From 71c5e3f39009e3e64c188562f286a4702b83a65e Mon Sep 17 00:00:00 2001 From: Lena Poepping Date: Wed, 22 May 2024 12:27:17 +0200 Subject: [PATCH 01/92] DOC: import notebooks from https://github.com/ComPWA/K-matrix-research --- Mulitpleqn_Toyfit.ipynb | 1832 +++++++++++++++++++++ SubintensityPlots_mitAgrand.ipynb | 1637 +++++++++++++++++++ Toyfits_DataFVector_MoreChannel.ipynb | 2166 +++++++++++++++++++++++++ 3 files changed, 5635 insertions(+) create mode 100644 Mulitpleqn_Toyfit.ipynb create mode 100644 SubintensityPlots_mitAgrand.ipynb create mode 100644 Toyfits_DataFVector_MoreChannel.ipynb diff --git a/Mulitpleqn_Toyfit.ipynb b/Mulitpleqn_Toyfit.ipynb new file mode 100644 index 00000000..ac444ee6 --- /dev/null +++ b/Mulitpleqn_Toyfit.ipynb @@ -0,0 +1,1832 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Genarate data BW $F$ vector and fit with Breit-Wigner for 2 poles and 1 channel\n", + "## Do not mind tho Sub-Intensity plots \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import graphviz\n", + "import numpy as np\n", + "import qrules\n", + "import sympy as sp\n", + "from ampform.io import aslatex\n", + "from IPython.display import Latex\n", + "from qrules.particle import Particle, ParticleCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Collect dynamics symbols" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Resonance | $m$ [MeV] | $\\Gamma$ [MeV] | $J^P$ |\n", + "|-----------|-----------|----------------|-------|\n", + "| $N^*(1440)$ | 1398 | 167 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1535)$ | 1530 | 210 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1650)$ | 1668 | 194 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1710)$ | 1749 | 263 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1880)$ | 1876 | 261 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1895)$ | 2045 | 240 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_particle_database() -> ParticleCollection:\n", + " particle_database = qrules.load_default_particles()\n", + " additional_definitions = qrules.io.load(\n", + " \"../../../additional-nstar-sigma-definitions.yml\"\n", + " )\n", + " particle_database.update(additional_definitions)\n", + " return particle_database\n", + "\n", + "\n", + "PARTICLE_DB = load_particle_database()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "db52c172f856481b820a5068d1db636e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Propagating quantum numbers: 0%| | 0/36 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "g0_edge0\n", + "0: eta\n", + "\n", + "\n", + "\n", + "g0_edge1\n", + "1: p\n", + "\n", + "\n", + "\n", + "g0_edge2\n", + "2: p~\n", + "\n", + "\n", + "\n", + "g0_edge-1\n", + "J/psi(1S)\n", + "\n", + "\n", + "\n", + "g0_node0\n", + "\n", + "\n", + "\n", + "g0_edge-1->g0_node0\n", + "\n", + "\n", + "\n", + "\n", + "g0_node0->g0_edge2\n", + "\n", + "\n", + "\n", + "\n", + "g0_node1\n", + "\n", + "\n", + "\n", + "g0_node0->g0_node1\n", + "\n", + "N(1650)+\n", + "N(1900)+\n", + "N(Fakestar)+\n", + "N(Fakestar2)+\n", + "\n", + "\n", + "\n", + "g0_node1->g0_edge0\n", + "\n", + "\n", + "\n", + "\n", + "g0_node1->g0_edge1\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reaction = qrules.generate_transitions(\n", + " initial_state=\"J/psi(1S)\",\n", + " final_state=[\"eta\", \"p\", \"p~\"],\n", + " allowed_intermediate_particles=[\"N(Fakestar2)+\",\"N(1650)+\",\"N(1900)+\",\"N(Fakestar)+\"],\n", + " allowed_interaction_types=[\"strong\"],\n", + " formalism=\"helicity\",\n", + " # mass_conservation_factor=5.0,\n", + " particle_db=PARTICLE_DB,\n", + ")\n", + "dot = qrules.io.asdot(reaction, collapse_graphs=True)\n", + "graphviz.Source(dot)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import ampform\n", + "\n", + "model_builder = ampform.get_builder(reaction)\n", + "model_builder.adapter.permutate_registered_topologies()\n", + "model_builder.scalar_initial_state_mass = True\n", + "model_builder.stable_final_state_ids = [0, 1, 2]\n", + "for name in reaction.get_intermediate_particles().names:\n", + " model_builder.set_dynamics(name, create_dynamics_symbol)\n", + "model = model_builder.formulate()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "selected_amplitudes = {\n", + " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i < 3\n", + "}\n", + "src = aslatex(selected_amplitudes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Formulate dynamics expression" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle X_{Q=+1, S=1/2, P =-1}$" + ], + "text/plain": [ + "X_{Q=+1, S=1/2, P =-1}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N(Fakestar2)+ 1.75 GeV 0.6 GeV \n", + " N(1650)+ 1.65 GeV 0.125 GeV \n" + ] + }, + { + "data": { + "text/latex": [ + "$\\displaystyle X_{Q=+1, S=3/2, P =1}$" + ], + "text/plain": [ + "X_{Q=+1, S=3/2, P =1}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N(Fakestar)+ 1.82 GeV 0.6 GeV \n", + " N(1900)+ 1.92 GeV 0.2 GeV \n" + ] + }, + { + "data": { + "text/plain": [ + "ParameterValues({\n", + " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{+1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{-1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " m_0: 0.547862,\n", + " m_1: 0.93827208816,\n", + " m_2: 0.93827208816,\n", + " m_012: 3.0969,\n", + " })" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", + " display(symbol)\n", + " for p, _ in resonances:\n", + " print(f\" {p.name:<20s} {p.mass:>8g} GeV {p.width:>8g} GeV \")\n", + "model.parameter_defaults" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Formulate Dynamics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phasespace factor" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from ampform.kinematics.phasespace import Kallen\n", + "from ampform.sympy import unevaluated\n", + "from sympy import Abs\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class PhaseSpaceCM(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"\\rho^\\mathrm{{CM}}_{{{m1},{m2}}}\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " return -16 * sp.pi * sp.I * ChewMandelstam(s, m1, m2)\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class ChewMandelstam(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"\\Sigma\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " q = BreakupMomentum(s, m1, m2)\n", + " return (\n", + " 1\n", + " / (16 * sp.pi**2)\n", + " * (\n", + " (2 * q / sp.sqrt(s))\n", + " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", + " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", + " )\n", + " )\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class BreakupMomentum(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"q\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class EnergyDecaywidth(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " gamma_R: Any\n", + " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2, gamma_R = self.args\n", + " return gamma_R * PhaseSpaceCM(s, m1, m2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Relativistic Breit-Wigner" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", + "\n", + "PARAMETERS_BW = {}\n", + "PARAMETERS_BW.update(model.parameter_defaults)\n", + "\n", + "def formulate_rel_bw(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " q = BreakupMomentum(s, m_a, m_b)\n", + " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", + " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", + " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", + " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", + " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", + " for i, (p, va) in enumerate(resonances):\n", + " PARAMETERS_BW[w[i]] = p.width\n", + " PARAMETERS_BW[m[i]] = p.mass\n", + " PARAMETERS_BW[b[i]] = 1\n", + " PARAMETERS_BW[d[i]] = 1\n", + " PARAMETERS_BW[L[i]] = 0\n", + " return rel_bw" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $K$ matrix " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "PARAMETERS_F = {}\n", + "PARAMETERS_F.update(model.parameter_defaults)\n", + "\n", + "def formulate_K_matrix(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " g= [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + "\n", + " kmatrix = sum(\n", + " (g_**2) / (m_**2 - s) for m_, g_ in zip(m, g)\n", + " )\n", + " for i, (p, va) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = p.mass\n", + " PARAMETERS_F[g[i]] = 1\n", + " return kmatrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $P$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "def formulate_P_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " beta = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", + " P_vector = sum(\n", + " ( g_ * beta_ ) / (m_**2 - s)\n", + " for m_,g_, beta_ in zip(m, g, beta)\n", + " )\n", + " for i, (p, va) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = p.mass\n", + " PARAMETERS_F[beta[i]] = 1 + 0j\n", + " PARAMETERS_F[g[i]] = 1\n", + " return P_vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $F$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def formulate_F_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1,variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " rho = PhaseSpaceCM(s, m_a, m_b)\n", + " K = formulate_K_matrix(resonances)\n", + " P = formulate_P_vector(resonances)\n", + " Fvector = (1 / (1 - rho * K)) * P\n", + " return Fvector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model relativistic Breit-Wigner" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import attrs\n", + "from ampform.helicity import ParameterValues\n", + "\n", + "dynamics_expressions_rel_bw = {\n", + " symbol: formulate_rel_bw(resonances)\n", + " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + "}\n", + "model_rel_bw = attrs.evolve(\n", + " model,\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_BW,\n", + " }),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "full_expression_rel_bw = model_rel_bw.expression.doit().xreplace(\n", + " dynamics_expressions_rel_bw\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model $F$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{array}{rcl}\n", + " X_{Q=+1, S=1/2, P =-1} &=& \\frac{\\frac{\\beta_{N(1650)^{+}} g_{N(1650)^{+}}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\beta_{N(Fakestar2)^+} g_{N(Fakestar2)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1650)^{+}}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar2)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1} \\\\\n", + " X_{Q=+1, S=3/2, P =1} &=& \\frac{\\frac{\\beta_{N(1900)^+} g_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1900)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1} \\\\\n", + "\\end{array}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamics_expressions_fvector = {\n", + " symbol: formulate_F_vector(resonances)\n", + " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + "}\n", + "model_fvector = attrs.evolve(\n", + " model,\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_F,\n", + " }),\n", + ")\n", + "Latex(aslatex(dynamics_expressions_fvector))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ParameterValues({\n", + " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{+1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{-1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " m_0: 0.547862,\n", + " m_1: 0.93827208816,\n", + " m_2: 0.93827208816,\n", + " m_012: 3.0969,\n", + " m_{N(Fakestar2)^+}: 1.75,\n", + " g_{N(Fakestar2)^+}: 1,\n", + " m_{N(1650)^{+}}: 1.65,\n", + " g_{N(1650)^{+}}: 1,\n", + " \\beta_{N(Fakestar2)^+}: (1+0j),\n", + " \\beta_{N(1650)^{+}}: (1+0j),\n", + " m_{N(Fakestar)^+}: 1.82,\n", + " g_{N(Fakestar)^+}: 1,\n", + " m_{N(1900)^+}: 1.92,\n", + " g_{N(1900)^+}: 1,\n", + " \\beta_{N(Fakestar)^+}: (1+0j),\n", + " \\beta_{N(1900)^+}: (1+0j),\n", + " })" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_fvector.parameter_defaults" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5591" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_expression_fvector = model_fvector.expression.doit().xreplace(\n", + " dynamics_expressions_fvector\n", + ")\n", + "sp.count_ops(full_expression_fvector)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Parametrized Function\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from tensorwaves.function.sympy import create_parametrized_function\n", + "\n", + "unfolded_expression_rel_bw = full_expression_rel_bw.doit()\n", + "\n", + "intensity_func_rel_bw = create_parametrized_function(\n", + " expression=unfolded_expression_rel_bw,\n", + " backend=\"jax\",\n", + " parameters=PARAMETERS_BW,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "unfolded_expression_fvector = full_expression_fvector.doit()\n", + "\n", + "intensity_func_fvector = create_parametrized_function(\n", + " expression=unfolded_expression_fvector,\n", + " backend=\"jax\",\n", + " parameters=PARAMETERS_F,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "new_parameters_fvector = {\n", + " R\"m_{N(Fakestar)^+}\": 1.95,\n", + " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", + " R\"m_{N(1900)^+}\": 1.9,\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", + " R\"g_{N(1900)^+}\": 1,\n", + " R\"g_{N(Fakestar)^+}\": 1,\n", + " R\"m_{N(Fakestar2)^+}\": 1.75,\n", + " R\"\\beta_{N(Fakestar2)^+}\": 1 + 0j,\n", + " R\"m_{N(1650)^{+}}\": 1.65,\n", + " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", + " R\"g_{N(1650)^{+}}\": 1.65,\n", + " R\"g_{N(Fakestar2)^+}\": 1,\n", + "}\n", + "\n", + "new_parameters_relbw = {\n", + " R\"m_{N(Fakestar)^+}\": 1.85,\n", + " R\"w_{N(Fakestar)^+}\": 1/1.85,\n", + " R\"m_{N(1900)^+}\": 1.9,\n", + " R\"w_{N(1900)^+}\": 1/1.9,\n", + " R\"m_{N(Fakestar2)^+}\": 1.75,\n", + " R\"w_{N(Fakestar2)^+}\": 1/1.75,\n", + " R\"m_{N(1650)^{+}}\": 1.65,\n", + " R\"w_{N(1650)^{+}}\": 1/1.65,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C_{J/\\\\psi(1S) \\\\to N(1650)^{+}_{+1/2} \\\\overline{p}_{+1/2}; N(1650)^{+} \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(1650)^{+}_{+1/2} \\\\overline{p}_{-1/2}; N(1650)^{+} \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar2)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar2)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar2)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar2)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+3/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{-1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'm_0': 0.547862,\n", + " 'm_1': 0.93827208816,\n", + " 'm_2': 0.93827208816,\n", + " 'm_012': 3.0969,\n", + " 'm_{N(Fakestar2)^+}': 1.75,\n", + " 'g_{N(Fakestar2)^+}': 1,\n", + " 'm_{N(1650)^{+}}': 1.65,\n", + " 'g_{N(1650)^{+}}': 1.65,\n", + " '\\\\beta_{N(Fakestar2)^+}': (1+0j),\n", + " '\\\\beta_{N(1650)^{+}}': (1+0j),\n", + " 'm_{N(Fakestar)^+}': 1.95,\n", + " 'g_{N(Fakestar)^+}': 1,\n", + " 'm_{N(1900)^+}': 1.9,\n", + " 'g_{N(1900)^+}': 1,\n", + " '\\\\beta_{N(Fakestar)^+}': (1+0j),\n", + " '\\\\beta_{N(1900)^+}': (1+0j)}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", + "intensity_func_rel_bw.update_parameters(new_parameters_relbw)\n", + "intensity_func_fvector.parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data with $F$ vector\n", + "### Generate phase space sample" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from tensorwaves.data import SympyDataTransformer\n", + "\n", + "helicity_transformer = SympyDataTransformer.from_sympy(\n", + " model.kinematic_variables, backend=\"numpy\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cece241e87b345c2b9e3bcd74d447ef6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating phase space sample: 0%| | 0/100000 [00:00:3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n" + ] + }, + { + "data": { + "text/plain": [ + "{'m_01': array([1.81857778, 1.80863875, 1.86758228, ..., 1.7217908 , 1.88162305,\n", + " 1.95955089]),\n", + " 'm_02': array([1.70745362, 1.77483717, 1.56984082, ..., 2.13907063, 1.99774363,\n", + " 2.0295025 ]),\n", + " 'm_12': array([2.33002756, 2.2870134 , 2.38733903, ..., 2.0276747 , 2.02981933,\n", + " 1.92170012]),\n", + " 'phi_0': array([ 1.97016286, -2.8765596 , 0.75357421, ..., 0.19730572,\n", + " -0.45861856, 1.57182959]),\n", + " 'phi_0^01': array([-1.97869891, 2.40627766, -2.02701505, ..., 1.42458459,\n", + " 0.78477173, 2.00132783]),\n", + " 'phi_0^02': array([ 0.98414884, -1.41787483, 1.80055274, ..., -2.62005351,\n", + " -1.37701865, -1.58606652]),\n", + " 'phi_01': array([-0.00476082, -0.46629838, -0.49331781, ..., 2.95178512,\n", + " 2.14918814, -1.97763388]),\n", + " 'phi_1^12': array([-0.5234414 , 0.53541189, -1.32700284, ..., 2.04917998,\n", + " 2.17445382, 1.30218432]),\n", + " 'phi_02': array([-1.98053067, 1.48563902, 3.08718583, ..., -1.7995076 ,\n", + " -2.40408988, -0.99517043]),\n", + " 'theta_0': array([1.69320513, 1.8732383 , 2.16807283, ..., 2.56300869, 1.02101855,\n", + " 2.0423608 ]),\n", + " 'theta_0^01': array([2.00195379, 1.79913544, 2.46359496, ..., 0.38143291, 0.96066346,\n", + " 0.54722468]),\n", + " 'theta_0^02': array([1.73936386, 1.72081689, 1.66448996, ..., 1.63126795, 1.19669709,\n", + " 0.64090765]),\n", + " 'theta_01': array([2.57060174, 2.32905607, 2.03576298, ..., 0.5128774 , 1.68637297,\n", + " 1.23809802]),\n", + " 'theta_1^12': array([1.34061414, 1.5044162 , 0.83272194, ..., 2.71169603, 1.82674819,\n", + " 1.82311053]),\n", + " 'theta_02': array([0.63817236, 0.51529239, 0.96856613, ..., 1.79764582, 2.3444856 ,\n", + " 1.03333824])}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from tensorwaves.data import (\n", + " IntensityDistributionGenerator,\n", + " SympyDataTransformer,\n", + " TFPhaseSpaceGenerator,\n", + " TFUniformRealNumberGenerator,\n", + " TFWeightedPhaseSpaceGenerator,\n", + ")\n", + "\n", + "rng = TFUniformRealNumberGenerator(seed=0)\n", + "phsp_generator = TFPhaseSpaceGenerator(\n", + " initial_state_mass=reaction.initial_state[-1].mass,\n", + " final_state_masses={i: p.mass for i, p in reaction.final_state.items()},\n", + ")\n", + "phsp_momenta = phsp_generator.generate(100_000, rng)\n", + "phsp = helicity_transformer(phsp_momenta)\n", + "phsp = {k: v.real for k, v in phsp.items()}\n", + "phsp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Sub-Intensities" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "from tensorwaves.interface import ParametrizedFunction\n", + "\n", + "\n", + "def compute_sub_intensity(\n", + " func: ParametrizedFunction,\n", + " input_data: DataSample,\n", + " resonances: list[str],\n", + " coupling_pattern:str=r\"(\\\\beta|g)\",\n", + "):\n", + " original_parameters = dict(func.parameters)\n", + " negative_lookahead = f\"(?!{'|'.join(map(re.escape, resonances))})\"\n", + " # https://regex101.com/r/WrgGyD/1\n", + " pattern = rf\"^{coupling_pattern}({negative_lookahead}.)*$\"\n", + " set_parameters_to_zero(func, pattern)\n", + " array = func(input_data)\n", + " func.update_parameters(original_parameters)\n", + " return array\n", + "\n", + "\n", + "def set_parameters_to_zero(func: ParametrizedFunction, name_pattern: str) -> None:\n", + " new_parameters = dict(func.parameters)\n", + " for par_name in func.parameters:\n", + " if re.match(name_pattern, par_name) is not None:\n", + " new_parameters[par_name] = 0\n", + " func.update_parameters(new_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "total_intensities = intensity_func_fvector(phsp)\n", + "total_intensities_1 = intensity_func_rel_bw(phsp)\n", + "sub_intensities = {\n", + " p: compute_sub_intensity(intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern= r\"\\\\beta\")\n", + " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " for p, _ in resonances\n", + "}\n", + "sub_intensities_bw = {\n", + " p: compute_sub_intensity(intensity_func_fvector, phsp, resonances=[p.latex])\n", + " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " for p, _ in resonances\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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92pZwOKyzzz5bCxYs8KwPBAI65JBDdNhhh2WNXrVhwwaddtppWrduXa+2DQD2FwPpuA4A6Fn19fX6yle+0m/PX1lZqdNOO03vv/++Z31eXp5mzpyp6dOnKxQKeR5bsmSJTjvttA5v4AOAg1V/B2gBAAcOzte7ZsBMHbhHaWmpLr30Up177rk6+eSTVVRUlHrMtm29+uqruvXWW/Xqq6+m1r/99tuaP3++nnjiiV5rVyKR0Lx58zwfkvLycv3v//6vLrnkklTAqqamRj/72c/0ox/9SI7jSJLefPNN3XTTTfrFL37Ra+0DgIFqoB7XMw0bNkyPPPJIl7aZPXt2L7UGAAaukSNH6txzz9Upp5yi448/XhMmTJBpeu/dqK2t1RNPPKE777xTW7duTa1/4IEHdNJJJ+mKK67olbZ985vf1Ntvv51aNk1Tt9xyi66//nqVlZVJkmKxmB599FF985vfVG1trSRp9+7duuiii/TOO+/IsqxeaRsADFQD+bie6ZFHHtGwYcM6XT9x4sRebA0A7J8y70ovKChQc3Nznz7//PnztX79+tRyKBTSXXfdpauvvlr5+fmSpObmZt177726+eabUzcur127VldeeaWeffbZPm0vAAx0/R2gBQDsm5/+9KddutY4cuTIXmwN5+tdZbgDYKz3o446StXV1frud7+rSy+9VHl5eXutt21bX/3qV3Xvvfd61r/00ks67bTTeqWN9957r6655prUcllZmV577TXNmDEjZ/2jjz6qz3/+86lln8+nlStXasqUKb3SPgAYSPaH4/r48eO1adMmSdK4ceO0cePGXnkeADhQfPjhhzrssMOyRjZpT21trc444wy99957qXUjRozQ1q1bsy7i76vVq1fr0EMPlW3bqXWPPvqoLrnkkpz1K1as0EknnaS6urrUugceeKBPwgIAMFAM5OP6/Pnz9dBDD6WWKyoqen36EwA4kC1cuFCnn366XNeVaZq66667dNNNN6Ue7+1+kRdeeEFnn312atnv9+vFF1/UKaeckrN+0aJFOvPMMxWPx1PrerOPCAD2R1/+8pd13333ScoO0Pb2cX3u3LlatGhRarnt6OIdmTlzpkaMGNHTzQKAAe3222/XHXfckVp++eWXNXfu3P5rUAbO17tuQEwdeMcdd2jNmjW66qqrOrwYLyWnnvrVr36lo446yrP+d7/7Xa+0LxaL6Qc/+IFn3U9/+tN2Q1aSdOmll+qyyy5LLScSCd1+++290j4AGGgG+nEdANB1s2bN6vTFeCl5Y8Ijjzzi2WbHjh16/fXXe7xtt912mydk9YUvfKHdkJWU7ND76U9/6ll3xx13eL4YAsCBbiAf1wEAPSccDutLX/qS9txv/fWvf11HH310n7bhe9/7nmf5v/7rv9q9aCNJp556qr797W971n33u9/tlbYBwP5o4cKFqb5z0zR122239Wt7zjjjjC79I2QFAAML5+tdNyCCVueee64CgUCXtrEsy3PXjST961//6slmefa7ZcuW1PL48eM7dbf77bff7umA/Mtf/qL6+vpeaSMADCQD/bgOAOgb06dP15FHHulZt2rVqh59jtraWj355JOpZcMwOnWDwxVXXKFx48alljdt2qQXX3yxR9sGAAeavjiuAwB61ve+973UFCBjx47NuqG4ty1btswzxXdBQYG+9a1vdbjdTTfdpIKCgtTyG2+8wd8cANDACNACAA4cnK93z4AIWnXXySef7Fmurq5WS0tLjz/PM88841m+4oorOnXX56RJk3TqqaemluPxuP7+97/3ePsA4EDRV8d1AEDfmTRpkme5qqqqR/f//PPPK5FIpJbnzp2riRMndridaZpZN088/fTTPdo2ADgQ9fZxHQDQc9555x39/Oc/Ty3/8pe/VGFhYZ+2oW3f+kUXXaSioqIOtysqKtK8efM86zhfB4D+D9ACAA4snK93z34dtCorK8ta1xsjRj3//POe5bPOOqvT25555pme5eeee65H2gQAB6K+Oq4DAPpOJBLxLJeWlvbo/jlXB4C+1dvHdQBAz4jH47rqqqtSU2zPmzdP5513Xp+3g/N1AOg5AyFACwA4sHC+3j37ddBq27ZtWesGDRrUo8+xa9cu7dy5M7UcDAY1Z86cTm9/4oknepaXLl3aU00DgANOXxzXAQB9x3VdvfPOO551baec2ldtz69POOGETm975JFHKhgMppa3b9+u3bt391TTAOCA0xfHdQBAz/jRj36kZcuWSUqGYn/xi1/0eRtc19WHH37oWdeV8/W2fesffPBBaqosADjYDJQALQDgwMH5evf5+rsB++LVV1/1LI8bN06BQKBHn6PtPJKTJ0/u0nPMmDHDs7xu3TolEgn5fPv1Sw8AvaIvjut7U1VVpa1bt6qhoUHFxcUaNGiQRo8e3anpYgEA2R544AFt3749tTxt2jQdc8wxPbb/eDyudevWeda1Pf/em2AwqEmTJmnlypWpdatWrdKQIUN6rI0AcCDp7eP63jQ3N2vjxo2qqalRXl6eysvLNWrUKE9gFgCQtHLlSv3whz9MLd99990aPnx4n7dj06ZNamlpSS0XFBRo7Nixnd5+3Lhxys/PT+2jublZW7Zs6dI+AOBAMRACtACAnhWNRrVhwwZVV1fL7/dr0KBBGjlypPLz8/vk+Tlf7779Ou3zwAMPeJY/8YlP9PhzrFmzxrM8ZsyYLm0/ZMgQhUKh1ND6sVhMFRUVmjJlSo+1EQAOFH1xXM+lsrJSM2bMyArXSlJ5eblOPvlkXXrppbrwwgtlWVaftAkA9ncPPfSQvvrVr6aWTdPU//3f//VoeHXDhg1KJBKp5by8PA0ePLhL+xgzZownaLVmzRqdcsopPdZGADhQ9MVxvT3nn3++Vq1a5TnmS1IoFNKxxx6r888/X1dffbWKiop6vS0AMNA5jqOrrrpKsVhMknTyySfr6quv7pe27Gvf+p5tMvezZs2ag+LCDQBkGigB2r2pr6/Xpk2bVFdXp8LCwtRNzPSnA0BuX/va17Rhw4ZUjmQPn8+nI488Uuecc46++tWv9upNwZyvd99+G7T6+9//rldeecWzbv78+T3+PJWVlZ7l0aNHd3kfI0eO1IYNGzz7JGgFAF59dVzPJRwO5wxZSVJNTY2eeeYZPfPMM5o0aZLuv/9+nXrqqX3SLgAYyD766CNt3rw5tRyPx1VbW6vly5frmWee8YSXAoGA7r33Xn3sYx/r0Ta0PVcfNWpUl/fRdpu2+wSAg8VAOK63Z8+d+21FIhEtWrRIixYt0p133qkf/OAH+o//+I8+aRMADFS/+MUv9NZbb0lKH6/7a6TunuhbHzVqlOfCDefrAA42AylA254jjjhCH374oRzH8awvLCzUiSeeqAsvvFBf/OIXGY0WADJk9rNkSiQSWrx4sRYvXqy7775bN954o2677bZeCa5yvt59+2XQqqamRtdcc41n3QUXXNArw9U3NTV5lgsKCrq8j7bbtN0nABzs+vK4vi/Wr1+vj33sY/qf//kffeMb3+jv5gBAv/rVr36le+65Z681hmHo4x//uH70ox9p9uzZPd4GztUBoOcMhOP6vqivr9fXv/51vf7663r44Yfl8+2XXV4AsE8qKir03e9+N7X8ne98R9OmTeu39nC+DgD7biAFaNuzdOnSnOubmpr0r3/9S//6179066236he/+IXmzZvXt40DgP1YOBzW97//fb366qv629/+psLCwh7dP+fr3Wf2dwO6ynEcXXbZZdq6dWtqXUlJSa/NRdz2gxAKhbq8j7y8vL3uEwAOZn19XM9UXFysiy66SPfff7/effddVVdXKx6Pq76+XqtWrdL999+vk046ybONbdu6/vrr9dhjj/V6+wBgfzdv3jzdcsstvXYxnnN1AOhbvX1cz+Tz+XTGGWfoZz/7mRYtWqSdO3cqEomopaVFmzZt0hNPPKFLL700K1D12GOP6etf/3qvtw8ABqIvf/nLam5uliRNmzZNN998c7+2h/N1ANg3Ay1Auy927typiy66SN/61rf6uykA0G8Mw9AJJ5ygH/7wh1qwYIG2bt2qlpYWRSIRbdu2TX/72990zTXXZJ03L1y4UJ/73Odk23aPtofz9e7b74JW3/rWt/SPf/zDs+63v/1tt+aL7Iy2c2IGAoEu76PtUJjhcHif2gQAB5K+Pq7v8ZOf/ETbtm3T448/riuvvFJHHnmkysvL5fP5VFxcrGnTpunKK6/Uq6++qieffFKlpaWpbV3X1VVXXaWdO3f2ahsBYH/35z//WSeddJJOOeUUrVu3rsf3z7k6APSt3j6u7/H5z39eFRUVWrBgga6//nqdcsopGjZsmILBoPLy8jR27FhdeOGF+uMf/6ilS5dqxowZnu1/85vf6G9/+1uvtQ8ABqL7779fL774oqTkBZx77723W+fHPYnzdQDYNwMtQJspFArpk5/8pH71q1/pjTfeUGVlpWKxmBobG7V+/Xo98sgjOvfcc7NG3/rpT3+qu+66q59aDQD956yzztLq1av1+uuv6+abb9YZZ5yhUaNGKS8vT8FgUCNHjtR5552n3/zmN1q7dq1OPPFEz/bPP/+8fvWrX/Vomzhf7779Kmj1i1/8Qj/72c8862666SZdfPHFvfacbVN7e+ZA7opoNLrXfQLAwao/jut7zJs3r9NDbH7605/WP/7xD08qu6WlRT/84Q97q3kAMOD9/Oc/l+u6qX8tLS3asmWLnnvuOV111VWeY+arr76qo48+Wu+++26PtoFzdQDoOQPhuL7HmWeeqdGjR3eqdubMmVq0aJEmT57sWX/LLbfIdd3eaB4ADDg7duzQjTfemFr+0pe+pJNPPrkfW5TE+ToAdN9ADNDu8c1vflNbt27Vs88+q6985Ss6/vjjNWTIEPn9fhUWFmrixIn6/Oc/r+eee06vvPKKRo0a5dn+5ptv1gcffNBPrQeA/nHCCSfokEMO6VTt6NGj9eKLL+r444/3rP/BD36glpaWHmsT5+vdt98ErR599FFdd911nnXz58/v9dRz24vwbVN9ndE2tdfTc2cCwP6ov47r3XXcccfppptu8qx79NFH5ThOP7UIAAaWvLw8jR49Wueee65+97vf6cMPP9Thhx+eeryurk4XXHCB6urqeuw5OVcHgN7TH8f17ho8eLDuu+8+z7ply5Zx8QbAQeNrX/ta6ng8fPhw/fjHP+7fBrXifB0AumegBmj3OP/88zVo0KBO1Z500klauHChBg8enFrnuq5nSkQAQLZQKKQ//OEP8vl8qXWVlZV64YUXeuw5OF/vvv0iaPXcc8/p8ssv99yJ+JnPfEa/+93vsoac7GltPwh7hujsirbbHCwfLgBoT38e1/fFN77xDVmWlVquqanptbv4AWB/N3nyZC1YsMAzFey2bdv0k5/8pMeeg3N1AOg7fXFc3xdz587VnDlzPOt6svMRAAaqv/zlL3rqqadSy/fcc49KS0v7r0EZOF8HgO4ZqAHa7po8eXLW94a///3vqqmp6acWAcD+YfLkyTr//PM963ozaMX5eucN+KDVyy+/rHnz5imRSKTWnXnmmfrTn/7kudjdW4YOHepZ3rp1a5f3sX379r3uEwAOJv19XN8XZWVlWRdv1qxZ00+tAYCBb/Dgwbrjjjs86x588MEe23/b8+pt27Z1eR9tt+FcHQDa19vH9X31sY99zLPMuTqAg8G3vvWt1P+fe+65uuiii/qxNV490bfO+TqAg81ADtDuiy9+8YsaMmRIatlxnNTUiACA9vVmXwfn6903oINWixcv1vnnn+8ZouyEE07QU0891WfzEE+dOtWzvHnz5i5tX1lZ6Wl/IBDQxIkTe6RtALC/GQjH9X2VeQe/JO3evbufWgIA+4dPf/rTntEKt2/frk2bNvXIvidOnOgZOjkcDnf5uNz2/H7atGk90jYAOFD15nF9X3GuDuBglDmF6/PPPy/DMDr8d9ppp3n2sWnTpqyapUuX7nPb2vatb9mypcv7aLsN5+sADnQDOUC7L0zT1Ny5cz3ruDECADrWm30dnK9334ANWn344Yc655xz1NTUlFp3xBFH6O9//7sKCgr6rB1tPwjr169XLBbr9ParVq3yLE+aNMlzMQgADhYD5bi+r/x+v2c5Ho/3U0sAYP9QWlqq8vJyz7qdO3f2yL79fr8mTZrkWbdy5cpObx+NRrVhwwbPuoPliyAAdFdvHtf3FefqADCwjBs3Tnl5eanl5ubmLoVzN23apJaWltRyQUFB1oUmADjQDOQA7b7ixggA6Lre7OvgfL37BmTQas2aNTrzzDNVW1ubWjd9+nT961//UklJSZ+2Zfjw4Ro+fHhqORqNasmSJZ3e/vXXX/csH3744T3VNADYbwyk4/q+ansRKXO4YwBA57T9crgv2p5fv/HGG53edsmSJYpGo6nlESNGHDRDGwNAT+rJ4/q+4FwdAAYWwzA0a9Ysz7qunK+37VufNWuWZ1RFAMD+hRsjAKDrerOvg/P17htwQytt2rRJZ5xxhiorK1PrJkyYoAULFvRbB9m5556r+++/P7W8YMECHX/88Z3adsGCBZ7lT37ykz3aNgAY6Abicb27otGo3nnnHc+6gyWZDQDd1djYqJqaGs+6YcOG9dj+zzvvPD3++OOp5QULFug73/lOp7blXB0Auq63j+v74rXXXvMsc64O4GDwzDPPdPlC9QcffKAbb7wxtTxs2DA98sgjnprJkyf3SPvOO+88LV68OLW8YMECXXLJJZ3alvN1ADiwcGMEAHRdb/d1cL7ePYbrum5/N2KPHTt26OSTT9b69etT60aNGqVXX31VEyZM6Ld2Pfvss/rUpz6VWh4/frw2bNjQYRpv/fr1mjJliva8xH6/X7t3797vRm8BgO4aqMf17nrooYc0f/781HIwGFRNTY3y8/P7r1EAMMA99thjni9mQ4YM0c6dO2WaPTO4bk1NjYYNG6ZEIiEpeRfOunXrNHHixL1u57quJk6cqI0bN6bWPf/88/rEJz7RI+0CgANVbx/Xu2vdunWaPn166u+BJP373//W6aef3o+tAoCBaeHChZ5ppsaNG+c5L+5JH374oWbPnp1aLiws1I4dO1RYWLjX7RobGzVixAg1Nzen1q1YsUIzZszolXYCwECxaNGiXgnQHnfccR0ee3vb5MmTPdcK7r33Xl199dX92CIAGNjq6uo0YcIEz7Sy999/v6688soeew7O17tnwIxoVVNTozPPPNPzB3bIkCFasGBBv1+MP/vsszV69Ght3bpVkrRx40b9/ve/7/ADfPvttyszx3bhhRcSsgJw0BjIx/Xu2Llzp2655RbPurPOOouQFQDsRTgc1m233eZZd9555/Xoxfjy8nJdcMEFeuKJJyQlA1S33367/vCHP+x1uwceeMBzMWncuHE644wzeqxdAHAg6ovjenfYtq2vfe1rnpDVoEGDdNJJJ/VjqwAAUnL6kKOPPjo1QnhTU5N+/OMf684779zrdj/+8Y89F22OO+64g+aiDYCD26mnntrlbXw+7+XeUCg04Po4Fi1a5LlWIEkf+9jH+qk1ALB/uPHGGz0hq0AgoHPOOadHn4Pz9e7p356wVo2Njfr4xz+uFStWpNaVlpbqhRde0PTp03v0uTZu3CjDMDz/OrpbJxgMZl1cv/HGG7Vy5cp2t3n00Uc9aXHLsnTHHXfsU9sBYH8xkI/rO3bs0G233aba2touPcfHP/5xbdu2LbXOMAzdfvvt+9ByANh/3HTTTVlTp3akpqZG559/vj766KPUOsuydP311+91u/Hjx3uO6Q8++GCHz3XHHXd4LvI//PDD+tOf/tRu/cqVKz13ekrS9773PQUCgQ6fCwAOBAP5uH7LLbd4nqMjLS0t+sIXvqAXXnghaz8c1wGg57Xtg1m4cGGH27S9SHPXXXfplVdeabd+0aJFuvvuuz3rfvCDH3SrvQCA/tfc3Kz//M//9Kw77LDDOhyNHAAOFHfddZeWLFnS6fpEIqEbbrhB999/v2f9tddeqxEjRux1W87X+8aACFqdf/75WR183/zmN1VVVaUXX3yxS/+6cuG8K6666irNnDkztVxbW6uTTz5Zf/jDHzx3TNbU1Oh73/uevvCFL3i2v+aaa3TIIYf0StsAYKAZyMf1aDSqO++8U2PHjtXnP/95Pfnkk9q+fXvO2nXr1um73/2uDj/8cH3wwQeex77xjW9ozpw5Pdo2ABioXnjhBR1zzDE69thj9bOf/UxLly7NOYy967pavXq1vv/972vq1Kl68cUXPY9ff/31Ouyww3q8fTNmzNCXvvQlz7rLLrtMt956q+fvSDwe14MPPqiTTjrJcyfQrFmzdPnll/d4uwBgoBrIx/U//vGPmj59us466yzdd999+uijj+Q4Tlbd7t279dvf/lazZs3KCtcee+yx+upXv9qj7QIAdN/HP/5xnXXWWanleDyus88+W/fcc49aWlpS65ubm/Xzn/9cH//4xz1/lz7xiU8w6gkA9JKuXpD/xje+0W5/ei5VVVU6//zz9eGHH3rWMzgFgIPJP//5Tx111FE68cQTdc8992j58uWejMke9fX1+tOf/qSjjz5aP/vZzzyPTZo0SbfeemuvtI/z9a4z3My57fqrEYbRY/t6+eWXNXfu3HYf37hxY9aUVRUVFRo/fnyH+161apVOOukk1dTUeNYXFhZq0qRJCofDqqioyOqcPOaYY7Rw4ULl5eV1+ucAgP3ZQD6u56qXklOLDB06VMXFxQqHw9qxY4d2796dcx/z5s3TY4891u9TpABAX8kVOA0EAho1apRKS0sVCATU2NioLVu2qLGxMec+Lr/8cj3wwAMdHjvHjx+vTZs2pZZ///vfa/78+R22saWlRaeeeqrefffdrHZOmDBBwWBQGzZsUFNTk+fxwYMH6/XXX+emCAAHlYF8XG9bL0kFBQUaOXKkSkpK5Lquqqqqsmr2mDZtml599VUNHjx4r+0CgIPZwoULddppp6WWx40b1+GsD3u07fPpqN9mj127dun4449XRUWFZ31eXp4mTpwo13W1YcMGRSIRz+OTJk3Sm2++qSFDhnSqfQBwMOrL47phGAoGgzrnnHP02c9+VieeeGLOvvgtW7boscce089+9jPt3LnT89gFF1ygp556qlPtA4ADwdy5c7Vo0SLPumAwqNGjR6ukpESWZam6ulobN27MebPZ8OHD9corr2jKlCkdPhfn633D13EJ9pg+fbpeeuklfepTn/J06DU1NWV1UO5xxhln6C9/+QshKwAY4Kqrq1VdXb3XmmAwqP/+7//W9ddf36NhMgDYH8VisawvXbkUFxfrrrvu0rXXXturx878/Hz961//0rx58/TSSy952rlmzZqc24wfP17PPvssISsA0MA7rmdqbm7W2rVrO6y7/PLL9X//938qLCzsg1YBALpi2LBhevnll/WpT33K05ceDoe1YsWKnNscfvjhevbZZw+6izYAMNBFo1E9/fTTevrppyUlvyOMGDFCJSUlisfj2rVrV7ujXp188sl69NFH+7C1ADAwRaNRrV+/vsO6T3ziE/r973+voUOH9mp7OF/vGobi6KLZs2dr2bJl+s53vqOysrJ266ZMmaL77rtPL7zwgkpLS/uugQCAvRo2bJjuueceXXDBBRo2bFinthk3bpy++93vasOGDfrmN79JyArAQedPf/qT7r77bp1xxhkqLi7usN4wDM2aNUs/+clPtG7dOn3lK1/pk2NneXm5FixYoHvvvVeTJ0/ea93NN9+sZcuW9cpUhgAw0A3k4/pPf/pTzZ8/X5MnT+7Uc5SVlenKK6/U+++/rwcffJCQFQAMYOPGjdPbb7+tu+++WyNHjmy3buTIkfrxj3+sxYsXa8yYMX3YQgBAdzQ0NGjNmjV6++239f777+cMWZmmqZtuukn//ve/GZwCwEHnlltu0bXXXquZM2fKsqwO6wsLCzVv3jwtWrRIzz//fK+HrPbgfL3zBsT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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from matplotlib import cm\n", + "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", + "ax.set_xlim(2, 5)\n", + "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV^{2}]\")\n", + "ax.set_xlabel(R\"Intensity [a. u.]\")\n", + "ax.set_yticks([])\n", + "\n", + "bins = 150\n", + "phsp_projection = np.real(phsp[\"m_01\"])**2\n", + "ax.hist(\n", + " phsp_projection,\n", + " weights=total_intensities,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=\"hotpink\",\n", + " label=\"Full intensity\",\n", + ")\n", + "ax.hist(\n", + " phsp_projection,\n", + " weights=total_intensities_1,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=\"grey\",\n", + " label=\"Full intensity\",\n", + ")\n", + "ax.hist(\n", + " len(sub_intensities) * [phsp_projection],\n", + " weights=list(sub_intensities.values()),\n", + " bins=bins,\n", + " alpha=0.6,\n", + " label=[Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ $F$ vector\" for p in sub_intensities],\n", + " histtype=\"step\",\n", + ")\n", + "\n", + "ax.hist(\n", + " len(sub_intensities_bw) * [phsp_projection],\n", + " weights=list(sub_intensities_bw.values()),\n", + " bins=bins,\n", + " alpha=0.6,\n", + " label=[Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\" for p in sub_intensities],\n", + " histtype=\"step\",\n", + " ls=\"dotted\",\n", + ")\n", + "\n", + "fig.legend(loc=\"upper right\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dynamics expressions" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{m_{N(1650)^{+}} w_{N(1650)^{+}}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2} - m_{N(1650)^{+}} \\Gamma_s\\left(m_{01}^{2}\\right)} + \\frac{m_{N(Fakestar2)^+} w_{N(Fakestar2)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2} - m_{N(Fakestar2)^+} \\Gamma_s\\left(m_{01}^{2}\\right)}$" + ], + "text/plain": [ + "m_{N(1650)^{+}}*w_{N(1650)^{+}}/(-m_01**2 + m_{N(1650)^{+}}**2 - m_{N(1650)^{+}}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(1650)^{+}})) + m_{N(Fakestar2)^+}*w_{N(Fakestar2)^+}/(-m_01**2 + m_{N(Fakestar2)^+}**2 - m_{N(Fakestar2)^+}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(Fakestar2)^+}))" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamics_expr_rel_bw, *_ = dynamics_expressions_rel_bw.values()\n", + "dynamics_expr_rel_bw" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\frac{\\beta_{N(1650)^{+}} g_{N(1650)^{+}}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\beta_{N(Fakestar2)^+} g_{N(Fakestar2)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1650)^{+}}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar2)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1}$" + ], + "text/plain": [ + "(\\beta_{N(1650)^{+}}*g_{N(1650)^{+}}/(-m_01**2 + m_{N(1650)^{+}}**2) + \\beta_{N(Fakestar2)^+}*g_{N(Fakestar2)^+}/(-m_01**2 + m_{N(Fakestar2)^+}**2))/(-(g_{N(1650)^{+}}**2/(-m_01**2 + m_{N(1650)^{+}}**2) + g_{N(Fakestar2)^+}**2/(-m_01**2 + m_{N(Fakestar2)^+}**2))*PhaseSpaceCM(m_01**2, m_0, m_1) + 1)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamics_expr_fvector, *_ = dynamics_expressions_fvector.values()\n", + "dynamics_expr_fvector" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "dynamics_func_bw = create_parametrized_function(\n", + " expression=dynamics_expr_rel_bw.doit(),\n", + " backend=\"numpy\",\n", + " parameters=model_rel_bw.parameter_defaults,\n", + " use_cse=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "dynamics_func_fvector = create_parametrized_function(\n", + " expression=dynamics_expr_fvector.doit(),\n", + " backend=\"numpy\",\n", + " parameters=model_fvector.parameter_defaults,\n", + " use_cse=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Weighted data with $F$ vector " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AAEwiOAEAAJhEcAIAADCJ4AQAAGASwQkAAMAkghMAAIBJBCcAAACTCE4AAAAmEZwAAABMIjgBAACYRHACAAAwieAEAABgEsEJAADAJIITAACASQQnAAAAkwhOAAAAJhGcAAAATCI4AQAAmERwAgAAMIngBAAAYBLBCQAAwCSCEwAAgEkEJwAAAJMITgAAACYRnAAAAEwiOAEAAJjUwEyn5ORklwd++umn5e/v7/LrAHhO+KQV5doOTe/ngUoAoGYyFZzS0tIUFxenRo0amRp0w4YNGjt2LMEJAADUKaaCkyQtXbpUgYGBpvo2b97cVL/169fr5ZdfVk5OjvLy8rR06VIlJiZW2v+jjz7SnDlzlJubK5vNpptvvlnPPvusEhISHH2effZZTZ061el1HTt21J49e0zVBAAAUBlTa5zmz58vq9VqetC33npLQUFBl+1XWlqqqKgozZ4929S469evV58+fbRy5Url5OSod+/e6t+/v7Zt2+bU7+abb1ZeXp7jsWHDBtO1AwAAVMbUjFNSUpJLg/7P//yPqX59+/ZV3759TY+blpbm9HzatGn6+OOP9cknnygmJsbR3qBBAwUHB5seFwAAwAzTt+pqIrvdrtOnT5dbS7Vv3z6FhobK19dXcXFxSk1NVZs2bSodx2azyWazOZ4XFxdXWc2Ap1y88JtF3wDgOrcFp6SkJB05ckSff/65u4a8rJkzZ6qkpEQPPvigoy02NlYZGRnq2LGj8vLyNHXqVN1xxx3auXNnpWuvUlNTy62LAnAegQsA/sttwal169by8qq+baEWLlyoqVOn6uOPP3ZatP7bW3+RkZGKjY1V27Zt9f7772vEiBEVjpWSkuK05UJxcbHCwsKqrngAAFAruS04TZs2zV1DXdaiRYs0cuRILVmyRPHx8Zfs26JFC11//fXav39/pX18fHzk4+Pj7jIBAEAdU+vWOL333nsaPny4Fi1apH79Ln/LoKSkRAcOHNAjjzxSDdWhruE2FQDgt1wOTsOHD7/k8Xnz5pkeq6SkxGkm6ODBg8rNzZW/v7/atGmjlJQUHT16VJmZmZLO355LSkrSa6+9ptjYWOXn50uSGjdu7NguYcKECerfv7/atm2rY8eOacqUKfL29tbgwYNdPVWgTqtol3AAwKW5HJxOnTrl9PzcuXPauXOnCgsLdeedd7o01pYtW9S7d2/H8wvrjJKSkpSRkaG8vDwdPnzYcfztt9/Wr7/+qjFjxmjMmDGO9gv9JemHH37Q4MGDdfLkSbVq1Uo9evTQpk2b1KpVK1dPFQAAwInLwWnp0qXl2ux2u0aPHq3rrrvOpbF69eolwzAqPX4hDF2wdu3ay465aNEil2oAAAAwyy0fg/Py8lJycrJeffVVdwwHAABQI7lt/4ADBw7o119/dddwAAAANY7Lt+p+u9+RJBmGoby8PK1YscLlr2YBUPtUtKicTxsCqC9cDk4Xf6Gul5eXWrVqpVmzZl32E3cAAAC1mcvB6YsvvqiKOgAAAGq8WrcBJoCah9t3AOoLtwWnp556Svn5+S5tgAn8FhsyAgBqOrcFp6NHj+rIkSPuGg4AAKDGcVtwWrBggbuGAgAAqJHcto8TAABAXXdFM06lpaVat26dDh8+rLNnzzod++tf/+qWwgBcOdaLAUDVuKJ9nO699179/PPPKi0tlb+/v3788Uc1adJEgYGBBCcAAFBnuRycHnvsMfXv31/p6emyWq3atGmTGjZsqIcffljjxo2rihoB1EIXz3qxPQGAusDlNU65ubl6/PHH5eXlJW9vb9lsNoWFhWnGjBl66qmnqqJGAACAGsHlGaeGDRvKy+t83goMDNThw4d14403ymq1sh0B4CGsaQKA6uFycIqJidHXX3+tDh06qGfPnpo8ebJ+/PFH/f3vf1enTp2qokYAAIAaweXgNG3aNJ0+fVqS9OKLL2rIkCEaPXq0OnTowK7hQDVgdgkAPMfl4NS1a1fHnwMDA7Vq1Sq3FgQAAFBTsQEmAACASaaC0y233KJTp06ZHrRHjx46evToFRcFAABQE5m6VZebm6tvvvlG/v7+pgbNzc2VzWa7qsIAAABqGtNrnO666y4ZhmGqr8ViueKCAAAAaipTwengwYMuD3zttde6/BoAAICazFRwatu2bVXXAQAAUOO5vB0BAFyJivaf4vvrANQ2bEcAAABgEsEJAADAJIITAACASS6vcUpKStKIESP0u9/9rirqAVCPsO4JQG3j8oxTUVGR4uPj1aFDB02bNo0dwgEAQL3hcnBatmyZjh49qtGjR2vx4sUKDw9X37599cEHH+jcuXNVUSOAeiR80gqnBwDUJFe0HUGrVq2UnJys5ORkbd26VfPnz9cjjzyiZs2a6eGHH9Zf/vIXdejQwd21AkCFuOUHoLpc1T5OeXl5WrNmjdasWSNvb2/de++92rFjh2666SbNmDFDjz32mLvqBGoEfkHXHvy3AlAVXL5Vd+7cOX344Yf6/e9/r7Zt22rJkiUaP368jh07pgULFuizzz7T+++/r+eee64q6gUAAPAYl2ecQkJCZLfbNXjwYG3evFnR0dHl+vTu3VstWrRwQ3kAAAA1h8vB6dVXX9UDDzwgX1/fSvu0aNHiir4YGAAAoCZz+VbdF198UeGn50pLSzV8+HC3FAUAVYFP7AG4Wi4HpwULFuiXX34p1/7LL78oMzPTLUUBAADURKaDU3FxsYqKimQYhk6fPq3i4mLH49SpU1q5cqUCAwNd+uHr169X//79FRoaKovFomXLll32NWvXrtUtt9wiHx8ftW/fXhkZGeX6zJ49W+Hh4fL19VVsbKw2b97sUl0AAAAVMR2cWrRoIX9/f1ksFl1//fVq2bKl4xEQEKDhw4drzJgxLv3w0tJSRUVFafbs2ab6Hzx4UP369VPv3r2Vm5ur8ePHa+TIkfr0008dfRYvXqzk5GRNmTJFW7duVVRUlBISEnT8+HGXagMAALiY6cXhX3zxhQzD0J133qkPP/xQ/v7+jmONGjVS27ZtFRoa6tIP79u3r/r27Wu6f3p6uiIiIjRr1ixJ0o033qgNGzbo1VdfVUJCgiTplVde0ahRozRs2DDHa1asWKF58+Zp0qRJLtUHwPPYjwlATWI6OPXs2VPS+VmfNm3ayGKxVFlRlcnOzlZ8fLxTW0JCgsaPHy9JOnv2rHJycpSSkuI47uXlpfj4eGVnZ1c6rs1mk81mczwvLi52b+EA3IqF3QA8xVRw2r59uzp16iQvLy8VFRVpx44dlfaNjIx0W3EXy8/PV1BQkFNbUFCQiouL9csvv+jUqVMqKyursM+ePXsqHTc1NVVTp06tkppRMX7xAQBqI1PBKTo6Wvn5+QoMDFR0dLQsFosMwyjXz2KxqKyszO1FVrWUlBQlJyc7nhcXFyssLMyDFQEAgJrIVHA6ePCgWrVq5fizpwQHB6ugoMCpraCgQH5+fmrcuLG8vb3l7e1dYZ/g4OBKx/Xx8ZGPj0+V1AxcDWbmqhbrpwC4ylRwatu2bYV/rm5xcXFauXKlU9uaNWsUFxcn6fwi9S5duigrK0uJiYmSJLvdrqysLI0dO7a6ywUAAHXMFW2AuWLFf/+VNnHiRLVo0ULdu3fX999/79JYJSUlys3NVW5urqTzs1m5ubk6fPiwpPO30IYMGeLo/+c//1nfffedJk6cqD179ujNN9/U+++/r8cee8zRJzk5We+8844WLFig3bt3a/To0SotLXV8yg4AAOBKuRycpk2bpsaNG0s6/ym3N954QzNmzFBAQIBTgDFjy5YtiomJUUxMjKTzoScmJkaTJ0+WJOXl5TlClCRFRERoxYoVWrNmjaKiojRr1iy9++67jq0IJGngwIGaOXOmJk+erOjoaOXm5mrVqlXlFowDAAC4ymJUtMr7Epo0aaI9e/aoTZs2evLJJ5WXl6fMzEzt2rVLvXr10okTJ6qq1mpTXFwsq9WqoqIi+fn5ebqcOqkurd2pyjUxdek61WasewLqNld+77s849SsWTOdPHlSkrR69Wr16dNHkuTr61vhd9gBAADUFaY3wLygT58+GjlypGJiYvTtt9/q3nvvlSTt2rVL4eHh7q4PAACgxnB5xmn27NmKi4vTiRMn9OGHH+qaa66RJOXk5Gjw4MFuLxAAAKCmcHnGqUWLFnrjjTfKtbPzNgAAqOtcDk6SVFhYqM2bN+v48eOy2+2OdovFokceecRtxQEAANQkLgenTz75RA899JBKSkrk5+fn9GW/BCcAAFCXubzG6fHHH9fw4cNVUlKiwsJCnTp1yvH46aefqqJGAACAGsHl4HT06FH99a9/VZMmTaqiHgAAgBrL5eCUkJCgLVu2VEUtAAAANZrLa5z69eunJ554Qv/5z3/UuXNnNWzY0On4fffd57biAKAmuHgHd3YSB+ovl4PTqFGjJEnPPfdcuWMWi0VlZWVXXxUAAEAN5HJw+u32AwAAAPWJy2ucfuvMmTPuqgMAAKDGczk4lZWV6fnnn1fr1q3VrFkzfffdd5KkZ555RnPnznV7gQAAADWFy7fqXnzxRS1YsEAzZsxwrHeSpE6dOiktLU0jRoxwa4FATXfxwmGJxcMAUFe5POOUmZmpt99+Ww899JC8vb0d7VFRUdqzZ49biwMAAKhJXJ5xOnr0qNq3b1+u3W6369y5c24pCgBqMmYZgfrL5eB000036csvv1Tbtm2d2j/44APFxMS4rTAAqO3Y/wmoe1wOTpMnT1ZSUpKOHj0qu92ujz76SHv37lVmZqaWL19eFTUCAADUCC4HpwEDBuiTTz7Rc889p6ZNm2ry5Mm65ZZb9Mknn6hPnz5VUSNQb1R0Cwi1A//tgPrB5eAkSXfccYfWrFnj7loAAABqNJc/VdeuXTudPHmyXHthYaHatWvnlqIAAABqIpdnnA4dOlTh99HZbDYdPXrULUUB9QG3dgCg9jEdnP75z386/vzpp5/KarU6npeVlSkrK0vh4eFuLQ6oKwhJAFA3mA5OiYmJkiSLxaKkpCSnYw0bNlR4eLhmzZrl1uKA2oqgBAB1k+ngZLfbJUkRERH6+uuvFRAQUGVFAQAA1EQur3E6ePBgVdQBAABQ413RdgRZWVnKysrS8ePHHTNRF8ybN88thQEAANQ0LgenqVOn6rnnnlPXrl0VEhIii8VSFXUBAADUOC4Hp/T0dGVkZOiRRx6pinoAAABqLJc3wDx79qy6d+9eFbUAAADUaC4Hp5EjR2rhwoVVUQsAAECN5vKtujNnzujtt9/WZ599psjISDVs2NDp+CuvvOK24gAAAGoSl4PT9u3bFR0dLUnauXOn0zEWigMAgLrM5eD0xRdfVEUdAAAANZ7La5wAAADqK9MzTvfff7+pfh999NEVFwMAAFCTmQ5OVqu1KusAgDqvoi9/PjS9nwcqAXClTAen+fPnV1kRs2fP1ssvv6z8/HxFRUXpb3/7m7p161Zh3169emndunXl2u+9916tWHH+L6WhQ4dqwYIFTscTEhK0atUq9xcPAADqjSv6rjp3Wrx4sZKTk5Wenq7Y2FilpaUpISFBe/fuVWBgYLn+H330kc6ePet4fvLkSUVFRemBBx5w6nfPPfc4hT0fH5+qOwkAuEIXz0IxAwXUbB4PTq+88opGjRqlYcOGSTr/lS4rVqzQvHnzNGnSpHL9/f39nZ4vWrRITZo0KRecfHx8FBwcbKoGm80mm83meF5cXOzqaQAAgHrAo5+qO3v2rHJychQfH+9o8/LyUnx8vLKzs02NMXfuXA0aNEhNmzZ1al+7dq0CAwPVsWNHjR49WidPnqx0jNTUVFmtVscjLCzsyk4IAADUaR6dcfrxxx9VVlamoKAgp/agoCDt2bPnsq/fvHmzdu7cqblz5zq133PPPbr//vsVERGhAwcO6KmnnlLfvn2VnZ0tb2/vcuOkpKQoOTnZ8by4uJjw5EYVLYgFAKA28vituqsxd+5cde7cudxC8kGDBjn+3LlzZ0VGRuq6667T2rVrddddd5Ubx8fHhzVQAADgsjx6qy4gIEDe3t4qKChwai8oKLjs+qTS0lItWrRII0aMuOzPadeunQICArR///6rqhcAANRvHg1OjRo1UpcuXZSVleVos9vtysrKUlxc3CVfu2TJEtlsNj388MOX/Tk//PCDTp48qZCQkKuuGQAA1F8e/8qV5ORkvfPOO1qwYIF2796t0aNHq7S01PEpuyFDhiglJaXc6+bOnavExERdc801Tu0lJSV64okntGnTJh06dEhZWVkaMGCA2rdvr4SEhGo5JwAAUDd5fI3TwIEDdeLECU2ePFn5+fmKjo7WqlWrHAvGDx8+LC8v53y3d+9ebdiwQatXry43nre3t7Zv364FCxaosLBQoaGhuvvuu/X888+zjgkAAFwVi2EYhqeLqGmKi4tltVpVVFQkPz8/T5dT6/GpOsA8NsAEqp8rv/c9PuOEuoegBACoqzy+xgkAAKC2YMYJAGqQimZsuX0H1BzMOAEAAJhEcAIAADCJ4AQAAGASwQkAAMAkghMAAIBJBCcAAACT2I4AAGo4tigAag5mnAAAAEwiOAEAAJhEcAIAADCJ4AQAAGASwQkAAMAkghMAAIBJbEcAALXQxVsUsD0BUD2YcQIAADCJ4AQAAGASwQkAAMAkghMAAIBJLA4HgDqA77MDqgczTgAAACYRnAAAAEwiOAEAAJhEcAIAADCJxeG4KhUtSAUAoK5ixgkAAMAkghMAAIBJBCcAAACTCE4AAAAmEZwAAABMIjgBAACYRHACAAAwieAEAABgEsEJAADAJIITAACASQQnAAAAk2pEcJo9e7bCw8Pl6+ur2NhYbd68udK+GRkZslgsTg9fX1+nPoZhaPLkyQoJCVHjxo0VHx+vffv2VfVpAACAOs7jwWnx4sVKTk7WlClTtHXrVkVFRSkhIUHHjx+v9DV+fn7Ky8tzPL7//nun4zNmzNDrr7+u9PR0ffXVV2ratKkSEhJ05syZqj4dAABQh1kMwzA8WUBsbKxuvfVWvfHGG5Iku92usLAwPfroo5o0aVK5/hkZGRo/frwKCwsrHM8wDIWGhurxxx/XhAkTJElFRUUKCgpSRkaGBg0aVO41NptNNpvN8by4uFhhYWEqKiqSn5+fG86y7gqftMLTJQBwwaHp/TxdAlDjFBcXy2q1mvq979EZp7NnzyonJ0fx8fGONi8vL8XHxys7O7vS15WUlKht27YKCwvTgAEDtGvXLsexgwcPKj8/32lMq9Wq2NjYSsdMTU2V1Wp1PMLCwtxwdnVT+KQVTg8AAOqTBp784T/++KPKysoUFBTk1B4UFKQ9e/ZU+JqOHTtq3rx5ioyMVFFRkWbOnKnu3btr165duvbaa5Wfn+8Y4+IxLxy7WEpKipKTkx3PL8w4AUBdc/E/eJiBAlzj0eB0JeLi4hQXF+d43r17d914441666239Pzzz1/RmD4+PvLx8XFXiQAAoI7y6K26gIAAeXt7q6CgwKm9oKBAwcHBpsZo2LChYmJitH//fklyvO5qxgQAAKiIR4NTo0aN1KVLF2VlZTna7Ha7srKynGaVLqWsrEw7duxQSEiIJCkiIkLBwcFOYxYXF+urr74yPSYAAEBFPH6rLjk5WUlJSeratau6deumtLQ0lZaWatiwYZKkIUOGqHXr1kpNTZUkPffcc7rtttvUvn17FRYW6uWXX9b333+vkSNHSpIsFovGjx+vF154QR06dFBERISeeeYZhYaGKjEx0VOnCQAA6gCPB6eBAwfqxIkTmjx5svLz8xUdHa1Vq1Y5FncfPnxYXl7/nRg7deqURo0apfz8fLVs2VJdunTRxo0bddNNNzn6TJw4UaWlpfrTn/6kwsJC9ejRQ6tWrSq3USYAAIArPL6PU03kyn4O9Q1bEAB1C5+qA2rRPk4AAAC1CcEJAADAJIITAACASQQnAAAAkzz+qToAgOdU9IEPFowDlWPGCQAAwCSCEwAAgEncqkOl2LMJAABnzDgBAACYRHACAAAwieAEAABgEsEJAADAJBaHAwCcsLcTUDlmnAAAAEwiOAEAAJhEcAIAADCJ4AQAAGASwQkAAMAkghMAAIBJbEcAALisi7coYHsC1FcEJwCAy9jrCfUVt+oAAABMIjgBAACYRHACAAAwieAEAABgEovDIanihZ4AAMAZM04AAAAmEZwAAABMIjgBAACYxBonAIBbsLs46gNmnAAAAEwiOAEAAJhEcAIAADCJNU71FPs2AahqfBEw6iJmnAAAAEwiOAEAAJhEcAIAADCJ4AQAAGBSjQhOs2fPVnh4uHx9fRUbG6vNmzdX2vedd97RHXfcoZYtW6ply5aKj48v13/o0KGyWCxOj3vuuaeqTwMAcBnhk1aUewC1icc/Vbd48WIlJycrPT1dsbGxSktLU0JCgvbu3avAwMBy/deuXavBgwere/fu8vX11UsvvaS7775bu3btUuvWrR397rnnHs2fP9/x3MfHp1rOpybiLyYAANzD4zNOr7zyikaNGqVhw4bppptuUnp6upo0aaJ58+ZV2P///u//9Je//EXR0dG64YYb9O6778putysrK8upn4+Pj4KDgx2Pli1bVlqDzWZTcXGx0wMAAOBiHg1OZ8+eVU5OjuLj4x1tXl5eio+PV3Z2tqkxfv75Z507d07+/v5O7WvXrlVgYKA6duyo0aNH6+TJk5WOkZqaKqvV6niEhYVd2QkBAIA6zaPB6ccff1RZWZmCgoKc2oOCgpSfn29qjCeffFKhoaFO4euee+5RZmamsrKy9NJLL2ndunXq27evysrKKhwjJSVFRUVFjseRI0eu/KQAAECd5fE1Tldj+vTpWrRokdauXStfX19H+6BBgxx/7ty5syIjI3Xddddp7dq1uuuuu8qN4+PjU6/XQAEAAHM8GpwCAgLk7e2tgoICp/aCggIFBwdf8rUzZ87U9OnT9dlnnykyMvKSfdu1a6eAgADt37+/wuAEAPCciz/AwteyoCbz6K26Ro0aqUuXLk4Luy8s9I6Li6v0dTNmzNDzzz+vVatWqWvXrpf9OT/88INOnjypkJAQt9QNAADqJ49/qi45OVnvvPOOFixYoN27d2v06NEqLS3VsGHDJElDhgxRSkqKo/9LL72kZ555RvPmzVN4eLjy8/OVn5+vkpISSVJJSYmeeOIJbdq0SYcOHVJWVpYGDBig9u3bKyEhwSPnCAAA6gaPr3EaOHCgTpw4ocmTJys/P1/R0dFatWqVY8H44cOH5eX133w3Z84cnT17Vn/84x+dxpkyZYqeffZZeXt7a/v27VqwYIEKCwsVGhqqu+++W88//zzrmAAAwFWxGIZheLqImqa4uFhWq1VFRUXy8/PzdDkuYbNLAHUR655QlVz5ve/xW3UAAAC1BcEJAADAJIITAACASR5fHA4AwOWYWb/JOihUB2acAAAATCI4AQAAmMStulqO7QcAAKg+zDgBAACYRHACAAAwiVt1AIA6wezSBT59h6vBjBMAAIBJBCcAAACTCE4AAAAmscYJAFCvXLwWijVPcAXBqRZhzyYAcL+K/m4lTKEyBCcAAC7CrBQqwxonAAAAk5hxAgDgMridhwuYcQIAADCJGScAAK4As1D1EzNOAAAAJjHjBACAm/BpvLqP4FRDsWcTAAA1D8EJAIAqwjqouofgBABANSJM1W4EJwAAPIy1UbUHwamGYE0TAOACZqVqLoITAAC1FDNV1Y/gBABALcCdiZqBDTABAABMYsYJAIA6grVRVY8ZJwAAAJOYcQIAoA5jAbl7EZw8gAV+AABPudLfQQSu8whOAADgslg/dR7BCQAAXJH6GKYITgAAwG3M3AqszeGK4AQAAKpVbZ6pqhHbEcyePVvh4eHy9fVVbGysNm/efMn+S5Ys0Q033CBfX1917txZK1eudDpuGIYmT56skJAQNW7cWPHx8dq3b19VngIAALgK4ZNWOD1qKo8Hp8WLFys5OVlTpkzR1q1bFRUVpYSEBB0/frzC/hs3btTgwYM1YsQIbdu2TYmJiUpMTNTOnTsdfWbMmKHXX39d6enp+uqrr9S0aVMlJCTozJkz1XVaAADgKlwcpGpKmLIYhmF4soDY2FjdeuuteuONNyRJdrtdYWFhevTRRzVp0qRy/QcOHKjS0lItX77c0XbbbbcpOjpa6enpMgxDoaGhevzxxzVhwgRJUlFRkYKCgpSRkaFBgwaVG9Nms8lmszmeFxUVqU2bNjpy5Ij8/PzcfcrqNOVTt48JAEBdt3NqQpWMW1xcrLCwMBUWFspqtV66s+FBNpvN8Pb2NpYuXerUPmTIEOO+++6r8DVhYWHGq6++6tQ2efJkIzIy0jAMwzhw4IAhydi2bZtTn9/97nfGX//61wrHnDJliiGJBw8ePHjw4FGPH0eOHLlsdvHo4vAff/xRZWVlCgoKcmoPCgrSnj17KnxNfn5+hf3z8/Mdxy+0VdbnYikpKUpOTnY8t9vt+umnn3TNNdfIYrG4dlL12IXEXlUzdagc195zuPaewXX3nLp47Q3D0OnTpxUaGnrZvnyqTpKPj498fHyc2lq0aOGZYuoAPz+/OvN/ptqGa+85XHvP4Lp7Tl279pe9Rff/8+ji8ICAAHl7e6ugoMCpvaCgQMHBwRW+Jjg4+JL9L/yvK2MCAACY4dHg1KhRI3Xp0kVZWVmONrvdrqysLMXFxVX4mri4OKf+krRmzRpH/4iICAUHBzv1KS4u1ldffVXpmAAAAGZ4/FZdcnKykpKS1LVrV3Xr1k1paWkqLS3VsGHDJElDhgxR69atlZqaKkkaN26cevbsqVmzZqlfv35atGiRtmzZorfffluSZLFYNH78eL3wwgvq0KGDIiIi9Mwzzyg0NFSJiYmeOs16wcfHR1OmTCl32xNVj2vvOVx7z+C6e059v/Ye345Akt544w29/PLLys/PV3R0tF5//XXFxsZKknr16qXw8HBlZGQ4+i9ZskRPP/20Dh06pA4dOmjGjBm69957HccNw9CUKVP09ttvq7CwUD169NCbb76p66+/vrpPDQAA1CE1IjgBAADUBh7fORwAAKC2IDgBAACYRHACAAAwieAEAABgEsEJpqSmpurWW29V8+bNFRgYqMTERO3du/eSr8nIyJDFYnF6+Pr6VlPFdcecOXMUGRnp2KU3Li5O//rXvy75miVLluiGG26Qr6+vOnfurJUrV1ZTtXWLq9ee93zVmD59umOrmUvhfe9+Zq59fXvfE5xgyrp16zRmzBht2rRJa9as0blz53T33XertLT0kq/z8/NTXl6e4/H9999XU8V1x7XXXqvp06crJydHW7Zs0Z133qkBAwZo165dFfbfuHGjBg8erBEjRmjbtm1KTExUYmKidu7cWc2V136uXnuJ97y7ff3113rrrbcUGRl5yX68793P7LWX6tn7/rJfAwxU4Pjx44YkY926dZX2mT9/vmG1WquvqHqkZcuWxrvvvlvhsQcffNDo16+fU1tsbKzxv//7v9VRWp13qWvPe969Tp8+bXTo0MFYs2aN0bNnT2PcuHGV9uV9716uXPv69r5nxglXpKioSJLk7+9/yX4lJSVq27atwsLCLvsvdVxeWVmZFi1apNLS0kq/Qig7O1vx8fFObQkJCcrOzq6OEussM9de4j3vTmPGjFG/fv3KvZ8rwvvevVy59lL9et97/CtXUPvY7XaNHz9et99+uzp16lRpv44dO2revHmKjIxUUVGRZs6cqe7du2vXrl269tprq7Hi2m/Hjh2Ki4vTmTNn1KxZMy1dulQ33XRThX3z8/MVFBTk1BYUFKT8/PzqKLXOceXa8553n0WLFmnr1q36+uuvTfXnfe8+rl77+va+JzjBZWPGjNHOnTu1YcOGS/aLi4tz+pd59+7ddeONN+qtt97S888/X9Vl1ikdO3ZUbm6uioqK9MEHHygpKUnr1q2r9Bc43MeVa8973j2OHDmicePGac2aNXV6kXFNdCXXvr697wlOcMnYsWO1fPlyrV+/3uV/STRs2FAxMTHav39/FVVXdzVq1Ejt27eXJHXp0kVff/21XnvtNb311lvl+gYHB6ugoMCpraCgQMHBwdVSa13jyrW/GO/5K5OTk6Pjx4/rlltucbSVlZVp/fr1euONN2Sz2eTt7e30Gt737nEl1/5idf19zxonmGIYhsaOHaulS5fq888/V0REhMtjlJWVaceOHQoJCamCCusXu90um81W4bG4uDhlZWU5ta1Zs+aS63Jg3qWu/cV4z1+Zu+66Szt27FBubq7j0bVrVz300EPKzc2t8Bc373v3uJJrf7E6/7739Op01A6jR482rFarsXbtWiMvL8/x+Pnnnx19HnnkEWPSpEmO51OnTjU+/fRT48CBA0ZOTo4xaNAgw9fX19i1a5cnTqHWmjRpkrFu3Trj4MGDxvbt241JkyYZFovFWL16tWEY5a/7v//9b6NBgwbGzJkzjd27dxtTpkwxGjZsaOzYscNTp1BruXrtec9XnYs/2cX7vvpc7trXt/c9t+pgypw5cyRJvXr1cmqfP3++hg4dKkk6fPiwvLz+O4l56tQpjRo1Svn5+WrZsqW6dOmijRs3si7HRcePH9eQIUOUl5cnq9WqyMhIffrpp+rTp4+k8te9e/fuWrhwoZ5++mk99dRT6tChg5YtW3bJhfyomKvXnvd89eF97zn1/X1vMQzD8HQRAAAAtQFrnAAAAEwiOAEAAJhEcAIAADCJ4AQAAGASwQkAAMAkghMAAIBJBCcAAACTCE4AAAAmEZwAAABMIjgBcLsjR46oV69euummmxQZGaklS5Z4uqR6g2sPVC2+cgWA2+Xl5amgoEDR0dHKz89Xly5d9O2336pp06aeLq3O49oDVYsZJwBuFxISoujoaElScHCwAgIC9NNPPzn1OXnypAIDA3Xo0CG3/uxBgwZp1qxZlR7v1auXLBaLLBaLcnNzq7yeqnbx+V7u2g8dOtRx/suWLavmaoHaj+AEwLSePXvKYrFo2rRpTu2GYSg2NlYWi0XPPfec07GcnByVlZUpLCzMqf3FF1/UgAEDFB4e7tYan376ab344osqKiqqtM+oUaOUl5enTp06XbKe/Px8jRs3Tu3bt5evr6+CgoJ0++23a86cOfr5559N19S/f3/dc889FR778ssvZbFYtH37dkfbsGHD9PTTT5sa+1LnW9G1f+2115SXl2e6dgDOCE4ATDEMQ9u2bVPbtm21Y8cOp2MLFizQsWPHJEm33HKLo/2nn37SkCFD9Pbbbzv1//nnnzV37lyNGDHC7XV26tRJ1113nf7xj39U2qdJkyYKDg5WgwYNKq3nu+++U0xMjFavXq1p06Zp27Ztys7O1sSJE7V8+XJ99tlnpmsaMWKE1qxZox9++KHcsfnz56tr166KjIyUJJWVlWn58uW67777rup8K7v2VqtVwcHBpmsH4IzgBMCUffv26fTp00pKSnIKTqdPn1ZKSoqGDh0qSerSpYskyWazKTExUZMmTVL37t2dxlq5cqV8fHx02223ObV36tRJL7zwgv785z+rZcuWCg4OVlpamuN4fn6+LBaLXnvtNcXExMjX11c333yzNmzY4DRO//79tWjRItPnVlE9f/nLX9SgQQNt2bJFDz74oG688Ua1a9dOAwYM0IoVK9S/f39HX7vdrtTUVEVERKhx48aKiorSBx984Dj++9//Xq1atVJGRobTzy0pKdGSJUucAtvGjRvVsGFD3XrrrY6xZ8yYofbt28vHx0dt2rTRiy++eMnzvdS1B3B1CE4ATMnJyVGTJk00ePBg7d27V2fPnpUkPf/88+ratatatWql4OBghYSEyDAMDR06VHfeeaceeeSRcmN9+eWXjoB1gc1m0969e5WZmamePXvq66+/1kMPPaQnn3xSpaWlkuRYkzRv3jylpaUpNzdXbdq00UMPPSS73e4Yq1u3btq8ebNsNpupc7u4npMnT2r16tUaM2ZMpYuqLRaL48+pqanKzMxUenq6du3apccee0wPP/yw1q1bJ0lq0KCBhgwZooyMDP328zhLlixRWVmZBg8e7Gj75z//qf79+zvGT0lJ0fTp0/XMM8/oP//5jxYuXKigoCCnWn57vpe79gCukgEAJkyYMMG47bbbDLvdbjRr1sz45ptvjG+//dZo1qyZ8e233xpJSUnGvffeaxiGYXz55ZeGxWIxoqKiHI/t27c7xhowYIAxfPhwp/G3bNliSDL+9a9/Odq2b99uSDKOHz9uGIZhTJ8+3WjYsKFx8ODBcq87fPiwo+2bb74xJBmHDh0qdx49e/Y0xo0b59R2cT2bNm0yJBkfffSRU79rrrnGaNq0qdG0aVNj4sSJhmEYxpkzZ4wmTZoYGzdudOo7YsQIY/DgwY7nu3fvNiQZX3zxhaPtjjvuMB5++GGn13Xo0MFYvny5YRiGUVxcbPj4+BjvvPNOufP4rd+e7+Wu/QWSjKVLl15yXADlNfBcZANQm2zdulW33HKLLBaLIiMjtWPHDr333nsaPXq0OnTooJycHP3hD3+QJPXo0cNpBuhiv/zyi3x9fZ3avvnmGwUHByshIcHRduLECTVq1Ej+/v6Szs843X///U4LuP38/MqN37hxY0kyvYC7onoqsnnzZtntdj300EOO2az9+/fr559/Vp8+fZz6nj17VjExMY7nN9xwg7p376558+apV69e2r9/v7788kunxfS7d+/WsWPHdNdddzme22w2x/PK/PZ8L3ftAVwdghMAU7Zu3ar/+Z//kSRFR0crLS1NR44c0XvvvaczZ85oz549TgvDLyUgIECnTp1yasvNzVXXrl2dboHl5uaqU6dO8vb2djxPSkpyel12drYCAgLUunVrR9uFj9+3atXqiupp3769LBaL9u7d69SvXbt2kv4bVKTz65QkacWKFU41SJKPj4/T8xEjRujRRx/V7NmzNX/+fF133XXq2bOn4/g///lP9enTxxHifvtzLsXV8wVw5VjjBOCyvvvuOxUWFjqCUUxMjLZs2aLU1FQ1b95c33zzjX799ddy65YqExMTo//85z9Obd98841j/6ELcnNzHW2//PKL9u3bp7KyMsdxu92utLQ0JSUlycvrv3+d7dy5U9dee60CAgKuqJ5rrrlGffr00RtvvOFYX1WZm266ST4+Pjp8+LDat2/v9Lh4C4YHH3xQXl5eWrhwoTIzMzV8+HCnoPjxxx9rwIABjucdOnRQ48aNlZWVdckaXD1fAFeO4ATgsnJyctSoUSPHvkdJSUk6ceKE45N0W7duVatWrcoFhcokJCRo165dTrM8FQWnbdu2Odp27Nghi8Wif/zjH8rOztbu3bs1cOBAFRYWltvz6Msvv9Tdd99t+vwqqufNN9/Ur7/+qq5du2rx4sXavXu39u7dq3/84x/as2ePYxasefPmmjBhgh577DEtWLBABw4c0NatW/W3v/1NCxYscPo5zZo108CBA5WSkqK8vDzH9ZOk48ePa8uWLfr973/vaPP19dWTTz6piRMnKjMzUwcOHNCmTZs0d+7cqzpfAFfB04usANR8kyZNMm655ZZKj48cOdK4++67XRqzW7duRnp6umEYhnHw4EFDkrF//37H8TNnzhgNGjQwvvzyS8MwDOOtt94yOnXqZGRmZhohISFGkyZNjD/84Q9Oi8INwzB++eUXw2q1GtnZ2RX+3IoWh19czwXHjh0zxo4da0RERBgNGzY0mjVrZnTr1s14+eWXjdLSUkc/u91upKWlGR07djQaNmxotGrVykhISDDWrVtX7uds3LjRkORYSH/Bu+++a9x+++3l+peVlRkvvPCC0bZtW6Nhw4ZGmzZtjGnTppk+38qIxeHAFeG76gB4xIoVK/TEE09o586dTrfZLsjJydGtt96qoqIiNW/eXGPGjNGpU6e0cOHCS447Z84cLV26VKtXr67weK9evRxrtFypp6rdd9996tGjhyZOnOjS6y53vpWxWCxaunSpEhMTXXodUN9xqw6AR/Tr109/+tOfdPTo0QqPb9u2Te3atVPz5s0lnV/vdGF37Utp2LCh/va3v12yz5tvvqlmzZo5beR5uXqqWo8ePZz2czLLzPn+1p///Gc1a9bM5Z8D4DxmnADUSGPHjlV+fr4++OADGYYhq9WqRYsW6d57772qcY8ePapffvlFktSmTRs1atTIHeXWGsePH1dxcbGk818IXNkGnwAqRnACAAAwiVt1AAAAJhGcAAAATCI4AQAAmERwAgAAMIngBAAAYBLBCQAAwCSCEwAAgEkEJwAAAJMITgAAACYRnAAAAEwiOAEAAJhEcAIAADDp/wMjgWOZBCb1UgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([1.81857778, 1.80863875, 1.86758228, ..., 1.7217908 , 1.88162305,\n", + " 1.95955089])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "figD, axD = plt.subplots(figsize=(6, 5))\n", + "c = axD.hist(\n", + " np.real(phsp[\"m_01\"]) ** 2,\n", + " bins=100,\n", + " weights=np.real(intensity_func_rel_bw(phsp)),\n", + ")\n", + "\n", + "axD.set_xlabel(R\"$M^2\\left(\\eta p\\right)\\, \\mathrm{[(GeV/c)^2]}$\")\n", + "axD.set_ylabel(R\"Intensity [a.u.]\")\n", + "figD.tight_layout()\n", + "plt.show()\n", + "phsp[\"m_01\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9334868e3e8e428fb3e986a7f411a14d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating intensity-based sample: 0%| | 0/50000 [00:00:3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n" + ] + } + ], + "source": [ + "weighted_phsp_generator = TFWeightedPhaseSpaceGenerator(\n", + " initial_state_mass=model.reaction_info.initial_state[-1].mass,\n", + " final_state_masses={i: p.mass for i, p in model.reaction_info.final_state.items()},\n", + ")\n", + "data_generator = IntensityDistributionGenerator(\n", + " domain_generator=weighted_phsp_generator,\n", + " function=intensity_func_rel_bw,\n", + " domain_transformer=helicity_transformer,\n", + ")\n", + "data_momenta = data_generator.generate(50_000, rng)\n", + "pd.DataFrame({\n", + " (k, label): np.transpose(v)[i]\n", + " for k, v in data_momenta.items()\n", + " for i, label in enumerate([\"E\", \"px\", \"py\", \"pz\"])\n", + "})\n", + "phsp = helicity_transformer(phsp_momenta)\n", + "data = helicity_transformer(data_momenta)\n", + "data_frame = pd.DataFrame(data)\n", + "phsp_frame = pd.DataFrame(phsp)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "\n", + "resonances = sorted(\n", + " model.reaction_info.get_intermediate_particles(),\n", + " key=lambda p: p.mass,\n", + ")\n", + "evenly_spaced_interval = np.linspace(\n", + " 0, 1, len(intensity_func_fvector.parameters.items())\n", + ")\n", + "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.hist(\n", + " np.real(data_frame[\"m_01\"]),\n", + " bins=200,\n", + " alpha=0.5,\n", + " density=True,\n", + ")\n", + "ax.set_xlabel(\"$m$ [GeV]\")\n", + "for (k, v), color in zip(new_parameters_relbw.items(), colors):\n", + " if k.startswith(\"m_{\"):\n", + " ax.axvline(\n", + " x=v,\n", + " linestyle=\"dotted\",\n", + " label=r\"$\" + k + \"$\",\n", + " color=color,\n", + " )\n", + "ax.legend()\n", + "plt.show()\n", + "# Multiply" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perform fit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define estimator" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorwaves.interface import DataSample\n", + "\n", + "\n", + "def safe_downcast_to_real(data: DataSample) -> DataSample:\n", + " return {\n", + " key: array.real if np.isrealobj(array) else array for key, array in data.items()\n", + " }\n", + "\n", + "\n", + "data_real = safe_downcast_to_real(data)\n", + "phsp_real = safe_downcast_to_real(phsp)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorwaves.estimator import UnbinnedNLL\n", + "\n", + "estimator_bw = UnbinnedNLL(\n", + " intensity_func_rel_bw,\n", + " data=data_real,\n", + " phsp=phsp_real,\n", + " backend=\"jax\",\n", + ")\n", + "\n", + "estimator_fvector = UnbinnedNLL(\n", + " intensity_func_fvector,\n", + " data=data_real,\n", + " phsp=phsp_real,\n", + " backend=\"jax\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "reaction_info = model.reaction_info\n", + "resonances = sorted(\n", + " reaction_info.get_intermediate_particles(),\n", + " key=lambda p: p.mass,\n", + ")\n", + "evenly_spaced_interval_F = np.linspace(\n", + " 0, 1, len(intensity_func_fvector.parameters.items())\n", + ")\n", + "colors_F = [cm.rainbow(x) for x in evenly_spaced_interval_F]\n", + "evenly_spaced_interval_BW = np.linspace(\n", + " 0, 1, len(intensity_func_rel_bw.parameters.items())\n", + ")\n", + "colors_BW = [cm.gist_rainbow(x) for x in evenly_spaced_interval_BW]\n", + "\n", + "\n", + "def indicate_masses(ax):\n", + " ax.set_xlabel(\"$m$ [GeV]\")\n", + " for (k, v), color_F in zip(intensity_func_fvector.parameters.items(), colors_F):\n", + " if k.startswith(\"m_{\"):\n", + " ax.axvline(\n", + " x=v,\n", + " linestyle=\"dotted\",\n", + " label=r\"$\" + k + \"$\" \"(F vector)\",\n", + " color=color_F,\n", + " )\n", + " for (k, v), color_BW in zip(intensity_func_rel_bw.parameters.items(), colors_BW):\n", + " if k.startswith(\"m_{\"):\n", + " ax.axvline(\n", + " x=v,\n", + " linestyle=\"dotted\",\n", + " label=r\"$\" + k + \"$\" \"(Breit-Wigner)\",\n", + " color=color_BW,\n", + " )\n", + "\n", + "\n", + "def compare_model(\n", + " variable_name,\n", + " data,\n", + " phsp,\n", + " function1,\n", + " function2,\n", + " bins=100,\n", + "):\n", + " intensities1 = function1(phsp)\n", + " intensities2 = function2(phsp)\n", + " _, ax = plt.subplots(figsize=(9, 4))\n", + " data_projection = np.real(data[variable_name])\n", + " ax = plt.gca()\n", + " ax.hist(\n", + " data_projection,\n", + " bins=bins,\n", + " alpha=0.5,\n", + " label=\"data\",\n", + " density=True,\n", + " )\n", + " phsp_projection = np.real(phsp[variable_name])\n", + " ax.hist(\n", + " phsp_projection,\n", + " weights=np.array(intensities1),\n", + " bins=bins,\n", + " histtype=\"step\",\n", + " color=\"red\",\n", + " label=\"Fit model with K matrix\",\n", + " density=True,\n", + " )\n", + " ax.hist(\n", + " phsp_projection,\n", + " weights=np.array(intensities2),\n", + " bins=bins,\n", + " histtype=\"step\",\n", + " color=\"blue\",\n", + " label=\"Fit model with Breit Wigner\",\n", + " density=True,\n", + " )\n", + " indicate_masses(ax)\n", + " ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set initial parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "m_1900=1.93\n", + "beta_1900= 0.9 + 0j\n", + "g_1900= 1.\n", + "m_1650= 1.65\n", + "beta_1650= 1 + 0j\n", + "g_1900= 1.\n", + "m_Fakestar2=1.5\n", + "beta_Fakestar2= 1 + 0j\n", + "g_Fakestar2= 1.