{ "cells": [ { "cell_type": "markdown", "id": "a94fc016-ea97-4686-a90c-c872bdd2713b", "metadata": {}, "source": [ "## ELETTRA-46: EU100 (symmetric: twiss, orm and advance)" ] }, { "cell_type": "markdown", "id": "d95a2b96-519c-4e2e-b5e6-158e26cd8b7d", "metadata": {}, "source": [ "### twiss (local & global)" ] }, { "cell_type": "code", "execution_count": 1, "id": "da919b16-9b9d-49d2-94a4-1f35eff76336", "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": "6d014e74-65d7-4799-acf1-030e034d3799", "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": "7db967a6-a22c-4a58-959a-e9e6c9ee9e8c", "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": "b9fbe8b7-7ff6-4747-b9bd-2794a9d0a6da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'BPM': 168, 'Drift': 708, 'Dipole': 156, 'Quadrupole': 360, 'Marker': 12}" ] }, "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'^(?!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": "fac9685b-0fd9-4a53-83d5-a1bd495dfa02", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux, nuy = tune(ring, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 6, "id": "603bfb3c-e527-4b40-804c-f8f40d85dce2", "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": "893138ef-fe58-47d8-88bb-4bee3303fda2", "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": "1d564de1-0ebf-4bdb-8644-7b445f2d89ae", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux, muy = advance(ring, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 9, "id": "f2052983-97c9-47d5-9803-eb646d1cd0c2", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c = coupling(ring, [])" ] }, { "cell_type": "code", "execution_count": 10, "id": "32889e48-2a73-4191-8640-06062e179e9a", "metadata": {}, "outputs": [], "source": [ "# Compute chromaticity\n", "\n", "psi = chromaticity(ring, [])" ] }, { "cell_type": "code", "execution_count": 11, "id": "d916af8f-e187-4d2c-8f35-7746aac9542b", "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": "b895cfb0-199e-4dfa-9183-e5cee5b1e82d", "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": "f3dbd60b-fe3b-4bef-a7cb-24a0f77cc54c", "metadata": {}, "outputs": [], "source": [ "# Several local knobs can be used to correct ID effects\n", "\n", "# Normal quadrupole correctors\n", "\n", "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n", "\n", "# Skew quadrupole correctors\n", "\n", "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']" ] }, { "cell_type": "code", "execution_count": 14, "id": "69c3848b-7b6b-4032-b0e5-bb3d8bb66527", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[1., 0., 0.],\n", " [0., 1., 0.],\n", " [0., 0., 1.],\n", " [0., 0., 1.],\n", " [0., 1., 0.],\n", " [1., 0., 0.]], dtype=torch.float64)\n", "\n", "tensor([[ 1., 0.],\n", " [ 0., 1.],\n", " [ 0., -1.],\n", " [-1., 0.]], dtype=torch.float64)\n", "\n" ] } ], "source": [ "# Define knobs to magnets mixing matrices\n", "\n", "Sn = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]], dtype=dtype)\n", "\n", "print(Sn)\n", "print()\n", "\n", "Ss = torch.tensor([[+1.0, 0.0], [0.0, +1.0], [0.0, -1.0], [-1.0, 0.0]], dtype=dtype)\n", "\n", "print(Ss)\n", "print()" ] }, { "cell_type": "code", "execution_count": 15, "id": "a479d29b-92d3-428b-9458-b843b6f48fa8", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Define twiss observable (full coupled twiss)\n", "\n", "def observable_twiss(kn, ks):\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": 16, "id": "17835a12-a4ef-45c7-ada2-5a1a8bbd8274", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Define dispersion observable\n", "\n", "def observable_dispersion(kn, ks): \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": 17, "id": "298a3807-b168-4d80-a708-946d620366e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([5712])\n", "torch.Size([5712, 5])\n" ] } ], "source": [ "# Construct full target observable vector and corresponding responce matrix\n", "\n", "def observable(knobs):\n", " kn, ks = torch.split(knobs, [3, 2])\n", " kn = Sn @ kn\n", " ks = Ss @ ks \n", " betas = observable_twiss(kn, ks)\n", " etas = observable_dispersion(kn, ks)\n", " return torch.cat([betas.flatten(), etas.flatten()])\n", "\n", "knobs = torch.tensor((3 + 2)*[0.0], dtype=dtype)\n", "print((target := observable(knobs)).shape)\n", "print((matrix := torch.func.jacfwd(observable)(knobs)).shape)" ] }, { "cell_type": "code", "execution_count": 18, "id": "de75f939-8eb9-46d0-8c4c-e895b6ffd9cc", "metadata": {}, "outputs": [], "source": [ "# Define ID model\n", "# Note, only the flattened triangular part of the A and B matrices is passed\n", "\n", "A = torch.tensor([[-0.03484222052711237, 1.0272120741819959E-7, -4.698931299341201E-9, 0.0015923185492594811],\n", " [1.0272120579834892E-7, -0.046082787920135176, 0.0017792061173117564, 3.3551298301095784E-8],\n", " [-4.6989312853101E-9, 0.0017792061173117072, 0.056853750760983084, -1.5929605363332683E-7],\n", " [0.0015923185492594336, 3.3551298348653296E-8, -1.5929605261642905E-7, 0.08311631737263032]], dtype=dtype)\n", "\n", "B = torch.tensor([[0.03649353186115209, 0.0015448347221877217, 0.00002719892025520868, -0.0033681183134964482],\n", " [0.0015448347221877217, 0.13683886657005795, -0.0033198692682377406, 0.00006140578258682469],\n", " [0.00002719892025520868, -0.0033198692682377406, -0.05260095308967722, 0.005019907688182885],\n", " [-0.0033681183134964482, 0.00006140578258682469, 0.005019907688182885, -0.2531573249456863]], dtype=dtype)\n", "\n", "ID = Matrix('ID', \n", " length=0.0, \n", " A=A[torch.triu(torch.ones_like(A, dtype=torch.bool))].tolist(), \n", " B=B[torch.triu(torch.ones_like(B, dtype=torch.bool))].tolist())" ] }, { "cell_type": "code", "execution_count": 19, "id": "63e34a5c-16b8-4cb6-b53a-5888e758c6cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'BPM': 168,\n", " 'Drift': 709,\n", " 'Dipole': 156,\n", " 'Quadrupole': 360,\n", " 'Marker': 12,\n", " 'Matrix': 1}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Insert ID into the existing lattice\n", "# This will replace the target marker\n", "\n", "error = ring.clone()\n", "error.flatten()\n", "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n", "error.splice()\n", "\n", "# Describe\n", "\n", "error.describe" ] }, { "cell_type": "code", "execution_count": 20, "id": "726796a7-3b49-462b-9222-6962b19186be", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux_id, nuy_id = tune(error, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 21, "id": "9003f374-2b32-4abb-a8e7-bbc04834c2f2", "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": 22, "id": "ac9e82b8-1f4f-44a4-8944-1deb13539051", "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": 23, "id": "526d13d1-d122-4ba9-9ba7-bdb26eee42ca", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 24, "id": "7cdec996-1a90-4727-b6bf-7ee9d0ad96c4", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c_id = coupling(error, [])" ] }, { "cell_type": "code", "execution_count": 25, "id": "120d1618-741b-456b-815e-191996addde9", "metadata": {}, "outputs": [], "source": [ "# Compute hromaticity\n", "\n", "psi_id = chromaticity(error, [])" ] }, { "cell_type": "code", "execution_count": 26, "id": "685e4ce0-9bcc-4b75-9f04-90774b65f111", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0260, dtype=torch.float64)\n", "tensor(-0.0114, dtype=torch.float64)\n" ] } ], "source": [ "# Tune shifts\n", "\n", "print((nux - nux_id))\n", "print((nuy - nuy_id))" ] }, { "cell_type": "code", "execution_count": 27, "id": "26bd8ee6-7b1f-4a3f-8485-9e2df5df503c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0., dtype=torch.float64)\n", "tensor(0.0004, dtype=torch.float64)\n" ] } ], "source": [ "# Coupling (minimal tune distance)\n", "\n", "print(c)\n", "print(c_id)" ] }, { "cell_type": "code", "execution_count": 28, "id": "fd66e836-33af-44fa-848e-f3a474d1cbe8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([-71.2093, -66.6787], dtype=torch.float64)\n", "tensor([-72.2597, -66.6681], dtype=torch.float64)\n" ] } ], "source": [ "# Chromaticity\n", "\n", "print(psi)\n", "print(psi_id)" ] }, { "cell_type": "code", "execution_count": 29, "id": "2ff7a9d2-a778-45d8-a292-57bc2f0a5876", "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):\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):\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, [3, 2])\n", " kn = Sn @ kn\n", " ks = Ss @ ks \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": 30, "id": "bd65f9bb-361f-41af-a031-9d9b2084911c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0008, dtype=torch.float64)\n", "tensor(212.9162, 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((3 + 2)*[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": 31, "id": "bdbb55e7-6ffa-486a-9c37-afec99428b15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(212.9162, dtype=torch.float64)\n", "tensor(12.4882, dtype=torch.float64)\n", "tensor(1.1070, dtype=torch.float64)\n", "tensor(0.3135, dtype=torch.float64)\n", "tensor(0.2637, dtype=torch.float64)\n", "tensor(0.2607, dtype=torch.float64)\n", "tensor(0.2605, dtype=torch.float64)\n", "tensor(0.2605, 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((3 + 2)*[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": 32, "id": "4ae191e9-d787-49c8-8222-d1f2b1636bdb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.0603765 -0.00614448 0.20563185 0.20563185 -0.00614448 -0.0603765 ]\n", "[-0.00051897 -0.00251072 0.00251072 0.00051897]\n" ] } ], "source": [ "# Knob values\n", "\n", "kn, ks = torch.split(knobs, [3, 2])\n", "\n", "kn = Sn @ kn\n", "ks = Ss @ ks\n", "\n", "print(kn.numpy())\n", "print(ks.numpy())" ] }, { "cell_type": "code", "execution_count": 33, "id": "c5a371f7-8930-4604-908b-30de0f91d2f0", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "kn:\n", "OCT_S01_02: -0.060376501089756965: -0.29359999999999903 -> -0.353976501089756, FSE: 0.20564203368445888\n", "QF_S01_02 : -0.006144478301794437: 5.479408293511701 -> 5.473263815209906, FSE: -0.0011213762458750498\n", "QD_S01_02 : 0.20563185272696213: -3.319999999999998 -> -3.114368147273036, FSE: -0.061937305038241686\n", "QD_S01_03 : 0.20563185272696213: -3.319999999999998 -> -3.114368147273036, FSE: -0.061937305038241686\n", "QF_S01_03 : -0.006144478301794437: 5.479408293511701 -> 5.473263815209906, FSE: -0.0011213762458750498\n", "OCT_S01_03: -0.060376501089756965: -0.29359999999999903 -> -0.353976501089756, FSE: 0.20564203368445888\n", "\n", "ks:\n", "SD_S01_05 : -0.0005189660946489763: 0.0 -> -0.0005189660946489763\n", "SH_S01_02 : -0.002510722646811896: 0.0 -> -0.002510722646811896\n", "SH_S01_03 : 0.002510722646811896: 0.0 -> 0.002510722646811896\n", "SD_S01_06 : 0.0005189660946489763: 0.0 -> 0.0005189660946489763\n" ] } ], "source": [ "# Apply final corrections\n", "\n", "error.flatten()\n", "\n", "print()\n", "print('kn:')\n", "for name, knob in zip(nkn, kn):\n", " print(f'{name:<10}: {knob.numpy():>20}: {error[name].kn.numpy():>20} -> {(error[name].kn + knob).numpy():>20}, FSE: {((error[name].kn + knob)/error[name].kn - 1).numpy():>20}')\n", " error[name].kn = (error[name].kn + knob).item()\n", "\n", "print()\n", "print('ks:')\n", "for name, knob in zip(nks, ks):\n", " print(f'{name:<10}: {knob.numpy():>20}: {error[name].ks.numpy():>20} -> {(error[name].ks + knob).numpy():>20}')\n", " error[name].ks = (error[name].ks + knob).item()\n", " \n", "error.splice()" ] }, { "cell_type": "code", "execution_count": 34, "id": "20439c2e-0f79-45cc-a15c-7f495c5e90b1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0001, dtype=torch.float64)\n", "tensor(0.2605, 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": 35, "id": "8cbf8a62-62f1-4e78-8c50-8903ea1ca277", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0001, dtype=torch.float64)\n", "tensor(9.0533e-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": 36, "id": "bb80bed6-721d-4931-936a-0148a16c4590", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.061526790825143986\n", "-0.02255439679848007\n" ] } ], "source": [ "# Knob values\n", "\n", "kf, kd = global_knobs\n", "\n", "print(kd.numpy())\n", "print(kf.numpy())" ] }, { "cell_type": "code", "execution_count": 37, "id": "2f44d208-73da-48b6-8282-fb207955d60c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "qd:\n", "QD_S01_02 : 0.061526790825143986: -3.114368147273036 -> -3.052841356447892, FSE: -0.019755786058567648\n", "QD_S02_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S03_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S04_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S05_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S06_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S07_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S08_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S09_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S10_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S11_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S12_02 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S01_03 : 0.061526790825143986: -3.114368147273036 -> -3.052841356447892, FSE: -0.019755786058567648\n", "QD_S02_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S03_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S04_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S05_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S06_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S07_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S08_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S09_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S10_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S11_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "QD_S12_03 : 0.061526790825143986: -3.319999999999998 -> -3.2584732091748543, FSE: -0.018532165911187892\n", "\n", "qf:\n", "QF_S01_02 : -0.02255439679848007: 5.473263815209906 -> 5.450709418411426, FSE: -0.004120831291888782\n", "QF_S02_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S03_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S04_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S05_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S06_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S07_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S08_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S09_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S10_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S11_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S12_02 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S01_03 : -0.02255439679848007: 5.473263815209906 -> 5.450709418411426, FSE: -0.004120831291888782\n", "QF_S02_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S03_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S04_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S05_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S06_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S07_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S08_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S09_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S10_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S11_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n", "QF_S12_03 : -0.02255439679848007: 5.479408293511701 -> 5.456853896713221, FSE: -0.004116210289564881\n" ] } ], "source": [ "# Apply final corrections\n", "\n", "error.flatten()\n", "\n", "print()\n", "print('qd:')\n", "for name in QD:\n", " print(f'{name:<10}: {kd.numpy():>20}: {error[name].kn.numpy():>20} -> {(error[name].kn + kd).numpy():>20}, FSE: {((error[name].kn + kd)/error[name].kn - 1).numpy():>20}')\n", " error[name].kn = (error[name].kn + kd).item()\n", "\n", "print()\n", "print('qf:')\n", "for name in QF:\n", " print(f'{name:<10}: {kf.numpy():>20}: {error[name].kn.numpy():>20} -> {(error[name].kn + kf).numpy():>20}, FSE: {((error[name].kn + kf)/error[name].kn - 1).numpy():>20}')\n", " error[name].kn = (error[name].kn + kf).item()\n", "\n", " \n", "error.splice()" ] }, { "cell_type": "code", "execution_count": 38, "id": "f4674039-c1c3-4859-8ca4-38fed31f0b5a", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux_result, nuy_result = tune(error, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 39, "id": "0cc73933-bec2-4b04-afa3-f0cfe422b4ec", "metadata": {}, "outputs": [], "source": [ "# Compute dispersion\n", "\n", "orbit = torch.tensor(4*[0.0], dtype=dtype)\n", "etaqx_result, etapx_result, etaqy_result, etapy_result = dispersion(error, orbit, [], limit=1)" ] }, { "cell_type": "code", "execution_count": 40, "id": "c034d214-783f-48c0-ba13-e26707765bfc", "metadata": {}, "outputs": [], "source": [ "# Compute twiss parameters\n", "\n", "ax_result, bx_result, ay_result, by_result = twiss(error, [], matched=True, advance=True, full=False).T" ] }, { "cell_type": "code", "execution_count": 41, "id": "6dc974db-0d76-407e-84ea-82631af97e7b", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux_result, muy_result = advance(error, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 42, "id": "23d2ceba-4457-4a30-a832-b9ae5229c773", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c_result = coupling(error, [])" ] }, { "cell_type": "code", "execution_count": 43, "id": "94c2491f-7910-4ada-8ad5-0b197b5cf897", "metadata": {}, "outputs": [], "source": [ "# Compute chromaticity\n", "\n", "psi_result = chromaticity(error, [])" ] }, { "cell_type": "code", "execution_count": 44, "id": "8f8ccba0-0540-46fb-b5b9-8f27cd8df7d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0260, dtype=torch.float64)\n", "tensor(0.0114, dtype=torch.float64)\n", "\n", "tensor(0.0004, dtype=torch.float64)\n", "tensor(0.0008, dtype=torch.float64)\n", "\n" ] } ], "source": [ "# Tune shifts\n", "\n", "print((nux - nux_id).abs())\n", "print((nuy - nuy_id).abs())\n", "print()\n", "\n", "print((nux - nux_result).abs())\n", "print((nuy - nuy_result).abs())\n", "print()" ] }, { "cell_type": "code", "execution_count": 45, "id": "67ce60b0-015a-4fb2-bafe-9ad06707c2af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0., dtype=torch.float64)\n", "tensor(0.0004, dtype=torch.float64)\n", "tensor(6.5744e-06, dtype=torch.float64)\n" ] } ], "source": [ "# Coupling (minimal tune distance)\n", "\n", "print(c)\n", "print(c_id)\n", "print(c_result)" ] }, { "cell_type": "code", "execution_count": 46, "id": "8457e28a-3839-49cc-9c55-7395731873af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n", "tensor(-72.2597, dtype=torch.float64) tensor(-66.6681, dtype=torch.float64)\n", "tensor(-71.1487, dtype=torch.float64) tensor(-66.7459, dtype=torch.float64)\n" ] } ], "source": [ "# Chromaticity\n", "\n", "(psi_x, psi_y) = psi\n", "(psi_id_x, psi_id_y) = psi_id\n", "(psi_result_x, psi_result_y) =psi_result\n", "\n", "print(psi_x, psi_y)\n", "print(psi_id_x, psi_id_y)\n", "print(psi_result_x, psi_result_y)" ] }, { "cell_type": "code", "execution_count": 47, "id": "ecc352d0-ad04-449b-83ea-38ced66a9982", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Beta-beating\n", "\n", "bx_ref_bb = 100.0*(bx - bx_id) / bx\n", "by_ref_bb = 100.0*(by - by_id) / by\n", "bx_res_bb = 100.0*(bx - bx_result)/ bx\n", "by_res_bb = 100.0*(by - by_result)/ by\n", "\n", "def rms(x):\n", " return (x**2).mean().sqrt()\n", "\n", "rms_x_ref = rms(bx_ref_bb).item()\n", "ptp_x_ref = (bx_ref_bb.max() - bx_ref_bb.min()).item()\n", "rms_y_ref = rms(by_ref_bb).item()\n", "ptp_y_ref = (by_ref_bb.max() - by_ref_bb.min()).item()\n", "\n", "rms_x_res = rms(bx_res_bb).item()\n", "ptp_x_res = (bx_res_bb.max() - bx_res_bb.min()).item()\n", "rms_y_res = rms(by_res_bb).item()\n", "ptp_y_res = (by_res_bb.max() - by_res_bb.min()).item()\n", "\n", "s = ring.locations().cpu().numpy()\n", "bx_ref_np = bx_ref_bb.cpu().numpy()\n", "by_ref_np = by_ref_bb.cpu().numpy()\n", "bx_res_np = bx_res_bb.cpu().numpy()\n", "by_res_np = by_res_bb.cpu().numpy()\n", "\n", "etax_ref = etaqx - etaqx_id\n", "etay_ref = etaqy - etaqy_id\n", "etax_res = etaqx - etaqx_result\n", "etay_res = etaqy - etaqy_result\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", "rms_etax_res = rms(etax_res).item()\n", "ptp_etax_res = (etax_res.max() - etax_res.min()).item()\n", "rms_etay_res = rms(etay_res).item()\n", "ptp_etay_res = (etay_res.max() - etay_res.min()).item()\n", "\n", "etax_ref_np = etax_ref.cpu().numpy()\n", "etay_ref_np = etay_ref.cpu().numpy()\n", "etax_res_np = etax_res.cpu().numpy()\n", "etay_res_np = etay_res.cpu().numpy()\n", "\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_ref_np, fmt='-', marker='x', color='blue', alpha=0.75, lw=1.5, label=r'initial, $\\beta_x$')\n", "ax.errorbar(s, by_ref_np, fmt='-', marker='x', color='red', alpha=0.75, lw=1.5, label=r'initial, $\\beta_y$')\n", "ax.errorbar(s, bx_res_np, fmt='-', marker='o', color='blue', alpha=0.75, lw=1.5, label=r'final, $\\beta_x$')\n", "ax.errorbar(s, by_res_np, fmt='-', marker='o', color='red', alpha=0.75, lw=1.5, label=r'final, $\\beta_y$')\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", "title = (\n", " rf'RMS$_x$={rms_x_ref:05.2f}\\% \\quad RMS$_y$={rms_y_ref:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_ref:05.2f}\\% \\quad PTP$_y$={ptp_y_ref:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id)}{(nux - nux_id).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id)}{(nuy - nuy_id).abs().item():.4f}'\n", " rf'\\quad C={c_id.item():.6f}'\n", ")\n", "ax.text(0.0, 1.125, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_x_res:05.2f}\\% \\quad RMS$_y$={rms_y_res:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_res:05.2f}\\% \\quad PTP$_y$={ptp_y_res:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_result)}{(nux - nux_result).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_result)}{(nuy - nuy_result).abs().item():.4f}'\n", " rf'\\quad C={c_result.item():.6f}'\n", ")\n", "ax.text(0.0, 1.025, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'$(\\xi_x, \\xi_y)$ = ({psi_x.item():.2f}, {psi_y.item():.2f}) $\\to$ ({psi_id_x.item():.2f}, {psi_id_y.item():.2f}) $\\to$ ({psi_result_x.item():.2f}, {psi_result_y.item():.2f})' \n", ")\n", "ax.text(0.0, -0.25 - 0*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_ref:.4E} m \\quad RMS$_y$={rms_etay_ref:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_ref:.4E} m \\quad PTP$_y$={ptp_etay_ref:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 1*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_res:.4E} m \\quad RMS$_y$={rms_etay_res:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_res:.4E} m \\quad PTP$_y$={ptp_etay_res:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 2*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "\n", "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=4)\n", "ax.set_ylim(-20, 20)\n", "\n", "plt.setp(ax.spines.values(), linewidth=2.0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 48, "id": "f4adc4d2-f5a8-468e-baf5-e6eb3e6e6071", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Beta-beating\n", "\n", "bx_ref_bb = 100.0*(bx - bx_id) / bx\n", "by_ref_bb = 100.0*(by - by_id) / by\n", "bx_res_bb = 100.0*(bx - bx_result)/ bx\n", "by_res_bb = 100.0*(by - by_result)/ by\n", "\n", "def rms(x):\n", " return (x**2).mean().sqrt()\n", "\n", "rms_x_ref = rms(bx_ref_bb).item()\n", "ptp_x_ref = (bx_ref_bb.max() - bx_ref_bb.min()).item()\n", "rms_y_ref = rms(by_ref_bb).item()\n", "ptp_y_ref = (by_ref_bb.max() - by_ref_bb.min()).item()\n", "\n", "rms_x_res = rms(bx_res_bb).item()\n", "ptp_x_res = (bx_res_bb.max() - bx_res_bb.min()).item()\n", "rms_y_res = rms(by_res_bb).item()\n", "ptp_y_res = (by_res_bb.max() - by_res_bb.min()).item()\n", "\n", "s = ring.locations().cpu().numpy()\n", "bx_ref_np = bx_ref_bb.cpu().numpy()\n", "by_ref_np = by_ref_bb.cpu().numpy()\n", "bx_res_np = bx_res_bb.cpu().numpy()\n", "by_res_np = by_res_bb.cpu().numpy()\n", "\n", "etax_ref = etaqx - etaqx_id\n", "etay_ref = etaqy - etaqy_id\n", "etax_res = etaqx - etaqx_result\n", "etay_res = etaqy - etaqy_result\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", "rms_etax_res = rms(etax_res).item()\n", "ptp_etax_res = (etax_res.max() - etax_res.min()).item()\n", "rms_etay_res = rms(etay_res).item()\n", "ptp_etay_res = (etay_res.max() - etay_res.min()).item()\n", "\n", "etax_ref_np = etax_ref.cpu().numpy()\n", "etay_ref_np = etay_ref.cpu().numpy()\n", "etax_res_np = etax_res.cpu().numpy()\n", "etay_res_np = etay_res.cpu().numpy()\n", "\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_ref_np, fmt='-', marker='x', color='blue', alpha=0.75, lw=1.5, label=r'initial, $\\beta_x$')\n", "# ax.errorbar(s, by_ref_np, fmt='-', marker='x', color='red', alpha=0.75, lw=1.5, label=r'initial, $\\beta_y$')\n", "ax.errorbar(s, bx_res_np, fmt='-', marker='o', color='blue', alpha=0.75, lw=1.5, label=r'final, $\\beta_x$')\n", "ax.errorbar(s, by_res_np, fmt='-', marker='o', color='red', alpha=0.75, lw=1.5, label=r'final, $\\beta_y$')\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", "title = (\n", " rf'RMS$_x$={rms_x_ref:05.2f}\\% \\quad RMS$_y$={rms_y_ref:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_ref:05.2f}\\% \\quad PTP$_y$={ptp_y_ref:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id)}{(nux - nux_id).