt, a + da, b + db))\n",
    "print(evaluate(out, [dx, dt, da, db]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "297a6157-b391-4534-8874-51e74cda8445",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.094303556041e-01, 8.841960703680e-02], dtype=torch.float64)\n",
      "tensor([1.988014274981e-01, -2.394392236666e-02], dtype=torch.float64)\n",
      "\n",
      "tensor([1.988479888736e-01, -2.304645602610e-02], dtype=torch.float64)\n",
      "tensor([1.988014166220e-01, -2.394421962623e-02], dtype=torch.float64)\n",
      "tensor([1.988014274804e-01, -2.394392240255e-02], dtype=torch.float64)\n",
      "tensor([1.988014274981e-01, -2.394392236643e-02], dtype=torch.float64)\n",
      "tensor([1.988014274981e-01, -2.394392236666e-02], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Given a step, its derivatives can be used as a taylor model\n",
    "# Note, taylor model is not symplectic in general\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True, linewidth=128)\n",
    "\n",
    "def fn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q, p - a*t*q - b*t*q**2])\n",
    "\n",
    "def gn(x, t, a, b):\n",
    "    q, p = x\n",
    "    return torch.stack([q + t*p, p])\n",
    "\n",
    "t = torch.tensor(0.1, dtype=torch.float64)\n",
    "x = torch.tensor([0.1, 0.1], dtype=torch.float64)\n",
    "a = torch.tensor(1.0, dtype=torch.float64)\n",
    "b = torch.tensor(1.0, dtype=torch.float64)\n",
    "\n",
    "dx = torch.tensor([+0.1, -0.1], dtype=torch.float64)\n",
    "\n",
    "print(yoshida(0, 1, True, [fn, gn])(x, t, a, b))\n",
    "print(yoshida(0, 1, True, [fn, gn])(x + dx, t, a, b))\n",
    "print()\n",
    "\n",
    "print(evaluate(derivative(1, yoshida(0, 1, True, [fn, gn]), x, t, a, b), [dx]))\n",
    "print(evaluate(derivative(2, yoshida(0, 1, True, [fn, gn]), x, t, a, b), [dx]))\n",
    "print(evaluate(derivative(3, yoshida(0, 1, True, [fn, gn]), x, t, a, b), [dx]))\n",
    "print(evaluate(derivative(4, yoshida(0, 1, True, [fn, gn]), x, t, a, b), [dx]))\n",
    "print(evaluate(derivative(5, yoshida(0, 1, True, [fn, gn]), x, t, a, b), [dx]))\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc468eb-c385-4e06-ada6-30507c355403",
   "metadata": {},
   "source": [
    "# Example-23: Direct invariant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e226694-c8e6-48d5-9593-f42df9a4201a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# In this example Taylor invariants are constructed by solving I(f(x)) = I(x) order-by-order\n",
    "# Mapping f(x) can be replaced with its derivative table representation of a given order n\n",
    "# Or mapping can be used directly\n",
    "# Invariant of order n+1 can be computed\n",
    "# Note, to avoid trivial solution, initial invariant guess should be provided, e.g. linear part invariant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bdebd4f9-da37-4542-932b-d9f6c5b504b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.series import series\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.invariant import invariant\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a030153b-722d-4047-9630-b91db536b855",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7bf99d01-873c-4427-b5e0-d673008b80d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=20):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "715fc081-96cc-4c90-9c2d-e00212bee50e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x):\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88ad8540-ba8f-4eaf-88c1-aa25d696a874",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d01a891f-01eb-4d92-a76c-3f9c36e293c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0.], dtype=torch.float64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set evaluation point\n",
    "\n",
    "# Note, zero is a fixed point and derivatives with respect to parameters are not used, i.e. no need to compute paraetric fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "fodo(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b2fade1-dd91-4dac-9daa-0f5ceb83e454",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Generate derivative table representation for given order\n",
    "\n",
    "t = identity(4, x, jacobian=torch.func.jacfwd)\n",
    "t = propagate((4, ), (4, ), t, [], fodo, jacobian=torch.func.jacfwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b7bf95a-350f-43cc-b398-a6413fe3b8b6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute linear normalization matrix\n",
    "\n",
    "_, n, _ = twiss(derivative(1, lambda x: evaluate(t, [x]), x, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee74d406-c65e-4737-a1e8-41f17d84a673",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set initial invariants\n",
    "\n",
    "def ix(x):\n",
    "    qx, px, qy, py = torch.linalg.inv(n) @ x\n",
    "    return 1/2*(qx**2 + px**2)\n",
    "\n",
    "def iy(x):\n",
    "    qx, px, qy, py = torch.linalg.inv(n) @ x\n",
    "    return 1/2*(qy**2 + py**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf938379-b041-47b1-af4e-549bf985b64a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 6.775970e-02, -1.148888e-15,  0.000000e+00,  0.000000e+00],\n",
      "        [-1.148888e-15,  1.475804e+01,  0.000000e+00,  0.000000e+00],\n",
      "        [ 0.000000e+00,  0.000000e+00,  0.000000e+00,  0.000000e+00],\n",
      "        [ 0.000000e+00,  0.000000e+00,  0.000000e+00,  0.000000e+00]], dtype=torch.float64)\n",
      "tensor([[ 6.775970e-02, -1.151856e-15,  0.000000e+00,  0.000000e+00],\n",
      "        [-1.151856e-15,  1.475804e+01,  0.000000e+00,  0.000000e+00],\n",
      "        [ 0.000000e+00,  0.000000e+00,  0.000000e+00,  0.000000e+00],\n",
      "        [ 0.000000e+00,  0.000000e+00,  0.000000e+00,  0.000000e+00]], dtype=torch.float64)\n",
      "\n",
      "tensor([[0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
      "        [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
      "        [0.000000e+00, 0.000000e+00, 8.242765e-02, 1.472561e-15],\n",
      "        [0.000000e+00, 0.000000e+00, 1.472561e-15, 1.213185e+01]], dtype=torch.float64)\n",
      "tensor([[0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
      "        [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00],\n",
      "        [0.000000e+00, 0.000000e+00, 8.242765e-02, 1.554312e-15],\n",
      "        [0.000000e+00, 0.000000e+00, 1.554312e-15, 1.213185e+01]], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check conservation of linear invariants\n",
    "\n",
    "print(derivative(2, ix, x, intermediate=False))\n",
    "print(propagate((4, ), (2, ), t, [], ix, intermediate=False))\n",
    "print()\n",
    "\n",
    "print(derivative(2, iy, x, intermediate=False))\n",
    "print(propagate((4, ), (2, ), t, [], iy, intermediate=False))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a01f463-242d-4d93-a013-eb24445d55ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute nonlinear invariants\n",
    "# Note, computation is not optimized and requires a lot of memory\n",
    "\n",
    "tx, _ = invariant((4, ), x, [], ix, t, jacobian=torch.func.jacfwd, threshold=1.0E-3)\n",
    "ty, _ = invariant((4, ), x, [], iy, t, jacobian=torch.func.jacfwd, threshold=1.0E-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89508b00-d209-4268-9493-4181cf94325a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABTIAAAKTCAYAAAAqt/eRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADtjElEQVR4nOzdf5ReVX3o/8+EkASBmSlqMuQaL7hEA4okgsZpqT+WswyX2JorXZ1Jo6ncCK0rUQGrwFoUXH5dNylqbalYxF4buirOSFutBIw3i/DjCjFgSBRCyLItFpROUHMzI5Sfyfn+Mfc82c/O+bH3PnufX8/7tVbWZJ7nPOecZ57n7P05n/2rL4qiSAAAAAAAAACgxmZVfQIAAAAAAAAAkIdEJgAAAAAAAIDaI5EJAAAAAAAAoPZIZAIAAAAAAACoPRKZAAAAAAAAAGqPRCYAAAAAAACA2iORCQAAAAAAAKD2Zld9Ak12+PBhefLJJ+XEE0+Uvr6+qk8HAADAShRF8utf/1oWLlwos2bRvt1ExKMAAKDpbGJSEpkFPPnkk7Jo0aKqTwMAAKCQJ554Ql71qldVfRpwQDwKAADawiQmJZFZwIknnigiM3/o/v7+is8GAADAzvT0tCxatKgT06B5iEcBAEDT2cSkJDILiIfv9Pf3EzgCAIDGYkhycxGPAgCAtjCJSZkMCQAAAAAAAEDtkcgEAAAAAAAAUHskMgEAAAAAAADUHolMAAAAAAAAALVHIhMAAAAAAABA7ZHIBAAAAAAAAFB7JDIBAAAAAAAA1B6JTAAAAAAAAAC1RyITAAAAAAAAQO2RyAQAAAAAAABQeyQyAQAAAAAAANQeiUwAAAAAAAAAtUciEwAAAAAAAEDtkcgEAAAAAAAAUHskMgEAAAAAAADUHolMAAAAAAAAALVHIhMAAAAAAABA7ZHIBAAAAAAAAFB7JDIBAAAAAAAA1B6JTAAAAAAAAAC1RyITAAAAAAAAQO2RyAQAAAAAAABQeyQyAQAAAAAAANQeiUwAAAAAAAAAtUciEwCaYvdukb6+mZ8AAABAEjVmJH4E0DIkMgEgFD1wdAkk1dcsXTrzWPyz6PkAAACg+fQYT40Z9fjRR3zq61zzzoXYFUACEpkAEIoeOLokItXX7Np15HGXFva8QBYAAAD1l5W4FDkSM+7a1f3/pG2LNpQXOdesc9Eb8eP9kOgEeh6JTABIU7QHZRwwjo/PPDY+PvO7mpDMowafS5YceTwp+DM9n7RA1hbBIwAAQDmyRunoMd6SJSJRNPNT/X/StupPH7GdS5I1fp36ux6fqr9nJTqJS4HWI5EJAKqsINEkQFJfEweOY2Mzj42NdQeSSfvTH8sKPvUA1PR8kvZl8t7yAlMAAACEkTRKJylxmUffVv3dx+idvCSrfi7q65YsSY911dfv2tUdf6q/M/IIaD0SmQCgUoOguAdl/NMkcZcUrOk9M7MSgXlBWFYLu8n5qPvLClyTZAWmBIsAAAD+6LGVPkrHNHFpw2X0TtZ5ihx9rq69SeP/x7+nJUh9jTwCUFskMgG0S9FJwtWgSO1JqT+XJimw1HtmZrVQFxn+nfRe84LHtOMmydqGYBEAAMAfPbYKlbxU5Q1DT4o1k+a1VKdEsk10upyrGn+6jjwC0BgkMgG0i8sE5novxXh4ij6nZVKglTTxeBqTwC0vgEw7b/U92vYYTTuXvGHuWUOcTBBUAgCAJslacdv3/l1iK9+yYr+kxKTJaKMQCdmkhKuaUKXBHWgVEpkAmk8N+vTh4LbzSKo/9Tkts16r/j8tsDUN3NKGf+edtwmb4DFv/0WHODFBOwAAqJO8kT1ZK24XHRWk77+MHpi2shbjETk6Dlffe8iErO3wdeJOoNFIZAJoPjVY0YeDm/Q0zFq9MU28jzhQU7cv2upr+vrQq06qwajJsHWVSTCf1oJPUAkAAKqQN7Ina9FFk1FBefFRHXphqpJG52S9bz0OTxryXQY9JtaHu9NDE2i0viiKoqpPoqmmp6dlYGBApqampL+/v+rTAdohKdiweY1I8tw86mN9fUde61oEZu3D9D2knXfSe/B5fibno/+dVCb704+fdz7qKvGxIu8fgBFimebjMwQKyoojbeJSfduk19rGR1Wzid+K/v1CSXoPefcOAEpnE8/QIxNA9dTW3qLDpZN6CeYNL3GRtQ/ToUBZw5OKDiVy6Z1p0+vA5vhJP7Na+PXzAQAA8Ckt9tRjOJvh3Sav1eMhfSh23eTFf3qPS3Wu+aQpkqoY0p30HpIWCioSd4aeOxVAFxKZAKqlB5BJwUbRgCDEMB0f8xYlJQpF/CTwXAK0rMWIbN9vXjCfdE7xNiwcBAAAQiq6YKEv+lBskWpjmryFHk2GwmfFnVUM6XZ5D6qkz6PsuVMBdGFoeQEM5QE8UId7pA3pyBsSYjt0Rd9fHYa9iPg9j7oPmSn6XkNMFwD0IGKZ5uMzBAwUiRt9MolBq4xp8o5tcm5Zf9s6xNx572FiYiaxPD4uMjqavH3W5yiSHaPq9z5V/z2AmmBoOYDm0Fe+jqmtlSatvTYtvHmTs6exaUF1aW31uTql66qXZbUSF32vWcPg4/dAazcAABA5Om6oakXwrNEvaduU2YMvr3eiSe/VrB6QJotwFmGyr7z3oPeSTdreZhRT1nRLLDoEOCGRCaAacaAhkhxI5iXiXFYaj7kGr3lDRbK2DSHr+K7DpHyfd3yOExN+g/Cs1SjVcycwBAAAVQ0fzxuqncRkOp4Q56YfO+95U3nn7/P9mewr6T2q71OftzTpPfuaO7XKpDXQYCQyAVQjL9DICziLTMZue6y07bLeg++AOSmwyTq+69/D5LxNgyx1/tO4VdslSM0LpNN6Z+r/BwAAvSOvJ2AZfCTpQswfr55T6ASjj16eSZL+Bln7yoql1f+Pjs58V0ZHwycWy0paAy1DIhOAH7YTWdsmKrOOVZRpcJs3VMRln6aSApsyFzFyWVlefT5u1XY5V9ukd/weqrhhAQAA9VCHpJCPWC0pNguVILV53lTecHLXlc2zFo5Miv+yYmn9/1mvCSlpqiR6ZwJHIZEJwE3Wan1JP/XXibgnmuoQmIrYJyuLBCRJwWSZvQtcVvdUt4tbt4v0mB0fT/77lZn0BgAAzVDlauSxULGay3vLGubuaxi5KduVzX30vsyKpdMSqWV/h7JGHAHoYNXyAlglEj1NX3Fv377uFf7SViV0XYmx7qtwm2jyytpJn2fZK0+6/P1sV9cUaeZ3C3BELNN8fIaAoupVsas8ft6xs2KismNU25XNbc+vyPupS7xOfIoew6rlAMLTVxDXV/hLa8XVJ9B2GT4ST469dKnZPI1lroSYpeqeAbbD/1WhhjXZcPn7mbxGfR+0fgMA0FxV1+NVHr/I/PNV9jyMxXHpvn1Hb++j96Up22OFQu9MIFUpiczrr79eTjnlFJk3b54sW7ZM7r///sztb7nlFlm8eLHMmzdPzjzzTLn99tu7no+iSK6++mo5+eST5bjjjpORkRH5yU9+0rXNgQMHZPXq1dLf3y+Dg4Oydu1aefrppzvP79u3T971rnfJggULZN68efKa17xGrrrqKnnxxRf9vXGgzfSV9kwrfT3haVox2yy0ox7XZqXxPEWDiLw5gkIzHf5vqowFjVQuQ5xMXqO+D9dEOwAAqF7VjcZVHj9tfkV9WieRcoeRm3JdINJ2rsw8tvNslqHq7zVQM8ETmRMTE3LZZZfJNddcIw8++KCcddZZsnz5cnnqqacSt7/vvvtk1apVsnbtWtm1a5esXLlSVq5cKQ8//HBnm2uvvVauu+46ueGGG2THjh1y/PHHy/Lly+W5557rbLN69WrZs2ePbN26VTZv3iz33HOPXHzxxZ3njz32WFmzZo387//9v2Xfvn3yF3/xF/LVr35VrrnmmnB/DKBt1IretNLXK2LTitlmoR39uOp2RQKQEEFE3lyiPhNorn/7NGUsaBRS0nytaqLdZmEjAABQjTqsTh6r8vhpPfh8N2SHos6Jrv4u0n3OPntf5tHj8aoSinnzmQK9JgrsrW99a7Ru3brO74cOHYoWLlwYbdiwIXH73//9349WrFjR9diyZcuiP/qjP4qiKIoOHz4cDQ0NRZ/73Oc6zx88eDCaO3du9I1vfCOKoih65JFHIhGJHnjggc423/3ud6O+vr7o5z//eeq5XnrppdG5556b+vxzzz0XTU1Ndf498cQTkYhEU1NTGX8BoEF27ZqZ7nrXrmLb+HiND0nHtT2X0Oeetv8jU4/722cdqedqet6+3l/S31jdt/p8E/6WgIOpqSlimYbjM0Sj6XW67e9RVCxmKqquMVdWfFWXc7Y5Dz0uK+uzrvK7laaO5wR4YBPPBO2R+cILL8jOnTtlZGSk89isWbNkZGREtm/fnvia7du3d20vIrJ8+fLO9o899phMTk52bTMwMCDLli3rbLN9+3YZHByUc845p7PNyMiIzJo1S3bs2JF43H/5l3+RLVu2yDve8Y7U97NhwwYZGBjo/Fu0aFHOXwBoGJPegS6tuCat0yFaF5OOa9tSHrrVOu18irT41rWlPZb2fTJdfdz1/Zm0qqvnoD7PxOoAABSn18V5vQVNehNWOS1MXWOuOJ4ROXqRGJu55kPK+tvFn6He0zZt+HwoefF4Fb0jGWYOhB1a/stf/lIOHTokCxYs6Hp8wYIFMjk5mfiaycnJzO3jn3nbzJ8/v+v52bNny0knnXTUcX/zN39T5s2bJ6eddpr89m//tnzmM59JfT9XXnmlTE1Ndf498cQTqdsCjWA6XCJraLgvdQgEfU8WnrZPE0WGJhU55zICMpfvk8tr8m6W8v7GVQ9PAwCgDbIaI/OmvcmaDieu303mXw8V39QhqZT13tLi66qm8zGNsdXzSpv7soz3kBcLVnH/wjBzgFXLJyYm5MEHH5Sbb75ZbrvtNvn85z+fuu3cuXOlv7+/6x/QKFmJnax5hfSeaSGSO3UIBItMFp4WSIQMcNKOWeQzypqDyPV8shLmpufq8pq8myXfCCYBAMiON/W6WK/TTX6PmSZD9XPwqQ6NnlnvLS32SXs8VCyTFWOLpCc5k85Rf7zMe4e6zJkZq0NHEKACQROZr3jFK+SYY46R/fv3dz2+f/9+GRoaSnzN0NBQ5vbxz7xt9MWEXnrpJTlw4MBRx120aJGcccYZsmrVKtm4caN8+tOflkOHDlm+U6Ah0hI76mOxsidOtz1GGYvh2EgLJEIEOPF7L5J0TJPWG9eEaat/qNXHdXk3SzZMvm8EkwAAHF0fqkO/k4Y163VsUp2rPmabDFW3LZq4q2OjZVasmRb7pD0eKpYx6X2ZlOTUz7HqhZ18xLQ+VZ1IBSoSNJE5Z84cOfvss+WOO+7oPHb48GG54447ZHh4OPE1w8PDXduLiGzdurWz/amnnipDQ0Nd20xPT8uOHTs62wwPD8vBgwdl586dnW22bdsmhw8flmXLlqWe7+HDh+XFF1+Uw4cP279ZoAnU1QDj1ZpNhpMXUWbLblEhhnOHCHDS3rOPv0WROYjyWv1Fyg38ff7tTb5vZc/bBABAXaj1nj5nZd7Qb5N5MPU5tW3neLRJ3LkM1S5L0rll9Wy0FWpKpayYzOaYJvNq9tKcmQwzR68KvfLQ+Ph4NHfu3GjTpk3RI488El188cXR4OBgNDk5GUVRFH3wgx+Mrrjiis729957bzR79uzo85//fLR3797ommuuiY499tjooYce6myzcePGaHBwMPrnf/7n6Mc//nH0vve9Lzr11FOjZ599trPNeeedFy1dujTasWNH9P3vfz867bTTolWrVnWe//u///toYmIieuSRR6J//dd/jSYmJqKFCxdGq1evNn5vrBKJxjJZ7S5vJUHTlQb1Y/la9bwuKy7a8HXOZa8+6WN1RJ8rLFbx2dteD6x4joYglmk+PkPUgr6idFbs57Iy+fj4zGPj40cfL+Yrxszad3weVdXtWfFUyNWsXeN+l324nkcdVvOu8hzq8P6BAmzimVK+5X/1V38VvfrVr47mzJkTvfWtb41+8IMfdJ57xzveEf3hH/5h1/bf/OY3o9e97nXRnDlzoje84Q3Rbbfd1vX84cOHoz/90z+NFixYEM2dOzd697vfHe3bt69rm1/96lfRqlWrohNOOCHq7++PLrzwwujXv/515/nx8fHozW9+c3TCCSdExx9/fHTGGWdE//N//s+uZGgeAkc0lo9Az7SydH2dyuY1dUxyxcpOboVInLru0+fnUsdALe2mTf28SWqihohlmo/PEJVQ67ky6jyThvG0+MD2fELuu6is44U8F9OOCVUmGusQa1V5DnqyP+l8yu4MAVioXSKzrQgc0VP04MM1ueirtdz0PIvsy+bcbJK6LglgFzaJ07omY5NU0SvCpYey+lgdk69ARCzTBnyGqIRar5XRYGey/7Rtkupg0/PNizmqqN/r0Gif9PnnnVvI864yIVfGsfPizChK/i7m3b9lJahJcqJkJDJLQuCI2jCp3Hwfw2dy0ae880oLvFzZ7sukBdtX4stmPyGSsbbnaftZmPYO8KHIe9Z7rQA1QizTfHyGKI1an8WJvZCxpi9Feli6NpAXfS9V9mw0oceDaYmxsmQdN3RCzvexTb+vJklIPQGfdz+nf6ZVf8/QU0hkloTAEbVhUrlVqS4teklBV9G/j6/3lhY4FN2vSVCjb2ezTx9cPwv9/ZjsJy2Ay3s/Rd5zna5FQEMs03x8hihN6ORGmfWlae9N11EgpolS29fb7MOHvERtXqKsLFUmfl2PXbQHcYj3nNUjsy73c2gtEpklIXBEpfISVGX00gwtZMIsb99l/f3SkqshPqe6trAmJRNde/zmvUZ/7y5/D9vvQdOuO/QUYpnm4zNEUGkjRkLUaXVInNgkl7K2t92P6XmULe18895HXc4/iqo9l6xEeNFkt8lxfb5nm/sowIFNPDOrvPXRAXi1dOmRn0uWzFQrY2NHP7ZkSfJrmsD3+e7adeRn0t8n7di7dx/9WF/fkceLSHpvu3YdObfdu92O5fo6V7bHU7fP+o6afPbq55r1merbqj/1/2ex/V7mfdcAAKgrtc5butSsrnWl15dVxK1pdXZS3CBif45p+xE5Ojbatas7Dg0lK4YbH+/+Gct6HyL1uOeI35dIeXFYfEz1c9Tv0WJpf0MfcWOI2FM93zp8vuhpJDKBpjBJFOUFFWnBSB2p79M0waS/Xv972VTq6jHTKmmX4FI/Lz2RZpJ81veR9F6TXhcfK/78Xf6uaZISv6bbq+eSlWhMo3+uWdeKvm38exTN/G6SjLU5NxdlJ6EBAFCp9VBa4i7EsXSh61ub8/GV4EzaT3zMtMZcnw3oSbKSUnECLv4Zy4upq/jsdFnvq2islfZ69Vh5ScumNXSr56u+H+JWVKGEHqKtxVAelEoffuDSpb9Jw1mKDn/2MXw6bYiTPidj3vYmw0hMzyFpH0n7DD1cOu31pkNOQn7HXD/7ugy5r8t5oCcQyzQfnyG8M4lxQh6rSr7Ox8cUOUnTDxU5Ttq2cVw7Pl5s/ybqML9nFBW/J8obFp523DpNl+BL3a5hNBZDy4E20lvyXFrxTFtH6zBcoGhLbtHep7t3HxlCpffeGx1NPr+slvSY7ftK+pxNei/aHsek52cSddiO2uMz7ztk8v11beFV37PNa+vQ01J9bny8e3gSAABlKLP3WB167ql8nY/LKCB9ZE68j6xzsul1mLZtWq9L2/dhosx7jKxzN+1RmHa+ecPC045bxT1W6GPW7RpGTyCRCTRFXDGKuCc2TIMRNSEVHysvCWMy3DlN0WHgSbKCMhMuiTiTBGOIeW+S9ml7HJPE7MTEzOc0MZG8jT4PkL4/W7bD1WNJQ/NNXxdyiI/JEDH1fNW/I3MQAQBC0+cUFAnfmGYzPUwZqhjum3dM04ScTo/j4sb9jRuTpzkqY5iw6RyhrrL2ET83MdHdWSEr5vc9j2UVSb/Qx1T/FlVfv+gdJfQQbS2G8qASZXbf14ezZB3XZLizyXHS2A5VLrqydB2G11dN/xuYDmFPWuHU5e+oD6kqcu5lyTqu7RCxoqu5AwaIZZqPzxDeFInlQp1DSGXXqWUcLysGM4npXYex+3hOP7bLPk3fW9JxklYW96GXYjeGmaMAhpYDTZbXkuXSqubaW1I9Vt5xiyzWYjIMPK+HpN57L68Ha95wm6ZNwB2C/jdI+pzyep0WGc6iTyZu+9oqPr+s92syREw976ThSXWY9gEA0E5FYjkbdVngp+w6tYzjJY2UiWMIk0UfXYex+3hOP7bLPrPOP+39Z60s7kOdYreyetyqo/qAAPqiKL7Th63p6WkZGBiQqakp6e/vr/p00BbxkB6RI4k43/v0fYyk+SRtmJxP1jHi51RqEjOmvlY/ZtH3gGT637WJf2ebcw79/pr490OtEcs0H58hGidErOui7DrV5/HS9hXyb2sSi4d4bnx8JsmobhPiswv1fXB9/yGUde3V5RpHo9jEM/TIBKqmt4yFaIkO3cJeRktjVg+7tNZY/f9Zi+7QAzMM/e9aRau0OieS6Tyv6v9tzjnU90ift4zvKQDAhyJznLuqy+Igoets/W/o63hZsUnIv23W+Yd8LqmnpO/PLmRC0eQepqy4uKxrry7XOFqLRCZQtbQh0T4nPTdZHKaIopWVz9fr7ytteDKJy2ron0XSAkJJspKReddIfI3lDRlSr8WkoU7xsfRjlnHTV6dhSUBDXX/99XLKKafIvHnzZNmyZXL//fdnbn/LLbfI4sWLZd68eXLmmWfK7bff3vV8FEVy9dVXy8knnyzHHXecjIyMyE9+8pOubQ4cOCCrV6+W/v5+GRwclLVr18rTTz+deLx/+Zd/kRNPPFEGBwcLvU/Ail6/lN043cbFQXz8DeO/S9LfJqlhPtbG+Db0AkEi1cVZZSf8yvp+tP0aR+VIZAJlS+uBKWI/r0zWfstUtFK0fb3+Xous9ohy6Z+Fvrp8WuCelYzMu0b0OZHi46Rtp88Jm7QKelrSMxRatoFCJiYm5LLLLpNrrrlGHnzwQTnrrLNk+fLl8tRTTyVuf99998mqVatk7dq1smvXLlm5cqWsXLlSHn744c421157rVx33XVyww03yI4dO+T444+X5cuXy3PPPdfZZvXq1bJnzx7ZunWrbN68We655x65+OKLjzreiy++KKtWrZLf/u3f9v/mgSxlzYuZpqwEUplxsunf0DRZmdXrshdi2zJ6NJaRLE3ap0j771FojEcIoVceajNWiYQTdTU3k5WITVeAtl0lrskr6LEiXnvEK0SOj8/8nraapv64+r21+S67fneyVrU3PX6Trzm0Vq/EMm9961ujdevWdX4/dOhQtHDhwmjDhg2J2//+7/9+tGLFiq7Hli1bFv3RH/1RFEVRdPjw4WhoaCj63Oc+13n+4MGD0dy5c6NvfOMbURRF0SOPPBKJSPTAAw90tvnud78b9fX1RT//+c+79v2pT30q+sAHPhD97d/+bTQwMGD13nrlM4RnZdZJrita+1Rl7GiywrZ+Xuoq3sQN6cr4boX47tThXqasa4/4F4ZYtRyos6QemKZzx2S1aNm2oBdpHat6iAC909pjdHTm+z06OvN72vymedMHmLZm+1oFPWl18bzj69NIhLyGqr5GgRp54YUXZOfOnTIyMtJ5bNasWTIyMiLbt29PfM327du7thcRWb58eWf7xx57TCYnJ7u2GRgYkGXLlnW22b59uwwODso555zT2WZkZERmzZolO3bs6Dy2bds2ueWWW+T66683ej/PP/+8TE9Pd/0DrOlxYMh6IyvmLGvUTIjY0XSambz5LJPOK/67tL23XlGhe2uqn2fI9QvU45UVv5XVU5KRcQiARCZQBrVSSpuz0UTW62wriSIBXdlJUNuh5GiutMA96zO3+U5V+d1Rrzmf82fp79t2gSKg5X75y1/KoUOHZMGCBV2PL1iwQCYnJxNfMzk5mbl9/DNvm/nz53c9P3v2bDnppJM62/zqV7+SD33oQ7Jp0ybjFcc3bNggAwMDnX+LFi0yeh3QRY8DsxrbkuqbrDpIf7wODdA+6n/9vRWdZoZkZVg+koX6AkO+pH0fy4zfyr4uaWSHRyQygTLolZJrMOV7tUOXuXXUymd83HwV6Fher7Sk+YJIyiBLU74f6vVrEjyaLmKkv++sRQAA1MpFF10kf/AHfyBvf/vbjV9z5ZVXytTUVOffE088EfAM0Vp6TJnV2JZU3+TVQVl1susCfjbJU9/7Ejn6val/s3gebnU+7lgdErm9yEeysOyek2V+V8pu3G9KvI5GIJEJlKFuAUxeRWISxInYrQIdy+uVlvT/uv39UC9VfT+K9C4WyQ8eTRcx0t+3vggALeDoca94xSvkmGOOkf3793c9vn//fhkaGkp8zdDQUOb28c+8bfTFhF566SU5cOBAZ5tt27bJ5z//eZk9e7bMnj1b1q5dK1NTUzJ79mz52te+lnhuc+fOlf7+/q5/gJGs+iCrsS2pvjGpg2JpCVHbBfxskqd5oxNcErHqe4pHWcV/M33xQhUjierFZmGfsntOtvm7wv0cPCKRCZRBrZTKSipkzduTV5GYzsWZt5+k5/Mq6KQguc2VOooren25XpMuQWzRXgD6NBVJ14X+96AFHD1uzpw5cvbZZ8sdd9zReezw4cNyxx13yPDwcOJrhoeHu7YXEdm6dWtn+1NPPVWGhoa6tpmenpYdO3Z0thkeHpaDBw/Kzp07O9ts27ZNDh8+LMuWLRORmXk0d+/e3fn3mc98Rk488UTZvXu3/Pf//t/9/AGAmOm8mFnzQqdtoz8ucmTfaQnRuPeiSYI067ms5KnrvkSOHlqvylpJHPXmOq+mer1k9cBFMu7n4FMJiw+1FqtE4igmq7KVtUqdvhJilSuap+2PVezgW9rK5qavseGyomjRVUhtz9Xl74Ge0iuxzPj4eDR37txo06ZN0SOPPBJdfPHF0eDgYDQ5ORlFURR98IMfjK644orO9vfee280e/bs6POf/3y0d+/e6JprromOPfbY6KGHHupss3HjxmhwcDD653/+5+jHP/5x9L73vS869dRTo2effbazzXnnnRctXbo02rFjR/T9738/Ou2006JVq1alnierliMoPe4KGZNWvSpz/F7Hx+1jUP3c9bqUOrW9TL8XVX+/fSv7e60fj+sKEauWA9Ux6flUVoutTc9Jne8Ws7S/Cy1z8C2pN0Y8B9fERPZrXFYy148V4jUq23NlmDkgIiKjo6Py+c9/Xq6++mpZsmSJ7N69W7Zs2dJZrOfxxx+X//iP/+hs/5u/+Zty8803y4033ihnnXWW/MM//IN8+9vflje+8Y2dbT71qU/JRz/6Ubn44ovlLW95izz99NOyZcsWmTdvXmebr3/967J48WJ597vfLeeff76ce+65cuONN5b3xgFV1ryYIuaL+pjUJa51q695LeP3mjYNkunoI/0nsWu7ZX2+WfdWWfO+FlVG7Fb26B2TeXiBDH1RFPf9h63p6WkZGBiQqakp5ifCjCKL6NTxOL407XzRbPr3TR0OFkXdw62LfiddvttVXg/63wI9j1im+fgMkUiva/J+F0muI0wfSzpmbGJiJqE4Pi4yOpq9bVY9lfZc1mvSjkNsCp/0qQdEumPOIt+zMmK3sq8Hk/IIPccmnqFHJlCU2ttLb8UzaUGz2SY+lm2rVZGWPB+tgLReo0z6902fxyhtcSmR/O+76STwWfuq8npgDi8AaCe9zlFjxaT5kvMWZLR9LG2fIskL4aRt62uOzJjJfNIxRi3AVfzd27hx5qcec+bNuWmy75CxW9mxqck8vEAGemQWQAs4RMSt5bjINirTVqsiLXkur6VVDXWW1SNT/b4nfX9Nrgd1/3nbAhUjlmk+PsMeZTr6YNeu5GSlr5EJWecUs+mR6fO4thi1gKKyrkObmLJXehLTMxMKemQCZcpatc6kBc1mG3V1R9NWK7WVz6Ulz6UVkHlOUGdxq2/SNZS24qn+fNb1kNa7RKe2xIfsBUIPEwBoB7U812MtvX5Sezjpczyq9VQZyYLR0ZlziZOY+vkV5SvuZNQCisq6DvO2VaV9p9Me9zW3rMlzPjFXJhyRyARs6QV7UnAWMwnSTLfZtWumNdu2haposGpyfvpiKgSCaCr9hi/p+bzrQX191rZqsBYycCMoBIBmyhoubpMwyVvkx5eq6hvb91PHqV/QDmnfoawFqWySnOrv6r6yrj2bpGh8nvpzRRYCy3pM7aST9BNIQSITsKUW7LYtVUVat1yDwzIqBH3uIwJBNFlZ39+k1S9FzMsH0/Ikq9c4vTUBoL6yel0WqatC1XNVJSFs3w8NfCib7Xcua25XfZ8i/uaWTRtVlHT+Ph4bGzt6ETH1d+JTpCCRCdhKm9jcRJHAyTU49B2s6r0vRbITJUCTuST6TK9z9dpMC0yzzsX0OEmLLGSda3wcAkgAKJ9a1tv0uszaT1ni8xOpdx1Cry80UVovRhG7HtlZj6eNKiqyEJjJY1lDzENPwYRGYrGfAphcvce5TEbclAmMkyZljzEROnpJmYtdZV13+rmoizfkHSfrfJKe0xcX4zpvNWKZ5uMzbBlfcVaV8VqoYzcljgZ0Pr67bV4cSI+BkxbOjOUtooTGYrEfoAwuPR3rMuQ6r1UrqwcXvS+BbK7XedZ1J9Ldeq0Pz8lqobZtpU9qMQcAhJXV28qF3uOpzB5NdZiDkx5cqBObuTPTlLU4UBWSpilLmoIpqQdn3d4LSkEiE2gbk8I8LxDMSlZmLW4EtI0eOCVNrRDqWLqkFWhjPuf50ld1J0AEgPD0OeOKNnrriZMQ80KWvWiOTYKUeTDRBDbf0yKLA6XFjSYJzqwph3zFiEnnqU/BlDbUnWu9J5HIBOrKtWIwKczzAkGSlcAMPXDK6zXp81gm25YxzxcBIgCEkTUfpm8h9l92/WBTTzIPJpB+zdgkONP+n7a9y2rmto0fSY37u3bR+N5DSGQCqlCFX9EFQ1yGHcTHTZJUWVDwA/mSeitXee2EXPjB9zBHAEA3Ndbz1YuxzF6SdUgWlt0rFPDJ1zXka1X0rIV59P+nbV9kNXP9ejZZgFJ9LzS+9wwW+ymAydVbyHRycnUCYpNJhl0mPdcX3LB5fdHjUSwAZvTJyE0nX6+C7TVOmdATiGWaj8+wwULUEVWV3b7fi+n+qKvQRrb3mnWKN5POxfQx/XrW74fjoeTxa/R9qL+L1OdvAiMs9gO4Mu2antXFPm+/JtRjJk1w7Pt4rq8Bep16/eu9ofv66tUyXLQcodc2APgRl6ci4XtJllV2+67vTPdH/Io2sr3XrFMP5KRzMX1Mp/cG1cuFrHKiTjE4vCORCaTNz5FV+Nms6uvSQqYeM2mC4zxNXlEdaBL1Bkq9dvRyIynJWXZCsGg5QkAIAH6ELE+rKrt9JxRNE7LEr2gjm3vNNtGve30BSv35rNXM48fGx2mIbyESmYA+F2UsKyDTC1XT/ceFaF4io4zW5ZCrLwO9wmSOIf350DeVoRKl9HoBAD/KLE99H6ushCKNaehlNveaaULEg6Eb4/PKEf35rNXMY/ECnZQdrcIcmQUwJ1FL5M1x53v/uqouQeYUAqph0ks73mbjRpErrphpTR4dNds/1zYsEMs0H59hg9RpHjtXbZmDE2gim7kzQ1yrVcaYtmVA0noT4+MziU3KkVpijkzARlI3dd/7j6LuY4Q4nm0LWdLqywDCMg3C4iD1iitmfsatySZc5uTNKzuYHxMAigvZq7CscjpkDBuPFkp6DwwhB+zmzgxxrVY5Ose2/KR3ZquRyERv0oO9MoIj9RghjpdVuMfvV33Po6Mz52DaywtAMWorumnwuXHjzM+kBge1HNPn+o0nRDe5oTUpO0y2IckJANlCJgHKGnodx7Ai/sr++JxJMgDZbObODHG/WWaDgh5f2paf6rmq82Wq+0djMbS8AIbyNFgdh14WHTKT9Xq9a31d3jPQS9Tr0MeQlqQhMyIz17dNGWdTduRtQ9nSOMQyzcdn2ABlDItWjyES/nhFyn797xH/zrBPwI3NkPOmCBVf+o7H4Q1Dy4E8aouOS2+iED2QirakZ7WQ9+rKd0CdpK1w7mN/eas4ZsnqwZm1aFHSNiL00AQAXRm9JdXeR2Ucr0jvUv384nOPRwuRWADs2Aw5b4pQPdjV/ZmOXkLt0COzAFrAW8KltSdEC5Gv1np6RwHNUKeFC3yVG6EXT4N3xDLNx2fYAGWX9z6PF+Lc61T/AW1g2iOTa28G8Wot0SMTsOHS2pP1mjJ6I2Udo8pJmAGYK2s+M5H0+TRjvsoN9b1QBgHAjLIXqvF5PNe6KqmuiR8Toecl4FN8zaddVyZznruoYhSOj2Oq82aKZC80hlqiR2YBtIAjkWvPJpvXmcxbB6DebMuKIq3oWfNp+pQ27xllVG0RyzQfn2GNhS4Dy5570+YYSXUco4aA8um9D0X8lRlVXNM+j5kUH1M2VYYemYAvLi0+rj2b1NXU8o6p77stc6EAvcR2rt4irehZ82n6pPcCKrPXKQDUTegysOy5N5Ok1V9JdQ2jhoDy6aNlfPaGLuOaLrp6eRZ9NXPKpsagR2YBtIA3mGnrcpk9plyOSW8noB1MVlBs4vXexHPuMcQyzcdnWGNt6JGZdyx6WQL1llVONCFOK7OMacLfo8XokQnk9W4ybcG2bfHx0TJuu9owcwwBzaevoJikKde7Wv425ZwBIITQZaC+/5Dz1aXFuPSyBOotrRxSh5zXefXuMnt9MpKoMUhkop3yCmWTYdwuLTJ6QZu3wEYSbvyB3qNOOq4GalVMoq6zPQeCQAC9rqqyu4ryNytJUnX9BSCdXk6ov9fp+i3j3jitgQa1RSIT7ZTXuykuEMfG0rdxCQaz5ofzFVzGq6pNTBTbD4B6SQrUfJUbtgGpur3tOdA7B0Cvq6pBx0f5mxZnVjFKCUA4WfNDuly/ZSc/fR5PLd/UxcnqkMhFIhKZaA99OKNJwJW1jY9gMG+BDZcCOE6+xj8BtFdaOWTboGEbkKrb25aFZQ51BIA6Ct2gk1au2vRcSttHWpxp2yuKRi2g3uJrenT06LgtZnP9lt144fN4LFbZOCz2UwCTq9eMyWIZdeMyefHExExwOT4+U/EA6D2hFyKz2T5vWxaCqDVimebjM+xBPsrVtH0QZwK9Lat8qXLhIH3/vo+n7k+ERX8qwGI/6E0mi2X44DLvZRqX1uq41YzgEuhdGzd2/0xSZNGduFe7yeTvea3Wei90AEAxNvFjWqyato84znz96+lND/SirPIlK+YLPZelfmzfx1P3z5oVtUciE+1hOpy8qLR5L10CPZNCMg5ACSYBxK64ovunytfKi6avzyt3k+b8BIC2KmM6DTV+jI8XTzmiHzetLM+LQU3rAKYPAdolrTE7b8h56LIg9H1+1qK9qB2GlhfAUJ4aCN2FPe+YIt0BXojLSe3eH+oYAJolq+zTywzX8tHn8PK056sow9GFWKb5+AxrpozpNNSyMynRqB7XtZw1fR3ThwDtk3Rd513rbSsL2vZ+GoCh5egdVUzEq7ZgL1lyZKW3+KdvaqsTE6YDEMnuSaOvvOiaILQZVqOXxXordtq+mEwdQNv47DWU1iMoaUG2pJWHRdyHSJq+jkV9gPZJuq7zrvW050P3bPS9/3h/aWUqaoFEJpqtjGAxj89VxJPOIQ4kmacDgC5p9XKbKSt8zPUrcnRZ7DIsnSE8ANrANXGYVAamlaVq2Zm28nCR49pgLjmgfZKu67RrPS5DRI5e/TxrqiNfcZ/vRvF4P2NjlG01RiITzZY0R5BrYehaCLokU01a2AEgj0tDSjxcUOTouX5d6cGtabmovo7yD0CvME1aps3ZJuLvBpuyF0ARaWVIWgNM3uvy6OWn717hzJXZCCQy0R5JhaFJwVO0+7hLS7RJCzsA5HGZ2kK/SQ5R7tiWi3kTyANA3WXFnPpzJklLkaPL0hBJRx8roAPoXWlliD4sXY8Jk15nUsaEXr28jHIXhbHYTwFMrl4zSZOSm0zS62thDBtMHgygKj4X8fGFMrEyxDLNx2dYE2o5Fi/CE5edehkXegEeWyzsAyCPS/ljW2akbZ+02G5ZC0WyMGVpWOwHvcFkMQmTVua0bu4h0fMSQAhJc2bqXBfxCdkThzIRQFMljezRe/DoZZyPBXh8lsku8xoD6C0uPRNty4y07dVjlzEvr1q+Mg9wLZHIRHOZFKYmBU+8je/gLCvApEAEEILPxcdEusvFkENrQt2cA0AIajmVtDCEr8RlFp9lssu8xgB6i8u9ct4CQXqsl7a9yTD0kI07xKa1QyITzZVXmNoWOL6DM+bTAFA2lzkzs6jlou99p6HsBFA3WXNcmsxtGYJrA3xSfEyCEkAen+WEbayXdGx9H/oooiL08pXYtHZIZKLe4mDLpsUmVnWB4zqBMQC4Gh2dKRdHR/3v27a3p2t5x9BFAHWQ1Osyaah4VUlA1+OaxMfEqwB8U8sVH/fJ+j7UfRW9/9fLV2LT2iGRiXpTCyHbAil0gZNX2Jq0HAFASD5vRm3LVJvyjrmIANRNVq9LvZwKmfjzvW+Tspx4FYAp0zIqb57LtHInaxh6PPVRHD+Guv8nNq0dVi0vgFUiS6C2gle9UljSamkx08uI1R4BlCmrzAm9CqPp/tVyXoSysWTEMs3HZ+iR68q4IeO7EPvOK59ZpReAKdMyyrXcydo/99atwqrlaLaknjmmLSAhW8TVViL1ptumxYdu6QDKlFXmhOxxoycns7iWpwDgm+vKuCHjuxD7ziv/6X0EwJSvxcJsFvoR6b7fD1H2JuUVmHajNkhkon6K3FzbvtamMFILUX1uJFMEhgDKlFTmxOVevGhPiODPZlqQpPI0PseJCQJGAOEVLRdDxnch9h2/v/FxylgAxYS+v03bvxpfhjh2Ul6BaTdqg0Qm6qdIcBVyDje1ELUpsOMb8YkJs3MCgJDi8m5sLFzgqZbBLi306jmqvwNACGWUi3USl7uUsQCaKmvBIB8NNEn7Z3RlbZDIRP0UCa5sW4TUwihUV3HblX4BIKQygrCi04KoDVrq7wDgS94Kui77qaOs8+OmHEAIJuVi0bIzqyFc/b/rcZL2z+jK2iCRifoqI7jat+/Iz1BdxeMb8fgnAFQpKwiraki3Xv7G5zg6evS50ssdgA+u82Fm7aeOss6Pm3IAIZiUiyHKzqTek0nHqXsDFHKRyES9JC30YxNc2RZKam/JUInT+EZ8dNTvfgHAB7XcrGpIt035Sy93AEX4nic4VPzo60abXpcAymZS7vgsm+LyUuToEUFJx7FJbuqPkwSthb4oYp16VzbLw8NQXACJzBRAoV8/MTFzMzw+bp5ojG/20xb6yXseAOpELTd37Zopv8bHjzTw6OVY1WWcS7mNVMQyzcdnaKlorFkWn+dJuQmgzWzLy6RYNm0f+uNNqUMayCaeKaVH5vXXXy+nnHKKzJs3T5YtWyb3339/5va33HKLLF68WObNmydnnnmm3H777V3PR1EkV199tZx88sly3HHHycjIiPzkJz/p2ubAgQOyevVq6e/vl8HBQVm7dq08/fTTnefvuusued/73icnn3yyHH/88bJkyRL5+te/7u9Nw03WpL22K4ubcOktmdcNvu5DjAAgTdaQbpHuXps+yzibcl4tt2kVB2CrKT0UfZ4nPdkBtJlteZk08lPfR1rv/abUIS0XPJE5MTEhl112mVxzzTXy4IMPyllnnSXLly+Xp556KnH7++67T1atWiVr166VXbt2ycqVK2XlypXy8MMPd7a59tpr5brrrpMbbrhBduzYIccff7wsX75cnnvuuc42q1evlj179sjWrVtl8+bNcs8998jFF1/cdZw3velN8o//+I/y4x//WC688EJZs2aNbN68OdwfA/myJu3Nu2kuq5dQXuFF4QagSWzKLL0c9pVAdE2O0nAEwEbVPcpt+Jy/kvnaAVQtZOOzj/JS34c63VLc81Idvl73OqTlgg8tX7ZsmbzlLW+RL33pSyIicvjwYVm0aJF89KMflSuuuOKo7UdHR+WZZ57pSii+7W1vkyVLlsgNN9wgURTJwoUL5ROf+IT8yZ/8iYiITE1NyYIFC2TTpk0yNjYme/fulTPOOEMeeOABOeecc0REZMuWLXL++efLz372M1m4cGHiua5YsUIWLFggX/va14zeG0N5SqD2/skLOqvo5t2kgBgAilLL5JiP8ta2LI23zxoCDyPEMs3HZ2iBIYEAUI2yy9+i9+n66/XzJw/gXW2Glr/wwguyc+dOGRkZOXLAWbNkZGREtm/fnvia7du3d20vIrJ8+fLO9o899phMTk52bTMwMCDLli3rbLN9+3YZHBzsJDFFREZGRmTWrFmyY8eO1POdmpqSk046KfX5559/Xqanp7v+wYOs1hn1hjmvgIh7E42PlzfUkN5AANrCpKU8bq323fPctiVdbyUngARgIsTiEnWa2iI+p7qdFwDklb++y9Si9+l6bKqfv77/OtYJLRY0kfnLX/5SDh06JAsWLOh6fMGCBTI5OZn4msnJyczt459528yfP7/r+dmzZ8tJJ52UetxvfvOb8sADD8iFF16Y+n42bNggAwMDnX+LFi1K3RYWsgoZm6EwcWFT5mq7DCMH0BY2AZ9t4tF3cEfZC8CGvqKtj8aPOjZmq+dSp/MCgLzY0XeZ6rvhSu19qcazSYlNkprBlbLYT93deeedcuGFF8pXv/pVecMb3pC63ZVXXilTU1Odf0888USJZ9liWYWMy+TkZd7g+py/CADaIA7eJiaOBHG2wWleAKiWvQSLAPKESDqGiDeLlmfquTAfJoAmqXq0Txa9DkkaNaqefx0bulomaCLzFa94hRxzzDGyf//+rsf3798vQ0NDia8ZGhrK3D7+mbeNvpjQSy+9JAcOHDjquHfffbf8zu/8jnzxi1+UNWvWZL6fuXPnSn9/f9c/WEoK0LIKGZcCzbTQMg0W1e24YQbQVr4CSHXYd/y77b5tAkCCRQB5QiQdQzRm25RnaTF1jBXKATSBzx7zoe7V01YsV+sUtU5Qnyd/EETQROacOXPk7LPPljvuuKPz2OHDh+WOO+6Q4eHhxNcMDw93bS8isnXr1s72p556qgwNDXVtMz09LTt27OhsMzw8LAcPHpSdO3d2ttm2bZscPnxYli1b1nnsrrvukhUrVsif/dmfda1ojoBsbzhD9ng0PRd1O26YAbSVr+Hi6nzF8e+2+7ZJOjDMHECWJi3IYFOepcWklIkAmsTn/XWoe3U9js2La9XnGXIeRPCh5Zdddpl89atflZtuukn27t0rH/nIR+SZZ57pzEW5Zs0aufLKKzvbf/zjH5ctW7bIF77wBXn00Ufl05/+tPzwhz+U9evXi4hIX1+fXHLJJfLZz35WvvOd78hDDz0ka9askYULF8rKlStFROT000+X8847Ty666CK5//775d5775X169fL2NhYZ8XyO++8U1asWCEf+9jH5IILLpDJyUmZnJyUAwcOhP6ToC5MAz0CQgC9KC/YSgsW4+BtdNS9Icok8RlizjsA7dOkRmibRp+0+JRpjwA0ic977TretzPkPIjgiczR0VH5/Oc/L1dffbUsWbJEdu/eLVu2bOks1vP444/Lf/zHf3S2/83f/E25+eab5cYbb5SzzjpL/uEf/kG+/e1vyxvf+MbONp/61Kfkox/9qFx88cXylre8RZ5++mnZsmWLzJs3r7PN17/+dVm8eLG8+93vlvPPP1/OPfdcufHGGzvP33TTTfKf//mfsmHDBjn55JM7/97//veH/pP0tjrNJ2Qa6KV1EweANssLtkKWhyblOsEgABN1ij19iuNTkerPBQBc+Wx8KbMhx7QeSMoljI9TbhfUF0VxDQhb09PTMjAwIFNTU8yXmSX0kJ64R47IkYDORpOGHAFAWaosG7PK9fi8xsdn5oCj7C6EWKb5+AwrUDT2tJFXFpd5LgBQFl9xaKh4tkjZS7mdyCaeYdVyhFe010xea4drS3u8X5Pzq0PLOwCUSW/VLrMczCrX1cWEklrdKa8BxMpa+CGkrDhVfV+MFgLQJr5G3vjaj16fFKkH6JlZGD0yC6AF3FDRVpC8FgvX/av7Fcl+Pa0mAHpdyHLQphxXtxU5EpjGr6W8tkIs03x8hhnaUB5klY9teH8AkMRX+earR2aI8pYyvAs9MlEvaq8ek5Zx29YO11YWdb9Mig4A2UL2QLIpx5NWglRfy1zGAGJNKA/iuHdiIjlGzprzrQnvDwBcFC3ffC8KGaK8pQx3Ro/MAmgBd2DS6mDbMlHGPG7MowkAR6t6/qL4dSLprzXZpocRyzQfn2HD6SOEROiZAwBFNam3I7kGEaFHJurMpNXBtmUiq6U6rQeo7ZxJZa6ABgBNofakNC1Tk8pf1zI2fl3Wa5N6bQJov6bMl6vOlab+DgBw16Tejr7m8ewhJDIRnhpImtysFk0aqsdLKxRY4AcAiotvvGMmAVjZwZoawDYhmAXgR6iyxkd8mBQbj44e6TWUtn9iUwC9zKYMLKMjkq8yWY1PKd+NkMhEeLY9dooWCOrx0lpiTFpoaBkBgGxjY92/myQKy24hN+m1CaB9QpU1PuLDrH24PgcAbZbVScn09T4bodTzKFomq/Ep5bsREpkITw8g8y7OogWCGrimtcQwVBwAinNZNK3K8pfeTEDvCFXW+EiQZu0j67m4F7zeGx4A2k7NDbiUvyEaoXw2mDVpKHwNkMhEeHEgaXpxFr2IfQWuFCYAkC2vvA2ROCyyT3ozAb0hZKOFTZyZdh5Z+8h6Lu4Fr/eGB4C20zsrFXm9j3MQ8dtgFu9LhEZ3A6xaXgCrRCZgxS0A6D1pZb++Gq+PuqHIKpTUUUchlmk+PsMEdVmtNu081CGSNuURZRgAHK1NZWNd6q8KsGo5qpPV2yVulZ6YqGcrA0MOAcBNWtlvO7VIEr1stm1Rt11wDkDz1WVUTdp5qGWhTblIGQYARzPJQTTlHr8u9VfNkciEX1kXXlywxMNhbAK3MgoghhwCgF+2U4sk0cvmtBv5tHqCsh1oP/36ryLhl1QGpZ2HWhZyswoAxZjkIELFgb7zFDRYGSGRCb+yLry4YIknKLcJ3NIKIJeCI+01tH4AgJu88rNIUGZaNuf1CqVsB9qrjAaLvJjTpkdQXCaWPccwALSRSQ4iVBwYov6h/M9FIhPF2FxkcQEzOmp/Q5s3NMem4FBfY9JqDgDIZlt+2kw1YrrvtHpCfT2BIdBO+vUf4lrX40d9/757BNGbHADS5ZXz8fMiYe/xQyRKKf9zsdhPAUyuLt2T0VYxua7LxL4TE92rPXIJAEC59EWAYkXrkbw6oYcnUE9DLNN8fIYJQlzravmi3lya7N8lXqW8AoB0eWVk1vN1Xxyo7ucXCIv9oDxqy0PRFgOX1nOXXpRqEpOhhgDgj2k5rk81Eitaj9CCDfSmoouCmexb5EjMmbb/tDLQJV5lWgwASJdXRlY5b2ZRcT2jjyBFBz0yC6AF/P/x1WJQVstzj7ZwAEBwruW4r3I5bz+U/0chlmk+PkMJG0Pa7JtelABQf02IB3uwPqFHJsrla27JslqemQsTAMJwLcd9lct5+0l6nnkzgebzFUPazn1pex6UNwBQnbgMrnsSU4Re+TlIZMJdE4OxJp4zADRFnRqKksr7pMfqPrwIQD5fi3ollQdp5VrScfLKQJuVzQEAfqllr6+4L1TZXaeYuoZIZMKd75s/H/vLWwmXG1YA6A1J5X3SY7R4A+1iE+sVmVvTJabMmluTGBUAwlLLXl9xX6iym8atTCQy4c4k2Mu6AENMzB4XIPGCPgSDAFCdKoOwpDol6TFavIFm85mMtOl96RK3pu0/qXEFAGAnL+6My2Cf0wyFahCncSsTiUy4MxnGk3UBmgaPWdKC13glXL1AoecNAJTH5zDKeHvT1+h1isnE7rR+A81jE0+6Jj1thpy7UM+DRhUAcOOa/CuSNAzVIE7eIhOrlhfAKpGKtFW1sm4cfawW1oOreQFAY2SV87blt7q96WtsjqcO7XTZf0MRyzRfz3+GNvGka9xIvAkA9WebX4i3Hx+fGdFJY1KlWLUc5fM5vCb0cQEA5cgaphkzLb+LzmuUV18wtBNophA9MHXEmwBQf7b5BXVaOp/DzREciUy4ixfUmZjIn/Mn1OS3ImaFlXquAIDq6D0fTYPNtHmNTOUFtwztBJpPn4LCZRojl9XIXc+TG2QAqE5WI1Ud5qikrkhFIhPu4gV14p9JfLZgqxeybcFicq4AgPDq2vORRX+AZkqKD0WODC8UsStrfN68Jt2EskI5AFQvbxh6HXriU1ekIpEJd/GCOvHPJD5vDNUL2bZgMTlXAEB4Tej5SAs40BxJ8aHIkTLGtgemz5vXpJvQujbmAEDbZMVzeUnCpPojbX+h4sY6JFNrisV+CuiZydV9LMrTpvMAALRbDy3s0TOxTIv15GeoxoRFF+kKeb0nxa7EswBQjqzy3aUsTttfD8WNIbHYD/yqS5dmhv0BQHuE7PVYdN+0gAP15jpKJ6lsyHt9kfIkKXYlngWAcmSV7y5lcdr+QsWNjBBKRSIT+WwvzLpccHU5DwDA0fRGMn2RDp/7ThMfM14QLj4uiQagftS4Tp+iwvR6TSob8l5flwZ9AIAd3/Fc2nDzUL3sqX9SkchEPtsCwOWCc006Fpn3AgBQH2lzyJVxzHghOOoLoL7UuE6NTfVYUP3dx/yXRXra+GygAQCE45qPCJlzYIRQKhKZ8K/MFSKzXseFDwD1pZfR+iIdOpsA07T8j5+PF4KjvgDqJa0XpkqPBdWf+mMuvXOK9OipooEGAGDPNR8RMufACKFULPZTQKsnVy97IvKJiZkeMePjIqOj5q9jYl0A6A2U90G0OpbpEa3+DE2uezVmFTlyE6r+XubCOlnnw80oANSTz/wHi7o5YbEfFKe2SPge8p0kHtYX/zRFr0sA6A0hyvukuipt3kwA5Ymvw7ze0vrNoj73ZRW9WZKGwNOjBgDqIS32K5p4VPfLFHfBkchEsrRhO6ZsL17XG9Q4QBThhhMA2ipUy3ZSXcW8mUD11OvQZiGeOjRw1+EcAADJsmK/IjGfug+9HgixHkiPI5GJZHGC0DUYs31d0RZzWj0AoJlMgrSiowTSZM3PybyZQPlMemJmzZtZh/nE6nAOAIBkWbFfkZhP3YdeD4RYD6THkchEtqYEY7R+A0AzmQRpRUcJpEmq4+LHRkePfo6WcSAsk56Y+tDtXbtm/p+0ankRtvthWgoAqL+s2K9IziNrH0m5CpM6hhxHKhKZSFY0CCyr9SA+T5FmJFwBAN1MgrSiowRc6XUhLeNAOGrMqV/jWb0ws1YtL8J2P0xLAQBIkpTkNKljmtKprAIkMpEs7cIyTXCWdbPJTSUANJtNkFZ2QFfH+feAttIX6klbOEEvB/Tr0td1arsfpqUAgOYra/SNr3k0e1RfFMUrpcCWzfLwjZO2sELc+1HkyCI7ZdLPq+rzAQA0V94iQqEWGaqRVscyPaIVn6GarEyK8eLh4y2+FgEAFVHjPbVRrcz8QlJeowfiUJVNPEOPTCRL6/Vi0zrtOrdQ2vZ6i7zt+QAA6sukDvBZp4jk9+pnSA9QDv0a1Bf8qfu1SE8aAGiurBXHy6IeN65TGH2aikQmzLjMRek6t1Da9urjdVqdEgBQnGkd4KtOEUkOGk0SESQtAL/Ua9FkwZ+qMX8uALSHr+RlkfhQzWvodQmdto5CIhPd0i4+NUAzvThd5xZK2159vo5BLQDAXVYdkLUISNK2em+uNElBY1oiIm2+PgDFxNfU+PiRnyLm8WMVDQt6XMy8mADQXDbxYBZf8aEaE8fDzGlA78IcmQW0Yk4iXdqck+qNW9LzAACEYjMfsuvcyXnzELV0vr5WxjI9pvGfoXptxWyu3SquTeJiAGinIvNShprTskfWBWGOTLhLG2YXt1LUaU7KiYmZ85uYqPpMAAAh2dQ9rvVU3lQl+qiAug55Rarrr79eTjnlFJk3b54sW7ZM7r///sztb7nlFlm8eLHMmzdPzjzzTLn99tu7no+iSK6++mo5+eST5bjjjpORkRH5yU9+0rXNgQMHZPXq1dLf3y+Dg4Oydu1aefrppzvP33XXXfK+971PTj75ZDn++ONlyZIl8vWvf93fm26CuDfjxo0zP22v3aRh6UV7w+T18qxjXAwAKK5IjBcqPnSdCqnFSGSiW1636jJu3kwvzrGx7p8AgPZIakwzqXtC1VMkLxttYmJCLrvsMrnmmmvkwQcflLPOOkuWL18uTz31VOL29913n6xatUrWrl0ru3btkpUrV8rKlSvl4Ycf7mxz7bXXynXXXSc33HCD7NixQ44//nhZvny5PPfcc51tVq9eLXv27JGtW7fK5s2b5Z577pGLL7646zhvetOb5B//8R/lxz/+sVx44YWyZs0a2bx5c7g/Rp3s3n0kjrviCrdrTL02fSUWTROilAsA0D51SRYmrVOi1k91Oc8KMLS8gMYP5ckTqmt03vFUWV/PiYmZ4Hd8XGR0NOipAQBKVuYwGtv6ruz6MaDWxzL/z7Jly+Qtb3mLfOlLXxIRkcOHD8uiRYvkox/9qFxxxRVHbT86OirPPPNMV0LxbW97myxZskRuuOEGiaJIFi5cKJ/4xCfkT/7kT0REZGpqShYsWCCbNm2SsbEx2bt3r5xxxhnywAMPyDnnnCMiIlu2bJHzzz9ffvazn8nChQsTz3XFihWyYMEC+drXvmb03hr7GepxX52uJ/UaF2nN9Q4AMKBPeVJV+Z8UC6v1k1qHtiCtx9BymMvK4pfdymy7Otfo6Mz5kcQEgPYpc8hmiBXRURsvvPCC7Ny5U0ZGRjqPzZo1S0ZGRmT79u2Jr9m+fXvX9iIiy5cv72z/2GOPyeTkZNc2AwMDsmzZss4227dvl8HBwU4SU0RkZGREZs2aJTt27Eg936mpKTnppJNSn3/++edlenq6618j1TWJKZI+QqlHe74AQE/RY8+q4r2kWDhpFML4eM/VTyQye12omzGXbs766lx1CmgBAOXSG9NCDp/JS5rqx2ZevEb55S9/KYcOHZIFCxZ0Pb5gwQKZnJxMfM3k5GTm9vHPvG3mz5/f9fzs2bPlpJNOSj3uN7/5TXnggQfkwgsvTH0/GzZskIGBgc6/RYsWpW5bW0262Uq7oe3hIX0A0Gp1mQc5r2NZ/Hw8RUsPNbCTyOx1oS5OlwRp3KoQz/cAAEAsZC/IvEBRPzbz4iGAO++8Uy688EL56le/Km94wxtSt7vyyitlamqq8++JJ54o8Sw90a/jItd16IRi2g0tPbMBALoqGrl6cDEgEpm9zuVmzOTicE2QmgSFPXJxAgBSFK0DbOoRdRt6YDbSK17xCjnmmGNk//79XY/v379fhoaGEl8zNDSUuX38M28bfTGhl156SQ4cOHDUce+++275nd/5HfniF78oa9asyXw/c+fOlf7+/q5/jaMOh1N/d+Fz4QObKZeq7qkDAAjLpcGqikauvAWbW4hEZq8qEuiZXByuvVXUYDDt3Hrk4gQAKGznK8qq52zqEXUbemA20pw5c+Tss8+WO+64o/PY4cOH5Y477pDh4eHE1wwPD3dtLyKydevWzvannnqqDA0NdW0zPT0tO3bs6GwzPDwsBw8elJ07d3a22bZtmxw+fFiWLVvWeeyuu+6SFStWyJ/92Z91rWjeWupCBfF850WuLTWhWDRGtHk9PbMBoN3U2HNiwix/UnUjV4/Mm0kis1e5BHrxTWHR1vO81m79HHVVFw4AgPKpk5qL5NcBWfWcTT2SFxAySqARLrvsMvnqV78qN910k+zdu1c+8pGPyDPPPNOZi3LNmjVy5ZVXdrb/+Mc/Llu2bJEvfOEL8uijj8qnP/1p+eEPfyjr168XEZG+vj655JJL5LOf/ax85zvfkYceekjWrFkjCxculJUrV4qIyOmnny7nnXeeXHTRRXL//ffLvffeK+vXr5exsbHOiuV33nmnrFixQj72sY/JBRdcIJOTkzI5OSkHDhwo9w9UJt+L5yQtfOAaI8YxbvwTANC71NyE6TyUvhq5XOPLHpk3sy+KWrBOe0VsloevHbU13PQi6+s78n/br416PPViStqPy7kBAKDyXZeodaC6zyJ1Yw00Opax9KUvfUk+97nPyeTkpCxZskSuu+66Ts/Id77znXLKKafIpk2bOtvfcsstctVVV8lPf/pTOe200+Taa6+V888/v/N8FEVyzTXXyI033igHDx6Uc889V7785S/L6173us42Bw4ckPXr18utt94qs2bNkgsuuECuu+46OeGEE0RE5EMf+pDcdNNNR53rO97xDrnrrruM3lfjPsP42lTV5dpRr2cRYlEA6HVxnTU+PpMcLKteKBpfqnVtQ+oym3iGRGYBjQsc05je7BW5KdRvAElUAgCaRE++xOFTwxvfWhPL9LDGfYZV3RSmnYd6/DonWQEA1Sk73vNxvIY1ttvEMwwtRzlzTqpDfWy7WzNsDwBQtbRhq8yTB9iJ482xsWqvnaT4N22FcgBAbyt7nQ4f8WWL58skkQnzYK3IxVvkQmRxHwBAVqNW/JzpROyukuoy9bxoeAPy+b6xcr3usuLfvGsdANBb9DqjCXVCi+fLJJHZq9QLL+5lsnRp9oVYRut0UoFAqzgAIKtRS+3hlbZNKOp50fAGZFOHyvm6Xl2vOzVZaXJDyvUNABDpnoYkqU6oW5KzhQvZkcjsVfqFZxKclTF8Tj2P+MJn2B4AIKtRS+3hlbZNKOp50fAGZFPjPF/Xi7of15tHkziY6xsAeldSw7VIcp3go+HLZzI0bjiMf7YAicxepQdjdQnO1OPT4g0AiGU1asXPjY66NXwVCRbV86LhDchWZM70NOp+XHvImMTBXN8A0LvSGq6T6gQfuRWfowDqkuvxiERmr9KDsVDBme3NYdpiCgAAmLKte5KCxboNCwLayPd1lhVDZt0Umk6zBADoTTYN1z4X6vGRE2lhHUciEzOKBJJZr7VpSYj3Ew83osUbAKALMZddUrDo2hJOAhRI5zK1kY2sm8e8m0LmwAQAFOEzBvTd0axldRyJzF5imnC0vfiyLgqblgT19S25wAAAnuUFYkmLxeVJChZdV1ZuWaAIeFXW1EZJMW98nYskX9OMCAIAmEjLq9Q5BmxZHUcis5eYJBz1bU1kXRQ2LQnq61tygQEAPDPtVSXSXfe4THUSRWYrK6v7blmgCHgVYmqjpGs7K+ZNey7pXOhhDQDQ6fVIXFdUseikqZYNLyeR2UtMEo4uN2C+J2tv0QUGAPAsr85Jq8dcW8lN6kV13ywIAqQLkRhMurazrluX0UJ17F0DAKiGXo/EdcTYWL1jwBbVaSQye0nazZUaVNbhBqxFFxgAoGRp9ZhrT0mTepFemICZtF4sRRKb6vUX708k/bp1GS3EtQ0ASBOirgjR8NeiOo1EJkgcAgDaL9QQVl/7BnpBWi+WIjGoOlzOd6KUaxsAoNPrmqypSSYm3OohcjSZSGS2nUkAV2Zm3uSCblFLAQAgoLLnrzMJKplTD0in3+z5ivn0a9JnohQAAJXNtEMmc627HsNWi+pEEpltZ/JltWltLnqDZnJB0/oNADBRdkCWF1Tu3t2qIBEIIsSURuq1GSJRCgBALK/uUnMlrgsAhciJtKhOJJHZdr6/rC43aEmrudZ5RS8AQDMk1XEhe0TmBZVJi40A6OYj2a9f52nXpsmNIL2oAQA+qfXb6Gh9OmnFdaJI4+s9Eplt5zuT75IYTVrNtU4XNACgmZLquCp7RKp1JPUbkEyPJV0SiWnXuc99AQCQJ6neqWvPx/hcW1DvkchENtMW7yx6Txmb4wEAEJLPeoepUYB8+nXickOVtFK569QOafsCACBPUr2TFQ9WWc+kzSfdQCQy28z0Isnaztdqkvr+0rSgdQAAUJGk1vA8ar1jE1wmbWv6GIAjTHquZDWsq9ewSy+YtH0BAJDHptOWSD1GDsXT/DUYicw2M71IsrYr0i06aW7MvP3UtRs2AKD+XIJCtd6xCS6TtjV9DOhlLqN9QsWqIfcFAGg/m05bIm6Ndz7EoxZ27XJfSb1GSGS2mY/kYZFhcklzY+bth2F5AABXaj1mGpyp9Y5NEiNpW9PHgF5WdPi3Sr0x89GjkjgUAGArK9aLk5ITEzM/RYo13rkqOnqhZoInMq+//no55ZRTZN68ebJs2TK5//77M7e/5ZZbZPHixTJv3jw588wz5fbbb+96Pooiufrqq+Xkk0+W4447TkZGRuQnP/lJ1zYHDhyQ1atXS39/vwwODsratWvl6aef7jz/3HPPyYc+9CE588wzZfbs2bJy5Upv77dW9GAsLbMf37zFw+qKcOmFCQCAD7bJyKTXmyYx1G3juk/k6NeTGAG6JV2jeb1P0q4j/WaP2BMAULasWC+un9J6QZa1WJC+IKWv/E9FgiYyJyYm5LLLLpNrrrlGHnzwQTnrrLNk+fLl8tRTTyVuf99998mqVatk7dq1smvXLlm5cqWsXLlSHn744c421157rVx33XVyww03yI4dO+T444+X5cuXy3PPPdfZZvXq1bJnzx7ZunWrbN68We655x65+OKLO88fOnRIjjvuOPnYxz4mIyMj4f4AdZOV2feV9XfphQkAgC9V1D0MHwfMJV2jrteQfrNH7AkAqBN9Xko9OWm7WJArHwvt1UjQROaf//mfy0UXXSQXXnihnHHGGXLDDTfIy172Mvna176WuP1f/uVfynnnnSef/OQn5fTTT5f/7//7/+TNb36zfOlLXxKRmd6Yf/EXfyFXXXWVvO9975M3velN8nd/93fy5JNPyre//W0REdm7d69s2bJF/uZv/kaWLVsm5557rvzVX/2VjI+Py5NPPikiIscff7z89V//tVx00UUyNDQU8k9QD3GWP+3iUR8rmvV32Q8LIQAAfKiqPrEZUkRdBxx9rbrGoUVu9og/AQChxfXU6GhyfVXVSIKGj2AIlsh84YUXZOfOnV09HmfNmiUjIyOyffv2xNds3779qB6Sy5cv72z/2GOPyeTkZNc2AwMDsmzZss4227dvl8HBQTnnnHM624yMjMisWbNkx44dhd7T888/L9PT013/GkHtzpwW7PnK+mftJy1gbHhrAACgJpLqkzKSFUWGFAG9SL9WTeNQn9ezeg4kMwEARbnUUWWNJHBZaK/GgiUyf/nLX8qhQ4dkwYIFXY8vWLBAJicnE18zOTmZuX38M2+b+fPndz0/e/ZsOemkk1KPa2rDhg0yMDDQ+bdo0aJC++s5aQnLhrcGAABqIqk+qTpZkTekCOhFrrGfz8YKl8XBAABIY9NBq+xRAXo83PARCaxabuHKK6+Uqampzr8nnnii6lMyEyJR6PLl1yd1jzW8NQAAUBNJ9YmvZIVr0Jc3pAjoRa6xX15jhe050JgOAPAlLd+RJKvuiqcjmpjwdmpd9V0LRsQGS2S+4hWvkGOOOUb279/f9fj+/ftT56UcGhrK3D7+mbeNvpjQSy+9JAcOHCg8H+bcuXOlv7+/61/PcvnyJ03qDgBASFnJCpvkZAuCPqDWkq5Hk6FwRZKRNKYDAHyxyXdk1V3xdETxTx/U+q4FjXjBEplz5syRs88+W+64447OY4cPH5Y77rhDhoeHE18zPDzctb2IyNatWzvbn3rqqTI0NNS1zfT0tOzYsaOzzfDwsBw8eFB27tzZ2Wbbtm1y+PBhWbZsmbf3V1tJQWCImy/XL38LLhoAQMOkJSts6kebVvYkLRjGAwSVdD2aXKPq9c11BgCokmm+I6shLZ6OKP5ZVAvrxqBDyy+77DL56le/KjfddJPs3btXPvKRj8gzzzwjF154oYiIrFmzRq688srO9h//+Mdly5Yt8oUvfEEeffRR+fSnPy0//OEPZf369SIi0tfXJ5dccol89rOfle985zvy0EMPyZo1a2ThwoWycuVKERE5/fTT5bzzzpOLLrpI7r//frn33ntl/fr1MjY2JgsXLuwc65FHHpHdu3fLgQMHZGpqSnbv3i272/DBJgV8Pnqi6K8RKdaCvW9f6y4mAEANmNZt6vMmjWumrewsbAeYMVm53LYBnOsMAFClpASlSWyqbhNPRzQ6av76LHrd2IK6si+KoijkAb70pS/J5z73OZmcnJQlS5bIdddd1+kZ+c53vlNOOeUU2bRpU2f7W265Ra666ir56U9/Kqeddppce+21cv7553eej6JIrrnmGrnxxhvl4MGDcu6558qXv/xled3rXtfZ5sCBA7J+/Xq59dZbZdasWXLBBRfIddddJyeccEJnm1NOOUX+/d///ajztflzTE9Py8DAgExNTdVnmPnu3TNfyF278pOMcUJSZOZCMeHymrTXF9kPAABJTOspl/rMpI5N269N/VyiWsYysNLYzzDpWil6ndT0OgMA9DCTmDNrm6I5GL1urGldaRPPBE9ktlljA8eYyxfYV4A5Pj4z50PNLh4AQMOZ1lOhgriG1XONj2XQ3M8w6RoserPmelwAAHxS6xqR/Honq27qkXrLJp6ZXdI5oS70i8A2SHR5TZLXv56emAAA/0zrqXi7eLiOr+Aw3m+ckFm6lPoOSJJ0rcarqYacS10dUse1CQAIQa9r8uqbrPjVVw5GpDVJ0aBzZKJEpvMmVD0fgnp85scEAFTNZ72o1sUsbgeY8bEIQQsXMgAANFgc/42P+6+fbOo8fduq80GekMhsC9MvZNU3VupxG37xAABawGe9qNbF+mTvJFqAo8U9Q0RmfrreYNm8rupYGADQfnEcODY287tNvRbHjBMTxReQ1LdtSR1IIrMtTL+QSatoxULdZKn7XbKkNRcPAKDGTOu0rHrRVlr9pidrgF4XX5/q9bBrl3uMaPM6n9c8AABZXOq1uG5MS4La7FPftiV1IIv9FNDYydXT+J5gXb1x87lfAADylLFoiCn1XGo2J1HrYpke1MjPUL0mRPxdFy2Z+wsA0MNsFo60qfdqXkfaxDP0yMQRLq0FWT1e0loOAAAILatOK3uYt3ouNQwcgdKp14TPniH0fAYANF3ca3J0NL+OLDLMvMFIZPaCkMPrsi6GUEEqAAB50uo0l2HeRROfLRnGAxSiTzUU4ppIasBgfloAQB0UqY/SXmvTcN+iKf5IZLaFSc9Ikxs224srazWurJtIAkoAQBX0OflsXlOkBVuv+6gL0WvK6AmSFHu2qAcKAKChis6XnvbarMUl9de0qGGdRGZbmPaMLLKfJC6rcRFQAgCq4jLM27QetWlUpC5EL1CviazFsEIm9VvUAwUA0FBqvDc+bv9607pMjS9bXP+RyGyLrC+pTea9jNUiW3xBAQBqJk6SuAxpjV8rYvYam0ZF6kL0AvWaSLv2QvR4VsXHFaEXNACgGmq8F3cCs2Eav+oN9vFrWjYSiERmWyR9sdUva8h5MuPX7do1E4SGOgYAALbU5IhtosQ2wWLTqEhdiDaL486410lWwt5HUt/kWqUXNACgKnG+RCS7vjPN26Rtl5a8bFkdSCKzzdQva6gvbosvDgBAC6jBYsjRBiL0/AJicSw4NpafsPeR1De5VukFDQCokkl9Z5pTsW3Aa1kdSCKzLZIy8uqXNdQXt8UXBwCgBeKg0XW0gcvrTINQfdg70HQ2PTF9Mhk+Ry9oAEDdmeZUTFYrV+viltWBfVEUdx2ArenpaRkYGJCpqSnp7++v9mTiObxEjvQGKUPcE9Nk0QSbbQEAaCrT+k6tu0XKrb//n1rFMnBSq8+wqng0po4QquocAAAomxp7JtWDDcjF2MQz9MhsCzUjX+ZErjYTyDL0HABQpbLqR9sJ2fX/A03joyemj+tTjTG5pgAAdRNqNE7WSNkWTgNIj8wCatUCrvLRGu6SsVePm/S6BrQCAABaLK+eKqKhdVxtYxkYq8Vn6CP2rCp+BQCgLPpoHBE/dVZW/afHvyK1rCvpkdmrfM5L5JKxV4+X9LqWzcsAAGgYvZ7y2RrespZuwFjS/OwufMy1TqwJAKizpDrOR+yYVf+p9euSJa2IWUlkNlXS8BubFSLzuASTS5aw4A8AoL7UeiqWFsTZDnOl/kOvUq+hIrFnqCRkmVMuAQCQRV2EMlTsqNd7ev0aH298vLH1I0PLC6h0KE/S8BuG0wAAYCavzgyxaIl+zBrU27UYloxCKv0M1Xm3qow/TYfUcdsDAGgzm0XvalY/MrS8FyRl701asqtolaYlHABQN1l1pslQWZe6TR/K04KhPehxvnpjFpV1LdFbGgDQFEVzJ2o9mNfjssH1I4nMpnIdfuNy01TkYmrhClkAgAayqctMkjNF5pJO+wk0ia+5MUNj3kwAQFMUzZ2oseXY2JF9JcW/cf0o0riOZyQye43LTVORi0l9TZ2DXABAu9nUZSZ1pW19mjT0lQQLmixEb0zXxnP1OmzQjRgAAF2KNnKrsWXeYsz6cw3qeEYisxeoQaHLTZPLzVp8PH2FLAAAqmBTl5nUlbb1aQODRCBTiB7FrteJeh1yjQEAmiLOnUxMHJmz0lcjt+lizA1c/IdEZhPZtlYXvXkqerNGbxMAQNXqOo80gCOKJEeZqgEA0BRxzBnnTNRh4D6p8W9anBtvE+ocAiCR2US2iUk1sCvjJs20CzMAAHUSstdkWpKF5CmaKsT1UmS6BaZqAAA0hV53jo/P/HRtjDOJJ/Pq7QZN00Iis4lsW5zVwK6MoW2mXZgBAKiTkHVX2oTqDDlHU8U3XfHPMuXdsNFAAACoMzXmjCKR0dFijXF6PJlUD+Z1cGvQNC0kMpuoSIuzy02abTCYtKABAABVy6vPyujRpQeaNPyhqeIhaPHPMuXdsNFAAACoM98xpx5PJtWDJh3cGjJfJonMNjFJOLpcMKbBoD7PA8EjAKBOTOqn0D259ECT4bBoqiqT8Hk3bDQQAACayDUO1ePJvHow7fmGzJdJIrPJymp9Ng0G07L5AADUgUl9ZluX2gaceqDJEFjgaLa9p2kgAAC0gRqHFokP8+rBtCmPYlVOH2OgL4ris4et6elpGRgYkKmpKenv7y//BPr6jvw/iqof0l318QEAKCKux0TM6zK9Lralvn7XrtLr0cpjGRRW2WdY9Ltf1b4BAKgrtf6LhawH0+rbCuphm3iGHplN5rP12UePEHqZAACaTJ9HyERSL0+b+k99PVOzoElCDt/W921yTRF3AgCaTp2jUv099PH0BYBqvoI5PTILqGUvBtdekSEy7rSmAwCaxNfIAtf6r4KRDbWMZWCl0s+wrO+syTVF3AkA6EVqXSziXi/r9WjJ9So9MnuZ2pvDJnMeolWdidYBAHWU1nMrbWSDbU8v1/qPef3QNGX1Ija5pog7AQBN4mskgVoXF6mX9V6YNa5XSWQ2UdYXXv2S2Xx5XW+ess6FGzIAQB3ZBnm22+dNoJ6GobFomrJuckxiSuJOAECT+GoMVOviIvWyWn/WfJojEplNlPWFX7KknKAyvtliPi8AQNu51quhE6YAutEYAABoCtP4Mq9uUxvyijbqqXN01jguJZHZRHlf+DJapPUvcw27GwMAkMg2Melar9Z4SA7gRcibHJukJA3sAICmyYsvq6jb4nMaGzvyWLzwUI2QyGwiX3N4uYiPoa6ilXbx0SoOAKgjtR4NWVfZJkBJfKJpQn5nbW7caGAHALSBGpdWWbepx1KTmjVBIrNNbDP1Ljdv8b7HxvJvzmgVBwDUXei6yqauZY4/NI3rfLC+qQlVriEAQFOpcWmVdVvN61ESmU2VdGNk2yrucvOmzpmQF7DSswQAUHfxCINQw2Zo1EMvCPE9t4kjaQQAALSBWveZDj3fvdv/CCN9PzUbZdsXRXEzKmxNT0/LwMCATE1NSX9/f/gDxt2Ld+3qDhRdP0J1f7aBX19f8eMDAFA1tT5zqQ/zFKlrS1B6LAPvavEZVv09r/r4AACUTY1hVT7yM0n7Dpz3sYln6JHZJGndjF0Vab2mtyUAoA3UeizE1CymrenMKY0mq7pHJD2fAQBNZdubMmndEp/5GfU81GPUCInMJrHpZpzEZ3fjqgNWAAB8WLKknKlZ8vbla39AVYrGmUVeTwM7AKCpTOLKpEWA1HVLfOZn1PN4/euL7y8AEplNUvTLSWs1AABHM61fk1rAiyZv1MQLSRg0WdE4kzgVANCLTBrjfI/ONT2fmtbNJDJ7icsX3uUGjWFyAIAmsK3jklrAbQK8pOPFSVRGOaDpit5YJb3e9Bqt6Y0WAAC5TBrUi47OdT2f0ItiOiKR2SRFkoq7d7t94V0CQ4bJAQCawLaOS0q02CRv9OP5XmESqFLRG6uk15sOt4vRqxkA0EZpdWyI1crV/Y2Ndf+sCRKZTVIkqeiaUMy7QUu6cBgmBwBoAtseZElBpE3yRj8evcjQRj5vqmyG24nQqxkA0GwmdWjSfJm+Ykl9f/TIRGF6MJf1JU+ax8tF3g2a+kWPz4NhcgCAJih74Tr9eEWG0gJ1VXS6BZXtcDsAAJrMpA4N2RCu16n0yERhejCX9QVOmscrBDVopEcJAKANQicT4/2LuA2lBeqsyHQLLspukAAAIBSTOlTdxmdjXtzDM56H0/R8KkAis8myvlRlLeyzZEltv9wAADgJnUzM2j91KpquyHQLLlhkEgDQFraNcz4b85LmctcTmzVBIrOp8r5UZS3sox5LhCASANB8oZOJWfundxnawLRx3Mf3nUUmAQBtYFp3hmpwb9Bc7iQymyjEpK4ixW7cQp0TAAAhZAWLenLFdsSC67x/zI+JtsiKCX19z/X54EXoyQwAaC7TfEqoBnc1Pk1bzLkm+qIo7koHW9PT0zIwMCBTU1PS399f3oHjebVE6tPNt47nBABAGrXeygqF1Ia6vG1t9531ul27ShnOU1ksA29q+RlmjRxyuT6S9ud6nQEAUEdVDeWuSR1rE8/QI7OJ1Ax8XRKGdTwnAADSmLZmq0lM0xZp15Zy9XWMckCTZQ0Zd7k+kq4H5pMFALSJ7XQrvkY4NLCOJZHZRL7npPRxAcSL/ixdypA4AED9mQaLLg11rnWiek41DyABZy7zYiZdD8zRDgDoNWruRk1AFqkH9Tq2xov8xBhaXkDlQ3mSuvu6fOl8dRtmiA8AADMaUidWHsugsFp/hnFcOj4uMjYW5qaoIdcaAAC58vI5SdMQqXzUgxXVqwwtb6OkXpNJrdMuQ9F89fqg9wgAoK1sRy8UrRNZ+AdtEMejY2Pdv/tE/AkAaIu8fI4+UiiK3OvBtFizAfUqicymSPpCJw3NcfnSuQzxETn6i++6HwAAqmCTLLRtKCwy7FUfLgQ0VRyPxiuL+7wpiq9fEeJPAEA75OVzknIurnmYtFizAXkdEplNYZqgLONLFweO3GQBAJrMph5zbZ12qStdFhgC6iiOS0dH/cWnSXEovZcBAG1Q5tojSbFtQ0YEkchsirwEZZlfOP1mjJssAEDbuTYUuiRAXRYYAuomVGya1ihAwzoAoA3K6jCWFNs2pLMaicwmyQoITb5wLgFl3tycNe9yDABAqjLmANKDRJO6uAFDeoBcemxaNLEZv14dpl5kbjAAAOoor17Lqk9N6tqsbRpSp7JqeQGlrBKprlqlJin1j81ktXKX1afU14jk9w5xWTUdAIC6CF2P1WyF5VqveA0jtf0M9Wup6He/ZtcOAACVyKoPTerKpG1qkMdh1fI2UVuzs7LjJr03igxv088nTUO6IgMAkCh0PdaQlm7AWdoiPEW/+1w7AIBepfaizKoPTerKpG0alschkVl3+jxZRYaaubw+fo1p8EiQCQBoGtPg0OT1edLq4ngfDZhgHcgUahXUvGuH6wYA0FZq3ZpVn5rUtUnbNCyPw9DyAmo7lAcAAJhTh9i4DKnxMeRVn8qlpPCMWKb5avcZlj08jSHnAIC2SKtDazD0OzSGliMZLdYAABwtaWiNy+uLtGKrr21IaziQKK83SJF4NG8RSgAAmsznqIaiC//UGInMXtKweQ8AACjFkiVuyZCJiZngb9++4quMxwEqq5WjiWxuhFzi0Xj/Sa8tOmQdAIC6UOPRoklGk/q2oTkiEplN4SNTHnreL5ftAQCoA5dkyNhY9888WXUk9SeazOZGyCUe1fcbv5brBgDQJmo8WjTJ6LrwTwOQyGyKIl/itNUjXY6bFzA2NKMPAOhxLgmR8fHun3my6kjqTzSZzY2QS6OBuv9du2auk3i+MBGuGwBA++h1q22s6rrwTwOQyGyKpABR/yKnfbGLBHn6cfP21dCMPgCgx7nUlaOjM8Hf6KjZ9ll1ZNFgFahCkcZyG2k9VIg7AQBtpScZfTbeNTzOJJHZFEmZcv2LnPbFLhLk6ceN9zE+nvzFb2hGHwDQ48pIiGTVkSGDVSCUKr6n6rVK3AkAaLs46RiPAHKJVfXEZcPjTBKZTabfdKXdhPkM8uJ9xfOBNfSLDwBAF7WuDN1KbbJ/epqhCYp+T12uNZKXAIBeEudcxsbc6z89cdnwOJNEZtOoAZ8eyLkEdq43aw3/4gMAkCp0K7XJ/knWoO7iOSrjnpEuTK+1tHi14UPjAADI5SP3ou+j4XEmicymUQM+H4Gb681a0hefYBIA0AahG+vy9k99iibwkfA3vRbSjtXwoXEAAHRJigF9JB3jfYi0IsYkkdk0eqBXNHDzdbPGypEAgLbIChhtk4wuAanvRkvAJx9zdcVMr4WYfixGCAEA2iQpp+KzgbslORsSmU0TB3y+AjdfXYrVC4FgEgDQFLbBoW0A6BIw+m60hIiIXH/99XLKKafIvHnzZNmyZXL//fdnbn/LLbfI4sWLZd68eXLmmWfK7bff3vV8FEVy9dVXy8knnyzHHXecjIyMyE9+8pOubQ4cOCCrV6+W/v5+GRwclLVr18rTTz/dtc2Pf/xj+e3f/m2ZN2+eLFq0SK699lo/bzgUH3N1mVLj3aRjNXxoHAAAXZLyPC1JPvpUSiKTwNEzH3MSpe3XNdOvryAJAEAT2ASHav1o2mjn0vDou9ESMjExIZdddplcc8018uCDD8pZZ50ly5cvl6eeeipx+/vuu09WrVola9eulV27dsnKlStl5cqV8vDDD3e2ufbaa+W6666TG264QXbs2CHHH3+8LF++XJ577rnONqtXr5Y9e/bI1q1bZfPmzXLPPffIxRdf3Hl+enpa3vOe98h//a//VXbu3Cmf+9zn5NOf/rTceOON4f4YRZX5vfTZOxoAgLpLqvfiERDxzyLaEltGgY2Pj0dz5syJvva1r0V79uyJLrroomhwcDDav39/4vb33ntvdMwxx0TXXntt9Mgjj0RXXXVVdOyxx0YPPfRQZ5uNGzdGAwMD0be//e3oRz/6UfS7v/u70amnnho9++yznW3OO++86Kyzzop+8IMfRP/n//yf6LWvfW20atWqzvNTU1PRggULotWrV0cPP/xw9I1vfCM67rjjoq985SvG721qaioSkWhqasrhL1PAzFd75l8d9rtr18xrdu3yez4AAIRmU4eZ1pMNqhcri2VK9ta3vjVat25d5/dDhw5FCxcujDZs2JC4/e///u9HK1as6Hps2bJl0R/90R9FURRFhw8fjoaGhqLPfe5znecPHjwYzZ07N/rGN74RRVEUPfLII5GIRA888EBnm+9+97tRX19f9POf/zyKoij68pe/HP3Gb/xG9Pzzz3e2ufzyy6PXv/71xu+t1M8w1Hfbdr/x9iHiYQAA6qRH6jubeCb4X6JNgeNzzz0XTU1Ndf498cQT5QSOenBnEuy5BJquwWmPXFgAgB5nWk82qF7shUTm888/Hx1zzDHRt771ra7H16xZE/3u7/5u4msWLVoUffGLX+x67Oqrr47e9KY3RVEURf/6r/8aiUi0S/suvP3tb48+9rGPRVEURf/rf/2vaHBwsOv5F198MTrmmGOif/qnf4qiKIo++MEPRu973/u6ttm2bVskItGBAwcSz62yeDSK6tOYrm7fgAYDAACsxXHn+Hix+i4tfq1Zw7tNTBp0aPkLL7wgO3fulJGRkc5js2bNkpGREdm+fXvia7Zv3961vYjI8uXLO9s/9thjMjk52bXNwMCALFu2rLPN9u3bZXBwUM4555zONiMjIzJr1izZsWNHZ5u3v/3tMmfOnK7j7Nu3T/7v//2/iee2YcMGGRgY6PxbtGiRzZ/DnT7szWQ+IJd5FFznGWpL92QAAJLEQ1hFzOrJovUiQ2a9+uUvfymHDh2SBQsWdD2+YMECmZycTHzN5ORk5vbxz7xt5s+f3/X87Nmz5aSTTuraJmkf6jF0lcWjLlMrmLK9ZpjSCADQdr7mpE7LDTV47s2gicy2BY5XXnmlTE1Ndf498cQTyW/cN5cborKSi6Hm6wQAoC5sA70lS2bqxaVL3ZKRDQ4sEV5l8aj6ffQd86mN6WoiPy2pzyI/AIC285XTSdtPgzuksWq5hblz50p/f3/Xv1LYBGu2vUZcxcfhZgsA0GQTEzP12cRE+jYugV6R+rHBgWUdveIVr5BjjjlG9u/f3/X4/v37ZWhoKPE1Q0NDmdvHP/O20RcTeumll+TAgQNd2yTtQz2GrrJ41OdiA1nUayfrOqLnMgCgzXw12qXtp8GNgkETmW0LHCtjE6jpAZ9LkGfyGj2g5GYLANBEY2PdP5O4BHomyUh6m5Vizpw5cvbZZ8sdd9zReezw4cNyxx13yPDwcOJrhoeHu7YXEdm6dWtn+1NPPVWGhoa6tpmenpYdO3Z0thkeHpaDBw/Kzp07O9ts27ZNDh8+LMuWLetsc88998iLL77YdZzXv/718hu/8RsF37lnJteKD+q1k3Ud0ZgOAGijtPgwVANeAxsGgyYyCRw9iIdui5gFanrA5xLkmbxGPQ43WwCApsrrZeYa3IWazxpOLrvsMvnqV78qN910k+zdu1c+8pGPyDPPPCMXXnihiIisWbNGrrzyys72H//4x2XLli3yhS98QR599FH59Kc/LT/84Q9l/fr1IiLS19cnl1xyiXz2s5+V73znO/LQQw/JmjVrZOHChbJy5UoRETn99NPlvPPOk4suukjuv/9+uffee2X9+vUyNjYmCxcuFBGRP/iDP5A5c+bI2rVrZc+ePTIxMSF/+Zd/KZdddlm5fyATZfXIVGVdR/RcBgC0ka85LU1j2CbGo6FXHhofH4/mzp0bbdq0KXrkkUeiiy++OBocHIwmJyejKJpZrfGKK67obH/vvfdGs2fPjj7/+c9He/fuja655pro2GOPjR566KHONhs3bowGBwejf/7nf45+/OMfR+973/uiU089NXr22Wc725x33nnR0qVLox07dkTf//73o9NOOy1atWpV5/mDBw9GCxYsiD74wQ9GDz/8cDQ+Ph697GUvi77yla8Yv7dSVvosuipjkdXLbVfHqtmqVwAAOIvrtJArkNeg3uyFVctjf/VXfxW9+tWvjubMmRO99a1vjX7wgx90nnvHO94R/eEf/mHX9t/85jej173uddGcOXOiN7zhDdFtt93W9fzhw4ejP/3TP40WLFgQzZ07N3r3u98d7du3r2ubX/3qV9GqVauiE044Ierv748uvPDC6Ne//nXXNj/60Y+ic889N5o7d270X/7Lf4k2btxo9b5K+wxDXgtVHAcAgDpKy8fYxo2m9WkN4tEosotn+qIoikInS7/0pS/J5z73OZmcnJQlS5bIdddd1+kZ+c53vlNOOeUU2bRpU2f7W265Ra666ir56U9/Kqeddppce+21cv7556vJV7nmmmvkxhtvlIMHD8q5554rX/7yl+V1r3tdZ5sDBw7I+vXr5dZbb5VZs2bJBRdcINddd52ccMIJnW1+/OMfy7p16+SBBx6QV7ziFfLRj35ULr/8cuP3NT09LQMDAzI1NRVufqIqF9OJ59oUmfn6+94eAIC6Uus0kbD1sFrXi5Ra75cSyyCo0j7DiYmZYeXj4yKjo+GOYxL7qiOWWHASANBGRfMrtrmkihdytolnSklktlXrg/+GffEBAPDGpU5zrQf1pGmshBCt9bFMDyjtM6xTg7V+zVR9PgAA+FZ2fqXiet4mnmHV8l5kOleC7WIDLE4AAGganwvuuM4xZLq4CVCl0N9N9VrMi1XVc+BaAQC0RVz/TUyU30msQTEoPTILaGwvhjq1qAMAUCW1TiwaLDZwZEJjYxl0lPYZhv5+V9gzGQCAWkiqC23qwQbGojF6ZLaV68qpugZl2gEACEqtC11XgYx/Fyk2MsFXPQ+EEHpVU3omAwB6XVzvjY93/27KR13dgHiURGaTFP1SFrnRMv0yN+BLDwBAx5Il9kkTvT7Oq59N68bQiSKgCPX6CBHnqdM5xNfl0qXpxyLmBAC0TVwXjo6m52yy6j8fDYENiEdJZDZJ2peyjBsk09c24EsPAEAX2/kw9fo4L2g0rRvV/ZCkQd2o10cZcV7edUPMCQBoo7wYMKv+87FuSQNGRZDIbJK0L6XLDZKtuGtz/DPEMQAAqJLrYnh5QaNp3ajuhyQN6ih0r8ykY6VdN8ScAIA2yosBQ9d/JqMiKsZiPwXUZoL8Mhbv0SedbeDksQAAZApdn9pMwF7SZO21iWXgrPTP0Od14vI9b/BCBgAA5KrD4swVnAOL/bRVWk8R24y8y3A1fd/0EAEAtE3oFm6bXpY+hgYBIfi8TtRrwmWqJKZgAAC0TRn5HdNziPdfMyQymyTpBsilVdpluFp8Q8UwHgAA3NjUoSRoUFdxTChS/DuqXhMuUyUxBQMAoE3Kyu/kKXtebEskMpsk6QbI5UtbJBlJDxEAQFuFTIrYBqYkaFB3Pr6jalyZFZ+qiX3T1wAA0DT6qAMToerCGtexJDKbJCmJ6PLlCpmMpAcJAKCpQvaYJDGJtomvk/FxP7FfVnyadv3QwA4AaJOkTmtZQs4bXeNFf0hkNo1+41SnAC6+iES4UQMANENaT688tvWdbcNjjVvBARE5cr2Mjc38HjL243oAAPQC29EGofMvNc3vkMhsmrK+SC49K9VzItAEADSBbb0a14/j4zO/m9Z3tg2PdWqoBLK4JBlt40z9emAEEACgrWxiwNANfTVd9IdEZhOowVoZwaJI8bk3ufECADSBbb0a14tjYyRW0HuSvucui/8UXXm8pj1EAADwIq9ujJ8XCdvwXdNFf0hkNoEarLn00Ai5IJDrkDwAAOrAtu5Kqx99JFZIhqLusr7nNtdA0ZXHGWoOAGijOBbMqxvLbNCrYZ1LIrMJin5xQi4IRIs4AKCXpNWPPobeUKei7rJiSpt403Xl8bJ6oAAAUAU9BkyrG2uYXCwTicwmcO3pWEaw1+MXEACgpVzm8Iu5JiKpU1F3WcPIXYaYq68rssgWvZkBAG2gxoJZdWOZo2Fr2NBOIrMp6jp/kGvQCgBAnVUx3JUpWtAUrkPMiyYc03o+1/AmCwAAK7t3z9RjLmuOhGzQixe4jH/WAInMpnBZVTUWemGg+IITIYAEADTDxMRMfTcxkfx8yGlZYvQiQ1NlTaWQde0UjRfTej7TmxkA0HRqHVmnTmxjY90/a4BEZlupX+DQCwMlBZIAANRZXlCmJiVDJRxpBERTZU2lkHXtqAlH1+sqKWlJb2YAQNOlLSQZy6o3QzXouXaQC4xEZlPYfjFdv8jx9uPj5sGlfix6lwAA6s5mmEyohGNSXU0vTTSFScyoXztqwpFEPgAAR8R1ZFouJ6veDNWg59pBLrC+KIonOISt6elpGRgYkKmpKenv76/6dPyKFwkSOTIHZsjXAQBQN/HUKePjMz03XeYsytu3vs+S69FWxzI9ovLPMOs7mzXfl+tcYMSaAIC2S6oji8yh6WpiYiYGHh8XGR0NeiibeIYemUhWtEdnjbodAwDgJG6FHhszb+U27VGZ1qpOPYqmyfrOZvUQce09wjUCAGgbPX5MihOrmEalhvNjipDIREy/cFwvEuYoAgC0hUvCxHS4bNq+qUfRNPF3ViQ9iZ+3uJaJOFYV4RoBALSLHj+6Ntr5nKKopvNjijC0vJDKhvKE6FLMMB0AQC8JNVSmimE/BVQ+LBmF1eYzzIolfcSZSfto2PUGAEAiX/WZz7xOjac7okdmE/mcHD3O2MeLHdQs0w4AQBA2Q2VMWrfV3mK7ds3U0bat4Sz0gybL6j2iLq7lc7VyNSbm+gEANJWvETk+pl9pQI6IHpkF1KZHZt7vWVyz7LSAAwCazKZHpkldqW6jcl0wT6SUOrY2vfngrJafYVac6LOHh3octYGf2xsAQFOEWBiviIpG69Ijs+30bL3eQ9Omx6Zrxj7vGLSKAwDqbHR0pi41GVZuUleq2xRdMC/mY+QFUIWsOFHtnVmUGhOzCBAAoIn0OlPNpfgcjWuqAfUpicw20L9oNl880y7MemIy7xhVXHAAALjKaoAzqSvVbUwWP8naRwMCSCBT/N0dHz/6Ggi1AioLZQEAmkiP+9RcStkxYUNG3jK0vIBKhvJU9cWyHe7WkAsAAAARCTOMpgEL6dVyWDKs1Poz9L1AD/ElAKDtfNR1rvuoMHZlaHmb+RjS7TLs23a4G63iAIC6U+vDEC3eLvtkaha0XZEYkRE/AIC2cx3Zo7KpL0PHwwGQyGwaH0O6XYJAhrsBANpGrQ9DNMDZ7rOquZCAUNS4MS1Jb9MIX+MVVAEAcJJWDxaJCW3yNqHj4QBIZDZN3hfLdkEC38cHAADdTHtZqoEqiRo0WfydFzkSN6bdkNk0wo+NJceh9GQGADRVWj1YVt6mgZ3VSGQ2kc8FCUIiqAQA1FmRwM2mjjNtUVfPhwZDNJlNLxIfjfD0ZAYANFXa6IXQeZukRseGYLGfAkqfXF0dchar88fXgEUOAABwYlPH1XiBklovFAMjtfwMk77zIa8DNUau4XUGAICRMnMoNcvXsNhPW6V1NXbh0luSHpYAgLYoUg9OTBx5zKQuZloW9Jqk77zJdeAaa6r7pFcmAKAp9HpPjSuL5l3y6tQGDimPkchsEvWLVvSGyGUIju1rGnxhAABarkg9ODZ25LEidTENhGibpO+06WMi5S1sAABAHej1ns+GubQ6tcFDymMkMpvEZ48Ol2Av6zVJASk9UAAAdVWkHvS1cjLz+qFtkr7Tpo+J2MeaKuJOAEDTJNV7vhrm0vbTgviTRGavKZJ9zwoQW3AxAAB6iEvSI37N6OhMULh0abHelPQgQ9uY3pClffeJNQEAvSRvKpYio3fS6tQWxJ8kMpum6DC0UEFgCy4GAECPcqlbfdSn9CBD25jOjWnz3Y+vT189oQEAaApf+ZsyV0MvAYnMplG/yHk3XEk3ZqESjkkXA3N/AQCawCVILFqfUkeibYp8p9Neq65GPjZmduPFtQUAaAJ1Ecm0estX/qZloxr6oqgG66w3lM3y8N6oAZ3ITECXJh5Cnrdd0v537Sq+gIHpeQIAUJakes5X3WfDpY4OoJJYBl7V5jNM+k6bXltp14P6uOn1WZNrCwCATGp9FQtRb6m5mTJjXUs28Qw9MptmyRLzrLxL9t5Xpl59PUOAAAB1oAZyaj0VeoiNyQgJepGh6ZLiTtO4Mi1mVR83vT6Z7ggA0AS+F5HUxbGlHvO2AInMJjK94XK5MVODvyI3VS6BJwAAIdk2svlKLpokT1s25Ac9KCnuNE0qpsWstrFsFb2rAQAo4vWvD9OgrseULWrgI5HZBj57cagBIzdVAIA2MW1k01uwi9aDJskcepGhydJiUZdG9SJxLbErAKApsuosHzkeNbZs+OI+OhKZbWCzAJCNuItz/NMGgSQAoG5MkyquLdhFkjktWEESPUyP+3wnI+P95e2TBgEAQFNk1Vku+RS97m1xbEkisw2S5iLyYWys+6cNAkkAQFO5tmDTiIdepcd9Ra4Fdc6w+IZM3U/WPlt80wYAaJmsOiupLsyTV/e2aD52EpltYLMAkI0i+ySQBADUiWnwZjvHnrpfl3qzRUElelTSNeMjhowb0uN9x2gkBwA0XV78l1QX5smre1vU4E4is5fY3iyVPa8RAAChmAZvtkGeur1Lvam+nvoTTWSymJULtTdKnMwkLgUAtIFpvGnTMJhX97Zo1GxfFEVR1SfRVNPT0zIwMCBTU1PS399f7cn09R35f9pHarJNGecBAEDZTOsn2x6ZRes9fdis634c1SqWgZPKP0P9mvG9cniRa4y4FABQR77qJ991boVs4hl6ZDaV3sJclxVR045BizgAoEqmdaBtTzJ1fy51XHy8FrWSo8f5HrpW5NrgugIA1JGv+qlFw8Vt0COzgEpbwKtqYXbN+NMiDgBoqwbXcZX35kNhlX+G+ve/rN4hLeqFAgCAE5e53Wtad9IjsxfYZPDTekO69JJ0ncuLFnEAQN34Gi3gWsfFx5+YYNQCmkv//vte8DHtOk3rhcIoIABA3eXVVaZ1mW2d25IenPTILKDyFnBTaT1FXHqQVDyXFwAATpJaoKvuSakev6LzaEwsg1SVfoah5sdU96PGnWqPz/HxmdVc9WNVfV0DAJAnr65Ke75oPUuPTNSCSaY+raeISw8S5vICADRRUgt01XWZuipzlecBuNKvK189PdT96Ndp/NzYWHIvlKqvawAA8uTVVerzas6naD3re9RERUhkNp3JFznty1rkS9ySCwAA0MNC12V5jY3x8UdHqVPRTHESXk/GF00iqvvRr9O8YxCjAgDqSI0L8+oq9fmsxr2i59FQJDKbTP3i+Wp1Dv2lbsFFAwBoIF8JFpt6rCXzEAGpxsa6f/pKImbtJ+25rGuT+BMAUDXXuDCrca/M86gREplNpn7xfLU6u3ypuakDANSdbeBnu8BIEoa4ou3Shr5VIevaJP4EAFRNjQdt6krfIw1aEJ+SyGyyEF9Al30SHAIAmsC14U19nU09qQaeVSd5gBCWLDmyIE8V8aDptdmCmzYAQMOpiUjTujJE/NiCKVhYtbyA1q306bqClc3rarxKFgCg5WxWM85aNTn0sUvUulimB1X+GarfbZFyY7yaXlcAACSyzYeEqudqmJdh1fJe4jND79qSrmf0s86pBdl/AEBDufSmTHp96GMDTeL7O63GkXlxbtp1RQ9oAEAd2eRDQqyJEmv4qFp6ZBZQeQu4iN9WcF9ZeVrHAQB15FLPtbxOq0Usg0Jq8Rnq8WiRa0Xfl8s+W37dAgAazDQeDVmX0SMTldIz82kZdZOWaV+9JbN6ndBCDgCoikvrs8+elNSBaCv1+hgf97OvXbvcrz/XBRUAAAjNNB4NuaBew0fK0iOzgFq0gMfyMup1aZmuy3kAAHpP1a3PNawDaxXLwEktPsP42orV4ftdw+sNANBjkmLPrHg07bkQdVrVcbGGHpm9KC+j7tKiTc8RAECT6fVYyNZnkzozZMs6UCW9V0mV3+v42op7hjJ/JgCgKkm9L7Pi0bTemiHmWm/wPJn0yCygFi3gIfVA1h8A0FJl9xCzrTNr0lus9bFMD6jFZ6hfbyLVfa/zrq2aXHsAgB5gm/8oM19Ss9wMPTJ7md7KXKTVOc72j48n78Nl3w2fiwEA0BBqUqWM0Qi2LeWsYo420eO6tNixDHnXFtceAKAsav6jzHVLTM9t166ZmLlhoxTokVlALVrAdXors49W57R92Oy7Ztl+AEDLubaAq0zqzYbXb7WMZWClNp+hei346A3d8GsLAIAudRwRUKNzokdmL9NbmX20Oqftw2bfDZ5/AQDQQLYt2mlzEZm+zrR+Y34+9IK8UT0miB0BAG2SlD/JiwtDx40NHaVAIrNt9Bs30xu5rAskbR82N4kNvUAAAC2TVt+p9ZRat+UFkLb1G8kZtJX63Y5jxLGx7udsFIkd1es2/v/EBI0IAIDqJOVP8uJC9XkawztIZLaV7Zc89I1Vg+dfAAA0SF79l1bfpTXOJW2vHoO5n4EZeg8T9TGXnplFri31uo3/XySpCgCAL2ocaTOvc4icTUMb2ElktpXtF7KMHpMNvUgAAA2SV9f4WJSnSH3GCAW0VVIPEx89M12o15maTFWfAwAgBJtG9bxGO/X5EDFkQ+NSEpltYpPZ15XRo6ShFwkAoEVs67ukEQVF6jN6cKLNknplqo/v2lXO0Dj1Oov/PzrKtQcACM93o3qMGLKDRGab2GT2Va4Bpe3r9HNijgcAgG8hGs30gNQ1kKTeQ9sl9cqMH4+vmbqM0OF6BACEkBWL7t49U//t2lWPhGRd6mRLwRKZBw4ckNWrV0t/f78MDg7K2rVr5emnn858zXPPPSfr1q2Tl7/85XLCCSfIBRdcIPv37+/a5vHHH5cVK1bIy172Mpk/f7588pOflJdeeqlrm7vuukve/OY3y9y5c+W1r32tbNq0qev5e+65R37nd35HFi5cKH19ffLtb3/bx1uunuvNm+uXt+iXvqEXDQCgxkK0VhdNjsYJE+o99IK0Xpn681nzZpaRZOR6BACEkBWL1q3uaeio2WCJzNWrV8uePXtk69atsnnzZrnnnnvk4osvznzNpZdeKrfeeqvccsstcvfdd8uTTz4p73//+zvPHzp0SFasWCEvvPCC3HfffXLTTTfJpk2b5Oqrr+5s89hjj8mKFSvkXe96l+zevVsuueQS+fCHPyzf+973Ots888wzctZZZ8n111/v/41XyfXmLS/gzHud6zChhl40AICGck2OFE2O6sEq9R7aLK1Xpvp83ryZIW/04nJg48aZ3+O5MwEACCWue2zna2b0QKK+KIoi3zvdu3evnHHGGfLAAw/IOeecIyIiW7ZskfPPP19+9rOfycKFC496zdTUlLzyla+Um2++WX7v935PREQeffRROf3002X79u3ytre9Tb773e/Ke9/7XnnyySdlwYIFIiJyww03yOWXXy6/+MUvZM6cOXL55ZfLbbfdJg8//HBn32NjY3Lw4EHZsmXL0X+Avj751re+JStXrrR+n9PT0zIwMCBTU1PS399v/fpgXLor9/Ud+b/LV6Lo6wEAKCqv/quqrqrbMCJFbWMZGKvlZxh/58fHZxKWSd/9rOsi5DWjlgMxYlcAQEiuMaj+Ot/1Y43yODbxTJAemdu3b5fBwcFOElNEZGRkRGbNmiU7duxIfM3OnTvlxRdflJGRkc5jixcvlle/+tWyffv2zn7PPPPMThJTRGT58uUyPT0te/bs6Wyj7iPeJt5HEc8//7xMT093/asll1Zs096RaS0C9K4EAFQt1OTqNpLqSSZnR68x6XWZNXe6j2smL2ZlFXMAQFlcY1D9dT5HLCQtytcQQRKZk5OTMn/+/K7HZs+eLSeddJJMTk6mvmbOnDkyODjY9fiCBQs6r5mcnOxKYsbPx89lbTM9PS3PPvus83sSEdmwYYMMDAx0/i1atKjQ/mrFNGBMu3DyXp/XJZou0wCA0MpIKNZt7iOgSibzYcZ8Xzt5MWu8irkIMSgAIKy47hEptmCyz0Z5fVG+BrFKZF5xxRXS19eX+e/RRx8Nda6Vu/LKK2Vqaqrz74knnqj6lJKF7HESakEhbvwAAEXZ1FGmDWg2DW0NbtkGgjDpmRkrOvd61v6yEIMCAIpKq7f0x4vWOb4a5Rses8622fgTn/iEfOhDH8rc5jWveY0MDQ3JU0891fX4Sy+9JAcOHJChoaHE1w0NDckLL7wgBw8e7OqVuX///s5rhoaG5P777+96XbyqubqNvtL5/v37pb+/X4477rjc95hl7ty5Mnfu3EL7KIWa7fepyHwMu3YdeS0AACHY1H9qIJn1GtPt1G3jcwEwI44D456ZSbFkfP3G8aaI2XWXxrQ8IEYFABSVVm/pj9elzml4zGrVI/OVr3ylLF68OPPfnDlzZHh4WA4ePCg7d+7svHbbtm1y+PBhWbZsWeK+zz77bDn22GPljjvu6Dy2b98+efzxx2V4eFhERIaHh+Whhx7qSpJu3bpV+vv75Ywzzuhso+4j3ibeR89QM/++hm0XaT3Iazlgjk0AgC8m9Z5a32S1ntvUT9RlQDKbnpnqc1xLAIAmSIsB9cfrMG96w3tjigRatVxE5L/9t/8m+/fvlxtuuEFefPFFufDCC+Wcc86Rm2++WUREfv7zn8u73/1u+bu/+zt561vfKiIiH/nIR+T222+XTZs2SX9/v3z0ox8VEZH77rtPREQOHTokS5YskYULF8q1114rk5OT8sEPflA+/OEPy//8n/9TREQee+wxeeMb3yjr1q2T//E//ods27ZNPvaxj8ltt90my5cvFxGRp59+Wv7lX/5FRESWLl0qf/7nfy7vete75KSTTpJXv/rVxu+xlqtExpJWZBQp1lPTtUdmjVdrBQC0kOkKjGnb1WgFx9BqHcvASKM+QzUmFEmOD8uOG/WYmXgVANBmNY1zK1+1XETk61//uixevFje/e53y/nnny/nnnuu3HjjjZ3nX3zxRdm3b5/853/+Z+exL37xi/Le975XLrjgAnn7298uQ0ND8k//9E+d54855hjZvHmzHHPMMTI8PCwf+MAHZM2aNfKZz3yms82pp54qt912m2zdulXOOuss+cIXviB/8zd/00liioj88Ic/lKVLl8rS/9fie9lll8nSpUvl6quvDvXnKJ+a+U9qHXDppenaesDcQwCAMsR1m+lqxKat5wD8UGNJ1wUkQyNeBQD4VHSErK/RtrZxco0F65HZCxrVAq4rMwtv07JO700AgKs6tTA3pD5rdCwDEWnwZ2jSO7PM8xgfnxn6XvNrFgBQU2mxX9H41Ndo2zrFyQlq0SMTNVdmbxOblnV6bwIAXIWs22xbwNX6zMc81UDbmPTOLPM8Rkern7cMANBcaXVZ0fg0b7Rtnhb1xIyRyOxVLsN2fC0aBABACCGHpNomWvQgkQY6IJ16YzYxMRNvTkwU26dL3EqsCwBwlZZkLBqfqq932Vccg46NtabBjkRmL8gKymwCNpfWctuAkHnJAAA2yko82NZPcaBJvQbkU2/M4pXN45+uXOJWRgYBAEzpMWhekrHsxrKknpgtabBjjswCGjMnUdZcCDbzJLjMZVTzeRgAAA0W10sx23qmIfNYhtSYWAapWvcZTkzMJDHHx2eGe7tyub7VMqWHywUAgAHbXEfZuZGk49U4P8McmeiW1RvEtaeIaYt13v5b0iIAAKiAWge59Hi07X1lU2dRvwFu4rkqR0eLXUcuw+/UbZnfFgCQxTaX4pp70evCvLoxbU5MdfuGjxSiR2YBrWsBz6Nm73ft8tOLpcYtAgCAmnPtURm/buNGkSuuMO/5ZVNnNaR+67lYpoVa/RmWeR3pq5eranwNAwBaTq8L8+rGtOdrHpvSIxPJivYOUVsQfC2ooLdK0IMFAGBKr4tM65C4B+YVV8z8NJ2Lj/kugXKVGSfqiyFwvQMAslQ1T3tS/aSeS9rz+v4ajB6ZBTSmBVyfQ0yklhl4Eal9KwEAoAbSemKa1iFJPa98z4fXkPk3GxPLIFWrPsO86yZknNiQaxYAUBMmdVJZdYtrL80aoUcmuulJTJcMfF1XhQUA9Ba1cS6tfsurQ+KenOpwct+rFPsauQD0krx5a0PGiVyzAAAbJnWS7XzsNvJ6YarbxVqSZyGR2QvUL7VrgBbyAlQRRAIAsmQt8ONSh/hIjNhOwg4gWd71aDOdBNchACAkk7gzZAOcmqPJOhc1dm5JnoVEZi/wkRw0vQB9BY0EnwCAJPp8zUX5qCP1xr6yGv+AtrG9HrOutSLXoRqHEpMCAFQ29YJarxWtT/S6KZaWo0lbvbwFmCOzgMbNSWQ7P4PLfA6+5l5owBwOAACIyNH1ZYPm2mtcLIOj9PRnmHWtFbkO1ThU1YBrGgAQmGuuokiOI2ndk7x9NSynwhyZSKa2TJu0Ari0ZKs9ZYq0ODBXJgDAVdk9qPReZEyTAthzuW6zhpoXuQ7VOFSNRellDQC9q2gPxyI5Dn1qpbR9mc6b2XD0yCygcS3gehY/76Mv0pJteywAALJMTMysMD4+3r1IT5IyWqAb1OsyS+NiGRylNZ+hj+vWxz6Sru2WXO8AgALK7uGo1j0iZvVQw3phquiRiWRLlthl5Yu0ZGctxpCGOYgAAGnGxrp/ZgnRAq3XUcyDCfjl47r1sY+ka5te1gCAsns4mi7mI9Lq+TCTkMjsNWUEYvrEsz4mbCfJCQC9LQ7M4p9pQvWc0uuoFg/XASrhI0ZN2odtDMm1DQBI4lJP2dZBrkPD4/h0bKwnGt4YWl5AY4fyhB4y49qdOesc1H0yrAcAkCZEHWTyfEM1NpZBB59hjgYPswMANJxtHWS6vTqVn83Q8xpjaDmyJfV89DlEzrUlO6uFg4nWAQAm1PrCpPU7bvnOqwcZWgqEFWL0jT5KyNc+GSUEADBhmxsxWcRH5OhcTo/FqSQye1HSxeFzGE2Ii8h2fk8AQG9S6568hi99YToR6higKiHmndXnuRQpNsxP3efSpSQzAQDZsnIjSfVR2vZq3aPOhSnSk7EricxelHRxxI+JlNfKbBtI9lgrAwDg/0mqL7LqENOGL31hOuoYoDohGqyT9mmbME2bH9dmHwCA5lFjzRC98W3qI71ujOfC7NHYlUQmupW5CqvrsRjSAwC9xXZKFNOGr6IJE+qjRjhw4ICsXr1a+vv7ZXBwUNauXStPP/105muee+45Wbdunbz85S+XE044QS644ALZv39/1zaPP/64rFixQl72spfJ/Pnz5ZOf/KS89NJLXdvcdddd8uY3v1nmzp0rr33ta2XTpk1dz2/YsEHe8pa3yIknnijz58+XlStXyr59+7y878YJNaJH32fRYX6MEgKA3qDGmj7yJHrcaFKXxK8RmanPqH9EhEQmdKZzMoQ8Vp4yk60AgOqFmhLFZhh6En2YDwnNWlq9erXs2bNHtm7dKps3b5Z77rlHLr744szXXHrppXLrrbfKLbfcInfffbc8+eST8v73v7/z/KFDh2TFihXywgsvyH333Sc33XSTbNq0Sa6++urONo899pisWLFC3vWud8nu3bvlkksukQ9/+MPyve99r7PN3XffLevWrZMf/OAHsnXrVnnxxRflPe95jzzzzDP+/xB1V1bDgG3CNGskUw/2ggGAnqHGmj7iTj2PYVKXpL1GpKdjT1YtL6AVq0SarsKatnqWz1Vci54LAAC2itRjSXNsNqxeakUsk2Hv3r1yxhlnyAMPPCDnnHOOiIhs2bJFzj//fPnZz34mCxcuPOo1U1NT8spXvlJuvvlm+b3f+z0REXn00Ufl9NNPl+3bt8vb3vY2+e53vyvvfe975cknn5QFCxaIiMgNN9wgl19+ufziF7+QOXPmyOWXXy633XabPPzww519j42NycGDB2XLli2J5/uLX/xC5s+fL3fffbe8/e1vT9zm+eefl+eff77z+/T0tCxatKj5n2GV8V1SOeAzxgUAwLReUbcTSX5NC3MirFoOc6a9G9NaIHz2jix6LgAA2CrSsyp+LfVSbW3fvl0GBwc7SUwRkZGREZk1a5bs2LEj8TU7d+6UF198UUZGRjqPLV68WF796lfL9u3bO/s988wzO0lMEZHly5fL9PS07Nmzp7ONuo94m3gfSaampkRE5KSTTkrdZsOGDTIwMND5t2jRotRtG6XK68h2+goAAPLoIw1MY061/lFfo+6vx2NPEpm9zvQCSLvo9NfHF9fEhH1X56LnAgBol7hOqfvQGeql2pqcnJT58+d3PTZ79mw56aSTZHJyMvU1c+bMkcHBwa7HFyxY0HnN5ORkVxIzfj5+Lmub6elpefbZZ4867uHDh+WSSy6R3/qt35I3vvGNqe/pyiuvlKmpqc6/J554InXbRglxHZkOVy8yfQVz5QJAe/gq09VRO3kNYqZzZ6YlOHsQicxeV/QC0F8fX1xjY92/l3EuAIB2SeodZUIPCH0nGoo02sGLK664Qvr6+jL/Pfroo1WfppV169bJww8/LOPj45nbzZ07V/r7+7v+tUKVK8IWmQeTnpsA0HxxHeSrTFdfn9cgZjp3Zo/3wlSRyMQMX8FjfFHFQXiRi4wWbgDobUkLz5nQA0LboDSv/inSaAcvPvGJT8jevXsz/73mNa+RoaEheeqpp7pe+9JLL8mBAwdkaGgocd9DQ0PywgsvyMGDB7se379/f+c1Q0NDR61iHv+et01/f78cd9xxXY+vX79eNm/eLHfeeae86lWvsvtjtEWIhGCRmz7TBhF138SsANBMet1TNFmo1j9qQjKpLnEdmdrD+RISmbDr9pxnyZKZC3BsrPjk6Fnn1MMXLQD0DHVlRhHzOkrt0eYyj1Beneiz0Q5OXvnKV8rixYsz/82ZM0eGh4fl4MGDsnPnzs5rt23bJocPH5Zly5Yl7vvss8+WY489Vu64447OY/v27ZPHH39choeHRURkeHhYHnrooa4k6datW6W/v1/OOOOMzjbqPuJt4n2IiERRJOvXr5dvfetbsm3bNjn11FOL/3GaKkRPkyKjfUwbRNR906gBAM2k1kEuyULT+TCT6hLXuqqHRwSQyIRdt2eb/RW9oLIC2h6+aAGg59gmOOKekiJu8wjlHS/e3+goU6LU3Omnny7nnXeeXHTRRXL//ffLvffeK+vXr5exsbHOiuU///nPZfHixXL//feLiMjAwICsXbtWLrvsMrnzzjtl586dcuGFF8rw8LC87W1vExGR97znPXLGGWfIBz/4QfnRj34k3/ve9+Sqq66SdevWydy5c0VE5I//+I/l3/7t3+RTn/qUPProo/LlL39ZvvnNb8qll17aOb9169bJ3//938vNN98sJ554okxOTsrk5GTiHJqtV7cphvRyIKtcYLgfADSbTeIxSRkLF5vOpdkDSGQivduzj/0VkRXQ9vBFCwCtFc85OTHR/bhrIlL/vym1J6jeCs+IgMb5+te/LosXL5Z3v/vdcv7558u5554rN954Y+f5F198Ufbt2yf/+Z//2Xnsi1/8orz3ve+VCy64QN7+9rfL0NCQ/NM//VPn+WOOOUY2b94sxxxzjAwPD8sHPvABWbNmjXzmM5/pbHPqqafKbbfdJlu3bpWzzjpLvvCFL8jf/M3fyPLlyzvb/PVf/7VMTU3JO9/5Tjn55JM7/yb0a6AX1O3a0sudrHKobklYAIAfpnmHtO1cVy5PYjqXZg/oiyJ1zBZsTE9Py8DAgExNTbVnonUAAKrS13fk/3UIT5LOp27nWBCxTPO15jOs67UVT8EU35zG/+/BG0cAgCWfdZtaHy1ZcvTvDWcTz9AjE0eEXoXVZ0t73VrtAQDFxXNO5qzaXJqk1nVGBABhhLi2fMSLag8Yk6GDxKgA0Ayu5bXJ6+JtfM6nrvfA7OHp9uiRWUBrWsBjamtBLOnr4Zr5z2qNsN1nXVvtAQBokNbFMj2IzzCDj3jRtkcmMSoA1J+64LGIXXmdVs6r9YXrvm3QIxMQ81VYXTP/aku73ophu096xAAAiqLnFFAvvq9JH/Gi2gPGZD6yuvUsBwAcrciCx2l1i5rTKGOUQQ/PkUkiE0eYrsLqelGqF5p6kavBquk+lyw50tLBDSgANFfaAj8qPXDzlezIa0Qj0QmUy/cwuRA3eXnlwthY908AQP2YLnicVOan1S36Pn3XP3oOpYdjVIaWF8BQngJ8dLtm6A4ANJ9JWa5v46v8zxuS0wP1DLFM87XqM2zCMDm9XGj54gsA0HNMchVVlPVlD10vGUPLUVxaht9X5l9toXDt4Zn0OpOePQCA+jAZhqlvk1Vv2NRTea3lTGMCQKeXC3ov0h4e6gcArWAyRLyKhXZ85FBagh6ZBbSqBVyX1gul7r1T6n5+AAB7NmW7z4XlekCrY5ke0arPsIlxHOUKALSLSblO2e8dPTJRXFqGv+6ZfyZYB4D2sal7sratovUcgDnfcabPOcTS9pXXA7PH5zEDgNowLY+TynWbhXZClfvqfnu8bqFHZgGtagGvC1o2AABFqXWJSPL/qWNEhFimDVr3GfqMBX328HTdVxN7mQJA28R1SyypPM6qf3yNDipC3a+qJXULPTIRhk3W37WFgJW4AKA3ZJXxRct/tS5R/287dx3zLgPl89lz2mcPT5t9qWVY3UczAUAvUOuUtPI4q/7xNTqoCHW/PV63kMiEOZvA0jUIVS/IvH2Y3OhyEwoA9ZRVxhdNZPgK9MbGun8CCC++VsfHizdo+1x4x2ZfTGMBAPWixoMuizza1AGhFn2L9yvS8yOMSGQim2uLsuuNo81KXCZBIjehAFBPWWV80VZmtS4pEkwy7zJQvviajWO3uiYDsxrUbRrmAQDhmcSDcQ4iHh1aJ2qdQ71CIhM5XIfkqa0FJq3pScFg3vFMbnS5CQWAesoq40O1ZNsaHZ05j9HRas8D6EVqfFe3G0qR7BtJm4Z5AEC50hqi6pwkVM+LeoVEJnIUvUhMCwKXAsPkRpebUABotjLnS2Y1SKA+1PiubjeUIuYxcl0aZgCg16TFcmm5B5N5NKuiTrvS48PKRUhkIk/R4EsN8kyH4AAA2i2uD0wShWW2jKctEgSgGj7mywzVKGEbI9M4AgDlSovl0uoWk3k0TYQo75sy7UpJSGTCnMvCOWqQl1aQxF24XQoMgkIAaBZ12I5IfiBmMrzUti5I257VIIF68XHj5rtRwjX2VM+D2BUAwkuL5dLqFl896EM0hsd1TzxlXo/Hp31RFE9kCFvT09MyMDAgU1NT0t/fX/XphNfXd+T/Ll+btIRlkf2avrZIshQA4I9abouYlct5Zb1NPaInUns8DOq5WKaFeuIzjK/b8fGZG0+beM53DOgat+plj+3rAQDmTMr+UDmCEPtV656W5jRs4hl6ZMJc0YVz0lo4ivR6MX0tQwQBoB7Uctu01TuprFd7RdnUI2nzH9HDH6ivIj0zfc9R6Rq3xudBb28ACM+kF3yoOYxD7FetM8hp0COziJ5oAW8LemQCQLsU7RXlc3RAgxHLNF9PfYbq9SvSvNiOeBQAytHGXvAtr0PokYmw6rKqq82xWTESAKphs7CPjaK9onyODgBQjrS516vsTW0Tj+ojhOgJDgBh6L3giywaV7W4rmhxEtMWPTIL6KkWcJU+v1ms7K+SSe+ZlrdaAEDt6XUGYUet9Gws0yI9+xma9rYJHQu6zNEbn0uP9gQHAO/yyvqs8rbuOYMeiaXpkYmwTFZ1dWlhtn2NSe8ZWr4BoFpqGR26tyNlPNA7kuacTCoDQs+TbtObW+8RTk9wAPAjr6zPKm991hO+Y1F9P9QXIkKPzEJ6tgXchEsLs74SV1ariGmrCS3fAFAvIVu9KeOtEcs0H5+hQi8D1F6bdexpU/deQADQFEXKe59lse9YtIdiW3pkonouc1GorSR5rSKmrSa0fANAuSYmZsr9iYnk521avUP01HfdN4D608sAtZzxmSj0VX6E7i0KAL1o6VK78tnnehq+8g1xPYNE9MgsgBZwA75XlTV9HgBQjbxyf2JCZGxspqFrdPTo59XyXb259x2u9FALdxZimebjM8wQapVzX+VHWjxLnAsAdpKSfk2L79JiYJGeqA/okYn68L2qrOnzeeIWjrjnED1yAMCP8fHun7qxse6fOrWHUshe9PTQB9ovbZVzkWK9KkOXH/TUBAA76ohQ9fcmSYqBRXoiiWmLHpkF0ALeYG1osQGAJrLtce86JzKMEMs0H5+hgaS50+rQKzvtHCjPAKB3xGX++PhMQ3+d6qkS0SMTzVP2XGVtaLEBgCay7XFv2jOJHkwA0iTNlZnUq7JIPOry2rSenT7nawOANopHVrZhdGVcR42NHUlY9vWRq8hAIhN+uQaAtjegrsdRJ82Nopn52QgUAaCYkI1RWUM41ePaDPXMW5AIQLsklQ9JycIiDSIuryVhCQBu1CmKXBuxfcevNvvLimH1xCZ1xFFIZMIv1wDQdq4h9Tg281zmnR83twBgz7TsdwkYs2701ePaJATy5ukE0C6m5YMej9qUWcy7CwDlUedidy13fY/mydufWqekxbBqfUN9kopEJvyIL0rX7s+2LdLq/uMbUZMCKC/I5OYWAOyZ3sD7DhhdEwd5CxIB6E0mU1uoN6Lq/330rsxKnJY9DRMA1NnrXz/zs8hcwr4boPIaw0wWtEyaCgVHYbGfAphcXVFkIlrXCc3TJsUtYmJiZl/j4zPDzgEA/pQ9aTkLZuQilmk+PkMDRWNN9XVJC0aK+CnTssrIHlv0AQAyNaFM1M8xqy4KkdtoGBb7QfmKtGYU7aHz+tf7mzsinjOTJCYA+Ff20Eu9fqFHE9CbXGPNpF6WajmmLh7po2zJKiMZug6gF8Wxmz6dXBPKRP0cTaZLYl5MIyQy4Yc+r4PNCmKuhRAr1AJAteqeGEybPJ16A+gtPm941Zg3/r/NNEdp8nqNsjAQgF6kJvjU35tQJuado+uilSCRiQDUIM4koEtKgtZlUvW636QDQJVsezyGSCRmHVMPIAkSgd4U+oZXLVvSeg/loaEFAI4Wl68bN878rPP85ra5A9dFK0EiEwGoN4ghe1naXuwuSUmCSgBIZ9vj0TaRaFJuq8dMW4QjRpAIIAS1bEnrPZSHhhYAOFpcvl5xxczvPhblDdVZyTR3UHShZJDIRABxYeNysxgyiFMLljr1+gSAprLt8WibSDQJCNVjqtvTEAVAVdYoG3XeTPX3PC4NLYwcAtArfN6Xh4oRs85RLa+ZD7MwEpkISw+w8gKukL1l9AIlRK9PAOhlPspM2/mC1GMmLcJBQxQAkXA3rnpsG5dJ8QKSanlok3i07ZEOAG2QVvbFcV48AqeIUDGiyWI+8VzIIY7fQ/qiqK5r1defzfLwPauv78j/o+jo36uQN5k6ACCZ2oocqgxV6wnK6eCIZZqPz9BQqPjPJrb1vS0xLYA2UeNMkaPLvjrkEkzp5TPldS6beIYemQhLb21oausDQ3cA4OjF3EKUiWr9QC8jAL6EGmVjE9umbZsUZ9r2SAeAplPjvqSyr+65hKTh401aZb1BSGQiLP2CNVmhPC9pWDSpmDYMJ2u/DN0BALcpOkTsym19iDgA1JnNzWnatnqcSc8dAL1Ijf+Syr66JQP1RSYZPl4aEpmoTlpyMC9pWDSpmFaoZO2XgggAjgSQallokpy0LbddA1V6zwPIUtcyQo8zaUAH0IvqlqjU6XVI0iKTIkcSsXV+Lw1HIhPVSUsO5iUN1eddAtK0QiXruBREAHpNXL6mTbgeM7nRLqsxiJt/AFnKKiNs41M9zrRtLCpybACoozqWZXodkrbIJDmD4EhkojppycG8pKH6vFqYFC3kSFYCwBH6fJg6m+Tkvn3dP0Oh9zyALE1pVLFtLPJ5bAAoQ1aDuUiYsswmOWoyd7GaPyCXUKqgicwDBw7I6tWrpb+/XwYHB2Xt2rXy9NNPZ77mueeek3Xr1snLX/5yOeGEE+SCCy6Q/fv3d23z+OOPy4oVK+RlL3uZzJ8/Xz75yU/KSy+91LXNXXfdJW9+85tl7ty58trXvlY2bdrU9fyGDRvkLW95i5x44okyf/58WblypewLfYMF/8pcFCKvsAWANlHL16I91cfGun+GQhAJIEvIMkK96fWRMNX3YXoDToMOgCbw2WBue8yk42UNG48RZ9ZG0ETm6tWrZc+ePbJ161bZvHmz3HPPPXLxxRdnvubSSy+VW2+9VW655Ra5++675cknn5T3v//9necPHTokK1askBdeeEHuu+8+uemmm2TTpk1y9dVXd7Z57LHHZMWKFfKud71Ldu/eLZdccol8+MMflu9973udbe6++25Zt26d/OAHP5CtW7fKiy++KO95z3vkmWee8f+HQDhlLgqRV9gCQJvEwZqPgG18vPunDZOb9zoOPwJQT6HKC/Wm18fNrr4Pdf9Z58+NNoAmMGkw37XLz8hL/Tjj40eXo1nDxvMQh5auL4qiKMSO9+7dK2eccYY88MADcs4554iIyJYtW+T888+Xn/3sZ7Jw4cKjXjM1NSWvfOUr5eabb5bf+73fExGRRx99VE4//XTZvn27vO1tb5Pvfve78t73vleefPJJWbBggYiI3HDDDXL55ZfLL37xC5kzZ45cfvnlctttt8nDDz/c2ffY2JgcPHhQtmzZkni+v/jFL2T+/Ply9913y9vf/naj9zg9PS0DAwMyNTUl/f39Vn8fSLUrMrocu6/vyP+Z+wJAG9VxpVy17E0LWSifnRHLNB+foSWTMsVF6PJTXRE3FuY2DgDqIVR5nbTfImV4qPPsMTbxTLAemdu3b5fBwcFOElNEZGRkRGbNmiU7duxIfM3OnTvlxRdflJGRkc5jixcvlle/+tWyffv2zn7PPPPMThJTRGT58uUyPT0te/bs6Wyj7iPeJt5HkqmpKREROemkk1K3ef7552V6errrHxypwVgVvRtdjp02gS9DzgG0RV7ZWEaLs34MkxbxMqcZAdBsoUbyhO4JGe+foeMAeoWP8s5krkuRYmU45XLpgiUyJycnZf78+V2PzZ49W0466SSZnJxMfc2cOXNkcHCw6/EFCxZ0XjM5OdmVxIyfj5/L2mZ6elqeffbZo457+PBhueSSS+S3fuu35I1vfGPqe9qwYYMMDAx0/i1atCh1W+RQbzRDXvBpN90uwxzTCjeGnANoi7xArIwGKP0Ycdkrkp5ELXOaEQDN1rSh13osm3b+WQ1NDHsEUDWXcshHeV3GXJdNq1dawDqRecUVV0hfX1/mv0cffTTEuQazbt06efjhh2U8J6l15ZVXytTUVOffE088UdIZtlBa70bf9IIrLkB9LjyRN78HADRFXiCmlnG7d4e5OU5LSCYFourxCSIBmCorsefjOKYNSFnbsZI5gCqZjMYsWl6mvZ6G7layTmR+4hOfkL1792b+e81rXiNDQ0Py1FNPdb32pZdekgMHDsjQ0FDivoeGhuSFF16QgwcPdj2+f//+zmuGhoaOWsU8/j1vm/7+fjnuuOO6Hl+/fr1s3rxZ7rzzTnnVq16V+d7nzp0r/f39Xf/gKOmGM6/wcinc9IJLLzh9FGg+F8QAgLLZlK1qGbd0qd+b4/g8RJLL06RAlJtzAC7KKjt8HMf0JjxrO27kAVTJZDRm0fIy7fUhFg1S0eO9EtaJzFe+8pWyePHizH9z5syR4eFhOXjwoOzcubPz2m3btsnhw4dl2bJlifs+++yz5dhjj5U77rij89i+ffvk8ccfl+HhYRERGR4eloceeqgrSbp161bp7++XM844o7ONuo94m3gfIiJRFMn69evlW9/6lmzbtk1OPfVU2z8FfIkv/rzCy6Vw0xOmaiAXMvE4MTHzniYmwuwfAHyxLVvVctTnzXHeeSQ1gHFzDsBFqLLDZY7fPKa9zbO2o8c6gCqZjMYsukp41utDNl7RqF6JYHNknn766XLeeefJRRddJPfff7/ce++9sn79ehkbG+usWP7zn/9cFi9eLPfff7+IiAwMDMjatWvlsssukzvvvFN27twpF154oQwPD8vb3vY2ERF5z3veI2eccYZ88IMflB/96Efyve99T6666ipZt26dzJ07V0RE/viP/1j+7d/+TT71qU/Jo48+Kl/+8pflm9/8plx66aWd81u3bp38/d//vdx8881y4oknyuTkpExOTibOoYnATHtJ6oVTVfNsmBzX59B1AAjJ9kZbLUd93hxnnUdaucvNOQAXoXropM3x67uMsomB6S0EoGomZaHJnOgx23kvQzZ806heib4oCrc+/IEDB2T9+vVy6623yqxZs+SCCy6Q6667Tk444QQREfnpT38qp556qtx5553yzne+U0REnnvuOfnEJz4h3/jGN+T555+X5cuXy5e//OWu4ej//u//Lh/5yEfkrrvukuOPP17+8A//UDZu3CizZ8/ubHPXXXfJpZdeKo888oi86lWvkj/90z+VD33oQ0feeDx8TfO3f/u3XdtlsVkeHhniOTOyWmiStlE/w3Bf46OZHHdiYiaJOT4uMjpaznkBgE8mZXOZ5xErs7zvAcQyzcdn6ChEHFlWuWlz7lXFywDgQi2z4gYnvUytS4wKr2zimaCJzLYjcAzENGkZsgBL27d6Q03BCaDNfNz8JpWltmW3HtBS7npFLNN8fIaOmnwjbHPuPsphADBVtHxRX09Ddk+xiWeCDS0HnCV1FU/qsu2yYFCRc9B/J/AD0GZquetatqplafz6InNyUu4C8MVmGGNRvuLTvEXRkiTFy8zpBiAUl/JFLSPVMqvuw7aZuqMyJDJRP6ZJyyS+ArO0QtOlMKWAA1A38YJkf/Zn6eWTWu66lq16WRm3sCc9l4Z5MAGEVEZSz9cxfMe5IsSnAKqTt+hv0Rgw9H04jUKVIZGJ+ilSYPlqtUk7B5dzo4ADUDfxQmRXXDHzM698ci1b4zJT71VJYhJAXYTo8RNi9XKf+0nqnQkAPtiUU6aL/roKfR9e9x6jLUYiE81h0qJS9g2yyTlRwAGom/Hx7t/zyqeiK/ySvARQVyGGmIdavdxnWUp8CiCErHIqq5EnRJwYupwjvq0MiUw0R9H5Nlye93FOFHAAqhSXc2pZNzpqP/dkFb3LmZoDQFl8lnFlJgldy8m0+JRyF4Ap2/IiqZGnSEN5nlD34ZSTlWPV8gJYJbJkLiug6avdmqyGHvqcAKBMajkn4ncF8tB8rJyOTMQyzcdn6ElTYzrf5STlLgBTtuVFUjnbxDKniefcAKxajnZyaVFRW8RNV0M3ZRrwqr2h4gU2aL0BUBa1fCvSO6iK3uUMfQRQljJXMffJVzkZx6vx1COUuwDyxOWFPmVRWo/FpFiyibFeE8+5ZeiRWQAt4A3ju6XdtCVG7w1l8hoAcJFXzvkqB0P1XGpqj6gGI5ZpPj5Dz0L1tKl7+UYPIwC20soNyhM4oEcm2qnoXBQ+exOp55DXEqM+Tys3gJDy5njzNQdckf3EPdMnJrL326QeUQDaI47RxseLlUN63FrFPMM26GEEwFZauZH2eFlzS4Y8DvNj1gI9MgugBbxkdWrZ8XUuceu8SH1b6AE0R16Pn4kJkbGxmRv00dFwx8mSVX6qZWLaNvCKWKb5+AwDyZtn3eb1UVT/Hpl5mn7+APyzLRfKup8PeZw65SRahh6ZQGi+Wq3VG/a6ttADaI68nudjY90/fR4nrYVafzxpPqV4G5GZ/dIzCEDV1PLHpTelXo5VMc+wT3XvUQogrKQ4z7ZcKCu+C3kcYtRaIJGJ5iiji7q+L5uJil34WoQDQG+yLf9CBl9pwaz++OjoTPmp9gjVt2n6DT+A5ksqf2yGm1dRjoUc8sjNO9DbfCycW9aiaiHKX73RnRi1UiQy0RxqgaQGaj5biPV9hWp91gtCtTBUVzln7g0AWWzLqKzArugNcN48SVkJAG6QAdSRWjZF0ZHe7HGZW7e50nzHrer7o4EJ6F1qGaeOqHEtF5rYw7uJ59xiJDLRTGpBktdTM15YwiTI1PcV4uY6L/nKcHMApnyWUXq5ZHuDnhbMxo/rCQCT1wJAlfTeQ/qijWq5aVpmNqnXZN6NO43vQG9Qy4Ci0xOJNLMBu4nn3GIs9lMAk6tXyGRiYXUi3lgdvu765PH6+bMAEIAq6OWq78nMWSiilohlmo/PsARp5aFarqk3+uriPuPjMzf+ocrWkPLKbT3Wrvv7AeCmyP1pmfEfsWajsdgP2s9koQl1OGP8M6/FuIwhQmprTlIBG7+3pOHmtHYDsC0PTHvM6OWq75bnJUuO3OxTlgFokrQpMtRyUy8z45t+tTd6Uq/OOsvrLc9c70C75K0PUfdh5KGnhSN+rQ16ZBZAC3jN5LVwm7SAF2klD9kC1KTWewBh2ZYHdeoxQ1lWO8QyzcdnWCKbMkzvkalSe2zScwhAXegxo4/yqQ09MolfS0GPTPSmvN5DJr2LXHogxS00IVubmJMDQMy2PFC3Uydo96FOq6YDQGhqGZZX/sU9mEZHZ36m9dhU40afvX6q7EEUz08/MVH+sQG40+MzH/e1Zc6D7vtYcTnapJ70PYIemQXQAg4RCdNyBQB50uZfS+KzJTlrTjg0DrFM8/EZVkSf89y2F9DExEzZPT4+k+zU95nWa9O0x1GIHkRVHhtAeUL0bGxiL3TKslLRIxMQKa8lWm1ht20BYr4NAK6S5l9LE2p1c3pYAuhVavlnurq3Gu/FZbc67Nyk12basdLmivdZPpuOPlLnpwdQPyY9yn3Pa17mXJm+EOfWFolMtFdZhaVLF/Yiw9FJfgK9JW2xHn1Bs6wgy+dQG33BMtP9MtQQQJukLfSTFKclxXtJN8gmi66l3VjrxwiRiDC9qY+H08c9TQHUi8k9qO976dBJQV/3yOp+yhwWDysMLS+AoTw159J9vawu7/pwdHVYkc1ruXyB9mva9BVp5ShlVy0RyzQfn2HNJA05N5kCpKiksjet3K1qiGcTh5YCbWRyLbper1Vd577iTOLVyjC0HBBxa0HRW55C9X7UW6L01SzVY8e9mEIOFQJQvbTyJsTE60XklYtpLfgMNQTQC5KGnI+NHbkhVstPn3FmUtxr2nszFnrUTxOHlgJtpJYXade9a2/Eqq7zovfILOzTKPTILIAW8BbSW5BCt8hktVjpvbBCnQOAesgrb+rSQpx3Hmq5JkLvm5ojlmk+PsMay4srTRb3KeO8YqHrmbrUY0CvMClTfF+XTep5zQKWtUKPTECVNr9cEpO5iXzKaunKm/8urccmgGbKK29ClEdqK7xpT5y881DLNXrfAOhlepyn90zXfyaVmUlls23PSX37tPgzrXxPO57teTCqCCiXSRzm+7o06e1ZFyxg2Vj0yCyAFvCG0Hs21mGOIB/osQk0Wx3Kn6RyRMS8LMnrfVmH94hMxDLNx2fYIC49MpN6SyU9ZjrKxyVWTHs9PSyBeqs6DiujjLB9j4wcqi16ZAKqpJUeYyatVHVtSdLnmVPfm00vVADVCNlb0aV3pUlLtL5f9T0kvR9WewSAI/RyVv9pOs9l0mNZvTn10T1160lZ11gbaLqq47Ayejna3s+r21f994EzEplov7iASppk3aRwrWuyU10gSG9FUs+VIZ1APbkkDk2ZJknVAM4kmNP3a5sIBYBeppezJuVu0jamCU99saF4e9Mh7FnHSztm3r6SMA0JUEzVjQG+FwyykVQOZTW8E6+2AolM9BY9UDIpXG2TnfGclRMTRc82m3peaYGl/n8A1TGdo0zlcnOnBpGhr3/bRCgAIEzSwWbVctMenXnnmlbuk5gEymV6zYVKeJZ9zavvY8mSI4v1JCUuRY6+byZebTwSmegteS3HSQvn2CY7456Sao/JELLOS+2Fqs5Vx7AdoDouQZ6+EITNcUT8B2m0YgNAcXp9ECpGS4sVbZKeLgnOrLoi6XXULUAxptdQqISjz2vYZIEz/X1kJS5FSF62EIv9FMDk6i3ic+GciYmZJOb4uMjoqPnrQk/GzITsQLVcrnGX67bqid3RKMQyzcdn2EB6OV1WjOZSP6S9Ju2c845h816pzwC/yrim0o6R9LjrAmfqXJcs2tMaLPYD2IoLwKKToYvMJC+jyC6JKVKshczkPGntBqrl0hqslk2mZVFZrc708gYAN3o5XVaM5hJr2s6PmXWMrKlPkuoUhqgD3YrGXr5jRJvrNunxpMdMFjjT14agx2XPoUdmAbSA94CyW8hF3FqS6LUFtFsde1TX8ZxgjVim+fgMG6zsWKyM42XVDbbPuY5yAtqqqtjLpme2TW/tImUS97KtQ49MwJeyW8hF3AriIvPo0coNhJPVcp41N68udFnk0sJPL28AKKasuTJjVa0gnPdcWk9Ndd55RgAA1cWDafeNJr0nY0nlT5EyiV6YPY0emQXQAg5virYo0SMTqCfT3idp24SklgFqYJp1DpQbrUMs03x8hg1W1VyZdZPXg0vVS38XoCib+SpFyutNCSSgRyZQRBXzvtm0KPla7ZFWLMAP0zJDfz5tbt6Q1HNVW9fVMiTr/dCTGwD8qWquzLrJ6sGl9/Rifmb0Al/fc5v5KkXselNWFRNSBkDokVkILeAtVffejSFa62lRA9xlXZPx/F5pz/tgc/2q5xr3xDRtjc97Do1ELNN8fIYtUEUc1pTYT693TOoxoOls4q2sa9mmh6WtqsoQYtHWokcmUITeEmXS6lNmi5Rpa7163nnvgV5WgDn9esq6JkdH7XrYuLQy21y/6rmYrkSrnlOv9hYCgJDKnisz6ZihFH0ver0Tj2LYuJH4Fe2VFG/Zzl+Zth8RPyPz1H2U2UuSWBRCj8xCaAHvAfrcPEmXS9KK41XPI5I0p1CMuYeAYvJagotc6yF7hOdtZztfElqBWKb5+AxboIq5MsuKS32/l6T5pZPeQ1N6nAKmQvauLIpYER7QIxPwRU3ypbX6JK04ntQyZttqXKRlSz/vtJarrHOxWVEZaCuXOWmL9BAJOd9t3nnZzpcEAPAjLsdFZuqcMuZOLmuudN91SNL80nqPMH0eaKApsu7/8npXilR3z1ZGrMjcmFDQI7MAWsB7gN7CZdrT0kePzCItW/G8fOPjM0Nb08TnND4+s716blWvqAzUgct1WEWrtEn5krdyZVI5gNYjlmk+PsMWoVeTu6S4VYQ6DfXmszd2m3pyJ6F8bD16ZAK+6K3VSa27Sa1gSa3cti3fRVq24sVF1EVGksTnNDrafW5qS1dSrwBaxNArXK7DKnowmvQ8SSuD4teMjZXTOwcAkMxlnnbMUP92+nzQsfjvyd8UVVKvaz1+0++3bJQRf1bZ05kRQlCQyARsZBWgLgV7VoBaZMhPkYJenzdTT3KKMFwHvcPlOixruJ6qyIJCBIYAUA9643hTFuSp+lh6L7G8hjv9/0CZ1Otaj8GS7rdMlRF/qudbdkNLFfE1aotEJmAjqwCNey7GP02EWqWySEFvMi8oPQbQZlV/n12Ob3PN6+UOgSEA1EvaXMWhj1dGci/EsUz3qf4dabxDaGnxnP7d02OwOjcwqzFjGxta0BgkMgFfTIdzq/SKqg49HdOG46hMhtwDTVX19zn08escIAMAjh4mvXRp2Bv5MuuFEMfK2qeaBInj16TGO4adwzc1nlMXT83rddmUBuayyo2q43LUEolMwBcfqw3XIcHgUnnW4byBtgh9PanXOK3cAFA/Sb2eRMLdyJeZOAlxrKx9miZBGHYOFyarjIsc6eiiz4VZJNYrK4ZLO05Z5Qb3mUhAIhPwxUdh3pQWOJ1+3nGr48RElWcFuDEJmOKgTm1hz6IHgbbz45oGq1nbJT1HKzcA1JtaF42P0/hkyzQJwrBzuFDjqKREX/xd0hdP9XHPV1YMV3Ws2NT7YwRFIhNoutCtcS77dxlmD5QtLRkZB55ZQ/nUlb7V39PoQaBtUGjboyRpu6TnaOUGgHpTh0OrdU6IuK9tvfT1RYCymAw7b8vfBf6o8VPWMPGkxVN9HTt0DFdFrMg1hxwkMgGfqih0TRIcRc7LpRXOZeEjIJS0739WMjJviJltMKcHgbZBoWkvUX37pH2oPXpo5QaA5tCTJr5jzip6XoWMnX29n6p7pKFaWfOnqr0uy24ULiuG049Txv0u1xxykMgEfEordPMKfJsKQd/WpPLUhz3YVD4uScm41XF0NPmcgZD071vadZl1zeQNMYuDOtPgVQ8CbYLPtB4l6vuMt1GPp28TH9O0FykAoF7UpElMX0ikiCoSMiETFmnvh7gUNvIat3tt7vEykoyMGEIOEpmAT2mFbl6Bb1Mh6NuaJETU87I51u7d3cPE8yrmvJ5voYZCASr9O552XWYlI7OGmCXtI2RreNo1qz6eNFw87bUEh+gxBw4ckNWrV0t/f78MDg7K2rVr5emnn858zXPPPSfr1q2Tl7/85XLCCSfIBRdcIPv37+/a5vHHH5cVK1bIy172Mpk/f7588pOflJdeeqlrm7vuukve/OY3y9y5c+W1r32tbNq0KfWYGzdulL6+Prnkkktc3yp6gV53iRw95Nw1kVJFL/2QdVLa+7FNxJAQ7Q1pc/zbzJ8aMslXl+9byGs2fo8ijBhCJhKZgE9pXe/1CZ5VecNBdUVXR7d5fdY8e6r4faZV3hs3pr++LpUymiuvl3LejVndh1enXbPq4+r/1feRlaSt6/sFPFu9erXs2bNHtm7dKps3b5Z77rlHLr744szXXHrppXLrrbfKLbfcInfffbc8+eST8v73v7/z/KFDh2TFihXywgsvyH333Sc33XSTbNq0Sa6++urONo899pisWLFC3vWud8nu3bvlkksukQ9/+MPyve9976jjPfDAA/KVr3xF3vSmN/l742g3NaaL46zx8aNjMZvF5qrQhOSpr4Qo6i1tjn/Txm2RsEm+Mr9vtotS+joO1xRMRXA2NTUViUg0NTVV9amgro5Ue0ce27Vr5vddu47eJn5Ofb5K6rnq561S30PSNqbPJR0XyJP0HQJgpO2xzCOPPBKJSPTAAw90Hvvud78b9fX1RT//+c8TX3Pw4MHo2GOPjW655ZbOY3v37o1EJNq+fXsURVF0++23R7NmzYomJyc72/z1X/911N/fHz3//PNRFEXRpz71qegNb3hD175HR0ej5cuXdz3261//OjrttNOirVu3Ru94xzuij3/841bvse2fIQwkxZJJcWbS72kxV1tjMZ/vi/ijmdK+A+PjM4+Pj1dxVvnKvCbL+m7rZVdbyx0YsYln6JEJhJTUKpc17DVvDpayqS1uWa1v6ntI2kadZ1N/zuRvVLfeAwCARti+fbsMDg7KOeec03lsZGREZs2aJTt27Eh8zc6dO+XFF1+UkZGRzmOLFy+WV7/61bJ9+/bOfs8880xZsGBBZ5vly5fL9PS07Nmzp7ONuo94m3gfsXXr1smKFSuO2jbN888/L9PT013/0OOyeoHlLTZnMn1Jm/h8Xww5bx51TnH9O6DP8W+yrzI/5zJ7L+vf7VDvVb//Y9QQDJHIBEJKKoyzhr3azMFSJ3mVjhoY6BWhyd+orcE03LgseJX1etfj+lR039w0AYkmJydl/vz5XY/Nnj1bTjrpJJmcnEx9zZw5c2RwcLDr8QULFnReMzk52ZXEjJ+Pn8vaZnp6Wp599lkRERkfH5cHH3xQNmzYYPyeNmzYIAMDA51/ixYtMn4tWkqNpfJiJj3uMpm+pCy+67Kk/anvR3083jZt4aSkfcV/SxGGxzZF2pziRfbVxs9ZLyd8vld9IUrmbocDEplA2bKSfjZzsDSVSUVIaxyyuCx4lfV61+OaML0pKxogtjmYBhJcccUV0tfXl/nv0Ucfrfo0Mz3xxBPy8Y9/XL7+9a/LvHnzjF935ZVXytTUVOffE088EfAs0ThZjcFZyTi9Dq0iFsury9LqVJPFJmPq+0kaCaUunGR6blnPZcUBNEKGk/a3TZtT3EUvJeB8vlf1emFhHzgikQk0WVUBUJHjprWEm7wm9PAG9AbXYMzldaYJRnX6BRNFe6UCDfeJT3xC9u7dm/nvNa95jQwNDclTTz3V9dqXXnpJDhw4IENDQ4n7HhoakhdeeEEOHjzY9fj+/fs7rxkaGjpqFfP497xt+vv75bjjjpOdO3fKU089JW9+85tl9uzZMnv2bLn77rvluuuuk9mzZ8uhQ4cSz2/u3LnS39/f9Q/oyOpxWeXUPUnHSqvLxsfNE5NZj5v2NlWPk7ZAp+vw/ay/uWsCFPnS/u4iRyfNXJPNIZP9VXz+Zb1X/RqiER4uSpizs7WYXB3euE5sbDphe+jj2qr69aiPpO9g3sID8WTsdZ0I3HSRLFXedzpv8QbAUdtjmXixnx/+8Iedx773ve8ZLfbzD//wD53HHn300UgSFvvZv39/Z5uvfOUrUX9/f/Tcc89FUTSz2M8b3/jGrn2vWrWqs9jP9PR09NBDD3X9O+ecc6IPfOAD0UMPPWT8Htv+GcKjvLpEf16vd5PqX9N6PKneSqvL0h4PtTBRyDrVZJHPvIU0s/bXa9K+o3rMlbfIlcrlOZfvos1nV0WcV/S+Mo++qG2vf5dxFJt4hjugAggc4Y1rZZUXHJV1XFt1DjgRlr4ipI8bm5geIKU9b/u9s32d7XVocoy04JIAEAX1Qixz3nnnRUuXLo127NgRff/7349OO+20aNWqVZ3nf/azn0Wvf/3rox07dnQe++M//uPo1a9+dbRt27bohz/8YTQ8PBwNDw93nn/ppZeiN77xjdF73vOeaPfu3dGWLVuiV77yldGVV17Z2ebf/u3fope97GXRJz/5yWjv3r3R9ddfHx1zzDHRli1bUs+VVctRKpsVzvV/MdPHXBouy6rfyjwP05Wx046dFQu1JTGUFeMkJYKT/ulJd9cEY1ojukusapMUreKzDNVonvY5ARoSmSUhcIQ3ppVV3nZJLZA+jls3WUGgaTBqGkjCL5PWXtcbirwAKSn4DZHwt7kOi1z7BIPwoBdimV/96lfRqlWrohNOOCHq7++PLrzwwujXv/7/27vX4Kqq8/HjT2JIDIMniUIIclEoCPVSoTLQ+MaqqULpaK2tQJmOWrwgOrVtagdGK/WV1jqd/sZxGDuO6LTaIEzVVkF0hNqiEQWhIhdrCxRBE6fSkNBGIMnzf8F/H/fZ7PvZt3Py/cxkwtlnX9Ze+4T9nGettVdP/v29e/eqiOiGDRvyy3p7e3Xx4sXa0NCgQ4cO1WuuuUY//vjjgv3u27dPZ8+erbW1tTp8+HBtbW3V48ePF6yzYcMGnTp1qlZXV+uECRN0xYoVrmUlkYlUxdkjM4ryJC2O+2yQZJadML040+RWXqdY3C2es8ZY5n14xV/FJKz9xK9e+wqanE47wRlFBwCnJGapffdEIkhkJoTAEbEoNkAJc9NJO1C08hMEOAUTfltIsxjslSNrkBo0gRzks2ltwXf6QpZEwt/vNkEDbrvgMCt/tyhJxDKlj2uIspV2rBbHfTZMT8ti9+31XrHCfnfxk6wMGs+FKYufWCxID8+ggnwmok56xtHoYJe4JF6FDyQyE0LgiFiEaUm0k0SPM783JL/JF7ubn1tZjX1Yl9nt37q82AQb/Ck2MC9m+6j+luLk9rcRJIAFQiKWKX1cQyQm6XtnmvfqcjvXYmOiqJOVqv5j77i/G0WZ/IyiF2PQZU7lsVvmFmu6HcfrtXk/dr1k0463URJIZCaEwBGx8LpROC2zCpLwCHqTCZpMcWqZsyuDV8udXVmjSO6QIDqZNcAME4wUmyAuJgCKsydlFLwSl2H/9oEAiGVKH9cQiRlMsVLS5xomyRbF/lX9dQgIm6yMovxJdjbw8z3MbXnQBKefThxhyu20zCnWtF5/63per516ZAIBkMhMCIEjEuHVwhZ34GMnjh6ZXgnMKMtjJwtJuzSZAw+nxJpXcJb12cT9SDL48pO4BGJGLFP6uIZIzGC6TyUdXzvFH3HEJW7JOq9EV1zJSrf9pBmbBRX0HKx165VALZZXwtNvedxeD6b/JxApEpkJIXBEIoLccJzWT0rSvefi5JVY9dszNI7kZhRJVyu7INXvcazblnILbJC6LLbes/aZx6BELFP6uIYYNLJw34wr1ok6ceq3B6bXdkkm2rwazNMYLRP3foMkPpOoB7/HcPucACGRyEwIgSMyIUs3knK6idkNtTDzm7SMItHsdY29kq5+9mnXIzNo+Yp9EHoWvqAEUU6fdwxaxDKlj2uIVKRxz87CfbdUYpUgyUo3SfaSLOWRTVHz22HCbl2vTjBhk5VZ7UiDshEknqkUAKVt6tQTt5GpU0+83rq18Hcxtm0Tqag48duPKI9drKBlt5o6tfA8pk0rfN/uXOfOPXEt5s79fFlbW+FvYz/W/bmxbmPdp105vc7fuk/jc2T+LPllbGucv7G917layximbtJk1Htbm3Nd+/kcFvtZBQAgaWncs9OMM417tUi4WClqRnlWrrSPIdzqyvrdwY3Tum7794prnN6fN6/wd7lzqye7eneqc+vfot3fpnmZ+d/G8Y2yuMXl5t/WsgT5TAERqFBVTbsQpaq7u1vq6urk8OHDksvl0i4OED0jYBM5cXMqJUHKvm3b5zdl6w3Y7b2oy+V0LD9lsK7jdf5Rn5efMllZyximTEmch9cx3era7r2g1wqIEbFM6eMaIhVp3H/TFCZ+i4rd/s3lcSpXWrziGqf3k/5Mpf0Zjir+s56H3XmZl4l4N0DYxeVp1xfKXpB4hh6ZQLkI0qvLqRXXuo8s9bAMym/ZjZuyiP1N3drCWGzvObtyGft0KoefVk63nrnmMkfVo8BPPXiV29qj0alMbseKskeI32trPabbZ83uvSDbAwCQReZ7fBojC5I+ptu9Ou7eqXb7N8dQTuVKi9OIFeOaPfDA5++bJd2rL+2RQFHFf9Z6s6tH8zLj3+bjm8vg1MuSXpfIEHpkFoEWcGRKkFY9p1bcrLSQmkVxbLd9mM/ZzzGiaD1165HntxxB2F1vkeJaf6PsRRik5V7EuYU5aC/WoOXwu2+/71vPBUgBsUzp4xoidWmMLMjSaIao4uQoYpos9ZqzXqOs9SLNUl3FzauXZrmfP0oCPTKBwShIq55TK67fZ68kKeyxzS31bvswn7Ofm3gUraduPfLiaOm0a3EttvU3yl6EXvty+jyKOLcOO11ztx4cfs/Jq0Xa7tjm45q3K5XngQIA4CSNkQVZGM0QdpSLUyxSzMgcr32kwen7hfX7R1rPCk+6h2Gaz0T3em4mUGJIZALlwm2Ij/W1eXKWrVtP3MCMBEvQB3obwtyc/WxjPXaY4b9RPfA8zPp2/D4gO6qAx244iVNPQb/HC1sPdsfw2pfdEBgvYZLyUQW0DCcHAAwmaQwzz8IwV7eYIsxjcaKIDdKIL/xe87CTQyZZxjilmTi0+1wQi6KEMbS8CAzlQWa5DeXwMxlJFMe0E8UkJ1EM/41jKEUcD8SOasKisMeLo57SHgoW1zCaYoebAykhlil9XENkStr3+SSFfXRRuU1q43TNi43Zoyx3Fj6XWbjuxKPIMIaWA4Od01AOv5ORRHFMO1H0Soti+G8cLaLWfUZxjCD1E8fx4qintHsK+OnB4afV3rqOV11loecIAABxM9/nk+oFl/bQZJGTj283KsO6XblMauMU2xUbs0dZ7iz0PszCdWc4OcpErInMQ4cOyYIFCySXy0l9fb0sXLhQjhw54rrNZ599JrfffrucccYZMmzYMLn22muls7OzYJ39+/fLnDlzZOjQodLY2Ch33XWX9PX1Fazz5z//Wb785S9LTU2NTJw4UZ544omC95cvXy5f+tKXJJfLSS6Xk+bmZlm7dm0k5w2kLsgsc1OnFg4vD8Nv65752ThhZ86OY2h3FKznFsUskkHONYpzcpv5XCSaLwppJPSCBm1+1meoOAAAJzPf55NKmqSdnDEf39xwmpXYIKpyOMWBTt8lio35nModJh4djA3KDCdHGYs1kblgwQLZsWOHvPLKK/LCCy/IX/7yF7nllltct/nRj34kf/rTn2TVqlXy2muvyUcffSTf+ta38u/39/fLnDlz5NixY/LGG2/Ik08+KU888YTce++9+XX27t0rc+bMkUsvvVS2bdsmP/zhD+Wmm26SdevW5dcZM2aMPPDAA7JlyxbZvHmzXHbZZXL11VfLjh07oq8IIOu8JicJs70dI4iYN8/f+l7CJtaKCWbcgjjzuc2bF2/A5PTc0yiPZ92nXaAetyiSp0GDNj/r+33OKQAAg1VSSZMkkzN2cYnT87izEhtEVQ63eD+OZHIxPTWz8EzMNMpgNwpJJNjIJKAUaEx27typIqJvv/12ftnatWu1oqJCDx48aLtNV1eXDhkyRFetWpVftmvXLhURbW9vV1XVNWvWaGVlpXZ0dOTXWb58ueZyOT169Kiqqv70pz/V8847r2Dfc+fO1SuvvNK1zA0NDfrYY4/5PsfDhw+riOjhw4d9bwNk0tatqiInfhtO3OZO/ITZPsr1nQQpY7GMMnsdM6pz89pvkuduLUOSdRDneZrLaT63IOWO63oDCSGWKX1cQ5SEUrxfBo2Pvc4xa3UQZfye5Lk5Hcu8PI042SqNMtgdMwt1AfgQJJ6JrUdme3u71NfXy/Tp0/PLWlpapLKyUjZt2mS7zZYtW+T48ePS0tKSXzZlyhQZN26ctLe35/d7wQUXyMiRI/PrXHnlldLd3Z3vTdne3l6wD2MdYx9W/f390tbWJv/973+lubnZ8ZyOHj0q3d3dBT9AWbBrnQvSuu32jCC/xwsjjhZ4p9ZTp5klrezOrZgWWWNbp6HMxlD2Yvbtd1uv2cPtylrsbPZx9rIwl9Pu+UFB9wEAAOwVGxsEEdX+7e7xbnGJV3ybtZjBqTxBh5Ab7yXV089PT80sDKFOowx23w+yUBdAxGJLZHZ0dEhjY2PBsqqqKjn99NOlo6PDcZvq6mqpr68vWD5y5Mj8Nh0dHQVJTON94z23dbq7u6W3tze/bPv27TJs2DCpqamRRYsWybPPPivnnnuu4zndf//9UldXl/8ZO3asSw0AJcoIXkT8ByR2CawkhlIETaD64RTUmYOAoIFaMYGrUzmiGKYftlxeAaTBCHaDHMN43qqxjZ/AeOXKE9d/5Up/xzCXz/ht9/wgozzGZ8v4t3E8gkMAAPwx3y/jTuiFiT3s4ki7e3zQhF1SjbNhOJUn6SHkUTGfTxzfEYJKKrlrN5zc/P2A4eQoQ4ETmUuWLJGKigrXn927d8dR1shNnjxZtm3bJps2bZLbbrtNrr/+etm5c6fj+kuXLpXDhw/nfz788MMESwskxKvF3G6ZUzATNsgJ2pIeNqhye86RtadjMUFAMb0nvRKobkGxUz0ay6OYkMhpf0ZZgwbtdr0evJifSxqE+Zoa/7bWsV2vTYJDAACCMd8v457Z3Gv/1mVOcWQU93i3xtm0n+PoNIrIYBeHZS0ZK+LeCSPJxGta19OuI0kWrxMQocCJzNbWVtm1a5frz4QJE6SpqUk++eSTgm37+vrk0KFD0tTUZLvvpqYmOXbsmHR1dRUs7+zszG/T1NR00izmxmuvdXK5nNTW1uaXVVdXy8SJE+Wiiy6S+++/Xy688EL5v//7P8dzr6mpyc9ybvwAZcerxdxrmI3b0GM/rD3ygpTZ2N4vu+NEPSGRn326BT5eQbTb+049ZM0JuSgf/m63v6BfAqyt6X4YCVTjd5Tsem1GlQAGAGAwcpvZ3G4khFNsZ33fLqHlJ5aNM+njtu8s9G50SuoarPWfxQZct3qMcubzYsoRNbuevuZyZPE6AREKnMgcMWKETJkyxfWnurpampubpaurS7Zs2ZLfdv369TIwMCAzZ8603fdFF10kQ4YMkVdffTW/7P3335f9+/fnn13Z3Nws27dvL0iSvvLKK5LL5fLDwpubmwv2Yazj9vxLEZGBgQE5evRosAoByo1Ti7nBzzCbYm6eYXrk2bW8+uEWXMYR1Dr1EIgr8HEaKhT1uUW5P7+fHXP9zZ17Ypu5c4MfzyuQteu1aRyP4BAAgOJYYwi7kRB2SU7runa//R4zzqSP276z0GvOLambhUSrH2GeW2p3bsUmN5O8ntbyF9uRBCg1cc46NGvWLJ02bZpu2rRJN27cqJMmTdL58+fn3z9w4IBOnjxZN23alF+2aNEiHTdunK5fv143b96szc3N2tzcnH+/r69Pzz//fL3iiit027Zt+tJLL+mIESN06dKl+XX27NmjQ4cO1bvuukt37dqljzzyiJ5yyin60ksv5ddZsmSJvvbaa7p371599913dcmSJVpRUaEvv/yy7/NjlkggBnYzSfuZATErMyn6ZZ5BMOyM2U6s55u1WTSjOF7YGRizMPs7kCHEMqWPa4iy4hYHWu/ZfuKdrMWAaZYnaP0Y77W1lV8dBp2RPs6yBD2W+ZoQw6KMBIlnYv3Uf/rppzp//nwdNmyY5nI5vfHGG7Wnpyf//t69e1VEdMOGDfllvb29unjxYm1oaNChQ4fqNddcox9//HHBfvft26ezZ8/W2tpaHT58uLa2turx48cL1tmwYYNOnTpVq6urdcKECbpixYqC97///e/rWWedpdXV1TpixAi9/PLLAyUxVQkcgZNEfTOP6gYd9Y0+jgDKXMakk3x2SdU4vwREcT3ClsnrSxAwyBDLlD6uIQaNYu/ZWbjnu8VAcZcv7LGzljCLqzxhYuA4YlqnZL7dd4W0P89AhILEMxWqqql1By1x3d3dUldXJ4cPH+Z5mYDI588lEjlxizWGTQd53qFZsdtb99PWduI5jsXuz3qeUZTVPMTcuu8g25uHZvkti3lbcxmM49udbxhRXwev49jt31zPcR0fKCHEMqWPawj4FFU8Uwy3GCXu8oU9dlTxeFSSLI/Xd5soymI9hvm1mTlOz8q1ACIUJJ4J/IxMAHDk9pwjO0GeT1iMqCfwsXvWZbHPETLKGPb5Nm6zYvo9tvm5qOYZ1sPMjO5WxmInGXJ6uL/b7KPmCQCs5QEAAOUvC88QTOqZmXbxWdhjO81untaM60lOZOP13caInadNi+bZmm6zxhvnndS5AxlGIhNAdMyBhfVGbBfweD3E3Y8g20QVINrNtmk9RhT7dmJ3zlGfmznp63dmdC9Okx0F5fVwf2tdWCdUysIXGQAAkCxrPJNmMs5gLkOU5XOLz4ImOYPuP0ppXyM/9eL1fcbrtd2+jGMTswK2SGQCiIf1RmwX8Hi1cvoJXpwCqWKCtLDJ0aRaSM3nbO5pGOXx/QZOQQIsuwRwmADYekyv2Uetn7kkW/IBAEA2ZWFWbrcy+C1f0AbuKM7bbv9xJB2zcI3M7M7b6/tMkN9eMS0AESGRCSAuXskmkZNvzkGHplu3MQdQYQOfoNt6BRhhgzq37cznHFeA5zdwChtg2fWa9FtP1mN6lcF8LAJBAAAgko3ebm5l8Fs+u1jQiI1E4hnFYxd7xRGTZuEaufWaFfH+PhPkN4lLwBcm+ykCD1cHYhb0AdrmZyAaSb6gySvrPooNJMI+uN3vdll7AHtYQeupXM4bSBmxTOnjGgIRyEpc4VWOlStPPP6nrU1k7lzvbZKc4CiKOszKdTDLwiRRwCDAZD8ASlvY4dJRtGhaW0eLHSIT9rmQflugwwyXj2LoT9TDh9yGKK1c6f18VQAAgLCKnaAyqXIYzzA3fouc/Ix6czmT7NEYxaRAWYzvstArFEABemQWgRZwICZ2LZ/mFlqRZFpro2iBNZfbHJQl/V+vdcbuYsuRROu0U5mT/AwAZY5YpvRxDYEIeMWZSfXKs/ZItL6265FplrXeg17l8TrftGSlHMAgQo9MAKXNa9bppFpro2iBzcps2eZjhy2HuVU96D6CzNhovG9oayt8z2smdQAAgCDcJiS0Pl8yrt6Zdskza1nmzj1RzrlzPy9HWj0w/fB6Jrr1/LIS32WxZyiAPBKZALLHa9ZpryDNLsAME3S6DdXxK4rh7lEEzOZjhy2HOagLOnzIa6ZGp/VFPg/asxacAwCA8uM0+aQhqiSXV1LP7/rmf2clEWhwiumN30ZjtbXROmlpDskHEBiJTADZZ00GegVpdoGgW3DoJ1EYNmiNIqA0HzupZzTZ8Qrq3OrIzyz2XsfKWnAOAADKj9ss1NbGdXOvSOszvb1Go1jjJrvYx+7Y1vWtZUkjRvTLmri0e+ZnGrLaMxSALRKZALIvaDBhN8GOETDZJc78JCmDtMzGORFO1ENdgpTV6zrY1bsR1BvLjWSs174IIAEAQBaYYxJr47o5HjOScUas4zUaxRpbWmMfr+c0Gus7DYnPImvi0m2yx7gTssU8MglAqkhkAig/ds85mjfv84eMh3meUJDEWtQzXzoF0F6K7Wka9LmWdvs1B/WlEGADAAA4ceqtKVI4PNr8bHSRE7GT1zDqoD02vYZDJ5EQ9PPMc7cy2sXXds8ojeI83OqXBnSgpDBreRGYJRIoAdbWbOtM2H7+Cww6o6LX+0nNKOnnOG5ltW7vtT/z+0bvy7a2E8lMZhoHMolYpvRxDYEM8Yo7DXZxlfV1sfFkEvFmHGVwq0NrvYi4x+jm1+aGdD/1CyBRzFoOAAa3lnO/w0fchgbZtRIHGYJtFrbF2Wm7MD1N7YbZGMuDPNfS2K8xUU8xkwwBAACUArfna3o9L9xPb0WzNHpoBj1GmCHbbnUoUhiHew3fN/8OWr8AMosemUWgBRwYJOxafkXsW3eLEbbl3Nra77fnqIjzuRllSKr3KIBUEMuUPq4hABGxHxljjgGD9vg0uPVq9PN+1ML2yCRhCWRakHiGRGYRCByBQSjocGsru+DLGH5t/BbxH3CZHyZv5jeYdBryRAAIDArEMqWPawhARPwNoxaxb7w2eDXam1/bDf22S6ACgA8kMhNC4AgMQsW29DolDu34+e/ZLni0vg7aI5PgExg0iGVKH9cQwEm8njMZJB41C/MsTwDwIUg8U5VQmQCgPBjP03F67cWcXBRxnxAn6P6MWc3Nr73KZl2Hti0AAIDSZo3vrPGnXTxqLHd67bRt0FgYAIpEj8wi0AIOAABKGbFM6eMaAgCAUses5QAAAAAAAADKColMAAAAAAAAAJlHIhMAAAAAAABA5pHIBAAAAAAAAJB5JDIBAAAAAAAAZB6JTAAAAAAAAACZRyITAAAAAAAAQOaRyAQAAAAAAACQeSQyAQAAAAAAAGQeiUwAAAAAAAAAmUciEwAAAAAAAEDmkcgEAAAAAAAAkHkkMgEAAAAAAABkHolMAAAAAAAAAJlHIhMAAAAAAABA5pHIBAAAAAAAAJB5JDIBAAAAAAAAZB6JTAAAAAAAAACZRyITAAAAAAAAQOaRyAQAAAAAAACQeSQyAQAAAAAAAGQeiUwAAAAAAAAAmUciEwAAAAAAAEDmkcgEAAAAAAAAkHlVaReglKmqiIh0d3enXBIAAIDgjBjGiGlQeohHAQBAqQsSk5LILEJPT4+IiIwdOzblkgAAAITX09MjdXV1aRcDIRCPAgCAcuEnJq1QmuBDGxgYkI8++khOO+00qaioSLs4qeru7paxY8fKhx9+KLlcLu3iwILrk31co+zjGmUb1yccVZWenh4588wzpbKSJw6VoijjUf6O4kX9xov6jQ91Gy/qNz7UbbyirN8gMSk9MotQWVkpY8aMSbsYmZLL5fgPIsO4PtnHNco+rlG2cX2CoydmaYsjHuXvKF7Ub7yo3/hQt/GifuND3cYrqvr1G5PS9A4AAAAAAAAg80hkAgAAAAAAAMg8EpmIRE1NjSxbtkxqamrSLgpscH2yj2uUfVyjbOP6AMXj7yhe1G+8qN/4ULfxon7jQ93GK636ZbIfAAAAAAAAAJlHj0wAAAAAAAAAmUciEwAAAAAAAEDmkcgEAAAAAAAAkHkkMgEAAAAAAABkHolMAAAAAAAAAJlHIhO2Dh06JAsWLJBcLif19fWycOFCOXLkiOs2n332mdx+++1yxhlnyLBhw+Taa6+Vzs7OgnX2798vc+bMkaFDh0pjY6Pcdddd0tfXZ7u/119/XaqqqmTq1KlRnVbZSOv6/OEPf5Cvfe1rMmLECMnlctLc3Czr1q2L5RxLzSOPPCJnn322nHrqqTJz5kx56623XNdftWqVTJkyRU499VS54IILZM2aNQXvq6rce++9MmrUKKmtrZWWlhb54IMPCtYJ8zkYzJK+Rvv27ZOFCxfK+PHjpba2Vr7whS/IsmXL5NixY7GcXzlI4+/IcPToUZk6dapUVFTItm3bojolIFVpxnNHjx6Vu+++W8466yypqamRs88+Wx5//PHIzzFNxMvxIt6NDnFqvIgx40V8GJ+06vbFF1+UmTNnSm1trTQ0NMg3v/nNYAVXwMasWbP0wgsv1DfffFP/+te/6sSJE3X+/Pmu2yxatEjHjh2rr776qm7evFm/8pWv6MUXX5x/v6+vT88//3xtaWnRrVu36po1a3T48OG6dOnSk/b1n//8RydMmKBXXHGFXnjhhVGfXslL6/rceeed+otf/ELfeust/fvf/65Lly7VIUOG6DvvvBPbuZaCtrY2ra6u1scff1x37NihN998s9bX12tnZ6ft+q+//rqecsop+uCDD+rOnTv1nnvu0SFDhuj27dvz6zzwwANaV1enzz33nP7tb3/Tq666SsePH6+9vb35dcJ8DgarNK7R2rVr9YYbbtB169bpP//5T33++ee1sbFRW1tbEznnUpPW35HhBz/4gc6ePVtFRLdu3RrXaQKJSjOeu+qqq3TmzJn6yiuv6N69e/WNN97QjRs3xnKeaSFejhfxbjSIU+NFjBkv4sP4pFW3q1ev1oaGBl2+fLm+//77umPHDl25cmWgspPIxEl27typIqJvv/12ftnatWu1oqJCDx48aLtNV1eXDhkyRFetWpVftmvXLhURbW9vV1XVNWvWaGVlpXZ0dOTXWb58ueZyOT169GjB/ubOnav33HOPLlu2rCwDs2Jk4fqYnXvuuXrfffcVe1olbcaMGXr77bfnX/f39+uZZ56p999/v+361113nc6ZM6dg2cyZM/XWW29VVdWBgQFtamrSX/7yl/n3u7q6tKamRn//+9+rarjPwWCWxjWy8+CDD+r48eOLOZWyleY1WrNmjU6ZMkV37NhRloEqBqc044W1a9dqXV2dfvrpp3GcWiZkIR4r53g5C/VrVsrxLnFqvIgx40V8GJ806vb48eM6evRofeyxx4oqO0PLcZL29napr6+X6dOn55e1tLRIZWWlbNq0yXabLVu2yPHjx6WlpSW/bMqUKTJu3Dhpb2/P7/eCCy6QkSNH5te58sorpbu7W3bs2JFftmLFCtmzZ48sW7Ys6lMrC2lfH7OBgQHp6emR008/PYpTK0nHjh2TLVu2FNRtZWWltLS05OvWqr29vWB9kRN1bay/d+9e6ejoKFinrq5OZs6cWXC9gn4OBqu0rpGdw4cPD+q/FydpXqPOzk65+eab5be//a0MHTo0ytMCUpVmvPDHP/5Rpk+fLg8++KCMHj1azjnnHPnJT34ivb29cZxqKtKOx8o9Xk67fs1KOd4lTo0XMWa8iA/jk1bdvvPOO3Lw4EGprKyUadOmyahRo2T27Nny3nvvBSo/iUycpKOjQxobGwuWVVVVyemnny4dHR2O21RXV0t9fX3B8pEjR+a36ejoKAgajPeN90REPvjgA1myZIn87ne/k6qqqihOp+ykeX2sHnroITly5Ihcd911YU6lLPz73/+W/v5+27pzux5u6xu/vdYJ+jkYrNK6Rlb/+Mc/5OGHH5Zbb7011HmUs7SukarKDTfcIIsWLSr4sgWUgzTjhT179sjGjRvlvffek2effVZ+/etfy+rVq2Xx4sVRnFomEC/Hi3g3GsSp8SLGjBfxYXzSqts9e/aIiMjPf/5zueeee+SFF16QhoYG+epXvyqHDh3yXX4SmYPIkiVLpKKiwvVn9+7dqZWvv79fvvvd78p9990n55xzTmrlSEvWr4/V008/Lffdd58888wzJwUqAAodPHhQZs2aJd/5znfk5ptvTrs4+P8efvhh6enpkaVLl6ZdFMC3UogXBgYGpKKiQp566imZMWOGfP3rX5df/epX8uSTT2a+V2bW67fU4+Ws168V8S6yjhgzesSH8RkYGBARkbvvvluuvfZaueiii2TFihVSUVEhq1at8r2f8mzCg63W1la54YYbXNeZMGGCNDU1ySeffFKwvK+vTw4dOiRNTU222zU1NcmxY8ekq6uroBW0s7Mzv01TU9NJs2AZswg2NTVJT0+PbN68WbZu3Sp33HGHiJz4oKuqVFVVycsvvyyXXXZZkFMuKVm/PmZtbW1y0003yapVq07qXj7YDB8+XE455ZSTZsQ0161VU1OT6/rG787OThk1alTBOsaspGE+B4NVWtfI8NFHH8mll14qF198sfzmN78p9nTKUlrXaP369dLe3i41NTUF+5k+fbosWLBAnnzyyaLOC4hDKcQLo0aNktGjR0tdXV1+nS9+8YuiqnLgwAGZNGmSr3NNQ9brt9Tj5azXr1k5xLvEqfEixowX8WF80qpbY/m5556bf7+mpkYmTJgg+/fv938CRT1hE2XJeDjz5s2b88vWrVvn6+Haq1evzi/bvXu37cO1zbNgPfroo5rL5fSzzz7T/v5+3b59e8HPbbfdppMnT9bt27frkSNHYjrj0pLW9TE8/fTTeuqpp+pzzz0X9amVrBkzZugdd9yRf93f36+jR492fVDyN77xjYJlzc3NJz0o+aGHHsq/f/jwYduHqAf5HAxmaVwjVdUDBw7opEmTdN68edrX1xflKZWdNK7Rv/71r4J7zrp161REdPXq1frhhx9GfYpAotKMFx599FGtra3Vnp6e/DrPPfecVlZW6v/+979IzzMtxMvxIt6NDnFqvIgx40V8GJ806tZ4bZ7s59ixY9rY2KiPPvqo77KTyIStWbNm6bRp03TTpk26ceNGnTRpks6fPz///oEDB3Ty5Mm6adOm/LJFixbpuHHjdP369bp582Ztbm7W5ubm/Pt9fX16/vnn6xVXXKHbtm3Tl156SUeMGKFLly51LEc5zsIYhbSuz1NPPaVVVVX6yCOP6Mcff5z/6erqSubEM6qtrU1ramr0iSee0J07d+ott9yi9fX1+Rkxv/e97+mSJUvy67/++utaVVWlDz30kO7atUuXLVumQ4YM0e3bt+fXeeCBB7S+vl6ff/55fffdd/Xqq6/W8ePHa29vb34dr88BPpfGNTpw4IBOnDhRL7/8cj1w4EDB3wxOltbfkdnevXvLclZKDF5pxQs9PT06ZswY/fa3v607duzQ1157TSdNmqQ33XRTMieeEOLleBHvRoM4NV7EmPEiPoxPWnV755136ujRo3XdunW6e/duXbhwoTY2NuqhQ4d8l51EJmx9+umnOn/+fB02bJjmcjm98cYbC1rVjT/mDRs25Jf19vbq4sWLtaGhQYcOHarXXHPNSf+Z7tu3T2fPnq21tbU6fPhwbW1t1ePHjzuWo1wDs2KldX0uueQSFZGTfq6//vq4TznzHn74YR03bpxWV1frjBkz9M0338y/d8kll5xUR88884yec845Wl1dreedd56++OKLBe8PDAzoz372Mx05cqTW1NTo5Zdfru+//37BOl6fAxRK+hqtWLHC9u+FwRDO0vg7MivXQBWDV5rx3K5du7SlpUVra2t1zJgx+uMf/7hsemMaiJfjRbwbHeLUeBFjxov4MD5p1O2xY8e0tbVVGxsb9bTTTtOWlhZ97733ApW7QlXV/0B0AAAAAAAAAEges5YDAAAAAAAAyDwSmQAAAAAAAAAyj0QmAAAAAAAAgMwjkQkAAAAAAAAg80hkAgAAAAAAAMg8EpkAAAAAAAAAMo9EJgAAAAAAAIDMI5EJAAAAAAAAIPNIZAIAAAAAAADIPBKZAAAAAAAAADKPRCYAAAAAAACAzPt/2Il+hg7kBtwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate and plot trajectory\n",
