{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c655da72-31cb-4b09-a1c7-55a4fefb6fa7",
   "metadata": {},
   "source": [
    "## ELETTRA-48: ID interaction (EU100, U46, F8, EU132, W96, SCW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90bc5aba-bc18-4dc9-b82f-382c664989b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import\n",
    "\n",
    "import torch\n",
    "from torch import Tensor\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "matplotlib.rcParams['text.usetex'] = True\n",
    "\n",
    "from model.library.element import Element\n",
    "from model.library.line import Line\n",
    "from model.library.quadrupole import Quadrupole\n",
    "from model.library.matrix import Matrix\n",
    "\n",
    "from model.command.external import load_lattice\n",
    "from model.command.build import build\n",
    "from model.command.tune import tune\n",
    "from model.command.tune import chromaticity\n",
    "from model.command.orbit import dispersion\n",
    "from model.command.twiss import twiss\n",
    "from model.command.advance import advance\n",
    "from model.command.coupling import coupling\n",
    "\n",
    "from model.command.wrapper import Wrapper\n",
    "from model.command.wrapper import forward\n",
    "from model.command.wrapper import inverse\n",
    "from model.command.wrapper import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7db88eb3-4d75-4ba4-b122-d443a7beba57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data type and device\n",
    "\n",
    "Element.dtype = dtype = torch.float64\n",
    "Element.device = device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33ac6d21-12b1-4009-98bf-6a49a1d5e987",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load lattice (ELEGANT table)\n",
    "# Note, lattice is allowed to have repeated elements\n",
    "\n",
    "path = Path('elettra.lte')\n",
    "data = load_lattice(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e0023e7a-625c-42e0-9c31-a862fae65af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168, 'Drift': 720, 'Dipole': 156, 'Quadrupole': 360, 'Marker': 24}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build and setup lattice\n",
    "\n",
    "ring:Line = build('RING', 'ELEGANT', data)\n",
    "\n",
    "# Flatten sublines\n",
    "\n",
    "ring.flatten()\n",
    "\n",
    "# Remove all marker elements but the ones starting with MLL (long straight section centers)\n",
    "\n",
    "ring.remove_group(pattern=r'^(?!MSS_)(?!MLL_).*', kinds=['Marker'])\n",
    "\n",
    "# Replace all sextupoles with quadrupoles\n",
    "\n",
    "def factory(element:Element) -> None:\n",
    "    table = element.serialize\n",
    "    table.pop('ms', None)\n",
    "    return Quadrupole(**table)\n",
    "\n",
    "ring.replace_group(pattern=r'', factory=factory, kinds=['Sextupole'])\n",
    "\n",
    "# Set linear dipoles\n",
    "\n",
    "def apply(element:Element) -> None:\n",
    "    element.linear = True\n",
    "\n",
    "ring.apply(apply, kinds=['Dipole'])\n",
    "\n",
    "# Merge drifts\n",
    "\n",
    "ring.merge()\n",
    "\n",
    "# Change lattice start\n",
    "\n",
    "ring.start = \"BPM_S01_01\"\n",
    "\n",
    "# Split BPMs\n",
    "\n",
    "ring.split((None, ['BPM'], None, None))\n",
    "\n",
    "# Roll lattice\n",
    "\n",
    "ring.roll(1)\n",
    "\n",
    "# Splice lattice\n",
    "\n",
    "ring.splice()\n",
    "\n",
    "# Describe\n",
    "\n",
    "ring.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30b55e04-8dba-4b6a-8799-abe3b9980977",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux, nuy = tune(ring, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b8bae6d-9fd3-4e51-ba59-efe989852f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx, etapx, etaqy, etapy = dispersion(ring, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "616a7e44-324a-4ed8-bc72-a862b5a01303",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax, bx, ay, by = twiss(ring, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ae582d43-2ad3-46e7-a970-6719f2970ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux, muy = advance(ring, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c20caaf9-9308-47d8-98b6-1861b320ff27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c = coupling(ring, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0cbeb927-5107-4d04-a248-ffc8ff51f812",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi = chromaticity(ring, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b0dcf31b-14f9-425f-b6d0-35b407fda758",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quadrupole names for global tune correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed327096-e6f9-4805-b474-28717c790708",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.2994, 0.1608], dtype=torch.float64)\n",
      "tensor([[ 5.8543,  2.0964],\n",
      "        [-2.9918, -1.2602]], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Global tune responce matrix\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(ring, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "knobs = torch.tensor([0.0, 0.0], dtype=dtype)\n",
    "global_target = global_observable(knobs)\n",
    "global_matrix = torch.func.jacfwd(global_observable)(knobs)\n",
    "\n",
    "print(global_target)\n",
    "print(global_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a3beea50-130b-411f-904b-65ae6fedcc67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local knobs\n",
    "\n",
    "table_nkn = {f'S{i:02}': [f'OCT_S{i:02}_02', f'QF_S{i:02}_02', f'QD_S{i:02}_02', f'QD_S{i:02}_03', f'QF_S{i:02}_03', f'OCT_S{i:02}_03'] for i in range(1, 12 + 1)}\n",
    "table_nks = {f'S{i:02}': [f'SD_S{i:02}_05', f'SH_S{i:02}_02', f'SH_S{i:02}_03', f'SD_S{i:02}_06'] for i in range(1, 12 + 1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "be9e3be4-dea3-4ee8-9eda-d9b8e4e1aa8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define twiss observable (full coupled twiss)\n",
    "\n",
    "def observable_twiss(kn, ks, nkn, nks):\n",
    "    return twiss(ring, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edce5a79-8367-4c87-b554-c826c511198b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define dispersion observable\n",
    "\n",
    "def observable_dispersion(kn, ks, nkn, nks):  \n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(ring, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abebbb7a-569b-4514-9d8f-bae047a36e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct full target observable vector and corresponding responce matrix (without symmetry)\n",
    "\n",
    "def observable(knobs, nkn, nks):\n",
    "    kn, ks = torch.split(knobs, [6, 4])  \n",
    "    betas = observable_twiss(kn, ks, nkn, nks)\n",
    "    etas = observable_dispersion(kn, ks, nkn, nks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])\n",
    "\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "sections = [f'S{i:02}' for i in range(1, 12 + 1)]\n",
    "\n",
    "ts = dict.fromkeys(sections)\n",
    "ms = dict.fromkeys(sections)\n",
    "\n",
    "for section in sections:\n",
    "    ts[section] = observable(knobs, table_nkn[section], table_nks[section])\n",
    "    ms[section] = torch.func.jacfwd(observable)(knobs, table_nkn[section], table_nks[section])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f954ec5-901e-4f7d-96a4-a0d441bf461a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction container\n",
    "\n",
    "correction = dict.fromkeys(sections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c6b5f2d1-c8e7-4d42-80a1-6bec58836250",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EU100_L01\n",
    "\n",
    "ARK = torch.tensor([[-6.87458321e-03,  0.00000000e+00,  0.00000000e+00,  4.10330598e-04],\n",
    "                    [ 0.00000000e+00, -9.32772931e-03,  5.39380718e-04, -1.45557324e-09],\n",
    "                    [ 0.00000000e+00,  5.39380718e-04,  1.59668799e-02, -3.45623108e-09],\n",
    "                    [ 4.10330598e-04, -1.45557296e-09, -3.45622909e-09,  2.21701384e-02]], dtype=dtype)\n",
    "\n",
    "EU100_POS = 0.0\n",
    "EU100_L01 = Matrix('EU100_L01', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0061bcca-8d9b-4cf9-87d8-d764ec5f713a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# U46_L02\n",
    "\n",
    "ARK = torch.tensor([[-0.000116398, 0.0,        0.0,      0.0],\n",
    "                    [ 0.0,        -0.000219837,0.0,      0.0],\n",
    "                    [ 0.0,         0.0,        0.0299782,0.0],\n",
    "                    [ 0.0,         0.0,        0.0,      0.0526229]], dtype=dtype)\n",
    "\n",
    "U46_POS = 0.0\n",
    "U46_L02 = Matrix('U46_L02', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9be32d3f-64e9-4905-b3f1-c133a75c7cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# F8_L03\n",
    "\n",
    "ARK = torch.tensor([[-0.00172735,   0.0,         0.0000718013, 0.0],\n",
    "                    [ 0.0,         -0.00267732,  0.0,          0.000491118],\n",
    "                    [ 0.0000718013, 0.0,         0.0225744,    0.0],\n",
    "                    [ 0.0,          0.000491118, 0.0,          0.0383271]], dtype=dtype)\n",
    "\n",
    "F8_POS = 0.0\n",
    "F8_L03 = Matrix('F8_L03', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e87759d9-a769-4f3d-8fa5-0a86561e3671",
   "metadata": {},
   "outputs": [],
   "source": [
    "# EU132_L04\n",
    "\n",
    "ARK = torch.tensor([[0.0102095,   0.0,        0.0,        -2.47806e-5],\n",
    "                    [0.0,         0.00542115,  5.1928e-6,   0.0],\n",
    "                    [0.0,         5.1928e-6,  -0.00352211,  0.0],\n",
    "                    [-2.47806e-5, 0.0,        0.0,        -0.00199629]], dtype=dtype)\n",
    "\n",
    "EU132_POS = 928.0\n",
    "EU132_L04 = Matrix('EU132_L04', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ebcfcd6-bb97-47b5-9182-26a5a56c9522",
   "metadata": {},
   "outputs": [],
   "source": [
    "# W96_S05\n",
    "\n",
    "ARK = torch.tensor([[-0.000248802, 0.0,        0.0,      0.0],\n",
    "                    [ 0.0,        -9.53447e-6, 0.0,      0.0],\n",
    "                    [ 0.0,         0.0,        0.0176392,0.0],\n",
    "                    [ 0.0,         0.0,        0.0,      0.000856368]], dtype=dtype)\n",
    "\n",
    "W96_POS = 0.0\n",
    "W96_S05 = Matrix('W96_S05', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f8b60147-2168-48ba-b25c-2908c046e532",
   "metadata": {},
   "outputs": [],
   "source": [
    "# W96_S06\n",
    "\n",
    "ARK = torch.tensor([[-0.000248802, 0.0,        0.0,      0.0],\n",
    "                    [ 0.0,        -9.53447e-6, 0.0,      0.0],\n",
    "                    [ 0.0,         0.0,        0.0176392,0.0],\n",
    "                    [ 0.0,         0.0,        0.0,      0.000856368]], dtype=dtype)\n",
    "\n",
    "W96_POS = 0.0\n",
    "W96_S06 = Matrix('W96_S06', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e66afab-ad7a-4e93-ae88-e433a9937145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCW_L11\n",
    "\n",
    "ARK = torch.tensor([[-2.84055e-6, 0.0,        0.0,     0.0],\n",
    "                    [0.0,         6.85987e-6, 0.0,     0.0],\n",
    "                    [0.0,         0.0,        0.049742,0.0],\n",
    "                    [0.0,         0.0,        0.0,     0.0107508]], dtype=dtype)\n",
    "\n",
    "SCW_POS = 845.8\n",
    "SCW_L11 = Matrix('SCW_L11', length=0.0, A=ARK[torch.triu(torch.ones_like(ARK, dtype=torch.bool))].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13a9bb7f-6438-4ce8-b6f3-c78985ec61ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S01 (EU100)\n",
    "\n",
    "section = 'S01'\n",
    "\n",
    "target = ts[section]\n",
    "matrix = ms[section]\n",
    "\n",
    "nkn = table_nkn[section]\n",
    "nks = table_nks[section]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "77d94293-cc57-4438-b58e-46039874a1f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(EU100_L01, error.next('MLL_S01').name, position=EU100_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b512d9ee-231a-45c4-b0c7-120c57c77a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id, nuy_id = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5ab9ac59-4afa-4185-b1b3-ea2258d9bebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id, etapx_id, etaqy_id, etapy_id = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e1630835-9bcb-4035-9cad-26937447564c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id, bx_id, ay_id, by_id = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e6cddd15-0e4a-42bb-b702-b93c9fed0150",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1a9a9114-6a9c-41ca-94a9-5b323b5945c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "49abe80b-6aba-4bed-8d12-0bc9a77a0d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1b745a11-51f1-4ac0-9447-43f6c8b75a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0052, dtype=torch.float64)\n",
      "tensor(-0.0032, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id))\n",
    "print((nuy - nuy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "39ae3652-89fd-45b1-8a67-b80b19323371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0.0001, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ec928b83-0352-4643-8e0b-3176c4182ded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2757, -66.6654], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ea25569-ed8c-41da-8db9-a4bb0ada4252",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parametric observable vector\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(error, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "\n",
    "def observable_twiss(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    return twiss(error, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)\n",
    "\n",
    "\n",
    "def observable_dispersion(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(error, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T\n",
    "\n",
    "\n",
    "def observable(knobs):\n",
    "    kn, ks = torch.split(knobs, [6, 4])\n",
    "    betas = observable_twiss(kn, ks)\n",
    "    etas = observable_dispersion(kn, ks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "219391fc-edb6-41fd-824f-cc7bf0fc210b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.7058e-05, dtype=torch.float64)\n",
      "tensor(9.2197, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check the residual vector norm\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "print(((global_observable(global_knobs) - global_target)**2).sum())\n",
    "print(((observable(knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "21cdbdd0-292e-4a73-8017-f813a846dc51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(9.2197, dtype=torch.float64)\n",
      "tensor(0.5917, dtype=torch.float64)\n",
      "tensor(0.0483, dtype=torch.float64)\n",
      "tensor(0.0140, dtype=torch.float64)\n",
      "tensor(0.0119, dtype=torch.float64)\n",
      "tensor(0.0117, dtype=torch.float64)\n",
      "tensor(0.0117, dtype=torch.float64)\n",
      "tensor(0.0117, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (local)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(8):\n",
    "    value = observable(knobs)\n",
    "    residual = target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    knobs += lr*delta\n",
    "    print(((value - target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "89ad67d8-8452-463a-ac68-f12a38da48e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.01216278  0.00350744  0.0272648   0.05190816 -0.00369919 -0.01582589]\n",
      "[-0.00014983 -0.0007111   0.0007195   0.00022932]\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kn, ks = torch.split(knobs, [6, 4])\n",
    "\n",
    "print(kn.numpy())\n",
    "print(ks.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3564719a-8b47-4fea-ac04-bcc095067f40",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name, knob in zip(nkn, kn):\n",
    "    error[name].kn = (error[name].kn + knob).item()\n",
    "\n",
    "for name, knob in zip(nks, ks):\n",
    "    error[name].ks = (error[name].ks + knob).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "853ecfde-cc6b-40f4-aca3-85e6bbe47c9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.1975e-05, dtype=torch.float64)\n",
      "tensor(0.0117, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check \n",
    "\n",
    "print(((global_observable(0.0*global_knobs) - global_target)**2).sum())\n",
    "print(((observable(0.0*knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "15ad11ee-b04f-4d0b-ac84-ff3580cc8149",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_a, nuy_id_a = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8b1362a5-98f4-4956-bead-2fee0d6d665e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_a, etapx_id_a, etaqy_id_a, etapy_id_a = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "61536daf-6014-449e-bdf4-5d62e83345f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_a, bx_id_a, ay_id_a, by_id_a = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e8d08c90-3176-4083-a806-af1418434d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_a, muy_id_a = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e17b8f34-c319-407c-a255-57c8757d9f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_a = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "08819dce-4cad-4022-a161-2658a0f5c0b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_a = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "75162eb5-409b-4c78-89b4-e0a46860b7bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0006, dtype=torch.float64)\n",
      "tensor(-0.0034, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_a))\n",
    "print((nuy - nuy_id_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0fc502fd-c88c-4596-ada9-0529c5fbaa8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.4725e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b9d9cf4f-f2c6-4ac7-9c3d-aa1d04c9b548",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2119, -66.6814], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6f7032b8-b02e-4397-b2a1-76fc2dc9e59e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.1975e-05, dtype=torch.float64)\n",
      "tensor(7.8273e-07, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f146de9b-2b5a-4e20-b982-07fc34d0012a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01858255842157612\n",
      "-0.006756373991750641\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cbefd673-6e88-4a06-875c-d8e6291b6304",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3b694bee-a6dc-4d98-b769-946e80217181",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "39fee79b-69e3-4518-9b46-2bcc7dfa3ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "70687e90-aef8-4b6e-b88e-451cc6d84dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7fac10b7-fcb4-4c66-b196-cb33aaad6ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c943b950-9a3e-4bac-a3a5-4b6a5ffab122",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9bc4f691-0b09-43eb-af35-5a5aebc0f8f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f45db29e-2bf7-42bc-9b5f-b90f9fc0cdcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-5.3984e-05, dtype=torch.float64)\n",
      "tensor(-0.0002, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fecfc495-e4bf-464c-bcef-643d41e81e1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.5044e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9af72e9f-e0af-4585-9656-f4a85434697e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1968, -66.6908], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d8963ff5-4caa-4935-bbac-23fc13822e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_a_bb = 100.0*(bx - bx_id_a) / bx\n",
    "by_a_bb = 100.0*(by - by_id_a) / by\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_a = rms(bx_a_bb).item()\n",
    "ptp_x_a = (bx_a_bb.max() - bx_a_bb.min()).item()\n",
    "rms_y_a = rms(by_a_bb).item()\n",
    "ptp_y_a = (by_a_bb.max() - by_a_bb.min()).item()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_a_np = bx_a_bb.cpu().numpy()\n",
    "by_a_np = by_a_bb.cpu().numpy()\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_a_np, fmt='-', marker='x', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local)')\n",
    "ax.errorbar(s, by_a_np, fmt='-', marker='x', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local)')\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_a:05.2f}\\% \\quad RMS$_y$={rms_y_a:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_a:05.2f}\\% \\quad PTP$_y$={ptp_y_a:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_a)}{(nux - nux_id_a).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_a)}{(nuy - nuy_id_a).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_a.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-5, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d6cc7fcc-47fa-4571-bcb1-3d293649d04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "\n",
    "ring.flatten()\n",
    "error.flatten()\n",
    "\n",
    "dkn = {name: (error[name].kn - ring[name].kn).item() for name in nkn}\n",
    "dks = {name: (error[name].ks - ring[name].ks).item() for name in nks}\n",
    "dkf = {name: (error[name].kn - ring[name].kn).item() for name in QF}\n",
    "dkd = {name: (error[name].kn - ring[name].kn).item() for name in QD}\n",
    "\n",
    "ring.splice()\n",
    "error.splice()\n",
    "\n",
    "correction[section] = [dkn, dks, dkf, dkd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "89a58030-ef23-4eae-b496-58a900114df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S02 (U46)\n",
    "\n",
    "section = 'S02'\n",
    "\n",
    "target = ts[section]\n",
    "matrix = ms[section]\n",
    "\n",
    "nkn = table_nkn[section]\n",
    "nks = table_nks[section]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "facba39d-a294-41d6-a646-82982b3fca0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(U46_L02, error.next('MLL_S02').name, position=U46_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1bf14463-6928-43b7-9c8c-8b62a575a0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id, nuy_id = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fea71fd3-4c5e-460f-a259-c9f40c0b9cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id, etapx_id, etaqy_id, etapy_id = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3523bd8e-ab58-43e9-9ff4-11b94c354d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id, bx_id, ay_id, by_id = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9c7dabfb-20ab-43f3-a684-9290024b48a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "cfd4e713-4a1d-400f-af74-e6c7b59c35f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "8d431978-9947-48b6-8adc-045586f9e22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8c6cc612-5b01-458f-8f92-369a4de464d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(8.9060e-05, dtype=torch.float64)\n",
      "tensor(-0.0065, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id))\n",
    "print((nuy - nuy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e402b9de-d182-45f8-8eb8-e119e4bb2354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0., dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "135054ec-e107-4a8a-abbb-59beaf1e9b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2098, -66.6617], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8a877768-d5b4-4047-ba4a-c2df3f9b372b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parametric observable vector\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(error, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "\n",
    "def observable_twiss(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    return twiss(error, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)\n",
    "\n",
    "\n",
    "def observable_dispersion(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(error, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T\n",
    "\n",
    "\n",
    "def observable(knobs):\n",
    "    kn, ks = torch.split(knobs, [6, 4])\n",
    "    betas = observable_twiss(kn, ks)\n",
    "    etas = observable_dispersion(kn, ks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dfb46461-c9d1-44f5-939c-f59e4a9f37ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(4.1768e-05, dtype=torch.float64)\n",
      "tensor(1.9027, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check the residual vector norm\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "print(((global_observable(global_knobs) - global_target)**2).sum())\n",
    "print(((observable(knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8f579c32-b5ed-41b1-aa19-eba15a30ba04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.9027, dtype=torch.float64)\n",
      "tensor(0.1028, dtype=torch.float64)\n",
      "tensor(0.0073, dtype=torch.float64)\n",
      "tensor(0.0022, dtype=torch.float64)\n",
      "tensor(0.0019, dtype=torch.float64)\n",
      "tensor(0.0019, dtype=torch.float64)\n",
      "tensor(0.0019, dtype=torch.float64)\n",
      "tensor(0.0019, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (local)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(8):\n",
    "    value = observable(knobs)\n",
    "    residual = target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    knobs += lr*delta\n",
    "    print(((value - target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "46204ddb-8b03-4591-98b0-5c23089311a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.00608958  0.00533462 -0.01241458  0.00040878  0.00160792 -0.00808087]\n",
      "[-3.11576979e-17 -1.24527230e-17 -4.55368550e-18 -1.49645087e-17]\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kn, ks = torch.split(knobs, [6, 4])\n",
    "\n",
    "print(kn.numpy())\n",
    "print(ks.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "0933615b-21f0-4eae-a283-2af719cdf27b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name, knob in zip(nkn, kn):\n",
    "    error[name].kn = (error[name].kn + knob).item()\n",
    "\n",
    "for name, knob in zip(nks, ks):\n",
    "    error[name].ks = (error[name].ks + knob).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dda79966-44d6-4dd6-85c0-0d772a85bf5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7.1108e-05, dtype=torch.float64)\n",
      "tensor(0.0019, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check \n",
    "\n",
    "print(((global_observable(0.0*global_knobs) - global_target)**2).sum())\n",
    "print(((observable(0.0*knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "96971c27-3015-4da1-82a3-3ee8e98b5d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_a, nuy_id_a = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c5646911-061d-4a4e-bc8f-0530ed1dd198",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_a, etapx_id_a, etaqy_id_a, etapy_id_a = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2f0ca2da-e6b5-4919-bf04-ce8fe30d53f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_a, bx_id_a, ay_id_a, by_id_a = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4b9f2d43-3fc4-4a60-8f23-e52172b350b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_a, muy_id_a = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ef99adab-28c2-4258-9b31-b00ec5789cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_a = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3bc8b73d-b18a-425f-8f1f-c5bd4e394a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_a = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "43239da3-031c-4e6a-9daa-101d7c67e692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-1.5048e-05, dtype=torch.float64)\n",
      "tensor(-0.0084, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_a))\n",
    "print((nuy - nuy_id_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1edaca53-be40-4793-a4d4-f0b2627b105d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.8091e-18, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "96cdd3e3-048f-4685-ba6e-c3142bd1b82e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2104, -66.6829], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7bb1f16c-1235-4bc5-8a96-2423e140486f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7.1108e-05, dtype=torch.float64)\n",
      "tensor(4.6876e-06, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ab326b25-7dd4-439b-a39f-8e541dca28b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.042298359886024206\n",