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id)}{(nuy - nuy_id).abs().item():.4f}'\n", " rf'\\quad C={c_id.item():.6f}'\n", ")\n", "ax.text(0.0, 1.125, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_x_res:05.2f}\\% \\quad RMS$_y$={rms_y_res:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_res:05.2f}\\% \\quad PTP$_y$={ptp_y_res:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_result)}{(nux - nux_result).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_result)}{(nuy - nuy_result).abs().item():.4f}'\n", " rf'\\quad C={c_result.item():.6f}'\n", ")\n", "ax.text(0.0, 1.025, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'$(\\xi_x, \\xi_y)$ = ({psi_x.item():.2f}, {psi_y.item():.2f}) $\\to$ ({psi_id_x.item():.2f}, {psi_id_y.item():.2f}) $\\to$ ({psi_result_x.item():.2f}, {psi_result_y.item():.2f})' \n", ")\n", "ax.text(0.0, -0.25 - 0*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_ref:.4E} m \\quad RMS$_y$={rms_etay_ref:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_ref:.4E} m \\quad PTP$_y$={ptp_etay_ref:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 1*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_res:.4E} m \\quad RMS$_y$={rms_etay_res:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_res:.4E} m \\quad PTP$_y$={ptp_etay_res:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 2*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "\n", "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=4)\n", "ax.set_ylim(-7, 7)\n", "\n", "plt.setp(ax.spines.values(), linewidth=2.0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 49, "id": "1844fe71-a07a-453c-b54c-af2dda27bfc3", "metadata": {}, "outputs": [], "source": [ "# Initial and final values\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", "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n", "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']\n", "\n", "ring.flatten()\n", "\n", "kni = {name: ring[name].kn.item() for name in nkn}\n", "ksi = {name: ring[name].ks.item() for name in nks}\n", "kfi = {name: ring[name].kn.item() for name in QF}\n", "kdi = {name: ring[name].kn.item() for name in QD}\n", "\n", "ring.splice()\n", "\n", "error.flatten()\n", "\n", "knf = {name: error[name].kn.item() for name in nkn}\n", "ksf = {name: error[name].ks.item() for name in nks}\n", "kff = {name: error[name].kn.item() for name in QF}\n", "kdf = {name: error[name].kn.item() for name in QD}\n", "\n", "error.splice()" ] }, { "cell_type": "code", "execution_count": 50, "id": "be7dda03-6c14-4173-a1e9-2b0d0a72ad76", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'DKF': -0.022554396798479814, 'DKD': 0.061526790825143785}\n", "{'DKF': -0.004116210289564881, 'DKD': -0.018532165911187892}\n" ] } ], "source": [ "# Global\n", "\n", "gkfi = [kfi[name] for name in kfi if name not in nkn]\n", "gkdi = [kdi[name] for name in kdi if name not in nkn]\n", "gkfi, *_ = gkfi\n", "gkdi, *_ = gkdi\n", "\n", "gkff = [kff[name] for name in kff if name not in nkn]\n", "gkdf = [kdf[name] for name in kdf if name not in nkn]\n", "gkff, *_ = gkff\n", "gkdf, *_ = gkdf\n", "\n", "dkf = [(kff[name] - kfi[name]) for name in kfi if name not in nkn]\n", "dkd = [(kdf[name] - kdi[name]) for name in kdi if name not in nkn]\n", "dkf_abs, *_ = dkf\n", "dkd_abs, *_ = dkd\n", "gk_abs = {'DKF': dkf_abs, 'DKD': dkd_abs}\n", "\n", "dkf = [(kff[name]/kfi[name] - 1) for name in kfi if name not in nkn]\n", "dkd = [(kdf[name]/kdi[name] - 1) for name in kdi if name not in nkn]\n", "dkf_fse, *_ = dkf\n", "dkd_fse, *_ = dkd\n", "gk_fse = {'DKF': dkf_fse, 'DKD': dkd_fse}\n", "\n", "print(gk_abs)\n", "print(gk_fse)" ] }, { "cell_type": "code", "execution_count": 51, "id": "8215abd5-6b47-4646-b9dc-187a88b5aba1", "metadata": {}, "outputs": [], "source": [ "# Local\n", "\n", "dkn_fse = {name: knf[name]/kni[name] - 1 for name in kni}\n", "dkn_abs = {name: knf[name] - kni[name]for name in kni}\n", "\n", "dks_fse = {name: 0 for name in ksi}\n", "dks_abs = {name: ksf[name] - ksi[name] for name in ksi}" ] }, { "cell_type": "code", "execution_count": 52, "id": "476fcc6a-9d78-43a1-866d-ccd3205d9e46", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "gap = 1\n", "\n", "n_kn = len(dkn_abs)\n", "n_dk = len(gk_abs)\n", "n_ks = len(dks_abs)\n", "\n", "y_kn = np.arange(n_kn)\n", "y_dk = np.arange(n_dk) + n_kn + gap\n", "y_ks = np.arange(n_ks) + n_kn + n_dk + 2*gap\n", "\n", "yticks = np.concatenate([y_kn, y_dk, y_ks])\n", "yticklabels = [*kni.keys()] + [*gk_abs.keys()] + [*ksi.keys()]\n", "\n", "fig, (ax_abs, ax_fse) = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", "\n", "ax_abs.barh(y_dk, [gkfi, gkdi], height=0.6, color='red', alpha=0.75, label='initial')\n", "ax_abs.barh(y_dk, [gkff, gkdf], height=0.5, color='blue', alpha=0.75, label='final')\n", "ax_abs.barh(y_dk, [gk_abs['DKF'], gk_abs['DKD']], height=0.6, color='black', alpha=1, label='delta')\n", "\n", "ax_abs.barh(y_kn, kni.values(), height=0.6, color='red', alpha=0.75)\n", "ax_abs.barh(y_kn, knf.values(), height=0.5, color='blue', alpha=0.75)\n", "ax_abs.barh(y_kn, list(dkn_abs.values()), height=0.6, color='black', alpha=1)\n", "ax_abs.legend(loc=(0.01, 0.775), frameon=False, fontsize=16, ncol=1)\n", "ax_abs.set_xlim(-6, 6)\n", "ax_abs.set_xlabel(r'$k_n$', fontsize=16)\n", "ax_abs.tick_params(axis='x', labelsize=16)\n", "ax_abs = ax_abs.twiny()\n", "ax_abs.barh(y_ks, ksi.values(), height=0.6, color='red', alpha=0.75)\n", "ax_abs.barh(y_ks, ksf.values(), height=0.5, color='blue', alpha=0.75)\n", "ax_abs.barh(y_ks, list(dks_abs.values()), height=0.6, color='black', alpha=1)\n", "ax_abs.set_xlim(-0.005, 0.005)\n", "ax_abs.set_yticks(yticks)\n", "ax_abs.set_yticklabels(yticklabels, fontsize=16)\n", "ax_abs.axvline(0.0, color='black', linewidth=1.0, linestyle='--', alpha=0.5)\n", "ax_abs.set_xlabel(r'$k_s$', fontsize=16)\n", "ax_abs.tick_params(axis='x', labelsize=16)\n", "ax_abs.tick_params(axis='y', labelsize=16)\n", "\n", "ax_fse.barh(y_dk, gk_fse.values(), height=0.6, color='black', alpha=1)\n", "ax_fse.barh(y_kn, dkn_fse.values(), height=0.6, color='black', alpha=1)\n", "ax_fse.set_xlim(-25/100, 25/100)\n", "ax_fse.axvline(0.0, color='black', linewidth=1.0, linestyle='--', alpha=0.5)\n", "ax_fse.set_xlabel(r'FSE', fontsize=16)\n", "ax_fse.tick_params(axis='x', labelsize=16)\n", "ax_fse.tick_params(axis='y', labelsize=16)\n", "\n", "plt.setp(ax_abs.spines.values(), linewidth=2.0)\n", "plt.setp(ax_fse.spines.values(), linewidth=2.0)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1ccf9649-3180-46ed-8603-afb1c18fded9", "metadata": {}, "source": [ "### orm (local)" ] }, { "cell_type": "code", "execution_count": 1, "id": "2363e4a2-b520-4e3f-8534-73d6a51aac76", "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.corrector import Corrector\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.orbit import ORM\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": "9f45b7be-0325-4f35-a0c7-191453249de2", "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": "a44b9ee6-e17b-478a-a163-2ae10c615c24", "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": "94454136-d99e-4dcb-882b-964650727f7f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'BPM': 168,\n", " 'Drift': 732,\n", " 'Dipole': 156,\n", " 'Quadrupole': 360,\n", " 'Corrector': 24,\n", " 'Marker': 12}" ] }, "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'^(?!MLL_).*', kinds=['Marker'])\n", "\n", "# Replace all sextupoles with quadrupoles\n", "\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", "# Insert correctors\n", "\n", "for name, *_ in ring.layout():\n", " if name.startswith('CH'):\n", " corrector = Corrector(f'{name}_CXY', factor=1)\n", " ring.split((1 + 1, None, [name], None), paste=[corrector])\n", "\n", "# Merge drifts\n", "\n", "ring.merge()\n", "\n", "# Change lattice start 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\n", "\n", "ring.splice()\n", "\n", "# Describe\n", "\n", "ring.describe" ] }, { "cell_type": "code", "execution_count": 5, "id": "fd300ded-3885-40f8-8b42-78c795df7598", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux, nuy = tune(ring, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 6, "id": "35c35c49-7fb3-49cc-a286-de7c28bb4ea8", "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": "860020f8-4089-4c9a-9922-4d6bdd6da235", "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": "e83df1bf-c688-429d-8583-c24c6e396576", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux, muy = advance(ring, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 9, "id": "9cc283b5-c17e-41c3-b51a-217cbd001d02", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c = coupling(ring, [])" ] }, { "cell_type": "code", "execution_count": 10, "id": "60d2fc1f-8e7f-433d-91dc-bc17c54e90c5", "metadata": {}, "outputs": [], "source": [ "# Compute chromaticity\n", "\n", "psi = chromaticity(ring, [])" ] }, { "cell_type": "code", "execution_count": 11, "id": "9f628332-7fc1-40de-989a-4de735059aa6", "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": "a7615bc2-054a-4d91-af9b-c51d4adcbe84", "metadata": {}, "outputs": [], "source": [ "# Several local knobs can be used to correct ID effects\n", "\n", "# Normal quadrupole correctors\n", "\n", "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n", "\n", "# Skew quadrupole correctors\n", "\n", "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']" ] }, { "cell_type": "code", "execution_count": 13, "id": "eb4e98da-5b7d-4e50-814d-beaff8991aa6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[1., 0., 0.],\n", " [0., 1., 0.],\n", " [0., 0., 1.],\n", " [0., 0., 1.],\n", " [0., 1., 0.],\n", " [1., 0., 0.]], dtype=torch.float64)\n", "\n", "tensor([[ 1., 0.],\n", " [ 0., 1.],\n", " [ 0., -1.],\n", " [-1., 0.]], dtype=torch.float64)\n", "\n" ] } ], "source": [ "# Define knobs to magnets mixing matrices\n", "\n", "Sn = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]], dtype=dtype)\n", "\n", "print(Sn)\n", "print()\n", "\n", "Ss = torch.tensor([[+1.0, 0.0], [0.0, +1.0], [0.0, -1.0], [-1.0, 0.0]], dtype=dtype)\n", "\n", "print(Ss)\n", "print()" ] }, { "cell_type": "code", "execution_count": 14, "id": "81b1b676-9d39-4b96-9b93-a0e15aecd3df", "metadata": {}, "outputs": [], "source": [ "# Define observables for 'mixed' knobs\n", "\n", "def knobs_split(knobs):\n", " kn, ks = torch.split(knobs, [3, 2])\n", " kn = Sn @ kn\n", " ks = Ss @ ks\n", " return kn, ks\n", "\n", "\n", "def knobs_build(knobs):\n", " kn, ks = knobs_split(knobs)\n", " return (nkn, kn), (nks, ks)\n", "\n", "\n", "def observable_orm(nn, kn, ns, ks):\n", " orm = ORM(ring, orbit, [kn, ks], ('kn', None, nn, None), ('ks', None, ns, None), limit=1)\n", " return orm\n", "\n", "\n", "def observable(knobs):\n", " (nn, kn), (ns, ks) = knobs_build(knobs)\n", " orm = observable_orm(nn, kn, ns, ks)\n", " return orm.flatten()" ] }, { "cell_type": "code", "execution_count": 15, "id": "10d9bca8-2907-4ae5-82a8-c622a0bbf4dc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([5])\n", "torch.Size([16128])\n", "torch.Size([16128, 5])\n", "tensor(5)\n" ] } ], "source": [ "# Compute target vector and corresponding responce matrix\n", "\n", "knobs = torch.tensor((3 + 2)*[0.0], dtype=dtype)\n", "target = observable(knobs)\n", "matrix = torch.func.jacfwd(observable)(knobs)\n", "\n", "print(knobs.shape)\n", "print(target.shape)\n", "print(matrix.shape)\n", "print(torch.linalg.matrix_rank(matrix))" ] }, { "cell_type": "code", "execution_count": 16, "id": "b57c5064-b6df-4c64-ad83-6eabcd95a86b", "metadata": {}, "outputs": [], "source": [ "# Define ID model\n", "# Note, only the flattened triangular part of the A and B matrices is passed\n", "\n", "A = torch.tensor([[-0.03484222052711237, 1.0272120741819959E-7, -4.698931299341201E-9, 0.0015923185492594811],\n", " [1.0272120579834892E-7, -0.046082787920135176, 0.0017792061173117564, 3.3551298301095784E-8],\n", " [-4.6989312853101E-9, 0.0017792061173117072, 0.056853750760983084, -1.5929605363332683E-7],\n", " [0.0015923185492594336, 3.3551298348653296E-8, -1.5929605261642905E-7, 0.08311631737263032]], dtype=dtype)\n", "\n", "B = torch.tensor([[0.03649353186115209, 0.0015448347221877217, 0.00002719892025520868, -0.0033681183134964482],\n", " [0.0015448347221877217, 0.13683886657005795, -0.0033198692682377406, 0.00006140578258682469],\n", " [0.00002719892025520868, -0.0033198692682377406, -0.05260095308967722, 0.005019907688182885],\n", " [-0.0033681183134964482, 0.00006140578258682469, 0.005019907688182885, -0.2531573249456863]], dtype=dtype)\n", "\n", "ID = Matrix('ID', \n", " length=0.0, \n", " A=A[torch.triu(torch.ones_like(A, dtype=torch.bool))].tolist(), \n", " B=B[torch.triu(torch.ones_like(B, dtype=torch.bool))].tolist())" ] }, { "cell_type": "code", "execution_count": 17, "id": "ef441ba2-4475-4f5e-abeb-294a482d1f10", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'BPM': 168,\n", " 'Drift': 733,\n", " 'Dipole': 156,\n", " 'Quadrupole': 360,\n", " 'Corrector': 24,\n", " 'Marker': 12,\n", " 'Matrix': 1}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Insert ID into the existing lattice\n", "# This will replace the target marker\n", "\n", "error = ring.clone()\n", "error.flatten()\n", "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n", "error.splice()\n", "\n", "# Describe\n", "\n", "error.describe" ] }, { "cell_type": "code", "execution_count": 18, "id": "d15e02e5-8e41-4441-a980-9d57751dd41f", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux_id, nuy_id = tune(error, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 19, "id": "8d45c52f-a323-45dc-aa1b-40a0a778b2c9", "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": 20, "id": "e29f618e-36ae-48d0-80c2-dc0e1e837822", "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": 21, "id": "2e13703b-3d3e-4191-a7e7-d88ab7002acb", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 22, "id": "426b8a52-af92-4da5-a72a-ab981e02f56e", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c_id = coupling(error, [])" ] }, { "cell_type": "code", "execution_count": 23, "id": "e5854e88-a5de-46df-aefb-f9a01fb847f9", "metadata": {}, "outputs": [], "source": [ "# Compute chromaticity\n", "\n", "psi_id = chromaticity(error, [])" ] }, { "cell_type": "code", "execution_count": 24, "id": "2215faba-657e-4f07-a3e0-8422cdef822e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0260, dtype=torch.float64)\n", "tensor(-0.0114, dtype=torch.float64)\n" ] } ], "source": [ "# Tune shifts\n", "\n", "print((nux - nux_id))\n", "print((nuy - nuy_id))" ] }, { "cell_type": "code", "execution_count": 25, "id": "3131a5e6-a63d-41ff-a96f-6ce81fa2b902", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0., dtype=torch.float64)\n", "tensor(0.0004, dtype=torch.float64)\n" ] } ], "source": [ "# Coupling (minimal tune distance)\n", "\n", "print(c)\n", "print(c_id)" ] }, { "cell_type": "code", "execution_count": 26, "id": "1623bd24-4a69-4d5f-833c-1dd42b379ae1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([-71.2093, -66.6787], dtype=torch.float64)\n", "tensor([-72.2597, -66.6681], dtype=torch.float64)\n" ] } ], "source": [ "# Chromaticity\n", "\n", "print(psi)\n", "print(psi_id)" ] }, { "cell_type": "code", "execution_count": 27, "id": "34a0a54d-c329-42e9-b11f-442708ee2376", "metadata": {}, "outputs": [], "source": [ "# Define parametric observable vector (emulate tune measurement)\n", "\n", "def observable_orm(nn, kn, ns, ks):\n", " orm = ORM(error, orbit, [kn, ks], ('kn', None, nn, None), ('ks', None, ns, None), limit=1)\n", " return orm\n", "\n", "def observable(knobs):\n", " (nn, kn), (ns, ks) = knobs_build(knobs)\n", " orm = observable_orm(nn, kn, ns, ks)\n", " return orm.flatten()" ] }, { "cell_type": "code", "execution_count": 28, "id": "7406fdcc-7d72-436d-9b6c-d30261d9c656", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(1498.9104, dtype=torch.float64)\n" ] } ], "source": [ "# Check the residual vector norm\n", "\n", "knobs = torch.tensor((3 + 2)*[0.0], dtype=dtype)\n", "\n", "print(((observable(knobs) - target)**2).sum())" ] }, { "cell_type": "code", "execution_count": 29, "id": "d46f98e3-6b22-44fe-9ad0-4868da8bbef4", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(1498.9104, dtype=torch.float64)\n", "tensor(94.2315, dtype=torch.float64)\n", "tensor(27.2219, dtype=torch.float64)\n", "tensor(14.5212, dtype=torch.float64)\n", "tensor(11.7324, dtype=torch.float64)\n", "tensor(11.2607, dtype=torch.float64)\n", "tensor(11.2023, dtype=torch.float64)\n", "tensor(11.1988, dtype=torch.float64)\n", "tensor(11.1998, dtype=torch.float64)\n", "tensor(11.2004, dtype=torch.float64)\n", "tensor(11.2006, dtype=torch.float64)\n", "tensor(11.2007, dtype=torch.float64)\n", "tensor(11.2007, dtype=torch.float64)\n", "tensor(11.2007, dtype=torch.float64)\n", "tensor(11.2007, dtype=torch.float64)\n", "tensor(11.2007, dtype=torch.float64)\n", "\n" ] } ], "source": [ "# Optimization loop (model free)\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((3 + 2)*[0.0], dtype=dtype)\n", "\n", "# Correction loop\n", "\n", "for _ in range(16):\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": 30, "id": "54147a00-a1b0-439b-a399-721ddf44cc0c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.03402113 -0.21871428 0.75520734 0.75520734 -0.21871428 -0.03402113]\n", "[-0.00038598 -0.0024136 0.0024136 0.00038598]\n" ] } ], "source": [ "# Knob values\n", "\n", "(nn, kn), (ns, ks) = knobs_build(knobs)\n", "\n", "print(kn.numpy())\n", "print(ks.numpy())" ] }, { "cell_type": "code", "execution_count": 31, "id": "c9916367-b3cd-4475-b0d1-702c310ca2a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "kn:\n", "OCT_S01_02: -0.03402112810450709: -0.29359999999999903 -> -0.3276211281045061, FSE: 0.11587577692270834\n", "QF_S01_02 : -0.2187142761729715: 5.479408293511701 -> 5.260694017338729, FSE: -0.039915674185469374\n", "QD_S01_02 : 0.755207336457441: -3.319999999999998 -> -2.564792663542557, FSE: -0.2274720892944101\n", "QD_S01_03 : 0.755207336457441: -3.319999999999998 -> -2.564792663542557, FSE: -0.2274720892944101\n", "QF_S01_03 : -0.2187142761729715: 5.479408293511701 -> 5.260694017338729, FSE: -0.039915674185469374\n", "OCT_S01_03: -0.03402112810450709: -0.29359999999999903 -> -0.3276211281045061, FSE: 0.11587577692270834\n", "\n", "ks:\n", "SD_S01_05 : -0.0003859825140469747: 0.0 -> -0.0003859825140469747\n", "SH_S01_02 : -0.002413604312380506: 0.0 -> -0.002413604312380506\n", "SH_S01_03 : 0.002413604312380506: 0.0 -> 0.002413604312380506\n", "SD_S01_06 : 0.0003859825140469747: 0.0 -> 0.0003859825140469747\n" ] } ], "source": [ "# Apply final corrections\n", "\n", "error.flatten()\n", "\n", "print()\n", "print('kn:')\n", "for name, knob in zip(nn, kn):\n", " print(f'{name:<10}: {knob.numpy():>20}: {error[name].kn.numpy():>20} -> {(error[name].kn + knob).numpy():>20}, FSE: {((error[name].kn + knob)/error[name].kn - 1).numpy():>20}')\n", " error[name].kn = (error[name].kn + knob).item()\n", "\n", "print()\n", "print('ks:')\n", "for name, knob in zip(ns, ks):\n", " print(f'{name:<10}: {knob.numpy():>20}: {error[name].ks.numpy():>20} -> {(error[name].ks + knob).numpy():>20}')\n", " error[name].ks = (error[name].ks + knob).item()\n", " \n", "error.splice()" ] }, { "cell_type": "code", "execution_count": 32, "id": "1d7ce006-ac10-4eee-8f01-504843298138", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux_result, nuy_result = tune(error, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 33, "id": "da8430e5-9f58-468f-b0d2-e904badcb066", "metadata": {}, "outputs": [], "source": [ "# Compute dispersion\n", "\n", "orbit = torch.tensor(4*[0.0], dtype=dtype)\n", "etaqx_result, etapx_result, etaqy_result, etapy_result = dispersion(error, orbit, [], limit=1)" ] }, { "cell_type": "code", "execution_count": 34, "id": "6a04ab3f-3e8c-4048-98d8-c057f2f163e5", "metadata": {}, "outputs": [], "source": [ "# Compute twiss parameters\n", "\n", "ax_result, bx_result, ay_result, by_result = twiss(error, [], matched=True, advance=True, full=False).T" ] }, { "cell_type": "code", "execution_count": 35, "id": "a0cadf63-639f-4a16-8df5-c32b4b6f9b82", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux_result, muy_result = advance(error, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 36, "id": "c0e8709c-8d83-460f-9790-53a3b2b9ae75", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c_result = coupling(error, [])" ] }, { "cell_type": "code", "execution_count": 37, "id": "216ea6c8-1f61-4019-b478-44a5add37e3f", "metadata": {}, "outputs": [], "source": [ "# Compute chromaticity\n", "\n", "psi_result = chromaticity(error, [])" ] }, { "cell_type": "code", "execution_count": 38, "id": "79295302-f4a8-4559-a5c6-50eddb3d8aa0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0260, dtype=torch.float64)\n", "tensor(0.0114, dtype=torch.float64)\n", "\n", "tensor(0.0003, dtype=torch.float64)\n", "tensor(0.0002, dtype=torch.float64)\n", "\n" ] } ], "source": [ "# Tune shifts\n", "\n", "print((nux - nux_id).abs())\n", "print((nuy - nuy_id).abs())\n", "print()\n", "\n", "print((nux - nux_result).abs())\n", "print((nuy - nuy_result).abs())\n", "print()" ] }, { "cell_type": "code", "execution_count": 39, "id": "adb3595f-a269-4a40-bf92-5d2af2ed791a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0., dtype=torch.float64)\n", "tensor(0.0004, dtype=torch.float64)\n", "tensor(7.5546e-06, dtype=torch.float64)\n" ] } ], "source": [ "# Coupling (minimal tune distance)\n", "\n", "print(c)\n", "print(c_id)\n", "print(c_result)" ] }, { "cell_type": "code", "execution_count": 40, "id": "543d6c5c-375f-4ca0-a7c7-12159174a192", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n", "tensor(-72.2597, dtype=torch.float64) tensor(-66.6681, dtype=torch.float64)\n", "tensor(-71.1664, dtype=torch.float64) tensor(-66.6784, dtype=torch.float64)\n" ] } ], "source": [ "# Chromaticity\n", "\n", "(psi_x, psi_y) = psi\n", "(psi_id_x, psi_id_y) = psi_id\n", "(psi_result_x, psi_result_y) =psi_result\n", "\n", "print(psi_x, psi_y)\n", "print(psi_id_x, psi_id_y)\n", "print(psi_result_x, psi_result_y)" ] }, { "cell_type": "code", "execution_count": 41, "id": "53ac3320-38ae-43ad-8eb4-49640cd972e0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Beta-beating\n", "\n", "bx_ref_bb = 100.0*(bx - bx_id) / bx\n", "by_ref_bb = 100.0*(by - by_id) / by\n", "bx_res_bb = 100.0*(bx - bx_result)/ bx\n", "by_res_bb = 100.0*(by - by_result)/ by\n", "\n", "def rms(x):\n", " return (x**2).mean().sqrt()\n", "\n", "rms_x_ref = rms(bx_ref_bb).item()\n", "ptp_x_ref = (bx_ref_bb.max() - bx_ref_bb.min()).item()\n", "rms_y_ref = rms(by_ref_bb).item()\n", "ptp_y_ref = (by_ref_bb.max() - by_ref_bb.min()).item()\n", "\n", "rms_x_res = rms(bx_res_bb).item()\n", "ptp_x_res = (bx_res_bb.max() - bx_res_bb.min()).item()\n", "rms_y_res = rms(by_res_bb).item()\n", "ptp_y_res = (by_res_bb.max() - by_res_bb.min()).item()\n", "\n", "s = ring.locations().cpu().numpy()\n", "bx_ref_np = bx_ref_bb.cpu().numpy()\n", "by_ref_np = by_ref_bb.cpu().numpy()\n", "bx_res_np = bx_res_bb.cpu().numpy()\n", "by_res_np = by_res_bb.cpu().numpy()\n", "\n", "etax_ref = etaqx - etaqx_id\n", "etay_ref = etaqy - etaqy_id\n", "etax_res = etaqx - etaqx_result\n", "etay_res = etaqy - etaqy_result\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", "rms_etax_res = rms(etax_res).item()\n", "ptp_etax_res = (etax_res.max() - etax_res.min()).item()\n", "rms_etay_res = rms(etay_res).item()\n", "ptp_etay_res = (etay_res.max() - etay_res.min()).item()\n", "\n", "etax_ref_np = etax_ref.cpu().numpy()\n", "etay_ref_np = etay_ref.cpu().numpy()\n", "etax_res_np = etax_res.cpu().numpy()\n", "etay_res_np = etay_res.cpu().numpy()\n", "\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_ref_np, fmt='-', marker='x', color='blue', alpha=0.75, lw=1.5, label=r'initial, $\\beta_x$')\n", "ax.errorbar(s, by_ref_np, fmt='-', marker='x', color='red', alpha=0.75, lw=1.5, label=r'initial, $\\beta_y$')\n", "ax.errorbar(s, bx_res_np, fmt='-', marker='o', color='blue', alpha=0.75, lw=1.5, label=r'final, $\\beta_x$')\n", "ax.errorbar(s, by_res_np, fmt='-', marker='o', color='red', alpha=0.75, lw=1.5, label=r'final, $\\beta_y$')\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", "title = (\n", " rf'RMS$_x$={rms_x_ref:05.2f}\\% \\quad RMS$_y$={rms_y_ref:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_ref:05.2f}\\% \\quad PTP$_y$={ptp_y_ref:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id)}{(nux - nux_id).