    "\n",
    "x = torch.tensor([0.005, 0.0, 0.005, 0.0], dtype=dtype, device=device)\n",
    "bag = []\n",
    "for _ in range(2048):\n",
    "    x = fodo(x)\n",
    "    bag.append(x)\n",
    "bag = torch.stack(bag)\n",
    "qx, px, qy, py = bag.T\n",
    "\n",
    "plt.figure(figsize=(2*8, 8))\n",
    "ax = plt.subplot(121)\n",
    "ax.scatter(qx.cpu().numpy(), px.cpu().numpy(), marker='x', s=1, color='red')\n",
    "ax = plt.subplot(122)\n",
    "ax.scatter(qy.cpu().numpy(), py.cpu().numpy(), marker='x', s=1, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "11833575-ff75-4a80-ab99-aac3dab0530d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(6.939136e-07, dtype=torch.float64)\n",
      "tensor(8.133688e-08, dtype=torch.float64)\n",
      "\n",
      "tensor(7.033768e-07, dtype=torch.float64)\n",
      "tensor(2.919483e-09, dtype=torch.float64)\n",
      "\n",
      "tensor(7.008737e-07, dtype=torch.float64)\n",
      "tensor(1.419347e-09, dtype=torch.float64)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1st invariant conservation\n",
    "# Note, for different initial conditions higher order invariants can give worse results\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "for order, color in zip([2, 3, 4], ['black', 'red', 'blue']):\n",
    "    sx = series((4, ), (order, ), tx)\n",
    "    vx = torch.func.vmap(lambda x: evaluate(sx, [x]))(bag)\n",
    "    print(vx.mean())\n",
    "    print(vx.std())\n",
    "    print()\n",
    "    plt.scatter(range(len(vx)), vx.cpu().numpy(), color=color, marker='x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94609104-d058-4be2-a561-38a3ffd66bc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.222613e-06, dtype=torch.float64)\n",
      "tensor(1.538842e-07, dtype=torch.float64)\n",
      "\n",
      "tensor(1.200414e-06, dtype=torch.float64)\n",
      "tensor(7.017794e-09, dtype=torch.float64)\n",
      "\n",
      "tensor(1.206937e-06, dtype=torch.float64)\n",
      "tensor(3.553736e-09, dtype=torch.float64)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABkEAAAG7CAYAAAB0PDLNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9e3gcx3ngC7/ADACJoiiJIgkQhMZj2JbEgCYNLBAz8u4jeZPsZqU4Ts7KyeZsHGW96yTOZr8cJ9nEiXOOYztxfEnyZGNlw6UuyVnHkvKdDSHC+ZLDiFSutABYlixzRr5EkTQzvEmyraEuFkkQeL8/Bj2oqanurq6q7qrqfn/PM49EAD3dXfXWW++tqgYQEYEgCIIgCIIgCIIgCIIgCIIgCCJnDNp+AIIgCIIgCIIgCIIgCIIgCIIgiDSgJAhBEARBEARBEARBEARBEARBELmEkiAEQRAEQRAEQRAEQRAEQRAEQeQSSoIQBEEQBEEQBEEQBEEQBEEQBJFLKAlCEARBEARBEARBEARBEARBEEQuoSQIQRAEQRAEQRAEQRAEQRAEQRC5hJIgBEEQBEEQBEEQBEEQBEEQBEHkEkqCEARBEARBEARBEARBEARBEASRSygJQhAEQRAEQRAEQRAEQRAEQRBELqEkCEEQBEEQBEEQBEEQBEEQBEEQucSrJMjf/d3fwTve8Q4YHx+HgYEBePDBB1O/56lTp+DHfuzH4Nprr4XLL78c3vzmN8Ojjz6a+n0JgiAIgiAIgiAIgiAIgiAIgtDDqyTIq6++Cvv27YM/+IM/yOR+L774IrztbW+DoaEh+Mu//Et48skn4Xd+53fgmmuuyeT+BEEQBEEQBEEQBEEQBEEQBEGoM4CIaPshVBgYGID5+Xn4wR/8we7PLly4AB/84Afh/vvvh3a7DXv27IFPfOITcMsttyjd4wMf+AAcP34c/v7v/97MQxMEQRAEQRAEQRAEQRAEQRAEkRlerQSJ42d/9mfhkUcegQceeAC+/OUvw7ve9S74vu/7PvjHf/xHpe9bWFiA2dlZeNe73gU7duyA6elpuOuuuww/NUEQBEEQBEEQBEEQBEEQBEEQaZCblSDNZhMmJyeh2WzC+Ph49+++53u+B77zO78TPvaxjyW+x2WXXQYAAD//8z8P73rXu+ALX/gC/NzP/RwcOHAA7rjjDiPvQRAEQRAEQRAEQRAEQRAEQRBEOpRtP4ApTpw4Aaurq3D99df3/PzChQtw7bXXAgDAV7/6Vdi9e3fk9/zyL/8yfPzjHwcAgLW1NZidne0mUKanp6FWq1EShCAIgiAIgiAIgiAIgiAIgiA8IDdJkFdeeQVKpRJ88YtfhFKp1PO7zZs3AwDA5OQkfOUrX4n8niBhAgCwc+dO+I7v+I6e3+/evRv+7M/+zNBTEwRBEARBEARBEARBEARBEASRFrlJgkxPT8Pq6io8//zz8C/+xb8Q/s3w8DDceOON0t/5tre9Db72ta/1/OzrX/86vO51r9N6VoIgCIIgCIIgCIIgCIIgCIIg0serJMgrr7wCTz31VPffzzzzDHzpS1+CrVu3wvXXXw///t//e/jxH/9x+J3f+R2Ynp6GF154AY4dOwZ79+6F2267LfH93v/+98NNN90EH/vYx+CHf/iHYXl5GQ4ePAgHDx40+VoEQRAEQRAEQRAEQRAEQRAEQaSAVwej/83f/A28/e1v7/v5HXfcAX/8x38MKysr8Bu/8RvwP//n/4RTp07Btm3bYP/+/fDhD38Y3vzmNyvd88///M/hV37lV+Af//Ef4fWvfz38/M//PLz3ve/VfRWCIAiCIAiCIAiCIAiCIAiCIFLGqyQIQRAEQRAEQRAEQRAEQRAEQRCELIO2H4AgCIIgCIIgCIIgCIIgCIIgCCINvDgTZG1tDU6fPg1XXnklDAwM2H4cgiAIgiAIgiAIgiAIgiAIgiAsgojw8ssvw/j4OAwOhq/38CIJcvr0abjuuutsPwZBEARBEARBEARBEARBEARBEA7RarVgYmIi9PdeJEGuvPJKAOi8zJYtWyw/DUEQBEEQBEEQBEEQBEEQBEEQNnnppZfguuuu6+YPwvAiCRJsgbVlyxZKghAEQRAEQRAEQRAEQRAEQRAEAQAQe4QGHYxOEARBEARBEARBEARBEARBEEQuoSQIQRAEQRAEQRAEQRAEQRAEQRC5hJIgBEEQBEEQBEEQBEEQBEEQBEHkEkqCEARBEARBEARBEARBEARBEASRSygJQhAEQRAEQRAEQRAEQRAEQRBELqEkCEEQBEEQBEEQBEEQBEEQBEEQuYSSIARBEARBEARBEARBEARBEARB5BJKghAEQRAEQRAEQRAEQRAEQRAEkUsoCUIQBEEQBEEQBEEQBEEQBEEQRC6hJAhBEARBEARBEARBEARBEARBELmEkiAEQRAF4ty5c3Dy5Enh706ePAnnzp0zeh1BEAThN6T/CYIgCILwEbJhCN8hGTYLJUEIgiAKwrlz5+D7vu/74Oabb4ZWq9Xzu1arBTfffDN83/d9X99EqnodQaQBGYIEoYbK2NHV/zReCZNQIQdBEAQhC9kwhEvYsMOJfigJQhAZoDOB0uRLmOLll1+G559/Hp5++mm45ZZbuhNpq9WCW265BZ5++ml4/vnn4eWXXzZyXQDJMMGjE8gqkiFIY4cwherY0dH/RRuvRLpQIQdBEERxUbGJyYYhXMGGHU6EgB5w7tw5BAA8d+6c7UchCk673cZWqyX8XavVwna7Lbxm//79ODk5ic1ms+d3zWYTJycncf/+/cavJfKLihwGBHIDADg5OYnHjx/v+TcvZ7rXkQwTPDoy0Wq1hHLHy6dofOiMG12ynjsIgkdn7Kjqf517EvlFVReryhPJIUHYwabdReQLHZuYbBjCBWzY4UVDNm9ASRCCkER18tVReDT5EjwmAqOs/AQfmQlU5TqSYYJHVyZUDEGbCQUbcwdBiNBxokzMG+S4Ebq6OOtCjuCZKZBLmMJHeaJCDsI2Jn2HLGwYH8c5IYeNYlT+2iQyXCQoCUIQhrGVvaUAAsFiKjB6/Pjxnkn0+PHjUvdXuY5kmODRlYmkhqDNhIKNuYOcLyIMHSdKdd5QuSfJcD4xoYuzLOSgQC4hQlU/+ShPNgo5SP8TInR9h6xsGB/HOSGHzWJURHUZLgqUBCGcx0cDx1b2lgIIBEvWAWTd63SvJdzFVDWMikwkNQR1x40vlT/kfGWDjjzozs+61+sms9MeryTD+cZEYURWhRy0Io/gKdqWnlkXcpD+J6Kw4cMiJps7aN7ILzaLUSmWEg8lQQin0TVwbAb4bWVvKYBAsGS9rNdm0IJwExN6JsvKcp3rGo0GzszMCP92aWkJq9WqM5U/5Hylj82zvkxuJyQrh0VauUVkg42iiqztJiKfFG1LT9VnFl1L+p9AzL6Qw0bxIM0b+YXkyV0oCUJkhspEplsJo2sI+lZFSQYkISKrikZb21fYrJamlVTx+LY/b0DScdNut3F6ehrL5XLfPRYXF7s/r1arzlT+kLEsT9Y2jO64yboq10bAT+c6wh+y3F7TRtCCyC8+JoZ17dosiwBJ/+eXrAs5bNkwKs9K+EOWPizF9eShJAiRCToTmeqkoqsIfKuipABCfsl6KyFV2bdxkKnNamnbFXc+kbV+spF4Ye/JJkIOHTrU/Xe5XMalpSXptsp67iDnS4wNG0bn2mDOEF1fqVRScaJsbf3C/x3JsHtkbcPYLORAzH41KxVypE9RtvQ0ZddmWQSoeh3Jvtv4tL2aibmDdkHIL1kVo1JcQh5KghCZ4GMlsE9VlBRAyC+2gm86hzmqXGciaFGtVrsBZv665eVl5yq1i0ZSPWNrJaCpoHWQ+Ag+5XIZFxcXpdsoi7kjQMX5KlIAweYcm/RaXvZF14+MjGCtVpO6XvQsUYlh1XnD1pZ5RLrY0MW2Cjn4Z5Yd67pJDCrkSJcibelpa0W3jt2FqLZyl2RfDle2Fs/CJrZlw9iKwxTJjtfBl2JU3WctEpQEITIjawNHdN+kDonomV2soqQAQn6xXdGYFbqBkmq12g00Hzp0qOcdFxcXMwl6Jw3SFNFIyfLMoqy3L+KfjX1PAMBDhw6FXqNzbxvOVxEDCLZsmKTXiuSIv358fDzV7VBUsFlpTaSHLRsm60IO0bNlsZrVViFHkWwYHwv5ENWLG5aWlkLvW61WjdvEtlfu+uAj2cIFW0+3ECSrZ1bViTrjvN1uY71eD9Xv9Xo9tZ0QiqL/fStGJeSgJAiRKVlXtAToBN5E93atilL3WtX2JQWdDTYcaxvoyNPS0lJf5T2bAEnLSVW51se+MYFOQlpEGjqG7ZtardZzb7ZvohwLRMRDhw71JUHSTCxk7XwVNYBgw4ZRuZa9plKp4Pj4eM/1lUolNwkCcjLTR7edimDD2FrNKvrbtAs5fOsbE6i2sa05Vre4gbWdg0+5XMbp6WnjCTkXVu4mvdZHbBQGmZpjVVb6+DC36yaiZ2dncWRkpM+mazabWKlUcGRkBOfm5oz2bdH0f1GKUYsGJUGIRJiYVLI+9CypIehrFaUqqu1btEkQ0Z0lwa4GkG3DB54PHDiQSFfYrNRGdN8wyrpC1hZBZZVIv7VaLazVapH6jT0EnQ0euDZmTQZZXO/TAN9smGZzY5Wb6NqlpaXIwGiwcpW1XeJWs/qEbgCBbJgNwuTfVDvl3YaxuZqVvUcWiVZfbRhdsmwnG4kB/nnDijmiAt5ZFnKktXI3D3Mjjw15sjl3+IJOG7VarR4bL0iE8LZfpVIxascXUf9TsUD+oCQIIY2JwZy1oW2icifPVZS2ltH7iAvLRmnLsnBEuiWJsZxlAIG/xocAsq6h7puuUH1mdkUSH8xiEyGiyqqsg3025nSb+GbDtFqtnsBocJ4Me225XMaZmZnQccfbLsePH3d63CWlaHpJB9W2MtlOebdhbK5mRcyukAPRPxvGFD5t6amysoifX9h3lTnXLEtbxlSQMe96CdH+lmNs3ISPsdTr9dDvKIKe0S0wYxMe4+PjPXafTLyqCD6sCfJeyFE0KAlCSGMjIWHTweUnlmByyVMVpa4BqTsJ+jQ52F426lOwUQcVmeD74MCBAz3tFHcWg44cm7rW9T4tYsV10r5VDVrbbB8bKyNs4ZsN0263cXp6WriKiF1tVK1Whc9cq9VwZGREqF+i7u3TvIxYnBVquujIv047Bf0jmu8qlUro1rJFRGc1a9aFHLr3tIHJbd1cDYIFc061WsWZmRnhWJ+ensZqtZrKatassdGnvqI736nYeqwdUqlUeu5ZqVSwUqmE2iHLy8vC51taWupZIZuXQgVVRPEq2QRIgErfqo4d32xMFl/8HSIeSoIQiVCdQHUDaCrKkjUEg0oY/j2mp6ex0WiEfnfeqygR7RmQtlZWZB0w0Q28tdt6hxT6hIpM8O0bto8xrwMC6vW6MLHJGpVh/WOiQtYng8pEIEyEy0ZvEv2mOuf4XJHuWwDBJxsmuFZF//OVlqL3FN3b14SlDjoybGsFl42kj2oAeP/+/d1gF3vf4N9RZ+wVCVH7yrazrUIORH9sGNtFX1miE0Bm5zr2w2/R5qI9khSf+tQUWSc8+S2b2AB9mI9lIpHnIzpzO6+Hk+hiHRtI5awWX21M3/wdIhpKghCJ0XGEslZ6jUajbwINCAzBsPsWpYpSl3a7jfPz88JJMOp9bayssLX9i6qhrVsJ7BsqMsH2Kb9txKFDh3raiG/ndlvvUDmTTnUSXWpTvxTRCEy6/YVK//jojPv4zIh+2TAqz0zbXCZHJZBro5DD9hZ2R44cCW0n0XPzATCVPct9I+vVrDbHu082TBH1osoc3W63cWZmJnJrNleDlEmwVdigi43Vu7q2XrPZ7CsqDf4tul53Szcf0Znb+fmUn2+jyLowoki6lHAbSoIQSqg6jFkbDarKlqoo5dA1llUmFRccGdVlo0H1lchYCAsg6OwJ7yOqTptK1RsrD6r71WYZ9HZFv/hS+WmCLJM+PiWYfA0gBPhiw7DIPnORKp5NUKvV+gIIUXZegI1CDps2jGphkOp2KD5iYzWrTrGXbuDNNxtGpyDJBdtLhaR2BeuriNpoaWnJ+Ap9G6j2qQlZ8CkJbmLOUQnSF80O0YlXqZ4JYmuLTJ1rdfWMyvW+Jm6IaCgJQiTGp0ANYnJl60KQ3RdUDwFmUZEnW5Ov6vMidibeqakpYeAhynhVdUh8RqWNVZ0DG4a2qp5wQb/4pv91sCEbviSYbAYQdPFRhpM+s66j6GMbqaAboM+6kEP1nmHXJ7G5+EBL8O+obU3YdlZJNPmGjdWsOtv+Bt+RVVDIZxvGtyA/i6xdYSOxaxNd2VdZpWAzCW4jccneU7QaRNZfz7MdEqASr9JZaanat6YSY1n5+cG19XpdeH2r1cJarea030KYh5IgBaVIW3YgJlO2VEUpBzsJsgkP1mmLql5jyfpALnaPdfbauISCTt+yCSP2nrzTG1dxUQRDEDHbSu2s2zfr6ktTFEW3IdoJ1vg2zn2sqPJRhm09sy8JOVVMbdWUdSFH1jYMv2KSTX7IVpwiqu0d7mPQWTXQqLuaNUt9atOGsbEdkM9k4f/antezptls9qzS589LidoJQbet2IQyP34qlUro+Uo2Vu+GzR3sJ25+LdJYRUw+XlW3dA76VNS37M9NJ/JYkvatbpKWL9hg7b2gGMb0GCDchZIgBaSoRk4W+7oH+BbMQkz+zmHVa8GnXC7j9PR0am3Vbic/i4R95kOHDvVcGxixaVTgsH/DJojY/8a9c5EMQRvjJ+v21dExOu1jwpnxSf/b3HYgCT4G51Ux8a4+JmBUsPXMPtowSQlzjGUO7eblj58/jhw5Ent/ldURNmwYXheKZCPucPOk8uR75WWWVa625g4dG0Z1ZZCN7YB8RjUhV6QiSVVMFbYlaSvRnMUmE6JWLtrQqTpzLPtcRRirLEnjVfV6PXR3knq9nsrKBh3972shB5EvKAlSQIq23BWxGEFVHXS2NgnkhH/f+fn5SEem1WoJJzM+Sx92vcpZJKLVK2zSJspoNZU8TLrfM/v9SR1qH6sWbDhSvhnaKgnA4DqdpcS+6X/dZ85qDPkYnNdFN5Hn61ZcSbHxzEUKZolsEdYhDkuA6CYFgu/gtwc5fvy4tD2RlQ3DtlNAkqSPbtDCV52Y59WsOqjKPqJ+NTsbeONlUbTdmM/QalY5dOw8PhF94MAB6blSpa3YPo06aDzsfWz4hfV6XejXBxX4Yc+sY4f46v8iZjOGbM6vfPyG79uoc05V7LUA9ho++RGXAPFZnohwKAlSQHQMQR8VAQVV4zG5z2jc+5pYlqhzFsni4mLPtQcOHOj5d3D4uAgV+RcFHnjneGpqymh1lQvBPl8qtX0L+KkmABH1D4L3Tf/7EkhzYbzaQLVQwMa2DibQWZWU1TP7MmZE6LaTyr75qlWFrVarr5KX/z5XbBieJPaeqRW0PszNLFR4FY6O7COq28TT09PCldfseKpWq87pNl9Wswb4IoeI5rZ14z+yY111K2jR+Imbc2yh0sa6Np5Ne1pnjs1yzrM1vy4vLwvtFd6uWV5e7rmO71d+7IyPjyutAowbe7bliUgPSoIUjEajgTMzMz0BM1YJxG1hREGweHx13FSfO+l1bJ+w+1jygdiwZb2iSsjJSbmzSHSqKHVgx41oEg5LPKrKr+1Ali+V2rbbSQWdBCCi+kHAvun+AF/0se7Sct/6RidI6GNFry+OlMnVAixZJJhMneUmI498ICrpQeGi62USKLZsGPa5k+jTrPvFBYpUeKUy5lVlnyXpdlqtVqvnDIcg8Ma2W1QFsi18Wc3KP5Mv41XVB+B/f+DAgZ53PnToUOy9VbeEU02C2J6fk9xbR+5tr3Lw6bltjFdRQppfCSKKQ4YVn/DyH9c+fPIk7r19jBMQclASpECIFA+/lDMIILiYXVeBgqrJSDohmjAg+UBsnBHI9mnSRB57bdL9tE2QZQDBZvDXl0pt2zot6bvqJADZ70hahWm7nXTxzTFPgo99o6ObdPR/cL2NQIBPtoGpFY8BWcihjRUHbEKZ1ytR76zjzNu0YVTbOKsVOqah1azR6FZ4qwSy2u3O/vei7bTm5+exVquFjoFms9lzwDzfTnF70dvAp3nDl4ITHp1Cvig7JMoOVy1GYu+dZDssU/Nz1v6ZDysq+OfS8X2LsnKLL2Ri7acoPaybQBclENkCljDZ8FW3EdFQEqRAiAJofCUZAODCwkLs9TaMMR+2krAdCDBB0gO5TC4llp1M2D5NchZJcK3K5KsrRzYCCDaDv74YDbaCoipjx1QAuNls9jlQo6Oj0k6fy454GD5t0ZAE3/qGfd5qtdoNFPDPu7y8LBVkFBVyuOZk8vdwXSeq4IIcqrSv7nMfOXIkVK/IJoySbutgy4axMX5s2TCq71qkwiuVe+vIfnDtxMQEjo2N9en9UqmEQ0NDke+sWn1vEx/mDRf0vw5J9EyYHR7YI+zWamFzDiuD7G4I/M/DkhlJD0Y30T+27aek2Jo7dIt8fFq5lXWBg0oRH/uuuqt3bcVTiHSgJEjBEA1iUQAhbFD7VHFkCx+SNWGoKHmd59YNTKo8r4oMm5C/IlV5BOTZaHAhKZYkAcg7biJDcnh4OPRgOR8c8TDyLIeIfvVNIIfVarW7NScv+9PT01itVqUS6KItgcLORHAhUJNnWXRBDpO2r+1CDtF3JDngM0sbhn1uEabtVJvyRKtZ5VCVRRXZb7U2trRiP6Ojo1gqlbr/HhoaEn5H0FYqB7Lb9s9cnzdsy6EJkhYALi8vC2V9aWmpZ8URLzfBtuR84PX48eO4a9cuBOhsFR02DthEHj/mosaPrj51wX5Kii3/1/XxiqgvD7pjXjXepKLDWdlV2YY9IK/FdEWFkiAFhN1Tnv0cPHhQKvim4rTZChj6ggsOatYOp66RkGXVpyn5863KwwR5NBpMjVedMZe0b0UrAXlDslQqRS7hd0GekuJCYDYLfOobnQBCoEPZQxTZBEjce7sgD3nUiQEuyGHS9lWZl03IkS0bRmUFVtbo2lwmbC0XdIUMPgboVdt2YWGh5z7btm3r82VLpVLfobqI6lXE7XYbZ2dnhVutNJud6uLZ2dnU29n1eUNHDk3IcNar5tkzVvm/C+yYsNXcgQ4W3Xd4eBgfeeSR0PfQ0Ys2/G5b2LZFXB6vJmIaOt+hI0cqq/lYn71Wq/U8E+uz1+t140kb28XNRDiUBCkg7XYbr7/++j5DcHJyY2ln1DYszabaEmabAUPXsb1UNeskE599T3ImiM7zmqr6VHnmrHFhvNg2QtPC5HjJMoDAJsBLpVJ3Cyw+IBD13CqGvS1DMO/Jcx6XnS4efksfXo5FW0lEbQUB0KkIDvbZNh0IMCXDedWJLHlffWgzgKBrw7CHQwdniMTZ/zbQeU+TVel5Hq8m5+UkY153lc2ePXv6dD+bAAnbEjRIWLB+q8x+8vV6vef8BvZ5g+8bGRnBer0u3V5Jybsc2lxpr7Kygk9miO43PT2NjUaj53ci2RfFU6KSu7ptZWMHhqyx7f+63kY243Kmkicq58lmWSSch9VxeYeSIAWj3W7jwsJCz9LhwCEKAg+iPYVZpZX0MDvbAUNf0J20dR0L1f2Pk04qJpYl6jrIqhOh6kGoWUNVlOlj8v2yDCDMzMz06f/x8XGpZfiqwWNbhmCRjFBbc2Oj0RBW3SIiLi8v9wUBENXPtmFlf3h4WLiiNdjn12QggAo55NGVQx9Ws5qoKrRlw4hWgvPjUNYeTxvV9zSd/PYpuSyLCRnm/152zKsGkNnr77rrLmESZNeuXUL7RdeH5c9qOH78eOz5DabI+7xhM6msmuDSDeQGyehKpdK3IjtMhgOyXvEiwmWdaLv4yZfxaquwR9X2MbWlVVKKtoNIkaAkSIFot9s4NTXVE2hgK8GCT7VaDZ24VapogmttBAx9Q9dA0WnnpIpWJ3HCV/OyiYRKpSJVzWtjYmi1WpF7UboSQLBdRWnbCM0K1pkRjVcZOczKgAyo1+t9K0ACGY7aEkJVt9iWhSIYkLacrkajgZs3bxaewxFsV7V58+bIashKpSI83LxSqQgra5vNJk5MTITaIbt27cKjR48aDQTY3jrAF3Tl0KfVrO12G+v1uvB5W60W1mq1WF1sSzfx4+3AgQNOBml0MKUT81p4ZSqgpNrOKlsJsfcMWwkYdl+d3Qyi7hnn++pShHkD0d72gjqrfFSfWbcYSRVT8QGRTgw7BN4GtgoMgr8pwnjl24nXp0eOHEl0PUvcllaqsSPVvjW1g0jWNjERDyVBCsTy8nLPpHv48GFExL69tffs2RPqZKrspxpgwpnIq0PCYmOpqu6KjqSTfRBAEG2HEsiSS0ZVgMgRCiqPXZNFm1WURZm4+cQyO15l3lPHicpyNRMftBbJRFjQWuc9Tb1znrHpdC0vL3dtBzYRwtoU5XJZuFKElUM+GFAqlXDXrl3CsVOv13F4eFhoh7ABBNOySM5MNLYTRTba18eAh8g+zKstbbOoyAd4e5a1ZWX8Oh35V702KhkRzBtxQTCRXMSdaxncmy+CGh8fT1UO8j5vsOiMV52+VT1oXPWZw2Iphw4dSm11kam5lV8xeOjQoZ6dRFzRiTpB9qB9RImfYKzJfgdLXsYr/46iMZBGEi+4Nz/O+diRqO11+8XW6isfbUyfoCRIgeCrD/jJvlwu4549eyKXIO/fvz+yEj7NvSjz7pAg2lmqaiPLrbodSnCtjWAoH/Dlx0Ga1WBZw7+ryvknRQha62wtYsO4aTZ7l+Hz/bq0tCTc/qLd7hwMylaMst8ZVODMzc2Fjlk24cm2lWj7RdH1eXcsVLHdNnzC48CBA8LECE+r1eoGrPhAVlQQgK3cDAsghCVBdMec7vycZ51oSg5NVavypNW+Ks/rgg0zOTmJBw4c6BtDUehUUtqSe1WfoyjBB53CHt0xn3TssH3CzhFsIDYqga5yT/5alSSIrvzned7gUT1zTmeVD6Le3K7yzEePHu2zYQIZTmNLH1OrLINn5P0c9ue+6kT2PavVanelGu/PBb5MWqtJXIcvimMT57K7w5ggyZi1PZ9T7NNNKAlSMHSDUWzFBK90ojLjOtU3iPYVWBaYUnRJjTkbgSH+nqLtUET3tBXwE7VRksPsfMSX809swco9G/TlHYK0AghJabVaPYfiBsFp/j1mZmaE96zX68LACBtEiRuz/DgPtmOk7dX0sB1k4VeT8jIW9r1RW5oE9oHouY4ePRp6rlmUTWFizOV5O05dTDn/popBsiLJ87piw4QVn/BnNOg+t80krW5leVES77xOS9JWumM+6diZnp7u0fXB37Fz0MTEhPEkuChZJBPs09n2q2iojtewoCzfR3H2ocrcrhITYd9TZMcsLi7GxlPC2iGtwjS2gIrfSp2dT3yX5Si/TpQQccXnyFoueH2YdBWhKZKMWVsFNibsWd9sYl+gJEhBUZnsVSveRHv4sUvX4pbNtdv6ey+7jqkgn6qS103A6MqTbACNDeSGtVPYmTY66Cz/9LUqpNXy4/wTG+gGlAKyDF7zAQR2/LABhKjxo6InRFVkfOA6Td1GRGMq2MdXlR84cCD2vqJ9sQE653qEVUKqzBv8fU0EAkSOiMv63HX4fkm6x7RtZG0gWwldnS1N2u02Li8vC587COSGPbfu+6qOVxPzha+2WxJEtmwSO95EGyXxH3SSCqpzHZ+wlz0Ynbe52IPgeZtreXk5F/Kkiu54FQVlk1SlqwQZ2UIxfmV12GoO1QJARPuJ2aLYPyJZECV+TPscOistbZwBartQUnXMqhRy2zoThIUKsMxDSZACYqJyX9aRERmPgYKU2fuSVT61Wq3nb1jlI9rCxSdMGDe6CQLVTLNOhjqsgjjsO4LAGfs3/JZuYZXsuohWNPEJPf6ZbRutOogq31w9/wQx24CFqF95A2VqairVflWRLX4lID9+ZPb0VRnvqlsm8X1K1TBm0QlusqisBGEdatEWI6KVqToBhOB9bVRCFiGYqoNOkYGp+2dVzc7/fZYJ3Xa7jceOHRPO4UtLS3jdddf1jSGd5Inu++oErdk5LQg8889BgWe9LV5N2Lc6uwuIiLtGdC3787BVsCqHZ7NyGCT7y+UyHjx4sMf+WVhYcNoXSBsTieGwlaUyVemqxT0qsY0wfcraTWFbQbuwIroogdiw1XFpzdE6ulRHLlRX+AccOXIkVB7StG1VxmzQxkm39NeJf7Lzq+i6qLM02blKZGO6dAaPr1ASpGBk7YwgqlVLBLgw6WcFq/B4Q52dTMImlkajgZs3bxYGn4Ig1ebNm0PPfEFMbuDoOPNs37IfPrjEtwOb8OCNx7TlIak8+iq/us6xKrYqYVTvadNA0TFekwbtdKq0Vcds0KdsEEt07z/7sz9L1G5EB94ZZxMefHAzaqtMlQSX6vYVOgEEU05m0j2xbSbCfUm+hMlDkj2mbcwd7N8ktYFsJHTb7eRnO5la9Zhl8RU7d/CrBoJrp6enC70VkYkxp2vf6viGSdAd4+12G+fm5oRtEYybsPPQRHLPjpvDhw877QtkgYlix6wDnMH9VHa50ClGMpFAN7G6Lqs5ywZRY1YmLqKCri7VSQqo7tRiSx5MFWWzc1waiVIVe4u9Vrf4hIiHkiAFwtaydES5ap+w77BVNWcL1Qrv5eVl4UoQtqLX5EoQE/LErupg7xlVVRtloGQRREjaN7rym3UgS9SvWZx/YqMSxkSA0oZ+0jVeEeUTnrpV2uz1Sc4EYVe2BcF0vsoOAHB4eDi1CnEVfAw8V6tV3LNnjzBJFVWlury8LEx48ImR5eXlnutsBBB05isdh8SW3eXbKkR+bCfZY9rXKsqsq2pVKxP5tkh6oHpAVtvwIqqvcvNFf+sSpoODOT4I0KQRfEPU3yUgCSaKkXRXEYp8lo997GO59mWT0G7rb3ud9VY3gUyI+jfqbDKTAe+kvq/q+xYlDsO/J2vfpR1n0G1jnRhO0rN0bMqD6paIIhsz7S3zVG1EncIrQh5KghQI205x1LK5uPsXpQIBMbmBZGu7AhMVlKwjyt9TtvqYDdjF7QdvAhVniH1XkfymYaCrPqtu0FsVG5UwfCA46bYZtlb66Biv/PPF6VNTVdps4Jq9Z9Q4X1pa6o7zUqnUc2g2e5ZEGiuTTIwdFlcDz7xBzX4OHjwYKcc6qw9NOAdJx5yO46az8ksnkGsjuG8rEKy6x7StKkrVKj/+nlnatKqyGBbITStAo3utKEAZvG8ety9FTD5uTQSeEdX7R7eQIwm2g7iirRvz7ssmhS18CdPjYYV8puZ20TPJjPckCV5VPcM+J3+/+fl5qedUmSt93dEgKVGrHkWHpEfZbSYTpkn0g85uHqJtaeN8wqzlgV3lya8+De4/PT3d53Po+swBWRZysHGjpFvwEnJQEqRg2HJu+QwsG7iSVZxF2YsSMZnS1N2uwESwRCRXUSt8TAWyfHEm2u02Tk1NCeU3reCO6QqnuACCCbKuhAmu0dnrX9eZ4ZHVwyrGK3+dbBvz+jtJlbbqPQMWFxf7Ds8O/i1zbxVU+9XmakvV66P0qYzD12g0+lZ6BCwvL4duv6gzdkxtX6Qzb+g4m7L3tRXcNxEIVpVlPljItmuUPOnOHUmriFVXVZh4Vl1UxwDfNwcOHJB25nXfVyfIKHpfUYDddrBPR/+bsPdU7hug4p+pVtKrYivxKCraYj9hK6mKsiopoN3uPUie1xVh213aHrc6SVoRYQlL1a1Ao55XRh/bKMYzcW1S2PfkVxsHbR5soxgmUybsJ9VYl6p+E8XneLuGf8fZ2dnI7QFnZ2dT0U+qY53vF9HuFmkk+3WvZeMTouvyOA9kCSVBiNThnUI2YCd70LIN49W2AZrknfkJIMl2BSYMHJXtu3SDjCpngtjqU7aine1LfuWOyQCyrmOgcr1qUizsfVXGuooBqdo/QSWlqA1brZZwD2OT1aZJjVcdmbBVpY2I+JnPfKbvHWXurYqN1QY29HBAVJVqmivsVPWx6iod3SrKANWgx/z8vFA3Rb1r1olhE+NVVRZVdYzqu/LPm3Q/eZW+MRmw07Fnks6TqnOkifc1IcOyW3qaSNio6icd/W8zEGzCP8uqyC3rYjp23PCHorNzLF+k5vuqJBVU/DtTq5lUySqhLWqbycne3R6iih15nZR0zKrONapbFwX3zHoMsD4qf+/g52n6OzrBclU5bDabwkK6sGvbbfUzkkyg42OpJt1NjPOkc0/wvKLrKPlhBkqCEFKoToCiCUEUuIvazkRH+egEWVwwQJMoTZFiTxKgUXWmdTLzKo6iSqVQcC3bp+z9+T41OcGw7cAvqZWptOa/IyvDKOkYCP7exGGkWVfCICavcrW1WoC/T1LjVUevRW1pmFZwJ0yvsYndNAxCXaciqRzaSFoiioObovkjrSrKLLBZRRncX+X8K9E9VYKMOqtWVBw+FVnk7cKkq81U3zV4XhMHZsoGk0xUUeoEl5I+c7OZ/WpJ0bMmkUX2Or74KonPkXS82bALRMGdoK3SWrlr8p4mkigyZHWfgFar91wz9hB0fi7gt1M0lYzOqsjAFEn8WHasqR6orkPWiUc+ocbq4agiwDCdxM+VR44cMfKc7H15fz24P7s6KmyLMxM6UYSsDCf9Dl2dqDrX6bSTiu1je+UVf68kulyljU0VciRZ0RGMWdHWXcHP8pYEtwElQYhYdJwY3clX5+BJnee2OfnqZKtVtyvQxUSWXAbWqQjrFxmDig3U85WMwTJYE9sY8XKUdKsyliwTAyorOsIqlZIe5GUj4aOSQLSxWoC/v0rgTlU/6QaGdO+pcgaQri7Weecs945VvZ4PboZVqcYlCGwGTWQwVUUpGvNstWnYmFMNXrDY2CJB5TqVQADbrqrnDum+q86BmUm3atKtotQJLiXVEbzdlPTcrOB5TRVRie4dZUvwc6Ts6nPV8Za1XcD7OaIxYPoMN1GAhr2/7KHqqu+sQlb3YWELgxYWFoS+wNTUlNBea7fbfVvysM8cdQYVe++k/q8LBYCyZz7aDsZm3VaqxRQmilGD+6v6vyp2F6IZnSj6vjRWkOjoYd2Yk+r7sqtvK5VKT/tGBdtN6VMd3yHpHK3axjoFJ8H3Jy0i4f37oG/4n/lcnOYClAQhYtE1NHglJ+uoBhVzqgdP6iRQRO+XxeQb5lzIHBaos+WSCXQChrKYrCoUrcaIaiuVe4uu4SfuqampVIOxonumcWgf/5wqq15UDSuTgYckW8npJl509vpUNV5VsBFA0HWgdA3XAN1kRtLxqpO0DLteFDDhg5tskIYfs1EBFxeCJjKYqKKs1WpCR3f//v2h226YDgQkkSfdcauyfF8lEGAikGti7lA5MDNp35gI3KnKlMq9bY5v3Xu3Wq3ILc7C2lrX3tK1C3TlSXbrL5YkwSj2nqx/xj67jA2SVRDbZrCcLRiS3V7HxMpF1Xc27e/z363i60SNAxu2KUvWBSDttv62mirbkqvqYpFPyMqxzFarOjpRNXGfFF09bCJ2lHRrZt72EY31sHPN+L9TmSezXM2KqBfDSXoQe4BqcSglQbKBkiCEFKYMjSTfo6OgdZIJYc+bhYMrUnyBcxGl/FQyzWmQxX67aRjZojaLChom6VuVAGXY86qMvaxlOKkTY+K+qgakiZU6qoZgu93Gqakp4XiJM3p1jdck2Aog6FQ761zLotK3JuZK3aSlTKKVvU71EEhE+9WYspiqogy23RC9n2j+0Qlo6VQDp6HH4+RXJxDAz+tJtuowOXckOTBTdayb0BEqwSWd6nBbK7107q1yxgw/h4r6xrVVhFEBTt5GEKEiFybaKasEmyuJellZ5vWZ6MwumTk1a/1kImkZpdPC3ls3GOsTOu/KxxFY3ZBmcizMJ4wqMONR0Yk6MRGVeceEHlad60wVacZdw6N6zoXIR1taWsJ2u218Navo/iLiVjOp2NFh9n+g18MKzUQxTFaOaDssM1AShJBG19BQUSaqhrZudR9L1vtpJ63wtlH1EPferhuifJ/KPrNO36pca3I1U5LxoyvDYe2b1gqU4PqkBqSqgRL3vjLJP52VW1k69SbvlbSPVCuF2HGT5TJ8EwkBnaQlv6KDDSAsLCyEBjeTVKmaaisbmKiiVHk/Vu55XRF2KLtuIs/Uij6deU4lEMB/h8wYMDV3iO4btgWpjSSTzHfEBZdsVC7bSKCo2D6mVjgEZLmKUBTglN36S1WWTchwVvJhM5GnAtu2pVKpbz4Pkpxxz64jT0mvMxEwZAsV+LmnXC7jzMyM8H11C/B8kA8TdlbUuX5p2gVhiTzZ5I2KDKv6WLorFFT1sA4mijRF32lSt4QVXrE+2Zve9Cbjq1l1MWn/i9osLL4QXCfSba7oJN+hJAiRCB1DQ9VRVanmYr8zmIT4SSnNyVf1ukDp1Wo1YfY3qEI1nc3XxYcgWFSgI4lMZ+UctNt628HpOvQmZV/2ehuOSLvdxuXlZWH/BAZvlGGl0k7s+FbZNix47qzaysS9sqpCFlXQiA7+fuihh0KfVcehsHUIML+1mkiuwgIIwbObXmHniu4P0HlGURuz15puo1ar91DdIOjFfkcafWrCyazVakKbKxiPUUll1TFgUn5l7muzipJFJ7iUNrZsU139PTExgbt27erzNSqVCg4PD0uNARVdo2NHt1pqW3+J7OLg3mmenULEs7i4KEyABH0iu8JftY9Uk3iszczLcFDtLYKd88LGbVxluIoOdMGHjiOrAps0kmphZ7ykrRMRk5+Tqlt8oqqHTZBlHEblXnxxsmhlNCsfJlezmnxfXTsriV5V8Tt8SOi6AiVBCGlMKAFVp1x1UhFl5QMFnCSYqzKh6O6nnWRbBptKz0ZmPimiwOjkZG9leBKZ1u1blrCJmzcYRO0ateVR2DuzDn3c4ZVRVUNxK0hUg/s2UDWsVHREvV7vWekVZggePXo01XfOmqz0hMwqwNHR0cj76BjaqrpYd7wvLCz0vefk5CQePny4ZwwuLy+HvrcuLgfEdOfzdlt9+zrV++sElFQxsapienq6L3h3/Pjx2CCCLVvCtypKUVAmaXApC2z1p44MB1XAoq10g8RIGufT6LaVSrEY304imYx6Vx8S377CygP7OXjwYJ+NHSUXqn2kel2wmoPX/5OTvdts8nvot9udMw1E4zYoDBTJsInAr08+rOq8bDJAnsTOUz2LDdHO1szsPVVWkasW7ZoiC52s0y/8TieiGAwA4Gc+85nQ+9uKd5nwb5L0D2v/y27pFugJ1TNMikZqSZC//du/xe///u/HnTt3IkBnyb8s//AP/4ClUgn37duX6J6UBEmPLDPMonurLi9sNpvCisS460xMvkmztzLBOxcPQvKlioZtz4mJCVxYWOhrY5ntj3Scg6QTt4nAnWqVX1JjLsx4jDsAzBWS9o+qjuCTILVarW+LnqGhoe4hd3mq2lCV56R9w95ndHRUqEfTGK866K78arfbeP311/e8J1v1JnOYqg4uB8REY7Xd7j9vI+hvUf/qbF+nY0/YaFcd2W+1WjgxMSEcc+zKLNEKFhVbwuYqNVV0g+SVSgUXFhZCEyAuzbOmfAeVFYEmktFJ7XAbqwj5RLnKtsHB38me4WPTJ8waVpZ4uWLb1aRdECQTRGP84MGDiX2VJH2k07fsag72wwe/Wf3Pyn6w2wH/LPv37+873Nlk8sIHeTah09g2FrWTyeS97mpWUzpxcnISDxw40CePMu+Y5DB3VT1smrSLkXT6hY/FhH1kfLQsMWGHJ9ExqvEU3TFXNFJLgvzFX/wFfvCDH+wuRZNNgrz44os4OTmJ/+pf/StKgjiCzSoJflJJsr90mLLlK7xEz607+aoeyMUnfMbHx3ve2bWJIcCH5XdsUH9oaKinUilIqk1NTQkrVXW3DNDBVBAsaQUCL4fBv8PGD7vyZM+ePUJdMTU1leqBXlnKoY6OYLfDGh4e7quam5iYkN7qwHX4PkkqzzqrdPj5gjccXdOlOmcAiRLvbPuGrRpQHTPsdbxenJ+fF25rYQtehth/8+NMJFMipzjJCjfdILvLK2x4ZGwYkSwG8iSSR/bnUZXs/HMk0Z9ZzR2sPZ30DDfWhuHnjMA+Dn7vkqOra8PYTFIl3UpXdxVhUPzA02q1+oLAwc91/DMVH8tkUNV1+AC9aOXM/v37Q1cqqMLO55OT4lXrUQkQVVvChL8vWpUalaC1kTgMa3MTvpZr6Mqwqv9roipdZV7m5Um02j7JmYDsJyx54spqoqxkWHcVrMg3c9VHM5EgTSofIh9FNBeI5gCd1VdFI5PtsADkkyA/8iM/gr/2a7+GH/rQhygJ4gi2Kv11qpQQkx8wLnpvnclXpRpeJ3FDxBO2V3mz2ew5/4ENILDBfdVtpXQxFQST+Z6wSkjeQRY5x8FWEiKjN2jftJZi2tBTpg3B0dFR4YHevo73sD7h5fDIkSOh36HqXCwtLfUFCoP2drltVQxu1ao31THDXsevpmC3vIg7TydL2LHKy1QQlA8L4uk61Pz9eaJ0hU+BGdUqelU5dCXwkAQ2MMQfvsoGhkSHr8rYh8H/u7blgY4No9PPOuMuyVa6JlYLqK6E0rV7RO8atdreVmLABlH2cFxhkIl7sm3Pn28QFldpt9VXlerKU1jwOG7+Ug00mk5e+1RwkISgnUSy1WptbDcWFoxV9X9tFEjqBJBZkpy3ZStOJrpP0jGUNWFz67Zt25zzf03ZmKpzu6jQLPhEya/rW6W6glNJkHvvvRfn5uZwZWVFKgly/vx5PHfuXPfTarWkXoZIju2JjDWu2UEdtl8tb7iKFFfU/uomnvmhhx4KPX/iuuuuEz53cH3UGSgmJ1ITjptviBJpUXuts7KkcsC4LqaCYLLfY2rcZR2Usnlv1YSpKCEnu/esD4j6RGQAx61wS2rct1rqW/K4QJIxrxOkVx0zfDV7EMzlHc4gQeJiEExWpkTOCz+HTE1NpfJ+tp3apHqNbyvZc810dLftNlKh3W7j8vKy8DnZYgzRO4uS5/wWr67ZayZsGJV+1glMybSzifuw6FTE6/hnIlsk7txF1aCqj/C2PttWslszJ+kjkTwlCYLp+r+q8sTLwsGDB/tssCib1nbC3/b9k2KisCJOl9r2f1XRmWMR1YLINuJk7Pf74P+yzzM4ONg3v/IFw7Z9NJdWGydN0CZJ4hUVZ5IgX//613HHjh34ta99DRFRKgnyoQ99qK+DZV6G0EdlMOtcI5q0ZZZhmlBcSQmWtPPVE8FnYmIi0mBgt8rhFZfpti1KNVdAXJVS2ATBG3xZ7fVpKsCT9Ht4eUpSuW8zKGXj3qq6RnTmCm9wp3mGQ1aYCCDw3xM3Xtm25ZOcMpXSNp2ZAFmDV7fqTXXMsNdVq1Wcn58XXudaQJYlSWJYtSLLVEApS6c2eG7VVUK8zRZ8gjOQROjobpl+dFEOVd5ZlETmg50uYXJOThqk1EnyJlmRbXKs2rBhdA/z9TERmRRV3wHRbBXw5KTcdpO2bWFRMQYA4Jve9KZIPWxrJYZvMpzGih3X/F9dO7zdbuPc3JxwbgzmUdFqKNXthGz6DbZiXknuy86TExMTODw8LJxjg+IpV1azuuAPJrV9wuarqLNwiogTSZBLly7h7Ows/uEf/mH3Z7QSxF2yXrJtqzpKB/aZo6rJRFV+SQ0x1ba1sczbJqK2FVW/yVwvMwnpYsqx1v0elfdOeo3JsepDP4mcCl4W5+fnnQvaqSIK3IkqyuLkWWU7N1F/xFVBurSsXXb86FS9qY6ZrMdaGiQJuqgkk3XOB7AphzZWZ+jI05EjR0L7Matxq4JqMCrpWRU2SCORlzRIqiKLKlvp2kz2BKjYUaoBTv5eeZgL4uBlT1YOdcaBKT2cVZ+02+2eLTJVD5bOUoZsFxyokPSZRbpB1fbJom9M2D/tdmdbuKgkyOzsbM93tFpqB0vbtteCZ8g65pVEDoM24mNMbFFzsMuEi0UrtkhqW/CyKqt/i4gTSZAXX3wRAToH/QWfgYGB7s+OHTsmdR86EyQbVAwGHSNDt4pG9PxZZW5F1WR8cI59LpU2MmUk61Rpu46ojaICs2FkWaVkyqjS3RJC1bFXqWQ3ZTxmXU2WpJ10ZNEFvaaKbpBS1gnTlSfbDrHqmNN9b9Ux4+Me2klXK6gmmXRlyfZ4Z1el8rIYtqpD951V5MlUktUWOmd18e/s0vuZntuzSNbqJNFNBgqTjgOVttYpMNM968s3RH2bpJ+znNfZeYPvk7SLa1iZYqvm47Z51fE1VOfJ4DpRGwc/9yWBHtVmovcTyXPcGRlZ2nom7HCV7wjaKulh7rb9BpskGbv1el0YV2JtuDy2U1YrwlX1b1FxIgmyurqKJ06c6Pm8733vwxtuuAFPnDiBr7zyitR9KAmSHSoGS1ZGjq7jZSrwkDQJYitYrWvc+wDftqLkD3u4W5z8ZtU2JmSx3e5szxZWZVav142uwOL/JknQwYTxaKOfktxXVRZVjXMXkGmbKHlOqt90x42OPtXBVtA8i+CiK4iq0YI+5qvR2L9XOcia/ZssZcnUvBG2pWfUHuCm7BiV4KKPhRyy258mlVtXMGlP64yjLIoy2u02zs/PC++j+q6y40An4Jf0XU0VFfmCKR2jOl+q+r9h54Oluc1qu93G6elp4fkJ7DkL1WpVGLhLaveoyjB/HdvG/HUuFxjJ2tZsO/LnLvABU1f8XxO2k8p3mLCjkzwvez/+3uz9fJdDRP351cciwCxXhKvo3yKTWhLk5Zdfxscffxwff/xxBAD83d/9XXz88ce7AZoPfOAD+O53vzv0epntsHgoCZItSZQeb1yw18RVHyTFhkEV9QxJtsPSUfI6RorqMm/bJHUOWi21gxxtBUV1yXLy5X+XpK1Mta/tfkoSaEkqi6rLtG3DPp/K2RG2KqxsJT2zXlKvOmZsjzVVWq3e/f7ZFQP8z/kxyiYg+fdfXl42nmRSwbYNEzxDUjtGRZ58D8Ym3XapqFWUunNA0vGnIr/tdhtnZmb6tp9gg44y405Hr9oK+PmYfJQlbAWW6rbBaVfTu1AJ3Gg0+goGAoKVk3zgLuvtsn2v3E+62of3E3j54M+Tc8X/NWE7ZWl/qcw1eTmbNan/KyJqfrW93VjwHFnEnFTviZhc/xaZ1JIgf/3Xfy0MqN5xxx2IiHjHHXfgzTffHHo9JUH8IE7piRQXf83U1JTxwag6aZsyjFSrKHVR3UqCn7SzDPypoqrok06mPhvLWVeWZ7V6JI131UXl+ZPKouqBfbbgA8jBeOUdrmAPadOV5SokdS5No7pySwV+zAR78YrGDF+R5qtOREwefDYRBMhqKwmTfSNKKKSRWFB95jDd4MO2PEmTcYjuBAKyxtYq6SSYmJtNjN2sA35Jt6HzrZrXZJAyq74xZSfq9FXSa00VACYZ474Wcqiu9mk2m91CKpH8uer/xtlOMnojy628ktzLdJLVBu127zlArGwtLS0Z0ekuyGESO4T/W1HRQNzuI7rP6tM8a4tMtsPKCkqCZIuMQRe1DJOdsNOoSlE1OE0ZRir7aeug8r6+VnPpLvlTXV7uY+AhS0Nfd+WJzlYSNvtJp42TGiusfLM61NVxGvQJaySzc0GwlVCUEZuVQafqXIZ9l8ozZynHqgGedjv5gZOuIbsNUYBOACvLwCR/Px2dn1USRHfeYJOGorY2vdrYBKJCmaCvora30tErPjnF7PPyfcwnZNPY0jMJpqrvTen+LAN+Sc768tWW5mWRlRdeFmUSIFnY4bqrknzrKxP+fhZzswl09E3ShIJtOVBJ3Ii+I6s+LlIcBrFf1/C6zeTuBLaTlknsCd9XKRcFSoIQSiRRRuzf8ssw0w7eqToDupNm1llrlcnB5wqErJd8+xxAQMx+OxZbW0nY6CcbFSqHDh3qC0q6aCAjhm+HODk5ifPz8z0JENvP70IwK2t5Ulmy3W63cW5uLjIJMjc355xe5ElqH+iutMzScTNpwyTdDksFE0lDfoUZO25dTYTwK69Y59hUoYztQFZSTFXfZ/XeLiTPA1wL+OVpBSGiWv/YeG/doiIf+0p1xUCWSUNTqKz2UdUNtvzfqNiRaL6P22EgbftL516ivvHBv1teXu7pm2CbZrZYr1wu4/LyspH72U5aqsY+g7/lbWkfdE2eoSQIkRiVfYnZCVsU4EzDoNJVljqGUZZOp4mtJHzci1JkILFOZzAZ28aUASn7PWGy5/IWIb5t88SSdYApzFg+dOiQke+XwdTWBS4a+C5sa+JC4Dzqvj4GSHiSyqGK3NpuJxM2TNZbeiYlLHEpOvw1rbY2McenGYizLYdJMVmgk+UqwuA+NrZRRPQj4Ge7mlcHVVvPRhLSRMDb9aIVFtUVA67bo2EkLRyzMe50dG/ULiKyRbRZznsm7uXj2ayiXTn4lSCyBQAy92q1WkJbKcti1CQ6w9fkVlGgJAiRiGAbjJGRkb4q0CAJMjIy0lcF2m63cWpqSqjU0w4Uqkz4JgwjG1u4JDWw2W0GRMu8g20GXFztgOhGQDgKU85Pku/xbRlm1qt60iCrse5C4s+ETLtceWdqVZILc5AKsvf1OZiV9NlV39VmBb4J+cl6S09V2AAYn7hMOm6TYqKPsxjrvo1X9nl92iLElt72KeDna+BZ572zrKY3NV+12+K9/qvVqrG9/k0gsotlVgz4phN5ZFf72FqJpDMviq4X6Y2oFZ5Z2l+69/I5WB6mJyYnzZ0J4lpRThIflv/b8fFx73RNXqEkCJEIvkJLNDlVKhWs1+s918VVaZg0Ak0a6NVqFf/kT/5E6HBF7UWcNSa2ksg6SGOKLLYGUm1fU8Zn0u/xKYCQdCsJVxNyaeNKsqgIARDdrSQCdN/VVrJI9r4+9CVPUvnVlXcbW0mYCO74tnogSxuTxZdVX1kEKkzjW2CI9x3m5+eFfZmGLPoU8DM1v9rA9cC5jj5ir61WqzgzM4OTk5N48ODBnr666667nPENVVcM+Da/iZC1v2z4+SbaV2Q7JbWJs7S/VO/F2i4if33Xrl2p6xcfVrOyyY9KpdJzflrwsyzGaxK/J6oA1Sddk1coCUIkJqsqSlV0JnzeCNy3bx+OjIzgrl27egLHS0tLoatefMJ3QzCLQ6JNVnjoyL/KuPNl8mUNMP6gTXYriTQTc7b2vZVFtOw46HN2HFSr1dT7U1WmXQ8gBJgM7ts6l0qVpPd1eVWPiKT63FahgO3Eu48FEklk0aS+V9VrWdlfbF/yRSPBqpks+lKlzX3ZIiQsgMxXoQeJKJd0Rpb3MrXSUhedtkoSfM7apjTpr/B9JPKxXPAdVFYMBLtaRJ1rNjs769T8xpJ0zvG1GAMxu7OzbNBqtboJkFKp1E14sCseSqUSDg8PpzbmfFjN6koSJIlMs387MjIiLEB11ZYuCpQEIZSQVXi2guwmVkYEiY7g/YJEyPT0NE5MTGSefU4LXwKTPFmdI2FChk0ZCEm/h08osEEDFydfW4kbE0ZgFk5Go9HoBlf451xaWsJqtZpZfyaVxaTjyFZSSkcf8s+sOu5t6WSdAocsEzW6JJWtrGVRRx+ZTF64nhhmSSKLaSR4VMZCVokm0SrCrAOaKu8qalNXdQ3vO7B6MwjuT09P95ypYHuuE71D2s/hwvlvJsZdXMLVZhJZtx/Dxt3HPvYx4SHItlFZMdBut3Fubi4yCeJqgaNPxYu6NqJoG6TANg3+PTIy4m0ihC9uY7eaZ4vbJiYmUtvyyYfVrCI5YGMEWWyHlaSd+L+t1Wp9iTz2b13UM0WAkiCEMjJVdz5WE7IGFR+UHR0dxbGxsb4Are/4FsxiqydYY5yvYpqZmTEiWyYmeVMV0ya3qnFp8mWfN+stvExseZPlVhQuBEwQk1dey7aRrXlDRw74ZxbJ88jIiPFkka13V9WJJuW30Wjg8vKy8HfLy8vYaDSkv8slTG7B1W5vnPsVfHfQxq2WO1t66pBUFtMaYypzfFb6nA2olMtlPHDgQKYBTR394vqWngG878C+V9ih0q74SFk8hygZNzmZzy09fQpUi+C3K7OROFUl7/3jis6QRcf3Fa0ACOxrG1shpUG73btVZbDKIfh3sIrJxeKvLMdRML/aOhg9SeLUtzFaVCgJQiiRtOrOlaCdCqLqdJcTIKrtbWsrCRWCCSY4oI8lkM3p6WmjQTCdRJGJJBNvKLHfw+/pbauSXBWRISUad2mOOZ0288GhMj1mVSufZZ7B5gpCE1spstVKYf8vm0xJ8gxZvbtq/5h8t0ajgZs3bxYGcIOA7+bNm71NhJjQ4cGWH2x1Ifv9Rd7S0/Qc6XIhiQsrQRDl29yULrWNajCWt/XY90vLvs5izmX1v8z5b2miOv6TXOebHR7QbPafr8R+stxCLylF6B9E+z64LCbmxVqt1i0g4hMEolUAvrQNj20bQtWny9JfUW0jEzIR2NOyW+j5KodFgpIgRGJ8NhxkkFlam7SaIStUJ6SkSS0XMtw2JhiVihZTwSz+PAj2e1jn0YeAPE+YTPHtfeTIkVSfw1SiyzW9aHqLHD5AI6pa0sVWe+roFfaZdfaAtWU8y95XVZ5M6qbl5WVhJTtf8R62UsQHdJ1iX6sok8i/jm4zFXRwWfcjunMmCKJcm7PPW6vVhCvs9u/fj7VazdlgbICMzcgGnavVKk5NTfXYekGRS9r2dRZyzI5tvm3Y89+yIOn4V5m/bAc2k8I+b7Vaxbvuukt4fgtfeOUCPvZPXlezIprVJ7Vara8oLvgOE7apK9g+Y8/l1ayq8mRKJnyMrRDRUBKkwKgorrwrAZGy5AMGfECBv95m5lfXCJSZWEzLgO02k0XFWDbVVkkCfr4agbwcyLa3afnRMUJtO1RhmJJDUTIu+K40Dmd3tT2jkK3+dW38JUV13Jl0jG1v8ZMFuk4xb7+Mj4/3bC3k2opWlflLZw7QbV9fbOIkK0nTRnYrXXZLN7b9+FWDrurSJDaM6LDwcrmMhw8fxsnJ8PNEbD2zL/eJI60tPVXvYZN6vd5TtMGu1mHnVVftMN/6J8+rWdOYF2X6yZf5WIRtnWj7/lHo9KtJmXC94IVIBiVBCoqqseBrgFUWXlkuLi72JUBKpZIwgOBK2yRR0ra3knClzeKwXYHAO8n8M/Dnn/iSWApDtr1Nb4tmwgh01eE1MWZbrWzP4kF0tz2j8PGZs0S2qk8GNhHCBg/zkAAxuVIhz4Ucqpho37zbMIjmZMKlREzaqNrh/MdG8Dnt+ct2ICmQZ9H4r1QqkYcsJx0LLgcWWfitE/kzCoIzzfbu3ZtJMk4Vn/rHh9Wsqrrf9LyYpJ9s6xcVbD+z7fvHoStPJt/PF51OxENJkIKi42jadpLThm0DNuERGIN8YiTYSsKlCoQkFWg2t5Jwqc3SekYKICQjSXubDMqbMJJcN45MPF+z2exxgvl2MimLrreniKKsBFElmHPY1QhBoE01aHzgwIGe7zpw4ECKb5ANpp1SmS09XQnoZ+GQm9RjrtvEuva+qUIO2S09fUelvUXJXP6TdTAsjfvatvkDeebPlDl+/Hj33yMjI5GJEFlcCCzK6ia2X4JEEN9OlUoF6/W6U8ldHXSK20zpe5dXs+rqflPtpNJPtn2HJO+uqhNNta9tnSyL7vualAkqdMsHlAQpMC4YaK7COshssoOdHESHirrUprJK2uZWEohutZkIVwJDAXmffJO299LSUo/TEOxxHvxbxoAzYQT6IMetVksoP0kdtywcDBfaU6eqUOdMkDzDn1PBzq8qBx27vBJEdW417ZTKrgRxyRlOU8fYWNFmEx0bxsaWnr6TtL15uQtLgqRt62Ux59q2p/n5J61zklzQpTpyODk5iUeOHOmzX9j381k36gSeTcuvqzaMCzKs8wy2fOWkMqK6BajJcx5dinGkiemYlY0EG2EOSoIUGNcry21X18Upy1arhfV63anltVk+g8ktTVxosyhsy2KA6+1kiqTtHeZEyLaPrhHogrMQhaj6kQ8+JzVy03QwXGjPpDLB7qfNV08Gqx5UA/15gg826ZxRkdcqSpNOadL2diH5GJCWjhGtSmg2m9hut3FhYUF4tlHcPO+KjRCGzvOZkImkW3r6jkoF/uRk/8H1Wdl6Wc65tsdKrVbDkZGRrv5j5VDVHuJxIbCo0qdF8jFU+ietceLqalbb9oBqP9mUYxUZSaoTTcuhbZ2cBSZkIqvxUIT+cAFKghQUdmLhje6gktpm5te2AamrLG1W62ehpE07Ee12G+fn54VtVlSFz0+CbL9WKhU8cuSIteCUi4iCB0naRcfosK2v4hBVP7LbPwQ/S1JZnqaD4UJ7JnEy2u3e/bTZFR9BgG9oaKirM4s6XvktN8JWJ8jIoeuV5S5so6ha9exCICwLHcMWAVWrVZyamupJgPB9lkXSylVM9IfrhVc2YGWHPYia/bzpTW/q2botjXHogwybDAyZLOLK4nlVUfEH877aPEC1f0z72K6uBAkwNRertrfOimxbhRxZPIML7+kLJla5ZVUo4MNcnBcoCVJQ2MEcVUFtq1LVZiWw7sTiWwWCyj1MLifnKwTZNgucwqIpfH4SFAUP9+/fj7VaLfXx4AOiMRckdLPCBYc3DJNJkCJVwsi+Kz8+g5+LnNu05wIX2i0MXq/xwZbx8XFpXd9oNHDz5s3CYEHQ7ps3b8ZGo5HW68Ri20kVJefYZxNt6RmQ90IO0b1EASgZ28mFlWtZYEom0pYtl3WgCD45xB+GXq1WexIkacmSy+3Wbrdxbm5OuFIw0GUiPRZFUYL9SXxSHf/VZfkxjSk/3+XVrCy6Y0VliygXthLVIYtYkMlkbl7HLy8TovOOApmISjJklZxwSYbzDiVBCozrk69JR1h1aXpS5eNC0CNtJS3aWid4V5WDBU2c6ZA3TE3aKvhmCPEV92HBrCJjajusIhpnso5MlO7PKtDiQwVRoF9E7RocyCpLo9EIXemxvLxsNQESYHtVRbvdxnq9Hhq8d21LTxs6RnRofFL7zbbtlzamZKIIqwiTwsp8tVrFmZmZniKgoI2CRIlrz58F9Xq9Z/U5qxdYv6Ner0t9n229nDUyQWwdHWZy3Pngg5jYvcD11awBJsZKq9USrmZjvzvYftKVrURNkGaiNXhXdntTAMD5+Xnhu8attnGp3Uxisqg0K92Ud3vSFSgJUlBcXwkSYGoJvqxy15kIXAkQZqGkTQWyRHI4OdnZoo01ApeWlrSf2RVk+8fE8k2VZ/PJEBLJT7VaxYMHDwrlxxUHygaB3OkcjO6bfJhC1pEJqyrPKtDiyhwUR9EMfJ8qjm33TdY6xuSYzWtg1ZRMZCFbvuhAFl7mWRuRl/mi2jA+rmZ1BZlqcd1xY2rc+WBjmtq9oEirWdvt/nO4+DOhpqenjciiK0m0tO0BXiey9kuQGAnayZf4VVrwMsG+l6vbi+fVnnQJSoIUFNbQcPFMEBbdAEJS5a6zb6Xrxlsa6PRP3H7IrGGUB5LKSNaToElDKKtkHGtYs1WUhw8f7vl5kasoA0wllV1wMLIiaZupVJWbbFPXAzyiVW58Qj3QMXmQJxuOjO9bSbhQbadq0/iU8JLBlExkKVsmA3dZzXVFm1dVYFd98B/RNlkiXNFxWSF7fmOj0dD2X02MOx/6x+TuBS6vZjXZF0kLb123Y+PI4vl5fTg6OoqlUqn77/HxcWw2m9L95Xuby2JyNXra5M2edA1KghQYfh9a1kF35ZBCUwEEthKGV+4mlV7RHBnTQVVe4c/Pz+eqzVSMyqwnQROGUJYJwUaj0U18sPpscnISFxYWsFqt4vT0dM9S7Lw4uEmwsbLId5KOhbCq8qjzBdIYKy5XELHvG2zzFzwb+77B0nSfk5YmnUpZ2yJPW0mkSdhcrHOOj8vjThVTMuHCCp+kCZCijAWfaDabfdu/BME+GYrUr6LVM8E8K1pBY8J/NaEDXQ7GFmn3AtNjRSQbUTLi63yaRSJPtKUT36blchnn5+cTjR1f2zwKVq/xMs1vaZfkXMK0yWNfuAYlQQqMy4aGyecztR8+0Ytp+SmCwucTj3y7VavVnve11Sa69826mku0fURwj/n5eeFetEVC94yZoiV3EZPLMPvzarWKU1NTPU4yfz27vUkaY8XlCqLgvUXv3mq1pPbmdR3Tq+pkgxF52UoibURtyrZRuVzGqakp6bnDdXtaB14m2H/zMhG3WjpL2dLRgT5UpBcR3SQIYvF0nKnzG2UxYXu46g8WcfcCk2NFVGQQdXajy3ZsGFkkWvl78O3ErghJOnZ8bPMw+HZi5/Xx8fE+WWSTwjbJsz3pEpQEKSiuG/hpLcMUGc+239VHTMtPXhV+WAXC4uJiX0KON55tt4muIWTz+V11oGwhMgSD4F7YwXAmDin0Gd3AsyjhGRY4LHJC2baeSwuT40YnIZenNjUNH8xn2yhYDS1qYz4wxFdlBkFFV+xpHUQJkECu+RXjLs0Hea9IDyhKQB/RzHZYvmCqX7Pe+sWk7eFqMDbJ7gV5G4M6FGUlCKL9s1lHR0eVxo7PbS5CZDvzifRSqdTTXuxcYmN+dT0+mycoCVJQXA9spbEMsyjGcxaI+oefkNn+8WEfctNEVSBUKpU+I4V9T9ttYsoQsmlQuepA2YJPyLFnqfAHw5k8pNAGpgMIcd9jYr5KY8y5GrjjyZvjFWDSgUq6pWde2zQtZMewaO98vuJadK6XC4mBpIjahN8OJpgnXJoPTOpAnXGUdgCF3QqUf56lpSWsVqteyp0I0dZOR44c6QlmsRW8Pgef0/LP07aJXRl3WdFsNrvFRKJn9Fn3m6ZoZ4JkhaidRLEumTbLa5vz7zU/Px+6AoS1sev1upU4qevx2TxBSZAC43oFkcnnyyIJ4np7miZqn0X2feOUdl4VflgFQpgcskuCbbaJaUPIRjJC1YEqyhjOs0Nia+yYkB3dseJjwipA5t2LMj55VLf0pERwMmTkK2yM8UkqfqWIj7IZtsJtYWGhZ/97dt9xfktPF54ZUU8HqoyjtOchvpAhrH+CFZ2I/sohImK9Xu854LtWq+Hc3Bzu2rWrmwgZGRnBer3etbPn5ua8fN80ZDjtpILJZ/bB3mSfkdWFwTOyW2W5anNlSbvdxpmZmZ42Yvu1XC7jzMyM1cIr3+xLmRjD+Ph46NmPcd+F6IfvIINI//Fbhh0/frxnbq7X69baxDdZ9BVKghC5J4vtsNJweHxSgkkmUNF7BT8TvZdr75oEkTHPyyBvPOdpGaaNaq4kDpSpRJ6PiPomqo98qMxD9NeYN9G+viaU2QAy/+7BOCxS1TOPig3jy3j1EbYCWDTH8NtE+QwrR/xZR6IEOrulpw0bJo1V5CrjKO15SFTIUK1W8frrr+/pj+CAZpf1vwztdhtnZ2e7RWtsUiRIhMzOzvbMJUFSxEfSWlWRVlLB1LjzwX7jn5FNeIgSIjTnYs8WvGH9anMLXh9t57jdJkTbHIeNHR/fPyl8MQO/G0eU7+9yQpZQh5IgRO5RraJMgq7hxjuL7ITk8r7LLDKTRREmWp6oYHPwOXTokBMGvsn+sWE8JBmHfFCVvXbXrl04MTEhbUD6Sl4PKfTNcDX5vD4lzxE7CRC2wpff5qlSqeDs7Czu27dPGFhgZZites4bSVaz2pD/IhU38FWtrBMdBMTyZMeIbJhyuYwHDx4UJtBtBbICTOlA3XGU9jgUVaOzn8OHDwufw1cdyfYrvz3Wrl278MiRI31bZvn6rohmEtlZJRXa7TbW63Xh97RaLazX61LjzgcfUfSMor6yvSrOJZL2a9Z2rA/JNxEmC/l88x2SEBaH4X2OKPmkgqL8QUmQApFnBRdH0v20VVB1eHzdd1lE3GTBVyqIDI1KpeJt9VYYfPCYX4bpUvDEhJ6wuZxZdl930VYSzWYTd+3a1e2XsbExqaXEPpLXlSABvjyvr86XCUT7vAfjMGxJPztmDx061FN5GVQ95xHZJIgNeRLpXb74JC/nZCD2trGLxQxpwNswosA7m0D3Xa+Zev6056G4edz1AgAdkiSGfUWm8CTKLs8iqWD6Hj7EKkSFi/Pz88K+cuWZbeN6v/pWPCXC9Ta2AR9f4s+RCnyOqHndlwJAIhmUBCkItJVENs6YisMT9nxshSu/77LLE3LcZCFT+ZsnWUxSgZCXbTRcrwIVbSURBFX5BJXr400F0fvzgS1WJ/rqHOgGEBDTdyp8qHxMC9EqzUC+2C1NarVaZNVz1OqlPJBkOywb8iSyYUQJrlarlUkiPIsgQNgqOtd1ogoyq1lF7+7rvIFoVi+nHUA5dOhQbILK9fZWpdlsCnViHt5VNO7CVv3ZrPD2PeGpS9FWBuYZmTEXUNSkgk/wuqlWqwl9DtY25ceqLwV1RHIoCVIA2m3xAXqIxdhKIuuggIrDE+Us+uTMyEwWfHCENTTCDhb1FRMVCL7iekVKVFBVdGBanuCdNpHOsX1IoS4mAghZzR2uj5U0Cd49rL/YVZphwdhDhw5ZfIP0Sbqlpw15EtkwfBIk7SB4VuO1SAk5vl/n5+eFWy+FrYLxOYBgYhyl/f5Rybg82zABskkQ3+ZY33wHnxOeuiwtLfUULLK6UFRUVBR8G3MBfAxHpE/yUKDka/8kIWyVcljSg3/vIuu1IkBJkAIQVfVclK0kslL2Og5P2LV8lZerzkySySLMccnTxGKiAkGVIhg3JggLqoYdmJYXXD+kUBdTAYQs99Om8SpXQCCqes7b+BSRxZaeuoQlssIOvTdNFuOVv0eYPOYh4CVqz3a7jXv27BG+c1jVc1G3kkg7gBIVfC2CjpTdDss3G0Y07vh3ZbeIdKVvfU54qpIkvlIkO8+3MRcQtXODLwVgMhRpdxjVcedrASAhDyVBCkKRKtdsYcLh4Z1FkVPjolGZdLIIc17yso8von4Fgsn7BrhsfNpCFMQKZDGvVR+uH1Kog+kAQtqBLBqvHWQCKEXagogdc7xMBwEVF50xUcA7yyB4luM1CPrz8hic4eY7It3UbDa7CfRyuYxTU1M9CXV+S88iBkYR0w+gsIUMrA/H68jrr79eWPDgO6Lt9kSrz1qtlnfBrDCbQOQ3udanRUt4ys4HjUYD9+/fj9Vqta/gNJDD4O/ygM1zIVV9FRdWs+q+g+z3F3l3GFl89M188tVdgJIgBaJIW0lkrQhMTPii/gnbrsY1wzfJZMG+Z1738g2wMSH55vDZRBRU5RNyeW03Wdn0zahKI4CQZiCPxqtc4LpIW07wMiwKtPDzqQvOmO2VIFHPYfKe7XYbl5aWQmW2Wq3mxo7RScYVeSuJtAMowfezQVW+Kn1qagobjUYu55J6vd5ztiA7b7LnSdXr9e7PfZLFMLvL5SSDqt71zcbkYZ+f75/5+fnu70VJS75ANdiG1lXa7TbW6/XQldP1er37/FmPOR2dG2WHiwo200yApB14p91h5PFJN/mYtLENJUEKhs9bSSQJ2mWtCHTvyRsL7L7LYQaTa86MTP+wk68oOMJWbxHq+ObwiUjb+OCDqp/5zGe6TnVwJkgwxopqQPhqVKURQEgz+FDk8SqTBKpUKjgxMeF9AEGWsG2I+MA7G5y2/d6uVFEGqIxXWRkuauIyyXzgYxuZtjmyqOhlv5/tn7AVOS7O1yq0222cnZ0Vrh4PApezs7O5WpXk8vOr2jC+2pgi4vrH90KOYMyNjIwIz9erVCo4MjKCc3NzwkRI2jKrM+fEySFfsJlW8jGreZO3ndl382V3mEajgcvLy8LfLS8v52ZVlSw+2ly2oSRIgfB5KwkfnC+TQaB2u//gYr66zjXDUOb9g37kz8VgAyYjIyNO7G/uO1k7TCYd/rQdo7CqLHbffT6o6kKgMWvyZFTpjIcsxlJYFb3oHq7Jos54lbl2dnYW5+bmCrOVBKJfiTGRnhBtV8MmlNPUHSrjNYkM5ylwlxSXi5F08O15w/CpclUXldWsYZX6ruPyfKBjJ+bFxpTtH5/jMKI5vdns326WL2T0ZTvMMH0iWg2SZl9lNdZFdhKAH7vDNBoN3Lx5szBhE4yxzZs358onkMHlecJFKAlSEHyvQFA5c8IXRRDmfLGVn7zz5Zozk8SBrNfrwv34WUPDZVn0iayMT9MBhLQdI9FWEvzz5i2oqkoaTgVitjpM5x2ynEv48SraHlAUkLXdxrrjVTaBbvs9dUn6Dr4kskX6ny94YPV/mkFl1fGaVIbzII9p41Mb5SUYS/TC6iZfz/DhV9CLZLNSqXS3/soaXR/WJ39dRFLd4fOOHHxCYHx8vGeVhGiFSJY2jOl72pJN1XdIOuf6KovLy8vC3VLYJGO5XA5dKZJnbIw5X6EkSAHIy16USScjnxSBT86iiCRGYB4q7nxYhmlK/pNuc2YqgJC28en7mEPM7h3SrqxOE1+qFMOqssKCHpOTk1iv151oY9Gz+RbISBvV8eBLIluki4KfiXRRGjpWd7ySDOcT2XmS+j9/sDrB1/3vVbYhsvGMOluzueCvmywCCODf1efq+wDRyoi4BEjW+tSE3WQ7MZ70HZLacD6vSkLsT3gcOHBAmBgpIi6fHeUSlAQpAHmqek5qKJEiyI4kBo/PAWgflmGaMj5VjXtTBq8LjpGrZJ1kSKpLbTsQAWlv1WSijUVjR+Y8BVfaWPQeNF57UemrLNvTNVlSwcR4JRnOF0llgvo/f/i+EwIiereCXmU+semvp1EEEBD4tHzhKR989iloy/cV31827QmTBYC2ioxU3iFJm+dBJyKGJ3JcGEu24lxkw8hDSZCC4HPQmaXdbuP8/Lxw4uXfgxRBcnTlpAht7voyTJPGp842dKb6nxKZYmyvUpDpV1cqa3X0WtpzZ1Q/iqrt+HZzpY0DaLyGk6SvbPSra7KkgonxWgQZzotPEIdvwdgsKYoMtNu9Zyyyc2mwRZbrq88R/dPPqvOdDd8xbXs6DyuSAmRWgthKIJgeIzZ0pM47yFybl91hAg4cONAjhwcOHLD9SMbln1azpgMlQQhvSGLIkiJIjimlXQQH0uVlmKYn36RjyWT/23aMXKfZbHaNWVHf8NsQqN5DR5dSH0YTN17Z/ZbDxpMrbezKc4hwJeAn00Z5qKL0lSK8vytbFWaFbjCW3+YlwOdEgSsykJVeTlJA5zK+6SeZ53XFX0/zOdrtNk5PTwu3G2L9yWq16nT1PZ8AiToTJGubKw+rWU28Q9yYy9PuMGmvBFGVYZOyKDtX1+t17+U/aygJQniD7PK95eVlUgQKZDH55okiLcOU7VeT/e+KYyTClYBq2hWOpgy5IiRGdQiTJ1HFXZjs225j18erCwG/gLi+sv28tmXJFlnIsAtzB1sJGqbXbQfjbNgwfP8fOXIER0ZGhIkQ35NFLgQMs9RzefJNovSzC/qFJ+p5XZBDljTlpNFo4MzMjPD7lpaWsFqtOq1PWq1W3zatzWazz06tVCpW5g7bdpMJsipGdVFPJCXtYlTdvjBlT8rqSJfOiPQFSoIQXpBkKamPE6ErE1LayzDzRprLMF2RiYA4o8pk/7vmGLG4pF/YdmI/bIJYp51oj3176FYvZ9nGLo9X156vVquFJrZYvU77CWdLFjLiShCMT57z+sXUVhhZHDSchKTBWFHQr9VqOaPXdGi327i0tBQ6x1Sr1dTHPNvmbJKJbd9KpYL1el3rPnnyTaL0s0u2qczzIrplTwekWQTgml+XhHa7jbOzszgyMiJMClcqFRwZGcG5uTlr72GrfU3el7YljyeLbcldKgyWncN81i82oCQIIYXtgcUaSkGFM6tUyuUyTk9PWw8gqOCaEaiitLMKMrnUr2muBPFNJkz3v2vvz+JSQBUxXA5NGb46Y8634IMr+iWJjLnQxi6PV/45bLZTrVbrqSxnn6FSqWClUrHaTi60kS3SlmGXtkMRFRWJ5g6d59BpzzTmWNVgLFvlPDIygkeOHPF+TCT1p9IkbZ3omr2mQ5x+5pNatt/Vx8BdEQLIOrTbbazX68L+arVaWK/XnYqvZIFL9m9RbLhGo4GbN28WxlwCe2rz5s3aW3qZaE9TSVXSTeahJAgRiysKnjWUeKUyPz/v5MQrY9zpGulpGJBJlXYWMuKKHCKmvwzTJcdNxghIo2905Dptp8oVQ5N9Dte2ZnNJhmVwSb/4uAesS4GMMGw6Ea5vJWFzvLoiO2k+R5LVzFkgGgsmx4SuPJmcY3WDsfwYzUJvZCWLgQyKZCALPcTrRVYPBj/XeRaX5nUdZMdT1OqeLG1T3+w/RHfsesIvXJF1V54jKxqNRuhKj+XlZWNnmuj4DaZ9jqJuVZsWlAQhYnFNsfqSDU1ifKsaX2kY+Krtm3Ygo16v9zhFrByacJZkyWIZJqIbBnmSse9SICuLhBzrbLLjxMRh5DK4FMgQ4VvwwbV5TmY8udTGroz/OGw5EUFf8cG948eP91Sa12q1TJ4n7Pl4WWJ1HS9LJvrVJRlOG9eS1mmfa6Zrw5iw9U3p9Sz1RhZjwiVZbDabPYcrA0D33yZsXVcKanSfQ8WftOUjyz5vo9Fwwm5wzf4j/MIFf71ItlTWqMz/pmXCBb2eNygJQkjhgoJ36TlkSGpUqSg404abq+3rUgApq2WYiPYnPR+NqrSdGbZN+MRDcBZHVivjXNnSIuoZXQggyF7vqv6LwoXkg+t6ImgjkT6tVCqZJR5ceY6452P/HfQrn9w11a9h+po9O4XX1y4l1ZIStgLj0KFDTjyHaV2na8PoJh9M6Kas7bCsArJpb6Upi2ilTaATbc65Juc1E9+VZK53oWI47nkbjYYzdoPrNgzhPrb9dcRs/AEXfI4sodhcfqEkCCGNbQXvY6VGUsVlM9vscvuyz8YmQniHKatny2oZJmL2FYh8GwY/Exk3rho8aRoMWeynLgu/IoV/1ywON80CXQfVtypK33B57nApgc7iQpAqjqz6lf++I0eO9JwTwI69PASkwlbt2bCls5jDVGXdlC7WCdrYCj6kfV9WBkSFHFnpbPY9RatBbM67JvVflnOkLzaMa3aD68Fd15/PVbJsNx/sOh2KlixUnYdNtpNrejJPUBKEiIWdQGyexeGr8pU1SHUMVxNGr+vt67KzlBZZOjOu939S0my7tM+EkaUoxpHueya93hdHxiWn2NVKJX7PeRfO4vAlSIWYXb+GrUxgkyB50GsuVN+32/2HtLP9anIVoaqsu6BPbM+vaeqJdruNMzMzfbI4ObmxsjRte8+14iYRJuUwC5l2YdwkwaXndcme4smbf5YVWbabT3adKrbnxCzRfVdT+oTGfnpQEoSIxLUtV1w2UqKIC6qZMARNBO5cb18+cCUKlOSFrJ2DPBo3aQSzXVsJUhTjSHc8yF7viyPjYt+72na1Wq1nZQHb90HwLau2cinoI0tW/crra/ZcAB/aKY6lpaWeZDlbdZ/l3NFqtbBarUbqvmq1anyFj2wfumKLuKBj00rIN5vNHhng+yaLs81cXaXHY1L/palLXRk3SXHBbnBhrEdhum9d9/VNkaTdTK8YFM2xst/nKkXZfQDRLZ1QlPGaNZQEISIRBfwmJzt74bPO3NLSkvR3Fm0wxxl4JowbF4zILGg20z1A0RVsOTM+BujCSGtMuHImCPs8RdGnun0ad71P8p+GjjAhS66uomHPmOD7Pqtx4muQCjH9fhWNTVF1uGvjUBY28cCuFmTfu1wu48zMTKbnSaXl3OvIOgUfOqRlw7ikh+r1ek8ChH2WsDOBbGBS/6WlS10aN0mxbTe4NCZEmAw+uy4npnWujF2v0yYi2eFX2rGy40Ibq5BGUbTr/qvrz0foQUkQIhaT1WuuT76mSXvylb1HHuC3NREFSlxwlkxgc5yYdLxtGRBpjwneIWHbKYsKyiKj6yyHXe+6EyzCpJyb0DmuJ+NtB1p8tX/S7tcoOeaLHlxJqiUl6PtqtdpXNBS8//T0tNHzxOKeJ8252cQ5TkUOPqRpw7ikh1x6ljB8WQmC6Oe4UW0TG8FyG5gOPrts66alD9IsRhU9M19AHPSPC22sCt9GYWebJVmN5LruJ/INJUGIWEzuHevy5GuaLJZhFqk9XdrSJAtsOjOmtlazYeBkMSZcdZZcIg35TXMliK8Guamgiu64cX1MuJKgySL47FNgKEruREUPLsiSKj4GKHUo2vuaIgsbxqW+celZeEzqP9fnSBuotomtYLkNTAefEd3YDk9EmrovzW3JRTqM/b5qtZqL8c6+k2g76CTnYRYpfkW4CSVBCCna7TbOz88LJ5CkRqrLhqBJYzyLoJqvgbuksJNl2EGplUoF6/W65Sf1HxtBVZ/GHRlu8ZjoA14m+LF+5MiRRPOG7Ko8V4MxUagkLU06ba6PCdM2h6tyYlr3ZaHDw56ZTYKMjIwkHu8EkSU+2TCEHCbnNdfnSBvotEnYtey2l/y1Pm/paTL4jGi2uNU0acSIZP1a00kwF5NquiwuLkaeh0mJYcIXKAlCSFGEiSENxyOLYImrARmT+No3vmHaIJENPPvUt749rw10HX6+jfkkaLDqq1arSX2frQBEFv2qMpdGybDIwdH5PttBO9N9T++KPd+5f/9+bDQaRrc+4sd7cDByEv2RJ31KuA3ZBPnEZL+6th2eC+i2Lz8fHDlypGe3AD6pLtNfLsYmAkwGn9k5lv2w2527Urii2w9J/VrTSTAXk2qqpCE3LpzXRxQTSoIQsaSVqXVtYshTpY6OA+Wq80WVdnrEtV+9Xk9F/uOMWR/HHcliPDrzBi8TtVoN9+/fLzz/RzbImnUbZ3FP1TaWGXNJ52aX5w1bqyNsYGPVi0uJprzqU8JdHeO6TiDUaLfbWK/XQ1ci1Ov1RDLXaDRwZmZGqJuWlpawWq06p5tc374xzGYJ2zEgahy6XJWeRvDZZFIlDUzEiJLq5iIU/OpgegVRYK+Jzn4rqr3mqp2TRygJQkSSlnHv6sTgshEkCwUQ4ima0yrTr7Ozszg3N5dK36e5F6vv5FkWdfS8qMqPTYCw3yFjGGZtWKbdr7rfHzXmkq4EcR3Tfe+6vrJhX9lIvoT9Lq/6lKdIzrLrtqnrOsEkIrkLfiaSOx9lMQ158003uT7mAnj/Igiomi4Msdk3poPP7HuZ2F7LNKZsmCQybFqH63yfq3N7s2n2LJlWqyU8+01U8FYEfNG5eYGSIEQkaQxI00rUNK4maGRJY59VVwxBk7guhyaR7dewqrfgO1TaQ3Y8+T7udMhzAEWnmst3mUizX03MzaL2ZSsB8ySHqoQ5o81m/8HdLrWRjZW2rozXPOvTgKI5yz7Ypq7If5qI5C74GbtVZSB3vspiFgWArusmX8ccG0CVHYeu61OT/irfryYOWjeJjYIK07Ku832uymIa+oC3o8fHx3tWhfDb2vmOrR05CDGUBCFiMZmRbrVa3YmcrTbgqxJmZmasGsyubdWVFB0jwicjXQfTlTWuY6Nfk97T93Eng69BVRVMBIV8l4k0A2Mm5ma+fcn43iDOGRUt4XcBm8FYV8ZrHgPS7HjnAxJBECzP41XGngjTie12G5eXl4U60WRlrSvynxaiQBhfzVupVHq2qvRVFtOymX3STS77g1HPpjI3u1p9n+Z2k4Gvy7ZVuVzG6elpK+9rK/FmOvGg832uJh/T3FpWlLhk55I8INt+/FmXLuncvEFJECJVeKOi3W7j9PR0N/BcrVa7A5rdn7JarTpRheC6gRqFznvkpQ2iSGOPVdfJsl9t78XqImFGULvdxqWlJaHj5uNWEohmnOe8yISrgbGwlSD8VgguVELaIEqHiRw2F2TTZtDKtfHq6rhTQTR38MVDU1NTPdXCtmUxDaJkLGp+DQpe+OCeSd3mmvynhUjH8EmQvARu0upTn3STi3Lt49ysShorAwKfI8xWYGMzWWJzFYTpJJjO97mafDTZRnxfi7a1y5PPkSQm4qLOzSOUBCFSI8ohWVhY6El4uDD5Iro78YQRNyEdOXJE2dD2yUiPI6ydXD8YLg2y6lebe7G6isgICluRxFbH+GYImqhkyotMuGrMito3KoDqazJOF5WAn62tFG1WELo2Xl0dd6qE9e3Ro0exVCoJ3zOvYzbMhglro6Wlpe78Wi6XcWlpCRHNjgvX5D9tRONLZRsiHzBtM/uom1zzB8P8CzYJMjIygkeOHMnFODQdoHd1tQGiuytyssZHPZGUoK/D5pNarWb7EZWQ2fEhzk5wTefmEUqCEKkhM8m6dACry0aBiLggc6VSwZGREaX2zdPkK2Ms8x8XDoZLg6z71cZerK7Dv9f8/HyPHhwfHxcGWn16f91qrrzIhKuBsby0b1aI9CY/T/B2jY0tPW1VUbomT66OO1349zpy5EifjRc4y3ldvRVnw0T1fVrnHbkm/2nC2nR8kGZ+fl6r8MpFTNvMLusm37Zq5Z+X314nCKDmcRzq4uq5E0UjzUJWX3BZJ6pgIjYnmneq1Wq3gIOnSMlBk1AShEiVOOXGH8hlU8H7ZhTILgcOW5puIlPtAzJ7GI+OjvYFEnw1ln3rV9/GnQnCgqrsePU5CYKoV82VB5lwOTCWh/bNGj7gF1Tfs23owpaeNqooXZInl8edDlEVk6I5hJ3rfX1XHlkbJixwLdr/3oTt45L8p0ncWQKDg4M4PDzc1762Vsbpohuc8ylIn4eVFUUZh6agFRd2SbOQ1RfyaK/JvFPUmUWieUd0ljL/96TbkkNJECJ1snZIdPDNKBApSz54KlLA1WoVZ2Zm+tqbTQ6MjIw4ZaTrEFVBOT4+3lflZPNgOB3CjCrX+9W3cWcCPqg6OjraJ4e+bodlAt9kQnT+VTAW+SCQC0arb+1rE5ENw84bfIDM5paethDJU/AzkTyZkLGwe+7fv1+4lYIL406FuL2zg8/OnTt7nGvW/vMFUzZM2FYSaW0xUQR9ygZ32FU1n/nMZ3radNeuXX0rcGysjNMhKpDFbhvJ93nQ1yI5ZnUTb9fZ1k0yxWKuHXhvY84hCFPoFrL6iG9+kipRCfSobSNtbulZRFJLgvzt3/4tfv/3fz/u3LkTATrLZKP4+7//e7zppptw69ateNlll+ENN9yAv/u7v5vonpQEcRfe8eAPn3Zdwbvs4IgCNCMjI0IHOPjb6elp4f7vtVqtmxzgq899npAQixHICptAfexXl8ecKlHVvOPj433BBJ8PRg9Dd4WIizIRdf5VcPgkP77y1q95RdWZKTppV+VGfX+tVgtNHvs47vgKctE2nvy5IKK53gdM2DCuFF65Ol/pwAdjDh061FfVOj4+jq1Wq2dlXLlcxuXlZduPL03UnB6c4cYXSrFyWK/XQ+U4bJWWbZmIKhbj/UnbfgOt+pAnj3ooL6gWsvoY8C6anySyQ/hVunzss1arheo1fqW563FTH0gtCfIXf/EX+MEPfrC73VFcEuSxxx7D++67D2u1Gj7zzDP4mc98Bjdt2oT/43/8D+l7UhLETUSKQHT4tKsK3gdji08yHTlyJNboiQvuiBSqrxNSYATy7RQ4b2zCw1U5lMVWv5o0tH0Yc0nhqwDDjN48B1R1+tVlmcjjkm5Cf1m7S2QdCEl7TBRtzPHVoWNjYzg0NNT9N7+l5/j4uHdzZICODRN1bVpngohweb7SgU0CsPI2PDzcTcTx2yb5vLJatNJDVCgl0ju622nZICxw55o/WDT9r0pe9VCeUClk9bHPijhmw2JOUe9fr9dD22BpaalbwJzXOEGWZLIdlkwSRMQP/dAP4Y/92I+F/v78+fN47ty57qfValESxDHysLed64o7rOpNRinqXOsLouAz67jFVXS5IodJyLpfTRvaro85FURbCzSbTWw2mz2B1HK5jPPz8847yyro9KvrMuFjwEMEVQ1u0Gg0hNtGInackYmJCS/2bLYVCEl7TORlzMnAzx+sU80n4oL5hd8OzCdUbBiXtpJwfb7Sod1u4/z8fF/h1dLSknBlnK9ngoSRRO/46GOltWWcaYqk/1XJsx7KgqzsYZVCVh8p0phNK7nli372AWeTII899hiOjo7iXXfdFfo3H/rQh/oMf0qCuIPunqou4ariNvFceVeoouCzzMHTLsphErLs1zQMbVfHnA78lh68HLIJubw6KTr96rpM+BTwKMp5CqoEbVGtVrtB04CgKj84BNhFWWSxGQhJe0z4NOZ0COSRT3gcOnTIm4OLk5LUhtHdwsi3JKAtosZc3v2JgCR6x6c28U2f+va8JpEN0OdFD2VdoJNV8UjRZLgI76uzmlX2e/PadlniXBJk165dODw8jIODg/iRj3wk8m9pJYjbxFVRVqtVrwIqrikfE4EN194pDVSTID5jul9ljM80DO08yie7JzRfHZKng+GiyPPqNRcCHnHjtdFoGDl8OM8kObTSh+pKm4GQtMeEC2MuC0RzR7DCgU1cuiyHsqjq+TDd1263cXl5WTiPplnw4vp8lZS44E6RzkiS0Ts+9b+vwfKiVNGzJA3Q+ySHImysZpWNsURtXxQnb76OOR6ZGAH7N/yYnZ+fz8W4REyv6CgvsuISziVBnn76afzyl7+MBw8exK1bt+J9990nfR86EyQ7ZIMsYVWUk5Odw7kbjUYWj2sMl5xtXaOgKAo1ajus4Gd5CjKb7tckcpaGoe3SmDNFEZ02Hp1+dVUmXHA0Zcbr9PR0z2rMqAB/XucFGaJ0qY97NtuQT1oJYgaRLPq2tawsebNNXZ2vkpIkMex7n8Uho3d8kmNft00S9YPM3NxoNLy2uVX6y2c9ZEs+48Zw1EHWcXOwr2OOR8bnmJ2dxbm5OZycnMTFxcW+MevruVEi0kjY5UVWXMO5JAjLRz/6Ubz++uul/56SINmgG2TxdcC66GyrLg8tmkINOxj9+PHjXhi8sqTRr0m/06Sh7eKY0yWP75SUNFeC2DrTwpWAh+x4XVpaCn3eolX0RhEmb7VazcsgSpaBkLTHhCtjLm3ytLVsHHmzTfM034f5fkVbQSijd3yTYx8P0Bb1g8wqzWq1GrpLhavvKkJGDgN7WKSHfDs3Ksn7ilCdD6N0uM4493HMiZBpA9anCFavTk5O4qFDh4TndfmOaTnMi6y4htNJkA9/+MP4ute9TvrvKQmSDSaCLL45AXlztvOkUFX2RfXdGQ0jrX6VlX+TbezbmLO1ZZhv6LRBmlVZOrgW8DAxXqOC5Xk4PD3JO/hcQcmS5RyY9lYSro25NMmTvRZHnt41j/N90c+SSqLXfJNjl+b1uGep1+uh/RC3KomPTfg8d0TN6aJdEPhkEZu09AGZ901jzEXZgDp63qUxp4NMGywtLfUkPA4dOtT9GzYx4sO4s0FeZMUlUkuCvPzyy/j444/j448/jgCAv/u7v4uPP/54d/ujD3zgA/jud7+7+/d33nknLiws4Ne//nX8+te/jnfffTdeeeWV+MEPftD4yxD62AiK2iKvznYeFKqs0VOr1XLnjIaRVr/GjWWTDr9vY66oq+OSotOvSauNsmxjFwN3snOvyLmz5Whmhe0t/myQdRVl0i0SkspSHuQwCXmw12TJw7tmsZ+8S+Shz2RIoneK0iamMTF3VCoVHBkZycRfsUm73cb5+XlhgH55eRknJiZ6EkLNZlOYKPLJ7whLSGRxFkOYDZgXO1GHuDZot9s4MzPTTXiwfxNskZUnm41wn9SSIH/913/dI+TB54477kBExDvuuANvvvnm7t///u//Pk5NTeGmTZtwy5YtOD09jf/9v/93XF1dNf4yhBl0giw+IWv0mt5jlAzoeFwOjOaRrIxPW2NOFZn3z8sSfB10+lW22tRWwtNFfR0394rmcFHVINt+eaiiLNpqVlv6SaeaV0aWXBxzBIGYfhIwrWem8RRPu90OTV4Feo3aSh1TCcQjR44ktn98mtdlgsp79+7tJoP4VTE+nomZZTFe0u/zPdZlgrg2iEra0RyjD83hychkO6ysoCRI9qgEWXwyMgLiFEtwELwpZ6ZoVY46xBkptrbIyRtZV4dnPeZ0ybrS2ld0+rVWq4U6bUXb+i4OFWdRZj/tvCQH4sariWSPK+Pd5ZVqUf0gOtwWMX+60hU5IcySdhLQ9LOmbU/lRc7JP0sf3WB2EYo0ZbcXOnbsWOgZbz6NO1mZMGX/JynuI59jwz+LkjNqp/SgeSk5lAQhlMk6I+8ypivhfdsOyDYyyzDz4nzZeA8XA/wujhEy8PQx1a8mnVvf9IdqgL9Wq/VUDbLvzBvReZD1NBO7rjkkMjJsy2YT9cPIyIgwCZI3Z841OUkb33Rp2rjiJ6VtT+VJzmVX1i0vL4de78N72kbVxsg6WG4DVgZlDpr2OdmDmFw/mXhf2m5bHt53EK044gttithOaeJiTMR1KAlCKJFFFaVv2FyGyVNER9N3Iy8OW06kyxOrKwEElrTksEhjOqsqQBl8C97obn0ku9oGMR86N60D4F3Wm1HYCgzx/TA+Pu5d26mQhZy4Mne4rktdKDKxGYxN057yVR+GEdVWLm596ooOSEpSG6MoW12yujTY+optp3K5jNPT07kpWLF1jptPq/ls0Wq1hKvIm83es2fYZJ3P7eSyLnUxJuIylAQhEsMaGWFbZkxMTOC+ffuUjUCXlUwUpo0Nle9z3dFMA5llmL5jy4l0XZ5cMvDTehbX+yAN0q4ClMW34E1W5+m4NO5USfsdfHVIsk5uifoh7mwaV9tOhTTlxKW5Q1eXpukX2G4nVxLKaepEX/VhGGFt5VoRoG3ZViUt/9fW1o+mYfUhrz/m5+etru5MAxdXs0bJW5KCIp8J2kBkswX/Hh4e1ooJuoIPujQPvllWUBKESEy73cbZ2VnhdgVB5ndkZASnp6exXq8LvyNqAvBByURh2plJqxLGdQNPFpllmDry4lJCzpZB61IbiHAhgEBVlOZxRff55kimPV59aw8RWb2Dbw5J1s8b1Q+sU+1D2+mQVru7Nneojru0/QKb7eSajkjTnnLtXXUJayuX5kjXdIAMOu3n23mCuviSjEsbm4WC/HeyiYFardbzOx9lLI6gDcIKWmq1mvNxBBl80aUuxER8gJIghBL1er0n48sqAvbnKorAFyUjwoWVIPx1tg3wNJFdhsnvby+Liwm5vDmRurjQHlnorKKM6YC0qgBVx6sLcuYCPs/PAVm/gy8OSdY6RqYfgm2xXG87E6QlJ67NHSq6NK9zbB76Jim+6MM44trKJZvBNTmLokhbBOoS1a8ubsuWJi7563mwk1XJi34Pw3Vd6tK84zqUBCGUSVMRuK5kRJh+Zt3vi1OEeTACZZZhjoyM9FViyOKqIZN3I0MWV/REIIfVarV7ECH/jNPT09hoNLTuUxTjJs0qQB29RuPOLUdTlazGK/t9ro9ZG3NdnCwFq4pdbzsTpL2lp2tyqKJLs5jvs2wn1+zLvLVvmsi2lYqcp2XD+NL2ebAxskD2/Lfl5eXQ6/PWhi7FNVzxT7PEFx2ji+p70ip9t6AkCKFFmgrPJ2Vq2pkx9X1hBniejEyZZZg6uDap+DQu0sS1AEKj0QitulpaWsJqtWpkTOU9EO9avwbQuNvAJUdTlSzGq2tzRxS2bIIwWQqSID60nS5pb+kZ4MrcoaNL87RSwSU7PIt5l0308WPahK2eFSqHb8vKatoy4YoOiCMPNkbauKQ/CDFpzleujZE07V3X3hUxuS6VGa+zs7NKxwgEv3fRd3YZSoIQ2qRpVPlksJk0Rkx8X9Tk6/IBlTpksZ2EzQCoT0G1tHHNAch6u468BuJd61f2vjTu8kPa49VHhyRsXm+327i8vCwcc2nM9z62nSpZbOmZZpFIUkzo0jydWeGKLZ32vFuv13sSfeyYZldthwWA0kC17VUO35aV8zR1X1qy7YoMF5G02576Vp805ivX/KQ09ZZr78reN4kulWkj0VnL7N9EvaeL7eQ6lAQhtKCVIBuYNhZ0vk/G0VR1Rl1VtGnLi+2EXNqBIR+NXdeeOc1geZ4C8TIHV7rSrzTu8kvalWsuzpNJsfEeWdzTlXGX5paeaW8XmhQTujTKztPt0zzNsSq0222s1+vCNmy1Wliv13OTBNHVMVGytry83JMASSrnachhWrLt4zzniu53HR/71jXSiku4ViiSpqy49q46ujTqWt5OU3lP0m3JoCQIoYxvAb+iKIckE0Za2ewsJyTRvU07ry4k5NI0Mmwau3kbl2nISqvVinWqq9VqpmMuzSpKlxyrvI47okOauj0Pus3WfJ9m27k27tJarZH2KpOk6LZ7lJ2newiwi3Zt1qQ9LlzaDsv1ymWT81Ka7+rbuHFN97uMb33rGlnGJdL4/qSkabO58q5pF3K48p5FgZIghBK+GVVFMnySvqurB1TKkrYzw+7rK3K8s06EpGFkpN2GYdfV63Wcm5vL3bg0vWqo0Wjg5s2bsVwu4+LiYs/vFhcXsVwu4+bNm40c5CyDjj710bHycdwR8the5SeLraSKS/O9CVwed6ZlMavzRmRRlWGZPiuXy8p9GjWnBcF7UTv5ksyUoWhbeqap10zoalO6IG3/16f5wUXd73KxhE99G0XWbZyVnLmkT9PGhXc1pUujdLsL71kUKAlCKOFbdayLhk+ayE74OsrWFUWdliy2222cnp4WOtZB4NlGBX5apGHsxvVNpVLpBmjyMi7TGBeurQTR1ad5caxMENUWor1hEe06xi477Cq4Mo/FYbuQw5d2ksVFHZRWG7MV+Pz3+jJmZeRf9RwG9h68bmO3FONXKvhcqBFGFuPCpaSzq3rN9HOlPW+72o4iXNL9tud1GXzqWxF53dIzwGSy1HXb3oW5w+S2m2HjyYX3LAKUBCGUSXu7AtPf7ZLh4wIm2sMVRZ2GvLCBZ7YCn688nJmZccI4iEOmjUwbuzLBctF+5b6OyzR1jGv6S/d5fHesTCJqC90D8tKYm31w2JPgeiUwf43tQg5X5ntTuKSD0tbveeg7V20Y3wo14khzXLg05gJcGxuu2XqyuNaOUbgih77oF58D7S5v6ZlFQF32WV237V0ZszrI6PY8vKcvUBKk4PiQ+TUJKZcOae9rmBeazWZkZeHS0pIXYySJgWPakSnKpF+0rSRMPI9PTnPa8G0xPj6uJEtpOjO+OOwyuL4nvAibwTHXdI8pXNBBaY+rvPZdGDZsmLyRxrhwsR1dGxu+zrGutaMMLuh+RDfHRdjz+Rpod7GN0zwjK+k7uax3XNuWXJWiFYX6ACVBCozLmd80kzOuGD42cWnydR0fjXseWQOHNTRMvqtMG/o+LrPSp661k+rzuDiuXDhzIfioGsNZBlR91v2+JotsjJu89DmPKzooTVnMa9+FkVafuiIrWZDGu7oYZHNxbLjsl4cR1Y6qW3q6un1XWs/lqn7JU6DdtTbWaY802tJVfZiXbcmLuD2461ASpMDYnpDCyMohdGEStEmaB1QmlRvXVyS5FnhWIc7AWVxcTNUA8uUgMB1ZdNVxSwvV53HV2LYRfIgLIITt5a/6nTYDgmmMD1fHa5p9kOV85KqdiKjXf67poLS29HS179Ig7T7Ngx0YR1pt6Fpw3+Wx4bo/xBLWjrVarRvQq1QqPe8T199py4qqjKf9XK7plzwG2l1rY9dk0TUfs0jbktfrdZybm3NmjiwClAQpOLYnJBFpGacuvquPmJ58XXOOeFwzCnQIexd+qalphzCqDV0aly7LokvtpPM8rgYfbDyXzD2DbbGSOm1p662kzmQaY8vl8co+g8k+yHo+crWNdZ7LVR1kGlf7Lg1oSzF90m5Dl4L7RRobaRLWjq1Wq1vAMTIygrVaDRHtVbmb+O68rfCMo9Fo4MzMjPA5lpaWsFqtehVoj7qvTd3kUlERopuJojxsSy6DS3NkEaAkCOHk5Gs64GfT6c2jUjP5Ti4HJFwLPJtAZODY2o6D3/LHdt+7KouuPZfO87gcfMh6vCdZHq0yN6flzKjYDGnIsGvjQoTJPrA1H7low7img1xsI0R3n8s0tKXYBqp97vLcnAZFGRtpE9aOgQ2jMm7SGnMubgXtYpA3aKdqtYpLS0t9zzs5OYnT09PYaDSUvj/rQHtUv1Wr1dBkT1Z6z5XEg4vxQJefi/AbSoIQiOiOAmYxqfRsGfdFcypUcdHJ9CHAlpSsK2Fk2nBkZES4b7Ct8eGiLLqmR3Sfx+Xgg41Ke9MBhDTfQ2d8pBVAcG28ip5Nt8KP1aXsQZAuzEcunKWTtO9NPrNr+rmoFG1LMdH7BrJYqVS61fcBPs/Nrm2lSMihY4ekZcPo9rvJ53J1u588rXqReRfRmRNZ6XhXAvwu29KIbsYpCb+hJAjhjAIWYVLp2TB4ZSbfarWKy8vLmT6Xi7gmh3kLbNgwcGTacHZ2Fuv1uvD6NOU/Sh8ES71dkUVE9xx2154H0dwz2Ta2dR3QtMa6Ccc4DT3v2tzBP1OSPgjTme12G2dmZrBcLuP09HSPLNucj2zPky70vcuBckIP2/Kd9Ll0tiFylSJupZgndOwp27ZYGKaeq9129+DnvBStyIz16enpyNU4adkTriQeXLdhXLDziPxBSZCC44oCjns2n5VeVBu7sAzTJVwzeNMK9GYdQKbt4HqRMYqnpqackkUdXOwD05gKargw77h63oGpNk5Dz7s0d6S17zjrpItWivi2LZUpXOh7l21pVaLmjXq9bqV4wQZx82ej0XCqwCpIggB0thz1XRaLupViHnBxJYgupp+r3W73ncvIxwlsvbPpVS8u+6EurlDJSgelvf2ZDnm0rQg3oCRIgXFJAfPoKD0XA35hk2vaB1L7hKsGr2lsVKBR1VsvSZZH+y6LRel7mT6tVCqhgWfeCbJtbLu6r7tLW0mk+Z06uLjveJrEFXrwTnWACXvMpb536Vl0iZLhWq2GIyMjPSsNAmTk20UbXRWb82vUuGPPW/NdFhHzU5UeR57Ghk77utg3aT+Xq/OHyVUvrvsiWRZUuNYejUYjtCg32B0h6/5Ju6grL7qWUIOSIAXG1cyvawdemiJscrUZQHAFvsrVhYPhdAmbYNntCrJMfNGE30vUuGOXprvifKnicrLbNFF9Gnf2DL8c3ud2cnWsFyWQhehmsihNRM+b9kpXF/vehVUpJkiy0iAvNroKtufXKD2RF1kMyHsCPW5sVKvVvq0QA1yz4dNaDWnTFsviuVwbs2msenHRNkW0owtcag8Xx11a9kLe7BBCDUqCFBwXM7+ubgWiQ9zkaiOA4AquHgynQ5wMVyoVHBkZcSp4U0RE487mAXlp4WKwMC1EfcpWxYb1a1H0rS1oS5PkuBYQiYN/3vn5+dT6x8W+dymgagLZlQZJ5hQX+00X2/OrSE/kTRYD8ryVosxWiOVyObRo0SX7RMePdzVAmfZzuTZmbeu1LKFi1A4u9nkaiaI82iFEcigJUnBcVQQ6Ss81JS77PFkGEHQwPSG5fDCcKrJb8+RpuwJf4cfd1NSUc86XCVxzsNIkLigUpoddqsrKG3S4bTJ8G69hz7u4uJiKPeZa37tmd5oiSg5VZTSPbWVrvMok/fPQvoj5XwnCPw/fdzIFOvV63RkbRseectUWS+u5XNOJrsaG0iBPyUcTuKYT00JnzLmqn4hkUBKEcG7yNYErSlzWkGDPBskigKBKmksTXT0YThWZceVKBVpRCVuBldeqnyLIWxpBO8IMaTgO7XY7NPDTarWwXq97N2Z9s8ninpe1Y0yOO1ccUdbOC9seqlKphB4k7jpR84bqnJJHXZz1/CoadzpblblMGjrRVT2rmlCu1WpOJYYJOVxMOLhWZJAmUe+6tLTUU4jpQt9kQRF8RUQ1O6RIYyPvUBKEQERySNJCRlnye9FnFUBQIW1jLW9ySAFZdyna8uciyBslHotH3hyStObYtBIGss87Pz+f23HXbrdxdnZWeO5Qs7mx/eXc3Jw3chiQpg2TJ12c9fwaNu6CQ+uDJAg75n3Uh4iI9Xo9dDvLiYmJUJ0YpddcDDyzyJwhycuZ6+9EiHHVhsnjSp4wop432BretWSpKnF9U6vVcuMryshhUjuE9Gx+oCQI0YUcknSIUsLLy8tSh/G6FEBIewlhnuQQUX1rHiI9irb8OQ15c83JoS3oikneHBLftg1LWuiR13EXFahlf+6LHCKmcyaI6Lt9lwkb9lzUuKvValipVIRj2sUAZBRRCcZarYbDw8MIALh3796e94rTa64Gntn7h42NKB+JfAs/cc2e1sHlsaVKXuarIp1XmqZt6qKezZMOyQpKghCImB8Fj+imcgrD1wBCWksIXXxXHUTtJHNIs2/BEt8o0vLnNILELjo5RTLuiV58mvNlMO3MpJ0ooirKDnl611arFVqgo7vlUp7Gq80kbJQs1uv10O3XfAqIRG01x8rhddddl7jtXQwaxY0NmZ0BfPLnXeyDKHx7XhvkrTAlIA8FmlF9k7etFGXkkD1nyedCDhd9ch+gJAhBDollfHWq01hCqDMhuUbUuBJVtbHX+DZZ2XIM0lqu7fK4S0qj0cCZmRnhcx87dgwnJiYSV4y6qmfD+rTVauUy8UgO+QYuOSQuYsPOc1VPqFKkQo5Go4GbN2/GcrmMi4uLPb87evQoAgAODAzgI4880vO7OBumiDLhWkGAbzZmmiuSXMKkj+RDwNY3OfXteW2Shr1h097Nk31ZpNhE1LuyulTVDnFFz+bNrsoKSoIUnLwNnDwZKS73japBkPaE5ApRVZTB+0xMTIQGbH2QzwBbYy7t++bB6A3aSHTGSbB3+PDwcOJAFvs3PgQf8jQvBOTxnXRxxSFxlax1Wt5ktEjnn6Rlw7gqE3na/95l30GVKN2VB1sN0dwKZV/awzc5ld1yNQ9+nQlMyqHNecMnXwdRbj4K65tarebUXGYC0btWq9XQ4kBZeXJNz/ompy5ASZCC46pDEkWcgm80GrlQ4q72ja6iTWtCcomoKsrFxUUsl8u4efNmbDQalp4wGSbOtTHtyGThQPkeVE176bNrRmAUPq5WisK3AAJiun1AsihH1jrNtQCxLnH2j8x2Nb6QllPtmky4amvrkMeASJTu8t1WCwgbG+12G2dmZnoSIAGsjLKHGvvQ777JaZGq6E1galzasnddtbN19UQez1+NQvSuOnaIq3rLJz/IBSgJQjjnkESRR2clCtf6xpRBYHpCcg2ZKspqtapkOGXdTkm3AMnSIEjTEMmLMRHVRia2kiiSIZ2ULFcruWSIi0izLagd5MiLTrNNWDuyCRDX5VCWvMiMi4UcaZOXvkMsxkqQOOJs/3q97mTANg7f+k/0vHTWYz+m+9WGnedizMnEirGlpSWvxpwOpuXQ1cRYAPnk8lASpCDkJcDsuvLJOyYMAt8MXlXSMNhsGGSyY86WUZWGPPkUVJUhrQBCUcZyHGmdRaKzrN21Pkhr7vbNJrC1pUbedJpteEdzfn7eKzlMgu9OtcuFHGnje98hFudMEF3S8g+yiB/4Jqei5y3SHBsnE2mtSLJh77oWP9M9OyiPxRph5CUOE9w3L/6gK1ASpAC4mMnWoUiGhovkcQlhWuSlAkG232w5Mvx9jxw5oiyjvgVVZTG9lUTRxnIYcfNrpVLBkZGRxO2UZN72JYAQJTOic2sCfDxjIIqodkhjS4286jRVdIMaonk9b1t6BuTBqXa9kCMt8t53Jrb0zBumA7ZZzK++yalqUZFrwXRVktq8pselL/ZumsT5X2HbcrJzXN5tQdqRo5g+eRIoCVIA8ugAhxkaeTzUySeKuOVAHKYNNlvB5zhHxZYjI7qvTiDRx6BqHKZXguRxTlFFtrKfDdiYbGPfAndpBJB9DC6I2iGtLTXyqNNU0W2LNBJ5rpKnRLfrhRymyUvfRY3XWq2GIyMjODIygrVared3RdNraaFr69laMZAWMuMqbLvnvMzBMjKR1vkoviXM0iSuLfIuh3Hk6WzWpKt/iuyTJ4GSIAUhLwYxi6j6Oy3l7mOQJWviJtdqtYqbN2+WOrArL6RlsNkyBMMCBC4kZoL7mqgOzNN4j+ob1a0kimRIy6DqGOt+r6/L2lW2EqpWq7i8vCz8Pt/GZECWW2rkSafpoBPUK1LyN4/v6mohhwid8Zq3votqi3q9jvV6Xfi7Ium1NFGdk2yvGDCNTsFL3irw44oBjh07FjqH1uv13J/9lhUyPjk/lxXFFkxzJYgN4sZcHlcipw0lQQqESwa+LlRF6R4yBmIeA1lhpG2wZV2xGKY/bBn3SbZJKKqxnOZWEkUxpGWJml915l7Xxp0uYe8TldDJo4GfhrzQmJRDdTVHvV7Hubm5XMlhGHm1e10r5BCh2/Z57TvCHipzks0VA2mgu/Wpr0UrYYhkIi1bLW+JXROo2NI+ypkOLs3rJqDkllkoCVIw8rDUO43K5iho8pUnbxOOKmnLTNYJTRcrEJI4JL4nfVWhrSSyJa3K/rwsa0+ysiUPCZ8wZGQiqa3mozzYRDWAMzs7W5jK87w51b4klE3Yj3nruyhopUg2pLGa1bctrMNkrdVq4cTEROSYDRLsWfpuaaOyqldFl7pq39jSs1HjirZF6iVPBeCI+YjjugIlQQpEHhRBWvuvx0HBfXnyIGe6pGmwZS2LLq/wiTNAjxw5YtxY8C24QAGCbBDpPROrE/NS+SMb2JufnxeO2bzMwbbPkCmS8xtHVgGcIuKabnKxkAMxvJ2CQo409J1rfaMDFXpkg45fVwSfMMn5A3kJYma9EsE1vWUrMRNl6wXbP5XL5b4VrUXWeXkfc3nSpVlCSZCCkJcAguykoxL8THKAGymfaPIy4eiQhsFmI9DlagVOHGkYC762hSquOR2uEjW/6mz3kJd5G1Fu7ExPT3edONGYzYMDoLulRtS7+iYvNvVL1gEcF0mr/V2bJ10t5LCxmtW1vtElzS0/iQ5prWbNE7LnD+RlJYjqql7f3jMKW4UnMuevTk9PC3V4Ef22PPgNiP7Z9z5ASZACkLcKwTSSFWkmV9LC1QBlXiacONJs/7DvbrfbODMzk/nh8q7KWhhpGQt506VR5C1YEoeqjMtW9odtoaDzvb7JWlQbLy0t9QQRwsZsHoIpUVtqpLlyyCVs6hcK4Jg5dyJMDpeXl2MDglnqLlfnMhkdPz4+blTf5XFeiRrPaWyRXCRMyIsvc5IuSeYVn2VRd1VvnrAVmI7zVxqNhlc+e1rkJXGQx3nbBSgJUgAajUboUu8g8JCXYJaqwrO1zZYqrjp1eZlw4kiz/anKQ48sz2PJs4y7anSlkZDTGc9p6QJXdXwayMpaXqoowzDV56YTRXlZ1ZjkvnkP4Oi0f9JVXS7Mk64WcsgG8E3quzzaMFGB9qIE4V2zjdi/yZOsRREma66dO6SDiVW9ecI1/VIk38GnYgwditSnWUJJkJwTDJxgGSYLO1k1Gg1LT2gOXac6rWqiPAUQZJ8pbOl5pVIJPYPAJ9Jsfxf71ieyMBZcM3rTwjUHNq2+1R1zaW4r42LgzjQ+BlXTQrfPTeumPJ1vhUgBHJY0i4aKkLQ0hWjMpr2CIY82TFTy16UVhC4mK9I4O66ovoxI1vIWxDSxqjdPuKRfijLuZApGN2/enPnOGWlRFH8wSygJknOKogwRzTjrMs6I6Yo5nwIIUbTbbZydnRXugd9sdvbnHR4exn379uViFUOa7e9a3/pGFsaCS0ZvmrgULElzPqMxZ5eiVHSlSRoynOXKuqz0CwVwNlBtf1lZK8o8qQvfTsE2WGnquzz1jS8rQVws5KDVrOaIkrUiBDFdjTml2fYu6RfRM+XVjtE960s1uUvkB0qCFIAiKMMAExMd7xgcOXJE2ZDLYwAhinq9HpowmpiYQADAcrkcuirJN4M4zfZ3rW+JDYrWNy4FS7JKPhahX10irhJ1bm6uUMGUpPicIHRFvxRpNSuLavvH6UvSp3KI2klUTMT+La1m3SBKP7l2JoiLejrNZypC4D+gSLGWMFxMfBWpGDXs2XzX8WGotr+MTMzOzlKSJOdQEqQgFEEZmiCsnWq1mrIhV5QAQkDU+5bLZecqRHRJs/1d61vCbaM3DVycO9J8Jhpz2UMOiTxhQaV2u40zMzOpLf1Pa8y5pF9kVrOOjIzg3NxcbuRNt/3D9GXR5klV4gL4onYyWbnse99EBfDZ81TCkpo2fA4XCznyJBM2cHUFhA10El8+bR/uQ58XwZ9R0XkyfZd2IQJhH0qCFIgiKEMdXDRMbX2vLmHPtbi4mCtDm1aCuItPxrRNfN0aJo35jMacHfI4rtJAZg/k6enp1LabND3mXAy8Ra1mZX+eB1nUbf8wfZmnQ4DTxIbec1XXqtprUTqxVqvhyMgIDg8PY61W6/ldMJ5tJTRdLOQg+0cdF1dA+IZvKzZc7/M0xrOrK7tUdF6SFYSuzJOEWSgJUhDIuIkmC8egCAEEFpkKwSSy6Nrkm1XSzMW+dZ20goWuG71JiXqfpaWl7sottrrcBSMwjfmMxpxdqP3jsRnAND3mXA3Gip4hj7Ko2/5RbVStVnFmZiY382Ra2LAnXLRh0jrUu91u4759+3BiYkL4vZVKBWdnZ70K3MWhq6epWFKdNA6XzyNh7dRqtVINPBcpKVCkpI9Ov0ZdWwQ7sOhQEqQA0ECOJ23lXqQAAv8covdNami7Nvmm2f6u960PRLVhsLJB9WwaF41e1WfSaSdbVZSJ5rN2GzFsnLRand9jSDu023iKq2jutjFzbR+S9zR+bQ6gYo140pD/Prjr2HveVKng4pEj2vd0bU7nyXvARKf9ZWyUiYkJ4YGkrVYL6/V6oYJ+UdgInLokh8E907B5XbalXSzk8Gn+dU2Go3B9rsuSuLYItpxMK15VhCRfkfSpifhmlEz4pBOJ5FASJOe4qLRcJS2jqkgZefb+Ye/LboklO6m4Jsdptr9TfWsoiJb6dYJrowJ3eTqbRldeosbr8PAwjo+P26+iXO9boR5otbBVq/X3XbuNuH8/4uQkIq9bms3Oz/fvR2y3+9uQufbU4mLvyiHu2p65IcE9he+oc21OkidFcFITIejbYMzuAsAtYXOoqjxx17Fj7m2VCq5UKoj792uPuc6tGJuLe8+ecWVJhk3KolPzOvNMaW1BNDIyItyCKLP39DwZ7aK8pElaxXouFgGm8UxpruxyLeiXtv9F2+imh0xbsFsRmQw8Jwpo25g/uOt6ZJG7ztaOBS7pCRPjSkYmrPskDtgjeYWSIDknqTJsN9rYWj4t/K7W8mlsNyIOtFK81ka8NBPabTy9vCxU0l859ijeOLFfqKRb9XPYrp8Uf2er1WnnlthYC64VTZDt+kls1cVjQ/aeIb/utjU/KdUfeRJby6d7JpogAP3Pxr8Tjx46FjqB8vLEfkfUtcpymPA6tv15WWTbXySLwbUiGa7X67i4uIjLy6f7rguroowcCxF9GysTs98tDGi1ay2sT3wvtqbf0fdyrfo5rO/9EWxX9woDYe3qXqzv/ZHO93NBsNb0O7A+8T3YrrV6ZXg9gNae/W7x865fy9+z2Wzi2yoVfAoA/wq24GbY1ZeI21ut4p/f9ZdYre7tl8MEst/3SBnpUnbMTVXejF9YeKz77uz4ePRzj4feU2QIquyJmoouZ+TwXK3WO5+ty1Nr+h3doGx3Pmu1sFW5CduwpVeGA1mCLdiq3NS9MSuLreXTHVkCwEvVKv6bPXuwWq3iqcXF7rX1ie/FLyw8JrxnHW7slcUgAAzQeV5R3yZ8XrbxRLLfcy03bnreldMP7LiL1A8p+IH9crgFK5WbsFar9c91reAegrlOZ+ylZY80wv8gtJ0j+vYLhw7h47AFH4RduEXgmLEynEieODkMxlyQAAlkn53XA/lvLZ/u6O8MZLjnkVXnuIj+7JXFLQjrcwdvR8eOg3VZFDnsreXTWH/kSaE+tWKLJ7iOtz8D+RdtadKqn8OvHHtU/J5x/WN43PA2SN89JewX1u6R1aVJnZKwAE+t1sJK5Sax75BWfUlGNjU75q6ELTgWNuYS3jP43jHYhVfCltCAXRY+LN+vtVoLWy2xXZWkP1lff2npVM91JlZ2JQrSxzRIpE0tocuFurTVGRs6tqkoXhLIhKgNk+hhvi3//K6/xKnKm6V8355nzUEcRiaQzgaetwDgF+bnxfeXnD9E9/zOiamuT8gXkGQ1f4Tdk5XFrt+xbgMJZZEbNz3xCa6N2BWGIv3E6ghVfZqVDcKOV1YW+2xTgSzKyGGSxFnWcZhIeUJH4qCOQ0mQnBIMxkYD8dixl4SG8vIy4uLiqa7Ca5xo48ymr2BlsIn33/k81usbA3pp4QxWy02cuuzr+NCD57Be7x1c7UYbZ6+o4cTgSTx8z3P48MOMMlo8hZVSC/de/lVcuO8lXFxEbDQ2gsD751awMr6C99+PuLiI+LnPncV2u93xfSsrOLV7BR96CPHYsc77BO+3fz9itdr5+eIidu8ZxJumpzs/D37f87ztjWsWFzttwbZPcM2xhZewsXiq57p6ff3aB57D+uK57nu2Wp3fHTuGuPhQZyL8ZuXN+APT0zg5OYmPPHKyc7/DZ3DfcB2vGzqNN058F+7fvx+PHeu0y9HDL+F1w2dxbuRL2K61eu95+Awu7HgPzmz6Ck5WL2Gz2XvPw599Casjp3D/yBe71zYanXc8et9Z3DP0FZwcaWGz1hkfnf5HvP+el7EyfLrnuv57PomT110Q33Pnedw/t9JnRH7hvr/vkaf77/8SlkqvQwDAPdtvw2qpI0/33/00Tkx8DwJs6RqujRNt3Hf517ryFPRPs9nEvaPfj2PQwDfAY3gZ3IqVyk3dCUlVhtuNNu7f/GWslhp47P6zvfK0eAony8/i9KYncfGhc33yJJLhjeftl+ETJzptVK3uxZmZ81itIh4+3Lnfgw9+HYeHJ3F4+A04MXEBp6cRFxbWZfHw1/E7J6a6k3rQt4uLiPt2X8DKxKXuHBn07dHDL+HE8BmcHX68r28DmagOn8Sloy/1yPD99yNWdq3g9PCXsQET3Qm40UBcuOc53DP0FRyG17BSPonNpdPdex4+jLhzxwqOwHmcgyXGoEOsHzuNR8d+FHfDCRyB83jdzgs4Pf0DWKnchEePnsb773wex0tncARew9nhx/Gt6+9ae+ir+PDYj+IizOLM8AmcrKz0v+f9Z7FabuJ++Hxf8Pm18XFswAROwaM4CE/jb/1WDZeX12WpWsW7YBYn4GkcgkdxdPQ9+MgjJ7vXNio34cymJ7Gy8wLefz/2ytMSYnXXBZx647fxoYewT57mNnX0YSBPXZlYPIWV8kncd/nXuvIU6DWhDC+e6xqFfTJ8rGMUNptNnKq8Gafg8zgBT+Nvf+AfsLrjR7pBhOXDj3VleOG+l3Bx4UyfEfrgZ/4Gr4WbEeBG3ASz+MefeLhrAG6HWXzD2A/gn/zJY/gd238YN8NEj+FZryMePYq4Z/clrE6sYLB7Vp88TZ3v0aeNRke+9+1DnNi50pVDVp6u27mC+4brXTk8V6thvX6yK0974Amslhq4tHB2PUl4LlyGjx/HRuUmXIBbcd9wvXNPZqev7j2vQ9y3+zw2KjchAuCz5TLOwRZ8cGA/Pgw34+zw4zg8tIrl8kkEmMBK5SZ84IGz+MgjiJVxTv6PH+8mQBqVm3Bmz4XuvNUnTxOd512E2U4w+chG0mVu5Es4sXOlqyu68+vS6Y48wWO4MPoeXDy8njxdnwgbMIEzwycY/XQOp6ffgZOTk/i5+57C6sgpnBp6Eh+67wwePXoaq9W9uH//fmw+8iTODX8JJ8qn8fBnX+q9ZxOxsvMC7rvh2139xM6vjcVTuG/3BZwYW+m8JyvDS52+3ju1gouL2CPDk5OTeCVswbeMvR1/7/e+jCNDX8ESPIXDw2/ASqWCX1h4DOuL57p9O7Pv1e7zttttbJxo48Kdz+C+TV/DidIpXFo4s9G3nzuLhz/9DF5XOoX7Lv8aNk6wzpSkDO+8gNO7v42NxoaT2ZXhG17DidELuLS04Uh25WnsIu677KsdeQqCK+s2yMI9z+HM8Ik+PXzsGOLhe57DarmJ0/Boz7XLDz6Gd8M7cAYexV3wNG6D2e54DO5ZrSJO79mQ4UD+A3maHX4cK+Mr+Mgj2N+34ys4O/w4HoObcXH8nXjy0LFuAmR2+HEcGV7D8fFOmzz88Bms1891rqsgjgyv4e6hr+FDcItRGd43VN+QJ3bcHO3McXuHarh4+HSvHq61OvccOiOW4etWcd/uC30y3Gw28TsnpnATzOKOHe/BN02ew+FSC2Fd7y0tner0z7p+mpte6bEH6ovnsP7wGTz64Dncc/k/4mT5WWwunsKlpVP4lrF/iVfAjfimre/D6wZb+OaBR/DKddunGcyx972E+zZ9rTO/MvpyeRnx8N1n8brSKZy7os4FBNbl6b6XcGbTV9Z14pleebq7M09Ob3qyR/7Ze/aNG+ae+y77KjaOfq3vng8/jDi751Ws7LyAjzyCuLBwZj1QfzO+YeydeF35JO6DZdwGN/fYa+1aC/ePfBGrwye7/RP0bbOJOFm91Jmz1mWC79vZ4cfFfbt0uuNzCOSpXd2Ls7CEw3Aex3b0ylMg+8NwHqfgCVwcfyfWj/ZeOwdLOFE6hYfveR4PH36pq0uXHvoqVoZP477hOi7ccxYPH97QpedqNWxX9+Li1E/gw4dfEvscC515mbUHms0mVqt7cQfciG8a/278vd/7Mg4NfQUBnsJK5W24tHQK63VGP1V6F3d1ZWIBcd+eFazs2rCdujIRJsP1XnkKZLjH/l+Xp/2bv9wjizoyfPDgl/AyuBXfCI/hTngGP3fwL/tkeKJ0CmevqPUXJjXaOHdFHSdKp/DY/Wd79MTnDv4ljsMz+EZ4DDfBLP7JnzwW6cOy/cP7sKy9xtv/QX90ZbiyglPXn8eH1p9ncfEczs5+dzcBMrvvPE6MX8LDhxHvv/8sVio3deze2jmsTFzCvbvP9+mndhtx/8x5rExc6vE52u02Li2dxslJ7Pgc95/tylMwJ7XbiPWHz2B98Vx37Nbr53D//v1YqdyExw5/HevHTnf92CB5uX//fmwufgUXj57r+B0LZ3H52Ia91vWbHzqHx258X99cp+rDsvPkvuE6VoZPY7PWCQQHOmZ09D/ijm3fxiuGH+/6kbVaa0OG730eZ/Zc6PoOPfc8jFi9bgX3z61gvX5yPeC5BScmvhcXPvtUR4ZLLZwdf+t6cdBNeOzYaTx6n1iGu2NA4MOy9nTgw75u+3/Y8DnQbBxGVYbjfNieOMzcClYnVsRxmMoKTu9bEcZhAt0GMIsANyPALI6Pv7MbeA76dhPM4u/DzXh88HWdZAAjT7E+7ERn/nh6/K34tvXEfLW6Fw8fPoML9zyHe8s1HINnu/7M8vLprt01UTqFszE+bKBP2XuOj/b7v40G4sP3n8XF8XfiPni8z28OfNjrSqe6151eWurq/ocG93T939fteg0rlbchwC6cmPiejiwe7h83XXl65CTODH8ZK+WTeP89L/fY/wsLZ3p8WNb+b7fb2Dj6NWEcBhHxjz7+cE8c5tChL2jLcF8c5ug5XP7c2a789sRhHjiLjRPtrh3eJ8NHz+H/749q4ljiA2dx6ejXeooH//r+L+HiIuID9zyNbxl7OwLswkqlgm8Z/Wd4BdyIY2P/Dv/oEw/jW8b+Je5k7LWeOZaLw3TnrM+aj8NsyNNjWCm1+uXpKOLEBOLsLCVCoqAkSA4JEgQTE4iXX45YKnUGKOJGguD66xHLZcRNmxA///nOoNn9pgs4CCsIgAiwhkPlVRzddhH/6081sMT8HGANh4fWcPcN38a/+qs2Pvww4uE/eh6H4ELP32zfjnj3J5/HnYNnen4+MLCGl1/+tY4CP/x1HC+d7fk9wAq+4Q2/itftvMD9fA2v2LS2btAhViuXmN93/PyxsY4jAIBYKq1hudT53cDAGk7vubAeqEd8y57z3esGBjp/t7jYaZ/xsUu99xx4BRuPnMR2G3F233kcGtr43dDARaxMrOLiYue+7O+G4QLOwRJ+s/Jm/MLhx/CKTasIsIYluNC9966x8/jZz76MpRL2vMsIvNatiJ+dRRwqryHAKgKsdvtoxw7EbdsQh4Z6rx2HJj6w/T/j5x88jVdcEfx8deP3O1bwIx9BHBzkr2vhA9v/Mx697wxOTyMOlVf777ltpXPP8hrTRojj5TP4wL0vY6PRCfL/8ae+grvLX8VBuNi9R7m8iuXyC3jdlR/uk6ehoTUcHv4y3njj+/C++17C3dVX1+/d+ZvBgTX86Ec78lTqfmfnnS4bWQmV4XJpFce2r+CnP/KN/nuW13B6b8fxay13nGb2ewHWcGz7pe7PS7CyLk8dGZ6e3gh4jJc3ZHgAOvJ0+HBnAuNleNNlK+sG3i4cKjW6stu59gICtBDgGQRAHBxY6/5uCM7jm2EJ3zoxhYuLp9b7tiPnQT9UJjrOlFievhebjzyJ+/adZ+Sp8/tyeQ3vued5nJi4hOUydp+pXFrDmeET2IYt2KjchFdcdqnnOgDE8XHEj3wEcWAAmXdBvK50Cj8Ht+LRsR/F6etfwiE433ctwAsIcAYBzvf8fAxa+HtwO96y9d/gFfAyAqxiGS6sv2tnnH/606wMr183eAYX4FasT3wPNg59Af9hx/fjAtyK18MTCIwsDg6u4Z13Iv7af/5CV54G4GJnvF+B+PkHT+PCjvfgbqj1yPDQUOfen/wkdp+lO96HO4nXo0cR//SjX8NheK1HJgYH1/AXf+pF3FXa0IeBPF1+2RqeONGRp+kbXukdN3ARx0un8fAnv4pl5ucdmbiA05u+gu1GG7+w8BiOr8tNVwfDKfzjX/08Tpaf7cgTrKw/zypOXfaP3UDY0Qe/jiPr7QxwHgdgFUtwET939wm891PBOF5d76dVvBxexsWHvtp53r0b44KVp8OHOw5BmZHPMlzEmeEvY7vWCR4HMrzx+xVcfKgTJOrt2zXcN1zHNmzpBLFu7JenUqnTL6Oj2CfDe4a+ggtwK34e5vAKeKnnusFBxDvv7BicvAy/sXoBH1pPwO2Fx3AAVnEALuLVW15DVoavumql57pAFh+A27EBE9iAiY48vfECMzY788X27ZfW5QnXn6fTryPwGk7Do3gUbsZ7r/2F9THLfP9Y57nHxtg27PTt3je8jIvjP4CLMIt7hr7C6JFO35RKZxHgpxF6dOIqDsB5hHUd8+XRW3pkGGANd411HNpKd27utOPAwIYubddauHeohqz+7srw3c/1jJuh8ioOw3l8y+VP4lTlzXglbMEb4DEsd/XBeh/BM7gd5nAUWj3z53Zo4RVwI05OTuKxB/8RNw18u1evwUon8HLPc13Z78rT7o490Gh0AoT8nNbRiZ2ETbncK8P7bngZZ2e/GyuVm3DTptXeZx1Yxe3b/wu+4Q0fxIGBNeaeq7gbarg4/k5cfPAM7t270a7BnDU4uIaf/GRnXmf7ugQruBtO4MLoe/DPf+thvHxdhge6/XcRAX4Kt2//L+v3DPoa8YbJjgzX4UZswAQuwiz+0bW/gMNDYnkaHd0YN4Ow0pEneAwXYRaPjf0o7tzRK+vlcscW2bGjd7wCdPo2kOE/3f6fQ++5a1e/DE9f/zK2q3uxDVvW5Wnj2qGhjr3Vmes22ncILuDI8BrOzeH6asXvCZfhidXee67LcL1+Et86MYXfAY8iawsAIA4PnkaAORwcPN37c7iAi0fPdWzE6U4QvdyV03UbqXQax7e9hsNwHgfhte7PR6GFbxl7OzabTTxxAnHT5Wt91zUXT+HCQmAPBPc8j4uHO8mKRgNxz+4V7JMnuIi/+rPfwq1Xr/R8ZwlWcPcbO8mfz38eGTsxGG8reOdHvrGuE3tl+I2lf8KH7juDi4uIe/d2dObA+lzUmUfXcHAQ13ViYGcgluE1LMEKXn7ZJfz85zsBpWNjP4q7oMn0Q6dvd+xA3LkTu8/SlaehNdy7dwX/9M46/tG1v4BDPX2LuH37JfzUp17pyrBIJy7DLE4MnuyRw+CeG7p0w+YagfM4t/ulTvUv3IjDcL7nb0qlMwgwt54kY+3HVRyCR/GtE1P4jYkpfAsjT+E+R+f3V2xaXU+0Iu7b/RIOwWtY4ua7XbtWcHwccXh4w88JbMCg0G3Dz2HkaV3+x8d7+7xHhmd7fZnAdh4bPIPbt/bP9xOlU3jsT5/Der0jT7wM7xg4i/ff+Tzee2+/DN/7qRdwcRHxxAnE698Q6O+N97wWTuIv/uSzuP3aFe55z+O9v/2N7rUPP9wJdrN+6FB5DXfsQPy1n30Gd8Appn8uIMAF3L37PJ440fEd/ui3v9Fz7eDAGt57L+L9dz6Po4PP9cjT5ZetdWS4jrhw/0s4Wnq++/tyqePP7NiBuOPa3vl1qLyKI8NrOD3d0TOLD55Zt2M2ZHhsbAUfeuglHN/J6yfs+hytpVNYGdwYNwMDHT18+HC33qJPnk6caGO9fhJn953v009jYyv44INncNfOCzgM59efaV0PD5/Arx57FJuPPIlvGXpiQ4ZhtWuvNZu8PK3hFfAyNio3bfiwpQ1/MpCnSqVzHe/DVuHpzmrEWgvf8pbe7wXo+KF33om4bVuvTAzBa/iGq9+P9wU+LCPDsj7sQw+dwsuGvowAryHrl1wLp7B69a/gtq3n19+lX4YbjY1iijdWLyBrAw0OrOGnP434X9/7GGPfd77n8ssR/+qvOnEaURxmx7WX8Fd/9lvCOMy+qRVlGT5xohMXSurDduMw9XNYGT7d9/uxsU4yOZhrAvuJj8O86U2BLb4uF4OrePjwmfUgf7NPno4Pvg6/cvQLXEyk1+eoVDj7H1ZwBh7F+vB2fOvEFO4R6MThUgunpt6DY2OXutcBII7A+e6qbpHPAdCx1bZt670na/9/fvydXb+5xMjTjh3IxWFwQ57g3Xh07Efxza//Fg7Ba1iG81iG8zg4EMjTKQR4HoeG1tbvuy7D67GfxiMnuwWLu8tf4+Iwa1gqPYcA70cAvm8vIMCjOD7+A/inn67hG0v/1CPDpcE1vO8+xN/4xX/qk+GBgVfwoYdO4eIi4p7JV3vvycRhygniMAPruuKeX32q68N24jDr42bg23ji8+0QGe7op8N3PyeW4cFX8W1vvhWnKm/Gt1z+ZI8+HIILOFxq4a7y23AUmljq2msdm3YfLOM4bMFqdS/u2f1qnzxVJi515tjRXv0UxGH643obMnzffR2dKIrD1OFGPDHxvUJ5CuIwvDyNjCA+8EB/oRrRgZIgOaTV6lQBskYFAOLdd7MG0sbvLxtZ6yoV/pref/M/DwJjHUXMBlr6r+U/FxCggVeVvorb4Kz0Pcuwggv3nMWH730a76/+VyYg3vvZCA5sfHYOnsWH73kaFz71JG4feL7v94ODa7hlc/91ZbiIy+PvxNbCYzhROiW+n/DdOxPhMbgZH7jmJ0OfVdRGo4PP4QNwOy5s/w84ce2rCdo1SLKs4mXwbSx1J824vlzDrfACBsZIx8hLcs+OMh4ZXsPLR3qdGNm+LQ1uBBwGBf3HTib8z4eHLynJ8BBcwPs+/Rwu3Ps83vD6CyHfL373naOrnWq3N72C2wZekL5nGS7iH//KcfzO7bfif4Nb1oPvonv2/7wCz+Az49+JD/zB8zg4KP+sY4Nn8QG4Hf+o9E68bOC01HMGn4mxFXxg60/hAtwqLcOlEuKnfu2b60b7KuPoyXwudR3+y+BVwT3D+7ijgzr3u7wb1F/FXmNPJE/szztjR0WGy6WOoT8IK7gV+nVMlHy98fWXcGG9Uj2xDH/yWVy493msjLelryvBCs7suYCPPHISLys/KXy2Qbgk/HkZLuLyvSfw2MJLONx1uuL7BwBxAhr4wLb34X2ffg5LQhkWj/PxHRfwgat/Eu+H23EnnEx0z8EgYSqUp/B7bhjF55mAW/Bz/r2ZdhtELA92DNXL4eV1WQy7R7hclOE13HAy5OfXMlxYf+bz63pVXp4uh5M4f/W78U/hdhwFXlcgDoBobkDcUfoGHv79Z3Bh+3/AMcF1Uc87DOfxlht/CN8y9i+5IOPGNf1z+hpug9O4efiL+OCDZ3D3G8P0t1iGx8dW8N7f+QbeMHmBCS5IyvBAC79ry3/Cy+B2hARz1SCsdPulPCg/N7Pf2S/DayH37P15r05c63OWouWpI/9DpUs4NNSfLIz6lOE1HIBVHIRV3LpV/p474SQ+DDfjwrV34Nh2URuHXzu+9du4sO0nImQ4xF4rv4BL9z2Oy8OTON4NzvOf/mcJdOkf/ubXcWybaK4LH/cDcBbf9LoX8f67X8Lr37Ai/Nsr4cW+n5dKa3jD5AV88LPncPcNKwKbSU6eRkbWQmyJKJ24iuXSKhf4CXvH/vteNnypG2wtw/mIa0U/D4KLq+vzk7wMD8NrOD5wqmOblKKetfczAc/iAtyKf7r1p3F0m0gWRc+BuANO4l9d/UO4ALfi9oEX+uVwYA23XNmvT0uwgg/ff7YT7O4GheTk6Xd2fgoX7/8nPHbP0zjWLUDjP+J73lD5Nt5390s4MS7froOwgtu3ruDwUCegNTISPZfy79+Vw+HVCFu4//vYa4P/T+6HruDwMON39F0b/i4dv7kTMA4KaGSffWLXKt7/yWfxhtLXhfZImD+5c8cKPnzP07i44/vxd+DnBN8d5nOs4D+v/AzOXv3eCJkQy/AYnMb62NuxNf6dITpR7DeXYAX/O/xkqA8bpoNLg2t479af7yTdR/8djl39bXmZgI2g49BQEjlgfdhVJpEgc88NObzsMmQKJpLqVP46ub4dgBUcUZThN7z+Av7Bp55V8GE7cZiF3/4q3lB+KrJd+2R48AwuL3RWHQ8PnBVcs4Ig8J1KcBGXYRYPD74Vx67hZSKszTqf8cHTuAC34l9tf1cieRrbvuH7TkAj5Lr+z+AgdhPF4T5HnDyF+c1RMtyZG0fgVbx8uON7J5UngAvrPgNfsBgnTxdxqPRPOFS6xCXr4u8ZH4cJf4fdb7yARx88pyTDDz/wT/g3f/plgc+x3o9C274Th3mk9Dq8pfofcHupf17vfPrn2DE43YnrbbsDx6+VleHOv8eufQ13Dp7B3XAikTxt3dqZE9nCXWIDSoLklKWlYDKOVprhv492aEo9FcnxSqNHEQycxbGrX+m5XpwI6f2U4SJ+Fv4dboJO5nUILuAonI5QCBufnXAK3whfxRJcWHfIL+I2eC72uvLgKi5sfw/eB7fjp+D/wN1Qk7ofQMeYK5dWsQSvYRleW79//LWj21Zw9OpO8GsAVvFGOCEI+sUrwBJcxDvhpyTuudYXsN0OZ5XuGW3kRbzzwNk+2ZGRpY3vTZK0YWUqcKjXuMr+mD4aeA53v+7lbsUUAIZMwP0y/Gn4KbwMvo0DsIKvh6/hODSZqomIcQMtPHTVu/GT8HM4tB7klGmj0cHncLT0HAaBg5HIxELvvzurqTrtswlexrvhjlh5CqqZ2e/cDmcjgkrsp/d9AhmWaR+RcyD+fZiRtO7Qw1kleSrBxXV5YsbVleGBctFzBcHrcqwRychUiTVY5a4LjNRtV30bJ678TRyEM6HfL5Lh++Fd+OuXfxS3XvFtSUN9XZ4GV7sJrkG4hKOl56R04njpDI5etZEQGILzOBaaCAnv7xJcxN+G/0NSh/d+3yicwm1wJuJ+4p8PwkVJXdb72Q5ncTsXwBUl9/nPNjibKHHIyzCbPBzd8ipTQRj1rGe6VXID6ysHO/0l965jO1bwgz/5DH7gXY8xK0ej+2bbettcc+W38eotK8xKmXgZHh86i6MDgc2x1n13KRmGi+uB21UcgEu4Q9IG4YMTJbi4bvfIyyArw70BYPmASdz38z/v6O4kgVh2zJzmZHgNt14lDraxn51wEnfDCRxaT6CUBlZxtBzmbPa3zwi8goGuGB04K0yEiJ51bOAMvmnga/gp+P/gXZt/FscH468LdOnVV72GACs4BK/ijoH4MQPQSYB0/p9PdMbMNYPs3zGr2iRsPdHPtg1+Q0knlgZW8Rr4RgJ5ChtrcvI0BifX5+eNn4kTIfx1pzkZXsNPfvBbIYnA/nvuhhMb8rR9JSQR0i/DHZu/s7JycGAVt10d38YluIi7oIVTgzX86OUfxfvgdtwpZTut4Sfh/d1Vm4NwHl8PXxfMk+Ey3JHJVbx+4uWQMd/7GYAVwTvJyXB/EFxWhsOvDf99vEzKy1OYbo1KhGx8xqGJ1499C9lxK+fDnsTdg1/d0Imwgp+86iOCe/LBvot4D/w4Xg6vYGBj9if9xZ/RgbMdnQh1vAd+HO+/6idxvK+ISixP264M7LVVfAN8VcqfLA2u4t3b/mtP8dQUPCElw/zctv2KV3EniIsWo9oraCOZ9omfT9XlUOb60DhMSBKt/3t7g93bQM6HZeMwvXNRnAyfwhvgSdwBZ/GqwX/CMTiJOyTm5jKs4P+67mfwY/DTeC2cxcvgFWl7bXzs0noRVEeedsMJKXkaHcV1PZ8sDsO3fRku4r0SfrNQhuGsVPuYisNsg9PMahN5GS7BCl4DZ7DXb5bzOTptxMRhQuVJ/O9OjG1D98rGYe7f+j6882fr+NH/crpvdXPUZ9f2V/EP4Efww/DLnRUjcB63Q7zPMgqncbTbRquJZJhdxT4Mr0nG9RCvuWql5zt3ja2EnllSVCgJkmMW7/snZvsUsTKswLNSRg2vQDoKWzwBRymgXdDCWnkvvn7gnwR/F66IynARj8IteAP0VyzHJTN2QQs/DT/Td90gXAyp1t645+GBd65//xqyE0tsIFjw+0FYwXvgDiwNhBvZ18A314MzvRPETjgp5ZRsfC7itfAcTsOj+BC8PeZ5eye97XAGx6GF0/BoouqHrtEBTelEUefvW1iBYLueuEmh/3c74RkcTdQ26/cdPI3buUCduDK997Nz8CzuuezrwsDmldCOlKf7tv5Mz3YuwX/jgjTb4DRuvXKjijf4+Sicimzra+Cbgu/uXB9edbfeFn2/X8WdcBIPw22R99w88ErPM5bhIo7BaXwI3o6V7nZN8Z8SXMSdcLIrw9HOcW+/jcFJHIXTXPV+tDwNwgruWpejpDIc6MQKPIPX9OiUMGO093n6V2zJyeL42Apu54IQcQH3wZ5EycanXA5bMdB77czVP42DjAwPwqpUkDGs2j6urUfhNI5u6a/eGoemhE5k32UF3zD4NE7CU7EyzLfBMJzHnVd8Cx+Ct+OolGO9IRef2fQT+CfwrkTyNAqncRc08MbtZyWSPb3XbehS2YD3xnjrXNf7nKNXfZsLdPbfU9QPg7CC2wfDrws+Y9e81rMKsAwXIqpVO59rIAiI91a67biG3UIz5HlLz/fNscFqmbg+UpXh/mTT+nYg8IzU2GH7aA98uSvDSXTiEJzHzXA6dD4Q9g2cxiZM4NEfPSgZUNr4jEMLJ6CB1w98Fa/uC5SH20C7oIULgvFZ6iaoo8eb6J1GB84yMiOW4Y1xtqET7/qVp2ISIesJkCvYOU9OhoeEtsIq7toRZlfz/cXJ1KB8EJmVp10Dp1BFhtk5Lz6Zt3F9FZ7GPfBEIn0I0FlBuAeewAloCO4X7XM0YQIfgNv75PDKy6JXqI5DC8fg5HriXl6eNkEL74Ef73uu8BWiGzK+q6tLN+Rwx7pNHvWsPwO/J7SfP/3eL0UWoAxAf+HEAKzg9fBkrP0vSjaMXv0aVsbO931nVH8Fc9VAyBwUde0uaOHhq39cIqDU/7ty6P2in7sCz+Do5pdjn00kwzfAk5ztHdjj8TqRf84SXMSfgd8Pva4MK3gYbltPRPNzUPT4G4UzQp34WfjhSHkaFCb35WT4A/Cbfc81BK/hUbglpoiq992uhhdwDE7i1Lq+iLqn6HvGrnxRomhLPKeryOHr4Sn8I3iHks8BijLcWU2R3Ic9OvA9fXEYmaTaLjiJn37v48x1688BJyPnjxJcxMNw27pf1XttvAyf6rODy3ARF+A2HL8y/D2vgW/2FfGoxGEG4CJ+FH4Z63AjLsJcovl1FE7jBDSkk4D89+jEYZLIcJC078hgMp8DoLPdZ18cJlaeOt87BOLV39EyvLIeh9nYFq1cWsWxa+N3qxi99jWcvPrnsW9LcGhG2vHXwAs4ysl4uL7o/fTL7wWswDOxPuwA94xjcBqXfvvvQg+kLyqUBMkzrRYe2vafIpReA+/f+tN476/9I44OyjgxG58KPIPjA6e4c0Dirzs++M9xAW5DfjKL+3zsyt/Epa3fJ13Bwn5+Hf4vFFd2R9//l+ATOBYS3En6DMFnJ5zCX4bfjPib8Iz+mHQQgq0wWsElmMUD8F6pa6+GjT11K/AMHhr4Icl7Bs/Ywnvh3Xj35p+VdsaDyWMcmngfvAuvlVihE3wm4Sl8evyt+L1X/XtJRzyQ/ZO4+Ib/fX3FjXwSDwDx4I8exV3dwIic/Hbk6TfX7ycvg9Ey17nuKvhmAnnCrlzIPjcvh1V4Gj8Gv5T4umE4j5+BH5W6rpOg7MhBGS4mkuFr4WxXhrfDWbw6IljBfrau/10VnsZ74d14F/xEooBS57ofxx19FTHxMnw33JFIlgA6TuriXV/Ga654VXBt9HeJVxSs4fZSdNB66+CLeNWm/oTEKJyWWlnH3y/YNilKhvu3X9rYo1zeIVlDVi9OwlPSMrwVnsNtmzvbOQzDa4lk+Fcv+xQOD63iUOkSfgh+RdopCXR9eXAVP/pvH5PWbePrztIYnGK2hpDvkwl4lnM24+Vpo99FfxN9/23wHG4TBAW3b3ltvaIr6nn7ZXgQVnAsZuWKSJ5Gr3wFK4NJE+lrPYnTqEQR+7kWnsP74XYm4Sp3345D/Su468pzCNCZn/9P+L+kru3o0k57bR94XlqejsN3Ya16K47AeSwPrCSamzfslWAOkNOJh+CdTLI8mV4L2imsv8KuCQtol+ASfnzbJyLvJ9qqCgBx21VJtoDsPF8JVvD/GX0vTpbjAnf97TK69QJWEgb8PgK/jJ+D27AKT6+3g5wMb4ezeCf8FF63fr9RiUpIgI7PcSf8ZETQJPxzEN7DBK1X1ldjycnwUbgF+fNdZD79MiwnT78Fv5hoy4peWUIUbYUalwCMsvU+Dr+Q4NqNBMFd8J4E8tS5dgzO4OLIv8BJeEr6GgDEsYEz+H//2mLI9l/izzY4i/f//BIu33sC74SfSSRPk/AUfubK/9hdeShr603CU7gIczgOTekVA8Hn4MB7QxK1MT4HvBcn1gOUSeTwp+H3cWIgeYFYp23Fuj5ensL1vIoMBz7scfguyWdnzwS7iHdd8XOS1wWB1fNYgWdxEeakCxQCe3oMTuOdW3553QeQa+cKPIN/DVP43df9NN515X9J5HOMwTN42cBpJg4jN8dOwlN4eOh/k169FHw+Br8UEYeJ/p5fh18Lfb6ocbsVvtH1BXm5kO1T/t+T8BQejNRtpuIwGyvXa7Bb2ofdCs91zzBKpoe5OAzcoRyHkVvNumEzbOxoIS9P43ASFz/8/+L4tmDVmLw8dXYRSB6L+6WrDwjjMB27OPqdrxaulOr8O/ra/mu2wgsJE7S9cpjEhx2DM7j4yb/ByfKzuH/zlykRwiCbNxhARATHeemll+Cqq66Cc+fOwZYtW2w/jnVa9Zfgln89DE+fuizkL9YAYGD9E3TvgNR3Xw3fgDZsW79O7hoAgM3wIrwKmwFhSPqaDghXwEvwKlzV8zO5e/N/Z+K6JO9t4v5J7rcKAKXuv66CNrwEVwIyP5O557XwPAAAfBN2SN4Xe/6/BKuwKtnPJVgBgAEYgEtwCUZA9l3/E/x3+O73vAn+93tvAYSy9HVb4EUYhEvQhu1Sf9/LpfX/ltf/qypPSa4Nuybp9SrXiq5ReW6AAViVksMBuLTen537bIPn4FtwLax12zzJ80KCZ70EE1tehpMvXQ3JZHgFJuAMnITrEtyrww/A/4LPwTsVdOIqbIGX4CW4Zv3fan3SSxZ6jf1bhE3wMnwbVOZrVZ2YVIZ5GVoDgMHYq8pwES7BELCyuAPOwvOwU+qJB+HSurwnk+Gr4JtwDrZK//0GK1ACkNbZvZiaG2V/p/qd4VwN34Q2XJv4ugG4BJvhFXgZrpa8AmEIVmAVBmEQ1uASDEte17HXBmGVuS6pDg/+KyfDV296DQa//Sp8C7Zx3yOLilxcApDS86bul+Q7TfxdNINwCX4Q/gwOwY8kvvYq+Nb62JelM8dV4Bl4EbYmkP+OTirByrq+kH3vjgxv2AHy7TUIK7AGQ5BUhq+EF+FluErqb8Xo2kxJrgPolX+7MrwxByVjG7wA/wY+B5+B9yS4CmHb1atwqf0ytLs2TRzrOnEQYW0t0G9y/fwzl98Nd732bljpyq9cO797+D74fy++HV6AnZC8f1YAeuZXGz4sSF6b5HvFdOz4ZONcxDXwTViDAUnd1vtssj7HxrWdq66F5+FbcK3CtcH95e21qwa/Dc21CUjqN6vGYf43uB8Owe0ACnGYLdBmfA5IcG8dnRj2PWo+R3L7P/n9On7Nld1/XzlyHl65MJQwDtP5bzI9nH0cpjPHboak8rQZXoQRuAjfhNFE13Xg538Tdpqq76ImyyW4CGtQShybSyrDhz6wBL/422Pw9KXXwcTgKfjtO0fgu27bBpVK4kfOHbJ5A0qCeEar/hLc8s9egqcvdCZWM4Y0T9ZBt41rtsI3Gcc8fVSdgY3rAwcuK3QCCTbQMYgAdCeja+Ab8KKSPMXdT/z7bfAclGENzkoGRE2wHZ6HF6QTWlGYMmZV75c2toxzHRB+BX4Dfgv+z8T3UNVtvwW/BL8H74fnEsrwFjgHL8FVkc8Uhp4eNhEg1e3XLOTCTCA4eUBV737b4DkowVpieboKvgWXwYXE1wGoy1MnEHA185NsEzb+6ES7994Gz8E3EjvYuD5XJnfMR+E0vADbE9t6V8GLcC4ysGRef+sVC9jCphzaIvk7XwMvwOVwAU7DRCb369BbfJW9nZgUU7rXNd0f9nud9tftO5XrTSXDbVyf5f1kro2W2U4QWzbxuIGOPa5y7dXwLWgntkkDso6JZJ3ANoEpncTPBXJEy0T4s5XgEqxG9q342lE4A6swqGAjxj9TGGp+leh+8vfuJMJKUIIVGCiV4dLqAFxxBcCTT0LhEyGyeQPVEhrCAidPAtxy6yYuAZJGDot3oMII+33cIF4NuSvCxQgFOwirsFGtnwSETXBO+Js1KSMjnBF4TfjzK+Fc7LVqdGp6xYT9XBed91Cd5NmKG/UJfCXWOAp7t7XIqy6Dl4U/vwgj8AqErdDqGAMqDMAqhD3rC7ETr+w47m3jfw5/LXGtufvJXavzDDqyGPcdcfdTe6/N8BL8AfxsxD3D3+mqEJ0X90yfhP8K52FE6vlYXoIt6/ItfqYoGY4ebRB6XfDN4TIl2+5J5gF+3k2qn5IkS0TXyQRLwjnXs+qSJbx/dGyNARiAfwv/T8jvwuesl+Cq9TUsUYif6XJ4JfR3Ude9BFdxz5SWPCW5zkWdKHu/sP7Vk+FvhBQ2DETaiAOAifT3BhfgMrgMzof8NlyGz8GVMNDzPKKAlfjeV8DLsc8lhp+vVGU4S0z4HOFsCrHX4q4bhNVQWy8aXPdXknNFxLx9DrbC98BfCX83EDOLboqUp6h2KEH//Mdepz6O08GE7lV9L4TLY+UlbM6Kug7h38L/N+R3cdZTtO00EKovVf1tgCuhHXnP8O9NS16y8DlE6MyvMjqRnz/Y+12KWcUaHkfYBK/EPJv4mQZgNTLYfSW8KPxN/AqxqDYoQ5g8XQEvhf5OnaDNo2wb2e9JQhZxmDCfI/hd8gRIRye+GnG/cBtoU+h1HcJ05veVj8o/Xugz9dPRlWJ5Ohe764GaDzsM3w69dnV9bl6FIbi02rnuwgWA556LeRSiS+KVIH/3d38Hn/rUp+CLX/winDlzBubn5+EHf/AHQ//+0KFD8Id/+IfwpS99CS5cuABTU1Pw67/+6/Cv//W/lr4nrQTpcO4cwNvfDvDEE+wS4QEYhdPr1ZIyii6rSoq0qmpkq2GS3D+NChsT1Qk6VXy67Z2kn0HzXmH/ThfdVUDJMVkhlvW40Hl20Lg2yXWq8mTqGXH9o1JboKovVPoF16tWkm8RZFaGVbCpE23cK+v5VhbR/VYhXIZV5t+4+0fdL+5am/OVTZ2Y9nX8taa+R+bnqt/XIX67uTTGlInvTCJPujKc1D5UvY+Ja2XlUCbgrSqHKteqPg//u2ztaf0+Bsnrs/JhTZG2PtT53izkUBXbc5fK32fhcyS5TxroxDairjUfh9nYflkHisOofU/a16l95yCswlXwLXgx8ZbpacZhZO8bfV25DPAP/wDw1rdKfnWOSW0lyKuvvgr79u2DP/iDP5D6+7/7u7+D7/3e74W/+Iu/gC9+8Yvw9re/Hd7xjnfA448/nvTWheeqqwAefBDgLd+xAhPQghvgqzAJ/wRfgLfCx+ADUt/x6/B/wg44tf6vIGgnwyXYBs/BhmJei7mWHagrzHVJYCcC0feG3S/494rEdaLfhU0oSZ5/Y+K9Cr4F4dU2PNj97zb4BgBEVeqIrkXYCt+A7evnfshfF/TnAOyEU/BDIZW7PP9t5JfgTnhfz3PLcDV8Ez4N74MyXOzefweclbz2hfXrAOLlkGUVroJvdv/Vuy+/DCvxfxJK2AQqQ+91nQpDkbyLvrt34r2yr8owzolBuBq+BZ+EX0i4igVhEC7Bjh6dIXfdEFyE7fAcbIcXpO82CJfgV+HXmWdEuBq+IXXt++F31p8zeO6LkX8fsAVehE/D+9b3Ww3aS9aw4WUpiaEuq9fC4PdgTjIGeuVJtdr1KviWQBbjQLgT3gfvhQPSfx/8dzs8D5+EX4CBRGMYoQSX4BfhE+t9LMcgrMDPwycgqU4EuAS/AL8FJUaGZfnR4f/FXScnF1eWvw1b4FvS99lANC9HyTDvAMj+Lur7Nu6XTA437jcIl2KqVsUMwiX4FPwCbBn+tuQVG7LwUfhV+MDI70peF1T5BvO6vE4EANgKz6/bbUnmSYCt8AJshed77i/HCnwEfgWugechqXO7pa9KlHX8RD/vcCW8KCnD0c+ykQAJe9d4OU0mT73t05mvZOUYu59Pwc/DB+A3pK4ahgsQPHsJLsHPwKcl7wcA66v8yrACHx35KIwOytmYV3ft2BUowQWQWffHshVeUJLh7fDculyI/AgWXi5WOHmS9TkA+qtE5WQ43seR8YE2frdhIyaZ2zt/ey08D1dJzwnIfAYS2oid/hyCi3AP/AQchP8kddUb4B975CHJ3Nyx/4N2kfd/r4ZvwPauf5JEDgPZC55RJdkfIDuHwvr9ZPpCzi++Cl6E/lWbSWQ4mV7bDs/BtQnnOlYOd8BZZRnelsB3/tDwx2AILkJHJtYYvzSa98On4G74CdiQJXkbsTMvJ5fhzfAi6PuwAMkTIOzzsWci8fOAXBxG3tbDngSIWIbDrw3++xH4IOMfyl7bsdeSyBJ7LcAAlGBlXc/F89/gZ5XjMJ+C9yv5HFfCi4y8J7H1LknOr2GEybDcHLkGJW6L9DTjMP0/H4RL8KPwGcl7ysdhSiWkBIgKOqevAwDOz88nvu47vuM78MMf/rD038ue8l4U2otfwdbwJLZhC7ZgFy7CHJbgIgJg7GcXtHAR5nAMWjgEF9Z/vhZ73QF4LzZhAifhKXw9fB0HYUXqfgCIh+CdOAlPCe61hlfAudDrfgl+M+Q6xEGJ9x2F0zjAPec18DyWIp594+97nzP4dznivoNwEbfCc33ftwtaEu3E3m/j+SrwDB6Cd0peh3g1vNC937jUfTufbXAab4AncRKewsNwWyJ5qsFu3ANP4Cicxh1wRuq6cTiJTbgO63AjPgC3491wh1SfAiAeh+/CGuzGUTiFZTgf2R4iGR6HJgKscn97KfZ5Rd+/A87gNq7Pe+XthZB+WMNrI67bkJ+LPdeU4QJ+Fn44Vg5FMhyM99HIPuLlHnEcmliBZ6RlKfgE42wSnsJfgt+UuKZzvzFodWVYVg4BECeggXvgCdwJJ/GT8H7pa6vwNM7Ao7gLWvhJeL+0buvI8ATW4Ub8RfhYpOzxn0PwTpyAZ7EMr63LItsG4d8zDi1cgNuEz3gNvBDTH6L2WMO74Y5ImRiF07i97/dr+Fl4V4xu4mVqbV0Oz+ND8PYY/cS3wUW8AZ7EGuxm5oSwMbPS9z1JZTgYQ6Nwirkuvn+Dfil3rz8tNc5ZWdwDT+C18BzXx9H3Dq7bCSfx6j45iJKnk3gUbsESnO/5O37eFH0Own/EMWhhr/wiboOzkXPsx+EXcRRO9f18B5zBj8MvRN6zX4bX8Fp4HhdhjtHRMvK0hiVYwUX4TjwO35Xouq3wHE7Do1iD3dJz7HY4gzvgFE7BE1IyHHyuhbPdsRfIlOw8GbTVODSZeTlehoN2DO7Hz0Fx11fgGRyF0xJt2fvp6MPzKJqDoq6twLN4P9ze9zeDcBG3w9nIe/bP3Rv6IkqGPwAfFfTDGi7AbYnlqQQXcQIa2IQJPADvlb72WjiL98Pt2Fi3y2VkYhucxTvhp3ACnsUpeAKr8LTUdQAdXTYBja7sR9sRvddV4NkemZSdKwNZHIVTAp0UrddYGRbLVP8n0Lkd/ZTc5xiDk6EyHGVXbIPnuHHW33aizzXwvEAHde43CU/hx+CXpOWpDBdwF7SwCROJZPgaeB5vgCexCk9L2gOsLP4k1uFGxi6Pv24nnMQlmMWH4Wa8B34cd0bq/d5PBZ7BCWjg9fBkrG5gP8fhu7rPOJTA5/gY/FKoDxs3x4pltyNHUc8eyNl2OC24NlqGB+Ci0NYbW9fnUTpxG5wVyvAuaKXow/ZeOwQXsALPSshw7/U74CTeAE/iBDQS+c0VeBaPwc14DG7GO+GnpH2OSXgKmzCBD8Dt3bZNEoepwtOJ4zDH4btwEebWZYC/V/j3jMIZRgf1Xhfl+7JywevDD8OvRLZzWBymDOdxLHa8rzDXbehe1ThMuWtHyfsrKnGYYGwOwXmswLN4D9yRSRymBrtxBh7FHXBGyu5n5WkjDnOB+31MHGagEqKfou8fFofZCafw47v/OLJttwl05iCsxPocHXlMHocR2XqDsCJpdyWPw3zs3XXEeh2x3bYdonYC2bwB6NxEJQmyurqK1113HX76058O/Zvz58/juXPnup9WqyX1MoWh3Ubctw+xVJJMgPQOxjE4zUwIG7+7El4M/Y4yXMRFmMPPwa1994tWBB1DoRO461Ukd8MdkRNEGS7iYbhtfeBfwo7DeKF7/2vg+dBrR7uBgyDIuIbb4GykQwHQmdR7J/zVroKfhKfw4/CLMe/6zLoRuYoAq12DOaqPNtq9V/ENd4OF4c7BNfBCX8BhCM7jODQTGXMAnSCy3MTbK0+7oLX+nLxxEm3UBcbqI/BWHGSSEAMxCYkKPItLMNsXSNsFrcgJjZVhdrKVMZDGoYUT0MDL4OWuPO2EU7gIcyEBnw05PAq34DAjhyU43zV8r4JvRtzz5Hp7XFyXh2/jZ+FdsQGPj8MvcI7XhgyHB6g2Pvw4TSbDG58BWMXroCGRVOsP9g7Da8KAp4wM35vAeAy+swpP911XgpXI/glkcUGgE+MCRJ3rvh939PTFGh6G2yITRqNwBmuwG/fBY1iGC1iGCzgM53EcWrG6LUi8bCQA1/Bu+HFswkSkTByCd2ITJpiA9ypeL5mQ6DdaL+Kd8FOxCYkPwEf72nQMWhIJiRVlGea/swQXcbAb8IgLom38LNAtJaHhHS7DwTw6Bid79FNU4IH9/QQ0+oIju6CF74Pfj+mjFqNL17rfFyfDk/AUHrn6h/B6ONGVqSCIFhV8q8CzeBRuYYKFnQTtQ/D2WHk6BD+EFXimO6+PwKv4VzAbK8MieRqFU7gEs5Hz64/BvX2yWoKL+ADcLpFU6+3r8fV5MiwoFnbdNjgbkgCJ14cluIjD0jIctEsn2B1+Tfj8vGELrob8PFqGx6GBVfgnZIsUopLogd1QhafxengSB+ECDsAqltbn+rhAWAWewTE4iQPr9xuA1a5dEifDh7s27SqW4DzuhhPribFk8jQIF/FzcGusLS+S4V7HWNynl8MrPb/bDqdxAW5jHPEoORLLcP879jv8YTLcm2yNKz5prY/r1b6fR83NqjLca/8H9trFbrtH6ZggICiS4Qo8ExowDNpgHJrrtl5njh2G81iD3XgQ/mPEe57BRZjDCjyDpXWdOACXuvIZJU+dZHW/PMXpxJ+G3+/7/U44iZ+D22ITGYNcuwdFeUnlKbiuXweHy/KGjmn2BVI78224LO6ClrBdSjGJ1sCH7cjias91cT7Hx+EXmDHakQnWVwkLWLIyXIbzWFq3E4O+FgUD2U+gEwe78rSqJcNp+rCjcCpUhkWFFmI56fz/BDQE41sm6f8sHoZ3YGmAl5/o+b0CzwiTC6pxmDh7LZDhMe4dR+F0rA+7MdY6cZghuCDlcwTPVGJsvevgaUau42U48GFfB091ZT9OhnuT2BsFgCpxmHDZMCPDfP+GybWMTpSbm/vl9364XVme+PuV4SJ+AD4SK8PH4GYmebIqkSyNjsNEJgjgl7AJE7gDTnblsAwX8D6JeIp6HOYX+9pmKzyP163bJXEJ6eQ+7BoulH4QcW6OEiHocBLkE5/4BF5zzTX43HPPhf7Nhz70IQSAvg8lQbAj3EtLiNUqLsOsdKCQN5jKcL5nkG2Ds7FZ2DJcxAfhNrwCXsJOYPV8aLUK/+9O9U2966DeA3dIVeaW4SIuwG14Anbjn8LtXQUiqtTqVSAdY3EWlvAhuBn/tCdoET0pBNVXU/AEPgi34Rws4SQ8hXfDHTHXbhg3D8DteBRuxr3wuFRSga0GGYBVvBFO4GEJpyJ43sBRDK6Nr5bof48yvCasbpSRp37ZkQvwdJ67NyEhTm71y9Me+BIGxvYuaEmtYOFleAecESYERf8ehxY+AnO4uF6FtrT5X0r1zzi08BjcjIswm0iGt68n7abhUVyAW3ERZiUCaJ3f3Q134CQ81SPD4gSI+J2DaqHL4BW8EWpSMrzhDHaSf3vhMbwP3iWVkOAn/CSVJaoyLH7nzs9KsIJ3wx1SlTHj0MRN60bZ0HpCQmyki2U40IkLcBsehttinzeo+qnDjViHG/Eo3CKl28bgNC7BLC7CLN4JP4ULcKtUBWanYn4OW7CrK8e9CZB4Gb4ensQyXMRN8LKUUxC0f2ndoR6C8zgCr0n1Z6knEbKGl8GreCPUJeRwpU+P9Vf1RskDG9xbWXf8Nn7fkYnooF8ZLuIe+FJXnjYSING6Kfi7oW6ytaMTxXNWf/sNw3mcgAYehltxBh5dD3hEOZobAeoa7O7qxBbskpLhoILyXvhxvB9ux6Nwi5Q8dWR4DutwIx6Dm7EBE9IyzMrTMJzHvfC4YJ7sb6u7122VoG0vh1ckHVuRPPH2ktw8WYYLkisyRPN6rwzLVI+Owmkch2aPs9lpx3hdXoKLeDm8gkNwHocS2IiBDFfhabwfbsc98ISU3cVe9/C6flruJsbiA2EVeAYfhNvwAbgdT8DuRDIczMt1uDEkARIvT1fAy/ighO0irxP77/mr8OGen/frlbWQ913rkxf5+TVahmWrmHfAqfVEzsb8GlfIoSPDgf3/FngU74F3d+21cWjGBC427HhWhivwbOzzsgF6VqZk7YGlzf+yqxNPwG5ckFzNPQqnlXVisKJ6AFZxCp6QuK4zL/JJg/42DZMLfZ9jQ+57ExIyFe074STug8dwCF7r2mwycliGi3g/vAv3wmM4DK/hGJyM8Dn627kKT+NeeAzvh9ulV0gHMjwLS3gYbu0mBON8DlYnHoWb8UG4FafX7QEVGT4Ktyj7sP0rfMTzR2fVXq8Mb+eKi+LkouNzyK7wiS6C6ZWzsL/bKPZi5VglDhOtE3ufK5DhYXgNh+D8uo6Mt2XG1wPeJ2A3Pgw3S/scbBzmPrgd/wB+spsAkZPhBk7BE3gf3N69TlaGg0TC5fCytA/buypnw9aTnW/iZThelsQrdeRkUTUOI/Jhk8hToBPLcJGxUaJl+CjcgrOwhBPrvkB44jImDgOzUgVJh+E2bMEuvA9uxzvhJzs+R1m8won9qMZhSrDCxPA2ksIbfRzVT7JxGF7vXMTF0Xcgtlq2I9XWcTIJ8tnPfhY3bdqEDz30UOTf0UqQENptxOlpxHIZEQAbE9+FV4ysYJCQ6K8S3BggQ3C+u0IhMFr3wBM4DK+FTLziwd1JStzaE8wNV9h80K/jNH4ObhUqrUG4JFxOW4aLuAyz2IYtuB8+3zcJ9laT9Tr7x+BmRAB8EbbgFDwhPSmMwil8Am7EswD4HGzBe+Hd3N+E//8eeALbcBW2Sq9jqtH4v+1XZmNwGqfgCXwIbsE63BhhPPYbSWNwGh+A23GBMXjj+mPj352Jb4oJvpXhgrQ8dYyFl7sT4SBcjAlib/x/GV7rOrg74Ezk6p4weXoYbhZW48vIcLghJ26zCjyDLdiFLdgVs/1K72ccmtiCXSEyHG7wbIezWIPdiADYFspw+P//EdyBbdiCCIB1uDE0ASLaZuIGeLI74YfJU/9Y7SRC9sJjuAizeAxuTrSdFTvhT8ETjDytCOQpeI7AmRXJ8HnhM4r+f3A9QB84qf//9v49Sq6jvBeGa6Z7psEWsixLmlFPq90ewJYZxfIMM1hAOIYDwdxy4YQcILzghOQkJyH3kxDy5iQm77eWwXAWIRASxYRbjC2SrGgsQTjok2Rj3iNresA24G4TvhjbPS1Lsrm4bUFsNNI83x/dtad27dq3qtq7qnY/v7V62ZqZ3bsuz/156qlPknckpqdx8jQcIdd6zsGWxO3G1ml4mczDwciAh//nU6Tr0WHQIAunp0lyElZIDYCQAQ0na0FRIqvQJPPec2neSWl4mczDYe9UlOg95wLv/CR5OywNEjdXkvuF67OR/CDw8xJZhV3kGwMaXoDaqMihFgVpzg50Yd9gnSFf9+RTuHPqpynqAPV5tS8ft4Y6t8Fx1EgHlsg8HCSvgyvIAxFrG5zzbnIvHCGvgDsGCYakz46Rpz1d2SJXcjQc/R3T5EHokikAQkJoOF6Wpqbh0UulaJjS01FybUTQLvjuT5J3eEHGaBr2PzviJUL68ulZ5Ech7wnSAa0869trXx/QP9vG8bxwDKNMoE+OhtfXuR/I+s9wBXlAKMv5ym76qZKOl5BIS8Nz5GvQIxvhCXJRYhpmnwNCBAmQjGh4pMPYA2EJkGh6Etsu4nFTGj5O5mFpy+sEMpF9p//n/5O81wsWPov80Nc20293+d+5fmKgT8PP9pIK5wOtB0cHlcIs/bM0vGug19PQIaWnz5E3wVFyrTBQEnaiZJ2Gr4UrRv4tMQ1PkEehTXZ69lo/ARLWntDfznKSnIQ9I8fhCbIR2mRnoPLaz6Ni2j9HCBwkrwtdjzB7AAgJKY4D6PsNwfdOkEfhIHmdlEyk9MvbiFHFI9vIKZggJ2GcPA3j5GkfPfXbCZ/33jHioyfaQlbN56BJ2vFBa50kBSv0s52c8PwN8b6eE65xmZyFOwZtk8Q+R5Qd/w7okY3QIxthlnxNuLeioPsEeRRa5Er47c2XwSZyN2wNBI+jfNhvwv/v7/8Zrt+yBb4iScPd0XpuPixNvARpOGyOlE6T+hx+eTpKVmGMPDNIJjw9oNPznF8sepbV32tw+ci3leIw1OeISvbzPhCl4aPkWqg+O+w0XfC91IcV22vxcZg1QqRomJXDwQRIBA2X2l7RlmwcZinyRJEsDYetdd9eu5J805OJ/TgMr3f8tEhpWEUm8j5sanq6+L/AEpmHT6Xwm6lM7JIpWCE7Upzc8vsO4acBg7LiIHk9AJH3YYGExWHEz5XIqieXjpBrYTe5NyIBEnz/ehzmRQF6KpOzcODCt0B9lDuJU1qD5WXDsWoLYF0SZN++ffDsZz8bvvCFL6R+D94JMkC3C9BoDLirBHDFFdAZqcM/kjf5Ms7byKOwj7wJPkXe4Qt8VskKfI68CZYGCYUe2TgwGro+xiuRVfgEuZ7LYvcF74XkDHRIzVNmrCNF+3EeIK/nqv/6wn2BLHvvvYrcC6yxS6uN/S0B+gbDhWM/hs4gcNcmL4Aqk3yhirNOHmEMrnUl0SAPQXe0Dt1PHYb6lh/6vpeQvvNAhSibga+Qp+FnyE54xeQkHC03fIbrKPNOv0G7ti6g9/2/0Pv7f4Z50mSqc9d/T4/v9dt6UEOuC21yJUC5DD2yEa4mX2Oe8z/L32tRIU97xoLonaPkLHyU/DpUyYrvnSVyFq4k93s00SE1KXrqDO5GCDoH/QDgbeQXfH0qR8lZGCPPwCz5mnfKx28w9+lpK3mMc1JYemp69+LUfMdy17wsvF9BntdGw22ykwmE+elpvbImuD99Gt7po2E6vv6zK4GfU4W/MlqHmpDe1ulwhKHhOnnEc4z99LQeDJsmDwrpqb7pCS9pk46GH/Xm2SE1uJCc6e/HyDmP37eRE/An5P9h5thv/bab3AsHyeu8vWlf2A9+f478grfWk6QLHye/zFRn9tebJl7CaJiQNdhOTsD/Ir/no+FRsuq9+/7B6YplMh+gJ0rDfkPkvEfDlA799NanJ/++imnYH7hY39v1Sk3/z1l+52mYvmcy8Kyfnvw0zNNhsFJxB5PIk30nfZalGUqr/dZyjwLrQO4gHY+G7ydXegkuVmaukBrTbrH/83HyNCwNkjYsDa9XcD46GC8Ngpwf8M95+AR5h1eJS+npdvI6JgjdbymzSH7GV5k5PmiJc5i8fKDnLoIl8iK4g1wLB8jrgNV3W8ljnjycJCd8lYwL5Xv6SXQfPa15NHyQvN4z4OmcaWED1ZM8PZUH7Qt4uVYeOPJRNExI3+AWBZXL5KwX9GgGaBi8yrDgaYDzsJvc5wvuCOlp5LQcDY88GkLDHY8OWXnIjtdfvbX+82WGnkQ0vJWcgn3kFwYJpHUa3kZOwr+Q10GL7IS7yQJcSM7ACDnrtWig+pUmXmn7kXHy9CBpswC90YuhRzbCEXIt7CLfgBp5xDseT2UipSeayN1E7oW7GZl4kKNhQtZgC3nMk4clctY7NTJCzns0AYTAEplneuGf9/pW+1vZrJ9OYHWzLhqm/B7UzX5+V6Fh0TvDaXjNR8Op6WnDKxg9+ZTv91QmHuDkGkvDayEycZT0Ww5+krzDZw+UyVn41CCBcv+gD3iVrPgq87eSU/DX5NeZuy36P58gJ+EgeR20yU7okBoskXn4JHm7V9lIaXiZzMM+ps99n4Z/CZbIPDwxWKf1hLa/gGGKdOGj5L/77P8x8mOoMHoyyO9rTGskfxurdRq+CGDrVo6G120kPw0z+rW0C+Czn4X2xMsZe2BdB1cZvc6uc5V0oV19JcD//J9C+VQNtHJap+HjAxrukCnYIbD1qoGWkEH7srPpqoHd5ac36l/56em8Gg0P5Ms8acI4eXpQjLcetNtGTsFWcjJw91mVrMBRci20yE54ZEBPf0N+zXcP0V+TX4flwalV6ueVyFn4G/JrsT7HNnIKPkp+PeBz8LbeEXJtgJ4myEnO1uPpScKHHfkhdC77T9Ab2cQFgdf3cIp0vZ8H7PjROsDnPw9Lv/+5gS2ekIYvfimcPHIE/mnsKphgfI7RGL+5Xj4B3faT8OjSEnyZvCAVDS9d9CqAajWUnhL5sJWvD3g1SIe8z8HKb/rOkUGx57qO7Rc2sVXZrI71fI7B6SuWnibJCdhH3gT7yJt89DQ5OOHfJju9ExKfI2/y6YlRsgr/i/yerzp+hJyFElmFC8gZuJ9cmTIO4/dhxbquH8hnfQ5Wx0bRMKt3eD1YIc9A+5KfFNprlD9ZGmbpqUEe6uuskRFYItekouEW2QnfJQTu5fzmWBoeWZH0YbueLF2nYX/yIWkcZtSXkOjrnq3klM/nIOQ8TJKTnA+7w/MbKO9tG9Dh58nr4AB5vUcX63aiWhwm3P5PRk+ycZgumYL6SMf3XFK/OYyGgydSgjpL1ocN2rTg0b7o59RvBtK39XaNUNvHH4c5SF4/SOD6aZSnYXriZoksAJB+wc/6yeI1uPCCNeh0TAerzcOqJMhtt90Gz3rWs+D222+Xeg8mQRisrKwnQkZHPUE9T5pQIU9DfdOTXqUvyyCsY8pIFZ8BO0lOwIFBVTIQAisbZ6BOHoZx8jTMkG/AQfIGz0GlDE1bZ+wjb4I22QndkR0AhECTzEODPLR+sqH2Kuh99B/6yZvBs7vJvVAjj/jeSZ+tkQ5cNfpNOPinx6Gz7YXr4932fNhT/irUycPwCfIOmCNf8y4Zoxe3z5BvwK3kLdAgD8Eecjf0xrZA7/Y7Yc/uH0FjtP++3eQ+qJOHfc/Nkq/B7eT1sIN0YIE04TGyEd68dSu0B1UI8e/8hf47y1+F3l/fAlAq9Q2rkRk4svFnYRf5pvccEOIFcfaRN0GdPNIf674vAtx+u7c3S2QeDm56O+we/aY3XvrsUXKt1292ntnb7226FL4xMPB3DS5hapJ5WBO8c5Z8DTqkBk+MjMD33/veID0N+mOnpacKeRpqpANHB5l+/tndjMJnnxslT8OzyArcSumJTPmeo0HGNnlB4FmWhmnVOn2W0vD+QZA9lIaf8yJvvAEa3vIy6N1+B8CWLdAjG2FhcJTTo6fRFY8m6uRh2E3uhQPk9bBj9AQslO9dH+/oxbCH3A018gg8j/ybb50pPV1OHoBJ8qhvnf999GLYSO6GOnkIbmPahfA0/AnyjnV6IhsBtmwBuOSSdXoatLzx0eLIDjj6+wfhAPnpdb5h1rdNdsKRTT8PuzafCKfh0RXfc3R9//Fdd8GOQSU+S8OfJ6/3gsg10oE2eYGPlii90mAZ+yydL/05NVr71siUnw5HunCUvCIRHdL9YenpiICGKT3xNHz14LkqWYF95E3QiqThnd6zHVKDDeSpQavAd8Bucq833yWy4LWSunXgnLP8TmlYRE+Uhvv0dIKhp4s8Gq4OLgod5+iwTh6GK8n9MHFBzwtEPTF6seCd3xa+87nkARgZ9JZmZQWlQ1oBx8qYLpmCI+TlsH3klC/45dHTRTNwkJPf9HfLIy+CAxvf5snv3sgmn1yizgGlOUrH3jsHTsX06EPeftNPl0x5x/HD6LBPwzt9z9Fn6d9Qx5nSBP05rWTk5doecjc0yENw4OJ39KumBvqV5XdaKdT7pd/18d2G8R9DqbQG+zb9uidLgRAfPfF02CMbYb58L1TG12By04+8C2+bg/ZClIafR/7NuyS5Q2qwxtDwKDkLzyffhtrg7iYYGfHe+SzyQ+8IvUgOp6PhjclpmJwcPLccoMN95E1wITnjcyyofu0Hv1dhw7NWoTNS99FTHA1vI496DtsTnEy8g1zrtSj00TCZh+PkGqiPrqzT/tgYwDvf6f1Nm+z0nC2eFtmff5nshPMDG5Gn4SrpwD7ypgAd1kgHPrfhnbB08XV+mTiQp7R1AUtP9L3UiefpyaNh8jq4Y+t/he7GF4TQ8Dz0qlfC6kUX+WQipSd2rjw9LZAmfI+zByrkaZgk3cQ0DKOj0PnIImwYOROkYfrO0XNiGt60CXqTVySkp7OwgTwVoP+D5HVCelom84PA3AkYJ8/4ZD99VvROasN8POSdVCaKbL0umYIWuTLW1hPRMEsT9Lm1wc9XmTFT+5+nJ8/+J/fC0nNeGaAn1u66Q6CbaTHF0ubX9J8bGQH4wAc8vV4lHbiStIS8s5PcD1Oku64//vqvoVe+xKPhRHbX2MD+HxQz+d/5SPg7R5bhxHs/BKsM39Q3n4FPbPkjnz3A+xw+e2B0FODIEegcuBcObnsn7Cb3+WgYCIHlra+DAxe+BXaQjkfDa4TAjZs2wTcGcu02hp4+T66Bs55cE9MwpaUlMg+7yb0ePT20cQZWBjbiEfJyqI10oVJeFdIT5XeehoEQWBrZA+XR87Bh5IyPhiN9jtFRz4cNtfU4ejo8eJbqs3U7vi+fvrf1eQCbNgVoONSHJffBwS3XQ+eDn1vv4EDqMEe+JqQnqutmydfgE2/6AtTLJwY29UUABw5A7/4VmB+7L56GRx5d13WTk/DYJdPpfNixr0Lvc/8boNZPDryQNGEiCd+U7oHe8QcAPvGJdT256To4OPErAZtN6MMObLbe6MXQ/v/8Cxy56I0+HzbgczA+LBAC8Pu/D73Ri71WOfOcfKI0fHwQ/OZlKU9PdfIIrGycCci1MJnYp+EzUB49D7eTn/HRBNVZG8hTXrEX/87x0ipMkpMePa0RAiuXXM3FYV7n4zuW9vsy8RUBn6MvE9/Qp//aDMD/9X/5aLhKOnBl6dtQL3W9taI0vHPkWzC17SwsvOAM/Pvf/zOslUo+v5mnJ9ZvvrX0i35/cnISeh/5DMyT5URymO7PKiHw+xt3wFwcDZfacCt58/o7Ry8G+JM/WfdhybWwa+T++DgM2QhQW1/jpS2vg4P/990+ORxKw4yuapGdcHDDmz2fgz7L+xy8D7s2MuLb3ygdG0fDMnEYPz1dG0JPrws+O/o1cSyR85vDaJj6zVGxn2+SnfCHmy711ojS8CRZgWeTH/r0B6Xh55J/6/PeyBkvnpjY5+B80R7ZCNcM6FDkc1BZOjFyGirlcwE50dv2/Og4zKY3woHBnW2iOAxt98rud5dMQfu9/whHJ98Cnat/Bu8EgQyTIGfOnIH77rsP7rvvPiCEwIc+9CG47777oDNIPb3nPe+Bt7/97d7f33rrrVAul+FjH/sYnDp1yvv0UmwSJkE4rKwATE8HhF57809Cd+vs+s8HDnB3tA7t8k8EBCXvDPkCP6USwMQEdMcu8zkhfYntDwZ4zzEON2VM77kPftAzAoXPch/v2Y9/HKBSARgfB9i+vf9caTN0GQHKfof3XKnU//9B0A7KZejtvAa6k/PRzxECDzH//71SyXNK2Kr6sGe7ZAp65Uu8ZI/3GRuD3sim6PmWLwGYmgKoVv37MHCmYteKEFgb7AF1Oulz9N9rIc+dHx0FuPJKb48CNDFQOKzREElPIzP+8VJajHj262QjvIzshAcF8+yWLvU9t5bgnWuEwFlCPCfsCebZbqkUWCPfOwfz9dHwxES/Hd3kZOC5SJr4n3vX6XCwFn0anhLyXpdMQW/TpdB9Xj9A+URpMzw2oKfHyEZ4ZBBEiqV9Ou6xsQA9BuY8OtqfHz9nwTonoUP+mT3k7r6yZwKJQNYDYXtKfkOB7s0T7LP1l4qf5RIvMDLi8Uy79BPQ3TYnHGskDddmoP3Roz7D8/yFF8Y++x2yEb7eeC10J14YoNG4d3ZIDZYHxhu/zstkHh7xHIUpeJxshPOCfQmlp8Hv22Qn9Eqb0z1Hea82A4995CNeICvu2QfIRvjPZAq+MZjz2siIT0cI9Q4hAFu2QLf5KLT3fd3PN3F0SGVM9UXQ2x5MRgRoUeBUCOkp5Lm1BM+tP3scprc+6bVxSvJOIAR6l0xDd9//29f5rF6I4VMgBDojdVj+X3d5fM1+lgdVY2Fr1N78k/11FMiY1mA/A+8fHR3Q8LxQ19F3CsedRCaSjdAd2RHgoWS0X4u0gei4Rb9bnngDdG6/V7iOYTS8Rgj8ZzIFLyA74btpZemWLdA9eC/0tj0/NQ3/e0oaTkqHvvFydl4cLfZIvzUKTAn0Ovfc2sD+OEn6AfMOqcHy1tfB9y6ZTkRPq4TA6tat/X0Z3QXdy14WoEVWDvvef8klAHNz0Nn2QljefJ1wf5bJPHQuuCI437Gxvl3AFPmE0lMc7w3oSag/Nv8kPMEkd8NoeG101Pd8kyxAZ8dLPPswCQ2vr9WV/rkytkSkPTCyw7+3o6NwPu7Z0dE+/QtkC31vYhtk61b/mAdFSaw+FD3bJVO+BLrvnQJbPMr+XyP9oEf3o4vQG704+p0X1ft+Dn1ubAt0P7qY7J3sWlUqfR/tIx8JX6uRkcBzZ0dG4LsjIx7dUHo6XyrBaUJ89kY8Db8AuvWXBOzOLpmC9p/8A/TIRWIa3nQVLG++rv/vzZt9zy8/9y3QufUr/aKepDRcKkG38ZPQ3vQS31xXuWd5v2GN8VdYvwEuvjjw3mhavMjv+1IaZmghdG/Hx6G75er1cZfLAJdf7tOTofS0dXbdVxobg8dGRuAHjC8YS09jWwC2b4e1ctn7++XBWkTScPmSvh/J7vvWrQDj4/FrVb5kndfpf0dHfbwXye+E9Plnc9C+5eWW8NmAbfoCob6LtONLJeh8/Euw/Ly3+n8+Ntan4U2v8hVT+GhpUx3al74msEbnJyag+wcfCr6TKYIV0v7oaNBeKpUC/MPKH17veHv7kf3wvckr4BmGRxL5v9UqdMsNj56ofOr9+rsjafjxAb19i6wn0NdGR+HxuHduqov3NhXPbgzQQ6JnNzDxCUaO+uyuzbu94D0Qsd3Fx2p6Y1uge9Ot8ePlx3rJdN+OZ8dbKnmyP4yGe+/8fWg/72egW7pU+L6ksZ8k/i+7Rn0aDuoU/rmzhMAPL7tMyOesD82v0fLoNdD5xP8X4PBhH+9E+g4jm6C7ddZ7/7nNm33xwMjnPnEI2tVXRfpoofRUrUJ3tB58lqGdwIfKt+lpvBMEMkyC3HnnnSC6tPz6668HAIDrr78err32Wu/vr7322si/1zmZocKxY+HMMDoK8MlPAhw86De+BEKdF1T8v0UGp5FPtRoIAsV+6nWAAweExlPchxrJ3nowTljkZ1Al5K35Jz6RftyS8z03CNLwSnSNcchEzwUSJFTQjowAbNvmnzc7v6jP+9+//lyp1P93zDPnCYHHybqztTY62l+/mtjZSvo5K5jrWdnvS0ID/IfyXQgPhn4GTvy5yUk4PTCiU4+Tfd/AAEo1Zvrcc56Tft6M7PCUvSCQ6AtiTUzACVGy9F3vE9KB9+zoaHBumzcD/P3fe4GFVJ+LL4ZVNoiSdr9TyM0AXwrmIkpiSn/S0CBDA+e3bYMfp3zu/GAea4TAqTTv/cQnfMn2NOOEAwciZWe0E1RL5Tytec9FJyR6my6F7sQLQ94Z8ezY2LoeSyp7eB2kQhtpnuf/NowH4nSIiO9GRuJ5KomDIENPks+fJQSuJQQelnnvrbf2951/7y/8Qp+eQmi4Qwi856IZ38kTIARWt2zxBYFT06HsZ/BeIW1FfB4tleDs9LQX+Esls8bH158T6JtE+y37ibM1U8reVSIn8390ww3wfXYu27b1kzzc353fGLHf27b1EztpbQ92HRj5n/jZiQk/L4+MeIUnqW35iQn/nia1f/7kT8R0EWcTDE4x0b37Ll27ajXd3tP3pZ2vKv1GfM6F/Fy4t5s2+ceedN1Z+lla6vuw7FpMTMSvyXvf6+f7lL4v6yM8zvyMTQJJ2VB0/GmfSzJn0adahXMcvcby4ejoOq8NPo8SAidl6KpUCnxXog+TTA580tIRIQB/8Rdy63fxxf7nmKBr7B6zcZeDB/uB1xRjWCN92nsm7m9l6DDmvfS/5wQ/B+JPIMZ+KP2ljGeskn4hBBACT198sW+eYXIoQHu8vXj99cnez/q6pVIg8Rm75oN9Ps/7vxfNCNeAtbsCRQojIwB//ddyvhAhvsLWVJ9Nm/zrVyoB/PEfh/49vyfsPLT4rNx3paJBOn7637T2DF2/sbF0sSj2nZddlk43p4m7sjKR6k1EPu2w8gImQTgIToIEPtXquuAMEYKnCIHHOKF7jnAB4jjGFSmEuGfCfh+mXEql9SpC/lnG6Qh8brklfp0EnzVC4IbNm711WCuVAD78YTlncHq6/2za56amxM9FGJbnt2+Hk6KA79iYlwhhP08Q4rXLECqqLVuiDZe430fQHv2sTkwEaBAYGlydEgfO6ed73LOhweKQcayVStHGbQititYz8Bw7NoFzkeizZYvHx2tpHYCJCbl3bt26vq9R+xcWQGH5NeXn1ycm4MUSz3nzTbh/3mdkRBgcoqeIgCQwuj/8YTgfxpPbtsWO+wc8/QvkzDnSl9ep55dkzcLkboSxv0oInEthqFGjVTr5mObDjTu1wTzY/+/LOA+2fNI4xxdfLCUnThMCPxu2p2FO78hIPM3yTif9cM8ldq7C1mJyMllgg5e7KeTwd8PGuXWrXIFJxNpF0fmvTUwEAmKJPzJ2DyHrlZ9RAWDBWnrrJcN/vM6No4G4d0UVv1x0UfRzCXgwsWzixzAyAucS0O7qxMT6PqSZN32mVgN43vPC/0ZEG2zQWvD9YXOO5efpaYCbb5ajQdkPHX+1miqA7SXHS6VUehIIWQ8cptXtdKw83UXR4ebN4TZ8mO2bZkzsd/ByPW5+CXzY0HfSvYp5jiY6whIiIt22Njqanq5Y+z/NvpbL0kVg3/urv4Iu86610VGAHTuinxsbA7jlFp9/c6Jchu/K+LCE9McukhFhNEn3bXIyuHeDMQXkx0UXRRc6Db5HSLfcaaOo51N/6HP1eqSf/N2REaFMPFUqwWoS+yQp/Yl+Xq8HilLYdeITId440wZ0KR+nlIUnCfGK4mILdtnPTTetvzNqrDJJtZh3f+/974dHZGkmzocV7ePWreG66YYbEsmP81NT8ASf+CckNHbEf77LrWOiRNXgE/B/BfsSaSfxhQ7sJ4reZAsMQmJOiZ5N+6HPpfHP6nU8CQKYBCku2ATI9DTA/v3RzBxhvJ9l/4Zj7CdKpXjhNz0N0Gz2kw3sz6OcQ/rzWi19VYxIECQRLo1GPxObVDiK1ijtJ00CJkEQNOl8z0UIS15Yr05NARw8CKtxSrJeB/irvwqOo9nsZ52j6O+P/ih8PKUSvLFahRoJJuO+NzoKJxKsXRIFHfns0lJfYfzd3yV+jirkc7Va+uChpHF0amxMWIWfaP6iMU5MxPMflS9pxzszA3DoUPTfiCpsSJ/fpKvOkvye/7ty2efQ8sbb9wiB1bg9np7u0xG/nqOjoQGQtXIZ3j45GV4lnmYN6vX0wSH63MpK32Hgxi38/8FnlRDoDv7/vMCJCDNYz4a9I+7DtZLwfUfMOtGxnCqV/FVWURXQhPSroEl8EjbRuNN+ZKuAJR06IP0E+mrEPEUJ2POjo/Bz1SosiPY8SdB5//7oJHqpFFt53SH9AH9qPVCvAxw50m8dE/V+fg9GR9cTvCHrLaL/NULgUbZFUZTjJvqwJysJCU8UhXxOjY3BmsyJOELiddy2bWL5U6/HVzCGrUOCJDpdS3qC1Ldv/J6FrTVb3ZeW3w4dird/wujq5pthNc1JX/70FJ1PtRpNR3v3BnlsYsLvN4gq5ScmoveOVhx+7nPB9zOta+P2LtUJx5tvlpOp09NBHyXpZ//+RHb897l/v4uQ5IEwfv1kikjou9Kuz7FjfRsgZaXxedIvyng6pRwKfJLYLiIfdsBDkfONsXHXCIHXEwJTg/1if9cl4XbM43S/arWgHxnl+9JnJJLKq7UanKN3gdLPb/xGome7pRKsSiQDT42PB05lh354+XH55et3l6b5TE5G0uLqxRcHZUW9Dg99/OOR/uJZQuAxft0//OG+73fgQPSYeB9W5EPcfHPw5xMT8fbN4Pc8raUJIkO12m8bnpb3p6f7vB/VVUT0CbPHk4xT0l/vlkrwXYFNKSwM4+co48MSAvChD0X+fpX0i1xEtPbOuO+WKR7eulUsOy6/3J/ouPTS8D0LGcPJ8XF4ab0O3+cSIecuuQTeHyXj2YJJ5rNGCDyR0MeTKVKjnxPlMpxsNsN1SLksjvWxxQ1pigCZPeKT51InXpLEYcLs6N/9Xf+/t2zp+zMLC3gnCGASpJjodv0JkKWl9X+nUUxs0IA6M4uLqZjXCyAnOZUSxdT1erJqTEL8lTRpHKL9+8VCJKpCZPD5v0UCNEkCJanyf7R/AgAAqURJREFUnZgIVrEkqB73xk/bZiTdezKoPKFO1vQ0wIEDXi/u0DHygZBqNUh/l1+ebAxjY97adysVeGO1uj6epB/myPH5Ugl+lqwfn038qVb79CtBw91SaX3Mk5PpApBcqwg6n6jKzlODdT05MhIIrD9CCHws6kSUyIGMaRkEhIgDAOVyUPnyAZrJyVT7SfuEs3x4olyGxyN4/Fyt1g9i8nOoVPrBKZFhMTm5zmuTk/4A69QUQL3e7zXKPBMqG0qlfssmuj51cc97+uENvVOjo7BAiN/JFAUEq9V+Kxv2WULgNGug1esArVZwr/77f4+mwaWlfiIzZRCL0t8jZP24/ioh0TKErmel0jfYr7wySKNRzzcaAHNz/bk+97n+McfIHB+vigLbYZ96vb9GvO5Iwuvlcr8VW9S7tmzx/34QkF296ipYjavYrFb9zz7veQBHjwIcPx7P1+wdQaWSFxzvlkoBWuM/PyZ+B311+3Z4Y7XqJcVC1/G97w3Ovdn0ry0vR26+WeygM/qxWyrBywfjYv9mlRA4KzjhRef8+Cc+sR5UEgXZwqqqP/tZgD17+rzGtmrZvh3ODcb1DCHwA45Gbrz4YthICPzi5KQ/2VQq+XW46HPTTWI5HEf7pRLA/v3+YHtUwOSSS/y0k7SFBVuxzSfwop7n12HnTv/pkZDn1kZHA+1Ku6USrJAE7UPYD93bJMka0WcQRDpz663pHOD9+wOBZ3oHSqIxswl9KtdCTl0k3i9Cgq1P+eDHxERft9KAC7tuPD1G0CVbZX89SVFoJEoWJF3vOPsuzKeIs+NDxnCWEPjDNAmC7dv9fJm0uIYNNssmiFZWUifz3koIzBOB/ZLkO1iZytJimn1lk3RpfN9SCeDyyz3/90FCYGHw3zTrdp4N1qdpdcbKPNYejfn8+sQEPCp5Iu+xxUWpQPCZQ4f6zyb5e37td+xI/s7xcb/Pm6DoZI0Q+BWynmjslkrRNojoI4qhXH55PA0z/qvvtEdSnmWfZ+y4syShDmBPOtF3r6yk22Nq/8vEb/h5J32ejd+8//3+FleDuxbCnn0rIfA0TwOlkrCLhO8jkv2lEsC73+3/mch/TiEPeXskcWCfvwOWfsISv4Ssr5uIBo8c6QfAkxTFjY8DTE159uHDhCRPeoZ0i0hsxwwSh2vsHPbvT1VUukoIvHNmBp5stYKnXcrleH1Qr0udsnuG9JPkfHvn0yMj8cVYtVrfh6Byn84/Subx68zvPzvnffswATIAJkGKiF5v3QFvNoMJkXo92qhiDU7aN5kKgyROYLkMj3z4w/DQ4N8/Zg3B6Wn4/o03xn8H67ix404qhGq1/txbreBcwwRQ1OkTmggJO/otUX0cWpU1OhrM5NPvokdmRftXLvcr2fhn9+/v0wV1qOmzlUr/70MU2mOLi37jZ/B3qzt2wA8jnvOENe980IBQnDPG7sPBg97711g6/OxnoxUhV11D9+dhQpIZwXQt2SRcSmfqLGFOB/DPxxkRpRLArl2BoPm5yclgpRL36RACVxIC3xwf910wenJ0ND7pMD7eXzeq9PkgsmjcPB1HzW1yMmDMnR4bg/NsMH1sLNiqaGIC3litwsPEH8BaIYxBJtiP7378433abzbXx7VjR18uxFU2Uqeh1erzSqUC0GrBqYMHYSVqjuVyP3lE6ZzK4Xp9vaKc0rBgzD8mfgNxlR1PvQ4wOxtIgH7vppsCJ2NOj47C1wbr4wU4RVVWorn81V/5g1gsPdx003q7hKjgZaUCT09MwJWEQJP0ee/rH/94fz2jniuX+0Z6twvQ6QDs3u0ln2DPnv66hT23tNTXf90uQLu9zjtU1oW88ywhcPq22/yBazrn8fFgK7TNm9flKKsv6/V+X+e4yr4rrhDzjagq8tChfmKHkYtPHT0Kr5yfhzdWq5FJpTP7968/OzPTX09WF4SNsVrt7xO1H2Zn4dSBA/BIuQxfI/27GMKefWxsDF5dq8Gpgwf73zOgee8y6/FxOPMP/xCku/37g/MfH/fToUj3lUpBXTA21qcbRu56rRMH/6WO6Hf5cWzZ4r3Ta3VXq62v19RUvBM2Pd1fv263T49zc/25razAk62WuB0lIbBar8NL63XYs2cPPHn8eH/txsf7/AAQXY1JE4DT030nh86r0RCfQGNo71yt5j/JxtB0aOBepBPp+vF/yydjaVBpdjbewWS/u1rt83WCqvTf2boVvjbYw2cGQeBn5ubgVbUavILEBNUrlXWZw46BTyqGfTgH9Fyt5u35qqDyd5UI2upxcmV1YsLTfYGx80GeD3+4Twssn9brfrkm+oyN+d9bqfT/zcrRVis6GE71Jr9H7L6Hvb/Uv+OF2mtnSb8KnxACD/B+Q7nsp2k2uD462pexg+Id4Vh5O4qXx3F+QtK/p2Njfn+a2cMoOjxLiL/opVbrr2uzGc839K4cQtZlwPR0Xw/IJMJYfq/VEiU+n2bubjtRLvdpn9pAcf7RIPjmew9Lw2E+7NiY386kF8DH+b5cYna1VoNfnZjwEh/Uln6QkOiq58lJWKO0xfo+aQPQ4+PrvHTkiLDN4znOLvF0XK3WT5axz7ByeetWYSFNrE8mCiaLElSjo+F8vn27zy4IjEN0qrNc7ttVvA8b8/neTTfB9PQ01Ij/zq2TpRK8lcTogPe9L1g8msSH3bzZHz9haY/OmdoQYb4ctYG5ApBVQuBrhMBb4uZOx8XrsaQxHNYH5ItRk55MpD4sjd/QNSuV4k85cb4//ZybnFy/P0nwofv5423b+nKOo63vjY3BeRHvppHlon3nE04CH/ZUqeQlUh8gXMswvtCD/e6JiXWZz8ZGqC8YpQf4tS+X+99DfYAwHqU+LFu812zCarXq2SCPlMvwwPveB49HJZiovGbiZ6dKJbibBE/VBYqcw/RsvR6wZQN3pND4zWBtzvH6asuW9UKaqMQCG4fkY3AxdPhyQuCnajV4ZvdugEoFzm3dCt3B3H8nrv0Wva/jyBE/TcbFb2q1dR+AtXvKZX/xxMAXQWASpLiggSA2IcI7JqLKFNHJDVrpxwbwOEXI92d+pFyGBcJd9lmve4GUSMVChQ1rvDJjOc9fxET/nzU+B0Lbe479zltvjTb2jhzprxl/JLlej79AnT8NUS73K8H5uYVVk3Dz8QloKhxFDhCrIPjqbGpId7ve3q9OTcFTt9++vj4iRUafY5Uv60yx7+U/9Pg8q3xZo2Z6ur8uomfZynlqdPLCe2Ul2hjj2picHB31VYAGLrQXraUo+cauM6OQ2O85SZggIQ0iMEnAyEAEW/3TaPSrtvfs8XiOVmOvEOI79rt6ySVelXO3VIJrBu3Dvkb6lfgnR0fX25zU635Hg6U5mjzkK/8HLdFCx80avMzlzGvE38tzdXIS4OBBX9IwEOTnx0T6BmSN9I2Kp668Ek6Uy7BC1qt+HimXof3+9wedGmr0cYFIn3yj8oyn5WPH1uVpuw3QbsOjS0ue/HqkXIbWX/5l8J2Li/1n6Dv27Ok/z9IATa7wzly5DN/88IdhnvRl59P8PLrdfhU/156HruGPySDhN/jeTqkE84TAS+v1fiKErSy56aZw/qV8ztI+pQ06t7BTc0xVVXewbxtJv53E9PQ0PCpKaNPn2MQRNdKoLut2+2vGPzs5uU6X/HOUdw4fDhj8a4TAtwijo2jgennZr/sYee4ztplAy9r4OJzfsSMoL3mHj9J1owHwyU/6eb3Z7O8tb1zTEzxUn+3ZAyfabXhpve4FaFZrtUCPbiD9oLo3J1r5w9P+LbcE+O1crdY/Pg6wbkcA9BOAg799pFyGb914Y4D+A88yhvzqjh39iqyVlXAd1mj0DXaaaOODWPRv+NYSNIFL9QJdU2Y/HiZ9mfgwIXD/hz8sbu1VqcDpW2+FDhOIfexznwvaUbz+GRvrJ6xY3cXT8AA/5BNyf/7n3nOr9Xp/jRi5E9g3lp5uvnn953TOvLxjaZL93Hqrd9LlGTKQz4LE1Rr9f5b+Dx4MOuZJ7luq18X8PDbmp302EFMu9/+9Z09/bqz8qVT6bSq593QrlfVkHGMPszL8HMff3rtoEpauOQ02ULkbFQTmAwjMuHxJe8F+rG7b5l9DGkRjgmEPE6bN4NiY+ERwudy3HY4eXU/gHzni5/tbbxW3BDt40E9r1LGmsoTi4x/3P3vJJWIdxxQQAACcCWuBOViPTqkEv33ZZR7/PUj6pwm6/FjZoISoaKvRAPj858U8Q/+fb2MYUnwCpZI/iFyr+ZMK7Hey+8rZ8Kukr9ObhMAvEP8dWHwbF88uqlSCOo4/HSf6NBpBm/vzn1/XsTwNs+8vldZ/x/t7rD0rKKrw7kiksmPQLsl3IoRdXz5gyN4NwNrxlIbifNjJyf768HqOXTNBIA3Gxjw9szr4uwcJgV+ZmPDmslqrwRurVfGJkK1b/UkiKleo7RYWdGT5lz31Qe0twXNskcwzW7b4Kt3XSiX/vrPrwyeVRCeySqX+KVqWng8cCCYfxsb8/+Z93LDTXnQvDh/2/45PDvP7SmUK6//yp5H5Z+p1+Or+/TBF+n4QkL7MPH3rrZ4O8OiV3xdRADmJD8uvf73et7942dhsBnUPW9DByNC18XF4hqwHVp8W6Nk1NvHO6J8fb98O39m3z5+Eobwpiv8k5HvW1mRP/XYJ4//yMojle/5E+9at6/REdQ21uehp/sH4V0XPMoF2WFrq202s7V4qwZnbbguXmyE+LBACwLZ/CovpUP4K8WGf3rIFaqSvyx5i38nOWWRTlErhNh2lzXpd3GmC+rA0FjM7u24/sTYjf6L85psjfdhHymVvLkKfm13nRgNg1y5YK5fhm+PjsJMQeOvkpK9A0/ts3+4vLmPtv4MHgzIoLgHD+5psYjnOh6VrS7sS0ORCVBHCLbfA2cHPHyQE7j1woL/e7TZAtwv3HjwIu0ZH4Tv8Pg1OHgaS5nTPo+ikXA7yDevDlsvr+87SzPQ03gkCmAQZDrBBJFYZsY4DXzHGBgpnZ9eFZFj1H+kbZm3msqdHymX4r1u3wtOk72Df/+EPBwwPHyOzjiNbeccYrucHAuax0VF/q66BUDgxOgqnqSNNKyPZFimsI1gu9x2Yeh3gggv8AmggtOD48f768E4kF8TyxlKvrzueA2X95NLS+jhYx4lVlhdc0BeqrONFv4sV+nw1Fh3b4cM+gX5++3Z4bGxsvWqeBvj27IHVeh1+rlqFLpvUYh0S1jCjQpWOX3SyqFYLKl+Whqjy7XTWjRreuGQDWuw67dnTf44PYLPKa3R0/f1sBdj0NDz2uc9Bt1TyVy8P9uxkqQSPipQnn7Sg+zk+Hqgu5o8p06D10+xJGFaBsutXra4b8GzFM//+dttnRK9Wq54zeapUgqcHTtQjZP2ejKerVZgi/eDzPB0PTz+sQ8TwkPfO3bvXx8YHUGhAneUbPuhQKgHs3AnP7N4Np0ol6NC1mZyEb42NQWfAr+cvvHB9b9kK01oNVrdu9VWePDqo9H+02YSfqtWgSYKtClbKXIss+t0iORhl1FAaHuDk8rJPtj06SBbzfZRXp6bWn6MBZFEymo6BqwijBuYU6Z/o6ZRK8MyuXUHjtV4H+PjHPQfdO82wvOxre/VIuQy/NDPTD9LT9WAdE5YvPvlJv5HEBtHYAJjIgGTkJ5vsfqRchq8uLnoVeV4SnP8v33aAN9L4d4ZVqbLPUcOP0ytU/zw0oJ1HeNlPkyeD71yrVODk+Hi/JR9XELA6NQX3jY/Dm6+6Cp5st/1yjTpjvN6ZmxPLtbDCA0o3lJ66XV+g5o3VKjw94F8+COULxidolbnG0zgF887vcDzn6XNKS3wCICoBKUr00/1m14MPInS7fn1Zq/Vpn78Prd2GH+/aBSsDvqIJuZfW6x7vrpF+K4UTzNwXeN5jExmiJAit0ubfzzsaooREEtrnbaM4vqHj5YsR9u/3BVRPHzwIj5TL0CQEXlWrwcmjRwF27/bo4EHSb8918ujR/ntYPd1u+20D1mml9tzsrL8KdG5ufWzs/or0X73ur0ildhlLP2yCk1ubVU6Gs8/6TvfwCRz2OXqqTCRr2Ip0toCB1fVXXQVQqcDa2Jhng3RKJThz+eVwslTyt4Vgg7WsfdZuwzNzc/BIuQxXEgJ3k34y5BfZlpLj4/6TvZTfaCKN1UEsvVQq/oAUz6fUBmPpUUTDNOjA0yGXyOPvOFkbBJ9ZnUETH4+Uy3Dm8su9k5fPkEEAjKUztmhrfn5d1rJ0NwjEJLKx2b+h9Dc7C08ePuwvsup2+/Jm8I41ugfHj/e/k6GRtYEt/vIBj9372c/6fCFq43TIug25ytqQlC74ebF7vWuXWDaInguxYX0yXETn/Mk8QRDJd+KY8mG1Cj9Vq8E3N2zw8x1/6phdX1be33GHmPfT+LCiufPPDVrfPtlqQbdSgXsqFThx/LjPf6M6MNDOhU2McbTja0MaFzxlk1PsWBkaZk/tPUwIXDugIS+Qz54KZJJITx04sL43rN/I0j89bXf0qH+vWZ44csT/e7qW9bq/aK1c7p9KpP5TGO1QH4O372q1vq8cRreszmDXlUlYnuBk5lsnJ3220+tJSDyC0h71VZP4sCw/8j5sWDFqDO2fXF6Gn6rVYCdhipkGz50eHfVkx8nR0XVfZMD3P67V4PjICFw1NgY/pr9jePKUKBCdgO8pDZ0l/gJYWlTyMGESnmz8RaRD2SAuS/tsHIYp4AyMj+Vl3o6fn1//OU9HSX1YeoKala+UFxuNYJKW82HZ4twHCfFo8ZFyGU7v29e/m4HSE50L36UjqQ/L26S8HcPbXTwds/TPF0Ex9sNX9++HGllP5pynp1noPrI2xp498OjSEvxstQobKQ0PaGmtXIY/37x5XedVq+tyYM+e/npS3mNtMP6EKuvDsqcNeR/2+HG/DR/hwwZkOaVFxhZf42JKnj3NFIo9OljHlZUVX+HaGq+rRUULfEcDnofofov4hu0GxLa+Yu06bImFSZChAlsZyxqYtMqUdbwoA9KWJPxzjGJjj+mzzsszc3PQbbXgp2o1uJasB0w6pRL8mCoQti2IyGCfn19XEq2WzxG8f3y8L0hmZ+Hk4cPwSLkMd5N+pcQqFaLUeROdjKHCgc6RqbT1CQeREzlQSKu7d8Oj9L4K+s5eD6DVgtVqFe6pVOCV8/PQY51Idk3pODqdYPCNfhcbRGPXg3VwWSOhUoFTR47AS+p1uJL4A3wnjx6Fl9TrsJEQuKdS6SsdVqiylVYsLdB3xJ0sigrcURpks9Q0eAIQzFKz1csi5U8rhGkFNW8k7dkDvU4Hfnp2Fl5Sr8MzbEJsehpOHjkC9wyCm+dqtX6wolZbV74sLbA0wCqYVssLsM0PDJwHB2t7kgZneDpknvX2m647/Xv+/dxzlA9qhECNe+dqvQ73VCqwkfTbSHh7Td/D7x81MkTvpM4nv+9UOfN8w9P14HsebTY9A+DuwZhfUq/3DQS6Nuze0tYx7Ta85aqr/EHqAR+trKzAVY0G/NfLLvNky4OEwFdp+7ewqoeohAQ1LHgaBoBepwPf3LDBlwChz61OTcGpUgmeock4NgjMyt8wA7bdhtO33SZMHEwRAlc1Gv214p8bBJjXBOtDx/fM3Bz07r8/ILsCzhxfJcTTPgU7/lqtnyzjjMZHBvxA9QClATo/TweIArgiOSzie9HesXKTf67RgB/v2uUFuNm15sfK8/mpgdwkA7qlvE3b62wk/VMuJ+gzrFyjYAMytC1VFE3QSnlRYHywfzShzdK/p4vYwA8bGI9qlTk9Dc8M5BlvxHdbLbinUoEHCYHXbdnie+fX6WkE0TsT0D7s3u23AzgaDug9kT3D7jtDw91WC65qNLyTSMeOHYOXMM7I6vbtcPq224Am6ejPHymX4cF9+/p7KtpHMri7RpSgCXM0RAkJwR5E0bAX3ErLNyxNcomaUwcPwlWNBpDBGn11/364f3wcHhw4rSusLKNydfCeXqcDJ6mu5mj/5PJy3/YJo31eF3Lrt7qwAKeo7cPpwtV6HZ5ik4vsmkTRocgGZm0Z3gaOGys9zcIG4OlYKc+12wBLSz69vZEQ2DmwY1e3b/fTMMv7AhpeOnQIfpbh+9WpqfUkdVS1H29/0fl0u+LnWNtNhYZ5vhkfh1OlkncK7+mJCXhpve7x390DnXeKaT1D7f6X1Ov9k2ZRY+VtbKrzZGzsbhd6nQ7s2bMHXl2r9W1mhhbpnv7mzp39giduvs/MzcHPVKuwc7B3K4Ox0OeuJQTuGxuDBwd20ZWD9RDagey8WFlA58f7T6LneBqmRU7lct8GZmmYff/SUv/DfwcTnD9Xq/V1KH2OGceJdrsvCzj72aeP2Pfxc4/iXxkfNua5J9vtddnP8c1qrQar1O7Zty94SoeOm9pOIvnNnFT3JbnYufO0yNDw0xMT8PCAVzYSAvd+9rP+vWN9znYbnlxaglfOz/d9AVZetNvwzO7d/cuDx8dhlb0wN43+nZ9f9xUoPVGbkq6D6PuOHvX7GCxv0p+LnmP9JzagPvBl+NY99372s+u+7/i4Z/dRGdotleAx2oWA1x+8L5qFD8vaepwPu2fPHnhJve4lb+hzjy4twX+enPROuXj32g10xosGgWc659VBQd5qvQ6vrtXgbjJoUzc5uZ4kSMD3qwsL0K1U4BcnJ+HHV17pX/eB//mWq67qnzKltKiD76NshdlZeGzfvqCt0G7D6c9/PugDpfFh6Rj4v2P4K5CkHfiwtGCP0tnTg4Qw1eVdvnKf+ulRulzShw08y9uQ+/dHt0wa+HGs/fwQIfBz1WrfT+D9lQH993q9IA0P+GZlZQVezbQLDfCNqJCw1fLTE+vDttvh8RuKBD6s0KZmnjsxPt633er+4o/Veh3eMDEB32HsmIDPwRbGxr0zAe2HxozCTnqI7LohBSZBhg18EJevkhMpoLDnGKV+7y23eM7LRkLgq4uL3vPHjh2DjYPfeUFK1kEUCRr2vywzD4Tw9PQ0bCQEfrZahaVDh4ANFq7wgXfRGsgKB+7ZE+22LzjWHSivlZUVz+Canp6Gruh9onGwhhI/DtF6sM+xAfXBGNjq66dmZnyBji6vSNLQgkzgjh8r/87+oMXvFD1LnXfRs9R4BoBer9dff562Bvv3JJvg4R3pGBqigXg+wEb3/VE+C8+uH////Dv593PPfXVxEQjpJzqmCIGlQ4cCdHfs2DGPV15Cg1lp3hk2bh58okvwd8eOHfNOpxBC4Bjbboo+J6CLXq/XD3oI6KLZbMJP1OuebKmxgQaAcHqSpGEv4Cd47uTycr+PfxTth8yz2+36eJUG4ykPk4Fse4YNerDfF5bAjdoX9t/8vsfRgUgPDNb7XKMB39ywYT1xw7yfBn5+ZnbWnxjm90lE+yn5nn/uZLPp49VHadB/bs77+VWNhl9WM3Nj92J6ehq+urjok6cezcnoFxmZOHjP0qFDPvo/duzY+ntEz7Lry79z8HPaMog14qkceVG1CjMDB5rlOW+vo2R42DyT0LCEvjzRbvv2jO4Rm9B5ab0O3W4Xjh07BoSsJ5WfuPJKeOX8vH9vGb5/pFyGt9DTP6LTHyKeEgVO+GdZB1C0Zhy/JeabGLpi6Zsmzz2dwWPwHurcssFdnl/27NkDPdHY2P3j/x8Anmy3vfXn9Wi31YKX1Ovwyvn5ftBOQMORfKNi9wjGmkaHsnqbDPS2Lhr2IMODcc/RucnQMK8vWy1fYQRNpu6p1eA1u3at2/HMWB9dWvLTU5KKRk02NtXPIjuf1SkBO59JoPjkyGAcjzabUK/XYev4OEwxNMG+I5JvRPMT2bLMc0+22357mH7f8nI/URFnB4v4gFnTXqfT/37BOLrdbv/7VXhJhX9lnmOfl5HDUc/RBKrouQi9R+Ufa0979BUifygN83KdpeGX1OvBpL+M/h3QU6gMkdTngef4PR3oiW63C3tqNa8IkLWn6wP/aIoQmKnV4KmZGS8xrWzH9Bc0Ex/2BJ9EGDzX7Xbh35gi0FfVal5MhBAC9Xrdm/NL6nVYOnTIi1l4NnoS/5dbd58c4YLWQjki+I7UfC9hK/jsEP5EI4sEPmzo2NgxMnTB6o2X0gTAIEHF6vQTYXKpPwH9cRi6Z/xzAJFFFHQtaWHl09UqvHRAS578CRlvgIaZeXa73X4L2LT6nP23Rh82TpbTu/t8snRlxVeASv0k1p72ilFl7HhV2xMhBCZBhhUqQp57jgpGkUHGOtZsX/iAY52SeXmHPRCIyhF8cIwGjJKMyQvQC+A5DEmRcm+inmMGkVyR+F8cbwjKvFP1WY1gDRx2TXl6CNtfFYjonxq7JsYjM14hb6TYWz5BQE9OBL47CxpWoX3BPNlgohYjPmvE7JMXCBE8d3J5WSzXsuL7wXOBgC3zXCBgG4JM9Y7E/FinZCpsPCn1OcWjg6RRmIxR4rmcaFi45/RVg0DSnj17oMU4pTSpPFOrBeVpr+er5vfJ0zTBD0v1JU0E0U8gUR14PDsdmPS7T9AihgTz88GAHSEjP5LScEBuZTF3zfry2LFjvtMfS4cOBW1iZqwBm5gbq1Z7mkMWdj4Nysp8pwyiaCmpDjT5/dzL8uf7hM+q0HAcVOhQ5Vmrwe1Lp9OBDRs2QLlchnvpnVDQX/upqSkghMDIyAgcP34coLdeWBnQV4bpKM1zIntNFIcxHS/JAiZ98QCYPQrIQ2ZvA/LQEp0c9hy/xt1BIk64xpbZXVrHMXhOJEvZAtSVlZWAPe0VvKR9JyIzYBIEoYQoo4oPyuo2uNI67FlCt3Or02HIdJ1sUWgGkKvDJ/huEV9VKhWoC6p3sxyPynhl5YBWo9eEMy38uuwCOIg+dKyxLXonj4AGP9dqtWqHo5kCcXve4qry4mwYtvI7dTGHxfpSNlCSJR0WKWhnTbGMKjTpS3Y9aJGOyp7mYY/pDCaaCNxl/U6rgpGGkCUd6ljfogfEAfrr1BCc0m21WlCpVIAQArVazbdOJn0kXYiyTW2xW7OCrbZCFrpbW5I1hS43Fe+wGVGydBjkbBGASRCENKIMMho4oIEE3cawjQLGpipKChvXqUjIOziRhGbqfGuMhOPJYi5Z0fiwGWRWBcEyhs1ztUWemtIdNiZZVZBUnrL2TBF1qGoAIUu+sIXnVJA1v9osM0WwvjAiArqCiTYW0ejgq7jCONH320ijssiSDnXRTNED4gDDR4cYkLXXVtCpn036vq7ZGXlAJEttTcghgsAkCEIaUcL4+PHjMD4+DpVKBVq0t+0AzWYTGo2GtKC2UcBgFSUiD2RlALn2vfS7h8EgG6aEj81zzUKeytJw1us0LAGEpOt46NChwgaNdAXtsgys6fxuE3ojCZ3Nz89Dm+/Hn2BcNstMEbIMEucZ4NcRZDNlw2QdLByGBHoUsqRDVZqxKVCcNf3bNNcsEWev5dlyzzRsS/Dp1s942s4eiOSLzW3JEUFgEgShBJERQ4V+rVaDI0eO+H5HBcHs7Cx0Op3U77NRAdhYRcmuU9hJnHq9Hup0h8HWwLOt48oCLp3YyGq8wwQbZV5WsHWuWYxL1TnKiq9s3YOskKZdVhEDKTqcdFdOgthaRdlut2FhYUFqXK7xa54JXJ20WLSioqyDhUVopagCGwPww1TIQWFbUFw3THbksA028lwW+rlougjAvThB1B4MU8LfdWASBKEdth8H1ilsba2i7PV6MD8/LxTG1DiqVCqwsLCQeL62VhxGjavVakE95OJQGxWrSRTRsCoKbN2bLAxXG+eaheyzNXhJdUeYEV+v12F+fn4oZKeNtAign+9Uvi/LNdL93bbynOq4bKXTMGQd8NBtT9tKN7IwcRJk2CrSAewKwNtWyIHtoPUgzv+tVCrCjhxFC8jarAOzGJuNtG17QlQXsmxLjsgXmARBZIIsFZKKA6Vb2NpcRdlut0OP5bE/T2pk2uoIho2LvQSPV0g2KlYbYKNhhejDtr3J0nC1ba4Aw5XwWVhYiEyCpEmeu4ok+q7RaMDy8nLo86ZaK+Wl17K0CbI6zWojz+kYl40y0wSyWAebeE4VWdN/1PfbeL9SVgk52/jRxkIOlxLoNiPulKFMm0WXoEqHeZxCyEIe2JRktT0hqhNFsgeGHZgEQWQG24xAgOyqYWysoszi+201LEXjGrajwLpgk2E1bIiTJTbdTZC14TosdGibnnTNIckKcY5Oo9GADRs2QKPRyNURsml/sr7zSfdpVn5stvCcrnENi8wMg63FV7Yga9mR5PtpWywbaDQr+WWrn2RjIUcWstgmHYkQQyctqvBxngFtnfrZNhvG5oRoFsjKHoj73k6n47wdYhMwCYLIFDY6ZbYI27wMNd3K0jblGzWuYTyCrwJb97YISGLcRBnjNOhn095kJUuHjQ5t05O26EjTiOLZ5eVlaDQaRgItNu1PlsFh3adZWdjGcxSy4xo2mckDA5/xyDrgF/f9ttkwWdBMt9uN1QuNRqNQdGhb8hYrtftIoptNJHez2B/ZebgYh7HJ/tM5rmG3YWwtvCoyMAmCyAw2CzQbxuZqBUIW36cLonHZsNcuAKsos0MSXp+dnQ11nvlTTbYYvez4dfEXvcdHRIf1ej3Q29h12CqfbB2XTTDpjA7L/mSxxrauney4bA2K5AkMfCZD1oHRsGf5xKUtNKqbdzqdDmzYsAHK5TIsLS35fre0tATlchk2bNgAnU5H5zSMw7bkLfoc8fJwfn4eFhYWcpeZtiWss9afOr/ftrXjYVtC1CUk2dtyuWzt3rsITIIgMoELTpkNwtbFXpQuBhBk93pYDOksDSsMTiRf32azGZCbLrR10yVL2+227x4fUSJofHwcjh49KnzeNZ60XU/aoCNNIansN6kPh2V/sIoyfFy2B0XyQq/Xg3a7HXoZabvdVtINw2IL2nh3RNbQKV/wJAgmb21A0subw05aZs2Xtu19HA3bctLEBX9ad0K01WoNhe4FiOeLpaUlq/jGdWASBKEdthu8/FhsCuTrhm5DwzbDJcm44i5jDDNuer0ezM3NDcXRw06nA3Nzc8I9bDab0Gg0pOfqgjzQCdVKSJFsEvXFZ//WJB3qlKVJkiAjIyOwY8cOK9ciDWzni2HRkSKkdTRNJCOGbX90rLGtPKcyriQtFGZnZ0MvJHVBVsbBdJsnl/ROHLK6N9H29cuqX79NflIWyDJ522g0YHl5OfT5IvBblkiyNyZp1TYbJkwG2HbniM0JedGeNhoNaDabwr+n442iQ5v93ywQxxe28Y3LwCQIQjtsN3iHxUDV7cy4GECIayPUarVCabXZbHpHD9lEiOn56gblV5GhQuc6OzurdHx/WHhOV09s3hg/dOiQlUZvFvsa1Q6rWq1CrVazTgbJwGY9OSz8GoY0us6EQzJs+6NrjW3lOdVxDXshhw0Xfruid5IgC/niWuBOVY4OQ6Aq6+Stbf3vbabhMCShQ5O0astp1qg1UJX/LtKNDER6g56KE7UHZOMLSVtBF133UsTxhS184zowCYLIBLYK/WFyZnQ7/K4FELrdrqdAK5WKd5cAu9dJjgOzPRiLGGxy8WI4FWQpm5KsZbVajTRebFmnOGRJN1FrUKQAsKlLKaPe2W63M9lXW22CMNhaRTlMNgyA/oCsrXQYlchYXl6WOskxTLSSNS/apHdcbKNrK7Lc16hAla1yKA2ySt4CACwvL8e2FctTdtnq/yZBkoDpMJ9mtdXWcwlhtgZbTFoul71CS3Y9G41GaCcKvnhwGNY+ji9s4ZsiAJMgCOuQpXHosiEjA91raavhLhoX3WvRZcrsXrdarUjjhu3BWFSFk8TA07H3pqsX8uD/qLWMa83mkqGd9VpG0QoagXIwdVmmq3o3y+pAWbi6ljIYpiC+CDr22iWdooqs9YINeidP/jdtr2UNU4UcRZLhWfqENskuV3VREpllQq7Zsre2n/p1BVEybWlpyddVQya+wMZpirr2vV5PeC8omyzCO0H0ApMgCGOBbZXANV5wiEiCpHsdZ9xk4QzaRodZO202GJAmT73wp45444U3flxwsrKi4SS0UvQATRZISv9hFwzT70i7r1nzXZayNIs+0aqwSXdg0Up20MU3NujevJC1XjCtd4bt5G6WyEq+xAV4XbT1TMEmOrQlcJ8Utp5wsCmhlFYGmJb/NiPKFqR3i6rwcZHXvtfrwezsrK/zCF0bNokk+j3qDXlgEmTIYcrJVGlhVCQmTxJAsCngUWSEKdgsjHBbgztha6BqtNrkPOQ1Fn4taRussPWLOxJc9IAfRVrHLS1PDos8DZvnyspKZDIuK17Miu+ylKVxdDYstBSGPPTYsK+xLr4Z9kIOAPXx2hKQzdqGscleyxq6aTiprRxV8Vu0NVaFTcHPrGSACTqMagfNtiLjx1W04gZdxZKIaKjwcdHXvtvtCu9PYeddKpXgwgsvtOqeJNeBSZAhh6mMfNR74y6zLorQM9WaBBFEmILN6uihTZUwFHFGhqxj7OJcs/j+SqXiu9iN/9s9e/ZAp9OxKqiUN2QctzQ8aZvzlRXi5sn22M3TociC77KSL8MUCJSF6trbFkS3Fap8MwyFHHH82mq1lMZrmzzIyoax0V5zCWn4QnYPh0lu2hj81J2UyUKWqsQXer0ezM3NQblchtnZWd97i1jckGQstsl/16DCx1muvU10yCcfRScIhz1GoBuYBEEYE+5R743rnV8EqFZqoEOiB1F0mOXRQ5uMqqRjkTFkbAuWUGRVXRYn10RrhcZLH2mC9zI8OSwBniTzpCeTdNN/HLLgO92yFIP7yZFk7U21Pi0SZPkmKzvDJlmatS1t01xZZHW6Z35+XmirtFotqFarMD8/H+DLosk1FaSR/2n30FZ7OgskCQradtpMBlnJF9lOE+x42Kpz0/IuCyThp9nZWR8dFnk9soCKDZKl7rVRltqY9C0yMAmCAABzjBf1XpuOwGaFJMoBg6rZIUrBskcTm82m7zldCtIGhZfWyJDhS9uCglhFaS+iaKXdbiufjLMp+ZglbCwyyFLe6fxuFefIRscqa0StPbY+VYcsbWetj2yRpUl5jr1cNc14beTprGRpr9eDhYWFgG/BVodfddVVmVSHDxtk9nBYbMwk7WHK5TLMzc1Zc9pMhfdskaW2jicrJOEnbFUsDx0FRVnpXltl6TDEPm0BJkEQHkwxnui9NgSH80KSuYr+Jkl7HVTK0YhTsI1GI3AcmEJX8N60wsvj+L5NyNK4tzFYUjToSKgVgY6TQDRPlXZiusaS1Xt1ylJZOrPVscoaMvdJDUvrUxWo8E0e+sgWWarS173RaAQKXfhnbSrkyFKWhvFrs9n0Xc5K16voci0rqBS3DUOAutdLdlFwo9Gw5rSZKv3bIkttHU9WSMJPNsl/l6DDBsly7W2TpcPCc7YAkyAIALDrJIipII1JJAnepL1oGR2SZDBp3Nii8IalJ2oejgway+aQZRsKV2GD3hi2AEIRZGUaxK19XMCv6K1Pk4KXXyzfsG3D0vBNHvrINVnKj3dmZsaZwoW8ZSnPr2xQuuhyLSuE7WGr1fLafdbrdd8eFrEgKQ69Xi/yAnnRBcFZjiWPAifbZKlt48kKcfxUFL/OxDxsXztbZOmw+Q02AJMgCGOMJ3pvrVbzJUNEVYNFC+7LngQZxmRRkeCSwkvSNkzElzYYOCzwpEZxMWwnmpLAlhOEWfOdjbJ0GGksau2j1mNYAi1REPEIe29KvV738Ygt+so1OheNN8u733QjLxsmbF+Xlpac2m8boatF4LDIzTD/N+q0TBbIOphrmyy1bTxZI4yfiuI35jEP2xMeYTAtS4f1BLlpYBJkyGGK8UTv7fV6sHv3bp+RQ99LkyCVSgUWFhasFaRpkSSA0Gq1QpMd1WrVlzgqupFSFLim8MKMJ7ZPNN82zFbj0FUjTQbDNNekPBVVWeiK7Ex6civsUtF6vS68VNTFAILNstS0Y5U1dNwn5VKgRfaiWf5vwn4nWkve/uNPitiSAHFBlhblhENeej1MfuUp14pqw4TNiy/4k0kqu4C0+yo60epyQJqHbbLUtvFkjSh+stnGTIOs5+FqssgGWerq2rkOTIIMOUwxnui9rIAulUpeUJUVUPV6HdrtdiEM4yQKqVarwfj4eKDyhTWUx8bGnAi0ZBlAyAJZjsVFhSdaD5aG2aPprhmHRYSLNKaKOMeNT4C4SK9J9nV2dtbTD7ZcKpoVbKVzGxyrrJF07Tudjo/32EKXqakpJwItSeY6Pz8PCwsL0rToUuDJtcBQkvHSREhR+TUNbDgJYqtszxpxusMlOSFC2n0VrYfIJ7ZR7iSBbbLUtvFkjST85DrPUWQ5DxfpxqZ9tSn+NSzAJAjCGOOJ3ssKpEajEXpBVREM4yTzuOqqq3w9YkVJEP5jo0LOI4CQ93iH9dgoD5uMCBtgy74mNUjb7bYV49WFqABCEXRHkn1tNBqwa9cuay4VzRoqPJcFvyaRibbICVXEzaPT6XinBdn1YO2X8fFxqfsu8kQSvuNblMoEAVxJnrkmS5OMd2ZmxomCoqxhy4kZF4NquhB22qYIa5JmDiJaZHVHvV533uewTZbaNp4sIUuL9NNoNKDZbIZ+t41rFGdjqNimLsUDiiBLEWrAJAjCOsQJ6CIJriTKJqodlitH+PMKIOQ5XldoLA+4ErjJGrY5D3EGaavVsmq8uhDVrqMIweekQXZbLhW1FVnwa9Ik1dzcXOH4ToTl5WXPTmFPJR05csTjz1KpBMvLy94ztq5BXhWjrrRRc02WRo232Wx67QOH2YYJk1/NZtPHxzTwl7VNPIwJZZE9Xa/XodVqWWdjyiLJvkbpUlERoMv8ahsNu9a5QRZp+YnXzTMzM07yYpb3n7gSDzApS3XzThF40QQwCYKwEnFOoEvZZh0QKRWXLnMEcO/IqU1jsQVRinZxcdGJwE2WyCJ5pmrcRBmkRUr2JQkgFAlJHQ1XHBITyIpfk7QrY+9rcZnv4tDr9WB2dja0YINtfcrCVsctCT+p8Bzya/5AW28dYfKr1zN3/1sUTxQlKUDnwReDsScf6GXpRQl4JalIj9rbarU69D4HhU6aSJoAKQLfASRfuyLEYQDy8QmxkCP6nSq8w4+Z/T7+rkfXeDFvYBIEYR0wuCNGESoQsg4gmBjvsCBKcbMtdoZ9nXQGVHQ5GlEGaRECQGkCCEVCUkfDFYfEBHTTf6/XC20x1+12vTvNisB3ScGfSmJ1BO+0uYAk/CTDc8NEE6bABxC63a6XkGST5fzJLdsCWXHIokVgr9eD5eVl4bNZB9+L3B4KoD8Pvs0TLQzjf277XNIgTk6G0aLoNMiwykidCYmk39VutwvBd0kRpZtd6cgBkE8xKsZNoqGis0T8yX4fW6RQVF7UCUyCIKxCWuE7LMEdkVJxsRclQHYBhKwy+sNCY3EIU9yHDx/2tUlYXFz0/q6IFfhJoMsI1OHg25p41MmvwxhAwGIBfdC1RmmDEcO2N0XQpVnJ06IEc22GiD87nQ5s2LAByuUyVKtVH3/S4o4NGzZAp9MxOfRUKFKVNkA8PxUledhqtXx3QLLzoAUeLu1bHGT1X1H2Wxd06g7V+1qKuA9J1sSFIkDV+0+QN/VBdp3C9pAtROXjMLju4cAkCMIapFXkwxJAKJJSySqAkJXTJ0tjRTmuzoOnxc9+9rPeurD93tngc6VSgXa7bXjk+UNXwE+F/9M8m2eAMgt+HaYAQtJ9LZLuyBo66F8mGFGExEASFMFey7KKsmiBaxsh4s9utwu1Wi2QKHf5JEiREmoyus5V+QIAvjsg+Xm47Dvw0B0EdJG2dUKnrZfmu1ziO1m/PIlunpmZsd6OU73/JG5OtrWDtj0Ok0WiyYVknE3AJAjCGqQR0MMS3CmSwZdlACGLdcJghhgixU0IgWq16lt3l5MgWd7DIYOsK4vzdmSykmvDEEBIunbLy8uF0R1ZQyf9FzWAoIIi2GtJ+I5vx5eW52y6LNP2AIIsRLTInxZ0kT55DAvPsfyUd0IZT58ng6gFHSszRS3owuRk0f0rFWRlx8R9lwv0qvseBhbNZtNrqWi7Hady/0ncnHTzpsr3uSInZHlHtD/1eh32798f+D6X7bWsgUkQhFVIIqCLlBiIgyuCPA55BBB0On0qNDYM9Mkr7omJCeG6u9gOK4rnaJBdxHNUPmUVfEhrLCWVHa1Wy0iwxJZ1cg1J97XT6WjXHUUMjGZBh0kcyCIEKZOgKPowCd/Nz8/DwsKCFfbaMAQQZBEWQCjaHQOiebrURle2MC6P/bPt9LmtEK0Tf4ebqEWkbEDaNhoGyHe8Ou3fJN/lCr1mZYcU0Y5TmZPuNsdFjsOo8o4oDsPbMEXrgqAbmARBOIeiO2g8XDP4RMgrgKDLIFOlMZsNoyxOORQpgBBmPPHtltg1pGsyOzvrVQRlZWinWd+4vTZ9uaFuB8oVh0wVSXlYp+4wpXez1H9ZOkpRAQQXHDRdCKObXm/9snSebmy1a5LQoi32WtEDCKoQ8WcRE+j8nGZmZpzynZLwkwl726bT5zYjyp5mi974kyI20aAK8rSbdNq/RSzk0D3eIupJ2+aksmc206fq2ET8ycZjRCdcXaLDvIBJkIIja4fMlMMX9t5erwfLy8vC9xbNsLLB0U6DvAIIuhxZ1UDjysqKdYmBLJM71Wq1MAEE0TxFF2/zf9toNGBubk6rw5OlIWdDQlmGX0U8x++DTQZvEWDCOcqaPk1V89rAd3mC51d2/s1m0zfPIs7fFIoaQFBF0Qs5KETzpD3DbQhw6YDJoJ1OHrEt+KgTRZYlcchrX3WucZLvcpVedSaKimjH2TgnlT2zsTBOlXf4v1tcXPTdBVKtVjEJkhCYBCkwXA0gyMK28fBjc70i1wXkrfDi9sK2xEBW1aF88MAGQ0MVcYGSMOfAluPASWEyoSrDryKeY9epXC7D7OyssEIUjUA1ZB3M4GlRpXe47DtZyNB/0jVysZBBF1wNoLiIJDLWpUIOADXeSVLgUIQgbZQcYhMhrs/VtD8k63MMW2LYxmBkXsjabtKpT5N+V7vddjYOobNQst1uh/rL7XbbyvnHwUbbVGXPbDvhqaKzRPzZ6/Vgbm4ucCk6G7OwlRdNA5MgBUbWjqZtjqxt46HQbaTbOk/TMFFt5GJiQHadwuiYnWulUoFDhw457VSzEBlPeTpzph38LCFLh0mMQFb+ub5OtjkkWdF/EeQL6ubkUNHXtvGE7YgKArhWyKE7gNDtdoUVky7zLJ88FskhPmCSpSwtakcCCl33tbnYIjAJ6P6I1snleYlgIqGs009I810uduTQZb8W2TezDVjI4X8uSneI7DXTPGczMAlScGQdGDYReHZpPADYOzYP2HYk3vbKQl3Va3lUaptC1BrlWVli2sHPArqPAx87dkx4Fwv7PhfXyVZHKwv6dzGhzEPHftnK71mMS0YP2coTtiJujV3jOxXdIaId+jPZS5ptRK/Xg/n5eahUKr4kCMD6vo6NjeViwxSdX2Vk2DAly3n+YtepaFXKJhPKurtNyH6XzfyuM24yTDxsEkn2rAgFVGngWsLHZmASZAiQdeVynpXRLo6HH5OupIWuQDYLDBbKQbQXUVV4NhhHOgKZptc9K0TxaxF7h+cNHXRjo5zXDRsdrSzXPYrvbKtID0MRAwhZjiutHrKRJ1jYZF8ltTtdK+RQsadF+0N/JtofV23idrvta93J7jUvS7PcU9v5VQUqdDgshWyik1ZF7VfvWkI5C9jK71iM6h6S7tny8nLg74p4wjMKSItywCTIkCDrymXbeu7ZNh6AbIJHuo5hs+NzMWgNYD74wO9FtVq1do110qLpddeNpI6MbYEh16CDbmyU87phk3Gbx1hEsmlYEo/DFEDgn0+zrzbxBAub7Ku0e+ZaIYctSXCb7R9bklu28qsKdMjEYbDDhykJAmAPz5mEjfxOdXOj0YBmsykc7+zsLHQ6nVTfa4seKiLS2FM8zR06dAgqlUrAhhE96zps9RtcACZBhgDDchKEGoFhQRTaqsckdAbtZNYdhWU2CKM50V7Y6BibNlBtQpjhxTpzlUqlUK2/XIQteidrsH3C+bnyF6hmiTx1hyihPCzyylb5rHtcqt9nI//bZF/JJGRcKuQAMJ8EtynpFQZbkls28qsKdO190U9kD1M7LApbeM4kbOT3TqcDc3NzwnE0m01oNBpStGhaD2UBPqnK/puPYWQZ00iT3HUpDqMTNst/24FJkIIja4faFoddZGjxVRhs4NIEWq1WaDVrWoGssu627FlRIKpAELUhADCveG0K0gDYW70W1jKDyhhejqChkS90yzCb6ZAat/v37/fpjv3790vRnO4L+QD00j8GEOwMIADos2F06SEbgw822VdFDiDYwCO22VNhsCW5ZSO/qkDVbtBFw7bTIV0nXRejq7aczMPWs4XnTMI2fs+6JZZNtpoKeFuf/ffS0pKPZm2jYdtoLi/Y6sPaDkyCFBhZG0Y6vl8X44qO3K6srMDKSrCFjQlDsNVq+Y7mie4YSKpEbDuGPczg96LVagmTcewJJZPGgk0VAzaNJWx8w9A7PAy2GlW69ZrNdMjOtVwu++Q1/Xeec82aJkQJ5aRH2m2lV1nY5sxR2hHdzZKWT2y/EyiL4KYrCQXTiZsksGm8No0lbnwmadGEz2GzTrDtZF3W0LX/KrrDZCGHzfI/C9gaY9DJJ7bznCx4H4s9ic76HezPbUj220pzCHuBSZACI2uFryOgonN8OhMNOqE7QWPTMew8YLMjw+8FazywNNdqtawxFmxZT5ur12wOjOcBm+eve2w20yEAwNLSkud4lMtl2Lt3r+/fS0tLib/L5rmGjY09fcCPjcork/SahTy10Znj7RjWbgnbnyiorFuWwYes7CtbK4Ftlgki2DheG/mVH5fJIJ2Jcdhsw2RRyGFzK2id+6+ydnnIDlt4ziRsXwMd8tpGPaQT/Dz279/v8zvoSXQb99RGmkPYCUyCFBxZBzxVvj8LJaKz5ZQuZNGqy5Zj2FmMjf8uWx0ZCn6+7NrW63U4dOgQKuMQ2Gq4uGjg6uQ72+evW8bwFU0sHTYaDSvoUMdJEAB7eU5F1pui1yz0k637wxdtVKtV36mQsEpX3XDhhLPIvuLXyCZZaruNxcLW8dpWVGSLDjc1DlvmL4JOGua/S5R8LVqSVUVHZqlfVedqqkBN53vb7bawHTRrP9jgO6nKa9tPVev4fpEdw/shNsQ0bJb1CLuBSRCEUWRhkNjmjADYVanjQhUlhavKzdbKQAB7ToJQ2LpWtgYjRQjjOzbAz/OdyWprWyDqdcsb/LOzs1a0r9N1JwiAvTxn6+mAMOjWT7YGEEQnHEUB/jzGlUcQXHeQjT/xa5sstc0miEOn04Hl5WXh75aXl6HT6eQyDpvsetHYbEgWmRyHzTaMLp6zSTaLkNX+q9gwWdk/LrTqyvK9vV4P5ufnoVKpCNuX1ut1qFQqsLCwYE0rOpX9l+Vh27u0sODjaXv37rUyvmaKd2RluGs2V1GBSRCEceg0SGwN7rAwmaSxvYpSpBjYZxuNhlWOTBRsTcbZ4BjTsdC95tdqcXHRCiPABXkCIOa7Xq8Hc3NzwhMDSffalfnLgl83PtFgQ5KVP6nCjqvZbErziY3ySRUm6FVXoM3mAIJt1cZ5OJAytBRl/4iCk0WSpXnBFhsmixPeWYzRhkBL0nEMS2tB3eDli6lTemHIig5VbJis7B/ZuRbhxBSfkBN9V71eh3a7rXUOaWBDYtT2OAyFKydBAPLXdS4mPBFBYBIEYQV0GCQ2KLc0YzShRGyuoowaG9sX3zblK4LpfQ6DLSdrbK/AZ+FKsJjfw8XFReHdEWn3Om7+tgRaZMGuh6jlVJo7N7Ieny69Zqt80gET/KpjPW0PINhc9Z4V0tJSnH0lulQekQ622DDdrt67/sLgun5Niix9E1dsOFnwtMjSnk0tiHTCxpMgqjAVw9D5XpvjMLboDtE7da+T6vfzz9t+JwiPrPWmCi3ZRIfDDkyCIIxDdwDBVqFii3HgYhUl/102OzK27LPN4xPddSAytJrNZuZjiYKtzlIYoip3VA1h0fyLUtEiSrKy62Zqv23rp207TPLrMBRy2D4+nZClpTD7SnQaxNY1sz3wbgsdtlotqFQqXsCZHQcNQNvS1sR2ZOXDuWbDyYDSiSjJWjQ6AbD3ThAdMEWvOt+b9RxsbUOVFlmvk+z387KYPYnO+kX8XYq2BO3z2uciy6FhASZBEEahSxDYptx4uJCk0Q2ZoFAUPbhwEsSVi/FscAybzWZoZYnspc864aqRwvMdu65p9jrJ/Isg19g5hK2bqTkU5Y6lvBPvefOrSwEEWRSB15NCNy25pEtst6X5sZjmk1arFZrcUpVtNvGcizI86ffZnvRLgizpEMCeNRqGCmxTJ5d0vjfLdmM2X0ieFlnvtcz382ss6txA19gmm4BCB58npRMVO8QWG2aYgUkQhDHoNkhsU24sXHEsdUG3YlCpZs8TLvWJNN0igL+zgqUT3tBSeYcN/XlNJ7f4uy7i9jrN/F0K8ImQBx2qjk8X7ZjQQ3m802RwIwv6Ny2bRbDJhslSnuqmJVcCbxSuFHIA2MMnWY7DBv2aJ+/rCgwlpeN2u22NXJNF1jRim+x3xceSganAqCuFHK7p0yhkvdcq38/rcfbfvB43HV8TQUUmppUTNt5NhEgGTIIgAMBMAsEFg0QnbEnSZD0OHQY5rxhcMnjyCLyr7qEtFQi9Xg8WFxeFRoAqLdriLNlwNDftKaq0Y5alJxtk4srKCjQajVB5pXL5uI3Ie83zcFpN2RJZtyuzrTrMBn7Neq91f7+Ldq6sDedisNyFcZiea96BRx2BoaS02G63nQ6q5rE3tgWeVfSQDTosDCK5y9qmPB3beAIrj6StDYlhVbAnt/g56LhjrQhrpApZvSlbBJhWN5vW6whMgiDArJNmi0FiyziyRtZ7rcNYFikG0QXFOmnTlv1PYrio7qFNxpHNFUO6aMKkk8redVEul2FxcTHxXqedv+5LhPMIDtrm3BcVecgcEb3Sn4noVVeLEJ00bJNsthV5JdV02gO22BdpIKOb85KntvBJnnJNpF9ts011v0fVJpRpaZJ2bqZ5O88im2EqFskbItnJn1JmZaeuvdUps/O0p10OILfbbd9dUuw60cRIpVKBdrst9f3o16xDNqGeRCeo6A1bbJhhByZBEKECk89UswLTVsdNBmmMSNMGryqyVo5ZBOizroSxIRgremeYcaeyhzYZR8NUMZT1OEQ0zO51uVyG2dlZ6PV6mey1zQGzKNjG+0VAmI5kHbw8nNa89talZGlRYItctwlZ2Ka673Vj9yaPE7NZIs8TbvSSdXYvdFy8nhZZBx5N2P+idyedmy32Qx5+KR+QZ9fIhrahrsOUDe/iqXcKV1sJZZ0EsUUumYaqvop6vihxmGEHJkEQABBkvkOHDgmFNPu3RRGiw9Q7FiCfgKxLzq2NCinOuJPdQ1uMo2GsGMp6HDzfsXvNV+nprhCUlSc2BDPzCiC4nDxPijj5Uq1Wc3NabZTrUbBFNrsCWXlaRF7MgnZU9FXcsyrjtYVP8hhHt9v1JT/q9TocO3Ys8DObbFNZmKqEZ5F2bq7pGBWwc2U/+/fvL9xcTUGkm1haajQauZ2ipUirE/NqVWaLXyeLrNthuWjn6ByzLt8yTCcUwYZBZJgEueuuu+ANb3gDbN++HQghsLi4GPn3J0+ehLe+9a3w/Oc/H0ZGRuB3f/d3074SkyCKECkVUaa6iAZPEoFZJIPXxgCCScVgQzBWNJaovbFxD5NiWCuG8h5H1nudVfs7l5yZOAyTwRtFD3wlcx77bJNcTwJMyKVDli34XFon3bapDr6J2hvV8dqyN3noV5uSIFnqahdPs/LPuaBjVMC2V6UfNkFVlLnaIl8oim4jA6TTzVknEPLCMOxrUuj0k3TZQ0mKOfJI+CGyQ2ZJkC9+8Yvwp3/6p7B///5ESZCHH34Yfud3fgc+85nPwNVXX41JEEPgHRdauVlk444KI5HA4xVqkQxeG3v4m1QMNhgkaenLluC+DPLaaxv21aZx6IQumeAyHceBNcjDCgrq9br00XfbIJJhokBeXnqziHwnQhJ5WqSEnMy+FvnUry7bNK/EdpFsaRmk4Vcb2mHlsV+mKuFV5zYMOoadoygRwt/Z6Cps1ZFFtpEBkuudO+64I9NWUnkjy311KdCus5BDBw8Pu30yLMilHVaSJAiLa6+9NlES5JlnnoEnn3zS+3S7XUyCKCAsCZBnH++8wQtLURKIFZa9Xg+azabQ4HXpYrgsAwgunIQJg0lDs9vtCvsf804g3w6gqLypA7YYMraMIwuoGtrDQMetVsvnuPGVa3n3ds8aoj2NSgJlrTeKHkBI6vS12+1UOsZWqMjTJM+6auvokKWqAYQ0ezMMsl8EmRNJJi9GN80PWdKJrrm5rGPibLh2u+1bC1rcyu+HbfKQRxJb1TStizAscjKJ7sj6Po08keW+2prMi4JOP1nFL7VRBiCygdNJkBtuuCGgiDEJIoco4ZNnH++8wVfpilp20OPmrFLhjUDaF9U2pSJC1gEEV2Ha0Ox0OrBhwwZhVRU9hr5hwwbodDqF3ockSOrM2BDwQ4MqHMNCx3xbE1GRge00kNapEBUUmHDGTMv1PJDmhAPfY5/luXK5DHNzc1bbMHhSIRo6grGyAQSZvXE5eCyLtOtkWobZEFDLik50Vw27pmOSzH9+fh4WFhZgenr9EnR2rmyrMluRZp9Fsl/kS1BkmYx0VQ/JIgkvFaEdVtb7apPf6dpdLzboO0Q+cDoJgidB9EC1j7dLR+5E4OdZrVZ9iR9abcD3p+WNQBcCWdjDXwwbDM2kQfvl5WVrjBsTSGqg3H///YmTSjaM13Y5qRs2Gemy6PV60G63hWOkFZR0X1dWgheDs+0mbZafaWlYpCPYqj0WvI1g4+WILiDtCQdXbZg8W/DZYOu4FEDQISeKxpdhSCqbbJFhRW5VqzI3W/ZHFu122xdQFvn/NIHOdkHg59poNKyeq47Eo8iXyNKGL4KNLANXdLMs8tpXG2STjL1mQ2GE63FNRDI4nQThgXeCyCFMSLFGUKVSgUOHDgUEaBECfGmqdNmL4crlMuzdu9f3b9v7omIP/yBsMjSTGC10DxuNBjSbTeHzs7OzmQf3TSHpfi0vL1txEgRguAyqpHN1XXf0ej2Yn5+HSqUSCPBT3VmpVGBhYcGrLgzTM7Y7bmlkpIrjZepyRNf5U/Zes7gCF1uRZws+k7aOavWyzcFyW8ZrEnF0aJNtagom6CQpDbu+P6I7Z+gas75/q9Vyfq4A6WmJl/1J567LnnDdRk6DNDYMhatxiDz31XSyyLVTj4jhAiZBEAAQVNp8myiqgESBRtcNI6qQRG2/WIXE9kV1tYoSYLh6+Lt4WWyS9e10OjA3Nyccc7PZhEajURjjmAd/Lw/vzLAVaRhokYOsjEjLSy4Hn/nkOU1m8MmOer3uo1fRaRAX6DDtSQMZe0BnoCUpLXY6Havkf1qkvdcMAHzFHKwNY3sRR1qI5AufBLf5zoo0CX+X7HDevxCNt16vO9HXXQYsXfL8uri4WJhCAVWYCLynva/F5f2RbQft4lwpksp00d+FtZFkn9e9TmmKily1pdMk4yhM62ZVqOyXamvavJNFSf1wF/11l/kOgUkQRAhcrEBTAdtjkleoomC5y3eCJIVKAMEGxZDWmTE9XhZxRksRqrJkwO5p0t7ErhnLpmlRxYkbNrrkEx6iVopsAiQu0GA74nhJRwBApz2RhJdcp9k0gSyAYpwESQIRLfLtwKiuEO21LXZtknG4FqBMe4quSEhrw5i2B0zCBF2n1Qeu7w87r7gCDdfnShHnX0XJXL54gKdN2xN3NiJNQZFNujkP8DzH7nWz2RT6uja2nIwbh0k7PK+iP4R9yCwJcubMGbjvvvvgvvvuA0IIfOhDH4L77rvPa9Hynve8B97+9rf7nqF//8IXvhB+8Rd/Ee67775UlUCYBNGLNILBFkErgzQKla9CZ+fKKySXoRJAaLfbVigGV4NbMpVKRTcEKfg95ZORYXtquhImKWwwqlT5ZtjoknfUeEdOpsrNZsTxko5ASd72hOs0myQZRws6woI5Lp1mTQKRHOv1eoGL4fnkEH/q1wbbIQk/uBagTHoXQRFokYWIB6ktw7bW5VudugSdtGiCrl3XB2kRZ8MUCTqCsXG+RN7046qvy6LVakGlUvHojr/svF6vW6mbs0RRCjkoonwHU76v6L18azb2vUVqh4jIMAly5513BpQFIQSuv/56AAC4/vrr4dprr/W/RPD3l156aeJ3YhLELFwJNLJQ6Vdog1LJCkUJILi2X2nH63LyURbsnJO0dHFpjUwZVXyggX1fvV4X3gcVBZfWXAdEvaNZ/VeUgF+e+5q3PeEyzfKVlGwQi7/cdnZ2NrSth+gkncsQ6VPRPVEUtrbIBHDHvpYtnnLBPtOFZrPpS3jQk+RFSEbayDsycFkfpMXKyorwFEjR5pxE3kTRr6iNZNj6YCFHeqTtyOGyfEmCMH+Qv5t2cXEx8He2BehtLeTg16nVagmL1sKSIkXgu2FGLu2w8gImQczBVYMxjUK1TalkDdkAQtizphSDK7QpS1+uBEd0IqynfdzauWCg5D3mMBkoqg5M8/5hocskVZRFcNzypMukMlu3Y+QqzVL6SnKvmUiHs+vdaDQKY8MAyNOSTScrXLFhZOScK3NTgailCVtQxM6btsiyXR+EIayAir/Hja6HLl7Kgl9d1QdpkDSB7rpOSONfqbSCZoGFHOEI49cka2aTbs4aUXZ3VELOJp/Ddj+cL/oTnaKOisO4xHcIPzAJglCG7QIuDkkVqk1KJS+oBKNsUgwuODNZBhCKZBiyzgz7Yaspp6fduyyWRZ68E+UcioKqto3fJPgESFgbIgC3Hbc8CwD4YMPMzExkglOXznWdZrGKMhy8/t+/f7/v9zbP3SX7Ggs5ggjjuV6vBzfffLNw3rbrgzjkLcOzkGuu64OkSNqGyGV6BMj/rjtT9OOCLM2q8KqoCKMlvh304uKidYUcrhQPi9ZYJBNtSXgi9ACTIAVH1kLQFQGnCzYolbyhEkCwQTG45MzItpLIM1hoGkmrKDudjtMBvyS8o0vmiAJtoupA0VrqbqXlCtJe5ugy8gqe8/YEWz3MyrawqmJZuBRoFiHN+IfNhhHpf7Ztos22qov2dVpecsk+kwG7h2xSPE1rHReRJ9+p8skw2TCdTgeWl5cBIEibn//856HT6QTmn+ZuVpuR1wXIpuwJV2SpiF9FBUWiFrLDCt4fvPnmm4Xy1bZ2pi4V3vBrzBcBhtHhMBajFgWYBCkw8hA+Lgk4RHqoODImDDLXnBlZo9xUsNAERHtKW7qI9rTZbPpOb7kY8BPxDn9JZR6V8HGVMLz8551qWkXYarWcp0MevV4P5ufnoVKpCPemXq9DpVKBhYUFa+ksDfLgJZE9wdJluVyGmZmZyLaMaeFioJmF6+PPEvwa8JdP86cHTdsDPFy1r5Pafvz+HDp0KDTwZbO+jgNfbc/T4cTEhPd7G+lQFqK7srIKCsvS0jDZMJ1OBzZs2OD5cOzcDxw4AOVyGTZs2OBLhNgoX0wgqf1jSh+7Jkt53zxtC6JhQphvFmbXNJtN00P2wQU/XLTGSTohDGsxalGASZACIy9l7IKAQ6SHSgDBRCWMa86MSoDDRLDQBFzbUx0w6RyIKmGi9IbqpXKuo9frQbvdFq5/t9uFdrtdmLnmhbh+3LoT6iJZSscgolnbbBpXA+VZI8mlojZXy1LYbl/L9nYfJt0Rdu9CuVz29HvR7mAIK6DKku9kikeGiQ6Xl5d9PhxNhBw8eND3c/akiIvzNAkT+thVGha18KT8SmnPpvGaAO938S0U2RMhbPDddf2RJ0TxqiSdEIapGLWowCRIwWEqGG2706YrcMUeLeaxvLwMnU5HaaymoBJAMFUJ45ohqPsIP/+sCwGeOLi2p6oweUxc9vSJaxVoKrBdtxUZWbZWZPdVlHjlad0GGcOPmaVLvjLV9FhNgO5jo9EIVEbyvbST0FJevO+SjFHp7T5sBQ4rK8E7tiYmJgK63hb5ooIovzMrGU6RtpBD9PMi2zCsD1cul2Hv3r2BxAhCDXnLcBdlKR2zqNpelKh0medkIWql2Ov1YNeuXcIAPdsOehjXSwYqPvewFKMWGZgEGQLkGRi1vSpRZwsT/mgxC2po0qPFriFqH+MCCCZpwDVnJoskpQ33sOiEa3uqApWgkgpU17hoyTcRer0eLCwsBPQGwPr+FKX1FYBdwdg86cuF9lK221m2oNPpwNzcXOg+0k+j0YikpbzW27V9VU3au9a+VAUiHS4qNHDdhjF5AkvE23SNh/1uGhaivcAESDRssodEcE2Whp2OK9qJOBWExat6vV7gRMihQ4cAwE5aZGHD+FioJhCHoRi1yMAkyJAgr8Co7QEEXvGqXGYrOloMEKy0CTspYjtUhDv/LPtvXgnqVoquKSAd4+Xbt/BGZavVynAG2UN0bFpUqV0EyLYXkYUumV205BuPdrvt6+vOrhOlzUqlkugSUdudBN3BWJX5mjjNauKdadBut4WBZZYWhz2AABCUbbRSktpm7H+j9jUvu9Z2+1kEPtim2r7RNfstCdg58ZXP7EmFIsBUdaxIZidpacKi6DYMi7179/rmunfvXtND0g5ddpZryWkKm2WpKFku0h1JYbtNLQuRrSdKqKddr6zgIq9kcRJ8mHSJy8AkyBBANnDNIo0SsT2AoFP5DtPRYtl9NaEUXVNAKuOl68tX0LBOYKVScTYRkubYdFGRpTOjgz9tdrZ0QZRA5wMtSZLnLjgJOoOxKvM1GRS2laZ1y/uitvSk4AOwfOKDTYxE0VJedq3t9rMIYcUX/GnGYQwgiCpL+cB8Er3BwvaAH1/0xNJvs9n09frXIcOj9ESSy23p39oo77OAjpMgLtCgLjsri9bF7HdnuVY2ylLdMtEFm1oFUYUGWbVHloVu38EE36i+d5h0ievAJEjBkdSh0q1EbBcCSY+mJ8EwHC1WUWx5B7Jspz0WvV7Pd2kWO17qLMZB5+kmG6Hz2LTtjpsIeQTFXKvUN4UwvZFGd7hS7a2yr7qCYKad26IHEIrc0pPFysqKV3kusglsS/a6ZsN0u10hr8ic+nVp7kmQRdKSb/FG0Ww2odFoWBXwy0OGq7YRHSYbRkfhHl1v0X1LdC1nZ2eN6g3ddpZLRYCiMdskS/k1Ed3jk2ZNhuFkbNTpjyL5DhSmbX9ZDJMuKQIwCVJgpDECsgjM2BhAYMGPT2WMRT9arKqQsIoyiF6vB3Nzc76qVHa85XIZ5ubmEin5Vqvla9XDfg/b19I2gyEJdJ3ccsFx42F7wFw0Pj6xx47P1kRTGoiqS9O2NHFFTsk40brboaRJ0OlMctoaQODHpkKLRW/pySLOHk1KH3nZtarvySPhLwrws3o5rd3hilxMC11Bul6vB7Ozs8IWbizPNhoN44EwFnnRosydCLbbWDqhS953u11Pf7Pfw+v5pP5LVtBVyCH6vqQ2gSn6sl2W6mrhXPROCCwOHToUahPYlhhQtZ/z5Btd+mmYdElRgEmQAiNt4Fqn0rQ5gACAJ0GSgu+VyApvVjnYUOHn2jFM3QGoIt6bobPq2RXHjYXt1TD8+Nh/0zYzdHw2jFcHdCRBANzh17TB2DA5nPXFuDp5xfYAAh2jDhtmGFp66rI9XDkJkpfe0NUikH5XUQMIuvaDXSM2EbJ//34fz/JFHsOGNLRku42lEzpP/jWbTR/N7d+/X0ibpvlVVyEHxf79+1Mnp9PYEzp8T1dkqQ47K4t2gzYiCR3b5DcAqBdy5GGH65T/w6RLigJMghQcaRWqDifP9gCCrspygGIHELIQ6FlWUeoab16KTFTVx58EmZ2dtWZ9TUD3sWlXHDcWtrfw0tn/2/a5hgWe0+oNSte233Mjaw9E2QBZySddjr9LAQRRMk7GzrK5kENnf2YVezQvu1Z3YChLGtaZBHGtzVNa6NJtfNGGjTxrGmlteJ12h+02jM47oML0hi2+PoUNhRxJbCnXfFgV6NRR7DM6CpRsgy7bI0/ZlHfhiezcdBfPttvt0Lbw7XbbuPxH+IFJEEQAKoFU2wMIOu9PcKGVhIrSy7KnqopSjIJrVTR86yB2XZLeCUJh++krWeg6Nk2Rh+NmuyOcJWQNdtsdN91Vz7ruuckKqo6XiF+zPAmiY8wA9tMhQDb0Y2NLT9W90KXL8+o5nlVgKKukja52WC62qjQJkWwlhMD+/ftND80amLDBXNAdusGfjLDN79BVyMGftmILqJLON0lLRp0BWZt9EN28ElagJNPdwyboook8iwx02x5xfKNKS8PiNyCCwCQIwgfVQKrtgqDX68H8/DxUKpWAcqRKtFKpwMLCQuwYbb9UVMde6FIO/B0B7Pc0Gg3rjJS8qj4pXDg2ahK655el42ZSBtri+MjoEdsT6O1223fvjigQWqlUoN1ux34X77CpnEbMArr2QnTvVtbySUcy2BY+CoPuO6BsPQmiSoc6ZLFOmzEOWQSGsi6MoLyS5GL0MNgu+21E1sFnG2SgDWNIAxf72KvA9mRcVoUcola6cfuaVBYX3ZdjoZOGV1b0nYzVDZV56rJh8rpLykTxrI53qtpKaMO4CUyCIDzoUr42GGdR0HlkTefRYt3QJZRVlEOeylc38ggg6HhP0ZVvHkaVTsfN1H6oGsu65bZMYs9mB5AGQ0UJChoMnZ+fT9XX3dYexroT6KoBhLTQdQm2jeDpRyRf6vV6omQcgP0tPVVlgqpc073ecbBBDqeFDlvJZtnPwgb/JuvTrDYUcojGQH9uQ0FdGPKgYxuKDdl5FjGBTsHLT95PyKLSPC/fsyiw+WS1riSGLhsm67ukdMqmNHyjQ+5iMerwAZMgCAAIGgytVivQgoYqEZcDCMMGXUJZVjl0u+5dRs0i6wCCjv2xwSHKElkZVVk6biaMIRUZblMFcp7VyyJE6TcdATB+rVXvuckKKnNl97DRaMDMzIwwSJeFfFpZWfF0joh+XJeJOnnVhZaeAOaDQq46t3msm861Mb3PcbDB1srjXjMbCjn40+NLS0swPd1vi0blOz8GGxJUANnTsenCpzyDqrLIqpBD9H1RtCW7V67c72hDIajuk7E6YZpXKfJMWuqQwzLrZoPfabsNg/ADkyAIAPAbDK1Wy2c8sAZDq9WyJoBgi8ErgzzHriqUdTzPOi28k8zffWHLvrrkyNiyZllBt1GVh+Nmwhhi31mpVKBarQZ62YscQNt60WfpANoQyKL0LKIRmXtubIKIlkQtESkt6ZRPIkeP5XUaSMvL8YyDyWQcgP0tPVmYDgq55tzmkbjJIsBjep+jkNfdMGHIs6jIhkIOVl6L5Dk7Bp16XYd8daGAShaunPDXVcihsr4ydOmKrrHBfsj7pKYM0sZAshwHT1eE2NO+joWsPLehA4HNNgzCD0yCIDxQg0Hk2HS7XS8BYkMAwYZAlixMjF1WKOtSDkmNOlv2Nc28VQJZNsy16AkUChOOmwljiA3IsIF1NgESV0Ujy+s29GbNY4w6ILvetvOrKbmmEkAzAVvkvw2VnHFotVoBmcbapnnRvCvObV4yzqYThFmDzpVvsXLs2DHfnVBZJrDpGPK6QN50Icf0tL9IJWoMumg+zxMEsrChiCLPi5bzhm75mcZmM5ncSjteG06S2mJHRaHX68Hc3JxQjlE7Na8xZn2XlE6k9XVk5G4e7baTrK/tfl1RgUkQhBA2KOIo2BLIkkHeY5cVyrrHmSSAYMO+phmDqgFmWvH1ej1YWFiIvOdAx4Wv9F267uKRRZ6Om8mAjqjFUhIZrjpmVX7IS++Y1m+ycs4Fhw/AjFwTrY2InhuNRuZVsknmboOucwG2tLpIIxtt0Otp5ITKeHXNVUUm57HefMWxqbuc8qYtU4UcPK8lGYMNhRxZ2xY2tdM0fZIxK5g6VWSDTZB27jbcKWY7PbH7yn7YVoZ52HpZ3yVlEipFZSbuMMlqDIh0wCQIIhQ2V2UBmA9kqcCFYF9WyiGOlkzva5p5u3AUNwrtdtsXYGLHz1Y3qo6/1+tfKl2pVAIJF/quSqWiLeESN5a0BrNKRUreNBwXQIh7v2rQQ9YhcSU5rAOystUGJzlPpKUl0d/nGcRLu6+mdZ0K8go8s8Fm9kQb//MsaT7NPologK/c5lsRZpUUTLI/eZ8uCBuPrFzLM4DAjqdarQaCz0XjV5sKOZKOQceYZeVyHvrZlmScLFwJ+OnQbzLJaNNrI0PDYcH1PBIgKsgzeWI6AZH2LinbE0ssVOWujrmqjGHY/DqbgEmQAkMHY+ddBZRmzL2ev884a/Dm1WNRBVm3eNAhWHXQkIxDkdSRMR1AALCnUlUGoiAT295Bl0NlSzBLBmkdE5MGjYjX+GBNlAx3MTGgApPtbWRla1p56pIzw0IHPeRNzzK8b3uxiQh58Sp9j8kWRGn3lP97esceP4ewpEje6Hbzu2ciDCr0ZCJ5HhZ4tpVnZdbXtkIOPnAXNQYdel1GLuclF3karFarPjvPZlocpoCfzFxtsNdkeH/v3r0+Xtm7d2/m41SBqeS5iWRRWh1vQzIuDWwo5HClCwLCD0yCFBQuBhDSjJn9W77HITWUbRLSPOj4RQFKXUrGBkWmYvDyjgx/eZctypgP8LOOSB6XZaoizKnX7Uy56rilpWFTfCcap2hvw9bZBiMsDweQr8rm18aFS8mT6mZVWjTpkNveliQMad5L11cUtLM5QZVnIMs0v6oGkOv1ulDP2RT0S1slmgVUZE2evL6ysiI8BWJzsEI1kWeqkEN0lxN715NoDDr9VplkiokTci76HHnxq+mkgspcTY49DR+5eBIkLxnHvyfsTo6sdWuaJIGLiUq+1TXLO3yraxuKZ0VwsSDKdWASpKBwMYCQZszs30YdMbRJSLPIy4jt9czewyAbhAurBBNVMNiwzy46xizCxq973C5WUQK4UX3P85ooEEdPLPFjtsHozSsBYrqyXBeyvmPJVDKPpQN+nIuLi15FW5RcNU3PSZwZES3ydoDpBH8UTNiIpk5u6WolRPfWxio/0+06VJFHAMHl4PPKyopPdvI0yJ6et6GQo9FoeEEtPvFBOwBkeXrF5oBUHkV0WSPN+srahjYUArLvSkNLNow9ib614U4QWaSRibJg95GVY6yOnZ2dzZwG0/IQS7ONRgMWFxeFstSWYh1V3WELTHYnGEZgEqTAkDUIbakCihuz68o3j6p4GwwpFeVLKyfCqhRtcUpcDe5T5JUEARD3enZB0dvsFFNQXhPJ8G63C61WSyjDTcuJvN7vcls2Fmlo0SU7QEQHornGXW5ump4B4p2ZvFoRZomsW3qycEH+8hBdWszbBzYlXMOqVPmEuW3Bg7xOVLnc+rTX68Hc3Fwg0cUGivj7CUwXcogS4mHVvDr1lQsnFfKUvVmg1+vB4uKiUEey43epVR47N/470wY3XSjkWF5eFsZc+NjM8vJyJmNURVqZqPIetnU7L1Pi7FlTYJNEIhqwLeHK0qzMKUKT4GmEXW9RMs70CbeiAJMgBYeLFQhJxuzySRB27FlfbGfakEoLfrytVgu63a6wStGWAAK7lnGJBBuVU1gCJ4skjsvJoqROmw2QkeEutz5KA5cDWQByARrZAHLe1f5hdMAHZxcXF2O/y/ZWEq4nQfKsRjZx6kQVogCCyD6wRdaEnVxh25DaFvQAkDtRJSMbeLtdpKPq9Tq02+1sJqoIdvz8/trkC5iu+s+71Z/qSXlX5CGLNMFn17pa6CrkMDH2tO/tdDqwYcMGYdEp9dk3bNiQ6V0MKshLJroWh2HB+7zUHrB17CJes7V4lqLX68Hs7KwvdsnyHH9ayHSMtkjAJMgQwNa+plHvihszKwRcuxOEF2CiakGdfdpdMpjZtaEXitIx8vtsQwCBd4xrtVpoQstG5ZRnIC6v009ZIK+KIR3jZE93sPvGygbbkjYAcnJq2Koou92usB0Uu3aNRkO4JrLHrPOuwI86CZjH+2Ug08LL9XZYebUGcjGAIKpILJVKgbXSqV91jZcfZ6lUgqWlJWvXO60NIxtAKELgwfWWZ3HQ4bfmuc8yss1Fecgj7R1Eqj5snjYMvz9s9bmoOt2WscueIu90OqEnPZaXl61NgFDkIRNd1R0mEgo6ZLio24StvgNA+hNVRdABtgCTIAVH3gGMtFCpmkh7fMwmUEEvmmvUCQcdlUM20gELkTGW9YkZlbHygSze0K1UKnD48GErlVO73fZVxbM8yN6RoFrd6GobIkqLIqeN329d+2q6EtIk0sgpPmHKrhk737B7j1zsvSpbeSeqSmfXNc65yHutXKrmYumQb3lAAyCzs7O+RAifqHTtYnSA4WrpmQaiIBhvu7CJBtPJf9Gp6nq9DhMTE77xTk1NWcV3FGmTICoBBJdbUIgSc6xstaV9sK41VvmePPeZP0kkCvDrag9lA0Tyhi92KJfLoRc4y/qwedowIl5TacuT9dijig9ZmqKJEJvpKw3ylImu6Q5eH95yyy2BAgndhWM6ZFvYiVab/Ty+yJJP9JbLZZibm/PN26XiZpuBSZACwxSTpBH2KlUTrguBNOOXrdJg4WLQz4XTA+1221f5qlr5kyd6vR7Mz88L15Gu/fz8vHIrCfqeSqUSeBd9T6VSgYWFBWuMwagEFx8kEjltKu+UMQJ1VoeYNNiTyil2vixd8UEFUUW9S0lhFjInQdgEiCiREHeSydRamTyGL2vDqF7m6wotyhQo9Ho9aLfbwnXtdruhyUr6rCsBBF6Gh52YqdVqVlymLWrFQOUoTXzYZG/xkDlR5brvkAa9Xg+Wl5d98wu7+0UXDZou5HAtUcC26OR1gGi8LslDHuzepL0o+tChQ6G2YdS8TejYuMITlaCu7rHzvgMtKhLZWrbTV1Lwc04qE13mvaQQ0cOePXt8Pi8h/ba0OuWpqg/L/50Lp8gpZIq6XfQdbAMmQQoKU8elZIxPmaoJ14+DpRk/v6Z8kK9arXoGdJgAdFVY5tVyA0Cv45b0NJMNSDNvlep7lSCYCYiC7KKq3iinDUAtKaxqBMoEd0wGENLKKVGSlP47TEa4HgBLM/6wqktRolZEV6bWSlcAQQaqNkyj0YDFxUXhOvH87iot8msU19KTT4KzuoNPgtumB2TAyny6VnncnSJrw3Q6HS95x9Icv6+HDh1SHmMWkDlR5apNnAaU9sKSs1nYMDYUcpj0f2X4r9vtRsoH2/3ZtGDXiefZxcVF4TqlTRRRmNSxcQU9cX5lnpdpu2qLyEImGRemJwH6Ld4ajYZVyVVZmCzkkKVDlYJqm5C2WNnF4mabgEmQgsJUIEvW+Ewb9HCt0odHmvGL1pQP/tGgX1wCJE/jRkfFRF4BBFV6Es21iMpJpfreRfDzOnbsWIAWw5w2APWAqgy/qgZ3TAUQZOctkoVsMoQPQpuYm24k3eMwR2962l8lJXKobaCDNAkFft6yukfFhknDd67TIl1j0bz5lp68Ez0+Pi5MKlO+LYLuYJHHHUS6bRjXkgQy4y2ijcaCP6V28ODB0MBQWFA1LV3ZUMih83uSQoX/0tgwRUJSnpUpdgEwq2NV5GfY6TwA/z0BYfe/pUUaXV4kpEk08XvSaDS80//8niwvLztvv7D2AM9/ExMTMDk5GRt7kn2vTJt7Vv6mbUtrC2SKAF2y0WwEJkEKDJXKdpXgtazxmbZqwvVjiWnGL1pTUWJA9D0mjECdSao8Agi616nIyknWIXEVor3MOtCpSj+qwZ28Awiq/Bd2ER4/VteT5yyS7jHvzPB0pSv4pQM65LCOcWdlw+gep2mkWSdRoI/tMc0H/WzQHTpszLxkqU4bxlThjCzSjHfYAn7sPGXuJ5ChK9OFHLq/Jwl02Hr0ZD9Pk7bxmw4kpRF2XVn/gl8r0TqZ0rGq9N/tdn0tTOndFDwv8/cEyCDtqc6473IpJpOGZ9m/pXZLuVyGm2++2SdXaaLZdtstKcL4j5dRon1PSw8sLfLtyWgb3ySFHCK+pz+31bZOKzNcs9FsBSZBED7oMhowo6kfMsFY104ERc05a0HPJlv4d6VxjodBOSWpXIuq2nANIscgzUmFrAOqYe9zJYCgWkUZJhdFa+aaoyaCyt6kDdTnuVY69FUWuicrG4Zvm8RXsbLV1bbRpWxwVORET0xMWFf1bJoWZXhPh+3h2gmlNOOle8oXa7AXqVcqlUImQlTaC8rQlelCDt3fEwVRYo2nK9Hc+fsEw4LQtvCaLsjwLP07EV1F8WzeNoxO+4OtWud5Tpd/pRLkZuFiUUfaMUf5GuVyGQ4cOGCljlSBbJJMhh5YWhRdVJ9mXV3x83q94J1ddL1oizV+3ryMGYY7fLICJkEQPuh02pL2Ex6GoLEO9Hq9wGWxNBgbZWibUgY6qmHyCiCInGPWAEzaosO1AIIK+EsKWUc37rJllyAK3InagEXta15JYd2yNM8Agohn2Z/HBWeq1aqwirJIyTiA9HssexJEFaZOogLI84EpG2YYAggUYae2bLP3TJ5Kos+xLTf452ZnZ6HT6QSek2kloWPMJtDr9e8ZE4232+1Cq9XyjZdvy1ar1aDZbAZ0fL1eL0RLExYq9xMA5NtuzKVCDtkgfa/nvydJJsDvKtLKmE6nA8vLy97f8HT1L//yL7mOPwo65adqYQWPKDuaP90fd8pG9N0u+r5p1yssEXLjjTdaGbfSYU/LnJ6UpQe2tVi5XIa9e/f6/k1PRRUBrJ3H3zXD2nn8XTPs3TTHjx/3PcveTXPkyJHC3FOTFTAJgghAxqGXNQRFlTB0DGlaI7iS9ZVFr9eDubk5X/sIqoBoX232cnRdlYWqUHFC8gwgsAqbD6TSfyelQ1cCCDJgjSFRxRAh68dWbTV604Cl30qlIuzTnHRfkwYEZCvAXGzppsIv7HzHx8c9Q5lvkxB36atLSLvH7PqmvROEhcqxdlNyMC39ytowOvhumAIIYXojTZA0L+hIKsvYXTLtUFRbSaiOOW+w86WVkBSsjGm3277xshcsj4+Pw+TkpKcvaMELDTyIEk0uQpcuT5PUMF3IkVdxnUh2JznNwVfgu3qRryySyhjVkyBp36cDOt8l22IzjUzkk8NsfCFNHGZYClp5/Zqlj6SCrBJySfc27TM6T4LIIk85wc6XjVfx68YWZLDxraNHj/ruqWHtl9tvv1373UFFBCZBEEKkNWDDjlRG9e0UVcKw76eB/YWFhUjBY0PAJWs0m02fUtiyZYu3xnzwb35+3qqglEo1eV4BBPr7sABNmr68LgQQAPSclqlWq4HEHP23TcagDHQeOU0qT1naLZVKcPDgwcDz5XIZdu3aJawCNmnwykD1tNeePXugWq0GKoVWVlYCiRC2ktA1sKdh0vS6FTkVfEIkzrmQoStbgvppdI+MDQOQTQvRIgYQeP06MTEhLOrQXeWqY9xZJ4NFYG2+crnsKy4Q8ewwBxDSyphWqyUsdmErKcPsRNeQRVIhjg/YQo56vS4s5OADsQD69Ebe+of93jT3evB2XZSetjEJngdk9TILlYI6k76cShwm7ISRqLMBa0vz9pKK72BjYkAV7EkF0Wf//v2mh+jBBnmahh50FnLIwEScTCVRxMpDliYnJiZ8+oQvBkasA5MgiFCkDV7zTm7chck8M4sEa71eh3a7HfleWwIuWYGdX9yFXPV6XZg0MrVGJgyiXq8HBw8eTBVAoGPVkQRxAbKBzTD+5j9xx1ZNOxdJYCK42ev1YHZ21sfndB35Y8Ki4IyOdTUZQEgTpKGtUNrtNszNzfn4enFxEXbs2FGIQBZPh+we83QYVn3PBvX4k1pxretk6cF0UF9G96S1YQDW6VDED91uVxjs0znmvCFbmBC2rnxAIUzXulrIIQO6xqKAS1R1uMlWEi4EEFjwe1oqlRLZiS5Bly5ndYhondnTqWwhByEEpqamAoHYUqkE4+PjAV9FFw2pfI+sDSXyH0Qnh6PWViTzi1LIp2Kbyuhl/vvT8oHp4kpZ2baysgK1Wi10raampoTzZe/E5Okwy9MrJiBLi3xhAhuDYfWzTUFnHXa4Ki+kPc2k2tJTFq7EyeKS7knjMAhMgiA4UOUgYsokl0OzR815ZhYJyyyqlEwEXLIEq4DY4D4r6GZmZoQXKLHIe41M7Am7VgcOHAit2ODbv7DKT6UdlurYba+iZPmbr1zjP2HrZdq5SAPVPZF1vmSSeLpgomourREoSgyIDGd6xNgGWpKFDsM8LtCSZn/SyHKdjnVS8LTAjzeu9VcaGyYLWWZzAEFFNtBTv3xgkLbzHB8fj2zp6YqDqgp+jUWtN+Laspk4CeLS/oieCVuvovgOsvIp7clqvpCD+o4rKyuwtLQU+DlPD7rsYJnvYXut8+vF9loPS/RGVdJH0b/qfS22Qwcdpo0t8Ehrw5gsrlQ9IS1qnV2tVr0ECK8/dPrqthdyyNIiLwdvu+220EKOWq0WGitz9TSrTOcImVgiH+AX0WKW8lCGF3TsqUzReZQNQ4hdp5JsBSZBEB6ocuArK9iLt5P03uQvTGaZOaxSVYfStF35qoAVsrxT/L73vc+XAEnr/CVZo7RC3pQByb/35ptvFjq4fKWGiPZZhZ30YnRZuFRF2Wq1fJVGYUGXsHsY8qYNU6dOaIW46H6abnf90tawvuNpq4D5d6vMWcbglbnIl4Vs6yJ2LXjZuLi4GDlPV6DDSVUNrssmqmRbLMjQMA3AifiE5aeoHrm9Xg8++9nPCteKP9nB06FKyzyZNc4bqsEZenJrfn7ed+KDritNmolaeoreU8RCjrB2B+xnYmJCGDymNsQtt9wScIYpv6U5mZQWLgQQRGOcmJgIrLFNfKcClfWlPCuSqd1uFw4fPiyUqTQRrXLZct52m6rukA3SJ5H5ridBdNn8cbGFONkmU3Etcz+fKlSTcXHBUTb5qNMfc6EYVXa+rI+zb98+j9fZ+3xmZmY8GVepVAJdTIblNCulX1EsMSwRByDfzk030sgJHXsqa/fze1pUGyZLYBIE4YFvWcAKIP7nYQpRtvJTVyWMzVWUOhCV/c0qGCYj5HUoBpVj6XSNRKcUbr75ZuFzLO3yFQi0ciErZ8SlKkoAsfLljd6oiuu8jGVTRif73uPHj/scGva9R44ciXRoRFXASRIgebeCSFstykO1mnd62n/RdxGNQJUAua5AS9pElexlm7qq9dLSIa2iFNFRWNsw9rtFJx2yqlQ1BdVx9no9WFhYEAZBqa0ZdQ9c0Qs56DtY3uGresPaHPR6PTh69KgwGNtsNr3E+7AGEER7yq81/Qx7FSW7N61Wy3e6jrbgoHQWZuuFrW2SBIgJG4b3G3i7IqzVDR+kS1q9rEt3uABVvcEmJER8nnSd0rbmSWsP8M/LFHKoFhRF3VtB7z7i36fqI5nUl2khS4t0P9vtti8JQvmbjSGIkiCu+fkyEJ0EpDTHnwSsVqu+NQorxGb1dNRpYZ1IKidU91SWFqNigbwOs8V3sBGYBEH4wFaziALBURlY2Wd1GTd5CnpZqFQ38cJy7969qZ22tGvU6/VgeXlZKJTZCyRFQp5WkYnmG1e1o2qYRRmBogCCDRUIMsrQlmOYYQHHpMo/K341eSKJvRhU1L+4VqtFBoPDjJw4PletdJLlubQX+YrmmdY5DqNF/t6LuICLiZNCMpBJ8mcRpE/Cr3wALE0lsArfsvaEiJ6iqjdVaFh0gpDXIWFjdimAAKAmu3XMteiFHGEJxLgLL0U8l6Z3vi7YGkDg95RPjsbZifx35aU3TOiopAmjuISGqGAmjl9N2TAy9CA71rATX2H/H8UHrtgwsnpDtC8yF8infX/YqQrevtRdyKFLJvLJc0LW7+jhx6pKPyZPOchA1f9kEx687kna7imtvyODvN8n4hn+/pRSqQQTExNQqVSE90KJTlOzHWmixq168pF/N6vndO+pLK+zv69Wqz4+n5iYCLRps8l3sA2YBEEEIHOaQ/YUSRbGTR6CXga6jrjy68IqmqgLuVQcRn4f2OqosCowFaNIxRAMM1xvvPHG0ACCLRUIaQwzE8cw2b9vNBowMzMjrDhI+n6ZYFZaA8eEbOAryPjLy+jFoGFjUAnOyM5ZR3AybQsvHe9cXFwMOKhJv0NFJucNWb2sEmiRdUjC3ss7FVH7qjs5FveMSiUwQFCWVavVVBWGrgQQKE2IZHfSgEmee+tiIYeompKucVhrniheT5p8HIYAAh2niN/r9TrccsstsfyeJ7+alA2iQJ+ovWEYREmTOBpknzVhw4jol7UreMjuT1RCjj9pUyTdwdtrbKvJtL6vKGaQxHdJI2dk29KqFnKoysR6vS5s85eU/2SQdzJO9X15t4hVfc7EaVZVn5v/lMtlOHDgQKLkroytp2JzqVwHILOnMuPleZy2Ji+Xy55uZucQdfIdgUkQRAhkK+3SCBBdxo0LVZTUqZXtN6uakJBZI9XEi6ljgr1eD3bt2hVQvtPT/svS+bW2oQIBIMh7i4uL2hNFMusrel+vF7yEmA3K6EzAqBg4skanLEQBlqTOiGowVmXOOhJGaVp46ajclD1FqCqT84TsKUuVQItKYph/ryhBkCQwo8K3KqcFwnRd2D1HUU5fVg6uKmTeJ6IJVpalOSmZh3xytZADAOD48eNeopxfH1GCNorX6SfKiR+GAAL/vKz8z9PnMOXfRPE6+0naCivtnSD8HG21Yehaychu/jmZ+brg/wLIt5YS8blonaLa7+qouM6rGEn0bFJaYO9ioHcvsB+++CosQRy2hlkGUtO+W1VfsX4ru75p73mRTaTkeZpVtrWabp+bkH4xahp5rOOuyCSyULaQW3asFCp032q1fPvL7kncnaOIPjAJgghANUstel5UCabDuAn7Hn4upithdAQ2VSsaZdaIVx5pW3CpOiQygXL2Qka+pUmj0YCDBw/GVnjnGaDXFXxLs74yxoJOPpMZt6qzJ2usyjoGcYHRsDHoDM6rtk9KI/+j5hwlJ3Ssb6PRgMXFRSF9JKkwVEk2ZQ1V41w20MKuj0yLQNnKcB550rBMMppfD1G1dNizpiAry0W0yAa645xEHlk7uK4Wcohoin8uzEFmx8Kv76FDh7SuL/usCwEEHionAXUE25Miz3dRsPTAyzN6QimsSE0HPVDkbcPIVv7rgup886KPtJBtNQkg5vOk69Tr9U/zifRdt9sNvSeJl4dhibE4+lWhxbT3l9CAKH9xN2vT0haJWbR/lIVqRXwafSXyr1h+ifK3ecjurUx8QTb2o7MDSVr7R8QzWa6TaGxpZKHKdQArK9EFeTqTiKw85GUj+56sE5dFACZBED7oMqbSGCiyxk3c91DYIghUq0oA1IPtuoO5WSkyFmloiTVu2OoHPni8vLwcezIjzbFt1QCCSjBWZn1l6UgHn6mslUpQScZAUXUMou6miRqzjjZNeTlfovfJyrek0FX9mOeYZaFaZS1C0v2VDcaKnpexJ/J2gtK+V5QoWlpaClROp3GsZZBWLsvyj84kSF6FBvycbC/kaLfbwpOnbAA5iWyTaZ9nMoAgY8PosPlVvifPcava0TLgkxbsh8o6kbzo9XowPz/vS57z3ynqB69zzjI2jEygnu6naF/Zn2cZVFV9NmvoLjhJOle+alpUDLJnzx5hW0OV4jQeeSTyRGvM8yj786hLu1me5W1A/jl2vWRPQ8kG+GX01fLyso/mqI3P+mrlchmWl5eF41V5t8xzYXTI84/OLiA65hnn+2Zpd6nIQlm7idKp6E7KqJNuCLPAJAjCQxaBpbSOgejZNFWjMpBR3roTCkkuNVd9twr4jP7evXtTBQJkq/DT0FK32/UpItFF3Un6I/Z6cse2VZS2SpUUgFuJQ+ogi9ohUAd5fn5eWzBAxUDRlbDhP6LLqUXrpCM4k4cBqerkqhwJlklOxc03rUzOGjr1owz/yDgUJpwvHTZMmveKElRsoIHtyZ3FqSLZKte084yaL0uHSdthycqnYSjk0BE8zjshwb5XJfGSNNFqsmqZh0y1tuy4TZxmjUqCrKyshI6byqawwp+o+3Tou/O2YdL6D2z1PV+0Qp+bnZ1NXbSSdr4UqnccZAWdCYU066RqD/R6wXa/7PviulTw70pKiyoBdpVgrK62q6K5xD0nE+CXWV/ev+fXN0mMQKWIJO1zqqdZRd+fdSEHH89gL0VPMmYddryKLJQ9oRx2n3FcAj3sezFhkj0wCYLwoOOUgUjYJgn2qVa6qlQhyPZZlHVk0vabNQ1WobHjlbm0XjbwkFRxs0ag6Jkk/T5VEhKyc5VNvPBzFr0zSdsMFlkr316vBwsLC5FJkLggT1IjRcVAYceUlg7Z9/KX29J/8/IuSfV0Epg4acOfwmL/Nq6Fl8lTSRQyMjnP5DkLFeNedn9lk6x536egqptl3kv3VKQ3+MRIGN/J0pLsnS38msjQvYgmktCwrmKbtHClkIMNOonWJqoal36HidZUMs+KTlKxgXVWT7J8a4qGWPABUnZvw+xMVd2c52lWdqx8Oyz6bzpW3TajKRsmbf98Hfal6nxNFhCmAavjkraa5CGzTioBYBcLOWTlBP2dKOkZ56voSgrIBPgB5O7XULkTREfSJ81z/DqmPc3Kf0dSW49FGp+bTcQdOHAgIAvpJ4u72FTnKpNUk0nk2VTIMczAJAjCBxPOuKrTlnewRcVIMdVvlu6raH/Zn4uUr0pliawBqrsCP+naqla0A6hV66VpwUXnmnZ/TCtfnY5F3P6qVhrJOph8UqBSqfhOf9DKt6hKbVnoCHrI7ItsCy/TAS0ZmWwiec7+XVrZ1uvpaTmQVp7mWZyg452ypyro8yoXv6a9vJIPmFPZUq/XU11AnHfPfRP6x7VCDhmbiUK1qMhEAEHGd1BdJxWoVBPL0oSsHS7bXk20N6xMSXrqSwambBgZ3cF+N+sjpPHtVIOqOltlxkG1kMOE7lCxm1wr5ABQL5SJSnqGrZmsPFYN8KvQk8o6qdiZMs+J5pn1fGX8X9aePXjwoFBPzczMxBYA6lintHaB7LPsWEVrJYovmPZ7EX1gEgShDNlqLhYqx/dVjHzZ0ysywlK15ZEsWKWU5sg2u69sgIafe9j9GqrVTabaBqgc284zgCBbhWaD8pXhH756h38uLpHBvzfKQKHPsTTI01S1Wo10gNjAMu1HzL6fnorIwmBWSWarOF9p36sig3VAVibLJs9ZmhA9E3XZvYqBLtuyw3SgUSXYkvZdsr3D+e9Jm8jmK+fStHHkbSw+eBGXAJHRVzpoIs+9VQkg0+dl5muqkANAPnlvMoCgaofLrJMsVPvKpxm3ymkD1SA5296M39Okpwxcs2FkoBqgpAl40Xy73fDWYaoFhGnR66md4japO/JuJWeikIP9blmZyNNOUltC5d2y/KNLX+WpOwDk91flNGva+aroDraolucB+vMs5LGKzaUzLiJz52Le/hWiD0yCILSAd8bTXqAKINdPWFZQ88JZpCDiKmjSOjI67quQgawTpepQ6HheV8WETAABIN2x7bwDCGEJm+np+FMONijfNPvU68m3W+Ih00pLVA0Z5VzqSijk7dCbCjzLymAVqMrktDxEq4dpOzT+mXK5DLt27QpU/PNjDTPQw2hfNpltQ7I0L+iaq6zu0d2CMUmgR0YHmKQJGdk0jIUcst9jQwBBhn9M3IMgskd4WR53x0Gae2Jk7TwVG0bHnhbdhmHB72dSelRdI5UCwrRot9u+d7E0wfrbonZ9Jk7pULCJcJFcyYou8qZFVb+OfV50GiSJXygrj9MG+HUVGeTt/8ryu8ppVllbT0eCVYUHZIrqVBKPOnRVWhvGlGxC9IFJEIQ26Misp1Wgska+SIGKKr11BVTZatw0LS90gQ/sif4bFrhTMeJcMwLDviuOnk0FEGRPOaSdX1aQ7TMqm0SUMVBYmVKtVhO3mlGl/aIHoXXJYNVKPRWZLJsI5z+sLBbRcKfTgQ0bNvhon4ImATds2BA6VtUkq8rpCFegM4Ag87xKq8ywAJxufWWq0lqWFoe1kEPme2wIIACk8wFM2jC9Xvq+8rIndHTZeWkCmzr2tOg2DEVUIjqOHnXY/6ICQkpPUQWES0tLwtNK3W4Xjh49KmzByAdA2YLDMH+bvs+EfGGDjDInAV2BKq+pJEwpZOWxTIBfliZMyySZ96vQsMp880yw8pDd3zyTLjxk/ayZmRmhzZOlTY3oA5MgCK3QdWlrGgUqa+Szz+XRSsJUhRKFiqHuAnQaN2mVmYsBBJm/1wmVignRnsRdaKejGoaVDVlcaq5jzC5BVQabClL2euvtK0SnxcISAyJnj37i2m+ptNLi/zap3KfzlL0nwzXI2iC6dA9fDZnk/Xyilto9Ua2EVPVV3nYMu75pW64OayGHrENuKoDAjzmO/vm9lzl9rgNpC6F03N+Q1m4TyYik+lXnnhbdhpG9E0S1SGFlJV271l6vB1dffTWMjIwECitWVtbvhLjwwgsDiZAwWsqSnmT1K/ucysX1LkBXwl/HXVJp+FwlwM/a4Ty6XXEbOVOFHPTdfBKcX2ORnc3a+GlPs+o4aZa2Q4sOmE5WpYXseFXvq0OoAZMgCG1QqcpSNZRljfxWqyVMnNDvElVY8RVg/HijqrJUoMMhkT2ynTdk5qrLuFGpkpXp6ys7Xx4ySQVZflWFisGcZ5CS0hQvI44dO5ZblYbJfcoDMkFcCl2nqETfG8Wr8/PzUKlUYGpqKjD2UqkE4+Pjob2xDxw4IJTBaWhfNpgkk/R0zRlRhcwa6dA9YUUKUReDrqysQK1WE/JNvV6Hqakp4f6o6CpT4OUEP9c8ktJZQxevmZCLqkgj31SSYlmNOU6Gi6ro6ZjTtBeRsQfYwDbLP3nZEkW1YXQG2dOukUrlfrfb9dEDTYTwdBJ2r02r1YKJiYkAPcUFRlVki2qiKG0rOVMwmYiWvV+Vp0WRvqnX64E2aaYC/CZ0HD9WEb8nad3OIqltqTpfU8WSLiXQZfZJRwIdoQZMgiC0QEVY6XD8ZIz8Xq/f15f2aGcFfNg9A/QZHXcTpEGWQRbbBK3pKlUVw8pUhUla/uON0zyrKFX5Pe8gpalKGBYmT+xkDd4hp/NLSg+qznFaehCdDqrX67B//36fLpmamgqMOUoGi1pdRc01rew2WaTgClTWSFeAR9QSS0QX7OkgSm+8cy1KyLG071qbM5WEad4wWcjhmg2T1iYIq1pmkwq0jUdWSTEZmajSXkS2+Mr0aVaKItowlA4bjQbMzc0JaXd2dhYajUYivlE94ZOmXSvvM5dKJdiyZUus3un1/Hebse+Oq9rPwoeNk/msHBY9b1sCxJQPqfJ+tjCIpzlWHvOFQaq+/vLyslDmNptN38lpG4oiRDouaftfU4UJAOYT2KbfnwZp9omnBzZJy8pgvlWzyjsRQWASBKEM1aCmquKXNfK73a6vilL0HN+jnQ08lEolOHjwYGCuURfcykJ1jV3KONtQeSyjWEyNO+17TVdRqvC7rEEkW/lsQ/DXJSNQBmxgiJ9fVs6xKq82m01fEGBiYkLYyot9XlQ1yn+SyAeZYJIOOi46HZri9bBqYrZSVeQM8QUZbPCBLcio1Wq+CkyT8j/L06w20aLpQg7Z7zFlw8isl0xrEV1QWSeZogqV4iuTfd0piqw7KB2K+I39edz6yqyRaoKLT4SwwbewggyVlp46ZItqMs3mZJyrvi+A/CkS1WIBV074AASLD9O2/5VB3qevsoDNPCsLkc3Dz3NmZib2hI/JpGkRgEkQhDJMH9eTNfKTVNGI7hlYWVlvQSHqYfmJT3witqJLZq6yCknnke28YIvyTQsT407LfyarKFUcRt6ATHp6RVY+2eCQuMoHSSG7pyLIVFHKrC1NqImqIQkhsH379tC7CdhAliiQEGf0ygZKdNFxEZ0RALO8rhLglK0OF9k+WbeWyvo0q020aIPukEXeOkdHEDnvILsqLaeVo2zxFRug5oub2IIt+hyrX0V0KGpToxOu2DC2VFuntUVU27WKksp79+4V/q0o6VKtVn2tsaKql1VpQZXPXUjGucIvIuQ1dl1V9CbA2168/aVznVRPvNtgw7jAs7JQPaVmyx65DEyCIDyoGIGmDEhZI59/Lk0/1V6vB7t27Qr8/fT0NBw4cCA2aKHiPMkoBN1HtvOCq8ovr3GzPMfzH8tzYYmBvKsodRlkaauXZQ0F01UWRTdwdM5PlufSPsfSRKvVgsXFRWEiQ3SyiA9k0XfFVfyLxioTKFGlYxfksawdYprXO51OQDdT0NYOuu0CmfvQKEycMjBRRamCogSzsrZhdPFd2sSCqs+iUszEtrHj1zaqCIRt8cLTk6hgy7Rcc8WGMblOqmuk0q51ZSXdSZB2u+0VSLG6gv2Mj49HJtV02GqqCRTb5bELtlYY8ho7v5979+71vTPqLjUVqOqNblet/W8aqMgW07pDNE5beVaVJlTm6coa2QpMgiAAwA6BJwPZcfPPiXozJgnIsp8bb7wxNpilwymQvRNBpdrOVJLL1crjrMetk19lE2t5BsF6vX7PWVHFDHXkok6vqASQTVUHquyxyXEnRRbBeRkjMA2v8snzsACCSParVPyr6g2TRnpeUKUn0zyj+v40dBxWQczS8OzsbKhs0dHOMA0dhRWt8DxocyLEtWBWHraXriC5SjI7T93aarVCT5HX63U4fPhwYv7J0lbTBVdsGNMnAfOWp/QEYdo7QagdTuUsLyMmJiZgfn5eq64CUN8fV5JxLFz1fQHyS0iL5GEaPZv2vTp8Fh3tf9NAxXY3qTtU4wV5jVuVJnTIJpftTNPAJAgCANw0EihUqj/5anjWmW61WqHvZINWogBC1AW3urK+eQk7UwkyVwV7Ho6qbn6VCaDl7bQtLCxAvV6X7gHrIj3JyDYT/Koqg9M+x/6NCh/I0MTKSvA4O98WKywYK1vx73Klal5wZZxZIC0di9qaiIIIorUywXOU/k1ehC0LF4NZeQbbVROsMs+rng5tNBqBRDd9dnZ2VngfIFtFz7elFVXyh62la/Tkig1jMtGfZ1ERLcYYHR0N+KxLS0uBny8vLwvHKjqBQv3mtIHrpPJflh5cK/J00VehyDMhDQCwf/9+37v27t2b2KdM+17dp1nTtv/VmSyynZ50FTgnfU4WOgrUdIzXNbvAFmASBOHBpBFoCjJzZp8RJUJuvvnmVO9NE3wzsT8qxrZsgNNVWpQZt6wS1LVGMgE0FaUvc3yff6dsD9hhMBTyDgabdDBNVaWLKuhFVelhVUrtdjs0wCxqpUWfM1GV5dIJC1f1hgpkbRhWBvNJvCSFHGlb87CQPc3abreFtEiDclG0KBuM1XWCypXgQ542jOidadZJRdfJJk9k7uYACG8lVK/XfXcq7Nixo1CFHDIwldB2aX1leY6lYSr7l5aWfHOniZALL7xQmNBTuYdTVj+rymKV5/PUHS7bMHkmpAEAjhw5EojBUP8w7l4z2YvcVfwGFZmm2jLVRd83z+SwClR5dhjtTFuASRCED8PETDJKkBewN998cyAYliQY2+v1Av3kqVLKo/I/LdIKeRWn2PRcZSE7bl2OvAy/yipvNpHBPxd1iorShcxFjvxY0/aAHSbZlqcjZZpf8zaUaUuIsbExIQ3TqvSFhQVrqlxVoXLSx2RFb9H5XIaO2Weq1Wroidao1lK9Xg/m5uZCAxBJ5bjM/uRVWa4zsO9KMMuEDUMhmxjLO/nSbDZ9bW/ZllbsCQ/RXFm7SdROsVarJUqAuEJPKjA1X5eChSqBdpoIqVarwiT20aNHhQkQ/hQhez8f//Mo39kVvy5P3eHqGgHkn5ButVowPj4ulMNUtobZ4WEnS9k7MSuVSqgfK1PIp6Kr6OktmVa6/PoW3SYGMKM7TK3xsNkFuoFJEEQALhmBsqCBLPaSZYqoQBaryNhL0PlP3KWiaQMINgTO0gh51SCj6bnKwERVOgDAoUOHQvk1yhFSbQnBGpCs4xNWBUbfKWrDwhujaVqpJDE4htFQyNMoc219VWUM29td5ASFneYAcNvRTQtTcx0GGwZArjowSn4T0u/rHtdait1X9sMGIkTPmpITqsmitLTrKo+bsmFUdJVKgrbb7QplRVz1pagdrihAlXSucXLKVXpSRd6BpWEKFsrOVTaA7Kpfl6fucHWNAPJNSPN+ZLVaFSbiqtUqtNtt4fNswiTMJ42KT8gU8smeAmfHy+oZthV0WPGta76ZLpiQ5Xn7HcNqF+hEZkmQu+66C97whjfA9u3bgRACi4uLsc/ceeedMDs7C+Pj4/Dc5z4XPvWpT6V6JyZB1DEsRiCvBEXCo16vCxVor9eDgwcPSleDyVaSmWqHwiKNkFdRvjbMVQa6WoAl5T2VS9Zkj9fylcTsu+m/w2hfZKSmvdg2bQ/YYTYU8jTKXNMdnU4n0O+aYnl5WVgFCaDHqRgmxyTvubpGh7KgzrjM/QTUGRcVYyS5oBkgfSDYtByWoUNZ2nU9mJWnDbOyotZaTQYqhRwUvB2SRs7wejnueZfpSRUmLlkuuk6mkLUP6RqL+D3uJLiLfl3eusPFNQJQbxvG0+Pi4mLkqQrZkxwA/v0R+bFh+6NSyKeryEBkd4nal5q2uUyj6P7vMNsFupBZEuSLX/wi/Omf/qlnKMYlQR566CG44IIL4A/+4A/ggQcegI9+9KNQKpXgS1/6UuJ3YhJkHTLKaNiMQNn5drvyfYFVMvqmIGvwAgxPQEoX0iaZ+IQC22ohyiBTCaCJ3s3TRFj1jmz1DQWbQGTpib8snf0O1bm6CpVKP1knzJUKfBvaFQyTbEw7VwxkxUOVFlWCzzIOuUk5bMKGcTmYlRYygSwANVuaf6/ou6Pey7fuYQNocbZI2GmOuJac9Fne9mFtt6hEiEwVscuQ0RsyMsbVYGHeCUsRXLH5VCGb4B0WG08GrB0eds/j7OxspOyX0ekUYX5sVDGeSiGfDpstjd4Z5iC5DO+55ncMo02gG7m0w0qSBHn3u98NMzMzvp+9+c1vhuuuuy7xezAJ0oeM4HPVCFSFrKBUqcCUVfr0+Twdax0VF8NiJMtCxpgLSyiILmeOS0jIGmNpkyC8XOLpolqtxh6XZgMmfOuVqF70qpfKuQYTFcwuOXy2tCsYJtmYdK42JKhcgUohh+xa8c+GVcSLnlWRwyptj1T0DsBw8WlaqAayZHudq8hilSSITAKQfVa1cGVYAloysk02qebi2ppqXRf2PVkHGk1BNsFLgbojHLqKQmXXeGVlJbKbAU/HOgr5VHkv7QlE1/hNB2TWWEdnjDz9Dhd1lo2wJgnyspe9DH73d3/X97NPfvKTsHHjxtBnnnnmGXjyySe9T7fbxSQIyDHlMDOU7IWMsopF1qhS2SOdFXMrK8Hem1EOI3VKRAq7qEo4KWSTTDwtiJyRNMeBZQNoadthyVbvsO9kk4/8HJaXl0ODHsMSIFUJ8C8vLwufpUZg2LMuVuCrJIp0OBXDEECgSDNXWxJUrkClkEPWlpANepvYW9XAhUvJXRNQDWT1ej1oNpuhsjjsfj0VnS6yu1haCGuHpTJX9ln2LkJ2vEnfjTZM+FxdbjecBrJrpIuOZOwn13S0atEi6o54yPIrheway7S10lFQoTJmlbuohgWy8U8TxRh5zxMRhDVJkOc///lw4403+n72r//6r0AIgf/4j/8QPnPDDTcEBFiSyQwDZA0Ul4xAAPUxmzRS0r7bVHCIvXeCVswlcdrYBEjayv1hgUqSiad93iA7dOhQ7PtVAmiy/bRNOU8uBuplILNWYc4eH+ARBaRMGmOuyv9hCCBQyMyV1R2iZ8JaNblow+iAiUIOmcA1gHyFt4qM4fVp0hYWfIBaNNYi01VSqAayANTbzaTV6ZT+01yMritY0mq1fL9n5XdUCwu0YZLpumEJFpqyI0wnYPKCStJT1oYZNvR6PeHdZEliBCryUDauIVvIxyOtzaaqY4fFNpaRbzoKOWTbUqnsy7DYA1nC6SQIngSJRtGrEFSNOZMCRPbdJgIIAH2DgQ94RwUBwpQKG2BN6hwXHbLGGAsVXpcJoLH0wNNh0hMdaWlRhxFXdJlIkXat+H0Jq3oTGYFUDufd619XUiDvdgXDEkAAUEvcyzrGrsHFRJ6OpESeAW+Zqk/Vyv1hgi5+lZHFKvQv86yJlm6qY3YRqmslc3G9iUChCfmv+k4V20tH4jLtuFXmKxN87nbV7joaNvR6PVhcXBTK/rD9UbFB+NOhomfr9Tq0223heFVjRzIFsKp3Z7lYQCULGX7n15L3faPaXMqur+7iziLbA1nBmiSITDssHngnSBB5VwjmCV1KMO+AkmsBBIo0tKRSWT6MSJtkYqGytzK0pKMNhUljDHv0isHv3969e33rFHXpq+qdKzI6R4cMN2FAmgog5A1aHSWaa7fbhVarFTpXdm95GrQ14SMDVws5dMjxPAPedLx8+8Zjx46Fjld04pG+a2Wlf7KEFi8UgRZVIRPIYpF3IYeJYKwuFNmvA8gmMRBnw6jKNJkxu1rIAZD/peymAo0yCV6VE2MuwlQiT0dLz7TP6ozfJNU7qoVmLhZQmYCM3gAwX9iGMQ15WJMEefe73w27du3y/eytb30rXoyugLwViwpklaisI2MyGOtaAEH2WXZPRc8XLQGiagjK7KmKEjXFOyadcZUgmisBBBWEGYFx62QqMaYrAZh3QiHvAILqO9OC3VPZ1i/D0NLE1UIOADM0DCDv8MkUGtA5imQFnxgpAkztqUuFHKbhkl8nA52J4TQVvSZsGFcLOXQgrRw3GWiUSfD2evItI12CyUIOFX2lcqrIRAJFZ6Ipb38nT5g4QQiQb3eXsOddkv+2ILMkyJkzZ+C+++6D++67Dwgh8KEPfQjuu+8+L1P5nve8B97+9rd7f//QQw/BBRdcAH/0R38E3/rWt+BjH/sYlEol+NKXvqR9MsMAWcYyIaR1KlFbg0I63513AEGX8kz7bpcCzyZp2ITz5dLeULiYMJWBbiNw7969mRpzJk7GuRwEA5CTpSpVc3mf0pENZKmM2RSGRS5R5B3wVn2n6rtdgalktIuFHKZgSsfmCV2JYZne7ibWV5c8VPHN8kbectxkoNHF+EQauFzIIQtTsS5VyCbQXaBDAPN3SemQa3nbtcOOzJIgd955p29D6ef6668HAIDrr78err322sAzV199NYyPj8P09DR86lOfSvVOTIL0YfK4nozw0aEIh+U4WN4BBF1GStp3mzYW0sKUEwRg9hi+KxgWQ111X9n+xyyf8i3tdCYk+OdkeCDPpIBp5CnHTQRG+bGGVWZFJWhdOs3KjquogRIKE3pSp/wuciGHqUprLORIBhN+HYCZamuVsaq2ITJhw5j0zfKGqYSEyUCjK/bpMHXkMAXTeke2rbkL+6MiE1UvnqeQjUXmdTIO4Ucu7bDyAiZB+tAhuNIaDKYMdJmxugpXK+Zk3m3KwdUVyMprniowbYzlCVNHtvMOIKjKCPbyPf7+haQX3MoagXk7ty7Svy49mZd8ktkbllfZ5Bv9lMtlmJ2dDd0fF0+zAgxHIYeJgLcuRz4tLbsWyGLHlUZG6JgnFnLEw4Rfp3qC0MTpaNX7yQDyt2Fk3uki/evyd1wKNOad4DJlwwyTHe4q8o7rycJEHEbl4nnRu7P2YV2U/zYCkyAFhQ7FktZgMFHhoaNKwxW4WDHnUuLGNScTkQ6qMtGVAIJqsJu9fI//ruXl5UyMQIq8dY5LMOWQq66x7CW+Kv208w7mmkgWuQoTAW+dsl9GpuZld5mwYWTHqgPDFkDL268zfYJWNtht4lSeypiHJYBsSj7JPmeqeEr2WZM2DMBwFHK4CllaVKFhV2wY+k7Zi+f5d9q8vgg/MAmCECJPQ4NFXga6q3CtYs5EdYmptg4URW6ZMexwJYBgIhmXdxJ82OS/LjmeZ5BG9jkTCR9Tp1mHKZGnAlN60qVCDtOV1gi7kXcSXNezMr6kDPK2YXS80zXkXeWt8pzKeIfJhpEZKyI/mD7R7YINY6LYd9h8WBuBSRBEAKpGWV5HibGKPjlMBtpV353nZe4mnCCEG3ApgADghhGoMtdhlP95n2ZikecpHVMJH0zkIXi4VMgh+hu0YRAA5gKcriQGTNgwKP+Tw7VA47DYMDreicgWOmgxz1ZyrtkwJruPINSASRCED6baOsgKPayiLzbydr50PYeGYLHgUgBB9Z0yMHUMH+V/cuRJwzoCD6YSPqq8U+SLJ4cVLhVy8M+iDYMw3erGhRP+JmwYlP/J4WKgcRhsGEzkuQETLQKHyYaRXV/0Yc0CkyAIH/LuRQ+AShQhhg5l5tIFegg74VIAQTQuF4xADARki7yrskzvqSoP5HkxLjpBxYaJJDgA2jCIdZg4zST7rItBa5Uxo/xPjmEKNLpiw5i29RDZwuSpDLRhEFkDkyCIAGQMBtVqXlSiCBYm+qmqPIc0XFy4FEBw1Qh00Ul1CSb687p4hwP/dy5UoCHshalCDrRhEDxk5bGuat40z7poD7g4ZoSdcM2GQdovJkzeMYY2DCIPYBIEoQWqwgeVKIKFzsBzGmNOxeFDGi4uXAkgoBGICIMMDbtIT3iaFWEDTBVyoA2D0AUVGkaZiEDIAW0YhA3QYf+jDYOwGZgEQWgDCh+ETsjSk2sX6CGKCVMBBJTDCJ1wkZ7wNCvCNEwUcqANg9AJ1TZPKBMRCDmgDYOwAbruEkEbBmEjkuYNRgAAiOV46qmnyEUXXUSefPJJsnHjRtPDQSAQBvDkk0+S17zmNeTxxx8nX/7yl8mOHTu833W7XfLyl7+cbNu2jXzpS18iF110kfJzCIQIKvSEtIhA5AtVnnvyySfJmTNnSK1WC/zuxIkT5DnPeQ7y6pBBliZOnDhBrr32WvLQQw+R6elpjx4pHdKf33XXXb7vRr2B0A0VuYYyEYHID2jDIGwB2jAIF5A0b4BJEAQC4QxkjTk0AhE6gQEEBMIdIM8hbIBqAh1pGIFAIIYPKP8RNgBtGIQLwCQIAoFAIBAIBAKBQFgADAQgEAgEAoFwEWjDIGxH0rxBOccxIRAIBAKBQCAQCMTQ4aKLLgoNEIiCCggEAoFAIBA2AG0YRFEwanoACAQCgUAgEAgEAoFAIBAIBAKBQCAQWQCTIAgEAoFAIBAIBAKBQCAQCAQCgUAgCglMgiAQCAQCgUAgEAgEAoFAIBAIBAKBKCQwCYJAIBAIBAKBQCAQCAQCgUAgEAgEopDAJAgCgUAgEAgEAoFAIBAIBAKBQCAQiEICkyAIBAKBQCAQCAQCgUAgEAgEAoFAIAoJTIIgEAgEAoFAIBAIBAKBQCAQCAQCgSgkMAmCQCAQCAQCgUAgEAgEAoFAIBAIBKKQwCQIAoFAIBAIBAKBQCAQCAQCgUAgEIhComx6AEkAAIQQQp566inDI0EgEAgEAoFAIBAIBAKBQCAQCAQCYRo0X0DzB2FwIgly5swZQgghO3bsMDwSBAKBQCAQCAQCgUAgEAgEAoFAIBC24MyZM+Siiy4K/f0IxKVJLMDa2ho5efIkec5znkNGRkZMD8caPPXUU2THjh2k2+2SjRs3mh4OAoFAFAYoXxEIBCI7oIxFIBCIbIDyFYFAILIByld7AQDkzJkzpFqtktHR8Js/nDgJMjo6Smq1mulhWIuNGzciAyIQCEQGQPmKQCAQ2QFlLAKBQGQDlK8IBAKRDVC+2omoEyAUeDE6AoFAIBAIBAKBQCAQCAQCgUAgEIhCApMgCAQCgUAgEAgEAoFAIBAIBAKBQCAKCUyCOIxKpUJuuOEGUqlUTA8FgUAgCgWUrwgEApEdUMYiEAhENkD5ikAgENkA5av7cOJidAQCgUAgEAgEAoFAIBAIBAKBQCAQiLTAkyAIBAKBQCAQCAQCgUAgEAgEAoFAIAoJTIIgEAgEAoFAIBAIBAKBQCAQCAQCgSgkMAmCQCAQCAQCgUAgEAgEAoFAIBAIBKKQwCQIAoFAIBAIBAKBQCAQCAQCgUAgEIhCApMgCAQCgUAgEAgEAoFAIBAIBAKBQCAKCUyCOIyPfexjpNFokGc961nkmmuuIcvLy6aHhEAgENbive99LxkZGfF9du7c6f3+mWeeIe9617vIJZdcQjZs2EB+/ud/njz22GO+71hZWSGvf/3ryQUXXEC2bdtG/uiP/oicO3cu76kgEAiEcXzlK18hP/3TP02q1SoZGRkht99+u+/3AED+/M//nGzfvp08+9nPJq961avIv//7v/v+5gc/+AF529veRjZu3Eg2bdpEfuVXfoX88Ic/9P3NN7/5TfKyl72MPOtZzyI7duwgH/jAB7KeGgKBQBhFnHz9pV/6pYBN+5rXvMb3NyhfEQgEIoj3ve99ZGFhgTznOc8h27ZtIz/3cz9Hvv3tb/v+Rldc4Mtf/jKZm5sjlUqFPO95zyOf/vSns54eIgaYBHEU//iP/0j+4A/+gNxwww3k3nvvJbt37ybXXXcdefzxx00PDYFAIKzFzMwMOXXqlPf5P//n/3i/+/3f/33y+c9/nvzzP/8zueuuu8jJkyfJf/kv/8X7/fnz58nrX/96cvbsWXL33XeTz3zmM+TTn/40+fM//3MTU0EgEAij+NGPfkR2795NPvaxjwl//4EPfIB85CMfIXv37iXNZpNceOGF5LrrriPPPPOM9zdve9vbSLvdJocPHyZf+MIXyFe+8hXya7/2a97vn3rqKfLqV7+aXHrppeSee+4hH/zgB8l73/tecvPNN2c+PwQCgTCFOPlKCCGvec1rfDbtvn37fL9H+YpAIBBB3HXXXeRd73oXWVpaIocPHyarq6vk1a9+NfnRj37k/Y2OuMDDDz9MXv/615NXvOIV5Otf/zr5vd/7PfKrv/qr5NChQ7nOF8EBEE7iRS96EbzrXe/y/n3+/HmoVqvwvve9z+CoEAgEwl7ccMMNsHv3buHver0ejI2NwT//8z97P/vWt74FhBA4fvw4AAB88YtfhNHRUTh9+rT3N3/7t38LGzduhB//+MeZjh2BQCBsBiEEFhcXvX+vra3B5OQkfPCDH/R+1uv1oFKpwL59+wAA4IEHHgBCCHz1q1/1/uZ//+//DSMjI/Doo48CAMDf/M3fwMUXX+yTsX/8x38MV1xxRcYzQiAQCDvAy1cAgOuvvx5+9md/NvQZlK8IBAKRDI8//jgQQuCuu+4CAH1xgXe/+90wMzPje9eb3/xmuO6667KeEiICeBLEQZw9e5bcc8895FWvepX3s9HRUfKqV72KHD9+3ODIEAgEwm78+7//O6lWq2R6epq87W1vIysrK4QQQu655x6yurrqk6s7d+4k9Xrdk6vHjx8nP/ETP0EmJia8v7nuuuvIU089Rdrtdr4TQSAQCIvx8MMPk9OnT/tk6kUXXUSuueYan0zdtGkTmZ+f9/7mVa96FRkdHSXNZtP7m//0n/4TGR8f9/7muuuuI9/+9rfJE088kdNsEAgEwj58+ctfJtu2bSNXXHEF+Y3f+A3y/e9/3/sdylcEAoFIhieffJIQQsjmzZsJIfriAsePH/d9B/0bjNmaBSZBHMT3vvc9cv78eR/DEULIxMQEOX36tKFRIRAIhN245ppryKc//WnypS99ifzt3/4tefjhh8nLXvYycubMGXL69GkyPj5ONm3a5HuGlaunT58Wyl36OwQCgUD0QWVilK16+vRpsm3bNt/vy+Uy2bx5M8pdBAKBiMBrXvMa8g//8A/k6NGj5KabbiJ33XUXee1rX0vOnz9PCEH5ikAgEEmwtrZGfu/3fo+89KUvJbt27SKEEG1xgbC/eeqpp8jTTz+dxXQQCVA2PQAEAoFAIPLAa1/7Wu//r7rqKnLNNdeQSy+9lPzTP/0Tefazn21wZAgEAoFAIBAIRDK85S1v8f7/J37iJ8hVV11Fnvvc55Ivf/nL5JWvfKXBkSEQCIQ7eNe73kVarZbvnlBEsYEnQRzEli1bSKlUIo899pjv54899hiZnJw0NCoEAoFwC5s2bSKXX345efDBB8nk5CQ5e/Ys6fV6vr9h5erk5KRQ7tLfIRAIBKIPKhOjbNXJyUny+OOP+35/7tw58oMf/ADlLgKBQKTA9PQ02bJlC3nwwQcJIShfEQgEIg6/9Vu/Rb7whS+QO++8k9RqNe/nuuICYX+zceNGLMA0CEyCOIjx8XHywhe+kBw9etT72draGjl69Ch58YtfbHBkCAQC4Q5++MMfku985ztk+/bt5IUvfCEZGxvzydVvf/vbZGVlxZOrL37xi8n999/vcyoPHz5MNm7cSF7wghfkPn4EAoGwFZdddhmZnJz0ydSnnnqKNJtNn0zt9Xrknnvu8f7mjjvuIGtra+Saa67x/uYrX/kKWV1d9f7m8OHD5IorriAXX3xxTrNBIBAIu3HixAny/e9/n2zfvp0QgvIVgUAgwgAA5Ld+67fI4uIiueOOO8hll13m+72uuMCLX/xi33fQv8GYrVmMAACYHgQiPf7xH/+RXH/99eTv/u7vyIte9CLy4Q9/mPzTP/0T+bd/+7dA3zkEAoFAEPKHf/iH5Kd/+qfJpZdeSk6ePEluuOEG8vWvf5088MADZOvWreQ3fuM3yBe/+EXy6U9/mmzcuJH89m//NiGEkLvvvpsQQsj58+fJ1VdfTarVKvnABz5ATp8+Td7+9reTX/3VXyU33nijyakhEAhE7vjhD3/oVR3Pzs6SD33oQ+QVr3gF2bx5M6nX6+Smm24i73//+8lnPvMZctlll5E/+7M/I9/85jfJAw88QJ71rGcRQvptCh977DGyd+9esrq6Sn75l3+ZzM/Pk9tuu40Q0r+s8oorriCvfvWryR//8R+TVqtF3vnOd5K//Mu/JL/2a79mbO4IBAKRJaLk6+bNm8lf/MVfkJ//+Z8nk5OT5Dvf+Q5597vfTc6cOUPuv/9+UqlUCCEoXxEIBEKE3/zN3yS33XYbOXDgALniiiu8n1900UXeCQ0dcYGHH36Y7Nq1i7zrXe8i73znO8kdd9xBfud3fof867/+K7nuuutynjXCAyCcxUc/+lGo1+swPj4OL3rRi2Bpacn0kBAIBMJavPnNb4bt27fD+Pg4TE1NwZvf/GZ48MEHvd8//fTT8Ju/+Ztw8cUXwwUXXABvfOMb4dSpU77veOSRR+C1r30tPPvZz4YtW7bA//gf/wNWV1fzngoCgUAYx5133gmEkMDn+uuvBwCAtbU1+LM/+zOYmJiASqUCr3zlK+Hb3/627zu+//3vw1vf+lbYsGEDbNy4EX75l38Zzpw54/ubb3zjG/CTP/mTUKlUYGpqCt7//vfnNUUEAoEwgij5+h//8R/w6le/GrZu3QpjY2Nw6aWXwn/7b/8NTp8+7fsOlK8IBAIRhEi2EkLgU5/6lPc3uuICd955J1x99dUwPj4O09PTvncgzABPgiAQCAQCgUAgEAgEAoFAIBAIBAKBKCTwThAEAoFAIBAIBAKBQCAQCAQCgUAgEIUEJkEQCAQCgUAgEAgEAoFAIBAIBAKBQBQSmARBIBAIBAKBQCAQCAQCgUAgEAgEAlFIYBIEgUAgEAgEAoFAIBAIBAKBQCAQCEQhgUkQBAKBQCAQCAQCgUAgEAgEAoFAIBCFBCZBEAgEAoFAIBAIBAKBQCAQCAQCgUAUEpgEQSAQCAQCgUAgEAgEAoFAIBAIBAJRSGASBIFAIBAIBAKBQCAQCAQCgUAgEAhEIYFJEAQCgUAgEAgEAoFAIBAIBAKBQCAQhQQmQRAIBAKBQCAQCAQCgUAgEAgEAoFAFBKYBEEgEAgEAoFAIBAIBAKBQCAQCAQCUUj8/wHU5GByZvfyvgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2nd invariant conservation\n",
    "# Note, for different initial conditions higher order invariants can give worse results\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "for order, color in zip([2, 3, 4], ['black', 'red', 'blue']):\n",
    "    sy = series((4, ), (order, ), ty)\n",
    "    vy = torch.func.vmap(lambda x: evaluate(sy, [x]))(bag)\n",
    "    print(vy.mean())\n",
    "    print(vy.std())\n",
    "    print()\n",
    "    plt.scatter(range(len(vy)), vy.cpu().numpy(), color=color, marker='x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe79e079-32b3-4012-af0c-7fb6d9dd4e08",
   "metadata": {},
   "source": [
    "# Example-24: Direct invariant (parametric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9efdcf3c-8670-4e3f-9453-ec5b85ef9dd8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.series import split\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "from ndmap.invariant import invariant\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "\n",
    "torch.set_printoptions(precision=6, sci_mode=True, linewidth=128)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "311b1789-dc14-47b2-ad82-af04914df777",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8dd2762-8fed-4586-a1b7-3f1821e68102",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=20):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "feab5a33-0a03-4b3c-bf01-97ca16688379",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x, w, k):\n",
    "    ksf, ksd, ksb = k\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5 + ksf)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25 + ksb, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5 + ksd)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5 + ksd)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25 + ksb, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5 + ksf)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7dff7f1-6136-4a8d-9e2c-63338879306f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k, w):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k, w)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9017ea5-1786-4ac4-9d52-f0232906e211",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n",
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Find fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor(1*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(3*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(32, fodo, x, w, k, power=1)\n",
    "\n",
    "print(fp)\n",
    "print(fodo(fp, w, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3d6d2a5-a778-4032-8015-29128039087f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set computation orders\n",
    "\n",
    "(nx, nw, nk) = (3, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d0d6b28-3fe1-4ae0-8859-e26e018bf0b1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Find parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((nw, nk), fp, [w, k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77a868ee-fed5-4b9c-9818-b9e5fbe8705a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Test parametric fixed point\n",
    "# Note, if fp is not zero, redefine one-turn transport to map zero to zero\n",
    "\n",
    "print(compare(pfp, propagate((4, 1, 3), (0, nw, nk), pfp, [w, k], fodo)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "766a6908-04e3-4f22-9fba-62d1512b70cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Define a fodo variant around parametric fixed point\n",
    "\n",
    "def mapping(x, w, k):\n",
    "    x = x + evaluate(first(pfp), [w, k])\n",
    "    x = fodo(x, w, k)\n",
    "    x = x - evaluate(first(pfp), [w, k])\n",
    "    return x\n",
    "\n",
    "print(mapping(x, w, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9b654a3-82b1-430b-ba00-9a99aed9a531",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[tensor([0., 0., 0., 0.], dtype=torch.float64), tensor([[0., 0., 0.],\n",
      "        [0., 0., 0.],\n",
      "        [0., 0., 0.],\n",
      "        [0., 0., 0.]], dtype=torch.float64)], [tensor([[0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.]], dtype=torch.float64), tensor([[[0.],\n",
      "         [0.],\n",
      "         [0.]],\n",
      "\n",
      "        [[0.],\n",
      "         [0.],\n",
      "         [0.]],\n",
      "\n",
      "        [[0.],\n",
      "         [0.],\n",
      "         [0.]],\n",
      "\n",
      "        [[0.],\n",
      "         [0.],\n",
      "         [0.]]], dtype=torch.float64)]]\n"
     ]
    }
   ],
   "source": [
    "# Compute derivative table representation\n",
    "\n",
    "# Note, no parametric part should be passed, parametric zero is transformed to parametric zero\n",
    "\n",
    "t = identity((nx, nw, nk), x)\n",
    "t = propagate((4, 1, 3), (nx - 1, nw, nk), t, [w, k], mapping)\n",
    "chop(t)\n",
    "print(first(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8f06797b-4e78-4ded-9593-35b2d377755d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute parametric normalization matrix\n",
    "\n",
    "def fn(w, k):\n",
    "    m = derivative(1, lambda x: evaluate(t, [x, w, k]), fp, intermediate=False)\n",
    "    _, n, _ = twiss(m)\n",
    "    return n\n",
    "\n",
    "tn = derivative((nw, nk), fn, w, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26fa48e4-34fa-449f-88e0-c1a2c1154b4b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set initial invariants\n",
    "\n",
    "def ix(x, w, k):\n",
    "    qx, px, qy, py = torch.inverse(evaluate(tn, [w, k])) @ x\n",
    "    return 1/2*(qx**2 + px**2)\n",
    "\n",
    "def iy(x, w, k):\n",
    "    qx, px, qy, py = torch.inverse(evaluate(tn, [w, k])) @ x\n",
    "    return 1/2*(qy**2 + py**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81a9cc2d-ddf8-45d3-936b-21fdeb472714",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1st invariant\n",
    "\n",
    "tx, _ = invariant((nx, nw, nk), x, [w, k], ix, t, jacobian=torch.func.jacrev, threshold=0.01)\n",
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1aba49aa-9193-4888-8a0c-c83222f00e84",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2nd invariant\n",
    "\n",
    "ty, _ = invariant((nx, nw, nk), x, [w, k], iy, t, jacobian=torch.func.jacrev, threshold=0.01)\n",
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9744a927-cdbd-477c-ad0a-4f7363285d26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Test conservation\n",
    "\n",
    "# Note, here propagate is used as composition\n",
    "\n",
    "print(compare(propagate((4, 1, 3), (nx-1, nw, nk), t, [w, k], tx), tx))\n",
    "print(compare(propagate((4, 1, 3), (nx-1, nw, nk), t, [w, k], ty), ty))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a5e75ebb-22b4-4f55-9c14-5e21e97d6427",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(2, 0, 0, 0, 0, 0, 0, 0): tensor(3.387985e-02, dtype=torch.float64),\n",
       " (0, 2, 0, 0, 0, 0, 0, 0): tensor(7.379018e+00, dtype=torch.float64),\n",
       " (2, 0, 0, 0, 0, 1, 0, 0): tensor(-6.047879e-03, dtype=torch.float64),\n",
       " (2, 0, 0, 0, 0, 0, 1, 0): tensor(-6.517039e-03, dtype=torch.float64),\n",
       " (0, 2, 0, 0, 0, 1, 0, 0): tensor(1.317226e+00, dtype=torch.float64),\n",
       " (0, 2, 0, 0, 0, 0, 1, 0): tensor(1.419409e+00, dtype=torch.float64),\n",
       " (2, 0, 0, 0, 1, 0, 0, 0): tensor(1.994743e-01, dtype=torch.float64),\n",
       " (0, 2, 0, 0, 1, 0, 0, 0): tensor(-4.344542e+01, dtype=torch.float64),\n",
       " (2, 0, 0, 0, 1, 1, 0, 0): tensor(-6.188913e-02, dtype=torch.float64),\n",
       " (2, 0, 0, 0, 1, 0, 1, 0): tensor(-8.813844e-02, dtype=torch.float64),\n",
       " (2, 0, 0, 0, 1, 0, 0, 1): tensor(6.534032e-01, dtype=torch.float64),\n",
       " (0, 2, 0, 0, 1, 1, 0, 0): tensor(-2.031422e+00, dtype=torch.float64),\n",
       " (0, 2, 0, 0, 1, 0, 1, 0): tensor(2.482422e+00, dtype=torch.float64),\n",
       " (0, 2, 0, 0, 1, 0, 0, 1): tensor(-1.423110e+02, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 0, 0, 0, 0): tensor(5.742237e-02, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 0, 0, 0, 0): tensor(7.908874e+00, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 0, 0, 0, 0): tensor(-1.249413e+00, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 0, 0, 0, 0): tensor(8.887557e+01, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 0, 0, 0, 0): tensor(-2.613173e+02, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 0, 1, 0, 0): tensor(-2.180292e-02, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 0, 0, 1, 0): tensor(-3.543561e-02, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 0, 0, 0, 1): tensor(2.292041e-01, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 0, 1, 0, 0): tensor(7.152281e+00, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 0, 0, 1, 0): tensor(1.584743e+01, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 0, 0, 0, 1): tensor(1.861577e+01, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 0, 1, 0, 0): tensor(-7.509155e-01, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 0, 0, 1, 0): tensor(-1.639441e+00, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 0, 0, 0, 1): tensor(-5.148461e+00, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 0, 1, 0, 0): tensor(6.392325e+01, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 0, 0, 1, 0): tensor(1.341707e+02, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 0, 0, 0, 1): tensor(3.638539e+02, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 0, 1, 0, 0): tensor(-2.452915e+02, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 0, 0, 1, 0): tensor(-4.911196e+02, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 0, 0, 0, 1): tensor(-1.064586e+03, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 1, 0, 0, 0): tensor(-5.835809e-01, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 1, 0, 0, 0): tensor(6.243185e+01, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 1, 0, 0, 0): tensor(1.617990e+02, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 1, 0, 0, 0): tensor(-1.601207e+04, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 1, 0, 0, 0): tensor(4.860999e+04, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 1, 1, 0, 0): tensor(-8.770463e-02, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 1, 0, 1, 0): tensor(-4.631656e-01, dtype=torch.float64),\n",
       " (3, 0, 0, 0, 1, 0, 0, 1): tensor(-4.557887e+00, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 1, 1, 0, 0): tensor(9.372351e+01, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 1, 0, 1, 0): tensor(1.993612e+02, dtype=torch.float64),\n",
       " (1, 2, 0, 0, 1, 0, 0, 1): tensor(5.704250e+02, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 1, 1, 0, 0): tensor(2.404182e+02, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 1, 0, 1, 0): tensor(5.584620e+02, dtype=torch.float64),\n",
       " (1, 0, 2, 0, 1, 0, 0, 1): tensor(1.295549e+03, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 1, 1, 0, 0): tensor(-2.229816e+04, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 1, 0, 1, 0): tensor(-5.022342e+04, dtype=torch.float64),\n",
       " (1, 0, 0, 2, 1, 0, 0, 1): tensor( -1.279140e+05, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 1, 1, 0, 0): tensor(7.791695e+04, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 1, 0, 1, 0): tensor( 1.680246e+05, dtype=torch.float64),\n",
       " (0, 1, 1, 1, 1, 0, 0, 1): tensor( 3.876573e+05, dtype=torch.float64)}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series representation\n",
    "# Note, generalized monomial is qx px qy py w ksf ksd ksb\n",
    "\n",