      "-0.015160235675036986\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "82a3b0be-25f0-450d-96ff-0e5141aa2585",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f55c5da5-66cd-494b-a06b-3ba13e7e9164",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "eff7908a-6a6e-48b9-a5c2-fa8beb1b5a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "15d579e3-12f6-4064-b8f4-c094c2b2751c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ea4448f4-233b-4694-8e1e-5cceb1bddd63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9eb5c07d-1e2b-4cef-bafc-d2d58b4a1980",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a6f2048f-50d3-490f-b446-1ad3579224d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "234212d9-5a76-4b06-8ab9-c3973ed34459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-7.4771e-05, dtype=torch.float64)\n",
      "tensor(-0.0006, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "90f42a3d-bdbe-4a17-bd4b-473c97684ffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.8082e-18, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c35c2e72-3ad0-4364-aba4-a05e2acb34e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1709, -66.6974], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "50cdc36d-3c5f-4138-a6ea-6b13c357a822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DdVZOw1QdQrW1tQkPBBN1CNm2aSQnqmiceY3dKfj9/vgBT0lJiV1VVWVXVFTEP2fsfMc2yGtra+2WlpZJv8NsVVVVxb+L0+AF7Nra2tOatqSkJOHOcSaf0dzcbDc2NsYbAhUVFXZjY+OUB5g+ny9hZe98xsjfJ+qoWyhLKZvNzc3xhs3Ig8SysrJ5acwmazad6ZzvXVtbGy+v5ZbNpdAxtJzry5nUgTOZ30jK5NzOcyHryYXM40zrvTMhj7W1tfH95nR/vF7vuI6Nqqqq+Peuqqqa9MTsQtZ3k1no9mAyTLuQFjpbC92+m0wyZ2tkG7u+vn7KzsCRFqOz0raHO1N9Pp9dU1MTP9nhLM98nOhI5nW41KYVWerUWTkNU3UI2bbZAY0dHTBRh1BjY6NdVVVlt7S0JJxXokrXGS3g7OiczqGxZ+hs2x712kKcZampqYkvl9/vn7RhMt1pvV7vhGU+k88AEv4kKnvnsydqjDk7h5aWlvjOe6LPWShLKZvOZ5aVlY3aoU41mvB0JGs2GxsbR3X0lpWVTZmlpZjNZO8Ysu3lXV/OtA6cybI50yuTczvPha4nFzKPM633lnsene82m5+R5VtSUmL7/X67pqZmyowsdH03mYXc5ybLtAtlobO1GO27ySRztpwOYGfa6XYwLVZnpW2beqOsrGxUm8LJxXxJ5nW41KYVWcos2x5xsw9ZVgKBAOXl5dN6urAYRUVFE5ZXKBTimmuuid9jq6amJv4UP5m+/Px8Xnzxxfj9e0pLS5XRaVA255fqy5lTJueP6smZUx6nT/WdzAfVWyIiMpc8i70AMn/GPsEvEAjM+GEnZ5rm5uYJ3/N6vTQ3NxMIBPB6vXri2iwEAgF8Ph9erxdI/JRJSUzZnF+qL2dOmZwfqidnR3mcPtV3MtdUb4mIyFzT08CXmYaGhvhT+3bv3h1/wmYwGKStrW0xF21JcBpZk/H7/Wf8gc5stbW1UVBQEP+9trZ21FMmZWLK5txTfXl6lMn5oXpydpTHyam+k/mkektEROaaLgNfZgKBALt376awsJCysjJqa2vZtm0bXq9XZzglKVRWVsYPkiorK2lvb5/WQabIXFN9KclK9aTMNdV3Mt9Ub4mIyFxSZ6WILJiRlwU1NTVRXV096aV7IiJnGtWTIrLUqN4SEZG5ps5KEVkQoVCITZs2xW++XlpaSk1Nje6TJSIyRPWkiCw1qrdERGQ+qLNSRBZMXV0dBQUFBINBSkpK1JAVERlD9aSILDWqt0REZK6ps1JERERERERERESSgp4GLiIiIiIiIiIiIklBnZUiIiIiIiIiIiKSFNRZKSIiIiIiIiIiIklBnZUiIiIiIiIiIiKSFNRZKSIiIiIiIiIiIklBnZUiQ0KhEIFAYMrpgsHgAiyNyDBlU5KNMinJRHkUERERWV7UWTkNlmUl/CkuLqayspJQKJTw7/Lz8+PTTSUUCsU/t7y8POE0TU1NlJaWxj+3qKiIyspKNb7nwK5du8jPz6e4uHjK9VVaWjrhOl9oyubytxyzWV1dPW45A4EARUVFM/45nXnK7CiTymQyWap5nEx1dTXV1dWLvRiyDClbMlOBQIDy8vJ4G9+pb6urq9XOF5F5pc7KafJ6vTQ3N9Pc3ExjYyP19fXs2LGDe++9l/z8fBoaGib820AgMGVlfu+99076fnV1NaWlpbS1tVFRUUFNTQ1+v5+6urpxB0cLbdeuXRQVFcUPwCYri5lMO5357tq1a8rpGhoaKC4uju9gx3a4BYNBqqurqampoaWlJf77RPMsKyvD6/XOernnmrI5sWTNZigUijf88vPzKS0tpampadx0yzGbJSUl7Nq1i02bNo3Kns/no6ysLP5TWVlJZWUlXq+XYDCI3++PvzZymtOZ52JYrEwGAoF4R1kioVCIysrKUScy6urqxk2nTC6vTC50Hqurq0d9RqKMjTVZdpd6HhMJhULxfchS6FidrbnI03zldzbLNt026WJStubnM+YzW1Mdw8y3Xbt2UVxcTCAQoKKigtraWioqKuLvTWdE+2znuxzWYTJMK7Kk2TIlwPb5fBO+X1JSYgN2S0vLqNe9Xq/t8/lswK6pqZl0Hn6/3/b7/TZgl5WVjXqvsbHRBuyKiopxf9fe3j7lZ8+niooKG7Crqqrs2tpau6yszAbs2tra05p2IvX19XZNTc20y7WqqipedvX19XZ9ff24cnSWy+Es11jt7e2T5mAxKJsTS9Zstre3216v1/b7/XZNTY1dVVVle73ehH+zXLPZ0tISz+BUnDJobGxcsHnOl4XO5Eh+vz+es7Gc/Pj9fru2ttaur6+Pz+9MqS/PxEwudB59Pp/t8/ns2tpau7Gx0a6pqbG9Xu+4/cpYk2V3KedxIlVVVaPKezmaizzNV35nMu1M26SLTdma+8+Yz3p0Oscw88lZ3pKSkoTvNzY22u3t7fM236W+DpNhWpGlTp2V0zBVh1Bzc3PCHb/TIVFWVjbp37e3t8cbOYk6hCZqfC8253uPrRxLSkpsr9c7agc2k2kn46wLpxNusoahU55THVA6yzD278Yuk9NYSCbKZmLJnM2ysrKEDT/nYGdkx/JyzuZ0t8+56hiayTznw2Jk0lFfX297vd5xHTuOioqKhAdAzvY9cn7K5PLI5ELn0TnoHjutc8Jrou8/VXaXch4TaW9vj2+LTgftfHQGLKa5yNN85XemyzaTNuliU7aWVrYWs71i28PLO1FH5XzPd6mvw2SYVmQ5SL5ehiQ01cFGS0tLwhEoTodQfX29DdjNzc0J/97ZITmfM7ZDyGkEJRvn4GMspyIdeWAwk2mnw/m7yUavJVoniUznYKe5udn2+/0zWsaFoGwmlszZnKi8nXUx8u+WczadToqpDu7msmNouvOcD4uZSa/XO2pEzVgTNW6d5RiZV2VyeWRyofPojNBPJNEJNcdU2V3KeUykqqoqfsLKKfflNgJuLvI0X/md7bJNtd9PBsrW0snWTI5h5otzRdVExwfzZbmsw2SYVmQ50D0r50BtbS1gbtyeSFlZ2ajpEv19SUkJPp8v4fvO5ybbvXAaGhrw+/3jXndea2xsnNW0c8G5D9Z0biLu8/lG3btn7969AKPua1VdXc2tt946p8u4EJTN0ZIhm/X19QnnV1JSAgznD5Z3Np379E2UreUyT8diZdK5N9nNN9884TQT3cOvoaEBr9c7almUyaU/T1j4PG7dujXhvTmd1xLd33g62V1OeQyFQoRCoXgWnO89nft6LiVzkaf5yu9CtwcWirK1tLI1k2OY+RAMBgkEAvj9/oTLPJ+WyzpMhmlFlgN1Vs5SKBSKPx3NuWm70/GTSFlZWcIHlQSDQYLBYMKb8o/8Wxh+kEldXd2iPyQCzLJv3bo14Xs+n499+/bNatq50NjYiNfrxefzxR8a4dyYeuxNxZ2y37VrF8FgkKampvjNoyHxAXsyUzaTO5sTrQun3AoKCuKvLbdsjlRTUwMMd9Iu13k6FiOToVCI6upqKioqpv1QEaf+KC0tJRgMjuvkUSaX/jxh4fNYU1NDQUEB5eXl8brO2U+VlZWNyhBMP7vLKY91dXWjOie8Xi9VVVXxh6JMJBgMUlpaGn9YWyJFRUXz9iCMmZqLPM1Xfhe6PbBQlK2lla2ZHMPMB+eBj8neVpqLz1jO04osB+qsnKZgMBh/GqXzRLbi4mKampqoqamhvr5+0r/fsWMHoVBo3BN/nRFtk3Um+Xw+mpub8fl8NDU1UVlZSVFR0bSfpLlYZrJDneudbzAYpKCgID5ao6amhu3bt9PQ0MA111wzalq/309NTU38KaU+ny9+AAnJPypD2Zy5xczmRJzyH/mkx6WezbGcnBUXFxMMBqmvr5/3J/MuxjxnYz4y6RycjszMZOrq6kbVH/X19eO2f2Vyac5zpuY6j16vl5aWlvh+2XmC6c0335xwHzXd7C6nPLa0tIwbYeuMgNu5c+eEf+fz+eKjaRJ1yjY0NBAMBhdlRPlszMU+d7728Uv1CdrKlrFUsjWTY5j50NLSAiQe8b7Ylso6XArTiiwFnsVegKXC6/WOavDu3r2bhoYGHnzwwWmdsR95ue3IM1V1dXWTdgY5/H4/LS0tBAIBdu/eTVNTE4FAgMrKSpqbmye8jHci1dXVNDQ0zOhvwOwwy8rKpqwMvV5vfPTETKadK87n1dbWjhplUVRUFP/uI8u9qqoq/r1Grk9nZKJzIBkMBqmpqaGoqIiqqqo5XebZUjaNpZLNREKhEDt37qSsrGzcmeylnE2nI30s5wBoPs7az8c8l2Img8EgdXV11NTUTLsjrKSkhPr6etra2qivr6e8vDxhh6UyubjzXIp5BLPcdXV1VFVVUVRURGNjY8KMzTS7SzmPjl27diW8isEZAbdr1y527do14XI7l/kmGv22c+dOSkpKpl0PnG6+JjMXeZqv/CZLe2CuKVvGUsrWTI9hksVC7psmkgzrMBmmFVk2FvummUsBCW6Q79z8eLKnpDkPMXGMfXJyopvhkuAhJhNpbm6OP0F4oZ8WN9XNn30+X7zMZjLtdE3nISYjb7o/drmnU8bt7e2jlsuZp9/vt71e74I/IS8RZXO8ZM9mIn6/f0Z5WirZ9Hq9dmNjY/ynubl5xk8qnOnDTOZinnNtMTI59sEjtm1P+JCSiTj1wnRusK9Mzu8859Ji5HGiJ9s6r0/2ECfbnnl2l0IeR5ps3+qsg0RtGofzgLaxuXIejrfQD8mYyFzkab7yezrLlswP2FG2jKWUrbk4hjkdtbW1i/IApuWyDpNhWpHlQpeBz5JzRrKpqWnc5bMTcc5sOmedamtr8Xq9sz475vf745dPTXWp71xzzqK2tbUlfL+trS0+zUymnUuJLkvxer3TPvNUXV097nIy57Ln+vr6Ga37haRseoHkzuZIxcXFFBQUzOim2EspmyUlJfEfv9+/IJe7LsY8J7PQmXTWf0lJSfzfTU1N8XqvoaFhWvlwMjadm/wrk8k3z4ksRh25c+dOfD5fwpHjMJyxucruUsrjrl27pnwAVkVFBaFQaMLbq+zduxefzzduXVRWVlJRUbGg9+wMBAKj1t3Isp6LPM1XfpOhPTDXlK1hSy1bp3sMczqcenqh70W6XNZhMkwrsmwsdm/pUkCC0Wu2PXyGY+QItZG8Y0avOa85Z8S8Xu+4syPM4owZU4yimy9er3fCMzhjv8dMpp2Oqc5i+3y+Ga2XsVpaWsZNw5izjExydmuhKJuJJXM2R5rpiErbXlrZ9E4yWmO6ZjOKLRktZCadURFT/bS0tEy53BPVMSMpk/M/z7m2kHmcajTQyM+fi+wulTw6prOPcUaxTZQlv98/7vvU1tZO2daZD36/P+E6c8zFPne+8jvbZUvWkZXK1rCllK3TPYaZC87VUQs9cna5rMNkmFZkOdDIytPg9XqpqakhEAhM+x4dzg2Sm5qaCIVCkz5p2THZPSqc92Z6ZrO6upri4uIZ/4z8ntu3bycYDI5bPucs544dO2Y17VwoKSkhEAiMm59z752JnqTmqKysHHUfSOcsZmFhYfw1n8+XtPcGUTaTN5uO4uLiUTevn66lns2laKllsqKiAtu2x/04I6Wd352RGxONnnAyNNXDE5TJhbXU8jjVaKBQKBTP2Eyzm8hSymNdXd209rU+n2/SEXCBQGDUPQUDgcC0HrCXyOnmq7m5OeE6dMzFPne+8rtY7YH5oGwNW2rZOt1jmLngrN/pXFnhWOh900SSYR0mw7Qiy8LC9YsuXUwxssTr9SY8I5no7NfY+yYlmtfYsyI+n2/C+4aUlJQkPPNVW1tr19bW2lVVVXZzc7NdX18/52dbnO8ydtmcZRp5f5uZTDuTeU90Fnui+VVVVU15prCxsXFcWTlnnkfOz+fzLfp9r5ZaNuvr6+2amppR98JsbGwc9ftcSOZs2rYZHTCb7XGpZXOpjGJb7vWlY+y9aR3eoXsqjuWU/WTbpzI59/NciHpyofPolNnYfa9zz8qpvttE2R1rKeXRtqc38s3hfI+x+/zGxsZR66G+vt4uKSmZcL0sRH03mbnI03zld7bLlowjKxc6WwvVvpvMcsnW6RzDzCWn3p4oSy0tLYvafp+Lz1jO04osB3oa+ByoqamhsrJy0qfpOfx+Pz6fj0AgMO2nUXq9Xnbt2kVdXR1bt27F7/cTCoXi93GqqakZ9wRM57OLioooKiqipKRkRmfHpsPv98efJAiwbdu2+NOgnXsezmZagNLSUrxe77gzt4FAgLa2tvhIoJaWlvjZpJH3who5v2AwSGlpKY2NjTQ0NIwrr7Gcp1iP5IziaGlpib8WDAaT8ml8IyVTNgOBQPzftbW1o55CPtdnApM5m6WlpQSDQXbs2BGf50g+n2/CXC2nbCaL5VxfTtett95KaWkpJSUllJeXU1BQwO7du+NPHJ0sS8rk3FqoenKh81hbW0tTUxPFxcVUVVWxbds2GhsbqaurmzJjM7GU8lhXV0coFJpRXeOMUB35NODGxsb4d6yurqawsHDCEfsLVd9NZi7yNF/5nemyTXe/v9AWOlsL2b6bzHLJ1ukcw8yl2tra+BPIi4qKqKysjC+nszxzWX/D8lyHizWtyLKw2L2lSwFTjF6zbXOW3uv1jjqjMdF9RRobG+2qqqqE91xigjNYzmgB5x4i3qGnWSY6uzbyNRbgLEtNTU18ufx+/6Rn2aY77UT35PB6vTO6h1V9ff20l81ZvonOjDtnNFtaWuKjQaZzz7f5tJSy6XxmWVmZXVtbO2pZ5iujyZjNyaaDie/xuRSzuRRGsS3n+nKsyUantbS02GVlZfF8+v3+UdvpRMunTM7tPBe6nlzoPFZVVY36jKky5pjOyMqllkenHGbzM7J8S0pKbL/fb9fU1EyZkYWu7yYzF3maj/zOdNlm0iZdKAudrcVo301mOWTLtmd+DDNfmpubR7UPnOWZz5HEy2UdJsO0IkuZZdsjbvYhy0ogEKC8vHzUaAKZXFFR0YTlFQqFuOaaa+Jn0GtqaqY9AlGG5efn8+KLL8bP4peWliqj06Bszi/VlzOnTM4f1ZMzpzxOn+o7mQ+qt0REZC7pMvBlrKmpadRlKCMv05DExl4+NpLX66W5uZlAIIDX653yoRMyXiAQwOfzxS9TGJtRmZiyOb9UX86cMjk/VE/OjvI4farvZK6p3hIRkbmmp4EvMw0NDfGn9u3evZvi4mLA3J+pra1tMRdtSZjOvT6cezvKzLW1tVFQUBD/vba2dtRTJmViyubcU315epTJ+aF6cnaUx8mpvpP5pHpLRETmmi4DX2YCgQC7d++msLCQsrIyamtr2bZtG16vV2c4JSlUVlbGD5IqKytpb2/XDaFlUai+lGSlelLmmuo7mW+qt0REZC6ps1JEFszIy4Kampqorq6e9NI9EZEzjepJEVlqVG+JiMhcU2eliCyIUCjEpk2b4jdfLy0tpaamRvfJEhEZonpSRJYa1VsiIjIf1FkpIgumrq6OgoICgsEgJSUlasiKiIyhelJElhrVWyIiMtfUWSkiIiIiIiIiIiJJQU8DFxERERERERERkaSgzkoRERERERERERFJCuqsFBERERERERERkaSgzkoRERERERERERFJCuqsFBERERERERERkaSgzkoRERERERERERFJCuqsFBERERERERERkaSgzkoRERERERERERFJCuqsFBERERERERERkaSgzkoRERERERERERFJCuqsFBERERERERERkaSgzkoRERERERERERFJCp7FXoBkl5WVRX9/P263m1WrVi324oiIiIiIiIiIiCwpJ06cIBqNkp6eTk9Pz6TTWrZt2wu0XEuS2+0mFost9mKIiIiIiIiIiIgsaS6Xi2g0Ouk0Glk5Baez0uVysXbt2sVeHBERERERERERkSXl6NGjxGIx3G73lNOqs3IKq1at4vDhw6xdu5ZDhw4t9uKIiIiIiIiIiIgsKevXr+fw4cPTusWiHrAjIiIiIiIiIiIiSUGdlSIiIiIiIiIiIpIU1FkpIiIiIiIiIiIiSUGdlSIiIiIiIiIiIpIU1FkpIiIiIiIiIiIiSUGdlSIiIiIiIiIiIpIU1FkpIiIiIiIiIiIiSUGdlSIiIiIiIiIiIpIU1FkpIiIiIiIiIiIiSUGdlSIiIiIiIiIiIpIU1FkpIiIiIiIiIiIiSUGdlSIiIiIiIiIiIpIU1FkpIiIiIiIiIiIiSUGdlSIiIiIiIiIiIpIU1FkpIiIiIiIiIiIiSUGdlSIiIiIz0NDQsNiLMKGmpiaCweBiL4YkCWVVkl0yZ1SmR9uyjKRteulLlm1anZUiIiIi01RZWcnevXvjv+/atYv8/PxFW56x8y8oKKC0tJRQKLRoyyTJQVmVZJfsGU1Ws1nOufxu2pZlItqmZy7ZtmdInm3as6hzFxEREZljDQ0NeL1eABobG6msrMTn853259bV1dHU1ERLS8tpf9Z88fv9lJWVUV5eTmNj42IvjkxBWVVWk92ZnFGZHm3LS4u2aZlKsmzT6qwUERGRZSEUCnHvvfdSUVERf62goIDy8nKam5tP+/Orq6upr68/7c+ZbzU1NeTn59PU1ERJScliL44koKwaymryUkZlJrQtJz9t0zITybBN6zJwERERmdSdd8Jdd41+7a67zOvJpK6ublQjHCAYDFJQUDAnnw0smYOw7du3U11dvdiLsfCWSFiV1WFnYlb37IHrr4fLLzf/37NnsZdoPGV09iorKxd7ERbFmbgtxy2BjVrb9OycqdszLP42rZGVIiIiy5Rtw8DA6X9ONAp33AHhMLzvffCd75j+n+uug/7+0//8tDSwrNP7jERnfkOhEDt37uTWW289vQ/HnGHevn37aX/OQikvL6euro5gMDgnl3fNq7kKKsxvWOciqCirYy2VrM5VTB96CP7mb6CrC7Ky4Ne/hn374L//G6644vQ/X/Xp4mtra1vsRVgUS2VbBuZ2vzOfG7X2O4vuTN2eYfG3aXVWAsXFxXMy9FlERCSZDAzAm988N5914gT8/d/Dpz9t2vhr1sA995if03X//ZCefnqfEQwGKSkpYdeuXezdu5dQKERbWxv19fVz0sAKBoOUlpZOa9pAIEB1dTX79u2joKCAyspKqqqqppwWYOvWrdTU1OD3+wkGg1RXV9PU1EQoFMLv93Prrbfi9/unXAbnoCQQCCT/QeNcBhXmL6xzEVSU1bGWSlbnKqbPPAOhkIlSZ6eJ6PHj8P73wwUXnP7nqz6d+4wutLq6OmpqaggGg1RUVOD1emlqaiIQCGDbdsK/mW45NTU1UVNTQ1NTEz6fj9ra2lGdWGfCtgzM7X5nPjdq7XeW/Da9VLdnWPxt+oy/DLyyspJAILDYiyEiIpLU1qwxJ/dt2/x/zZrFXqLRnCcWlpSUUFpaSk1NDT6fj2AweNqf3dTUBDCtxl1TUxPFxcX4/X4efPBBampqqK2tpby8fNy0gUCA4uJivF4v9fX11NfX4/f72b17N2Bugl9QUEB9fT0tLS34fD6uueaaaT+d0efznZkPO0jysCqr451JWe3rA7d7eLCUZZnf+/oWd7lGUkYXT0NDA5WVldTW1tLc3EwwGKShoSG+3IlMt5xCoRA1NTVUV1fT2NiI1+ultLR01HrVtjwLS2Cj1ja9OJb69gyLvE3bZ7Dm5mbb7/fbkxXDunXrbMBet27dAi6ZiIjI6YvFbLuvb25+brvNtl/3Otu++mrz/9tum7vPjsVO/7vW1taOe62lpcX2+/1z8tkTtRVqampsr9cb/93n89lVVVXjlgOw6+vrR73u8/nssrKyaS9He3u7DYz6rmPnP5Lf75/R5y+auQzqfIZ1LoJqK6uJLIWszlVM3/c+2z77bNu+8koTzyuvNL9/4APJE1Nl9PScTpZLSkrsioqK+O/Nzc02YLe3t8dfm0051dTUjCv39vZ22+v1jprfWMtxW7Zte273O/O5UWu/M+3lmK9t+kzenm177rfpmfSvndGXge/evZsdO3ZoZKWIiCxLljUnVw9x113mCtoPf9jc+u+uu8xtAVNSzO+LbaInFRYUFMzJPn66Z6ADgQDBYHDczdh9Pl98NEBZWRlgLs0JBoPU1tZOezm8Xi/AhGfjxyooKJiTURPzbq6CCkkfVmU1saWQ1bmKaWUlPPEEvPyyub1dTw/k5cFNN83dZnA6lNHF1dbWNqPLLWdSTmN5vV5KSkriI+MmmgaW17YMzO1+J8k3am3Ti2epb8+wuNv0GdtZuWvXLm6++eb4k6tEREQksVgMbrhhuK/H+X8stnjLNFIgEEjYEA8Gg/GG2UJw7qmU6MmaYy+3cv49VSO2qamJ2tpaAoHAjG/y7vV6z7wbwyd5WJXVxM6krF55JXz963D77fD887Btm+lbv/LKxV4yQxmdvsrKyoQH8fv27Ut4/77KysoJOxpGTlNdXU15eTlbt25l586d+P3+Cct+JuWUiM/nG9e5oW15hpJ8o9Y2PT3anhNbzG36jOysdG4QOpON8+jRo6xfv37K6T71qU/xqU996jSWTkREJLlcf/3415JgkNqUmpqa2Lp162l/znTbC06jOhgMjrt309gnKY6cdqLGeHl5efzm6c79pawZPBU0FAolbPAua0s0rMrqmZXVK69Mmn6MaTvTM5rIRKO+ysvLqa+vn9VnOveVKy8vJxQKUVJSwoMPPjjh9DMpp0TGTqNteZaW4EatbXo0bc+JLeY2fUY+YGey4bMTicViHD58eMqfzs7OeVpqERERSWTv3r0JX6+traWmpib+eyAQoK6uLn72vK6ujurq6ik/32mkTXWpU0lJCV6vd1yDNxAIEAgE2LFjR/w1n88Xf3LjWKFQiFAoRENDA7feeisVFRWzegpjW1vbgo6akKlNN6sjcwomE8qqLATVp4srEAhQU1NDe3s7tm3HH5wxkZmU01ihUGjUJcLalpcn7XcWz1LfnmFxt+kzbmSlc/n3TLlcLtauXTvldLm5ubNZLBEREZkF535LoVBoVGOqvLyc6urqUWemd+/eTU1NDQ0NDZSXl9Pc3ExRURGVlZWTNuImO9M91q233hp/YmN5eTnBYJDq6mpKSkrGnSitra2ltLSUysrK+Fn3xsZG9u3bR3NzM16vl507d+L1eikoKGDnzp0zKptgMDhpw1YW1nSz2tDQwPbt22lsbIyPkmhqaprWfcGUVTkdqk8Xn8/no7q6msrKyngnkHO/uonMpJxKS0uprq4e1RHlHBt7vV5ty8uM9juLa6lvz7DI2/ScPdZnCWhubh73lKlET1IaSU8DFxERSV7OEw1ramrs2tpau76+3q6qqrJbWlpGTdfe3h5/+mJNTY1dU1Mzo/kw5umJjkRPUWxubrZLSkpswPb5fJPOy5nW6/XaXq/XLisriy97fX19/HW/32/X1tbaJSUl03qKo/PEx8bGxhl9T5k/082qY+R6raioGNeGnYiyKrOl+nTxnwZeVVVlA+N+fD5f/LvMppxqa2ttv99vNzY22n6/3wbskpKScetW2/Lyov3O4j4NfClvz7Y9P9v0TPrXzqjOyrGPgLdtdVaKiIgsZYkax1Px+/3xBp1zwD2VkpKS02qwLrTa2tpJ2zey8GaS1fr6+lF58/l8yqrMO9Wnc2O23625uTlhx0Bzc7Pt8/nsioqKuVi8eaNtOflov3P6ztTt2bbnZ5ueSf/aGXMZeENDA4FAYNxj4J0nLjmv19TU6D4bIiIiy0xdXR0tLS1UVlbGH7TnvF5VVTXl31dXVyd8GmSyqq+vp6KiYrEXQ2apra2Nbdu2AcNPRZ1u+1RZlfl2ptWnMzWbW46BOS71er3jntzs9/spKysb95TfZKNteWk7k/Y7M3Gmbs+w+Nu0Zdu2vWhzTwKVlZXU1dUxUTGsX7+ew4cPs27dOg4dOrTASyciIiITCQaDBIPBcQ3BRJqamggEAvj9foLBYPz+TTNphDn3Y5vOwfhiCgQCFBcXx59CKYtvJlmF4QcblJaWsnv3bgoKCiZ8UmkiyqrMlOrTxRcKhdi0aRMlJSVUVlaydetWgsEgTU1NVFdXU1tbm7SdgdqWk4/2O4trKW/PMH/b9Iz61+Z0TOcSVFFRocvARURElqD6+vppX6I0F1paWmyv17ug85yNsfcjksU306yOnLakpMRubm6e0fyUVZkp1afJob293a6oqLB9Pp8N2F6v1y4pKUn6+0BqW04+2u8svqW6Pdv2/G3Tugx8Btra2hZ7EURERGQWxj4Vcb75fD7q6+u55ppraG5uXtB5T1d1dTU+ny+pz9afiWaS1WAwSGlpKS0tLfEnuU71hNWxlFWZKdWnycHr9c5oNFsy0LacnLTfWXxLcXuG5Nmmz9jLwOvq6mhsbKShoQEwG+PWrVvHhUmXgYuIiIjIQqqrq6OgoIC9e/dSU1Oz2IsjIiLLnPY7shBm0r92xnZWTpc6K0VERERERERERGZvJv1rrgVaJhEREREREREREZFJqbNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoI6K0VERERERERERCQpqLNSREREREREREREkoJnsRdgIYVCIXbu3EkoFCIYDNLW1sbNN99MWVnZYi+aiIiIiIiIiIjIGe+M6awMhUJUV1dTU1OD1+sFIBAIUFxcTFlZGfX19Yu7gCIiIiIiIiIiIme4M+Yy8J07d47qqATw+/3U1NTQ0NBAU1PT4i2ciIiIiIiIiIiInDmdlQ0NDRQXF497vaSkBEAjK0VERERERERERBbZGdNZ6fP5aGtrG/e6M9Iy0XsiIiIiIiIiIiKycM6Ye1Y2NjYmfD0QCACwbdu2Sf/+6NGjrF+/fsr5fOpTn+JTn/rUzBdQRERERERERETkDHfGdFZOpLa2Fq/XS0VFxaTTxWIxDh8+POXndXZ2ztWiiYiIiIiIiIiInFHO6M7KpqYmmpqaqK+vH/XgnURcLhdr166d8jNzc3PnaOlERERERERERETOLGd0Z2V5eTm1tbWUlZVNOe3atWs5dOjQAiyViIiIiIiIiIjImemMecDOWOXl5dx8881TXv4tIiIiIiIiIiIiC+OM7Kysrq5m27ZtVFVVLfaiiIiIiIiIiIiIyJAzrrOyrq6OwsLCcR2VdXV1i7REIiIiIiIiIiIiAmdYZ2VTUxOhUCjhiMpQKLTwCyQiIiIiIiIiIiJxZ8wDdoLBIJWVlZSUlFBdXQ0Md1A674mIiIiIiIiIiMjiOWM6K0tLSwkGgxNe7l1TU7PASyQiIiIiIiIiIiIjnTGdlS0tLYu9CCIiIiIiIiIiIjKJM+qelSIiIiIiIiIiIpK8pj2y8rHHHmPfvn3zuSzceOON8/r5IiIiIiIiIiIikrym3VnZ2NjIZz7zGQBs257zBbEsS52VIiIiIiIiIiIiZ7AZ37Py3nvvnfOF2L17N/fdd9+cf66IiIiIiIiIiIgsHTPurLz22mvnfCGCwaA6K0VERERERERERM5w037Ajt/v59Of/vS8LMR8fraIiIiIiIiIiIgsDZY9HzegXEbWr1/P4cOHWbduHYcOHVrsxREREREREREREVlSZtK/Nu2Rlaejs7NzIWYjIiIiIiIiIiIiS9i8dVbedtttbN68GbfbTX5+Pm63my1btvDlL395vmYpIiIiIiIiIiIiS9iMH7AzHW984xtpbGwEwOfz4fP5aGtrIxAI8OlPf5rGxkYeeOCB+Zi1iIiIiIiIiIiILFHTHlnZ0dHBgQMHppzuvvvuo6mpibq6OmKxGPv37+eXv/wl+/btIxaLccstt9DY2MgPfvCD01luERERERERERERWWam3VnZ1tZGSUkJH/3oRye9B6XzvJ5t27YlfH/btm3Ytk1bW9sMF1VERERERERERESWs2lfBr5p0yb2799PXV0dfr+f7du38+///u/jpisrK2Pjxo34/X78fj8+n4+CggLa2toIBoMEAgGKiorYvn37nH4RERERERERERERWdpm/ICdiooK9u/fTzQaZfPmzdx+++3jpgkEAtx44400NzdTX19PbW0t9fX1NDc3c9NNN7Fv3z5yc3Pn5AuIiIiIiIiIiIjI8jDrp4HX1NSwd+9e9u7dy+bNm/nhD38Yfy8vL4/a2lpisRjNzc00NjbS3Nwcv2dlXl7enCy8iIiIiIiIiIiILB+z7qwE8Hq93HLLLfziF7/ge9/7Htu2bePxxx8fNc2ll17KNddcw6WXXnpaCyoiIiIiIiIiIiLL27TvWTkZn8/HvffeSyAQ4MMf/jCFhYXU1dVxzjnnzMXHi4iIiIiIiIiIyBngtEZWjuX3+9m3bx+f/vSnueaaa6Z8criIiIiIiIiIiIiIY8adlZ2dnXzpS19ix44dvPGNb2THjh18+ctfHtUpWVJSwv79+7n00kvx+/3cfPPNc7rQIiIiIiIiIiIisvzMqLPy1ltvJT8/n6qqKurr62lsbKS+vp5Pf/rT5Ofnj3syuPPk8Pz8fDZv3sxtt902pwsvIiIiIiIiIiIiy8e0OysfffRRKisrufTSS6mvr6e5uZn29naam5u59957ueSSS6ioqOCxxx4b97dVVVXs27eP/fv3j3tyuIiIiIiIiIiIiAjM4AE7+/btw7IsfvWrX5Gbmxt//dJLL+XSSy+ltLSU/Px89u3bxyWXXDLu7/Py8vjiF79IRUUFn/nMZ8jLy+Pqq6+eky8hIiIiIiIiIiIiS9+0OytLSkqwbZuSkhIqKyspLi6Ov7dv3z5qa2uxLIuSkpJJP8d5criIiIiIiIiIiIjISNPurNy0aRP33nsv27dvp7m5edR7tm0DUF9fz8aNG+d0AUVEREREREREROTMMO3OSoCysjJisRh1dXUEAgHa2tooKCiguLiYm266ab6WUURERERERERERM4AM+qsdFRUVMz1cojIcrFnD9x+Ozz/PGzZAh/+MFx55WIvlYiIiIiIiIgsAdN+GnhnZ2fCJ33Phfn8bBFZQHv2wMc/Dr/5DXR0mP9//OPmdRERERERERGRKUy7s7K2tnbUQ3Xm0nx+togsENuG//xPOHoULAtycmDjRujsNCMtRURERERERESmMOPLwLu6uuIP1Jkrra2tc/p5IrJATpyAffvMTyAAv/41RCLmx7Zh0ybIyjKXhIuIiIiIiIiITGFGnZW2beP1eudpUUQk6fX2wmOPDXdQHjw4+v2cHDOS0rahqwtiMejpgcsuW5TFFREREREREZGlZdqdlT6fj5KSkvlcFhFJNtEoPPfccOfk00+b1xyWBRdcAFu3mp/WVvjEJ6Clxby3fz8UFsKHPrR430FEREREREREloxpd1Zee+21XHvttfO5LCKykCZ6avfRo7B3r+mcfPRR6O4e/XdnnTXcOXnppZCdPfr9b3wD/uqv4MABKCqCf/s3PQ1cRERERERERKZlxvesFJFlwHlqd2cnZGTAAw/AL34BF144euQkmM5Ivx+2bYPiYli7duLPvfNOcLng85+H//kf05n54otmpOX118/nNxIRERERERGRZUCdlSJnottvNx2VOTlw6JC5x2R/Pzz1FFx0kem0dEZPnnee6YCcDpcL7rgD3vY28/svfwnNzWbUpoiIiIiIiIjIFNRZKXImev5585TuU6fM7+npZoTlihXw4x9DZubMPq+nBx5/HDo6zMjMf/kX87plQXk5XHfd3C6/iIiIiIiIiCxL6qwUORNt2QJNTdDXZzoUN282T/beunV6HZWDg/CnP5lRk4GAeQhPLGbec7vNCMvBQUhJMfe4FBERERERERGZBnVWipyJPvxh+PWvzaXfWVmmozI3d+KndkejZjRmIGB+nnrKdEaOtH69ubfliRPmfcuCSAS+9z246ab5/04iIiIiIiIisuSps1LkTHTllfBnfwa//S3k5cFrXjP8NHAw97B86SXTMdncbC7x7ukZ/RmFheaBO5deajopV62Cu+4yl5G//e1mtOXTT5snitfWQmXlwn9PEREREREREVlS1FkpciaqrTWjHy+4AO6+24yK/OpX4Yc/hIIC08HY1jb6b7Kzhzsm/X7YsMGMnhwpFoMbboA3vQne8x5ITTV/99JLC/fdRERERERERGTJUmelyJnowAE4csRcAn7vvVBfD88+C2vWmB+AtDR45SuHOyc3b576qeDXXz/87yuuMJeXgxl1KSIiIiIiIiIyBXVWyvK0Zw/cfru5z+KWLaMvcRYz2nHNGnOp9he+YC77XrsWrr7aXNrt98MrXmEekDNb73wn/PSnZlTlI4/M2aIvC8qnnGmUeRERmSntO+RMo8yLxE0xTEpk7uzZYwbeXX65+f+ePVO9cRoz+vjH4Te/gY4O8/+Pfxz27JnzWS0VI7/3DddFaf35I+DxmE5Kl8uMmty3D772NXMZ98UXn15HJZhLxs87z1wa/tRT/PEnJ8/4sr/+enj8axPnc05nNPR5yvwCfG+V/SiLnfkp3lq2lPnFk/B7z0dhKPOjLFi7cpLPPBPLHRYo85O05ydchmVuQTM/02VY5pT5xaHMn9ks27btmf7R9u3bue+++wCorKzkG9/4xpwvWLJYv349hw8fZt26dRw6dGixF2fJcurezk5z5XFPj3n49N0Ve7i4LsEbX//68FmkSAS6uoZ/ursn//3nPzeXOKelQUYGFBXByy9z7IKreMPhO+nogPR0GBgYP6vlaGzZb2h/gn/p+CRFBClM6YSNGyE/33RSXnfd3M78Bz+AT3yCUH8an8n5BvfbbyI7O/FqXo4S5f5/Oq7njWm/Jj3dMiF0uUyGN22C970PMjOHf7KyTIazska/7vykpJj7hk6wgT1e8XU+UHclHR2mbzoSMc9Tmqjcl8vJ3Enrm32n+QVtG/r6zIf29sLvfgef/7xZhwUF5rURZd/ebgYy9/VNnPnlUu4wWeZ/Q3phlikfgFDIfNkbbjD3tnV+UlJG/z7RTyAAn/mMKfcxlcoeruTjH4f2dvPSmVDfTJT5r38drmSOA7ZnD3zsY2ZmI8reyXxbm6m2wuEzN/NXuffwdT5OVn+rqeP7+02dfdNNcOGFpt52fpzbq4z8t8s1ehrLgiefhK98xWxDOTmjCtjJfChk3jqTMz+tduUczczJfChk2pWDg8p8VqTDtL97esybn/2sOQEejZrMRiLT//dtt5kCKygw28Tq1eb2QlddxZ4P38nHPw6trabMz4R97IJm3pnhmIJz6pqODlOlTVbuy8mkmY+OKfevfAW2bTP1fl9f4p+R74389733mivSsrNNvX/WWXDy5KjMnzpl6vnJjmPPmMy3tZnK1wnil79srhJMSxv/XIXpznCSzGdkmNW13Mt9vs2kf23GnZU333wzBQUFlJSU0NLSwhe/+EUsy2Lv3r3x9xsaGggGg3i9XkpLS7n55pu5+OKLZ/+NFpE6K+fG9dfDr35lKprubnPF8YkTcI/neq6I/MbUur29poHS3m4aJJdeajof+/tnNrNAwHxOaqr5PT8fMjJ44WQepZm/B0yFk5Vl3i4pgTvvHP8xy6XCuf56ePBBU8QeD1zffwuvabmHQ6yj/MrjZsXccw/cccfcd1j29MCrX80zz7v5cWoZ/5H+T1iWuQK9qwuuump82S+Xcgd473uhsdFEce1a0/b48h8uZ5PnJQpSe4cnHBw0K8fvn9kM3G7TWnzySdOYycszwV67Fl56iT2eq/hA5E4syxzEpqSYP3njGxOX+4SdHUus/D/wAfjFL4aP8c89F9bu38N/DnycDXmdpiB6ekyr42MfMxM4nY+9vaP/Pfb3vr7RM3vmGeJHqtnZ5uTIUNm/d+BOentN/2ZhoVnNr3/96LJfTuUOw2WfmgorV5pIfvkPl7Mh9SirPKHhCWebecfIck9LM539x47BVVdxPXfys5+ZY96sLFi3zuxvlnN988EPwi9/aYo0GjXFcfgwVFywh88e+pgpgNRUsz9NT4e//Esz0eCg+QmHR/9/5M/I18JhaG4ePkBYscI8oO3AgXjme3pM5nNyzMD6sfvY5Zb5977XZN7lMtv5mjXwV/uu5/X2gxSmDRWGbZuy93rNg+1mY2TmnYPYnp545u+/3xy8Zmaat06dWt6Zv/56aGoy1fjgoHnm36FDQ+3KwV+Zej4aNWXf0QHnnANvf/vw+nA4vzuvjf03mJPgBw+aoBYUmJ+hzL8/fCd9fcN9x7EYXHPN8s78e95j2jaWZb7H2WebzF9l/5oV3gjxHd/pZn5sez4nx/zk5XH9lt/HM+/xmMOGoc1h2Za9k3mnb2bDBrPbu8dzPVf0N5kNIRo1E/f3m/vFX3GFafilpJiCcv4/8t9O43Dk7y++CN//vvmc/Px4wP993depe+ZKYjHTji8oMG9dffXyrWvAHBr94hemaDIyTLvir/Zdz5/zG1ZuzDZt8GjUBCwvb+4yn5Jidiz5+Vy/5fc88IBZJS6X2f0ODCzvduX115u2TUqK6T84+2zTTXCP53qu6P2lqWssK3F9k55uVpbz/0Q/I98LBs0DaPv6TOaHOkCdzDvHUmdK22Y+zaR/bcb3rLRtm09/+tMAXHrppZSVlbFr1y527NhBMBgkPz+fiooKWltbCQaD/PKXv6S+vp66ujo+/OEPz+4bzbFdu3bR2tpKYWEhLS0tlJaWUlZWttiLtTgWYE9y9Cj89rfm2Kary7x25Iipy/Nefh7yY6aF6YhGzYHVyZOjPygra7ihkp09/O+xv3/5y+Zp1qtWmQfJtLVBairPcRkejzkLC6YSGRw0A6OcE8Aji6Wy0jT2CwvNKPxAIHkrnESr8bWvhf/7P9OwaW83lTzAFnsPbjvCbzwllH/9crPHczooY7G5XbCsLHjDG+h99iEu7G8m6rHBsuKr+9FHx3+Pj33MlHtmpslJMpf7RA4dgoYGc3zjDBI4eNDsP0+lncVF3Y9CSppp7aSmmi964YXwoQ+N7iQb22HmjOhzOvCjUbNRtbWZHbUzrW1DVhbel58nZY0pTzDL0tNjDjIOHTJ9DI7bbzcfk5Vlts2VK83mc/vtMyj7BWyZJprVxRfD//4v/OxnpgHntPVaW+Hv+2/H09cJq9JM/WLb5gv/x3/MrlHpdBRHIqaRY1mmcE+ejJd9OGt4kzp1ykz6xz+a///hD2b5f/5zs8pWrzYfsXHjLMo9CYRCo8s+HB4+638kfROv6HocclPiJ484edLcJmLHjsQdZFP99PebdWBZ5vcDB8xR0/PP83TEvO3xmLJ94QWThaefHr3Me/bARz9qFiU/f5b1/AIfjY2d3Qc+YL6jkyMn80Mx5JLm2yFy2ARxcNDkvqsL7rpr9gdTPT3DZd/aava/Q5mPZA/373R1jd7HBgLDme/pMZmPRMzBx8svz3HmF2C9HDkCu3cP1/OpqXD8uGkzFEWfxxXpg1TbdKZnZpqdcEqK6U2JxYYLKtG/E/0895xZqenpZmUfPRrP/LOx4QPYvj5oaTH5f+qp8cXy0Y+aKtDrneU+Ngl6IP70J3MStr3dZAlM2WdlDbUrMweGG5xgVtCBA/DQQ7Ob4aFDwx0RPT1mnQ5lPuY1Hw/mIDUchocfNvVfWpp5/fbbzXsrV5r1c845ZvBUMmc+0cetX28Gfj3wwHDm29tNp0lR7Hmi0dhw50FKisltNGq+8MhOsen+u7/fLMCKFaZS6+w0n3/ZZTz37HDmo1GTZds251J+9zv41rfMn544Yf4sP3/4JFoyt20m8txzw5l36vmjR031m/fy85DaN74N39Y2+vhqJkaeHGltNbeLOn6ci4/djivbXLHjzMJp00QiZrXBcMdNW5t57fDheWjPL8B66eqCH//YtG36+03Z9/SY+rMo9jx9dor5cg6nEgazHWRmTt5ZNrbD7JvfNAMQzjrL9ET39Zlwv/rVPPecWQanb+7kSbPK9+41vzsDCW+/3ay6rCxT9kuxXWnbJlO/+IXZfkdmvrAQ8l5+Djx95ks7oYtGRw8o6O+f2YCnkZk/ccJk6uhRLj52O57cK+P9B07b5re/NefB8vLM6049v2aN+f/69WbzW0rlnoxm3Fm5YsWKca9VVVWxdetWPvKRj3DjjTeOe7+hoYGKigqARe+wrKyspKioiJqamvhrpaWltLW1xZfxjDGyZygtDZ54wjwV+sor4R/+YVZb1sj9xqpVpnHw0kum7ohEzE51cNC02WMx6C1cD8cfNfNfscJUEKdOmeHz//Ef5kg3J8fUuFM9idrh8RC/BjB7+GzXS5e+iROPmY/xek1FePy4Wbb3v9/ch+Kpp8yyB4OmvsrIMO/PqqJfoMbN2DM5Dz5ofjZvNt/RuZrG44FVgwfJ736ZftI4vvZVpoNsyF1cRwy4fq4XsKICz9f2kBNuZ33kAP1rN9HWZnb2J06Ys2a9vaZCP3zYrP6UFLN/Ofdcs/rmpUG/b9/wKKOtW2e9fkau5sJCky2nbZiebsre5TK5b2+ziUZsbLd7+ODUGUn8+c9Pf/6xmAmm0zn5139tHmJUUGAaNSdPQno6odWXceKE2Zfn55tyPXjQ/PkNN8All5gd//795uAvEjHrpbMTzj/f5On552dQEM51iBkZ43t+5nB7GJv5piZz5nXDBtMuTE01B4r5+aZ4T5yADX3PQ3qq+QXMynG5zEHRlVeOvtTe+Xeiy/CdfzuX4F9/vfmuubmmcI8cgawsThZeRuiYmc26daat39dnGjZvfavpQItGzXdw+toyM83izKjcp1tg85T5detMw+z55833cMreue1Aayu0xbxYbmu4Y7e314z+3blz9hu2U+7r1pnC7O83ZX/ZZVjPmAPp7GxTpq2tptyPHIHPfc7U7/v3D5/LSk8372/ZMsMOBCeIzlD9ecz8yNl1dppl/slPzJ02iopMbmIx02ju6DD1aHY2XBh9YrjnPi/PZL6316yH97xn9KX3Iy/BT3Q5vvPa3/2dOYrIyjI70ZdfhqwsTq0wmU9LMx2QoZDZ3Pr64E1vMpsHDGf+pZfM+nG55jjzIwsK5rxts3q1qVsOHDDVuFPPp6ebGB4/Dt12JlmxbiADfD5TbgcOmCEw//zPs/tera0mYxs3msJrbzcV+GWXkXbYrOacHLMPOnnSHFAdOQJ/+7dmmQ4cGJ359nbTXz2jjuKxlW+iHv556jR77jkT4YwMk61w2JS7005zLo2MeAvhZGC48rUsE8RXvhL+5m9GXxo48jL7iV6zLNMeffxxM7OODlOQGRm0rbyM1mNm9a5ZY9q2p06Z/19/vfnajzwC991nlrW1dbhunFXmJyrbOc782NX8i1+Yzpr1602+0tNNfeOMHjp+HI5wFhdGHgV7qAJwNpLXv95cuTMbF188vCA5OWZGHg+89rWkBoczv3atyXp3t/n/u99t6iGv12wqTpPJ4zGZn1XbZrLMz7GRqzk/38z25MnhzDvl3tExNJAyNwdah64W2bzZVKoHD8KrXw3/8i/DZ82dH+f3sf8f+e+dO4dvTdHbayqK/Hy29D7PiROmLPPzTZ3X3m6K54YbzEDOP/zB5MXpZPJ4zCFeZ+cs2vNTZb6jwyznHNfzZ51lcvXCCyZnznGJU98cPw5B17lcMPAE2B4TtjVrzIHM615nMu+0E2di1arhY9icHFPZud2wfj2ZB80yZGWZTezwYVOmbW1w7bXDx7VPP21Wo3O+Jj19FvXNIp2Idc7JeTym3G3bbL/5+ea79vcP1aNZ6RDqMxOff74powMHzMnAW26Z/iX4I39/4QXTcHK7hxspIzLv9B/EYmaX0t9v7t61bZs5QfiDH4yu53t6zMfNWT1/hppxZ2Wr062cQEFBQcLXy8rKKCkpoaSkhPLycnJzc2c62zkRCASoq6tj7JXvNTU1FBcXn3mdlbffbraori6zF3QqVGcHUFFhDm6nubE4f3bypNnGAwGzsW7ebC4P+L//M/s/MHVvaorNpgvccGroKCstzbQ0Vq2CqipT+czGlVeaRoSzoXu94PFQEv45/xx9Oz3hjPhBxfr15kDvwIHhS1rc7uH2Xmqq2Q86Iy/HVTjT2YlmZk58EDsHnQfOmZzVq81JOOfK+XAYLrsM3nvxU/z8iQ30peaxtWMPkZiLZ6wLWBs9zF13mUGVd901fBX4nDv/fFq9Pjwnj/Gqwb08lrqJrCyzurOzzQjEaNT826leXC6TnSNHzOqb0wb9xz5m9uadncOngU+dMutnlpk/ccLsVJ3G8JYt8OY3m1FP//Vf5nhycBDOe/4nrPC0Mbj5QtJftck0/rZtm/l6d47uneHAf/d3w41pp3cmGmXwXW8hercpX6cTaeNGc+Xt00+bE7i2PTwCLRYz+QfzET09JkOTlu1f/qVZeZ/+tLlsyO0ebiGdOmWWq6TEHK0NDpqjzQcegB/9yBxIziL3TubPOsuUbXe3yXwsZq7ye8c7zDRdXeb79fRA0LOZi8NPQqrbhMo5A/H615vG/Gx9+MMmO51Dl5cPVR77LnsfnlPDk6Wnm3bshg3w2GOmDszLGx4IYdtm/Rw5YlbhuHKfSqLMX3EF3H+/6cw+eXJ4Jc9B5ltbx9fzf/Zn8K53wa23mk1scBByWh7jAvfThDdtJv38DWa+l112+o0vp9wPHzYdxYcPg9tN15pziT5llsmyhhvpbrc5yPjSl4Y7mNrbzUc5ByHOyOIp6/nt203LubraZMjjMevd4zEfWlUFZWVQV2c+ODv7tDMPZhHa283ynjgxPFiyo8Osk927h0fQ9/ZCdmaMlVk90BM1Ydu0yXx5p0FfWTm7sv/EJ+DZZ813drnMBmZZ7H31B3GfHB5M5XIN72Mff9xk3rlMFoYzf+yYOSY77cx/6EOm3bBzp6kY3G6zUp2ZPfSQKaivf324QKeReyfzp06Z5R2Z+Te+0exD//M/h7eJvkOtZLt6caUOXWrpNCJyc80yzpaTeefMxlC7LTwQIzIYw+Nx4XKZuj472/w/K8tsj04TyxkN5RxDnzgxzcxfd53J7uc+ZzpBnJMOaWlm2/vCF8zlcwcOwCc/OaedZh/7mMnI4ODwiZDzzzd1za9/PTx4prcXcjMjnLM+DO2e4RPc3d2m8v3c52Zf56SnD7fpnNHJLhfPXHEDrh+bpoQzGPCss8xAwmefNScUXC6z3JGIKXOnjz87e4aZT9Rp9sgj8Fd/Bd/7nmlPON/XGXrlFKDzbIFpZt7ZxxYUDA/u6u83/37HO0yM/+M/zOY1OAjHj9l4Um1cqe7hs+QHDpx+5se251euhGiUyI9+RlbfO/F4VsRHVjqDjvv7TRE4/W6WZfLvZOfwYfPalG2bD3/YTPTlL5svnpc3nPkTJ8x9CS+/3Kz4OWzTO6vsxImh+mSoXXneeaZ989vfDn+Xri7IT+tlw9oB6PCYZXFG/xYWmjMVF100u7J/+GGTsfXrTQ9SXx8MDDBQ9Gp4xixberpZ3U7b5qmn4Ic/HO7fjEbNesjMNPXnqlWz6DAbe2b6d78zJ9p+/nNz5hHMsjnHsL/7nRkx8M1vmt9nWM8natts3WpOMt95p9mPDQ6a9XM8awOeKOaFjAyTk/x8+MhHhocCztTYzG/cCCdPEnvo92zqepRHPJfGB0A4gzazs80J+0jE/N7VZTLvDHA+fNh8l2ll/sorTRl+5CPDw3hffNEcxN9yi+mInexvZ2Giev6VrzTHUT/72fCm190NK6xWzjqrH7o9w/t5Zx/74Q+bQkhPN239mXj++eGT4M89Z2Y6MEBky2XEnjT1iVPHbNhg9kOHDpk/sW2z7CPr+Y6O4WPxGRWGM/AjJWXpXmI4h2Z8z8pbb72V0tJSNm7cOOr17du3c9lll/H3f//3E/7tiy++SF1dHTt37pzVwp6uyspK9u3bR3Nz87j3LMuivr5+3OXgy/melZ2rzyWlt4PU3hCuWATL7SYWi4ENNuassrvAC3l59B/vIDYwQP/qjRScnQNbtvBE6zo6V2/mituuB+BN5+7n9wc34MpIi+8zIr0DFK89yhdKfsPjx9ewt/BNPPWUqTjf2vN9vhCuJjPbTc7rt5otPsHnztRDN94JLhdX1A1d2hwKcerSUo4cjNKY9hc0vOpfsSxTt25rfYALVx7nb37+Rp45tYKMbI+5mrMzwp/FHqLCup3NrhZeSt3CdwavZXPaQb5U/n/w/PO0vdxFxomXsFJTSV9XCJ2d9HcN0nb2JeQdeYb07lO4szPjZ+gjXb0MZBfSseY8Cl9+DFekH3dkEJcFUdsimpZJOD2XF7IuxnXJxbyq8DCd9z9EX94ajp/35/EyeajiLojF4uVz0eoTtPZmMJiaEz8IjPX1U5DRx3PH83nk43fR+st9/O9ZH+fq5l0UhZ/jR+53UfjKddR2vIfeXlPXv+Y1Zme8fr2pp9etMweQs7k/8UitrfCVjf/JX/R8nwOZF/Lfr7ydLee52Nb6APf+eiX7Bl9JSmYqlgVdHVGitkVu6iCe7HRzwNvdwauznuL+jtfG12/2L+9jbVo7q1fGxmXmibd+lqKHv01WeszsZLu7GeyL0LlqM9mnDpDS14llx7DsKFZKCrFIlJg7FTsWw7JtPOtWQ24u/S8dn1Xmo70DbD3rCL96cRMP3Xgnjx9fw+9z38Qff3qK2v4PstEOMrByHRcebhrO7Jh1OlMP3Xgnucdf4FWFh83OtqODruPd7O08n7/JuY2Mczfgdg9n/lWrjvHpX17D48fWkJaVgmVBpGeAvrAblwtS0s15rFWRw3zJ+wWufXPfcOaPH8DtcZHqzYSuLiIDYfry1pDReQLsGJ7szPhyRbp7wXIR86TiHujFnZNFpKcfVzSMZdtEUtNJWbuK7qOdHPWez+Y3b4Hnn+f4SRdHB/LpfsO18TIZWUYXrT5BqD+NgdS8+AmQWF8fKzP7eKG1gIdvujNe3zzyiGlvfab17/ir6FfwpHnMKemBAXo6o7Rc/gFe9dN/n1W5jyv7Z56BEydobbe4u+fdfDPvM7zyqhUcOTJc9lsKTnH9D99OW18G6dkpDHQPEo6541d6pqS5yXX38E8FX+PSN6/lirrr4vNY/dxvyeg4Ru6br5g48x4PdHcT7gvTm7eWjK4TuAd6sWzbZN7lwo7ZxFwebDuGhYV7ZQHk5NB/rJ3YwOCkmX/z5v388dA67LSM+PFwuHeQS9ccZc9L58TL/pH8N/HLnwzwtf4P86roo7i9ORSd+ONwuc115tPT6XqihUODa/intBracjZy9us2xtvRW1sf4M4HN/DU4BbSs03mu4fqm4yUMKlZabjd4O5o5QOpu0fX88cP4PZYpOZmQHc3kcHIjDIf7gvjjgxCLEp0lpkH2LKilcOd2aRmmWtLs7Ig2tFFYWYft7zt5/HM791r6t3t/d/mC31/R6bdg2fNyviTV04396PK/k9/gqNHaetO5b96K9idexMXva6QY8dG72Ovu++dnOrNIC07dXTmgZRUN1muXj5X+NXpZ/4vbubch79NZmokfoAe6Q/Tl7sqvl5c0ajJ/Mi2jSeFAVJxp3pIy0qhd8CNO9yLKxJhMCufrHeUTCvz0T7TtvnNgY3xen5v4Zt44H6bqp5/5k0D/8uqtA5WXHuVGa0xR22bUZk/6yy6fvcoJwbyuNv6AL/IKee8a9bHM7+t9QHu+fVaHht4Rbye7+6I8Br7YSqtWznXFaTFtYXvxco5L+2liTPf00NkIEJf3upJM+957Z8x8MijuMP9RFPS8Qz24va4iIbD2C4P/a6sGWe+tKiF3x/cEG8juN0Q7u7nsnWH+efX74mX+759pjPkg33fpDr8Bdy52eSVbDOdGXNd9o8/DseO0TGQxud6q/hZ9g6KthXGR2dva32A8wtP8tf3v4HnWwvJzPYw2DPIYNSNbVu4XDHcKR4yXX18vvAr0878iXNfg/fIM6SuWxm/siLa0UUkLdPULxNl3u0hTAquFA+pWSlmPz/Yjys6ceYvWnWCEz1ZhFPNCVHLAqu/l5WZvTx7asWozD/4IJR2/YC/7f5Xzkk5TO47rjYdp/PRnh8Y4NjFb6DnWBd7w5fwlfz/x5aSjaMy/5XG8zhhrYH0DADCPQNsDf+BD/Ettrj28wJb+KH1Lt5X+MD4tk2Ki9R889CwcN8g/TkrJ8/861/HqWeOk3PqAK7IIO5oGJdlx9v0g1H3jDP/xnP38/uXN+DOTIsP8DXtysN8/urfxcv98cfNOZmPdu/iY9Gv4inMI/9qv7lsYA7K/om3fpai399NVq7bbHiHDxOJwjdcf8XXcm5mzSsKiUSGy/28wpP89c/fyAttBWRme+jpNPtXl2WT4o6RlpVCpHeQKzP+j3/YEZw683XX0bqpmNzjL5BSkBs/Ix3t6pk68y4PEcuDleIhNSuV3rAbd7h/ynr+kcNnYaeZ9WzbEOkd5JI1x9jz0tmj2pW/+hWs6Q1S27GDc10vknNVsekhnI/MAy++egc8/zwv9q3lX71fYsMbXuGsZra1PsB9vylg78DF8cwMdA+wLfIHbuRbbB7K/A+sd/P+wvtHZ/7ES7hSPKStyjPHSj2DdK70kXPqAJ7+btw5WfETH9GuHiLp2aS9r5xjv32W/ENPYcWiRN0eMhgg2h+mY80WXnZtBOCSqwunnfk3nNvC71/egGeonvd4INzVz6vXH+KfrnooXu5PP226Cj4d+izviX2PlDX5rLjqVfEO0znNvMsFR44Qidp80/0Jvpp9Myu2FALD5f7KVcf41P1v4KkTK0nPTmGwZ5CBqAds8LijWJ4UUu0BvrDqf/C/ec3kmV91Llf841V0vOaNZLUdxJOZZpbhoovof/RpOtacz+oXZnkbkyQ0r/esvOmmm/jMZz7DZz/72VEjJEOhEN4perA3bdo04ejLhdDU1IR/gpv5e71eGhsbz6h7V/blrSHt5CFsLGK4IAoubGxcWMSwbAh39uKJRHH39pBmh0k98iyxvC0MNvyUzX39PP+WvzaXc/fCvqNrCQ/G8DDA2nPSSO88Tk9nL6195hLuyx+q4eLyE1yx7zr++uJfU/7k12n35NLbG+Nx71u54t7reKjiLrIeugPKz5v9F3O5yKq/g4fAVAxVP+bI8QvYaD/P6/t+zlXZAxT/soaHKu8ia8+d9Gy/nojt4h2xH1Le+0POdR+gI5bK2byEy47Ra2dzWd+vuYxfsaK3nf6GNNLXeMk7/CIuYkTDHjgRJdwzQGq4n8ID+8xONBol3BcmJTOVcM8ArkiYtJ42VrzUjKfPjDyzsIkBFqYhmNLXx8b+ZtJ+/mv6M9Jxp6RR+MIfWfHC7+nPW8PxX9ey5lgvx679GIOnOs0gKSK0daeS4hnAu9KDd+A47YPwylXHSd0TIJKVhys8yNefu5rM2AmetTbTaJdw+PDFRCLD9zlpbYVvf3t0UWZnD3dcjuzEXLfOnGQe25F5552jb4EJ5sTuSbbxXruOd0Xq2f6v7+Ohe4+Q9dAdfDN6G2uih/F4conkFZLTc4Lj4Xz6Bt2syoATRyOkxVz8TfcXOL6li9UrYlzw+EG8vUcIezIh9Wz663/Clv5ejr+yhOjN+9n8mzrSetoIp6Tjzkwj2juAJ9xPzvEXcEdNhwHY2IAdjmBh44oMMNRFT/jYKdxd3Vi9vWTYg6QdfoZo1mbC9T9hc38/z5V8nN5TvXR1W