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id)}{(nuy - nuy_id).abs().item():.4f}'\n", " rf'\\quad C={c_id.item():.6f}'\n", ")\n", "ax.text(0.0, 1.125, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_x_res:05.2f}\\% \\quad RMS$_y$={rms_y_res:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_res:05.2f}\\% \\quad PTP$_y$={ptp_y_res:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_result)}{(nux - nux_result).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_result)}{(nuy - nuy_result).abs().item():.4f}'\n", " rf'\\quad C={c_result.item():.6f}'\n", ")\n", "ax.text(0.0, 1.025, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'$(\\xi_x, \\xi_y)$ = ({psi_x.item():.2f}, {psi_y.item():.2f}) $\\to$ ({psi_id_x.item():.2f}, {psi_id_y.item():.2f}) $\\to$ ({psi_result_x.item():.2f}, {psi_result_y.item():.2f})' \n", ")\n", "ax.text(0.0, -0.25 - 0*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_ref:.4E} m \\quad RMS$_y$={rms_etay_ref:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_ref:.4E} m \\quad PTP$_y$={ptp_etay_ref:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 1*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_res:.4E} m \\quad RMS$_y$={rms_etay_res:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_res:.4E} m \\quad PTP$_y$={ptp_etay_res:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 2*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "\n", "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=4)\n", "ax.set_ylim(-20, 20)\n", "\n", "plt.setp(ax.spines.values(), linewidth=2.0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 47, "id": "e37bdcc4-61c2-469d-9cd5-2e1a59cb7ed6", "metadata": {}, "outputs": [ { "data": { "image/png": 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ZzLGxoEym/5qpJlEdqSJ9FvuJF9FuGItHCi/UlqXIf67IoyKRcVWNLfFkyeVyzbuf9vb2Rd/dlWC59tlyzrGYPGXSzswEq022XC5XTP2jWEnZStU8KBU6NxWoXUeZjuDNdB+m49hk+jBV/a3mENkyrhGSKpjDc4VxOBzhZPnROc7isX//fnR0dKCnpwc+n2/BCtiK6Fx/0X8DEosKi6S1tXVJK4QHDx5csRye9fX16OjomPc8VH6hyMp56jlHH6vu+cCBAwteq7m5eU5eTLXSVllZGf7M6XRmTY6maNa6bC63/5eKOn9tbe2ixzocjjnRTip3z2J9leuymYukQiY9Hk/Md0H1U6yootraWjidziVFSSejL5M5NhaUycySSR3Z1NQ0J5ehYt++fejq6oJhGIted7FI4XgytlT5zyV57OjoSGisdTqdaGpqQkdHBzo6Oub1icfjmRPJ5/F44Ha7k44KB9JvH6ZifF7KORKRp0zamelmNcpWb2/vgsetlGylklTo3FTQ2dmJ2tpatLa2JtzX2WK/J3OOdBybTB8m29/x5l0qGjeR+QchOcXK+lvXDlgg+sYwZMUn1sporMiY6DxSsa4VvXrldDrj5lGpr6+PuQLX3t5utLe3Gy0tLUZvb6/R2dmZ1tXmTER4qu9FP4uWlpZ5zyDesep5LZQ/rbu7e96zUivgkW12Op0rHlmba7LZ2dlpuN3uOVGM3d3di0Y1JstS+3+5EZ5KFpO9H5V7p729fcHjck02cyWaLhP60nE1j2M06v6iZcblci2rDanQl7GOjYYymfprZkJPLmeMNIz5eZ8XQz2zaFlSOTxTJf+5JI+GkVgEnkLdR/SYryJ7VJ91dnYa9fX1cfsw0/ZhNMuVvaWcI1F5ypSdmQkyLVuZsu8WYiVkK/p76ZoHJatzU4HS2/Fkqa+vL2vs96WeI13HxiKZPox3bDxbUs33VlrvEJJqGOGZJbjdbjQ3Ny9Y5VDhcrngdDrh8XgSrhLqcDjQ1taGjo4O7NmzBy6XCz6fb0416OjKpOrc1dXVqK6uRn19/YI5spaKx+PB0NBQeGWpr68vvNIVnasrmWMbGhrgcDjmrMS7XK5wRUmv14uGhgZ0d3ejq6tr3jOIPBYA9u7dG64grvJTxkNVF48kVm4Ur9e7YtGuiZJNshkZ4dbe3j6nOnyqV8yT7f/lyqZCRbXGy98JiNzU1tbi4MGD4SrNqnJxrFXeSFaTbGYLmdKXjz76KBoaGlBfX499+/ahoqIChw8fDkdgR/ZXQ0MDvF4vDhw4EJbhSJxO57zjU6UvFzs2GspkasmUnlzOGLkYseSxvb0dPT09qK2tRUtLC/bu3RvWfcuV/0hySR5VpFAyukZFykbu3Oju7g7fY2trKyorK+NGZGXSPoxHsrK3mH5b7BzJyFOm7Mx0k2nZyqR9txCZli0gffOgbKC9vR3V1dVobW1FdXU1mpubw/ev3oto/b1cMt2H6To2XaiK7I2NjThw4ACGhobgdrvh8/nQ3d29onqHkLSw0h7XtQIWiaIzDIkWiF5ZiRVFZxiy4tnS0hIzHwfirKSpqAWVU8VxtcporMibyM+Q5lVmh8ORcL6RZI+N98w7OzvDz8Hlci24uuh2uxM+Vh0fb8VVrfD39fWFo1LSnUNnMXJJNtU5Gxsb50QypnNFMtH+T5VsqhXZxeSipaUl3C6n07loZKe6l1yTzVyIpsukvuzr6zMaGxvD8uZyuWL2/ULyCMzPi5sqfZnMsYZBmUyHTGZaTyY7RioWilRZSB4jdV+q5D/yXnJJHtVzWMpPdDVgl8tluN3uRWUkk/puMZIZn+PJUyLnWIo8pdPOzASZlq2VsO8WIlOypc6RjnlQNCsR4ano7e2dY7uoZ5LOiuCZ7MN0HhtJKiI8DUPmXSqfreNq/YWVHs8ISReaYWQokQfJSTweD/bt28eKbUlQXV0d93n5fD7ccccd4ZVZt9udcCQkMSkvL8dLL70UjiZoaGigjCYAZTO9UF8mD2UyfVBPJg/lMXGo70g6oN4ihBCSSrilnSxIT0/PnC0S8YpmEJOFEqI7HA709vbC4/HA4XAsuG2ZxMbj8cDpdIa3XETLKIkPZTO9UF8mD2UyPVBPLg3KY+JQ35FUQ71FCCEk1egr3QCSfXR1daGhoQEAcPjw4XC1Nq/Xi6GhoZVsWk6QSO4TleuSJM/Q0BAqKirCv7e3t4fllSwMZTP1UF8uD8pkeqCeXBqUx4WhviPphHqLEEJIquGWdjIPj8eDw4cPo7KyEo2NjWhvb8fevXvhcDi40kqygubm5vBEq7m5GcPDw0yyTVYE6kuSrVBPklRDfUfSDfUWIYSQVEKHJyEkp4jc4tTT04PW1tYFtyESQshag3qSEJJrUG8RQghJNXR4EkJyBp/Phx07doQT2jc0NMDtdjNvGCGEXIV6khCSa1BvEUIISQd0eBJCcoqOjg5UVFTA6/Wivr6exjAhhERBPUkIyTWotwghhKQaOjwJIYQQQgghhBBCCCGrBlZpJ4QQQgghhBBCCCGErBro8CSEEEIIIYQQQgghhKwa6PAkhBBCCCGEEEIIIYSsGujwJIQQQgghhBBCCCGErBro8CSEEEIIIYQQQgghhKwa6PAkJIX4fD54PJ5Fj/N6vRloDSEmlE2SbVAmSTZBeSSEEEIIWV3Q4ZkhNE2L+VNbW4vm5mb4fL6Y3ysvLw8ftxg+ny983n379sU8pqenBw0NDeHzVldXo7m5mQZ8Cmhra0N5eTlqa2sX7a+Ghoa4fZ5pKJurn9Uom62trfPa6fF4UF1dnfTPcq5JlgZlkjKZTeSqPC5Ea2srWltbV7oZZBVC2SLJ4vF4sG/fvrCNr/Rta2sr7XxCSFqhwzODOBwO9Pb2ore3F93d3ejs7MSBAwfw2GOPoby8HF1dXXG/6/F4Fh0QHnvssQX/3traioaGBgwNDaGpqQlutxsulwsdHR3zJliZpq2tDdXV1eFJ3ELPIplj49Ha2jrnHB0dHTGP8/l84QG6vLwcDQ0N6OnpmXec1+tFa2sr3G43+vr6wr/Ha39jYyMcDkfS7U4XlM34ZFo2AaCrqwu1tbVhozCekzgROV6NsllfX4+2tjbs2LFjjuw5nU40NjaGf5qbm9Hc3AyHwwGv1wuXyxX+LPKY5VxzJUhEznp6euI6yjRNS/haierAZNpGmVxdMpms3ktUv8UiGXlcK+N3LHw+H9ra2tDW1pYTztmlkooxN11jfKLH+nw+NDc3z1lEjmeTZgOUrfScI12ylcz4nS7a2tpQW1sLj8eDpqYmtLe3o6mpKfy3RCLrl3rdXO/DdB6b6FyYkJzHIBkBgOF0OuP+vb6+3gBg9PX1zfnc4XAYTqfTAGC43e4Fr+FyuQyXy2UAMBobG+f8rbu72wBgNDU1zfve8PDwoudOJ01NTQYAo6WlxWhvbzcaGxsNAEZ7e/uyjo2H0+k0nE6n0d7ebnR3dxtut9twOBzzntnw8LDhcDgMl8tluN1uo6WlxXA4HDH7QrVLodoVzfDw8IJysBJQNuOTadk0DMNoaWkJP4/Ozk6js7Mz5rNJVI5Xq2z29fWFZXAx1DPo7u7O2DXTRaJypt6rlpaWsBxF/iRCMjowmbZRJlePTCar9xLVb7FIRh7Xyvgdj5aWljl9sxpJxZibrjE+0WOVTLlcLqO9vd3o7OwMH5voe5FpKFupP0e6ZCuZ8TtdqPbW19fH/Ht3d7cxPDyctuvmch+m89hE5xCErAbo8MwQizmVent7YxoParBqbGxc8PvDw8PhQSyWUymeAb/SqPuOVsb19fWGw+GYMwgmc2w81GQr+ljlHIicfDY2NsYcoJWTL9IBqNqgUP0QfR01ycsmKJuxybRsGoYpN4s5QZKR49Usm4k+r1Q5l5K5ZjpIRs5iyUKyJKMDk2kbZXJ1yGSyem+57UxGHtfK+B2L4eHhsLNMOTjS4VBYSVIx5qZrjE/m2KamppiOTWUXZVu/UbZyR7aS0YHpQrU3nrMz3dfN9T5M17HJzCEIWQ1kn5dhlbLYhKWvry/miq5yKnV2dhoAjN7e3pjfVwa6Ok+0U0lF6WUbSulGoxR35OQimWPjoSINYxHt1Iv3vFVfRK6QJjJh6u3tjXvtlYSyGZtMy6ZyDCcS1ZGMHK9m2VTG2WLRCql0LiV6zXSQjJylwnBNRgcm0zbK5OqQyWT6PBn9Fo9k5HGtjN+xaGlpCTszVB+ttki8VIy56Rrjk30vFmpbPLtqpaBs5Y5sJaMD04Xa2ZVpOV4tfZiuY5OZQxCyGmAOzyyhvb0dgCTDj0VjY+Oc42J9v76+Hk6nM+bf1Xnb2tqW29SU0tXVBZfLNe9z9Vl3d/eSjo3Hnj17YuY6U59F5ovs7OyMeb36+noAwNGjR8OfOZ3OObmM1N8i83y1trbi0UcfXbSN2QZlcy7pkk2VOyeRQgDJyPFqlk11v/Fka7VcU5EKOUuGZHRgMm2jTOb+NYHk+jwZ/RaPZORxrY7fPp8PPp8vLAsHDx4EgFWXmy0VujBdY3wyx8bLBdvV1QWHwxHzPCsFZSu3ZCsZHZgOvF4vPB4PXC5XxuV4tfRhuo5NZg5ByGqADs8VxOfzhavWqUT4ynkUi8bGxpjFX7xeL7xeb8xCB5HfBcziMB0dHSteeAOQtu/Zsyfm35xOJ44dO7akY+PhdrtRUVGBffv2he9f9UFjY2M4iTaAuH2hvldRURH+TD37trY2eL1e9PT0zDlXNhqvC0HZzLxsdnd3w+FwwOl0hgsYqIIe0YUBkpHj1SabkbjdbgCmAb9ar6lYipx1d3eHC8Qkm7Q/GR2YTNsok7l/TSC5Pk9Gv8UjGXlcq+N3R0fHHKeyw+FAS0tLuNBMPLxeLxoaGsKFTWJRXV2dtuIiyZKKMTddY/xS26bsroaGBni93qxzsFO2cku2ktGB6UAVR8oVW2k558i1Y5OZQxCyGqDDM4N4vd45VXLLy8tRW1uLnp4euN1udHZ2Lvj9AwcOwOfzzauwpyLrFnJIOZ1O9Pb2wul0oqenB83Nzaiurs76qmzJVIBM5FiHw4G+vj5UVFTMqUx38ODBRZ+/Qj3/yMqyLpcLbrc7XPHO6XSGJ6FA9keHUDaTJ9Wy6fV6w3IJiEGyf/9+dHV14Y477phzbDJynOuyGY2Ss9raWni9XnR2dqa9YvJKXHMpxJKztrY2OJ1OtLe3w+l0hhcxlkMsHZhM2yiTuXnNZIns82T0W7IkI4+rcfyOpK+vb16kr4rEO3ToUNzvOZ3OcARQLMduV1cXvF7vikS2L4VUVA9P9Ri/0LEdHR1z7K7Ozs4F7aaVgLIl5JpsRbOU8Xsp9PX1AcjOaMFc78PlHpuKuTAhuYR1pRuwlnA4HHOM5sOHD6Orqws//vGPE4ociNw6HLli1tHRkZBh5HK50NfXB4/Hg8OHD6OnpwcejwfNzc3o7e2NuyU5Hq2trUlFCyncbjcaGxsXVdQOhyO88pTMsYvR2tqKjo4OtLS0oLq6Gt3d3di3b19CBqbP58OhQ4fQ2Ng4b9WypaUlfF+R/akiJNVk1Ov1wu12o7q6Gi0tLQm1Od1QNoWVlE11THt7+5zV1erq6vD9RD7LZOQ4l2VTOeOjUZOodEQPpOOamZTJSFpaWsLOm6amJjQ0NKC1tRVNTU1LcpDF0oFLaRtlcmWvmWl5TFa/JcpCY3Iyx+ayPCra2tpi7qZQkXhtbW1oa2uL2261ZTlWFN6hQ4dQX1+fsM5YrnwtRCrG3HSN8UttW319PTo7OzE0NITOzs6EbdJMQdkSclG2otuWqL5cKVbKVookG/ow3bKxnLkwITnHSicRXSsgRtEBlcR/oep1qjCMIrqidbxE1dGFYeLR29sbrtiX6apsixUxcDqd4WeWzLELEa9KrPp8scTaLpcrqWqDw8PDc9ql+svlchkOhyPjlQtjQdmcz0rIJoA5hTOi2xL53JYrx+q8uSCbDofD6O7uDv/09vYmXRU22QIxqbhmqlmKnMVq83ILFsTSgal6ByiT6b1mKkm2z5PRb8mQzJi8GsfvSBZ6huo5x+oDhdIN0XKlCg5mSwGdVOibdI3xqdKFyp7KlmdO2RJyXbaS1YHLob29fUUK4KyWPkynbKRiDkFILsEt7SuIWhnt6emZtxU4HmqFVa1+tbe3w+FwLHk1xuVyhcPXMx3GrlZzh4aGYv59aGgofEwyxy7EoUOH4HQ6Y0Z3AAsXVKitrUVFRUVShUFaW1vnbY1TW7g7OzuT6vtMQtl0AMisbAKxi444HI55q7PLkWNFLslmfX19+MflcmVk6+5KXHMhliJnsdqsZExtN0uGeDowVe8AZTL7rhmPpfR5ovotUZIZk1f7+N3W1hbeXhwLh8OBpqYm+Hy+uKlijh49CqfTOa/fmpub0dTUlDU5TFOhb9I1xqdKFyq5W06Rr1RB2TLJZdlaig5cDso+zXRu1tXSh+mUjVTMIQjJJejwXGGUEZGoclHbPg4fPgwAeOyxx7B///5ltUEZGitRKMbhcMQdDH0R1SCTPTbeMdHb1aLbEu8ZLMVQ8Hq9OHbs2ByHX09PT/h3NdBka74UymbmZBNIvMLycuRYkeuyuVZJhZzFM4gXYzEduNy2USZzj2T6PNX5+dLt7Mw1eTx69OiiTiM1lscb03t6euZNgDs6OjA0NJR0Wpl0kwpdmK4xPpX2QDYUcKRsmeSqbGXa2QmIDKvc/Cvh9FwNfZiOY1MxhyAk12AOzxXG4XCEk+UnmsNq//796OjoQE9PD3w+34IVsBU+ny/uipbK/ZHsCmtra+uSohsOHjwYvk91L9HtU+c9cOBA+LNkjo3FYpEkPp8vZoW72tpaOJ3OpCc2zc3Nc/JiqutWVlaGP3M6nVk7sFA2MyebgEygY51DGSdKNpcqx5HkumzmIpmWyXjvlTKIa2trE25DIjpwue8AZTKzZFoeE9VviZDMmLwWxu+Ojo6Exlqn04mmpiZ0dHSgo6NjXiVej8czJ5LP4/HA7XYvyUmSCvlaiFSMueka45M51uPxxLRvlFytdCEfypYj/HmuyZZiqTowFXR2dqK2thatra0J93Wmx6Z4ZEMfpuPYVMwhCMk5VnpP/VoBMfIkRuJwOGLmv3FE5Uk0jPl5pGJdKzrfjtPpjJtHpb6+PmbOjvb2dqO9vd1oaWkxent7jc7OziXn2YqHupfotqk2Reb7SebYeKi8adH3qvKWROabNAzJd7OUe+7u7p73PZWrKDJ3ntPpXPE8YLkmm52dnYbb7Z7TV93d3fP6brlkWjbjnaOlpWXeM0hWjiPJNdmMJUfJspR8icmSbfrScTXnYzTqfU40/2OiOnA57wBlMvXXzISeTIWOjKXfFiKZMXktjN+GsXB+xWjUfUSP+d3d3XP6rLOz06ivr4/73mZC3y1EOsfc5Y7xqdDTSj+k2q5JlkzLVqbsu4VYLbJlGEvXgalEyXK8dvT19a2o/Z6Kc+TascuZQxCSizDCM0twu91obm5esMqhwuVywel0wuPxJFwl1OFwoK2tDR0dHdizZw9cLhd8Ph96enrCVUejK5Oqc1dXV6O6uhr19fUpz+vhcrnCFR4BYO/eveEq3SoH5FKOBYCGhgY4HI45q5rt7e3o6elBbW0tWlpasHfvXnR3d4eriUeu/jY0NMDr9eLAgQPha0bidDrjrhar6uLRxwNzc+d5vd6sr4aXTbIZGQ3R3t4+pzp8Iiu2yZBp2Yw8h9frRUNDA7q7u9HV1TXv/UxGjqNZTbKZLWSjvlQV2RsbG3HgwAEMDQ3B7XbD5/Ohu7s7IZlMRgcm+w5EQplMLZnSk0vVkYvpN2D58rhWxm8V0ZOMrlHRPZE7N7q7u8P32NraisrKyrgRWZnSdwuR6jF3sXOk69hHH30UDQ0NqK+vx759+1BRUYHDhw+H+2Yl5SvTspVJ+24hVotsLUcHppL29nZUV1ejtbUV1dXVaG5uDrdTjQGplvXV0ofpOnY5cwhCcpKV9riuFbBIFJ1hSLRAdORNrCg6w5AVz5aWFqOvry/mtWKtpKmoBVX52nG1ymisyIrIz5DgathycLvd4Xa5XK4FV5cSPdbhcMR95i0tLXPO0d7eHvP7AOL+xIvscLvdcSsgq2iWvr6+8EparD7MJLkkm+qcjY2Nc/osmYi1ZMm0bHZ2diZ8vUTkOLp9uSabuRBNl636sru723C5XOF7amxsjNunsWRyKTowmfdFHU+ZTO01M60nk+nzRPXbcuVxrYzf6lku5Sfy+dbX1xsul8twu92Lykim9d1CpGLMTccYn8yxfX19RmNjY1hmExnLM0GmZWsl7LuFyHXZWqoOTBe9vb1z5Fy1PZ6+TQW53ofpPjbZOQQhuYpmGIYBQuLg8Xiwb9++JVX0XatUV1fHfV4+nw933HFHOI+e2+1OOBKSmJSXl+Oll14KRxM0NDRQRhOAspleqC+ThzKZPqgnk4fymDjUdyQdUG8RQghJJdzSThYkurJivATrxCR6K1wkDocDvb298Hg8cDgcK56QPhfxeDxwOp3h7Rmxqn+S2FA20wv1ZfJQJtMD9eTSoDwmDvUdSTXUW4QQQlKNvtININlHV1cXGhoaAACHDx8OV/P1er0YGhpayablBAvlqlOoXJckeYaGhlBRURH+vb29PSyvZGEom6mH+nJ5UCbTA/Xk0qA8Lgz1HUkn1FuEEEJSDbe0k3l4PB4cPnwYlZWVaGxsRHt7O/bu3QuHw8GVVpIVNDc3hydazc3NGB4eTmiiSkiqob4k2Qr1JEk11Hck3VBvEUIISSV0eBJCcorILU49PT1obW1dcBsiIYSsNagnCSG5BvUWIYSQVEOHJyEkZ/D5fNixY0c4oX1DQwPcbjfzhhFCyFWoJwkhuQb1FiGEkHRAhychJKfo6OhARUUFvF4v6uvraQwTQkgU1JOEkFyDeosQQkiqocOTEEIIIYQQQgghhBCyamCVdkIIIYQQQgghhBBCyKqBDk9CCCGEEEIIIYQQQsiqgQ5PQgghhBBCCCGEEELIqoEOT0IIIYQQQgghhBBCyKqBDk9CCCGEEEIIIYQQQsiqwbrSDVgLFBUVYXp6GhaLBevXr1/p5hBCCCGEEEIIIYQQklNcuXIFwWAQdrsdExMTCx6rGYZhZKhdaxaLxYJQKLTSzSCEEEIIIYQQQgghJKfRdR3BYHDBYxjhmQGUw1PXdWzcuHGlm0MIIYQQQgghhBBCSE5x8eJFhEIhWCyWRY+lwzMDrF+/HufPn8fGjRtx7ty5lW4OIYQQQgghhBBCCCE5xZYtW3D+/PmE0kWyaBEhhBBCCCGEEEIIIWTVQIcnIYQQQgghhBBCCCFk1UCHJyGEEEIIIYQQQgghZNVAhychhBBCCCGEEEIIIWTVQIcnIYQQQgghhBBCCCFk1UCHJyGEEEIIIYQQQgghZNVAhychhBBCCCGEEEIIIWTVQIcnIYQQQgghhBBCCCFk1UCHJyGEEEIIIYQQQgghZNVAhychhBBCCCGEEEIIIWTVQIcnIYQQQgghhBBCCCFk1UCHJyGEEEIIIYQQQgghZNVAhychhBBCCCGEEEIIIWTVQIcnIYQQQgghhBBCCACv17vSTSBrDMpceqDDkxBCCCGEEEIIIWsar9eL8vJyVFdXo7W1FQCgaRqam5vTcr22tjaUl5en5dwkN6DMpRfrSjeAkKwlFAJOnAAGB4HKSmD3bkDnGgEhhBBCCCGErDaam5uxf/9+uN1uDA0NAQBaWlqwd+/eFW5Z4nR1dcHhcAAAuru70dzcDKfTubKNInGhzKUXOjwJicWRI8DDDwMnTwIzM0B+PrBrF/DAA0Bd3Uq3jhBCCCGEEEJyjmyOKenp6UF3dzccDkfYgeN2u1e2UQni8/nw2GOPoampKfxZRUUF9u3bh97e3hVsWRaQxUJHmUsvdHiS1UEqldiRI8A99wA+H7B+PWC3A9PTwFNPyeePPEKnJyGEEEIIIYQkQTbHlPh8vpVtwDLp6OhAS0vLnM+8Xi8qKipWqEVZQhYLHWUu/dDhSXKfVCqxUEjO5fMB27cDmiafFxUBhYXA6dPA5z4H3HZb1qwKEUIIIYQQQkg2w5iS9NHT04P6+vo5n/l8Phw6dAiPPvroCrUqC6DQpY1ckTl6bEhuo5TY8eNAaSlw7bXyr1JiR44s/P1AABgeBs6cAZ59Fvj3fwd6e4G8PODKFeDCBWB8XI7VNKCqCnjuOYkmJYQQQgghhJBVjmGIn2ipP5OTQFubTLu2bgUKCmRqVVAg0zefD/jMZ+S45VzHMJZ2f5GFXBoaGqBpWriATGQxGXVsbW0tPB5P+Fj1eyRerxf79u1DeXl53GNShdfrhcvlQltbG/bt24eGhgbccccd6OzshMvlSss1084qFzrKXGZghCfJXaKjMQMBYGoKCAaBkhJxVt53H/DHfyyKbGxMfsbHzX9nZuaec3gYGBiQKFEV3Tk8DLziFfJ/u13+PjiYyTslhBBCCCGEkBVhZgb43d9d+vfHxiS2xGoFRkfn/z0YBHp6gNe/XqZxS+X735fpWrK0tLSgvr4etbW1aG9vx/79++MeOzg4CI/Hg7vvvhtutxutra1obm7GHXfcgeHh4fBxXV1dqKioQGdnJ5xOJ1pbW3HHHXfgpZdeCudqTBVqa3R9fT0cDgf27NmDQ4cOwev1Zk3xmKRZ5UJHmcsMdHiS3OXECdnGvn69rK48//zcvweD8vd//dfFlVhRkRxTUSHRnna7OD19PnGkKqanAZtN8oQSQgghhBBCCFmQQEAC4eJlBNN1+XvktCvTKCdNRUVFQs4ht9sd3tKrHFA+ny/83ejcho8++ijKy8vnFXlJBeqaLpcrHF3ndruzqnhMxskBoaPMpR86PEnuMjgoKz92OzAyIp/puvxusUiE5vg48IY3AHv3AsXF4tQsKTH/X1wszk6lCEMh4B3vkC3xW7aIw9MwzFD1/n6gpkaKIhFCCCGEEELIKic/XwLZlspTTwF/9EeSeayoaP7fJyYkCK+9HbjlluW1M1NE5i9UjquhoaG4jiv1eV9fX0rbESuXIiBOtHRtZ84IFLp5UOaShw5PkrtUVoqCmZ4WRyUgTswdO+T/ExOSo+OP/kiclImg61Ls6J57gLNnJUpU10UZDg8DZWXA/fezYBEhhBBCCCFkTaBpS9sqrti7F7jpJvFBFRebmcMAiSsZHJTp2t69q2ua1dPTg/b2dng8HgwNDaXlGh6PJ6bzyev1pnwbc0ah0C0JytxcVk/PkrXH7t1Sjb2/XxyTgKmsDEM+v/HG5KMx6+qkYltNjZx3ZkYcnjU1rORGCCGEEEIIIUmgYkrKyoDTpyUuJRiUf0+fzr2YkkScOvv27QsXc+nu7p6TazET9PT0YM+ePRm9ZlaxyoSOMrc0cqN3CYlFpBKLdHqmQonV1QHf+hbwqlcBN9wA/N3fAd/8Jp2dhBBCCCGEEJIkKqbkllskluTcudUbU+Lz+dDV1YVHH30UTU1NaS3icvTo0Zift7e3w+12h3/3eDzo6OhAc3MzvF4vOjo65lQCX5WsIaHLRpmLlDfVxkzLHLe0k9xGKbF77gFeeEEUmM0mSuz++5enxHQd2LRJ/r99e86s/hBCCCGEEEJItlFXB9x2m9SeHRyUDGW7d6++aZbD4YDD4cChQ4fgcDhQUVGBQ4cOLfo9n8+HQ4cO4cCBA+FCMAuh8iVGFq4BJNKvtbV1zjkOHz4Mt9uNrq6ucGGZ6upqNDc3Z1VV7ZSzRoQu22Suq6sL+/fvR3d3d7hye09PT7i6e6agw5PkPnV1wLvfDXzta8Bv/zbwrnelTokVFsq/k5PLPxchhBBCCCGErGF0PfHyCrnMo48+irvvvhv79u2D0+kMV9Surq6O+52hoSG0tbWhuro6IefTsWPH0NnZiba2trCT6+jRo3C73XOcmD6fDwcPHgQgeRYPHDgAIPXFbLKWNSJ02SRzjY2NAGSbe2dnJwCgu7sbDQ0Ny7zL5NAMQ5WfJuliy5YtOH/+PDZv3oxz586tdHNWJ5/+NPDDHwLNzcA735m68953n6wGffzjwO23p+68hBBCCCGEEEJIBLW1tXj00UcTcj51dHSgqakp6fN3dnbC6XTOi9Ija5N0yVxXVxcOHz4cdnhWV1ejt7d32TKXjH9tdcXxkrXL1JT8u5xKbrEoKJh7fkIIIYQQQgghJA34fL6EHE/JoPJ1er1eeDyecCReR0dHSq9DcpN0yBwg0aN79+4FgHAez0w72LmlnawOpqflX+WgTBV0eBJCCCGEEEIIyQDt7e0JHafyIiaCiub0er1ob28Pb0duaWlZTlPJKiEdMgcA+/fvR2trazjSs76+fqlNXDJ0eJLVgXJ4pjrCkzk8CSGEEEIIIYRkgESdQh6PJ+Fj6+vrV8TZRHKDdMicQjlT29vbw3lkMwm3tJPVQbq2tNPhSQghhBBCCCEki2hsbGT+TZJRkpE5r9eL2tpaAGZl93Rsm1+MNRXh2dDQAJfLhQMHDsDlcoVDun0+X8JhvADQ1taGwcFBVFZWoq+vDw0NDeEqVGSFUA7PVG9pp8OTEEIIIYQQQgghJCGcTmd4O/vRo0fR3d29Iu1YUw7PoaEhtLW1oa2tLfxZfX19Ug+/ubkZ1dXVcLvd4c8aGhowNDSUdIU0kkKYw5MQQgghhBBCCCFkxVH+sZUMDlxTDk+n04kDBw7g6NGjcDqdaGhoSCoHgcfjQUdHBwzDmPO52+1GbW0tHZ4rCXN4EkIIIYQQQgghhBCsMYdnRUXFsiqRtbe3x8w7oD7r6uri1vaVgjk8CSGEEEIIIYQQQghYtCgpenp64HQ6Y/7N4XCsWF6CNU8gAASD8n/m8CSEEEIIIYQQQghZ06xJh6famq6qRSWK1+tFRUVFzL9VVFTg2LFjqWgeSZbI/JqpjvBkDk9CCCGEEEIIIYSQnGJNOTyHhobQ2toaLjA0NDSE2tpaeL3eRb/r8/kW/LvD4Vj0mIsXL2LLli2L/nzuc59L4q5I2BlptcpPKmGEJyGEEEIIIYQQQkhOsaZyeDY0NMwpLFRfX48DBw6goaEBfX19ab9+KBTC+fPnFz1udHQ07W1ZVaSrQjtAhychhBBCCCGEEEJIjrGmHJ6xqqjX19ejtbV10YJDDodjwXMvFt0JALquY+PGjYseV1pauugxJIJ0VWgH5jo8DQPQtNRfgxBCCCGEEEIIIYSkjDXl8IyFKkLU3d29rArrQ0NDcQsaKTZu3Ihz584t+RokDumq0A6YUaOGAczOAvn5qb8GIYQQQgghhBBCCEkZayaH5759+1BbWxv370NDQ4uew+FwxD3O5/Nhz549S24fWQbp3NIeeU5uayeEEEIIIYQQQgjJetaMw9Pj8cSssK4cmHv37l30HPv371+wwFFDQ8PSG0iWTjojPDXNdHrS4UkIIYQQQgghhBCS9awZh2djYyO6u7vnfd7V1QUgdn7PaPbt2wePxzMvX2dPTw8AyQdKVoB0RngCLFxECCGEEEIIIYQQkkOsGYfnwYMH0dzcPOczj8eDQ4cOobOzc15RourqalRXV8/5rL6+Ho2NjTh06NCcz91ud8xzkAyRzghPwHSkqusQQgghhBBCCCGEkKxlzRQtcjgccLvdaG5uhsPhCG9N//GPfwyXyxXz+Fhb4Ds7O9HW1obW1lZUVlair68Pzc3Nyyp4RJYJIzwJIYQQQgghhBBCyFXWjMMTECdme3t7Qsf29vbG/VtLS0uqmkRSgXJ4pivCkw5PQgghhBBCCCGEkJxhzWxpJ6uYdG9pp8OTEEIIIYQQQgghJGegw5PkPsrhma4t7czhSQghhBBCCCGEEJIz0OFJcp905/BU52WEJyGEEEIIIYQQQkjWQ4cnyX0ytaWdEZ6EEEIIIYQQQgghWQ8dniT3YdEiQgghhBBCCCGEEHIVOjxJ7pPuLe10eBJCCCGEEEIIIYTkDHR4ktwn3VvaWbSIEEIIIYQQQgghJGegw5PkPumu0s4IT0IIIYQQQgghhJCcgQ5PkvtwSzshhBBCCCGEEEIIuQodniT3YdEiQgghhBBCCCGEEHIVOjxJbmMY6Xd4MocnIYQQQgghhBBCSM5AhyfJbWZnxekJMMKTEEIIIYQQQgghhNDhSXKcyKjLdDs8p6ZM5yohhBBCCCGEEEIIyUro8CS5jdrOnp8P6GkSZ+XwBLitnRBCCCGEEEIIISTLocOT5DbKAZmu6E4AsNkATZt7PUIIIYQQQgghhBCSldDhSXIbFeGpCgulA01jHk9CCCGEEEIIIYSQHIEOT5LbpLtCu4IOT0IIIYQQQgghhJCcgA5PkttkYks7QIcnIYQQQgghhBBCSI5AhyfJbZTDM51b2iPPzxyehBBCCCGEEEIIIVkNHZ4kt8lEDk+AEZ6EEEIIIYQQQgghOQIdniS34ZZ2QgghhBBCCCGEEBIBHZ4kt2HRIkIIIYQQQgghhBASAR2eJLfJ1JZ25vAkhBBCCCGEEEIIyQno8CS5Taa2tCuHJyM8CSGEEEIIIYQQQrIaOjxJbpOpKu3c0k4IIYQQQgghhBCSE9DhSXKbTFdp55Z2QgghhBBCCCGEkKyGDk+S27BoESGEEEIIIYQQQgiJgA5PkttkOocnIzwJIYQQQgghhBBCsho6PEluwwhPQgghhBBCCCGEEBIBHZ4kt2HRIkIIIYQQQgghhBASAR2eJLehw5MQQgghhBBCCCGERECHJ8ltMrWlnTk8CSGEEEIIIYQQQnICOjxJbqMcnpmK8JyZAYLB9F6LEEIIIYQQQgghhCwZOjxJ7hIKAbOz8v9MFS0CGOVJCCGEEEIIIYQQksXQ4UlyFxXdCaTf4ZmXB1it8n/m8SSEEEIIIYQQQgjJWujwJLmLirTUNMBmS//1mMeTEEIIIYQQQgghJOuhw5PkLpH5OzUt/ddjpXZCCCGEEEIIIYSQrIcOT5K7qEjLdG9nV9DhSQghhBBCCCGEEJL1WFe6AZnE5/Ph0KFD8Pl88Hq9GBoawsGDB9HY2JjwORoaGuByuXDgwAG4XC54vV60t7fD5/Ohvb09ja0n81ARnnR4EkIIIYQQQgghhJCrrBmHp8/nQ2trK9xuNxwOBwDA4/GgtrYWjY2N6OzsTOg8Q0NDaGtrQ1tbW/iz+vp6dHd3p6PZZCEit7RnAubwJIQQQgghhBBCCMl61ozD89ChQ3OcnQDgcrngdrvR2tqKnp4e1NfXL3oep9OJAwcO4OjRo3A6nWhoaEjoeyQNcEs7IYQQQgghhBBCCIlizTg8u7q60NXVhb6+vjmfK2dlZ2dnQo7LiooKtLS0pKWNJEmUwzNTEZ50eBJCCCGEEEIIIYRkPWumaJHT6cTQ0NC8z1XEZ6y/kSxnpba00+FJCCGEEEIIIYQQkrWsmQjPeDk2PR4PAGDv3r1Jnc/j8eDYsWPYs2cPXC5XQt+5ePEitmzZsuhx999/P+6///6k2rMmWamiRczhSQghhBBCCCGEEJK1rBmHZzza29vhcDjQ1NSU0PFDQ0NobW1FQ0MDmpqa0NPTg9raWnR2dsLpdC743VAohPPnzy96jdHR0YTasubJdA5PRngSQgghhBBCCCGEZD1r2uHZ09ODnp4edHZ2zilmtBDK0amor6/HgQMH0NDQMC8/aDS6rmPjxo2LXqO0tDShtqx5VirCkw5PQgghhBBCCCGEkKxlTTs89+3bh/b2djQ2Nib8nViRoPX19WhtbUVXV9eC59q4cSPOnTu3pLaSGLBoESGEEEIIIYQQQgiJYs0ULYpm3759OHjwYMJb2RdCbWWPlyeUpImVcngyhychhBBCCCGEEEJI1rImHZ6tra3Yu3cvWlpakvrevn37UFtbG/fvrPSeYTK9pV05VunwJIQQQgghhBBCCMlaEt7Sfvz4cRw7diydbcFdd92V1vMDQEdHByorK+c5Ozs6OhaN9vR4PDELEylHZ7KV3skyUQ5PbmknhBBCCCGEEEIIIVdJ2OHZ3d2Nhx56CABgGEbKG6JpWtodnj09PfD5fDEjO30+36Lfb2xshNvtnvd5V1cXgNj5PUkaYdEiQgghhBBCCCGEEBJF0kWLHnvssZQ34vDhw/jmN7+Z8vNG4vV60dzcHC4wBJhOTvW3SKqrqwFgTuX1gwcPorm5Ge3t7eHPPB4PDh06lFSld5Ii1NZyOjwJIYQQQgghhBBCyFWSdnjeeeedKW+E1+tNu8OzoaEBXq8XHR0dMf8eHbnpcDhQUVEx7zO3243m5mY4HA54vV4AwI9//GO4XK70NJzEJ9NFi9R1AgH5sSb9+hBCCCGEEEIIIYSQNJOwx8blcuHBBx9MSyPSeW5FZKRmIvT29sb83OFwzInwJCvISuXwBCTKs7Q0M9clhBBCCCGEEEIIIQmTsMPzjjvuwB133JGWRqTz3GQVk+kt7RYLYLMBs7N0eBJCCCGEEEIIIYRkKXomLjI6OpqJy5C1RqaLFgHM40kIIYQQQgghhBCS5aTN4fnlL38ZO3fuhMViQXl5OSwWC66//no8/PDD6bokWUsEAkAwKP/P1Jb2yGup6FJCCCGEEEIIIYQQklWkperKm9/8ZnR3dwMAnE4nnE4nhoaG4PF48OCDD6K7uxs/+MEP0nFpslaIdDgywpMQQgghhBBCCCGEXCXhCM+RkRG8/PLLix73zW9+Ez09Pejo6EAoFMKpU6fwox/9CMeOHUMoFMKXvvQldHd341vf+tZy2k3WOsrhabVmtlo6HZ6EEEIIIYQQQgghWU3CDs+hoSHU19fjAx/4wII5OQ3DAADs3bs35t/37t0LwzAwNDSUZFMJiSDTFdoVdHgSQgghhBBCCCGEZDUJh8bt2LEDp06dQkdHB1wuF/bv349PfepT845rbGzE9u3b4XK54HK54HQ6UVFRgaGhIXi9Xng8HlRXV2P//v0pvRGyxliJgkUAc3gSQgghhBBCCCGEZDlJFy1qamrCqVOnEAwGsXPnTnzlK1+Zd4zH48Fdd92F3t5edHZ2or29HZ2dnejt7cXdd9+NY8eOobS0NCU3QNYoyuGYaYcnIzwJIYQQQgghhBBCspolV2l3u904evQojh49ip07d+Lb3/52+G9lZWVob29HKBRCb28vuru70dvbG87hWVZWlpLGkzXMSkV40uFJCCGEEEIIIYQQktUs2eEJAA6HA1/60pfwwx/+EF//+texd+9ePPXUU3OOufXWW3HHHXfg1ltvXVZDCZmDivDMdA5PdT06PAkhhBBCCCGEEEKykpSUt3Y6nXjsscfg8Xjwvve9D5WVlejo6MC2bdtScXpC5rNSDk8V4ckcnoQQQgghhBBCCCFZybIiPKNxuVw4duwYHnzwQdxxxx2LVnQnZMmsdNEiRngSQgghhBBCCCGEZCVJOzxHR0fx2c9+FgcOHMCb3/xmHDhwAA8//PAcx2Z9fT1OnTqFW2+9FS6XCwcPHkxpowkJOzxXKsKTDk9CCCGEEEIIIYSQrCQph+ejjz6K8vJytLS0oLOzE93d3ejs7MSDDz6I8vLyeRXbVUX38vJy7Ny5E1/+8pdT2niyhmGVdkIIIYQQQgghhBASg4Qdnk8++SSam5tx6623orOzE729vRgeHkZvby8ee+wx1NTUoKmpCcePH5/33ZaWFhw7dgynTp2aV9GdkCWx0lXamcOTEEIIIYQQQgghJCtJuGjRsWPHoGkafvKTn6C0tDT8+a233opbb70VDQ0NKC8vx7Fjx1BTUzPv+2VlZfj0pz+NpqYmPPTQQygrK8Mb3/jGlNwEWYOsdJV2OjwJIYQQQgghhBBCspKEHZ719fUwDAP19fVobm5GbW1t+G/Hjh1De3s7NE1DfX39gudRFd0JWRbM4UkIIYQQQgghhBBCYpCww3PHjh147LHHsH//fvT29s75m2EYAIDOzk5s3749pQ0kJCbZkMPTMABNy+z1CSGEEEIIIYQQQsiCJOzwBIDGxkaEQiF0dHTA4/FgaGgIFRUVqK2txd13352uNhIyn5XO4RkKAbOzQH5+Zq9PCCGEEEIIIYQQQhYkKYenoqmpKdXtICQ5VmpLe+T1pqbWlMMzFAJOnAAGB4HKSmD3bvk8+jM94VJoK0us+8mVtpPYrMY+XY33tBZIVb+p8/T3A0NDQEUFUFVFOchFcvldzuW2k7mkWjdRJnKP1dZ3qbif1fZMcp1c7o/ltj2X7z1bSdjhOTo6Cq/XG7Mg0XJJ57nJKmWltrRrmlxzelq2tTscmb3+CnHkCPDww8DJk8DMjPh5q6rkb/395me7dgEPPADU1a1sexcj1v3kSttJbKL71GYDNm0C3v524Pbbc9NgoJzmJqnqN3UejwcYGAACASAvTwxgl4tykEvk8rscq+033CC6dds2TshyiVTrplyU57XOauk75RR6/HHgO98BLlwwN94lez+r5ZmsFnK5PxZq+223Le7IzOV7z2Y0QyXgXITPfOYzeOihhxAMBlPeiHSeOxvYsmULzp8/j82bN+PcuXMr3ZzVwR/+IXDxIvAP/wC84hWZvfadd0qozaOPAtddl9lrp5F4K0pHjgD33AP4fMD69eLv7e8H+vrke9XV4vycnpbPy8qARx7JXsUc635ype0kNtF9OjMDnDkDjI+LDG/YkP0Oouj3b2QEuO8+ymmukSr9os5z6RIwOiryYbEAwaDIdFmZyDXlIPtZjkykI9IjmXPGG/9Pn5bzVFQApaWckOUCqdZNHJtyj8X67gtfkH+zPbIscjHw0iUpqVBcLAswNltyskh5zi5yuT8WarvFAmzcGD9IKBQCvvpV4JOflJiuLVty695XgmT8a0lvaR8bG0OCPtKEGRwcTOn5yBpgpba0A5LHc2hoVVVqj7ei9KEPAZ//vCjv7dslwDUYBK5ckf8bhqyq2mwS7Lptm0yEPvc5WcnKlKGU6AQuFJL7jLwfACgqkm5dibaTuSQ7wY/u08FBwOsVObXbAb8fGBsDnnpKDJFsNBhiRaeOjsr7tXMnMDEh0X0LySm3wKw8qdIv6jzDw9LvoRBgtcpPXp7IiN8vTnHqq+wmWiYmJ8V8SUQm0hHpkcw5Y7X90iUZ80Mh0U+zs+LwzGb9Sub3pVpEWapuWk021FoZOxfru+efB975TnmflxopmQmUU2l4WGw7XZdxcWoKePFFiT5PdC6insmVKxK4YRjimMplec5llqtfVvJdjmz7tm2iY/1+cVOUlQHPPitj5803m45MNW7edRfQ3Q385Ccix3l58g5u2CCOU8ri8knK4WkYBhxrZAsvyXJWqmgRYDpZ1bb6HCfeitRTTwFNTTLJKSoCzp4V42JqSj5TA9H4uDiY1Db3igrguedk0MlElopkJnAnTshx69dL+y9dEudSRYX8XlWVXNvXiqGcKZYywY/s06kp+X8wKEYrIP06MyP9MziYfQZDrPfv0iVZ0dU0cXZarXI/N9wg8hotp9wCkx1EyiIgUfC6LsavpokePXYM6OwE9u2LL4PqPCUlMhELBuVnZsZ0fE5OAtdck1ldS5InWj+9+KJ8XlkJbN4cf8xZaFxeqmMx2XNGtn1sTOR5YkLGvfx80UmTk+IkWKnFTpIYkX05MgK8/LJMqq+7zrTdFtMloZDort5emcAbhvR5IAA4ndLnydpQK81aGjujx6eXXhKnTHGxvMs+n/RlZaU4Wparb9JBpFNp3TozunNmRv7m98t91dQkJotf/Srw4x/L94aHzZ0U118PlJfnnjznOtFztOFh6dsNGxafo630u3zihDg1rVZpo98vnxuGjJPBoPzf55M5Z+RCw0c/Kr8HAuJiCIXk3oeH5TwL2QokMRJ2eDqdTtTX16ezLYQkhmGsrMNTVWpfBRGe8VbTNE0Gi9OnZZWpuNicwKgAb02Tz4JBUfDBoBgf6vO+vvQr5WQmcKEQ8KtfibFfVAScPy9OJUCMd4tFvj8wII6xRK69VgzlTLDUCf7goDx/u136NBQyZVUZGICcK9sMhsj3b/NmcW6NjppOBaXqSkvlXrxeMcQj5TQdjpFsIBcXEyJlcWpKnESA6YSfnBTj9cMfBr7+9fi6Qp1H00T/apqpmwMB8zhdl79zk0z2ovoyPx84dWru52Njsm1N9WFkgapPfSq1UXRLiZxRbTcMcSIEg3IeTRM59vvlWL9/aQuGJHNE6qbLl+Uzv19kcufOxW0fZe/09oqdNzAg45HFIvbf4KD0fzI21EqzWsfOeETqopdfFlsYMG0OlVXO5xObOBujHCMdYleuyHioxsfIIIzxcXEcLSbTavtwQYHoOaXXXnhB7Pni4tyR59VApJ4CJNAmFJJ+3rxZ/h0ZkblcpE240u+yzwf8278B585JUIKmyYJSXp6M8+rdCoUkyrO/X9put8v9zM6KnQvIPSq5Vsdv3pxbujUbSdjheeedd+LOO+9MZ1sISYzZWdOLQYfnsoi1mnbunKmcdV0eta7LMcrxcvKkGLmAKObqavn3yhUxNAIB4DOfEYP4ne8Err029W2PnsD5/ebKWXS0yS9+IcceP24OGIYh4mO1isFXXi4DpM1mDjzxWOnBdbWxnG0slZViwE9Pi+wB0odWq5xXrfyfPy/RLNnkIFLvn1rljXzvLBb50TRg61Z5L6en5VmsXy/3WF4O/M3fmM9tYkKM9+Li7JuoJEOuLiZEyqLaABAIiH5Ri0hWq0wmF9IVlZXS9xcuyO+aJv2pojxV8aJQKDF9RVJPog55JRPnz4tcWK0yHp47J7roxRdFz730ksjCyZMiLwMD8vnFi6LXZmbMcwYCsv3tjjtElpS+VEQuXKp/fT6ZJOblSRQKILJz7bUyfsVyVlZWyj0pR21BgYyzFou0QUUeK1sgekKWi4sWqxUlhxMTIl+A9P/srPTvpk3xdUmkvaPyO0ZOygsLxYlaWZm4DbXSrMat+YtRWSl909cn45Omyfs/Pi5yoZ7ByIjooa1bJVVVNi1kRDrEJibkM4tFZFvtiAmFzPuJJ4uq/6em5BgVJaoCNgIBcbbt2JEb8rxaiLShlA0PyCLL5csy/gQCwKFDwPe/bxYCUimAqqpkam61ZuZdvngReOwxacvAgHxmtYqDsrxc5GloSOx7lRJORXKGQqKL1aLixYtzF7jVLjU1t1lInsniJJ3Dk5AVJ3IrOR2eyyLSeAiFzNU0XRfnZlGRuV1940Yzb2dhoelcKimRH0CMo+efl7/b7cB//ZcMBHV1wLveJdFpqSLaWfvSSyIaKtmzMtK++lXg7/9eDNuqKvlX5UecnjZX2BwOWXWrqZGJWTxiOVrVdtXVaiinm+itVmrb53XXLR45tHu3OMOeesp0CihjQRmuFov09wsvSD9ni8Fw5YpZfVvTxKFwzTXisHz2WXnHDEN+duyQianPJ8b8614n51DPbXJS/q5pkiPIYsmuiUqi5PJiQqQsAtJvgYC5Pjc7a04g160zdcVrXgM8/bTpGNq0SWRZGf3KALZa5bNQSHTuxMTi+oqknmQc8rt3ywLcz34mx23eLE6j4mIZby9elL596CF5/zduFKfkwICMU6OjMq5Yo6z1YFAmeImi8sFaLOZWO79fxutrr5U29feLU1TJ4ebNcn9TUzJ527RJ9InFYkauKB0LzHV25eqixWpF6ab/+R/RR0VF5pgyPS1j7utfL2PH8eOmDNx881x7B5DoQDVmWa2i1ywWkdnJydzQSZE2x/CwjMWbNondu1qjlV/xCtMBU1Ag/a/uVzmXCgpEN01MSD+vWyc2SbYsFCuHmM9nLr4oJ6Wy+9Qi49RUfFlU/b95s9zb8PDcSFFNE/vr3Dng1a/OfnleLUTaUCqDYiBgpk5RC8br1wNPPgm8971iPz3+uOlABGQs27UrfWk2vF7ZpfOTn5hO2T175P05d850dgIyVqrdkGVlwE03yefT0+LEPXNGfrdaTUeo3S6yPDFhOuPHxnJDt2YrdHiS3EM5PPPzV8ajtIpyeEaupqmJtJqYqNXSdevklk+fNrcsVVWZA8u6daLIVSU5VTm4ogL42teAJ56Qyd7PfgbU1orjs6ZmflRKskQ6a1VuUUCM7tJSM1eKcnZu326upCkDCZDvDQ3J5w4HcP/9C4tVpKE8MyMTxqKixJxzJDaRfTk5aa7cDw6KfC20lUPXZRL9/vdLRJwyLFQEitUqEchDQyKfk5NmuoWVjEAaGBBnvFqN3rBBjG/1Xlx7rciWWtEuLRXZUjnT7rgDOHpUnPUFBWY0oGGYUZ65tgUmejFhZkb6Qy0mvPwy8LGPAR/5iDyLbIsYU7J4zz3iSFDOaptN+lEZxsPDpuOztxdoaBDHl8rRqd6FggLRx2Njol+V415RVra4viKpZSkO+aIiM1rDZjPHS0CcLD6fTK5DIdEHQ0NmtDcgfX7DDaZumJyU4z/1KXPypOQiuqao+v2ZZ4APftCM/lYRJWNjMuHKy5PzHjpkbsebnRUZLCiQduu6fHdsbO6Wvf7+uQuGIyPAfffl5qLFakXppj/4A+mLdevks02bZCEQkLHibW+TybxyUm/cKOPlNddIn6vc0sq5ZLWKbpudFf183XXZqZOix3pVKXloyBwfz50z36dcGztjEXnP5eXA974n73JenulQibSV8vIkF2tpqegGtSA7MiKfZXKhOJ5tphxiP/+5HFdRIbpwelrar+z7wcGFZVHZnOvXyzg6PGyO1bOzcv1gUJ5XNsrzaiXShjp3ztxJoLZ3qznqmTPmvPW73zWLbCkZmJ2VXRVqB0Mq3mXDAH7zG5nX/upX5ud79gB/+Icy9v3859L2yPmyGrcNQ4JxFHa7yO/Zs7KAvXu36KUzZ+S+I9PIXbokupqyuHTo8CS5x0rm7wQQshdiYhw475nE9O7sm3QnQ+RqmnqslZVmJGd/vzgpP/hBqdZ+8qQMHDabrHoCcozKW1JTIwpZTWY++Ukxnr/xDUkM3tsrP7t2yQDxutct3fEZ6axVOalUBNTTT8t5AwEZ5FSk6vi49FVFhdzfxIQYPTMzstr9N3+z+ERMGUrBoAyofr8Ziadpq8NQzjSRfanyHgIyyFdULL5Nrq4OeN/7xAEwPS3GzvS0TO63bZPvWixyrg0bgH/4BzFMXnppZSKQjh6Vd8PnE0NH0+Y6OwExwsvK5P9+v/mOXX+9ODQ/8QkzEkxtpVEpGtS958r2QkXkYsLly9L/BQXSh9PTMvH6xS+A97xHnls2RozV1Uk6jz/8Q1kUCgaln4qLzVxUp09Lf9rtMqmcnZXtg3l54nwYG5O/PfigyIrHI/rE7zcjPjdtouMo08TaBhsILBzd/x//IZPpV75SnA5erzmG1tQAb32r6C2bTfTQ+fNmGhlA9JKafBcXm5Ofmhrgd34ncdtjxw7gn/5JxnpVpK+6Ws519qy8W7ouOqe0VBz2w8Ny/QcflGJbJ0+a+cksFtFZ/f0iry++KBM8ZSuoZxQMzl204A6IlaO62kynoGnmmPL614scqFQHN9wgOnh6WhZvh4dFZvLzxRFmtYq+GhszI68A0dUHDmSfTooVbbx+vWmjqSjB2VmRW4cj98bOaKLvWdmo27YBbjfwox+Z9nxenjjAATPic9Mm0TcvvyzjmK7Ptc0y2fZo2+xDH5KIvulpcchv2CBOImXfq4XCt70tviwqm3NyUu6rsNDMTawWKgsKpJhMtslzLrGUoIK6OrFtmprERleBC3l5ZsFGQPpL2VVlZSILDofIwalTck2HQ6630Lu8WBsNQyLjv/Y1WThU1779duB//2/JgRzddiW/aqyvrRXd6fOZCw7T0/J3tSNS00SW8/NlTJ6cNHNmb90q4yplcenQ4UlyD+WZU5GWGeTIEeDJrxTg1SeB7r5J/NsPsnPSnShqNe3uu0UZ5+fLADExIRMZFUFUVyc/0YMCsPhg5nRKRNZ73yu5Tv7rv2Qg+NjHRIn/7/8N1NebW/YSHSCVs/boUTNfz/r14jCInGRNTorBpowah0MmYhaLfP7ii2Lc/cEfJNaHp0/LIHXxork1UNfls6qq3DeUV4JIx/vsrPm5ygs7Pb34Vg5dl+13r3mNGA3f+Y5EPY6OSn/ceqsYymfPAp/9LPDooyJz111nTu7SHYEUDEpU57//uxhRO3fK9T7xibkrwpHR0l/4gpk3rbJSIlIOHDDluaDAnMyoLf1TU+aCRS5tgYks1qMWMaamZAFDvWuaJs/Bbs/eiLGqKtk+ODMjToWyMvnM75f+GxgQOb90ySyspZzagYA4c+12cTJ1dcn99/fLd69cAf7v/xXnhUprQDJDdBqVS5fkZ/v22LnuLl8WPQPIlvW3vnX+2Pbf/y0ycP318vmlS9L/mzeb0dyR+eUix+VkHIaRkTORuqa42NySbreLDBYWig5S+qW315TDwUH5/ne/K89COX1LS+U9LCkxn9HoqExY16+XMZY7IFaWnh6R0zvuAP7P/5m7bf33f192FOTlybh53XViP23ZYvZ5RYXYZ4WFIvOGYUYAHjgg+ur4cdFleXkrfLNXiRWRPTYmMj0zI3bgjTeaC+dXrpjpHaLHzlzJSRt9zyqa1e+XvnrlK2VHTOS9qKjsSN1gsYgMaJo4kz72MWDfPnFERafYSFfbY0WHFxfLAs7Fi9InY2NybE0N8Pa3i876+tfNStmxZFHZnE88YUay33ijzCWef16e12tfK4urZGksJ61JXZ0E1QSDIqPbt4veuXjRlMviYjn27FkZXy5cMNPFrFsnuunMGelblyu2HbxQG1/zGgnU+frX5b0ARJZ+53dE323eHL/tt902X1eoWhKRjtBbbxV9/OUvm+9eSYnI9/nzcv8Oh1wzm2zcXIQOT5J7qL3LGY7wVAPxbecL8VorcE3pJEpLs3fSnSh1dbJS1dUlhs3Fi7GjNXU99gQl0UnLxo1iUL373cA3vymRL2fOyGrzP/8zsH+/DFb/8A+JDZBqArdvn7lF6/LludsH1BYI9ZnFIo4C9bvVKlE3JSXAL38p/bhQxOmRI+KAUsUaADMRdV+fDIY+X245mbIB1Zd/9mfigM7LE3m5eFGMgOrqxSf4zz4rffHmN4s833tv7MlJKAR88YtmsY2LF8Upn+4IpMFBcWyq/I5ve5vcr80mxnq0IRT9/gHS9ne8Qwy4yGgEFXGltrOPjsp95NqWZ1VU4exZuZfiYlMnBYPmFsr8/OyOGFOOoIYGkcGnnhL9lJcnTuz1683txGoL+8svy3eVEz4YlAnb00/P1bFqC9eVK3KOTZtW4g7XJtEVZFXE08WLMimJjO43DHmnp6dF97ztbSIT0eNlZHT7+vVmHmNA3oWXX5ZJ+OCgjFOx9EKixIo+CYVELrduNdOJqPzcO3bIOxdLDt/zHpHt3/xGxsTSUokM/M1vzGekcjEPD5tyyh0QK4NhiMMTEL0U2ZfHj4uc7dwpToPpaYlEvv560cHFxSLrKtIqcqI/MSFbOv/2b8WJ2t8vedvf9rYM3lwcYkVkT0yIUz8vz4y+HxkROxCYG60cOXbmSk7a6Hs+d07+b7GInTMyYo6X0booXmTaffeJfdXZKT/PPivOz0hdlY62xysktWeP6Nt3vUuCJaJtvEBAHFX9/fLv7/zO/Gvputhf//3f0p8bNpiR9ao4zO/9XvbYFLnGcnOxBwIiu6WlIhfK9osugDsxIe/i+94n6cuU03DDBlkgHh2VdzyWHRyvjcePA3/0R/K+qOj1wkLgf/0v4M47ZeFnMWLNl+M5QnUduOWW+e/eq18tgTj//M/yzilbnywNOjxJ7rECW9ojB+LSawphOQfYjamsnnQnytiYRGHcfLMof1XUJV2r1+XlwF13SWTnf/6nRH1euSIGs9crin7r1sSi7rZvl4mUilBRW2+KiuR3VekakEFxdlYGSLU1UK3iT02ZzjWVmD8aJQMjIzIReOYZcwBSUS4vvCCThlxyMmULdXUSdfCxj0k/qWqFhYXA7/7uwsbR1JRZSfgVr5B/4znoT5wQg+K666TPJydlRX/nTpG9dEQg9faKfPt84qx84AFZ1Y2893iGUHTbT56UfGq6LvJmtco7q5Lsz87KO/3a12bfZGwxdu8WQ/X0aXlO114r93P5srxrKpeTigLO1oixkyfl3xtvFOdCdFSd2p6vnJuBgBjnapux3S73GssxZLdLnrkTJ0SuIh2euRKBlKtEOieLikxTZGbG3Kqmovu7u2X3QV4e8OEPx5+oREa3q22VCpXWoqYmdXlro3XNqVPApz9t6pRLl0QWVQGXeHKo9GtNjRSPePJJ4Nvflklafr6Mrer5+P2mkyjZHRCU6cRY7Dm9+KIsJNls88eEyHyGBQUyHk5Oyk9hodhjTz8tx5SVmRWyI6ON7XZJ4/HII7KD4fd+L31RgIkSHZE9PCyL7KrwptMpsr19u1k8TEXYR9qbuVRIL7oYk3pvt20T29tmiz9eLmSH3H67/P/Tnxbb9667ZLu3Sm2V6rYHg+LwqqiYW0jqmWdkvmCziV2o7L1IrFZZGG5vFwftm98cW/+eOycyEJnD1WaTZ2W1mhGEJDkSdVwvNF8+e1ZkYMMGiTKPNT5GzuHe8x6x4ZXTcHbWTBe1fr1Za3ihNgYCYjuPjZkLf69/vQTjvPWt5rxyOcSbl8R79wIB4F//Vd7ly5dlnCZLgw5PklOEQoD3N1OoHAb8YwVYF8qM8Rs5EM/OFsA/C0wPTSK4LXerISt++EMZHK5zhvAO5wloQ4MAKgHsBhDxcFM88ygqAt75TjFMvv99WW1V2/ZeflkusXGjGB/xBshvfEMM7qYmOe6v/komaiUlEqmgaTLJ8nrNaLjorYEPPgj8v/8nSaifeOKqwzPGvZ44oc+pJG63m1uIVbSnrktkYbYYv7lGMCiO99pamTCdPStbwCO7IhYnT0rfVlXJz0JETu5Ki0Owv3ACxZOD8L9Uienrd8Nu11MWgRQKyfbjf/1XaZ/TKTIavUoNxDeEYrVdbTdTE7aNG0XOR0dlQrB1K/B3fxfDeZ/l3oNAQCYcVqtZbVW9YxaLNFUlrPf7xRjOxogx5fDctUu2LMWKnLnxRokKLyqSn02b5q7gL+QYqq01HZ5vfat8duQI8LnPhmA8dQJF04OYsFdCu2U37v+wvrb1UQplPtI5qSLEFJcvi2zW1Mj797GPyed//Mex33dFvK3mKq2Fw5FYbulkiNQ10U7cjRvlR5GIg7KxURye3/ueRMbs2iXVa1W0FCCTSJstuTQbcaPqPhRCXVn26rG0EkOej/xCX/TdV9Gdr33t/Ml/tAyoIi6XLomTTC3OFBWZaQ9i7UJ4y1vE2XnlitiVb3lLxp5KTCLHy/5+2SIKyP1t22YWtlSLCc8+K3am2lIKpMZ5kzSxdBaQkB6LvGd1vxs3mhGsi42XC9khr3udpOj4678Wp/hDD4mT+0/+xAwsWM59DvbvxsyMLrmtL4WwZeAEqoYGUVFdDk0HimaG8fRQJQYLdmP7zXq4wFQs3vIWsb28XtFNLtfcv/t84gx1OKT/ysvNR9vXJzvNXnppCfdE5i009PeLTnA6ZUFlznx591UZUDl7KiqAqiq8NCDzT6dTHI7xxsfIXUyxnIb//d+yI6atTSIlle6LbKNhyHxxYMCM6FS67uBBYO/eFD6cBewRHSHU4AQAcw5us+m47jpp6zPP0OG5HOjwJDmDMn43HJvGewaBp84W4AfvyEwUU6QRMR0olOIR2iQGB80V32ybdCeCYchg8ErfERx89mFo/zvOfp14M48PfWhugsElTDzUiqrKyTI6KhF7/f3yb3V1bIfywIAkXgdka4vfL01RCegjt11ZLPO3Bt56Swh/8bYTcM0OIrSxEr82duOJJ3T84bWx7zV4+wOYmamD3S6OOKtV2mWzmQVISkrkXsjS6O2Vvnv722Vl1TAkL9gzz4jT8IMfjP09VWxhzmp/HMNCTe529R/Bu/sfxrWTJ2HMzsA/lY/zU7vwtWsewBVbnUzwl+EsGRqSqM4nn5Tff//3gT//c7n2UomemJaUyI/CahWDsqREjPU5Ds9E9uStsEP08GFxIu3ZI/fw4oviKDEMud/t20UnXL4sETmBgExYsilnrs9n5h+9/nr5N5YhfvPN4iiKjFyITMexkGOotlaMd48HMIIhPPl/T+B//uJxPDj0HWzCBdgwi1ktH89378I/PfMA8M915hiZ5U7vpFjMMRCZbDIF+1AjnZMvvSSyqnYODA7KZOQNbwD+4i9kHNu5U3J9LUa8QgfL2b6eKAtFmCaaB/i3fksc9ufPS1TV9dfL2KwWoUZH5fmMjyeeZkNF1Y0Mh/DakhOoyhtEv1GJwC9H4H/b5zFZdhKFlqt9esMNMmhs25b7Mh0LJeePP24mp75alnigaheOeOvx/uEe3GCchM2YwSxsuHBpE35+9O0o+tvbUfPu3fjJT+R5NDTMP320DOTlmRFOly/LpaxW2YGxf3981ZGfL4vY//iPwL/9m0TWrWSUZ2SKFJ9PPquqMm3DiQlzV4eKVv7NbyS9UVeX2BuRjpFQSGTc4ZgbdZiSYIcF+ji8iquScS6gx5SNMDpqFneJHBuXm2N+40bRVV/6EvCtb0khl6efBv7yL83CRwkRwx6p3bgLrw0+gMIrwEOnH8bOwEkUGyMoPT4u6QgsJfAFS3EusAtTb3kAmnEb8FTssaykRCJAv/1t2UUW7fD8938XW+L66yV6NVYEKB2eSyM69Ut/v8zPLl2ShQQ1Xw4+fgT4q4fFkBkYEIMuLw+orISz0oXdxoewx1aGutlB/NO9lfjEd3bjuef1BcfHaIf9DTfITouLF4H/7/8T/ad2NszMmDulIkuDbNgg8nPunLxHSyKWbRKZxDP6PQbm/s1mk0H17W/H60tvx/PGbjz7rD5nZxhJDjo8SU4QuaXknfYpcRoU2DO2pSTS0TBjKZQKfjAdntlYqGaOvi0PYTdOQB+eaxgcPw44fnMEH+y7BxvKfcCGGPt17rpLMipH7+f51a8kUZPyMi5jMjk4KLbdtdeKQTUyInPV8XGJ5tqyZb5DuatLxsfdu8XRFQolvjVw+9kjuO67D0Nrk8HlNks+/sa3C70X6hH46ZdhHfPN27t003P34LXBR/DsWF3YeF6/XgbMy5dlQM82Gcgl+vulzzVNouIA+f9dd4lf/XvfE+dBZPSRQlVOvGlXCDgeZ9JwVTZ331aHd1Qdwf/51T0o133w2dZj1LDDGpiGc+wpfHDiHqx79SPYPQLgHUtz8j/5pOTrHB4WEXrgAck1tVwWck4gFMK6cyewu1xWh/te3I3ffgPMSdQXv2iGtsbakwesaJKyK1dkEgLI5Om3f9tc+P/Up2TBoqxMJptWq0w8r1wRvfD612dPzlwV3bl169wtULEiZxaK7JvnGIpQ6LsclSgq2I3tZ3+B8Tc/jC1PePDBqUvQYWDSUowr9m3w6zbsnn0K1565B//2kUdw20/roP8i+53ei5KoY2BkRF5AXRdH2LXXxt6HmuT9Kufkhz8szhFdlx/DkMu1tclQWVSUXHGPRNNapJrFIkwTcVDqukQs//CHovtUXuv8fHM7eyAgk81EHLihQAjf+tgJ/MHpx/E24zuo7L+APGMWeiiIouAIJo0CnNO3Yucr7dD6+yV/wA9/aO5/jaW3ckWuo9unHEMej3gMVHLjbdtg2GzQf/0r3D/7Q0zkleGKfSv8oRlcM30Gm0Ne7Ln4BEb+fANG/sWFTSMPYPbaOrzqVfMvHSkDzz8vr45KH6KKxVksshPmd38XC0663/Y2cYJduiTd8ru/m57HlQivfKXYZKdPi0xv2iTODCC+M//AAXF4fv/7Umgz0nlz+bIsZE5OipgBKQp2WKCPMTMjtjYgq/9Kjx0/LsbRBz5g7jfX9bCN8POfy2nKykwdlKpChnl5IiuvfCXwmc+I2N59t2xx37MnxheiZVtVSIqaU5S9/BQ+M/hezFwA9FAA01oBSowxWOGHZgAFoREM6mXYNf0U1n3tvcCRjaYTOMJBpJ7HnXfq+I//kMd35oyMyYDYDf/xH/L/u++e7+x0OuVflfIoOiKaLEzkfNlqNVMQjYyYY8FrAkdw0xfvAUYuiVdRJZO+mkOlqv8IPun/b+jny4BvWeDKz8e3du3CqZYHcGZbXcIqvKAAaGmRd9ntFqenppnvwsCAtNFqFflQ77VaDFnSfC5WcEFVlblKH22Dv/e98j31t5kZEVivF3jiCfwvxwZcG3ThV44HgD8zjYRQeSVOYDcGh/WsHNKyDTo8SdYTuaVkx7YQrjvzPEoCw6jEELZvDeHlM3ra82dGOhqmqgphALBjCjMz4pQbGMiuQjWR+nb3yBE0jT+MCziJypIZFJSak9zv9tyG/WcfRqXVB33H9vn7dV5+GfjkJ0VhR+7n8fsl9Gp6Wh76K1+5rKRGsbZU7dghW0uGh2UMLCgwB5+xMQncAWRLDbD4xK28LISH/+gEXM88Ps/5Y52exq5Lx1Hj/W8ESgtg3b1r3rOwnz6Ne/yfw++fvg2GoaO0VNqkmJkRQylbZCDX6O2Vf2+4YW7UYk2NGNHHjklU20c+Mvd7hiERnq/0HcHvdDwMvBhj0mCzhWVT/8IXcD8+jxn48DK2Iw8arDZgIlCE8WAhqrXTaBn9CPR7R8VCS8LJHwpJZMtXvyqX37FDtrArQ3u5xJPxXf1HcOfph3F96CTKC2YwejkfwU9UAf8Kse7PnjVDoCsqzDKXak/eRz4iRmf0/WYwSdk//qO8Q7t3A29849wCL3b73HuuqJDbOXNGbsXhMLfDrzSR29kXI+HIvigD2pKfj3+YqIJ14CKC+X7kz4zB0HTMaHnID03jmolTOFdwPS7Zt2HD1Gn89vHPoe+fQ9j5yPxJZkJO7xRE8ifFEpw/cxwDTqeZwyQUEvkvKJB7iNyHGgoBn/980k7+ujoplhcMig/i2LG5+TytVrl0W5v4JxJ9dRJJa5EOlhtheuSIRFFNTsq9q1QypaXyuMvL5d9Pf1r0+2InG/3Yw3jwiAfrQpegGQYmtGJcyd+KjcEzsAcnoGsBDEwFMHVlDIXnzko/GobMrGNVklwsuj2TztBkIn/q62WxeXhYjB5dF6fA9DRw6hSmNu8EAn5Y4YceCsA/6cfWUB+sCGBWs8Nq+JE3M4ag5yncG7oHvbc9Aqs1dmfW1UnxqXe+U14d5cTXdVEV110nqmMxW1tFeX7pSzIWvulNS9zuvEwCAZE35czIzxdVEQwu7My/5RaJ+nvhBVlP2b1bvjsxIe8FIN8PheR7yw52UNEcsfpYVXBUDRwYkDByv18mHufPi5dxyxZZcXjgAeh1dfjQh2Q9aHpaDl/snpfK7bdLFPtf/ZVEzLW0SEqLP/7jiPNHv3s2m9gahiEyHmFnawUFWH/xKGaDwFHsxc3aM7AigEmIoW3HDCpDA7gS2owN558FrlyQrRJRDiJs2AC4XNj8wAN47Wvr8ItfSIDE/ffLpb76VZGPmhrZLRFNaan0p9okcOONy39Wa4nI+XJ03ssrV2RB6xH/w7Brw9IRoZAoGeWJnJqC3T8CwwCMGR24UeaX2vHj2PncXdipnPzRadfiMDYm1x0fl0vddJP8rt7jigp555X9uKyFgVgJf6emZIITCEh0jnooRUVilxw9Kr+/6lVig/f1ybF2O+D3wzYzhurpp7DjR+9FsG4jLIP9mBqdweBYPgawCx3FD+BEWV1WFlHLJjTDUBkLEmf//v345je/CQBobm7GP/7jP6a8YauJLVu24Pz589i8eTPOnTu30s3JOY4fFwPq9ZpsQb3B90uUBEcwaS3Fs2Wvwb9UPYCfGXX4xjfSO1lQeiy//xw+c/mPMKUVYl/B/4PFIvOtbElcHrkV7L36V/G+i59EXmgKF7AFgTw7rt86jdLpfgSKyvD54L145+lPY/1WO/ILLDIC+P3mBGJsTAwxlaleqYuREZlcKEt22zYJvdM0sRBqaqQUe4JWlao+/dRTciplAw0Pi89VVbp94gnZPnP4sEQP7N4NfOUrc1doI3PZFc8MYjy/Eq+4dgQfwuex7spzc50/W7fKvwAG+4ZRdvZpBPPsyN+2Ue4vFBKLsbgYmJjA+MVRvHnoG+gN1qC6Woyi6WnZ0mQYstr9gQ+kph/XGn/7t1JR813vksCFSF54AWhulv8/8ABQkB/ClqET2FkxiP5QJT7VOoJ7++6Ds2IYmsqJkJcn/axpYgDn58uEweEARkYwY9gwNqkj5A8BMBA0dISgI9/iRyWGodnz5XsWi1hCgQBw5gyM6WkECkswvv2VsIWmUTjRD62sDGOfegR//ZO6sOP2d39XghiWs4U9HpEyXjv2OP7P2BdRaJ1Bwbb10IvsGHi2H5un+5Bnhezfu3zZzMquaSK4KkR0Zkb+XlAgERKqCpKa8S7hfU4Wj0f6VdNkfq8iLKLvOTIhvapuPzkpDnKXS6JqVzoa46GHxO92zz2i0xJhQX/LT38qoYIjI7JnsKICmJpC4MQzMGYDuFK0A5UTZ67uiZeTWeFHADaM2NZBN4LQA37YN5WjaHpIZFolSc3Lk5/z52XSHMvpffasPOQURPInRDznVKTzJ/IdDwTkYVkscnwoJMKh3n0l93l5co9qsjE4KMISHfWsvAKLDOj33Sfjld8vj0/TzG2zNps49s6eTfurk1KW4vdTY/fx4/KIlVPI4TDzb9tsYh68//1iy8XlqvEydXEYM/2jKMAU/EYe8iAhQhYthJChQ0cQAViRlwdYQgHTyxoKiQemvFz2I9bUyL7kGNFk4X6+6y5JcJmJyPZkIn+uXDG3CFRViUdJyXMoBAQCCGoWGIEgDGjQEEIQFlgQQkDLg6FZYADQjBBest+E8tAgCl9bg7IembvF6ujjxyXCMS9PdOzFi/JalZSIw3NiQl69xWzt6Wnp55ERyYH3pjel9jEuxswM8PGPix62WCSdzOOPzx07brwxvjP/xz8We8ThAL7+dbmXn//cHD4BcZAUFCxzeIx8edatM3PzqIWaQED+r+TbMMwISVW6OhSSVZeJibDe6i2sw113AZcuhPAq+wmUzIodrNfsxoceSH1O59lZyXn5n/8pv996q6T1qHgmhvNnaEjCiPPyxP62WExbe2oKxpUrCAaBUZSiFKMIQYcBTWQZBnSEEEAe8uCHbtGhb6iS8weDps1XUCCdV1aGUx98BHf/3zrYbNKms2dljauoSBZZ4+UBffBBWcj68IdXPhdtLqLmoS+/LF1bWiq6w+8H3rL5OP5l9p0oLLWKXlOrB1f1mzEzC4SCmNYKxA/6ilfIu3D2rMi+zTbHyb+QQKtX7MknxWTw+00V6/fLZ/n54jdP0gSIf7Hjx2XwUyGtk5OiTINBaXt5ubma5PfLOwGIbebzmTtWLBbAMGAEAjivX4sNky/Bkm/F+Pab8cIZO6yzU9iE85i1FuCfrvkovmq8B6UOPWt8EZkgGf9a0hGeBw8exN69e3Hw4EH09fXh05/+NPbu3YujVz3UBw8eRFdXF7xeLxwOBxoaGnDw4EHccsstS7ubNNDW1obBwUFUVlair68PDQ0NaGxszPg5SGIMDkqU4oNj96DY78NUKB95yMekVojq8afw4PQ9GCl9BIOD6X3DVRTEIx+3o/DiGEoxjJtmnsTLZbegrW2+EbESO6hUNOzOS0dwr/+zqPX9BLbQFAJaHvItsxicrYDvfB5KqnSE+rxoCjyIAmMStkv5sZPYRFb6Uc5OVUlE7VtTGZ+HhuRGy8uTTmoUL3LNZjN9TcPDkh9NJZ83DBkTfv7zucq9DkfwOu1hzOAkDMxAnw7CdmIEWkGBmWTIZpPlvmeekZE4FIJjagYaQtD8kzAuXYKm9gH19UmoXlERMDOAmzcO4pxf2qSS9197rbRFJYbPWVZo259hmBGesbZEXX+9BBj/538CX/zDI7g38DDyAifRnzeDvEIb/nJkFHl5BrSqLSIgui5GQzBoJt4qKpL/Xx0U8wsKYLNrCOYBRgiABsxMA1owAIT8gNVizt4NA5iYgBEMImBYYYxNYuDkAIatVSgo3IbtY6fx7F2fg+f625Bv13H//Smc5MXokzr8QmTceA7WibPQQ35ohWXQCioQLNTh8PdfnSiEZMun2peoJlJXrsjzUM6g2Vl5ZhcuyHFDQzKJSmMZdHVbV67IZMkwxFaM5ewE4m/5PX5cJjLKaep2m9uS0kW818QwZC4HJBcVEjey76c/ldn2wIAYv6OjYjgXFUEPBWAgiA0TfdAQgmHogCETQ0BDHmZRHPAhCAvyjSnkXRkD7DazjyNRJXHzrzr5x8fNyIsURvIvSrxyyMePS/WBggKZ6Fy5Iu0JBMzwJfUgNU1mMZG/AyLjly6J8yAUknu02+V8ymmaYCUSw5D8buPj8qqsXy/doxyeah6fa8UMlxJhGpnjMNLhuWGDqT7On5dn1du7gMMzYitPqLIK+f39MKDBBhmXdQShG4BE9Ih864GrjVZ2iGHIDFtVa3n8cck7MDoqg3Re3tzo9ueflwi58vL0R7YvFvlz/fXy+9CQyOr4uFkueGJirgP/KnrADwNACBo0GLAigBB0WI0AAHGWaTBQHriC6cJKXHP+OQlvi5PXdnC2DrOz0ne6LvN1v9/MeZno9m27XRynHR3Av/yLmF3Dw5kxKcbHZcPCb34jt/bXfy2VxO+9N3HT5g1vkLZfviz/vuY14hMPBOScquL7lSvLjJiMfHlmZsy9v9GJdFV+gVBI7Bvl7FSfqbQdV/XWD2tvQx1+gXvXP4wtY2IHa8hHPnZBwwMAUhvVbLPJM7jlFuCznxXn0t3vC+Ervofh8PlgbN2KqYFJGJcHkDc+iLxQCNrUlFm1L2LnmBEKQQdQrE9BD4l0G1DPQxye+ZhBCBoQDMC4fBlaKGSG1eflybO8GqJZ/Z3PobT4Nvzif3QcOCCv2MyMqIOF5HjHDnF4er1LeiTJk+0pN5JERYwfOGDmwVRD7O+9ehCFT8wAM0FTr6n5pWEAoSAAwGbMQpuF6PTJSdNhqLa/J6Cn1Su2YYOc+tQp01zYskUufe6caVIsK3f2iRMyp9R1WbwIBORzv9+scDg7K3pdzS9VgBFg6n4ViHAVLRTCNdrLgBGC4Q9g5NwYCv0z2ITzsIcmYZ3ux4fP3Ic3ln8Xfz/zAD73ubq07njNVZJ2eBqGgQcffBAAcOutt6KxsRFtbW04cOAAvF4vysvL0dTUhMHBQXi9XvzoRz9CZ2cnOjo68L73vS/lN5Aszc3NqK6uhtvtDn/W0NCAoaEhNDU1ZewcJHEqy0NoGn8YRX4fXsJ2bNTOAtAwoxVg1L4eG6ZO466xz6Gy/DYkEt6+HOpwBLdqbbDgWegw8M28A/CGboLueQB4k6kdE6kPkjIiBsoXBypR3DuCj43eh/LAlXAeHKsWRHFgGMUYxvR4IYKGVSLmgxOwWCEKtqhIGmqzmYnJRkfNCJiKCvnM5xPDShkYKllXMCijhlq5euqppGZP8bbV3XabXOr735eBqbLSnLecP391vPvC1aqtjz8OfPGL0GZmYF+/Xtr19NMyYQgEJFQhFDKdtcFgOOGabtGuOokMzBaUIf+aCjPiyetFaMMGjEzZENxaiX/8a5kEKNvk+HGpCHn6dIr7NpNkVGjn4vVKN+TnRxUeimjar34F1Iz8FH8XbEK5ZQQ+6zq8HFiPUt8QrgsMQDN0wDtrOuPVT16eyGRpqTlj03WgshJaYSGsyikSCmF8MITZSwOw+6egVa2Hbs8XeRkdRWh0DCFDh4YQLDCwOXgG60OXMDZTgsshGzbZfoPXlZ7A+x6pSV3hqgUigrRAAPaSYqAfQP5VB/6zz8JSVISS4NXtQEFAU5UL1H3m5cnv5eXygvl8YkxWVMjvIyPmdrktW9JSkS3ytoaGzGImqshPPGI5ZFwu8U21tMj57r1XJl1JFVBYYtujX5OdO+VxWiziL172hZqaTEVotcoFh4eB4WFoQDiyC8olpFkRMnSEoMGGWQzo6xGCjkrLEGyFQenjyOghv99c1FKLAyrawDBELlTE5MyMzBaLi1NXnjhykldeHnZ4Gdu3Y3xCQ2DEgC2ko9CaB218XGYqk5PmpEBNlHXd1OuR+2fVQAGYUf2GITJ+dYKlIor8BaUIbHWiuFiDtoincmRE5iyRu/EsFpkkq132QO4WM0yGyByHFossWIRCZroXu930Tf/mN9INSgXNIcL5UzAzi5AxG3beG9ARhAYNAYRgRUCzwmbMAhaY+xDVeG6zmc7PqSn5sdlMz4WuS+PsdtOZX109d5uhku+HH5bfl+uti1fqe2rKdNo///w850/4u0rWLRb5UW2YmUEwqCFgWGCBTKj9yIOuGbAgBB0y4S4NDaNiZhTaOb84eG028QBEOXi33vsI8vPrwqmFdu6Uy6tdCsls337722Uh6wc/kHUbta08nSbF8LCMA6dOSfsPHZI1GiA5Z77VKnbIj38szjsVMK4e/9SU6IDXvnaZRcUiXx6VokPTTBs8GJSLqYh8patV/6vfX35ZFE1eHkLHelF68h+x/+yXsbXMB9vmOHn50xDVfMcd5hZ3h+e/4X/uGEbzQtAuPA0jaMAAYBgBWAxA03ToqhphSQmg6zBmZhB86SwMANOOa2AbvijWlqbDCAE6gpK+QQOChizoWgwDmlptjHASYWYGqKrCVO9zCIZOYGJsN260nUC5MYhBrRJnxnfjnnviR8OphdeMFC5aQds7naxfL7cRCkle9r4+iQ5/+mIlQsEQ9P6LcqDyNBrGHOegphlAyJAXWykilRTUap3j5A/bIVGO48H+3ZiZ0cPj0zXXyKuyebOYHGq99KGHJIp9yWr+7FnJ46EWjpWtXVpqVmxS7du4UWwRtfCqhKy83JyfKDvt6limG35Z2AoGsW78ZWyEHwZ0zGj58MMCm+HHDWO/xt9a7sFf9z6CEyfqcmKRNZMk7fBcF2MW0dLSgj179uD9738/7oreiwigq6sr7AhcSaenx+NBR0cHonfxu91u1NbWJuSsTMU5yCJEKazdoRAu4CQuGeuBUBD5hizPBK+u9F9BFW7Ec9iEEwBq0teuqyv0eVd8COhWhDQdBVXFuO7iUwh89h7gtkeA226D56sn8K+fHMTsVCXKNu9G/no9fUExUQPlFr8ND18chdVioF/bgFIMIs+YgcUwrk6ODWgIYbRwA4ZDNpQFB1Fxy7XAlctz95ID5pZ29c6XlJgrccoACwREcb/iFeIcvHJF/p2dlRt98kkp6/ma18SOII1ioWrGKhfTxIScatMm+fv654/A9s6HYZQ8B+3c2bm5CkMh04Dw+2UwioyUsFplcNm6FVp5OfxHT8A6NY5h2zW4prJEznHmDDA0BMN7GkMFLvi27sab6kPIe+4EgEEAlRjZJrlkctbhGS+yKkM5HFV0Z03N/MmwmivePPhTPGK8ExXGAIIBC4pDo7hGOwt/UIeOEPRAABi9uoqqJg1qK9jMjOnQU8Lz8stmGoarlBUbGL5yBTNaAaby1qNyk0yCjeFhBAZGMWNYkacHYTECCOg2IGig2BhFMQzY/TP4+NiHYfneH0hIye7dy0squVhE0E03iSyrVeRQSH78fmiGAeOqfgwbXsoLoZ6HqgA0NiYT/E2bZPIxOirOgYEBuWZBQUqrcUXeVkWFqAwVdNfaKk1JVtR27TILyZw+Dfz5n4vTc8uWBL6cRGTFYq/Jn/yJHOd0LjOfaKSDROmoqSlzsmsY0AwDQWs+EAgiaLNDm53BjGEDoMGqBRE0LBgIluMa6yCCN++GNnFB+jw6qZZhiLF++rTIgNrPqpIna5r8bhjiBC8vlzYVFspz+5//kZl/Avp93sOMnOQZBjA4iInyTRjwDMI+OQS7MYkggGnND7thQFNeB8DMdae2dU5Omu+83z+3aoKKiNq4Ue7/ueeAYBATVdsx2j+NwulhYGIU/b4reLl0Pa7dbIdjNr6n8uWX5d9Nm+QSkbmnI8nGYoapJjr/dnR09fS0iEpFhXTzs89KJNg8rjp/fLN2TJ4axAaIzTKDfARgBWCgBOMADISgQ8uzQNNhOgBDIdFnN99sOu4vXRJZUJNOlbRtYkI8VuqdeuEF05FfWSn/t9vF43X8uJmUNFknhGGIPj1yRGyh/Hyz7PnYmBm1BJgT+vJy01556SX53Ok0w2TVGBYMyi6U/HzkTYxjFMWwYxZBWOA3LNA0A3ZtGrNaPiYtpVin++T+AwG5T12X2X+Eg/e6734ON95wG46f0MPV2iNvJZm8dr29Ym6Nj8vvN92UhEmxhGi3S5dE/ysV9ZnPLH3R6cgRyfmoctIqdVJQIM+kokJyP37968uMolIvz6VLIieRi5KRizmAPJPCQnNPfihk6jc1PkxOwpiYxnuMj8KKIPLWbzCTCqc7qjkQAJ55Blt/+Us8GvglBq48iRL/IKZn8wFoCFnzMG0pxhQKsG72POzGNKw6YKmqCm+NGhsJwaZdBDQN/o1b4feNodgYx6yWh5AGWI0ApvVi2DAD3QhiSitEkXUWyL9a7CYyHdeZMzDWrcP4QAB78x/Hn+KvsHPyJPIwg6AlHxdmduGRy/Gj4XbskH/T4vCMlO/TpyUUcgXzp6eLY8cAHSHs33UCDfogfntPJf77x7sRfHEE/pEp5KsXS0XoatrVbd4BBKBD063QC/LMHSZqkVbXTZshcnFybGye47h24y68NvgAXpyuQ1GRqLxrrjHbOD0tTXj1q5ewC8MwpJ8ee0zsoLEx8/3dvFnGIzVnnpgwc/SWlZmrosXFYn+peYmy+a5uZw+/4zOzCAY0GAjCgiB0hBCCRMHOwAYDGkasVSgJjuDdg5/DYH/6A8ByjaQdnoMLLFVXVFTE/LyxsRH19fWor6/Hvn37UJru/WZxaG9vh8vlmve5+qyrq2vRbempOAdZgBgrXXplJaqsPkxPBLHdGIIOCf+eMfIxPQPYrHZUlgxIBfJ0ETEBnd6wHegfQ54eQmmVHU/7tmHD+GlM3v8RFGyqxPqfnMRfTM0Atnycmd2Fx659AL9x1KUsKCZMdG639euhnx9CRWgARkhDnjZ1dYuTuDkDej5CsEA3AhgJlmDWosFSUgK9uQn4+7+PXenH4TCrtKu/FxTICDE2Jv9ee60o67IymVicOiX/lpXJYPDUU+J5aGwE3vxmOf8CRK/EHz8u4rBzJ3D5YgjbRk6gSh9Eua0Sxb4R/NnIfSj0+zBdWCypzW02adszz0j7VMQfYG5dNAxph4qaumpoaoV2TM+GoA0PwpjQodntwLp1MHw++A0LbKEpfCTvM8g78D9zZHTP1l14pe8B9J2uCy/S5wzxok/SEekSh2PH5N9YCeRPnACKPEfwqZEmVIQGMAsbgrCiwJiCLTR9VRsAhm6BpmnmpEBNGpTTxmo1Z2wqr1uUzOv9/ci7pgqnhqqx/sI5BDYWwmrVMOm3Qjc0aDqgGQbGLWV4Fq+ATZ9BiTGKSm0QtpAf01d8KOrqktlSfr7c0KteJdZUpJUVizjRbuE+URFLKpLpuedE1tX9RTg2DU3HjJEHq1VHviUge2zPnRN5VQ6jQMC8/+pq+Xthoby7mzbJ1uezZ+Uz5cBdJtGiduaM3FZJibzfy9GPKo+ymvTee28Ck94kIisSeU3++Z9F7S27yIGKdquqMqPQALloQUE4+tJqBDCrWTBo24jK4DnkB6fhRx6shl8mlhhE/oYylH/+ryRx2VNPmblbI5makvNWVJgO0ZISkTE10VALYGq7uIqo+bM/k4laZaX8VFSY/4/8fd06eTiaFttzfPo0jIkJ2MdPoVIrREi3yuurWTAWssFqzMJiMaBv3SrO+PFx8x1XSldFqZaUyJjz4ovy7JSQqRzTDgemkQ/vlSKMh9ajKr8IG2bPYn3wAl4aLcLZCcC2zobCOJ5KtbBVU2PWQ4t+rKmqiJztRBaoWOgZ3HqrZCXo7Y3j8KysxGQwH1dO+rB+diicu88PKwANFoTghxWabkEhpqAXFotOV5NMq1VsEUB+Hx8X5+eFCEe/mkBOT4ueVbKsFoFUBLXFYqZ1sNtFRqOdELW1Zviu+re/f+7vAwNmPp4rV8zIH4Vy1qoFqY0bzbw4hiHpK5ScX3utKc8RuQrz7Hb4Z2YwG7BDg4YCTGEWebAhAMNixfm8ahilpdiAk0DQL+9BICDv3vi4JJvNywOqqqCdfA5/2XICf3K6JqY5mOj2baUrAXnswaB0k8ORQHD4EqLdTp8Wva9q+nz2s+Y2/GRRbR8dFRXS3y+fFxZKM/r65NGNjCzt/HPYvVvG2v/5H7nPTZtEbiL7WOUZNgzRpZcvm6Hl+fmyLaKwMJxfwz8RQF5oBlq+DdrwEDB8NWLfahUdr3ZtpSKq2eeTrTe//KUUXpmYACCB1+u32DB2xY7BYAWGLFWY0Qpgt4nIB602XDv5PIyAH4Y/AO1qmF3Q248B2yYUFwPFQ2dw1lKFguA08kNTgAEENCuGbRuwYfYMAAP9lmtQpJ2Vtths8sympsL2fPBSP0qnZnHv7CcxrdlxQduIadhRbpvGdeNP4W/1+NFwaoz3+eRRpCxdVaR8Kz0UDMp7GKs/0l2RN40Mf/cI/ubph3HLqZNA9wys+fn4B2Mdhi5M4aJtM7aV65KsYHRUnsXVIJqQpkE3DBjWqyGZ586ZEc4qYOXiRXkXCgvls+98B/j2t+etRpe9/BT+duQetPofQf/1t6F64gTKAoMYsVair2g3+vv15MfoQEB2Ez72mOhkQNr0pjeJ/fzSS6azU/1tyxZZ7VOLGpGVxDZtkuMGBuSdjsxPbrUCW7ZAO3MGWjCIKb0EeaEZBGGF1fBDQwgFmIJhaNA0A4OWKuz0Pwf/0AkgtHtVpUlYLkk7PJ1OJ15++WVs37593ufeBZJdOBwOdHZ24tChQzh06FDSDU0FPT09MZ2VgLSvu7t7UWdlKs5B4hBrEjQ+Djz9NPLGxrAJY/BrNkxpBbhoXIMJlKKsGNi2bhoFRprDKCK2WwVnNGiaBdBCsI77UFFSjpkZO2xPHoX/5QoM+bfAX2BHIaZw/civ8cDIe/C1HR9F96b3oKpKT01Or1i53c6dg12XbR4GQrAbksReg4EpFELTdBghA1bNQHBqFo7gKPJfWwO85z3ibVioROstt8ytGKK2hhcWikKOVN7r1smE4IYbZAD63vdkwPq7v5MKQ297G/AHf5Bwf6ldP/X5R3Bg+mFsnjmJfG0GwWdtKAyOQoOBFyy7UGYMm9ESauKrflfbwUIh2co1PGxO4oGw8ydv8wZ8Zf1d2H2lB46Bk8iHPIuRW96AH7+4FbeM/Ay3/r9PyH1v3y6D0/AwCp/6JT48dhceNjowOPiGtG2nTQtKth0OiTTw+czCNhUVi0e6LDP30OysfB2Inb9zsD+Edw88jOKQDyFYEYIFdkxJhBss4W2PFosuC5rr18t9RE0MMThoztgWKEtc/MH78T03sP9n96Dw2dOwbKzC0EQBSpCP4tAYZrV8nDGuRUjTMKvbMWHPR9nsJJ7QX4Xy9z2A2uCvZRIwOChVtp54Qm5k2zZxHP7Wb8k+O2vE8Bsn2g1btkh/DA7KZGJ21ozoVGXJ1WRZ7R2dnoZhzYd1cgZaAEBpiUykCwrmJn33+813HJibQFcVRhgYkLb88R/LMcePL8t4ik5ZNjwsn6tcSsvNeXjNNdKtalvjffdJpd6bb45xcJJRzZFtV/a2rs/NVXjqlARjJVKhfUGU0lu3ztxurqIqlYPPYgGCBkKGjosTpRg0qrEVZ1CMcWgWHVphCU7bavDiHffjfW+ok8ZGJ0lWOjva6a0cSGoPp9qnXVVlRlmo7bgqBOrSJflZCItF9MyxY9L5KrnfyAiMqSmEDA1ACHnw40L+NoxbyxDQZKubbewpFIfGoRUVSz7maOdPpGNg3ToZo669Vu5XbcsfHQVqahC694M4/oefx+bZpxAoKsSYVolCYxwlgWFsDb2EyWAhTgRceNXNu2PGSKgIz+3bpThZvMeayorI2Uq8/NvRz2B8XByeHo8ZCR1J6ObdeGbmOtw08zhCuhUjlnWwB8ZgxzQCyIMVfsxohSitskObmpSJpaoiY7FIX18tLhi+8F/91XxHf0GB/FitIq8lJTBecTOmhqcAnw+2sUFYxkfECQOInF++bE62T52Sxdsbb0x8ZVNFbRYWIhw6WVgo7X3mmbnVuSOx20V2BwfNd/TMGXmYui4yXluLn+t3wPrTHrxy1gPrxAT00DQmjGKcDW7DTNCKzcHTmKosQkGFLvs2Z2els6anRZivuy6cf8G1bTDe0Jjw9u3IvHmFhaZqUD6AuHp+CTtNTp4UfT82JkPsctOZROp5m810eCo9v3GjdNnAgCysKR97QkTbSqriulqE3LBBHlJkH5eXI5wj58oVM+I+qugmHA4EBkfwYmENqmbOorK6HPBPyXmUrh4eNheAXnhB3qGiInlgdvvitp763i9/KT/PP2/a0IC05dWvBl79avwmrxZXfu+PcePMU5iFPbxeW1gITFjLMGEpQ8gASif8sE+dQ9BiwzO2Gjy243489BBQ9vWHYf/xSYxNlaIEY4AGTFpLoGnAyeJaOCYvojhvBnq0gygvT+Yzug489wI0hFAYGoNV86NKz8NQ3jWYySvCJWshrpk+HTcaLj9fnObnzsmGl1iL8Qn3s7KVouW7sFB0SzAoQqd0lMMhtneuJYGOYKbnCN7yg3tQ5Pchf8t6oNQOXL6Mdad7URywoKfkDzH18Xfhxu9/XgaFwcFwvpMxOGANTiO/pNBcJFK7W+x2eTZ+v7wHqhLS5z8v/b9xo3lsURG0Qunn1tGPYPBoJa43TsJmzGBWy8fz2i58Y9MD+JP76xIbo8fHZT77zW+ayarz84Hf+R0ZE7ZsMfs4ejAcGZH3deNGUSrRihUQpevxyBg2PS3jw7ZtQF4eNAC6ZmDEWon1s+cxjXz4NRvyjBnYMIsQNFRNn4UPDthtIaw7/zjwjr9adWkSlkPSDs+7774bDz30ED7ykY/MidT0+XxwOBwLfnfHjh1xo0AzgdfrRX19fcy/VVRU4JgKM0rzOVYrCfk94h0UHT4TCMiMcnDw6g4FSV7vd16PgO5A4CKwrgS44QYD2ukMhFFE5NoJTgLQ8mDX/MDFi9jkv4DQ9BR0w49ZPR8BWFBsjGHDzFnYApMoRz/uPXUf6oa+i29sfgDfn7kNM79axqpLrNxuU1PA9LSslmlA0LDCgI4z2jZsNC4gDzMIBG3QNQO6ZqAs0A9/2XoUfvTqbCxeRRDVrlh/HxmRQWYhq7i5GXj3uyWRU1eXRFv8+79LmfU3vlEGiZ07595flIxUlu/Ga4O/wL0v3oPSkA/D9vUY1e0onR0MF2e53vIsCq5MmxOgyFyFdrv0Xd7VbS/XXCMGXgznj37//bjcU4eP/fRPcd9vn8DbXydtaPvGbvwyZOAbp2qhDQZkoL1yRQbAyUlohoFNM4P4y2fficFvfQPrmt6wPHnLFGoAv3BhboEPQKz506fNysexIl0SzAW1kG545hlzx3nUOhoAYMvQCeQFTmLYUoXi4CjshkTlhKBhRi+AETJgwwysGqR/7fbYE8No2Ywj87qu47dtwKfe/QgazzyMXS+chCU0i1GjBBpCmDIKEYAVNj2IUts0HLP9GNPL8M/rHsSDb6wDat4gBpfXK5OCX/9a8siePi0/jz0mE+7aWpkcGIYkOIqc5F24IPvpXnjBXFQAzIT8apuu0yn39+KLIpOqkEFFBfTJ84ABhCrXQVcVTIuL5T4/8AHg9tvndkT0LLewUIwzh0MqN3zlK3KdZRhPkSnLVO2csjKzsnoqch6Wl4taUoUrPvxhs3BFmCVENUfmghofl3kKIKJVWGjatYGArPUsC7XV8fx506EYWaDC70coZOCyfg282I4qox82bRYDWI9nbTX4RcXbcct9t8P9/d2wnNfROAKULeDkj+n0jo7k37HDnFwbhhxXVwd87WvyHIeGpOPUT/TvqpLuyy+beQxUvlAAQd2GGc2AzZiFAQ3jwQJMabawGg9Y7JgKhWC7OIj8LTGcP5GOgf5+s6Lcm94ki2zbtoXf8RMndDySp+PjtnuwYeY0LoeqcNm2CUWBURQGx2BYdDxi+RAefFqPOc9UEZ7bty+4drK8/H6KdBSzSPE5E3kG6n157jlRbeqdV5z4jYbzUxV4hQ5YEcBpyyYYoQCuNc6gyBhHCDpGUQLjplo4Pv5BkUW1JVQV4VF9HnnheI7+qzpuRrfjhZMWTE6WIGSUoNQoxfXBZ2BFEJrFIjaCqroByPnGxkTuNmwQW0L9qEjmyP9XVIhefsc7zN0ukePsQpE/GzbMHWNnZ2WMqKmRJJlXdfg379Xx7M1/ivftPYHz//44Xn35O9hoXEApRjELG05YavCE5a14SGtDoco9sGOHOKwmJkQZOxzh/At1NQubg4sRqeftdnndp6fldVdrqGE9r2Sxvx/41KcW1slR0W5PPim7s6emZDhKRcG66Jy0W7dK21WEn/o8EBDfd8IOz+hFTZU6pLxc+lHTzHE8Rh8DkOf0+OPAF79o5jGMkJdRrQz/dc2f4L39bcgrtAFFEdHCk5PSEZFRzSrHrYosU+moIm29J58E3vteoL5evq9WKRU7d0rKqle/Wjrhat/0/xjoKH4AnzTugTNwGpeCVZg07NAmp3GNpR/D+RvwlyVfwIcOluHV1w2i+2gl2n6wG7tu0rHpAIB9t+HKV0/gS58cxPnJcpRXAOv0YfQblfif8d14Q9Uv8JmND0M7HdtBhP5+BKo2YfT8KDRdg92YQjmGURoYw7m8nfDr+XOj4WKkQ9uxQ1TKSy+ZDs9FVWe8COUPfUgMk0j5HhgwgzMCAfkpKjLHTJXXsq8vtxyeoRAmPiF1NwZLt2NDuSb3c+UKNHs+NL8Ox2w//vnFOrR9q858/4eGMFlQgQ//TRWKAiN4+NrPAy88F9/JPzUldrYaC6xWeaYDA2ZdiqIiFOQbqPH/Gj69EuexBVOwo8CYRo3+FG7DPSgOfQE4Xha/Uy9eFCfnf/2X+f6Ul0vgztveNjeXzWKD4UKKVf3t8cclYvXCBXknbTagthbjJy/CPjIJXQcsoeDVBWJgGnaMaaWwh6ZRgUEUBGahuw/JexwjV3Mup0lYDkk7PAHg05/+NN7//vdj//79eOMb3wgA+NGPfpTQd7UV2uvpU+Uz4+BwOBY9ZrnnuHjxIrYkkFTs/vvvx/1qApIjJLQLZaGDSkrEKWC1inJXiX8AjGslGLeVoypwEaVTl4HSPAxY7SgITYuzMxNhFBGJqoLBIgzYt8GSP4gC2wQsIyPQDamWaR0ZwA2zA8ifmYYBDdPIRxAW5F1NKNx68r14j2Ujbj7UD2hLcBxE53azWs0V26uROJquQzN0GAFgSitEn7YTm0NnUaRNwqb5MWPYcbLkVSj92APYFHnNxbK6x/p7Xd3iVnFBgQwMb3+7RLt1dsp3fvQj+bn1VnF8vuY1UnI9SkZuueEGfHRiCAWzPvQXbkJpcBiVMxdQFBiBlOcIoiw0DIul0HQEFRSYeV6qqiS8YGpK5KygwDSOYjh/XjsJ/OxnOr53rgZvv0Ps7P/5FXDd5HGU26dk+0F/vzhMVQEEXUcwqMPhH0D+x+8GrA/NmWBnVYhPMCgRVj/4AfCLX8gsREVqqZVlVcl7fNyMkh0akoG3pETuLVYuqKkpcfC95z3yt/e8B0d+oS+oGyK3s8caHnZWDKI/bwZn/OXYaoRgQRB+WDGNQhiGBlW6RSsqNHOfTU7GnjRE90Mcmff7gf/01eHruA2vLz+BG9cP4lenKpE3PYL78XncjJMo1AcQCNrwYnEN/iHvfvhr68w1F00Th0x1NfCud8kEubdXIj9/9SuZNPz856ITn35a5HHTJjHqLl82HUGhkHy2ZYtZZOjpp82IIJtN5HjnzjkOfEuBDU87Xg0jCOya7YftqiPAqLkVp952P85sq0MlgN2IiGuI5QDetAnYtw/42c9EPq6/PhzVjF/+UibjHR1S1jYBlBqdmDBvsarK/PuiOQ8TdNQUF8t29o9/XB73Rz8qDtCr5oqc49ln5cCBAZEXXTe3dMeIdInMBTXYH8LNgROoMAbhP1uJ6et3Y2RE2lFYiOUXrdq9W+T317+WtuzYIc98chIwDBiBAHzWKtxX9m94srgO11yRtmjrJJ/wy2d01PwS2HmD+Mx7eoA778TiC1uRhvpikfxq3FURcxs3LnxPKsLoe9+TDqmokPMFAkBxMcZCZTh/cgTOwIuwhaaRPzuG0UABijGN8oBMkP/Nfhf+bEcP1g/Gd/6E+3cBGRkcBJ6w1OHvdz6Ct516GDv8J2GbmsWErRQhzYJ+2yasmzmPwf4QcHz+uVReN9XPiz3WJZOOYhZpKpCx2DPYsEEips6fl9fqtb819122fOt5bPCfxenCXZiwOuAYOwcrZjFsXY9TBTX45uzb8ZPQ7fhEy2686Q1RD/Y971l4sTbWBPTWW3Fq2x0Itn8ZDv9paPlV8OfZUTgzCc0IYUorAK7ZjEJbUGyrvDz50XU5x9/8DfB7v5f4A4oXBrtY5E9dHfCnfxr3/kIhcbyFoONfTtTgeVsN/vm6e7HuguiE6aJKzO7ajdOngbfMfBev6n8KWuHVsXLbNvHmqNzrr3lN+B1KpshPNJF5XYsLQ7jdcQKBy4MYO1sJw7Eb09M6bDZg6+kjwDuu9svoqNx7UZE8h5kZM3K7oEDGv2efDUe7/fznspAVCEjhuk98Yr4TfbltLyoSNRXJ9LSZDenFF4Hf/u0EThorcrWvT/pzeFgikd/61gUVSCgEnEANBl9Zg62ttbjuuw9Di5Bno6YGj4zdj58GbsN7130XuBAR1aycP+vWyQtYUiJJVScn5fpqF0kwaKZ8GhyUPlEpVbq6ZKtEUZEYa695jaTriTNYV1YCJ8rq8BnHI3h3/8O4dvIkKmYGMGvYcNxag29tuR/HUYf8VwPGLcC/fxUwNOAtb7l6Al2H609q8EdXN58dOwnMTsmrccutwJ/cX4d1CzmIampgq3sDjL/8Al4KXosS2yzWz5xBfmgKm6f6cLbgekwE7ViXN4D1FbFXWHfskK5Tm1cXVZ0LRSg3NYl9XFVlPtcrV8yURCq9xaZN5vYXFZn76U+LvfU7vwO87nVmFbFsIdouC4WgnTyJYdt6lBWHoF24LPcKAFVV0IvKse2F5zD0+Al4X66BM0LRnDoB9JXIeGH9Wt2iTn5s2iT6/+/+TmySsTGzouD4uPx/YgKWQAAV5dMoKh6GP78EKCtFcfE2aM8/L7slS0vl/JGdWlEh89Wf/cyMZN6xQ2zi+vo41few+GAYT7EqpVtTIzmZor5/6gu/wNTffhZ7Rn4CuyGpSya0YlywXIvhUBnKrBO42fIcLIGQjCtXbSsUFq6aNAnLYUkOTwD40pe+hG9+85v4wAc+gIaGBrzjHe9I6HsL5QBd7YRCIZw/f37R40YjV5NzgIV0/L1/HsJX7jsB1+jjorBUxW910LFj4hBQlnBkjqOiIkyWXYO+8yXQCoKoss1C27EDtvODWD8zAA024LU1KQqjWISIRFVBayH8ej6myjcBmwBtYADGs89hBnbMWMtQPHsFOoIIQYcdklNNg4FZ2LFh9gyq9AvIr7oZKIjtIFpQCUXmdhsdNZPuA2ZZ1JkZwACsCKLKEcB4fgVOX9qBjcZ5zGgF+LT+UXwv7z3495tToOySsYp1XQbr171O7qGrS/a4PfmkWQrzhRfMbedXZUQ7ehS7RgcwapSgdOI5sd8AGBAjPwQdVqsGbds2MRoio90AOU9JifxeUmJGgdx6a0zZUZFgTz4pTXziCXnEr7l+EHlPzJqJnVQl2EDgavXAPIT8fhT0n5a9tOvWZdc2gpdeAn74Q6C7e05kFW6+Wd65y5clYlC9f6qCt9oeMjsrTjVAjI7xcZmQbN0qg6nPJ3+fnJTnc999GPin7+KfLj2Ap/y34bUlJ1CVNygr9MfNCpnK4RlrOzsA6FWVKKnIg/NiH2Zhg47g1YI8IRiGjjz4oSOIqeIqFP5rh7nau0SvQygk9oDNBuQX6Pj1bA1OTQJXrMCkDvwiVIfXFpxA7fbBcLRBqUPHIwutuZSUiEPm9tvleb74onjivvc9ef9VflGFxWI6b3VdjBZl5MaKCIqK3tRuvx1f/spu/OY3wCffeQKv2TkIz+lKfOI7u/Fcmx7fzxH9Pqtk8mpL9aVL0r9XHW8YHBRj8RvfSMjpqdToE0+Y6xIqd/uiOQ+TdNTk5wN/+7cyV3jih2M43PoiSt/wAvaUvSgpQc6enZ9Tb3BQnsHExLz8fSoX1D+N3YVXj/fghpAUQPBP5ePC1A34dvDtcOZtw29tq4RFm+NKTp5Ll8xFCItFJvvXXCOToIEBzOSX4d5QB17e+AaUBICnh2oAADdtBmy6uRPu9a8Xlfpf/yUBZpqGhXX2UiP5E8FqlYbdeqtMLpQRrv48DkxYHLgQuhbXhM4hH9PYbJxDaNaGU6U1+Neq+/Ezow5/8MifYr2+iGdxkTFJOTV+ZavDYftt2KWLcyh/UyWus53BO858HnePfhbbPvooMHphjryNNz8An0/ue+tW85zLcRCFSXcxi8W2DH/hC8vSn4s9A5dLzLyLjx0B2iLeZQC7fON4MbQFX7nmr/Djwrei2Hs1V/d1lXipZDd6n9Th9wMz/iVcOIZch27ejQ836sgruwX3Bh7GtsmTsM4OQEMIs7od5y1bMD19DW6+TuyNMBMTZpG3ZFhO5M8C93fmjOkbvHhRbcXW8fQVOf7aDUClDqxbD/z9pQfwZds9KFBO1+Ji6e9Ll0T23vWulEyClZ63/eoI7vHLszVmZjA7lY/Tnl3oKH4A110HXPeFe4ARnzQ6L0+eyeio/KhFFjUfUhWcP/1pnNj4Zhz+2Q0otF+PW95Yib/8mBbX9xAmwcWyRHLSOp3y6pw6lcDDiLWbQI2jBQVyka9+VRyecfp4/tBXhxtvuA1/2XICrm1yP7/Bbjz+IR2FRYDj/geAB2I419XODbtdnrfDIT+Dg3JywLQFI/MPFhfLOPT+95sVRBdBPcfup+rw4iskd2LB5CBOnK/ECewGLut4wxvk8Xz5yzJeVVVFLEpeZWHf0cIOIv3ECZT83ZdQODCNEX8RZm3V2DrzAqyhWWyY6MOAbRNKK23Qq2I7bSMrtSvVOTIcim3LfiGEus8/PD9C2WoV+++ll2S+NjJiyl10QUlVkEqtDqliakVFYigfOyb998Y3ivPzpptWvmBALLusshLa6AgKNWD9oBfQrwZNVFUBmzfDFgyiJH8AZYFBfOMbsm6kuk0tJu7YgbkOwNra+LqzpEQqpOfnmysU09OiqwcHw3meNb8f9rEB2McGgJGraUVGRkSvVFbKc5+eFtv8D/5AwrfVzuU9e4ADB+JHZkSzXIMgxvfPbq9DE27DnZavoiX0SdiNKQzat6Csyg69fwKVoX5omzYB4yPmYvKFC6K0rr8+nKs5V9MkLJclOzwB4M4778Sdd96JJ598Eg899FA4erOhoSEc+RnJk08+uWJb2hfbbr9Y9GYqzqHrOjYuFgUBrFhRp6UQOZbv2CYFZcqnBzFmq0Rh2Qje8sLnsf7e52CEzkqF1bIyUexX83ZhdlYUzOhoOJLGWLcOk9YyzGj5UP7hDaXTyCssBR55BBde0vGPnxiEbWMlDn0zQ9FzEYmqis6chj1YBYtmByamw6H0/dZrMaUVodg6hBm/BRYEocGADbPQEML62TPQITk0tREfMFsg1n+Egwjf/e7CzrHI/TZKoVksZtXNq8UbZiyFmNLyYdP9KBg4BwM29FpejS8W3I+fa3XID0jdlhWLbN+1C/iLv5BVz29/W+77V78SJ1pRkVlNenQUmJiAHvCjVB/BpF6MUZRiVHNgSi+C03gRxca4iICuy+AUFe0Gv1/yJn7wgwlN5n7zG7ETL12S4E9ll657YyXQm29WL7bbzbxeoRAsIUBHAKHQ1S2oaivb8eMSCRdrG/FySMSAHxmRSLUf/MBMrg2Iw6G+XopI7dwp0YbR0ScqujM/XyIVg0F50Scn5Z1VlZtfeklmXVNTZt4niwWG3w/t2K/x8cB78cGijSi73C8VMjUbBvI34Xujb0fnR2/Hi/puQNPj50i66SYU2g3k66MYQxGeD27DelxCISahw4AFAQzpVTho68B79TegrmZ5j1WtKWzeLIvSIyPiH1Y1MUZGdPxqogbeIbGzbontN4+Pponxcf31Mmn+1a+kP1SurZIScZZPTZl5CsfGzMjkeBFBUQ5853XAiaeBp1CDgA245++TSo1mPozz5+V6Fy7I/y0WM1+oina6+27gocWjmnVddnb99Kdy/fXrYwcN6jqW7vwZHpaJwosvwvrCC/joiy/i3KVLknLpFHBpI7ChcEzsFV2XZ68mG0NDonNUVLPaKnc1F9Sm0efx4PBHMWSUY8i6HrO6HQ5/P/b6uvFb+CFGLBXAj0phvGMXtA99KHHnUeS9lpQA7e3yHr3pTdKW5///9v48vK3zvBP+v1i4SiIPSS3WZlug5C2JTINS2sTVJFMBaWc67XRqUEozM50ktcmkjbM4MRH2fZtpO20VIHHaNL0SA3b7ps38ppFBp0u6JYSStB5nqURYVrwotgntli2KJEhxJ4Hz++PWgwOCAIl9Ib+f6+IlEQTOOTjn2c59nuUnxs3QO96BF9/1CJ794iHsvnkZ1LQAamV4NWR0715p44bDcnOePHtISrn25M9UmojCxo1ATY0O68wsvms5jK9s/gxqJscwbm0D7tmPC5duLi7QYQbMHSvuItND+NGPgIWoGS9YZXt1E8C1O+/F2y58E7+8+Feof1GXN27ZEu/VbH3uQexv8ePNu94Vf8aYsZXK7HSLWezda9woA3JBL1+WG/wPf3jpdVC9udT/E1+PxYA//VPJw21tRs+t5majx366ni6p5mp+61ult3kWacJuB85/7Rkc/MuHgaaIEeh6+WVYZ6ax3XQVF2+04OoNM+asHdi+HdjWZDxbTJzdI2tJ6frMaTnVTbcewv9svB8bw2dQPzWC+u0t6H7j92C78TxGJ3VMTpqw6eZDmbxXosq1588KXnlF/t26VYpoNeR6+3YpvhKHYv/AeggvffhL6PxeQuBATVbY1AT8wz9I9OGVV7IvtxLeazYDv+t4BvXfehiNCxGM123FTH09TLOyWMzvzX8E28ebYJqISB04NSXRWlXuAvL/226Lrz6OqSlA1zF68jVER9/EBwG0tQK7X2iB6bfvkHlE7rxT6tW2tqXpf7WHZQnfw9zWhk9+Yj8e/pg57Zy0v/mbwJ//uVTPyxapTNHbLT4pKCDnXM11vHu3bHyFAES6ZxSnz5jxwQsdUvV1AP/slfe/611A7eE0wfX77gMOH166CGl9vdGWu7lIZ/wcNTXJj9UqZc6WLRlnwMS5fc9fNGNqSwfqNwMjJmD2Zo/Js2eB971P4qtzcxJgPHVqeVsko9hRqjft349G+13Y+6Pn8dpCI6anrThnbsee2KvYYJpGm+lVWO3/Pm1ejgc8h2L4xmfO4L9c+B5+Sf9btF57HdbYPKKWOlxovAtfmvskvvE7m/Azr5+FqaVF2hFq2gs153osZgxdV3OnNjfLa6qdp9okah5iNSn53r3SWeFb35K08/d/Lz+7dkng8z3vWTpURinE1CWptgEs7XmZ2JFpZgaxF1/Gptlx1JknYK2tlde3bzeGfs/OolGrxeWZNnztj2WxR9VvpKZGktiePUnHsVLZGYstb1Oo+TRMJmnIb9ok7dipKSP2oO7lzGb5d3TU6F0+Oyt15dGjwJEjRmIok2eekR7tUzNm/J+6D+JCzT58ZO4x3LVwFpY3rqO5phavNXZg3PEu3PWtL6qbFSlX1Vz/QGHmjapSeQU8lfvuuw/33Xdf/PcTJ04sCYC2tbVhaGgImqaVbcGi1YyOjsKWZ4JebRvbt2/H5cuX89pHpTlzBvjJyzF80PRV/OfQn0GbugKz2YRaSxQbouOYNTVg1LQZMTNgqakxlrxTLVc1jNBkAt72Nsy8dhmvvbkF09MmmdIkCljNOprrhoHODqCjA9Y2M0ItQFMt8upEk7WbT+ivPvgYNlw6i01j1wHUAm9/O8wjI2h47jImpmsRjemYQx10mGDFAuoxixgssCAm9Zkek4Cc6r2lWqgLC9LbK/EGPrmy0TQpjMNhqR0ShrKrJ+B6NIqxui3wtvsx39CMiXMjmKpvwzPj+6GbzLBA2oSvv14BPdu3bpV5Pu+9V8Zc6rp85zffNCb9ujlvndlswoZ7bode24KGBcBaA2xc3C1DEhYWpMbMZK7CFajG5Y0bxtR5Fouc8v/59H68c8td2PzqD+U46+qMYfMLC7DEYgCAKMywqoVGYjFp9Fy5Ij14d+2SxQ6Sg9orNUxS/e3ZZ9M34N/xDhn+8s//LP+qmwiLRf72cz8nAeDEhmuq3idq3s5du4xhS1u3yutXr0qgU/X+nJ42Gg4355jUY8Ck3oCdsYvYduN1vGR6K2oxh9tNF7F9Loy34vsY/v42vLLRjh+885Noazu0/Pu2tkr3tI0bYa6rxeJiPeZMjThvvRuNc2PYrF/HpKUZX3yLH9+deBfGCpCeE58p7Ngh8S9dl/ZDW5sks3BY4udOZ57x67Y2o2GWPDSsrk52evmyNL6S56dbZQytWp38tdckKaiOBzMz8v0yGuGiTsauXZKGVf6MRo1hngsLspHEXs133inDjFMEQGtq5DjeeD2Gu+fOYNOrI5isa8Oujv34xCfNxtCwTFYyVdG8j31M0vWrry5ryJkghz/fth3/+uad+Afsw50/uxeubb8L00svyTGqO9bmZplUVn0/ddNys5yunRmHRZ/HRfPtmDVvwKZYBDv1SzAhBh06TNF5XL7RBOs//QhNA78Ea1uz0Vs33TlJvhmfnJTzfPfdMl1Aa+uy62w5Y0bd48awy+SbAzU1wO7dkrW/8x3JRh/7WG7JtDDdFxO2dfNOWL9wAdMbtmDeLHfxt8wO47qpGV9p+CSGd9tx/rycksZX5N6vUDPXqEPo6pJztWWL0XH5J2d1bDe9IQvAq+BiNBrv1Vy7MILfvvpePH33/wFOt2QeAFwp6AIsX8xCBYBeeslYZEdRQXC/X27kMnHjhuzbajXqVkDaIqo3fyy2tKdLurmao9H4AhPx9K3mqUsM8iedk/ve9lZYLz2G2ukIFu65HTU1Jsm/ug5TUxMazHV48MYf4ZuLP4PaejNaWox7/6YmuU5DQ/lefZFYzkcmzHjuRgcAwHwVqN38SbhnH8aO2QuYHt6Cxbl61MZm0Tg1DFO+UygVMi/BeI55xx0SN1NlwrZtS9+nygTLuw8BH02qO3btknbY6dPSgykWi/e8xc6dwK//uow+ApY+hFJzpyan5/vvx73BxzDbHMGri7djetqEWAwwmWowYdawF+fQeHba6GEFLG3L1tUZvd62bJFpPM6fx+sN7fCZfwM7W1/Dv9v2CnbXn4MpMmZMFaO0tMgJufNOqfC+/GUpV1M9LEsxD/mhu+7CXzz4SfzOwP3Qnz+DjXNSR+2+9634f/7zC3jr5hH862QbhvT9GL0OtF1Z4Zy0tRkjZc6fl3wDGPO8RqNpAxDJnUNvTtW/rO4+cEAeIgIS/wKwcoAocRHS5LbeLbcsT2RTU6vMNZNauk7Ne/ZI8/H8eWNaEKtVbg0LOsXgzYK+4eGH8dbxC5i+Reqauplb0HB5CCaT2VgY8PTp+DySasGgHW/Zj/smn8UvvvoY7pwMYXPsDZh1HZPYiIvm22Cy1mLf5HN4DB/A2ZNvRSx2GZZa6/KHTY2Ncu4uX5Z7kzvvXPqevXvlIYPFIiehrm75KIr3v18Wj3z+eWnQfe97sr0nn5T51Ts7lw55zyLIvyx4uFIeV4HVa9eM+9jmZslzs7PAG28gOh+FGUBtbA7m2/YunRfi5kOjG7s78J0X9+PGlKTn3bvl42fPGrdNKa9nqrIzMbqe6imFxSIFYkuL/OzcKfXr0JCxINLly0Yda7VKgNZqlXNa5mBnYjmwebNU5T+0HMK5HffjZ5rOYObyCKJaG97cth9H9DO4SzUOW1slZqCC7kAG80atXQUJeCY7fPgwDh8+HP/9xIkT0HUdIyMj+PznPw+73Y4DBw6UvCejpmkYTRzKmSASieBAujGVBd7GWhL93jP46vnfwn0LJ2HWF7EAK6ajG1Cvz6MuNgOzaRFTlg0wLS4CiBkfjMWk1tu0KV7YDHU8gMVTX5X5lGq3YDxWjw2YxdbYMC5NNGP28CO412yOz9OjRlWWtEf/oUN48j/ej/FnzuA3jo7A7pSK4vkvP4vaf3sYbbFr0AFYEIUOE2oQxRzqcb1mO3bGLkkDXwXmbgbIMDcnBZBavnJ8XFoxsZgxlFBNcm4yGas179xpDNFWJ2NxEdHWLfjcdj9ONb5LFqa+TT6qT8jumptldxXVs101qNrbpaa7dk1e27RJCu2bPTZN0I3eFgCgNxtPDRcWVh2uvpLESqW9Xe4nAakrb7sNiIyb8YWmT+IPmh6ESQ19VU/NZmdlGzDDqi9CX9RhCoflmHRdjikWk/cn90pb7UY4+W9btkhlvbi4fHqI975X8lXi+K477pBg0OHDSyfXTpbcQG5pkXnKnn9+aUZTveIAuT7t7XJTqxpLN2+WTNEYduACzIjBCh3bcRmaPg4zopg318MaW0Bj9AbumH4ebz3zMPDMl5Z/XzWH0W234fLHPoeffPGfsBdnUYt5TJtrcdL0DvyfWx7B1dZD2FJXmPScPIfXHXcYD4wBOaxNmyTYmXe+WW383OysXLfPfCa+eE6mPYL27pV/BweNoY6Li8YN8t13Z1AOqJMxNmasdB2NSvkTjRpTOqinA62tUhYNDEhvhNZWSSsJDe1vfAM4hGfwiVsew/bxs9AxBxPqUIe7YMIngWeQeiXTWEwy5c0b4HjaUCucms1yYUwmaT3v2ycXb98+mPbuRfumTQgFgBNfBk6cBBbu+jR+9bI0kFXQrX5iGvWxmKwCfuutcrHHx2XfExPA7BzM0NFuGoK1/gowMw0di5hGA8zQsQHT0EwTqFu4AczPYt5sRu19b5NGd6pz4nDIDYv6rtPTxmql167JhTl0aNmFyWTYpeqEtrgoAc9gUJ7/qF6gZXXoEJ5/8EuY+YPHsPXKWVhj1zEbq8Wr1g58u+MR1Ow+hBs/ke+1uChxj0KPRjhwQMp1lXzNZvn/fZYzeLv2KqyaTe7Kr19fMlfzYtQMbWEYH/jGLwLPtkoFkS4AqG4uVxpK/pGPSHoYGZG/TU4a5TtgDOW95RbjQsdisq13vENunhNXSlb/T/73lVfk+zQ3G+XHzIwxoa56MDY8LCeksVHywCuvLJ2reW7OmDamtlbyWG2tBJzUAg5pzknz9u24e3YI12puQcPoLJoXRozRPTYb6mIx3PPyy/iphjM4W98R75zS0SH38X//90sHKuRDFW0zM4iPJFKH/Y3hQ4hs+BL+x+xjuOfKWdRcuY5FUy2utXWg4ZOP4N5yT0+TQPXwfPe7pXjMpExIGTj4xV+UNLqwIHfVakjolStSiag58oaHpUwcG5Pt3HabEa1QbZuPfhR48UXUtzTgrfqbWBiXpwm6ZRHzc4BpcR66vij3BbW1kreamyWtDA1BX1yEHtUxdX0O5hkTGieHMbygwdPyO/ixdggPPgjc9T7AND8nT/ReecX4OXdOju1HP5IHvi+8IHlq40ajm/DGjanTdkK+vPeFD+Abt2zHHIahYw7m2Shq/20BpuckPf/eaB2GsQU1TgCzac5JJCIV69SURCnUIqNbthg9PlcIQCSuGB+NGj1K77lnad39F38hVcf27cDb3pawgXQBokzbeikTT3bS7UrNoqCmdtywQarsgk8xeDPqanrsMWw4exYb5m9GXX/6p6TsGRyUrudqkT210ntbGyy33Qb3T65icWYBjbEb0GHGrKkGDfo07oidxdTMJtSaF1Abm8Wm+etAzc32b1OTtEXUioaJ920NDann8d23T55IrjRKxmRaOoT/X/9Vgp/PP790yPvtt8vILjWMJoMgf7w9ol5PlZ6Hh40HCzt3yr+1tXIeX3ghPrntvLkOk7VNaItdk8/U1S0JQOrNzfiC/ggsNbII5M0R5/Fn2FNT8rU+/vEs0kC66Prb3y7X9fJlI22bTHId1EK2aj0MdX/V2moEQSugJ2RiOaCmJQWA1s1mDNV1YOpW+bqtU8A3L+xH9x13wfLj542Rn6rRl+8IhSpXlIBnsuQA6HPPPQefz4eRkRGYTCYcPHgw4zlA83HkyJEVV1F3Op0l2caa8cwzuOfLH4G+ILXwFDbADB0bcQM1sUVZyVifw47FizCZYoDZZBQssZgxSfPUFPSaWvzpC+/G5eZOfHTxMeycOIuNujQyz2s3FwU5cQhP/4bRwWdxUdpmpb6BuzFlxtCmDswfAtAhX+V/Bg+hpuVL+MDI5/HvFr+DesxgETWYtW7EJezGYsyMW/QrsMaiMG3caKy0fHM4dHzlt5ER+YI/+pEM91ZDBUw3e0KoxUrUwjK1tRK5uDm3G5qbMfigH2f+8V24pUXum1QH0pYWeZtqZ1VUz/bEKFNz8/LA3Nyc0dUjqfLEtm15zz0GLK1U1ALFKhatFjD9xvAhfKDXj32//V5jhUWzWYa8zs5iPlqDmugcdJMJppkZo6eY6rlgtUrjQbXqYjFp5KS6Ef7AB+TAEgObMzPSQFtclFZvTY2xIrJ6/D83J5P3vec9EuhcNjZkBckN5HRPTRPnglIrSKuI4MICMDcH3WKBORqDDhPMiGKrfg0mxLAIK0wAFs21qIvN4XVrG9qjI7KqzMSEMWx5clK+282hJ9c234Putofw7tYzaImN4PKM9Fq+bZcZ9Shcek4OJiUOWy14e2G1J9PNzfJ3uz3rTe/ZI5dFJY2tWyVuqOIfw8PSZl3xvKmT8cMfGkPeFheNSePUQxvVo/mNN4wAoa7LtWtqije03/h/voT5E8BHX30Yu1sisG5PE/xRXVqiUeMmRAV+Xn9dykh1U9bYKPv5T/9J5hdrb0e6scZdXXIP4vUCT5w9hPM7voSfffNm0E2/jinE0KbXI7Z5FzYk9nSZnQXefBOxqYvQAZhrzLDG5hBbmIMOEzZgGjGYYUYU2xcvwoJFzKEO1uk56G9eg+nK5eXn5PRpmcO4oUHO8fS0fDeLRS7M7GzaO79Mko3qhGa3y7W/dk06hme0yEaRPfMM8PCThzBeez/esVcWNLkyK3O73b5gxp8+It/hwgXg2DFjKtVk+YzYe/ZZSTo/93PApz4l9zaf/Sxw1+QIzMNzQPMWo/eHrsdvDHUdMnP0wozUx7femjoAmDgvplqdd8cOScvj4zfn2tYl6LKwIOlAdWtRwU6r1ZjjTdOMCW+npuRvv/7rmT91OX1aIt9qOKUyOioVn/qe09PGMvRqX4uLsv/ZWXn4GIvJNubnJSC2a5cxRN5sTn1OrFbg+efRMj2BWtMEas6ZgJtVBnbuBBoaMDkehXnxOt5+xwj+1x/Lx9V1PX9eAp6vvVaYB92qaHv2WaMdeddd8vWGh4FvRA7h763344E7zmCLKWG+5ifN+NK95Z+SG5DzoOaRvPPOzMuEZWIx4G/+RtKgrks5rgL3tbWSJtSiojab8SA3dnO0klrQqa5OLtSjj8pn6upgMplQCwBmQK8FZk0NuDHfiNrFN2HZulXSSsLFvDFjBs6dR93iFKYujWDavAlDdR34q1sewfmWQ/jExyWuDkD295a3yI8ylxAE/d73jPnho1FjflBlelrS9s6dRmBKpeuXXoLp9ddR/9a3pgzyWyfncPfIj2B+AcDehHMSjco5uH5dtq3m2p6bk6eQLS3G912lQZHYC3l0NH3d/Z3vyOs/93NZ5ItM23qrJp7sdnX6tBQ37e3SHlHBGzULQVE6YqTr7frFLwK//dtyXVUgTAXBhoeBa9fQthDDJesetCwOI2ayoEafhxlRmBHDJj2CWX0DZswbEdXNmLPZ0Dg9YjS+FHWdOzslileIObEbG6X34c//vNzofetbEiV84w3g+HGpS5qb5d+aGqNbcKrFRtW8ld/6lnxm9245tsQ8Xl8v30GlgcR2mXrf/Dz0227D+Tc3Y8EaQ4s1KudiZGTJd33tFx/BN7yHcOutktzUbEJqhH9trbyedRpId52ffXZ52l5cNILbd90l9WLiNcuxV3MxJJYDZrNc7uZmY1r/+nrjWc7MnBk/+S+fxD0Xi5OXq1lJAp7JkofAn1Oz1BZZV1cX/H4/IpHIkvk4g8EgAMDhcJRkG2vCze5w9ZPDmDcBC7oVNViABdH43JVWLCAGC8yIGl3FE7tJqQbC8DDG93Tgn6/sx6Zbzeg13Q/rS7KIwMbb2nB1y35MTpsxcbMSvPde4z5ElUmlNDUl/6p7DxUoa7r1ED6x6X44rnwVn5r9A2ywzmCkbhcWUI/o5Ax0E4CYLgWoWqCptlbOxfy8FEDq8c3kJDA6Cv2WWzB3fRLmkWsw61FYNm6Eqa5OGp1tbcZqujfndsMjj+AHg1Jp3nWXjIZTPdVuvVXutxI7JVZIeZ5ZT7eDB43vnE9DIY3ESgWQmPLVqxJPVfG869eBi3vehX1f/7oEpMfHpTdEba3MQ7a4gHlzHaLbdmLT9fPGxtXNbThs7OBf/kUmDB0fl947atgkYKxICkhgMxqVdKEm2I7F5O+J58pslvNjtUqDJu2kmFlYYYXb+FxQ6hG9Wm3y5vAo0y23YPHcZSzGzLBiASbo0GGWeTdjs9ABmKBjx+Il1JqsMp1DQ4O05G/cMG669uwBZmfR/ndfQF3d/XjB2iH36y3AnQnrRhQqPWcTTCqI1RazyDFt19VJHELd36nOg8rIiPQIWfG8qZPx4IPGgj6qAJmbi083sWQqCrXQkcUi5dj4uDQm33gDsUfd6BnVcYt+FVZtq9GLaHHReKgzOyvv//GPjTmvZDykcdLVU/iGBvliExPAL/+yDKFdxc//vJTdH/848IcnD8Fjuh+/sPcMtlpGcD3Wgl8793t4y9XnsdCqQ9Nu5q36esxvaoUJlzBj2YTae/dj9o03YZ0MSwAU+s16LwZLbBYxmFGHOZhiMehDQzBBN3rxT05KQlKriM7MSCWi7mabmqQ8mJpa8c4v02RjNst3/su/lGHt5Q54LhmmuceM75/vwHgMsDQAb90n2f6P/xh4+mn5Li+8IJ1O/uZvALfb2E6+i43fbLLB4ZDi7L77JOg5+GdtGJ+pQ/3YmLFwlSpzp6dRE43Konmmm/X1a68ZE5AtLkoPs507Jf2fPw/86q8aXVjU8N1EqswEZF9q7rzXX5ftJ/aWBHJ/6pKujlX5Sg293LLFCF4uLBg9P9WDtelpowEGGPM6q96c09OSj9U5WViQ4NPN82jSY6jXJzEXbYK+aRNMrS3xiSbHXp8FTLXoONy27BreeqsUK9PTkkYymA5/RWazxIu/8x1pQqnnG2oha10HrLVmnJzviHdmunVzZS1yq6aAr62V56g2W45ViWrE3nabTOmh0qOadkk9rNF12al6XfW0f/ll4yFULBYf8YKaGmOBssZGmBoaUDNjwusvTWJjdAI1U7NIGIuCyDjw6pVm3BJrxhsbO/D/u/W38OK1Lfi/E/thfcOM3/9IQrAzncQgaFOTDMnduVMusurRrNK3CthcvCiJSvVGVtM7LCwYTwMTy4Lz59F08/ZFj928EGoeTPU5tUJyW5v8qMVDkh/Yr9CgSOwHkDiwMLHuNpulyNm4UZ5x56xI7ZBkqp29das801TFpxr1XLSOGMkB3lhM2uANDXJA6vqquclnZ4FoFGaYsXPhPCxYhK6boMMEwITFmyn3ovl2TFmacJvlMloe+jXgL7+6csPx0KHCzokNSGJQQ96fekoeHNfVSZq8elV+VNstEpGpz9o2Y+bGIhYj07BadDTMzMCk1tSYmjLqHkDOz8svG+009eBWnVe1bZMJ8+YGzC+a0KDPwtrWJGnKbF7yXS9+1xy/19qyRbLP6KgRwGtokCyUUxpINwd5ctquqZHCHlge7KywnpDJo87e+lbj0gDyel2d3CKHQsC3Zw7hnhLk5WpTloBnsj3Z9EDKg8PhgMvlwrFjx+DxeOKvezweBAKBZYsStd+cBG0oYdKgbLexZt1sIE1bm1CrD6P2ZvgCAHS5vQMALJpqUGPVYbp1lzQoZmeNnLq4KBVDczOGfukRzH7RjC31wNCQGdPWDmgacHuK3oiqN7qax1xNyl4qqhOGCngmBsqsW8z4+ugHMdy8Dx9deAy3Tp9Fc+w6Js21eKm+E3c2XcVGFdRSASLACPSoCZZ1HYuwYPb1ccSi49ABzJobMVKzB7ua5tE0PCxPJ5MqEpjNePl/yyYdDomJJd7fqFhFhZXnmUWZ/vAPV523MB/JlYqa8kUF1JcEhjreJcE+VaGoHjfRGK7U7oNm0rHp5rx/+uIiMDsL3WRBTDfDsrAAk7pRUHcryXP7Li4aDYpXXzUeGCT2qlPTIjQ1GXO1ALKtDBZhy9hqc0F9/vNy1zgzY8yfuns3TGYzLDWvQ5+LYhqNqMc85lEDK6KwYgFmxGCCjgZMw3wDxoSpr79u7Hvz5njwp/nKy/j5HWfwN+c7Vh+yV4CvXNL2wmqLWeSovV3u5Uwm+VfFIFR7/vp1SYIrnrdDh2SuwPe+F7h+HXo0ihjMiNU2woJZmBplMYr4fJ7qRlg90LpyBaipgb4YRdu1ENoAWBpSpHlA0nQ0akzhAUhmVNtTCyyo3go5Xvh3vlOSaSwmQY1/Ge9Ae7skv2/WfBK7XnoY1lcuQH/LFphulkNzl6/DZJZezfX1JkzUbQRMVsRMVugwoTY2g6ipBjX6ws3enlILmmI3e4Gr86GOGTCmBFhYkDy+caMxqVkGd36ZJhsV8BwclOolVW/JUlGxleZmudmdmoqPaI7fACXGeX/5lyU4+Z3vyJD8pqbVFxtfbfh7JAKcPCn/T3xGfeQI8NdP78erlrvQ+sYPUaPmE6ypkd4rC4sS7MTN8icalXLPbDYCHpOT0iNGjWRR108txFdTI4WJCnwsLEiAcMeOpSt/WyzpF7PI5alLujo2safL7bcvHV1x7Zoxhry11aizACMIpkaoJE5posoBdU7m5uT7NDQA0ShMM/O4Uns7bt/VHL/JnZrSURMZxlBTBw5/YnleVgtZvPqq/OQb8AQkvbS3SxxajWCMxYyRjWo9yZkZeXiUKn2WkxrOrsouIMeqRDVi1XxRVqtRNqlrDMi/6om/CpKrIKfVKpmzvt5YWO+NN6S3WEJl3digY7v1Ol7d2IEt+gR23EyLen09rp2bxS1zw5hr0PAXtt/D34wcwtQiUH9zpMX3vifJN+Nkrxp1qs7YsMEIcly7JidQ12/O+XQziKNGMADy2tWrRg9A9eB6YgK1kPnaTcDyc6JGr91+u7TL1Mp8KXq7rdSgUM8oBgeN5wy1tXI4IyOy27Y2+Vr79xcgTxSpHZIo3ZRBSsk6YqiKaNs2Y+V0tQgpYJRvphgsegwmyJRVC6jBoqkGJrMJ1uicrF9qnpXV3n/23cDBzsyeQhaj8DCZJH1v3Gh0HFAJReXdhQXoMR2zF9/Eoj4KHUBMX0RMn5Zq5sYNiaCrkVspApw6AOiAbrHI6ucNDbJmy9wcZiaknbMVwzDf0yHfMyn9JKaBlhbjwY16OG+xFCENpErb4+Mywq7Ce0ImP6tMnM47sQnscknA89lngY997BBMRc7L1SbjgOeJEyfg9/vxoQ99CP++3F0E8hAIBOD1euF2u+OLKfX09MDlci17r6ZpKVeVz2Yba9bICGYm5nB+rAXtuvTiXLwZyojCjEZMw4rFmw9MzVKqbdq0fPXsmxWBZdMh1D0u7aPpacmT6qk6sLwS3LDBWGSglNT9DGAEPJMr8Le9DRg2HcJn9PthmzyDuskRvDLShsit+/Eb9z6LD4ykDhChuVl2EIkgWluPV0x3oVafxgbrNOYtDbhuuQXz0ybMXDTjnubraBgbk152CebnjUn93/KWEvdUy1emUaYi3WWk6gCz4tQnyRXohQuY+f0vYuPrESzOSm+HxflFxOZjWNAbcN6yD7OLG7DJuoBdzRNotA5LQ7ilxejBo3rNqN4Iug69thYLlnrEdBPMNTpqFhZgsliMeXATy6hiDcNYbS6or34V+IM/WHpnODMDixmImXRcwy3YpV8CAMyhFvOoQQNmMIMGDFtuxe6666ifnpbvUltr9I5QKy/U18N0/To++J9H8N2vliY9l6Dtv1QRGsE2G/DNb0pQ6JVX5H5t82Y5bxcvSge0vXszOG/vehfw9a9j+r93Y+7aOIZjmzEXrcW+6Msw35iFua4G1l3bJWKghuCq4PzGjUBtLWamYjBNR2AyA5aWZsByc4cWizHVydyccXG3b5dtmc0SoSpg8OfMGalr9u6V+Pr0tGx+717ghZZD+OP2L+HI5cfwM9fOot58HbGaWrxYdx9+uOcwHtSfBC5cgKVxM2ZNjdgQuwEdJiyaazFctwvb5i4iCgus+iLm0YgN1lmYrBZjuKOuS563WmXnui7BgS1bcrrzyyTZbN8u7zl9Wkat/ff/nvGpKrg33jA6TAHylW+7zRhlnRznvecemd7s1VeBf/onmZYgcSEPFUPOaBGum/7lXyRp3nGHVL1KWxvwc//BjKdGPok7LjyIrYsJvZpvBu0Bk8z3WgvjJvDmjV/8Gm/caKxufO2a/LtjhzH3caLJScQnMkscq61pmS1mkY1MerokUkOcN26UqNqNG5LvVGBHBesBI0im6jB1TgA52bt3A9u2wTQ6CrzwMjbPv47p61bU3SKF+Pwrw5iyNuP8f3kEP7M99YVT6eDVV2XWlnz8+McyzV1LC/D1r0vVNTIiQfjPflYOd2JCysnJSRkJum2bnCoVcCq3xAWLEmVdlahGrJoLvrHR6F2v5kkGjN7G6v9q8tuFBan31SpTsRjw0EPAn/xJysp6w45mfLXhDxGNAr/b9BgaLpzF/OR1WCZr8dqmDgR2PYK/vnYo3oNRLcCXdaB5pZFDatqTTZvkwa0Kdo6NyTGrAP6GDUYAX+XxmyMOYroZgAlmky4BH7UacmJwFDBGLaTo7bZSvaWeUbzvfcYaJC0tcht17pycckDSZ169O5N3WsRIfjbzTxeVCvKrXr1Wq5G2gXggX7dYEV3UMY+6m1PV1MKkm2CJyToNMZMV283DqLd3GNeznIGmxCB/a6sxH+X8PDA8jNi581gEMI4mWCwm6Z4Um4UeNSGqm2A16TAnPrxW5fjNAOiipQYLC2bAFMUV063YHr2Mmsk5mGstsAKYnVzELbMXYLolfbssMQ2oun9y0pjXFZCpNwueBlKl7SroCZnpqLPOTrn0169LPbZvX3HzcrXJOOB5+PBhRCIRHDt2DC6XC93d3Th69Cg6qvBk9vb2ZvS+wcHBvLexVsVa2jByow4LixbMWDaiPjqJedTBbDHBpAMLMenJZV6cBxqbpHExO5t29ez9MWnUfPe7Ut6oe10gdSWoHkSrB6ulMjtrFMgq4JmuAtdNZry2sQMXRoA9bwM2xIBvRg7h/d+4H6a/+OryANHNG3i9uRmjE7WYX7RgbsMtiCSM5qy3AKaZWYzcqMWOlrZli9S/9prU2ZomQY5t26qiPDeUPMpkyGkoc1IFOmLdhyuPPIY9sy9jMWaCPjePG6ZmXG+4FXM1zdBjwMiMFRumrmHurW9Dy9TrsuHEOdUAubF88UVEozrCMRtGZzYipsvQ2bswh00LN2C2mpfO51CubrtmM/DBD8rdaFJCW+zoxLXTV7EpOgezpQ61MzOYRw1qsIgF1OCC2YapaBOax6/DWtcI644dy88FEA/+vO3dbfhSBg/QC/nVqrCKAyC94J58Unp5J45SVIss67oUyx/8YGbn7Rnzu/DntU/ivebHcKfpLDbGphCNWRGLxXBB34fNCxa06legRwFLjRWmaFRu8u6+GzqAqz+eQmONFc3NgGnH1tTXWS1YlriQG1Dw4E/isLoNG+Qh0eysEfQ8u+UQembvx//59Bn81N4RnHytDb/1V/vRutmMhz9yL/DHj6Hx5bOYramFPmdCzGzBcN1ujNVsRuv8m2iI3sAc6jC+cTs2Ri8agYG5Oak49u6NP9zC5KTR+08pQl7+D/9BAp7/9E/Af/tvJV7s76bnnpP6SHWIb2mR6i+xGEuO85pMsrj95z8P/O3fSnBHzbUMGFNg3nln5ovxJQ5nT/arvwr8t384hD/Q/Xjs9ffCOnY9PlfzYt0GIDojPVv0RQmWqPm4VTBocVG+1MaN8kVbWyXYef68saiWoutSiHV0SHQt18UsspFNT5fEuZpV4LOxUdKs6pqpzoHFIt+9qSn1OVH5va4Os9otuBhtx+7Rq8DidcybavGCtQP9tkfwyG+lz8v79sm/at7KXMViEosDgF/4BUk7SnLvow0b5DnOxITckA8PG83YclMLKyYHPLOmGrH/9m9Gj00197i6rhaL0SNZnSD1cEs9FEgst97//pRtAnR0oOaRR7D3zCE8+SRw37n7ca/pDGqiIzivt+G16H7oF80wmyUJqSmZV1jMPL2VGnWJaRuQ76RWh1fBr+ZmyXcvvbQ0Pc/PwwRgIVoL6IDFaoalvu7mSB4TYgsxQDdhbqEGDboOkzonKXq7read75RzMDMjh3Czk178fFy8KJt8/HHJehXXpk9S8imD0lEZXT1kUovWKIuLiEWjiC0Ci7DiimkXduqX0YBZLEBGcqChAdsxgklzMy4eegRvVwddzoZjqhtSk0nmn25ulplZTBtxdeOd8azbEL2B26dfQjRmQsyko27fHTBdOG+U8zd7uy5Y6xCdnIUJUcxYN+HGhu1YnG/AlrlLaJodR7SuFgszCzi/sQN7jj2CljSJMTkNqOnpVLtULU3w7LMlSM9lvOfMRqb9gd7+dmn7P/usUV+SyGpI+wMPPIAHHngA4+PjeOqpp/Dggw9ifHwcPT096O7uLvmq61Q+Z7Af13EX7jE/j6vYhVujr6Ees4iiFjGYYEEUc6iFtb4ONWol8RVWz1Y9Os1mKfRUAyddJajazqXu4al6d6r53NWxr1aB/7//r/QaGBsDrlw1Y1eaABE6OvDaL3wcFz7xR7gn+jyuLDRidl5ukBobAbNJhgq8iA5cx350JB3fyy/Lv/fcY9xXVUl5bihjYyHfocyb/8sh/PqfS8/et898D45XvoIGyzzmdWlAbzDNQsMwxtCMLzf+Dn7/1j+CKdWj7g0bsKibsLBoxvXpDaipA2rMQCxmwqXZXbhj8SVYLGZYTKaVM0oppUho37u2H3/nfhbvH3kMlushaJhCA2YxiY24ZL4NiyYr9pgv4Hp0C643tOPu4cswrfLY/1CZH6BXAzXkd3TUiLWp+7jGRnneNDEhHc3VeiUrBcDUvIunFw/h/MH7cdv4GTQvjmDHwgX88sUvomE2ggsXN8NiasSGuRuImUww11lhvdl97sa4jsbJYZxr7sDb3w7ghTTdO0oU/Enulb93rwRR1HoXO3bIUPfzWgcmAfzZvwAxyLpIlncfAv7d/TCdOYPXvzeC/scu4P6Rv8Md+llsnbuCScsm6LEY5iyNaNq5CabrjcYcjlbr0i6F9fXGsOebU2AUKy+/610yC8rVq8Zc2KUyPg585SvSu1TXJX6tOqhnEud1OOSG/upVacirKWSuXTOmC7xyRba32kwAb7wh84KaTMDP/uzyv+/YIa+fOPEufK3j6/jV73XDNDGOWMtmjOotaLr4AjYs3gBq6qSL6aVLxo3hwoJEw9SK0OrLfPzjKw+d+8M/lJ2X80nOanM1q2PfuVOeDKjRBLfeKlGXGzfkHGRwTqIdnfjkQgC26Rfw4SMjGAi14VtX9+PQu8zxGR1SUTdw+a7U/g//IPl840aZxzNRcrygtlZ6ykci0oxVi6z8wz8YI5bLQdeN85D3ja1qxH7kI/JFVQZTU4mo3o3z8xLg3rXL6HGv60Yg/+YUVfFya4XGZ/uUPGianzej7rYObGiTfLk4KZtqbZVAdN5zzmcyD3livkxcUEg9uEgM8qv0DKA2MolYFFhs3ATLnl1YPPsqYjdmocd0TJk24fKQCdvOXcCGHc3YmGNZPjgoh/TOdwKf+Qzwf/+vLOSmpnOvq5PDeeGFzKbzqAQlnzIoFZXRT582rq+6/roOfXER06aNqMUMADMmTU04Z2rHrbiIRn0Sum7GODZh/p4O/En0EYyEDuEvFpcONy6LFW5I569cx7ypEQvmeszN6liImmC1AKjfiFlzIzbGIpjQNSyYNmHT7t1L8ri+aRPemN2CbRiCCUDEuhmxhSh0aw3moxtxMdaGf979YTzX/G6M7tqPp9+7clpXaeC3fkues6jpQS0WqVIuXy5heq6Sng2Z3Mu/853S/v/+9+WZExlyyprNzc146KGH8NBDD+HcuXPw+Xyw2+1ob29HV1cXHnzwwUIfJ1WYkTEz/Bs/iT+48TCaF8Zx1bIbmzGMxtgULPoiorDglOnt+PEv/S982N28amTiueekbL3jDmNOj5GR9JWgCniWuodn4nD2xBu1TCrwv/1bKaief/7mUJQ0pdfF75rh32jG/xp/GFtnLuC6eQtmUQ/z9CxusQxjsqYZT256BN1jy8+jCnjeddfS16ukPK8I+QSIN20CWtrMOD3ZgRPTHTi1txPvff0x7J48i024DtTU4rWNHfjalkfwr9cP4f0fNGPf+eUNE314GNcsOzBvAW43XUAEWzCHejRgFpp5HK9bbsX0hu24e3wYpkrqtpuU