    "s, *_ = split(clean(series((4, 1, 3), (nx, nw, nk), tx)))\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0f4f3b7-822b-44f3-bfa9-8b55f7d28262",
   "metadata": {},
   "source": [
    "# Example-25: Composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3301dc2f-580b-4fd2-a8d1-925ca0f0d39a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# In this example the following homomorphism is illustrated\n",
    "# t(f o g) = t(f) o t(g)\n",
    "# Meaning, table of composition is equal to composition of tables\n",
    "# Mappings f and g that map zero to zero, i.e. are without constant part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ae27036-ddeb-43de-b016-40f4a0f0f492",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df9b1ff3-9833-4fe6-a6cb-62a49cc26d1a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59ce7622-531a-4006-b23f-99a4b55de3dc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=20):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "665d2e9c-7131-4092-b87d-f30ca256fd70",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x):\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    return x\n",
    "\n",
    "def map_02_03(x):\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c7ea02f-3249-41de-94e7-6189cbfe7ee5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set computation order & evaluation point\n",
    "\n",
    "n = 4\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "997b9f76-2ce8-4da8-8eaf-1902052923bb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n",
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Note, both mapping map zero to zero \n",
    "# Also, parameters that effect closed orbit are not used\n",
    "\n",
    "print(map_01_02(x))\n",
    "print(map_02_03(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cfcb6ce8-7f63-4602-a221-e091d0911ccc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Direct table generation\n",
    "\n",
    "T = derivative(n, lambda x: map_02_03(map_01_02(x)), x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "61525252-bcd6-45ed-acb5-999669d9e82c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Propagation of identity (equvalent to direct table generation)\n",
    "\n",
    "t = identity((n, ), [x])\n",
    "t = propagate((4, ), (n, ), t, [], lambda x: map_02_03(map_01_02(x)))\n",
    "\n",
    "print(compare(T, t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc7de821-ba85-45fc-9105-3039a6eebf62",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Propagation (split)\n",
    "\n",
    "# Table is propagated through mappings\n",
    "# Note, this evaluation is inherently sequential\n",
    "\n",
    "t = identity((n, ), [x])\n",
    "t = propagate((4, ), (n, ), t, [], map_01_02)\n",
    "t = propagate((4, ), (n, ), t, [], map_02_03)\n",
    "\n",
    "print(compare(T, t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c1ffa18-d121-4e04-9375-f73eb879b12c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Composition\n",
    "\n",
    "# Note, given an evaluation point (e.g. closed orbit at each element)\n",
    "# Identity can be propagated (or just perform direct computation) for each element\n",
    "# This can significantly reduce computation cost\n",
    "\n",
    "# Note, evaluations of t_01_02 and t_02_03 are independent and can be performed in parallel\n",
    "# Also, propagation of table through table might be computationally less expensive\n",
    "\n",
    "t_01_02 = identity((n, ), [x])\n",
    "t_01_02 = propagate((4, ), (n, ), t_01_02, [], map_01_02)\n",
    "\n",
    "t_02_03 = identity((n, ), [x])\n",
    "t_02_03 = propagate((4, ), (n, ), t_02_03, [], map_02_03)\n",
    "\n",
    "t = propagate((4, ), (n, ), t_01_02, [], t_02_03)\n",
    "\n",
    "print(compare(T, t))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b962a2f-bc1a-41ca-abb4-4d2e9513de7a",
   "metadata": {},
   "source": [
    "# Example-26: Composition (closed orbit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd01440b-4859-4832-9cec-8b17878cf0e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Composition illustration with non-zero closed orbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fcd5d12-e0ed-4a7e-9a2e-f7513a7fe7b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13128e0c-948a-430d-b28e-b1873f0eec92",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0600ed8-cbec-4b15-886f-07985ecd9d6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=20):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a2da85da-c7ba-4c9c-b7bd-5216b1d929a9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "# Note, kick is used to generate non-zero (dynamical) closed orbit\n",
    "# Momentum deviation is used as a parameter (coupled to closed orbit via dispersion)\n",
    "\n",
    "def map_01_02(x, w):\n",
    "    x = kick(x, +1.0E-4, -1.0E-4)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    return x\n",
    "\n",
    "def map_02_03(x, w):\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa1f172c-86b6-4690-bccc-680eb3385c8b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor(1*[0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1296cdef-385f-49a0-85f5-8b9ffc59e3cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 8.418072943377e-04, -5.000000000000e-05, -2.043309959087e-03,\n",
      "         5.000000000000e-05], dtype=torch.float64)\n",
      "tensor([ 8.418072943377e-04, -5.000000000000e-05, -2.043309959087e-03,\n",
      "         5.000000000000e-05], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Find (dynamical) fixed point\n",
    "\n",
    "fp = fixed_point(32, lambda x, w: map_02_03(map_01_02(x, w), w), x, w, power=1)\n",
    "\n",
    "# Check fixed point\n",
    "\n",
    "print(fp)\n",
    "print(map_02_03(map_01_02(fp, w), w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a66341c9-0d88-40ba-937b-f5212110d8d7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set computation orders for state and each knob group\n",
    "\n",
    "(nx, nw) = (4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b32dc37f-d144-48ca-a965-cfd07feb97f8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Find parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((nw, ), fp, [w], lambda x, w: map_02_03(map_01_02(x, w), w))\n",
    "\n",
    "# Check\n",
    "\n",
    "print(compare(pfp, propagate((4, 1), (0, nw), pfp, [w], lambda x, w: map_02_03(map_01_02(x, w), w))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfe3b573-d15f-4942-802b-72add4c38436",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Set parametric fixed points at each map entrance\n",
    "\n",
    "pfp_01 = identity((0, nw), [x, w], parametric=pfp)\n",
    "pfp_02 = propagate((4, 1), (0, nw), pfp_01, [w], map_01_02)\n",
    "\n",
    "# Check\n",
    "\n",
    "print(compare(pfp_01, propagate((4, 1), (0, nw), pfp_01, [w], lambda x, w: map_02_03(map_01_02(x, w), w))))\n",
    "print(compare(pfp_02, propagate((4, 1), (0, nw), pfp_02, [w], lambda x, w: map_01_02(map_02_03(x, w), w))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cb9c63ac-3ebc-42ad-9790-f766e6dc6e4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[tensor([0., 0., 0., 0.], dtype=torch.float64), tensor([[0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.]], dtype=torch.float64), tensor([[[0.]],\n",
      "\n",
      "        [[0.]],\n",
      "\n",
      "        [[0.]],\n",
      "\n",
      "        [[0.]]], dtype=torch.float64)]]\n"
     ]
    }
   ],
   "source": [
    "# Define transformations around parametric fixed points\n",
    "\n",
    "# Note, this transformation map zero (parametric) state to zero (upto given order)\n",
    "# This is true by construction\n",
    "\n",
    "def fn_01_02(x, w):\n",
    "    return map_01_02(x + evaluate(first(pfp_01), [w]), w) - evaluate(first(pfp_02), [w])\n",
    "\n",
    "def fn_02_03(x, w):\n",
    "    return map_02_03(x + evaluate(first(pfp_02), [w]), w) - evaluate(first(pfp_01), [w])\n",
    "\n",
    "print(propagate((4, 1), (0, nw), identity((0, nw), [x, w]), [w], fn_01_02))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16af664b-09da-4cda-9db5-c8aee3332da2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate identity sequentially\n",
    "\n",
    "T = identity((nx, nw), [x, w], parametric=pfp)\n",
    "T = propagate((4, 1), (nx, nw), T, [w], lambda x, w: map_01_02(x, w))\n",
    "T = propagate((4, 1), (nx, nw), T, [w], lambda x, w: map_02_03(x, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c923c4b-3539-4ee6-832f-325f649e9cb5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Composition\n",
    "\n",
    "# Note, parametric part is zero, other elements of t should be equal to corresponding elements of T\n",
    "\n",
    "t_01_02 = identity((nx, nw), [x, w])\n",
    "t_01_02 = propagate((4, 1), (nx, nw), t_01_02, [w], fn_01_02)\n",
    "\n",
    "t_02_03 = identity((nx, nw), [x, w])\n",
    "t_02_03 = propagate((4, 1), (nx, nw), t_02_03, [w], fn_02_03)\n",
    "\n",
    "t = propagate((4, 1), (nx, nw), t_01_02, [w], t_02_03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6d8a68a-5c71-4812-a8fe-9952ae2c863f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare phase space trajectories\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "qx = torch.linspace(0.0, 0.01, 10, dtype=dtype, device=device)\n",
    "px = torch.zeros_like(qx) + 1.0E-12\n",
    "qy = torch.zeros_like(qx) + 1.0E-03\n",
    "py = torch.zeros_like(qx) + 1.0E-12\n",
    "\n",
    "x = torch.stack([qx, px, qy, py]).T\n",
    "\n",
    "w = torch.tensor([1.0E-3], dtype=dtype, device=device)\n",
    "\n",
    "count = 256\n",
    "table = []\n",
    "y = torch.clone(x)\n",
    "for _ in range(count):\n",
    "    table.append(y)\n",
    "    y = torch.func.vmap(lambda x: map_02_03(map_01_02(x, w), w))(y)\n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qx, px, *_ = table\n",
    "for q, p in zip(qx.cpu().numpy(), px.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='gray', marker='o')\n",
    "    \n",
    "\n",
    "count = 256\n",
    "table = []\n",
    "y = torch.clone(x)\n",
    "for _ in range(count):\n",
    "    table.append(y)\n",
    "    y = y - evaluate(first(pfp), [w])\n",
    "    y = torch.func.vmap(lambda x: evaluate(T, [x, w]))(y)\n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qx, px, *_ = table\n",
    "for q, p in zip(qx.cpu().numpy(), px.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='red', marker='o')\n",
    "    \n",
    "\n",
    "count = 256\n",
    "table = []\n",
    "y = torch.clone(x)\n",
    "for _ in range(count):\n",
    "    table.append(y)\n",
    "    y = y - evaluate(first(pfp), [w])\n",
    "    y = torch.func.vmap(lambda x: evaluate(t, [x, w]))(y)\n",
    "    y = y + evaluate(first(pfp), [w])\n",
    "table = torch.stack(table).swapaxes(0, -1)\n",
    "qx, px, *_ = table\n",
    "for q, p in zip(qx.cpu().numpy(), px.cpu().numpy()):\n",
    "    plt.scatter(q, p, color='blue', marker='x')    \n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b5f45a-b6f7-4e7e-8551-d47a24ec034c",
   "metadata": {},
   "source": [
    "# Example-27: Inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6abab690-eea1-4753-a698-e7d2732e0d3c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.inverse import inverse\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e3fd75b-78b7-4b39-8a5a-992d3955826f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "706cc9c8-4f41-4d85-88c1-d02b80ef1d3f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=20):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcebaee3-d16b-427a-aafa-7c488169cdbc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x):\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6a3a2cc9-4fed-4cfd-967f-a9625d369265",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set computation order & evaluation point\n",
    "\n",
    "n = 3\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(map_01_02(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72e39b4f-c121-40e2-a353-fe3c00956009",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute derivative table\n",
    "\n",
    "t = identity((n, ), [x])\n",
    "t = propagate((4, ), (n, ), t, [], lambda x: map_01_02(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0085cbaa-f1a3-4e76-bd4e-5f5f5c0199b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute inverse\n",
    "\n",
    "t_inv = inverse((n, ), x, [], t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e7f286aa-dc5d-48c0-9827-d0aa560362bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[],\n",
       " tensor([[1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
       "         [0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
       "         [0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00],\n",
       "         [0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00]],\n",
       "        dtype=torch.float64),\n",
       " [],\n",
       " []]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check\n",
    "\n",
    "out = propagate((4, ), (n, ), t_inv, [], t)\n",
    "chop(out, replace=True)\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd37bbff-419e-48cd-af75-7026011537df",
   "metadata": {},
   "source": [
    "# Example-28: Inverse (closed orbit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf632036-4f4b-4a3b-9aa7-ac6825fd7935",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "from ndmap.inverse import inverse\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c3ccd36-534d-43a5-8595-e5774f40432c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c720780-6a29-432d-a86a-378072dfa8aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=20):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6ea4258-880e-49c5-bf57-9c39dc132535",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x, w):\n",
    "    x = kick(x, +1.0E-4, -1.0E-4)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df8af8aa-944d-4124-aa65-2c17e6d682e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor(1*[0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6de412c5-e4c8-406e-890c-124e566dd141",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 8.418072943377e-04, -5.000000000000e-05, -2.043309959087e-03,\n",
      "         5.000000000000e-05], dtype=torch.float64)\n",
      "tensor([ 8.418072943377e-04, -5.000000000000e-05, -2.043309959087e-03,\n",
      "         5.000000000000e-05], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Find (dynamical) fixed point\n",
    "\n",
    "fp = fixed_point(32, lambda x, w: map_01_02(x, w), x, w, power=1)\n",
    "\n",
    "# Check fixed point\n",
    "\n",
    "print(fp)\n",
    "print(map_01_02(fp, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7edc1a27-7361-40e3-8356-912651f0bca7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set computation orders for state and each knob group\n",
    "\n",
    "(nx, nw) = (3, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc7c07ed-fa2d-4154-9b63-014f648de216",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Find parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((nw, ), fp, [w], lambda x, w: map_01_02(x, w))\n",
    "\n",
    "# Check\n",
    "\n",
    "print(compare(pfp, propagate((4, 1), (0, nw), pfp, [w], lambda x, w: map_01_02(x, w))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b8c6b5d-6cda-4e79-92eb-357098ff6d75",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[], [], []]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define transformations around parametric fixed points\n",
    "\n",
    "# Note, this transformation map zero (parametric) state to zero (upto given order)\n",
    "# This is true by construction\n",
    "\n",
    "def fn_01_02(x, w):\n",
    "    return map_01_02(x + evaluate(first(pfp), [w]), w) - evaluate(first(pfp), [w])\n",
    "\n",
    "out = propagate((4, 1), (0, nw), identity((0, nw), [x, w]), [w], fn_01_02)\n",
    "chop(out, replace=True)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb80f040-10d2-4f0a-b2f9-654c71ac62f6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute derivative table\n",
    "\n",
    "t = identity((nx, nw), [x, w])\n",
    "t = propagate((4, 1), (nx, nw), t, [w], lambda x, w: fn_01_02(x, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a5cc048d-61e2-4d74-8f2b-3ab1eb16dce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute inverse\n",
    "\n",
    "t_inv = inverse((nx, nw), x, [w], t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15165c76-6665-4098-86b3-9a6d06b198ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[], [], []],\n",
       " [tensor([[1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
       "          [0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
       "          [0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00],\n",
       "          [0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00]],\n",
       "         dtype=torch.float64),\n",
       "  [],\n",
       "  []],\n",
       " [[], [], []],\n",
       " [[], [], []]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check\n",
    "\n",
    "out = propagate((4, 1), (nx, nw), t_inv, [w], t)\n",
    "chop(out, replace=True)\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a64f7bd-e716-4653-95f6-ddc3ed0108d3",
   "metadata": {},
   "source": [
    "# Example-29: Momenta generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd671059-7f65-4e37-a093-63e5b90b6c35",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# In this example initial and final monemta are computed from given initial and final coordinates\n",
    "# Given an origing preserving mapping, its table representation can be computed upto some order\n",
    "# This representation can be considered as exact\n",
    "# Next, given initial and final coordinates, corresponding momenta can be computed using momenta generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8cfbb8f-8e87-4e6e-9e5d-b30fe8d87318",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "from ndmap.momenta import momenta\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "701fc087-f666-43e0-a90a-8b0580102a7f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3094551a-2a92-464e-9121-d37f190864a4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=20):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2df7f71e-228a-40af-b2c6-70f56611689b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x, w):\n",
    "    x = kick(x, +1.0E-4, -1.0E-4)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = quad(x, w, -0.21, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, -0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.1, +0.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, 0.19, 0.50)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "590d16c4-55b4-4ab2-9c69-2ada48a3b17b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor(1*[0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47848d70-3c44-4d28-909b-e0d24dc8fcc4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 8.418072943377e-04, -5.000000000000e-05, -2.043309959087e-03,\n",
      "         5.000000000000e-05], dtype=torch.float64)\n",
      "tensor([ 8.418072943377e-04, -5.000000000000e-05, -2.043309959087e-03,\n",
      "         5.000000000000e-05], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Find (dynamical) fixed point\n",
    "\n",
    "fp = fixed_point(32, lambda x, w: map_01_02(x, w), x, w, power=1)\n",
    "\n",
    "# Check fixed point\n",
    "\n",
    "print(fp)\n",
    "print(map_01_02(fp, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "003f4e9f-2ae3-412d-bf0c-d298d0776eff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set computation orders for state and each knob group\n",
    "\n",
    "(nx, nw) = (4, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e4c1d58-c7c8-4b46-8ee0-3b2679a764db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Find parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((nw, ), fp, [w], lambda x, w: map_01_02(x, w))\n",
    "\n",
    "# Check\n",
    "\n",
    "print(compare(pfp, propagate((4, 1), (0, nw), pfp, [w], lambda x, w: map_01_02(x, w))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29309988-0a58-4ecc-90b3-64fcf7974daa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0.],\n",
       "          [0.],\n",
       "          [0.],\n",
       "          [0.]], dtype=torch.float64),\n",
       "  tensor([[[0.]],\n",
       "  \n",
       "          [[0.]],\n",
       "  \n",
       "          [[0.]],\n",
       "  \n",
       "          [[0.]]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define transformations around parametric fixed points\n",
    "\n",
    "# Note, this transformation map zero (parametric) state to zero (upto given order)\n",
    "# This is true by construction\n",
    "\n",
    "def fn_01_02(x, w):\n",
    "    return map_01_02(x + evaluate(first(pfp), [w]), w) - evaluate(first(pfp), [w])\n",
    "\n",
    "out = propagate((4, 1), (0, nw), identity((0, nw), [x, w]), [w], fn_01_02)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3863c3f4-cc4b-422a-a25f-344054aac225",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute derivative table\n",
    "\n",
    "t = identity((nx, nw), [x, w])\n",
    "t = propagate((4, 1), (nx, nw), t, [w], lambda x, w: fn_01_02(x, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "287c366c-dbe7-4675-8f79-209e7c9e1f78",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute momenta generator\n",
    "\n",
    "# Note, computation order can be different from that of the input table\n",
    "# Accuracy is strongly related to computation order and magnitude of initial coordinates\n",
    "\n",
    "m = momenta((nx, nw), x, [w], t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa9a6e65-5a23-4d45-a76b-756da0491388",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 1.536782226285e-03, -2.360460895977e-05, -1.128298475344e-03,\n",
      "        -6.800430927953e-05], dtype=torch.float64)\n",
      "\n",
      "tensor([1.536782226285e-03, -1.128298475344e-03, 5.000000000000e-04, -5.000000000000e-04],\n",
      "       dtype=torch.float64)\n",
      "\n",
      "tensor([-2.360460895977e-05, -6.800430927953e-05,  1.000000000000e-04,\n",
      "        -1.000000000000e-04], dtype=torch.float64)\n",
      "\n",
      "tensor([-2.353065307569e-05, -6.780339520304e-05,  9.993585406462e-05,\n",
      "        -1.001716171999e-04], dtype=torch.float64)\n",
      "tensor([-2.360460892419e-05, -6.800430928312e-05,  9.999999996933e-05,\n",
      "        -1.000000000050e-04], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Recover momenta from coordinates\n",
    "\n",
    "# Set deviations\n",
    "\n",
    "xi = torch.tensor([0.0005, 0.0001, -0.0005, -0.0001], dtype=dtype, device=device)\n",
    "dw = torch.tensor(1*[0.0001], dtype=dtype, device=device)\n",
    "\n",
    "# Evaluate final state using table representation\n",
    "\n",
    "xf = evaluate(t, [xi, dw])\n",
    "\n",
    "print(xf)\n",
    "print()\n",
    "\n",
    "# Set initial and final coordinates\n",
    "\n",
    "qi, _ = xi.reshape(-1, 2).T\n",
    "qf, _ = xf.reshape(-1, 2).T\n",
    "qs = torch.cat([qf, qi])\n",
    "\n",
    "print(qs)\n",
    "print()\n",
    "\n",
    "# Set initial and final momenta\n",
    "\n",
    "_, pi, = xi.reshape(-1, 2).T\n",
    "_, pf, = xf.reshape(-1, 2).T\n",
    "ps = torch.cat([pf, pi])\n",
    "\n",
    "print(ps)\n",
    "print()\n",
    "\n",
    "# Evaluate generator using coordinates\n",
    "\n",
    "print(evaluate(m, [qs, 0*dw]))\n",
    "print(evaluate(m, [qs, 1*dw]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dcdb26f-f41a-428b-b3f1-0de458403dc3",
   "metadata": {},
   "source": [
    "# Example-30: Factorization (kick)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c9c3ebf-1980-4108-b5b0-58ffe61cb8fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given a derivative table representation (taylor series) of an origin preserving mapping\n",
    "# It is possible to factorize it into composition of linear and nonlinear table representations\n",
    "# If original mapping represents a single turn, linear part can be brought to its normal form\n",
    "# This can be done using linear theory\n",
    "# The nonlinear part is a near identity transformation, it can be represented with Lie transformation\n",
    "# t = tl o tn and tn = exp(-[h])\n",
    "# In this example (generator) h is computed for a single kick\n",
    "# It can be used to construct nonlinear normal forms or as a redundancy free representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "186b19c2-a6a4-4eb7-b239-e9e0695cd7e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.taylor import taylor\n",
    "from ndmap.inverse import inverse\n",
    "from ndmap.factorization import hamiltonian\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56cd3c16-afee-4687-b711-9a248e9c9f0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46182ddc-f0a7-47f0-873a-57c2eb80da81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transformation\n",
    "\n",
    "# Note, this transformation can be exactly represented with a taylor series (i.e. polynomial)\n",
    "\n",
    "def mapping(x, k, l):\n",
    "    (qx, px, qy, py), (k, ), l = x, k, l/2\n",
    "    qx, qy = qx + l*px, qy + l*py\n",
    "    px, py = px - 1.0*l*k*(qx**2 - qy**2), py + 2.0*l*k*qx*qy\n",
    "    qx, qy = qx + l*px, qy + l*py\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "# Test origin propagation\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(1*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(mapping(x, k, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "281e3c38-c941-4622-8a3b-5f0619409511",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute table representation\n",
    "\n",
    "t = identity((2, 1), [x, k])\n",
    "t = propagate((4, 1), (2, 1), t, [k], mapping, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63a04154-d5da-4968-aa34-e6bcbbcf0576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.009811250000e-01, 9.622500000000e-03, 5.102537625000e-02, 1.050752500000e-02],\n",
      "       dtype=torch.float64)\n",
      "tensor([1.009811250000e-01, 9.622500000000e-03, 5.102537625000e-02, 1.050752500000e-02],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compare for some deviation\n",
    "\n",
    "dx = torch.tensor([0.1, 0.01, 0.05, 0.01], dtype=dtype, device=device)\n",
    "dk = torch.tensor([1.0],dtype=dtype, device=device)\n",
    "\n",
    "print(mapping(x + dx, k + dk, 0.1))\n",
    "print(evaluate(t, [dx, dk]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52aff165-c437-4e34-9d62-78dd396522d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.000000000000e+00, 1.000000000000e-01, 0.000000000000e+00, 0.000000000000e+00],\n",
      "        [0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
      "        [0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 1.000000000000e-01],\n",
      "        [0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00]],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Linear part is not near identity\n",
    "\n",
    "print(derivative(1, lambda x, k: evaluate(t, [x, k]), x, k, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b25e630-6870-4be2-86d3-8d8dce231b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set linear part and compose with its inverse\n",
    "\n",
    "l = derivative(1, lambda x, k: evaluate(t, [x, k]), x, k)\n",
    "t = propagate((4, 1), (2, 1), inverse(1, x, [k], l), [k], t)\n",
    "chop(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c631a032-59f2-40b7-ace1-00b9e1243b25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
      "        [0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
      "        [0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00],\n",
      "        [0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00]],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Now table represents a near identity transformation\n",
    "\n",
    "print(derivative(1, lambda x, k: evaluate(t, [x, k]), x, k, intermediate=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6852653d-0193-4d28-8454-05530ab38812",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 0, 0, 0, 1) tensor(1.666666666667e-02, dtype=torch.float64)\n",
      "(2, 1, 0, 0, 1) tensor(-2.500000000000e-03, dtype=torch.float64)\n",
      "(1, 2, 0, 0, 1) tensor(1.250000000000e-04, dtype=torch.float64)\n",
      "(1, 0, 2, 0, 1) tensor(-5.000000000000e-02, dtype=torch.float64)\n",
      "(1, 0, 1, 1, 1) tensor(5.000000000000e-03, dtype=torch.float64)\n",
      "(1, 0, 0, 2, 1) tensor(-1.250000000000e-04, dtype=torch.float64)\n",
      "(0, 3, 0, 0, 1) tensor(-2.083333333333e-06, dtype=torch.float64)\n",
      "(0, 1, 2, 0, 1) tensor(2.500000000000e-03, dtype=torch.float64)\n",
      "(0, 1, 1, 1, 1) tensor(-2.500000000000e-04, dtype=torch.float64)\n",
      "(0, 1, 0, 2, 1) tensor(6.250000000000e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute single exponent generator\n",
    "\n",
    "h = hamiltonian((2, 1), x, [k], t)\n",
    "chop(h)\n",
    "\n",
    "# Compute series representation\n",
    "\n",
    "# Note, each coefficient is proportional to strength\n",
    "\n",
    "s = clean(series((4, 1), (3, 1), h))\n",
    "for index, value in s.items():\n",
    "    print(index, value.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0bd97ea3-8875-4b60-904a-59e29890b8ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.009811250000e-01, 9.622500000000e-03, 5.102537625000e-02, 1.050752500000e-02],\n",
      "       dtype=torch.float64)\n",
      "tensor([1.009811250000e-01, 9.622500000000e-03, 5.102537625000e-02, 1.050752500000e-02],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# In this example, this hamiltonian generates exact solution with taylor intergator\n",
    "\n",
    "print(mapping(x + dx, k + dk, 0.1))\n",
    "print(taylor(1, 1.0, lambda x, k: evaluate(h, [x, k]), evaluate(l, [dx, dk]), dk))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4168324a-1472-4574-9087-e60922704759",
   "metadata": {},
   "source": [
    "# Example-31: Factorization (fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d50ae1b9-9c66-4f96-b57c-3b6d9be7dae6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.taylor import taylor\n",
    "from ndmap.inverse import inverse\n",
    "from ndmap.factorization import hamiltonian\n",
    "from ndmap.factorization import solution\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1824145-7e9f-45c8-8473-294bb7db7cf5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48d3e9e8-cde9-43f6-a74d-6caa49e5d233",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=1):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a877a9b8-31a2-4329-8053-386b10f72dde",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set fodo\n",
    "\n",
    "def fodo(x):\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = quad(x, [0.0], -0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, -0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.25, 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.1, +0.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19, 0.50)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62358986-4d24-49f8-9e60-5facf10c0104",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0.], dtype=torch.float64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set computation order & evaluation point\n",
    "\n",
    "n = 4\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Note, origin is preserved\n",
    "\n",
    "fodo(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b43b5788-9b64-414a-99ca-296fc0a1b0b9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute derivative table\n",
    "\n",
    "t = identity((n, ), [x])\n",
    "t = propagate((4, ), (n, ), t, [], lambda x: fodo(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d23b3ce-06ab-482a-939e-1a798c735974",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       " tensor([[8.259928915375e-02, 1.470387298165e+01, 0.000000000000e+00, 0.000000000000e+00],\n",
       "         [-6.754528950779e-02, 8.259928915375e-02, 0.000000000000e+00, 0.000000000000e+00],\n",
       "         [0.000000000000e+00, 0.000000000000e+00, 8.239443265215e-01, 6.857644998910e+00],\n",
       "         [0.000000000000e+00, 0.000000000000e+00, -4.682595072274e-02, 8.239443265215e-01]],\n",
       "        dtype=torch.float64)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set linear part\n",
    "\n",
    "l = derivative(1, lambda x: evaluate(t, [x]), x)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac3be7e9-12ad-4bf8-986c-3dc318dd5b96",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       " tensor([[1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
       "         [0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00],\n",
       "         [0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00, 0.000000000000e+00],\n",
       "         [0.000000000000e+00, 0.000000000000e+00, 0.000000000000e+00, 1.000000000000e+00]],\n",
       "        dtype=torch.float64)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set nonlinear part\n",
    "\n",
    "u = propagate((4, ), (n, ), inverse(1, x, [], l), [], t)\n",
    "chop(u)\n",
    "derivative(1, lambda x: evaluate(u, [x]), x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e614b69d-6596-4ea3-9a69-1eafc23c4994",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute hamiltonian\n",
    "\n",
    "h = hamiltonian((n, ), x, [], u)\n",
    "chop(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d2a22f41-7484-4fb9-a743-dff32cf7c6d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 0, 0, 0) tensor(5.024988945080e-02, dtype=torch.float64)\n",
      "(2, 1, 0, 0) tensor(-8.027849817894e-01, dtype=torch.float64)\n",
      "(1, 2, 0, 0) tensor(1.242554019904e+01, dtype=torch.float64)\n",
      "(1, 0, 2, 0) tensor(-5.976214487541e-01, dtype=torch.float64)\n",
      "(1, 0, 1, 1) tensor(3.719621673904e+00, dtype=torch.float64)\n",
      "(1, 0, 0, 2) tensor(-6.659517059512e+00, dtype=torch.float64)\n",
      "(0, 3, 0, 0) tensor(-1.465736828313e+02, dtype=torch.float64)\n",
      "(0, 1, 2, 0) tensor(7.616595688746e+00, dtype=torch.float64)\n",
      "(0, 1, 1, 1) tensor(-6.812652464723e+01, dtype=torch.float64)\n",
      "(0, 1, 0, 2) tensor(1.637189795764e+02, dtype=torch.float64)\n",
      "(4, 0, 0, 0) tensor(-2.670453079972e-02, dtype=torch.float64)\n",
      "(3, 1, 0, 0) tensor(1.592046859279e+00, dtype=torch.float64)\n",
      "(2, 2, 0, 0) tensor(-4.015290100155e+01, dtype=torch.float64)\n",
      "(2, 0, 2, 0) tensor(1.098921583040e-02, dtype=torch.float64)\n",
      "(2, 0, 1, 1) tensor(-1.150214563563e+00, dtype=torch.float64)\n",
      "(2, 0, 0, 2) tensor(5.596016324538e+00, dtype=torch.float64)\n",
      "(1, 3, 0, 0) tensor(2.720415502394e+02, dtype=torch.float64)\n",
      "(1, 1, 2, 0) tensor(9.092563694884e+00, dtype=torch.float64)\n",
      "(1, 1, 1, 1) tensor(-7.311435065924e+01, dtype=torch.float64)\n",
      "(1, 1, 0, 2) tensor(1.043406910282e+02, dtype=torch.float64)\n",
      "(0, 4, 0, 0) tensor(-8.872464473277e+02, dtype=torch.float64)\n",
      "(0, 2, 2, 0) tensor(2.515413272498e+01, dtype=torch.float64)\n",
      "(0, 2, 1, 1) tensor(6.993401944277e+01, dtype=torch.float64)\n",
      "(0, 2, 0, 2) tensor(-3.593047920669e+02, dtype=torch.float64)\n",
      "(0, 0, 4, 0) tensor(-1.258999636146e+00, dtype=torch.float64)\n",
      "(0, 0, 3, 1) tensor(1.898846058062e+01, dtype=torch.float64)\n",
      "(0, 0, 2, 2) tensor(-1.062500223737e+02, dtype=torch.float64)\n",
      "(0, 0, 1, 3) tensor(2.608144282259e+02, dtype=torch.float64)\n",
      "(0, 0, 0, 4) tensor(-2.419706859670e+02, dtype=torch.float64)\n",
      "(5, 0, 0, 0) tensor(3.720153703396e-02, dtype=torch.float64)\n",
      "(4, 1, 0, 0) tensor(-2.470046738149e+00, dtype=torch.float64)\n",
      "(3, 2, 0, 0) tensor(5.465213020046e+01, dtype=torch.float64)\n",
      "(3, 0, 2, 0) tensor(6.994399435987e-02, dtype=torch.float64)\n",
      "(3, 0, 1, 1) tensor(-9.734770779462e-01, dtype=torch.float64)\n",
      "(3, 0, 0, 2) tensor(5.509140066922e-01, dtype=torch.float64)\n",
      "(2, 3, 0, 0) tensor(-7.728896235979e+02, dtype=torch.float64)\n",
      "(2, 1, 2, 0) tensor(1.614402519455e+01, dtype=torch.float64)\n",
      "(2, 1, 1, 1) tensor(-1.014114400601e+02, dtype=torch.float64)\n",
      "(2, 1, 0, 2) tensor(2.099498728183e+02, dtype=torch.float64)\n",
      "(1, 4, 0, 0) tensor(6.599464974629e+03, dtype=torch.float64)\n",
      "(1, 2, 2, 0) tensor(-1.831472433171e+02, dtype=torch.float64)\n",
      "(1, 2, 1, 1) tensor(1.658609587412e+03, dtype=torch.float64)\n",
      "(1, 2, 0, 2) tensor(-4.455648121228e+03, dtype=torch.float64)\n",
      "(1, 0, 4, 0) tensor(-2.372154036991e+00, dtype=torch.float64)\n",
      "(1, 0, 3, 1) tensor(3.120364861986e+01, dtype=torch.float64)\n",
      "(1, 0, 2, 2) tensor(-1.392873544053e+02, dtype=torch.float64)\n",
      "(1, 0, 1, 3) tensor(2.280297561995e+02, dtype=torch.float64)\n",
      "(1, 0, 0, 4) tensor(-5.830536198793e+01, dtype=torch.float64)\n",
      "(0, 5, 0, 0) tensor(-1.712807218852e+04, dtype=torch.float64)\n",
      "(0, 3, 2, 0) tensor(-5.278629070820e+02, dtype=torch.float64)\n",
      "(0, 3, 1, 1) tensor(3.474912442117e+03, dtype=torch.float64)\n",
      "(0, 3, 0, 2) tensor(1.999757814650e+02, dtype=torch.float64)\n",
      "(0, 1, 4, 0) tensor(2.874466602579e+01, dtype=torch.float64)\n",
      "(0, 1, 3, 1) tensor(-5.199961828427e+02, dtype=torch.float64)\n",
      "(0, 1, 2, 2) tensor(3.267795295152e+03, dtype=torch.float64)\n",
      "(0, 1, 1, 3) tensor(-8.677457594504e+03, dtype=torch.float64)\n",
      "(0, 1, 0, 4) tensor(8.128231575922e+03, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute series representation\n",
    "\n",
    "s = clean(series((4, ), (n + 1, ), h), epsilon=1.0E-9)\n",
    "for index, value in s.items():\n",
    "    print(index, value.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5bfe2b0-5796-45f5-8785-f30ed8a32cd6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Restore mapping table nonlinear part from hamiltonian\n",
    "\n",
    "print(compare(u, solution((n, ), x, [], h)))\n",
    "print(compare(t, propagate((4, ), (n, ), l, [], solution((n, ), x, [], h))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d8236a-9e84-47e0-ab5f-003cc8fd7f92",
   "metadata": {},
   "source": [
    "# Example-32: Factorization (factorize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba8720f6-ac3f-447d-b74a-5f2a7cc5ed65",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import torch\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.series import split\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.bracket import bracket\n",
    "from ndmap.factorization import hamiltonian\n",
    "from ndmap.factorization import solution\n",
    "from ndmap.factorization import factorize\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f459d7a8-0c87-45a8-be50-3c6e0eaf4815",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "017b340b-4d9c-42ba-ad8b-c6a410514e10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(3, 0, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (2, 1, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (1, 2, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (0, 3, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (3, 0, 1, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (3, 0, 2, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (4, 0, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (3, 1, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (2, 2, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (1, 3, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (0, 4, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (4, 0, 0, 1): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (5, 0, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (4, 1, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (3, 2, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (2, 3, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (1, 4, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (0, 5, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64)}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set test hamiltonian function and compute corresponding table representation\n",
    "\n",
    "# Note, test hamiltonian is near identity\n",
    "\n",
    "def h(x, u, v):\n",
    "    q, p = x\n",
    "    u, = u\n",
    "    v, = v\n",
    "    h1 = (1 + u + u**2)*q**3 + q**2*p + q*p**2 + p**3\n",
    "    h2 = (1 + v)*q**4 + q**3*p + q**2*p**2 + q*p**3 + p**4\n",
    "    h3 = q**5 + q**4*p + q**3*p**2 + q**2*p**3 + q*p**4 + p**5\n",
    "    return h1 + h2 + h3\n",
    "\n",
    "x = torch.tensor([0.0, 0.0], dtype=dtype, device=device)\n",
    "u = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "v = torch.tensor([0.0], dtype=dtype, device=device)\n",
    "\n",
    "h = derivative((5, 2, 1), h, x, u, v)\n",
    "chop(h, replace=True)\n",
    "\n",
    "s, *_ = split(clean(series((2, 1, 1), (5, 2, 1), h)))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fcb9c0c-4136-49b9-9639-9532c15155b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute solution\n",
    "\n",
    "t = solution((4, 2, 1), x, [u, v], h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c30313c7-b21d-4a38-9a1b-36651a60691e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute hamiltonian from solution and compare\n",
    "\n",
    "compare(h, hamiltonian((4, 2, 1), x, [u, v], t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7e423a0-73e0-4c22-8d83-6648b670f1f7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Perform factorization\n",
    "\n",
    "h1, h2, h3, *_ = factorize((4, 2, 1), x, [u, v], t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a14155f-01b8-4224-a93f-55610417aa9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(3, 0, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (2, 1, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (1, 2, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (0, 3, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (3, 0, 1, 0): tensor(1.000000000000e+00, dtype=torch.float64)}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine series representation\n",
    "\n",
    "s, *_ = split(clean(series((2, 1, 1), (5, 1), h1)))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c099717-836f-48b6-9dbb-84a3b9ce5455",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(4, 0, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (3, 1, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (2, 2, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (1, 3, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (0, 4, 0, 0): tensor(1.000000000000e+00, dtype=torch.float64),\n",
       " (4, 0, 0, 1): tensor(1.000000000000e+00, dtype=torch.float64)}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine series representation\n",
    "\n",
    "s, *_ = split(clean(series((2, 1, 1), (5, 1), h2)))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f97e5ac-3cdb-4b24-a8bf-0e8c15d8a17f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(5, 0, 0, 0): tensor(5.000000000000e-01, dtype=torch.float64),\n",
       " (4, 1, 0, 0): tensor(-5.000000000000e-01, dtype=torch.float64),\n",
       " (3, 2, 0, 0): tensor(-2.000000000000e+00, dtype=torch.float64),\n",
       " (2, 3, 0, 0): tensor(4.000000000000e+00, dtype=torch.float64),\n",
       " (1, 4, 0, 0): tensor(2.500000000000e+00, dtype=torch.float64),\n",
       " (0, 5, 0, 0): tensor(1.500000000000e+00, dtype=torch.float64),\n",
       " (5, 0, 0, 1): tensor(-2.000000000000e+00, dtype=torch.float64),\n",
       " (4, 1, 0, 1): tensor(-4.000000000000e+00, dtype=torch.float64),\n",
       " (3, 2, 0, 1): tensor(-6.000000000000e+00, dtype=torch.float64),\n",
       " (5, 0, 1, 0): tensor(1.500000000000e+00, dtype=torch.float64),\n",
       " (4, 1, 1, 0): tensor(3.000000000000e+00, dtype=torch.float64),\n",
       " (3, 2, 1, 0): tensor(4.500000000000e+00, dtype=torch.float64),\n",
       " (2, 3, 1, 0): tensor(6.000000000000e+00, dtype=torch.float64)}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examine series representation\n",
    "\n",
    "s, *_ = split(clean(series((2, 1, 1), (5, 1), h3)))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fef41af0-cc19-4eaf-bc96-def01749691d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compute solutions\n",
    "\n",
    "t1 = solution((4, 2, 1), x, [u, v], h1)\n",
    "t2 = solution((4, 2, 1), x, [u, v], h2)\n",
    "t3 = solution((4, 2, 1) ,x, [u, v], h3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d3545d6-d9d4-40ee-bb5f-6b97bb268509",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compose individual solutions and compare with initial solution\n",
    "\n",
    "T = identity((4, 2, 1), [x, u, v])\n",
    "T = propagate((2, 1, 1), (4, 2, 1), T, [u, v], t1)\n",
    "T = propagate((2, 1, 1), (4, 2, 1), T, [u, v], t2)\n",
    "T = propagate((2, 1, 1), (4, 2, 1), T, [u, v], t3)\n",
    "compare(t, T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a3070ac-166d-4d99-9a7e-0c730be43a9e",
   "metadata": {},
   "source": [
    "# Example-33: Nonlinear mapping approximation (gradient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "43645f7a-fe2d-479b-9838-cefcac6e83fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.gradient import series\n",
    "from ndmap.series import clean\n",
    "\n",
    "torch.set_printoptions(precision=12, sci_mode=True)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58c626af-cc72-47c3-902b-409166eafee3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7991bb3-9707-4e8c-899d-7e6eef5ba24a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set test mapping\n",
    "# Rotation with two sextupoles separated by negative identity linear transformation\n",
    "# Note, result is expected to have zero degree two coefficients due to negative identity linear transformation between sextupoles\n",
    "\n",
    "def spin(x, mux, muy):\n",
    "    (qx, px, qy, py), mux, muy = x, mux, muy\n",
    "    return torch.stack([qx*mux.cos() + px*mux.sin(), px*mux.cos() - qx*mux.sin(), qy*muy.cos() + py*muy.sin(), py*muy.cos() - qy*muy.sin()])\n",
    "\n",
    "def drif(x, l):\n",
    "    (qx, px, qy, py), l = x, l\n",
    "    return torch.stack([qx + l*px, px, qy + l*py, py])\n",
    "\n",
    "def sext(x, ks, l, n=1):\n",
    "    (qx, px, qy, py), ks, l = x, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px, qy + l*py\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px, qy + l*py\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def ring(x):\n",
    "    mux, muy = 2.0*numpy.pi*torch.tensor([1/3 + 0.01, 1/4 + 0.01], dtype=dtype, device=device)\n",
    "    x = spin(x, mux, muy)\n",
    "    x = drif(x, -0.05)\n",
    "    x = sext(x, 10.0, 0.1, 100)\n",
    "    x = drif(x, -0.05)\n",
    "    mux, muy = 2.0*numpy.pi*torch.tensor([0.50, 0.50], dtype=dtype, device=device)\n",
    "    x = spin(x, mux, muy)\n",
    "    x = drif(x, -0.05)\n",
    "    x = sext(x, 10.0, 0.1, 100)\n",
    "    x = drif(x, -0.05)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0af10da0-8d18-47ca-8329-5cf890b48073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 0, 0, 0): [0.55339155 0.83292124 0.         0.        ]\n",
      "(0, 1, 0, 0): [-0.83292124  0.55339155  0.          0.        ]\n",
      "(0, 0, 1, 0): [0.         0.         0.06279052 0.99802673]\n",
      "(0, 0, 0, 1): [ 0.          0.         -0.99802673  0.06279052]\n",
      "(3, 0, 0, 0): [-7.53257307e-09  2.82424677e-03 -0.00000000e+00 -0.00000000e+00]\n",
      "(2, 1, 0, 0): [-1.96250238e-08 -1.27525063e-02 -0.00000000e+00 -0.00000000e+00]\n",
      "(2, 0, 1, 0): [-0.00000000e+00 -0.00000000e+00  9.21186111e-06  3.34331441e-04]\n",
      "(2, 0, 0, 1): [-0.00000000e+00 -0.00000000e+00  1.59704766e-05 -5.06941449e-03]\n",
      "(1, 2, 0, 0): [-1.11004920e-08  1.91940679e-02 -0.00000000e+00 -0.00000000e+00]\n",
      "(1, 1, 1, 0): [-0.00000000e+00 -0.00000000e+00 -2.98671134e-05 -9.79459005e-04]\n",
      "(1, 1, 0, 1): [-0.00000000e+00 -0.00000000e+00 -1.48185697e-05  1.53623878e-02]\n",
      "(1, 0, 2, 0): [-1.05857397e-06  1.97282603e-05 -0.00000000e+00 -0.00000000e+00]\n",
      "(1, 0, 1, 1): [ 1.48154798e-05 -1.18570682e-03 -0.00000000e+00 -0.00000000e+00]\n",
      "(1, 0, 0, 2): [ 2.88067554e-05  9.18409783e-03 -0.00000000e+00 -0.00000000e+00]\n",
      "(0, 3, 0, 0): [-9.21338045e-10 -9.62979589e-03  0.00000000e+00  0.00000000e+00]\n",
      "(0, 2, 1, 0): [0.00000000e+00 0.00000000e+00 2.40366928e-05 7.09979811e-04]\n",
      "(0, 2, 0, 1): [ 0.00000000e+00  0.00000000e+00 -1.38786549e-05 -1.15294375e-02]\n",
      "(0, 1, 2, 0): [ 1.80396305e-06 -2.59196622e-05  0.00000000e+00  0.00000000e+00]\n",
      "(0, 1, 1, 1): [-2.98509155e-05  1.72488752e-03  0.00000000e+00  0.00000000e+00]\n",
      "(0, 1, 0, 2): [ 1.66318846e-05 -1.38269522e-02  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 3, 0): [-0.00000000e+00 -0.00000000e+00 -1.47704279e-08  4.12534350e-06]\n",
      "(0, 0, 2, 1): [-0.00000000e+00 -0.00000000e+00 -3.31103168e-09 -1.96719688e-04]\n",
      "(0, 0, 1, 2): [-0.00000000e+00 -0.00000000e+00  3.93325786e-09  3.12683573e-03]\n",
      "(0, 0, 0, 3): [ 0.00000000e+00  0.00000000e+00  2.56887204e-10 -1.65665408e-02]\n",
      "(4, 0, 0, 0): [ 6.48869023e-07 -3.91844462e-06  0.00000000e+00  0.00000000e+00]\n",
      "(3, 1, 0, 0): [-3.91685700e-06  1.51001133e-05  0.00000000e+00  0.00000000e+00]\n",
      "(3, 0, 1, 0): [ 0.00000000e+00  0.00000000e+00 -4.36165465e-07  5.76316391e-06]\n",
      "(3, 0, 0, 1): [0.00000000e+00 0.00000000e+00 6.56410859e-06 1.31668166e-05]\n",
      "(2, 2, 0, 0): [ 8.85501662e-06 -1.48880894e-05  0.00000000e+00  0.00000000e+00]\n",
      "(2, 1, 1, 0): [ 0.00000000e+00  0.00000000e+00  1.92232731e-06 -2.78183189e-05]\n",
      "(2, 1, 0, 1): [ 0.00000000e+00  0.00000000e+00 -2.96894787e-05 -3.16635607e-05]\n",
      "(2, 0, 2, 0): [1.81838643e-08 2.18816315e-06 0.00000000e+00 0.00000000e+00]\n",
      "(2, 0, 1, 1): [-9.93314202e-07 -3.25277215e-05  0.00000000e+00  0.00000000e+00]\n",
      "(2, 0, 0, 2): [ 7.67316422e-06 -2.51871510e-05  0.00000000e+00  0.00000000e+00]\n",
      "(1, 3, 0, 0): [-8.88618620e-06 -4.33921249e-06  0.00000000e+00  0.00000000e+00]\n",
      "(1, 2, 1, 0): [ 0.00000000e+00  0.00000000e+00 -2.81910856e-06  4.44963121e-05]\n",
      "(1, 2, 0, 1): [0.00000000e+00 0.00000000e+00 4.47107608e-05 6.22767407e-06]\n",
      "(1, 1, 2, 0): [-5.02653030e-08 -6.69892371e-06  0.00000000e+00  0.00000000e+00]\n",
      "(1, 1, 1, 1): [2.90625131e-06 1.04277202e-04 0.00000000e+00 0.00000000e+00]\n",
      "(1, 1, 0, 2): [-2.28808176e-05  2.60346923e-05  0.00000000e+00  0.00000000e+00]\n",
      "(1, 0, 3, 0): [0.00000000e+00 0.00000000e+00 2.09236592e-09 3.51323891e-08]\n",
      "(1, 0, 2, 1): [ 0.00000000e+00  0.00000000e+00 -6.41657941e-09 -1.78350571e-06]\n",
      "(1, 0, 1, 2): [ 0.00000000e+00  0.00000000e+00 -6.10954936e-07  1.86435774e-05]\n",
      "(1, 0, 0, 3): [ 0.00000000e+00  0.00000000e+00  3.05503037e-06 -5.00150901e-05]\n",
      "(0, 4, 0, 0): [3.33982092e-06 8.88114110e-06 0.00000000e+00 0.00000000e+00]\n",
      "(0, 3, 1, 0): [ 0.00000000e+00  0.00000000e+00  1.37553970e-06 -2.36217768e-05]\n",
      "(0, 3, 0, 1): [ 0.00000000e+00  0.00000000e+00 -2.24184174e-05  1.77513110e-05]\n",
      "(0, 2, 2, 0): [3.54703897e-08 5.11986525e-06 0.00000000e+00 0.00000000e+00]\n",
      "(0, 2, 1, 1): [-2.15414781e-06 -8.31702815e-05  0.00000000e+00  0.00000000e+00]\n",
      "(0, 2, 0, 2): [1.72952174e-05 1.78791226e-05 0.00000000e+00 0.00000000e+00]\n",
      "(0, 1, 3, 0): [ 0.00000000e+00  0.00000000e+00 -3.32576455e-09 -2.16775859e-08]\n",
      "(0, 1, 2, 1): [0.00000000e+00 0.00000000e+00 1.73891518e-08 1.32003603e-06]\n",
      "(0, 1, 1, 2): [ 0.00000000e+00  0.00000000e+00  8.58583303e-07 -7.35197022e-06]\n",
      "(0, 1, 0, 3): [ 0.00000000e+00  0.00000000e+00 -4.60205741e-06 -3.44817289e-05]\n",
      "(0, 0, 4, 0): [-2.95410616e-10 -3.91436795e-10  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 3, 1): [ 1.72261161e-08 -3.76703368e-08  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 2, 2): [-3.76632244e-07  1.04173914e-06  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 1, 3): [ 3.81179356e-06 -5.44741177e-06  0.00000000e+00  0.00000000e+00]\n",
      "(0, 0, 0, 4): [-1.51552013e-05 -3.46854944e-07  0.00000000e+00  0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "# Set evaluation point\n",
    "\n",
    "x = torch.tensor([0.0, 0.0, 0.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Compute and print series\n",
    "\n",
    "n = 4\n",
    "s = clean(series((n, ), ring, x, retain=False), epsilon=1.0E-12)\n",
    "print(*[f'{key}: {value.cpu().numpy()}' for key, value in clean(s, epsilon=1.0E-14).items()], sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6adb4e51-5548-45a3-ae31-b3857fad7a42",
   "metadata": {},
   "source": [
    "# Example-34: ORM optics correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3790881b-6df5-4a3e-9269-bb0b201e91a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# In this example orbit responce matrix (ORM) is used to correct linear optics in a simple FODO cell\n",
    "# Two gradient errors are introduced into cell quadrupoles\n",
    "\n",
    "# This example illustrates one optimization step\n",
    "# Given a measured ORM, the model knobs are fitted to reproduce it\n",
    "# Next, the corrections should be applied and the matrix should be remeasured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b330e440-4b94-49ba-aad4-04f7747fcaf5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edeb8ffc-d3ac-4981-b2e1-95c60b9222ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11392423-7345-47e9-a6dd-a5c4ab15f0d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=1):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4bebf740-d585-4caa-991c-e623d86d0200",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "# Note, transport maps are expected to have identical (differentiable) signature\n",
    "\n",
    "def t_01_02(x, cs, dk): \n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = quad(x, [0.0], 0.19 + kf, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf1, cysf1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def t_02_03(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd1, cysd1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21 + kd, 0.50)\n",
    "    return x\n",
    "\n",
    "def t_03_04(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = quad(x, [0.0], -0.21 + kd, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd2, cysd2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "    \n",
    "def t_04_05(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf2, cysf2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19 + kf, 0.50)\n",
    "    return x\n",
    "\n",
    "ts = [t_01_02,t_02_03, t_03_04, t_04_05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2557a45b-6b31-4bdc-b7fc-4f603321239b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set deviation variables\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "cs = torch.tensor(8*[0.0], dtype=dtype, device=device)\n",
    "dk = torch.tensor(2*[0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eab7987b-31b6-49f0-8ff6-5b2af18c80a8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define one-turn transport at the lattice entrance\n",
    "\n",
    "def fodo(x, cs, kq):\n",
    "    for t in ts:\n",
    "        x = t(x, cs, kq)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6f22dcd7-0f99-4ad9-92ed-abaad6e8364f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test one-turn transport\n",
    "\n",
    "print(fodo(x, cs, dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77ed531f-c6ce-437d-9419-8fa52fab1a6e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute (dynamical) fixed point\n",
    "# Note, dynamical part is assumed to be fixed during optimization\n",
    "\n",
    "fp = fixed_point(16, fodo, x, cs, dk, power=1, jacobian=torch.func.jacrev)\n",
    "\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84c99315-9b7e-4cbf-9c32-171c464ebcbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 8\n",
      "torch.Size([10, 8])\n",
      "\n",
      "tensor([[7.577e+00, 5.936e+00, 7.577e+00, 5.936e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.216e+00, 4.039e+00, 6.749e+00, 4.566e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [5.110e+00, 2.611e+00, 5.110e+00, 2.611e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.749e+00, 4.566e+00, 6.216e+00, 4.039e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [7.577e+00, 5.936e+00, 7.577e+00, 5.936e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.344e+01, 2.158e+01, 1.344e+01, 2.158e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.801e+01, 2.744e+01, 1.849e+01, 2.792e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.509e+01, 3.667e+01, 2.509e+01, 3.667e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.849e+01, 2.792e+01, 1.801e+01, 2.744e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.344e+01, 2.158e+01, 1.344e+01, 2.158e+01]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Define parametric responce matrix\n",
    "\n",
    "def rm(dk):\n",
    "    \n",
    "    pfp = parametric_fixed_point((1, ), fp, [cs], lambda x, cs: fodo(x, cs, dk), jacobian=torch.func.jacrev)\n",
    "    chop(pfp)\n",
    "    _, (dqx, _, dqy, _) = first(pfp)\n",
    "    \n",
    "    out = [torch.stack([dqx, dqy])]\n",
    "    for t in ts:\n",
    "        pfp = propagate((4, 8), (0, 1), pfp, [cs], lambda x, cs: t(x, cs, dk),  jacobian=torch.func.jacrev)\n",
    "        chop(pfp)\n",
    "        _, (dqx, _, dqy, _) = first(pfp)\n",
    "        out.append(torch.stack([dqx, dqy]))\n",
    "    \n",
    "    return torch.stack(out).swapaxes(0, 1).reshape(-1, len(cs))\n",
    "\n",
    "print(2*(len(ts) + 1), len(cs))\n",
    "print(rm(dk).shape)\n",
    "print()\n",
    "\n",
    "print(rm(dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a2426368-738a-4b97-abbe-738497626a3f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Test responce matrix\n",
    "\n",
    "dc = 1.0E-3*torch.ones_like(cs)\n",
    "\n",
    "o = fixed_point(16, fodo, x, cs + dc, dk, power=1, jacobian=torch.func.jacrev)\n",
    "\n",
    "os = []\n",
    "qx, _, qy, _ = o\n",
    "os.append(torch.stack([qx, qy]))\n",
    "\n",
    "for t in ts:\n",
    "    o = t(o, dc, dk)\n",
    "    qx, _, qy, _ = o\n",
    "    os.append(torch.stack([qx, qy]))\n",
    "    \n",
    "print(torch.allclose(torch.stack(os).T.flatten(), rm(dk) @ dc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8259e696-8769-4728-b604-576ddfab8282",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set quadrupole gradient errors\n",
    "\n",
    "ek = torch.tensor([-0.010, 0.005], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c44c7d7e-193d-4055-a223-6c8aaa8edd96",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[8.038e+00, 6.338e+00, 8.038e+00, 6.338e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.658e+00, 4.415e+00, 7.190e+00, 4.942e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [5.488e+00, 2.923e+00, 5.488e+00, 2.923e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [7.190e+00, 4.942e+00, 6.658e+00, 4.415e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [8.038e+00, 6.338e+00, 8.038e+00, 6.338e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.470e+01, 2.316e+01, 1.470e+01, 2.316e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.943e+01, 2.915e+01, 1.990e+01, 2.963e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.678e+01, 3.865e+01, 2.678e+01, 3.865e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.990e+01, 2.963e+01, 1.943e+01, 2.915e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.470e+01, 2.316e+01, 1.470e+01, 2.316e+01]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Measure ORM\n",
    "\n",
    "erm = rm(ek)\n",
    "\n",
    "print(erm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2dd37d3b-ee46-4e71-84cf-83a3b83a030c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5.298e+01, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Define objective to minimize\n",
    "\n",
    "def objective(dk):\n",
    "    return ((erm - rm(dk))**2).sum()\n",
    "\n",
    "print(objective(dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b127458-c62b-47ff-a300-3a5df652743d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set model class\n",
    "\n",
    "class Model(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, knobs):\n",
    "        super().__init__()\n",
    "        self.knobs = torch.nn.Parameter(torch.clone(knobs))\n",
    "        \n",
    "    def forward(self):\n",
    "        return objective(self.knobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2b49c0be-15dc-4950-9b7e-ce1e966933bb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5.298e+01, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# Set model instance\n",
    "# Note, initial knobs are set to zero\n",
    "    \n",
    "model = Model(torch.zeros_like(dk))\n",
    "\n",
    "print(model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7b4cf56-6505-4fc4-85d8-e295a91907b9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set optimizer\n",
    "\n",
    "lr = 2.5E-3\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63ae71c4-247a-42a2-a580-517bcb6d9f48",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([0., 0.], dtype=torch.float64, requires_grad=True)\n",
      "tensor(5.298e+01, dtype=torch.float64, grad_fn=<SumBackward0>)\n",
      "\n",
      "epoch: 0, error: 52.97690929514796\n",
      "epoch: 10, error: 9.061476544019179\n",
      "epoch: 20, error: 4.116026193086178\n",
      "epoch: 30, error: 4.4716138801849885\n",
      "epoch: 40, error: 2.5934201513661606\n",
      "epoch: 50, error: 1.320221084167608\n",
      "epoch: 60, error: 0.7104114404644974\n",
      "epoch: 70, error: 0.39073287332555584\n",
      "epoch: 80, error: 0.17765207040232386\n",
      "epoch: 90, error: 0.07092862791751303\n",
      "epoch: 100, error: 0.024482773974344098\n",
      "epoch: 110, error: 0.006731421279765544\n",
      "epoch: 120, error: 0.001272143234908415\n",
      "epoch: 130, error: 0.00019181854968944713\n",
      "epoch: 140, error: 2.3799691903263396e-05\n",
      "epoch: 150, error: 2.155612473667951e-05\n",
      "epoch: 160, error: 2.4284316948196817e-05\n",
      "epoch: 170, error: 1.359976274459628e-05\n",
      "epoch: 180, error: 4.543935327150297e-06\n",
      "epoch: 190, error: 1.1354899250982208e-06\n",
      "epoch: 200, error: 1.718342123892043e-07\n",
      "epoch: 210, error: 2.551350446132945e-08\n",
      "epoch: 220, error: 2.8003866265404305e-08\n",
      "epoch: 230, error: 2.1762818183897804e-08\n",
      "epoch: 240, error: 9.432013407780834e-09\n",
      "epoch: 250, error: 2.3809020247549156e-09\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64, requires_grad=True)\n",
      "tensor(5.351e-10, dtype=torch.float64, grad_fn=<SumBackward0>)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fit model\n",
    "\n",
    "epochs = 256\n",
    "\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print(model.forward())\n",
    "print()\n",
    "\n",
    "knobs, errors = [], []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    error = model.forward()\n",
    "    with torch.no_grad():\n",
    "        knobs.append(model.knobs.clone().detach())\n",
    "        errors.append(error.clone().detach())\n",
    "    error.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'epoch: {epoch}, error: {error.item()}')\n",
    "\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print(model.forward())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d8f2614-7dd1-46e7-b957-6dac5eb5fee5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot error vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(range(len(errors)), torch.stack(errors).cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12735314-2de6-482f-af5c-0d32721c0885",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot knobs vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.hlines(ek.cpu().numpy(), 0, epochs, linestyles='dashed', color='gray', alpha=0.75)\n",
    "for knob in torch.stack(knobs).T:\n",
    "    plt.plot(range(len(knob)), knob.cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "89e52553-d9ba-4ce6-9bb5-a910afad0b04",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Higher order derivatives can be used to create surrogate Taylor model of the ORM\n",
    "# Taylor model can be expensive to compute, but computation is performed only one time\n",
    "# Note, model can be also updated duaring optimization\n",
    "\n",
    "n = 5\n",
    "t = derivative(n, rm, dk, jacobian=torch.func.jacfwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce1a8c1b-c36b-422b-816a-dfb7a2dc6707",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5.298e+01, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Redefine objective fuction to use Taylor model\n",
    "\n",
    "def objective(dk):\n",
    "    return ((erm - evaluate(t, [dk]))**2).sum()\n",
    "\n",
    "print(objective(dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3880778a-c2e3-4ffa-8291-5b8f8387deb5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5.298e+01, dtype=torch.float64, grad_fn=<SumBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# Set model instance\n",
    "# Note, initial knobs are set to zero\n",
    "    \n",
    "model = Model(torch.zeros_like(dk))\n",
    "\n",
    "print(model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "18d43692-84ae-47f7-be66-23d53f645780",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set optimizer\n",
    "\n",
    "lr = 2.5E-3\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1a9dbe49-6ff0-4f8a-9b48-c6bfe8c409a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([0., 0.], dtype=torch.float64, requires_grad=True)\n",
      "tensor(5.298e+01, dtype=torch.float64, grad_fn=<SumBackward0>)\n",
      "\n",
      "epoch: 0, error: 52.976909295147884\n",
      "epoch: 10, error: 9.059850191301422\n",
      "epoch: 20, error: 4.116647404613835\n",
      "epoch: 30, error: 4.472951117480212\n",
      "epoch: 40, error: 2.5922560001567434\n",
      "epoch: 50, error: 1.3204603071831524\n",
      "epoch: 60, error: 0.7100392325724316\n",
      "epoch: 70, error: 0.39053200207952343\n",
      "epoch: 80, error: 0.17757259750915444\n",
      "epoch: 90, error: 0.07088795313036353\n",
      "epoch: 100, error: 0.024459899278350294\n",
      "epoch: 110, error: 0.006720239692606007\n",
      "epoch: 120, error: 0.001269417957035261\n",
      "epoch: 130, error: 0.00019187232318368966\n",
      "epoch: 140, error: 2.3597651642216307e-05\n",
      "epoch: 150, error: 2.1673080207299973e-05\n",
      "epoch: 160, error: 2.426302907045522e-05\n",
      "epoch: 170, error: 1.3607180530103824e-05\n",
      "epoch: 180, error: 4.539802198450803e-06\n",
      "epoch: 190, error: 1.1314656087725447e-06\n",
      "epoch: 200, error: 1.710558210516473e-07\n",
      "epoch: 210, error: 2.5644830807954306e-08\n",
      "epoch: 220, error: 2.8132779788222537e-08\n",
      "epoch: 230, error: 2.1802735069022923e-08\n",
      "epoch: 240, error: 9.435223715979277e-09\n",
      "epoch: 250, error: 2.377689322793694e-09\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64, requires_grad=True)\n",
      "tensor(5.331e-10, dtype=torch.float64, grad_fn=<SumBackward0>)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fit model\n",
    "\n",
    "epochs = 256\n",
    "\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print(model.forward())\n",
    "print()\n",
    "\n",
    "knobs, errors = [], []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    error = model.forward()\n",
    "    with torch.no_grad():\n",
    "        knobs.append(model.knobs.clone().detach())\n",
    "        errors.append(error.clone().detach())\n",
    "    error.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'epoch: {epoch}, error: {error.item()}')\n",
    "\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print(model.forward())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94e43d64-e39e-427b-b7d1-b03a3c705e7c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot error vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(range(len(errors)), torch.stack(errors).cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c491943-8551-439a-8f5a-14dd95eba142",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot knobs vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.hlines(ek.cpu().numpy(), 0, epochs, linestyles='dashed', color='gray', alpha=0.75)\n",
    "for knob in torch.stack(knobs).T:\n",
    "    plt.plot(range(len(knob)), knob.cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65723b5a-0a81-4644-bb52-c3d30ed8c069",
   "metadata": {},
   "source": [
    "# Example-35: ORM optics correction (training loop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9143b674-3dbe-4dfc-a181-b3a3de1cf9c1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# In this example orbit responce matrix (ORM) is used to correct linear optics in a simple FODO cell\n",
    "# Two gradient errors are introduced into cell quadrupoles\n",
    "\n",
    "# This example illustrates one optimization step\n",
    "# Given a measured ORM, the model knobs are fitted to reproduce it\n",
    "# Next, the corrections should be applied and the matrix should be remeasured\n",
    "\n",
    "# Fitting step mirrors neural net training loop\n",
    "# Elements of measured responce matrix are used as targets\n",
    "\n",
    "# Note, full ORM is computed for each batch\n",
    "# It is also possible to define elementwise computation (see the next example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "421f32ce-a17d-4a59-b07f-b287f463f0c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from torch.utils.data import TensorDataset \n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4800ee8-62ef-4529-815f-470bad780ce8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79e61d8a-89bf-4c21-bfb1-f96385c6e8f4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=1):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31b6fc56-33ab-43af-a66a-47e6b3355648",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "# Note, transport maps are expected to have identical (differentiable) signature\n",
    "\n",
    "def t_01_02(x, cs, dk): \n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = quad(x, [0.0], 0.19 + kf, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf1, cysf1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def t_02_03(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd1, cysd1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21 + kd, 0.50)\n",
    "    return x\n",
    "\n",
    "def t_03_04(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = quad(x, [0.0], -0.21 + kd, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd2, cysd2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "    \n",
    "def t_04_05(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf2, cysf2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19 + kf, 0.50)\n",
    "    return x\n",
    "\n",
    "ts = [t_01_02,t_02_03, t_03_04, t_04_05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e99dd7e-676c-4f05-b26d-a906461dc4d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set deviation variables\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "cs = torch.tensor(8*[0.0], dtype=dtype, device=device)\n",
    "dk = torch.tensor(2*[0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9275a226-36c9-4beb-aaa9-b517561285fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define one-turn transport at the lattice entrance\n",
    "\n",
    "def fodo(x, cs, kq):\n",
    "    for t in ts:\n",
    "        x = t(x, cs, kq)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "564f3bf6-2bab-4c43-978b-a7798a7b545e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test one-turn transport\n",
    "\n",
    "print(fodo(x, cs, dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4110332b-9f67-4ecf-81d4-3965ba19c5e2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute (dynamical) fixed point\n",
    "# Note, dynamical part is assumed to be fixed during optimization\n",
    "\n",
    "fp = fixed_point(16, fodo, x, cs, dk, power=1, jacobian=torch.func.jacrev)\n",
    "\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ccb9fd5-7e2c-4d97-8723-4a1db9702413",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 8\n",
      "torch.Size([10, 8])\n",
      "\n",
      "tensor([[7.577e+00, 5.936e+00, 7.577e+00, 5.936e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.216e+00, 4.039e+00, 6.749e+00, 4.566e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [5.110e+00, 2.611e+00, 5.110e+00, 2.611e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.749e+00, 4.566e+00, 6.216e+00, 4.039e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [7.577e+00, 5.936e+00, 7.577e+00, 5.936e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.344e+01, 2.158e+01, 1.344e+01, 2.158e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.801e+01, 2.744e+01, 1.849e+01, 2.792e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.509e+01, 3.667e+01, 2.509e+01, 3.667e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.849e+01, 2.792e+01, 1.801e+01, 2.744e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.344e+01, 2.158e+01, 1.344e+01, 2.158e+01]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Define parametric responce matrix\n",
    "\n",
    "def rm(dk):\n",
    "    \n",
    "    pfp = parametric_fixed_point((1, ), fp, [cs], lambda x, cs: fodo(x, cs, dk), jacobian=torch.func.jacrev)\n",
    "    chop(pfp)\n",
    "    _, (dqx, _, dqy, _) = first(pfp)\n",
    "    \n",
    "    out = [torch.stack([dqx, dqy])]\n",
    "    for t in ts:\n",
    "        pfp = propagate((4, 8), (0, 1), pfp, [cs], lambda x, cs: t(x, cs, dk),  jacobian=torch.func.jacrev)\n",
    "        chop(pfp)\n",
    "        _, (dqx, _, dqy, _) = first(pfp)\n",
    "        out.append(torch.stack([dqx, dqy]))\n",
    "    \n",
    "    return torch.stack(out).swapaxes(0, 1).reshape(-1, len(cs))\n",
    "\n",
    "print(2*(len(ts) + 1), len(cs))\n",
    "print(rm(dk).shape)\n",
    "print()\n",
    "\n",
    "print(rm(dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "43f1f2b2-ee29-4921-b007-07edb9cbd4da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Test responce matrix\n",
    "\n",
    "dc = 1.0E-3*torch.ones_like(cs)\n",
    "\n",
    "o = fixed_point(16, fodo, x, cs + dc, dk, power=1, jacobian=torch.func.jacrev)\n",
    "\n",
    "os = []\n",
    "qx, _, qy, _ = o\n",
    "os.append(torch.stack([qx, qy]))\n",
    "\n",
    "for t in ts:\n",
    "    o = t(o, dc, dk)\n",
    "    qx, _, qy, _ = o\n",
    "    os.append(torch.stack([qx, qy]))\n",
    "    \n",
    "print(torch.allclose(torch.stack(os).T.flatten(), rm(dk) @ dc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a4750a0-6938-4ea2-876a-90946c4489fe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set quadrupole gradient errors\n",
    "\n",
    "ek = torch.tensor([-0.010, 0.005], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4fb45fb-a282-4507-9f92-29366b75f675",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[8.038e+00, 6.338e+00, 8.038e+00, 6.338e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.658e+00, 4.415e+00, 7.190e+00, 4.942e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [5.488e+00, 2.923e+00, 5.488e+00, 2.923e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [7.190e+00, 4.942e+00, 6.658e+00, 4.415e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [8.038e+00, 6.338e+00, 8.038e+00, 6.338e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.470e+01, 2.316e+01, 1.470e+01, 2.316e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.943e+01, 2.915e+01, 1.990e+01, 2.963e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.678e+01, 3.865e+01, 2.678e+01, 3.865e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.990e+01, 2.963e+01, 1.943e+01, 2.915e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.470e+01, 2.316e+01, 1.470e+01, 2.316e+01]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Measure ORM\n",
    "\n",
    "erm = rm(ek)\n",
    "\n",
    "print(erm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c52743af-570a-47e2-ad6d-34fb982cb10c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data\n",
    "\n",
    "i_max, j_max = erm.shape\n",
    "i_val, j_val = torch.arange(i_max), torch.arange(j_max)\n",
    "X = torch.vstack([*torch.stack(torch.meshgrid(i_val, j_val, indexing='xy')).swapaxes(0, -1)])\n",
    "y = erm.clone().flatten()\n",
    "\n",
    "batch_size = 16\n",
    "dataset = TensorDataset(X.clone(), y.clone())\n",
    "dataset, validation = random_split(dataset, [0.80, 0.20])\n",
    "\n",
    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1ed1485-5e05-4b9f-9251-06e44a0cf75f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set model\n",
    "\n",
    "class Model(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, knobs):\n",
    "        super().__init__()\n",
    "        self.knobs = torch.nn.Parameter(torch.clone(knobs))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        i, j = x.unsqueeze(0).swapaxes(0, -1)\n",
    "        return (rm(self.knobs)[i, j]).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec5dd971-d5c0-4e36-8508-9c13f53dd6ab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set model instance\n",
    "# Note, initial knobs are set to zero\n",
    "    \n",
    "model = Model(torch.zeros_like(dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa56067f-b4c1-4f9d-88ee-63067704749b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7.577e+00, dtype=torch.float64)\n",
      "tensor(7.577e+00, dtype=torch.float64, grad_fn=<SqueezeBackward0>)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Test model\n",
    "\n",
    "i, j = 0, 0\n",
    "\n",
    "print(rm(dk)[i, j])\n",
    "print(model(torch.tensor([[i, j]])))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7e389ab-5c8d-4b86-a827-6ae9be871cb7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set optimizer\n",
    "\n",
    "lr = 1.0E-3\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "61f79a95-6c76-4230-bf4d-30c94fe01af5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set loss function\n",
    "\n",
    "lf = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f41cd5b-5997-4424-8f16-35692ae1fe38",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([0., 0.], dtype=torch.float64, requires_grad=True)\n",
      "\n",
      "epoch: 0, error: 0.37529107626509794 / 0.72679523282213\n",
      "epoch: 10, error: 0.019561891190916357 / 0.08595406190271199\n",
      "epoch: 20, error: 0.028442131733101884 / 0.059218224485797756\n",
      "epoch: 30, error: 0.024756037639704742 / 0.03382898705876869\n",
      "epoch: 40, error: 0.007808066790505313 / 0.014454231605437087\n",
      "epoch: 50, error: 0.003117435961114121 / 0.006542069654623646\n",
      "epoch: 60, error: 0.0012404708769704496 / 0.00196001107485135\n",
      "epoch: 70, error: 0.0002826792552836003 / 0.000523081505929046\n",
      "epoch: 80, error: 6.0312874463179874e-05 / 0.0001471941670079705\n",
      "epoch: 90, error: 2.3739166186638856e-05 / 5.9827396259044656e-05\n",
      "epoch: 100, error: 6.057427185883081e-06 / 7.80155998736023e-06\n",
      "epoch: 110, error: 1.3473974612042764e-06 / 1.5022630951121078e-06\n",
      "epoch: 120, error: 7.86204138851082e-08 / 1.297239564506323e-07\n",
      "\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([-9.994e-03, 4.998e-03], dtype=torch.float64, requires_grad=True)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fit model\n",
    "# Note, each epoch loss is computed for full validation set\n",
    "\n",
    "epochs = 128\n",
    "\n",
    "print()\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print()\n",
    "\n",
    "knobs, errors = [], []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        y_hat = model(X)\n",
    "        error = lf(y_hat, y)\n",
    "        with torch.no_grad():\n",
    "            knobs.append(model.knobs.clone().detach())\n",
    "            errors.append(error.clone().detach())\n",
    "        error.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "    model.eval()\n",
    "    X, y = validation.dataset.tensors\n",
    "    test = lf(model(X[validation.indices]), y[validation.indices])\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'epoch: {epoch}, error: {error.item()} / {test.item()}')\n",
    "\n",
    "print()\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "84d1afe9-3b5c-40cf-b9cc-0dd886ea1bfc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot error vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(range(len(errors)), torch.stack(errors).cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f6cdfc4-6fc6-4f6b-9849-5a1cb7ef02b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot knobs vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.hlines(ek.cpu().numpy(), 0, len(errors), linestyles='dashed', color='gray', alpha=0.75)\n",
    "for knob in torch.stack(knobs).T:\n",
    "    plt.plot(range(len(knob)), knob.cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c317a652-f2c2-4fd6-84ab-4d428fbc6016",
   "metadata": {},
   "source": [
    "# Example-36: ORM optics correction (training loop + elementwise computation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0935be0c-6270-4c36-ba44-4a71a0a4f31b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# In this example orbit responce matrix (ORM) is used to correct linear optics in a simple FODO cell\n",
    "# Two gradient errors are introduced into cell quadrupoles\n",
    "\n",
    "# This example illustrates one optimization step\n",
    "# Given a measured ORM, the model knobs are fitted to reproduce it\n",
    "# Next, the corrections should be applied and the matrix should be remeasured\n",
    "\n",