+w7spbLBvdwQ/jbXJC2n0Ns4Dvha/lTz1WE+yLYNly+p4ZXlh3nb17zAvw6xMvhszjn5CH2VNzFFbXX8VDlXWTX30F3+Q0J7/vv/CPhaaWh12MxiO55mN+9+3r+rOk29t5Uy1m7/4scO8QnQ//KWt/rKLl1B4984m5y9txF17uuo3sglbNih4jhJX11HmkvHeDc8OO8O/a/nDtwgBZ7E+5YmLee/BH9DdmkrS3Ae7gFC5toxA19FpHBKK5IGM9gH325q0lvPUJnSiG5r1hP59MHSY/00LN2I+ldp7BivUQHbTzRCJZtetXd4QH6T3WRPtiB78Qf6W94jvRz1pB34GUKI7089UsA00GfVX8HPeVm6G9W6iAvnMgjJWWA3BVpeCMnCXVE2HJOD1CAjWXK/dpjfP2RD7D9wqfYGnmEEHm48wrJ8xZwotVN52A3oZWbGeiJDJfpiMv/LJc16mUsa1zmY7iI7vk9e8pv4PLf38ljZV8g/Uff57XWHlxdcGnBq7iifmg9P2TW8yvXnODR/bmk26lkF4SxT5zkAOfgTvWQ6hpg1cBLnJ12nFXf/T7Hfl3HJScPkNFxFBuLfu9q+hp+ypa+Xg5f8lYG/+UY5/72NtK7TxFOSceTmUakdwB3uJ/0zhO4ooPYMRvLyfzQv62Yyb4FhNs78QwM4OnpwU2EtMNPE8vwMfjEc6aef+MniPSFicYsHju2muL+h7lh4Nucn9LCQWsD34u8mz8NvN7kd6iuf1X5CT72zj7c332R1nAuq9rbeKjiLlMnj1mf0zJmw7AsiO15mIfLPshrf3cbD1fexcqH/5Me3FSGv8xAXyb57jfw2l+9j4c/9h2y99zFLfbtvCP2A8p7f4TP9SIvso7f8VqKw49yXtcLPBfdzDOcT3VkF/0NbtJX5OA9/PJw5tNcRMKxeOb7c1eR1nqULo+XnC1n0fXcYdIjPfStWEN6TxvEYkT6BnGHB7HsqMl8JExv+8BQ5h+hv+E50s5ePZT5Pp76RYzBrr/kkU/eQ84P76Lzndcx0DnAHx6xaOtNM/eJ6+vl3IvSCR86zoke2LCmi1g0xuV7vshF7zzC1x76AO+/5AnKj99GW2oB3a4crNR1rMqOcmIgn67BbtpWbKa3M5L4bNQEZ6ic7SESs4ju+T2/eff1vPq3d/D8u6tJ/eXPeKv1I9I6B7nCu5FXf+c6Hvn4XeTtuYOOd9/AK1ad5LGWHNLtNDLzw3DSZN6T6sZjDbBy4CArU9rJuvcBfhuFvGMvcN79X8Jlxxj0rqJ/KPNHi/8C/vUgW357K6lD9XxKVhrh/gju8ACpvR2E03NJ6WrFtkytbkWd/LsgEiGdMEQgEk4j1Y6Z/TVgdcfob/gJm/v62f/mT0I0io3F48dHZD61hUPWBr4bfjdP973eFMyI9s3f3Hw24eoH6Yuk0EYmz6ZdwxW///bpt21sG1ym3B8uH8r8R+4mq/sI0Sj8BQ1sG3gIr/stvPZX7+P3H7uHrD13883o7bw9+gO29/6QTa6X6LTT4m2bPjuby8O/5nJ+w4reUwzUp5C2Ihfv4YOjMz8YxRUZJKW/i4GsAlJDJ+i2sskuWkPP/iOkRboZyCvAk56OOzKAFQmTEhk07ZuohQUQi5JOp8n8fc+THs/8UD1vf9C0ye69k97yD0Ikwksvwd7DZxELR7H7+9lwbiqe1uN0hvs51pWNKxbhij07ufTdB7njkffx3gsf553H76YzNYtoVypPp1zJa37xTf7wye+Ruece3O/eaBqrTsbHXuo98vWx07gsog/9nofKb+C1gTt57pqP4vntg5TxPfo603jvuhSu+Nl1PFx5F9l7TD0PFmvtI0RjXtJyIqS1H+MQ6whbmWS6elk5eJAVKZ1k1T/AQ0Du8Rc472fjM3/yldfAJwMUvPw4VniA8MtD7creQVwx01bvz11NWusRYpaFhQti4LKHMh+NkkoEohCJppNix3BHBsx37InRP9Sef+Etf2227whEbBcX9v6RD/fdyStSTT1/R/9fMrjhImDFqMz/w3dKOfGGrxGOWhyjkCdy38IVvz+N9vyIhxtZFmTd+y0ejkV47Tfez8MfuwfXoRRikXyK7Gf58skPYrvez+VN7+P3f/Udsvbcw09T/40Luh7ng7Hvscn9Mt0RD+t5GTcxel25/HnkV1zDL1h54hQD9dnjM59qDe1DB0jtCRFJzSClp5O+SAoZG1bQd/AkqZEIkcxcPP395J58EU//cJs+ijXUpu8n3Y4O1fPPk37OavIOvERhpI8//cKGwffy8EfvJvu+u+i59joYGCDUbrPv8FlEwjHoG2D9xhTSOo7T1dHPie4s3LEwV+z5d/zvepnX/O4v+aR/D2853kBHSg52KIVn3a/lNQ98jT/89ffI3PMd3O86Jz7yHTAN9ET5TvB751nnczi8grzUbFaviNLZbdF3spuLYwFe2fEQH9/cxdW3vz++nnvKb8CyYK19hEgsn/Qs6Ox2E7Y9xGIWvd0RPNEoryzYT1b9tyfI/E84r7eX9s2XwTt/QN7RZ7HCg4RbbZP5/gjEwHan0J23lszjQWA485ZtY1tuiEVJGcp8OJo+VM87mbfpv++nbO4dzrxtw5MnVuHv+z039I9u2zw98HpTJCMz/8v38Xjx54hFYnS4Mnn87Pdyxa0fTJj5mQ0JA9uyyL73DvbEYlxxywd4+CN34XomRFtsM6vtw/xX63X0uCu5/FfXDdXzd/HNyO28JfK//GX/DznHeonuaApncwAL6Hfn8OfhX3E1v4xnPnW1F+/hAyavYTd2e2zoWGmAnJPBoZOrMQYHbVILcxhs7cQdi+EOm3vIFhx8HE9fFzFcpBCNH8fmHX6GV/AsFhb9DbkjMt9L5/cehadu4Xirm9VHezix/RPQ10drK+w7vJZoOIqrr59zzk0hpfU4HeF+jnRm44oMmMy/8yVe/cu/5Euv/QFXHv0lbZ5cXKdcPGe/hlf/9Cs88rffI/t338F659lEO8zo8ngbfsy/R+Z9ZDu/a+0WDg+uIC81i9UrYnR2W/Sf6uai2GOcF3qET5wX4g13vHdU5vujHs6yD2NbXty5MVLbTD0fsTLI8XST33eEvlg62ffezkORMLnHnue8+/8bFzEG81YwsPuHXDDQR/eqIqLP3kFW20GT+V4Ld34u3c8dIaW3j4zO4zML0jIy45GVjo985CNs376dq6++ekZ/96UvfWnS0ZfzybIsKioqqK2tHfdeUVERXq933KhLp+fX5XKxdu3aKefxqU99ik996lNztszzas8eut9URmbvCUYemkSxhjotLcxOwDT0HTFcMNTw70hZwR8yr+EbfTfwSNjPJXaAT/AVighykPXcSiXh1Gz+Med/yOs+xOrBg8Rsi0FSOcpZvOjysdrTyivCj2Nj4SLGibT1dOU4T/4Ynrc13HNjfsce8RpD05nfc7qOUhg2B/dR28WHXHdyCY/x8dhXWclJYriI4eKUZy2ncn18t/Mv+GDkdry0M0gqqzmOhygnWUEEDy6irOYkbqLxuY8sE3uoBGNYRF2pDLgyyIh0Di2P6fwFi15PDqmxftyxMB6i41ZJLMHnuoZ+t4d+j1gpvJT1CjrcBXx/4Fp+Fi5hffQlPsS3uIBn2U8RP+RdvCHvEbalPWlGC3WF2Nj9FG4i9JDFu9Pu5w+eK8nPNx2PAwPmEs6iIjPq9fDh8c83Giszc3Tn5bp1ZqT6/ffDTTeZUZr/8R9m5HpWRoxA+zmsiRw2nTu4OZaygY+5biFloIsKatnAIQ6ynt/yOraxj83s53m28CznU0kdhZyKZ8QakQLnXxErlX53FpmRTtPZPmKaGC4iQ+slK9KBm8iozNujPskakaeR69dsEwOuTIKp5/Gj8F/QEc3iRm4jhy76yCCDPkLk823Ph9iR/XMAVnUHWRk5gpso7eTzX/wtV6Q8wmsiD5kDaWxOpaylM3PNuPnORG7vMQrDx8zJB9vic9bn+QB3c7Z9gEJasTDb7knPWk5k+/h/PdV4wr3cRB0bOEQ3WZzFMbLpIo0BwqSSQR8phOP5HVsP2FhEcRNxp3Mkw8fZPU/jscPx7xWxUng56xUUDhwlJ9wK2LiJjil7a8R/wY4n3unMdxPD4oRnHcdzNvPLwT/n2/3bORhdh4cwazlGH+lkWANUZX0Nv+fJceXRYm/CAtooYCt7wXJhYdOasiZe7rPhbPe5vcdYMVTf7LP97OXVvNH6BZt4EZcdJWa5sbA5mXIWHVnreDR8Eb/pLuZd/IBzaaGDPNxEuJA/8QwXcBfXc17qi3ws/N+k2APxTsWR5QUTZd6pi1yEXen0u7PICbcmzPzw/4cOrhJkHiwGXBmcSFnPL6NX83JkLddxF9l0J8y8jcWq7iCrIodxE6UDL7dxI5s8BymJ/mJU5juyzoqvZzB1/Oj826OWeRQLcnqG17GFzT/zeUppZLO1n7PsQ6TZA7iIxXP/VfsTfDBq6vk+Msilgxx66COdAdJJo58M+vAQGVqOyTN/NNPHhu7xmT+Y/QoKBo6SM9g6VPvEppl55zVXfJmPZ/loCv859YNvp8vO5kR0BZn0kEM3PWSSYfVTlfV1/J4nR2X+ZXs9EVI45lrLua4XWR09El/G0809jN6+nrQv4hkuYKvVzNm8TIo9iD1mGwtEXsmuno/TZ6eTRS8X8hTv5x7+nN+xnyJu4yYiaVl8PvqPrIocwUN4VPmYOggiVgoD7iwyIl2jMm8DMdyE3Rnx9ZJiD45r24BrqKQZKuXR9a2zPQy6MjiZdha/jFxNS/gcrudOsummn7QRmf8wO/IeACxWdL7AqvARUojQQS4/4h10eFbwgeidw5lPXUdn9sjM2yP+7bRxErV5hmWPyXyN62ZW2id4o/0APjuIm8ikmV/LUTxEOMlKbFy4iLKSk7hwHswxu3r+paxX0OPO45zep8kNt+FO0L5x9rNmThNn/kS2jycjF3Br/3U8E91Mr53BK/gTGfRjAy/iozj1CT6fuWtUDo/aq+kjk5dcm7jQ9cy4zHdlrRm5khOs98mNnNcxexWPcwnnWi0U0orXbo9n3qnnP9f99zwWvpBNtAAWF/Mor+e3vIon6CKbOirpTSvg36NVrIocHtrXTpz5tGgPHjsSf99ph3emrqA9dQ0behJn3sbsf5z20/C6HvndTdvmaNpGfhB5GycjXj5om8wPkE4GffSQxfOZl7A2owOA1Z0vsCp8EA8RuslmH9v4rftq/jr2X8Pt+dT1dOasG1O327iGTlg628HY9v1IY+v5GtdnOMUK/ib2XxTZ+3ERxYUdz89jKVs5t+9JsuilnzTO4jCphIngwU2ULrLJpXNUS2P8MQ5EcRN2p3Mko2iCts0FDLoz8XU9jic2iIfIuGWfbuZPZvs4HF3DV/tvZF/kErrtTM7nWXLoHpX5f8ncRY6TQxt6yOI4q3nMupStnkc5JxIcnfnTrOeze4/H2za9dhqPUswa6zipDLLBfnmobRPjZMo6OrLX8fnuv+OxweHMn8vzlNDEFl7gGKupZwddaSv478hfsTZ6kJShfa1T8znrwWnbpEd7cdvhcZnvSF3JqdR1+HqeGGojDTNpcMc/d6LM27jodedyIP08doev5anBzfwr/0j+0HGgU8/f6bmJd+U+CMDazudYEzkYP5Y6wEZ+5Ho3H7e/Fl/+Eynr6cheF5/TyGPVeN7HtXlGt31G1jUWNl93fYI/8Bo+F/t/XGQ/Gc+ak5+v2J/g+qF6foA01nAMDxE6ySWXLjrJGcp84np+aCnjx0r97kyywyFGHx1ZdKd4OZm+gbN7nsETCyes58eWsWnFD6+bkccGz1kX8NX+m2iOvIpL7Ef5JP/NJg4M9R9UEE3NGF3P22Z97udcfuO6mkvdj+OP7J23tk3YdhOgmBVWKy5i+OyWEW2btXRlrub/9VTTHH4lmwhiAa/isaF6/nFcxPgPqngh5RXcFvsQ66MvjWrbjD6GTaHfnU1KbICUmBlAY+Oi35VJSmYK6WVvm/29f5PQvI6sdNxyyy3cd999fPSjH6W0tJR3v/vd0/q7ye55OZ9CodCk73u93kmnicViHB75tK8JdDr36VkKrryS4x/9ZzZ8+a/NGShMxed0EAySxouczQCmoZjK4JgDWpuccBtndT7LJ/lPIraLYprxEKGHTC7hCb7IZ3jBfRFp0R7cKRZZA114hppP/aTzeevznO8OclH40Xhjqi+9AE+4d8ZfZ2QTpzNzNQUdx7CwuZ83c9S1Dp8dpMBqx7LtocZNlJXRo2R09nJzNEAqgwySQha9QwerMVZhOnJ7ycQiFu+yGZ6fxQCpPM2FpNPL2dZhBj1ZtKauYUPvC6TGerGJYWEz6MrgWPpGCgeOkm2HsO2xnTbm88A0kgZJJYVBrKF148zVbUdZ2xtkj2sb7lgvN9m1fIB7cBOmkxxez8tc7fotL1oXc8BVRPZgG1t79+Eiio2LdAa4LfYhPsK3CJ93JY2NcPfdpg58zWvAOZfg3D/P6bw8fHj4ITjO7e9eeMH8jNTaau5h/7d/O/zUutfEHiaT/vju0EOMVdFj/IP1rxRyjCx66CeNy/g/SvgVTqfWBTzL2/hJ/IDJFW/kmTLqIwM3saEdt0Vr6lpiuMiMdmPZEZwdQcxy0+vJHVov/ea2B2PKPYornvkoHrbwHKlDOxXia8DCExtkcADeyP1sJDjUmE8jk14iuDmbl6mO7mSgLwe3HSYv2koK5gAjkz5u4Nv8gO38GQ/HM9+VuXpUw2E2OjPXUNBxHAubRt7AH92v5ZLoYxSzL37w6iZCQayVnkgBH3XXcl74CbIxZ0fP4zk8RIjhwoVNHzapDOKctBhZVv2k8TSvIMPJvDuTbo+Xl7JewTndT+MmSgSPOYD1eLGBjGg3qbHh7Xq4IWOaoDFc9JNOFBcphEnDaYCatV8YO8n/9ryLH8beQq7VTbXny1wVaSSfdtrJpyt9Jes8bfHPj6amkzHYg5sol/AoLWxmp/sfiEQ9vIY/YmOddqPGWWddmasp7DgGts23uZ797vMo4dekM0hqtBdsky9SU/DYg1zFryhzfZtozGKQFF7Jk0Pbp8U29rGRl+gezIo3LEc2bCK4CQ/VDQDtKauwsciI9ozLfJ8nm9bUNWREuxNmPjYi831kcmG8nh/ZXWwy3zGYzqXs4528SA5d8cxHR2S+vz8PdyyMN3pyROZ7eDc/5DtcRwm/wEUMG4vuzFW47fEHd2NNuFXYozP/J/sV/N5zJS/aRdxlX0em3TtUZ5vcF8ZO8rf2l/AQJkQeGfSTQzcphPEwSC5d9JM2tNzWqPnbWAwkyHyXe4LMu73EUiEjMjrzQLzb0jX0734yRmR+aDQaNm4i5Mda+UHftdTH3gHAX6Q08ecpvyav/3jC3I/M/EWEaOFcdlr/yDVpD/Oe3m/F65vTzT0Mlz22zZ1DmX81+8iij9RoH9jmwCSamg6A3/MkX07/LDl9J1nHIdZwHA8RPES4mCe4mZ38ZvAqvLTGG/MMlX0/aYA1lHmLtpTV5OEic0zmbctF/4j1srH7qXFtm0FXOoOuVFzRCC/b6ziHl0llMN4uGZX5/gwuJsDbuS+e+Qz6CONhPQf5+1gNfb35uGODFEaOk0IYgGy6uYKH+Yb9SaK2ixTLZKonoxB3dGDKsp2422Z05g/a62mySnATYYd1L6nR4c55DxFWRI9Txb/jJkIvmXjpiJ98Ws1xLGCA1HguRtYPE2V+snoe4FTaOjKivbhiPePaN6bTZvLMF8RO8du+q/lq9EbA4hL3kxRGT1Bm38u5tLCfIn5glVOS+jAwOvO5hNjPZu6wbuCNab/jfb23js786e1iR5X9PXyA/3O/hl32p1ltnSQl2oc1lHmnnn932k/IjbTxbruei3mcVZwgTArpDLCRfqrYxS8G30QebUOdNk5Zjcx8GHBxIm092DZn9R8YqlNM90LYlUZr6hp6PBNnPuxKmyDzzjydzA/Q3e/mKprYaAfJo5M+0uknA4ixglOcNXCcY9ZGUmN9rIweHZV5H0HuYzs9dia5Vpdpz2cUkBLpG1uU40y2asZm/kGr1CyvxyYlHB7KvIWHKKuiRymJ/gyLGP2kkQvxjKUNtWey6MM5yTF15rOmzHyvO2eoTT/6hKAp2ckyb9rChbGTPN3/Sr4Y/iT9VjrnuoKcHTtAub17XOZthjPvIUoeHfSQxfdd7+VIShF/FfmPOa3nnbaNhc2PeBc/c7+dz8f+iSIrSKo9nPlTqR5SYgO8O/Un5IYTZ/5SHuVCnuH+wbfgjWd+uPu2n3TAwk0EcHE4owh3LMyGvv1DmXeOpdJpT11FvyeLl7IumCDzqVNmHky71Ort4Z3cyyc5QBY9tJOPBXSSywpO8VH763RG1pIe7WZV7NjQ/skmm25Wcooj1ll0xnLIt0Lme2Tmkxab+THsSCMzf8ou5KfW24jgIZqSRsrg+Mz/k/05XEQZJHXUMWweHVhAFr0J63nTnjfHsBljjmHTo31Dx7DWULlncjJtPT3uPHrceWTbIVxDV4yMzb0N9JFJFBcZ9AGjp3PZNhl9bXyXdxIFtnMvn+R/4ifBX8VTfJGbecFt7rc6MvMWNrlWFz+y3knQfT6XRvbNS9vGwuanvI0fuMv559jnOM96dlTmo6lp2MA70n5OVqQjYeYz6eWz/Du/iLwRL20J2jbpRPGY9WVZBLMuIjPaFe8/gBip9gBpm845vXv/LnGz7qwEuPbaa7n22mt59NFH+cxnPoM1NLS2tLQ04YjLRx99dFEvAz8d0x1ZuVgPD5qto6EMunNfw4rel1gROTp0gJvLU5vezqr9D5Mb66J3aOfsIkYP2aS7BnHFzN3z2z0rIC2bjRndnBt+FkIh+u0U0hgwnYHWKc7N7yNywUVYD+0bNYphVWoH6zem8dvQ23iH68dku/txxaJ0XvUOzvuHssSX5Yz8PcFrzkHmM5/fjesXEXqtDO7ovYlw/ir+atMjRJ/Mgr6uoeo6hsuyyfW6sPptIr027lhsqBvTTcpQR8GAK4P+WAaeoYPYQSvN3DA96iGXDmK4yaGLDPo4bK3nZPV/seX9f8bTH/kXNu65hzS7nwErnZde937Orvt/uPf+nvBHbsTVcXJotJM5+xchhcG8VfRuegWRx//EcXsl5/MsZixIFOfMl+1247bAn/UCxa4XuIBn8HS0Ycds8izzRBELi0s8TzGwIUra0wFcsR4GSCfiTqPL42V1+DAVka/z0aeu5KKLzDNeLr98+MEHYG5kvGmT+RlrcNDcK2dkR6bzY1nm5vHRqPlZswb+Mfd20jsHTEPecpl781mD/JlrL1ErwoCdQgw3GfTiGRq9GsONiyge7HhH4nBHik0EDy/io5d0NlhHsV53BSvv+B88+/5I+KaPkNLZStRy47ajRPIKybv7NvIufy3PvPdfOOtXd5MdDuEhTIRUulO8PHnuO1nz3O/IiXXRRyYRUnBh00Ueae4IViyK7UnhhcxX8bsVZWzOPcalB28l0u7GHTUHiGmESXFFycyMEHvVOVgvPIfdGiNqu7AtN31kkmn1c577BR5JfT1rPK1YsShdbyrn4i/+5fAOP8HlxvFLk63E/w58+ru8/OMo/VYGt/d8hEjhaq7y9RPbl4YdHsC23MRsixRXhHPzTnFu+CixgTb6h8rePeLcf8RKAVwM2qm4idJnZeJyWfQPZd7GRe6IzLf+41c574Ov4YnPfIfen91NzPLgikVoe8v7eOW//aXZJis+zzm/u4eMWDdpsX5sLMKuNLN+XB66opkc4GwKaGcdBwGnaep0Eoe5lh+wqjDKKzf34mu+D3ekm6jLbS4hSl0BtbcSffXlePb+HvvGm8w+yTad3GtSWsnLtrizu4LVmQO47Cjdb38v2/7zvcQvsx86+z3y3/HXYiPOQtujX3/s5u9z8Gdhfh99Nc/1v4KU1QUU/V0FqdUfwYU9lEObcyJBePu1uB/di72/BzsWHTGKj6HyNyOdVnGSqOXGZbuGTpOYBlIED89yHqutk7j+/ErW3v3fWI/8kcEPf4SUzrbhzOcWknNnHdl/djnPX/cF1v3mHrLimU8xmS96B2uef4jcmDmJFMVNFDdd5JHqHroPVIqHZ7K3cn/B+1nvOcarTvwXsQ4ProhpHKYOZT4jM0LORetx7X8eu81kHstFP5lkWn1c6n6SX6e+haKUg1ixCN1vuJZX/dsOU4wj6vP47wyNP0lUzw9N88TN3+Wln0WJWh6+2vdxwvmrKb7xQjL2XAoP/9bUZ5YL246RQphV7jbCMRdpsYGhDh2nAW0x6EonYqfQZ5sDyk4rj1RXhMGoO2HmT/7jNzj3g6/lqc/cTc/P7oqX+6m/uI6L/v19APzpxv/Hpt99m4xo16jMu+wotstDdzSDI6zBSwerOQYMndqyXGDbeGJh3m39iJWrLbZcsZqLNoSI3vIb3HQQdXlw21HCaStI+c6dYNuE3/+heOatocyvPT+fn/bexGtO/h+2y2yXPW97D5f913vHV+wjjMz76OAbe//ue7z84wj7Yn6e7LsE9+oVbPyrD5P6//4WFzHCViqWHWMDh/F8+i+hv58LvvzfuOkaGl1tD23jpp4/yzrCe9iNy47FDziHT0x5CLKJFVYbvO515H7ra1j/9wcGP3ITqUP1vMuOEs4thG98izVbL+fpf/wuvQ98hxW9L7MyeoSY5aHXncuLr7uOniveyOovfIJcu4soblzY9JNGryuHdPqxUjw8m7ONn+a+lw3WIV7Z+hXocuGOmq7mFMKkuiJkZQwQecUq3MHnoT1CzHYBFoNWBlmuPv7c8xD3pb6f16Q047IjdFz9Li74p3Jz2dmYNoyT9VGXqo2ZxnJZPPmP38f6eZSYy8MtfdcR9q7hivKVFB7YBg8cM08GHsq8x4qS7+omHHPhjsXiV5R4iAE2/a4ss53aprTbrQJy3T0MRNxDbRsXOXSROZT5tn/8KufdcDlPVN9D70/vIeZy44pFaX/r+3nVzvfGM/LMhz9P0W9uIyfagTW0r45ZbmyXh95oGsdZSS5drOTkcOaHRk25Y2Guth8ku8BN/uaVFG/uIPq9eiIDZvTe2bzMNRl/JOVrX8FOfzOuypuwLBfY5lPWpLaxYUs2P+q+ide0PoLt9uCORWh9y3Vc8qX349xrxbKI/9vJ9cjX4iOfGP798Zu/z8s/i3Agdja/7HszkcLVrHjPDlK//k8m8640LDvGOtdxrF2f4vz+fq6t/ltifabLL31oZKiNB7A52zrIh/jWUOad0e0M1fUe9lPEKusU0SteR/YtXwHgmY/9G+c8/J14u/LAFe9n/S3/RB7wp3/6Pn1DmV8RNe35XncuB678AL1XvpGVX/hrckZlPp1eK5sM+rFS3LyQdQm/znk751gvc3FbC1avi8xoH5kMdTa63aSmRznngizcLx6BgfDQKESLCClkugd4o6eJuz038ta0RlyxKF1Xv5NX/HP5qPa8bbkSZ3yCS5Sf/IfvYf1sOPOD3jVcVb6Ks49egv3jFqKRcDzzLsvG44oRtS0yo+ahK5Y93Gk44MoiZkPMdmHj4qS1kgJ3x4SZb/3Hr7Ll+tfyRPV36PnZXdguD1YsQvtffIBX1Zh63v3Iw4RvvB6r40R8f+606WPuFHqj6RxnJdl0s4oTw5m33Nh2DLcdwR/dx5eyP4e1ooALi/rh178mGo7SSyabCPKGtD24qz8DqedjfeHzIzpbbdamtbN5s4cHQu+jtOPXeNwxXLEI7W+9jku+9IFxOScWG92+GbkNMPr3Jz/7XQ7+PEyH5eV7PR+gu2ADGVe/jbSGJ3ARY9CVhsuOcZbrOPZXP8e50Sjv/NhfY/f2kUHv0GhomxgeLOBs6yA3chtWvJ4fvgoqTArPs4WzrOPYV1zB2tv+G4DnP/LvbNjzXdLsAQasNA5e+V7OueWzZvn+6V767/8eK3oPUhg9hm256XXncPDK99J3xRvw/tvfj8t8v5VFOn24PG5eztjCMxl/xvroSxT0PEdsIIo31jF00j5GqitMYXo/sfNW43rxCHb/IM6VEDFceNzwFk8jd3lu4l3p9+OKRegteTsXfa58VI4nrNdH/m5Z2KZRH69rYi4Pd/eV0Z93FtvespJLu88h9qNHscP9ozKf5R4kHHMRi0XjV6+lEMMG+l2ZQ6vUXONxglUUeoYzbw8dwzqZb/9/X+GCGy7n2Q9+jrN/exdpsT4GXOm89LoPcvbt/0yBk/mKD+HpOBo/qW5j0W9l4LGiRGIugmwkiz7Wc3DE0YWp52MuF15C/H3WN8nKsikKP4vd1k5XLBMPEVxEWeFpp2hLNpHLLod77hnVf7Amo4NXnRvl4RNv4f96fs4KTweuWJTQ2z/I1v9+/6hsj2q7x4bzHW/Hj9gWbBse/8x3eflnZn9zV++H6CjYRMqfv420Hzw2OvPuE0S/8W/4gHfc+BHs3l4y6CVzKPM2Hmws1llHuY67sewY0aG+FKcOGCCdZzifc6xDuK55PRf88HawLJ4p+yc2NH2LtFg/A650njvnbbzqDH24DpxmZ6Xj0ksv5dJLL43//uCDD47qvCwsLKSlpQWv17toD9eZ6n6aU428XLt27bJ7wM5DFXeRdd9ddKw9n32HL6He9R6O9uSyJr2Tk89nsIp3837Xd9lstXAouoEi9uMmRltaLukDnXTHMtmVU8P5lVfx129/kcE3voUUICUzlbSsFMIdPbgGo9gnT2K1P4xnoMfcdD0rjYGBGCn9PVz74pfYa+/kp5d/jp2/fS0PVdxFbv0d7F9ZMOrmwjP9XvlN9XS95yaCV1xH6DPHWHfqCVY80YQ9OEjfqnPIzXPTe6Ibd2cbkYiH/nwfOT3PEF21lrwL1tH1hydJHTQH/hnnb8R6+Tgd3XkMkorLgv7UXFL7OukkmxfYQmFKN09YG7lncDuD3y/is8d+RNajAZ654T+4ou46Hhu6R9uzNT8CYFV0Fbm5WaR2tdJHBr12Bi+mbKZ7II+fPv1WPmXvpIB2BkgnhW76SeeQdTa43ayIHONAymYe2/Ru3nllG9z2KHbMxk7LICXDQ7h3EGuwH7uzi6y1uYSfiDLoSoPsHHIKs+hJO4fW/THWhV8kFDL3aW9vNw9Nu+WW6ZVxaqp52uU554x/7447zKjMtjbzZLeiIjj3Tw9jRyPYqenxey1Zg/0QjeDCIi01hZRMN7GQs9Nz4SnaSO+pHlI7TgAWL7AZd2Ya9PawhReI4ibVFSE/doxOO5tvtbyNL21ayUM721kVXUXeuT5Wr4hy/JSbjqPdnPjxSfjx/5L1SDN/8r2DFw6nxzO/Nr2TE89ksJJ38gHru2x27edgdAPnsh83UUKpuaQPdNEdzuBrnr/h3A+/kzf8PZy68Dd4W/cRXbOBvKKVdD5/BE4dot17DgXVf0/fB27CbbuI5hSQcaEP90shOBpiU/+zfPOyO/jmI8U8VHEXOfV38GRO9mllPvun36drx40cfP11tP7dMVadeprCjiYiUQt3Rjapaa54ucdOmMdUW3aU1IwMUld6CR/sx2PHiFpuUv/8cnqfPUr42BEsoIs8BlJzRmW+IKWbJ9jIPeHtDN59Dp892ED+/d+le8eN8fsS5tffwXO55ib3WQGzPQAMfOserGiEfFcXJzPOZl/XFt7Oj1nFSXpd2VgxCOMhRAGuFDeecD9p9JEbbuVNZz1BRnMAT18X0ZR00rPSCPcMkNlxlMEPvZ/sd76F3p8+SFrHSaIZOXjSYLAvQvpAF29rv5MPZXyP5za+i7W9+yn7wVew0u6bdbnHy/7+etrKK/lN9weI/u4Y7wrdRvjzd2FHYkSde+r1DJDS30X4h/dBZBBXeJBYajqEo8ONJctFNCMHenuJ4GLQTqPdvY7BqJt1HCKNAQZIYyWtdNg5fOuFt/Hl9at46F9CrIquJu/cotGZ/3kr/PwnZO0N8FSCzLc9m0Ih7+T9ru9xbryeb4lnPmOwk+6BDL6Z/TFSr3knH/sCtL/mx3jbTOa9566k87kjcOogobxzKPj039J3/Ufw2BbR7ALSzzsHjvTgOtrOxoFn+fIl9/DtR19lyqz+Dp7Myz2tzHsf+D7d77kR9w3X8fy1x8g4eZDig3sYeO4lLNuFlZoar28YHCAWM9017pw8ss9eyeAz+0mJDTDoSiP9da+m/9mjcOwwEdLpI4OOBJl/nI18J7yDwbvX89mD32PF/XfTs+ND8cyvqL+DlmzTtMpq/j+euX7XuMyfSD+bvd3n8U5+TB6d9LhyIHaMCB468OJyu3BHBkhlgLzBE7x5dYC84zD44wBpQ7nPGMpURugo4e07sC036d1tRNKz8aS7GOyLkDrYyxuf+S8+z+d45C2fofJHb4nfL/T/0jNOq+yf+9+nqV9zJ82hIqLRXt5x6of0f/G/yRrKfNrQ8qV1nyL8hX/DAlKHlp3I8MGy5YKYJx1rcAA3MbrJps+dw2DUzUpOkELYjLgjRKedw7da3sqXfPk89MVTiev53xyH3/yQgsbv03nW+TSPaNuszezkLX/8Md/6w3pWxP6H6627KWYfZ9mH6SOddruQPFcn3QOZfCP7o4Rf+05u+iKErrofb8c+oqvPIm9TIV0vHMVqPUJnzll4r38v/VX/hDsGsYwc0nzr6W6LknL0BJuiz3LL5n+n8rlzebjyLvLq7+CFQu/ptW1+YTJf8DfXsa/kGCknj/Da1j8wuPcJsN2409JIyc1goCeMq7cb27bNPYLz88k9dy1dj+8nZbCdqOUh8/xz6H/5OP3dLrrIwbZdnPCcRWrk/7d353Fx3/edx99zcWsYQLdkWwJJluQjEYjE1lqJbcGj3myTtjFY2WYT25ENdtzubt1YSNndtN12rUDcNE3qxpDYchw3XRuSxkk2TgV2k8pJfCAkJ4psy2J0C10MAwJxzvz2jy/DDDcDA4zE6/l48JDm+s33953P9/h9flc45j2Odh2wrdTzfZ9S4LvX6Ysnq+X56ffUvnXbQMx7qnfrndTE8PVo99Xr7VXFajvRrIS+Ti3sPaNzjmWq7/uA/kAvaZ7a1Wb3aH7wgvrklF8ZsjsdcgS6lGh1KbW3RbcsaNQCV6N6X2hQUreJm4xUc7MUx+UW9T58v2ySGQcSkuVMSVZPZ58Se9r1n979W/2l/kqv37VDj/z4Lr1W8pyyqnfr3VTHlOp+3ssv6NLWB/WrjM8q8N2z2tTy/+R69ttSoD/m05LU296l5Laz6v1vfyKbJHd/zDv6egaSZpbdUsCZKFtPl5wyCalOe6Z6gk5l9R9Z7FCf5stnYv7o7+uJ9Vmmbt/eP2xe+f7f/kiSlFH7glqXrlP96Q2DYv4/vvFjPf3rFVoY/Lrusz2nXKtey9Qf8+qP+Z4UfX3ef5cK/lB/vEvy3VavjCP16pp/rdzL09Vx7LwS/OfVkZSleZs/rJ7f/kaOYNDM6RZmqj2QpoSms7o2cEQlqbv143VfUobviIr2fF32rLop1fvRnxzUV5Y8o7Opq3T8vcta2HxSmy6+rt7X3pSCdtnSMpWUlarOlk7ZL/llD5hUQdf8a+Veu0yX3jyktB6fLJtDKWuvVdeJc+ppl1rlkc2yRoz5/baV+qeBmH9RmS8/r46ImM+o3q130xLM478+o4WBBXK7UwbP6Z2r1dB3kz6unyhN7Wq3p2th0Bx11aws2V0u2Xu7lWK1K6GvQ0uyerR8+Vn17W2Qs9fEjTu1R70dvXJ0XVLv438Zjvn+bamerqASu9v0iXcr9Jb+Wq/evlNfrL1Dr5U8p8zq3XoneWox7/7XarV9qkQHbvqsOnadVU7zm8r66XelQMDMv9JT1d3Ro+S2c+orvV82K6DUgZjv7j/NWbL1x7y9p0v2/pi/bE9TT9ChrP4jzlzq1WKdV6uVpme8n9DfrlloYn7/2zp0/xO6reqz2t8f8wef+JkkKW3P9+VfeoPeOp03KOYL3/ipdv96jRZHxPxSnVbXQMyb+Xy5+3+q7/f+ULt2Sb7Nt8hztEE2j0fuBSnqOuOTo6NL3bZEJc9LVl/rJdkCARPzaUlqc2Up4dx5rQi8p/Jrv6rH3vmiGv67Kd9vM7KmVO/v//iQapbs1vl5q3Tsncta0HxKt13ap669b8oVkKz0hUpZOE+XL3TI0dose6A/AbZwsYn51w8qocenoM0x0M/3tnfLL4/sCsf8pYiYf9u2Us/3bVXfd1boi8drlFr/lt65r2LwNuz/MfPl1/7PWS0MLFBWik3uy2fVK6d8ypRTQfUF7eqVS2619//GCUqQdEZLZUtKlq27UxlBn5od85VybZZWL2hR8LVW2YN9mpfQI1eKuWmjvadL1qFDsh8+MhDzjtREdXdLCZ2tKnr3b/RGsFw/zS/T3//qQwNj0duJ9inVffrL/1eXtj6od/I/q7a/OKtrLh7Q4p89F455T5q6OvqU3HpWPSX3yh7o07zO1mExb0XEvFMBtStN7fZ09QVtWqCLcqlHierScp1WqzVP3zr4MX0t1Wli/te/Hlb3oWu+z0UxSVYOtWXLFm3ZsmXg8SuvvCLLstTc3KwnnnhCubm52rhxY1wdhejz+ZSdnT3bxZhZwaB+dVuZntt/o97uXCR7gksuR5feu+xQjxxy6Qa94f6PkqTu9h5t7DN301vf+77eVb6ec9yr5csz9IW/8chm26CWBTlKD/QqKe9GyWaTKxhU976DupR1nZLbziloc5q7ZgcCSly0SB3vHNf6y++o1X2tnvdm6d9ukdau/azyb1uoDwTPTnm9ftl+l17+b1Ji4mJ9ZONxdR10K8kZkHvdNZLNppSllrr2HVT7gpU6t+YjymlvVnqyufC/y2VTZzBNXYtXKjM9TUl35+uZHy3VD1ru1Gf1vNb3HdZB5esZ3ad65yYlpiZIknqsHnnOdqmlw6W3byvTWz13afsmac2awetVe/suU+8di+RMcinZ6lBXr1OBPruCgYBOOJbqfvt39MHet7REZ9StRPVZDqX2dahJS/V0xhf0v2uLlDVfOvdyrdLPvhuud8usV+uStVr00ks6ftt9Wvzuz5V24wrJZtNiy1LTUZuO2NfL7ZZ6e80p3T6f9J//s3TXXeEb6oT+li2TMjMHH+Q0kueeM3+f+Yz0ksnLyu+XztsW6ZqUFrnWZUu9vXLZbOp+zyurp0+2hAQlblgvORzqe/OAnF2XFEhKleOaa5Sy3FLH3nb1BB1KV6s6u1KVrA6d0HId0Rpl2NrU4MrX7uC92nfxFt32Qymtx6ba23fpray7zA3iNpm7toXqPjLmHYkuOZ2deu+ysz/m+/Rm+l3DY74nHPPXXJOhHTv6jyBd8xGlXTyu9MRuqaVF7pSgOjyLdOoDH1fmJz6htoUVSg++q+TcdZLNJs+aRXKeb9KvrOv10sk81edLN9wQ25j/2SNSQsJibco9qa5D85Tk7FPCTWuklha5HA71eE+pbdEqBe1Ouc8dUdJGEzdd7ZZSfafNXR/Pn5cnsUvnnPO0q+8LyrW9PXrMt5uYb708dsz/qv+1X7zUoouOrXI4g3LYg+rsS1WXgqpToba5ntOq4GGd0jVKVYfe1fWyB82p4tlqVHvmdVq3a4c6iz8jJVnmrp09PXKlJqqvPSBnT6d0+rQSLvslp0OJ5qbJSli3Ss0Hz2hp31ldDqbo9GnJn7pKj7v+So+e+bVum3zND9R99ZG7tH+/5PEsVsrt+Up+9Rvq8SxQ6lKP1Nkp13yHus9cVNCZoM70JUrxn1HCmpXqea9Rjl5zfZpAYqoS8z+grn0H9V7XCrn6ujUvcEm9StQlzRu41u+B/ph/6+Kt+tALUma3PaqYdzk7dbjTqe7+mH/d/TFJg2P+hr739Y5lYj4pa6m+/zVztPVv+mPek9gl+XxypwRMzH/w48r85CfVVvZVpVvvKinvhv6YT1PHhTP6ZfB6/WvTzcrLk266KXYx/2b3XXr5DyS7fbHuXHdRngRzs6eEjhYl3ZAjOZ1yOZ3qavidOoLJSkqUudOjZclKSFSwu1e2xISBmD/vnKfH+x5Tru1tres7rN+NEPNd7b1KP9OlEy1paokq5i05bEFd7ktRt4J6VQWDYj5FHXpP18tumes85ahRHZnX6vq/2S41NSn41m8USEiWKz1V6usbiHtHjznyyXI6lZhkjlpKWJujjuMXdGP7IbWmX6uvHd6g74xQxsl4+8x8fdX1V7rUmqHLlyVLKfp5ysdkXf6iejwLlep2Sh0dcmXMU6+//yhZm119SWlKWLpAfSebZO/rkWWzyXI45bplo7oafid/h0vdVoKOBVfKkuSTR6t1RJ1K0V7XHQP9/JaXpeQ+K6qYT0q4rCOdTv3CypcVCMjmzNa+tEJ1t/cov+/Xul+7db31nt6yPqTnHPcqOG+FXnrS3EA9FPPpST1SW5vmpUkdwUU6kfdJeT7/ebX+3fcGjcGe+ZYuXzilxuBq7W9bpZtvlvLyYhvze7ZIlrVYt6xu1eLUSybmL7fI1V+GxP55QCjm09zmjukul33Y3OZHP03Vty/+of6L7Z+0tu+wDkXGfFo45lNP9eo3Z+YrcFuZ9o0S84PmlT1mXumwdasn4FCv7HplrJi3LK2UV/7MbN38rcel8+cV+MyDpq/PckuBgFwJCerzWQM3RQo6XXKlmDImrF+tjqPndEPHIbW6r9XXD2/QP8Uo5kPr9a9n79IvXpBSUhZr2W3ZSn6tVd2ehUpNCkpdXeE22Weu2xpwJco136O+882yB83ZOZbdKVfuzer67WH5LyeYmLdMzF9Q1ogxf/tPpOQe27hjbCjmnUkuJSV2qLHTNRDz7ztXaF9awagxb/dcqx9+w1yPPBTz7tSg1NOj1HSXOmyLdfSWP9bNX31cLT9+08R8/9zNY1lqPX9WBwM3qrUvTadPS2cdUx9jQ31NoCdDl5qly8EUXUi4RhfaGwdiPjTWJK8wMd/WH/Pu1KDk8404n//JT5P1rYt/pE/bvjduzP/ubKZ6bytT/RgxHzmntye45Ap0qTtgtqX26PeGzW0Oa40cQbuClqXrdEynPTdq095vydZ8Ub0fuSu8vRQMyuUJqKfpoiSbZFkKJCTJlZ5mYn71CnW8f1o39sf8t45s0I9vla6/PnYx/wv/Xdrzv6SkpMUq/PB8Jb/VZmI+ISD19Cgx0a6+XqccvWYcCjpdcrlTFPD33wxHNsnukOuDN6rrd0d0qdOpy8GkiJj3D4v5Ny/cqtVPSYvbEycc8yP1843O68aMeaVfq5f+UUpNlX6z7k6ltpw28wOXS0nz09SRkKzGTZ/RzT/6G/lW3ar0c4eVuGaFlJwsd0KCOpubdNzKVmP3NfrgB6VNm2I3vgZ6MnT5pNQRTFHQdY0udR0ZiPnkD14fsR3bHY755D7p4sURY/7//TRZVRf/UJ+2/fOYMT/vdI+8F91qm2DMp3W9r/ts39Ga4Ls6rDX6lrbJqcBAzP9St2mV3jc3uOp1KNmy6bhW6KUVf6Yv/uZzsllBXVy9Seln31Pi2mwT84GAet4/pk7PEiV0tMhKtilx6XzJspS4eLE6Dh3TusvvqDX9WlUfy9KvPyStXx+7mN/bdpd+9piJ+Ts+5FHyvtZBMZ+UIPX1OOXqbg/HfFqSAm2RMR/q599XR6dd7cHkiLlNxkDMv9Yf869f3KQl5dJKf4oujBHzc9Gkb7AzFfv371ddXZ2am5tls9mUn58/4WteTkVGRoYKCgpUXV097LXRbr4TzQVArzR790qPPCI1NpprCwaD5iyM5GTz2OGQbrrJJGV++1tztJzLZTp1m81suP7e75k7QA9aYFubeVNHh+R2mzusPP209POfSytWmA9blnTsmJrW3aH1r+9WZ6eUkWHKkJoq/emfSjfeOOxMzKFHbI/43O9+Z44Q9PtNIs5uN1/7fOlefaBqlPJt3mzK//TTMls+a6Rt28zz/e67T/qXfwkfsd/RYe6amJEhLVwoXbwoXbpkHi9daq7v6HCYx5FfJZlq8npNnYZOvU5ONstLSJCSkqTrrzf1fnPrXt2vp7XWdlhe5xo9n7BN8/9os7773QnUe2i9hrx+5Lxbf7XwSb2/eLOamszv3d4uOZ1Sbu7I8ZKUNDiJGXmX8IwMUyfPPmuuYflv/2Z+h0WLpKIi6Tbt1cd+MkIZS0qkqqrw883N5ofzeKSsLKmjQydb3aq0SrTucr1WBQ/r3eAafdvapt+mb9a6deb6madPm4+sXWvW5fhx87unp5v1crulf/gHU8Y/+ZNwzIfuhB4Z8x/4gInzAwdMEnfMmA/V/2hxM0Lddzjc2nrxSdV1b9a8eeHvLymR1q0bfFZO6E7fUvjMnZH+3nvPXHf00iUTQzabOX1/zJgPBWLkaw6HWYfWVmnNGj1+bpu+8vrmgXrq6DBtKiNDWrJEOn8+HPPLloVj3uMZ/ataWsxnJPO1Dof5jRwOE3utrdKiw3v194FH5FabOu2pSlWHOuxu/UvBk/riy5tNY4zsT4JB6ehR6cMflr70JWn7dmnfPmn+fNOgPB5dqD+mV607VOraLZvNhFdLi7R6tbl7/Wg3gx3vucOHpeefN6Frs5l1WrxY2rPU7CQY2ufpjjvMtWdCFSKZi8NKphItS3K79deLn9QvfiHdbz2t7L7Des9ao2cd2/ROlrkx1okTJr5DMX/ihIl5t3v0/qazc3DMd3aael+/3lTTwYPmuVDMW5bpE37/96ce859qflK1XSbm7XazQfxf/6vp5yONNyMJvX7woGnTbW3htrxypen7N2uMPlEaXPaNG6X6+oHHj5/bpq++tXmgHUXG/PLlpr9paTGPFy829e50mlAbLeZ9PhPjQ2Pe6TRff/mylHFwr/6uNxzzaepQu8Otf9nSH/PS6HF/yy3m2hxvvGEGI6fTZNmOHdPJVXdo3eu7B8aq3l7Tl//xH5vVH3qGYKiOh46rkY+ff97083Zzo2EtXWrW/Xnnfbqtb5SYt6xw2VtbTX13d5typqdLbreeTSzRxn1Vmqc2tVum3V+SW19Mf1LvLtis06fDMR8Mmpi22cwiRov50NwmFPN9faYfT0yUrr1WOnQoHD8JCeY9Npv0iU9I3/nO1GL+v7Q+qZfbNys52fzmycnmJ1y7duyb8o722qFDUmWliZ3OTrNeK1ea56KK+RHmNi+/bH6OyLlNZqaUnW36+bNnwzF/9KjpI+bPN7Hrdkvf+EZ4uvGnfzp4jI2cV4bmGF1dw2N+WD8/UsyHYur2200hf/EL0286TIJBx47p5Gozr+ztNWXs7TX9zec/b/qbyLNgh54RGxpvh/7/0CHpmWdMW7YsEz/Ll0f089dea77IZjMTkjvuMB8eL+aTSrSxfnjM73Q/qXfmb9aZM+G5TXe3mdvYbObj0cwrExNNu1+1SvrNb8xnI2Pebpc+/vGpxfzJVrc+bz2pvdqshARTRp/PfOdnPhOO56HG2gn+3HOmr0lPN/2uJKWlSVu2SM9um1rM79lj4jeyn8/KMuU9d85c6igU88eOmboa2s9HTrFH25ay2822VG+vNP+dvfpaX0TMW6af/+FEYn5oPxrx2pnr79DaX+9WT48pcyBgYv6RR4aPsUOvrDLab3DwoGnXPp+JIafTnE01EPPLlpnKsNtNhf2H/2De9Mtfmo5plJh/LrlEufVVmmcN7+ePLNmsEydM/a5daxYRmldmZkbXzyclmb81a0zMt7XFPuY7HG7df/lJ/ajFxHxioln25z4XHl+lkfuc0f6efdZ8fUaGmVdalon5goLpifn09HDMX7gweG7jcg1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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_a_bb = 100.0*(bx - bx_id_a) / bx\n",
    "by_a_bb = 100.0*(by - by_id_a) / by\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_a = rms(bx_a_bb).item()\n",
    "ptp_x_a = (bx_a_bb.max() - bx_a_bb.min()).item()\n",
    "rms_y_a = rms(by_a_bb).item()\n",
    "ptp_y_a = (by_a_bb.max() - by_a_bb.min()).item()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_a_np = bx_a_bb.cpu().numpy()\n",
    "by_a_np = by_a_bb.cpu().numpy()\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_a_np, fmt='-', marker='x', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local)')\n",
    "ax.errorbar(s, by_a_np, fmt='-', marker='x', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local)')\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_a:05.2f}\\% \\quad RMS$_y$={rms_y_a:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_a:05.2f}\\% \\quad PTP$_y$={ptp_y_a:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_a)}{(nux - nux_id_a).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_a)}{(nuy - nuy_id_a).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_a.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-5, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a8468fd9-aed9-41ee-bf72-3cba1eaab010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "\n",
    "ring.flatten()\n",
    "error.flatten()\n",
    "\n",
    "dkn = {name: (error[name].kn - ring[name].kn).item() for name in nkn}\n",
    "dks = {name: (error[name].ks - ring[name].ks).item() for name in nks}\n",
    "dkf = {name: (error[name].kn - ring[name].kn).item() for name in QF}\n",
    "dkd = {name: (error[name].kn - ring[name].kn).item() for name in QD}\n",
    "\n",
    "ring.splice()\n",
    "error.splice()\n",
    "\n",
    "correction[section] = [dkn, dks, dkf, dkd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5e1114b2-13ca-4a5c-93a5-0ad0d8c387e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S03 (F8)\n",
    "\n",
    "section = 'S03'\n",
    "\n",
    "target = ts[section]\n",
    "matrix = ms[section]\n",
    "\n",
    "nkn = table_nkn[section]\n",
    "nks = table_nks[section]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7bd8a0f1-f13b-40e9-8317-9bc38bb89a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(F8_L03, error.next('MLL_S03').name, position=F8_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "14ce675b-7cdd-43d3-a923-0e88f90fe76b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id, nuy_id = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e432b6ab-ece0-4eb2-8787-7e11a20b464b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id, etapx_id, etaqy_id, etapy_id = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e6ee99c3-e12f-4154-bcab-d980fdc83c7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id, bx_id, ay_id, by_id = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "d45bbfcf-be83-4456-b802-4cdddc3cc399",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "adeb42a4-0550-49e8-871b-6fae37e151ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "520e5e6e-7c2f-478d-a889-e615608edcaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c74038c7-0de4-4473-8eb7-d76638de23f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0013, dtype=torch.float64)\n",
      "tensor(-0.0048, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id))\n",
    "print((nuy - nuy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "a9f9296a-635d-4e9f-bf21-2b5d3eecbb34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.9883e-05, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c6e03d3c-e692-4649-be9b-a4a5084e1280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2187, -66.6646], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "fe238ea9-b20e-49f2-88ff-3266806440f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parametric observable vector\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(error, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "\n",
    "def observable_twiss(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    return twiss(error, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)\n",
    "\n",
    "\n",
    "def observable_dispersion(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(error, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T\n",
    "\n",
    "\n",
    "def observable(knobs):\n",
    "    kn, ks = torch.split(knobs, [6, 4])\n",
    "    betas = observable_twiss(kn, ks)\n",
    "    etas = observable_dispersion(kn, ks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "be18de4d-01a5-4a80-88ab-34058506a483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(2.4803e-05, dtype=torch.float64)\n",
      "tensor(1.7504, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check the residual vector norm\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "print(((global_observable(global_knobs) - global_target)**2).sum())\n",
    "print(((observable(knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f5750ce0-d345-4664-b131-846940e6dd9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.7504, dtype=torch.float64)\n",
      "tensor(0.1079, dtype=torch.float64)\n",
      "tensor(0.0088, dtype=torch.float64)\n",
      "tensor(0.0027, dtype=torch.float64)\n",
      "tensor(0.0024, dtype=torch.float64)\n",
      "tensor(0.0023, dtype=torch.float64)\n",
      "tensor(0.0023, dtype=torch.float64)\n",
      "tensor(0.0023, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (local)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(8):\n",
    "    value = observable(knobs)\n",
    "    residual = target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    knobs += lr*delta\n",
    "    print(((value - target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3a648b02-4025-4447-aa8d-cbc3368f80ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.0067783   0.0042135  -0.00124323  0.01278984  0.00012627 -0.0089216 ]\n",
      "[-6.08025617e-05  2.68331844e-04  2.65530542e-04 -8.39538522e-05]\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kn, ks = torch.split(knobs, [6, 4])\n",
    "\n",
    "print(kn.numpy())\n",
    "print(ks.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "63484da1-16de-4d42-8c90-d3401493833c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name, knob in zip(nkn, kn):\n",
    "    error[name].kn = (error[name].kn + knob).item()\n",
    "\n",
    "for name, knob in zip(nks, ks):\n",
    "    error[name].ks = (error[name].ks + knob).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "34255780-65bf-41c8-9247-2540618e3015",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.7282e-05, dtype=torch.float64)\n",
      "tensor(0.0023, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check \n",
    "\n",
    "print(((global_observable(0.0*global_knobs) - global_target)**2).sum())\n",