0J79XeDH2iH87X+6H89/7Qz+Xex7+PcTf4tNk6+jSZ9A1GScj6kp4Mnah9GQwWN/puf0EheFsdnkRigalYbknXdKwOdf/gX4sz+T4NHZszK8c6WOhGq6q61bgflFM755qUOtTYWgvg8fMT2Gu3AWUXMtoJuwqFtwNbYbbbGN0KamsDgkw1Xf/K+fhPW/YPW5eoGi3g0lBzXq6ozhsrOzUo42NckNplrsacOGhJvtmwnwbR1ApBP4wuffD/35M9g4N4LJuja8Zfc4PoE/QtPwWaOnqsUiwU7V8294WLrgP/igdDks8p1fXZ0E8v7+72XxolIEPHVdgpxf+YrEsFVvzXvuAXp7M+/hU1cnPVQDAeDUKWNqvWvXjA4s4+MSjDKbVw6MnDgh/3Z0pH/P+94niyb1vvAu/HPNk/jg7GPYd+ksavQrmDRvQn1dDOamRonGJAcAd+5cPt1Cpnf45XySs9pczerY5+dlXLdapOjGDan4YjHJTBmck/B/fgQv/oEVP7zRgX/8Y7l2DQ3Ag90rH6LNJtd6ZETKt2yCjbGYfLVLl+SrAMAHPrB8G+niBTU1RgB02zbJsj/6EfChD0naLHWP6TfekPaomts0b4cOAX/6pxKBOHnS6Ia9caMUflevykncvFmu9+7dcoLUssoTE6nLrRSVdSwmDzDUfNJjY0tHgKuR4wWbcz7TtK3yZWenfN9IJHV63rULmJuDOTIBABiv34wZfRNGYruxPXYBFlMMMWstmkwTOI0OfB2P4IM4hFxK8299S/51OAC7Hfj935dDSIzN3nKLXKZKWlBrNWXviJGY0efmjInNb/bijcGMaTRirHYL3jBvhzY/jFrMY7x2K4bqOvC9pv+MfzW/G5/+k/249DkzIq8DAwNSFpRdmvpmvP0+/On8YRy98SS2z9+8t1ysh3V+FoumGkRNNVhEDSyTU8DWpXl8wVSLhXkdL2z8KZgAbJweRhsuQ7fW4rVN9+FrWx7BNyOHsLMZ+OUDmZWH999vrOk1P28MDFBTNVRTei6V1e59fvqn5dy/+mr552uvNHk/i9izZw8++9nP4rOf/Syee+45+Hw+9Pb2wul0oqenBz+b6hE6Vb22NuBM8yF8TvsSfm1YFueZiW3CLDbiet0u/J/6D+JPJt6Pw1NmfOjelQu/hQXgj/5I/v9rvwZ89KOrV4KVEPBMtloFfu+98rczZ2QYFYCUpVdbGzDYeAgfv/ElfMQigQRr7Drm9Vq8UC/zGz2vH0p5s6YCnnffXZCvu27lE1C77TbpuTAzA7y86xDeN3I/9sXOoFUfQcTShjnbftTWmzF/Gbh42yHsS9Uw2dOBR6OPYMMG4NeGH8POG2fRuHgdUWstXt3Ugf+95RH8a/R+fPGDZ7DFPIINt7Zh3wP7ZZh7EaibxWwapouL8uQWAG7bY8ZfmjowensH/t78UWx+/Qzmr46gYVcb3ty2H4sxMy7PAS99+Evo/F45H/tXv8TgpFrgbXLSWE9CDfm9eFHm/fr7v5eFPVe6kVRDwKNRudGen5frf/06cCJ6CP9quR/7cQb33TqCdqv0eNw+fhazr17Gwi21eLGmA/3tj8D9qUPADpQ9+JMuqLFzp5yb+Xm5f9+5U9Kx1Sr11G//tgRIEpOilPtmnDnTsfRQcWjJ/L74u7+T76tGOyR+39/4jZLc+f38z8ucrt/8puxi587i3WReuiQ3K6dPy+82m5zze+6R3xsasotp/9IvAU88IcH5mRn5ASRosnmzsVZQQ4N03kqXnlXAM9VwduXKFZle9cYN4AdbD+GNzvuxa+wMIq+NYNTUhs98dBz/4eU/Sh0AnJhI/WUyucMv95Oc1eZqTjz2t75VnqYkDov/o9XPyfOHH8EHnpT5GdW0vRaLvPVzn5PePemKerW24+XL0kPzwIHMvtYzzxhpTS1CtHnz0umvk79uqiLKbpdLumWLTLEwNCTH/K1vyesr9U7NR6r6V/Xu3LOngL3KDh2Sp2Ff/ao8EbtyJT4cFj/1U/Ke4WGjDHvPeyRjZtnjPnFR+IsX5dqPjxtraMVikp/Hx+W7FaTDezZpe/9+eRqYLsh/Mz3feMtP4eJ5YMv8MBZfvQzEanGq9T34weZfwrWG2zBubcPQhv04f9GMyBeAd7xjaZZZ7XRNTUmPTgD4uZ8zztv27ZJnxsflcDZtkvdU0oJamSh3cbcko4dCcmFuXuP5jW147kYnvrnvEfxbzf2of+UM7tw8Ar21DeGNN9usl4GpGZkG5StfkYUBnc4K6OUJAIcOIfaO+/Hq02cwdVHuEUZ27sef/pIZP7Lei4/iMdyln4Uldh0L87V4qfmn8cONh2EfDeJnZs8Cl68vyeMvjNyG//eP2jB+23689hpwB+Se5kZtG2L37EcMZkxdkfzb2ZnZIar0vGuXPFi+cmXp7D7Vlp4rgabJ+h0vvAD84AfygJlEQbPlfffdh8cffxyPP/44nn76aTz++OPo6urCkSNH0NPTU5XzfVJqqofMwPOH8Opb7kf7lAxxVBV8+LwZG2QxQ5w7Jzc76Xz963Kj0tIinV0yqQTVHJ7lGtKeKuAJrHzs6gbs+edX3seePdK++u7iIZzZfD9+fscZWCIjePlaG14w70fjhDnlhM4TE8YC14lzUlFp7d4t129+XqZtm5034ycNHairk/RjCcuwSWPF9+UN7sHh/fh+txm7twC/YbkfzXNn0FojN9vhmv1oghmXrgAP/mlHfOqhu/5KAgqFjgsm3izOzd3c112r7+v0acmfLS0SgDCGEJsxvLMD2AmM33yvGq5mefch4KMcr54PFZxUU5Nt3iyNULWCa+KQ364uCXh+//tyD6uerCe7cEE+c/WqsUaJmv7KbAaiuhkhvQMXbkgv/W9tfz+2Xz+DmvERdBxqw9+d34+ffqfZWIS6AoI/6dZvaW6WsrSuTsrThQW5ubTZZIqAVD0OUh9q0ovvf3/671uiO7+REVkQ/Pp1GcqraZnl5WwsLAB/9VfA174m6a6uTr66y7X0RjDbHj7nzskN0eSkPPBUo2kXF+W6mM0SyGpoSB8YCYdlO1Zr+kVvVA9pq1XyyuSkpO/LbR14/rLs98svAD/314dgfmGFAGCqL1P2O/w8pDr25N8PHVoxKBp76378T5cZkYiUNa+/bkwX2d4ub1utR8++fVJWvfpqZgFPNb1HJCIPfaJRubaLizLTQLohk6ulT59PegH/+Z/Lex58EHjve2V+3MSpAPOVrv5V83YWvK2n5uROVV4BBambE+dQ3rFD6peaGrm2CwtyPzAxIfVNc3MJnnmmStsZBPlntuzHx/8rsHPkDMyRESw2t+GSth+XXzejBhJQrzdL4GZwUIJhV69m3o767nelHXnbbXK9v/Mdo26/5RYpi9S6jsDq03lQConXeXhYKpPWVlwc3YKPf2Y/NtWZpYNNZwcSpw5OnGLhZ35G7mPfeAP453+WqW/KTcoNM86e7Yj3nFRzvz6DQxh9m8ydOx4ewRsLbXi9SYKW9x3+DfzsZ84AY0aZfeYFM07+CHixBpgZAhYWgVc3dMTXprxlWPY5Nyfp9b77MjvGxLZqQ4P8v7nZ+DvTc27uv1+KqWefZcAzUdGeQ6w036fL5cLtt99erF1TCST2kDl/0YypLR2ob7o5LO2iBDne/W5pyHz1qzJ/WKr20ZUrcmMEAL/5m+kDickqsYfnat7yFvnub74pP9u2LX9yf889wO/8jgQpbtwALDVm/NjSgfqdwLk5YOY6ALN0CEpuZ6renbt2GU98qbSeeUYaPufOSUNgbExu5m67Ta7L0JCkoaEhaWfFg9ZJDe6209IgfvNN4No1My5ZO6BpkibmJoE3rsn7mpok3czOSiC90HPeJN4sbt1qDD3NZF/f/778+853Su/mjFfmrOagQAVInp9S05YO20xsqN96q/Q6+cEPgP5+CQAke+YZmfsxGjUWMTWbjZ44ah0Ls1kau5cuATabGZdaO3BxUsqlxkbgV34lacMVcJ2T72mvX5dh7Dt2SDBmbk7eV1cnec1iyaPHQZm/7zPPyOgJtfiFxSLfqZDlxvPPS3Dm0iX5/eBB4BOfMILtyTI9JSoIqdZNUaOnb66lgPl5Y/Tttm2STk+cWN7mUIsVveMd6etw1etk504JTqh1tFpajB7Tr74KnHnBvPwh/novt1YJip45bfQ+T2y/NDZKm8VsXj1/7d0rgaDEeTzTjUBInN7j9tul3lXzQ95+++pDJldKnxYLcOSIMTfuD34A/O//LUGpT3wi896nK1mp/v3+9yWNFm1hinRfvgBpPHlhKLVekMUif1e9Ovv6pGNp2Z55rpKeb9GBxo3AjyMdGAGwp1V6rI7ffJJ77Zoxy4MaGXHrrau3o1R6fvJJafO95z2SbpPr9uSRXDnPc7repbjO+2LAnX+VWZvVbAb+63+VGSG+9jXpjaumY0gllxFT2UguNywW6RF/44b8f8sW4PqoGWe2dCC6R8rS+atyn/KJT5phtnfEt/OYS8rs2VlJw2r9OZtNOjQMDcmPqosvX5ZpPjJ5iJqcnpMfuDM95+ad75QHcs89J9dIdRBb74pehaj5Pk+dOoVvf/vb0HUdDocDBw8exJNPPomJiYliHwIVieohc++98jRWrWTZ0SGv2+3ylOGP/9h4+v0rvyKFKCCF4x//sdwsd3amXkAgnXL18FQB1lQLDK+mocF4Kn/mjJyHX/kVOS8PPggcPSpPxr73Pbnh9vnkHKpz29gogdDbb5enxcnOnpV/1ZBBKi3VyLh0SRo79fXGIlxXr0ojeNs2Y3LuhYX0Ty7375drffGi5JMtW+S633mnbC8Wk21EIpIH6uulsTI+LjdwakXufCTfLG7YII2lDRtW35euy9NFQCpf9YCkuVluMtWKjFNTy9c5oPyo3vfDw8acaIpqqN99txFsP3JE/v3nf5ayJpFKA+PjUnapudUsFmmI6ro0StUcmGazbGNkRDpKTE5KI3vXrsyHOZWautc5fFjynFqXY+9eY/XwzZvl3/p6+Xu19ThIzMt33inXb3raeBiTb7kxMQF4vRIwV6M1PvMZwONJH+zMhgpC7t5t9J6rqZHrds890jtw61bpgDU0JD2Xk9scum4MZz98OP2+EnudqN5Tw8Pyo0a0zs1VXxqoBMk9elRQQJ3nTPJX8krtye2oxGt+5owssN3QIKMtJm8uiLNzp/ybOGQyV9u2AX/wB8Dv/Z6UE6+/Djz6qMy1ODaW+3YT8+zu3fKgQgX629oyGz1VqZLrqMZGI9ip1q677z7goYdyWti8ZEwmKXvm56UevHhRrhcgD5MAaeNcvmw8FFQ9mtO1o1R6fuABmSrhpZckkP7MM9nX7ZS7bNus/+k/Sb68dk3myE5npfKqEBLLjV275JjDYbnv2LRJHvbceqtx3z42JuXjxo1Szrz97cZxPvywjNRS0yFZLJLOZmbkc+r/Cwvy/4YGqftVIH+178T0XBy7d8u1X1yU6ZhJlHSmiT179uDRRx/Fo48+yvk+14iVpr/5sz+TyqGmRhqCJtPSJ5rRqCxAUFMjN0rZTPquAo6qx2Wp5NPDE5BK5uxZ4G/+Rs5R4pP711+XitRqlSdkR4/KjVviuTWZpPfAt74lFVNrq/G3l16SfSQvWETFl7xQzIsvSuNArVUyNSVPUXftkp7PCwvSSP7Up6R3SFPT0us8PS0NCYtFGlSaJvlFrcypJq1XT1iBm0PCLcAPfyhzCf3H/yg3dNnkq8Qnz9evy83g1q3y+htvyP62bZPjVTeLp0/LMY6MSGMHkO8/NCTfRQW6KmJlznVghUU6U86Fdu+9xoI9f/M3Un6rdBiLJSxWNG8MYzeZpDFlNsvvO3fKe3RdAl5q5XddlzSzaZPMRVbp1zi5x8Edd0iZr3rIVmuPg8R5XWtqJP9OTBhl0mpzZaXrkaLrslDDl79s9Gr6xV8EurtzryNTSQ5CXrpkTAtSWysBk9dekxu7yUl57bbb5BhVm+PjHzd6XP30T6ffV2Ia2LTJmCtPPQyYm5Oy+cKFwn2/9SI5f6npe9Qwxkzy19698u/ly9Jj91OfWt4D8rnngP/xP6QuvnRJ9qnqwe3bjQcZhRoyaTJJ2dbZKe3ev/5rCa7/6EdAT4/M2a72v1Jv1MTXVdm7cSPwk59IvX/5srx3etqYNuc3f1OCq5VetibKto6qVM88I72Nz52T+nBkRI55505Je4uL0q6fmDAeMg0NSRm8Y4e0lxLbUadPS/B8Zsbo9drQIH9X901r4bxVi2zarHV1Mp3FF78o7e8dO+S6J+bxlXpsf+QjwMc+lvV0uACkHr52DXjlFSl31AO+F19cenzt7VKODA/Lcap2e2OjBN2vX5cpOn7qp4BPf1q2eccd8j3efFO2sW2b/P/SJSNIb7FIOrVa5dibmjJbcGitlAOVxmSS8/71r8vIrcVFzg4GACZdT46rl96JEycQDAZx7Nixch9KUezatQtXrlzBzp07cfny5XIfTtHFYvLESi1WMD4uBdnOnfK3V1+VQr2xUYI4H/iANE6z8Y//KJPFv+MdxqK+pfD448Dx49Ir6sMfzv7z3/++DNP5yU/kdzX8PBKRHgi6LpXKO98p80OlKpz+7M9k6MTVq9IYXlyUz924Ief5r/+avTxL7fRpeVLb1CQ3cj/+saTtTZuk4Xv9uuSDz39egtjXr0slf+2aERh49VVjpdLpackvnZ3yXjVv/sKC9Jzbt89YOHVqSv4GyGfn5qQXV0uL7H/fPrlJVP/u3m30pkiUPFeYasBv2WI83Vaam+XG8cIFuWkdGZHvpx4IWCxyTLt3y7CsVMO1OEVncSVez/l5aajffXfq4PKJExI4ePNNSTMquNnWJumyttbo3b5pkzTWFxclnZ4/b/QamJ2VBy9qVfj6emkENzdLeizkdAvFoOqu55+XOip5GNuFC3Kzk65srlQnTkhvEpX35+flJl0t/LNpk3zXP/9z4N//+6X5M3EtmsS55/77f5f6LBSSbdx+u6Sht7yl8MefXL6q9KVMTsrxbdokaU71WrnzTqPNodLzAw9IHZxOYhpobpabyNlZI8CrVoHfu1fq4UpOz5WmUPnrgQek3InFpMy64w4pj8bHpS1144ZcM9VjVPWK1LSlw/umpuRm/utfL+xsBD/5iZS9atj9W94iN/aXL6eej9PhkOBt4uttbUbAQj1gUiOa6uqM6Ta2bJH5HCu9bE0lmzqq0qjglXoQHIsZvXDVaK7aWun1OTYm7aRoVP6v2lIbNkg76soVKT/Vgmw1NbKd2loJUmna0ryRvJ5SNZ23apRpm3VhQaYf+PGPJY/W1hp5/BOfkHr09Gkp+2Zn5TrX1EiaeOUVqdM2b155ftdYzJjDOPHnxg35+9iYlD/qIU9dnaSzHTukLRaNyuefeGLpSIfvf18enqj1yK5dWzqFjDq2Xbukvr1yRV5X06+pB4Jve5vRySPTsrWay4FK9dWvSntsdlbqiEzXXqg22cTXKmEtMRw+fBiHVxpjRFUlsTdJYiO0sVEK06kpaaTW1EhG3L07+32oRms1zeEJSMVw5oyx+qRaHEPdwG3ZIoGBlXrbtLfL07XJSWO15MlJaQBHItK75tixtVWoVbrkhWKam+Ua79hhzBk2PS2NA7NZ8sbnPw+8733SQ8Bslgbyxo3SU2l2Vj73R38ki2skzzGoejapVWGjUWOYyfi4NJ7VcOJQyAhKAPI5m21pEPTqVakIE588X7wox3HpkjSYNm6Uf1Vwc3hY0p+uIz6/qFrQJhqVdB2JLJ+jqgKmblwXslkUxmKRAJjq1bZ7t5StL7wg17WhwXi6nzj/Xm2t3Gy3t0tPFtXeUAu+WK2S5nfuzOyJf7mt1R4HyT3ramulvFHzSUcicvPyj/8oeVUFXlSv8oYGY+65mRm54T5xQsqOrVuBX/s1GZFQrNVp1dA3NZ9aYrBT1+XmC5Cbsbo6qVdnZuQmcm7OqCvVQ8Kf+Zn09aNKAx/5iHxeLXKj5iirqZFzF4lUfnquNIXIX888IwGFixelfWm1ykghk8lIfyaT0Tv37rslKLV9+ypzRxfQnXfKqs1//dfyEOHFF2XRrmvX5Ltt22Z87x/9SEbsNDcbeWxyUgIjahGurVuNHtRqOhyTSba1a1dmiz1VomwXLqsUiSN6brvNCFapNDg5afSev/tu6dWpHtbs3Cnp4I03pI598UUpm9RD5oYG2b4qf4Hl0y9U63mrVpm2WX/4Q2PqjGjU6MH+/PMyPcPEhNQfL75oXNuaGmljxWLy09oqaUn1+vz0p6V9/eqrks6GhqTcSGa1GlNPDQ/Ldlpbl6eJdL3o1dRbN24Y8+maTEYgVbXjAEmjaqRPa6uUtVNTRpkLZNd7num5sJ55Ru4dp6clXSSmqUKv81BNKiLgSWtLYvDHbJaMNjMjN89msxTw8dV9o9LAzTYDqiHtlbZK+0qeeQZwu43KSj1tGx+XikNVKLFY+ooiFpN5T9WKduPjxjlVlcNzz0lFyd4npZMcULj1VqNBAKRuZOzcaTw9tVqlAawCnVu2SL754hcl4KkaW7GYrICcPJG6mhPq+nW5mX/6aWk8nz8vAVT1FFg1ls6eNeZ8VUNfZmZkv+rp7dSUbFfNOXrnnUY6vXhRGm+6bgx3V4111dt4YUECIq+/Xp03Y2tBJg11Vaao+fQSVygH5JrPz8vQ98TVh1XAoLMTCAQkzX3qU3LjHo1KmgCMaRVWGzZdKdbi1AvJAUOTSX5uuUVuxF96SfK6z2esmL1li9TZU1OStxcXpexQ89EtLEhZ8c1vLl9soNBWC5Q1NMj71DxjO3ZIAF+lQTWE2WqVz6/W6D90SIYXfuxjxrA9XZfP790rN6A1NdWRnitNPvlL9aobHTXqkmhUyid18711q5RBVquk1aNHpbdLqR9gWCwS5Px3/w74kz+R0UGTk8ZK8RaL5EWVl9RiIDduyENGq1W+UzQq7YY33zTafarezWaxp0pVjQ9AEzt1NDQYc1vv3CllqhrR83u/J72RXa6lZe+2bZJWL1+Wa63rxgraVqvRkcFkkrZYS8vyAFI1nre1TAXBdV3yuBqNpTpBXLggeXzjRrl26v5X9fhV13t0VMoGtUjQww/L3NSJD2vq6qQe2rtXHr7t2yfBTtUz+Px5SW/JQc10D3nUsavpYVRaVPcwJpOxbgBgzNuu/r9hw/JjzHb6H6bnwkicd3/bNmPEw86dcm2roeNBseQU8Dxy5AiefvppAEBPTw++/OUvF/SgqLolB3/UUA114zA3ZzRO9+7NLQNWWw/PxCfCamJ71UhXFV00Kv+uVFGohtYtt0iBpubJUxOiWyyyr+Hh9VuolUO6gAKQvpFx5ow0dtvbjQAjYKyAODOz/CYmmx4yqheXWihLHcuVK0YQ9LXXZFLrqSlpaEcixqT7FovRG2FmRhrbra1L5+2prZWHDmoBlJkZ42GGWj25WgJd65UqU269VRrKqlcvIGm5tVWe/J8/L4GtVOlN9eRUT5PVKu5qSBdQuPnySmGt9ThYrdzYs0fq5YsX5RpevCjl0NSUfHZuTh6KqJv7mhqpg9QCI8UOeAIrB8p+8Rdl0STV5mhtle8ALO0R19YmPbIyaXPcdpuk6dZWY7GKhgajXq6m9FxpcslfyYtxDA0ZC12oua4BCUIBcr1qa2XO7M7O8j3A2LpVAl5f+5p8h4UFmWtW1ZETE5I2x8eN1ZABKXu3bJGg2LlzkgZraqQ8nZmRz6h8x7RYWomdOtQiWLOzUiYmj+ixWlOXvWoOVhX4jkaNkTqqp6iaSkYttlWN80evF4lBcNWGunrV+LvVKnm/sVHy7YYNUpa9/LL8XT3EuH7d6KleUyPb2rVLyst9++Rn9+7CzouZeF+pFt8C5HjUSB2VDlUPUtWWz2QVeyqdxHS4sCBpbGIi9UJ96+1+LOuAZ19fHw4ePIi+vj4MDQ3hs5/9LA4ePIiTN5eC6uvrQ39/P8LhMDRNg9PpRF9fH+69996CHzxVpuTgD2A0SBcXjaeYt9+eewZUAcdqCXgmFkLz8/LkTp0H1TtlelqexIyMpK8oVEOrsdEIKCTOrah6jTY1rd9CrRxyaWSoa7l1q9zEnDsnacFmk/elu4nJp4eMuklSiycBMjT1gx+URrpakKOmRp4OqvkXX3tNbsimp40h8YAESs6dM+awSmz0qEBDUxNvxipZYjpsa5P0arFI43fzZmN4nZqrNV16S37QlRwEq7YFf9Zaj4NMAoZ33CGvT0/LtVYP4QCpb2IxSSc7dshrly+XNl+nC5QBwN/9ndHmUEPYVW8VFVjYti3zNodKzxaLlGFqQTal2tJzpck2fyUvvKXalGqVb11PfVOuAqnlfIChFrO55x55gDg8bAQ/1UNrQNp/apHPHTuM+by3bpVe2LOz8r2bm+U7qNXtmRZLK7mu27x56d+Tr0e6sle1o9rbjXkc1TQNqmegenA8McEAUiVLDII3NkoeV/N0aprcM6rFHDdsMOpV1dZX721tlfsANY3Qm2/KOhfZzPqX7T1C4rFv2CBt/WjUqPNVMPbGDdlWc7Pc7wBrb/qfapd4LdUDahUXUHOyrtf7sawDnrqu49FHHwUA3HfffXC5XPB6vTh69CjC4TBaWlrQ3d2NkZERhMNhfPvb30YgEIDf78ev//qvF/wLUOVJDv7U1UlhqYayWyxy86yGUOaSAVUgdXp66dDhYss14JlcoezcKRWZauiqHp6XL0ugIV1FoRpa6nurRpFauEit9tnYKE921mOhVi7ZNjKSVwN+61uXXvOVbmIK2QOtrU0qRtUwS1ZXJ8Gr3/s9adirFWTf9z65YduxQxrrapgOYDSQ1BNq3oxVrsR0uGOHMVer6mUwOysBny99yVjVM1V6S9XLWeET/8qQrtz47neNoHdLi/wtEpH8bbHIz8KCPKRU+Vj1oCt1vk4XKEvV5lDzkNXVSZ2rpsLJpM3B9FxZEttQFov0SJ+elofHc3PGcM7Em/LEdlQ5H2CoMnZ+3hj2vLgoQazXXjOGRN9yi+S/xAf6TU2y8N+jj0qQXi10qTAtll4uZUOqsle1o+bmjGmMXnnFaD+pYPjwsJTNDCBVruQg+L59Umeqe7SpKWk/NzQYAUJ1LVWw8447pNxSpqaMBcyylc09QvKx22xS109MSE/VqSlJj7OzwH33Lb2fWWvT/1S75Gv5trdlfl+51mUd8Nyc/CgLQG9vLw4cOIAPfehDePDBB5f9vb+/H93d3QDAoOc6kRj8OX3aeJqtFsRILNRzyYDqxmVxUbar5ugqtlwDnsmF0J490rC9dEka7Wq+lHvuAX73d9NXFKqh9W//ZsyrYrEYweS5OTk2NRxmPRZq5ZRNIyO50Zz4nkxuYgp1A5dp472ryzjGWMz4zK23SpqenDQeYqh0uGGDsboob8YqU+L1v+22pWVz4vXv6Fj5ZmutLviz1qQqN1L1WGprk97ek5NGbzo1PUElBlkK3eZgeq4syWm0rU1+Nm1a/aa83FItuqVWPX7jDXm4oGlLF/BMzGN2uzxwfPhho7co02L55Fo2JJe9ie0oNX3MXXcZ9wUqYPb2t6+91ZXXmnR5HFg63/nHPy4LyqiFAdWCePv2pW975VrHZnqPkOoewGyWMqmpSaa/stlkLuLkduBam/6n2uV7X7mWZZ0kR1Z4JN7a2prydZfLhXA4jK985SuYmJjIdpdUpQ4dAr7xDfk5eFAq87e8JXWhfvfd2WVANQwcKN3CRbpuBDxVwDVTqhAaHjZ6dWqa9Oq75x4Jft5/P/Dtb6/cqFENrS1b5Pe5Oakso1H5v8UiPQByOadUGKqRcfjwykEidS2bm6XRPDVlzBV34ULpbmJyOY7Ez1y8KOnRapUAycyM/H3z5tJ+D8pNIdOhCjrde6/0Drh82RiKt15XhqwGqeonk0kCMCpf19VJvVvq8ikbhW5zMD1XjlRpFJB21FveIm2ot79drv3TT1fWtUlXxk5Py0NC9bNS2cu0WFkKcT1SpYtNm6RDhKbJw+QvflHSNK9vZcu0HfWud8n1/PrXpef2F78oa1lEIpV5D3DxovQuPnZMHrykOpZM73mo+CrlvrISmXQ9semwuieeeAJOpxO33377ktePHDmCt7/97fjUpz6V9rPnzp2D3+/HsWPHcjrYarVr1y5cuXIFO3fuxOXLl8t9OGWhVtccH0/9NDSXBtsv/II0GL/2tdIsmjA3B/z8z8v//+EfjGH1mSrkOXjmGeC3fksWnFFz/mzcKNudnc39nFLpPfOMMSRkfl56Ht19d+l7qORyHImfmZiQ4YSANNqbmsrzPSg3hUyHsRif+FebdPWT6m2kVr4uV/mUrULWt0zPlaEY7chSSlfGHj4MBIOZlb1Mi5WlENejUtqAlL9829HVdg9AlWm9XMts4mtZBzwB4NOf/jR+67d+C01NTfHX3vOe9+DIkSMph7Qn+tznPhefA3S9YMBTFDoDHjkiDV2fb+lK1MUyMiKrbZpMstBLLvOGFjqo8NWvAn/2Z7LytpqrbC0WamtdpdzE5HIciZ9RC3uMjfFmrBpVSjqk8khXP3384xJQqrZ0sV4a/etJtV/TdGUsy971jdd/7ci3HV1t9wBUmdbDtSx6wBMAPvShD+HIkSP42Z/92aw+9/nPf37FXqBrEQOehkJmwPe/X7pof+ELMmdTsV24IPvctElWhM1VoQuh9VCoERFR8a21+mStfR/iNSUiIlrvsomvZb1okfL444/j6aefxoc//GE4nU78yq/8SkafW2kOUFr7CrlSpppHs1RzeOa6YFGyQq8WWs7VR4mIaO1Ya/XJWvs+xGtKREREmcs54AkADzzwAB544AE899xz+PSnPw3TzTG+TqczZc/P5557Lu3CRkTZUnNoTk2VZn+FCngSEREREREREVHx5BXwVO677z7clzCm+MSJE0sCoG1tbRgaGoKmaetuwSIqHtXDkwFPIiIiIiIiIiJSChLwTHb48GEcPnw4/vuJEyeg6zpGRkbw+c9/Hna7HQcOHFiy6BFRtqp1SDsRERERERERERVPUQKeyZIDoM899xx8Ph9GRkZgMplw8ODBjOcAJVLYw5OIiIiIiIiIiJKVJOCZLHkI/Llz58pxGFTl1Bye7OFJRERERERERESKudQ7nJiYWPbanj17Sn0YtAaoHp4qEFlsDHgSEREREREREVW+kgU8JyYm8KEPfYirtFPBsIcnERERERERERElK/qQ9vPnz8Pj8cDv90PX9fjK7UT54hyeRERERERERESUrGg9PM+fP4+jR4+ivb0dPp8Phw8fRnd3d7F2R+sQV2knIiIiIiIiIqJkBQ94nj59Oh7oDAQCeOCBBzA0NIRvf/vb6OzsLPTuaB1TQ9rZw5OIiIiIiIiIiJSCDWk/ffo03G43gsEgdF2Hy+WCx+PhgkRUNBzSTkREREREREREyfIOeH7nO9+B2+1GKBSCruvo7u6G2+1moJOKLnHRIl0Hijk9rK4z4ElEREREREREVA1yDnimCnR6PB40NzcX8viI0lKBx8VFYGEBqK0t3r7m52U/ifslIiIiIiIiIqLKk/Ucnt/4xjewb98+OJ1ODA4O4tFHH8XY2Bgef/xxBjuppBoajP8Xe+Ei1bvTZFq6XyIiIiIiIiIiqixZBTyffvppuFwujI6Oore3F2NjY/jsZz/LQCeVhclUuoWL1PY3bCju0HkiIiIiIiIiIspPVgHPBx54AKdOnUJXVxfa29sZ6KSyK1XAk/N3EhERERERERFVh6yHtNvtdjz++OM4fPgwPv3pT+PJJ58sxnERZaRUK7Uz4ElEREREREREVB1yXrRoz549+OxnP4tz587h05/+NDZv3oxPfepThTw2olWpgGep5vBkwJOIiIiIiIiIqLLlHPBUVOBzfHw8Hvjs7u5GU1NTIY6PaEUc0k5ERERERERERInyDngqzc3N8cDnH/7hH1Zc4DMSieDYsWOIRCIIh8MYHR1FX18fXC5XxttwOp2w2+04evQo7HY7wuEwfD4fIpEIfD5fEY+e0uGQdiIiIiIiIiIiSlSwgKeSGPj0+XwwmUxlD3xGIhG43W54PB5omgYACIVC6OzshMvlQiAQyGg7o6Oj8Hq98Hq98dccDgcGBgaKcdiUAdXDk0PaiYiIiIiIiIgIKELAU2lubsajjz4KAPjc5z6H0dFR6LperN2t6NixY0uCnYAsvuTxeOB2uxEMBuFwOFbdjs1mw9GjR3Hy5EnYbDY4nc6MPkfFwx6eRERERERERESUqGgBz0Qq8On1etHc3FyKXS7R39+P/v5+DA0NLXldBSsDgUBGgcvW1lb09vYW5RgpN1y0iIiIiIiIiIiIEplLubPe3l6Mjo6WcpcApGdmqv2qHp/lOCYqjFL18FTbV/sjIiIiIiIiIqLKVJIenuWWbo7NUCgEADh48GBW2wuFQjh16hQOHDgAu92e9/FR7rhKOxERERERERERJSppD89K4/P5oGkauru7M3r/6Ogo3G43RkdH0d3djdHRUXR2diIcDmf0+atXr2LXrl2r/nzhC1/I52utK5zDk4iIiIiIiIiIEq2LHp6pBINBBINBBAKBJYsZrcTpdC4JjjocDhw9ehROp3PZ/KCpxGIxXLlyZdX3TUxMZHQ8xDk8iYiIiIiIiIhoqXUb8Ozq6oLP54PL5cr4M6l6gjocDrjdbvT396+6LbPZjO3bt6+6n6ampoyPab3jkHYiIiIiIiIiIkpUFQHP9vb2rBYWam1txcDAAGw2W8q/d3V1oa+vL+Oh7CtR+xgYGFg14Ll9+3Zcvnw5732SoRQ9POfn5QdgwJOIiIiIiIiIqNJVRcAzk+HimXK73Th48CB6e3uz+lxXVxfC4TAGBwdT/p0rvZdHYg9PXQdMpsLvQ/UeNZm4SjsRERERERERUaVbV4sW+f1+tLW1LQt2+v3+VT8bCoXQ2tq67HUV6Mx2pXcqDBWAXFwEFhaKsw81nL2xsTgBVSIiIiIiIiIiKpx1E/AMBoOIRCIpe3ZGIpFVP+9yuTAwMLDs9f7+fgCp5/ek4lM9PIHiDWvn/J1ERERERERERNWjKoa05yscDqOnpye+wBBgBDnV3xK1t7cDWDqUvq+vDz09PfD5fPHXQqEQjh07ltVK71RYJpMEPaenZeh5MS6DGtLOgCcRERERERERUeVbFwFPp9OJcDicdui6x+NZ8rumacuGr2uaBo/Hg56eHmiahnA4DAA4ceIE7HZ7cQ6cMpIY8CwG1cOT83cSEREREREREVW+dRHwzHbRo3QLE2matqSHJ1WGDRuA69eLH/BkD08iIiIiIiIiosq3bubwpLVLzePJOTyJiIiIiIiIiIgBT6p6aqg5e3gSEREREREREREDnlT1VMCTPTyJiIiIiIiIiIgBT6p6akg7e3gSEREREREREREDnlT1OKSdiIiIiIiIiIgUBjyp6nHRIiIiIiIiIiIiUhjwpKqnApHs4UlERERERERERAx4UtXjHJ5ERERERERERKQw4ElVj3N4EhERERERERGRwoAnVb1izuG5uAjMzcn/GfAkIiIiIiIiIqp8DHhS1StmD0/VuxMwAqtERERERERERFS5GPCkqqcCnsXo4akCno2NgMVS+O0TEREREREREVFhMeBJVS9x0SJdL+y2OX8nEREREREREVF1YcCTqp7q4bm4CCwsFHbbDHgSEREREREREVUXBjyp6jU0GP8v9LB2BjyJiIiIiIiIiKoLA55U9cxmI+hZ6IWL1PYY8CQiIiIiIiIiqg4MeNKaUKyFi9jDk4iIiIiIiIioujDgSWuCCniqAGWhqO2p7RMRERERERERUWVjwEiazRIAAEG5SURBVJPWBLVSO3t4EhERERERERGtbwx40pqgemAWeg5PBjyJiIiIiIiIiKoLA560JrCHJxERERERERERAQx40hrBHp5ERERERERERAQw4ElrREMDcOMGcOoUcPo0EIsVZrsMeBIRERERERERVRcGPKnqPfMM8P/9f8BLLwF/+ZfAe98L/MqvyOv5iMWAy5eBsTHg0qXCBVGJiIiIiIiIiKh4GPCkqvbMM8DDD0tg0moFNm0CmpqA55+X13MNej7zjARNf/AD4Cc/AT75ycIEUYmIiIiIiIiIqLgY8KSqFYsBjz0GRCLAtm2AxQLMzcmPpgHDw8Dv/z5w7Zq8puuZbVcFUU+fBsxmoK4OaG7OP4hKRERERERERETFZy33ARDl6swZ4OxZYOtWYHFRXpudBS5elP9HoxKc/I//UXp+Wq0yF+emTca/6v/q98ZGwOuVIOmOHcD4uGxLvffCBeALXwDuv1+CoUREREREREREVFkY8KSqNTIiPTfr6yX4uG2b/B6Nys/ioiw6FI3K+xcXpTdoJJJ+mzduyFygViswNCSvmc2AyST/37IFePllCbZ2dBTxyxERERERERERUU4Y8KSq1dYmw81nZ4ENG4Dt25f+fWoKmJgA/vzPgTvvlODnjRvGv+r/ia//+MfAK69IT89YTIKkLS3GNuvrgevXJdhKRERERERERESVhwFPqlr79wN33SVzazY2Gr0wAZmvc3hYemHee6/00mxslOHvKzl9GnjuOVn4aMOG5X+fnQVqayXYSkRERERERERElYezEFLVMptl9fTmZplbc2pKhq9PTcnvzc3AI49kN9emCqIODy9f5EgFUe++W95HRERERERERESVhwFPqmqHDgFf+pL04pyYAC5fln87OuT1Q4ey214xgqhERERERERERFQ6HNJOVe/QIVk1/cwZmVuzrU16YOYalFRB1Mcek1Xgr1+XYewdHRLszDaISkREREREREREpcOAJ60JZnNhV00vdBCViIiIiIiIiIhKgwFPojQKHUQlIiIiIiIiIqLiY381IiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1Y90MaXc6nbDb7Th69CjsdjvC4TB8Ph8ikQh8Pl/G2/F6vRgZGUFbWxuGhobgdDrhcrmKeORERERERERERESUqXUT8BwdHYXX64XX642/5nA4MDAwkPE2enp60N7eDo/HE3/N6XRidHQU3d3dBT1eIiIiIiIiIiIiyt66CXjabDYcPXoUJ0+ehM1mg9PphMPhyPjzoVAIfr8fuq4ved3j8aCzs5MBTyIiIiIiIiIiogqwbgKera2t6O3tzfnzPp8Pdrt92evqtf7+fg5tJyIiIiIiIiIiKjMuWpShYDAIm82W8m+apmU1NJ6IiIiIiIiIiIiKY90FPNXQ9FAolNXnwuEwWltbU/6ttbUVp06dKsThERERERERERERUR7WTcBzdHQUbrc7vsDQ6OgoOjs7EQ6HV/1sJBJZ8e+apq36HgC4evUqdu3aterPF77whQy/FRERERERERERESVaN3N4Op3OJQsLORwOHD16FE6nE0NDQyU5hlgshitXrqz6vomJiRIcDRERERERERER0dqzbgKeqVZRdzgccLvdqy44pGnaitvOpHcnAJjNZmzfvn3V9zU1NWW0PSIiIiIiIiIiIlqqKgKe7e3tGB0dzfj9ra2tGBgYSLvIkKL+PjAwkNcK66Ojo6vuCwC2b9+Oy5cv57wfIiIiIiIiIiIiWllVBDzzHXLe1dWFcDiMwcHBlH/PJJiqaVra90UiERw4cCCvYyQiIiIiIiIiIqL8rYtFi0KhUMoV1lUA8+DBg6tu48iRIysucOR0OnM/QCIiIiIiIiIiIiqIdRHwdLlcGBgYWPZ6f38/gNTzeybr6upCKBRaNl9nMBgEIPOBEhERERERERERUXmti4BnX18fenp6lrwWCoVw7NgxBAKBZYsStbe3o729fclrDocDLpcLx44dW/K6x+NJuQ0iIiIiIiIiIiIqvaqYwzNfmqbB4/Ggp6cHmqbFh6afOHECdrs95ftTDYEPBALwer1wu91oa2vD0NAQenp68lrwiIiIiIiIiIiIiArHpOu6Xu6DWOt27dqFK1euYOfOnVylnYiIiIiIiIiIKEvZxNfWxZB2IiIiIiIiIiIiWh8Y8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjOs5T4AWhu+8IUvYGJiAk1NTXjkkUfKfThEaxLzGVFpMK8RlQbzGlFpMK8RlQbzWmUx6bqul/sg1rpdu3bhypUr2LlzJy5fvlzuwymK9fAdicqN+YyoNJjXiEqDeY2oNJjXiEqDea34sjnHHNJOREREREREREREawYDnkRERERERERERLRmMOBJREREREREREREawYDnkRERERERERERLRmMOBJREREREREREREawYDnkRERERERERERLRmMOBJREREREREREREawYDnkRERERERERERLRmmHRd18t9EGtdbW0tFhYWYDabsX379nIfTlFcvXoVsVhsTX9HonJjPiMqDeY1otJgXiMqDeY1otJgXis+dY5ramowPz+/4nsZ8CwBi8WCWCxW7sMgIiIiIiIiIiKqamazGdFodMX3WEt0LOtafX09ZmdnYbFYsHXr1nIfDhERERERERERUVW5du0aotEo6uvrV30ve3gSERERERERERHRmsFFi4iIiIiIiIiIiGjNYMCTiIiIiIiIiIiI1gwGPImIiIiIiIiIiGjN4KJFlBev14uRkRG0tbVhaGgITqcTLper3IdFVJWcTifsdjuOHj0Ku92OcDgMn8+HSCQCn8+37P3Mf0Sri0QieOihh3D06NEV80c2+Yl5j2i5TPIa6zmi/EQiERw7dgyRSAThcBijo6Po6+srSH3F/EZkyCavsW6rXAx4Us56enrQ3t4Oj8cTf83pdGJ0dBTd3d1lPDKi6jQ6Ogqv1wuv1xt/zeFwYGBgYNl7mf+IVtbV1YXW1lYAQH9/P44ePZr2vdnkJ+Y9oqWyyWus54hyF4lE4Ha74fF4oGkaACAUCqGzsxMulwuBQGDJ+1m3EeUm27zGuq1ycZV2yonK8MnJJ93rRLS6rq4uHDx4ECdPnoTNZoPT6YTD4Vj2PuY/osyFw2G0t7cjEAikfHqeTX5i3iNKb7W8BrCeI8qH2+1GX19fPACjeL1euN1uDAwMxPMT6zai3GWT1wDWbZWMc3hSTnw+H+x2+7LX1Wv9/f2lPiSiqtfa2ore3l4EAgF4PJ6UFSXA/EdUSNnkJ+Y9ovywniPKXX9/Pzo7O5e9rvJRYq8z1m1EucsmrwGs2yoZA56Uk2AwCJvNlvJvmqal7L5NRIXB/EdUONnkJ+Y9otJgXiNazmazYXR0dNnrqhda4t9YtxHlLpu8lg3mtdJjwJNyEg6H4/M1JWttbcWpU6dKfEREa0coFILf70coFEr5d+Y/osLJJj8x7xEVBus5ouwNDAxgbGxs2esqHx08eDD+Gus2otxlk9eS/866rbIw4ElZi0QiK/5d07RV30NEy42OjsLtdscnrR4dHUVnZyfC4XD8Pcx/RIWTTX5i3iPKH+s5osLz+XzQNC2+4AnrNqLiSM5rCuu2ysVV2omIKoTT6VxSgTocDhw9ehROpxNDQ0NlPDIiIqL8sZ4jKqxgMIhgMIhAILBsgRUiKpyV8hrrtsrFHp6UtdUqUz6ZIMpN8tNCQCrMcDgcn8Sa+Y+ocLLJT8x7RPljPUdUWF1dXfD5fHC5XPHXWLcRFV6qvKawbqtcDHhSwY2OjvIJI1GBqImtM53EmvmPqHCyyU/Me0S5YT1HlJuuri709fWlDLashHUbUXZyyWus2yoDA56UE03T0q5OFolEcODAgRIfEVF16+rqQmdnZ9q/J+Y35j+iwskmPzHvEeWO9RxR4bjdbhw8eBC9vb0p/866jagwVstrrNsqGwOelJMjR44smYQ3mdPpLOHREFW/UCiUctU+VSkmrgbI/EdUONnkJ+Y9otyxniMqDL/fj7a2tmUBGL/fH/8/6zai/GWS11i3VTYGPCknXV1dCIVCy+aaCAaDAGTOCiLKnMvlSjnkQc37kjiEgvmPqHCyyU/Me0S5Yz1HlL9gMIhIJJKyt1lifmHdRpSfTPMa67bKxoAn5cThcMDlcuHYsWNLXvd4PFwlkCgHfX196OnpWfJaKBTCsWPHluUp5j+izKlGZbohRNnkJ+Y9ovRWy2us54jyEw6H0dPTg6GhIbjdbrjdbvT09KCnpwdOpzM+ZyDAuo0oH9nkNdZtlc2k67pe7oOg6uX1ejEyMoK2tjYMDQ3B6XSmXLmMiFYXiUTgdruhaVp8uENfXx/sdnvK9zP/EaXndrsRDocRCoUQDoehaRocDgdaW1vh8/mWvT+b/MS8R2TIJq+xniPKXXt7+4rDYQcHB5flJdZtRNnLNq+xbqtcDHgSERERERERERHRmsEh7URERERERERERLRmMOBJREREREREREREawYDnkRERERERERERLRmMOBJREREREREREREawYDnkRERERERERERLRmMOBJREREREREREREawYDnkRERERERERERLRmMOBJREREREREREREa4a13AdARERERFRq/f39CIfD8d+7u7uhaVrBth8MBhEKheK/OxwO2O32gm2fiIiIiNJjwJOIiIiI1h2fz4dgMBj/vdAByYGBAXi93iX7Y8CTiIiIqDQ4pJ2IiIiI1q2xsTHoul7wYKTH44Gu6wgEAgXdLhERERGtjgFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiIiIiIiIqI1gwFPIiIiIiIiIiIiWjMY8CQiIiKiquJ2u9He3g6TyYSWlhY4nU74/f6CbDscDqO9vR1utxvhcBhdXV1oaWlBe3s7enp6EIlEAABerzd+DJ2dnejv7y/I/omIiIgofwx4EhEREVHVcDqd8Hq9sNvt6O3thcPhQDgchsfjKdg+wuEw+vv70dnZCQA4cuQIRkdH4ff70dXVha6uLvh8PjgcDjgcDoRCIXR1dSEUChXsGIiIiIgod9ZyHwARERERUSYikQiCwSBcLhcCgcCSv4XD4YLuKxwOw+fzobu7GwDg8XjQ0tKCYDAIu92OoaGh+Ht7enrg9/tx/Phx2O32gh4HEREREWWPPTyJiIiIqKqoYeWJbDZbQfehaVo82Kl+dzgcACTAmairqyvtcRERERFR6THgSURERERVQdM02O12BINBtLS0xHtWFiPQuFIA9cCBA0t+b21tBQCMjo4W/DiIiIiIKHsMeBIRERFR1Thx4gRcLhcikQj8fj96enqwZ88eBIPBgu5HBTFT0TStoPsiIiIiosJiwJOIiIiIqoamaQgEAhgbG0MgEEB3dzcikQicTme5D42IiIiIKgQDnkRERERUdTRNg8vlgs/ni6/QXuhenkRERERUnRjwJCIiIqKqoIaxJxsZGSnD0RARERFRpbKW+wCIiIiIiDJx6tQp9PT0wO1248CBA7DZbAiHwwgGg7DZbPFV1ImIiIhofWMPTyIiIiKqCg6HAwMDA3A4HDh16hT8fj/C4TC6u7sxODhY7sMjIiIiogrBHp5EREREVDUcDkdRe3LabDboup7ybwMDAylft9vtaT9DRERERKXHHp5ERERERERERES0ZjDgWSbhcLjch1AQoVCo3IeQlXA4nHKxA1p/gsFg1a/my/RMCtMzrSVrIT0TERERUXkx4FkGoVAIPp+v3IdRMG63u9yHkJFIJAKn04nW1tZyHwpVgAMHDqCrq6tqb6qZnikR0zOtJdWenomIiIio/BjwLLFIJAK32w2Px7Pi+/r7+0t0RPnt0263o62trSp65XR1dcFut8Plcq363kgkApPJtOJPS0tL2s97vV54vd6sjzESiaCrqwstLS1oaWmB0+nM6IYv1/3lq7+/H52dnfHz0dXVVZD35rsNt9uN9vZ2mEwmdHZ2pkyfmqYhEAjA6XRWZY/rYqfnXNNivtsoRDrJVSWl52yvGdNz8cvnTLaR77EVUiWl50SVWD4fO3YMXq8XkUikoNsNBoPwer04fvx4QbdLRERERKvjokUl1tXVlVHvzoGBAYyOjgKQyfltNluxDy3nffb29qKzs7Nkx5kLv9+PYDCIsbGxjN6vaRp6e3uXvNbW1hb//7Fjx5Z91/7+foTDYfh8PoTD4VWD2skikQj27NkDm82Gvr4+jIyMwO/3w+l0wuPxLDuefPeXL7fbDa/Xi+7ubvT19QFIv5hDNu/Nd3/t7e3x99tsNoRCIbjdbgwMDCAQCCx5r1r4oqurq6pW9y12es42LaaSyzYKkU5yVWnpOZcyiOlZFKN8znQbuRxbMVRaelYqtXxWwWuHwwG73V6w7Q4MDJTlYSQRERERAdCpZAYGBnSHw7HiewKBgG6z2XSXy6X39vbqvb29us/nK+pxFWKfgUBAd7lcRTrC/NlsNr27u7sg2woEAjoAfWhoaMnrAHSbzaY7HA4dgO7xeLLarsvlSpk+bDZbUfaXD4/HowPQBwYGCvrefLfR29urA9DHxsaWvD4wMJD284ODgzoAfXBwMOfjK7Vip+ds02Iq2W6jEOkkV5WantNJVwbpOtNzscrnQmxjpetWSJWantdL+UxERERElYEBzxJyuVwr3hD09vbqmqYV/WaoWPvUNG3ZjUwlUDeZhbhhGhoa0gGsGBBWN2jZ3gynO0Z1/Om2l+v+cjU2NqYDyChAkc17C7ENu92u2+32lH8DoPf29qb8mwpilEK+6bAU6TnXtJjrNgqRTnJVyek5lUzKoFKl50KkwWopn/PdRibHVgiVnJ6roXwmIiIiorWDc3iWUDAYhMPhSPs3r9eLJ554omTDwgu9T4fDUZFzeaphdYUYptbT0wObzYbu7u68t5UsEAikPEaVZk6ePFnwfeZCXeNMFqvK5r2F2MaBAwdSzvemXlPDKZM5HI6SLY7h8/nyyielSM+FSIvZbKMQ6SRXlZyeU8mkDCpVes43LQPVUz7nq1THVsnpuRrKZyIiIiJaOxjwLJH+/v4Vg4pqXs9MFmwolELv0+l0lmy+vWysFGjORn9/P4LBYNHmykx3HdTNYKWsXjwwMABN02Cz2dDT0xNfkKarq2vZgg/ZvLcQ+/N4PGhtbUVXV1f8vIVCIXR1dcHlcqUNNnR2dsbfW2w9PT0ZzeObTinScyHSYjbbKEQ6yVUlp+dkmZZBpUrP+aZloHrK53yU8tgqOT1XQ/lMRERERGsHA54lcvLkyVVv6jRNK83BFGmfBw4cqMheGOFwuCA9WNUiC6UMSgOIn9NSrla9knA4jNbW1nhvHI/HgyNHjqC/vx+HDx/O+b2F2J+maRgaGoq/X60C3NfXt2xBjEQHDhwAAJw6dSqzk5AH1ZMt17xSzvRciLSYahuFSCe5quT0nCzTa1aq9JxvWgaqv3zORCmPrZLTczWUz0RERES0hpR7TP164XK5VpzvK3FOsEAgoPt8Pj0QCBT1mAq9TzWXVzbzePb29uo2my3rn0yPU82blu/8lmpRhUy2U8g5NcfGxnRN01ZcEKrUc3gCSDkXnVq4IvHaZPPeQuxP142FMdTiWy6Xa9V9qXSSbg65QhsaGtJtNlvWc+eWIz0rmaTFXLdRiHSSq0pPz0o216yU6TnXtKw+W43lczbbyCWv5aPS03M1lM9EREREtDYw4FkiDodj1cUKuru746ulezwe3eVyFX0RoELvEyVYgTYb6sY034Uiuru7M/5uhQxA2u32VRdrKEfAU9O0Za+rgHdiMCub9xZif+lWC1avp1sYpRyL5gwODuo2my2rtFmO9KxkkhZz3UYh0kmuKjk9J8rmmpU6PeeSltXnqrF8zmYbueS1fFRyeq6m8pmIiIiIqp81s36glC817Cudnp4edHV15T0fWjaKtc9iz7mXjdHR0bR/C4VCKf+eauqBp556Kj5PWal0dnaitbW15POiZnJeUp0HTdOgadqyRSmyeW86mW7j2LFjsNlsy65hb28v3G433G53yvOppnZYKb0kc7vdeS/YEolE0NPTg6GhoYzm9ytXei5EWlxtG4VIJ6lUc3pOlM01yzY9lyMtA9VdPmeq0MdWzem5lOUzEREREREDniWiaVraxrrX60VnZ2dBFm7IVDH3WUk3nSrInCoI+9BDD6VcBEHX9SW/h8NhRCKRkq78W65gJ7D6ecnm+hYiLWS6jUgkgkgkkjZNZ3IDn83CUB6PJ69FSILBYHzRl0zzYTnSc7mCnYVSrek5Ua5lUKbpuRxpGaje8jlTxTi2ak3PpS6fiYiIiIi4aFGJ2Gy2lDd1kUgEx48fL+nNWrH2qb5fNgshud1udHZ2Zv3T39+f0fbVsYyMjCz72+DgIHSZ1mHJTzK1IIdaKbbYOjs7YbPZyrbi/WrnxeFwIBQKLUvP6oZWLTCR7XvTyXQbq/VKikQiaW/Oc0m7+QiFQujp6cHg4GBWAaJSp+dCpMVMtlGIdJJOtabnRNmWQaVMz7mmZaA6y+dsFOPYqjU9V1P5TERERERrAwOeJdLa2prypu6pp57K+Gbe7/fD7/fD7XYjFAqhv78/p9WSM91nf38/vF7vkuBiMBhMG2wMh8NZ35B4PB4MDg5m/ZPparfqBiqfYfZDQ0MAStO7RAWHVlqxttx6enoAyPDEROp39fds31uI/R05cgShUGhZDyiv17vi/tRNuFppuNgeeughBAKBrPNLKdNzIdJiptsoRDrJVSWnZyXbMqiU6TnXtAxUX/mcrXIcWyWn52opn4mIiIhobeCQ9hLp7OxMO1em6gWyEq/Xi97eXgDS6G9vb4fD4YDb7V72XnVToG62ctlnKBSC3W4HAPh8vniA0efz4ejRoyk/c+rUqbx6YhWLzWbDqVOncv68uhlfbeiemltN3cwNDQ3Fz3Nyzyen0wlN05YEgpxOJ8LhMI4ePRq/AUz+HomB3nz3lyu73Y7e3l54vV6Ew2E4nU4MDAygv78fHo8nnm6yfW+648xmGz6fD8FgEJ2dnejt7cXBgwcxMDAAv98Pl8uVNlCubqhLkX6DwSBsNtuy756pUqTnbNNivum5EOkkV5WcnpVMyyClVOk537QMVE/5nO02sjm29ZKeq6F8JiIiIqI1pLhrIpEyNDSkpzrd6nWbzaYHAoG0n09cvRTAiiup2+123WazpV0VNpN9qs+6XK4lK+hqmpZ2393d3XmvtlsMapXcXLlcroxW2dU0TQeQ8if5s5qm6TabLePPA1i2unW++8tXIBDQbTabDkC32+0rpt9M37vScWazv97e3iXvXS1d5ptGstHd3b1sleJsP1/s9JxLWsw3Pet6YdJJrio5PWdaBimlSs/5pmW1jWoon7PdRrbHtp7ScyWXz0RERES0dph0PcWkWFQUnZ2d8Hg8y3qChEIhdHV1xYeE9/X1obu7O+UQQfXelXpvAsZiCel63mS6z5aWFpw7dy4+95bT6Uy77/b29lWPqxzU0P/BwcG8eiJVM6/Xi4GBgbLNC1rp2tvbSzZvaiQSyWsuOqZnpufVlCo955uWAaZngOl5NaUsn4mIiIho7eAcniXU19eXcli73W7H0NAQBgYGcODAAbjdbrS0tKQcrh4MBpcETFOt1gpIwHOlYXSZ7DMUCsFms8VvaJP3nai/v79ib1ZdLhdsNlvaKQXWg5MnT+Y03+t6EAqFEA6H81qlOhv5BoiYnpmeV1LK9FyIRWSYnpmeV1Lq8pmIiIiI1g4GPEvI5XIhEomkXaXU4XBgYGAAY2Nj6O7uhtfrRSQSQX9/P5xOJwDg+PHj8RVfw+EwRkdHU24rFApldDOabp8AMDo6umSxBZ/PFz+OZD6fr6JvSNxuN/x+f16LY1SzUCiEI0eOlPswKpLb7Ybdbq/YgH0qTM9Mz+kwPVcfpuf0qjE9ExEREVFl4JD2EguHw+jp6Uk5NCtxeGAkEsGePXswNjaGUCiE48ePo62tDS6XCz6fDwcPHoSmaWkXSnjqqafQ3d296vGk26fS09MTD7D29PRgbGxsWSBV3aiqRZUqVWdnJw4cOLCuexLRUsFgEE6nsyqH0zI9UzKmZ1pLqjk9ExEREVH5MeBZBsFgEAMDA8t6REYiEfj9fmiahsHBQfT09OTUyPf7/RkFO1fbZ+IQ9mAwCLfbjcHBwSWfV8HYSu7dqUQikfg8qulWg6X1o9rTQ7UfPxVWtaeHaj9+KiymByIiIiLKFwOeZRIKhSq6x4Lq7akWLHI6nfB4PMuOudK/R7JwOIxgMJhxQJjWrmAwiEgkUtU300zPpDA901qyFtIzEREREZUXA56Ult/vR2trK8LhMBwOR1UFNomIiIiIiIiIaH1iwJOIiIiIiIiIiIjWDK7STkRERERERERERGsGA55ERERERERERES0ZjDgSURERERERERERGsGA55ERERERERERES0ZjDgSURERERERERERGsGA55ERERERERERES0ZjDgWSImkynlT2dnJ3p6ehCJRFJ+rqWlJf6+1UQikfh2u7q6Ur4nGAzC6XTGt9ve3o6enh6Ew+F8vh5lKV16SPxJdw0pd8yHlIj5sDxWyodut3tZPgyFQmhvb8/6J599UvEx/5UH8x8x7xER0XphLfcBrCeapuHEiRMAgNHRUUQiEYTDYRw7dgx+vx+BQAAulyvlZ0OhEMLhMGw2W9rtP/XUUyvu3+12w+v1wm63o7u7G21tbTh58iT8fj/8fj90Xc/9y+UgEomgpaVlxfdomoaxsbGsthsKheKBqXTfye/3w+fzIRQKwW63w+PxwOFwpDzGhx56CMFgEABw4MABuN3ulO8F5Bz39/cjHA7Dbrejp6cH3d3dab+bx+NJ+z1WutapeL1e+Hy++L77+vqWpKdine9qw3y4Oq/XCwDo7e3NeRuZ5MNs99ff349jx44hFApB0zQ4HA4EAoFl76vkfJjrca41qfLhyZMn4fV64ff7MTg4GD/3NpttyTlsa2sDABw/fhyhUAgulwsHDx4EAIyMjCx5T677LLVIJAK3242nnnoKkUgk7/SwWp7Kpm7L5L251i/lzH9A5mXKWsP8t1y+aSGTtKcedqaj6spc8lO2ZQjrPiIiWhd0KgkAus1mS/t3h8OhA9CHhoaWvK5pmm6z2XQAusfjWXEfdrtdt9vtOgDd5XIt+dvAwIAOQO/u7l72ubGxsVW3XSy9vb1LfjweT/xH0zTdbrdnvU273a5rmqanS94ul0sHoPf29uqBQCD+eyAQWPK+sbGx+DF4PB69t7c3vt1U58tms+k2m033+Xz6wMBA/DskXwtdXz09ZKu7uzv+nXw+X/w7+Xy+Je8rxvmuJsyH6QUCAd3j8WT8PVezWj7Mdn+9vb3xcxcIBPRAIJDyPFZDPsz2ONealc770NBQPL+tRp3vgYGBku2zGMbGxnSbzabb7Xbd5/MtqZdSpfF0Ms1T2dRt2bw32/qlnPlPHW8mZcpaw/y3XL5pIdO0p9oAqv2Z/JN8TJnmp2zLENZ9RES0XjDgWSKrNS4GBwfjjYVEqlHjcrlW/PzY2Fj8BiRVoEU1PqpFIBBIGXjK5HOapsUbX8l8Pl/K4KbD4dA1TVvymsvl0h0Ox7JtqJvJxGNTjeWxsbEl71WN2+QbgkI2NlXaSW5Yqu+UfEyp5Hq+qw3zYXrq3Kigbz4Bz9XyYbb7U+dztRvrasmH2R7nWrPaec/0ehcq4JLNPouhu7s7ZVBClReZlOG6nnmeyqZuy+a96aSrX8pZD5bzepcb819h951N2itEGZ8qP2VbhrDuIyKi9YJzeFYITdMAIO08RkePHkU4HEYoFEr5d7/fDwBph49U0/xI4XAYXV1d8Pl8WQ+peeihh1YcFuPxeKBp2rLzpOaQ6u/vj7/W39+fcriPei3xvcFgEHa7PX4dFTXkb2BgIKvvkY3jx48DwLLv7fF4EIlE4sMQ08nnfK816zkf6rqOoaGhFYe4ZWq1fJjN/tQwve7u7rRTSSjVkg/LeZzVwG63A0DafLZW9ql4PB74fL5lr6vyONO5fTPNU9nUbdm8N5VS1S/Z5L9sypT1aD3lv0KkhXzbYNlIl58KVYbkgnUfERFVMgY8K4RqqKSb20cFUFI1aNTrDocj7Q2F2q6a16uS9fT0wGazZT2fj9frRSQSQV9fX9r3hMNhHDhwYNnr6rXExlYgEIg3whOphtnJkyeXfD5Vg1K9ljyBfyH19/enPE712moNyFzP91rEfJi/TPJhNlQQ2e12r/reasmH5TzOaqDOQykfwJRjn0ryzb/S398PTdNSpqt8ZFO3ZfPeVEpVv2ST/7IpU9aj9ZT/CpEW8m2DZSNdfip1GZK8D9Z9RERUqRjwLKNIJIJQKISuri54vV64XK4VJ9d3uVwpF0QJh8MIh8Po6elZ8bOANOqcTif8fn9Frgjd39+PYDCYdS+zxKf06Rp+Sqq/q9cSz0m6a6He09raGn/N4/GgtbUVXV1d8b+ra+tyuYp6s5cuiAvIzcOpU6fSfjaX8x0MBtHe3o5QKASv14v29naYTCY4nc74k3y/3x9/vbOzsyy9pjLFfFg42eTDTA0MDEDTNNhsNvT09KClpQUtLS3o6upa1mO2WvJhIY5zreXDRKo8KmXvu3LsMxVVHjmdToTDYTzxxBMF30c2dVs2702Wa32ei2zyXzZlSjrMf9W/T6AwaSGXNtjAwAA6OzvjaWO1ntJA5vmpFGVIItZ9RERU0co8pH7dAJD2R9O0tHNtJU5MrubtSZ7jRs2Jk7ivVJN/Dw4OxufdUj9qgvNKoSYzz5aaS0rNC5Ru7sB0C/OouRczWbRnpfme1H7VT/JcocpK6UH9ZAorLGyx2vnM5XyruZbsdrvucDh0n88XT4Mq7aV6vRIwH65OzceVyxyemebDbPan0qjNZtO7u7t1n88X3266/Fot+TDT40yl2vNh8vkYGxvTBwYG4gt+ZXIu8p1DMJd9FpOaYzqX9JAo1zyczVyGmbx3tfqlXPkvlzIlGfPf2sh/hUgL2aQ9lW5UGklc4Ge1/JpJey3TMoR1HxERrRdWUMlomrbkSevx48fR39+PEydOZDTcJHE4beJTcL/fv2KPNMVut2NoaAihUAjHjx9HMBhEKBRCT08PBgcH0w7TTcftdmf0VDqZx+NJebzBYBDhcDjr3iDhcBh+vz8+P+dKHA4H+vv7EYlElrxXDWta7Yl+JBLBsWPH4HK5lvVEcLvd8Pv96O3tRXt7OwYGBtDV1YVAIJDy+yanh1ysdryapqXtQZjr+U7c9+DgYPz3trY2uN1uhEIhDA0NLXs9GAyWvfcUwHyopMuHucomH2a7XUDOd2Lvj/b29vh3T/we1ZIPsz3OlfZdjfkwHA7DZDIte91ms2FgYKAox1iMfRYy/zkcDgQCAYyOjiIQCOSUHnK1Ut2Wy3szrV/Kkf+yLVNW2zfzX3n2WYi8l29ayLUN1tvbG88b3d3dcDqdK46OyDQ/ZVOGsO4jIqJ1odwR1/UCaZ5uA0i5AqqS3CMxeZVn1ZMj8eko0vQsSyWxt1m5V0dUT3yzXSk81Qrr6XqWqfNlt9v1oaEhfWxsTPf5fPFzsNK10HU9/kQ5WbreLur1wcHBJa+nSg+5UGkolx6euZ5v9XQ9eSXz1V4vd+8pXWc+zPRYkEPvsGzyYTb7w83et8nUdUs8x9WSD7M9zlSqPR9qmqYPDAzEfwYHBzNejVzJtodZIfZZSqqcySQ9JMolD6er23J9byb1S7nyXzZlSjrMf2sj/+WbFnJpg6X6zmrkSLo8m2t7LV0ZwrqPiIjWC/bwLCNN09Db2wuv15vxk8eenh709/fHnzr7fL6Uq45nym63IxAIoLOzE4FAoKxPP5966qn4XEqZCgaDCAaDcLlcS1aCVE+U1YTt6nvZ7Xb4fD709PTEJ0dPPAcr9fDr7OxEa2trygnojx07BpvNtuz89fb2wu12w+125zxxfSgUwujo6LLXHQ5HvCdAqr+r19Odz1zOd6J0k8tX26TzzIf5yzYfZitVGtU0bVnvkWrJh4U8zmrOh+VI59WUtzweD/r7+/NKt5lYqW7L9b351i/JCl0PZlqmrIb5r/L3uZp80kIuaS9VD071nsSeiYlyzU+FKENY9xERUTVjwLPM+vr64PV64Xa7lwzNSEc1MI4fPx5fPOXIkSN5HYMK8pVz8ZRwOIxIJJL1oiKJAZVUQ5u6uroASCNSNbq6u7tx5MgRnDp1CjabDTabLR6kOXjwYMr9rHSTF4lEEIlE0jbks72BSvbQQw+lnHBd1/X49tNNyB6JRFI2kHM934nSLVax0iIWlYr5MD+55MNMZfr+asmHhT7OtZQPaSmVZopZJhQj2FmI+iVZIevBQq4EzvxX3QqRFnJpgyVLFzAE8stPhShDWPcREVE1Y8CzzDRNg8fjyWreqCNHjsDv9yMYDCISiay4KrSSPGdl8t8AZDR/YSI1J062+vr6ln1PtZ3Ozs6sttXd3Z2yEdjV1YX+/v54gyxZcm8zNe9gqvPf2dkJm82GQCCQdlsrNdQikUjaFSwzsVoATqWH5GuszunRo0eXfSbX871WMR/mJ9d8mAmHw5EyfaubJ5W3qiUfFvs4qbQKkf9CoVDKfK/SSCEDdIlWq9tyfW8x6pdC1oOZlilU2QqR9wqRFrJJe+naACpgmCrPZJKfilmGsO4jIqKqVs7x9OsJVpkvR9O0lPMIJc8dqOtL56FM9RmkmHfIZrMtm9dGcTgcKefO8fl88VUOBwcH9UAgkPGchNlSKykWao6d5DkWV6LmTkq1Srbdbs/oO6v5lZLPoZqXKPl7rZYesqHSQ/L1Vdc11XxR+ZzvdPMhZft6OVRbPgwEArrH41ly7gYGBop6LvNZpT1ZJvlwtf2lS98qDSeer2rJh9keZyrVng9T5Zls5TKHYLZKUQ+quQ2Tqe+X7TXLJA9nWrdl+15dz7x+KVf+y6ZMSYf5r/j5rxT1XyHSQjZpL11eV22PXNtr2ZYhrPuIiGi9YA/PCuHxeNDT0wOv14ve3t4V32u322Gz2RAKhVZ9r6JpGrxeL/x+Pw4cOAC73Y5IJLJk5cfEp8OJx9He3o729nY4HA643e7cv+QKVO+21Z5CO51OaJqWUS+TVMLhMDo7O9HX1xdfGVStFpncQ83pdCIcDuPo0aPwer3LtmWz2Zas2B0MBtHZ2Yne3l4cPHgwvm2Xy5WyJ93o6GjK7SbK5Pra7fb4HJSADMtXq3+ruSWTZXq+15tKyoeJPTZ8Pt+StJaq126+1DxdqqfJ0NBQvIdG8hC0fPNhNvtLTN/hcBhOpxMDAwPo7+9fVm5VSz7M5Tip9EpVDz7xxBNwOp1wOBzo6upCa2srjh8/Hu9tnpwe0uW/TPNUNnVbNu9VsqlfypH/silTqDxKVf9lmxZS5b1s0p5akd3lcuHo0aMYHR2Fx+NBJBLBwMBAzu21bMsQgHUfERGtE+WOuK4XyOBpqs1mW/aEN1XPMl2XJ5a9vb0pV2xEmpUlBwYGdJfLFV8NWtM03eFwpHyCnfga0vQSLCTVE2y1FSg1TcvoqfRKPct6e3vj58Bms6Xs2an2BSDtT6pVahO3bbfb0257pe2qn2x7Q3g8niX7XulpdqbnO5VqfrpeTflQbdPlci1JR+l6geRrpfSe/P0KkQ+z2Z+uS2+fTNN3teTDTI8zlWrPh9XQw6yU9eDQ0JDucrni+WKl9JAu/2Wap7Kp23KpBzOtX8qd/7IpU5Ix/xU3/5W6/ss0LaxU92Wa9gYGBnS73R4/Jy6Xa8W8kml+yqYMKXfeW691HxERlZ5J1/OYXI3WvFAohK6urrQrRxJRcbW0tODcuXPxua+cTifzI1EJsR4kKg/Wf0RERJQPc7kPgCpbMBhcMhQu3UqMRFR4oVAINpstPiQsOT8SUfGxHiQqPdZ/RERElC8GPGmZ/v5+OJ1OAMDx48fjK0OGw2GMjo6W89CI1pXR0VG0trbGf/f5fPG8SUTFw3qQqLxY/xEREVG+OKSdlgmFQjh+/Dja2trgcrng8/lw8OBBaJrGp+tEJdbT0xMPtvT09GBsbCzlwgZEVDisB4nKj/UfERER5YMBTyKiCpU4hC8YDMLtdmNwcLDMR0VERFRcrP+IiIgoXwx4EhFVoEgkgj179sQXbHA6nfB4PLDb7eU+NCIioqJh/UdERESFwIAnEVGF8vv9aG1tRTgchsPh4M0eERGtC6z/iIiIKF8MeBIREREREREREdGawVXaiYiIiIiIiIiIaM1gwJOIiIiIiIiIiIjWDAY8iYiIiIiIiIiIaM1gwJOIiIiIiIiIiIjWDAY8iYiIiIiIiIiIaM1gwJOIiIiIiIiIiIjWDAY8S8RkMqX86ezsRE9PDyKRSMrPtbS0xN+3mkgkEt9uV1dXyvcEg0E4nc74dtvb29HT04NwOJzP16MspUsPiT/priHljvmQEjEflsdK+dDtdi/Lh6FQCO3t7Vn/5LNPKj7mv/Jg/iPmPSIiWi+s5T6A9UTTNJw4cQIAMDo6ikgkgnA4jGPHjsHv9yMQCMDlcqX8bCgUQjgchs1mS7v9p556asX9u91ueL1e2O12dHd3o62tDSdPnoTf74ff74eu67l/uQLxer0AgN7e3qw/63a70d/fj3A4DLvdjp6eHnR3d8f/HolE0NLSsuI2NE3D2NhYSbaraRo8Hk/az6x0rVPxer3w+Xzx4+zr60ubnlb7TmsZ82FqkUgEDz30EILBIADgwIEDcLvdcDgcOW1vtbwciUTgdrvx1FNPIRKJrJoOM0nfKpCcTqpzW458mMtxrjWp8uHJkyfh9Xrh9/sxODgYP/c2m23JOWxrawMAHD9+HKFQCC6XCwcPHgQAjIyMLHlPrvssp3zqQSUUCsUf0KyWnrLZX6HfW+r8l2t9vdYw/y3l9/vh8/kQCoVgt9vh8XiKVvclyiafZvLebNqBrPuIiGhd0KkkAOg2my3t3x0Ohw5AHxoaWvK6pmm6zWbTAegej2fFfdjtdt1ut+sAdJfLteRvAwMDOgC9u7t72efGxsZW3XYxBQIB3ePxZPw9U7HZbLrNZtN9Pp8+MDCgezweXdO0Zeeht7d3yY/H44n/aJqm2+32kmx3tfSQre7ubh2A3tvbq/t8Pt3lcukAdJ/Pt+y9mX6ntYj5MLWxsbF4OvV4PHpvb6+uaVrW+THTvDw2NqbbbDbdbrfrPp9PDwQC8TSb6txkmr7V+e3t7dUDgcCyn2TlyofZHudas9J5Hxoaiue31ajzPTAwULJ9Fksh6sFEdrs9nofz3V+x3luu/Jdtfb3WMP8tpdKJKo/V79mUxbnm39XyaTbvzaYdyLqPiIjWCwY8S2S1xsXg4GC8EZBINb5dLteKnx8bG4s3slIFWlTjoxKpc6OCTdne6PX29uoA9LGxsSWvq4ZVJo3xQCCwLNBVrO3qemEbmyrtJDcsHQ6HrmnakuMvxHeqZsyHqblcLt3hcCx7Xd28JaffdDLNy93d3SkDm+r8JKbPbNJ3tum4XPlwveS3dFY77yr/rHZ+ChVwyWafxZJvPZgoEAjomqbFz0+++yv2ewshm/yXTrr6eq1h/jP4fL6UwU2VbjKVS/7NJJ9m+t5s0z/rPiIiWi84h2eF0DQNANLOY3T06FGEw2GEQqGUf/f7/QCQduhKJc+PpOs6hoaGVhxas5JgMAi73R4/h4oajjQwMLDi58PhMLq6uuDz+ZYM4SnWdgvt+PHjALBsKLDH40EkEokPUQby/05r3XrNh/39/Snzn3qtv78/o+1kmpc9Hg98Pt+y11U+SZzLNJv0XU7VcpzVwG63A0DafLZW9pko33ow0UMPPbTqFCXZ7K9Y7y2kfPNfqerrarCe8p/H44GmacvqbDWvaKHrvkSZ5NNM31vO+od1HxERVTIGPCuEuvlPN7eNaoylChKo1x0OR9qGutqumltoLTlw4EDKxV7Ua8mT5yfr6emBzWZb1lgr1nYLrb+/P36zkEi9lhjEzPc7rXXrNR8GAoGUaUgFwk+ePFnQ/SUH3JX+/n5omrbkWLJJ3+VULcdZDVR5VMrAUzn2WQxerxeRSAR9fX3lPpSSyjf/laq+rgbrKf+Fw2EcOHBg2evqtWKV29nk00zeW876h3UfERFVMgY8yygSiSAUCqGrqwterxculyttzzBAgi2pFkQJh8MIh8Po6elZ8bOAPLV2Op3w+/1rZkVoj8eD1tZWdHV1xb+TOq8ul2vFG5j+/n4Eg8G0vduKsd1CS9dgB+Tm4dSpU/Hf8/lOSjAYRHt7O0KhELxeL9rb22EymeB0OuNP8v1+f/z1zs7OsvWaygTzYfoeqerYWltbi7Zvdf6dTifC4TCeeOKJZceQafpWBgYG0NnZGU9/mfbSyUepj3Ot5cNEqtzMddGQatlnoamFwLq7u9M+VFircsl/Si71NfNf9e9TSZVX1GvFqJ+zyaeZvjef9J8v1n1ERFTJGPAsoXA4DJPJFP9paWlBZ2dnvKEdCARW/PzRo0dTDg9Rvc1WCtLYbLb4CpjBYBA9PT1ob29HZ2dnfBhutdI0DUNDQ2htbV3SwOnr61v1nLrd7mUrkBZ7u0pyekj+KZTEYdT5fKfkY3/ooYcwMDAAt9uN3t7e+OqbXV1dCAQC8dcTVxatBMyHmVPfsaurqyjb9/v9S85/IBBY8fylkmqaAK/XC5vNFh+eqoLZqZQjH+ZynOmOvVrzYTKVpzo7OxEOhxEIBIoetCvHPovJ7XYDQMmHk+ejnPlPyaS+ToX5r/r2mUzTtJRBTZVeRkdHC77PbPJpofJ0qvTPuo+IiNYDa7kPYD3RNG1J76Xjx4+jv78fJ06cSDkcJFnicNrEp+B+vz+jhrrdbsfQ0BBCoRCOHz+OYDCIUCiEnp4eDA4Oph2mm47b7c6p55TH48n6xiKTY/H7/ejt7UV7ezsGBgbijZ50+woGgwiHwys2JIu1XWB5esjFanNCpmrM5/Kd0u17cHAw/ntbWxvcbjdCoRCGhoaWvR4MBiui9xTzoVgtH0YiERw7dgwul6to183hcCAQCGB0dBSBQGBZOswlfQNAb29vPP91d3fD6XSm7SVTrnyY7XGutO9qzIfqZjuZzWbDwMBAUY6xGPuslHowHA7D7/fH5ySsFuXMf0Dm9fVK+2b+K88+C5H3HA4H+vv7EYlEluQb9QCy0PNuZ5NPM31vrumfdR8REa0HDHiWUGtr65IbHNXQcrvdGc9x43K5ljTwQqEQIpEIjh49mvFx2O32JRPEd3V1we/3o6urK6vGgMfjqYieJF6vF16vd0mDubu7G16vF11dXRgcHEwZyFI9+dLddBZru0pyeiiGSCSyZDhyrt8pleRjV59L93qlLNjDfJiZw4cP48CBA1n1/M2WzWaLz9nW3d2Nrq6urNJhcvp2OBwYGxtbdsPU09ODYDAYD/QnKkc+zOU406nWfKhp2pK01draCpvNVtRgXTH2WSn1YE9PDzRNyzjdVIpy5L9EmdbX6TD/lW+fhch7fX196O/vx+HDhxEIBNDa2oqnnnoq/uCx0HOKZpNPC5Wn06V/1n1ERLQeMOBZRqoh4/V6M37y2NPTg/7+fvT398PlcsHn86VcYTJTdrsdgUAAnZ2dCAQCVfn089ixY7DZbMuOvbe3F263O20g66mnnoKmaWkbtMXabrZCoVDKYVUOhyPeaEw37Gp0dHTJceT6nVJJt8BRtS18xHy4XGdnJ1pbW0u+2IDH41kSfM42fQOp52NT70ns9ZGtQubDQh5nNefDcqTzcuetYggGgwgGg3C5XEum2lA9q9RiYNX83Qud/5R862vmv8rf50rsdjt8Pl98ehn1mqqPM30AnIls8mk2780n/WeCdR8REVUzBjzLrK+vD16vF263e8nQjHRUA+P48ePxxVOOHDmS1zGoBl0lLJ6SrUgkgkgkkrYRnW44TTgcRiQSSbtIT7G2m4uHHnoo5YTruq7HjyXdhOyRSCTekMz1O6WTrsdMMRe4KRbmQ0O5gp2AcdOTeA4yTd8rKcQ8bIXKhyvJ5TjXUj6k3CQGQVIN8VVz8A4NDVXtKvTFyH+FqK+Z/6pfd3c3jhw5glOnTsVHHagg48GDBwu2n2zyabZ5uhD1Tzqs+4iIqJox4FlmmqbB4/HE5yLKpIfYkSNH4Pf7EQwGEYlEVlwVWkmenyj5bwCyfpKt5sTJVl9fX8GG0WiatmKgLhKJpFw9Uh13uonMi7XdXKwWgFPpIfkaq2NRw6xz/U7rAfOh6OzshM1mK+owdkB6jKT6niptJt4gZZq+gfTnV92M5ZMvC5UPi32cVFqVUA92d3enDNp1dXWhv78/HpioZoXMf8l/Y36rToXMe8k9oNW8mYUc8p1NPs02T+eS/jPFuo+IiKoZA54VoLe3F8eOHcNDDz2UUeOqp6cHfr8fbrcbmqZlFCDp7OyEy+VKOd+RelKc3CBSk7YPDQ3h6NGjCIfDOH78eDwYUgnzlgFGYys5iKJWfEwViFLDZlZ6Elys7RaaSg/Hjh1bck3U/xMb8bl8p/WiEvNhf38/wuHwkhWEVYBV/V6ofFiqYCeA+Hxpyb2N1XdJTIfZpO89e/ak3O6xY8egaVrevXBXUi3HSdmplnpwvcsm/ynlqK8pc6vVf8XKe/39/QgGg1kvIFhOuaT/cuybdR8REZUaA54VwuPxoKenB16vd9UJu+12O2w2G0KhUMaTe2uaBq/XC7/fjwMHDsButyMSiSxZoTQ5AKa23d7ejvb2djgcDrjd7ty/ZBpqfiD1hHdoaCj+ZDi5UeR0OpdNfO/z+RAMBtHZ2Yne3l4cPHgQAwMD8VWzUwWvVG+6lYbaFGu7yujoaDzQmE4m19dut8fnoARkCJZa/VvNLZnPd1pPKikfJgalfT7fktXh8+mtkYrT6UQ4HMbRo0dTpsnEG071/uR8qI45k7z8xBNPwOl0wuFwoKurC62trTh+/Hi8d23ivrJJ32q1V5fLhaNHj2J0dBQejweRSGTJnKCJypEPczlOKr1qqgeLub9ivRcoT/5TsqmvqbRKVf+Fw2F0dnair68vvlq8WjgnVQ/LfOu+Yskl/bPuIyKidUGnkgCg22y2Fd9js9l0TdP0sbGx+Guapul2u33ZewcGBvTe3l59aGgo5b5cLlfKz7hcLt1ms+kAdE3TdIfDoQ8ODi57b+JrAJYcU6FpmqYDSPmT/P00TUt7Hnt7e+PfzW636z6fL+0+XS5Xyu2Xarvpvm/ij6Zpqx5bIo/Hs+Q4A4FAQb5TsoGBAR3Asu1n+3o5VFM+VNt0uVxLrk/ysRXCSnkQgO5wOJa9P9V5zCYvDw0N6S6XK/6Z1dJhpul7YGBAt9vt8XPrcrnS5sdy5sNsjjPd56s5H2Z7XlPp7u7WAegDAwNF2Wc11oOJVH1UiP0V473lrgezaQckY/4rbv4rZf2X2B6y2Wwr1kOFqPuSrZRPs31vpumfdR8REa0XJl1fA5M7UdGEQiF0dXXltboxEeWupaUF586di8+/6nQ6mR+JSoj1IFF5sP4jIiKifJjLfQBU2YLB4JLhOOlWYiSiwguFQvEVWIHl+ZGIio/1IFHpsf4jIiKifDHgScv09/fD6XQCAI4fPx5fNTEcDmN0dLSch0a0royOji5ZUMPn88XzJhEVD+tBovJi/UdERET54pB2WiYUCuH48eNoa2uDy+WCz+fDwYMHoWkan64TlVhPT0882NLT04OxsTFO7E9UZKwHicqP9R8RERHlgwFPIqIKlTiELxgMwu12Y3BwsMxHRUREVFys/4iIiChfDHgSEVWgSCSCPXv2xBdscDqd8Hg8sNvt5T40IiKiomH9R0RERIXAgCcRUYXy+/1obW1FOByGw+HgzR4REa0LrP+IiIgoXwx4EhERERERERER0ZrBVdqJiIiIiIiIiIhozWDAk4iIiIiIiIiIiNYMBjyJiIiIiIiIiIhozWDAk4iIiIiIiIiIiNYMBjyJiIiIiIiIiIhozWDAk4iIiIiIiIiIiNYMBjyJiIiIiIiIiIhozWDAk4iIiIiIiIiIiNYMBjyJiIiIiIiIiIhozfj/A/NxKFCJpur5AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Beta-beating\n", "\n", "bx_ref_bb = 100.0*(bx - bx_id) / bx\n", "by_ref_bb = 100.0*(by - by_id) / by\n", "bx_res_bb = 100.0*(bx - bx_result)/ bx\n", "by_res_bb = 100.0*(by - by_result)/ by\n", "\n", "def rms(x):\n", " return (x**2).mean().sqrt()\n", "\n", "rms_x_ref = rms(bx_ref_bb).item()\n", "ptp_x_ref = (bx_ref_bb.max() - bx_ref_bb.min()).item()\n", "rms_y_ref = rms(by_ref_bb).item()\n", "ptp_y_ref = (by_ref_bb.max() - by_ref_bb.min()).item()\n", "\n", "rms_x_res = rms(bx_res_bb).item()\n", "ptp_x_res = (bx_res_bb.max() - bx_res_bb.min()).item()\n", "rms_y_res = rms(by_res_bb).item()\n", "ptp_y_res = (by_res_bb.max() - by_res_bb.min()).item()\n", "\n", "s = ring.locations().cpu().numpy()\n", "bx_ref_np = bx_ref_bb.cpu().numpy()\n", "by_ref_np = by_ref_bb.cpu().numpy()\n", "bx_res_np = bx_res_bb.cpu().numpy()\n", "by_res_np = by_res_bb.cpu().numpy()\n", "\n", "etax_ref = etaqx - etaqx_id\n", "etay_ref = etaqy - etaqy_id\n", "etax_res = etaqx - etaqx_result\n", "etay_res = etaqy - etaqy_result\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", "rms_etax_res = rms(etax_res).item()\n", "ptp_etax_res = (etax_res.max() - etax_res.min()).item()\n", "rms_etay_res = rms(etay_res).item()\n", "ptp_etay_res = (etay_res.max() - etay_res.min()).item()\n", "\n", "etax_ref_np = etax_ref.cpu().numpy()\n", "etay_ref_np = etay_ref.cpu().numpy()\n", "etax_res_np = etax_res.cpu().numpy()\n", "etay_res_np = etay_res.cpu().numpy()\n", "\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_ref_np, fmt='-', marker='x', color='blue', alpha=0.75, lw=1.5, label=r'initial, $\\beta_x$')\n", "# ax.errorbar(s, by_ref_np, fmt='-', marker='x', color='red', alpha=0.75, lw=1.5, label=r'initial, $\\beta_y$')\n", "ax.errorbar(s, bx_res_np, fmt='-', marker='o', color='blue', alpha=0.75, lw=1.5, label=r'final, $\\beta_x$')\n", "ax.errorbar(s, by_res_np, fmt='-', marker='o', color='red', alpha=0.75, lw=1.5, label=r'final, $\\beta_y$')\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", "title = (\n", " rf'RMS$_x$={rms_x_ref:05.2f}\\% \\quad RMS$_y$={rms_y_ref:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_ref:05.2f}\\% \\quad PTP$_y$={ptp_y_ref:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id)}{(nux - nux_id).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id)}{(nuy - nuy_id).abs().item():.4f}'\n", " rf'\\quad C={c_id.item():.6f}'\n", ")\n", "ax.text(0.0, 1.125, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_x_res:05.2f}\\% \\quad RMS$_y$={rms_y_res:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_res:05.2f}\\% \\quad PTP$_y$={ptp_y_res:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_result)}{(nux - nux_result).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_result)}{(nuy - nuy_result).abs().item():.4f}'\n", " rf'\\quad C={c_result.item():.6f}'\n", ")\n", "ax.text(0.0, 1.025, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'$(\\xi_x, \\xi_y)$ = ({psi_x.item():.2f}, {psi_y.item():.2f}) $\\to$ ({psi_id_x.item():.2f}, {psi_id_y.item():.2f}) $\\to$ ({psi_result_x.item():.2f}, {psi_result_y.item():.2f})' \n", ")\n", "ax.text(0.0, -0.25 - 0*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_ref:.4E} m \\quad RMS$_y$={rms_etay_ref:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_ref:.4E} m \\quad PTP$_y$={ptp_etay_ref:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 1*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_res:.4E} m \\quad RMS$_y$={rms_etay_res:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_res:.4E} m \\quad PTP$_y$={ptp_etay_res:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 2*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "\n", "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=4)\n", "ax.set_ylim(-7, 7)\n", "\n", "plt.setp(ax.spines.values(), linewidth=2.0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 43, "id": "32b57441-b15b-4262-af3e-56e7d357a8bd", "metadata": {}, "outputs": [], "source": [ "# Initial and final values\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", "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n", "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']\n", "\n", "ring.flatten()\n", "\n", "kni = {name: ring[name].kn.item() for name in nkn}\n", "ksi = {name: ring[name].ks.item() for name in nks}\n", "kfi = {name: ring[name].kn.item() for name in QF}\n", "kdi = {name: ring[name].kn.item() for name in QD}\n", "\n", "ring.splice()\n", "\n", "error.flatten()\n", "\n", "knf = {name: error[name].kn.item() for name in nkn}\n", "ksf = {name: error[name].ks.item() for name in nks}\n", "kff = {name: error[name].kn.item() for name in QF}\n", "kdf = {name: error[name].kn.item() for name in QD}\n", "\n", "error.splice()" ] }, { "cell_type": "code", "execution_count": 44, "id": "85682460-b571-42d3-9bbd-ef77cbe2e1b6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'DKF': 0.0, 'DKD': 0.0}\n", "{'DKF': 0.0, 'DKD': 0.0}\n" ] } ], "source": [ "# Global\n", "\n", "gkfi = [kfi[name] for name in kfi if name not in nkn]\n", "gkdi = [kdi[name] for name in kdi if name not in nkn]\n", "gkfi, *_ = gkfi\n", "gkdi, *_ = gkdi\n", "\n", "gkff = [kff[name] for name in kff if name not in nkn]\n", "gkdf = [kdf[name] for name in kdf if name not in nkn]\n", "gkff, *_ = gkff\n", "gkdf, *_ = gkdf\n", "\n", "dkf = [(kff[name] - kfi[name]) for name in kfi if name not in nkn]\n", "dkd = [(kdf[name] - kdi[name]) for name in kdi if name not in nkn]\n", "dkf_abs, *_ = dkf\n", "dkd_abs, *_ = dkd\n", "gk_abs = {'DKF': dkf_abs, 'DKD': dkd_abs}\n", "\n", "dkf = [(kff[name]/kfi[name] - 1) for name in kfi if name not in nkn]\n", "dkd = [(kdf[name]/kdi[name] - 1) for name in kdi if name not in nkn]\n", "dkf_fse, *_ = dkf\n", "dkd_fse, *_ = dkd\n", "gk_fse = {'DKF': dkf_fse, 'DKD': dkd_fse}\n", "\n", "print(gk_abs)\n", "print(gk_fse)" ] }, { "cell_type": "code", "execution_count": 45, "id": "129ad990-1b90-4bde-8d7c-2b590b9fdfb7", "metadata": {}, "outputs": [], "source": [ "# Local\n", "\n", "dkn_fse = {name: knf[name]/kni[name] - 1 for name in kni}\n", "dkn_abs = {name: knf[name] - kni[name]for name in kni}\n", "\n", "dks_fse = {name: 0 for name in ksi}\n", "dks_abs = {name: ksf[name] - ksi[name] for name in ksi}" ] }, { "cell_type": "code", "execution_count": 46, "id": "305fb768-d5a7-473c-b97d-3c9540064bf0", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "gap = 1\n", "\n", "n_kn = len(dkn_abs)\n", "n_dk = len(gk_abs)\n", "n_ks = len(dks_abs)\n", "\n", "y_kn = np.arange(n_kn)\n", "y_dk = np.arange(n_dk) + n_kn + gap\n", "y_ks = np.arange(n_ks) + n_kn + n_dk + 2*gap\n", "\n", "yticks = np.concatenate([y_kn, y_dk, y_ks])\n", "yticklabels = [*kni.keys()] + [*gk_abs.keys()] + [*ksi.keys()]\n", "\n", "fig, (ax_abs, ax_fse) = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", "\n", "ax_abs.barh(y_dk, [gkfi, gkdi], height=0.6, color='red', alpha=0.75, label='initial')\n", "ax_abs.barh(y_dk, [gkff, gkdf], height=0.5, color='blue', alpha=0.75, label='final')\n", "ax_abs.barh(y_dk, [gk_abs['DKF'], gk_abs['DKD']], height=0.6, color='black', alpha=1, label='delta')\n", "\n", "ax_abs.barh(y_kn, kni.values(), height=0.6, color='red', alpha=0.75)\n", "ax_abs.barh(y_kn, knf.values(), height=0.5, color='blue', alpha=0.75)\n", "ax_abs.barh(y_kn, list(dkn_abs.values()), height=0.6, color='black', alpha=1)\n", "ax_abs.legend(loc=(0.01, 0.775), frameon=False, fontsize=16, ncol=1)\n", "ax_abs.set_xlim(-6, 6)\n", "ax_abs.set_xlabel(r'$k_n$', fontsize=16)\n", "ax_abs.tick_params(axis='x', labelsize=16)\n", "ax_abs = ax_abs.twiny()\n", "ax_abs.barh(y_ks, ksi.values(), height=0.6, color='red', alpha=0.75)\n", "ax_abs.barh(y_ks, ksf.values(), height=0.5, color='blue', alpha=0.75)\n", "ax_abs.barh(y_ks, list(dks_abs.values()), height=0.6, color='black', alpha=1)\n", "ax_abs.set_xlim(-0.005, 0.005)\n", "ax_abs.set_yticks(yticks)\n", "ax_abs.set_yticklabels(yticklabels, fontsize=16)\n", "ax_abs.axvline(0.0, color='black', linewidth=1.0, linestyle='--', alpha=0.5)\n", "ax_abs.set_xlabel(r'$k_s$', fontsize=16)\n", "ax_abs.tick_params(axis='x', labelsize=16)\n", "ax_abs.tick_params(axis='y', labelsize=16)\n", "\n", "ax_fse.barh(y_dk, gk_fse.values(), height=0.6, color='black', alpha=1)\n", "ax_fse.barh(y_kn, dkn_fse.values(), height=0.6, color='black', alpha=1)\n", "ax_fse.set_xlim(-25/100, 25/100)\n", "ax_fse.axvline(0.0, color='black', linewidth=1.0, linestyle='--', alpha=0.5)\n", "ax_fse.set_xlabel(r'FSE', fontsize=16)\n", "ax_fse.tick_params(axis='x', labelsize=16)\n", "ax_fse.tick_params(axis='y', labelsize=16)\n", "\n", "plt.setp(ax_abs.spines.values(), linewidth=2.0)\n", "plt.setp(ax_fse.spines.values(), linewidth=2.0)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "80a7bfe1-6bf9-4dc9-9bed-e9fe5a392f86", "metadata": {}, "source": [ "### advance (local)" ] }, { "cell_type": "code", "execution_count": 1, "id": "3d7b6132-4782-4d32-94e2-4f55c0916ada", "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": "07bcf3a4-52b5-4bfa-9da1-905080c826d7", "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": "ae4cb963-d14b-4d6a-9f1b-729715051363", "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": "c7b4d4a1-9b46-41a1-8410-8af7117dc180", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'BPM': 168, 'Drift': 708, 'Dipole': 156, 'Quadrupole': 360, 'Marker': 12}" ] }, "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'^(?!