    "# Fitting step mirrors neural net training loop\n",
    "# Elements of measured responce matrix are used as targets\n",
    "\n",
    "# Note, elements of ORM are computed in forward method, but computation is sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc79eada-b30e-41d2-ae4f-451f43ab4bbd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from torch.utils.data import TensorDataset \n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "from ndmap.util import flatten\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70df77f6-6762-4b13-bd49-b5bd52339aab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c05d1e4-c0b6-4558-b3b5-21b3de1a3e4b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=1):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89951d35-fd38-4ec7-bceb-efd7bbc3cbdd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "# Note, transport maps are expected to have identical (differentiable) signature\n",
    "\n",
    "def t_01_02(x, cs, dk): \n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = quad(x, [0.0], 0.19 + kf, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf1, cysf1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def t_02_03(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd1, cysd1)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.21 + kd, 0.50)\n",
    "    return x\n",
    "\n",
    "def t_03_04(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = quad(x, [0.0], -0.21 + kd, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsd2, cysd2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "    \n",
    "def t_04_05(x, cs, dk):\n",
    "    cxsf1, cxsd1, cxsf2, cxsd2, cysf1, cysd1, cysf2, cysd2 = cs\n",
    "    kf, kd = dk \n",
    "    x = bend(x, [0.0], 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = kick(x, cxsf2, cysf2)\n",
    "    x = sext(x, [0.0], 0.00, 0.05)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.19 + kf, 0.50)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7bdbeecf-3673-40ca-93a8-47862ab928c4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set deviation variables\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "cs = torch.tensor(8*[0.0], dtype=dtype, device=device)\n",
    "dk = torch.tensor(2*[0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04bda73e-bfc8-46e0-abe8-8a356976c9f7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define one-turn transport at the lattice entrance\n",
    "\n",
    "def fodo(x, cs, kq):\n",
    "    for t in [t_01_02, t_02_03, t_03_04, t_04_05]:\n",
    "        x = t(x, cs, kq)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abe0f3d2-66b7-41b2-8273-4f79a7b1d9a3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test one-turn transport\n",
    "\n",
    "print(fodo(x, cs, dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5d96912-d52f-47e7-9305-ff488ff0699c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set rings at observation points\n",
    "\n",
    "def ring(x, cs, dk, s=0):\n",
    "    ts = [t_01_02, t_02_03, t_03_04, t_04_05]\n",
    "    ts = ts[s:] + ts[:s]\n",
    "    for t in ts:\n",
    "        x = t(x, cs, dk)\n",
    "    return x\n",
    "\n",
    "rs = [partial(ring, s=s) for s in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26e2a7d0-0c5f-4c8e-85be-6e3b580e987e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "# Note, dynamical part is assumed to be fixed during optimization\n",
    "\n",
    "fp = fixed_point(16, fodo, x, cs, dk, power=1, jacobian=torch.func.jacrev)\n",
    "\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fbe7da39-2803-42cf-b4e3-2f1b09dec384",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0.]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed points for all rings\n",
    "# Note, dynamical part is assumed to be fixed during optimization\n",
    "\n",
    "fps = torch.stack([fixed_point(16, r, x, cs, dk, power=1, jacobian=torch.func.jacrev) for r in rs])\n",
    "\n",
    "print(fps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53cb9eb6-9444-440d-937c-16dd45ef23b4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 8\n",
      "torch.Size([10, 8])\n",
      "\n",
      "tensor([[7.577e+00, 5.936e+00, 7.577e+00, 5.936e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.216e+00, 4.039e+00, 6.749e+00, 4.566e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [5.110e+00, 2.611e+00, 5.110e+00, 2.611e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.749e+00, 4.566e+00, 6.216e+00, 4.039e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [7.577e+00, 5.936e+00, 7.577e+00, 5.936e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.344e+01, 2.158e+01, 1.344e+01, 2.158e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.801e+01, 2.744e+01, 1.849e+01, 2.792e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.509e+01, 3.667e+01, 2.509e+01, 3.667e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.849e+01, 2.792e+01, 1.801e+01, 2.744e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.344e+01, 2.158e+01, 1.344e+01, 2.158e+01]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Define parametric responce matrix\n",
    "\n",
    "def rm(dk):\n",
    "    \n",
    "    pfp = parametric_fixed_point((1, ), fp, [cs], lambda x, cs: fodo(x, cs, dk), jacobian=torch.func.jacrev)\n",
    "    chop(pfp)\n",
    "    _, (dqx, _, dqy, _) = first(pfp)\n",
    "    \n",
    "    out = [torch.stack([dqx, dqy])]\n",
    "    for t in [t_01_02, t_02_03, t_03_04, t_04_05]:\n",
    "        pfp = propagate((4, 8), (0, 1), pfp, [cs], lambda x, cs: t(x, cs, dk),  jacobian=torch.func.jacrev)\n",
    "        chop(pfp)\n",
    "        _, (dqx, _, dqy, _) = first(pfp)\n",
    "        out.append(torch.stack([dqx, dqy]))\n",
    "    \n",
    "    return torch.stack(out).swapaxes(0, 1).reshape(-1, len(cs))\n",
    "\n",
    "print(2*(len([t_01_02, t_02_03, t_03_04, t_04_05]) + 1), len(cs))\n",
    "print(rm(dk).shape)\n",
    "print()\n",
    "\n",
    "print(rm(dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c010ee73-cbe4-4f5d-bbb9-f84a66dcb799",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Test responce matrix\n",
    "\n",
    "dc = 1.0E-3*torch.ones_like(cs)\n",
    "\n",
    "o = fixed_point(16, fodo, x, cs + dc, dk, power=1, jacobian=torch.func.jacrev)\n",
    "\n",
    "os = []\n",
    "qx, _, qy, _ = o\n",
    "os.append(torch.stack([qx, qy]))\n",
    "\n",
    "for t in [t_01_02, t_02_03, t_03_04, t_04_05]:\n",
    "    o = t(o, dc, dk)\n",
    "    qx, _, qy, _ = o\n",
    "    os.append(torch.stack([qx, qy]))\n",
    "    \n",
    "print(torch.allclose(torch.stack(os).T.flatten(), rm(dk) @ dc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5122a2a-8812-4be1-a41d-0889d792cc6f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define parametric responce matrix element\n",
    "\n",
    "def rm_ijk(dk, ijk):\n",
    "    i, j, k = ijk\n",
    "    t = lambda x, *cs: rs[i](x, torch.stack(cs).flatten(), dk)\n",
    "    v = tuple(torch.eye(len(cs), dtype=torch.int)[j].tolist())\n",
    "    fp = fps[i]\n",
    "    pfp = parametric_fixed_point(v, fp, list(cs.reshape(-1, 1)), t, jacobian=torch.func.jacrev)\n",
    "    _, (dqx, _, dqy, _) = [*flatten(pfp, target=list)]\n",
    "    return torch.stack([dqx, dqy])[k].squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd989d83-7f72-4d59-abaf-88b89247d48f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set quadrupole gradient errors\n",
    "\n",
    "ek = torch.tensor([-0.010, 0.005], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3913dcb9-8f2e-45d8-8f1e-2ce77c9f0bbb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[8.038e+00, 6.338e+00, 8.038e+00, 6.338e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [6.658e+00, 4.415e+00, 7.190e+00, 4.942e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [5.488e+00, 2.923e+00, 5.488e+00, 2.923e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [7.190e+00, 4.942e+00, 6.658e+00, 4.415e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [8.038e+00, 6.338e+00, 8.038e+00, 6.338e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.470e+01, 2.316e+01, 1.470e+01, 2.316e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.943e+01, 2.915e+01, 1.990e+01, 2.963e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 2.678e+01, 3.865e+01, 2.678e+01, 3.865e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.990e+01, 2.963e+01, 1.943e+01, 2.915e+01],\n",
      "        [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.470e+01, 2.316e+01, 1.470e+01, 2.316e+01]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Measure ORM\n",
    "\n",
    "erm = rm(ek)\n",
    "\n",
    "print(erm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e16ffba-540b-4ed1-a3f4-b27bbd08ae82",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data\n",
    "\n",
    "i_max, j_max, k_max = 5, 8, 2 \n",
    "i_val, j_val, k_val = torch.arange(i_max), torch.arange(j_max), torch.arange(k_max)\n",
    "X = torch.vstack([*torch.vstack([*torch.stack(torch.meshgrid(i_val, j_val, k_val, indexing='xy')).swapaxes(0, -1)])])\n",
    "y = torch.stack([rm_ijk(ek, ijk) for ijk in X])\n",
    "\n",
    "batch_size = 16\n",
    "dataset = TensorDataset(X.clone(), y.clone())\n",
    "dataset, validation = random_split(dataset, [0.80, 0.20])\n",
    "\n",
    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ca24bc7-83b9-45b9-a705-2dc81a8abf7d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set model\n",
    "\n",
    "class Model(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, knobs):\n",
    "        super().__init__()\n",
    "        self.knobs = torch.nn.Parameter(torch.clone(knobs))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return torch.stack([rm_ijk(self.knobs, ijk) for ijk in x]).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6894796-bbb0-45e3-87f2-db2cd66fcd1f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set model instance\n",
    "# Note, initial knobs are set to zero\n",
    "    \n",
    "model = Model(torch.zeros_like(dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "242427a4-c13f-4b6c-bbd0-864338f3d1db",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set optimizer\n",
    "\n",
    "lr = 1.0E-3\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6fe6053-1877-4ad4-b521-63e5d86de8f9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set loss function\n",
    "\n",
    "lf = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dc34bd61-d6d5-4a5c-9062-43e56c180fa5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([0., 0.], dtype=torch.float64, requires_grad=True)\n",
      "\n",
      "epoch: 0, error: 0.3207854421804211 / 0.4834669895527817\n",
      "epoch: 10, error: 0.06071210114479893 / 0.04106920029560601\n",
      "epoch: 20, error: 0.017744182732083627 / 0.01457174468546367\n",
      "epoch: 30, error: 0.003963890419609968 / 0.0034633084736078864\n",
      "epoch: 40, error: 0.0009804767999389498 / 0.0005130453666292978\n",
      "epoch: 50, error: 8.948304821381796e-05 / 0.00014339307948138367\n",
      "epoch: 60, error: 4.0143085845154395e-06 / 2.272965287854233e-05\n",
      "epoch: 70, error: 3.4453470618958206e-06 / 3.3214685216885225e-06\n",
      "epoch: 80, error: 1.387860215946706e-07 / 9.034775495586135e-08\n",
      "epoch: 90, error: 6.611469669425412e-08 / 4.719136099019071e-09\n",
      "epoch: 100, error: 3.0221850571449367e-10 / 2.1032181485124297e-09\n",
      "epoch: 110, error: 1.9253040326146467e-11 / 2.887542634043959e-13\n",
      "epoch: 120, error: 1.9606914010193334e-13 / 5.526546647035777e-14\n",
      "\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64)\n",
      "Parameter containing:\n",
      "tensor([-1.000e-02, 5.000e-03], dtype=torch.float64, requires_grad=True)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fit model\n",
    "# Note, each epoch loss is computed for full validation set\n",
    "\n",
    "epochs = 128\n",
    "\n",
    "print()\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print()\n",
    "\n",
    "knobs, errors = [], []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    for batch, (X, y) in enumerate(dataloader):\n",
    "        y_hat = model(X)\n",
    "        error = lf(y_hat, y)\n",
    "        with torch.no_grad():\n",
    "            knobs.append(model.knobs.clone().detach())\n",
    "            errors.append(error.clone().detach())\n",
    "        error.backward()\n",
    "        optimizer.step()\n",
    "        optimizer.zero_grad()\n",
    "    model.eval()\n",
    "    X, y = validation.dataset.tensors\n",
    "    test = lf(model(X[validation.indices]), y[validation.indices])\n",
    "    if epoch % 10 == 0:\n",
    "        print(f'epoch: {epoch}, error: {error.item()} / {test.item()}')\n",
    "\n",
    "print()\n",
    "print(ek)\n",
    "print(model.knobs)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7f6fd8bb-f601-40c7-bbef-07bb35c3e4e2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot error vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(range(len(errors)), torch.stack(errors).cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a4d5e5aa-aebc-4502-9358-f42fd129084d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot knobs vs iteration\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.hlines(ek.cpu().numpy(), 0, len(errors), linestyles='dashed', color='gray', alpha=0.75)\n",
    "for knob in torch.stack(knobs).T:\n",
    "    plt.plot(range(len(knob)), knob.cpu().numpy(), color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c51475-fffb-464f-8328-787d12d993d5",
   "metadata": {},
   "source": [
    "# Example-37: Normalized dispersion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bcc54c7a-4bdc-4bc8-a264-62c05f6d22d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Normalized dispersion can be used for calibration independent correction (10.1109/PAC.2007.4440536)\n",
    "# In this example derivatives of normalized dispersion with respect to quadrupole amplitudes are computed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75e9c661-86b5-4d01-8b58-420c87966d53",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from torch.utils.data import TensorDataset \n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "from ndmap.util import flatten\n",
    "from ndmap.util import first\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.evaluate import compare\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "from twiss.wolski import propagate as propagate_twiss\n",
    "from twiss.convert import wolski_to_cs\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9229b9e-cad4-4c82-a130-c9798711a40c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f361876e-71d9-472e-ad9f-883e5cc8b362",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=5):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=1):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def kick(x, cx, cy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx, px + cx, qy, py + cy])    \n",
    "\n",
    "def slip(x, dx, dy):\n",
    "    (qx, px, qy, py) = x\n",
    "    return torch.stack([qx + dx, px, qy + dy, py])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "791aa64b-569d-413f-adcd-96d453b347c7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set transport maps between observation points\n",
    "# Note, transport maps are expected to have identical (differentiable) signature\n",
    "\n",
    "def t_01_02(x, w, dk): \n",
    "    kf, kd = dk \n",
    "    x = quad(x, w, 0.19 + kf, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.00, 0.1)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "\n",
    "def t_02_03(x, w, dk):\n",
    "    kf, kd = dk \n",
    "    x = bend(x, w, 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.00, 0.1)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, -0.21 + kd, 0.50)\n",
    "    return x\n",
    "\n",
    "def t_03_04(x, w, dk):\n",
    "    kf, kd = dk \n",
    "    x = quad(x, w, -0.21 + kd, 0.50)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.00, 0.1)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = bend(x, w, 22.92, 0.015, 0.00, 1.5)\n",
    "    return x\n",
    "    \n",
    "def t_04_05(x, w, dk):\n",
    "    kf, kd = dk \n",
    "    x = bend(x, w, 22.92, 0.015, 0.00, 1.5)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = sext(x, w, 0.00, 0.1)\n",
    "    x = drif(x, w, 0.45)\n",
    "    x = quad(x, w, 0.19 + kf, 0.50)\n",
    "    return x\n",
    "\n",
    "ts = [t_01_02,t_02_03, t_03_04, t_04_05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20842ab6-8669-4778-80c0-52046e77aa3e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set deviation variables\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "w = torch.tensor(1*[0.0], dtype=dtype, device=device)\n",
    "dk = torch.tensor(2*[0.0], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24a40d8a-7ec3-4e07-b58a-5e23af39933d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define one-turn transport at the lattice entrance\n",
    "\n",
    "def fodo(x, w, dk):\n",
    "    for t in ts:\n",
    "        x = t(x, w, dk)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f64db496-e281-407b-aadd-57a7ae533b88",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Test one-turn transport\n",
    "\n",
    "print(fodo(x, w, dk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7ea3d4c-05c9-40ef-8b03-6d768af9ab63",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute (dynamical) fixed point\n",
    "# Note, dynamical part is assumed to be fixed during optimization\n",
    "\n",
    "fp = fixed_point(16, fodo, x, w, dk, power=1, jacobian=torch.func.jacrev)\n",
    "\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a4e9da8-9224-4b6e-b71e-922daf96d5b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Normalized (horizontal) dispersion\n",
    "\n",
    "def dispersion(dk):\n",
    "    \n",
    "    # Set container for parametric fixed points\n",
    "    \n",
    "    pfps = []\n",
    "    \n",
    "    # Set container for x and y dispersions\n",
    "    \n",
    "    etas = []\n",
    "\n",
    "    # Compute parametric fixed point and set dispersion at the lattice entrance\n",
    "    \n",
    "    pfp = parametric_fixed_point((1, ), fp, [w], lambda x, w: fodo(x, w, dk), jacobian=torch.func.jacrev)\n",
    "    chop(pfp)\n",
    "    pfps.append(pfp)\n",
    "    \n",
    "    _, (etax, _, etay, _) = first(pfp)\n",
    "    etas.append(torch.stack([etax, etay]))\n",
    "    \n",
    "    # Propagate fixed point and set dispersion values\n",
    "    \n",
    "    for t in ts:\n",
    "        pfp = propagate((4, 1), (0, 1), pfp, [w], lambda x, w: t(x, w, dk), jacobian=torch.func.jacrev)\n",
    "        chop(pfp)\n",
    "        pfps.append(pfp)\n",
    "        _, (etax, _, etay, _) = first(pfp)\n",
    "        etas.append(torch.stack([etax, etay]))\n",
    "        \n",
    "    # Set dispersion at all observation points\n",
    "\n",
    "    etaxs, etays = torch.hstack(etas)\n",
    "    \n",
    "    # Define wrapper for transport maps\n",
    "    \n",
    "    def wrapper(x, w, dk, transport, pfp_in, pfp_out):\n",
    "        x = x + evaluate(first(pfp_in), [w])\n",
    "        x = transport(x, w, dk)\n",
    "        x = x - evaluate(first(pfp_out), [w])\n",
    "        return x\n",
    "    \n",
    "    # Set containers for beta functions\n",
    "\n",
    "    bxs = []\n",
    "    bys = []\n",
    "    \n",
    "    # Compute beta functions at the lattice entrance\n",
    "    \n",
    "    pfp_in = first(pfps)\n",
    "    pfp_out = first(pfps)\n",
    "    \n",
    "    matrix = derivative(1, lambda x: wrapper(x, w, dk, fodo, pfp_in, pfp_out), fp, intermediate=False, jacobian=torch.func.jacrev)\n",
    "    \n",
    "    *_, m = twiss(matrix)\n",
    "    _, bx, _, by = wolski_to_cs(m)\n",
    "    bxs.append(bx)\n",
    "    bys.append(by)\n",
    "    \n",
    "    # Propagate twiss\n",
    "    \n",
    "    for i, t in enumerate(ts):\n",
    "        pfp_in = pfps[i]\n",
    "        pfp_out = pfps[i + 1]\n",
    "        m = propagate_twiss(m, derivative(1, lambda x: wrapper(x, w, dk, t, pfp_in, pfp_out), x, intermediate=False, jacobian=torch.func.jacrev))        \n",
    "        _, bx, _, by = wolski_to_cs(m)\n",
    "        bxs.append(bx)\n",
    "        bys.append(by)\n",
    "    \n",
    "    bxs = torch.stack(bxs)\n",
    "    bys = torch.stack(bys)\n",
    "    \n",
    "    # Set normalized dispersions (exclude lattice exit)\n",
    "    \n",
    "    *etaxs, _ = etaxs/bxs\n",
    "    *etaxy, _ = etays/bys\n",
    "    \n",
    "    return torch.stack([*etaxs, *etays])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0fc1d92e-b4d4-4373-9395-7fa27e1e6d3e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-8.795e-01, -1.390e-01],\n",
      "        [-8.732e-01, -2.077e-01],\n",
      "        [-4.797e-01, -4.941e-01],\n",
      "        [-8.732e-01, -2.077e-01],\n",
      "        [0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00],\n",
      "        [0.000e+00, 0.000e+00]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute normalized dispersion derivatives (responce matrix)\n",
    "\n",
    "rm = derivative(1, dispersion, dk, intermediate=False, jacobian=torch.func.jacrev)\n",
    "\n",
    "print(rm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dcdc2acf-2f97-4227-a018-0e8b2ec1205c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.169e-01, 1.568e-01, 2.615e-01, 1.568e-01, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "       dtype=torch.float64)\n",
      "tensor([1.176e-01, 1.575e-01, 2.615e-01, 1.575e-01, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "       dtype=torch.float64)\n",
      "tensor([1.176e-01, 1.575e-01, 2.615e-01, 1.575e-01, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00],\n",
      "       dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check covergence\n",
    "\n",
    "ek = torch.tensor([-0.001, 0.001], dtype=dtype, device=device)\n",
    "\n",
    "print(dispersion(dk))\n",
    "print(dispersion(ek))\n",
    "print(dispersion(dk) + rm @ ek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4f72419-624e-4865-867e-e9eb0d558d01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-0., -0.], dtype=torch.float64)\n",
      "tensor([9.987e-04, -9.924e-04], dtype=torch.float64)\n",
      "tensor(1.198e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-0., -0.], dtype=torch.float64)\n",
      "tensor([1.000e-03, -1.000e-03], dtype=torch.float64)\n",
      "tensor(3.185e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-0., -0.], dtype=torch.float64)\n",
      "tensor([1.000e-03, -1.000e-03], dtype=torch.float64)\n",
      "tensor(1.955e-11, dtype=torch.float64)\n",
      "\n",
      "tensor([-0., -0.], dtype=torch.float64)\n",
      "tensor([1.000e-03, -1.000e-03], dtype=torch.float64)\n",
      "tensor(2.021e-16, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Correction\n",
    "\n",
    "# The target values (normalized dispersion) are associated with model response matrix\n",
    "# Given measured values, the goal is to alter knobs to get target values\n",
    "\n",
    "# Set target values\n",
    "\n",
    "vf = dispersion(dk)\n",
    "\n",
    "# Set initial solution\n",
    "\n",
    "sol = torch.zeros_like(dk)\n",
    "\n",
    "\n",
    "# Iterate\n",
    "\n",
    "for _ in range(4):\n",
    "\n",
    "    # Compute current values and set difference\n",
    "\n",
    "    vi = dispersion(ek + sol)\n",
    "\n",
    "    # Set difference\n",
    "\n",
    "    dv = vf - vi\n",
    "\n",
    "    # Update solution\n",
    "\n",
    "    sol += torch.linalg.pinv(rm) @ dv\n",
    "\n",
    "    # Verbose\n",
    "\n",
    "    print(-dk)\n",
    "    print(sol)\n",
    "    print(dv.norm())\n",
    "    print()\n",
    "    \n",
    "    # Continue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a58cb1e-9b13-4fe8-9e6f-60d0f6dc6710",
   "metadata": {},
   "source": [
    "# Example-38: Coupling (minimal tune distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff382636-60b0-49b1-ac4d-3e6a3f8116d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example, minimal tune distance is computed using TEAPOT expression\n",
    "# The value is compared with some approximate analytical expressions\n",
    "# Computation dQmin of derivative (gradient) is illustrated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9944188d-4d08-4ca0-98fe-96092904616b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "from twiss.convert import wolski_to_cs\n",
    "from twiss.matrix import symplectic_conjugate\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61c00b83-fd9a-441b-b069-6f5584c6b47c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0611ac96-c53f-4ba2-87b8-ece288d83711",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def roll(x, a):\n",
    "    (qx, px, qy, py), cn, sn = x, a.cos(), a.sin()\n",
    "    return torch.stack([qx*cn + qy*sn, px*cn + py*sn, qy*cn - qx*sn, py*cn - py*sn])\n",
    "\n",
    "def kick(x, kn, ks):\n",
    "    (qx, px, qy, py), kn, ks = x, kn, ks\n",
    "    return torch.stack([qx, px - kn*qx + ks*qy, qy, py + ks*qx + kn*qy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b04a6805-0910-449c-87f1-6ea258651dee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x, k):\n",
    "    kf, kd = k\n",
    "    x = kick(x, 0.0, kf/2.0)\n",
    "    x = quad(x, [0.0], 0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = drif(x, [0.0], 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.18, 0.50)\n",
    "    x = kick(x, 0.0, kd/2.0)\n",
    "    x = kick(x, 0.0, kd/2.0)\n",
    "    x = quad(x, [0.0], -0.18, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = drif(x, [0.0], 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.21, 0.50)\n",
    "    x = kick(x, 0.0, kf/2.0)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(2*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bc46030-6a86-47f3-a067-4920a09201bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(2*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d2fb82ec-7efb-4cec-919e-dac35e0f91ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "269724b3-9cc5-4d1d-808a-1a8917a885ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 2), (0, 1), pfp, [k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c253111f-bd69-410e-abd5-a33b6e1c9ba9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 1), fp, parametric=pfp)\n",
    "jet = propagate((4, 2), (1, 1), jet, [k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e2ccc2e1-df88-416e-a1c8-1efae9f9651d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2.192e-01, 1.772e+01, 0.000e+00, 0.000e+00],\n",
       "        [-5.372e-02, 2.192e-01, 0.000e+00, 0.000e+00],\n",
       "        [0.000e+00, 0.000e+00, 5.733e-01, 6.018e+00],\n",
       "        [0.000e+00, 0.000e+00, -1.116e-01, 5.733e-01]], dtype=torch.float64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute uncoupled one-turn matrix (zero skew quadrupole amplitudes)\n",
    "\n",
    "m = derivative(1, lambda x: evaluate(jet, [x, k]), fp, intermediate=False)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1277c136-defe-4b2e-951e-4433d371c6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7.539e-16, 1.816e+01, 1.423e-15, 7.345e+00], dtype=torch.float64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute (uncoupled) CS twiss parameters \n",
    "\n",
    "(nux, nuy), _, w = twiss(m)\n",
    "\n",
    "mux, muy = 2.0*torch.pi*nux, 2.0*torch.pi*nuy\n",
    "\n",
    "ax, bx, ay, by = wolski_to_cs(w)\n",
    "\n",
    "torch.stack([ax, bx, ay, by])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0fbd822f-4284-47bc-8b55-2b7d8fd0eaaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 2.192e-01,  1.772e+01,  8.859e-03,  0.000e+00],\n",
       "        [-5.372e-02,  2.192e-01,  3.963e-04,  3.009e-03],\n",
       "        [ 3.009e-03,  0.000e+00,  5.733e-01,  6.018e+00],\n",
       "        [ 3.963e-04,  8.859e-03, -1.115e-01,  5.733e-01]], dtype=torch.float64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute coupled one-turn matrix\n",
    "\n",
    "dm = derivative(1, kick, x, 0.0, 0.5E-3, intermediate=False)\n",
    "dm @ m @ dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3bf2dfb-ba74-4abb-8fd7-4878bd08b2c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 2.192e-01,  1.772e+01,  8.859e-03,  0.000e+00],\n",
       "        [-5.372e-02,  2.192e-01,  3.963e-04,  3.009e-03],\n",
       "        [ 3.009e-03,  0.000e+00,  5.733e-01,  6.018e+00],\n",
       "        [ 3.963e-04,  8.859e-03, -1.116e-01,  5.733e-01]], dtype=torch.float64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coupled one-turn matrix from jet\n",
    "# Note, jet is first order in skew quadrupole strenght\n",
    "\n",
    "dkf = 1.0E-3\n",
    "dkd = 0.0\n",
    "\n",
    "dk = torch.tensor([dkf, dkd], dtype=dtype, device=device)\n",
    "\n",
    "m = derivative(1, lambda x: evaluate(jet, [x, k + dk]), fp, intermediate=False)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e79583ad-cca4-4770-8e43-4f31b30f22f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.838149e-03'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# |dQmin| (Edwards & Shyphers, first order in amplitude and unperturbed tune differenct)\n",
    "\n",
    "f'{abs(dkf)/(2.0*torch.pi)*(bx*by).sqrt().item():.6e}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d38a280-58d7-4bdf-8791-0cd441393e39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.831163e-03'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dQmin (first order in amplitude)\n",
    "# Note, tunes in [0, 1/2] are assumed\n",
    "\n",
    "f'{abs(dkf)/(torch.pi)*(bx*by).sqrt()*(mux.sin()*muy.sin()).abs().sqrt()/(mux.sin() + muy.sin()).item():.6e}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a96f0df2-3215-4118-9bf1-8ba54291468b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.831193e-03'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dQmin (TEAPOT manual, appendix G, 1996)\n",
    "# Note, \n",
    "\n",
    "(NUX, NUY), *_ = twiss(m)\n",
    "\n",
    "mux, muy = 2.0*torch.pi*NUX, 2.0*torch.pi*NUY\n",
    "\n",
    "B = m[:2, 2:]\n",
    "C = m[2:, :2]\n",
    "\n",
    "f'{torch.linalg.det(C + symplectic_conjugate(B)).abs().sqrt()/(torch.pi*(mux.sin() + muy.sin())).item():.6e}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f21341c8-f3d9-4870-a1e2-22866f21cd1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.275e-05, 1.314e-05], dtype=torch.float64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Effect of skew quadrupole on tunes\n",
    "\n",
    "torch.stack([nux - NUX, nuy - NUY]).abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "839affeb-bf67-4665-bf2c-63b5b1068890",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.831e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([nan, nan], dtype=torch.float64)\n",
      "\n",
      "tensor([1.835e+00, 1.715e+00], dtype=torch.float64)\n",
      "tensor([-1.835e+00, -1.715e+00], dtype=torch.float64)\n",
      "\n",
      "tensor([1.831e+00, 1.725e+00], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# dQmin derivative (TEAPOT manual, appendix G, 1996)\n",
    "\n",
    "def dQmin(k):\n",
    "    m = derivative(1, lambda x: fodo(x, k), fp, intermediate=False)\n",
    "    (nux, nuy), *_ = twiss(m)\n",
    "    mux, muy = 2.0*torch.pi*nux, 2.0*torch.pi*nuy    \n",
    "    B = m[:2, 2:]\n",
    "    C = m[2:, :2]\n",
    "    return (C + symplectic_conjugate(B)).diag().prod().abs().sqrt()/(mux.sin() + muy.sin())/torch.pi\n",
    "\n",
    "print(dQmin(k + dk))\n",
    "print()\n",
    "\n",
    "# Derivative a zero is not defined\n",
    "\n",
    "print(derivative(1, dQmin, k, intermediate=False))\n",
    "print()\n",
    "\n",
    "# Derivatives at points near zero are valid (note the sign flip)\n",
    "\n",
    "print(derivative(1, dQmin, k + 1.0E-16, intermediate=False))\n",
    "print(derivative(1, dQmin, k - 1.0E-16, intermediate=False))\n",
    "print()\n",
    "\n",
    "# Derivative at a point\n",
    "\n",
    "print(derivative(1, dQmin, k + dk, intermediate=False))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b05181f7-6c8d-4a52-ba0c-c4568187fa89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAKACAYAAADqwVVOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5wURdrHf9WzmV1yzkkFBBQMKGZFUDHnnBOKOeud5929Zw6YwZzOiPnMilnEAKioIJJzTruwabreP3Z7tqvqqerqCb0jzI/PfGa7+qmnnupZdp5vV1UX45xz5JRTTjnllFNOOeWUU045JSmnsQPIKaeccsopp5xyyimnnP7aykFFTjnllFNOOeWUU0455ZSSclCRU0455ZRTTjnllFNOOaWkHFTklFNOOeWUU0455ZRTTikpBxU55ZRTTjnllFNOOeWUU0rKQUVOOeWUU0455ZRTTjnllJJyUJFTTjnllFNOOeWUU045paS8xg4gW+W6LhYvXoyysjIwxho7nJxyyimnnHLKKae/pDjn2LBhAzp27AjHyd3P3lyVgwqNFi9ejC5dujR2GDnllFNOOeWUU06bhRYsWIDOnTs3dhg5ZUg5qNCorKwMADBtVheUldVRNXN9dM1ZfZn37pU3mMhl3jHzbFy/LSdtBRupHtmm7Fv2ZzpH+EN9/xK2XCoHEteiob50LPjR2HK/P10dtU1e74e7AOeAA/UzkvvAXWLkKeFPE6fBhvtjl89Jbdf9KMel95Mok2P3++N0m5y4prJtPA44jmQvX4NEfD53mtjFNqV45HiJuJTrL/gOY0tfP+oaULFrbbzfNw7EOUeMMUObRFyubOu/FLQf0p/iR9+HhusG1UbnByZ/8jUhPnPp3SXic10ODg7GY/pr4tUPujYAXFesQ/2/cpVrQsdZ1y+Q56jfY1fjT772Ypx0TF5fqfpyDHHXhcMcoazhq4RJx57/Bmm+LsivAq7YcOu6nq1qQ/vQlfvLFB9MOlbalmJgHJy7QP31a+hPg51SxmifrvBzw7vri00sr7P3bFwm1o3Xv8fA4IDB4XXvAOCAgSV+bpDj+73x/wwADgcp2S6sOHcBMDDGErHL8j6XOK/EzPjfE7lVY6myshLV1dWRt1tQUICioqLI241aOajQyJvyVFbmoGlTRwIKDzJY/btX7qsv/eVTgEI4pwOKBmMtUBigww4o5CTfbyueyxhQCDa6OlSy2nB8/th5KCmI4a7TOtd9djZAoUnGxbjkNoNBwAgUmsQ9FFBQ9WyAQmrzzd8W495vZuK1k4aiaWGhpm0iwUkGKIiEVk3YDcm4DigMcZBAESIZJ4GiLv9FTZzjoolvY6fWXXDG1jvYA0VM7ZMpHjKpdxngWNpqrl/yfojPLGa2dUl/wNhl4+FyjvPbHQMvPaLaFoCENbwLNi4a+sJoWzdRBtLG9fuV/Al1IfsF6cdrB0BDgu9COEfZNgCUxrb++vzm/oDP+Xicwm5AMSsTY/EDha9OnX80+If6p9+UyGcSKBr+1DaUxwjbmO/dDxQx0o4r5XX2HDV8LX7kd2FrdjxasH5w6u0dqZ+JMtZQ5tl6Ph0fMHjtMu9nhoaf68sZeH1MvP6Lta6+l2U0vLPEu/eSy/3v8s9APVCIRaRdWHHOMY8/gUK0Rjvn0MRn5cn7Ey2XN+Z08srKSnTvUYplS+ORt92+fXvMmTNnsweLHFSEVVRA4VNkQOFXNgGFXwRQAMD23Ztg9GPz0bFlPq48pAPpN91AISiLgEKQAQS2al2GOWsqcOJLk/DKCbuhOJZH+q0r0/tR2xTjaVSg8IeVKlD4bPIchr7N2+HuX79Cs4JiHNFtW7pNDeylBBRB/bQFCts2TUARYKsDCgDoVdgFDy59EWVOE5zU5uBgoNDEJ49QkKMYut8l8vdEF4NiqoxQULZqnHRMgAQUlK2v7dbogPVYjf+6t+EU5wbko7jexosFSh1boBD6qNiYgUKsmxxQ+KW1DTlC4QcKAMhDKYrQEj+7D2B75wqUsZ5kP+viEsvkayCf049QSC9W9+4fmfCUAAZuDxRRijGGYtYNS93X4bglaOMMS5yTv4r816QxVV1djWVL4/h1VheUNY1uXceG9S627bUA1dXVOajY0sVc3yiFDigE+/ofUgWK+nNaoPC3mS6gkJN6KvaEbYRAoSTsqp/zh7fBotXVuPa5RejYvAAn7tZa8JsJoNBCApHcRwkU2vpSAtW3TVO8eNyuOPL5r3H269/j6SOHIMZiiq0NUOhHTKDKkJzK/lIGCil2m2Q8CCi899F9d8Gaqk3455SPUZpXiGEdtzL2zw4AkgMKvR+lybQAhU0fTEDBXYZhzXdBRbwST614A01iJTisxX5k/WSAwi8boEjYWABFw7kQtmkGCpcDrVknnORch6fdf+Ml9y4cz65GjBUKsQnXIRGTdCydp8qCgMIv2dYWKMhYMwQUHIDD8tDfGYWp7j34yR2D7Z2r0YR1lvrZ0EMdUHjTmBR74eukASRkxeuhIgaWeJeBwlMQSEQ17cnrRxtnP8T5Jix130AMxWjp7EYCRbaprDQfTUsjXCzuUr/lm6dyS/BtZQIKOYmPAiiUEQQxFkHpAgobSAiyJW00dYgyCig8m38d2wmn7dUKZz08Fx//sk7zjRchUMh3iC1GOqIACn9StVPnVnjyiCH4eNZSXPruFHApC00XUOgTdhMkSHWFwNIDFEpZiOQZcHD9dvtg/05b4dof38OkFfO1/RPbpP2FG+kIBxSpjXQQbVqAiepPrXtoy31wdMvheGbFm/h43UQtUFD+TEChS/STAQqybQ1QmOOE1jYIKARb31dHB9YDx7GrsBB/4DX+AFweF4BC+XrQAIUQt1wncazenUeAbRBQCO0ybrRNJ1B4djFWiP7ORShCK/zs3oNNfLkCCOJXQzBQ1I1ANNjLMEGNUvhHKzzJ6yiCpjrZAkWqkiGhnTMSrdieWOS+iDX8R6NtTpu/clARJM6QClAIihAodIuvmXwbwV8vVaBwA2yFMg1Q+BUCKMDrFouNPbs79hvQFEff/SemzK2oryfFQyW0ch8iAAr5nAwEgjIAFF7bw3q1x/0jd8QLv8zDTZ/+4mtL70dtM/NAYbWWI2Es1kkWKOREU36PMQf/GXwAdm7dBZdMegu/rFmSMlAo55Ia6SBskgKT5IBCHWUQ6/p1fKuRGNFsd4xb9iK+3TCVBAplFCQFoPDLBijk+iagUGwNQCEvzDYBReLPJFePu7G+OJJdhj8xGe/wceD1nQkDFLpRCxNQBNnqgMIvedpTlEDhveezEgxwLkMMRfjZvRtVfK0CFMKUJqI/JqDw2+imPflFLcymZAKKTImCBMYYOjhHoxnbEQvjT2OD+6timy3Tnzwxty4Xiu7V2D2OTjmosFSyQKHCgfSt4FeUQKF8ExAAAOlcFEChTdipNuW2GPLzHLx4cS/06ViMg2/7A7OXVos2hoRWCxT+LkQIFNrRAWNyT9f121BtH7NtV9w8bDs8+N1M3D9phhEotMm46XpFABS6a5ApoPB+zndiuHPHQ7B109YY/e3rmL1hleZa2PkT3kkwCU7qFdsUgELwlyag8NdjjOGsNsdgaOlgjFn6NH7e+HsooKD6awIKxSYFoBDblmwloKBsdUAh2Na/U0BRd8ywFRuMQ9go/Iwv8QmehculBFgCCr9sgYKMSWMrn6fayzRQ6GOWRiIAFLCm6O9cBg4Xv7h3o4aXK+so6Bg1U5+kcgooPHnTnWL1L7+SXT9hGqVIBkBMUOAygDsOOsVORhnbFgvij6HcnSnUzWnLUQ4qAuSRpqI0AYX8pCfqcbMJRQgUysJsChJkf5kGCqFNFSg8lRbF8NZVW6GsKIaRt0/HyvW1kr+QQKFJxslpTDqg8JukCSiUhNNwN9gEFJ7O3WErXLFrH/zzs1/w/C9zkwYKJZEzJKlRAYXQpA2kWQKFV78kLx/37XwEWheV4vxvx2PxxnXGflmNOiQJFLp1KMkChY0fT2GBwnt3mIML2p2KASVb444lj+KPyjmkLQUUik0KQOGXDVDo2qKAQvfoWPKxs55t4hjksV/9sDtG4Ax8j/fwNV4zAoUeCuRjKqE221JfYTZPejLaSUBhI3mUgpI/9iLWCv2cS1GLcvzm3oc4r5TAQI6RGKlgNFxQIxT+UQoPKPyjFLZPeMrktKegEQb/58FYDB1jZ6CYdceC+Dhs4gvI65fT5q0cVISQbt8J6n9LuoBCgRYDdEQKFHKZMdFOE1DoknpilKBNWQH+d/U2WLcxjsPumoGKSuKCpQko5Cc9kUBhkdSr/lIECl2iSEJyXdk1u2+L07bricve/wHv/blINEkTUOghgQgrTUChTZrJ5FnTX39cRP+aFRTjoZ2PRj6L4YLvxmNV5UahX6GSegswTAYowvuzBxMdUNi06XKGPBbD5e3PRvfCzrh1yUOYX7VEtKH+CwcABWVrAgotJCQBFH4FAQVpmzgGeVxXJsY3iA3HnjgOX+EV/Mjfp6+D5yfgWIgpsK4IFGJ7qQEFpXRMewJRVsLao2/sEmzCUvzuPgiX1wi24faikEYrIgCKdCpohEH+E+WCw2H56Bw7FwWsLRbUPogqviw7Ryo4i/61hSgHFZayAQrd5naMuK2UNqDQxWP1zaD+oqcNKOTpSjJQ+JVmoPASoF5ti/HWlX3w68JNOPGBmaiNc/XuOzWtSmozEqCQocCU0KYJKNTEuG5+7K37DcbIrTrj3Le/xTfzV9B+yCRV55e6XvoEVEmsrZJ70X8mgEILVT6bNkWleHjIMaiorcbo71/Fhuoqa38UUNgk/g22cl9CtGmCjhSBImjKlH+6UQErxDXtR6F1XkvcvOR+LKteVWfj+1sQ5Mf0uF4ZKPwKAgorWyFO2pYCCt2TnkyzZXWPjh2Cw7ETRuITPIVf8UV9m6KNDVAEPTqWGs2AYpMcUPiViXUU/nKhjAGlrCu2iY3GBj4bf7iPgvO4EJcOKPznwwCFA3WDO4AGhsDzacjfbdY/UEDhKcaK0Dk2CjE0xcLaB1HDVwNo+Hxy2ryVg4oA1S3oqT9IFSiU+hJQSO0KfpIECu3mdsToRaRAoYMDQ3IfBig8mx26l+Lli7bGh9PWYdQTs8E5V/tgkfSS05iiBApdomiAtLBA4fmPOQwPHbQzdu7UGqe88TV+WbaWrCPGQ/sVwkoTUKjJveTf32aEQOG137mkBR7Y6Wgs2rgWl09+HZtqa63jCgsU8jVMFSh08ZigIxWgoPyVOE1wXfvRKGQF+M/Se7Gqel1gH0xPXzIBhQ5IKKAItI0AKGx2y2aMYW+cjAHYG+9hLP7AD4oNdexXMkCh2oQDCrF9yUeGgEKInzWUNWVbYSvnXKzjv2C2+ww4dxH06FjTk56CZFqYbbueIlWgsIUJE1B4PmKsCTrljQIQw6LasYhz9W9gYyraRdqaKfSbqXJQYasogMKVbaDaBMWTbqAQ2pT8ZRAolIXZxri8OpJN/fvwgc3xyJm98NSXK/DP1xfScRL1jZCgAwohruiAIsyTkUxA4akwloenD9sNPVuU4vjXvsCcNeVCHTEe2q8QR4hRh1SBIrm78Rr/ZJuEjfTZbFXWFvfueBSmrV2C6396G7Wum1xcKQBF8v40v2+EjfHRrxZAoduLolmsGa5vfwlqeS1uW34/KuIVVkChm2qWDFAIfQllK/et/phICoOAwi8boGgQw3Cci97YEf/DvZiHXwUbw1eKNVDQdb1jO6DwS/0qiwYodFOhmjsD0MM5E6v4d5jvvox4/S9PMk968sub7iRPeQLsF2anMu1Jt4A8sB7RJHXtvJ/zWDN0zBuFVrFDwJlj1UZOf33loCJIHIm/QqbN7iIBCosRk4a40gQUSnJP+NHaZhAo5KTeAB3cZTh5tzb4z9FdcfNbC/HohKVEm2kCihAjHZEChVJfTHjEuOrONckvwPNH7Immhfk49tXPsKy8UmkzVaDQJsQWiXbSQKEkz1BttG0GA4XXp4HNO+P2QYfj6xWz8e9p78Pl3JzUByThYYEiJYhJEiiovSh0beuAwntvk98K17S9GGvia3HH8odQ6ValBBRUf02QELS5nWCb+B2S+1Z/DCpOyVZqlyoz7ZYt+wccHISL0Bl98CbuwGLMUuroRh7CjGaEBQohVp1txEAhfuU1+Gzp7IiuzglYwT/DUvedQKDQLcz2zntPd/JvcOcplXUUYUcpwib41OiECSi893zWGsXOttbgEpW8GShRvrYU5aDCUuRjXU1TkzxFCRSJcxqg8CtdQCGfMyXRpulLXvV0AQWRpF55UCeM3q8DLnpuNt6cvMrXpiGp1/qDamsBFNqE2KJN093gVIFC56dVcTFePHJv1LguTnj9M6yrrI4UKLRJKfE7nS6g0Lepv/66JH+3Nj3xzwEj8e7iabhn+gRwAixoPzYAIB0nPXVKviamNpMHCr+CgAKoS9Q7F3TAVW0uwoKaRbh35TjU8BrNNTHHWRcXyHPJAIXQlwCgoOOUbFXTUECR+LPJxeMY8nEIrkArdMEbuBWrsFCtkzgOBgrZ1hYo/AqEj0YCCjExbmi7jbMHOjqHYyl/FyvcCeSIhP9nGSjkvSioHbNtgaIxZJruJB/L1zbbYCKnzCsHFRYKCxTaze3IMW3ZRvLr950qUEjnFKAg4koZKHTQQo4AGPzIfQgJFADAOMOdx3fHETu0wimPzMTXM9ebgUI+R4286GyTBAol8U92ypQUVzJA4b13adoELxyxDxZv2IhT3voCm2pryURM7oN51CHY1gYowiTj6QIK3d183ROHRnToh6v6DMeL83/AE7Mnqm1a9EE9p8Ye1IewQKGzoe/8m/tC1TdCh++z6VXYHZe1vhDTK//A2FVPwvWmoejgKgmgENq2AIrEOQugCNrcTvgTLQGDDiiEeBMxScf173kowuG4BqVogddxM9ZjuTVQ+BUWKIS6jbi5nbGcqeVqjBztnOFow/bHYvdVrHYnah8dmziWgMIPFjJQyOV+6UYzhLIM5ewuSx9QuEydBpbT5qkcVAQoE0Ch3dzOfFtHb5suoNBCAhFXlEAhl8lJNRGX7ilPjsPw5FlbY+cepTji/t/w++KNdFJuARQKOBiSpCiBQo7L5k67caSDM/Rp1QzPHbo3flm+Gue++xVq4q5iI9Q3QgJoW39caQIKFaak3zerNsMBBeXn6K6DcW6vPTBu1hd4dcHkpPpgAorkRjpSAwolnhSBQk3UG2z6FW2DC1qdix82TcFTa/+LuJTMG/trARQN50LYNiJQKAm9Big8FaEUh+N6xJCP13AzKrBWCxR+6eBDBxRk3TQDRZBSAQpP1KNj2zmHoiXbDYvc57HWnZIop0YrPPmnOwE0UNiAg648k0ChlNmO0MjXhKnXt9HlNsJrC1EOKmylAwqf0gUUus3tyHl5UQKFJlE3LZ6OBCg0ybhuSlFRvoNXLuiHzi0Kcci9v2LhmioxGUkXUGjWQZiS6CiAQrdbtvHuvsuwQ4fWeOygPfDZ/CW44pNJiV170wYUumTXAEOpAoXNXX3FNiRQeO9n9tgNx3fdCXdM/wAfLf3VappQuoAiqX6mABSUv2SBwtMOxdvjrBan4rOKL/HK+teMQBG0W3YYoCD7ogEKv4KAQrANAAohhoR/ya90Xi5rguY4DDegFlV4EzejEuUkUGi+fqyAQgGFFIGCUtAoBSVboHAF/2LSzBhD+9hxaMoGYXH8aWxwf6eTa80deRNQZMu0J2p0AlCBQv5Z+56NQJFTRpWDiiBxmIEicU4DFD6lDSiUcwQAJGwjBAo5+bZaLyC3mQGgkNrgLkPzkjy8dVF/AMCh903D2o21ZMwZBQq5HpVoJ2yl+hkECj3gAPt064h7998V42fMwb+/nqw+NSmTQGFxR1zbh2SBQgNMoUYA6t8ZY7h4q2E4oH1/3PTr25i4alZIAFCaTBtQpLSmg7gmOiAxQocBKLyfdyvZDSc0Ow7vlr+Pdza8lxRQkG0bgCJot2zK1gYoVICQj/19kmyU9lSpUAA0Q1sciutRjtX4H25HDSqFOmGBgmovWaCgJI9SpGsvCsrWJWzFKU0AYw46xk5BCdsai+KPYZM7J2HnBwpqtAKoA4rEzyGAIopRCuprB9g8gYLx6F9binJQYamkgUKzuZ0NdCQNFHJSbxhdsRp1SBdQKAm7oc0MAoV33KlFId6+pD8Wr63GMQ//isoqTtv6w4oQKLT1TUl0BoHC0+Fb9cC/99gRj0ydjoem/EZDguQvCqBQyqwS49SAQne3XNemwxiu73Mwdm3VC9f9Mh6/rFsQGij0IybJ9TO5JzXRdan69KJuycYCKDw/w5sMw6GlB2P8htfw2cbPhf7aAIXNbtlaWwkoKFsdUAi2iXjpY7FPko3SHuHXcNwKXXAwrsUqzMf7uAc1qA2oqyboDe1pQCGJ3bIzvY6iLi7a1h8nuTbAV4+zGDrGzkQh64LF8XGo5IsSQKFbmOw9Ojbxs+XeE7Y2qSqdQOGtn/BfC3nTv5w2X+WgIkD+x4FlBCiUEQQI/gSlCyisICGMbZqAQjdVygRDSQKFpz7tm+DVC7bF93M34Mynfkc8DtHW174OKARpgIKKPVWgUBIw41102k9YoPBszhzYB5fuOAA3T5yCF36bGQAJkn8hsOiAQrcXhVVcSQKFcs5lyHNi+He/I9G3rCOu/Pkl/Fm+zOgnLFBYAVPimqQOFIK/NAAF5U+etnRY6WHYt2QfPLvuOXy38QfRxuhP9qvaBm1uJ6yZkGxNQBG0uZ24ZoLRNlI/hL7JNppjAGiHXjgQV2AxfscneADentE2QBE48pBFu2WTIw4GoKBs/ECR8MPy0T52NvLQEktqx6KGryB6VyebKU1hpz2lc5RCN90pWaCQ7YGGvTmyRYxn/hGywmsL4qkcVCSjCIFCt7kduUNjVEDhlw4oSJvUgEJJrIlkXOmDwcZLFob2bI7nzuqLN6auxJXj/4Qb9xuJthRQ6EcJ9LFHAhS6JNCQvFmN0tS/X7HTdjh1261xzeeT8N6c+WT/TGsAjIvDE8ZinUwCha4+fS3s/VEgUBjLx+0Dj0WHoua4/OfnsXDjmsBrkS6gCDPSYQMUurasdrkmAEAPJg1+GGM4sekJGFI0BI+uexQ/V06rj0v1F7Rb9l8ZKIIAggIKL+HriP4YhosxB9/jSzyWuJNM3eFX2wsHFEYfEQGF+BWnAx/NrtnSz3VrLIrQPu88OCjGstqxqOFrlb6muo4ik6MUNusn5OOgKU9UfUAcnclp81YOKiyl3dyOItAogUL5FrEBgBSBQpf4G9dDRAAUmn4Zpx3Vnzt4QBvcd/zWePjzRbjzIy9BBmmbbqDwSwsUhE2qQKFNxoWEUY6roQ5jDP/eYyeM7NkNF338BSYuku+4S3WJOMzJvVjHmGhHABS6ftnERfWhSawYdw88EcWxAlz683NYUVlOxIVAP9o2kwQK3ShDskChtUkSKLxzDDGc0ewMbFvQHw+vfRAzq2Yq/oKAwq8goDDZykBB2iaOIRz7ZbdbtlgWBBRiHTGZ7o4dsTfOw3R8iu/wPJkQqv7tgIKKtbGBQkx+5baJqU9M9CU/2YixUrTJPxcAx8qacajh5Qn/fqBQypJ8fGydP7I4lGymO/mPyWvjvx7EI2M9kEjsGk78zcpp81MOKgIkLLIxAIV2czvDX9gogEK7uZ3xL3+GgUJoUwMU/iYzDBRe+dm7d8QNB3bHjW/PxnPfLqFt/XGlCSiURJECCl2iSI5YZR4oPDmIYcy+u2HnDu1w5vsT8POK1UJdIaw0AYU2aaYS7QiBQpvUG353mueXYszAk1HrxnHFL89hXfUmnw1IP2FHJhTbFICC9JcCUJj8mYDCe3eQj/Oaj0K3/O54YN29WFi7IGFrAxS6tqg4g3bLFuOUbBMxQTgWy4KBQvekJxNQqIm2WN4be2JXnIaf8T/8hDcF27BA4ddfbS8KyPU1QOG3cxlHHmuBVvnnwUUF1tQ+Dpc3LH4PWkcRZm1FOqQbnQDUaxA0dYxakE2OUnCWfSMVLo/+tYUoBxW2ShEotJvbEV+ukQKFXGZaL5AuoNDYiH7q3zRAQcclHocBCs/+hgO748yhHXD+CzPw/q+rtf0zjhL4/JF1MgAUamJM+JFsUwUKz77AycOjw/dB7+bNcOq7H2H22vVqW4a1HGofRP9hgaKhTU1/yTYtkvEUgUJn066wBe4eeApWVW/ANb8+j03x6rQBhc2IiWxrAop07IchJuoaGwt/nk0+L8CFTS9G61gbjFl7F5bHl6UFKMS2Qdq6RKIUBBR+2eyWrfiFaENNcYJiQwOFd9wfB2AwjsIPeAm/4SONfxoShP4E2GYDUHjSPTrWH59c7j/2J9P5rA1a5J2DWr4Cq2qfBOO1CfsgoKCUiVEKE0wETV/yl8tAIdcXNvrzAUVWQUVOGVMOKoLkQgUKn9IGFJy2Jf9/RwkU8nQlYmqS6i/zQKGco6ZVSW0GAQUAMDDce8zWGNGvFU568hf8MHdDoB8hCdHEboKOSIDCIhlv8Kfzq7/+JXkFeOrAYWheVIiT3/0ASysqrIBCjYeIXWObLFBoocoAe0ntYG0BFN5795I2uL3/yZi9cTn+/ttLqHFrST+2/kzxmGxSBQrdXhTJAkXQbtne51zilODippej2CnBmHV3Ym18jdVnZgKKoM3t/EARtLkduXGdBVDI6yjs1kzINmag8MoG4Sj0wwH4Bk9iFr6R6nClTqJuhnbLtlVYoHCFtlXYcH31SJgAvSC5yOmMVnlnoYbPx8raZ8BcN6mF2elOvm1HJ+Rj05SnOr/05xTzT3nywQTLIqjIPVI2c8pBhaUY8a2gBQqhnmcD0SYkUGg3tyP+WkQKFNr1EPrkPgqg0CayxulVdW8xJ4ZnTtsWAzqW4ahHp+LPFcSu28kChS7BM0BaFEAhP+nJCB2a/rUoLMJzBw6Hy4FT3vsIayurUgYKm+RZsY0AKJKKKwAA+pZ1ws19j8eUdXPxnz/eQJy7qV+LFIBC8JcGoLC5JmGAQvZX5jTFxWVXwYWL+zbcgXK3XIoPpJ9MA4UQpxxLGoDCL1ugoGLiAIbgZPTGHvgCD2M+ptSXq0ChW0eRLFBQsh2lEOsEA4VsKz69qOGcaS2Frv1Cpwda5Z2OSj4Dq+IvgnNXGaXIhqlOQDBQeO9U/ymoUtZQNPJGfjk1jnJQYaHQQOHKNhBtMgEUShs2yb3BNk1Aod3cjozLqxMBUEjJVqLt+vKSghjGn70dWpbk49CxU7F0fZXiJxNAEebJSFEChc2oAwB0LG2CZw4cjhUbN+HMDz/Gxpoa8rNOG1AoyTNUG22bmQOK5KYJAYOb98SN2xyDz1f+intnvQteH0BSIxMpAoVuLwqqDzZAoduLgr4m5jjr4oJyrmWsFS4uuwob3A14cMPdqOSb6s+ptv52hL4EAAUdp2Tr9ycBgw4ohBgk/3ZrJuRjfeKuswUc7IZz0BmD8BnGYCl+09dNEigopWvaU11ctK0/nmSAIhFr/ZQfee8FL6FuwvqidewkVPDJWO2+Ds651YgFdSycC3G323StAXugkN/9C7JloKiLkSlTnhpeWSS3EV5biLLqc85GpR0o/NIBhdB+moBCSe4JP1pbyiZNQCEn9QboiAooPPtWTfLx5rmDUFXr4sjHpmJDZS19LSxiTxtQKPX1CY8VmKQJKPz1ezdvjieHD8f01atx4YRPUVOfpWmTSTJZSw9Q6Ns0XYvMA4V6rqF8j1Z9cWXvQ/HWsu/xxIJP0gIUQj/TBBS6+iR0WACFbuTAFii8uu1iHTC67Aosiy/GuPL7UOVWa22VvlgARdBu2ckChQIHGqDwKwxQBNk2+I9hL4xGG2yNCbgLqzBHBYWQQCHGLNumByjMibEaj6me9zO1MNsDCkrewuwyZxDaxI7FevcrrHXfazgfwbSnIHhLBSh0PvyS15Dk1lRsWcpBha3SBRTSX3QSKBLnNEDhV7qAQpeoE1OT7J7YpPETFig0CS0FYDbrGGyBwrPt2rIYr58zCLNXbsIJT/2E6loxQTbFriT+1AhHQOxhgcLKjxIPcQ2kPtgChWe7XZs2GLffMHyzZDGu+vILxHVTkojYbe5kpwsodHfzwzztKJ1A4enANjvg/G7D8dyiL/DKkq8D7uoHA4XOhp5KZO4LVd9okwagEOIzAIWnLrEeOL/0MsypnYmnNj6M2vogTAvAdUDhVxBQCLYBQCHEkPAv+ZXOU2X6UQeDf8lWPh9DAfbBZWiGjvgYt2EdFocGCr9s11GkChTi154uTvtHxwrHTF2QTO0U7SXPTZ1d0dI5FKvdD7EmPiHjQBF2upNcZgsUOulHKBoAI6fNXzmoCBJH4i+YDBSCogQK6ZwCFERckQCFlODZjXTUvyULFBZJr7pwXE1otbacYUDHMrx0+vb4evYanPfytMSu2ykDhaY+mURHCRS65NSUgBK2u3fshHv22htvzZ6F//vuW3VfgpBAofZd+n0T4koPUFiNmAT0wRwXFFvPz7Ed9sCJHffEw/Pex/srfrQCpmSBQonHBMBpAAq/bIBCt1u2CRJ65fXBmU1GY1rNVLy46QnU+v5uB21uR63tsAEKFSDkY3+fJBulPVVhgMLWlvozyMGRj2Lsg6tQhGb4BLegAisDgUL2AUQPFPQ1UJNiE1D4bWSg8CDC2yVaXpgMNCTQrWL7oYUzDCvdN7HWnUhcpdQVBBNeP+RjcvG15l0neQ0FNUrh/ZxNYi6P/LWlKAcVlqKAQru5ncW3QUaBQgsJRFxRAoVcJifVRFxRAAVX4lL97NmrJR47YQBemboUf3v3DzX2DACFHBeVGCdsIwQKXUJMfY4HduuJf++6G576/Vc8+POUjAJFKABIE1Do/dgAAFRbyc+ZnffHwW13wl2z38BXq39Nvp8pAoWuvhE6DEChG2VIFih0j47dNm8QTio+F5NqvsSbVS+Acx4IFGJf6mMw7JadsE3ESx/7FQYo9FAgHgv+A+tSd609m4YkvxBl2AfXgiGGCbgFlXydYmf00UhAIayxoO68S/Ep5b7RCt0aChkoPClg4RyM5s7uWBZ/ERvcKUmPUnjwIL8C6xFAYfrZdg2FvFu4PBIjwAVPz6Z9OWW/clARIOYmDxS6ze0Ymdx75+Qk32eTLqCwWJsgxxUJUGiScZs1CpkECu/ckQM74PZD++C+L+bivi/mGuerRwEUus3tyJGOKIFC8nPC1v1w+aAdcc/UH/H8DHHhZ6RAoelfmGQ83UAhtEn4YYzhom6HYo+W/fGfWS9h8rrZwXGFmEaWLqBQbFIACr9SAQrveKeCoTiq6FR8Xv0+Pqp6SxMn0aYFUARtbieumWC0jdQPv8IAhTrdR1fXn3DLNnLyD5SgBfbBtajBJnyK21HNNwox6tZRNDZQuEIbamLs+uqRoxM+oKiGK0BFTEiYfT8Td+ZjzEFb52iUscFYHH8G5e7viEK26yf8P8t9p2ztFmWLQJF1chvhtYUoBxW2ygRQKOcIAEjYRggUcvJtldzLbWYAKKQ2jNCRZqDwykYN7YYr9u6B69+ZgZenLqbrUYl2olGQsWcCKPSAo4alBQq/SRJAkbhu/Qfh9L798Y9JX+GdObOkeIKTXhMwpQsodHfLw410hAMKGz8x5uDankdjYFlP/GPms5hevlDfpgVQ6PaiSH5RtxkoKH8moFBsjP7kOqqtyxn2KNgfBxYchXerx+PLqo+lOCHY+ssaAyiCoUCVLVD4ZQMUAMAZRxlrh31wLTZiJb7Enaip3zU6m4ECmjp1CXPDOe1aCt8IhQuOGhaHXzZAkdijgTnoFDsFpawPFsYfw0Z3tmKbTtmun/D/LAIX/RhdWbopTw3TokS7nDZ/5aAipCIFCjmpl4HCryiBQknYCT9KXJkHCi0kmEYUQgCFF/s/hm+NEwZ1xPnjf8GEP1ZJNmqbcn3zGgUNUAg2tJ+wQKEfATBBglRXCEyf0DLGcP2Ou+LQHr1x5def4stFi0hbsoxMnmlbq7iSBArlXFIjHYSNBZjksXz8o/dJ6F7cDjfMfArzNq5Q20wTUOjqW+1yTQCAHkwUd1ZAEbRbts52/4LDsFf+AXit+mn8UP2N0nYQUJC2iWMIx0KfAoBC8AvahqqjJtrmun5bW6Dwqynrgj1wFdZiHr7Bfait3zU6WaAIq7BAYfPoWL9vam0BtRBblu3jYhmLoVPsTBSzrlgQH4tKvtDoN1mlAhTyCIVOpjUUDSMZIMuzQbnN7zKnHFQEyUXi20ALFD5FChRhpjFFCRS6qVImGIoCKOSkw2KkQwYCAGCM4cGj+mOfXq1x0n8nY8rC9do2bYBCSf7IKVOaJNAEJhEARZh9NRww3Dp0bwxt3xkXfvEBpq5YrvpLE1BY9S9ha+/PDCbBSb1iG2Kko8gpxL+3Oh0t88pw3czHsbx6rZUfT+kCCq1NkkChtUkCKOj+eiUODik4ETvG9sCL1ePwW3xKA0BYAEXQbtnCn+ZEWTBQhNktG4qNGSj8SgYo5IXZrVgv7IZLsRy/4TuMRbz+Q8rk5nbme2h6oGhoR7SRyyig8C/M9tZN5PNY4MLsIMBwWAE6x85FAWuN+bUPoYov116fsNJNdzIBhfLSPOWJ2tjOOO1JAoqcthzloMJSRqBQpiTVv1N/DaMECjfAVogrOqBQEmJDwqcFCp/SBRTapJ6IPd+J4ZkTt8c2bUtx1NPfY/ZKcZ4xVT+TQKFNxm3WfWQQKORrkIcY7t9jGPo0b4VzPnsXf65bHdyHDACFrl9Wow5JAoVu2liYkY6yWBPcvPVZyGcxXPvHY1hbU57ko1+VJtMCFCZ/yQCFXzZAoWur4XfCwTEFZ6NfbHs8U3UfZsenG3fLbvArxZCIiYqzvo4GKEi/EG3oUQbZJhgoZNsgoBBi0zzpqQ3rjyG4AAvxHabgKbj1H5YMFH6le9oTVab2ye7RsXKc1F4UNk960j0yVk6qY6wY3WIXIg9NMK/2ftTwNcr1Cqug0Qn/sW5URjdCYVpD4bcxAUXWwUWy6yJSeW0hykFFgIShqySBQrdbNqOS6CiBQpf4k8l9hECh6Zcp6Y0SKLx6Jfn5GH/qTmhWnI8jn/kOK8qr1ESRuBsstxkFUOg2t6MS2kwBhVenKFaAcXsfhLbFTXDmp+9gccUGbR+EsCIECi0kWHyOyQKF7UhHq/ym+M9WZ2NjvAp/+/MJVMQrkwaKoClTYYHCxo/OlgKKIEgIBRT1irEYTsy/EN2drfBE9V1Y5M4hgSJoczty4zoLoEj8WQsACr9sgcIvW6Ag2wl4dGwHthMG42zMxaf4FS8bn2aeqXUU4jk5TgI+GA0U1MJsecqTHyj8ohJlm+Q5j5WiW96FABjm1j6AWr4hsI5OYYFCLvee8GRaQ0GNTgDBU568n3PacpSDClsZ/tKnDBTKNwYBAJDOUZAg+0sVKLQJu01cVFIYDVCIjYK29ZskARSeTauSQrx26s6oqIrj6Ge+R3lVrRkodAkjCZgaoCBiTxUo9JBAhGVahyLFJV8Dr7xZQSEe2/tgxJiDMz/9H1ZXbiKBoiEJ1/SXbDPYJlWgsAUAsq2kRzoayjoWtsJ/ep2NpdWr8c/ZT6HKrSH7mQpQUP5SBYqg3bLDAIW5v6qtf2F2PivAqfmXoy3riMerb8cKd3EooBDa9vxrgMKvMECh+1qhFiMr/iVbE1AoX1GWm9t1Y3tgAE7ETLyDmfx/GtvMAIVLxB/m0bH+nymgMD3pSfcIVf+7XE6V5bMW6JZ3EVxsxLzaBxHnmxTbICUDFBQ8UCDlxSo/LjfMlKfEQvX6fzlt/spBhY18f721m9sRf+HTBRTa3bJJSNDYgrJNESh0ST01SqABCjou8TgsUMhPeiITd40fMmHRJdH1791blmD8KTvjz1UVOOXFH+t23U4SKNTEOIQfi4Q2WaBQEmuLZDxolKZdSRM8sfehWF9dhXM/fwcVNdUICxRaqCL6Zxp50flLFii08JL0SIcaT4/iDripx5n4Y+MC3DrvOdTUZ806oBDatAAK3eZ2oaAjBFBQ/oyQoIxaqL+3CVsfUHi2hawYp+dfhSasDI/V3Iq1fKXUlnQd/P6kEQgTUCgJvQYo/AoDFEG2NiMgYYHCU292ALbGYfiNv4w5fIJkm1mg8CfFYR8dS62lkBWToMH/pCe/bBdqUypkbdAtbzSqsQrz42Ph8urAOnJf5HLKRvsurR3xx05Nb9JubhcAFA6yK9n0tgqI8rWlKJs+5+xUWKDgtC35dytKoJCnKxFTk1R/mQcK5Rw1rUpqMxKg0MSuS9AGdmiG547fCV/OXYnRb06F6/JGAwrtXhQhgCkTQEElnt3KmuHRvQ7B3PK1GP3Vu6iu3648SqAIN+qQHqBIqU1fn/o16Y4bup+KHzfMwL0LXka8PpulH/2quyZqmzZAoduLgu4DbStAh+TPCAkWQCGPplCLspuwMpyVfy0cOHii5hZscNfRtlTbynFqQKEbtZCTbL+SAQrTo2NFXyrAKD4Y0IcdiR4Yhp/501jIJ0ntZxYoXKEN4o68JVD4k2ugIcGWpzzZrqPQibIpYp3QLTYKm/gCLIg/Drf+qVo66WDCtCBbfvdvaqeLUbcPhWcTBihy2nKU+7wtlS6g0G5uR9zBjBQotOsh9Ml9FEChTWSN06ugt00XUBAJ3l49W2PcEYPw8s+LcNPHv6u2FoljFEBhAwlhbJMFCu+9b4s2eHD3gzB55VJcNelD1NZnZeb9CehrkAmgSG6akHSc9EgH0/rxtENZX1zR5QR8unYyHl3yFuLEdbMBCt0IR7JAEbRbdligaDgXwlYDFH6VsVY4s+A6VPKNeKr2NmxyN2ptg4DCryCgEPzq6iSOeWhb8n5RSKCgY2wACgAAA/qzk9AJu2IKH4el/Ce6rSzci8KLi0rUY0LibL8wmzoOUonTE11j56CCT8ei+DPgxBxjm9EJ+VgHFDp/cn9tgII6D4hA4WTb9CeOuv+Qkb0au8PRKQcVARJ31K4vyyRQKG3YJPcG2zQBhXZzOzIur45kkwmgUJJeiLZCXLSfdACFV/fI/p1wywHb4r5vZuGhb2cp9QU/Sn01OdLFnkmg0CbEVsm9GAtlQ12Lndt0xj27HoBPFs3GvyZ/BjfOibiCr0HCNkKgUM+pbYVq0wIo5HN7NBuEUR2PxNurvsRLKz6U2jT3xf+zDij8SgdQCPGlASjE/jIpTii28sLslmiPM/Kuwxq+HM/F7xTWqKgAIR/7+y3ZKO354pRtNMd+JTOaETSVSQYKqq4OKLxjzhxsx85CGwzAZP4AVvEZYv0MAYXto2Pln71jCii8Rdme5DUFstI1HarU6YvOsdOxnk/BEvclcK6PXVduNUKhgZNEnFwFCBNQ+PumA4qcthzloMJWcrLvV5RAoST3hB+trZj4kW0kCxRyUm9KRKMEihAjHTaxm4DC0/k798IlQ3vjhg9/xfhpC5MGijB7QEQJFPrkXgnLmHg2GNWV7dOxJ/694354ZfavuO/Xb0MDhRyznOTbxJUJoEjHgmgbSDug5W44ue2BeH75B/jfyq/EeExrhiyAQk3U9f5sgEK3F4V5fYXeVnkqVAig8I7bsa44NXY1FvE5eMkdgzivDQQKsd+SjdSe0CfZRnNcVyYn0rq6DbVUGzNQiLFpbDVAkYARFsMgdgGaoyd+5GOwls9TfIeRLVCQaweYlEwTIxW6hdme5HUUNlOdUk2gmzqD0DF2Ita4X2O5+5bQJ1nJjlAA9KZ+1KNy0wUU2fZIWcYjXlORG6nIKSEOGiikv+jUztqeIgUKXaJOQoec3BOJWrqAQpekEtfLZlpOuoBCe4edSKi0QEHE/o99++GEgV1wwZuT8dns5ZkBCiUe4hpIfYgCKMIk43Ich3Xti6sG7IZHZvyAZ2ZOCWiTiMsAFDYgkUwfUgWKUHFpbLyk+Zg2w3BYq73xyNLX8NnaH8kYTPWTBQrdKIMROgxAoXt0bDJA4ZcOKLz3rs7WOMG5HLP4z3jNHQuXu0agCNrczh9DMBSosgUKv5IBikDbAKBILPZlBdiBXYwmaIcf+V2o4EszvheFYsuCgYJSTE6mfUDhV9h1FGHVwtkV7Z2jsNL9CMvjH5I2OqAwAZRcj+pvpoAipy1HOaiwVGigSJxjZB1BUQKFrg1yjYLGTyaAwiLp1e5FQV5TjZ9MAAVRnzGGMSMHYc8ebXDq+O/w05K1QvuRAIVc3ziNCbStYKNL7tXYkwEK7/ft9K0H46ytd8Dtv3yJN+dN118DOa4UgULvx+ZaKCZpA4owIx2AgzPaHYr9mu+Mexc9j+/X/xbYpvEpUWkCCsUmBaAQ49PF2WATtFu2bNvbGYijnIswjX+Dd9ynwHnDJJsogEJNss11/ba2QOFXqkDhFwdHHivGYHY58lGGH/gdqOSrQwOF+NVnBgpxwbVaz2+jW5itU7rWUYRJrF1wtIjtjdbOAVjhvoU18a+UflDH2vIAoAAyN+UpcY5vWXfrt2TloMJCGQEK6ZwCFH5FCRTSl77dSEf9GzmlSJPQZgAoFHCwSJYyARSeTX7MwZNH7YytWpXh+Je+wdxVG0m/QlxJjNIkCxS6O+w2i8NTvbuvAwqv7NJth+Kobtvixskf4dPFc6z7F+buflig0Psh2ooQKFQoYLigw7HYqbQ/7lj0FKZVzNK2ST8lSrKRAMCvdACF6E+uo9pqN8JLAij88j/paVtnCA5mZ+MH/hE+5a+ocQYAheAXtI15lMEMFKJ/O6AQ2rF8dKwOKOj2G3wUsDIMdq4AwPCjeyeq+frQQEEvTg8GCu3deinJ9isoyY5K/j63dg5CC2cvLHVfxjr3R+3oBPWzDE8UQEUJFFkntxFeW4hyUBGgRgMKLSQQQUYJFEriXv9G2RgSWrVNQzKdLqDQrIMw3ZVPFig8NcnPx4vH7Yqygnwc89JXWF5eJdjW2Qf70cejhK5NuDMCFFLsNnf3g4CiTg7+tv2+2Lt9L1z1/Tv4YcWiwP5x6prawFBEQEG2mWag8OJxeB4u73gKtinujlsWPYrZleranmSBQu9H6aYVUATtlh0GKPyyAQrdo2NdDgx29sUwdiK+5K9jovtO4G7ZwtcAF8vMowyyTTBQyLY2QCFPewoLFH7J056oJz0VsRYY7FyJWmzEZPdu1PKNoYFCXCeiAkUiHl89Cjga7MIl2dR5+Wfq2PacEJsUE2MM7Zwj0YzthMXxZ1Du/kra6qZ3mdaMJPuUJ6FuCKDItjUVOWVOOaiwlQ4o/NIBheBHPJc0UOju/JOJu6aOYCMleJkECqkNI3RECRRyPSrRTthK9Q2JY+viIrxy/G4or47j+Fe+RnlV3GdD+wkLFPqE3ZTQQm+bJqBQykIk2jEWw607HoCBLTvg4klvYvq65dr+hQUK5VxSIx2ETVJgQrSZAlB4dQucfFzb6Wx0LGiLfy18GIurG9b2mBZEpwoUWpskgMIvG6Cg9qIAGoBC7K9km2inwWaocwh2Y4fiI/4cpvJPtUBB+oVoYzeKQQOF4F+y1QEF2U5IoBDblW31QOHFVcLaYXvnclRiJX5y70OcV4UGChGMiJEKqV6iXE62DY+O9eRI/y8y8fhYv4LWeTDmoF3sBJSy/lgcfwIb3T+1QCG8G6Z3UUDhlWcKKLJN3nSsKF9binJQEST/0BUFFMo5Kcn3ndMChV/pAgo5+TauhxC/9E3QEQVQaCHBNKIQAVBo65uS6Hqbrs1K8dKxu2HW6nKc8fpEVMfdaIFCl4CaANOQ9KYLKHSb23nvhbE8jNn5UHRt0hwXTHwdCyrWRgIUoQAgTUARZqQjCCi8c8VOEW7odD6axprgnwsfxKqaNdL6Bbot6s5/OoCC7gOkOqo/mwXgNkAhr6MwAYX34944HoOxH97hj2I6n0QCReLPmgVQqBDgHatJtOJfsjUBhQIKIXfLBvx/7sMDhWdXyrpggHMJNmAefnUfhstrQwMFh7p2wOpuvQ8oPMlPPQJoaLABChuRIyYGkJDtGIuhQ+w0FLMeWBQfh0q+IHFOftdtauc9JrexgCI3UrHlKAcVtsokUIQZdYgSKHRTpUwwFAVQyImFxUiHDARU7JkCCu94QLvmeObIofh6wQpc8u4PcDmnr5PSppxsGmySBAqbxeG6a2Bzdz8ZoPB+Ls0vxIO7HInSvEKcN/FVrKgs1wCmpm1DXMkChW6UJ9xIB9GmBZjogELXZtNYKf7e8UIAwL8WPYT18XJjPOKdf42NRT8poAgHCcG2pt2ygdSBwuV101BGsLPQF7vgDX4/5vCfIwGKIFvKvxyTLVD4ZfukpzCb2zVjvbCtcyHWYDqm88fBuRsKKED49GLUTXeijimlso4i7ALsMDAh2LE8dIidhXzWHotrH0YlX5qwS9hrNrXTrReh1lFkCiiyTplaN2F6bSHKQUWQOOhENEqgcANshbik5DmDQKEkxFRCK/chAqDQJfWm2OXk1690AIVnv2e3tnjgoJ0w/rf5+OenvwT7MVzTbAMKbdJMXVNLoPDqtywswcO7HI0aN44Lvn0V62sqjf2yggTj1DBTXJJthCMdJqAwTZlqld8CN3YajQ1uOW5Z/BA2xqtI27BAobvuyQJF0G7ZYYDCryCgEGzlWMDgMAcH40J0w7Z4FXdhEZ9ZZyP5t5u2JB8nDxSif3rkIZXdstMBFF55S7Yt+jjnYAX/ATP5f8E5twYKefTBi1E3akGtLQDUUQp/oi2XmX6mjtMl3YJshxWhU+w8OCjD4tqHUcVXJc7714ro9qAAzEAh2qUXKFj9K6fNXzmoCCHd5naMSqKjBApd4k8m93KySrUptxWclNMAQPcr1DSmRgIKJVGUgYKIPQgo6sqAI/p2wf/tuz0e+uEPPPz9H3o/xDXQbW5HJas6oBBMIgSKhuRZ019/XET/OpY0xUM7H43lleW45PvXsbG2VuhXskARDgDkvoRo02RjMdKRLFB4ap/fDjd0vBCLa5bjzqXjUBWvVWyC+hAGKMx9EMvrzplt/bIBCt2jYymgCNot2+VAjOXhSFyOduiOV3ArltVPQzEBhW7UQk6+/QoDFIFrIwxAoX6dpQYUNmrNBmMrdhqW8S8wj78h+DUBRUPMYoxyfWV0wgAUnuREO1Ge4rSnZKQDCk+MlaBj3igwxLC09mHU8HXK1K641N+gRdm2QOH9SwYoctpylIMKS1kBhfItQAAApHNRAIU2YbeJKzgpTxooNMk4vaYDtK3fJEqg0CVvJGBKyY3LcM7grXDJkD74x+c/Yfzv89IGFHpI8IUj+7ZK7kX/mQAK7TXw2fQqa437dzoKM9YvxzWT30J1PG7tL1mg0I6GJD3SQcdjsjHuJWEBFF797gVdcXX78zGjcg7uX/4Ean1DE7q9KKzgioIOyZ8REiyAQh5NSQUoyLaVY9U2jxXiaFyNpmiNl3Ez1vBl9e0RffLi1h6rCX4Y+AgLFFTdZIGCkmmUwn/c1hmK7uwYLOLvYZH7vlRHbp8YqWBQy+TRCd/de50cMKuF2bq66ZYJKPx9c1hTtM87HxzVWBYfB9f3VC2gYbqTaQ2F14cwIxR1P4M8FwQUWbemIjf9KWPKQUWAvG3WAaQMFNrN7Yx/9Q226QIKXVJPJU0aoBCNpFiTBAr5SU9k4q7xY0q+ogAKNTFW/Vy/2wAcv213XPz+d/hs7lIyngZ/Or/U9ZLj8/thRluyXyFGaTIJFF77/Zt3xF2DD8e3K+fin7+8hzjx/8cuMQ5Oxm2AItxIh2STJFDIv0+mNuX6fYu2xqVtz8aPG3/BoyvrpqHYAIVulCFZoGg4F8JWAxRCfy2AQgUI+djfb9GmgDXBMbgOBSjEy/gP1mON6jfgWIw3qG5DLdXGDBR+pbpbNhVzsrtld4ztj85sJOa5r2GZ+4WmT8RIBWvwZ1xLQTxO1VusHCOSa/+7rEwnw7qF3LqfXXDEWCu0yTsPtXwdVtQ+BpdXCfVtFmVHMeXJf5zTlqEcVNgqCqCQy4x37uVkXKpD2ojJnclG9FP/ZgAK5Rw18iK1GQlQaGK3ueObSaAAr9t1+85hO2Hvbu1x5ttf46dlq8lroAMKKvYogCKpJFpKMkkbC6Dwyoa07o7/2+5gvL/4d9z1+yfgnNtBgsVdfTUuuS/hroXNiInONlmg0NX33ndoMgCj2pyGz8sn4tlVrxqvn+iHjlPoQxqAgromJqAI2i07DFD4pduLogTNcQxugItajMd/sAnloYBCvTOvt1XiThwHA0WgbQBQiO1LPpIECu+j6OQcgnZsb8xxn8cq9wepDWKkgon+EuXyaIUPKCilso5CV5asaGBTgYJaJxJz2qFl3tmo4UuxuvZpcF4rQJPDWcpAkeqUp2x9+lPdY15ZhK/G7nF0igQqHnzwQXTv3h1FRUUYMmQIvvvuO6P9K6+8gj59+qCoqAgDBgzAu+++K5znnOPGG29Ehw4dUFxcjGHDhmHmzJmkr6qqKmy//fZgjGHq1Knhg/cPXVndZpKSaqQRKOREnZiapPrTJ6JRAoU2kTWOhkBvmy6gkBI8OZkWA7NPRIOAwrPNjzkYN3I3bNOqGU58/XPMXrNBiof2K8QRYhpTJEChJM9QbGxGOBK2SvJcVz6sfR9c029/vDR/Mh79c2JgXDZAoZ5TYw9zLWyAQmdDJ9jmvlD1dTa7l+6M01och/c2fII3172v7acNUOj2ojCvr9DbBu2WHQYohLYT8dLHdWWMtpHaa4Y2OAo3YCPW4TXcimpUaiHBrzBAoXvSk9XjYANsk90tu65OakDhgoMxhq7OsWjFdsZs9wmsc6eRdUxJtr+cmvJk2vTNO/4rAIVg44Mmx+mM5nmno4rPxura/4LzuDCtK2gNhcPNQFH3M5RzQDigyGnLUcah4qWXXsLll1+Of/zjH5g8eTK22247jBgxAsuXLyftv/nmG5xwwgk466yzMGXKFBx++OE4/PDDMW3atITN7bffjvvuuw9jx47FpEmT0KRJE4wYMQKVlZWKv6uvvhodO3ZMvSO+/9fa3bKJBDlSoNCuh/AnVjo/xH/8KIFCSXoh2gpx0X4yARQ2C5nVNRxicmOKnXOGJvl5ePawvdCiqADHv/4plm2oFOonCxTaO+wWCTIFVVEChRyznOR7OrrrIJzfew88MusrvDJ/ijaudAFFciMd8jUJvhb0XhJ0DFSbQUAB1CXqw5vujaOaHYJX1r2FjzZ8YfBDxyvYGIAiaLfsMEAh9rc+BgNQ6B4dmwpQeGWt0AlH4jqsxiK8iTtRixoSKNQkm/bvly1Q+BXVbtl0n+yBwntnzEE351Q0Zf3xpzsO6xNP1ZLAgtFAQS3OBhpggkrc/Qm3UB4AFOlUWKAwjcLkOb3QPO8UVPJfsS4+Hqz+l5eECAkovJ/l93QDBUOWLdjOranImDIOFXfffTfOOeccnHHGGejXrx/Gjh2LkpISPPHEE6T9vffeiwMOOABXXXUV+vbti3//+98YPHgwHnjgAQAA5xxjxozB3/72Nxx22GEYOHAgnnnmGSxevBhvvPGG4Ou9997Dhx9+iDvvvDO1ToQFivpzWqAQfEv+TH/lowAKOak3JaJRAkWIkQ6b2KMEChs/rYoL8cIR+6A67uKktz7D+qoatS3PX5qAQp/cq7HbJNFRAoW//pk9d8XxXXfEHb9/iI+WTFfDihAorMArwDYsUOjqG6HD99kc3vQgjCjdF8+seQETN34fCBR+pQMoxPh0cRJtaoCCjC9xDOFYaDsAKAS/aLBph544FFdhMWbgPdwPF3HJr1cnGChkWxugkKc92QKFGKNsm3mg8Pw6LIaeztlownpgVvwhlPP5og0LBgqXcdQyV0m4HWkdhQwUYe6mpwM0dOsngoACgHbheQwMTZxt0TJ2PCrc77HGfRuMExDRiECRU3ile4bPa6+9huHDh6NVq1bGmTsTJ07EvvvuiyZNmqBp06bYc889sWnTJuu4MwoV1dXV+PHHHzFs2LCGBh0Hw4YNw8SJE8k6EydOFOwBYMSIEQn7OXPmYOnSpYJNs2bNMGTIEMHnsmXLcM455+DZZ59FSUlJYKxVVVVYv3698AIgfPukDSiU5J7wo0vUSeiQk3siUUsXUOiSVOKbN0qgkBNi4z4YOqCwiD0TQOGd61xaiucP2wfz15XjrHe+QGWtq9jYjTog0DZdQKH2XUwyxbh0bRJxWQJFnRxcus1+GNFhW/xj2lv4dtVsgx+b5B6KbbqAIqU1HUkChWIjAQBjDCc2PwZDS4Zg3Kon8NOmX4xAobRlXC8j11FttTtrE79DNkARtLmdcC8mUcZIGyHh52KZ36Yz+uEgXIrZ+AGf4FHE662CgMIvW6DwKyxQCHWZzjY6oPDacVg+ejrnowjtMCf+ADZ5m7sJX5ti4k3dvfcDhX9htl+NsTCbggmvnLIR15KI07qoPjpgKHN2REvnSGxwP8c696OsAgpvfUdOdsrEDJ+KigrsvvvuuO2227TtTpw4EQcccACGDx+O7777Dt9//z1Gjx4Nx7FHhYxCxcqVKxGPx9GuXTuhvF27dli6dClZZ+nSpUZ7791kwznH6aefjvPPPx877rijVay33HILmjVrlnh16dJFOK8FCr+iBApdG+QaBY2fTACFxsZ4Z9u0NiRKoNDUJ2NPM1B4tn1aNceTB++J75eswGUfT4TLOZGwG5JxHVD4FCVQJHXn3mJ6j/zO4ODv/UZiSKseuGbqa5i2bpHVugMdUAhtJgUmRJsW11S3F0Xyi7olG83okcMcnNXiVAwsGoD7V4/DH1UzkwYKLSQkCRRBu2WHAQq/goDCL5vdsntiBwzHKPyGz/AVnjMk0b4YFH/BQKGMPDTybtnGckaXq7FyxFgResQuQAylmBN/AJVYnTinW0vhlcWll6eYkEzTO2ZnepSCggm5XLtuxLdLtg6a/CMwzWJ7oKVzENa472Ft/POsAYqsUyNNf5JvXFdViU/t8pTuGT4AcMopp+DGG29Ubtr7ddlll+Hiiy/Gtddei2233RbbbLMNjj32WBQWFpqvp0+b5dOf7r//fmzYsAHXXXeddZ3rrrsO69atS7wWLKjb2KjukbJS8ix8K4jnFKDwK0qgkBM8A3REChTyOQIoFHCwSOaiAAo5LupOe8I2CajyEpddOrbH/fvvhrdmzsNNX06G8GSeZIEiTHIfIolOGSg0wCRed7M/T3lODP8ZcCS2adoOl015CbPLVyQNFDYjHUH9ywRQ6OqbFkQHAYX3nsfyMKrleeiR3x33rLof82sWkP5SAQq/0gEUYnySbaIdn41UZgKKxJ9OC6DwyrbGHtgTp2Mq3sWPeENJov2yBQqqThigUM4x2tYWKMLKBii89zxWih6x0QAczKt9ANV8PQkU/lEKAIn1E/7kPJFwg1k96Yk6TlWpAIW/jAImAELfvL62dIajubMPVrivYb37nc+uoY78nmmgcJCbBgUAXbp0EW5e33LLLYpNJmb42Gj58uWYNGkS2rZti6FDh6Jdu3bYa6+98NVXX1n7ADIMFa1bt0YsFsOyZcuE8mXLlqF9+/Zknfbt2xvtvXeTzYQJEzBx4kQUFhYiLy8PvXv3BgDsuOOOOO2008h2CwsL0bRpU+ElKFmg0EICEUSUQKEk7vVv5BoFTULbWEChWQdhupsbBVAoSaDJjwYoPI3s3RX/2WtnPP7zdDw4+TfSr99fykAhxW6TRNvAXhRA4S8riuXjjgHHoV1RU1wy9QUs2bTWMi7CJgWgENq0gLR0AYXWJgAoPD8FLB+XtByNdnltcdeqMVhas5xsi5reZgMU6qiKGleDLe3XDxRBu2VHCRRe2UAcgCE4Bt/iJUzDh0ZbG6BIZrdspR9JAgWlsKMUxniI95jTHN3yRiOOTZhf+xDifJOSZOsWLVPSLcxW7NI4Hco03cl2hEI3rQsAuUbEezHG0NY5HM3YrlgS/y/K3Z8bHSiyTrwRXgAWLFgg3LymbnxnYoaPjWbPng0AuOmmm3DOOefg/fffx+DBg7Hffvtpn65KKaOfd0FBAXbYYQd88skniTLXdfHJJ59g1113Jevsuuuugj0AfPTRRwn7Hj16oH379oLN+vXrMWnSpITNfffdh59++glTp07F1KlTEwtWXnrpJfznP/8J1wnf0FVGgEKXqFN/kaMECk3ya7Pomf420fnzuUkXUOiSXxKYpPqGBDkKoPDbnNZ/a1y640Dc8u0UvPz7bG1cpsQsSqDQbW5nFVcagMLzU5ZfhHsGnogCJ4ZLfvovVldttI4rXUARxo8OKMi4UgAKkz9/4l7sFOOylpeghJXgrtX3YHXtWtGGGI2yg4Rg26DdssMAhdC2518DFH4FAQXpVzoejCMwEAfiCzyFmfjaaCsn56J/O6Cg4k8WKARfaZ72JLavvnsx5rPW6Jo3GjVYhYXxcXB5dQNMoGGEwi8vqbZZmC3/nE7ZjE7Ix9QaET8w+XfJNgGFdxxjDjrEjkdTtj0Wxp9EuTujUYGC+drakiXfuA4zrSjTcuv/+J533nk444wzMGjQINxzzz3YZptttNOuKGUcIi+//HI8+uijePrpp/H7779j1KhRqKiowBlnnAEAOPXUUwVau+SSS/D+++/jrrvuwvTp03HTTTfhhx9+wOjRowHULSq89NJL8X//939466238Msvv+DUU09Fx44dcfjhhwMAunbtiv79+ydeW2+9NQCgV69e6Ny5c+qdihIo5OTbKrkn/ChxBSflRliQ2jBOO9IBhT+sCIFCW9+URBvn+dN+UgEK7/iKnQbipL5b4arPvsGEeQvVZJxIzKzWciSMxTqZBApdffr3wd4flcC3KizFvdufhE3xGlz+8/Mor6k2+IHWjxoXHU8mgCIdj691CQAwgYmnMqcprmh1GVzu4u41d2N9vKI+JtWfERJ4CNsAoBD7q7H12QTtlk2OYkj+7dZByMccDAy74WRsgz0xAQ9hLiZrbWXppjKZgCIdm9vJ7WdqHQV9jivnClgHdIydjyq+EEviTyDOa0Il3Z4oeMjEtCfd6IS/T/KxMnIRMDphAxTezzEWQxfnNDRhW2GeOw4VfE6jAUW2ibks8petMjHDx0YdOnQAAPTr108o79u3L+bPn2/tJ+NQcdxxx+HOO+/EjTfeiO233x5Tp07F+++/nxiqmT9/PpYsWZKwHzp0KJ5//nk88sgj2G677TB+/Hi88cYb6N+/f8Lm6quvxkUXXYRzzz0XO+20E8rLy/H++++jqKgocx2Rk3oZKARb0SajQKEk7CY/cuJuiD0KoJATC4uRjqwBCjk5shnpCAkUdT84+L89hmC/bp1x/kdfYMoy/zoBqS4Rhzm5F+vYJKuZBApdv6ySaOKPdseilrh74IlYvGkNrpn2IirjcSIukH7CjkwothEAhW4vimSBQr4WLZ3WuKLlFVgfX4/71tyDSrdS8ZcKUPhlAxRBu2WHAQq/goBCiFNXh7DlcLAXzkE37ICPMAaL8JtU13/3XvZnBgqxneSBorEWZpM2TC0rdrqjfewsbOR/YHn8v+A+qtUl3Z78C7MB/VOfUgUKE0x456ljZaRCAgpqYbYtUHhrKByWh+7OOShCZ8yJP4RNfHGjAIU/tpzMysQMHxt1794dHTt2xIwZM4TyP/74A926dbP2wzgnt+ja4rV+/Xo0a9YMqz/tiaalsbpCE1BYQUIYWzHxyyRQKAkxCQB0rJkECm1Sb4hdTl79NlEChTYZF5Jo0DZE7Jtq4jjpnY/w55p1ePWwA9GrWXOtbapAoU1kKZsIgUKb3JN+Gt5/Wjcfl//8HHZq0Qv/7ncsYojRbSUJFKYRE51NpoECgLKOIgxQ+G3m1czDnWtuQ/e8Hrio2aXIQ4EUg1i37hwUP0o/JfhJBSgA359KC6AI2tzObh2EeFxXJifRQBw1eBe3YwVmYST+jtboIdjaAgUA7ToKW6AQbRsHKOQF1/44dU962uD+hOXxp9DEGYJmsaPgsjqo8I9QAL5kl0vHhpELU7IblAibYEI+H/RzLXOVTfuo6VwyRFDTmuQ1FC7fhD/jY1CD9egTuwpFrE2kQAEANXwTPuDnY926deqa1Yjk5XVrPu3VkNdF0W55HC32mWXd95deegmnnXYaxo0bh5133hljxozByy+/jOnTp6Ndu3Y49dRT0alTp8RC72+++QZ77bUXbr31VowcORIvvvgibr75ZkyePDlxQ3716tWYP38+Fi9enLDZZptt0L59+8SIxpgxY/CPf/wDjz/+OLbffns8/fTTuPPOOzFt2jT06tXLqq9ZuYYmK5VpoKDa+qsAhU9/OaAgYs82oABnKM7Lw+Mj9kObkmKc8u5HWFpRIcaeRUAhxGXon9qm3l+yQAEAA5t2w7/7HYuJq2fi9j/eRt3TtOS+BP8+ZAoohDYjAgqxn+Y4u+V3w+iml2BWzZ94bP0jiPO4edQhDUAh+IsYKPxKFSgAgCEfw3EFmqMT3sctWItFkQKFX9kAFJB9+OpSQOGCo9gZgBaxY1Hhfov1cXFTL0/pBgqTbEYnrBZko2HjPpNSAQoHDAWsCbaKXYQYivBHfAxq+VrFBsgcUPjPZYV4I7xCKBMzfN566y0MGjQII0eOBAAcf/zxGDRoEMaOHZuwufTSS3Hdddfhsssuw3bbbYdPPvkEH330kTVQALmRCq0SIxWf+IjW9/9euxdFskChS/zJ5F5OVk1+NH6hJqJJA4UmGaeSey1QGPxkFCh0SRc5ZUpKbmz8pAEo/OcWb6jAkW+9i7KCfLx88EFoVlhotZYjSqDQXgPDZ2PaEyEVoPD7+2DZL/i/Ga/huE67YlT34fC+5sL6CYonLFDoNrej/KQLKHSjDKRNvb+plT9h7Ib7sUvhbji5yZnwPySyIT46XqotE1Do9qIQRi+8+BLHII/ryhhtI7UnlAUci2U0UPhtN6Ec7+BfqEYFRuIfaII2Uh2u1NGto7DZLVs37amxgUIABl9dHVD4F2avj3+OdfG3UBo7CKWxfQCoe1F4P/vf5Z+pY5MSd/wtssOgBdmJfkl7UHii9qLI407SQOG9MzBU8dWYEb8DMRShX+xK5LPSyICihm/Ce9kyUjGhEUYq9rUfqfgrK6vgMasVBiioetkAFH7TvxpQGGLPdqAQYk8BKDgHOpQ2wTMH7o+lGzfinI8+wabquN5PAFCIcekTUMUmC4DCJnbZ3/C2A3FRzwPw0qKJeH7h10IsYfyQ7xbXNFNA4VemgIJzhu0Kt8eppWfjm6ov8drGl+Ddj0oHUPi1OQIFB1CEUozAtWCI4X3cgk1Yl1ag8CvTQJHoF6PtTUAhx2i6oy8vzC6N7Ykmzv4oj7+LjfGJRqBIp4JGJqj45WPdWgp5DYUHEgVwkA8HebwhTUsFKACgmLXENrFLUINyTI/fD87rNl/bYkYoPHFW9wsY1Yv4W725Kis/76xTWKCQy6jpS4qtmPhlBCjkpNKQ/BpHXiSbSIBCE7sJOrIFKOSF2UboMACFp97NW+CJ4cPw84qVuPTzzxGPi8kdHY8au1XiriTPUG20bar+0gUUyT0ZCTi64y44rcteeGTex3h7yY/h2rQACp0NvTmduS9UfaONxXS0ZIHCKx9SOBTHlpyEjyvfxweV76QNKIJ2yw4DFH4FAYVf4RZhezY0UOhsS9ACI3A9alCJD3ErqlEhjC4osYQAiig3tzNP/ZHbJ0YqEl8vhtEJ3918v0pj+6PE2R3r469jY3yKdjQiE9OedKKgwwQU8qLsGhYX7OU1FPLL60PiRUCB95IXZTdhHdA3djEqsQy/uw/AdauVukB6nvKUe6TslqccVATJ93cibUAhJ+rE1CTVn5SoZQAolHMEUGiTeuNoCPS26QIKKcGTk2kxMPtENAqgUJJTi7gGt2mP+/fZBx/Pn4+/T/wG/lmMUQKFtr4F7GUSKNRzDeWnd9kHh7ffGffMfhufr/o1JJjI1yQ4dnovCbovVJupAoXqB4qNJxNQeNq7aH+MLD4cb20ajy+rJihAIfZBFyfRpgYoyPgSxxCOxTJG2gh/OrlYZgIK1UYPFDpbDqAp2mF/XIdyrMQnuBO1qBLq2AKFX6kChV+pTnsS2zGMVBiAQo7HfyefMYbmsUNR4uyANfEXsMn9TdgxG4geKExlQUDhgqNW8mELFAC0QAFAAQqvvIx1Q9/YRSjnczDDfQTgceE8g+pTKDcAhQP9uayR2wivLUQ5qLBUpEChgwPiSzvrgEJJkCHaErFHARRWezco9fXJTZRAoVtUPKxLN9yy2+544Y8ZGDNliiYeIvQIgUKOWU6qbeISbFIECqBur5uLuh+EvVv1x39mjsfkdbMs/RjiShNQ6OoboSNFoFBsDEDh1T+w8HDsXbg/Xtr4DH6s/lbwp91Zm/hitQGKoN2ywwCFXza7ZQM6m+SAwjvfAl0wDNdgNebic9yLGtSKdSPeLVsLDrpyRpeL07c0YMH0kCGvOZATdm9qEGMOmseOQTHrh1W1T6PSnWW1jiLdMgEF2R+iTw4YCnkMwpOeuAEiUgQK770ZeqOPcwHW4lf8wZ8Cq/8PwTT2ifIkgMLhDbHmtHkrBxUBYtwAFH5FCBTa3bLJuLw6eptIgSLESIdN7FEChZUfydZmh+FkgMKzPWarrXHV4J1w309T8N/pv0t9oOIJjl0HFGTsKQKF1ciE1o9Ncg/FlrsMDnNwTa8jMahpT/x9xvOYXr4wNFAkOwWLqkvVp9dgSDYW09FSAQrRX8M5xhiOKDoRO+bvimc2PoLfan42tiXumQHSNhmg8MsGKOR1FHZrJuRjfXJuAxSeWqMX9sblWIJf8Q0eRm39WRNQKKMZlkBBqbH2oqDOUQm4SQ4YYiwPbWKnoJD1wPL4Y6jiCwIhIp2QQY2oaGFJWpQtb9onAIVpVCJNQOFd3lasP7ZxzsYK/h3+5C8k/mOkGyhy2nKUg4oAbajgGHX7Mqxa433bCn8VxTIZKPyKEijkpJ6y0SSp1DdllEAhJ8TGfTB0QGERe0aAQokHehvd9ffHHgAU3rnzBwzEaX23xY3ffo335s6pP6HGbpO4m4AiqTv3EQCF3o8SluAn38nDjVudgB4l7XDdjGcwb9Ny+7gsrqluL4rkF3VLNhajR+ZdqSUbwp8WEjjgMAcnlZyFfnkD8fjG+/BnzUzaNgRQiPFJtomYfDZSWWMARZAtlVN5iXd79MfuGI35mIQf8CTcei8mYLEFCqo93TqKdAMFlWD711HopjvJCTigbnLnyeEMjOWjTewMFLB2WFo7DtW8YVfhTI1ShFk/YVqU7UlelO2fypVJoPCO27GdsDU7BYv5p5jDX88IUGTdmgreCK8tRDmoCNCylXG8/lk5Rly2AGvW+/5c2wCFfI5KxhP+5OSeSCCjBAqNjfHOdrYAhaY+GXuWAYXuDrspEQUc/G2nXXFQ95647MsJmLR0sRJ7JECh6Z/diIIhiU4RKEx+imMF+M82p6BlfhmunfEUllWu0/fP85cmoNDVpxd1SzZJAoXWxmJqkh8oPDnIxxklF6JbrBce2XgXFtbOU+JqaJv26wcK3V4UdGLu2XjHanyJvsgxSD5Iv8qxah0GKHRrIzpjJ+yMc/AnJuAnvCz5l2BALme0nRhD9EAhx6ADCr9dEFDIG9x5ymPFaBc7FzGUYXHtw6jhq0mgSAdkhF0/AYBcQ+Ede/3yr6HIg5P2NRQ6oPDeOzp7ohc7BvP5u5jnvp92oMhpy1EOKgLUu0s+PhzTGfOW1uCAyxdgXXk8fUCR+OvN9DaeaSgw8eqoSWvagEI+R4286GwNSVMUQCEDjexXiCsJqMooUGgSUQYHt++2D3Zu1wHnffoBfl+9KtR0oSiAQvfEoXAjHcFJuS1QeCqLNcGt25yOGBxc+8cTWFNdoW8rQqDQ2lhMR0sWKHT1KaDwymIoxNkll6G10w7jNt2B5fGGu8VBu2WHBYqgze3IUQzJv906CPmYJ2Wr1qEhoSfbE4NwMn7H2/iV/89oa9otu6G9xgUKMamGWuazo+7oA/oRCgDK42PzWSk65o0CQwxLah9GLV8v2jcCUPjXUMh9UaY78eimPOlGIro5B6AbG4lZ/BUscj9PK1A4yLJk02XRv7YQZdXnnK0a2LsIH97TFbMWVeOgKxZgfUWcTsY9pQso5C99g40WKIi4ogAKZSTC4EcLFP6wIgQK3W7Zxrv7ElCIwUvJoXH6C/S2Acl9YSyGB/faH93KmuHMT97DgvL1Vsm4DexFARTKuSRGOkgbCzBpld8Mt25zJsprK3HDzKdQUVsdyo8nHVDYxB4WKFIa6UgCKCh/nm0hinFe8ZUoRgnGbboNa93VgUAh+tPY+mxsdstO+PNsJP926yDE4zC2/iRTtaEhwVMfdgD64XD8jBfxJ59A2v4VdssW10aodbXTg6Q7+p685BtoeCqS97NfeawZOuaNgosqLKodizjfqF6kJERNd/L3Se6f1xfZxutLNgKFd9ybHYnObF/8zp/BUvc7BSj8CgMUOW05yn3eQeIM4AyDti7C+3d2we/zq3Hw1QtRvtEVvxV0iTrxhRwpUGiSX5tFz40OFLrklwQmqb4hEYoCKPQjAIZk3JDEBQFF3QFQml+Ax/Y+EMV5eThzwrtYXbkpNFDoNreziosEpmB/NkBhAzg6KLMBk46FrXHz1mdicdUq3DTraVS7NaHAxAQUyS3qFt/DAoXuupugwwQUur0oPNtSpynOK74WLlyM23QbKvgGI1Do9qKg4rMBiqDN7ezWQYjHdWVyEq231fvXA4V/YXZ/HIXe2B8/4knM45NIH2I7st/0AIVOYYBCrSOWyQuzqbZjvs9X91Qn/8+FrA065Y1CLdZgcfwRuLwqpVEKHUzYAoUMSF6fshEoHNQ9gKEPOxEd2C6Yxh/FCv6TkCTKm9vZAkXWjVTU53WRvrYQZdXnnNVygR37FOO927vi59lVOPS6BajYVP+n1QYo5OTbKrmX/GQCKKQ2jNChAwp/6BEChba+KYk2Jpm0n8YCijBPm/Jfg9bFJXhin5HYUFONcz57DxU1NXT9JIHCpn/akRebJDqDQBHkp1dJB/yz12n4vXw+bpn9IuI8HglQ6PaiCAUdIYFC9+jYZIDCO9fCaYVzC69BOd+AxyrvRCXfREOCBVCoACEfE/El/Et+fW0HAYUYpz1QqAm6ZyP68Et+0hMYMAgnoyuG4js8jMX8Z8EH4P+TnxpQmKQbpaD7pXlnmtELAig8aRdlJ5JWRpb7fy5kHdEpdj6q+GIsiT8BzmsD+0spaHTCf6wDCkBdF5KtQOGVM+agP85EawzAVP4gVvM/pLbENm2AIqctR7nPO0guhMR7SL9ivHNrF/wwYxMO/9sCbNrEE+cS9v5jwA4olISd8AOpLAKg0EICMZVLZyso24BCTqRsRjqyBCh014Bzhq5lzfD43iMxe/0aXPzVB6iqdY3x1dUX/WYSKLTJvXFqmCkuyTZpMGEYUNYDN/Q8Gd+u+x33znsdnHNjch8FUOj2ojDCcQpA4ZctUHhxtnU64uzCq7CCL8ZT1WNQw6sF+3QABRmfd5yISbW1AQr1br7eFpKtDVA0/Nml7/pz5mBHnI12GIhJuBcr+YyGuky0DQsU5pil0QtGl1M+KKCgbJTRCk0S7k/EEwk4l5Na4vulXkVON3SMnY1N/E8siT8DTj2JwKB0AIV/YTbZnywEiro2GRyWh0HsAjRHL/zIx2A9nyfVgRiDBihy2vKU++xt5fvC261/Cf53S1dM/G0TjrhxASqrpNthmQAK3VQp6tsiy4BCm9QbYs8WoNDe3bdZ92EAwkwChae+LdrgwT0OwKTli3DDdxMQN4CqDihs+pcJoLABgAZbqU7SYNJQNqRZX1zR9Vh8uPp7PL74Xa0f4+Z0EQKFbpQhWaBoOEfbCv30AYXnp7PTA6cVXIF57h94oeZB1Lp1O/bqgELwl+gTfVxXxmibREwNtsHrIIi2LYBCttUBhV9BQOEdOywPQzAaLdAL3+AurOVzQwMFpXSvoyBtfHVNaym8KU/UQmb/Ogq/dEBBAUaJszU6xE7HBv4zlsVfBCcXvImyXT9hs4ZCuwdFlgOF5zfG8rEDuxhN0AHf87uwkS8R2rQBCr8t8W3YaGJu9K8tRTmosBGRjO85oAne+r+u+PKXjTjmXwtQXUX81kQIFEpCbEiwtEDhU5RAoZ1uZExspbqETRRAodst2w4SlNCtnjZlAxTez7u064LbdxmG/82biTumfk33zwAU2mtgiMtqw78UgUILL0mDiRrPvi0H47yOh+HVFZ/jleWfWgGFbnM7qk0dUAg2aQAKoZ9pAgrdXhR+Pz2dvjg5/2L87k7Ga7WPobb+JAUUQbtlhwEKv5JbB0EDhcnWBBTyOoogoPB+jrEC7IJLUYr2+Bp3oJwvCQQKv7JxLwoZKAB1ypO8jkK3MNtvo1OZMxDtYydiHf8WK9w3tWBhgglq/YTYZ3WEwt+XvxpQ+JXPSrATuxyFaIpv+e2o5CvFGCyBIqctRzmoCBIBFN5fzn23L8VrN3XFx5MrcPzNC1FTy/WJP5ncy8kq1aaU7CYLFA3fFIE2JkhosEWgbbqAQkmaKaDQJV3UddcBhd8kQqBQEmKr5N4+9gO6bIXrB+2Bp/74CU9Mnyy1qfFvugaGz8a0a3NSyX0GgcIGcA5rsztOaDcMTy55Fx+smpQyUOjqkzYWo0c2QKHb3M68YFtvawMUnn0fZzCOyTsfP7pf4L34f+G6XGurAwohPgugCNrczu9XtdEDhc7WBmqCgIKqw8GRz4qxC7sSBSjDN/x2bOKrFKAg22vkvSjkc7p4dZvBxXxJNKBfmG2SZ9fM2RltnaOwxp2A1e6Hip1ujYlpupMfKIJGWyig8L+8WLMFKGQYKGJlGMKugoM8TOS3o6r+cb1hgCLrFmq7LPrXFqKs+pyzWhJQeH99R+xQivF/74J3vyvHybcuRG2cm4FCk4STUz6zDCjkJz2ZEnfjKIEm9kwChZoYh/BDJl86v9T1kv36/TCjLR2PGntQEn3y1gNxXt8dccfP3+CNOdPr62v8G65BJoAiuScjSccZAAqv7KS2IzCy1VA8sGg8vln3syYZN/fF/3OqQKEbZUgWKHR7UdDXXxdng40c13ax3XFI3mn4Ov4ePnPfFPzZAEXQbtlhgEJoW7FRRwyCbMkYlNGE+nIDUCi2vkS9kJVhV3YVAIZv+B2o4uuFhLexHx3rxSmf0/5MbAYny2ZhNnWsU4vYXmjlHIiV7jtYE/9CiUtW0PoJoAEoAJCLsr34KKAQ+qgBCtm+MYDCsy1hLbAruwa1qMJEfjtq3QoyJtpHTluScp93kFxogcIrHzmkDC/d0AVvfLMep92+EPE49/3lVpNWI1DoknoqcdcAhWgkxppRoND4EZITOSE2JZBZBhTywmwjdGQRUHi+L+k/BEf16Ie//fAJPl80V7XRthn82WQSKNRzaluh2gwBFJzXPWbxvA5HYPdm2+H2Bc/hp/I/6HhMAB0RUPiVLqDQboRHzfiUbL01FLs4I7Bf7Gh8HH8Z39Z+JNom6kI4FsuY1kbui906CPlYBYogWzIGS6DwywQU3nExa4ld2NWoxUZ8x+9ETf0eDI0NFHWQoNZNFihiQoItJrbJAoWnVs4BaOHsjeXueKx1v9Pa6YBCKAsACv/oRB7X75BtAgp/HxsTKLzyUtYWQ9nV2ITVmIi7EOdVwnnaR/0xz7JVFbwRXluIclBhK+NUIIbDdm2K56/pile+XIezxiysG+bPBFBoknIaABJ/7Y2xA74korGBQkrw5GTaFHtjA4UNJATZkjZpAIo6OfjH4H2wV/vuuHzSe5i6ammgH1P/KFAKE1e6gCIde0AE2TjMwSWdTsSAJr1x8/wn8cfGBWI8xO+77Me4qDtNQKHYuKpNg61cR7W1AYqg3bK9432cIzDUORD/c5/C1Pg3decS7SjhWQFF4k9bIwKFbm2ECSiS2S27CWuHIexKVGAFfuT3orY+qWtMoGi4Bvoy72dqLwrtOgkJKNIhzoBWzuFoyoZgWfx5lLu/KDZBU578u2TL8k93kmVaRwFkP1B45c3RGUNxJdZjIb7FveC8xuCj/pjKO3LabJWDiiD558P5/3PIibrLcNTuzfDMlV3w30/X4rz7FyXmD2thwa8IgUKb9BpHQyDaErFHARRh9m7QAYUp9kwChXbKjjGJVq9BoK2hfzE4uGPIAejbvC0u+OZtzFq/Om1AYQU4nq1hmpDeHxTb5MCEsLHwk+/k4bqup6NrYQf8c94jmF+5jKxL1afXYEg2RgCQ/RDXwgIotJBAJvUa2xBAIYrhAOckbMf2wGvuw5jhTlHaliFDBgohPjkGyYdfQZCQrK0tUIj+7YCCaqeUdcFO7DKswxxM4Q+htn4PhnRtbqfvl+adaUYvqGQc4voJ09ORvHJPyYxSUPEwxtA2dhyasP5YGn8KG92Zij1Vv66v4rHNzt/GtRN/MaDw4mzFemNXXIaVmIHv8DBcHg8ECsfXdk6bt3JQYasAoKg7Bo7fqzkev7QznvxoDUaPXdQwEkGOAOj8UMmhV0eyyQRQKEkvRFsiVhOYRAoUSn0i+Q0DJjqg8NtECBRhkmilf/W/D0WxPNy/68FoW9QE5339BpZu2hAJUOj9BF8D05OyogAKL2kuZEW4sds5aJHXFDfNG4vlVWvVuNIEFHo/xLVIE1DYwEuDLe3XDxTqk55iOMw5F1uzQXjJvQfz+PQG20Sc3jERn2cjxyCdp8pMkBDGVrWhIcGvQPgggEL3pKfmrDcGsdFYiWmYxh9HHHEyVhNABI1SiO3rgUL2ZwMUpqcjUUpl2hN1DRiLoX3sNBSzXlgcfxQb3XnqqIoFUPglwMRmDBTecRvWD7tgNBbjR0zGE+DcNQJF1sm7WRzlawtRVn7eWSdLoPB06j4tMW50Z4x7bzUufXQxeFz1YwUUclJvmiqTZUAhJ8Sm2LMOKJR4oLcxQELCNIuAwitrVlCEh4ceDgaG879+A+uqK62AqbGBwsZPqLhCAIVX1sQpwU1dz0Mei+GmBQ9hXW25tr7pCUupAoXWxmKtQ7JAIS/MDgMUnq3DYjjauQhd2Fb4b/x2LOFz0gIUQn91dVK0DQMUQY+ONT0BKujRsa3YAAxk52EJJmE6/y9cwx4M2tELRpfT15MGCioBp8rlNRT+0QrTOopMyWF56BA7CwWsA5bEx6K6fg8GckqXBijIRdl88wcKz6YjG4ydcS7m4kv8hBfAOTcCRS7Z3DKU+5yDZAMUftWXnbV/Kzw4qhMe+N9KXPXkYjGpSxdQ6JJU4lvBZp5/lEChJL/UlKkwsWcZUMgJsWLr71/EQOG9tysuxdihR2BV1UZc9O2b2FRbY+xfmMSdtIkAKGxAImFrASa63bJbxJrjH11GYWO8Ev9aMBYb45WKH9ciLmqxc7qAItyoQ7BtEFCI/jS29efzWQGOY1eiFTriOfdWrOJLFKAQ/HkxSP7t1kGIx3VlcsIfbBsEFEK8lkDhl7yOQgcUXnlbZ0f0ZadhIf8Us/hrUr/kmMMBRbKPjk2MTkgJuSx5LwqABopUF2cbxQrQIXYu8tAMi2sfRlX9HgxAMFD4ZXpkrBdzNgCFX6kChffeje2GwTgVf+B9/IbX6+MTfck/Z4U4i/61hSjrPuuslQkoEn+5xaTu/BFtcN85nXDPWyvwt/8uAec8WqDQ2BghgRh50dpmEig09ckkWgMUfmUdUKQ5+Q0LFN57j7KWuH/I4Zi+bgWu/uGdukciEzGL113vT9tWEmCSCaAI40cHFJ46FLTG3zuPwtKaVbhl0aOodmuSBoqURjqSBArdXhQUJNgAhW63bKoPLgcKWTFOdK5GCcrwrHsz1vFVAlAE7ZadbUChW5htHM2QEvWwu2V3dvbEVuxYzOXvYK77nibm1IBChAS1LvVzna15YXbQCEUmgcKLK8ZK0CFvFBgKsLT2YdTydaGmPMnrQLw4sxEo/Ltli21pygOAwju3FdsfA3EMpuE1zOAfCOfln3Pa/JX7vIPkIimg8I4vHNkGd53RCbe+ugz/emmp3k+iPU1S7leEQKGAg1Viq489SqDQ7ZZtM0pjtag7k0AhxW6TRNt8NpSfgS074O6dDsE3y+fhXz991JBAZhAoQo0opAQmRJspAoW/fo+iTri+07mYWTkPdy95CvHEXEdzXGGBQnfdzRvY0b+LdefMtlSsRkiwAIrEn0ofLJSwpjiRXQ8G4Hl+MyrqN9YKAgqhv7KNdCzGaQYKylYHFKJt/TmL3bIhn2O0rQwUQnu+hdndnAPQjY3En/wVLHQ/l2JOHSga2lTran8mHrnqjU7IT0iidnLO1PQnGXwAwGFlaJ83Chy1WFo7FnFeYQQKalM7L+YtDSi84344FH0xEpPxLObgc6UOQ5Y9UtZthNcWohxU2CoJoPDKLj20LW4+uSP+9dJS3DJ+GWkj+ql/o2wMCa1sGwlQaNZBGPfBoBLthK1UP4NAoQcc6G0MseuAQjSySKbTBBS6ze0oP7u17YGbth+ONxf8ivunf6Xpn72/VIFCB2XpBpNUgMJ771vcC1d2OAuTK37F2GUvoDaxa3SIuFIECt1eFOSoQ5qAQvfoWFug8I6bspY4id2ASlTgRX4rNrkbRRupPaEs4FgsCwYK2dYEFMraCAug0C3MtgGKxDniSU892RHoyPbBH/xZLOc/SP1NDSh005zkMu9n6klPnuQnJPllAol0QAb1dKeGEYsWaJd3PuLYgOXxR+HySu0IhRdPDij85Q62xwnohX3wLR7FfHyv2Oa0ZSgHFUHiTJO4178HAAVQ94V/9RHtcNNxHfD3FxbjrjeWKTYpA4U0mmKEjiwDCm19QxJGPrY2YUP7yQhQaBJQMbnX+DH8XkUJFF79gztvi8v77YUn/vwOz8/50dgvqwQ5xPqRTACFDeDItmGBwnsf3GRbjG53Cj7dMAnPrXrDuN+E7pzxdzlFoGg4R9uS1yIFoBD8watPHwNAC9YBJ7DrsQZLMR53oJpX1/tX/Sa3sJoGCpOteRRDatuwW7Yatx1QkO1pHh3LGbAVOwFt2c743X0UK/kvynn/cVigcBl9zvSkJ8D8lCQgumlPFFDIymft0CbvXNTwZVhZ+yQ4ryGBIhEXz/41FMkChdBP6V33lCcGhiE4E12xM77CA1iMaYJtViWbXl4X5WsLUVZ9zlkvOfm2Gi1oOP7bMR1w/ZHtcc1zi3Dfu8t8f+lFv5kACi0kmKYoWYBJFEChJF3klCkpubEBkywBCt01sEnc6fUjtK0RqurfT+mxE07ruRPu+O1TvLdoOtkvq+Se+IyTSqIzCBS6RJ2MywAUnnYr2wlntj4G/1s7AW+s/SBpoNBNW6LjEm2SBQrdXhSmRdgmoAjaLVt8qlOdbRt0w7G4BosxC2/iXtTU78Hgl906CNlGDxQ6W3oUQ07+68sNQKHYBgCFX2F3ywZzsA07Ay2wLX53H8ZaPrM+Pto+DFA09EcPFLLSARTpkAko5EXlBU5ntMo7E1V8LlbVPgcuTWeUF2V7MW9uQKHGRZf7N7arAwcHu+MCdMC2+Ax3Yzn+yCWZW5hyn7etbIBCSdih2PzzuI648tB2uPyphXj4g+WqX7+iBAo5IbbwIyfDVOzZAhTau/s26z5M0BgBUGgTUMrGcIc8qH9e25f02RMHd9oWN/70LiaumBsYe6pAobuGyY900PGEBQrdbtnUNfXqj2i2N45ucRBeXP02Pl7/VUaAQrcXhXl9hd7WBiiCdssOAxRCfBDj68S2wZG4HLMxFe9hLDh3fW2KfpIBCljYpgIUlP8wQKFbmG27uR1YDH2c81CK7vjNvR8VfAERWXigkCHCH68/Kfef8091sgGKdIuCHfppVlxYQ1Ho9ELzvFNRxX/H+vgrcDhX1lDkgEIFCu89hjzsjUvREj3wCW7HaswHk+LLafNVDiqCxFnagAIuA2MMt5zYGRcf1BYXPTEfj09YIXwrKgmxIcHKNqDQTjcyJrZSXcImCqDQ7ZZtBwlK6FkHFNprQAETHPx9wAgMad0dV05+A9PWLlX7FyFQhBvp0Mejs0kWKHT1j2o+EiOa7oXHV76AbyvqppGZpkN5ShdQ6PaioK+XZJsEUJB9SBxDOBbLGGnTk22PkbgQv+FrfIJnwMG1QCG0rdioSbsOSJTkXKhjDxS2m9vR06OSAwpZMVaAvs6FKEJb/OqOwSa+LLAO5VdemC3bmIBCiEdKaHWizicLHfQUMhoovGP/Gooipy+axY7HJncy1sXfAuPIaqDwqzGBwrPNQyH2x5UoQ1tMwN1woY46NqY4Z+BuhC/ye3bzVA4qbJUGoPDKGGO46+SuGLV/W5z/2Fw888XK+nqSbxIAEn/p9TbeKQMkyP2KAiiUpJkCCl3iSEFVlgGFkshaJL9hoCqTQCF/Nnkshtu2PwS9S9vgkh/GY17F6tSS+wwCRSqAQ9/dp6+JDVDw+v/fp7U6BkOb7IgHlj+JqRW/hfADxcZTuoBCuxEekanbAIVuLwqXyDGDgKKuDaAvG4r9cRYm4318jfGiX59tECQkCxS6xdYyUPgVZrdsuZ2wQCG2K55zwZHHitEvdjHy0AS/xu9BFV8TOO1J8MFUn7rpTvKTngLXISSSXn9iSiTHEQKFJ3/sxbFBaB47AhXuV1gf/yCrgSJxLguAwjsuQAmG41rshdFwkJfRUamcskc5qAgSh++vNvWtICerhA852a0fsbj3tK44c+/WOGvcHDz/1SrRJlmg0CXjZIIM2tanbAMKNTEm/Ei25hETnV/qesnxEW0ZgEKNR409qcQ9zUDh9asoVoh7djgKLQpKMPr7l7GicoM5ubdYd6DGBQub4OQ+VaBQ4rFY10IBhSeGGM5vcxoGFPfFmBXjMLNyjjZ2E1AoNq5q02Ar11FtbYAiaLfssEChjlpI8fn64F+YvT2GYXecgIl4FT/iXbW/cp3EsSnxDrYNAxSBtgRQ2O5FIQOFGKN59IIzIJ+VoU/sEgDA7/ExqOEbjNOebIHCHwP16Fi/bJ70lM5kM1mg0MFQk9hQNIsdhPXuR9gQ/zwBCboF2kBdoq47t6UAhRdXMZqiLXpnH1BwFv1rC1EOKmxFJvdSoibcFqNt/H4ch+Hhs7rj5N1b44yxs/HqpNWSP6pNBNqkDBQaP2QiY3NXPkqg0Pkhk1/ar03/Gg0olOQZqo22zeDPRr4mzQuKcd8OxyIOjot/fAUbatSdo2k/wdfAtA4lpdGQDACFrr4pLofHcHHrc9CtoCvuWPEAFlQvUWxSBQotJJCjBBrbEEAh9lOyTbTjs5HKZKAQ4pP9AtgZh2JHHILP8Ax+wxfaaVAUJISxVW1oSCDjDQAKv8IChdieBA66ctZQXshaok/sEtSiAjPi9yPON5FAkWiD0T7lMg8oPNkARZh1FGGSUHkqllyu/Gy5S7a3hqK5MwzNnH2x2n0T691vSSCSgYI691cHCr9sgKLB1v6zzOmvrxxU2CgDQOH9HWNw8Og5PXDMkFY4+aFZeOuHNWr72QYUckJsSn4NIxy6NhsbKGwgIciWtIkAKLT1Df2jQMlv2764Ke4bfByWV27AFVNeRWW8RmwzQqCwuk4BtpkACsWm/rMpYIW4os0FaBVridtX3IsVtSsjBYqgtsIChW4vCvK/dSJO75iIz7ORY0hYMOyBE9Ef++IDjMUs/KCFBL9sFnVrYzAARZjdshOxMJ0tDRR0bOGBwnsvYu2wdewSVGEFZsYfgsurlXo6ePDetVOfyCsKYXM7k1JNOHXt62Kkpjx58m9q58XmTXlq5RyCps5QrIi/hA3uVOMIhVCWhUDhVxigYNzO1t+Kvx+pfdJpltsIry1EOagIUgaBwktgYszBk+f1wmGDW+D4B2bivZ/WqEkqtU5DapOEBM8ky4DCZiFzwlYDFKKRRTKdJqDQrgGwSu6J0C2uQZRAQfnpWdoadw86Br+vX4K///ImauuzUZsnI0UJFClNwbKYQhcGKLzjJk4JrmpzMfJZPm5bcS/WxdcbN6fTAYVf6QAKsU3ab1ig0D06NjmgqBMDw744G72xM97FvViAX0lIUNcMgDz22wYBhV9hgCJdu2WL/QoPFF6bJawzescuRAXmYpb7KDiPq3aMBopEXKAXZpseHZvJUYp0AEXQxnaJpJgxtHWOQSkbhKXxZ7DRnS7EGgVQ+JU4lwRQyJvbpRMo/KKeSpXT5q8cVNhKBws2Ngag8GzyHAfPjuqNg7ZvjmPu/QMfTVtrBApt0ktBgg4oiNhNYBIpUCj1ieQ3DJhkGVCESaJ1QCH0L4NA4b0PaN4ZNw88Al+v/BO3TX8PbpyuY/ZDxJ4UmBBtWlxT3eZ2yS/qlmw0sNc81gzXtL4M1W41bl85BhXuRuN1p4AiHCQE2+o2tzPtTZEKUAj+PBvJv25htQMHw3EhOqEv3sYdWIZZkQCFvI7CCj6SAIqghdmpAIXXVinrhZ7OedjAf8dc92lw7gpAAY1v05OeTIuyPemAIpWF2MkAhVcvDnq3b39M1MZ2MRZDx9gpaMK2wcL4Y6h059TbRgMUyrksBAo5buark0s2twzlPucgcQYTUChPejJCh1dHsql/z89z8Nz5W2O/bZvhqHtn4LPf12UWKCz8yAmxaZpP1gGFEg9UG6mf5mlMwbZRAoVNoq3rX7KJ+26tt8INfQ/G24t/wrjZnyUNFHrAsfFDxB4hUGhtAmCvTV5rXNH6UqyuXY0xq+5HlVtlbCssUOj2okgWKHR7UVBwZQMUik2ib4o7EgrykI+DcDlaoQvewi1YjYX1NmagEOIMAArRtv6cBVDoFmanY7fsdACFV97U6YeuzulYy3/EQvdlcM6Ne1EEPelJp6ARinQ+2Yk6R41QmOStnwBEoPBi9X6OsTx0jp2FYtYV8+IPo8pdlAOKRJkmVmShvLwuytcWoqz8vLNSyQKFnNSbklaXoTDfwYsX9sEeWzfF4WN+x1d/rFds/PUbGyiU5DeJRd2NBhRKwq7vXyobszUaUGj6J7YVEDvRvwPbD8To3vvh6Xnf4OWF3zUaUNj48RQlUJj8dM7vhMtaX4x5NfPx8JpxqKkf7sk0UAjXIk1AoQKEfEzEl/Av+hXKDMf5KMIhuAZN0BJv4masxXJjXbEsGCiUxdaG3bLVuO2BItnN7XRAESR/vebOYHRyTsBq/iWW8reV2LRrKQIWZgPiWgpHE1xYoNAtxJbPK/FLm9rJcfvXUNgAhfeexwrRNXYeCtAac+MPoIovzwGFLlbf+XCfek5/VeWgIkguyyxQEAltUb6DVy7cBjv3LMWhY37DpD/LyfpU8pR1QKGpT8auAQqhe9kGFDbJvRS7TeKeLUChnKv3c2KXXXFil11w78wP8fGyafbXIEKgUBNsfaIdBVB46pXfCxe1uADTqn7DE2ufRLw+4zbviK3/XbYBCjlW46hDGoBC8JcmoPBUiFIciusQQz7exs0ox1rB1q8goBDilNs2AIViGwAUYkypAQWloFEK2balMxTtnCOw0v0QK+IfJ8qDgMI/fUiWzeNjbeSHCFOfPVvqmNrUjorVH58NUHjv+ShGr9iFiKEEs+L3o5qvyQGFASiyTl5eF+VrC1EOKpKQFij8ShIo6uoCJYUxvH5xX2zXtRQjx/yKH2aXi3VMkCC1TccVnPhFARQy0FBAodvczgaqrBZ1ZxIopNhtkmibz8aUuGcaKLzjC3rthxHtBuDf09/Ed6tmG/zA6Cds/9IFFOl4fK2NDbU53baF/XFO87MxqfI7vLjhRcT9YCKtyzCOOqQJKHR7URinJhmAQrcXhSmZ1wEF1XYxmuNQXI8aVOEd3IJKVBj86YEizG7Zsv8goBDrcNHWKw+5W7bgi4nHpmlPsk+XAa1j+6GVMwLL3Tew2v1a8U/97IcJ+a4/kLlpT5RsgMIfq3ZRNmehgMJbQ5HHytA7dhEAYFb8ftTwDVkBFEL/LIHCr80eKHLKqHJQYatEIiMem58OVf9GQYcBKDw1KcjDW5f0Rd8OJRg55lf8NL/uizMsUCgjEQYw0QKFP/QsAwp9PIaYKUiQbE1rMKIECt3mdsZrYOyfpm3Bhvbjt2VwcN02h2CnFj1x/a8v47f1i4m4EOgn1MhECDBJFSh0m9sJNhafTUNcou1ORTvjlKan4JONn+Dtirfq49L1geofbUteixSBQrcXRbJAIS/MNgGFDhJK0RYH4zqUYzXew+2oQWUgUPgVBihsN7ejRzPCAYUYo3guVaCoG3VoqNvGGYnmzu5YFn8J69zJ9EgFa1jgDNQl6P5YsxkoTDt8e0CR+DkEUHhlBawlto5dglpsxJ/xB1DLNzU6UMh7UdgAhby5nRV86GJVLOvKsirZ5Cz61xairPqcs1K+X4iUgUKT/JK/cPVlpQX5eOvibdGjdREOvOcXTFu4scEmSqDQJb8kMEn1DclO1gCFJgEVk3uNH/LzE+s0NlBoR15sknsDeMVYHv7V72j0bNIOV017HvMrVvlsQPrJBFDoRgfI5D5CoNBNW/Le9yrZC0eUHoW3K97EJxWfaPrgi90CKHR7UZie6pQKUAj+0gAUfukgwStviS44ENdgFebhQ9yDOGqNQBH0OFhOLOoNs1u23I4tUIh1zaMXqQCF0gYD2jpHo4wNxtL4s9jo/h4IFIAeGHTrKNIlav2ECShk+ddQpAoU3nsRa4utYxejCisxK/4g4rx6swUKeQTCBBRUWU6bv3Kft6UiAQqpDc+meXE+3rm0Pzq1KMSBY37GjKUb9UDhjzlCoNDWNyXRxrvNtJ9sBgrdNbAZCaDXj9C2jQUUOpsipxC39z8RzfNLcMW0Z7Gyar0WKEzXIJNAodvcjvpsogIKz+bAkoOwf/EIvFj+X3y76Vuhrl/pAgrdXhT09Kr6c4ljCMdiGdPayPHZLayWj6kRBKAdemM4rsRi/IZP8SBcuEpiLvoLBopAWwIoot4tW3yXY9C8C183nk+GdrGTUML6YGn8CVS6sxNx+tdQUPtR6BZmZ2KUQjc64cVK2VCxphMogLqku4R1xtax0ajAfMxyH4HLa3NAIfhjafkdyCn7lYOKAHEXKlAIBmKCnG6g8I5bNsnHO5cMQOuyfBxwz8+YtXwTmdwn2tcAhaBsAwo5CbMZ6bCBqiwBCm0CStloktaw/cs0UHjvTfOLcWf/UxDnHFdOew4bajcl5YfqXxRAoatPQkcagaKukOHoJsdiaOHueKr8MfxU9ZMauwYo/EoXUOj2okgWKOSF2akABTS2ndAf++JizMUkfI3HIa9rCIKEMLbUmolkgEILDrpyRpeHBQp5EXadbwdtYqeikHXF0vijqOSLBKCQpzvJSbpfmUgeTdOd/HtQeNKt98gEUNQdA2WsJ7Z2RmEdn45Z7pPg3A0ECr82Z6DIOoVdZJ2O1xaiHFTYSgAAKRkkph0p9VIACqAuUWhTVoD3Lt4OTYvyMOLenzBn5abQQKFN6g2xG++mRwgUumRcTFrluPTJoQ4oBJMsAwrtNTAm5bCwsfFjvgbtCpvhzv6nYGX1Blz32wuoiteEBgobwNHZ0nf3IdRPFijURF1/vWw2p1MfHevg5NLTMSB/ezy64UHMrJlhBRTanbWJbDkdQCH6q6+TAlD4ZQMUpjUY3bETdse5mIEJ+AEvEjZ6oAizW3YiFqazpYHCr8YEikT8PhsXHA4rQJvYWchjrbGidhyq+HLEwVHD4kpd3ToKSqkmlaYRCv+xaVO7RCwZAgqvvLnTD1s7Z2MV/xGz3f+Cc24ECu3oRQaAQrgOXt2IgIKBBqmcNj/loMJGIYFCSYgNiZEWKPzN+9pq17QOLArzHBxw70+Yv7pSaD9VoNBON/L7sRnhkNrMFqDQQwIRs2namGQb5hpkEijU5BmqjU1yb7HuQPUHdC9pg1v6nYSZ5UvwzxnjEedxo5+wQKGzoR/9SveFij1VoFD9INhG8hdjMZxVdj565PXGQxvGYEHtvJSBQrcXhUt8xdsAhTpqwRSbRHyyX8kHVZYsUHjaCnthCE7BL3gbP+MtLST4FQYoktktOxF/BJvbke9Efe06C6cQbfLOAUMJVtc8ghq+BrXgiDP1ylFAke5RC5spT0Dwzt5hRygcHh4ovPdWzmD0ZqdiGf8S8/lrUj0xJupcpoBCPhclUGSdeCO8thDloCJImQCKhm+JQBsKEjo2L8T7l2wHADjw/qlYvLZKa5twFyVQ6BI0Cqp0QOE3yTKgUONRY7dJotU2Nf7JNvX9iwIo1HMN5duWdcE/+xyHSWv/wF2z3ra6ux8lUOjqG6Ejw0DhnctnBTiv7GK0ddrhgQ13YXl8qWaUgG4rLFDo9qJIFiiUhD8FoEBIWy/h7IcDsT2OxI94EX/gExIodE96stpfgtG2FFAEPenJFihsZQMUlG3dU6Hqjhlrgpb55wBgWF/zGBxegRh34IDeLM5TOoFCns4k9knd1E6WvLFdWKCQy/zvNo+Nbefshp7sOCzkH2Ch+159vQYlCxR+ZQIo/AoCCjEWsQ5lm/xvQ05/JeWgwlZZAhSeOjcrxvsXbY/quIuDHpiKpetqRFufjKMEmtgzCRRqYhzCD5n40X5t+tdoQKEkz1BttG3q+9fYQOFp52Zb4+peh+O9FZPx2IKPLP0EJ/dRAIViY/HZpAMovPrFrBgXlF6BEtYE92+4A2vd1dZtNbSp+gXCAYXgLxGnd8yEY8FGjoHwFwYSwth6ZQNxFPpgBCbiCczBxKSBQrcw27Rbtlw3is3tSFhgurrqJnfy5nYx1hzN888BxyZU1DwJ8EphUbasdM6bp2CCesKT7ilPqa6hoMrCAIV33MkZhm7sEMzhr2Gx+5nWdyIhtwAK5VyagUKxlWL2f8qqTbBttoi7LPLXlqJs+6yzT/5FNkSC7ClKoPDsu7cqxvujt8e6TbUY+eAUrFhfE+hHTGzp2E3QkS1AIT/pibSxgIQwtlEChba+oX8UKAX689ukCSg8P/u3HoRR3Q7AC4u/xPglX2cEcFR/dF2qfrJAofejhKUFCr8oSChzmuLC0qvA4eLB8jtQHi/X2ib8SAuzzXFpbIW4IJTJQCH482wk/3aLsFUFAQVl67dhYNgJJ6MnhuIrPIxFmCr6SyNQ+JXsbtlkfxhdJ2jakz/WIKCQ2/WvS4ix1miedw5cvgbra58C59XaUYp0yDQ6UdcnMW7dE6kAdWpWpqc8AfRTnrqxQ9GJDcNM/l8scyflgCKnzV65z9tWFFBoknLzU6IQaGMDFF7bPVs3wXujB2FleTUOfngKVlfUpA8oNEkrfSvTPvHLFqDQPvbUmLTqr0G2AYVV4q71E3wNTAvbPZuj2++O4zrsgYfnv4ePV061j8sCKHSb2yW/qFuySRIotDauamOChBZOK1xYejU28A0Yu/EubHKrtLY2QKHbi4K6FjZAoduLgvy9kG2kY7EsGChkW9U/B4ODoTgXHbEdvsC9WIYZdec0QOFXGKAIetKTDijE9iTYYHS57TqKMOfkR8f6Veh0RIu8M1HLF2Jt7bNg9WukTNOedGUmma4JQAOFTn6gSIxWRDDliSxnDL3ZsWjPdsV0/jhW8p+2SKBgUnlOm69yUBEkzpAsUCjnCKDQJr3G0RAIttu0KcU7FwzG4nVVOHTcVKzbVBsJUNg8GUm3uR2VeFiBSYRAoU/uqXiCY9cBhWCSZUCh90O0pfFzTpfhGNF6MO6Y8yq+WzszOK40AYWuPj1lSrKx+GwyCRRewt7W6YhRTa7E0vhiPL5xDGpc32ikZ5smoFABQj5W4zMBRfAi7AYFAQUMtqZRDCAPe+AitEIvfIY7sRpztba6R8fKQCHWCQcUpn6ke2G2zoYCCqpP3l3/YqcHWuadgSo+E6vjL4D55rw1JlDoRimABqDw4qHgInEuQ0BRVxdgzEEfdjpaY3tMcx/Gaj59swUKGGyzSl5eF+VrC1EOKmzVWEChJL0Qbeu1bYdS/O/8wZizciMOf2QKNlTWkmCSbUBh5UeJh74GQv0sAQq17/ok0wZMdAk8aZMFQFEnB5f3OBw7N9sG//rzv/htwwJ97BYjJlEChY0fnW2qQOGVdXZ64pySyzE7/geeq3wYLneVWI2jDmkACsFfGoDCLxug0D0VigYU0Y+DAuyFy1GGdvgUt2EdX6q1tQGKoEfHNuZu2f5pTzZA4cmfoPsTRC9RL3G2Qau8k7HR/Qmr3VfBybtC4URNd/LHW9cfrpTJsVEb29msnYgCKBLtsBgGOOehObbCT+69WMfnpBUo/GpMoJDP+Y9zyeaWodznHFY6oPCbRAkUPj/bdSrDW+cNxu/LKnD041NRUV0r2lvE/pcECiVhN3w2OqAQbDTxNBZQaPpnBKUkwCQTQOH97CAPN/Q6Hr1LOuFvM5/CvI0rkvKjAwqyfxEAhe66m3fE1v8u63bL7hXri9OKR+OX2h/xcuWT4JwbgUK3F0WyQKHbiyL5RdiyTfJA4ZcOEvJYMfbCVShAKT7DLdjIV1kDBdW3MEAR9klP6QAKXdvez/LCbFnCXf8EWAxEq9ixKHcnYq37TugRCb90MCE/4ckrM+3q7cWYrUCRaI/lYztnNErRGVPcu1HOFynQ4FcYoFDBpP44BaCgYxGPbYEi6+StlY3ytYUoKz/vrJL/F8KUlOsSWmoaRQaAwjs3uEszvHHWYExZuB7HPjkVG6tdsb7hrrwWKGxi9yvLgEK5o00BhU1yL8Vuk7hnC1Ao55IY6SBtQoBJASvAP3ufilb5TXH9zMexvHpt2oAiuUXd4nsmgEJeM2NaPK0DCs+2f95gHFd4DibVfoa3q14SzoUBCr/SBRS6R8cmAxSwsKX9aSChPvsrYk2xN64BAHyG21DFN1gBRTK7ZUOyjRootOsnAoDCf9dfftqTwxlKnSFo4RyG9e4ErI1/ovTXBjSCRifkMt36CWpRdrYChVeex4qwA7sMhWiBH/id2MhXCEDhSO2mEyj8sgGKhjL6OIwtgzjqkdPmqxxU2CpVoNDYkEm5BigEGcBkSPfmeO2sQZg0by1OfGYqqmpcbexWQKHpO3lXXgMUQveSuAaNDhTSNbBJok39s0l+owQKG8DRXcMw/SvLK8bNW50FhzHcMPMJrK+tiAQodJvbJQ9VtG1YoGg4R9vKce2UvwcOyz8Zn9X+DxOq/6eNywQUur0o6Dv/jLQJAxR+2QCFDkhSAQqvrIS1wl64FtUoxxe4AzV8k+LDr7/ibtna6U4SaJh2nzbtmN08tjeaO8Oxxv0f1rvfJMqDgMJmulNdfxqO/esnqFj/SkDhxZnPmmBndiViKMD3/A5U8rVSHc+X1zfpWOg/fY4CCq2tFLNYRh//ZUcoPHEg2jUVjd3h6JTVn3tWiENJKDMCFPI5AiiUEQ1D0rNbj1Z4+fTB+HLWapzy3FRUVRNfmBEChW63bNKPBijE4KXEzziNCXrbCIFCt7md8RoY+6dpW7Ch/WQCKOz8MLQqaIr/9D4b62orcOOfT6EyXm2M3bg5XZqAQre5He2HthXj0vWB6h9tS7XlusAe+QdgWN7heLf2RUyq/VS7F0WyQKHbi4L8b2gBFEGQEMbWryCg0NmWsfbYA1ejHMvwNe5BnFcLQCGvo7ABinTtlm2rsEDhj1Nuy/axrH41dw5AU2cPrIqPR7k7JTBWXf+U2CzWUHg//xWBwvNdyJphZ3YVXNTiO34HanmF0O7mBBTqb09Om7NyUGEpKqnLOqCQEqx9erfCC6duj4//WIkzX/oJtXFXrWdIWrMNKKwSbiUplOoK/QtONrMNKLSgZJPch1g/kimg8N47F7XBv3qeiXmVS/Gfuc+htv5Rlbo7/2Q8FtPQogQK3eZ25vUVelvdbtmcA8PzjsKusWF4teYJ/ByfpAUK8lokjiEci2VMbyPFlwpQIKStaqMHCtNeFM1ZNwzFFViDWZiEB+DyuvVntkDhV7JAQcl2lMIfq24EQy7TLcyWk3VAP+rgf1RqS+dwlLIdsCL+HDa6vzdMr5JeQX2t64sKQ1SM1I7ejQUUfjFdHa9cSvQZGEpYG+zMrkI11uM7fjdcXinGkYVAQfdZb+svyyWbW4Zyn7OFqKRVCxRCRdomCqDw6g/bqi2eOXEQ3vltOc59+RfEXW4ECrm+zd4Nxp26swworNZyJIzFOjYjAfR0L9q2sYDCBgAabKU6SYOJGs/WJV3wt+6nYWr5TNyz4GXE6zPYVIFCV5+EjgiBQoEEA/CagAKoS+oOzTsN2zm74MWaBzHT/YUECt3mduanOknx+W1kv1BtIJWZICG5p0R5x+qIQcI2YHM7F0ArtjWG4BIsw8+YjMcQrz8b1W7ZiT+fChTIMdtBg/xOAYUpyad2zDZtcMeYg9ax41HC+mF5/ElUurO0tn4pU7EsNrXzx5ctQKGAQwig8NSUdcLO7AqUYxG+4/fC5TX1dUTbbAEKtc96W6osa8QZIl2kbfh/pNODDz6I7t27o6ioCEOGDMF3331ntH/llVfQp08fFBUVYcCAAXj33XeF86+99hqGDx+OVq1agTGGqVOn6i8P5zjwwAPBGMMbb7wRKu6s/LyzSaGBQnrSkxE6dEDhb18DFJQfHRCM7NcWTx63HV6bthQXvvYLXJcnDRRKQkVOmZKSMBswsbkGEQCF7hrYJO7kddIkrWH7FyVQaNtKGkz08Qwq2xpXdj0Bn62ZgseX/C+RPGcSKNREXR+76UlNik2KQKFri9ozw+UMDnNwTN756O30x7M192C+OzM0UKijFkyxSbQp+5V8UGUmSLBb1C0n556NChTyOgob+GjHBmBHnIcFmIhf+HNw6z9ECiiyYbdsedqTycb7WV6Ybdo4DtBPeyLv0LMY2sRORSHrjqXxx1DFFxh9y9IBhS7GzQkoPNuWrAeGsMuwGjPxA38IcOOKbUMd0U8qQOFXQz/oY7JvBlvFhuWmQYXRSy+9hMsvvxz/+Mc/MHnyZGy33XYYMWIEli9fTtp/8803OOGEE3DWWWdhypQpOPzww3H44Ydj2rRpCZuKigrsvvvuuO222wLbHzNmDBhL7hPLQYWt0gQU2lEHIUE221Ln5GRYjv3wAR0w7uiBeH7KIlz25q/gnDcaUOiScTEeOS594qcDCsEky4BCew2MSTksbPQJfJRAESaePZptj1GdjsAbK7/A+BUTNNOFINTPJFCofhBs46o2SuwpAgW1uV2M5eGEvEvRgXXD0zW3Y5m7gLZNtOPzJ5VRQKEk8xJQ+BUGEhoTKORzHZ1dsB07HXPwMWbw1+mRj5BA4VemF2brbKmF2bLkJz2FAYrEOZaPdrEzUcDaYmntOFTzZaRd2BEKefpTGKBIvLIIKPySF2W3Zn2xM7sYy/ATJuMx4UtMfhysDihE/xpbIgam1BGPw9oqNvpfnUYT59G/AGD9+vXCq6qqiozv7rvvxjnnnIMzzjgD/fr1w9ixY1FSUoInnniCtL/33ntxwAEH4KqrrkLfvn3x73//G4MHD8YDDzyQsDnllFNw4403YtiwYcZrM3XqVNx1113atoKUgwob/QWBgkqij92uEx46YiCe/GE+rn7nN3gbGGmBwn8Jsgwo9JBAxGyaNibZZgtQqMkzVBuL5D4wdtJG7ku4NpMBnINa7oYT2g7H08vexYerv6XjsZiGZlzUHSFQaCGBAN5kgQIAXDAUsEKcmn8VmrHWeLL2Vqxyl4u2apNpAwodFCQDFCBsdUAh2taf0wAFaSsl6N3Y3ujLjsUfeBOz+QdSDHZA4ZcONtINFNSCbKGMWJgty3YdhUkOK0K72LmIoQxLa8eilq8RzgctyPYAQl7fkcwaigZbKOf871EDhby5nZz4d8B29aNm3+AnPAfOeSigCLT1xZJOoPBLsWENx7lkE+jSpQuaNWuWeN1yyy2KTXV1NX788Uch+XccB8OGDcPEiRNJvxMnTlRgYcSIEVp7nTZu3IgTTzwRDz74INq3bx+qrqe8pGptiaIAwJMOKPzVGxkovOOTBndBdZzjkjd/QUHMwb/271f3H58CCl3iSC7qphPAbAYKNR7Jv797YZLyLAEKmycjqefUtoLatLGxAZPj24zA2tpyPLT4FZTFSrFL0wFGoJDr01OmJBvjiILsR7VNF1DYtNVgS/t1fV/rRWiCM/KuxSM1N+Gp2ltwVuwmlLHmYlKfAAj5mGjTs5FjMNlAtUm0bQEUciJuAgp5YbYJKBRbKUH3jrdiI1HNK/Abfx55KEZntkcooAj7pKd0AIXctg4ogqY8+WVaR2FSjDVB+7zzsaT2PiypfRgd8y5CjJUZR2/Cjp5srkDhxduF7YpavglT8CQK0QTb4qisBwrt3hRM9Zc14kzITSJpD8CCBQvQtGnTRHFhYaFiunLlSsTjcbRr104ob9euHaZPn066X7p0KWm/dOnSUGFedtllGDp0KA477LBQ9fzKys87q+T75SPXV5iAQpeMU7/MEQCFp9N36IbbD9oW9389G/+ZMCNpoFATY8KPZEvF02Cj86smylEAhdVIgJI8Q7XRtknYZBlQJOeHuE4Bti5nYIzh3A5HYpemA3HnwmcwrXwWGYOufqAN8dno/SihRwoUut2y6bjq3pugGU7Pux41qMYz8Vuwsf4xlf72TUCh24uCbFO2kY7FstSBwq8goDDaaoDCs+nDjkEX7I2f+RNYwn8QfRC7ZTfUbRygUBZkG4BCnl7k3+gumWlPlPJYM7TPGwWOyvoRi42Jc/4RCjkuWcJO2dxup+x0A4VfUQCFd9yD7YsBOB6/4w3M5O+SMWcKKMx9pvyBtgn3a7PFqGnTpsKLgorG0ltvvYUJEyZgzJgxKfnJQYWlMgEU8pOeTCMBWqCgYjQldfU25w7piX8P74c7v5iJOz7/IzNAofNDJq2039D9yzKg0NanbDRAQfbP4jPOFqCw8ePJQQyXdzoZfYp74D8LHsPsjYsDr0EmgUJro1k87a+f7GiIDVDo9qJohrY4LXYd1mM1/hu/HdW8Mm1AETTFKQxQwGBL+5NGDLxyAii0tlKC7pf/T3l/dio6YCdM5WOxgv9aX0d/t70xgUKORY7VlLh7CgsOQcpnrdE273zUYg2Wxx+Dy6uEmPzrO6j4ZKDwxxglUGjBIQNA4ZcDYBs2En1wCH7CC5jDPxfrZhAo5OtiY6vYMNomvb9lm69at26NWCyGZcvEtUnLli3TTklq3759KHtKEyZMwKxZs9C8eXPk5eUhL69uItNRRx2Fvffe29pPDirCKkqg0PgRE1vZnyH5lepfNLQXbthnG/zn0xm475s/yfqNDRR2kGC29UsHFCabxgYKq8Rd6yf4GpgWtkcBFDIU5LE8XN/lTHQoaIN/LRiLpdUrtfVNe0DIQGG0SRIorCBBGbXQX/dUgMKzbcu64OTYNViGeXgpPgZxXqsAhdBmGoDCLxugUP0lDxSwsSWAQvvoWMYwkJ2DVuiLKfx+rMafhr4lBxS2CgIK06NjTSBBraNIdZQiETM4ClgHtI2di2q+GCviT4F7+4D4Yoobrok87cj/8p/fXIBCjQ0YgGPQC/vhBzyOhXxSfYz+6yK+U4+flfvT0A8Ix6I/e1vFhultskoui/5lqYKCAuywww745JNPGsJ1XXzyySfYddddyTq77rqrYA8AH330kdae0rXXXouff/4ZU6dOTbwA4J577sGTTz5p7ScHFQHiLlOTVAkoxApS0kSNOmQBUHh1r9pra1yx+1b4x8e/4ZFJcwQbU0IqA4VopEkgGwko9Mm9L+QQd9N1QCGYZBlQ6P0QbSUFJvrf8zBA4cVTxIrxt87notgpwj8XjMWa2vVGoNBCBwEASYFJBoFCjlWGBL+CgAKo+7XuzHrjOOcKzMGveJ0/BJe70iJsL56GOnX+VdktwpZt1BGEIFsaUIKBQvekJ9OC7cDN7VgetmcXogxdMcUdg3K+MGmgoGQ7SmE6lwxQ+BdCZwooPBU63dA67wxU8llYFf8v4jxOPi7WARPWUVBTnvwxZStQ+JUqUACAwxzsgNPQFbtiIh7CYv6TL0bxnQIKOW6m1PH32WwLwlbxxww2rO6Vk50uv/xyPProo3j66afx+++/Y9SoUaioqMAZZ5wBADj11FNx3XXXJewvueQSvP/++7jrrrswffp03HTTTfjhhx8wevTohM3q1asxdepU/PbbbwCAGTNmYOrUqYl1F+3bt0f//v2FFwB07doVPXr0sI49BxW2MgCFNumlIEEHFERbmQYK79wN+/TB6F1745oPfsGTP85T/Sj1icRIu9bBlGyC9CvUzxKgUPuuTzKtEm7dZ0PZZAlQpCseTzqg8Oo2zyvDP7qMQpVbjf9bOBYV8U0ZBQrl98kCOqIACnlhtgwUQly+tnuyATjSuQi/8W/xnvskEk97swAKeWF2qkChq08BRSAkJAkU8joKHVB45TFWiO2dS1CMVvjRvQsb+XJroPAr1WlPJhvqZ3kNRRxcWD8hb3KX7MJsv/yAkyhjHEXOVmiZdzI2udOwJj4etdwVnvQkv/6qU578/tMBFF55jDnYBeeiAwbiK9yL5ZjRKEChq28LFNkmzlnkrzA67rjjcOedd+LGG2/E9ttvj6lTp+L9999PLMaeP38+lixZkrAfOnQonn/+eTzyyCPYbrvtMH78eLzxxhsJMADq1kwMGjQII0eOBAAcf/zxGDRoEMaOHZuGK9ogxjl5r3mL1/r169GsWTOsfGhnNC2uf0hWJoHCwo+cEBsfW2sJFJ4t5xzXfjANj3w/Gw8cMhgnDOwmtJ8yUCjxENdASdj1/ftLAoWmf0ZQsknu/0JAoSbY+t8n79zcyiX4+4J70a2wE/7W8QIUOPnavSgyCRS6ze1ISIgQKHR7UXjHU9xP8TZ/BLvhcOztHE/ahAEKqiwVoKBtaEgAoH3SE2krJeiBIxT+c/XtVPJ1+NG9FS7i2MG5FvmsubYfjf3oWD9QuOCJRNF2Pwr/OVtRMCGfK4//gHXxF1Hs7IkmsZFgTIUcGSh0kLClAIU/qY/zGnyG27EGc7E//obW6K61leNuOBbPi23Ttqb6YYCCgaOKb8Qd/CysW7dOeAJSlErkdQ8Oacjromh3Uy1aXzipUfselXIjFbbaHIDCJ7W+g1uGD8Bpg7rjorcn49VfF/z1gMLfP1M8UuybE1AI/bO5Bin4iQIoOGfoVtgR13U8H39WzsOYpU+hpj4LD9U/ow1tm26goOKSgYKKKxWgAIDt2D7Yj52Er/EGJvF3SBu1TdEmLCSEsW1soPCLetJTIWuG7ZzLwRHHFPdu1PBy8to1NlB4ioPemyJoP4qgc5RMa0T8T58qiA1GaewwbHK/QIU7gYwpaqDwK5uBgnGGPBRgH1yBpuiACbgN67A4KaAQ+xxsK5fZ+EvYMO84C+9bZ/Gair+6clARJBfiN1W9bJ5ElHVAoanv2TDGcOeB2+O4gV0x6s0f8faMRYpfoXvZBhRWyb0Yu01CavW0KZvfgwiAwg4AJNsk/Si2GQAK771PcU9c0eFM/FAxDY+ueNG4gZ3unGCj+30if891fVCvgQkodJvbkZCgAQq/bIDCK9uFHYKhOAwf82fxM/9MtJHiSxUogp8SZQ8Uftnslg35HKNtxRikc8ReFMWsNQY6l6MG6/Gzey9qeaXgJ9NPetKWSY+ONT2eFRAT6lSnPVEjFLpN7QCgOLYbimP7Y1P8A1TGvxFiagygkPeiyFag8MryUYz9cDUKUYaPcAvKsSI0UMhlqQKFvBdFwoZ5x1kIFDllVDmoCCNN0muEBAkoBGURUHg/O4zhvgN3wGF9OuGcN77DB38ukdoK9tNgLMejmmQbUChllI1F0qof4aCScnt/2QIUNvHItsbdri1GjQaVDMCotidhwvqJeGH1WxkFCt1eFOb1FXrbdAGF8lSoRF0lLOXRsXvieAzCMLyLcZjBv6tvQ/LrHavu0gIUZn8qJMjrKKxsA4BCjCEYKLz3JqwDBjqXYSOW4Ff3AdTyasEmU0ChHanwTXmSgcJLwOUF0OmQbv2E/5w/Hn9cTZxhKHH2wIb4G6iKT94igMKvZIHCey9CGYbjejiI4SPcgk1YR8bNfHUa2hbLMg0UfuUWam85ykGFrdIEFAo4WNwBVoCAtDEkd5ZA4cUVcxgePHgnjOjdAWe8/i0+nbO03iaEHwNQhEu46QRQNApOSLMNKLSgZPg9sLtOprgk2wwChWxD7yUBsj51Dbz6e5btglNaHYk3136Id9aKj9CLEih0m9vRn59km0GgUEctGuJjjGE4zsQ2GIK3cB/m8F+kNkUfVJkJEuwWdctJu2cTHVDoFmabdsv2VMq6ob9zMdZjFqa7jyBe/6jUTAKFHLcMFIDdztR+JbOWgoKJutjFMtNme3nMQVnsEJQ4O2NN/CVUxn9NtL25AoW8uZ0OKPyigMKzLUELDMf1qEElPsStqEa5ph8+f1KZbAuDLemPaWx8Dr1RiqyECc6if20hykFFkHy/EJEAhWZhtXEfDEOyaRrh0MXFOUN+zMEjh+6Cvbq3xSmvfYOv5i0n/WQEKJSkUKpriIOGDrGOTeLe2EChTe5JP8HXIJV1KI0FFLr6I5sNw6HNhuPZ1a/iiw31d9wN06E8RQkU2p21idvlUQCFJ8YcHILR6Ipt8RruxGL+Z32bog+qzAQJ6QAKCLb15zRAQdpqgEK0tQMKuk6dmrKt0Nc5H6vxM/7kz8Ct95IpoKBGKvzy1lD4z+kWZgPJTXvSrZ0wbWpHQYgHFi1jx6CE9ceK+DOocv/MeqDwK1NAkSgzAIWnZmiHEbgOFViFj3AH4qiS+tEguYzqm3YRNuWPaWx8DmWgYKCvZU6bn3JQYSlTUp5tQKGtb0j8qMfWFsQcPHn4rtilcxuc+OrXmDh/leJHbVNONg02GQQK3TWwSdzp9SN0HcEmy4BC21bSYGL43DQ26QYK7/2Elodh79KhGLfiGUyumBbCDxQbTzqgEPsn+1VtbYBCt7mdS3z16oBCsEm07R2L8fltGMvDYbgMbdAV43ErVmCh4i8dQAHC1gQUukfHWtkagCLo0bEUUChrJaTy5mwAtmJnYDn/FnPdVxDnIliElQkoFBtp2pMLjjhTr3oYoDCNUtgChSc5Jrl+3bSsGFrHTkYx64Vl8cdQ6c5VQCKbgELe3K4xgcIra4kuGI6rsQYL8DHuhosayGJSnWSAQvDHNHV8xhRQZJs4z+5Hyv6VlYOKAHGX2PwOBkgwTVGyAJNsAQrPvjAWw9NH7IpB7VvixNe+xOTFq/V+bK6BIWnNNqBoSJ41/TXUNyflar+C4mosoEglHnr9AYT6pmugA4o6OTi79YnYvngA7l3xCP6onKON3QQUig0BFFpIIHKtdAGFbnM7IfFNAIR8TFxTz6b+PR9FOArXoBQtMR43Yx2WawHCBAl2i7ppoBD9SW1LQGG0lYCCtjUDhVjHDBTee+vYEPR0TsAS/gkW8XfIdtOxF4XpSU+eYrzhq9y/J4XtCIWyRkIz0pCIQbOGQpjuVB+Tt7ZD3ocixvLRPnYmClknLIk/giq+mJ4GlQMK1bb+vR22wv64EksxHZ/iAbiIayEhWaCQHx2bDFA4DMKjZ3PafJWDCluFBQo5gbEAEzkZptqPEii8hKY4lo9nj9gdfVs3w3GvfoFflq2xAgrd5nZ2kEBcgr8gUKh346HaWCT3gbGTNnJfwrWZLqBQ4klyypRsy3gMo1ufiZ4F3XHnigewsHqxYhMFUNi01WBL+80EUOj2ovD8FqAUR+E65CEf43EzKrA2FCSkChRBm9tZ2RLJue3mdn6g0IGDtry+vfbO3ujiHIaF7ttY7H5CxxYAFKYnPFFAQS3K9t6p9RO2MoFEwoaY7mRaKK4DisR5VoiOsXORhxZYVPswqvlKsU9JAoVfmytQeDF0wrbYD5dgLn7AV3gU3PfXJFuAIuvkNsJrC1EOKmzUSEChXb9gSLTIEQ7JJgxQeH5K8/PxwpF7oGeLMhwz/nP8vnJtJEChJLIWyWaYa/BXAIow+2mkMhpiY9NYQKHY1H82+awQl7e5AK1iLXHbivuwsnaVNsmn+hcFUOg2tzPtTZFpoPBUihY4CjegBlV4FbegEhVJAQUMttkIFKZ4g4DCLxccHdmBaM/2x3z3ZSx3v5XaswMKE1jU2at7UchJfLrXUcgKmu5EreswAUUiblaCLnmj4KAQC2sfQpyvr4+5oW/UuwkoEjZ/YaDwSwcU3nE37IB9cAFm4AtMxHPg4BkHCr/+UkCRU0aVg4ogZTFQKAkalUzrEkhyUTedAHpqWliAF4/cCx3LSnD0+M8wc/X6RgcKNblXY09qJMA4r57+TLIZKJLzY3GdrGzoGKj6YYDCOy5xinFVm4uRjzzctuJerI9vMO4loQMKv6IECt3mdtT0sXQBhR8KmqEtjsD12ICVeAu3o6Z+0Sd8NnV19EChjlpwxabBnxkorGwJz2GAwvToWKpcaIeJ5zgDujhHojXbDXPdZ7DGnUr6tFmYTZYF7EXhn+5ks8FdMqKAQl7T4V/XYQsU3nsBa4ZueaPBUYt5tQ/AdcsVG/97tgAFhDqyLfS2nl8DUMh+5LiFeAFshd2wB87AL3gPk/GaYEu2rTkWypjBhnllwUDBQMNITpufclBhKRMkNBhpbA1+Ghso1MTY7Kd5UQFeOmoftCouwjHjP8XctRuk/tF+BZtsAwr5HAEUNiMccv+ofgX6M/qxAQEi9jQBhY0f3eZ2YRZ1G200n03zWDNc1eZSbHI34c6V92GTWxkaKGwgIcjWr3QBhQoQ4rHQZkig8I5bowsOw7VYgbl4B/egBrWSjf5Ovw1QqFOCpHIhaQ2wFe7g62y5YttQxw4ohDo+gCDrMIZuzklozrbHbPcxrHOnk3ZBT3qSy/zJvG5zO086oEh5kzvNCIWNbIDC+7mQtUL3vNGoxQbMjT8MzqsUWyC7gELe3M4EFAokpBEovLJtsT+G4Dh8j/H4Be/5/Km21LFQxgw2zCujgcKv1H77MiTOon9tIcpBRYA4963cp0YdpCc9kUChSepNiaRxmk8jAYX33qq4EK8cuQ+aFOThqFcnYP7ajWJ9CoIsICHIlrTJEqCQYzaCkk1yb3gykt6fYpJ1QKGrT0+ZkmyIjNZv0y6vLS5vdQmW1S7H/aseQg2vMbYVFih0m9uRO2trgMKvdAGF/KQnGSjENmkbDqADtsJIXIEF+AUf4SHEIT69h54OJR+HBwo6PgtbprNVgUJeR2EDFLp1FCoQ1Isx9HBORynbCrPdsSjnc0Q7bX16pELenZqSaS8KIDWg8C/I9sehW5Qd405iGpbtCIVcVoz26BG7EFVYhrnuOKB+H5BMAoVffwWgEOJVbIDBOAyDcAi+xDP4HZ8FAgXpj9F1ADugUEctOBxiJDKnzU85qLBVJoFCTr5MyW8Si7rTCRReedsmxXjlyH0RYwzHvv4JFm/YSPoV6htHHcy2VByma2AzEvBXBAq9H6KtpMCEsMkyoDD56VbQFZe0uhB/VM/EI2seR7w+06bakv1FARS6vShIIJTblo7ryhhtI/RPLDNBRxcMxAhcjD8xEV/gSR9YEHEpx8SoQMJGDwlBm9uR6yuYzlYfQxBQiHXCAUUCYFgeejjnogidMDv+EDbyxUK8JqCQ2/b/bLu5XbrWUchPxTIBhe2ibH98pqc8NWFd0SM2ChV8Dua6j4PV/2HOFFAkzv1FgKKhjD6OwcFQnIh+2BcTMA5/YpIRKBR/jPYPJA8U2SbvqZ5RvrYU5aAiSC4DOY0py4DC5slICVsNUIhGwcl0hyZNMP7I/VAdd3Hs6xOwvKJSsYkSKMLcldcBhXAJQlzLvxJQpCseT9kCFJ62KdgGo1qchx8rJ+O59f9N1BfakgDSOI0pQqDQbW5H/hdNE1DIZb0wBPvgHEzDR/gOr0ht6vxxg03yQCG2bQ8UyeyWrR29YHS5GnPde4wVokdsFPLRHHPiD6ASKzX1DSMV0joKWeQu2WmaaCKPTlDrOZJdlK0tk2CgKdsKPZxzsI5Pw1z3mcQvcg4ooLERbRkY9sHZ6I1d8QHuwzz8lBRQ+JUKUDCG3CNltxDloCKMlKS3/o26I5RtQKHUJxKjMGBSb9u5rBSvHLEfyqtrcPwbn2D1psoGm78gUNgk3Gp9w3VKCUyCr0FjAYWaYOuT8lDXIAkbKnEfVDQIpzc7DZ9t/Bxvlr8ZGigaztG2VFwyUIg2de+pAoVuczsSOpIACu+4D/bBrjgJP+J1TMG7RtswQCHEZwEUuic9WcGH58MAFLaPjtVOeSLkgiOPlaB77EI4KMDc2gdQw9dp/AQDBTU64Cnd6yhMT3jyA0XQomz/6IU/Phug8N5bOAPQwzkDq/n3mO++BJYAi4a+/ZWBAiZbKW6xjD6WbR04GIEL0BUD8T/cjcWYERooEjbMO04OKLJOnEX/2kKUgwpb2QCFLnmmhr40QGFqs9GBQomnrrh7s6Z4+Yj9sHJTFY5/cwLWVlYZISHhTgcUgo1FspllQKHbLduY3GcITGz8ZAIokpsyRdhoHutL90+03b14DxxdejTerngbn2z8uD4uXR/8frxztC3VFgUUur0o6NGQ+nOJYwjHYhnT20jx2T36VT6u+2kQDsYgHIqJeBbT8XkS/gyQkGag0D3pidotW60jHgcBhV+mR8fms6bolncRvCcaxXmF1jYRk7QwWxb1pKd0SLd+QhsHV1MHP1B4x/K7LVB4762cndDNOREr+OdY5L6ttAdkFij8CgIK0tY7JoBCbVOKLQRQiG03nIshDwfjMrRDT7yJ27Acc2l/TPWXsGHesQgUfv2lgCKnjCoHFUHyUWaqQCFDgemuvBYo/KFZLHrONFB4Nr1bNMOLh+2HRRsqcNLbE7Chujpg1EE6tljLIcYjxm6TcP+VgMIOACTbJP0otn8hoNCtg/DeDyw9EMNLDsCLG57Htxu/JftAXYMogEK3F4X5qU5SfH4b2S8MNppjSLY743j0xX74HOMwC98T/uTE3PMXHijktgU/zGRrDxRBj47VAYUxNt07AwpYS3TNuxC12ID58Yfh1j/RSPfoWLlNYTG0BiJSHaUIWj8hx2FaQ5FOoPCS7XbOHujiHInF/F0sdT8WbTIMFMo5A1BoISECoFD73WCbhwIciqvQHO3xGm7BGiwWbZjqL+GHeccqUKg23rH6fybbpj9xziJ/bSnKQYWlsgYoNPWpxE8HFHS/9MlmEFB4Nn1btcALhw7D7LXrcer/PsXGmppGAwqlzCaZtrkGVMwJP8H+sg0obOKRbY27XSe1BkO1TQdQeDZHlx6DoYW74ckNj+OXql+EulR9GSjEayDZZhAo1FELptgk2pT9Sj6oMtpGhgGG3XEmemAXTMB9WIhpiq0OKMj4DEChe3QsBRS6Jz3JQCHGEA4o6LpSbAag8I4LWXt0zjsf1XwpFsYfRS2vVurIQBH06Nh07UdhAgrv3TRikmmg8BKUDs5wdGQHYr47Hivcr+tsLIHCr8S5LAYKMl7NsT9mnS0AFKMER+JaFKEU43EzyuvX+WQSKDzf2QQTOWVeOagIkLByn7ydp0lasxgolIW0Jj8aoBDkqz+gTSs8O3IYflu1Gme+/yk21dYK/rIFKHS7ZRuvARGztl828BJiuldjAYVsQ29OB7I+5UcHFH6lEyjqChlOKTsd/QsGYOz6B/Fn9Sw1dgNQaDfCIybW64DCr3QBhZLMS0AhxKWrI9jIibMnB3vjAnTEtvgId2I5/rQCCgUSiN2ytbZSck7bBgOFFhw05WI7MjBIsVkAhfdexLqiQ+xcVPI5WB5/BvH6R6XK6zt0owO2U51sRynk6U5yLP72/es9/DARFVB457o4h6E92xuz3Gexyv1BU1f0TZ7LcqAIgoSwQOGVlaApjsYNcMDwCm7GRqxT/CX8MO84PUDhgMMxgHJOm49yUGEr4S6jmLjb3AVXgIC00Sd32QYUpoR7cLs2eOrA/fDjshU498PPUFnrim1TX3ybKVBok3uLJyPZ3ClPHkzoeBoLKNQ7//prYLM5nbwXhYM8nFM2Ct3yuuPBDfdgUe3CtAGFbnM7P1Do9qLIJFDoAIJO6mmg8I5jyMN+uBQt0Q0f4DaswcIAf1LbBFAEbW4n3G9QbGmg8CsZoEj8CU0jUHjvJU5vtIudjgr+K1bGX0acx+vriE9SAvQjFf4RCiEJ5iwUUChlvvbkRdlUDIkpUBqg0C7UThIoACDGHPRwjkcbtjP+cB/HWv6rVFe0J89toUDhlTVDSxyDG1CFjXgFt6Cyfp2P4Id5x+kDiqwTZ0g82TOKl+X/zc1BOagIkMs5npm4BLVxLzH2vjXopNN/zggUuuQ3yUXd2QIUnnbp0B5PjNgXExcvwehPPke1fP0McdDQIdaxGQmgp3vRttkMFNq2kgYTfTw6m8YGCtUPgm0kfwWsABeUXYqWTkvcv/5OrIqvVIDCr6iAwi8dUJA2cgwmG5hsdCMUom0+irA/rkQJWuID3IINWG4HCREChe3mduSia0bXSQdQeO9NnG3RJnYiyvn3WOO+iThcZQ2DaepTqjIBBdW2P+n3j04AMI5QUGWpAEXDu4Pe7DS0QH/87j6MDfzP+rqivegXiXj9/jMNFLCxJfrKlDqqkgEK77gla49jcT3WYwVew+2o4VVaoPBLtfGOg4Ei29ZU5JQ55aAiQNMWVeDCF2fgvOenIx6vL4wAKLT1DUmrcafuMGCSIlB453bv1Anj9t8XE+YvxGWffoHauO+OoAVQ6K6BTeJOXieLu+CmEQ61Tb2/bAOKVOKhFzRDqG+6TtkAFP/P3nmHSVGkf/xTPbOBZVlyDoIREQRFJJiVE7OY9cw5YsCsd553vzNjzllPz3wG9DwMmBVRFFSQoJIlSdqFXTZN1++P3Z7trq7q6ZnNbH+fZ5/ervrWW9XDAu9nq6vKqWtl5XFhm8vJElncv2ECG+wi7wxAiL7Uz6C2QGE63M4NFKazKAIPzTPce8vMQKHbFSqHfPbnGmJk8x63sIn11Z5goPCOLxgogr3S6E3ntGwUT0MAhXPNs3amg3UkG+zPKbLfB4J3elJ3e8pkHYXpdacwaygsZRwQDBS1maFQ+/V4qmPERRbbW+fShn78ZN/HRrnY4/PGrRlvVTkB/XnrVKAI9Dr3Uvg8dQUU/rMoUnt9nuqCzqIPx3INK1nI69xFQlZqgcIPEP6+HAUBRZOTFA3/1UIUQUUK7dgzn6dO2Z4Xp63k0tfmIR2w0P2QNDWgUBOjMHF0kOAblzmRVOv26d2LB/bdi3cXLOLKz7/AlrLJAYXxM3B7fL+Nx+dJOa6MPeqzpP4M6gMofOPJ8JUp1asChdZTB0DhtG9rteOi/CvZJDfx4MY7KZWbQvdlOtwu6GyKhgCKdHZ1yhQonPs82jGGa6mknA+4lVI2etrogMK0K5R2LYYwef1AYdrpKQgowm4dmwooVIUBCket46NoGzuQosT7FCU+A1Ivzs5UYV53MvWvgk19rqGoKtN71UXZcZHNQGsceXTlR/tuNsmVrhhO3Or7AKCo6cdbpwOKlJDQiECBxuuLJ7yeHmIbjuZKFvMzE7kfWybSBgp169gm/cpTpAZRBBUpJBEcvVM3Hjq+P098+TvXTfxVuxWpCSi8wVIn8E0NKHyJVUDC7YeEquID+m3BPXvvyZu/zuf6L6YgpQwEk+YIFKGSe8NrQsFx/H2l1WcTAwqfRwMA5ji+j8AIFG7pIKFzrCsX5F/BH4mVPF58H+V2pdGbjBMCKEyH2+lmexoSKEyQkC5QOGpNF/7EtRSzlo+4g3JKvW3TAAq3UgGFx5tsEwwU3jaZAYVO6ixFUD+6nZ5aW/uSZ+3FhsREihPfGscc0yTBbgWtpUgXKNRF2c61vtdQVJXpvaZdnrJELjtal5JNG2bYd1Iq1zQLoHCrroDC1N4EFI6nrxjIEeIS5vIt78rHq/5vpu6AIlkmpHar2caSlA3/1VIUQUVInbprT+48Ylvu/WQx/3xvPuBKOAKAwrh+IY1kWudpLkDhlB261ZbcvvvuvDh3Ln//eipSyhTJffjPIDDh3syAIkycVOPK3KMfg659fQKF0aOB/SBI6BXrw7n5l7Kg8hf+VfIwldUZfH0ChR8gvPdumYDC41HjKvd6jzdxDo7nBwrH046ejOYq1rGYz7iXCioD4pmBItXhdp6F1GpdwGnZNW28dWGBwvscBkgwxDC1cW8dawtoEzuYXGtXNiRepcyu2a7XUSqgCFJYoAhSmDUUxlmLegQKp2WWyGeINR6BxQz7TspkYZMHipqEXyj33npvPL03qH2oWQwB24ldOEycxw98wofy+eRshdvjG1eaQBGp5SiCilRyVu8D5+/Rh38cvDW3vL+Auz5aVFNP5kBhOi07zFar+pO69QmgxxIKcNB6MgEKx3vUNttw08jdeHb2z9w6bVrytyJhgCJM4u5f54Hfk85nYACKoDiNDRR1NR7T4XbpLOoO9DQyUDgJ+1ax7Tkt7yJ+qvyeV0ufxtZk9yag8HjqCChMh9t5knDpLUsPOvyJswlIgmYHnOS+A1uxF+NZwc9M4SFsbN/C7NoABTqvEycAKFLt9GQCCn1/6QFFEFg43ydcMfJjR5JjDaKo8t9U2lULj9XXncIChbN2Ih2g0B1sF3ZRdmMChVOWI9qzs3UFlZQy3b6LClnSYEDhVl0AhT6e2auWaT3C4HF9kIPEHhwgTmcq7/K5fKPOgSJaqN1yFEFFmrpiv75cNboff3nnFx77YgnQ+EDhT/zTiKMdjz6uW+kCheP9c//+/HXXETw280funfF9gwKFsb3OEwAUocbjeAN2RjLH04y9iQGFqb3+lSnFo8kYMwKTWgKFU7ZDfGeOzz2LKRWf8G75a9qxaiEhmdyrfWmeT+1bua8qE3qPO04IoEgHEsJ4/R5vct+dgezGOJbwDd/yFFJKo9fbd2qgMO30pDst29/Ge28CCm9bdSyZAYX7NaKaNtIDFM5VCIs2sePJEltTWPksCbtq4XHYdRTJ2Q/Db4TdY3H3qy7K1p1BAXUPFG7VFiica57owlDrcspYywz7HiplWYMAhbp1bG2BQi1rCKBwZiZ2tfZnH3Ecn8hXmWpP8nhItssMKJqcarM9bKZfLUQRVISVK6G54YCtuGD3Plz2xhye/3aZ3xq0O5TiaU5AoYME4/MZvACnDxjIlTsP474fpvPoTzO0UOaL04yAwhwn9Xi0CWlGYJL6Z66xgSJcHL03CDqC1mCoQOF4h2XtwWE5J/Bh+UQ+KZvkGWumQGE6i0KX+jUkUKR6ZSodoHC+780u7MrZ/MbH/MArgV5PnSDAGx4oUm0dqwKFW/W105O3jT+xt11fQsRpGz+ZLNGDdZVPYtsrkt4wsxRBQOHu07Qo2w0w9Q0UasJdW6Bw6vJFL3YW49nAUn6Q95OQFZ5nTAUUXi96r8vT0EBBgFcbTxg8roDqWRR7iMMZJQ5hknyW6fanSrvMgcKyqr4ibf6KN/YAmryk8CWtQlrcfth2lJQnOP/VmeRlxzhiULcqT1DCbUg29VmGIfFrwkBh3KVIE+e8QUMoTSS4/ftvyY7FOa3/IM14UifKJqDwWJohUNQuTnrjcdQcgUIFyKBXpkxA4Wif7IPYaG/grfJ/04p8donvUa9AYTrcTvvPQQMBhadPnycAEoRkS/akTJYwg+fJknn0F4d44qUCCq83GCh03nSAIt2dntIFCs9MhWvMTmJvI0mIql5jsirbiosc2sfPYF3lI6yufIzOWReSLTr7njcTmV53spGehN99Sjbo4aApAkVV/4J2YkuGcjHT5F38IB9jJ85HiFgooPCPTV9eNc40vMq4hdJGV6Z/br1XG08YPK6AKlCI6ob7iz9TZpfwlv0oubRigLWrESjcCgKKpiYphT6fqMf+Woqa4B9305Q/qbe498gdOHpwd05/4Ucmzf6j1kCRzmFwJqDwmkLEaQSgcK4X7ziUs3fYkX9+O4WX5s1WxpM6UQ4CilAzHIbnS2cmoLkDhT8ZN/88pfVZZuBpLKBw93Vw9nHsGtuLV8ofZ1bldM3Yq64qULhVV0Ch7vSUHgA49+kBhT8xdzy6ZL66zrWGYjtxAAMYy4+8zG/y4xoYUOLogMK0dawOKIzgYCj3jrv+gSLZl2thtu4MCAcoHGWJPDrGz0aQw+qKR6mUhb7xh5EbakxAAd6Ev7kDhRO7o9ieIeICVvE9M+UzCGdHI3Uc9QQUbtVkC8p4tR6919uH/t7TpzC0cZm1QFF9L4TgEOtMdhAjeNW+n/nyR09c705SCkA05VeeIjWIIqhIIQ/RKslZzBI8euxADty+Myc+P4NPfl1Tv0Dha69JosLE8Y0HvwdvWfCheam9ukRSCMFVOw3nlO124K9TP+c/v84zetU4tQYKX3tdX8HxvJ+BOU6qcYWJ01hAkdkrUxqPYRcu/Weg93o89QAUzvWo7DPY3tqJ58rvY35ijvFwO29fymfhPLcWOqrb1BIo0pl1qCug8EGCZlH2DhzJ1ozmO55mqZxqBArd+ExA4VZYoPC0Efo29QUU6k5P2jMgqoHCWRidLBdt6JR1LiD5o/JRErJY8ynoZVo/oX6f7Av/GgpnTOq1MYDCrTBA4aib2JkdxVks5XN+li8lwaIhgMIPAMp43fGUskyBQt06NhA6AoDCUUxYHGldwFZiIP9O3MUSOc8Tv6qP1EDhzFIIIZsWaEjR8F8tRBFUhJV2pyXIilk8ffwQ9ujXgeOf+56vF61TPIZksykDhSHh1kMCeq9LQYmkEIK/DhvFsVv359qvP+Wdhb8ZvU0NKHx12jipx2P6DBsCTJoKUJgWVms9BqDQfQYqUHg9irc6rkWME7MvZAtra54uv4tl9qJaA4V/1kL4PMnnVOMqMXRlQZAQbpeo2gOFIyEEgzmZ3oxgKg+zUv6oBYpUh9t5XrMKebiddgcooW+TCihMygQo1FkKJ0l3A4UbLLJFBzpnnYsti1ld+Ti2LA0ck27cJqDQndTdFNdQVLU1tHHKNUDh9Ndb7MZAcTILeI95TMwIKNyqC6DwxFPa1CdQqKdlBwGF8222FeP42CX0FP14tvJ2VshFrj7TA4pILUcRVISRdh1D9cUW5MQt/n3izgzpUcBRz0xj+tKi6soQSWKIRc/NEihCJffO1eIfu+7JoVtswxVfTeaDJQt9H4EJKHSezQ0o0pqZSANMAk+7boZA4YMEzc+3qS91R6oskc0pWePpKLrwZPltrJEr6xUofMm8AhRu1RVQqIl4MHQofWmAwg0DQlgM5Ry6MoivuZc18pd6BwrveDMDCl0MdZZC69HEUV95UmcRQL/Dk5NcZokudI6fTYX8g9WVTyOVhcdBSjVD4fS9uQKF491SjKa/OJq58nV+k++nDRTqWRS1BQoTJDQmULiV7Kv6DybHyuHk+BV0FF15quIWVtvLMwKKaEvZlqMIKlIpBVBUfSPIy47x6qm7sE3n1ox95htmr9jo8eiS6eA1Cs7/ZEpdpmDSRIHCaSewuGXE3vyp15Zc+uX7fLpskb+d7nNK47Ua4/O5LQGJrf8zMCfwTQ0oVI/+cDq07XWfgQkoPB7Dn40+jtnbkEDhhoRckcfpWVeRK1rxZPmtFMl1nrGbgMLjSfbt3HvH5/GocXVxDPdu1RVQmM6XkJrfPOq8loizKxfRjn5M4U4K5WKD1wsUnmdJAyhMC7NTAYWnP9VrjCED2vjhQQck7pmCmCapBsi2etE5fiblciGrK59DyoQvjhrftGWs2+Puz5LBJ2WrZWAGCrcaAij0/Xn73ZZD2ZqDmCmfZ5H83DA2bwxPWQMBBSG82ngiwCOcMjNQ+GctvJCQK/I4Letq8kQbnqy4mfVyTdpA0dQkbdHgXy1FEVSEVBBQOMrPzuI/pw6jR0Euhz09lV//KPG2DWifLlCYTsvWxjEAhfcBwyTchgSwlkDh1MVEjAmj9mOP7n0Y98V7fL1yaa2BwtRev6ORPl5QH+m+7lXjUbzNAChM7bWeAKDwx8HncdRYQOHc54u2nJ51LZVU8kzFbZTYxXqvZ8x4ylSgcKuugMKfdHvL9X3WDihMu0K5y2Mim5GMJ48uTJF3sEGuULx+oDBtHZvO4utUQBHUNp11FEFAoVuY7SSQYc+gAMixtqRj/DRK5WzWJl5Gut73DFpDoUpdu1HXi7Ld3zcUUPjXPwhfuRCCAeI4+rI33/MEy+R3yti8MTxlBqBwq66AQq0z3XvKRIBHOGWZA4XzfWtRwFlZ12Bh8WTFLWyUhdqTsk1AYQkZvQbVQhRBRQpJ6VqorSbyig+gQ6sc3jptOAW5cQ5/5msWr9vkMintGxkowiTcfkhQ2no+hNTJtAkonGuWFeOeUWMY1rkH53/2Lt//sbxZAEV6AKDcZwwmAX9uBk9zAAqfx/Z7fGNXgMKtTIECwEbQXnTm9KxrKJRreK5yAmV2mdfr6QtPmQ4oTGdRBB6aZ7j3lpmBwrQrlB46ag8UbsWsVowUV5BFHl/L2ymWaz1jcCsdoDDOXghzG/8zZg4UNWPWA0UYhYWLVtb2dIz9mRL7e9Yn3qTqgEEvTKS7hqIlAIX7MxjCafRkGN/yIH/ImUZvGKBQn1EdtxYAlPt0gMKthgaKqj4kbUUHzs6+llK5iSfLb2NT9QYC6taxTXmGIikJDbtQu7EfuOEUQUVY6YBCTZqqr53ycnnr1BEIAWP/9TUrN5T62wckic0JKMKs5TBBmSlRzo7FuG+3AxnUsSvnfPY2P61d6fWEeK2mOQJFRjMTacTRL2jG0z7oswxcg9GAQGGEBM3Pd5jZEBNQuNVF9OKU+FUskwt5OXEfFXbVayj1ARTp7OpUV0CRDiSE8aoLs7NFG0aKK7GxmSrvoFQW+b1OHANQuBUWKILHXTug8MxUaM6i0C3MBrQLsx1PkPJiO9E+dhQb7S8pTEzy9OeWChTu71siUCTHLWIM43w6M4CvuIe18he/12nfxIDC5xF+T7JP4bSpO6Bw7jtaXTkn52rWydU8VX4nFVT9giUIKHQzGpE2X0VQkUqStIDC8fRs24qJp46kpDzB2Ge/Zm1JeaitVn1A4R5KOmDSjIBClwS3imfx0G4Hs03bjpz92URmr1td3V7fRlsWIgkOtZ3uZgYUvvFk/MqU4gmcUVDj+L0NCRSm07K9yb23rLfYhj/HLuVX+SNv2Y9hu19DaUCgMEFCuEXdmQGFbutYo1dJzp37HNGREeJqytnAt/IuKuWmtIDCtNNTEFCE3ekpE6BI9pHiLAq3MgUKR3mxERTEDmaD/SEbEp/6ZieCFmU7/TQVoHBLTczrGiicsriIM5KLac8WfM4E1lev8/G0rwVQuNWQQJH0COfeCxSecQm1TTigcLzdrN6clXMVy+3FPFt2D7aoqPaYgUI0sdefpBQN/tVSFEFFSKUDFE5d3w6teeuUEfxRXMaRz31NYWlF+kBhSAAD4+ggQfGGWtQd8Jvb+gQKJ27rrGwe2+NQerVuy1mfvcUv69em7Cvo+fyv9+D3hEjuU45d6/H3lU6fzREozHF8H4ERKNxqTKBwrluJIRxpXcgP8gves/+NlNIIFN6xKx6/pc6AwtQ+3G/v/eNLBRRumYDC8eaLbgwTV1DMCqbJe0nICiNQeMcZDih0beoLKHRbx6p9u5XOOgqdnJhtYvuQb+1LUeJtShLfAMGvW6UCCveXWgf1BxQ+cKhnoHA82SKH3bmc1nTmM25ng1xRZ0DhAwDlvrGAwu9x7v0/N0FA4WiL2FaclnMZv9lz+Hfpw0gSHq/b35RgIlL9K4KKdGVIXj0WVzK9bec2vH7SSBasK+G4F6dSXF7p8TQEUJhOy9Yl3EFA4UtkQyTT2lkew9h1n2l+Vi6P73EYnXLyOOvzN1m0odA/9gYEijBbrfrrNH2FiJNqXJl79GPQta9PoDB6bL8n3GtM5r4cmYDC4zEAheMdZI3kIOs0psh3+cx+q8rjtHU/H8JTpgMK01kU6cw6pAsUfk8AJIQACl+dCPJK2ootGCouZT2/MUM+gi0TWqAw7fSUzoLt+gQKtU+31N9cq68hBXl1UvtoHTuAVtZIihKvUWL/4POr6yiCgEIt81wDZhcaAii8/aH05+3X4zUARQ1Y5LEXV5NFHp9yKyVyTb0DhVthvD6PCPAI5z4zoFBPy1aBwjP26rLtsnbglNyL+CkxjVfKnvTM3KpAYQn9rEmkzU8NAhUPPvggffv2JTc3l+HDh/PNN98E+l999VX69+9Pbm4ugwYN4t133/XUSym54YYb6N69O61atWL06NH88ssvyfqFCxdy5pln0q9fP1q1asVWW23F3/72N8rLy9Meu2c7sIDk15eguRK3Qd3a8tqfRzBrZSEnvfINmypsr3czAQr/eJT4Gk+YJLhtVise32Ms+VnZnPH56/xeXOTzbG5AUVfjMR1ul86i7kBPIwOF6bRs7cnaAUBhOtzOtHh6V2t/9hZH85F8mWn2ZM9YqjzCU5YpUKQz61BXQKEuzK4NUODxymRdB7EtQ8SF/MEPzORppDI1lQ5QJP8ZzRAoUskMIv6F2aZTq3Xfh+1btyBbCEFBbCy51mCKKl+k3J6bjK++YhX0ypOuLAgo/Mm/mogr9bUECvUsiiCgUA+3MwGFU5crCtiXawD4mNsopcgAAOoY/M9UU6a/1z2fboG1MZ7QPUN1mXDu6w8o1FeknDUUg7J25oTcc5ha8RkTy19ASqkFiiYnWzT8VwtRvUPFyy+/zPjx4/nb3/7G999/z+DBgxkzZgyrVq3S+r/66itOOOEEzjzzTKZPn87YsWMZO3YsM2fOTHpuv/127rvvPh555BGmTp1K69atGTNmDKWlVaeOzpkzB9u2efTRR5k1axZ33303jzzyCNddd13mD5IhUDjapWcHXjxuBF8vWcMZ//mW8kono9D8sGWQAOpnTNB6mjRQqHXVmUHHnNY8sfsRxC2LM794g5WbNob7DAxA4fGEAZzNFChM7fWvTCkeza+2MwKTZgAUusPt9hRHsqsYw3/lk/wsp9bEa0CgyGxRt3OfPlCg8wYAhWnrWBvoLAazgziT5fIr5smXsKU+eQ8EKYG2TSqg8DyHMYZ/NqKugcI0S6HChC6+LQT5sePIFttSWPkvKuyFvr7qag2FztOcgcIpyxMd2YdrKaeYT7iNckoMfTr3/meqKdPf65/b6w1qr50tcDzCuW94oHCgYZes3Tgq9xQ+LX+P98vf8NR5xhO9BtUiJKTUnlpQZxo+fDjDhg3jgQceAMC2bXr37s24ceO45pprfP7jjjuO4uJi3nnnnWTZiBEjGDJkCI888ghSSnr06MHll1/OFVdcAUBhYSFdu3blmWee4fjjj9eO44477uDhhx9m/vz52vqysjLKysqS90VFRfTu3ZvfbxhNQW4WkDlQuBPlD35dyUmvfM0h/Xvw6KG7ErP0ibEWKEwJpHY8mD0pIKGpAYXb83vxBk797FVyY1k8vfvRdMjJT/l8gc8VJrkPsdWqCSjCxGnuQBEujt7r8Sh/3oGQ0MhA4dxLafOGfIif5dccL66inxjs8yTj+PoM8Bjv0wMKf2LueHSJf3VdAFD4vBkCRdIjJEvsj5kjn2NLMZa+1qGGcfsT/HSBQus1xvBf1Z2earMwG4JfewoCCrU/KSsorHySSrmMDvHzyLV6ueBATcQze+VpcwSKqnFW3ReymA/4P9rRm9FcTZwcI1DonwXtfRhvUHtPcq96hHNf8/PgBwj1vm6Bwl33YdlbvFP2Gkfm/pl9csf4gGKT3MTl686nsLCQgoICGkNFRUW0bduWpX/ZP5nXNUi/pRX0+uf7jfrsDaV6nakoLy/nu+++Y/To0TUdWhajR49mypQp2jZTpkzx+AHGjBmT9C9YsIAVK1Z4PG3btmX48OHGmFAFHh06dDDW33LLLbRt2zb51bt3b099mN9epwIKgNFbdePxI4YxcfYyLv3f99hSNimg0D1zmN2m1M8gyJMpUEgp6JFXwBO7HU1xZTlnf/k668s3NWugcKs2MyZutVSgcMsEFF5P1TVToAAQwuJQzqMfg3hV3sXv8pd6AwrS9DY0UOjGlwooAHpZ+7CVOJL58k2W2B/6xu2Nmx5Q6NrWJVAEKZOF2boZilQSIouC+GnERCfWVT5BhfwDiIBClQkoLKA9fdiHq1jLQj7lXuzqhcebE1C4VddAYQnJn3IOZb+cg3i99AWmln/m8kQzFC1J9QoVq1evJpFI0LVrV095165dWbFihbbNihUrAv3ONZ2Yv/76K/fffz/nnnuucazXXnsthYWFya8lS5Yk67TJrwEoPDIkXIf278kDhwzlxR8Xcd0HPyKlbDJAYdz2NESy6f4M0lmIbAIKj8Xl6ZPfjsdGHcWashLO++p1iipKtWNuDkBR1+MxAYVboeClFp7GAgrT4XZaSDAAhVsmoPB6ICbiHCUuoyt9eUnexmq5tF6Awp84e71ovLUBCm88fRzPP1PK+MIAhTOaPuIgeov9+UW+yAr7K8NzhwMK77gNoGCMob+6x6yucdApnXUUDkjoYCLoUDsndgxBlmhFp/jZWOSxpuIxbHs90HhA4VZjAoXxNSbp93ZlG/ZmPMuZxRc8hPMT5QUA9VnwedLxNiRQqJCgAgUB3lRA4XgOzz2W3bL35t8lTzG9/FvPNrPBfwsaVlJGW8rWlzb73Z9+//13DjjgAI455hjOPvtsoy8nJ4eCggLPF6QPFGG2WsUWHDuwD3eM2YnHv/uNmz/7udqjGViY5K4ZAoU6Rh1QBCXcW7bpyKOjjmJpSSEXff0GxRUV2r7dSitR3syAIrNXpjQeBQCCP0u91+NpYkDh22Y22dblUcokgiyRw9FcSRs68BI3U1j92+KGBAqpeIOhQ+lLAxTGk7U1SUgqoPD0rTmLQgjBluIYuondmSOfYbWc7omTCij0Y6kboLCRPqAI88pTKqWalQg61E7tJ4YgJlrTOavqF2crKx9Fyo1A4wCFcXvYWgCFW3UBFGi8PRnEnoxjEVOZwpO4/xamAwkmr65PXzwR4EnCQt0DhQoJKlC45fc6V8Hxeaeyc9auPF38MD+X/9S0T9aOVOeqV6jo1KkTsViMlSu9JyKvXLmSbt26adt069Yt0O9cw8RctmwZ++yzD6NGjeKxxx6r1bPUJVA4ZafttCX/2GdH7p4yh7u+mp1eHN948HvwlgW/xpTa21SAwtF2BV14ZMRR/LphDRd/8yYlFZU+jymxDUzu6wlMwsRp7kBhWlit9RiAwi0TUHg9ireBgcK5byXyOZ5rsYjzMjezsfrUaBUo3GpIoDBCQoZAYdo6VgcUyTrD4XYOWGwnTqETO/Gz/Shr5WxvPOFvo8ZviK1jgxZmq8r0bAp3H2HWbTjgkE17usbPQ7KJFZWPImVpkwUKt8IAha+ulkChrrlwvP0Yxm6cyzw+5lteQCLrDChM7ZsDUKiH2+nOoajaOtbitPyz2T5rII9uvJ/fKn7BEpLoZO2WoXqFiuzsbIYOHcrkyZOTZbZtM3nyZEaOHKltM3LkSI8f4IMPPkj6+/XrR7du3TyeoqIipk6d6on5+++/s/feezN06FCefvppLB1uh5EU9QIUjveCXbfl6t0GcPPns3jsu1/qDigMibIeEtB73R9DmGS6AYHCab9Du+48sOsRzFy/nCu+e4uyyoQrTuqxhwGKcACgeDOM4/OGABPdWRJJTzMECtPhdmH60u1IZQIKt2oLFI5ai/Ycz3WUUcKr3EqpLKnuUxPHcO8ZuwEoPGNXvJkABWG8aQCF9xmcNnqg8EhYbG+dRVu25Wf7AQrlgupxpo5fX0CRaqcn95jcC7PrAihMcvejLsrOEV3oFj+PStayIvE4tixvkkCRrGtCQOHcb8ueDOcUZvJffmKi8ize8Xvjqc9dI1P7ULMYmh+lpggUTllMxDm7zYX0jffjmY2PkZCVmqdsREVbytab6v31p/Hjx/P444/z7LPPMnv2bM4//3yKi4s5/fTTATjllFO49tprk/5LLrmESZMmceeddzJnzhxuvPFGpk2bxkUXXQRUTa9deuml/POf/2TixIn89NNPnHLKKfTo0YOxY8cCNUDRp08fJkyYwB9//MGKFSuMay7CKDC5C7HoWQcUVfdw+ajtuXDYtlw/+Qf+/dOCpgMUoZJ772cQJlEOtTg86LNU6oa07809uxzBtDVLuGbG21TYiWYJFJmMR384Hdr2us/SBBQeTzMECh8kaIDCdLhdYOKuAIXO01Z04xiuYz0reJM7Kafc50ln1iHcK1PS56mJlxoo/Im5f1xJrzB5a+L56gKAwjd2EWeAdQF59ORn+16KWWZs09hAAfrfvGeiVDMS4D+HwrTLU67oSY/YeZTJpaxIPI2UlUag0D1LSwYKRztwAEM5hmm8zM+8p7Tx91lboFDPokh6XJ2pW8eqHs+4GgkoHG+2yObCgku4sOASYiIeLdhuIap3qDjuuOOYMGECN9xwA0OGDGHGjBlMmjQpudB68eLFLF++POkfNWoUL7zwAo899hiDBw/mtdde480332TgwIFJz1VXXcW4ceM455xzGDZsGBs3bmTSpEnk5uYCVTMbv/76K5MnT6ZXr1507949+ZWu3IffBSXTQVutmoDCkRCCG/bckVMHb8n496bx+uzFwcl0MwIKX5nOEyJpNc9wVF2Hd9qCCTuP5YtV8/nrD++SkHa4mQDds5s8mwlQmNprPYY/G7fCnHZtAgq3mhpQ+M6Z0ACF6SyKzmzBkVzJMubxX+7HdqWLDQEUqSAhXaBIdVp2XQGFcy9ENgOscWTTnp8T91AqV/vapAsUJgUBhaN0Zg8yUar1E2r8IKBwXoVqZfWlZ+wsSuRclieeQ0pbCxSmWYxUQOFWYwGFdwxqnOr7NIHCPfYhHMEgDuIrnuFXPjf22RSAwjMuA1C4VZ9A4XhbWa3oEe/Z5IBCymihdn2p3s+paK5y9jNect0YCnKz6gUo1ATQtmHcpG94ffZinjx0Nw7cuqc/mVaAQhc7HCQobQOep6kDhbts8vJ5XDNjIgd234EbBh2IcP3TbUzuQ2y1qn8u5T7jOPrxNGWg8MchtUcDFEZI0Px8Bx6Ep6yjaEygcP/d/IXveIs7GcCe7M+5yfZhIKG+gAJqoKIugMI7jmCg8D6fHijc0FFGITMTtyOBHWJXEhcFhnH729b11rEJl09NrGtzanbQ7k5qzJiSeIfZNnaj/QO/J56ivRhJ99gJnvHVBijUrWMbAyhSQUJtgMK5l0i+4HHm8in7cxn92EWJpz63Lp7+HuoOKGrKzEChbh2regHfTk+q110XBBSqd5O9iUvWXNgkzqlYfM2BDX5ORZ9b/xedUxGpSg0BFEiBJQT37L8rB27dk3P++xWfLFzp7aMRgcIjQ/LqsTQCUEgJ+3bblr8POpj/LpvJbT9/gMPMzQ0o3DIBhdeDp33Qn0lLAwq3TEDhVl0BhQ1sxVDGcD6z+ITPeMHTxtMuee9P2FN59Z7MgcKtxgQKKSBbtKV/7FJsKpiduJdKWVxvQJFsG7B1rM5f30ChxtYBhfvL40FQYA2hR+xE1smvWJV4K/lvYlMDCrfqAih08dIFCqj63PfgLPoxjA+4l6XMNAKFWzXx9PeweQKFW8k6q+qr6UiQXC/bEF9p/rvQnNWk/pibosIAhdafJlA43rhl8fBBI9i9d1dOm/g5U5eu9ngbCyhq+vK2CZMoaz+nEK/VBAGJv8+a+wN6DOAvOxzIf5bM4J65H5nXBDRhoDB59IfT4Wmf7gyH6lU/L62nGQGF6XA7z2eZHJf+3q2wQOF4BrAHe3Eq3/E23/KW1lN170/YfX0oXr2ndkDhqxNB3mCgQOM1AYVbyX9GkeSKTvSPXUw565mbeJCELPMBhbYfodyHmanQnJatAoVJ9QUU2pkFBSg8dZpre2sE3a2jWC0/5A/5foMAhVvJugCgsNDXZQoU6lkUmQBFTVuL/biIngxgEhNYya9aoDDu6oRfmytQ+LaZjbLMFqXojzusAoDClzgG/rbYHMdRlhXnyUN3Y+duHTnprU+ZsWKdp60ndjMCipox6tsEtdeNOei5Duu1I1f2H80Li6bxyK+fh4KXlggUPo8GAMxx8MkEFG6FWYPR1IBC3elJTeQ9z6fGBXbiQIZzJF/yIjP5qMGAwq26AAo83tRA4X8+M1AEbR3bSvRg29g4NvE7v9mPYMsK7VjqauvYVGsonIQyk3UUqQ60cxR2Ubb7+6BdnjpZ+9DFOoiV9tustj+rd6Dw1TUhoHBLHXvNvdcTI84BXEYn+vIOt7KaxWkDhbp1bCB0bEZAEW0p23IUQUUqSWoPFIYEMChOq1gWzx62J9t2KOCENz9izpr1Pm/gblMGoPBYNlOgcL4/ps8ujNtmH55e8BXPLPgqRRzzZxnUV8rPIISnuQOF0aPZJjgQEmSw1y0TUHg8DQgUpsPt3J7hHMMg/sRHPM6vTA3+Tb3SPlOgUBdm67aH9XkDgEJdmB386pTXG7RDVRBQOPFbi75sbV3ABvkLC+xnSMiE3muMkeJavdOTGyhs/Kdd64AiLFyoMBFmDUWYRdlqmfvqVjdxEJ3EPvxuv8xa+5tmARRu1RVQ+AHAO163ajyQRS6HchVt6MxEbmY9KwzxvPeeMhHgcRJ2A1BoxxXw+lFTAYqmJimjhdr1pQgqQqohgcKpax3P5vnD9qFnfmuOe+Mj5q8vCpVwBwGFL5ENkQT7oErjaWpA4VxP6juCs7fcnYd//ZSXFn9jiJN6PEGzPOnFSf15pwU4aazBaO5A4R8rHq+nzAAUbpmAwusRWk+6QCGpSmr25HS2ZgTvcT9L+En5Dbm+vX5cqiczoPDtCqUk5m6FAYqgw+3UZ/HVCX25O34ba1u2tM5ivZzBEvsFEtU/aHUBFG45QJEQXlyqS6AwqTaLsnUw4D6LQghBL+soOoiRLLL/xXr7hyYPFP7x6curxhns9Zbpx6v31HhzaM3hXEs2rXiTf1LMGq1XG08EeJyEPQAo/LMW/uRe3TpWBwnJeAagcGtzA4rmogcffJC+ffuSm5vL8OHD+eabbwL9r776Kv379yc3N5dBgwbx7rvveupff/119t9/fzp27IgQghkzZnjq165dy7hx49huu+1o1aoVffr04eKLL6awsDCtcUdQkUJSilCJW10DheNtl5vNC4fvQ0FONse9+RG/byiuV6Dwj0eJr/GESoJDvKdf10DhXM/otwcnbzGSe+Z9yBtLpqcce0MCRWaH03nbBrUPOqVaTdIDPWHiNAOgMB1u50l2k2XC6PH36fWYXmOysBjNhfRiB/5b/W52UHtdEm4CCjze6rpaAkWq07LrGyjccvossHZkC+tk1sivWG6/UQMWhhhhgcK9jsIBikoNWGT6ypPue5MyXZTtvla1w1cnhEVf60TaiR2Zbz9BkT3XBxRu1SVQuNUcgcJRHm0Zy/UAvMFNbKJI29ZTJjB7krBQ/0Chbh0bBhJUoHArDFAIIZvWtrJhD6yry6809PLLLzN+/Hj+9re/8f333zN48GDGjBnDqlWrtP6vvvqKE044gTPPPJPp06czduxYxo4dy8yZM5Oe4uJidt99d2677TZtjGXLlrFs2TImTJjAzJkzeeaZZ5g0aRJnnnlmWmOPtpQ1yNl6bNHVB1KQU731mCnx0yR3RqAIiqMm/a72yzZs4sjXP8ASgteP+BNd8lrXeJoaUKh1IX4LHjzDEeK5UiT3UkrumvsBry2dxl+2P4wDu+0YEgA0Y2+GQGH0aAAgOI7e6/Eof96BkNAMgUJdmB1u61fvfQWlvMlNFLKcw7mR9vSsNVD4IEEBCre/LoDCU5cCKILqgoAiaKen1fYn/G6/SlfrUDrFxmhjpAsUgA8q4ghi0spolkIHEKleeXK+z3RRtiMdULivUlbyq/0QG+V8+scuo0D0q2nrJNt1DBTq1rG1AQpPWQMChbtuHct4jb+TTweO4a/kkKdtv7kBhencCveibLVuk72JC1eOaxJbyi668pCavK4h+i2rYIs73gn97MOHD2fYsGE88MADANi2Te/evRk3bhzXXHONz3/cccdRXFzMO++8kywbMWIEQ4YM4ZFHHvF4Fy5cSL9+/Zg+fTpDhgwJHMerr77KSSedRHFxMfF4PMSTRjMV4dVAQOHp0pVwdc/P46XD9qO0spITJk5mXWmZN3YEFIHJPdLi0m3255DuQ7hp9ttMXjk7RBx8Sufk8AgoMgMKt0xA4fVUXVWgcKspAQVAjBwO5iryaM873EwRqz1ez9gbCCgI9DYMULiVauvYDtZedLEOZqX9NmsTn2tjeOOFAwpHMWklgSJZlsYshe5Au6YCFAKBJbLYxjqPPHoyL3E/JXKZ15MCKNxqiUAB0IEeHMF1FLKKN7mdCspSAoVbLQUomtpMhZQN/wVVUOP+Kisr842tvLyc7777jtGjRyfLLMti9OjRTJkyRfs8U6ZM8fgBxowZY/SHlQNBYYECIqhILSnMIFAPQGE8LVsKtmjbhpcPH83qTaWc+PaHFG6q0Mb1DN8AFFqPAhRBHl1fTRUonDgCi6u2O4jRXXbgxtlv8OXqeaGAwjye1M+l/TMJASYmoPDECdNXMwQK3zazGqAwHW7n7UvxJtvikwkoPB41rnOv8xjuq8qq7rLJ52CuwSLOf7mZYgprxq541Xi6cdUWKExbx2YCFG6lAxTqOopUp2V3EQfQwdqH5fYrFNrTAr2eshSH2zlygEL3WlCQwi7IhvCLst3fBy3GDgMUVfcQEzn0j11ENu2ZnbiH8uqTy8MAhXH2ogkAhVv1BRTOfVe24AiuZiULeIe7SVAZCBQ1cODcexN4z9gNQOFWcwCKSFXq3bs3bdu2TX7dcsstPs/q1atJJBJ07drVU961a1dWrFihjbtixYq0/GG0evVq/u///o9zzjknrXYRVIRUYwOFo63ateOFQ0ezqHAjp/3vI0rKK71eTfswazl0QJHOaz4moHArrSS4joHCubeIcX3/w9m947ZcP+s1vl37W4MARTpxgoAisxmOEJ93oEfv9XiaIVCoZTqg8MGBAhRupQMUTl0eHTiYaymnhEncRjkloYDCCAma/8DrAijcCgMUpq1jVaDwxk0PKACkJehmHUFbsSvLEs9RZP+Uso3pcDt1lyfwzhCosxS6WYuw28WqMWqzhsLbDq0naJenuGhN/9glxMhhVuJuyu31hrY1qkugQOvF6A0DFL6TtZVxu1UboHDue7Ath3M5i5nJJB7Exs4YKPwe596f3JuAwi0TULhV30AhhH5sLU1LliyhsLAw+XXttdc29pC0Kioq4uCDD2bAgAHceOONabWNoCKFpBShkjIVKLymEIlbCKBwygZ06Mi/Dt6PWavXctb7H1Naabs8Xm9jA0Wo3+D72tc2CQ6OE7csbtz+KHZp349rZr3MjPWLWhRQGA8D1MbRez0eA1C4ZQIKr0fxNgOgMAFE0GtMqrct3TiQayliJe8xgUrKaw0Upl2htOMSJq8reVfr6ggoTDs9BZ2W7Ru3EHSPnUC+GMSyxNNstH8xxPaP0b0w2y0VJtTXk0xAYZJua1p3PKdP8AOFp66OgcJRrmjLgNhlSBL8bN9DQhYrbWtU10ChnkXREEChQoF671YYrwD6siOHcDFz+ZoPeAIpZaMBhfqKlA4oTJCgmzExed0KAoqmJieva8gvgIKCAs9XTk6Ob2ydOnUiFouxcuVKT/nKlSvp1q2b9nm6deuWlj9IGzZs4IADDqBNmza88cYbZGWlt/YkgoqQ8iRBarIakAAGAoUv2dT0pfTveHfu0oVnDtyPb5av4sIPP6HCtjdboDAltoHJfYqzG7KsGP+3/bHsUNCbq2a9wM8bluJWfQNOSwAKHyRofr6NJ2trsl8TULjV1IDCn1h7y52yjmzB/lzJH/zGR9yHbUhR6wooUp2WnQ5QuJUKKHTeVEChbSOU/oRF99gp5Ip+LE88Rom9SN+HqEnsw+zCpJPudaawC7JVUGkKQOF834qO7GBdSgUbmGXfhy3Lqtv6x99QQOFWQwKFf0G61+uW27Mtu3Ig5/EjH/Ep/8bZB6cGDpw2TR8oTOdWeOJY+rrolafMlZ2dzdChQ5k8eXKyzLZtJk+ezMiRI7VtRo4c6fEDfPDBB0a/SUVFRey///5kZ2czceJEcnNz0x5/BBUh1GhAYUiUnfKRPbrx6J/24eMlvzP+4y9I2Lbfq3mOwCS4BQCFc82JZXHzgOPZqnVXrpj5PPM2LM8oTibjUb26sySSnozWYGhAoBkChQ8SNECh7vSkAoVbJqDQetQxBHkI8gQDhVPWjf7syyUsYQZf8DiyujatWYcQ3lRAgc7rxNEAhWnrWO3rSkJfFwQUfjAwxBdxesTOIkt0Y3niUUrlCqWdHyicqy7JhuCF2Q5chFmQrXuNqrGAQh0X1KyhyBc9GGhdQgkrmGU/4Dm5PBVQuFVXQOGDhCYCFKb2QsBAsRf7cSrf8A5TeLNFA4W7fZM6s0KKhv9KQ+PHj+fxxx/n2WefZfbs2Zx//vkUFxdz+umnA3DKKad4Xp265JJLmDRpEnfeeSdz5szhxhtvZNq0aVx00UVJz9q1a5kxYwY///wzAHPnzmXGjBnJdRcOUBQXF/Pkk09SVFTEihUrWLFiBYlEIvTYI6hIoaYKFE7dvn16cd++e/L2/AVc98XXSClDreXw9uUde5jfvIdaHB7gMT6fJ07qeJkChXPNi+Vw+w4n0jO3PZfPfI6FxaszilOb8egPp0PbPtwMh/+zNAGFx2NI8rUezSJ839ibCFCYTst2A4XpcDvtAnnV47ekDRSOtzc7sQfn8Suf8y3/TiangZBgOIsiEBKE2asuzFaBAo3XBBSevoW+TdAuVGGBogYssukeO4cYBayofJhyucY3dt1OT47CAoVJdbWGwlOnGUdtgMLnUWIUiL4MtMZRxHx+th/FlpWhgMK3+LqegYIgr/Ks3jL9vdtv8ga1dyf3w8SB7MExfMbLTJOTfEDhHXt1HOG993iE92rpfnabMFBESk/HHXccEyZM4IYbbmDIkCHMmDGDSZMmJRdjL168mOXLlyf9o0aN4oUXXuCxxx5j8ODBvPbaa7z55psMHDgw6Zk4cSI77bQTBx98MADHH388O+20U3LL2e+//56pU6fy008/sfXWW9O9e/fk15IlS0KPPTqnwiBnP+MFV1TvZ2wAiqqy6msDA4W7/WvzfuPyTz/nzB124Ppdd0UIUWdA4SvTeUL8Ftw8w2F+rlCJe2CcMM8FhRUlXPLTMxRVbOLeHc6gZ6uOzR4o1EQ9XBxSezRAYYQEN3CH6Mt0FkVjA0W4Rdiqx5+wm9qr26/+zHtM5Vl24lgGcbinTbpA4asT3ntv7NRA4X8+M1CkOotCv1bEcDXG8F8rZSHLK+8HBF2yLiQmCjBtHWtSulCh2z1KBymNOUORCijc/ayVPzHTfoAuYhgDxJnERKw6Lp5xu/swAYWnro6Awt+nc+9/XuFr4++zroAiGVNKPuJ5vuG/HCouYLDYo8YjlDjKfVWZN7nXAYU/8a++dz1YKkhoCKAQQrLJ3sQ5v1/aJM6pWHDp4bRpwHMqNpRV0O+etxr12RtK0UxFKrlOQ8wEKDwyAIXWE/gbfH9SdtQ2W/OPkSN5ctYs7p4+PQKKkONx+iqIt+bOHU4lL5bN5T8/w8qy9SnjuRUBRf0DhVsmoHCruQIFQH/GMJijmM4rzOVD/axDEwQKtxoLKAAsUUCX+HlIyvmj8jEqqxcegzf5182oQOZAYVJzAwoBdBSDGCDOZqWcyi/yBaSs+ZuzOQAFaXrTAQoBCCEYLU5iMPvwjnyEufLbKo9Q4ij3VWUNAxRumbweT4ZAEanlKIKKkMoUKHzJtAYowiTcpsPt3H2dvP32XL3LLtw/YwaP/vRjYBIcAYW/r/bxNtw54DQsYXH5z8+wpnxDyDgBf24Gr36HJDztg+AlcA3GZgoUptOyPWs5kuPS37tlAgqPR42r3Os9tQMKx7MjR9Cf/ZnKMyxkire9ASj0Y88cKNwKAxSmnZ6CFmxnChSpFLM60Dl+LpWykDWVT1Ih/QdN6V4tSveAOxNQ1MUaCl28hgAKp6yrNYztxSkslR8zX77hGbe7j+YIFOqiddWLxhsWKKAKFoQQHCzOoj/DeF3ex0Jm+jy+cTUgUKhbx+rPuKiuMwCFW4FAEcFFi1AEFakka7YDa8pA4fjPGzSYiwYP4dZp3/LcnJ8N4/G2CfObd+2heSHe028uQOHE6ZzTlgnbn06ZXckVs59hfUVxnY9Hf9o1nvbpz3AonsA1D/okP9ATYjem5gAUptOyPQAgvWXpQYcuaU7t9XskAsFQTqYfo/iKh1nKj1V1AUDh2xWqlkCR6rTsTIEi1daxJqBwK2i2whl3zOpKp/hZVMgVrK98lkrXwuPaKggm6goofHUNCBTOtae1F9uIY1kg32GRPckV12mTOVCg8zr3jQwUpvahoCMJCxJLWIwVF9GXHXjZnsDv8hfF44rTDIEi1CtTTQwopGycLWVbgiKoyEAmoPB6Gh4oHO9lQ4Zy2vY78Levv+L13+YpfXnbhEmU9add69sEtQ+GF/9zpRxXxh71Wbxj757bngn9z6CosoSr5zzDxspNzR4ozHF0n48ZKMJAQiqvWyag8HiaGFCEgYQwMxzmeDXJvcBiJOfQjR35nHtYxVyt111mAgq36gIovPGCgULXd5jD7VL1E9TW+T5m9aZd/DTK5XyKKl9ESrvWrz0llH50r1I1FlC4VVugcMr7WgfQTxzCPPkKS+SndQYURkjQ/DvQ1IBCPdxOBQq3sqw4x1qX0Z2+/Nu+jVUs8nkioIi0OSiCirAyJIBBZ0CoQOEJFyLhzgQopBQIIfjLsJEcu812XP3lZ/xv4YJqj7dNYwGF6bTsUIl7YJwwz+XvS+ft3aoTt293BivL1nPt3GcpSZSl7CvV2FsCUJhOy9b1FQQUpsPtAndjUmDB69EDhcfTgEDhX9zseHQji7M7F9OBvnzKBNZK/04cYYDCdBaF9tWpEEBhqtMBRaqtY7VQINS2Ia+anZ7i1lYUxE+kXM5iY+I/6PYnyWS3p1Rw0hhAoSbNJqBwKwgoHG3DkfRmX36Wz7JcTm0QoFBfkWpOQKFuHZstcjjBuor2dOa5xC2skctd7fRA4ZYJKLweMyQ4agygEEI2LbiQouG/WogiqEgl1w9EbYHCl4QFJMomoPAMLeDVKyEE/xyxOwdvsSWXff4Rnyxd7BuPaewtCSiC4vTL68pt/U9nUekq/vrLv9hUWVGr8fjH5W2r91Rdg06pbolAYTrczpO4J8uE2ePr0+9BKWsIoFAhISay2JPLaU1nPuU2NspVGkjwx0EpCwIKdWF2bYDCrbBA4Wkj9PHDAoVup6ccawfaxI6h1P6WksR/Pa94pzNDodvlCWoS1SCgcGAi1NqJegYKf2KtxnWNSVb9smoHcSI9GMEP8nFWyR8MgOIdd30BhVtNHSicqjwrj5Nj15JLa55N3EyhXBMIFOpZFDqgULeODYIEE1C4ZQIKrcc3Lj1QRGo5iqAipJoKUPhetQlIpi0R4/bd92HPHr258NMP+Nq1r3GYxN3fJ6k9mwlQONomrxc3b3Mac4uX8o/fnqes+hCYdOOYTsvO/JUpPVC4lRGYBHgCX2PaTIEindeYwq3BSB8onDUUcZHHXlxFnFw+5VY2yfWBQJHqtOx0gMKtMEBh2ukpCCgy3elJBxSOVADIjQ0lP3Y4pfbnlNgf+Z7NpKAF2RAeKDxlQTMTTQwonPhCWOwozqILO/K9fIA1cq6n74YECnXsOgDwx/M/nz+e/h5qBxTOfWtRwKmx6xDAs4mbKJZFjQ4Uvt2cNEChQkLgLlHqtrUWnle0GltSRmsq6ktN6I+5aUrK5gkUzjVLWNy3534M7dyNcz+ZxIzVK8MBhVoX4rfg6vO5ZQIKjycM4KRxQF9tgcL5fkB+X/6x9an8sGEBtyx4gQpXBt/UgCJcHL3X41H+vAMhwQAUbpmAwuupuqpA4VZTAwp/0uz1esv0QOGWCSic+1zRlr24igQVfMbtlMvijIBC22cAUPjHXnug8I6hboFCBwBOXavYbuTF9qck8R6bEl/5fKqC1k+ksyjbU9YMgcLp0xIxdhYX0J5t+FbeTZFc6BmPChRuNSRQqGXa5zN4dSlgXQCFU9ZWdOS0+LWUUsIziVsplSXNHij8nhqgiNRyFP1xh1SYV1T0C7ZJ7aknoHD+L8wWWTy81xi2a9eBsz7+H3PWrWkQoFDHHPhcIZL7dLa/DYSXDMBkSJut+etWJ/FN4VzuWPgyCWmHitOSgcK3K5QGKEyH23n7UrzJtvhkAgqPR43r3Os8hvuqsroBCuNrTJrfAEokrUVn9uBqNrGOL7iTSlmaNlCYzqLQ/S4+FVDovCagCPTWMVCYXlMCyLP2o5W1BxsTb1KemK55ar+CFpCns4bCraYGFJ6xaYDC8cZENsPExbShB1/LCWyUyz3j1o9PjVN934SBQt06NhA6DEDh9VTHEZJOojunx69lvfyDf1XeQXn1lsebK1A0pdegpN3wXy1FEVSEUPBuTJpk2nRadkCiHGqHpBC7TalA4SgvnsXjex9Er9ZtOP2jd5hftK5FAUXt4lRdh7ftz9V9j+fzdT9y76LXsaUdGMcEFG6Fmp2phaclAIVapgMKHxwoQOFWOq8xNQRQ+H/TDwWiJ6O4gkIW8zX3YcvKqro6AgrfTIShvGqs+jr9q10mMEhRr/yDlilQOPdCCApih9LKGkZh4mVK7Z/RSY2TEDYJ4f2JSHcNhREkQgKFW3UNFMm6AKBwlC1aMVyMJ4e2fCVvY5P9h8frHZ8ax6/mCBTurWO99zqPc1+TnHez+nBK/CqWy4W8UHEPdvWWx4ELrQ1A4VYEFJEaSxFUpFBjAYVx29NQyb1//FIK2mTn8MQ+h9AhN5fTP3qbpRuLlIfVA4XHkk6CuxkBhfP9Hu13ZHyfY/lg7TQe/f1tpJRpA0Vmr0xpPIZF8/o/E73X4zEAhTfO5gkUPg9BHj1QEODVezIDCufaQWzFSC5jNbOZxqMkqmuDD7CrPVB44tUjUOjk3R0qGCh08iTQQlAQO5ocMZD1lc9RZv+abG9aQxGTFjFpJWM19BoK8CfbJqDQPXddAYVTlyPaMEpcRYwsvuQ2SmWhzxMICVJ9Fq/Xrc0NKBxtEduGk7Iu5zc5i5crH0JW/+3xJu7V7QOAQoUEFSjcMgGFWxFQRMpEEVSEVTMHCufaIacVT+59GNmxGGd8MpGVJSX65wvxWk3wDEcI6MgATMLEqS+gcOLs12EoF/U6komrv+SZ5ZNwtqdsakBh2qlJ6wkACtPhdmHgRbeA3AQUujjNCSj87XUJu+PRAwVpejuLAezC+fzON/won8Wu/oN0e9SF2SpQoPEGAYW6jsIEFEFxTUARqo1mLG6gUNc9ONLuVCQs2sVPIFtsyfrKZyi1F3ti6+I4sdIBCt046hsoTGslMgEK7/i9dXm0YzeuJkEFX3Ib5XJjnQGFHwD8kOB7hgBvUwMK5/ttrR34c3wcs+xv+U/lEwjX36K6AgrTTk1uoPDVhdklKmCXp2hLWddXC1EEFank+oEI/i2vPoHzeBoZKJxrl1ateXqfwylP2JzxyVusKy1V+tSMuZZAYUpsA5P7NM7TqGswSRXnwI4jOKv7Ybz6x0e8vGpyiwYK48nanp8jvdcNFKbD7fS/5Xc8zr3mz83xKPEDPcq9t6z+gcJ0WnbQrEN3axhDxBks5GPmyP+kDRT+56sboMjktOx011GAHyh0UpN79xayQsRpFz+VmOhGYeWTVNorAuOkAgpdn3XxylNjAoWFvs4Zd77owu5cTSnr+YoJVMrSBgEKEyQEvSqmerUeoW8DdQ8UzhqKgfFdOCbrHKYlPuXtin8jpWxyQGH21PTlH2sTgolI9a4IKkIqMJkOAgpDohxmUXdaZ0q4PSGS1x55BTy1z+GsLyvlrM8mUlReXt02dV9NGShMn2HGMxMBcY7ovCcndz2A51ZO4s3Vn2oPp0vGy2hRt8ZjAAp9HLO3OQKF6bRsN1CYTsv2vHqFwYNfJqBwqyGBwldX/eh9xJ4MEMfzC2/zm/yfMo7MgcL7nMFA4VZYoNDFT2dhtu6VJ3Xdgy7B9I1XZNE2fjqWaMv6yidIyLXGOOkuyt7cgcJRgejJ7lxJEb/zFfeQkOWNDhRqnQ4ofB7h9yT7NACF3uPcpwYK536X+O6MzTqVzxOT+DDxZpVnMwCKpjZTIWW0pWx9KYKKFHL/QNQHUKi/0dYCRajkHmU8AQludVm//PY8udfhLN1YxHmfv01J9eFuKftK8XwtASic74/r8ieO6bwfTyyfyKS1X+nHE+LVqyCgUBP1cHHweRyZgMItE1Bo4wT01RyBwgQJ4V6Z8n9QoWYdagEUzv3W4kC25hBmy5dYJD9L3acyZj0MKXUBQOEfu/7qVm13eoLgXZ50cs9SuNdOWCKPdvGzEGRRVPE4tiwytg2zKNtT30SAwq26Agr3+DqILdmd8axhHl/zELZMePtshkBRAwfOvR8o/B7n3v8zaQIKR7vF/8SBWcfwfsV/+KJyUrXHH8cEFNq+GhkoIrUcRVARUs0dKHxl1ddt23bmsT0PY27hasZ9+V/KEpWhEm7j87kUanFwqDhNEyic60ldDuLQjnvw8LL/8PH6b73jaSJA4fNogMI46+D6PyFMX6bD7YLOptAdhFczrmqP777pAoXfEwAJBqBA5zUAhePZThxNb/bmJ/kUy+U0f5/KwuzaAEXg+AKAoi63jjUBhbOYWifTQmwbCSKfgqyzgEqKK55G2FWvhoZdQ+FWUwQKX10dAoVz7SK2ZxQXs4wZfMvjiOp/bExA4dbmChTq4XY6oHDPaOwbP4x9sg7mzfLn+bbyU1+cIKAIc9q1v89aepoZUDTEzIT61VIUQUW6MgCF1tPEgcK537FDNx4adSjfr17G5VMnUWEnMgYK43OFSe5DnN0Q5rfqGb82lgHg2FIghODMrkcwut1w7vv9Rb4snOFpHwGFmtyr7b1e0AGE994tE1B4PGrcUJ7GBQrfrlCBrw458WCgOIXuDGOGfIQ/ZM1WqamAwhsvNVCYdnoKjNvAQBHm1Sd1MXZMdKBN1lnYcgMbKp8CWRYaKFKCRAqgcCsVULhVF0Chj6eMJQVQOOU9xE6M4DwW8iXf83zyh04HFL5zKzRjrxmX6vF7mztQAMRigkOyj2dk1j68VPoEP1Z8W2dAoW4dqwMA4wyHZuenMGsoLCGTzx1p81YEFSnkocwAoEjvN/jmpKzG1DBAUdUehnXuyT0jDubzFYu4btoHVYe7NWGgCANl6cVJfzzuJFgIwfk9jmH3gp24c+lzfFv0s68vNV7gGowIKAKBwnRatgcApLdM68Hk8f8HaPIGe+oPKIxnUQjBjuJsOrI90+X9FMr5oYDCVKf75ykdoDAtzK4PoHArLFA4cq+hiIuutI2fSUKuYkPlv5CyIuWi7LoAiprEPzVQCEOcTIFC3To2U6Bw1FeMZBinM4/3+Yn/ZAwUYSAhlTfQ08SAwknSLQuOzjmNIfHh/Kv0QeZU/tSsgKJmjBFMtCRFUBFWtQUKX2KqtHX7Q/xW3QQUHosBKDweV5K5R7e+3DbsAN5b+gv/N/0j1K1SfEDy9gABAABJREFUI6DQA4VTZmFxcc8/s0v+Dtz++9P8sHGeMY4OKHyewDUP+iQ/0BNie9dM+9pcgSLcK1Nqwu54/B9UfQOFTCY/cQaLC2lDL76Td7NR/h4SmMxAYdrpSQUK7/PWPVC4YzsgoC6oDgMUbiWETaUy/iyrF+3jZ1AuF1JY+W+kTPiAoq7WULi/b4pAgdaL0euUbcN+DOEEZvIGP/PfegEK/7N7vUHttVulOh4FKII9mji1AIqahNzipFbnsl18EE+W3MP8ynnNEiia2kLtaEvZ+lMEFank+oFobKCo6cvbJkyirD8rw99m/57b8I+dR/OfRbOYMPOzml18AgAnzMxL4IxLRh71WVJ/BvUJFM54YjLO5T1OZYdWW3PL708wu2R+nQGFOY7u81E8ml29AmcdZLBXP1Y8XrdMQOGWCSi8HpHa08SAwgcJClC4ZQIKt1IBhVMXFzkMsS4hh3Z8b9/FJrlaO4NQ10Bh2ukpzMJtnVSgCNo6Nh0l4zqzE0oiHkOQbW1Jx/gplMrZrE+8gpS2Z1G2W5szUDhjNwGFW8n21X0N5BAGcjjf8wK/8LE2jm7sNWX6e+3zKd6g9p7kXvUI577m58w/I6HpS0nuVaAgwGtpPkwhICbinJF3EVvEtuTR4rtYUrlI8TR9oIjUchRBRUgFvca0uQCFo8P67MB1O+7Dc79N5+G5U7RAYTotO1TiHhgnzHP5+0r5GdSRJwgonGeJiyyu7nkmW+f24abfH+W30sUtEihMp2VrT9ZW+8btccrMQKEuzE7vFSW/GhIojCdru+MIk9c/evfWsVmiNTtZl2ORxff2BMqrTzxOBRTeeMFAEeg1AEVwfKdtjTfVadnuU65TybeGwnVKdkxanhgxBLnWADrG/kyJ/T2FibcCF0c3NlC41ZBA4YMEpa/BHMN2jGYKT7KIqUpf3vF6y/T32udTvEHtGwso/Il79b3rwXSQkC2yOSf/UjrHuvJQ8R38YS83elWZgMLjCQASdVyZAEXVTIUvZKNJyqr/rxvsqwVxVQQVIeT97bVz1YOAt6E+GdbGbiJA4Xx/XL8hXDJgdx6dO5Vnf53WpIEisziZjccfz9vWUY6VzbU9z6Fndlf+sfQhFpct0/4G3wQUgZ4IKNIGinQgIdyibj1QeD1KX/UIFKazKCSQI9qys3U5CcqYYd9NmSxWxu5P+J0/yjBAYYYCc/x0XntSgcIEBGEUNMPhBooYwrOGIi+2Ex2so9hgf8F6e1KyPNS1AYHCOHvRiEABVZ/nrpzKluzG5zzIMmb4xl4Tz9tHSwcKp7yVaMUFra8gX7Thvg23s9b+Q+v1xAkAilAzHOoaDgUo3IpmKCJBBBUplS5QmCHBFTOZ8KUGExNQ6DyhkuAQ7+k71zO23pWztxnOXT9/xqsLf/D03xKBwnRatumVqVZWLtf3uIBO8fb84/cHWV6+KrAPWwMAQTMlJm+QJ3BdhAEotHEUoPB6quNtZkChJuF6jwESNP/BBh2ElxxXLYHCUY7oxBBrPKWs4Sf7PiplmSdOOkDhGV9IoAjVRgMUjupiYbZpUba7fcz15Y5pSUGb2CjaW4ew3n6fosSn3vqQQKEbrwko3KoLoPDEa0CgqGkXYzfOoSdD+Ih7WMkc39iDICGV1+shfU8TBgpHbWL5jGtzFTER474Nd1Ak13u8njgNABQ+T0CcpiQpoy1l60sRVIRUQwKFf7ZBia/xaIFCrQvxWo36fBf2H8Wf++3MzT99yDuLZxufK2U8t6cFAIVzzY/lcX2Pi2htteIfy+5nVflarTddoDAtiPd4lD/vwFmHAKAwnZathQQDULhlAgqvp7oPBSg8HmV8zQEoTLtCaeMYgAKd14mjAQpnzHmiJztal7KRJcyyH6ZSVqAqDFA01GnZEG6np3QXZrthwvneAQrwbhtrSeFZlN0uth/trP1YY7/JBntqslx71QCFP/E3A0XSU0dA4dvNSenbrboGCscbJ87ejKMLW/MBd7CaBWnNOgQBham9xyMMHs2PjwkovJ6GAwqn//axdlzS5krKZRn3F93JJrnRH6eJAUWTW6gdqd4UQUUKSVouUFTJ4ood9uawXgP524//Y/LyX8zPFSa5T+PE70B4ySiOxhMiTqZA4bRtG2/DX3uOI06M/1t2H2sqCr2eFgAUptOyvbs6ect0QOGDAwUo3ErnNaZwazCaBlCoC7NVoEDjda5txJYMtC5iPXOYK59CSrsmXh0DRV3u9FQbqWso3HJeeTKdQ+HInbR3sA6hrbUbqxIvUWz/4KuH5gkU6tax6vjQeZ37EEDh3GeRzWgupy09eJ9bWcfvTQYo1K1j9R6nTA8UbpmAwuvRJ/luJcdTXdc53oWLC65kvb2O+4vuplSWNmmgiNRyFEFFSOkT7uqiNF5j0h2E5/M0EaBwyoS0+OuO+7Nvt225dsY7fLVqQVrjCQMU5jiazyejOJmNxwQUboWBl/ax9vylx8VUyEpuXn4/hZUbfZ5QEBQBRSBQ+DwEecxAYdoVSg8dZkjwx6t/oPA/n3fsbUV/+ltns1pO41f5AlJKI1Don1N/9T5v3QBFmFmKIOnaOeswdIuy3THdMxTua0xYdLaOpo3YmWWJZymx5yjt/GOrS6DQPX9DAIUJEoTm3ymjt/qaRSsO4Bpa0Y5J3MwG/thsgELdOlYHFP7Fzl6vu04FCsfbI96TiwvGszzxOw8X3UeFrPABhVuhFnUbgCLQEyKOsCTC0v8dbhTJRvhqIYqgIoWk9B9+55RDMFAYtz0Nldzrx6L1ekx1CxTOmC1i/HPwwQzvuAWXf/8m09cubdFAkckrU53jnfhL90vYYBdzy4r7KU6UGA+308fRe90yAYXHYwAKr0fxajLllgwUoSBBqH34IQG1rp6BwlEnsTNbi5NZIT9lkXxT20Y/djNQ1PVZFLWdqVDXUKhKtYbCc+8DixjdYyeRL/qzNPEEpfaC6nZ4fLq2tQUKgb6usYFCfUUqaKbDQpBLPgdxLRZx3uUmilkXAQXeOhNQOP6+Wf24sOASfqv4lcc3PEJCJvTrK0L0FQQURk+IOE0KJiLVuyKoCKtGAop01g2YgMLzGKF+G65PvLOsGLftdBiD2vXgkmn/4efC5RnFCTOezQ0onGv3rG5c1+1iVleu5dYVD1Bql9YaKEynZQfOOmiAwniytuf1LL3X9qQDBq/PYQYKrUeJG+hR7r1lTQMofHUKUODx1g1QuOu6WruzhXU0v8v/sdR+X9OnOnY/SCS9aQJFTbtgoAh6dSloliIIKJw1GKZXnlIBRQ1YxOkZO4NWog+LEg9TZi/1jauhgcL7nE6bpgEU6tgFkEd7DuF6KinnXW6hlI1KPPPzmYDC49mMgcKp2y57O85rdwE/lf3Is0VPYVf/R9DUgKJpbSkbLdSuL0VQEUbNHCjCJNymszbU58qNZXHnzkewZX4nxk17ld82/hGc3Kex/W1dg0mYOA0JFM61T3ZPrupyEUsrlnPnqkcot8s3G6AwnZbtSWyTAKHeC7NHie8pM9y7ZQIKt0xAoffUP1CoOz0Fz3R4vamAomqc0NPanx7iQBbbr7HK/kIDXGYoMO30lM7rUW6FfeUplcK0C7MoO8w1LnLoEzuXHDqxMPEgZXJlso9UQOFWXQGFunWsChRuNTZQOJ4CunAI11HCOt7lVirZZH4+XzzMHgNQuGUCCq+nOk7AuggTUHg89QAUAMKCHXMGc2bbM/m69Gte3PAizt/gCCgiNbQiqEgld0IZAAk1HoPX42meQOGUtY7ncM/QY+iSW8CF377MkpJ1+j4yBArTZ5jxzEQaYKI7nC7pyWhRt8Zjw1Y5fbmi84X8Wj6fe1c/RqWsNMTRP4vHEwFFWrMO4V6ZkgGegOTeABRu1RVQqDs9pbPGwfnjt5H0tg6ni9iT+fbzrLW/MwKFNl4KoAgag27r2NrINKvhPssi3UXZYa5ZtKJv7EJitGZB4gHK5bpQQOHzNABQ+OoaGSgcdaAXB3EN61jG/7gTmwr/8/niYfYEAIW6dax+m1ilL01yr+70pE/cq8sMQOGWCSjc0gGFo+GthnNywcl8VPIRb2x8M1RfLRUoopmK+lMEFSEVbtbB4HX5Q/1WPY2djUItDq9DoHCuBVm53L/zceTHc7hw2ous2LQhRJymBRSqR3/aNdr24WY4NB4XAPTP3YZLO13ArNI5PLTmSSqrK4MOp3NkAgq3TEChjRPQlwko9H0pXk9feMo2F6BQd3rSAYVvVyglKXerroDCVKf+EQsh2MI6ng5iKL/ZT1Fo/6wFCtNOT4HrLEyzFxkuzHakgkAYMKmLV55MuzzFRT5bxcYhsJifuJ8KuaHZAYVb4dZFpPaGAQqnrBtbcTBXsoJ5vMd92NT8omVzBQoVEnRAYdqpyQ0UTt3erffkmDZH887G//K/je8Fz3AoV7fqzlPjjXaBahmKoCKF3JRZr0CRRvIaBBSm07K1pGxIvD0WDVA46pDTmgd2OR6B4KLvXmR1abH2GYLG01KBwrkOyh3AuE7n8N2mGTyx7l9UVmfVQYcKBgGFcdbB9e+5f8yaOMpOTzqgMB1up1scbgKKQI/fYgSKYE/TBwrT4XbamY46Agp3nRAWfa3TaCP685v9CBvl/IyAwjPOegAKZz2Erh9H9bWGwgQUTlmWaMfWsXEk2MRvifuplCUZAYVbDQkUoSBB6Uv1Bo1d8z+QDxJ6MYADGc8ipjOZR5DYEVBovDqgcOoOajOGQ/IP4pWi1/ik+DN9ew0I+Meq8YSI48xSCM3zRNr8FUFFSG0uQGFqr9+21tC36/suOW15YOgJlFSWc/H0F1lfVoqqULMzQZ9hWnHMz2XyNBZQONopdwjndTiDr0q+5rnCF7CTYNG8gUIFCB1QmE7L9gCA9JZpPZg8/v/QTN5gjy5prq5rAKBQF2aHe75wQOHEt0ScraxzyKM3vyYeYJP8Xe9VgMKt+to6Vifd6046oAD9GgqoO6BwrjmiC9vGLqactfyWeIiELE8bKPy/1fdDQtLbxIDCDwAaCPJ5vN6+7MSfuJC5fMlnPINE+rxumYDC42khQOFcjyoYy36t9+HZ9c8zddNUrachgcISUrvOpLEkbdHgXy1FEVSkkOkHIgKKmmvPVh24f+cTWFNWzPgfXqK4sizNGQXlPs1zOYyekIfTqTIBhddjBhKfJ/AVpaqyXVsN5/R2p/Bx8ae8XPQa0pXBR0BRe6AI98qUmrA7ngBIUIDCrYYECv/YzUm6DiicPi2RzVaxC8imI78m7meT/MPr1QBF2B2f6hoodHLWTkDDA4VzbSV6sk3sIkpYym/2I9jV66XqCiiSdZshUDh12zKK/TibH3mfKbzkH48aTwMUNXDg3HsTeLdMQOGWCSi0nkYECiFACMGJbY9nVN4IHlv7FDNKf9B69WOte6CI1HIUQUU68iWmeO6DvTpP9SWNnY30h+/pvQ0BFM61b14X7h58PIuK13DVj69SlqhoVkDhG0/Gr0wpHg0AmOLskbc7JxYcz3vF7/PWhnf0fWmy13DrIvRA4YljAAqPJwKKQKAwnqytnVHQA4VbdQUUzh+PCSjcskQrtopdhEUu8xP3USbXVY/B721ooDAtxnbLfahdXQOFWyaPQJAvtmRr63w2yHnMt59CVP8w1zdQeMfnrWsuQOHUDWRf9uAkvuUtvmOi1gOZA4Xf49z7k/sgoFBnNHQnUPu9ZsAJOu3aFEfniVuCs9qfwpBWO3L/mseYXTbHH6elAoUUDf/VQhRBRViFAArTNrP65N7bJkyirN/aVt/GM3QDUOg8YRJTUwLfv6A7E3Y8jllFv3PdzP9QYScioAgdp6p+dP5+HNXmSN7aOJFJG9/3ejS7egXOOshgr36seLyeMgNQuGUCCq9HpPYYgELbl+++6QKF6XA773oIpU4BCrfqCijUhdkx0YatYhchSbAg8SDlFPv6bIwZijCqT6BQD7cz7fIE0M7qz9bWWayVM5hvP58EifoEipp23jrd+WOh1kUYgEIXRx27CPR4vbo6AQzlEIZzBJ/zAj/yYbMEChMkeMajxtEm7vq6oL7ilsWFHc9iu5ytuOuPh/itbGFN/wagcCsMvIRZQxEt1G45iqAilVyU2ZyAwtReN+bA5wqR3Lu9g9v14daBxzFt3QL+PvtNKpVfrdcGXsJ4mjNQOHUH5x/Ewa0P5pUNr/BR8SfV4/I/e1MBCtNp2Z7EPVlmBgp1p6dwuzr5Pam8Xo8eKHRxmgpQ+NZKaOIk42UAFM41LjrQN3YRlWxgceJhErKszoHCLd2p1+mqqQCF8x9rR2sntrJO5Q/5JQvt10CzQ0FDAoX/8D1dnOqyAKDwbTNreBa9x+vV1bnvR3EsQziAD3mS2XwZAYWnTeq+sq04l3Y6n95ZPbnjj/tZUv57IFCoswxBniCgCJppaWxJGW0pW1+KoCKkIqAw9KVZC7Jrhy35+4Cj+OyP2dw+920SvrFq+krjXI7aQIfe4x1DUBz9Ggw9UNQmzhH5R7Bfq9G8sOF5pmz6yueJgCJ41iHcK1NmoDBCguY/yKCD8JLjMgBFYJwMgUJdmB20vsIEFM41V3Rji/gFlMkVLE48ii0rUoJEKiU0Y3WAIl2wSAg72aapAYVT19UaST/reJbLD1gq/2tor4/vqWsmQOGWCSjcMgGFu799OYUB7MEkHuI3vtOvZ3DiREDh8+RaOVzZ9UI6xNpz66r7WFnxR4MCRTRT0XIUQUUKuSlT+7pHwMna7hhV3yhtNJ7mDhTOdc9O/bm+/1j+t/IH7v9tki95rQ+gyOy0a2/boPb6GQ4zUISLY/BicWyb49ktd3eeLnqS78qmBUJCzfN54wXBi/pn4vVUxyOoLz1QaMezmQGFb/F1UJwAoFAXZqtAgcZbW6Dwj90MB7aAVqIPvWPnskku4PfEM0iZCASJoFkK9ytPurbuhdZh5Bxs11SBwrn2EPvSRxzOYjmR5fZHSntvG7eaI1CYIEG3C1YQULjLBBYHci5bsTMTuYfFcpa+DwUo3DIBhcdjAAq3TEChi9MUgML5vrWVx7Xdx9HKyuWmFfeytnJ9gwFFpJajCCpSKGHb/GvWHMoTNalATXIfkLj7knu8bTSeUL9VD7GTkA5Ikh4DUATFyTSB/1OXHbl864N5bdk3PLHoowgofHH0XsdjCYuT8k9ll5xhPFH0KDPLfwyedQgACtNp2dpZhwCgMB1u512E7S1TgcItE1BoPQR59EDhVnMECnXM6tjdqgug8MRzxcmztqZH7HQ2ypksT7yIlLYWZsIChSr3qdfpqjGBwjsOvdf54+wlDqKn2J/58iVW2VOq6pQ2nnbJMXgTed2zb85A4dxbxDiUi+lFf/7DHayUv3k9GqDwz0ho+lKSex1Q+BP36nvXg4WBhFRej8cAFN444fuyLGgXK+D67hdjS5ublt9HUWKj11NPQCGErPmL0ATk/LK4Ib9aiiKoSKG569bz9ynfMP6Tz0nYdsMChVoX4j19HVCoY9YBRVqAk8aJ34d1H8b5/f7Ec0s+54WlX2Qcp6UBRdXYqsDi9PyzGZg9iEeKHmBexZxmDxRqMq8DCp+HII8ZKEyLuPXQYYYEf7ymARS+OgUogseugw7v2N2efGsgXWMnskF+y2r7LZxtj8Ouo/CUVb+2VJt1FBY1B+E1FlD4E3Y9UADEhEU/cTTdxO7Mlc+wWk739xkCKNStYwMXWm8mQAFVSWxcZDGWy+lCb17mFlbLJVWeZgAU6tax+jMunMTdHCfUDIc6Q+J6PatTvAPXd7+YjXYxtyx/kE12iTGOf1zevt2KZigiQQQVKbV9hw7cv+9evDN/Idd98TVSyhYJFOY4qcdzfM/dOLX3Xjy68EPeWPZtxnEyBRwTUHjihOmrFp4wQOF9rhpPTMQ5M/8CtsraloeK7mFB5W9KbJT7CCj07XVJueMJgASh9uGHBNQ6BSjcakig8H8+mQGFcy2wdqGzdRTr7U9YZ78fGigS1LzupIJEbRdo1xVQuFXXQJGEH2GxrTiFzmJnfrYfZb2c7fPUFijUrWND7dyk6cv0nJp8skGAwlGuyOUYrqaAjrzEzRSysrpNBBRejx8oHPXM6cJ13S9iRcUf3Lb8ESpkWco4OqBIZ4ajKUnKhv9qKYqgIoQO7LcFd+y5Oy/Nncc/v/626jd0IRJK3a9DWxpQOH2d3mdvju4+gnvm/5dJK3/IIE5m4wkCisxmODQewxoXPXTovZ6+lHi2FGSJbM5rczG94n14YMOdLKlYUl2HL46pL90CchNQ6OKoQOHxJOM59y0HKHx1GqBQd3oKBhOvN3DdQi2AQhvPABRuFcR2p4N1EOvs/1GU+DwUUDhyAKJS+1MUXnU9Q+FtS0pvKqDQjTV5FRbbi7NoT39+sh+gUP7WoEDhgwRNX77XqZL3/ucKgoRUXq+H0J5WIp/juZZscnhB3sQGubbGYwAKTxwDULi1uQKF832/3J5c2/185pct4Y7lT1BJhTFObYFCWDIZI9LmrQgqUkjKqvfhjtpma/4+YgRPzprFfdNneOp1V/f/mWGS1xqzHijUMaWKY0q8w8Spa6CoksWF/Q7g4C5Duf3XN/h09c9pxGneQGHaqSksUDjKJocL8i+ls9WF+zfezrLKZaH78r6epfe6gcJ0uJ0nKU8ChHqv+VlzPEr8QI/fYgQKt5ojUJgOt/NuN6uv0/0zEgYo/LMM5r7VsgJrNAXW3qyxX2ejPS3tsyjirp+RdNdSNAegEIY4NYlunEHWBeTThx/se9ggl2x2QKF+PjpYMLX3eITe08Zqx5/FX7BJ8IK8iRJZFAgUNWVmoFB3elK9boVZF2ECCm0cBSjS7SsdoHCu2+f14+oeZzNz0zzuXf4vEtKuF6BoanLyuob8aimKoCKkpC04ZcAArhq6C/fMmM4TM3+qM6BQZxl0QBEm4Tadlu3tKzie55lrASY6D9Lisi0PZa+OA/nnr6/yzfpf0nq+lgwUTvtc0ZoL86+kjSjgwY23szqxOmVfjQUUptOyPa9eYfAQ5PEn5ab24dYW+GUCisA4jQwU/teVgsYeDBSBXiRCCNpZh9Ja7MqaxEtssmeGBopMVR9rKLxtSemtC6BwymIih52sS2hFJ6bbd1IiVybH71YEFBpP9TdtRSdOFNdRwgZelLdSJkvqFSj85zt4ve66IKDwJeMaoPAvdg7j8QKFWz5P9XXHvP6M73YaUzf+yKOrXkJKGQgEJqDQ9mXVeJria1CR6l4RVKSQtIVnDcX5Ow7mgh0Hc9O33/DSvDmbHVD46tI4TyOwL9v5TyrGtVsdxbB2W/O3eS/yY9GCOgcc3eF0SU9GazA0nkYACqcuT+RzYf5VZIlsHth4K+vttRFQBHj1Hk1y79QFAIVvVyglKXfLBBQeTwMChWnrWB1QGL0KWLSPH0MrMZDVlc9RZv+abK8ChWlRdthZCnfS3pBA4VZdAoVzjYs8drbGk0VrvrMnsEmu8fRhAgq3TEARNPbNASiqyiQdRQ9Osq5lLSt5yb6DClkeAUUqjxJneJvBXNjtBCYXfs0zf7xRBRaW26M+nx8ozDMcEUy0JEVQkYEu32kXTu4/gOu/+oK3F/wWAUWqvpQ4cSvGDVsfz4D83lw/93nmblxaZ+PRn3aNtn24GQ6NxwAUbgUdTufzaNbM+MaeHF/VfVurHRe2vhqbBA8U384GuzCwrwgoGgYoTKdla+MYgMKtINjAUJcJUATH018BhLDoGD+RHLElayqfZpO9yBdHhQln+9hMtpBtaKCoSYzrHiicumxRwC7iCgC+kxOokEVKn34AULeO1QGFERI0/z6YvF5P0wMKp6yb6MuJ1tUsYwGvyruplJVKuwgoTEDhXPdpuytndz2Kt9d9yqtr33d51OfLDCiaFFzYouG/WogiqAgrVwIphOCGXUcxdqttuOLzj5m8ZJHXmsZWqWEOzQuK09yAwvk+28riH9ucxBatunDN3GdYULKi2QOFPw6pPRqgMM46uKFVCjpYnbgg71o2yWIeKbmDTbLYG0fZ6Sno0DwTULhlAopAj99iBIpgT1DC7b0P9jR9oFDXgGhnL4S+Tv9ZpgaKdEAi2cZ5BhGjQ/xU4qIb6yqfpEyuSHpqu7MT1CSotQUKXczaAIVbmQKFM/5c0YFdxBVUUMI0eSeVcpPSxtVXHQGF+oqULk5yjAGQ4I/nf3Z/PMweEcLjAgrH01tsy3HWeObLmbxhP4Ss/sc1aF2ECSg8nhYAFM71oPZ7cGLng3jxj//x9ppPN0+giFSviqAilaTwJ+5SYAnBLSP3Yr/eWzDu0w+Zsuz3qro0FjTrT7vWe8PNcOjAxNC3xxMmThhQUrwpQKBVLIebtj2NztntuGbe0/xeujpwPBFQ+ON0jnXj/FZXs85ew6MlEyiTpcpY9XGhZpYiCChUgNABhem0bM+MgvSWaT2YPP6kPAgo/Em549Ekxk6dAhQ6T1CcpgYUqU7LzhQoTDs9SZFN+/gZWKIthRWPU1H9Gk9tVZdA4aurJVAkPQFxTH27gcJRG9GNXcUVbGIN0+Q9SFnu6dszniYCFGqZHqb0Xq0nACjUsyh028RuY+3IMdbFzJJf87b9BKL6b4V3kbHTR1DiXl1mAAq3TEDhlgkodHFCLeoOs54hA4+wJEd3Gs0RHfflyZVv8uG6qZslUEgZLdSuL0VQEVK6pDpuWdy1x34M79adcz95j+9XrdJ6tWX1CBQ17Qx9B/SR7nkaxr5CgkmbeCtu3e508mK5XDPvSVaWFaYdR/dbdRNQ6OI0V6BwvN1jvTm31VWssJfyZOldlCXKPXEioDAk906dBiiMJ2u74wiT1wwvKlB4PQ0DFLo+0/IqQOGsn5CiFe3iZwFZFFU8gV39Gk8mqstF2dq6BgAKNSkP+g2+00eB6MMu4jKKWMQ0eT9Uv8bjGU8EFIZzJ6quA6xhjLXO5Tv5MZPsF3D/basroFATbB1QmLZ+dQOF+QTqMJ6avvxj1XhCxKm6F5za9RDGtB/Jg8te5svCH0LtNhW0qDtaqN1yFEFFCrkpU5dQ5lgxHtprfwa078RZH7/L7LVrfZ6WDBRh4rSLt+HWbc8A4Np5T7CuvDh0HP1p13japzvDoXpVoNB6NIm70aP5dXiYNRhBffWObcnZra5gUeJX/lV2HxV2pcfr6SsCigYBCtPhdroxm4DCrboCiqDD7Xxe3wyHHiiSZ1GIfAqyzkJSQVHlk9iyxPccqVSXi7K1dU0EKNStY50+O4ptGCYuYQ2z+V4+gpR2BBSEAwqnbGhsTw62TuVL+798Yr+l9BEBhc7j3jpWCMF5PY5ij7Y7cefS5/luw5zA9t5F3cHQ0hQUzVTUnyKoCCntD0X135VcK4vH9j6APvltOf3jd5hftN7XTgUCT5gWDBTO952z2nPrtmdTYpdx3a9PUFS+KWWc+gAKn0cDFOY4pPZoFuEHAYXptGxdX31Ff07LuYx59kxerHiIShcBqAuzVaBwywQUXo8eKDweA1Bo+wrwhFuDsXkChbrTU9BMQl0AhVvpAkUyhtWO/KwzsGURxRXPYNkVnoXZzk5QqdZbNARQuNUUgMLxdhI7MFRcyHK+4wf5DFJKH1B4x67G8csEFG6ZgELv8Xr149Hfe8rqGCicBHpUfAyjY8fwYeIVptjvVfcRAUUqoHDqYsList7Hs1P+dty86BlmlfymjxMCKKKZipajCCpCSJ/cV1+q/5fOz8rlib0Pon1OLqd/9A6/F28IBAqpJJm6hNvfZ2pPmN90h+krnEd9lkzBpOraPbsTN299FqvLC/nr/KcoSZRGQBGyL2es21qDOCn7ImYmpvFa5ePY0g4FFOpOTzpYMJ2W7U2UvbHD7eqk88hAr7dMDxRumYDCraCD8JJxDEARGKcegcK/AD0AEkIAhXkNRkigqFbM6krrrNNJyJUUVf6LSso9EGHa/akhX3nSeZoCUDh13cXO7CTOZjGf8rN8ESmlBwRCzTpI9Xm8Xm+Z8pzuMfs8Xq+uTr13ywQUHo8BKPQe5977c7i3NZbdYwfzTuWzTE985unTLRNQePvSA4U2TsBv6ZsDUDht4yLGNX1OYbu8Pvx9wVP8tmmJN05IoIjUchRBRQqFAQqnvENuK57a+1DilsVpH01k1SbXbjxpAoUJSDwe3wJk85gDk/s0tr+tb3jp26ob/7fVmSwuXcnfFzxLuV2RHuBkvAaj+QOFUzfAGsaxWefxfeJz3q58DilliwQK46yD6z85065Q2jgBQKEuzFaBAo1XHbvOU1ugUBdmZ7S+ItUrTy65wSFu9aJ11qlUyoWUVL6ElAnAfDZFbYFCF6s2QKGL11BA4dT1FruxIyfzK5OYx0SfpzkBhc+jAYoaOHDu/Qm83+PcuxPc6jJLcGDszwyz9ua1ykeZZX9b81xKMq8/gVr1asajxtEm7vq6dKDD4zEARbpxTEDhVm4szl/7nk6vnM78Zf4TLClb4fM0N6CQMnr9qb4UQUVIpQIK5/uuea15eu/DKE0kOPOTt1lfVtoigCKzOPrxbJvXm7/3O4O5xYu5eeFzVMqEknAbxpPxGgw9ULiVEZgEeILWYJiAQhtHAQpHO8V2Y2z8DL5KvM97iVcD+tIDhXY8EVBkDBTqmHVA4atTQACPVx1PZkCRcuenEDMUuvE5yrK2pHX8z1TYs9lU+QZSs6CoLhZlB9VlChQ+jyaOIxNQeJ4zTaBwxr6V+BMDOJqf+Q+/yvcioPB4nHs/CDjXmIAj4mcyyBrOCxUPMC/xU5MDCuMrUx6P1I5V6wkRJwgo1Pat4zn8Y8sz6ZDVhut+e4KVZWt9Oz0FwouQyT/PSJu3IqhIIQ9lapJXt8+59sov4Km9D2N1aQnnfPY2xRXlXk8EFCnHMzB/S67vexrfb5zHnYtfJFH9oTUkUISLo/d6PMqfd+CsQwBQmE7L1s46SBge25eDYifySeItPql8yxcnCCjUMhUo3DIBhdZDkEcPFG6ZgMLr2TyBwv/5+CHB560DoAgr0xqJrNj25MWPodz+nk2J/yJdP7B1sSg7qK4+gcK/kNk/rmSdASjc0gGFo+04jG04iB95noXy88A4EVC4+6iqi1kWx2WdzzbWQP5VcTcLEvMioIBQQOFc22blcdNWZ5Et4lw7/3HWVBQFv1bVpGcrGnqWwv/3dHNVBBVhlUbyKqVgq4IOPLHXYSzcsJ7zv3iHTZWJ6vbetp4uDEDh8RiAIihOcwMK5zq0zXZc1fskvij8gQd/f42ED8a8bYP6aGlAAWAj2D1+MPvGjuL9xMtMSbxXZ0ChwoEOKHwegjxmoDDtCqX3mCEhldctE1B44+iBwhvH661PoPBDgT+O0RsAFGHWUaRadJ0dG0Kr+GGUJ75ik/2hr76lAoWFvs4NFIKqA1d35Hj6sTff8ThL5bd6SDAAhVsmoAj2+GUCikCPxlTfQOHcx0WcU7IvprfVj6fKJ/C7vSgCCsWTag1Gh6wCbtnmLCrsCq7/5QmKKotDAUW0UDt9Pfjgg/Tt25fc3FyGDx/ON998E+h/9dVX6d+/P7m5uQwaNIh3333XU//666+z//7707FjR4QQzJgxwxejtLSUCy+8kI4dO5Kfn89RRx3FypUr0xp3BBWpJEkbKBwNaNeFR/Y4lJlrV3HpV+9S5oBFiARX/8qUGSjCjCfpTePE70B4yShOeuMZ1XZHLul1PO+vm8pTKyYaX/cJ3VcLAgrnfl/rSHa3DuadxLN8Z3/SIoHCdFq2d9ZBqdMAhbrTkzaO8I4xnXUMmh/ZOgOKlCARMD4TUITZxQmq1lHkWaPIix3ApsSHbEp80SCvPLnVXIHC8QghGMrp9GJXvuZBVsifvO0DgMK3zawyBm+Z/t7tDwIK485PIsCjAIVbJqDweAxA4ZYlJFkimzNyxtPJ6srjZbexyl6mjRO00NoEFB6PASgy7StoAXlDAYXTvltOB27Z5izWVW7kL788TUmiLCVQNDlJ0fBfaejll19m/Pjx/O1vf+P7779n8ODBjBkzhlXVZ6Gp+uqrrzjhhBM488wzmT59OmPHjmXs2LHMnDkz6SkuLmb33XfntttuM/Z72WWX8fbbb/Pqq6/y6aefsmzZMo488si0xi6k1KVnkYqKimjbti3TjzuNNtnZaQOF+7Wlr1Yu5vwv3mbf7lty+64HEKv+F6chgcIcJ8xzafrKKE5m47Gl4L9rvuDR5a9zQucxHNf5gBpPiJkS0+F2Ho/hlTR9HL3X4zEAha69ChRBfXnhR++18fdl25KJ9lNMsz/iGOtidrBGePqGCCg8dcJ7741dO6Aw1bn/+fAn/LrxGOBAeOsDvTqP8NbVZobCkbM4W0rJpsT/2GR/SpvYsbSODauqDwkUurIgoDB59LsoKZ4mBBTuqy0TfMndrGIO+3A1ncW2mx1Q+GckNH0pyb0OKPyJe9X9JrGBBzb9kzK5iXF5f6WD1SkwyVe3jtVvSesk7uY4oWY41BkS7WyK4gkzW6AARVD7YCCpuv5aspQr5z3ONnk9uWnbU8mxsgKBoiSxiUO+vZnCwkIKCgpoDDl53ffHnEV+VnaD9buxopydX30i9LMPHz6cYcOG8cADDwBg2za9e/dm3LhxXHPNNT7/cccdR3FxMe+8806ybMSIEQwZMoRHHnnE4124cCH9+vVj+vTpDBkyJFleWFhI586deeGFFzj66KMBmDNnDttvvz1TpkxhxIgRoZ41mqlIU2F+G66ugxjZuQ937noAHy77jRu//whbyggoQsZxEuaDOuzByV0O5sU/3uOtNZ8Eto+AQu2r6recB4szGCRG8Zr9APPs6bhlAgqtR4kf6PFbjEDhlgko9B5znJYKFG41FaAAiAmL1rGDyLV2ZUPiVUrtn5o0ULhlAgq36hsoBIKYiDOKS+hAPz5lAuvlIq1XFycCiqokvbVow3m512AR4+GSW9kg1yveCCj07Wuu27TuyT+3Po05xUv4568vkqg+/T0oTktXUVGR56usrMznKS8v57vvvmP06NHJMsuyGD16NFOmTNHGnTJliscPMGbMGKNfp++++46KigpPnP79+9OnT5+04kRQkUJSirTWDfgPp6sq3q/nVtw09E+8uehnbv/xc6QGLNR+q9qH8GzmQOHcH9VpNEd1HM3TK9/ivbVfp+wrAgqv1xIWY63z2VbsxMv23fxmz6ryJPuuboN7PHg9SnxPmeFe7/En5ab2+qTcuQ+ABANQoPMGJuXecWjjGIDCrYYEinRedWoooEgmpkLQNnYUudaOrK/8N6X2vCYLFP723nJtnQIUbpmAwi0TULiVLbLZi/G0oSsfcRsbWObzRkChBwpH7WLtOD/vGioo55GS2yllY7U3Agp9e79nUMEW/G3rE5lW9Au3zX8Nm4QxTlOStBv+C6B37960bds2+XXLLbf4xrZ69WoSiQRdu3b1lHft2pUVK/zb+QKsWLEiLb8pRnZ2Nu3atatVnAgqQqo2QOGUHdKnP38Zsg/P/zaDB2dP9cUxnZYtAxLT+gKThowTBiic5z6py0Ec2G53HlnxCp8XTjfGiYBC4wViIsYx1sVswfa8ZE9gifzFO54WABTqK1I6oDCdlq2NEwAU6itb6ayv0M/g1B4otPHqGSg85QiEsOgQO4EcsQ1rK5+hzF642QKFunWsdvZCql7vOHV12bRmb64mlzZM5laKWW30+v81DoYE4/NpPfp4gR5NZyag8Hr0QOGWCSi8nqq6zrEunJ93NYVyHQ8XT6CcTf7xREAR6Nm13TZct9WxfLr2J+5dMBH3v1zuOEK30KmFacmSJRQWFia/rr322sYeUp0rgopUkiIw6QwLFM712H47cukOu/Po3Kk888t3nn687TMDCl9diNOl9XEUb8ZxUo9H9epOu64xWZzV9Uj2KtiFe5c/xzcbZvosJqDweAxAoY9Dao9t9piAIt2+TECh70vxujwWWRxrXUZXtuAF+zaW24uqPRFQ6GLXJ1A4f8xBQGE6iyKTXZ20dSmAwq1MgUK3KFuIOJ3ip5IterG68gnK5bKMgELXz+YOFM4aijzasB/XYGHxIbewicJAoEhn1iEIKEztPR5h8LgTXKSnTO9xysxAoe70pAMK/+5JVfc94j04P+8KViR+57Hie6mQ5UagcMsEFFqPBkj8Hi9QBHpCxNEl8UFA4osTAiic614dd+DyLY/gv39M47HF7yOlDBxHY0vKxjn8rqCgwPOVk5PjG1unTp2IxWK+XZdWrlxJt27dtM/TrVu3tPymGOXl5axfv75WcSKoCKmgJDgsUDjXM7bZhbO33ZW7Zn3Oawt/bNFAoXp0QKFuHSuIcWG3E9glfxB3Ln+an0rmGdtrZ0ECgMIfh9QeDVAYZx1c/76G6Uvd6UkHFKbTsnWH5jn9Z4lcTrCuoh1deEHezBq5zOdRE3lPHDLx+P9zCQISs0eXTFfXKUCh8wTFaY5AYTqLIhR0GIBC1ybTV56CdnmyRDZdYmcSpwOrKh+lQv6RNlCoW8cGQ4fe2xyBwrnPowN/4joqKeVDbqOMjRFQqB4DUDjePvEtOS//MhZU/sIzJQ+TkAktUPh+u68BCv9i5TAeP1AYPSHiuBN5daen9GY4QsBL9fWAzjtz0RYH8fLyL3hh2ae+OE3xNaimqOzsbIYOHcrkyZOTZbZtM3nyZEaOHKltM3LkSI8f4IMPPjD6dRo6dChZWVmeOHPnzmXx4sVpxYmgIoXclOncu6+6w+lMXrfnwv6jOKHfEP75w2TeXTrbl3h746SO11KAwqmziHNZt1PYodXW3Pr7Y8zbtCACCo0XXImtAgvZ5PFncQ2taMPz8ibWy1WBQGE6LTvdWYdwMxxqUu54NMm9U6cBCuNBeJqk3AQUbpmAwusxw0ZyXA0IFOZXpszwods6Nl25k/CgbWMt0Ypu8fOwaMWKyoepdBbN1hFQ+BP2IOjQA4XO0xSAwvm+DV34E9dRwhomcwcVlEZA4XhSAIVTt3W8P2flj2NmxQ88X/IEtrQjoEjVl+I5svtITu+9L08umcwbK7/2eZqKGmumIqzGjx/P448/zrPPPsvs2bM5//zzKS4u5vTTTwfglFNO8bw6dckllzBp0iTuvPNO5syZw4033si0adO46KKLkp61a9cyY8YMfv75Z6AKGGbMmJFcL9G2bVvOPPNMxo8fz8cff8x3333H6aefzsiRI0Pv/AQRVKSlMEBhOi1bTUyFEFw1cB8O7T2Av0yfxCcrfjVsW2voO2BcqU78NsdRvBnHqV+gcK5ZVhZXdj+bfjm9uWnZwywsWxoBhcsLZqBw7vNEAX8W1xMni3/Lm9gg10ZAoYzZdFq2bsxBQKEuzA6aUWgMoAg6iwKgEkmlFqNqpFtHEeYcipjIp1v8fACWVT6ELYu93noECmGIo0vKTUDhlgko3DIBhT6OM04dBNV429OLP3E161nKR9xFggr9mJNt/HFaKlA4/Q/MGsxprc/h2/IpvFLyPM6O+xFQpAYK53pyz704tsco7pv/Lu+tmu4bT6TUOu6445gwYQI33HADQ4YMYcaMGUyaNCm5GHvx4sUsX7486R81ahQvvPACjz32GIMHD+a1117jzTffZODAgUnPxIkT2WmnnTj44IMBOP7449lpp508W87efffdHHLIIRx11FHsueeedOvWjddffz2tsUfnVBjk7Gf83dFnkp+VXadA4W5fkZBc8/07fLZyPvcPO4pdO/UJBRTG2YI0z9Mw9pVxnPTHoz/tGk/7oL42Vpbyj2X3saZyPTf2uIwe2V39fWgAwAw2pPZoXnFrbkBRVVblX2uv4nluJIc8/swN5IoC15idPpU4NWEioNCOPT2g8K8XCYCEwDjBQJLOadnuGQoHKOKa39+HWUPhLlcXZTvfl8s/WFZ5H3Ha0js+jpho1SyAwrc9rAYW1K1j9Yfv6euEpi+TdwWzeZ9b6cUQ9uMSrOSnHwwJ+mfXe7UeA1CAGyD0QKEr00GC6bRs3cnaJqDwxlbGU33/Zdmn/Lv4aca0OoQjWx/ljWOZ44R6ZWozBgonjpSSO3+byLsrp/P3/sewV+cBFFeWcuCUW5vEORXfjD23wc+p2PXNRxv12RtK0UxFCrmnruoaKKQUxC2LmwYfzC4de3PptDf4Ye3y6jp9vKA+WiJQ2FKQF2vFtd0voo3Vmn8uu49V5Wu9nkYECreC+nJkAgqPxwAUnr4crwIUXk8N9LQTXTiev1BCEa9wK2WyxDsOguKk9oRbg6EHCl0cFSi844mAoq6BwlF9AgVAruhCz/j5VLCG3xOPgazw9RcBhdkL0J3t2ZdLWcz3fM6jOD9dEVCosfVAAbB77l4cnXcC7216h0kl/603oHCr7jxeb20Xdac1niSYCMZvfQh7dxrA44smU2knfG0jbZ6KoCKkVCDw1IXYSSgISLJjcSYMPZz+BV0Z9+1rzC1cZYwXAYUuORcUxPK5vsc4YiLOP5ffx9qKoipPIwOFunVsUF9BQKFuHasDCnWnJx1QmE7LlkBH0YPjuJ51rOQ/3Ea5LKvuUxNHKQuadQg3w2EGCiMkeBLkYK9bJqBwywQUnjgGoNB5VKDwetQ+6wcodOOqLVC4lWpRNgQDhXNtJXrRK3YepXIJSxNPImWlq72+jVstGSgc9xbszN5cwC98wVc8m0zoPc+hPGcEFN44f8rbn4NbHcYbJa/xyaaP6wUo/GPVeMLMFihbx2Y+wxGir5BxYsLiL9sdyb07nkZWzIpef2ohiqAihdwzFfrTrpX7gORVP8NR9X2ulc09uxxJr7x2XPjtqyzYsFY7FlNfmXkI4Wn6QOGofaw9f+l+MRWygltW3EdhxcY04pifvSUAhaPOYguO4RpWspA3uZPy6veymztQmE7LdnvUnZ50QKGOWQcUvjoNUPg/n9QAoAKF1hMEJsJbFnYNRdBaCmeWIuyibNVrmm1obW1Jr9jZlMh5LEs8h5R2IFCoW8eqCbNbJqBwK9yCbZQ+mwZQOOPYmlHszpn8zPt8yyuKx+uNgEKJU112WOvD2a/VaF7c+DxTN32tjRMBhSkOxK0YnXLyaWqSsia3a5ivxn7ihlMEFWFVj0DhlOXHc7h/2NG0y27FhdNeYfmmwnDJfRonfgcBRZg45nh1BDghFofrXgly6jrHO3Ndt4tZnyji9lUPUGJvyihOSwQK5xG7sw1HcCVLmM1/uY8ECZ8nAor6BQr/a0tBceoHKFIp00XZjlLt7pRv9adX7HSK5HSWJ15CSpkxUKhbx+pfmdLXeZJg6e1DBQq3TEDhVhAkqB7dDlKmsTv327MfIziR6bzJD7ytxAsaj/de6xEBHgNQ6D3OfdMCCiEkQgiObXM8o3J346miJ/ixbLrSRwQUJqBQvbrXpyJtfoqgIowaACicJLFdVmse3OVY4iLGBd++wuqyjU0GKOoKcEynZWc+w+G9do9355quF7OiYhV3rnqYMrs8RRx9vCDoCFwXYQAKbRwFKLye6ngE9aUHCu14FKDweJQ+ezOQQ7mU+XzPezyCxA6EhJo4eqBwywQUXo8ZEnxj1kCCL44CFLoxb05A4Xm+kEARJPXVJ/UsCkgNFG6F2i4WQYE1mF7WiayTX7HSftMDFm5/UwEKX50GKELNOkj1ecxxTEDheAZzCDszlq95gdlM9o1ZBQgdUPg8GqCogQPn3g8Ufo9z7/8ZNAGF1qMk6W6ZgEIbR5vcW5xccCo75ezEw+sfZnb5z6H70noMQJFuHBNQeJ8rRJwGAIomJyka/quFKIKKVAoBFB67ASg8HgNQJH/bntuGB3c5jtJEBRdNe4XCik0RULjaBnqq/2z6Zvfmii4XsqB8EfeufoxKWZkxUKh/3oGzDgFA4dsVSgMUamwdUJhOy/bu6uQt0wGFCgfqouytGMoBXMhsvmAyTyNdKXnQrEO4NRi6pNzxmIHCdFq2d9ZBqRPee29sPVC4ZQIKt0xAoff4x2OKkylQqAuzwwCF6SyKugKKMCDh80pob42gh3U0f8jJ/CHf83kioDADhVM2jGMZxP58ypP8ypSavozxMHtEgMdJKDMECnXrWB1QqDMaOqAwQYJnPGoc7UxA1TXLsji73dn0z9mO+9c9wPyKX1P2pUvKg4BCnWUITO4DgCK9GY7MoUPv8Y8nUstSBBUhFQQUxiRaCyT6pFrtq2deO+4fejxryjZyyXevUFxZ5vUYEnjtmIP62gyBwinbNmdrLul0Pj+XzuHhNU9hSzsCCtWjxsXr6c8oRnM2P/IBX/ACEtkigMJ0WrZ3dyh9nfuvoj/x143HAAmBYJI5UOik2zoWzECRzqJsT1mKmQlv+5ryztY+dLMOYbn9Nn/Yn0ZAoRm7CSig6s9md05lO/bgQx5kId9HQBHgBXxnUWRbcS5sdwF9snpz15r7WFKxdLMFilCzF744/vE01defpGzah981Z0VQkULuH4j6BApdnC3zO3HvzsezqGQtV8x4jU2VlYY4YQBA01dGYKLxhIhjAoq0+0rTMyh3ABd2PJtpm6bz5LrnsKv/ECOgSA0UTtlA9mVPTuE73uYb3tR4IqCoT6DIZFenIKAIcxaFTpmsofCUZQgUjrqJA+ki9mOp/TJr7KnapFyNpwKFWyag8MQxAIW3L2+dZnfOcOsiDECh78s79mBPlWLE2I9z6cvO/I+7WcqsCCg0XvADhVOXG8vm0g7j6BTvxIQ1d7OicmUEFIbxNOnXnyLVmyKoCKmGBgrn2r+gG3cNOYZZhcu4/qc3qKi0td7gOJq+MgKTMM/l9wQBRWYzHBqPYY2LlIKhrXbi7Pan8XnJl7xY+Ap2dYYdDEr6Z/B4DEDh9ShezRsmJqDQxVGBwuNJxnPuNXEcjxI30APsxEGM4Bi+5mV+YJIRKNyKgKJhgMLf3gwdYU/LjiM8sxT1+cpTGKCwEAgh6GUdRUcxioX2v1hnz/D8B+abbTDEgWCgSNYFAIW605MOKPyzKLo41WUBQOHbFUoZg7dMf1/TX4wDuJge9Ocd7mAFv2r+lTADhdajAEWwRxPHABSecRuAQhdHBwk1Hj1QeDwGoHCUZ+VxRcdLaW3lccfqu1hduSawr6D1Hi0FKJraTEWk+lMEFSkk7ZoE01PeAEDhaHC7Ptw2+GimrpnP33+eSKWSFAfH0fTVgoDC0ci8kZzS9kQ+KJ7MmxsmBvcVABSm07L1Y1a8ntez9F43UJhOy/Yk5UmAUO+F2aPE95QZ7gF24Uh24mA+4xlm84kWKEzt9b9VdzxmMDEBRWAcTeZiAgqPxwAUnvEodeG2d9WNxwwJqTzpAkW6Z1HUZg2FW3UJFM5VCEFf6yTaiyHMt5+g0J6tjRMBhTeO+9ljZHEol9OJ3rzJLaxmSWB7z2yB6tEAhX9GwntfVaYmpE58d2Kq9+pPoFa9mvEEAEVN4m6O425fEGvD1Z0vQyC4ffU9FCaKFI8ZKHyeMBCkAIVb6S3qDgNc6cTxjydMnMZW0GtK9fXVUhRBRRoKk3D7vJozF1LF03mGt9+K/9vhCD5aNZvb576LdGWEEVCEi7NP6705us2RTNz4DpM2vhcBhVpmuHfKBIJRnMQO7MdHPMpvTE0bKPwef+Ktbh2r/y2/IY7rr5DptGxtnACgUGdYtLMXQl+n3do2CBKMcUJARy2BQqd0gULdOrYugQKqfgaFsNjKOoMCsR2/2A9TLOd7PRFQGIHCqcsil8O5hjZ04nVuopAV+ngRUBjbd4i155oul1Fql3L7H/dSIjd6vJkChS8p1wBFZjMcIfpKK45/PGHiRNq8FUFFSIUCCrVOe4hbcDxtXXWcvTpvz/XbH8rby2dw/6+TkVI2CuCEiaM7nC7paQSgcD73g/IP4uDWB/HKhlf5tORTrycCCiNQOGUCwR6cwdaM4gPuZzEzAttHQKGOp3ZAYX5lyv8fdligcF57qu0MRSqgcMsEFEEeT1Iu4mxtnUtr+jAn8QCb5NIqT0CcCCi83lxacyTXkUMr/sM/2cgarycCipR9dY135uoul7I2sY4Jqx6kTG7yjDUCCv/n0BTkvIHSkF8tRRFUpJBz8mLV940HFM79gd12ZPw2B/DS0q95auHnAXEIjBM0jnTGo/PogMJ0WrYOOkxA4fEYgEIfx+s9ss0R7Ju3L88VPceUkqnV48IYxwQUYfrSxQk648IEFN6+qj3KfaDHb0kbKKruJRYWe3MefRjCe9zF7/xs9OIrc+4joKgPoFAXZocFClX1ARTG2QvXEEweXVKeJXLYLnYRuXTi58Q9lNorjXF0SbkjE1C4ZQIKbxwzJKhjV4EiKI6/JzNQ6Masgw1H+bTlaK5HAq9xE6UUmcfs3Gs6MwGF17N5AYWj3tndubLzOJZULOPuPx4lISoC4kRAEWnzVgQVIdUUgML5/qiewzi33748tehTXlrydeq+mgFQmF+Z0ngCgMIfB58HLE5ocwIjckfxdNGTzNg0w+cxzjq4/n0M05fptGzvbIHa3usFP0DogMJ0WrYnwZXesvSgo6azGHFGczHd2I5JTGAlv3m8bpmAQtuXAhQ6T1AcE1Bo4yhA4fWYYSPZlwEE9OMxexoKKHRSZyegeQCFU5ct8hgQu4Q4rZll302ZXBMKKHx1miRf3TpWO+sgVa93nLo6HVD4Zj+S926PHhK8nmCvrqyAThzD9ZRSzKvcTBklroRfaeNOKJGeMr3HKdMDhVsmoPB6zJDgyAQU2jgKUKTbl+PZOncLruhyPnPLfuP+VU+RkAlNHJkyjprI6zzprcEI0VcDAIWwpPaZGktSRmsq6ksRVKSQ+wdC+4NhSLy9MfB6tL/FTi+BP7nP7pzYezcemP8+by+fbu4rAgqtB2KcWnAag7OH8GjRQ8wum530tGSg8AOAc+9OXqsUI5sxXE57evE/bmVN9YJPtz8IKHyQoAEK065QnjjC5K3xmE7Ldv8359vNSRNH3elJDwBqnwGQEAJM0llfkenhdg5cNCegSIIFbdjBugwQzLLvoVwWRUCh8Qa170h3juE6CvmD17mNClnWIEChbh2rAwr/dqxer7suCCh8v93XAIU/QQ7jqbrukLctl3Y5m+9LfuTR1f/ClnYgUBiTcs8p1erzBSTuAUARBgB83gzAJChOpJahCCpCSpuYBgCFNCSboZJ7LZj4vef23Y+x3Xfhjl8mMvmPmUagSBUnpSdEnOYEFI7HkjHOLDiHbbP681DRfcyv+C0CCq1HlzBXKUYuB3AVrenI/7iZQlZEQOHpMzOgCLXzk/CWhQUKdftYR2GAwq2mABROLpMj2jPIGk8lpcy076FSlkRA4RuPvr1z34UtOIZrWMlC3uQuKmVFBBQpPdIzzp3zBnFhl9P4YuO3PLvmFaSUPiAIitNYQBEuTnC8lHGi16BahCKoCKHGAopUMwpCCC7d6iD+1GVH/jn3daasnZdRnNqOR3+GA572QfASuKjbABTBcXwWv6c6XpbI4vyCi+gd78N9RXextHKJFhLS6csEFB6PASg8fTleBRa8nsYDCuc+m3wO5FqyyeN/3EQxayKgMHoaBijcCnu4naMgoKiBAjNI+Ly19ui9apxWoguDrfGUsYaf7HtJyDJtXypQuGUCCk+cZgIUbgW1T3oE9BDbcBRXspifeZv7sV2v8VS1i4DCBBRO2aj8XTi78wm8V/QZL62bGDrOZg0UTUz1+ZqT6aulKIKKFGqqQOF8bwmLq7c5nJHtt+WGOa8wvXBBowGF79kzfmVK8WiAwhyH1B7b68kWOVzQ5jI6WZ25v+gOViZWeGcdlD/LwJO1A4DCdFq29mRttW/cHqdMDxRumYDC4yG1J9UajFa0ZQzXAjCJm9lEYbh1EZr/cIIOwkvGMQBFYBxNXyag0HlUoPB61D4D+gqMUzdAoVuYHea07NoCRaitZGWwF3QJu8ajidNa9GRH61KKWcpM+yGQlR6PDij8yb0fANSdnoJO8zYBhVsmoPB69EChi6MDCtOMhNYjau77ioEcwSXMYxr/43FE9U+3CSjcMgGFd8zmxD3pMQCFpy8DUHjiGIBCFyfUGowwr/sIyX4Fu3FyxyN4c937vLXugzTjqM8XAUWk5qMIKkKqKQKF01dcxLhhu2MY1KYP1/38ArM3LM0gTssECqeulZXHRW0uJ0/kc1/R7ay111TX+b2mvpoKUCQT3ACgCLfzkwz0essk+XRkDNdRzibe51bKKa726CHBDRSmXaG0sxcBQKHu9KQDCql4dUDhq9OAgP/z0QCACgeBccwgkfQagMLj0QAFeBdl607Lbu5AUdUWCsSWDLTGUchcZtmPJf/S1ydQGGcdNH0FAUUNAJiBQi2rS6BwPNuIXTiUC/iRT/lAPpf8DPXbxCp9aZJzdacn/UxAdVkAUKgJtg4oTDs1uYHCfAJ1GE9NX/6x1lwPbb8fR7c/kH+veYv31n8RnJQbTsuu7aLusBBkjqP3homjQkdTAoxopqL+FEFFCnl+IDRJsNtXdSWEJ0yc9PrKtuL8c/sT2DKvK1fPfo75xasy7qulAYXTvo1VwLg2V2KJGPdvuI31icLQfbV0oHDuC+jGn7iWYtbwIbdTziZPuwgo0gcKdacnHVAEbR3rnqFQT8ze3IDCUUexPQOt81nND8yWz9QkxRFQeMs0QOFokLU7B4gz+Jb/8Zl8LQIKpa8goHB0XMcDObjd3jzxxyt8WvRtxkAR7pWp1GNOL46+Tdg4qjdSy1AEFWGlS4J9C5Dxe8LMFoQ4DM5f5+8rV+Rw6/Yn0TWnLVfOfoalm9ZmNJ5UXr3H+yxBcfRrMPRAUes4AR4dJLSzOnBx/lWUyjIe3HgHxXZJ6jgKUHg9VVcVKNzaXIDC8bSnN6O5mnUs4WPuooJyb7wIKPSeegSKoIXZKlC41RyBwinrInZiB3Emy+VXzJUv4p64iYAiGCichH0XMZp9xQl8zutMsf8bAYXjCQEUlpAIITit81j2KdiVB1b8m282/NQigELnaUpwEc1U1J8iqEglKZoFUDhxWsfyuG37U2kdy+XK2U+zsrSwzsfj36nJ2zaovX6GwwwU4eLovR6PMusRNOvQwerCRflXUSjX8XDxBErsUmNfOqAwnZbt7UvxJtvikwkoPB41rt8SCAk1Hj1QuGUCCrc6siX7cgV/MI/PeYBKKgPG44cE1DoFKHRjDo7j9YZZz6D7f8AEFEFxNJwd7lUnA1C4FXaGQlUQUKhbx6pA4VaohdYGEHDLBBQeT4g4al13awTbi5NZIifzq3zD04cKFNrxKEDh7Us/dp1Ht4NUTV/esevSDxNQ6Mfjvdd6RIAnCQtVD7+7dRi7icP5QD7P9/ZHisdp4/9ZMwGF1qMk6d7x6IFCGyfgNZ2gA+NSeTIBCqfMEhbndzue4fk7MmH50/xYPK+qrgUBRaSWowgqQqo5AIVT1i7ehjv6nw7A1XOfZl3FxggoNHXa15iqy7pZvTi/9ZWsSPzOkyV3UyHLGxQo1DIdUKhwoAMKn4cgjxkoTLtC6T1VpV3Ynj25lN+ZwRQeRWIbT8u2A+LogELd6UkbR3jHqEvgTXVuoPADgG48ekiwA+P4x+Nv7/eEPS3brTCvPLm/DwKKUDMTSlIe5AkCCnXrWP0Mh77OKe9l7c224ljm8zYL5f+UPt194a3TAEWoWQepPo85ThBQmCBB/+z6e61HAxQ1cODcexP4fcVxDBP787Z8glnyK6WNO6FU+/ADhem07DCzDp5dptQ42leL9HWhZjh00GEACo9HEycmLC7tcTKD8rbhlqVPMLd0gafvUGsVGsRjfr7aAk4EFy1DEVSkKRNQeDwBSbk5jsaTAVA4107Z7bi9/xkUJ0q5Zu4zbKzcFAGF5xn8famLsntZW3JO3hUsTPzGMyUPkJCVEVC4vEFA4dx3Zwi7cSGLmcK3PIOUMgIKw3gioKh7oHCeo584kK04hLnyFZbITzV9OfEioFCBoup7wYHiVAaL3Xndfoh59vfV5Zs/UCT7DwAKdZZB58m2Ylzd8wy2bNWL/1v8GAvLfvd5wsyU+E+5TgMAAuPg86SKl66nqai+X3XSfbUURVCRQu4fiCCgCDUTYIyTOuHWwksKMOmZ25Fbtz2DVWXruX7ev9iUKM8YcExA4YkTBqZq4WlIoHC8W8W34/RWlzAn8SP/Ln0MW9oRUBg93jjOtQ/DGcZZ/MpkZvAyUsoIKJTx1AYo3HIOtqtEhl6U7f5+cwQKp49txJFswX7MlM+yXH7t6suJFwGFDiicb2OWxeHWuWwnduJl+x4Wylk1nggoQsXJjWdxfe+z6Jbdkb8tfIRl5SuTnqYGFJnFMY8nUstQBBUh1VSAIt04/fK6cdM2p7Ng03Ju/PU5yu2KtOMEAUVmMxwajwIAwdCh93o8BqDwxtEDhdrX9vEdOSn3QmZUfs1rZU8jFaIyAYVuzCpQeDzJeM69Jo7jUeIGeoL60iTlJOv0Xr3HHEcKyVZiL4ZwEnN4h5+ZmDqO/9GNQOHxGIDCMx6lLsz6iuD1J+nECeHR/Ccc5rTsdNZQeOoMQOFWqLUTBqDQeUKdzxAQxwQUnjhqki8sBogT6ckoZsjHWSmnG4HCOx7vVbsuwgAUujjq2IM9fpmAItCjMZmAIsgTFzGOsS6mj9iO5xMTWGr/FgFFyDhOAp4fb8WNW5xLQbw1f134CH+Ur9vsgUIIqf35aixFMxX1pwaBigcffJC+ffuSm5vL8OHD+eabbwL9r776Kv379yc3N5dBgwbx7rvveuqllNxwww10796dVq1aMXr0aH755RePZ+3atZx44okUFBTQrl07zjzzTDZu3Jj22KUtmi1QONf++b35+9anMmvjQm5Z8CKV1Rl8cwIKNXEP9AQAhW9dhAYodH0Nju/Ksdln83Xlx/y34iXs6oZBQKFuHasDCt9J2BqgMJ2W7ZkpweAhyONPmE3t9b/ld+79cdSdnmxgO3EAO3AkM3mVX+QHvhkNHVCYTsvWzl4EAIV/lyp/cu78CAQBhem0bE9fxjj+8QQBhbrTU10Chbp1rA4o0pqZCAAK/0yCHyh8noA4QUCRrFOSfKdPISwGizPpyhC+kw+yWs7WAoXptGztrEMAUJhOy0531sG0K5TweAzxRIBHAxSm07Kd+7jI4qTYeLqK3jybuI2V9hIfULhlAgq3wiy0NgGFx2MAikz7ClpAbgIK7ZiVRdkF8Xz+0fdcYlj8deHDrKvYEPxqkSGB1/bVBIEiUstRvUPFyy+/zPjx4/nb3/7G999/z+DBgxkzZgyrVq3S+r/66itOOOEEzjzzTKZPn87YsWMZO3YsM2fOTHpuv/127rvvPh555BGmTp1K69atGTNmDKWlNbv0nHjiicyaNYsPPviAd955h88++4xzzjkn4+eo9as8jQQUznVwm634y1YnMXX9HO5a9BqJhEwZJwIKl9eGYfE9OTzrZD6p/C8fVU6MgILwQOF4BzCWbTmQ6fyLRfKLCCjc1wyBQifntacIKFQQEFgixlBxPh3ZlqnyHtbJ3yKg8JXp76vKJNkil1PjV9FOdOTpyltYI1d4gMKfuFffux4szKyDunWsfktaJ3E3xwk1w6HOkGhnUxRPmNkCBSicuo5Z7bhpy/Motcu5YeGjbKgsCZzhyHRmIuk1AIXHk1GcMICj76/RJEXVP9YN9dWCZiqEVN/lqGMNHz6cYcOG8cADDwBg2za9e/dm3LhxXHPNNT7/cccdR3FxMe+8806ybMSIEQwZMoRHHnkEKSU9evTg8ssv54orrgCgsLCQrl278swzz3D88ccze/ZsBgwYwLfffssuu+wCwKRJkzjooINYunQpPXr0SDnuoqIi2rZty2d/upj8rJxkuSmxDUzuQxwGp4+jeDOOU1P26dofuHXhixzcaQTn9zgCIfTtIqDwAoU7zocVb/Je5WscFj+ZUbEDqjwRUHjrNEDhKCEl3/MUC/iU4VxMDzG0yuv6dzcCivBAoc5SREBhBgp3XUKWM0XezgaWsQfX0Vb0joBC63HFURLSYgp5rOIfVMoKzsu+gXaiYwQUnnHpgcLdfknZcq7+7SF65HTipi3PoVUsp86AwhzHP57M4oQZT1X9xooy9vzgPgoLCykoKKAx5OR1n+x3GfnxnNQN6kgbK8vYe/LdjfrsDaV6nakoLy/nu+++Y/To0TUdWhajR49mypQp2jZTpkzx+AHGjBmT9C9YsIAVK1Z4PG3btmX48OFJz5QpU2jXrl0SKABGjx6NZVlMnTpV229ZWRlFRUWeL1XNHSgA9mw3hIt7H8k7q6fw7IpJWo/utOtkvIwWdWs8BqDQxyG1xzZ7TECRbl9O3T6xsewRO4iJlc8xLfGpoS9vXG9f1R7lPtDjtxiBItjj7ywISMwev8sEFKpHCMEQTqcXw/mWB1kpfwoFFLq+VKDwesywkezLAAIeTwAkJPuqBVDoxhz2lSfTadnpAIVbaa2daESgcMsEFPoxO20EcZHDCDGePDryJbdTLFd6PBFQBAOFEJAv2nJW1rWA5MmKWyimUOuNgAJj+z653fi/Lc9mUelK/rHwGSpkudK++QOF6o+0+apeoWL16tUkEgm6du3qKe/atSsrVqzQtlmxYkWg37mm8nTp0sVTH4/H6dChg7HfW265hbZt2ya/evfu7anfHIDCiTOmwwjO6nEIr6z6iFdWfuzx6IBC3To2vVemNJ4AoPDHIbVHAxTGWQfXv2th+lJ3erKlQAjBQbE/s6u1D/+pfJwfE98q7b1jAN2MhPe+qkzoPbg80lum9WDyuBPcYK/bHwQUvnURGqDQ7QolhMVQzqELA5nKvayR8wxeaY6jAQrfbk6aOOpOT3oAUPsMgIQQYBI8w+EtU4HCLdMaCt0uT24FAYXptOxQOz9p8gQTUAR6AuLoknJfnQYoTKdlu8eTQ2t24yqyyOMLbqNUrvHFqemzun0AvJiAwushpccEFNrxpOPRdGYCCq/HDxSO2omOnJV1HZtkCU+W30YZG30eR0GQ4BtPmNd0FKBIty8TUAR6wrzuo/mLYQISISTb5PXixn5nMLt4ITcvfJ6EqNTG1fYVAUXakjJaqF1finZ/qta1115LYWFh8mvJkiVAVRK3OQGFc39k5704ocufeGbFu/x3ddUMTwQUqYHCuRdCMDZ+BgOt4bxceT/zEj9ovRABhdaLxBJxduUi2rMVX8s7WS8XKt7mDRTGV53SBIpkXRqLsqEmMW0IoDB59NuymoHCtD1sfQCFM/5c0ZbdqXoV93Nuo1wWauJU32v68u0glbz3P1cQUKifjw4WTO09HmHwuJM8pKdM73HKzEDhrKPoHOvGWdnXsE6u5snyCZRTs77Rv3uSN667LggofMm4BihMiW2YU6r1sylmoDAm057D4NTn8yfljnZs04+/9DuF7zbMY8LCV7BJ+Dyh1jwYQKCu4jR3oIhUv6pXqOjUqROxWIyVK1d6yleuXEm3bt20bbp16xbod66pPOpC8MrKStauXWvsNycnh4KCAs+XW0Gv6ZgS+ECPNo7iTSNOJoBzYtf9OazjHjy87HUmr/tO40HbvqUDRVIixrHxC9ha7Mi/K+9mQWKOxwsRUGi9rjjCymZXcSn5dGOKvIMiucw35ggo0luUDRFQeON5y93jd8ryRQf25BoqKOEz7qBcFkdAQTigcMp6xnpzVs5VrLCX8GzZPVTKiggoNO2DkvJd223H1X2P57N1P/Dg4reQUgYk7gHj0QCF6bTsdOOk+gy0HktqZ28aS9FMRf2pXqEiOzuboUOHMnny5GSZbdtMnjyZkSNHatuMHDnS4wf44IMPkv5+/frRrVs3j6eoqIipU6cmPSNHjmT9+vV89913Sc9HH32EbdsMHz48rWeQuA6/CzNbEOJ0aX0c5T7NOEZvAJiAxVndD2PfdsO4d+lLfF000+XB0z6or8A1GAagCI7j9zY1oLCTSUScE+KX0EtszXOJO1hqL6zpKzku/X1VWcsFCudHMy5yGSGuIIe2fC1vZ6NcZY4TAYVHEVDUHigcT4Hoyl5cTQlr+Jw7qZSlEVC44gYBhePdIrYVp+eMZ749l3+XP0hCJiKg0DxDUPs9Owzi0i2O4r+rp/LUsv8FgkVQnPoAinCAo3ibEExEqn/V++tP48eP5/HHH+fZZ59l9uzZnH/++RQXF3P66acDcMopp3Dttdcm/ZdccgmTJk3izjvvZM6cOdx4441MmzaNiy66CAAhBJdeein//Oc/mThxIj/99BOnnHIKPXr0YOzYsQBsv/32HHDAAZx99tl88803fPnll1x00UUcf/zxoXZ+0qkpA0VtxgMxLup5LCMLBnH7kmf5YeO8QKBQ2+tfmVI8GgAwx8EnE1C4FW6hdeq+TEDh8eBtHyebk2KX01n04F+Jm1kllxqBwjMeA1B4x+z0GRQntSfcom49UOjiqEDhHY8eKDxxhNcbF/kMF1dikcU38g5K5XojUOjGrI5d+1wKCOieSwUKbV+BccwgkfQYgEIXR12U7T4pO47/Z9MEFB6PASh0cWrv0XuD4vgdZqBwywQUnjgGoHDUVvRmT66kkCV8wT0kZHkSKHTPFeoQP6WNLk7QswcBSdJjAApvHD1QaPvSJPc1YzYn7gBbxwZwau7FzEpM5+Wyx7GlrY1jAgpPXwag0MXRAYnZ4++rKQCFcz+m0y6c3/sQXlnxGS+t+MQ49uYCFEJI7WfeWHJea2/Ir5aieoeK4447jgkTJnDDDTcwZMgQZsyYwaRJk5ILrRcvXszy5cuT/lGjRvHCCy/w2GOPMXjwYF577TXefPNNBg4cmPRcddVVjBs3jnPOOYdhw4axceNGJk2aRG5ubtLz73//m/79+7Pffvtx0EEHsfvuu/PYY49l9AybK1A4CXNMWFzW8yQGt96Wmxc/yZyShY0OFEaP7fcEzjrIYK9+rHi8njLUvqquOaIVJ1nX0Ib2PJu4mXVylRYW1K1jtR5fnwEew31VmQz0esvMQGGcdXD9J2HaZlYbR+i9uaIdu4orsangG3kH5XKjFijUMeuAwlenAQH/5+MHAf/Wr0FxzEChbh2rAwp1p6egGQq31J2edEBRM8tgBgHjzITmt/xBHv+2rBqPIY47HzTOXkj3mNU+/Um+eridfieqmrqOYiv24nJWM5eveBCbSuV5zHFqAMAMFGqZ/tn1Xq0nACjUnZ70uzopfWmSc9Np2ZYmobQs2CG+Eyfmnst3lV/yZtnzODvXm07L1iWmQUBhPoE6jKemL9Np2Zm/7uP11mZR9xFdd+PUnqN5+vf3eWvVVxnGST32hgCKSC1H9X5ORXOVs5/xx/tdmtzPODC5D3F2g79OKW8EoHCXlSbK+fviR1lUupz/63MhfXN6G+NEQOGN4/iL5HqeSvwdic3p1o3ki/b+8URA4fO6x7FBLuNreTOt6MwwcSWWleuKoz7f5g8UYXZ6ioCiboDCfb9M/sBn3MUWjGA3zkNgRUDhvjcARdV9Vd3XFR/zculT/Cn7MA5tdbSnrjkARZg46taxmc9w+D1SSh5f+j9eXfEFV/U7ijFddkojjj5ubcZjjqPca+JsrChjt/892CTOqZi89+UNfk7Ffp/cGZ1TEalG9Q0UYfuqPw/kWNlc3/McumV34sYlj7CsfJW2fQQUeqCQQBvRjpOt60hQyXP2zZTIDd7xREARCBQSSb7ozi7iCkpYwffyPhLV+7a3RKBQFQGF41H7rFugEFLQkyGM4gIW8RXf8EwyOY+AIhxQCAEjs/fh8Jw/80H5RD4oeycCCs9zpfZYFpzT+wAO6rwLExa8zmdrZzVLoEBItPtGN5ZkAy/S1uRem6siqEih5A8F9QsUmcXJbDym07KlLciL5XJDr3NpG8vnxiUPsqp8ncerW2NgAopAT5g4AZ7AdREGoNDGUYDC66mOR1BfNUDh9N1OdOZk6zqKKeIF+zY22SXVHi9QeOL4+gzwEOTRA4VbJqDwesyQ4B+PHxJ8cRSg0I3ZHadAbMEQ6xIKmc+P9kMkZKXH25KAwgGJMEDhlgkotB4NACQ9BhBwywQUHk+IOOHWV6h9BoxHAYrgOH71YwTDOYtfmMz3vKyNYwIKvcfr1Y/He6/1iACPASj0Hue+7oHC0X65BzAm53DeLn2Fz8smR0DhahvoSV4Fl/Y7jL06DuSmX1/h2/W/ND+giNRiFEFFSLUEoHDUJtaGv/W+AEvE+PvSB1lXscHTNqgPWzOjEAQLpjNAPB5l1iNw1iEAKHy7QmmAQo2tAwrfrlDJtjWejqInJ1rXsIblvCzvoFxW+DwqHOiAwuchyGMGCtOuUHqPGSjUnZ70sw5KnQYoamL7gcJRe7Etg62LWMMsZsknSMiEp41bJqDQe/zjMcXRbJ4WCBI+jzB7Ur3ypJ6W7SgIKNSdnnRAEWpmQknKgzxBQKHu9KSf4dDXuT96dacnHVD46jRAEWpdhNLXtuzNLpzELN7mR95U2ivP6R6zz+P16up0QOHzaICiBg6cez9Q+D3Ovf9n0wQUWo+SpLvlxD449wj2zt6fVzc9xzdlX+rjaJJXNY4uwU3LYwCKdOOYgMLjCRMnZAIfExbXbnUUu7TdmhvmvciPRQubFVAI0dQWakdbytaXIqhIIdMPxOYKFE5d+1g7/tbzQsrscv7x+4MUVZakHE8EFHjKutKP48VVLGcB/5F3U2FX+jwRUBjiVPfVUQxkoHUuK+W3zJXPk3C9B+d/dj9Q+AFANx49JNiBcfyw4G/v96hbx6ZaQ6Gup4iAomGBwrnuwEEM5iim8wpzeE879uYOFOrWsTqgUGc0dEChgwQhBEflncDw7N15ruQJfqiYZvQm41j6ulAzHJokOAgo1FmGwGQ6ACjSm+FIDzriVowbtz2WAfm9uG7Ov5m7cZni8Y9HF6duPMp9CqCI1HIUQUUaSia/AYfB+bwBr+lkFiczwEkHKJxr16zO/LXnhayrLOSWZQ9TapdFQOH2Jtu6PEpZL9Gfo8XlLOQn3uYBbGm7kmElLpo4hntv2eYJFE6rzmIo24vTWCY/Zb58DSml5tk3D6BQFTRD4VYEFPUHFI53MEeyAwcxlWf5lc/MY/Y9n/c5dXWbM1A4fVvC4sS8MxiSPZSnNj7MnIpZWi+0bKAImi3IiWXxz/5/pk+rTlw56zkWbVplHE+oWYeMxuPvS/XqgKIpwUU0U1F/iqAipMIAhXlGoa7iaDwhwMQEFNrnU669c7pzfY8LWVq+gtuXP0q5XVF7CDIAhcdjAApdXypQBPWlW+9hAgpdHBUoPJ5kPOe+ytuXHTmci5nDVCbxBFLKCChMcRSgcLxdrd3YWhzPEvkei+S7nrrmDBRBSgUUpsPtdGc4hFo7YQAKnSfU+QwBcUxA4YljAApvX9463TlbdQEUUPU5D+NEtmUfvuBRFvGN51m87dS+6wYo0HmaAVA4ilmC01qfy3ZZA3hkw70sqJznjxMBRaCndVY2tw04iU7Z+Vw+818sL1uXUZy6Agrf1rFNHCgi1a8iqEglF2U2J6Dw/3ZfM56QMxxb5vbhqu7nMbd0AfesfIpKmfB6FAAIninRez2eAKAwnZYdpi/vbIre6wYKdacnHVCYTsuWSgK0ndiVgziPH/iIyVTt266FOyeOcq/3+JPy5LgMXr3HHMcEFIFxNAmQCSg8HgNQOOpljaavOJyF8g2W2pO1HndZ8OfTNIDCtDBbBxRuhQGKtGYmAoDCP5PgT8p9noA4QUCRrAsACnWnJx1Q+GdR/HGSXgNQuOWGg1GcST9G8DH3s5QffB4dJGCo0/xVCQQKXx8KUGjjKEDh8RiAwi0TUOjiBK2LcMqyrDjntLmQLeL9uL/obhZXLqrxGIAi076C1nuYgELnaSpA4cRpm92KCQNPJsfKYvxP/2JNeVFGcSKgiFTXiqAipFoqUDhlA1ptw/iu5/w/e+cdH0Xx///n7F0agYRQA4qCigKKKKiIvaDYxY+9Ye+9t89XP+Vn771+7B17QWzYRVDEggpiQUAInYRA6u38/kj2sjs7s7d3uSQXci8feVx29zXvee/liO9nZmeG79b8xP2Ln8JurPyzQBEMFG7PULEzozmeb3ibL3mlyUOTR3es9/gLZlN7dxHr9/jjqEvH6v/Kb4jjqlz8ox9+oIhfCwAK9wjLemI/1hF78rt8jjK7aUMo5yMQBBTqSk+hJlon40kRKNRJ2W65gcK0dGwWKJIHCvfSsTqvLo7TOkqEXTiTdRjG+9zGQmYGAoVpmVnh8ejbJwsU/hEJTV++ItGJ7y4E9V43UKjFpq44V1d6crz5Vi5nFZ1Hr0hv7iq/lYX1CwOBIkxfahGsH01RPGGKaQUo3EpuUncIeEkiTo+8Ltw+dBz1MsYFPz7Fyro1KcVpDaAQIjtRu6MoCxUJJG2h3V06fj2gmPZ5UoiTCUDhvG7ZaVPO7nUCX67+mseWPU8sJpOIo79Pj2ctBoqGPmC42IsdOJwvGM80JmSBgvBA0ZAXDBCH0FvswGz5BMvk9LQBhbp0bKiRCQUo3Ep2hMIEFpAYKHTeZJZuzQJFOKBoyiHKaM6jFxvxLjexhN+zQIG3oDQBheMtsAo4r/hCulhF3F5+C0tjS9sFUKT2yFSYwj2ZOA3XS/O7cvvQcZTXreHiH5+msr465XzMefksRqAIipNVx1AWKkIq+REFxasBijBxfN4QYKLbnC7uSWkORpNn28IRnNrjGD5Y9RnPrXzVOFLhjRPC0wGAwom7LWPZhv2ZxJPM4OMsUBhyVoHCPXdiQ3EM3cVwZtkPs9L+OWOAIj75OkmgAO8cCki8uZ2ncJfec6FGJjRFucmrn8+gBwqdRy3K3TIBhb4vp03bAYVzLkoue3ExJazDBG5gOfOyQKH2b/A61zpbnbmg5CJyRA63r7iFlbGVWaBIGMebz3qdunPb5kfzd/UKLpvxPNWxujTm47/3IKAIui8dnLSVnD8Wt+ZXR1EWKkKorYDCdy1EHP1u12jbhxvh8Ht2KtyOY0sO5e2K93mjYmICMMEXx+exzR4TUCTblwko9H15vd6+Gj3KcaBHuS4Q7MBRbM5o3uNBfuWrlOdFBAGJ2eN3mYBC5wmKYwIKbRwFKLwePVA4r0JYDLROpFgMYqZ9P6vk7+0SKJzRiUwACpNHv4qSGShMqzkFjl5ogMK0W7Z+AzozJKi5q0ARFEdXCrgBIJcC9uEyCunGW1xHOYs8OauwoM0nGY8mIRNQeD16oHDLBBRejxkSfPmEiWNB10hXLi65iDpZx60rbqXSrgzdlwkoAj1rEVA4rwM7l3Lr0CP5ddVCrvppPHV2LA35aPpKESiy6jjKQkUCZYFC8TTWRGO67M4/ivdjfPnrvL/qI0MczHECgMI46uD63RSmL3XpWB1QqEvH6oBCBQgdUKi7ZeuAwr3Sk0CwGyeyMaOYwN3M4TttOx1QhHtkSi3KHY8/ThBQmFaFkpqiPAgofNc0QOFbzUkTJx5PgCWiDLROo5B+zIzdwxo53+sJgAQ197YcoVCVCCjc6shAoS4dG2rUQdOXaVUo4fHoIUEA+XRmX64gh3ze4FrWsFzrNbX3eYTB4y7ykJ5zeo9zzgwUpt2yPR7f6kler/taEFCYdsu2hKRHtAcXdbuICruC25bfQbVck7CvIKAwesI87qMZugsCEl8ctfAO6iuNcTYrXpcbhh7KtBVzuObnV+ObhWaBwi8pG2qF1vtq6ztuPWWhIqSyQIHvUaexXfZjr8678+TK5/ms8islDuY4HRwoHI+FxZ6cyfoM4y1uYz4zfZ6G4yxQeK65QCAichkYOYs8ejAzdidr5CKlzwBIMACFWy05QqGbQxEGKNSJ2UnNncgCRVqBwlFnunIAVwGS17iWKso1+ejbd3SgcNQ3p5SLul3IovpF3Ln8HuqoNfbVXKAwFsGeTdzU+wtRlGtAIKmRicA4wfEAtu62Af/d9B98tnQWN8x8G9n4e6e1AceXu9X0M89q7Vb2x5xADmk637tfvT68Hs0zdOHi6IEiyKPfwwFP+6C+UgEKKQVCCI7qeig7d9qBR1Y8wddrpnv7zgKFN7YaF4gQZV/Op5SBvMGNlPGn4skCheeaBgSiooBNIucQpZBfY3dSI5dp43rOBQBFoqVkmwMUbphwlo51Hn0K2i3bURigCLPyUyKv16P3BsXxO8xA4ZYJKDxxMgwonHPF9ORArqKW1bzO9dSx2pWPvn0WKLzt+uf244Ju5/Jn3RzuWXY/NnU+TxYoguPs3Gtj/m/wAUwo+4E7fn0f92/2cPkoXh1QJDEykYWJjqXsjzukgkcUlOOAXapDjUwkEccOyifFEQ7VqwKFVxYnlBzDVgXDuX/5w8yo+tkcRwEKt8LNi9ADhSeOASg8HgNQePqK5+U99nr0QOHN2enT7ImQy/5cTFf68jrXsYy/G9tkgcJzLQAEIqILG0fOA0QjWFQY4zQHKNxqjUee3N+nCyhMu2V7JyKrBbvGY4ijK8qDgMJf3PsBwLRbdqh5EZrfASag8Hr0QKGL43hL6MOBXEUlS3mdG6mjOhBI4jkbgMLjMQCF19MYR1PcN+VshoS4xwAU3pz1QOGJYwAKXRzndeP8jTiv21n8XDOTB5b/D1vaWaBIMs5efTfl0kF78eK8b3jw90+TyEeJlyJQqD9vIbJLynYUZaEigXQjFe7v1zag8Hk0QOG/jwinlpzEkLzB3LXiXn6r/d3cl2c1Jj0keEcvgr36XPF4PecCgEJd6UkHC+pKT1qPr88AT+NrlAIO4HI60ZXXuZYKFhu9+knUeqDwepR4ml/yQTtrx+MYgCIwjqavdAGF00eO6MrAyLnEqOb32N3UydW+OM0FCnUvilQmZUPi3bLj1xIAhVsdGSiMow6avoKAogkAzEChnlO9PViPA7mCpczlTW6hntrAEY4goDDtlq33OMd+EDDtlq3bWTsIKEy7ZesK0yCgMG8Y13B90/whnNX9FL6pms6jK5/SgoVbST3KE+jxeps7B6MtgMKJ849+W3L2wF157M8veXrOVyHioI2TDqDIquMoCxUhlQWK4DgRopxZchoDcvpz+7K7mFs3LwsU7mOdRznOpwsHciVRcniNa1nNikCgUItwvUcPCW6gMC0zq40TABTq0rE6oFBzVnN3ywQUXo83n1zRk40i51DLcv6I3U9M1mQcULiVym7ZKT/qlAWKFgcK51opG3Igl7KAWbzDncSozwKFIY6prxEFW3Jqt+P4bPWXPFs+Him9f+325+ovcJPb7dobL/kRjsR9Gb1aMNF7k4lzbP9tOXGD7bh79ke8On96QBwlXrqBQki0a063kbIjFS2nLFQkKRNQ6D3+wj0dnkwDCqfvPCuPc0vOoVe0F7ctv52y+rIsUJg8huNCStifq4hRx5tcTzWrOhRQ+K5pgEJdOladlF0g1mGDyFlU8zdz7IewZZ0PKNxqK6BwyzQxu7lAoU7MDoIOE1B4PCHihJtfofYZkI8CFMFxzH21FlA459ZlCAdwIX/yHe9yH3bjJzYLFImBwrm2feG2HF9yJO+umsQrFW82edohUCQXR98mlXxO23BHDl9vBDf8/C4TF85I3Jdu45pkQCk7QtGhlYWKBNI9/qSdiBxil+pQoxch4vjjedvqPX4gMXo0z7OEjVNgFXB+yfl0tjpz6/JbWRZbmgUK1WM4bjgnKaIn+3Ela1jJBG6klqosUPg8aj5eTyfRn/WtU1ktf2Oe/QRSxpIamUgGKNwKM4ci0cRsE1C4lS6gUFd60gGFutKTfoRDf81TBEtvHzqg8F3TAEUoSJDq/fjjmHLXQZAJKNzSTcIewJbswznMYjIf8EgcElSg8MQxAIXe4xzrijynD3/h7vMEFIImoNDGCSg6kwUK53X3LjtxZNeDeLV8Am+Xv58FiiTzsSy4cNDu7LfOUP714wQ+XjQ7FFB4RhnC5qP7HGUgXGRHKlpOWagIqUwBCv9KTd62Qe31IxxmoAgXx+/tYnXhgq4XYQmL21bcQrldrm2vnWhtAAq3TEDh9TTGI6gvPVBo81GAwuPx9RngIcjTVDCXsA77cAUrWcC7jc9le9v7i3JHHR0onNfO1iasGzmRCvkDf8eeQzbSbVKTsUMARfxawE7ZYVd6CgKKlFZ1ygKFFij8AKDJ2efxenXX1GOATdiWPTmNH5nExzyN88nXjoIEAIXf4xy7izzvqw4oTLtlhxl18OSjxtEW7vpryUAHwH7Fe3Jg0V48u/IVPqz4LOU4TXnpi2qPZy0ACuc1YsFVm45hl14bc8V3bzBl6ZxWBQohMmuidnvQvffeS//+/cnPz2fkyJFMnTo10D9+/HgGDRpEfn4+Q4cOZcKECZ7rUkquvvpq+vTpQ0FBAaNHj2b27Nkez6+//sqBBx5Ijx49KCoqYocdduCjjz5KKu8sVCSQlP7iNQsU+vfD7ekaKeHCrhdTK2u5Y+WtVMZWK/fg7ysIKHzLzGqAQo2tAwrfMrPxti6Pck4HFCoc6IDC5yHIoxbM0JMB7MWlLOI3Pmx8Ltvt9RblTjszJCTyumUCCm8cPVB443i9YR4/SgdQOLkXWZvTN3IM5fIrFtuvEksAFt72wUBhI7X341Yqe1GkChS+awoIuJUFitYBCufc5uzC7hzPN7zNl7ySBYoEfemK18O7HcCYol3437Ln+Lzya60nud2u/fceZoTD/x6ky+PPJx19Oa9Ry+LaLfZl6+7rceG3r/H9ir9bDSiySk4vvPACF154Iddccw3ffvstw4YNY8yYMSxevFjr//LLLznyyCM56aSTmD59OmPHjmXs2LHMmNH0uNtNN93EXXfdxQMPPMCUKVMoLCxkzJgxVFdXxz377bcf9fX1TJo0iWnTpjFs2DD2228/ysrKQueehYoEKqtZydk/PMq8NQ1r32eBwvHovW5PD6sX5xdfxEp7JXdX3M4au8bT1ttOPe7YQOGoN4PYgwuYz/d8wv3UE/N5Qo06CLUPPwj4rmmAQl06VhtHeKFHV7ibrulGiU1Aofd4c7eRFFtb09s6lOX2xyyzJ6YNKNzSjVI0ByjcShdQ+At/f1GuelWgcMsEFJ44BqDw9uW9pnukuz0DhXNuhNiLnTiCLxjPVPm215MFisDi1bJACMFx3Q9hx84juW/xE3yz+gclx5YHCtPu1KFGFALj+PNJLU4wdORYEW4ecSCDi3pz7tcvM6t8sc/T8E0S8KL7BaLJJ5PgItMff7rttts45ZRTOOGEExgyZAgPPPAAnTp14tFHH9X677zzTvbaay8uueQSBg8ezH//+1+GDx/OPffc03i/kjvuuIN//vOfHHjggWy++eY8+eSTLFiwgNdeew2ApUuXMnv2bC6//HI233xzBg4cyA033MCaNWs8cJJIWahIoHyRS3ldFRfNeJLFNeXx80G7VMc9AZCQTBwTUCTdVzM8yQKFM4+iT2Rdziu+iIWxBTxQcSe1dq2vryxQ6IHCObcuW7ALZ/MHk5nMY0oR7LRbu4DCX/jr8tHDgWZqEV0jO9LD2o9l9jusiH2cNqCICbvZe1HogMK0F0Uo6EgRKHyegDhBQBG/FgAU6kpPOqAwbW6nnRdhAAq3TEAR7PHLBBSBnsYT24qxbMuBTOIpvpcfNngUoNDGUYDC4zEAhVsmoNDFCZoX0VZA4RxbwuL0XkexdeHm3LHof8yomhk6TkcECv81KIjkcNfWB7FuYVfOmDqevxr/YBoEFMZ8XJ+jMCMcHV0VFRWer5qaGp+ntraWadOmMXr06Pg5y7IYPXo0kydP1sadPHmyxw8wZsyYuP/PP/+krKzM4ykuLmbkyJFxT/fu3dlkk0148sknWb16NfX19Tz44IP06tWLESNGhL7HLFQkUHFOJ27d7FgALpzxFCtqKwNBINTIhHMuRJwgoEhthEPjUVZ6CoYOvdfjUeL1iwzgzC4X8Ef9b/yv8n5ist4IFEF96SaQm4BCFyfTgcIttX1/RrI9pzCLD5nG84onCxQ6oFCvdbVGU2LtzlL7VSrsKSkBhVuJYCKZORRuhQEK4+iF5v/faytQxM8FAIVpt+xkRx1Mq0IJj8cQT3g9O3MEw9mTd3iEX/iisY27wFXiaIDCtFu2GyhMu2XrN4xTvZp8DF63TEDh8SQBL7rRlIiIcG7vE9i0YGNuWvggs6v/SBgnCxTevjrn5HHvNgdTktuJ0yaPZ2FVeePFlgOKTIKLthqp6NevH8XFxfGv66+/3pfb0qVLicVi9O7d23O+d+/exseQysrKAv3Oa5BHCMEHH3zA9OnT6dKlC/n5+dx2221MnDiRkpKS0O9tFipCqFdeMbdsehyV9dVc8tNTVNZXdSigMK3UpPUo8ZzzA3M24ZTOZzOj7geeXN2wS6oOKEx9eR/P0nvdQKGu9KQChVsmoNB6lPiBnqC+NEW5GUgavtuYXdiGY/mRN/meNxo9ZjAxAYVbJqDQedozUNhIhBCUWPtRZI1iSex5Vts/KO0TA0WiORQ6Be2W7ci0dGyqQGHy6B8/ygJFawEFgCUEe3I8Q9mR1+W9zJZfN3naIVCoRWbwDtRhPGagcL7PsSJcVHoyG+T147oF9zOnZp4xjgoUbiU3qTsxBCUXx59PanGSAwpH3fILeGDUIeRaEU79cjxLalaFiNP+gaItNW/ePMrLy+NfV1xxRVunFJeUkrPOOotevXrx2WefMXXqVMaOHcv+++/PwoULQ8fJQkUCOZS5bkF3bhl8HItqyrn8l2eoitVmgUL1GIDCab9Z7jCOLzyNb2q/4oU1TyGlbDOgUJeF1QGFz6PE95wzHOs9yQOFc25T9mYY/+BbnucX3vPFUVd60hflal/+otw/+uEFCrdMQOFWWwOF8yqEoId1KIViCxbHnmS1PdNzD2EnZTf3kSf394mAwq10AYU6T0AFCl2fuqLckQko9Lk7bToeUDinIpbFfuI0NmErXpZ38of8MQsUrtcgoHBeCyI5XNH3NEpzevKfv+9jfu2iUEAR7yNMUR4AFKFGFIxx/PmkFicM4Pgs8Ti9Cwp5cLtDqLFjnP7lK6ysrUqc11oAFG01UlFUVOT5ysvL8+XWo0cPIpEIixYt8pxftGgRpaWl2vspLS0N9DuvQZ5Jkybx1ltv8fzzz7P99tszfPhw7rvvPgoKCnjiiSdCv7dZqAgpaQs2KOzNDYOO5Y81ZVz967PU2g2r8YR7bEgPFDrP2ggUzrURuSM5suAEPq+dxJvVLyfsKwsUfs8wDmYwezGVx/mNz/19tQJQqEvH6nJWV6lKZgUo/SNhzQOK+P0JQc/IUeSLgSyJPUaV/NPjSWVStk663bLj15IAikQTtVsCKEyrOQWOXmiAwrRbtn6/CDMkqLmrQBEUR1NTBUJCU196oNDmk4wnXlxHOEicQ382Zby8lXnyV5/HE8cAFG51FKBw4hRG8/m/dc+gONKF/8y/l8V1yzokUJjjaPpS4qxTWMRD2x3Mspo1nDX5VSrrasKNmBiAQucRQpKJ+1VkonJzcxkxYgQffvhh/Jxt23z44YeMGjVK22bUqFEeP8D7778f9w8YMIDS0lKPp6KigilTpsQ9a9asAcBSJlxZloWte/bcoCxUJJC0haeQH9ylH/9vk6P5oeIvrv3tReob3+xQj0OluNu1OxdTX45MQOHxGIBCH4fEHtvs8UGChFF5O3NQ/lG8X/Mm79e8lQWKAK/OA7AVR7MhOzOZB5nHN+0SKJyPSxBQqEvHNhcomkZIIvSMHE+uWJcl9Y9QY/8NtAxQhF06NhWgcKs9AoW6dGyoUQdNX6ZVoYTHo4cEryfYG9Te4xEGj7vIQxIRUQ6zLqAPA3jWvpGF8k/Dyk9moDDtlh38eI3X65YJKLRxFKBIti8TUAR6lDhdIoVc0+8MckSUf827j+X15VmgMPWlxHH+4Q/o0o0HtjuIvypXcM5Xb1BVX6fvSwW+JPLJJEkp4rVdq3xpaqMgXXjhhTz88MM88cQT/PLLL5xxxhmsXr2aE044AYBx48Z5Hp0677zzmDhxIrfeeiszZ87kX//6F9988w1nn302AEIIzj//fP7f//t/vPHGG/z444+MGzeOvn37MnbsWKABTEpKSjjuuOP4/vvv+fXXX7nkkkv4888/2XfffUPnnoWKkHJ/KLYo2pBrBh7B5JUzueWPV4k1VqEtARTq0rHJPTKl8QQAhT+O/959Hg1QGCHB9btl17y92CtvLG9Wv8jntR8EwosJKHQe3c7aTXk1epTjQI/fYgSKYE96gELGi3WLkZxMP7biM+6mjBkdDijcSgYo4uesHHpETyQqurG0/mFqZMPyirpJ2dA6QOFWGKAwbW4XtLJRMmCSBYqWAwrnXI7I4yjrErrTh6ft61ks5yvtmgcU/h2ovV73tSCg8BXjGqAwFbZhdqnWj6aYgcJ9rVu0mH/3O4M6Wc+/591PRf1q3DIBhc7jK7x1njaMk26gcM4N7tqL+7Y7kJ9XLuKiqW9TZ8fSBhTCyqwlZTNdhx9+OLfccgtXX301W2yxBd999x0TJ06MT7SeO3euZ57Ddtttx7PPPstDDz3EsGHDeOmll3jttdfYbLPN4p5LL72Uc845h1NPPZWtt96ayspKJk6cSH5+PtDw2NXEiROprKxkt912Y6uttuLzzz/n9ddfZ9iwYaFzF1LqSrSsKioqKC4u5q2tr6RTpCB+3l2MT1r2Pdf/Pp4De23LGf32Rwh9UZ8FCm8B75yL2fBqzTN8VvseR+efxvDoDk1xlKVjdUChLh2rAwoVIHRAoa70pIMFdaUnrQeTx1+Um7xuvwko3OfqqOdTbmUJs9iVK+gmNnLlo0KCJo4weTMfKGxjnMRA4Z5DEZOVLKm7D0ktXXPOJCK6+tq3FlAkfNQpSaAwbW7n8Rji6IpyE1C4+zABhedaFih8cyaqqeTx2H9YQyUnRa6hm+idBQpPfonj/F1XxlVz7qFnTgn/7X8mnSL5gUChK34TelKOExwvvfkoXh3gGNpPXvwXZ01+g936bMDNI8cQEVazgQJgVW0tW77wOOXl5RQVFdEWcuq614dfQ2Ekv9X6XR2r5sBv/92m995ayo5UJFAQCOzabQvOW/9AXls8mScWvK/1ZIFCDxS2FAghODD3KLaO7siz1Q8xo36akqs+LmSBwnmNEGUHzqOE9fmEm1kp53r8zQEKt0xA4fW0P6AAQBTSLecUAMrrHqFertK+B8koLFDo2oQadVgLgQKdN56n7v1KHCeZeRG6e2/KR+1T4zEAhTeOHigAOonOjItcSS55PB67llUs9cTNAkWwR1iSdfN6c836p7Owdgn/nfswtbJGew+m9gk9azlQCCHZrvd63LLN3nyw4HeumTYpPgKuG+FIlFemKp0TsMN+dRRloSKkTBOa9+21DSevuzfPLvyIFxd+5vVo1uc0AYXXg6e9/pEgM5D4PBkIFI7XEhaH5Z3M0MhWPFl9D7PqZnjiZIHCDBQAUkiiIo8duJhO9OATbqRCLtR7kwQK3zUNUPhWc9LEicdrJaBwKxFQxB91EsUU55yCzRoq6h/Flg2roITd3C4irdCb23mL8WCQ0I5iGEDArfYIFMZRB01fQUDRBABmoFDP6e9d79V6AoDCtFu2ztNFdOW4yFVIJI/VX8dqylFlAgqPxwAUbpmAwhPHABS6OGH6CvW4Twoep3AG2LBgHa5e/zT+qJ7P9XMfJybrfPGyQBEcZ/Q6G3DtVqN59a9fuPH7z4j/lk42n6Z/8N4PfFZrrbJQkUBuyjQV5YeV7sSRpbvyyN8TeHvJ1Ibztteje7U1hXsQUIQb4VA8gZOo9UAR6NHUV2EmdQf1ZQmLo/PPYENrEI/V3M5fsd88Xk9fBqDw9BXPS3/ccG7tAArnOFd0YmcuI4dCPuFGKuVyc5xWBIqmidH6Nm6lCyjMk7r1QOG8RkVPiqInY8vlrKp/gnr8u52qcsMEJAYKt9INFOrEbLVgdssEFEH5ZYGiZYHCOdfN6s6J0SupporH62+gSla6+jADhbrSU5hRBx1QGHe7dr2J5g3jwnia+vLnqvGEiKPmPrhwfa5a70R+WD2bm+c9Q0zGXB4zkBg9YQBHGydxvEwDCuf7A/oP4urhu/DU7O+556epzQOKDJOUrf/VUZSFipAK+iu/lILj+u7JAT234+65r/LRsu+N7doaKMxxzPeqA4owIxzq5nZBfVkyh3F559HXWp9Ham7m79g8l6cxXgBQmHbL1v1bNgGFxxPvMyhOYk+4Sd1qUe60cRfKyjUXUDjKE13YmcsA+IwbqZIV/jgZBhS+ieNBow4tCBTOtajVhy7RE6iX81ld/wzSVYwkkgkoPB7n8x4CJNS4zQUKdaUn/SNT+mu6usAEFG6ZgCI4jrmvtR0onAK6h1XKidErWCmX8UT9TdTI6ixQGDym3bK37DKQK9Ybx1cVM7jr7xexpR0IAimNTATG0bdxK32Ao3g1cZIBCufnfeRGm3HR5ttx389f8+jM6YHtg4BCiOxE7Y6iLFQkkMQ/UuH+vumcxenr7s9uJcO55a/nmVr+SxYoQvbl5JpLPifmXkyJ6MkjtTew1C5LG1CoS8dqPfG8lGOdx3DccE4Ger3n9EDhlgko3HKK8nzRjR25jFpW8wU3UyfXNMUxAIUuTqYARRMU6OOkAyhsvBvb5Vjr0yU6jnr7N9bUv4jUDc8p0hXU6rVkgMJ3TQEBXfzWAArTbtnevpRrGqAIBQlSvR9/HFPuup+GCSjcCpqE7fO0EFA4cXtb/Tgh53IWy795qu4W6mRtFijUnA1A4bQdWbQZF/U7gkkrpvHQgteRUqIrvFsTKJKLEyYf5VgTJ2hzOl885Wdz8qDhnDZkBDd//yUv/j4jcRwNUGSabCla/aujKAsVIRVUnDuvQka4YL1DGFk8hGvnPMUPlb9ngSJBX+qk7DwKOSn3MgpEIQ/XXk+5XNYhgcI40dr1C9pflDeoUJSyA5eymsV8ya3Uy5pAoFBXelKBAo13bQIKnSKRjegUPYI6+0eq6huKEZ9Hs7ldsntRhHosSgMU/sLfX5Sr3rUNKPwAoMnZ5/F6ddd0QOHzCL8n3qcBKPQe51hX5MG61gYcn3Mx8+RvPFN/J/Wy3ucBf5Hu7UsPFNo4AcVi0P4OiTxtARTOuV1KhnP2uv/gzWVf8GTZO562QX10BKDw54HPg5Cct9lIjhk4lH998wlv/TXLHCcAKDIRLrJKv7JQkaSC5lcARESEy9Y7mk0LB/DvPx9j1pq5Po+3feNxypO69UDR7DgBnpYCCsfbWRRxcs7lADxSdwOVsiILFIQDCue1WKzHdlzMSv5iCndjy/qUgULNWc3drfYOFO6J2bmRzSiMHEytPZWa+vc0d9ugsBOzkxqZaCZQ+DyaOI5MQOG5xyxQBAJFExw4x/4C3u9xjt2Fl9qHZIA