    "print(((observable(0.0*knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7b8e398d-e193-40c9-997c-9c95d442cd0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_a, nuy_id_a = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0dfe7b7f-2618-4099-b799-1f6dec127438",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_a, etapx_id_a, etaqy_id_a, etapy_id_a = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "8aa38112-921a-4ef8-aab6-cbc567f3167a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_a, bx_id_a, ay_id_a, by_id_a = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "eef8db19-fe24-4e96-b97e-5971e066a4fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_a, muy_id_a = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7bd32050-c06f-4573-85b9-fc8a321ecbe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_a = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "a4fd3162-df14-4a95-b4c3-5e79f4c2a0d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_a = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "0185bed6-c5c1-4cf2-b4a4-5abd3482f7f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0002, dtype=torch.float64)\n",
      "tensor(-0.0061, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_a))\n",
    "print((nuy - nuy_id_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "10f4f345-98ff-4158-92be-528e6e09bcd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.5376e-07, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "00165816-59f5-42b6-9361-9c2c0e18ba59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2106, -66.6820], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "37e7de61-e27e-4d75-97bd-2df88eb65a6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.7282e-05, dtype=torch.float64)\n",
      "tensor(2.4343e-06, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "28bd54ae-5cb1-4078-a59d-fc1e4f412585",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.030918693208962598\n",
      "-0.011102519274665108\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c6bd69d1-be92-4d60-85bb-f1671e45ae15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f4cc1ee8-66ed-4d31-95d2-ce4c10e11651",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0c739961-b6fb-4806-b36c-35f488d465af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d810dadb-51cd-431a-98b9-806b2e0d0a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1c862753-e406-432d-a1fa-4d42b04673de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "8aec46eb-04bb-4dda-a0a2-368c46ee1a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f4b7b87b-42c4-401b-ad41-46e2754007fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "b6839017-8bb0-4a25-b160-b8fb57caf8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-4.9308e-05, dtype=torch.float64)\n",
      "tensor(-0.0004, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c8affcfb-fe9b-488b-8631-a144228d2a5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(6.7433e-07, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3f3cfb47-1bd0-42d1-a959-b83d511a773e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1823, -66.6932], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "42cfb73b-770b-4feb-9d21-98a2cc126dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_a_bb = 100.0*(bx - bx_id_a) / bx\n",
    "by_a_bb = 100.0*(by - by_id_a) / by\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_a = rms(bx_a_bb).item()\n",
    "ptp_x_a = (bx_a_bb.max() - bx_a_bb.min()).item()\n",
    "rms_y_a = rms(by_a_bb).item()\n",
    "ptp_y_a = (by_a_bb.max() - by_a_bb.min()).item()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_a_np = bx_a_bb.cpu().numpy()\n",
    "by_a_np = by_a_bb.cpu().numpy()\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_a_np, fmt='-', marker='x', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local)')\n",
    "ax.errorbar(s, by_a_np, fmt='-', marker='x', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local)')\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_a:05.2f}\\% \\quad RMS$_y$={rms_y_a:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_a:05.2f}\\% \\quad PTP$_y$={ptp_y_a:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_a)}{(nux - nux_id_a).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_a)}{(nuy - nuy_id_a).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_a.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-5, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "be800d46-264c-468b-8a50-b55315e329e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "\n",
    "ring.flatten()\n",
    "error.flatten()\n",
    "\n",
    "dkn = {name: (error[name].kn - ring[name].kn).item() for name in nkn}\n",
    "dks = {name: (error[name].ks - ring[name].ks).item() for name in nks}\n",
    "dkf = {name: (error[name].kn - ring[name].kn).item() for name in QF}\n",
    "dkd = {name: (error[name].kn - ring[name].kn).item() for name in QD}\n",
    "\n",
    "ring.splice()\n",
    "error.splice()\n",
    "\n",
    "correction[section] = [dkn, dks, dkf, dkd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "1cbc0be7-6445-47e9-be5c-603e28d8b3d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S04 (EU132)\n",
    "\n",
    "section = 'S04'\n",
    "\n",
    "target = ts[section]\n",
    "matrix = ms[section]\n",
    "\n",
    "nkn = table_nkn[section]\n",
    "nks = table_nks[section]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "180b736f-2156-4a17-89cc-cd61d10234f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(EU132_L04, error.next('MLL_S04').name, position=EU132_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2bb757d9-3ad0-48d0-bc76-0998ca17fa83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id, nuy_id = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8b519fcb-bb3b-4feb-8cbb-016a267303d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id, etapx_id, etaqy_id, etapy_id = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "7779b507-a783-42d8-b768-26206f0414e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id, bx_id, ay_id, by_id = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "f8b0137c-baa5-49d4-9fda-ba2d6daec5ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8bed54b0-00ff-411c-a2b2-70257403d8d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "fd1af370-d22c-4684-a041-62355ed2a927",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "0cdb8a34-ba1a-4579-b6cb-88fd6494d5d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0078, dtype=torch.float64)\n",
      "tensor(0.0007, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id))\n",
    "print((nuy - nuy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d5ad75cb-aa38-40a1-a75f-fb91e6721675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.0048e-05, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "d430d7cc-f5f4-4819-8cb8-07072211b551",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2623, -66.6823], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "6a4ef6f5-2094-47f8-9f84-02ce986edc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parametric observable vector\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(error, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "\n",
    "def observable_twiss(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    return twiss(error, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)\n",
    "\n",
    "\n",
    "def observable_dispersion(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(error, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T\n",
    "\n",
    "\n",
    "def observable(knobs):\n",
    "    kn, ks = torch.split(knobs, [6, 4])\n",
    "    betas = observable_twiss(kn, ks)\n",
    "    etas = observable_dispersion(kn, ks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "7aed7b13-abc5-4979-884e-cce41e3d331f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(6.1851e-05, dtype=torch.float64)\n",
      "tensor(19.8483, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check the residual vector norm\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "print(((global_observable(global_knobs) - global_target)**2).sum())\n",
    "print(((observable(knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "742a59bd-7032-48c4-9359-757fb766f087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(19.8483, dtype=torch.float64)\n",
      "tensor(1.2198, dtype=torch.float64)\n",
      "tensor(0.0952, dtype=torch.float64)\n",
      "tensor(0.0249, dtype=torch.float64)\n",
      "tensor(0.0205, dtype=torch.float64)\n",
      "tensor(0.0202, dtype=torch.float64)\n",
      "tensor(0.0202, dtype=torch.float64)\n",
      "tensor(0.0202, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (local)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(8):\n",
    "    value = observable(knobs)\n",
    "    residual = target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    knobs += lr*delta\n",
    "    print(((value - target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "25af5140-cc90-471e-92a5-81d973b12e4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00891094  0.00346326 -0.04715884 -0.08306084  0.00258604  0.01972254]\n",
      "[-3.70389961e-05  2.73780764e-06 -1.13472190e-05  3.55587507e-05]\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kn, ks = torch.split(knobs, [6, 4])\n",
    "\n",
    "print(kn.numpy())\n",
    "print(ks.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fff1cfe2-b58e-4420-b28d-095e7dc5034a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name, knob in zip(nkn, kn):\n",
    "    error[name].kn = (error[name].kn + knob).item()\n",
    "\n",
    "for name, knob in zip(nks, ks):\n",
    "    error[name].ks = (error[name].ks + knob).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "f3d1c890-c963-458a-9633-069586a2430d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.7606e-06, dtype=torch.float64)\n",
      "tensor(0.0202, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check \n",
    "\n",
    "print(((global_observable(0.0*global_knobs) - global_target)**2).sum())\n",
    "print(((observable(0.0*knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "57ea7b62-1860-41c6-af3e-9b8925226912",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_a, nuy_id_a = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "91cdaddf-ae36-4983-bf76-e0860ba4f726",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_a, etapx_id_a, etaqy_id_a, etapy_id_a = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "1c963492-1708-4a0b-b231-fa2ede198f30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_a, bx_id_a, ay_id_a, by_id_a = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "6d8fbd26-0bcd-47d9-9599-fe22158f7ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_a, muy_id_a = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "ecc093eb-d74c-4545-8757-49f6e6eafb70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_a = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "2162121c-fb3d-44d2-8f0b-b13b188d77f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_a = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "cc2e2e08-182c-43ea-80fa-a3c85a044933",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0009, dtype=torch.float64)\n",
      "tensor(-0.0009, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_a))\n",
    "print((nuy - nuy_id_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "546c2611-86bf-4839-a36b-b613189e97ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(2.9784e-07, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "02d60ee2-3b10-43d5-81f2-7e6ed06261ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2069, -66.6785], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "60c986bf-f265-4c4e-845b-5995f02e2ad3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.7606e-06, dtype=torch.float64)\n",
      "tensor(1.0971e-07, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "451b00c3-05f8-4795-9581-396d31b5c135",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0023295305998528772\n",
      "-0.0006851113772105288\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "b5ab9f52-e2a7-4554-b7c6-a842ce7da9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "8894e7cf-52c5-4059-9c15-4276b7366b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "fcf1b034-971d-40d1-a202-38d03b9f53f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1b63abca-39c1-47c8-b1d4-c4c4ea0cab83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "91100b6c-caef-41b5-a1ca-bbb239db5951",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "a93d432a-cf16-42de-82f4-f65dc144f3da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "fb98532b-fc44-4084-a8d8-4f07bfca68be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "46bb0527-726b-4cd4-84c8-2eccc2608872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(5.8243e-05, dtype=torch.float64)\n",
      "tensor(-5.8701e-05, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "bd590060-7dc0-4280-b024-fcce6597abf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(3.0304e-07, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "fb18dfc6-83a2-4a66-9235-2933d487c925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2012, -66.6745], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "97ed28d0-d9a0-4ac5-9f36-f32394ee5da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_a_bb = 100.0*(bx - bx_id_a) / bx\n",
    "by_a_bb = 100.0*(by - by_id_a) / by\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_a = rms(bx_a_bb).item()\n",
    "ptp_x_a = (bx_a_bb.max() - bx_a_bb.min()).item()\n",
    "rms_y_a = rms(by_a_bb).item()\n",
    "ptp_y_a = (by_a_bb.max() - by_a_bb.min()).item()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_a_np = bx_a_bb.cpu().numpy()\n",
    "by_a_np = by_a_bb.cpu().numpy()\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_a_np, fmt='-', marker='x', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local)')\n",
    "ax.errorbar(s, by_a_np, fmt='-', marker='x', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local)')\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_a:05.2f}\\% \\quad RMS$_y$={rms_y_a:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_a:05.2f}\\% \\quad PTP$_y$={ptp_y_a:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_a)}{(nux - nux_id_a).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_a)}{(nuy - nuy_id_a).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_a.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-5, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c12611ff-a544-4034-8556-51fe5a02289c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "\n",
    "ring.flatten()\n",
    "error.flatten()\n",
    "\n",
    "dkn = {name: (error[name].kn - ring[name].kn).item() for name in nkn}\n",
    "dks = {name: (error[name].ks - ring[name].ks).item() for name in nks}\n",
    "dkf = {name: (error[name].kn - ring[name].kn).item() for name in QF}\n",
    "dkd = {name: (error[name].kn - ring[name].kn).item() for name in QD}\n",
    "\n",
    "ring.splice()\n",
    "error.splice()\n",
    "\n",
    "correction[section] = [dkn, dks, dkf, dkd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "05730dfc-115a-4a71-82dc-f73e0ac6e98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# S11 (SCW)\n",
    "\n",
    "section = 'S11'\n",
    "\n",
    "target = ts[section]\n",
    "matrix = ms[section]\n",
    "\n",
    "nkn = table_nkn[section]\n",
    "nks = table_nks[section]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "b324142d-d558-45da-a944-e51f33b0be74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 721,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 1}"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "error.insert(SCW_L11, error.next('MLL_S11').name, position=SCW_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "219ca9f3-e4c4-44ff-87fd-65d62339e7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id, nuy_id = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "3097e0f2-bdb9-4a99-956c-fd3ecf9a8b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id, etapx_id, etaqy_id, etapy_id = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "1af0d3ee-fee9-4e97-a34f-3fdf82cafc80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id, bx_id, ay_id, by_id = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "6a1e5834-5e5c-4abe-b65e-45ac1c5c1376",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "7c855138-ef55-41d9-bbe4-79c9f3070511",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "b2d4eab8-cec7-41cf-bbde-f2d08c570923",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "3495ba38-6874-4391-86bb-3b8893d9f833",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(2.0874e-06, dtype=torch.float64)\n",
      "tensor(-0.0086, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id))\n",
    "print((nuy - nuy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "ebea52f0-ee6e-4de1-8e66-8b37dccf8938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(0., dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "c8a14b72-cb09-440e-99c8-6986da3b6360",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2093, -66.7275], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "69344946-2563-4458-b6da-4541287924d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parametric observable vector\n",
    "\n",
    "def global_observable(knobs):\n",
    "    kf, kd = knobs\n",
    "    kn = torch.stack(len(QF)*[kf] + len(QD)*[kd])\n",
    "    return tune(error, [kn], ('kn', None, QF + QD, None), matched=True, limit=1)\n",
    "\n",
    "\n",
    "def observable_twiss(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    return twiss(error, [kn, ks], ('kn', None, nkn, None), ('ks', None, nks, None), matched=True, advance=True, full=False, convert=False)\n",
    "\n",
    "\n",
    "def observable_dispersion(kn, ks, nkn=table_nkn[section], nks=table_nks[section]):\n",
    "    orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "    etax, _, etay, _ = dispersion(error, \n",
    "                                  orbit,\n",
    "                                  [kn, ks],\n",
    "                                  ('kn', None, nkn, None),\n",
    "                                  ('ks', None, nks, None))\n",
    "    return torch.stack([etax, etay]).T\n",
    "\n",
    "\n",
    "def observable(knobs):\n",
    "    kn, ks = torch.split(knobs, [6, 4])\n",
    "    betas = observable_twiss(kn, ks)\n",
    "    etas = observable_dispersion(kn, ks)\n",
    "    return torch.cat([betas.flatten(), etas.flatten()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "fba450c9-93a9-42d7-b1f3-43df1e33f120",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7.4360e-05, dtype=torch.float64)\n",
      "tensor(48.9830, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check the residual vector norm\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "print(((global_observable(global_knobs) - global_target)**2).sum())\n",
    "print(((observable(knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "6f5391b3-a3de-4fa3-b7cf-0d7a9bd41e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(48.9830, dtype=torch.float64)\n",
      "tensor(3.8238, dtype=torch.float64)\n",
      "tensor(0.4350, dtype=torch.float64)\n",
      "tensor(0.0999, dtype=torch.float64)\n",
      "tensor(0.0635, dtype=torch.float64)\n",
      "tensor(0.0595, dtype=torch.float64)\n",
      "tensor(0.0590, dtype=torch.float64)\n",
      "tensor(0.0590, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (local)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "knobs = torch.tensor((6 + 4)*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(8):\n",
    "    value = observable(knobs)\n",
    "    residual = target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    knobs += lr*delta\n",
    "    print(((value - target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "7fb5e43a-39d7-4e1a-8c78-08129f71de4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.0385348   0.05106073 -0.1382038   0.11085802 -0.03556687 -0.00120526]\n",
      "[2.06946804e-16 1.24152820e-17 1.14493189e-16 1.19702447e-16]\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kn, ks = torch.split(knobs, [6, 4])\n",
    "\n",
    "print(kn.numpy())\n",
    "print(ks.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "cb94010a-dc5d-4a5a-9082-7b0f0411ebaf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name, knob in zip(nkn, kn):\n",
    "    error[name].kn = (error[name].kn + knob).item()\n",
    "\n",
    "for name, knob in zip(nks, ks):\n",
    "    error[name].ks = (error[name].ks + knob).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "59e9258b-c83e-4185-8357-67fa4e9a4bdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(0.0590, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Check \n",
    "\n",
    "print(((global_observable(0.0*global_knobs) - global_target)**2).sum())\n",
    "print(((observable(0.0*knobs) - target)**2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "0b03c250-61b4-4523-8948-f790463c1c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_a, nuy_id_a = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "a50ddc86-3f61-4889-8b3e-c4c53d27c7ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_a, etapx_id_a, etaqy_id_a, etapy_id_a = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "33adb736-26a5-456f-8c03-3679ec60bb0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_a, bx_id_a, ay_id_a, by_id_a = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "4d182150-403d-4867-b667-07268ef12128",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_a, muy_id_a = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "8d7e27a3-cec0-4327-a5d2-80e114158183",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_a = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "6e019452-542e-475c-9b6d-151bb272fab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_a = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "fdc2e990-819d-4576-a939-651a9b9474b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3.8400e-05, dtype=torch.float64)\n",
      "tensor(-0.0145, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_a))\n",
    "print((nuy - nuy_id_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "9409621c-1c1c-4b68-a837-d8c6083923a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(2.6504e-17, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "c933b58e-26d3-4213-b2fc-bd9d1f58ee19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.2123, -66.6945], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f4dbea67-3244-4bee-97d7-426cb63fcd6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0002, dtype=torch.float64)\n",
      "tensor(1.4458e-05, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "7f6125ef-4fe3-4c66-a39b-658ade22fd26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07300308463644578\n",
      "-0.026168149352525136\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "fabc0ce9-6513-4971-a0da-5ec171ec3caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "66fdddce-7367-4662-bf1c-785d18d80671",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "6e3dd5c3-da7d-4e9f-9f68-763cf624dc92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "416599b7-cee8-4c20-ac81-330465214ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "d771e078-5ede-4a37-9034-a946eb1e0501",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "ec481792-fcb2-41ea-942d-3374065f7726",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "8495f197-6edc-4f9f-b88a-03b0d20763d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "416c1d54-b9e0-4b7a-ba88-c2a77b387cbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0002, dtype=torch.float64)\n",
      "tensor(-0.0010, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "30942eb0-2f13-4c07-a023-fd2278275f11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(2.6027e-17, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "d8061cd8-d202-4044-a8de-049851cd86d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.1439, -66.7209], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "28468e3f-170b-4c3a-bf77-b9957af50e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_a_bb = 100.0*(bx - bx_id_a) / bx\n",
    "by_a_bb = 100.0*(by - by_id_a) / by\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_a = rms(bx_a_bb).item()\n",
    "ptp_x_a = (bx_a_bb.max() - bx_a_bb.min()).item()\n",
    "rms_y_a = rms(by_a_bb).item()\n",
    "ptp_y_a = (by_a_bb.max() - by_a_bb.min()).item()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_a_np = bx_a_bb.cpu().numpy()\n",
    "by_a_np = by_a_bb.cpu().numpy()\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_a_np, fmt='-', marker='x', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local)')\n",
    "ax.errorbar(s, by_a_np, fmt='-', marker='x', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local)')\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_a:05.2f}\\% \\quad RMS$_y$={rms_y_a:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_a:05.2f}\\% \\quad PTP$_y$={ptp_y_a:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_a)}{(nux - nux_id_a).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_a)}{(nuy - nuy_id_a).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_a.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-5, 5)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "123deef0-1d59-451b-aa19-36d3a0fce69c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correction\n",
    "\n",
    "QF = [f'QF_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "QD = [f'QD_S{i:02}_{j:02}' for j in [2, 3] for i in range(1, 12 + 1)]\n",
    "\n",
    "ring.flatten()\n",
    "error.flatten()\n",
    "\n",
    "dkn = {name: (error[name].kn - ring[name].kn).item() for name in nkn}\n",
    "dks = {name: (error[name].ks - ring[name].ks).item() for name in nks}\n",
    "dkf = {name: (error[name].kn - ring[name].kn).item() for name in QF}\n",
    "dkd = {name: (error[name].kn - ring[name].kn).item() for name in QD}\n",
    "\n",
    "ring.splice()\n",
    "error.splice()\n",
    "\n",
    "correction[section] = [dkn, dks, dkf, dkd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "1d5f1ea1-3c7b-485b-8fa6-46c17a6fe4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 725,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 5}"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "\n",
    "error.insert(EU100_L01, error.next('MLL_S01').name, position=EU100_POS*1.0E-3)\n",
    "error.insert(U46_L02, error.next('MLL_S02').name, position=U46_POS*1.0E-3)\n",
    "error.insert(F8_L03, error.next('MLL_S03').name, position=F8_POS*1.0E-3)\n",
    "error.insert(EU132_L04, error.next('MLL_S04').name, position=EU132_POS*1.0E-3)\n",
    "error.insert(SCW_L11, error.next('MLL_S11').name, position=SCW_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "bf322687-2284-4e39-90fd-43b1b213444c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply correction\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for section in sections:\n",
    "    \n",
    "    if correction[section] is None:\n",
    "        continue\n",
    "\n",
    "    nkn = table_nkn[section]\n",
    "    nks = table_nks[section]\n",
    "\n",
    "    dkn, dks, dkf, dkd = correction[section]\n",
    "    \n",
    "    for name in nkn:\n",
    "        error[name].kn = error[name].kn.item() + dkn[name]\n",
    "        \n",
    "    for name in nks:\n",
    "        error[name].ks = error[name].ks.item() + dks[name]\n",
    "    \n",
    "    for name in QD:\n",
    "        if name not in nkn:\n",
    "            error[name].kn = error[name].kn.item() + dkd[name]\n",
    "    \n",
    "    for name in QF:\n",
    "        if name not in nkn:\n",
    "            error[name].kn = error[name].kn.item() + dkf[name]\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "d007211e-0b44-43e4-922c-8420b64ef2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "bd7e9ec4-6ba2-4530-83d2-5e638b1c837d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "5c6b5aea-c701-4c79-9158-cf1ee1508e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "7ea7a864-03c1-4904-97d6-e1d3b5ce169b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "e28dd564-c97e-46fb-a313-cbd6fc190210",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "7e87ea18-5cf7-4cc1-bb73-95ac5b19471d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "59da8960-cf5e-4fcd-9553-28cc6546d2aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0018, dtype=torch.float64)\n",
      "tensor(-0.0032, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "9ae79432-be2b-483c-a71a-b4b86c2c396d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.2819e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "edf3dcd4-ffb7-4851-9bb1-dd24f412a1a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.0556, -66.7690], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "998214ed-9cb8-4404-b263-4e6be8849a36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(1.3918e-05, dtype=torch.float64)\n",
      "tensor(2.0316e-06, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "11bd3af9-7878-4b70-ba6d-44cce06e9f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02280581419001947\n",
      "-0.008521360336182646\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "1d1921f9-2643-4a71-9e09-8fedd1087239",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "3f9e1aba-2a0d-416a-a855-be8a987c0b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_c, nuy_id_c = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "74e2b1f0-c847-49ef-8599-2f749f5e14a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_c, etapx_id_c, etaqy_id_c, etapy_id_c = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "a3f4ceec-b970-4fe6-aebe-280c20bd873b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_c, bx_id_c, ay_id_c, by_id_c = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "12b552e0-161b-4911-811d-b9c2cdf9ef27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_c, muy_id_c = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "cfdedd31-cc3a-45a7-bb3e-a42c334c251e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_c = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "9f1485ce-822d-410f-8760-24c522975bd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_c = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "341593ed-4988-4074-85a0-aefc3bf9e0c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0004, dtype=torch.float64)\n",
      "tensor(-0.0004, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_c))\n",
    "print((nuy - nuy_id_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "f619e68b-f6c2-430e-9f90-f8a7132e97bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.2891e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "f4fa5058-8adb-4426-960b-775cffdb1553",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.0408, -66.7922], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "e89372ac-f020-447f-94a1-7ca2d3006e66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n",
      "tensor(-71.0556, dtype=torch.float64) tensor(-66.7690, dtype=torch.float64)\n",
      "tensor(-71.0408, dtype=torch.float64) tensor(-66.7922, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "(psi_x, psi_y) = psi\n",
    "(psi_id_b_x, psi_id_b_y) = psi_id_b\n",
    "(psi_id_c_x, psi_id_c_y) = psi_id_c\n",
    "\n",
    "print(psi_x, psi_y)\n",
    "print(psi_id_b_x, psi_id_b_y)\n",
    "print(psi_id_c_x, psi_id_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "10494bcf-bf6a-4e47-bb44-37334b6b3d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "bx_c_bb = 100.0*(bx - bx_id_c) / bx\n",
    "by_c_bb = 100.0*(by - by_id_c) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "rms_x_c = rms(bx_c_bb).item()\n",
    "ptp_x_c = (bx_c_bb.max() - bx_c_bb.min()).item()\n",
    "rms_y_c = rms(by_c_bb).item()\n",
    "ptp_y_c = (by_c_bb.max() - by_c_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "bx_c_np = bx_c_bb.cpu().numpy()\n",
    "by_c_np = by_c_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "ax.errorbar(s, bx_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global + tune)')\n",
    "ax.errorbar(s, by_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global + tune)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_c:05.2f}\\% \\quad RMS$_y$={rms_y_c:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_c:05.2f}\\% \\quad PTP$_y$={ptp_y_c:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_c)}{(nux - nux_id_c).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_c)}{(nuy - nuy_id_c).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_c.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-6, 6)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "d3b8838d-4ec5-495f-878b-3580a95009e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jVVQhfyg0UUrC94RsEW/hVfL4CQ5FwvcEQcg8akc5cCC9ZkOUEk+pwqTQRCnJKSWo3qlAZjyleJJz/PHAsmX02fnnUyVTsxxS48cDRUXAYYdZV0UVchMRpQQhM8RLdC55/ASHIqKUIAiZhydHXi8wYAD9no3wvUQ9pWRgmV8UmiglAqvQ3R0dupkJUYqLSxQX66JUZ2d0cQl1MvTCCxTCd+KJwF57iTCVTxSSKKVp+rMm9rbwqK8nbyMzuxUMUtvgHKd2kEiKikCAxr833QTcfTdQVSWClJB1xEdPEITMwx1laSlQUhL9md2k4ikl4Xv5hXGioA7i8hERpQSjAJ8Jz9SZM2mSYxYyHQjQ92pVVF4ICIfpfW2t5EDLFwpJlBLP1MIm2+FxieZNnTSJ/nbLFlogFkFKyDLiKSUIQubhjrKkJLuiVEcHrWq6XH3/Tjyl8pdCTnQubbkwyYYoxcTK46d6DHA75VeZJOUPqs3Nd3ur2lgRVQuPbIfHGcP3rMa4zz+v53blAhRic4UsIqKUIAiZhwWo4mLylgIyN0lSO2xNo7CW4uK+fycT+fyl0ML3ZOVeMIbrZbL6XkuL/nusxQdeCBDP1PyjkDylZOwgsLhz553A7bcDI0dmLjxOte1WY9xgEHjxRSpAcfTRwFFHSbi0kHUkfE/ILFKKVAB0ASrb4XvqsRiRgWX+UmiilLRlwShCOcVTSkUN3xPyi0ISpWQRQACo0ujmzcDWrXpIciYwPl/GsbVagMLvp/EBh0ubhR0KQoYQUSrbFJpIk+1Ya8EZOCV8D7CeJKnhe7Jyn1/wRMHno9d8nySJKCUY7VwmPaUSEaU0TTyl8hnV7qiec/mI2FsBAO6/n8I3XS7yVsqU2GO07cYKfJzH75hj6L0aLi15/IQsIuF72YZFGiBaRVfjj/MJPsd//hP45BPgN78BnnxSSpEWGtkUpYxJra32KwPL/IXvZ1UVsG1bYYlSMuAsTLIZvpeIKCXeJflNIeWUEnsr8BzO76efE07IXHhcvDEu5/F77DF6VdurzMGELCKiVLbJdkK8bBAIAG+/DfzrXxTTPGRI/p6rYI4qSmU6p1QqnlIyScovCk2Ukgm/4NRE54xUO81vVLvT1madfDkfEHtb2PAc7rzzgGeeoc+OPx4YOzYzwlSyC6/SRgWHIKKUEwgEyL3yD38gocbtzn+RZv/9SaXv6pJSpIUId5KlpdkXpZL1lKqvp2fUrM0Gg7QyqlaUEpwH38/KSnptb6fPPJ7sHZOdiNef4BRRysreSrh0fqPaHU0jmztgQPaOx05ElCpsODzuoIN0UaqtTR8z2u09Z/SCtbK53d30Km1UcAiSwMcp+P3Apk3A+vWFIdIsWUIDE7dbL0UqFA65llNK7bQlL1ruo3pKMZkMZ8o0IkoJ3L7ZOyVT7T0Uis5pIp6phUkhFZcQe1vYzJxJczjVY4l/DwTsX7TkZyve2JrbqSwCCA5BZk9O4YUXSKQJh/NfpAkGKXzP7weuukoqPhQiThCluERustX3zKqU5HvIbb7B97OoSG9/+TxJUtuv5DgpTFiEqqmhV6eFS0v4Xn5jtDv5bG8lp5QARItSmWrvmqbb2EGD6NWY6JyRwhKCw5DwPScQDAKvv04izXHHAYcfnrmEeJmGJ+9TpwJbtuilSIH8PWehL9xpZlOUGjKEPBNTCSfhNnr33cCf/wwMHCiCVC7BEwWPBygvpzZQKJMkWbkvTNjm1tQA27dnzlMq0XBpDiUBpI3mCsmEsheSp5SE7wmAuaeU3XR0kDAFkCi1YUP8hQARpQSHIJ5S2YZFmqOOIlGqudncEyNf4FjryZPpPXfYUoq0sMhWTilN0wcHgwfH3m+8iXwgAKxbB6xZQ9sVQSp34PvJohQgkyQhv2ERilfPsyVKiadU/pBMKDvbHe7v89neyiKAAGRHlOL9eL1ARQX9Hs9TStqo4BDEUyrbsEgTDgMrVwItLfR5phLiZRpeNbvtNno1hkUJhQGLUsXF0Z5Sdlfk6ezUn6khQ+jVbJLEobSMWacdDNLg0+WiTj8YlDacKxSaKCWTJIHtXDwxPt0k6iklic5zj2SqR6t5/Do68tveGpO653OlQcGabIpSZWXxoxBElBIchohS2YZFmrlz6bW1lSbNVi7R+YKUIi1szHJKRSLULnw++/bLA2GPB6iupt/NJmfGdmmcJPHA2++nn5NOkvDTXEJEKaHQUMP31Pd209/CEoKz4f7ur38F7rgDGD4cuPLKvv2gWvG0oUFfgM1HjOOFcJg8V4TCQrV9mRpfsCg1YEDiic41TZ93CkIWkRboFLjD1rT8nhwxotAXNjwxKS3VO071c7vgZ6u8XA8jMOuw1QkSEN1OWZC64goSpABg2rT8DbnNJerrra9/MEjfA31zSgH5bXcl8a5gDN9Tc4/YCT9XPCmX8L38IxCgHKFbttD9NVuYMVY8zWd7axzXyji3MMmmp5QqSsUL3wPE5gqOQEQpp6B2Ws3N2TuOTCGeUoWN6inl9eoTFruTnXOHXV6ud9hmkySzlU6GQ24vu0z/rL1d8qI5gURznBSap5TklBJYlOLwPU2znqykE/aIiRUuDUii81xGDWXv6TFfGCgkUSrW+EEoHLIhSrGdV0WpRLxTRZQSHID4kzoFEaWEQkIVpQDyWmppyZyn1IAByXlKqR02h9yqEzoeCEjoXnZRc5w0NwNXXw38+999c5yw3XG7C0OUkvA9gW3rwIEkHmga2S3VU9UO1GqnmzaJp1S+wYL/+PFAURFw6KHmoexsdzhsvlDsLSA2t1DJhiiljnGT8ZSSNio4ABGlnIJqEPI51p6R8L3CxihKlZRQu7fbU0oN30vVU8rss0wNOIT4BALAzp3ALbcAf/4zMHasddJdj4cGb0DhTJLE5hYmLJxzAtyOjszklVJFKUASnecTalLzF18k0fGEE4C9944WpjjZN0A5pYD8trdGGyve04VJNnJKqZ5SxcX0e7ycUsbfBSFLiChlJ/X11gnLg0HqqNjrQjUITU2ZOLrsIp5ShQ13kuytFC8hY7pQw/d432YTs1g5pRh1oJmp8upCYhx+OHmDtLdTaKhV0l0RpYRCQc3jV1ZG7zNht4yiFOeyMlYjk1X73IND2QMB4Jln6LPu7r7Vo9W+shDC9ySnlABEL1Z2dVE/bHfCe7X6XqxoAEBsruA4JKeUnSSa3wQoPE8pEaUKGzNPKfVzu0g0fC8RTykRpZzL00/rK/OhUF8bXGiJziWnVGETCuk2TZ2sZMNTKhLpK/rzMTLSRnODmTN1AYrvH78GAvqiq3o/C0GUkvA9AejrQZ8Jj3p14ZU9pSTRuZAjiKeUnaj5TVpbgf33B1atAh54wDqcBJCcUkJ+E4noSW3VnFJAZqvvxQrfE0+p3CUYBJ57jioj7rMP8J3v9M1xUmiJzmXCX9io9qm0NDuiFCdYB2ghoKgo+u/UROcyQco9uM80ExxFlMrOcQjZQ9PMRSlu/3ahekrFS3Qu4XuCwxBRym54EvSb31BY3vjxwE9/ah1OAhSGKCU5pcxJJuQzV1E9k7LlKRUvfE9ySuUm7IV60knA+++TnVEXBwB6L4nOhUKCRamiIhJiy8qiP7cT9vyuqqL9d3eTzeXcQoxMkHIbvmdmopS6gCM5pYRCoLtbfyZKS8nmZdJTShKdCzmIhO9lAp4EaRp1TmaCQ6GJUuIpZU4yIZ+5CgtPLpe+Wp4NT6l0ilLiKeUMOMfJ8cfTezWUpLZWnxyIp5RQSLCNYzEqk6JUKt6pIkrlHrFEKTNPqba2/BVrxFNKYHHI5dJDlzMxxlBFqXxMdF5f33d+xASD9L2Qs+TBDDcHYA8Xl8s8vwkgopRA8ORZFabUCjdmgmauwR1kcbGe7DabnlKdnXr+IcY4sDbrsCV8z3lwjhMOBVJDgsxynKiiVHt7/k6SVDubr+coWMP2iW1eNsL3jDbXiKza5y6apt+zeKKU6iGXrx7GkuhcUMPoeIyRifauVt+Ll+hcHR/lShsthIX7AkbC9+yGH5SJE+n9fvv1zW8CRE96C0GUkvA9a7hd/OUvwK9/DYwZA3zve/khSAF9k5yrv2ey+h7vkxPvqjlOkk10nq+D61xFTbprVunLTJQC6D5WVGTmGDOJeEoVNmrlPSBznlLd3Xpfn4x3qrTR3EK9d7FEKY8H8PloQaqriwTLfLe3gLTnQkT1WOIKv5kYJ6rFfPLRU0pNx9DcDBx1FPDJJ/m1cF/AiChlJ6qHyxtvAKtXA0ceCRx0kHXiXaAwqu/x+UpnbU4gANx0E3UwbW35ZWiNEyT190yF76nx9rxfVZSSROe5jTEUyOeL/l7NKeX1FtYkSWxu4WEM38u0vXW54ifelUTnuUu80EvuK9mLobxct7f5iOSUyj3Snc9VHWtmUpRSPaXiLfbmasg036Mbb6Tz3XNP4Ac/yK95UoEifm52wvlNAoHoyiTG/CZA4YXviadUbIJBukYuF71axVDnItn0lFJDSTweXYgy7tfYQZt12JLo3LmoE9x4K/dA/ueVUtuq2NzCgycq2RKlBgygvixRT6lcmiAJ0fdLtb1Modlb8ZTKPdIdFqZ6SmWyvZtV3wuFzNtgLi9W8byaQ4dFkMoLxFPKTlRV3ZgEMlb1vbY2es8deD4iOaWs4U5wxAhKCrr33uYhn7kKVwLJdvge75erQakk4imlftbRYR4mJmQH4ySJJ+OM2SRp+/b8FRfV6yGr9oWHMadUpsL32OubvQ9jiVK5umovxL93Ikpl5ziExDGr0tuffK6qx1KmPKVCIX18bYwG6OqKHgdpWm7bXHXhvrub3ufD/KjAEVEqU8SqTAL07bRaWoDqalsPKWtEInpiaemso+FOcOZM4IEH6DodfDBw6KH5I0zxhCTTolR3t76Ky4Pi0lLyTIznKRUvfC8U6puXSsgeqp2NVaK8UCZJ4ilV2GSr+p7qmQpIovN8Rb1f4pkqic5zFR5b33cf8Kc/0Rws1Xyuqu3LlCil2vOyMnreXC6aR3R2RotS6jwMyC1RKhgE7r8fGDYM8PuByZPzZ35U4Ej4XqZQw/fMMHZa+RzCJ4NPazjkc/p0vcNobzcP+cxVeEKS6ZxSaole7pytcpwkW30PkLxSTiKeKFVok6RcdtMX+k+2qu9ZiVKS6Dy/SNTeqjmlgPy1tyJK5S6BAHlNr1sHNDamLnJkI9E5b7+4mHJlulzWyc5z1ZuPF+4vuogEKQA45JC+VcuFnEQ8pTJFrHK56vdMPic7l8GnNRzyuWmT/hkP4PNlBcAsp1QmRamyMj3Mzmq/yXpK8fbz1bsx1xBRKhqxuYVNtqrvGUWpVBOdpzsJsZBe4uUDK2R7C4jNzSWCQbJFaj7XVMbe2cgppe6TKSmhMbdRlDLmfssVTyleuD/xROCxx+iztjZg1iz9eyFnEVEqUyTrKdXUZO/xZBOZIMVH9ZTLNw+cbCU6N06QgP6JUsbP8u0+5TKqnZXEu5JTKhdJpxCT7ep7iYTvxRI2OAkxEH091JwvQvYQexuNiFK5CduTAw4AduwAxo9PPSwsG55SxoIWgPXYOlfbKPd5X32lf8Z2JF8W7gsYCd/LFInmlOKcNOIpVdio9z/fxI5s5ZRSK0ExIkrlJ4mWKC+USZLklMo90lkNKtvV9xIJ34v1zHL4+v33A7feSkl7+5OEWEgv4ikVjYTv5R6qPdl3X/rs0ENTDwvLRk4pYyEfQB9bcwJ0JpEUFU5GtR35WqCmABFPqUygaYmLUtXVwJYtklOq0BFPqfQTy1PKuF9uo8XF1JknGr4nOAN1gCXhe7IQkIuo1aAiEXr/0EOpCTHZqr5nFb6XSqLzQAD48EPg7ruBxx8Hhg4VQcopSLh0NLnqhVLIcFhYIADceCN9Fg7r9iVZD+Ns5pRKxVMq10Qp9Vrmqx0pQESUygTxKpOof1NTk/+ilHGCpGl6jh+BUD2l7F7NzjTZyillJkpZ5TjhNlpSkrgolW/iYS6jhpDECicphMS7miaeUrlKIEB2ZvZs4OabgYkTUxNiYlXfs7P/TTXRuaaZH9d++9FnHR2UyFcEKWcQbxGgkD1TAQmZzgXUUGhuw3wfcz2nFGC98Mrk2rhAvZb5akcKEAnfywTxOmz1bwYOpNdCCd8DpMM2o5A9pdQytekkFU8pPi4J38stxFNKR0JJcptzziFhvK2N2msqEySr6nuRiPWYJB2kmugcMF+5X7xYF6xCIam05BSStbcVFfSaj/YWEE+pXMcoSqWCmadUV5e9HklmopRV9T0J3xMciIhSmSARUYqNH4tS+ZzoPNcV+kygilL5ZnCNlaAAfbISidjXOabqKaW+VxFPKeci4SQ6IkrlNg8/rHsNcTWoZDHaXNX22mm3UvWUMnsfDAJLllAZ8LPOkhLgTkLsbTRic3Mbtj39uW+q7VPD6ey0t7xts7yp+ZLonJHwvbxEwvcyQSqiVD57SkmHHR9VlMq38D1OuGjmKQVQ5+nzpX+/ZkkgE8kpBSTmKZVv4mEuE2+SVEjhJLk++CxkgkHgkUdIiPH7gUsuSa0alDF8z+3W8+V1dFAuSztINdE5EN1uOQnxoYcC69bR+EjNuQVIKF82SdRTqhDCpQGxubkOe232J4pD9VryenV729YGVFb2/xjNMCvmw2PYfEt0ro63ZeydN4golQniTZCA6ETnQH7nlBJPqfiooiTnNOIJdK5jFr7n9dJPKEQTFnbvTydmHXainlKSUyq3kBLlOjJByk1YiDn/fOCZZ+iziy6iSUayQoxZqfDSUl2Usov+iFJqO+UkxI2NJEoZS4BLCoDskmq10/b2/BrbMJKiIrfpr6dUT4/+TPB4s7yc7K2dYwwzTymrMW6ui1LqdezoyE87UoCIKJUJUvGUymdRSiZJ8THe/46OaA+fXIY7R1WUAmjC0tJi3yQpVk4pqw6bV5lilblmRJRyDomGkxhX7u1O/JwNxN7mJizEHHmkLkqlUg0qHNaFWTVsr6yMRB677Jam6YsrvMhg5ZkKxG6nnIT47rvpVZ2QiIdU9lHvXSKLAOqkub3dnkWobCLRALlNf3NKqZ47vBAwYACwfbu9Xj2xqu/ls6cUkJ92pAARUSoTJJPovKaGXkWUKmyM97+9PX9EKZ6QqBMkgDrPlhbzCUs6iBW+lw5PKXEhdg7J5jjhSZKm0X3Ml2cNkEpQuQoLMcuX65+lUg1KtW3qZIV/t2sRQK1aauYpZRR/E0l0ztvr7KTvvTKEdQTqvYp139jeer3Ut3Z2Up+fb5NJPl+Xq2/1U8H59FeUYtG8tLTvwlcmRCl1/GKV6DzX52FGj7PW1vyzIwWIJDrPBMmE77GnVEdH7inXiSLhe/Ex5hRzQl6p+nrrpLLBIH2fCGbhe+p7u0Qps/C9dIpS4inlHJKtBuXz6YO3fBMXc33wWeio7TeVe8d2iUOkGbZ9dtkttrdut25HYxW0iPceiD7/fHtOc5lkc0oBhREyXVREr2Jzc4v+hu+ZVcHj3+1s72aeUvHypjK51kbNRCkh5xFRKhPE67A1TZ/gVlbqq4f56i0lk6TYaJp+73kS4QTBw+02r3bEuU/cCZqTbItSqSQ6TyR8TyZJzkH1ukhElALyd5Ik9ja36a8oZVbtFNAnLnaLUuXl+phGtfmp5DhRzz+fi8HkGsacUpoW/b0xpxSQv/YW6CtKiXdqbsHjBztEqUx4ShVaonOz90JOIqJUJognSqkdls+nuyDm66BLJkmx6ejQ28TQofTqBFEqENDLcN91Fx0jC1K1tYmFlGha7JxSgH1eYWauzelIdM6dvhO82QQiWU8pIH8nSWJvc5t0iVLq6jlgv701WwTwePTKqvFEqXgVT/PtOc1l4nm5FZK9BfTzFU+p3ITbb6piotlYMxPt3UyUijfGtXrvdPhcuR8TUSovEFEqEyQ6QQKo0+ZyoU1N9h5XtpBJUmzYS6q4WK/G6ARRCiDhaf/9gd//HjjggOQEKYDaP6+iZtJTKhw2r0wSz1MqlihlzJcinaJzSDbROZAZ9/psIDmlchu1/aZy78wq7wHZEaXU/ao2V9P0cQE/kyJK5Q5GG2slMBaKKMVtmResZIybW6Qr0XkmPaU0LXb1vXzylOLcnwAwbBi95qMdKUBElMoEiU6QAOq0891TKtdjme2GRamKCvuT0abCnntSOEZnJ4UXJpN0V52IZFKUUkU9K08pNeQgGU8pFpGdIhwK0TYmkWpQQP5OkqQSVG6TrpxS2QrfMyafNRPDwmHd/vL38cL38u05zWUS9ZRSFwG4XeTjfRRPqdxFFchzSZTq7taP2yx8L5lE5+nKH2sX6rmyKCWLwnmBiFKZwFiZxBhvr36vekpJTqnChO97ZaX9yWhT4a239MpJoZB152UGd4xeb7QgANi7cs8D3+Ji82S/4bC5R2OsQSWLUjy4bm/v+2wLmUfTYi8EaJp+nwpBlBJ7m9vYFb5n94IHL6oZPaXMwknUNsrfiyiVOxjvlXhK0at4SuUealvOJVGK5wguV/QChFU0gHGxTj3XdOWPtQu2GS4XMGRI9GdCTiP1dDOB2SoS51UArMP3RJQqTHgwX1lp/2p2sgSDwDvvAH4/cNVVwKBB1EkBiXlMccdoXLUH7PWUsgolUb21Ojv15zIRTylj+B5AEy3j5E/ILMZ7ZZwgqSFQhTBJ4rYs5clzE7s8pTJVfc8qfE8VpdRzjCVKqc9uvj2nuYyE70UjnlK5S3/tLWBu++xu72qOJS4sAVgnOo/l3chjeXVsn2z+WDtRRb98tiMFiG2i1IcffohZs2bhgw8+sGsXuYNZh20mSrlc9MOilITvJUd9PSn4ZgYzGKQB7cyZ6dmXnZiF7zlBlOJO6cADge3b6b6ZdV6xsKq8B2TGU8o4QeIy6aEQ7Ze9now5pWKt2peW0mCb81aJKJVdEp0gAYUxSVJX7Ts7ZYKUa/Q3p5QTqu+pxBOlYnmXiKeUMxFPqWjEUyp3yVVPKbPk6oB1onN+RnmxyvgM81j+zjuBP/6RPJKcIEgB0X2L5HTNK2zxwZs/fz4AEqYExF+5N3bY+Z7o3K4cJ053OU0UNXzPSTmlIhHqlPbdV38P6FX5EpkwxRKl7PSUsuqwAfNJkpmnVKwy13yfpGPMPsbBVSw39UKYJBlX7SXReW6RT9X31P2qdp6fWZ9PD68WUSp36I+nVD4uvnJ7Fk+p3CMdnlLZFKWMdt4q0blxjGu28HrRRUBDA7BhA4lXThCkgOjrm68FagoUWzylpk+fbsdmc5dkO2zxlEoN1WtH04DTTgNefNE5LqeJwve9osJZOaXYy+ymm+hVvW+JXlueAGValLKaIAF0jVtazEUpXukE9DxaDE/u3W4aCLS0OOM+FTpGEUrC9+hVVu1zk3SF7zlNlFL3y8+sKkpJ+F7uEM9TSl3AYfLV3gKyEJDL5Loope4TiB5Xq2NYPs/iYrLFZuf6wAP6/3R20gK/E+ZRZuF7siCcF0hOqUyQqGszD8Y4hEhySiUPG8w5c4Bf/AIYNw742c+cYUgThe97VZU+kXSS2METiFTuW7ZzShk7bPVYzFbuVfEsFNIHmUC0KMXbddJ9KlQStbdAYUySZNU+t+lv+F62q+8lk+g8niglnlLOJF55+UIL35OcUrlLrueUshKluAAMt0k+z9JSoLGx77kGg8CDD1L+WL8fOOWU5PLH2ol6fe0W+4SMkvV4pq6uLjQ3N0f95B3JxttXVdFrPl4LwP5E54EAuaqyEc62AU0W1VPKiWIHt9/+iFJOySmlHotZjhP1OK3CTtlTCnDWfSpUEvVM5Rx+TL5OkoyeUmr1QcH55Gr4ntqPme1XXQTgc1SrsooolTsY26VVyHShiFLinZq72O0p1dVlbtv6C489jaKU6u2vhvDxMbA9Vo+JU55ccgkJUgBw4okUcWKWIiXTmIlS+WhHCpCsi1Jz5sxBVVVV78/o0aOzfUjpJ9nwvXz3lLIrfI8JBsnAulz0mm0DmiycS6yy0v6JQyrYJUo5MaeU2qFbiVIej6zWOIlE7a0xx1y+TpKMq/bqZ4Lzsav6nhMTnRcV6eOgeDml8jW9QS6SqKeUanPz1d6qFU7FUyr3sEuUUhcF7LC5VtEAHo/ufRpr4VV9Zjl/7IwZ+mctLcnlj7UTqb6Xt2Q9fG/27Nm45ppret83NzfnnzCVrKcU55TKV1HKTk8pVvgnTaLrPH68c1xOE0VdYeZr4yQPnP6IUonklLLTU8osfM9MDEvEU0rNk+Gk3F+FTipJd4H8zU1gDN8D6Bp4s979C4nQ32pQTqu+Z2Zv+Ry5Gipgfq6SU8qZ9CenVGcn/X++2CO1jUpOqdzDrup7Xi8tcnZ10fc8z0sXVp5SANnc1tZoT6lYohTnj92yRf+M5yVOmEep43kRpfKKrHtKFRcXo7KyMuon70hVlOrq6usGnQ/YJUqxIFVbS2IUABx0kHNcThPFrPqek8SOdOSUihW+l41E54A+eVPL4xYV6SFexnYrOaWcSTxRymyCBEQPbvIpvC0Rrz/BuahjADvC90Kh9IeTaJo9ic4lfM+ZpLIQoE6e82khQG23Er6Xe/TX3oZCuvhjFIjsFFCsqu8BiS28xvNMdZKThBr5wNc0FMqN+XJ9vfVcNBik7wsY20WpxsZGu3fhfJIVpcrKdDfnfHRRt0uUYpfTQEA3vt3dznE5TQRN0+95LFEqm4bNLlEq2+F7vN9IRBcl1BwnVp5Sak6pfBpc5ypG+5pIfhNAH0BqmrPCZfuLiFK5jd3V94D0t/eODt2GJiJKqYnOE80p1d2dG5OQQiDZMS7/zm0ynwRGtY2KKJV7GMPYkkUdAxptrp1pHmKNcWN5p5rllGKcGi6teqKVluoLx7kw/na7zZ0k2KnCmFaiwLDFX3bhwoVYsGABAMoZdfDBB2P69Ol27Co3iLeKpLquA/SAVVZSRYTmZmDQINsPMaPEyz+QKuxyCugrFTxodYLLaSKopVkrKvRO0ThpYMMGRJ+b6i1mF3z/Uumws53oPFb4Hu9XbY8cThIKxU50ztsQT6nsk+gEydj5FxXRpLinh9qL2YpjLsLnK6JUbtLfcBKrnFJer97eOzr6JiTvD2xvvd7osFEgdmGJeOF7xs/a2vpuX8g8/cnj196eX6KU+rxKTqnco7+LACyMFBf3DUnNhChll6eUk0Qp1QvX5aJzbmujzwcOzO6xxYPna+r8TZ235cpc1SZsEaWmTZuGadOm4Q9/+IMdm889UllFqqjQRal8w+7qe0C0p1Quwfe7qIg6Ne5gQiFqNz4fvc+mYUtHonPjBAmI7jg1LboyWn9JJnxPfT7VlftYic7FUyp16utpsmLWZoNBEj9VwTke/MyXlFBbSjSnFEDtY+dOai9Dhya+TyejLnq4XPRs5YLXqEDYFb4HkO3r6Um/mG6cNBj3qR4XYJ7oPN7KPe/H6ZOQQkD1xuzqSi5kessWZ014+4vaRlmUEHubO6RLlDIba2ZClDJbeOUFqViiVDx766S5qHE8X15O558r428e6/7ud8BNNwETJgBXXlnwghTggJxSBUEqolQ+JzvPhChl9JTKFYxltFXxxjhxCASAiy4Cbr0VOOywzCntdlffi0TSn+MkGVFK3bfHEz98TxWl8insK1Ok252Z7x/fk2RFKSA/V+5jhaIKzkVtv8lObjUt9kKAXTkLk7G3QOKJzo2f5ZOYkcvECwWKV1wiX+1trLYsOJP+eqbG8sq3s73HSnTOz6Wa6JzPM1FRyknPqFGA41cnHWM8AgFyPOEE9CJIARBRKjOkkgQyn0Upq/NPF+pAPNdEKb7fVVX06vHoqxxmE4dx48izY9MmGgBlwrDZnVNK/bt0ECvpLmAtSrFnidXA0izRea6s1DgJzvlWVwfceCOwYkX/vP7YvvCEO9GcUkD+T5JElMo9+jNJUoWfWKJUusX0ROytauOTTXTO3lf59JzmMmxz+d4mW/E0n+4jt1uPR19QEXubO/TXMzWWx5ITPaX4mc2lROdG4S8X7UgwSNfX5aK5Xa4U4rIZEaUyQarhe0B+rgRahUGlc/ucZDXXRCmjpxQQO9fSs8/SuXLFOLsNm1qZLhWXdD4HM1FKXVlM5yQpVtJd9Vi4w1bzmwDW4SRqngwnVknMJQIB4Nxzgb//HTj22P55/RlFqVAouppeoYlS6vnKJKkvTq+G059JEttRt9s89xL3LU7xlIqX6Jz7HO4f8+k5zWWMnlLJ5JQC8us+8rnKIkBukq4cfk4SpRLJKZUric41Tb/GavgekDuLwrzoOmwYMHkycPDBuVUh3kZElMoE8UQpY6JzQPeUaWqy77iyBZ+/XZVJzFZgcwW+3+wpB1gLHsEg8MorgN8PnHGG7m1ip2Hrb4cdK5QEME+C21+4ozJLuqsei9FTivN3JRO+lyudohM58URaNeru7p/XH9sXdXCm3jurCRJQOJMkyXGi4/RqOP2pBqVW3jPL0WdXcYlYopQxdyCQfKJzHh/l03Oay4inlI56riJK5R79CZcGYts+u0QpVahJVZTKFU8pdZE5F8P3eFxx2WU0dwOA8eMzM3/LAUSUygTiKRWNsUR5ujtsNW4610QpM08pM1GKDdsRR5Bh6+6ODoOyy7D1NwlkrPA9wDy0o7+orr6xJmZWnlJWkyTxlEov8+ZFezim2oaNEyTeHlNonlISvhcbM7vppGo4/bG5VpX3mGyG74XDettMNtG5iFLOIlFPqUIQpTJpb53u5ZmLGO2t6mWdCLE8luxq76pQY1bQwix8L9mcUj090XOrbGFW2TWXPKUiERpXnH++/tnGjfo4pMAXDG2pvicYkJxS0dgtSuWypxTf73ieUmzYOjqAVav08+QJlF2GTb2eqeyD741anl7FbEWnv8SaIKn7jOcpZey0VU8pHoCIKJUawSDw1FMksPr91GGrlSWTwRi+p34GWFeCAmSSVKgEAtQu7rwT+Mc/yD45QZAC0hO+ZzZRAewL3zNbXGHUBYmODrKzySY6d5Iole7qobmIcSFAEp1TO7Y7XJq9PIHo9qeK6kJyGOdnyVaCTkSUSrd4kmg0QKycUolUO21psR67Zwqzyq65lNOV+4ItW/TPNmygVyeMN7KMiFKZQJ3k9vSIKGV3+F4+eEqpopRZiAUbtr/+lV7Vc7bTsKWrPHm88L10ilKxSvSqx2L0lOLnMV74nuop1d1Nz7tXTGvC8AD6xBOBDz6gz04/nZ6BVIQpVfT2eOi+mXmbFJooJeEksdlnH6ChgUKo997bOQPE/oRMJ+oplcmcUh6PPhbq7KTnPNFE52xzWZRygie5CAN6u0y2uEQ+2ttMhu9xe1Pbn5O8PHMRs/lZMiHciSQ6T3d7jxemzXMtdZ6QbKJzgOajgwf371j7i9n1zaXwPUa9F42NNDey6qcLCJk5ZQLVtTlZUcoJg650Y3QbtdNTynitnQ6LkPHC9xg2bJkS3zIVvpfOcJJ0eUolEr4H0H1SRUUhNuz1N2CALkq1tKTu9cfPAntdFLoopeaU4sF1gbuIm/Lww3ohBy4a4YRJXX9ynMRbBMhG+B4fT0+Puc3NtfA9VRhoaQGuvhp45JHCEgbieV1Yeafmo73N9CJAIABs3w5cdx3wl78A1dWF0+7swMzLj8eCiZCNnFLGanRGzBZ7jfMwTaPnVBXgjO3WCc+pmSiVi3bEuPC+aRPllipwRJTKBGo4SXNzcqJUPiY6l5xS1iQavsewYctUrHd/RKlIpG9yRSN2hu9ZddhGISzR6nvqQNvjofbc1SWiVLKw19+DD+qf8XPQn+p7RUX009UlOaUACd+LRTAIvPgihY6OGQNccknq4aPppj82N1vhe4ksBDQ3m9tcq/A9TXOmKAVQG9myBbj5ZhIGRo8uHGEgEtH7Qm5nhZxTKhuFJcaOpT6uoYE8WQqh3dmFVdtNlEQ8pdItSsVKcg7ETnSuhuOFQtHhf2aeUtnGrG/JRTtinJtu3CiiFCTReWYwujZbVd8rNE8pEaX6kmiic4bPtasr+YSMqdCfCZLaIWZSlEo0fI89JFLxlAIk2Xl/Ua9bfwYXqgjD91BySokoZQWHu+y3n1404sILnVMNJx2JzuOJUtnwlFL3m0iic7V/4/GRk57Tffah0JnW1v5VD8011Pskic6zY2+fe46ej64uGjtl22blMnaKUna1d96nlZ03Jjpnj2Ag2ovWaozLOGE+Git8LxdySjFGR4KNG7NzHA5DRKlMkEq5XBYlurudUfEgnSRSirQ/5EOic14JBmJPHNROJhMTzf7klOJjdbnMkzEC2Ul0rnbKHR3Rg0ogfk4p/j4XO0YnoYpS/Rn8qPlpzESpQvWUkpxS5nD46OjR+mdNTc6phpNv1ffU4zGG78XylFLvg9M8pQAq1MD9MId/FgLJiFLG3Dz5aG8zmVMKoHb2xhskqE+eDBx5pDPE9FzF2HaTtf+JeEp1dZmHJ6dKsnlT1aqCat9gVaCAcYIoFctTKpfG3sY5johSACR8LzOkUi63tFRP0tvcDAwZYv9xZgq+HixM2OkpFQ7Tj9kE1GloWuqeUgBNxu1OsJ0OT6mSEutqJnbmlLJybeaJUChEx2j0lLKaJBkH2naFwhQK6nXrj5u4ev+SFaVyMWFmPNTztbsaVC7C4aMLFuifNTUBw4Y5w9slF8P3uB9LdJKkCslWnlLquTtNlAoGgVde0auHXnaZc8I/7UZtn1Zj3Hg5pbq66H+Syd3jVDLpKcVenvvso/eZQ4boXp5A/re/dNNfT6lY4027co8m6inF8wXVtqrhe/E8pZwavpeL4zaz8D1BRKmMkIoo5XKRwdq5M39FqUx4SgF0vXNBlFK9dJLNKQVQh2PVKaWLdIlSVmQj0TkfU2sr7dcqp1SinlIiSqVGusL3+P75fLrwbdZuzSrqqCv3yZaCdioSvhcfTQO2btXfNzZm7VCiUPP1ALlRfU/T9O2piysqVp5SsarvWYlS2X5OWRjYd1+93ZxxBp1LIQgDfJ/cbn2Cm2j4njpxb2ujJN25jplnql3eluzl+f77wGef0WfLlgEPPWTvfvMZO8P3vF4992hbW/pFqXg5pYzh0oC+EMAenipO9JSKlei8rS37/UGi8HyI28OmTdk9HocgolQmSCV8D4gWpfIJY4I9Oz2lAFKkYwkhToENvjqZBpLzlLKbdFSCinUvshG+B9Cz2dpq7ikVT5Qy5pTKJRdiJ6EKkf2xeaoolWr4nqbR8dgt8mYCEaXi09oabUudUmCkvxOkbFTf44kBkPwkKVb4npkoFQpRv6eu9mcaFgaWLtVFqR07Uq8emmskIygabS5Xrm1vp2cwH0Qps0TndtlbMy/PDRso6X4+C6F2Ek+YiUU4rI9drWxfeTn1Nen06klUlDLzlGLxNBFRyglz0Vg5pXJp3Mb3YrfdgBUrqEiBsfphAVLYZ58pEvWUMoZesYruBEOQLiIRfcBqlyhlFDRyJa+UWnlPVfpjeQ+pn2Ui91g6ckpZTZCA7IlS6iTJ6CmVaPieeEr1j3TllIonSsVKdF5UpN/vXHIFj4WIUvHZsiX6vVNFqVQXAjIZvsfPDVe/jLVftvOJJDpX22x5ud5HZvs5nTmTBIAdO/TPtm+n10BAFw7yFbW/ZHtrHHMl6p2aD2QjXJrFUJ4zLFtm7/7yGau2mwiqHbUab9qRezTZ6nvqGMnlshaTjf2NE55Rs/F8Lo7buJ2NGqWnDzGOQwoQEaUyAT/oyZbLZdd3J7hMpgvV6NkVvmfmKZUL8H02uvQ6yVMqHaKUUz2lgOgQSqOnVLzVX/GU6h/qdUtH+J7VJCmWp5TLld+TJBGlzFFD9wDnilK5UH0vWXsLJJboXBU2nPicshAFRAtU+Y5ZDj+rya2ZzeVxrlPuY3/J9CJAKKRfu2OOodcPP7Rvf/lOfzylVEHeKr+rHaJUvLypsUQpIH6Faf5/JzhImHlKqf1Broy/1fnQ8OH0u4TwiSiVEVLJKQXoLupOMATpwizBnohSBN9nYx6OZHJK2Y3R40Qt0x0PNYbaCjsmSfEqkwDRnXayOaWM4XvprmRVKKQ7fK+oKHb4npWbtNMmu/3FbJKU7yFFyZLvolS88L2urvS1iWQ9U4HkEp3zc+uk57SjI7ovLiRRituox2Nub4HCqniaaXvLtsrtBo49ln5ftiy5sZmg0x+bGy+MTv0uG55SPT3UFo0VpuMtBAwcSK9OcJCw6l9yLdk5z9eKi4ERI+j3DRuydzwOQUSpTJBqTikWJ0SUSo5cF6US9ZQyxoBnOqcUkNzAJ15+E8BeT6lYAwUzT6l4HbZVovNcWalxGukK31PvX7I5pYDCmCSJp1Q07DbPz7pTEp2nK6dUPE8p9W/7S7zKe+p+2c6b5SWKZ2+d5EluFKEKSZSKV+0USEyUcsJ9TAeZ9kzduZNeq6qA/faj52frVpngpordopQd44tEq+8B0QuvxgrTVgsBnOvNCc+olSiVa55SZqKUeEqJKJURUg3fy8ecUnzuLpduECWnFMEG3+gppQomqgiUDfGtPx12NsL3urv1Y040nMTKtdkqJMHoKSU5pZKHE1QyXFUrFVSvCxGlRJRKBPaU2m03enWqp1SyHhfxPKVUz6R02S1+bqwq76nHEyvRebxwaSc9p0YRSg3ly3fMFgHiebmpOOk+pgO1+l4mckqxgF5dTRPcffah9x99ZN8+85n+hO8l4pVvx+JlvP0WFek5+Lq6UhelWluz72VtJfw5ZVG4vp4qspoRDNL3gD5/U8P3Nm60++gcj4hSdqNpqYfv5aMopa6q2TVByldPKU2LPjfjeWY60TmQ3L1TjbAVxrCO/sIdlMsVuyJHLE+pePH2Un2v/xjvt6alfh35/qmJlhPNKQXk3yRJckrFh0Wp3XenV6eKUumuvqfaxXTZ3FTC95JJdO5EUcooQhWqpxT3mVbjhEII38tk9T0gWpQCgClT6FXySqVGf8a42Qrfi+cp5XLp3lKxUlTEE6WA7D6nsaobOiV8z+0G6ur6ClPBIH3O8wWeDxUVASNH0u8iSokoZTuqQbMSpYyTYMZJ7unpIhOr9rnuKWUUpUpK9FUOdTXbeJ6ZzikFJHfveAISS5QyhnX0F+6gysqiKxrG2q9VonMrUcqY6Dyex0GiKymFBF8zdfCUqt0zS3ReyJ5S6iSJB0TpWu3Ml7acj6KU6n2YiCCfbk8puxKdO1GUYhHK76fXQvKUMrO38QRFFSfdx3SQac9Uoyh14IH0KnmlUiMdnlLZEqVi7VeNQkh2jFtUpPch2ZyPqtfMeK5OsSOBAFBbSwLU/ffTM8iCVG0tfQ9Eh++Jp1QvIkrZjWrgkg3f40TnThkgp4NMdNi56inF99koSrlc5hMHJ4TvJTO5ZaEpVk6pdCc6T2SCBESv3Ce6imSVUyre5C7RlZRCQk3U2V8xXnVNl0Tn9trcfGjLmqbnlJo4kV6bm50xoevPBKmrSz+HRL1E00EqolQiic6N9tZJzymLUixq7tzpjPaTCdKVU8oJ9zEdmHmmapp97YFFKU5GvddeNNFtbAS+/daefeYzuZZTKhTS5wKJiFJq+F6iic49HmfkfuPrW1zc14nDSXYkEAAuvBC47jpgwoS+ghQQHTnCOaVaW/PLCSUFcmDEmOOoA6tUE53nUyNVJ4ziKRWNVU4pwHzikGueUrFySrHHhbqaw4O4VDwueHtmsfZm2zML34u3imSVUyreCpi6ksKTebOVlEJCLV2fKVEqnqdUvoRh2ilK5UNbbm3VbeeECfQaiThjcGvsu5JZBDDzPjQjG+F7qSQ6t/KUcsL4iEWp8ePpNRzOr7QLsYjnmQoUliil2ltVlLfLW4pFKV7E9nop4TlA3lJCcqh5b4HkbG4iti/dnlKq3Y61+MB9gLrwyukNEsnj54R0MrGur1NySjGTJ1PbaW6m62scC6nhe8XFwKBB9L7AvaVElLIb9SFXy3KqJJJTKl9W3dQkkHZ7SvH1zhVRyiqnFGAeGpYNT6n+xNvHEqXY4+I//6H3XLY2VY8L3t4TT9B77rCstqeKYVaeUomKUomEwQQCwPnnAzfeCBx0UG5N4u1AzX2TTlGKB12qzTV6XBjJ10mSXTY3EKCfW24B9t0399oyh+5VVpKd4OfYCR7K6QiXLi1NLHQ5m+F7ySQ6Z3vLdsIJzymLUsOG6eJAoYTwmSU6j0SiJ/OxbG4h2FvAPlGKq++xpxQgeaX6A9uiVKqDZyN8j58bM+8hFba5XV3WFaZjiVJOcJKIVUnbaYuJjzxC83Z1LqNinKdKCB8AIEYLFtKCOtgymyAB8UWpUIgmy7HCnnKFTCY6r6yk65YropRVTinAXPDIhkeYXTmleAJ7//3A5s2Um+P664FXXwVOOgkYOhR4/nky8Gzojb+r710uCsV55BG6dkccEduDQ50kcbs0ViaJl+hcDd/TtNgTQQAYN47+dssWGlDmyiTeDpzoKZUvkySznFLptrmHHUb3q6WFBle51JZZlBo6lF6rqqg9NjUBo0Zl77iA/oXvxau8V19P7cHMUyoYJDs6c2Zi++JtBQJ9RSmzbRmrrKor93yOuRi+N2gQUFNDbWfHDt1zysmo985IIu3AzMsNoHtqnNgXgihllugcsK9qGYvnajJqziv10Ue031wIo3YKbItKSsg+pUuU4ueMvdhU8SRZe6uipj6IhVmi83gVpp0mSsWqMuiUROcA3c8XX6R5jN8PXHQRzT0A85xSAIXwffaZiFLZPoC8x2wVyWpib1S5i4vpf3p6yBDkgyilinR2h+9VVNCEPxdEKU3TPaXMwvcS8ZRyevW9eDml2Fj//OckTH30ERn0pUvpJxXKyoCGBmDePKpwYeXBoYaTcCfB7TPZROeRCF2nWCEzAPDSS3quCV5JyaXJfDpRB3MsyqY6+ImX46TQRCmz8L10T5B4VdDlIhuVS22ZRakhQ+i1qgrYtMkZnlL9sbfxKu+xN+ngwfSe+xZVvE8U3hYQLUpZbUtdBFArFOd6ovOaGhKmvvkmdyrwqfdOfWYTbQdmC6/8eSKilJM83tKBWU4p9fN0w55Sqig1cSL1pW1twKpVwB572LPvfIRtUbo9pfg5O+00es/tPRV7a7bPWKF7gHlqjEQrTDslfC/W9XWKpxTfzz331I/lzDOpz1PtrBq+B+h5pUSUEmzFTJTSNHrYjeq0scN2ucgQbN9OA2Reyc1lMlEuV/WUAnJDlFIrYph5SiWSU8rpnlJ8vLHEmkAA+J//oQG9203G3OWi393u6N+N710ualPGz+69lyYLZnHdjJronDu8RHNK8fdcJVHTqDOKdZ7BIPDKKyS6HXYYcPzx5hODQkGdQPc3Vww/B6rNVZ8NEaXSa3ODQeCpp/RVwWOPza22bCZKAc4QpdLhmWo1WeF787vfUdtgMTGV8Ev+27o62k5ZGbBwIQnvsTxTw+HohOw+n/6sxqvg5pTnNBLRhYGaGvoBckeUUu/d+vXANdcAjz+eeDsw835XP2fvZcDcY8dJucHSgZm9BezPKaWKUh4P5bRZsoRC+ESUSgyemwH6mDCZ+xYrvIyfo3/8g+xFSUl68i/G8h5SUUUp9uJPNBrAaZ5SZtfXKZ5SkQjdz1de0Y+3uVm/v2wLjeF7LEpt2pS5Y3UgIkrZjVkSSP7cOEGwWkXavj1/OmzVbdTKEPYHTYv2lFL36WT4/nq95mKGUzyl7Ep0zgSDNKgfOpQGd/vt17+JbTBIE2WvN7Y3kir6JVqZxDjQ5iqJ7e30w5MTs2OqqwOOPBJYuZL+Vp0YALkxmU8n6Qrf4xBOgFagzDylCimnlDohtCOnFLflI44AVqygz/bdl8KWcqUtO1mUSkf4XqwV9EAAeO014L//BW64gWzdpEnAO+/QT7K43cBXX5Et7OoCvv998/uv9gHqynsiic7Z3jrlOW1q0kPGq6t1u59LOaUCAWD5cuCuu4C5c4Hddkt8oqz2ly6X3teyuKjmQ40VvtfTQ/+jelvlIqoo5XLpC1V2iFI9PfpzropSAOWVWrKEkp1fdFH6952PqOMEO3JKBQI0xrzhBooGWLkSOOEEykW3bBm9DhkSPVe0gsMBR46k96qdNwsHVMP3ePtGUSqRROdOyCllJsA5pT+YOZOe94ce0j/jPk61p8ZFehalNmyw/RCdjIhSdsMPtRpKApDxMyrxZh22E1wm04ndq/ahkD4IyiVPKTXJuVkuokRySmUjfC+ZMKB44STGVSN+D6Q2sU1me4lU34uXeBegwQiLUlbwSkpnpy5KqcdkV+4JJ6NOoPtj89RBpVXIdDKeUonkBnMyqm21Q5TittzaqotSa9eStwV/73S2bKFXNacUoHsgZBO2t8XFZN/TmVOKmT6dPN26u+k5iUSAL79M7XgBfSJeWmpttz0ePTWB+pwbw/fU58/43KphX9l8TtkjqqqKji3XPKUYTojf2EhtLdE+15g02eejz/hzow0yUlamCzetrdaLObmCsZ16PHQt7LCFbKM8nr4Tdc4r9ckntP9YSbAFQh0npOIplUii81mzgJtvpnuyYweNAe+8M/pvBg4kgWroUF2oUt9XV+vhgJMnR+8zXsi0Ok9IJtG5EzwaExGlsh2+B9BCl9qWzMayat8O6KIU/28iwmQeIlbKbtRVJA4n0rTEJ0kiSiWHKtTwoDUXRKlYSc4B51Tf64+nlNFdVcXMjbk/3kPJbk/NKWUcZCdafQ/Q71OsjpFXr+65h17VxOhO9yqxC1WU6s+Kl9o++5tTKhLJ/QIT6iDTjkTn3JbvuEP/bO1aes2Vtmz0lGKPAyd5SrEolcoiQLxcI1u3AhMm0O/hMHDUUXrek2R54QW6buXldKyxcouVlOj5MgE9/NoY8mT0nDKG70UidK7xztMu1HxSgF7aO5c8pSIRqnzL/dCGDYnnhTN6Fvt80R7H8UQpl4sm1K2t+SFKGdNxsChlh6eUGrpnFGXHjSOhtKmJvOD23Tf9+883zDylkrG5iYTSPfQQhVN2dNDPsGHA6NG0OLJ5M9n5nTvpZ/ly8234fCRQFRdTqG1VFT1DscIBVU8pHgckk+jcCXPRRML3eAyfTRHWmBfKKORpWt9E59XVenL9zZuzX2QlS4goZTdGV17OmVCoopTdic5ZmPF49EFqLohSPAGyEqVi5ZTiCYvTRalY4XvscWHsSFP1Hkp2e2pOKaOnVKKJd4HoCnzx4PuVaGL0fCZd4Xtq+/R49FAQs5xSVhWJior0EJTW1vwSpezK46d6FbEolQtomu4p5cTwPbUSVHNz+j2lgkHggQeAn/wk2pt04sTkRcVgEFi8mApVJOLpWlqqV2wE9DFSPFGKn1vjc+oUUSoXPaXuvBP49luaHA8eTHlN/u//6Lt47UCNBlBfzUQpK5tbXq6LUrmOsXCRXRVPAd3uss1ScbnIW+r11ymvlIhS8TFLt5LofYtE4lfCY5t49dXRNvL00+m9ppE93LxZF6n4detWet2+nY6Tw7yqqqiYzwMPkMBlFXar5pTicVG8vKlOyykVy1NKtf/t7dZzqUxgFKWMYwl1PMrjfpeLvKVWr6Z7K6KUYAvGCa6ZKGWV6BzIP1HKLCmmHZ5SxcXmE1KnwoberPIeENtTqqoqc1UGUxWlNE0X1MxEqVilcFPxuEh2ezxx6+nR25DRUypeiXIgMU8pRvV0a28XUQpIX/iez6cvAqifA/E9pVwuGvQ0NtIgiMWKXCRTohQneubfW1qsbZmTaGvTn3cnilJs01MJJUk2XBpI3Ts1lW3xcfFzblwEAKj9WlVwMz6n2SoEk+uiVDAIPPgg5V48/3y6vq+8QnY4kXZg5ikF6LZHXQAqhDx+RlHKLpsL6HZ34EDz71mU+uij3PFczSaqE0GyYqI6NjcTpRK1kZWV9DNxovUxbtsWLVrdcUfixXxU7+98SnTu9eqeRm1tzhKljNdMnaupOfRYlCrgZOciStkNd9jcMSU7ScpXUcquCZLqEplLopSaU8qMWDmlKiupY3JyTqmeHj3XlxM9T9Rj4g4k0ep7ZuF7qkebFeq1bGuzHlgWAomE73FiT7NB16xZ9Prb39KrugjQ0BCdbDdeonMgerKby3Cb5Tw/mfCUAoB164C9907vPuyAQ/cqKnTxw0miVH/Kk8cL30und2oq22Kba7S3qiilnq/Zc+uE55TD9Dhsj1/b2qhPdvpiQyQCDB9ONvKQQ+i5ffVVGpOccUb8dmDlWcz9W6KeUkDu21vAPHwPsCenFNsoY5JzZsoUev3ss9xoi9mG22wqi+YsmHi95sn602VvvV690i1AYtewYfGL+aiilDFFRTKJzpubs5fDL17OrgED6PyybUdYlBo0iPoH4/yd52oeT3R/N3x49P8XICJK2Y2ZpxSQuCjlBHU6nWQqp1RJSW6JUv3xlOL/cXL1PTXXV6zqe9mCV8YikehwEiC+a7Mx0TmQvKdUIiJWPqN6dfDgp709OjcAJ/YEogddHDYEAI89Rq9sZ196iUSpwYP1v4/nKQXkzyQp0fxo/UHTdFFqzBgK31u7NrdEKdUbzkmJztnesoCTzMQ2XvW9dHqnprIto6eU+pxz7k11kmT23DrhOTV6SpWV0diju5u+44mGUznnHAr9AUiUGjQIOPZY8rBpawN++cvY/2/0lOJxlzHROd9XM5xwH9NFojbXapGlvp6q5h1xRN/nylhVjT2lrESpkSOp79u2DfjiCz35uWBOfyI54uWTSnc0AJBcMR9VlFLFNyAxUYrnGaEQjV2zMY6PFb7Hn2/fnn07wqLSXnvR2NQoShkr7zFcSbGARSmLZQshbZhVJgEK11NKDa8RTymdeJ5SsXJK8SQqE+dp3EeyopTPZ71amk24WhSgX+NY4XtWZa7NxEMrVFEqXRVD6utpYGJGMEjfOxEzTykgenARCNDgq65OP0cehM2eTT+PP04ilNdL3z3zDK0ojhmjb0dEqfSu2re26td0//3pNVfyShnzSQH6BK+tre8gPdP0J3wv0ep72cIoSqneBWY2N1dEKZdL95bKhRC+99+n191314/78svp9Y03gDVrYv+/ldeFMadUrH5fraSY6xg9pazCwHiRxdhfL1kCzJtHryrc16nXUU10bgbnlQIor5QQm/7MT7jtxqq8l06swgGNYySGBZCuruQrTHs81Afx32XLSSIRUUr9u2zBotKkSfRqnL8bK+8xXIFPRCnBNkSUikbtsMVTSqc/4XvZ8JSyikG3IlY+KadgPLZY4XtWIQnJ5JRS22UiIlYiWA10zQa0TkJ1y3a79etoHFzwoOuuu4CxY4GbbqKO/aOP6Ke0lESpxYvpfKdPJ1HKzN7GuhZOKi/cHzLhKcWr9eXlwPjx9HuuiFLsKaXmIyov1z06st3v2hm+l23Y3ho9UwHzdmoVvgc4S5RSf3eiKGVcuHj3XXo95BB94WL8eKrCqGlULSwW6kRefTWKUoksAuRDRECiOaWsFllWrgRmzKBX4+KLMfwrnigF6KLUsmWpnlHhYCZKJbqAEy+0LN3ECgesrbUu5tPZ2Xccn0iic5cr+5E7iYTvqX+XDdTiHXvuSa+Jekqp4XvqwncBIeF7dhOvwwb6dmIq+SpKiadUNP0J3+M2kslE58XFdC8T7bDZCDt11R7oe2zGlV+zCRKQHk+pdIlSauJMTQMuuohC2qzKBGeCWLmgOBzBmJS5spKuiZndO+kkqvDFeQ26uqIH3Jzg3OslUeqFF5JLdA44Y7KbDoznaofNVSdGY8fS77kmSqmeUm43tb+mJvrJZol6tfoekN+eUjw2AujZ7eoyXwhwmihlzCkF6G2Gv3MSahj0ZZfpnlJbtgALF1I/AZC9XryYkp5fcYV1Nah4C6+FZG+B5GxuIEA25pe/1Pu04cOpD9u8GfjZz4BrryWR8Ec/6tuHxhOl6uv1seWXX5JN4DGKMRRQ6F96kXiV99JNsuGAZjmlkgnfA6hfbGzMznxULRAWz1Mqm6IUezkNHKgvdlnllDKKUn6/PqbduTO7Y48s4dBl8zwiXZ5SLS35oZyqCr2dolRJiX6tc0GU6o+nVDbC95KdJFmtDDgJ4+QtFU8pHpAkIjLZ4SkF6Ctlv/sduQP/4x/ZE6SA+N5bLlff/DexVuSuu45sYUUFiSCHHQb8+tf0c9hh9Nnuu5Ptffpp+h/1WicySeL7mOuTJGP/Y0d5crUCFIdJbtyY/dC3RDATpQDnJDtXFwGA5MIu41XfyzZGTymjKAU4P3yvq0u3XbniKaV66PzxjzT22LEDWLAgup+YOJHsqaYBDz9svb14eVMLTZRK1jt1zz3pf3iRZdgw+n3YMHofCtEk26z/ZlHKqkiK2w385z80/opEgE8/pc+z6Tnt5BQD/Ul0Hi+0LNvE8pRKpPoekF1PKRaaXC5r718njNtYlBoxQp/PtbVFX1ur8D2vVxeyCjSET0Qpu0lXovNwOD+SIateYXaG7xUV5ZanVKI5pbq69IlJphOdq4ln+XiSFaWcHL5n5SkVK5RE/R5ILnzPDk8pJhCgjjkSoW1nsxx0rFxQtbXAxRfr15avn1U4x733As89RytKTz4J/OIXwDvvULW3devo91/8AnjtNdr2vHkUzlfonlJ25pRSV+sHDaLnKBwGNmyI/jsnTkbMwveAvsnOs3XsxkTn+RS+Z5XoXP3dTJRSJ9LZfk5ZdCoqir7O2fSUSqStsk1+4AHg449JfL3yyr79REUF2c+XX44uU15fD1x1Fb0aJ7jLl9PfiihFr/EWAp59lsZW1dVUHGLGDGD+fHodMoQm4R0d5veUFwTYXhnh+7x1K93HZcusQwEzhZNTDKjztWQXcDIdvpcsZqIUz5ES9ZRygihVWhq/YIITPKVGjoyOfFFtW6z5UIHnlRJRym744eeHOllRqrhYV1OzvWqbDsxitjUtfV5gqqdUrohSmpa4pxSgTzbYsKnhe3Z606kdVqqeUk5dtQesPaXihe+Z5ZTKVvge869/0fZdLuqgrSYpmSIQoPLi118PHHxw9KBYPXc1fA+IHvwEg8Bf/0oryEcdReWuedA9Zw79GJN+XnopDcbXrdOfjWRySuX6JCmZSlBWbWTWLPoxg5PJAzSpcrl0byljCJ8TJyOJekpl69iNnlKphO85XZTiZzxeonOznFLZznGi5pNSJ0rZTHSeaFtl26tp5GljJlCMGkX9x8aNwCOP6J+rybjViXwwCHzyie7hA5jfNyP5Ym+B5ML3gkHyUPP7gR//mIS+efOoaMe8ecB3vgNMnkzeVMZ72tWlj6usPKUAuq/nn0/94O23Z1eQ4uPhRar77yfhP9tCGdOfROe5Ikqpic5jFfMBzMP3gOyE7yXiieY0TymPR+9/1WvGY3+1z2MKXJSSnFJ2019PKYAGXl1dNPByennheJjFbAN0DcxyaiWLGiaWK6KU2klY5ZTiTjIcpoHkgAG6YVNXyXp6zA1dOlDbbLKTpFzwlDIeW6wOO93he+lc2QkGgblzaaDr99N5WZUJziTDh1Ob3baNBAw+Fr5WJSX6tTSbbHZ300S2spJyZfEkMBAAFi3Sf1e5/HLgnnto4sU2ppBW7o2VoKwG2mqeGfUaBoOUV4Z/N35XV6cPonhiNGYMsGJFX1FKzXfG77M5GWlv19ueUZTiHC0sSqnHvmULlWtftcr+Y081p5SmOT+nlDpJAuInOndi+B6LTmo+KSC74XtqW21vB849F3jppb5t9YEHqN9xuag/Nz7fvK3166mwxP33AyecQCLKJ58AJ59MrzzpeuUV4L33gKlT6by5fyskewskbnPZ9h18MC2alJfrebzmzSNPqUsuAZYuBQYPBqZNi7ad7MXp9cYXnq+5hsYEbW3099kcBwD6/n/zG+BXvyLR7fvfz/5x5bMoxWP2jo7oMEUg+fC9bDyniYhSTrAjqigF0PzMmB/VKqeU+n+qZ2oBIaKU3fQ3pxRAk7Bt2/Ij2bndolS6PKUSSc6crgSRaviClWjDcdQtLfpkw1h9D6Dzt0uUUq9jIYTvxcopxau/Llf0CnkynlLq9UxXaC4PdI8/nkLZAOqoL744+8LUggW6V2QopE+CzDw6zEQpv59s4dChwLHHRm977lzzfRYV0f8BZHO93sJauU+mEhRAbSQSoev7xBMURvLd79J3//d/JMj89KfAo4/qk9zVq2kg9uGHtH0zTym2mVdcQYOt226jyomjRgE/+EF22uSWLfRaUdHXLpnllOJjvO46enb32gu4+mp7jz1VUaqnR2/nTveUYsxySpmJUk4M3zMmpGWRKluJzgMBsrPXXQfcfDOwzz70HHNbDQaB++4jMXbUKBI/rPqHG28E3nyTqvSdeCJt1++n56epiQRol4tEkl/8gp7vF1/Ux3qFJkolGjLN1dM2bdJFKYAEb34dNox+37KFFljU7ahh01bhTMxzz0UXBjETIDPNJZdQe+FCJ9k+HqB/ohS3XaeKUtyHaJo+3oznKWUcK/G4LBtz0UREPyeF77G4VFFBz7iZKBUrfM+Y/qBAEFHKbhIRpYx/YySfKvCZJToH0pdXKhlPqVjC05Ilfb0v6uvp85Ur9Qo1TH+EKrXyXqzBRWkp/W1HB3UsbNgGDKD/0zR7vcLUe2e1smIFd4JOFqXieUrFmyABiZekjUSiBwDp6kR5oAvootS2beS+z99ng2CQnie/nyY2hxyiT4IOOIBeY4lSmkZVBAFaQU5UwFYnuj09er4joDAmSVb5TczaAdu53/6WJjw8+XzuOfq8qQm44w7g7rspCTJ7XfzsZ/R9eTnd08MPp/csSgWDwD//Sfd58WLg669pMq9ptJ9sTUasQvcA60Tnl1+uV8kKh+0/9lTD91RR3Kk2NxFRyumJzq1EKX6/c6cuBmSa88+nSb+mUTtSBam6OvJ6WryYCkPU1tJ1tRKm7rmHhOpQiM5l333p88GD6Rp4vbr365//TN8Zc0olGi6dreuVLhJdCOCx4i230Cv3eTNn6t/xNezqIluk3pd4lfeYYBB48EFaLKipAc45J/sLVAC1k0hEL3TiBKGsP4WYcsVTCtDtZbLV97IZLu3k8D2eS158sT6uGDmSXtetI1FKvWYSvmeJiFJ2oyrv6muy4XtAfohSajijHaKUmadUKESdn3FQFCtkZeVK4Oijo7/nPAozZpiHsRiFqkSJl0+KUb1wenr0HDmlpXSuXV2ZEaVSSQKZizmlYlUmsfK24W1wtRurgbgxKX26ckrxYPaOO6I/X7s2ewM+fj72248G1molobo64Jhj6Hf1+hsHP2+9RWEk5eWUmypR3G76iUQKM/FuspWgAgHghhvItni9lHiXqa6mMuXGEBCeHF14IXkP/f3v9FlZGVWArKuj37ny09atNEDu6tIT+GajbaYiSs2dm1mPA6OnFHsaxpu08yJAcXF2EwfHwsreqr/nqijF3iuRCLWheMKBHdx3n95W1Em/unCxeDFVKgX0dmwmWL/9NoVYeb10T846Sw+/3bZN/zwY7DvGTcYzNRSiMYyTq/TGI9HwPYb7OLPJts9HXnfbt5O3lNqOEhGl1LHp88+T/T7pJAq1zqYwxUIZpxiorHSGUMb3rqgo/0QpXkwOhfpWPE00fC+bDhKJXN9EF4XTDc8lt28nm1taSmOIYBD48kvqv9WxRCKeUjt30jzCqYtKNiGilN3E85TiFVfAutPmAXI+iVJeb/TAOt2ilOopBdD1Ng50jDlOLrsM+Mc/yJCceiq5T//3v5Qc8s476f9PP50EKx7gpZoTRfXSMopSVl5XqiilihpFRfokz05RyqxcbqKeN7kQvqcem8ulT+ZiJd218pQCaGJo1YHaJUox69bRKwsya9aQYJANeBL00UdU/YcHDPy8fP45vcbylHr0UXo9++zkhU2fL/rZSFaUyuWV+2QnSMEg2UqXC5gwga4336d//Qt4/XX6rrtbt4FcAYqTJYdCwLXX0gRoyRKadAwZAowfT8/DRx9RnpvFi+naZmsykqwoxZXLeCI1ebL9x24UpQB6nmK1XcD5lfeAvs9xvETnsUSptrbsPKccnmcUpbxeakONjSRcZVqUCgYpMTm31YkT9bbK44pf/5peJ0zQ/88qXYE6xuH3ixfrXuPq52PH0v8lswjAFbU0jWxuPohSiS4EsKBqlU906FBqZ2xPedyo2l3AfNzIfW8gQAs7mzfTeDOWAGk33E722ku3wQMHkmdftoUp1VMq1ep7sTx5sk1JCbU3PtZkE507ofqeE8P3uL3+9a80lj/iCBJd6+ro940bzT2lzOxceTld55YW+r/x4+0/fgchopTdJCJKMfE8pbJVYSadGEUpTt5tNIapYiVKWa2+ce6FW27RwzL8fuCNN+gHoJVAHvBWV9Mg88YbyQvA7aaVJ78feP99+p5/1HAEI6qXFg9oKypie12pohSLPLz6wedmFDvSSX/i7XNBlFInSeq9SyZ8z+fTV6Pa2qw7UKN4mE5RStN0UWryZBKC1qxJ3/aThQfJnDBbHTAEAlQS+733oq+VavM+/5x+vF4SM5KFRalUVu7DYfpfJ7fbWCRbCaqujmzZ4MHAeefpNgoAHnqIktUPG0beonV1tB3ul3jifeWVNOFta6P9zZpF3m3vvEOizve+R16oixeTED99enYmI5xTKhFRiq/NmWdSbjQAOPBAqgBp57GbiVLhcHxRyumV9wDrcGkgcZvLz6mmUXvL9ITQylOKP2tsJEEhkxMLNa/g0qX02fjxVLFUbaurVtHv7CkVa1vGqqZqMm71cwD4/e9prJSMKOVy0b1raaFJszFxfC5htRBgJQDF8pQCyN5++SXZK3XcyMJzdbX1uFEVqHjR01i8IdOwUPbKK/pnGzZQpVz+Plvkc04pgOYJvNAGpJ7o3Gnhe7zIf+qp+t/ynC2V1Cqp5BQOBGgc+/DDwGuvUa7N2lq6hsFg4tX3APKWWrGCwv5ElBLSiqq8A31FKdUIxEp0DuSHp5QxnJFFqXTnlCopoW2zp0gsD6KTT9ZzL7hcwKRJNKCsqQG+/ZYmLTzo3bqV3q9bp1eu4UmzkbKyaJGquppWhKqqyOgcdxwlD2YPlpUraSXMyuuKRZOOjr5KOxs3O0UpvoapuDbnQk4pVZRKpRIUM2AADfxiCU12ilI7d9L2XC7K75NtUYrhgQznRGOvBm4bVuF77CV1yimpTVasbG6sSVJxsW6bWlud3W5jYZVTyqoS1OWX0wofQOJSVRUwZw69nz2b8nq1t1Op8vJyChHavp3EKtXTc+xYauNlZSRw7bknhQCxbeN73tKSvXxnyXhK8URq9911UaqpiZK+8/d2YCVKxcPplfeA5BOdm4nJRUX0091Nz6nTRKnVqzNfgY/bqseji1IbN1JYLn/f2qpXd4olSqmeNipqMm4VFqzefLNvovN4YaSqKJXLJJpTiknEUwogL6cf/pB+r6vT7dannwLLl8f31ndKxMXMmXTOLK55PNRWNm92Tk6pfKy+B8Qv5hPPUyqbc9FYohSLtTz/4TDgxx5LLbVKrNQuVttbu5bG2S4X9dec4uCJJ+j7RBOdA7ooVYB5pUSUsgNVZeWHmh/+99+nwYCZKFUIic6TDSdJFjOxprMztih17716HPAee5A3BrujL11KVaL4/f33kyfBjh26F0VNDbDbbrQq2tREr+GwXnI8lmFpaqIQQZeLJm8332zdMZt5SrFR4/PNVE6pfAzfi+cpZRa+ZyZslJXpuZOssDN8j72k/H59wuEEUYoHFVz9xVipsKxMt50nn0yf7dhBYQcAXev6+uSLCSQjSqm2u7yc7mNrKz2b6a66mQkSDSXhyeeZZ5IoxdU+A4Hogg/PPKPbtUCAVu//+U89hw4P2rgqHb8Hoq8b51xoagIaGpybU6qri3742F94Qf8bzumSiZxSqpdvIn1lLobvpZLoHKDndMeOzIsZmqaHUFmJUkDmK/BxW/1//0//jMcg3FY/+YRehw61FkPUbZl9bvXdccfRAlsynqlA9vODpQtjO40VBhaJ6P1fLE8pgEQbgO5hVxeNFXt6qO1fe218O2SVJy8brFhBr8OH05jwm29o3ML5dLJFqonONU2/j04WpYzRIskmOuc22t6emMduOokl+qmpWDZvpmfmjjuoCug55wBTp5o7DlgxdSrlMP3b32i+/stfkgeUVaqWxkYS/Vetoj53+HA9xx5XfzYTpYz3g8efZsnOc3H8mQIiStmBqrKqRi4YpNVi1bVZNQKF4CmlJjoHrA2/lfskV8A74oi+D2cwCHz8sa5UA/FFqWCQyp77/cAFF1B+BbN8CUBft/X77tMnXWruFc6L0Nho/bNzJw0OBg7UBxtcvcYKs5xSmfSUKqTwvXieUlY5pQD9PvHE0Ay+Tz4fXdd05kRhUWr0aD2/x6ZNtM9s5epgT0Omrc1clDKueLGbeUUF8PTTqRUTMFbhjLVyr9puVZTqbzGDbJGoKMW2lCvmlZfrbXHuXP3vjPfsuOOoOt/AgdahPoD5qqPfT9d306bovDaZgsP32BNBpbRUD8NtbNQnhuqELhOTO6ucUvHIBU+pWOF7uSBKNTfrx2cmSrFXZ6Y9pZht2/Tft2+Ptv+JhO6lSiqeqUBuiFLxwnrUVBSJeEqpfWI8UYrtlaaRB39PD+2jpiYxYdxJotSXX9LrXnvR9WJR6tBDs3tc3GbVaIBE7C17fwPOzymlEquYj/reGL4H0HPKbSoTxPNE42fgmmtooeujj2iMsWCB7t2cLC0tlE/4nntoLM2LbSrd3SQQL1tGtv7WW4Hvf18fD3Ehn0REKR5/TplC71mUytXxZwqIKGUH6kCc82wsWkRutscdR662hR6+F6/DtnKf5Ap4QLQoxQ9tOBydY8k4IVXh/9l/f5oYjR4dnRfhmGPMq/LNmKG7rZtNulwuMt4VFbTNWASDJFBxqFCsak6xPKVinWe6yHdRSj22RENJzISNRCqAcKc0cCANNjUtfVWHVFFq4EA9aeL69dmZ/APReQwAujbsoaKKUmYrXg0N9Pc//nFqXinJTJLU/bMoM28ehaMkW8zACSSTUwqIX3bZKEqxt1BVlXWoj1VS3eHDacW8oSHuaaQd9vYCyAvOiMtF57R9O03izEQpPnc7UT2lOBF0Mp5SuSRKpZLoHMiemMFiU2WluZc7C1VOEKUAmuCMG0e/iyiVGvHCeq64Qv/MOMY1Ezc4pL242DpSQg3fA2g///kP/c73L5EqoE4UpSZN0o+Hxy3ZRBUUkxnjcpv1eKzzBDkBo81NNnzP46ExQHs7td1MilLxxiYAPQN//CN5QbvduriTKiNGAC+9ROPPTz7R7SajacAf/kDPY3MzcNttJEjxsQDkbdXSoo8hAGtRiv/nnnvofDduTL2YVo4iopRdcOP59a+psW7fTi6ApaXmopTLZe0lwep0PohSicbbm4k9qihkVQGPy94mItbwJGrZMhKlxoyJ3rdxEJHspCseVlVt1G2qxMoplYnwPTWnVLKVSViUcvIkKZ6nlFqOPZa3jXHibgZfy6oq6kDZkyjdopTLRe36888phC+bopSKKtgZkzIbV7xKS8k1OtUOOdkS5byfm2+m4965k44lFwcEqVaCshr4qTYI0IWZgQNju5WbXTt2a+fcNpmEQ/cGDLAOcVNFKSaTnlKaFn3/3O7E8y/mQvie16t7ivJ7JpZ3qvG5zVby3Vj5pADdUyrT4XsM75c9/rItSiWSUwpwtiiljku7uoDTTgNefVUfx110EfDAA/Q3iSwEJJK8+Zxz6P3OneTxcddd5NF6zDEUThxv3MgYE51nC03TRam996bFMsAZopRZhelE7K3qxePkSr3xwvfieUoB1I5YlMokieTsCgZpsZPD5045pX/jtmCQvBLXriV7/z//Q+31n//UBepHHqFx0Gmn0ThRJRCg//vHPxKvvscV2W+5heaz69cXjCAFAHF6CaFfqOFcnPQslVUk7kxaWqK9DXIRs0TngLnhDwRo5en226kCwa9/TROF4cPp9de/plLH992nP7RWYW1mYs3MmfQ/3BmyKMX7Nk6y+O/NMPv7WFiFutTW0ufBYN//ScRTKhPhe8m6NgO57SmlPpvcTmM9t3yfYnlKcXssLtYn+unKK2VszxzCl828UsaJhnquZpXCAgG6H5pGq1XqCnSypGJzAwGy2Wxvc3VAYCVKpVoJyhiayqJUKiXvWZTKhqcUi1JmoXsMn1MsUcrO/jgcjq6SlIzNzYXqe0By3qlO9ZSyEqWy6Smlabooteee9MqhIBwuBYinVCrwOO2228jL/t579XGcWTqOWAt4bG/N8nrxpPfJJ/Xx7M9+RhPkigrg4oujj8dq3Mg4JdH5li16dMDuu+vRBE4QpdT0IqmKUk7GuChsDN+L5ykFZM9JIt6CmTqnWrAgsWciFmp+zG++oWrEACVPP/10Svty1100fvnhD/WE5kauvJLmrO3t+vWNJUoBtD0u0hWJ5O74MwXEU8pOgkEa2JaX00Q+GNRXz1IRpThXUazElE7HOEmyUuiZyZPJDZ09VJqaKJQGIKOoaTSw5YfWWNUgXlhbe7s+eIsXapdOUvG6ipVTyugplUpJ03jke/ielaeU+juHh8ZLdA7EFpnU+8fu0OkQpXhFHABGjaJXJ4hSxlW1WJ5SALXRUaPofMrLEwtPsIJtQDI2lwcynP+vP/vPJnZ5SvE940TPAwcmf2zDh9NrNkUpsyTnjFm4i/p7KBSdsD/dcHsFkq94mgs5pQA6PrYNiSY6N3rcxBMz7OgLAV1ssqoIms2cUk1NdL1cLmC//chTdsMG+m7tWrquZWW6MJxO+D4aq+9Z2Vu+P2b30anJfQMB4Fe/ovGnOmk0K1yUqqeU6pXV00PXdedOGkPdeWd0e07EW98pnlLsJTV+PI1/eNy9bRvZ02zarFTHuDyecXI+KcDaU8osfE8NFTcTpZL1lOqPHVZzkppd42TzWcbDbHsPPgiMHEnheIsWAe+9R8d17rnAn/5kvS3Oz6lpdM0GDowvSj30EC2Y+Xx0XXJ1/JkC4illF9yor7kG+OwzUj7r6oDXX6fvjYnOY02QfD59Mp9pl8l0k2hOKeaxx3ThaeRIEql+/nN6ZVfZ7m663pqmix88EY0nSnFi35qazK5ypOJ1pYod7KlgrL7Hxo5X2YyrBNwu47nSm5GqazPQ93idSLzwPUB/XhPJKZWIKFVUlNjfJ8qmTXRsJSV6rhwniFKxwveM+W+4jf7wh1T9sr8rXsmGk/D+Dz+c7MwRR/Rv/3ZSX299XMEg8MYb9LvdOaX66ymVaQ/gZEQpNXeUcUJnZ14pdYLA4XtA/oTvAfEXAtKRU8qOvhDQF7PieUqpns2Zgtt3dbU+6efFCjV0z45QI753xsISVmNcvj9Ll9J7vo/9vT92EgxS21QXLYDoZ5OPO5bNjeUpBeheUGvXUhGfSITynZqND+N566ueUtmMuFBD9wA6dz42DuXLFv0VpZzuKRUv0blRlGLUZzDVHMf9scOdnfqY2+wax1rkr61NPrWK1fbuvJM8JH0+uj41NcC//hV7W6rgzteM+wMzUYqvx+zZlMdq1iznjj9tQDyl7CCWavuXv1CDTNa1ubKS/q+5OftlU/tDMiv3wSBVdvL7genTKVSvro4GeStXAiedRG6Vo0fT56GQbkgT9ZRS8+84HdVLwcojjD9XVwk0jcKf+pswT+2w8zGnVDLhe4lU30sk0Xm6w/eM+aQAXZRavz7zZXyZRD2l0r3iBSQXTqLuv6MDePRRSpZ59NGp799O4iXe5Rwy6fKUMopS/fGU4sSfnJ+CB7uZIB2eUvzerv6Y+yyXi+5bvnpKMfESnVt5p8YTpazyU/Y3eWy88L3SUrLvXV30t5kct7FgNnhw3/LiduaTAvT7mKinFF//P/+ZrlVLi7OT+/KxDRtGP8cco7et00+nV49H739T9ZRiAgHgf/+XxPsxYyicKBXYvobDZB+yJaAsX06vkybpn40eTbZ03Toa52cLs0TniQgauShKcb8CmLdRqyJcqYbZ9mdOwtfX7TYXcpLNZxmPWNsbPJgWK7u66Ho++GD8fVRWkl1jUUpN36Fix/g3xxBRyg5iqbZffUWxqKmIUlu2ZD8evL+oMduAdYfND+eRR1KFpooKun6LF1M1LK6A9803NGmcNo2Sz23dSiJWIjmlAN1TSs0n5VTMckrFOs9AgNrijTcCv/kNTVD7M8hTE50nM0GKRMxLmzuNoiLdzVZdtXe7+1a+Slei83R7SnF75tA9gAbOPDnatCn6u0wRS5RSB3TpLiYA6Lamu1tPVg+Y21x1/7wy1dEB/OAHqe/fTuJNuNvayEZye+b2anUeqSY6T8VTqriYJvQ7dlC7NIpS6Q67UrfH5dU5p5TZ9oyiVCikt+Nhw6ighp2eUsnkXzSSC9X3gPjeqenKKcVt6I47KBflbrtRro/+DPDjiVIuF323aVPmRSmuvKeKUg0NdA3tFqX4PiZaWAKg+7BqFVWdevRR4IMPnC1IXX45TUYBWhydMIE+Z3tgHD8AsUWpWCk5gkEaU+yxB9mgVEN5iov1cUBzc3YElHCYxvMAsNde+uejR1NESbbzSqUj0bmTUcffXq8unMbK4Qf0TXQOpDYXDQQocfg11wB//Sv1sYk85+q4JJuJ5Pn5/+53EytOxRjzcFmF79kx/s0xRJSyg1gD5bPOAt56K3lRKlsVZtJNouF7/HC2t1MnxgPPI47QX1UhJhCga3PPPbRN3n4+eUqZ5ZSK5xF2xhnUAbS39z9hXqquzWrogpNFKZeLJknt7dGeUgCdbyiU2OpvIiKTulKSiGdVorD7u9qeuQLfypU0IMiGKBUr0bk6gU73ihcQ7SllNdBi1P0bBRinTZCYQIDa02230Yp6WZk+sPnb3+hvnOgpBVBeqR07aMLMCZmZeF5gtbXJ7UvdnuopZbU9oyjFfa/LRc/X5s325mdJdAHHjFwJ30sl0XmyOaWYQID6wnCY+s/+Ps/xckoBuiiV6Qp8LEoNGkRtnKscbtlivyiVaqLz6dPJXoVCemEgp8Hj0nPP1UWpUEg/Vr7u6rn2x1Mq2QrN8aiqojbQ1KTn9Msk33xDz15ZWfRCsFOSnas2N5loAL6PuSRKJZrDD0hPTilm8GBaGGxqIvuUSDt2gujXHy8mY+isVfieHePfHMM2UWr16tWYP38+xo8fj9WrV+Oqq65CdSqrqflGqh22WShBLsLnG2+SxA8nJ5BjQzhzpv7dvHn0yhP8s88mLzT1QVe9JMwwq7yXAVJyAlArXxmNmjGnFPPww3qSeHaLT9W4pSpK8QTJ5eor9jgNFqW8BtPIopQxfC9dic4B/Tr1B6v2PHYsiVJr1gBHHdX//SQLD9q4PDkPMniCCNg3gbYSpeLlKjGKUk5m4kSaJHd1UVgEP+PGnIWJilJWK/dGb022Q6n27X4/JWHetKnvd+kOu1K319hIx/zmm8Azz5hvz1h9j18rKnQRzk5PKXXVHkhukpQr1fdUT6lEJ0mpVt+rq9OTf7e19T95bCxPKe7gzZKdJ+vll8pggUWwIUPofIcP1/MStbTQNeSw7nST6hj35Zf1xOH98QiyE77O7GkJ6M9pIED96zPPmHv9mXk5xBKl7AjlUUWpbKCG7qkeL04RpdQxLh+fQz2lUppDqHMjszaaCVHqxRd1b/VEn/NEwlztpj9eTKqnlFpV1yrReQFjmyg1Y8YMfPDBBwBIoJo1axbmsYiQ57CxiET6Go3f3uHDRSsAX3cPFtUD1RvCOKwB8I/w9jEkwSCNmY85BgiYGIJZs+h17ty++wb6GqV0jYXq64ElS8hZSd2W8XP1/3nfJ6/tgVsD3viPDws+BH75rQeTACAcjjq+3nMbEZ0IkvcRiQCXlhfheKC3I5l9bTeuWAGUjizBa/X0b/t9WoSpABa90o2v26Kv7aI3Irj13XUYMQR9JvF2X1srJ4BZsyhCcfbs6L+vrwc+eKMMNzYAw/0hfPhmK0Y0AP6SEgSDwJh3irFnA7Dp3W58tqvdzHQHcdQnjwJ+PxrgR1PlROxZV4f77wfenhiIOjfjeR95ZN97/3B9D6Z+A+z8wIfPvnHjrM2Af1fHpZ5/VLsNILrynstl+7VVt6cev9U+otptSQk2NQCbP/fhAGXfJ27ywNcNLHo0jJc+A873R3AaALjdfdrt2K0DcDPQx/NJvbZ7vdmFQwHycCsrw/IVwLv/1wZXt+Haxbu2BpY9tRal3cAkxVOqvh6YsHYsJjYAG55ci6mXpv/a9rmOM6M/H/hQCw5pA9qrh2FA4wb4d12bR/7VgSkrgOoqYMETZXhjifW5m7UbJla7ffQ/PhywBtj+eg8eXhjGrxt25dj2eGJfW4Mola12y5/Hsrk/W/MUdu/SEIKGAbsGekEEUPLvMI5pBla940X9W8DJkz24ALC2t96+gz/12g5fUYpT+Jo0NmL5CiDsLsIH80rx5qIU2u2uZOeP/rUBr7xidm0DGD8R2OuWuzHoV7+iDY4ejbV//Q9w93+SWktYuxaACxijNZHHoMsFRCJYtMd38eDiAI4w3KsnFlZh3xVASXMTXqsHBq1vwlkAUFWFd1dUo2oFsPOlJqyM88ym2m5vviGEy1YAJaN8uP17wE1rPRhXgj73jn8HlG0Ycko5dawwq6kUoxoAFwD/rklSMAhoD3lwynZgzVshfLlrcf/gT8LYB8CCVz3Y0KCPMVY/X44fNADDh0aLUuq13X1JEEd9eT+1N78fb60fg0E31GHbYqA+EojZbvtcW4DGHLvGYj/+dQ26iqKv7aK33Ji4uA5tA0egbCcwnEWpYBANc+qw8qhaHJ3gteVt+YGoC7xoVhBDn6vDljOit1VfDwx7cBsO6AI+XzYIGwEERowA1q7Fp397A74VgGfiWLz9aFGfczPed6u2q47DjM/0Tb/x4vIVQGlrD16rB8a9HcaeDYDf0Ffyte4dq3z+JN2fsWOBSy4BLMYqTmi30/bswtHcbndN5INB4Itnw/h5A9DV4ukdg+75vgeHA3jnrTCWF0Vf2/Pfa8GpNYiyt7yPw5ZHsMekWhylHBzdjwCumAjgzQhWRazvnanNVRa3s2ITvvgCmxqABe/vhUi9sq3Ro7GpAdj29Tp8UKdhZq3LdP4A6MfXH5v71Vd6BJZ6vBNe68GQZmDli5TI+uAGwL97OP61NYhSmRgrmM0huO2sXBnt+MufT68swckANjUA3a0+jN2176JGL45tADRPCAt3ndu0qWFcxMf3oAsRjY7vj/+oxHdWAJP2ae5zTLH6s1mzgMNXBnHRR++jEX5s3/tc7DdjElBXh8WLgVVHBKzHCooolbX5A2ZSZTiT7c1aRH84d2bf/w0GAe+zlTihEVj9Ugsefr4Tt/AYtLg44Xbr1EKk6SbOUnFqrF69Our9+PHjsXDhQjt25UjYWCxZEp00PxgE3lvmw/btwIpPe+B2A24tjE0NwNvveaIKEKgFCerqgCWfVdIXu2JSg0ESLxYvjk7K73YDc+bQj+oIkEohE6tiCUuWkJPSkiWxP+f/v+qqXft2aXBrEaxZC/zP3R643cDnyz1oaABefC7ce3zquX3xbrQoxftYtQp46VUfVRPv6UEwCHz6fie2bwc+/aqYrq0bePuDIqxYASx5vbvPta3u2ozNG3qwcatPT7qboWvLRSGM7WPxYvO/X7IEeOTJUjTvWuAqad+BTQ3A3+8vpryBviK4ALz9WhfmzAGOWBXEgHl1+HDMOWiAH5sagK6qYVg8sRb7vF+HQc8FTYtg8Hmbtd21q7qxfTvwzIuUU2pTA/Dh0kjU+RvbbTCIqFDDTFxb3p6x7VrtI6rdlpbCBWDpx1693boBuD1Ysxb4+9/CcLupvTY0AMu/cvdpt+9+UkrtUvGUMl7bD97uor8pLsaSj8rQ1ASs/Lgdc+YAR66qx4Y5dH+M13bDnCCOXFVv+lw+cl8rurc2oqkJePgNPUTP7QbufXEs1qwFKrZ/a8u17XMdDZ9/+1krmpuAzuph2NQAfP5+O4JBYN4D7XABWL3Wi9/f5YvatvHczdqN2bU1ttuvvinC9u3Ay892w+OKYFMDRYs9+IgndrtVRKlsttt419b9cBDV772Mjio/Xi8+FYsn1qJhTh02zAnCo4WwZi3w5LNeuN3AI4+60dAAbFpvbm+//iRalDJe26deLutt2/+tp7b29fZqzLnTFfPe9bm2zPDhaGgAWr5qsLy2sxYFUNTRSB4J4TAaOqqwfXUTijqaaMU/wZ+ijiZsX92Eho4qEqSKi4GiIjzoCphe27c+q8KWrUB7QxPcLg2vPNFIz/ymKix8vwouAG8919R779Pdbj9e2kP92Qp6LlZ+Q33l0/+NmF7bqOurhO85eaywan0JXKBJ0tMvFvXuW/N4sWYt8OrLod5z++LTMFasAF542dN7bEuWAE+8VE79ouIppZ5zpJ76wvdGn0cLNA3AZztH4v/aa+F7uA5HrArGbbd9zm9X2OrGrV688m55n2v7zdEB3N1UC3ywDKVNDcC77wLf+x6abpiDpY0T4NbCtMEEftxaGEsbJ6DphjnA975HHkVBEqT+0lSLB13RisqSJcDmL7ejuQnorhyMujrgvfUj0NAAdC1ZCheAZ77cvfe+9xmnufteQ6v7umpV33b7zodkb1ct3zXGBdnceU+aj3F5rPL2xMtpptbVBVx6qeVYxQnt9pP3Onvb7Wsv9/Tu2wuyt1985e29touXeLBiBfDuknCfa7tjbSvZU8UzlfcRdM/ELSsDfcaHzz1HNnH1MTNj3jtTm7srH9AbTzdlxyYsX47mJuCRZXtFX9sRI9DU5ELjpg58/NqOqP9Xz814fKna3JUraRvq90uWAGtXkc2FzweXl8a4b70Zjn9tWZQqL8/YWMFsDsFtZ+LEaEGFP/94BSn8LgCr1vp6r63L64ELwNpvwr3H9++HaIy7aYsHdfWu3mv79mcV1O9/FO0pFa8/G/RcEHu+XYe1HUPRUubH8o86EUQAiyfWYsC8Ouy+JGjdbndd30++HuC4+YPxfpu1y67iSqxZCyx6tglFWhc2NQCbNrsQfMSbcLtN5TxyEVs8pRYuXIgag0tzTU0NPvzwQ0yZMsWOXToK1cOWC8axITziEB+8zwBejbx7zjwtjK/+F/hijQdjjgDOOYcqTD700C5PwUg9FsONBW9VYnwE8Dc39zbQuUcH4dIiuLluJnp66H+3btWrZ378MfDUU8BrrwGvvAKceCItlDz9dN9jNssdV1MDHHggRdB99BHlc3zrLWDZMuC006iya329vqK4ciXlH1+5kt4b85IHLglh050ANKAz7MMeewBjl3vw7fvAQw+Ecf5sWoB86CHdU2j1Da2oqQL8u4y9uo9ho4uwaQnw8XM9qHsbOOGALng3A10oxs6dwKmnAosqi7D5W2Dg8G4cfTSlLJg3j3KcXjFpLRo+AJZtH4WFD7mjokN4//ffT+P7M8+kdD2cmmnxYvr87bfJAB9+OC2c/u1vumcme6Lz72Y/Q4cCt94K/O53ZHBOOokWW/7f/6N7+d3vAk8+Sed7/gUeNM0vRkNDF/Y+bCdWlAKvvV2CiZcAx59ajIaXAZ/WTc4EoyLYMrIW/7tof9y47SWMGgl0blyJnzb9Dr+7FDhLi+BmZZXFeN5mbfdHu/XA+xnQ4yrCHnt5MHwj8OhrYfzjE+BXvyKv1GCwb/qDqvUdOBvAN5tK+uxDfV4Aul5NTeTVMH8+3avp0ynibPXq6GtnvK7q+6lT6X797W/0euGFFGnD3ouMsU196SvFXn5gYLtPb7cBYO1fyX3Zo4Vw1FHAsEERrPl/wIrNHtTerZ/P7NlA2ZYB2HQH0L28HaM12kd9PXDDDXRsDzwAnDqwG5tWAk/XF2HzJi9mVAHVPSRiTdjdjVM31+GvNwD/rQz0/t/GO4OoRR38V9bCY7h+wSCw8J/r8ccxQFf5INzwSBm6PMB551Gai28iYxEJA6Fv1uKRhzUsWuzCG28Axx5LXtyPPNL3+bfC6yXv+zvvpPZ/2mnAO+/EtguXjG9B+yYgUjkMI/3A62+3Ys43wO3fa0fVP4H1LRRmdNRRtDr9+9/Tvq69Vm9XZu0mkXZbu4cP3g8AL3pwxKFh+N8B1qwF7rjTg1/+ijzIH3ywb7utOaEUZwJY9WkH6r7su//LL6f5UyhE7errr8nWvvwyeaifeipFJbz2WmK5OV0uchI4/HAq0rp8OUUkv/oq5YA9+2xa4WXbGgwC4xYFcV5VHT7EETi4aiX2HBzCWYsCOLsJ+HlVHbq0QWgAEIYXRx4JePfzYM31gOaK4NJf0HE/8IB+bhuvbcWAwbq9NV7b43YrxaZXgW9f68CCpTvx0yrg245qaBrVmzjgAOD22+nvf/ELuj4PP6znlOZt8v17dqkfIxuA4/dtQPEsPcJq+nQ9GvGc5iAinZ3oggvbikbioR1nofmM83HIIfp1s7qext/ffRcof/kJzCjqRMXQAWj5KoThq4I49dQAvvxSj5YJBoFP11Ti6iFAe3sE3s5WfOfYJqyZCyzbWoXDAlWoegEo39qESISK8ey7L9lxAPjZz/Tijam22xMO6IZ3MxCCD1OmAKM/9uDbT4F/3hvG+ddRX/Tvf/eNKKirA6ZtbceIIcDjz5Si7ono/UQiNFbYtk0fKyxbRmODV1+ln+OPp3nyk0/2rR6v2lp+LS0F9tkH+OMfgffeo7oj774LfPghcMopFKH5r39Rf6ba3PVvlgIuYLgfuOV+L74upmMdv9gDLKVFOwC47DLg1T+EsWUFMHR/Ny64gNrtypXAtHPK0f400LC6Hf5wGMGHPVHXduPvI1g2uRaLVk1F1fqn0d4BTB68Dnd7f4+yJuCsiRHMPIomG5pGNqenJ9omqNcWAAKH7EBDA7Biew1m/8kV9d2ll9K9mV8WwLj2zxDYMRc9/56PUI+G7fBj72Ffo+aDr9H4gXm7NbIPgB1FwPbNQNFDT8D7+H+wpWoPPFr6Q7SdGMDaFXQtuJjVypXAD4ZvRXsT9QW1tcBDN47EpTuBsWPIq2dts55P6vLLyTPh8ceB88+n4zdrn2rbVftMY7s9+FAvvJsArxZCVxcw5YAw1j4NLPvYg7EX0rPNkzYa40aweEktfrviYjy05TH4/cAj/2zH3JWBqLEKj3G3bKF2Gw4DS5dS23vjDeD114HjjqP38+fT8fJzb7QR/L6yEth/f+Cuu6itTptG47lly6jdfvEFjQGvvDL6vDctI9F3uB+omxfCgpfoWlVtCAHPAWGXF5oGXHwx8NBcD7Z8DQyaEsZ3ztHPffZs4NB7W7DpC+D918px1lHxr+3RRwOLFtGxt7dTaquXXwYee4zS1Z5xBnDffdSfm9rcqio0NADvbWrG7Bv1e2tmEz74gJzb1WtbXEz7MtoERv3c54seJ5xwWDsOWPgtmluA4cdPwmefAffeS94hD/3bhxHh4Rg9ZCNavliHYHBQ3/lDIHqyfsQRwKGHUu0Cfm65L0/E5rLAwCxaBJzm64HXDYTgxaknA9/+Hfjymwh2O4LGUv/6l24X1OJxB/tasReAl94agLp3zPcfDtN11TSKrv36a2qnTzxBffu++5Lt5CwRPT3675zOVH3P2QjGjqU+9667aGx73HHAp5/Stb3qKjpeblOrPyhBQztpv9+GvL3X9sJLvWi4B3CBBtBHHulC8T5hrJkN9Lg8uOwaOu76euAnP6rE2H8BX61owVvBxPuzwB4RLFh+IU5reQxjq4D9RnbjnN8DQAD3nge4w5He/rdPu/W1oaEBeGfNAMz+bfS1ZXvb00PXd8UKeiYWLKDXk0+m6OUFC/raAquxw8iR1Lb+/Gcaz55zDrBhA113t1uPbDQ779pa+uG2NXs2MHppJfAeMCDSgin7dmPou8DKNcW44/cu/PKXfcdhZu3WiXUf7MClaVbmJXX++Mc/YsGCBVjArQDAhAkTcO+992LatGlRf9vV1YUuJQ9Oc3MzRo8ejaamJlRmskS0DQSDZCy2bSOht7QUGOdZi/u6r0CbqwLnep7GgeGluLXjV1jnm4AfFf+zt8gXezGfsjmIkzbU4RPvVIzd/gE+KjoEt5X9Ad8vC+L8ljq8NLwW94cCaGhA1P8C6PMZf54sDQ3m2+LPOdx43DjKnbl2LQkIHg89vAMGkINXcbgdjzSfgaIiYNaoF7FuSzFu6/gV9u9Zirt8N2FJ2TRoGuVh3msvGiRf+uzFKG5swG1D/oYV3n16Jz7LlgG+JW/gx9t/i48i++OWir/icLyN33bfiNVFk/AD/B0uF3BZ5z9xYfhhPOU5D/8s+Unv8Y8YAZzcPB9nrf1fvF96DG4O3wq3m4zObruREWttpRQBdl5bgMRDTvs0ebL5NR89mjr4H796LrwtjWj1VKE80oR/DrsZL/aciCM6XsHPW2/HqsoD8Yehf+793/3a38VtPTf0CjE/H/c0Bo6pQGkp5eldu5buUyRCncjEibS/VauoI+nsJINfWgr8quf3OMW9AI8P+gEatnkxq+sevO46Hnf6fo3i4uhr4XbTdjdtAoas/QC3dfwSa9zj8Zd9/4Xx4+n7NWso7yVA133oUNpGOq+tWdsF6Ny9Xvrsh2X1qKpx44FIAN/96jpMCb2PF31noW7gNTirMQgPIji551n4fdvx2+H34Z3tE3Fs6BX8ov12fOI+EL+p+HPUsVZ3b8ENH12I9m4vzq9Y0Oc8GhqAwLe34oju13EPfgq3142fRO7GJ1XH4G9DbkVDA3BRdxAXddbhKc/5eKrsYpzU8xyuRB3e3L0Wb44NwOulTvLbb/VOckbVy7h6xxysHHAAbh7wl6jzHjkshLnrTkWoO4zvDngMW11Dbbm2ql3gNjd2LPCX7ZejfOd61LtqcVFHHdZoY3B1yQOY7PsCc1p+hJYBfvxq5L+xcSNtT025pWn0PI4dS+83bqR263ZTu50wARg/nv5+1SqyPZ2dJAqUlpKwH8ADeK3ibPyjPYAH2s5HJOLC6SWvRrXb4cOpTXi91G5Lvl2OP7X9AFtcw3D7vo9i5Ej627VrKfXFrugvW2xCrGu7ebP+zP5oQD2qa9xo296B4xoexRbPcFzhJYUx4ArijM7/YEhREx4a+BM82HYeKtGEYPM5AIBzK1+FBhdGjqTrV1Ks4VcLTkZ3Rwg/HPQ4trmGYOJEsumRCNmDni9X4a7GWdgWqcFDJd/Dz3v+iOVVh+LPQ+7sPV7jvfP7aZDHxZ82bqRn3+0GBndvxP1dl8JVWoybp7yAb9e4sGmTft5XlwZxblMdSjob0eyuxkveM3CW+zm8OqYWL/uTH6Gd3BDECWvrUIdaPF4cwAVdJPS+OqYWQQSi2u24ccDcjadDa+vArJIHcWT3a5jecj9e8p6BT0oOxXVtv8a68r3xO///xjz33XajPnDTJnpevV66nnvtRQW1IhESIJcvpzbL9vZgvI87eq7DGt/uuMo1F/e0fxejQ6txY9Gf8GXp1Kh24fXSBLK4GGhYH8Iflp0EALis8hkM3q0cI0bQoH3NGvQ+Y9kYK7hcet2FCROAfT5+GCev/ScA4Me++/C1eyJKS4Fz2h/B97S5WFR+Gn7XdR1cLuDOtp9gH3yGO4puwzu+o8mujQQmjA3hlkUnoasTuHLQU2hGJXbfnbbPfdnXXwP7tL6LW9p39YVuN67d5yV0R7y9aWzM7p3fr9dN4f5s3Trg0NBbuKHtZmyqmoQHD/87NI0+X7dOXxjx+wFfpAuPrZqKInQhAjfmDf5hnyKTidLcDHx36xwUowvfusbh4j0+jLIJbjftd+zIEOo3nISuLuAC35No8VRjv7Z3cGv3bPobAP9vzP/gi5IpUW2htJTaH78fM4bsQnExLeysXq3bPL6+K1fSte3ooJ+SEmCEdwuCXRciBC/OKFqAy7rvxwXdD+LlsnPxN/dPe7c/fDjto6iIfjZsAO788GR40YOrKx9Fydhh8PvJQXLdOnvHYcn0ZRMmAJXL38OVK64HXMC/PFdjvvcilJQAe3Z/iv8J/RQN3lG4wvUgXC7ggq4gLg/V4Tn3d/CP0l/0jnEnTgRmLz0P3pad+EnJP7GuaAIiERrn7bknCWJGm+Dz0XGFQtHHa7x3bHNLSuiHxwoXdj6AGW31eN9/Fl6fcg26u+lz1ebadW33Dy/D7R3XoLV0KP449bGoaw4Af/LegEO0d/HAoGvwUNNZvf17ebmeAisSoXYTDuvHbDZWGDeOrsnGjfoYice4u+9Of7dyJYk37FRaXAzM91yA6u6t+IHnPpS4unBn+0+w2TcKs4ofjLoWw4cDp22uh8vrxsPuAK798nsYF/kat5b9AdvGH4LLtSAQiaAeM3trz9g1VgD6ziHUdsvP8m67AaUrP8GPVv6MvKI843Cj/360tgJlWhseajoTxcXArDEvY+0mH4ZrG/H3lkvRiVJcXPU8NI3mTZOHb8Hsjy9ER7cX3yl5GW6PC5qmj8PY5qrjMO6bjqhZjps2/ABdXcC37gn4vvefUfeO2606Dlu3Dri8cy7O7XwEbw07H28d+GOEwzQO27CBzt+uuZnV/IHnvfwsc+2g9evpx+2OTvt7ZOcruFG7HV8WH4i/hH6Cv3VciZ1aNS4ufrL33EeN2jUOK6F2y3PoMWNIXMx1Qaq5uRlVVVVxtR3bckqZ0WiSFHTOnDm49dZbM3kYGSMQAH7zG91jxuUiDyGvFyh198Cl6SuBFdUeuDp1w8JezE9XB9DZCZy//m5ooQ6UohnnRYI4p7sOz/lr8fLgAPygQYmmUad18MG0AvTcc/SZ2w1ccAEdU6ISpPHvHn5YV/oPOkjvJDZv1nOHnt9SD+0jNxr8AXR16ecyaRIw6b0gSjVywSwqAkbt5kXDTkDr8sDjAcqK6Tq4XFScYetW+inubkEoBKxvqkCjh1YYXnmF9l3c5qNzjlCiSU+oC74iwDugGJ7OXQbZV4TqCqCsvbt3Iuf37+rsdq5DVzewpXo02rbqx1tUpOcJ9fvpHPnzE0+kHLePPUbfezzAz39Ov7vd0StzPJDhgbjZz6JFtPLndtP9rqmhgc+OHXT/QiH6u0GD6Hp0e8rgijSiJNKMMIABg0rQ+S3QHiqCBqCqpBt+v56Hs7K4C54wDUQBYErlKqwKH4jWVpoodXfr5+3xkCHk8+LOntuuJ9IDTxFQXO5DeBudXLEn3GtUud3y/0QidC+L13RC04AOlKCiQi98VVISvX+OoNyyRb8+e+yh/268lom8HzGC2gvnFlRFqZ4e+ruSMjeO/7YOXX6gE6XQAPTAhx9XBnHUljo8XFSLMLwoKgKG1ITh2gG4tAjdX4+nz7l3egagqAjo7g7BE+lB2O2L6hj9fqD42y6655Fi+HxeoAsYVNbe294eLQrAhx78puN63ND8O6z0TMIru38XL9cEgF1e02VldA68/9086xCOAJuLRvduR9Ooc59yiBdu1yh4v12DfQZ8i8/KhiZdvMyMuXP1FbuDDiJvNN43t5uqKsC9rhVuD7Ax5EdEA8pdbfD5AF8P574pw9ChNAjRND3/o9o2OK+2sd0WFelFB0tK6Hh4MOZ2A10hH7xFQFVpNzztZGc0t6dPu9U0+t+eHspxvT1SiogGFKMDlZV6Or+aGhoo8fmNHUvn2NKiP/ennaYfv0q898z++5OXCnthjR9PExO+tmwXFk2YCQCY3voXAPSMciHOf2sBjPOuwrSiRagc6IGrHQhH9NAntxZGxO3FkCF07J2NXfC6QuiMAJvbK9C9y559/TX9vdcLbA+VAS6gFO0Y6Gqkc6isjmpvxnvn91P7aG/XJ1fcP2zEULg8Lri6u+Bt2Ylhw2rQ0EDfXRwK4vJwHT46+BKMf/cR+AD8d8SPcPhBQ3Hp0jrsPxT4fGqgj+eO1XXd98Mg9l9Th49Pr8XCZQFUhoDnIwEcOBo4c0UdMBS4yxXova8VFcCOcBVq3B0o7mxCSU8T5cj2VGFHpBoAMKSoqffcAT3ik7fh9+vttqiIBuh8XXp6yIOI4UFsr70N98BXBBSV+eDqAMKge1dWpPeVbFdCIVpEaW0FKn0dvR66LaFS7FauV6JWnzGvl8YK1dXAs8/qz8tFF/W9dkaPEyvPE5eLPEu43R5+OI0VvF69fQC7nq/tgFZc2nusEY8PXg892z2aF14fUFkWgmvXs+5xhVFWCkTCus0dMgRobvci7CtBpKMT7vZWdHkq4fWSAAdgly0GKtGsH6sWwYSSDdhcMrZXSDJrtwC1XV4Zr6qiSW5Fzw5oGtBVVtNrFwYOpIkS///EicDp2x/DzjVDEIIXXoQwZvJAvL9XarOLg78MouPVchRHujHA1YHvlwXxjCfQ2/54HDa2YgfCEcBV5MXpnU8i7PLgreLj4Arp44COERNw2bYg1ndH8EjRTLhcJISok9uaGj2Pv89H7ZO/4+vL15btrccDdEV88BUBPoTggtY7xh081A3X9mib3tUVXUi4VStDpdYEb3c7Kir0qCjVvvh8wGGHURt6+mm93V52WWJefVbfP/igPsY9+GB6Zlyuvn3Ztm3ACG8nIhoADSjy9sDr3dXvRELw+YCiYk9vu43Ag7IywNsV7j33wYOBnTs0uFqp8WztKEdzJ30XCul2wWgTwmH6X0C3OR4PCRHvv6+3Aba5bW30U1JC13lbdyUiGjDQ3dQ7hh82DL0212gTALq2l19u7VVi5nli/Nu5c4EDmpbD2wNE9twLI0ZQP8731OUCWqpHo2fruxgZWddn/rB9V7u5pLseo0JuvOwP9B4zP7cXdNEC4hfDZvb2NcXF0e3W7dbHuF6vLlbx+1JPD3wAwhEvwruiWQZWhOHqibYLmgaEIm6curYOPX6gNNKGiAa0agMwoyOI4xrq8KK/FjV+Xajm//V4oos0HnigLsKw+O316vWE4n23dCmJGJEI3eNwmMbNarsdOJDscEV5CYnmoOd0t93ome+MkBTg8wH+wSGsa/DBHaFwU82l29uhQ6kNhUKAzx2Cu7sTHSiFy0X/ywK/cRzGDgoTSjfC56PjLNK6LMcKXP+mspKeh6KeNnpWBwzotbeDB5Mopdrbqiry6uN7feaZfdtrsmOyJ57QvbAmTKDnSbW3NTV6pg4eG4ZCer2HcBhodVeiyAMMKW5GSZgMXthdFDUGHTyYbE5zM91XHis4tRCpXdgiSlVXV2OHWm0EwI4dO0yr782ePRvXXHNN73v2lMoHgkG9+EkoRMLC6EofBjUD8PRg7yHApPYwylcDoWoPJg+jh7enh8KNzj2XDGpXVwAbfrQOU1/7E76j/Rf7e5Zj48m1uPSuAK4spslLJEIDhFCIXJAB6ti42NWECak37GCQJl68rZNOom3V15M7Kw+w9xvjxnHf1GHvGuB34wO9Bum8tiCO8NTh9aEzMGAb4Pe7cNElbnR0A4O/9cC3BRhYGcbUSTTAOfdccp1uatRQc0E7dnQClaMqEAGFCBx7LLmbb1lbBM8GoBg9qKkBJvg6MSQCuCeUYL9OOt4hG4rgbQRGDunGARPo+C+7jNxlfdevhfdT4NM9xmDMZ7rH17RpdP0HDNAHPT4f/S+HjLCaHwrt8sZI4doGg7QSdv310W6aZ59N369ZQ8fU3U3uo6eeCrRdUoZwOwBQz+IrL8a4ccDw7mKUrQX2GNeNyy+nAZbXCwxd0QVfaNdKRAS4aOpKTL7jQHR0UOhHa6teWO7443Uj/uyzNFHduVNvu8PauzGoDJh6mA+dYaB8E1DuiWDIYFL5e3poQnPBBdQew2EKX1m5pRMDNgBlZSU44wzgO9+h7//zH9o/FwGZMYOO86GH9Gt7/vn9M8jBIHXYvL1AgK5nd7c+yCueEcAYF3DZ/9VhZdlglIeBY31LsdvOtfjPuFosHxHAeV+8hKHVwGknh/FNMbB7UwS+VUBJiRsHTKZtX3jhrgldpBRth1O7OXTfNjS5qnHhhZS7FaDrXr2yC0U7AberCCWDSjGgCdhvfDsuuoiOyesFPvr2FHjX/ApF7jAGF7eg4kcBPHye7tY9fz51kCy2Hlm1DnvVAOOvHA33rkmhz0fbmzYNKGkei0jDGkwZtAaNgw6B39//aztqVF+7UFdHE1O3m471rDM1TO1uxdYGoKR0GMrWAZGeNtTUAHuXdaBsDVA5sRTnn69XA+dibCNH0jZmzKBnoLubBGFut+EwCcXf+Q7dz2eeoWuyc6c+gB/iKsLgCLDfpB5M2hJB+UqgtduDoUP17V94Ie2D3eYfewxY0FyKAauAYrTjzDMp7MTtpnbb3a23W76vW7bo1+KAA/p/bT/5RN/ehReS3Zo7l/bNz+zZZ9Nxrbm6AyVbAc0XwqCBtI3hw4Fhq0MYUgYcfrQXb3wKDHB7UPQ+fX/QgWG0hbw4/3y6ft0bW+G9BOgOezB692JENEo2etZZdN7PPQe82lgG33KgTOvE6AE7UOYF9jh0IC49kbbp9eoCoWoTzj+f+rLubgoTam3lCYEXpV1DsFvZFsz9XQOe+LIG4TD1ZWPWR7DzmFqUTTwCJR8/gnZfFYaMKcX2MwIYcwYwJhLBGTOTuKj1EeCsWryBAEZvU+x3bQD7AWh6M4LnV+jt9owzgCnDqtH0XgMmFTVhXFsTvNuA0IAq+MdWYcBXwJihjbjsMutzv/BCuketrdSmmpp0b5PDDyfx0u0GXnqJBqONjbq9HVfUgyEhwDfah8kRoGalB0U7gUEDI5g6Ue/LZszQr21nJ/Dc/e0o/RgIuYswYQ8PTj+dbLLPR203tKs/YLsAkMcAX49x4/o3Vhg9Wt/WccfRth54QLe5PT3Upr7zHeCD20tRuhZwARhQ5cVA3652u8WDwRqw3x4h7NNMxz7kyzBKXcD44R70VNP2uO22n1mO1p5O7DO2FQOKgBNOoL7M7aa+rK0NGL62Be5O2pbPC5wzdR02TxyL7m76jD0vRo6kY7/oIrp/aujM44/TZGGfHTtQthkYPbkG9ffQfp54giZSPNG6zh/E7ivrUDeuFq+OCuCE9UHUrquj9pLsBQ4G0fByHd4ZejJ26/wSTZ4aXNheh5/+FLg/FOidAIZCwHnHbMPeGrCmowbDWj04c2sdqotD8Ha7UFykYad3CH6221PYa3Md6kfW4oCR1BbGjdPD2zlU7qyz6DrPn68X7QuH6fp+5zv6tVXt7ZgaH4bspGt5oD+McVsiKG8ABg/zYPIoffszZtBYj/vjefMAzydlKAs1YdKYdhx0Bt2DoiJqt+EwXVseqwCUVoLb2ujR6RvjTpumt1teAOjpoTb1ne8AS3/fidKvqd1WFocwsJLa7fimMIZ0AsVDvNjHQ+1qVIMbZY3AyJowpo6jcz3vPOCMad0YekUIW7cAAysrUO7T7cLpp5vbhMGD6ZrxZJbHpaNH07PP4thFF9E+urqovc6bR/evsq0KpRuAvUc1o66Oru2TT6LX5lrZhFGj+ndtR40CDg5/iZIeoOLgSXj4/+jz1la6tt3dwG6TRmPPz4GO4nUYX6mPawYPJsHB5wOGbXbj8lAdDhkD3BoOwOcjYeKc5iAu0urw/LBa7DOD7lFnJz2vLS26+HTccXQPXS7qz779VhecBw4ERpb0oKgHmDDQh3JXN8qXA6EBYew/JvrazpgBhEIB+P4NXHZPHdp9jWjzVuOC6pdwXs8zKLulFpdfEEDZE/pYQdPoGno8u/KP7bq2p57av2u7Zg1w003Rc4hhw2hhC6BjPvtsep5fvLcYZV8CcAEDSnwYOpQErBKvB2WfAkMGA+efHUK7BowKhVH0MdDl8WDKFN3ennN2MWou9mLrphD2rWhBc0lpQuOwwYOBk3bfhCFtNBcZVtyFIQN1m8v95fTpur197DFqv+M2tqKsEdj7kHJcfHv0OKyoiK7t9Ol0vuvX69d2v/36Pw5btkzfHs9t7r+fvg+F6LryvufNo2eOx7DsXTastQJD24Cqymbshy5UfA3sCBdj2DD6nm3CWWdRu503TxfF+TgKRZiyRZSaNm0a7r333j6fH3TQQX0+Ky4uRnEelkVk43DAAbvyHkyj173G+rBpHgCEcdWNGgLjQlhxEfDe115MPIriwfl/y8p0Q/N862V4oPh/UFnag26/F7/aEkDt67Svxx7TK0kEg9GxrGZ5PFI5D2OcL8cJz5qlf/6nugA2lwEHvlqH/zujEYdcfSDuv2U1Jr5Vj4+PrMW1j5+KhmPn4eu1Xsy504XZs4F9/uNB5wJatb/8ctpnXR2t1Hg7WrFXs4YxY4C3Pq5A8BH6rrub9n3cAUUo3QHsObgbY0YBE31d2PAWsHRHMWr/TMf195OKsGUlMGz/bixYoB+/zwcEmtahoQVY+NUYXHNN9PmxLvroo9Gx+em6tmZxwvxqtY/PPwdO2VyGI0fRisKKFcA7H5Vg6kXAfT8sQsPFwDufduGuu/TY5LdvIlXev8sL6YMFK7HhSPr9hReAn/wkeh+77UbfvfYa5YhZuZI6m5UrgRE7urFpFfDMy0W4eEYYw58HPFtpNeWKK/R7xyJdMEgDnptP6cCUZYC3ogRzn6PVFoBivNX9p7Pdml3jWPtYPjGAcV7gR97foVLbjuE9G/Hb9psQ+U4Ar98HfH2kB+s/Ah59OIxZtwIjPw7D9TWgudy9Hkf8zAJu7LmtBGOHd+LZeR0ILqhWvqM29euqLhS1AUceVYyVm8owoAf4Ylkb7t6s37st1z4MFzQUFbkwqmgrWv43iIWVgd7jfvFF4Mc/1s+j8fp1aKwGPlw7Go8t6msTzmsci5/4gVuuWIuxQ9J/ba3swkP/7MIZ34TQ3gGcc+tQjPw7sGZtJ4q8EZx7SjtGPgs893kZ/vd/o/M9ANHtigsUvfhi33bLVdgWLNBt7vHH0+tIrxcblwAfNvYgcEsYw/8BrFjngcsVvX3V3j7zDDDrqlJMeRRoaAjh+adDGDKEusv//he4+urMtlv12qr7rqujMI+jP2vHSSOAUHE3vLs8D773PWDKYyFseB148hkvZt0BuHvcwFL6/vJLwggX0zYqK4EBW1sxficwfGI53l/q6t3+hAn096++Cuw1pRTF39IAfrxvE8q9wMNvVuOFj/reO9WeqzbhpZei798XN/oxsGcLPnlxE+a9tze+9z3+biZmzQGmvrQIf/ID/uOGo/bwXde2NoDAzCQv7MyZ1td2YgArv45ut3V1wD47q+DfCVxyZROGf9mI1jeAzuIqTP9eNUbcDaxZ3Ya/3BXCdTd6Lc+dn/tXXiGvWnX7kybRd4sWUR481d7uUdaDDa8Dn2/3ofYvwMSgB01v9+0rfb7oSj/vvNqOC0cC/kll+NG5+iQFIOFEPcdMjhVUe1RXRwsyZR+V4tiR9P+uUBGKvBSqMPpDLzbOBT7YGcKsXX35oklhbP0aGHmoB//3pL6dTz4Bzt1ajgNHbsP8+lYEP6PPx46l7S5cSDZhTHMzhrmB9g6grBR4/8l1eL4yts1RF5zYLvzwh0Bg+w403AfUfzUIa96g7596Cvj+93fl0rwqCN/Ddbi7qhZ73xnAggAQDAZQNweondO3kl68C9swh0JOD6wdhwOW/BorXENQt+lsnDe7DluqgKtm6/3C+3/ZjkM2A1+4hmC3OQG0LwbOfbgOJVo7hg0vw5CSDmz8Tx3+tuvYXgvQNef8MuoYdOAugfuVV4Cf/jT6/q1bR/fVaG/3HO/Dhkfp/678VQ8u7Q5jxe3A+x96MPGy6O2Xl+vbXLAAmLHfAEx0AZcf04Y/KWOFxx6DYhcy227V/dbV0bhr6NIOHL+r3ZY0knfqVVcBg78OY8OfgG+2eTHrr/R/957owZaVwNADwli4UN/OUE8rjm0Bdja58MNbSxG4Qv9u2zbat9EmTJwI/P3v0edude9Um7tgwa77t3clGi4Hln3dhC/fpO3Mn6+f41uz6vHMDW48VRXoc23HLw7iKGOJ0ljU12PxEjfqVgZQWwtc9syXaPgM+Nv7e2PVcUFsaYjgyhtn9u7j2f83GpM2Am3hdfjpXVbnFsDfbgDOm3c//vf4z/Hpd27GJ7c+iXOb6tB6aS1GHRWIGiv0nrdyHuPG0XePPEIePffcQ+/nzKFE56Ve4MKf+HDBaR6sOAZYvj6MPaZZXNuBAWzwargGP0exx4WRxT2433MVRg4OAK/aO1awmkOoebjUY/7iC2Db5yU4ZyTNH3Zu8+Lll3f93b0eNOxNuTb/eW8YP74ZKN8cBj4hTz/VJlZWunBsZwV27NiJ73+/Bef9amhC47CJE4FP/7sJNW3A2DHAkJJOuHd5AlqNw559dpe9/aIVDU8Bj39Ujp2v098++WTmx2FW+2A7yX0AoP/t974HlDZWYf1soNvVjHOv7YL/ceCrTSV9zp2LY776KuWlTMd55Bq2iFLjOcHHLlavXo2DDjrI1FMqH+FGNHGiXpqTG9df7vDhgl3eAO5wD156IYyhHcCEPTx4dGW0Iqp2jL/f4wUUL9eASAQjBvfgdxODmDWH/pAfECvU7anvEz2PWEbP+Pm18wL4n8nAlW/chK4n23C4Z3e8feSP8aArgIpHNuBoUBJIgP4+/LEHp40BLjg5jNvqopPEDQm14nE/4B9bDHi9UfuePBlYvc6H4X7Af0APak8Dls3uwgEhoMtdgopdx7+2oQgnDAHWbOuOuraP/LMdh67ajvYO4NRrRuOSOMJQuq9tJBI7cd1RR5lf83PHlsI/kNyt2zuAE04rxlsrgadeLMahIJdYvrYrVwK/ObcbYxcA324pw25D23HcyJU4y+TcjOd99NF92+7mn/egPESJdz/7QsOgBuDII8IovTA6wV9Ukula4MSyTmAZsP8hpagdY/+1BRJPDhjdngOo/PTvaF/ejm3aIEQuDfQm7T/CQ23WgzAWLwYGvR3Bz8YApRM9+Fld3+SGL44fAH95J9DWZnpt21Z0Yy8/cN3NRfjv62VovA3wRCi5weLFlMB6Vvk8RHx+fNHhx7iSBnw3dC/+NQe4anEg6t4AQOByDRv+tAFrvgb+8MhofPfX+ncTFtXj7CY3Nvp2zdLWrEHgWvp1w5wg3loUwZFzZyZ8bd+aVY8Ni92o3TURUq+j++EgAgdHENilGAQCwEcLW7HlbQrf6CwfjE0NNCi5+edtePOeNoxuAtrdA3rPfeXK+Ikjk2m3b9/kw+QQJTpf+m4YuzcA4yd6MPsXsdvtJZeUAo/SAO67F3Xgd3Mq+uzfjHS323g2d948YProDqAH2LS2B7Pv1vc/fFsIlSCbu3gx8PUKDx7eNXC8NRjGRbP0azuxqxX3+QH/XuWW1/bLr4rgH+7C8GEaPOGN2P4BsKlkIFAZ+94Zr616Dh8+48ea5z/BggcbUHtn3+s1ONRAv/j9Gb+27z9YhQsHAi0dTfjmoyYcNAY474Jq3PXvChzY5AKgoSLShMWLB6W93S6+qQeH7LK37