\n", + "m_Fakestar1= 1.94\n", + "initial_parameters_fvector = {\n", + " R\"m_{N(Fakestar)^+}\": 1.95,\n", + " R\"\\beta_{N(Fakestar)^+}\": 0.9 + 0j,\n", + " R\"m_{N(1900)^+}\": 1.91,\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", + " R\"g_{N(1900)^+}\": 1.,\n", + " R\"g_{N(Fakestar)^+}\": 1.,\n", + " R\"m_{N(Fakestar2)^+}\": 1.7,\n", + " R\"\\beta_{N(Fakestar2)^+}\": 1 + 0j,\n", + " R\"m_{N(1650)^{+}}\": 1.67,\n", + " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", + " R\"g_{N(1650)^{+}}\": 1.6,\n", + " R\"g_{N(Fakestar2)^+}\": 1,\n", + "}\n", + "\n", + "initial_parameters_relbw = {\n", + " R\"m_{N(Fakestar)^+}\": 1.8,\n", + " R\"w_{N(Fakestar)^+}\": 1/1.85,\n", + " R\"m_{N(1900)^+}\": 1.93,\n", + " R\"w_{N(1900)^+}\": 1/1.93,\n", + " R\"m_{N(Fakestar2)^+}\": 1.7,\n", + " R\"w_{N(Fakestar2)^+}\": 1/1.65,\n", + " R\"m_{N(1650)^{+}}\": 1.6,\n", + " R\"w_{N(1650)^{+}}\": 1/1.6,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "original_parameters = intensity_func_fvector.parameters\n", + "intensity_func_fvector.update_parameters(initial_parameters_fvector)\n", + "intensity_func_rel_bw.update_parameters(initial_parameters_relbw)\n", + "compare_model(\n", + " \"m_01\", data_real, phsp_real, intensity_func_fvector, intensity_func_rel_bw\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "61398b9f63a14c89b142003b6f3d36c6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Fit Breit-Wigner:'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "FitResult(\n", + " minimum_valid=True,\n", + " execution_time=4.677215337753296,\n", + " function_calls=460,\n", + " estimator_value=-12198.959244284237,\n", + " parameter_values={\n", + " 'm_{N(Fakestar)^+}': 1.8497722024190237,\n", + " 'w_{N(Fakestar)^+}': 0.5782959510425077,\n", + " 'm_{N(1900)^+}': 1.8979233143190917,\n", + " 'w_{N(1900)^+}': 0.5133596540336253,\n", + " 'm_{N(Fakestar2)^+}': 1.750670984933865,\n", + " 'w_{N(Fakestar2)^+}': 0.5846494642322556,\n", + " 'm_{N(1650)^{+}}': 1.6498642685938685,\n", + " 'w_{N(1650)^{+}}': 0.620015291718532,\n", + " },\n", + " parameter_errors={\n", + " 'm_{N(Fakestar)^+}': 0.0014323314749189518,\n", + " 'w_{N(Fakestar)^+}': 0.01925746470118625,\n", + " 'm_{N(1900)^+}': 0.0019038409162150155,\n", + " 'w_{N(1900)^+}': 0.019434730126103774,\n", + " 'm_{N(Fakestar2)^+}': 0.000892931269048051,\n", + " 'w_{N(Fakestar2)^+}': 0.013270544273614748,\n", + " 'm_{N(1650)^{+}}': 0.00047548447999302897,\n", + " 'w_{N(1650)^{+}}': 0.011881206933894169,\n", + " },\n", + ")" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "470eae98802c48d49beed64bc11f4aee", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Fit F vector:'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "FitResult(\n", + " minimum_valid=True,\n", + " execution_time=47.1162691116333,\n", + " function_calls=2975,\n", + " estimator_value=-12202.383067702911,\n", + " parameter_values={\n", + " 'm_{N(Fakestar)^+}': 2.0582987246008804,\n", + " 'm_{N(1900)^+}': 1.8231026455013533,\n", + " 'g_{N(1900)^+}': 1.0730078384977024,\n", + " 'g_{N(Fakestar)^+}': 1.3133876970845961,\n", + " 'm_{N(Fakestar2)^+}': 1.7778710952273362,\n", + " 'm_{N(1650)^{+}}': 1.6396907094273547,\n", + " 'g_{N(1650)^{+}}': 0.9545451645245374,\n", + " 'g_{N(Fakestar2)^+}': 1.0945401433722264,\n", + " '\\\\beta_{N(Fakestar)^+}': (0.9055666944002341+1.0500508390638388j),\n", + " '\\\\beta_{N(1900)^+}': (1.273746344719463-1.4101342023704395j),\n", + " '\\\\beta_{N(Fakestar2)^+}': (1.5062984460066997+0.6457898290798657j),\n", + " '\\\\beta_{N(1650)^{+}}': (0.9570822021506897-0.8803373376651846j),\n", + " },\n", + " parameter_errors={\n", + " 'm_{N(Fakestar)^+}': 0.02416835891604554,\n", + " 'm_{N(1900)^+}': 0.006926974109042624,\n", + " 'g_{N(1900)^+}': 0.023515895445379586,\n", + " 'g_{N(Fakestar)^+}': 0.0916024840987184,\n", + " 'm_{N(Fakestar2)^+}': 0.002272635372427852,\n", + " 'm_{N(1650)^{+}}': 0.0007383030652937115,\n", + " 'g_{N(1650)^{+}}': 0.009734006074575145,\n", + " 'g_{N(Fakestar2)^+}': 0.01991299259213779,\n", + " '\\\\beta_{N(Fakestar)^+}': (0.1350094868896259+0.12215287033507256j),\n", + " '\\\\beta_{N(1900)^+}': (0.17203402316801028+0.1326094786089237j),\n", + " '\\\\beta_{N(Fakestar2)^+}': (0.11676784512423925+0.10945244771190928j),\n", + " '\\\\beta_{N(1650)^{+}}': (0.08848716894518248+0.08786639182501035j),\n", + " },\n", + ")" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from tensorwaves.optimizer import Minuit2\n", + "from tensorwaves.optimizer.callbacks import CSVSummary\n", + "\n", + "minuit2 = Minuit2(\n", + " callback=CSVSummary(\"fit_traceback.csv\"),\n", + " use_analytic_gradient=False,\n", + ")\n", + "\n", + "fit_result_BW = minuit2.optimize(estimator_bw, initial_parameters_relbw)\n", + "display(\"Fit Breit-Wigner:\", fit_result_BW)\n", + "fit_result_F = minuit2.optimize(estimator_fvector, initial_parameters_fvector)\n", + "display(\"Fit F vector:\", fit_result_F)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optimized_parameters_BW = fit_result_BW.parameter_values\n", + "optimized_parameters_F = fit_result_F.parameter_values\n", + "intensity_func_fvector.update_parameters(optimized_parameters_F)\n", + "intensity_func_rel_bw.update_parameters(optimized_parameters_BW)\n", + "compare_model(\n", + " \"m_01\", data_real, phsp_real, intensity_func_fvector, intensity_func_rel_bw\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters for $F$ vector v.s. sum of Breit-Wigners" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "m_{N(Fakestar)^+}\n", + " initial: 1.95\n", + " optimized F vector: 2.06\n", + " original: 1.95\n", + "m_{N(1900)^+}\n", + " initial: 1.91\n", + " optimized F vector: 1.82\n", + " original: 1.9\n", + "g_{N(1900)^+}\n", + " initial: 1.0\n", + " optimized F vector: 1.07\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Precision not allowed in integer format specifier", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[42], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m initial: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minitial_parameters_fvector[p]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m optimized F vector: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moptimized_parameters_F[p]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m original: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moriginal_parameters[p]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6\u001b[0m latest_parameters_F \u001b[38;5;241m=\u001b[39m CSVSummary\u001b[38;5;241m.\u001b[39mload_latest_parameters(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_traceback.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 7\u001b[0m latest_parameters_F\n", + "\u001b[0;31mValueError\u001b[0m: Precision not allowed in integer format specifier" + ] + } + ], + "source": [ + "for p in optimized_parameters_F:\n", + " print(p)\n", + " print(f\" initial: {initial_parameters_fvector[p]:.3}\")\n", + " print(f\" optimized F vector: {optimized_parameters_F[p]:.3}\")\n", + " print(f\" original: {original_parameters[p]:.3}\")\n", + "latest_parameters_F = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", + "latest_parameters_F" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for p in optimized_parameters_BW:\n", + " print(p)\n", + " print(f\" initial: {initial_parameters_bw[p]:.3}\")\n", + " print(f\" optimized Breit-Wigner: {optimized_parameters_BW[p]:.3}\")\n", + " print(f\" original: {original_parameters[p]:.3}\")\n", + "latest_parameters_BW = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", + "latest_parameters_BW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/SubintensityPlots_mitAgrand.ipynb b/SubintensityPlots_mitAgrand.ipynb new file mode 100644 index 00000000..9dea70df --- /dev/null +++ b/SubintensityPlots_mitAgrand.ipynb @@ -0,0 +1,1637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Sub-Intensity plots " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "import graphviz\n", + "import numpy as np\n", + "import qrules\n", + "import sympy as sp\n", + "from ampform.io import aslatex\n", + "from IPython.display import Latex\n", + "from qrules.particle import Particle, ParticleCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Collect dynamics symbols" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Resonance | $m$ [MeV] | $\\Gamma$ [MeV] | $J^P$ |\n", + "|-----------|-----------|----------------|-------|\n", + "| $N^*(1440)$ | 1398 | 167 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1535)$ | 1530 | 210 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1650)$ | 1668 | 194 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1710)$ | 1749 | 263 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1880)$ | 1876 | 261 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1895)$ | 2045 | 240 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_particle_database() -> ParticleCollection:\n", + " particle_database = qrules.load_default_particles()\n", + " additional_definitions = qrules.io.load(\n", + " \"../../../additional-nstar-sigma-definitions.yml\"\n", + " )\n", + " particle_database.update(additional_definitions)\n", + " return particle_database\n", + "\n", + "\n", + "PARTICLE_DB = load_particle_database()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb66f384c6034cff9b69aa4ad8239413", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Propagating quantum numbers: 0%| | 0/36 [00:00\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "g0_edge0\n", + "0: eta\n", + "\n", + "\n", + "\n", + "g0_edge1\n", + "1: p\n", + "\n", + "\n", + "\n", + "g0_edge2\n", + "2: p~\n", + "\n", + "\n", + "\n", + "g0_edge-1\n", + "J/psi(1S)\n", + "\n", + "\n", + "\n", + "g0_node0\n", + "\n", + "\n", + "\n", + "g0_edge-1->g0_node0\n", + "\n", + "\n", + "\n", + "\n", + "g0_node0->g0_edge2\n", + "\n", + "\n", + "\n", + "\n", + "g0_node1\n", + "\n", + "\n", + "\n", + "g0_node0->g0_node1\n", + "\n", + "N(1900)+\n", + "N(Fakestar)+\n", + "\n", + "\n", + "\n", + "g0_node1->g0_edge0\n", + "\n", + "\n", + "\n", + "\n", + "g0_node1->g0_edge1\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reaction = qrules.generate_transitions(\n", + " initial_state=\"J/psi(1S)\",\n", + " final_state=[\"eta\", \"p\", \"p~\"],\n", + " allowed_intermediate_particles=[\"N(Fakestar)+\", \"N(1900)+\"],\n", + " allowed_interaction_types=[\"strong\"],\n", + " formalism=\"helicity\",\n", + " # mass_conservation_factor=5.0,\n", + " particle_db=PARTICLE_DB,\n", + ")\n", + "dot = qrules.io.asdot(reaction, collapse_graphs=True)\n", + "graphviz.Source(dot)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\sum_{m_{A}=-1}^{1} \\sum_{m_{1}=-1/2}^{1/2} \\sum_{m_{2}=-1/2}^{1/2}{\\left|{{A^{01}}_{m_{A},0,m_{1},m_{2}}}\\right|^{2}}$" + ], + "text/plain": [ + "PoolSum(Abs(A^01[m_A, 0, m1, m2])**2, (m_A, (0, 1, -1)), (m1, (-1/2, 1/2)), (m2, (-1/2, 1/2)))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import ampform\n", + "\n", + "model_builder = ampform.get_builder(reaction)\n", + "model_builder.adapter.permutate_registered_topologies()\n", + "model_builder.scalar_initial_state_mass = True\n", + "model_builder.stable_final_state_ids = [0, 1, 2]\n", + "for name in reaction.get_intermediate_particles().names:\n", + " model_builder.set_dynamics(name, create_dynamics_symbol)\n", + "model = model_builder.formulate()\n", + "model.intensity.cleanup()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,0}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{\\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,-1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{3}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,0}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{\\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,-1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{3}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right)$" + ], + "text/plain": [ + "-C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 0, -phi_01, theta_01, 0)*WignerD(3/2, -1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 1, -phi_01, theta_01, 0)*WignerD(3/2, 1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, -1, -phi_01, theta_01, 0)*WignerD(3/2, -3/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 0, -phi_01, theta_01, 0)*WignerD(3/2, -1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 1, -phi_01, theta_01, 0)*WignerD(3/2, 1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, -1, -phi_01, theta_01, 0)*WignerD(3/2, -3/2, 1/2, -phi_0^01, theta_0^01, 0)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "amp, *_ = model.amplitudes.values()\n", + "amp" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "selected_amplitudes = {\n", + " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i < 3\n", + "}\n", + "src = aslatex(selected_amplitudes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Formulate dynamics expression" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle X_{Q=+1, S=3/2, P =1}$" + ], + "text/plain": [ + "X_{Q=+1, S=3/2, P =1}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N(Fakestar)+ 1.82 GeV 0.6 GeV \n", + " N(1900)+ 1.92 GeV 0.2 GeV \n" + ] + }, + { + "data": { + "text/plain": [ + "ParameterValues({\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " m_0: 0.547862,\n", + " m_1: 0.93827208816,\n", + " m_2: 0.93827208816,\n", + " m_012: 3.0969,\n", + " })" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", + " display(symbol)\n", + " for p, _ in resonances:\n", + " print(f\" {p.name:<20s} {p.mass:>8g} GeV {p.width:>8g} GeV \")\n", + "model.parameter_defaults" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Formulate Dynamics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phasespace factor" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from ampform.kinematics.phasespace import Kallen\n", + "from ampform.sympy import unevaluated\n", + "from sympy import Abs\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class PhaseSpaceCM(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"\\rho^\\mathrm{{CM}}_{{{m1},{m2}}}\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " return -16 * sp.pi * sp.I * ChewMandelstam(s, m1, m2)\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class ChewMandelstam(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"\\Sigma\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " q = BreakupMomentum(s, m1, m2)\n", + " return (\n", + " 1\n", + " / (16 * sp.pi**2)\n", + " * (\n", + " (2 * q / sp.sqrt(s))\n", + " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", + " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", + " )\n", + " )\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class BreakupMomentum(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"q\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class EnergyDecaywidth(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " gamma_R: Any\n", + " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2, gamma_R = self.args\n", + " return gamma_R * PhaseSpaceCM(s, m1, m2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Relativistic Breit-Wigner" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", + "\n", + "PARAMETERS_BW = {}\n", + "\n", + "\n", + "def formulate_rel_bw(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " q = BreakupMomentum(s, m_a, m_b)\n", + " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", + " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", + " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", + " dummy = [sp.Symbol(Rf\"Dummy_{{{p.latex}}}\") for p, _ in resonances]\n", + " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", + " rel_bw = sum(\n", + " (w_ * m_ * dummy_) / (m_**2 - s - m_ * w_s_)\n", + " for m_, w_, w_s_, dummy_ in zip(m, w, w_s, dummy)\n", + " )\n", + " for i, (p, va) in enumerate(resonances):\n", + " PARAMETERS_BW[w[i]] = p.width\n", + " PARAMETERS_BW[m[i]] = p.mass\n", + " PARAMETERS_BW[b[i]] = 1\n", + " PARAMETERS_BW[d[i]] = 1\n", + " PARAMETERS_BW[L[i]] = 0\n", + " PARAMETERS_BW[dummy[i]] = 1\n", + " return rel_bw" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $K$ matrix " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "PARAMETERS_K = {}\n", + "\n", + "\n", + "def formulate_K_matrix(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + "\n", + " kmatrix = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", + " for i, (p, va) in enumerate(resonances):\n", + " PARAMETERS_K[m[i]] = p.mass\n", + " PARAMETERS_K[g[i]] = 1\n", + " return kmatrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $P$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "PARAMETERS_F = {}\n", + "\n", + "\n", + "def formulate_P_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " beta = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", + " P_vector = sum((g_ * beta_) / (m_**2 - s) for m_, g_, beta_ in zip(m, g, beta))\n", + " for i, (p, va) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = p.mass\n", + " PARAMETERS_F[beta[i]] = 1 + 0j\n", + " PARAMETERS_F[g[i]] = 1\n", + " return P_vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $F$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def formulate_F_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (p1, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " rho = PhaseSpaceCM(s, m_a, m_b)\n", + " K = formulate_K_matrix(resonances)\n", + " P = formulate_P_vector(resonances)\n", + " Fvector = (1 / (1 - rho * K)) * P\n", + " return Fvector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model relativistic Breit-Wigner" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{array}{rcl}\n", + " X_{Q=+1, S=3/2, P =1} &=& \\frac{Dummy_{N(1900)^+} m_{N(1900)^+} w_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2} - m_{N(1900)^+} \\Gamma_s\\left(m_{01}^{2}\\right)} + \\frac{Dummy_{N(Fakestar)^+} m_{N(Fakestar)^+} w_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2} - m_{N(Fakestar)^+} \\Gamma_s\\left(m_{01}^{2}\\right)} \\\\\n", + "\\end{array}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import attrs\n", + "from ampform.helicity import ParameterValues\n", + "\n", + "dynamics_expressions_rel_bw = {\n", + " symbol: formulate_rel_bw(resonances)\n", + " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + "}\n", + "model_rel_bw = attrs.evolve(\n", + " model,\n", + " parameter_defaults=ParameterValues(\n", + " {\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_BW,\n", + " }\n", + " ),\n", + ")\n", + "Latex(aslatex(dynamics_expressions_rel_bw))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ParameterValues({\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " m_0: 0.547862,\n", + " m_1: 0.93827208816,\n", + " m_2: 0.93827208816,\n", + " m_012: 3.0969,\n", + " w_{N(Fakestar)^+}: 0.6,\n", + " m_{N(Fakestar)^+}: 1.82,\n", + " b_{N(Fakestar)^+}: 1,\n", + " d_{N(Fakestar)^+}: 1,\n", + " L_{N(Fakestar)^+}: 0,\n", + " Dummy_{N(Fakestar)^+}: 1,\n", + " w_{N(1900)^+}: 0.2,\n", + " m_{N(1900)^+}: 1.92,\n", + " b_{N(1900)^+}: 1,\n", + " d_{N(1900)^+}: 1,\n", + " L_{N(1900)^+}: 0,\n", + " Dummy_{N(1900)^+}: 1,\n", + " })" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_rel_bw.parameter_defaults" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3071" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_expression_rel_bw = model_rel_bw.expression.doit().xreplace(\n", + " dynamics_expressions_rel_bw\n", + ")\n", + "sp.count_ops(full_expression_rel_bw)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model $F$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{array}{rcl}\n", + " X_{Q=+1, S=3/2, P =1} &=& \\frac{\\frac{\\beta_{N(1900)^+} g_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1900)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1} \\\\\n", + "\\end{array}" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamics_expressions_fvector = {\n", + " symbol: formulate_F_vector(resonances)\n", + " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + "}\n", + "model_fvector = attrs.evolve(\n", + " model,\n", + " parameter_defaults=ParameterValues(\n", + " {\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_F,\n", + " }\n", + " ),\n", + ")\n", + "Latex(aslatex(dynamics_expressions_fvector))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ParameterValues({\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", + " m_0: 0.547862,\n", + " m_1: 0.93827208816,\n", + " m_2: 0.93827208816,\n", + " m_012: 3.0969,\n", + " m_{N(Fakestar)^+}: 1.82,\n", + " \\beta_{N(Fakestar)^+}: (1+0j),\n", + " g_{N(Fakestar)^+}: 1,\n", + " m_{N(1900)^+}: 1.92,\n", + " \\beta_{N(1900)^+}: (1+0j),\n", + " g_{N(1900)^+}: 1,\n", + " })" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_fvector.parameter_defaults" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3575" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "full_expression_fvector = model_fvector.expression.doit().xreplace(\n", + " dynamics_expressions_fvector\n", + ")\n", + "sp.count_ops(full_expression_fvector)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Parametrized Function\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from tensorwaves.function.sympy import create_parametrized_function\n", + "\n", + "unfolded_expression_rel_bw = full_expression_rel_bw.doit()\n", + "\n", + "intensity_func_rel_bw = create_parametrized_function(\n", + " expression=unfolded_expression_rel_bw,\n", + " backend=\"jax\",\n", + " parameters=model_rel_bw.parameter_defaults,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{Dummy_{N(1900)^+} m_{N(1900)^+} w_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2} - m_{N(1900)^+} \\Gamma_s\\left(m_{01}^{2}\\right)} + \\frac{Dummy_{N(Fakestar)^+} m_{N(Fakestar)^+} w_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2} - m_{N(Fakestar)^+} \\Gamma_s\\left(m_{01}^{2}\\right)}$" + ], + "text/plain": [ + "Dummy_{N(1900)^+}*m_{N(1900)^+}*w_{N(1900)^+}/(-m_01**2 + m_{N(1900)^+}**2 - m_{N(1900)^+}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(1900)^+})) + Dummy_{N(Fakestar)^+}*m_{N(Fakestar)^+}*w_{N(Fakestar)^+}/(-m_01**2 + m_{N(Fakestar)^+}**2 - m_{N(Fakestar)^+}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(Fakestar)^+}))" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamics_expr_rel_bw, *_ = dynamics_expressions_rel_bw.values()\n", + "dynamics_expr_rel_bw" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "dynamics_func_bw = create_parametrized_function(\n", + " expression=dynamics_expr_rel_bw.doit(),\n", + " backend=\"numpy\",\n", + " parameters=model_rel_bw.parameter_defaults,\n", + " use_cse=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "unfolded_expression_fvector = full_expression_fvector.doit()\n", + "\n", + "intensity_func_fvector = create_parametrized_function(\n", + " expression=unfolded_expression_fvector,\n", + " backend=\"jax\",\n", + " parameters=model_fvector.parameter_defaults,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\beta_{N(1900)^+} g_{N(1900)^+} \\left(m_{01}^{2} - \\left(m_{N(Fakestar)^+}\\right)^{2}\\right) + \\beta_{N(Fakestar)^+} g_{N(Fakestar)^+} \\left(m_{01}^{2} - \\left(m_{N(1900)^+}\\right)^{2}\\right)}{- \\left(m_{01}^{2} - \\left(m_{N(1900)^+}\\right)^{2}\\right) \\left(m_{01}^{2} - \\left(m_{N(Fakestar)^+}\\right)^{2}\\right) + \\left(- \\left(g_{N(1900)^+}\\right)^{2} \\left(m_{01}^{2} - \\left(m_{N(Fakestar)^+}\\right)^{2}\\right) - \\left(g_{N(Fakestar)^+}\\right)^{2} \\left(m_{01}^{2} - \\left(m_{N(1900)^+}\\right)^{2}\\right)\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right)}$" + ], + "text/plain": [ + "(\\beta_{N(1900)^+}*g_{N(1900)^+}*(m_01**2 - m_{N(Fakestar)^+}**2) + \\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+}*(m_01**2 - m_{N(1900)^+}**2))/(-(m_01**2 - m_{N(1900)^+}**2)*(m_01**2 - m_{N(Fakestar)^+}**2) + (-g_{N(1900)^+}**2*(m_01**2 - m_{N(Fakestar)^+}**2) - g_{N(Fakestar)^+}**2*(m_01**2 - m_{N(1900)^+}**2))*PhaseSpaceCM(m_01**2, m_0, m_1))" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamics_expr_fvector, *_ = dynamics_expressions_fvector.values()\n", + "dynamics_expr_fvector.simplify(doit=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\frac{\\frac{\\beta_{N(1900)^+} g_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1900)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1}$" + ], + "text/plain": [ + "(\\beta_{N(1900)^+}*g_{N(1900)^+}/(-m_01**2 + m_{N(1900)^+}**2) + \\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+}/(-m_01**2 + m_{N(Fakestar)^+}**2))/(-(g_{N(1900)^+}**2/(-m_01**2 + m_{N(1900)^+}**2) + g_{N(Fakestar)^+}**2/(-m_01**2 + m_{N(Fakestar)^+}**2))*PhaseSpaceCM(m_01**2, m_0, m_1) + 1)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dynamics_expr_fvector\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "dynamics_func_fvector = create_parametrized_function(\n", + " expression=dynamics_expr_fvector.doit(),\n", + " backend=\"numpy\",\n", + " parameters=model_fvector.parameter_defaults,\n", + " use_cse=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "m_res1 = 1.82\n", + "m_res2 = 1.92\n", + "g_res1 = 1\n", + "g_res2 = 1\n", + "\n", + "new_parameters_fvector = {\n", + " R\"m_{N(Fakestar)^+}\": m_res1,\n", + " R\"\\beta_{N(Fakestar)^+}\": 1+0j,\n", + " R\"m_{N(1900)^+}\": m_res2,\n", + " R\"\\beta_{N(1900)^+}\": 1+0j, # 0.5l\n", + " R\"g_{N(1900)^+}\": g_res2,\n", + " R\"g_{N(Fakestar)^+}\": g_res1,\n", + "}\n", + "\n", + "new_parameters_relbw = {\n", + " R\"m_{N(Fakestar)^+}\": m_res1,\n", + " R\"w_{N(Fakestar)^+}\": g_res1 / m_res1,\n", + " R\"m_{N(1900)^+}\": m_res2,\n", + " R\"w_{N(1900)^+}\": g_res2 / m_res2,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+3/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{-1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'm_0': 0.547862,\n", + " 'm_1': 0.93827208816,\n", + " 'm_2': 0.93827208816,\n", + " 'm_012': 3.0969,\n", + " 'm_{N(Fakestar)^+}': 1.82,\n", + " '\\\\beta_{N(Fakestar)^+}': (1+0j),\n", + " 'g_{N(Fakestar)^+}': 1,\n", + " 'm_{N(1900)^+}': 1.92,\n", + " '\\\\beta_{N(1900)^+}': (1+0j),\n", + " 'g_{N(1900)^+}': 1}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", + "intensity_func_rel_bw.update_parameters(new_parameters_relbw)\n", + "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", + "dynamics_func_bw.update_parameters(new_parameters_relbw)\n", + "dynamics_func_fvector.parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data with $F$ vector\n", + "### Generate phase space sample" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from tensorwaves.data import SympyDataTransformer\n", + "\n", + "helicity_transformer = SympyDataTransformer.from_sympy(\n", + " model.kinematic_variables, backend=\"numpy\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ce51704fbe054fb7822ba057f14e0683", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating phase space sample: 0%| | 0/100000 [00:00:3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", + ":3: RuntimeWarning: invalid value encountered in sqrt\n", + " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n" + ] + }, + { + "data": { + "text/plain": [ + "{'m_01': array([1.81857778+1.e-08j, 1.80863875+1.e-08j, 1.86758228+1.e-08j, ...,\n", + " 1.7217908 +1.e-08j, 1.88162305+1.e-08j, 1.95955089+1.e-08j]),\n", + " 'm_02': array([1.70745362+1.e-08j, 1.77483717+1.e-08j, 1.56984082+1.e-08j, ...,\n", + " 2.13907063+1.e-08j, 1.99774363+1.e-08j, 2.0295025 +1.e-08j]),\n", + " 'm_12': array([2.33002756+1.e-08j, 2.2870134 +1.e-08j, 2.38733903+1.e-08j, ...,\n", + " 2.0276747 +1.e-08j, 2.02981933+1.e-08j, 1.92170012+1.e-08j]),\n", + " 'phi_0': array([ 1.97016286, -2.8765596 , 0.75357421, ..., 0.19730572,\n", + " -0.45861856, 1.57182959]),\n", + " 'phi_0^01': array([-1.97869891, 2.40627766, -2.02701505, ..., 1.42458459,\n", + " 0.78477173, 2.00132783]),\n", + " 'phi_0^02': array([ 0.98414884, -1.41787483, 1.80055274, ..., -2.62005351,\n", + " -1.37701865, -1.58606652]),\n", + " 'phi_01': array([-0.00476082, -0.46629838, -0.49331781, ..., 2.95178512,\n", + " 2.14918814, -1.97763388]),\n", + " 'phi_1^12': array([-0.5234414 , 0.53541189, -1.32700284, ..., 2.04917998,\n", + " 2.17445382, 1.30218432]),\n", + " 'phi_02': array([-1.98053067, 1.48563902, 3.08718583, ..., -1.7995076 ,\n", + " -2.40408988, -0.99517043]),\n", + " 'theta_0': array([1.69320513, 1.8732383 , 2.16807283, ..., 2.56300869, 1.02101855,\n", + " 2.0423608 ]),\n", + " 'theta_0^01': array([2.00195379, 1.79913544, 2.46359496, ..., 0.38143291, 0.96066346,\n", + " 0.54722468]),\n", + " 'theta_0^02': array([1.73936386, 1.72081689, 1.66448996, ..., 1.63126795, 1.19669709,\n", + " 0.64090765]),\n", + " 'theta_01': array([2.57060174, 2.32905607, 2.03576298, ..., 0.5128774 , 1.68637297,\n", + " 1.23809802]),\n", + " 'theta_1^12': array([1.34061414, 1.5044162 , 0.83272194, ..., 2.71169603, 1.82674819,\n", + " 1.82311053]),\n", + " 'theta_02': array([0.63817236, 0.51529239, 0.96856613, ..., 1.79764582, 2.3444856 ,\n", + " 1.03333824])}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re\n", + "epsilon=1e-8\n", + "import pandas as pd\n", + "from tensorwaves.data import (\n", + " IntensityDistributionGenerator,\n", + " SympyDataTransformer,\n", + " TFPhaseSpaceGenerator,\n", + " TFUniformRealNumberGenerator,\n", + " TFWeightedPhaseSpaceGenerator,\n", + ")\n", + "\n", + "rng = TFUniformRealNumberGenerator(seed=0)\n", + "phsp_generator = TFPhaseSpaceGenerator(\n", + " initial_state_mass=reaction.initial_state[-1].mass,\n", + " final_state_masses={i: p.mass for i, p in reaction.final_state.items()},\n", + ")\n", + "phsp_momenta = phsp_generator.generate(100_000, rng)\n", + "phsp = helicity_transformer(phsp_momenta)\n", + "phsp = {k: v +epsilon*1j if re.match(r\"^m_\\d\\d$\",k) else v for k, v in phsp.items()}\n", + "phsp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Sub-Intensities" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "from tensorwaves.interface import ParametrizedFunction\n", + "\n", + "\n", + "def compute_sub_intensity(\n", + " func: ParametrizedFunction,\n", + " input_data: DataSample,\n", + " resonances: list[str],\n", + " coupling_pattern: str = r\"(\\\\beta|g|Dummy_)\",\n", + "):\n", + " original_parameters = dict(func.parameters)\n", + " negative_lookahead = f\"(?!{'|'.join(map(re.escape, resonances))})\"\n", + " # https://regex101.com/r/WrgGyD/1\n", + " pattern = rf\"^{coupling_pattern}({negative_lookahead}.)*$\"\n", + " set_parameters_to_zero(func, pattern)\n", + " array = func(input_data)\n", + " func.update_parameters(original_parameters)\n", + " return array\n", + "\n", + "\n", + "def set_parameters_to_zero(func: ParametrizedFunction, name_pattern: str) -> None:\n", + " new_parameters = dict(func.parameters)\n", + " for par_name in func.parameters:\n", + " if re.match(name_pattern, par_name) is not None:\n", + " new_parameters[par_name] = 0\n", + " func.update_parameters(new_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "total_intensities = intensity_func_fvector(phsp)\n", + "total_intensities_1 = intensity_func_rel_bw(phsp)\n", + "sub_intensities = {\n", + " p: compute_sub_intensity(\n", + " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " )\n", + " for p, _ in resonances\n", + "}\n", + "sub_intensities_bw = {\n", + " p: compute_sub_intensity(\n", + " intensity_func_rel_bw, phsp, resonances=[p.latex], coupling_pattern=r\"Dummy_\"\n", + " )\n", + " for p, _ in resonances\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from matplotlib import cm\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", + "ax.set_xlim(2, 5)\n", + "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", + "ax.set_ylabel(R\"Intensity [a. u.]\")\n", + "ax.set_yticks([])\n", + "\n", + "bins = 150\n", + "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", + "ax.hist(\n", + " phsp_projection,\n", + " weights=total_intensities,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=\"hotpink\",\n", + " label=R\"Full intensity $F$ vector\",\n", + ")\n", + "\n", + "ax.hist(\n", + " phsp_projection,\n", + " weights=total_intensities_1,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=\"grey\",\n", + " label=\"Full intensity Breit-Wigner\",\n", + ")\n", + "ax.hist(\n", + " len(sub_intensities) * [phsp_projection],\n", + " weights=list(sub_intensities.values()),\n", + " bins=bins,\n", + " alpha=0.6,\n", + " label=[\n", + " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\" for p in sub_intensities\n", + " ],\n", + " histtype=\"step\",\n", + ")\n", + "\n", + "ax.hist(\n", + " len(sub_intensities_bw) * [phsp_projection],\n", + " weights=list(sub_intensities_bw.values()),\n", + " bins=bins,\n", + " alpha=0.6,\n", + " label=[\n", + " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ Breit-Wigner\"\n", + " for p in sub_intensities\n", + " ],\n", + " histtype=\"step\",\n", + " ls=\"dotted\",\n", + ")\n", + "\n", + "fig.legend(fontsize=\"9\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Phase" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "epsilon = 1e-8\n", + "x = np.linspace(2, 5, num=400)\n", + "data = {\"m_01\": np.sqrt(x + epsilon * 1j)}" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "total_phase = np.angle(dynamics_func_fvector(data))\n", + "total_phase_1 = np.angle(dynamics_func_bw(data))\n", + "sub_phase = {\n", + " p: np.angle(\n", + " compute_sub_intensity(\n", + " dynamics_func_fvector,\n", + " data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", + " )\n", + " )\n", + " for p, _ in resonances\n", + "}\n", + "sub_phase_bw = {\n", + " p: np.angle(\n", + " compute_sub_intensity(\n", + " dynamics_func_bw, data, resonances=[p.latex], coupling_pattern=r\"Dummy_\"\n", + " )\n", + " )\n", + " for p, _ in resonances\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig_phase, ax_phase = plt.subplots(figsize=(10, 6), dpi=500)\n", + "ax_phase.set_xlim(2, 5)\n", + "ax_phase.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\", fontsize=12)\n", + "ax_phase.set_ylabel(R\"Intensity [a. u.]\", fontsize=12)\n", + "ax_phase.set_yticks([])\n", + "\n", + "# Plot histogram\n", + "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", + "ax_phase.hist(\n", + " phsp_projection,\n", + " weights=total_intensities,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=\"hotpink\",\n", + " label=\"Full intensity $F$ vector\",\n", + ")\n", + "\n", + "for i, (k, v) in enumerate(sub_intensities.items()):\n", + " ax_phase.hist(\n", + " phsp_projection,\n", + " weights=v,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=plt.cm.viridis(i / len(sub_intensities)),\n", + " label=Rf\"Resonance at ${k.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", + " )\n", + "\n", + "ax_phase1 = ax_phase.twinx()\n", + "ax_phase1.set_ylabel(R\"Angle [a. u.]\", fontsize=12)\n", + "ax_phase1.set_yticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", + "ax_phase1.set_yticklabels([R\"$-\\pi$\", R\"$-\\frac{\\pi}{2}$\", R\"0\", R\"$+\\frac{\\pi}{2}$\", R\"$+\\pi$\"])\n", + "\n", + "# Plot total phases\n", + "ax_phase1.scatter(x, total_phase, s=22, color=\"red\", marker=\"^\",label=\"Total Phase\")\n", + "\n", + "colors = [\"green\", \"yellow\"]\n", + "point_styles = [\"v\",\"o\"]\n", + "marker_size = [20, 9]\n", + "\n", + "for i, (k, v) in enumerate(sub_phase.items()):\n", + " ax_phase1.scatter(\n", + " x,\n", + " v,\n", + " color=colors[i % len(colors)],\n", + " alpha=0.5,\n", + " s=marker_size[i % len(marker_size)],\n", + " marker=point_styles[i % len(point_styles)],\n", + " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", + " )\n", + " ax_phase1.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\")\n", + "\n", + "# Set labels for twin axes\n", + "ax_phase1.set_ylabel(\"Angle [rad]\", fontsize=12)\n", + "\n", + "# Add legends\n", + "fig_phase.legend(loc=\"upper left\", fontsize=\"9\", bbox_to_anchor=(0.1, 0.9))\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "total_dynamics = dynamics_func_fvector(data)\n", + "sub_dynamics = {\n", + " p:compute_sub_intensity(\n", + " dynamics_func_fvector,\n", + " data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", + " )\n", + " \n", + " for p, _ in resonances\n", + "}\n", + "\n", + "sub_dynamics_bw = {\n", + " p:compute_sub_intensity(\n", + " dynamics_func_bw,\n", + " data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"Dummy_\",\n", + " )\n", + " \n", + " for p, _ in resonances\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_1 = np.linspace(2, (m_res1**2+m_res2**2)/2, num=500)\n", + "x_2 = np.linspace((m_res1**2+m_res2**2)/2,5, num=500)\n", + "\n", + "#x_1 = np.linspace(2, 5, num=500)\n", + "#x_2 = np.linspace(2,5, num=500)\n", + "\n", + "data_1 = {\"m_01\": np.sqrt(x_1 + epsilon * 1j)}\n", + "data_2 = {\"m_01\": np.sqrt(x_2 + epsilon * 1j)}\n", + "\n", + "y_imag_1 = dynamics_func_fvector(data_1).imag\n", + "y_real_1 = dynamics_func_fvector(data_1).real\n", + "y_imag_2 = dynamics_func_fvector(data_2).imag\n", + "y_real_2 = dynamics_func_fvector(data_2).real\n", + "fig_A, axs = plt.subplots(1,2, figsize=(10, 5))\n", + "colorsA = [\"red\", \"blue\"]\n", + "axA,axA1 =axs\n", + "for i, (k, v) in enumerate(sub_dynamics.items()):\n", + " axA1.plot(\n", + " v.real,\n", + " v.imag,\n", + " color=colorsA[i % len(colorsA)],\n", + " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", + " )\n", + "\n", + "axA.plot(y_real_1, y_imag_1, label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \", color=\"red\")\n", + "axA.plot(y_real_2, y_imag_2, label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \", color=\"blue\")\n", + "axA.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", + "axA.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", + "axA.axhline(0, color='black')\n", + "axA.axvline(0, color='black')\n", + "axA1.axhline(0, color='black')\n", + "axA1.axvline(0, color='black')\n", + "plt.tight_layout()\n", + "axA.legend(loc='upper left')\n", + "# Save the plot as PDF\n", + "#plt.savefig(\"_func_plots.pdf\", dpi=750)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig_phase_bw, ax_phase_bw = plt.subplots(figsize=(8, 5), dpi=500)\n", + "ax_phase_bw.set_xlim(2, 5)\n", + "ax_phase_bw.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", + "ax_phase_bw.set_ylabel(R\"Intensity [a. u.]\")\n", + "ax_phase_bw.set_yticks([])\n", + "\n", + "# Plot histogram\n", + "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", + "ax_phase_bw.hist(\n", + " phsp_projection,\n", + " weights=total_intensities_1,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=\"grey\",\n", + " label=\"Full intensity BW\",\n", + ")\n", + "\n", + "\n", + "ax_phase1_bw= ax_phase_bw.twinx()\n", + "ax_phase1_bw.set_ylabel(R\"Angle [a. u.]\")\n", + "ax_phase1_bw.set_yticks([-np.pi,-np.pi/2,0,np.pi/2, +np.pi])\n", + "ax_phase1_bw.set_yticklabels([R\"$-\\pi$\",R\"$-\\frac{\\pi}{2}$\",R\"0\",R\"$+\\frac{\\pi}{2}$\", R\"$+\\pi$\"])\n", + "colors_bw = [\"magenta\", \"cyan\"]\n", + "\n", + "# Plot total phases\n", + "ax_phase1_bw\n", + "ax_phase1_bw.plot(x, total_phase_1, color=\"blue\", label=\"Total Phase Breit-Wigner\",linestyle=\"--\",)\n", + "\n", + "\n", + "for i, (k, v) in enumerate(sub_phase_bw.items()):\n", + " ax_phase1_bw.plot(\n", + " x,\n", + " v,\n", + " color=colors_bw[i % len(colors_bw)],zorder=999, linestyle=\"--\",\n", + " label=f\"Resonance at {k.mass} GeV Breit-Wigner\",\n", + " )\n", + " ax_phase1_bw.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\"),\n", + "# Set labels for twin axes\n", + "ax_phase1_bw.set_ylabel(\"Angle [rad]\")\n", + "\n", + "# Add legends\n", + "fig_phase_bw.legend(loc=\"upper left\", fontsize=\"7\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "y_imag_1_bw = dynamics_func_bw(data_1).imag\n", + "y_real_1_bw = dynamics_func_bw(data_1).real\n", + "y_imag_2_bw = dynamics_func_bw(data_2).imag\n", + "y_real_2_bw = dynamics_func_bw(data_2).real\n", + "fig_A, axs = plt.subplots(1,2, figsize=(10, 5))\n", + "colorsA_bw = [\"red\", \"blue\"]\n", + "axA_bw,axA1_bw =axs\n", + "for i, (k, v) in enumerate(sub_dynamics_bw.items()):\n", + " axA1_bw.plot(\n", + " v.real,\n", + " v.imag,\n", + " color=colorsA_bw[i % len(colorsA_bw)],\n", + " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", + " )\n", + "\n", + "axA_bw.plot(y_real_1_bw, y_imag_1_bw, label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \", color=\"red\")\n", + "axA_bw.plot(y_real_2_bw, y_imag_2_bw, label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \", color=\"blue\")\n", + "axA_bw.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", + "axA_bw.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", + "axA_bw.axhline(0, color='black')\n", + "axA_bw.axvline(0, color='black')\n", + "axA1_bw.axhline(0, color='black')\n", + "axA1_bw.axvline(0, color='black')\n", + "plt.tight_layout()\n", + "axA_bw.legend(loc='upper left')\n", + "# Save the plot as PDF\n", + "#plt.savefig(\"_func_plots.pdf\", dpi=750)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Toyfits_DataFVector_MoreChannel.ipynb b/Toyfits_DataFVector_MoreChannel.ipynb new file mode 100644 index 00000000..7fb9df09 --- /dev/null +++ b/Toyfits_DataFVector_MoreChannel.ipynb @@ -0,0 +1,2166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [R990] Genarate data with $F$ vector $F$ vector for n poles and n channels \n", + "## Working plots Remco" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "from __future__ import annotations\n", + "\n", + "from dataclasses import dataclass\n", + "from typing import Any, Iterable, Mapping\n", + "\n", + "import ampform\n", + "import attrs\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import qrules\n", + "import sympy as sp\n", + "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", + "from ampform.helicity import HelicityModel, ParameterValues\n", + "from ampform.io import aslatex\n", + "from ampform.kinematics.phasespace import Kallen\n", + "from ampform.sympy import perform_cached_doit, unevaluated\n", + "from IPython.display import Math, display\n", + "from matplotlib import cm\n", + "from qrules.particle import Particle, ParticleCollection\n", + "from qrules.transition import ReactionInfo\n", + "from sympy import Abs\n", + "from sympy.matrices.expressions.matexpr import MatrixElement\n", + "from tensorwaves.data import (\n", + " IntensityDistributionGenerator,\n", + " SympyDataTransformer,\n", + " TFPhaseSpaceGenerator,\n", + " TFUniformRealNumberGenerator,\n", + " TFWeightedPhaseSpaceGenerator,\n", + ")\n", + "from tensorwaves.estimator import UnbinnedNLL\n", + "from tensorwaves.function.sympy import create_parametrized_function\n", + "from tensorwaves.interface import DataSample, Estimator, Function, ParameterValue\n", + "from tensorwaves.optimizer import Minuit2\n", + "from tensorwaves.optimizer.callbacks import CSVSummary\n", + "\n", + "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", + "\n", + "_ = np.seterr(invalid=\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect dynamics symbols" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "| Resonance | $m$ [MeV] | $\\Gamma$ [MeV] | $J^P$ |\n", + "|-----------|-----------|----------------|-------|\n", + "| $N^*(1440)$ | 1398 | 167 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1535)$ | 1530 | 210 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1650)$ | 1668 | 194 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1710)$ | 1749 | 263 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1880)$ | 1876 | 261 | $\\frac{1}{2}^{+}$ |\n", + "| $N^*(1895)$ | 2045 | 240 | $\\frac{1}{2}^{-}$ |\n", + "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def load_particle_database() -> ParticleCollection:\n", + " particle_database = qrules.load_default_particles()\n", + " additional_definitions = qrules.io.load(\n", + " \"../../../additional-nstar-sigma-definitions.yml\"\n", + " )\n", + " particle_database.update(additional_definitions)\n", + " return particle_database\n", + "\n", + "\n", + "PARTICLE_DB = load_particle_database()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "680eabb7bba743b09c1cbe16ad28890d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Propagating quantum numbers: 0%| | 0/36 [00:008g} GeV {res.width:>8g} GeV\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "@dataclass\n", + "class TwoBodyDecay: # specific to the channel\n", + " child1: Particle\n", + " child2: Particle\n", + "\n", + "\n", + "DECAYS = tuple(\n", + " TwoBodyDecay(\n", + " child1=reaction.final_state[0],\n", + " child2=reaction.final_state[1],\n", + " )\n", + " for reaction in REACTIONS\n", + ")\n", + "s = sp.Symbol(\"m_01\", real=True) ** 2\n", + "\n", + "PARAMETERS_DEFAULTS = {}\n", + "for model in MODELS:\n", + " PARAMETERS_DEFAULTS.update(model.parameter_defaults)\n", + " del model\n", + "\n", + "resonances, *_ = COLLECTED_X_SYMBOLS.values()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Formulate dynamics expression" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle X_{Q=+1, S=3/2, P =1}$" + ], + "text/plain": [ + "X_{Q=+1, S=3/2, P =1}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " N(Fakestar)+ 1.82 GeV 0.6 GeV \n" + ] + }, + { + "data": { + "text/plain": [ + "ParameterValues({\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to K^{0}_{0} \\Sigma^{+}_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to K^{0}_{0} \\Sigma^{+}_{+1/2}}: (1+0j),\n", + " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to K^{0}_{0} \\Sigma^{+}_{+1/2}}: (1+0j),\n", + " m_0: 0.49761099999999997,\n", + " m_1: 1.1893699999999998,\n", + " m_2: 0.93827208816,\n", + " m_012: 3.0969,\n", + " })" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", + " display(symbol)\n", + " for p, _ in resonances:\n", + " print(f\" {p.name:<20s} {p.mass:>8g} GeV {p.width:>8g} GeV \")\n", + "MODELS[0].parameter_defaults" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Formulate Dynamics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phasespace factor" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\begin{array}{rcl}\n", + " \\rho^\\mathrm{CM}_{m_{1},m_{2}}\\left(s\\right) &=& - 16 i \\pi \\Sigma\\left(s\\right) \\\\\n", + " \\Sigma\\left(s\\right) &=& \\frac{- \\left(m_{1}^{2} - m_{2}^{2}\\right) \\left(- \\frac{1}{\\left(m_{1} + m_{2}\\right)^{2}} + \\frac{1}{s}\\right) \\log{\\left(\\frac{m_{1}}{m_{2}} \\right)} + \\frac{2 \\log{\\left(\\frac{\\left|{\\frac{m_{1}^{2} + m_{2}^{2} + 2 \\sqrt{s} q\\left(s\\right) - s}{m_{1} m_{2}}}\\right|}{2} \\right)} q\\left(s\\right)}{\\sqrt{s}}}{16 \\pi^{2}} \\\\\n", + " q\\left(s\\right) &=& \\frac{\\sqrt{\\lambda\\left(s, m_{1}^{2}, m_{2}^{2}\\right)}}{2 \\sqrt{s}} \\\\\n", + "\\end{array}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@unevaluated(real=False)\n", + "class PhaseSpaceCM(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"\\rho^\\mathrm{{CM}}_{{{m1},{m2}}}\\left({s}\\right)\"\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " return -16 * sp.pi * sp.I * ChewMandelstam(s, m1, m2)\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class ChewMandelstam(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"\\Sigma\\left({s}\\right)\" # noqa: RUF027\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " q = BreakupMomentum(s, m1, m2)\n", + " return (\n", + " 1\n", + " / (16 * sp.pi**2)\n", + " * (\n", + " (2 * q / sp.sqrt(s))\n", + " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", + " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", + " )\n", + " )\n", + "\n", + "\n", + "@unevaluated(real=False)\n", + "class BreakupMomentum(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"q\\left({s}\\right)\" # noqa: RUF027\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))\n", + "\n", + "\n", + "s, m1, m2 = sp.symbols(\"s m1 m2\")\n", + "exprs = [\n", + " PhaseSpaceCM(s, m1, m2),\n", + " ChewMandelstam(s, m1, m2),\n", + " BreakupMomentum(s, m1, m2),\n", + "]\n", + "Math(aslatex({e: e.doit(deep=False) for e in exprs}))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\begin{array}{rcl}\n", + " \\Gamma_s\\left(s\\right) &=& \\gamma_{0} \\rho^\\mathrm{CM}_{m_{1},m_{2}}\\left(s\\right) \\\\\n", + "\\end{array}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@unevaluated(real=False)\n", + "class EnergyDecaywidth(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " width: Any\n", + " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\" # noqa: RUF027\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2, width = self.args\n", + " return width * PhaseSpaceCM(s, m1, m2)\n", + "\n", + "\n", + "s, m1, m2, width = sp.symbols(\"s m1 m2 gamma0\")\n", + "expr = EnergyDecaywidth(s, m1, m2, width)\n", + "Math(aslatex({expr: expr.doit(deep=False)}))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\begin{array}{rcl}\n", + " CM_{m_{1},m_{2}}\\left(s\\right) &=& - \\frac{2 \\left(- \\frac{\\left(m_{1}^{2} + m_{2}^{2}\\right) \\log{\\left(\\frac{m_{1}}{m_{2}} \\right)}}{2 m_{1}^{2} - 2 m_{2}^{2}} - 0.5 - \\frac{\\sqrt{\\left(- s + \\left(m_{1} - m_{2}\\right)^{2}\\right) \\left(- s + \\left(m_{1} + m_{2}\\right)^{2}\\right)} \\log{\\left(\\frac{\\sqrt{- s + \\left(m_{1} - m_{2}\\right)^{2}} + \\sqrt{- s + \\left(m_{1} + m_{2}\\right)^{2}}}{2 \\sqrt{m_{1} m_{2}}} \\right)}}{s} + \\frac{\\left(m_{1}^{2} - m_{2}^{2}\\right) \\log{\\left(\\frac{m_{1}}{m_{2}} \\right)}}{2 s}\\right)}{\\pi} \\\\\n", + "\\end{array}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@unevaluated(real=False)\n", + "class CM(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " _latex_repr_ = R\"CM_{{{m1},{m2}}}\\left({s}\\right)\" # noqa: RUF027\n", + "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2 = self.args\n", + " return (\n", + " -2\n", + " / sp.pi\n", + " * (\n", + " -1\n", + " / s\n", + " * sp.sqrt(((m1 + m2) ** 2 - s) * ((m1 - m2) ** 2 - s))\n", + " * sp.log(\n", + " (sp.sqrt((m1 + m2) ** 2 - s) + sp.sqrt((m1 - m2) ** 2 - s))\n", + " / (2 * sp.sqrt(m1 * m2))\n", + " )\n", + " + (m1**2 - m2**2) / (2 * s) * sp.log(m1 / m2)\n", + " - (m1**2 + m2**2) / (2 * (m1**2 - m2**2)) * sp.log(m1 / m2)\n", + " - 1 / 2\n", + " )\n", + " )\n", + "\n", + "\n", + "s, m1, m2 = sp.symbols(\"s m1 m2\")\n", + "CM_expr = CM(s, m1, m2)\n", + "Math(aslatex({CM_expr: CM_expr.doit(deep=False)}))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### \n", + "\n", + "Relativistic Breit-Wigner" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "PARAMETERS_BW = {}\n", + "\n", + "\n", + "def formulate_breit_wigner(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (_, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m_a = variables.outgoing_state_mass1\n", + " m_b = variables.outgoing_state_mass2\n", + " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", + " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", + " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", + " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", + " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", + " for i, (p, _) in enumerate(resonances):\n", + " PARAMETERS_BW[w[i]] = p.width\n", + " PARAMETERS_BW[m[i]] = p.mass\n", + " PARAMETERS_BW[b[i]] = 1\n", + " PARAMETERS_BW[d[i]] = 1\n", + " PARAMETERS_BW[L[i]] = 0\n", + " return rel_bw" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Define matrix symbols" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "n_channels = len(REACTIONS)\n", + "I = sp.Identity(n_channels)\n", + "K = sp.MatrixSymbol(\"K\", n_channels, n_channels)\n", + "P = sp.MatrixSymbol(\"P\", n_channels, 1)\n", + "F = sp.MatrixSymbol(\"F\", n_channels, 1)\n", + "rho = sp.MatrixSymbol(\"rho\", n_channels, n_channels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "\n", + "\n", + "### $K$ matrix " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input", + "full-width" + ] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}\\frac{\\left(g_{N(Fakestar)^+,0}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}} & \\frac{g_{N(Fakestar)^+,0} g_{N(Fakestar)^+,1}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\\\\\frac{g_{N(Fakestar)^+,0} g_{N(Fakestar)^+,1}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}} & \\frac{\\left(g_{N(Fakestar)^+,1}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[ g_{N(Fakestar)^+,0}**2/(-m_01**2 + m_{N(Fakestar)^+}**2), g_{N(Fakestar)^+,0}*g_{N(Fakestar)^+,1}/(-m_01**2 + m_{N(Fakestar)^+}**2)],\n", + "[g_{N(Fakestar)^+,0}*g_{N(Fakestar)^+,1}/(-m_01**2 + m_{N(Fakestar)^+}**2), g_{N(Fakestar)^+,1}**2/(-m_01**2 + m_{N(Fakestar)^+}**2)]])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def formulate_k_matrix(\n", + " resonances: list[tuple[Particle, int]], n_channels: int\n", + ") -> dict[MatrixElement, sp.Expr]:\n", + " Kmatrix_expressions = {}\n", + " for i in range(n_channels):\n", + " for j in range(n_channels):\n", + " resonance_contributions = []\n", + " for res, _ in resonances:\n", + " s = sp.Symbol(\"m_01\", real=True) ** 2\n", + " m_a_i = sp.Symbol(Rf\"m_{{0,{i}}}\")\n", + " m_b_i = sp.Symbol(Rf\"m_{{1,{i}}}\")\n", + " m_a_j = sp.Symbol(Rf\"m_{{0,{j}}}\")\n", + " m_b_j = sp.Symbol(Rf\"m_{{1,{j}}}\")\n", + " g_Ri = sp.Symbol(Rf\"g_{{{res.latex},{i}}}\")\n", + " g_Rj = sp.Symbol(Rf\"g_{{{res.latex},{j}}}\")\n", + " m_R = sp.Symbol(Rf\"m_{{{res.latex}}}\")\n", + " parameter_defaults = {\n", + " m_a_i: DECAYS[i].child1.mass,\n", + " m_b_i: DECAYS[i].child2.mass,\n", + " m_a_j: DECAYS[j].child1.mass,\n", + " m_b_j: DECAYS[j].child2.mass,\n", + " m_R: res.mass,\n", + " g_Ri: 1,\n", + " g_Rj: 0.1,\n", + " }\n", + " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", + " expr = (g_Ri * g_Rj) / (m_R**2 - s)\n", + " resonance_contributions.append(expr)\n", + " Kmatrix_expressions[K[i, j]] = sum(resonance_contributions)\n", + "\n", + " return Kmatrix_expressions\n", + "\n", + "\n", + "K_expressions = formulate_k_matrix(resonances, n_channels=len(REACTIONS))\n", + "Math(aslatex(K_expressions))\n", + "K_matrix = K.as_explicit()\n", + "K.as_explicit().xreplace(K_expressions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### $P$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}\\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+,0}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\\\\\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+,1}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[\\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+,0}/(-m_01**2 + m_{N(Fakestar)^+}**2)],\n", + "[\\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+,1}/(-m_01**2 + m_{N(Fakestar)^+}**2)]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def formulate_p_vector(\n", + " resonances: list[tuple[Particle, int]], n_channels: int\n", + ") -> dict[MatrixElement, sp.Expr]:\n", + " P_expressions = {}\n", + " for i in range(n_channels):\n", + " resonance_contributions = []\n", + " for res, _ in resonances:\n", + " s = sp.Symbol(\"m_01\", real=True) ** 2\n", + " m_a = sp.Symbol(Rf\"m_{{0,{i}}}\")\n", + " m_b = sp.Symbol(Rf\"m_{{1,{i}}}\")\n", + " g_Ri = sp.Symbol(Rf\"g_{{{res.latex},{i}}}\")\n", + " beta_R = sp.Symbol(Rf\"\\beta_{{{res.latex}}}\")\n", + " m_R = sp.Symbol(Rf\"m_{{{res.latex}}}\")\n", + "\n", + " parameter_defaults = {\n", + " m_a: DECAYS[i].child1.mass,\n", + " m_b: DECAYS[i].child2.mass,\n", + " m_R: res.mass,\n", + " beta_R: 1 + 0j,\n", + " g_Ri: 1,\n", + " }\n", + " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", + " expr = (beta_R * g_Ri) / (m_R**2 - s)\n", + " resonance_contributions.append(expr)\n", + " P_expressions[P[i, 0]] = sum(resonance_contributions)\n", + "\n", + " return P_expressions\n", + "\n", + "\n", + "P_expressions = formulate_p_vector(resonances, n_channels=len(REACTIONS))\n", + "Math(aslatex(P_expressions))\n", + "P_vector = P.as_explicit()\n", + "P.as_explicit().xreplace(P_expressions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phase space" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left[\\begin{matrix}\\rho^\\mathrm{CM}_{m_{0,0},m_{1,0}}\\left(m_{01}^{2}\\right) & 0\\\\0 & \\rho^\\mathrm{CM}_{m_{0,1},m_{1,1}}\\left(m_{01}^{2}\\right)\\end{matrix}\\right]$" + ], + "text/plain": [ + "Matrix([\n", + "[PhaseSpaceCM(m_01**2, m_{0,0}, m_{1,0}), 0],\n", + "[ 0, PhaseSpaceCM(m_01**2, m_{0,1}, m_{1,1})]])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def formulate_phsp_factor_matrix(n_channels: int) -> dict[sp.MatrixElement, sp.Expr]:\n", + " matrix_expressions = {}\n", + "\n", + " for i in range(n_channels):\n", + " for j in range(n_channels):\n", + " if i == j:\n", + " m_a_i = sp.Symbol(Rf\"m_{{0,{i}}}\")\n", + " m_b_i = sp.Symbol(Rf\"m_{{1,{i}}}\")\n", + " s = sp.Symbol(\"m_01\", real=True) ** 2\n", + " rho_i = PhaseSpaceCM(s, m_a_i, m_b_i)\n", + " matrix_expressions[rho[i, j]] = rho_i\n", + " parameter_defaults = {\n", + " m_a_i: DECAYS[i].child1.mass,\n", + " m_b_i: DECAYS[i].child2.mass,\n", + " }\n", + " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", + " else:\n", + " matrix_expressions[rho[i, j]] = 0\n", + "\n", + " return matrix_expressions\n", + "\n", + "\n", + "rho_expressions = formulate_phsp_factor_matrix(n_channels=len(REACTIONS))\n", + "rho.as_explicit().xreplace(rho_expressions)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### $F$ vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{note}\n", + "For some reason one has to leave out the multiplication of $\\rho$ by $i$ within the calculation of the $F$ vector\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\left(\\mathbb{I} + - i K \\rho\\right)^{-1} P$" + ], + "text/plain": [ + "(I - I*K*rho)**(-1)*P" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "F = (I -sp.I* K * rho).inv() * P\n", + "F" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "full-width" + ] + }, + "outputs": [], + "source": [ + "F_vector = F.as_explicit()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "full-width" + ] + }, + "outputs": [], + "source": [ + "combined_expressions = {**K_expressions, **rho_expressions, **P_expressions}\n", + "F_expressions = np.array([\n", + " perform_cached_doit(F_vector[i].xreplace(combined_expressions))\n", + " for i in range(n_channels)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Model $F$ vector" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "DYNAMICS_EXPRESSIONS_FVECTOR = []\n", + "for i in range(n_channels):\n", + " exprs = {\n", + " symbol: F_expressions[i] for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " }\n", + " DYNAMICS_EXPRESSIONS_FVECTOR.append(exprs)\n", + "\n", + "MODELS_FVECTOR = []\n", + "for i in range(n_channels):\n", + " MODELS_FVECTOR.append(\n", + " attrs.evolve(\n", + " MODELS[i],\n", + " parameter_defaults=ParameterValues({\n", + " **MODELS[i].parameter_defaults,\n", + " **PARAMETERS_DEFAULTS,\n", + " }),\n", + " )\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "FULL_EXPRESSIONS_FVECTOR = []\n", + "for i in range(n_channels):\n", + " FULL_EXPRESSIONS_FVECTOR.append(\n", + " perform_cached_doit(MODELS_FVECTOR[i].expression).xreplace(\n", + " DYNAMICS_EXPRESSIONS_FVECTOR[i]\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Parametrized Function\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "UNFOLDED_EXPRESSIONS_FVECTOR = []\n", + "INTENSITY_FUNCS_FVECTOR = []\n", + "for i in range(n_channels):\n", + " UNFOLDED_EXPRESSIONS_FVECTOR.append(FULL_EXPRESSIONS_FVECTOR[i].doit())\n", + " INTENSITY_FUNCS_FVECTOR.append(\n", + " create_parametrized_function(\n", + " expression=UNFOLDED_EXPRESSIONS_FVECTOR[i],\n", + " backend=\"jax\",\n", + " parameters=MODELS_FVECTOR[i].parameter_defaults,\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "new_parameters_fvector = {\n", + " R\"m_{N(Fakestar)^+}\": 1.71,\n", + " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", + " R\"g_{N(Fakestar)^+,0}\": 0.8,\n", + " R\"g_{N(Fakestar)^+,1}\": 0.9,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "for i in range(n_channels):\n", + " INTENSITY_FUNCS_FVECTOR[i].update_parameters(new_parameters_fvector)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate data with $F$ vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate phase space sample" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "HELICITY_TRANSFORMERS = []\n", + "for i in range(n_channels):\n", + " HELICITY_TRANSFORMERS.append(\n", + " SympyDataTransformer.from_sympy(\n", + " MODELS_FVECTOR[i].kinematic_variables, backend=\"numpy\"\n", + " )\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import re \n", + "re.match(r\"^m_\\d\\d$\",\"m_01\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-05-18 22:07:37.952367: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-05-18 22:07:37.952389: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-05-18 22:07:37.953198: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-05-18 22:07:38.561912: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b1a52872669e4e21a1a11a21ef4fe83f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating phase space sample: 0%| | 0/100000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(n_channels):\n", + " fig, ax = plt.subplots(figsize=(6, 5))\n", + " intensity = np.real(INTENSITY_FUNCS_FVECTOR[i](PHSP[i]))\n", + " c = ax.hist(\n", + " np.real(PHSP[i][\"m_01\"]) ** 2,\n", + " bins=100,\n", + " weights=intensity,\n", + " )\n", + " ax.set_xlabel(R\"$M^2\\left(\\eta p\\right)\\, \\mathrm{[(GeV/c)^2]}$\")\n", + " ax.set_ylabel(R\"Intensity [a.u.]\")\n", + " fig.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "339f1c4dc3444581b81c002bd8e0adec", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Generating intensity-based sample: 0%| | 0/50000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(n_channels):\n", + " resonances = sorted(\n", + " MODELS[i].reaction_info.get_intermediate_particles(),\n", + " key=lambda p: p.mass,\n", + " )\n", + " evenly_spaced_interval = np.linspace(\n", + " 0, 1, len(INTENSITY_FUNCS_FVECTOR[i].parameters.items())\n", + " )\n", + " colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", + " fig, ax = plt.subplots(figsize=(9, 4))\n", + " ax.hist(\n", + " np.real(DATA[i][\"m_01\"]),\n", + " bins=200,\n", + " alpha=0.5,\n", + " density=True,\n", + " )\n", + " ax.set_xlabel(\"$m$ [GeV]\")\n", + " for (k, v), color in zip(new_parameters_fvector.items(), colors):\n", + " if k.startswith(\"m_{\"):\n", + " ax.axvline(\n", + " x=v,\n", + " linestyle=\"dotted\",\n", + " label=r\"$\" + k + \"$\",\n", + " color=color,\n", + " )\n", + " ax.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Perform fit" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Set initial parameters " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to K^{0}_{0} \\\\Sigma^{+}_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to K^{0}_{0} \\\\Sigma^{+}_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to K^{0}_{0} \\\\Sigma^{+}_{+1/2}}': (1+0j),\n", + " 'm_0': 0.547862,\n", + " 'm_1': 0.93827208816,\n", + " 'm_2': 0.93827208816,\n", + " 'm_012': 3.0969,\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", + " 'm_{0,0}': 0.49761099999999997,\n", + " 'm_{1,0}': 1.1893699999999998,\n", + " 'm_{N(Fakestar)^+}': 1.71,\n", + " 'g_{N(Fakestar)^+,0}': 0.8,\n", + " 'm_{0,1}': 0.547862,\n", + " 'm_{1,1}': 0.93827208816,\n", + " 'g_{N(Fakestar)^+,1}': 0.9,\n", + " '\\\\beta_{N(Fakestar)^+}': (1+0j)}" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "initial_parameters = {\n", + " R\"m_{N(Fakestar)^+}\": 1.9,\n", + " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", + " R\"g_{N(Fakestar)^+,0}\": 0.8,\n", + " R\"g_{N(Fakestar)^+,1}\": 0.6,\n", + "}\n", + "INTENSITY_FUNCS_FVECTOR[0].parameters\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "def indicate_masses(ax, function):\n", + " ax.set_xlabel(\"$m$ [GeV]\")\n", + " for (k, v), color_F in zip(function.parameters.items(), colors_F):\n", + " if k.startswith(\"m_{N\"):\n", + " ax.axvline(\n", + " x=v,\n", + " linestyle=\"dotted\",\n", + " label=r\"$\" + k + \"$\" \"(F vector)\",\n", + " color=color_F,\n", + " )\n", + "\n", + "\n", + "def compare_model(\n", + " variable_name: str,\n", + " data: DataSample,\n", + " phsp: DataSample,\n", + " function: Function[DataSample, np.ndarray],\n", + " bins: int = 100,\n", + "):\n", + " fig, ax = plt.subplots(figsize=(9, 4))\n", + " ax.hist(\n", + " data[variable_name].real,\n", + " bins=bins,\n", + " alpha=0.5,\n", + " label=\"data\",\n", + " density=True,\n", + " )\n", + " intensities = function(phsp)\n", + " ax.hist(\n", + " phsp[variable_name].real,\n", + " weights=intensities,\n", + " bins=bins,\n", + " histtype=\"step\",\n", + " color=\"red\",\n", + " label=\"Fit model with $F$ vector\",\n", + " density=True,\n", + " )\n", + " indicate_masses(ax, function)\n", + " ax.axvline(DECAYS[0].child1.mass+DECAYS[0].child2.mass, color='grey', linestyle='dotted', label=rf'${DECAYS[0].child1.latex} \\, {DECAYS[0].child2.latex}$ threshhold')\n", + " ax.axvline(DECAYS[1].child1.mass+DECAYS[1].child2.mass, color='grey', linestyle='dotted', label=rf'${DECAYS[1].child1.latex} \\, {DECAYS[1].child2.latex}$ threshhold')\n", + " ax.legend()\n", + " fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ORIGINAL_PARAMETERS_F = []\n", + "for i in range(n_channels):\n", + " resonances = sorted(\n", + " MODELS[i].reaction_info.get_intermediate_particles(),\n", + " key=lambda p: p.mass,\n", + " )\n", + " evenly_spaced_interval = np.linspace(\n", + " 0, 1, len(INTENSITY_FUNCS_FVECTOR[i].parameters.items())\n", + " )\n", + " colors_F = [cm.rainbow(x) for x in evenly_spaced_interval]\n", + " original_parameters = INTENSITY_FUNCS_FVECTOR[i].parameters\n", + " ORIGINAL_PARAMETERS_F.append(original_parameters)\n", + " INTENSITY_FUNCS_FVECTOR[i].update_parameters(initial_parameters)\n", + " compare_model(\"m_01\", DATA[i], PHSP[i], INTENSITY_FUNCS_FVECTOR[i])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Define estimator" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ESTIMATORS_F = []\n", + "for i in range(n_channels):\n", + " estimator_fvector = UnbinnedNLL(\n", + " INTENSITY_FUNCS_FVECTOR[i],\n", + " data=DATA[i],\n", + " phsp=PHSP[i],\n", + " backend=\"jax\",\n", + " )\n", + " ESTIMATORS_F.append(estimator_fvector)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "class EstimatorSum(Estimator):\n", + " def __init__(self, estimators: Iterable[Estimator]) -> None:\n", + " self.__estimators = tuple(estimators)\n", + "\n", + " def __call__(self, parameters: Mapping[str, ParameterValue]) -> float:\n", + " return sum(estimator(parameters) for estimator in self.__estimators)\n", + "\n", + " def gradient(\n", + " self, parameters: Mapping[str, ParameterValue]\n", + " ) -> dict[str, ParameterValue]:\n", + " raise NotImplementedError" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "combined_estimators = EstimatorSum(ESTIMATORS_F)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Optimized fit" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a1cf71730e9844c8befd2d7ccf3282a4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "FitResult(\n", + " minimum_valid=True,\n", + " execution_time=3.351858377456665,\n", + " function_calls=162,\n", + " estimator_value=-18694.71978168161,\n", + " parameter_values={\n", + " 'm_{N(Fakestar)^+}': 1.7102099446837458,\n", + " 'g_{N(Fakestar)^+,0}': 0.8145358423603044,\n", + " 'g_{N(Fakestar)^+,1}': 0.8963220998212011,\n", + " '\\\\beta_{N(Fakestar)^+}': (-36.888094928163476-4.678879465472437j),\n", + " },\n", + " parameter_errors={\n", + " 'm_{N(Fakestar)^+}': 0.000913074977897192,\n", + " 'g_{N(Fakestar)^+,0}': 0.010543626222578407,\n", + " 'g_{N(Fakestar)^+,1}': 0.0036131105694627934,\n", + " '\\\\beta_{N(Fakestar)^+}': (13106208.4666789+13641374.272541337j),\n", + " },\n", + ")" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "minuit2 = Minuit2(\n", + " callback=CSVSummary(\"fit_traceback.csv\"),\n", + " use_analytic_gradient=False,\n", + ")\n", + "fit_result = minuit2.optimize(combined_estimators, initial_parameters)\n", + "fit_result" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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initialfit resultoriginal