1iGNzLmC2PYPn6+4DYl6PphA0QYInH6Uw1a6QZOmvhRrh0EGHASg8njB/VTcAhcfjar939205pe9+vLD4I15c9JHWEyZOYo/5/torUEDDz+3K4Ttw0IBNuOyrSUz6+09/nCxQZEUWKhIqaKK2ZyTANcqQY0X5Z//jWT+/lKv/fJg51Y2r8fjae9sG9aEf4TADRbg4eq/Ho4x6BE3qNgGFWyag8HqgWHTnpNwrqZKreazuBqqkdzMjE1Bo81GAwuNx9ek51nkMxw3nMgconGvdxEaMFOezlJ+ZJu9HSrtFgMJ3TQEBPF419+SBwhsvNaAwyZl47YxC5ERHkB/Zl2r7Y6rrP/FdTxUo3EoEFEHeVIHCP+/An1/8mgEovHl5r+lz13vdygKFFyic400im3N0zjn8ZH/NS3WPIBv/JbUHoBCuwjShJ0wRHAAUQSMc/+i1E0f3Hs1jC9/hjSVfpjYyoR0pSQ5w0uNJ3Fe6gMI5F7Hgv9vswh7rDuDcz9/ji7L57RYonLquNb86irJQEVJhgcJ5zbfy+Ff/U+iZU8L//fkgf1cvUzzetkF9ZDJQNF3Te3V96YBCjW0j6C5KOSF6BSvlEp6qv5kau9rrjbd15aOc0wGFDw4UoHArmYnW4SZ1+4vyJk/6gAIaivKeYlNGiLNYyDS+k4/FH+NpTaDwvz/NAwojSCQBFKbHnnTKj+5AXmQ3qmMTqY5NiZ9vDlAkXPnJlZ7Joy/uWw8o1JWedEBh2txOCwkGoPDen5JfIOCY45iAItCjMbUGUDgaGh3B4Tmn8U3sM96oezqpIr+jA4WjY/qO5qBeO3DfvNd5f9m0FgWK1OKEyUfTl2G37FDwov0Hos8nalncst3ubNt7Hc78ZCLfLikLBRRCZCdqdxRloSKBgkYqQA8UzmvnSAH/HXAqnax8/m/OAyypLW+85m1rag8dFyic41JrPY6LXsZC+RfPxm6n1m54prglgcLnIchjBgrTMrN6jxkSEnndkkpRLpH0EcMZJk5mHp/ys3yOWOMPNxOAwnt/LQ8U6qTssMqPjCY3MorVsVepiX2XUUCh/hVeBQq32iNQmHbLTnbUwbQqlPB4DPFEgEcBCrdMQOHxGIDCLadgHpGzPQfnnsDn9e8xofZFbZygeREmoPB4DEARpq9MBgphSYQQnLbuvuzVY2tum/MSn62Y0SGAwpiP6x9I2BGO3EiEe3fag0279eDkj97h5+VLtR/soBGXNldrj1JkRyqyUqUbvgra7dpRUaSI//Q/HYnkmjn3s7Ku0tNW1z4LFF7PumIjjolczBw5k5fse6hvXOazIwCFunSsfl6Eck14jwHWFduzqTiGP3mX33gjAZio99dyQOGHAv15rTcFoEhVQggKIvuRa21JZewFauxfQgOFW2FWfkrkTRYoTJOvA+dXKEW+Pi+nTRYo3EDhH5HQ9OUrEp347gJO790udzcOyD2KD+ve5IPa1w1eTT4BQKEWmcE7UJs9qjdoArkJKHQeFSjcSm4ORgNYnLf+WHbqNpTr/3ier8t/1bbPAoU5TkE0h4d324v+RcUc/+EE/ihfGXw/QqL9K0dWa52yUBFCUlfcBwCFWmD3yOnGf9Y/g8pYFf+e+yCrY1X+eAFgkj6P3uvxGIBC15cKFEF9pQoUzvf9xWYcZp3LTPkNb9gPE3PRVBYogoHC8awvRrOx+Aez5avMke974whvjsnMY1jbgcJRlCidI4eQKwZTUf8UtfYfgbtlO0o4MuEutGVwG29cvTdZoPBd0wCFaXM7/fKuZkhQc1eBIiiOpp7scEDhFOm75O7DmNyDmFA7ns/q3lO8bQMUaiGpn++heMIUwRqgSG2Eo+k1Iiwu7X8YWxUP5N+/PcOMyj+NceLxDEDh8SQJOEaPASiC4rQWUDh9d8nN5dHd96Z7fgHj3n+bv1dX6NtnIEy05iiF+2mXjqAsVCRQc4HCOe6T25Nr1j+NRbXLuHbuI9TYtYEg4J/UrfEoABAcR+/1eAKAwgcJGqAw9aVbkcoEFG6pq0JtYm3FQdaZfC8/5V35FFI2lc4qUHjiOB4lbqBHOfaea39A4eSxAfsxgL34RT7L3/LzxnaJgcJ0Tfe7MtOAIhk5qz85K0A5ciZmR0SUouhR5Ir1WVH/GLX2fC1QJAMS8TYhgMK0F0WYkY6WBArTbtmBow6avkyrQnkBIDEkJPJ6PSTv0XRmAgqvp/lA0XBOMib3IHbN3ZtXqp9iSt3H8fO+fLJAofXkRCz+b8MjGdK5H1fNepJfV89PIo4/n1CjDgH5mONo+jIAhVuhACcASIxxXHMoSvLzeWL0vuRGIhz73gSWVK1OmFdWa7eyUBFSzQEKp4gdkL8O/+x3Kn9Uz+fGvx+jTtZ3KKBQIUEHFOrSseqk7M2t7dlbnMAUOZGP5cvefHDng6edDijCrOqEz+Mv7s3x/JHaEigkDUP/A8VhrMvOzJCPUsY3hvtLDij8Bb+ae9sARTJzKFSQALQrPUVFLl2jxxMVPVlW/zB1crGnTVIjE8qjQ80FCmGIE6rgXkuAQn1/dLBgau/xCIPHXeQhPef0HuecHijcMgGF1+MU7oID8o5k+9zdeL7qUb6t/SruMQGFLo4KFFqPBkj8nvYDFM73eZEo/xl4DOt36sVlsx5nTlVZiDjme29roAiVj/rzSWqEw//z711YwFN77kNdLMa4995hRU2VzyMsqd/lO6u1TlmoSCD30FVzgMJpP6hgAJetczI/rvmVOxY8RUzaWaDw9aV4m8JgS9ja2oPdxBF8Kl9msj2h0ZN+oDBBQrhHpnRFuXNsBhMTULiVKlA454UQDBHj6C225gf7QZbKn5T7azug8N5n84EiXVInZueIArpHT8YSnVlS9yC2vdLjy1SgMI5eaIacskDROkChrvSkAwrTbtkRCw7NH8eInFE8WfUgP9ZNDwQK027Z2tGLMCMcBqBwywQUQR5dARoEJL44auEd0FenaC7XbzKO3rnFXPLL4/xdvSwLFLpX76Qpz7V+XTrz1Ji9WVZdxfHvvcuq2tqm2BkIE9nHn1pOWagIKd1u1/FrBqAwtR9WuAkX9TmBKZU/8MCi57Ebq3oTULjV0YHCObeDdSDbcQDvy6eYLj/Wehr68Mb1nDMc6z0tBxTq0rE6oPAtM6sU5W4FAUU8L0uwmTiZbgzme/tuVsjZAbmnDhTevBIDRUKQaCGgcI9S1CttTSs95YjO9IyeBlgsqn8AKSs9/lCPJhlAwK3WBAp/ce8HgLjXABRBuQvN7zQTUHg9WaAwFfmWsDim08lslrMFj66+l5l1P7U5UBg9SRavpt2yw41whC+4u0QLuHHwcRRG8rn458dYXFOeFJikC3DaI1A4rxt2LebJMXvx16oKTvrgParq65UJ8pkHF1mlX1moSCBpCy1QqIW6DijUpWPdbbbpvDlnlx7NpIqveGLpq8QMsODpI8DjKGi3a5/HNntMQJFsXy0BFM7xruIIhrMHE+RD/CK/6tBA4X+cygsUbtnx/xlE2dw6ky6sz/f2nayS8zS5Nw8ozFDgvR7obeURiqgDD7Lp16NppadcutI7ejqSasrqH0TKas/1wKVkA4BCXelJLZjdMgGFW+kCCtNu2aFGHTR9BQFFU8FvhgR/PD9M+eNh9hiAwhsnc4DCuRYREU4sPIONo4N4sPIOfq+bnQWKRH0pnpKcztw65DiEEFz88+Msr61UPMHx0pmPz9tcwDEARZg4YYDCeR3SrTuP7bknPy1bxumTPqQmFstImHDqutb86ijKQkVIpRMoHO3UZRtO7nkob6/8mJdWvJMyUPjzMOccBBTGUQfX74QwfakrPQXtwm0CCrd0QAEghGAvcQJD2I7X5N38Lr/zFfKeOE6fSXnWDqCIXxPOtQYJkcsw61wK6Ml39q2skWXtGihSlRsg3McWIuHSsXmiJ6XR06lnBWWxh0HW+eInAgqdNwgo1JWe9I9M6a/p/vfWHoFCPacfndF7tZ4AoDBtbhcILxpIaOrTXLjHPQag8PQlmvLJETmc2uVs1osO4N7K2/irfo6SRxYoEoFAz7xibt30ONbEarnkpydZVV+lzaclgcLXlw4okrmvAKBIBnBMQOH1SIb36sXDe+zOVwsWct5Hn1DfWBDoPr9ZrX3KQkUItQRQON/vWbwzR3bbn/HLJ/D2ykkdAijUpWN1QKEChHrccE4ghMW+nMEGbMEr3MY8+YsXAJzYaly3B5PH/0vT5A32+F2ZABSONyo6MdQ6nxwK+d6+jWq53FfMe3PPTKBIZlK2Ts6k7GSAwjnOF+vQJ3IqNXI+C2OPIWW98TEoHVD4C/+WBwrT5naBj2ApQOFWRwYK027Z3sdYnD6CCvfGcwFAYdotWwhJrsjjzC7nUxrpw90Vt1AW865opNs9OejRG7PH/+8sfR6vN5k5GM0BCifOOgXduHWzY1laW8GlPz1NVawmpTip5aN42xgoTLtl6/pyz6HYfp2+3Dd6F97/ay6XfvqF9v+BbansnIqWUxYqEqkFgcJpP7ZkLw7sugdPLnuZjyq+9HqyQGEECudcREQZy3n0ZSDjuYky+ac3thoXdxyTx1+UBwFFUzGvegIgQQEKt1oLKJzvc0UXNrcuBAQ/2LdRKyu8Iy8KDGQiUKQi3fKx4C2sw+6WnW/1p0/kJKrkLBbFnkFKOy1AocurNYDCd00DFGEgIZHXrSxQpA4UjgqsAs7uciElVgl3VNzCErthRSM3UJh3oA7jaerLtFu2x5NE8RoEFKEez0ngDfS44gwo7MXNmx3LX2uWcsXPz1Fr1yYdp6MChXN+j/XX4/ZddiRqWUjdYwpZrZXKQkVImYDC62l8TWFS95HdDmR0lx15aOkzTK78tsETOOchCxRuT1TkchAX052+jOc6lsq/G/tU4uCOg8HTNkBhWmZW91eedAKFcy1PdGOodSH1VDHDvoN6uaYxV69X90iYGs8EFKG8KQCFCQ6CZIIJ98TsZHfLLrQG0SdyPKvkdBbb45FS+oBCbeN+1T9SpHg0YOIo04DCtFu2dmdtJXfNR8YIFG4FTcL2eUSApx0ChePvEinkvOKLyBf53L7yFlbI5cY4awtQpDQyERBnUJe+3LjpUfy8aj7/98t4YrI+qTiJvF6P4tX9skgGlNoYKByNHbgBN++8PdGI0Oab1dqnLFQkkHvoSr9CklIopzipGyxO7HE42xVuxT2LH2P66p+SiKPP2+PR1FvhJlon7ssEFB6PASg8fcXz8h57PX6gaIgPeaKAQ7iCQrryIteygsU+j68v33HmAoW6dKwKFG6ZgMLr8cNGgejFptZ51LCMX+x7qKfG49XPMUkEBf4+0z1C4X5sKaziG9op+1BA0+iE+3zY3bK7WMPoEzmSlfYXLLXfbGwb3AZSBwr/X/UDwMcAFN578l5ra6AwQYJ+dEZ/rPVogKIJDpxjbwHvlgko3DIBhdajFoKevvRAoY3T6CmOFHFh8cWA4NYVN1MeK88CReg4DcfDuq7HdUOO4JsVf/Dfma8Rk3YL5KMca+Ikt9s1Pk+ivDweA1Do4gQtG6veVyYp+/hTyykLFSHVkkDhfC+IcHrP4xhWMIQ7ljzIL1W/hYjjzzEIKEKNOshgr+daAFCoKz3pgEJd6UkHFOrmdkHQkU9nDuVKouTwEteyihU+z9oKFL5rGqDwTb7WnC8U6zLEOpdK5jHLfoD6xonHmQoUzZEORNR5FKqMcyVcr12tUfS2DmKp/T5LYx8kbKsv7tMDFPFrAUChTsxWgcKbF3qvy7O2AoXf4xy7izy1j6DCvfFYUwiadrD25KPGUbwlkW5cVHIxtbKG21feQqVdGVhQBm0qF/cYgCLZOCag8HjaECic45HdN+BfQw7m4yU/c/Ovb2FLmcZ8lOOWAIpk8gkACl+7JN5DhES34lRWa5+yUJFAbspsSaBwXi0Z4Zyep7BR7gbcuuRe/qydmwUKgoFCXTq2EyUcwlXUU8fLXEcVlaEgwRdPOfZ61j6gcKuztQGDrDMpl7P4zX6UmIwF5N66QNHcCdk6uR95cr8mmkcR9Nojsjs9rDEssl9juf1FKKBQi2YVKHQ5twZQmDa3086LMACFN3clvxQfY8oChdkLDUVmz2gvLu52MRV2BbevuJ0quypUX7qCMggo1FGGMMWrDijCjHD434N0efz5AOzcYxBXDTqAt8u+4+7f30O6wSILFIH3lYkwkR2paDlloSIFtRRQOEV6Drlc0PMM+uaUctPiu/i7bmEWKEweX58NKqIXB3MVa1jJq9xALdWhRh3CzcHQA4VbJqBwK9OAIn6tsc8iaxADrVNYLqczx36GmNT12fpAAfr5EG6550YkUrqBwq1SsR/dxI78bT/PSvvb4EeTlHx0QGGaKxE4v6IVgEJd6UkHFKbdspMddTBN4tb9tE1AofUoQBHscY4zGyica32ifbio2wUsrl/MHSvupMauCWyfyUBh2p061MhEYBx/Pu52Y0o35+KN9+bF+VN5eM7HqEoHvHjOJQNKWaDIKkOUhYqQ8u923XBet6lJ0BwMo0epkQqsfC7ucS7FkSJuXHIHS+qXhouTBYq4pzvrcBBXspy/eYObqae2RYHCCAmuX6y+1Zw0ceLeNgQK57irNYwNrHEskV8w334ZKWWbAYUqE1iEhQm3N51A4Z6YLYRgXeswuooRzLUfp8L+WSmi1SK8eUDhu6YU+bp7D16Nqf0BhXHlJxHg0QCFaXM7LbwYgMItE1Do4uggocmjBwqPx/JeE0KyXs56XNj9XObWzePuFfdRR522ryxQmOMctM4IztpwNx6f8wVP/zU5xXw0fRl2yw51X9ohPHM+vngKUGg9zQQKYUn95PM2ki0b6pDW+2rrO249ZaEigZwPBQQDhVrcBz8yZQYK97XOkUIu7XE+OeRw45LbWRlb6e0/CXgJnGhtAAptHAUovJ7GeAT1pQcKbT7NAArnXC8GcACXspBfmcBdxKjPaKBQl47VbZpn7DPNQOG89rC2ZT3rcBbJD1go39F69JPF0wsU7j50E7OTGZ1w/NB8oNDFdC8dK4RFf+s4uojB/Gk/RKX8A0gMFNq4zQQK0+Z2HkiQqhfPqzevRm8AvKytQGHaLdsNFKbdsvUbxqleTT4BQKEWmbo4G+ZuyPndzuHXmtncv/wh7EawCCo6gyaQm4BC51GBwq3k5mC0DVA4r8esP4oT+u/Avb9P4qV50zIGKEztA+NogMJ4PyEAN+jnllXHUBYqQqq1gcJ57RrpyiU9LqRO1nHL0jtYFVuduC9l6djAUYcAoPAtM6sBCjW2Dih8y8zG27o8yjkVKNwyAYXWA6zDYPbhQv5iOh/yALLxansECnXpWBUo0HibAxROH72sXelr7c8C+w0W2R+ZvYY4LT2Hwl2g2iHapBMofNek/7wQEQZYJ9OJ9fktdg9VjUseBwGF0MQB7R8UWxUoTJvbeQHA4NXkHgQJTfGyQNFcoHC+H5y/Ced0P4Mfqn/koeWPI4lp8jEDhc+TRPHqXWpX3z54hCMAOlICE58lFJicuuGOHN5va26Z9S4TFvzYoYAiDJTplpnV/TyyWvuUhYoEck+yaW2gcOL0jPbgkh4XstIu5/Zld7KmcYfPjgQUajGvAwqfx3Xcny3Zk7OZxRd8yuPE4mDh9bplAgqvJ7OAQs05HUDh9Fkq9qan2J359osstb/ye1sIKBIpmdEJtz9VoNDFCgIKR1GRx0aRM8ijB7Njd1ErFxu9YYDCOHqhGWnMNKBIBhJMXjTepDyaj40JKLye9gsUTvstCjbjzO6nMLXqGx5d8TS2ZqnU9gAUoUYmjHH8+YQFEyEEF2yyOwf0HcZ/f3qbjxbN8noMQKHG8fSRBCi1N6DINGUnarecslARUkF7QaivYTxhgcJRw0S781lYX8bdK+6h1q73ebJAETzqsBGj2JVT+JH3mMILxkemvO39QJHUvIgQ3tYCCm9eyQGF413H+gfdxHbMs59mhf1dk7cFgSJo1+tkgUJVWKBQ+9W+BgCF831UdGJg5BwiFDAzdif1cqUvfrqAwl/c+wEg7m2HQGFq7/EIg8ddTCE95/Qe55weKNwyAYXXY4YERyag0MZRgCJMX9t02pJTux3Hp6u/5JmVL+Je0SgLFInjCCG4YtMx7F46iKt+eJ3JS383xNH0FQAUSQGOASh0Hh9QBHnSDRRCev+BZ7XWKgsVIeQplJWVnnRA4Z+DkTx06PpfP6c/53U7lz9q/+D+lfdT79rh0wQUur5UoND1tbYBhXNuMLuyHcfwLa8znTdaBCjUpWNDzYtoBaDwXUsCKNxt1rWOpFgMY579GJX2rBYBikRKFSbUUQr3uUS7ZQe+KiCga+sU2nmiiE0i5wE2v8TupE5WNnkCwMSUS6pAYdrcLtS8CM3vjo4MFKbdsj0e32Rnr9d9LQgofMW4BihMha0KHdsXbsvxJUfyXuVHvFT+uidPt0xAEeQJKl7bO1A4rxFh8e/N9mPbHgO49LtXmb5ibuK+mgkUpt2yQ70/3mXdEvbV1Kf5PWwyGe4vA5UdqWg5ZaEigVoCKGwFAILjeL0DczbmzJIzmVEzg/+VP4ot7UCg8EGCBihMfakrUum8tqboMQGFxxOP5xxr4jgeNW6QhyBPw3ebsy8j+AdTeI6f+dDQvuWAwndNAQo83swBiqa2gnWt4ygUG/OX/SBr7DlpH6FQH3tyT8oOAgrT41LuUY1Em9up/aQCFP7C31to54luDI6cTx2rmBW7m5isDgSKpqJZDxTevJ02zQMKIyRo4MXk9eZlhgR/PPP9ZYGi+UDh9uzeZSeOLjmY1ysm8kbFOwFx/EARpng17ZYd+Ff1gMI0mb/gt1QctzcnYnHDsLFs3rUvF0x7mZ/LF3juOzCfTAaKJKAsECgyGC6ySr+yUBFSmQAUzuvQvKGcXHwqX1dP5dmKp5FStihQ+CBBAxTq0rE6oFBXcdIBhc+jxHfLBBR6j7coH8EhbMYYPuN//MoXWm9bAYW6dGzwSId6f8kDBR6vmrsfOgAsEaVf5GTyxbr8FbuParmgWUDhfOlyCgsUYZTOze0a2vn7SAQUzrUCUcqQyHlUUcbM2H3Ysi5loDDtRaF7v1oTKNTctQDgi6e5P4NXCx0GoPB4DEDh9TTGWYuBwnndp2g0h3bdnxdWvME75ZNaFShM7VMemQiMExwvbBzVmx+NcOvwf7Bhlx6c881L/LZqiT9eCoCztgCFEJk1UVvKhqc7Wu0rc269xZWFigSStsgooHA8W+VvzbGdj+OT6o95dfXLnra69msLUJhGJPQA4Bz7i3KBYDvGMZAd+Zj7+YtvPV63Mg0o1KVjmwsU/lWwzEBhu3K3RC7rRk4jh27Mrb+XWrkk5REK55wzf0KdQxFm/kTQKAW0LFCYPCagcF47i/UZbJ3FKn7nV/thpIzhVrqAwndNAQq32iNQqCs9aT1xgDADhWlzO+0yqAag8HgMQOGWCSg8cVoQKJw8D+q6N/sX78mTy8czqeKLLFAkmU9hTg53bXUwpflFnPX1eOauXhEMFCHuK241AIXHEyJOWwFFVh1HWagIqaA5Dyag8HgMQKGPY/a6N7fboWAnDi08golVE3hnzduuXL3ts0ChAED81WInTmU9hvMhd/A3P/k8oeZFdECgcI4johPrRs/AIp959fdSL1em/MhTOjazS6R0AYUuPxNQBHmFhGKxCYOs01jGD8yWT8XrBRNQeOKlCSiSe4xJ73VrbQUK027Zup21g4DCtFu2rgg2AYVbJqAI9kjluuCobgeyZ9FOPLz0WT5f9XVSf1X3erzeZOZgtEegcF675ORz3zaH0jmax+lTX2BhVUXKQKH+vHVAEW6Ew/sLJRg6AiBB6VO3EZ75/tD8lshqbVQWKhLIPcnGXZT7V1gK8AQAhWmlpkDocMUbXTCG/QoO5LU1L/FJ9SQjUGjjdGCgcM4JIuzGOfRmE97nFpbweyigiF8zAAU6bwsDhbfPlgcK5zUqurBO9AzAZn79fcRkZUqPPLkfc1I3tguSbg6GI/coRTqBwrTSkw4o1KJZNym7uxjGJuIEFskv+EO+GP/wBz4O1YpAYdotW7uztnK/umLCBBRuJbVMbAcECvMO1GE8TX25cxVCcEKPQ9mpyzbcu/hJplZ+HzqOabfsZEc4EnkDPdo4ieOlD3CgJK8TD257KBEsTpvyIkurV2c0UIQZdTDtlq3bWVsHFJmm7ETtllMWKkKqrYDC57H9nn0KxrJr/h68sPopptZ8qQWKZODFBBQ6j+p1ywQUgR4lvuec4VjvSQwUzrEgyu5cSAnr8h43soL5zQYK9REpFSjcShdQmK61JFA4rzmiG32iZxJjNQtiD2LL6oRAofYD/p2yU33kyd02zEpPSY1MpBEo3Cq1RrGROIr58n3myrdTAgrv/Xuv6ed/GLwuT7qAwgQJ+vkj+mOtR/g98T4NQKH3OMfmwlQFCq2nnQKF82oJi9N7HcU2nYdxR9lj/LDm54Rx0gUUKY1MBMbRt3ErfYDT5O2V34UHRx1Cdaye0796iZW1VY0XOy5QZHfW7hjKQkUIZTJQ2FIghODggiMZmbs9T65+hB9qv00aKNSlY3VAoS4dqwMKFSB0QKEuHasW+24Frepk9oQHCsebQz57cCmdKOFdrmMVi1oUKPzzM9ovUDivuaIXpdHTqJdLKIs9gi1rEwKF6ZEnSA4o1HkYunkUyQKFLpdEQOFto/e66xH12rrWbvQXB/KnfI35jTuXOwoDFOpKTzqgMO1FoX2MqR0CRRMcOMd+oPB7nGNdMeX0EVS4Nx6rhSA6rxlwgjbCM8VJJ1A4rxER4dzexzGscBA3LXyYn6t+NcZpj0CR/nyUY0uybmFXHtz2EJbVrObMr16hsr7aGMcXL+BzFApwskARKFuKVv/qKMpCRQLpN47LHKBw2lvC4qhOJ7J5znD+t/o+ZtX9HLqvTAMKdelYrQeTx//LK4xXAnl0Zg+uIEoe73E9a1jRJkDhVjqAwhOvhYDCec0T69Izcgq1ci5LYk8iZSwhUKQyKdstE5jolo5tid2yvYWxWoSHBwonygCxP+uK0fwqn2GxPcVzrTWAQp2YrXrdMgFFsMfvXVuBwgQJnnzUONrCXX8tGejweAxA4VauFeGi0hMZVLAB1/39EL9Vz/F5skChBwpHGxZ14/5RBzO3ciXnTHmd6lht4nx0QJFMPgagcCsIEpo8ib3tESiyallloSKkTECh9WgAwBzH700WKJxrERFhXKfTGRgdxEOr7+DPuj8S9rW2AkWYR6Z08Qroyh5cgU09H3ADNazytm8FoIhfSxNQ+It/NRcNzKQAFM5rvjWA7tHjqZazWBZ7Dilt7SpPAPVK3+mYlG0htCs9tdRu2Y4SAUVQP+6iXAjBQHE4fcQofpL/Y6n8oTEHb3x9PCWXFgAK0+Z2yUJCIm+gJwsUofrSFZ1BQKFCQV4kymV9T6Z/fl/+M/9+/qye7ytetTsr++7PDC/N8WQyUDjnBnftyT2jxvLLysVcMPUt6uw6c18tCBS+dkm8h0GrTQXu5m25PJrrbaXsnIqWUxYqEsj9gdABhToSoQMKn0dTuKteFSjcCoKEHJHDyYXnsG5kPR5Ycwvz6+cb++rIQKGuluQutDvTkz24girKmcRN1NDwPKwKFG61FlC4lW6g8E+uNsVI8CokBdYgukWPosr+jhWxV5FSesDCmTcRRcS/DwsU6sRsZx5GRFqeGOrEbJPCLh2rnbAdh4LEQCEMcXTZRUSEweIEejCUH+z7KLdne+J7+9QDBVovnle3WhMoTKtCCY/HEE/zhpmAItijiWMACrc6ClA4xwWRXK5a5zRKc3vw7/n3Ma+mLBAokpvUHaZwTwY6/Pkkipe6J3Ffzj/6Lbv34c5t9+ebpfO57JuJ1Nt2uwQK0+Z9us+jByiy6jDKQkVItSRQGD223xM46tB4Lod8Ti28kBLRgwfW3Mji2CJ/HANQeO7ZABRumYDC62k/QOGoC33ZjcuoYCGfcCt11GjyUSFBOd8CQOHPPfOAwjkusIZRFDmEKnsyq2ITjTCQjMJOzE73XhRBow0moHArDFCo1yIiwlDrDIrZgG/lHVTKuU3xQgCFaXO7QEgIGA3JNKDweTRAYdot29OXr5hy4gcV7o3HmjcqzLwIE1B4PAagCNNXuoDCeS2MFHB1v9PpGi3imnn3sbB2aZsDhWkfhebHCZOPpq8EBfe2Pdfj1m324eOFf3L19PexZdOGcLplgk1A4bG0E6AQIrM2v8uq5ZSFigSyEe0KKJxz+XTm9E6XkEcB96+5gZX28lBAoS4dqwMKdelYHVCoS8cGQocBKDwe9J6WAArnXAkD2JmLWcbvfMHd1Mt6Vz7BQOHNveMBBTTMoegU2YbCyH6ssSdRFfsEncLOoVAnZqsxoOWBwrTSkw4odEvHetv681ev5ZDLcHEunejN1/JWVsuyVgUK06pQup9WECSo97m2AUWYUQfTbtna+QcBQBGmL9UbNIHcBBSqpyhSyH/WP4MCK49r/rqPJXUrPJ4sUCQquCW79NmA67bak7fmzuTa7z9GSukBClP7wHwUoHDLBBR6j/++krs/rzeTN7/LPv7UcspCRUi1J6BwvJ1FMWd0uhyQ3L/mRiplRcYAhbp0bLhlYv2eZLzJAIXz2lNswg6cRxk/MIUHkdIOBRTqXhQqUOhyX9uAwnnNj+xIXmQ31sQmsMb+Kt63DiYcKIhoS9IGtSZQ6PpJB1AEx8WTe1QUsJW4gBwK+VreQrVc4QMKT7xWBIpkIMHkReNNyqP5qHRkoFALSf3jWYonZPHaNdqF//Y/ExBcPed+VtaXa9uHKUyDR3CSiZP4/jIBKJzXffptzL+H78YLf/zI7T99iZTB7ZMFCuP9JAllxlWhtD83b9zsI08dV1moSFImoAj0aCg1GU8qQOF4S6zunF5wBVWs5sE1N1El16w1QOEvmr1eNN5kgMKZQ1EqhrEtZzKPr5jGE0gpmw0UvmvNAArdfWYKUDgqtPYkNzKKqvrXqIl978tbBYmY5t50ammgMHmCd5xODBQC/TUVKJw+8kQR24iLkcBUeTO1slIPCQagQOd1jtsYKEztPR5h8LiLKaTnXDB06IHCrSxQ+ItXIaBHTlf+2/8Mqu0arp7zAJWxSm0bffswhXsycfApNTDRFcrpBwrn9eABm3LFFjvyv1nf8uAv37RLoPC11wCFO49MGrHIjlS0nLJQkUDuD0QQUBg9msI90KOMegRN6jYBhZp/T6uUU/IuY7lczKM1t1IraxRPY7y1DCjUIlznUftUgcJRPzGSEZzE73zID4xX+nYV6m0AFM6PPRFQeO+3dYDCOS+EoLN1ALnWllTGnqfOnhXPJWhUQs0X8E3KTsfmdsmsBKUr2H2eZgCFNwenjaBAdGdbcSm1rGKqvJV6WeXNJwAojJCg+Z1h8no97Q8oms6ZgcK0W3Zw8er1umUCCm0cBSjC9NXaQOGoT153/tv/DFbWV3L1nIdYY68xx2kFoGhenDD54FO4v+Cb+zp24Bacu9m23PXTVzzx63daIDHGSWaDvgwAiqw6jrJQEVKZAhRN1/ReXV+2DX2t9Tgp71L+tufwVO1d1Noxj1cFCrdMQOH1NPbZjoDC+BiT65eguzDfQOzM5hzFLN7kF/mWJwakByjcSjdQmMEgwfUUgEKdlA0ghEVR5FByxSAq6p+i1v5TCxTqKEXQ5OxU9qIIBAkDDKhA4WmTACjcCgMUpr0oLASdRSkjxcVUspCp8i7sxnk+zQUK9REp7c7aSu46APDHMwNTewQK027ZulGHIKAw7ZYdvL+D32Py6ieQ64EiyBNUvK5f0Iv/DjiNhbXLuObP/1EVq/HHWcuAwhQn+d2uG15PHzyCkzYZzg3ffc74338Ol493neiEfTX1ab6vJlMLAYWQWuhqK0nZuhvfZUcqsopLN1LR3oDCOb++tRHH5V7AbPsnXqi7j1jjRR1Q+JaZxTl2eZRzKlC4ZQIKrcdw3HCubYDCOd5Y7M0gxvITL/CHnNTkTRNQ+OZKGM435Kq/lglAocqZPyFEhKLo0eSI9aiof4w6e77P65Yay4ljkdxeFKFGJJIACtPSsTqg8BfhqQGFoxLRn5HifJYzm2nyfmj8A4EeeNQ4jcctABTqOf2EdL1XW98YgMLjMQCF19MYZy0DClMhqX88ywwUYYpX0+Z2G3bqw38HnMKc6oX896/HqLXrzIW3S6GK4GbEaQ9AIYRECMFFw7blyI0245pvPuKtv2YHx0kWKJKAsqCdtf33h8er9/jfn6zWfmWhIqR0pNmegMLRRtZQjs45hxn217xS/z9i0u9JF1D44EADFCaACJoX0VZA4bwO5h9syJ58x+PMk5OzQKEBipiwtTtdRxBERS4l0eOJip4sr3+EernY51PvVxfH/Rq0QlOoR5sSAIW3rd6bClB44oYACudaDzGYrcXZlPEt3/GY54MUBhKS8bYmUKgrPWk9cYBQCnitxzn2F1NNfZoL97gnCxS+9s7xJp3W498DTmTm6r+47q8nqbPrtYVpUgAQUOCaNrdLN5iEitMMoHDaCyH4vxE7ckD/Tbjsqw+Y9Pcfge9Fw0GI3NMFFIbPozcfQx6eezf/Ps9q7VEWKhJInWSjrvSkvnrb6r0eTysCheMfYm3FIdFT+cb+mHdiz2C7SCDTgMJfWHvPE+DVx1OKe+d8SKAAEEIwlKNYj+2ZxkOUyek+rzcvJc5aChRuufeg0M59EPmURE8iIjqzvO5hYnIFMbwb5KmFtxrHNDHbpEQTs4OAItHmdro/xoUBivg1A1B443mv9WVLRnAqf/EpM3geKWU4SJDqvZj7NAGF3mP2ZhpQmHbL1u2sbQIKt0xA4ZYJKHRx2gtQON9v1nkD/m/AcXy7ajY3zX2OmIy1GVCEixMmH8XbXDDRAIVzbAnBtdvsyuh1B3DeF+/x5aJ52r4bDkL0mQWKQEnZ+l8dRVmoSEJhgMK0UpPWYwAKt0xA4fUoXk1lq+a1RWQn9o8ex+exCXxkv+q51p6Awt9e+jxN8bxxUgEK55oQFsPESfRmC76W97BU/uLxNOWhxEkBKDz30A6Awi3dX+Tj10QhJdFTEFgsr3sIW1YmjJMIKFJd6SloBCEZoDCu5pQiUFjorzl59xPbMYxjmc0EZvOW3uu+lxBA4QeA8JAQNLKjerWedgQUpt2ydYVpEFCY93fAp6T+qt5sj9ebaFL3iKKNuWrAMUxe+RO3/TUeu/F/cGstUCRzXwagcCsnIrhl1Gi27b0OZ34ykW+XLPR5TEDh9QTkrOTVEYEiq5ZVFipCqjWBwgcJGqAw9eWd76H3OnMoRkXGMDpyGB/GXuLL+ne9Xs170B6BIgwkJON175ZtiQgjxBl0Y2OmyDtYKf9Q8kgPUMSvJQAK7720LlC4J2a75z04Mq3wFBHFlOScgqSG8vpHsBtXNHKki9EaQGFa6SmZR6rUotytdACFc25DsSeDOYgZvMifjfN8tKMOrQgU6jUdUPg8wu+J92kACr3HOV47gCLMpnKm3bI9njBFsLLSk26Eo+n+/AX8qK5DuLT/EXy0/Dvunvs6zm+npABH+5x+4vtrj0ChtsuLWty74xiGdu/JyR+/w0/Llxh3y9b1FQQJpt2ytUV/CMBNBiiEMMRoI0lbtPpXR1EWKhLIttdOoHDO7WIdyA7WvrxtP8G02KcN5933HwcI9dj/j8QEFIEe5dh7ruWBwhmlMHm9uXsLc4kkInLYSpxLF9ZhiryFcjlf36fmf6bpBgp1L4rWBAqTEm1kByBEd4qjpxCTKyivf4yYsuRx8OTq9ACFLnYYoBBSf00HFL5rSpGvvT+nHwNQOJ4hHMRG7ME0HmeenOKK4+SpBwpvn05cfb56j9eru5YqUDTBgXPsBwq/xzk2F6YqUGg9muVYHXVkoDAW5a4P5i7dN+eC9Q9mwtIpPDj/bVSwCBvH9xfyZoOJ+d6bPIq3FYHCeS3IifDgLnuxQVFXTpj0Nr+uXN4qQOG7poNyw27Z2vdSsxRxVmu/slARUmsjUDRIMMY6ihFiV16zH+Qne6qrr0av71iYPb74ZoDQlaKZBhS+a8LvjYp8thEXkU8JU+XNrG6ceKwDCv/9rV1A4X5MKcz+E46iVinF0ZOolwtYVf8UsnGpVEfuUYqWAArTSk9Bj/UkAgq3wgCFOjE7eK6D1xMRgi05hvUZxRTuZ6H8IRRQmDa3S/UxpkwDCnWlJx1QmDa3CwMJuhGToL0k/H0m78lkoHDOjek5grPXO4BXFn3B43+/3yJAkVycEABgAAqPQoGJASh0HsNr55xcHt19L3p3KuT4DyYwp6K8fQCFLw/Xec370FbKbn7XcspCRQLplpR1K2i3a5/HNnvaAijir1jsb53MpmJbXrLv5nf7h1YFChMkhFt21luUez3eOC0BFI6iohPbiIuJkMtUeTNVcmVjm6bM/Pcntec91zIYKFQFzZ8wyR0rx1qPouhx1MnfWRV7Htn4DyYde1GoQKHLOwxQmFZ60v9VX38tVaAw7UXRVJxbbMOp9GYoX3AnS+XsZgNFGEgwrQql+zSsrUChFmHa+QeW/lqoEQ4ddBiAwuNJ4S/KzQUK5/XA3ttyyrp78ezCj3l2wScpxDHfX2sChbngDuhLBxQh8olbLUnXvHweH70PhTk5HPf+2yxY7Z1zFgQJTZ7E3lYBiqw6jLJQEVLu4t60UlOgRwMURkjQ/BsMAy8moNB5VK8QFgdZZ7KB2Izn7NuYJ3/NGKAwtdc9dtNaQOHts+FcrihmG3EJknq+kbdQzSrNPaxdQJHM/AlVOjjJsTaiKHo0NfYMKmOvxAvpVPaiSGb+Q2sDhVvpAArnmiWi7MA5dGN9PuEWVjLPMKLQekBhXPlJ8zExAUWwRxPHABRutUegiPcfABQqFAQ/nqMvqt0KVQQbPIf12Ylx6+zGo/Pf45VFXyQRJ/H9rW1A4etfQI+CTjy15z4IAePem8DS6tX6vEJAWdCO34G7eRuAwmtqX0DRmhvfOV8dRVmoCKG2AoowfalLx+qAQt3cTvU6ioooh4oL6MsAnrVvokz+1S6Awu/xx4l70wQUTbAiPdfyRA9GiEuooZzp9u3Uy6qEQOFWawKF2mcqk7JVJfPIk0l51mZ0iRxKtT2VytjboR5XCgUSBhgwAYWnbQKg0HmDgMK/QlNA3yGBwjkXFXnszMV0ojsfcQOVLDb06Rz71ZpA4fNogMK0W7anL18x5cQPKtwbj7XLu6rexEXw2gIUyT0yFdzXsX134/A+O3LfXxN4e/HXIeL48/HlngLg6D2J+2oroHDO9ynszJN77MvqujqOe28i5bXV3rhJAoVprw3d5zEIKExgY1y1LEMBI1N177330r9/f/Lz8xk5ciRTp04N9I8fP55BgwaRn5/P0KFDmTBhgue6lJKrr76aPn36UFBQwOjRo5k9e7Yvzttvv83IkSMpKCigpKSEsWPHJpV3FioSqQMARbzolZAjcjnCuoQSevGMfR3LpHdZOxNQeDxqXOVY72k5oFCXjm0poHDOdxZ9GG5dxBrK+M6+h5isCwQK37VWAgo3HDR3Unaycyh0oxTu2PmREXSJHMhq+xMq7Umux5cSg4VbiVZ6CgIKnycAKISpjXM+SaBQJ2aHBYqGPAW5FLI7lxMljw+4gRpW6r2a+w2ChERerwetJ5OBIgwkmDa3004ONwCFx5MEvLQ3oHByPqXfnoztPZLb/3yDD5Z+FxDHn0+oUYck8gnsK0TBbYynHRJsHlA46l/chafG7M2iNas5/r13WVVX420TdF9tBRQB711WwXrhhRe48MILueaaa/j2228ZNmwYY8aMYfFi/UaxX375JUceeSQnnXQS06dPZ+zYsYwdO5YZM2bEPTfddBN33XUXDzzwAFOmTKGwsJAxY8ZQXV0d97z88ssce+yxnHDCCXz//fd88cUXHHXUUUnlLqTsSNtyhFdFRQXFxcU8tM4dFFgFHQIo3MeVdgVPyv9QSzXjxL8pFj0CgUJdOlbrweTRFc3BXr3HHycRULjPpQMoGto1nFsuf2W6fRvdGMJm1hkgIp683XESAYX+Pg2vCYCiOY88ufNxT8pORkH7WjhyYq6Ovc+q2HuURA6myNre029zl45VgaLhnN6rAoW3vaGN634S7ZadDFDo4zQeK/BSyRLe5d/k04W9+D9yKUwZKNT3RgcUpvYejzB41hKgUIvM4P0dwniaivOEnlB/TfcXgImApPkFvMSWNrf88RrvLfmOqzc+jF16DtG+F5kMFMZ8NJ+j5EY44v/YA/uasXQpR737DoO7dePxPfekU24k+L4yBSgsSUVNHQNueYvy8nKKiopoCzl13T297qHAKmi1fqvsKs5efDbz5s3z3HteXh55eXk+/8iRI9l666255557ALBtm379+nHOOedw+eWX+/yHH344q1ev5q233oqf23bbbdliiy144IEHkFLSt29fLrroIi6++GIAysvL6d27N48//jhHHHEE9fX19O/fn3//+9+cdNJJKd9rdqQigdwTtYPnKuiBwq32AhS2hE6iiKPElQgsnpXXssou93o89+49p/Vg8vjfzDBev0dT3DvX2gAoJFAiNmaodSbL+JFf5GNI6b3b9gYU0FBQuudQNPdxJ93P3z0xu9jak87WjqyIvcJqe1oWKLRxGo81oyFF9GQPLmcNy/mQm7Gp8eeXQUDhlgkovB61mPJ7skDRPKBwy1TAezyGOJawuHjDA9m5+6b8v9kv8dXyX7NA4eo7EVAAbNazO4/tsQc/Ll3KGZM+pCYWC7yvRPfn8bQgUGTVoH79+lFcXBz/uv76632e2tpapk2bxujRo+PnLMti9OjRTJ48WRt38uTJHj/AmDFj4v4///yTsrIyj6e4uJiRI0fGPd9++y1///03lmWx5ZZb0qdPH/bee2/PaEcYZaEipHSFu7oqlA4owkBCIq8nj1YACkddRHeOEldRQxUvcD1VcnVjfHfOeM6pxb7unA4SUlt21jluGaBwK1mgcLzdxVAGi5NYJKcwWz6HMzDYHoFCVSpAYRr1UGM6S8cKIehmHUih2IqlsedYY/+UNqBwK51A4YmbACjcSjdQOK8l9GN3LmU5f/ERd2JT7/KaIcEfr+WBwrRbtn7lJ7WYcudsKtzxKQgS4vEMQKGNs5YBhb99EgW34okIi6s2/gcjSwZy9cznmbbyjwQ/E3+cuCclwNH01U6Awok7ondvHtljdyYvLOO8jz6h3rb1QGH4uen3yvC+F6EmdYdYctcNFLqfT1uprZaUnTdvHuXl5fGvK664wpfb0qVLicVi9O7d23O+d+/elJWVae+nrKws0O+8Bnn++OMPAP71r3/xz3/+k7feeouSkhJ22WUXli9fHvq9zUJFArln7rcEUKhLxwb1ZQIKT74GoHArDFA4E7O7UsqRXEU5S3iJm6iWTc/ftSZQ+FdLcjz+G0wXUPhXkJLa8w3t1PyaCvVe1jZsLI5lgfyIOfK1VgUKVemYlK2bP+GcCwINE1A435uWjhXComfkcDqJTVkce4Iq+7eUgcKtRJvb6f4fGAYo4tdCAIXvmvQee/vW5x0EFI63NxuxGxeygB/5jAeQ2ElBgm4ExuTVejIMKEy7ZetGHYKAwrS5XfAO1GE80pNnoKedAIUTJ2pF+NegQ9iiuD9X/vwcP5T/lVKc5PPx33u4v+Cb+1I9yc3BiP9jT9yX+/4syfbr9OX+3Xfhg7/mcvEnn2NL2SJAYcrDO5dD6TPDgaItVVRU5PnSPfrUVrIbC8yrrrqKgw8+mBEjRvDYY48hhGD8+PGh42ShIqQyGSjUvSh0QBGPrfatu1eEx9ND9OMwrmAxf/Eat1Ev6zIOKHyQkCFA4aiPtRMbiEOZK99mvv1uqwGF+1wYoHC+1DiOdNCgA4xkpFuOVl06VogIvSPjyBcDKIs9TLWclxJQJFo6VgcUxsnXKQCF976913RAYYKE4BEFvXcdhrIzZ/MHk5nCk0ik616aFAYojJOwdR4DULhlAgqvpzFOOwIKU2EbZg8I/eNZiieZ4lXjaW2gcI5zrSjXDjmCwV3W4dKfnmFW5d8pxQmfj+b9CVVwB/SljiAlk0+qO367ch69fj/u2G0nXv/9T/75+eT4KHgWKBIrk5eU7dGjB5FIhEWLFnnOL1q0iNLSUm2b0tLSQL/zGuTp06cPAEOGDIlfz8vLY4MNNmDu3Lmh889CRQh1BKBQl45VPX3FRhzExczjF97ibmxi7QYo3EoHUHjihQAK57t1rD3pJ/bhT/kSZfZnPqDQ5dlcoHB/edt5j3WPOqnzJ3TSAYQaSwUVE6xENEW6u3C3RA59IyeTK0pZUH8/NXJRQqBQ7wcSA4WujQko3AoDFOo8iuYChWlzO/0u3DCAkezASfzMe0znZX88X98tBxSmze30HufYXyQ29akv3DsCUIQpXk2b2wX+VV2zQ3OiPsPGyYvkcP2QIxnQqRcX/vgUv68uM8Zba4HC9xnTvJfGe4cDNhzAjTtux9O//Mq1X01DNv6fI9zPDY+3JYFCCKnNJSu/cnNzGTFiBB9++GH8nG3bfPjhh4waNUrbZtSoUR4/wPvvvx/3DxgwgNLSUo+noqKCKVOmxD0jRowgLy+PWbNmxT11dXXMmTOH9ddfP3T+WahIoDBA4fWvfUDR0AesLzbjAM5jNt8wkYcN5WG4eRGtCRSmjfBSBYr4tSSAwjnXTxxIqdiF3+XTLLW/MYwkqLkYXkMAhb6d9zjMHAr10SbTo046oFDzceQAS0RXdEvhBwYElshj3cjpRChifv291MnlPqBQ+3C/hgGK+P8bQwCFaenYVIDCmzd6r8sTBij8ACAYxO5szRF8y8vMYGJTPF/fOsjSe9sKKNSVngLnRRiAwi0TUHjirKVAYWqf8shEYJyG18KcXG4eejR98rtywQ9PM3fNElQ1B3DWZqBwdMTgjfjP9tvw4A8/cee0H/Tvk+F9CTNiEjSpO5nHvbIKrwsvvJCHH36YJ554gl9++YUzzjiD1atXc8IJJwAwbtw4z3yM8847j4kTJ3Lrrbcyc+ZM/vWvf/HNN99w9tlnAyCE4Pzzz+f//b//xxtvvMGPP/7IuHHj6Nu3b3wfiqKiIk4//XSuueYa3nvvPWbNmsUZZ5wBwKGHHho692ia3oO1XkFAoS4dGzjRWuq9ur5UoPB6GuO1ElA4sTdiK/bmTCZwL3kUsDPjEIikIKGjAoWNRAjBAHEEMbuK2fb/2Jg8SqzNfPGTBQq3TI8/ufO2XaMHkBgogo4TKQ4vwiYiG/6PrK4e5UzMBki4F4UoZL3oWfxVfwdz6+9hQPQCoqIouA1moHArnUDhVhigMO1FoR11SBEonONhHEANlUzmCQrozMbsoMRzQZYxnu4+Gz0iwNOKQKGu9BRm1EEHFMbN6ULMeUh5InIzPO0BKJxzRTl53Lb5MZzz3ROc+/3T3LvlOPp1KklLPj5vc0deDEARJk5LAIVz7oShg1hdV8eNU6dTmBvltC0H++MEAYUpDw9MKf3rcjaNJAmpfa/aSlLq66qW7C8ZHX744SxZsoSrr76asrIytthiCyZOnBifaD137lws118ftttuO5599ln++c9/cuWVVzJw4EBee+01Ntusqba49NJLWb16NaeeeiorV65khx12YOLEieTn58c9N998M9FolGOPPZaqqipGjhzJpEmTKCkpCZ17dp8Kg5z1jO/tfXd8PeNUgUJdOlYbR72mAQo1tgoUnmstABTu4+94jw95lFEcykgO9vbltPHEkdpr+kem1ILdua4p7p1rKQAF4Fs6tiWBoiHPhvP1sp7Z9oOUy5/ZJHIuXcTAZgGFzhsGKJKBCdM5t3SgYiOJCZt6JFEEEWl5Rih0QOEZndCMJlgIauVS/qq/nShd2CByHhHRKSWgMK30pH/0xwAbGqAwLR2bKlCoE7NVry6ODiiavJJPeJBf+Yy9uIgBDPd4ve31x1qPBiia4KCpb/ex3uMc64oppw/vtbYCCvP+DmE8TX2ZNrfzeEL9NV0pIBWgCGrfmkDhfl1WU8lZ3z1BnYxx/5bjKC0oamY+ijfUX/AD+goAinAjHE47fRtd7DDvodt7w1fTuefbGdy4y0iO2XRg5gAFUFFTx/o3vpMR+1Tc0f2+Vt+n4vxlZ7bpvbeWso8/hdTaABRuNQcoJDCMPdmBI5jMeKYzocWBwpO72lc7AwobiSUiDLROoVBswK+xe1kl5yq5GF7TABSOwmxC5yjMjtmJdsk2xQ0CCp2c8/miJ+tHz6aOFcyJPYCUtdr2wZOb2wYo0HoxetMBFG5Zjf6dOYX+jOA97mABMzMWKNwyAYXWoxaCLmWBwgwCbqU0YqKNExyve15n7triaADO+e5pltasygJFgvtSvZeN3IITh27C5R9P4ZVf/1D6UnIJeA/SDRSZprZaUrYjKAsVCaTb/A7aJ1Co51SgcCsIKByN4EBGsD+f8CQ/83GLAoUREjIAKNxSr5lWeQJARNnYOoN8Spkdu4squVDbNixQNGdStip1/kQiqSMUah8RaflGKcIARaLXTvSlf+RMqpnPX/bDIGOe6/q9HhQ4kPrzBLVxzmv+ZxEGKEx7UQQ+UtRMoNBBgkWEPTibUgYygZtZyl9Gr6rWBAp1pScdUJg2twsDCbqN9YL2kvD3qb6G8bhgIcOAIqWRicA4+jZuCSHpnV/MPVseTXWsjnO/e5YVtat9nsRxFG8HAQpo+Bz9e8etOHzwhpz/wWQm/jHX09ajMO+lASg8CgEUwspO1O4oykJFClpbgUIt5lWgcMumoZDZnqPYjN35gAf5jaltAhSE8bYgUPhzTwwUdrwoymdg5GyidOHX2F1Uy6XaGGGAQt+u6dhd7JvAQjchOxkFbW6nPvbkKFWgcP5f2lkMoL91GpVyNn/ZjyMaP7j6ZVzDA4VamAcBhX9CdfOAwrQXhe4XdqpA4RxHyWVvLqKYUt7gOsop03pN7UFftLRHoFCLYO38A0t/LRno8HgMQBEmTnsHCl2cdQpKuGfEUaysW8N53z7Pqvo1SeSjHIfYoC1w5KUdAUX8523BTbtuw74b9eP0iV/w0V8LNPkEvAe+PLznPf4wGxBm4I7ambykbHtXFipCSl06NtwKUN5jr0fxair3TAMKnwcaH6E4iY0YxbvcyVy+b/RIn9ccTy3K3R618G887/rl5Z+LoD8PiYHCrXQChTeut++IKGRg5FwEEX6L3UWNLPf6EgCF6Vyyk7JTnZCd6LEn99K06jyK5gKFc1xsDaa/dSIr5XTm2k07lzd40MZQgcKtREDhViKg8Hil6sXnDQMUplWhtH+Q9LXx95lPIQdwObkU8jrXsoblvnhhgMLnUYBCm5cCFB5POwQK4yNTmqIsCChUKEjmL8qBhXKYYrqNgMKJ07+wB3dteRRlNeWcP/1F1sSqQ8TBF0f1JLfbNT5Potw9HgNQ6OKkCyica9GI4O49tmPX9ftw0tuf8eX8Ra0LFGqOoul9yGrtVhYqEsizo3YAUPggQTl2+4OAwgcJ8eLeDCZtBRTOsYXFHpxJPzZnArfxNzO1Xk/u8fYtCxRuhQEK37U0AYV/JMHbX1QUs1HkXGLU8kfsHurkak/OnvvQjEqEnZQdBijCzJ9wZJqYbepDfewJmg8Uzms3a0v6W8eyVH7OAvu1xmt6rw4oTCs9aedXKIV+EFCY9qIINeqQIlD478XvUb2dKGIsV2IT4zWuo4ZKTZ/KcZJAYdot25O7r0h04vs/VyagcGttBQq1eNYBRXIjHCEK5aQ8/nwSxXEXyhsX9eSu4UcwZ/UyLpz+EjV2rfk9aA2gSAa4AoDC106Tc5NXfw9ej/7+ciIWD+69A1v37cG4Nz/h27JlbQcUWXUYZaEipDIFKNSlY1Wg8OQc79s5Tj9QOLKIsjcX0IsNeJubWMwcX17+9mpR7u5TDwnJAoW6dGwmAoVzPkd0Z4OIM/H4fuqpCYgRbg5Fsqs8JfO4U5i5Ge6lY9XN7Ux7UQS+KjCgjjJ0t0bRzzqUMvkeZfZErVfz/+W0AoVbYYDCCAmaYa50AYUKW861YnpwEFexmnJe50bqqA4c4fDn1XjcgkBh2txOv7+D6k1cBGsnhxuAwhtH72nvQBFq9MKw7GmycVTvkOJS7hx+KDMryrhk+ivU2vWaePr3xBO7HQGF8T0MeL/1e2VAfjTC4/vvyJAeXTnq1Y/5eclK7fttBAq3pRlAIURmzamQsqGma7WvzLn1FlcWKhLI+VBA+wAKdRUnFSg89+Z41ByCPJg9Fjnsw8UUU8pbXM8KFjbl5WvvLcq9eamFf+P5tRgonNc80adx4vEC5sYexpZ14UYmNEARRs2ZP+GWGzDUgty0uZ3JHxYo3HJPzO5t7U5fsS/z7ddYYn/qvS79bRIBhaefEEBhWjo2VaAwrQoVuJqVr42/T5O3G+swlstZyjze5nZi1GuBwrS5nW7Sc6YBhWm3bO38gwCgSAVeglak6shAYcpnWMk63LblwUxfMZ8rvn+dejsW2JfaZzIbtOn2HWmvQOF4C3NzeHrsTqxXXMhhr3zEb8srAvPwAIUaO2Cfj+wIRVaQhYrQCjUvoh0Ahc+jxPfcn+pRjr3nGr6L0ol9uIwCuvA217KKpaGAIuG8iA4AFM5rvtWf9SKnsUb+xvzYE0gZCw0UzvVEk7LTOSFbF18FiqCVntzfJwIKXR+6lZ7WsfajVOzKn/ZzLLe/brgug9rqz3uupQEovPmrcRqPkwQKNXcdAPjjmb0CKGVDDuQi5vETE7kPiZ00UJg2t9NDh1pM+T3tESjUQlI/30PxhCmCA/6inNwcjBCFchsChfO6Vff1uXnLsXy55A/++cNb1Nu2vq9QBbehL83nKLlJ3eZiuq2AwvEU5+fy/ME7070gj0Nf+ph5FZXaPJIFCuP9uH8P6ACkjZVdUrbllIWKBFI/EEZI0Py7aY9AYQII3a8Ff2HdoHyK2JcrEVi8zXWsYWWzgIIkvYmAAp3XiSP8+TX17b3WEkDh5Fxobcw6kRNYJX9gQew5pLS1cKMCBZBwDkVzd8h2K8xjVcnuRaE7p14LAgqAiLBY3zqMnmJbZtuPssL+0RhDLcrdSgQU3ty813RAkdyoQ2JvGKBQzwVNOnd712Mo+3IuvzKZSTwe/+ymCyiazqnFVJPHtLld8C7VXq9b7REo/MWv/z1IbYQjRKGcFJj4LGkBCuf77XpuwA1bHsBHi37lvzMmYksZGKclgcL0M9EV0yagcCvUyIvh/rxxvF73PXQvyGP8wbuQG41w8PiPKVu9RonrDpReoBCWzMhVoLJKv7JQEUL1suE5zjBAYVpmNmikQ7ckrerJFKAwjTqoBXsh3diHq6iline4gRpWpwwU6l4UzQUKdenYIKDwgYPpfBqAwi0bSRdrc0ojR1Mhp7DYfg0pg+dQAKGWjHV/nwpQhNnkzg0UjsKu9BT3K8V5WKCInxcWG1nj6CqGMst+gHI5OxRQmJaO1S4Pq44yNBMoTHtRtCRQGCdhAwPZhtGcwne8xxeMzzigMO2WrYMEE1C4ZQIKrWctA4pQAGCM488nNTAJer8lu/YeyP8bti9v//0T1894P77KW3sEijCjPMZVoQIAVwUKt0q75PPyoTtTb9sc8uInLKuu9nlaAigyTdklZVtOWahIoLLYAq5ecQW/1/4BNB8o1L0odEChLh2ret3KNKBwty+iN3tzJatZyrvcRB3VaQcKt9oKKAhok8wIhbp0rI2k2NqantYhrLQ/Zpn9buAcimSBIhWZHnuyNLHdE7PTtXRsWKBwvhciwiBxKl3YgF/se6iUcwP/Up9OoPDGRe91eRIBhVutARSOtmA3duJIJvMK0+Q7Wk9Tn0oBr/U09pUhQGHaLTt4fwe/x+TVr0ileELEWVuAonn5wJi+g7l66F68PO97bvn5IxC2t32Y9zLAk9wcjIBiOkOAQs2jX3EnXjp0Z1bU1HLoi59RXl3b9DM0AIXuPQhetUp9f6T255LV2qcsVCRQV6sHXa0S7ll1G/Pr5gFtDxTx4lbqj91qK6BwjrvRjz25jOXMZRK3E6NO8XjjJAsUvmttABTOj6a5jzyZPEWRHehm7cMK+x3KY58a51CY1Nz5E46S2YlbXenJ/X1LAYVb7onZlshhiHU2BfRmhn0Ha2SZUlircRvPy8Txg4DCtBeFFhJCAIUfAMxwZAIK7z3ovTro2FYcyDbsz4c8wU/yc68nDhBKAa/1OMf+grSpz8SFe6YBhamQ1M/3UDyh/ppuLtySm4ORTAEfBgQS3186gcLR2PU244pNd+fZOd9yz8wvEhTcAX2pI0jJ5BNUTAf9TNoQKJxrG/XozEuH7cTfFWs44qXPqaytDwQKUxzh/YXn9QaMmGS19qrFoGL58uUcffTRFBUV0bVrV0466SQqKysD21RXV3PWWWfRvXt3OnfuzMEHH8yiRYs8nrlz57LvvvvSqVMnevXqxSWXXEJ9fdMyc6+88gp77LEHPXv2pKioiFGjRvHuu++mfB+55HFml/PpZnXn7lW3sKh+MZDZQKEuHdtUEPvVkkDheHuyEXtwEWXM5FPupb7RlQUKr4JWeSqyRlNk7cJy+1Uq7a+1cyjU7yG5+RMWQlt86voImqvRkkvHBgGFaelYC0FU5LO5dT45dOEH+zaq5XJDXOI5q/ET7ZadLFCoE7ObCxQmSNBOOjccuxX3NJp25Sg2Zxfe5n5+k982eNIEFKbN7XQ7a5uAwi0TUHjurx0ChWm37MAiOGDFnmT+gt9ScZIFCufc4RtswUVDduZ/v03hoV+/ajug8EGr5j0IAWWJvB5LM4DC8Q7pWcyLh+3AzKUVHPPyF1TVNa2q1RJAobuPNpNsqK9a6yvE3+PWGrUYVBx99NH89NNPvP/++7z11lt8+umnnHrqqYFtLrjgAt58803Gjx/PJ598woIFC/jHP/4Rvx6Lxdh3332pra3lyy+/5IknnuDxxx/n6quvjns+/fRT9thjDyZMmMC0adPYdddd2X///Zk+fXpK9yGBAlHIOV0upkB04u7Km1hhL2+6bgAKtzINKNSlY3UeMHmkP14CL0Apm7IL5/IX3zCZR7CJGftsbaBA420toNA98uS8ur8XQtDV2p/OYiTLYi+wunHisVPg6wr9RPMnHIgIgolUlc6lY01Aoe1PAxTOa47ozObWBQgsvrdvo15WKnHx5ewoEVDovRi9iYDCe29Oe+89tQRQ+Dyi6VgIwV6cwkYM51Vuj29ymSlAYdrcTujiGIDCraDC1uyRnjwDPW0EFEmNKATGCY4XNk6ivLSexjjjNhrB2YO2596ZX/LEb9PC31+GAYWpYNfGMQGFSyYQ8Hpgyz7deO7Q7Zm2YDknvjaZ2pitePRx2jVQZNWiElLqyuDm6ZdffmHIkCF8/fXXbLXVVgBMnDiRffbZh/nz59O3b19fm/Lycnr27Mmzzz7LIYccAsDMmTMZPHgwkydPZtttt+Wdd95hv/32Y8GCBfTu3RuABx54gMsuu4wlS5aQm5urzWfTTTfl8MMP98CHqpqaGmpqmjYbq6iooF+/ftxacj8FogApBSvsZdy66lpyyeW8zlfSWRQDekhQV3rSQkK8uM8soEgECckChdvzB5/zGfczmDEM55h4oaUuHdscoHB7wgCF//5k4PmGPPXXWgIo3K8xYiyLPUWV/RMl0ZPIswamNIciCCLUUZREy8e645tWekoXUICuaE8MFE1tYY1cxHf2jeRRwgjrEqKikxYoTEvHaiFBql5vDN21IKAwbW6XDCSkEyjcnnpZy4vcQBlzGCeuprdY3zAJWz3WFaROH95Cqa2Awry/QxhPU1+mze08nlDFr1JArmVAES4fxauJc/cvn/PI7KlcufmuHLHBsOB8DEARJp+MBgpTHgGfJ+fax3PKOGr8ZPYa2IeHD9qKqGWFipMMUFRU17HOfz6gvLycoqIi2kIVFRUUFxdzfeeHyBedWq3farmGKypPbdN7by21yEjF5MmT6dq1axwoAEaPHo1lWUyZMkXbZtq0adTV1TF69Oj4uUGDBrHeeusxefLkeNyhQ4fGgQJgzJgxVFRU8NNPP2nj2rbNqlWr6NatW2DO119/PcXFxfGvfv36xa85gFBidefswktZI9dwX+WtVMk1LQYUbsWLW6k/bjjXOkChzSvAq3oGsD0jOZ5fmMiPvOptnwWKQKCwhUQKQUnkKHLFRqysf5xq+y9UNWcOhe7nnEhhVnpyf58sULiVLFB42zaosyhlC+tCqljCd/Zd2LLWk7M7vgko3MpEoHArnUABkGvlcqi4mBJ685y8gZUs8nmaAxSe3A1A4VZHBQq3Aotfk2ctAQqE5OzB23HMhlty3Q8f8drcGea+mgEUHhneA69Hn3OmAYUQkl0H9ObRg7bh7VkLOO/t6UjMk98TAoX7LXC3D/+/oazasVoEKsrKyujVq5fnXDQapVu3bpSVlRnb5Obm0rVrV8/53r17x9uUlZV5gMK57lzT6ZZbbqGyspLDDjssMOcrrriC8vLy+Ne8ed5J2dBQsPeKlHJm4SUssxfz4OrbqZU1LQIU6tKxKiy41ZpAkWgORhBQOMX2QEazBYfxAy/zMxMbrhmAwq32DBSe+0gRKJq+j1ASHUdU9KW8/n/U2w07l+secTI98qSTLtdEoxTJrPSkO2cCCjVnSA0oTEvHFon12MI6nwrm8IN9H7asd7VB6c8PFIl2y04GKAjyKnl7z+mP3X6T19un4tEV946n8Zt8UcCR4jLyKOAZ+zpWyRVpAwrT5nZhIEG3sd7aChRh/pqeTKEcDkz03jBxWgoooOF9v2SznTi0/2Zc8+0HvDN/pr+vZgKFsZjWxElmXkTQKkqtARSO9t6kD/cfOIIXfpjLZRN/IP5/tWSBIiCPTFFrzqeIz6voIEoKKi6//HKEEIFfM2fOTByolfTss8/y73//mxdffNEHOary8vIoKiryfLnl3i17nch6nNrpYubH/uSxNXfF97FoTaBQl47NBKBwy++RnuNNOYDB7Mu3PMXvfKL1us+1FVB47qkZIxTqSk+pAkUsfi6HougJREQJ5fWPEJPLPLmqgJHKvIl0AkUQLOgUdulYFSh0MdTC3HktERsxzDqbZfzEz/ajSGmnBSjQeZ1jDVD4+3SOdfHUNt5jd85BQGFc+UkEeOKw0HDjhaKYo60rqKeeZ+0bqJarMwYo1CIsaIWkMDtQh/K0Q6BILo6+Tdg4ibxej+INUXBbFvxzi93Yf71BXPHNe3zw9+8ZBxS+lZ4C3m8TUAS9B9r3MgRQOL/MDtlsXe7Ydwv+N+1Prvnw5/iIhSfHFIAiu/ldx1FSUHHRRRfxyy+/BH5tsMEGlJaWsnjxYk/b+vp6li9fTmlpqTZ2aWkptbW1rFy50nN+0aJF8TalpaW+1aCcYzXu888/z8knn8yLL77oeaQqWdnSCxQN5wQDogM5seB8fo39zDPVDxBrNGUCULhlAgqPB70nWaDwF+aOx1t8O7lvwZFsyK5M5WHmyq89Xre/LYEifi0DRijUCdmWKKA4ehKCPMrrHiYmy4Hk5k+o9+koCCjc3ubsRRHPTxLoSwQUnli+tsFteojN2FycxkKmMFM+jZTSBxRuhQEKIySkCBTJjDq0FlA457qKnhxjXU45y3jOvplaWaOHIQNQeHJvRaAw7ZYdpi9dIWkCCv39hS8AQ40EtAOgSC4f5ThUwe18DgT/GT6a0X034uKp7/Dpwjl+oHCpPQKFbrds3/358vCe90jJddzw9blhzGbcPfk3bvns18D3wKMAoMiq4ygpqOjZsyeDBg0K/MrNzWXUqFGsXLmSadOmxdtOmjQJ27YZOXKkNvaIESPIycnhww8/jJ+bNWsWc+fOZdSoUQCMGjWKH3/80QMs77//PkVFRQwZMiR+7rnnnuOEE07gueeeY999903mFo1SV3kC2CQ6lHH5Z/F9/VTG1zxKLNZYgGr+DbUmUKh7UYRbJlY9lqG9br/foymunW8EbMUJrMtIJnMvC+UPPk86gMIt9f5aGijSPUKhyhJd6JZzKhKbFfUPg1zjvZ7k6IQqXb+WZhRE/b6llo7VTr42xAgCivg1KSgVW7OZOJ65fMRs+armnpw2zQcKwng9nvCQYPLq+1SOUwAKx9NL9OOYyGUsZA7j7TuJNY7cmja3cwOFaXM7/YZxqjeoSDR7WxMo1FGGwGI6ACjCjHD434PkPe0ZKJzvo5bFjdvswU6l63PeVxOYvGievuBOAvKCiun2CBRxv2F539O22ZB/7jqI6z6exX1TfjO+B6Z8MvGRJ7ekFK3+1VHUInMqBg8ezF577cUpp5zC1KlT+eKLLzj77LM54ogj4is//f333wwaNIipU6cCUFxczEknncSFF17IRx99xLRp0zjhhBMYNWoU2267LQB77rknQ4YM4dhjj+X777/n3Xff5Z///CdnnXUWeXl5QMMjT+PGjePWW29l5MiRlJWVUVZWRnl5eUr34v5A6EYdhka35oi8U5hS/zFv1T2H7Zo9rU7iVoHCrUwCCpL0hgEKHyTEf/lYjOR0erMpX3AHS+XshEChyy8IKNS9KDIdKGwhtUChWzI2giAiSuiWcwq2rGRZ/SPYsjqpx51MoxSJNrtTV3pqLaBwKyxQeNo411wjB/3ETgwSh/M7b/KHfCfuTQQUboUBCnXORagN/Hxt/H0GAYWpfSjo0HyE/I84QT8xkCOtC/ld/sBr9oPQuGR0c4EiDCSYNrfTTmg2AIXH0w6BwrT3QygACAFnrTNiohynABTOa44V4bZRe7NNz3U4+4u3+GbJgsD2QT8TbTFtKKLDzEMJ2vE7cDfvVgIKx3/Rjhtz4Q4bcdW7P/P4tL98njBA4XkPO05d3aHVIlAB8MwzzzBo0CB233139tlnH3bYYQceeuih+PW6ujpmzZrFmjVNf1m9/fbb2W+//Tj44IPZaaedKC0t5ZVXXolfj0QivPXWW0QiEUaNGsUxxxzDuHHj+M9//hP3PPTQQ9TX13PWWWfRp0+f+Nd5553XrPvRAYUDGyMiO3FgzrF8Uj+BSfVvePxBQOHbtyLeF55j77mWBwp/4ZzY6/e4i2vlmvDGt0SEUZxDCQP4jFtYKecGAoV/0zypPd/QTs1PKm285z3XWggo3F9B7U2bzIF/udg80Zvu0VOpl0tYXv8YtqzTtlOVCCjceTp9+eZrtOBeFDqgSLR0rA4ofNeUR5EEgg3E3mzIfsyULzBXfhoKKBJBQrJAoeYeBgCaCxTqSk86oDDtReH2bGRtzj+ss/hBfsE79tM4/7raGiiaikRznDB9qd61DSjC7PScGpiE8fj7Uj1hgaLhXiA3EuGu7fdmaLfenP7Zm/ywvCzxe5AmoDDeV8D7rd8rw+sNgo50AoUT5/92H8SpIwdwwVs/8OIP83y5hwaKDJPzWHtrfnUUtcg+FWuDnPWMbyx6kDya1jNWgcI9h+L9uld5r/5lDowex3bRPZsFFLpzKlB4PGpc5bruXCpA4T0nDfGaIiYCCre/Rq7hE66nihXszD8pFA0re7U0UGivJQkUWm+SIxROzg5QQPAEafV7gDr7T5bUP0SeGEiP6PEIEfG1d8udvzqPwn3NKVzdQKHOo2gpoNB6DDFEUBvnvAYoHAkJM+QT/MXHbCXOZh2xtSdnXX6BkJBgt+xkgUI9p79vvbclgaLpnGRK7H3etB9jdOQwdouOdeXlLXZ0G+GZ9qJoa6BQiy/9fA/FE6r49b8HqQFJCG8rAEVq+Wj6ClVwG/pSwHR1XS2nfPoGv1es4IndDmRwSY9QP5P2ABSmPDyTutV2Aatx+e5HgG1Lzn3je577fj6PHzacAzcr1ccNAAohJBXV9fT5v0kZsU/FfwsebvV9Kv6v6pTsPhVZ4XkWLggoAEZHx7JjZB9er3+Cb+o/a/BkgSIUUNhAjujE9lxCLoV8xg2skcs6PFCoIwkmoHCK+jxrA7pHj6NazmR57Hmk1M0sCVbQ6EjQxGz3980FCrdaEijUfoQQbC7GsQ7b8K28n8VyRosBhVutBRSePlsAKAC2je7B7pFD+CD2IlNiHzT24S12UgEKt7JAYS7g3Qo1WrAWAwVAYW4OD+60H+t1KeLEj9/gt4pl5jgBf103AYXeg9HbnoACwLIEdx24OQcO6cOJ47/lvV8Xa/NR708HZZmi7JKyLacsVISUCSg8Hiz2jR7F1tYuvFz/EDNi33jaur9vaaBwq7WAQtdnWKBwXvNEEdtzKQLBF9xEjawweKX2PLQ/oFDnULjlLtiDgMKtAmsw3SNHs8aezsrYa+gGI925uB+zMk3MVo/TsRdF8CpOeq8JKHT5hgEK3dKxQlgMF6fRgyFMlXeyXP6meL05aiEhBFD4AcAPCfg8Zm8YoFAnZgdCRwpA4cTdLXIQoyJjeKP+MWbEvvL2mSJQNBXceq8ndwNQaD0B8NIegSIUABjj+N6CFMEkTF74PWkGCqd9UV4uj+y8P70KCjl+0hv8WbEyFFAY7ycQpvz3lej+PJ4MAgrHE7EEDx86jD0G9uKYZ6fx2R/LvPdnaO/xZFeB6hDKQkUCuSlTBxT+pWMFB0VPYlNrG56rv5vfYjOM3njR6/q3pp5LFSh8HsNxwzm1cA6Kpxbsbo83TrJA4ZwvEN0YJS6ljjVMljdTK1crXqmNAZkJFEFzKGzR9H1QcZ/sDtmdIltQEjmESvsLKmITE/p18zfcjzQ5oxRBQBEIFlJ/3q1kN7fTruaUACj0/Xn7FQgsEWVrcQ7FrMeX3EqFnK/k5o3hOZcmoDBBQhCENRco1JWeVKBwywQUDd8L9o0cyzBrFC/U38ts+0ejN11AYdrcLnh/hzAe6cnT8x6spUDRvDi6QtncV9zTQkDhHJfk5/HYrvtRnJvP8R+9wbzKitYBCsP7pN8rwxs31KTuEHt4NAconGs5EYvHj9iCUf27cdhT3zB17orA9lmg6JjKQkVIhQGK+B+ERYRDo2eyodiUp+tvZa49O6OAwq1MAwpHnUUpo8QlrGEJU+Tt1MkaJQf/L6lMBQp9XD1Q6Ir7RI88mdQ5si3Fkf2osD+gIvaxL2/wwosKP05/bqBw9x30GlaJJmabgMKtZIDC/7iSHygc5ZLHKC6kE935gptYIxcbvYmAwnvP+ryTBQoTQAT22UygMG1u5y1IG16jQnBI9DQ2tDblqbrbmd844uPJqx0ChdETppgO2Awu1FK57QgozHES31fwo0WJ3yfT4znd8zvx+G77kWtFOG7SmyxcU9kugMKUh3cuh9KnAhRuhQETP3BJ8nMiPHfMlmzep4iDH/+GHxaWKx7Ne5CBQGFL0epfHUVZqEighpGK8EDhzKGIiihHRs+nj+jPE/U3Uibn+oDC0w9OPOe45YDCX3R7z2vzakWgcIrzItGPkeIiyvmLb+Q91DeuaKSdt9EKQOFWc4HCHSeZVZ4chS3eiyK70sXanfLYm1TGpmgfsXLP3YiJpp+We8fsphGH1ls6NggoTCs9BT1ClAgoPN7GPvNEZ7bnUqLk8xk3UiNXmuMGAIVpL4pkRh2SBQqfR/g98T7TDBTOHIocK8qxOefRx1qP/9XczCL77yaPASjcMgGFN3e1cDfHaQ2gMP813V8ApjbC0byCO5U4bQUUofJRH0lLkE/vToU8NXo/pJQc9+FbLKny7u0T6r1sh0ARJo4KBbp8CnOjjD9uBBt078QBj37NrCWrPG09ct277n3Mau1TFipCKhmgcM7lijyOjVxCCb14vP56lsmF3uK28UD6jv3/OtViviWBwt9eLdjd8bxxfPtQhPFqgMI511VsxFbiPJbxM9/JB7FlzJdna41QmCdY+2MkGqFQJ2W7PY50QJFodEKn4sjeFFrbsSI2nir7+/h5FWTcQOHu1w0UjlobKNxKBihMKz3pgMK0dGwnUcyOXIZNHZ9yIzWy0g8JzQSKZEYd0gUUTXDgHHsLeLdSAQrnON/K48Tci+giuvJw7Y2ssJcGAoVpt2xvkaj3uoHCvByr//7aI1AkBQCBcYLjudU8MAkRp4WBwnntW9iFJ/fYjzV1dYx7/22W11Qnfg+MMOV7C8xA4ba0Q6BwrhUXRHnlhK3o3SWP/R/5hj+Xr/F5dI9cZYqyE7VbTlmoCKFUgMJ5zRedGBe5gnwKeSJ2HeVyWWP7Ro/TR/y4KY7Po8R3y+dRjr3npNarby8D4nnj6IDCX5zrz4MfKBxPdzGELcQZlPENM+QTSClDA4VbmfjIk1u6SdnqnhCpSAhBSeQgCqwtWF7/LNX2LO3cjYi0iEgLC/NeFG6oaWmgcMv4aFNQmzQAhePpInqwM5dTTTmfcyt1stoIFG6lCyhMq0Jp6xnVkyJQ+D3Osa5AcvrQF1qdRGdOzbsMiwgP19xIpVzV5kDRWrtUez1eb3sAinTl4/M2d8QkRaBw+l6/SxFP7rEfy2uqOe79t6mo1YOFJ3aIx8YCgcLweQx8/EgDXE0efdyWBArnXI8uObx58lYU5kbY76Fv+HtltRkohNTmn9XapyxUJFBzgMLxFooixkWuBODJ2HWssp0VjRxPY5skgSLRJOx0A4VbrQUUTg69xXCGihOZz6fMlC82gEUIoDBda4tHnkxAYZpDocKEaYRCNwlZlRSCksgR5IuNWV7/OLX2HE+/Diw4QOE534JLxwYBhTDECAKK+LUEQOFWIqBwVCT6shOXUs58vuAOYrJOCxSmZWZDAYBy7M45CCiME7V1f5V3PM0ECtNu2frCHYqtEk4vuIw1rOaRqpupllWevDINKEyb23k8oYpfpYBUgMKttRUoTLtlBxfcGo9jNQCFxxMAFI427NqVJ/fYl4VrKjnhg3eorKsx59UaQGG694DPU+DyriHAJBWgcDy9i/J469StkEj2e/hrFq2q0QNFhik7UtFyykJFSBknZWs8uknZxaI74yJXUsVqnravp0quafQ0tkkzUHjyinuk0RsGKBJCQgsBhXN+XbEDg8RRzGEif/CW4d6a4pquJQMU6XzkyfneNCnbPToQdkJ2vHgOARZCRCiOHktU9GNF/f+otxf4POo8Ct1KTy0FFJ5cDTHSBRS+a1L1ovV2EwPYiQtZwiwmcx+2B93DAUUyow7pAgqfRwEKt1oCKKChSO9plXJawSUsthfyWNWdxKhTvNLTtycvA1C41R6BIkzxm8gb6NHGSRwv04BCBUhtwZ0EmDgf7EHdSnhij334o7yckz54lzV1df682hNQmOa6aOIYgcKlwBEOYN2uBbx96lasqomx/8PTWLa6NqOBIquWVRYqEshNmTqgUDe38y8x26QS+nCsdQUrWcJz9k3U2A1/FWkJoDBBQiYAhVuJgMLjBdYXe7CBGMtv8hXm2h8q99bUxn/fjfeUIUChW2VJ973psR7nK4zc/QiRQ1H0eCKiOyvrH6FeLvH1m2ilJ925VIHCc1+OxxBDBQq3EgGFtx/vNRUoAr1AbzGEHTiH+Uzja/7X9Jd+jdedm/5edJ5grz4/5ThJoDDtRaH7hKUKFA3HknUj/Tmp4AL+iP3K01UPALH4NXff7nOBm9NZ+mvJQIfHk2FAkdLIRGAcfRu3mgM4mQwUpmJ6aI/uPL7HXvyyfDmnTnqP6vr6YEhQ+kxmedfAORiZAhSG9qZHpjbo0Ym3ThvOoooaxj48nYrqeu/9WRJ0701Wa52yUBFSqQJFvLht/KaXWJ+jrMsoYw4vyduJyaZ/fK0FFITw6uOphX9QPLNXKsV5EFD4rgnJBmJ/1hN7Mks+wwL7S00bb5xMAopECgMUycj9vjj9WyKf4ujJWHSivO5hYnJlUis9NRcoPPejegwxQv0VPwAo1HkUQUChTrbWTcpelxFsy6n8zsd8y3PxAj0UNPniuz3B3qD2LQkUKiSoQEGAV7e/w8CcwYwrOJPv679mfPWTOP8KWwIojI9MaQpSE1Do7y+g2GyHQJGufJq8SptQBXdAXi0EFM7rlr168egee/Lt4sWc8dGH1MRioR4b00+eVq5pgMK0uV2Yz5N+BQtDnBYGCqfvwaWdefP04fyxdA0HP/Idq2vrPXllkrJLyracslCRQO4PRHOBwjnuy0AOFRcxh594Td6LLe1WBQpT+1SBQl06tiWBAgAhGCgOp4/YgV/koyyW0zX35i3w2wooHI86MdpdnJseeVIVBBS25n8yOqCIxxKFFOecDAjK6x7GlpWhl471xAkJFN42em8YoDCOXrQSULi1ITuyFcfyC28zgze13qaCP/yoQ3OAQhdPhQWPJ01AYdotW796UsPxFrkjOKLgRL6oncSE6lc9eWUaUKhQEFz86gt4/f0FFeVJFNwZAhTGvkIV3AHvgQoUOk+IotwEFM7325SW8vDo0Xy5cCHnfPwRdbbddkBh+Dxp5zoELO+b8D1IA1A4x8PW6cJrp2zJd/NXccSjP1BjN41C6h61zGrtUxYqQipdQOEcbyCGcpA4j5lMZYJ8BCmlqyD2KwsU3vykgEHiOLqzJT/ZD7BCzkwZKNxKN1AAgZvapQMoEsm4B4boSrecU5FUsaL+EZDVvr4SzZ8Iq+bslm3KxQQU+jZKHpq3JAxQqBOzh7AXw/gH03mBWXyYNFCo55oLFKbN7byTsKXnXDB0qEWiEz+ocG88DgAKx7td3k4cmH8YE2te55Oa9wK9bpmAwuPJMKBI7pGpJAruUJ7E95cpQOHPA58nuREOfTHtljuvHdZZhwd235WP583nwo8/Ida4Wku7BIok3gMTUHg9wX0DbDOgiJdPGcYXf67kmMdmUG/H1ChtLtkGXx1FWahIUukAioZzgk3E1uzL6XzPJCbxtGepVE9sNa5yrPdIz/mg9qb5Cw0eb5xMAArHK4TFEOtkurIJM+y7qZB/pAQU6tKxLQEU6nscdg+KZOZOuOXu352HChhR0YMe0VOpl8tZWv8o2HW+XFp6pacgoFAL8zBAkWi3bO8f3dTc9Ocb8tR7h3EwgxnDVzzKH0zW5p0KUBDgbUmgaDqnFomavJoBFM753fP2Yfe8vXmp6hm+qf1S6/XECQCKMCMcqlcFCq0nQ4AiVHFvmFTcfDAJ41GOMxgofO1cbXZbrx9377YLE/78i0s+/SL+/4iggtt0fx5PhgGFKZ8wj0xp/zLTeG2njbvy3IlDee+XZZz41C/EbKn/xZbVWqcsVCSQZ6K2ASg8/sbXREDhnBsqdmI0xzOVt/mS1zz9NvSpxNHkmC6g8Hu8cTIJKJzvLZHDptaZFLIuP9p3slr+3eZA4UgFCh1YRJQC2FGqMBFWTt8RBDlWX3pGT6ZWzmdp7EmEtNsMKNwKCxTeNsFAofeS2GsACtH439Ycy4Zsz6fcx3y+0+arvzeMXuOcCV28VgQKdWK2biO8prz0XrciluCggsPZNncHnlj9CD/V/eDzpgso1OJLBxQ+T5hiWi0AXco0oEgtTph8/H2p3mSWP20roHC094D1uH2XHXll9u9c+dlk4v/nCHif9HtleL1B74ERKNyWDAMKX7vGa3tt2o0nj9+UHp1ztPfRlrJl6391FGWhIqSCgMK9dGzDMdrjhnPC59lK7MUOHMZnvMC3cmIooFDPNR3LpL16jzdOMkDhViKg0HqdPhMAhXMtIvLY1DqXPEr40b6darmkTUcoTJOy3XMogh53SgYmnN2548d4R0l0Uh+5shAUiAH0jJxAlZzFktizyDBg4fw/pJlA4VaipWN1QGFaOlY7v0KqXn0Mz7kAoHAUwWIHTqMfWzCJO1jMr558ve28x6kChToxOxA6DEDh9TTGaSZQmHbL1kGCk49lwdGFJ7BZzuY8tOoe/qj7zefNFKDwF7/+9yDMCEfcG+Z5+HYIFOEKbkNfusokzHtpKKa1ngCgcK6NHbgBt+y8Pc/O/JV/ffk17mcJ0gUUpjw8hbjaLmBFKhNQeCwtDBTx929YT24/dGMs3bOVWa2VykJFAtmIZgGFWzqgaOgDRnEQW7EvH/A4M/i0WUBBkl6/x1+Ax70hgMJ3LQAomuDAey0sUDjHUdGJIdb5RMhjhn07tXJl2oDC/WVq4wCFbg8K3aRsR80FCpNMm+vpgMJRoTWIXpFjWC2ns8x+GSlliwGFW4mWjg1eAQqlv9SBQhvXABQ6b4QIu3AOvdiI97iJ5fylydN7HDSRvblAYdrcTu9xjtUi0d2nqXB3eVIACuc4akU4qcsZrB/tz72rbufv+vlGoHArzGhIpgGFv30SBXcoMPFZ2iVQmNqH+Zmg+ZmYgMIt9b4OG7QR1++4LY/O+IXrp3yLlDLjgMIISLr3IMSISTqAwtPekh5oy2rtVRYqQipVoFA3tzNBhxCCXTiGoezKRB5gNl+nDBSJ5mAkCxTqXhSZBhTOa64oYtPIBUhi/BS7nTpZmRagMHnjXxqgUOV+3CjokafmKNEIhTsXd//ulZ66WFvSM3I45fYXLLff9vrSCBTCECMMUCTa3E5b+EvVi8+rWzo2tLfxNZc89uAiiujNO1xHOQtDAYUJIAKBp4WBwtun95qucG8OUDjeXJHLWUXn0c3qxl0Vt7IstlQLFKbCNsySrbolbtsjUJjjmO8r/SMm+LztESgSjfIcM2QT/rXd1tz/3U/cPu17Y5ymPr1x2wVQGNoHfQ5UoPCa8MbLIDWsRdW6Xx1FWahIIPecipYACvfSsQLBaE5hINvwNncylx/TDhSee/N5/AV4ewGKhhwhT3RncOR86qhkZuwuYo0rGjXds9S2NT3yZPI2+b1AYSOpEzFP+zArPCW9/4Tyi143SgX6UQp3/7qlY7tao+hhHchy+31WxD5U/Er7kEDhVrJA4VYyQJFot+xkgIIgr5J3Lp3Yi8vJpTMTuI7VLG82UPg8wu9xlC6gMG1up9tZ2wQUbiUCCid2J6sT53W9kCg53FF+K6vsihYBCqMnTDGtKZTaI1A0Lx/fW2AECo+nnQGF4z9p88Fcse2W3P7ND9zz7Y/+OGp7K/F7kMymeW0OFKafrXb0An+fmvchq7VPWahIQS0BFM6xhcXenE0/NuV1bmEhs719gXLsLn7Va7o+1cLc8UjPsedaGwEF2jbeY92k7AJRyiaRc6hmEb/a91Ev65T+DK/CHzsMUISRaYWndE/IVld7MuUR79sAB90ju9PdGsNi+3VWNm4wmCxQuJVopSe1MHcrfi0BUHjbBAOF1usca4DCtCqULm8BFFDEflwJSN7mOqqoMPStxvceaz0aoGiCA+dYeo7dak2gUAtsFSi8eTV5i61iLii5iCpZxZ0rbqfKrlL6CMpLX0gmCxTm4tf/HgSNcPjiBTwPH2pEIcOAwnhfSYKS6k1uUncS72UKQOF4zxq+GRdtvTk3fPUdD3//i6Yvb9yg90A/l0PpU1O4q/GCwCRjgCLDJCWtOklbt6jP2qosVISUunSs8xlJJ1A4nghR9udCetKf17iBxY3PZnc0oPCBg+m8BgYKxXpsHDmb1fJP/rAfxm7cuby5cyhMIxSeEQFpeeZQpPtxJ9MohbrBnrt/NY8we1H0tPalxNqRhbHnqYh9G+jVKezSsbrCPNHSsTqgMC0dqwMKIySkABRuuQGgM93Zj6uoYhXvcBO1VOnvLWQ8SB0o/B7nWFeIOH14C6WWAArjXhIW9Ir24ryuF7I4tph7Vt5DPbXGvpri6QvJtgIKU/uURyZaHEzC5JU4TvK7XaNtHyof7xrR4d+DEPNQ/I8mSS7YeihnDR/Cvz6fxhM//tqqQBEmju89CCruDUDh0VoGFFm1rLJQkUCeJWUNQOFWc4HCUYQ8DuQyiujJa1zHcsp8Hl88w3HDObUwdzzSc+y5ZgAKfd/e4rwtgcJRodiIDa3TWClnMMd+kpiMab3JzKFw8jUBhTopu6XmTzhKaaREur43QIKFQAhBH+tQisVWzLefYJX9szF+qntRaP8fFhIovG2UPGQSXudY8zMJAxRBkNCVPuzP5axkAe9wKzZ1Rq+/T+VYV0Q7nmYChWlzu8C9KXRLfiqxUwUKR/1z+3FOyTn8VvsbD618BEnM01bbPpm/cCfhyWSgMG1u1xZgktFAYYQp/32ZvYIrRm3BScM24cpPvua5n39vV0BhbK97D5J4nC0QKCypHW1pK8k2+OooykJFSAUBhXquuUDhnMulE2O5klwKeY3/xyqWuTxS2z4VoMDjbbwWABTqMrMmoPDGlV6vc76FgMKJX2RtygDrRJbLr5lvjycmbW+bZgKFOqoBtOiEbHX5WEe6UQp3Pk4eYYHCeRXCop91DJ3FYP6yH6ZS/hZ6HkUyQJFo6Vj9kq+G0Qup86L3OseaJWqbCxSOejKAfbiUMn7lPe6OF8apAoXPowCFW+kCCtPmdmEgQfcIVhigcK4NytuYM0pOZ3r1dJ4sfwb3b4swQGHaLdvjSQpMvPGSmYORyUARLh/F21wwaWdA4VwTQvCfHYczbrONuPjDKbwya06oR7CCJnUn87hXmwOF7xr+fBRvVh1DWahIIFuKZgGFJ5bTRirHOk/jaz5FjOVKAF7nWqqoSDtQ+CAhTUChbm7X2kDh9NfVGk4/60iWyk8os99KK1DUiBgx0TQeEwQUzZ0/YVpCNggo4n0rQOE+H/gqQYgI/a2T6ST6Myf2AGvkvGYDhZqbp41zPmCydFsBhS6HMKMO6zKYvbmAOXzLJB5CeMbw9O1TBQrT5na6T15rAoVaxAXNP1CvDS8YxvFdx/HJmk95ZdVrWk9LAoW6Mo8OKEIVvwm8gZ72ABTJgJIBKALfA93vwFYGCuecEILrd92awwZvwLnvTebN2XMD3wP9XA5/3LhMj6aFeQ/aGig0cbQ/uzZSdvO7llMWKkLK+UwkCxRxj3MsvcdumSChMz04kKuoYTVvcD01rDF6vfGkPocOBBTOtW7W9vSxDmKxnMgS+4OUgUJVVFuqNUgFiuYo0TwKnRy4MUFEGKCIe0UOG1pnkEdPfo/dQ7VclDRQuCVMbZzzGqBItBeF9o9kymswJCT2qnmrAKCP1+AawHBGcwa/8Cmf8TQS6WsfJl5zgcK0F4Wl+Sy1JlCYNrdzXnfstD2HFR3Cm6sm8EHlB1qPJ/cMA4qURiYCwUTfxuvRx8lkoAiVj2FSsR46AnJW+gwLFA2vYAnBbXtszQEbr8cZ73zJu3/M1+fTAkBhfA+aOam7JYAiq46jLFQkkKRlgcLnMRx3pQ/7cQUVLGYCN1NHjRYoms5JQzx/Ad6egcItE1A4rz2s3ekp9qTMfo0V9pcpj1Cok7KdidlBIxTplG5itgoXvl27NSs9ub8PAgrnNSLyGRg5hyiFzI7dSa1cnhRQJFo6VgUKt5IBikS7ZXsgIcFu2ckChXpO9Q5mB3bheKYzgW94TelTE0+g92g+UukCCtPmdvr9HVRvUGFr9iYCCqf9Pl3GsE/nPXm6/AUmr5mi8UhProHQEdBXU17q+yU9bXXtWxIo1Pey+WAS4j0IUbRmGlCEgQTfSk9J3V/TpYhlcfeYbRmzwTqc8vYXfPzXAvP9tSZQGNoHPTIVCAIGoPAoCEyycNEhlIWKFBQveKVz3LJA0XBO0oP12ZtLWcKfvMcdxKjv8EDhfxRMats6rz0j+1Ni7ciC2HOstKcZvclMym5poNDNo3ADRaL5HEFLx+rOBS0dGxWd2ThyHiD4NXYndbLCCBRupQIUppWegiZsJwIKjzcBUHjjmu5D59F7HQ1jDKM4jC95gR94zxwvBFCY9qLQQ4dakDrx/YVWEFCEgQTTXhTa+QchgcLxHF58MDt2GsVDyx/jh+oZvryCgEKFguDiV1/Ae3IPVZQnUXC3AlAkl49yrF3xJ4n3oB0Bhfn+XPfV6I9GBPfvM4qd1y/l+Ne/4It5i/zvgQEoPMo0oDBCgv++2tMIhWyDr46iLFSElDrpuqWBwtO3q3AuZWPGcCF/M4NJ3Ifd2EIqXn986YqnXFOAwtu3Pk5zgEJ/b2reyvk0AIUtQAhBb+sQuojhLIw9xSr7J583XZOyTUDhhpGgJVlVqaMzbqBQc4r3k2Dp2GSAAhoK7lxRwqDI+cSoYlbsbupllfY+Ei0dG7wCFPH+vG285905hwEK09KxgV4lb+0f6JQ2+snoTd5tOIjh7M0kHmMmn/vjpQkoms6pRaLmHloRKNQiM3ikw+uxLDip27EMK9iMu5Y+yOyaP1oVKJJ7ZMrs8egfAkcAALMkSURBVPUZypP4/jIFKPx54PMkysvjMRTTujitCRSOJy9q8ch+27Htuj045tXPmPL3Uk0eeNp4+jIBRZLvQcYBRQbDRVbpVxYqEsjZJAXMQOHxOx5Xe/ex1kOQRy2cYV02ZzfO4U++4gv+F1CY+xUGKHwwoJ7XFCTJAIX/3tS8lfNpAgrnWApBn8gxFIrBLIw9ymr79/i1IKAAjJOyPQW6YUK2CSKCwEIHYuqIiTsnT18hVnrynJNoPbpJ2fmiF4Mi51HDMmbF7iEmaxo8hhhhgCKZ3bKb2qhePK/JAIUnbry9N28TJHjjJfZGEOzMsWzKjkzkfv5gepOnFYHCtBdF8B4QqtddcAd7PdcCgCJRXxER4ezuJ9M/dz1uWXIP82sXBi5xm2lAkcxf5U1Fvu7+UoGXNgOKZPIJKKZNf9UPMw8lXUDheApyLB4/cHu27NONI1/5lG8XLm8+UIR5Dwz5hJnU3RpAISypf6ysjZSdqN1yykJFSDmfCR1Q+KDDaSO9x1oPQR5p9A5gG3biNGYyiak8S8w3YuGO542TLqDwe6X+fAYBRVNci9LI8eSJ9SmLPUSVnOcDCrecQtE9f0K9BsGjEybp5oc4eage7ZKxypyOZFd60uVpAgq3CsW6DIqczWrm8qv9ENKuN7TVx/b0mwRQJNrcLhASAoAi0W7ZyQCFtm+f12JPTmMAW/ImtzOfmUagcMsEFF5PY5xmAoVpczvdY0wmoPDklQagcL7PtXK5qNcZdI+UcOPiu1havzyw2Exut2t/7pkGFKnFCfEeJDMS0A6AwnhfSdyfx2Mq4BvVKSfKUwftwOAexRz+0qf8sGhFmwOFsX3A/JGgx5iSBYqsOo6yUJFAkuYBhRrL41GOvefUwtmvgezMtozjR97iB97IAoXuVQMU8XgiSp/IyURFL8rqH6ROLvaNULjlnj8B+FdWSgEoTEo0j0KXU0TpJ5WVnhJJLZ6LxUYMss5kpfyZ2fJxROMH3wQUbsWvhdgtG/WaVL3e/HTXmgMUboUBCvWaetwUK8IBnEtfBvIKN7FIztEChWlzO73HOQ4okNMEFE0Fu97rje0tMoP3d9C39XokhVYnLi89h6iIcH3ZXVTEKn2eMHEa8vLnHmaEI+4N8/hKhgGF/5qmL8NqQKHuT1dhZBhQhLk/0+Z2unwQks65UZ4/dAc2KOnMoS9+xi9Lyr1vQYYBha+dFhL0fQaBicer+by0lWQbfHUUZaEipFIFikSTsJMFCvXcEPZiSw5hGi/wC+9ngcL9GgAUTWCRR6/IKUQoZFH9A9TK5cZVldyFexigCDNnQruKlQEodKCj5qWbmB0WKMI89qS7XiKGsLF1Mkvl1/wmn43/v0NXmJsmZgcBhWmlp6DlYRMBRaBXk3ciSEgWKNznouRyEBdTQinjuZ7lcmGLAIVpLwrtqEMrAoWpsA2zqZxlQUm0mCv7nMNqew03lt1LtaxS8jMXgM0FCn/7EH0lBSY+S8YBhTEfzVyc5CZ1K79IdJ4MBArHW5SXw/jDd2SdLp34x/OfMnt5udcbNKE5zHuQYUBh/FlkEExk1fLKQkUCuZ+HyzSgcLzDOIhN2ZuveIw/Gid9rg1A4VZLAIVzLSIK6ZFzGiBYWvcQMVmpXVXJeU0EFMlOwE4kN0iYJorH+03D0rHu17B7UfQSWzFQjKNMfsIc+ar27sMChbeN91oQUJiWg9UBhdGr5Oo9pz92K1mggIaiJU8UcBiXU0Ahz3MtFXJZg6cVgcK0F0VQYdsaQGEqWt0TyEtzenF56dksqF3ErWUPU0+tL44xXhsDhTlOQM5JgUmI96CNgcL0MyHEexD0GFPgo1wtCBRO2675uYw/fEe6d8rjoOc+488Vqxq82gnkpqJcc18JHsHStW9zoMhWmx1C2R9zSKUbKLyxHY9aOAfFayrMBYIRHMNG7MznPMBcvvG0UYHC27fUe5sBFMH3JoPPu/rzQ4Cah+FVNBXfiYCiwS+xRBE9ck5DUs2K+oepl2t89xJmQnZzYUI3SgGGuRQhgCJwpML5f0MzgcI57mvtyAbiMObKCcy13zXEbGwTABSmlZ5UoHArnUDhVhhI8D/qlUQ80XTcSRRxOFcB8DzXUU1FYxunwPXHa02gMG1upytoWhMonHMb5K/LJX1OY2b1b9yz6CkksYRxgvZuSG4ORhIFdwsARfPy8d1euwSKUBPbDe+THky8cZMFCkc9CnN5+YgdKcyNMva5z5hbURk6TtJAYWgf9BkOfowJbZ/tHSjsNvhKVvfeey/9+/cnPz+fkSNHMnXq1ED/+PHjGTRoEPn5+QwdOpQJEyZ4rkspufrqq+nTpw8FBQWMHj2a2bNna2PV1NSwxRZbIITgu+++SyrvDPtRZ77SBRQmSEgWKBwJBKM4mX6M4BPuZgE/edt6ilUnnrdP9bxbyQCFuhdFWwCFyeODDdH0vRDdKYmeQkyuYFX94whZB/g3kjMpWaAwTdB2X0s0ryN+LmDpWN25dAOF89rP2pP1xL78IcdTZn+mxGxsE/AoUjJAYVrpSQUKb3yDV5NDuoDC1F6dlA1QLLpzBFdSxSqelzdS2wi3+lWd1GNzYdQaQGGaB6F7rj5UYZsEUDivm3UayLm9j2dK5XQeW/ISUspQQGHa3C5M8RtuJCAgXoYAhfG+krg/nSe5ORgBxXQrAIWvfcAjPMZi2mNqOFfaJY/XjtqRHMti7DOf83dFVeI4LQEUpnvXQgLaPj23F+Y9yFaZSeuFF17gwgsv5JprruHbb79l2LBhjBkzhsWLF2v9X375JUceeSQnnXQS06dPZ+zYsYwdO5YZM5r28bnpppu46667eOCBB5gyZQqFhYWMGTOG6upqX7xLL72Uvn37ppR79sedQBKBuhdFU1HceOz6d9QWQOF4BBY7cha9GcTH3MZiGpZKTRUo1L0o1gag8Pr9k7JzrD6URE8mJhdQUf8kloz59qBIxwhF0FyKoJWeAN+8jpZcOlZ7XWnvlgAGiLGsI3ZlpnySJfKb+PmGvv1AEXa37FSBIpHXrWRGHdIBFG45p3pafTlSXMFyFjBe3ord+CiPJ04AUJg2twvcmyJgOdZ0AYV59aQwnvCF7aguwzil1+FMLP+Ml1a8o/e2AFCEAQCfNwUwCROnJYAiFOCon6Nk8gkYNQp8D1oRKExgo/s8qffetzif147ZgZgtOeiZzyhbVR2qKM84oDCNYln+fFy/kNFBSVtJtsEXQEVFheerpqZGm99tt93GKaecwgknnMCQIUN44IEH6NSpE48++qjWf+edd7LXXntxySWXMHjwYP773/8yfPhw7rnnnob7lZI77riDf/7znxx44IFsvvnmPPnkkyxYsIDXXnvNE+udd97hvffe45ZbbknmLY0rCxUhZQIKjwe9RwZ6ZBLxZEC8xn/Y5LAz59OVfnzMTaxgrtHbUYHCFtIDFOCdCJ1rrUfX6PHUyt+piD2HlLYHKNxK1/wJE1A4Oamb7CW7dGy6gcIttbiOCIuNxVGUipHMsB9imfyxsW8dhIQDCm9/6L1+ayhvU8EfHhJMXl3fQUDh8zR+00f05zBxCfP5lVfse7BlLG1AYdrcLgwk6B7Bak2gMG1upxabexRvz1Hd9+OFZe/w7srPtB53/60BFOHiJL6/MHES56XxZApQhHgPTJCgA2cjULgtLQwUzi+yfsWdeP2YHVhTF+Mfz3zB0tU1hqJc6TNo0rMBKDxqa6DICoB+/fpRXFwc/7r++ut9ntraWqZNm8bo0aPj5yzLYvTo0UyePFkbd/LkyR4/wJgxY+L+P//8k7KyMo+nuLiYkSNHemIuWrSIU045haeeeopOnTqldI9ZqEgg70TtxnOu6+peFKrH/c8pDFCY2ocBCufYIp9duIROdONjbmSVXNShgcId3128uwt3deSgk7Ux3aLHUmXPoDz2MlLKtM2fUPvSbXDn5KWTb6K4Dh50IxXS73e/JgIKt4xzJRzoERGGiBPpzmZ8b99Luf2bqy1Kf4mBItHmdh5ISLBbdjJA4b3nxF4TkCQDFM4civXFYA6xzmcW03hLPtzwGVSAwq3WBAq1CAuaf9CaQOGO849ue7Bvyc48vPglvlw1vSl35bl3FSjcao9AESYfn7e5j3JlGFCYil5tHBNQuJQOoHDODehWyOvHbMfyqloOeuZLVlTVJg0Uxjx070ESj7O1FFAIK8M2v2uDL4B58+ZRXl4e/7riiit8uS1dupRYLEbv3r0953v37k1ZWZn2fsrKygL9zmuQR0rJ8ccfz+mnn85WW22l7SeMslARUk1Fseuc9J5TPe5/Qq0FFM5rjujELlxGlHw+4gaq5IqMAwq3WmMOhToa4Fa8GHb9Vi60htItcjir7SlUxN5Cup5zawmgSPTYk5Of5zGsEEARDgrMf+131PT/i2CgaAKLKJtbZ1DMAL6Vt1Mh54YGCk+/CYDCm2MSXiXvMJDQHKBwKxFQOOc2FsMZK87gO/kJH8hnGuYIuOOoBZECFAR4WxIoTJvbJQcdrvciSaBw2pzQcyw7Fg3njgVP8cPqX0MBRajiN4E30NPGQGHaLTu44A64PwNQeDztEShMeWjvT9/G62m6NrBHF147dhQLV1Vz8DOTKa+u078HLQkUvmt447kVAkyCgCKrBhUVFXm+8vLy2jqluO6++25WrVqlBZ1klIWKBJKkBhRutTZQOCoQxezM5UhsPuYGauSqtACF596aARTqXhQt/ciT+7q6/4T66hSPhZGtKLEOosL+mAr7w7Q97qQqaNK2Lq/mLh2bSGEnZpuAIp67yGW4OI9O9GKavJXVsiwUUKjzKIIgwbTSU6BXyTsVoCDAq40nDB5NQPeIxObWDuxjHc+X8m0+l6+7PGqB7MT3F5AmoPDcQysChfGRKR10GIDC4zHEiViCs/scydDCgVz/9//4rWpes4EipZGJQDBJfH+ZBhRN7b3HyT4y1WTWt/F69Dm3FVD4VnrS5W6a6yIkg3sV8eoxo/hz+RoOeXoKq2rrvd4AiG0VoAgRx78qFD5PVsmpR48eRCIRFi1a5Dm/aNEiSktLtW1KS0sD/c5rkGfSpElMnjyZvLw8otEoG220EQBbbbUVxx13XOj8s1ARUskChXrOX1yH9+ryCAIK914UhaIHO3EZNVTwKTdTJ6uaDRTxa2sBUDgyFu5SUBTZkWJrDCvtCVTEPve1DSvTKIU7rzCb7ZlWemqtpWPDAEX8mhRERQFbiQvJoRNfy1uokst9QOFWWwGFW2GAwviIk3IM4YDCtBeFJWCkNYZdrIP5wH6Br+0P0gYUYSAhkdfjSRNQuEcbEnoSFK05Ispl/Y5nvbxS/jv3If6uWRyucG9BoPAVtM0Gk8SFe6YBhWlSsa6vZOZFhFmNqa2BwtHmfYt49dhtmbVkFYc9M4U1dXUJ8wkzqTtwY71WBAqPJ4NAQ9JQx7XaVxK55ebmMmLECD788MP4Odu2+fDDDxk1apS2zahRozx+gPfffz/uHzBgAKWlpR5PRUUFU6ZMiXvuuusuvv/+e7777ju+++67+JK0L7zwAtdee23o/LNQkaTaAij8Hm8cE1A43iLRhx24lEoW8QW3E5O1uNVRgCKmfLmlbmqnzp8oscZQZO3MMvsVKu1pNFfqPIpEjzw5Clrpyf19awOFW26gcGLniaL/3955x0lRpH38Vz2zGXaXtCw5SBYFAwJmFBXDa85ZUVAxYwDv1FPPM2E4I6JnFgPqYcYAngEBBUWQJEhOS1x2FzbNTL1/7PZsVXVVdfWkHaF/fPYz091PVT3VPcw8365+qnAQuRUUwM90HGpouTAqwPurAgpWbkAhtRX8NoGEWICCVbxAYWsIOQODyDB8EnkJ8yMzHEAhbTNOoFAtbie9u68ACr1f6kAyXqBgg+dsKwt3drwSTYN5+Meq8dhau0Nb3hN0NBJQePPHeU6cHfRwDtIEKFSrZcvOkwoo+A4anIMEAYVdz37tC/HeRQMxb8MOnDdxNqrCIaU/Jknd8gXsFMc05yChQOHLk26++Wa88MILePXVV7Fo0SJcffXV2LlzJy677DIAwMUXX8w9pnTDDTdgypQpePTRR7F48WL84x//wOzZs3HttdcCAAghuPHGG/HPf/4TH330EebPn4+LL74Ybdu2xamnngoA6NixI/r27Rv969GjBwBgr732Qvv27Y1996HCRWySTSKAAh5tnTZ8AO4GFPZ2M9IZB2M0tuNPzMLTCNevwbAnAQXAB+/2e9midqwsEBBC0Nw6BU3IAGwOT8SuyO/wItXjTbrHnkQf3KaOTTVQiP6xx2SL2+WQFhhEbkMNKjCLPopaWsn7QcFvR+tukNvidlpbha9yG94WBrbS+hRAwcoEKAjqfhhOCFyIfckhmBR+Fn9EfuMCI9VaFPo1IETbhs9jooAiltGQRAKF3XZ+MA/3dBqBCCj+sfJ57IrslJaXBb/xQIfcxr1/qQAKdcCtaUuSi+PmF2eTZkChmqpVFpSrgIKTCigYiedgYMdmmHThQfhpzTZcMHEOqkPhlAKFanE7ve/RHwh3mzQDi8ZK1DbVOeecg3HjxuGuu+5C//79MXfuXEyZMiWaaL169Wps2LAhan/wwQdj4sSJmDBhAvr164f33nsPkydPRt++faM2t912G6677jqMGDECAwYMQEVFBaZMmYLs7GyP3ulFKJt96iuqsrIyFBQU4Aa8hCySmzCgcEvqTgZQsK8b6TzMwGNoj4HYj4wAIdYeAxS2WJiQrUFhSxZQUhrGpvBrqKQL0TowEjlWN4eNKJPHnuxXcZE9WR6FLiE7lUChHL1QrJZt2+2gqzCDPogCdMRgcgsCJFMJFPJ66rdjWC1b+oiWYtuLrVegEBOzZbM6qaaOjSCEiaEn8CddgMszxqKT1cMIKFSL28kgIZVAIQZf8gRywcYo+JUH1WuqSjBmxVPokFWE+7qOQLaVGTNQeLkrrwryTepJa6Dw4o8CKKTl/kJAoQrqdfXIzsH/lm/G2W/8jKO7tcRr5+2PrAwiLysrn0ygcPge/YFwt6l/LdsVRosRc7Bjxw7k5+ejMWTHdVfhJWQhtilTY1E1dmE8Lm/UvqdK/kiFiyjUIxSincwmnYACAFqTfXAgrsIazMA8+joiNCLYUq4O7thfEChkEtd8MAUKACAkgKLARcgiXVESfhHVkdVSO1tuQKGS7HEst4TsZAMFK69AwaoZ6YSB5CaUYjlm02eASEjql6yeVAEFKy+2yQYKQupm1To3eD3akS54tfZhlERW8W0mCShYqYBCapMGQMGqU04R/tH5CvxZuQ4PrXodYfAJsnx5DwF3koCClTcbZ1uizV8FKHjf5X0w6x9jk2ZAQSyKId1a4s3z98fXS7fgiklzEQpHpP1TAQVvI/erMYCCO5YGoo3wt6fIhwpDmU0TK27TmGydNpTbjhUo7GNtrYHoTy7DSkzFYvp+UoCCVTomZccCFLYICaJ14HJkkmJsDD+PGiqfO1qUDCh0uRSqmZ5UsFBnC+kxr0AhkxicuwGFtC/12y1JTwwg16ME8/ALXmwgd66M3W78QCHvh3yb9dfE1mFDnDbReuMECluZViYuzrgFzUkRXqp9CNtoCWfLtZkgoFAtbqefPclpo7JNJlCI5XvldcTfOl+KOeVL8OSaSdx00Sqg0PmeTKCIDXAkbSmekTfqXyMDhbJfsfZPARS8keLzlCSgsG2P7VmE187vj08WlmDke/MQjriU14zOeHuMycT36A+Mu02aAoWv5MqHChdxORXCfnZfPEDBymlDuW3ONgagsHMoOpIj0Ieci6X4GMvop1IYiAcoosdSDBT2PjYZWzZ9rCqHQgcUdhkLBEGSjTaBkQiiEBtD41FLtzrs3UYjZPkdrI+2b0YzPLmMYgDuQCH2lS/rsl8S6LtNHdsG++JAjMQazMBveKNucbdoO2Id9duSVblNgMILJLjZsjIBigY4sLcpt83KBCjs97kkB5dn3IYskoMXax9EBbY5bFRAwfUhhUChCkDlU9wKNgkCCnv7wPweuLnjufh6+xy8vPFTobxJ4O4esP2VgELpj3T4zuAcpBlQqFbLlvmjBApJ/7QzLXkECvvYib1b4+Vz++H9+Rsx6oPfQe1fzHiBQnEOdCMdPlD48iIfKgzF/rfwAgkmts7g3LbhA3DumAtQ8L43BOjsdldyPLrjZCym72I1/Z+0nr8SUNhSBeu2uBEASfKzzF52LEBy0SZ4FQgysDE0HiG6Q1q+ri+8/7LZpxxtxrgWheyYCihYNfxmxAYUXF0uQGH72p4MQn9ciuX4Covxgdw26h97DqC3ZXzxAgkmtsq8ihiBwmljb8sCNbuNumNNrQJckTkGYYTwQvVD2EUrjIBCtRaFLDBKJVAobXRBawxAYb8OabYfrmp3Mt7b9C3e3/w/qY3OL91deROQ8wYmJn7BaZMgoFCVN4E82X82FVDIbeQ+mPePt40ZKMRy0nwPeT3Sc6Dyh1Ccuk8xJpy1Dyb+sg43Tl4Y/b1MJlCofXdeGxMbru/q+3UpF0XDDeNU/EnO3m4rHyoMxH4gvEBCYwKF4xiR23Yjp6ETjsbv9FWspzO5Y+kIFOxfdB8TsKumirXfq2Z4kkkHGlEb0hTFwatBUYuNoecRpjud58QFKERf2ZmebD9Ef9xmejKVaWK22QxQqD9mBhT2vq7kKPTF2ViEyVhGpwh1qNtRAYW0f44ysvriBwpWiQIK1VoU9nYz0hIjMsegjJbipZpxqKZVcQOFcupXgxGFZAKFOvhVB6Rus02d0upQnNt6CF5c9xm+3jbbYZsooIivHl3ALQl+bZs0Awpl3zWQoM0NUfSPs0kzoHCeH/U1Pmf/NnjuzL54adZa3PrRYshuG6YUKBru8KhtDGDK1+4rHypcxP43392AIgIKQgj6kAvQFoPxG30BJfS3+jKib3xgLO7njsUBFBGiH6GI2tl/xHlclNsaFLrHf3Sy28wgzVEcvBphlKEk/AJCtKrBRvihYB/Jki10J45IGI1UUMV+l1GKRACF20xPuoRt1q9e5P/QEydiLt7ESvo9V7bOR7EPzjoa6uX9jgcoILHVAYXDRgAKVokACtu+yGqLK7Nuw8bIWrxW82+EaK00SEkUUKjXd3A0aXZXPkFAoVot2y34vaTtcRjWcgAeW/k+Zu1YHJvvKQAKdT3u/uhHAtz7lzZAYdA/R3nd3XRVMM0ZCW0lEygEmwsHtMO/T++D8dNX42+f/lGX/6Pqu9R3SNvkTJINFBaV+9ZISuUoBfsI/Z4gHyoMtTsCRVSEYB9yOVphX8ylz2Arlgi+Ue5V3M8dixMolMdkoxSE39aNUqjyJ2IFClGZpDWKAyNQQzdic/glUBrigMIkOdtkpqfGBgrO3xiAouG3xgkJ++IcdMUQ/IwXsZbOjpYxWS07aiv4bQIJOlsVkMQKFKq1KGSfOFOgsPd1CHTF5dk3YVl4Ed6sfi46s5sKEmQ5HYkCCpNF5VRrUXiBjkQAhe3L9R1PxaDCXrh/+ZtYULHKDABUAa6J740EFEb+iInZXq6NZtQoXYBC5Yfs86QCCln/Eg0UdvkrBrfHuFN64d/frsJ9Xy6T91066gBpm6y9EVTFCxS+9hj5UOEiWaJ2soFCbB9IHlBE2yQB7EuuRiG6YW7k39hBVwn94n3mfaw/FgdQiHWpcieidoSHCVlSti3ThGyTx510yrI6oigwHNV0BTaHXwelYc7vsKSfgLfEbL4/wnaSgIJVw++EGVCw0gEFAFjEwgG4DO0xADPwNDbS342Awhnwm0NCPEDB9U20iREoxMRsU6AA6gLj7sG9cUn2KMwL/Yz3ql+B/T9WBxRiEKZ7XCiVQOFttWu+Pm9J3XWvARLAHV3PRY/c9rhz6StYsWtjzEChWtzOy0iH1CZNgMIE8uDlHBjkoaQbUJjU47D1lKPAl7360I64/6TueOjrFXjo6z/5epIBFA0/EK79+6s98kQb4W9PkQ8VhrI/FLEABSS2zvooYyMcSyBQsBKPESuIfta1yEUb/Bp5FOV0vdRndl+yH3mS2+tHJ9hXL0DhRbLHrSKgyLa6oWXgElTSBdgWnoRwPViI0CO2J0vMlsFCqoBC9I095gUovKyWDdSBxWBcjdbog+/xOLbgz5QBBTS20vqIwkZSYSKAAhpbNkjfJ+NAnJN9BWbUfoNPqyfFDRSqxe28QUeDbaKAQnzuXQQKVqZ30zOtDNzX/SIUZTbDHX+8gpLq7UkBCrN63M+BwzbeEZNUAoXCZxkoKYFC2j9wto0GFOI50PhjFJRbFDcO6Yy7ju+Kez9fjsemrVT6lRKgMPGdbV8GUr52O/lQ4SIKM0jQAQUVbEWbWICC94E/pgOKBh/4Y/ajOgGSg/2sG5GJAsyNPIpddAvnM1tHKh55Yt+Lszyp1FhAYSs70AfNA+dhV2Q2SsMfI0QjjhwK2082Mdskf6KuP/JjiQQKt5meVEDBygtQsMcCJIjDcCOaoRP+h4dRijVmowMeIEHeZ7mttD4DoFCtRSGHDjFAtut3BjsqoGA1KPNwnJJ1Pr6u+RjTqj83CsJiBQrlI1My6EghUJgEvzKbvGA2Hux5CTKsIMYseQU7ane61iOei1QAhWq1bGngbgImCqDg++d+TlMKFI42wNnqfNcldacLUIjtE0Ix5piuGHNsZ/z9k2V4+tvVkvrg9MdWYwGFrz1GPlQYSgcJjQEUDtsEAIVde4Dkob91MwiC+C3yKKqZqVIbCyhYP+PJoWAV7+NOMtk+5gT2Q37gVOyKfI/yyFeOx53imelJti9eoGDlFSj4svwxFVCwkk0dG0QWhuAW5KE5puJBlGOT3Fbw2QQSYgUK1dSxsQJFwz4xQLbr1wXu9dvS2ZPqto/OHoZjs07G5Kq3MLP6O96fFAJFNOBXAAUrEzBJJlDYr80zm+KhnpeiPFSJO5a8jqpIlUE97v1LN6BoKM9ve31kqsFYX4bzKxVAofKD+5wL7Xu4fvLHmATbBAKFXebOYV0x+qhOuG3yUkz4Ya0eKDT1OH2P/kC423gBCj9Re4+RDxUG2pOAomFGowL0s25GGDWYF3kctXRnowOFW1I2IATjmjUoEgkTqpmncgMHIzcwDLvCX2FX+HsHWIiQYAIU7PdyrEAhkxicewEKVWK2DihUszexORSZyMNRGIMgsvAVHkAlSoWyfPus3IDCxNYLULBKBFBw9cUAFLbtSdmn49DMIZhY+RLm1c7W2nJtKoBC75c6aNUBhQgF+uBXHsDzfimCYEOgsNUupzke7HUJVldtxt1/vI0QrdXU494/s5EO98A93YBClQMghw6Nz0KbaQMUquR5k3MQZ1K3cmSBENx7cleMOqI9bpz0B16ZsT5moHBOMwt3G03/pEDha4+RDxUuopL3uztQ2K/ZpBX2tW5CNbZjfuRJhGl1owJFGFT6GJEtbvYkBihEm3iAQgQIMXnc3mf7mW0diazAYagMf4Kq8BzeD9rw2JPRaASFw8aWyeJ2qnq9AoW8LsEPym/zZeQ+i0nZAJCHAhyDsYgghK/wIGpRIfWZ3yff9mIr9VcDFKq1KOTQYZcRA1K2Tf6YbGXtaH0KSCCkbsros3Iuxv4ZB+Hlnc/hj9ACqS1XjwYoTEY4RNtUAIW3pG7zgLtHkza4t8f5+K1sBR5c9l9QElaei1iBwos/KqDgO+jhHKQAKEwgQbW4nVn/nCZpBxSK8lro0IAXrLr/24+c3g0jDm2La95egjdnbWw8oGj4sZC2LdbZ2KKN8G9PkQ8VhnIG3uJ+ta1o81cACnt/HmmHvtYN2IW1WBh5BhFa2yhAoZNqDYpEj06YAgUrQgjyrBORaQ3AzvB7qI0s4Gd7UkCEG1DoHl9i5XWmJxOgMF3cTpZQ7QYUvO91aooiHIOxqMR2fIWHEUIV1z7fD/k2KxNbh02cQKFaiyJWoFAtbicbdQhaFi7OG4EeGb0xvvxJrAovd9gmCijE4Ev0k1UqgUI1VasJdBxQ2AV/634mvtn6O55ZMQWU0rQDCuedbAN/DK5NugCFun+Mz5b7ORBt0wYoxHOgGZ1hjxFC8PhZPXDJoDYY8eYivDu7RGmr9j364+BusxsAha/kyocKF1G4A4Voz9o2bDuVrkDB+0jRlHRBb+ta7MBSLKEvgtJwyoBCJTEvAUgfoGBHUiwQEEKQHzgD2VZflIbeQE1kaVpOHasCClZeVstu6AfktvY2d93kts3RDsdiDLZjLabicYRRy/gkluG3pX3T2LoBBde3BAAFV1+CgKIhYK/bzrACuLLJtWgXbI+nyx7DhtB6h20qgMJhYxLciwEgo0QBhVsQfUSLvXFj1xPw/oZZeHPdd5w/XoJyE5tGAwoDf6KmimBa1r+0AQrV50n2O6MCCrZ/jQwUdlnLInj63J44d0BrXPbqIkyeu0ldj8P36I+Du81uBBR+TkXy5EOFoXRA4Ry1EIP2BjnXjKjfnwCgkPkbC1DIjhWQnuhpjcQ2+huWRV4DpZGkAUWEUG0OhexOfzKBwkSsn+IjWhYIgiSA5oHzkU26YUvoZdREVkePyWChsYGClWqmJx1QqGZ6igUo7O1W6IpjcAtKsBjf4VlEEEkIULAyAYoGOLC3KbfNygQoVGtRyEYdYgEKezvbysI1TW9EgVWIp8ofwbbwViVQsDIZDUkUUKju6suSeL0ldXsIuBVgckqbg3BphyPw4qpp+LRkTsz1uNk464OrTSqAwhE0S/xS3dU3mSlLN9tUKoBCFdTr6tFBlQooeBt921w5CVDYCgaBFy7qhdP3b4WLXlqEz37fYuB79MfB3UbjuwlQEKvhc+dr95Z/mV3EB711otxxfl8igIJvX2yn3lYCFA22lLe198cIFHU+As2tfuhmXYbNdCZWRiaBUpoUoGBtTHMoxH2q7VhkMkqh8pUdUQmQDLQKXIpM0g4l4QmopRu1jzOlGihYxbJaNhy2kNrq8hbcbNugD47C9ViJnzEdL4KCaiHBBCiUidoxAoXTxt6WBch2G+rAXbYWhVhPgy3vj2ibZzXBjQWjEUAQT5Q9grJImRQonMGliQ3l/JT5lQqgcJY3Cdw19TH1XNrxSJxSfCDGLf0EP2xdFHM9rr5rgMJxx99L/xoZKJT9irV/aQYUKn9MHpmSDvMaAIVdXyAAvHRJL5y4bwucO2Ehvlq4TeN79MfB3Ubju3baWOEz52vPkH+5DZVKoFDaNiJQ2NstrYPQyToXJXQa1tHPhLacr14febK3VTChmjJWTMCONyHbRKo8CnubXYgvmphNstA6cCWCaIYNoedQS7fIRyqi3/mpA4qG3wnvQCHmUZhAgttq2fJHkgg64UAcgauwBN/gJ7wJCiqFhEQABatEAAVXX4KAQrW4nSwIswhFgVWIGwtHozJSiSd3PIbKSGVSgEJpYxT8OvuZKKAwAYCorSX2j+DGbifg8Ja98Y9F72Fu6aq0AwqlP9IEI4NzkGZAoVrcTjtaoHmELt2AwlHOw2NMGRkErw3vhWP6NMNZzy3EtEXbUwsU4jlg8zRk16eRRBvhb0+RDxUuYp+Ho8J+dt+eABT2a2vrCLSzTsH6yMcoiUyVlkt0UrZuDYpkPu6kAga3PAoZUNjHMkgu2gSvgoVsrAs9ixDdIR+pSDJQsPIKFKxMgYJrzwUopL4x/eiOw3AILsE8fIq5+LDBF4mt0m9H/fXbRGMjAIXUTw1QqNai0E4lGydQyPIgigJFuKnZaGwOb8bTpU+ihtYIbVBpWbkN5fzU2hgFv5Jzm0KgUNdTdzxALNzZ6zT0ze+AsQvewrKKEk9g0thAoSqvrUcMKNn+pRlQqPzg/o+J5TSBe9oBheIc2LaZQQtvjeiNw3sU4IxnF+L7P3Y4bbX9c/fdM1D42mPkQ4Wh2P8WbkDBl7ODT6HsXxQo7LqLyTC0JkOxJjIJmyMzHHax5lCIYmECSH+gYH3VrUWRSfLRLng1KMJYG3oWIbqT6ZuzP/EChUxuMz3pgMJ0tWzZDFAmQOEM+J2Q0BfDcCDOxE94BwvwlRFQqEYtYgUK1VoUsQKFai0K1agDe8wUKIC6H/z2GR1wfeGNWFm7As+XPo8IQryNQQCZTKBQLW5ncjddaxMHUNj7Mq0gHuh7DtrmNMPo+W9ifeV2z4Cj9sthknZAoTyH2v4JPnjuH2+bTKBQApLm2pg9piXUn2CgsO2zMiy8e3UvDOzaFKc+tQAz/ywTAABceR10KIGC1V8MKPxE7eTJhwqPMgEKZ56BsL+RgYKVeMwEKAAABGhnnY4W5BCsjryO7ZFfo3aegQI8UMgee+KTl2MDCruOWB6NUgGG6hEtcVYq2QhEFmmBjsFRCKEca8PPIUyrpGtRxAoUrMTg3CtQ8HWZAQWrRAOFrQNwOvbBMHyPl7EU0x22vN+q+jX+2tsxAAXXdoKAQgUJsiRxHVDY6pHVDdc0uwbzq+fj5R2vgCKs8SuWANlDcO8RKFTlvY5MOGxdgMJ+zQtm4dF9z0O2lYGb503EtpqdLvWY+OXujz5wd+9fKoBCdQ75ANlL//h6jZK6kwgUzvMjAwB5eZNrI8+vgLRNFZjkZAbw3+t6Y79OTXDSEwsxe0V5vb3KP3ffpbM6qYCClUXlffK128mHChdRyNaZsLf5oJ234YPwdAIK56NalN/vAhSUsetgnYcCsh9WRV5GWWRRTEAByGdNkj3uJAMKHSQkAiJ0idkycSMrmqljLRBkkWJ0Co5CNS3BmvAERGitdpRBBRQymc70pFstW2zXC1C4LW5n9kiSHhIICA7FReiJQzEVz2ElfuFslSMSMn+JwkbSuAlQqNaisCSfm0QBhRjE6e7u28f2ze6L4YWXY0blTLxTNgnsN5oJUKgWt+NsPAX3fH3JBAqTenT1Nc9sgsf7XYCd4WrcMu9t7ApXSetJBlAY9U/8HHmBPc1jaKkACkd5SfCr8kPme9oBharvUkiAtE2uexLfc7MCmHx9b/Rpm4sTHluIX9dUKPxz990zUOgAqZFFQUFJCv8UscLuKB8qDJVIoGBlChQyXxoTKBrsCdpbFyOP9MCqyATsiqwwBgpbXhKy2X3ie1GxPgqlyutQJWazYv02XYsih3RA58BV2EVXYE3kFVAaVj72pFL0+1sFDjEAhSoxWwUUnD8egMJhK7TvBgkEFo7GVeiE/fA5Hsc6LFLaivVFfTAACtVaFLEChWotCvnsSaIt7wu7T7uWhCU/Njj3IFxYcD6+3Pk1Pi7/VGqTTKAQ7yqLQMGfC3nwmwygUC1ux9q0zy3EY/uehzW7tmHs/PdRW7+GiimYsO1o/UkCUCjr4QJI93PQ0Ka8X40FFMrF7TTApAOTtAMK1WNjTD1NcwL4+Mbe6NY6ByeMW4j5ayvU9SULKAiFDIR87X7yocJFskTteIHC1VYCFE5bKt+fQqCwAcEiQXQIXIFs0gGrws+iiq5rgAfZnyY4d0vINgEKk5EJJTgoHnOKBShk/kghgwJ5pBs6W1egjM7H2siboDQS82NPsQIFq1iAQjV7kwgUrNyAgvdJtGmwtRDAcbgebdADn+BhbMEKqa0UUBIAFJyfCQIKL6MO8QCF/XpU3pE4vekp+KD8Q0zb+Y3UhutnCoHCKPh1s00CUNjve+a3xsP7noXfdqzGvQs+RsNk22b+OGzjzQ1JJVAofJblhiiBQto/cLbJAAqjkQ7xHHhKeubLsu2nAijsfQVNgvh0dB90aJGFYQ8vxKINO419l9l4Bgpfe4x8qDCUGIiz/032ZKCwfbVIJtoHRiIDLbA69Ayq6CanHeQ5FLbcFrVjpQOKREkHFLL1KOz23SCI20cbjuVbfdHZuhTb6E9YH3kflNKkAwUr08XtjB6PsrclQKG0FXzm98m3WdsMZOJE3IJmaIvJeAClWC8ty+1TAAUrE6Bo2CcGyJL6EgQUqrUopHf3XYDCLv9/TU/EsU2Oxuulb2Fm5U8SG8r5qoWOFAJFTIG7Fkz09cr2HdCiI+7pewqmlSzGY0u+qvv/awQ4Qn3SBF0P/VMAhfYcJBEoVEGvvn/gbHW+NzZQKMvLvkw85MckCijsL7dmTQL4/NbeaF2YieMeXIQ/NlQ22Oh8V+VwaKBKWiaNHoPyE7WTJx8qXEThA4UbUNj7AiQH7YNXw0IO1oWeRQ3dzvlgA0VY+BMVyyNPseRNiNKNQrDSLcYnAwrlSAWFY18z60B0tM7DJvoNNkTqH0OB0w5AzEDB+4z6Y2ZAISvrBhT69uxt3md+n3xbZpuNHJyC25GDfHyAf2Entqh90ACFai2KWIFCtRaFfoYl0bbhIpgARUMgpK6nwbbuNWAB5xechYNzB+L5rS9hXtXvjvp0QCFCgT74Fc9XbMFvTCMT2nrU/VPWV1/PkKJeuLX3cZi0Zg5eWfmjgV+Q1uMVKMRrLQtavYBX2gGFyg/NZ9lL/kgygUIEGqnvjmNw+qO0lfke/XHgtlvmB/HF7b3RLC+IYx9ciD9LqrS+mwCFY6Ynf4Rij5YPFYaSBZw+UDjLWqQJ2gWvAUCxPvQcamm5AyhkMnnkSQYOiZjJSWejmz5W9F3mkwooWFvxtZV1GNpZp2ID/RSb6tcBiRcoOP88Lm7H/87yNiZA4Wrr8JDth3xbZ5uLfJyOO0AAvId/YRfKnPXFARTStuMECtVaFMkEClX5gEVwRfOLsW92X/x7y3j8Uf1nwoDCGTzDYZOI2aaSARSm9Zzefj+M2OtQjF/2HSav/VXju+qcuAfTyQAK1SM7cujQ+Cy0mQqgcMz0JPNdlTxvAlWNDRQm9ThsIbUpys/AF2N7ITcrgGMfWIRVW6qTCxRpNEJhyx+pSJ58qDAQG4TS6D57mw/C92SgsN8HSCGKg1chjJ3YGH4eIVRCJdNF7VSjE4mS7Lx5AQo2MdsIHtjvXAcYELSxhqENORarI5OwpX4dEK9AwSp6LIbVsuGwhdRWN5uTCVA4+wHptoltPprjDPwN1diJ/+JBVGOXEig4fw2AQrUWhWzUIRFAIfUvSUBh7w+SAK5reQW6ZnbGuM1PY3XNOu2aGakECtXMSvGPdMjLeK+HYnjXQ3Bmh/3w4MIv8e2mP9zbkgVeXvqXAqAwgQTV4nZm/YPTNoVAoTwHcSZ1pwIonNPMQmvTpjATX47thWCA4Jj7F2Pt1hre+UQDBWnom6/dWz5UuCgCitr6oLghOEf9Nh+Ey2g0lUAh+s37zAfIyQIK+30GKULr4EjU0i3YEnoJYSp8aUGfQ5EsoJCPOFHHe3Z2KkCeQ6Ga6Yn1TzpSQXkb9j0bdLe3TkMROQzLI69he2Ru3XGhrP7RJiHwjgEoVInZOqBwW4tClxiugoRYbJuhDc7AHSjFRnyIR1CLGilQNMCBvU2FbaZtA6BQrUURK1A0BOxyW1aJAAr7faaVidFFV6Mo2BIPbXoSJbWbtYG2t8Xp4LBNFFCYBNGqcxE/mNSfCwu4pfdQDGndA3f89hF+2bbGCCgcwapJ/wxgL12AQt0/h+vacyD6nhKgUJTXPTKlXam7kYDCfm3fIhNf/a0nIpRi6D8XY/32Gq4tWQJ5Q31y/5RAkXaiKf0nj9J2T/lQ4aJtWI13cQPWYT4AM6AQ16JwAwpW8QCFAxxU+5MMFPb7oNUWLYLDUUNXY1voNVAajvrKBeRpBhRuZURf3GZ6Yt/L8ihsiUF3gFjoYp2PFuQA/BF5ATsiC5VlHe3ECBRcXVS0BfcqHc3wABQNUOAOCWIehYktAVCETjgdt2MD/sTH+DfCNMT7a5dRAAWrRAAFV1+CgEK1uJ1+OlZ5Wd6GItfKwZjia5FNsvGvjU9ie2iHw8aknjq/1AFkKoHCZNrT2MCEfw1aBPf1OxH9Ctvh5l8+wNKyTakBCi++K4JpWf9SARSqqVql/VMABad0AwolJDhdV9qyJjEChb2/U6ssfP33XqiqjeDY+5egpKxG2jZfTu4fJxEoLCq/Pr52O/lQ4aJ8tENL7IWpeAwbUTeMnWigEBe32x2Aws6hCFqdURi8GFX0D2wLvw1KI54WtWOViGRsE6keexITy01nenIDCl2fCLHQ3bocBaQnFkWeQzld7rSRtMntT8Jq2dIVsA1Wy1b5HA9QQGLL2rRHT5yKm7ECc/E5xoPQCG+jAQqnjb0tC77sNvlgh4eE+n0KoODr4/2JFSicwaWuD7ytZQEFgXzcUXw9QjSEBzY+hZ0RfuXoWIFCVV5/R9p7cJ8MoDB9BCvTCmLc/qeiXW4Brp39Htbv2mEWuCcRKBxBs8E50D/mo+hDIwGFKqiPFapSCRROSHA/Bw6gkPZP7XuX1ln46u+9sKMyjGPvW4LN5cKIBVdO3hfpPhYofO0x8qHCRQEEcBRuRHN0wVQ8jG1YBcAHChOgsAPwLKsXCoPnoSoyFxXhyaC0wXsvCdnxyOtjT6o8CvaxJ9Y33eNOgDtQ6KaOtUgQvchVyEN7LIw8iZ10rSTAjh0oVInZutWyHWUNgEI105MJJOhsVUDC5lB0Jf1xEq7FQkzHV3gVoHZAa5eRBBx2PRqgUK1FoZ1KVgMUqsXtZMFOooBCFYCyU9wWZTbHHW2uw7bQDjy08TnU0GpHPcr6YgQKZ3mDQNsTmMBhmyigYPc1ycjC0wPORHYgA6N+noRt1buk9XiZraixgUJ5Dj0AE2eTSqAwOQcKf0ySuhsdKFQ2Gt+7t8vCV3f2wObyEIbdtxRby5kR3UQBhaz9RpKfqJ08+VDhoggAC1k4GregCYrwFR5EKTZGj9na04Ei+kfka1BkW/1QGDgTOyMzUB7+3HGXX6dkjE7EChSsT7LEbBk0xAMUQN13cYBkoa91PbLQAr9HnkAl3RwzULCKBSi8rJbtbIf32QQS4gEKVn3IYAzDcPyCL/AD3pMChdtq2V6BQrUWRaxAoXpsiUg6nCigsPd1yCrG2DbXYFX1Wjy28UWE61eO1tWjS2BNBVCo63G4lRSgsNtqkZWHZw86E+W11bj+5w9QGanmy3npn3QKNE0QrTgXyQQKE2BSLW6nHS1IE6BQlpfdlddBgqLNZAKFY6an+u1e7bPx5Z09sG5bLY6//w+U7gxBCRSa/qU7UPhKrnyoMFQGcjAUtyMTeZiKB1CBrdFjuxNQsH+sn25AYfuoykEIgCAvMBCFgZNRHpmG8tA3DptkjFCwvpscY4FCNUrhdaYn2T4vQGFvB0ku9rVuQgDZmBd5FNW01BgoWKkSs1VAwcoLULitRSG7sm5AobOVQgfh9+1HhuJInIcf8D5+op95Agqu7QQABatEAYU6N0HWB7mtfBG+utfuOZ1wa5sRmL9rCZ7Z+AYiNGIEFKrF7UyC32QCRXwjHRrfJYF3h7xCPHPQGVi5cxtGz/4IITu/J0agUAXs2no0ybPpBhQqP3SfZW1idAqBwlFOCgnyNnVgkiygsG36dszBF3d3w4pNNTj+n8tQtissBwqVz9IvS/Xvb2MptWnadrL2niEfKlzEfiAykY+jMQYAMA0PogplKQUKVskaoeBsYgAK2aJ2bA5F08ARKLCOQWnkE1REZkgDcHs7WfkT0kehmHPM+m+/xjPTUyKAwn7NJPnoZ90MijDmRR5DLa1QAgUrr6tls18MqmRr2WrZzvbsbb4vrIcqKPBiawIUts0h1ikYjP/DV/Q1zKPf1dkYAIVqLQpL8nkyAQoVJMiSxBMFFIlYA6J/Xk/cUHwJfqz4BS9tniRfOdojUKjKe/ErGUARH+A4XEfvZkV4YsApmL11Le78dQoikgcjUgkUyr5rz7vgA2uTSqAQy0kTyOX1SKHKYMQklUCh9l0SoBrYuAGFva9f51xMuasb/lhXjZPu/xMVlWGHjdRn7stStIEfbe4h8i+zoeyv/jy0wBCMRQ124hs8hBq6yxUouHriAAoq2Db2I08NfeKBgpUsKbvAGoZ861BsDb+HisivSRmdiPom+OP22BPbB7GsCBSujz5Rviz7GgtQ1JUDskkL9LdGowZl+C3yBEK0UnrG3GZ6UgGFvo76bQlQKG2FPsrr57e92OpGEhxl6t8MIedjPxyFj+nzWIrZUhuvQKGaOjZWoBCDON3d/UQBhWpxO7HNwU33w4iiczBlxw94d9tnvG0SgMLTiELSwcTEL7U/B7XqgAf2Px5T1i3Bo79/K4cyg/6lAihM8lCcgb8msFUF07r+JREonOdHct4V5U2uTVKBQhhBkNqo+i4bDWHKH7BXLj6/ey/MX1WJk+//E7uqIw4bflteD+dnGsnPqUie0vByp58iwvt8FONI3I4KbMJ3eBQhWq0FCioE6rsDUIg5FG4LwwENd9ADxEJz6zQ0IQdgc/gN7KyfKpW1TYS8AoVObkDB2gEN36uyY/EAhb2dR9pgP2s0dmIjfos8jTCtrW9XAQ4uQMH3FXJbe1syEuIFKNxGGbzYSkcxXIDCfn88GY7eZADeizyJlXSBw6bBVgyo7PqdwbMKKFglCihUa1F4g44GWxOgYOs5pvBgXNjyJLy79Qt8uv1/yn6mAijM6nHvn6O+BAOFvX1Mu+4Yu+9ReGP5r3hl2c/u/RPB1MR3CeS5+Z4MoHCUlwS/Kj9kvqcdUKj6LvUd0jYbDSgccGCXAQb2yMOnd+6F2X9W4tR/LUdlbVhRhumfCigI5X3ztdvKhwoXiUAB1OVQNCMdcThuxTasxPT6+e//ikChy6FwHBNhgjhzKGyQcFslmxALRYHzkEv6oCT8MiojfyYVKGTHVCMUrO+q2Z7E9xxkUOcxW4kAClv5pBP6WzdgB5ZhfmQ8EIlwdXsBCtPF7VigcLWFU40FFLwPdR0PWhZOJaPQGb3xdmQcNuBPZ9sKoODqMwAKL6MOiQIK5SNTMujwCBT262nNh+KU5kPwn03/xf92zHbcVRaBQt5P78FvMoDCpB43v6Q2iqDznC79MLLnQDyxcDomr/5d7ZcBUCjPE/+f3Nz3NAMK5eJ2ms9MugBFwzFI2zTznW1T5Z+776ZAYeuQPnn4+O9d8eOinTjjgZWossHCZaRD5mc6iZLU/+0p8qHCUCxQ2NstSXccghuwCQsxC+NBaeQvBxScjQAUYj3i6ITbLEkqoLADcEICaB24BNmkMzaGX0AVXQOd4oEOsT/sflUeiFtitheg4EFEbuMGFKzsY81JD+xrXYMtmIcF9CU0rMGgBwpWXlbLdvZBbssH/CIYQbrN+2RuawIUqrUoMkgGzrZuQmt0wBvhB7GZrjMCCrfVsr0ChWotCmkwniCgiAb8CqBgparHsoBLWp2MowsG4qkNEzG7fIEWKEyCXxOQUNomESjMAEewdQGTa3oNwpmd++KeX6fi241/OutLJVAofNaudp0mQOFlpEMFFLr+JRUoVHkoumFknU2CgMLed8S+efjozi749vcKnP3gKtSEw4wNpPX4keWeK//Su4hCDhR1xyiKyT4YiGuwFj9hDl6JrsGwuwOFLhAH0BCIS4CCVZBkom3gCmSQIqwPjUcNLYEo1aNGKukee4ruE/ohApIXoIj6Gf0+lfdZFtB7kQo2WqM/9iFXYD1mYBF9K/oBEYGCqyv6e2MOFKarZccKFF5sHTYxAIVtk0mycUHgNjRFM7wW/hdK6ea4gIJVooCCCD/6+pEOuY1XoBChQBXcE0JwTduzMKDp3nh47atYsPPPpAKFURAtnIv4wcTEL8FW+jw9v8+ygL/3H4Ij23TF6Fmf45et6xtMFUCh9T2JQKEKevXnFJytzvekAoXi2sgAxySpWwkUkv7FDRRioK6zMfHdA1DYNkf1b4IP/tYZX/1agfMeWoPaEGW+hIV+qeqRAZCv3U4+VBhKN8tTOzIAB+AKrMQ3WIB3dlugsLfDkEOFLTsQj75XAEF0P8lGu8BVCKAJ1oWeRS3dFi2XjEeiZLNpscfFfoiPO8mgwRQo4n3sSTXTUzsyCHuTi7AKX2MpnSwFilhWy46WNQQKVm6QEKutG1Bw9boAhb0vhzTBxYGxCCIDr4bvRwXdIYUEE6BQrUUhG3VIFFCYwItoGy9QsAFagARwS/uL0DO3E+5f/SJWVK6TtpkKoHAEtHGDiYlfznMi2qgSq4MWwcMDjsM+zVvj2h8/xrKybVqgMPGnwVhfhvMrXqBwtAHOVncOdEndXvJHkgkUItBIfXccg9Mfpa3MdyFQjxUolAnWss+M3Oa4/Zti0t864tOfy3HhI2sQClNzoEgz1SVP0xT+7TnyocJFFNRo2thO5HDsiwvxBz7FEvqxZ6Dg2+RtGwMoHH+EcsG4mDsRvasP5+iE7L0ICwGSh3bBawAEsC70HEK0XHJm3KUapVDlUbBicyh0idlsf3T7UgUUdr2dyFHoQc7EMnyI5fRLrm+xAIVqpiftaIbgsw4SxPNhYmsCFFEbBVCwYiGjKWmGS4NjUINqvBJ+EJV0p9AGHyzFChQNAbvcllWigEIMvnRrZngFCrvtTCsDf+t4OdpktsTdqyZgY81maXmj0QIPNskACi+BezxAYb9mBYJ4avBJKM5pgpE/fIgNu8pjBgrVIzty6ND4LLSZCqBwzPQk890BP7oAme9DowOFST0OW347GUChXC1b6N9JB+Xj7TEdMHnGDlz62BqEw9TFd6YPfrS5R8i/zIZSAQWrvcix6IXTsBCTsIJO9QQU4uJ2jQ0U7Hsxh8KWCBQAXIFCp0xSiA7BaxBBFdaGnkWY7jIqZ8vtsSfdTE+mQCEdqaCNDxR2O3vhRHTFCVhI38Ra+gPvA+VtRUjgz0e9rQtQsGosoJD6oAEKp429TdGctMZlwbHYQbfg9dAjqEV1fZt8sCMddUgQUKjWotAnO8vL8jZU6qfUxiS4lyRl5waycXfnEWgSyMadK57HtlCptH6+vCZA9hTcO/0xri/mERP5OeEdM/M9PzMLEw4/GRYhGPH9hyitrpIGv/EChQkkqBa3M+sfnLYpBArlOYgzqTsVQOGcFQoOG0d9KQIKu+5TDy7AG7d3wLvf7sAVT6xDJKLw1RL2p5H8KWWTpzS83Okl9gMhhQHhWA9yKrriOMyjr2EtnbHbAIXqcScvQKF6nIndn0laoUPwGtRiO9aFJyBCazhbGRDI9rPb4nu3pGyVbyqgEPuaaqBgFSAWepOz0QlH4Df6H2ykv3B+6oDCdLVs6WiG4LMIAKzcgEJmqwMKh02MQGGrtdUBFwdvwwa6ChNDTyBSP12vHBLq9ymAglWigMIZVMrLymxjBQpVEC7rZ7OMPNzbeSTCCOPOFS+gIryTK8uXNwnc3YNoVZDPKpVA4QhWjc4pUJTTBC8ecTK2VVfi6h8+QWWoVm7byECh7h+cSjegUJTXQodmJCeVQOGw0cFeEoDC3j7r8AK8elt7vDG1FFf9e33Db6wOKGTnztduJx8qDGUCFBECEEKwNzkXHXAofqUTUEJ/3S2BQgzCTYBCJtn+LNIW7QNXoYquxbrwi6A0JC1rIvaxJxYu3OBI9bgT+17Mo2DfJxsoWIkzPQUIwb7kUrTBgZhNn8GWyAKpT/I66rcVQMHKC1CI+7zYxgMUrEyAwq67U6A7Lsy4GX9GFmBSaDxkT8WaAIVqLQpZcJIIoOD9E9qWXMREAYVYvnVWIe7rMgLba8tw94r/oCrccHMglUAR20iHiY2wnQCgsLe7NG2G5w/7P/yxYwtu+vELhGnY3S9FMC3zPRVAoZqqVee7CBScUgkU4jnQPO6lAgq+gypbme9CMO4VKJjAX9Y255YKKFhJgMIuc96QAvzn5nZ46YtSXPf0hobvyL8AUNBG+LenyIcKF1GYA4W9TYiFfchlaI39MYc+ja100V8eKFSPDAFOoNAF5Ow+3WNROVZntAtciUq6FOvDr4FS9QCiaR4FAKPRFt3ohAoo2L4kCyhYOfMahDZJAAeQq9ASvTETT2A7XW5QR/22BijcFreT/qYK9Xix9TSKIQEKJ0CI27KAqu61h9UX5wWvxbzITHwYqpvZzW21bK9AoZypyQAAtEndGqBQ2pgE2gZAYW93yinCvV2vwKqqEty/6lXURkJKoND3UxL8JggoYgMcZ1uibaxAYWufFkV48pDjMX3jGtz18/9gf7tL69EAhSoIN5kpK6lAofo8SXxXAgXre5oBhRMSTHyXBOMqm1iBQvx+0dgogYLRxccVYsLNbTH+4+248ZkSUGKDBeu8s5yv3Vc+VBjKFCjs4xYJoB8Zieboidn03yily/9yQGHLDrpNcihkjw+JMs2xyLN6ok3gMlTQeSgJv42wBixs6RKz2f6o+qJ6XIvbT8Htkydty228AgUrr6tlBxHEQFyPAnTEjxiHHXSN8lEmFVCw8gIUKijwYisdxSAKmxiAgpVqtex9ggNwevAKzAxPxVfhSbyNJtk5UUChXgmbPy63oZyfWhuTYFzzeIjKzx65HXBXl0vwW8WfeHRN3f/hWEcmorYKoJD5lQqgUK3VYARM0v9odccObdMBDww8Ch+sWIzH581KClAo++UBmDibVAKFwTlw2Gr6l1KgcPge/WFwt9H4nhKgEHy9/PhCPHtjGzw9eRtuHb8pOq2+w9aPNvcI+ZfZQF6Bou6VIkAysB+5Dk3RDrPpY6ig6+rL0PoyDbbsq3J/I4xQ2Eo1UNhqau2L4sD52EFnYkvkI/4LCzwseEnMBtRAYcOEDBrcgILPN/AGFDI1fKd7AwrbzyDJwsEYjRw0x3Q8jJ10kyegUNoKfWSVSKBg5QYUnA8GQKFai8ISPi8HBY/EicHzMDX0Ib4NfVZnI81NEOu1A25NX+IACr4+uW0ygEK1uJ0qiO7ftBvGdD4f32+fj/FrPwSl1DNQOEdKnOciPjDRtBknUKjPqdN3sfzJXbpjzH4H4/mFv+LVxfOk5yKZQGHWP95WBx2pBAqlP1Ibedsy39MFKByJ2bJ8CEd98r5I9xkAhb098uRCPHV9azw+aRvueHFz3e+0rF9pIj9RO3lKw8udXooVKOz9QZKF/clNyEJzzKbjUIGS+mO8bToDBeCeQ2GakB2LCqyD0Mo6HaWRadge+Sq6X5eMLXsvgyS7L26Pbun2qfIovMo0MdsUKOx9mSQPh+E2BJGF7/AQKmmpEih4f8T27G3eX36ffNsLfEihwwAoVGtRxAoUbOB/ZMaJOCr4f/ikdiJ+Dn9bf1xuyx7j/BPajBco1DaSIDqFQCErf2izvrih0+n4ePNMvL7ha/c2kwAU6npMzhcctskGCvv1sl79cEXvfrj/lx/x6aqlXNuyr9R0AwqVHzo4ThegcJTz9BiTie+SwDtRQCGeA5OZpGIACtvmmlOb47FRrfHwW1txzyubnbaydn3tdvKhwlCxAIWtDJKHA8loWMjEL5FHUUVLOdt0A4paRKLvgYYAXJd3YCuW/AkTFQaOQHNrGLZGPkVp+HulnSwxWzXqUjfyIIch6UgFNQeKRCdmmwIFK9anbFKAI3A7IqjFd3gI1bRC+p/f9fEoSRkvQOFm6wUoeB/sIFeoVzqKIQZUdv3OYIm1PT7jbAwOHoV3q1/Eb6GfNLa8L+w+k8XpEgUUqsXtvEBHIoDC3h7WcgCGtzseb26Yiv+WTPfmVwqAQl2Pwy1lwC31PQ6gsN/f0n8QTuvSA7fN+AbTN66pP+Delt4vwQeD/iUFKMRymsBdBRQy31MCFIpzoBvpkK2V4WqTDKBQ+Sz9go3+gKl9t4AbzmqOB0e2wn2vbsX9r29R2zayonFPCv/2FPlQ4SLplLIegMI+lmHl4wDrFkQQwi+RR1Fdv7hbYwEF98cAhWyFbKAhAJflHcje6/Z5le17c2sYCq0jsDnyPnZEZjuOi3kUgHyWJ5nfJkAh9ildgIJVw++C6BOQR1rhSIxBJUrxPcahllbV++e0ldXBB/xiHyDdZuXF1gQo3FbLlk8lKwZUEj8lQAEAgQDBGVmXol9wIN6oehZLQr8bQYIJUKgWt/MGHQ22JkDhbaSDr89bUneD7TltDsdZrQ/Hc2s+wddbfjUL7nV3h+MCE02bOnjRAIUJ4Ii2JkndAQu4f9DhOLi4HUZ9+xXmb93sfg4kgagWKAz619AmX28ygUI54qK5NmZ5H0L9SQAKte/cD4q7jcL3pAKFajRFVg9z7NbzW+DeK1rirhe34OE3tzr64Gv3lg8VhooHKOwgN5u0xP7WaNRgB36LPIEaWllfhq/DC1CIJOwFKEIkghBxjkrYQbW4GByghohkAwUAEELQ0joVTckAbAq/iZ2RBUqgkN0ZcBtxEX0WgUIGG40FFKy8rJadT9rhCNyGHViL7/EEIvXT9boBBSsvQOHF1mETB1CwMgEKt9WyrWjQb+GC7JHoHuiDlyqfwMrQMikkqNaiiBUolI9MyaAjQUAhBoEyoHAb4ZAFh1d2OA7DWh6AR1a8j5mliz0DhWpxu0SDSTKAwjHTkwkM1b9mBi08dfhQdCtshuHTPsfKsh0pBQpHeV3wqwqmOSOhrWQChaK8CezJRx0gbTMpQKHqu8wvFVBobBIJFPb23y5pib9f0gJjx2/GE+9sq7OV+dtIoiT1f3uKfKhwETvHcDxAYR/JIW3Qz7oJO7EBv0eeRrh+Ya1YgUJqQyCFDe49aRidqEHEaDE4U6BIxONOKlFC0CpwDvJIX5SEX0FlZFn0WETyA2HL5BEu6fvo96Szv6kGClaqPAodUNhqSbricNyMzViCH/EMgLC8TqY90WcVJPA+mtu6AQVXrwFQqKaOjQUoOD8JRZAEcXnu9WgX6IQJleOwIbzWE1Bw9SUIKKIBvwIoWKUKKLh+MjaEENzU5RQMbtYT9y59C/PKVmrrSQZQxDKikAqgcDunuRkZeOGoYSjMzMJlX3+GzZW79L6nEChUYCN7hE4FFJzvqQQKVd+lkABpm2a+s22q/HP3XZ9gbZeBq40JmHgFClv/uKIFbr+wOT78vgKhkPq32dfuJR8qDOUGFJxttAytL8PbNiGdsI91PcrwJxZGnke4/m6x10eepDZEVk4+oiGuQcGOVrhNtWq/yoAiUVI9h0hIAK0DFyOLdEFJ+EVURlZzZcT+iIp36ljAGXgnCyg4v4U2TYGCr6NOxdgbh+J6rMUczMJ/ogG6PDeDb18HCaqZnrQ5E+K2BCga4MDepsI2U68LULAyAQrZWhSZJAsj825CM9ICz+58BFvDm/m2NUBBhB99/aNTchuvQCFCgVkgKi8rKy8DCl2gHSAB/G2vc9CnaQf8/Y83sHzXemU94rlIBVCoFrfzNtLB1JcAoKjbAJpnZ+OVY45HTTiMy7+egvKaGiNI0K52bdQ/cLbJAAqTehy26QIUqjwUaeCu8s/d96QAhVi3BBIafBeOyWxJ3c2D+69ugSlPtEMgg+xRd+v3ZPlQ4aIIMQMKxzEFUNivBaQH+lhXYxvm4w/6SnTV1HgfedIBRXSb8EBRTcKOAF6WQ6EKvtkyyRR3vkkArQOXI4MUY1N4AqqxgbOVAUW0Twoo4vZT5z72te693CZeoGBlOtOTDihUMz11xP4YjJH4E99iDt7k2lXN9CQCgKxMIoCClRtQcD4YAIWYmK0bSVAtbmcRilySh6vzbkEGycDTOx9Gef0EDLogzgQoVECiC/jiBQpn8AyHjdM/dfBrEmhnBYK4t/sFaJfdHLctfg3rq7Zo6lH3T9lmMoHCoJ6oqQIotL4rgMI+1q5JU7x87DCsKS/HVdO+QnU4zPulAQpV0KvvHzhbne9JBQrFtTFLenaapBQoxOBbBx0mvqcSKMRyuoRtxpYQgsys9Asz/UTt5Cn9rnaaKpFAYe9vRvZBTzIcm+gsrKDvqB9nYtpxe+RJPKZKymaVRQNwy6FI9eNOoo/sdvQ9yURR4EoESD421z6PGloXlIgjLuJjXKLPjve0wUb2Gs9aFKr9vG/g2vEKFKzc1qLohkNxEC7BQnyO+fhQaIf3WQYU4j4v8KEDCoeNBiicNva2LKCy65cH7tw+DVDYKggUYFTebailNXimYhwqaYWzL45AUb5f15YuMNYtwpcooEjE9LWyNptkZOHBXhejSSALtyx6FVtrdyjPhdHIhCZQM/NdsI0RKMRrLQMKk/PUYOws06tZc7w49BjM2bwJo7/7FpH6X4qUAIXKZ805TQVQKMvLzoHmcS9nfVC2mTCgMPBdnQ8h+8wobJIBFI5Ri7pX7j6ZP1KxR8iHChdxORX1+xIBFPZ2C+tA7EUuwAb6DdZEPkoIUChhgjTkUdgyScp2A4pEywgo6hUguWgVHAEgA1tqJ6CG1gUlAYnPbB6F2+iLG1BIk5GpvKwYnMsU61oURvkW9rZilqdeOBb9cRZ+xbtYgi+lPktvlAn1eIGPVAEF528CgIKvr+5YUbAVrmt6G0oj2/BsxeOoQXVDX1yAQlZfvEDhsDEBgDiBQjWzkgl0NMvMxSN9LkGYRnDrwtdRHqpMGFB4ARwVUPDOe6gvTqBQPbLD2hxUXIynjjgSn69aiXtmzgRIxGEjtpkKoHDM9OTh8yAPkAXbZAKF4xjfJ05pBhTKxe005zQlQJF2kSZN6b+GyM9czzzzDDp37ozs7GwMHDgQP/30k9Z+0qRJ6NWrF7Kzs7HPPvvgs88+43tMKe666y60adMGOTk5GDp0KJYuXRo9vnLlSgwfPhxdunRBTk4O9tprL9x9992oqanx5HfaXep0VTKAwn5tHTgcnazTsZZ+ig2Rr+IGCvY9OzpRi0g0KTsMyt3JTxZQeB3FMB0mbOg/BSFN0TJjJChqsT30AiJ0l8NHLzM9mQJFrEBlmpjtBShUidkqoGBFQLAvTsXeOB4z8QqW4wepn3z9Yrvm8OFpFEMACmm9GqBQrUWhnUpWAxSqtSjaBttiVNPRWBdagwnlTyNEQ0ZA4QwqnTYq21iBQhWES3NOEgQUbkF066xCjNv7YmytqcDYhW+iMlwjBRzX+pIAFI6A0QjS5GWN/TIACvv9sZ074b7Bg/HaokV49rd5go2iD0bABKdtooBC8ZmR1aMECtb1VAKFST0OW7jbpAIoVPkZWt81ffhLAkX665133sHNN9+Mu+++G7/88gv69euH4447Dps2bZLa//jjjzjvvPMwfPhw/Prrrzj11FNx6qmn4vfff4/aPPzww3jyyScxfvx4zJo1C3l5eTjuuONQVVU3tfzixYsRiUTw/PPPY8GCBXj88ccxfvx43HHHHZ58J5RSsyhuD1NZWRkKCgpwPBmPAMkBkBygYHMoVof/i/V0CrpYF6GFdUi0nVgfeYpukzqgYJOy2dEJezuRQKE67gYNJqMULFDY22FQhGgJttY+iyBpiZbBkbBIVrRf2sedhGBdNnrR2GtRqFbL5n2S75cBhcyWgmI6JmAZvscxGI2O2F/oA6TbXuBDOopBFDYSoEjEatlySKjfjgEo2GBpSWghnil7DP0z98fwpiMRqDeKFShUgafFnEClTYxAoSovvxtvECB7CKIXV6zBTfNfxb4FnfCvPuciMxCIqZ5UAoXznMJh422EQxFMS2zZQPTfv8zF47/8iocPPwTn9OxuBBTq/jGui8/syyBB9XlKJlAoyusemZKvN6E4FiNQOKeZhbuNxveUAoWqHunohcIWDFREfyQoyirCaD5kOXbs2IH8/Hw0huy47ijrWQTr47pUKEQrMS1yDdasWcP1PSsrC1lZWQ77gQMHYsCAAXj66acBAJFIBB06dMB1112HMWPGOOzPOecc7Ny5E5988kl036BBg9C/f3+MHz8elFK0bdsWo0ePxi233AIA2LFjB1q3bo1XXnkF5557rtTvRx55BM899xyWL19u3FefIV2UzBEKMSm7nXUKisjhWBF5A9siczyPUKiAQhfIWyAIUsvxaJAumVn7GI/HkQlWsQKFrSBpjYLgcIRoCbaFXgGh4d0KKFh5BQpWKlsLBIfgCnTCgZiKJ7ABC42BQl6/fJvrhwtQcPWmGCj4tvkgQgYUhAC9MvrgiqZXYU7Nz3hr5+uglEI2V3yqgELbZpKAQt9PSX1MPb2btsf9fc7FnNLleOCPyYhQfXkvQCH3S94nrlwaAIXMdzEQvWH/fXFh754Y+/2P+Hr1Gq2tvn9MWwkACk5/MaDglE5Awbr1FwAKX3Xq0KEDCgoKon8PPPCAw6ampgZz5szB0KFDo/ssy8LQoUMxY8YMab0zZszg7AHguOOOi9qvWLECGzdu5GwKCgowcOBAZZ1AHXg0b97cUx99qDBQKoAigrqZEjpY56I5ORArIi+hNLJAWtbokSeY5VCw+0V5GZ0wgYlYZ0BwwAZp6KO4Cni21REtgpehmq7AttAbIDQSPabMn6Bw7ksToGAlPvbkBhRcWRdb268gghiCa9EaPfElxmEzlhsBhRt8SEcxDIDCbbVsL0ABja38USLRlveFFWu7X9YBuKjJpfiu6n+YvOsDSZvqIK6hPrmtV6BQBuOaR0jiBQoTAIjaOuoBDmy2F+7qdQambv4d//7zc9jftvECheq8GwXuRpAmqS9BQKEKwnlIILj34IE4tlNHXPP1/zCnpERjK+9fMoBCFdTrPg/pAhROSDDxXRF4y2xiBQrhM6eDDiVQcDaKehIFFATced1TtWbNGuzYsSP6N3bsWIfNli1bEA6H0bp1a25/69atsXHjRmm9Gzdu1Nrbr17qXLZsGZ566imMHDnSrHP18qHCRakCCrteQix0ti5BU9IbKyITUE7/5G1MgILIgUKVQ6FLUBb3y5SIZG3VKIU4WsGOvIhAAdT1McfqjpbBi7GLLsCW8LsglCr7KAIFHyg3DlCw8rq4Hf8bYDiaIbQZQAaOxWg0QztMwYPYjnVpCRSsTIBCtRZFrEChGmUghOLQnMNwVpOz8fmuT/HFzilGQJGIGZa8ryUBaXltm3EChbqehraGtOqDW7qfhA/W/4yXV32XEqBwJC576Z80Wcn7uYgdKOreBwMW/n3UYehf1BKXTpmKP7Zvd7qVbkBhcA4ctkIfeBt921y5eIHC4bskGFfVl0ygEPfJgMIAErzYaoEizaSc0CaJfwCQn5/P/ckefUoHrVu3DsOGDcNZZ52FK6+80lNZHyoMlQqgsNuhxEIX6wrkoCOWh5/FTrqmvk13oLAlu4sPQAkUqrv4sm12fzKBQreP7Rs7haztTx7pi1aB81FBf8a2yEeg9WBhAhQ8gMhtEg0UrNwSs1WQwCoWoGB9zUA2jsftyEUhPsW/UI7N0i8LN6CQ9ksBFHy9dpAr1MtBh71PDBTt+p3BswoouLbjBApbx+YNwwl5J+K9infx/a7v4wYK1VoUXqDDK1Coyntq0yNQ2PtOabs/RnY5Ci+t+hbvrf1Ze3fY27mAwzZRQKEO2DV+JQgo7H7lZATwn+OOQpu8XFz46dfYULHTqH/iudDZpBIolP5IbeRty3xPCVCobDQwlDZA4TgmbDP6KwFFuqtly5YIBAIosUca61VSUoLi4mJpmeLiYq29/WpS5/r16zFkyBAcfPDBmDBhgmf/fahwkXRK2SQCRTR4JhnoErgKWWiJleFnUIkSI6BgRyhUQTebQwGIQbQ5UCRDbnkUbN9EkIr6Vt+3fOtAtLLOQGnkf9ge+VLbX1keRbKBgvPZEex7BwpVYrbZ41FiH4BsNMGJGIsAgvgE/8IulCptZdtSGw1QuK2W7QUoWJkAhQkkeLG1f/RPb3Iajsg5Aq+WvYrZlXOk9SUKKLyNdPD1xQoUJiDhsDUACvv1oo4H49z2g/D4sin4suR3l3pM/HK4lVKgUEKe9HOrDqKdNvx2QXYGXj9xKCxCcOGnX2N7VbVL//hzoTsHSqCQ+KV7XCiVQOEo5+kxJoltgoDCcf1luQ6O+uBqkxKgsPi4xgEUrCxA+sPTSKKN8GeqzMxMHHDAAZg6dWp0XyQSwdSpUzF48GBpmcGDB3P2APDVV19F7bt06YLi4mLOpqysDLNmzeLqXLduHY488kgccMABePnll2HJ7rq5KGmXedu2bbjggguQn5+PwsJCDB8+HBUVFdoyVVVVGDVqFFq0aIEmTZrgjDPOcJDV6tWrceKJJyI3NxdFRUW49dZbEQqFpPVNnz4dwWAQ/fv3j7s/qQQK+5WQbHQOjEIAeVgZehq1dLsxULCKZcpY1ShErKMTqnwKt/1iYrZK4uiL7WNh4DC0tE7E1shn2Bb+tqHfVA8UouIFCpkSsVo2hH1uQMG3z/ssg4Q8NMNJuAO1qMIneBC12KW0Vddfvy0ABee/C1BI69UAhWotiliBQkzMNgEKi9TlSV1UcAEGZB+ICaUvYEH1Qm15KXQkCChUa1EkEyjiHekghOC6bkfjhOJ9cd+ijzBz21JpPV6BQumPoV91ZZ028QKF8lxKr6P8etoqzsvFm/93NDZXVmH459+gMhRyBuds+XiBQiyn+cyogIJVSoFCcQ50Ix2ytTJcbbwChXgONMG9EigkfqUEKGR3mXwZ6eabb8YLL7yAV199FYsWLcLVV1+NnTt34rLLLgMAXHzxxVw+xg033IApU6bg0UcfxeLFi/GPf/wDs2fPxrXXXgsAIITgxhtvxD//+U989NFHmD9/Pi6++GK0bdsWp556KoAGoOjYsSPGjRuHzZs3Y+PGjcqcC5WSdrkvuOACLFiwAF999RU++eQTfPfddxgxYoS2zE033YSPP/4YkyZNwrfffov169fj9NNPjx4Ph8M48cQTUVNTgx9//BGvvvoqXnnlFdx1112OukpLS3HxxRfj6KOPjqsfFI0DFParZTVBh+AoEBCsDj2NGloWPS4DClaqFaVlwbRutMJtf6xyy6MQ+6ZLOpcBhf2+uXUsmltHoSTyHkojPwnfn3KgkOUOqMvI93Nl4lyLQv7okVDWACicowr6UQcAKERr/B/uQDm24BM8jFD94m7anAlxWwIU4giEDijcVsv2AhSsEgEUXH0Wf4wQCotYuLLZ5eid1QtPbXsWy2uXS9uSBV+pAgr5ORECI51NzGDi3j9CCMb0PAmDW+yFsfPfx/yyNRpblV8S3zVAYdI/0TZtgIJpo1uzArx24hDM37wNo778AaFIw8QVXPk0AQrn+dGc9xQChdp3dTCug46UAoVqNEUGQ0kCCmpR0DSCi2jeaQr/vOicc87BuHHjcNddd6F///6YO3cupkyZEk20Xr16NTZs2BC1P/jggzFx4kRMmDAB/fr1w3vvvYfJkyejb9++UZvbbrsN1113HUaMGIEBAwagoqICU6ZMQXZ2NoC6kY1ly5Zh6tSpaN++Pdq0aRP986KkrFOxaNEi9OnTBz///DMOPPBAAMCUKVNwwgknYO3atWjbtq2jzI4dO9CqVStMnDgRZ555JoC6xTh69+6NGTNmYNCgQfj8889x0kknYf369dGTO378eNx+++3YvHkzMjMzo/Wde+656N69OwKBACZPnoy5c+d66oM9n/HRpG4+42QABVUF0aCICOWq6CasDj2BIArQJjgKhGRHE5dtO9kIBcAErgaPPCUDKExGKVSPPYmJ2TLJoEKEC0KB9eGJKKWz0Mm6AoVWP2OgSHZithtQ8D6JddRvK4BCaiv4K7uh5Pi9AMFGLMWH+Cfaojf+D7cggKDUNtVAwbfBH5PZqtaikCU5mwCFeOdadee/OlKNR7c9gfW1G/C3olvRPqNtTEDB++ce/JoAhfpuvHmgnRygaNhXFa7FTb+9hT8rNmP8gRegW9MiQ7/grC9OoFAtbmcEOJLgKxlAwR6btnotLv3kW5zbey88fNQAEEKUQKHzQ5aTk1KgUJTnbFTXtrGBQtV3bT4E/92SFKCwFMckj3LFChQAUFYRQYtD02OdisMCT6d8nYrvw9c2at9TpaSw44wZM1BYWBgFCgAYOnQoLMvCrFmzpGXmzJmD2tpabh7dXr16oWPHjtF5dGfMmIF99tmHmxbruOOOQ1lZGRYsWBDd9/LLL2P58uW4++67jX2urq5GWVkZ9weAo0z7v02ygcIWuy9IWqJd8GrUYis2hF9AiFY7gEKc6QlAQ7AtAQqTEYpEJWOLMgEKVmFFHzNguQKFBQJCCNpb5yGf9MPqyEuoiPwRPQ7ABwqujNhu3Z426I4TcAvW4nd8iWcQQcQYKFi5AQWr3QUoACA7kIUbW1yHFsFmeGTzE9gS3qy0jQcoWKUKKLg2dUF01EZfr2xfTkYQ4/qfheLsfNzwyztYX1kam18eH8Fy+JUqoGD9igMoQCiO6tQO444ahDcXLsMjs+YnHSg439MIKHjHIG3TzHe2TZV/7r57AgpW6QQUrBRAASL45mu3VVKgYuPGjSgqKuL2BYNBNG/eXDvPbmZmJgoLC7n94ly7snl27WMAsHTpUowZMwZvvPEGgsGgsc8PPPAAtyhJhw4duOP2f8NUAIVqlqcM0hatAyNQQ9dic/gVUBpSzvIEMEABOVBwdsL/+ETBhGqUQmerS8y2xY1OUCdM2H2IvqcAIRY6WZcgj3THish4VNJVAJB2QMHKK1CwMgUKvozYLm/bGftgGK7HUszEN/gPKKgRUDhsNEDhtLG31cGpCihYJQIo+Lb5H319bkLddtNADm5peQMyrAw8uPkJlIZ3ONuOEyicwTMcNk7/1MFvTCMT2nrU/VPWV19P04ws/Hv/c5EdyMB1c97CluoKF79U50QS8BkF93x9SQUKhc+yz6ISKBid06cL7ji4P574+Xe8Om9pUoHCpB6HraccBb4s274OKBqOQd2moh6Z7ykFCoWNfm2K6I+X069EAYWjX3UvVOZXmoh9fDxVf3uKPEHFmDFjUDdsqv5bvHhxsnx1VTgcxvnnn4977rkHPXr08FR27Nix3KIka9bUPbdLkdpHntymjc20OqFl8DJU0aXYGp4IWr+4G7uQnZc1KHQ5A8mSbmRCl5ht50/IckRkoxPR95SxIxnoao1ADtphafhpVNEN9ccabNjXVAEFq2Sslq0DChUUqOCjGw7CUIzE75iKH/G2tKzcp/ptAShYmQCFaupYOSTU71MABde2AVA4gnkBKGT1iX41C+bj9lY3opbW4pHNT2JnZJcSKOT+GQT3GqBIxHoYyQAKo2AcQIusPDx1wDmoioRwwy/vory2KqlAIV5rR9DKKJVAoQp6df279oDeuKJ/T4z938/4ZOkare9JBQrFtZEBjm6EI6pUAoUiUJdCRyqBQqxbBgkQbHT5FYkCCn+EYo+SJ6gYPXo0Fi1apP3r2rUriouLsWnTJq5sKBTCtm3btPPs1tTUoLS0lNsvzrUrm2fXPlZeXh7NeA8GgwgGg7j33nvx22+/IRgMYtq0acq+ZWVlORYmYZXqHAol8RKKbKsHmgcvRGVkHkrD70f/X8tmebLVGEAho3PVY0/sNvtYly1xBXAAjlEYwAkU7H77NUCy0C1wDTJRiD/C/0Yt3eKwAZB0oOD7ItqCe9UBhaut4C/XrsOGt1XBx944EkfgIvyMD/EzPnL0SzV1rAwo3FbL9gIUrEyAQrUWRaxA4QwU5WWLgi1xe6vrsS28HY9ufgbVkRrFInyif5LAUbSNEyhUMyvFP9IhL+O9Hop2uYV46oCzUVJZhtG/vo+qcK3gl+qcqAPuWIHCBLwajNXnwOFXgoGCEIAQgnuP2A+n9OiIa6b8iB/Xlcj9kV5reb2NBRRi+yawlxKg8AJDqQQKsZx09EJuK7l/5Q0oLMGXRhZthL89RZ4uc6tWrdCrVy/tX2ZmJgYPHozS0lLMmTMnWnbatGmIRCIYOHCgtO4DDjgAGRkZ3Dy6S5YswerVq6Pz6A4ePBjz58/ngOWrr75Cfn4++vTpg/z8fMyfPx9z586N/l111VXo2bMn5s6dq2xbJ3G0AEgtUHDbTLCdafVFQeBs7IrMQln4U1gU0mlj3YJsVo0FFNx7CVCwcpvpyfGYF3UG/xYIgiQPPQI3wEImFoefQKh+Vq1kAwWreFbLhmDjBShEgFABhawd1vYAnIhBOB3fYyLmo+H/bSKBgpUJULitlu0FKFglAij4vlC0z2yLW4tGYVXNWjy5ZQJCNOQZKFRBuAyyEgUUXoLoZACF/dqtaSs8ccAZWFRWgrFzP0KIhuRt6e4gpwAoVI/syNoyeYwpVqCwbS1C8O9jB2Fg21a49KPvsWjLdnX/xM+T0bV21tNgJNgmEygcx/g+cUolUCjzIdSf07QBCjExWzZ64fBLcszXbqukXO7evXtj2LBhuPLKK/HTTz9h+vTpuPbaa3HuuedGZ35at24devXqhZ9++gkAUFBQgOHDh+Pmm2/GN998gzlz5uCyyy7D4MGDMWjQIADAscceiz59+uCiiy7Cb7/9hi+++AJ///vfMWrUKGRlZcGyLPTt25f7KyoqQnZ2Nvr27Yu8vLyY+5ROQGHnT+QEDkRB4BRURP6HnZFvHMG2Mr9AvOMv2CZDKkiQJWazeRRhUMfUuCqgsOUGFEBd8J1B8tErcCMoQlgUfgJhWr8GA4WijMv+JK6Wzf8+GI5mOFp3fs/rgMLNlgA4GGehP47Fl3gRSzDTFShYmQCF22rZXoCCb1sM2J22yrUkJN+aJkAhDYyzumB065H4vXIJntv8KiL1jzMmCihU5aVBYYKAwnQdBW19hjkd+zZrh4f7n4Ift6zAfb9/Ef2u1dWjAgqpXwaPo6UCKBzn0BMwwWGblWHhpf87FJ0K8nDeB99hbXkFV5b3Q14vAEcwrgMTJVCwrqcSKEzqcdjy28kACuVq2RpI8wQmSQQKqjhGLaTXlLKqJ0GS+LenKGmX+c0330SvXr1w9NFH44QTTsChhx7KLfldW1uLJUuWYNeuXdF9jz/+OE466SScccYZOPzww1FcXIwPPvggejwQCOCTTz5BIBDA4MGDceGFF+Liiy/Gvffem6xucB8IyuxjXxMNFGLbMqCwX5sEDkN+4FjsCH+GnaEfpUG0G1AkWm7/gXR5FGwfRanyRKS5FJQvx76ywXcWaYHegRtRizIsCv8bkUiVooyqrvr90vFhsQz49qmwHbXn97OKBSicfYB0m2vHxTYaZ4BgKC5FbxyMT/AUVtDftEDhtlq2F6CAxlb+KJFoK/jiESjUd/4d7ikDT8sC9snpjWuLLsOMnXPw8tZ3UPfNwfpnENDGCBTO8gaBdpxAEdtIh9rmkKIuuHffE/DZugV4fNE3iJ4/j0DhPKdO370ldSuC6SQAhbp/DreiNvnZQUw87XBkBi2c89532FZZ7fA9JUChKK+FjhQAhXOaWThsHPXpYC9RQKHKzzCABO1wdxKBwteeo6SsU7E7yJ7P+FDrKQRJTlKBQpeYLXscyA667Tv3lFLsCH+E8sj3aBW4EPnWAQDcASJZoxOqXAn2vQwo2G22j7avsse6pBAV/f50HlfN9LSLrsaC8KNogs7oY12HIMl0BQruWIIWt+NGJMSyhkAh89kEKLzYivsiCGEyHsNqLMA5uAMdSM+GeqPAQIVt/ji/TwxK7facwVYigILdlyigcHvcSFzVe1rZdEzY8iZObzYM57Y4iatDV48skTZRQGEEEsp6oLb1BCYmfgHvrPwFDy2cimt7Hobh3QZKp1Y1ri9GoHBcE4NzkAqgUE3VSgjF8u3lOOmtaehUmIf3zjoCTbIDfHkZBKUSKMRzoHncSwUUsnMQL1A4bDS+6x9fEr5fUgEUqn4xfinXoogRKMoqImg5MD3WqRgUeDLl61TMDF/vr1Phy2X2p0YACgCOGZACsNDCOgVNyABsDr+JnZEFyrv57L5kyAQoxG1dPwE1UNhSAYV4XDd1bBPSCb2tUSjHn/gj8iIoDbv2NdFAwdftDSikfrlAAqt4gIIQIECCOBU3ohhd8R4exqb66XobCyhYJQIoWCUCKHj/6vYdXXAwLmxxGj7YPgWflE5LOFDI2kw2UEjbTAJQAMC5XfbDVd0PxtNLvscHa37jy5mcizQACt4vSG1lvscKFADQtVlTTDzjUCzasgMjPpmB2nAkNqBg/fqrAYXUd0XgLbNJFlCwSiegYGQ0QkHkZRtL/uNPyZMPFYZKBVBwf0KgzT4SFA2smTUaCLFQFDgHeaQvSsKvYFdkWdSWlWn+hGw0wPQcebFzS8x2SzzXAQU/oqAGCqDue7WA9EQvayS2YR6W0tdAKbu4m2Bv708gUCgfZTIAClVitg4oxHNhAh8yoLC3M0gmzsStKEQR3sa/UIoN9WXsINegXgVQcLYGQOEGCV6BQrUWRaxAoVqL4uRmR+PUwmPw2pYP8E3ZTG09XoFCVd4o0I4RKEzqidrGART29ojug3Fu5/64f97XmLphSf1Bk3qE4I5zzPu5iBUoVP2S5zoI9RgEyNLzTyj6FzfHK6cOxjcrS3DLl7+AUgrPQGFwDhy26QIUDt8lwbiqvmQChbhPrJdVKoEiCg6iX0630gkmfCVfPlS4SAz+geQBRYNdw3vlwnZMoG2/EhJAceASZJMu2BCegJoIPw+5F5iQ7fcqk8eexPe2HNPHSkYnZPAgexzKy+J2LUg/9CSXo4TOwHL6LiilSQMKvn9iHfXbmtWynWX5/um+570AhTKvgvDbAJBNcnEOxiIbeZhI70e5PV2vBCjcVsvmF8/jj8UDFKwSARSs4gEK+/W8FifjmPxD8FzJRPy08zdlPQ194OuLFSg8AUCCgEJdj+5aOduyZQWA2/oehePa9cCYXz7HrC2rzPsp+Qx5S+pWB9OpBApHYrbGd9aPIV2K8dQJAzBx/kr86/vfHX7FCxRKf6R+yduW+Z4SoFDZaHxPCVAYQIIX20QBBZX9aKRRtBlphL89RWl0mdNbyX7kqcFOP1ymmv0oeowE0TZwBTJJMdaGn0N1dHE3NRR4HZGQyUseRXQ/aeinlxXBpRBB5UDRYAfpMRYo7NfW1kB0J+djLf0aq+mnvL1dTyOslq3NtxD6JwMKx++FB/gwAQrbpomVj/PJHaAAJtIHUIUy7jjgDhS8D/wxna0JJKjWopAmRBsAhTIHQwYdLkABAAELuKLobAxq0h+PbXgZv+9a4mzT4n0XgYLrQ4KAInUjHSZ+SdqqrydgAfftNwwHteyAG2d9hN+3b3SvzyNQqM6F7CtUBRRyG7kPbBvxAoUKbAgBztq7I+4Zsi8en7EEL8xepgQK3nn5OfAKFMrymvwRb48xSWwTBBSO6y97NMlRH7+dFKBwHBO2GSmBglWsQOFrj5F/uV3EBvipeuSJlWw6VVuqu/VBko32gasQQAHWhp5FiG5V9i8RjzZ5zaNQ9ZX1iQUKcb/jPVUDBQHxBBR1ZYB21hB0JadhBZ2MtZGp0f117XkDClZeF7czyrcQ+ie7ol6AQjVqIQKFtP76NwWkBS4gd2AXyjEx8jCqaWXU1gQo3FbL5kYxHIE6b8vKBCjEQFM/0iG3kQVxKqDg/avbF7QIrm9zEfrmdMcD617EsqpVDfUZAIU6eI4TALRgoq9P2maCgcLezghYGDfgJHTPb4lRM/+LFeXbPPkVL1Ao29JAgnbq3RQAha1RA7vj6gHdccfXv2HyorXSYFw54iL7bkglUCjOge7OfUqAQjwHmuBeCRRSG0UZ6TFJmxYf20iBQjgWC1BQolhAr5FEG+HfniIfKgwlAwp7WCvRjzyJYoNsdps9Lu4PkDx0Co4CQQZWh55BLd0hrTdV0uVRiPkiAKTwpAIKR1kGKJTHqKpMw/5O5ER0JMfhDzoRGyLTOb/cgIKVKo/CDSi4OlS2Qv+ItAy/7QU+dEDhsInCQl2HW1ltcIE1BluxHu9ExiFEaxICFKxMgKIh6Da31QGF22iIV6AQocDezrQCuK3d5eiY1Qb3rX0ea6o3JgwoTEBCaRsjUCRmpENiowi4c4MZeHrwKWiRlYeRP36AjbvK5X4J18ZbUrcz8EsYULj0j/eLrzdWoLCP3Xt0X5yxdwdc/fFsfL9ys2egcJ4fzTlNIVCofVcH9ykBCpXP3Be6aCOpJ2oLd9tEAAUjf4TCF+BfdlexhC0CBZCapGwAUqBQjVTY20GSj47Ba0FRizWhZxGmOx3lTBTvKIUpPEV9N5jpqc4OnB17XJ4IbQ4UABAgFrqTs9GWHIaF9GVsifzK1ZmM1bIJB1MutoK/rLwAhRIghG1t/QJQ2GpDuuA86zasxVK8R59EmIYdQMHKBCjcVsv2AhR82/yPfixAIasvFqCwX7OtLPy9w5VoFszHPWvGY1PNNv0d7QQBhVs9yQCKmEBHE3ATQlGQmY3nDzkVAWJhxI//xfbqSs9AYXJO3WyTARSO8poAWRlMc0YNbVmE4Kn/2x+HdGqJi96bid9Ldujr8QoUivK6a50SoBCCb+051T2aBGFfKoDCUhzT3OmKGyiix+RluDZ87RHyocJQsryAeIECgCOHQgUUsrv1op1sO5O0QMfgKISwA2vC40FpTTynQSu3x55k77UL3WkAigUKtgzgDL7lQbceKKKvhKAPuQRF2B9z6XPYThdK/WXbi2e1bLEuL0DhBgXxAoXbatksULAjEp1IL5xj3YSldC4+jDyPCI1IISERQMHKBChUa1HEChRiMKlb1dsNKOz3TQN5+EfHkQgSC/9YPR6loXLOxi05PNFAwfdBXkbWz3iBwtGW4UhH65wmmHDoqdhRU4Wrf/wQO2tr6svztjEDhQFURU0VQMEbJQYoVGAjmzXM0XeLIjNg4eXTD8JezZvgrLd+xJodO+X1JAMoVH2XwpDiHCQDKFR91+ZD8N8tnFIJFEIbDqBglWigsISyjSwqu7mbxD//8SdfUTmhQP3IU8PIg6qskJhNnAnZdv6ELTavwCtQ2MoibdApcA2q6UasCU9AhNZ6PQ1SqUDBkXwuABQrWc6IV6Bgj5vO9MRKBRTR31tiYV8yAs3RE3PoUyilyx2jFLECBedHDKtlO/sg3/YymiGtP0agsLd7WPvhdOtqzKM/YErkddhrbroBhdQHDVC4rZbtBSj4thMDFA4bk2DcomieUYB7Ol+NykgV7ln1PHaGK+V98BLce7AxmaUo0Xf3PQOFpp5OTQrx/CGnYGV5KW6Y+Rlq69ehkQXsnvpgAhQKn2WPcimBQuoXeH8SCBS28rODePucQcjNCOKMN3/E1soq5zkQ20wBUDQcU58Dp636M5hSoFDYmIx0JBUoFLkSVHO+jIHC1x4j/3Ibyu2Rp4bAGc59mhwKmWSJyrEChV1XjtUJHQMjsIv+ibXhV4wWd2P7aLpfZcP2VzZNrggUov/2e3a/uM/L1LFiWZ0sSmCRDOxPrkNTtMds+hjK6bqEAIX42JMXoFAlW3u4AaUFCjGPQgUUrFSL2xEA+1qH4ETrMsykU/BN5L9GQOG2WrYXoOD9dAcKZ6DotFHZxgoU6rv6QJvMlrin80iU1GzDP1f9B7X1o47aYNwRyHoAgCQChQlINNgKZTwCha0+zVrh6YNPwpwt63H7T18iYn8HejgXvGP6Mpxf8QKF4rNoAmmxAoW9r3XTLLx//mDsqK7FORNnYVeIuSmluDZmOQpC/5n24wYKRT1ECgAK/wx8TwpQiHXLIAGCTSqAwnFM2M9IbINawmJ4jawIoSn/21OURpc5PdUwTNawDcQGFKocClt8AnJigIJVntUDHQLDUU7nY314IiiNbfbkePIo3KaO5fbJIII6z4cFb2tRcPtV9vZ+JtgPkiwMIDchG83xE30Eu+hmz0DB97ve1hAo+LLmQGFqGytQuK2WzdY7KDAUQ62zMS3yHmaEv3AABSs3oJDbCr5wAaPcVvZYgglQqIJJ+areon8GwbjFtw0AnbPb4q7OV2BZ5Vo8uPo1hBGSluXLq4Neb8G9ENAmESiUbbnkArjVd1Drdnh00DB8te5P3PfLt8w3u6FfioBYDh1ySEgKUKh8ll5HeRlZ4G6/dmmRi3fPG4QlW8px6btzUBuOOM+BZsRE98hUVJrHvZz1QdlmwoDCi++pBAqxnGz0QmGbVKCwj6lsfe0R8i+3gRIGFDa1SnIoOJjwABSqpGvVXfim1j5oF7gIpfQnbIz8N/oYirzf6mMyGxlQsMfCQp+j/lPno07SR5+oHCga6oL0WCKAom4/QQbJwyByKwLIwkz6EKpoqStQsIplteyGsry/iQQKVm5AwfskBPF2HRJjO5A+MnAKDrVOxCfhV/Fr+HsOKNwWtzOBBFnbJkDhtlq2V6BQ2mgDdjG4dNrsndcZd3S6BL9WLMETa94BhXPUMVFA4Wna0xQChSNgNIGh+msztH0X3HfgELyzfAGe/P0nz+dCBxRGkODFNkFA4fg8GcEjX0//toV4/ZwB+N/yzbjh43mglMYNFCLQSIHCcUxyDpS2zs9OwoBCmQ/hHtwnFSgcoxZ1r9KE6VQCRRpFm/7id8lTGl3m9FSigKKhPskXTr1Usx7JAmx2P1eHAjJYFVoD0MY6G9si32Bz5HOtrSjTx6FUeRSqxGwgdqCId+pY6X7N1LFZpAAHk9sQQQgz6COooRUOoOD7JdZRv20AFE4I0EMC3645UDhsNEAh5lHogKJhX0NQQwjB8cHzcaB1JN4PPY+F4Tl8mwkCChUUxAIUrBIFFG5BuAwo7PIH5vfE6A7n4X+lv2LC+o+kAZ7ZqIN7EK0DithGOjRtJggonOeU3396lz64pd9gjF84G6//Mdf4XMQLFKrF7cxGYOC0TRRQqD4zQj1D9mqFZ07rj4lz1+C+qYuVvqcUKEzqcdjy28kACuXidlK/VPXKfJf3QbpPBhTRHxyhXwbPy+4OQOErufIvtYvYhUtMgEL2V2fnTFIG4FjUTvnYjxA2ehmdkKl54DAUWf+HzZHPsDX8jeO4zFclOEgSs9lRGTaHwiQx2+4LCxRiH1WJ2dwxqirjst9gLYpc0hIHk9tQjR2YSR9FiFZx58Y1N0KzWrZYhxtQyNs1hw83oODqdwEKab2OQBEghODU4BXoYx2AiaGnsDyyUGorf5RIrJf3hd1nMvWrCVAkIiE63sXpRB+OaNYPo9qfho+2TMdbm77iysc6MhG1TRBQmIyYOOtztiXaxAMUdvnhvfbHFb32w79+nY6PVjpXLRf7IAKF1q8YgULdP0eT2nMg+p4IoKh7A5y9b3v887jeeOz7ZXh+1nKlPzpgSiVQOGeFgsPGUZ8O9hIFFKr8DC0MCX3g/JLbegUK1eJ20seYfKDwJci/3IZSAYUUICRAIYoNpFmgEG10+932uamldSxaWEdjY+R9lEZmeiqrgg7VI0+sTGd6AtjvUPe1KFibZAKFvb8paYfB5BaUYx1m4t+I0JDgj1hH/XYCVsvWjTKYAIUyr0ICFOIIhA4o3Ba3Y4OjICE4NzgKna2eeLX2Uayjf/I+SO/8i/W6B6fS/AVLfixWoFCtReHpjj0XXPK2qv6e0GIwLmkzDG9s/AofbflB2banUQJNDoCn+mJ6BAtOG9UjLSbnVgEU9r7R/QbhzK69cMesb/C/9SuV50IGFKog3GTq3ViBQjWzkrR/CqDg5BEobJvrDtkL1x7SBbd/thD//X2D85rIzoV4DjSPhqUSKBw2Gt9TChSqeqSjFwpbVokCCvGYwV2udFtRO57pYWP921PkQ4WLZLAANACFyoa9W2/vl0kECtWjT1H7BAEFABBC0No6FYXkYKwLT0RZ5Delr6pHuKSjFaRhhKIWEQ4sZEDB9oM7D7Rhv9hX3VoUyQIKmZqjCwbhJmzFH/gZzwL1M8p4AQqlraPPTpkCBat0AAo7jyLDysDFGTehNWmPF6sfxia6zuGvCSS4rZbtBShYJQoovK0lwdfnltR9TtEQnFF0GJ5b+xGmbfvF0YdEAUV8Ix36Psj6LatHBhTqc+pwy1HesoB7BhyBIe064YYfvsKczRsSBhTKfmlgKBVAoQrqvY7S/PO43jhzn7a4ctJcfL98a/xAoYQEh1saW+dnJ+lAwbplFNyLgbvkc5EgoFAubic5p4kCCmopbHztEfKhwlAxAYWCUrmkbDj/RDv7vQwwYgUKW4QQtA2ci3zSD2vDL6M8sljad5lUMz2xjzyFSYQbsRATs9l+6ICCG4VQAIUOuGIFCr4u/pjtYxHpg4G4FhvwC37BSyD1s2rpVstW1hndlvsvt1H1tUGqUQsRKFi5AQXXDxeg4GzBH8uxsjA86xbkk0JMqH4Q2yKbXVfL9gIUXJ8MgEKZgyGDjgQABeefR6AAACsAXNHuRBzb4gCMWzkJs3Ys9gYACQIKdT2aNlMAFOqAve41aFl47JCj0a9FEUZ++zkWb9+aEqDwNAKTSqAwgSqLwrIInjtjXxzSuRnOe2MO5m8o0wbl8QKFExLMz5cs0kkYUIifOY2NEihYpRIoouCQZKCwSFpFm7QR/u0pSqPLnJ5yPspkBhRu8jrDk6N8nDDBihAL7QKXII/0wJrwBFRGVkaPueVRyEYqZGIX9VONyrDHRLuory5A0fBdqz5nujv/6jJC+5Tfbkf2xwCMwEp8h9/wFiD5HWiok381WS1bLOsGFKy8AIXDRgMUTht7WxaU2vXLA/88qwlGZN+OIDLwQvVDKI/sMIIEE6AQA814gYK9yy2z8QoUYhAoAgUr1QiHZQE3djodgwt7459/voH55SvMAEABAto2YwQKdT2OJlMGFPb7rEAQzx15HNrn5ePybz7F6vIy6X+mVAKFanE72TVKNVDYtplBC29euD+6tsjF6a/MxurtlYJf8rZlvscNFA7f1cG4CihkvqcEKAwgwYttIoCCb1MoIx3pUNjI7kL52m3lQ4WL3IDCdJYnWQ6FvZ991b3X7YtXFgmiXWA4skl7rA4/hyq63mHjOmJBnKMyGTQAC0SZkC4bibCo87xYiH8tCmXfPeRR2P7xdnXqTA7BfrgYf2AKFuJDrg2vi9vxICWW4be9jGbofjfcgIKVG1Bwti5AYSufNMNVOWNQjSpMqHoYlXSnFhISARRSP+MAClaJAgp18My3GSABjO16Dvo06YQ7l72KZTvXxwwUqrUovI10eAMKpT9eQCcGoLBfm2Zk4qWjhyE3mIFLp36GzZW79OdA65fgA2uTIKBQgY0uB0abx+ARKOxjTbOCeP/SA5CTYeHUl2djW2W1vLwmf8TbqEPygMIx05Ps0SRHffx2UoDCcUzYZpQooFCtli1L/P6rPfLk51QkTz5UGEoFFNw2kY9S8InH8uRkqa0QDificSeVIqCwSCY6BEYiA82xJvQMquhm7rj4ngcu59ob9uhEJqwoUKhGJ1igYPezr3Xv5cfcgCIRidmsf6rF7XqQY9EXZ+J3vIdl9Mt637wBBStToGDlBSjcFrdjgcJtcTs+EBbr1wX+9fssoIVVhKtybsP2yBa8WPkYami1J6BgZQIUKiDRBc86oBChQBuMJwgo2GOZVgbu6X4hOmS3wtg/Xsbaqi3SPsQLFIkZ6ZDY6J6RN4A00dZbUjdFy5xcvHrMiagKhXD515+jvLZK3pYUDDVA4QmY+HpjBgqxnPRay+uRjtKo/CEURU2z8N/LDsT2XbU465VfsKs2LG1b2ncpJMjb1N25TwlQiOdAE9ynFCjq7ZVAwSqVQFH/RZ1uK2r7Sp78y+yiCFE/8sRuswnZ7HEuf0LzyJPqrr1Yj04Wlf95UYDkomPwahBkYW3oGYToDu64W2K2mJSd6Jme2H3pBhS2+uAU9MTxmIPXsJJOl9omYrVsL6MZUmBpJKCQ+sAE6m0CHTAi9xasDa/Cy5VPIkT5VaN1QKFai0L/KJG8LG9DHX6q6jMZ6VABhdw/M6CwX3MDWfhXj4uRH8zF7YtfxpaaUtd6GurTt62rx2xkQtPPOIHCMdOTie+Sc9G+SVO8MvQErN9ZgRFTv0R1uFbtV4KAwlFec4c8lUDhOD+a69itKBcfXL4/FmyswCVv/oZwJMy1Le17jECh9l0d3KcEKFQ+S7+AFZCQDKAQ4UDjVzKAIt3kj1QkT2l4udNPXpKyZTJZ1C5qK73zrAeKWOBBpyDJR4fgKFBEsCb0DGppBQDDxGxmlML23Q0oxMRsFVBYcJ6zxgYKVvaxACHYD+ejK47ATDyPdZijaIsty/trMsrgLKvf5va5AAUrN6DgfDEACrfF7QgBOge7YXjujfgjtBBvVE6AvWp0vEChulseK1A4bOIEikSsh0EIRUFGHh7qdRkoKG5b/ArKQhVSH2TnIhVAoVrczms/ozYJAgr7Q92zeTO8cPRxmL91M67/dhrC1GTVcsEH1o84gUI1UiJdRyGVQCHY7N+hAG9e1B9fLdmC695fBErVbSYVKITgWwtpqQQK1WiKDIZSCRQiJOh+cGIBCiK052u3lQ8VLnLLodAlZbs9yuRmo9oXPZYgmJAlY2eQ5ugQvAZhVGBdeDwi9Yu76RKz2WRs2/dETx3LHWskoGClAgULFg7CFeiAA/A9nsImLFDWEQtQxAIfXoDCbbVs/vdDDKrs+r0DBaveGXvj0tyr8WvtLEyqfD3qk8w2EUDBKhFAIa0vTqBwm6qVtS3KysfDvS5DWWgXxix+HZWRKq4O2bnwBDHSkY4EAYUXuEowUNjHDmxdhGeHDMX/1q7B2Ok/1AXICp+1i9OlACiUi9tprlGigcIuf0yvFnju7L3x2s/rcN+Xy+R9l8IQpG0mBShUfZc+LpRCoLAUx6Rf6KkDCuXidlK/JEDha4+RDxUu0g5pyYIb5o687JEnk1wK1lalRI1MqGZ3AoAgKULb4NWopZuwLvwiIrSWs2H7z8KE+MhT1GeD/rPHG2MtChVQsHLNjahvIwgLh2AUWqMnpuExbMFyRx2mQMG3bw4UDhsFULByAwrOFxeg0NnKA3Xetn/mgTgv53L8UDMNH1e9r7UVgYLrkwFQqAJP+are7kChvqvvrC8RQCHzr2NuCzzY+2KsqdqCO5e8hZpISOKf3F9dW26PYKn9EsrECBTitZYF7F76IAKF/TqkQ3uMO+wIvLdsKR6Y/ZPUZ1luiBIopH6B9ycJQOFlpEMFFLyRPCi3y55/QFvcd2J3PPT1Crzw4xq+HikkwLVNo1GaRAGFAw7U3y3a2aGUNkI7rI0OKIQ2tAvPpRAoxJmeZEBB02gWKP/xp+TJhwoXRaBIzGa+DGQfGNWidtHjBqMYMnkdnYho/h/rgMJWNmmP4sCVqKYrURJ+FeH659tlj3zJcijs/qj6b1EnAADOQN0NROrKuOw3BAqZVHkUbovbZSADR+ImFKI9puIhlGKtMVCwEs+DCXx4AQq3xe3kU8m6A4Uz8K/fNgAK+9gh2YfjtJxz8UXVx/iq8nOtLfuj77ZatlegUNrECBSq8vK78R4CbeG1Z5M2uL/n+fi9bDXuXzYJIRpOKVAo2zJ4dCdWoPACOiqgsF9P2Wsv3D1oEF74/Xc8P3+etE8yP8yACZwt55YCBHgbeb2egUJ1bSTn1CSpGxbFTUM6YdRhHXDTB4sxeX6J0i8tUChGeYgUAKC2geJYrEChsJGPEiiAglWigMLRr7oXKjsXKqBgtDsCha/kyocKA8mAQkagXFI2nH+infheth3d7xEm4hXb3xxrLxQFLsUuuhBbw28jjLA6f0Qx4iLNpZAAhQXzqWNZxQsUfF0N/vG24F5NVssGgAxkYyhuQy6a4Ss8iHJsNgIKcZ8JUCjzKgSgYJVIoGDlBhR6W96XodnH47jsk/DfynfwY/W3UlsvQMG3HT9QaOuLEShUd/M9AUD9a/+CLvhHr7Pxw9YlePzPj0EpdQT5JuckGUDhCBh1cJUgoFDdYZdDB8Wlffrg+v364cGfZ+PdP/5Q+hU3UKh8lviebkAhtm9ZwEOn9MDp/Ypw2esLMP3P7ZL6hHOQDKDwAkOpBAqxnC6/IlFAoYIEyY/I7gwU4s3i5P/tOfKhwkXsSogsUKikW4PCDS50QJFo6UYpZAnZuVYftAxcgJ30F5SGJ4NSfqYnQA9V9nEWKBzluPMhP9bw3ayGAgfYGEya7ZZHYQoUqjqz0ATHYgwCyMAX+Bd2odQBCaxMgULenrAtAQoRGFRAIfVJAxSqqWNlQKEaORCBwt53cs4ZOCxrCN7c+Qrm1vzM2bIyAYpEJETHspaErHwsQGHinxhEH9y8J8b0OAWflvyK51d9rW4zjnOSDKBwnlM4bLyei7o38jJSvyyKm/bfDxf27okx3/+IKStWOaFMc81TBhRsk5p6GowE2wQBRfQzHgBevGBvDOpcgLP+Mw8LN1SogYJVKoFCmQ/hHtwnFSgcoxYCULBKFVAw8gQUFiD90fK128m/zC4yScpmcyjYbfu9yp7dVikeoFA9+mQCFOJ2BBR51n5oFjgDFZHpKA9/wfupAArxeJ1Nwz7nyALxDBTiXX9bYve95lGYAoWsDZltLpphGMYihGp8iQdRjQqjUQYvoxkqoGDlBSjcVsv2AhScDy5AIbO1LIJz8y7CgZkH4T/lz2NR7e8NNpa8nliBwm21bO9rSUBaXn833kOgrauPqee4on64ruswvLV2Oiau/cFTfbHkdKQEKGI4F16BAqj7TNx78EAc37kTrv/mW8xYv0Eb9Kr753BLfQ68AoXqM2MCVRoANsnBUIFAVtDC21fsgw7NsnHK+N+wZntVQ9uax470MJQ6oFAubqeBtJQARfQHQg4Ucr/qXmIGCocNcdrALie5m5UmooRZLiAFf+m+GGAilYaXO70kAoX4uBMbTDv2ye7Qi3fRNaMTyQAKbRkFXLCJ2U0Cg5EfOAHlka+xM/xdna8SoLDlGKlwAQq2HHfMECgSlZjtBSi8LG7XFEU4AXdgJ7bhCzyCWlQ5bPi6+P6w8gIUDhsFULByAwrO1gAoZFPH8rbM/y1H0Fe3HbQILm16BXpl9sZzO57G8to/XYGC71P8QMEqUUChKh8rUKjrAc5qNwiXdDgc41d8jY82zEkYUKhATnfevNxpjxUonHfhPfgl9DtgWXjiqMMwoLg1hn8xDfM3b5UGtCZAoZpZSQ+G8n4DSBxQKMrrHpnSgoBFUZATxOSr90XAIjjlud+wfVetZ6BwzgoFh42jvlQAhSo/QwtDmj4kCChUi9tJ141IFFAQuxxxlNmTAus9WT5UuMheJVubP6FZJTvW/IlkySQ5W5xGFwAHVU0DR6GJNQTl4Y+xM/xTvc/uMz2xQMHaAc4AnDsmlGtsoGAVy2rZzdABx+F2bMdqfI3HQRES+iP0nymrBAhhm5UXoHDa2NuSoCj6u6GCBMaHBACFbRskQYzMvwYdgh3x5I4nsC601uVRInlbUugwAApvj07x9cUCFHx93oNoWUA7vPOROL3tADy69FN8s3mhYUAbH1CoplY1Aibp3dfEAIUqCJfflQeyAgG8eNwQ7FWYj4s++xrLS8uSAxSO68jv55ROQKHwq21BFj6+ph9Kympw1oT5qKwJ8z7GCRQOG811NJmxKWFAoapH+2WdAqAQjxn8iMQKFL72HPlQ4aJaRFynA1OBhFegiHd0wk1e8yiix4hzX37gBORag1AanoTK8LzoftPEdNZGlpidbkDBSsyj8AIUdvut0Q3HYDTWYyGm4eloKpcIFNJ2XbYBNuAXbASgYOUGFJytC1BwfrsABWfrAhT2sSySheua3YAWgRZ4fPuj2BzaHDNQRAP+BAGFai2KWIHCBCQcthqgIISCEIIbuw3DUUV7455FH+CnbcsNwUTTZgqAQh2wu5+LWIFC1q+8jAy8dsLRaJadhQs//Robd+5y8Y/xK0FAoQrqdWCoG6VJGFAoIQHo0ToX71+1L35ZXY5LX1mEMI0obJMAFKrAXwZVqQQKVb+kMKRqu8EkUUChXNxONqz9FwMKf0rZ5MmHCgO5JSRz+1yAQpWQnUiYMHn0SZdHYWJDCEFh4HTkWv2xJfwGqiJL5KMTTNBuChTRNhoZKLj+imUNgYKV6GcH9MVQXI8V+Bnf4cWGkQP2HDjql29zvnoACrfF7fhgXqxfF/jX7zMAioag29yWWECulYubmt2IbJKNR7c/itJwKVglAihYJQIoeP8SAxSxjnRYhODOXifjwGZdcceCd7FgxzptPWZ+OZpMKVCozoXsP0qsQGHbNs/JxpsnDUUoEsEFH0/FjuoqZf/Ec6GzSRhQmECVyp8kAIWtwXvl443hffDxvC246Z1loCTirEfhn+yLNWVAwUoFFJyNoh6PQKFc3E7ymU4lUIh5GbocCmopHrvytdvJv8wuYleJZhOyY1nUrjEed7LlBRzEx56k63CAIEACaGmdjxzSA5vCL6M6stIIKGw15loUusXtGtqW++kGFKxEf8Xv764YgCMxEovxDWZgIjfbmBegEPMoVEDBqrGAgpUbUMhs2RyKgkABRje/GSEawqPbHsdOWiH1Sxdg64BCNcqgC4B0QKEOng2gI0agUK1FQQhF0Arg/r3PRPcmrXHzvLewYucmaT1egULpjwlcJREolOdS65fcBwBol5+Lif83FJt2VeLSz/6HylCtxC8I/qh9VwKFxK9kAwXfqLxt3sYdKOw2T9y3BZ45vwcmfL8eD09Z4+K7OhhXAYXM97iBQtwnAwoDSPBiawQUUXCQAwXfplAmiUBBiWCbRvJHKpKnNLzc6SlZQjb7Ktrp3kf30cQDhWyUIt48CoAfrWHPRYAEURy4DFmkA9aHn0cVXecKFBYaZy0KffCvBwUvQOGEADlQ2Nu9cTgOxSWYi0/wKz7S2mofxzIACre1KGTY6wYUMlsdUKimjjWxFZOyAaBlsAVGN78JOyI78MTWJ1FDq6Vl+TYSAxTO4FleVlZeBhQmIKG09QgUtnKCQTyyz7lonZ2PG+ZOxIbq7eZtxggUsfQzFUChemxMB5E9WuTj9ZOGYMGW7Rgx5XuEImGmTd42ZqAQy+nyGBIEFMrymvwRnV9im5cdVoy7/q8T7v5wJV6dvlHhe2xA4bj+skeTHPXx20kBCscxYZtRooBCtVq27NGkpAGFJfjta7eVDxUGkgGF6o89Lpbn6mwkcI0nj4IVl5xOMtE2cCUy0AJrQ8+ihm6qt2HspaMQ8mMN37eyMrEBhbwPoi3vlylQcHVGy/L+qiDB1r4YhoNwJmbibfyOLxV1NchRnwIoWHkBCrfVsuWBf/12HEDBygQo7GPtMtvg5uY3YF1oHZ7c9izCqHHU6wYUrBIBFLL64gUKbyMd+jLs+/zMLDzR7zxkBzJw/S9vYVtNhSe/HLZxAoVqcTsvcJUUoFD0b7/iFvjP8Yfj+zUbcfPUmYiwiwumAChU9ci+CFIKFIpzYNuOPaEjrji8GFe/sRSf/75VYcs6nyCgEM+BJrhPKVDU2yuBglWigEJ8RMoEOrwCRZopDJryvz1FaXi500sqoOCOaUYnHCMZNHlAYTJKodrPjVQIQBEWXsWpYy0QZJBcdAxegwBysDr0DMKR7dFjThCIfS2KWKV67Cne1bKlttG65EAh88sCwQCcgX4Yhv/hZSzFdGVZL0DhZbXshjJiUGXXrwv867c1gboJUCjzIDRAYb92zeqMG1pciz+q/8D4bf9BhEaUtjI/HTaegmdnfYlYYM8rUKgCYpM2W+bk4cn9z0NVuBY3/PI2ymurDP1SnZMY+2kAFN7gStFWgoDC3n94xzZ46tjB+GDJSvzjh1/qVi3X3CFPJVA4gUtzjVIEFEDd/8Mnz+uGE/ZpjvOfX4zZK8t4W84xeR+SAhQqn6V3axSQkAygEOFAm1+hKOMVKBSL28lWylYBhZ9TsefIv8wusvMnAO9A4awreX56eexJtx6FbITCzikJyCCJGaUJkqboHLwOAMWK8DOI0J1SoGDLcsdcgCLZidluQMH3W2Er+GoCBWy/DsPF6I3D8CWexUr86mg3WUDRsM8dKNxWyzaBhFiAgpUzUKzbv3d2T4xqMQJzKufi5dLX6wK7BAGFKoCNFShMpmpV9zc2oHCrp21OIZ484FyUVJVh9NxJqArXxg8UXuAqUUBhMErT0Gb8QGHvO7l7Jzxw5AC8OHcJnpqzQGoj7Z/mmfuUAIWivO6zGC9Q2PaBAPD6lT3Rv2MeTnlyIf4o2dVQjxB8ayEtlUChGk2RwVAqgUKEBIM7WnEDhVAunUcofCVf/mU3VDoDhUyx5FGwQCEO19lAIa7JIfY7C83QJXAtwtiJ5eFnEKaVDqBgP3R/JaBwtRX7BKdUQGHvD8DCUIxEZ+yHT/AY1mGRpP76bQEoWJmslu3wKQFAwdXrAhQmtixQuK2WbR/fP6c/rmx+Mb7dOR1v73ifAwu5n/EDBatEAYXJXX2nrbystj4h0NurSSs8tv9ZWFJWgjvmTUYoEla0CWk9XoFCvNaygN1LH4yAQuGzdrVroxEY4JJ9u+OWQX3xwI/z8Pr8pUrfZUChXNxOMwKjA5OEAYUKpqQwBGmbOt9zsy18cG1vtGyagZMeX4iNO2rMgEIFU9LHhRRAobGJGSgsxTFNm6kACuXidpofkXiBIh1HKPxE7eQpDS93ekk1s1MygUKXByCT2xSypnkU9rbjcScXoBDhIocUo2tgFGqwGcvD4xGhNZIgvsE+XYGCVTxAofqOV41mWAjgBFyPtuiBD/EwNmGFEVCIIxA6oHBb3E6bhK0BClWgLwMKN1svQMH7SXFo3mBcWHgWPiv/Cp+Uf6HxUw4UYn28X862VUBidmc8NqDwlFRsCBT2676F7fDIfqdh5pYVuPf3zxCh+rZM7rTHChRe4CpRQKEK2PXAhKjt6IF9cXm/7rh92hx8snSN048YgcJopEN1baTnKzFA0XCs4Rw42lTUY9s2b5KBT27qg5oQxclPLEJZZUjin0egcMCBBCgUNiZgEjNQCG1oF55LIVAoF7eTxRhegIIo6vC128mHChdZIAjT0uh79lX3HoBx/oQqUDdRLHkUsld76lhVYpFs1XDW9zqbBr/zSEd0DVyDnViJ5ZEXEaFhRzBtgaQdULDyuridFCgdNrytauQgiEycglvQDG3xPh7ANqx3AIVYBkg8ULitlu0FKDh/PdiaAIXMZlj+0Tit4AS8UzoZ08q/l7StBgq3INwrUKju5nsCAI9A4VaPW5L4oJZdcN++J2HKhoV4dNFUAPZ6AvI+ce2bwFWCgEIVEMuhQ3UdnH7FChR12wT/HLI/Tu7RAdd8PgPfry6R9q+hTXm9yQAKZXlp4JgaoLD3dWyRhY9v7o3lm6twzjNLUBOK6IE1FUAh1p0KoHD0q+6Fys5FCoFCtbidbDRCCySNLH+kInnyocJFVZE1WB36JyoicwDwQa4bULhJlsgdr0zyKMT9qrUoon5KgMKxTflzAwD5pBu6WVehjC7EqsgroDSiBbPGAgqur3bZGFbLhrDPC1DIbDORgzMwBrnIx3u4H2XYIgT8QhkFUHD9cwEKztYFKFiZQILb4nZ8oCg/ZgoU9usZBSfh2KZH4qVtb2FGxeyEAIWs3/EChZe78YkAClkfZK/HtOmFMX2OxTurf8GEZT/GDRTOc+r03UsfTIDCCBK82LoAhW1jEYKnhh2EQ9oX4ZIPf8Dcjdv44F4EVA/XKJlAIQKNHoYSBxR1tsA+HfLw/vU98f2SMlzxn2WIRIQ6TWAoVUDBSgUUGr9iBgoVJEjORUqBQuGPn6i958i/zC7KIR2QTw5ESfhN7Ir8DkACEB6BIlEw4eWxJ3afCijs0Ql7wT82MVs1OgHw/RXtC6y9sZd1ObbS2VgdeQeU0oagWlIu2UAhU6yrZfNQIPoP6baX0QwCIAdNcTbugAUL7+J+7KQ75GU0QOG0sbedAbrb4nZcMC8EhKkCClZu+QuEEFzS4iwckjcAz2x+Bb/tWhhbgC15TEUFFNL64gQKdT2OJj0F4ybQcWanfhjV4zBMWPYj3l75S3KBIga4ihcoVIvbmT3SBactY5MZCOClUw5Gr5b5OO+D77BsW7ngh7xeAI5gXBfcpxQoHMck50BpqwcKe/uI3gV4ZWQ3vDNzK8a+sxpGQKHMh3AP7mMGCrGcdPRCbptUoBACf4cto1QARbopTCIp/9tTlIaXO81ECIoC56AJ2Qfrwy+jMrKMOxwLUCRLulmdZPvER55YiYnZtu+O0Yrod6l66tjm1gHoal2IEvot1kU+qjvGlLOVLKBg5QoKhkDB1+kdKNxs2e0maI6z8DfUoBKT8ABq6C7eRgAKVm5Awdm6AAUrN6DgbF2AgvPBACiUORgKPy1i4aqii9AvtzceK5mAJVXLBeDxEMgaAIUKSGK+G28AFG71eAUKcd/lew3EhV0OxMMLp+KzdQulfUgFUDiDZkk9Kr9iBAp1/xxNSvuQlxHEm6cdhhY5WTh70nfYsHMnXz5GoFBClWZEzSSpO26gMKnHYctvE4virIEt8NgFnfHY5xvwxOcbnL57BArl4nYaSEs6ULBSAIXcr7qXmIHCYUOcNrDLJQYopI9u+drt5EOFiywAARJEm8DFyCXdsDb8PCojq6WjDbr/M4l+1EkcpTAFCnamJxlQsCMUbjM9sUDB7gcagnB7X5F1KDpZZ2It/Qzrw19ytvK797EBhUyJXC27oU6Fn47jMn/0trJPSXNSjLNxB3ZgM97DwwhFV422y7BBslCvBijcFreT2ZoARUPQbW6rAgpWJkARbZvxM0gCuKn1cHTN6ogHNzyLldVrtfXIg3Cxb7EBhUng7zw3sQMFV18MQEEIQAjBzb2PwMnt98bdv03BD5v/5MuZ9CEFQKEKwuVBNFxtTYBCNbMS61fz3Ey8e9bhAAHOfvcHbK+q5spwShRQmJwL8RykACic08zCYWPr2uNa49aT2uDWiavx7sytyQUKVX6GFobiBIroj40aKFSL20lHABIFFFEokAOFtE0fKHzBhwpjBUkm2geuQBZpizXhZ1FFN0SPWVQNFLHChC7Hwe2xJ7EOVV6FDRTiFLJeZnqS7WeDdfZ4O+tYtCfHYyV9D5siP3DlxSA9ViVqcTvN93VMQGFqK/u9sQOY1qQjzsbtKMEK/BdPgCLk9PEvABSsTIBC1ZYpUNivmVYmbiu+Cq0zWuL+9c+gpHaTtB4vQCHtS5xA4VaPV6BQ12PQB6bfhBDcuc9xOKx1V9wy+2P8um1t/UEP/ZRStge/4gQKWb9YW1mb8QKFfaxdQQ4mnXUYtuyqwvnvTcfO2lpnWybXKFVAwUoBFLxjiQEK0eafZ7fH+Ye0wGXjl+ObBWVSv1ICFKp6tD8WKQAK8VgKgEK2uB3nC1jb9AMKf0Xt5MmHChdxATXJQqfA1QiiAKtCT6OGbnEdnUiFVKMUMhtVYrbJCIUuMVsGFLbYHIqO5FS0IUdiKX0dW+mc+jK8XapmejIBCq+L28lv5HgfzRCBwt5uT3rgdNyMFZiHj+mzoDQCt8XtpL//LkAhs1UBBW/rDhRuq2V7AQpp2xKgsJUXzMbf2l6DPCsH9617GttC29X1GQCFOng2Cdi9A0XM9cWU0+FoEhlBggf3PxH7Nm+D62b9F4vLSszrYz7o6oDd/VwkEyhUi9vpg3t3oLBtu7dsirfOOhSLtpTh8skzURuOmAGFwTlw2MYLFEpIUJ8DHZjEAhQgFJZF8OLIzji8d1Oc8dgy/LZ6F9eWNB8iWp+8L0kBClW/tM/hJhcoZG2mAiho9Jjdhwh/Pn3ttvKhwoMsEARILjoFr4WFDKwKPY3a+sRZmW0yFMtjTyJQsBKTsb1OHcvnCYjBOQMb9c0GiIW9yHkoIgOwKPICtlM++b2xgYJVLKtlQ9jnBSgcNgJQ2Me7kn44lVyHhZiBL+hLdcnvBkDhtridPPCv39YAhdtq2V6AgpUJUIgBmA4o7PeFwSa4s90oREBx77pnURGpcNaXIKBwC/y9AoVqcTuTu/uxAoVYT3YwgH8fdAo65hXi6hn/xaqK7QbnxFlfPEAh7WcKgMKRmK05/85cjrrd+7dthtdOH4TvVm7CdZ/OrlsDJE6gUPoj9Uson0ygcPguCcZV9TFlM4MWJt3YDd2Ks3DSg39g1dYqadt8OXlfOKUQKJSL2+nu7scJFMrVsqUrkNplkgAUaSZx+vxk//lTyvpyiA2eM0g+ugSuQwQhrAg/jRDdydnFCxTK0YYYgILdZ49Q2PsD9X7aoxQy/2VTx7LH7Fcvi9sFSAA9yeVohr3xe+RZlNM/ueOpBgpWSluhXyZQ4GU0wxQogLrfg95kEE4gV2IOvsa3eJevQ9KAyWrZKlsxUJfbyoFCZysDClVwGwtQsBKhoCizGe5qfw3KwuX459rnURmpdgUKWX3xAIW2vhiBIjEjHRIboZ68jAw8M/g05Gdk4aof30dJZYWnfsYLFMq2tP0UfGBtEgQUKrARR32O6Nwaz588AO8vXIO/TZ3HrfqeDKBQlpcF2qkECpWN5Bo1zQ3go9t6IDuT4IR/LcXWilpnH0yAQtwnAgUrEzBJFFA4gnGhDNemUCZGoFCuli1pMx6goFYE1Eo/uPCVePlQ4SJVQnYmaYEugWsRQhlWhp8DpdVJG53wIl1itkziY0+2pKMVlD9mv28oA26fKvcCqEt+39u6CvnojN8iT2AnXQOA/X5PLlCwcoUPoQ8mUOBlNMMNKLgy0REIiv3IEBxDLsAPdDJmRD7hjnP1GgCF2+J2suDKBCjcVsv2AhSsTIDCYSNst8tsjTvbX421NRvx0LoXURsJaYHC2yxR7rYmYNJgqy+jq8dsZELTB0XA3TwrF88ffBrCoLj6x/9iR02V3C/x2sQAV16BwmQtD2fgrwMTvt6YgYIpd0rv9nhkWH9M+PlPPDZ9iaPNVAKFo5ynx5iSBxSi78XNg/jsjh7YWh7CKQ8tw65qJlBNFFAYQILzmLDNKFFAoVrcTpY8nSigUK2WrVvsTgYU6aYwoSn/21PkQ4UHWZT/XskmxegSGIVqbMQW+r+EtBHvKIWuLtlsT1F4kORReJ06lt0nPvbE29cpg2RhX+t65KAIiyNvALQ++NvNVsvWPuIkbkuAQvw9YHMoBlsn4VByKqbSt7GdbpKUSS1QsHIDCt7WHShUwaRlUJ9qpGOv7A64o/2VWFS5HNPKZnJtewUKt5mVvAKFKiCOfaTDxC/B1mWko01uPiYcchq2Vu/CC3/8JDknfH3ekroN+plEoHCUlz0aI9iaAgUAwKK47IAuGHtELzz03WL8ua1CWY/sTkZKgUJ1DjR37hMNFHZb3dtm4aOx3TBvVSX+M22zUB8c9YhtpgQo6u2VQMEqUUAhBvwm0BEvUAg26fzIk6/UKNjYDvzVlUM6onvgFmShqLFdicrr83vsWhTJkK7uIMnBftZNoIiASCd+T6Qf8SseD2Mpa1LmKHI2+pLBaGE1/mdQFnyqJBuBiKdeL22ztn1zu+GRzqPRKbu1cXlHfbJgIQ4lur9mbZoYOdvs2rQ5Xj38LLTPaxJDmwZ9aIQB4FSe/1sO7YkTe7ZBtxZ5KWvTkwzOf6r9OqhbHmY+2BO92mXrDRP8/9LoR8SkzUR/po38SnCjBtWl4ygFAFBUI5VpDhTVqWuskeVDhUK0/q55mFYx++S2GShEBDUJ+ZCqRx3Udvx7gKoegWLyKRru3pP6WxIN27T+Xjit30dBon2n9cfBHCdoWCUhIhmpUCZhR/dnAABCtLJum/J28Y9U8NusrXPkQD9SIbkx5DpSIb7qyjpufknLUIdNIVqiqn5RPL5eym2L+3W2spuk7KdE7gtTr90X8e4y+P3cMcVIhfQuMxHa1tTXkIzuvINMCEWrYDNURqq5YyYjFVFfqK4vzj4Qhz+KO+ecP/L6dfXI77yL10N+TqT+KOprlZ2LmkgYNZEwX4/LSIX28aHotsSvpI1UOG2jn/kEPfokGxFoX5CLsupah63WL+VIBSQ2QnmTkQro6nP2weF7LCMVrIlLEnX7FpmoqIoI9fH1Gz36JPFdOVIh6YNuVIM6vnglX6qqHAUvIxVEYmuSb+FlpEKxze6jwjVioaK8vP5xKFUglQJlZmaiuLgYGzc+mPK2i4uLkZmZmfJ2Uy1CG/MKp7HWrl2LDh06NLYbvnz58uXLly9fu4XWrFmD9u3bN1r7VVVVqKmpSXm7mZmZyM52GVnbDeRDhUKRSATr169H06ZNk/5YTmOrrKwMHTp0wJo1a5Cfn9/Y7vhKsPzru/vKv7a7t/zru/tqT7u2lFKUl5ejbdu2sGSJcL52C/mPPylkWVaj0nRjKD8/f4/4cttT5V/f3Vf+td295V/f3Vd70rUtKChobBd8JVk+Lvry5cuXL1++fPny5Ssu+VDhy5cvX758+fLly5evuORDhS9kZWXh7rvvRlZWVmO74isJ8q/v7iv/2u7e8q/v7iv/2vraHeUnavvy5cuXL1++fPny5Ssu+SMVvnz58uXLly9fvnz5iks+VPjy5cuXL1++fPny5Ssu+VDhy5cvX758+fLly5evuORDhS9fvnz58uXLly9fvuKSDxW+fPny5cuXL1++fPmKSz5U7Ibatm0bLrjgAuTn56OwsBDDhw9HRUWFtkxVVRVGjRqFFi1aoEmTJjjjjDNQUlLC2axevRonnngicnNzUVRUhFtvvRWhUEha3/Tp0xEMBtG/f/9EdctXvRrr+n7wwQc45phj0KpVK+Tn52Pw4MH44osvktLHPUnPPPMMOnfujOzsbAwcOBA//fST1n7SpEno1asXsrOzsc8+++Czzz7jjlNKcdddd6FNmzbIycnB0KFDsXTpUs4mls+QL+9K9bVduXIlhg8fji5duiAnJwd77bUX7r77btTU1CSlf3uyGuP/ra3q6mr0798fhBDMnTs3UV3y5St+UV+7nYYNG0b79etHZ86cSb///nvarVs3et5552nLXHXVVbRDhw506tSpdPbs2XTQoEH04IMPjh4PhUK0b9++dOjQofTXX3+ln332GW3ZsiUdO3aso67t27fTrl270mOPPZb269cv0d3b49VY1/eGG26gDz30EP3pp5/oH3/8QceOHUszMjLoL7/8krS+7u56++23aWZmJn3ppZfoggUL6JVXXkkLCwtpSUmJ1H769Ok0EAjQhx9+mC5cuJD+/e9/pxkZGXT+/PlRmwcffJAWFBTQyZMn099++42efPLJtEuXLrSysjJqE8tnyJc3Nca1/fzzz+mll15Kv/jiC/rnn3/SDz/8kBYVFdHRo0enpM97ihrr/62t66+/nh5//PEUAP3111+T1U1fvjzLh4rdTAsXLqQA6M8//xzd9/nnn1NCCF23bp20TGlpKc3IyKCTJk2K7lu0aBEFQGfMmEEppfSzzz6jlmXRjRs3Rm2ee+45mp+fT6urq7n6zjnnHPr3v/+d3n333T5UJFjpcH1Z9enTh95zzz3xdmuP1UEHHURHjRoV3Q6Hw7Rt27b0gQcekNqfffbZ9MQTT+T2DRw4kI4cOZJSSmkkEqHFxcX0kUceiR4vLS2lWVlZ9K233qKUxvYZ8uVdjXFtZXr44Ydply5d4umKL0GNeW0/++wz2qtXL7pgwQIfKnylnfzHn3YzzZgxA4WFhTjwwAOj+4YOHQrLsjBr1ixpmTlz5qC2thZDhw6N7uvVqxc6duyIGTNmROvdZ5990Lp166jNcccdh7KyMixYsCC67+WXX8by5ctx9913J7prvtD415dVJBJBeXk5mjdvnoiu7XGqqanBnDlzuOtiWRaGDh0avS6iZsyYwdkDddfJtl+xYgU2btzI2RQUFGDgwIHctfb6GfLlTY11bWXasWOH/380gWrMa1tSUoIrr7wSr7/+OnJzcxPZLV++EiIfKnYzbdy4EUVFRdy+YDCI5s2bY+PGjcoymZmZKCws5Pa3bt06Wmbjxo1cwGkft48BwNKlSzFmzBi88cYbCAaDieiOL0GNeX1FjRs3DhUVFTj77LNj6coery1btiAcDkvPu+5a6uztVzcbr58hX97UWNdW1LJly/DUU09h5MiRMfXDl1ONdW0ppbj00ktx1VVXcTcEfPlKJ/lQ8RfRmDFjQAjR/i1evLjR/AuHwzj//PNxzz33oEePHo3mx19V6X59RU2cOBH33HMP3n33XUeA6suXr8bXunXrMGzYMJx11lm48sorG9sdX3HqqaeeQnl5OcaOHdvYrvjypZR/O/kvotGjR+PSSy/V2nTt2hXFxcXYtGkTtz8UCmHbtm0oLi6WlisuLkZNTQ1KS0u5u9klJSXRMsXFxY7ZLezZg4qLi1FeXo7Zs2fj119/xbXXXgug7vEYSimCwSC+/PJLHHXUUV66vEcp3a8vq7fffhtXXHEFJk2a5BjS92Wuli1bIhAIOGbhYq+LqOLiYq29/VpSUoI2bdpwNvZMbLF8hnx5U2NdW1vr16/HkCFDcPDBB2PChAnxdscXo8a6ttOmTcOMGTOQlZXF1XPggQfiggsuwKuvvhpXv3z5SogaO6nDV2JlJ2HOnj07uu+LL74wSuR97733ovsWL14sTeRlZ7d4/vnnaX5+Pq2qqqLhcJjOnz+f+7v66qtpz5496fz582lFRUWSerxnqbGur62JEyfS7OxsOnny5ER3bY/UQQcdRK+99trodjgcpu3atdMmfJ500kncvsGDBzsSPseNGxc9vmPHDmmitpfPkC/vaoxrSymla9eupd27d6fnnnsuDYVCieySr3o1xrVdtWoV9/v6xRdfUAD0vffeo2vWrEl0F335ikk+VOyGGjZsGN1vv/3orFmz6A8//EC7d+/OTRe5du1a2rNnTzpr1qzovquuuop27NiRTps2jc6ePZsOHjyYDh48OHrcnnL02GOPpXPnzqVTpkyhrVq1kk4pa8uf/Sk5aqzr++abb9JgMEifeeYZumHDhuhfaWlpajq+G+rtt9+mWVlZ9JVXXqELFy6kI0aMoIWFhdFZuC666CI6ZsyYqP306dNpMBik48aNo4sWLaJ33323dGrKwsJC+uGHH9J58+bRU045RTqlrO4z5Ct+Nca1Xbt2Le3WrRs9+uij6dq1a7n/p74Sp8b6f8tqxYoV/uxPvtJOPlTshtq6dSs977zzaJMmTWh+fj697LLLaHl5efS4/WX0zTffRPdVVlbSa665hjZr1ozm5ubS0047zfFDtHLlSnr88cfTnJwc2rJlSzp69GhaW1ur9MOHiuSosa7vEUccQQE4/i655JJkd3m31lNPPUU7duxIMzMz6UEHHURnzpwZPXbEEUc4zu+7775Le/ToQTMzM+nee+9NP/30U+54JBKhd955J23dujXNysqiRx99NF2yZAln4/YZ8pUYpfravvzyy9L/o/5DCYlXY/y/ZeVDha90FKGU0pQ+b+XLly9fvnz58uXLl6/dSv7sT758+fLly5cvX758+YpLPlT48uXLly9fvnz58uUrLvlQ4cuXL1++fPny5cuXr7jkQ4UvX758+fLly5cvX77ikg8Vvnz58uXLly9fvnz5iks+VPjy5cuXL1++fPny5Ssu+VDhy5cvX758+fLly5evuORDhS9fvnz58uXLly9fvuKSDxW+fPny5cuXL1++fPmKSz5U+PLly5cvX758+fLlKy75UOHLly9fvnz58uXLl6+49P99+V+6YCkzSwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dQmin and its derivative norm on a grid\n",
    "\n",
    "dkf = torch.linspace(-5.0E-3, +5.0E-3, 128, dtype=dtype, device=device)\n",
    "dkd = torch.linspace(-5.0E-3, +5.0E-3, 128, dtype=dtype, device=device)\n",
    "\n",
    "dk = torch.stack(torch.meshgrid(dkf, dkd, indexing='ij')).swapaxes(-1, 0).reshape(128*128, -1)\n",
    "dQ = torch.vmap(dQmin)(dk).reshape(128, 128)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.imshow(dQ.cpu().numpy(), cmap='plasma', interpolation='bilinear', origin='upper', extent=(-5.0E-3, +5.0E-3, -5.0E-3, +5.0E-3))\n",
    "plt.colorbar(fraction=0.045, pad=0.05)\n",
    "plt.contour(dQ.cpu().numpy(), origin='lower', extend='both', linewidths=1, extent=(-5.0E-3, +5.0E-3, -5.0E-3, +5.0E-3), colors='black')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56009808-6961-4e78-bb02-90c27980696b",
   "metadata": {},
   "source": [
    "# Example-39: Coupling (minimal tune distance correction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25645806-27ec-4b1f-97bb-6cccd1e1e23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example point derivative of dQmin (minimal tune distance) is used for coupling correction illustration\n",
    "# A pair of skew quadrupole errors are added and correction is performed with GD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ae8da28-97a6-466e-9b55-eac1c2b4d10b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "from twiss.convert import wolski_to_cs\n",
    "from twiss.matrix import symplectic_conjugate\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49eb723b-812e-4c94-a64e-ea51e3782cde",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59cfa31c-1648-44a6-a4e1-90819ac4268c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def roll(x, a):\n",
    "    (qx, px, qy, py), cn, sn = x, a.cos(), a.sin()\n",
    "    return torch.stack([qx*cn + qy*sn, px*cn + py*sn, qy*cn - qx*sn, py*cn - py*sn])\n",
    "\n",
    "def kick(x, kn, ks):\n",
    "    (qx, px, qy, py), kn, ks = x, kn, ks\n",
    "    return torch.stack([qx, px - kn*qx + ks*qy, qy, py + ks*qx + kn*qy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "409c755d-0b79-445b-9918-433ee329e4af",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points (close tunes, bends are replaced with drifts)\n",
    "\n",
    "def map_01_02(x, k):\n",
    "    kf, kd = k\n",
    "    x = kick(x, 0.0, kf/2.0)\n",
    "    x = quad(x, [0.0], 0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = drif(x, [0.0], 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.18, 0.50)\n",
    "    x = kick(x, 0.0, kd/2.0)\n",
    "    x = kick(x, 0.0, kd/2.0)\n",
    "    x = quad(x, [0.0], -0.18, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = drif(x, [0.0], 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.21, 0.50)\n",
    "    x = kick(x, 0.0, kf/2.0)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(2*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fdf8754d-9490-46f7-a46b-b3d6860f4f66",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(2*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b32a563-4760-4042-93b0-3065b90aee49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87593b5d-5745-4e32-80ce-03674871a046",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 2), (0, 1), pfp, [k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a915b96-a5d1-415f-b260-2afec812d698",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 1), fp, parametric=pfp)\n",
    "jet = propagate((4, 2), (1, 1), jet, [k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a1dfa00f-f762-43bd-815b-d21af93302a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Minimal tuen distance (using 1st order parametric matrix arounf closed orbit)\n",
    "\n",
    "def dQmin(k):\n",
    "    m = derivative(1, lambda x: evaluate(jet, [x, k]), fp, intermediate=False)\n",
    "    (nux, nuy), *_ = twiss(m)\n",
    "    mux, muy = 2.0*torch.pi*nux, 2.0*torch.pi*nuy    \n",
    "    B = m[:2, 2:]\n",
    "    C = m[2:, :2]\n",
    "    (m11, m12), (m21, m22) = C + symplectic_conjugate(B)\n",
    "    return 1.0/torch.pi * (m11*m22 - m12*m21).abs().sqrt()/(mux.sin() + muy.sin()).abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6f162256-4768-4300-84d2-fff428a7e563",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set skew errors\n",
    "\n",
    "dkf = 1.0E-2\n",
    "dkd = 1.0E-2\n",
    "\n",
    "dk = torch.tensor([dkf, dkd], dtype=dtype, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b9d4bd6-ea43-4ca1-ba6d-6d855d4d48d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0., dtype=torch.float64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note, the sign flip doesn't change dQmin value\n",
    "\n",
    "dQmin(+dk) - dQmin(-dk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26f7d999-a006-4072-8265-2a3d7a254dbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor(3.574e-02, dtype=torch.float64),\n",
       " tensor([1.871e+00, 1.751e+00], dtype=torch.float64)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute dQmin and gradient\n",
    "\n",
    "derivative(1, dQmin, k + dk, intermediate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc4f5653-83b4-461d-ae06-59755fb6fb4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(3.574e-02, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Correction setup (minimize dQmin)\n",
    "\n",
    "# Set objective\n",
    "# Note, this objective represents a model with errors, the task is to find knob values that minimize it\n",
    "\n",
    "error = dk\n",
    "objective = lambda knobs: dQmin(knobs + error)\n",
    "\n",
    "# Exact solution\n",
    "\n",
    "print(objective(-error))\n",
    "\n",
    "# Set initial guess (zero knobs values in this example)\n",
    "\n",
    "solution = torch.zeros_like(error)\n",
    "\n",
    "# Evaluate objective for initial guess\n",
    "\n",
    "print(objective(solution))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4161c6ff-47d9-4f05-8a9f-d937c32a4da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.000e-02, 1.000e-02], dtype=torch.float64)\n",
      "tensor([9.462e-03, 8.845e-03], dtype=torch.float64)\n",
      "tensor(2.966e-03, dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correction loop (gradient descent)\n",
    "\n",
    "# Note, small learning rate is required here for convergence\n",
    "\n",
    "ni = 2048\n",
    "lr = 2.5E-6\n",
    "xs = []\n",
    "\n",
    "for i in range(ni):\n",
    "    solution -= lr*derivative(1, objective, solution, intermediate=False)\n",
    "    xs.append(objective(solution))\n",
    "\n",
    "xs = torch.stack(xs)\n",
    "\n",
    "# Evaluate objective for final solution\n",
    "\n",
    "print(dk)\n",
    "print(-solution)\n",
    "print(objective(solution))\n",
    "\n",
    "plt.figure(figsize=(16, 4))\n",
    "plt.plot(xs.cpu().numpy(), color='blue')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5a4b55b-58bd-4b6e-adf1-871481b73392",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another (prefered) approach would be to fit our model to observation\n",
    "# In this case, observer value is computed first, the task is to find knob values that fit our model to the observed\n",
    "# The next step is to interact with experemental model by applying fitted negativd knob values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d46c2dc-436a-497d-bdab-247cb47aa363",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.574e-02, dtype=torch.float64)\n",
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0., dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set objective\n",
    "\n",
    "dQmin_observed = dQmin(error)\n",
    "objective = lambda knobs: (dQmin(knobs) - dQmin_observed)**2\n",
    "solution = torch.zeros_like(error)\n",
    "\n",
    "print(dQmin(error))\n",
    "print(dQmin(error - error))\n",
    "\n",
    "print(objective(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "24291c7a-0cd5-47b5-92ee-7ffefda01201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([nan, nan], dtype=torch.float64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The derivative at zero initial guess is not defined\n",
    "\n",
    "derivative(1, objective, solution, intermediate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b84ac4a6-124a-49f1-8a6d-dac1309a6bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-1.113e-01, -1.049e-01], dtype=torch.float64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set random initial guess\n",
    "\n",
    "solution = 1.0E-3*torch.randn_like(error)\n",
    "derivative(1, objective, solution, intermediate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af627647-f59b-443b-a1fe-9101e1fc6813",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.000e-02, 1.000e-02], dtype=torch.float64)\n",
      "tensor([-1.170e-02, -8.184e-03], dtype=torch.float64)\n",
      "tensor(8.324e-19, dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABjUAAAGGCAYAAAAzegNcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABTiUlEQVR4nO3df3xU9Z3v8XcmIQkimYiRJGMjBBsEBIlLYAjFH5VZww9bU+ndBHML0pRsXUKhgSKwEKxlN0rllk3hNhVroVtQpFspRjY1DSpWxhACqCAgUBS4OgFMMwNRIMmc+8dsBkYCZALJyY/X8/E4j3NyzufMfM488DySeXu+3xDDMAwBAAAAAAAAAAC0cxazGwAAAAAAAAAAAGgOQg0AAAAAAAAAANAhEGoAAAAAAAAAAIAOgVADAAAAAAAAAAB0CIQaAAAAAAAAAACgQyDUAAAAAAAAAAAAHQKhBgAAAAAAAAAA6BAINQAAAAAAAAAAQIcQZnYD7YnX69Wnn36qnj17KiQkxOx2AAAAAAAAAADoEgzD0OnTp2Wz2WSxXP55DEKNi3z66adKSEgwuw0AAAAAAAAAALqkY8eO6Wtf+9pljxNqXKRnz56SfB9aVFSUyd0AAAAAAAAAANA1eDweJSQk+L+nvxxCjYs0DjkVFRVFqAEAAAAAAAAAQBu72tQQTBQOAAAAAAAAAAA6BEINAAAAAAAAAADQIRBqAAAAAAAAAACADoFQAwAAAAAAAAAAdAiEGgAAAAAAAAAAoEMg1AAAAAAAAAAAAB0CoQYAAAAAAAAAAOgQCDUAAAAAAAAAAECHQKgBAAAAAAAAAAA6BEINAAAAAAAAAADQIRBqAAAAAAAAAACADiHM7AbQflVVSZWV0g03SPffb3Y3AAAAAAAAAICurkVPaqxcuVJ9+/ZVZGSk7Ha7tm/ffsX6DRs2aMCAAYqMjNSQIUO0efPmgOOGYSg/P1/x8fHq3r27HA6HDh48GFBTXV2trKwsRUVFKTo6WtnZ2Tpz5kxAzZ///GeNHDlSPXv21C233KKJEyfq448/bsklQlJxsTRhgvT002Z3AgAAAAAAAABAC0KN9evXKy8vT4sXL9bOnTs1dOhQpaWl6cSJE03Wb9u2TZMmTVJ2drZ27dql9PR0paena8+ePf6apUuXqrCwUEVFRSovL1ePHj2Ulpams2fP+muysrK0d+9elZaWqri4WFu3blVOTo7/+JEjR/Twww/rgQce0O7du/XnP/9Zp06d0iOPPBLsJeJ/3H67b33okLl9AAAAAAAAAAAgSSGGYRjBnGC32zV8+HCtWLFCkuT1epWQkKAZM2Zo3rx5l9RnZGSotrZWxcXF/n0jR45UcnKyioqKZBiGbDabZs+erTlz5kiS3G63YmNjtXr1amVmZmrfvn0aNGiQKioqlJKSIkkqKSnR+PHjdfz4cdlsNv3hD3/QpEmTdO7cOVksvqzm1Vdf1cMPP6xz586pW7duV702j8cjq9Uqt9utqKioYD6WTun4cSkhQQoNlb78UmrGRwgAAAAAAAAAQNCa+/18UE9qnD9/XpWVlXI4HBdewGKRw+GQ0+ls8hyn0xlQL0lpaWn++iNHjsjlcgXUWK1W2e12f43T6VR0dLQ/0JAkh8Mhi8Wi8vJySdKwYcNksVj029/+Vg0NDXK73frP//xPORyOZgUauJTNJkVGSg0N0tGjZncDAAAAAAAAAOjqggo1Tp06pYaGBsXGxgbsj42NlcvlavIcl8t1xfrG9dVqevfuHXA8LCxMvXr18tckJibq9ddf14IFCxQREaHo6GgdP35cL7/88mWv59y5c/J4PAELLrBYGIIKAAAAAAAAANB+tGii8PbI5XJp2rRpmjJliioqKvTWW28pPDxc3/3ud3W5EbYKCgpktVr9S0JCQht33f4RagAAAAAAAAAA2ougQo2YmBiFhoaqqqoqYH9VVZXi4uKaPCcuLu6K9Y3rq9V8dSLy+vp6VVdX+2tWrlwpq9WqpUuX6u6779a9996r3//+9yorK/MPUfVV8+fPl9vt9i/Hjh1rzsfQpXz96741oQYAAAAAAAAAwGxBhRrh4eEaNmyYysrK/Pu8Xq/KysqUmpra5DmpqakB9ZJUWlrqr09MTFRcXFxAjcfjUXl5ub8mNTVVNTU1qqys9Nds2bJFXq9XdrtdkvTFF1/4JwhvFBoa6u+xKREREYqKigpYEKgx1Dh82Nw+AAAAAAAAAAAIevipvLw8rVq1SmvWrNG+ffv0+OOPq7a2VlOnTpUkTZ48WfPnz/fXz5w5UyUlJVq2bJn279+vJ598Ujt27FBubq4kKSQkRLNmzdKSJUu0adMmffDBB5o8ebJsNpvS09MlSQMHDtTYsWM1bdo0bd++Xe+8845yc3OVmZkpm80mSZowYYIqKir01FNP6eDBg9q5c6emTp2qPn366O67777Wz6nL4kkNAAAAAAAAAEB7ERbsCRkZGTp58qTy8/PlcrmUnJyskpIS/0TfR48eDXhiYtSoUVq3bp0WLlyoBQsWKCkpSRs3btTgwYP9NXPnzlVtba1ycnJUU1Oj0aNHq6SkRJGRkf6atWvXKjc3V2PGjJHFYtHEiRNVWFjoP/7AAw9o3bp1Wrp0qZYuXaobbrhBqampKikpUffu3Vv04SDwSY2GBul/Hn4BAAAAAAAAAKDNhRiXm0W7C/J4PLJarXK73QxF9T/q66Xu3X3rTz6RbrvN7I4AAAAAAAAAAJ1Nc7+fD3r4KXQtYWFSYqJvm3k1AAAAAAAAAABmItTAVTGvBgAAAAAAAACgPSDUwFURagAAAAAAAAAA2gNCDVzV7bf71oQaAAAAAAAAAAAzEWrgqhqf1GBODQAAAAAAAACAmQg1cFUXDz9lGOb2AgAAAAAAAADougg1cFV9+0oWi1RbK1VVmd0NAAAAAAAAAKCrItTAVUVESLfd5ttmCCoAAAAAAAAAgFkINdAsTBYOAAAAAAAAADAboQaa5eJ5NQAAAAAAAAAAMAOhBpqFUAMAAAAAAAAAYDZCDTRLY6jBnBoAAAAAAAAAALMQaqBZmFMDAAAAAAAAAGA2Qg00S79+vvXf/y5VV5vbCwAAAAAAAACgayLUQLP06CHZbL5tntYAAAAAAAAAAJiBUAPNxrwaAAAAAAAAAAAzEWqg2RpDDZ7UAAAAAAAAAACYgVADzcZk4QAAAAAAAAAAMxFqoNl4UgMAAAAAAAAAYCZCDTQbc2oAAAAAAAAAAMxEqIFmaxx+qqpKOn3a3F4AAAAAAAAAAF0PoQaazWqVYmJ82zytAQAAAAAAAABoa4QaCArzagAAAAAAAAAAzEKogaAwrwYAAAAAAAAAwCyEGggKT2oAAAAAAAAAAMxCqIGgEGoAAAAAAAAAAMxCqIGg3H67b02oAQAAAAAAAABoa4QaCErjkxrHj0tffmluLwAAAAAAAACArqVFocbKlSvVt29fRUZGym63a/v27Ves37BhgwYMGKDIyEgNGTJEmzdvDjhuGIby8/MVHx+v7t27y+Fw6ODBgwE11dXVysrKUlRUlKKjo5Wdna0zZ874jz/55JMKCQm5ZOnRo0dLLhGXcfPNktXq22aycAAAAAAAAABAWwo61Fi/fr3y8vK0ePFi7dy5U0OHDlVaWppOnDjRZP22bds0adIkZWdna9euXUpPT1d6err27Nnjr1m6dKkKCwtVVFSk8vJy9ejRQ2lpaTp79qy/JisrS3v37lVpaamKi4u1detW5eTk+I/PmTNHn332WcAyaNAg/a//9b+CvURcQUiINGCAb3vfPnN7AQAAAAAAAAB0LSGGYRjBnGC32zV8+HCtWLFCkuT1epWQkKAZM2Zo3rx5l9RnZGSotrZWxcXF/n0jR45UcnKyioqKZBiGbDabZs+erTlz5kiS3G63YmNjtXr1amVmZmrfvn0aNGiQKioqlJKSIkkqKSnR+PHjdfz4cdlstkve97333lNycrK2bt2qe+65p1nX5vF4ZLVa5Xa7FRUVFczH0qVMnSqtXi099ZS0aJHZ3QAAAAAAAAAAOrrmfj8f1JMa58+fV2VlpRwOx4UXsFjkcDjkdDqbPMfpdAbUS1JaWpq//siRI3K5XAE1VqtVdrvdX+N0OhUdHe0PNCTJ4XDIYrGovLy8yfd9/vnn1b9//2YHGmi+gQN9a57UAAAAAAAAAAC0paBCjVOnTqmhoUGxsbEB+2NjY+VyuZo8x+VyXbG+cX21mt69ewccDwsLU69evZp837Nnz2rt2rXKzs6+4vWcO3dOHo8nYMHVEWoAAAAAAAAAAMzQoonC27tXXnlFp0+f1pQpU65YV1BQIKvV6l8SEhLaqMOOrXFOjQMHJK/X3F4AAAAAAAAAAF1HUKFGTEyMQkNDVVVVFbC/qqpKcXFxTZ4TFxd3xfrG9dVqvjoReX19vaqrq5t83+eff14PPfTQJU9/fNX8+fPldrv9y7Fjx65YD5/ERCk8XPryS+noUbO7AQAAAAAAAAB0FUGFGuHh4Ro2bJjKysr8+7xer8rKypSamtrkOampqQH1klRaWuqvT0xMVFxcXECNx+NReXm5vyY1NVU1NTWqrKz012zZskVer1d2uz3gtY8cOaI33njjqkNPSVJERISioqICFlxdWJjUv79vmyGoAAAAAAAAAABtJejhp/Ly8rRq1SqtWbNG+/bt0+OPP67a2lpNnTpVkjR58mTNnz/fXz9z5kyVlJRo2bJl2r9/v5588knt2LFDubm5kqSQkBDNmjVLS5Ys0aZNm/TBBx9o8uTJstlsSk9PlyQNHDhQY8eO1bRp07R9+3a98847ys3NVWZmpmw2W0B/L7zwguLj4zVu3LiWfiZohsYhqAg1AAAAAAAAAABtJSzYEzIyMnTy5Enl5+fL5XIpOTlZJSUl/qGejh49KovlQlYyatQorVu3TgsXLtSCBQuUlJSkjRs3avDgwf6auXPnqra2Vjk5OaqpqdHo0aNVUlKiyMhIf83atWuVm5urMWPGyGKxaOLEiSosLAzozev1avXq1XrssccUGhoa9IeB5mOycAAAAAAAAABAWwsxDMMwu4n2wuPxyGq1yu12MxTVVbz4ovToo9Lo0dLbb5vdDQAAAAAAAACgI2vu9/NBDz8FSDypAQAAAAAAAABoe4QaaJH+/aWQEOnzz6WTJ83uBgAAAAAAAADQFRBqoEVuuEHq08e3vX+/ub0AAAAAAAAAALoGQg20GENQAQAAAAAAAADaEqEGWmzAAN+aUAMAAAAAAAAA0BYINdBiPKkBAAAAAAAAAGhLhBposcZQgzk1AAAAAAAAAABtgVADLdYYanzyiVRba24vAAAAAAAAAIDOj1ADLXbzzVJMjG/7wAFzewEAAAAAAAAAdH6EGrgmzKsBAAAAAAAAAGgrhBq4JsyrAQAAAAAAAABoK4QauCYDBvjWPKkBAAAAAAAAAGhthBq4Jgw/BQAAAAAAAABoK4QauCaNocbBg1J9vbm9AAAAAAAAAAA6N0INXJOEBOmGG6S6OulvfzO7GwAAAAAAAABAZ0aogWtisUh33OHbZggqAAAAAAAAAEBrItTANWNeDQAAAAAAAABAWyDUwDUj1AAAAAAAAAAAtAVCDVyzxlBj/35z+wAAAAAAAAAAdG6EGrhmAwb41vv2SYZhbi8AAAAAAAAAgM6LUAPXLClJCg2VTp+WPv3U7G4AAAAAAAAAAJ0VoQauWXi4dPvtvm2GoAIAAAAAAAAAtBZCDVwXFw9BBQAAAAAAAABAayDUwHUxaJBvvWePuX0AAAAAAAAAADovQg1cF3fd5Vu//765fQAAAAAAAAAAOi9CDVwXQ4f61h98IHm95vYCAAAAAAAAAOicCDVwXfTvL0VESGfOSEeOmN0NAAAAAAAAAKAzItTAdREWJt15p2+bIagAAAAAAAAAAK2hRaHGypUr1bdvX0VGRsput2v79u1XrN+wYYMGDBigyMhIDRkyRJs3bw44bhiG8vPzFR8fr+7du8vhcOjgwYMBNdXV1crKylJUVJSio6OVnZ2tM2fOXPI6zz77rPr376+IiAjdeuut+rd/+7eWXCJagHk1AAAAAAAAAACtKehQY/369crLy9PixYu1c+dODR06VGlpaTpx4kST9du2bdOkSZOUnZ2tXbt2KT09Xenp6dqzZ4+/ZunSpSosLFRRUZHKy8vVo0cPpaWl6ezZs/6arKws7d27V6WlpSouLtbWrVuVk5MT8F4zZ87U888/r2effVb79+/Xpk2bNGLEiGAvES3UGGq89565fQAAAAAAAAAAOqcQwzCMYE6w2+0aPny4VqxYIUnyer1KSEjQjBkzNG/evEvqMzIyVFtbq+LiYv++kSNHKjk5WUVFRTIMQzabTbNnz9acOXMkSW63W7GxsVq9erUyMzO1b98+DRo0SBUVFUpJSZEklZSUaPz48Tp+/LhsNpv27dunu+66S3v27NEdd9zRog/D4/HIarXK7XYrKiqqRa/RlW3ZIo0ZI91+u3TokNndAAAAAAAAAAA6iuZ+Px/Ukxrnz59XZWWlHA7HhRewWORwOOR0Ops8x+l0BtRLUlpamr/+yJEjcrlcATVWq1V2u91f43Q6FR0d7Q80JMnhcMhisai8vFyS9Oqrr6pfv34qLi5WYmKi+vbtqx/84Aeqrq6+7PWcO3dOHo8nYEHLNT6pcfiwdPq0ub0AAAAAAAAAADqfoEKNU6dOqaGhQbGxsQH7Y2Nj5XK5mjzH5XJdsb5xfbWa3r17BxwPCwtTr169/DV/+9vf9Mknn2jDhg363e9+p9WrV6uyslLf/e53L3s9BQUFslqt/iUhIeFqHwGuICZGstl82xeNLgYAAAAAAAAAwHXRoonC2yOv16tz587pd7/7ne655x7df//9+s1vfqM33nhDBw4caPKc+fPny+12+5djx461cdedD/NqAAAAAAAAAABaS1ChRkxMjEJDQ1VVVRWwv6qqSnFxcU2eExcXd8X6xvXVar46EXl9fb2qq6v9NfHx8QoLC1P//v39NQMHDpQkHT16tMneIiIiFBUVFbDg2jSGGu+/b24fAAAAAAAAAIDOJ6hQIzw8XMOGDVNZWZl/n9frVVlZmVJTU5s8JzU1NaBekkpLS/31iYmJiouLC6jxeDwqLy/316SmpqqmpkaVlZX+mi1btsjr9cput0uSvvGNb6i+vl6HDx/213z00UeSpD59+gRzmbgGQ4f61oQaAAAAAAAAAIDrLcQwDCOYE9avX68pU6bo17/+tUaMGKHly5fr5Zdf1v79+xUbG6vJkyfr1ltvVUFBgSRp27Ztuu+++/T0009rwoQJeumll/Tv//7v2rlzpwYPHixJeuaZZ/T0009rzZo1SkxM1KJFi/T+++/rww8/VGRkpCRp3LhxqqqqUlFRkerq6jR16lSlpKRo3bp1knzhyvDhw3XjjTdq+fLl8nq9mj59uqKiovT6668369qaO7s6Lm/PHmnIEKlnT6mmRrJ0mgHOAAAAAAAAAACtpbnfz4cF+8IZGRk6efKk8vPz5XK5lJycrJKSEv9E30ePHpXlom+yR40apXXr1mnhwoVasGCBkpKStHHjRn+gIUlz585VbW2tcnJyVFNTo9GjR6ukpMQfaEjS2rVrlZubqzFjxshisWjixIkqLCz0H7dYLHr11Vc1Y8YM3XvvverRo4fGjRunZcuWBXuJuAZ33CGFh0unT0uffCIlJprdEQAAAAAAAACgswj6SY3OjCc1ro+775Z275ZeeUVKTze7GwAAAAAAAABAe9fc7+cZHAjXHZOFAwAAAAAAAABaA6EGrjsmCwcAAAAAAAAAtAZCDVx3jU9qvPeeuX0AAAAAAAAAADoXQg1cd41Pahw+LJ05Y24vAAAAAAAAAIDOg1AD190tt0hxcZJhSHv2mN0NAAAAAAAAAKCzINRAq2CycAAAAAAAAADA9UaogVbBZOEAAAAAAAAAgOuNUAOtgsnCAQAAAAAAAADXG6EGWsXFT2oYhrm9AAAAAAAAAAA6B0INtIo77pC6dZM8HumTT8zuBgAAAAAAAADQGRBqoFWEh0sDB/q2mVcDAAAAAAAAAHA9EGqg1TBZOAAAAAAAAADgeiLUQKthsnAAAAAAAAAAwPVEqIFW0/ikBqEGAAAAAAAAAOB6INRAq0lO9q0PHpTcblNbAQAAAAAAAAB0AoQaaDW33CL16ePbrqw0txcAAAAAAAAAQMdHqIFWNXy4b11RYW4fAAAAAAAAAICOj1ADraox1Nixw9w+AAAAAAAAAAAdH6EGWhVPagAAAAAAAAAArhdCDbSqf/gH3/qTT6STJ83tBQAAAAAAAADQsRFqoFVZrdIdd/i2eVoDAAAAAAAAAHAtCDXQ6hiCCgAAAAAAAABwPRBqoNURagAAAAAAAAAArgdCDbS6i0MNwzC3FwAAAAAAAABAx0WogVaXnCyFhUknTkjHj5vdDQAAAAAAAACgoyLUQKvr3l0aPNi3zRBUAAAAAAAAAICWItRAm0hJ8a0JNQAAAAAAAAAALUWogTbBZOEAAAAAAAAAgGvVolBj5cqV6tu3ryIjI2W327V9+/Yr1m/YsEEDBgxQZGSkhgwZos2bNwccNwxD+fn5io+PV/fu3eVwOHTw4MGAmurqamVlZSkqKkrR0dHKzs7WmTNn/Mc//vhjhYSEXLK8++67LblEXGeNocaOHZLXa24vAAAAAAAAAICOKehQY/369crLy9PixYu1c+dODR06VGlpaTpx4kST9du2bdOkSZOUnZ2tXbt2KT09Xenp6dqzZ4+/ZunSpSosLFRRUZHKy8vVo0cPpaWl6ezZs/6arKws7d27V6WlpSouLtbWrVuVk5Nzyfv95S9/0WeffeZfhg0bFuwlohUMHixFRkput3TokNndAAAAAAAAAAA6ohDDMIxgTrDb7Ro+fLhWrFghSfJ6vUpISNCMGTM0b968S+ozMjJUW1ur4uJi/76RI0cqOTlZRUVFMgxDNptNs2fP1pw5cyRJbrdbsbGxWr16tTIzM7Vv3z4NGjRIFRUVSvmfyRlKSko0fvx4HT9+XDabTR9//LESExO1a9cuJScnt+jD8Hg8slqtcrvdioqKatFr4PJSU6V335V+/3spK8vsbgAAAAAAAAAA7UVzv58P6kmN8+fPq7KyUg6H48ILWCxyOBxyOp1NnuN0OgPqJSktLc1ff+TIEblcroAaq9Uqu93ur3E6nYqOjvYHGpLkcDhksVhUXl4e8Nrf/va31bt3b40ePVqbNm264vWcO3dOHo8nYEHruXgIKgAAAAAAAAAAghVUqHHq1Ck1NDQoNjY2YH9sbKxcLleT57hcrivWN66vVtO7d++A42FhYerVq5e/5sYbb9SyZcu0YcMGvfbaaxo9erTS09OvGGwUFBTIarX6l4SEhKt9BLgGTBYOAAAAAAAAALgWYWY3cL3ExMQoLy/P//Pw4cP16aef6uc//7m+/e1vN3nO/PnzA87xeDwEG62oMdTYuVOqr5fCOs2/PgAAAAAAAABAWwjqSY2YmBiFhoaqqqoqYH9VVZXi4uKaPCcuLu6K9Y3rq9V8dSLy+vp6VVdXX/Z9Jd/8H4euMCt1RESEoqKiAha0nv79pZ49pS+/lD780OxuAAAAAAAAAAAdTVChRnh4uIYNG6aysjL/Pq/Xq7KyMqWmpjZ5TmpqakC9JJWWlvrrExMTFRcXF1Dj8XhUXl7ur0lNTVVNTY0qKyv9NVu2bJHX65Xdbr9sv7t371Z8fHwwl4hWZLFIw4b5thmCCgAAAAAAAAAQrKAHAMrLy9OUKVOUkpKiESNGaPny5aqtrdXUqVMlSZMnT9att96qgoICSdLMmTN13333admyZZowYYJeeukl7dixQ88995wkKSQkRLNmzdKSJUuUlJSkxMRELVq0SDabTenp6ZKkgQMHauzYsZo2bZqKiopUV1en3NxcZWZmymazSZLWrFmj8PBw3X333ZKkP/7xj3rhhRf0/PPPX/OHhOtn+HDpzTd9oUZ2ttndAAAAAAAAAAA6kqBDjYyMDJ08eVL5+flyuVxKTk5WSUmJf6Lvo0ePymK58ADIqFGjtG7dOi1cuFALFixQUlKSNm7cqMGDB/tr5s6dq9raWuXk5KimpkajR49WSUmJIiMj/TVr165Vbm6uxowZI4vFookTJ6qwsDCgt5/97Gf65JNPFBYWpgEDBmj9+vX67ne/G/SHgtbDZOEAAAAAAAAAgJYKMQzDMLuJ9sLj8chqtcrtdjO/Riv5+GMpMdE3Sfjp09JFuRUAAAAAAAAAoItq7vfzQc2pAVyrPn2kmBipvl567z2zuwEAAAAAAAAAdCSEGmhTISFS49zuTqe5vQAAAAAAAAAAOhZCDbS50aN967/+1dw+AAAAAAAAAAAdC6EG2tzFoQYzugAAAAAAAAAAmotQA20uJUUKD5eqqqTDh83uBgAAAAAAAADQURBqoM1FRkrDh/u2GYIKAAAAAAAAANBchBowxT33+NZvv21uHwAAAAAAAACAjoNQA6ZgsnAAAAAAAAAAQLAINWCKUaN8648+kk6cMLcXAAAAAAAAAEDHQKgBU9x0kzR4sG/7nXfM7QUAAAAAAAAA0DEQasA0DEEFAAAAAAAAAAgGoQZMQ6gBAAAAAAAAAAgGoQZMc889vvXOnVJtrbm9AAAAAAAAAADaP0INmOa226SEBKm+XiovN7sbAAAAAAAAAEB7R6gBUzEEFQAAAAAAAACguQg1YCpCDQAAAAAAAABAcxFqwFSNoYbT6RuGCgAAAAAAAACAyyHUgKkGD5asVunMGen9983uBgAAAAAAAADQnhFqwFQWi/SNb/i2337b3F4AAAAAAAAAAO0boQZMx7waAAAAAAAAAIDmINSA6S4ONQzD3F4AAAAAAAAAAO0XoQZMN3y4FB4uuVzS3/5mdjcAAAAAAAAAgPaKUAOmi4z0BRsSQ1ABAAAAAAAAAC6PUAPtQuMQVEwWDgAAAAAAAAC4HEINtAv33utbb9libh8AAAAAAAAAgPaLUAPtwr33St26SUeOSIcPm90NAAAAAAAAAKA9ItRAu3DjjVJqqm/7L38xtxcAAAAAAAAAQPtEqIF2w+HwrUtLze0DAAAAAAAAANA+tSjUWLlypfr27avIyEjZ7XZt3779ivUbNmzQgAEDFBkZqSFDhmjz5s0Bxw3DUH5+vuLj49W9e3c5HA4dPHgwoKa6ulpZWVmKiopSdHS0srOzdebMmSbf79ChQ+rZs6eio6NbcnkwyT/+o2+9ZYvU0GBuLwAAAAAAAACA9ifoUGP9+vXKy8vT4sWLtXPnTg0dOlRpaWk6ceJEk/Xbtm3TpEmTlJ2drV27dik9PV3p6enas2ePv2bp0qUqLCxUUVGRysvL1aNHD6Wlpens2bP+mqysLO3du1elpaUqLi7W1q1blZOTc8n71dXVadKkSbrnnnuCvTSYLCVFslqlv/9d2rnT7G4AAAAAAAAAAO1NiGEYRjAn2O12DR8+XCtWrJAkeb1eJSQkaMaMGZo3b94l9RkZGaqtrVVxcbF/38iRI5WcnKyioiIZhiGbzabZs2drzpw5kiS3263Y2FitXr1amZmZ2rdvnwYNGqSKigqlpKRIkkpKSjR+/HgdP35cNpvN/9pPPPGEPv30U40ZM0azZs1STU1Ns6/N4/HIarXK7XYrKioqmI8F18l3viNt3Cj9279JCxaY3Q0AAAAAAAAAoC009/v5oJ7UOH/+vCorK+VonPxAksVikcPhkNPpbPIcp9MZUC9JaWlp/vojR47I5XIF1FitVtntdn+N0+lUdHS0P9CQJIfDIYvFovLycv++LVu2aMOGDVq5cmWzrufcuXPyeDwBC8zVOAQVk4UDAAAAAAAAAL4qqFDj1KlTamhoUGxsbMD+2NhYuVyuJs9xuVxXrG9cX62md+/eAcfDwsLUq1cvf83nn3+uxx57TKtXr272UxYFBQWyWq3+JSEhoVnnofU0ZlvvvCN98YW5vQAAAAAAAAAA2pcWTRTeHk2bNk2PPvqo7r333mafM3/+fLndbv9y7NixVuwQzZGUJN12m3T+vPT222Z3AwAAAAAAAABoT4IKNWJiYhQaGqqqqqqA/VVVVYqLi2vynLi4uCvWN66vVvPVicjr6+tVXV3tr9myZYueffZZhYWFKSwsTNnZ2XK73QoLC9MLL7zQZG8RERGKiooKWGCukJALT2uUlprbCwAAAAAAAACgfQkq1AgPD9ewYcNUVlbm3+f1elVWVqbU1NQmz0lNTQ2ol6TS0lJ/fWJiouLi4gJqPB6PysvL/TWpqamqqalRZWWlv2bLli3yer2y2+2SfPNu7N6927889dRT6tmzp3bv3q3vfOc7wVwmTMa8GgAAAAAAAACApoQFe0JeXp6mTJmilJQUjRgxQsuXL1dtba2mTp0qSZo8ebJuvfVWFRQUSJJmzpyp++67T8uWLdOECRP00ksvaceOHXruueckSSEhIZo1a5aWLFmipKQkJSYmatGiRbLZbEpPT5ckDRw4UGPHjtW0adNUVFSkuro65ebmKjMzUzabzV9zsR07dshisWjw4MEt/nBgjgce8K3fe0+qqpK+Mt0KAAAAAAAAAKCLCjrUyMjI0MmTJ5Wfny+Xy6Xk5GSVlJT4J/o+evSoLJYLD4CMGjVK69at08KFC7VgwQIlJSVp48aNAWHD3LlzVVtbq5ycHNXU1Gj06NEqKSlRZGSkv2bt2rXKzc3VmDFjZLFYNHHiRBUWFl7LtaOd6t1bSk6Wdu+WtmyRJk0yuyMAAAAAAAAAQHsQYhiGYXYT7YXH45HVapXb7WZ+DZP95CfSs89KU6dKl5kSBQAAAAAAAADQSTT3+/mg5tQA2krjvBqlpRKxGwAAAAAAAABAItRAOzV6tBQeLh0/Ln30kdndAAAAAAAAAADaA0INtEs33OALNiTf0xoAAAAAAAAAABBqoN1yOHzrv/zF3D4AAAAAAAAAAO0DoQbarcZ5Nd54Q6qrM7cXAAAAAAAAAID5CDXQbt19t3TzzZLHI23bZnY3AAAAAAAAAACzEWqg3QoNlR56yLe9aZO5vQAAAAAAAAAAzEeogXbt29/2rf/0J8kwzO0FAAAAAAAAAGAuQg20aw8+KIWHS4cPS/v2md0NAAAAAAAAAMBMhBpo1268URozxrfNEFQAAAAAAAAA0LURaqDdaxyCilADAAAAAAAAALo2Qg20e42Thb/7rlRVZW4vAAAAAAAAAADzEGqg3fva16Rhw3wThb/2mtndAAAAAAAAAADMQqiBDoEhqAAAAAAAAAAAhBroEBpDjddfl7780txeAAAAAAAAAADmINRAhzB0qJSQ4As0ysrM7gYAAAAAAAAAYAZCDXQIISEMQQUAAAAAAAAAXR2hBjqMhx/2rV99VfJ6ze0FAAAAAAAAAND2CDXQYdx3n9Szp+RySTt2mN0NAAAAAAAAAKCtEWqgwwgPl8aN820zBBUAAAAAAAAAdD2EGuhQmFcDAAAAAAAAALouQg10KOPGSaGh0gcfSEeOmN0NAAAAAAAAAKAtEWqgQ+nVS7rnHt/2n/5kbi8AAAAAAAAAgLZFqIEO55FHfOuXXjK3DwAAAAAAAABA2yLUQIfzT/8kWSxSebn0t7+Z3Q0AAAAAAAAAoK0QaqDDiY2VHnjAt83TGgAAAAAAAADQdRBqoEPKzPStX3zR3D4AAAAAAAAAAG2nRaHGypUr1bdvX0VGRsput2v79u1XrN+wYYMGDBigyMhIDRkyRJs3bw44bhiG8vPzFR8fr+7du8vhcOjgwYMBNdXV1crKylJUVJSio6OVnZ2tM2fO+I8fOHBA3/zmNxUbG6vIyEj169dPCxcuVF1dXUsuEe3cI49I3bpJe/b4FgAAAAAAAABA5xd0qLF+/Xrl5eVp8eLF2rlzp4YOHaq0tDSdOHGiyfpt27Zp0qRJys7O1q5du5Senq709HTtueib6KVLl6qwsFBFRUUqLy9Xjx49lJaWprNnz/prsrKytHfvXpWWlqq4uFhbt25VTk6O/3i3bt00efJkvf766zpw4ICWL1+uVatWafHixcFeIjqAm26Sxo3zbTMEFQAAAAAAAAB0DSGGYRjBnGC32zV8+HCtWLFCkuT1epWQkKAZM2Zo3rx5l9RnZGSotrZWxcXF/n0jR45UcnKyioqKZBiGbDabZs+erTlz5kiS3G63YmNjtXr1amVmZmrfvn0aNGiQKioqlJKSIkkqKSnR+PHjdfz4cdlstiZ7zcvLU0VFhd5+++1mXZvH45HVapXb7VZUVFQwHwtM8NJL0qRJUr9+0qFDUkiI2R0BAAAAAAAAAFqiud/PB/Wkxvnz51VZWSmHw3HhBSwWORwOOZ3OJs9xOp0B9ZKUlpbmrz9y5IhcLldAjdVqld1u99c4nU5FR0f7Aw1JcjgcslgsKi8vb/J9Dx06pJKSEt13333BXCI6kG99S7rhBulvf5MqKszuBgAAAAAAAADQ2oIKNU6dOqWGhgbFxsYG7I+NjZXL5WryHJfLdcX6xvXVanr37h1wPCwsTL169brkfUeNGqXIyEglJSXpnnvu0VNPPXXZ6zl37pw8Hk/Ago6jRw/p4Yd920wYDgAAAAAAAACdX4smCm/P1q9fr507d2rdunV67bXX9Oyzz162tqCgQFar1b8kJCS0Yae4HiZN8q3Xr5caGsztBQAAAAAAAADQuoIKNWJiYhQaGqqqqqqA/VVVVYqLi2vynLi4uCvWN66vVvPVicjr6+tVXV19yfsmJCRo0KBBmjRpkp5++mk9+eSTarjMt93z58+X2+32L8eOHbvS5aMdSkvzTRr+2WfS1q1mdwMAAAAAAAAAaE1BhRrh4eEaNmyYysrK/Pu8Xq/KysqUmpra5DmpqakB9ZJUWlrqr09MTFRcXFxAjcfjUXl5ub8mNTVVNTU1qqys9Nds2bJFXq9Xdrv9sv16vV7V1dXJ6/U2eTwiIkJRUVEBCzqW8HBp4kTfNkNQAQAAAAAAAEDnFhbsCXl5eZoyZYpSUlI0YsQILV++XLW1tZo6daokafLkybr11ltVUFAgSZo5c6buu+8+LVu2TBMmTNBLL72kHTt26LnnnpMkhYSEaNasWVqyZImSkpKUmJioRYsWyWazKT09XZI0cOBAjR07VtOmTVNRUZHq6uqUm5urzMxM2Ww2SdLatWvVrVs3DRkyRBEREdqxY4fmz5+vjIwMdevW7Xp8VminMjOl55+X/vAHacUKX9ABAAAAAAAAAOh8gg41MjIydPLkSeXn58vlcik5OVklJSX+ib6PHj0qi+XCAyCjRo3SunXrtHDhQi1YsEBJSUnauHGjBg8e7K+ZO3euamtrlZOTo5qaGo0ePVolJSWKjIz016xdu1a5ubkaM2aMLBaLJk6cqMLCwgsXEhamZ555Rh999JEMw1CfPn2Um5urH//4xy36YNBx3H+/FBcnuVzS669LDz1kdkcAAAAAAAAAgNYQYhiGYXYT7YXH45HVapXb7WYoqg5m5kypsFB69FFp7VqzuwEAAAAAAAAABKO5388HNacG0F49+qhv/corktttbi8AAAAAAAAAgNZBqIFOYcQIadAg6csveVIDAAAAAAAAADorQg10CiEhUk6Ob3vVKolB1QAAAAAAAACg8yHUQKfxve9JERHS7t1SZaXZ3QAAAAAAAAAArjdCDXQavXpJEyf6tp97ztxeAAAAAAAAAADXH6EGOpXGIahefFE6c8bcXgAAAAAAAAAA1xehBjqVe++V+vf3BRovvWR2NwAAAAAAAACA64lQA51KSIj0gx/4thmCCgAAAAAAAAA6F0INdDpTpkjdukkVFb5JwwEAAAAAAAAAnQOhBjqd3r2l9HTf9qpVprYCAAAAAAAAALiOCDXQKU2b5luvXSt98YW5vQAAAAAAAAAArg9CDXRKY8ZIiYmS2y1t2GB2NwAAAAAAAACA64FQA52SxcKE4QAAAAAAAADQ2RBqoNOaOlUKDZW2bZP27DG7GwAAAAAAAADAtSLUQKcVH39hwvBf/MLUVgAAAAAAAAAA1wGhBjq1vDzf+ve/l1wuc3sBAAAAAAAAAFwbQg10aqNGSamp0vnz0ooVZncDAAAAAAAAALgWhBro9GbP9q1/9SupttbcXgAAAAAAAAAALUeogU4vPV3q10+qrpZWrza7GwAAAAAAAABASxFqoNMLDZV+/GPf9i9+ITU0mNsPAAAAAAAAAKBlCDXQJUydKt10k3T4sLRpk9ndAAAAAAAAAABaglADXUKPHtLjj/u2n33W3F4AAAAAAAAAAC1DqIEuIzdXCg+Xtm2TnE6zuwEAAAAAAAAABItQA11GfLyUleXbXrbM3F4AAAAAAAAAAMEj1ECXMnu2b/3KK9Lf/mZuLwAAAAAAAACA4BBqoEu5805p7FjJ6+VpDQAAAAAAAADoaAg10OU88YRvvWqV9Mkn5vYCAAAAAAAAAGg+Qg10OfffLz3wgFRXJy1ZYnY3AAAAAAAAAIDmalGosXLlSvXt21eRkZGy2+3avn37Fes3bNigAQMGKDIyUkOGDNHmzZsDjhuGofz8fMXHx6t79+5yOBw6ePBgQE11dbWysrIUFRWl6OhoZWdn68yZM/7jb775ph5++GHFx8erR48eSk5O1tq1a1tyeegCfvYz3/q3v5UOHTK3FwAAAAAAAABA8wQdaqxfv155eXlavHixdu7cqaFDhyotLU0nTpxosn7btm2aNGmSsrOztWvXLqWnpys9PV179uzx1yxdulSFhYUqKipSeXm5evToobS0NJ09e9Zfk5WVpb1796q0tFTFxcXaunWrcnJyAt7nrrvu0n/913/p/fff19SpUzV58mQVFxcHe4noAkaNksaNkxoapKeeMrsbAAAAAAAAAEBzhBiGYQRzgt1u1/Dhw7VixQpJktfrVUJCgmbMmKF58+ZdUp+RkaHa2tqAcGHkyJFKTk5WUVGRDMOQzWbT7NmzNWfOHEmS2+1WbGysVq9erczMTO3bt0+DBg1SRUWFUlJSJEklJSUaP368jh8/LpvN1mSvEyZMUGxsrF544YVmXZvH45HVapXb7VZUVFQwHws6oMpKKSVFCgmR9u6VBg40uyMAAAAAAAAA6Jqa+/18UE9qnD9/XpWVlXI4HBdewGKRw+GQ0+ls8hyn0xlQL0lpaWn++iNHjsjlcgXUWK1W2e12f43T6VR0dLQ/0JAkh8Mhi8Wi8vLyy/brdrvVq1evYC4RXciwYVJ6umQY0pNPmt0NAAAAAAAAAOBqggo1Tp06pYaGBsXGxgbsj42NlcvlavIcl8t1xfrG9dVqevfuHXA8LCxMvXr1uuz7vvzyy6qoqNDUqVMvez3nzp2Tx+MJWNC1PPWU70mNl1+W3nvP7G4AAAAAAAAAAFfSoonC27s33nhDU6dO1apVq3TnnXdetq6goEBWq9W/JCQktGGXaA+GDJEyMnzb+fnm9gIAAAAAAAAAuLKgQo2YmBiFhoaqqqoqYH9VVZXi4uKaPCcuLu6K9Y3rq9V8dSLy+vp6VVdXX/K+b731lr71rW/pF7/4hSZPnnzF65k/f77cbrd/OXbs2BXr0Tk9+aRksUibNkkVFWZ3AwAAAAAAAAC4nKBCjfDwcA0bNkxlZWX+fV6vV2VlZUpNTW3ynNTU1IB6SSotLfXXJyYmKi4uLqDG4/GovLzcX5OamqqamhpVVlb6a7Zs2SKv1yu73e7f9+abb2rChAl65plnlJOTc9XriYiIUFRUVMCCrueOO6Tvfc+3vXChub0AAAAAAAAAAC4v6OGn8vLytGrVKq1Zs0b79u3T448/rtraWv/cFZMnT9b8+fP99TNnzlRJSYmWLVum/fv368knn9SOHTuUm5srSQoJCdGsWbO0ZMkSbdq0SR988IEmT54sm82m9PR0SdLAgQM1duxYTZs2Tdu3b9c777yj3NxcZWZmymazSfINOTVhwgT96Ec/0sSJE+VyueRyuVRdXX2tnxG6gPx8KSxMev11qaTE7G4AAAAAAAAAAE0JOtTIyMjQs88+q/z8fCUnJ2v37t0qKSnxT/R99OhRffbZZ/76UaNGad26dXruuec0dOhQ/eEPf9DGjRs1ePBgf83cuXM1Y8YM5eTkaPjw4Tpz5oxKSkoUGRnpr1m7dq0GDBigMWPGaPz48Ro9erSee+45//E1a9boiy++UEFBgeLj4/3LI4880qIPBl1Lv37Sj37k2541Szp/3tR2AAAAAAAAAABNCDEMwzC7ifbC4/HIarXK7XYzFFUX5HZL/ftLJ05Izz4rzZ5tdkcAAAAAAAAA0DU09/v5oJ/UADorq1V6+mnf9k9/Krlc5vYDAAAAAAAAAAhEqAFcZMoUafhw6fRp6aKpYQAAAAAAAAAA7QChBnARi0X65S9926tXS+XlprYDAAAAAAAAALgIoQbwFXa79Nhjvu0ZMySv19R2AAAAAAAAAAD/g1ADaEJBgdSzp1RRIa1ZY3Y3AAAAAAAAAACJUANoUlyctHixb3vePMntNrcfAAAAAAAAAAChBnBZM2ZId9whnTghPfGE2d0AAAAAAAAAAAg1gMsID5eKinzbv/619Je/mNsPAAAAAAAAAHR1hBrAFdx/vzR9um87O1vyeExtBwAAAAAAAAC6NEIN4CqeflpKTJSOHpV+8hOzuwEAAAAAAACArotQA7iKG2+UXnjBt/3cc1Jpqbn9AAAAAAAAAEBXRagBNMP990u5ub5thqECAAAAAAAAAHMQagDN9PTTUr9+0rFj0uzZZncDAAAAAAAAAF0PoQbQTD16XBiG6vnnpT//2dx+AAAAAAAAAKCrIdQAgnDffdKMGb7tqVOlqipz+wEAAAAAAACAroRQAwhSQYE0aJD02WfSo49KDQ1mdwQAAAAAAAAAXQOhBhCkHj2kDRukG26QtmyRfvpTszsCAAAAAAAAgK6BUANogUGDpFWrfNtLljC/BgAAAAAAAAC0BUINoIUefVT64Q8lw5CysqRjx8zuCAAAAAAAAAA6N0IN4Br84hfSP/yD9PnnUkaGdP682R0BAAAAAAAAQOdFqAFcg8hI3/waVqvkdEpPPGF2RwAAAAAAAADQeRFqANeoXz9pzRrf9vLlF7YBAAAAAAAAANcXoQZwHTz8sLRggW/7Bz+QysrM7QcAAAAAAAAAOiNCDeA6+dnPpMxMqb5emjhR2rvX7I4AAAAAAAAAoHMh1ACuE4tF+u1vpdGjJbdbGj9e+uwzs7sCAAAAAAAAgM6DUAO4jiIjpY0bpf79paNHpYceks6cMbsrAAAAAAAAAOgcCDWA6+zmm6XNm6VbbpF27rwwJBUAAAAAAAAA4NoQagCt4PbbpU2bfE9uvPaalJMjeb1mdwUAAAAAAAAAHVuLQo2VK1eqb9++ioyMlN1u1/bt269Yv2HDBg0YMECRkZEaMmSINm/eHHDcMAzl5+crPj5e3bt3l8Ph0MGDBwNqqqurlZWVpaioKEVHRys7O1tnLhrX5+zZs3rsscc0ZMgQhYWFKT09vSWXBlw3I0dK69ZdmGvjn/+ZYAMAAAAAAAAArkXQocb69euVl5enxYsXa+fOnRo6dKjS0tJ04sSJJuu3bdumSZMmKTs7W7t27VJ6errS09O1Z88ef83SpUtVWFiooqIilZeXq0ePHkpLS9PZs2f9NVlZWdq7d69KS0tVXFysrVu3Kicnx3+8oaFB3bt3149+9CM5HI5gLwtoFd/5jvT73/uCjeefl374Q4INAAAAAAAAAGipEMMwjGBOsNvtGj58uFasWCFJ8nq9SkhI0IwZMzRv3rxL6jMyMlRbW6vi4mL/vpEjRyo5OVlFRUUyDEM2m02zZ8/WnDlzJElut1uxsbFavXq1MjMztW/fPg0aNEgVFRVKSUmRJJWUlGj8+PE6fvy4bDZbwHs+9thjqqmp0caNG4P6MDwej6xWq9xut6KiooI6F7iSdeuk733PF2j88z9L//f/+oIOAAAAAAAAAEDzv58P6mvV8+fPq7KyMuBJCIvFIofDIafT2eQ5Tqfzkicn0tLS/PVHjhyRy+UKqLFarbLb7f4ap9Op6Ohof6AhSQ6HQxaLReXl5cFcAmCKRx+V1qyRQkKkX/9ays2VgosTAQAAAAAAAABhwRSfOnVKDQ0Nio2NDdgfGxur/fv3N3mOy+Vqst7lcvmPN+67Uk3v3r0DGw8LU69evfw1LXHu3DmdO3fO/7PH42nxawFX87//t+9Jjccek371K9++X/5SCg01tS0AAAAAAAAA6DC69AA4BQUFslqt/iUhIcHsltDJTZ7smzQ8JMQXbGRkSBdNHQMAAAAAAAAAuIKgQo2YmBiFhoaqqqoqYH9VVZXi4uKaPCcuLu6K9Y3rq9V8dSLy+vp6VVdXX/Z9m2P+/Plyu93+5dixYy1+LaC5pkzxzbERHi79139J//iPUnW12V0BAAAAAAAAQPsXVKgRHh6uYcOGqayszL/P6/WqrKxMqampTZ6TmpoaUC9JpaWl/vrExETFxcUF1Hg8HpWXl/trUlNTVVNTo8rKSn/Nli1b5PV6Zbfbg7mEABEREYqKigpYgLaQmSn9+c+S1Sr99a/SN74hffyx2V0BAAAAAAAAQPsW1JwakpSXl6cpU6YoJSVFI0aM0PLly1VbW6upU6dKkiZPnqxbb71VBQUFkqSZM2fqvvvu07JlyzRhwgS99NJL2rFjh5577jlJUkhIiGbNmqUlS5YoKSlJiYmJWrRokWw2m9LT0yVJAwcO1NixYzVt2jQVFRWprq5Oubm5yszMlM1m8/f24Ycf6vz586qurtbp06e1e/duSVJycvI1fERA67j/fl+gMW6ctH+/lJoqvfaa9A//YHZnAAAAAAAAANA+BR1qZGRk6OTJk8rPz5fL5VJycrJKSkr8E30fPXpUFsuFB0BGjRqldevWaeHChVqwYIGSkpK0ceNGDR482F8zd+5c1dbWKicnRzU1NRo9erRKSkoUGRnpr1m7dq1yc3M1ZswYWSwWTZw4UYWFhQG9jR8/Xp988on/57vvvluSZBhGsJcJtInBg6V335XGj5fef1+6917pd7+THnnE7M4AAAAAAAAAoP0JMfjG38/j8chqtcrtdjMUFdqU2y1NnCg1jsI2Z45UUCCFBR07AgAAAAAAAEDH09zv54OaUwNA67Bapf/+b2n2bN/Pzz4rjRkjuVzm9gUAAAAAAAAA7QmhBtBOdOvmCzM2bJB69pS2bpXuvlt6+22zOwMAAAAAAACA9oFQA2hnvvtdqaJCuvNO35Ma3/ym9MwzUkOD2Z0BAAAAAAAAgLkINYB26I47pPJy6dFHfWHGvHm+ScQPHjS7MwAAAAAAAAAwD6EG0E716CH9/vfSb37jG45q2zZp6FDpl7+UvF6zuwMAAAAAAACAtkeoAbRjISHS978vffCBb+LwL7+UfvQjyeGQPv7Y7O4AAAAAAAAAoG0RagAdQJ8+0uuvSytXSjfcIL3xhjR4sLR0qXT+vNndAQAAAAAAAEDbINQAOgiLRfqXf5Hee0+65x6ptlZ64gnprruk0lKzuwMAAAAAAACA1keoAXQwX/+69Oab0urVUu/e0oED0oMPShMnSp98YnZ3AAAAAAAAANB6CDWADshikaZMkT76SJo1SwoNlf74R2ngQGnBAunvfze7QwAAAAAAAAC4/gg1gA7MapV+8Qtp927pvvt8E4kXFEiJidKSJdLp02Z3CAAAAAAAAADXD6EG0AkMHuybPPxPf5KGDJHcbmnRIqlfP+n//B9f2AEAAAAAAAAAHR2hBtBJhIRI3/6276mNF1+UkpKkU6ek2bN9T278+78zLBUAAAAAAACAjo1QA+hkLBYpM1P68EPpN7+RbrtNqqqS/vVfpYQEaeZM6eOPze4SAAAAAAAAAIJHqAF0UmFh0ve/Lx06JP3+99LQoVJtrVRYKN1+u5SRIb31lmQYZncKAAAAAAAAAM1DqAF0ct26SVlZ0q5dUmmp9OCDktcrvfyydP/90sCB0rJlvqGqAAAAAAAAAKA9I9QAuoiQEMnhkP78Z+m996ScHOnGG6UDB6Q5c6Rbb5UmTfIdr683u1sAAAAAAAAAuFSIYTD4TCOPxyOr1Sq3262oqCiz2wFa3enT0ksvSc89J+3YcWH/LbdI//RPvpAjNdU3TwcAAAAAAAAAtJbmfj9PqHERQg10ZTt3+iYWf/nlwKGobrvNN//Gww9LI0dKoaHm9QgAAAAAAACgcyLUaAFCDUCqq5PKyqQXX5T++EfpzJkLx2JipIce8gUc//iPUo8e5vUJAAAAAAAAoPMg1GgBQg0g0JdfSq+9Jm3c6FvX1Fw4FhEh3XOPb54Oh0O6+26GqQIAAAAAAADQMoQaLUCoAVxeXZ30179KmzZJf/qTdORI4PGbb5YeeED65jel0aOlO+8k5AAAAAAAAADQPIQaLUCoATSPYUgHDkilpdJf/iK98YZv0vGLRUdLo0b5Ao5Ro6Rhw6QbbzSlXQAAAAAAAADtHKFGCxBqAC1TVydVVPgCjrfflpxOqbY2sCYkRBo4UEpJkYYP962HDGFeDgAAAAAAAACEGi1CqAFcH/X10nvv+Yar+utfpXfflY4fv7QuJET6+td94cZdd/mWO++UEhOlbt3avm8AAAAAAAAA5iDUaAFCDaD1uFzSjh2+paJCqqyUqqqarg0N9QUb/ftLSUm+deN2QgJzdQAAAAAAAACdDaFGCxBqAG2rqkr64APp/fcvLPv3S19+eflzIiOl22/3hRx9+/pCjsbla1+T4uJ8oQgAAAAAAACAjoNQowUINQDzeb3Sp59KBw9KH30UuD582Dd/x5WEhUk224WQozHwiI2Veve+sPTqRfgBAAAAAAAAtBetGmqsXLlSP//5z+VyuTR06FD98pe/1IgRIy5bv2HDBi1atEgff/yxkpKS9Mwzz2j8+PH+44ZhaPHixVq1apVqamr0jW98Q7/61a+UlJTkr6murtaMGTP06quvymKxaOLEifqP//gP3Xjjjf6a999/X9OnT1dFRYVuueUWzZgxQ3Pnzm32dRFqAO1bfb109OiFkOOTT3xzdRw75ls+/VRqaGjea1ksUkxMYNDRu7d0yy3STTf5lujoS7cjIlrzCgEAAAAAAICuqbnfz4cF+8Lr169XXl6eioqKZLfbtXz5cqWlpenAgQPq3bv3JfXbtm3TpEmTVFBQoIceekjr1q1Tenq6du7cqcGDB0uSli5dqsLCQq1Zs0aJiYlatGiR0tLS9OGHHyoyMlKSlJWVpc8++0ylpaWqq6vT1KlTlZOTo3Xr1vkv+MEHH5TD4VBRUZE++OADff/731d0dLRycnKCvUwA7VBYmNSvn28ZO/bS4w0Nvrk7jh0LDDuOH5dOnLiwfP6574mQxp+D0b37hZAjOlq68caWLz16+CZEDwm5Hp8OAAAAAAAA0PkF/aSG3W7X8OHDtWLFCkmS1+tVQkKCZsyYoXnz5l1Sn5GRodraWhUXF/v3jRw5UsnJySoqKpJhGLLZbJo9e7bmzJkjSXK73YqNjdXq1auVmZmpffv2adCgQaqoqFBKSookqaSkROPHj9fx48dls9n0q1/9Sv/6r/8ql8ul8PBwSdK8efO0ceNG7d+/v1nXxpMaQNdQXy+dOuULNE6eDAw8Tp6U/v73Sxe3W2qNwfosFt88IS1dunW7/ktYmG9orsbFYrn0ZyZrBwAAAAAAwPXUKk9qnD9/XpWVlZo/f75/n8VikcPhkNPpbPIcp9OpvLy8gH1paWnauHGjJOnIkSNyuVxyOBz+41arVXa7XU6nU5mZmXI6nYqOjvYHGpLkcDhksVhUXl6u73znO3I6nbr33nv9gUbj+zzzzDP6+9//rptuuimYSwXQiYWF+SYUj4tr/jler+TxXBp01NZKZ84Ev5w/f+F1v/jCt3Q0Vwo9mnOs8eeQkMClqX2X298a519p/8VP1Xz1CRuOBX8M4N8Evop/E/gq/k0AAIDOLjNTGjjQ7C46lqBCjVOnTqmhoUGxsbEB+2NjYy/7NITL5Wqy3uVy+Y837rtSzVeHtgoLC1OvXr0CahITEy95jcZjTYUa586d07lz5/w/ezyeJq8BACyWC0NOfeVW0yLnz/sCkXPnpLNnL12+/LLp/V9d6uqu/1Jf37ynUhoamj+HCQAAAAAAAC51992EGsEKek6NzqSgoEA//elPzW4DQBcUHu5b2ivDuBBaeL0Xtpv6uaU1jfsMo+nlcseC3X89X+viz+ernxfHgjsG8G8CX8W/CXwV/yYAAEBXcD3+59muJqhQIyYmRqGhoaqqqgrYX1VVpbjLjOMSFxd3xfrGdVVVleLj4wNqkpOT/TUnvjKbb319vaqrqwNep6n3ufg9vmr+/PkBQ2N5PB4lJCQ0WQsAXUlIiG+YrrAuHX0DAAAAAACgvQlqqtfw8HANGzZMZWVl/n1er1dlZWVKTU1t8pzU1NSAekkqLS311ycmJiouLi6gxuPxqLy83F+TmpqqmpoaVVZW+mu2bNkir9cru93ur9m6davq6uoC3ueOO+647HwaERERioqKClgAAAAAAAAAAED7FFSoIUl5eXlatWqV1qxZo3379unxxx9XbW2tpk6dKkmaPHlywETiM2fOVElJiZYtW6b9+/frySef1I4dO5SbmytJCgkJ0axZs7RkyRJt2rRJH3zwgSZPniybzab09HRJ0sCBAzV27FhNmzZN27dv1zvvvKPc3FxlZmbKZrNJkh599FGFh4crOztbe/fu1fr16/Uf//Efl0xSDgAAAAAAAAAAOqagBxbJyMjQyZMnlZ+fL5fLpeTkZJWUlPgn5T569KgslgtZyahRo7Ru3TotXLhQCxYsUFJSkjZu3KjBgwf7a+bOnava2lrl5OSopqZGo0ePVklJiSIjI/01a9euVW5ursaMGSOLxaKJEyeqsLDQf9xqter111/X9OnTNWzYMMXExCg/P185OTkt+mAAAAAAAAAAAED7EmIYTL/WyOPxyGq1yu12MxQVAAAAAAAAAABtpLnfzwc9/BQAAAAAAAAAAIAZCDUAAAAAAAAAAECHQKgBAAAAAAAAAAA6BEINAAAAAAAAAADQIRBqAAAAAAAAAACADoFQAwAAAAAAAAAAdAiEGgAAAAAAAAAAoEMg1AAAAAAAAAAAAB1CmNkNtCeGYUiSPB6PyZ0AAAAAAAAAANB1NH4v3/g9/eUQalzk9OnTkqSEhASTOwEAAAAAAAAAoOs5ffq0rFbrZY+HGFeLPboQr9erTz/9VD179lRISIjZ7bQLHo9HCQkJOnbsmKKiosxuB0A7xz0DQLC4bwAIBvcMAMHivgEgGNwzzGUYhk6fPi2bzSaL5fIzZ/CkxkUsFou+9rWvmd1GuxQVFcV/yACajXsGgGBx3wAQDO4ZAILFfQNAMLhnmOdKT2g0YqJwAAAAAAAAAADQIRBqAAAAAAAAAACADoFQA1cUERGhxYsXKyIiwuxWAHQA3DMABIv7BoBgcM8AECzuGwCCwT2jY2CicAAAAAAAAAAA0CHwpAYAAAAAAAAAAOgQCDUAAAAAAAAAAECHQKgBAAAAAAAAAAA6BEINAAAAAAAAAADQIRBq4LJWrlypvn37KjIyUna7Xdu3bze7JQDtxJNPPqmQkJCAZcCAAf7jZ8+e1fTp03XzzTfrxhtv1MSJE1VVVWVixwDa0tatW/Wtb31LNptNISEh2rhxY8BxwzCUn5+v+Ph4de/eXQ6HQwcPHgyoqa6uVlZWlqKiohQdHa3s7GydOXOmDa8CQFu62n3jscceu+R3j7FjxwbUcN8Auo6CggINHz5cPXv2VO/evZWenq4DBw4E1DTnb5KjR49qwoQJuuGGG9S7d2/95Cc/UX19fVteCoA20Jx7xv3333/J7xo//OEPA2q4Z7QfhBpo0vr165WXl6fFixdr586dGjp0qNLS0nTixAmzWwPQTtx555367LPP/Mtf//pX/7Ef//jHevXVV7Vhwwa99dZb+vTTT/XII4+Y2C2AtlRbW6uhQ4dq5cqVTR5funSpCgsLVVRUpPLycvXo0UNpaWk6e/asvyYrK0t79+5VaWmpiouLtXXrVuXk5LTVJQBoY1e7b0jS2LFjA373ePHFFwOOc98Auo633npL06dP17vvvqvS0lLV1dXpwQcfVG1trb/man+TNDQ0aMKECTp//ry2bdumNWvWaPXq1crPzzfjkgC0oubcMyRp2rRpAb9rLF261H+Me0Y7YwBNGDFihDF9+nT/zw0NDYbNZjMKCgpM7ApAe7F48WJj6NChTR6rqakxunXrZmzYsMG/b9++fYYkw+l0tlGHANoLScYrr7zi/9nr9RpxcXHGz3/+c/++mpoaIyIiwnjxxRcNwzCMDz/80JBkVFRU+Gv++7//2wgJCTH+3//7f23WOwBzfPW+YRiGMWXKFOPhhx++7DncN4Cu7cSJE4Yk46233jIMo3l/k2zevNmwWCyGy+Xy1/zqV78yoqKijHPnzrXtBQBoU1+9ZxiGYdx3333GzJkzL3sO94z2hSc1cInz58+rsrJSDofDv89iscjhcMjpdJrYGYD25ODBg7LZbOrXr5+ysrJ09OhRSVJlZaXq6uoC7iEDBgzQbbfdxj0EgI4cOSKXyxVwj7BarbLb7f57hNPpVHR0tFJSUvw1DodDFotF5eXlbd4zgPbhzTffVO/evXXHHXfo8ccf1+eff+4/xn0D6NrcbrckqVevXpKa9zeJ0+nUkCFDFBsb669JS0uTx+PR3r1727B7AG3tq/eMRmvXrlVMTIwGDx6s+fPn64svvvAf457RvoSZ3QDan1OnTqmhoSHgP1JJio2N1f79+03qCkB7YrfbtXr1at1xxx367LPP9NOf/lT33HOP9uzZI5fLpfDwcEVHRwecExsbK5fLZU7DANqNxvtAU79nNB5zuVzq3bt3wPGwsDD16tWL+wjQRY0dO1aPPPKIEhMTdfjwYS1YsEDjxo2T0+lUaGgo9w2gC/N6vZo1a5a+8Y1vaPDgwZLUrL9JXC5Xk7+PNB4D0Dk1dc+QpEcffVR9+vSRzWbT+++/ryeeeEIHDhzQH//4R0ncM9obQg0AQNDGjRvn377rrrtkt9vVp08fvfzyy+revbuJnQEAgM4oMzPTvz1kyBDddddduv322/Xmm29qzJgxJnYGwGzTp0/Xnj17Aub4A4DLudw94+J5uIYMGaL4+HiNGTNGhw8f1u23397WbeIqGH4Kl4iJiVFoaKiqqqoC9ldVVSkuLs6krgC0Z9HR0erfv78OHTqkuLg4nT9/XjU1NQE13EMASPLfB670e0ZcXJxOnDgRcLy+vl7V1dXcRwBIkvr166eYmBgdOnRIEvcNoKvKzc1VcXGx3njjDX3ta1/z72/O3yRxcXFN/j7SeAxA53O5e0ZT7Ha7JAX8rsE9o/0g1MAlwsPDNWzYMJWVlfn3eb1elZWVKTU11cTOALRXZ86c0eHDhxUfH69hw4apW7duAfeQAwcO6OjRo9xDACgxMVFxcXEB9wiPx6Py8nL/PSI1NVU1NTWqrKz012zZskVer9f/xwWAru348eP6/PPPFR8fL4n7BtDVGIah3NxcvfLKK9qyZYsSExMDjjfnb5LU1FR98MEHAYFoaWmpoqKiNGjQoLa5EABt4mr3jKbs3r1bkgJ+1+Ce0X4w/BSalJeXpylTpiglJUUjRozQ8uXLVVtbq6lTp5rdGoB2YM6cOfrWt76lPn366NNPP9XixYsVGhqqSZMmyWq1Kjs7W3l5eerVq5eioqI0Y8YMpaamauTIkWa3DqANnDlzxv9/NEm+ycF3796tXr166bbbbtOsWbO0ZMkSJSUlKTExUYsWLZLNZlN6erokaeDAgRo7dqymTZumoqIi1dXVKTc3V5mZmbLZbCZdFYDWdKX7Rq9evfTTn/5UEydOVFxcnA4fPqy5c+fq61//utLS0iRx3wC6munTp2vdunX605/+pJ49e/rHs7darerevXuz/iZ58MEHNWjQIH3ve9/T0qVL5XK5tHDhQk2fPl0RERFmXh6A6+xq94zDhw9r3bp1Gj9+vG6++Wa9//77+vGPf6x7771Xd911lyTuGe2OAVzGL3/5S+O2224zwsPDjREjRhjvvvuu2S0BaCcyMjKM+Ph4Izw83Lj11luNjIwM49ChQ/7jX375pfEv//Ivxk033WTccMMNxne+8x3js88+M7FjAG3pjTfeMCRdskyZMsUwDMPwer3GokWLjNjYWCMiIsIYM2aMceDAgYDX+Pzzz41JkyYZN954oxEVFWVMnTrVOH36tAlXA6AtXOm+8cUXXxgPPvigccsttxjdunUz+vTpY0ybNs1wuVwBr8F9A+g6mrpfSDJ++9vf+mua8zfJxx9/bIwbN87o3r27ERMTY8yePduoq6tr46sB0Nquds84evSoce+99xq9evUyIiIijK9//evGT37yE8Ptdge8DveM9iPEMAyjLUMUAAAAAAAAAACAlmBODQAAAAAAAAAA0CEQagAAAAAAAAAAgA6BUAMAAAAAAAAAAHQIhBoAAAAAAAAAAKBDINQAAAAAAAAAAAAdAqEGAAAAAAAAAADoEAg1AAAAAAAAAABAh0CoAQAAAAAAAAAAOgRCDQAAAAAAAAAA0CEQagAAAAAAAAAAgA6BUAMAAAAAAAAAAHQIhBoAAAAAAAAAAKBD+P/nUNtVSkltvwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correction loop (gradient descent)\n",
    "\n",
    "# Note, in this case learning rate is not as small as in the previouse case\n",
    "# Thus, the number of iterations is also reduced\n",
    "\n",
    "ni = 256\n",
    "lr = 5.0E-3\n",
    "xs = []\n",
    "\n",
    "for i in range(ni):\n",
    "    solution -= lr*derivative(1, objective, solution, intermediate=False)\n",
    "    xs.append(objective(solution))\n",
    "\n",
    "k1 = solution.clone()\n",
    "xs = torch.stack(xs)\n",
    "\n",
    "# Evaluate objective for final solution\n",
    "\n",
    "print(dk)\n",
    "print(-k1)\n",
    "print(objective(k1))\n",
    "\n",
    "plt.figure(figsize=(16, 4))\n",
    "plt.plot(xs.cpu().numpy(), color='blue')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f99c4ed-a9ef-4b13-9936-f5da3e5a5c9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(8.324e-19, dtype=torch.float64)\n",
      "tensor(8.324e-19, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# While objective is minimized, the knob values do not correspond to the set errors\n",
    "# Note, objective values with '+' and '-' are the same here\n",
    "\n",
    "print(objective(-error))\n",
    "print(objective(+k1))\n",
    "print(objective(-k1))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fcbf3d5-e406-436b-b62e-cf34fa9b6a73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.574e-02, dtype=torch.float64)\n",
      "tensor(2.672e-02, dtype=torch.float64)\n",
      "tensor(1.777e-02, dtype=torch.float64)\n",
      "tensor(3.969e-04, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Now, if we apply the obtained negative solution to the observed system, dQmin value will be reduced, but not as much as if the exact errors were recovered\n",
    "\n",
    "# Note, a fraction of a fitted solution can be applied\n",
    "# Also, new correction step can be performed using new observed value\n",
    "\n",
    "print(dQmin(error - 0.00*k1))\n",
    "print(dQmin(error - 0.25*k1))\n",
    "print(dQmin(error - 0.50*k1))\n",
    "print(dQmin(error - 1.00*k1))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "06187aa0-5677-483f-8ae7-d3f9d730de22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform next correction step\n",
    "\n",
    "dQmin_observed = dQmin(error - 0.5*k1)\n",
    "objective = lambda knobs: (dQmin(knobs) - dQmin_observed)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a817ceaf-87f7-458b-bc98-8560929b525a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-5.780e-02, -5.374e-02], dtype=torch.float64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set random initial guess\n",
    "\n",
    "solution = 1.0E-3*torch.randn_like(error)\n",
    "derivative(1, objective, solution, intermediate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4c5f503c-c897-457f-ac28-6f235b4b87fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correction loop\n",
    "\n",
    "ni = 256\n",
    "lr = 5.0E-3\n",
    "xs = []\n",
    "\n",
    "for i in range(ni):\n",
    "    solution -= lr*derivative(1, objective, solution, intermediate=False)\n",
    "    xs.append(objective(solution))\n",
    "\n",
    "k2 = solution.clone()\n",
    "xs = torch.stack(xs)\n",
    "\n",
    "plt.figure(figsize=(16, 4))\n",
    "plt.plot(xs.cpu().numpy(), color='blue')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "83b31ae8-94a2-479c-9415-41b62f23a317",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.574e-02, dtype=torch.float64)\n",
      "tensor(1.777e-02, dtype=torch.float64)\n",
      "tensor(8.875e-03, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "print(dQmin(error))\n",
    "print(dQmin(error - 0.5*k1))\n",
    "print(dQmin(error - 0.5*k1 - 0.5*k2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "47379a46-4d55-4b08-b279-75573bc38cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note, there seems to be no guarantee for the above procedure to converge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349ea254-fd76-4d92-bdba-20f04c61c0ec",
   "metadata": {},
   "source": [
    "# Example-40: Coupling (amplitude ratio correction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dfdc170a-b0f8-4ef6-88ee-72de5d700373",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example an objective function constructed from ratio of coupled and uncoupled amplitudes is used to minimize coupling\n",
    "# Amplitudes can be computed from simulated TbT data at one or several locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47452d3d-8b7c-4e6b-b003-e5c135e5b09a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# Import\n",
    "\n",
    "import numpy\n",
    "import torch\n",
    "\n",
    "from ndmap.derivative import derivative\n",
    "from ndmap.signature import chop\n",
    "from ndmap.series import series\n",
    "from ndmap.series import clean\n",
    "from ndmap.evaluate import evaluate\n",
    "from ndmap.propagate import identity\n",
    "from ndmap.propagate import propagate\n",
    "from ndmap.pfp import fixed_point\n",
    "from ndmap.pfp import parametric_fixed_point\n",
    "\n",
    "from twiss.wolski import twiss\n",
    "from twiss.convert import wolski_to_cs\n",
    "from twiss.matrix import symplectic_conjugate\n",
    "\n",
    "torch.set_printoptions(precision=3, sci_mode=True, linewidth=128)\n",
    "print(torch.cuda.is_available())\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa35ce5b-bff8-44ca-beee-538ed0fdf4e8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "dtype = torch.float64\n",
    "device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53e07eda-75bd-46c6-93ee-a61917432998",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set elements\n",
    "\n",
    "def drif(x, w, l):\n",
    "    (qx, px, qy, py), (w, ), l = x, w, l\n",
    "    return torch.stack([qx + l*px/(1 + w), px, qy + l*py/(1 + w), py])\n",
    "\n",
    "def quad(x, w, kq, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), kq, l = x, w, kq, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx, py + 2.0*l*kq*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def sext(x, w, ks, l, n=10):\n",
    "    (qx, px, qy, py), (w, ), ks, l = x, w, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 1.0*l*ks*(qx**2 - qy**2), py + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py])\n",
    "\n",
    "def bend(x, w, r, kq, ks, l, n=50):\n",
    "    (qx, px, qy, py), (w, ), r, kq, ks, l = x, w, r, kq, ks, l/(2.0*n)\n",
    "    for _ in range(n):\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "        px, py = px - 2.0*l*kq*qx - 1.0*l*ks*(qx**2 - qy**2) + 2.0*l/r**2*(w*r - qx), py + 2.0*l*kq*qy + 2.0*l*ks*qx*qy\n",
    "        qx, qy = qx + l*px/(1 + w), qy + l*py/(1 + w)\n",
    "    return torch.stack([qx, px, qy, py]) \n",
    "\n",
    "def roll(x, a):\n",
    "    (qx, px, qy, py), cn, sn = x, a.cos(), a.sin()\n",
    "    return torch.stack([qx*cn + qy*sn, px*cn + py*sn, qy*cn - qx*sn, py*cn - py*sn])\n",
    "\n",
    "def kick(x, kn, ks):\n",
    "    (qx, px, qy, py), kn, ks = x, kn, ks\n",
    "    return torch.stack([qx, px - kn*qx + ks*qy, qy, py + ks*qx + kn*qy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "826107f4-d1d2-41f9-8e98-83023b448111",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Set transport maps between observation points\n",
    "\n",
    "def map_01_02(x, k):\n",
    "    kf, kd = k\n",
    "    x = kick(x, 0.0, kf/2.0)\n",
    "    x = quad(x, [0.0], 0.21, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = drif(x, [0.0], 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], -0.18, 0.50)\n",
    "    x = kick(x, 0.0, kd/2.0)\n",
    "    return x\n",
    "\n",
    "def map_02_03(x, k):\n",
    "    kf, kd = k\n",
    "    x = kick(x, 0.0, kd/2.0)\n",
    "    x = quad(x, [0.0], -0.18, 0.50)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = drif(x, [0.0], 3.0)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = sext(x, [0.0], 0.0, 0.10)\n",
    "    x = drif(x, [0.0], 0.45)\n",
    "    x = quad(x, [0.0], 0.21, 0.50)\n",
    "    x = kick(x, 0.0, kf/2.0)\n",
    "    return x\n",
    "\n",
    "transport = [\n",
    "    map_01_02,\n",
    "    map_02_03\n",
    "]\n",
    "\n",
    "# Define one-turn transport\n",
    "\n",
    "def fodo(x, k):\n",
    "    for mapping in transport:\n",
    "        x = mapping(x, k)\n",
    "    return x\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(2*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "print(fodo(x, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fca0c8b1-c181-402c-8a8e-a1f4186f4519",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0., 0., 0., 0.], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Compute fixed point\n",
    "\n",
    "x = torch.tensor(4*[0.0], dtype=dtype, device=device)\n",
    "k = torch.tensor(2*[0.0], dtype=dtype, device=device)\n",
    "\n",
    "fp = fixed_point(16, fodo, x, k, power=1)\n",
    "print(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b7a3598-2849-4132-bc27-4abe94c16f79",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute parametric fixed point\n",
    "\n",
    "pfp = parametric_fixed_point((1, ), fp, [k], fodo)\n",
    "chop(pfp)\n",
    "pfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff02e6bf-18ef-45ab-83c0-019e7ee318a7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[tensor([0., 0., 0., 0.], dtype=torch.float64),\n",
       "  tensor([[0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.],\n",
       "          [0., 0.]], dtype=torch.float64)]]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Propagate parametric fixed point\n",
    "\n",
    "out = propagate((4, 2), (0, 1), pfp, [k], fodo)\n",
    "chop(out)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "289c70af-912c-42c4-b049-d2584d4c89b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Propagate parametric identity (surrogate model for linear dynamics)\n",
    "\n",
    "jet = identity((1, 1), fp, parametric=pfp)\n",
    "jet = propagate((4, 2), (1, 1), jet, [k], fodo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2ec09f9-2c19-4139-b55f-8677dcca9d6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dQmin (TEAPOT manual, appendix G, 1996)\n",
    "\n",
    "def dQmin(k):\n",
    "    \n",
    "    m = derivative(1, lambda x: evaluate(jet, [x, k]), fp, intermediate=False)\n",
    "    \n",
    "    (nux, nuy), *_ = twiss(m)\n",
    "    mux, muy = 2.0*torch.pi*nux, 2.0*torch.pi*nuy \n",
    "    \n",
    "    B = m[:2, 2:]\n",
    "    C = m[2:, :2]\n",
    "    \n",
    "    (m11, m12), (m21, m22) = C + symplectic_conjugate(B)\n",
    "    \n",
    "    return 1.0/torch.pi*(m11*m22 - m12*m21).abs().sqrt()/(mux.sin() + muy.sin()).abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b637ae8-5ff3-4cd8-89c5-0c5c871770fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.881e-03, dtype=torch.float64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set skew errors\n",
    "\n",
    "dkf = +1.5E-3\n",
    "dkd = -0.5E-3\n",
    "\n",
    "dk = torch.tensor([dkf, dkd], dtype=dtype, device=device)\n",
    "\n",
    "dQmin(dk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47fdf24f-b900-47e0-9f2c-ebc7c9d15d1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given qx TbT data, in linear approximation qx(n) = (cxx cos(mux n) + sxx sin(mux n)) +  (cxy cos(muy n) + sxy sin(muy n))\n",
    "# Amplitudes axx^2 = cxx^2 + sxx^2 and axy^2 = cxy^2 + cyx^2 are used to compute ratio axy/axx\n",
    "# Similary, ayx/ayy can be computed using qy\n",
    "# These ration are computed at each observation point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f9943a07-4556-4a25-a62c-fb36b9e1f044",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Observation function\n",
    "\n",
    "# Fit 'experiment' to 'model' ('experiment' is 'model' with errors)\n",
    "# The goal is to fit knobs so that 'experiment' observation matches 'model' observation (ratios are zero)\n",
    "\n",
    "# Fit 'model' to 'experiment'\n",
    "# The goal is to fit knobs so that 'model' observation matches 'experiment' observation (final ratios are non-zero)\n",
    "\n",
    "# Set initial condition for TbT\n",
    "# Exact value is not important, since the underlying model is linear\n",
    "# Also, this initial should be set relative to (parametric) closed orbit\n",
    "\n",
    "initial = torch.tensor([1.0, 0.0, 1.0, 0.0], dtype=dtype, device=device)\n",
    "\n",
    "# Normalized window for computation of amplitudes\n",
    "\n",
    "def window(n, *, s=1.0, dtype=dtype, device=device):\n",
    "    t = torch.linspace(0.0, (n - 1.0)/n, n, dtype=dtype, device=device)\n",
    "    f = torch.exp(-1.0/((1.0 - t)**s*t**s))\n",
    "    return f/torch.sum(f)\n",
    "\n",
    "def fn(k, n, x, error):\n",
    "\n",
    "    if error:\n",
    "        k = k + dk\n",
    "        \n",
    "    w = window(n)\n",
    "    t = torch.linspace(0.0, n - 1, n, dtype=dtype, device=device)\n",
    "\n",
    "    matrix = derivative(1, lambda x: evaluate(jet, [x, k]), fp, intermediate=False)\n",
    "    (nux, nuy), *_ = twiss(matrix)\n",
    "\n",
    "    mux = 2.0*torch.pi*nux\n",
    "    muy = 2.0*torch.pi*nuy\n",
    "    \n",
    "    xs = []\n",
    "    for _ in range(n):\n",
    "        for mapping in transport:\n",
    "            x = mapping(x, k)\n",
    "            xs.append(x)\n",
    "    xs = torch.stack(xs).reshape(n, len(transport), -1).swapaxes(1, 0).swapaxes(1, -1)\n",
    "\n",
    "    cxx, cxy, cyx, cyy = [], [], [], []\n",
    "    sxx, sxy, syx, syy = [], [], [], []\n",
    "    \n",
    "    for x in xs:\n",
    "        \n",
    "        qx, _, qy, _ = x\n",
    "        \n",
    "        cxx.append(w*qx @ (mux*t).cos())\n",
    "        cxy.append(w*qx @ (muy*t).cos())\n",
    "        cyx.append(w*qy @ (mux*t).cos())\n",
    "        cyy.append(w*qy @ (muy*t).cos())\n",
    "\n",
    "        sxx.append(w*qx @ (mux*t).sin())\n",
    "        sxy.append(w*qx @ (muy*t).sin())\n",
    "        syx.append(w*qy @ (mux*t).sin())\n",
    "        syy.append(w*qy @ (muy*t).sin())\n",
    "\n",
    "    cxx = torch.stack(cxx)\n",
    "    cxy = torch.stack(cxy)\n",
    "    cyx = torch.stack(cyx)\n",
    "    cyy = torch.stack(cyy)\n",
    "\n",
    "    sxx = torch.stack(sxx)\n",
    "    sxy = torch.stack(sxy)\n",
    "    syx = torch.stack(syx)\n",
    "    syy = torch.stack(syy)\n",
    "\n",
    "    axx = (cxx**2 + sxx**2).sqrt()\n",
    "    axy = (cxy**2 + sxy**2).sqrt()\n",
    "    ayx = (cyx**2 + syx**2).sqrt()\n",
    "    ayy = (cyy**2 + syy**2).sqrt()\n",
    "\n",
    "    return torch.stack([axy/axx, ayx/ayy]).T.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f51a641-5c7e-4e79-9baa-879d75dafc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The above function returns ratios (as a vector, so that the derivative is a matrix)\n",
    "# rx_1, rx_2, ..., ry_1, ry_2, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "788191af-12ab-4360-b7f0-1fde5c06a91f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1.041e-04, 1.041e-04, 1.041e-04, 1.041e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([7.097e-09, 7.097e-09, 7.097e-09, 7.097e-09], dtype=torch.float64)\n",
      "\n",
      "tensor([3.210e-02, 6.907e-03, 2.517e-02, 8.844e-03], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.007e-03, 2.508e-02, 8.942e-03], dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Without errors, the ratios should be zero\n",
    "# The accuracy is determined by the TbT data length\n",
    "\n",
    "print(fn(k, 128, initial, False))\n",
    "print(fn(k, 256, initial, False))\n",
    "print(fn(k, 512, initial, False))\n",
    "print()\n",
    "\n",
    "print(fn(k, 128, initial, True))\n",
    "print(fn(k, 256, initial, True))\n",
    "print(fn(k, 512, initial, True))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5b599d7-ff36-4254-892a-d718daf7c00e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.094e-03, 3.652e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor(4.220e-02, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.396e-03, 4.657e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([8.467e-03, 1.954e-03, 6.640e-03, 2.491e-03], dtype=torch.float64)\n",
      "tensor(1.121e-02, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.474e-03, 4.915e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([2.153e-03, 5.078e-04, 1.689e-03, 6.460e-04], dtype=torch.float64)\n",
      "tensor(2.850e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.493e-03, 4.980e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([5.430e-04, 1.326e-04, 4.266e-04, 1.674e-04], dtype=torch.float64)\n",
      "tensor(7.156e-04, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.498e-03, 4.996e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([1.385e-04, 3.790e-05, 1.093e-04, 4.665e-05], dtype=torch.float64)\n",
      "tensor(1.791e-04, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.499e-03, 5.000e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.719e-05, 1.418e-05, 2.991e-05, 1.639e-05], dtype=torch.float64)\n",
      "tensor(4.491e-05, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.001e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([1.188e-05, 8.259e-06, 1.005e-05, 8.816e-06], dtype=torch.float64)\n",
      "tensor(1.167e-05, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([5.555e-06, 6.783e-06, 5.081e-06, 6.915e-06], dtype=torch.float64)\n",
      "tensor(4.302e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.961e-06, 6.415e-06, 3.839e-06, 6.430e-06], dtype=torch.float64)\n",
      "tensor(3.323e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.551e-06, 6.324e-06, 3.529e-06, 6.305e-06], dtype=torch.float64)\n",
      "tensor(3.249e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.445e-06, 6.301e-06, 3.452e-06, 6.272e-06], dtype=torch.float64)\n",
      "tensor(3.245e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.418e-06, 6.295e-06, 3.432e-06, 6.263e-06], dtype=torch.float64)\n",
      "tensor(3.244e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.411e-06, 6.293e-06, 3.427e-06, 6.261e-06], dtype=torch.float64)\n",
      "tensor(3.244e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.409e-06, 6.293e-06, 3.426e-06, 6.261e-06], dtype=torch.float64)\n",
      "tensor(3.244e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.408e-06, 6.293e-06, 3.426e-06, 6.261e-06], dtype=torch.float64)\n",
      "tensor(3.244e-06, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.500e-03, 5.002e-04], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor([3.408e-06, 6.293e-06, 3.426e-06, 6.260e-06], dtype=torch.float64)\n",
      "tensor(3.244e-06, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Correction ('experiment' to 'model')\n",
    "# Target system is 'model'\n",
    "\n",
    "# Learning rate\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Target values\n",
    "\n",
    "vf = fn(k, 256, initial, False)\n",
    "\n",
    "# Initial solution\n",
    "\n",
    "solution = torch.zeros_like(dk)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(16):\n",
    "\n",
    "    vi, jacobian = derivative(1, fn, solution, 256, initial, True, intermediate=True)\n",
    "    dv = vf - vi\n",
    "    solution += lr * torch.linalg.pinv(jacobian) @ dv\n",
    "\n",
    "    print(solution)\n",
    "    print(vf)\n",
    "    print(vi)    \n",
    "    print(dv.norm())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9e6b733c-ccf8-4872-adfe-d1076c51ef98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(4.220e-02, dtype=torch.float64)\n",
      "tensor(3.244e-06, dtype=torch.float64)\n",
      "\n",
      "tensor(1.881e-03, dtype=torch.float64)\n",
      "tensor(5.079e-07, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Test final solution\n",
    "\n",
    "print((vf - fn(0.0*solution, 256, initial, True)).norm())\n",
    "print((vf - fn(1.0*solution, 256, initial, True)).norm())\n",
    "print()\n",
    "\n",
    "print(dQmin(dk))\n",
    "print(dQmin(dk + solution))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2213356-31d7-4a6c-a825-e6741bf4f861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.019e-03, 3.612e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([4.081e-06, 4.081e-06, 4.081e-06, 4.081e-06], dtype=torch.float64)\n",
      "tensor(4.220e-02, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.443e-03, 4.881e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([2.008e-02, 4.856e-03, 1.556e-02, 6.267e-03], dtype=torch.float64)\n",
      "tensor(1.563e-02, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.553e-03, 5.221e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([2.867e-02, 7.133e-03, 2.241e-02, 9.127e-03], dtype=torch.float64)\n",
      "tensor(4.267e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.582e-03, 5.310e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.087e-02, 7.729e-03, 2.416e-02, 9.878e-03], dtype=torch.float64)\n",
      "tensor(1.869e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.589e-03, 5.332e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.142e-02, 7.878e-03, 2.459e-02, 1.007e-02], dtype=torch.float64)\n",
      "tensor(1.603e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.590e-03, 5.338e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.156e-02, 7.916e-03, 2.470e-02, 1.011e-02], dtype=torch.float64)\n",
      "tensor(1.585e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.159e-02, 7.925e-03, 2.473e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.927e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor([-1.591e-03, 5.340e-04], dtype=torch.float64)\n",
      "tensor([3.200e-02, 7.011e-03, 2.507e-02, 8.946e-03], dtype=torch.float64)\n",
      "tensor([3.160e-02, 7.928e-03, 2.474e-02, 1.013e-02], dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Correction ('model' to 'experiment')\n",
    "# Target system is 'experiment'\n",
    "\n",
    "# Learning rate\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Target values\n",
    "\n",
    "vf = fn(k, 256, initial, True)\n",
    "\n",
    "\n",
    "solution = torch.zeros_like(dk)\n",
    "\n",
    "for _ in range(16):\n",
    "\n",
    "    vi, jacobian = derivative(1, fn, solution, 256, initial, False, intermediate=True)\n",
    "    dv = vf - vi\n",
    "    solution += lr * torch.linalg.pinv(jacobian) @ dv\n",
    "\n",
    "    print(solution)\n",
    "    print(vf)\n",
    "    print(vi)\n",
    "    print(dv.norm())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "211f3b2e-3c75-4947-a84e-61113d4c737c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(4.220e-02, dtype=torch.float64)\n",
      "tensor(1.584e-03, dtype=torch.float64)\n",
      "\n",
      "tensor(1.881e-03, dtype=torch.float64)\n",
      "tensor(1.989e-03, dtype=torch.float64)\n",
      "tensor(1.078e-04, dtype=torch.float64)\n",
      "tensor(3.870e-03, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Test final solution\n",
    "\n",
    "print((vf - fn(0.0*solution, 256, initial, False)).norm())\n",
    "print((vf - fn(1.0*solution, 256, initial, False)).norm())\n",
    "print()\n",
    "\n",
    "print(dQmin(dk))\n",
    "print(dQmin(solution))\n",
    "print(dQmin(dk + solution))\n",
    "print(dQmin(dk - solution))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5b28dc5-b48c-43bb-8e73-08df3b0433d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "\n",
    "factors = torch.linspace(-1.5, 1.5, 128, dtype=dtype, device=device)\n",
    "plt.figure(figsize=(16, 4))\n",
    "plt.scatter(factors, torch.stack([dQmin(dk + factor*solution) for factor in factors]), color='blue')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "351bf1ae-7f98-4360-8e45-2822ae4c2421",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5fb855e-c792-4edb-8b97-c3b131dc4a75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c1331d7-3f94-4ffa-88b0-c4cdbbeb5a27",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "myt0_gMIOq7b",
    "5d97819c"
   ],
   "name": "03_frequency.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}