ywGype7ccQYIHBuGH8wXFumrg74+dkd8L8MoLQ0Y/1Zqu32umNLgRVUxewnd3jRXU7bOa/Ig0MAuBHu3f6QpgjGDwG+Xefp7ct5OxeNKId/KIDWVstr+72DW7DneqomtfbbMGo8axNqt+q17T2/m7fD7wcOmVyDH5lcW5cWwZNVtXiqKoC9lWsbBAlTxyyK4MgEr+1biyJ4E7UYOTuAU4/+BlgC7Fm2Ht8cdTn+8ghw6B6RqGvb+uA2bPkM8O4/CABwy66E4fvOvwnhVW1oGzAUT1bd2HtsxuTa6jjJqu2q4zBju73z9z5cumuM6wqH8N7bYfg6gAMP8qA+xhi3thaY+EEZ8AlwxvHt2D7Bue32D1M6gY3Ubs84qwe7T6PtnFoRwnGgiTxvf+NmD44bAny1LRJ17vfd1ordNgEj9ihH4ApXQtd2zhzytuTJa6x7Z2pzV1bB7wf2Lm/GqSbXVnO5cW4TN/pA7zGNX0wVEhejFkfNTOzaLl7ixoB5dfjdDOCo008H6rbBP9yFY0LvY/xbDwFH1kZd248WjsaWj4FRQzfhpEtCCAa9fc4NAJ6qCuDwtgU4aeF9qHztaZww1o9VZ9bilpUB1B6VmM3la6h+v3gx4PqqByEAmseL/zzlwbgOYMyIcMxr+71rZ6D4+l8AmoZho4sx8opARsa4ZnMItT0ccYS+TW5TgbNL4F9D84dtbT792j7owkluN6jl0hi34+Mw7hoDdFZ4MMfQn1VsrMThI3biwGnNfa4tYN6fzZkDTG3fCICuf/PmrqixSsyxwk/a4PcDxx8+AL9w2PxBPW+1D1F/33e3ClwGmp+tWNqCEQ3A/gcVYXYg8bFCoQhTtohSADBv3jxcf/31OPjgg/H+++9j3rx5du3KcbCxiET6igtL3vBhUAtQXQWs7OkBQmEM9wMHnuLBxv31+Gb+nzffBH43MYjDv3pUz9p24YU46rE6/GoY8PbEQFQjjUT0h4G3pW5P/SzR8zA+BGzs+NX4ufuIAHDDdQA0DBms4crXA/AGAWwJQQMwbqIXs39B53bAFA/8OwD/MWGsm0r7nDmTVo6HNbXAvxX6soeyj0gEOGNPH/zPA+jpQSAAhO7vxCANOPq4Yny+6zxPOLUIey4Fesp6sFS5tpUb10L7f0DN7jW4ZNaAqPMIBPQKJ3ZdW6vFJt6H1TX3by0DGikmfLgfuP1PJQi+BvQ0FEMDsM8e3Zj9U7q2tbXAcb4u4FNg+7i9oX2zFLv71uK4w7sQ8hT32Yd63kcc0bftvnZnDwY1AhcHfPj8A2q3/r0j2Fc5fz4v3n8gAOChXcshJSUZubb898a2a7UPvraXa0GgpASNI/bG4EgI9x0VRPAoyjETcXkwdgzw3QtDeHI9cPKJYfg/BvwHuFG7e3S7BYCxXWXAuu29webGazvpy274vQCKi3HOJWVY/leg1NuG2bOBnn8FcQbqMPiSU4AlSzC8AeiAH8OP2h21i+vw3ArgqO9GP/fYsgUjB3WhabsXex7rjz5vuPHzqjqs3/80aJ+AfNY1DQE8iAbUYSVqk7u2cFMVQDqz3s8v14IYWlWHLROjt3fEfi0YuhAoHVaBsNuHYaOK4B/cjUvOaceiZR3QngaOOKEUVWcY2o1y39Rrm2y73f7vIgz6GDj6gG58E97VbnfzRLUt03YLb6/f9iXnduC1pRV99p+Jdsvnpb4aPx+zqh3aJmCUP4SDL9d6/b5dfwhj7BjgonM8mLcVuOJKD/z19D+XXxBGl3Jtx29phb8ZUfa277V1YfjjZUBbG3Yv24hQFbDvwdU47fzY9w4w/x4AppwxHMvfAg4e3YALTK7tpAUN0JaC4mOQ2Wvr+6IKlduB7W1N2Hd0E/xlwOkXV2HbBBfaf12FsWMa8avvNuH55YPS3m6bH+rBoDBw6CE+vBcBJu3jgb8bOPXkCLZMir62fC1qa4Gz924HdolSxv04ZawQiNTjsMluDJp6ALQV1JcdeLkPqKAJcNvbH2PsGGDAhHBvv73HhDAm+ICzTvRgRyR6u4M3lAPt6E38ZnZtD1nXDKwHhh8zEU07l+Pwgesw+vr47Va9tr1/sys9xQnTa3BUW/R1A4CVR87E3kcDe6PvtQ0igJUR4MhELuyubY08etf2u0bQh21tOO7AJrzuCsBluLaTR23H4CGAb6/B2L7rvhwVCADv/wUdX3ehtXgQ9r4zEHVs6uRRHYNatV11HHbMMdHtdtEiNwY1u1BdpWF1Tzewy+YeWOuGy2M+xu29tl/ucitsb3dku+X3e0U6oW2kduuf0oNDd/1/w2MhjB0DVI724t1dx3HciR7s+QnQUR3GR8q5f/1UC6ragdF7lZvuw/za0u9HHx3/3gEmbXuXmjW2uglHTaZ+Qj33VUcEsDuAny+qw8pF6N3wUSvrsHhGLVYdEcBRca9q9LaOWlkH/GUFfRgK4eStD+GFI2vhnhl90fc/fhAGvlaC6vJOYONGRCJjTM/t1h804OD7GqCtdGFQWQfKR3nhvy+A2mDiNverr3TPTebIw8LwP6tBA/CNqwhueOj+Tgyjdrr1tb20+X49sVkkggCCWHRUoM/+0z1WMJtDxGvPUw8uAdbQ/GGfyT5cdJ9+bSMuL8aO6cYvfhjCyx8D088Nw/8SAL8HtadFX9uqJRW0CMAJnhC/P1u0CDjw1U0YUwQ0NgGjh3VHjVVijhV2JZc79fwBeGJr9HXj/83W/MFop+rr+/7vm28MwJjd3HBFIviofesuZ4oSTDKMQWO121TOIyfRHEZTU5MGQGtqasr2odhDJKJpxx1HPzt3atqLL9Lv111n/vcPPEDfP/CApp15Jv2+Zk30507jgQc0bdAgTRs4UNMmT9aPcdUqOubzztP/9ve/p88ee6zvdpYupe9mzjTfz5o19P1ZZ9H7++6j93/7m/43r71Gn/3sZ9H/+/LL5p87nbvu0tvPccdp2ubN9Pm6dfT+9NOj/76ujj7/85817Zxz6Pcvvkht37Nm0f+/846mvfoq/f6LX8T/v7lz6W/vuSe1/WYC4/NkfH/11fT+7bfp/X/+Q+9/+1vz7fHfL1li/v1FF9H3n3+uadu20e/HH0/2oa6O9vv88/T5tGn0euGFmlZfT98bef99+ptAwPr8jjlG0/bai/6utlbTJk2i43z4YU179FFNe/xxTXviCU178klNe+opTXvmGTqGF1+k52XhQnqe3nhD0266SdMOPJDOv7Mztj36+GP67tJL6T23w6+/1p9ZO9sGt9Wf/czcBsXirLPo77/91r7jSwdXXKHbhJ4e/fMf/pA+W7RI/+z44+mzbduit8E28dprY+9rxoxoG/TVV/079hdeoO388pfm38+eTd8//XT/9pMKDz1E+/7DHzTtlFPo940b6Tu+5h9+aM++58+n7d92G72/9lp6v3Ch+d+z3XjzTfq7H/9Y/+6BB8ztRrZge/GHP+jtqKND//y66/q2xUsvpc8+/bTv9u64g7579FHrfV5/vW5r1HFDKlx4oW6/M80FF9C+P/vM/HseUz38sP4ZX1fuS+weN/Kz0tCgaX/6U+L7vO02+tt58+w9vv7yj3/o7fauu/TPFy7sOy566in67JZborfx9tv0+axZmTlmTaO+mo+7rc367+rqNG30aE0bMkTTBg/WtClTqN9O5WfKFE2rqaH5wIQJsdsBjzEXL7b+m9//nsYu1dV0jOlqz+q1aW/XtPXrzcfUKg88oGmHHkrHc845zp6XaVr0/NPYHk8/nT5fv57ef/pp9LhNhfvkZ59NfN89PZp2wgnRY4fOzsT+l23e8uWJ789pnH12dP9z443ZPqKMkqi2Y5unlGABly4JhfSyVYCeZduIKtm+9BJlBmxqcq502hswP5QSWUydqvsdcpZwr9Ls+Lz5OqiwCl9RYb4vLm/QTdX3ompwMlyKiv+GWbuWXjkIOlfgBCVMCcUlW56nWi934kQq0bJyJdUjTxbedlGRvl2z+2aEa+7ysTqNWAm+uO1ym+Xz5VcuZWakTF/xNUW9L/y3mkaf83IRe5cedhhlW9y8mTI3TpnSd3tc9sSqSASfz3XXke/2Rx/piclWrDD/n3h0dAB3301Z9UePtvZ1bm2lV36Oy8spe2tbm359jO06nbCd4BIqgLW9NVJaSnaI27BTUY+vp0dvr6EQvRptbijU99k13icrjPeqv2H5uzygerPbG+HP+e8yCcfJbN6sP7P8Gb9yGat009NDr3zv2NZY2Vy3m+wV5+7kUoCqfXMKbCf+8Q/Khuv3UwWIYJCOc/Ro4L339PYLxH522buP27AZPJ7gvq+lJToWKlE0rddTCoMGJfe/6WDkSKqqsH49VX4xwqWDuUybVdIkwL5YEK+XnpdExrgq8fpNp2C0twyfayJj3ETtbTopLqafri5q+1b97jHH0PFxabDKSr0UY7JUVtJ2NI2S78Rqc6NH0/iUxzNGvvmGxhsNDWQzDj6YEvamoz2rY2efL7695efozDMp6RonSAOcG2vlcun3n8dFDLdTtrmx7C23WcVTKi5bt9I2ef4LRFeGiAU/KwMGxP47J1NZSc8clx/nOZsQhYhS2YBLZqgdttfiVqg+mpWVVEqM6zs7zeCxkb7oIsrmDFDntv/+9DmXJjLrsNXBJxOv0+aHmgcFam1h498YxZp4k3inwhMNhg06v3Itbu5QrUSpVODrXFQUv8NWMbsvTsLK51kVfpPpsAG987QaXHN7LC6m6+Jy0aCtvV2/TlwPu6aGBl5PPQU8/3xqohSfz9//TllU3W7KwBiJ6D9cDzvR92PHUnnK9vbYdWv5OeaJozrxyLQolcwECdCfN6eLUmo7U21pLFHKuKBhvE9WGG1Qf0UpPwWC9tawVoVeTdNFKf67TMKCBS9ieL36+fN3qU7W4qHaWyD2Ag6gP3933UX3t6ws8eQY2SAQoDZ3000ksrAgpcZDqG051rObiCjF46YhQ2jBbPNmuq/77Zfccbe16feGM9xmklGjaJGCx1NGuN784MGJLbjY0S5Stbncb3Lf51R4ARQwF6XUc7V6bnlCH8/eppvKSpoYNzVZC/0PP0y2t7iYqt+ccQaVB0uF//yH+s/iYrLtanImIzx+sRKlfvYz6g/224+ua2Nj+tqzams8nvj2lseNEyaQKMXzFKc6DDClpXqJOBXj+cZ6bisr6ZVtaiJspHxSGDGCfg+F6DnibVkRiejjm0w/K+mEz5NFKafOh7KMiFLZwOcjI8213YHEOmweBCdjCDIJG+kTT9RFqe5u3Uh/+y29qgp9LMMfb5LE2+EJcyF6SvH5qap7d7du8FRRatQo+n3VqtT2zYMvny9+h63C98WpRjhWNRluu598Qq98vjzgiOcpZTW45vtSVESCVGmpLtLU1NB36urQMceQKLVoEX1ufCYSEaWClDMLu+32/9s79zC7qjLNv6euSeVWCQQqIQkSuYw6tkzEVpRpxyYoTo92z9NxuqdnPAJyUdsLok1Eu6Xpxokw9mgrbSsoCSfqiMRbq9wSW8QRRaDUbvAKUQEhEJKq3CqppKrO/PH1l73Oqn0/+77f3/PUc+qcU3Uue+/1rbXe9X3vkrizdGl3E5Jrr5XPND0tr+c12LQH3zrx2L8/G1HKjAFVFKXa7c7PZ8a6KJOkOKLU0NDsFdeoLF3qCGW7dsl9Zc8eaSuNhrOFXJZon6sT/UWLnH2aVYxLO1NKj2+YmNtsAt/7nvS/n/2sxPwiClLKW94CXHedxFFT2Hb7rt2KUmbm9cqVIko99lh0UUozkebPz2e1W/txL1FKP98xx4RbcEmDuKJUWTKlTFEqzCIAUIxMKUBi2M6d3vOIVkuEpJER4JxzJPNSqx+ixpFWC/jGN/5tC7UQWXo6fnG7tn/yEzGDWrZMtsy79FI5htPTyVzPZrxtNILjrY4bb79dbs1+s6jxFnDmR3bssrOr/dptnEwpM+N59245dzoO9sOMBWXPlAIcUSpMhlgNoSiVB3E77LTLBbpFg/TPfuY8phOkZhO4/37gW98KN0ECgsv3zKB65Ej4TKmZGafTK7MoNTjoTJDCiFInnyy/b98uxzvsxFyJK0oVvXwvDF6rSHHK92ZmnI5fO6Z58zozhwBH0Jo3DzjtNNnL+Fe/kv1iX/vaztcMEqWSLuFotSRLamREVr5e/3rv17MH32YWWZaZUmbJmtd5symDKGX2I3pfiTNJChKlzHOVRKZIT49MeJ58Un5MUUoHsscc0734FQe7tMu8n1WmlJ67sDF3zRrg5psdq4AiT5BaLREbtaRDhW27XNr83a3tBolS7bYzCV+4UPr9++/3zsjwQ0v3dPEga1SU+u1vZz83Oekcg2OPDbfgkgZxS6bLKEoFxVuvrPI8M6UA93mEjgte/nLgxz+WGBc3EylOlp6OXx59VByje3rkb9pt4PrrRdB49auB0VHpG5Ytk+M4PNz99ey1CKClhzrWtgmapxQNHYfbmVJeFhVpiFIPPxxelNJ4NjCQzxggKfSY6aIBRSlXKErlQVVFKcUMVKYYpB122EypoE7bfB1TlArKlHrqKfn7/v58Vt+7wZwQmiKPphtPT3cGetMH6oQT5P8nJqTTP+mkaO+trxU3U8ou+ykTdvleULv1K98zr0W9Pt0G4ypKzZ8vA6JXvxr4+MeB227rFKUmJ6X8BXAXpZIu4dDXu/BCZ+uWP/ojx9PGfj170GZmkbF8r3vsayxo5b7bSZJ5rrot3VNGRmTQumOHlHsrefpJAf6iVFaZUhojwpZMf+tbMokaGPDPYMwbP6FcvZKilu95TZImJuSY6N+ak9+o5OknBUg/DsjCmj1Z1oy+OXPSjalBVL18z+wPgq7RImZKAe6ZUppZ12g4ohSQ7NaGfq+lguv4uIyT/u//lfvPeY58nv5+pyxZ++Y9e5Lph+y+0jyH09PeFit5nce46LzBFniiZErFKd/Tvnz5cmeOFkaUMhdny4y2Je2HKEq5QlEqD+J22H4rHEXCDFTmBNxe+QW6y5Tq7XW8eA4fDp8ppQPRFSvCZ0wUBVPYsYPa4KAMvs1Abwp1jYbUv//rv4qvVFxRKoynlLnKZZfvqZDht4pbNOzrNGj112/F1zw/eg7d/t7OXDnnHOCTn5RMxF//WsrwAFkxb7elnbiZ9iZdwmG+3le+IvFo927v17PFDr09cMAZ3KcpWJobIlRRlLI/m1v5nlvMjesplXSmFCCi0w9/KKKUSd6i1MKFTh8DdE5+ssqUilK+12pJ+d7ICHDVVdLGimi6GySUn322/B5WlNIxglemlMYgNXrWDOluRKm8MqWWL5dr8uBBMYk3P4dpcu6V2ZEFbtmpYWJuGeIt0Jkp5bbwmmS5dNL4zSN0THbddXJrjieixo84WXpDQyL27tollgVz5kg8UPF12TLgi1+UuLF1qwizSc2HzEVXoPMc+o2R8jqPUTDH4zrm1DGBjsejLLx2mymlcwGzHXlRFVHKnsdSlHKFolQedCtKFdVTSgnKlAorSoVZgdCd4A4fDu8pVVaTc6BzQmhP5AcGRNQwv6st1J1yiohSDz8MvPKV4d+33e7MdPOa2Cpm1owpShVxJ6gw2KnNQZ5Sfiu+en76+pz/9xOl9LWGh4GXvlR8pW67DXjzm+VxU2R1m4gkXcJhvt7ixTIoHBvzfj27HdPoPFnsz+ZWTpKWp1SSmVLAbFFK7+dhcg5I+1ywwOlz88iUslfuvWKuxtbnPlfa1fz5wB//sTxXNGEqSCjXPjqp8j17gUv7/ieflDbilQXhRt6ilGZ479ghk3Lzc6hfSV5ZXIoez6gLAWXJlPLylEpjESBpwnjTakwLMqFOg5UrRZR69FGJBz/9qezSqb6DF10kj//gB8mKUnYlh50p5UVeZZhRMMfj2n/393eOx6MYnccRpdTofNkyZ14WpXyvyMc3DHZboijlCkWpPDAnSVUyOle8MqWilu+FCUYqSgWV7x054qy2lNXkHJjtKWXiJsCZhtqA4ysVdQc+c+AVpnzPXPU+dEgEqTvukNWtIhvveuGVKeUlSmnH75cpZfqAuRmjm+V7yrnniih1550yOOvrc/zR8hBZFy+WrC0Vpdyw27FpdJ7FKljdRKmwk6SieEoBTiaUrqYqeWdKAdLvuolSaZfTR919T4Wee+8VU2A9j0XcDSpIKP/Zz4Bt25Lbfc/0kwIkk2jOHOmbnnwyWuzMW5QCpIRvxw7JkjXLXc1MqTwxM6WC+kqTsntKRYm3eYkZYeKWW7zLihUrgB/9SITpqSngN7+RcfvSpdJmNZ75fQ8zM8jmoovk9oYbOh8/ckTalI6fw4pSZSjfM8fjetzuvRf4+c+d8fh3vyuPp5EpNTHhXFNmphRFKWJRstqlilD18j1zYNhNplQYA0HzWLrt8maboQPVyZSyjcM1yLmJUvrcKafI7cMPOyUpYTAHXmE9pZpN6fAeflj8AP7pn8opSAHRjc7DeEqZnZKeV1NgcBNsXvximQyNjwPf/748ptdzHiKrTsz8RCmv3feyLt8z423Ysl2381I07GssbswtQqZUUUUpv9/37IkWS8MS1ej8vPMktrqdx2azXOXSbkbnfiXT+l0nJzuFLMUeSzQa8X2liiBKee3Ap55SRRGlqrr7npenVJzM1Lw8pfzmEfpcHqKUjmMef1zGjD/8oRzD5csdjzzzs7mVT2tmkP6t0moB/+//yY/93Je/LKKUnq8qZUoBznj8wQdlPP7d73aOx6MYnetcdGLCPd7aaD++aJG08Tp6SlGUCgUzpfKgW6PzsmZKJe0pBXQeS79MKf0sAwPlzpQK8pQCvD2lAPEh6uuTAdGOHeEne7Y5d1ij82YTuOwyx3i3jIIUEK3DBtwzn5QwmVLt9uzyPX2/V75Stny/7TbgrLM6y/eyRjNldKLmhlf53u7dzmQ+zfI9Pc7qPQdUK1PKnsCF3Q3KzJwxr7c8M6V27nR2Bp2ZcQz8iyJKuXlKTU3JOUh60OzlKRWU8VSFlWXb36Td9helzGO/f/9ssVTHJOZYYtUqyRiOugNfEUQp0+zcRDOl8i7fq7rReZTd98qYKZVH+Z5mN516qtz/xS+Ar35Vxqm/+7vAf//vnWVoft/DzAyamQFe9zrgc58DNm+W8SgAfOpTcu7e+MbO3YSf+1x5vtFw/ATDiFJFzpRSmk3g05+W+cDSpZ3j8ShG5+Y16xZvbczSPSCaKFWF/gygKBUSZkrlQRKiVBors0mRhKdU2EmSWbLmlimlZuj6NxMTzqCyaplSbnXatijV1+cYnD/8cPj3NQXFRiP8BGnTJjm3eg7s1amyENbofNMm+Y5umVIXXSQ/buLpj34kq0n696YXh339n3uu3H7/+3It55n5p6JEmEwpHbTp99FV/UZj9rWcJGa5sMaIKolSfuV7YVfuDx50+hSvwbVe226777Va8nxcliyR82QKUc884/j95Jn54ZUpNTjoXLdpZC9HLd9TqrCybE+QzH7Gre329DjXpVsJn9vEMW6mlAo/zJTypluj86mpTrGnSMzMdH62sB5+9iKA9vVZixlhKi7yyJRSwUkzwEdHgV/9Sq7lyUmnHO/88+Xvfvzjzs9q02wCf/InwF/8hbT1yy+XRY/PfU5+du6U5170Inm9c88VUSrsorlShvI9pdWS47h8uVyD5ng8itF5T4/Tv4Qp4VNvSBWl6mh0TlEqFMyUyoNuy/dmZiQQFjUIJuEpdfCg04mHyZQyd98zG3uj0WmGrhOeJUvKGeTmzHFWb7wypcxj7lYqdvLJskL8y18C//E/hnvfuDtB3XijdPQjI8Ab3lA8w92w2B22l0+GDqxU+NQOVdPGAWeVW89JqyUDsUbDGajqQKfRmF3aduKJspr3k58At9wi79FoOK+bJTox88qUmppyBh52ppRO7ubOTXenqKqLUl7le+22IzQFiVJ6vfX3d2bwmei1/Xu/5zy2eHEymxc0GmLe/PjjTganpvwfd1w+u6Tqyr2bKKU7Fi1aJNfU+LgM9JPEa4tyv5hrZgyXeWXZKzMV8G678+dLW/ATpcyJgYpSYTKl9Fr4sz9zJsEa+/LYTVZFKd15VeNnUUQp0+g8jqcUIOcyj/KxIOyJdBwPv/37ndic9TjUXty2+94jR5z+Lsvjr2PCG28Uwajdlv53wQLgggtm79IZJEoBsgA7Pe18T3PDjJER4Kmn5P+Hh4G1a4F77uns/9Rcvey77wGzdzzV+4Dcj2J0DkgsPXAgXOWOZkppHxnF6LwqohR33wsFRak8cBOlwuz+0t8vQfrgQQkERRWlTOXcbUUpSmpzX5/3JAlwnpuYcDp5twwiFaXKXLoHOCLFxES8TClAfKVuuy2a2bktSukA02uCpB3eunVSqz9njgzazfTrMglTYY3O9TvdcINMEJYtAy69FPjmN4FXvUqe+8xn5Dg+73nOcXrlK2WQZYtS8+bNHjRu2uSczy99SW6PP14ey3qCFJQpZU4QdVCht3oM0yzdA5xsyXa7mqKU1+575mQpzCQJ8B9Y67V93XUSn0dGgNtvB26+ORmvuGXLHFEKyN9PSmOVlnMAMkkzB/fDw87EJmnsLcqDYi7gDOAbjXIP4r0WAczngE5D4/nzZdFJr2XT0Ngu32u1nOsrTKaUXgs6LuntnX0tZMmyZXKOJydF3D/2WIlvRTE617FI1Eyp3l7pxyYniytK+e126vZd3dqtXqMDA/7j2zTQY6o+rPail7aVnp7shRbtQy6/XOLf0BDwjne479L5ve9JNpWbp5Ry443SLkZGZIzUbAKvf708d8klkqHeaMh1euut8niUTKmyLALYghTQWeIIRCvfA4I3lzDRWKuiYJDRuRnX7bFJHosASTB3rhxjPb4UpVyhKJUH3ewGtXChI0rlkRkRBq/yvTg7QS1Y4J9FocfSVOvtTt4s8SuzybmiolTQ7numh475t1qzH0WUsidIYXeCOvNMEaWKvBNUGLxW7t1Wf5tNadvvfrdMWH/4Q+mMdWWvv18m3l//OvDQQ3Kc9HkVpfxWh3p6gAcekOwknXysWJHPBCnI6Fzb8dCQc6zs75S2KAU4wrQOguosSvlNkoIG1s2mTPw/8AG5toHkNi/QAWtRRCn9Tn/3dxL/RkbEd2TzZuc7P/SQ/M0tt0g8jbLbU9BzDz0kxyCKp5TZ3tLMPkwbv0wpM+aaixzmJMnMTG21OjOlNE7+z/8pj+3dKz9+/jl6Xv/hH+Rv//2/l+vAnuhlRV+fXI9PPilC7rHHyvfW+JZnaaF+PiDeGHdoyBGlioidKRXHUyrPkq/BQYkpR47ItWyLUqafVB4xpNkErrlG+pdVqyTD3o0gb6xWC7jrLmknmzbJTnMbNzrn43vfk+fe+EbJxv3Qh+SYmJnVQQsBZkZ7kUUpHY+7iXv6fFRRSuNlmEwp7cs1U0rnI17le2ZcN8cmeS0CJEGjIe1dx8ppWlaUGIpSedCtKOW3Muu3FWpUhTnOa7XbwUbnYcr3wpoHqhCjgau3d3bWmSnW6Kpo2UQp81wMDcmKqAY1PRd2+Z5tTq6sXi0BctcuWWUKs4OWvpbtb+I1QdLr4oc/lFtTiChThpQSpd4ekIHOxz4mx7enR4w2TT77WTGj7evr3I7XFqXcBjp6/P76r+XzjIzIRP7++7OfIJmZUm6lAG7t2BahshCl+vtlosNMKfeYG8V0901vAj74QYkFev0mgYpPOoBVccosuciaZlO89z72MSkpMQUpwImdhw65Z4Da4kiU5372M3n9KCXTZSklCUK/qxqce5Xvmav9GmN0x64rrnCe02N4zz1SKq3n8M47RWR97DHJXPWj2RThUTMFn3km391kV6yQtvLb3wKnn+5kSS1YkP8qfDdj3HnzpD8pqtm5nyjlt/BqjpXybKeNhgg6zzwj84jjj+98Ps+d9wCJfccd17nbnlsb09jrNhdqtYDrrxdxdtkyEZFf/GJ5bsMGuX3FK4Cf/lTOX7MpC32tltgiKEExV/vNoi8C+M359NhedZXchi3f03gb5CnVbs9eYHKzGnH7TBs3OsLg3XfP3jGwbCxc6IhSWWdIlgSKUnnQTYcdtAOfV3lUHIU5zmsdPtw5GXLzlAqzXW5YUUqPpf6922DMLVOqbOV75rnQSbyWa+m5sMv3zNRY87jMnSsD2scek0H2i14U/P5xPKWA6k2SgozOlVZLBJulS+W6P/lkpw21WnL9aSpvqwU8//nynA7E3XbeM2k2gd/8Bvg//0dE6meeEdPOrDtrHRjOzEhMsgeybuff/k72Sm0a6CRBBZwqiVJeu+95TeS7Xbn/0peA5zxH4o3fpCEqKj7pADbvTCnlggtEiOjvny3C6fX+ghcAL3mJs9vTmWcCX/sa8JWvSLvs6+vsSzVum8KJ/dzq1XKMo5TvVSXempN629PFnvyZIv34uOza9bKXOT4mS5cC//zP8hr79gFvf7vzPytXiij16KPBohQgbUt9KpMUZOOwYgVw332O2XlR/KQA9zFuWF+4osdcc0OdQ4fk+tQFmbBj3Lzb6cKFjihlk6coFeR7ZKKf79AhGeuaY9yZGdmZ+N57ZeylY45mE/jOd+T33/99EaX0nJ11lojUZnwJGueWyeQ8iKgLr5opFSRK7dolcaCnR8RGIJzRuV3KuX8/8La3lVeQAjqzcfNeOCgoFKXyIAlRym/HCcB9kBtVYY7zWrZYFtfoPGynbZfvuaVEqlhz6JAzgCubKGWeCw1m997rlH81m8BHPyqP6zE3S5Xs6+uUU7oTpcJMkAD/jJ8yEdZTCvAfWAHuz+mOejoQD3Pc/vIvgY9/XK7r+fPz6az7+mRAtm+frADZA1k3cbmnxxnQA9mV7wHVzJTyEqU03vb0BA+0w8ZbvV4vuih40hAVFZ+KUr6n3H038KxnyXVqi3Bmf3zJJfL7hg0iFKuXyebNcr1PTADveQ/wV38lzz3vecC//Iv8z5w50p6vuUYG7uefL75/O3bUM1PKFKWmpzvHSW4ZCVryMzYmbXXnTuCOO5znZ2bkmA8NdV6nq1ZJhkQYX6n775ct6tttEYSSFGTjYJqdA06m1DHH5PN5TLrNlAKKmymlfcHChU5/Mj3d6RcT5CkVJTM1DfwWt83yvSwJ43tktrWhITnO09PymVXwACQz6CMfkd9f8ILO99FS6a9+VW71nB05IvH6JS9x/jZsplQVRKkoFhWA852Dyve0Hz/+eOd4hjU6bzaBd73L2dipzIIU0NmmWL7nCkWpPOi2fA/wN1ZtNmUQduWVshI7MyMixF13yU9UpqeB9etlML1yJXDxxd7BwVbN3cr3ohidB3XadvmeX6bUY485NeN2ynIZ0GN+5ZVy/nfvloCtj+t3tzOl3I7JKafI6nFYXymvTCnd4csrdbkqkySvVSS7w/YbWGna+BVXzH7uE5+QCZUOyMMct82bZXeZI0ekg8trgrRkiSNKPetZnc95DdrmzXMG9FkYMut1q20i6qr95KTE0Tx2gQtCJ0kDAxJvbVHKLmeOK0pFnTRERTOldu0SAUcn2XmKUvqdL73UXYTTyZ2a7TabIi6123Kcly+X6+bQIZlEHTok56vRkN8feMB5r/37pb0cd5y8zj/9kzxeR1HKHA+ZZtle7a/VEjFGvXKe8xxnl8i775ZsKJ3Earl7T4/7Dnxu1gTT08A73yki4WteI3+TpCAbB/UULUumVBRPKaD4nlJqrA/I9+zri7b7HpCfmOG3uK0iQ9aZUmF8j0y0DHH37tmiFOB4eP7O77i/n+2h5OcH5mVTUZV4C0TPlApbvue2uBRkdK588pPyeRoNJ3aXWZgyRSmW77lCUSoPzA7ba+LgRVD5nvLiF0ugVsGgtxf41a/ifd7eXsePZf9+/6Cgn0tXMNwypaKIUlE9pfwypR55RG5XrCjm5DIMzaYE6oEBGYSb58JefXAzOVdOPlluH3443Pt6eUoBziqhG1XZzjVs+Z7fwErTxt2e279fMt10F8mg46YToje9KfmMlagsXiylhLt3z37Oa9A2b54jOmRRvqcxN275HuAIC0VDJ2+LFkmGSFhRKqrHSdRJQ1QWLXJ23vrXf3VWR/P0NgkS4Vavllud3N10k7TdRsMp2f3jP5bnN292+sfpaeB3f1d23QSkbER3gjpyRN476uYSQHDZb1mw+xe/CZKepwsv7IyFmvHw05/Kopr53CmnyILMf/pP8jcqSnlZE7zrXcCDD4ro/g//II8lKcjGwcyUareLKUpF3X0PKI8oZY5N7Yl8kh5+aeAnSuVVvhfG98hmeNgRpUz27AF+/Wv5Xa0RbEwzfmB2vAXCl+9VQZRKy+hcy6hNUSrI6ByQWHzjjbJY9dznAn/4h+XcuduEolQgFKXyIO1MKQD4/OdloDJnjqyoveIVwH/5L/E+79e/LoLW3r0yYfBTq7WzXbxYBkndlu8l6SmlAkzZTM5NWi35PqtWzS4fsHff04DvlSkFyCrrxETwZFs77qiiVFU67bCpzX4DK7cdtpTzz5cdvNptaWN+k8u0M1aiombnUUQp83rLyugciF6+198v53hmRgStIopSZjmJKUp59S1xM6XiTBqi0GjIwPXXv3Y2SFi2LD8D2TAinJkppea6xx0nfe7/+B+yQYV+/jvucLY31zasPkY/+IH0S8ccA/zX/yrP7d4tWYh19JRqNJx25ydsxM1MVWHqzjvlOPf2ymOt1uxzfuCAZFuNjEi2uDkmyXM32ZER+dyHD0u7V5G/CKKU9peHDwf7L9oUvXzPzPBtNKTPthcCkvTwS4MiilJx8PoeWhZ94oneG/l4ZUpF2X0vb3ExSbzGuHa71U2XTjpJ7uu1DLjvJquZUg8/LP973nnBRuca13//92VjiuXL818E6AY9ZtreBwedcUHUDcgqDkWpPEjT6ByQi/yrX5VByx/8gaSvbtwoq3xRG3KrBXzrW8A550hQWbbMPyhokD72WEeUsk0gk8yUiuIp9ZvfyG3Z/KSUIBNIW5TyKt/TALl0qQxmH3nEWU3y2qL8yBEpXfjpT+W+KcbUaeU+rNF5VObMcQa4ExP+mVJpZ6xERbcf111FTLzasTmIK7KnVKMh2VIHDhTXV8oUpYB45XtFGVyPjIgoNToq9/Ms3QsjwmmGzUMPyYrw2WdLRuSqVdJGe3v9xRHzuS1b5Dz80R/JROrSSyWW25lSfu27KqIUINetbpziNU6Km5mq//uylwGXXSYeYPv2AX/+57P//jOfkXHX858PvPa1sz9nXpOj3l5pH48/LtlSO3fK40XwlNJ4202mVNHj7dy5co2aFQ9uMbeInlJ+i9t5eUrFIUiUsv2kTMxsPiDa7uBKlTylwpbv6aZLZ58t93Xu5bWb7JNPytxhfBx46UvlsSCjc43rvb2OKAXkuwjQDXrM/sN/kPsaH+NsQFZxKErlQRKZUl6ilF7kv/d7UgKxYEF8hdlsMIsWiXHgySeLKbPXa+nnMgdGU1OO1wMQTpQKO7iO4imlwbaMmVJhsmN0RUhFKbvkTtEAqR3pL38pA26/LcrvvFM6ltNOk/vm9erXQdTR6DwOKn5MTHSKUm7HLe2MlahoppSbKFXUTKko563oopRZvgd05ymV9+BaRSjNalWfqaJi7gDVbEr/853vOGV9QeKI+ZxpvPv614tg1W7X01MKcEQps3wvqcxU81z8zd84JUD2OXrySRELAeDNbw5vs5AVJ5wgotTjjxczU6obT6miZ0rNmeOMa3Ws5ZcpZfpv5h1v/Ra38/KUioOXKKV+Un6ilF2+F0eUyvs8JknYTCmNkR//uBz3oSHgrW8Vv2IVqq69VjaGuPJKEZV27HBKqIFgo3ON69deK7fqn2e+f5nQz/zhD0v8WLo0/gZkFadgPWxNSHP3PVWY220RpVTEiqMwm6uQ3/62PDY25v9aunJgilK62hsnUyrs7nthRCmljJlSYbJjwhqd26v0v/xl8Bbl27bJBPGMM+Rxu3zPi6pkSkXdmSQO8+Y5olSZJpdxMqXM6yFLUcrckTIsRd+BL2qmlNvKfVGuNxWh2m25zXvnvSAWLHDKDl/7WuAf/1Ee19IGwF8cMZ8zJ0lTU86xqKsoZa7cRx0nhaXVksWcsTERdbS8RPnEJ+T9zzhDfDqLxooVsgvvY48VS5Sqg9G5ilJAOE8pQMZpvb35t9MwmVJlFaUOHHAWNbxMzoHZY7oo8xMl74y3JIlidN5synF+73sly/See6S/eugheX7XLuDTnwa+9CX5u5EREfUBibm6w26Q0bnuLGqKUmWl2ZQ4/aEPyfW6cycFKRcoSuWBW4cd1ejcNDE30QHVxz4mt27+B2ExB2eaDWHuMuSGBmmdqAIiSs2bF81TKqrRuU5i/Mr3lDJmSoXJjvnWt+TWFqXcjkmzCfziF2La+r//txyj004Dvvtd52/e+16ZZM2ZA5x1FvDznzvHstFwys3qMEmyO+yky/eAzhXiMol5fp5SRROlopbvAcUWpczNJOxMqSBPqahG51lgi1BFz5TSHaDGx6VP3r5dHn/2s6O/ljnB1XNoPl5XUWp6Op14qwsxb30r8JWvSH943XUi2jabUgJ0991yjt/ylvy8zfxQs/MHH5Rj1Gg48ThPzDFu1KziMolSdraNW8y1F/CKIEr5ZUqVsXxP5yWAtIV2W0q+/ARa8xoFujM6r1KmVFij8ze/WUT7ffukbb/+9c5zxx4rMXVsTBbFV6+W60lj7h/+ofydn9E54Jika/le2XnPe+QYDA3J8aYgNQuKUnngtvte1PK9qSl/492ka539Jp5u77twobNFuQZ7v/I9PQ7264T1lFKCMqWWLCnHRD8OtnmgilJeuzxcdpmITtPTTufwi184z09MyJbHq1fLTkY///nsDttcxXYj78FXUtiDkzQypUwvjTKVPZahfE/bQNTd9wB/UUr92dwGF3EMLKO+nvmZ7EwprwWPuEbnWWCLUEXPlAIcUeqZZ4BHH5XHtHwvCqY5tJ8oVSdPKSCdTCm7dGLvXvk59lh5vN2W1X9ANogxM9+KhIpSP/+53C5enHw2WRy6yZQKMjpPMubGeS3TU8rOlPLLtgGcY5F3ho1XptSRI873K0OmlFpWmN8jjJ8UMHvuEWXRXKlqvAWC222rJfOp446T/1mxwmlHrRbwve9JFhUg1/vmzU7MffWrpVzdy+gckPmL7ihaFVHqs5+V79LXN3ujKgIASHBGRULTTYc9ODjb3NsNUxxKAg3+ExP+gUQ/k4pSgPP3YbfLbbfDr0DYgktQplQZS/fCYtdp63F3E+oA2VXxtNNkF6KTThIfsg9+UH5GRjoN6u++W/7HrcOug6eUnerNTCkHM4tSMxYVr0GbeV9FnzSxB1xJiVLqz9ZqdT6uk96oomXU19Nsgr4+5/qJ6ik1M5N/O920Sb6jmyjVasnzRUUnbw8+KMd03jzxjIiKW6ZUb6+ToVO3TCkz5iYtStnl8OecI7f79smuif/yL7JAMzQEXHBBMu+ZBlrWov1REUzOgc5rOenyvSRjbpzXcivf81sIcBOl8s6w0Zh1+HBnCZWO33t6yhFD3Mr31E/Kr3QPCGd0Xqfd97wWXt3arSnqb90qt9qO9Lmrr5YKCwC47z6xC9GYq/O0qanZCQmK7to3b145svaC8Dtm5CjMlMqDbkQpLRd45hnpQLzKG5LOlJo/31F3x8dFHfd73/nzZ4tSYY0EdccdfR0/omZKlbF0Lyz28dbBk9sx0QD5pjd17uSnZScPPijX1uteJ+b2V18t5yrKKpIpLpa9084yU+rAAWdAXiZRampKzrfGHD9xOS+jcyWq0TngLkqZmw3s3i2x+TvfkSyLl75U2uKnPhXts550EvC//pcYh77nPWIU6mWIae8EBUT3lDIzEvJqpzo5BOSaP3BArpkvfrH4u9Pogo3uGHjSSfFKvczzF2VTEJO8xcUkMYXkpBcBzOyXTZvkfC1fLuUiIyPA7bfLcyeeKGUoRd2u+/jjnXEZEE8MTYMkjM69RCkz5j7xhGRx33GHnLNzz5WFx7vuCvdeq1bJ/3/4w7Kz8J/9mbTjTZu8/V78RCk/o3NArmOzeiCvdqr9xdSUCDo6njdL94pYrmpjl+9NTgI/+5n8HpQplYTReZV23wtrdO636ZK90+wrXgG8/OVyLWnWFNA5Jzl82N2+xizdK8O16EeYjaqYMQWAolQ+dCNKAdJhPPOMt9k50JmxlASNhgy+n3kmnCjllikV1khQX6OnJziLwp5sBmVKVVmU8irfs0WpMJ3Kf/7PMjg7ckSeu+MO4NZbZcXjkkvkb4JWkSYnnclEGcQVP7L0lNq1y8k4KsPksr9fPuf+/SLM6ADtwIHZ30PLJcyMRb024pS7hcXOqEzSU0rbzvvfL3G33ZaJ7a9+JT9xaDSAL39Z2t2pp3pPkExRSr+j3wTJvK/XsLlJRF67i5kDtJkZuUb27i3H7jQ6MdLJUBw/KcA9U8q8boPi7dSUM2EuQ9wIwoy5GkfSKE1TQfTkk+X+Rz8qx//QIeAnPxHRoqj09Eg24WOPyf2iZUqZWfVJeko1m2IUfPXVjrflyIgI+N//fvTPe+iQ+ON88pNyHbz1rd4xx81Tyi4BM69T239T422jkd+4SBe3d+1yF6XKULoHzPbY/clP5BgvXRrsRxgmU8pPlGq3nWu0SqJUUPme36ZL9k6z3/62LNL09ckOod/5jjzX3++0iclJ94XJKpmch9moigCgKJUPpigVZ7DlZ1KopKHgL14sopSbd4yin2nBgu5FqfnzgxVye7Jp3tcJsDlA18lwmhPgvPAq37OPUZhO5dxzHVEKAE4/Xe67rQAG1dv39nqXEJYFt5InIPnd9wDx8QKknXj5gRWNxYsdUerEE+UxPf8DA8730Angy1/u/O/cuZ1CaRrY4nXSRufNJvC+90k8Hx6WSU037Nsn2yFPTvobYuqgeGjIu5TES5SyS0nyFjJscW/hQuBv/qbYghTgZErp8YzrP+SWKRWlXNrMeMsi+zBtzOtUx0lJxltFr69PfELGNiMjsjvUnDli5lv062/FCkeUKsLOe4Bz3ZoxM6qn1MSE+2Y+yplnOoLP/PnAq14V//O+4AXi96KCsN85d/OUCvLx6+lxylDNLOg8M0BMUUopqyilJehm6V7QsbVFmKjZqfv3l2vxMIiwu+/5zZnM3WTtxW+9rz5KAwMyvvEyO6+SyXmYjaoIAIpS+WB3ZEA8UcorU6rd7hSHkkIH316i1NSU0+G6ZUqFDfpRBDU/TymdAJ9+uvPYqlXpT4DzwitTys4eC9OpaPq7mdo8MgKcfbbzt2EnSXkPvpIgi/I9FT927pTbMh23JUtkYmTGBrd2rB3wddfJ8yMjUqJ1003pZsSkLUq1WhL/Gg3JVliwoLvv8vGPSxyfmfE3xPSbIIX1lCqKKAXId/zgB6VvGxoqx4DNnsDFMTkHnL7MzJSKUr6n53FoqBhm191iTxqB9L6XXmdXXCFlJkNDUjpb5OtPF93U7BxwRKm8F900Fpl+RVHL93RnUa8Fra98RWLkggXS5tasiX++Wi0xXt6zR9qenwGxOa4KK0r19jqiVFF8iNzMzssmSg0MSN938KB89rAm54B3lps5pwgSpYBOn98yE9Xo3I8w5WqDg9KWzBhhUqVMKRIailJ5YHZkOqmNWr4HeItSBw86QkHSmVJA5/arJhqkAXdPqbC7W0QxgfTzlNIg+Hd/J59h5Uoph0l7ApwX+t01yHuV74Uhrh+YSZEmu92SRfmenSlVpuPmtgOf1/lvNiXj8m//Vr5rFu0xTVFKB2Annih/+6pXdecT0GoBX/iCCHYjI8Cf/qn36/llSpVRlGq1RNTr73e2kC56nNbFGiXJTKko5XtFOo9JYHqc2GbvadBsym60Tzwhi1dveEN675UEuuhmTsCPOaYYi24ai8wsiLDnzrRsmJjw9sS87bZO38u4MVeP15lnirnyKaf4v5Zb+V6Ukum8Tc4Vt4oL01OqLCxaJH3zM88ADz0kjwWZnAOz+0u/hQC3hdeqxdsoRudBhClXmzNHrj0vUapKmVIkNBSl8sAMhhoAkyzf05WYgYFky6b8tn4HOncM6+mJX74XJdgHeUo1m2Jg+YlPyPGqqiAFOMdbsys02McpAbM7bD2HUXYmKdMOckEk2WF7oSvEZqZUWViyRG7DiFIA8I53AH//93Jt+pWnJUVanlLmBPBLX5JB/Z/+qQyk4kyS9PUuuAD43OekDb/mNfIZ3F4vjihlt9uiTJL0u190UWe6P1DseG1mFRx3XPxJipunVF0XAYDOlfs4i3dRabVk/HDqqeXYrls/20c/KlnJIyNSfv+Nb+Q/xnHLlAqbVdxoONkvExPOuFPRuHDWWeLjNm9efNNgM34fPAh8/vMi8p11lvdraT9gZkrZ2TZ+CwFFaadui9s6pyhLphQgiwI7dgD33ivj1OHhcLtsm4JTux095hYl4y0pwhqdhyFMudo//7PcuolS09NyTgGKUjWDolQemJOHOCuA2pl4iVJplO4BweV7trl6t55Sccr33ES4yy8HPvMZGdxkMQHOC/NYmFv9xhEmvUSpsKnNQHEGX0lgd9hpeEqpKKXHukyilE4cdu92HvNrx5/9rJRc6A5AaU8Au8mU0vPiJkqZK4Kf/aw8NndufANL8/W+9jVpw/v2eb9eGH+TMnhKlXF3Gi2fOvNM5zEt3YtTPtXt7ntFOI9JYman6u9peEoB3v4nQPGuO5NmU0SF979fyg6nphxRN0/cNkyIGnNVlLLRGHn4sIhSGp/jxFy3+H3gAPDnf+79Wpop5bfjqf1dzYWAorRTv0ypMolS+lnVDzWMnxTQOSaIsxBQlMWcpEiyfC8MOi9x85R6+ml5//7+4uwoSjKBolQemJMHbfBJlu+ltU1pUKaUvXIQpgTMnuybrxOnfM9t972vfU3Su7OaAOdFmqJU3VfuszA6t82Jy3Tc3GKD10piHhPAtMr3VHRQ/xPz7+N8F1PE0F1W9ThW2VOqjLvTaPmUOalbvTp++VTQ7nth422ZxGw/3L5vGhOkMgqiJm99K/DhD0u2x5w5xfis9mKhGpKHZd48MeE2zfsVjZEf+5jcmv1m1O9uxluNf9qO3F5LdwsD5FjbO576eUrp80XJsHHzpi1r+R4gu7sB4fykgM5zFGchIK15Vl6ENTpPCttuxET9pJYvL4+vKkkEilJ5YE4e9PcoW3EHle/ZGUtJEeQpFTZTym0Ht3bb2WklSqcdlClV1hXQODQacj0dOdJpIJiXp5QOKPMefCVBFkbnVRClzEwpt5XEvCaASYhSfluUm6t9pidKN+j513joRlVEqTLuTqOf69OflpXdkRHg4YeBH/wgXvlUUKZUXT2l0jY6L6MgarJ5s/jZFWnRzSvmhEX7Qr+Yq+OLpHaaVDHX9Ea1OXzY2XHN9JQKmsi7le/lLWZUpXzP/qxh/KSAzms0zkJAUcTFpMgrU8pNlKKfVG2hKJUH5gRJG2SSu++l1emFzZTS941idA5IEOzri/b5/XbfK/sKaBwGB6VzPXzYOe5pZUrVyVMqyXp7L+zBdZmOm5unlNtKYl4TQFuUiiImhtl9T5/r7Y22wOBHUJk2kIzRedUG11mi1+1ll0n51KFDwJvfHK9fMc+f3yJA0G6nVTmPbkbnaZTvlVEQVYq66NbNIgAQTpQyY18SaH/rlp2lmH2AvfueehMB5cqUqkr5HiDnMOzupz09Elfabekrw9qLKEURF5MiC99UE52rUZQiBhSl8sDssOM0/CBRKq1MKfWUGh93sppMtLN1y5Rqt91Tm91EqSidtt/ue2VfAY2DHvNuM6WieErVYZKU5e57XveLjClYa2xwy9zIawKY5u575nNz5yaXbq6DXb+Vez9RKmjVXq/hqg2us6bZlFKigwflWo97HZsr1d2U71Uh3gKdxyOL3ffKRpEX3boVpbTvCyNKJdVPhhGlNCN2cFCuSfMaNdul3+YSRYm3fuV7ZRCl1NNPF8QA4PnPl8fCePrp+TtyRM6f22Y+ddp9L4uFVxNmShEXKErlgd1hA/E8pVR4sEWHtFZiVJTSjtXuVG2DdVOU8uqwbVEKiNZp+4lSZV4BjYt+/7Q8perqcZKFp5Rd9lWmwY6KUlNTTmwo0qAtS1EqKTT++WVKuZXvzcw4O3AC5SjfKzOtlhy74eHuyqfMmBt11R6o3nl0MzqnKOVQ5EW3bsv3wpRMJ12+p+0mjCilWR7aZg8f9i8zLXKmlApRR444/UkZPKXU0++lL3Uee8ELonn6qd2FGXPDVgMU5TwmRZGMztVT6oQT0nlvUlgoSuVBT4/8mIOGqDuT9PZK0Ni7d/buBGllSvX3SwDev18yImzRyBaTTFFKRQ19HcVNlEra6LxOuGVK2SWOUV5nairedrlAtTKlsth9r8yZUgMD8nkPHHBiQ5GMQO02EEeUOnTIPUMUSFeU8vOUcsuUAryFDYCiVJIkWT5lTgr8Vu3rJkpNT6c/QSojRV500ywUr93ogohSSpe0KLV/v3ec9xKlzPIvoFyeUjpW1M/V01OOGKLX+HXXSR85MgL8+tfAHXeE9/TT82Jm63D3PbnNK1Oq3XYypShK1Q6KUnnR39/ZGKM0/EZDOpSxMXdRKs3J4PCwI0qtWtX5nJ/Rudlhu5m3AvE8TuzJZhwBpkqYxzyJTCkg2OOkTplSdoedpCilJQHqS1GGgaHJ4sWOKLVqVbFEqSQypXTnJTfhOy9Ryi1TCvAXpezV36qJGVmRdPlUt55SVTuP5kIARany0d/vxKCo/WSeRucqCruNmzTeah9gbk7gt0uk2XaL0k7txW3TfqMsO541m2IncuWVwM6d0kdfcEH4uKvx1bzOwo5xizS+SYKsd9/z8pQaGxPxt9EQoZHUihRcI0ko7ElSVHNcPxNc29spSfzMzv2Mzk1RyhygNBrek6Qwwb631+lA+/qSMxkuK27le3Gyx+wJbtR6e6A4g68kyMIEstHoHGCX7bjZZudFOv/dGJ2rWAh4l/AVQZQyr0VzkhTkKVW1MoSs8CufOv/86OVTblkXdc6UcvProShVHrxsGsKQh9H50JAT570ytDRTSuO82WbNa9QWddwypfJup41GZwlfmfykTN7+duBZz5Kf/v5oCwF6jZr9etiS6ar1m1kbnXuV72mW1HHHcT5XQ3jG86JbI0g/s3Pb2ylJTLPzoPd1K9/r63PvsGdmJAgeOeKIKWGCfaMhx9JrZatu6DE/dMjdByosXplSdTXeNSfy7XY6RueADIx1QFy2DDMVrHfvlmNUpJXEbuJtoyHC7sGD8qPf0yQNUUoXFcKIUjqhMj0y3DaWADrb7dSUMyisQjvNkqTLp8ysi27K98oWN7wwRam04i1JD7exQliCyvfabSf2JXW966LQgQPyYxpoK17le2Zmqtt3NbNQijQuWrhQ+us9e5x+pmyiVKsl8xItF43i6WeLUo1G5/lj+V76opT2dQr9pGoNM6XyIk1RKs3JoNvW717v65Yp5WfybppANhrhO219zbr7SQFOoDcnsnHEOnNnGVNUDGsCCVTLU8psn2okDSS/Rbm56lu2yaUpSpltvgjnvxtPKSDY7DwNUUqPWxhPKXvlPqynlDnpK9v1VjXMrIs48bZIk90kMCfyzJQqH91kSoWJt1rmnlSmFODEQK8dT8OIUm7ZHfr91a8KKEY71XnE3r3lzJQyS6i3bpXbjRvl8TDo+dPrzGteZsfcdrt68Tav3fe8MqW4814tYaZUXtjBL2oNt1/5XlpG54CTKWWLUmZmhO0pFbbDNlOb580Lf0wGBmRyVXc/KSA5UQpwPCHMgWHYlfuqZWCYHbO5ck9RysEs7TVNU5MUauLS7SJAHqKUX4wHOrMF9LqJKkrpeVJ/EZIfZqaUX/meW1ng9LRzLVQh3gLuRudJx1uSHm6CaliCMqX0Wu/pSXbcN38+8PTTwe9re0qZJbd+mVIq/PT3FyOzX/uYPXucfqYsolQSnn56/nRxx6vM3465RVt0S4KiGJ0zU6rWUJTKC3uwGVWU8sqUMsvf0siU0omnXb538KATxPwypcKKUlE+OzOlHPSY6wCjry/+QL6/X86rOUALO0kyvSCSXMnMC/O6TdN41xSiyjbYMbMoTb+FIpimepl9hyUPUUpj4OSkxHV7wGyuMPplStnXqDnQrtpqb5kxz51f+V67PXt3MDPelk3M9oJG5+XGa5flMAR5Spkm50n2L+YOfG54eUr5lUsDzvfXcXNR4q2ZKZXmYnYa+Hn66fNB2OV7YTOldHxTlEW3JCiK0TkzpWoNRam8MINfHDM3r1V0s/wtjcGpV6aUvm9/vyOMuHlKBZXvxZkk6fsUYeUpb/RY6PnoZhXRbWeSsJ5Seh5tA+ayYn6H6WknBT/plXsd4AwOls/k0cyUKpKfFFDO8j3NFtUsVNvjRNtlo+HEPrdJkt/25FUzay0zQZlS9k61ZnzQeDtnTvnihhf0lCo3aRqdJ21yrgRlaNnle2HN+LXt6iJyUfpFc3G7bJlSSXj6dStKLVhQjEW3JLAXPfIq32OmVK2pyOilhHSzigR0pt2apJ2h4LX7ntt2sm6ZUn6pzXEnSfo+zJSaXb7XzTHR46oDNNuk3s/jpGqmu+aE0DRmTKrD3rRJ3kOPl3ncWi2ZlPkNwoqAiia7dxcvA6eM5Xt6Pezf7y5Kme+p7TJu+V5RzlOdCfKUsoVxN1GqSufRbcLP8r3ykKbRuYpSSY8vwnpK2YsAYasBdLxelHZaZlEqCWxPKb++0qTK8RbIxsfPzej8wAHnOly2LJ33JYWGPXxedCtKmWm3Jmmn4AaJUuYKUDdG53HK95gpNTtTqptjYmdKhV1FAqplcg7IpF8nRGYnmtQkqadHfBAefFDu63FT34QyTMaKnClVRlEKcI6f2yTJ7T0pSpWXsLvvAfWYJNHovNyYcaebcmnNSjYxy/eSJKyXlV2+Z16jYUSpovSLpiiln60s5XtJYGdKeWVU+2VKVQUvi4q0xp66YG5mSmnp3vBwNWw/SGRKMNOpKEmJUl6ZUmkFSy3fm5jonJyrGOYlSunKb5CnVDeiFDOlHBFKz0cS5Xs6QIsiSlUtUwro3I1QSarDbjbFH+G++4AdO+S4uRl5FhkVpY4cke8AFGeSnLYolVY5icZBN7Nzt/cMI0qZGY5VFDPKisZq0zSZohQ9pcpKN2NcHTe027PLe4DZGzwkhbafqOV7fh5+5mNF85Qqs9F5EtiiFDOlhCwWArS/Mz2lWLpXeyhK5UXa5XtprXbMn+8EL9PsPChTKswqUrdG58yUKk6mVBU7bf2+KrCajyVBswm84hUi6Nx6a7kEKUCuNZ0kPPqo3Bbl/NubSZQlU0rjuLmbpt97RvWUqmI7LSvmBNetfM/2lDKp8iIARaly0s0Yd3DQidduvlJpZUqFNTpXUcrMlPITpWxPqaLEW7PiQj9bnUQpu3zPa/e9OohS9g7TmqGYpdE5RanaQ1EqL5LKlJqYcDpDwD1jKUkaDXezczcxLE2j802bJJPEfB8VYFoteb6O2J5S3YhStqeUnXUVxlOqip12GplSyn/7b9LG+vrkpyyClKLZUo89JrdFSW9vNLqLuXmJUtp+3ESpuJlSFKWKSZCnVKPhTNTtnaWqeB7pKVVuuom3jYa/2XnaRudRRamwu+8VbWMJHa/v2uX0YXUSpZIwOq8KZv9iCkVZGp1z573awx4+L7oVpUwjc3PCkkWw1ImnmSkVVL4XxgQyitG5evC0Wp1G52Xy4EkDPRbaidJTKjnMTAYl6Q77N78BTjoJWLVK2owKr2VBY4NmShVp0FZGUSpMphRFqWoQlCkF1KuchJ5S5abbMa6fv1NaolRQ+Z6Xp1RYUUqzT4rSL6oApX2FudlKHYhbvlc0cTEp9PtnKUodPuy0C4pStYe77+VFtx12T490bJp2q5PBtI3OAf9Mqaw8pTSDZONGZ1eqBx4Atm8vV8lT0tjZTEmIUkGeUvaqPVDNchK3TKkkd7hstYCbbgLe8Q65flVgBcpzPWsc0tWvIg3ayihKaRz0y5RKwui8KJOkOuOWKeV27kyRRqmiKGVmSmkfQ1GqPHQ7xlXByS3m6pgkrd33onpKBZXv2Y8VpZ3OmydzCW1f5u7ZdUDPn/alYY3Oq9pv9vVJ35OFKKVtqN2W9xwYoChFKErlRrcdNiAdyN69nSa4eWdKuZXvmWaVSRqd60T9qqtEIHv6aWD9+vJM4NPAFqGYKZUctiiVZGftZmpuCq/m/SKjArFSpEGblz9PGIooSnXrKTUzU00xo6y4ZUpFnSRV6TzS6LzcmGO9bkQpN4EoLaPzbsr3/KoB7P6mKP1ioyFjdh3L16l0D5jtKRU1U6oo5zEp3DbzSdvoHHBEsJ075ZaeUrWlpjVOBSAJUcptB760jc4BR5Tavdt5zG3lwAw6Xtk2QHcr980mMDIi2VvHHluOiXuaJClKBXlK1W33PdvoPMkS0ZkZ9ww/3ZXPLRutiGhsUIo0SU4iU8rN38QU3dMSpdx23wsSpVi+Vy7McxdUvlcnTymKUuXEHC90I0rlYXQeNlPKLd6WKVMK6BSi6iZKxfWUqmK8BdyrAdKKuX19zmtPTgJPPiljqaGh+l2H5CjMlMqLtEWpNIOllu8FeUqZkyGvlQigu0mSeko9+9mOB0+dhSlbOLLvR8HOlPISpdwEkzpkSiUpSp13nvdzZbqe7UypIp3/tMr3TKPOtEQpt5V7Gp1XC7MUyK98D6jHJMnN6JyiVHkwr904faUuaGVpdG6KUu327FI2eyHAzG4M4ymlFCnDhqJUsChVh0UAYPYY1zQ/T4PBQWnLhw51lu7VqYSUdMBMqbwwg59bJxYGzYYyV9Gz8JTSbIggT6lGY3a2jV+HffiwM9gI02mbJU9bt8qtmp/XlTTK94I8peqWKZVG+V5VsDOlijT47mbl3s/fRB8z411ShMmU6kaUqqphaxnRc2dm3rF8j55SZSUpT6ksjc51vDIz07nYAHS2SztTKkg4ZaZUMTFjrnlfqWv5npbTpR1vdX4yOUk/KQIgRVFqdHQUL3zhC9N6+fKTlKcU4J4plYWnlJsoZYthOqj28iUCnO9vfo8gMcPLg6fuwlQaRude505XP/1EqSINvrrFrrev6w6PfhRZlEorU8pcPU96hc9v970go3OvSZJet4cOOddykc5TXTHFw6CYW4eVe7dMKcbc8lBGo/PBQecas7NTTXHUNjoHwvmmKkVqp+aYvW6ilH2uoopSRTqPSZC1KKXtaHIS+O1v5Xf6SdWaVMr3tmzZgtWrV2N0dDSNl68GSZbv6Sp6u+10pFlkSmn53tSUM3CwJze2KOX2XTUQ6uvNnRucPebnwaPP15EkRSk7yy1OplSVOm3bU4qr9rOxy/eKlCmXltF5WibngNN+9u2bXU4SVL7nVU5it9tGI/mMAxId8/oMk+VmUuV4S0+pcpJmplRaRueNhvRZ+/bJ+y5d6jxnZk7pZNocb0URpYrUL5pCVJrzhiJin6sw8XZqyjnXVVvMyVqU0vbDTCnyb6QiSq1bty6Nl60WSWZKqSi1f7+ThppmsDQ9pdptZ9Wg0Zg9KA4SNgDn+6soFeazV8WDJ2nSKN9TgS+sp1S7XW1PqTSMzquCmSk1NFSsSWRSmVK2OJSmKKUxfnpaBsLme3RrdK7Mm0cPhyLgdk2GKd9rt51roUrx1jQ6Z/le+ehmEQDIx+gckDakopSJ6Yuq16ZbplRQ+d7QULHGDnUu34uTKWVm0BVJXEyCrC0qmClFLGh0nhdpGJ2rODRnTnyfqjCoKKVGuSqKzZ8/e3JjZ0r5rSLp96jSwDpr0ijf87rvtWp/6JAziajSuWT5XjBz5sjPoUPFO/dm24ibKTUzI4KP+VppilKDg3LdTU1JfA8rSh0+HF6UKtp5qiuNhnOuFfvcuZVMqykzUK1JEo3Oy023Y1wvo3PT7yktUQqYXb5n+0kB0h4bjU5hOEiUKkp2zaZN8vmPO855TOcUrZYcZ7/F3yoQZ4yr10XRFt2SwB7jZuUpNTEB7Nghv1OUqjW5z6omJyexd+/ejp9akIYolYXJOSCfXTvu3budIO3W2dq+RH6iVJRMKeJOGplSXve9PKX0eujtTd74OU9odO7Ppk0ymNVsKbMdt1ryfJ7o9asTiSiYExG7hC9NUarRcI6j7SvlJ0pNTXlvUW4LchSlikM3k6TBQfdM5LJiGp3TU6p8mGO9JMv3zPibhiilYlgYUUqFZPN5tzGued0WJd729Ij/6j33OI8tXOj4tdahrXWTKVWU85gk+n2zNjp//HGJ8319wLHHpvuepNDkHnU2bNiARYsWHf1ZuXJl3h8pG9LYfS/LHSHMEj4/MSyO0TlFqfjYIlA3opD9v2EzpczSvSqVBdFTyh8d5D79tNzXQVtRBrmmKBWVnh5nAJWlKAV478AX1lPKvk6ZKVVc7LFAmPK9qk6SmClVbpLylLIzpcydnNMQYbUd2WKYmygFOJ8hrKdUUca3ujHQ7bc7WSpbt87eQKjKBIlSbhtLVHXnPSA/o/Pt2+V22bL8x4kkV0KrIddffz0eeeQRz+fPOeccrF27NvIHuOKKK3DZZZcdvb937956CFNJZkrt2ydBUyctWQTLxYtF3R4bczJH3AbFtqdUmEypqg2us6Svz0knB5LNlIq6PXmVSkkAlu8FoYPY979fBJqzznLfJTMv9HqOG2/nzpXBWl6ilLly3277i