$m_{N(Fakestar)^+}$1.9+0.0j1.710+ 0.000j1.71+0.00j
$g_{N(Fakestar)^+,0}$0.8+0.0j0.815+ 0.000j0.80+0.00j
$g_{N(Fakestar)^+,1}$0.6+0.0j0.896+ 0.000j0.90+0.00j
$\\beta_{N(Fakestar)^+}$1.0+0.0j-36.888- 4.679j1.00+0.00j
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" + ], + "text/plain": [ + " initial fit result original\n", + "$m_{N(Fakestar)^+}$ 1.9+0.0j 1.710+ 0.000j 1.71+0.00j\n", + "$g_{N(Fakestar)^+,0}$ 0.8+0.0j 0.815+ 0.000j 0.80+0.00j\n", + "$g_{N(Fakestar)^+,1}$ 0.6+0.0j 0.896+ 0.000j 0.90+0.00j\n", + "$\\beta_{N(Fakestar)^+}$ 1.0+0.0j -36.888- 4.679j 1.00+0.00j" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "original_parameters = {\n", + " **ORIGINAL_PARAMETERS_F[0],\n", + " **ORIGINAL_PARAMETERS_F[1],\n", + "}\n", + "df = pd.DataFrame({\n", + " f\"${p}$\": (\n", + " initial_parameters[p],\n", + " fit_result.parameter_values[p],\n", + " original_parameters[p],\n", + " )\n", + " for p in fit_result.parameter_values\n", + "}).T\n", + "df.columns = (\"initial\", \"fit result\", \"original\")\n", + "df.round(decimals=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FitResult(\n", + " minimum_valid=True,\n", + " execution_time=3.351858377456665,\n", + " function_calls=162,\n", + " estimator_value=-18694.71978168161,\n", + " parameter_values={\n", + " 'm_{N(Fakestar)^+}': 1.7102099446837458,\n", + " 'g_{N(Fakestar)^+,0}': 0.8145358423603044,\n", + " 'g_{N(Fakestar)^+,1}': 0.8963220998212011,\n", + " '\\\\beta_{N(Fakestar)^+}': (-36.888094928163476-4.678879465472437j),\n", + " },\n", + " parameter_errors={\n", + " 'm_{N(Fakestar)^+}': 0.000913074977897192,\n", + " 'g_{N(Fakestar)^+,0}': 0.010543626222578407,\n", + " 'g_{N(Fakestar)^+,1}': 0.0036131105694627934,\n", + " '\\\\beta_{N(Fakestar)^+}': (13106208.4666789+13641374.272541337j),\n", + " },\n", + ")" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fit_result" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-37379.43956336322" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_real_par = fit_result.count_number_of_parameters(complex_twice=True)\n", + "n_events = len(next(iter(data.values())))\n", + "log_likelihood = -fit_result.estimator_value\n", + " \n", + "aic = 2 * n_real_par - 2 * log_likelihood\n", + "bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", + "aic" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-37335.34067194117" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 40d11cfa83444075df8a613a86f894381a95ba2e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 22 May 2024 11:24:52 +0000 Subject: [PATCH 02/92] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- Mulitpleqn_Toyfit.ipynb | 830 ++++--------------------- SubintensityPlots_mitAgrand.ipynb | 768 +++++------------------ Toyfits_DataFVector_MoreChannel.ipynb | 852 ++++---------------------- 3 files changed, 381 insertions(+), 2069 deletions(-) diff --git a/Mulitpleqn_Toyfit.ipynb b/Mulitpleqn_Toyfit.ipynb index ac444ee6..c2bb241d 100644 --- a/Mulitpleqn_Toyfit.ipynb +++ b/Mulitpleqn_Toyfit.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [] }, @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,112 +69,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "db52c172f856481b820a5068d1db636e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Propagating quantum numbers: 0%| | 0/36 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "g0_edge0\n", - "0: eta\n", - "\n", - "\n", - "\n", - "g0_edge1\n", - "1: p\n", - "\n", - "\n", - "\n", - "g0_edge2\n", - "2: p~\n", - "\n", - "\n", - "\n", - "g0_edge-1\n", - "J/psi(1S)\n", - "\n", - "\n", - "\n", - "g0_node0\n", - "\n", - "\n", - "\n", - "g0_edge-1->g0_node0\n", - "\n", - "\n", - "\n", - "\n", - "g0_node0->g0_edge2\n", - "\n", - "\n", - "\n", - "\n", - "g0_node1\n", - "\n", - "\n", - "\n", - "g0_node0->g0_node1\n", - "\n", - "N(1650)+\n", - "N(1900)+\n", - "N(Fakestar)+\n", - "N(Fakestar2)+\n", - "\n", - "\n", - "\n", - "g0_node1->g0_edge0\n", - "\n", - "\n", - "\n", - "\n", - "g0_node1->g0_edge1\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "reaction = qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", " final_state=[\"eta\", \"p\", \"p~\"],\n", - " allowed_intermediate_particles=[\"N(Fakestar2)+\",\"N(1650)+\",\"N(1900)+\",\"N(Fakestar)+\"],\n", + " allowed_intermediate_particles=[\n", + " \"N(Fakestar2)+\",\n", + " \"N(1650)+\",\n", + " \"N(1900)+\",\n", + " \"N(Fakestar)+\",\n", + " ],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " # mass_conservation_factor=5.0,\n", @@ -186,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "tags": [] }, @@ -197,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [] }, @@ -216,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "tags": [] }, @@ -237,77 +146,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle X_{Q=+1, S=1/2, P =-1}$" - ], - "text/plain": [ - "X_{Q=+1, S=1/2, P =-1}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " N(Fakestar2)+ 1.75 GeV 0.6 GeV \n", - " N(1650)+ 1.65 GeV 0.125 GeV \n" - ] - }, - { - "data": { - "text/latex": [ - "$\\displaystyle X_{Q=+1, S=3/2, P =1}$" - ], - "text/plain": [ - "X_{Q=+1, S=3/2, P =1}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " N(Fakestar)+ 1.82 GeV 0.6 GeV \n", - " N(1900)+ 1.92 GeV 0.2 GeV \n" - ] - }, - { - "data": { - "text/plain": [ - "ParameterValues({\n", - " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{+1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{-1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " m_0: 0.547862,\n", - " m_1: 0.93827208816,\n", - " m_2: 0.93827208816,\n", - " m_012: 3.0969,\n", - " })" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", " display(symbol)\n", @@ -334,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -410,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -419,6 +262,7 @@ "PARAMETERS_BW = {}\n", "PARAMETERS_BW.update(model.parameter_defaults)\n", "\n", + "\n", "def formulate_rel_bw(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", @@ -452,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "tags": [] }, @@ -461,6 +305,7 @@ "PARAMETERS_F = {}\n", "PARAMETERS_F.update(model.parameter_defaults)\n", "\n", + "\n", "def formulate_K_matrix(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", @@ -468,12 +313,10 @@ " s = variables.incoming_state_mass**2\n", " m_a = variables.outgoing_state_mass1\n", " m_b = variables.outgoing_state_mass2\n", - " g= [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", "\n", - " kmatrix = sum(\n", - " (g_**2) / (m_**2 - s) for m_, g_ in zip(m, g)\n", - " )\n", + " kmatrix = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", " for i, (p, va) in enumerate(resonances):\n", " PARAMETERS_F[m[i]] = p.mass\n", " PARAMETERS_F[g[i]] = 1\n", @@ -489,12 +332,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", "def formulate_P_vector(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", @@ -505,10 +346,7 @@ " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " beta = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", - " P_vector = sum(\n", - " ( g_ * beta_ ) / (m_**2 - s)\n", - " for m_,g_, beta_ in zip(m, g, beta)\n", - " )\n", + " P_vector = sum((g_ * beta_) / (m_**2 - s) for m_, g_, beta_ in zip(m, g, beta))\n", " for i, (p, va) in enumerate(resonances):\n", " PARAMETERS_F[m[i]] = p.mass\n", " PARAMETERS_F[beta[i]] = 1 + 0j\n", @@ -525,14 +363,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def formulate_F_vector(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1,variables), *_ = resonances\n", + " (p1, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", " m_a = variables.outgoing_state_mass1\n", " m_b = variables.outgoing_state_mass2\n", @@ -552,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "tags": [] }, @@ -576,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "tags": [] }, @@ -596,26 +434,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{array}{rcl}\n", - " X_{Q=+1, S=1/2, P =-1} &=& \\frac{\\frac{\\beta_{N(1650)^{+}} g_{N(1650)^{+}}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\beta_{N(Fakestar2)^+} g_{N(Fakestar2)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1650)^{+}}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar2)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1} \\\\\n", - " X_{Q=+1, S=3/2, P =1} &=& \\frac{\\frac{\\beta_{N(1900)^+} g_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1900)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1} \\\\\n", - "\\end{array}" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamics_expressions_fvector = {\n", " symbol: formulate_F_vector(resonances)\n", @@ -633,67 +454,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ParameterValues({\n", - " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{+1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1650)^{+}_{+1/2} \\overline{p}_{-1/2}; N(1650)^{+} \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar2)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar2)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " m_0: 0.547862,\n", - " m_1: 0.93827208816,\n", - " m_2: 0.93827208816,\n", - " m_012: 3.0969,\n", - " m_{N(Fakestar2)^+}: 1.75,\n", - " g_{N(Fakestar2)^+}: 1,\n", - " m_{N(1650)^{+}}: 1.65,\n", - " g_{N(1650)^{+}}: 1,\n", - " \\beta_{N(Fakestar2)^+}: (1+0j),\n", - " \\beta_{N(1650)^{+}}: (1+0j),\n", - " m_{N(Fakestar)^+}: 1.82,\n", - " g_{N(Fakestar)^+}: 1,\n", - " m_{N(1900)^+}: 1.92,\n", - " g_{N(1900)^+}: 1,\n", - " \\beta_{N(Fakestar)^+}: (1+0j),\n", - " \\beta_{N(1900)^+}: (1+0j),\n", - " })" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model_fvector.parameter_defaults" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5591" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "full_expression_fvector = model_fvector.expression.doit().xreplace(\n", " dynamics_expressions_fvector\n", @@ -710,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "tags": [] }, @@ -729,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -751,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -772,57 +544,21 @@ "\n", "new_parameters_relbw = {\n", " R\"m_{N(Fakestar)^+}\": 1.85,\n", - " R\"w_{N(Fakestar)^+}\": 1/1.85,\n", + " R\"w_{N(Fakestar)^+}\": 1 / 1.85,\n", " R\"m_{N(1900)^+}\": 1.9,\n", - " R\"w_{N(1900)^+}\": 1/1.9,\n", + " R\"w_{N(1900)^+}\": 1 / 1.9,\n", " R\"m_{N(Fakestar2)^+}\": 1.75,\n", - " R\"w_{N(Fakestar2)^+}\": 1/1.75,\n", + " R\"w_{N(Fakestar2)^+}\": 1 / 1.75,\n", " R\"m_{N(1650)^{+}}\": 1.65,\n", - " R\"w_{N(1650)^{+}}\": 1/1.65,\n", + " R\"w_{N(1650)^{+}}\": 1 / 1.65,\n", "}" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C_{J/\\\\psi(1S) \\\\to N(1650)^{+}_{+1/2} \\\\overline{p}_{+1/2}; N(1650)^{+} \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(1650)^{+}_{+1/2} \\\\overline{p}_{-1/2}; N(1650)^{+} \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar2)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar2)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar2)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar2)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+3/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{-1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'm_0': 0.547862,\n", - " 'm_1': 0.93827208816,\n", - " 'm_2': 0.93827208816,\n", - " 'm_012': 3.0969,\n", - " 'm_{N(Fakestar2)^+}': 1.75,\n", - " 'g_{N(Fakestar2)^+}': 1,\n", - " 'm_{N(1650)^{+}}': 1.65,\n", - " 'g_{N(1650)^{+}}': 1.65,\n", - " '\\\\beta_{N(Fakestar2)^+}': (1+0j),\n", - " '\\\\beta_{N(1650)^{+}}': (1+0j),\n", - " 'm_{N(Fakestar)^+}': 1.95,\n", - " 'g_{N(Fakestar)^+}': 1,\n", - " 'm_{N(1900)^+}': 1.9,\n", - " 'g_{N(1900)^+}': 1,\n", - " '\\\\beta_{N(Fakestar)^+}': (1+0j),\n", - " '\\\\beta_{N(1900)^+}': (1+0j)}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", "intensity_func_rel_bw.update_parameters(new_parameters_relbw)\n", @@ -839,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "tags": [] }, @@ -852,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "tags": [] }, @@ -867,79 +603,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cece241e87b345c2b9e3bcd74d447ef6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Generating phase space sample: 0%| | 0/100000 [00:00:3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n" - ] - }, - { - "data": { - "text/plain": [ - "{'m_01': array([1.81857778, 1.80863875, 1.86758228, ..., 1.7217908 , 1.88162305,\n", - " 1.95955089]),\n", - " 'm_02': array([1.70745362, 1.77483717, 1.56984082, ..., 2.13907063, 1.99774363,\n", - " 2.0295025 ]),\n", - " 'm_12': array([2.33002756, 2.2870134 , 2.38733903, ..., 2.0276747 , 2.02981933,\n", - " 1.92170012]),\n", - " 'phi_0': array([ 1.97016286, -2.8765596 , 0.75357421, ..., 0.19730572,\n", - " -0.45861856, 1.57182959]),\n", - " 'phi_0^01': array([-1.97869891, 2.40627766, -2.02701505, ..., 1.42458459,\n", - " 0.78477173, 2.00132783]),\n", - " 'phi_0^02': array([ 0.98414884, -1.41787483, 1.80055274, ..., -2.62005351,\n", - " -1.37701865, -1.58606652]),\n", - " 'phi_01': array([-0.00476082, -0.46629838, -0.49331781, ..., 2.95178512,\n", - " 2.14918814, -1.97763388]),\n", - " 'phi_1^12': array([-0.5234414 , 0.53541189, -1.32700284, ..., 2.04917998,\n", - " 2.17445382, 1.30218432]),\n", - " 'phi_02': array([-1.98053067, 1.48563902, 3.08718583, ..., -1.7995076 ,\n", - " -2.40408988, -0.99517043]),\n", - " 'theta_0': array([1.69320513, 1.8732383 , 2.16807283, ..., 2.56300869, 1.02101855,\n", - " 2.0423608 ]),\n", - " 'theta_0^01': array([2.00195379, 1.79913544, 2.46359496, ..., 0.38143291, 0.96066346,\n", - " 0.54722468]),\n", - " 'theta_0^02': array([1.73936386, 1.72081689, 1.66448996, ..., 1.63126795, 1.19669709,\n", - " 0.64090765]),\n", - " 'theta_01': array([2.57060174, 2.32905607, 2.03576298, ..., 0.5128774 , 1.68637297,\n", - " 1.23809802]),\n", - " 'theta_1^12': array([1.34061414, 1.5044162 , 0.83272194, ..., 2.71169603, 1.82674819,\n", - " 1.82311053]),\n", - " 'theta_02': array([0.63817236, 0.51529239, 0.96856613, ..., 1.79764582, 2.3444856 ,\n", - " 1.03333824])}" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "from tensorwaves.data import (\n", @@ -970,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -983,7 +651,7 @@ " func: ParametrizedFunction,\n", " input_data: DataSample,\n", " resonances: list[str],\n", - " coupling_pattern:str=r\"(\\\\beta|g)\",\n", + " coupling_pattern: str = r\"(\\\\beta|g)\",\n", "):\n", " original_parameters = dict(func.parameters)\n", " negative_lookahead = f\"(?!{'|'.join(map(re.escape, resonances))})\"\n", @@ -1005,44 +673,35 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total_intensities = intensity_func_fvector(phsp)\n", "total_intensities_1 = intensity_func_rel_bw(phsp)\n", "sub_intensities = {\n", - " p: compute_sub_intensity(intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern= r\"\\\\beta\")\n", + " p: compute_sub_intensity(\n", + " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " )\n", " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", - " for p, _ in resonances\n", + " for p, _ in resonances\n", "}\n", "sub_intensities_bw = {\n", " p: compute_sub_intensity(intensity_func_fvector, phsp, resonances=[p.latex])\n", " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", - " for p, _ in resonances\n", + " for p, _ in resonances\n", "}" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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92pZwOKyzzz5bCxYs8KwPBAI65JBDdNhhh2WNXrVhwwaddtppWrduXa+2DQD2FwPpuA4A6Fn19fX6yle+0m/PX1lZqdNOO03vv/++Z31eXp5mzpyp6dOnKxQKeR5bsmSJTjvttA5v4AOAg1V/B2gBAAcOzte7ZsBMHbhHaWmpLr30Up177rk6+eSTVVRUlHrMtm29+uqruvXWW/Xqq6+m1r/99tuaP3++nnjiiV5rVyKR0Lx58zwfkvLycv3v//6vLrnkklTAqqamRj/72c/0ox/9SI7jSJLefPNN3XTTTfrFL37Ra+0DgIFqoB7XMw0bNkyPPPJIl7aZPXt2L7UGAAaukSNH6txzz9Upp5yi448/XhMmTJBpeu/dqK2t1RNPPKE777xTW7duTa1/4IEHdNJJJ+mKK67olbZ985vf1Ntvv51aNk1Tt9xyi66//nqVlZVJkmKxmB599FF985vfVG1trSRp9+7duuiii/TOO+/IsqxeaRsADFQD+bie6ZFHHtGwYcM6XT9x4sRebA0A7J8y70ovKChQc3Nznz7//PnztX79+tRyKBTSXXfdpauvvlr5+fmSpObmZt177726+eabUzcur127VldeeaWeffbZPm0vAAx0/R2gBQDsm5/+9KddutY4cuTIXmwN5+tdZbgDYKz3o446StXV1frud7+rSy+9VHl5eXutt21bX/3qV3Xvvfd61r/00ks67bTTeqWN9957r6655prUcllZmV577TXNmDEjZ/2jjz6qz3/+86lln8+nlStXasqUKb3SPgAYSPaH4/r48eO1adMmSdK4ceO0cePGXnkeADhQfPjhhzrssMOyRjZpT21trc444wy99957qXUjRozQ1q1bsy7i76vVq1fr0EMPlW3bqXWPPvqoLrnkkpz1K1as0EknnaS6urrUugceeKBPwgIAMFAM5OP6/Pnz9dBDD6WWKyoqen36EwA4kC1cuFCnn366XNeVaZq66667dNNNN6Ue7+1+kRdeeEFnn312atnv9+vFF1/UKaeckrN+0aJFOvPMMxWPx1PrerOPCAD2R1/+8pd13333ScoO0Pb2cX3u3LlatGhRarnt6OIdmTlzpkaMGNHTzQKAAe3222/XHXfckVp++eWXNXfu3P5rUAbO17tuQEwdeMcdd2jNmjW66qqrOrwYLyWnnvrVr36lo446yrP+d7/7Xa+0LxaL6Qc/+IFn3U9/+tN2Q1aSdOmll+qyyy5LLScSCd1+++290j4AGGgG+nEdANB1s2bN6vTFeCl5Y8Ijjzzi2WbHjh16/fXXe7xtt912mydk9YUvfKHdkJWU7ND76U9/6ll3xx13eL4YAsCBbiAf1wEAPSccDutLX/qS9txv/fWvf11HH310n7bhe9/7nmf5v/7rv9q9aCNJp556qr797W971n33u9/tlbYBwP5o4cKFqb5z0zR122239Wt7zjjjjC79I2QFAAML5+tdNyCCVueee64CgUCXtrEsy3PXjST961//6slmefa7ZcuW1PL48eM7dbf77bff7umA/Mtf/qL6+vpeaSMADCQD/bgOAOgb06dP15FHHulZt2rVqh59jtraWj355JOpZcMwOnWDwxVXXKFx48alljdt2qQXX3yxR9sGAAeavjiuAwB61ve+973UFCBjx47NuqG4ty1btswzxXdBQYG+9a1vdbjdTTfdpIKCgtTyG2+8wd8cANDACNACAA4cnK93z4AIWnXXySef7Fmurq5WS0tLjz/PM88841m+4oorOnXX56RJk3TqqaemluPxuP7+97/3ePsA4EDRV8d1AEDfmTRpkme5qqqqR/f//PPPK5FIpJbnzp2riRMndridaZpZN088/fTTPdo2ADgQ9fZxHQDQc9555x39/Oc/Ty3/8pe/VGFhYZ+2oW3f+kUXXaSioqIOtysqKtK8efM86zhfB4D+D9ACAA4snK93z34dtCorK8ta1xsjRj3//POe5bPOOqvT25555pme5eeee65H2gQAB6K+Oq4DAPpOJBLxLJeWlvbo/jlXB4C+1dvHdQBAz4jH47rqqqtSU2zPmzdP5513Xp+3g/N1AOg5AyFACwA4sHC+3j37ddBq27ZtWesGDRrUo8+xa9cu7dy5M7UcDAY1Z86cTm9/4oknepaXLl3aU00DgANOXxzXAQB9x3VdvfPOO551baec2ldtz69POOGETm975JFHKhgMppa3b9+u3bt391TTAOCA0xfHdQBAz/jRj36kZcuWSUqGYn/xi1/0eRtc19WHH37oWdeV8/W2fesffPBBaqosADjYDJQALQDgwMH5evf5+rsB++LVV1/1LI8bN06BQKBHn6PtPJKTJ0/u0nPMmDHDs7xu3TolEgn5fPv1Sw8AvaIvjut7U1VVpa1bt6qhoUHFxcUaNGiQRo8e3anpYgEA2R544AFt3749tTxt2jQdc8wxPbb/eDyudevWeda1Pf/em2AwqEmTJmnlypWpdatWrdKQIUN6rI0AcCDp7eP63jQ3N2vjxo2qqalRXl6eysvLNWrUKE9gFgCQtHLlSv3whz9MLd99990aPnx4n7dj06ZNamlpSS0XFBRo7Nixnd5+3Lhxys/PT+2jublZW7Zs6dI+AOBAMRACtACAnhWNRrVhwwZVV1fL7/dr0KBBGjlypPLz8/vk+Tlf7779Ou3zwAMPeJY/8YlP9PhzrFmzxrM8ZsyYLm0/ZMgQhUKh1ND6sVhMFRUVmjJlSo+1EQAOFH1xXM+lsrJSM2bMyArXSlJ5eblOPvlkXXrppbrwwgtlWVaftAkA9ncPPfSQvvrVr6aWTdPU//3f//VoeHXDhg1KJBKp5by8PA0ePLhL+xgzZownaLVmzRqdcsopPdZGADhQ9MVxvT3nn3++Vq1a5TnmS1IoFNKxxx6r888/X1dffbWKiop6vS0AMNA5jqOrrrpKsVhMknTyySfr6quv7pe27Gvf+p5tMvezZs2ag+LCDQBkGigB2r2pr6/Xpk2bVFdXp8LCwtRNzPSnA0BuX/va17Rhw4ZUjmQPn8+nI488Uuecc46++tWv9upNwZyvd99+G7T6+9//rldeecWzbv78+T3+PJWVlZ7l0aNHd3kfI0eO1IYNGzz7JGgFAF59dVzPJRwO5wxZSVJNTY2eeeYZPfPMM5o0aZLuv/9+nXrqqX3SLgAYyD766CNt3rw5tRyPx1VbW6vly5frmWee8YSXAoGA7r33Xn3sYx/r0Ta0PVcfNWpUl/fRdpu2+wSAg8VAOK63Z8+d+21FIhEtWrRIixYt0p133qkf/OAH+o//+I8+aRMADFS/+MUv9NZbb0lKH6/7a6TunuhbHzVqlOfCDefrAA42AylA254jjjhCH374oRzH8awvLCzUiSeeqAsvvFBf/OIXGY0WADJk9rNkSiQSWrx4sRYvXqy7775bN954o2677bZeCa5yvt59+2XQqqamRtdcc41n3QUXXNArw9U3NTV5lgsKCrq8j7bbtN0nABzs+vK4vi/Wr1+vj33sY/qf//kffeMb3+jv5gBAv/rVr36le+65Z681hmHo4x//uH70ox9p9uzZPd4GztUBoOcMhOP6vqivr9fXv/51vf7663r44Yfl8+2XXV4AsE8qKir03e9+N7X8ne98R9OmTeu39nC+DgD7biAFaNuzdOnSnOubmpr0r3/9S//6179066236he/+IXmzZvXt40DgP1YOBzW97//fb366qv629/+psLCwh7dP+fr3Wf2dwO6ynEcXXbZZdq6dWtqXUlJSa/NRdz2gxAKhbq8j7y8vL3uEwAOZn19XM9UXFysiy66SPfff7/effddVVdXKx6Pq76+XqtWrdL999+vk046ybONbdu6/vrr9dhjj/V6+wBgfzdv3jzdcsstvXYxnnN1AOhbvX1cz+Tz+XTGGWfoZz/7mRYtWqSdO3cqEomopaVFmzZt0hNPPKFLL700K1D12GOP6etf/3qvtw8ABqIvf/nLam5uliRNmzZNN998c7+2h/N1ANg3Ay1Auy927typiy66SN/61rf6uykA0G8Mw9AJJ5ygH/7wh1qwYIG2bt2qlpYWRSIRbdu2TX/72990zTXXZJ03L1y4UJ/73Odk23aPtofz9e7b74JW3/rWt/SPf/zDs+63v/1tt+aL7Iy2c2IGAoEu76PtUJjhcHif2gQAB5K+Pq7v8ZOf/ETbtm3T448/riuvvFJHHnmkysvL5fP5VFxcrGnTpunKK6/Uq6++qieffFKlpaWpbV3X1VVXXaWdO3f2ahsBYH/35z//WSeddJJOOeUUrVu3rsf3z7k6APSt3j6u7/H5z39eFRUVWrBgga6//nqdcsopGjZsmILBoPLy8jR27FhdeOGF+uMf/6ilS5dqxowZnu1/85vf6G9/+1uvtQ8ABqL7779fL774oqTkBZx77723W+fHPYnzdQDYNwMtQJspFArpk5/8pH71q1/pjTfeUGVlpWKxmBobG7V+/Xo98sgjOvfcc7NG3/rpT3+qu+66q59aDQD956yzztLq1av1+uuv6+abb9YZZ5yhUaNGKS8vT8FgUCNHjtR5552n3/zmN1q7dq1OPPFEz/bPP/+8fvWrX/Vomzhf7779Kmj1i1/8Qj/72c8862666SZdfPHFvfacbVN7e+ZA7opoNLrXfQLAwao/jut7zJs3r9NDbH7605/WP/7xD08qu6WlRT/84Q97q3kAMOD9/Oc/l+u6qX8tLS3asmWLnnvuOV111VWeY+arr76qo48+Wu+++26PtoFzdQDoOQPhuL7HmWeeqdGjR3eqdubMmVq0aJEmT57sWX/LLbfIdd3eaB4ADDg7duzQjTfemFr+0pe+pJNPPrkfW5TE+ToAdN9ADNDu8c1vflNbt27Vs88+q6985Ss6/vjjNWTIEPn9fhUWFmrixIn6/Oc/r+eee06vvPKKRo0a5dn+5ptv1gcffNBPrQeA/nHCCSfokEMO6VTt6NGj9eKLL+r444/3rP/BD36glpaWHmsT5+vdt98ErR599FFdd911nnXz58/v9dRz24vwbVN9ndE2tdfTc2cCwP6ov47r3XXcccfppptu8qx79NFH5ThOP7UIAAaWvLw8jR49Wueee65+97vf6cMPP9Thhx+eeryurk4XXHCB6urqeuw5OVcHgN7TH8f17ho8eLDuu+8+z7ply5Zx8QbAQeNrX/ta6ng8fPhw/fjHP+7fBrXifB0AumegBmj3OP/88zVo0KBO1Z500klauHChBg8enFrnuq5nSkQAQLZQKKQ//OEP8vl8qXWVlZV64YUXeuw5OF/vvv0iaPXcc8/p8ssv99yJ+JnPfEa/+93vsoac7GltPwh7hujsirbbHCwfLgBoT38e1/fFN77xDVmWlVquqanptbv4AWB/N3nyZC1YsMAzFey2bdv0k5/8pMeeg3N1AOg7fXFc3xdz587VnDlzPOt6svMRAAaqv/zlL3rqqadSy/fcc49KS0v7r0EZOF8HgO4ZqAHa7po8eXLW94a///3vqqmp6acWAcD+YfLkyTr//PM963ozaMX5eucN+KDVyy+/rHnz5imRSKTWnXnmmfrTn/7kudjdW4YOHepZ3rp1a5f3sX379r3uEwAOJv19XN8XZWVlWRdv1qxZ00+tAYCBb/Dgwbrjjjs86x588MEe23/b8+pt27Z1eR9tt+FcHQDa19vH9X31sY99zLPMuTqAg8G3vvWt1P+fe+65uuiii/qxNV490bfO+TqAg81ADtDuiy9+8YsaMmRIatlxnNTUiACA9vVmXwfn6903oINWixcv1vnnn+8ZouyEE07QU0891WfzEE+dOtWzvHnz5i5tX1lZ6Wl/IBDQxIkTe6RtALC/GQjH9X2VeQe/JO3evbufWgIA+4dPf/rTntEKt2/frk2bNvXIvidOnOgZOjkcDnf5uNz2/H7atGk90jYAOFD15nF9X3GuDuBglDmF6/PPPy/DMDr8d9ppp3n2sWnTpqyapUuX7nPb2vatb9mypcv7aLsN5+sADnQDOUC7L0zT1Ny5cz3ruDECADrWm30dnK9334ANWn344Yc655xz1NTUlFp3xBFH6O9//7sKCgr6rB1tPwjr169XLBbr9ParVq3yLE+aNMlzMQgADhYD5bi+r/x+v2c5Ho/3U0sAYP9QWlqq8vJyz7qdO3f2yL79fr8mTZrkWbdy5cpObx+NRrVhwwbPuoPliyAAdFdvHtf3FefqADCwjBs3Tnl5eanl5ubmLoVzN23apJaWltRyQUFB1oUmADjQDOQA7b7ixggA6Lre7OvgfL37BmTQas2aNTrzzDNVW1ubWjd9+nT961//UklJSZ+2Zfjw4Ro+fHhqORqNasmSJZ3e/vXXX/csH3744T3VNADYbwyk4/q+ansRKXO4YwBA57T9crgv2p5fv/HGG53edsmSJYpGo6nlESNGHDRDGwNAT+rJ4/q+4FwdAAYWwzA0a9Ysz7qunK+37VufNWuWZ1RFAMD+hRsjAKDrerOvg/P17htwQytt2rRJZ5xxhiorK1PrJkyYoAULFvRbB9m5556r+++/P7W8YMECHX/88Z3adsGCBZ7lT37ykz3aNgAY6Abicb27otGo3nnnHc+6gyWZDQDd1djYqJqaGs+6YcOG9dj+zzvvPD3++OOp5QULFug73/lOp7blXB0Auq63j+v74rXXXvMsc64O4GDwzDPPdPlC9QcffKAbb7wxtTxs2DA98sgjnprJkyf3SPvOO+88LV68OLW8YMECXXLJJZ3alvN1ADiwcGMEAHRdb/d1cL7ePYbrum5/N2KPHTt26OSTT9b69etT60aNGqVXX31VEyZM6Ld2Pfvss/rUpz6VWh4/frw2bNjQYRpv/fr1mjJliva8xH6/X7t3797vRm8BgO4aqMf17nrooYc0f/781HIwGFRNTY3y8/P7r1EAMMA99thjni9mQ4YM0c6dO2WaPTO4bk1NjYYNG6ZEIiEpeRfOunXrNHHixL1u57quJk6cqI0bN6bWPf/88/rEJz7RI+0CgANVbx/Xu2vdunWaPn166u+BJP373//W6aef3o+tAoCBaeHChZ5ppsaNG+c5L+5JH374oWbPnp1aLiws1I4dO1RYWLjX7RobGzVixAg1Nzen1q1YsUIzZszolXYCwECxaNGiXgnQHnfccR0ee3vb5MmTPdcK7r33Xl199dX92CIAGNjq6uo0YcIEz7Sy999/v6688soeew7O17tnwIxoVVNTozPPPNPzB3bIkCFasGBBv1+MP/vsszV69Ght3bpVkrRx40b9/ve/7/ADfPvttyszx3bhhRcSsgJw0BjIx/Xu2Llzp2655RbPurPOOouQFQDsRTgc1m233eZZd9555/Xoxfjy8nJdcMEFeuKJJyQlA1S33367/vCHP+x1uwceeMBzMWncuHE644wzeqxdAHAg6ovjenfYtq2vfe1rnpDVoEGDdNJJJ/VjqwAAUnL6kKOPPjo1QnhTU5N+/OMf684779zrdj/+8Y89F22OO+64g+aiDYCD26mnntrlbXw+7+XeUCg04Po4Fi1a5LlWIEkf+9jH+qk1ALB/uPHGGz0hq0AgoHPOOadHn4Pz9e7p356wVo2Njfr4xz+uFStWpNaVlpbqhRde0PTp03v0uTZu3CjDMDz/OrpbJxgMZl1cv/HGG7Vy5cp2t3n00Uc9aXHLsnTHHXfsU9sBYH8xkI/rO3bs0G233aba2touPcfHP/5xbdu2LbXOMAzdfvvt+9ByANh/3HTTTVlTp3akpqZG559/vj766KPUOsuydP311+91u/Hjx3uO6Q8++GCHz3XHHXd4LvI//PDD+tOf/tRu/cqVKz13ekrS9773PQUCgQ6fCwAOBAP5uH7LLbd4nqMjLS0t+sIXvqAXXnghaz8c1wGg57Xtg1m4cGGH27S9SHPXXXfplVdeabd+0aJFuvvuuz3rfvCDH3SrvQCA/tfc3Kz//M//9Kw77LDDOhyNHAAOFHfddZeWLFnS6fpEIqEbbrhB999/v2f9tddeqxEjRux1W87X+8aACFqdf/75WR183/zmN1VVVaUXX3yxS/+6cuG8K6666irNnDkztVxbW6uTTz5Zf/jDHzx3TNbU1Oh73/uevvCFL3i2v+aaa3TIIYf0StsAYKAZyMf1aDSqO++8U2PHjtXnP/95Pfnkk9q+fXvO2nXr1um73/2uDj/8cH3wwQeex77xjW9ozpw5Pdo2ABioXnjhBR1zzDE69thj9bOf/UxLly7NOYy967pavXq1vv/972vq1Kl68cUXPY9ff/31Ouyww3q8fTNmzNCXvvQlz7rLLrtMt956q+fvSDwe14MPPqiTTjrJcyfQrFmzdPnll/d4uwBgoBrIx/U//vGPmj59us466yzdd999+uijj+Q4Tlbd7t279dvf/lazZs3KCtcee+yx+upXv9qj7QIAdN/HP/5xnXXWWanleDyus88+W/fcc49aWlpS65ubm/Xzn/9cH//4xz1/lz7xiU8w6gkA9JKuXpD/xje+0W5/ei5VVVU6//zz9eGHH3rWMzgFgIPJP//5Tx111FE68cQTdc8992j58uWejMke9fX1+tOf/qSjjz5aP/vZzzyPTZo0SbfeemuvtI/z9a4z3My57fqrEYbRY/t6+eWXNXfu3HYf37hxY9aUVRUVFRo/fnyH+161apVOOukk1dTUeNYXFhZq0qRJCofDqqioyOqcPOaYY7Rw4ULl5eV1+ucAgP3ZQD6u56qXklOLDB06VMXFxQqHw9qxY4d2796dcx/z5s3TY4891u9TpABAX8kVOA0EAho1apRKS0sVCATU2NioLVu2qLGxMec+Lr/8cj3wwAMdHjvHjx+vTZs2pZZ///vfa/78+R22saWlRaeeeqrefffdrHZOmDBBwWBQGzZsUFNTk+fxwYMH6/XXX+emCAAHlYF8XG9bL0kFBQUaOXKkSkpK5Lquqqqqsmr2mDZtml599VUNHjx4r+0CgIPZwoULddppp6WWx40b1+GsD3u07fPpqN9mj127dun4449XRUWFZ31eXp4mTpwo13W1YcMGRSIRz+OTJk3Sm2++qSFDhnSqfQBwMOrL47phGAoGgzrnnHP02c9+VieeeGLOvvgtW7boscce089+9jPt3LnT89gFF1ygp556qlPtA4ADwdy5c7Vo0SLPumAwqNGjR6ukpESWZam6ulobN27MebPZ8OHD9corr2jKlCkdPhfn633D13EJ9pg+fbpeeuklfepTn/J06DU1NWV1UO5xxhln6C9/+QshKwAY4Kqrq1VdXb3XmmAwqP/+7//W9ddf36NhMgDYH8VisawvXbkUFxfrrrvu0rXXXturx878/Hz961//0rx58/TSSy952rlmzZqc24wfP17PPvssISsA0MA7rmdqbm7W2rVrO6y7/PLL9X//938qLCzsg1YBALpi2LBhevnll/WpT33K05ceDoe1YsWKnNscfvjhevbZZw+6izYAMNBFo1E9/fTTevrppyUlvyOMGDFCJSUlisfj2rVrV7ujXp188sl69NFH+7C1ADAwRaNRrV+/vsO6T3ziE/r973+voUOH9mp7OF/vGobi6KLZs2dr2bJl+s53vqOysrJ266ZMmaL77rtPL7zwgkpLS/uugQCAvRo2bJjuueceXXDBBRo2bFinthk3bpy++93vasOGDfrmN79JyArAQedPf/qT7r77bp1xxhkqLi7usN4wDM2aNUs/+clPtG7dOn3lK1/pk2NneXm5FixYoHvvvVeTJ0/ea93NN9+sZcuW9cpUhgAw0A3k4/pPf/pTzZ8/X5MnT+7Uc5SVlenKK6/U+++/rwcffJCQFQAMYOPGjdPbb7+tu+++WyNHjmy3buTIkfrxj3+sxYsXa8yYMX3YQgBAdzQ0NGjNmjV6++239f777+cMWZmmqZtuukn//ve/GZwCwEHnlltu0bXXXquZM2fKsqwO6wsLCzVv3jwtWrRIzz//fK+HrPbgfL3zBsT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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "import numpy as np\n", "from matplotlib import cm\n", + "\n", "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", "ax.set_xlim(2, 5)\n", "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV^{2}]\")\n", @@ -1050,7 +709,7 @@ "ax.set_yticks([])\n", "\n", "bins = 150\n", - "phsp_projection = np.real(phsp[\"m_01\"])**2\n", + "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", "ax.hist(\n", " phsp_projection,\n", " weights=total_intensities,\n", @@ -1072,7 +731,10 @@ " weights=list(sub_intensities.values()),\n", " bins=bins,\n", " alpha=0.6,\n", - " label=[Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ $F$ vector\" for p in sub_intensities],\n", + " label=[\n", + " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ $F$ vector\"\n", + " for p in sub_intensities\n", + " ],\n", " histtype=\"step\",\n", ")\n", "\n", @@ -1081,7 +743,10 @@ " weights=list(sub_intensities_bw.values()),\n", " bins=bins,\n", " alpha=0.6,\n", - " label=[Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\" for p in sub_intensities],\n", + " label=[\n", + " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\"\n", + " for p in sub_intensities\n", + " ],\n", " histtype=\"step\",\n", " ls=\"dotted\",\n", ")\n", @@ -1100,23 +765,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{m_{N(1650)^{+}} w_{N(1650)^{+}}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2} - m_{N(1650)^{+}} \\Gamma_s\\left(m_{01}^{2}\\right)} + \\frac{m_{N(Fakestar2)^+} w_{N(Fakestar2)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2} - m_{N(Fakestar2)^+} \\Gamma_s\\left(m_{01}^{2}\\right)}$" - ], - "text/plain": [ - "m_{N(1650)^{+}}*w_{N(1650)^{+}}/(-m_01**2 + m_{N(1650)^{+}}**2 - m_{N(1650)^{+}}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(1650)^{+}})) + m_{N(Fakestar2)^+}*w_{N(Fakestar2)^+}/(-m_01**2 + m_{N(Fakestar2)^+}**2 - m_{N(Fakestar2)^+}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(Fakestar2)^+}))" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamics_expr_rel_bw, *_ = dynamics_expressions_rel_bw.values()\n", "dynamics_expr_rel_bw" @@ -1124,23 +775,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\frac{\\beta_{N(1650)^{+}} g_{N(1650)^{+}}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\beta_{N(Fakestar2)^+} g_{N(Fakestar2)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1650)^{+}}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1650)^{+}}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar2)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar2)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1}$" - ], - "text/plain": [ - "(\\beta_{N(1650)^{+}}*g_{N(1650)^{+}}/(-m_01**2 + m_{N(1650)^{+}}**2) + \\beta_{N(Fakestar2)^+}*g_{N(Fakestar2)^+}/(-m_01**2 + m_{N(Fakestar2)^+}**2))/(-(g_{N(1650)^{+}}**2/(-m_01**2 + m_{N(1650)^{+}}**2) + g_{N(Fakestar2)^+}**2/(-m_01**2 + m_{N(Fakestar2)^+}**2))*PhaseSpaceCM(m_01**2, m_0, m_1) + 1)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamics_expr_fvector, *_ = dynamics_expressions_fvector.values()\n", "dynamics_expr_fvector" @@ -1148,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1162,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1183,31 +820,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([1.81857778, 1.80863875, 1.86758228, ..., 1.7217908 , 1.88162305,\n", - " 1.95955089])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -1227,50 +842,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9334868e3e8e428fb3e986a7f411a14d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Generating intensity-based sample: 0%| | 0/50000 [00:00:3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n" - ] - } - ], + "outputs": [], "source": [ "weighted_phsp_generator = TFWeightedPhaseSpaceGenerator(\n", " initial_state_mass=model.reaction_info.initial_state[-1].mass,\n", @@ -1295,23 +871,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "from matplotlib import cm\n", "\n", "resonances = sorted(\n", " model.reaction_info.get_intermediate_particles(),\n", @@ -1358,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1377,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1400,12 +964,10 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", "reaction_info = model.reaction_info\n", "resonances = sorted(\n", " reaction_info.get_intermediate_particles(),\n", @@ -1493,27 +1055,27 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "m_1900=1.93\n", - "beta_1900= 0.9 + 0j\n", - "g_1900= 1.\n", - "m_1650= 1.65\n", - "beta_1650= 1 + 0j\n", - "g_1900= 1.\n", - "m_Fakestar2=1.5\n", - "beta_Fakestar2= 1 + 0j\n", - "g_Fakestar2= 1.\n", - "m_Fakestar1= 1.94\n", + "m_1900 = 1.93\n", + "beta_1900 = 0.9 + 0j\n", + "g_1900 = 1.0\n", + "m_1650 = 1.65\n", + "beta_1650 = 1 + 0j\n", + "g_1900 = 1.0\n", + "m_Fakestar2 = 1.5\n", + "beta_Fakestar2 = 1 + 0j\n", + "g_Fakestar2 = 1.0\n", + "m_Fakestar1 = 1.94\n", "initial_parameters_fvector = {\n", " R\"m_{N(Fakestar)^+}\": 1.95,\n", " R\"\\beta_{N(Fakestar)^+}\": 0.9 + 0j,\n", " R\"m_{N(1900)^+}\": 1.91,\n", " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"g_{N(1900)^+}\": 1.,\n", - " R\"g_{N(Fakestar)^+}\": 1.,\n", + " R\"g_{N(1900)^+}\": 1.0,\n", + " R\"g_{N(Fakestar)^+}\": 1.0,\n", " R\"m_{N(Fakestar2)^+}\": 1.7,\n", " R\"\\beta_{N(Fakestar2)^+}\": 1 + 0j,\n", " R\"m_{N(1650)^{+}}\": 1.67,\n", @@ -1524,34 +1086,22 @@ "\n", "initial_parameters_relbw = {\n", " R\"m_{N(Fakestar)^+}\": 1.8,\n", - " R\"w_{N(Fakestar)^+}\": 1/1.85,\n", + " R\"w_{N(Fakestar)^+}\": 1 / 1.85,\n", " R\"m_{N(1900)^+}\": 1.93,\n", - " R\"w_{N(1900)^+}\": 1/1.93,\n", + " R\"w_{N(1900)^+}\": 1 / 1.93,\n", " R\"m_{N(Fakestar2)^+}\": 1.7,\n", - " R\"w_{N(Fakestar2)^+}\": 1/1.65,\n", + " R\"w_{N(Fakestar2)^+}\": 1 / 1.65,\n", " R\"m_{N(1650)^{+}}\": 1.6,\n", - " R\"w_{N(1650)^{+}}\": 1/1.6,\n", + " R\"w_{N(1650)^{+}}\": 1 / 1.6,\n", "}" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "\n", "original_parameters = intensity_func_fvector.parameters\n", "intensity_func_fvector.update_parameters(initial_parameters_fvector)\n", "intensity_func_rel_bw.update_parameters(initial_parameters_relbw)\n", @@ -1562,132 +1112,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "61398b9f63a14c89b142003b6f3d36c6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'Fit Breit-Wigner:'" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "FitResult(\n", - " minimum_valid=True,\n", - " execution_time=4.677215337753296,\n", - " function_calls=460,\n", - " estimator_value=-12198.959244284237,\n", - " parameter_values={\n", - " 'm_{N(Fakestar)^+}': 1.8497722024190237,\n", - " 'w_{N(Fakestar)^+}': 0.5782959510425077,\n", - " 'm_{N(1900)^+}': 1.8979233143190917,\n", - " 'w_{N(1900)^+}': 0.5133596540336253,\n", - " 'm_{N(Fakestar2)^+}': 1.750670984933865,\n", - " 'w_{N(Fakestar2)^+}': 0.5846494642322556,\n", - " 'm_{N(1650)^{+}}': 1.6498642685938685,\n", - " 'w_{N(1650)^{+}}': 0.620015291718532,\n", - " },\n", - " parameter_errors={\n", - " 'm_{N(Fakestar)^+}': 0.0014323314749189518,\n", - " 'w_{N(Fakestar)^+}': 0.01925746470118625,\n", - " 'm_{N(1900)^+}': 0.0019038409162150155,\n", - " 'w_{N(1900)^+}': 0.019434730126103774,\n", - " 'm_{N(Fakestar2)^+}': 0.000892931269048051,\n", - " 'w_{N(Fakestar2)^+}': 0.013270544273614748,\n", - " 'm_{N(1650)^{+}}': 0.00047548447999302897,\n", - " 'w_{N(1650)^{+}}': 0.011881206933894169,\n", - " },\n", - ")" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "470eae98802c48d49beed64bc11f4aee", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'Fit F vector:'" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "FitResult(\n", - " minimum_valid=True,\n", - " execution_time=47.1162691116333,\n", - " function_calls=2975,\n", - " estimator_value=-12202.383067702911,\n", - " parameter_values={\n", - " 'm_{N(Fakestar)^+}': 2.0582987246008804,\n", - " 'm_{N(1900)^+}': 1.8231026455013533,\n", - " 'g_{N(1900)^+}': 1.0730078384977024,\n", - " 'g_{N(Fakestar)^+}': 1.3133876970845961,\n", - " 'm_{N(Fakestar2)^+}': 1.7778710952273362,\n", - " 'm_{N(1650)^{+}}': 1.6396907094273547,\n", - " 'g_{N(1650)^{+}}': 0.9545451645245374,\n", - " 'g_{N(Fakestar2)^+}': 1.0945401433722264,\n", - " '\\\\beta_{N(Fakestar)^+}': (0.9055666944002341+1.0500508390638388j),\n", - " '\\\\beta_{N(1900)^+}': (1.273746344719463-1.4101342023704395j),\n", - " '\\\\beta_{N(Fakestar2)^+}': (1.5062984460066997+0.6457898290798657j),\n", - " '\\\\beta_{N(1650)^{+}}': (0.9570822021506897-0.8803373376651846j),\n", - " },\n", - " parameter_errors={\n", - " 'm_{N(Fakestar)^+}': 0.02416835891604554,\n", - " 'm_{N(1900)^+}': 0.006926974109042624,\n", - " 'g_{N(1900)^+}': 0.023515895445379586,\n", - " 'g_{N(Fakestar)^+}': 0.0916024840987184,\n", - " 'm_{N(Fakestar2)^+}': 0.002272635372427852,\n", - " 'm_{N(1650)^{+}}': 0.0007383030652937115,\n", - " 'g_{N(1650)^{+}}': 0.009734006074575145,\n", - " 'g_{N(Fakestar2)^+}': 0.01991299259213779,\n", - " '\\\\beta_{N(Fakestar)^+}': (0.1350094868896259+0.12215287033507256j),\n", - " '\\\\beta_{N(1900)^+}': (0.17203402316801028+0.1326094786089237j),\n", - " '\\\\beta_{N(Fakestar2)^+}': (0.11676784512423925+0.10945244771190928j),\n", - " '\\\\beta_{N(1650)^{+}}': (0.08848716894518248+0.08786639182501035j),\n", - " },\n", - ")" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from tensorwaves.optimizer import Minuit2\n", "from tensorwaves.optimizer.callbacks import CSVSummary\n", @@ -1705,20 +1132,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "optimized_parameters_BW = fit_result_BW.parameter_values\n", "optimized_parameters_F = fit_result_F.parameter_values\n", @@ -1738,38 +1154,9 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "m_{N(Fakestar)^+}\n", - " initial: 1.95\n", - " optimized F vector: 2.06\n", - " original: 1.95\n", - "m_{N(1900)^+}\n", - " initial: 1.91\n", - " optimized F vector: 1.82\n", - " original: 1.9\n", - "g_{N(1900)^+}\n", - " initial: 1.0\n", - " optimized F vector: 1.07\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Precision not allowed in integer format specifier", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[42], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m initial: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minitial_parameters_fvector[p]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m optimized F vector: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moptimized_parameters_F[p]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m original: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moriginal_parameters[p]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.3\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 6\u001b[0m latest_parameters_F \u001b[38;5;241m=\u001b[39m CSVSummary\u001b[38;5;241m.\u001b[39mload_latest_parameters(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_traceback.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 7\u001b[0m latest_parameters_F\n", - "\u001b[0;31mValueError\u001b[0m: Precision not allowed in integer format specifier" - ] - } - ], + "outputs": [], "source": [ "for p in optimized_parameters_F:\n", " print(p)\n", @@ -1809,6 +1196,9 @@ } ], "metadata": { + "colab": { + "toc_visible": true + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", diff --git a/SubintensityPlots_mitAgrand.ipynb b/SubintensityPlots_mitAgrand.ipynb index 9dea70df..15403775 100644 --- a/SubintensityPlots_mitAgrand.ipynb +++ b/SubintensityPlots_mitAgrand.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [] }, @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,105 +69,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eb66f384c6034cff9b69aa4ad8239413", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Propagating quantum numbers: 0%| | 0/36 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "g0_edge0\n", - "0: eta\n", - "\n", - "\n", - "\n", - "g0_edge1\n", - "1: p\n", - "\n", - "\n", - "\n", - "g0_edge2\n", - "2: p~\n", - "\n", - "\n", - "\n", - "g0_edge-1\n", - "J/psi(1S)\n", - "\n", - "\n", - "\n", - "g0_node0\n", - "\n", - "\n", - "\n", - "g0_edge-1->g0_node0\n", - "\n", - "\n", - "\n", - "\n", - "g0_node0->g0_edge2\n", - "\n", - "\n", - "\n", - "\n", - "g0_node1\n", - "\n", - "\n", - "\n", - "g0_node0->g0_node1\n", - "\n", - "N(1900)+\n", - "N(Fakestar)+\n", - "\n", - "\n", - "\n", - "g0_node1->g0_edge0\n", - "\n", - "\n", - "\n", - "\n", - "g0_node1->g0_edge1\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "reaction = qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", @@ -184,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "tags": [] }, @@ -195,25 +101,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\sum_{m_{A}=-1}^{1} \\sum_{m_{1}=-1/2}^{1/2} \\sum_{m_{2}=-1/2}^{1/2}{\\left|{{A^{01}}_{m_{A},0,m_{1},m_{2}}}\\right|^{2}}$" - ], - "text/plain": [ - "PoolSum(Abs(A^01[m_A, 0, m1, m2])**2, (m_A, (0, 1, -1)), (m1, (-1/2, 1/2)), (m2, (-1/2, 1/2)))" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import ampform\n", "\n", @@ -229,23 +121,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle - C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,0}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{\\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,-1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{3}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,0}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{\\frac{1}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}} X_{Q=+1, S=3/2, P =1} D^{1}_{0,-1}\\left(- \\phi_{01},\\theta_{01},0\\right) D^{\\frac{3}{2}}_{- \\frac{3}{2},\\frac{1}{2}}\\left(- \\phi^{01}_{0},\\theta^{01}_{0},0\\right)$" - ], - "text/plain": [ - "-C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 0, -phi_01, theta_01, 0)*WignerD(3/2, -1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 1, -phi_01, theta_01, 0)*WignerD(3/2, 1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, -1, -phi_01, theta_01, 0)*WignerD(3/2, -3/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 0, -phi_01, theta_01, 0)*WignerD(3/2, -1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, 1, -phi_01, theta_01, 0)*WignerD(3/2, 1/2, 1/2, -phi_0^01, theta_0^01, 0) - C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}*X_{Q=+1, S=3/2, P =1}*WignerD(1, 0, -1, -phi_01, theta_01, 0)*WignerD(3/2, -3/2, 1/2, -phi_0^01, theta_0^01, 0)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "amp, *_ = model.amplitudes.values()\n", "amp" @@ -253,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "tags": [] }, @@ -274,53 +152,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle X_{Q=+1, S=3/2, P =1}$" - ], - "text/plain": [ - "X_{Q=+1, S=3/2, P =1}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " N(Fakestar)+ 1.82 GeV 0.6 GeV \n", - " N(1900)+ 1.92 GeV 0.2 GeV \n" - ] - }, - { - "data": { - "text/plain": [ - "ParameterValues({\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " m_0: 0.547862,\n", - " m_1: 0.93827208816,\n", - " m_2: 0.93827208816,\n", - " m_012: 3.0969,\n", - " })" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", " display(symbol)\n", @@ -347,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -423,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -470,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "tags": [] }, @@ -505,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -539,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -566,27 +402,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{array}{rcl}\n", - " X_{Q=+1, S=3/2, P =1} &=& \\frac{Dummy_{N(1900)^+} m_{N(1900)^+} w_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2} - m_{N(1900)^+} \\Gamma_s\\left(m_{01}^{2}\\right)} + \\frac{Dummy_{N(Fakestar)^+} m_{N(Fakestar)^+} w_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2} - m_{N(Fakestar)^+} \\Gamma_s\\left(m_{01}^{2}\\right)} \\\\\n", - "\\end{array}" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import attrs\n", "from ampform.helicity import ParameterValues\n", @@ -597,79 +417,32 @@ "}\n", "model_rel_bw = attrs.evolve(\n", " model,\n", - " parameter_defaults=ParameterValues(\n", - " {\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_BW,\n", - " }\n", - " ),\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_BW,\n", + " }),\n", ")\n", "Latex(aslatex(dynamics_expressions_rel_bw))" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "ParameterValues({\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " m_0: 0.547862,\n", - " m_1: 0.93827208816,\n", - " m_2: 0.93827208816,\n", - " m_012: 3.0969,\n", - " w_{N(Fakestar)^+}: 0.6,\n", - " m_{N(Fakestar)^+}: 1.82,\n", - " b_{N(Fakestar)^+}: 1,\n", - " d_{N(Fakestar)^+}: 1,\n", - " L_{N(Fakestar)^+}: 0,\n", - " Dummy_{N(Fakestar)^+}: 1,\n", - " w_{N(1900)^+}: 0.2,\n", - " m_{N(1900)^+}: 1.92,\n", - " b_{N(1900)^+}: 1,\n", - " d_{N(1900)^+}: 1,\n", - " L_{N(1900)^+}: 0,\n", - " Dummy_{N(1900)^+}: 1,\n", - " })" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model_rel_bw.parameter_defaults" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "3071" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "full_expression_rel_bw = model_rel_bw.expression.doit().xreplace(\n", " dynamics_expressions_rel_bw\n", @@ -686,25 +459,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{array}{rcl}\n", - " X_{Q=+1, S=3/2, P =1} &=& \\frac{\\frac{\\beta_{N(1900)^+} g_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1900)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1} \\\\\n", - "\\end{array}" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamics_expressions_fvector = {\n", " symbol: formulate_F_vector(resonances)\n", @@ -712,69 +469,28 @@ "}\n", "model_fvector = attrs.evolve(\n", " model,\n", - " parameter_defaults=ParameterValues(\n", - " {\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_F,\n", - " }\n", - " ),\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_F,\n", + " }),\n", ")\n", "Latex(aslatex(dynamics_expressions_fvector))" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ParameterValues({\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+3/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{+1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(Fakestar)^+_{+1/2} \\overline{p}_{-1/2}; N(Fakestar)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+3/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{+1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " C_{J/\\psi(1S) \\to N(1900)^+_{+1/2} \\overline{p}_{-1/2}; N(1900)^+ \\to \\eta_{0} p_{+1/2}}: (1+0j),\n", - " m_0: 0.547862,\n", - " m_1: 0.93827208816,\n", - " m_2: 0.93827208816,\n", - " m_012: 3.0969,\n", - " m_{N(Fakestar)^+}: 1.82,\n", - " \\beta_{N(Fakestar)^+}: (1+0j),\n", - " g_{N(Fakestar)^+}: 1,\n", - " m_{N(1900)^+}: 1.92,\n", - " \\beta_{N(1900)^+}: (1+0j),\n", - " g_{N(1900)^+}: 1,\n", - " })" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model_fvector.parameter_defaults" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "3575" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "full_expression_fvector = model_fvector.expression.doit().xreplace(\n", " dynamics_expressions_fvector\n", @@ -791,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "tags": [] }, @@ -810,23 +526,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{Dummy_{N(1900)^+} m_{N(1900)^+} w_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2} - m_{N(1900)^+} \\Gamma_s\\left(m_{01}^{2}\\right)} + \\frac{Dummy_{N(Fakestar)^+} m_{N(Fakestar)^+} w_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2} - m_{N(Fakestar)^+} \\Gamma_s\\left(m_{01}^{2}\\right)}$" - ], - "text/plain": [ - "Dummy_{N(1900)^+}*m_{N(1900)^+}*w_{N(1900)^+}/(-m_01**2 + m_{N(1900)^+}**2 - m_{N(1900)^+}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(1900)^+})) + Dummy_{N(Fakestar)^+}*m_{N(Fakestar)^+}*w_{N(Fakestar)^+}/(-m_01**2 + m_{N(Fakestar)^+}**2 - m_{N(Fakestar)^+}*EnergyDecaywidth(m_01**2, m_0, m_1, w_{N(Fakestar)^+}))" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamics_expr_rel_bw, *_ = dynamics_expressions_rel_bw.values()\n", "dynamics_expr_rel_bw" @@ -834,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -848,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -863,23 +565,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\beta_{N(1900)^+} g_{N(1900)^+} \\left(m_{01}^{2} - \\left(m_{N(Fakestar)^+}\\right)^{2}\\right) + \\beta_{N(Fakestar)^+} g_{N(Fakestar)^+} \\left(m_{01}^{2} - \\left(m_{N(1900)^+}\\right)^{2}\\right)}{- \\left(m_{01}^{2} - \\left(m_{N(1900)^+}\\right)^{2}\\right) \\left(m_{01}^{2} - \\left(m_{N(Fakestar)^+}\\right)^{2}\\right) + \\left(- \\left(g_{N(1900)^+}\\right)^{2} \\left(m_{01}^{2} - \\left(m_{N(Fakestar)^+}\\right)^{2}\\right) - \\left(g_{N(Fakestar)^+}\\right)^{2} \\left(m_{01}^{2} - \\left(m_{N(1900)^+}\\right)^{2}\\right)\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right)}$" - ], - "text/plain": [ - "(\\beta_{N(1900)^+}*g_{N(1900)^+}*(m_01**2 - m_{N(Fakestar)^+}**2) + \\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+}*(m_01**2 - m_{N(1900)^+}**2))/(-(m_01**2 - m_{N(1900)^+}**2)*(m_01**2 - m_{N(Fakestar)^+}**2) + (-g_{N(1900)^+}**2*(m_01**2 - m_{N(Fakestar)^+}**2) - g_{N(Fakestar)^+}**2*(m_01**2 - m_{N(1900)^+}**2))*PhaseSpaceCM(m_01**2, m_0, m_1))" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dynamics_expr_fvector, *_ = dynamics_expressions_fvector.values()\n", "dynamics_expr_fvector.simplify(doit=False)" @@ -887,30 +575,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\frac{\\frac{\\beta_{N(1900)^+} g_{N(1900)^+}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}}{- \\left(\\frac{\\left(g_{N(1900)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(1900)^+}\\right)^{2}} + \\frac{\\left(g_{N(Fakestar)^+}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\right) \\rho^\\mathrm{CM}_{m_{0},m_{1}}\\left(m_{01}^{2}\\right) + 1}$" - ], - "text/plain": [ - "(\\beta_{N(1900)^+}*g_{N(1900)^+}/(-m_01**2 + m_{N(1900)^+}**2) + \\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+}/(-m_01**2 + m_{N(Fakestar)^+}**2))/(-(g_{N(1900)^+}**2/(-m_01**2 + m_{N(1900)^+}**2) + g_{N(Fakestar)^+}**2/(-m_01**2 + m_{N(Fakestar)^+}**2))*PhaseSpaceCM(m_01**2, m_0, m_1) + 1)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "dynamics_expr_fvector\n" + "dynamics_expr_fvector" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -931,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -942,9 +616,9 @@ "\n", "new_parameters_fvector = {\n", " R\"m_{N(Fakestar)^+}\": m_res1,\n", - " R\"\\beta_{N(Fakestar)^+}\": 1+0j,\n", + " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", " R\"m_{N(1900)^+}\": m_res2,\n", - " R\"\\beta_{N(1900)^+}\": 1+0j, # 0.5l\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j, # 0.5l\n", " R\"g_{N(1900)^+}\": g_res2,\n", " R\"g_{N(Fakestar)^+}\": g_res1,\n", "}\n", @@ -959,35 +633,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+3/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{+1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(1900)^+_{+1/2} \\\\overline{p}_{-1/2}; N(1900)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'm_0': 0.547862,\n", - " 'm_1': 0.93827208816,\n", - " 'm_2': 0.93827208816,\n", - " 'm_012': 3.0969,\n", - " 'm_{N(Fakestar)^+}': 1.82,\n", - " '\\\\beta_{N(Fakestar)^+}': (1+0j),\n", - " 'g_{N(Fakestar)^+}': 1,\n", - " 'm_{N(1900)^+}': 1.92,\n", - " '\\\\beta_{N(1900)^+}': (1+0j),\n", - " 'g_{N(1900)^+}': 1}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", "intensity_func_rel_bw.update_parameters(new_parameters_relbw)\n", @@ -1006,7 +654,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "tags": [] }, @@ -1019,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "tags": [] }, @@ -1034,89 +682,19 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ce51704fbe054fb7822ba057f14e0683", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Generating phase space sample: 0%| | 0/100000 [00:00:3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n", - ":3: RuntimeWarning: invalid value encountered in sqrt\n", - " return select([greater(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2, 0),True], [1j*sqrt(sum(x0[:, 1:]**2, axis=1) - x0[:, 0]**2),sqrt(-sum(x0[:, 1:]**2, axis=1) + x0[:, 0]**2)], default=nan)\n" - ] - }, - { - "data": { - "text/plain": [ - "{'m_01': array([1.81857778+1.e-08j, 1.80863875+1.e-08j, 1.86758228+1.e-08j, ...,\n", - " 1.7217908 +1.e-08j, 1.88162305+1.e-08j, 1.95955089+1.e-08j]),\n", - " 'm_02': array([1.70745362+1.e-08j, 1.77483717+1.e-08j, 1.56984082+1.e-08j, ...,\n", - " 2.13907063+1.e-08j, 1.99774363+1.e-08j, 2.0295025 +1.e-08j]),\n", - " 'm_12': array([2.33002756+1.e-08j, 2.2870134 +1.e-08j, 2.38733903+1.e-08j, ...,\n", - " 2.0276747 +1.e-08j, 2.02981933+1.e-08j, 1.92170012+1.e-08j]),\n", - " 'phi_0': array([ 1.97016286, -2.8765596 , 0.75357421, ..., 0.19730572,\n", - " -0.45861856, 1.57182959]),\n", - " 'phi_0^01': array([-1.97869891, 2.40627766, -2.02701505, ..., 1.42458459,\n", - " 0.78477173, 2.00132783]),\n", - " 'phi_0^02': array([ 0.98414884, -1.41787483, 1.80055274, ..., -2.62005351,\n", - " -1.37701865, -1.58606652]),\n", - " 'phi_01': array([-0.00476082, -0.46629838, -0.49331781, ..., 2.95178512,\n", - " 2.14918814, -1.97763388]),\n", - " 'phi_1^12': array([-0.5234414 , 0.53541189, -1.32700284, ..., 2.04917998,\n", - " 2.17445382, 1.30218432]),\n", - " 'phi_02': array([-1.98053067, 1.48563902, 3.08718583, ..., -1.7995076 ,\n", - " -2.40408988, -0.99517043]),\n", - " 'theta_0': array([1.69320513, 1.8732383 , 2.16807283, ..., 2.56300869, 1.02101855,\n", - " 2.0423608 ]),\n", - " 'theta_0^01': array([2.00195379, 1.79913544, 2.46359496, ..., 0.38143291, 0.96066346,\n", - " 0.54722468]),\n", - " 'theta_0^02': array([1.73936386, 1.72081689, 1.66448996, ..., 1.63126795, 1.19669709,\n", - " 0.64090765]),\n", - " 'theta_01': array([2.57060174, 2.32905607, 2.03576298, ..., 0.5128774 , 1.68637297,\n", - " 1.23809802]),\n", - " 'theta_1^12': array([1.34061414, 1.5044162 , 0.83272194, ..., 2.71169603, 1.82674819,\n", - " 1.82311053]),\n", - " 'theta_02': array([0.63817236, 0.51529239, 0.96856613, ..., 1.79764582, 2.3444856 ,\n", - " 1.03333824])}" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import re\n", - "epsilon=1e-8\n", - "import pandas as pd\n", + "\n", + "epsilon = 1e-8\n", "from tensorwaves.data import (\n", - " IntensityDistributionGenerator,\n", " SympyDataTransformer,\n", " TFPhaseSpaceGenerator,\n", " TFUniformRealNumberGenerator,\n", - " TFWeightedPhaseSpaceGenerator,\n", ")\n", "\n", "rng = TFUniformRealNumberGenerator(seed=0)\n", @@ -1126,7 +704,7 @@ ")\n", "phsp_momenta = phsp_generator.generate(100_000, rng)\n", "phsp = helicity_transformer(phsp_momenta)\n", - "phsp = {k: v +epsilon*1j if re.match(r\"^m_\\d\\d$\",k) else v for k, v in phsp.items()}\n", + "phsp = {k: v + epsilon * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}\n", "phsp" ] }, @@ -1139,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1174,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1196,24 +774,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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LtPyrr77S2LFjG6XGWbNm6ZZbbnG34+Li9N133ykpKclj/wULFujqq692t+12uzZv3qx+/fo1Sn0AECjN4Rjes2dPpaWlSZJ69Oih1NTURtkPADRXGzZs0JAhQ6qNfFKTrKwsjRs3TmvXrnUv69Spk/bt21ft5n5Dbd26VSeccIKcTqd72YIFCzRlyhSP/Tdt2qRRo0YpOzvbvey1115rkhABAARKMB/HU1JSNG/ePHd7z549jT51CgC0FCtWrNDZZ58twzBktVr1xBNP6P7773e/3tjXOL744gudd9557nZISIiWL1+uM88802P/lStXavz48SotLXUva8zrPQDQHNx8882aPXu2pOrB2cY+jo8ZM0YrV650t6uOHF6XwYMHq1OnTv4uCwCCyowZMzRz5kx3++uvv9aYMWMCV1AlnI/XX6uaUnDmzJnatm2bbrjhhjpv1Etl01S99NJLOumkk0zLX3311Uapr6SkRI899php2T//+c8aw1aSdNVVV+maa65xtx0Oh2bMmNEo9QFAIAX7MRwAULfk5GSvb9JLZQ8fvPnmm6Z1Dh48qO+//97vtT3yyCOmsNW1115bY9hKKrsY+M9//tO0bObMmaYPmQDQ0gTzcRwAUD+FhYW68cYbVf5c9h133KGTTz65SWt46KGHTO3/+7//q/HmjiSdddZZ+vOf/2xa9te//rVRagOA5mDFihXu695Wq1WPPPJIQOsZN26cT1+ErQAgsDgfr79WFbiaMGGCQkNDfVrHZrOZnuaRpKVLl/qzLNN29+7d62737NnTqyfkZ8yYYbp4+d577yknJ6dRagSAQAn2YzgAoHEMGjRII0aMMC3bsmWLX/eRlZWlDz74wN22WCxePcRw/fXXq0ePHu52Wlqali9f7tfaAKC5a4rjOACg/h566CH31CHdu3ev9kBwY9u4caNpWu+oqCjdd999da53//33Kyoqyt3+4Ycf+PsCoFUKhuAsAKD54ny8YVpV4Kq+Ro8ebWpnZGSooKDA7/tZsmSJqX399dd79eRonz59dNZZZ7nbpaWl+vTTT/1eHwA0R011DAcANJ4+ffqY2kePHvXr9j/55BM5HA53e8yYMerdu3ed61mt1moPSCxevNivtQFAS9DYx3EAQP38/PPPeuaZZ9ztF198UdHR0U1aQ9Vr4pdffrliYmLqXC8mJkaTJ082LeNcHEBrFOjgLACgeeN8vGEIXHkhLi6u2rLGGEHqk08+MbXPPfdcr9cdP368qf3xxx/7pSYAaO6a6hgOAGg8RUVFpnZsbKxft895OAA0rsY+jgMAfFdaWqobbrjBPa325MmTddFFFzV5HZyLA0D9BUNwFgDQvHE+3jAErrywf//+asvatWvn130cPnxYhw4dcrfDwsJ04okner3+GWecYWqvW7fOX6UBQLPWFMdwAEDjMQxDP//8s2lZ1ampGqrqufPpp5/u9bojRoxQWFiYu33gwAGlp6f7qzQAaPaa4jgOAPDd3//+d23cuFFSWRD2ueeea/IaDMPQhg0bTMt8ORevek18/fr17im1AKClC5bgLACg+eJ8vOHsgS6gOfj2229N7R49eig0NNSv+6g6n2Xfvn192kdSUpKpvXPnTjkcDtnt/IoBtG5NcQyvzdGjR7Vv3z4dO3ZMbdq0Ubt27dS1a1evpowFAEivvfaaDhw44G4PHDhQp5xyit+2X1paqp07d5qWVT23rk1YWJj69OmjzZs3u5dt2bJFiYmJfqsRAJqzxj6O1yY/P1+pqanKzMxURESE4uPj1aVLF1NQFgBao82bN+vxxx93t5988kl17NixyetIS0tTQUGBux0VFaXu3bt7vX6PHj0UGRnp3kZ+fr727t3r0zYAoLkKhuAsAKBhiouLtXv3bmVkZCgkJETt2rVT586dFRkZ2ST753y84UjjeOG1114ztS+88EK/72Pbtm2mdrdu3XxaPzExUeHh4e5h+ktKSrRnzx7169fPbzUCQHPUFMdwT44cOaKkpKRqgVpJio+P1+jRo3XVVVfp0ksvlc1ma5KaAKC5mTdvnm677TZ322q16oUXXvBraHX37t1yOBzudkREhBISEnzaRrdu3UyBq23btunMM8/0W40A0Fw1xXG8JhMnTtSWLVtMx3hJCg8P16mnnqqJEyfqpptuUkxMTKPXAgDBxOVy6YYbblBJSYkkafTo0brpppsCUktDr4mXr1N5O9u2bWtVN3gAtE7BEpytTU5OjtLS0pSdna3o6Gj3g8hcCweAMrfffrt2797tzneUs9vtGjFihC644ALddtttjfpgL+fjDUfgqg6ffvqpvvnmG9OylJQUv+/nyJEjpnbXrl193kbnzp21e/du0zYJXAFozZrqGO5JYWGhx7CVJGVmZmrJkiVasmSJ+vTpozlz5uiss85qkroAIJhs375dv/32m7tdWlqqrKws/frrr1qyZIkpxBQaGqpZs2bpnHPO8WsNVc/Du3Tp4vM2qq5TdZsA0FIFw3G8JuVP+1dVVFSklStXauXKlXr00Uf12GOP6Y9//GOT1AQAweC5557T//73P0kVx+ZAjcLtj2viXbp0Md3g4VwcQEsXTMHZmgwfPlwbNmyQy+UyLY+OjtYZZ5yhSy+9VNdddx0jzwJo1SpfM6nM4XBo1apVWrVqlZ588knde++9euSRRxolsMr5eMMRuKpFZmambrnlFtOyiy++uFGGvs/LyzO1o6KifN5G1XWqbhMAWpOmPIY3xK5du3TOOefoX//6l6ZPnx7ocgCgSb300kt69tlna+1jsVh0/vnn6+9//7uGDh3q9xo4DweA+guG43hD5OTk6I477tD333+vN954Q3Y7l8kAtGx79uzRX//6V3f7L3/5iwYOHBiwejgXBwDfBVNwtibr1q3zuDwvL09Lly7V0qVL9fDDD+u5557T5MmTm7Y4AGhGCgsL9be//U3ffvut/vvf/yo6Otqv2+d8vOGsgS4gWLlcLl1zzTXat2+fe1nbtm0bbQ7kqm+88PBwn7cRERFR6zYBoLVo6mN4ZW3atNHll1+uOXPmaPXq1crIyFBpaalycnK0ZcsWzZkzR6NGjTKt43Q6dffdd2vhwoWNXh8ANDeTJ0/Wgw8+2Gg36TkPB4DG1djH8crsdrvGjRunp59+WitXrtShQ4dUVFSkgoICpaWladGiRbrqqquqBasWLlyoO+64o9HrA4BAu/nmm5Wfny9JGjhwoB544IGA1sO5OAD4JtiCsw1x6NAhXX755brvvvsCXQoANBmLxaLTTz9djz/+uJYtW6Z9+/apoKBARUVF2r9/v/773//qlltuqXZevGLFCl155ZVyOp1+rYfz8YYjcFWD++67T5999plp2SuvvFKveSu9UXVuztDQUJ+3UXXozcLCwgbVBADNVVMfw8v94x//0P79+/XOO+9o2rRpGjFihOLj42W329WmTRsNHDhQ06ZN07fffqsPPvhAsbGx7nUNw9ANN9ygQ4cONWqNANDcvPvuuxo1apTOPPNM7dy50+/b5zwcABpXYx/Hy1199dXas2ePli1bprvvvltnnnmmOnTooLCwMEVERKh79+669NJL9dZbb2ndunVKSkoyrf/yyy/rv//9b6PVBwCBNmfOHC1fvlxS2Y2eWbNm1evc1584FwcA3wRbcLay8PBw/e53v9NLL72kH374QUeOHFFJSYlyc3O1a9cuvfnmm5owYUK10bj++c9/6oknnghQ1QDQdM4991xt3bpV33//vR544AGNGzdOXbp0UUREhMLCwtS5c2dddNFFevnll7Vjxw6dccYZpvU/+eQTvfTSS36tifPxhiNw5cFzzz2np59+2rTs/vvv1xVXXNFo+6yaFiyfe9kXxcXFtW4TAFqDQBzDy02ePNnr4TwvueQSffbZZ6bkd0FBgR5//PHGKg8Ags4zzzwjwzDcXwUFBdq7d68+/vhj3XDDDaZj5LfffquTTz5Zq1ev9msNnIcDQP0Fw3G83Pjx49W1a1ev+g4ePFgrV65U3759TcsffPBBGYbRGOUBQEAdPHhQ9957r7t94403avTo0QGsqAzn4gDgvWAMzpa75557tG/fPn300Ue69dZbNXLkSCUmJiokJETR0dHq3bu3rr76an388cf65ptv1KVLF9P6DzzwgNavXx+g6gGgaZx++unq37+/V327du2q5cuXa+TIkabljz32mAoKCvxWE+fjDUfgqooFCxborrvuMi1LSUlp9HR11Rv0VdOE3qiaFvT3HJ4AEOwCdQyvr9NOO03333+/admCBQvkcrkCVBEABFZERIS6du2qCRMm6NVXX9WGDRs0bNgw9+vZ2dm6+OKLlZ2d7bd9ch4OAP4TiON4fSUkJGj27NmmZRs3buRGD4AW6fbbb3cfezt27KinnnoqsAUdx7k4AHgnWIOz5SZOnKh27dp51XfUqFFasWKFEhIS3MsMwzBNlQgAKAsuzZ8/X3a73b3syJEj+uKLL/y2D87HG47AVSUff/yxpk6danqa8fe//71effXVakNc+lvVN175kKC+qLpOa3szA2jdAnkMb4jp06fLZrO525mZmY321D8ANDd9+/bVsmXLTFPC7t+/X//4xz/8tg/OwwGg8TTFcbwhxowZoxNPPNG0zJ8XLgEgGLz33nv68MMP3e1nn31WsbGxgSuoEs7FAcA7wRqcra++fftW+0zw6aefKjMzM0AVAUBw6tu3ryZOnGha1piBK87HfUfg6rivv/5akydPlsPhcC8bP3683n77bdON8MbSvn17U3vfvn0+b+PAgQO1bhMAWqpAH8MbIi4urtpNnm3btgWoGgAIPgkJCZo5c6Zp2dy5c/22/arnzPv37/d5G1XX4TwcACo09nG8oc455xxTm3NxAC3Nfffd5/73hAkTdPnllwewGjN/XBPnXBxASxfMwdmGuO6665SYmOhuu1wu95SJAIAKjXndgvPxhiNwJWnVqlWaOHGiaYi0008/XR9++GGTzX88YMA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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from matplotlib import cm\n", "\n", "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", "ax.set_xlim(2, 5)\n", @@ -1278,7 +843,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1289,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1318,22 +883,10 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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oZrhp+IzMIKW4R8EvmXVrobTJTPPjM50lCENpuXIOp1TCizVf1bOgUs6BE4bS/DJOe/N1AQIvj19xzPEKfKwoBK3ePuOXiVQxwbZqBd8FX1E1EVmiFFUTERERERERETmKTU9P89vf/rZhuWVZnHrqqU0eISIiIiIiIiIisn+mK9Pszu9iujLdch13aA9PLZua9abS/eP4MJCGwDK3bWBnp/nZCmDTTpiMQbp0AGNPTxEdeJzysccf2OREREREREREREREREREREREnqUWGj6ryZazDE4Pkitn4ZZbYOtW8DxwHNi0CTZvBstiMDvItqFt9fhZEMCOHTAwYH62LFizhurxx7F76kmiThTHDitfIyMwNgh9ta36MPYw3rbRuaE0P4DMYNP4WWl6mKKbAtumUC3QZsdxW4XPJjNUEtGZqFsuN4ETmPe3NAiAfJYgFobP9hFKC4ByOU/gmH1zKmW8FrWfAPB9H6wqBD6BBVYEVrRDZxwcC7wAMkUYykKlCpbvzxzjqAPLU5BONK7rVQCvSizqcNoKn419cPO3Yfee5nMREXkmU1RNREREREREROQo9utf/7r+LSuzPPe5z6W9fS9fNSIiIiIiIiIiIrIAv5newfcHv08Q+Htdz6rk6VoL1kFsq+DCUJN/0urPwta18JM18EePwClP79+4gWXhdXQexMxEREREREREREREREREREREnoFGR2FoCPr7oadnZvFCQ2lPTDzB1l1b659dCgK491549FHwfbBtOOkk2LgRLIuJ4gS7MrtMxGtkBCYG4dTaBjyYuAP+dztezzLGCmNE7Ai2Zde3n83M+kK/ALI7Ke0cpRAzwbE2tw0Xp2UorTw9TMFNmSBbBdpKVaKNH7uaGT4olyBmBprIj+LGWr/3xcuPU3HCuXpliklarhx4efyKM3PMLN8niLaeh+V7BFZgQmk2RCImftYslOZVIe74bOiDDSugP2l2t8mwDE/B9qcDPAtO7jXxNYv6lx7OXnlsCoLApy8dEPEh4h/c+4BERI5miqqJiIiIiIiIiBzF7rvvvqbLTz/99MM7ERERERERERERWXKy1SzfH7ytadR/vqCtjcpzjiH65O4D3t4JY5B3YWrWN/N2lMxyAN+Cm06G1XdDqrzwcfPn/B+8ZT37XlFERERERERERERERERERETkKNIQP2sSPqvJlrMMTg+Sq+RM/OyWW2DrVvA8cBzYtAk2b2YwN8S2oW0mfAZm3R07YGDA/GxZsGYN1eOPY+fkALZlE3NiOLZjQmljg9BX26oPYw/Dg+NzQ2l+0DJ8RmaQUtyj4JeAWaG0yUzzgzCdJQhDablyDqdUwos1X9WzoFLOgWNyOTm/jNPkC/7ARMd8vwzlangYfMrR1iGxwK9i2Q74HtgQtWB5R/PwWaWKCanZNgQ+VgCu0zyUNjIFVQ8iYSjtjOVmvfkTscLT1e5CWwzG8uD5YAcw/6sSrQBs4IUr4fUnm4ja7kkYnDCnuOLA7HcDpW04Zx20x8DzA8ZGYXgMnL1/B6OIyDOWomoiIiIiIiJHgYcffphvfetb/OQnP+HRRx9lbGyMcrlMT08Pvb29rF+/npe97GVcdNFF9PQsjQ+Gbd++ne9///vcfffd7Nixg927d5PNZqlUKiSTSVasWMG6det4wQtewObNmznllFOOyDyP9Ll56qmn+O53v8v999/Pr3/9awYHB5mammJ6ehrHcWhra5s5XmvWrOG4445j48aNnHPOOaxevXrR5yOHX6uo2oYNGw7zTORoft168skn+f73v8/Pf/5zHn30UQYGBshkMhQKBWKxGB0dHfT09HDiiSdy8sknc95553HeeecRi7X4LdNRavfu3Xz3u9/lJz/5Cdu3b2fXrl1ks1mq1SrJZJLly5fz3Oc+l+c///m8+MUv5txzz8Vq9nU8h9g999zDrbfeys9//nN+85vfMDIyQi6XI5lMsnbtWt72trfx7ne/+7DPS0RERERERGS+gdzOOUE1P9j7OyWnLnwZ7XfcdsBhNQvYMATjCShFIFaF7sLc94gGFvxmGZw5uLAxc+eex+Dff+aA5iMiIiIiIiIiIiIiIiIiIiJyQEZHYWgI+vth1mfKGiJp0DKU9sTEE2zdtdW8dyMI4N574dFHwffBtuGkk2DjRrAsJooT7MrsqofSRkZgYhBOrY3mwcQdeLc/wli7bcJnll3ffjYDy2rrBpDdSWnnKIWYMzOfNjuOmxluGUorxz3yXhGAZMnDbfUdfgGQzxLEwvDZPkJpAVAu5wkcMxenUsZrUcMxoTQfrCoEPoEFVgRWtDePn3lVsML3w1iAa0N/k/DZUBa8CkRtj9P6YUO/GbNZgS0Ahqdg21DAI+MeJy7beyjN8SEZg0QUxgoQeGYQb964yQic1AvpqPliwqoPezIwNA5lGzy7vm57BE7pgVSsPqfj07AyDjuGYcIHP1w/FYUNfRCzzYqRANZ2wYokxN3mx1lE5JnOChbyVbMiiySVSpHNZucsa29vZ3p6+gjNSERk6dm+fXvTb5K3LIsTTzxx8Te4a3Lf68jSsjq973We4dauXcvAwEDD8jvuuIPzzz//sM3j/PPP58c//vGcZWvWrGHnzp1Hxdg7d+7k2GOPbVj+kY98hI9+9KOHbYxDYTHmtWXLFi677LKG5fOvo/vuu4/3v//9/OAHP1jQuIlEgve+97184AMfoL29xVco7Md8Dtb+/idltVrl61//Ov/8z//Mr3/96/167O/93u/x4Q9/mFe+8pX79bj5jrZz08qPfvQj/uEf/oEf/vCH5h96D8Cxxx7L6173Ot785jcvOO50INf/4bi+giDg+OOP5/HHH5+zjuu67N69m+XLly/adovFIitXrmRiYmLO8o6ODp566imSyeSibWshTjvtNB588MGG5d/5znfYvHnzYZ3LgSqXy6xYsYLx8fE5y9vb2xkaGlrUYzo1NcWKFSvI5/Nzlvf29rJnzx5cd//+tf9oeN1qxfd9vvnNb/KZz3yGn/70p/v9mpxIJLjooou4/PLLeclLXoJt23PuP1r+7gDzGv2JT3yCH/7wh/v1+LVr1/Le976Xt7/97QcVkFvo3x3f+ta3+NjHPrbPa+WSSy5hy5YtBzwfERFZfIf939REROSQeWB3hld9/qcLWve/3/VCNhzTeWgnJHKI7Lj3d4syzmNTj3H74G0ztx+ZfITp6r7fw2EVC8QffQhrL/+dHlgW5WOfS+Ds+zsfO6w2TnHXzNz+w9hZnOges49JQOn45+F3dje9e93G5+5zu0e7B0eGj/QUjnqn9i3evw2LHBF77oX/vHBh617+I1i18dDOR0RERERERERERERERORZYn/iZ9lylsHpQXKVnImf3XILbN0KngeOA5s2webNDOaG2Da0rR4+CwLYsQMGBszPlgVr1sC6dVQDj92Tu4k6URzbMZG0wSbfQLdiBV7PMsYKY/VQmufBniehyds2Sg4UulNg27S5bbg4LdctO5DvTpl5AeVSDrfkNeuIAeDFIlSc8LMn5TJ2YOJfzQSA7zozx8HyTACt1coWEDg2BD62B5GIiZQ1i5+VqxAPQ2EbVkB/cmYXGuYwPAWPjJqfT+5tHkqzwmPT4UKbC08Xw65dEO7HvPXtAFwfVnVCfyc8NQWDE+YUV5y5hzptw4l90B6rBeFMKG3PBHiWOQdgwmen9pueXdg+w7PBxez/eB4CFxwbIjb0tgFVyJXDufrmvngcbAdG81AOj+vydnPJFEtm3bgH6STEY/CKi+C+e+funxowIrIU7PtdiyIiIiIiIrLogiDg4x//OB/72MfwvPnfKdBaoVDg7/7u7/j617/OD37wg6bxq6PV3XffzRVXXNE0DrUQ99xzD6961at4+ctfzpe//GV6e3sXeYbGkT43U1NTvOMd7+DrX//6AT1+tieeeIJ//Md/5Le//S033XTTQY93JFmWxRVXXMH73//+OcsrlQrXXHMNH/zgBxdtWzfeeGNDUA3gzW9+82EPqhWLRR599NGm923YsOGwzuVgRKNR3vjGN/K5z31uzvJsNstNN93EJZdcsmjbuvHGGxuCagAXX3zxfgfVjubXrZ///OdceeWV3H///Qc8RqFQ4Oabb+bmm2/mfe97H//4j/+4aPNbLKOjo7zrXe/ixhtvPKDH79y5kz/5kz/hc5/7HNdccw0vfOELF3mGxvT0NJdccgnf+ta3Dsn4IiIiIiIiIofKysRKLMuaCcyualvF9qnt+3xcZHx0r0E1ACsIsPM5Kiv3EUcD+jtOpOqaNyNblkX3sRdRcFML2AMRERERERERERERERERERF5VhkdhaEh6O+Hnp45dzWE0lpE0p6YeIKtu7bWv5A3CODee+HRR8NClQ0nnQQbNzJRyrArs6seShsZgYlBOLU2mgcTd+Dd/ghPJ8HCwnVcEz+bnIRsBpbV1g0guxMez1Bqj1OoFABoc+K4mWFT1JovM0g57pH3igAko0nc6WzTSFptE0G5BLE4uXIOp1TCa/Ed9QFQLucJHFP2CjwPL9LQHKuv71XxbQd8D2wT+1rZ0Tx8VqmC5fsEtgmlWQG4TvNQ2sgUVD2IOD4b+uCM5Wa9VvGzdhfaYjCWBy+Mn/k0rmsDL1wJrzsFAguezJj4WcWaG0pLRuCUHkiFx6kSwFMT5n8BJlQ3OwjXacGJKyEZhtKOT8PKOOwYhgkf/LA7lwrDb7GwkmaHkzyuG47vgYkwlBaLwIqUOR5eGfJlc0l2JSAZNcG4ng4Yzpl1+lPm+NlR6EhAoWwib7EoeOFc41EYzsKyJMTCcBsJ8CoQs8wlHtB4jEVElgpF1URERERERA4z3/e55JJL+OpXv3rAYzz++OOcd955bN26leOOO24RZ3dofPrTn+Yv/uIvqFarBz3W9773PX7v936P73//+5x44omLMLu6I31uJiYmeOlLX8qvfvWrA97+UvbHf/zH/PVf/zWlUmnO8v/8z//kAx/4AFazrxQ5AFdffXXT5VdcccWijL8/tm3b1vR509XVxerVqw/7fA7GpZde2hBVA9iyZcuiRtW2bNnSdPlll122X+Mcza9bn/zkJ/nQhz60KHOrmf+8Oho88sgjvPzlL2fnzp0HPdaOHTu44IIL+PznP8/ll19+8JObJZPJcMEFF/DrX/96UccVERERERERORxSbooTUuvYMfUYAGk3TSqSYrra+ttmrWqFyMjQgsaPjAxT7esniLSO3afcFGm3/sblE1LrSCmoJiIiIiIiIiIiIiIiIiIisiQ1hM+gZfwsW84yOD1IrpIzlalbboGtW8HzwHFg0ybYvBksi8HsINuGtpn4WRDAjh0wMGB+tixYswbWraMaeOye3E3UieLYYW1qZATGBqGvtmUfxh7G2zbKWLtNxI6YSJrnQWawafysND1M0U2BbVOoFmiz47iTmRYHIQOJ5TNRt1xuAscFz26+upcfp+KYO4vVIna1jN/efN0A8P0ylM1nTizfJ4g2Xxcg8D0sK4DABxtc20S7WoXS4rbHaf2wYQWsSNI0yhUAw1OwbSjgkXGPE5ftPZTm+CZQlojCWAECzwzizRs3GYGTeiEdNVG0qg97MjA0DmV77vFrnxVKCwDPqsfPto/ARBhoS0Xh1H5oq00ciAfw3G44IQyfEQHLMQE334f+JFQq5mcnDKWl4nDuCSZ8ZtkmlLYyZR5TrUC+BNEAOpLgJuaG0rrbIBpeilYEupMQcUwAzbPMtGwb+trBD8x5scLpug4s7zC3i6X6MXNseE7arF/jONAZhuOKJROPExFZqhRVExEREREROcze/e53N4122bbN8uXL6evrw7ZtRkZG2LNnT8tx9uzZw6WXXsqPf/zjRYtJHQp/+Zd/ySc+8Ym9rmNZFv39/SxbtoxEIsHIyAiDg4OUy+Wm6+/atYsLL7yQu+++mzVr1izaXI/0ubn44ov3GVSLx+Mcc8wxtLe3E4vFmJ6eZnJykpGRkZbHa6lYtmwZr3vd67juuuvmLH/iiSe4/fbbeelLX3rQ23jooYf42c9+1rD83HPP5dRTT23yiEPrvvvua7p8w4YNh3kmB2/jxo2ceuqpPPjgg3OW//jHP2bnzp2sXbv2oLfx29/+lp/+9KcNy8844wxOO+20BY9zNL9u/emf/imf+cxn9rleNBqlt7eX5cvNL9nGx8d5+umnyefzB7ztw+mxxx7jggsuYGRkZK/rdXR0sHLlStra2njqqacYGRnB9+d/v5BRqVR4+9vfjud5vOMd71iUeXqex2te85qmQTXbtunt7aWvr49EIsHY2Bg7d+7E8+b/Wk9ERERERETkyDqr+yx+M71j5o26q9pWsX1q+8z9cSdOX6yvfvvRB4mNLvRdlT7FPXlKJ9X/bW2kNEIx/PZkgFWJVTM/W5bFWcvOPsA9ERERERERERERERERERERkUNqdBSGhqC/H3p6ZhYvNJT2xMQTbN21deY9CgQB3HsvPPqoKVTZNpx0EmzcyEQpw67MLhNJAxM+mxiEmbcgeDBxB/zvdryeZYwVxurxs8lJyGZgWW3dALI74fEMpfY4hUoBgDa3DRenZSitPD1M3k2BZZGMJnGnszPhrQYBBOUSxMxAE/lR3FjT5phZPT9OOWKbY+GVcSKto2qBV8WvBeB8D9+CiAv97U3iZ9MmShYE5rMVVhCGt1qE0qoViDo+G/pgQz+saKdp+Awg5ULShaeLJhZmB2a//XnrOwGsbYMXngn9nfDUFAxOmHlVnLmHMG3Dif3QHqsF4Uwobc+ECYqVw92uxc/imO1aAWDD8d3wvGUwnofANTGxiA29bUAVcuV6/MyxoD8NK7thxFwCrEiZ+6sVyJfNJdkbh2jcjN/TASNZKPrmmK9oN9uwAhNKK2YhlTChtMCGdBsMZ2FZEmLh3HGhJwmuZcJ0HuBjLvfVaagGJn5mYcJnSTc8BY6ZUzmAqmfWj0dgqmjGjrnQ5oIdnrJEzITYClUoe9CVgFLFjO86Zl3MbpGIQbEIFX3ERUSWKEXVREREREREDqNrr72WL3/5y3OWvehFL+Id73gHL3nJS+ju7p5z365du/ja177G3//93zM1NdUw3l133cUXvvAFrrjiir1ud/ny5bzwhS+cuT05OclDDz3UsN4JJ5xAX19fw/ID9a//+q8tw0SWZfGHf/iHXHbZZbzoRS+iZ9Y/pAPk83l++MMf8m//9m/cdtttDY8fHBzkzW9+M3feeSe23eJfjPfDkTo3NTfddBO33npr0/ue97znceWVV/Kyl72ME044oWmorVqtsmPHDrZt28btt9/O9773vX3GiBbL4by+rrzyyoaoGsDVV1+9KFG1q6++uunyxQow7a/777+/6fJnYlQN4NJLL+XP//zP5ywLgoCvfOUrfOQjHzno8b/yla80XX7ZZZcteIyj+XXrb/7mb/YaVFu+fDlve9vbuOiiizjnnHNwHGfO/b7v88gjj/DLX/6SW265hVtvvZVSqbTX8Y7E3x1TU1O88pWvbPkalkwmefe7380b3/jGhlje+Pg43/jGN/jc5z7XdK5gAprr1q3jwgsvPOi5fvKTn+TOO++cs+zcc8/lHe94B3/4h3/YcI3kcjluvfVWnnrqqYPetoiIiIiINBeN2Kxd1rbgdUUEumPLOCG1jh1TjwGQdtOkIimmq9MAlLwSXdEu4k4cq1ig64HfYVUXPn6w7XEmTj6PIJ6g6BXZnd89c1/KTZF2698wfUJqHd3R7mbDiIgsTU4Muo9b+LoiIiIiIiIiIiIiIiIiB6EhftYkfFaTLWcZnB4kV8mZ0tQtt8DWreB5pvy0aRNs3sxgbohtQ9vq8bMggB07YGDA/GxZsGYN1eOPY+fkALZlE3NiOLZjQmljgzDzkQQfxh7G2zbK00mwsHAdF9sPWobPyAxSinsUfPP5iDYnjjuZaXEAMpBYPhN1y5VzOKUSXotfxXkWVMo5cCIUq0Xsahm/vfm6JghWhnI1PAw+5WjrqBp+BcuPEPhVsCHiwKoUpBON4bNKFSzfJxox4bPTl5tIWpOPmEEAw9Pw8KiZ0/oes26rUFqHC4kojBbCrl1gol+zJSNwUi+ko+Z2JYAnMzA0brZRciCYNX6nBSeuhGQYSjs+DSvjsGMYJnzww7dtpaKwoQ9itpm3bTpwHNcNx/fARBhKi0VM/Mwz/bmZ+FlXApJRcxx6OmA4Z9bpT5ljaEehIwGFMkQCcGPmOAQWrI6ZkFnEDg+NC91JcG0TJguAKiZk1tteD6U5dv18p+MmbFeqQO3TOY4Nz0mbsWscB9oiYFvgulAsQRHzVIpHzHHPlszPqXCOVvjUiUdgZBqmS7AqbWJ2Fc8cm2M6zXac8Nh7gRlreMpE08JLkaEp6EtBexSwzbGr+ObPYNY8RUSWEisI9BInh08qlSKbzc5Z1t7ezvT09BGakYjI0rN9+3aa/fVuWRYnnnji4m9w1+S+15GlZXV63+s8w61du5aBgYGG5XfccQfnn3/+YZvH+eefz49//OM5y9asWcPOnTuPirF37tzJscce27D8Ix/5CB/96EcP2xiHwmLMa8uWLfuM+KTTab7yla/wqle9ap/jPfnkk1x44YX85je/abhv3bp1PPbYYwuaV82dd97JBRdc0LD8y1/+Mpdeeul+jdXK3XffzXnnnYfnNX5dwcknn8z111/P6aefvqCxvvvd73LxxRc3jZf93d/9HR/60IcWPK+j9dxccMEFDWEegA996EN8/OMf3+8Ak+/7/PjHP+bTn/408XicG264YUGPW4zr/1BfX2eeeWZDbCwSibBr1y5WrFhxwOPm83lWrlzJ5OTc/3/T1dXFU089RTze7Dcvh9ZZZ53FPffc07D8mmuu2a9Q2NFiZGSEVatWUa3O/dTtcccdx29/+9umwcCFCoKAtWvXsmvXrjnLo9EoTz31FMuWLWvxyLqj9XUL4LbbbuOiiy5qel8kEuGDH/wg73vf+2hvb/HbuSampqb46le/yj/90z/x6le/mk9/+tN7Xf9w/N0B8La3vY0vfelLTe/btGkTX/nKV1i7du1exyiXy3zsYx/jE5/4RNP/PlqxYgXbt2+no6NjQXNayN8diUSCL3zhC7zpTW9a0JgiInJ0Oez/piYiIiJykHbc+7tFHW+8NMbXBq6f+f9Ek5VJtk9tn7m/N9bLce3H0farn5H49b37PX7+9I0UznoBj2cf5+nS0zPLT+w4cSaqZlkWb1z7pkWLqq3b+NxFGedIenBk+EhP4ah3at/yIz0FEREREREREREREREREZFDb3QUhoagvx9mffl3QyQNWobSnph4gq27tpr3BgQB3HsvPPpoWNKy4aSTYONGsCwmihPsyuyqh9JGRmBwsGFaXv9yxtptInYE27Lr289kGtYtdbZTiNW/PL7NjuMODUOT4knZgXx3vRrWVqoSncy3PDzldBv5WCTcUBGnUMZr8VGsIOZSjtgz75FwintZF/AtTCkr8LE8iLgmpNUZb4yflatQOwwEJtDVn2q+rleBeAzW95lQ2op2E/cK5n+0JoDRKXPKetJgY8Jn1OY2ix2A68OqTljVZZY9lTEBtIo1d/1kBE7pMRGvABNKG5yApyZMTK4cnqpUFE7tNz27WbtmomW2iXsRAcsxwS/fh/4kVCrhpRWG0nAgGjXhM8s2obSVKfOYagXyJYhWoSMJbsKc+mpg1u9ug2g4HyuAiGUidF7FzDUIj5sXzAulheegzTW382UoB4Blth9xTMysdorbXbOPQWDCZyVMfKzk1Zt0Ucc8zrEhFYbSggCyFXMMPc/E5PIlKITRtM4EOLOOX9WD3VMmXtfdZuY5WTRjdiYgETH76FUhV4EyJqrWmTD77wP5ignK2YE5lwC58ONauRI4EYiHx2yyAKmE2X4iauaSLZv7XvESeGDeW4HUgBGRpSBypCcgIiIiIiLybNTX18cPfvADTj311AWt/5znPIfbb7+dU045hXx+7j8A79ixg7vuuovzzjvvUEz1gFQqFf74j/+4aZjooosu4hvf+AbJZHLB473iFa9g69atvOhFL2oIXn3sYx/jbW97G319fS0evX+OxLkZGxvjrrvualj+hje8gb/7u79b+ORnsW2bCy64gAsuuKAhbv1Md+WVV3L55ZfPWVatVrnmmmv4y7/8ywMe94Ybbmi4vgDe8pa3HJGgWrVa5cEHH2x634YNGw7zbBZHX18fL3/5y/nv//7vOcsff/zxmef4gfrRj37UEFQD2Lx584KCakfz61Yul+Ntb3tb0/sSiQQ33ngjr3jFKxY8t5qOjg6uvPJK3v72t/O73y3uB7EP1K9+9Suuueaapvedd9553HbbbSQSiX2OE41G+du//Vu6u7v58z//84b7BwcH+chHPsK//Mu/HPScAZLJJN/73vcO6hoWEREREREROZK6Y8s4IbWOHVPmSyLSbppUJMV01bxBcrQ0yiqri+6HHjig8RMPbyNz8kmMlkZnlqXc1ExQDeCE1LpFC6qJiIiIiIiIiIiIiIiIiIgcTfYnfpYtZxmcHiRXyZla0y23wNatptbkOLBpE2zezGBuiG1D2+rhsyCAHTtgYMD8bFmwZg2sW0c18Ng9uZuoE8WxHRNJGxuEmY81+DD2MDw4jtezjLHCWD2U5nmQGTRVrXlK08MU3BTYNm1uGy4OTGaaH4TpLIFbD6VN5EdxY/VY1WyeBZVyzpShgJxfxtnLd9AHXh6/4pj9DnysKAQtQmn4FSw/Yo6b7xHYEInA8mbxs2kIPIg6Phv64PTlZj2ryaQDYHgKHhk1P5/ca0Jp83fQCk9Xh2tiX6NFExarhdLmf6ol5cA566A9ZqJdT2ZgaNxso+TMjbB1WnDiShP1qrXqjuuE5QnYPgIT4cJaKK2tNnFMcOu53XBCjwmlBa4Jj61ImePhlU2ULAhgWcLEubChpwNGslD0TXBuRbuJg1mBCYsVsybo5SbMOUm3wXAWliVhprHnQk8SXAsqVXMMfEy0bXXaxNX8IIyfOZAMI2k49VBa1TPrxyMwVTRjx8JjbIenoS1qQmXlClgRc2kTmP1LxcxKVvjUibowMgmZsrm9qgOyRRjNQcKFtV31cxtYEI3A4DjYjom62RZM5KCzzcTN4q4ZpxZK64hBtXZNYK69wIKqb0JvccfE5gLbhIF6kmYfLbPbdCbMsQKzvzDzdKEtGsbewvG72kzgDgsK5fBYWuFYzZ6AIiJLgKJqIiIiIiIL9J73vIf0rH+cPFB/+Zd/yUUXXbQIM5JnKsuyuPbaaxcc7apZu3Yt73vf+/joRz/acN+3vvWtoyqq9qUvfYlHH320YfkZZ5yx32Gimg0bNvCFL3yB17/+9XOWl0ol/v3f/52PfOQjBzzfmiN1brZv39405PRnf/Zn+zWPVtrb9/Jbg2egN77xjVx11VUNoar//M//5IMf/CC23eq3Hnt39dVXN11+xRVXHNB4B+uRRx6hVCo1ve+d73wnruse1PiWZXH77bcvKFC1mC699NKGqBrAli1bDipItWXLlpbbW4ij+XXr05/+NHv27GlYblkWN998My972cv2e26zRSIRnve85x3UGIvl4x//+My3Pc32nOc8h1tuuWW/r9f3vve9PPbYY3zhC19ouO/qq6/mAx/4AMuXLz/g+dZ86lOfUlBNREREREREnvHO6j6L30zvmPlv81Vtq9g+tR2AgIDRHXezslrd2xAtWZUKT++4m2B1PX6/KrGqfr9lcdaysw9i9iIiIiIiIiIiIiIiIiIiIodXQyitRSTtiYkn2Lpra/298kEA994Ljz5qSke2DSedBBs3MlHKsCuzqx5KGxmBiUGY+aiTBxN34N3+CE8nwcLCdVwTP5uchGwGZn41H0B2JzyeodQep1ApANDmxHEzw00jaWQGKcc98p6pNCWjSdzpbL3ONF8AQbkEsTi5cg6nVMKLtVyVcjlP4JiSVuB5eJHmUbUA8H0frCoEPoFlIlgr2puEz7ImLmX5/sxxizomfpZONK7rVcDyPSKuxYZ+OCMMpTWLn9kBJKOQiMFY3gTWCEzwq2Fd4IUr4XWnmDjWkxkYnICKBf6ssZMROKUnjHhhIlp7MvDUhNnvig1e+NGoVBQ2LIe4ZbYbxUTSViVN+MwK5+bY4NrQmzBzzJWpF7sc6E/Dym4YMZcAK1Lm0qtW6qG03jhE4ya21dMBwznobjPHEszx705CxAG/Yh5TxVy+ve31UJpj189hOg797VCqQO0TUo4Nz0mbsFeN40BbxITAXBeKJShiomfxiJlrtmR+nh8/i0dgZBqmS7AqbQJ1Fc8cn2M6zXZq4TA/MGNNl802a4G7YhUKFUiEc6iFz1Jxs24iasboiMNUyUTV8mUTL4vaUPFNrCzpmrCZhZlbOgbtUTOnXMmE3mqhNMcywbdCxawfjZjrqlw1t5+cMDG0iB0G4VzI+uF1EDHnofY2nrG8+bOvw/wZsU2grhheqPEIlMomVlfxYKwAfe3Nn3siIkuFomoiIiIiIgv00EMPLco4w8PDizKOPHO9+c1v5qUvfekBPfatb30rf/M3f9MQnLn33nsXY2qLIggC/umf/qlhuW3bbNmy5YDCRDWve93r+OIXv8j//u//zln+7//+73zwgx8kGo0e8Nhw5M7N0NBQ0+Xr168/oLksdW1tbVxyySV85jOfmbN8YGCA73//+wcUrnzggQf4xS9+0bB806ZNnHTSSQc814Nx3333tbzvl7/85UGPf/zxxx/2oBrAy1/+cnp7e3n66afnLL/pppv43Oc+d0CvEdPT09x8880Ny/v7+xcUHDuaX7fK5TL/+q//2vS+d7/73QcdVDua7N69m+9+97tN7/vUpz51wHHbf/iHf+Dmm29mdHR0zvJCocCWLVt4//vff0Dj1rzgBS/g7W9/+0GNISIiIiIiInI06I4t44TUOnZMPQZA2k2TiqSYrk5jVSuMjz1BIQKJA+iqFSIwMf4E1soOgohLyk2Rduv/rX9Cah3d0e7F2hUREREREREREREREREREZHWRkdhaAj6+6GnZ2ZxQyQNFhZK20skreJXefjph+duf2QExgahr7bAh7GH8baNMtZuE7EjJpLmeZAZbBo/K00PU3RTYNsUqgXa7DjuZKb5/k5mILF85jNPudwEjlsPd83n5cephGWsYrWIXS3jtzdf18TPyqYERRg228vHJALfw7ICCHwIQ2D9qeahNK8KccdnQx9sWAH9SROqajaH4SnY/nSAZ8HJvSa+ZmHiZvNXHpsynyPpSQfYmHAagQlOzZZyzFjtMRNF83wTSdszAWV77vFrnxVKCwDPguPTsDIO20dgIvy4WSoKp/ZDW23iQDyAdV1wQk8YSouA5Zj597WD75nImO+DG0A6CfGYme9I1oSzIrbZZ8c2obCOBFRNF49IHLDMsVgdM2GxWqgL14TSXNvE5jxM2Mu2YXUaqoFZ38JEyJJu+DjHzKkKVD0oeeYcep5ZFnHmhtIS4fiVIAylxSBfgoI3K5QWsi2IuuaceuF2Ccx2CgHEIvX4WbUWP4vVH2sFJubmOuYQ50omZAeQC9/z0pM041lmV+hMmDBfzmsMn3UnmofSLExILXChWDHjtkXNHGqfLuxqM+etVi+bHUpzbBOsK1XN/jk2RCwzj6pnQm9jBRP8c2wT7Ys65qlWW9cKj38hvHirXnjsCc+xZ867Y9WfX6Vw7KpvzruIyFKlqJqIiIiIiMhhdtVVVx3wY4855hjOOOOMhtjSr3/9a4IgwGr2L8OH2Q9/+EOeeOKJhuVvetObOO200w56/KuuuqohTjQ8PMzPfvYzzj///IMe+0AdzLmpVpt/CrFcLtPW1nbAc1rK3vnOdzZE1QCuvvrqA4qqXX311U2XX3HFFfs91mLZW1RtMZx11lmHdPxWXNfl4osv5tOf/vSc5dlslm9+85u85S1v2e8xv/GNb5DP5xuWv+lNbyIS2fc/fx3Nr1vf/va3GwJ0AJ2dnfz93//9Qc/taPJf//VfDWFKgNNPP53Xve51BzxuV1cX73//+/mLv/iLhvu++tWvHnRU7T3vec9R8feviIiIiIiIyGI4q/ssfjO9Y+a/0Ve1rWL71HYiI0MQ+Ayk4cSx/R93IA34PpGRISorj2FVYtXMfZZlcdaysxdpD0REREREREREREREREREZKnbn/hZtpxlcHqQXCVn4me33AJbt5qyk+PApk2weTODuSG2DW0jqOWQggB27ICBAfOzZcGaNbBuHdXAY/fkbqJOFMd2WkbSeHCcfFc706VpXMfFtmyCvYTSytPD5N0UWBbJaBJ3OluvM80XQFAuQcwMNJEfxY3NtJsaV8+PU47Y5v0AXhkn0jqqFnhVfNsJd8XDtyDiQn97k/jZNAQeBIEPmKCV65gQVLNQWrUC0Voord+EwOZP2gr3ud2FthiM5U3QzA5M8Gv+ujbwwpXw+pNNQGr3pImfBQFUnLmHMG3DOetMKK3qw54MPDVh1qnMCqWlorBhOcQtc6ytwISqjuuG43tgPG9iWo5t4lW9bUAVcmUT0XJ8s+/9aVjZDSMFM+6KlLm/WoF82cyxp81ExnwHejpMKK3kQX+HiWhZAAkTEItZYUws/LO3HYazsCxp5hJeGkRtSHea2/kylMOD0Oaa6Fa2ZG7XQmk2EERMKK1IGD6LmLlmS7PCZ2G0zArDZyOTkCmb26s6IFuE0RwkXFjbVT+3gQXRCAyOg+2YY2ZbMJGDzjaIOxB3zTheGErriJlAG+EwnXEzTrU2p1nxswitQ2kAU2FgrhZHa3Mh65uDFY2YY1L7eN/s8BnsPZQWAIPTZm4W5jGuZUJ3YI5bqWzicPNDaXYYRYvZ5nw74Tm1gULVhPwKHlR885yyMCG1Mmbd2u1ctR4PLHn1Y2vb9eeMVRvbN8/H2rqu3fo1Q0TkmU5RNRERERERkcPotNNO49RTTz3oMebHlqamphgdHaW3t/egxl4MN998c9Pll1122aKM//u///u0t7eTzWbnLD/YqNqRPDfLli1ruvy2227jDW94w0HNaak68cQTufDCC/nRj340Z/n3vvc9nnrqKVauXLngsXK5HNdff33D8p6eHv7oj/7ooOd6oA51VO3ss4/cB1Uvu+yyhqgawJYtWw4oqrZly5aW21mIo/l169vf/nbLuSWTyUWZ39Hitttua7r8kksuOeix3/SmN/GBD3wAz5v73VEPPfTQfr9mzJZOp3nNa15z0PMTEREREREROVp0x5ZxQmodO6YeAyDtpkkFMaojQwAMt8OaSUg0/56IpgoR8ziAyMgwiZUnkHbrb2A/IbWO7mj3ou2DiIiIiIiIiIiIiIiIiIg88zSE0lpE0p6YeIKtu7bWv9A7CODee+HRR03pyLbhpJNg40YmShl2ZXbVQ2kjIzAxCDMfH/Jg4g682x/h6SRYWDPxMyYnIZuBmY/8BJDdCY9nKLXHKVRMJavNieNmhptG0sgMUo4vJ+8VoALJaJLS9ATVWPNj4FlQKefAiVCsFrGrZfz25usGgO+XoVwND4NPObqXQJJfwfIjBH4VbBORWpWCdKIxfFapguX7RCMmfHb6chOBavpd5AEMT8PDo2ZO63vMuq1CaR0uJKIwWghPV2CCU7MlI3BSL6SjJixVi58NjUPZnhuDa4/AKT1h8Cs8LsenYWUcdgzDhA/+7FBan4lYEUA8gOemTdhtIg92FBIxiEUa42e2B+mkiZ9ZlomfDefMcetPhUGsKHQkoFCGSABuGCELLFgdAz8wwSsLwIXupIlaeZVw7oG5fHvbzTZngmqY+Fk6bo5jsVSPjQUBdCegXDHRrpgDMRfaIiaiBSYK5lXNOlbEBNMIzNybhdKGp8w5cRyzXtWDQmCOSyIctxqGz1JxmC6bc+pY0BGHqZKJquXLZttR24TBCmUTcKuFzSwL0jFoj5poXq5k5l8LpTmWiZ8VKmb9aMQEwspVc3t2/GxfobSxvPmzr8PE52zM8S1XwzBeGCmbHz6DvYfSKj5MFs0+uLaJuUVssDxz3h2r/vwqtwqlhVE1aqE0zPozobSqOZ+1iBrh08vCjD37LTxeYJ5TtZUsIBIeEzBj1q4Lf36hUERkiVFUTURERERE5DD6P//n/xz0GMcff3zT5VNTU0dFVO32229vWNbT08OmTZsWZXzHcTjnnHP4wQ9+MGf5z372s4Ma90iem/Xr1zdd/hd/8Rc8//nP59hjjz3ouS1FV155ZUNUrVqt8qUvfYkPf/jDCx7n61//OlNTUw3LL730UmKxFr+pOsSCIOCBBx44pNs4klG10047jTPPPLMhHHfnnXcyMDDAmjVrFjzW448/zk9+8pOG5WeddRYnn3zygsY4ml+3fvjDHzZd/ta3vnVR5na08DyPu+++u+l9r3/96w96/P7+fs4///ymx/Ouu+464G08//nPP2KvEyIiIiIiIiKHylndZ/Gb6R0zb0A/fmCCx3zzc2DBQBpOHFv4eAPp+hs9Ld/nuJ3jM286tyyLs5YduX+nEhERERERERERERERERGRgzQ6CkND0N8PPT0zixsiabCwUNpeImkVv8rDTz88d/sjIzA2CH21BT6MPYy3bZSxdpuIHTGRNM+DzGDT+FlpepiimwLbplAt0GbHcSczzfd3MgOJ5TO/U8/lJnDcuaGv2YL8OOWITRAEFKtF3HKZSovKhwml+WBVIfDxLYi40N8OnfHG+JlXBSswZSQLE3XqT7VYtwJxu8r6fhNKW9FuHlP7ff7sSYxOmc+19KRNfMoOzPL5DSY7ANeHF6yE155ilj2VgSczULFMQKomOSt+FgCVAAYn4KkJE5MrO2a9VBRO7TenyQ7CGJsNx3fD85bBeB4CNwxh2dDbBlQhVzaXi+Ob+zrSsLIbRvNQDsy+Lm83l0GxZNaNBtCRBDdhAlkjWRO36u+YFTQL42cx28TmqpaZv21DX7sJpTlhvCrAhLKWd5jb+bLZNpjgV8SBbMncdhwTGLOBIGLm5IUBs7JnYne5IrgRM2aba8a0LRN+Kxbr813VYYJpYwVz/+pOc65ql6QfmP2dLpvt1gJ3xaoJltVCaV4YSuuI1UNdFuZ6CsK4XbYE8Xnhs+5E81CahQmpBS4Uw3BcW9Rsq9b96moL417htTI7lObY5jyUqubg1uJnlWpj/GxfobRCWO6rerNiZhaUqZ+/iAX5+eGzfYTSSuG4Rc+E12zLXBt2+Hyxasv81qE0J4zaEYbtnFnhMzCPq50zZq1bi6Y5s9a1w2V+uI7tm6ia75uHBJY5fzD3uhURWYoUVRMRERERWaA77riD888//0hPQ57hNmzYcNBjdHR0NF0+OTnZdPnhNDExwe9+97uG5Rs2bMC2W/x24AA0Cy5t27btoMY8kuemv7+fDRs2NES0nnzySc444ww+8IEPcPnll7Ns2bIWIzw7vepVr2LVqlXs2bNnzvIvfvGL/OVf/uWCr7mrr766YZllWbz97W9flHkeiB07dpDNZhuWJ5NJpqamFvX5dKRceumlDVG1IAj4yle+wl//9V8veJyvfOUr9W+YmuWyyy5b0OOP5tet3bt3MzIy0rC8s7OTU045ZdHmdjT47W9/S6FQaFh+zDHHsGLFikXZxvOf//ymUbUHHnjggKNqGzduPNhpiYiIiIiIiBx1umPLOCG1jh1Tj2EVC/Rv285QD0yGbywfboc1k5Co7n0cgELErF+TLsKKBx9j4pRzCOIJTkitozvafWh2RERERERERERERERERERE9ml/4mfZcpbB6UFylZyJn91yC2zdakpVjgObNsHmzQzmhtg2tI2glusJAtixAwYGzM+WBWvWwLp1VAOP3ZO7iTpRHNtpGUnjwXHyXe1Ml6ZxHRfbsgn2EkorTw+Td1NgWSSjSdzpbOt6UABBuQQxM9BEfhQ3NtN5alx9VigNr4wTaR1Vw6vg2xFzLHyPwIFIxISgGuJn0xB4EHV8NvTB6cvNelaTiQTA8BQ8Mmp+PrnXhNLmT7oWY+pwTZhrtAieH4bSMDGx2VIOnLMO2mMmfPZkBobGzTZKztwIW6cFJ66EZKx+aI/rhOUJ2D4CE+HCWiitrTZxTADrud1wQg9MhKG0WARWpMzx8MomShYE0JWAZNQch54OGM6ZdfpTYYAqCh0JE/ByfYhFwQvnGo/CcBaWJSEWxqRImMhcLAxgeZhAWG+72eZMUI16/MzBBM5qobSqZx4bj8BU0YwdC4+xHZ6GtqgJlZUrYEXM04TAzD0VYyaQZVkQdWFkEiZLkIybcapVeDpnIm/JqDloQWBiX0Fg4mLJmDkGHXGYKkFnwtyXLZmQHUAufH9HT9LM28LsT2fCjJHzzPxnh9LaXBNcs4BoxATCylVze3b4DPYeSguAwWlzrVuYx7gWFMNCXzwCpbI5B/NDabVAWMw28bJabMwGCvPjZ3sJpeWqs2JmHiSC+n2E56oWJ5sfPnPd1qE0wlgd1P+sDWhhYmazr/f5obSZSqFlxg48TAHIMse8xrFmBQ0DM24QVvN8Zl3XmOeQb4Hlm+d5xA/DfV79ue46JqwWU21IRJY4vcyJiIiIiIgcRosRxWpvb2+6vFmA6XB7+OGHmy4/8cQTF3U7zY7j2NjYoo+5vw7m3Pz5n/85b3nLWxqWT05O8sEPfpCPfOQj/P7v/z6bN2/mggsuWPRj+kwUiUS4/PLL+ehHPzpn+a5du7j11lt5+ctfvs8x7rvvPu65556G5RdeeCEnnHDCYk11v82PjdUsdujrSHrjG9/IVVddRblcnrP82muv5cMf/jBWs9/6zRMEAddee23D8lgsxv/9v/93QfM4ml+3HnvssabLzz777AUdn2eS3/72t02Xn3HGGYu2jVZjNYvqLdTxxx9/wI8VERERERGRpWvHvQf+35pHi7O6z+I30ztIPHg/VrXK2gw80G/uCywYSMOJC/gn2YH03DdUr82AVakQf/B+ime/kLOWnX0opi8iIiIiIiIiIiIiIiIi8qy20FDaExNPsHXX1voXnQcB3HsvPPqoqQTZNpx0EmzcyEQpw67MrnoobWQEJgbh1NoGPJi4A+/2R3g6CRbWTPyMyUnIZmDmbfUBZHfC4xlK7XEKFfMF3W1OHDcz3DSSRmaQcnw5ea8AFUhGk5SmJ6jGmh8Dz4JKOQdOhGK1iF0t4zf/2A8B4PtlU40CgsCnHG0dVcOvYPkRAr8KNkQcWJWCdGJeJC1rolWO7xFxLTb0m1Baf4qm8TM7MPGsRAzG8mFkKZgVVJq9LvDClfC6U8zv5Z/MwOAEVCwTVapJRuCUnjDiBVR92JOBpybMflfsehAuFYUNyyEeBqOimEjaqqQJn1nh3BwbXBt6E2aOuTJQ+2I2B/rTsLIbRsLvXV+RMpdTtVIPpfXGIRqfG0rrbjNBMzARsu6kObZexZzPAHNJ9rWDH9TDWQEmFLW8w9wuluoBKceG56TN+jWOA53h8SiWwsBVeCF0tUEmD1YYTEvNiuvZllk2Mg3TJViVNuGqimeOzzGd9XmB+dn3YbpstlkL3BWrJliWiJgxvSrkKpCKQ64E7eG1l0pAvmL2LVcy8TIHs8yxoTsBTiSMglmQjtUfCyb2BvV12lzIhqGvaMQc22p43maH0hzbnIdS1azr2CZAVqk2hs9g76G0ig+TRTMv1zbzj9hgeea6daz6c6bcKpQWRtVmYmU0xs/aIq1DabO/M9AL6uehFrUjqP88P3xmz16XWaG0cAO19Z3wtheOZ4dBs1psLaAxlFYNzHOBABwfc8DCgF0tfFb0TPystq7vg+uZ510QMQ+prYtljnGAWdcr14NCQRmqbn37/U0CjCIiS42iaiIiIiIiIodRZ2fnQY/RKqTj+/P/ifzw2717d9Pln//85/n85z9/SLedz+cpl8tEo9EDevyRPjcXX3wxX/3qV7n99tub3l8ul7n11lu59dZbAejr6+Occ87hBS94Aeeffz4bN24kEnn2/Wf+29/+dv72b/+WarU6Z/nVV1+9oKja1Vdf3XT5FVdcsSjzO1CtomqLGZg60pYtW8bmzZv55je/OWf57373O+666y42bdq0zzHuvPNOdu7c2bD81a9+9YKf00fz61aruR3J4N+hMjg42HT5cccdt2jbaBVAe+qppw54zPSsb10TERERERERWUq6Y8tYZ6/k6YceAKCrCOkiTIZvXB9uhzWTkKi2HqMQMevVpItmHIDEw9tYfc5r6Y52H6I9EBERERERERERERERERFZWhpCaU0iabDwUFrl9NN4ePSRuRsZGYGxQeirLfBh7GG8baNzQ2l+AJnBpvGz0vQwRTcFtk2hWqDNjuNOZlrsVAYSy2fmmstN4Lj1yNd8QX6ccsQmCAKK1SJuuUylxUdpTCjNB6sKgY9vQcQ1MaHOeGP8zKuCFZjP/1iYOFF/qsW6FYjbVdb3w4Z+WNEexr3mf6wogNEpcw560gEWJtJkBfXoV03KgZN7oT1mwlKebyJpeyagbM89Ju2zQmkBJjh2fBpWxmH7CEyEpz4VhVP7oa12QIB4AOu64ISeMJQWASuMV/W1g++Z0JjvgxtAOgnxmJnvSNaEsyK22WfHNvvSkYBq+H6ASNwcwMCC1TETFovYYb/JNaE01w5DaZiAlG3D6rQJR/lBGMNyIOmGj3NMjK0cmKiXHQbPpooQcyDmmmCYHZ67RMyMX6hC2YOuBJQqZnzXMeuCWT8Rg2IRnp4y8bFu2wSrxvNmrrGIiZ9Zs+NnYZDNDkNc6bgZN8DEz5LhNZkL31PRkzTztsyu0JkwgbKcZ+ZfrUBgm+ussw2idj2UtixZ369COTw+YeCrLWrmUOt+dbWFEa/wOhzLmz/7Osy5sjGxtHK1HkqzaAyl2eHyWBgzc8J4mI05pnPCZ/sIpZXCC73omfCabZnzZ4exQKu2zG8dSnNmxc8sqz6fmr2G0qhH7mrnrPbSSGDCZ4FvLoZm4bPACoN880JptU8KxsMIYCx8XDUwz92Ib45X4IVxvTB4NjuUVhs7CNenDEG0/trQ3QbjBfO42th44bhlCGJmW7V1Z/OAIAOR9vrxL3iQCF+zUzEzXqGCiMiS9ez7tLWIiIiIiMgR5DjOkZ7CITUyMnJEtz8+Pk5/f/8BPfZInxvbtrnhhht45StfyV133bXP9UdGRvjOd77Dd77zHQBSqRQvfvGLufjii3n5y19OLNbiq3aWmBUrVvDqV7+am266ac7y//mf/2HPnj2sWrWq5WOnp6f52te+1rB8+fLlvPrVr17sqe6X+++/v+nyM8888zDP5NC67LLLGqJqAFu2bFlQVG3Lli1Nl1966aULnsPR/Lo1Pj7edHlXV9ehnNIRMTEx0XR5R0fHom2j1ViZTGbRxxQRERERERFZCl5y+yN8vVKdecPq2gw8EP4zRmDBQBpOHGv9+IH03DeMr83Uf7bLFV7yv49C8wa6iIiIiIiIiIiIiIiIiMjSMToKQ0PQ3w89PTOLGyJpsLBQWotIGhs3UvGrPPz0w3O33yKUln94mOlkxETSLJvA81qG0srTwxTclCkaVaCtVCUaNK4HQABBuQQxM9BEfhQ3NtN5alx9VigNr4wTaR1Vw6vg2xECAvA9AgciEROCaoifTZugUdTx2dAHpy8361lNJhIAw1PwyKj5+eReEw2bP+lauKkjjHiNFk3UyA6XNwulnbPOhNKqPuyehKFxs43KrFBaKgoblkPcMpOxAhOqOq4bju8xga/ADUNYNvS2AVXIlc0l4Phm3/vTsLIbRgpm3BUpc3+1YqJkQQA9bSaS5jvQ02FCaSUP+jtMcMsCSEC1CrEwXlUN/+xth+GsCX05dv3YRW1Id5rbtfgZmGMUcSBbMrdroTQbCCIm3lYEPM9E0nzfrBuPhOGyMJBlWWbZyDRMl2BV2hz3imfCcMd0mjhVLZ7lBWas4SkTTSuHcbOhKehLQXsUE9EKTBgsCMz+ptvM/qcSkK9AMmrmNj9+FqF1KA1M7A1MpM0Kj0PWNwcrGjHHpBqu++SEiaFFatdCHCy/fi21x8x2fdvs71jBxO8szGNcy4TuwByjUtk8thpGtAh/dsNzG7GgTBgqC2/n54fSnPr4JY+ZOFmExvCZ67YOpRGeB6j/CbPGC48J5nQ0hNJmymWWGTvwwklYjfGzmeHnhdJ8zLmriUVMTCwI5+Z69XE9zL7PD595fmMozQvn6jqQjtXDZ4EF1TK4tQ2WwXPMdQONobSSB244LoE5x7VQWtQxscFyeB7LHriVMOAYQLFc36/OuDmUtUhaoQxOHhJtZtvZMuRLEI2a51YtsmfbsyJzIiJLjKJqIiIiIiIismjy+fwR3X6xWDyi2z9YnZ2d/PCHP+TjH/84n/rUp/breE5PT3PzzTdz8803s2zZMq666ire8573kEwmD+GMjw7vfOc7G6JqnufxxS9+kY985CMtH/e1r32NbDbbsPytb30rrus2ecTh0yqqdsYZZxzmmRxaL3vZy1ixYgWDg4Nzlt9000187nOfo62trcUjIZvNNg2yrVy5khe/+MULnsPR/LpVKBSaLu/s7DxEszlySqVS0+XpWW8EOFitxjqYvzuO9GuFiIiIiIiIyKFiZ8Y57us3sX4tPLjcLOsqQroIk+Eb6IfbYc0kJKqNjy9EzP016aJ5fM36ETj+Vzfx+P99F35n9yHbDxERERERERERERERERGRg7U/8bNsOcvg9CC5Ss6Uam65BbZuNWUmx4FNm2DzZgZzQ2wb2mbiYGDW3bEDBgbMz5YFa9bAunVUA4/dk7uJOlEc22kZSePBcfJd7UyXphcYShslH4bSktEkpekJqrHmx8CzoFLOmUoUkPPLOO3N1w0A3y/PlKyCwKccbR1Vw69g+RECvwq2CU6tSkE6MS+SljXRKsf3iLgWG/pNKK0/RdP4mR2YKFYiBmP5MJwUzIovzV4XeOFKeN0pJor0ZAYGJ6BimdhUTTICp/SEwS9MKG1PBp6a2HcoLWrBCWlYlTQhMDucWyzSGD+zPUgnTfzMskz8bDhnjkV/yhwXOwodCRNPigTghhGywILVMRMZi9jhoXGhO2lCTl4YXQoCE1TqbTfbnAmqYS7VdNwcm2IJam8LCALoTkC5YkJdMQdiLrRF6kGstih4VbOOFTGXPoGZ+/xQWtQ14TMv3CZBGAMLzHFJhONWq5Cr1I+7HY6RjpuoVQDkSub8AOTCCXcn6mEzyzLxq6hj1k1ETQwtXzGBqc42E4errbssaa6L2jGZHT/bVyhtLPyYSl9HPV4VdcxTwrFNyMzC7GuxakJpy1PhsbfMWEXPnMtoBPyKOZ+lcP2qb86lQzifcF3Hqj9nStVZ8TMPEkE9oka4Xxb19WeH0toi9fuswOyjFY49P3xWCwo2hNLCDdTWd8LbXjieHUbKarG1gMZQWi1+RmDCgYTxM5/G+FmzUFowL5SGZY5bgDmegRdG0soQRM0Y0Bg+8y3wyvU4T6VqQmk1K1NQCM99EEA2C13ha65XhoIF0fDjUfNDaaUqOFVzLn0glwc/Yq6TNtfM3Y1AsQLTOehwIIp5nclMQbId2mL182rbJp6Wy5ptFXPgdEChZI5v7ZqpXQPzY3YiIkuJomoiIiIiIiKyaKrVJp+ek/3iui4f+9jHuPLKK/n85z/Ptddey65du/ZrjLGxMT74wQ/yb//2b9xwww2ce+65h2i2R4cLL7yQE088ke3bt89Z/sUvfpG/+qu/wnGcpo/7whe+0LDMsiwuv/zyQzLPhdq5cyfj4+MNy6PRKOvXrz8CMzp0HMfhTW96E5/85CfnLJ+enuamm27iLW95S8vH3nTTTeRyuYblb3nLW1qe82aO5tctq9lXYIH5Bq5nicXc12fTcRMRERERERE5WN1f/RJ2Ic+mAXior/7m2bUZeKDf/BxYMJCGE8caHz+Qrj+m9rgaK4AXDYCdz9F1/TWMveuqQ7UbIiIiIiIiIiIiIiIiIiJNNYTSWkTSnph4gq27ttbfixwEcO+98Oijpt5j23DSSbBxIxOlDLsyu+qhtJERmBiEU2ujeTBxB97tj/B0EiysmfgZk5OQzcCy2roBZHfC4xlK7XEKFfOF3W1OHDcz3DSSRmaQcnw5ea8AFfYZSguAcjlP4DgUq0XccplKi/qECaX5YFUh8AksE8xa0Q6d8cb4mVcFKzC1JAsTJ+pPtVi3AnG7yvp+2NBvxrSY+zvn2iRGp8w56EkHJggUmN9Be/NWTTlwci+0x8Igkm8iaXsmoDwrfAbQPiuUFmACcsenYWUcto/ARHg6U1E4tR/aagcEiAewrgtO6DGhNCsCVhgn62sH3zNRMt834bPuBLgJM9+RrIld9XfMCpqF8bOYbcJNVctsyrbD8YIwdBVOwXVgeYe5nS9DOZxXm2vCXNnwu88dB5JuGJCKmDl5YcCs7JmAXa5o4k2uYx5vYWJLiRgUi/X5ruowwbSxgrl/daeJQdUOqR+GrabLZrtWOKdiFQqVeijNC0NpHbF6tM3CXCOBZcJh2ZKJYVUrENgmjtKTNPO2MIGozoQ5VgBT4Ze9he0/2qJmW7VPEnS1hREvy8To/DAWZgGpOFh+/Vpqj5nt+nZj/GxfobRCOEjVM8ezFjMrUz9/EQvyVXN9FjyohKE0KzDHza6aa9EOz7/th0EyzHlw7Vn3hbFAK4yY2X79mIJ5XO081KJ2tfCZFTSG0mbKg5YZOwhjZliN4bPAMsdxfiitNkQ8/HhPLHxcNTDPx4gfXo9eeM2EwbPZobTa2EG4PmH8rHaO5sfPqgEwO5QWM9uqrTtbqQpuOdzNAIrl+n3zw2c+UJyE9qT5ueSZyFkibp4r0TDYVqiY51a+ZK5rx4JSEaZ8E1lrFkrL5sErmevYs6GQN8G25d3gWvW4XSIKfjm8JiImjJakHrqrnUI7jN/1ps216ZfNtdXTWX8+FkthsBGozPqCRBGRpUZRNREREREREVk08Xiz38rAMcccw+rVq4/Y9p+J+vv7+fjHP87HPvYx7rnnHn74wx9y5513cvfddzM1NbWgMXbv3s2mTZv4zne+w0UXXXSIZ3xkvfOd7+RP//RP5yx78skn+Z//+R82b97csP4999zDfffd17D8JS95Cccee+whm+dCNJsXwCmnnILruod5NofeZZdd1hBVA9iyZcteo2pbtmxpuvzSSy/dr+0fza9bre7LZDKHaDZHTizW/Df1C329W4hWYy2lvztEREREREREFoOdGafzhusA6M3D+hF4cLm5r6sI6SJMhv85PdwOayYhMevduIWIWV6TLprH1awfgZ7wW5m7briOiYvfit/ZfQj3SERERERERERERERERESWuoZIGiwslLaXSFrFr/Lw0w/P3dDICIwNQl9tgQ9jD+NtG2Ws3SZiR0wkzfMgM9g0flaaHqbopsC2KVQLtNlx3MlMix3LQGL5TNQtl5vAcedGwWYL8uOUIzZBEOwzlAbg+x6BZapFgQORiIlGtQqlxR2fDX2wYQX0J03EqWEOwPAUPDJqfj6514TSmLduLfLU4ZrQ0GjRBJDscHmzUNo568LYlQ+7J2Fo3GyjMiuUlorChuUQD8tjVmDiW8d1w/E9MJ6HwA1DWDb0tgFVyJXNJeD4Zt/707CyG0ZMz44VKXN/tWICZkEAPW0mTuQ70NPRIpSWgGoVYmHsyMMEwnrbzTgz61GPnzmY6FItlFb1zKUZj5hwWMyBWHjc7PDQtkXNOSpXTNzN88z+e4EJxtUCWpYFURdGJmGyBMkwDFWtwtM5M/dkFLDNPlb88M8qJMNQVEccpsIQVBCY+FkyvM5y4fsHWsXPcp6Zfy2U5lhm+4WKWTcaMeGqctXcfnLCxNAidrifLmR9s2/RiInHVcNtjoXvRejrMH9GbBOmKoZhrXgESmVzDiqeiaT1tdfDVZEw6BWE8TC/YqJe5fnxs72E0nLVehCw5EEiqN9HeK5q2/OCWetWwXXDKFq4r064n3YYL6vFz3y//lyjtm2/tqA+fo1tmfNUe1DEN6EybPOQ+aG0avjSSBDOIYyq+Zh9nx8+8/zGUJoXbt91IB2rh88CC6plmPl0VNlExILw/vmhtJIHbjgugTlvtVDa/PhZ2QM3vI6CeaG0zrjZ10LF3J7KQ1f4nPSBiWmItbUOn1VLkEyAFzHhs6oP3R31edbCalOTZtvTRUjHYapijnO+0DqUVg5M7DDimlCabUO5DJFo+Bob7otTO4Yu+GHML/BMuC0efiSnWDLn1nEg4UDFgclpWBb+FRgJ43zZqnl+ttcKjSIiS5CiaiIiIiIiz0KeN/+flUUWR3rWL5lme+1rX8unPvWpwzybpcGyLM466yzOOussPvCBD+D7Ptu2beOuu+7irrvu4gc/+AETExMtH1+tVnn961/PAw88cMRjYYfSJZdcwoc+9CFyudyc5VdffXXTqNrVV1/ddJx3vOMdh2R+++P+++9vuvzMM888zDM5PE466STOPvtsfvnLX85ZfueddzIwMMCaNWsaHvPEE0+wdevWhuXnnnsuz3ve8/Zr+0fz69ayZcuaLl+KUbWurq6myw9HVK3VtkVERERERESerbq/+iXsQn7m9qYBeKiv/kbetRl4oN/8HFgwkIYTx+qPH0jP/cbwtZn6z1YALxqo37bzObquv4axd1216PshIiIiIiIiIiIiIiIiIs9sCw2lzYmkwf6F0lpE0nhwnHxXO9OlaVzHxbZsgr2E0srTw+TdFFgWyWgSdzrbOlQTQFAuQcwMNJEfxY01NMfqq88KpeGVcSKto2p4FXw7QkAAvrf3UNo0UIVIGEo7fTn0p2gZP2t3oS1mwlW1+JlP47o28MKV8LpTzO+On8zA4ARULBOmqklG4JSeMPiFiRTtycBTE/sOpUUtOCENq5IwkQc7ComYiTzNj5/ZHqSTJjpkWSZ+Npwzx6E/ZY6JHYWOBBTKEAnADSNkgQWrYyasVYt64UJ30sSUvDDQFATmMmsVSkvHzbEplsx2a9dBVxtk8mCFwbTUrOvAtsyykWmYLsGqtDnuFc/s8zGdZl61eJYfmP2eLptt1s5bsWpCUomIGdOrQq4CqTjkStAeNdtMJSBfMTGpXMlE2hzMMseG7gQ4kTAKZplQVu2xYGJvUF9nb/Gz2aE0xzbHq1Q16zq2CZBVwuhTsWriZ8tT5j4bs365Wl/XwqxbCD8yWfXM9hzC8xaG0hyr/hwohWNXfXMurcAcN7tqgmA25pzafmP8rC3SOpQ26zvp8IL6eahF7QjqP9ciabV17cCcx9pBjXphQC28FuNOeIzDffbC68gOg2a12JpvmXBdTSxSD6X5PrieiXERMWM0C6X5prVoYm1lCKL10OH88JlvgVeux2sqVRNKq1mZgkJ4YIIAslnoCl9HvTIULIi2hfs8L5RWqoJTNefSB3J58CPN42fTOehwIIp57chMQbLdvG7VzpVtQ75k5hBxTeyvakGhYM57y/BZ2qwXiZnwmWObOFw0fK0gMK853Snw2sz1WapAPA0dEbCd1qG0RAzKVhh0DI+pVYW8Z/bN8+vXMEA2Z8aw7TBCWTXnA8LQYni9+D5kpk1gcWLKzM32oVKCp0sQqZrrINBHjUVkiVJUTURERETkKGU1+aqOmX/cP0h7CzCJHIxjjjmm6fKxsbGmy2X/2bbN6aefzumnn8573vMeqtUqP/nJT/jyl7/MjTfeSLFYbHjM9PQ0H/nIR7j22muPwIwPj3Q6zRvf+Eb+8z//c87y2267jd27d8+5Nqempvj617/eMMbKlSt5xSteccjnui/33Xdf0+VnnHHGYZ7J4XPZZZc1RNWCIODaa6/lwx/+cMP61157bdO/Ey+99NL93vbR/LrVam6/+c1vDvNMDr2VK1c2Xf74448v2jZ++9vfNl2+YsWKRduGiIiIiIiIyDOdnRmn84br5izrzcP6EXhwubndVYR0ESbDN7gOt8OaSUhUoRAxt2vSRbN+zfoR6Kn32sx4N1zHxMVvxe/sPgR7JCIiIiIiIiIiIiIiIiJHk8UOpVVOP42HRx+Zu5GFhtL8gKBFJI3MIOX4cvJeASqQjCYpTU9QjTXfL8+CSjkHToRitYhdLeO3N183AHy/bKo7QBD4lGfFqRr4FSw/QuBXwTaxqFUpSCfmRdKyJmbk+B4R12JD/95DaXZgoliJKIwWTFjHCurhpJpkBE7qhXTUxJNq8bOhcSjbcwNv7bNCaUF4XI5Pw8o4bB+BifB0pqJwaj+01Q4IEA9gXRec0GOiYVYErDBO1tcOvmeiZL5vwmfdCXATZr4jWRPJ6u+YFTQL42cxOwx0WWZTth2OF9TjVAEmzrS8w9zOl6EczqvNNcc8WzK3HQeSroluBZF6KK3qmRBUOgG5ookhuY55vIUJHSViUCzC01MmPtZtm+M+ng/nGjHxM2t2/Cy85uwwxJWOm3EDTPwsGRZDcmG0qidp5mJhQlSdCbP/Oc+EtqoVCGyz751tELXrobRlSbNfFiYw5wdmuYWJrNlWvRXY1VYPfoGJ7QH0dew7fjY7lGaHy2O2OYeOZZbZmBCXb5lYWsU3+12LmZWpn7+IZfZ/JnzmmZiaPSs85Yf7aFv1UFptXdeuR9QsTNyKoD6P+fGzlqE06pG72jmb+ehLYAJlgQ/Y5iHRWbUXZ1YIKwhDaU5gro9ajLAWP4uFj6sGJrgV8cPr0QtjaFZ9XSyzfwHhfV64bhhKq4bzmx9KK3nghuMSmJifF0bb5ofPfKA4Ce1J83PJM5GzRNxc/9FwHoWKeb7kS9ARM/tcKsKUbyJrzUJp2Tx4JXMdezYU8ibY1ip+5llgR8z5T1K/nmbHCh0LetPm2gwi5pz2dO47fOYBfrUePnMqUABirgmoORWoOuY1IhLAaB660+a6gr2H0soVmMpCV4eZn+vBZNUEIKO+iVp6CTPWWMacz852iJagUoBJN7zMKhBJQCUOk9Pm+eYH5ufCLhOYdGMmKBevhqE9RdVEZIlSVE1ERERE5CiVTCYbluVyuUUZe3x8fFHGEZnvec97XtPlTz755GGeybNHJBLh/PPP5/zzz+ejH/0ob3rTm/jZz37WsN6NN97If/zHf9DW1nYEZnl4XHnllQ1RNc/z+OIXv8jf/M3fzCy7/vrrm76e/vEf/zGRyJH/p5JWUbUzzzzzMM/k8HnDG97An/3ZnzVEAb/yla80RNVqsbX5EokEr3/96/d720fz69aJJ57YdPkvf/lLgiBoGmB9pjr++OObLr///vsXbRu//vWvmy4/4YQTFm0bIiIiIiIiIs903V/9EnYh37B80wA81Fd/M+faDDzQb34OLBhIw4lj5s9g1j9ZrM3Uf7YCeNFA4zbtfI6u669h7F1XLdp+iIiIiIiIiIiIiIiIiMgiGx2FoSHo74eenjl3NYTSmkTS4NCE0vIPDzOVdLCwcB0Xyw9goaG0kkfJhardZF0gyI9TjtgEQUCxWsQtl6m0+MiBCaX5ppoT+PiWidb0t0NnvDF+5lXBCkyhyMKEjPpTLdatQNyusr4fNvTDivYwBDb/7eQBjE6Z49qTDkzIK2geSks5cHIvtIfxs2oAgxOwZ8JEicpOuF4YP4vPGgsbju+G5y0zMbDADaNZNvS2AVXIlc0pdXyzP/1pWNkNIwUz7oqUub9aMQGzIICeNojHwHegp6NFKC0B1SrEwjCShwmE9babcWbWox4/czARo1ooreqZSy0egamiCY3FXBNzqgXF2qLmHJUrJu7meeb4ekEYOQtjWpYFURdGJmGyBMkwIlWtwtM5M/dk1ByzIDBhsCAw96fbzLZSCchXzHqe1xg/i9A6lAZmH8BE2izM9rNhFCwaMUG4arjukxMmhhYJr/lUHCy/fn20x8x2fdtEtMYKJkBnYR7jWlAMw1rxCJTK5rFVz4TPCH/eW/ws3yqUFkbVanGyCOZ4z4TSqtAWqY9Te+7UolrVWde3F0YDaytZmPBYrQgXWPVQmu/XH1O7eKJefXmz+JlfuzEvlOZjzl1NLGKeW0FgxnM9EzUjYo5bLXxW9My6QRhhq1TC+WIiWVW3PmZ/e32eYK7RmdZjGfMaFd7fPe8jZKUquOXwsITBsJr5obRSFZyqud58IJcHP9I6fFYtQTIBXsSEz6o+dHfUI221sNrUpNnH6aIJBE5VzLHLF1qH0sqBiZ9FXBNKs+3W8TMwr0m1+FngmXBbPDxIxZI5X44DCcdEzizL3Ia9h88qVchMw7K0mZ/jQz4Lw+G+pFwTfvMCs57nwcTUwkNp5XD8FVFwPMg7EDgQLZuwXKYAj+2BQvicT9mwvgdibfXjbHmQfxoeHoWpWS/8sSqsyELWgnSfOdblGJQKJsomIrIUHflPCouIiIiISFPpWb80qJmenj7ocUdGRhgcHDzocUSaOe6441i2bBljY2Nzlv/85z+nUqngum6LR8piOPbYY7n11ls5+eST2bNnz5z7SqUSP/nJT3jJS15yhGZ36J1++umce+653H333XOWf+lLX+Kv//qvccJ/4b766qsbHus4DpdffvlhmefeDA0NMTQ01LDctm1OO+20IzCjw6Ozs5NXv/rV/Nd//dec5b/73e+46667OO+882aWbd26lccff7xhjNe85jVN/+7cl6P5dWvVqlX09/c3XBOZTIaHHnqIU0899QjNbPEdf/zxtLW1kc/P/dD27t27GRoaor+//6C38Ytf/KLp8g0bNhz02CIiIiIicvR48MlJXnf13fteEbjxinM59Tn7/9+SIkuVnRmn84brmt7Xm4f1I/DgcnO7qwjpIkyGH0QYboe+nPmzJl0069WsH4Gexl6bGe+G65i4+K34nd2LsCciIs8gT90PX/7Dha172f/AyjMO7XxEREREREREREREZMlqCJ9By/hZtpxlcHqQXCVn6je33AJbt5pCjOPApk2weTNYFoPZQbYNbSMgLAft2AEDA+Zny4I1a2DdOqqBx+7J3USdKI4d1mv2EkqbTkZwHRfbsgk8r2UorTw9SsFNmW1VwCoVCWKN69XMCaV5ZVwHKk6Llb0Kvh0x++Z7BA5EIrC8Wfxs2sR7oo7Phj44fblZr9n3aAfA8BQ8Mmp+PrnXhNKYt64VBqA6wuDXaNFEfmqxqGahtHPWhWEsH3ZPwtC42UbFNrEfMKG0DcshHsaIANwAjuuG43tgIgylxSImfuYF4JXr8bOuhAmAWZaJnw3nzDr9KXNM7Ch0JKBQhkgAbhghCyxYHQM/MAEtC8CF7qQJAnmV8PgEJjLUKpSWjodBqFIYwgoPalcbZPJghcG0VKz+ONsyy0amYboEq9LmWFY8s7/HdJp5ObXQVhjBmi6bbdbORbEKhQokImZMrwq5iomT5UrQHp0bSnMds7wtauJU+YoJPXW2QTQ8BpYFy5L1oBvMjZ/tK5Q2Fv4evq/DjG1jjlk5jEpFwkhV1TPzHyuYaxPM/kbCqFdgmbH9itn/Urh+1TfnxyGcT7iuY9WfA6XqrPiZB4mgdfysVSjNCUN1hC8dzqzwGZjH1c4Ds9atRdOcWeva4TI/XMf2TaTM981DAsvEzGrHwKIeP7N9cIIwfoYJwLn23PhZs1BaMC+URvi4IJxH4JlzE5QhiJoxwITPxgv1KJZvQbkAbeHtKiY4Fg9f/1Ix89hCxcxhKgfLombsasUEwRLhup1xs/1C+NyaykOXVQ/DTUybIFerUJpXMgE/zzahNM/ZS/gsDVULIjETPnNsqJYhGj7/Ccz8ulPgtZnrs1SBeBo6ImA7rUNpiRiUw4hZLZy2t/hZNmfGsO3w9bJqjjHhc8kOX498H8anzO39CZ9lc7CqDYpFcy68GFQmYMoCKw57CuG2LbOP+xNKi1WhkjPhs/Z+IGqux1IBxp6EwqyPOLVNmRhmug/al5t9nhiCyRETjJua9R6eztr7dwKYHIZ198BjHjyVNteRiMhSpKiaiIiIiMhRqlkYplwus2vXLlavXn3A4/70pz89mGnJEhGLNf8NUblcbrp8f/zBH/wBN9xww5xluVyOu+++m02bNh30+LJ3HR0dvOMd7+DDH/5ww31PPPHEYZnDoby+9uWd73xnQ1Rtz549fPe73+VVr3oVv/jFL3jggQcaHnfRRRdxzDHHHPL57ct9993XdPm6detIJpOHeTaH12WXXdYQVQPYsmXLnKjali1bWj7+QB3Nr1svfvGLue66xg8zf/nLX+af//mfD+tcDuVz27ZtXvCCF/CDH/yg4b4bb7yRP/mTPzmo8UdGRrjzzjub3jf7+hIRERERkWc+PwgoVOa/bbj1uiJS1/3VL2EXWlTPgE0D8FBf/U2oazPwQNhBDyx4aPncb0Jfm6n/bAXwooHW27bzObquv4axd111wPMXEXlGCnyotH7tbVhXRERERERERERERGSWhYbSnph4gq27thLUfk8eBHDvvfDoo6YqY9tw0kmwcSMTpQy7MrtMSAxM+GxiEGa+D9qDiTvgf7fj9SxjrDBGxI5gW7bZdjYDy2rrBpDdCY9nKLXHKVRM1abNbcPF2WsoLR+G0pLRJKXpCaotQmkBUC7nCcIvYQ+8MtbeCgq1UJpfBdsEpValIJ2YF0nLQqUKju8RcS029JtQWn+KpvEzOzChsUTMRK4Cz0xu/r/uW4GJH71wJbzuFPM71iczJoxTsUxQqSYZgVN6TEQJTNxqTwaemth3KC1qwQlpWJU00TA7nFstlOb7YYCpDLYH3QlwE3NDad1tJs4FYEVM/CzimPiZZ5k52Db0tdeDZLVOm+vA8g5zO1+Gcng5tblmjGzJ3HYcSLphbCpSD6VVPSh75rzkiiac5Drm8RYmVpSImajR01PgRKDbNsd9PG/mGouY+Jk1O34WHks7jHKl42bcABM/S4bXTi4MlvUkzVwsTFSsM2Gui5wHMcccw8A2+763UFqhbI6RFR6jVBwsvx7Ga4+ZsXy7MX62r1BaIRyk6pl9qcXMytTPScSCfNVcXwUPKmEozQrMObCr5pza4Tm1/XqwruSZdWfuC69rK4ye2b4Jf808xfYWPwsaQ2kzTxLLjB14mApKGHmrqYXAwIwX8cNfn9lmeWxWHDEWMftq+Sa4FfHDa8yrH/Na/CwWbiOwzLmN1DZSBs+tvw9ifvysGgCzQ2kxs63aurOVquCGH7uwwlhYTdSB1WlzvRMejsoEJLrMtVXyITsNbtRcv86sczGZNcG1dAyiAWTL5jpvi0NbrB61s23IlyCbhYhrruOqBYWCOe8tQ2mBeV5GXBNKs+29h888wK/Ww2dOBQpAzDVxMacCVcdcc5EARvPQnTbjwt5DaeUKTGUXHj8LgM52iJagUoDJMEYWVCCSgEocJqfN88YP9j985legZ42J0qVKJnxWzMPow7DLhdKsa3d/QmmdRWbCZ9Oj0BuDpwIT2nOAWLsZO1aFRNX8df7ko5D6mnnc9JnQ3mXChrFq47rZCUj+EpLfhmMcuOUsmBpFRGRJUlRNREREROQodeyxxzZd/sADDxxUVK1ZmEWefTo6Opouz+VyBz32H/3RHzXEiQD+5V/+5YjHiZ4tTj/99KbLx8fHD8v2D+X1tS+ve93reO9738vo6Nx/0f3CF77Aq171Kq6++uqmj7viiisO+dwWolVU7cwzzzzMMzn8/uAP/oDnPOc5PPnkk3OWf+Mb3+Czn/0sbW1t5HI5brrppobHHnPMMVx44YUHvO2j+XXr1a9+ddO/u7ds2cLf/u3f0tbW1uRRh8ahfm6/7GUvaxpVu+666w46qnb99ddTrVYblm/YsIH+/v6DGltERERERERkKbAz43TesPffH/TmYf0IPLjc3O4qQroIk+GHHGa/uT9dNPfXrB+Bnn00g7puuI6Ji9+K39l9AHsgIiIiIiIiIiIiIiIisjQsdiitcvppPDz6yNyNjIzA2CD01Rb4MPYw3rZRnk6ChYXruNh+0DJ8RmaQUtyj4Js6VpsTx53MtNipDCSWz8w1V87hlEp4CwilFatF3HKZyl6qCL7vEViBKSzZELFNOKwz3jyU1mZXWd8PG1bAimQYApsXSiOA0SlzXHvSgQl5hbGo+V/1lnLg5F4TxvItEwManIA9E1CeFT4DaJ8VSgswQaPj07AyDttHYCI8nakonNoPbbUDAsQDWNcFJ/SYUJoVASuMaPW1g++ZKJnvm2BRLZTmASNZE8fq7zARJwvADeNjNlQqYRAJc+msTptolB+EoaUwfmYBOPVQWtUz68cjMFU0YauYa+JntaBYW9REzcoVM2cvDM55QRg5C2NblgVRF0YmYbIEybgZp1qFp3Nm7skoYJvLveKbP6tVSLeFsbIE5CtmPc9rjJ9FaB1KA7MPYCJtFmb7Wd/MNxoxQbja29KfnICuNnO9wd5DaRXPRNL62uvhqogDxTAaFY2YSJMfmHDanPjZXkJpuWr92i15JqRUu4/w+Ne25wWz1q2C64ZRtDAE54T7aVtmvVr8zPdnnhLmXBGGx4L6NpxZzx/bMuel9qDZ8bOAxlBaNQjXD8I5hFE1n3r4rOiZ+FltXd83Ua3AM9Evb9a6WCZ8FmDW9cr1qEpQhuqsgFV/O3MiifkCdNRu+5ArQDRhbs6Pn5U9cCvh68e8UFpn3Gy/UDG3p/LQZdXDcBPTJsbl2OYacx0TEitWIJuHClDMQSQNhbw5Zg71a80yh5N4BNxO8xpjOyZC1ps027Gt+q7Z4bZ70+b6CiLmeujpNI/bWyitbJnz4iwgfFapmjjZsrTZnuNDPgvDRRMwTLlmrl4YMfM8mJhaeCitXN2/+FnKhvU95ljXYniWB/mn4eFRmJr1Yr6/4bNKFQZ/Y9ZfF4cdRZgcMcev04Ph9rnr728orRY/67kdbjwNbBfau6HUDb/tg+NHYGgcsuPmteNdvzLX3L89ubB1KUEv5v08teeyiMhSo6iaiIiIiMhRauPGjU2X33LLLWzevPmAxvzNb37Df//3fx/MtGSJaBXG2b1790GP/epXv5r+/n6GhobmLP/v//5vfvWrX3HWWWcd9DZk7/zabyzm6ezsPCzbP5TX177EYjHe+ta38k//9E9zlt92221s27ataThr9erV/OEf/uEhn9tCtIqqnXHGGYd5Joefbdu85S1v4ROf+MSc5dPT03zzm9/kzW9+M9/85jfJZrMNj73kkkuwbbth+UIdza9bmzdvZvny5QwPD89ZPjExwYc+9CE+/elPH7a5HOrn9hve8Abe9773NbyG3XPPPXzrW9/iNa95zQGNOzk5yT/8wz80ve/iiy8+oDFFRERERERElprur34Ju7CP6hmwaQAe6qu/IXZtBh5o0itfm6n/bAXwooF9z8HO5+i6/hrG3nXVguYsIiIiIiIiIiIiIiIi8kzREEprEkmDQxNKyz88zFTSmQmlWXsJpZWnhym4KVPWqUBbqUq0VWwlAPJZgpjJFeRyEzju3IDYnNXz45Qj9sy+OZUy3gJDaYEDkQgsbxFKq1Yh7vhs6DOhtP5kGAeaxarFylwTBxstQtUPQ2k0D6Wdsy4MY/mwe9JEaQKgMiuUlorChuUQD2NEVmAiOcd1w/E9MJ6HwDVBoogNvW1AFXJlc0od3+xPfxpWdsNIwYy7ImXur1ZMwCwIoKcN4jHwHejpaBFKS5jjEQsjSh4mJtbbbsaZWY96KM0hDDqVoIgJHcUjZvvZkvl5fvwsHoGRaZguwaq0iStVPBN7O6bTxMFqoS0/jGBNl802a+eiWDXRqUTERJ+8KuQqJk6WK0F7dG4ozXXM8raomXO+Yo5rZxtEwzCYZcGyZD3oBnPjZ/sKpY2Fvzbv6zBj25hjVq6G5zAMVVU9M/+xgrkuYe+htFK4ftU3cScrMMfCrpqwno15ett+Y/ysLdI6lDb7a8e9oH5sa+eKoP6zbc1d1w6jebUDFfXCgFoYVYs79f2aeY4EZo4Rvx5b8y0TrqtpFT9jXvysFkoLLDOPIByXMgTR+nOyuw3GC/UoVjUwk7ExkbQgZq6/2rqzeUCQgUgYuCp6JlaXCF//UjEzXqFi5pvJQrI9PLYeTOWgK9Y8fjadgw4HopjXg8yUeWxbrH6ubBvyJchmIeKagF/VgkLBnPfl3eBa9fOZiIJfhnKn+ZMIJMP4mhWY52g8ZtavhKEuJzz2xcDcB+Z4l4r128WSOb6OAwnHRNEsq/7YvYXSyhWYyu5f+Cybg1VtUCya4+vFoDIBUxZYcdhTCK93C0qV/Qul7U/8rG3KBC7TfdC+3OzzxJCJnyUcmDrY8Nk4+LdC8laYuAhyZ0N7l7k/Vl2cUFrPrC9AnBwBRqA8BMWMeb0GOHXWFx3uz7pg3gfErOeviMhSoqiaiIiIiMhR6swzz8SyrPovQkI33XQTn/zkJ0nP+qXJQlSrVS699NKWsSV5dlm5ciWJRIJCoTBn+T333HPQY7uuy1VXXcVVV8394FsQBLz2ta/lF7/4BcuXLz/o7Uhr9957b9Plq1atOizbP5TX10K84x3v4P/9v/835/XO931e85rXkM83fjD0bW9720EFuRbT/fff33T5mWeeeZhncmRceumlDVE1gC1btvDmN7+ZLVu2tHzcwTiaX7dc1+X/+//+Pz74wQ823PeZz3yGl7/85bz4xS8+LHM51M/tVatW8cpXvpJvf/vbDfe9973v5cUvfjHt7e2ND9yHv/qrv2JkZKRheVtb20FfOyIiIiIiIiJLgZ0Zp/OG6xa0bu+sN2sCdBUhXYTJWR+4SBfN8pr1896QuTddN1zHxMVvxe/sXuDsRURERERERERERERERA6/hkgaLCyU1iKSxsaNVPwqDz/98NwN7SWUNp2M4DoutmUTeN5eQmmjc0JpVqlIEGu+X54FlXIOnDCU5pdx9vL23cDL41ccs1+BjxU1Aa+m/AqWHyEgAN8jsPcSSpsGqhAJQ2mnL4f+FPVKVmh2KK0tCqMFEwSyA5j/6alkBE7qhXTU3N6fUFrUghPSsCppomF2FBIxE4SaHz+zPUgnw/CRZeJnwzmzb/0ps592FDoSUChDJAA3DJYFFqyOmcBULQCGC91JE5TyKuFxD8yl0yqUlo7XA0xe7WNpgQmLZfJg2fVQWu1xtgVRF4anTIjKccxjqh4U/n/2/j3KrvSu7/zf+3bup05VSaVLi263m2652263bLe77eDYcRjiZbwYAiEhQ0gHYjN4ArmQGWYYSGgCYcVrkjWzknjoGc8MTjLGzNjGxox/BOyJycQLbGIwpt3IfTHpq9RS6VaXc9+35/fH9zl1SqqLqiSVpJY/r7VY0jnn2Xs/+9n7lBtVnXc5O9dJ/CyfxM+q020DZ8dNIlvP/tjWHKDvy1/7m7a/AGv4zNYhy6FfWJQrz+z+iYLtQ2nD1NYo8DGqdg2CchrhalVtX2W4MX52uVDa0O8kL+xcJjGzlGlcLA5gkFtMbFhAtsNQGlhcLQmnrwVYWA8fPQvZGD/bMpTGNFw3uQ5u3fWOSwtpEdomlXVlkcjfb2Xp76fSwlSusPOCafzMdxMtjuXDZyF+rA/ATcYS2Pk5/Gvr42cVH0RjYyhtXEDi94uzQN8klFaJ4I4OpP7apAUkmZ2/czBKp+c1W7NjD/17ZZhCNIB6w+6tXmqRs0rF7t9o3bVY6UFZQHdk92YvtbkMhlvHz4oAwtjer03/Whhc/L6KAljo2P3mYrum+2chjCBNIa74CKQ/lyi0ry9ULepW9++z2N9Hvdzuz1YAQWTrn+UWJtvX8fecA3JbY7AgXLjuml9Ytce7CaXtNnxWZrD/VVBtQHts99doAOeOw4uJBccmdhNKu5L42Yknof2rtl33TT585q5d+IwBvP4z8NjpvQmlXfoLEO9anq7Hpb/ocDdjwX4OqLHJfzuIiNwKFFUTEREREblJzc7O8u3f/u18/vOfv+j5paUlfuZnfoZf+qVf2vG+yrLkb//tv80Xv/jFaz1NeYWKoogHHniA//gf/+NFz3/xi1/kySef5L777ruq/f+dv/N3+NCHPsQ3vvGNi55/4YUX+M7v/E4+9alPceedd17VMSa+8IUv8Nu//dubhpheKX7qp36KdrvNj/3YjzE/f3UfFjxz5gwf+tCHNjwfxzHf9m3fdlX73qm9vr8u59WvfjXvfve7+bf/9t9e9Pyzzz67YWwcx7zvfe/b0/ns1NLSEs8///ymr73xjW+8vpO5Qe655x7e9ra38Xu/93sXPf/v//2/5wtf+AL/3//3/23Y5u1vfzvf+q3fetXHvpm/bv3dv/t3eeyxx3jppZcuet45x/d+7/fya7/2a7z73e++4vnkec6zzz7L0aNHtx13Pd7bP/uzP8tv/MZvbIjKPv/883zP93wPv/mbv0m1usVPcWzil37pl/if/+f/edPXfuzHfoyFhYWrmq+IiIiIiIjIrWD+V36ZcLjD6hkbfwDzzmV4/ND09TuXp3/f7AcytxMO+sx99MOc//GfvPxgERERERERERERERERkWtop6G0iyJpsLtQ2haRNJ64wGCuRXfc3XEobeBDac1Kk3F3iXyLH7F1QJoOcFFkj4uUYIvCgMM+g0SQgytxAQQxHG5tEj7rWcgnKEsLpWHxo4Nt6NQ3ji0yCMqCOAk4dgjeeNDGbhZKCx20KhYuOje0MFPgptGsifWhNAdkDk4uW8CmCCC1U6ZdgdcfsqUM/WWrBPCaWXjNPgulBbEFigLgQMviSqOxXdLYwXwdkrrN4UzPIlCHZtYFzXz8rBrauuSBzSkM/f7cNMrlsAjVwRl7PEgh9fNqJBBH0Bvb4yiCZuLDVPE0lJYXFpfq1KE/giS2fTYS22cYWIxpNIKzq9bJmw9tLS8M/Fx9KC0IoPChtJkq5JNrgY9UBRah640tnDWJn8VsHUoDWPV1oCi21xsJ9HxArBLbeeZ+7Ikli77Fk7DdNqG0rLBI2oHWNHIVRzAqbK6V2OJFpbMY1EXxs21Caf18+n3wcWHRpclrk/WYHK9w68bmkPho1WahNBdMQ2mlD4UVbnrvV4rp85vFz9YCgZeE0kosRjdRjS1a5pztLynsehPbOk7CZ6PCxjofYcsy2y+AyyBfF7A61OKi92iawdqXmhSyePr6fIOLjHNIfPAsuCR+dmkobZxDlNs9VAL9AZSxBcYaic09iWGUQbcPMxFUsBDi8io0W9CoTq9VGFo8rd+zY436EM3AcGzrGzG91wJbTmoxLMz5+FkEQRUWmtvHzwBcAqUP9LkCxiOLKoK9X11p7+N6BIPC9uG/HBPk9lwSW0QuKKb3VZbbuY7GMN+28F02hrNjiHMfrGtAVoOlVbvmp8/DbAsqY8iGsJJMr2tct7ErXXvPlm73obTdhM+yHE59w8YfrcEzIx8QczBbwGLr4vG7DaVdSfzMAY+d2Jvw2cIehtIWBnBsEf543c/mTBxbvPgXHe5m7MT6X5woInIrUVRNREREROQm9t73vndDVA3gscce48477+S//W//28vu4+zZs7z//e/n13/91/diivIK9s53vnNDGKcoCr7zO7+Tf/bP/hnf9V3fRb1ev6J9VyoVfuVXfoW3ve1t5JPvcHhf/epXeeMb38j/9D/9T/z1v/7XSZJki71s7cyZM/zGb/wGH/rQh/jKV77CsWPHXtFRtcXFRf7pP/2nfOADH+CRRx7hB37gB3j7299OGG71K5o29/zzz/OX/tJfYnFxccNr73rXu9i/f/+1mvJl7eX9tRM/9mM/tiGqtpn//D//z7ntttv2bB678Ud/9EebPn/nnXcyNzd3nWdz4/zNv/k3N0TVnHP8wA/8wIbQFsAP//APX5Pj3sxftxqNBv/H//F/8O53v3vDGvT7fb77u7+bf/AP/gE/+ZM/SbPZ3PGcer0eH/3oR/kf/of/ge/+7u/mn//zf37Zbfb6vf2mN72JH/3RH900Dvn5z3+e97znPfyrf/WvuOOOO7bdT5ZlfOADH+Dnf/7nN339yJEjPProo1c8TxEREREREZFbRbh8gdmPfWRX26z/QVCwH67sjGClZn+u/2HL+89s/gOZ25n72EdY+sH3Us5e3S+gEBEREREREREREREREbnWobTsDQ9w/NzXLz7ITkNppcNtEUlj+RRp7SCDYggZuwqljfIRSZpa3GgLZVngAmd1n9BiQofbm4fSihxqUcmxA3DsMBxq+pDQJnNYXIWnzjqKAF67YPG1gGkkZv3g86v2c+H7O44QHzfbJJTWjmxfraodI3dwaglOLl0+lEYAd89PQ2kusXjU4badY5FawMz5SFozgTKC/TNbhNLqFv2qBn6dsJjYQsv2szaOafwswgJJk1BaXtjtU4stMlaNoJpYKCrEx8Yqtu5pZnG3ovBr46BdtUGBs+tQSeDMCqyMoVmz/eQ5nO3b3JsVIPSxrNL+zHPoNOxY7ToMMhtXFNAvbE6TUFoU2D6HmY2vxBa5SnN7vD5+drlQ2nn/veIDMxacCrE1S32AKvZBs7yAUW6htINtv57bhNLGfnxeQhLa2kQRhLndIyG25mG5MX7WiLcOpa3/FEHhbL+TQYG/Xyd/D4OLx4bO5rYhlOajarVoel4B0/hZWFqYyvk3QhnYOa2Pn20WSnOXhNLw2zlsHq7wIb4UXMX2ARY+uzC0sZPjpUNo+Mc5MBhCzX+daldt22Fmc1jtw76K7TvPLAhW92Nna3b8YWaPVwcwF0zDcEtdqDY2D6X1Bhbymq1bKG04gCKCg/OQBNNrVK9AmfrrHNt92PSvhcH0/Rj64y50fFgvtffD/lm7VwJnsbJa1bbJfNRrEj9bGtprk491bRc/6/XtvRmG/mtRbmuMf39MwnplCRdW7fHcjM0vKWAlh8U+VEpoJVDUbV/nl23uK10YvgidJiRViBOo+bBYugxPLMJyNr0f2yHcv9/WenKdgwIGZ+H4OVhd90V3t6G0XYfPLkD5W9D8LVj6Tug/DK05e72aX5tQ2m7jZ3sVPtvt+N3Gz97zDTjXgBMz0+e+ZdWev9RuxsKGtqmIyC1DUTURERERkZvYX/pLf4lXv/rVPPfccxte++/+u/+Oz372s/z0T/8073jHOy4KvDjn+MpXvsKv//qv88EPfpBut7v22v3330+j0eDLX/7ydTkHuXm9733v45/+03+6IdDzwgsv8P3f//1EUcSrX/1qZmdnqVY3/07c7/7u7265/4cffpj/9X/9X/mRH/mRDa8tLy/z3ve+l0cffZQf+ZEf4Z3vfCcPP/zwliGel156iccff5yvfvWrfPazn+VLX/qS/fajW8xgMOBDH/oQH/rQhzh06BB/8S/+Rd761rfy4IMP8trXvpZo8qtI1hmNRvz+7/8+n/jEJ/jwhz/MaLTx10PEccw//sf/+Hqcwpq9vr8u5zu/8zu3/Pq53vvf//4rPsa1tlVU7Y1vfON1nsmN9f3f//383b/7dxkMLv4OwMsvv7xhbLPZ5Pu///uv2bFv5q9b73rXu/i5n/s5/tE/+kcbXsuyjH/0j/7R2tzf85738Ja3vGVDmNE5x9NPP82Xv/xlPvOZz/Cbv/mbDIfDXc3jery3/9k/+2f8h//wH3jqqac2vPY7v/M73H///fztv/23+Wt/7a9x//33X/T60tISn/zkJ/ngBz/I1772tU33H0URv/Irv0K73d52HiIiIiIiIiKvdN2sy0uDF+lm3S3HdD79MU4c3GX1DPut2i90pj8IGpewXIN9A3h+1p4LHLzjeVipQme8832Hgz5zH/0w53/8J3c9LxEREREREREREREREbn1bQilbRJJg70JpQ2OL7LajAgISKKEoHSw01DauGCcQL7F7153gwukcYhzbtehNBdBHFuQarNQWr6DUNokINVKoFG1IFZR+lgUG8eGwNtug7/6Wvu+4UsrFj9zDrLI4koTnRDeetRCaXkJJ5fh5SUbk4UWUQILpR07CLWAtR0kDu6ah7v3Xz6UtlCHetXObf+MhYrmGxbyAguWzTct1lVmdgzn7FbYKpTWqU0DTMXkpJyFxZYHEPhgWrt6cdCpFsOZLnTHcKRja5kVdg63z1r4KvIblD6Y1U2nwSewcNgwg3ps+yxy6GfQrkF/DK3KxaG0JLLnGxWLHA0yC1jNNqDiI2hBAPua06AbXBxKi0Jbg3Fu5zmJn2X5xvjZ5UJpQx9vygub2yRmljKNi8UBDHKLew0LC1/tJJQGFldLwulrAfa9bHz0LGRj/GzLUBrT6zG5hm7d9Y5LaxLiw2WVde/NaF0069JQWun3OYmfVf12zq9pPHlzpVAk0+/BXxo/yx2wPpRWtXtqMna9cQ5J6k/TwSidvlaJ4I4OpJOIG5AtQX3OonrjEnpdSCp2T0fr1nelZ8G1ThUqDnqp3Y+Nmn3NmITqwhAGY+j1LAA2W4c8gOHQruVWobTUWSwuTiyUFoaQphBX/Ncrfy6RXxOXQOnvO1fAeGQhNLD3qyunkbQssjjZPv8/EbG/j3r+vm4FEETT67Lah+F45/EzB8y2oDKGbAgr/mOeLoO4DlnNjp8X9n5f7sLhCkQFDCJwEVRSC8stD+HpkzD0Hw+r5nC4B73A4mcH5yGtWvjs3PNQLYDW9Bo3Vu1rcecAtA7a2i2dtohYPYLVdWOvKJR2BeEzBvD6z8Bjp/cmlLabmNlehs/2MpRWKeB9fwR/Om8/nzM7grsvbB5E281YEZFbmaJqIiIiIiI3sVqtxv/+v//vfMd3fMemr3/+85/n85//PM1mk9tvv52ZmRmWlpY4ffr0RSG1ifn5eX7t137tpooIyY1zzz338EM/9EP863/9rzd9vSgK/vRP//SqjvG+972PwWDA3/t7f29DgAfgxIkTa4GgOI45cOAAc3NztNtter0ey8vLLC0t0e/3r2oer0SnT59eC6wBVCoV9u3bx9zcHDMzM4zHY5aWlnjxxRcvG2p69NFHedOb3nQ9pr3metxf2wnDkPe///389//9f7/lmFe/+tW8613v2rM57NZXv/rVTZ+/3tfuRmu323zf930fH/nIRy479vu+7/totVqXHbcbN/PXrZ/7uZ/j/PnzfPCDH9z09dOnT/OLv/iL/OIv/iLVapUDBw5w4MABnHMsLS1x5syZq57X9Xhvt9tt/p//5//h7W9/O4uLixte73a7fOADH+ADH/gAnU6H2267jUajwalTpzh9+vRlvyY+9thjvPOd77yqOYqIiIiIiIjc7L7RfYbPnvoszm3z/yc7x9yznyW888qO0U/gzLp/mnHA+Yb9H8CBHnzhTvjdV8H3fR1ed3bn+579tV/l/N/6r6e/9llERERERERERERERERuWRsiabCzUNoWkTQefJCszDl+9vjFB9omlNZtxiRRQhiEuKLYMpSWds8xTNpWqckgGI9wm/8eYuCSUFqRkvi4z6aKjDKMcTgoi+1DaV0gh9iH0t5wEA612VBMmQSk2omFts4Ntw6lNWO4bwE6FQtBTeJnpy9Aui58BtCK4XX7LboE9r3CuztwWw2eWYSlEsr1obQDUA1tYM3Bt3bgcMsCY2HFQmiTUFpZQp5Z4CwsYL4OSX3nobTCrixhaPGo3FnEKMACS83EL1M0DaXlhUWmOnXojyCJLYLV8GPDwOY4GsHZVYhimPchpwsDO341tvhZsD5+5tcn9AGvTs3267D4WdOXHvq5/bm/aXMJbHrM1i3w1C+gGtm6uNDuhe1CacPUn7MPV7VrEJS2NuDjdpldo0tDaaHfphpatCwKpoGy4aXxs21Caf18Gi0aF1B309fw85xEtQq3bmwOSeKPuUkozQXTUNrkx8YLN733K8X0+c3iZ2v3/SWhtBJb44lqbPeO88G7pLDrTWzrOImkjQob63yELcumoTSXQZ5M93moxUXv0cEQZiaPS+gPoeJ/5/ul8bO0gCSzzd0lobTZmp3rMLPHqwOYC6bnu9SFasPiY43E5p7EMMqgN4AMGPUh7sBwYGsWYZG9ybUKsVBgMmtfC8IIggIWmtN7ZH1UMApgoWOxPRfbNdo/a9ttFUqrVyEN7HpPwmlBDoPC5luUdszJvdLr2z7C0L6mkVuIDuw8J/dLWVrErChgaRXm2xa+y8Zwdgxxbtc2bFj8bGnVthlnu4uftUO4f7+t9SSGFxQwOAvHz8Hq5A2IRcqyvg+fHQIqFuIbD+H8CRiuu29mJxUxHz87+ofwdAEvd6A5C/XA9ndp/OzEk9D+Vduu+yZozdn7cLOxVxJK2234bGEPQ2m7iZntZfhst+N3Gz8LgHsubPHixNGj8MgjBPU692w37q671v776rnv+i6rHoqI3GIUVRMRERERucn9Z//Zf8Y//If/kF/8xV/ccky/3+epp57adj+zs7P823/7b3nNa15zracor2C/9Eu/xNe//nW+/OUv79kx/s7f+Tt8y7d8C+9973tZXl7eclye57z88su8/PLLezaXV7I0TTl16hSnTp3a1Xb/8B/+Q372Z392j2a1vetxf23nve99Lz/3cz/HeDze9PUf/dEfJbj012vdQH/0R3+06fNvfOMbr/NMbrwf/uEf3lFU7W/+zb+5J8e/mb9u/ct/+S85ePAgjz766LbxsPF4zEsvvcRLL710zedwPd7b99xzD7/zO7/Dd33Xd/Hcc89tOW5lZYWVlZUtX18vSRIee+wxfuRHfuRaTVNERERERETkptTLe3z21G9vGoxfL1pZIkzTbcds51UrcKbJ2k9zzo3WvejgTv//spcB/Npr4Y4vQXuHh4u6q1ReeJb01Xdf8fxERERERERERERERETkxtlpKO2iSBrsLpS2RSSNJy4wmGvRHXd3HEob+FBas9Jk3F0i3yKU5oA0HeAiqy+5IiXY7tP6k1BamUNo4a8jbYt3XRRJ61k8KyoL4iTg2KHtQ2mhg1YF6j6U5gp7vrjk8OtDaQ7I3DSUVgSQ+ohUuwKvP2TLEzofYwvh7nl4zT4Lh7nEQkdxCAsNIId+apdpEkGa6cBt83BuAKmPaB1sWcxoNLaxFQczTQulFcCZnkW3Ds1YSCoASHyoLLRI1W5DaSPsmLXYjtkb29/bVVvPwAfHKgmcWYGVMTRrFmLKczjbt/k0K7YOzllEzDl7vdPwsbI6DDIbVxQb42cxW4fSAFb991ij2F5vJNDzAbFKbPdL7seeWIK5xjR0tV0oLSssknagNQ2XxT4A5gLbd5nZGqZbhdJ8VI1JKI2N8bNGvHUoLV//NpjcU0zXHzf9exhcPDb013dDKM1Hsmr+vp1E3Cbxs7C0MJXzi1IGFrdaHz/bLJTmLgml4bdz2DxcYVExl4Kr2D7A4n4XhtOAVhlAOoSGf5xj4bSa/7rTrtq2w8zmsNyDZsuvVwGrfZirbh4/6/ZhJoIKFjVbXrVtG9Xp+ochDMbQ60Gc2L2WB9ZKKgI4OA9JML1G9QqUKaSz9icxNH18LXD2XqpVbXw2smvmv/QxcvYa2PqMR9PHo7HF6qII6pFF0YJguu12obQ0g9UezM3YHJMCVnKLKVZKaCVQ1O1czy/bNZptQWUM2RBWfIzMZRDXLZK20rX1LZ39ffgidJqQVG2daj4sli7DE4uwnE3vx93EzxqrcGrJjz1o57x02gJi9QhW1/3Sulm/niuL0D0HC1V42dn9EwHV1tbhs+an4fYIPvPQzuJnDnjsxN6E0nYTPtvt+L2Mn+1l+GxPQmlvfjM8+ig0m1uPCQJ4/eth//7L7Gyj/uTNKSJyi1FUTURERETkFeAf/+N/TKPR4Gd+5meuaPt7772XX//1X+fee++9xjOTV7pGo8Hv/u7v8ou/+It88IMfZGlpaU+O873f+708+OCD/P2///f51Kc+dU33fffdd/O+973vmu7zemu329d8n7fddhv/4//4P/Jf/Bf/xTXf905dr/trKwsLC/zlv/yX+ehHP7rhtSRJ9izIdSV6vR7f+Mbmv6bkTW9603WezY335//8n+fOO+/k+eef33LMq1/9av7cn/tzezaHm/nr1j/4B/+At7/97fxX/9V/xZNPPnlN5jQzM3P5Qd71em+/9rWv5Q/+4A/4W3/rb/GJT3ziqvZ19OhRPvzhD/O2t73tGs1ORERERERuVoc7NX76O3f276CHO5v8tLzILeCF/vMXBdVKt3mY3VUqFMHWP7h5OfUcDvRhsbXxtYN9+8HjtWMF8I198KYd/s4IFwQUM7NXODMRkVeQmSPwF35h52NFREREREREREREbqBrHUrL3vAAx899/eKD7DSUVjrcFpE0lk+R1g4yKIaQsatQ2igfkaQp2TafwC/LAhc4qwb5yNjhNszWNg+lNcKc+w/BscNwuGnfn3OXfpPOwblVW6v9HUfANG52aSitHcFrFyyi5bBI06klOLm0fSgNgGAaSlvyobRqbPMvHBQpDFK7ZHN1i4UFAeyfsahR4Sz0FgUQVmCmDsMUkhKqFSgiH72qwGIP9jUtMgZAHYoMqoHdAgUWHlto2THXgmpMQ2kRPiq1y1Da4qrtP4psbfMChs7OtR5bxCvPoZ9ZnKw/tkjd+lBaEtnzjYrNY5BZbGq2AZXQB8QCO8eQ6dzXx88uF0o776NAB2Zs3yG2DmnuA3aTaFgBo9xCaQf9x0+2C6WN/fi8tOBX4GNYYW73SIhdg7DcOpQWrYufBYG/5pfEz7YMpfntJ8LA7qnJvR6X9vbBh8sq695vkQ+nleXGUFrp9zmJn1X9di6w91o8+dZ4CkUyPa9L42e5A9aH0qoW95qMXW+cQ+J/eVjgYLTuF4lVIov8pZOIG5AtQX3O7u1xCb0uJBW7TyfXOAxhpQdlAd2Rxc96qa3NYLh1/KwIIIzt3moyvSaTpQ79dVro2D3kYrtO+2chjCBNIfbvafy5RCHUq0DVom51/3Uy9vdGL7f7rxVAEE3XerkL+zr+PnJAbmsMds+H667jhVV7vNNQWur3f7gCUQGDCFwElRSKMSwP4emTMPSVr3YI9++HamN6jYMCBmfh+DlYXfdFtJrD4R70AoufHZyHtGqRtHPPQ7UArjJ+duJJaP+qbdd9E7TmoO5szFahtP2fg48/sLPwGWNYYHfxs70Kpe02fLaXobTdxMz2JHx29Cg88gjU6zZ+q3F33bX230uXdRWhNBERUVRNREREROQV46d/+qd55zvfyX/z3/w3fOlLX9rRNnNzc/zET/wEP/VTP0W1usV3f+SbXpIk/PzP/zw/8zM/w2/+5m/yxS9+ka997Ws8++yzrK6u0u12GY1Gl9/RZdxxxx188pOf5A/+4A/4F//iX/CpT32K4XB4Rfu67777+I7v+A7+6l/9q7dEIOeDH/wgP/ZjP8Zv/uZv8lu/9Vv8/u//PoPB4PIbbuLuu+/mh37oh/iJn/gJWq1NPs14nV2v+2sr73nPezaNqn3P93wPBw8e3LPj7tbjjz9OWW78cOvBgwc5fPjwDZjRjRUEAT/0Qz/Ez//8z2855m/8jb9BEFzpR3535mb+uvWOd7yDxx9/nI9+9KP8i3/xL/jjP/7jXe9jbm6Ov/gX/yI/+qM/yp/5M39mV9ter/f2vn37+PjHP86///f/nn/yT/4Jn//85y/6UPjl3Hnnnfz9v//3ef/736//FhIRERER+SZxYKbG+//ct97oaYjcUHGQXPT4qdWn6ObdTcdWXz9DtLp6xccqsajaeN1PIFVz+8H3cQxvPD19Prn00x7bGLz1z1Ls0w+Fisg3gfYheNvfu9GzEBERERERERERkW9iG0Jpm0TSYG9CaYPji6w2IwICkighKB3sNJQ2LhgnkIebn5cbXCCNQ5xzuw6luQji2OJVm4XS8hxqUcmxAxZKO9T0kaJ1JrGpdmJxsHMjC1yFzuIsm4XS3nrUQml5CS+tWEjHAVkIhT/PdgWOHYSajyIBJA7umoe7918+lLZQt2jS+lDafMPiUABBDPNNi3UVmUWcHHZ5D7Qs2BX5iJPD4lYHZ+zxaDw9ryiEb+nY+Ikogln/47yjsc3NLpZFyJYHEITTUNr6UNRuQ2kzVZj8DqgAu44usLXtjS3IlWcWvoqC7UNpw9TOI/Dn3a5BUE7PtVW1fZXhxvjZ5UJpQ7+TvLC1DLDXU6brHAcwyC0oNiwg22EoDSyulqwLeQVA5ANv4WSbS0JpTD7WENj96gqsSBFsjJ+tfQLiklBaybqYHnaNcmf3YFna944n+y2YRtJGhY11PsKWZdNQmssgX/et8EMtLiogDYYwM3lcQn8Ilbo9vDR+lhaQZP4+viSUNluze3uY2ePVAcwF0/Nd6lq8KwotHpZEPvyXQW8AGTDqQ9yB4cDWLMJCaJP7McTu84U5Hz+LIKjCQnP7+BnY+7v095IrYDyC2rr3lSvtvqhHMCh8EG/y/s7tuSS2iFxQTMNZWQ7dvu1jvm33UTaGs2OIcztW2ICsBkurdh1Pn4fZFlTGkA1hJZleq7huY1e6dn+XbnehtGoOWd/CZ61DQMXieuMhnD8Bw3X3QmPVwpKdA9A6aOe8dNoCYvUIVi+NpPl7dmURjv4hPF3Ayx1ozkI9uHbxMwc8dmJnY/cPdhc+g93Fz/YylLab8Nlux+9l/Cyo17cOn8HO42cKn4mI3LQCt5tPIYpcpXa7Ta/Xu+i5VqtFt7v5D++KiMjuPfXUU5tGBoIg4N57770BMxKRvfDlL3+Z3/qt3+J3fud3OHnyJGfPnmUwGNBqtbj99tt5wxvewLvf/W6++7u/+6aIKolsZjAY8O/+3b/ji1/8In/8x3/M888/z+LiIv1+H+ccrVaLdrvN/Pw8R48e5d577+V1r3sd73jHO275yFSe5zz++OP8x//4H3nyySf50z/9U5577jmWlpbo9XqMx2OazSadTod9+/bxute9jje+8Y28/e1v5+GHH77R07+pfN/3fR+f+tSnNjz/+c9/nm//9m+/ATOSV7Kb+evWU089xW//9m/z+7//+zzzzDO89NJLdLtd8jyn2WzSarU4fPgw9957L6997Wt55zvfyVve8haiKLr8zm8iL774Ip/5zGf4vd/7PZ588sm18yyKgkajwcGDB7n77rt5+OGHede73sW3fdu37Xl8T0REbg36NzURERG5lp75yn+6ocfvZl3+zXP/au2/b1ayFZ5afWrTsUFRUHnuG1cVVnPAMLYPrMSl/VBxABw7DXP+p3sDBz/x+9AZX35//T/zdk594F9SttpXPCeAow++8gOLT5xZvNFTuOm9/sDN88szRERERERERERERERuBhsiabCzUNoWkTQefJCszDl+9vjFBzpzBk6d2nD8waH9dJsxSZQQBiGuKODkibUA2HppBIP59lqRLBiPcON040DPVZO1UBppSlJAts2PwpZJjMNBWRAW24TSuhYpiqtw7AC84SAcarOh2rI+lNaowLmhhYlCZ8Gr9Zox3LcAnYo93nEozQudhbSWBhBWLIQ2CaWVpUW8BimEBczXIanbMuZuk1CaszBXHFnAp8Dm6wILbuXOR8OwUFMr8afu7Bip80G40AJSqyMLZlUTGzuJjzkHvQyGuUWr5uowzmz/SWTxsQgb7xwsjeBsD6LYzqE7gKWxRd1m6xZKiwMofCgtdxZKS6LptZgEggI3DaVN1rWaWBgrxMJWJRawCoGzqxZyi30kLAx8KM3vrxJNQ2lZYZG0Ay3bNgmtMTbyQapWFUZDyANbr7MDe/7IjEW3GontY5D5NQigP4aVEZz1v+97oWH3ZbNi13c1tbBcFFjU7dwAzvvvve5v2LrXQovNDUZ2TzVrkGYwLmE1g/MDuwYzVahg99BgaEGzqG6hq6yAkYOT3em+2wnM1HxsbABJAknV3wulvV9GhZ1fEkKnCkUB5cDCYHEVeqm9N0537eY41Jre62UGwdjWKG7Biv8eclrAheF0bOBsXMNfzzSE1Rxqtem+cmfX2DlYugBHWrZmWQ4n+zC37+L4We4sftYfwEwEFWfvxaURNFvQqNo9BzZ2MIaz52A2sXsyDeDUMlQacHAeEh++K/3/9Xt2DSopVDrTkF7gIIgsfhZgIbR83dfEwdBeC8PpubtgGj9z6+JnhQ+lhZP9Oru3wM7T+fd2WcL5Fbu352ZsXZICVnJY7UOltPuoqNu+zi/bfEcjaHah07Tr3k+gllvQrMjhiUVYzqZzb4dw/34LzfV96KyZWfzs+DlYXVeQrOZweF0oLa1Mx544AS+tC6Ud7NnPHoCF5haqsOQg9e+ZU61p+Oxwbxo+a38VcNB9E7TmbK22Gtv8Mnz7p+FsBI89tHn8rLpJ/OyT91n8bOLZ2YsjZa9fhO970v6+m7FnGzaPybVeL3DT46/36Xs3j5+94TR8z1NXPjaN4P88tjF89jcet9DZpRw7D59dyfjLevOb4dFHodnceoziZ1tSA0ZEblWKqsl1pf9BFRHZe/oAqIiIiIhZXFzk9ttvJ8uyi56/5557ePrppxVaEhEREZE1+jc1ERERuZZudFQN4LOnfptnVp9ee/z1la/Tzbf+2YxgNKT25J8Q7ODHiFwQkL76W3FRvOWYmaDB65JXrT2+N76d99Qe2n7HAYzvfg3l7Pxl57ATiqp9c1BUTURERERERERERES+Gew0lHZRJA12F0rbIpLG4cMM5lp0x91dh9KalSbj7hL5eLTpeTkgrUY4/0uCXZpeNqpSJjGuzMFBNbBIWqd+SSStZ1GlKAyIk4BjC+W2obTQQasCdR9Kc4UNKy4Zuz6U5oDMwcllC6UVgZ07WOzp9YeghkW4JmKAyEJpQWyRpQCLdZUFjMZ2mWI3DaUVwJkejAs4NGOxr0nwLA6gGkKW2fF3GkobjWGExZqaVchKC6XVYgtHhYGtSYCFt17sQncMRzr22vIQemO4fdbWLZlEqErop/DisgW89tXtFjyxAgfasK8xDUxlhe3z9Arsm7FIm3OwuAoHOtNQW/UKQ2lRaOG77tjOuV6xnk5/bFGqUysWJDvof8/TdqG0XmZRs9t8RKwa2HUYpjavmaqtaV7CSnpx/Gxum1DamSEs+UDUfB321yzqVTrojnyALbR9n18XYJurw1wCcWghrotCaSWMSlvfk10fYKvYmreqMB5YTMtV7d4al3a+l4bS2hVwpQ+l+fhZL7N75tL4WbMCrAulRW27n8DGwjTwVwkhGU+jeFkVxuu+jlSi6TbOQXYa5ubAhdDHImFz+2wdK5G9D3MHKz1YXoLZqt3DSwO7FtXm1vGzMLD3dBFaPC1K7L6ddBoLYDiGIrV7bLZqAbqytMhXENk9FgZ2zQap38jrD21/YWjXpwgsCpgX0AosCOl8AO78sgXUdho/c8BsCypjyIaw4mNknQziOmQ1WOnasUoHeQ6HKxAV0Ivs2O2xhdKWI3j6JAz9NZuExwgsfnZwHtKqhc/OPW+RtMXW9DwP9qBe+FDaQbtfuqdh5QwMo03GXkEorflleP3n4LEHdhY+c+wukgZ7Fz/bbShtN+Ez2F38bK9DaZd19Cg88gjU69uPu+uuiwKw21Io7ZpQA0ZEblVb/3SjiIiIiIiIiMgr2C//8i9vCKoB/OiP/qiCaiIiIiIiIiIickt7aP4hvtF9Zu0DM0caR3hqdfoTtrWoxoHqgenjF56gen6nv5fRMZqPGN93/9ozZ8ZnGBXTD8McmrmXPLEfcg2CgGN3/nWGlWsTSxMREREREREREREREbkVXOtQWvaGBzh+7usXH+TMGTh/Cta+LVTC+ePwxIWLQ2mlwy2fsgrYpZZPkdYOMiiGkDENpVU3Py8HpOkAF0WM8hFJmpJt82n2sixwgbOCU2ihqMNtmK1tHkprhDn3H4Jjh+Fw0wIvG2I1Ds6t2lrt7zgCfCTMXdQ8AqAdwWsXLDg1CTSdWoKTS9uE0ibfVgvg7nl4zT6LOLkEqrHNv3AWYxqkdsnm69BMoIxg/8wWobS6xY+qgT93LCa10LL9rI0Dosj2F2ExprVQWmFxtLK08FkttgAVgZ1/EFhMbHHV9h9Ftl55AUNn86/HForKc+hnfnvsucBBpwaJD1r1xxamAuj7WNJ8HaLY5hoE0PEBrP7Y4mYRMMgsGDbbsNDWZOy+5jSoBnBiyYfSQhtfiWCc25yj0MJyWW7zH+VwfmihtNBHtKqhrXMU+FgcMMwtQDcsLCiX+NBbFEGY23UPsbdWWNq1BNtPEk5fC4CotLlM9h2uuxcLN42HTdYf5/+OzWkiDOw+mdy/cWlvCULbpLLuPRT5eF5Z2jZhaXEsV9h5gV2fWmTXE6bxrrj0O0mhSKbvnfmGhdKScN25lj4K6CN4hd+2EsEdnWn8LC0gyfx70cEonc51tmbzH/qPdawOYM7f3yWw1IWqj+01Ept3EsMog94AMmDUh7gDw4GtQ4TdD35ZCbH7fGHO4mcugqAKC00Ld6UpxD6qh59f5Ld3CZS5PXYFjEcWqgN7T7nS7ot6BIPC9uEbkAS5PZfEtjZBMV3PLIdu3/Yx37ZrlI3h7Bji3I4VNix+trRq13KcwXJ3Gj8bRHYulRSKscUM18fP2iHcv9/Wb3LdggIGZ+H4OYufTVRzyPo+fHYIqNg9Mx7C+RMwTNZds8m3/R2sLMLRP4SnC3i5A81ZqAe2v0n8rJ77eOKT0P5V2677JmjNQd1tPnYSStv/Ofj4FqG005vEz+4/Y+GzlTPAGUhPw2jZvvYBvP7MNFC2m7EA73gB/uTA9BquD6oFDv7cC9PHCwM4trh5/OzY4sX73c1YgPd8A841NobP3vONjduDxdDe90c7i5/tZizY8/dc2OLFiTe/GR59FJrNrccofCYiIjeIomoiIiIiIiIicsspy5L/7X/73zY8X61W+eEf/uHrPyEREREREREREZHraL66j3vaR3lm9WkAOkmHdtymm9tvkR0XY+Yqc9SiGsFoyNzj/4kg3/n+3deeZem1b8fV6oyKES8NXlp7rZ206STT3xp8T/so8wqqiYiIiIiIiIiIiIjIN4EbGUobHF9ktRkREJBECUHpYKehtHHBOIE83Py83OACaRzinNt1KM1FEMcWutoslJbnUItKjh2wUNqhpo8frTMJU7UTC4mdG0Fe+lAam4fS3nrUQml5CS+tWKDHAVkIhT/PdgWOHYSajy0BJA7umoe7918+lLZQh3rV5rt/Bhb7FqeqTKJLMcw3IY4sDITzEaxtQmmdmp3vaDyNeeEsLLY8gCCchtIm24VXEEqbqcLk24MBPn4V2Hr1xhbkyjOLusXA/qbtL8CiVrN1C0cBrPpKUeTviUbFR8L8/ucadlsTwDCF0sfdAqBdg6CcXsNW1Y5bhlcZSvPrGvuxk5BZjK3rJJw0ziFJ/H42CaW5YBpKK31UrPBhNLBI0eT5zeJnk4bZpaG0EqhG07HV2IJ+ztn+ksLCW8S2NpNI2qiwsc5H2LJsGkpzGeTr4liHWlxUSxoMYWbyuIT+ECp1fx6XhNLGOUS5XesS6A+gjDePn3X7MBNBBXtvLa9CswWNqkXvwNZ2MIZeD+LE7p88gOHQInYH5yFZF5urV6BMIZ21P4mh6eNrk/dHzb8HspGt7yR+tjS018JJaGyb+Fmvb/diGPpYYm5xObDznNwDZQkXVu3x3IzNMSlgJbf3faWEVgJF3fZ1ftkCbytdGL4InSYkVTv3mg+LpcvwxCIsZ+vug13EzxqrFoDsHIDWQTuPpdMWEatHsNqajp31a7SyCN1zsFCFlx2kQ7vG1dbW4bPmp+H2CD7z0M7iZw547MTehNJ2Ez7bzVjY2/jZbsbuNnwGO4ifHT0KjzwC9bqN3WYod9219t8q21IoTUREXgEUVRMRERERERGRW87//X//37zwwgsbnv8rf+WvsF//aC8iIiIiIiIiIt8EHpp/iG90n1n7MM6RxhGeWn0KAIfj5eHL3NW6i/oTXyXId1FUA4Iso/bEVxk+9G28PHwZt/aRCDhSPzIdFwQ8tO/ha3A2IiIiIiIiIiIiIiIiN8aGUNomkTS48aG0tHuOYdK20EkGwXiEq259XheF0oqUJIIs2mJwkVGGsX1PqCy2D6V1gRxiH0p7w0E41GZDEWZ9KK1RgXNDCx6Fbl2IymvGcN8CdCr2eDehtEoA93TgSNMiaWHFQmiTUFpZWsRrkEJYwHwdkvrOQ2mFXS3C0KJUufPRMCzy1Ez8qUfTUFpeWLyqU4f+yGJPSWQRpwALN9WrMBrB2VWLlc2HFtm6MLDjT0JpQQDFFYTSosCON8xsbCW280hze3xiyWJosQ+NNRLo+dhYJbbzn3yL8byPDR3wwaA4tEDWyF/IWgzj1NYqKyySdqA1DWjFPhbmAj+PzNYwvYpQWiOe7j9wNu8gmIbY1m5t5++5zUJpPqhV89c+8sebxM/C0oJXzofIygCS8OL42WahNHdJKA2/ncPm4QoLxbkUXMX2AXYfXhja2Mnx0iE0/OMcC6fV/NeHdtW2HWY2h+Wexc6iwO7B1T7MVTcPpfUGUIwtflaEMBxAEW0dPysCCGO7Nk2m67w+/BcFsNCx6+piW/v9sxBGkKYQV3xQ0cEotXnVq0DV5lr3X8/i0rbt5XYerQCCyK5VltvY4Xjn8TMHzLagMoZsCCs+XOYyiOuQ1SyOlhd2fZa7cLgCUQGDCFwEldTWa3kIT5+EoS+CVXM43INeYPGzg/OQVi2Sdu55qBbAVcbPTjwJ7V+17bpvgtYc1J2N2SqUtv9z8PEHdhY+YwwL7C5+tlehtN2Ez3YbSYO9i5/tNpR22UgawJvfDI8+Cs3m1mMUPhMRkW9yiqqJiIiIiIiIyC0lTVN+4Rd+YdPXfuInfuL6TkZEREREREREROQGma/u4572UZ5ZfRqATtKhHbfp5l0Azo3PcSSYY/5PHr+i/dePf43l197HufG5tefaSZtOMv3w0D3to8xX5q/iLERERERERERERERERK6tDZE02FkobYtIGg8+SFbmHD97/OIDbRNK6zZjkighDEJcUVyTUJoD0nSAi6wA5YqUYLtPkU9CaWUOoYWtjrQt9HVRJK1noaKoLIiTgGOHtg+lhQ5aFYstnRtaICpwFpFab30ozQGZg5PLFvQpAkh9yKpdgdcfsuUJfVyqEsBrZuE1+yyUFsQWVQqwWFdZWMCsLCF201BaAZzpWZDr0IyF0gKAxIfKQsiuIJQ2AorC4mHlJGAWW9QKH/UKAqgkcGYFVsbQrFnIKs/hbN/m06wAod1qWWl/5jl0Gna8dh0GmY0rCugXUL3CUFoU2vmPc7t5ohBiH6XKCxjlFj872LbXQmx8mk/HBtjYob+4eWH3UYSPsflQWhRM76mx33deWhwscLamYe7DXH7dw/LqQmlrZb7A7htXYFUFH26biHw4rSw3htJKv89J/Kzqt5vEu+LJMVIokulcL42f5Q5YH0qrWkBwMna9cQ5JOn0/jdLpa5XI7sd0EnEDsiWoz9n1H5fQ60JSsXtvct3CEFZ69r7ojiyU1ktt/QbDrUNpqbP4WZxYKC0Mt46fAbgESn9/uALGI6j5r1ejMbjSrnU9gkHhr90kVpjbc0lsazO5d8DWutu3fcy37RplYzg7hjj3EbqGxc+WVu1ajrPdxc/aIdy/H6qN6XULChichePnYHXdF7BqDlnfImmtQ0DF7pnxEM6fgGEyHTs7qYj5UNrRP4SnC3i5A81ZqAfXLn7mgMdO7Gzs/sHuwmewu/jZXoXSYHfhs92MhT2Knx09Co88QlCvc8924+6666L//tiSQmkiIiI7pqiaiIiIiIiIiNxSfvZnf5ann356w/N/4S/8BR588MEbMCMREREREREREZEb46H5h/hG9xn7oA9wpHGEp1afAsDhOPfMl7ht8ivkdynIMs4+8yXcHfvWnjtSPzJ9PQh4aN/DVzF7ERERERERERERERGRndlpKO2iSBrsLpS2RSSNJy4wmGvRHXd3HEob+FBas9Jk3F0iv0ahtLIscIGzelFoYavDbZitbR5Ka4Q59x+CY4fhcNMCMe7SeoyDc6u2Vvs7jgCLVG0WSmtH8NoFaFVt7rmDU0twcunyoTQCuHt+GkpziUWsDrdt3kUKg9Qu2XzdwmZlBPtntgil1S1IVp0EvbDw1ELL9rM2jmkoLcKiTrsNpS2u2v6jyNYrL2DobP712IJfeQ79DNo16I8tPLc+lJZE9nyjYvMYZBanmm1AJbSxQQD7mhbKmsz9SkNpoY9oVUNbu0mYLASGuQXFhoXF3RK/VnEAKT5q5h/383Xhs8LmEQY+jOYs/BVM9u1DaZOxybroVwBEpc17Mo+rCaXlvoOI8/v1Y0umkbRRYdfIBT5kl01DaS6DfF0c61CLi8pKgyHMTB6X0B9CpW4PL42fpQUkPnTnLgmlzdbsvTLM7PHqAOb8PVsCS10LfUWhBfOSyN+jGfQGkAGjPsQdGA5sHSZRO79UhNi9uzBnUTwXQVCFhSaE0dahtHoV0sDWbxJO2y5+1uv7a+2vPbnF5cDmHq4L2F1YtcdzM3auSQErOSz2oVJCK4Gibvs6v2zzXunC8EXoNCGpWuyt5iNk6TI8sQjL2XRtdxM/a6za16rOAWgdtPNYOm3BsXoEq61112zEWiStew4WqvCyg3Roa19tbR1Ja34abo/gMw/tTfxsN2N3Ez6D3cXP9jKUtpvw2W4jabCDUNqb3wyPPgrN5jaDUPxMRETkJqComoiIiIiIiIjcEk6cOME/+Sf/hP/lf/lfNn39F37hF67zjERERERERERERG6s+eo+7mkf5ZlV+yUEnaRDO27TzbsEecaF888xjO2HuXdrGMPShecIbpvBxQntpE0nmf7m5HvaR5mvzF+rUxERERERERERERERkW8y1zqUlr3hAY6f+/rFB9lpKK10uC0iaSyfIq0dZFAMIWNXobRRPiJJU7JrFErLc6hFJccOWCjtUNOHktYJ/DK1EwuJnRtBXvpQGpuH0t561EJpeQkvrVj4xwFZCIWPLbUrcOwg1HyYCSBxcNc83L3/8qG0hbpFnILAQmmLfZhvWKAKIIhhvglxZMEhfDQr3CaU1qnZ+Y7G05gXziJkywMIwmkobbJdeAWhtJkqTL7dFuBDWYGtV29sAa88s6hbFGwfShumUPpgW4BF2IJyel1aVdtXGV6jUJofyySUhq3VWigth0Y8jahNzjHAx8vW3SuFuziSF+AjZZO3ZjANpZXldJvJ4leK6fOOqw+llf5tE5dACq4yXcf5hgW/En//loFFsRr+cY6F02r+Pd+u2vGHmc1huQfNlp9XAat9mKtuHj/r9mEmggr2flletW0b1emahiEMxtDrWSxstg55AMOhBQkPzkMSTNe9XoEyhXTW/iSGpo+vTe75mr+vMx8BiyILhC0N7bVwB6G0NIPV3s7jZw6YbUFlDNkQVny4zGUQ1yGrWRwtL+z6LHfhcAWiAgaRBd8qKRRjWB7C0ydh6Itg1RwO96AXWPzs4DykVYuknXseqgVwlfGzE09C+1dtu+6boDUHdWdjtgql7f8cfPyBnUXSGMMCexc/283Y3YbPYHfxs70KpcEOwmcAR4/CI48Q1Ovcs924u+5a+++JbSmSJiIi8oqjqJqIiIiIiIiIvOL82T/7Z9f+XhQFZ86c4dlnn91y/F/7a3+Nt771rddjaiIiIiIiIiIiIjeVh+Yf4hvdZ9Y+SHSkcYSnVp8iPnMaXMkLHbj3/O73+0IHKEviM6fJbrudI/Uja68FQcBD+x6+RmcgIiIiIiIiIiIiIiK3ig2htE0iabA3obTB8UVWmxEBAUmUEJQOdhpKGxeME8jDzc/LDS6QxiHOuV2H6xDtWAABAABJREFU0lwEcWxRrGsRSmtU4NzQ4kihs+DUes0Y7luATsUe7yaUVgngng4caVokLaxYCG0SSitLC34NUggLmK9DUt95KK2wq0UYwh0di1iVPvYWRRaBCwCiaSgtLyAtoFOH/sjCUElkYasAi3jVqzAawdlViGKYD8EVcGFgx5+E0oIAiisMpTUSC24FWAysBNLcHp9YspBbPFnXbUJpWWGRtAOtaUAr9rEwF/h9Z7Yu6TUIpUWBv398yG0SYpso3PT+Yt3Yyb6jdWMnUbfSjwl90KwsbRMX2LoxOS7TUFpYQuTsuoCdVxJeXShtXEBiLUJwtraFf0NUIrvH0snxgGwJ6nN2Tccl9LqQVCyoFoW2nzCElR6UBXRHFj/rpbb/wXDr+FkRQBjbtWkyXef1Mb8ogIWOXVcX29rvn4UwgjSFuOLf+w5Gqc2pXgWqFnWr+5BjXNq2vdzeH60AgsjWL8tt7HC881Bamu8uftYO4f79UG1Mr0VQwOAsHD8Hq+vKjdUcsr5F0lqHgIrdB+MhnD8Bw2Q6dnZSHPOhtKN/CE8X8HIHmrNQD65d/MwBj53Y2dj9g91F0mDv4me7DaXtJnwGu4uf7Uko7c1vhkcfhWZz6zGKn4mIiIinqJqIiIiIiIiIvOL83u/93o7HHjp0iH/+z//53k1GRERERERERETkJjZf3cc97aM8s/o0AJ2kQ9tVyc+cBmCxBa9asR8q36lhbNsBxGcWqd92D51k+kGne9pHma/MX7NzEBERERERERERERGRm9OGSBrsLJS2RSSNBx8kK3OOnz1+8YG2CaV1mzFJlBAGIa4otgylpd1zDJO2BVcyCMYjXHXrc7solFakJBFk0RaDi4wyjHE4KIvtQ2ldIIfYh9LecBAOtdlQmrnSUJoDMgcnly3+UwSQ+nm3K/D6Q7Y8od9/JYDXzMJr9lkoLYgtwBRgYa+ysIBZWULspqG0AjjTs3DVoRmLVAUAiQ+VhZBdQShtBBQF1GI7Zm9sf29XWYt6BQFUEjizAitjaNYsapbncLZv82lWgNButay0P/McOg07XrsOg8zGFQX0C6heYSgtCu38xzng7HHsA1Z5AaPcQmkH27bm24XSxn58XlqQKnC2TmHuw1x+LcPy6kJpazdRYPeCK7DqgJ/PRBSsu9+cBbtcaWtbYms2UY0thhaUdq/GPmjmimn4LIkslFb1x5iEvuLJQVIokul5XU0obZxDlEPkT7c/gDK269NIbC5JDKMMegPIgFEf4g4MB7YOEdMYXmCnTS2GhTkfP4sgqMJCc/v4GYBLoMztsStgPIKa/xo0Gtu6RhHUIxgU/tpNAoS5PZfEdr5BMV2jLIdu3/Yx37Z7IxvD2THEuR0rbEBWg6VVe1+Ns92F0nYTP2uswqklP/agncfSaQuO1SNYbU3Hzo5Yi6R1z8FCFV52kA5t7autrSNpzU/D7RF85qG9iZ/tZuxuImmwt/Gz3YzdbfgMdhA/O3oUHnkE6nUbu81Q7rrrov9O2PqgCqWJiIjI7imqJiIiIiIiIiK3rFarxac+9SkWFhZu9FRERERERERERERumIfmH+Ib3WfsQ0rA3S8s8XRpf3cBvNCBe8/vfH8vdKY/EB6UJXc9fwH2+cdBwEP7Hr6W0xcRERERERERERERketop6G0iyJpsLtQ2haRNJ64wGCuRXfc3XEobeBDac1Kk3F3iXyLUJoD0nSA85UiV6QE233KehJKK3MILYJ1pA2d+iWRtJ5FjaKyIE4Cjh3aPpQWOmhVoO5Daa6w54tLDn81oTQCuHt+GkpziUWsDrdt3kUKg9Qu2XzdwmZlBPtntgil1S1IVvWBrgILjy20bD9r45iG0iJ8sGqXobTFVdt/FNlFywsYOpt/PbY4WJ5DP4N2DfpjW8/1obQksucbFZvHILOQ1WwDKqGNDQLY17RI1mTuVxpKCwPbRzW0tZtEzEJgmFtobFhY3G0noTTw8bBw+loARKXNZbLvqwml5b5tiPP79WNLpuGzUWHrPhlblpAUtl8X2yaTsQQ2X4eNLdJpxMClkK+Lbh1qXfzeGAxhZvK4hP4QKnV7eLWhtGIMs3UoQgulFREcnIckmAbo6hUoU0hn7U9iaDZsn4Gze7hWtfGZj4BN4mdLQ3stnITXtomf9fp234Whf6/mFowDm3sY2NiyhAur9nhuxuaYFLCSw2IfKiW0Eijqtq/zyxZ4W+nC8EXoNCGpQpxAzYfI0mV4YhGWs+m67yaUdiXxsxNPQvtXbbvum6A1B3VnY7YKpe3/HHz8gZ1F0hjDAnsXP9vN2N1G0mDv4me7DaVdNpIG8OY3w6OPQrO59RiFz0REROQmo6iaiIiIiIiIiNyS3vCGN/Bv/s2/4YEHHrjRUxEREREREREREbmh5qv7uKd9lGdWnyYYDTn0tac4vR9W/IePFlvwqhX7ofXLGcY2fqIzgsNPPM3S696Kq9W5p32U+cr83pyIiIiIiIiIiIiIiIhckWsdSsve8ADHz3394oPsNJRWOtwWkTSWT5HWDjIohpCxq1DaKB+RpCnZNp+cLssCFzhwpYXSQguNzdY2D6U1wpz7D8Gxw3C4afEZd2mZxsG5VVur/R1HgAWTNgultSN47QK0qjb33MGpJTi5dG1DaQt1qFetcbN/xkJM8w0LVAEEMcw3LRRXZnYOztnl3SqU1qlNA1OT8BfOImTLAwjCaShtsl14BaG0mSrk01NmtmZrnk9ibBHkmUXdomD7UNowhdIH2wIswhaU0+vSqtq+yvAqQ2l+rWI/Fr9tjK3V5J4Z55D4eNZmoTQXTENppQ+kFY61Ba0U0+cdVxdKc4GtjSshLoEUXGW6NvMNC34lPhaWO9tfiEXSXNWiYZOx6xWAW4bYf09xVNha1f17vl21/Q0zm+9yD5otfw4FrPZhrnqNQmnOAnBxAk1s3dMU4ordFzgYpXasehWo2vHr/mtO7EN3PR/TawUQRLZ+WW5jh+Odx88cMNuCyhiyIaz4cJnLIK5DVrM4Wl7Y9VnuwuEKRAUMInARVFJbg+UhPH0Shr4eVs3hcA96gYXSDs5DWrVI2rnnoVoA677PeyWhtCuJnzngsRM7G7t/sLtIGuxd/Gy3obTdRNJgj+JnR4/CI48Q1Ovcs924u+5a+9/9bSmUJiIiIq9giqqJiIiIiIiIyCteGIa0222OHDnCW97yFv7yX/7LvPvd7yac/MonERERERERERGRb3IPzT/EN7rPUH/iqwR5zp3L8Lj/AXAXwAsduPf85ffzQufiDwvduQxBllF74quMHn4bD+17eC+mLyIiIiIiIiIiIiIil7iRobTB8UVWmxEBAUmUEJQOdhpKGxeME8i3+DFfN7hAGoc453YdSnMRxLFFsTYLpeU51KKSYwcslHao6cNK6wR+mdoJNBM4N7KgV+gsarNZKO2tR32gq4SXViwU5IAstPATWCjt2EGo+ZATQOLgrnm4e/+1D6UVdrUIQ7ijYxGr0p9DFNm5BQDRNJSWF5AW0KlDf2QhqySysFWABb/qVRiN4OwqRDHMh+AKuDCw409CaUEAxRWG0hqJBbcCLBxWAmluj08sWcgtnqzrNqG0rLBI2oHWNLYV+7CYC/y+M1uX9CpCaY14uv/AR82CYBpimyic3UflZqE0H1Wr+esZ+eNNQmlhCZGztQabaxJeXShtXEBifUFwtl6TUFolsvsm9RunBST+mjgfIpuYrdktPczs8TCFaAD1hl3TXgqDMVQqFlSLQjtmGMJKD8oCuiMLpfVSm8tgeO1CaWlgkbnIn3eQw6Cw+7soISim1zPLodu398R829Y9G8PZMcS5rX/YsPjZ0qpdu3G2u/hZO4T790O1Mb0WQQGDs3D8HKyu+yJTzSHrWyStdQio2H0wHsL5EzBM1l2HSXXMh9KO/iE8XcDLHWjOQj2w/V2LUNpu42e7GbubSBrsbfxsN2N3G0mDHYTS3vxmePRRaDa3GYTiZyIiIiKbUFRNRERERERERF5x1n6oQ0RERERERERERHZkvrqPo+FtnP2TxwGYG0FnBCv+w02LLXjViv3w/FaGsY2b6IxsPwD141/jjrf+FeYr83t0BiIiIiIiIiIiIiIit75NQ2mbuNGhtLR7jmHStpBLBsF4hKtuPd+LQmlFShJBFm0xuMgowxiHg7LYPpTWBXKIfSjtDQfhUJsNFZv1obRGBc4NLaYUOgt2rdeM4b4F6FTs8W5CaZUA7unAkaZF0sKKRZ0mobSytODXIIWwgPk6JPW9C6WNgKKAWmzH7o3t7+2qrVHg41+VBM6swMoYmjWLmuU5nO3DoRloVoDQbrOstD/zHDoNO167DoPMxhUF9AuoXmEoLQrt/Me5rWkUQhxY7CovYJRbKO1g29Zou1Da2I/PS4tXBc7WKcyhCKZhr7C8ulDa2k0U2D3lCuwT/H4+E5EPp5XlxlBa6feZRBZKq/rtnD/3eHKMFIpkOterCaWNc4hyiPwp9AdQxrbmjcTmksQwyiw0NhNBBbv/l1eh2YJG1a4Pfi0HY+j37FijPkQzMBzbOkRMY3iB3VLUYliYs3vBRRBUYaEJYXTtQmlpBqs9mJuxa5AUsJLb+61SQiuBom7zP79sc1npwvBF6DQhqVrAreYjZOkyPLEIy9n02u4mftZYhVNLfuxBO7+l0xYcq0ewuu77sbMj1iJp3XOwUIWXHaRDW89qa+tIWvPTcHsEn3lob0Jpu4mf7WbsbiNpsHfxs92G0i4bSYOdhdIUSRMRERG5aoqqiYiIiIiIiIiIiIiIiIiIiIh8E3jX577O/5Xlaz+sfucyPO5/GN0F8EIH7j2/9fYvdKY/6D7ZfiJMM971/z4Jd1/rWYuIiIiIiIiIiIiIfHM4fuY4n3zyk5RuXeZrMIBeD1otaDQAGOdjvnTiSxdv/OKLcPI5WAvKlHDyON1kieXOtObjiuKahNIckKYDXBT5/aYE231ieRJKK3MILYJ1pA2d+iWRtJ4FpKKyIE4Cjh3aPpQWOmhVoO5Daa6w54tLDr8+lOaAzMHJZYsKFQGkPmLWrsDrD9nyhD7EVgngNbPwmn0WSgtiCCKbzoEWlIUFzMoSYjcNpRXAmZ5Frg7NWGQqAEgslFYNIdvjUNriqu0/iuyi5QUMnQW76rHFwfIc+hm0a9Af23quD6UlkT3fqFjEaZBZyGq2AZXQxgYB7GtaJGtyma40lBYGto9qaGs3iZiFwDC3+NiwsLjbTkJp4ENj4fS1AAty4ab7vppQWu4sZoXz+/VjS6aRtFFh6+4CH6fLpqE0l0G+Lrp1qHXx/T4YwszkcQn9IVTq/v68ilBabwDFGGbrFkobDqCI4OA8JME0KlevQJn69Y3tmjaZXpvJ1EIfnVvoWJisTO067Z+1axQ4u4drVdsm88GwKLKY2NLQXgv3KJS23IXDFYgKGEQWcauktgbLQ3j6JAx9Payaw+Ee9AKLnx2ch7RqkbRzz0O1AK4yfnbiSWj/qm3XfRO05qDubMxWobT9n4OPP7CzSBpjWGDvQmm7iZ/tNpS2m0ga7FH87OhReOQRgnqde7Ybd9dd0OlcZmcolCYiIiJynSmqJiIiIiIiIiIiIiIiIiIiIreEpX7Kf3jm7I7G/rmjC8w1K3s8I5GbR7h8gbv+r1/j/jvth+YB5kbQGcGK/+DUYgtetWI/nH+pYWyvT3RGtv3E/Wfg7j/4NZ79gR+nnJ3fs/MQEbmlDC7An/67nY29+zugoa+vIiIiIiIiIiKvNCujFZ5depaV8cq6J1fgwgWYn18LsQyyAZ84/gkcvgLlHHzlK/Dkk1bOCkO47z548EHODy+wPFpe253LMlh8btNQ2vD8y4zrB4jjCmEQ0uueI79GobSyLHCBA1daKC2Ew22YrW0eSmuEOfcfgmOH4XDTojbu0uKNg3Ordv77O44AC1ptFkprR/DaBWhVbe65g1NLcHLp8qE0Arh7fhpKc4kFrw63bd5FCoPULsN83cJmZQT7Z7YIpdUtTlb1UakCcCEstGw/a+OYhtIifNxqD0NpM1XIp6fMbM3WPJ8cI4I8s7lGwfahtGHqY28+otWuQVBOr0uravsqw6sMpfm1iv1Y/LYxdm0m98w4hyTx+9kklOaCaSit9PGywrF2ISrF9HnH1YfSSv9WiEsgBVeZrs18Ay4MLewGdr7pEBr+cY6F02r+Pdyu2vGHmc1huQfNlp9XAat9mKtem1Ba6sDFECcWSgtDSFOIK3atcTBK7Vhg75Uyt8eugPHIQmhg97Irp5G0LIKVLuzzvanYh+56PqbXCixQ6Hxgb7UPw/HehtKyvkXSWoeACkTOQmnnT8BwXdhudvJ9UB9KO/qH8HQBL3egOQv14NrFzxzw2Imdjd0/2F0kDfYulAa7i5/tZuxuI2mwg1Dam98Mjz4KzeY2g1D8TEREROQWoaiaiIiIiIiIiIiIiIiIiIiI3BJevDDgJz72xzsa+xs//jZF1eSbyvyv/DLhcLDhh+bvXIbH/Q/FuwBe6MC95zdu/0Ln4g823bk8/fvkh+3DQZ+5j36Y8z/+k3t1GiIit5al5+BT/+XOxv6Xv6OomoiIiIiIiIjITWKnobTnlp7jCy9+Aed2Fkp7afWltd25xUU4fxoOTJ4p4fxx+Np5hvNtuuMucRhbKG3lDPm6SM16DkgH53FRRCVMIB2RXqNQWp5DLSo5dsBCaYeaPsK0TuBPvZ1YSOzcyIJeobMAzmahtLce9YGuEl5asaiQA7LQIlFgobRjB6Hmo08AiYO75uHu/ZcPpS3UoV61+e6fsWjTfMPiZwBBDPNNiCOLGeEDW+E2obROzc53NPbxLn8B5hqwPIAgnIbSJtuF1yGU1kgszhVg4bASi1EFwIklm188WddtQmlZYZG0A61pmCv2YTEX+H1nFhdLryKU1oin+w981CwIpiG2icLZfVRuFkrzUbWav56RP94klBaWFtNy/kTLwIJnVxNKGxeQ2NsGnK1X4aNtlQju6EA6OR6QLUF9zq7TuIReF5KK3R9RaPsJQ1jpQVlAd2ShtF5q+x8Mr00orV6FNLBw3CScFuQwKCzUVpQQFNNr1OvbPsLQRwpzWwewe20StCtLC54VBSytwnzb1j0bw9kxxLmtf9iArGZjyhLG2R6H0nwkrXsOFqrwsrPAXQRUW1tH0pqfhtsj+MxDexM/283Y3UTSYG9DabuJn+02lHbZSBrsLJSmSJqIiIjINyVF1UREREREREREREREREREREREbmHh8gVmP/YRwH5ofvJD+QBzI+iMYKVmjxdb8KoV+6DAxDC25yc6I9tu4v51P8Q/97GPsPSD76WcVfhHRERERERERERERF45rnUoLXvDAxw/9/WLD3LmDJw/tTGU9sQFhnNtVserJFFCWDq6g9MUs5tMdHQat9pj7DIAkiCGdES2zaeFCx9KGxYpUQRRDAevUSitUYFzQwsvhc5iUes1Y7hvATr+dx3tJpRWCeCeDhxpWiQtrFgAahJKK0sLfg1SCAuYr0NS33korbArQBha8Cr3ga4Ai5s1Ex/7iaahtLywMFanDv2RRaeSyGJSARaUqldhNIKzq7bW86HFoy4M7PiTUFoQQLHHobQotPMf57amUQhxAFlu5zLKLZR2sG3H3i6UNvbj89JCYoGzdQpzKIJpBCwsry6UtnYTBXZPuQL7NLyfz0S0Lt51aSit9PtMIlu7qt/O+XOPJ8dIoUimc72aUNo4hyi3OFcJ9AdQxrbmjcTmksQwyqA3gAwY9SHuwHBg5xYxDdwF2HFrMSzM2fV1EQRVWGhCGF27UFqawWoP5mZsXZMCVnJ7D1VKaCVQ1O36nl+29+5sCypjyIaw4sNlLoO4bpG0la7dY6Wzvw9fhE4TkqrF3mo+WpYuwxOLsJxNr+31CqXt/xx8/IGdRdIYwwJ7Fz/bzdjdRtJg70JpsMP42dGj8MgjBPU692w37q671v73dlsKpYmIiIjIZSiqJiIiIiIiIiIiIiIiIiIiIiJyC5v/lV8mHE5/ev7SH8q/cxke9z907wJ4oQP3np9u/0JnOnYyfuLSH+IPB33mPvphzv/4T17z8xARERERERERERER2Y0NobRNImmwN6G0wfFFVpsRAQFJlBCUDpZPQW2TiS6fomwEDPMhg2xAc1xQy6FX2eLEspQwiSjKgjQfUnVQCeFAGzo1iygVJaz4UJorIPahtDcchENtNtRxrjSU5oDMwcllixAVAaQ+YtauwOsP2SmHfv+VAF4zC6/ZZ6G0IIYgsukcaEFZWMCsLCF201BaAZzpWeTq0IwFrQKAxIfKQsiuIJQ2AorCwlXlJGAWQ7tqaxT4+FclgTMrsDKGZs0iWXkOZ/s2n2YFCO3WyXzkK8+h07DjteswyGxcUUC/gOp1CKWFgW1fDW3tJhGzEBjmFh8bFjbnnYTSwIfGwulrARbvwk33fTWhtNzZ+uH8fv3YkmkkbVRYKM0Ffs2zaSjNZZCvi24dal18vw+GMDN5XEJ/CJW6vz+vMpRWjGG2bqHA4QCKCA7OQxJMo3L1CpQppLP2JzE0G7bPwNl9Wava+MwHw6II6hEsDe21cA9CaWkOy104XIGogEFkEbdKaue1PISnT8LQ18PaIdy/H6qNaYQuKGBwFo6fg9ViuubVHA73oBdYKO3gPKRVi6Sdex6qBbDuFzxdr1Da/sHuImmwd/Gz3YbSdhNJgz0Kpb35zfDoo9Bsbj1G8TMRERERuUEUVRMRERERERERERERERERERERuUWFyxeY/dhHLnpuYd0HBADmRtAZwYr/INdiC161Yh9CGMb2eKIzsvET95/Z+EP8cx/7CEs/+F7K2fk9OCMRERERERERERER+Wa1IZIGOwulbRFJ48EHycqc42ePX3ygbUJp3WZMEiWEQYgrii1DaWn3HMOkbUGZDILxCFfd5uR658h8KG1UpFQrkEUWvdqgSCFMiKOCYwfLy4bSZhILOZ0bWswqcNapWu9qQmkEcPf8NJTmEgteHW5bUKtIYZDaZZivW9isjGD/zBahtLoFyaqBxaAKLDy20LL9rI1jGkqL8HGrXYbSFldt/1EEOIuSDZ3Nvx5bHCzPoZ9Buwb9MbQqF4fSksieb1RsHoPMAlmzDYvdBdjx9jUtODaZ+3UJpfm1iv1Y/LYxdm0mYahxDkni97NJKM0F01Ba6eNlhZueTKWYPu+4+lBa6cCVPpSWgqtM79n5BlwYTkNeZWCxrYZ/nGPhtJp/X7ardvxhZnNY7kGz5edVwGof5qrXKJTmwMUQJ9DE1jJNIa7YPYCDUWrHqleBqh2/7r82xD5e1/PXvRVYdND5e2G1D8Px3oXSqjlkfQuftQ4BFYicxc/On4DhulhdYxVOLfmxB+38lk5bnKweweqlkTR/c6wswtE/hKcLeLkDzVmoB3bsGxFK200kDfY2frabsbuNpMEOQmk7iaSBQmkiIiIi8oqgqJqIiIiIiIiIiIiIiIiIiIiIyC1q/ld+mXA42PD8pR8QuHMZHvc//O8CeKED9563P926n76/c3n6980+SAAQDvrMffTDnP/xn7xm5yEiIiIiIiIiIiIit6ZNQ2mbuCiSBrsLpW0RSeOJCwzmWnTH3R2H0gY+lNasNBl3l8i3CKU5IE0HuMhqZK5ICbb7RK8PpRVlDiEUVTjSgnbDh8UcrAxhsWun+47bS95yhyMJLG62VSjtdfstKjUJpZ1agpeXrm0obaFuUaggsFDaYt/CVxW//yCG+SbEkYWP8IGtcJtQWqdm5zQa+3iXX9S5BiwPIAinobTJduEVhNJmqhbh8qfMbM2+L5JPYmwR5JlF3aJg+1DaMLUYWOBjW+0aBOX0urSqtq8yvH6htEY8DX8FPmoWBNP9r91+zq59uVkozUfVav56Rv54k1BaWFp4y/kTLQMLnl1NKG1cQFLa+eMgK6Dw0bZKBHd0IJ0cD8iWoD5n12lcQq8LScXujyi0/YQhrPSgLKA7slBaL7X9D4bXLpSWBhaOi/y5BDkMCgu1FSUExfQaZTl0+3afz7dtLbMxnB1DnNuahg3IarC0atdjnO1dKG12xFr4rHsOFqrwsrNoXQRUWxvDZyeehPav2nbdN0FrDupu+0ha89NwewSfeejGh9J2G0mDvYuf7TaUdtlIGuwslKZImoiIiIjcghRVExERERERERERERERERERERG5BYXLF5j92Ec2fW1h3YcPAOZG0BnBiv+Q2GILDvTtz4nOyMZN3H9m8w8SAMx97CMs/eB7KWfnr8GZiIiIiIiIiIiIiMit6PiZ43zyyU9SuvLiFwYD6PWg1YJGg3E+5ksnvnTxmBdfhJPPwVrUpoSTx6EyoHtwjuXRsj1dFLgtImksnyKtHWRQDCFjV6G0UT4iSVOybT6lW5YFLvAlqdAiWIfbFu6ahNKWR3C6Z3GlZphz/yHHA4fhsI91lZfUdOox3N6GcVaw2IUUH1WbHNP/ZRJKa0wmju3vW+fhnv3XPpRW2BUgDC14lftAV4DFzZqJn2M0DaXlhYWxOnXojyw6lUTQ8GPDwOYwGsHZVYhimA8tNHVhYMefhNKCAIorDKU1EhhmNrYS23mkuT0+sWQht9gHsrYLpWWFRdIOtKZhrtiHxVzg953ZuqTXKZTG5K3l43uu8Dv185mIfDitLDeG0ib3VBLZ2lX9di6w+zaeHCOFIpnO9WpCaeMcotxCXiXQH0AZW6iskdhckhhGGfQGkAGjPsQdGA7s3CKm1y3w938thoU5W38XQVCFhSaE0bULpaUZrPZgbsbWNSlgJbf3UKWEVgJF3d4r55dtLitdGL4InSYkVQu41XyILF2GJxZhOZter70OpU3iZ/s/Bx9/YGfhMwc8dmJnYxnDAjdPKG03kTTYo/jZ0aPwyCME9Tr3bDfurrug07nMzlAoTURERES+6SmqJiIiIiIiIiIiIiIiIiIiIiJyC5r/lV8mHG5RPWPjhw/uXIbH/YcLXAB/cnD62uT1iUs/nHCpcNBn7qMf5vyP/+QVz19EREREREREREREXplWRis8u/QsK+OVdU+uwIULMD8PnQ6DbMAnjn8CNyl+gdV8vvIVePJJqzuFIdx3H+fvu3MaSQNclsHic5uH0hafY9gOGBdj4jAm7K7QSyAPN5+rG1wgjUOcc7sOpbkI4hgObhFKy3OoRSXHDsCxw3Co6YNN6wT+9NsJNBPHhSGkbhpKC9zFobQHDkFtEhWahRPLcLYLCy1o1qFWnYbSytKCX2uhtBpUansXShsBRWHhqnISMIuhXcUiYT7+VUngzAqsjKFZs0hWnsPZPhyagWYFCG3OmY985Tl0Gna8dh0GmY0rCugXUL3CUFoU2vmPc8DZ49jHwvICRrmF0g62bY22C6WN/fi8tJBY4GydwhyKwKJeYWjBsusVSsudrR/OomD4sSXTSNqosHvGBX7Ns2kozWWQrwt0HWpxUTlqMISZtaIf9IdQqdvDqw2lFWOYrUMRWiitiODgPCTBdK3qFShTSGftT2JoNmyfgbP7sla18ZmPi0UR1CNYGtpr4R6F0pa7cLgCUQGDyCJuldTOa3kIT5+Eoa+HVXM43INeYKG0g/OQVi2Sdu55qBbAul+EdL1CafsHuwuf7WYs3DyhtN1G0mAHobQ3vxkefRSazW0GofiZiIiIiMgeUVRNREREREREREREREREREREROQWEy5fYPZjH9l2zMK6D0IAzI2gM4IV/yG0ct0nBToje33i/jMbP5xwqbmPfYSlH3wv5ez8FZyBiIiIiIiIiIiIiNxMNoTSLomkTTy39BxfePELOOdrYduE0l7qnljbzjkHZ87A+dNwYPJsCeePM/zT06w0IsIgJA5jBqvnyNeFci7lVl5aC6VV0hQiSKMtBhcZZRhb3K0stg+ldYEcYh9Ke8NBONRmQ3lnfSitUYFzQ4s0hW7aw5poxnDfAnQqfjqlhdJeXgKHxbjyCGoJvP6Qj3WVPioVwsO3QZDA+T6MnW1zaMaCVgFAYqG0JIRij0Npi6u2/ygCnEXJhs6CXfXY4mB5Dv0M2jXoj6FVuTiUlkT2fKNicahBZuc524BK6ENzAexrWqRssvRXGkoLfaCrGloUbRIxC4Fhbt8rGRYWd9tJKA1sP0k4fS3AB83cdN/XM5RWWgPQQmkpuIptAhbVuzC0+YKdbzqEhn+cY+G0mv/eUbtqxx9mNoflHjRbfl4FrPZhrnqNQmkOXAxxAk2/lmkKccXHCR2MUjtWvQpU7fj1qs019tek5697K4AgsjXJchs7HO9tKC3rWyStdQioQOQslHb+BAzXxepmJ9+D86G0o38ITxfwcgeas1APbH83IpS2m/DZbsbCzRVKu2wkDXYWSlMkTURERETkpqComoiIiIiIiIiIiIiIiIiIiIjILWb+V36ZcHiZ6hkbP9xw5zI8vskHF+5cnv59sw89bCYc9Jn76Ic5/+M/uaM5i4iIiIiIiIiIiMj1syGSto2LQmlbRNJ48EGyMuf42eMXb3zmDJw/tUkobZHVZkwSJYRBSHe0SjFagtmNx3ej84xDX1JyjihLKbb7dGyRUYQxrswZxlAL4UgbOvVLImk9iytFZUGcBBw7tH0oLXQWAav7UJor7PniksOvD6U5IHNwctniRkUwDby1KxZKq2H7Bott3deBe/bD0gCC2Jb44Iy9luUwGEMlgP1N+7MIYGEGzvQsvlZZF5CbhNJCLFC1l6G0mapFuPDLN1uz7z/kk2NEkGfgQrsO24XShqmPvfnYVrtmMbnJWreqtq8yvMpQmo/PxX4sftsYu08m3z8Z55Akfj+bhNJcMA2llT6QVrjpfVQpps87rm8obVxAUtr54yArLN4Hdq/c0YHUb1wC2RLU5+w6jUvodSGp2P0RrYvFrfSgLKA7slBaL7X9D4bXLpSWBrYekT+XIIdBYaG2ooSgmF6jLIdu3+7x+baF7rIxnB1DnNv7NWxAVoOlVbse42yPQ2k+ktY9BwtVeNlZtC4Cqq2tI2nNT8PtEXzmoRsfSttN+Gy3kTRQKE1ERERERPaGomoiIiIiIiIiIiIiIiIiIiIiIq8A3azLS4MX6WbdbceFvS6Hv/ivCe/c+b6fn53+PQuhX5k+bqawUrP/q+bwthc3/9DDZuY+9hGWfvC9lLPzO5+MiIiIiIiIiIiIiOyp42eO88knP0npyotfGAyg14NWCxoNAMb5mC+d+NJ0zIsvwsnnYC2AU8LJ41AZ0D04x/JoeW2oKwpYPmXVsEuUK+cYVmYYZNCsNKmNUnrbzDksSooohCInDy2YdaANnZoFl4oSViahtALqYc79h+DYYTjctLiOu7S84+DcKuAc+zuOAIubbRZKa0fw2gWLeTksgnVqCU4uXT6URgB3z8Nr9lkozSUWxzrctvBWkcIgtajSfB1aicXC9q8LpbX8v9sHCexrWAyr8BGrwEEYWUwrL6E3sljVjQilNRIYZja24qNgaW6PTyzBXMMCZrB9KC0rLJJ2oDUNc8U+LOYCv+/M4mLpVYTSGvF0/4GPmgXBNMQ2UfioXrlZKM1H1Wr+Hoj88SahtLC08JabhMsCC55dr1DaOIcot5BXCfQHUMb2vmkktl5JDKMMegPIgFEf4g4MB7bWEdPrFmDHrcWwMGfr7yIIqrDQtHvxWoXS0gxWezA3Y+uaFLCSw2IfKqW9V4q63e/nl20uK10YvgidJiRVC7jVfLQsXYYnFmE5m17b6xVK2/85+PgDO4ukMYYFbo5QGuwufLabsaBQmoiIiIiI7A1F1UREREREREREREREREREROSW8LrbZnj85961o7HNSrTHsxG5tr7RfYbPnvos7tIPuG2i8Qdf5Bu3jXe8734Cz3dY+3RCHtqHFiaq+bromrMPMxxfgNedvfy+w0GfuY9+mPM//pM7no+IyDeNQ8fgp164/DiASmtv5yIiIiIiIiIit4SV0QrPLj3Lynhl3ZMrcOECzM9Dp8MgG/CJ45/A4aZjnIOvfAWefNIqUWEI990HDz7I+eGFaSgtz3GLz20aSWPxOYbtgHExJg5jwiCk1z1HXt16vtl4QBGFjPIR1TQliyw4tRlX5CRxwLHbHMcOw6GmDzatE/hTalWgFcO5kcW/Qmf/BL5ZKO2tR33Mq4SXVixu5LBfQFJM4l8VOHYQaj4QBZA4uGse7t5/+VDaQt1iUkFgobTFvkWyJt+uCmKYb1o0rMy4KJR2oGWXY+0cgUbFgl9BCKMxjDM414cotnNf6tm5zjcvDqUVexxKi0I7p3Fu5xCFEAeQ5RZuG+UWSjvYtmNvF0ob+/F5aSGxwFkELswtYhdi6xKWVxdKo5wuRDgJn8X2uLLuE9iRD6eV5cZQ2uSeTSJbu6rfzvlzjyfHSKFIpnO9nqG0YgyzdbunhwMoIjg4D0kwXat6BcoU0ln7kxiaDdtn4Oxeq1VtfObjYlEE9QiWhvZauEehtOUuHK5AVMAgsohbJbXzWh7C0ydh6Etj1RwO96AXWCjt4DykVYuknXseqgWw7p9br1cobf9gd5E0uHlCabsJn+02kgY7CKXtJJIGCqWJiIiIiMgaRdVERERERERERERERERERETklhBHIZ16ePmBIq8wvbzHZ0/9Ns65y44NRkNqf/L4rvbfzOBAH874D5DUc/tgyPoPiEwc6NvjX3st3PElaKeX3//cxz7C0g++l3J2flfzEhG55UUx1Gdv9CxERERERERE5Ca3k1AawHNLz/GFF78w/bfkLUJp5++7k5e6J9Z25ZyDM2fg/Gk4MHm2hPPH4WvnGc636Y67FkpbXaHXsl/OsRm38hJpHOKcoxImkI5It/sUa5FThBGUBaPIwlGHZizyFfmA1coQzvTh4SPwtjvdWoAqdNMe1kQzhvsOwExikZ68hBdWYfECFqnaJpRWCeCeDhxpWiQtrFgIbRJKK0sLjQ1SCAuYr0NS33korbBVJQwtjpU7i4cFWJiq6edMZPGqwlmILAgsWNYdWqCqXZuGwYIA4hjOr1p4asEff6YKvRSaFdtHv4DqdQilhT7QVQ0tEDaJmIXAMLf42LCAbIehNPChsXD6WgBEpc1lsu+rCaXlzt4qOL9fP7ZkGkkbFXYfuMDGZtk0lOYyyJPpPg+1uKgeNRjCzORxCf0hVOr28LqG0hy4GOIEmn4t0xTiio8TOhildqx6FajCat//HTvfwkHPX/dWAEE0Dcet9mE43ttQWta3SFrrEFCxqN14COdPwHDdNZidFMp8KO3oH8LTBbzcgeYs1ION3we7XqG03UTS4OYJpcEOwmcAR4/CI48Q1Ovcs924u+5a+9+ubSmSJiIiIiIiV0hRNRERERERERERERERERERERGRm9gL/ecvCqqV7tKPiU01nvgjXJGvfRhjp25fhcXW9HFnZI87I1ifcrvTf2bPBfCNffCmU5ffdzjoM/fRD3P+x39yd5MSERERERERERERuUVd61Ba9oYHOH7u6xcf5MwZOH9qQyht+KeLrDQjAgKSKKE/7lGMlmB2k4mOTuNWe4xdBs6RpClEkEVbnFiRUYQxDscwS4ki68ofbE9DaYWD5RGc7oLLoRYWPHAI3nDQ4mWX/vt2I4b7FiyedaYLL/ehKC2WFWChLrBI2usPWRwt8stVCeDoHNw3b6G0ILYQVAAcaEFZWMCsLCF201BaAZzpWdDr0IzFrwKAxEJp1dDCWrsNpY2AooBabMfsje3v7SoQWFQpCKCSwJkV6I5goWMRqzyH5QFUE1vHOLR1GGawrwWLK9N5tn3sLWAaSLouoTR//NiPxW8bY9d9cm3HOSSJ388moTQXTENppf+WSOGmJ1Mpps87rj6UVjpwpQ+lpeAqtglYKO/CkLWYXxlAOoSGf5xj4bRazd+HVTv+0N4yLPeg2fLzKiw+Nle9MaG0NLD1iPzcgxwGhR2/KCEoptcoy6Hbt/t2vm2hu2wMZ8cQ5xaqCxuQ1WBp1a7HONvjUJqPpHXPwUIVXnZ2LSKg2to6ktb8NNwewWceuvGhtN1G0uAmCKW9+c3w6KPQbG49RvEzERERERG5ySiqJiIiIiIiIiIiIiIiIiIiIiJyE4uD5KLHT60+RTfvbhzoHPXuHxO86sqOc64B/cr08SiGM+tCawt9aGTTx0nBjs3+2q9y/m/91/aJHhEREREREREREZFb0KahtE3sRShtcHyR1XWhtKB0sHwKahuPX66cY1SZsV+okUF1nJFvN+EsJUwiinxMGkHLweEZC3dFPuy1MoLTPcgKCMuCOAk45kNph9psKPoEzqJorYqFpc6OLNIU+BjWhlBaaLGlzjzcOeeDbBk0a1CtWRzrcNvCW3kGw7HtZ7YB7QTKEPbPbBFKq1uwrBr46BvgQlhowSBdN45pKC3Ch7B2GUpbXLX9RxHgLLI1dDb/emwhsTyHfgbtGgzGG0NpRQH9AhoVi5OVft77WramBDauXbNjE1y/UFojnoa/Ah81C4Lp/icKf/3LzUJpPqpW8+G+yB9vEkoLS7sXnP8eRRlY8OxqQmnjApLSzh9n93Hho22VyIJ56eR4QLYE9Tm7T8Yl9LqQVOyaR6HtJwxhpWfhvu7IQmm91PY/GN6YUFqawWoP5mbsmEkBKzks9qFSQiuBom7HPL8MaQ4rXRi+CJ0mJFWbV81Hy9JleGIRltd97+h6hdL2fw4+/sDOImmMYYGbJ5S2m0gaKJQmIiIiIiJyJRRVExERERERERERERERERERERG5id1Wv40gCNY+YHekcYSnVp/aMC4cjwgmn/K5Ap0R9BPWPoVRX/8pOgdvPDV9GDi4Y/vPBV4k6q5SeeFZ0lfffcXzExEREREREREREblZHT9znE8++UlKt+7faAcD6PWg1YJGA4BxPuZLJ7508cYvvggnn4O1wE4JJ4/TTZZY7kwLRK4otgylpd1zDJO2RXAyCMYjXHXr+WbjAYWvMI2KlDCahsw2KFKqccQbbis5dhgON+2fkd2l4x2c6UJYOvZ3HAEWzgrcNGY10Y7gtQvQ8nPMS3h5GU4ugQPGEdR8UC2JICosVBUEMN+Eu+YsfHW+DyM3jaQBBDHsa0AcWaAsLH2Ma5tQWscHyEZjC35NzmeuAcsDCMJpKG2yXXgFobSZKmsBuwCYrdk65pMYW2RROBdePpTWHVrgK/bn0qwCxTR01qhAOra5pdcplEY5PblwEj6L7XFl3aeZIx9OK8uNobTJfZhEth5Vv50LIMt9JA0ghSKZzvVqQmnjHKLcQl4l0B9AGVuorJHYXJIYRhn0BpABoz7EHRgO7Nwiv56Taxti98zCnK2/iyCowkITwujGhtKWu3C4Yu+rQWRzq6RQjGF5CE+fhKEvjVVzONyDXmChtIPzkFYtknbueagWwLpf0HO9Qmn7B7uLpMHNE0rbbSQNdhBK20kkDRRKExERERGRbxqKqomIiIiIiIiIiIiIiIiIiIiI3MTaSZt72kd5ZvVpADpJh3bcppt3Lxrnohj/WaorUimhlUGvsvG1A324rTd9fP8Z6Ix3vm8XBBQzs1c4MxEREREREREREZHrb2W0wrNLz7IyXvcbJlZW4MIFmJ+HTgeAQTbgE8c/gcPXuJyDr3wFnnzSqlFhCPfdBw8+yPnhBZZHy2u7c1kGi89tGkobnn+Zcf0AcVwhDEJ63XPkW4TSHJCmA1xkZTFXpATbfHrUFTllGEFptbIogNtmLPIV+djVyhDO9OHhI/D2VxXEkR0ndPbv0JeG0poxvOUeC4/lJbywCosXbHJZCIUPRLUrcOwg1HxMCqBZwl3zcPd+WBoAMXzLnMWsssJCY2UBsw2I/T+CV2JYmIFhNg2qgcXNmomPVUXTUFpeWESrU4f+yAJVSWTRrAALhNWrMBrB2VWIYpgPLfR1YWAxt0koLQiguMJQWiOxOQf+HEosdBUAJ5Ys5DaJc20XShvn0M3gQNtuuQALrLkcisD2kfnrdb1CabmzueAsCoYfWzKNpI0KW0cX2Ngsm4bSXAb5tCPIoRYXfdNjMISZyeMS+kOo1P39cJWhtGIMs3W7T4cDKCILiCXBdK3qFShTSGftT2JoNmyfkyhfzYf3Mh8XiyKoR7A0tNfCmySUlvUtktY6BFQsajcewvkTMFx3DWYnhTIfSjv6h/B0AS93oDkL9cD2dyNCabuJpMHNFUq7bCQNdhZKUyRNRERERERkU4qqiYiIiIiIiIiIiIiIiIiIiIjc5B6af4hvdJ/BOfuE2ZHGEZ5afWrt9VpU40DjDhrxSZIzZ674OHcu2Qc+Trcg9x9Cq+bw8MnpmM0+iHI5g7f+WYp9+kCHiIiIiIiIiIiI3FibhtI28dzSc3zhxS+s/Zvs5UJpL62+tLatW1yE86fhwOSZEs4fh6+dZzjfpjvuEoexhdJWzpDPbDi87QdIB+dxUUQlTCAdkW7zidCyLHCBA1dCaFGtw+1pKK1wsDyC0z3IcqiFBQ8cgmOH4XCTDfWfRgz3LUAInOnCYhdSplE1gNL/pV2B1x+Chp94JYC7O3B7wyJpYcWCZdXY5lSWFhobpBAWFixLakAI+30oreHDTkkIcdVCaXkBeWqXI/dRrvmGBatKLHjWqPpzcRYIqyRwZgVWxtCs2X7zHM724dAMNCt2XOcgK/2+c+g0bDftOgwyG1cU0C+geoWhtCi0+Nc4t/lFoUXistzObZTD+SEcbNu5bxdKy7HxeWHPBz7gFTANpYWh3X7XM5RW+lswLoEUXGUa4JtvwIWhXdPJ/ZMOoeEf51g4reYjg+2qHX+Y2RyWe9Bs+XkVsNqHueo1CqU5cDHECTSxtUtTiCs+0OdglNqx6lWgasev+9BhXNra9vw1aQUQRLYmWW5jh+ObKJTmI2ndc7BQhZedXYsIqLa2jqQ1Pw23R/CZh258KG23kTRQKE1EREREROSbhaJqIiIiIiIiIiIiIiIiIiIiIiI3ufnqPu5pH+WZ1acB6CQd2nGbbt4FYFyMmavMUX/b99L6f/9/VF4+ud3utnWgD//hVZBF9iGgQz3YP5y+fv+ZzT+IspX+n3k7pz7wL694PiIiIiIiIiIiIiLXwvEzx/nkk5+kdOXFLwwG0OtBqwWNBuN8zJdOfOniMS++CCefg7UQTwknj0NlQO/QPMujZXu6KOgOTlPMbjKB0Wncao+xywBIghjSEdk2n/IsfChtWKREEUSxBbc2C6XlOdSikmMHLJR2qOljUOsEvhHXTixSdnYERWlhLdzGSFoltDBTZx7umoXTqxYWa9YsJhX4iNeBtj0/HlkwLQhgoQaVOQtqnenBuLCIWcVvQ2IxtWoIWWYRuRyLSh2cseBa4Szq1khsn5UIKjXoD+DsqkWdosiiYqe7Fu2qxlBLIMHWpJ9Buwb9MbQqF4fSksieb1QsJjXILJo127BzD7Dj7mvaPCbLeaWhtNDHvKqhrUcU2HMhMMxt/YeFxd2SbUJprrRtwfYT+yhc6ONkcenbeoGt//UMpY0LSKzrBw6ywu4xsHW6owOp37gEsiWoz1mgblxCrwtJxYJqUWj7CUNY6UFZQHdkobReavsfDK9dKC0NbD0ify5BDoPCQm1FCUExjX1lOXT7MBrDfBvCErIxnB1DnFuoLmxAVoOlVXtfjLObM5S2/3Pw8Qd2FkljDAvcPKG03UTSQKE0ERERERGRbxaKqomIiIiIiIiIiIiIiIiIiIiIvAI8NP8Q3+g+g3P2qbcjjSM8tfoUAA7Hy8OXuat1F8WBQ3AFUbXRtx5lfO/r+E/5ywTlChX//P74DlZC+yBIANzd+Au8FLYvv8MAxne/hnJ2ftdzuVrPfOU/7en+jz74rXu6fxEREREREREREdm5ldEKzy49y8p4Zd2TK3DhAszPQ6fDIBvwieOfwOGmY5yDr3wFnnzSikdhCPfdx/n77pxG0gCXZbD4HNQ2OfjicxSdCuNiTBzEhN0Vajn0KpuMBchSwiSiKAvSfEjVWbzrQBs6NYs5FSWs+FCaKyD2obQ3HIRDbTbUf9aH0hoVODechtIuycfRjOG+Bej4+WUOTixbOMkB4whqPqiWRBZ+CvFhsRZ86xyMSjjfh7GzbS4KpdUsZJaEFjUrsFjWQssiaWvjsFhYM7HgUxJbnCrHx+J8EKw3toBW6DcKsHjb5Cq2/HlUIpivQbMCeWnb1UPIMzt+FGwfShumFg4LfJirXYOgnIbDWlXbVxleZSjNn3/sx+K3jbGA3CQ6Nc4h8cGszUJpWQDVyJ4vS9tHvu7WTgq7d8COn4TXL5Q2ziHK7bqWWACvjO3ebiS2BkkMowx6A8iAUR/iDgwHFoCL/BpNrnkI1GJYmIM0txBZUIWFJoTRtQulpRms9mBuxq5jUsBKDot9qJTQSqCo2zU5v2xzWenC8EXoNCGpWsCt5qNl6TI8sQjL2fTa3IyhtP2D3UXS4OYJpe02kgY7CKXtJJIGCqWJiIiIiIjcxBRVExERERERERERERERERERERF5BZiv7uOe9lGeWX0agE7SoR236eZdAM6Nz3EkmGP+Tx6/ov1XX3yOC295mMUROOyTKu2kTXPmNeR+zNGZ11A//C6GV302IiIiIiIiIiIiIlvbEEq7JJI28dzSc3zhxS+s/TKK7UJpL3VPrG3nnIMzZ+D8aTgwebaE88cZ/ulpVhoRYRAShzGD1XPk6+I+Gyw9R+ZDaZU0JaxAGlnMaoMihTAhjgqOHSwvG0qbSaDuQ2musOeLS3a5PpTmsFDayWULJxWBzQWg7WNpNSy4BlAB7pmD1+yDpQEQw7fMWfgqK3xIrLAgWRzYdtUIFmbgTA9qiUW1JqLI4nCBs0haMQl9OZhrwPIAgtACWe3q9LTDACoJnFmBWtVv5+zY49wCYLXYLu8otVDavpZdXueXb18T0tI/dnBiyY45iXNtF0rLCoukHWjZvqIAYh8hc4GFvsrM5pFeRSitEU/3H/hzDIJpiG3tNnE+irdZKC2wfSaRhdIifzw3idH5+JnzobTJ8a9nKK3wMbwitFBaEcHBeUiC6fnXK1CmkM7an8TQbNg+J/dPzd8jmY+LRRHUI1ga2mvhHoXSlrtwuGJRwUFkEbdKaue1PISnT8LQ18aqORzuQS+wUNrBeUirFkk79zxUC6A1vY43ayhtN5E0uLlCaZeNpMHOQmmKpImIiIiIiNwyFFUTEREREREREREREREREREREXmFeGj+Ib7RfWbtA4JHGkd4avUpAByOc898idvyfLtdbCnIMs4+8yXcHfvWnjtSPzJ9PQh4aN/DVzF7ERERERERERER+Wa1IZK2jYtCaVtE0njwQbIy5/jZ4xdvfOYMnD+1SShtkdVmTBIlhEFId7RKMVqC2Y3Hd6PzjMOKBXacI8pSiu0+iVmkuDChKHOGMdRCONKCdsMiToWDlSEsdu0U3nF7yVvucCQ+UrZVKO11+y08NgmlnVqCl5cuH0ojgLvnp6E0l0A1hsNtm0uRwiC1pd1Xt7gVIeyfgWFmkSyw+FZchWYCeQF56nfvLKzV9Es0SC20lkR+28BCXfUqjEZwdhWiGOZDi4JdGMB80+ZUj20fRQ79zMJnm4XS8hL6YygKqPioWMUfc5D5iFlkG41zGzfKLZR2sG3z3i6UNvbj89LOO/ABrzC39Q6xeYXl1YXSKKfXKHQ+khbb48q6eyzy54jbGEorqtNx8w071kQ/hUY5vU7DEYR+/HUNpTlwMcQJNP3apSnE/p7Bx/Gi0O4TqrDa938HYr/OvdzuvVYAQWRrkuU2djje21Ba1rdIWusQUIHIWSjt/AkYJtM1n50Uynwo7egfwtMFvNyB5izUA9vfzR5K220kDRRKExERERERkZuXomoiIiIiIiIiIiIiIiIiIiIiIq8Q89V93NM+yjOrTwPQSTq04zbdvEuQZ1w4/xzD2D6Us1vDGJYuPEdw2wwuTmgnbTpJZ+31e9pHma/MX6tTERERERERERERkW8Sx88c55NPfpLSlRe/MBhArwetFjQaAIzzMV868aXpmBdfhJPPwVq0p4STx6EyoHtwjuXR8tpQVxSwfMoKY5coV84xrMwwyKBZaVIbpfS2mXNYlBRRCEVOHkI1hANt6NQsBFWUsDKC0z3ICqiFOfcfcjxwGA43LYBVXlILqsdwexvGWcFiF1J8VG0yR/+XSSitAVYXw/b3rfNwz/7Lh9IW6hanCgILpS32Lb5V8SG2ILaoWRxZrMk5yLEA1cEZ20/h7JiNxPZTiaBSs/DWuR50S2jWoFWxaFoBHJqBpo9iOSAr/b5z6DTsPNt1/DWw8Fm/sCBbnoELLx9KO70CBzuQYMGyJII4syhYFEAYQZZZIK0MYFjYPHYSSgOLqyXh9LUAiEo7oXCyzVWE0nLfCcT5/fqxJXYuNR99q8YWwnI+jFbzx3AOBiNIKvZ4Ej8bZva4N7T7jMD2udSF2eTGhNLSwM4xCv3y5DAo7PhFCUExjX1lOXT7MBrDfNuuSTaGs2OIc1vTsAFZDZZWLU44zvY4lOYjad1zsFCFlx2kQ4vQVVtbR9Kan4bbI/jMQ6+8UNpuImmgUJqIiIiIiIjcvBRVExERERERERERERERERERERF5BXlo/iG+0X0G5+xTXkcaR3hq9SniM6fBlbzQgXvP736/L3SAsiQ+c5rstts5Uj+y9loQBDy07+FrdAYiIiIiIiIiIiJyK1gZrfDs0rOsjFfWPbkCFy7A/Dx0OgyyAZ84/gncpA4GViD6ylfgySetjhSGcN998OCDnB9emIbS8hy3+NymkTQWn2PYDhgXY+IwJgxCet1z5NWt55uNBxRRyCgfUU1Tsmhj+GxtikVOEgccu81x7DAcavpw1DqBP6VWBVqx48LQwlOTUFrgLg6lPXAIapMQ0iycWIazXVhoQbMOteo0lFaWFhpbC6XVLGq201BagUW1whDu6FjMq/TziiJoJj58FFnIKscCWkVp0bCej21NAmIBFjVzQO7jYa2KHX+mCr3Ugl29se07wsJoUQizDaj4QFkQwD4fnZss54klmGtAHF4+lOaA80M42JrOqxLCqLQHkZ0ShZtGp8Y5JImPom0SSnPB9DxLHy8r3HSClWL6vOPqQ2mlA1dCXAIpuIptAnZNLwwt7AZ2/2QDC5sRQh7C6jLMzvt4WezDcCEMUwuEdRO7dqPSjjMY3phQWprBag/mZuyYSQErud27lRJaCRR1O+b5ZUhzWOnC8EXoNCGp2rxqPlqWLsMTi7CcTdf/eoXS9n8OPv7AziJpjGGBV2YobbeRNNhBKG0nkTRQKE1ERERERESuKUXVRERERERERERERERERERE/v/s3XmcHHWd+P9XVfXdPdMzncxMJiEQjkQiJAFDAGG5VEQFwf35VZFLyBddFLyQReW7nqyurgcq4K6uQrgXQhAVFkEQRBQWCFcYQgBzkWNmkjl6uqfPOn5/vKunJ5meZM5kou/n45EHmTo+9anqzpD0dL1aKaX2IanwNObWzeO1vjUAJINJ6rwwdmc7AB0JOCAtN/2MVD4g+wEEOjuIzpxLMpgcWD+3bh6pUGrCzkEppZRSSimllFJKKTU11Qyl1bCuZx2Pb3x84MMfhguldc2fw5uZTQP7eZ4HnZ3Q1Q7NlaUudLXBS13kU3VkihkJpfWlySYkJFWLl36TUsDE8zxCZhBKBUq7umPSsXFMC1yHgiVBsBn10BCR6JPrQjoPnf1w9Cw4fo43ELcyPYlkDRYPwPxmqPcDZY4LG9OwtQfwwDHAtiAShAUzJJRluH7YyoSjZ4IRhK5+KHoS7JpRL/MyAIISSgua4IwhlFZAQmiRgJxbtii/rwsDhsSaDANCQehMS9TN8eNgriMxMteTfTw/ruWZMC0BHenqPOuiMk4lJJcv+fPyl9VF5Lwr4bBEWIJxrgm2I7G0rjy01Mn64UJprh+pK9hgu9XjB0yJeeEf0xoUSQM5J9O/VkNCaX5ULeLH6Sx/zpVQmulKpMvzJ+8a8niMJ5RWdCDoSlgOD8qOPHdAzmn/JJQqxwOcLBTCEK6Doh9K683AftOrcTPLkLlMT0qczAvIzk2NEqXbm6G03gy0hsByIGeBZ0GoBE4RevOwZjPk/dJY2IbWLGQNCaW1pKAUlkja9vUQdoBE9bHdU6G06bnRRdJg3w2l7TaSBiMLpWkkTSmllFJKKaXUXqRRNaWUUkoppZRSSimllFJKKaWUUkoppfYxS1JLeD3z2sANi4ds6GGNK7/3DNiQhEO7Rj7ehmT1xh7DdTlofTdM8782DJZMO3oip6+UUkoppZRSSimllJqC2jrbWLF6Ba63Uz4sl4NsFhIJiMUo2kWe3PTkjtts3Aib18FA5MeFzW1kg730Jqu3MWYKfTiFHmioMYFCO15flqJXBs8jWCqBBWVrmAk7ZRwzgIdHvlzCssAKSJyrEkpzPOgtQHsGPBsipsPCGXBEC7TWVV8XrYgFYH6TBK86M7ClX8JSph8vc/3t60ISSgsZEt0CCXm9pQHeMg16cmAEJC7VUi/ryjbkirLP9Lj81zGgqR46sxJfCw0610oozUQiWaMNpXX0SdjLsgBPAmZ5T+Jb0YDMybahvyzhM9OUsJsBTItDyZV4WX9RjhnyA2SWIWE109/YMPxwmv912ZFIWnPCD74ZEPAjZJ4BoYDEqlxPoluuAXkHyi4EdxFKs4zq41VwJGxmIPM2PbkeAcs//0F3zlb2c92hobTK4xm0ZLywv5/nP16Byh+FEjjB6vHHE0or2mDZfigO6M+BG5CoWSwocwkGoFCGbA7KBlj94MUlWBcKQl1MQnuu6QfkPCiX5LGOWlBEHoupEkor90skLTEDCMn1L+ahaxPkg9XHqqFSKPNDafOehTUObElCvAGihoy3N0Jpo4mkgYbSlFJKKaWUUkqpvUmjakoppZRSSimllFJKKaWUUkoppZRSSu1jUuFpzK2bx2t9azAKeWa89Crt0yEdkfUdCTggLTcU7U4+INtXJAvQumoNPYcdixeJMrduHqlQanJORCmllFJKKaWUUkopNenShTRre9aSLqb9BWno7oZUCpJJAHLlHMvbluPhVXf0PFi5ElavliKVacL8+XTNn0Nvobe6WbkMHesgMvTYzrbNFKJNGKZFwAwQKZTI7mqy5RJm0MKxi5QsSHjQWg91UQlEOS6kC9CelXCV6ToEggaL/FDajDqGlIUMT6JbiZBEq7YVJKhleHKKQ0JppkSfkimY0+gH2coQj0A4IuGt1joJttllyBdlnIYY1AXAM2H6oFBaIuTPIwjTYhLZckpIMMwD05JIl+1CtiCXeSJCafVhqLxEbCChOc/wj1OUiJhdlvmGLIl55cp+1MySnYq2BNXa09CShEr/Kh4GHLkGngGxEJSKMreiAwVbjhM0Zf6WBaYtETkTP4TmVvcHOdZwoTRzp1Ca68pzwauE3UBO1pLxKpG0giPXxjNk23K5GkrzymAPCnrNSOz43Mnlob7ytQv9eQhF5cvxhtKcIjREwTEhnwPHgpYUBI1qiC4aArcEpQYIlqDkQTQi4xienG/OleeFW/afz/58M/0SS5syoTQ/kpbZDk1h2OJBKS/XK5wYPpIWvxdmW/DbJXs/lDbaSBpoKE0ppZRSSimllNpbNKqmlFJKKaWUUkoppZRSSimllFJKKaXUPmhJagmvZ14juup5DNtmTi+86N/M4xmwIQmHdu1+nA3J6g1DAHN6wSiXiax6nsLRx7Nk2tGTMX2llFJKKaWUUkoppdQYDYmk7cK6nnU8vvFxPM8bNpLG4sV05bt5s+/Ngf08z4POTuhqh+bKUhe62si/0U4mHiRgBjANk2y6E7u+5uEBKOd6cCwTgFCphG1VQ2ZDOCXCAYsjZrosaoXWuMSDvJ2396AzA6brMT3pSYjLj5Q5O21aZ8FbmyARlq9tF7b0wuYe8ICiBRE/qBa0JCplIq2hVBwOapSAW1c/FDyYUS9RLQAjIKG0gCUxM9MF1w+lNSfkElcYSHzMNMAwoVCEYhm294MVkPhaT1bmn4rvGEpzxhBKswwJeuX9UFooIMGvki1fb+qBxhgETDlG0IJAWbaxDDmHclkCaR7QlYeWhOxbCa95figtYELZfwwcv8tXdCSqVomoGYDlymNnVuJqgx5Xx5PYXa1QGoaE2SqhNMufhOv5ETYXDAe8UPW5lYpBd17mALK8lIeY/7WNhNMifgywLgy2J9fL86A3C/GEHMt2oK8fGsMTFErzwAtAIAhx//qUShAIyfMODwolOVY0DIQh1y9ht8Z6CPvP9TywdRvE8hCISijNA9IZuZ5TMZQ2/SG4a+HIImkUoYmpE0obTSQNNJSmlFJKKaWUUkrtLRpVU0oppZRSSimllFJKKaWUUkoppdTfhDc6s/y/X60a0bbf+scFHNKcmOQZKTW5UuFpzDNnsu3lFwFoLECyAGn/BrCOBByQlhuWhpMPyHYVyYKMAxBte4n9j/0QqVBqks5AKaX+zm17De77/Mi2PeMaaJo3ufNRSimllFJKKbVPaOtsY8XqFbieu+OKXA6yWUgkIBYDoGgXeXLTk9VtNm6EzetgIAjkwuY2COXIzkjRW+gd2DRT6MMp9EDD0Dl4hS6KptSfgkYASgXKu7hT0XNsHNMC1yEfgLApYbKGiISmXBfSeejsh6NnwQkHOBLswg+lMTSUFg/AMXP9EJYLG/qgoxvwoGxK2AqgLgSLWiDih6oA4i4clIJDpkNPDgjAfo0SySo7EiVzHWiIQcCvH4UC0FQvwa1KUA0k9BUP+iEsS0JpjicRLsOPjWXyEt+qi1QjYoYBgQB09UnkrCkm49aHIVuCeEjG6HcgPMZQmmXKmEVbzt0y5XzKtoxdsP1QWp0/JyBkQsGVLyw5JVxPgmQFW6510JQ4lWX5gTVPxjdNOffKObr+U9TxI2kgkanKcs+f/8C1NCSEVTOU5m+TisGWjATnQMJtIQMCruxTrkTZkHPfPwkl/8njAuUeiDbKtSy6kM1AMCTPI2tQAC6dledApiChtGxJJpzLT1worWRIZM7vDWLYkHPkueJUzt2/bmVbgmoAvT3QHAXHkT83eLKtk5VQ2uo3oViSbadiKG16bnSRNJg6obTRRtJgBKG0kUTSQENpSimllFJKKaXUKGhUTSmllFJKKaWUUkoppZRSSimllFJ/E/qLNv+7bncf317dVqm/Be9+6BXuKNsDNxLN6YUX/RuFPAM2JOHQruH335Cs3oRU2b/CLJV59+9XwyETPWullFIAlDKw4YmRb6uUUkoppZRS6m9aupBmbc9a0sX0oIVp6O6GVAqSSXLlHMvbluNV6mAgZaOVK2H1aqlVmSbMnw+LF9OV766G0mwbr2MdRGocvGMdTjJE0SkSMAKYhkmkUCK7i/majotjmZTsPGFPYlzNdZCMSCTKcSFdgPashK4ipsPCGbCoFVrjDKkQxQIwv0miVp0Z6MhAiWpUDSTsBRJKWzADYgCeRLUOScLsmETSzJBEq8IBaK2Ty2KXIVcC04FUHIIRwITpfigt5selgiYEwhJKsx2wS3KJbUP+m4pBT14CXdEAxML+ufgxsVAQOtMS42pK+mPY0JuDcFAiXAH/+uTLMC0BHWkJgBlAXVTaScagSzTWUJrpjxM2JT5mGbLMBPK2XM+8A2VXgnIGcgzDkbENQ/apxM4ACo4ExUCeaiBBM8+V7T0g4kfnLP/4tifXznQl6OVVImeGXO+IJeOGA3Kcsi3zqITSHLsa1QtZMLNO9gMJpgXkocRFYmIl5LrEgnJewQAUypDNQRko9EMgCfmcRN0s/7zxr7kJRALQ1CixOs8CIwxNcTCtiQullcrQl4XGerlWQQfSNnT0Q8iFRBCcqFznrl6ZS6EA4W7o6oJEIyRaZE7416n7r1AcFP2bqqG00UTSYGqF0nYbSYORhdI0kqaUUkoppZRSSk0ajaoppZRSSimllFJKKaWUUkoppZRSSim1DzJ7uznojrs5fI7cqATQWIBkAdL+jZEdCTggLTdD7SwfkPUVyYLsX3F4JxzyzN2s/eiluA2pSTsPpZRSSimllFJKKaX+VtUMpdWwrmcdj298HM/zY2nDhNK65s/hzcymgf08z4POTuhqh+bKUhe62uClLvKpOjLFDAEzgNmXJpsA2xxmEj3rKActHNchZAYxi/2UrGrIbAjHJhAwWDTT44gWmFHHkAqR4Z9OIgSJAGwrSFjK9ABvaCQtZEp0K5mCgxqgvQ8cB+IRCVYZfviruU6WFwtyeQwDmiIQapT4VmdWImIz6quxMoISUwubUC5L/MtGLm1LvQTXHE+CWrGgjBmyIBSB/hxs65O4lGVJgKs9I4G1cAAiQQgi8bT+MtRFIFccGkpzHOh3IBaSKJrrB8umJfxrYsh2dRH/2hkTF0orOv74yE2ljlcNWhVtCZsZ/vUwPbmuAcsPxQ26C9V1d/wvSAzMq8TCPDluxJJrA4NCaZV9SuAEq8dPxaA7Xw2lFR0JsFWeqqWSHyvzF6Sism+hLNenLwsNYXBMeQxyZWhJSfzNQK5RNARuCUoN8l8CEI/JmIYHhSJEwrJ92Q+RWRZELYnoRcLViNxEh9J6M9AaAsuBnCURt1AJnCL05mHNZsj7r92HbWj1a4fpDohsgMh06EtC3zYwS/tGKG20kTTQUJpSSimllFJKKaVGTqNqSimllFJKKaWUUkoppZRSSimllFJK7YNSt/4SM58bcqPSnF540b8RyTNgQxIO7Rq6/4ZkdZ/KfhWVG5zMXD+Nt91A16VXTNZpKKWUUkoppZRSSin1N6mts40Vq1fgeu6OK3I5yGYhkYBYjKJd5MlNT+64zcaNsHkdDMSDXNjcRjbYS2+yektgptCHU+iBhhoTKLTj9WUpemXwPIKlElhQtoaZsFPCM4M4uOTLJayAxLFm1ENDRAJRrgvpPGzrh7e3wjEHeQT9CJfhSdBssHgA5jdDfdCPVXmwMQ0d3YAHRQsiflAtaElUykS6RNMScHAjFFzo6oeiJyG0HUJpEYloBU2JmjmAZ0JTQiJpA9shgax4ECwkhlUoSlTNMvzQmwHZIjRE5fcg+zqOHBckDgcybioC8RDYruwXNcEuy/F3F0rL5MFFomUGEA/L5Cuhs1gISkU5n9I4QmmxQDUqZvghO8OohtgGHnrPj6J51fHk4sjXlh9JK/gRNwzZ1nGr8TO3DG6oOuaMBDsUrHJ5qK987UJ/HkLR6vXcPwklZ2A1+V6JnrnIefbmIVUv1zxgyvJ4GIolyAbAC0AgJCGxQEBCbIGQnC8eFEoSUIuGgTD09fu/R2JvjgdZWyJtCUMCfpUYXF8/5IuTG0or90OyGRIzgJDEBYt56NoE+WD1OjYM+mAUgP23QvkeuMWWOe9LobTRRNJAQ2lKKaWUUkoppZQaOY2qKaWUUkoppZRSSqlh9fX1sXLlSp577jkymcyQ9Q0NDXzuc5/b8xObINu3b+fRRx/l6aef5tlnn2XLli309vaSTqcxDIOGhgYaGhqYPXs2S5Ys4eijj+Yd73gHdXV1kzYn13V58cUXeeaZZ2hra6OtrY2NGzcOzCsYDNLY2EhjYyOzZ8/m2GOP5dhjj+W4444jvqs39kwx/f39/O///i9PP/00q1atYsOGDbz55pv09fWRy+WwbZtIJEIsFqO5uZnW1lbmzp3L/PnzWbJkCW9729sIh8N7+zQm1NNPP83//M//DFluWRZf/vKXCQT23Mu5a9eu5eabb6657rOf/SyNjY17bC4Vv/jFL1i2bNmkH+fMM8/kyiuvnPTjKKWUUkoppdR4mb3dNNx5CyA3KlVuggJoLECyAOmIfN2RgAPSELWr++cDsrwiWZD9Kg4fdNNU45230HPuUtyG1CSekVJKKaWUUkoppZRS+4Z0Ic3anrWki+lht8mVcyxvW443kONCKlQrV8Lq1VInM02YP5+u+XPoLfRWNyuXoWMdRIaO62zbTCHahGFaBMwAkUKJ7K4mWy5hBi0cu0jJgoQHrfVQF5XAlONCugDtWSg7YLgOkaDBwhlwRAu01u34wQwgoa63TpeQVkcWtvQDdjVs5Pq/qfNjaSFDAlEAIWBuI8xPQU8OCMB+jRIKKzsSJXMdaIhBwJAoWdiCpnrozEIkKAGuCsuCpB8rKxQljCUXERpj0JsDw4RIAOrC1TmaBoSC0JmGSNjfz5NjF21wPdnH82NcnulH0kwJrBnAtDiUXP9rDzb1yDEDphxjV6G0og2ZMjTXyTEMJLDm2eAYMkYZOf/xhNKo9Pz8a+k5cs0xIDTorUiWIcdw/bmYLhgOeKHqEKkYbMlAOFB9nPMFiPgbOEh4LOgHwOrCYHuQl6YfvVmIJ+RYtiOhssawPA9jQXkOBANQKEM2B+U8hIPghSGfk/AZVK+v4Z+jY0scrWRI2K7oyjrDhpwjYzqV8/GvT9mGTL88Z1J1cr7lImwrQsCW62TGoByBnj7541osT3IozYN0B2S2Q1MYtnhQykvXLpyAYkDGjNpyPbM9EH8a4vcCRThs/r4XShttJA1GEEobSSQNNJSmlFJKKaWUUkr9jdOomlJKKaWUUjuZM2cOGzZs2P2GOwmFQiSTSerr62loaOCtb30rRx55JEceeSTHH388wWBw94MopZRSe1Eul+OFF17gmWee4dlnn+WZZ57htddew/O8Yfc54IAD9smo2p/+9Ceuv/56fvWrX1EqlYbdrr29nfb2dl599VV+//vfA5BIJDj77LO57LLLWLRo0YTMp6Ojg3vvvZff//73PProo3R3D/+un1KpRH9/P5s2bWLVqlUDEa76+nouuOACLr30Ug499NAJmddEK5fLLF++nP/+7//m97//PYVCYZfb53I5crkc27dv55VXXuGRRx4ZWBeJRDjttNP40Ic+xPvf/37q6+t3MdK+IZVK8Y1vfKPmuiOPPJIzzjhjj83l+uuv54c//OGQ5fPmzePrX//6HpvHYPfffz9//vOfJ/0455xzzqQfQymllFJKKaUmQurWX2Lmq3cr7XwT1JxeeNG/yckzYEMSDu2q7r8huePNkHN6q7/f+aYpM9dP42030HXpFRN+HkoppZRSSimllFJK7UvaOttYsXoFrufuuCKXg2wWEgmIxdia2cq63nUDqz3Pgzc3wub1MPA2Fxc2t9Ef7KGnvnqbXzbdib2Lt8KUcz04lpSlQqUStlUNmQ3hlAgHLI6Y6bKoFVrjEiXaOZSGB50ZMF2XpqQsMj15rdDzdgylLZwBEf/r+hQc2ACdfRLKikcgHJHwVmudRK/sssS2PD90Fg8BBkyvl+BWzH+LddCEQBjiQRnL9t/WZXgS9oqHpIOUK0loLWj5+xoSeIuGoVCAbX1gBSBlShyrOwepuMwpGpAxHBv6y1AXqR1Ks13oL4LjSBTOMyTmFrQgV65G0DAkdOY4ULChKw8t/ud17iqUZiPb244sNzwJxBlUQ2mmKU+R8YTSbP/xwwPLRcpnARkiaEHEgoIj18YzJDYWMCVgZiDXyfHHC1kws04eJ/znUCkH0RC4JtgG9PVCQ0qCapYpj4tpQjorwbpMARqikC3JnHJ5aElB0KiG4qIhcEtQapD5hEIQR8Zz/X3CIbm2RX8ckPPc3guRkIwTdCBtQ0c/hFxIBMGJyny6eqFkQzoD+Y2QjEMwDIEgRPxoWakXVnVAb7l6TfdUKG36Q3DXQjCDkEhBMQVvNMMhndDeDdlucMtw6TNAUY63r4bSdhtJg5GF0jSSppRSSimllFJKqUE0qqaUUkoppdQEKZVKbNu2jW3btgGwcuVKbrnlFgCampo477zz+MQnPjFlIydKKaXG57HHHuOUU04ZsvzGG2/kwgsv3PMTGoF8Ps9NN900EFFra2vDcZzd77gP6+7u5nOf+9zA/6PHIpvN8otf/IIbbriBz3/+81x99dVEo9FRj+M4Dv/1X//FXXfdxeOPPz7ua9/X18d1113H9ddfz2WXXcZ3vvMdYrHYuMacKMVikZ/85Cdcc801bN26dULGLBQK/PrXv+bXv/414XCY008/nauuuorFixdPyPh7wyGHHMKJJ57I448/PmTdsmXL9lhUzbZtbr311prrLrrooj0yh1qee+65PXKco48+eo8cR+179sX/1yullFJKqb9dZm83DXfu+PpGUw4O74RVLfJ1YwGSBUhH5OuOBByQlpuz8gH5uiJZkO0rDu8cetNU45230HPuUtyG1CSckVJKKaWUUkoppZRSe1e6kGZtz1rSxbS/IA3d3ZBKQVIqY7lyjuVty/EqFSeQEtLKlbB6NbiuFJvmz6fnrQfRW+gd2Kwru41ydgvU6P24/VvIBWJgGFgYuKUC5V3c9ec5No5pgeuQD0DYhBn10BCRmJTrQjoPnf1w9Cw44QBH4l74oTSqoayKeACOmSsxLMeFjWnY2gN4EgSzLYgEYcEMiWoZfnTLMKAlBgemoOxAVz8UPJlPyJKxjQBMi0kEy7ZlX9uQS9VSL5E0x5MAVywoY4YsCEWgPwfbs5BxJdiWCEk0zUGOEQ/71wQou/Jw2DYkYzK/uqhE0OIhCZ/1OxJks8vgmbsPpbWnoSUJQSRuFrQgUJYomWWAaUG5LIE014C8I/MI7iKU5rmyL0DRj6p5lYgaEjTz/Jiaw/hDaa4n4wVcoAReqPr4p2LQna+G0ooOmK48FgClEpQMCZoBpKIyZqEsQbh8ASJlCCTl66AlwbCAv72BjBUJQFOjhMw8C4wwNMXl+pVKEPCDeXhQKMnxomEgLEG2SEjGCznyXMwVoC8LcQ/cCAQCcvztPRJiaw2B5UDOkuOFSuAUoTcPazbLvEGCZq1ZyBoSSmtJQSkskbTt6yHsAINeS99TobTpg17vT3cCnVBqh0IvVF7KX7DT6/gaSlNKKaWUUkoppZSq0qiaUkoppcbltZV/3dtTUHvYvMUH7+0p7JO2bdvGNddcw7XXXssXv/hFvvKVrxAOh/f2tJRSSv2d6+jo4JOf/OTensYes2bNGt7xjnewZcuWCRnPdV1+8IMf8Lvf/Y4//OEPNDc3j2r/TCYzKdff8zyuvfZaHnjgAZYvX84RRxwx4ccYjYceeohLLrmEdevW7X7jMSoWi9xzzz3cc889nHHGGXz961/fZ+NqS5curRlV++1vf0tXVxfTpk2b9Dn8z//8D52dnUOWW5bFBRdcMOnHr6Wrq4uNGzdO+nEikQiLFi2a9OMopZRSSiml1Hilbv0lZj43ZPmJG+DlZrmpDWBOL7zo30DlGbAhCYd2yX+9QXc7zemt/t7w4KQNQ49p5vppvO0Gui69YsLOQymllFJKKaWUUkqpyTIkkrYL63rW8fjGx/E8b9hIGosX05Xv5s2+Nwf28zwPOjuhqx0G3jrkQlcbzuvb6EqYBIwApmHiZtPkreHn4JZLOJZJ2bEJASETmusgGZHAlONCugDtWYmXRUyHhTNgUSu0xhlSN4oFYH6TBK06M9CRgRLVqBpIBAygLiSxtBhI+MuAQ5IwKw49OTBDEAvDfg0SISvb8st1oDEKoUHxr6Z6yJerQTWQsFjcj6VhQaEINhIIc/x4WLYIDVH5Pf7pOI7E0mxD5p0Iybj1YciWJOCVLcrYFhJGs0xoiMn1q0TfpsXlOlTOe1MPNMYk/LW7UJoHdOWhJVGdV8iEgitfWHJKOF71NdeiDcGg/L5WKK1sSNgN5ClmIKG0iqADnlN9jILm+ENpwUoozZPnj+PKupAF+yehVDkekO+FeEx+X3QkRJaqlzBdwJTl8TAUS2DXgVsCIwhhD4IBeY25UIRIWM6t7IfILAuiFvTkZV3l2hg25BzZ13HBcKrXsmxLPK3fglQcwgbkijJGwJYIWaYIRQ9e3yBzCttQ7pdIWmIGEALLk1Ba1ybIB6vXuqFSKPNDafOehTUObElCvAGihoy3N0JpO7/ef1Bvdd7DvY6voTSllFJKKaWUUkopoVE1pZRSSiml9iDbtvnWt77Ffffdxx/+8AdSqdTenpJSSin1d+H111/nlFNOYevWrbvcLhgMMnv2bFKpFI7j0N3dzcaNG+UNkMNoa2vjXe96F48++uikBa8SiQSzZ8+mrq6OUqlEV1cXmzZt2uW83njjDU4++WQefvhhjjrqqEmZ1644jsOXv/xlvv/97+9ynoOFQiFmzZpFfX090WiUUqlEf38/W7ZsIZPJjGiM++67j/vuu490Ok19ff3ud5hiPvShD/HpT396yPmWSiVuv/12Pv3pT0/6HG688caay0877TRmzpw56cev5bnnntsjxzniiCMIBoO731AppZRSSiml9iKzt5uGO2+pua5p0E1ZAI0FSBYgHZGvOxLQ3C//rUgWZLuKwwfdtLWzxjtvoefcpbgN+vMNpZRSSimllFJKKTV1tXW2sWL1ClzP3XFFLgfZLCQSEIsBULSLPLnpyeo2GzfC5nUw8LYTFza3QShHdkaK3kLvwKaZQh9OoQcaakyi0EnZiuDgETQCWMV+ylY1ZLYzz7EJBAwWzfRYNANaEwypGxn+W3ASIUgEYFvBj5J5gDc0khYyJSaVTMFBDdDeJ6GyeASiYTAsOURznSwvFvzIlwFNEQhFJGjWmYVIEGL+WyqCIbAiEjPzbAlZmZ5s63kS9erJS3wrGpAgGwYDwbZQEDrTEtZyKnN3JEbmehAJyDiFEngmTEtAR1oCYAZQF5U5Gv41yZdkv8qyuggYbjUylgiDXQbXBNuRWFpXHlrq/OtK7VCa61/Tgg22Wz1+wJTwF/4xLaMagwM5J9OT/SsGQmmGRLKCloTSLH/OXiUw58fPPD+UVglqjSeUVrTBsv1zAvpz4AYkQBcLylyCASiUIZuDch7CQfDCkM/54Tb/vCvXy/HAseV5RFiuq+s/vgFX1mdtWZ4w5LnmGX4krR/yRWisl/MPOpC2oaMfQi4kguBEJbrW1QslGwoZ6F8LURNi08GLQsT2z7cIr3TI8xD8UJofSctsh6YwbPGglJdrEE4MH0mL3wuzLfjtkr0fSmvKwaIOeGEGQyzqqP06vobSlFJKKaWUUkoppYRG1ZRSSimllBqhww8/nGQyOez6QqFAT08PW7ZsoVAoDLsdwIsvvsjpp5/Oww8/THxXP2BUSiml1Li5rss555wzbFDNsiw+9KEP8YlPfIJjjz2WaDS6w/p0Os1jjz3Gddddx8MPP1xzjFWrVvGpT32KO++8c0LmPHv2bE4//XTe9a53cfTRRzN79uwh23R3d/Poo49y7bXX8sc//rHmOOl0mjPOOIMXXniBGTNqvLtqkhQKBc4++2x+/etf73K75uZmzjzzTE499VSWLFnCnDlzMIzab+Havn07zz33HH/+85+5//77Wbly5S7Hdl13l+unqlgsxkc+8hF+8YtfDFl34403TnpUbdu2bdx///011y1dunRSj70rzz//fM3l//RP/8R//ud/7uHZKKWUUkoppdTelbr1l5j5YapnDL0pa04vvOi/LOAZ8HJLdV1lfcXON23tzMz103jbDXRdesWY56+UUkoppZRSSiml1FilC2nW9qwlXUwPWpiG7m5IpSCZJFfOsbxtOR6DilaeBytXwurVUg0zTZg/HxYvpivfXQ2l2TZexzqI1Dh4xzqcZIiiUyRgBDANk0ihRHYX8zVtBydgUirnCQMRA1JJSEYkaOU50JeDbf1w1H5w7EHeQDTL8Bh8BgDEAzC/GeqDEkMqe7AxDR3dgAdFCyJ+UC1ogeVIdMswJEx2cKOEw7r6oeiPP6O+GgsjArYt4a4gEuAyTWhOyH8rDCTGZRgQCIBlSahrW5+EqCxLAlztGYmBhQMSZQvKJaa/LOEz05Q5GMC0OJRciZf1FyXyFvIDZJY/f9Pf2DD8cJr/ddmRSFpzQsayDAhYUHBk/1BAIliuJ4Eu14C8A2VXrtNwoTTLqL6WWnAkgmb418T05KkUsGT70KC7NAf284aG0pxwdbtUTPat6C9BzK0+/vkCmP724w2lOUVoiIJjSijNsaAlBUGjes2iIXBLUGqQ+FkoBHFkTNeDXB7CITnnYokdnqCZfiiVIVUn51suwrYiBGx5npsxKEegp0+uW7EMvRloDcnzNGeBZ0GoJHPtzcOazXINQAJorVkJlaXbITwdokmwge0bwA0Cw4TSpj8Edy0cWSSNIjQxdUJp73sdtsdg06DPFt2vT5YPZ8ShtO9+d9fbaChNKaWUUkoppZRS+zCNqimllFJKKTVC1157LSeffPJutyuXy7z00kvceeed/PKXv6S7u/ZPJZ966im+9KUvce21107wTJVSSqnxM02TQw89lKOOOoqjjjoK13X53Oc+t7enNSY/+9nPePbZZ2uumz17NnfeeSdvf/vbh90/mUxy1llncdZZZ3HXXXdx8cUXk8lkhmx311138YlPfIJ3vvOdY5pnOBzmQx/6EB//+Mc54YQTho2LVaRSKT74wQ/ywQ9+kN/+9rdcdNFFdHV1Ddmuo6ODT33qU9xzzz1jmtdo2bbNBz7wAR588MFht5k/fz7/8i//woc//GECgZG9RDl9+nTe/e538+53v5tvfOMbrF+/np///Of8/Oc/r3ne+7KlS5fWjKo9//zzvPTSSyxcuHDSjn3bbbdRLpeHLJ8+fTpnnnnmpB13d5577rmay4844og9OxGllFJKKTXlJaNB3rdgZFHpZDQ4ybNRauKZvd003HnLLrdpylVv+AJoLECyAGn/RlB30EsOyYKsrzi8c+hNWztrvPMWes5dituQGsMZKKWU2kGkAd561si3VUoppZRSSqm/QTVDaTWs61nH4xsfx/P8ktMwobSu+XN4M7NpYD/P86CzE7raobmy1IWuNnipi3yqjkwxQ8AMYPalySbANoccXvSsoxy0cFyHkBnELPZTsnZ8zW0wwykTCJgsnOlyRAu01u34gQcAsQDMb5IQ0rYMbOn3Q1wAXnXsOj+WFjLA8i9BCJjbCPNT0JMDArBfowS1yg7YZXAdaIhBwJAQWNiCpnrozEroLGRV52JZEnwzPCgUwfFkDkFLLm9vv3SWGmJg+vPykGNU+lqJkD83C1IRiIcklJYtQtSUOXmmrA9akCv7UTMLMCQW5jjQnoaWpITYAOJhwJE5eQbEQlAqggMUHSjYcpygKfO3LDBtcAy5lqYpsa/K/iDHGi6UZu4USnNdeVy8StgNpOhlyXhBS8YqOBKR8wzZNl+AiFt5LkKuAEH/GlXiZ3n/rTrZPEQDMr4L9GSgIThBoTQPvAAEghJKM00olSAQ8sNuHhRKcqxoGAhDOiuRPoCQA7Yl8+/LQtwDNyJRPduRWFq+APmNkIxDMCzHiviBs1IvrOqA3kFvSwrbUO6HZDMkZsgT2vKgmIeuTZAf9GOchkGvY7sOHLQS7uuETWsg0bDrUNr03OgiaTB1QmkhB/7vc/BGCnojch0O6faff2N12mlw111QX7/7bZVSSimllFJKKaX2URpVU0oppZRSaoIFg0EWL17M4sWL+eIXv8jSpUv5zW9+U3Pbn/3sZ3zuc5/j4IMP3sOzVEoppaoMw2DevHkDAbXFixfztre9jXg8PrDNY489tvcmOE4//vGPay5PpVI8/PDDzJs3b8RjffjDH2b69Om85z3vqRmeuuaaa0YdVaurq+OTn/wkl19+OS0tLaPat+L9738/Tz75JMcffzzbtm0bsv5Xv/oVTz31FMcee+yYxh+NT37yk8MG1QKBAFdffTVXXHHFiGNqw5kzZw7f/va3+Zd/+Rd+9KMf8d3vfpe+vr5xjTlVvP3tb2f+/PmsXr16yLobb7yRa665ZtKOfeONN9Zcfu655xIM7r3gxHBRtUWLFu3hmSillFJKqaluzvQ4Pz138d6ehlKjkilneDO3kUx5aMR9Z8l772RTy26qZ4DlwoZk9YavgCs3XO1sWg7WN8jNa6n8jjeCDcfM9dN42w10XXrF7jdWSim1a9MOhg/fvLdnoZRSSimllFJ7TVtnGytWr8D13B1X5HKQzUIiAbEYRbvIk5ue3HGbjRth8zoY6AK5sLmNbLCX3mT1fSmZQh9OoQcaakyg0I7Xl6XolcHzCJZKYEHZqrEtgFPCM4M4uOTLJayAhK5m1ENDRCJWrgvpPGzrh7e3wrEHuhLp8iTG5NUKpflBr4YUzGmErRnwbEiEIRyRSFdrnQTB7DLkizJOY0yiZRgwvV7iXDH/7R1BEwJhiAclemWXZLnhSbAr7se0ciUJrQUtf18/QhYNQ6EA2/ugPi4RNNeBbMkPdXkSAPP8GJdnwrSExLr87hjT4lBy/a892NQjcw6YEi0LWhAoy3wsA0wLymUJpHlAVx5aEjJWJbzm+aG0gAll5Lo5ftGt6Mh5VyJqBvJaKZ4cz6QagwM/sObUDqVhSJitEkqz/Em4nh9hc8FwwAtVH89UDLrzMgeQ5eWcXC9MifX19crjbJly/UxTfuVLEhPLBCWUVnDBcyGXn7hQWsmQ62H58zNsyDkSanMq5+OfS9mWeFq/Bak4hA3IFaEnDwFbgmWZIhQ9eH0DFEvyOnNrFrKGhNJaUlAKy3ltXw9hB0hUr39DQeaY7oDMdmgKwxYPSnl5CMIJKAZk3KgfZ8v2QPxpaLoX6g8CZ4ShtNFE0mBqhdIMYG7tz3ivOuoo+OpXYdB7PocOZMCCBTB9+m4GU0oppZRSSimllNr3aVRNKaWUUkqpSTRt2jTuuecezjrrLO6///4h68vlMj/4wQ/46U9/uhdmp5RS6u9dU1MTjz76KIsXL6aurm5vT2dStLW1sWbNmprr/vVf/3VUQbWKd7zjHVx66aX86Ec/GrLukUceIZvNkkgkhu64E8uy+PSnP81XvvIVmpqaRj2Pnc2dO5cVK1Zw4okn1lz/n//5n5MeVbvtttv4xS9+UXNdXV0dv/3tbznppJMm9JixWIyrrrqKiy++mE996lOsWLFiQsffWy666CKuvPLKIctvv/12vve97407SlfLc889x0svvVRz3dKlSyf8eCOVyWR44403hiw3DIMFCxbshRkppZRSSiml1MR5PfMaD259EG/nm0ZrMAp5Gt94AGPOyMbuD0LnoJco8gG5Aa0ibENXTH4BzMhCR3zozWC1NN55Cz3nLsVtSI1sMkoppZRSSimllFLq70q6kGZtz1rSxfSw2+TKOZa3LcfDqy70PFi5ElavljqZacL8+XTNn0Nvobe6WbkMHeugxgcJONs2U4g2YZgWATNApFAiu6vJlkuYQQvHLlKyIOFBaz3URSU+5biQLkB7FsoOGK5DJGiwcAYc0SKxM2+nGlIsAG+dLhGvjqzs67qDokkGRIISVAtaYLhgedI8SsXhwBTYNnT1Q8GTaFvID70ZAZgWk8CYbcu+tiGXqqVeImmOJ8eOBWXMkAWhCPTnYHsWMi7EI5AIwbY+cJBjxMP+9QXKrjwctiP7G8g1Mfy4l+NAv+OvN+QahPw4W65cjaBhQNGW7Qu2H0qrG7gMhEwJiGFISMtCwmWuIdvbrkTKDE8iZwbyezw5Z8erhtJc/2VWp1J1QwJWleUeEBr0Gqnlz7tmKM3fJhWDLRkJ24GE20KGfJCFgTwnPP8pHLJg/ySUHH8+gJOFQhjCdVD0Q2m9GdhvejVuZhky/vQklGwJpeFCU6OE5iYqlFYqSyitsV6OGXQgbUNHP4RcSATBicp17eqVuRQy0L8WoibEpoMXhYjtn28RXumQoBr4kTT/Qqc7YN6zsMaBLUmIN0DUkNelhwulTX8I7loIZhASKSim4I1mOKQT2rsh2y0xt0ufAYqjC6WNNpIGGkpTSimllFJKKaWU2pdpVE0ppZRSSqlJZlkWy5Yt48ADDySbHfq2jN/97nd7YVZKKaUUxONxTj755L09jUn1pz/9qeby+vp6Lr744jGPe8UVV9SMqhUKBZ588klOPfXU3Y5RV1fHT37ykzHPoZYTTjiBD3/4w9x1111D1t1///14nodhDPdWrfFpb2/nsssuq7kuHA7zwAMPcPzxx0/KsQGam5u5++67ueWWW7jkkksm7Th7ygUXXMBVV12Fbds7LO/s7OT+++/nrLPOmvBjLlu2rObyxYsXs3Dhwgk/3ki98MILeJ43ZPnBBx88ooChUkoppZRSSk1VWTvLg1t/V/PfPLVEVz2PsdO/E3flgDR0xhm4a6uhAB2D/hk1cIMbgAf7p+Hut8L+T0Jdaddjm7l+Gm+7ga5LrxjxfJRSSimllFJKKaXU34e2zjZWrF6BO/iDBHI5yGYhkYCYVP63ZrayrnfdwCae58GbG2HzehiIGLmwuY3+YA899dXb4LLpTuxBoaOdlXM9OH5lKlQqYVsS6arJKREOWBwx02VRK7TG5SW1nUNpeNCZAdN1aUrKItOTeJPnVcevC8HCGRDxv65PwcFJaM9IgCwegUgY9muUAFnZgXIZPAcaYhLHMjwwA9BUD/lyNagGEhaL+7E0LCgUwUYiWY4rgbFsERqi1dhYJYLm4QfYPAmqhSyoD0O2JHPJFmVsCwmjWSZMS8j2GHLMuogfNTPkfNrT0JKEIHK8oAWBsgTFLEOiYOWyBNJcA/KOBNuCfiAtYEr4Cz8qZxnV2BlAwYGg/3vTD4cF/DiZYcg5RfzrY/nBN9t/TEw/VudVImeGBNoilowbDshxyrbMoxJKc2wJzYFco5l1sh9IMC2AROtcJChWQq5VLCjnFQxAoQzZHJQNsPrBi0O+BKEg1MXAKYNr+o+RB+WSPLZRC4r+gzZZobTeDLSGwHIgZ4FnQagEThF687BmM+T914/DNrRmoQCk2yE8HaJJec5t3wBuEBgmkha/F2Zb8NslIwulTc/B4Z2wqgXSnUAnlNqh0CvHB1jQWQ2gjTaUNppIGmgoTSmllFJKKaWUUmpfplE1pZRSSiml9oDp06ezdOnSmuGUdevW8cYbb3DIIYfshZkppZRSf9u2bNlSc/lJJ51EMBgc87izZs3isMMOo62tbcTH3FMuvPDCmlG17du38/rrrzNv3rxJOe7Xv/51ent7a6677rrrJjWoNtj555/PokWLiERqfBTxPqSlpYX3ve99/OY3vxmy7sYbb5zwqFqpVOL222+vue6iiy6a0GON1nPPPVdz+RFHHLFnJ6KUUkoppZRSE2xD//odgmo73GS6E6OYJ9T24vA3ftYQteWmsk7/3qyILTe2FQLy+4gtNxwCtPRDrCw34r0+Dd62dffjN955Cz3nLsVtSI18UkoppZRSSimllFJqn5QupFnbs5Z0Mb3L7XLlHMvbluNVXnnyPFi5ElavBteVstP8+bB4MT2FXnoLvQP7dmW3Uc5ugRodIbd/C/lAHNO0MDwPp1SgvIu74jzHxjEtcB3yAQibMKMeGiISnnJdSOehsx+OngUnHOAQsOT1MtPzw1o7jRkPwDFzoS4sYauNadjaA3jgGGBbEAnCghkS4DL8QJdhwIwYzElByYWufgiHJcAFsm0gLDEz25FIluFJ/MzzIBWDnrwEvKIBiIXxq28S5AoFoTMtoTbHk+WuA0UbXA8iARmnUALPj6R1pCUWZgB1UZmjgRw3X5L9KsviYbkYjievH8ZCUCrK9Sn5rzF25aHF/0AHAwiZUHDlCwv5VdkfZG4R//imKdfcdSFg+ec06LF13R3/CxIO8yphMU9CZhFLImkwKJRW2acETrB6/FQMuvPVUFrRkQCbWdm8BKVBUbNUVPYtlOUx6stCQxgcE2xbAnQtKYm/GchzLBoCtwSlBgiWoORBNCJhNMMDbMi58vi5/mNemV+mX54HkxlKK/dDshkSM4CQxOeKeejaBPlBb6sb/OEcrgMHrYT7OmHTGkg07DqSRhGaGF0o7cQN8HJz9Voc1Fs9vuHBSRvYwWhCaaONpMEIQmkjiaSBhtKUUkoppZRSSiml9jCNqimllFJKKbWHnHbaaTWjagCrV6+elKjaq6++yoMPPsiTTz7Ja6+9xptvvkk2m6VcLhOPx2ltbWXevHkcd9xxvP/97+ewww6b8DmAxGXuu+8+nn/+eV544QW2bt1KX18fmUwGy7KIxWID8znggAM46KCDWLx4Mcceeyz777//hM/nzTff5L777uOJJ57g1VdfZePGjWSzWWzbJh6P09LSwsEHH8wxxxzDqaeeytvf/nYMYxR3qk2QtrY2fvWrX/HEE0+wevVqurq6KJVKTJ8+naamJg4//HDe85738N73vpfpk/RDdsdxeP7553nyySd55ZVXWL16NRs3bqSvr4++vj6CwSCpVIpUKsWsWbM4/vjjOfHEEzn66KMJh8OTMqfdmQrXbWebNm3iwQcf5KmnnmL16tVs2LCB3t5e8vk84XCY+vp6pk+fzqGHHspb3/pWTjjhBE444YQJv4ZT5XvCRNsXn6dqz+ns7Ky5/MADDxz32AceeGDNqFpHR8e4xx6Pk08+edh1f/3rXyclqrZu3Tp++ctf1lz3rne9i4svvnjCj7krCxcunPAx98b30KVLl9aMqv3P//wP27Zto6mpadzHqPjNb35DV1fXkOWRSIRzzjlnwo4zFsNF1RYtWrSHZ6Km8t8lpsrftybTvvLvGIBnn32WBx54gKeeeorXX3+dzs5O+vv7icfjzJkzh4svvpjLLrtsr8xNKaWUUmoqCRg7Bt9f7XuVjJ2puW1wy5sEZ9mjPkbJhC11DNwVVjahPwR2CTY0yLJIGY7ZPOhYO98tOgwz10/jbTfQdekVo56XUkoppZRSSimllNp3tHW2sWL1iqEfCpDLQTYLiQTEYgBszWxlXe+6gU28jRtg83oYCB65sLkNQv3Ys2bSW+gd+OABN5smbw0/D6dcpGSZhBwP05NwV3MdJCMSv3JcSBegPQtlByKmw8IZsKgVWuMMKSfFAjC/SUJanRnoyECJalQNGPiQg7qQxNJiMBAzOyQJs+LQkwMzJLGz/RokVla25ZfrQGMUQobEx0IBaK6XGFblahpIYM3wY2IhE/pzsK0PzCBYFgRMaM9IDCwckHhbEAl69ZehLiJjev540+IScLNd6C+C4/hzMCTSNS0h54khx62L+KEvQ65dVx6aE35oDYmdebbE4wImlP3r5HpyjQq2HKsSaguYYDhyrQz/mOag6+94fhStMgcAG6mvGXLOEQsKjuyLH5hz3Gr8zC2DG6qOOSOx42Ocy0P9wAMJ/XkIReXLkAX7J6HkDKwm3wvxmPy+6EiILFUP8ZCcj4sE5oolyAbAC0AgJIGyQEBCbIGQnC9+xM4yIRoGwpDrlzk01kPY8wN2wNZtEMtDICqhNA9IZ+QaTWoozYN0B2S2Q1MYtnhQystDEE5AMSBjRm259tkeiD8NTfdC/UHgjDCSBqMLpTXlYFEHvDCDIRZ17DgujD6UtttIGowslKaRNKWUUkoppZRSSqkpzfAGf+StUpOsrq6ObDa7w7JEIkEmU/tNwUoppUbv1Vdfpdb/3g3D4NBDD53w47228q8TPqaa2uYtPnhvT2HSzZkzhw0bNgxZ/uijj+4yUrI7mzZtYvbs2TXX3XDDDVx00UVjHnsw27a54447+OEPf8gLL7wwqn2POuoovvKVr3DmmWdOyFz+8Ic/8J3vfIdHHnkE13V3v0MNBx54IB/+8Ic5//zzxx1lePTRR/n2t7/NI488UvN75XDmzJnD5Zdfzic+8YlxRReWLVtW83He+bn13HPP8cUvfpGHH354RONGo1Euv/xyvvSlL5FIJMY8v4re3l7uvfde7rnnHh5//HHS6V1/smQt06ZN47Of/Syf/vSnaWhoGNd89pXrtjPXdVmxYgU/+clP+POf/zyq51xlfu9973v5+Mc/zrvf/W5M09z9TjVMpe8JE2kqPE+He26O11R9qeaxxx7jlFNOGbL8gAMOYP369Xt+QiP0mc98hmuvvXbI8quuuopvfetb4xr73HPP5fbbbx+y/Pvf/z5f+MIXxjX2eKVSKXp6eoYsv/322/noRz864ce78sor+d73vldz3YsvvjgpkbM9YW9/D7Vtm/32269mqO+HP/whn//858c89s7OOOMM7r///iHLzz77bO64444JO85YLFy4kFWrVg1Z/pvf/Ib3v//9e2FGo1cqlWhtbaW7e8d3ZSYSCdrb24nv7tNqR6Gvr4/W1lZyuR3fRdrU1MTmzZsJBoPD7Fnb3v5zsCuT/fetqfL/+r397xgY+d/Jf/WrX/HNb35zt8+Vj33sYyxbtmxcc5poe/o1NaWUUkr9bRvpz88y5Qw3rbtx4O8h6XKaV/teHbqh5xF9aSWGM7bX2bfFJKRWkQ/IjXAVh3XCki3ye8ODzz0FyeLIxnbq6vnrI8/KHZt/Z/bEz8xWde7deP++YEFzy96eglJKKaWUUkoptc9KF9Ks7VlLujjovVfpNHR3QyoFySS5co7lbcvxGPSzNM+DlSth9WpwXXltaP58WLyYnkIvG9L+e3Btm651bZRrvXRkANObKLplXM/F8DzIZChZ1ZDZzjwgEDZY1OxxZAu0DPowgYFh/WkmQpAIwLaCH+Lyl+8cSQuZEp4C2a69TwJk8YjEsAw/EtZcJ8uLBTllw5AQVSgCtgGdWQmdJaPV07NMiAclRlYs+ccwAAuCFnTn5JwaoxANVufv2pArQns/1McltIYH2YLMq/LZWlET7DI4phwraEGuLMGxaEgiZ0U/7NaehpakbAMSCcMBBwldRYNQKsr8smXYnoOZ9RA0IWxIhKxQkvUJPyzmupApQ0dexpwWh4YQ1IVlXX8BwmHZN1+ErA2b/dvHpsegLihjGUAhB0EPAgnIlyUIV3JgSwZm+fMwPAm1RfyoWtmErAeVt0HUhcD2ZH/Pg55umJWQKFvZhs390DhNrlUsKNfC9qBQhmwOyj0SvPPCEsorO1DfINE+04/iOUAhz0AsLhqGXKH62HkGBAPyXPKcakSsbENXr/w+ZEBzVB6X7XmZa7ws63otWP1m9fkStqF1UCitFJJti3nYtAneHPQWkJZs9XVf05LnZ48fSgPYOiiU1prdMZS24CH46UKJ+CVSUEzBG81wSCeEuyHbLRG7S5+RqNm2GPx0SfX8dv4zWNlusHsPrR1KO6IdPrDTy+IlC25eBJvqq8v264MLXpSI2pgddRR897vDr9dQmlJKKaWUUurvkDZglFJ/qwJ7ewJKKaWUUkr9vZg2bdqw67Zt2zYhx3jyySf5p3/6p5rRiZF49tlnOeusszj99NO58cYbaWpqGtM4fX19XHLJJRMS4Fi3bh3f/e53eeONN7j77rvHNMb27du59NJLueuuu8a0//r16/nMZz7Dddddxw033MDxxx8/pnF2x/M8rr76ar75zW/iOCP/qX8+n+db3/oWd9xxBw8//DAHHnjgmOfwhS98geuuu45SqTTmMQC6urr46le/yve//31+8Ytf8KEPfWhc4+3KVLhuO3vqqaf41Kc+xfPPPz/mMfL5PPfccw/33HMPV155Jd/d1Rs5hjFVvidMtH3xear2nubm5prLawXHRqurq6vm8qnwZ2W88ZzRsG172DDOe97znn02qDYVvocGAgHOP/98vv/97w9Zt2zZsgmLqm3dupXf/e53NddNRlBqNAqFAqtXr665btGiRXt4NmMXCoU455xzuO6663ZYns1mufvuu/nYxz42Yce66667hgTVQEKQow2qTYU/B8OZKn/fmkz7yr9jADKZDB/72Mf41a9+NWnHUEoppZT6W1QXrGNu3Txe61sDQDKYpC5QR8be8U2JZrEw5qAaQLIA/UEGbjAdHFSLlOGwQT+iOLxz5EE1ACvTR2jDWkoHHjLm+SmllFJKKaWUUkqpiVMzlFbDup51PL7x8eoHDw0TSuuaP4c3M5sG9vM8Dzo7oasdBt6W40JXG7zUhTN9Gl2FLgJGALMvjYtE/mvKpXFDIcpumaDtYgIRA1JJCVlZpoSp+nKwrR+W7AfHHOQNBLZMTyJXg8UDML8Z6oPycljZg41p6OgGPChaEPGDakELLEcCXYYB0xJwcCMUXOjqh6InIa0Z9RIGMwAiYNsS+QrKmWOa0JzY8XMHDCTcZRgQCIBlQX8OtmcgngDHk7DZ9oxs53oSZTNdiZd5pszHNGUOBhItK7n+1x5s6oHGmIxjGnI+gbLMyTIkrFUuQ8GWfbry0JKQsQwgYEnwzTFkjDL+NfWfEkVHzhOvem4BFzxXjufiB98qzwLXj4n5EzYAbMDyY2MWRCwoOBAOyLKyLccOuLK9Y1cf05AFM+v8OSDbl3ISjHNNidn19UJDSkJulimPpWlCOivRskwBGqKQLcl55PLQkoKgPz/L8McrQalB5hMKQRwZz/X3CYfkehX9cSp/ZLb3yvPJMiDoQNqGjn4IuZAIghOV+XT1QsmGQkEiZV1dkGiERIs8Tvjn3v1XeY5WNBTkeOkOyGyXUNoWP5RmAeFBobSovWMobfpDcNcwobT2GqG0wzthVQukO4FOKLVDoRf8ZhwLOquhtKYcLOqoHUlb1DE0qAbwvtdhe2xoKO19rw/dNuTA/30O3khBb0SuwyHdQxqKo3PaaXDXXVBfv/ttlVJKKaWUUkoppdQ+T6NqSimllFJK7SEDbzwZ5bqR+tGPfsQ///M/Y9v27jfejfvvv5+jjjqKBx98kEMPPXRU+/b09HDaaafxzDPPjHseE+GVV17h9NNPZ/369eMe67XXXuOUU07h+uuv5+Mf//j4JzeI67p87GMf49Zbbx3zGGvXruWEE07g8ccf56CDDhrTGCtXrhx3qGqwvr4+PvKRj7Bq1Sq++c1vTti4FVPlug32ve99j6uuumpC/ixWFIujuIvRN1W+J0yGfe15qvauJUuW1Fze1tY27rFfeeWVmsvf9ra3jXvs8SiVSmzfvr3musbGxgk/3p/+9KdhA7EXXnjhhB9vT5hK30OXLl1aM6r20ksv8dxzz03I8+2WW26pGSbdf//9ede73jXu8cfjpZdeqvk4NDY2sv/++++FGY3dhRdeOCSqBhLIm8io2nCRw9EG8qbSn4OdTZW/b02mfeXfMQC9vb2ccsopvPDCCxM+tlJKKaXU34MlqSW8nnlt4OcEs2KzeLXv1YH1EStCS6yZuu5V47phLFaGrhhkQmAPuinvkG5ZB3Ij5kkbRjeuZxg49Q3jmJlSSimllFJKKaWUmihtnW2sWL0C19sp0J/LQTYLiQTEYhTtIk9uenLHbTZuhM3rYKA35MLmNrLBXnqT1dvPMoU+nEIPNNSYQKEd0v2UPQfHtQmWSlghKFvg1npxyymBAwHTYcFMhyNbYEadxLMGiwVgfhNYHnRkYUu/xNZM/623lbHr/FhayJBtAULA3EaYn4KeHBCA/Rol8FV2wC5LfKshBgFDxgxb0FQPnVkJnYUGvZ5mWRJ8MzwoFP0AmSfjmSb09ktIrSEm4TFkNa7j/9eojheyqpEz24VsEVxb5u/52wUtyJWrETQMKNrgOBJL68pDS52MZwAhU6JwGBLdspAwmGvI9rbLQJTO8kNxhn8OpinnU5m36z+NBn/eQ9CRa185L2tQKM3ya22uJ+OaLhgOeKHqY5SKQXe+GkorOrJdpUdXKkHJkKAZQCoq16JQBtuBfEE+KCKQlK+DlpxjwKxeAxOIBKCpUUJmngVGGJriEjArlSAQkscJTyJ2lgnRMBCWIFsk5D9GjryemitAXxbiHrgRCeXZDmzvkRBba0gCfTlLjhcqgVOE3jys2SzzBomftWbl9+kOiGyAyHToS0LfNjBLey+UduIGeLm5+ufvoN7q417rtePRRNIq13I0oTQDmNs9zMqKo46Cr34V4vHhtzEMWLAApk/fzWBKKaWUUkoppZRS6m+JRtWUUkoppZTaQ7q6uoZd19DQMK6x/9//+398+9vf3uU2hmEwY8YMpk2bRjQapbOzk61btw4bJtq4cSPveMc7ePLJJznggANGPJdzzz13t0G1SCTC7NmzSSQShMNhMpkM6XSazs7OCQ0lrVmzhlNOOYXOzs5dbldfX8/MmTOJxWJs2bKFzs5OXNetuW25XOYTn/gEjuNwySWXTNhcL7vsspphMNM0aWlpobm5GdM06ezsZPPmzcOOs3nzZi688EL++Mc/Yhjj+ky2murr62lsbKShoYFIJEI6nWb79u3DxntAooFXX301zc3NXHbZZRM6n6l23T772c/yk5/8ZLfbhUIhmpqaaGlpwfM8uru72bZtG7lcjY/nG4Op9D1hb5hqz1O1d/3DP/wDdXV1ZDKZHZY/+eSTdHd3k0qlxjTuCy+8wJtvvjlkeWtrK4cddtiYxpwoK1euHDY0dMghh0z48e6///6ay8PhMO9///sn/HiTbap9D50/fz7HHnssTz311JB1y5Ytm5Co2nARro997GOYgz9GeS947rnnai5ftGjRHp7J+C1evJgFCxawatWqHZb/8Y9/ZP369cyZM2fcx3jjjTf485//PGT5kUceycKFC0c8zlT7czDYVPn71mTal/4d4zgO//iP/1gzqGaaJk1NTTQ3NxONRunq6mL9+vU1I45KKaWUUn/PUuFpzK2bx2t9awBIBpPUBerI2PJaRtEp0tDQSlP9bEKbhr4WMVIzM/DMTOgZdN9YQx6ObK9+ffigG+hGKnfsP+BM05vRlFJKKaWUUkoppSZTupBmbc9a0sX0sNvkyjmWty3HY9CH/HoerFwJq1dLIcs0Yf58uubPobfQW92sXIaOdRAZOq6zbTOFaBOGaREwA0QKJbK7mmypiBkK4TgOJQsSHhxQB7G4hKscFzJ56OyT3x+3n8Ox+7sEDAk3mZ5Me+dQWsSQyFJ9CuY0wra0RK3iEQhHIByA1jqJgtllyBdlnMYYxEOAAdPrIV+GWFDGDpoQCEM8KGPZ/o+7DQ9cZD/DgFxJQmtBy9/XkHBXNAyFAmzvg/q4RNBcB7IliIYkLhYNyDwKJfBMibWBjG8AdVEJhrmAY0NHGlqSEETiZkELAv56y5AoWLksgTTXgLwDZVe2M5C4mOEAnszd8n9VQlkFB4L+7ytvCQm44LmyvYdE0vCPZyDHcdxq/MwrDQ2lbcnIYwASSgsZMq6BxOsqnz0dsmD/JJSc6nXI90I8Jr8vOhIiS9XL9Q+Y/mMRhmIJ7DpwS2AEIexBMFAN3EXC/vEKcv6WBVELevKyrnK+hg05R/Z1KuE3/1zKtsTT+i1IxSFsQK4oYwRsCZZlilD04PUNMqewDeV+SDZDYgYQkqhfMQ9dmyAfrP7xaCiwg/23QvkeuMUGw9q7obSmHCzqgBdmMMSijqGvHY82kgYjCKWNJJIGGkpTSimllFJKKaWUUrulUTWllFJKKaX2kFo3mVcceOCBYx73xz/+8bDBA8MweN/73sdFF13ESSedxPSdfnicy+V45JFH+OlPf8rvfve7Iftv3bqV888/n8cee2xEQY27776bBx54oOa6t7zlLXzqU5/iPe95D3Pnzq0ZrrJtm9dee42XXnqJhx56iPvvv3+3IYHh9PX1ceaZZw67fzwe57LLLuOcc84ZEpjo7u5m+fLlXHfddbz88ss197/sssuYN28e73jHO8Y0v8Fuvvlmbrzxxh2WnXTSSVxyySW8+93vHhL82bhxI7fffjv/9m//Rl9f35Dx/vSnP/Hzn/+cf/qnfxr33BYvXsyZZ57JUUcdxaJFi5g1a1bN7TZv3swTTzzBDTfcwO9//3s8zxuyzRVXXMHxxx/PkUceOe55wdS7bt/4xjd2GfhoaWnh4osv5r3vfS/HHnsslmXtsN51XV555RWefvppfvvb3/LAAw9QLBZHPY+p9D1hT9lbz9OWlhaOP/74ga/T6XTN7xlz586lubl5FGekJlI8Hmfp0qX8+Mc/3mF5uVzmO9/5Dv/+7/8+pnG//vWv11x+ySWXTErUcjRWrFhRc3lTUxMHH3zwhB/viSeeqLn8mGOOIRaLTfjxJtNU/R560UUX1Yyq3X777Xz/+98nFAqNarzB/vd//5fVq1cPWW4YBhdeeOGYx50ozz//fM3l+2JUDeDCCy/kC1/4wg7LPM/jpptu4mtf+9q4x7/ppptqLr/oootGPMZU/XMAe/7vW3vj//X70r9jAL73ve/x2GOP7bDs7W9/O5dccgnve9/7hjxH+vv7eeCBB9iyZcuEHF8ppZRS6m/FktQSXs+8NvB61azYLF7texUAD48t+S1E3/k+kvfcjrVTOH6kLA+SBWjuB9uUGxqP3STLYegNdCPR//YT2Ppvu48eK6WUUkoppZRSSqmxa+tsY8XqFbjeoA9YyuUgm4VEAvz3ZmzNbGVd77qBTTzPgzc3wub1UF9Z6sLmNvqDPfTUV28py6Y7sesZVjnXg2PJz3hDpRK2VQ1qDeGUiHgui2c6LGyF1riEuHbePhqA/eqgVHbozEDJk+CSWXlLlwGRoATVgqYEvQykpTQ9Bgc1Sqyrqx8KHsyol2AXgBGAaTEIWGDbYLhgGxLVaqmXSJrjybxiQRkzZEEoAv052J6FjCvBtkQItvWBgxwjHvavLxIz8zwJsoWsaiTN8GNkjgP9jr/eD5sFLYnL5cvVCFrIkphY2ZZxu/LQkhi4DIRMKPgVNgv55XjVUFbRlhCagZyj6UlDL2BJmC006O7Byud0Df68rqADXiUs5skcI1Y1kuYZEqmL+PsYHpTy4PkRvpAFM+vkcQIJpgXwH3ckPFZCzjsWlPGDAYnJZXNQzkM4CF4Y8jl57RL/2lSugeNJdC4aBsJyTV1Pzi/gyvqsLcsThgTKPMOPpPXL/BvrJRQXdCBtQ0c/hFxIBMGJyrXr6oWSDYUM9K+FqAmx6eBFIeJ/xmWoCK90yHmBH0rzIN0Bme3QFIYt/jWygHACigGJr0Vtec5keyD+NMTvBYpw2PypEUp73+uwPQabBn0/2K9Pltey20haxVFHwXe/O/x6jaQppZRSSimllFJKqQlmeLXuIFZqktTV1ZHN7viZNIlEgswY3/SrlFJqqFdffbVmIMQwDA499NAJP95rK/864WOqqW3e4okPcUw1c+bMYcOGoXcOPfroo5x88sljHvfSSy/lpz/96ZDlhmGwffv2IQGokXjyySc54YQTcBxnyLq3vvWt3HbbbRxxxBEjGuu+++7j3HPPrRmb+ta3vsVVV1212zFOOeWUITfUA1x11VVcffXVow4nuK7LH//4R370ox8RiUS48847R7zvxRdfzC9/+cua60488URuuukm5syZs8sxSqUS3/zmN/n2t79d83tra2srr776KvX1u3g30SDLli3bbdAimUxy0003cdZZZ+12vE2bNvGOd7yD118f+m6FefPmsWbNmhHNa7CTTz6Z1atX85nPfIYLLriA2bNnj3qMJ554gqVLl9ac1wknnMDjjz8+qvH2hev2u9/9jve+97011wUCAb785S9z5ZVXkkgkRjxmX18ft956K//+7//OBz7wAX70ox/tdp+p9j1hskzF5ynAY489ximnnDJk+Y033jglwkTjNdz5HXDAAaxfv37PT2gUurq6OOyww+jo6NhhuWVZ/PrXv+b0008f1XjXXHMNl19++ZDls2bNoq2tjWQyOa75jkc2m2X//fenp6dnyLqLLrqIG264YUKP5zgOdXV15PP5Ieu++MUv8p3vfGdCjzeZpvL30L6+PlpbW8nlckPW3X333Xzwgx8c1XiDXXLJJfzsZz8bsvzkk0/m0UcfHfO4E2XJkiU8++yzQ5bfcMMNowqFTRWdnZ3MmjUL27Z3WH7QQQfxxhtvjCvK6Hkec+bMYePGjTssD4VCbNmyhWnTpu12jKn852Aq/H1rT/y/fir+OwZG9nfyaDTKz3/+c84777wRjzuV7OnX1JRSSin1t20sPz97cOvveK2v+trkK+lXyNjyXgoDg0WRubTe9d8YO/17YiQ8y2LbKafwvLEZD/k7T70R47DgAQPbHBqYzfsiS0Y2oAHFQ96C2zD6n2v8LdkTPzNb1dmx+43+zi1obtnbU1BKKaWUUkoppUYtXUiztmct6WJ6l9vlyjmWty0feE0Hz4OVK2H1aqlimSbMnw+LF9NT6GVDuvre167sNsqdW6DGXWOuAflkHNO0MDwPJ5uhbA3drsIDnKAFrgMuhE1oroeGiESqXBfSeejsh6NnwQkHSGDLNSTw5be6BsJqdSGJpUUq8TEXNvXCtgw0JSAelXjWfg0S4LIdKJfl8A0xCPljeQbYngTKYuHqfAOmhLIMA3ChUIQicrlCAYmx9RWgIQrRoOxj+tGuXBHa+yVU1pSUYFgmB9kSzGiQMeNBiWVl/ahWsewHyIzqw2QgX9sOtKehJenHxCyJxeXy4BiQiMj2+Tz0lSSo5hkwq14CZLGgzKu/LJE3y4B8AXqKsM1/u05jFBqDEnTzkLFDFoTCUChJkK09AwVHgnSJEFguREJQLkhYzDWhFAK7ElvzqucDkM/A9CAYpsx3ay/EmySUBjKmZ0gozXbAyUJDGMp+EC5bhpYUBP3H3PV/9WehVJZgWSgp87VMWWcC4ZDE4XIlCb9VZPplv1SdxN6KRegoQsCGhANmDMoR6OmT56frSWCvNQSWA1kLyhbUFeWDJ3otWLNZri1IAK3Vv/3NtCA8HRr8t4Rt3wAbg9VQWmt2x1DagofgpwvBDEIiBcUUvNEMh3RCuBuy3eCW4dJnqlGzbTH46ZJqKG0ww9txW4B7D60dSjuiHT7w6o7LShbcvGhoKO2CFyE09O0heMAbKeiNSDDukO4dngqjd9ppcNddMIr3KCillFJKKaWU2nO0AaOU+lulUTW1R+n/UJVSavJpVE1NNo2qnTymMTs7OznwwANrhjBOPPFE/vjHP456zHK5zKJFi1i9evWQde9973tZvnw58Xh8VGO++OKLnHTSSaTTO75RJxwOs3HjRpqbm4fdt6uri5aWliEBhrPPPps77rhjVPOoJZvNjjiQ8Mwzz3DMMcfU/H54wgkn8OCDDxKNRkd87B/+8Id84QtfqLnuc5/7HNdcc82IxtldiKC5uZmHH36YBQsWjHhu69ev57DDDqv53Hr88cc54YQTRjwWwB/+8AeOO+44IpHIqPbb2fbt2znllFN4+eWXh6x7/vnnRxzjgKl/3fr7+3nLW97C5s2bh6yLRqPcddddnHHGGSMeb2e2bfPXv/6Vt7zlLbvcbqp9T5hMU/F5ChpVm+oefvhhzjjjDIrF4g7Lw+Ew3/nOd/jMZz6z2/hnPp/ny1/+Mj/+8Y+HrAsGgzzwwAO8853vnNB5j9a//Mu/8K1vfavmuj//+c8cd9xxE3q8119/nXnz5tVcd/PNN3P++edP6PEmy77wPfSCCy7glltuGbL89NNP57777hvVWBWFQoHW1lZ6e3uHrJsKj59t2yQSiSF/bgFWrlzJ2972tr0wq/H7wAc+wK9//eshyx977DFOOumkMY/7yCOP8K53vWvI8g9+8IPcfffdu91/Kv85mCp/35rs/9dP1X/HwO7/Th6Px7n//vvH9Rze2zSqppRSSqmJNJafn3UXu7h9w20DfydJl9O82le9+22/jV0sfHbsP5d78aiD2bx/NbZ8aP2hJINyF6BhGJwz5zxSob/vSNpoaVRtatComlJKKaWUUkqpfU1bZxsrVq/A9dwdV+RykM1CIgGxGABbM1tZ01UN8XsbN8C69UMHPXAO9qyZbM5sHnh9yU53kS32DzuPcjiAY5mEHA+zWMYLQXMdJCMStXJcSBegPSsRsmAYFjbDolZojTOkshQLwPwmCWF1ZuRXyatG1QAcU4JiR7RKdC3oyjrDgFRU4lnbc1D0oD4CSf/HowYyp3gQHAfKRQk/2aZEwGIh6MlLiCsakFiaaUiMykQiX5vSEAlLVA0PsgWJlAUtiARknoWChM5sDzrSMKPRD8L52yei/nyRSJfhB8ICJuCAg0SxokEoFWWsXAm6+iEYgpaExM6iQSiXJHIWj8hccznIlKHDD6VNi0NDCOrCEgXrL0A4LPvni5C1YbN/K9b0GNQFIRGW+RRyEPQgkJDgXMmFkgNbMhJrC5pybQxHgmQmEj/LehD0I3N1oWqwzvOguwummxBPQNmGzVkwwpCqh3hI5uX6D1axJE/lhjAQku0dFwxLHivTkOuXK8l1G/gjUJDQm2lCwAHbkv36shD3wI1AICCPZ2eXBNDiGUjG5fnZH5Q4m+UH8lZ1QG+5On7YhtZ+SDZDYoZE5OJlKOZh0yZ4M1jdtiUL0UGfb3HYJrivEzatgUTD7kNpK+bDqkEvW61tgIN6q18v6IAP7vT2jH0qlHbUUfDVr8Ku3kdiGLBgAUyfPp4jKaWUUkoppZSaZNqAUUr9rQrs7QkopZRSSin1t862bS644IKa8SaAT3ziE2Ma95e//GXN4MGRRx45puABwKJFi/j5z3/ORz7ykR2WF4tF/uM//oOvfe1rw+776quvDgmqAXz+858f9TxqGWlQDeDqq6+ueTP8fvvtx29/+9tRhQgALr/8ctasWcPPf/7zIet+9rOf8aUvfYmWlvHdtGMYBjfffPOowmAgEcArr7ySr3/960PW/epXvxp1VO0d73jHqLYfzvTp07n77rtZsGAB5XJ5h3XXX389//Vf/zUhx5kK1+1HP/pRzcCHYRjcc889vOc97xnV3HYWCAR2G/iAqfc9YTLta89TNTW8613vYsWKFZxzzjn09fUNLC8Wi3z+85/nJz/5CUuXLuXEE0/kLW95C42NjTiOQ3d3N6tWreKRRx7hxhtvpKura8jY4XCY22+/fa8H1VatWsX3vve9mutOPvnkCQ+qAWzcuHHYdQcfvO/EePeF76FLly6tGVV78MEHaW9vZ8aMGu+q3I1f/epXNYNq9fX1fPCDHxz1eBPtlVdeqRlUA/jkJz9JMBisuW6kDMPgoYceGvXfDcfrwgsvrBlVW7Zs2biCVMuWLRv2eCMxlf8cTJW/b022ffHfMRU/+MEP9umgmlJKKaXUVJAKT2Nu3Txe65ObZJPBJHWBOjJ2BsMu0921jnxgxxv5RiofgJ7udRgz6/ECQeqCdQNBNYC5dfM0qKaUUkoppZRSSik1TulCmrU9a0kX08NukyvnWN62HI9BPxf0PFi5ElavlnqWacL8+bB4MT2FXnoLvbKdbdOVXk+5Vp8oux66+im6ZVzPxfA8KPZTtsAdppzkOTaBgMGCmR5HtkBLHUMqS4Y/zUQIEgHYVpDIlekBXnXsuhAsmAEhU4JWyRQc1ADbeiWsFo9ANCyn1lIvUa2SLXGwkAHTEhDyj9lUD9uyElUbmAcQC0qjKWRBMAr9OdjWB2YQLEvCZu0ZSMUgHJB4W1AuG/1lqIvI8T1/vGlxiY3ZLvQXJdYWMiSKZvlzMv2NDUP2N/yvyw505aE5UY2sBSzwbAmpBUwoI/u7/nUq2HKsgFmNxJk2OJ4czzT94JvPdeVae5U5ANiAJXMMWhCxJMwWDsiysi3jB/xYnWNXm2UhC2bWSVANZPtSDqIhcE2wDejrhYaUhNwsU2JrpgnpLHgupMsSdsv6b7UL+F3AgFl9nBw/aBYNQ8mAqAnFSjzPhpwDwYAfWXNkHiBz78tCvwWpOIQNyBUllhewJViWKUpw7/UNEm4L29CahawhobSWFJTCEknbvh7CDjDorccNBXnepjsgsx2awrDFg1IeLCCcgGJAxo3afkyvB+JPQ9O9UH8QOC2Q7gQ6odQOhV4o+OMv6JSgGsCJG+Dl5ur5DQ6qGR6cNPSzv3nf67A9NjSU9r7Xh24bcuD/PjfyUJoBzO0eZmWFhtKUUkoppZRSSiml1N8QjaoppZRSSik1ibZv386FF17Igw8+WHP9UUcdxTnnnDPqcT3P49///d+HLDdNk2XLlo0peFDx4Q9/mF/84hf8/ve/32H5f/zHf/DlL3+ZUChUc7/29vaayw8//PAxz2Us3nzzTe67776a637wgx+QTCZrrtud73znO9xzzz1s3759h+X5fJ5ly5bxxS9+cUzjVpx//vmcdtppY9p36dKlfOMb3xgSYFi5cuW45jReb3nLW/jYxz7GL37xix2W7/zcGo+9fd1KpRI//vGPa6677LLLxh34GKmp+D1hX7Ennqdq6jj99NN54YUXuOSSS3jooYd2WLdu3Tq+8pWvjHrMBQsWcMstt7Bo0aKJmuaY5PN5zjvvPEql0pB1pmnywx/+cFKOWytyVDGWyNfesK98Dz3ppJM4+OCD+etf/7rDctu2ueWWW/jnf/7nUc/vxhtvrLn8Ix/5CDH/U6/3pueee27YdU8//fS4xz/kkEP2eFAN5HtRU1MT27Zt22H53XffzXXXXTem51wmk+Gee+4ZsnzGjBkj+vvIVP5zMFX+vjXZ9tV/xwAcd9xxY46FK6WUUkqpHS1JLeH1zGsDr1nOis3i1b5XCXS2g+eyIQmHDu2979aGJOC6BDrbKc+czazorIF1hmGwZNrRE3QGSimllFJKKaWUUn+f2jrbWLF6Ba7n7rgil4NsFhIJiMXYmtnKut51A6s9z4M3N8Lm9TAQUnJhcxuE+rFnzaS30CvbpXtwkYB+Tbk0bihE2S0TtF1MIGJAKgnJiESyPAf6crCtH5bsB8cc5BE0JfJketUAV0U8APOboT4oUaayBxvT0NENeFC0IOIH1YIWWI6EuAw/SnZwg4TLtuUkhlUfkUAbQDwI02MS+yqXwXAleGYaEl4rlCVeZhnQEJPlA1fIYSBLVxkvZEEqAvGQxMuyRQl62WXwTD/GZkGuXI2gYUDRlqBaexpakhJiA4iHAUciYZ4BsRCUinKNik41kla5fpblh8P84JxpykNpGdWwVsGBoFFdbyAhNiMk18waFEqz/Fqb68l2ZiVCFqrG7FIx6M5XQ2lFR7bzv6RUkrCZ5S9IRWUuhTLYDuQLEClDIClfBy2Jiw2OpJlAJABNjRLCK5XAC0JTXObrepDLQzgk17RYYuDB8TzY3ivPEcuAoANpGzr6IeRCIghOVK5FV6+MX8hA/1p57GLTwYtCxP+giVARXunwj4EfSYOBUNq8Z2GNA1uSEG+AqCGBtOFCadMfgrsWSpgvkYJiCt5ohkM6ob0bst0Sc7v0GaA4ulBaUw4WdcALNd4+taijGl8bTENpSimllFJKKaWUUkpNHI2qKaWUUkopNcFs2+bFF1/kzjvv5Je//CXd3bV/Yt3Y2Mitt96KYQz34+7hPfLII6xbt27I8vPOO4+FCxeOerydXXHFFUOiBx0dHfzlL3/h5JNPrrmPbds1l5dKpT0a5fjv//7vIZEsgCOOOIIPf/jDYx63sbGRL37xizWDJbfeeuu4YwRXXHHFmPedPXs2Rx555JDwyAsvvIDneWN6jk2U/+//+/+GxKo2bNhAe3v7hMR29vZ1u/fee4cEUQAaGhr4t3/7tzHPbbSm4veEfclkP0/V1HLggQfy4IMP8tRTT3HDDTfwm9/8ho6OjlGNYVkWJ5xwApdccgkf+tCHME1z9ztNsk996lO89NJLNdddfvnlHHnkkZNy3N7e3mHXTd9H3mi3r3wPNQyDCy+8sGb8b9myZaOOqm3atIlHHnmk5rqlS5eOaqzJsquo2kRYsmTJpI4/nGAwyLnnnsuPfvSjHZZns1lWrFjBBRdcMOoxly9fTi439B235513HoHA7n8MMJX/HEyVv29Ntn313zEAn/70p/fqvzmUUkoppf6WpMLTmFs3j9f61gCQDCap88LYnfKhJh0JOCAtN/+NVD4g+wEEOjuIzpxLMliN9s6tm0cqlJqwc1BKKaWUUkoppZT6W5IupFnbs5Z0MT1oYRq6uyGVgmSSXDnH8rbleAz6eZ/nwcqVsHo1uK5Uo+bPp+etB9Fb6B3YrCu7jXJ2C9R6i0V2PXT1U3TLuK6NUcxCCMpWNaq1A6cEDgRMhwUzHY5sgRl11QBURSwA85vA8qAjC1v6JbZm+tOvjF3nx9JChmwLEALmNsL8FPTkgADs1ygxrrIjATPXkQhawJAxwxY01cO2rETVKgwkVGYaEDahUIR8GbqyUB+X5X05+W80JPGuaEAubaEkobRpCbm0nj/etLhE3DwkcrWpBxpjEgkzDZlnoAwuEvky/aBbwZZ9uvLQkvDjaEgkzLPBMWSMMn58zr8eRccPmlUiakDABc+V47lAaNCP7F2/ued60rECMP1ImefH1lIx2JKBcKB6jJAh4xrIda78aDlkwf5JKPk1PBfI90I8Jr8vOtCbh1S9xOYCpiyPhyVMZteBWwIjCGEPggG5boUiRML+8QoyL8uCqAVZG4r+epAQmG1BrgB9WYh74EYgEJBQ2/YeKNvQGpLoXs4Cz4JQCZyizG/NZgm8gcTPWrNQANLtEJ4O0STYwPYN4AbleVcrkha/F2Zb8NslIwulTc/B4Z2wqgXSnUAnlNqh0CvHB1jQWQ2gjTaU9r7XYXsMNtVXl+3XJ8uHM+JQ2ne/u+ttNJSmlFJKKaWUUkoppf7OaVRNKaWUUkqpEfr0pz9NMpkcdn2xWKS3t5fNmzeTz+d3OVZzczP33nsvb3nLW8Y0l3vuuafm8osuumhM4+3sne98J4lEgmw2u8PyXUUPpk2bVnP57373O84+++wJmddI/O53v6u5/GMf+9i4xz7vvPP40pe+hOPs+HmML7/8Mlu2bGHmzJljGnfhwoUsWLBgXHNbuHDhkPBIX18f27dvp6mpaVxjj8dw5/W///u/nHXWWeMaeypct3vvvbfm8osuuoj4rj7Zb4JNxe8J+5LJfJ6qqauvrw/HcWoGbHbnmGOO4bzzzuPUU0+dEkG1H/zgByxbtqzmuoULF/Kv//qvk3bsXf2dJxKJDLtupM4444xdhtt259prr91tUG5f+h564YUX8rWvfQ238i5b3yuvvMLTTz/N0UcfPeKxbrrppiHjAMyfP59jjz12VPOaLJMdVRvN9ZpoF1100ZCoGkggbyxRteG+B4z0eTyV/xxMlb9vTbZ98d8xAMlkkn/8x38c7xSVUkoppdQgS1JLeD3z2sBrFods6GGNK7/3DNiQhEO7Rj7ehmT1xlnDdTlofTf4L+cbhsGSaXvv30ZKKaWUUkoppZRSe0PNUFoN63rW8fjGx6vvLRkmlNY1fw5vZjYN7Od5HnR2Qlc7NFeWutDVhvP6NrbHDQwMAmYAN5smb+1iErk0bihEuZQn6MfHWusgFgfLBMeFTB46++T3x+3ncOz+LgFD4limJ9PeOZQWMSTeVJ+COY2wLS0BrHgEwhEJerXWSTzMLkO+KOM0xiTOhQHT6yWCFgvK2EETAmGIB2Usu1Q9DdeDVBwKZcCT0FgsIuExkP5TIABdfRLgCvnXpN4PdxnImP2O/DdkyGteIUtCablyNYKGAUUbHEdiaV15aKmrjhMyoeDKFxbyy/WvUcEG25VzMfyQmIH8vhJOc7zqvCtvu3AGvf0i6EikDtkFy4KIBQVHQm4Y/jkGwHTll2NXH6OQBTPr/FgbEkwLACZ+KK0EJeTxjwXl/IMBubbZHJTzEA6CF4Z8TmJsIEG1yjVwPDlmNAyE5Zq6/nkFXFmftWV5wgDDkutdtiHrv1WoKy2PadiAXBF68hCwJViWKULRg9c3yHzDNpT7IdkMiRlASEJ9xTx0bYJ8sHr9GgrV37sOHLQS7uuETWsg0bDrSBpFaGJ0obQTN8DLzdXXUA/qrR7f8OCkDexgNKG0kAP/9zl4IwW9ETm3Q7rlMRiz006Du+6C+vrdb6uUUkoppZRSSiml1N8xjaoppZRSSik1Qi+//PKEjHPGGWfw05/+lNmzZ495jIceemjIsunTp3PiiSeOZ2oDLMvi2GOP5eGHH95h+V/+8pdh9zn88MNrLv/nf/5njjnmGA488MAJmduuOI7Dk08+WXPdRz7ykXGPP2PGDE4++WQeeeSRIev+9Kc/jfkY//AP/zDeqXHIIYfUXN7X17dXo2otLS01l69fv37cY0+F61bruQCwdOnScc1rtKbi94R9yWQ+T9XU8/vf/54rr7ySF154Ycxj/OUvf+Evf/kLn/3sZ/nEJz7BVVddxfS99Kme9957L1deeWXNdfX19dx9992Ew+FJO36xWBx2XTAYHHbdSD311FN0dY3iTvGdpNO7fgM07FvfQ/fbbz9OPfVUHnzwwSHrli1bNqpI2HgjXJPN8zxefPHFST3G3oyqLVy4kLe97W1DwnGPPfYYGzZs4IADDhjxWGvXruWJJ54YsnzJkiW89a1vHdEYU/nPwVT5+9Zk2lf/HQMSGp3M/88opZRSY1EoO2xNF3a/IdCajBAJ7uqORaX2vFR4GnPr5vFa3xqMQp4ZL71K+3RI++3yjgQckIaovfux8gHZviJZgNZVa+g57Fi8SJS5dfNIhVKTcyJKKaVGplyAvs0j27Z+FgTH/2EWSimllFJK/T1r62xjxeoVuN5OH0KWy0E2C4kExGIU7SJPbtrpZ3gbN8LmdTDQMnJhcxvZYC+9yertWZlCH06hBxpqTKDQSdmK4CC1s2CphGNVg1pDOCXCnsvimQ4LZ0Brwo9r7bR9NAD71UGp7NCZgZIn4Saz8lmDBkSCElQLmhLyMpCY2fQYHNQIZQe6+qHgwYz6atjMCMC0mATLbBsMF2xDAmMt9ZAr+aExJPJl+LGzUAT6c7AtKzG0pqSEvbb1SbwrHJQwmGVIlCxfhmkJ6O6vRqfqolCyq1/bDrSnoSUJQWScoAWBsgTHLANMC8plCaS5BuQdKLuynYHMwXAAT+Zq+b8qUa2CA0H/95XPWgy44LmyvYdE0vCPZ/iPh+MH0kzAK4EXqj5OqRhsyUisDqDoB9YqwbNSCexBL1WnojKfQlnOuS8LDWFwTHkMcmVoSck8DX8e0RC4JSg1+NG2EMSRa+x6kMtDOCSPY7Ek51+R6YdSGVJ1cg7lImwrSiTNc8CMQTkCPX0SknM9KGSgfy1ETYhNBy8KEf81y1ARXunwj4MfSvMg3QGZ7dAUhi0elPIStQsnoBiQ+FrUloBftgfiT0PTvVB/EDgjjKTB6EJpTTlY1AEvzGCIRR07jgujD6UZwNzuYVZWHHUUfPWrsKsPljMMWLAA9tJ71JRSSimllFJKKaWU2tdoVE0ppZRSSqk9IBqNcvbZZ3PxxRdz3HHHjWusnp4e/vrXvw5ZvmjRIszKOzgmQK2Qw0svvTTs9jNmzGDRokVD4hebNm3iyCOP5Etf+hIf//jHmTZt2oTNcWdvvPEG+Xx+yPLZs2fT2to6Icc45phjasYIXnzxxTHHCBYtWjTeaVE/zKfOjSQmMxL9/f28/PLLrFq1ildeeYXu7m4ymQx9fX0UCoXqp3GOUG9v77jntLev25tvvklnZ+eQ5Q0NDRx22GHjnttITdXvCXvDVHyeqqnDtm0uv/xyrrvuul0+F8LhMDNnzmTatGnYtk13dzdbtmzBtofeqdzf388111zDf//3f3PTTTdx6qmnTuYpDPHHP/6Rj370o7iuO2SdZVncdtttzJ07d1LnsKuQTrlcJhQKTerxx2tf/B66dOnSmlG1O+64g2uuuWZEcaMnnniCN954Y8jyQCDABRdcMKZ5TbTXXnuNbDY7ZHk8Hqevr29CH5+95cILLxwSVfM8j5tuuomvfvWrIx7npptuqvl9baSBvKn852Cq/H1rsu2r/44BWLx48XimpZRSSk2KNe0Zzrr+zyPa9teXHs+i2Q2TOyGlxmBJagmvZ14juup5DNtmTi+86N/U5xmwIQmHjqBBviFZvXEQYE4vGOUykVXPUzj6eJZM23uxaaWUUr7ONvivd4xs24//AWbpv8WVUkoppZSqJV1Is7ZnLeni8O/7ypVzLG9bjje4JuV5sHIlrF4ttSjThPnz6Zo/h95Cb3Wzchk61kGNzrGzbTOFaBOGaREwA0QKJYb+tLvKtB2cgInn2JQsiAHTkpCMSADLc6AvB9v64aj94LgDbYKmBLoMP15meNVgV11IYmkRP7B1UANs6oVtGWiqg3gUomHYr0HCYrYj0THXgYYYhJAgWSgATfUSNwsNCnxZFsT9WBoWFIpgU42hmQZki9AQld+DzMNxpNtlGzLnypj1/tsaHAeyroxd9ANolgGNMTk/A3ltKx6Sh8ZDAmse0JWHlkT1WCETCq58Yck0cbzqa2NFW0JoBvIQm56MGbBkzqFBd9dV3oYz+O04QUceF88APLmOEasaSfMMyBch4u9j+MEwz3++hCyYWSdBu4HtcxAz5No7QHcaGuvlfAOmLI+HJUyWDYAXgEBIwmOBgITYAiH/cfGgUJLnTzQMhCGdhYj/tp2QI9G2XEECbXEP3IiMYzsSS8sXIL8RknEIhiEQlEia50GpF1Z1QG+5ek3CNrRmJWyWbofwdIgm5bmxfQO4QWCYUNr0h+CuhWAGIZGCYgreaIZDOqG9G7Ld4Jbh0meA4ugiaTD6UNr7XoftMdg06O2k+/XJ8lpGFEoDiaV997vDr9dQmlJKKaWUUkoppZRSk0ajakoppZRSSu0B+XyeN998k2QyOe6x2traai4/9NBDxz32YLXiZ11du75L6wtf+ELNEEc6nebLX/4yX/va13jnO9/J+9//fk455ZQJn3OtOAjAkUceOWHHGG6sWiGKkZqI0Fwikai5vFaMZKTy+Tz33nsvt99+Ow8++CDlcnn3O43QRMSq9vZ1W7NmTc3lRx99NIYx3OcOTryp/D1hT5jqz1M1Ndi2zTnnnMPy5ctrro/FYixdupSzzz6bJUuWDAmB9ff389RTT7Fs2TLuvPPOIc+zrVu38r73vY877riD//N//s+kncdgzz77LGeeeSaFQmHIOsMwuPHGGznjjDMmfR7RaHTYdYVCYcpH1fbF76FnnXUWqVSK7u4d353Z29vLvffeO6I40o033lhz+Xvf+15aWlrGNK+JtnNsrGKiQ1970znnnMMVV1xBqVTaYfnNN9/MV77ylRH9fcLzPG6++eYhy8PhMB/96EdHNI+p/Odgqvx9a7Ltq/+OATjkkEPGtb9SSimllKotFZ7GPHMm216WDzFpLECyAGn/JsyOBByQlpsRh5MPyHYVyYKMAxBte4n9j/0QqVBqks5AKaWUUkoppZRSas9p62xjxeoVuN6gClYuB9ksJBIQiwGwNbOVdb3rBjbxPA/e3Aib18NATMmFzW30B3voqa/ecpVNd2LX/vxOAMq5HhxLfpYdKpWwrWr0bAinTDBgsnCmy6JWaI0jlaZBYgGY3ySLt2WgMwMlPzRmVppwBkSCElQLmmC6st4y4ahZEgzbnoOiB/URiAVlt5AJVlhiZo4D5aKMaRsSvkrFoCcvUa9oAGJhf36eHyALQmcaImEJl+FJoK1og+tBJCDjFErgmTAtAd391VOsi0oczUD2zfjHCvjRs0gQcGQMxz9eqSAxNdePyRVssF2JlRlIhMxwZDzDj7OZg66p4/lRNE/OxQC/DCdfW34kreDIvvjXwnHluppI5Msd9DaYGYkdH7f+HESDgCn7ptMSSPOfFqSiEgUrlCVk1puGUBy8sHxt+U/fgDnw8OJ44NgSSisZEDWh6D/Ohg05B4IBmafhVKNjZVviaf0WpOIQNiBXlMc1YMu5ZIry3Hh9g4TbKpG0rAHJZmhJQSkMxTxsXw9hBxj0emPDoLcsuQ4ctBLu64RNayDRsOtQ2vQcHN4Jq1og3Ql0QqkdCr0SaQNY0FmNn402kgajC6WFHPi/z8EbKeiNyLkd0j3kj+XonHYa3HUXDPPBv0oppZRSSimllFJKqcmlUTWllFJKKaX2kIcffpijjjqKm2++mQ996ENjHufNN9+sufz666/n+uuvH/O4I5HL5SiVSsMGUs4991xuvfVWHnrooZrrS6USDzzwAA888AAAzc3NHHvssRx33HGcfPLJLF68mEBg7P9M2bp1a83lBx100JjH3Nlw0YAtW7aMecyGhoYx71sxXFTCHfxxiaNw9913c/nllw/7fBuvvr6+cY+xt6/bcNdm7ty545rTaE3l7wmTbV94nqqp4Utf+tKwQbVTTz2VW265ZZchqXg8zjvf+U7e+c538tWvfpWzzz57SPDJtm0++tGP0trayvHHHz+h89/ZqlWrOO2004Z9jl5//fWcf/75kzqHil0FY7u6uqif4m8M3Be/h4bDYc4991yuvfbaIeuWLVu226haLpcb9s/D0qVLRzWXyTRcVG0iI1N727Rp03j/+9/PihUrdlj+17/+lT/96U+ceOKJux3jscceY/369UOWf+ADHxjx35Wm8p+DqfL3rcm2r/47Bnb9/wGllFJKKTU+737oFe4o2wM3Qs7phRf9mwY9AzYk4dBddIo3JKs3UVb2rzBLZd79+9WgjVyllFJKKaWUUkpNUelCmrU9a0kX07vcLlfOsbxtOR5+aczzYOVKWL0aXBdME+bPh8WL6Sn00lvoHdi3K7uNcnYLTB86rtu/hXwgjmlaGJ6HUypQ3sVbGz3HxjEtcB3yAQibMKMeGiIS6XJdSOehsx+OngX/cIBLwJJ9zUqYzH8tpy4kobSQKesaUnBQA7T3SXwrEZHIlmlCS73Ew0o25IsQMiRiVvkJbFM9bMtKVK3CQAJrhiFRsmBUgmDb+sAMSmAsYEJ7RgJr4YCEzoKAbUN/Geoicny/Uca0OJRcCZ31FyXWFjLk9SnLgMYYGH4UzjMkNua6sn/RhkwZmuvk4TOQwFrZln0sP4JmIL+vvOZVcCSEZiBzMT0ZM2D55zbo8ars51bCdJUIWUiCbiDnuiUj5wvyeOQLEPE3cJBrHPTjdHVhsD3Il2XeuSLkyhBPyONUMqCcgVS9nG/AlGPFwxIxs+vkHMNhCZZNC8j8cnkIh+Q8iiV5blSe2tt7IRKS8wk6kLahox9CLiSC4ETlWnT1ynOikIH+tRJji00HLwoR/4MaQkV4pcM/BoMiaR6kO2Des7DGgS1JiDdA1JDwWjEg/43aMqdsD8SfhqZ7of4gcEYYSjtxA7zcXH08D+od9Bz14KQNO/4ZG00kDUYfSjOAud3DrKw46ij46lchHh9+G8OABQtgeo1vLEoppZRSSimllFJKqT1Go2pKKaWUUkqN0KOPPsrJJ59cc12hUKCvr481a9bwl7/8hdtuu41Vq1bV3O6jH/0ohmHwf/7P/xnTPDo7O8e030Tp7u5mxowaH/cGmKbJnXfeyZlnnsmf/vSn3Y7V2dnJb37zG37zm98AUFdXx6mnnsq5557L6aefTjgcHtXcenp6ai6fyKDLcGP19vaOeUzLssa870Qrl8ucf/753HnnnZN6HMdxxj3G3r5u3d2130HT2Ni4R+cxlb8nTJZ96Xmq9r4nnniCH/7whzXXfeQjH+GOO+4YNrBYy9y5c/nLX/7Cu971Lp544okd1tm2zQUXXMCqVauI+Z9yPNHWrFnDqaeeOuz3oO9///t88pOfnJRj1zJr1qxh17W3t3PggQeOa/zt27ePaLs5c+awYcOG3W+4k331e+jSpUtrRtUeeughNm/evMvH5e677yaTyQxZ3tzczBlnnDHquUyW559/vubyt73tbXt4JpProosuGhJVAwnkjSSqtmzZsprLL7zwwhHPYSr/OZgqf9+abPvqv2N2Na5SSimllBofs7ebg+64m8PnwCq/A99YgGQB0v4NsB0JOCAtNy/uLB+Q9RXJguxfcXgnHPLM3az96KW4DalJOw+llFJKKaWUUkqpsWjrbGPF6hW43k4fjJnLQTYLiQT478vYmtnKut51A5t4GzfA5vUw8GMsFza3Qagfe9ZMegu9eJ5Uqtxsmvwu3oLmlIuULJOQ42F6EjlrroNkBCwTHBfSBWjPQtmBiOmwcAYsaoXWOEPqTbEAzG8CE+jM+Pt5EgMzkO0jQQmqBS0wXLA8aTRNS8CBjRLr2paDoiehtIRfT4sHYXpMImnlsuzrIcG1lnoolGWOlgENMVk+cIWcgW7XwHghC1IRiYHZLmSLEuayy+CZfozNkohYJYKGIYE0x4H2NLQkJcQGcl44EuFyDAgFoVSQ49pAwZZzC5jykFkBKCPXxnMkFOa6O4bSXFceA88vuxn4g1kS6gpaEl0rOBJK8wyJmAVMCLiyvWNLLK1yzjPrIGj6zyUDSjmIhsA1wTagr1cid3VheQ6YyNzSWfBcSJclkpYtyxgB/ykc8Mc0AMeT40bDQFjiZ64rj0nIAduCXAH6shD3wI1AICDXZ3uPnENrCCwHchZ4FoRK4BShNw9rNksMDiR+1pqVqFm6HcLTIZqUy7R9A7hBYJhIWvxemG3Bb5dIbC+RgmIK3miGQzqhvRuy3eCW4dJngOLoQmlNOVjUAS/UeLvAoo5qfK1itJG0yvXeZShtJJE00FCaUkoppZRSSimllFL7II2qKaWUUkopNQEikQiRSITm5mZOOOEErrzySm699VY++clP0t/fv8O2juNwwQUXMHfuXBYtWjTqY+Vyud1vNIkKhcIu1zc0NPDII49w9dVX84Mf/GBU881kMtxzzz3cc889TJs2jSuuuIJPf/rTxHf3hgVfsVisuTyZTI54Drsz3Fi7uy77gnK5zIc//GHuvffevT2VfUI+n6+5vKGhYY/OY6p/T5ho+jxVo/XNb35z4M2wg731rW/lpptuGlVQrSIcDnPvvfdy6KGHDol+rV27lhtuuIHLLrtszHMeztq1a3nnO99JR0dHzfVXX301X/jCFyb8uLuy//77D7vujTfe4O1vf/senM3o7avfQ4844giOPPLIIeEx13W55ZZb+NKXvjTsvjfeeGPN5eeddx6BwNR5uXi4qNqRRx65h2cyud7znvfQ2trK1q1bd1h+9913c9111+0y0JjNZmsG2WbOnMmpp5464jlM5T8HU+XvW5NtX/53TLDyMexKKaWUUmpCpW79JWY+N+QmxDm98KJ/k6FnwIYkHNo1dP8Nyeo+lf0qKjcvmrl+Gm+7ga5Lr5is01BKKaWUUkoppZTaQbqQZm3PWtLF9LDb5Mo5lrctx2PQez08D1auhNWr/fqUCfPnw+LF9BR66S30yna2TVd6PeVa3aPseujqp+iWcT0Xw/Og2E/ZAneYt454jk0gYLBgpseRLdBSx5CCk+FPMxGCRAC2FST0ZXqAVx27LiSxtJApobRkCg5sgPYMuDbEIxAJ///s/Xl8XHd96P+/zjL7jEYaa/GWzdghTuKYJHYgQBKSCxhCCQm0LDcEsvRHgRRu6TdtLy2EQFNoSkuhabnQ9kJoSNs4MQ1bWS5hKw1ZcBYS78a24k2SLWlGs8/Zfn+8z2ikaGRL3u28n4+HH9Kc5XM+52gsS2Odl2Bhl8TAHE/iaIEnETTLBMsHy4KeDthXkqja+DyAZFTCXDETanWoOjBcgo6ULB+ryNtEFPwAErZc2lpDQmlz0nJpw0YZc1LQCONsRgC7RqErKYEwM4yW2Q74SKzNDINuNVf2Ga5CX1rGaobXHFfGsozWpbTCd+phVM33wQgjZJYPeHI8Dzn/ZijNCgf2wyid6YPhQRBtXfdcEkaqrVBa3ZPtwoc0GtAw5PoC5BLyulrNkYhZtQZxB+ysPI5YYDE5kmYCcRt6uiSQ1mhAEIGelMzXD6BShVhUrkG9AROf3sWSfAxyKYgZUKnDaBVsV4JlxbpE9Lb0y74xF5wyZHshPReIynOqXoXhXVCd8F+4nRP+O9j3YNFa+M4Q7NoE6c6DR9J6kF/Q8GwfFIaAIWgMQC0voTaAZUOtANpsQ2lXb4H9Sdg14Xd5LRyT5e0cNJLWtGIF3HXX9Os1kqaUUkoppZRSSiml1CnvxLlLTimllFJKqVOIYRjccMMNnHvuuVx55ZUUi8VJ66vVKu9617t48sknicfj04zSnuu6R3KqR0UkEuFTn/oUH/zgB/mHf/gH/uVf/oXnn39+VmMMDw/z0Y9+lC9+8Yvcf//9hxVlaRfTORHGOtH87d/+7QFDVc1Ax4oVKzj77LNZsGAB3d3dZDIZotHotCGWQwkWnQymO69j/Rw5GT4nHEn6PFWz8Zvf/Ib/9//+X9t1d955J7FY7JDHnjNnDh/96EfbRszuvvvuIx5V6+/v58orr2T37t1t1//Zn/0ZH/vYx47oMWdi0aJFxOPxtlGe9evXH/P5zNbJ/Dn05ptv5kMf+tCU5ffcc8+0UbXt27fzs5/9bNrxThQ7duxgZGTqT8FGo1HOP//84zCjo8eyLN797nfz2c9+dtLyYrHIgw8+yHve855p933wwQenBJwB3vOe92BZB/h16i9wIv89OFG+3jpe9PsYpZRSSqkXJzM/Quf99wJyE2LzpkWArhpka1AI/1thMA1nFCAx4cv6qi3Lm7I12a/p/Ak3OXbdfy+j19+M35k7imeklFJKKaWUUkopBeuG1rFmwxr8wJ+8olKBUgnSaUgm2Vvcy/b89vHVQRDAzudh9w4YDy/5sHsdRMu4C+aTr+Vlu8IoPvL6SFuVAn40iuM7RFxfYlgG5LKQjUtYK/AkPravDCsXwssXBURMCYGZgYS9JkrZsLQXOiISfHICeL4AgyNAAHUL4mFQLWKB5UmEyzAkYrYoBw0PhssQi0EyDGJFTLBjkIpIzKvhyPgBEunKpST8RQBRG5JxCY8Rjm3bMDwmEbNo+N/HHeGPyRjImGVP3kYNCYlFLZljxWlF0DCg7oLnSSxtuBrG5cJxoibUfHlgIX/8MCZXc8H1w6CZD5YNDnIdA08Cbk4AsXB+vi9j+oa8D2D7QAN4QShtTxFi4ce5Hp6DHe7veBKLAzmn07NyjcNnDtU8pJLyft2DfBVyHZCKhlE3IBWTgJmbAb8BRgRiAURseS7U6hLBMwCnJh8Hy4KEBSUX6uF6gKgHrgWVGoyVIBWAH5ePkevB6JjE28rbIGFCshuCBMTD1/yidVg/GMbYCENpARQGobgfemKwJ4BGVa5/LA11W+JrCVeuRWkUUo9Dz0PQsQi8GUbSgCm/+GFRvrWu+QscJppNKC3qwS1PwtYc5ONybotHprQLZ2fVKli9Gjo6Dr6tUkoppZRSSimllFLqlKVRNaWUUkoppY6iiy++mH/913/lmmuumXIT+4YNG/jkJz/JZz7zmVmNOV2E7bTTTuP0008/5Lke7vHbmTt3Ln/+53/Opz71KX71q1/x8MMP89Of/pRf/vKXjI2NzWiMnTt3cvnll/Otb32LN77xjQfcdro4zkyPNRPTjTXbON6JZs+ePfz5n/9523VnnXUWX/jCF3jTm96EaZptt5mO4zhHYnonpOk+5vl8/oSYx4n4OeFw6fNUzdbDDz/cdnlnZyfXXHPNYY//7ne/m9tuu23Kv/GbN2/m+eefP2J/B3ft2sVVV101baD0//v//j/uvPPOI3Ks2bJtmwsuuIDHH398yrpHH330OMxodk7mz6HXX389t912G/V6fdLyTZs28ctf/rJtkPaee+5pG1a65JJLOO+88w55Lkfak08+2Xb5eeedRyQSabvuZHbTTTdNiaqBfLwOFFW755572i6/8cYbZ3X8E/nvwYny9dbRpt/HKKWUUkqpiXJf/7+Y1dZdiy+8afHMPDwzV94PDOjPwjnDrf37s61tm9s3vfAmR7NSpuu+rzB8621H/DyUUkoppZRSSin14lCoFdg2uo1CvTDtNhWnwgPrHiBgwv9XBwGsXQsbNkhByzRh6VJGz11EvpYf32y4tA+ntAe62wxc2gHDZeq+g++7GPUSRMGxWgGuSbwGeGCbHsvme1zYB3Mzk19LAUjasLQHrAAGS7CnHEbAwuk3x86EsbSoIdsCRIElXbA0B6MVwIaFXRIrczxwHfA96EyCbciYURt6O+QSNJNzBhJYMwxZHzWhXIF9JYmh9WQlALZvTGJqsYhE4SwDPB+qjkTbRsqtQFUmAQ239dj1YKAAfVmIIONELLAdmYdlgGmB40ggzTeg6oHjy3YGMgfDAwKZqxX+aV7TmgcRQz7cRvgjXZYPeLJ9gIwVt2Q/A9nXccD0JUBHAI0aBM1ImQXzM2GsDQmm2ci2PhIfayDXIxmR8SO2ROhKFXCqcr2CGFQrYbgtPJfmtfcC8FxIxICYXCs/kGtk+7K+5MrytAGGFc7bhVI1fO4WJH4XM6BSh9Eq2C74DhTrUA9gS7/MN+bCvJKEzQoDEOuGRBZcYH8/+BF5LrULpXX/EFZfAGYE0jmo52BrLywegoERKI3IMW99AqjPPpLWU4Hlg/D03Bf+hZLlEwNsMPtQmgEsmfp79yZbsQJuvx1Sqem3MQxYtgy6232yUEoppZRSSimllFJKvdhoVE0ppZRSSqmj7Ld+67d43/vex5e//OUp6z73uc9x0003cfbZZ894vGw223b57/zO7/A3f/M3hzzPo8kwDFauXMnKlSv53//7f+P7Pr/+9a/5r//6L/7rv/6LH/3oR4yOjk67v+u6vOMd7+CZZ57hrLPOmna7rq6utsuPRYxgumOfLL72ta9RKpWmLL/wwgv50Y9+RC6XO6RxR0YO9tMuJ685c+a0XX6sIx8n4+eEQ6XPUzVbv/rVr9ouv/zyy7Es67DH7+3t5fzzz+fZZ5+dsu6xxx47IjGigYEBrrrqKrZt29Z2/e///u/z13/914d9nMNx2WWXtY2q/fKXv6RarZJIJI7DrGbmZP4c2tXVxVve8hZWr149Zd1Xv/rVKVG1IAj4l3/5l7Zj3XTTTUdljofqqaeearv8oosuOsYzOTaWLl3KJZdcMuXv0U9/+lP6+/s544wzpuyzfft2fv7zn09Zfumll/LSl750Vsc/kf8enChfbx1t+n2MUkoppZRqMvMjdN5/76RlPRU4fwie7ZPHXTXI1qAQ9nEH03BGQW6mrNryuClbk+2bzh+aepNj1/33Mnr9zfidh/bamlJKKaWUUkoppV681g2tY82GNfiBP3lFpQKlEqTTkEyyt7iX7fnt46uDIICdz8PuHdDRXOrD7nW4kSL5rDH+C8P8UoHqgX7Eo1LAj0ZxGlUiBsRMmJeBZEqiWp4PxSoMjcn7r1zo8YrTfWxDAlJmIIGqF4bS4mHgqyMHZ3bBvoIEtFJxiMUhZstxvEBCadW6jNOVhFQUMKC7Q+JmyfB3h0VMsGOQishYbkOWGwHYlsS/RioSGutKSMBLLpiE2ALANSQeFg2vSUcYGvM8KPkydj0MoFmGzMcIWrGyVFQadgESWAuA4Sr0ha8pGUjArebLAwv54wWtCFfdlQiagYTgzEDGtC2Zc3TCnWq+P/V92wcaYSQtjNHlktLEaio1INucEPJ0aobjAHIJmU/NkWs5VoLOGHgmuC5UHOjLSdDNQK5FIgp+AxqdEj+LRiGFjOkHUKlCLCrnUW+05gZQLEPDgVxGYm9OHfbVJZIWeGAmwYnD6Jicpx9ArQjlbZAwIdkNQQLirowXrcP6wfA4SHhs/Dp5sGgtfGcIdm2CdOeBQ2ndE14/LAwBQ9AYgFpeIm0Ayya8LjjbSBrA1VtgfxJ2dbSWLRyT5e3MKJQGEku7667p12soTSmllFJKKaWUUkopdYg0qqaUUkoppdQx8NnPfpZvfvObDAwMTFreaDT46Ec/ypo1a2Y81mmnndZ2+fDw8GHN8VgyTZOXvexlvOxlL+NDH/oQruvyi1/8gq9+9ausXr2aWq02ZZ9iscgnPvGJaWMgAPPnz2+7fLoQzaHYunVr2+Xz5s07Ysc4Hr71rW9NWWZZFvfdd98hh6rg1I5VTfd3ccuWaX5S6BjP42T6nDBT+jxVs7Vv3762y6f7e3MoTjvttLZRtcHBwcMee9++fVx11VXTfl553/vex9/93d8d9nEO15ve9Ka24aV6vc53vvMdfud3fuc4zGpmTvbPoTfffHPbqNrq1av5whe+MClo95Of/IQdO3ZM2TaRSPCud73raE5z1p588sm2yy+88MJjPJNj56abbpoSVWuG8D7+8Y9P2f5f/uVfxm8imOjGG2+c9bFP5L8HJ8rXW0ebfh+jlFJKKaWacl//v5jVqXctXt4Pz/W2blw9Mw/PhDc9Bgb0Z+GcYXkbTLj59Mx8630jgCv6px7TrJTpuu8rDN962xE7D6WUUkoppZRSSp3cCrUC20a3UagXpt2m4lR4YN0DBBPLU0EAa9fChg1SljJNWLqU0XMXka/lxzcbLu3DKe2Bdp2k8vPUoh24gYeFgVEv41it6NkUXoNY4HPxfI8L5sK8tETHXrh9woaFGWg4HkNFaIShMbM5fQPiEQmqRUwJZxlI06k7CYu6wPFguAy1AOZ2tMJmhg1zkhLjcl0w/DB+ZkJfB1QaEiUzkcCaYci+0TiUK7C/CKm0bGOb8jgZkTBXPCJzqTUgMGFOGkbK460xMgmJoxkAgQTkfGQuBrI/nnxoPAOiEWjUpBfmhzG5mguuL3MykDkYnoxnhHE2c8L19AIJiQWBXDcDwEXqawZYlkTXap7siyHbOo6cixkeu9oAK7yrLRmRqFzVkcd1F2p1uS4+UPWgWoRch4ThbFOWp2ISJivZENhgRyHmgm1DoyGPDUPOpdaQgFoiBsSgUIJ4VI4X9cC1oFKTQFsqAD8u47iexNKqNag+D9kURGJgRySSFgTQyMOzg5B3Wtcp5sK8koTNCgMQ64ZEVi7V/n7wI4At2yXCcUqjkHoceh6CjkXgzTCU9sLXDxflW/No97rgbCNpUQ9ueRK25iAflwjc4pHW8/CQrFoFq1dDR8fBt1VKKaWUUkoppZRSSqlZ0qiaUkoppZRSx0Amk+GOO+7g/e9//5R13/jGN/jVr37FihUrZjTWS1/60rbLd+3adVhzPJ5s2+Y1r3kNr3nNa7jjjjt497vfzSOPPDJlu9WrV/OlL32JZDLZdpzFixe3Xf7UU08dsbk+/fTTbZcvWbLkiB3jWKvValMiHgCvec1rWLp06WGN3S40dKo455xz2i5//PHHCYIAwzisHxmasVPxc0I7+jxVh6JcLrddns1mj9gxOjs72y4fGxs7rHGHh4f5H//jf7Bhw4a262+66Sa+9KUvHbPPNQfy6le/mu7ubvbv3z9l3T333HNCR9VO9s+hr3vd6zjttNPYuXPnpOWFQoFvfOMbXH/99ePLvvrVr7Yd461vfesR/TtxJEwXVbvooouO8UyOnXe+85185CMfmRIX/trXvjYlqtaMrb1QIpHgHe94x6yPfSL/PThRvt462vT7GKWUUkopBWDmR+i8/96263oqcP4QPNsnj7tqkK1BIS6PB9PQW5a3TdmabNd0/oSbLF+o6/57Gb3+ZvzOQ//FBUoppZRSSimllDo1rBtax5oNa/AD/4Db7S3uZXt++/jjIAhg5/OweweMN5J82L0ON1IknzXGf3GUXypQtaYf269XcW0Tx3WIGhA3IJeFbFyiWIEHYxXYV4YVC+GVZ7lETAmEGWG8zAhaYbVMVGJp8TAAtqgTduVhXxF6MpBKSGhrYaeEvVxPAmC+B51JiMqZELWhp0PCX9EJ87csSIWxNCyJgblIUMzzJUhWqkNnohUnMwDPC+NmRmu8qAV9aVnv+rKf70LUkGCWZUBXUs7PQJalotKwC5AYWdGB3oxEugwksOa4so8VXgMjnF8zwlXzJIRmIDE4M5AxbSuMwE24+6y5n98M0/kSYQuicp0AcknYU4RYuJ9vSMwtbcrBPRNKJUh3hKEzOzyuKbG1RhUKDsRi0LDk3CLh4LY54RoG4Lny8WsYkDCh3gziuVDxIGLLx8HwWufruBJPK1uQS0HMgEodRqtgu+A7UKxDPYAt/RJua0bSSgZke6EvB40Y1KuwfwfEPGDC63OdE16b8z1YtBa+MwS7NkG6E+o52NoLi4dgYARKI3LcW58A6rMLpfVUYPkgPD2XKZYPTn1d8FAiaQaw5EC/u3TFCrj9dkilDrAR8oRatgy621UVlVJKKaWUUkoppZRS6sjQqJpSSimllFLHyC233MLnPvc5Nm/ePGXdxz/+cb73ve/NaJxFixYxZ84choeHJy1/9NFHcRyHSCRyROZ7vJx11ll873vf49xzz2X37t2T1tXrdX7xi1/w+te/vu2+ixcvJplMUqlM/gmQnTt3MjAwwNy5bX5iZJYee+yxtsuXL19+2GMfL4ODg/j+1B+Cu+yyyw577HZxvFPFggULmDt3LgMDA5OW5/N5nnvuOZYtW3ZM5nGqf05o0uepOhRdXV1tlxcK0/8m5dkaHR1tu3y62NpM5PN5Xv/6108b/Lv++uv553/+5xMmJhSJRLjxxhv567/+6ynrvve97/Hss88es8+Js3Wyfw41TZP3vve93HnnnVPW3XPPPeNRtWKxyDe+8Y22Y9x0001HdY6zNTAwMOXfVpBzveCCC47DjI6Nzs5Orr32Wv793/990vLf/OY3/Nd//dekf+9+/vOfs23btiljXHfddYcUyDuR/x6cKF9vHW36fYxSSil1ZJ3Vk+LeWy6Z8bZKHS1Fp8jOyvMUneKMts8+dD+7+qapngGWD/3Z1k2Uti83PDb91xmtdQBzKrA3DV1VSLiTb7J8IbNSpuu+rzB8620zmqtSSqkjaM5iuOE/Zr6tUkoppZRSR1GxXuTB9Q8SEExeUamEBaw0hL8Q1TTMSZsMFHZTz++AzjYDF56nHM/hBJ6UuuplHKsVPZvCc7Btkwvm+yyfC/PSTKk9JW1Y2iOL9xVhqAiNZuSrOX0D4hEJqkXMMP6FRLxWLJBg2P6KhLM64pAM/2s0aoIVk1Ca54FTlzFdQ0JluaTEt3wkBpaMhfMLJJoWjcBQAeIxiX4RSFSr7kqILG7LOLUGBKaE2kDGM4BMAmqOPPZcGCxAXxaa/3MbjwCejOGFx2vUJDzmAjVXwnC2KYNaNjjhdQk8CZf5/uRQmu9LeCwIZBIG4WCWvOYUsSS6VvMklBYYEiazTXmdygjn6oXjRS2Yn5HrDrK914CGC7GMXEvXgXwRFnbLxwQk2BazoTsr2zou+FHo6ZJ4nR9ApQqxqHz86g25viBz35+HeFTGiXhQcGGwDFEf0hHwEnL+w3kZv1aE8jaJsSW7IUhA3A3PoQ7rB8NjMCGSFkBhEM7+FWzyYE8WUp2QMCS8VrflbcKVOZVGIfU49DwEHYvA64PCEDAEjQGo5aE59LIJvxhhtqG0q7fA/iTs6mgtWzgmy9s5aCStacUKuOuu6ddrJE0ppZRSSimllFJKKXUC0qiaUkoppZRSx4ht29x+++28+93vnrLu+9//Po888givfOUrZzTWa1/7Wu6///5Jy8rlMr/85S+5/PLLj8h8j6eOjg7e//738/GPf3zKuu3bt7fZQ5imyStf+Up+9KMfTVm3evVqPvzhDx/WvIaGhvjpT3/adt2RCDsdL0NDQ22XH268IQgCvvvd7x7WGCe6173uddx7771Tln/1q1/lc5/73DGbx6n+OQFOjudpLBZru7zRaByR8dXs9fT0tF2+c+fOI3aM6cbq7e09pPHGxsZYtWoVTz75ZNv173jHO/ja176GaZpt1x8vH/jAB/j85z+P67qTlgdBwG233cYPfvCD4zSzgzvZP4fedNNN/MVf/MX4b/Ru+vGPf8zOnTs57bTTuP/++6fEmgDOPPNMrrrqqmM11RmZ7rl/9tlnkzrYbzM+yd10001TomoggbyJX2vec8890+5/qE7kvwcnytdbcPT+rdfvY5RSSqkjqyMe4bIl7b8fVOpY2VLczA/2/oAgmPpLAtoxalW6tn4P48wDb1eOwFC69bhqy02aLxRzYTgpf4wA3rJx6k2WL9R1/72MXn8zfmduRnNW8OzQ4PGeglLqVBDPwktOrNfolFJKKaXUqadQK7BtdBuF+oF/Cd3m4c1sz7d+Ps/3fXhyLWzcIHUwy4BzlsJFF9PwG4zWRseDVsboKFVr+rHNao0gZkOjDkDcgFwWsnEJagUejFVgXxlWLIRLz/Kxw/GMQP40I2yZqITSoqaEwjpzsKgTBsYkJpaOQyIm8ay+DgmdNVyo1iFqwJw0RMN59XTAvpJE1ZoMJLBmGBIHiySgXIF9Y2BGJO5lmzBQlMBazJbQWQRwXSg7kInL8cNGGXNS0PDB9aFcl1hb1GgFyywTqo5sa5ty3LonUbEAGK5CX1rWG0hQzHHlulhGqzlnhe/Uw6ia70Ozf2f5gCfXw0POoxlKs8KB/WaYzgfDgyDauu65JIxUW6G0uifbNX+CpdGAhtEKpOUScn41Rz4utTqYNQhScvyIDZkkeA74psyLAJyGzC1hyfELY9CVkTGjHrgWVGowVoJUAH4cbFuOsX9Ursu8KFgeVCwILIg2wKtDvgqbdkM1rJjFXJhXkqhZYQBi3ZDISk9ufz/4EWCaSFrqITjNgm+vlOdFOgf1HGzthcVDMDACpRHwHbj1CaAOl/fDc72tX46wKD/heRdM/cUIswmlRT245UnYmpNfxtBZg8UjU3qEs7NqFaxeDR0dB99WKaWUUkoppZRSSimlTiAn1t2XSimllFJKneLe9a53sXTp0rbrbr/99hmP87a3va3t8r/92789pHmdiF72spe1XT4ycuBfjfeGN7yh7fJ2IYbZuu+++6bEYgCWL19+2GGn4+mFEZamWq3WdvlMfec732Hr1q2HNcaJ7tprr227/J577mkbrzlaXgyfE06G52nHND88Vi6Xj8j4avYWLlzYdvnPf/5zPM9ru242BgcHWbduXdt18+bNm/V45XKZq6++mscff7zt+re+9a18/etfx7IO8FPIx8miRYu45ZZb2q774Q9/yFe/+tVjPKOZO9k/hy5atIgrrrhiynLf9/na174GMO31v/HGGzGMw/rx2SNuuqjaRRdddIxncuy99rWvbft564EHHhj/uqJcLvPggw9O2ea00047rEDeifz34ET5eguO7r/1+n2MUkoppdSpo1gv8oO9359xUA0g8exTGG2+ZnuhMwqM36gMcmNkOxOXB8CeDBSj7bdtMitluu77ykHnoJRSSimllFJKqZPLuqF1fOGxL/DNTd/kpzt+2vqz/j/56eOr5W247Km9T7Ejv2P8z2PPfo+fl9fz89MCfn4m8ra8np8/+20e3fUoe4p76C/00z+6nYHGCMUYFOLT/PEqeF6J8+c1uOEV8JEr4T0Xw1vOg99aCm8+H/5/r4R7/yf8weUSSYuGsTATsAJ5m4xIUC1ihUGxACygNw2XniF/5mYkctadgnRU9ulKwGldcNociYI1/6fcNCS81nChVJPwWsQKA18h32u9JJMOX2OJWpCLQyoqobRSXeJh1YYEs6I2JCJA0IqgRS157HkwUID6hNd5IhbYzceGBMcsXyJnvgE1V44jE2qFy8xAgnSmKdvFwh8p8X05rm/I+wC2D4S/L2piKA0kDAcSSvN92dYIwPEkJNY859OzE64LUM233q97kC/K9smIRN0sA1IxiNuQyEAkC054fXJZCePhQqUKDQcaVTAmvKxWqoDjwHABvIZck0ZF4ml+FfwiFAswsB9++TSs/w08/xt4/lcwsltiahFPnif1KgxvbAXVYPLraL4Hi9bCI/8Oqz8Jjz8E/Q/DL56St099D37xr/Dkt+CCbwN16KnA+UPguVAYgtpGaPxa3hbC5ecPtX7hQU8Flk/T6V8+OPUXIzRDadf/Gt60Wd7e8qQsb8cAlozAyj3ytu1PhKxYAd/6Fjz88PR/fvxj2LcPvv99DaoppZRSSimllFJKKaVOSm1+X61SSimllFLqaDFNk9tvv513vetdU9Y9/PDD/OxnP2sbxXiha6+9lrlz5zIwMDBp+Te/+U2eeOIJVq5cecTmfLz4fvsbzjo7Ow+43zvf+U7++I//eMr+v/rVr/iP//gPrrvuukOaT6FQ4C//8i/brrv++usPacwTRXd3d9vlmzZtOuQxHcfhjjvuOOT9TxZvfvOb6evrY3Bw8k86jY6O8qd/+qd8/vOfPybzeDF8TjgZnqfThVZ27tx5xI6hZufKK69suzyfz/Otb33rkP9NaPra177WNviXSqVm/feuWq3y5je/mf/+7/9uu/6aa67h3//937HtE/flvDvuuIP777+ffD4/Zd0HP/hBli5dyite8YpjP7GDOBU+h95888389Kc/nbL8nnvu4e1vfzuPPPLIlHWGYXDjjTce/cnN0nRRtQsvvPAYz+TYM02T97znPXz605+etLxYLLJmzRpuuOEG1qxZQ6lUmrLve9/7Xkzz0H+Hyon89+BE+XoLju6/9fp9jFJKKaXUqWPLyJZJrxf4B4mrGfUq0XXPjN/IeiAJV268HErJ47gLMRdqE14uiLvyJ0BumuwtQ9KFLXPgor0HHr/r/nsZvf5m/M7cwSejlFJKKaWUUkqp46pQK7BtdBuFemHabSpOhQfWPUAwsdIeBLB2LWzYIPUs04SlS+Hii2n4DvlaXrZzXcz9+6nF2wxczkMigmmYBAQYjToAcQPmZCEbl+iX70GxAvvKsGIhvOIsH9uSEJgRyFSar4lkohJLi5qyvjMHZ3XCnhIELqRjEI/Bwi4JkDmexLYCDzqTcjzLB8uCng7YV4KOCXM3gGRUYmkxE2p1qDowXIKOlCwfq8jbRFRiZglb5lhrQGDCnLRcrubrLnNS0PDDxwHsGoWupMTETCMMpTkSHLMMMC2Zcy187Wa4Cn3p1vyiFtQ8IAi3D/cLwmtU8yBiyJyM8L+HLR/wwDBkzIgFcUv2M5B9HQdMX8YjgEYNgpjsH7VgfkZCcwANT240MwlDaQ3psFmmhNIilgTfak4YPKtCLCLjVSthuA25Bs3z8gKJiyViQEyu1f4CdGUgFj4XqsDefZCsgp0ALyHnUyjK/rUilLdBwoRkNwQJeQ0MIFqH9YMyVwhDaQEUBqG4H3pisCeQYJsFxNJQt+V1tYQr17M0CqnHoech6FgEXp9E0RiCxgDU8tDsry0bmhw/u7wfnuttfZwW5Sc87wK4on/yX5+rt8D+JOya8N/PC8dkeTvNUNpBrVgBd901/XrDgGXLYJqfw1NKKaWUUkoppZRSSqlTyYl7F6ZSSimllFKnqLe//e3ceeedrFu3bsq6T3ziE22DGC8UiUS47bbbuO222yYtD4KA3/md3+Gxxx6jr6/vSE35uFi7dm3b5QsWLDjgfgsWLOCaa67hoYcemrLuD//wD3nd615HOp2e9Xw+9rGPMTQ0NGV5Mpk8IYMkszFv3jwikQiO40xa/q1vfYvPf/7zRKPRWY/5qU99atooyqkkEonwB3/wB3z0ox+dsu7v/u7veNOb3sTrXve6YzKPU/1zwsnwPJ0/fz6JRIJqtTpp+a9+9asjdowXm3vuuYebbrqp7brt27dz5plnHnD/lStXksvlGBmZ+pOFH/vYx7j66quJxWKHNLehoSHumuYHEa+88spZPScbjQZvfetb+clPftJ2/dVXX80DDzxAJBI5pLkeK3PnzuXuu+/mhhtumLKuVqvxhje8ge985zu8+tWvPg6zm96p8Dn0t3/7t/n93/99xsbGJi3/zW9+w/ve9762+1x11VWcccYZx2J6s/LUU0+1XX7RRRcd45kcHzfeeOOUqBrI58MbbriBe+65Z9r9DseJ/PfgRPl6C47uv/X6fYxSSiml1KkjYk7+/n3j2EaKbnH67ffsJLLAnfH4DRP2ZJC7KQHHhPKElyHcBvR3yk2h84pwZnhfdcQ7+NhmpUzXfV9h+NbbDr6xUkoppZRSSimljpt1Q+tYs2HN1Jh7pQKlEqTTkEyyt7iX7fnt46uDIICdz8PuHTAedPJh9zqIluG002l4DcqNMhRGwZbXHtx2v9+pUoBYHJM6L+ttsHwe9GbAD6NjIC9fpC14aa+8P1iCgZJEsswJG8UjElSLWGD4YAXSf5qThrPmgOPCcBliMQl7gUTA7BikIuB60HDCiBgSRMulJPxFAFEbknEJnYGMbdswPCZjRy1Z3hFrzdv1oOzJ22gYNotaMsdKeCzbko3rLniexNKGq9CXaY0TNaHmywML+eOHMbmaC64vATIDCZeZrlwfy5CAW3TCXV8Tfz9T833bBxphJC28prmknGNTqQHZ5oSQp0gsIscDyCXk/GqOnO9YCTpj4JngunK+fTkJuhnI3BJR8BvQ6AyvYRRS4Tn4AVSqEIvKNao3WnMDKFck9JYfhd6EXLtCVbYxPPBKkK/Chp2tUFrMhXklCZsVBiDWDYksuMD+fvAjwDShtO4fwuoLwIxAOgf1HGzthcVDMDACpRHwHbj1CaA++0haTwWWD8LTc5li+eDkABtA1INbnoStOcjHJQK3eGT8w3NoVq2C1athml8UppRSSimllFJKKaWUUi827f5rQymllFJKKXUUmabJJz7xibbrfvazn/Hwww/PaJwPfehDLFmyZMry/v5+3vjGN7Jjx47DmeYkP//5z/nTP/3TA27zJ3/yJ9x5551twzWzNTQ0xJe//OUpy23b5pWvfOVB9//4xz+OYUz9EZMdO3Zw7bXXUq/XZzWff/iHf+Dv//7v26774Ac/SE9Pz6zGO9EkEgle8YpXTFm+e/du/uqv/mrW433xi1/kL/7iL47E1E4KH/7whznttNOmLA+CgOuuu47vf//7hzW+67ps3rz5oNudaJ8TjrST4XlqWRYXXHDBlOWPPPIIGzZsOKLHUjNjmibXX39923Xr16/nve99r/zA8CxVKhWuueaaaf/Nm+6Y7biuy9vf/vZpP1esWrWKb3zjG4cUDjwe3v3ud3PLLbe0XVcoFLjqqqv47Gc/i+vO/Gbxg3Fdd0pwcbZO9s+hiUSCd77znW3X/exnP2u7/Oabbz6iczgSRkdHp73WF1544bGdzHGyZMkSXvWqV01Z/pOf/ISf//znbQPMl112GS95yUsO+9gn8t+DE+XrraP9b71+H6OUUkopdWo4PXv6pK/rFiSn/0UhhutgDw3MavyoD6kJ3wZHfLAm3EQbCd/vrEFvGZKO3Ox5emFm43fdfy9m/vBf51dKKaWUUkoppdTsFWoFntr7FD/d8dNp//znlv/krx/5a7aNbmNHfof8Gd3Ojh89yI7/+zfs+Lf/I29/9CADxb3ka/nxP9uGt7KptINN3Uz9U9rBpn0bGK4Mk6+OUqgXKcShYUHdbv1pWOAZEDgNXrGgzocvaXDVOdDTARjyOkRTOgrLFkA6Bh1RWJKDSxfCki44swPO64VLFsDVL4UzuqAnBR1xCXbNzUIqJq97RG3o7YCuRGtsAwmsGYasT8ckpLWvCIN5ed82YbQM+8ag4UmsDMDzoepItC0akbEMIJOQ2FjzsevBQAHq4X6mIVG1ZufMCkNrZgANV0JpVQ8cvzVH2wyvSdAKkllGK9hV8yQqRiARNQMI3HAOBlgWxK3W8TCkT+Y4YPpyU1gQQLXRujbJSCsQBxJ9q5XlfR+ZY74o+yUjMkfLkOsdtyEI/9hxCaN1JqDRkO1B5lqrS0AtEQMjBYVSKwoWDeP+lRrsGoT6iGzvelBrwOgYFIowtgM2/AJ2b4RIDeLhj5J4LoxsbgXVQF7ravI9WLQWHvl3WP1JePwh6H8YfvGUvH3qe/CLf4UnvwUXfBt6RuH8IRm3MAS1jdD4tbwthMvPH2rFz5qRtHbaRdIArt4CCyf/HjwWjsnydgxgyQis3CNvpw2qrVgBDz88/Z8f/xj27YPvf1+DakoppZRSSimllFJKKTWBRtWUUkoppZQ6Dn77t3+bZcuWtV13++23z2iMaDTK17/+dWzbnrLuqaee4sILL+SrX/3qIUdGhoaG+Kd/+idWrFjBFVdcwX/+538ecPvBwUE+/vGPc9ppp/H+97+fn/3sZ/gTfzXiDO3YsYM3vOENDA5O/amU17/+9XR3dx90jIsuuoj3ve99bdc9/PDDXH311Tz//PMHHcdxHD71qU/x4Q9/uO36BQsWzPjjdaK75ppr2i6//fbb+dKXvjSjMcrlMn/0R3/ErbfeOh4paheFONUkk0n++Z//ue25lstlrrnmGj75yU9SLpdnNW6pVOLLX/4yZ599Nl/84hcPuv2J9jnhaDgZnqevec1rpizzPI83vvGNPPDAA1Sr1SN2LDUzH/vYx0in023X3X///dP+mzOdjRs3cumll/LYY4+1XX/RRRfxjne8Y0ZjeZ7Hu9/9br75zW+2Xf/a176Whx56iFgs1nb9iepLX/oSq1atarvOcRz++I//mAsvvJD777//sOJqjuPwta99jaVLl7Jnz55DHgdOjc+hs4mkZbNZrrvuuiM+h8P15JNPtl1+5pln0tXVdYxnc/zcdNNNU5YFQcC73vWutiHIG2+88Ygc90T+e3CifL0FR/ffev0+RimllFLq1JCNZ1mSObv1OJIlY2fabmsPDWD4sw++Z2vI3bOh5g2nsQlvEw6cGYbUzh+C7AwbvWalTNd9X5n1nJRSSimllFJKKXV41g2t4wuPfYFvbvrm5JDa+v/kp4+vlrc7fso3N36T7fnt40G17aPb2f7sz9m+ex3bO3y2dyJvd69j/5ZnyNfyjFZHGa2O4pcKVC2o2m3+WFCtFKi5NZxGFc8A24LTO2BZL1wwD87rg4VZmJeGm1fAlac5JIIAMwyGmQFYgdyolIzAsrkSIbP8cB3Ql4ZLz4BLzoS5GejLyLYRU97OScMZ3RL4MpE/RiBzidkSD6s0wphZ878PAwltBYBryD5RS/50xCTu5nlQCsNelTCAZpnQlZTxzUDGS0XlfSMMpQXAcLX1UowBRM3WA8uQ8/KCViit7oLvh9fElPHGH4cRuKbmj1j6AeNlLTMMmDXHyyXlbSzczzegWgMrjJd5JpRKEosDSNhy7qmYHK9RhUIdXAdqvpyL3Yz0m63z8gMJjCVi0AivT3PehguVqoTOanV53OS4MFaC4QJ4Dfl4NyqwfxT8KvhFKBZgYD/88mlY/xvY0w+1vbJ9YRDcx8HcAmP7YO9mMIsveK3LlWtSHAH/+9BzJ3T8KNx/BqG0y/snB/8W5VvvGwFc0T/57+NsI2lRD255Eq7/Nbxps7y95clWYO6QrFol4bSrrpr+z5VXwgx+plYppZRSSimllFJKKaVebKbeHaWUUkoppZQ66gzD4I477uBtb3vblHWPPPII3//+93nDG95w0HEuueQSvvSlL/G7v/u7U9bl83luvvlmbr/9dn73d3+X17zmNVxyySUkEok2I8HOnTt55plneOqpp/jBD37AL3/5y0OKolUqFb785S/z5S9/mblz5/KWt7yFV7ziFVx88cWce+65WJY1ZZ9arcajjz7KAw88wFe+8hVqtdqUbWzb5s///M9nPI/Pfvaz/OxnP2Pjxo1T1v34xz/m/PPP5/d///f5n//zf3L++edPWj86OsqaNWu4++67+fWvf912fMuy+PrXv04m0/5muJPN7/3e7/GXf/mXDA8PT1oeBAEf+MAH+I//+A/+8A//kCuuuIJ4PD5pm02bNvHQQw9x9913s3v37knrPvzhD/OFL3zhqM//eHv961/PJz7xCe64444p6xzH4Y477hj/u3r11Vfz8pe/HNOc3DkPgoBNmzbx+OOP8+1vf5vvfve7s45ynIifE46kk+F5esstt/BXf/VXU6I3/f39vP3tb8eyLM466yw6OzunDWX94he/OCJzmamnnnqKD33oQwfdrlAotF0+MDDAq1/96hkd61ifG0Bvby+f+MQn+KM/+qO263/4wx/ykpe8hJtvvpl3vvOdrFixgmg0OmmbcrnMI488wj333MPq1aunDYFZlsXnPve5GYf6Vq9ezf333z/t+pGREV772tfOaKyZuvnmm2cV3zoUtm3z0EMP8Y53vINvfetbbbd57rnneOc730lfXx/XXnstr3vd61ixYgWnn376tNevXq+zefNmnn32Wb773e/yn//5n+Tz+SM275P9c+jLX/5yzjvvPNatW3fQbd/1rndNO/fjabqo2oUXXniMZ3J8vf3tb+fDH/4wlcrkX2/dLh6YSqV4+9vffsSOfSL/PThRvt462v/W6/cxSimllFKnhpW5lWwpbh7/unFBcgEbx1pf48WtOL3RHjo2PznpBtTZSLgwEn45XoyCa0Ik/JK7swa9ZUg67W8KPZjOB/+V4Q/8odz1q5RSSimllFJKqcNSqBXYNrqNQr39z10AVJwKD6x7gGBiRT0IYO1a2LBBylamCUuXMnruIvK1/Phmw6V9OKU90K6rVH6eWrQDN/CwMDDqZRxLolxteQ1ivs9F81yWzZN4molEuJqBr0wUlvVJcGxf0WeoCIEv8bS5GcjEIBmDhZ2yzPEk5uV70JkE25DIWNSG3g45reb/IhpIWM0Iw2NRE8oV2F+EVFrCZbYpj5MRiYDFI2D6UGtAYEqUbaQ83icjk5C5GsiJFKtyPNuSZfEI4Mnl9gyIRqBRk3P2A7lWNRdcX0JlBjIHw5PxjDCsZk64pl4AQTgmRnhsF6mvGWBZELeg5sm+GBImi9pyLqYvUbDmxylqwfyMhOcIPxZeQ84rlpGQnOtAvggLuyWGBjJ2zIburGzruOBHoadL5uAHEkqLReV61BuM1+OCAPbnIR6VcSIeFFwYLEPUh3QEvIR8/IbzMn6tCOVtkDAh2Q1BovXLAKJ1WD8YHgN5/Wqi0/eC8w241wXDgnQO6jnY2guLh2BgBEoj4Dtw6xNAXUJpz/W2npsHCqX1VGD5IDw9d+rTfvlgK77W1Iykbc1BPi7zXTzSel61YwBLRg6wwYoVcPvtkEodYCPkSbVsmcbSlFJKKaWUUkoppZRS6jBoVE0ppZRSSqnj5LrrrmP58uU888wzU9bdfvvtM4qqgdzUX6lU+F//639NubEfYNeuXePhAdu26e3tpauri0wmQ6lUIp/PMzo6SrlcPqzzaWdgYGA8sAYQjUaZM2cOXV1ddHR0UK/XGR0d5fnnnz9oYOH222/noosumvGxM5kM3/rWt7jssssYHBycsr5YLPKZz3yGz3zmM2SzWebPn08ymWTv3r0MDAwcdD5f/OIXec1rXjPj+ZzoMpkMn/70p/m93/u9tut/+MMf8sMf/pBoNMqiRYvIZrOMjY0xMDDA6Oho231WrlzJX/7lX74oomoAn/jEJxgeHubuu+9uu35gYIA777yTO++8k1gsRm9vL729vQRBwOjoKENDQ0fk7+GJ/DnhcJ0Mz9MlS5bw3ve+l3vuuaftes/z2Lp16xE51pFSKBT47//+70Pev16vH9b+x8Jtt93Gli1b+Md//Me268vlMnfffTd333038Xic+fPnk8vl8DyPkZERdu/ePW1IbaJ//Md/5IorrpjxvOr1+gHXTxeYOhxHOtI2nXg8zpo1a/iTP/kT/vZv/7bt5yOAwcHBSV8rxGIxFi5cSEdHB/F4nHq9TqlUolQqzejf54luuOEGXvayl81q3if759CbbrqJ22677aDbHe2w3qF66qmn2i6fzdeAp4JMJsPb3vY27r333oNu+7a3vY10On1Ej38i/z04Eb7eOtr/1uv3MUoppZRSp4ZcbA5LMmezeWwTANlIloydoegWAah7dbprBnNHDrGoBuSq8MR8cCwYjUtkDSDmQsKBM8P7tM8fmnpT6MFYxTGi/dtonLX4kOenlFJKKaWUUkopWDe0jjUb1uAHE/4Pp1KBUgnSaUgmAdhb3Mv2/PbxTYIggJ3Pw+4d0NFc6sPudbiRIvmsMf5/eX6pQHXq7zkd59eruLaJ4zpEDYgbkMtCNi7xrcCDsQrsK8OKhfDKs1yiZhj0CiSqBuABiQhc0CcBrrgBnTlY3CVxLhuJmvlAR0KiZyAhMDsGqQi4HrhhVMsIZJyIBSMVaXl1JSbEyQIJsQXIXKLhOUYt6EtLwMr1oVQH34WoIXEty4CupIxvIMtSUenSBUDdhaIDvRkJhxnIPBxX9rHCCJqBvN8MdtU8CaEZSEjMDGRM22pF4Jqa+/nh+KYvEbYg2grI5ZKwpyjRM4B6GFizww0aDXAnfFxzCRmz5sh1rNXBrEGQko9NxIZMEjwHfDO8jgE4DQmoJSw5fmEMusLfvxT15BiVGoyVIBWAHwfblmPsH5XrMi8KlgcVCwILog3w6pCvwqbdUA0DaTEX5pWgBhQGINYNiaz05Pb3gx8B7PD1K1euf2kUUo9D6iGgDucthWf7oDAEDEFjAGp5GRNg2YTXumYbSrt6C+xPwq6O1rKFY7K8nYNG0ppWrIC77pp+vUbSlFJKKaWUUkoppZRS6pjTqJpSSimllFLHiWEY3HHHHVx33XVT1j3xxBN8+9vf5s1vfvOMxvrQhz7EwoULufnmm8nn89Nu57oue/bsYc+ePYc67cPSaDTYu3cve/fundV+H/vYx/j4xz8+6+MtWbKEH//4x/zWb/0W27dvn3a7QqFAoTD9bwKdKBKJ8MUvfpHf/d3fnfV8TnTve9/7eOqpp/jSl7407TaNRoONGzcedKzzzjuP7373u8Tj8SM5xRPe3/3d39HX18ftt99+wKBFvV5n586d7Ny586jM42T5nHAoTobn6T/8wz+wfv16Hn/88SM6rjo8/+f//B983+ef//mfD7hdrVZj27ZtbNu2bcZjRyIRPve5z52woarjxbZt/uZv/obXve51fOADH2DHjh0H3ader/Ob3/zmsI776le/mk9/+tNcdtllh7T/yfw59IYbbuCjH/0ojuNMu815553HypUrj+GsZm66kOCFF154jGdy/N14440ziqrddNNNR+X4J/LfgxPh662j/W+9fh+jlFJKKXVqWJlbyZbi5vEbnBckF7BxTF6zCgjYFeTpQ27OPBQpB+aW4bmeyYN01mR50pEbga/on/3YgWHgdXQe4syUUkoppZRSSqlTW6FWYNvoNgr1A/8/TcWp8MC6BwgIf5FREMDatbBhg9S4TBOWLoWLL2a0lidfy4/vO1zah1PaA+0aTOXnqUc78AEjCKBexrHCCFo7noNtm1ww32f5XJiXZsoLEkkblvbI4n1FGCpKsCygFVUzgHN7JILWfBy3YE4abENiXoNVaAAdE34Ux0QCa0YYRovGoVyB/UVIpcELwDblcTIiIbJ4REJktYaE2nrCCJYfHjeTkLiYD3guDBagLwthx414BPDkknsGRCPQqMn5uEDNlWiYbcqglg0OEkoLPPnQ+P7kUJrvg+fLmIThNVzAkthZxJLrUfMklBYYEiSzTQmlGeFcvXC8qAXzMxKdA9m+WoGkEZ4XMFKArg6JwtlhsC4Vg3oD3AxEYuAYELcllGeEJ1jx5Zz98PWhZhiuVAHHgeEC5FIQM6BSh9Eq2K5sX6xDPYAt/XKcmAtOGbK9kJ4LRMEKoF6F4V1QjbSuUWet9b7vwaK18J0h2LUJ0p1Qz8HWXlg8BAMjUBqRY976BBD+XsLL++G53tacF+VbY7Z7rWs2obSoB7c8CVtzkI/LfBePHPrrcwCsWgWrV0NHx8G3VUoppZRSSimllFJKKXXMaFRNKaWUUkqp4+jaa6/loosuahuQ+MQnPsFv/dZvYRgz+5GN6667josvvpiPfOQjfOMb3zii81y8eDG33HLLAbfJZDJH9JgA8+fP52/+5m945zvfechjnHvuuTzxxBN84AMf4IEHHjis+Zx99tl85Stf4VWvetVhjXMi+/u//3s6Ozu56667xm/2m61rrrmGe++9l44X6Q8K/dmf/RmXXXYZ73//+9mwYcMRGfNQruXx/pxwNJ3oz9NkMskvfvEL7rzzTu6++25GR0eP+DHU7JmmyT/90z/xpje9ife9733s27fviIy7bNky7r33XpHB6KoAAQAASURBVJYvX35ExjsVveENb2Djxo18/vOf5/Of/zwDAwNH/BiWZfHmN7+ZD33oQ1x11VWHPd7J+jm0t7eXN73pTTz00EPTbnOixv9KpRJbtrT/9c8XXXTRMZ7N8XfllVdy5plnHjBGeNZZZ3HFFVcctTmcyH8PjvfXW8fi33r9PkYppZRS6uSXi81hSeZsNo9tAiAbyZKxMxTdIgD7KFJcMI+O3bP7JSQTLSzAU32txzEX+sqwZFgenz8E3ZXZj1t5xavx5rS7a1sppZRSSimllHpxWze0jjUb1uAHL/jlP5UKlEqQTkMyCcDe4l6251u/QCd4vh9274Dx/5byYfc6iJZxF8wnX8uP/xyMXypQtaafh1+v4tgmEdfHBOKGRLWycbBMCYONVWBfGVYshEvP8rGbMbRA/jQjbJkoLJsLUVOiYp05WNQJg2Pgu5BIQCIGuSRkk9DwoBFGybqTYBoSQLNs6M3AaA2qTqsb15WQbSAMi3kSN/PDyBrI2760rHd9KNXl2FGjFSyzTBnXQAJjUQvqnoTLAmC42hrDAGxL1hkBWEYrnGWF79TDqJrvgxGGzaywZmYaEjWzJoTSrHBgP5CxTB8MD4Jo61rmkjBSbYXS6p5s1wzTNRrQMORcAHIJOb+aI9czX4BoCoKYPLbCp5ltTrh+gcTZEjEgJue3vwBdGYiFH9sqsHcfJKtgJ8BLyDUqFGX/WhHK2yBhQrIbggTE3fBjUYf1gxJUgzCUFkBhEIr7oScGewJoVMECYmmo2/K6VMKV6FxpFFKPQ89D0LEIvD4oDAFD0BiAWh6a/bVlL3j9qqcCywfh6blTn/fLB6e+1jXbUJoBLBmZZmXTihVw++2QSk2/jWHAsmXQra+hKaWUUkoppZRSSiml1IlIo2pKKaWUUkodZ3fccQfXXHPNlOVPPfUU//Ef/8Fb3/rWGY91+umns2bNGp544gm+8IUv8I1vfINqtXpI81q6dCmvfe1recc73jGjm+/vvvtuPvjBD/Ld736X733vezz66KNUKodwtxYSWXjve9/LH/zBH5BOpw9pjInmzJnD6tWr+clPfsKnP/1pHn744VmFmM4880w+8pGP8Hu/93vEYrHDns+JzLIsPvOZz3DllVfysY99jCeeeGLG+15wwQV8/OMf57d/+7eP4gxPDpdffjnPPPMM9913H1/4whd4+umnZz1GV1cXb3nLW3jf+97HpZdeekjzOJ6fE46mk+F5GolE+OQnP8mf/umf8t3vfpdHHnmEX//612zbto2xsTGKxSK1Wu3gA6kj7tprr+Wqq67i3nvv5Utf+hLPPffcrMcwDIMrr7yS97///Vx33XXYtr7EdjCxWIw/+ZM/4SMf+QgPPPAA//Zv/8aPfvQj6vX6YY35qle9iuuuu463vvWtzJ8//wjO+OT9HHrzzTdPG1WLRCLccMMNR/X4h+qZZ57B9/0py/v6+pg3b95xmNHxZRgG733ve/nkJz857Tbvec97ZhxgPlQn8t+D4/311rH4t16/j1FKKaWUOvmtzK1kS3Hz+NdxC5IL2Di2EYCAgB19cS7Yfejj7+qAuWWo2uCachPqmfnw5uEAruif/ZjlSy9j72f+7tAnpZRSSimllFJKnYQKtQLbRrdRqBem3abiVHhg3QMETPj/miCAtWthw4ZWRWzpUrj4YkZrefK1vGznugwXduC06y+VdsBwmbrv4Ac+RhBAvYxjtWJdU3gOtm2ybL7PhX0wNyNxromSNiztkdcJ9hVhoAROIIEvMwAMiEckqBaxwPDBCqQVNScNZ3VJ2GywCg0gl5J1tgnpCKRjEkBzGnKMhgvDNUglYLQqUbCIAcmIhMjitlyuWgMCE3rCuJwvUyGTkLiYj0TDBgvQl4VIeD4RS47lhXGziA2uI2P7BtRcmU/ElEEtG5zwXANPPjROALEw5Ob7clzfkPcBbB852ReE0vYUIRb+aEjdk9ibHe7veHJeIKG307MSnmueWzUPqaS8X/cgX4VcB6SiYdQNSMUkYuZmJAQXi0HMgzm2nF+lCrGohOLqDZj4FCxXwHEgPwq9CYnWFaqyjeGBV5JjbtjZCqXFXJhXkrBZYQBi3ZDIggvs7wc/AkwTSuv+Iay+AMwIpHNQz8HWXlg8BAMjUBoB34FbnwDqcHk/PNfben4uyrfmPt3rV1dvgf1Jee2raeGYLG9nRqE0kFjaXXdNv15DaUoppZRSSimllFJKKXXKMILZ3IGj1GHKZDKUSqVJy9LpNMVi8TjNSCmlTj0bN25se4OtYRicc845R/x4m9f+5oiPqU5sZ1/8kuM9BTULlUqFH/3oRzzyyCM8/fTT7Nixg8HBQcrlMkEQkE6nyWQy5HI5zj77bM455xzOO+88Lr/88sOOV7iuyzPPPMNjjz3Ghg0b2Lp1K9u3b2d0dJRSqUS9XieVSpHNZpkzZw7nnXceF154IZdddhmXXHLJEboC7T3//PN8+9vf5r//+7/ZsGEDO3fupFgs4nkeyWSSvr4+Fi9ezCWXXMLrX/96XvnKVx71YMWJ6he/+AU/+MEP+NnPfsbzzz/P8PAw1WqVVCpFT08PL33pS7nkkkt44xvfOO3HbWBgYMqyRCJBNps92tM/YWzcuJHvf//7PProo2zevHn8Oee6LqlUinQ6zbx58zjnnHM499xzec1rXsPLX/5yLOsAv3L2EBzPzwlHkz5P1eF49tlnefTRR3nsscd47rnnGB4eJp/PUygUsCyLzs5OstksfX19XHTRRVxyySW86lWv4vTTTz/eUz/plUolHn30UZ544gmeffZZduzYwa5duxgbG6NareK6LrFYjGQySU9PD/Pnz+clL3nJ+NcML3/5y49pJOhU/Ryq1GycyH8PTpSvt44m/T7m6DrWr6kppZRS6tQ28f/PfrD3+2we2zT+eH1hPUW3iOE6JJ59hpfv9Em4sz9G1YbHTjOon/USAsumw0hyXuSM8fXn2KdxdXzlzAc0oL74pfidudlP5kWqftrh/1IYdfiW9fYd7ykopZRSSimlTnLrhtaxZsMa/GDqL4GaaG9xL5uGW9/jB0EAO5+H7TumbnzWmbgL5rO7uFu2K4ziFouUotMMHoviR6M4vkPE9THrDpYNuSxk42CZEgYbq8C+MqxcCK84SyJbRhhKC2iFwDJRiaVFTYmKGUhwbE8JAleCaPEYLOwKY2WehLkCDzqTcjwrjIbVgZIDmXhruomIBMRAYmyFIgyMgW/CvJxExfaNQUcMMknAkHl6YZDMN+W4lglVR46TjkmYre5BtQ7DZYhEoS8tx0pEJKpW8yAVB9OASgWKjoTfAOakIGODHUAiDtUamD5EIhIMq4f/FTRQlOOlw49HNAA8iIZPgUoA/oQfR2h4rfMFSAJmIwyl2dJhs0wJyEUscAMJxJUq4IxCbwcEMRityLXu6JSPq2nIx80DamEITS5q2OgzwPbAtcDzYawEqQD8ONi2hOuGhuU8YyPgDEO6C9J9YFoSyfNc6O+H5yfMv6/EpNejztsF3xmCXZsg3Tk5lBZ7QSituwJrlsKzE74d39Y5OZa2bBDetqH1+KFz4Om5U5/2LxuAazdOXU54KbbmIB+HzhosHpHnySFbtQpWr4aOjoNvq5RSSimllFJKKfUiog0YpdSpSqNq6pjSf1CVUuro0xtAlVJKKaWUUkoppZSaHX1NTSmllFJH0sSo2kh9mH/tv2/8a42CU2Dj2EYie3YSGdjL3CKcMzz7Y2ycAwMZcObOw5l/Gud0nEM2Ir8gwDAM/ueZ7yYX1UDa0aRRtRODRtWUUkoppZRSh6NYL/K5X36OgDa31VQqUCpBOg3JJIOlQTbsb5Wi9uZ3UX/+N7TbFQNYsJCyX8Nx6hDeL9GwWuGzKaJRbNPjgh6PC/tgbgaCF2ybtGFpj8SyBkswUJLYlhnOwTMhHoGXzZO4l+lLYMwwJJaWjIHjSrAsFoNsojW2bUIqIqGuhiP7BkAtgGgUijUgkLhZNtkKXAUeVKowUJbHPR2yX7ECURvi0TD45sFAAfqyMreIJXOtVMEzIB2HIIBqFcYaMFyV81/QAXFbAmieC9UGROMSjKvWoOrBzvB2lK4EZKNyzrEo1BsQdeX8nRg0wmhaw5NlETN8XIcsYIUnNVSEWJeE0kCOHRgSSnM98ErQGQPHhJov0bm+HEQMuS5++KdckmsZdyGahVpDxvSREF4sKmG8SkOuY1OxLNvmUpA0JBw3WgXbhZQDxagE4rb0yznGXJg34TadvhrEu6GYhbF94DZgb1oCcM1tgwBKo5B6HK56CNYsmnkobV8Svrhy6vMTJJ7XjK81NSz4l+Wwa0LPbOEYvOcZiHpTx5ixFSvgrrsOvI1hwLJl0N19GAdSSimllFJKKaWUOnVpA0Ypdaqyj/cElFJKKaWUUkoppZRSSimllFJKKaWUOhXlYnNYkjmbzWObAMhGsmSCGO7QAACDaTijAAl35mNWbdkPwB4aJDF/yXhQDWBJ5mwNqimllFJKKaWUetEr1ApsG91GoV6YdpvNw5vZnt8+/tgPwpLYk2th4wbwAiltnbOUxvJljNZGxyNqxugoVesAEyiOYKbSBI06IBGxeRlIpiSs5flQrMLQmLz/yoUerzjdxzYkTGUGEr5qRtgyUVg2F+JhuKsjB2d1yv6uB6k4xGOwsEuO5XjgOBLr6kzKMU0fDBt6O8AM414g4yUj0p+K2hIsK1dgX0miZfNyEl3bNwZeDCK2BNEsA6ouBCbMSUOhKmMZAWQSMv/m45orl264Cn3p1nGjFtQ8ILzUJvK2GeyqeRIrI5A5G0DgghGV+VoWxMMxLEMGDQI5d9OX8fxAYmxWeAdVMiLXqOrI47oLtTqk0nJNqh5Ui5DrgFRUzt0HUjGJmJVsCGywoxIps21oNOSxEc61GVBLxIAYFEoSmAMJibkWVGowVoJUAH5cxnE9GB2TYFx5GyRMSHZDkJA4G0C0DusHZS4AnbXJT73T94LzDbjXBcOCdA7qOdjaC4uHYGAESiPgOxJAow6X98Nzva3rPjGoZgRwRX/rcU8Flg/C03OnPu2XD04OqjXP95YnYWsO8nGZ7+KRVpjvkKxaBatXQ0fHwbdVSimllFJKKaWUUkop9aKjUTWllFJKKaWUUkoppZRSSimllFJKKaWOkpW5lWwpbiYI5K7rxf2jbPLl/cCA/iycMzzz8fqzrRtcDd9n0Y4RmBM+NgxWzrnkSE5fKaWUUkoppZQ66awbWseaDWskkjZRpQKlEqTTkEwyWBpkR37H+Oq9xb3UR4agnIfTmksDKK+H9XsoJ20czwHfh0aRRqwVPZvCqxBrNDh/vsvyPgmqGUzdPmHDwgw0HI+hIjTCEJkZxtswJGC2bC5EmmE0JN7Vk4JFXdDwYbgMsZgEw0C2tWOQikioq+FIYCwIwLYkKjZSkdBZVwLM5rwC8D1Z7hoSUGvGzzpiEl3zPCj5kLDA9eV1CsuAjrjsbwWAKcfxPIly+WEgrubKPnYYSLNMMN1Wv8405RhNfvgh9IMwWAaYDQgi4esjAeSSsKcIsXA/34BGDdKmXD/PDD/sHWHozJbjmKbE1hpVKDhy/RqWnHskPK5tjn8Y8ALwXAmlNQwJntWbHw8XKp5cL88Hw2u9fuO4Ek8rW5BLQcyASh1Gq2C7Ejcr1qEewJZ+iaXFXJhXghpQGIBYNySy4AL7+8GPALZsl3Dl41oahdTjkHoIqMN5S+HZPigMAUPQGIBaXsYEWDbUCqDNNpR29RbYn4RdE5pmC8dkeTsGsGSk/bpJVqyAu+6afr1hwLJl0N09g8GUUkoppZRSSimllFJKvVhpVE0ppZRSSimllFJKKaWUUkoppZRSSqmjJBebw5LM2Wwe24RRqzL31xsZ6IZCXNYPpuGMgtwAezBVW7ZvytZg3rObGD3vFQTxBEsyZ5OL5o7OiSillFJKKaWUUsdZoVZg2+g2CvXCtNtUnAoPrHuAgKC1MAhg7VrYsEEqXaYJS5fSeNky8rX8+GamH1Ar59vfaVPOY8Tn4PkeZqOBAcQNmJOFbFxiXb4HxQrsK8OKhXDpWS4RUyJfZhhLM4JWWC0TlVha3JB1izphVx72FaEnA6mEBLwWdkoEzfXAceQ4nUmIhlOL2tDbIafVzMgZSGDNMGR91IRyBfYXIZWWQJhtyuNkRKJl8YhE22oNCEyYk4ZCtTXvTEIupQEYPozWwLblTxDujxceP5A5B24YTzNakbGaB5EwiGaGcbXABSMq87UsiFuynWXIyTiunIfpyx/PbV3HqAXzMxKSAzmO14CGC7GMxOFcB/JFWNgtHyvCOcVs6M7Kto4LfhR6umQOfgCVKsSiEoirN2TOzafU/jzEozJOxIOCC4NliPqQjoCXkPMbzsv4tSKUt0mMLdkNQQLi4etB0TqsHwyPAXQ2y2fIx3vRWvjOEOzaBOlOqOdgay8sHoKBESiNSJzt1ieAuux3eT8819u67otaT3WMAK7on/wUn00oLerBLU/C1hzk4zLfxSPysTxkq1bB6tXQ0XHwbZVSSimllFJKKaWUUkqpA9ComlJKKaWUUkoppZRSSimllFJKKaVOCXvyVe55ZMeMtr3xlWcyvzNxdCekVGhlbiVbiptJPPsUhutyZh6emSvrAgP6s3DO8MHH6c+2boQFODMPhuMQf/Ypape8ipVzLjka01dKKXW0FHbBY1+a2bYvfz9kFx7d+SillFJKncDWDa1jzYY1+IF/wO32FveyPb99/HEQBLDzedi9A8ZbTT7sXgfRMo05CcqNsiwujOIY4FrTDF4tYscMzpvX4KI+mJsG3xzvbGEAaQte2ivvD5ZgqAiNMERmTtgwHpGgWsSUSJiBxL5WLJCA1/4K1APoiEv0DCSMZsUgFQHPAyeMZxmB7BOxYKQi8+lKgNl8DSGQMFeAhMii4flFLehLy7FdH0p18F2IhgE0y5DjE4AVAKYcx/PkmF4AY1XozcjYQSBjOu6EiFw4heiEu5f88EPoBxJRAzAbEETC1z0CyCVhT1GiZwD1MLBmh/s2GpM/TrmE7FtzJD5Xq4NZgyAlnbeIDZkkeI58zMzwOE5DAmoJC4IoFMagKxPO2ZNjVGowVoJUAH5cInKuB/tH5VznRcHyoGJBYEG0AV4d8lXYtBuqYSAt5sK8EtSAwgDEuiGRBRfY3w9+BLBlu4Qr17M0CqnHoech6FgEXh8UhoAhaAxALS/jASwbgu5K65r0VGD5IDw9d+pTefng5G2b5zubUJoBLBmZZmXTihVw++2QSk2/jWHAsmXQ3X2QwZRSSimllFJKKaWUUkqpmdGomlJKKaWUUkoppZRSSimllFJKKaVOCfuKdf7x59tmtO2bls3TqJo6ZnKxOZxtzmffc88A0FWDbA0KcVk/mIYzCnLD7HSqtmzXlK3JOACJdb/m9Ff8Drlo7iidgVJKqaOiNAiP3D2zbc+7TqNqSimllHrRKtaLPLj+QYLxfNkElQqUSpBOQzKJaZiTVg8UdlPP74DONgPnd+Al51F0K1L6qhfxLHAmxLqMACxfIlyXzm/wikUGdniIIJD1zQB6OiqhtGi4fkkUFnXCwBg4PmRikIiBaUJfh4zZcKFal5DZnDREw+P2dMC+Uhg1a84FCawZYRgtkoByBfYXIZWWyJltyuNkRKJl8YhE22oNCEwZF8APx8skJETmA54LgwXoy0LYcSMeQapk4bWIWBC4YSTOgJorgTHbgsADM9qaK66cq+dLuCxuQS2Mo2FIkCxqy/xMX47vh9cyasH8jETnQK5xtQJJI5wrMFKArg5IReW8fSAVg3oD3AxEYuAYELchl5X540LFh2gEfGfyx69UAceB4QLkUhAzoFKH0SrYrmxfrEvsbku/HCfmglOGbC+k5wJRCdDVqzC8C6rNC4lEypp8Dxathe8Mwa5NkO6Eeg629sLiIRgYgdKIHPPWJ4A6XN4Pz/W25rsoP/l5ekX/1Kf41VtgfxJ2dbSWLRyT5e3MKJQGEku7667p12soTSmllFJKKaWUUkoppdRxpFE1pZRSSimllFJKKaWUUkoppZRSSimljrLX/3A9/+a44ze+npmHZ+bK+4EB/Vk4Z3j6/fuzrZtmm/s3mQ2H1/+/DbD4SM9aKaWUUkoppZQ6ugq1AttGt1GoF6bdZvPwZrbnt48/9gMfAuDJtbBxg9TELAPOWUpj+TJGa6M0+2vG6ChVq/24AJQKGPEoXqOGAcRMmNchsTHLlPhV1IdLz4TuFBgEeGGIywDMADAkPrZsrkTHCENsVhhKOzMHrgv7KxLk6ohLgA0gFYHupETEHAeM8NRMQ8JrNQecMETWmZTl49fBk239MLIG8rYvLXNzfSjVwXcl2hYYMj/LhKoj29im7FP3JHIWAMPV1hhBEM7NlXMNe2gESBANZF/bnPy6hemD4YERbYXScknYU4TYhP0sA2xfHjca4E74WOUSMmbNkXBbvgDRFAQxeWyF+zUjdwbyVPBcidcRk9jY/gJ0ZSAWRvCqwN59kKyCnQAvIedTKMr+tSKUt0HChGQ3BAmIhyH8aB3WD0pQDcJQWgCFQSjuh54Y7AmgUQULiKWhbkt8LeHK9SyNQupx6HkIOhaB1weFIWAIGgNQy0Ozv7ZsCLor8n5PBZYPwtNzpz6Nlw+2tpso6sEtT8LWHOTjMt/FI2H07lCtWgWrV0NHx8G3VUoppZRSSimllFJKKaWOA42qKaWUUkoppZRSSimllFJKKaWUUkopdRSZ+REW/duDnH8mPNsny7pqkK1BIS6PB9NwRkFusH2hqi3rm7I12b/p/CFY/MSDbHvXrfiduaN2HkoppZRSSiml1JG0bmgdazaskUhaU6UCpRKk05BMAjBYGmRHfsf4JnuLe6mPDEE5D6c1lwZQXg/r91BO2jieA74PjSKNWCvsNYVXIerWOX++x8v6YF4mHC3cPhMNY2kmlOuwrwiDRcCCeSnoiEEyBgs7JVjmeNBwAVciaJYpIa+4BT0dsK8kUbUmA0hGJZYWM6FWl+DZcAk6UrJ8rCJvE1HwA0jYEueqNSAwZVwAPxwvk5AQmY8ExgYL0JeFSHjMiAVOA7ywkBaxwXVkbN+AmitBtogJgQdmtDVXXDBNcAJZH7fkMhvhNXPC1zXscJlbhyDcP2rB/Izs17zG1QokjXCuwEgBujogFZVYmg+kYhIxczMyfiwGMQ/m2DLnShViUbCtMHYWtK5vuSKxuvwo9CbA86BQlW0MD7wS5KuwYWcrlBZzYV5JwmaFAYh1QyILLrC/H/wIME0orfuHsPoCMCOQzkE9B1t7YfEQDIxAaQR8B259AqjD5f3wXG/r+bYoP+G5EcAV/ZOfrldvgf1J2DWhZ7ZwTJZPxwCWjEy/HoAVK+Cuuw68jWHAsmXQ3X2QwZRSSimllFJKKaWUUkqp40ujakoppZRSSimllFJKKaWUUkoppZRS6kVj89rfHPNj5r7+fzGrlSk3yp6Zh2fmyvuBAf1ZOGd46v792dY+zf2amjfYmpUyXfd9heFbbztap6GUUkoppZRSSh1UoVZg2+g2CvXCAberOBUeWPcAQbOAFQSwdi1s2CCVLtOEpUvh4otp+A75Wn58X9MPqJXz7e+IKecx490EBBiNOgBxA+ZkIRuXyJnvQbEC+8qwYiFcepaHbUmLywwkQuUBiQhc0CdxLyOAbARyc2DlAomKlcPgeToOybBYFjEltJaKgOtJ3MwMT9EPIJeSZQQQtSEZl2AaSLPKtmF4TOJhUUuWd8TC9ciYZU/eRg15vSBiyXlVHdnGNmXfuifjBMBwFfrSrXGiFtQ8mYdlgIm8bb7+UPMgEi5vXXcJkRnRVrcsl4SGFwbXkCBbLGjtV65ItM0KF+QScoyaI+eQL0A0BUFMHlthX882W3P1AonDJWJATKJ1vi/XLeqBa0GlBmMlSAXgx+U6uh6MjkG1BrERGB6GdBek+8AMr63nwshvoG61zrNzQsje92DRWvjOEOzaBOnOA4fSuisSv3+2DwpDwBA0BqCWl0gbwLIh2Q6gpwLLB+HpuUyxfLC1XVPUg1uehK05yMdlrotHWtf/kKxaBatXQ0fHwbdVSimllFJKKaWUUkoppU4CGlVTSimllFJKKaWUUkoppZRSSimllFLqKDHzI3Tefy8gN8o2b6wF6KpBtgaFuDweTMMZBUi4rf2rtixvytZkv6bzJ9yI23X/vYxefzN+Z+4onpFSSimllFJKKdXeuqF1rNmwBj/wJ6+oVKBUgnQakkkA9hb3sj2/fXyT4Pl+2L0DxrtOPuxeB9EynHY6Da9BuVGWVYVRHENiWm1VxzBjBi/rbbB8HvRmwDdbITADSFvw0l55f7AEg0VohOuaYbVzeyRY1twnYcGcdBgICyATgZEKdCRahzYMCawZhkTTIqac/r6SxNB6shIM2zcmUbBYRMazDPB8CaPNScNIuRXKyiQkJNZ87HowUIC+LIQtNyIWOA3wDJlsxAbXkZCbb0jszPXDQBxyTNOVYJllSMMuOuEOI3/Ch9AJX6ewfdm37jB+N1LUgnQYOgOo+2CUIZYCP3xcLkKuA1JROb4PpGJQb4CbkfFjMYh5MMeWOVeqEIuCbcl24x88oFiCWkMCdTEDKnUYrYLtStysWId6AFv6Zd+YC/NK4VNnEOL9EO+GsSyM7QOzAbE01G3ZNuFK4680CqnHoech6FgE3gxDaS+M6i/KT3h+hHH8ia7eAvuTsGtC02zhmCxvxwCWjLRfN8mKFXDXXdOvNwxYtgy6u2cwmFJKKaWUUkoppZRSSil18tComlJKKaWUUkoppZRSSimllFJKKaWUUkdJ7uv/F7NaGX/8whtrz8zDM3Pl/cCA/iycM9zavz/b2ra5fdMLb8Q1K2W67vsKw7fedsTPQymllFJKKaXUi1ehVmDb6DYK9cK021ScCg+se4BgYv0qCGDtWtiwQSpdpglLl8LFFzNay5Ov5WU712W4sAOnXduptAP2lQkMg5pXw/ADqBfxLHAmRNWMACxfQmWXzm/wikUGtimBtOb65vfX6SgsmwtRUx4vicKiTthTAs+FTAzmdUIuLYEvxwMCSEdaYTMrgJgFfR1Q88LomQm5hMwB2QXfk7euASYSIQPoiMlbz4OSD6kI1F0JoFkGdCVlzgYy71RULmGAHCsAhqvQF4bYjXDsWjhXKzyeZbTOu+ZBxJD1ZhhXC1wwotLXsiyIh2NYYZwtCKDhSLzNRIJopQokwricbULClqhbzZGQW7EGiSj4MQnA2eHxbbM1Vy+Qa52IAWGUzQ8/flFPgnmVGoyVIBWAHwfblvFGx6Bag/I2SJiQ7IYgAfEw7Batw/rBMMYGdE6I0wOcvhecb8C9LhgWpHNQz8HWXlg8BAMjUBqRQNutTwD12YXSeiqwfBCenssUywdb8bWmqAe3PAlbc5CPy3wXj7Sea4dk1SpYvRo6Og6+rVJKKaWUUkoppZRSSil1itGomlJKKaWUUkoppZRSSimllFJKKaWUUkeBmR+h8/57Jy3rqcD5Q/BsnzzuqkG2BoW4PB5MwxkFSLhQteVxU7Ym2zedPzT1Rtyu++9l9Pqb8TtzR+GMlFJKKaWUUkq92KwbWseaDWvwA/+A2+0t7mV7fvv44yAIYOfzsHsHjHedfNi9DqJl3AXzydfysl1hFB/5PritSgFicRzfwXJcTCBmwrwOyCQk7uV7EPXh0jOhJwUQ4IVRsmZYDQPiEQmqRSwkPuZLQGxOGs7MSUBtpALd6TA0ZkisLBOV4JfjSmTMc2G0AomMxMKG6xIgS0UkxBaPSGyr3oDAlPFHyq1QViYRhthkqhSrEiyzLVkWjwCejOkZEI1AoxaG2gKJr9VccH05voFcB9OVYJllSDgtOuGa+uGH0A/k3ADMBgSRMBYWQC4Je4oQC/fzDXBdMKoQS4Nnyrnni5DrkKCagRwvGYbUGp1ynWwbujsl1uYHUKlCLCrnWG/I8ZqKJag1IJeCmAGVOoxWwXYlblasQz2ALf2yb8yFeSWoAYUBiHVDIgsusL8f/Ahgy3YJV65jaRRSj0PqIaAO5y2V12cKQ8AQNAaglpcxAZZNeN1ltqG0q7fA/iTsmtA0Wzgmy9sxgCUj7ddNsmIF3HXX9OsNA5Ytg+52hUKllFJKKaWUUkoppZRS6sVBo2pKKaWUUkoppZRSSimllFJKKaWUUkodQNEpsrPyPEWnOKv9sv9xP7v6KlOWWz70Z8MblgHbh3y8tf6J+XD6GDzfAaOJ1j7n7GttYwRwRf/UY5qVMl33fYXhW2+b1VyVUkoppZRSSqkXKtaLPLj+QYKJ9aumSgVKJUinIZnENMxJqwcKu6nnd0Bnm4HzOyDlUvZrOE4d6kWIgmNJxGsKrwEuRE2f8+e7XNgHczOyqvm9dSYaxtIMqNZhsASDRQmhzc1AJgbJGCzslKCa40nUDBc6kxIkMwKIWTC/K4yZBdL9StgyDkDEBtOHvWWoe5C1AAO6UxIFM5DQWaku8bGYIXO0DOhKyjGMcN6pqITOAqDuQtGB3owc20DiY44r+1iGLGsGzJrnXfPknAkkomYAgQtGNIzCWRC3ZDsrHMBxJbZm+vLHc1vXPWrB/AxEzNb19RvglMCJgRsH15OInee14mwGck0tExIxIAbFMsRj4bgeuBZUajBWglQAflzCa64Ho2NQrUF5GyRMSHZDkIC4G+5fh/WDYYwN6JwQnfc9WLQWvjMEuzZBuhPqOdjaC4uHYGAESiMSZ7v1CaAu+13eD8/1tq7lonxrzHavu8wmlBb14JYnYWtOXvPprMHikVZU75CsWgWrV0NHx8G3VUoppZRSSimllFJKKaVexDSqppRSSimllFJKKaWUUkoppZRSSiml1DS2FDfzg70/IAj8We1n1Kp0bfkuxpnt15cjMJRuPa7aUA9/kicfg3IUBlOM320bc2FTN5gB9Fbg/CHontprA6Dr/nsZvf5m/M7crOaslFJKKaWUUurFo1ArsG10G4V6YdptNg9vZnt++/hjPwgLYE+uhY0bpDpmGXDOUhrLlzFaG6XZXzNGR6laB5hAcQQzlSZoSOEqYsG8DCRTEubyfChWYWhM3n/lQo9LTw+wmwGx8DgekIjABX1gmxIl74hC5xxYuUCiZWNhRCsdh2RE3o+Y0BGDVESiXjVHxgzCuQQBFCsSDcvEWtP2fWg40NUBfi2MmCExsnhc3g+QMYcK0JeF8JDEIzLhIADPgGgEGrVwe6Dmyn62Cfhg2eAQzsuTaJrvSxBt4nzkY9MKnJkNCCJhLCyAXBL2FCEW7lcPA2t2uG+jIcGzplxC9q054bWpQxIYKUugLWJDR0pibpUqxKLyMXPrE+YVXr9yDXIpictV6jBaBduVwFmxDvUAtvRLLC3mwrwS1IDCAMS6IZGVa7O/H/wIYMt2CVeuY2kUUo9Dz0PQsQi8PigMAUPQGIBaXsYDWPaC11J6KrB8EJ6eO/XpuXxw6ususw2lGcCSkWlWNq1YAbffDqnU9NsYBixbBt3dBxlMKaWUUkoppZRSSimllFKgUTWllFJKKaWUUkoppZRSSimllFJKnSIilsmCzsSMt1XqYEpuiR/s/T5BEMx638RTT2D404fYzijA0IRoWmcNBpuRNWPyuub6wID1PdC5E67on/7YZqVM131fYfjW22Y9b6WUUseQFYXs6TPfVimllFLqCFk3tI41G9ZIJO0ABkuD7MjvGH+8t7iX+sgQlPNwWnNpAOX1sH4P5aSN4zlheaxIIwb+dMUpr0Ks0eD8+S7L+ySoZjB1+4QNCzPQcDyGiuCGXbfmKzsGcG6PhNCaj+MWzEmDbUiQLBmFkQp0THjZyDAksGYYEimLmFCpwHAJsqYMZJtQa0CpBrYlUbSaE4bUTDmGH0jcywBScSjX5bHjyDyHq9CXlvUGMo7jghH26Jqna4Xv1MOomu+DEZ6k5QMemIZE5CxLzrEWxtEwZMyoDaYvfzy3dS2jFszPyDmCvL5QrUDSgHBoRgoSiktFw+MDqZjEztwMRGIQRCWw1pmR40Y8aNiyrJyXcJqXANuGUlmuXa0G5W2QMCHZDUEC4m44rzqsH5RjgLz20eR7sGgtfGcIdm2CdCfUc7C1FxYPwcAIlEYkznbrE0AdLu+H53rDmBywKD/h4x20fy3l6i2wPwm7OlrLFo7J8nZmFEoDiaXdddf06zWUppRSSimllFJKKaWUUkodNRpVU0oppZRSSimllFJKKaWUUkoppdQp4dz5Hfz3/77qeE9DnUL6yzsmBdUOdrN5k1GvEl3/6+lvHAcSLvRUwngackNxzIVa+NM8wYR9465sL4NDVw26KweeQ9f99zJ6/c34nbkZzVkppdRxMHcZfOTZ4z0LpZRSSr3IFOtFHlz/IAFtAuKVCpRKkE5DMkk2np20utNOM1jY3H7gQp54ah4ODjTqAMQNmJOFbBwsU0JZxQrsK8OKhXDpWS4RU+JfZhgmM4JWDCwThWVzZRwDWNQJu/MTAmAJmJ+FXBg3cz0IfEhHZXvTl7cxC/o6JELWcCVWlktIpAwkfuZ78hY7jKYhMbJ4XN53fchXJFaWjMj37bYJ0TC8hhEG2iw5RhCeR82VfSMm4INlg4Ocb+CBaYITyBwhjKkh+zZb7bYPNIBo69rkkrCnCLHwdYR6GFizw30aDXCt1ocnl5A51xy5TvkCRFMQxOSx1TxWGF4zAC+Q803EgBjEAhgryfn1JsC0ARdGShCvyPbFhvzZ0i/XIObCvBLUgMIAxLohkQUX2N8PfkSueSx87SMIoDQKqceh5yHoWAReHxSGgCFoDEAtL+MBLBtqvUbSU4Hlg/D03KlPz+WD7V9LiXpwy5OwNQf5uITdFo9M6tzP3qpVsHo1dHQcfFullFJKKaWUUkoppZRSSh1xGlVTSimllFJKKaWUUkoppZRSSimllFKqDduITHq8cWwjRbd40P0iu/qJnN7m5vQXaJiwJ8P4nbqOCeXo1O2siTf9BvDyXQcdGrNSpuu+rzB8620H31gppZRSSiml1CmhUCuwbXQbhXph2m02D29me377+GM/8KUa9uRa2LhByliWAecshYsuJiAgX83LxgN78YDGdHeilArEkxbn9ja4YB70ZsA3Gc+3GUDagpf2yvuDJRgqQiMMqpkTNoxHJKgWMVtxNMuEixdA9DQYqkpnrDstMTPLgJgJ6ZjE0lwXnLq8Ha1AIiPBsOG6hLtSEQmxxSMScqs3IDBhThhoC8I5peJQDvdxHBgtg5WFSBgsi1gSCPMCIICIDZ4j82kG02seRIxwzDBaZvmAJ3MPwnHiluxnIPs6jpy7iYzdqEkEDSTeNj8TxtrC7asVSBoQDt2Kz0Xl3H0gFZNzdTPguBCLQcyDObacd6UKsSjYlmw3sb1Xrsg54EClBOVAPr5EwAq3i9ahf2+4HRIpG3+uebBoLXxnCHZtgnQn1HOwtRcWD8HACJRGwHfg1ieAOlzeD8/1tq7lonxrPCOAK/onPwWv3gL7k7BrQs9s4Zgsn44BLBmZfj0AK1bAXXcdeBvDgGXLoLv7IIMppZRSSimllFJKKaWUUupo0qiaUkoppZRSSimllFJKKaWUUkoppZRSbcxPzMcwDILwTuAFyQVsHNt4wH0M18HeNzij8aM+pJxWSC3iy03VntnaxvahZ0JUra8My2c2PF3338vo9Tfjd+ZmtoNSSimllFJKqZPWuqF1rNmwRiJpTZUKlEqQTkMyCcBgaZAd+R3jm+wt7qU+MgTlPJzWXBpAeT08uwcvk6bYKILvQ6OIFwfHah3CCOR7WdOAS+dXeMUiA9tsBdKMoBXESkcllBYNv+9dEoVFnTAwBo4PmRgkYmCa0NchYzZcqNYhakjwLIpEsHozUHJkm6Z4pPU4aoERgeEC1D3IWrJjdwpqDRnD9aFUB8+FWBhBs02ZX60h2xuGjNVwJRQWACNV6A3jawYSRAtcCatFTFkWnXC3ju9Pfd/2gUYYSQuvVS4px2sqNSAL4zH2UgliEYnLAeQSMueaA64H+QJEUzKm64XhNuScmsN4gZxvIgbE5Lz88OMX9cC1oFKDsRKkAvDjYNsy3ugYVGsQG4H8fonHmRb0paA+F+woDG9DKnA2xFxIhNetNAqpx6HnIehYBF4fFIaAIWgMQC0Pzf7asiHoDl8L6anI6yBPz2WK5YOt7ZqiHtzyJGzNQT4uUbfFI+OX8NCsWgWrV0NHx8G3VUoppZRSSimllFJKKaXUcadRNaWUUkoppZRSSimllFJKKaWUUkoppdrIRDIsyZzN5rFNAGQjWTJ2hqJbnHYfe2APRjDzY2RrUI4wfndv3G1F1gA6anIDMgABvGELZOszG9uslOm67ysM33rbzCeklFJKKaWUUuqEUagV2Da6jUK9cMDtKk6FB9Y9QNCscwUBrF0LGzaExSwTli6Fiy+m4Tvka/nxfU0/oFbOt7+7pJzHSESxTRu3Jt8Lx02Y1wEdCYl7eZ5Ewy89E3pSAAFeGFIzfelrYUjwbNlcCZARhtisMJR2Zg5cF/ZXoB5AR1wCbACpCHQnJWzmOGD40iAzTehOQ6EKjicxs3SsNXXPh4YDXR3g1+Tb7gAZJx6X9wMkFjZUgL4sRMJ9Ixa4SICMACK2BMQsA3wDaq4E2QzCcwlDar4Hhilzs4C4BTVP9sOQD4vjtK6LH0C1AVZ47ZMROXbVkcd1F2p1SKXBB6oeVIuQ64BUVGJpPpCKQb0BbgYcF2IxiHkwx5ZjVKoQi4JtyXZMeN2iWJKAXC4lYblKHUarYLvgO1Csy8dkS7/sG3NhXqm1f/cYLHwefrIdNj4i51LPwdZeWDwEAyNQGpGxbn0CqMPl/fBcbyu2t6j1dMQI4Ir+yU/Dq7fA/iTsmtA0Wzgmy9sxgCUj7ddNsmIF3HXX9OsNA5Ytg+7uGQymlFJKKaWUUkoppZRSSqkThUbVlFJKKaWUUkoppZRSSimllFJKKaWUmsbK3Eq2FDcTBHLH8YLkAjaObRxfH7fi9MZ6ATAadTKbHp9VVA0kmjaSkPeLUajZ4Jlg+9BTaW3XV4Y3T3PD8HS67r+X0etvxu/MzW5HpZRSSimllFLH1bqhdazZsAY/8A+67d7iXrbnt48/Dp7vh907YDxC5cPudRAtw2mn0/AalBtlWVUYxTHAtaYZvJInEre5sLfB8nnQmwEM8MIgViYKF8yVqFm5DoMlGCwCFsztgGwMkjFY2CnBMMeDhgu40JkMg2SBBMh6OmBfSaJqTQaQjIJpQMyUyFjVgdGSxMVMA8Yq0BGDUk3CYQlbwmcAgSnhNj+QqJkBpOIy12bkLACGq9AXbmcgcw1cCatFwmhazGqFwOphyC0IJKIGEDjIXTqmXPJcEvYUIRbeueMb0KhB2gyvoQmlEqQ75DokbBnLNCW21qhCwZFIWsOSeUbCp4Nttq6PF4DnQiIGxOT6+r5cm6gnH9tKDcZKkArAj4NtS1BudAyqNShvg4QJyW4IEhJ9B4jWYf1gGGMDOmuTnx5nDEHqJ1AfgVJPuHAIGgNQy0Nz82VD0B2+xtFTgeWD8PTcqU+35YOt7ZqiHtzyJGzNQT4uc1g8Mt6nPzSrVsHq1dDRcfBtlVJKKaWUUkoppZRSSil1UtGomlJKKaWUUkoppZRSSimllFJKKaWUUtPIxeawJHM2m8c2AZCNZMnYGYpuEYC6V6cr2kXcipN8+uckCodwjCo8MR8cC0bjcuNyOQodNQmuARDAG7ZMvbH4YMxKma77vsLwrbfNfmJKKaWUUkoppY6LYr3Ig+sfJKBNtbtSCUtcaUgmATANs7XecRjY30+9s83A+R2QcvEMKDaKUt6qF/Es+Z60yQjACqNcl85vcOkiBzuMn02UiMCyuWBZEhdLxeClMVi5AJxAImcGkI5DMiL7REwJoKUiEvWqOWCG4/oB5FKyjACiNiTjMg8Aw5AY2PBY+BiIWjJe1JbHrgeFhsTbkhGJoNkmRE2oNWQnw5D9Gq7M2zckwub6YagrCGNvgO+BEYbOLEPibzVPLh2A40I0PDfblbhZM7wWtWB+Rs4ZZLnXkOPGMuAa4DqQL8LCbjkmyHFiNnRnZVvHBT8KPV1yrf0AKlWIRSUiV2/InMefPyU511wKYgZU6jBalfn5DhTrUA9gS7/sG3NhXkkCaIUBiHVDIgsusL8f/Ahgy3aJ8JqVRiH1OKQeAupweRKe626d+6L85OfTFf2TnztXb4H9Sdg1oWm2cEyWt2MAS0bar5tkxQq4667p1xsGLFsG3d0zGEwppZRSSimllFJKKaWUUicjjaoppZRSSimllFJKKaWUUkoppZRSSil1ACtzK9lS3EwQyB3KC5IL2Di2EYCAgD3VPSxKnUV8w7OHNH7Kgd4yrO8BDIj4cqNyz4SAWl8Z3jzNjcUH0/ngvzL8gT+UO8CVUkoppZRSSh1XhVqBbaPbKNSnr3JvHt7M9vz28cd+4Es068m1sHEDeIGUt85ZChddTMNvMFoblW0G9mL4AdXYNIMXRzDTHViGhduoAhAzYV4HZBIS9vI9iPpw6ZnQkwIICHwJZjUDaAZwbg9EJsTYkiZ0p2WMAMhEYKQCHYnWNoYhsTPDkBBaxJRO3L4SlD3oyUoEbd+YxNRiERnPMsDzoerAnDQUqjIHI5B5B0Hrcc2FkTJY2db8IpZEwrwACCBig+fIuM0QWM2DiCFjNb+FDhzAlvkGQC4Je4qynwHUPRnb9MEEGg3wJtypk0vI+DUnjMjVwaxBkAIPmUcmKXPxzTAgF4DTkIBawoIgCoUx6MrImFEPXAsqNRgrQSoAPy7BOdeD0TGo1qC8DRImJLshSEjEHSBah/WDYYwN6Ky15ut7sGgtfGcIdm2CdCfUc7C1FxYPwcAIlEYkznbrE0Bd9uupwPJBeHru1Kfc8sGpkfioB7c8CVtzkI/LHBaPhFG7Q7VqFaxeDR0dB99WKaWUUkoppZRSSimllFKnLI2qKaWUUkoppZRSSimllFJKKaWUUkopdQC52ByWZM5m89gmALKRLBk7Q9EtArC/vp/TShaG5x/yMfpK8MSC1uPOGiTCm50J4A1bpt6APFNWcYxo/zYaZy0+5PkppZRSSimllDp864bWsWbDGomkHcBgaZAd+R3jj/cW91IfGYJyHk5rLg2gvB6e3QPZLGWnjOPUoVGEDDQs8NsVqrwKVF2ips8F81xeNhf6wlhXMy6WicKyuRIYq9ZhsARDRbCA3ixkYrAgC7k0+AE4noTIMpFWFMsMIGZBX4fEyhouGKZExkxj/AzwPXnrGhIli4YRtI4wCud5UPIhFYG6K+dkGdARlwGsADDBtmRbI5A5BcBIFXrDORpI/CxwJawWMWVZdMJdNX74YXFdiETkfdsF3w2vTSDz605CzG5ds0odYj74yPzyJQmlpaISiPOBVEwiZm4GIjFwDIjbkMvKnHGh4kM0IsEyI2h9PEoVcBwYLkAuBbHwmKPVcH4OFOtQD2BLvxwn5sK8EtSAwgDEuiGRlbDc/n7wI4At2yVc+fiVRiH1OPQ8BB2LwOuDwhAwBI0BqOVlPIBlQ1Nfp7h6C+xPwq4JTbOFY7K8HQNYMtJ+3SQrVsBdd02/3jBg2TLo7p7BYEoppZRSSimllFJKKaWUOtVpVE0ppZRSSimllFJKKaWUUkoppZRSSqmDWJlbyZbiZoIgAGBBcgEbxzYCEBAwtOsZ5h7G+PtSkHKgFJXHnbXWurklePM0NyDPRGAYeB2dhzE7pZRSSimllFKHq1gv8uD6BwkIpq6sVKBUgnQakkmy8eyk1Z12msHC5vYDF/KQThO34jhliX9HLJiXgVQKLBM8H8aqsG9M3r90ocelpwfYhsS7jHBKHpCIwAV9EgOzfOiIQuccWLlANsg3pGHVnZK3liGhsUxUYmmOCzUHPAcKFUhkZKzhuoS7UhGJnMUjctx6AwIT5qRhpNyKsmUSYYgNIIBiVeJktiXL4pFwwsg4zWCagczJN6Dmguu3xrDCkJrvSeDNNCUUF7ck/GYZgCHHtS0wfQm9eTVwoxM+HnGIRcLz9KFYlkhbEJOYG45sZ5vh/JCQm+dCIgbEZM77C9CVgVj4MagCe/dBsgp2AryEjFcoyv61IpS3QcKEZDcECYiHQfZoHdYPyvWEya8r+B4sWgvfGYJdmyDdCfUcbO2FxUMwMAKlEYmz3foEUIfL++G53lbYbVG+NZ4RwBX9U5+KUQ9ueRK25iAflzksHml9TA/JqlWwejV0dBx8W6WUUkoppZRSSimllFJKKTSqppRSSimllFJKKaWUUkoppZRSSiml1EHlYnNYkjmbzWObAMhGsmTsDEW3iOE6jOR3UrUh4c5+7KoNg2nIVcAJb7hujtNRh2s3Qnfl0OdeecWr8eZ0H/oASimllFJKKaUOqFArsG10G4V6YdptNg9vZnt++/hjP/ClmPXkWti4QapZlgHnLIWLLiYgIF/Ny8YDe/GAxnR3gBRHSGRSXNJd59wF0JsJQ14m4wk3A0hZsKADqq7HUFGCY4EBZtDa5tweCZQ1H8ctCZ7Zxv+fvTuPr6uu8z/+OufcPTe5SZqkLaVQSwsFCQVplcqwOY5VNhfGFRWouIIzjjK4/MZxRkdHxnUUnRlHoYjAiC1WBQUVAUVBSstSutDW0kBpmzTLvbn7cs75/fG9aRJy02ZpusD7+YBHcr7L53zPuTe3OUnO+4LjQywEOdcEkoGpHwmYQDWAYMA0dvZB0YWEYwq11EGhZGpWPMgUTchY2DJrcCxoilVD3jBtdSHwqqepWIF02Ryb75v/Q44JcbOrcwbCu8LOYBhY0a0GnvlD1lzG3E1jAx40x2BnGsLV8+tZkCtA2DPbrgXJDMRj5jijARMoVxc2oWqVApTLEA6b89TcYILjcnkIh0xAW7EEQ/P0sjkzJ9kHbVFwXUjlzRjLBTcDyTxsfG4wKC1cgZkZKACp3RBugWgCKkB3B3hBc1zhivm5gu9Dpg/qHoHWVdAwF9zpkOoCuqC0GwpJUw+gvWvw5w+tOVjYCY/XSJBf2Dn6zyksYH5v7b5hVqyApqbR+y0L2tuhRT/PEBERERERERERERGR8VGomoiIiIiIiIiIiIiIiIiIiIjIGCxuXsyW9GZ839wFPSs2i039mwh07QbfoyMBC3rGX7cjUb2JHZiRgTlJCLkQqcC0HLz2mf1VGF12yVns+vdvTbyAiIiIiIiI7NP6rvWs3LjShKQNlctBJgPxOMRidGY62Z7cvrd7V3oXxd4uyCZh9kCrD9kNsG4nbn2cdCltUsVKadwIlJ3B8pYPjmdCvpYclWPJ3DxBa7Dfr44ZCBeLh6B9BoRsE442txGeT0J3P0xrgLooHJWA5rgJBKu44HtmngXY1cMLOxCLQqoIpYoJDKsPD+7X9aBcgqYG8ArVgDRMAFokYj73MfW7UjA9AcHq3EgQcE0YmGtBKAilQnU8UKiYeQEbfBfsUPVcYAbYtjldjmPC4Aqu2QYTvhaq7ihQMYFuA+cm5MBR9SZ8DUx7PlMNiAtCxYJizgS0zW4Gxx48Ds+DSAgImbUNPAQhFyqOCWfrz0CdD14EAgEzrq8f8gUI90JPD8SbID4d7Opj7Fag9y9QHPKYNxYGP/dcmLsG7uyCHU9DvBGKzbC1DeZ1we5eyPSCV4arVgNFOLsDnmobPO65yeHPp3M6GOb8LdAdgx0Ng21H95v2SVm6FC65ZJJFREREREREREREREREalOomoiIiIiIiIiIiIiIiIiIiIjIGDSHpzG//ng29z8NQCKYoN4PU+naDUBnHI5NQbQy9pr5gJkH4FsWkZedTHN03t7+mYHZ5N+xmOfGu1gLivNOwGtsHu9MERERERERAVKFFNv6tpEqpkYdkyvn+Mn6n+DjDzb6PqxZAxs3msQt24YTT6R0ajvJQnLvMNvzKWSTte/qyCYhGsTCwisVsYCIDTMboCFqQr1cF0IeLJkDrXUAPlTDyCx/MNwLy4SVtc+AoAOOa0K9HRtOOxoiFnTloQS0xMGywLEgbEM8bELbKhUoF83HVBbqm0ywWU8RYkHIFEy4WiQIhXI1SM2GadWANr+6nroIZItmu1w2oWQ9eZgeN/0Wpk65Ug2NswaPw6l+UqyGqg3Jj8P2wHLBClXDzixojsHO9GCNomuO3/bM8ZdK4A45981RM69QNqFnhSIEMhBuM9vhAMRiZt1OdZ2+bwLkBlRc6E1BUwzCFuSK0Jc3IW5eGdJFKPqwpQOKJQhXYGam+nzrhEgHRFqgPwH9e8AuQTgOxYAZG62YfWb6oO4RaF0FDXPBnQ6pLqALSruhkISB/LX2LmjJmc9bc7CwEx6fMfIpt7BzcNyAkAvvWwtbmyEZMaFu83qHn/txW7oUbr99MhVERERERERERERERET2SaFqIiIiIiIiIiIiIiIiIiIiIiJjtLh5MVvSm/F9c8P8vI4+nvbM574FHQlY0DP2eh0JMw/A8n2OTfpUjjvabFsWC+e8m3xIwWgiIiIiIiIH0/qu9azcuBLP9/Y5bld6F88kn9m77fs+PPcsPL8dGgZaPXh+PYSylKZFyZaypjnVR9mCijNK8VyKYCTAyTNKnDYDZsQBy4SmAdSH4JQZELRNUFlnBrrSELSgrR4awlAXMplu0xtMOFqxYsLCQpYJPAtY4PhmfKZsxgyIBAe3Qw5YQehJQaUaUubY0FJncuMsoOJBMmeC1+qC5lo3YEPIhkLJrN2yTK1SNRzMs6BQMXODtjlVTgDKgO2D75r1l30IV8/TwP48y4SvAQSqbW4BvNDgmltiJgwNzHpyRQh74FXnJzNQHzPnKWCb9rqwCTyr1INTgqIH4SA0J0zQm++b/iJQzJvwuEDAHFsqDT19kPkLRG2ItYAfhUh1naEibOg088GElA11zC4o3wE3V8ByIN4MxWbY2gbzumB3L2R6TUDbVauBIpzdAU+1Df5sYW5ysJ7lwzkdw/dx/hbojsGOhsG2o/tNey0WML+3dt8wixbBddeN3m9Z0N4OLS1jKCYiIiIiIiIiIiIiIjJxClUTERERERERERERERERERERERmj5vA05tcfz+b+p7EKeWY8uYndLZCKmP7OOBybgmhl/7XyATN+QKIAM9c9Td/Lz8CPRJlffzzNClQTERERERE5qNLFNCs2rMDHH96Ry0EmA/E4xGIA2JY9bMju1PMUk9uhsUbh5Hbc2EzSlZxJBiumcR0oDwlVs3xwPBNmtuSoEkvmlgnY4GPCvAYyz6JBaJ8BjmPa68JwQhgWzwLXh0zehGx5QEMU4tWgsVgQAjETOFYsg+2Z2rYNLXFI5aHsmoCzeHhwXa4HpTI0NUCqZNbh+6ZOMGj6fd+EpSUzEEhAsHpcQQcqmHXhQzAAbhkcazAIrOCaMDjfh4FT6niAa7K4/GqdiGPmWZi5pTIEgYFHoZQBv8mMAWiMmEC0QtmsMZ01x+aHTU3KZlygWsCqrtOtQDQMhM1jsicJiTjEqqF2ZSDZD5Es5ILg2pArwJYOCJVhZgYKQGo3hFsgmjDnoLsDvCAQgHDF/OzA9yHTB3WPQN0qoAgvPxHWTYdUF9AFpd1QSJqaAO1d0JIzn7fmYGEnPD5j5FNuYefguAEhF963FrY2QzJigt3m9Q4+tyZk6VK4/XZoaNj/WBERERERERERERERkSmmUDURERERERERERERERERERERkXFY3LyYLenNRNc9hlWpMCcJT1RvXvYt6EjAgp791+lIDN5ADjAnCVa5TGTdYxReeSaLp71yKpYvIiIiIiLykpQqpNjWt41UMbXPcZt7NvNM8pm9257nwdo1sGmjSdxyLFhwIrzidEpeib5CHwP5a1ZfH3lnlMKAnenHiYaplPIAhG2Y2QD1UXBs8FwIebBkDrTUgYWP75nQLguwq8FqJ7UOhpYBxKqhaE41HCzhQGceSkBDZMj+gVjIhLaFbCgWIV+Gvgw0N5j2/hw0hCFTgIAD0QAUqsHhvg2JGHgvCHjLFKtBZ9WQtt48tMUHxwUd8Cvm9AWrxxIacjeL5438POABJROANnB+m2MmZG1A0QMrC+FqYHnBh3zahL/Zllm7ZZnQOdeDSgHKZQiHIWCZY/Z8yOUhHDLHWywN7g8gm4NKBZK9EAiAH4DuMgQq4PgQqUDKgY6dJiCtsTA413Nh7hq4swt2PA3xRig2w9Y2mNcFu3sh0wteGa5aDRTNvLM74Km2wZ8ZzE0O1rR8OKdj+PPq/C3QHYMdQzLNju437bVYwPze2n3DLFoE1103er9lQXs7tLSMoZiIiIiIiIiIiIiIiMjBoVA1EREREREREREREREREREREZFxaA5P43j7KPY89QQATQVIFCBVvVG9Mw7HpiBaGb1GPmDGDUgUTB2A6PonOeaMt9Icap6iIxAREREREXlpWd+1npUbV+L53n7HdmY62Z7cvnd7V8d6itlumD3Q4kN2A6zbCYkE2XKWsls2aWClNKUweFatyoCbJVQucMpMl1NnwPT6asXq+PoQtM8wwWPZIuxJQ1caAjbMiJuws1kJaK4GlpVdE+RVHxwMObN8E1jWXAedWVPHtiHsQCxiwsbAfAwEoKe/Og8IOWYfoYDZrriQKpn9xIJmnQHbBLIVSoP7DDlQrpigMs8yIWwVr9rvm7A3CxMyZtlmPQ4QcaDgmpw6LHMs5TLYngmA83zIl8Cp3vkSC5qAtnzZbJcqQNaEpNlByHvgFqEnDbObzX59swQ8DyIhIGSOa+/aXag4kCtAfwbqfPAi5txUXOjrh3wBwr2Q7DbBceWZMDNuAt/sEuzohGIZwhXzswDfh0wf1D0CraugYS640yHVBXRBaTcUkjCQv9beBS25wadJaw4WdsLjM0Y+hRZ2Dh87cAzvWwtbmyEZMcFu83oHj3FCli6F22+Hhob9jxURERERERERERERETmMKFRNRERERERERERERERERERERGScXvfrDdxWruy98X1OEp6o3uzsW9CRgAU9o8/vSAzeND8wf4BdKvO632yEeQd61SIiIiIiIi896WKaFRtW4OOP7MzlIJOBeBxiMQASkcRgf7lM484eOmM1CqeSEI8TcSImVK1UBCBiwbQEJCIm1MtzIZ2DPVlYdDQseZlLwDHhZ1Z1SS4QDcIp001omeVDIgjN02DxLKj4kCmAZUFLnfnoWCbMrD5kAtLKFSiUwS1DXxbSPkyrh768CQcLWhAOmjU5FrieCSebFodU3gRwWT7UR00o2MB2oQK9WXASJtDM800YWgUzzvUhGDD7DTuD17pF14TD+b4JUQPwy5i7WGzAg+YY7ExDuHpni2dBqQBxG7DAtasPT4NZdzRgatm2CVsr5cEPQC4JdpMJT4sEzUNZLpvgtoBTDWsrDT50FRd6U9AUg7AFuaI5T4EKeGVIF6How5YOKJZMWNrMzOD85q3QFYFMEp5bB6kQbG2DeV2wuxcyvabOVauBIpzdAU+1DZ6bucnBWpYP53SMfHqdvwW6Y7BjSKbZ0f2mvRYLmN9bu2+YRYvguutG77csaG+HlpYxFBMRERERERERERERETn8KFRNREREREREREREREREREREXhSeej7FO//34TGNve39Z3DyrMT+B4rUYCd7mXvbCk6eA+umm7amAiQKkIqY7c44HJuCaGXk/HzA9A9IFMz8ASd3wbzVK9j2zqvwGpun7DhEROQQ2/k43HTx2MZe9nM46tSpXI2IiMgRKVVIsa1vG6liatQxm3s280zymb3bnu+Z9K21a2DTRpMK5liw4ER4xelggY9PMp+E3bvA9nEtKDk1iqd7seMNxByLBW0lTpkJbfXg2eyNcLOAuAMntJnPOzPQmTahZLY/OOakVhNaNrAddUzgmVMNWWsIQM4dElAGRAImUA1MsBk+dPZBvjouWg1R29MP8RC4LmQ8qAtCsWJCzBwLGiJmruMDtgkic12zX883++rNQ1vchLGBWWu5YkLjgkGzZscxgWsFF7zquHIFQkHzeaACbmUwXCzkwFH1JnwNTLtbglIFwvVQsaBShmQajm4xxwJmzeEAtCTMWKcEPVkTZhcMmnX7vglEKwLFPPg2BAImMyyVhp4+yPwFojbEWsCPQqR6DR8qwoZOMx+gccg1O8Dxu+DRbbBmJ/jV4yzthkISBoa2d0FLznzemoOFnfD4jJFPoYWdg+OGCrnwvrWwtRmSEbOGeb3mPE/Y0qVw++3Q0LD/sSIiIiIiIiIiIiIiIkcohaqJiIiIiIiIiIiIiIiIiIjIi4Lr+aQLNRKsRhkrMlHNP/oBdj7H2R3wVNvgzeBzkvBE9QZp34KOBCzoGTm/IzE4Z2DeAMuHczrAzmVpuuUGeq66ZqoOQ0REDjXfhX0EwIwYKyIiIsOs71rPyo0rTUjaPnRmOtme3L53e1d6F8XeLsgmYfZAqw/ZDbBuJyQSuJ5LutgPpX5oBNeC8pBQNcsHxwO7nGNJm8sZs0qEGAy8svzB6754CNpnQMg2oWUNzTC3EbpSUPagLgIzG6E5bgLMyq5ZTjw4WM/2IexALAqpogkSCzhQHx5ck+tBuQRNDVDMglNdT8iBhjCEAmbb9yGdBw9TwwIiQcAdXHvQAb9SDUqzTPhaoQIVzwSg+Z4JOCtjQtKsgAlxqwDNMdiZNvMsoOiaerYHNlAqgTvkTpbmqDlXhTJUXCgUwS6AX2eWFAxAfQzcsgmrsy1zfsolE+IWdcAPgd8LyQw01kPMqj5mQLIfIlnIBcG1IVeALR0QKsPMjAlBS+2GcAtEE+YYujvACwIBCFdMYLrvQ6YP6h6BulVwkgP3Lx580Ocmhz8/zukY/jw8fwt0x2DHkDyzo/tN+2gsYH7v6P0ALFoE11237zGWBe3t0NKyn2IiIiIiIiIiIiIiIiJHPoWqiYiIiIiIiIiIiIiIiIiIiIiMkZ3spfHHNwPQmoOTu2DddNPXVIBEAVIRs90Zh2NT5ubrAfmAaR+QKJh5A07ugpZctd6Pb6bv0mV4jc1TeEQiIiIiIiJHnnQxzYoNK/CpEZqey0EmA/E4xGIkIolh3Y2BOJ2pzbULp5IQj+M4DsFimTImEOzoOMTqwHJMeFkqD5ThghNhRmMZXB/XMmFaezO0LRNW1j7D1HBcEypmWTAtDsc1QcGD3hy0xE27Y5kQtPqQCQ8rV6BUNsFlqSzUN0HAhp4ixIKQKZhgtEjQhJJZgG+b+qm82bZ8qI+aULCB7XwF0mVoqzftvm/2W66YADeLweMIO4MBcUXXhKq5FRN2BmC74FUwB+eZOi0xCFf7fQtyRQh7JsjNs0z4WX0M6kLmeDygLgzFElTqIRiGsgWRADQnzJqpQM6DUBC88vDgukzOHIPrQrIXAgHwA9BdhkDFhNlFKpByoGOnGds45Frcc2HuGrizC3Y8DfFGKDbD1jaY1wW7eyHTa/Z71WqgCK3Awk54fMbIp9HCzsFr+wEhF963FrY2QzJi9j+vd8jzZSKWLoXbb4eGhv2PFREREREREREREREReYlQqJqIiIiIiIiIiIiIiIiIiIiIvOSky2meyz1Lupwe17zET3/MjumDd0Y7HnQkBm/kDnjm5ugBq4+CY/oHt59tgL7o4NwFewb7LB/O6RjctnNZmm65gZ6rrhnXGkVERERERI5kqUKKbX3bSBVTo47Z3LOZZ5LP7N32fA98YO0a2LQRXN8klC04EV5xOj4+yXzSDN69CxcojXY3RbqXaH0dr2zOM/9oaGuohpVZ4Fav/epDJiytXIbOjEdXGnzPBI3NiEN9GGJhOLrRBKqVXaiUTXhXYwwClgkvCztwVFM1EMw3hxANmEA1MPVsD3ZloWKZdTg2tNSB55ntigfJHFQqUBc063QsaIgAvgkUwzbha65rrj0rQKECFdeEmvku2CGzTwugArZt9uE4EHGg4JptgFLFBJeBuQ52C+CGBk9hYwTC1aA314N01oSx+WFzjJSrc+3Bfbq+CWuLhoGwWWd3CprqIexXw+CAXXsglodAFNyoqZdKm/mFAoR7IdltHo/yTJgZN/u1S7CjE4plCFdMALrvQ6YP6h6B1lXQMBfc6ZDqArqgtBsKSRjIX2vvGh6Wdv4W6I7BjiGZZkf3m/ZaLGB+7yjPu6EWLYLrrhu937KgvR1aWsZQTERERERERERERERE5KVFoWoiIiIiIiIiIiIiIiIiIiIi8pKyJb2Ze3bdg+9745pnFfI0bbkLa87w9mwQuuKD2/kAFKt/lZMMm8+DHpRteL6e6h3q5ibup1vMjfRtOTj5BTdnAzT9+Gb6Ll2G19g8rrWKiIiIiIgcidZ3rWflxpUmJG0fOjOdbE9u37u9K72LYm8XZJMwe6DVh+wGWLcTtz5OupQ2qWClNG4Eys5gPcs3wde2BUuOyrFkbp6QZUpgVYPAqqJBE6gWcCBkw/wQzGsCr5rjhgUe0BCFWNDMCdoQCJvQs4oLldLgfoOOCfdK50xWVn14cF+eB6UyNDVAqlQNd/Mh5EAwaALLfN+EnCUzEEiYehYQCQLuC/ZTMdtO9bq0WA1Vs4Ycn+2B5YIVMsftW9Acg51pM88CPAuKJTPWBsoW9GchGjXnMBowx1IXNmusFEwAXThsAuWaG8z5yuUhHDLnslhi2InO5sycZB+0RU0gXCpvxlguuBlI5mHjc9W5mOvsmZnBGs1boSsCmSQ8tw5SIdjaBvO6YHcvZHrBK8NVq4EinN0BT7UNBqfPTQ5/jgwNQgcIufC+tbC12QSsNxZgXu/w8zluS5fC7bdDQ8P+x4qIiIiIiIiIiIiIiMgIClUTERERERERERERERERERERkZeMTCXDPbvuxvf9/Q9+gehjq7G8kTf2H5uCrjr23jXdWIDOgZA1C1JhaMmbj0PvrG4smBu1N7RC43Mjb84GsHNZmm65gZ6rrhn3ekVERERERI4k6WKaFRtW4FPjei2Xg0wG4nGIxUhEEsO6GwNxOlObaxdOJXHicYJOkHKhH4CIDTMbTPCZY5vArpAHS+ZAax2AP/CfCfGqlrKAk1pNQBmYoK2IA9Pipo5Xgs48lICGyOASbEzAmmWZQLRQxISG9WQgYZvCARsKJcgUTMhYJAiFcjVIzYZEzASRDawlGoRMsdrvmbX25qEtPjx8rVwxYd7VvDdcC8LV9XveYEhauRq4Fqi2uQXwQqYt5EBLDMLVO1B8C7IFiLvgBcG1oVyEXAVmN5tz4ZtTh+dBJASETKDcwPpDLlQcyBWgPwN1PngRCATMuL5+yBcg3As9PRBvgvh0sKtrdyvQ+xcoDgnHaywMf+iP3wWPboM1O805AijthkISBoa2Dwk4b83Bwk54fMbIp9HCzpFB6APPifm9I9trWrECmppq91kWtLdDS8sYi4mIiIiIiIiIiIiIiEgtClUTERERERERERERERERERERkZeMjuz2YYFqnj8yJK0Wq5gntOFJPGtkX7RibrzuqjPbkQqEK1Co/mVOJgSxsvk4sOdIxcwzxaGpUPvmbICmH99M36XL8Bqbx7RWERERERGRw02qkGJb3zZSxdSoYzb3bOaZ5DN7t72BpLC1a2DTRnB9cCxYcCK84nR8fJL5pBm8excuUBrtDol0L9F4lPa2EqfMhLZ6wDIBYwD1IThlBgRtyBahMwNdabO7tjjUR6AuBM0xE25WcqFYMWFsLTGwLROwFgqY2n0FyJdNoJhtQ1PUjIFqYJlbvT4MVAPQMMFlkYj5vOJBMmeCw2JBE2IWsCFUDV7bG0xWDU0LWCYYrVAxc4M2+C7YocF9UjFrqWBC4SKOOT4LU79UhiAmAA6glAG/yYwBaIxAuBr05npQLkO+H8JtZs2OBfGYaXcwwXC+D+XS4MNQcaE3BU0xCFuQK0JfHgIV8MqQLkLRhy0dUCyZa+uZmepzqBMiHRBpgf4E9O8BuwThOBQDZmy0YvaZ6YO6R6BuFZzkwP2L2XvS5iYH12P5IwPOz98C3THY0TDYdnS/aZ+UpUvhkksmWURERERERERERERERET2R6FqIiIiIiIiIiIiIiIiIiIiIvKSEbCCw7Y39W8iXUnvd15wRwfBY/xR+0s27KzelA9QtiEbGuxPRgb7AJyhAWo+vGrH6Pu2c1mabrmBnquu2e86RUREREREDjfru9azcuPK/YZad2Y62Z7cvnd7V3oXxd4uyCZh9kCrD9kNsG4nbn2cdCltkstKadwIlJ3BepYPjmfCzJYclWPJ3DwB27QPFQ1C+wxwqiFgdWE4IQyLZ5kct1wOXNsEjzXXgVUNOKsLQn3YhJgVS2D7UKpATwHqoiYsrOJC0DLBaJ4PkYDZR6EEvg3T4qbd980lY13EhLr5vgkn68uCkzAhaJ5vgtAqmH7Xh2AA3DKEHbM+gKJrQtXsIcdoe2C54IeB6sPQHDPHMqDogZU1IWUABR/yaWhqMOcwGjDj68ImVK1SACdu5sUjZpxVPZZiCYpAMW+OM1Cdm0pDTx9k/gJRG2It4EdN8DhAqAgbOs18gMbC8MfqmF1QvgNuroDlQLwZis2wtQ3mdcHuXsj0moC2q1YDRWgFFnbC4zNGPucWdo4MOA+58L61sLXZXMs3FmBe77BL+vFbuhRuv30yFURERERERERERERERGSMFKomIiIiIiIiIiIiIiIiIiIiIi8ZR0WPwrIsfN/cRT8rNotN/Zv2OceqlAns6dznmJAHdeXBILWgZ27edwfuYh9y93XAg9YhN21Pz5obufel6cc303fpMrzG5n0PFBEREREROYyki2lWbFiBT42Q6lwOMhmIxyEWIxFJDOtuDMTpTG2uXTiVxInHCTpByoV+ACI2zGyAhig4NriuuVZbMgda6wB8/Op1mu2ZyzQLOKnVhJYNiNnQEjc1ABIOdGfBDptwMVPJhLHZFoQcCIWhPw2d/eDZps+xYU8/xENmPxXPBKa5LoQsE4IWsCFkm5A1LBM8FnJMOJvvm/305qEtboLMwKy1XAHPhWDQ1HYcE7hWcE3G3MAay9WwsoBntotlsKvHGguaWvmy2S5VgCyEw2AHIe+BW4SeNMxuNsfjV+t6HkRCQAgcoDcF4RDELHAtKAPJfohkIRc05zxXgC0dECrDzAwUgNRuCLdANGHC4ro7wAsCAQhXIFo9D5k+qHsE6lYBRXj5ibBuOqS6gC4o7YZC0tQEaO8aHpZ2/hbojsGOhsG2o/tNey0WML+3dt8wixbBddeN3m9Z0N4OLS1jKCYiIiIiIiIiIiIiIiIHgkLVREREREREREREREREREREROQloz5Yz/z649nc/zQAiWCC+kA96Up61DmB3Tuxatz//0KJAmSD7A1Qi1QGQ9aGaiiYG8MB8OH1WyBR3HdtO5el6ZYb6Lnqmv0vRERERERE5CBIFVJs69tGqpgadczmns08k3xm77bnV9O91q6BTRvB9cGxYMGJ8IrT8fFJ5pNm8O5duEBptLse0r1E41Ha20qcMhPa6oFqqBdAfQhOmQFB24SZdWagK22CzKY3QCIMsxLQHAfPh7JrArzqg4O52JYPoQA0xSHrQboAtg3RwPAgNt83wWZONWTMwoSjNYTNfKs6xnVhdwqmJyBYnRt0TKCYW00sCwbALZvT4llQqJhAtqANvmfCzcqAWwErYNZTAZpjsDNt5lmYuW7Z7McGyjbkshCtBsZFq3NtG/IlKOXBD0AuCXaTeZgiQYjFoFw2xxVwzHGUS4PH7vmQzUM+A4GAqdFdhkAFHN9cG6cc6Nhp5jYWhsx1Ye4auLMLdjwN8UYoNsPWNpjXBbt7IdMLXhmuWg1Ur53P7oCn2kwwHcDc5GBNy4dzOoY/VUIuvG8tbG2GZMSsYV7vsPzz8Vu6FG6/HRoa9j9WREREREREREREREREDhqFqomIiIiIiIiIiIiIiIiIiMiLwoxEhGted/yYx8pL1+LmxWxJb8b3TVLarNgsNvVv2tsfcSK0hdsAsEpF6p9+ZEyhamDC0nqjg9s9HmRC4NlmO+BBa26wf3oWLtoyttpNP76ZvkuX4TU2j22CiIgc3uqPgtf809jHioiIHEbWd61n5caVJiRtHzoznWxPbt+7vSu9i2JvF2STMHug1YfsBli3E7c+TrqUBs+DUho3AuUh4WWWD44HtgVLjsqxZG6egM2Ia7ZoENpnmKAz34e6MJwQhsWzoOJXw9EsaKkDyzJBZCHHBLHZFpQrUCibULK+LGR9mN4Eu9NQcaGpGpYWCZq5+Qr4NkyLQypvwrosH+qjZv8D24WKCSvrycP0apibhQlW8ysmWC1om7awMxgaVnRNu1sxoWsAtgteBZOY5pn1t8QgXO33LRMUZ+UgGAe3Oj+ZhqNbTLAamPWHA9CSgFIFnBL0ZGFaAoJBs27fh2LJZJoV8+ZYAwFz7lJpSPZDuBeS3Sb4rTwTZsbBD4Ndgh2dUCxDuGKum30fMn1Q9wi0roKGueBOh1QX0AWl3VBIwkD+WnsXtAy5lm7NwcJOeHzGyOfcws7hY/c+d4D5vbWfpyOsWAFNTbX7LAva26GlZYzFRERERERERERERERE5GBSqJqIiIiIiIiIiIiIiIiIiIi8KExviHD1a+Yf6mXIEaA5PI359cezuf9pABLBBPWBetKVNABFt0hTqImIEyH2+O+JpsZROw+rj8LcrQ3YHvSHB/sbCuYGcgB8eP2W2jd712LnsjTdcgM9V10z9gWJiMjhq2EmnP2Ph3oVIiIi45YuplmxYQU+L0gyy+Ugk4F4HGIxABKRxLAhjYE4nanNtQunkjjxOEEnSLnQD0DEhpkN0BA1IWCuCyEPlsyB1joAH98zgWG2Vw0vA05qNUFlA2I2tMQHg8TqA5B3wa5u+0AkYALVoBpc5kNnnxkXDJnwsZY62NNvAtVcFzIeRByoeCbEzLGgIWLmOj5gQ8AxYy3fhKh5lglXq1TXi2/WZQGeC5Zt1uVYpnbBNRlzYELPAtW7QAIeuAVwQ0PObwTCwWognAflMlgZsGKmvm1Dc8yExXl29Xh9KJdMAF3UAT8Efi8kM9BYDzELXAvKmPC0SBZyQXPOcwXY0gGhMszMDK6jeSt0RSCThOfWQSoEW9tgXhfs7oVML3hluGo1UISzO+CptsEQubnJwVqWD+d0jHy6nL8FumOwo2Gw7eh+0z4pS5fCJZdMsoiIiIiIiIiIiIiIiIgcKgpVExEREREREREREREREREREZGXnMXNi9mS3ozvmxCAWbFZbOrfBICPz878To4LzCSy4clx1a0rQ1sWuuJmuxgwN9K7lrnhvXVIgNr0LFw0zpu9m358M32XLsNrbB7fRBERERERkf1IFVJs69tGqrjvZOnNPZt5JvnM3m3P82DtGti0EVzfpIEtOBFecTpY5hormU+awbt34QKlUe5ksNN9xBvqmNtWon0mtNUD1VAvgPoQnDIDgjZki9CZga40BGyY3gCJMMxKQHPcBJiVXfB9qA/uzb7G8k1YWV0UUsVqUJkD9UMCsV3PBI01NUAxa+pbQNiBhrAJbBsIREtmTdBZIGD2FQkC7uC+gg74FTPesQaDwwouBC0zZ2+4WxkIgGWZoLfmGOxMm3kWJpCtWDIBcjZQtqA/C9GoCUiLVufWhc0xVAoQTJhx9UFzPJYPVCDnQShows0sf3BdmZxZk+tCsrd6XAHoLkOgYq5xIxVIOdCx04xtLAx/HI/fBY9ugzU7wR8IhNsNhSQMDG3vGgwZb83Bwk54fMbI58TCztph5CEX3rcWtjZDMmLWMK938HGekKVL4fbbJ1NBREREREREREREREREDjGFqomIiIiIiIiIyKj6+/tZs2YNa9euJZ1Oj+hvbGzkYx/72MFf2AG2adMm7rrrLh5++GE2bdrE888/TyaTwbIs6uvrmTVrFieeeCLnnHMOF1xwAcccc8yUrcXzPJ544glWr17N+vXrWb9+Pc8++yzJZJJUKkUwGKSpqYmmpiZmz57NGWecwRlnnMGrX/1q6urqpmxdB1o2m+XPf/4zjzzyCOvWraOjo4PnnnuO/v5+crkclUqFSCRCLBajra2NmTNnMn/+fE488UQWL17MK17xCsLh8P53dAR55JFH+OUvfzmi3XEcPv3pTxMIHLwf527bto0f/vCHNfv+/u//nqampoO2lqG+//3vs3z58infz8UXX8y111475fsREREREZFDqzk8jfn1x7O5/2kAEsEE9YF60hXzM5DuYjfHPbUdy/PHXfvYFHTVQdmBTMjcbJ4NQUMBopXqIB9ev6X2jeH7YueyNN1yAz1XXTPudYmIiIiIiIxmfdd6Vm5ciTeQgLUPnZlOtie3793e1bGeYrYbZg+0+JDdAOt2QiKB67mkS2nwPCilcSPmemmA5YPjmVCwJUdlWTI3TwhGpGNFg9A+AxzHBHnVheGEMCyeBRUf0gVTo6XOBIs5FoQcE8RmW1CuQKEMbtkEodU3mbC0niLEgpApmHC1SNCMswDfhmlx6M+bNTge1EcH1+R4JkeuP28C4HzM2kKO2Z/tmzoDhxIa8is/r3qqKxUIBs3ngQp4lWrAWbVOSwzC1Xm+BdkCxF3wguDaUC5CrgKzm8Gxq2uo1o+EgBA4QLIfImEI++ac54FdeyCWh0AU3KiZl0qbYyoUINwLyW4TjFaeCTPj4IfBLsGOTiiWIVwx17q+D5k+qHsE6lbBSQ7cv3jw4Ocmhz/m53QMf3zP3wLdMdjRMNh2dL9pH40FzO8dvR+ARYvguuv2PcayoL0dWlr2U0xEREREREREREREREQOd5Y/8Ha7IgdBfX09mUxmWFs8Hq95U7aIiEzMpk2bqPXPu2VZLFiw4BCsSOTIM2fOHDo6OvY/8AVCoRCJRIKGhgYaGxs56aSTOO200zjttNM488wzCQ785ZuIiMhhKpfL8fjjj7N69WoeffRRVq9ezebNm2t+fzng2GOPZfv27QdvkQfYypUrue6661i9evWY59i2zfnnn8/nPvc5Fi1adEDW0dnZyapVq/jNb37DfffdR2/v/v7qe6SGhgbe+973ctVVVx223/uXy2V+8pOf8H//93/85je/oVAo7H/SKCKRCEuXLuWtb30rF110EQ0NDfufdJjbunUr8+fPr9n3i1/8ggsvvPCgreUTn/gEX//610e0H3/88Tz99NMHbR0v9OY3v5lVq1ZN+X6+853v8JGPfGTK9yMihw/9TE1EROSlZfOav+z9vLfYw60dt+z9XiBVTrGpf5Pp9H3mPvgoC/ZM7E9rNrTAhlbIVDPB8wGYkxwMVZuege/8cvyhagBufQN/ufdRsO0JrU0mrzg7fqiXIEB72/RDvQQRERGRF4V0Mc3XH/o6PjWuf3I5yGQgHodYDIBCpcDDOx42/eUy+UcfojNWY64FzDoaHIdsOUs52w/FEkEHmhLQEDUhYK4LIQ+WzIHW6vtI+UNKgAkTWzgL4gPvu+RDnQ0tcVOjuhTyLsSjgzWiQQgPCXArl6FzD1QsaG40dUsVE0AWDZrtimuCzuqq22DaAjYEfHAtcznmeeD4kCrDnjwc1WBC2QIuRENQLJm5IceEvrmeCUHbnYaCa8LS4iETzBYLQzFvzoNnQSEEAxnfARvC1aA314NcGiL9EG4DzzFhadG4Oc5I0KzBA/KFwRPp+bCnF4JAWxQ8F7rz1XC6shmTdGDjc4PrDldg5pA//U8UIBWBTBKeWwepEGxtg3ldJnwt0wteGa5aPXitu2oBPD5j5FPj1N3wpk0j231gazMkI9BYgHm9I7L1xmfpUrj9dngR/D5ZRERERERERERE5EBTBoyIvFgF9j9EREREZHTrujoP9RLkINONCaMrlUrs2bOHPXv2ALBmzRpuvvlmAFpbW3n3u9/NBz7wAd2MLSLyInX//fdz3nnnjWi/8cYbufzyyw/+gsYgn89z00037Q1RW79+Pa7rHuplHRTPPPMMl19+Ob///e/HPdfzPO68805++ctf8uEPf5ivfe1rhMPh/U98Add1+d///V9uv/12fv/730/63Pf393P99dfzne98h6uvvpovf/nLxKo3NRxqxWKRb33rW3zjG99g165dB6RmoVDgZz/7GT/72c8Ih8NccMEFfOYzn+H0008/IPUPhXnz5nH22WfXfF4uX778oIWqVSoVfvSjH9Xsu+KKKw7KGkazdu3ag7KfV77ylQdlP3JkORL/rRcRERGR/WsOT2N+/fFs7jcB0olggvpAPelKGjuboTPmc2xgMAhtPKZnYPWswe3GwpA6Prx+y8QC1QCcdD+hjm2UXjZvYgVEREREROQlI1VIsa1vG6liatQxm3s280zymb3bnu+ZdKu1a2DTRnB9cCxYcCK84nSwwMcnmU/C7l1g+7gWlJwaxdO92PEG4o7N3LYS7TOhrR6wTDgZQH0ITpkBQRuyRejMQFfa7LKtHurDMCsBzXETDlZ2TRhYfXAwcMvyIeJAXRRSRROUFnDM3AGuB+USNDVAqmTm+r4JPQsGTb/vm7nJDAQSEHTMuFAAbM/Usf1qkJtrTlOoeldGsRq85g9JAbM9sFywQiYsDaA5BjvTEK7O8yzIFSBcre9aZv/xGNgWRANgWVAXNmusFMCJQ9GDeMQcj+WbtRdLUMQEtPk2BKpzU2kTvhbuhZ4eiDdBfDrY1cfMrUDvX6A45DFsfMF7ZB2/Cx7dBmt2gl9da2k3FJIwMLS9a/i17vlboDsGO4Zkmh3db9prsYD5Y3kPskWL4LrrRu+3LGhvh5aWMRQTERERERERERERERGRFxOFqomIiIiIHAR79uzhG9/4Bt/+9rf55Cc/yWc/+9kJha+IiIgcSJ2dnXz4wx8+1Ms46O677z7e/OY3k0qNftPAWHiex3e+8x0eeeQR7r77bpqbm8c1P51OT8n5932fb3/72/zqV7/iJz/5CaeeeuoB38d4/PrXv+ZDH/oQzzzzzP4HT1CxWOSOO+7gjjvu4MILL+Rf/uVfjthwtWXLltUMVfvFL35BT08P06ZNm/I1/PKXv6Srq2tEu+M4vPe9753y/Y+mp6eHZ599dsr3E4lEWLhw4ZTvR0REREREDh+LmxezJb0Z3/cBmBWbxab+TTh9PfgWdCRgQc/46+6pg7oyZEJme+jN6DMycNEoN5CPhW9ZuA2NEy8gIiIiIiIvCeu71rNy40oTkrYPnZlOtie3793eld5FsbcLskmYPdDqQ3YDrNsJiQSu55Iu9kOpHxpNEFh5SCCX5YPjgV3OsaTNZcmsMiGfwRS0qmgQ2meA45hdNISgcRosngW+Ww0/s6Clznx0LBOCVh8ygWPlChTK4JYhmYX6JhNs1lOEWBAyBROuFgmacRYmbCwRMwFt1pB1ZIrV/mqmXG8e2uKmzfMg7Jj9WZhgNQ9z3EHbBLp5XnV+dV0AgWpbpQh+9fow5MBR9WYe1fH5DAQDQBAqFhRzJqRtdrMJcPPN6cHzIBICQuAAvSkIhyBWDakrY8LTIlnIBcG1TWDblg4IlWFmxuwz1QmRDoi0QH8C+veAXYJwHIoBCFdMMLjvQ6YP6h6BulVwkgP3Lx58HOcmhz/m53QMf3xDLrxvLWxthmTEXBvP6x3xNBifpUvh9tuhoWH/Y0VEREREREREREREROQlR6FqIiIiIiIHUaVS4Ytf/CJ33nknv/vd78YdviIiIiKTc9999/GGN7yBYrG4z3FtbW1Mnz6dQCDA7t272b17994bq19o9erVvPa1r+WBBx6gvr5+KpZNPB5n9uzZ1NfXUyqV6OnpYceOHaOuCWDr1q2ce+65/Pa3v2XRokVTsq59cV2XT3/603z1q1/d5zqHCoVCzJo1i4aGBqLRKKVSiWw2y86dO0mn02Oqceedd3LnnXeSSqVoOAL/iP6tb30rH/3oR0ccb6lU4tZbb+WjH/3olK/hxhtvrNm+dOlSjjrqqCnf/2jWrl17UPZz6qmnEgwGD8q+RERERETk8NAcnsb8+uPZ3P80AIlggno/TKVnDwCdcTg2ZW4mH6t8wMxrzkG5epP8wPyGIrxpE7TkJr7m3Bl/hTutZeIFRERERETkRS9dTLNiwwp89v+7ukQkMWy7MRCnM7W59uBUEuJxHMchWCxTBoIOHB2HWB1YDrgepPJAGS44EWY0lsH1zEqGBJlZwEmtZj6YUK6IA9PiJkjM8aAuBDkX7IEAMiASMIFqUA0i86Gzz4SRWZi5LXWDIWcVD5I5qFSgLmhCzAI2hGwolAbXE6qGpgUs8CwoVMzcoA2ObwLGBtglsALsPbvNMSi5g7UKFQj7UF022ZxZq1NtaI6adRTKUHGhUIRABsJtZjscgFgMymUTnhZwzP7LpcE1eD5k8yaQLRAAPwDdZQhUzHojFUg50LHTzB0a9g1wzC4o3wE3V8zjFm+GYjNsbYN5XbC7FzK94JXhqtVAEVqBhZ3w+IyRT42FnbWvdS1gfu/I9ppWrICmptp9lgXt7dCi62EREREREREREREREREZnULVRERERETG6OSTTyaRSIzaXygU6OvrY+fOnRQKhVHHATzxxBNccMEF/Pa3v6Wuru5AL1VERERq2LZtG29+85tHDVSbNm0a1157LZdccgnHHXfcsL7du3fzs5/9jC9/+cts3759xNzHHnuMK664ghUrVhyQtc6ePZsLLriA1772tbzyla9k9uzZI8b09vZy33338e1vf5sHHnigZp1UKsWFF17I448/zowZNf6qfYoUCgXe8Y538LOf/Wyf49ra2rj44ov5m7/5GxYvXsycOXOwrNrvSd7d3c3atWv54x//yF133cWaNWv2WdvzvAmv/1CKxWK8/e1v5/vf//6IvhtvvHHKQ9X27NnDXXfdVbNv2bJlU7rv/Xnsscdqtn/wgx/kv//7vw/yakRERERE5MVmcfNitqQ37w0Gn9fRx9Oe+dy3oCMBC3rGXq8jYebZwIwMzElCyDU3tE/LwWufmfhas0vOYte/f2viBURERERE5CVhS++WYYFqnj/k92e5HGQyEI9DLEbICdEaa6Ur2wVAZHcX4TIUat1t4APpfsKN9bQ35TluNrQ1mOAs3wK3+uu++hC0zzChYJ0Zj660CVsLOdBWD/VhmJWA5rgJByu7gAfxkKllVcPXwg7EopAqQqliwsXqw4PLcT0TNNbUAKlqQJrvm/0Eg6bf983cZAYCCRPi5lUD3CrV8a5vQs/cstmnXz2OomtC1WxMSBuA7Zn/S2Wwqu/VE3IgHjb7ASh6YGUhXAdedTubhuYGExQXsE17XRiKJajUg1My48JBaE6Yc+D7pr8IFPPg2yZAzbIglYZkP4R7IdkNvgflmTAzDn7YBL/t6IRiGcIVE/bt+5Dpg7pHoG4VUISXnwjrpkOqC+iC0m4oJGHgL+Dau4aHpZ2/BbpjsGPIe1wd3W/aJ2XpUrjkkkkWERERERERERERERERkZc6haqJiIiIiIzRt7/9bc4999z9jiuXyzz55JP8+Mc/5gc/+AG9vbXfZvPhhx/mU5/6FN/+9rcP8EpFREQmz7ZtFixYwKJFi1i0aBGe5/Gxj33sUC9rwnzf5z3veQ+pVKpm/8UXX8yNN95Ic3Nzzf4ZM2bwwQ9+kMsuu4xPfOITfPe73x0xZuXKldx4441cccUVE1pjOBzmrW99K+9///s566yzRg0XG9Dc3Mwll1zCJZdcwi9+8QuuuOIKenpG3t3d2dnJRz7yEe64444JrWu8KpUKb3rTm7jnnntGHXPiiSfyT//0T7ztbW8jEBjbjyhbWlp43etex+te9zr+9V//le3bt/O9732P733vezWP+0i2bNmymqFqjz32GE8++SSnnHLKlO37lltuoVwuj2hvaWnh4osvnrL9jsXatWtrtp966qkHdyEiIiIiIvKi1Byexvz649nc/zRWIc+MJzexuwVSEdPfGYdjU+YG9P3JB8z4AY0FmJN2SL9mKX4ozMzAbPLvWMxz412kBcV5J+A11v75hYiIiIiIvHSkCim29W0jVaz9+0+AbX3b2J7cvnd7S88WsqUM7NoF3XtMOJoFtLTCzJlUfJfuXLdJDsvvoRKFQnCwnu1DwAXbglfN7GfJcVnCHrg2eBZD4tsgGjSBagEHQjbMD8G8JhNkFgQ824SWtcRNOJhjmXH1YVO/XIFKCSoVSGWhvsmEkPUUIRaETMHUjgShUK4GqdmQiJl9WEPWkSlW+z2zxt48tMVN2BqYgLVyBTzXhLBZgOOYwLWCOxik5mOC3ywLQp4JRMsVzDjHNuuLBky9QhkqZUgXIBoCLwwVFwLVhQVs89HChLm5FYiGgbAJUtuThEQcYtWQujImPC2ShVzQnPNcAbZ0QKgMMzOD5755K3RFIJOE59ZBKgRb22BeF+zuhUwveGW4ajUmqQ04uwOeahsMkpubHKxn+XBOx/DnVsiF962Frc2QjJjr3nm9g+d9QpYuhdtvn0wFEREREREREREREREREUChaiIiIiIiB1wwGOT000/n9NNP55Of/CTLli3j5z//ec2x//M//8PHPvYxjjvuuIO8ShERkUGWZXH88cfvDVA7/fTTecUrXkFdXd3eMffff/+hW+ABcNttt/GnP/2pZt/f/u3fctttt40p3CsSifCd73yHaDTK1772tRH9n/zkJ3nLW95CIpEY89rq6+v58Ic/zMc//nGmT58+5nlDXXTRRTz00EOceeaZ7NmzZ0T/T3/6Ux5++GHOOOOMCdUfjw9/+MOjBqoFAgG+8IUvcM0114w5TG00c+bM4Utf+hL/9E//xDe/+U2uu+46+vv7J1XzcLFkyRJOPPFENm7cOKLvxhtv5Bvf+MaU7fvGG2+s2X7ppZcSDAZr9h0so4WqLVy48CCvREREREREXqwWNy9mS3oz0XWPYVUqzEnCEzNMn29BRwIWjCHXuyMxeCM6wJwkWK5LYE8nhVeeycI57yYfUjCaiIiIiIhMzPqu9azcuBLP9/Y5rlApDAtVq3gVkl3PQjoJ4SED03vAKUMigeu7lHP94JhwsKMS0BA1oWGua8LElsyB1joAF9cGxwPswXIWcFKrmQ8mlCviwLS4qeOVoDsLdjVADUxgWTQ4uB1ywA5ATxIqVjXozIaWOhNyZgEVD5I5E7xWFzTXYQHbhLMVSoMBX6FqaFrAMuFvhYqZG7RN0Jpjm9AytwJWAGwbKkBzDHamTeCbRTU4zodSFmJRKFfnJ9PQ3GAC1SzM+Fg1SK3UWN13AFoaTVib50MuD+GQCYYrlhiWSJetHlOy18zzA9BdhkAFHB8iFUg50LHTrKexMPxxP34XPLoN1uw06wMo7YZCEgaGtndBS25wTmsOFnbC4zNGPo8Wdg4fO/Rxnl/7vUZHWrECmppq91kWtLdDS8sYi4mIiIiIiIiIiIiIiIjsm0LVRERERESm0LRp07jjjjt44xvfyF133TWiv1wu87WvfY3vfve7h2B1IiLyUtfa2sp9993H6aefTn19/aFezpT68pe/XLP9mGOO4Qc/+MG4A76uu+46HnzwQf785z8Pa9+zZw/f/va3+ad/+qf91nAch49+9KN89rOfpbW1dVz7r2X+/PmsXLmSs88+u2b/f//3f095qNott9zC97///Zp99fX1/OIXv+Ccc845oPuMxWJ85jOf4corr+QjH/kIK1euPKD1D5UrrriCa6+9dkT7rbfeyle+8pVJh9LVsnbtWp588smafcuWLTvg+xuPdDrN1q1bR7RblkV7e/shWJGIiIiIiLwYNYencbx9FHueegKApgIkCpCKmP7OOBybgmhl9Br5gBk3IFEwdQCi65/kmDPeSrMC1UREREREZILSxTQrNqzAH5rCNSCXg0wG4nGIxYgEIrTVtdGV7QIgSoBwT4qiU6NwKgnxOIlAgGPbSrTPhOn1JqjMq6aT1YegfYYJI8sWoTMDXWkTFHZUHOIRqAuZMLJEDEouFCsmjK0lZgLTLB9CAWhNQMGHbMmEpAVtqB8S9OZ5UCpDUwOkqgFpvm8C0oJBcL1qwFkFkhkIJEyIm1cNcKtUx7s+BAPgliHsDAZgF12zT7di+gFsF7wKJiDOM/tqiUG42u9bUHbB64dyANyoOTbPNR+tam0LM86xIRoGwpDOQqR6fCEXKg7kCtCfgTofvIgJUKu40NcP+QKEeyHZbYLRyjNhZhz8MNgl2NEJxTKEK+Ya1fch0wd1j0DdKjjJgfsXszdZbm5y8NxaPpzTMfIpcP4W6I7BjobBtqP7TfukLF0Kl1wyySIiIiIiIiIiIiIiIiIiY6dQNRERERGRKeY4DsuXL+dlL3sZmUxmRP/dd999CFYlIiICdXV1nHvuuYd6GVNu9erVrFu3rmbfddddR0NDQ82+fXEch+uvv57FixeP6PvP//xPrr32WkKh0D5r1NfX861vfWvc+96Xs846i7e97W3cfvvtI/ruuusufN/HGvhr/gNs9+7dXH311TX7wuEwv/rVrzjzzDOnZN8AbW1trFixgptvvpkPfehDU7afg+W9730vn/nMZ6hUht+p39XVxV133cUb3/jGA77P5cuX12w//fTTOeWUUw74/sbj8ccfx/dH3hx03HHHEY/Ha8wQERERERGZmNf9egO3lSt7b7Sfk4QnZpjPfQs6ErCgZ/T5HYnBm/QH5g+wS2Ve95uNMO9Ar1pERERERF4MUoUU2/q2kSqmRh2zuWczzySf2bvt+R74wNo1sGmjSRFzLFhwIrzidAD6Cn1mzO5dWJ5Pfkh4WcCDaNkEnp3elGTxMRWsCrjVYDGAsg2RIYFqtg8NIWicBotnmV3mcmaOb0FznQkYC9hQFzRhaRUPiiUzt1SB3gI01kNP1gSJNYUhU4CAA5GACWMD8G0T0Ob5e/PBiAYhU6wGrVUPvzcPbXETtgYmYK1cMYFnwaAZ6zgmcK3gmtA2MGsZeC+jgAduAdwhv+ZtjEA4CIWyqV0uQ8iH3izEw2Db0BqHoAW5PIRDJkytUhys4fmQzkG2YM5N2IJcEfryEKiAV4Z0EYo+bOkw5ylcgZlD/syseSt0RSCThOfWQSoEW9tgXhfs7oVMr6lz1WqgCK3Awk54fMbI59DCTmjJjWwPufC+tbC1GZIRaCzAvN7B8z4hS5dCjd9di4iIiIiIiIiIiIiIiEwlhaqJiIiIiBwELS0tLFu2rGZwyjPPPMPWrVuZN093UYmIiEyFn//85zXbZ8yYwSWTeEfsRYsWccYZZ/Dwww8Pa+/u7ubOO+/kLW95y4RrT8bll19eM1Stu7ubLVu2cPzxx0/Jfv/lX/6FZDJZs+/666+f0kC1od7znvewcOFCIpHIQdnfVJk+fTrnn39+zefvjTfeeMBD1UqlErfeemvNviuuuOKA7msi1q5dW7P91FNPPbgLERERERGRFzU72cvc21Zw8hxYN920NRUgUYBU9TKzMw7HpiBaGTk/HzD9AxIFM3/AyV0wb/UKtr3zKrzG5ik7DhEREREROfKs71rPyo0rTUjaPnRmOtme3L53e1d6F8XeLsgmYfZAqw/ZDbBuJyQSZMtZyuUilNJQDyUHPGtw6IxGeNPJMKM+QwnwAyaozB4oZ8EJbSbwzAdcIOpAW50JEANoCEBPBuywCWirliYaNNshB0Jh6E9DZz84IZP91lIHe/ohFDDhXRUPUnmoVCAWNCFtARtCNhRKgwFfoWpoWsAyx1KomLlB2wStOTaUAbcCVsCEn1WA5hjsTJt9W5i5xRLYnjnesgX9WYhGzbqjARMQVxc2oWqVAgQT4IfAdSERN7WCLpQCUCiahyJsgRs1gW2ZrFl7oQDZbRC1IdYCfhQi1WvLUBE2dJq1gAk0G+r4XfDoNliz0xwfQGk3FJIwMLS9a3hY2vlboDsGO4a8x9jR/aZ9NBYwv3f0fgAWLYLrrtv3GMuC9nZoadlPMREREREREREREREREZEDT6FqIiIiIiIHydKlS2uGqgFs3LhxSkLVNm3axD333MNDDz3E5s2bee6558hkMpTLZerq6pg5cybHH388r371q7nooot4+ctffsDXALBz507uvPNOHnvsMR5//HF27dpFf38/6XQax3GIxWJ713Pssccyd+5cTj/9dM444wyOOeaYA76e5557jjvvvJMHH3yQTZs28eyzz5LJZKhUKtTV1TF9+nSOO+44XvWqV/E3f/M3LFmyBMua1PuuTsj69ev56U9/yoMPPsjGjRvp6emhVCrR0tJCa2srJ598Mq9//et5wxveQMsU/RGi67o89thjPPTQQ2zYsIGNGzfy7LPP0t/fT39/P8FgkObmZpqbm5k1axZnnnkmZ599Nq985SsJh8P738EUOBzO2wvt2LGDe+65h4cffpiNGzfS0dFBMpkkn88TDodpaGigpaWFBQsWcNJJJ3HWWWdx1llnHfBzeLi8JhxoR+LzVA6uP/7xjzXbzz//fILB4KRqv+lNbxoRqgZw6623HrJQtXPPPXfUvr/85S9TEqr2zDPP8IMf/KBm32tf+1quvPLKA77PfTnllFOmpO7Bfh1dtmxZzVC1X/7yl+zZs4fW1tZJ1R/q5z//OT09PSPaI5EI73rXuw7YfiZqtFC1hQsXHuSVyOH6/cTh8v3WVDtSrmUeffRRfvWrX/Hwww+zZcsWurq6yGaz1NXVMWfOHK688kquvvrqg74uERERkf1p/tEPsPM5zu6Ap9rMzfsAc5LwxAzzuW9BRwIWjLyEoiMxOGdg3gDLh3M6wM5labrlBnquumaqDkNERERERI4w6WKaFRtW4OMP78jlIJOBeBxiMQASkcSwIY2BOJ2pzbULp5IQjxNxIpSzaQCCDsysh7pqIFpzFP76ZeB55n8wwVqBAMyIQ0MYZiWgOQ6eD2UXfB/qg4MBZ2BCzprikPUgXTAhZtGA2d8A3wfHMYFqAdvMDztmH0GnWs+Hchl6s+AkTLvnQ8QxoWi+D64PwQC4ZTN/4Dqs6JpQNbdi+gFsF7wKJjHNM+tsiUG42u9bkC1A3AUvCK4N5SLkKjC72Zwj3ywLz4NICAhBFOjPQLEMbVGwA0AFejMQyZk1pkvm/y0dZt3hCszMmBC01G4It0A0YY6ru8Psn4AZF62YOZk+qHsE6lbBSQ7cv5i9J35ucvDcDlxzDhVy4X1rYWszJCMmqG1e7/DHbdyWLoXbb4eGhv2PFRERERERERERERERETlELN/3/f0PEzkw6uvryWQyw9ri8TjpdPoQrUhE5MVn06ZN1Prn3bIsFixYcMD3t66r84DXlMNbe9v0Q72EKTdnzhw6OjpGtN933337DCnZnx07djB79uyafTfccANXXHHFhGsPValUuO222/j617/O448/Pq65ixYt4rOf/SwXX3zxAVnL7373O7785S9z77334nn7fifd0bzsZS/jbW97G+95z3smHchw33338aUvfYl777235mvlaObMmcPHP/5xPvCBD0wqdGH58uU1H+cXPrfWrl3LJz/5SX7729+OqW40GuXjH/84n/rUp4jH4xNe34BkMsmqVau44447+P3vf08qlRp3jWnTpvH3f//3fPSjH6WxsXFS6zlSztsLeZ7HypUr+da3vsUf//jHcT3nBtb3hje8gfe///287nWvw7bt/U+q4XB6TTiQDofn6WjPzck6XH9Uc//993PeeeeNaD/22GPZvn37wV/QOLS1tbFnz54R7d/73vd4//vfP6naDzzwQM3vDxKJBN3d3QQCh+Y9DZqbm+nr6xvRfuutt/LOd77zgO/v2muv5Stf+UrNvieeeGLKQs4OhkP5OlqpVDj66KPp7Bx53fX1r3+df/iHf5hQ3VouvPBC7rrrrhHt73jHO7jtttsO2H4m6pRTTmHdunUj2n/+859z0UUXHYIVjV+pVGLmzJn09vYOa4/H4+zevZu6uroDtq/+/n5mzpxJLpcb1t7a2srzzz8/7kDJw/X7ian+futw+rf+SLmW+elPf8rnP//5/T5PLrvsMpYvXz7h9UyFg/0zNREREZk6qUKKbX3bSBVH/3nRc08/P6LNzvRz1CevxnLNz7IfOBaeaRrs39IM2ZD53PJhQTeE3cH+ogObWswN+Y4H0zPwyp2D/e2dcMlG87kXq2Pbz+/Da2ye8HHKoVecfeB/rizj91L43aWIiIgcucZyfQKwuWczf3ruT3u3Pc+DtWtg00aTzuVYsOBEeMXpYEFHqoNkPmkG795Fb3oPJad2bbuujrpEA8cFdnHi0dBWb0K1XBsiQThtZjXQzIdsEbqzEA9DcwwsC2zLhKoNvH+HZUF9yLSXK1AoQ6Vsst+yPkxvgt1pqLjQFIamerMfx4JcETwXKj7056GpDmwPKtXatmW2+0vQl4NgCNriJg+tPgKVktmnE4Rw0GTOuRbsykDBhaaoCWgLeBCLQL4AoQpgQTFkTiWYQLdw0Kzd9SCXhkg/hNvAcyDZD9G4CWyLBCHggIepN5B75/mwp9eEtsWLJgDNs6EYhLqyGVP0Ye0uKJbM9vSMCUsb8PIdcGcX7Hga4o1QbIatbTCvC8K9kOkFrwxXrYaW6q98Vi2Ax2eMfJxP3Q1v2rTPp9n+rVgBTU21+ywL2tvhIL15oYiIiIiIiIiIiIgcHMqAEZEXq0NzV6eIiIiIyEvQtGnTRu2rFfQyEQ899BAf/OAHawZOjMWjjz7KG9/4Ri644AJuvPFGWltbJ1Snv7+fD33oQwckgOOZZ57huuuuY+vWraxYsWJCNbq7u7nqqqu4/fbbJzR/+/bt/N3f/R3XX389N9xwA2eeeeaE6uyP7/t84Qtf4POf/zyu6+5/QlU+n+eLX/wit912G7/97W952cteNuE1fOITn+D666+nVCpNuAZAT08P//zP/8xXv/pVvv/97/PWt751UvX25XA4by/08MMP85GPfITHHntswjXy+Tx33HEHd9xxB9deey3XXXfduGscLq8JB9qR+DyVQyefz4/67+wJJ5ww6fqj1UilUjz66KOcccYZk97HREwmOGe8KpXKqME4r3/964/oQLVD/ToaCAR4z3vew1e/+tURfcuXLz9goWq7du3i7rvvrtk3FYFS41UoFNi4cWPNvoULFx7k1UxcKBTiXe96F9dff/2w9kwmw4oVK7jssssO2L5uv/32EYFqAJdeeum4A9UO9dfBaA6X77em2pFyLZNOp7nsssv46U9/OiX1RURERMZqfdd6Vm5ciefv+00+enp6R7Q13HkHW2YPzssGYXsCkzYAVGxIRgbHr2uDlvzgdncUMkMux4MudMWgLWfCCc4Z8l4udi5L0y030HPVNeM5PBEREREROYKM9foEoDPTyfbk9r3buzrWU8x2w973bvQhuwHW7YREAtdzSZfS4HlQSuNGoDwkVM3yTdizbcGSo7IsmZsn6psgtWo1bB9ObDWBYT6ABc11sKDV5GelC5DOQ13EhJoNzIsEBgPWggETJpbsg7xrQtAcC1rqYE8/hALgupDxIOJAxasGUVsmJA3f1HYA2zaHY2P6PQsKFTMnaIPvgWNDGXArYAXMnAomAG5n2syzMHOLJRPQZgNlC/qzEI2acxKtHkNd2ISqVQrgxKHoQTwCTQ3mHPq+qVMEinnwbQhU56bSJnwt3AvJbnDLYDswvQ6KMyAQgp5tmAUEIFwxgWq+D5k+qHsEWldBw1xwp0OqC+iC0m4oJKFQPeftXYOBagDnb4HuGOxoGGw7ut+0T8rSpXDJJZMsIiIiIiIiIiIiIiIiInJ4UKiaiIiIiMhB4vv+hPrG6pvf/Cb/+I//SKVS2f/g/bjrrrtYtGgR99xzDwsWLBjX3L6+PpYuXcrq1asnvY4DYcOGDVxwwQVs37590rU2b97Meeedx3e+8x3e//73T35xQ3iex2WXXcaPfvSjCdfYtm0bZ511Fr///e+ZO3fuhGqsWbNm0kFVQ/X39/P2t7+ddevW8fnPf/6A1R1wuJy3ob7yla/wmc985oB8LQ4oFovjnnO4vCZMhSPteSqHVio1+ru+NzY2Trp+02jv1A2sXr36kISqlUoluru7a/bta70T9Yc//GHU4LrLL7/8gO/vYDlcXkeXLVtWM1TtySefZO3atbziFa+Y9PpuvvnmmsGkxxxzDK997WsnXX+ynnzyyZqPQ1NTE8ccc8whWNHEXX755SNC1cCE5B3IULXRgg7HG5J3uHwdvNDh8v3WVDtSrmWSySTnnXcejz/++AGtKyIiMlHJXIk/bu0Z09gz502jMRaa4hXJwZIuplmxYQU+4/95u53qI7Dr+WFtdWVoy0JX3GxHK+Ym+GL1L20yIUgUIehB2TbbA8IViLiwoRUSO2DRzuE3wQM0/fhm+i5dhtfYPO71iojIi0iuF555YGxjX3YOxPTvhojIkWC81yeJSGJwo1ymcWcPnbEaA1NJiMdxHIegE6Rc6AcgYsPMBmiImuAx14WQB0vmQGsdgIdvQcAzgWMAsxuhfkgwdDgA02MmmAygMQoNDpQsyJZN4FnAMXOqQ3A9KJWhuQF2ZyFgm76wAw1hCDrVsT4ksyaQLFANYgsFTOgZmIA3xwaqv64KOyZ8DaDomlA1t2JC3ABsF7wKJrDMg5ADLTFzDGDmZgsQd8ELmjC5chFyFZjdbPblm2XheRAJASET7tabgnAIYha4lglxS/ZDJAu5aq1cAbZ0QKgMMzOD57ClH45+Fu57Bjb9CZwAFJthaxvM64LdvZDpBa8MV60GinB2BzzVNni8c5OD9V4Y0A0QcuF9a2Frswn+bizAvN7Bx2RCli6FCb65i4iIiIiIiIiIiIiIiMjhSKFqIiIiIiIHSU/P6DfyTTbU5f/9v//Hl770pX2OsSyLGTNmMG3aNKLRKF1dXezatWvUYKJnn32W17zmNTz00EMce+yxY17LpZdeut9AtUgkwuzZs4nH44TDYdLpNKlUiq6urgMalPT0009z3nnn0dXVtc9xDQ0NHHXUUcRiMXbu3ElXVxeeV/udgsvlMh/4wAdwXZcPfehDB2ytV199dc1gMNu2mT59Om1tbdi2TVdXF88//3yNCsbzzz/P5ZdfzgMPPIBlTepPJmtqaGigqamJxsZGIpEIqVSK7u7uUcN7wIQGfuELX6CtrY2rr776gK7ncDtvf//3f8+3vvWt/Y4LhUK0trYyffp0fN+nt7eXPXv2kMvl9jt3LA6n14RD4XB7nsqhta/Anbq6uknXD4fDOI5TM5Bq7dq1k64/EWvWrBn1uOfNm3fA93fXXXfVbA+Hw1x00UUHfH8Hw+H0OnriiSdyxhln8PDDD4/oW758+QEJVRstgOuyyy7Dtu1J15+s0b6WFi5ceJBXMnmnn3467e3trFu3blj7Aw88wPbt25kzZ86k97F161b++Mc/jmg/7bTTOOWUU8Zc53D6OhjqcPl+a6odKdcyruvy5je/uWagmm3btLa20tbWRjQapaenh+3bt9f8N1NERORA6ujJcdWtY7se+9lVZypU7UVkS++WYYEFnl/7+6JaffEHfoNvMSLuYHY/dMYHtxOFIdsWJMPQkjcf/SE/VmwsDI7pi468CR7AzmVpuuUGeq66Zv8HJyIiL159z8BPLh/b2Pf/TqFqIiJHiH1en+RykMlAPA4xk5wWckK0xlrpynbBjmeJlHzCISi88C/9fSDdD4lGGgIB5rSVaJ8JbfVANQQMoD4Ep8wwYWTZInRmoCsNIWBaPSSicOL0agAbUPFMCJrnsvfCqFKGXBbqEtCbhZJrxmSKJjwtEoRCxQR/+TZMi0N/3sx1PKiPDi7b8cD1TX9b/WCYWdiBcsUEgtk+DJwlx4GIAwXXjAMoVUwgG5hwOLcA7pBL+sYIhINQKJuwt3IZ8v0QbjP7cyyIx0y7gwmI830oD/mVhudDNg/5TDX8LQDdZQhUwPEhUoGUAx07zdy9135Vx3ZB3X1Q7IVMa7WxC0q7oZCEgeHtXYPB2605WNgJj89ghIWdIwO6MQ8183tHtte0YgWM9sZblgXt7dDSMsZiIiIiIiIiIiIiIiIiIkcGhaqJiIiIiBwktW4yH/Cyl71swnX/8z//c9SwA8uyOP/887niiis455xzaHnBH8Hlcjnuvfdevvvd73L33XePmL9r1y7e8573cP/9948pUGPFihX86le/qtl3wgkn8JGPfITXv/71zJ8/v2ZwVaVSYfPmzTz55JP8+te/5q677tpviMBo+vv7ufjii0edX1dXx9VXX8273vWuEeESvb29/OQnP+H666/nqaeeqjn/6quv5vjjj+c1r3nNhNY31A9/+ENuvPHGYW3nnHMOH/rQh3jd615Hc/PwmyOeffZZbr31Vv793/+d/v7+EfX+8Ic/8L3vfY8PfvCDk17b6aefzsUXX8yiRYtYuHAhs2bNqjnu+eef58EHH+SGG27gN7/5Db4/8t2Wr7nmGs4880xOO+20Sa8LDr/z9q//+q/7DPiYPn06V155JW94wxs444wzcBxnWL/neWzYsIFHHnmEX/ziF/zqV7+iWCyOex2H02vCwXKonqfTp0/nzDPP3LudSqVqvmbMnz+ftra2cRyRHEj7Ci6t9VowXtlsdtRwmM2bN0+6/kSsXLmyZntrayvHHXfcAd/fgw8+WLP9Va96FbHqjSBHksPxdfSKK66oGap266238tWvfpVQaOIhEH/+85/ZuHHjiHbLsrj88ssnXPdAeuyxx2q2H4mhagCXX345n/jEJ4a1+b7PTTfdxOc+97lJ17/ppptqtl9xxRVjrnE4fh3Awf9+61D9W38kXct85Stf4f777x/WtmTJEj70oQ9x/vnnj3h+ZLNZfvWrX7Fz585J71tERETkhYJ2cNj2E7ufIFVM1RybS+f3fm4V8kSinVij5P52xyA75LIrEwK3+q1sKgzJKGSDmLvagUgZokOyzo/vrn0TPEDTj2+m79JleI0KyBEREREROVKkCim29W0b9XoDYFvfNrYnt+/d3tKzhWwpA7t2Qfcek/JlAS2tMHMmWBYVr0J3pgvye6AFKhYUhlzm2D4EXLCL/ZzR6vHqmUUTRPaCP7+JBqF9hgkmw4eGEDROg8WzABf6ixCLQMgxa7AxYWkhBwhCpQJ9WUgnIRgA24bWOHSmIRwwIWoVD5I5KLsQD5iQaceC+ojZp40JLrNtE4pmA7ZlQtgqrgk0sz0TTLb3+EpgB0wgmm9Bcwx2pk1dC3OcxZKZZwNlC/qzEI2a2tGAyQirC5salQI4cSh6EI9AU0M1AM43dYpAMW8C4QLVuak0JPsh3AvJbvA9KM+EmXHww2aNOzqhWIZwxVz7+T5k+qDuEahbBRTh7Bg81TIYvj03OXiclj8yePv8Lebac0fDYNvR/aZ9UpYuhUsumWQRERERERERERERERERkSOP5de6g1hkitTX15PJZIa1xeNx0un0IVqRiMiLz6ZNm2oGhFiWxYIFCw74/tZ1dR7wmnJ4a2+bfqiXMOXmzJlDR0fHiPb77ruPc889d8J1r7rqKr773e+OaLcsi+7u7hEBUGPx0EMPcdZZZ9UMcznppJO45ZZbOPXUU8dU68477+TSSy+tGTDzxS9+kc985jP7rXHeeeeNuKEe4DOf+Qxf+MIXxh2a4HkeDzzwAN/85jeJRCL8+Mc/HvPcK6+8kh/84Ac1+84++2xuuukm5syZs88apVKJz3/+83zpS1+q+do6c+ZMNm3aRENDQ43ZIy1fvny/YRaJRIKbbrqJN77xjfutt2PHDl7zmtewZcvIv6I8/vjjefrpp8e0rqHOPfdcNm7cyN/93d/x3ve+l9mzZ4+7xoMPPsiyZctqruuss87i97///bjqHQnn7e677+YNb3hDzb5AIMCnP/1prr32WuLx+Jhr9vf386Mf/Yj/+I//4E1vehPf/OY39zvncHtNmCqH4/MU4P777+e8884b0X7jjTceNsFEkzHa8R177LFs37794C9oHMLhMKVSaUT7b3/7W/76r/96UrV37Ngx6nNwxowZ7Nq1a1L1xyuTyXDMMcfQ19c3ou+KK67ghhtuOKD7c12X+vp68vn8iL5PfvKTfPnLXz6g+5tqh+vraH9/PzNnziSXG3kX/ooVK7hkEjdDfOhDH+J//ud/RrSfe+653HfffROueyAtXryYRx99dET7DTfcMK6gsMNFV1cXs2bNolKpDGufO3cuW7durRk+PFa+7zNnzhyeffbZYe2hUIidO3cybdq0/dY4XL8ODofvtw7Wv/VH6rVMNBrle9/7Hu9+97vHVPNwc7B/piYiIlPnieeSvPE7fxzT2J9ddSYLZzdO7YLkoEkVUnzz4W/iY/5N78v38UTnEzXHDg1VCz+9HiebHbVuyYad9ewNTSvbw0PW9oYhVM3pg8Zqdq/lww9+NvwG+hfqueJD9Fx1zegD5LBVnD326w+ZOi+F313Ki9zza+B/xxh8/v7fwazTp3Y9IiKyT+u71rNy40o839vnuEKlwMM7Bt8wJ1/O07lzMySTIwc3NkIiAUA22UW5mCfowPQ4xOrAckxIWCoPlOGCE2FGow2uh+MNhj6DCfFaOAviYbNtexBzYFocHBscD8oV8AJgV9+XxMKEoQ1c1rgeZHKQ7gffgcYG8ICSawLOgrbZzpYhmYHpDRB0zHzbMvvEN5dKThDccvWc+PB8GqbVQV0QHB+CgOebOSEP8j54ocEwskzJBLkFq8dYLkDcNXVLAehOQdmB2c0QCZh9+pY5xvKQ91TJ5CAcgpgFrgVlTHhaJAu5oDmHuQJs6YBQGWYO+VP3RAFSEcgk4bl1kArB1jaY12XC1zK94JXhqtXDA7VXLYDHZ4x8uE/dDW/aNLLdB7Y2QzICjQWY1zvsUnP8li6F22+HMf4eQERERERERERERERempQBIyIvVuNLNBARERERkQnp6upi+fLlNfvOOuusCQWqlctl3ve+99UMO3jDG97AI488MuawA4ALL7yQ3//+9ySqf6g51Oc//3m6urr2Ob+np4c//OEPI9rf8Y538MUvfnHcgWoAtm1z3nnn8bOf/WzUUIFaVq9ePWpozFlnncXdd9+93xACMOET//Zv/8ZXv/rVmv27du3ic5/73JjXtT9tbW384Q9/GFMwGMDRRx/Nr3/9a2Kx2Ii+zZs313w89uef//mf6ejo4P/9v/83oaAqgL/6q7/iT3/6EyeffPKIvj/84Q88/vjjE6o7mkN93rLZLFdeeWXNvmg0yk9/+lM+//nPjyvgA6ChoYGPfOQjbN26lQ9/+MP7HX+4vSZMpSPxeSqHVktLS832devWTbr2k08+OWrf7t27R4QmTbUvf/nLNQPVgFFfqyZj27ZtNQPVAF7+8pcf8P1NpcP5dbShoWHU4LQbb7xxzHVeqFAojBpau2zZsgnXPZAqlcqoX6sLFy48yKs5MNra2rjgggtGtG/btm1CoZ5D/e53vxsRqAZw0UUXjSlQ7XD9Ojhcvt86GI7Ua5m6ujp+9atfHbGBaiIiIvLikIgkOLlt8Gc9TdEmEuGR34sOZRXy2PsIVANzY39deXA76JkggsEig59GyoOBagCLdu47UA2g6cc3Yyd79z1IREREREQOuXQxzYoNK2oHquVy0NVlPgKRQIS2ura93VEChHtStQunkuC6hKlwRlOeyxfDx8+Fdy+CN50EFy0wH69eAv/9Nnj9ApgZ93BsEwYWseHoRjhxOvzNfFjQBkc1QGudCTBrbTCBapZvLl9CAWiIQLEC6YIJNRv6fhOFCgQsaGoApxoobQEhB6LBwW2vGp7Wm4eB6Z5nAtYGxtjVfXqWaY84ZoxFNfzMNfsPVE9pJQuVIWtpjEBj1KwfoFyGfBLKVjW0zYKmmGkvV8x6fA/KQ95vy/Mhm4dkL5RSUMlAdx9QNMFukQpUXOjYac5DY2H4w3P8LrDuhTW/gM5tUNgEpSfNx1QXuBU4uWt4oBrA+Vvg6Be858zR/aa9FguY3wuLd5qPowaqLVoE9947+v+/+x3s2QN3361ANREREREREREREREREXnJChzqBYiIiIiIvNhVKhXe+973ksvlavZ/4AMfmFDdH/zgB2zcuHFE+2mnncZPfvIT6urqxl1z4cKFfO973+Ptb3/7sPZisch//dd/7fOm+02bNtUMX/iHf/iHca+jlvEEJHzhC1/AH/oXn1VHH300v/jFL4hGo+Pa98c//nGefvppvve9743o+5//+R8+9alPMX369HHVfCHLsvjhD39Ie3v7uObNmTOHa6+9ln/5l38Z0ffTn/6Us846a1z1XvOa14xr/GhaWlpYsWIF7e3tlMvlYX3f+c53+N///d8Dsp/D4bx985vf5Pnnn6+5tjvuuIPXv/7141rbCwUCAU444YT9jjvcXhOm0pH2PJVD77TTTmPnzp0j2h966CE+9rGPTar2ww8/vM/+3t5e2tra9jnmQFm3bh1f+cpXavade+65vPrVrz7g+6wV3jTguOOOO+D7m0qH++vosmXLuPnmm0e033PPPezevZsZM2q81f1+/PSnPyWZTI5o31eI28G2YcMGisVizb4Pf/jDBIPBSdW3LItf//rX4/7+cLIuv/xyfvazn41oX758Oeecc86E644W5Hz55ZePaf7h+nVwuHy/dTAcidcyAF/72tcm9dwVEREROVDOPvZsnup6Cr96S/+cxjk80fnE3v5oMMpR8aNIuebO9ro19xKonU0+zFH9sKnF3PQPECtBbxRKDnhD3k9kxpA3bbV8uHLt/mvbuSxNt9xAz1XX7H+wiIiIiIhMiVQhxba+baSKowSfAZt7NvNM8pm9257vmWSvtWtg00ZwfZPyteBEeMXpAPQV+syY3buwPJ98eLBewINoGWwLTm9KsviYCtECFANQGZLbVrYhEoL2GRCsft7QDPOaTGBYEHNd4lvQEgfLMssI2VAfNvXLFaiUoFKBbBFiNgRs6CmajzbgOCY4reKa+ViQiJp9WJhrnHDA9FtACBOWVqiY9QZtE1I29EfcdgmswGDoWnMMSu5gYJgLuBmIVX/0XQIyaRPoZlsQDZjjqQuD60GlAE4cih7EI2acVd1nsQRFoJgH34ZAdW4qDcl+CPdCsrsaujYTZsbBD5s17uiEYhnCFYhWTL1MH9Q9AnWr4CQH7l/M3oUPDc+2fDinY+TzJeTC+9bC1mZIRkxY27x9haWNxdKlcPvtCksTERERERERERERERER2Q+FqomIiIiITKHu7m4uv/xy7rnnnpr9ixYt4l3vete46/q+z3/8x3+MaLdtm+XLl08o7GDA2972Nr7//e/zm9/8Zlj7f/3Xf/HpT3+aUChUc97u3btrtp988skTXstEPPfcc9x55501+772ta+RSCQmVPfLX/4yd9xxB93d3cPa8/k8y5cv55Of/OSE6g54z3vew9KlSyc0d9myZfzrv/7riPCFNWvWTGpNk3XCCSdw2WWX8f3vf39Y+wufW5NxqM9bqVTiP//zP2v2XX311ZMO+Birw/E14UhxMJ6ncuidc8453HXXXSPa77rrLvr7+2mY4B+d+77Pbbfdts8xPT09ByVULZ/P8+53v5tSqTSiz7Ztvv71r0/JfmuFHA2YSMjXoXIkvI6ec845HHfccfzlL38Z1l6pVLj55pv5x3/8x3Gv7cYbb6zZ/va3v51YLDbuelNh7drREwgeeeSRSdefN2/eQQ9UA7jgggtobW1lz549w9pXrFjB9ddfP6HnXDqd5o477hjRPmPGjDF9T3K4fh0cLt9vHQxH6rXMq1/96gmHhYuIiIgcaK11rZzcdjLrutYB0BRtIhFO7A1GKJQLtMRaiEXrsFN9NHb0jfmG9ooNXUPe+6PkQPeQS6dIGRqHZEIv2gkn9IytdtOPb6bv0mV4jc1jXI2IiIiIiBwo67vWs3LjShOStg+dmU62J7fv3d6V3kWxtwuySZg90OpDdgOs2wmJBNlylnK5CKU01FeDma3BoTMa4U0nw4z6DCWgFAHHA3dIeDMWnNAGAceEk3lAnQ3T4uDY4JahJwN2NUCtWppocHA75IAdgJ4k+I4J9nIsaKkzHy1MWFqqYILRotUf2wcsiASgUDLtnm9qlSsQdgaDp4uuCVWzAa96Gm3P/F8qgxUcXEc8DKVKdZ4HpKESADsIBcAtQk8aZjeb4/Orx+N5JlCOEDhAbwrCIYhZ4FpQxoSnRbKQC5pzmCvAlg4IlWHmkBDs5q3QFYFMEp5bB6kQbG2DeV2wuxcyveCV4arVQBFagYWd8HiNX38u7ISW2u+ziQXM763dN8yKFdDUNHq/ZUF7O7S0jKGYiIiIiIiIiIiIiIiIiNj7HyIiIiIiIuNRqVRYs2YN1157LSeccELNEBeApqYmfvSjH2FZ438P0nvvvZdnnnlmRPu73/1uTjnllHHXe6FrrrlmRFtnZyd/+tOfRp1TqVRqttcKd5lK//d//zciJAvg1FNP5W1ve9uE6zY1NY0aNvCjH/1ownUH1DrnYzV79mxOO+20Ee2PP/54zXNxML3lLW8Z0dbR0TFqCN94HerztmrVqhFhKACNjY38+7//+4TXNl6H42vCkWSqn6dy6J133nk127PZLD/4wQ8mXPfOO+9k69at+xyTy43yF/QH2Ec+8hGefPLJmn0f//jHa77eHQjJZHLUvpYj6I/6j4TXUcuyuPzyy2v2LV++fNxr2rFjB/fee2/NvmXLlo273lTZV6jagbB48eIprT+aYDDIpZdeOqI9k8mwcuXKCdX8yU9+UvM1593vfjeBwP7fX+Vw/To4XL7fOhiO1GuZj370oxO6rhURERGZKmcfezbWkKi0OY1z9n7u49OR6gAgfv9vxhyoBnBsCnMnf5Xtw9ACM4bcoG/5cOU4LmfsXJamW24Yx2pERERERORASBfTrNiwYr+BagCJyPA3vmgMxCGVrD04lQTXJeJEoPp3M0EHjq2Hk6fDwplw3nFw9Vlw1AveT8MJwuwEnDgdFs2GpcfDCa3QWgcNYYgFoaXeBI6BCSprTUBdCLIlSBegWBrsBxNIVipDUwM41cA0qzo3Ghzc9jzozUDZNZc/rmfWHRjodyEYMNdDjgMRZ7A+mDll14SvBTwTAJcrmDoAARuiAagLm/VVylB2IJc0oWgVFyJBaIpBuWzC2/DB96A85M+PPB+yeUj2QikFlQx09wFFE/4WqZhaHTvB96GxMPwcH78LrHthzS+gcxsUNkHpSfMx1QVuBU7uGh6Wdv4WOLp/eJ2j+037pCxdCpdcAq95zej/n3eeAtVERERERERERERERERExmH/d1KJiIiIiAhgbhRPJBKj9heLRZLJJM8//zz5fH6ftdra2li1ahUnnHDChNZyxx131Gy/4oorJlTvhf76r/+aeDxOJpMZ1v6nP/2Jc889t+acadOm1Wy/++67ecc73nFA1jUWd999d832yy67bNK13/3ud/OpT30K13WHtT/11FPs3LmTo446akJ1TznlFNrb2ye1tlNOOWVE6Eh/fz/d3d20trZOqvZkjHZcf/7zn3njG984qdqHw3lbtWpVzfYrrriCurq6Sa1tPA7H14QjyVQ+T+XwsGjRIk477TQee+yxEX2f+9znePOb38ycOXPGVTOdTvPRj350v+MORrjo1772tVFDtU455RT+7d/+bcr2va/veSKRyKTrX3jhhfsMbtufb3/722MKlDtSXkcvv/xyPve5z+F5w2/s2bBhA4888givfOUrx1zrpptuGlEH4MQTT+SMM84Yc52pNtWhauM5ZwfaFVdcwTe/+c0R7cuXL+e9733vuOuN9jow1ufx4fp1cLh8v3UwHInXMolEgje/+c2TXp+IiIjIgdRa18rJbSezrmsdAE3RJhLhBKliCoDOTCeJrEegq3NcdevK0JaFrjiUbSg54Hjg2hApQ2NxcOyinXBCz/jW3fTjm+m7dBleY/P4JoqIiIiIyIRt6d2CPyQ9eVi4Wi4HmQzE4xCLEXJCtMZa6cp2ARDZ3UW4DIVaf43vA+l+oo31nDStyIlHQ1u9CSZzbRMcdtpME1hm+ZAtQncW4mGYFjPvmu5bYFnQXGc+BmwTnFYfgopngtMs3wSP9RagsR56siZMrCkMmQIEHIgEoFh9n0TfhkTUhJJZmPnhgJljAaHq0nvzZr2+bwLTgo7Zj1uBcDWEzbegOQY70+BY1dA1y8wpZSEWNddOvgfJNDQ3mEA1CzM+FgIvDKVGcEqQKZkxwaBZl++bYywCxbxZeyBgzkUqDcl+CPdCsrsaujYTZsbBD4Ndgh2dUCxDuALRiqmX6YO6R6BuFZzkwP2L2RuWPTc5+PBZPpzTMfwhDbnwvrWwtRmSERPUNq+XcYV1j7B0Kdx++2QqiIiIiIiIiIiIiIiIiEgNClUTERERERmjp5566oDUufDCC/nud7/L7NmzJ1zj17/+9Yi2lpYWzj777MksbS/HcTjjjDP47W9/O6z9T3/606hzTj755Jrt//iP/8irXvUqXvaylx2Qte2L67o89NBDNfve/va3T7r+jBkzOPfcc7n33ntH9P3hD3+Y8D7+6q/+arJLY968eTXb+/v7D2mo2vTp02u2b9++fdK1D4fzVuu5ALBs2bJJrWu8DsfXhCPJVD5P5fDxyU9+smbIZzqd5i1veQu//vWvaRnju3sXCgXe8Y530NHRsd+x5XJ53Gsdj1WrVnHttdfW7GtoaGDFihWEw+Ep23+xWBy1LxgMTrr+ww8/TE/POO9CHyKVSo1p3JHyOnr00UfzN3/zN9xzzz0j+pYvXz6ugLDJBnAdDL7v88QTT0zpPg5lqNopp5zCK17xihHBcffffz8dHR0ce+yxY661bds2HnzwwRHtixcv5qSTThpTjcP16+Bw+X5rqh2p1zKvetWrpvTfGREREZGJOvvYs3mq66m94QhzGufwRKe5vvDx6V37a2ZOoO6xKeiqg1QYsCBSgWwIZgzJDrZ8uHIC+dB2LkvTLTfQc9U1E1iZiIiIiIi8UKqQYlvftr0By7Vs69vG9uT2vdtberaQLWVg1y7o3mMSxiygpRVmzqTiu3Tnuk3SWH4PlSgUhvxKzPYh4IJtwatm9rPkuCwhDyqO6ferY05sNYFnPqZ+cx0saDWBYemCyXPzLIhETK2BudGA2Q45EIxAfz909oMTMkFlLXWwpx9C1fCyigepPFQqEAuaIDTbMkFrhRI4vglYC1VD0yKO2W+hYoLWAraZG7ShXF2EVwbLMZ+HHGiJmWA2MPXLLnj9UA6AGwXXBc81H63qsViYcY4N0TAQBjcDyYwJh4tZ4Fpmn8l+iGQhFzSBdLkCbOmAUBlmDrkWa94KXRHIJOG5dZAKwdY2mNcFu3sh02vWftVqoAitwMJOeHzGyOfFwk5oyY1st4D5vaM+nYZbsQKammr3WRa0t8MYfz8tIiIiIiIiIiIiIiIiIuOjUDURERERkYMgGo3yjne8gyuvvJJXv/rVk6rV19fHX/7ylxHtCxcuxLbtSdUeqlaIw5NPPjnq+BkzZrBw4cIRwRc7duzgtNNO41Of+hTvf//7mTZt2gFb4wtt3bqVfD4/on327NnMnDmRW+RGetWrXlUziOCJJ56YcBDBwoULJ7ssGhoaaraPNUxmf7LZLE899RTr1q1jw4YN9Pb2kk6n6e/vp1Ao4Pv+/osMkUwmJ72mQ33ennvuObq6uka0NzY28vKXv3zSaxurw/U14VA4HJ+ncvj427/9W0477TQee+yxEX2PPfYYZ511FsuXL+dVr3rVPuts3ryZ973vfTXDi2oJhUITWu9YPPDAA7zzne/E87wRfY7jcMsttzB//vwp2z+wzyCdcrk8pcd/oBxpr6PLli2rGap222238Y1vfGNM4UYPPvggW7duHdEeCAR473vfO+41TZXNmzeTyWRGtNfV1dHf339AH59D5fLLLx8Rqub7PjfddBP//M//POY6N910U81/58Yakne4fh0cLt9vHQxH6rXM6aefPtlliYiIiEyJ1rpWTm47mXVd6wBoijaRCCdMmEKpxB43RT4A0cr46taVobEA2xvNdtCD+iI0DskcX7QTTphgPnjjilvp+fDH4UVwvSMiIiIiciit71rPyo0r8fyRv0cbqlApDAtVq3gVkl3PQjoJQ3/lkt4DThkSCVzfpZzrBweCDhyVgIaoCQhzXQh5sGQOtNYBuHg22B4mkQuY3Qj1Q2qHAzA9ZkLRABqj0OBAZx/4EROyZtsmCC3oDM7zfRPM5oRM+JkFhB1oCJtxFoAP5TL0ZsFJmHbXg1gIBi6HPBeCAXDLg/vJu1AcEqoWqq7X9sArgDfkV4CNEQgHoVA2tctlCPlmn/Gwqdkah6AFuTyEQ+ZcVYZcR3k+5IvmmJK9EAiAH4DuMgQqJvwtUoGUAx07zbjGwvDH8vhd8Og2WLMTBh720m4oJGFgaHvX8LC087dAdwx2DPnTkaP7TfukLF0Kl1wyySIiIiIiIiIiIiIiIiIiMlEKVRMREREROQjy+TzPPfcciURi0rXWr19fs33BggWTrj1UrfCznp593wn2iU98omYQRyqV4tOf/jSf+9zn+Ou//msuuugizjvvvAO+5lrhIACnnXbaAdvHaLVqhVCM1YEImovH4zXbawWRjFU+n2fVqlXceuut3HPPPZTL5QnXeqEDEVZ1qM/b008/XbP9la98JdbA2ysfBIfza8LBcLg/T+Xw4TgOP/nJTzj99NNrBidu2rSJJUuWcPHFF3PJJZewZMkSZsyYgeM4dHZ28thjj7Fq1Spuu+22Ec+z4447btR/ByKRyJQcz6OPPsrFF19MoVAY0WdZFjfeeCMXXnjhlOx7qGg0OmpfoVA4IkLVjrTX0Te+8Y00NzfT29s7rD2ZTLJq1aoxBSPdeOONNdvf8IY3MH369HGvaaq8MGxswIEO+jqU3vWud3HNNddQKpWGtf/whz/ks5/97Ji+p/B9nx/+8Icj2sPhMO985zvHtI7D9evgcPl+62A4Uq9l5s2bN+G5IiIiU+WkoxpY80+vHdPYhmhwilcjh9LZx57NU11P4WMCiOc0zuGJzidg+zNgQUcCFkzkx1svyDM+Kj34ueXDlbUvZcbESfcT6thG6WX6PktE5CVjxinwj2O8No9M/nfNIiIvBelimhUbVuy9FtiXSCBCW10bXVnzBh9RAoR7UhSdGoNTSYjHSQQCHNtWon0mTK8H3wKv+iPr+hC0z4CgDdkidGagKw22BW1xE5h24vRqABtQ8UwImuey91qjUoZc1vT7Ff4/e3ceHVd93///ee/sM5JGkiXZ8oKMMcQGGxsCZGkwhARMTCEQslESAia/tCkl3+835dumbUJokm+TX9PlJJT8wmkOISHkWxw7AdI0NGUNO6QYx+w2xrKNbY21zGj25d77++Mz0khoZGvxIpvX45w50v0s7/u5M6PRvfboNWSr/4XQHDQBbOGACWDLl8GzYVYDDFY/s8PnQuOI/77zuSawzAP682YNFuC6JmCtXAGnYkLRLMCxoCUK+bQZY2GOrVRNYPO74NiQzkEkbI4r4gfLgljIhKpVChCIgxc0IXPxBrPegAMlPxSKkE1CyAInYgLUMlkolKBQgFA/JHtNMFq5EzobwAuBXYJdPVAsQ6hiQrI9DzIDEHsGYnfDyT54+EyGA+wWJWv3heXBOd2jH9KgA9c+B1tbIRk2QW2L+4enT83q1bBu3XQqiIiIiIiIiIiIiIiIiMg0KVRNREREROQwuf/++znjjDP48Y9/zMc+9rEp19m5c2fd9ltuuYVbbrllynUnIpfLUSqVxg1IufLKK/nJT37Cb37zm7r9pVKJX//61/z6178GoKOjg3e/+928973v5dxzz+Wd73wnfv/UL1P27NlTt33RokVTrvlW44UG7N69e8o1m5ubpzx3yHihEq67/09dHs/69ev54he/OO7zbboGBwenXeNI32/j3TcnnnjitNY0WTP5NeFQOxqepzKznHDCCfz4xz/mIx/5CI7jjOn3PI977rmHe+65Z8I1Ozs7ufXWW/ngB+v/wf7BCFR9q82bN7N69epxn6O33HILn/70pw/6fuvZ3/H19fXR1NQ0bv9McbS9joZCIa688kpuvvnmMX233377AUPVcrkcP/vZz+r2rV27dsLrOBzGC1U7mCFTR9qsWbO4+OKL2bBhw6j2119/nUcffZRVq1YdsMbDDz/M9u3bx7RfeumlEz5fmqk/BzPlfOtwOFqvZQ7F7zkREZHpCvhsZjWEjvQyZAZoj7WzrGMZmxObAWiJtBC3wqR6egDoaYCulPlD/InK+yEVgYYyZIK1P+Qfctab8I5pfA6BZ1k4Tc1TLyAiIkcfXwBibUd6FSIix5Qt/VtGBaq5XvX//3M5yGSgoQGi0eH+BU0L6Mn2mOSxXTuI5z16Rnw+W8CBxpIJEFvZkmHlcSWcClRsoFq6bEN4RKCa7UFTEJpnwZnzwPEgk4dYGII+wAIbE6gW9AEBqFRgIAvppKkZjMCsRtiThopjAtUcBzIuhP0mwMyzTGBZYxjwTE0fYNsmFM3G9LsWFComxC1gm30FbChj5rllM6eCWU9b9e6xMPsolsB2ANuEqrkl6CvDglYTEOeZMriuuR8IQgQYzJgQtI4I2H6gAv0ZCOfMfZIumduWbhOQFqpA54jPwGvdCokwZJKwczOkgrC1AxYnYG8/ZPrN2q97FihCO7CiB56fM/Z5saIH2nJj2y3gxP6x7XWtXw8tLfX7LAuWL4c2/V4XEREREREREREREREROdIUqiYiIiIiMkEPPfQQ5557bt2+QqHA4OAgr776Kk888QR33nknmzdvrjvuiiuuwLIsPvrRj05pHYlEYkrzDpb+/n7mzKnz7kPAtm3uuusuLrnkEh599NED1kokEtx7773ce++9ADQ2NnL++edz5ZVXctFFFxEKTe6PHwcGBuq2H8xAl/FqJZPJKdf0+ep9xPGRUS6X+fSnP81dd911SPdTL8xoso70/dbfX/9dtS3jvYH2EJnJrwmHytH0PJWZ55JLLmH9+vV86lOfIpvNTqvW3Llzuf/++ymVSuOO6ezsnNY+3urVV1/l/PPPH/c16B/+4R/4/Oc/f1D3uT/z5s0bt2/v3r0cf/zx06rf29s7oXELFy6ku7v7wAPrOBpfR9euXVs3VO03v/kNb7755n4fl/Xr15NOp8e0d3R08Id/+IeTWsehtnHjxrrtp59++mFeyaF1zTXXjAlVAxOSN5FQtdtvv71u+9VXXz3hNczUn4OZcr51OByt1zJHQ3imiIiIvL2t6lrFC4kXhgMVFm7tZZNrvvcs6I7DkkmEoHXHzbzWnAlNaC7U+poK8D+emt56c+9+H84s/QG+iIiIiEg9qUKKbQPbSBVT+x23bWAb25Pbh7e39G0h270FeveZ5C8LaGuHzk4ThAUkC0kKpRzk90Ez5Pzg2tUCHvgDcPGpMLsxScUx1wUVe8ROLXhHB/h9ZhcOEPFBR8yEjgE0+jGJZ9XPfrMxAWZDHwVn2xDyQSkAOQdCtglEa4vBvkEIVMd6nglfC/jA7zfbQT/Y1YA326vus/rf3SGfWS9A0TFhak4FgtW35NgueAVwguZYAWJBiAShXDHhbUUX7H7wtZt+vw0tESiXzSH5fWYd5RH/bep6kC+aMLZcBrJe9T4NgK+6n2ARuveYuTD6GgvgpD3wu23w37thKBuvtBcKSRgaujwxOixtzRbojcKuEf98PX/QtE/L6tVw+eXTLCIiIiIiIiIiIiIiIiIih4NC1UREREREDoJwOEw4HKajo4Ozzz6bv/iLv+AnP/kJn//858cEtjiOw1VXXcWJJ57IihUrJr2vXK7Ox6YeRoVCYb/9zc3NPPDAA3z961/nH//xHye13nQ6zc9//nN+/vOfM2vWLG644Qauv/56YrHYhOYXi8W67fF4fMJrOJDxah3ofjkalMtlPv7xj3P33Xcf6aUcFfL5fN325ubmw7qOmf6acLDpeSoHw6WXXsqzzz7LZz/7WZ544okp1Tj//PP58Y9/zJw5c3jggQfqjmlpaSEcDk9nqaNs27aND3zgA/T09NTt//rXv86f//mfH7T9TcRxxx03bt/WrVt5z3vecxhXMzVH4+voypUrOe2008aEjrmuyx133MGXvvSlcef+8Ic/rNv+qU99Cr9/Zv1z8XihaqeddtphXsmhdeGFF9LZ2cmePXtGta9fv55/+Zd/IRqNjjs3k8nUDWSbO3cu559//oTXMFN/DmbK+dbhcLReywQCgSnPFRERETkc2mPtLOtYxubEZsjlaHn698RnQap6ud7TAF0piFQOXCvvN+PBhB+8o9fMLfghXIFztsPczNTXmn3P2ez55nenXkBERERE5Bj2YuJFNry8AXcoWWs/CpXCqFC1yps7SKb3wcjP9UvvA18Zqv9u6rgOhdwg+ExY2bxGiFUD0Voj8IHjwXWh4kIFCDgQtqGtCRpDMC8OrQ0mSKzsmJCwxkAtMA1MuFkoDH15KFWgIWTGVXPdKFTAb0FLExRHvNUobENTqDbO9sxa+kvQ0Why0FzX1C9XzD5tD4buKZ8Pwj4oOGYcmDml6nWQ3zXb6RyEw2BbJngtaJvgNdcFXLBiJlwtXv1vC8sz6y+WoAgU8+DZJujNsiCVhuQghPoh2QtOGWwfzI5BcQ74g9C3DXOB5YdQxVybeR5kBiD2DMTuhpN98PCZDN+Zi5K1+8by4Jy3fO5S0IFrn4OtrZAMm6C2xf2jH4tJW70a1q2bTgUREREREREREREREREROYxm1l/JiYiIiIgcIyzL4tOf/jQnn3wy73//+0mn06P68/k8V1xxBc8999ykw1YqlQn8ddcRFggE+NrXvsaf/umfcsstt/DjH/+YHTt2TKpGX18ff/VXf8X3vvc97rrrrmmFsnhDH2l7EBzMWjPNP//zP+83qGoonOOMM87gpJNOYt68ebS1tdHY2EgwGBw3iMWypvXW1BlrvOM63M+Ro+E14WDS81QOlqVLl/LYY49xzz338N3vfpdHHnkE1z3wH2GceeaZfOlLX+IjH/nIcFsikag7dsmSJQdtvd3d3bz//e/nzTffrNv/N3/zN3z5y18+aPubqEWLFhEOh+sG8rz00kuHfT1TcbS+jq5du5brr79+TPvtt98+bqjaG2+8wSOPPDJuvZlk+/bt9Pf3j2kPBoMsW7bsCKzo0PH5fHzqU5/i29/+9qj2dDrN+vXrueqqq8adu379+jEhzgBXXXUVPp9vwmuYqT8HM+V860jStYyIiIjI9K3qWsULiRfwnnwCymUWJmHTHNPnWdAdhyV9B67THTfjhxyfhJYCFE44idKSUzgxej477cbJL9CC4uJ34Da3Tn6uiIiIiMjbQLqYZv1L6/Go82+cuRxkMtDQANUPKQn7w3TEOkhkE1AuE+neTSgKxbf+V3Eqaeb5fERtj5NnFVk6H2Y3mgAux4ZwAFZ2mqA1y4NsEXqz0BCE9qgJEbMsaGswX30WBH3QGDThZOUKFMpQKUO2AFEb/Db0Fc1XGxN6FvRBxTHzsWBWA6SqnztiexAPg1Ndts8ztQsVM8fvA9s1YWRD7BLYfnBccx3TGoXdaVPfAlzLhKHZDtg2lG1w8tBbNvtuDJpxHua4/BbQCD4gkzPhcVELHAvKmPC0cBZyAXO/5QqwpRuCZegcET7dNgjzd8BDb8ArT4DPD8VW2NoBixOwtx8y/eCW4bpngSK0Ayt64Pk5Yx/+FT3QVudzYyzgxLH/zVTf+vXQ0lK/z7Jg+XJoa5tgMRERERERERERERERERGZCRSqJiIiIiJyCL3zne/kpz/9KZdccsmYP2B/+eWX+du//Vu++c1vTqrmeCFsCxYs4LjjjpvyWqe7/3rmzJnD17/+db72ta/xu9/9jgceeICHH36YJ598ksHBwQnV2LlzJ6tWreLee+/lQx/60H7HhkKhuu0T3ddEjFdrsuF4M83u3bv5+te/Xrfv+OOP5zvf+Q4XXXQRtm1Pqm65XD4Yy5uRxnvMk8nkjFjHTHxNmC49T+VgsyyLSy+9lEsvvZSenh4efvhhNm7cyBtvvEF/fz+lUoloNMq8efM49dRTWb16Ne94xzvG1Nm6dWvd+gcr+GnXrl2cd9554waU/vmf/znf+MY3Dsq+Jsvv93PqqafyzDPPjOl76qmnjsCKJu9ofR298sorueGGGygWi6PaX331VZ588sm6gbS333573VCls846i1NOOWVK6zhUnnvuubrtp5xyCoFA4DCv5tC75pprxoSqgXnM9heqdvvtt9dtv/rqqye1/5n6czBTzrcOB13LiIiIiBw67bF2lkWOY/PT5tq1pQDxAqSqp0E9DdCVgsh+sobzfjNuSLxg6gCEdrzB/PPXEll8AflDdAwiIiIiIseqVCHFtoFtpIqpcce81vcabyTfGN52PdekfT333/DKy+B4Ji1syVI4/Z0mUQsYKAzAnj0QdLE8c14/xO9CpAx2Os2Zyxt5V3wAf9GEglH9HCafByfPhaANeKZsexSWtJusrVwOUkWIhk04GmYYYb/pBwj4TdhZcgDwmRo+C9pitYCzigOpgtlfJFjdtwWxoCloUw04s0ywG0CweixFx4SzeRYMfX6U7ZqbUwS3Wi/og7YohKrzPAvKDtAPoVZw/OA65q6sVEwtu3oMFad6YFXZPBRz4PeD5zdBbP6KWX+4AikfdO82x938ls9l6kpA7CEo9kOmvdqYgNJeKCRhaPjyxOiwtDVboDcKu5pqbfMHTfu0rF4Nl18+zSIiIiIiIiIiIiIiIiIiMtMoVE1ERERE5BD7wz/8Qz73uc9x6623jun7p3/6J6655hpOOumkCdeLx+N12z/2sY/xj//4j1Ne56FkWRZnnnkmZ555Jl/60pdwXZff//73PProozz66KPcf//9DAwMjDu/UqnwiU98gk2bNnH88cePO65lnE+OPRxBBOPt+2jxox/9iEwmM6b9tNNO4/7776e1tXVKdfv7J/rRv0efWbNm1W0/3CEfR+NrwlTpeSqH0uzZs/nEJz7BJz7xiUnPfemll+q2v+td75rusti7dy/nnXce27Ztq9v/Z3/2Z/zDP/zDtPczHWeffXbdULUnn3ySfD5PJBI5AquauKP1dbSlpYUPf/jDrFu3bkzfD3/4wzGhap7n8eMf/7hurWuuueaQrHE6Nm7cWLf99NNPP8wrOTyWLl3KWWedNeZn6eGHH6a7u5uurq4xc9544w1++9vfjml/z3veUzcAcn9m6s/BTDnfOhx0LSMiIiJyaK26+3leKJXxqqEAC5OwaY753rOgOw5L+saf3x1neO7Q/CF2qcwF//UyLD7YqxYREREROba9mHiRDS9vMCFp+9GT6WF7cvvw9p70Hor9CcgmYcFQqwfZl2Dzbqj+m3e2mKZc6odmM6LkA9eqDZ/TDJcuSzFnVo5cqUzAb/qHxixohliolicW9EF7rBY2Fo9ALAAlC7JlE0Tm90FjaDjXDceFUhlamyBV/awvq1or6DOBZRZmbioLHXFTw8IEp/mozfFbgGvWF7Ah7DPzhgLXyhUT5has3p2VLLgBE9AG0ByGUAAKZbOuchmCFehLQ6wFbBvmxMz+iyVzXJYNuYIJbrOqwW09fRDohWQveC6UO6GzAbwQ2CXY1QPFMoQqJrza8yAzALFnIHY3UIRVUXihrXadtShZe7wtD87pHv0cCDpw7XOwtRWSYRPWtri/dj9PyerVUOf/2URERERERERERERERETk6GcfeIiIiIiIiEzXt7/9bebMmTOmvVQq8Vd/9VeTqrVgwYK67X19+/mLrxnGtm1WrlzJ9ddfz7p160gkEjz00ENcddVVhMPhunPS6TRf/epX91t37ty5ddvHC6KZiq1bt9Zt7+zsPGj7OBLuvffeMW0+n48777xzykFVcGyHVY33s7hly3Q/CvngrONoek2YKD1PZaZ6+umn67afe+6506q7b98+zjvvvHFfVz73uc/x3e9+d1r7OBguuuiiuu3FYpF///d/P8yrmbyj+XV07dq1ddvXrVtHPp8f1fbQQw+xffv2MWMjkQhXXHHFoVjetDz33HN120877bTDvJLDp1643f7C8H784x/jed6Y9quvvnrS+56pPwcz5XzrcNC1jIiIiMgh1NtL+3d/wLJEramlAPFCbbunAfLjfCxh3m/6h8QLZv6QZQlY/H/XYyf1b0wiIiIiIhOVLqZZ/9L6AwaqAcTDoz8YpNnfAKlk/cGpJDgOAOF8CTwI+GB+HE7tgBWdsGw2nDEfPvMumNMIVEzaWdkHEcuMPWUOvKsLFjTB3CboaIAFcRM8NsSpQCZr6qcLMJA3YWKZImRL4HhQqJiQMM82IWxggsBsD0L+2nYQE97WnzcN3tC4EYfmM4c1HETWGjWBacMBbkAxU5tTApJpcKv/lRDxm/GxkLkFAxCIQ6DBhLs1RCFog6/6kLguJPdBegB6B2BfP7ywBXa9AYU94JTBdaB1KyRegRcfgd/8f/DKv8NjG6H7Adj4a3jsp/DcvXDqL4Giqd2egxU99R/CFT3QlhvbbgEn9sOZu83X/QaqrV8PDzxQ//bgg7BvH9x3HzQ17a+KiIiIiIiIiIiIiIiIiBylxnlLqIiIiIiIHEyNjY3cdNNN/Mmf/MmYvp///Of87ne/44wzzphQrXe84x1123ft2jWtNR5Jfr+fc889l3PPPZebbrqJT33qUzzxxBNjxq1bt47vf//7RKPRunUWL15ct33jxo0Hba3PP/983fYTTzzxoO3jcCsUCjzzzDNj2s8991yWLl06rdqbN2+e1vyZbMmSJXXbn3nmGTzPw7Km9ZnIE3YsvibUo+epzFRbt27ljTfeGNN+0kknccIJJ0y5bl9fHx/4wAd4+eWX6/Zfc801fP/73z9srzX78773vY+2tjZ6e3vH9N1+++187GMfOwKrmrij+XX0/PPPZ8GCBezcuXNUeyqV4uc//zlXXnnlcNsPf/jDujU+8pGPEI/H6/YdSeOFqp1++umHeSWHzyc/+Un+1//6XxQKhVHtP/rRj/jKV74yqm28sLVIJMInPvGJSe97pv4czJTzrcNB1zIiIiIi+5cqpNg2sI1UMTW5iZ4H110HHTl8LnTHawEEfheSIz7j49m5cNzg2BI7mmAgYoIFImVYmKz1WR6c0w12LkvLnbfRd90Nkz42EREREZG3oy39W/CofXDIqHC1XA4yGWhogGiUoC9Ie7SdRNYkJYf3JgiVoVDvXfAekB4k1NzI0uYMixfArPiI4DEbwgE4rRP8NuSKkMhAfxaOa4E5McCCWBiC1fo20BSCkM+Ur1QglYV0Enx+E7TW3gA9aROUZnlQcSGZg7IDDX5zHWJbEPZDoQQ+z4SdBX1QrkDYB65lQtgqjlmbzwPHrQW5+R0oeGCHzHbQBw0hKFXMdtEF0lDxgx2AAuAUoS8NC1rBZ5v1e5jAtHAQCEIEGMxArgCzAhCNQhlIDkI4D2ELskHTn+iHjtH/jcFJe+B32+C/d8PQw1jaC4WkWQPA8sTYoLQ1W6A3CrtG5JrNHzTt07J6NVx++TSLiIiIiIiIiIiIiIiIiMjRTKFqIiIiIiKHybXXXss//dM/8dprr43p+8pXvsKvf/3rCdVZtGgRs2bNoq+vb1T7U089RblcJhAIHJT1HinHH388v/71rzn55JN58803R/UVi0Uee+wxLrjggrpzFy9eTDQaJZcb/U7MnTt3snfvXubMmTPt9T399NN121esWDHt2kdKT08Prjv205/PPvvsadeuF453rJg3bx5z5sxh7969o9qTySQvvPACy5cvPyzrONZfE4boeSoz1YYNG+q2X3HFFVOumUwmueCCC8YN/Lvyyiv5wQ9+MGPChAKBAFdffTX/8A//MKbv17/+NZs3bz5sr4lTcTS/jtq2zWc+8xm+8Y1vjOm7/fbbh0PV0uk0P//5z+vWuOaaaw7pGqdi7969Y36/gjneU0899Qis6PBobm7m0ksv5d/+7d9Gtb/++us8+uijo37n/fa3v2Xbtm1jalx22WVTCsmbqT8HM+V863DQtYyIiIjI+F5MvMiGlzeMDlmYqIcfhvzLsNBsZgOQaKh15/1QrL5zJhky3wdG7KZsw5uNDCcwhMumbciyEcEALXfdwcCVa3GbWye/ThERERGRY8hEQpG3DWxje3L78PaWvi1kSxnYswd695nkLwtoa4fOTiqeQ2+u16SB5fdRiUBhxD9d254JHbMteFfnIO85IUuoem5fthiOb7M9WNoOfp/ZjoVgWRRaopArmTw3y4WG4IjamAAzqkuybQjbZv+ez7T5LGiLma8WJt+56EAyA4EmCPhMQFo0CNUMNFwHAn5wytWafsg7Zp7fNnWcagqav3oAuSJEAiYgzW9DxG9qF8pQKZv15JIQajfhbOGhkLQy+DDH7XlQLtWOz/UgXzRB0pVB2LsTelyIN0HUZ46JMnTvhmAZIhVTIzMAsWcgdjec7IOHz2T42mlRslZ/KIz6rYIOXPscbG01gdfNBVjcXwvAm5LVq2HduulUEBEREREREREREREREZFjgH3gISIiIiIicjD4/X5uvPHGun333XffpEJ9PvjBD45py2azPPnkk1Ne30zS1NTEn/zJn9Tte+ONN8adZ9s2733ve+v2rTsIb5pMJBI8/PDDdfsORrDTkZJIJOq2Tze4wfM8fvWrX02rxkx3/vnn123/4Q9/eFjXcay/JsDR8TwNhUJ120ulUt12Ofq5rssPfvCDMe22bXPVVVdNqebg4CCrV6/mueeeq9v/iU98gh/96EfY9sz6Z73Pf/7z+P1jP7/B8zxuuOGGI7CiyTmaX0evueaaugF7Dz74IDt37gTgrrvuGhPUBLBw4ULOO++8Q77GyRrv+X/SSScRi8UO82oOr/FC7m6//fb9bh9o/kTM1J+DmXK+BYf2d72uZURERETqSxfTrH9p/dQC1XI5ePTRUU1dKWppCpg/2h9mQeotp3ypEKP+oj9ehJfaoegbGwxg57K03Hnb5NcpIiIiInIMeTHxIt95+jvc8+o9PLz94XFvr/W9xvbk9uFbxa2QTOwgmd5HMmRCtpIhzHZiB5lSBsdzKORSFHxgBWFuMyzphFPmwUmdsGQufOY9cPYJ4MfBsatha675anvQFYfGkDnNtzBBZu0NJjStOQJzGiFsmTfYZ0uQLpjQMW/EdUShGoLW0gS+aviahakRCdS2XRfwoD9fuwxxXROCBuBUaiFsrmWC3YbGDLVZFmRz5hgc21yHJNNmPRF/LdAtGoSmEESawdcAmRK0NkFrHCKWmVcsQTYPyX4oFEzomuNCctDcsjth10uwbzs4u6D/JdixGXa+CI/dCQ88Dd0PwMZfw2M/hefuhVN/CRShPQcreuo/J1b01MKo38oCTuyHM3ebr+MGqq1fDw88MP7twQdh3z647z5oahr3+SkiIiIiIiIiIiIiIiIibw8z668vRURERESOcVdccQVLly6t2zde4Fo9l19+ed32f/7nf57SumailStX1m3v7+/f77wLL7ywbvsdd9wx3SVx5513UqlUxrSvWLFi2sFOR5I38t2/IxQKhbrtE/Xv//7vbN26dVo1ZrpLL720bvvtt99eN7zmUHk7vCYcDc/TpnHenJ3NZg9KfZl57rzzzrrPn8suu4xFixZNul42m2XNmjU888wzdfs/8pGP8JOf/ASfzzfp2ofaokWLuPbaa+v2/eY3vzki4UeTcTS/ji5atIhzzjlnTLvruvzoRz8Cxg+fuvrqq+sGsh1p44WqnX766Yd5JYffBz/4QebPnz+m/Wc/+9nwuUU2m2X9+vVjxixYsGBaIXkz9edgppxvwaH/Xa9rGREREZGxtvRvwRuRguZ67sRvv33YfLUYvkUq5o/9PcwtXIFQpbadCULJNt+XbLM91BeqmPlY0B+BZYmxwQAtd92Bndz/v2GLiIiIiByrJhOKHPaH6Yh1DG9H8BPqS9UfnEqC4xD3+3lnR4mrz4IvngtXnw6XngwXL4FPnwbfuAguXQ4r50FnHHy2CV87rgmWzoazjoN3L4R5TdAeg+YwdDQwnORledU31gdgIAt9WRjImzCyTMGErJWrQWSuBZ4N8Uh1Lia0LeSvbQcx4woVqLjmuqJSMesCwAO3GtDmWSaUrS1aC1rzLBOeVsqAUzahah7glsBxTODa0L4qjqkbCYGvsRqWloFypRoq55q2viRU+iGdgt4B6N4Nz74Au7uhsMfsB2Be2twGe+HJddD7NJR+D4VXIJUwgXBvvSZaswXmD45+6OYPmvZpWb0aLr8czjtv/Nv73w9tbdPckYiIiIiIiIiIiIiIiIgcKxSqJiIiIiJyGNm2PW542gMPPMAjjzwyoTqXXnpp3T98v+eee3j22WentcaZwnXrv8m2ubl5v/M++clPYttjL3V+97vf8Ytf/GLK60mlUnzrW9+q23fllVdOue5M0DbOG0tfffXVKdcsl8vcdNNNU55/tLj44ouZPXv2mPaBgQH++q//+rCt4+3wmnA0PE/HC1rZuXPnQduHzBx9fX38xV/8xZh227b58pe/POl6+Xyeiy++mMcff7xu/yWXXMK//du/4ff7J137cLnpppvG/T39p3/6pzz11FOHd0GTcLS/jq5du7Zu++23385rr73GE088MabPsiyuvvrqQ7yyqRkvVO200047zCs5/Gzb5qqrrhrTnk6n2bBhAwAbNmwgk8mMGfOZz3ym7nnwRM3Un4OZcr4Fh/53va5lRERERMYK2IFR25v2buK33b898G3rA/w28Sy/7WLM7c1G6I5Dd7O5ZYKQCptbMgJbW0371laznQqbMc0jsv19LpzTPXa9di5Ly523Hcq7RERERERkxtpvKHI2g9uz13ytti1oWoCHh+d5eLt2EM97w6HGHuB3oCUPs/LwgZYM/2P5IBcvgvkxCLgQdMy5ecwPy+dAwDbBZk1BeMcsuGgJnH8SLGqF2RFojULAZ8ZFA9DZCM0RiATNeksFSCRNWFp7gxmHZ8LOhoLLerNQKNWCmy0LwgEzzueBOzTeg5DPBKMBFB3z1anUwtBsF7wCOCM+/yYWhLaGWvBa0TVh0P1ZcF3T3h6HiAW5vAlN81yoFGs1XA/yRRO8luyHUgoqGROiRtGsM1wxx9O92wS3Nb/ls8y6EjD3Htj475CrBqUtStb6LW/sNVHQgWufgyt/Dxe9Zr5e+5xpn7LVq2HdumkUEBEREREREREREREREZG3I4WqiYiIiIgcZh//+Mc55ZRT6vZ99atfnVCNQCDADTfcMKbd8zw+9rGP0dPTM601zgT//d//Xbd93rx5+503b948Lrnkkrp9X/ziF+sGUEzEl7/8ZRKJxJj2aDQ6YwNJJqqzs5NAIDCm/d5776VUKk2p5te+9rVxA1GOJYFAgP/5P/9n3b7vfve7/Nd//ddhW8ex/ppwNDxP586dSyQSGdP+u9/97qDt4+3m9ttvx7Ksurft27cfsXVVKhX+6I/+iL17947pu/baa1m5cuWk6pVKJT7ykY/w0EMP1e1fs2YNP/vZz+r+DMwkc+bM4eabb67bVygUuPDCC3nssccO86om5mh/Hf3oRz9aN+zp9ddf53Of+1zdOeeddx5dXV2HemlTsnHjxrrtp59++mFeyZEx3rnl7bffPurrROdN1Ez9OZgp51tw6H/X61pGREREZKzj4sdhUUsYWNi8cGITu7czIsthlKALsXJtO+CaIIYhJR+UbfN1SLwAkUp1w4N374K2XP36LXfdgZ3sn9g6RURERESOEqlCio17NvLw9ofHvT2/93m2J7cP3x7Y9gD3vnIP9z70fe791T9y78O3mq8PfZ97X7mHB954gO5kN6/ue5lX87vY0QwDYeiLmtveBsjPhvM+CEsXJCm6ORwLKnbthgXv6AB/NcDMsSDgh84maApDSwRmN0EEaAjVjsfGBKxZmBC0SBByBXCq76y3bWiPmgCyoRA0C/Ac6MtA2TGXHI5r6vir/a5j9m974PNBuHpdMfS5gh5Qql5b+F0TaJbOmSA0MEFuQRtiIbPeUADCTRBsNDuIDQXDVYPKCkVI7IFS0nxfcWAwbYLfUmnI7oStz8LWx6F3J1hps0+7BLu2Q7EEoYq53vE8SPeDex/E/l9o/i84dU/958OKnvrXRBZwYj+cudt8tcYOqVm/Hh54oP7twQdh3z647z4Y5wNPRERERERERERERERERETGo1A1EREREZHDzLbtccPTHnnkER544IEJ1bn++us58cQTx7R3d3fzoQ996KCGzfz2t7/lr//6r/c75i//8i/5xje+QX//9P9YLJFIcOutt45p9/v9vPe97z3g/K985StY1ti3Zm7fvp1LL72UYrFYZ9b4brnlFv7lX/6lbt+f/umf0t7ePql6M00kEuHd7373mPY333yTv//7v590ve9973v8n//zfw7G0o4KX/jCF1iwYMGYds/zuOyyy7jvvvumVb9SqfDaa68dcNxMe0042I6G56nP5+PUU08d0/7EE0/w8ssvH9R9ydS4rnvgQQeQz+f56Ec/ym9+85sxfQsWLJj087FSqfDxj3983NeK1atX8/Of/5xgMDil9R5un/rUp7j22mvr9qVSKc477zy+/e1vU6lU6o6ZikqlQrlcPvDAAziaX0cjkQif/OQn6/Y98sgjddvXrl170PZ/MA0MDIx7X5922mmHdzFHyIknnsgf/MEfjGl/6KGH+O1vf8vDDz88pu/ss8/mhBNOmPa+Z+rPwUw53zocv+t1LSMiIiIyWjwcZ1nHsuHtlkgL8VB8/5PKZdi9e/91C4wKXQuPvEy1IBtg+K//fS60jwgLmJ2FNVvHr23nsrTcedv+1ygiIiIichR5MfEi33n6O9zz6j37DVV7re+1UaFqFbdCMrGDZHofyRAkw5iv6X2mvZDEcR0KuUEKPnBCMK8ZlnTCKfPg7BPhs++H9haoVMPUyj4TOja/GZbOhvNPhCUdMLcJ2mPQFoM5jeAb8Q75oA+aGqqhYQXIlsBvjQ78KlaguQl8wdHzmkNmHpigNL9lLiX680D1e7carAbgVMBXLexZ0Bo13/uq+3MtE2Tmlsyb+B0bnBL0pk1AW8RfW5dlmf35GiESAseBviRQrP4BQAX6U2DlwMlAOgU79sDTm+Hl12F3NxT2gFM2YW+tWyHxCrz4CPzm/4NX/h0e2wjdD8DGX8NjP4Xn7oVTf2n2AbBmC8wfHP18mD9o2qdl9Wq4/HI477z6t/e/H9raprkTEREREREREREREREREXm7UqiaiIiIiMgR8NGPfpTly5fX7bvxxhsnVCMYDPKTn/wEv98/pm/jxo2cdtpp/PCHP5xyyEgikeBf//VfOeOMMzjnnHP4j//4j/2O7+np4Stf+QoLFizgT/7kT3jkkUemFF6zfft2LrzwQnp6esb0XXDBBbRN4E2Tp59+Op/73Ofq9j3wwAOsWbOGHTt2HLBOuVzma1/7Gl/4whfq9s+bN2/Cj9dMd8kll9Rtv/HGG/n+978/oRrZbJb//b//N9dddx1e9V3F9QIhjjXRaJQf/OAHdY81m81yySWX8Ld/+7dks9lJ1c1kMtx6662cdNJJfO973zvg+Jn2mnAoHA3P03PPPXdMm+M4fOhDH+JnP/sZ+Xz+oO1LJu+ll17i9NNP54c//OGUHotHH32Ud77zndxzzz1j+gKBAHfeeSfNzc0Truc4Dp/61Kfq1gP44Ac/yN13300oFJr0Wo+k73//+6xevbpuX7lc5i/+4i847bTTuOuuu6YVrlYul/nRj37E0qVL2X2AP5afiKP9dXQyIWnxeJzLLrvsoO7/YHnuuefqti9cuJCWlpbDvJoj55prrhnT5nkeV1xxxfDvr5Guvvrqg7LfmfpzMFPOt+DQ/67XtYyIiIjIWKu6VmGNiDtY2LxwVH8kEOGElhNqtzcGOKEPTugf/7a0F04YgJa8uXVkoakA4TLYLqPSFeIFiAxdvnpw4VZoy7FfLXfdgZ2c/geQiIiIiIgcaelimvUvrcf1Dvz+j7A/TEesY3g7gp9QX6r+4FQSHIeo7fGuWUWuPgu+eC585p1w2cnw8eVww9lwVhe8ZyGsnGeC1JbMhjO6oKsV2hrMzWdBwIZYEDoboSkC4YDZTaUMfSkYKJiQtHQR+jKQKUCuCI5nQtFcF1wfzGowbWBC1OJhqLi17bDPBKMVKlBxTKhapTIixM0DtwxWNWQt6IO2KISq/+zuWVB2oNhvQtQc2wSeOWVTx3VNDTxTf2QYdL4AlCG3D/p3Q2YvUARfdUywCN27ayFwzYXRd/lJe8B6AP77l9CzDQqvQOn35msqYQLhliVGX+8EHbj2Objy93DRa+brtc+Z9ilbvRrWrZtGARERERERERERERERERGR/Rv7l1EiIiIiInLIWZbFTTfdxOWXXz6m74knnuC+++7jwgsvPGCds846i+9///t89rOfHdOXTCZZu3YtN954I5/97Gc599xzOeuss4hEInVr7dy5k02bNrFx40b+8z//kyeffHJKoWi5XI5bb72VW2+9lTlz5vDhD3+Yd7/73bzzne/k5JNPxufzjZlTKBR46qmn+NnPfsZtt91GoVAYM8bv9/P1r399wuv49re/zSOPPMIrr7wypu/BBx9k2bJl/Nmf/Rl/9Ed/xLJly0b1DwwMsGHDBm6++WZ+//vf163v8/n4yU9+QmNj44TXNJP98R//Md/61rfo6+sb1e55Hp///Of5xS9+wRe/+EXOOeccwuHwqDGvvvoqd999NzfffDNvvvnmqL4vfOELfOc73znk6z/SLrjgAr761a9y0003jekrl8vcdNNNwz+ra9as4V3vehe2PTrn3PM8Xn31VZ555hl++ctf8qtf/WrSoRwz8TXhYDoanqfXXnstf//3fz8m8Ka7u5uPf/zj+Hw+jj/+eJqbm8cNynrssccOylomauPGjVx//fUHHJdK1f+Dh7179/K+971vQvs63MdWz8aNG1m7di1/9md/xgUXXMDFF1/MGWecwdKlSwkEAmPG79q1i/vvv58f/ehHPPzww+PW/cEPfsDZZ589qbWsW7eOu+66a9z+/v5+PvjBD06q5oGsXbt2UuFbU+H3+7n77rv5xCc+wb333lt3zAsvvMAnP/lJZs+ezaWXXsr555/PGWecwXHHHTdu0GGxWOS1115j8+bN/OpXv+I//uM/SCaTB3XtR/Pr6Lve9S5OOeUUXnzxxQOOveKKK8Zd95E2XqjaaaeddphXcmR9/OMf5wtf+AK53OikhnoBgrFYjI9//OMHbd8z9edgppxvHY7f9bqWERERERmtPdbOso5lbE5sBqAl0kI8FCdVNP9WUSgXaIu2EQlEIJeDp1+FCZx2tubh2bkMB6gFHdjdCIUR76TxudA+4rR8dhYufu3Ate1clpY7b6PvuhsmeJQiIiIiIjPTlv4teCOSvUaFq+VykMlAQwNEowAsaFpAT7bHhIHt2kE879HTUJsScKCxBLYFK1syrDyuhK8IuQDD5/E+D06eC0Eb8Mwpe3sUlrSDZUG6AOk8xMK1Txf3gLDf9AME/CZcLDkAeQdCARO+1haDfYMQ8JlQs0zRvJnewwSe2TZEg6bBBrDAB1jVdfhts5+8A0XHbFcqEKz+c7DtglsAN1g75uaw2X+hbPZZLkOwAn1piLWYfc6Jgd8HxRKULLBsyBVMfcsyYWs9fRDohWSvCWGzfTA7BsU54A9C3zbMov0QqphwaM+DzADEnoHY3XCyDx4+k+HroEXJ2jotD87pHvscsIATJ5oZfdtt0NVVv8+yYPlymMCHKYqIiIiIiIiIiIiIiIiITIdC1UREREREjpDLLruMFStWsGnTpjF9N95444RC1cD8UX8ul+N//I//MeYP+8GEwQwFD/j9fjo6OmhpaaGxsZFMJkMymWRgYIBsNjut46ln7969wwFrAMFgkFmzZtHS0kJTUxPFYpGBgQF27NhxwHCFG2+8kdNPP33C+25sbOTee+/l7LPPpqenZ0x/Op3mm9/8Jt/85jeJx+PMnTuXaDTKnj172Lt37wHX873vfY9zzz13wuuZ6RobG/m7v/s7/viP/7hu/29+8xt+85vfEAwGWbRoEfF4nMHBQfbu3cvAwEDdOWeeeSbf+ta33hahagBf/epX6evr4+abb67bv3fvXr7xjW/wjW98g1AoREdHBx0dHXiex8DAAIlE4qD8HM7k14TpOhqepyeeeCKf+cxnuP322+v2O47D1q1bD8q+DpZUKsXjjz8+5fnFYnFa84+UXC7H3Xffzd133w2Y31GdnZ00NzcTiUTIZrPs2rVr3OfOEJ/Pxy233MJVV1016TUUi8X99o8XLjUdBzukbTzhcJgNGzbwl3/5l/zzP/9z3dcjgJ6enlHnCqFQiPnz59PU1EQ4HKZYLJLJZMhkMhP6/TzSpz/9aVauXDnptR/Nr6PXXHMNN9xw4D/YP9TBetOxcePGuu2TOQ88FjQ2NnL55Zdzxx13HHDs5ZdfTkNDwwHHTcZM/TmYCedbh+N3va5lREREpuf1fRm+es+Bw4YB/vbDp3BC+8E9l5JDY1XXKl5IvDAc5rCweSGbesy/rXt4dKe6WdK2BH77iEkbmIBYGTqykKg+BSIVcC2GwwUA4gXTXt0RF26FttxbK9XXctcdDFy5Fre5dWITRETk7al3C/zHBEM41/wDtJ14aNcjIm8rqUKKbQPbhgOL69k2sI3tye3D21v6tpAtZWDPHujdZ9LILKCtHTo7wbJIFpIUSjnI74NmyPnBHZF+5g/AxafC7KYkRRd8PnMu7lbPxRc0QyzEcJRb0A+zYrU3vTdHoMEPFSBbNpcAfh80hmqn844LpTK0NsHeEf8sHLahKVQLX7NdGCxCMAB+v9lnNAheBZOmhtmv5ZrjdC1oiUI+bfZrYdpK1esGvwuODekcRMImPC5SDXuLhcy6KgUIxMELQsWBhqgJkLNccz+5LqT7oFgxtQD27INkD3RmasfSNgjzd8BDb8ArT4DPD8VW2NoBixOwtx8y/eCW4bpngSK0Ayt64Pk5Yx/rFT0Tv96py7JgzRqYPXsaRUREREREREREREREREREpk+haiIiIiIiR4hlWdx0001cdtllY/qeffZZfvnLX3LxxRdPqNb111/P/PnzWbt2LclkctxxlUqF3bt3s3v37qkue1pKpRJ79uxhz549k5r35S9/ma985SuT3t+JJ57Igw8+yB/+4R/yxhtvjDsulUqRSo3/JuGRAoEA3/ve9/jsZz876fXMdJ/73OfYuHEj3//+98cdUyqVeOWVVw5Y65RTTuFXv/oV4XD4YC5xxvvud7/L7NmzufHGG/cbZlEsFtm5cyc7d+48JOs4Wl4TpuJoeJ7ecsstvPTSSzzzzDMHta4cWqVSie7ubrq763z8+jhaW1u5/fbbJ/z7+u3G7/fzj//4j5x//vl8/vOfZ/v27QecUywWef3116e13/e973383d/9HWefffaUaxytr6Of/vSn+au/+ivK5fK4Y0455RTOPPPMw7iqyRkvTPC00047zCs58q6++uoJhapdc801h2T/M/XnYCacbx2O3/W6lhEREZm6TKHCY1t7JzxWjg7tsXaWdSxjc2IzAC2RFuKh+HD4Q0+mh65gO5Fnfzepul0pSMQAC8o2WCMyhX0utI8IFJidhYtfm3htO5el5c7b6LtugkE5IiLy9lQchG0PT3ysiMhB8mLiRTa8vAHX238ocaFSGBWqVnErJBM7IJ2E0IiB6X3gK0M8juM6FHKD4IOAD+Y1QiwGPhtaI/CB401wWKUaVFaxIWLBrCaIR2DpbDPWwYxpCoHjMJyyVnYgn4FYHPqzUHLMmEwR/DaEA1ComPN7z4ZZDZDKm7m2B/GwqQ3g88BnQX8eOhrNeiyvmtVcDVULWVAsgBUBz4KgD9qips/CtBVLYDuAbYLQ3BL0lWFBqzkWr7p814VwEAhCBBjMQK4AswIQjUIZSA5COA9hC7JB05/oh47C6MemKwGxh6DYD5n2amMCSnuhkISh4csTo8PS1myB3ijsaqq1zR807dNywQUKVBMRERERERERERERERGRGcE+8BARERERETlULr30Uk4//fS6fV/96lfxPK9uXz2XXXYZmzZt4iMf+cjBWt6wxYsXc+211+53TGNj40Hf79y5c/m///f/8vWvf33KNU4++WSeffZZPvaxj017PSeddBIPPfTQMR1C8C//8i986Utfwhr6WOYpuOSSS3jiiSdob28/8OBj0N/8zd/w0EMPsXTp0oNWs6mp6cCD3uJIvyYcSjP9eRqNRnnssce48cYbaWlpOej1ZWb48Ic/zO9//3sFqk3AhRdeyCuvvMK3vvUt5syp87H3B4HP5+PSSy/lgQce4NFHH51WoNqQo/F1tKOjg4suumi/Y9auXXvQ93uwZDIZtmyp/xc7450zH8ve//73s3Dhwv2OOf744znnnHMO2Rpm6s/BkT7fOly/63UtIyIiIjLaqq5VWNT+PWhh88Lh7z08up/6z2rywcTFytCRNd+nQuD3TJgaQLwAkaHcPQ8u3Do6iGAiWu66AzvZP7lJIiIiIiKHWLqYZv1L6w8YqAYQ9ofpiHUMb0fwE+ob54MeUklwHKK2x7tmFbn6LPjiufCZd8JlJ8PHl8MNZ8NZXfCehbByHsxvhiWz4fSFcHwrLGiBoN8EkQWqIWzRADSGTVhavgT7BsDxwLahvcEEqYX8Jgyt4kIyB+mCCTvzLFMrFgQ888Z5H2BXw9MszP4KFag4tUPxXHCrb9nxu+BloDLiLTyxILQ1mNoARdeEm5Wrlyx+G1qiUC5DuWL27blQLtVquB7ki+YapDIIe1+FV18wgWi2CwEXKEP3bgiWzfWJ50G6H9z7IPb/Ao/CqjdGB0QvSta+tzw45y2fKRV04Nrn4Mrfw0Wvma/XPmfap2z1ali3bhoFREREREREREREREREREQOHoWqiYiIiIgcYTfddFPd9o0bN/KLX/xiUrWOO+44NmzYwDPPPMOVV15JJBKZ8rqWLl3K9ddfz2OPPcaWLVu4/vrr9zv+5ptv5qWXXuLb3/425513HtFodMr7Xrx4MV//+td59dVX+eQnPznlOkNmzZrFunXrePDBB/ngBz846SCmhQsX8p3vfIff//73/MEf/MG01zOT+Xw+vvnNb3Lfffdx5plnTmruqaeeys9+9jPuueeeKYWAHUtWrVrFpk2b+OEPf8jKlSunVKOlpYWrr76aJ554gq997WtTqnEkXxMOpaPheRoIBPjbv/1b9uzZw4YNG/jzP/9zzj//fE444QTa29sJh8OHbN8yvuOOO44vf/nLnHbaaVMK5QsEAnzkIx/h8ccf5+6772bevHmHYJXHplAoxF/+5V/S3d3NT37yEy666CJCodC0a5533nncfPPN7Nixg1/84hecd955B2nFxtH4Orq/0LRAIMCnP/3pQ7Lfg2HTpk24dQIQZs+eTWdn5xFY0ZFlWRaf+cxn9jvmqquumlbI6ETM1J+DI32+dbh+1+taRkRERKSmPdbOso5lw9stkRbiobjZKJfp2bOVvH/ydbtSULEgEzTb4YoJNWgfEaA2OwsXvzb52nYuS8udt01+ooiIiIjIIbSlfwsetRQu13P3e1vQtAAPD8/z8HbtIJ43s4dufgda8jArDx9oyfBnywb48CKYHzPBYH7XBHad3A5BuxZm1h6F9y2E0+ZCS9iMa6yGn4F5k3vQZ763MCFqQT9EfWD5TZvPgraYGWdh5hYd6M+YoLOhowz6TZjaUC2/BbZn+n0+CPvMPDwzwOeHbM6Em9mAZZuaQ0FrAdscSywEDSEIBSAYM/uMhKAxBpFqcFuxBNk8JPuhUDDhbY4LyUFzy+6EXS/Bvu3g7IL+l2DHZtj5Ijx2JzzwNHQ/ABt/DY/9FJ67F079JVA0a2nPwYqe+o/1ip764dAWcGI/nLnbfN3vvzyvXw8PPFD/9uCDsG8f3HcfvM3fJyIiIiIiIiIiIiIiIiIiM4fleZ534GEiB0djYyOZTGZUW0NDA+l0+gitSETk2PPKK69Q79e7ZVksWbLkCKxIRI6kXC7H/fffzxNPPMHzzz/P9u3b6enpIZvN4nkeDQ0NNDY20traykknncSSJUs45ZRTWLVq1bSDKyqVCps2beLpp5/m5ZdfZuvWrbzxxhsMDAyQyWQoFovEYjHi8TizZs3ilFNO4bTTTuPss8/mrLPOOkj3QH07duzgl7/8JY8//jgvv/wyO3fuJJ1O4zgO0WiU2bNns3jxYs466ywuuOAC3vve9x7ysIqZ6rHHHuM///M/eeSRR9ixYwd9fX3k83lisRjt7e284x3v4KyzzuJDH/rQuI/b3r17x7RFIhHi8fihXv6M8corr3Dffffx1FNP8dprrw0/5yqVCrFYjIaGBjo7O1myZAknn3wy5557Lu9617vw+XwHLj4JR/I14VDS81SmYt++fTz55JM888wzvPTSS2zbto09e/aQyWQoFArDj/8JJ5zAySefzDnnnMP5559PW1vbkV76MSOTyfDUU0/x7LPPsnnzZrZv386uXbsYHBwkn89TqVQIhUJEo1Ha29uZO3cuJ5xwwvA5w7ve9a5pB7NN1rH6OioyGTP152CmnG8darqWOXT0b2oiIseOTTuTfPiWxyc09p7r/oAVC5oP7YLkoNqX3cf3nv3ecADEQH6ATT2bYNs22LmTOWlY0jf5uv91PLw54p+BImUTpAaAB5/ZBNc8P7U1O41NvP7A70wChBxTigsajvQSBFjeMftIL0Fket78b/jXCX5YxP/zIMx756Fdj4i8LWzu2cyGlzcMb2/cs5FUMQXlMpRKEAxCIDBqzr7sPrKFQXhzF3iQDoIzdIrrwXEB+MPl0BGvtZV84Fb/eXJBMyyYVasX8sPsGIzMRXbKYPsg44DrmrCyxrAJTgPIlsCrgOVCqmz6hoLdgj4TVuYBg0UYzEIgCB0N1U8g9yAADP0ToO2BizkGz4KSA/kKNIVMwJjnQF8fzIuZu6I/C8kyEIZZDdAcNqf4nmVq5vMMJ7g5jglfi1rgWFDGhKeFs5ALmH3mCrClG4Jl6BzxtuqO6nXI60V4+RHIDcK2ZliUrI1Z3gOXvzz6MS354McrYNeIXLP5g3DVJhNoN2WrV5vANBEREREREREREREROSYpA0ZEjlUKVZPDSr9QRUQOPf0BqIiIiIiIiIiIyOTo39RERI4dClU79m14aQObE5uHtzd2P0Xqt78B18Py4Kw3IVKZeL28Hx5bAG82ARaEKqNDDWZn4Jb/gLbc1Ne8/Wf3UTp+8dQLyIykULWZQaFqctRTqJqIHESpQoptA9tMQNp+ZEoZNry0YTisOF0c5PUXH4PefSYYzALa2qGzE6of2lBxK/T2vAG5HAEftDZBuAF8NrRG4LxFQDUMzeeaEn4/NMchHoGls81YB6i4JrzMccCq/pNc2YFyDiIx2JM1IWdtMYgGwO8zoWm5sqkNYNkmuAwAD2JhyBbNZr4IiZwJPJvbBAG7Os+tZR0HK5B3wQvX7hfbNusHE9CW2QdNDsRmwb4iFIpQ9kFrI7RGzbo8q7r2Yq3OYAaKOWjzg+eHfWXwVyBWhrINKZ8JVCuWzPXGyOuXM9+A8pPw3cqI4xvB8uC6Z+tfn3jA1lZIhqG5AIv7zeMwZatXw7p10NR04LEiIiIiIiIiIiIiInJUUgaMiByr/AceIiIiIiIiIiIiIiIiIiIiIiJy5K3qWsULiReGAyAWbu1lUzVtwLOgOw5L+iZerzsOAQ8aypAJmvCBYR5c9Nr0AtU8y8Jpap56ARERERGRCXgx8SIbXt6A67kTGp8tZ0lkE2Zjxw7yA/sohkYMSO8DXxnicQBCVFjRlmPpfJjdaMK6HBvCAVjZCQGfCfzKFqE3C3G/CUVzbRN4Fqy+Y93GBKoFfUAAKhUYyMLAIDQGTFhaRwPsTZsxNibgLFUw30cDpo7fgrAfCiXweSaALOiDcgVC1bAzgKJjQtVsoOIBHvg9s50vQCBowt4AYgFwgUIZ8iXT5jrQl4aSBcEAzGowYWrFkmmzbMgVwG+b/DnXhZ4+CPRCshc8F8qd0NkAXgjsEuzqgWLZBDpHKuB5kBmA2DMQuxsowqlL4Pk5Yx+3FT3jX59YwIn9E3jw16+Hlpbx+y0Lli+HtrYJFBMRERERERERERERERERmXkUqiYiIiIiIiIiIiIiIiIiIiIiR4X2WDvLOpaxObEZcjlanv498VmQCpv+ngboSplwggPJ+814gNYc+J3R85b2widfnN56c+9+H84shRGIiIiIyKGTLqZZ/9L64eDhieiKd5lQtXIZurtptmvnxgABBxp6Utjt81k5v8DKaC++IuQCmOQxTJjZyXMhaAOeCfVqj8KSdpPLlc9BOg+NQdOPZcLMgj4z3wJs2wSuRX0mgA3AZ5lANp9VG+e6kMxBoMmEmjkuRIMwdPruOhDwg1MGfBD2QcEx8xjavQXZHMwKmUNwLMimoaXJrMlnmX3FQiYgzh+HQAm8wIhxNliuWavrQroPihUTMAewZx8ke6AzU7svW7dCIgyZJOzcDKkgbO2AxQnY2w+ZfnDLcN2zQNHMWbMFeqOwq6lWZ/6gaZ+W1avh8sunWUREREREREREREREREREZGZTqJqIiIiIiIiIiIiIiIiIiIiIHDVWda3ihcQLeE8+AeUyC5OwaY7p8yzojsOSvgPX6Y6b8WDCHVZ1m+2CHyJl+MpvIehMfZ3Z95zNnm9+d+oFREREREQmYEv/llGBaq7n1jpzOchkoKEBotHh5kggQnu0ncSLT4HrEnYhVDHnwgBlG+YEPD6w4E1mzY3Bm1lwTThZqRqKNq8FGgMM7znkh/YY2NVz7KYIxAKABZmyCSFrCIHr1QLTChXwW9DaBKmSabOAkA/8tglPs6i+4d2D/jx0NILnmXoBH5Qr4FQgFDDzPQtao7A7XQtKcy0zp5QxgWheyKzbKUJfGhbOqu3b9QAXIiEgBP6KCWTrS8KsgLkby0ByEMJ5CFuQDUKuAIl+6CiMfnxO2gO/2wb/vRuGHprSXigkYWjo8gS05Wpzgg5c+xxsbYVkGJoLsLjfrG/KVq+GdeumU0FERERERERERERERERE5KigUDUREREREREREREREREREREROWq05y2W9dlsfvppAFoKEC9AKmz6exqgKwWRyvg18n4zbki8AK3VRAPP52PeB6+h8OFz2DmVBVpQXPwO3ObWqcwWERERERmWKqTYNrCNVDE17phtA9vYntw+vL2lbwvZUgb27IHefSY9zALa2qGz0ySEAZVSgd7cLmgz8yoWFKrBZJ1NcNapgH+AvsE0vmA13MwP7XGIR2DpbPDZ4AAVF5pC4DoMp6xVHCgUIeKHdAFKjhlvAX4fBH1mng/wbGiM1I7J50IgYELVACI+E4xWqJi6fhsqFQjYJuAMD9wyWD7zfdAHbVET9AYmaK3sQLgC6RSEOsBxTLhyUxCKRSBo1uWMOAaAfKG2psog7N0JPS7EmyDqqwbElaF7NwTL5jrE8yAzALFnIHY3nOyDh89kOBVtUbJW3/LgnO6xj6sFnNg/7sM+2m23QVdX/T7LguXLoa1tgsVERERERERERERERERERI5uClUTERERERERERERERERERGRY0JTJMD5J8+e8Fg5MiYSDFGX58Gtt8LPf47PLtO9xIQjAPhdSIZrQ5+dC8cNjl9qTwOUbfBXwxIWJmt9dsXh3K1l8p9/z+TWJyIiMlnhZnjHRRMfKyJvKy8mXmTDyxtwPXe/4wqVwqhQtYpbIZnYAekkhEYMTO8DXxnicbOdHMCxgCDMbjBBaa4foiG48CQTQlZ2wHIqEIQTW6CjAVwbYmEIVt+FbmMC1YI+IGDCzgaykElCOASWbebtTZsxNiYYLVUw30erl2YWEA5AoWTaPdeML1fD08J+yDtQHBGqFqwen+2CWwA3WDvc5jCEAlAom3C2chmCHuQsyA4CHrQ1Q