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": "9070d24f-c2f0-4c9c-8f53-549341ca9a75", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux, nuy = tune(ring, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 6, "id": "97f7ba78-72b6-4a27-9418-66c150ad6236", "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": "f6b3f66c-310f-4664-b952-e10bb9b7271c", "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": "8233fbd3-0d21-4a6e-b4cb-df665217c209", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux, muy = advance(ring, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 9, "id": "794caf4d-1564-4c55-975c-17849b42061f", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c = coupling(ring, [])" ] }, { "cell_type": "code", "execution_count": 10, "id": "1807751c-cf95-4969-bbf0-728b8b8b351f", "metadata": {}, "outputs": [], "source": [ "# Compute chromaticity\n", "\n", "psi = chromaticity(ring, [])" ] }, { "cell_type": "code", "execution_count": 11, "id": "bb722e0a-070d-4c34-aca4-aa1f9f9cec0d", "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": "06a1406c-5e8d-4e49-8430-f0efc1e46b93", "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": "d4fe73cd-ead3-4922-95fa-9097cd787ab9", "metadata": {}, "outputs": [], "source": [ "# Several local knobs can be used to correct ID effects\n", "\n", "# Normal quadrupole correctors\n", "\n", "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n", "\n", "# Skew quadrupole correctors\n", "\n", "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']" ] }, { "cell_type": "code", "execution_count": 14, "id": "3fc3a736-d4ec-454f-8e4b-1dcac9ebf1ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[1., 0., 0.],\n", " [0., 1., 0.],\n", " [0., 0., 1.],\n", " [0., 0., 1.],\n", " [0., 1., 0.],\n", " [1., 0., 0.]], dtype=torch.float64)\n", "\n", "tensor([[ 1., 0.],\n", " [ 0., 1.],\n", " [ 0., -1.],\n", " [-1., 0.]], dtype=torch.float64)\n", "\n" ] } ], "source": [ "# Define knobs to magnets mixing matrices\n", "\n", "Sn = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 0.0]], dtype=dtype)\n", "\n", "print(Sn)\n", "print()\n", "\n", "Ss = torch.tensor([[+1.0, 0.0], [0.0, +1.0], [0.0, -1.0], [-1.0, 0.0]], dtype=dtype)\n", "\n", "print(Ss)\n", "print()" ] }, { "cell_type": "code", "execution_count": 15, "id": "f9c79f36-993e-4e19-a45f-70bb904c2251", "metadata": {}, "outputs": [], "source": [ "# Define advance observable\n", "\n", "def observable_advance(kn, ks):\n", " return advance(ring, \n", " [kn, ks], \n", " ('kn', None, nkn, None), \n", " ('ks', None, nks, None), \n", " matched=True)" ] }, { "cell_type": "code", "execution_count": 16, "id": "2e3a3b59-f7a8-4f3f-bb3f-9b505ad53451", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Define dispersion observable\n", "\n", "def observable_dispersion(kn, ks): \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": 17, "id": "15789b64-e1dc-4f3f-95b2-ca0cde06b65d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([672])\n", "torch.Size([672, 5])\n" ] } ], "source": [ "# Construct full target observable vector and corresponding responce matrix\n", "\n", "def observable(knobs):\n", " kn, ks = torch.split(knobs, [3, 2])\n", " kn = Sn @ kn\n", " ks = Ss @ ks \n", " betas = observable_advance(kn, ks)\n", " etas = observable_dispersion(kn, ks)\n", " return torch.cat([betas.flatten(), etas.flatten()])\n", "\n", "knobs = torch.tensor((3 + 2)*[0.0], dtype=dtype)\n", "print((target := observable(knobs)).shape)\n", "print((matrix := torch.func.jacfwd(observable)(knobs)).shape)" ] }, { "cell_type": "code", "execution_count": 18, "id": "0526859b-53b1-4fd8-b52c-a9d928fec324", "metadata": {}, "outputs": [], "source": [ "# Define ID model\n", "# Note, only the flattened triangular part of the A and B matrices is passed\n", "\n", "A = torch.tensor([[-0.03484222052711237, 1.0272120741819959E-7, -4.698931299341201E-9, 0.0015923185492594811],\n", " [1.0272120579834892E-7, -0.046082787920135176, 0.0017792061173117564, 3.3551298301095784E-8],\n", " [-4.6989312853101E-9, 0.0017792061173117072, 0.056853750760983084, -1.5929605363332683E-7],\n", " [0.0015923185492594336, 3.3551298348653296E-8, -1.5929605261642905E-7, 0.08311631737263032]], dtype=dtype)\n", "\n", "B = torch.tensor([[0.03649353186115209, 0.0015448347221877217, 0.00002719892025520868, -0.0033681183134964482],\n", " [0.0015448347221877217, 0.13683886657005795, -0.0033198692682377406, 0.00006140578258682469],\n", " [0.00002719892025520868, -0.0033198692682377406, -0.05260095308967722, 0.005019907688182885],\n", " [-0.0033681183134964482, 0.00006140578258682469, 0.005019907688182885, -0.2531573249456863]], dtype=dtype)\n", "\n", "ID = Matrix('ID', \n", " length=0.0, \n", " A=A[torch.triu(torch.ones_like(A, dtype=torch.bool))].tolist(), \n", " B=B[torch.triu(torch.ones_like(B, dtype=torch.bool))].tolist())" ] }, { "cell_type": "code", "execution_count": 19, "id": "094eb1bf-0f79-4168-afbd-4e7e99782f91", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'BPM': 168,\n", " 'Drift': 709,\n", " 'Dipole': 156,\n", " 'Quadrupole': 360,\n", " 'Marker': 12,\n", " 'Matrix': 1}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Insert ID into the existing lattice\n", "# This will replace the target marker\n", "\n", "error = ring.clone()\n", "error.flatten()\n", "error.insert(ID, error.next('MLL_S01').name, position=0.0)\n", "error.splice()\n", "\n", "# Describe\n", "\n", "error.describe" ] }, { "cell_type": "code", "execution_count": 20, "id": "953fb8d0-60fe-48aa-a697-c496fe56ff78", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux_id, nuy_id = tune(error, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 21, "id": "c4386f93-60f2-4f4a-8266-5c494a0f0f79", "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": 22, "id": "1a25e2d2-0e8d-40fb-a0ae-fe9923925631", "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": 23, "id": "7cda1e60-6698-4d61-8218-ae731d73fe21", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux_id, muy_id = advance(error, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 24, "id": "2429c2d8-4b9c-4188-8668-243333f36b2a", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c_id = coupling(error, [])" ] }, { "cell_type": "code", "execution_count": 25, "id": "7fa7a5da-53c3-4c87-960f-04dbb45dfe0e", "metadata": {}, "outputs": [], "source": [ "# Compute hromaticity\n", "\n", "psi_id = chromaticity(error, [])" ] }, { "cell_type": "code", "execution_count": 26, "id": "7d907ce8-16f0-41bb-91d2-0eb20f4b9c20", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0260, dtype=torch.float64)\n", "tensor(-0.0114, dtype=torch.float64)\n" ] } ], "source": [ "# Tune shifts\n", "\n", "print((nux - nux_id))\n", "print((nuy - nuy_id))" ] }, { "cell_type": "code", "execution_count": 27, "id": "1425bc6b-5a20-42fa-b681-42a16dc840e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0., dtype=torch.float64)\n", "tensor(0.0004, dtype=torch.float64)\n" ] } ], "source": [ "# Coupling (minimal tune distance)\n", "\n", "print(c)\n", "print(c_id)" ] }, { "cell_type": "code", "execution_count": 28, "id": "e79ace15-f3fb-4794-b411-4a557aa03055", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([-71.2093, -66.6787], dtype=torch.float64)\n", "tensor([-72.2597, -66.6681], dtype=torch.float64)\n" ] } ], "source": [ "# Chromaticity\n", "\n", "print(psi)\n", "print(psi_id)" ] }, { "cell_type": "code", "execution_count": 29, "id": "9f71bb37-ab46-4f59-befc-49b9b639c747", "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", "def observable_advance(kn, ks):\n", " return advance(error, \n", " [kn, ks], \n", " ('kn', None, nkn, None), \n", " ('ks', None, nks, None), \n", " matched=True)\n", "\n", "def observable_dispersion(kn, ks):\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, [3, 2])\n", " kn = Sn @ kn\n", " ks = Ss @ ks \n", " betas = observable_advance(kn, ks)\n", " etas = observable_dispersion(kn, ks)\n", " return torch.cat([betas.flatten(), etas.flatten()])" ] }, { "cell_type": "code", "execution_count": 30, "id": "44680570-5da7-480b-82f0-e1c62ae7f29b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0008, dtype=torch.float64)\n", "tensor(0.4812, 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((3 + 2)*[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": 31, "id": "1a5f5875-f98d-4cbb-8ab4-4165dec8d813", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.4812, dtype=torch.float64)\n", "tensor(0.0262, dtype=torch.float64)\n", "tensor(0.0007, dtype=torch.float64)\n", "tensor(0.0001, dtype=torch.float64)\n", "tensor(0.0001, dtype=torch.float64)\n", "tensor(0.0001, dtype=torch.float64)\n", "tensor(0.0001, dtype=torch.float64)\n", "tensor(0.0001, 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((3 + 2)*[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": 32, "id": "87de8f59-71ea-4d1f-807d-008926ac5de2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-0.03953849 -0.18218742 0.65489026 0.65489026 -0.18218742 -0.03953849]\n", "[-3.65464381e-09 -2.14039357e-03 2.14039357e-03 3.65464381e-09]\n" ] } ], "source": [ "# Knob values\n", "\n", "kn, ks = torch.split(knobs, [3, 2])\n", "\n", "kn = Sn @ kn\n", "ks = Ss @ ks\n", "\n", "print(kn.numpy())\n", "print(ks.numpy())" ] }, { "cell_type": "code", "execution_count": 33, "id": "9ac935d0-d510-46cc-a4f3-64b9071c537d", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "kn:\n", "OCT_S01_02: -0.03953849341840344: -0.29359999999999903 -> -0.33313849341840246, FSE: 0.1346678931144536\n", "QF_S01_02 : -0.18218742155108172: 5.479408293511701 -> 5.297220871960619, FSE: -0.03324946997777378\n", "QD_S01_02 : 0.6548902571935666: -3.319999999999998 -> -2.6651097428064316, FSE: -0.19725610156432738\n", "QD_S01_03 : 0.6548902571935666: -3.319999999999998 -> -2.6651097428064316, FSE: -0.19725610156432738\n", "QF_S01_03 : -0.18218742155108172: 5.479408293511701 -> 5.297220871960619, FSE: -0.03324946997777378\n", "OCT_S01_03: -0.03953849341840344: -0.29359999999999903 -> -0.33313849341840246, FSE: 0.1346678931144536\n", "\n", "ks:\n", "SD_S01_05 : -3.65464380817924e-09: 0.0 -> -3.65464380817924e-09\n", "SH_S01_02 : -0.002140393567505922: 0.0 -> -0.002140393567505922\n", "SH_S01_03 : 0.002140393567505922: 0.0 -> 0.002140393567505922\n", "SD_S01_06 : 3.65464380817924e-09: 0.0 -> 3.65464380817924e-09\n" ] } ], "source": [ "# Apply final corrections\n", "\n", "error.flatten()\n", "\n", "print()\n", "print('kn:')\n", "for name, knob in zip(nkn, kn):\n", " print(f'{name:<10}: {knob.numpy():>20}: {error[name].kn.numpy():>20} -> {(error[name].kn + knob).numpy():>20}, FSE: {((error[name].kn + knob)/error[name].kn - 1).numpy():>20}')\n", " error[name].kn = (error[name].kn + knob).item()\n", "\n", "print()\n", "print('ks:')\n", "for name, knob in zip(nks, ks):\n", " print(f'{name:<10}: {knob.numpy():>20}: {error[name].ks.numpy():>20} -> {(error[name].ks + knob).numpy():>20}')\n", " error[name].ks = (error[name].ks + knob).item()\n", " \n", "error.splice()" ] }, { "cell_type": "code", "execution_count": 34, "id": "d41d8c78-f8e8-4078-960d-f5b88f1ad720", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(6.9220e-06, dtype=torch.float64)\n", "tensor(0.0001, 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": 35, "id": "803eed5d-2d4b-49f4-8862-f3af44f1841d", "metadata": {}, "outputs": [], "source": [ "# Compute tunes (fractional part)\n", "\n", "nux_result, nuy_result = tune(error, [], matched=True, limit=1)" ] }, { "cell_type": "code", "execution_count": 36, "id": "19ef5ba7-2da3-425d-8d39-25088e50ead4", "metadata": {}, "outputs": [], "source": [ "# Compute dispersion\n", "\n", "orbit = torch.tensor(4*[0.0], dtype=dtype)\n", "etaqx_result, etapx_result, etaqy_result, etapy_result = dispersion(error, orbit, [], limit=1)" ] }, { "cell_type": "code", "execution_count": 37, "id": "b95aac55-230d-477e-b2e9-d7758fb917e1", "metadata": {}, "outputs": [], "source": [ "# Compute twiss parameters\n", "\n", "ax_result, bx_result, ay_result, by_result = twiss(error, [], matched=True, advance=True, full=False).T" ] }, { "cell_type": "code", "execution_count": 38, "id": "e92ced0a-9be4-4a72-a67e-824e5157bd09", "metadata": {}, "outputs": [], "source": [ "# Compute phase advances\n", "\n", "mux_result, muy_result = advance(error, [], alignment=False, matched=True).T" ] }, { "cell_type": "code", "execution_count": 39, "id": "a9414693-096e-470c-97d9-e0c5fc4b85f9", "metadata": {}, "outputs": [], "source": [ "# Compute coupling\n", "\n", "c_result = coupling(error, [])" ] }, { "cell_type": "code", "execution_count": 40, "id": "42f4eec1-45ec-49af-a3cb-c3572c5f0f1c", "metadata": {}, "outputs": [], "source": [ "# Compute chromaticity\n", "\n", "psi_result = chromaticity(error, [])" ] }, { "cell_type": "code", "execution_count": 41, "id": "5c88b28d-5c3e-4dba-b0c1-4ce17864a871", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0260, dtype=torch.float64)\n", "tensor(0.0114, dtype=torch.float64)\n", "\n", "tensor(0.0011, dtype=torch.float64)\n", "tensor(0.0024, dtype=torch.float64)\n", "\n" ] } ], "source": [ "# Tune shifts\n", "\n", "print((nux - nux_id).abs())\n", "print((nuy - nuy_id).abs())\n", "print()\n", "\n", "print((nux - nux_result).abs())\n", "print((nuy - nuy_result).abs())\n", "print()" ] }, { "cell_type": "code", "execution_count": 42, "id": "dcb91707-c448-4586-8ff7-1a88eb2d96e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0., dtype=torch.float64)\n", "tensor(0.0004, dtype=torch.float64)\n", "tensor(4.0273e-05, dtype=torch.float64)\n" ] } ], "source": [ "# Coupling (minimal tune distance)\n", "\n", "print(c)\n", "print(c_id)\n", "print(c_result)" ] }, { "cell_type": "code", "execution_count": 43, "id": "9157a4d3-0a8b-48f9-9a80-2e40d55bb929", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(-71.2093, dtype=torch.float64) tensor(-66.6787, dtype=torch.float64)\n", "tensor(-72.2597, dtype=torch.float64) tensor(-66.6681, dtype=torch.float64)\n", "tensor(-71.1746, dtype=torch.float64) tensor(-66.6894, dtype=torch.float64)\n" ] } ], "source": [ "# Chromaticity\n", "\n", "(psi_x, psi_y) = psi\n", "(psi_id_x, psi_id_y) = psi_id\n", "(psi_result_x, psi_result_y) =psi_result\n", "\n", "print(psi_x, psi_y)\n", "print(psi_id_x, psi_id_y)\n", "print(psi_result_x, psi_result_y)" ] }, { "cell_type": "code", "execution_count": 44, "id": "f0d24d60-455f-4688-837c-212b11ac4e19", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Beta-beating\n", "\n", "bx_ref_bb = 100.0*(bx - bx_id) / bx\n", "by_ref_bb = 100.0*(by - by_id) / by\n", "bx_res_bb = 100.0*(bx - bx_result)/ bx\n", "by_res_bb = 100.0*(by - by_result)/ by\n", "\n", "def rms(x):\n", " return (x**2).mean().sqrt()\n", "\n", "rms_x_ref = rms(bx_ref_bb).item()\n", "ptp_x_ref = (bx_ref_bb.max() - bx_ref_bb.min()).item()\n", "rms_y_ref = rms(by_ref_bb).item()\n", "ptp_y_ref = (by_ref_bb.max() - by_ref_bb.min()).item()\n", "\n", "rms_x_res = rms(bx_res_bb).item()\n", "ptp_x_res = (bx_res_bb.max() - bx_res_bb.min()).item()\n", "rms_y_res = rms(by_res_bb).item()\n", "ptp_y_res = (by_res_bb.max() - by_res_bb.min()).item()\n", "\n", "s = ring.locations().cpu().numpy()\n", "bx_ref_np = bx_ref_bb.cpu().numpy()\n", "by_ref_np = by_ref_bb.cpu().numpy()\n", "bx_res_np = bx_res_bb.cpu().numpy()\n", "by_res_np = by_res_bb.cpu().numpy()\n", "\n", "etax_ref = etaqx - etaqx_id\n", "etay_ref = etaqy - etaqy_id\n", "etax_res = etaqx - etaqx_result\n", "etay_res = etaqy - etaqy_result\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", "rms_etax_res = rms(etax_res).item()\n", "ptp_etax_res = (etax_res.max() - etax_res.min()).item()\n", "rms_etay_res = rms(etay_res).item()\n", "ptp_etay_res = (etay_res.max() - etay_res.min()).item()\n", "\n", "etax_ref_np = etax_ref.cpu().numpy()\n", "etay_ref_np = etay_ref.cpu().numpy()\n", "etax_res_np = etax_res.cpu().numpy()\n", "etay_res_np = etay_res.cpu().numpy()\n", "\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_ref_np, fmt='-', marker='x', color='blue', alpha=0.75, lw=1.5, label=r'initial, $\\beta_x$')\n", "ax.errorbar(s, by_ref_np, fmt='-', marker='x', color='red', alpha=0.75, lw=1.5, label=r'initial, $\\beta_y$')\n", "ax.errorbar(s, bx_res_np, fmt='-', marker='o', color='blue', alpha=0.75, lw=1.5, label=r'final, $\\beta_x$')\n", "ax.errorbar(s, by_res_np, fmt='-', marker='o', color='red', alpha=0.75, lw=1.5, label=r'final, $\\beta_y$')\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", "title = (\n", " rf'RMS$_x$={rms_x_ref:05.2f}\\% \\quad RMS$_y$={rms_y_ref:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_ref:05.2f}\\% \\quad PTP$_y$={ptp_y_ref:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_id)}{(nux - nux_id).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_id)}{(nuy - nuy_id).abs().item():.4f}'\n", " rf'\\quad C={c_id.item():.6f}'\n", ")\n", "ax.text(0.0, 1.125, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_x_res:05.2f}\\% \\quad RMS$_y$={rms_y_res:05.2f}\\% \\quad '\n", " rf'PTP$_x$={ptp_x_res:05.2f}\\% \\quad PTP$_y$={ptp_y_res:05.2f}\\% \\quad '\n", " rf'$\\Delta \\nu_x$={(lambda x: '-' if x < 0 else '~')(nux - nux_result)}{(nux - nux_result).abs().item():.4f} \\quad $\\Delta \\nu_y$={(lambda x: '-' if x < 0 else '~')(nuy - nuy_result)}{(nuy - nuy_result).abs().item():.4f}'\n", " rf'\\quad C={c_result.item():.6f}'\n", ")\n", "ax.text(0.0, 1.025, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'$(\\xi_x, \\xi_y)$ = ({psi_x.item():.2f}, {psi_y.item():.2f}) $\\to$ ({psi_id_x.item():.2f}, {psi_id_y.item():.2f}) $\\to$ ({psi_result_x.item():.2f}, {psi_result_y.item():.2f})' \n", ")\n", "ax.text(0.0, -0.25 - 0*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_ref:.4E} m \\quad RMS$_y$={rms_etay_ref:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_ref:.4E} m \\quad PTP$_y$={ptp_etay_ref:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 1*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "title = (\n", " rf'RMS$_x$={rms_etax_res:.4E} m \\quad RMS$_y$={rms_etay_res:.4E} m \\quad '\n", " rf'PTP$_x$={ptp_etax_res:.4E} m \\quad PTP$_y$={ptp_etay_res:.4E} m \\quad '\n", ")\n", "ax.text(0.0, -0.25 - 2*0.1, title, transform=ax.transAxes, ha='left', va='bottom', fontsize=16, fontfamily='monospace')\n", "\n", "ax.legend(loc='upper right', frameon=False, fontsize=14, ncol=4)\n", "ax.set_ylim(-20, 20)\n", "\n", "plt.setp(ax.spines.values(), linewidth=2.0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 45, "id": "4333fa4a-6553-4a2c-8c46-d4dde71334e6", "metadata": {}, "outputs": [], "source": [ "# Initial and final values\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", "nkn = ['OCT_S01_02', 'QF_S01_02', 'QD_S01_02', 'QD_S01_03', 'QF_S01_03', 'OCT_S01_03']\n", "nks = ['SD_S01_05', 'SH_S01_02', 'SH_S01_03', 'SD_S01_06']\n", "\n", "ring.flatten()\n", "\n", "kni = {name: ring[name].kn.item() for name in nkn}\n", "ksi = {name: ring[name].ks.item() for name in nks}\n", "kfi = {name: ring[name].kn.item() for name in QF}\n", "kdi = {name: ring[name].kn.item() for name in QD}\n", "\n", "ring.splice()\n", "\n", "error.flatten()\n", "\n", "knf = {name: error[name].kn.item() for name in nkn}\n", "ksf = {name: error[name].ks.item() for name in nks}\n", "kff = {name: error[name].kn.item() for name in QF}\n", "kdf = {name: error[name].kn.item() for name in QD}\n", "\n", "error.splice()" ] }, { "cell_type": "code", "execution_count": 46, "id": "d2e66f37-eb76-4c42-af00-f19319b2446d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'DKF': 0.0, 'DKD': 0.0}\n", "{'DKF': 0.0, 'DKD': 0.0}\n" ] } ], "source": [ "# Global\n", "\n", "gkfi = [kfi[name] for name in kfi if name not in nkn]\n", "gkdi = [kdi[name] for name in kdi if name not in nkn]\n", "gkfi, *_ = gkfi\n", "gkdi, *_ = gkdi\n", "\n", "gkff = [kff[name] for name in kff if name not in nkn]\n", "gkdf = [kdf[name] for name in kdf if name not in nkn]\n", "gkff, *_ = gkff\n", "gkdf, *_ = gkdf\n", "\n", "dkf = [(kff[name] - kfi[name]) for name in kfi if name not in nkn]\n", "dkd = [(kdf[name] - kdi[name]) for name in kdi if name not in nkn]\n", "dkf_abs, *_ = dkf\n", "dkd_abs, *_ = dkd\n", "gk_abs = {'DKF': dkf_abs, 'DKD': dkd_abs}\n", "\n", "dkf = [(kff[name]/kfi[name] - 1) for name in kfi if name not in nkn]\n", "dkd = [(kdf[name]/kdi[name] - 1) for name in kdi if name not in nkn]\n", "dkf_fse, *_ = dkf\n", "dkd_fse, *_ = dkd\n", "gk_fse = {'DKF': dkf_fse, 'DKD': dkd_fse}\n", "\n", "print(gk_abs)\n", "print(gk_fse)" ] }, { "cell_type": "code", "execution_count": 47, "id": "bf200589-dcd2-4fc0-890f-c991045e7464", "metadata": {}, "outputs": [], "source": [ "# Local\n", "\n", "dkn_fse = {name: knf[name]/kni[name] - 1 for name in kni}\n", "dkn_abs = {name: knf[name] - kni[name]for name in kni}\n", "\n", "dks_fse = {name: 0 for name in ksi}\n", "dks_abs = {name: ksf[name] - ksi[name] for name in ksi}" ] }, { "cell_type": "code", "execution_count": 48, "id": "0147c9b7-70dd-45e5-92db-6abfa2283860", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "gap = 1\n", "\n", "n_kn = len(dkn_abs)\n", "n_dk = len(gk_abs)\n", "n_ks = len(dks_abs)\n", "\n", "y_kn = np.arange(n_kn)\n", "y_dk = np.arange(n_dk) + n_kn + gap\n", "y_ks = np.arange(n_ks) + n_kn + n_dk + 2*gap\n", "\n", "yticks = np.concatenate([y_kn, y_dk, y_ks])\n", "yticklabels = [*kni.keys()] + [*gk_abs.keys()] + [*ksi.keys()]\n", "\n", "fig, (ax_abs, ax_fse) = plt.subplots(1, 2, figsize=(12, 6), sharey=True)\n", "\n", "ax_abs.barh(y_dk, [gkfi, gkdi], height=0.6, color='red', alpha=0.75, label='initial')\n", "ax_abs.barh(y_dk, [gkff, gkdf], height=0.5, color='blue', alpha=0.75, label='final')\n", "ax_abs.barh(y_dk, [gk_abs['DKF'], gk_abs['DKD']], height=0.6, color='black', alpha=1, label='delta')\n", "\n", "ax_abs.barh(y_kn, kni.values(), height=0.6, color='red', alpha=0.75)\n", "ax_abs.barh(y_kn, knf.values(), height=0.5, color='blue', alpha=0.75)\n", "ax_abs.barh(y_kn, list(dkn_abs.values()), height=0.6, color='black', alpha=1)\n", "ax_abs.legend(loc=(0.01, 0.775), frameon=False, fontsize=16, ncol=1)\n", "ax_abs.set_xlim(-6, 6)\n", "ax_abs.set_xlabel(r'$k_n$', fontsize=16)\n", "ax_abs.tick_params(axis='x', labelsize=16)\n", "ax_abs = ax_abs.twiny()\n", "ax_abs.barh(y_ks, ksi.values(), height=0.6, color='red', alpha=0.75)\n", "ax_abs.barh(y_ks, ksf.values(), height=0.5, color='blue', alpha=0.75)\n", "ax_abs.barh(y_ks, list(dks_abs.values()), height=0.6, color='black', alpha=1)\n", "ax_abs.set_xlim(-0.005, 0.005)\n", "ax_abs.set_yticks(yticks)\n", "ax_abs.set_yticklabels(yticklabels, fontsize=16)\n", "ax_abs.axvline(0.0, color='black', linewidth=1.0, linestyle='--', alpha=0.5)\n", "ax_abs.set_xlabel(r'$k_s$', fontsize=16)\n", "ax_abs.tick_params(axis='x', labelsize=16)\n", "ax_abs.tick_params(axis='y', labelsize=16)\n", "\n", "ax_fse.barh(y_dk, gk_fse.values(), height=0.6, color='black', alpha=1)\n", "ax_fse.barh(y_kn, dkn_fse.values(), height=0.6, color='black', alpha=1)\n", "ax_fse.set_xlim(-25/100, 25/100)\n", "ax_fse.axvline(0.0, color='black', linewidth=1.0, linestyle='--', alpha=0.5)\n", "ax_fse.set_xlabel(r'FSE', fontsize=16)\n", "ax_fse.tick_params(axis='x', labelsize=16)\n", "ax_fse.tick_params(axis='y', labelsize=16)\n", "\n", "plt.setp(ax_abs.spines.values(), linewidth=2.0)\n", "plt.setp(ax_fse.spines.values(), linewidth=2.0)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] } ], "metadata": { "colab": { "collapsed_sections": [ "myt0_gMIOq7b", "5d97819c" ], "name": "03_frequency.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.0" }, "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 } }, "nbformat": 4, "nbformat_minor": 5 }