1KTk04MCCrfq+Lguqx4Cf/2/TqIUjQ6Lzfdekp5le+ZJudpLHrp+3plaNlx3halgsr3itROm02pdrjqKuCpp6SdXXhh/n11VnSTmVqk85gUeRmdqyhFk/PaE1qUuvjii1P5AIODgxjsZuJcVpIQpXQyobvu+ZXRJY2ZKaVlImEypfxEqSw/f1VpNOSYa6dir+pFIa6nVBVNzoHZ5XsUpWbTbMrK6223AZ//PLBiRTEEKSAZUWp8vBiZUkeOOKu3buV75mekp1R5MM+VLjCYuJVMV3WSZBqdU5QqH92OcTWueWVKpbVjaJTyPcC5Tk0jdJsiZkopb34zcPXVTjZwEfrqrLCvyzC771U5Uyovo/PHH5db+knVntRnVeOa/UI6SUKU6utzOuY9e7LzlAKcrd/HxvyDtIpSXqv2ACdJSWNmNHVTvheUKRXkKVW1TCmKUuH4kz9xxNG+vuIMcpMQpYDsRSmN56anlDlRcxOlzO3LKUqVBzPm+pW6m+UkVY+39JQqJ2kZnbtliCZJ3GJnxucAACZGSURBVPI9v+ySInpKKTffDJx2GvDc50pba7Xy/kTZEacaoMqiVF5G5zo/pChVe1Lp4bdt24b169cDEM+oLVu2pPE25SYJUQpwSvj27s3HUyqsKKX4DbSVKgb7LDEzD7vJQozrKVXVlXsanYdjbAw49VTxByjSILesopS2I1OU0vccHOyc8LhlStHovDyYAqJfX1mnTCl6SpWTpIzODx7sFGGzEqXCZkpF9ZQqUjvV8vpLLgHuukuymjduLE6fnTb2uQqTKVXVeAvkZ3SusHyv9sR02PZn7dq1WLt2La655po0Xr4aJGF0Dogo9eST2WdK6Q5b4+OOeOFXvqeEyZSiKNUdSYlSYcv3zAEjUN1Om0bnwbRawE03AW95i2RI6aAXyD9jSmNR2UQpt0ypIH8TP1Gq0ej0nataOy0zZn9JUUpuZ2YoSpUR81ruRpQCJJ7ZXk9pZQbG9ZTS58viKeXm96i3Remz04aeUp3kZXSuUJSqPamIUiQESWdK7dmTbaaUilK7dzudeJhMKYpS6WMe8zyMzqvuKaWiFEtJOin6ILesmVIaD93K9+xsAbcJkpsZcG+v4wVYtXZaZuJkSlU93gLMTi0j3WZK9ffLa0xNSbzTcWba8bYunlIzM+5+j3rfXmysItx9r5O8jM4BGacsW5bu+5HCQ1EqL5ISpXQVPa/yvfFxJ2CHyZQKU75XtcF11phCVJKeUlHL96rqcUJPKXeKPsitkigVdtXeKwuXolQx6cZTqmrn0bx2KUqVj27HuI2GxLd9+zp9pfLKlPLaPMb2lHKLuUX0lDrvPO/nqp4hpbB8r5O8jM4BYOlS9z6P1AqKUnmRhiiVR/ne2JizEu8WpL2ya/weq2KwzxLzmHez+15cT6mqrtzbJpAUpTop+iC37KKUufteUKaUCk5e37WI5SSEnlImzJQqN0mMcefNE1HKFIjyMjrXOB/kKRU0xq1ihk1ZCVsN0G7LT6NRj0yprI3OAZqcEwAUpfKj7OV7mik1MeF4kySVKVXFYJ8laZXvRfWUqmqmFD2lysWmTSIg6o6hppjYasn16yeoKXmLUmY5SVCmlOKXKaVUTcwoM2EzpeogSpnXblblJCQ5zOs37gKOCk9mplTeRud2zNXrVMfBZfGUIrPPld9OtdPT8nwdRKk8jM7pJ0WQ0u57JARpZkplESznz59dS09PqWKggb6vz91PJiz2ubLPpQ40ufseKTI9PeJpddddcl/Pm3pghZ0w5S1KRcmUUihKlYugTCk3j5OqxtueHqf/0j6G2anlIYkxrp8olXb53sREZzsL2n1PKdPue3Un7MIr4MSgqsZbILvd9zZtkvGX2ZY0U6rVkudJLWEPnxdJZ0o9/bRTspGFqNNoONlSShKi1OAg64q7RY95N1lS5usoLN+TW5bvlYtmU7yutm0DduyQ69bNlD2IvHffO3DAmSQlmSnFRYDi0E2mVNUyU4HZYyMuBJSHpMr3AHdRKm2jc/t9g4zO3e7r5Nst3nLynT9BRue2KNVuV1uUyipTShcKt251Hlu+PPpCIakcPPN5kbQo9dhjzmul1VnbqK8UIAKIm6l21PK9Kgb6rFExqltRKmy9fd3K9yhKlY9mE/iDPxBR6rbbogtSgBNXzYkKkL4oZcZEbVthRSmvvkWv3Z6e7nznSLLQU6oTv3IaUmy63X0PcI+5aRudDww4bc/0lfLylPLzTdXJ9513yv2+Pvl7Tr6LQdjd9wCJuQcPOmWaVVzMyaoaQBcKv/ENGZMBwL33xhuXkUrBiJgXSZfvaae9YEF3JVtRMDOlvAJ01EypKg6ssyYtUSrq7ntVO5f0lCo3F18ssXFgQOJQ1IGPW6ZUu52+KNXX57y2+lkkVb43f352/QUJJihTyi6Zbrerm5kKMFOqzJhjv7jii9tOeGl7Spnva/pKBXlKud3Xyfett8rke/58YPNmTr6LQtTyPe1/+/u729m6qGRpdN5sAuvWSbv48Y9FoGKbqD0UpfLCbOxJZEopWar3ZqaU145/UUWpKq4+ZE1S5XtBHbabp9TUlJP6W7VJEsv3ys23vw0873nA6tVynbZa0f7fTZQ6csTJFEwzQ1Xjog6Kkyrfq1obLTtRy/fMlfsqnkv7+mXMLQ9JZErlYXQOuO/AF9ZTyv6uzSbw2tfK5Pv736cgVST8BEVAFmxMX7sqm5wDzvcP2r03KS68UI6vinxsE7WHPXxeaEMEksmU8rqfJqYo5TUgpiiVHepf4CZKxfEvsM+dV5q6KUqZg7g0B415YJcrctW+PGi5xEUXibfU+efL/SjClJsoZf6eZhmcLUolmSlFikPU8j3N5Kj6yr3CmFseenudCX2ZjM4B9x344nhKKZddJt9l8eJ4WbokHYIWXoHOmFvVKgAl68zUr38dOPlk4LTT4i0UkspBUSpPNAB6TRzCMDDQKT5kKeqY5XthM6WCPKUoSsVH/Qt+8AO5r9dFXP8Cu0MKU76nnfbcudWbQNjfh6v25cDN1FzLKqIIU36i1OBguteDvQOfvm+QKOXVBvVxxttiYZ4/vwWcOuwEBbB8r+x0u/DqV76XZmaq2/t6eUqFiblf/jJw6qmywxgn38UhaIxr/k2dMqWUNOOtjsve+lbg7rvjLRSSytGFGkK6JolMKUBK+J5+Wn7PK1OKnlL5oxPua66RbJ4zzoi3y5ii2XzqoRRGlKK/CSkaMzPu17/et436vfATpdLeXELjq4oQXhOzRkOuS22TXgseKqBVsZ2WGfN8uWU+2SXTdROluBBQLvr7pdw97nlT0V3j7PS0Yw+QZqZUFE+poOxUewym9wFmTOUNM6U6yWqM67VQCLBt1ByKUnmShihFT6l602wCo6PATTcBX/wicP/93fkX+IlSbp5SVd15D+AEqaycd573c3F238tTlLIzpdzet7/faZNBmVJVHVyXlbCeUiqkVn2SxPK98rFpk/SNzebsMW6rJdeuX0w2UVFKF7vMMr4sjM71fWdmnHFQlPI9Tr6LTU+PLOSoL1/dRams4m1SC4WkclCUypokO2zFFISyEHX0O5x55uz3tb9D1PK9qgb7LPnLvwS++lVgyZLu/QsGBpyBYBRPqSqeR4pS9UYFoKkp+enry06U0hgfZHQOSIzVVX1zkGn2PbYoFbfvIclinq8o5XtVXAQAKEqVEbURAJzz19vbKdCExfaU0tv+/u5sL4Kwjc41ngLRyvc4+S4+fX0iOJoeaCbmQgDL95IhqYVCUjkoSmWN2WGbolScDlsxd+DLIljqd9BVe31ft+/gJWR4PVbVYJ8lt94KPOtZ0sGof0E3mVKAe4dtr9oD1Z4kcYJUb0wB6OBBiVVZ+JsAziQpyOgc8PYlMvseU5Tqpu8hyWKeO7fyvbp5SjHmlg8zE+jQIRFxbr8duOuu6FnbXqJU2uML2+hcFwHMDYoUP1GKk+/io9UAQZuC0FOKkNShKJU1Zoc9MyMThW9+E7j33vhlVmamVBaeUvoZb7wReOopYGQE+N73ZLtb+zuEyZQyA2FVg31WJO1foOcrzAQJqPYkiZlS9aavzxF6VZTKO1MqSJQyr1mz79G/uf9+4Mc/5hblRSHs7nt1Kd+jj1850Viyfr14QO3fD7ztbdFjjF1Gl9UigO0pZe68Zy/Ohd3xlBQTPV9eu5fWqXyP8ZbkDKNnHmjHfMUVMrkYHwfe+c74k4KsM6UA57NedpkIU/v2AW9/++zvQKPz7EjDv0AHXGG2Jweq3WmzwyZz50qsU1Eoa0+pffukvR0+7P2+fju4afv/q7+S1+q27yHJwt33OjFjbKPhXl5DikmzCXzkI8DYmIjqcWKMbXSu4lRWmVJ2+Z5dugf4e0qR4qPni5lSHOOS3OFSf140m9KxtttyG6fD3rRJhAg3T6lWS55PkzDfgeV72eHnX3D++fH8C/T8Ba3aq1FklSdJzJQittl5HqJUkNlvkLDRbAIrVwLLlgHHHENBqkgE7b5XN1HKPB6cIJWLVksWTE8+WcYIcbZ61/g2OSnXvF/ZcpLYGVp+opRf+R4pPhpj3Ma4QL0ypVi+R3KGs6q8aLWAE04ATj9dgmGcDls9Qu6913ls4UInYybtSXOrJd5Fv/M78l5u38EcWPf1da50qqjmJkplIapVjfPO855gNpvxTIzDlO8BjiiV1UpmHtgdNkWp+pG3KLV3r/OeWk5oEyRKtVryN8uXy0A7Tt9D0sHuL23qLEox3pYHM2t761a53bgxeqwxxaeJiexFKdtTKigzFWCmVNnwqwYAOneZrlO8BShKkcxhL58HZof9rW/F77A1A+ab3wR27JDHvv712SVcaaDf4Y1vBL79be/v4DdBUlHt1ludx0zjXQ5C88evwzbPTx0mSUxtJnmLUvv3B7+nl6cUkNxkkaRDkKeUxty6eEoxU6p8eNkIxIk1fX2OUGuKUkUu3+N1Wi7Clu9x9z1CUoeSftYk7fvTbIogtWGDeDu128AFF2QjSIX5DrpbidvuFvo3110nwX7FCuDmmyVDisa7xSCMpxQgolR/vzOIq+IkiaIUyVuUmpwE9uyR372yBbwWAtLwnCPJwt33OjFjLONtOfCzEdDnozA0JB56Bw5kv9upm9G5DTOlyk0co/OqilIc45KcYfTMmqQ7bAB405uAa66R4Nrfn/7EIup3GBjw3nK12QSeeEI+/65dsrMVBaniEMZTCpg9SWL5HqkieYlS8+aJwN9uy+KD33t6iVJp9D0kWczzxfI9ilJlxM8mIK7Z+fi4xNqs7AH09ScnZUzKTKnqov1lUKbUwYPOBiN1iLdu9wlJGYpSWZN0hw0AX/kKcMopwOCgdKCtVrqiTtTvMDAggwmvmu13vlOypebNk46BglRxCOsppRPaOmVKUZSqH3mJUo2GtKl9+4Cnn5bHomZKpdH3kGQxz51f+V5dRCl6ShGNc2amVNqeUubrHzjgH+fD7DBNiktYo/PxcbltNNK//vKCAivJGfbyZUdLMt7yFn9vpzzRTturs775ZhHVVq1yRDVSDPzK90zT+jpkSlGUInmJUoCzy2qUTCkOKstFkKeUmSnVbtdLlOK1XE9UAMjS6LyvTxZ5ARGlomRKUZQqF2FFKS2bnz+/c+xbJShKkZxh9CwzZfEI8ROl7O+g94FifPa645cp1WiIMDMzU49JElObSZ6ilPpYxM2UIsUnKFPKFKUOHnR2Pa1ivAUoShFngStLo3NA2tTkZLAoZbdTXqflIkiU0sVHU5SqKhSlSM5wxFpmyuIR4uVLVBZRrY5s2iSdsdu5a7Xk2jrvPOm0VJSq+iSJohQpgigV11OKFJ8omVJaKm3uUFY16ClF3Mr3soi38+eLz+n+/fSUqjJ+1QDA7PK9qpqcAxzjktzhiLXMlMUjRAfMdoAri6hWR3p6RBhcuVLua4dtComAnNMjR+oxSWL5HqEoRdIkrKfUzExnVmpVy0nMmMt4W09UlMrS6Nx8j/37/eO82U57eqrbFquK9pFBRueaKUVRipDU4IiVpI9XplRZRLU6osf/Ax9wMqbcMttM411dTdSdwqoGd98jRRCl9D3DlO9xUFkuopTvVblUWmH5HlFxKEujc/t9w5bvcRGgfMTxlKoqzPojOcMIStInyOicFJNmE7jnHjGiv/FGYNmy2ZltbuUkVe20mSlFiiBK2Z/FhpOk8qHl0uee6zxmZqea5dIARSlSHzTOZWl0DjjtKorROa/RcqDxttmcXb5nxlugXuV7FKVIznBWRdKHolR5ufpq4PjjgSVL5PzZGWzaaZnlJFXceQ9gajPpFKWmpuTHfDxNdPc9hZlS1UHLpb/4Reex/n4nO1UFcDdRqqrxFuCEn+RndM5Mqeqi8bbV6syUsuMtwEwpQjKEEZSkj1f5Hik+t94qGVJ9fTIBb7XqmynFDpuYopRmSZmPp4ndrpgpVR00pn7qU8DOncDIiAhUt9zSmZ3qtghQ1XgL0OicOOL7+Lj4VwLZxtsonlK8RsuBuaHSsmXy++go8Oijs6sBVKA6fFhu6xJv3e4TkjIcsZL0YaZUObE9pPQ+MHuSVIeVe5bvETdRqq8vm9gWJ1OKMbc8NJsy6X73u8XMfnoauPBC70UAilKkDmic27Vr9mNpYopSzJSqHhpXr74aeOYZ4Mkngfe+17saQGH5HiGpwQhK0kPrtt1EKbtumxQLN1Nzc3VJ79dpkkRRiriJUlms2gPxPKU4qCwXF1wAXHmlxNPBQe8JUh3iLcDyPeIscu3cKbeDg9lcC2HL93iNlpdmE7j+esmCWrrUfYOlOolSzJQiOcNZFUkPrdv+4Q/lvnbebnXbpFjMzMxOYwbk/vnny/NAvcpJuIpE3ESpLFbtARqd14HNm4FTTgGe9zynXNqkTuXSQOf1y/FCPTGNzoHs4q2KUkGZUj09zm7DjLflotWS/vKUU+Tc2fEWmD3Oq0u8BTjGJZnDCErSQwWND3xAOm7TSNBN8CDFwS+Dza3evg4r98yUIjpBmpx0RIG8MqVYvlctwpRLa8ypwyIAwCwUMtsOICt7ADNTyi8rttGQmHv4MONtmQgTb4F6ZUpRlCI5wwhK0qXZBL7zHWDLFuDTnwZOOIGCVJWo08o9U5uJOSkZG5v9WJowU6q6sFzaHXpKEVt8zyremp5Sk5Pyu1umFCBx9vBhXqNlIWy8BeqVKdVoyE+7Lfd5PZOM4YiVpM/69cDttwOLFknnTUGqOtDonNSJgQE57zMzwO7d8lhWk6TBQRGcdAeqMJlSHFSWA79yaX0eoChF6ocd57IaX2i7Mg3WvUQpjbm8RstB2HgL1EuUAmSOpmMMXs8kYyhKkfS55x7g5JMl2KlPBoWpamB6StUtU4qiVP1oNESEOnAge1EKkB34dJLE8r3q0E25dFUXAQB6SpHZcS5rTyn1kwL8M6XMW1JswsZboH6iVG8vRSmSG+zlSbqYabJbt8rtxo3uhoKkfNRpkqSpzQo77HqiIlQeopQOiBsNZ1dTG/NxTpKqRd0ypegpRXp6JEtUyVqUUgYHO/t/E10IYLytHmbcmTOn+ueYMZfkSMVbF8mVKHXbpJzUyVMKkO87NSW/c+W+nuQpSi1c6Lyn1wTJHFRWfQBdN8x4q7uRVTnecoJEABGI1NcpS1HK9NfxypICWL5XZcxzWmWTc4Uxl+QIR6wkPaLUbZNyUidPKcApQQXYYdeVPEUpHRT7vSc9paqLns+JCYm5QLVFKfP65SJAfRkacuJtVqKUlmqr+BtGlOIiQPWomyhFHz+SI4ygJD2i1G2TcqKd1uSks5LJSRKpMnmIUps2yfWmg2JzYtZqicCv8Zble9XFLJfW+36T5bLDCRIBOuNdlote8+eHE6U0zvIarR7mOa3y2Fahjx/JEV5xhJD4aKe1d6/zWJUzpThJIipC7dnTeT9Nenqk5PknP5H7OknTEmlz8MjyveriZrrrVcZZBVhKQoDOMUVWmVJApwgRJjuV8bZ61FmUYswlGcMISgiJj3ZaOkEfGqr26gpXkYg9OclClNLM0g0b5Pb00909+wDuvldl6rYTFCdIBOgUorIUpUwxjJ5S9cQc57F8j5BU4YiVEBIfW5SqcpYUwPI9ko8oBYjwNDoK3HQT8NWvAj/6UacgpSV+557r/I9er3aJHykndROlOEEiQPFFKRVPuQhQPZgpRUhmcFZFCImPdlpavlf1TpuTJJKXKAUA738/sGQJcMwxMng0M6S0xG/LFuexvj73Ej9STuyYU/VFAE6QCFB8UYrle9WFRueEZAZHqYSQ+NiiFCdJpOrkKUp9/evAqlXA8LDsAtlqOc81m5I59fnPAzt2yGO33OJe4kfKCTOlSB3J0+hc8YvzNDqvLnUTpTjGJTlCWZ8QEp86Z0ox86Se5CVK2R5Seh9wBKdmEzhyBHj3u4GnnpJd2i68kIJUVaibKMUJEgGKYXRuZ0ppuXSz6ex4ynLp6sHyPUIyg7MqQkh86ClF6kYeopSbqblmRm3c2JkxdcEFsqI7dy4wOEhBqkrUWZRivK0vRSzf03LpVqvTU4rl0tWibqIUs1NJjjBTihASHx14qShV9U6bohTJQ5SamXEvwdP7MzPOY5s3A89+tkyQtMSPwlQ1sGNOneItJ0j1Q7ORjj/eeUxFqSyykfxEKY2pGzcCIyPy+wMPAI89xnLpKlG33feYKUVyhKIUISQ+2mlNTcktJ0mk6uQhSvlNvMzJT5gSP1JeGo3O+1WPt5wg1RvNRnrpS53H5s7tjHNp4le+Bzgx9W//Fti1C3jiCeB972OsrRLMlCIkMyhKEULiU7dyEnbYJE+jcz+8SvwAClNVodGQuDM9LffrFG+ZmVo/NF599KPAgQPASScBn/lMdps3hNl9r9kEPvQhoN2WvoAxtlrU2ejcXgQhJGUoShFC4lM3UYoeJ8QUoXp6nO3A8yZKiR8pLz099RGlmClFmk3ZTXTDBmBsDDh4MLvyuKBMKUAWA5YsEcFiYIDl0lVBS0dPPNF5TEWpKhvZmztJUpQiGUNRihASH1uYodE5qTqmKDV3bnEGbmFL/Ei56e2VHRYBilKkHvzFX0h21OCgXBNpxzMVJF7+cucxjfumIKHZqRdcwHLpqqGloy97mdzv63NExyxKR/PCFKUIyRiKUoSQ+NQtU4qiFLFFKUKypE4eJyyXJoBs3jAykt3mDSpI7NvnPDZnTqcgwXLpaqPn7u//HpiYAP7dv5PrMKvS0bzQOMt4S3KAohQhJD52x1WnTCl22vWEohTJkzqJUiyXJnls3qCv++lPA08/LYLYnXcC27Y5n2PTJpZLV51mE3jkEfE0Gx+XnyoLUgBFKZIrFKUIIfGpW6YUy0kIRSmSJ6Y4U/V4y0WAepNnNlKzKebll14KPPWUeFm95S3O+7Fcuh68/e3AP/6j+EllUTqaNyzfIzmSiig1OjqKbdu2AQDuu+8+3HDDDRgeHk7jrQgheUJPKVI3TMNbilIkazQGNRrVv/4oStWbvDdveMMbgPe8Bzh0SOJ+1QUJMptvfAN43vOyKx3NG4pSJEdSEaW2bduGyy+/HABw7bXX4uyzz8YDDzyQxlsRQvKkbplSFKVIoyETlEOHqi8KkOKhMWj+/OKY7KdFoyFxdmaGk6Q6knc2UqsFnHSS/N5oVF+QIJ3kUTqaNxSlSI4kPqsaHR3Fhg0bjt5ft24dRkdHsX379qTfihCSN2bHpbuTVBmW7xHAEaMoSpGsMUWpOsBJEskDFSDe9CbgnntEmNi4UR4n1cerdLTq1wE9pUiOJJ4ptWbNGtxwww1H74+PjwMAlixZkvRbEULyxjbdrfrKPTOlCAAMDQFjY3JLSJbUUZQ6fJiTJJId3FmP5F06mhcUpUiOpFK+t27duqO/33zzzVi7dq2np9Tk5CQmJyeP3t+7d28aH4kQkgZmx1V1PymAHid1ZtMmESKbzdmZUq2WDFL9yk0ISQKNO3WItwAnSSR76ipIEIe8S0fzgpmpJEdS3X1vfHwcW7Zs8fWT2rBhA6666qo0PwYhJC3qtD05wEypOtPT46ySm6KUuapOSNrUMVMKYLwl2VFXQYIQilIkR0KLUtdffz0eeeQRz+fPOeccrF27tuOx9evXY+vWrb47711xxRW47LLLjt7fu3cvVq5cGfZjEULypM6ZUlUvVSSdmOUbWrZ3333Az37mvqpOSBrUQZQysxLtTClmJRJCSDpQlCI5ElqUuvjiiyO98LXXXov169dj9erVR32l3MSpwcFBDA4ORnptQkhBMFevqzxJUrSj7umhKFVHVHj6678GxseB3buBd72LghRJF1Ok0Zir8baKIo2ZlWhOkpiVSAgh6cFyaZIjqZTvbdmyBWvWrDkqSH3hC1+ILGoRQkpA3cr3WEpCmk3gU5+S0r1jj6UgRdLHFGnMTKmqijRmVuLhw7Kr6+23A9/+NrMSCSEkaXThQ8fx5ti+igsfpJAkLkpt374dr3vd6zoeGx4epihFSBWpa/keV5HqS6sl53/5cmBqSu5zkkzSxBRpVBi//37gwQerK9Lod1q/HpicBPbvB972tmp+V0IIyRNd+HjBC+S+WS5dxYUPUkgSF6VWr16Ndrud9MsSQopI3TKlzPI9Uj/srcL1PsDJMkkXvb7e9z7gwIF6lI42m8BHPgKMjQELF1b7uxJCSF5obP3Qh4AjR4DTT5893iEkZTizIoTEp26eUjSBrC9uA7RmU+5v3CjPE5ImzSYwPCzxZ9Gi6k8UWi35niefLOUjbGOEEJIOzSbwqlcBO3YAX/oSBSmSORSlCCHxYaYUqQszM+4DNBWmZmby+VykPrRa4mN2+umO8XdVMUXgrVsp/hJCSNpceikwOAgsWCCLsBSkSIakYnROCKkJdfWUoihVP/xMPjlwI2lTp9JRr6xEoLrfmRBC8uab3wSe8xwRpOiZSTKGohQhJD51zZRi+R4hJCvqJtL4ZSXq84QQQpKjTgsfpJBQlCKExMfMGKpDphQ9pQghWVM3kYZZiYQQkh11W/gghYSiFCEkPnXNlGL5HiEkKyjSEEIISYu6LXyQQkJRihASnU2bRJh5/vOdxzRTqtWSDsxvIlVWKEoRQgghhJCqwIUPUgA4syKERKenR1J6b73VeWzePCcFuKqiDUUpQgghhBBCCEkMZkoRQqKjKyfXXQfs2wesXg185jOza9KrgmaGnXCC3DfLFqucGUYIIYQQQgghKUJRihASj2YTePpp4AMfAMbHgYmJagpSgJMZ9pKXyH0VpUxzSEIIIYQQQgghkWANCiEkPu96F7BihWQQ9fVVU5AC5Hudfz5wxx3Ajh0iUrntVkIIIYQQQgghJDQUpQgh8dm8GTj2WGDxYmBqSoSaqtJsAq95jYhSt91GQYoQQgghhBBCuoSiFCEkHmam0NatcrtxY7WFqfXrgUWLgGOOqXZmGCGEEEIIIYRkAD2lCCHRcStd09uNGzvvV4ktW4CTThJBSjPDqvg9CSGEEEIIISQDKEoRQqIzM+Neuqb3Z2ay/0xpYwtxeh+gMEUIIYQQQgghMaAoRQiJznnneT9XRYGmrplhhBBCCCGEEJIiFKUIISSIOmaGEUIIIYQQQkjKNNrtdjvvD2Gyd+9eLFq0CHv27MHChQvz/jiEEEIIIYQQQgghJAJhtR3uvkcIIYQQQgghhBBCMoeiFCGEEEIIIYQQQgjJHIpShBBCCCGEEEIIISRzKEoRQgghhBBCCCGEkMyhKEUIIYQQQgghhBBCMoeiFCGEEEIIIYQQQgjJHIpShBBCCCGEEEIIISRzKEoRQgghhBBCCCGEkMzpy/sD2LTbbQDA3r17c/4khBBCCCGEEEIIISQqqumoxuNF4USpffv2AQBWrlyZ8ychhBBCCCGEEEIIIXHZt28fFi1a5Pl8ox0kW2XMzMwMnnjiCSxYsACNRiPvj9M1e/fuxcqVK/HYY49h4cKFeX8cQgoJ2wkhwbCdEOIP2wghwbCdEBIM20kytNtt7Nu3D8uXL0dPj7dzVOEypXp6erBixYq8P0biLFy4kBc0IQGwnRASDNsJIf6wjRASDNsJIcGwnXSPX4aUQqNzQgghhBBCCCGEEJI5FKUIIYQQQgghhBBCSOZQlEqZwcFBXHnllRgcHMz7oxBSWNhOCAmG7YQQf9hGCAmG7YSQYNhOsqVwRueEEEIIIYQQQgghpPowU4oQQgghhBBCCCGEZA5FKUIIIYQQQgghhBCSOX15f4Aqs337dmzZsgWrV6/G9u3bcfHFF2N4eDjvj0VIroyOjgIA1qxZg+3bt2N8fBxr1qwBwDZD6svo6CguuugiPPDAAx2P+7UJthdSN7zaCfsVQhxGR0exbds2AMB9992HG264IVS/wbZC6oJfG2F/khNtkhpr1qw5+vsjjzzSXrduXY6fhpBicPHFF7cBtAG0165d2x4bGzv6HNsMqSO33HJL+4EHHmi7dcl+bYLthdQJv3bCfoUQh2uuuabjd7MNsE8hxL+NsD/JB5bvpcT27ds77q9evfqoIktInXnhC1+IsbExjI2NYevWrR0rdCZsM6QurFu37ugqnIlfm2B7IXXDq50A7FcIUUZHR7Fhw4aj99etW4fR0VFs376dfQoh8G8jAPuTvKAolRLbtm3DkiVLOh5bsmTJ0ZRAQurM8PDwrHRXthlCOvFrE2wvhHTCfoUQKTm64YYbjt4fHx8HINc9+xRC/NuIwv4ke+gplRJ6gdvs3r072w9CSMEYHx/Hli1bAEgd9yWXXILVq1ezzRBi4dcm2F4IcWC/QojDunXrjv5+8803Y+3atRgeHmafQsi/4dVGAPYneUFRKmO8LmhC6oJpCrh69Wqcc845eOSRRzz/nm2GkE782gTbC6kj7FcImY1Oru2NAdz+Ls5zhJQdtzbC/iQfWL6XEsPDw7OU0927d9Ohn9QesyZbd6/Yvn072wwhFn5tgu2FEAf2K4TMZv369R2eOOxTCOnEbiMA+5O8oCiVEmvXrnV9/Iwzzsj4kxBSHEZHR3H22WfPenzJkiVsM4RY+LUJthdCBPYrhMzm2muvxfr164+WHY2Pj7NPIcTArY2wP8kPilIpsXr16o7727dvxxlnnEE1ldSa1atX45prrjl6f9u2bVi3bh2Gh4fZZghBZxq4X5tgeyF1xm4n7FcIcdiyZQvWrFlzdLL9hS98IbA9sK2QOuHXRtif5EOj3W638/4QVWX79u345Cc/iRe96EW47777cMUVV/DCJbVHd3kZHh7GI4880hH82WZIHdm2bRu2bt2Ka6+9Fpdffjle9KIXHTXh9GsTbC+kTvi1E/YrhAjbt2/Hs5/97I7HhoeHMTY2dvR59imkzgS1EfYn+UBRihBCCCGEEEIIIYRkDsv3CCGEEEIIIYQQQkjmUJQihBBCCCGEEEIIIZlDUYoQQgghhBBCCCGEZA5FKUIIIYQQQgghhBCSORSlCCGEEEIIIYQQQkjmUJQihBBCCCGEEEIIIZlDUYoQQgghhBBCCCGEZA5FKUIIIYQQQgghhBCSORSlCCGEEEIIIYQQQkjmUJQihBBCCCGEEEIIIZlDUYoQQgghhBBCCCGEZA5FKUIIIYQQQgghhBCSOf8fFZ7jh6lvKwwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.098085414602516e-08\n",
      "1.4623824460407997e-07\n",
      "\n",
      "1.0108095324680851e-05\n",
      "4.458100331938437e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dispersion\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.errorbar(ring.locations().cpu().numpy(), (etaqx - etaqx_id_c).cpu().numpy(), fmt='-', marker='x', color='blue', alpha=0.75)\n",
    "plt.errorbar(ring.locations().cpu().numpy(), (etaqy - etaqy_id_c).cpu().numpy(), fmt='-', marker='x', color='red', alpha=0.75)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "etax_ref = etaqx - etaqx_id_c\n",
    "etay_ref = etaqy - etaqy_id_c\n",
    "\n",
    "rms_etax_ref = rms(etax_ref).item()\n",
    "ptp_etax_ref = (etax_ref.max() - etax_ref.min()).item()\n",
    "rms_etay_ref = rms(etay_ref).item()\n",
    "ptp_etay_ref = (etay_ref.max() - etay_ref.min()).item()\n",
    "\n",
    "print(rms_etax_ref)\n",
    "print(ptp_etax_ref)\n",
    "print()\n",
    "\n",
    "print(rms_etay_ref)\n",
    "print(ptp_etay_ref)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "9a5d9c4b-3dc7-4568-8be9-af0667098cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BPM': 168,\n",
       " 'Drift': 727,\n",
       " 'Dipole': 156,\n",
       " 'Quadrupole': 360,\n",
       " 'Marker': 24,\n",
       " 'Matrix': 7}"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert ID(s)\n",
    "\n",
    "error = ring.clone()\n",
    "error.flatten()\n",
    "\n",
    "error.insert(EU100_L01, error.next('MLL_S01').name, position=EU100_POS*1.0E-3)\n",
    "error.insert(U46_L02, error.next('MLL_S02').name, position=U46_POS*1.0E-3)\n",
    "error.insert(F8_L03, error.next('MLL_S03').name, position=F8_POS*1.0E-3)\n",
    "error.insert(EU132_L04, error.next('MLL_S04').name, position=EU132_POS*1.0E-3)\n",
    "error.insert(W96_S05, error.next('MSS_S05').name, position=W96_POS*1.0E-3)\n",
    "error.insert(W96_S06, error.next('MSS_S06').name, position=W96_POS*1.0E-3)\n",
    "error.insert(SCW_L11, error.next('MLL_S11').name, position=SCW_POS*1.0E-3)\n",
    "error.splice()\n",
    "error.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "0d210b31-8a1f-4b63-bc37-f8cd495188f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply correction\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for section in sections:\n",
    "    \n",
    "    if correction[section] is None:\n",
    "        continue\n",
    "\n",
    "    nkn = table_nkn[section]\n",
    "    nks = table_nks[section]\n",
    "\n",
    "    dkn, dks, dkf, dkd = correction[section]\n",
    "    \n",
    "    for name in nkn:\n",
    "        error[name].kn = error[name].kn.item() + dkn[name]\n",
    "        \n",
    "    for name in nks:\n",
    "        error[name].ks = error[name].ks.item() + dks[name]\n",
    "    \n",
    "    for name in QD:\n",
    "        if name not in nkn:\n",
    "            error[name].kn = error[name].kn.item() + dkd[name]\n",
    "    \n",
    "    for name in QF:\n",
    "        if name not in nkn:\n",
    "            error[name].kn = error[name].kn.item() + dkf[name]\n",
    "\n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "65728672-3a58-421f-b841-5006da3d1c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_b, nuy_id_b = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "ee68df54-2857-4f46-9792-102885e0c9b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_b, etapx_id_b, etaqy_id_b, etapy_id_b = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "2f2baf43-0645-4fd6-a2d6-7df46629df8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_b, bx_id_b, ay_id_b, by_id_b = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "058195eb-8e35-4604-8e27-7a72355539e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_b, muy_id_b = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "4c9e44a1-bb92-4dfd-ae2d-d3849e578016",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_b = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "46a83547-0d61-4924-8498-97b86d7508ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute chromaticity\n",
    "\n",
    "psi_id_b = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "6ce89aa4-ebec-4826-9c29-db8900813a89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0017, dtype=torch.float64)\n",
      "tensor(-0.0089, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_b))\n",
    "print((nuy - nuy_id_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "f76df538-01f7-48b6-a448-c01a87d86c1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.2180e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "931bbdc7-2fbe-4434-bbc1-92eb3e512441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.0514, -66.6208], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "8dff415b-7e5f-460d-a8c6-832d4f138b04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(8.1540e-05, dtype=torch.float64)\n",
      "tensor(1.0706e-05, dtype=torch.float64)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Optimization loop (global)\n",
    "\n",
    "# Responce matrix (jacobian)\n",
    "\n",
    "M = global_matrix.clone()\n",
    "\n",
    "# Weighting covariance (sensitivity) matrix\n",
    "\n",
    "epsilon = 1.0E-9\n",
    "C = M @ M.T\n",
    "C = C + epsilon*torch.eye(len(C), dtype=dtype)\n",
    "\n",
    "# Cholesky decomposition\n",
    "\n",
    "L = torch.linalg.cholesky(C) \n",
    "\n",
    "# Whiten response\n",
    "\n",
    "M = torch.linalg.solve_triangular(L, M, upper=False)\n",
    "\n",
    "# Additional weights\n",
    "# Can be used to extra weight selected observables, e.g. tunes\n",
    "\n",
    "weights = torch.ones(len(M), dtype=dtype)\n",
    "weights = weights.sqrt()\n",
    "\n",
    "# Whiten response with additional weights\n",
    "\n",
    "M = M*weights.unsqueeze(1)\n",
    "\n",
    "# Iterative correction\n",
    "\n",
    "lr = 0.75\n",
    "\n",
    "# Initial value\n",
    "\n",
    "global_knobs = torch.tensor(2*[0.0], dtype=dtype)\n",
    "\n",
    "# Correction loop\n",
    "\n",
    "for _ in range(1 + 1):\n",
    "    value = global_observable(global_knobs)\n",
    "    residual = global_target - value\n",
    "    residual = torch.linalg.solve_triangular(L, residual.unsqueeze(-1), upper=False).squeeze(-1)\n",
    "    residual = residual*weights\n",
    "    delta = torch.linalg.lstsq(M, residual, driver=\"gels\").solution\n",
    "    global_knobs += lr*delta\n",
    "    print(((value - global_target)**2).sum())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "a461437f-ecb1-4616-9a2a-6d1c080132d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05324241824456308\n",
      "-0.019481179604359446\n"
     ]
    }
   ],
   "source": [
    "# Knob values\n",
    "\n",
    "kf, kd = global_knobs\n",
    "\n",
    "print(kd.numpy())\n",
    "print(kf.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "46bee3ef-188d-41c6-83c5-97589f0d9acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply final corrections\n",
    "\n",
    "error.flatten()\n",
    "\n",
    "for name in QD:\n",
    "    error[name].kn = (error[name].kn + kd).item()\n",
    "\n",
    "for name in QF:\n",
    "    error[name].kn = (error[name].kn + kf).item()\n",
    "    \n",
    "error.splice()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "e5c2b83c-a8eb-4a1d-8c26-c5823d9c6f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute tunes (fractional part)\n",
    "\n",
    "nux_id_c, nuy_id_c = tune(error, [], matched=True, limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "ea33284f-71b2-4c63-b876-f6a67ae79f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute dispersion\n",
    "\n",
    "orbit = torch.tensor(4*[0.0], dtype=dtype)\n",
    "etaqx_id_c, etapx_id_c, etaqy_id_c, etapy_id_c = dispersion(error, orbit, [], limit=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "a55b4999-200d-4c4f-aefc-0a6da324caeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute twiss parameters\n",
    "\n",
    "ax_id_c, bx_id_c, ay_id_c, by_id_c = twiss(error, [], matched=True, advance=True, full=False).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "38696e1f-e0f8-47e0-bbb6-f739abd4222a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute phase advances\n",
    "\n",
    "mux_id_c, muy_id_c = advance(error, [], alignment=False, matched=True).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "fa57571b-768b-4595-9933-257fcafcea30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute coupling\n",
    "\n",
    "c_id_c = coupling(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "1aeac3bc-fb5b-445c-82b9-30b3fb9c00b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute hromaticity\n",
    "\n",
    "psi_id_c = chromaticity(error, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "0b9979d1-dde8-4012-980d-c1de5512048e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-0.0009, dtype=torch.float64)\n",
      "tensor(-0.0010, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Tune shifts\n",
    "\n",
    "print((nux - nux_id_c))\n",
    "print((nuy - nuy_id_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "da6ffece-d654-4e31-87e7-ae058cfe1c28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0., dtype=torch.float64)\n",
      "tensor(1.2775e-06, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Coupling (minimal tune distance)\n",
    "\n",
    "print(c)\n",
    "print(c_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "10959647-6a0f-4cfb-8b50-98d0a2c0ca06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-71.2093, -66.6787], dtype=torch.float64)\n",
      "tensor([-71.0080, -66.6634], dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "print(psi)\n",
    "print(psi_id_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "3d937ffe-555f-4051-985d-f3c61b1ab433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n",
      "tensor(-71.0514, dtype=torch.float64) tensor(-66.6208, dtype=torch.float64)\n",
      "tensor(-71.0080, dtype=torch.float64) tensor(-66.6634, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "# Chromaticity\n",
    "\n",
    "(psi_x, psi_y) = psi\n",
    "(psi_id_b_x, psi_id_b_y) = psi_id_b\n",
    "(psi_id_c_x, psi_id_c_y) = psi_id_c\n",
    "\n",
    "print(psi_x, psi_y)\n",
    "print(psi_id_b_x, psi_id_b_y)\n",
    "print(psi_id_c_x, psi_id_c_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "33980564-164a-4783-b8bc-fa1d50802f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beta-beating\n",
    "\n",
    "bx_b_bb = 100.0*(bx - bx_id_b) / bx\n",
    "by_b_bb = 100.0*(by - by_id_b) / by\n",
    "\n",
    "bx_c_bb = 100.0*(bx - bx_id_c) / bx\n",
    "by_c_bb = 100.0*(by - by_id_c) / by\n",
    "\n",
    "def rms(x):\n",
    "    return (x**2).mean().sqrt()\n",
    "\n",
    "rms_x_b = rms(bx_b_bb).item()\n",
    "ptp_x_b = (bx_b_bb.max() - bx_b_bb.min()).item()\n",
    "rms_y_b = rms(by_b_bb).item()\n",
    "ptp_y_b = (by_b_bb.max() - by_b_bb.min()).item()\n",
    "\n",
    "rms_x_c = rms(bx_c_bb).item()\n",
    "ptp_x_c = (bx_c_bb.max() - bx_c_bb.min()).item()\n",
    "rms_y_c = rms(by_c_bb).item()\n",
    "ptp_y_c = (by_c_bb.max() - by_c_bb.min()).item()\n",
    "\n",
    "s = ring.locations().cpu().numpy()\n",
    "\n",
    "bx_b_np = bx_b_bb.cpu().numpy()\n",
    "by_b_np = by_b_bb.cpu().numpy()\n",
    "bx_c_np = bx_c_bb.cpu().numpy()\n",
    "by_c_np = by_c_bb.cpu().numpy()\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    1, 1, figsize=(16, 6),\n",
    "    sharex=True,\n",
    "    gridspec_kw={'hspace': 0.3}\n",
    ")\n",
    "\n",
    "fig.subplots_adjust(top=0.85, bottom=0.35)\n",
    "\n",
    "ax.errorbar(s, bx_b_np, fmt='-', marker='o', ms=4, color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global)')\n",
    "ax.errorbar(s, by_b_np, fmt='-', marker='o', ms=4, color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global)')\n",
    "ax.errorbar(s, bx_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='blue', alpha=0.75, lw=1.5, label=r'$\\beta_x$ (local + global + tune)')\n",
    "ax.errorbar(s, by_c_np, fmt='-', marker='s', ms=6, markerfacecolor='none', color='red',  alpha=0.75, lw=1.5, label=r'$\\beta_y$ (local + global + tune)')\n",
    "\n",
    "ax.set_xlabel('s [m]', fontsize=18)\n",
    "ax.set_ylabel(r'$\\Delta \\beta / \\beta$ [\\%]', fontsize=18)\n",
    "ax.tick_params(width=2, labelsize=16)\n",
    "ax.tick_params(axis='x', length=8, direction='in')\n",
    "ax.tick_params(axis='y', length=8, direction='in')\n",
    "\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_b:05.2f}\\% \\quad RMS$_y$={rms_y_b:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_b:05.2f}\\% \\quad PTP$_y$={ptp_y_b:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_b)}{(nux - nux_id_b).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_b)}{(nuy - nuy_id_b).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_b.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.15, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "title = (\n",
    "    rf'RMS$_x$={rms_x_c:05.2f}\\% \\quad RMS$_y$={rms_y_c:05.2f}\\% \\quad '\n",
    "    rf'PTP$_x$={ptp_x_c:05.2f}\\% \\quad PTP$_y$={ptp_y_c:05.2f}\\% \\quad '\n",
    "    rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id_c)}{(nux - nux_id_c).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id_c)}{(nuy - nuy_id_c).abs().item():.4f}'\n",
    "    rf'\\quad C={c_id_c.item():.6f}'\n",
    ")\n",
    "ax.text(0.0, 1.05, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n",
    "\n",
    "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=6)\n",
    "ax.set_ylim(-6, 6)\n",
    "\n",
    "plt.setp(ax.spines.values(), linewidth=2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "5811b866-545f-41b8-87cd-388658ccbaa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.963778040361568e-05\n",
      "0.00012536550004436584\n",
      "\n",
      "1.0080771427201566e-05\n",
      "4.4483455191515144e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dispersion\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.errorbar(ring.locations().cpu().numpy(), (etaqx - etaqx_id_c).cpu().numpy(), fmt='-', marker='x', color='blue', alpha=0.75)\n",
    "plt.errorbar(ring.locations().cpu().numpy(), (etaqy - etaqy_id_c).cpu().numpy(), fmt='-', marker='x', color='red', alpha=0.75)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "etax_ref = etaqx - etaqx_id_c\n",
    "etay_ref = etaqy - etaqy_id_c\n",
    "\n",
    "rms_etax_ref = rms(etax_ref).item()\n",
    "ptp_etax_ref = (etax_ref.max() - etax_ref.min()).item()\n",
    "rms_etay_ref = rms(etay_ref).item()\n",
    "ptp_etay_ref = (etay_ref.max() - etay_ref.min()).item()\n",
    "\n",
    "print(rms_etax_ref)\n",
    "print(ptp_etax_ref)\n",
    "print()\n",
    "\n",
    "print(rms_etay_ref)\n",
    "print(ptp_etay_ref)\n",
    "print()"
   ]
  }
 ],
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  "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": "3.14.0"
  },
  "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
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}