9iCClAsQd4DJw1OyATIAaQykMtDqB+SveCUwbGhH8gAtg82bYbnQ7A4AXv7IdNvQt6uexYoQjuwogeenzP28VvRA225/T7E+2dZsGYNzJ7YtbCIiIiIiIiIiIiIiIiIyLFOoWoiIiIiIiIiIiIiIiIiIiJyTDi+Lca/XnXGkV6G7MdEgyHqevxxePp+mGc2swFINNS6834oVt8JkwyZ7wN1dlO24c1G8317DuamoaVQ61+WgMXPrmfbFdfhNrdOfp0iIiITNesEuOKnR3oVIjIDpYtp1r+0Hg/vgGPD/jAdsQ4S2QQAEfyE+lIUfXUGp5LQ0EDI57G0eZAlp0C82eRygQktXjEPGkLgAdki5IrQFjNBZrhge9AwIrzMxoSfWUPbNoR8UAqYADYAn2Vq+KqDLMB1IZmDQBP4q/Ntq/bmdqcCobAJMXMtaIlCPm3mWZi2UsWM9bsm6Cydg0jY1In4zXHFQiZUrVKAQBwi1bVncmC55rhcFxwLMgMQzEE6aOrt2Qc9fRCqQGemdszz0ubr60V4+RHIDUKpGQpJGLq0WJ4YHZa2Zgv0RmFXU61t/qBpn5YLLlCgmoiIiIiIiIiIiIiIiIjICApVExEREREREREREREREREREZFDbjLBEGPkcvDII6OaulKQiDGc3tBcgJ6hkDULUiFoy48tlQrV5uyLwhm7a32WB+d0g53L0nLnbfRdd8Pk1yoiIiIiMk1b+reMOm8eFUqcy0EmAw0NEI0CsKBpAT3ZHpOEtmsH8bxXOzcGAg40lkzY2MqWDCuPKxGopn8VHKhUw8/mt0BjEPDMKXNbFGa3m750weza55kgtWzJhJE1hMDzasFshQr4LWhpglTJtFmYoDW/bQLOLKpvYvegPw8djaZGxYGAD8oV0+c5Zr5nmeC2tmitnmdBsQS2A9gmBM0tQV8ZFrSCzzZ3h4dZZzgIBCECDGbAcSDXC3YEAlHozUOoev0QrsA+FxL9Zrt5RAgzQFcCyk/C+gq41YdpUbLWP3RdMVLQgWufg62tkAybmov7a2F0U7J6NaxbN50KIiIiIiIiIiIiIiIiIiLHHIWqiYiIiIiIiIiIiIiIiIiIiMght99giAN58nGolEclDkQq0J6rBqthgg9CFShU3w2TCUJTEQIjdlO2TfuQkAPuiJrLEtCWM9+33HUHA1euxW1unfg6RUREREQOIFVIsW1gG6liatwx2wa2sT25fXh7S98WsqUM7NkDvftMUpgFtLVDZydYFslCkkIpB/l90Aw5P7jVsDQ88Afg4lNhdlOSogsVvynhWiZUzWdDZ0t1uGUC2NpiJizNBpojEAa27YRSNcQMTFCaDfh8Jvis4oDPMutrjNSOyedCIGBC1QAiPrPvQsXM8dvmlD8QGHFH5MEb8W73WBAiQRO65rhQdMHuB197bS0tESiXwQf4fSasrVyq1XA9yBfNevwO9O2EgT0QbAVf3ISs2SXYu8fMDVXMtYfnQWYAYs9A7G6gCKcugefnjH38VvTUritGsoAT+8d71N9i/XpoaanfZ1mwfDm0tU2wmIiIiIiIiIiIiIiIiIjI24dC1URERERERERERERERERERETkkAuYLiltAAEAAElEQVTYgVHbm/Zu2m+QxLByGXY/BV1ju0o27G5kOGytbEN2RGhaLgDRyohtvwl/aKyGKjQXwK7mvFkenNNdG2vnsrTceRt9191w4DWKiIiIiEzAi4kX2fDyhgMGDBcqhVGhahW3QjKxA9JJCI0YmN4HvjLE4ziuQyE3CD4I+GBeI8RiJiytNQIfOB5cFyouUA1Si1owtwHCEZgTh/YGEx5WdCHsNyFlnmduhRJk0jC7w8wtOCZ8LWib0/GKA6kC+DwTfIbZDeGAmWsDnmuC18oVCNhmH3kHitVQNQsoO2au7QIepPMQiZiQt4Bt9hcMmWPBBStm1huPVvdZXW+xBEWgmAfPBr/fZJGl0pAchFA/JHvBKZt5J2yGFzpMsNu2/4Z+C7Z2wOIE7O2HTD+4ZbjuWUxhYM0W6I3CrqbaQzJ/0LRPy+rVcPnl0ywiIiIiIiIiIiIiIiIiIvL2pFA1ERERERERERERERERERERETnkjosfh4WFh0kxW9i8kE09mw48cdfOamLCWEEXYuVakFrABZ9rgtMASj4IOSbYwbHMdqwamhCqQKQM8YLZXpaAttzo+i133cHAlWtxm1sne7giIiIiIqOki2nWv7R++Hx4f8L+MB2xDhLZBAAR/IT6UhR9dQanktDQQNTncfKsIkvnw+xGE1Dm2CbUbGWnCVqzPMgWoTcLDSGYFQV/dTmtURPABmZsY9h8X3YgXYCBQfPGc8sP7VHoSUPJNWFnYPbnupDKQkcc/D7TZlu1N6w7FQiFTZCZa0FLFPJpM8/CtLkVsx2rrt+rQG8aZjVAY9CM8zABaX4LaAQfkMmB65mgOMeCMiY8LZw1YcuODbkCbOmGYBk6M7W7sCNrrgWatsP9GyE3aNpLe6GQhOolA8vfcs0QdODa52BrKyTDJrR5cf9w5vPUrF4N69ZNp4KIiIiIiIiIiIiIiIiIyNuaQtVERERERERERERERERERERE5JCLh+Ms61jG5sRmAFoiLcRDcVLF1PiTymXY9eb+6xYgG2A4uSBcqYWsYUHRB9GK+erzTPAamMCDjiyEHRMucU732Np2LkvLnbfRd90NkztYEREREZG32NK/ZVSgmuvVDw4esqBpAT3ZHpMgtmsH8bxHT0OtP+BAY8mElq1sybDyuBK+ogkQo1ra58HJcyFoA545ZW6PwpJ2E0qWLkAuZ8YFRwS2BX21YLCgz4SsRXwmXMwP2DbMisG+FHjVQ7KAoNkN/XnoaDR9FcfML1dMp+eY8Z5lardFa/O9amJaNgnRJnD8ZtupQKUatjYU4lZxqjuryuahmAO/Hzw/9JbBXzHHFq5Aygfdu82amguM0pUAHoIFGyH/ToYPflGyNma8awYLOLG//mM4xm23QVdX/T7LguXLoa1tgsVERERERERERERERERERKQehaqJiIiIiIiIiIiIiIiIiIiIyGGxqmsVLyReGA6TWNi8kE09m4b7I4EIcxvm1ib87lno3X/YBECkAv2R2nafCyVf7RaobsfKpj9UgUgZFlbz3JYloC1Xv3bLXXcwcOVa3ObWSR2riIiIiMhIATswanvT3k21gOFyGUolCAYhUBvXm+0lWxiE9C5ogEwQHLva6UE0Dh9aDh3xJI4Djg9cy9wAFjRDLFTLHgv6TRja0BvImyPQ6IfUIOSLUMEEpoWjtXW6ngkia4lDIgOV6ul50ActISg74PeZ0LFwdf+Figk989tQKY86JMib0LMhsSBEgiZ0zXGh6EKgCH1paGgBnw1zmsw+iiUoWWDZkCuY+pZlwtZ6+iDQC8le8Fwod0JnA3ghsEuwqweK5eq1QMUcU2YAYs9A7G6gCO3Aih54fs7Yx29Fz/jXDBNiWbBmDcyePY0iIiIiIiIiIiIiIiIiIiJyIApVExEREREREREREREREREREZHDoj3WzrKOZWxObAagJdJCPBQfDpMolAu0RduIBCKQy8HTr0D5wHVb8/DsXKAaHhF0YHcjFPymLRsAn2fC1QCaC9CRhWjZhD+c0z1+bTuXpeXO2+i77oapH7iIiIiIHNNShRTbBrbVQtLqyJQydCe7hwOG/bafZH4A9uyB3n0m+cwC2tqhsxMsC8uyKAz2gw8CPpjfBOEGEzTWGoHzFgFOLejM50HUguY4xCOwdLYZ62DGNIXAcRhOWSs7kMuA54NMEQoOeBaEbXACEAqA45lluTbMaoCelAkk83nQEIHBIhAE26sGsvkh70CxGqpmVfcDYLtm3+k8RCJgWxCwIWhDMGTC0XDBaoJS0MwJh0y/5Zo1uC6k+6BYqQXM7dkHyR7ozNTu79atkAhDJgk7N0MqCFs7YHEC9vZDph/cMlz3LFCszVuzBXqjsKup1jZ/0LRPywUXKFBNREREREREREREREREROQwUKiaiIiIiIiIiIiIiIiIiIiIiBw2q7pW8ULiheEwiYXNC9nUswkAD4/uVDdL2pbAk09AeQKJakCsbELSEg1mO1IB12I4ZA0LwtVSoQpEyrCwmnexLAFtuf3Xb7nrDgauXIvb3DqJIxURERGRt4MXEy+y4eUNuJ57wLHZcpZENjG8nU/sppjuhdCIQel94CtDPE6ICivaciydD7MbzemtY0M4ACs7TdCa5UG2CL1ZaAhCexSwIBaGYPWd4jYmUC3oAwJQqcBAFgYGwe+CP2rm9aSh5JqwM8c1da0R59SWDfGomR/wmTZ7KAgNcCxoiUI+bcLPLMx5uVsx27Hq+r0K9KZNSFtj0IzzMPvyW0AjRIByBfqSMCsA0ajJW04OQjgPYQuyQcgVINEPHYXR9/VJe+B32+C/d8PQQ1PaC4UkDA1dXudaIOjAtc/B1lZIhk0g8+L+2qXFlKxeDevWTaeCiIiIiIiIiIiIiIiIiIhMkELVREREREREREREREREREREROSwaY+1s6xjGZsTmwFoibQQD8VJFU3KWU+mh65gO5Gnn5lU3a4UJGKABWXbhEsM8bkQqAYpNBdMAFu0bMac033g2nYuS8udt9F33Q2TWpOIiIiIHNvSxTTrX1o/HBh8IF3xrlqoWrlM8+4+eqK1/oADDSWwS0lWLm9lZVMvviLkAkD1fNbnwclzIWgDngn7ao/CknYTSpYuQD5rwspMUpkJVQv6zHwLsG0TuBb1QR7zhnLbhlkx2JcCz6uNzRfA5we/3zREg6Z+pLruSBCcPPgi4FlmP23R2nyvmpiWTUK0CRy/2XYqJpzNrYa4AVSc6pqr8gVzLl8ZhL07oceFeJNZt88CytC9G4JlE6zseZAZgNgzELsbTvbBw2cynIi2KFmrvb9rAQs4sf+ADyfcdht0dY3fb1mwfDm0tU2gmIiIiIiIiIiIiIiIiIiIHAwKVRMRERERERERERERERERERGRw2pV1ypeSLwwHD6xsHkhm3o2AeDh0f27+1lSLk+qZqxswtISDZAKgd8zAQyODeGKGROqQKQMC01+G8sS0JabWP2Wu+5g4Mq1uM2tk1qXiIiIiBy7tvRvGRWo5nrV5LNcDjIZaGiAaC01LRKI0B5tN8Fqu3YQLnmEglCovqO7bMOcEHzgdJjVnIBMFlyTM1aqhqLNa4HGQC17LOSH9lgtmKw5Ao1+s50pm9CyhhC4XjWIDChUwG9BaxPszUKluuygD1pCUHbA7zPBYyEfJPLQ0VjdoQW2Z+rZFoRsyKagEh7OLiMWNGFr5Qo4LhRdCBShLw0NLeCzYU6T2UexBCULLBtyBfDbJovMdaGnDwK9kOwFp2zO7fuBDGD7YNNmeD4EixOwtx8y/eCW4bpngSK0Ayt64Pk5Yx+7FT0Tvxaoy7JgzRqYPXsaRURERERERERERERERERE5GBTqJqIiIiIiIiIiIiIiIiIiIiIHFbtsXaWdSxjc2IzAC2RFuKhOKliCsplet58jS4/RCqTq9uVgt2NkAma7XDFBFQEqiERzQUTvBYtm4CIc7onXtvOZWm58zb6rrthcosSERERkaNOqpBi28A2c366H9sGtrE9uX14e0vfFrLdW6B3n0k9s4C2dujsNCFcQMWt0JtJQH4ftEHFgkLAzO9sgrNOBXzQV8ngC5oSfj+0xyEegaWzTSiZgwlDawqZ4DKq57xlB4oFiMUgXYCSY8ZbmBCzoM/M8wGeDbMaoCcFngc+DxoiMFgEgiY8zQyESjVoDcAXgGwOGmPgd8G2oT8DLU0maC1gQ9CGYMiEo+GC1QSlofP0kOm3XHBtMybdB8WKCU4D2LMPkj3Qmand3/PS5uvrRXj5EcgNQqkZCkkoVMcsf0tw8pot0BuFXU21tvmDpn1aLrhAgWoiIiIiIiIiIiIiIiIiIjOQQtVERERERERERERERERERERE5LBb1bWKFxIv4OEBsLB5IZt6NsGunXiuS3cclvRNrmasbLIrMHkVBFwIFUxQQ6gCkTIsrOZiLHtL2MJEtNx1BwNXrsVtbp3cRBERERE5aryYeJENL2/A9dwDji1UCqNC1Spv7iCZ3gehEYPS+8BXhnh8uMnJZSj7IOCDOQ0QjUFzDC5eYsLN8kXwXMAPJ7ZAR4M5p42FIVh997eNCVQLVoPOKhUYyEJfBpr8YNlm3t60GWNjgtFSBfN9tBrk5rMgHjXzAz7AAntESJtnQWsUig74q+FsPj+kBiEWMIFqlSA4RehNm5C2xmoYnIfJkvNbQCNEgHIF+pIwKwDRKJSB5CCE8xC2IBuEXAES/dAxlJRW1ZWA8pOwvgKuuYxgUbLWXy84OejAtc/B1lZIhk3Q8uL+4UuGqVm9Gtatm04FERERERERERERERERERE5RBSqJiIiIiIiIiIiIiIiIiIiIiKHXXusnWUdy9ic2AxAS6SFuBUmtetNAHoaoCsFkcrEa+aH3gnjAZYJUmsumFrNBejIQrRcP2xhIuxclpY7b6PvuhsmP1lEREREZrx0Mc36l9YPB/8eSNgfpiPWQSKbgHKZSPduQlEovvUd2qkkNDSAz0eICstb8pywAGY3VcPHLFgxDxqGwtg8cF2zXawADtgeNARrJW1qgWoWJtws6IeIz9QDE5jWFjNfh8a5LiRzEGgCv8+0RYOQz0IkasZFguDkwapuB30Q8MPQ3eLZJnOt2A+hVqj4wK5AuWzC2VwX7Oo+K05tHkC+AD4XKoOwdyf0uBBvgqivus4ydO+GYNlcC3geZAYg9gzE7gaKcOoSeH7O2MdjRU/94GQLOLG//mM4xvr10NJSv8+yYPlyaGubYDERERERERERERERERERETncFKomIiIiIiIiIiIiIiIiIiIiIkfEqq5VvJB4YTi0YuHWXja5LmCCILrjsKRv4vW64xDwIFaGbNAEqUUqEC9ApAwLU2bcskT9sIWJaF7/U/o+/0WTWiEiIiIix5Qt/VtGBaq5nnvAOQuaFtCT7YFdO8B1iVdDfYcEHGgsgZ0usvK0OCtj+wgUoOCHSrX8/BZoDDIcPhbyw+yYCSZzPcjlIJc3b/zOFcHxTOCa69XCywoVc4ra1gCDJdNmASEf+G1wXLPtx+ynPw8djSa0zMKEtg3VC9mQTgGRWiBb1G/qF8rVoDTAqkBf2oSqBQMwq8EEtRVLULLAsiFXMPu3LBO21tMHgV5I9oJTBseGfiAD2D7YtBmeD8HiBOzth0w/uGW47lmgaPa7Zgv0RmFXU+1+nj9o2qdl9Wq4/PJpFhERERERERERERERERERkSNJoWoiIiIiIiIiIiIiIiIiIiIickS0x9pZ1rGMzYnNkMvR8vTvic+CVNj09zRAV8oEox1I3l8Lr4gXwLFq81oK0JGFaBksD87pnvqafelBgt3bKB2/eOpFRERERGRGCtiBUdub9m4iVawm85bLUCpBMAiB0eN6B3vIpndBs9nOBE1YGAAeROPwoVP20dGWx8nkcPzgWlCxwWdDZ0t1qGVCzdpiJoTMwoSaxYJQzkMqBelqXV81qMxvQ9BnAtosABsaI7W1+VyzXKca4BbxmX0XKiYczW+DzwO/H7I5aIyB3wUnCJk0tDSZNQ0Fo8VC5nuvEQIh8IJQzEFzAwRtsFxwbROglu6DYqV2X+zZB8ke6MzU1jcvbb6+XoSXH4HcIJSaoZCEQnXM8reEIgcduPY52NoKybAJU17cXz3+qVq9Gtatm04FERERERERERERERERERGZARSqJiIiIiIiIiIiIiIiIiIiIseEYsUhMVic0NiOphAhv+8Qr0gmYlXXKl5IvID35BNQLrMwCZvmmD7Pgu44LOk7cJ3uuBkPEHRNqEImVO30YGE1C2NFz+hAhsnyLAunqXnqBURERIZUipDeO7GxjXPAHzrwOBEZV6qQYtvAtlpIWh2ZUobuZDceHgB+208yPwB79kDvPvAwyV1t7dDZaVLGAKuvl4INAT90NMJxEagETZBZyA8fXARRGyr5LD7PlAj5YE4c5rfA/GZTygWCfgjYUK6A50GhBJkkREIQa4BSDoqOCVKzPBOMliqYmrGACUyzgHDAzLUBzzXjyxVTO+yHvGPq+G0z3u+HZNrUwAY3AE4RetMwp8mEu3mYG55ZDyEIA4MZ6EvCrABEo1AGkoMQzkPYgmwQcgVI9ENHgVG6ElB+EtZXwDV3O4uStf7xQpEt4MT+CT74t90GXV31+ywLli+HtrYJFhMRERERERERERERERERkZlMoWoiIiIiIiIiIiIiIiIiIiJyTHhlT5oP3/L4hMbec90fsGJB86FdkExIe6ydZZUWNj/1NAAtBYgXIBU2/T0N0JWCSGX8Gnm/GTckXoBTe0w422AIOrIQLcP8QVizZXrrzb37fTizFLggIiIHQc8L8K/nTWzs//MgzHvnoV2PyDHsxcSLbHh5A67nHnBstpwlkU0Mb+cTuymme2FkrmF6H/jKEI8TosLSlj5WnwptcRP2BeDYJthsZacJLssVIZHx6M/C8XHoaAAsaI/DUN6zH2gMVkPR/FAoQ0+/afcAy2fm7UmbLLChfbkuJPMQbDS1LMC2am8UdyoQCoNTNqFrLVHIp808i2oQW7VYsR987dUaQLls5pX9tXU6I87NK9XwN58LlUHYuxN6XIg3QdRnwtgoQ/duCJbNeb3nQWYAYs9A7G6gCKcugefnjH08phuKjGXBmjUwe/Y0ioiIiIiIiIiIiIiIiIiIyNFCoWoiIiIiIiIiIiIiIiIiIiIiMi2pQoptA9tIFVOTm+h5cOut+O5dR/dJHl41yMHvQjJcG/bsXDhucPwyO5pMeFqkDH4PFibB58Fpe2AgDBe/ZtoW99eCJ6Yi+56z2fPN706jgoiIiIgcbulimvUvrcfDm9D4rnhXLVStXKZ5dx890Vp/wIGGEtilJCuXt7KyqZdAwQST5UZktvk8OHkuBG2z3RiCjqgJNMuVoJAFy4OgrzYn6Kudr1qA68GcJkilwal2+Cxoi1FNWasFqFku9Oeho9GcZlccCPigXDFjPcfM9yyzn7ZobT+eZcYEHHAd6E3X1tQRMXWKJShZpr9YBr/f5JUNZsz6Qv2Q7DUBbI4N/UAGsH2waTM8H4LFCdjbD5l+cMtw3bNA0exnzRbojcKuptq+D0YoMhdcoEA1EREREREREREREREREZG3EYWqiYiIiIiIiIiIiIiIiIiIiMiUvZh4kQ0vb8D13AMPfqvHH4en74fZkA1AoqHWlfdDsfrOlmTIfB+os4uyDW82Uk2DgEUD0FIwfRZwTjecvzsMP/0pxOPsfG335NdpQXHxO3CbWyc/V0RERESOqC39W0YFqh3ovDUSiNAebTfBart2EC55hIJQqJ6blm2YE4IPnA6zmhOQyUK1pAeUqiFp81qgMcDwnkN+aI+ZALRgBBw/5HLgOFBwwXXBFzFBarZlvlYc8PmgpQn6CrU1hu3Ra476oN+CQsXM8dtQKUMgMGJQHrwR7xyPBSESNKFrjgtFF8IVc95drsC+fjhhATQETL9rm9rFHsj6THCa48JLW8Ffgs5Mrfa8aijb60V4+RHIDUKpGQpJGDqM5Qloy9XmBB249jnY2moClpsL0w9FZvVqWLduOhVEREREREREREREREREROQoo1A1EREREREREREREREREREREZmSdDHN+pfWjwqpmLBcDh55ZHizKwWJGMOpCc0F6BkKWbMgFYK2/NgyqVBtDpYJZyv6IOSA5ZlQNQoFePZZ+Lu/Ix9/ffJrFREREZGjVsAOjNretHcTqWLKbJTLUCpBMDgqgazklNid2gXpXdBsgtSyQdPX2QTvPRX8PkiVMlBtD/ihrRHCUWgMQVcrYEHJgbJj2kayAHzQn4WCA54FUT94Ltg+E6xmYdqxoSlSm2t74A+Y2gABC8J+yDtQrIaqWZj9Atgu4EE6D5GIqR2wIWhDMGQC3XDBagI7CE1+sCzIpiESgGADOBZkkyb8LOyZ+yPRZ4LV2kYEvgF0JaD8JKyvmHA4gEXJEcc+dJ7+FhZwYn+9R7GO226Drq76fZYFy5dDW9sEi4mIiIiIiIiIiIiIiIiIyLFCoWoiIiIiIiIiIiIiIiIiIiIiMiVb+reMClRzPXfik598HCrl4UC0SAXac9VgNSBcgVAFCtV3t2SC0FSEwIhdlG3T7mHKhCoQcaA/Ap0ZWJaAtlx18M03wxe/ONVDFREREZEZKFVIsW1gWy0krY5MKUN3snv4vNVv+0nmB2DPHujdVzuZbGuHzk4TyAWQSlHwQcAHcxpNsFhzDC5aCnhQLJgANL8Ni1qhvdFMdSyIhc08sz+IRCHoMyFnxRLkSzCYgkAE2hugJw0l14SdATgODBQhaEE4CK4FPssEuVUqYANuxQS5VSomeK0lCvm0CUizMHPcitmOAY4NXgV60zCrARqD1dA2zLr9FtAIEaBcAc8z5+TFEuRTsKcALX4IhMw5eTkHif7qOXh1fGYAYs9A7G6gCKcugefnjH1MVvSMOE+fCsuCNWtg9uxpFBERERERERERERERERERkWORQtVEREREREREREREREREREREZEoCdmDU9qa9m/YbaDGsXIbdT0HX6OaSDbsbGQ5aK9uQDdb6cwGIVkZs+6HkB58LjSVoLph22wPLg3O6RxTPZOCf/gkuv3bCxyciIiIiM9eLiRfZ8PKGCQX7ZstZEtnE8HY+sZtiuhdCIwal94GvDPE4ISosbx3khBUwu6kaPmbBinnQMDTHM4FlDSEoVsBzzDjbg4YR57A2JlDNqn61g9CXMqFnALYNs2KwL2WCyaA6FujLQEcc/H7TZzHizd/Vc17LA9c2tduitfleNTEtm4RoEzh+s+1UTBCbOyLEreKYvuH7p2DOsf2OCUrrfxMas5AIg2WD7YPnNsLmGCxOwN5+yPSDW4brngWKps6aLdAbhV1NtdrzB037tFxwgQLVRERERERERERERERERESkLoWqiYiIiIiIiIiIiIiIiIiIiMiUHBc/DgsLr5rAsLB5IZt6Nh144q6dJsXhLYIuxMq1ILWAa8IcHNtsl3wQcsDngWOZbYBwBUIViFQAD+IFWJaAttxbdnDzzdgfuAy3uXVqBywiIiIiM0K6mGb9S+uHz0MPpCveVQtVK5dp3t1HT7TWH3CgoQR2KcnK5a2sbOolUICCHyrV09b5LdAYZDh8LOSH2TETTOZ6kC5AIQuligkpK1ZMYFpTeDgzGICiA22N0JOp1Q76oCUEZQf8PhOUFvaZXfXnoaPRfF+uQNiuhqABlMGhVj8WhEjQjHNcKLoQKEJfGhpawGfDnCazj2IJSpYJScsVwG+DZZnT9J4+CPRCshecsqn9jj7Y3gxv+uH3/wXpPig1QyEJ1Wxjlr/lHDzowLXPwdZWSIZNCPLi/tH3x6StXg3r1k2ngoiIiIiIiIiIiIiIiIiIHMMUqiYiIiIiIiIiIiIiIiIiIiIiUxIPx1nWsYzNic0AtERaiIfipIqp8SeVy7DrzfFrFiAbYDhpIVyphaxhQdEH0Yr5imVC1wKuCWgA6MiacLVzuusUz2RoufM2+q67YbKHKiIiIiIzyJb+LaMC1VxvbGDvSJFAhPZouwlW27WDcMkjFDShaQBlG+aE4AOnw6zmBGSy4ILfgUzIhKPNj4NdDfy1LWiLmRAyC/BZJtAsl4aeHrCCtWBgG7A9CAXN2IpjgsxmNUBPCjzPhAY3RGCwCATNeJ8NQT/kqyFtfp/Zf7li9gvmXDhdhIZquFvAhqANwVA1w9gFqwlK1fPpcMj0Wy64thmT7jMBcEPr3bMPkj3Qmandfx1ZiJZhwSvw7zsgN2jaFyVrYyyv/jm4BZzYf8CH1LjtNujqqt9nWbB8ObS1TbCYiIiIiIiIiIiIiIiIiIi8HSlUTURERERERERERERERERERESmbFXXKl5IvDAcarGweSGbejYN90cCEeY2zK1N+N2z0HuA0IsK9Edq230ulHzm+5LPhF4MbYcrEKqYOXiwMAXLEtCWq1+75a47GLhyLW5z62QPVURERERmiIAdGLW9ae+mWrBvuQylEgSDEKiNKzkldqd2QXoXNJtzyqHw3s4meO+pJrgsVcpAtT3gh444zG+Buc0m18vFhJ0FbCg54LlQKEFfBgIWLOqCig0FBzzLhJg5LmSKJnzNwrTbNsSjUKlAoBoYbA8FoQEe0BKDfBqKDvhtM891TBBbyAbXAsuB3rQJaWsM1uZaFvgtoBEimDC2viTMCkA0CmUgOQjhPIQtc1/kCpDoh47C6Pu7KwE8BI2Pw0knwfNzxj4mK3rGPwefEMuCNWtg9uxpFBERERERERERERERERERkbc7haqJiIiIiIiIiIiIiIiIiIiIyJS1x9pZ1rGMzYnNALREWoiH4sOhFoVygbZoG5FABHI5ePoVk+CwH615eHYuJhECCDrQ0wCOBQU/ZAOmz+dCwIXmauhDRxZiJTine/zadi5Ly5230XfdDdM7cBERERE5JFKFFNsGttVC0urIlDJ0J7uHg339tp9kfgD27IHefdVUMaCtHTo7TWAXQCpFwWdCzOY0QrABmmNw0VLAg2LBhKT5bVjUCu2Npk573ASugXnzdWM1dC3sh0IZdg/U1mb5oT0KPWkouWBXd215kCxAxA9+v1liNAj5LESiZkwkCE4e/BETmBa0oS1qQtmGwtgsCwYGobMBKgGwAadswtncEfurOOaYhuQL5vy5Mgh7d0KPC/EmiPpM2Btl6N4NwbIJLPY8yAxA7BmI3Q0UTZ01W6A3CruaarXnD5r2abngAgWqiYiIiIiIiIiIiIiIiIjItClUTURERERERERERERERERERI4JC9ti3Hb1GRMeKwfPqq5VvJB4YTjUYmHzQjb1bALAw6M71c2StiXw5BNQPkCiGhArm4C0RIPZjlQgVIGBMMNBawDhanukAniwMAXLEtCW23/9lrvuYODKtbjNrVM4WhERkYOk9QT4o3UTHyvyNvBi4kU2vLwB13MPODZbzpLIJoa384ndFNO9EBoxKL0PfGWIxwlRYXnrICesgNlNtZCyFfOgYWiOZ4LJGkJQrIDngA8I+molR35vAa4Hc+KQrIaoAdg2zIrBvpQJJxsaGwD689BRDWuzPLA9U8O2IGRDNgVOCLzqu7xjQQj4Tdib44ID2CXoH4SGFhPQNidqQt+KJShZYNmQK5hwOMsyx9TTB4FeSPaaEDbHhn4gA9g+2LQZng/B4gTs7YdMP7hluO5ZhgPVwAQeX/scbG2FZNgEHC/uH3WaPnmrV8O6Cb4eioiIiIiIiIiIiIiIiIiI7IdC1UREREREREREREREREREROSYEI8EOG/J7CO9jLel9lg7yzqWsTmxGYCWSAvxUJxUMQVAT6aHrmA7kaefmXDNrhQkYgynMzQUoWdEFp7PhYBrQhzAhLDFSnBO94Fr27ksLXfeRt91N0x4PSIiIgddpBlOWn2kVyEyY6SLada/tH44qPdAuuJdtVC1cpnm3X30RGv9AQcaSmCXkqxc3srKpl4CBSj4oVINP5vfAo1BGNplyA+zYybgzPUgXQC3CFQg54LjgS8CAa82puKaELO2BujJ1moHfdASgrJjAs8sz9Qv5KFSbQMTipbNQWMM/C74LRhIQ1Oz2YcFhH1gB0w4Wt6CeBTKQdMXCYHPAssF1zZj0n0mFM6xzT727INkD3RmavfPvLT5+noRXn4EcoNQaoZCEqqn2CwfJ7DYAk7sP8ADFIvB3XebhLnxWBYsXw5tbQcoJiIiIiIiIiIiIiIiIiIiMjEKVRMRERERERERERERERERERGRaVvVtYoXEi8Mh2AsbF7Ipp5NAHh4dP/ufpaUyxOuFyuboLREg9ku+sHngVMNWQtXIFSBSAXwYGEKlo0T+lBPy113MHDlWtzm1gmvSUREREQOnS39W0YFqrmeu9/xkUCE9mi7CVbbtYNwySMUNKFpAGUb5oTgA6fDrOYEZLLggt+BTMhkfc2Pg+2a8DHbgraYyfmyMEFlsSBkSpBMQdoGz4JIABzXhKLZ1ogF2TCrAXpS4Hnm3LUhAoNFIGhC1Xw+E5BWdMBvA5YJVUumIRao5o/5oVSG3rSpF/KbtXiYfr8NVqN5E7gH7OuDVj9Eo1AGkoMQzkPYgmwQcgVI9ENHYdTdR1cCyk/C+ooJhwNYlKz1W97EAovHlc3CvHmwdOk0ioiIiIiIiIiIiIiIiIiIiEyOQtVEREREREREREREREREREREZNraY+0s61jG5sRmAFoiLcRDcVLFFJTL9Lz5Gl3+agjaBHWlIBGDsg8yQROklg2Cz4WAC83VYIiOLMRKkwt9sHNZWu68jb7rbpjEUYqIiIjIoRKwA6O2N+3dNHwuSakEwSAERo8pOSV2p3ZBehc0myC1bND0dTbBe0814WepUgaq7QE/dMRhfgvMbTYhai4Q9EPAhpIDnguFEvRloMkPjc1QzkLerYWuOQ4MFMFnm0A0xzLhZ/EoVCoQ8AGWCW3DNYFsAK1RyFdMDQDPNl+L/RBuhbIf7Ooh96Tg+FkwlDXnAc6I8+lKBWwHKjnYuxN6XIg3QdRn1kIZundDsGzOwz0PMgMQewZidwNFOHUJPD9n7OOxomfigcV1WRa0KsBYREREREREREREREREREQOL4WqiYiIiIiIiIiIiIiIiIiIiMhBsaprFS8kXsCrpj4sbF7Ipp5NsGsnnuvSHYclfROvFyubwLSX2gHLBKn5XBOuFqpUA9o8WJiCZYnJhz603HUHA1euxW1W2IOIiIjIoZIqpNg2sM0EpO1HppShO9k9fC7pt338/+zdeZxcVZ3//9e9tVd1d1Wv2Ui6gQBBO+xuLEkQJIKK6JdxBscfbig6MC7MuIy4oCPBZcAloo4z4jiKCwaNMIiICogom0DIwpIQ0tm70lt1LV1Vt+69vz9OpbuT7k56g4Tk/Xw82u57ls89Vd3goerW+/ZtXAddu0yamAU0NcOsWSawa/AEGYoBE2I2sxbCNZBKwBuOB3woFU1IWtgyAWVNtaZWc9IEroG5oLq2GroWDULRge29Q6ewAtBcAztyJphs9+ltC7rzEK6FYND0xcMwkIdY3IyJhaEyAMGEOQ4HIBoCzzPHLhD0wKpAfy8MREx7yIfauAlRK7gQCILjmmPLMufa1QvlLdDXBa4Drg09QA6wA7BqNTwRgflp2NkDuR7wHLjiEaBkznPBeuiKw9a6ocd7RL9pn5LzzoMZM6ZYREREREREREREREREREREZGIUqiYiIiIiIiIiIiIiIiIiIiIi06I50Ux7Szur06sBqI/Vk7SiZLZuA6CzBloz1TC0cZqRg0fmDB0ni+DZkCqa45Y8JMqwuGPi67ULeepvvonuK/514pNFREREZL/Wptdy61O34vneuMbnnTzpfNocbN7MQO8uSpFhA7K7IOBAMglAhAoLG/o5+kSYUWdy13wLTpwDNbvn+SbArCYMFcccW5hws92G/2wBng8zk9CXBae6dNuG5jhUKhDeHcAWAMuDngFoqTXZbzZg+6aGbUHEhq4cJGIQsM28RAg8THjbQHno3AVgw2aYOxNaGkxQnI8JUCuVoKfHhLABuB489wy0DMuqm5M1358rwVP3QaEfyiko9kF1+8zCvcKIwy689zHY0AB9UbPPnt9jnodJW7oUbrllKhVEREREREREREREREREREQmRaFqIiIiIiIiIiIiIiIiIiIiIjJtFrUuYk16DT4+AG0buljlmSQK34KOJCzoHn+9XQmIO5CvBlc0F6AYrAaz+dCWgfa9giEmIrXiJ3R/8CqTkiEiIiIi0yZbyrJi3YrBfeF4tCZbTaia40BHBynbBPPuFnKhpjOD3XwEJx1R5KR4F6Gi2R9WquFnR9RDbRh2nzYShBkJE3Dm+TBQgLIDVKDggetDIAYhf2hMxQPLhqYaSOdNqJllmfC1YflrhG1Tf6ACFReCAbA9CAYhX4DaBAR88EMmoK2+rlrDMqFliQhYPgSTECpDLASzfbM1LRUhFgEnZM6f74NoeWhfnO6G2vxez18anL/Ciop5HABH9Q31W/7oYcQWcEzPOH9JN90Era2j91kWLFwITU3jLCYiIiIiIiIiIiIiIiIiIjK9FKomIiIiIiIiIiIiIiIiIiIiItOmOdFMe0s7q9OroVCg/qEnSTZCJmr6O2ugNVMNRduPgaAZnyxCPgQR18zbPbclD4ny6MEQ4xXI9hPu2Ej5yPmTLyIiIiIiI6zvWb9HoJrne/udEwvFaI43k177IHgeUQ8iFROaBuDYMDPkc87cbTTOTsC2PHgQdCEXMWFkRyRNsJlrm5C0poTJ+rIwYWaxMHhl6MtA1jbBv7EQuF41FM0atiAbGmpMyFooALYPARvcarflQ30CBrJQciFYrRcMmhC1RAi8IAQCUCpBdxbaGqtzqQafeSY8jQgEKxCqPlavH3I+lOPQX4ZUxQS0BTzIliGThpkVE7iW64XEw5BYCZTghAXwxMyRz++JnZMPIzaLtuCCC2DGjCkUEREREREREREREREREREReeEoVE1EREREREREREREREREREREptWi1kWs2fQw/u9/D45DWx+sqoY6+BZ0JGFB9/7rdCTN+LAHNY4JUBvkQ1tm6sEQvmXh1qUmX0BERERERhWyQ3scr9q5ikwps9955WKB7f3bIGWOHRvyYfPzrDo4/QQIBnvJ9Oeg2h4KQksSjqiH2SmT/eUB4SCEbHCq4WPFMvRnoCYMtXXg5GHAGwpdc13oLZngtEQIXMsEseEBPtjVNfquCXDzLQjb0BQ3oWwWps2qBrMN9ALNpt0G6sImXI2wCXBzXVN3t4Gi+R6oBsVle2HnWugNQksRwjETMPfrZ2HmJtjRA7ke8By44hGgZOZfsB664rC1bqj2Ef2mfUrOO0+BaiIiIiIiIiIiIiIiIiIiclBTqJqIiIiIiIiIiIiIiIiIiIiIjJApZtjYu3FcwRd78H34z/+Eh3/BpqQ3ahhGJmKCJSLu2GVKAdjQANEKBH04sseEU/RHTH9LHo7tnnowROHVZ+I2Nk2tiIiIiIiMMC85DwsLv5oa1pZqY1XnKtPpOFAuQzgMoT3D18I7dpIo+5Rj0FwLdTEoRiAehXOPhYoHXgXskkMgAG2N0FQLWNCcNGFlYC6Srq3uP6NBKDqwvRfCvslIswLQXAM7cmYLuzsIzbagOw/hWggGTZ9tgT0s/AzXtO++EjsRNsFuvmfC1Vwg5JqMs2K/eYhNKYhaUAFKZRjwwc2CGzHnAcjkoDAAkR7o6wLXMaeY3w+ZqAlQe/Ju6HEh1QfVDDYWpvcMGg678N7HzH66LwqpIszvMXvwSVu6FG65ZSoVREREREREREREREREREREXnAKVRMREREREam65ppr+PznPz+i/fnnn6etre3FX9BBrK2tjY6Ojj3aFi9ezL333ntgFiQiIiIiIiIiIiLTam16Lbc+dSue70188gMPwEO/Jx+CTUkGkxsqtgl02G11CzQNjF2mKwa5COBDcwFO3GnCIHpiJnDtvY/DK7dNLRgi/5qz2HHdN6dQQUREROTwNO4AXgs29W4aPHRch3zHeujaBb7pp6kZZs0CyyLslzm2ZgtnHwfxxqGgM9+C9rlQEzbTCiUoFqEpAUF78FSEA0On3v3z7v2i58OsOsj2Q7kakGbb0ByHSsXkuwFEA2B50DMALbXmfDbgVsCuXnkdsWFXFmpTJnDNqs6zQ+B5MGBBMg5OGMJByOZNzaBt+l0Lcr0QLkA2DK4NO3ZBZzdEKjArN/Q4WvLQ2gf3RGHdn6DQD0cNf4p9WLzn2/eDj/uYnn3/egbddBO0to7eZ1mwcCE0KYhYREREREREREREREREREQOfgpVExEREREREREREREREREREZFB2VKWFetW4ONPfHKhAPfdB0DCMQEQ6RrTFauYgIhS9WqVXBiSJQiNktvm2KYfAMsETcQdEwzROAALO+FV3VH45U8gmZzQErc8ux0sKM0/Di/VMPHHKCIiInKYm0gAb76cZ1PfpsHjyrbN9GV3QcQch1yoSe/CDgY46aQaTorvIFQ0fUUfKtVEtLkpE6gGZk/YkIAZLea4UID8gAkv81wY8ExwWSAGYd9kgnk+VDwI2FBfB5158Kt94QAMy2IjbEMkCAMVqLgQDIDtQdmtrjkIAd+ErXVlobHGjA9Y1QA224SnWbXmQm0fKDkQyIEdg1AcugYgUg0YjlZglwfpagBaqrjnc9iahsQ9UOmGQsvI5/jETmgq7PdXMTbLggsugBkzplBERERERERERERERERERETk4KBQNRERERE5LO3cuZOLL774QC9jVFdffTXnn3/+gV6GHESs3bfenqBYLEYymSSVStHU1MTJJ5/Maaedxmte8xqOOeaYaV6liIiIiIiIiIiIHCrW96zfI1BtPGEZg/76AFSq6WfA3H7orBnqThaHHVvQF4GmgZFl+iLgW4NlSJagJwazcmD5sLgDKBbhkUdg2bKJPDwGks9NaLyIiIiIDJloAG8inKAl0UI6nwbHIdaxnUh8KGjXsWFWBM5p66Rxlg3b8+wuHXIhGzFBaHOTYHng2WBb0JwwWWA2UJuAsAWbd0LBA9cye8lYCHwP7ICZg2/asaGhxoSshQJg++Yc1cw0LB/qEzCQhZJrAtJ8C4JB6O2HljrwLNNeKENnBo5sZHDdPuBWhp6DSgVsF4IudG+B3h0QboBA0oSs2WXYucOEvEUqJozY9yHXC4mHIbESKMEFAeiKwta6odpH9MMF6yf5y9ztvPMUqCYiIiIiIiIiIiIiIiIiIocMhaqJiIiIyGGpWCzywAMPHOhljKqzs/OAnv9//ud/ePe73z2i/Z577mHJkiUv/oJk0gYGBhgYGGDnzp0A/PnPfx7se/WrX83ll1/OP/zDPxCNRg/UEkVEREREREREROQgFLJDexyv2rmKTCmz/4mOA9sfhNY9m7vikA8PHefC4Nrm50wE+qMQGJbJ4VqQDZu22vJQsIRdHdOehqZCdfDy5XDVVdDUNLEHKSIiIiKTMpkA3rl1c+nMd8LWzeB5ewTtzkjBuSdCveXjd3UNBaoFoSUJR9RAUwqaak3QWMmDSAgClhnq+1AsQyYPDQ1QscGvmD1ldcuJ60JvCUIWxMImEC1gAR7gD43zXbCrAWphG5ri4Hom6Ne3TIib60K+G6JNpj3kQ23chKgVXAgEwXHNsWWZ9e3qhfIW6OsC1zHnOno1rGkxecQb/wY9Fmxogflp2NkDuR7wHLjiEaBk5oRdeO9jsKEB+qKQKsL8nqEg4klZuhRuuWUqFURERERERERERERERERERA4qClUTERERERE5DD344IM8+OCDXHvttfzwhz/k9NNPP9BLEhERERERERERkYPEvOQ8LKzBsIy2VBurOlftf+LWLeCNDNVIFiEfYjDtIVoZFrJmQSkA8crQ+FLAtEergROpIuCbOpYPizuGFc/l4IYbYNmyCT5KEREREZmMfQbwOg6UyxAOQ2jPcV39neSzWyFljnNhSNbA+adBOQS9Lth+mUAC2hpNiJpVDU5L1oAVMNvJOFBbvW+Y40K2CN05CFpgBaE5DumsCUjbHdBmYS6Y7smZoLZg0ISd2dZQcC8ArmnffXV1ImzC3XzPhKu5QMiFIpDPQMCG5iSEAkMBb6US9PSYsWDmPfcMtAzLKG7Jm5Dguk3w+8eh0G/ayzuh2GfqAywcHibM0GM5pmc/vySARAJWrjQpcaOxLFi4UOHEIiIiIiIiIiIiIiIiIiJyyFGomoiIiIiIyGFsw4YNLFq0iGuvvZZPfOITB3o5IiIiIiIiIiIichBIRpO0t7SzOr0agPpYPclIcigsYzSOA1u3jdoV9iDhDAWphTwIeOBW8x3KAYi4EPDBtcxxwDPjIhWIVUzwRNSF9lGCJVi+HK66SoEQIiIiIlOUKWbY2Ltxn/u+XDlHR1/HYABv0A7SN9ALO3ZA1y6TLmYBTc0wa5YJ7wKs7i6Ktgkpa6mFtjicfxI0xKFUgUIRUiE4dgZEAoN5vNhAODB0/r1/DgUgHIRKGSqeyRBrSEBvnwldC1bHxwLQA/QMmPP71dpuBezq1dQRG3ZloTZlAtcsIBoAO2SygwcsSMbBCZtz9mWhVIRYBJyQCVXL90G0PLT3TXdDbX7P57A1DdwDcx+HgVMZfLBH9Q2NGREmPFH5PMyZA8cfP4UiIiIiIiIiIiIiIiIiIiIiLz0KVRMREREREXmJOu2004hEImP253I5enp62LZtG57njTnOdV0++clPkkqluPzyy1+IpYqIiIiIiIiIiMhLzKLWRaxJrxkMy2hLtbGqc9VgfywUY3bN7KEJjz4CXWO/Djm7H55uAr8aGBEvQ09sqL9imVC1UgCwIOqY9lQR8KEts49giVwObrgBli2b3IMVEREREdam13LrU7fi+WPv6XbLO3nS+fTg8UB6O6VsFwx/+zq7CwIOJJNEqHB8fTdLT4CmpMkQm5uCuY1DwyMBqI9CwYF8GSzXhJ75PgyUoeKbwLRIfChwzfNNOFpTLXRlwamY8eEA1EWg4gBhs48M2yYIbaAClWrYmu1B2TW1QkGzH/UxtRprIBKEgFUNYLMhaINVay6+9jF1vALkfCjHob8MqYqpE/AgW4ZMGmZW15XrhcTDkFgJlKAZOLETnpg58jk+sXOUMOGJsCxoaJhCARERERERERERERERERERkZcmhaqJiIiIyGGpra0N3/enVMOyrFHb77nnHpYsWTKl2iLj8Ytf/IK2trb9juvr6+P+++/n29/+Nr/97W/HHHfFFVfw8pe/nDPPPHMaVykiIiIiIiIiIvLi2ZEZ4McPjpa6NdI7Xt3KrGRs/wMPU82JZtpb2lmdXg1AfayeZCRJppQBoOgUaYo3EQvFoFCAh54GZ981Kzaka4aOXRtKQXAtyIbBdqAcMAEUIQ8iFYhVoCUPcQfa0/sIlli+HK66CpqapuHRi4iIvEgy2+DR749v7GnvheScF3Y9ctjKlrKsWLdiMFB3f1qTrUOhao5Dans3nfGh/pALNWWwy32ctLCBk+q6CBXBs6DgQcCGeUkTauZZYFvQnDChZbGgCVfblYNnN1aDzGabvaRvmfA1z4VICNzdy7VMCNrODDguRG1IxKGrD/z4UFhafQIGslByTV3fgmAQevuhpc6sJWhDoQydGTiyEXY/JT7gVoYeY6UCtgtBF7K9sHMt9AahpQjhGBSD8OtnYeYm2NEDuR7wHLjiEaA0VOeC9dAVh611Q21H9Jv2KTnvPJgxY4pFREREREREREREREREREREXnoUqiYiIiIiInKIS6VSvOlNb+JNb3oTd911F+94xzvo6uoaMc51XT7+8Y/zl7/85QCsUkREREREREREZOrS/SVuvOe5cY0972UzFaq2H4taF7EmvWYwXKMt1caqzlUA+Ph0ZDpY0LQA/voXcPaTqAa0ZiCdAKr3LEkVobMGSgHTlg+Z71FnqB8f2jJg+bB4X3l5uRzccAMsWzbZhysiIvLiy+2E+68f39gFb1Comrxg1ves3yNQzfO9fY6PhWI0x5tNsNrWzUTLPpGwCRIDcGyYGYFzToHGVBpyeaiW9IGGBgha5sAH6uMmaG23sgMVB45ohFzWhJ/ZvgnjtSxwPciXMPtK33y3bEjFoVCEeNy0RUPgDEAoauaGbWiKm/kWpq5lgetCvhuiTaY95ENt3ISoFVwIBE1Ym1sx430fdvVCeQv0dYHrmAuy5/dDJmoC1J68G3pcSPVBsfq4Fo4SEhx24b2PwYYG6IuaPfD8nsEt8+QsXQq33DKVCiIiIiIiIiIiIiIiIiIiIi9ZClUTERERERE5jCxdupR7772X17zmNWSz2RH9f/3rX7njjjt4wxvecABWJyIiIiIiIiIiIgeT5kQz7S3trE6vBqA+Vk8ykiRTygDQmeukNdxM7KGHx1Uv4UBLHtI15jhWgaAL5Uh1gAUBD0IeRCqmvyUPcQfaRwmgGGH5crjqKmhqmsSjFRERETl8hezQHserdq4a3PONpeyW2Z7ZCtmtkDJBavmw6ZtVB6efAMEAZMo5qLaHgtBUC8cdAXURE1AGUBM1QWdlF8oV6M6Z9kQEkjVQscGpmKBdPCBgQsdyZQjZpi4WxMPQkwcvBrYFsSiUusBtAb96xXQibMb7njmnC4RcE3yWz5hwt+YkhAImr833oVSCnh4zFsy8556BlmFPUUseWvvgniis+xMU+uGoYc/XvkKCLeCYnn0+3cZNN0Fr69j9lgULF2o/LCIiIiIiIiIiIiIiIiIihzWFqomIiIiIiBxmXv7yl/Otb32Ld77znaP233rrrQpVExEREREREREREQAWtS5iTXoNPj4Abak2VnWuAsDHp+PR37PAccZdrzUD6QQmOQKw/aGfAaIV8z1VBHxoy+w7gGIPuRzccAMsWzbu9YiIiIgIzEvOw8Iadc+H40C5DOEwhIbC18KBMIlMgbxvAshm10KgDlIJOP9lgA/OgAkvCwSgrdEEqtkWNMQY3ANGAtVgNBuiQdjlmGww3ywF14bmGkhngQpUXHM+gKgNXQPQUltdlAXxEOQLUJuAiA2uC71ZqEuZc1tANAB2CDwPBixIxsEJQzgIfVkoFSEWASdk1pHvg2h5KDQu3Q21+T2fw9Y0JO6BSjcUWkY+xyd2jiMkeF8sCy64AGbMmEIRERERERERERERERERERGRQ59C1UREREQmo6sLdu6EmTN1d1cReUn6x3/8R774xS+yfv36EX133333AViRiIiIiIiIiIiIHIyaE820t7SzOr0agPpYPclIkkwpA45D57ZnaQ1CrDK+egkHWvKQrgHHhnIAAp4Jywh4EPIgUjH1WvIQd6A9PYEAiuXL4aqr9P6NiIiISFWmmGFj70azf9sXCzb1bho8dFyHfMd66NoFu4Nwm5ph1iywLMJ+mRNqujliIbTUmW7fgva5UFMNH8M3oWo1YXAqJqAsFKyOrQ4J2ENL8H2IBGFGCjL9UPbAq5g8sYYE9PVCxQGq9UO2KVRxIVgNWguGzNxoBBKAFYKyA11ZaKwx9QOWOb9tQ9AGq9ZcUO1jankFyPlQjkN/GVIVCPhmv5otQyYNM6uPJ9cLiYchsRIowQUB6IrC1rqhx3VEP1ww8q35iTnvPAWqiYiIiIiIiIiIiIiIiIiIjINC1UREREQmwvfhE5+Ab34TSiWIROBDH4Ivf9lcwSkyTXzf54EHHuD3v/89Dz30EM899xzpdJp8Pk8wGKS2tpa5c+dy/PHHc+aZZ/KmN72JOXPmHOhlT8nWrVv505/+xOrVq3nqqad49tln6e3tpb+/H8dxqK+vp76+nqamJk455RQWLVrEWWedxQxdNDwpgUCAt7zlLXzlK18Z0bd161Y6OjpobW2dlnMVCgXuuOMO7rzzTp544gk6OjrIZrPEYjFaWlqYPXs2S5Ys4fzzz+dVr3oV1gvw71PXdXn88cf561//yrp163jqqafYvHkz/f399Pf3EwqFaGhooKGhgTlz5nDGGWewaNEiXvnKVxKJRKZ9Pbvlcjl+85vf8Mgjj/DEE0/w/PPPD67JdV3i8TjxeJzm5mba2tpobW3lxBNP5NWvfjUve9nLsG17/yeZAM/zeOCBB7jnnnt45JFH2LBhAzt37iSfz2NZFjU1NcydO5eXvexlLFq0iIsuuoiZM2dO6xpEREREREREROTgs6h1EWvSa/Cr0RdtqTZWda6CrVvwPY+OJCzoHn+91gykE5CJABZEK5APm+8AqSLgQ1sGLB8Wd0xgsbkc3HADLFs2gUkiIiIih6a16bXc+tSteL6337H5cp5NfZsGjyvbNtOX3QXVt0tDLtSkd2EHA5x0Ug0nxXcQKkIxCLvzdeemhgWqAZEQzEiAbYHnQ6EAngt+BQY8qPgQCZhgNdsCp7pM24amWujMgVsBx4VwAGqjUBgAPw42JsStIQ4ltxqOBgSqV0bn+yBRB5Uw2BUol6EzA0c2Mpjo5mPqDz7mCtguBF3I9sLOtdAbhJYihGPmsf76WZi5CXb0QK4HPAeueAQomRphF977GGxogL6o2dvO7zFrm7SlS+GWW6ZSQURERERERERERERERERE5LChUDURERGRifiP/4CvfnXouFQyx83N8LGPHbh1ySFjYGCA//zP/+RrX/samzdvHnVMpVKhWCyya9cuHnvsMW6++WauuOIKzj33XD796U9z1llnTeic11xzDZ///Of3O+7ss88ed83W1lY2bdq0zzGe53H//fdzyy238Lvf/Y4NGzbsc3w6nSadTvPMM8/wwAMPsHz5cmzb5q1vfSuf+tSnOPnkk8e9PjHOOuusUUPVAHbu3DnlULVyucx//Md/cMMNN9DdPfITlY7j0N/fz4YNG/jTn/7EF77wBV7xildw/fXXT/jveDR9fX2sXLmSX/7yl/zpT38ikxn7zuuO41AoFNi6dStPPvkkd955JwCNjY18+MMf5p//+Z9JpVJTXtNuzz33HJ///Of55S9/ST6fH3Pc7oC1nTt3snr16j366uvrueiii7jkkkt43eteN6X19PX1ceONN/Ltb3+b7du3jzmup6eHnp4eVq1axU9/+lOuvPJKLrzwQq655hpOOOGEKa1BREREREREREQOXs2JZtpb2lmdNq9R1cfqSVpRMlu3AdBZY4LSYpV9VRmScEy4xKaUOQ55EHXM90jF1GnJQ9yB9jQ0FSa44OXL4aqroKlpghNFREREDh3ZUpYV61YMBuPuTyKcoCXRQjqfBsch1rGdSBxK1SuNHRtmReCctk4aZ9mwPQ++CVvLRkww2twkWB541ZC05oS5R6EFBCyIRmEgC30ZyFvg2hAPQ6kCIZuh5DHf1GisgZ0ZKJQgGoNEHApFKJYgXg17CwcgFGQoKM0GDwiVoL8XSlHTHvKhNm5C1AquCV9zXHNsWeY+i7t6obwF+rrAdcxF1vP7IRM1AWpP3g09LqT6oFhd6sJR9qsWcEzPeJ70BKxcaVLkRmNZsHCh9rUiIiIiIiIiIiIiIiIiIiITMMa78CIiIiIyQlcXjBU89YUvmH6RKbj33ntpb2/nox/96JiBamPxPI/f/e53LFq0iHe84x309va+QKucHvfeey9z585lyZIlfPvb395voNpYPM9jxYoVnHLKKbz73e+mWCzuf5IMmjVr1ph9XVP8d9ozzzzDKaecwtVXXz1qoNpYHnnkERYtWsQXvvCFKZ3/X/7lX5gxYwbvfve7uf322/cZqLYv3d3dfPazn6W1tZVf/OIXU1oTgO/7XHvttbS3t/OjH/1on4Fq+9Pb28sPfvADLrnkkimt6Yc//CHHHHMMn/70p/cZqDYa13X51a9+xSmnnMInPvEJKpVxfmpWRERERERERERecha1LsLCgkIB0mnant4JngeAb0FHcoIF98r2mJkz31NF09eWAcuHxR2TWGwuBzfcMImJIiIiIoeO9T3r9whU83xvv19z6+bi4+Nv3YzveSSLZtvmAy0pOHcJNNT5+F1d+L5pjwRgXhJOORLm1MOcFMyug3n1EAtXA8uAgTJ0ZkzoWX0KwkGz3/N9E0LmepApQqFsjn3LZI2l4iZUDd8EtcUiUMxAZdh+Mh6Emmqwm19tD/hQADZtN9vW5iTUVgPifN/cR7ErDV29sKsHOrthw9NQ3GEC1cAE/R7bDbluePw3kO2Go/qGzjvp/epu+TzMmQOvfe3oX2efrUA1ERERERERERERERERERGRCQoe6AWIiIiIvGRcf725mHE0uz+cs2zZi7smOWQsX76cj370o7iuO+VaN998Mw8//DB33HEHxxxzzDSsbvpt2rRpwuFN+/M///M/rFu3jl//+tfMnDlzWmsfqurq6sbsm2wIGcBDDz3E61//evr6+iZd43Of+xwDAwNcd911k5r/t7/9jXK5POnz762/v5+///u/Z/Xq1ZMOfPN9n8svv5z/+q//mrZ1TUWxWOR973sfP/7xj6dcy3VdvvKVr/Doo4/y61//mpqammlYoYiIiIiIiIiIvBAyxQwbezeSKU3wNUDfh9/9jk3PPDgYpuY0QD5crRsx4ReRcbzM7fvQnYCEY+ZHKpAqmeCMWMWEV8QdaE9DU2Fiyxz0ne/AF79okjhEREREDkMhO7TH8aqdq8a1B+zq7ySf3Qopc5wLQ7IGzj8NyiHodcH2ywQS0NYITbWAZULL7OpVySFMyBlANAhFB7ZX7w3nemAFoLkGOrPguBCubtkiNmzPQrgWgkETxhYPw0AO8gWoTUAsCuUC9GWhvs4ErQVtE96WiJif/VoIRUyo2+yAqV0qmkA2J2T2o/k+iJaH9rPpbqjd67Kg1jQk7oFKNxRaRj5XJ3ZOYb8KZtENDVMoICIiIiIiIiIiIiIiIiIiIntTqJqIiIjIeHR1wfLl+x6zfDlcdZXuECsT9vWvf52PfvSj+xxj2zYtLS3Mnj2bgYEBtm3bRn9//5jj169fz5IlS7j//vs56qijpnvJL7hwOExTUxPJZJK6ujoGBgbo6+tj27Zt+wyee/jhh3nLW97C/fffTzCo/9zZn30FpyWTyUnVfPrpp7ngggtGDVSrra1lxowZNDQ00NfXx9atWykUxr7C/Etf+hLnnHMO55577qTWsi91dXXU19eTSqWIRqNkMhm6urro6uoac47v+/z7v/87LS0tXHnllRM+51e/+tX9BqoFAgGOOOIIUqkUsViMQqFAf38/XV1d5HK5CZ9zLAMDA7zpTW/iD3/4wz7HhUIhZs6cSWNjI5Zl0dnZyY4dO/B9f9Txf/zjH3njG9/I3XffTSgUGnWMiIiIiIiIiIgcOGvTa7n1qVvxfG/ikx94gPyjf2HTbEx6GlCxoS86NGR1CzQN7L9UV8yEc6SKgF/9DtRXj9syYPmwuGPiyxzU1wfPPAPHHz+FIiIiIiIvXfOS87Cw8DHv7bWl2ljVucp0Og6UyxAOw17v6yW7s+Q9CAWhuRZqauC8k6E+DqUKDBShNgTHzIBw9W15CwgHhmoM/xnA82FmErpzUCqbUDPbhsYEFMuYFDYgFIC4DT0D0FINa7N8SIQhPwDRiAlgc21wS9CVhZl1ELBMAJuP+Z9YBIhAFOjPmfN5/ZDzoRyH/jKkKhDwIeBBtgyZNMysmLG5Xkg8DImVQAkuCEBXFLYOu2/ZEf1wwfop/pLOOw9mzJhiERERERERERERERERERERERlOKQMiIiIi43H99ZDP73tMLgc33ADLlr04a5JDwp133slVV101Zv/ChQu58sorufjii2nY6+7Eq1at4ic/+Qk33ngj+VH+Prdv386b3/xmHnzwQRKJxJjnmDdvHmecccbgcTqdZv36kVf+tre3jztoa9asWeMat9uMGTO48MILWbx4MSeeeCILFiwYNRQtn8/z+OOP8/Of/5wf//jHowZ3Pfjgg3zqU5/iK1/5yoTWcDjasWPHmH1NkwiILBQKXHzxxfT09Ay21dXV8c///M9cdNFFnHrqqViWNdjnOA733Xcf1113HX/84x9Hrfn+97+fdevWEY1GR+0fr1NPPZULL7yQ0047jRNPPJE5c+aMOm7btm38+c9/5qabbuLuu+8eNTzsX//1XznjjDM4+eSTx33+zZs38/nPf37UvoaGBj7wgQ9w4YUXcvLJJxMOh0cd19HRwerVq/nTn/7E7bffztNPPz3u8w/n+z7veMc7xgxUi0ajvPvd7+atb30rZ5xxBrFYbI/+zs5OfvOb3/CVr3xl1DXcd999XHPNNVx77bWTWp+IiIiIiIiIyFQEAxYz6iLjHns4yZayrFi3YjBUY0IKBbjvPhIOtOQhXWOaYxWIVKBUfTk3F4ZkCUL7yGxzbDMOywSyNRZMnd1a8hB34MROaBr7ngz7Z1mw1+vqIiIiBx07BLXjfG/V1g1tZEimmGFj70YypbFvpAWABZt6Nw0eOq5DvmM9dO0yCWQW0NQMs2aBZRH2yxxbs50lL4f6lOk+oh7mNA6VDAehIQoDZRgomRCyUBBcD4oueB4EYhDywbZMf8UDy4bmGtiVA8c1dcIBE7jm+2b7ZvkmvG1rDiouBKvhbKHqfrMvC811EAyCXzHZcK4DTnBorDtsb1mphqQFPAi6kO2FnWuhNwgtRQjHoBiEXz8LMzfBjh7I9YDnwBWPAKXqY3bhvY/Bhgazh00VYX7PYNbw5CxdCrfcMpUKIiIiIiIiIiIiIiIiIiIiMgrLH+0T4iIvkNraWnK53B5tNTU1ZLPZA7QiEZFDz9NPPz1qAIxlWSxYsOAArOgQ0NUFbW37D1UDc3ve55+HSYQRyUvP8HCo4e655x6WLFmy3/k9PT0sWLCAXbt2jVr7c5/7HJ/61KcIhfZ9cfxzzz3HpZdeyl/+8pdR+//pn/6JG2+8cb/r2e1//ud/ePe73z2ifbyPa7znuOyyy7j44ou58sorOf3007Fte0I1uru7+chHPsKPf/zjEX2WZfHss88yf/78CdW85pprRg2+ev7552lra5tQrek01t/aVNf1iU98YszwuU2bNtHa2jrm3La2Njo6OvZZ/+KLL+Y73/nOuALavvzlL/PJT35y1L6bb76Zt7/97futMdySJUt46qmn+NCHPsSll17K3LlzJzQf4M9//jPvec97Rg0ZPOuss/jTn/407lqf//znueaaa0a0L126lJ/97GekUqkJr+/pp59m+fLl3HHHHWzatGnc87761a/y8Y9/fNS+t7zlLXznO99hxjjuhl6pVPjyl7/MZz7zmRF7D9u2uffeeznrrLPGvS4RERERkQNJr6mJiMjh4LEdj3HbM7cNHnv+PpLP9vbHP8AD5jXofAgenT3UNRCEzpqh45oSNA2MXaorBvlhuXev3ArFEPRHAB9euR2O7YZLV5nwiklbuhR++9tRu57923NTKCxilObW7H+QvOAWtuz/9WwRkUPN2vRabn3q1nHt5/LlPI9sf2TweGDzc3T2bh08DrlQUwZ79kxOOqmGk+I7CBXzeBYUQhCw4bRWsG3wLBOSNqsOgrYJQPN86M3Ajl3QXA/lgBk3u86EnAVtM6dc3dfZHrg+ZIuQiptjfCg4EI2aY8+GXBl8CxIhsAEc6OqFoAdW9R5VTgXqayBSvT+XZYHnQskxoWuWBX39Zl6kB5wuE8AGkCxCJgpOCZ68G1a5cFTf0PO2sBP+31OT/AUlErBypXnSRmNZsHChri0SERERERERERERERGRA04ZMCJyqAoe6AWIiIiIHPSuv358gWoAuRzccAMsW/bCrkkOCZ/97GdHDVQDWL58OVdcccW46hx99NHcfffdnHPOOTz44IMj+r/73e9y2WWXcfLJJ09pvdPpVa96FRs2bJhSIFhjYyM/+tGPmDNnDl/+8pf36PN9n29961t8/etfn9pCD2Ge5/GrX/1q1L45c+bsM1BtPP7pn/6Jb33rW2MGwu3tE5/4BJs3b+bb3/72iL7vf//7Ew5V++xnP8vpp59ONBqd0LzhzjzzTP7yl79w9tlns2bNmj367r//fp544glOOumkcdVauXLliLajjz6aW2+9lUQiMan1LViwgBtvvHHE3/++bNiwgc9+9rOj9n3uc58bNfhtLMFgkKuvvpo5c+aMCGL0PI8PfehDPP744+OuJyIiIiIiIiIiL6yQvecNPFbtXEWmlNn/RMeB7Q/CsJcMu+KQDw8d58LgVjMjMhHoj0JglFv8uZYJYauphllEKtA4AC1d0BODI/rhrU/B/B4Y3yuLY1i6FG65ZSoVRERERA5K2VKWFetW4DO++yknwglaEi2k82lwHGId24nEoVS9etixYVYEzmnrpHGWDdvz7C7tA40NELLMgQ/Ux01Q2m5lB4oezEqZ8DTHByyTG4YPrgu9JRPOFg+ZwLWABeEA+F41MM0Cr2IC0Sx797pNKJvvg+eBP+ycTgUyWZg7E2pC4FaD2CoulDohHzB7U9eDdRsgWIZZwz4H0pKH1j64Jwrr/gSFfjhq2HNm+bB43/cX27d8HubMgeOPn0IRERERERERERERERERERERmawxboMmIiIiIgB0dcHy5RObs3y5mSeyDzt27OC///u/R+278sorxx2otls8HueOO+5g5syZI/o8z+Pf//3fJ7XOF8rxxx8/pUC14b70pS+xaNGiEe0/+MEPKBQK03KOQ9HNN9/M+vXrR+173eteN6XaZ555Jt/85jfHHai227XXXksqlRrRfs8999DX1zehWq997WunFKi2W1NTEytWrCAUCo3ou/HGG8dVw/d91q1bN6L9/e9//6QD1YarqakZ99hPf/rTFIvFEe3ve9/7JhSoNty73vUuPvjBD45of+KJJ7jvvvsmVVNERERERERERKbfvOQ8rGFRZW2ptvFN3LrFJFkMkyzC8ByPaGVYpwWlwOilSgGIuEPHqQFTywKaCvDex+GYHrAuuQT+8IeJf/3xj7BrF/z2t1BXN77HJyIiIvISsr5n/R6Bap7v7fdrbt1cfHz8rZvxPY9k0WzlfKAlBecugYY6H7+rC78anhYMwrw6ePkRMDNpQtPmpqC5BsJBs38ru9BdDSsLRaA+Ve3zTRiahfmyLejKg+OaBh+IhaE4MPS4YmHI98HuraIFhAJQEzFfkTAk45BIQnM9zGiCfBbK/RBwzfnyfWC7Q3vTdLcJVkvt9fZoaxoSd0HlJhOotrcTO83edNIsCxoaplBAREREREREREREREREREREpiJ4oBcgIiIiclC7/npzB9mJyOXghhtg2bIXZk1ySPj+979PqVQa0T5z5kyuvfbaSdVsaGjgK1/5CpdeeumIvttuu43t27cze/bsSdU+2C1btowzzzxzj7b+/n4efvhhlixZcmAWdRB7+umnufLKK8fs/3//7/9NurZt2/zgBz8gEBjjU5P7kEqluPjii0cEDvq+z+OPP87ZZ5896XVNxXHHHcc73/nOEeu6++67xzW/p6eHcrk8or29vX1a1jdeGzdu5JZbbhnRPnfuXL75zW9OqfZXv/pVfvazn9Hb27tH+ze+8Q0WL148pdoiIiIiIiIiIjI9ktEk7S3trE6vBqA+Vk8ykiRTyow9yXFg67YRzWEPEg7kw+Y45EHAA7d6a79yNTwtMCx4zbXAs8xYgEgFWjMQrSZntKeHhVfcfjt885vQ1DSFRywiIiJy6AnZe94MatXOVfvez1V19XeSz26FlDnOhSFZA+efBuUQ9Lpg+2UCCWhrhKZaE4aWTMDuXN5IwASdhYBoEHblTH6Y75vwMitgQtc6syZALVzdG0YDJmitdwBaak2omg3ggVOBUBAiNvQ70JWFxhoI2BCqnte2IRCAaHxwKbjV8LRSGQYysKMI9UET7hbywClAusfsOWMVs8ZcLyQehsRKoAQXBKArCluHZfEe0Q8XjH5vsvE77zyYMWOKRURERERERERERERERERERGSy7AO9ABEREZGDVlcXLF8+ubnLl5v5ImP46U9/Omr7Jz/5Serq6kbtG493vOMdowY1ua475jkPBaeffjozZ84c0f7ggw8egNUc3H7/+9+zaNEi+vtHueU28KpXvYo3vvGNk65/0UUXMX/+/EnPv/DCC0dtf+yxxyZdczq89a1vHdHW0dHBzp079zu3UqmM2j5a0NoL6aabbsL3/RHtn//854lGo1OqnUgk+MAHPjCi/Te/+c2oAZIiIiIiIiIiInJgLGpdhDUYRQFtqbY9+mOhGEfXHz30tTnL0V0eR/cw4uvEndBQgPoB8zUjB1Gn+lWBkAtBd6h2qRq0tltqANqq+R+WD4s7hi1k9w1sRERERGQP85LzRt/POY65aaDjjDov2Z0Fz4SizU7BqS3wT2fAvHqoi4IfgNoYnDgXmutMWFooyLAzmaCz3TwfIkGYkYJwEErV4DLbhsYEuMOWEQqYELZiBSouYIHtm/p9WfC8ahhvGCpl2JkxYWs2mAQ2wHOH1uIDvmP2mrke2PoUxNdA+mnYvBq2rIW//ATufww6/gCP3wl//gk8dhuccDtQffsy7MJ7H4N/fBLe8Kz5/t7HTPukLV0Ko9zoSkRERERERERERERERERERF48wQO9ABEREZGD1vXXmwtOJ2P3h32WLZveNckhYcuWLaxbt25EezAY5O1vf/uUaluWxaWXXsrHP/7xEX133XUX//Iv/zKl+gcry7J4+ctfPiLg6qGHHjpAKzq4ZDIZ7r//fr7zne9w5513jhqsBWDbNl/60pemdK53vOMdU5p/wgknjNq+cePGKdWdqoULF47a/tBDD/HmN795n3MbGxtHbf/tb3/LRRddNNWljdsvf/nLEW3xeJy///u/n5b6b37zm7nuuuv2aCuVSvztb3/j9NNPn5ZziIiIiIiIiIjI1DQnmmlvaWd1ejUA9bF6kpEkmZJJNys6RZriTcRCMSgU4KGnYfRcDgAqNqRrho5dG0rBoT4fCACuBZ4FIc/0RSrQmoF4tXZ7GpoKexVfvhyuugqamqb8uEVEREQOdpliho29Gwf3ZftkwabeTeZn38fZuJ5813az+bKApmaYNcskowFhv8yxNdtZ8nKoT5khR9TDnGFvY4aD0BCFYgkKZROQFrTBdaHogutDJGCC1SwL3Oq+zrahuQZ25cBxTZ1wwISu+b4Za/lQH4eBLJRcU9e3IBg09bO9EEhBIADBCpRtE7JWKEIgCNhQLg0+XAoDkNsMvV1D4W3HdcOmFGwLwpN3Q7Ybyiko9kGx+hgXjrLntIBjesbxC0okYOVK84BH/Z1YsHCh9q4iIiIiIiIiIiIiIiIiIiIHAYWqiYiIiIymq8t8WGcq9GEfGcOf//znUdtf+9rX0tzcPOX6l1xyyaihag888ACe52GPdZHvS9yMGTNGtG3atOnFX8iL6O/+7u+IRCJj9ufzeXp6eti6dSue5+233te+9jWWLFkypTWdccYZU5rf2tpKKBTC2esO6v39/VOqO1Wj/X3B+P7GgsEgCxYs4Omnn96j/fvf/z5vfvObOf/886djifu0detWnnrqqRHtS5cuJR6PT8s5TjnlFGKxGAMDA3u0/+Uvf1GomoiIiIiIiIjIQWRR6yLWpNfgY26+0JZqY1XnKgB8fDoyHSxoWgB//Qs4+0hUwwSjpROYNAogVYTOashasXpFSrwCpQBE3KF5qQFoq+aFWD4s7hiluG5gIyIiIoeJtem13PrUrXj+/t/TBciX82zq22QONm+msmM7fcOCbkN9u6gJBLFntXDSzDwnxXcQKpqQ2wImGG1OCmzftNkWNCdM2Fk0CKkY9PbD9p0mmKxiQSUA8XA1FM0azGszbGisgWzRhKrZPsSjUChBNGqGhAPQFDdhbBYmVG13DbsE6X7wPLCBlrgZ7/tQcSBfgmx26HTp7RDeMXTckjdhvXOfhv/bDIXqW8tH9Q2NGXPPOV75PMyZA8cfP4UiIiIiIiIiIiIiIiIiIiIi8mJQqJqIiIjIaK6/3lwQORX6sI+M4cknnxy1/VWvetW01D/iiCOYPXs227dv36O9UCiwYcMGjj322Gk5zwvB8zw2bNjA6tWrefLJJ9m+fTvZbJb+/n5yudw+g8HWr18/oq2vr+8FXO2B9+ijj05LHcuy+NznPseHPvShKdWZNWsWLS0tU15PbW0tPT173g48kxnHHdnHIZ/Ps2bNGlavXs26devo6ekZ/BsrFov4vj+heuP9G1u6dOmIULVKpcIb3/hGLrvsMq666iqOO+64CZ17Isb6WznllFOm7RyhUIjZs2fz3HPP7dE+1r/zRERERERERETkwGhONNPe0s7q9GoA6mP1JCNJMiXzGlxnrpPWcDOxhx7eb62EY0Is0tUQj1gFIhUohKAcMG0hz4R1hKov70YqJowtXs1ra09DU2GME+gGNiIiInKIy5ayrFi3YjDwdjwS4QQtiRbSfdugo4OYZ/ZYpeoVwY4NszI7OOe1MRrjZdiaZ3d5H2hsgJBlDnygPm4C1XYrVsAtw8w6E7hWsUwome+bOZ4PuepeLh4yAWm2XQ1CqwajYYFXAc8FO7B73RAKmjGuBy4QcsEJgOtCuhfmNEOtbfqgOq6XwaudXQ/8zj2fj9Y0cA/UPgDHHgtPzBz5nJ3YuY8953hYFjQ0TKGAiIiIiIiIiIiIiIiIiIiIvFgUqiYiIiKyt64u8yGd6aAP+8goNmzYMGr7ySefPG3nOPnkk0eEqgE899xzB2Wo2sMPP8zNN9/Mz3/+czo7O/c/YZwO9VC16XDkkUfygx/8gMWLF0+5VmNj4zSsCGpqakaEquVyuUnXGxgYYOXKlfzkJz/hrrvuwnGcqS5x0Hj/xj70oQ9x4403UqlU9mj3PI/vfe97fO973+MVr3gFb37zmznnnHM47bTTCAan7z/Z16xZM2r7ggULpu0cYP4G9g5V6+7untZziIiIiIiIiIjI1C1qXcSa9JrB8I62VBurOlcB4OPT8ejvWTDO19FaM5BOAJY5ThWhNzp0nA+Z8LXdUgPQVr2HguXD4o59FNcNbEREROQQt75n/R6Bap4/9o3GhptbN5fO1Q9C9cZkySJ0VoNuZ6Tg3BOhvtKF3+8PBqqFgjCvDtqOgNqoyQnDgngEXB+cCpRd6M5B0IJYAOqS4OTBr/aFquFrERu2ZWFWbTUoDYiFoViAUMyMiYWhrx+SKRPOZgHRANghs+wBC5JxcMKQDJo6XhkKneDWQjAKxSyEK1CywbWhNw2Rkgl4y/VC4mFIrARK5pwXrIeuOGytG3qujug37VNy3nkwY8YUi4iIiIiIiIiIiIiIiIiIiMiLQaFqIiIiInu7/nrI56enlj7sI6PYsWPHqO1HHXXUtJ1j/vz5o7aPFrR2ID333HN8+MMf5o477nhB6vf3978gdQ8Fr3jFK3j/+9/P29/+duLx+LTUTKVS01LHsqwRbZ43vg8P7G3FihVcddVVbNmyZarLGtV4/8aOOuooPve5z/GZz3xmzDGPPPIIjzzyCJ/+9KdJJBK84hWv4DWveQ2LFy/mzDPPJJFITHqdYz3+v/u7v5t0zfHq7e19wc8hIiIiIiIiIiIT05xopr2lndXp1QDUx+pJRpJkShlwHDq3PUtrEGKV/RTCBKa15CFdDfEIeuANe4kv4EOo+vJepGJC2OLVkLX2NDQV9nMC3cBGREREDmEhO7TH8aqdq8yebH8ch66ereRTQ025MCRr4PzToByC3nI/NhBIQFsjNNWacLPksEDcSABCAQgB0SDsypmwNQsoVwAbmmsgnYWKA1Z1uaGACV3rHYCWWlPP8gHPhLOFghC2oVKBriw01kAkCAHLBLDZNgRtsGrNhcw+UHEhXjGBaZVeeKwLUl2QCEMlBpt82H4nhLsh1wOeA1c8wmCgGkDYhfc+BhsaoC9qAn/n9ww+3MlZuhRuuWUqFURERERERERERERERERERORFpFA1ERERkeG6usyHc6aTPuwjexkrYKiurm7U9skYq1ZfX9+0nWOqbrnlFt75zndSLBZfsHNMNojrUBGNRkkmkySTSZqbmznppJM47bTTeM1rXsNxxx037ecLBALTXnOyHMfh//v//j9+/vOfv6DncV133GOvvvpq0uk0y8fx/zP5fJ57772Xe++9l+uuu45QKMSrX/1qLrnkEt72trfR2Ng4oXWm0+kJjZ9OPT09B+zcIiIiIiIiIiIytkWti1iTXoOPD0Bbqo1Vnatg6xZ8z6MjCQu6x1erNQPpajhHJgIRFyrVlwujw4LZUgPQVs0IsXxY3DGO4rqBjYiIiBzC5iXnYWGN3JMBOA6UyxAOQ2jP8DW2biE54FOOQHMd1MWAELz2BKiJQK4MrgfJCBwzA8LVq4VDQRMw5lfLBOyhkr5vgs9mpCDXBwMuOK6Z25CATAb8mAldA2iIwbacCUMLBobq92WhMQlBC8IByJehMwNHNg6d2AfcYfvESgVsF4Iu5HqhZxvUYY5LDjg98NcHoGULDFTnLBwjoNcCjtnfW5SJBKxcadLdxmJZsHChrvcRERERERERERERERERERF5iVGomoiIiMhw118P+fz01tSHfWQvpVJp1PZkMjlt5xir1gsZYDYRP/vZz3jHO94xoUAqGen555+nra3tQC/joOM4Dm9729tYuXLlgV7KHizL4pvf/Cann346H/vYx9i6deu45zqOw/3338/999/Phz/8Yd7znvdw9dVXM3fu3HHNLxRG+TTBi+Rg+feOiIiIiIiIiIjsqTnRTHtLO6vTqwGoj9WTtKJktm4DoLPGhKXFKvuqYiQcaMnDtjrIhSHkQaB6z4tQ9XukYurFHXPcPkYIxmi8b3yTjee8BS/VMJGHKCIiInJAZYoZNvZuJFPK7HugBZt6Nw0eOq5DvmM9dO0y6WMW0NQMs2aBZRH2yxxbs4XjXgs1DeBWA82OqIc51Xsz+ZhQtfoIlMomMA0gaIPrQtEF14dIwASr2RY41X2bbUF9HZTzUCiZULVwAGrCUCxBLGoCckNBaIpDyTV1sSAYNPX7+6AuBYEAhFyojZsQtYILgaAJa3MrJrfM92FXL5S3QF8XuNX94ilpWNMCTgmevBtahgX+jjugdyz5PMyZA8cfP4UiIiIiIiIiIiIiIiIiIiIicjBSqJqIiIjIbl1dsHz5C1N7+XK46irdvVb2yd99FfNBVmu6dXR08J73vGfMQLVIJMKSJUt4zWteQ3t7O62trcyYMYO6ujri8TjBYBBr962vh3nXu97FD3/4wxd6+fIS8LWvfW2fgWqzZ8/mda97HaeddhrHHnssc+bMoampidraWsLhMMHg6P+pPNrf3WT8wz/8AxdddBE/+MEPuOmmm3j00UcnNN9xHP7zP/+T//3f/+Ub3/gG73vf+/Y7p1IZxydfRURERERERETksLOodRFrNj2Mn8tCTQ1tG7pY5Zk0Dd+CjiQs6N5PkarWDKxrxoR+ANG9XpJKDUBbNU9koiEYdiFP/c030X3Fv45/koiIiMgBtDa9llufuhXP9/Y7Nl/Os6lv0+BxZdtm+rK7IGKOQy7UpHdhBwOcdFINJ8V3EKre18jzoFANRpuTAtsHz4KAZULWgjb4HhQK0N0H23pgoN6McW2Ih6FcGQpFo/rNC0BjDXT2Q50PQR8ScRN+Fg5DsDo2EYZgwASjeR74dvUxVKA7Y+o2JyEUMEFvvg+lEvT0wO4rBlwPnnsGWoZlz7XkTQBv6nn43RNQ6N/zOTuxc/wBvaOyLGhQYK+IiIiIiIiIiIiIiIiIiMihSKFqIiIiIrtdf725E+0LIZeDG26AZctemPrykhKJREZt7+/vp7GxcVrO0d/fP2p7NBqdlvpTcdVVVzEwMDCiPRAIcPXVV/ORj3yE+vr6Cdd1HGc6licvcdu3b+ff//3fR+078sgj+cY3vsEb3vAGbNueUN3p/vuKRqN88IMf5IMf/CDPPfccv//977nnnnu4//772b59+7hqDAwM8P73v59NmzZx7bXX7vd8o2lvbyeZTE54/RMxa9asF7S+iIiIiIiIiMjhLlPMsLF3I5lSZv+Dh/N9+M//hEdWsKnOBdsCH5x6yIertSMmVCMy+j0yBkUqEN3rJbSasvleCpr+1gzEq2Pa0xMPwaj/+Y/o/cf34KUUfiEiIiIHt2wpy4p1K/AZ383QEuEELYkW0vk0OA6xju1E4mYfBeDYMCsC57R10jjLhu15hpf2gcYGCFnmwAfq49WgNMw2LxgCywWvbMJzLUzQre+bfDHXg5xj2uMhM8a2IRmDXAEaYoAFsTD0ZyCZgt05bKEAhAMmVK1cgWQcCEA4CNkClIoQi4ATMufL90G0PLTnTHdD7V6X67SmgXvgZQ/CowuhUDfUd0Q/XLB+Ar+Q0Zx3HsyYMcUiIiIiIiIiIiIiIiIiIiIicjBSqJqIiIgIQFcXLF/+wp5j+XK46ipoanphzyMHvbECw8YKQpuMsWpNJqxsOm3fvp1f/epXI9qDwSC/+c1veN3rXjfp2j09PVNZmhwifvjDH5LL5Ua0n3zyyfz+97+nYZJ3G38h/76OPvpojj76aC6//HIAnn/+ee6//37uv/9+/vCHP/D888/vc/6yZcs4+eSTufjii8ccM1Zw2rJly3jTm940+cWLiIiIiIiIiMgBtTa9llufuhXP9yY++YEH4KHfkw/BpiQmVQOo2NA3LKN/dQs0jbxPxgjdMQh6mBQPC1JF095ZA6kBaKtmvlk+LO6Y+HLtQp76m2+i+4p/nfhkERERkRfR+p71ewSqjWevNrduLp35Tti6GTyPZNHsowBmpODcE6He8vG7ugYD1UJBaEhCNAZz5kC0GlrmeFAbA8c1Q8sudOcgEoOZCSAITgVsHyoVCFfDzSI27OyHUB0Eg2ZuPGzmOiFzvlgUij3QlYXGGgjY1TA3TAhbIADRuAlbA3BdCBQg50M5Dv1lSFUg4EPAg2wZMmmYWTFrz/VC4mFIrARKEAbe+xhsaDB71FQR5vcM1Z+UpUvhllumUkFEREREREREREREREREREQOYgpVExEREQG4/nrI5/c/bipyObjhBli27IU9jxz0Zs+ePWr7xo0bOfHEE6flHBs2bBi1fdasWdNSf7Juv/12fH/k3bg//vGPTylQDRSqJsZtt902oi0QCHDzzTdPOlANXty/ryOPPJIjjzySSy+9FIA1a9Zw8803c9NNN5FOp0ed88lPfpI3v/nNhEKhUfvnzp07ant3d/f0LFpERERERERERF502VKWFetW7BHYMW6FAtx3HwAJB1rykK6GdsQqEKlAqXpFSS4MyRKE9pEF4tiQrYZxRCvgW6YOQLIIrRmIO+a4PQ1NhYkvGSC14id0f/Aqk9ghIiIicpAK2Xu+Z7dq5yoypcx+53X1d5LPboWUOc6FIVkD558G5RD0umD7ZQIJaGuEplqwLBMwFosA1Z/rAhAJmZA1x4VduWoOmwUVC2bWwK4suBVwHUxyGRC2IRaAngFoqa3W8yERhr4sNCbNGN+GShl2Zsw4O2IC0bDAc4cCz3zAdyDoQrYXdq6F3iC0FCEcg2IQfv0szNwEO3og1wOeA1c8ApSGnhcLOGY8b9decglcdtnY/ZYFCxfqZogiIiIiIiIiIiIiIiIiIiKHOF1lKiIiItLVBcuXvzjnWr7cnE8Oa/Pnzx+1/fHHH5+2czzxxBOjth9zzDHTdo7JuK/6Ib3hLMviAx/4wJTquq7LU089NaUa8tJXLBZ5+OGHR7QvWbKE448/fkq1V69ePaX5U9He3s51113Hc889x/ve975Rxzz33HP86U9/GrPGcccdN2r71q1bp2WNIiIiIiIiIiLy4lvfs36PQDXP98b/9dcH8CoOngWeBXP7q6EX1a9kcdixBX2RPfv3/spUQzywIOxC/cDQOusHoK2aIWL5sLhj8o85kO0n3LFx8gVEREREXgTzkvOwBqPFoC3VNtTpOOamf44zYl6yOwsehAIwOwWntsA/nQHz6qEuCn4AamNw4lxorjMZYQChIMPOBoFhVwYHbWiIQygEHmbv5nrQkDChueWBaiAaYPuQikOxAhV3qEYoCK4Lvf1mbDAIQc/8HPChUISyA+UKlEumfsWFbA5ym2HrOujaDMEyzO8B3zMBao/dDD0PQvFpyKRNyNtUAni5805YsgRe+9rRv84+W4FqIiIiIiIiIiIiIiIiIiIih4HggV6AiIiIyAF3/fXmgtUXQy4HN9wAy5a9OOeTg9IJJ5wwavtoYVCTsW3bNrZt2zaivaamhqOPPnpazjFZO3bsGNHW2trK3Llzp1R39erVZLPZKdWQl77Ozk48zxvRftZZZ0259l/+8pcp15iqmpoavve979HZ2cltt902ov8Pf/gD55xzzqhzX/nKV47avq8gNhERERERERGRl6I12zJcetP4Xmv93/e8kvY5yRd4RS+ckB3a43jVzlVkSpn9T3Qc2P4gtO7Z3BWHfHjoOBcGtxrIkYlAf9SEZuwt6EJlWHBH4wDMyMPOGnPckod4NTPkxM4phGQAvmXh1qUmX0BERORgs2MV/Ogt4xv7//0KZp34wq5H9itTzLCxd+P+910WbOrdNHjouA75jvXQtcskm1lAUzPMmgWWRdgvc2zNdpa8HOpTpvuIepjTOFQyHISGKAyUYaA0FIbmeebYAWwb4qGhkDXHMyFrzbXQ7Zvgs0LJhKfVRiE/AMUSxCNmfDAITXEouSaQDcu0Afhl2NVvAtZsoCUO4YBZR8WBfAmGv22f3g7hYZcItOShtQ/uicK6P0GhH44a/pRNMYCXvj545hmY4g23RERERERERERERERERERE5KVNoWoiIiJyeOvqguXLX9xzLl8OV12lu98exsYKePrjH/9Id3c3jY2No/aP189+9rNR28844wwsyxq1b7hIJDJqe7lcntK6ANLp9Ii2mTNnTrnu7bffPuUa8tI32t8XTP1vzPd97rjjjinVmE4f+9jHRg1Ve/7558ec8/KXv5xZs2aNCDZ84IEHGBgYIBaLTfs6RUREREREREQOBNfz6cmP77VM1xslIewlZF5yHhYWPuZxtKXaWNW5av8Tt24xyRt7SRYhH2IwgSNaGRayZkEpAPHKyHK2PzQHH47rguYCFELQH4a2at7IEf1wwfqJPMKRCq8+E7dR76+IiMghxKtAoXv8Y+WAWptey61P3Yrnj9xL7S1fzrOpb9PgcWXbZvqyu6D6dnzIhZr0LuxggJNOquGk+A5CRfAsKGCC0OakzF7Ls8C2oDlhgs6iQUjGoDcDT20yoWfzZplxbrU/GoBoaCh4zQIaaqGzD/JlqI9CIg6FIuSzEAuCFzBjE2EIVsPSPA/8YQG6rgtdvTCrGWptcHe3e+D2MnhVsuuB37nnc9KahsQ9UOmGQsvI52yqAbxYFjQ0TKGAiIiIiIiIiIiIiIiIiIiIHArs/Q8REREROYRdfz3k8y/uOXM5uOGGF/ecclCZM2cO7e3tI9odxxkzEG0ifvSjH43a/vrXv35c8+vq6kZtz0/DPyu+P/JDisVicUo1y+Uy3/nOd6ZUQw4No/19wdT/xv7v//6PDRs2TKnGdDrppJNGbe/p6RlzjmVZvOUtbxnRXigU+N73vjddSxMRERERERERkRdRMpqkvWXoteb6WD3JSHLfkxwHtm4btSvsQcIZOg55EBiWF1IOgLvXfTuCrmnfrbkAM/MQ8OHkHXDxOvi7tfCPT8J7H4Owy6TlX3MWO6775uQLiIiIiExBtpRlxboV4wpUA0iEE7QkqslhjkOsYzuRYbl4jg2zIvD2tk7OmlWgNp8nWoG4AzEHZiUgZJl9leVDfcwEqu1WdqDowbwGiNvgWyY4zfJNGJrrQb4EjgtUQ3BtG1JxwINCwQS1xSIQqkA+MxSQZgGhANREzFckDMk4JJLQXA9HzASvDIVOKPRDuQz5XghXhvaPvWmIlMxasj3g/RYSXwbuhwueMYG7w01HAC/nnQczZkyxiIiIiIiIiIiIiIiIiIiIiLzUBQ/0AkREREQOmK4uWL78wJx7+XK46ipoajow55cD7u1vfzuf+tSnRrQvW7aMd77zndTU1Eyq7s9+9jNWrVo1oj0QCPAP//AP46oxVqjali1bJrWm4ZpG+ZvfuHEjlUqFYHBy/3nyrW99ix07dkx1aXIIGO3vC+CZZ56ZdE3HcbjmmmsmPf+F4Hmjf1AjlUrtc94HPvABvv3tb49o//KXv8x73vMeamtrp2N5IiIiIiIiIiLyIlrUuog16TX4mBsOtKXaWNU59BpxLBRjds3soQmPPgJdYweBzO6Hp5tMKAdAvAw9saH+SjXYYze7GtABQDVIbXjfP6yFpsKwE1xyCVx22X4f15Zntw8dWFCafxxeqmG/80REREReKOt71g/uuYBxhavNrZtLZ74Ttm4GzyNZhM7qpQAzUnDuiVBv+fhdXewuHQrCvDpoOwJqo2BZgAXxCLg+OBUou9CdM+MjMZiZAIKmz/ahUoFw2PSXK+D5pi6YELWBHOQHIBqBWBQCGRgA8llorIGAbQLdwASxBQIQjQ9t+1wXohUTmFbphce6INUFiTBUYrDJh+13Qrgbcj3gOXDFI0DJzA+7JnB3QwP0RSFVhPk9Q/UnZelSuOWWqVQQERERERERERERERERERGRQ4RC1UREROTwdf31kM8fmHPncnDDDbBs2YE5vxxw7373u/nCF75AsVjco3379u1cc801/Md//MeEa2YyGT72sY+N2vfWt76VmTNnjqvO/PnzR21/9NFHJ7ymvc2bN29EW39/P3fddRdveMMbJlxv7dq1o4bTyeFp1qxZhEIhHMfZo/22227j61//OuHdnxyYgC984Qs89thj07XEafG3v/1t1PY5c+bsc97ChQt54xvfyP/93//t0b5jxw4uueQSbrvtNmzbHmO2iIiIiIiIiIgcjJoTzbS3tLM6vRqA+lg9yUiSTCkDQNEp0hRvIhaKQaEADz0Nzr4qQsWG9LD7frg2lILgWpANQ7BsgtWCLpQDw9ZSgNm5oeP29F6BagC33w7f/OZ+bzozkHxufw9dRERE5EUVskN7HK/auWpwz7UvXf2d5LNbIWWOc2FI1sD5p0E5BL0u2H6ZQALaGqGpFmwLkgkGU8YiAQgFIAREg7ArZ8LW/GrAbcWCmTWwKwtuBVwHqL41GragpwzJYVcLx8NQGIC+LLTUgmNBJQCVMuzMmDY7MlTfc/fI0cV3zF4w1ws926AOc1xywOmBvz4ALVtMUBvAwlH2hRZwTM84nvhEAlauNOluo7EsWLhQNzUUERERERERERERERERERGRQfq0tIiIiByeurpg+fIDu4bly8065LA0c+ZMLr/88lH7rr/+ev77v/97QvWKxSJvetOb2Lp164g+27b59Kc/Pe5as2bNYsaMGSPab7vtNtLp9ITWtbfXvva1o7b/27/9GwMDA6P2jeXZZ5/l/PPPp1QqTWlNcuiIxWK8+tWvHtG+bds2vvKVr0y43re//W2uvfbaKa/r5z//OZdffjnPPvvslGt5njfmmhYvXrzf+V/96leJRCIj2u+44w4uvfRSCoW9P+U6OY7jcPPNN/Nf//Vf01JPRERERERERETGtqh1EdZgzAW0pdoGf/bx6ch0mIO//gWc/SSqAa0ZTFpGVap6b5BSALCq3wG7GrJRPREn7xiaY/mwuGOU4rtvOiMiIiLyEjMvOW/MPde+JLuz4JlQtNkpOLUF/ukMmFcPdVHwA1AbgxPnQnOdyQgLBRl2JggMu9LX8yEShBkpCAfBw2zdXA8aEhDyoDxQDUSrSoSg4pqfLSBQDVxzXRjohlI1Ly7omXkBHwpFKDtQrkC5ZOpXXMjmILcZtq6Drs0mcO2U6j7QKcGTd5tAtd3G3BeOVz4Pc+bAa187+tfZZytQTURERERERERERERERERERPagUDURERE5PF1/vbnw8kDSB4cOe9dcc82o4WUAH/jAB1i2bBmVSmW/dTZt2sTSpUu5//77R+2/8sorOeGEEya0tiVLloxoy2QyvO51r+Ouu+7CGccH70Zz/vnnEwwGR7SvXr2at771rWQy+7+TN8Dtt9/OWWedxZYtQ1djW5a1jxlyuLjwwgtHbf/sZz/Ld7/73XHVyOfzfOxjH+OKK67Ar37aYCp/XwMDA3zve99jwYIFXHjhhaxYsWJS4WWFQoFLL72UP/zhDyP66uvrOf/88/dbY8GCBVx33XWj9t1888284hWv4L777pvw2nZ7+umn+eIXv8iRRx7JO97xDtauXTvpWiIiIiIiIiIiMj7NiWbaW9oHj+tj9SQjycHjzlwnA/3d8NDD46qXcKBl2FsosQoEXShXw9TKAbC8oWOA5gLMzg0dt6ehaayXwHTTGREREXkJSkaTY++5HMdcg7LX++hhr8SJoR2861XwT+fC214F7zsTXt0Kx7XACbPhrGNg8QITiLb7LcmgbQLPCiXIFk2g2e6QNNcz320bmmsgWA1IK5QgHIDaKPgeFEsm0My3THskCPjVULVqGFvIBbcCm7pMiJoNtMTNeN+HigOZftjVY766emHTRijuALf6UFvyZt+Xeh4e/w1ku/d83k7s3Me+cDwsCxoaplBAREREREREREREREREREREDjcj0wxEREREDnVdXeYDOweD5cvhqqt019zDVCqV4oc//CEXXHABnuft0ee6LldffTW/+MUvuPLKK3nrW99KfX39HmPWrFnDzTffzLe+9S1yuRyjOeGEE8YMUNqXyy67jJ///Ocj2p988kle//rXEw6HOfLII6mrqyMcDo8YN2vWLH7xi1+M2v7ud7+b//qv/xrR99vf/paFCxfyr//6r7ztbW9j5syZe/RnMhnuvPNObrrpJu6+++49+ubNm8epp57Kr371q4k+VDnEXH755XzpS1+iu3vPq/V93+eDH/wgv/rVr7jqqqtYvHgx0Wh0jzHPPPMMK1euZPny5Wzbtm2Pvg996EN84xvfmNLafN/n9ttv5/bbbycej3PBBRewaNEiTj31VE4++WRisdiIOa7rsnbtWm677TZuvPFGdu7cOWrtz3/+86P+sziaj370ozzxxBP87//+74i+devWsWTJEk499VTe9a53ceaZZ3LCCSdg2yMz2V3X5dlnn2XVqlU8+uij/N///R/PPPPMuNYgIiIiIiIiIiLTa1HrItak1+Bj0jbaUm2s6lwFgI9Px6O/Z8EEbpbRmoF0ApO6Adj+0M9Y4AQgWA32wIeTdwzNtXxY3LGP4rtvOrNs2bjXIyIiIvJCyhQzbOzdSKa075uABewAHX0dg3uuoB2gb+M66NoFPkRci8YZbdgLjuNlLb28zHqWUNKnYkPGg0AA5qTM3qoC2FY1GM2CWBBSMejph+07oZyCigWVAMTDUHLNuD3uBWVDYw3szEC+DPVRSMRNQFo+C5EQ2NUg3HAAgiEoOiaYzbUhWoJSEJwK7EjDgqOg1ga3Wt71wO1l8Epj1wO/c8/npDUN3AMvexAeXQiFuqG+I/rhgvWT+pUMOe88GONmdSIiIiIiIiIiIiIiIiIiIiKjUaiaiIiIHH6uv97cIfhgoA8OHfaWLl3K1772NT784Q+P2v/EE09w2WWX8f73v58ZM2Ywa9YsisUi27dvp6+vb5+158yZw8qVK4nH4xNe1znnnMPZZ5/NPffcM2p/uVzeZ3hSa2vrmH2f+9zn+NWvfkVXV9eIvi1btvDhD3+YD3/4w8ydO5fm5mYAdu7cSWdnJ67rjpgTjUb5yU9+MmpQmxx+amtrWbZsGZdffvmo/b/73e/43e9+Rzgc5qijjiKZTNLf38/OnTvp7e0ddc4rXvEKvvSlL005VG24QqHAihUrWLFiBQC2bdPQ0EBDQwOpVArXdclms3R0dFAqlfZZ65xzzuHKK6+c0Pm///3v4zgOP/3pT0ft/9vf/sbf/vY3AGKxGM3NzTQ0NBAOh+nv76evr4+enh7K5fKEzisiIiIiIiIiIi+M5kQz7S3trE6vBqA+Vk8ykjTBII5D57ZnaQ1CrDK+egkHWvKQrgHHhnIAAtXwjYA3LFANaC7A7GH3/WhPQ1NhPyfQTWdERETkILE2vZZbn7oVz/f2PxjIO3nS+bQ52LyZgd5dlCLVTt+nsf95zk7209gQg629ONVQs4APjXVDIWcAqTgEbPABDxN45jgws84ErlUsE1jr+4APng+5ak5uPAS+BbZt6vTloFCA2gTEIlAuQG8O6qu1bCBgQSICkSA4NRANgR2GI0MmMM0rQ6ET3FoIRqGYhXAFSrbZB/amIVIy68n1QuJhSKwEShAG3vsYbGiAviikijC/ZyiXd1KWLoVbbplKBRERERERERERERERERERETkMKVRNREREDi9dXeaDOgcTfXDosPehD30I27b5yEc+MmpoGIDneezYsYMdO3aMq+YxxxzDHXfcwZFHHjmpNVmWxU9+8hPOOussNmzYMKkaY5kzZw6//OUvOffcc/cZyLRlyxa2bNmyz1qRSISf/vSnnHHGGQpVk0Hvf//7efzxx/nud7875phyuczTTz+931ovf/nLueOOO4hGo9O5xBE8z6Orq2vUsMF9Wbp0Kb/85S+xrIl9HCEYDHLzzTczf/58rr32Wjxv7A+JDAwMsHnzZjZv3jyhc4iIiIiIiIiIyItrUesi1qTX4GMSz9pSbazqXAVbt+B7Hh1JWNA9/nqtGUgnIBMBLIhWIB823wf5cPKwl60tHxZ3jKO4bjojIiIiB4FsKcuKdSsG90/j0ZpsNaFqjgMdHaRs6KwxfTNScO6J0OB1Q6aG3WWDQWitg9YjoDYKlgVYEI+YoLRyBcoudOeAAESAZBKcPPjVvpBtakVs2JaFWbUQCppTxMMwAOQHIBqBWNSEqrkl6MpCYw0EbRNwVs1nIxEGKwwxoDBgQtWiFROYVumFx7og1WXGVWKwyYftd0K4G3I94DlwxSPAsPtDWcAxPeN4Ei+5BC67bOx+y4KFC3UdjYiIiIiIiIiIiIiIiIiIiEyKQtVERETk8HL99ZDPH+hV7EkfHBLgyiuvpL29nfe+971s3LhxSrX+8R//kW9961ukUqkp1Zk5cyaPP/44//Zv/8YPfvAD8tP4z85ZZ53FXXfdxd///d+TTqcnVWPOnDn8/Oc/54wzzpi2dcmhY/c/A1/+8pfx/fF/CGK4Cy+8kB/96EfU1dVNaS01NTVYljXpdYwmGo3y8Y9/nE9/+tOEQqFJ1bAsiy984QssXbqUf/7nf+bxxx+ftvUBnH766bzhDW+Y1poiIiIiIiIiIvszoy7Kh885ZtxjDyXNiWbaW9pZnV4NQH2snqQVJbN1G2DCPlozEKvsq8qQhAOpImxKmeOQB1HHfB88ZwFm54aO29PQVBjngnXTGREREaidBYs/Of6xMq3W96zfI1DN88e+EdFusVCM5ngz6bUPgucR9SBSgVgSLjoV7JAJM7OzOQIBmNMCDXUmJ6w+Bv7ucLQghAMm4CwShF05M8b3oehB0obmGkhnoeKAVX1LMBSAWAB6B6ClFrBMsG0ibELV+rLQVAeuDTZQLkMmD3VhsAPVx+maADQw56+4EPAg6EKuF3q2QR3muOSA0wN/fQBatpjwNoCFE9n37e3OO+HHPwbbnmQBERERERERERERERERERERkbEpVE1EREQOH54H3/3ugV7F6L7zHfjiF3XB6GFuyZIlrFmzhu9+97t8/etfZ/PmzeOea9s2r3vd67j66qs566yzpm1NNTU1LF++nOuuu45f//rXPPTQQzz55JN0dHSQzWbJZrOUy+VJ1V6yZAmPP/44n/nMZ/jxj3887jqpVIrLL7+cq6++mtra2kmdWw59gUCA6667jrPPPptPf/rTPPLII+Oee8IJJ/CZz3yGiy++eFrWcvHFF7Njxw5+85vfcMcdd3DffffR1dU1qVqNjY287W1v42Mf+xhHHnnktKzvjDPO4NFHH2XlypXceOON3HvvvXje/j8wsrdgMMgrX/lKli5dytvf/nbmz58/LesTEREREREREZmImckoH33dsQd6GQfMotZFrEmvwS/kIZej7emdrKq+1uNb0JGEBd0TKLjXfQJm5qAvNtR38o6hPsuHxR0TqK2bzoiIiEDdbDj73w70Kg5bIXvPmxet2rmKTCmz33nlYoHt/dsgZY7dAJx/KlgxKAKODQ0ROHoGhINQwnz3bbMnA3N5yO6tluebYLUZKejJguOA45o5DQnIZMCPmdA1gIYYbMuZMLRgNSgtVL0a2HXNeMsG34OQD6kolMpQtiAQgMIABGxTr+xAXz9EeqCvC1zH1DklDWtawCnBk3dDy7A95IT3fXvr64NnnoHjj59CEREREREREREREREREREREZHRWb7v+/sfJjI9amtryeVye7TV1NSQzWYP0IpERA49Tz/9NKP937tlWSxYsOAArOgg8tRT8LKXHehVjG3dOl0w+hJzzTXXjNr+rne9i7a2tinV9n2fP//5z9x99908/PDDbNiwgXQ6TaFQIBAIUFtby9y5c3nZy17GGWecwYUXXsgRRxwxpXMeSNu2bWPlypXcc889rF69mu7ubvr6+ohEItTV1XH00UezcOFCzj33XF7/+teTSCRG1MhkMgwMDIxonzlz5ovxEF5QY/2tfeQjHyGVSr2oa3mp+vOf/8xdd93Ffffdx+bNm+nu7mZgYIBEIkFzczPHHXccr3zlKzn//PN55StfOWqNnTt3jmiLxWIkk8kJr2f9+vU8+OCDrF69mg0bNvDcc8+xa9cucrkchUKBaDRKXV0dqVSKY489lpNPPplXvvKVnHvuuYRCof2fYArS6TS//e1veeihh1i7di0dHR2Dz5dt29TW1lJXV0dLSwsLFixgwYIFnHjiiZx55pkKOhQRERGRlyy9piYiIgebTDHDxt6N4wr12IPvc99/fZrnn3nQ3GwGWN8A+bDptnxY0AURd1yl2NgAPTEzP1KBWTnYUQOlIDTn4Q3rh8Yv7IT/99TElktNDTz/PDQ17dH87N+em2AhkRdfaW7NgV6CAAtbZhzoJYjIS1immOHrD34dvxpv1jvQy6rOVfuf+PxGdnVtoRyD5lo4/RhoPwpcoFQB24KFLRCwhqbEw1AXg6ILrg9NCROaZlkmQG2gGmaGB11ZCAcglQB8GMgBIYhFzX7OtyBXNt8TIbABHOjqhaAHFRu274L6WpjTBNEweNV7/JULkMmBWz3e2gnZXWaft1tLHl62C57w4XdPQKF/z4d/0k646OmJPtvDWBbs2AEz9O9wERERERERERERERERkQNJGTAicqhSqJq8qPR/qCIiLzx9AHQfdu6E2bPNp4AONrpgVERERERERETkgNFraiIicjBZm17LrU/diud7E5/8wAPk7/s9j8wGqiEeA0HoHJb9VFOCppH3pxihKwa5MKSK0BeFGXmIVar1EnDeczC7+va/5cMVj0BTYeJL5t/+DZYt26NJoWryUqBQtYODQtVEZKpuXXcrq9OrB48f3/E4mVwXlMsQDsNeNzwKeyWO3v4QbfN8ws0QDMApbWDZJrjMtmBmHfgeDBQhX4Z8CbIDMCMJPibQbE4SAjYEbbNtc3Zv/Xwzt7MfZiYh6Ju2Xb2QaoCgZcLUfMy5fd9k6foudHcPhap19UJNHObUQDwOTghcD0qdJtQtHzbH6zZAU7/Z5+32iuchcQ+UH4T/XQhb64b6juiHS1dBeBwhvWNauhR++9spFBARERERERERERERERGR6aAMGBE5VAUP9AJEREREXjQzZ8LrXge/+92BXslI552nQDURERERERERERERkcNctpRlxboV+EziBjGFAtx3HwkHWvKQruY9xSoQqUCpeoVILgzJEoT2kdnm2GYclglUaxgYCtqIVaC1byhQDaA9PclANYDvfAe++EWw7UkWEBEREdlTpphhY+9GMqXMfscG7AAdfR1m/+X7BNc9RV/3epNaZkGkaSaNx56Mbfu8rLmXl1nPEkqavVquAqmkCTqrTqc+Xj0OQDQOERs6O6HsQLAWXAs8f+h+gK4Lecf8HKvuv2wbkjHIFaAhZtpiYejPQDIFtmkiFIBwwISqlSuQjJvzhoMQjYAzAG4WcjlwEjBQgBoXQhYEPNjRDcGy2d/5PuR6IfEwJFYCJQgD730MNjSYPWGqCPN7BrN7J2fpUrjllqlUEBEREREREREREREREREREdknhaqJiIjI4eUXv4C3vQ3uuutAr2SILhgVERERERERERERERFgfc/6PQLVPH8fyWd7++sDUHHAgrn90Fkz1JUsDju2oC8CTQNjl+qLMJSWYZkQtd5qoAc+vGr70FjLh8Ud41/myJP1wTPPwPHHT6GIiIiIiLE2vZZbn7p1QvuovJMnnU/D5s2w9XkGaoYCaenfSeP2Jzn7zDiNkQHY2osTMF1BD5qS4Ff3TUEL4tVgNICiC/05mNcAFRfCFhQssH0ouxC0zdCIDduyMLMakuZj6nTnwAlBKAixKBR7oCsLjTUQsE04GpgQtkA1xG33Fs51IVoNxa2UYccGKOZNUFo4Bpky/Go9HNUJO3sg1wOeA1c8ApSGnhsLOKZnHE/iJZfAZZeN3W9ZsHAhNDWNo5iIiIiIiIiIiIiIiIiIiIjI5ClUTURERA4vdXXw29/Crl2wZs3QrX8PBF0wKiIiIiIiIiIiIiIiw4Ts0B7Hq3auIlPK7H+i48D2B6F1qKkrDvnw0HEuDK5tfs5EoD8KgVHeJnEtGAhCjWOOIxWYmYeIBztroCUPcWdo/Imd0FQY5wMcjWVBQ8MUCoiIiIgY2VKWFetW7BFSOx6tyVbSfdugwyTFpoYF0s5IwbnHbqMhcgT097O7dDBo+lobzM+WBeGgCUBzfShXoHcAnIAJNwsFoD4FTh6ciulPVLd+oQBEA9BbgBm1gGWCaxNh6MtCYxLCNvi2CUjbmYGWWrAj1cteLPDcoUA1H/AdCLqQ64WebdDUb/rSCROg9uRvoGhDsQ+K1XkL01PY1915J/z4xybhTUREREREREREREREREREROQAUqiaiIiIHJ6am+Hssw/0KkRERERERERERERERAbNS87DwhoMAmlLtbGqc9X+J27dAp63R1OyCPkQg+ka0cqwkDULSgGIV0aWKgUg4g4dpwZMraYCFILQNizj7Yh+uGD9+B/fqM47D2bMmGIREREREVjfs36PQDXP9/YxekgsFKN5Szfp6n4qWjHBsrEkXHQq2CFwM73YuTyBAMxpgYY6CIVMMJprmyCzcBCCAXNhbsiGeBgyFRNa6/vg+NBcA+kslIpAlMG9WkMctmeh4poaYALa8gPQ2w+NtSa8za1A2TbhuIUiBIKADeWSmeP7UBiA3Gbo7QK3Gobb1mfWuD0IT94NhX44athzYPmwuGNST7vR1wfPPAPHHz+FIiIiIiIiIiIiIiIiIiIiIiJTp1A1ERERERERERERERERERERkYNAMpqkvaWd1enVANTH6klGkmRKmbEnOQ5s3TaiOexBwhkKUgt5EPBM6AdAuRqeFhjKHcG1wLPMWDBhIq0ZiFZD1i5dBSd2Ql8UUkWY3zOYAzI5S5fCLbdMpYKIiIjIoJAd2uN41c5V+95H7eY4lHc8y/Ykg5sbNwDnnwpWDIqAU8nTkISjZ5jwtBIQCJu9lYeZZ9tDJV0faiIQC0E2A6UKFEqQSkBDAvp6oViCWNSEnYUC0BSHkgtB29QLVq/w9cuwqx9cF2ygJQ7hgAlQqziQL0E2O3Tu9HYI7xg6bslD3AF2QPn/IBsd+RSc2GlCdCfNsqChYQoFRERERERERERERERERERERKaHQtVEREREREREREREREREREREDhKLWhexJr0GH5N21pZqY1XnqsH+WCjG7JrZQxMefQS6vFFrze6Hp5vAr4aDxMvQExvqD7lg+1AJmONSNWhtt9QAtFVzSCwflnTsFbZxySVw2WUTf5CWBQsXQlPTxOeKiIiIjGFech4W1pj7qDFt3UK44pPyIN4IdTE4YR4smAlFF3JlSERhwQwI7JUoW3FNEFrFh2jQhKPZlgk8AwjYUF8Hu3KQL0NdNRCtNgr5LITDJozNAhJhCFbD0jwP/OEhbS509cKsZqi1YfeWzfXA7WXwamDXA79zzzW2poF7gAfgXAu2nwhb64b6j+iHC9aP7zke03nnwYwZUywiIiIiIiIiIiIiIiIiIiIiMnUKVRMRERERERERERERERERERE5SDQnmmlvaWd1ejUA9bF6kpEkmZJJNys6RZriTcRCMSgU4KGnwRm7XsWGdM3QsWtDKTjU5wMBwLXAsyBUzWeLVKA1A/Fq7fb0XoFqALffDt/8psLRRERE5KCQjCbH3kc5DpTLJsUsFBqcE/ZKHB3ayvwlkExBLmKC0E5pA8sGzzZ5sI1xKDswUDKhZ4Uy7OyDmSlTx7UgFoKgDaHqHAB8E47WWAM7M5AdgPooJOJQKEJvFhrqzHgLE8oWDphQtXIFknEgAOEgRCPgDEChE9xaCEahmIVwBUq22ef1piFSXWOuFxIPQ2IlUKo+XuC9j8GGBuiLQqoI83vMuSdt6VK45ZapVBARERERERERERERERERERGZNgpVExERERERERERERERERERETmILGpdxJr0Gnx8ANpSbazqXAWAj09HpoMFTQvgr38xASH70JqBdILBpIxUETqrIWvF6lUj8QqUAhBxh+alBqDN5Lhh+bC4Y5TiuRzccAMsWzbJRyoiIiIyPpliho29GweDZscSsAN09HUM7qOCdoC+jeugaxf4EHEtGme0YS84jpe19PIy61lCSTOWigkmSzVC0MIEovnQEId4yHw1RGFHN2x4HuIRsJLgW2D7UHZNoJrrgeNByYVEyPRbNqTi0JeDuiCEghCLQLYEXVkTuhawIVTds9k2BAIQjQ8FnrkuRCtmTZVeeKwLUl2QCEMlBpt82H4nhLsh1wOeA1c8wmCg2m4WcEzPfp7wSy6Byy7b9xjLgoULFbArIiIiIiIiIiIiIiIiIiIiBxWFqomIiIiIiIiIiIiIiIiIiIgcRJoTzbS3tLM6vRqA+lg9yUhyMESkM9dJa7iZ2EMP77dWwoGWPKSrQWqxCkQqUAhBOWDaQh54lvkOpr81A/FqXlt7GpoKY5xg+XK46iqFaYiIiMgLZm16Lbc+dSue741rfN7Jk86nzcHmzQz07qIUqXb6Po39z3N2sp/Ghhhs7cUJDM0NetBUDUoDE64WDzOYbFbywQba50DHTvCqXRZQccAKmXEhG3rzELYhGDQD4mEYAPqy0JiEaBTyRXDKsDMDLbVgR0xoGhZ47lCgmg/4DgRdyPVCzzaowxyXHHB64K8PQMsWcw6Ahfvaw+3PnXfCj39s0t1EREREREREREREREREREREXkJ0tYOIiIiIiIiIiIiIiIiIiIjIQWZR6yKswRgNaEu1Df7s49Px6O/BccZVqzWDSeKoShWhFGAwASQfgog7rH8A2kx+G5YPizv2UTyXgxtuGNc6RERERCYqW8qyYt2KcQeqAbQmW80PjgMdHaSKQ30zUnDuEmj0uiHTN7hHCgahpRGOmQPzm2FWCmYmYXYKosGhcLPeARNMG4tCWytUhi3LGagGolU1RqFnYOgYCxJhcF3o7YeADbZlgtx8HwI+FIpQdqBcgXIJXA8qLmRzkNsMW9dB12YTuHbKjup5S/Dk3SZQbfBU+9vD7U9fHzzzzBQKiIiIiIiIiIiIiIiIiIiIiBwYClUTEREREREREREREREREREROcg0J5ppb2kfPK6P1ZOMJM2B49C57VkGguOrlXCgJT90HPTAG8prI+BDqBoIEqmYELZ4Na+tPQ1Nhf2cYPly6Ooa32JEREREJmB9z3r8Yemwnu/t9ysWitEcb8bfuhnf84hWzB4nlYCLTgU7Bq4PfjaHHYC5s+DlR8OsZmhIQdg2gWeBAMTDEA1BbRTCASi7Zh9VscGqtgcDJsTMcqFYMuu0fBPUVhM2oWi7BUPme6kC3RmoVMyFvC3x/5+9O4+Pqr73P/46sy9JJjubkKCgoAHEvbYCblj33vbWXSvUervobUt7be293ay1/dUWW2nrbW9bcaltKVrrUrVqcQdBEWQTUSDsGUgyk0yWWc/vjzNZIBMyySQhgffz8ZhH5ny/5/s53xkmw3cmM+9j1TdNSMQh3AB766zLvnrYuhlad0MyvUYrb7LWaIVb4J1/QGPt/vfbtJos1nAHYxhQXJxDAREREREREREREREREREREZFDI8uP14qIiIiIiIiIiIiIiIiIiIjIYJpRMYO1wbXtQSKVhZWsrlkNO7ZjplJUB2BSbQ9F0irCEPQDBoTd4E5Cwm71eRId+xW2QGXYum6YMLM6i+KRCMyfD3fdlfVtExEREcmG0+bcb3v1ntWEo+Eex8Vam9nVsBMKre2kHS48GQwvtAJxGxS74ZgR4HJAOgsNuwuSNkgBGGDrdOpiuw1GFcC+JkimA2mjCSjJg4aQtd3UCC4XOIGUDfwucDoglYKUCfZOn9pNJaG2AcaUQb4N2rLXkilI1tP+Cd9kCsya/W9fRRBYAscvg7emQHNBR99RDXDRph7vooObPRtGjMixiIiIiIiIiIiIiIiIiIiIiMjgU6iaiIiIiIiIiIiIiIiIiIiIyBBU5i+jqryKNcE10NxMUSROIG4nvGMnADV5VliaN9FDIcAfh/Im2FkAERc4U2BPh4E40z/dCaueL25tVwWhtDnLyS5YAPPmQWlp726kiIiIyEGMC4zDwOgaMtsD1+49+GMmMS+U5cOZE6FqlBVcFk2AzYBJ5WA39h8XT0BzFJImYLdC0WwGGIBpWsFqZXmwz4RE3GqLxSDfA00tgAmNYSgMWPUMrPFeN5gpiDvAnwcOwwpz87oh1gLNNZDMB4cHWhvBlYCozQp4qw+CO2odK1IP/uXgfxyIggv47Er4oBhCHihshQl11nH77IILYNGiXCqIiIiIiIiIiIiIiIiIiIiIHDIKVRMREREREREREREREREREZHDQrg5zrIttVnte8b4EgI+5wDPqEO4Nczm+s2Eo+FejbMbNqpfWIy5YQOkUjjcECrq6F8xGsY1ZFfLk4B6D+0pG54DwtgKW6AyPT3DhJnVvZhoJALz58Ndd/VikIiIyBGupR62vp7dvpUfBW9Rz/sdZgKeQEfILFDkLSLgDlhrqnjcSjRzucDZsa5zpaIc49zBhaeDqwwcdjipEgwbpGxWyNnIAivkrKUVmmLQFIXqveC0w4SRVp2kAW47eJzgdXQElRkGlORBMGQFnbU0gz8fmlutbWczBO3WPnYb2LHGGjZwAj4v2K2MOEysNZlpQqIeVu6Dwn1WmFvCC1tN2PUMuGohUgepOHxpBRDtuI8MYGJdFnfm1VfDTTd1328YMGWKQnJFRERERERERERERERERERkWFOomoiIiIiIiIiIiIiIiIiIiBwWttY28R8PvZ3Vvn//0keZ5isc2AmlrQuu49ENj5IyU70f/PrrNG1cR7Cgo6nFAdH0Jz5Cbuu6M4vScRvs84EjBa4U5MWs9qgD3AmoCIMvbrVVBaG0uZdzXbAA5s1TEIeIiEi26jbDX67Nbt/P/QvGnDyw8xlk2YbO2m12qkPVmFhJZA6bndDm9bBvL5jgThqUjKjENuk4ji+v53jjfZwBa99IAgoD4DAA0wovK/Klt+3g9oHbBjU1EGsEp8cKlzUNsJkQS4LTBk1JcDlIz8AKSyvwQ0OjFZDW0AQuJ6RaIGmDRAz2hKHQB/ku69gYkDStuu0S4EhCpB7qdkIB1nY0DvE6WPo6lG+HlvTuU/qyRmvzzDPw8MNgs/WxgIiIiIiIiIiIiIiIiIiIiMjQp1A1ERERERERERERERERERERkQHSGG1k8frF7SEgvdLcDC+/TAUQ9AOG1VzYCjV56X0MCLuhtKWbGp2E3eBOQqMLHFGrDli1ClugMp1nYpgws7r30yUSgfnz4a67+jBYREREjiS9DZ1tijcRbApaG9u20VK/l6g73WmalDRs4exAAyXFXthRT9xudTlSUBqwQtLAClPzuWhfV0WTUNsCY0qg1AfVeyCV7jaARBwMp7VvLAGYYKQzyXwuSC+nSCYhGrNCa1u91nFjNmiJQqsL7A4w7BCPQyplhbslEhDeCrEgJNPBticFYW05xKPw7vNQXttxH/R5jdYmFIKNG2Hy5ByKiIiIiIiIiIiIiIiIiIiIiAxtOt2ciIiIiIiIiIiIiIiIiIiIyADZVLdpv0C1lJnK/rL0dVKJON4ElDWDiXXxJMCd6NiOuKzQjrbtTJeYzdrPboIzZX1gxJuwLoFWqAiDLx3mURWE0uY+3uD77rOSQkRERES60RY6m22gGkBFoMK6Eo9DdXV7OCzAiEI4bxaUpGohHKJt6eVwwOgSqCiGkQEYVQjjisHvArfDCk2rb7F2j9vB64HKCkh0mla8xQpAA2s/o9OcDMDj6dj2mrBpOzS3WmstuwnF3nSAWtwKWKvZC3vrYF897K2Blp0dgWrlTdYarHALvPMPaOwUqAYwrSaHNRqAYUBxcQ4FRERERERERERERERERERERIY+x6GegIiIiIiIiIiIiIiIiIiIiMjhymlz7re9es9qwtFwzwPjcdi1DNL5ITEb7MqnPckjboMmV8fuzU7wJQ5SzmaFqYEVylbQKYikqAUq01MyTJhZ3fP0uhUKwcaNMHlyDkVERETkcJYpdLYnXqeXMl8ZwXXLIJXCk7JCZr0B+MTJYHNCMgq2xgh2O4wph+ICcDrB74RkOoDW5QCH3frwrNMGPheEE0AKEjYwbJDvtILVkgkwktAatQLXAGwGOO0QTVrLMocTWrBC1FwpaHXDpq0wohQmVYDL3nEbEs3Q+aYmgvvfxoogsASOXwZvTYHmgo6+oxrgok29uZczmD0bRozIsYiIiIiIiIiIiIiIiIiIiIjI0KZQNREREREREREREREREREREZEBMi4wDgOjPTiksrCS1TWrex64YzukOlI3XCnwxzuC1JwpsKesgBCAmB3cSbCbXUslDSssrY0/BhPqoM5nbZc3gS9uXZ9WA6XNvb2VnRgGFBfnUEBEREQOd30NnY21NrOrYScUWttJO1x4MhheaMUKkS12wzEjrPC0KGB3WeulFIABNltHvaQJeW7wOqG+0cq0BYgmoCQPGkLWdlMjuFwdY+02yLNDNG4FrCVt4I5D3G6FuOUZUJgPiRA0x8Hps8LYUiGwp/c3EhDfC6YJkXrwLwf/40AUXMBnV8IHxRDyQGGrtXYz+nRvp11wASxalEsFERERERERERERERERERERkWFBoWoiIiIiIiIiIiIiIiIiIiIiAyTgCVBVXsWa4BoAirxFBNyBgweHxOOwY2fXWq3Q5KQ9UcOT6AhZw4CoHXyJruVShhXC1uaYejhhL6weCQ0uqExP5agGuGhT72/jfmbPhhEjciwiIiIih7O+hs66du/BHzOJeaEsH86cCFWjIIkVhGYzYFI52A9IH0skIZqEhAkehxWEZhiQSofO2m1Qkg+1jRBLWEFnsRjke6CpBTCt0LXiAmscJjhs4HCD22GN8bVC3AUTXNbx7ClwxCDZCHtbYc8WKGqGhBfq/dC4EoLLIVIHqTh8aQVWClyaAUysy+LOvPpquOmm7vsNA6ZMgdLSLIqJiIiIiIiIiIiIiIiIiIiIDH8KVRMREREREREREREREREREREZQDMqZrA2uLbb4BCv08vovNEdA95aAftSB5ax9k1AnbdjuzYFMbt13QDyW6HFCYl0W9IAw+zY3xO3AtXsJkzfDWVNcPpOKGyFCXXteW19c8EFsGhRLhVERETkCNCX0FlXKsoxzh1ceDq4ysBhh5MqwbBBymYFqo0sADMFLa3QFIOmKOwJwchCq0bSAK/TGuuwga3zAWxQkgd7wlaoWksz+POhuRWSJpgx2Ndo7WNgXUys4/pdYHdaH8iNxqxQNU9b0G0Mdm8HV70V7EYT1G6Hbf+w5gowJQilzX28M595Bh5+GGy2nvcVEREREREREREREREREREROQIoVE1ERERERERERERERERERERkAJX5yw4aHNIab6XUV4rX6YXmZnjzPYhnrlXcAitG055+5kpCTV5Hf8IOiU6ZGikDnJ3y2Y6pB1+6ts2Eq9YdEOJx9dVw0029u4GGAVOmQGlp78aJiIjIEavb0Nl4HGIxvL4Ao4srMEgxsSTIhJb3cHitfUOtkF8GboCUtWwq8oPTAOzg8YHbBjU10NQMjgIrUM0GxJJWqFoyCdGUFXTmdVlzMmxQ6INQxAqgbWgClxNaYtZ2LGaFrh1TQnsiW9K01lRtkkmwp8CRhEg91O2E0U0Q9lj98SjsfbYjUM0wYWZ1DndkKAQbN8LkyTkUERERERERERERERERERERETl8KFRNREREREREREREREREREREZIB1GxwCmJhUh6uZVDoJlr5hhYl0wx+H8iYIpoPUvAlwJyCa/gRIrRfcSSv4I2lYQR1tPHE4YW/HdlXwgEA1gCefhHvvVUCaiIiI9Em4Nczm+s3t4bEHZcDW+q3WddMkvnkTTft2gQlhDMaOPZozLyyj0NkIu4KY6VBZfxxKCjrKOAGfu2M7loRII4wrhhY/uAxoNqxM2lgCfE7rutMGdc1WyJrLbs3H54KWdJ1kElpj1nWbHRwpsLsgkUiHpzkgGgczaWXMmibUhsBVC6F9kEwv6Y6rhW0B2O6Ed5+H5oaOuU6rybAe6w3DgOLiHAqIiIiIiIiIiIiIiIiIiIiIHF4UqiYiIiIiIiIiIiIiIiIiIiIywMr8ZVSVV7EmuAaAIm8RAXegPXCkJlJDhasM75vLe6xVEYagHysNBChshZo8K0QtZrfafAlIGeBMdYw7ph586XAPw4SZ1RmKRyIwfz7cdVcfb6mIiIgcqdYF1/HohkdJmamedwaaYk1sDW21NrZtI7F7F6F0cOyIgMmkyR9CQy2hVApcVlKswwElhTCmzLpuGOBygNMBSdMKTatthbgDXEnwusGfB/EoxBPQEoUib8ccirxQ2wwj8tMNBvhd0JROVrPbYdNWGDsSfB4oTYe3mSbEY1BfB4l0iG1zK+zcCqMiHfXLm6z114R1sOJ9aOx01xzVABdtyvLO7c7s2TBiRI5FRERERERERERERERERERERA4fClUTERERERERERERERERERGRw8LkUQUsu/3crPYt9rsGeDZdzaiYwdrgWkys5I3KwkpW16wGwMSk+q0XmBSP91jHH7cCOoLp0BFvAtwJqPcA6WA1Z8oKTmvjicMJezu2q4JQ2tzNARYsgHnzoLS0D7dSREREsjZiCszbkN2+vqH9/3JjtJHF6xe3r3Oy4Xf5KfeXEwzthOpqvClrTeMLwL+dDDYnJMMh7KYVbjamHIoKwOUEvxOSNquOywEOOzgBpw18LggnIA4YSWhJQFkeBBshmYDWVvB6rLFOO+S7IJG0agA4nEALpLAybG0GfFAN0yeAB6suQLwF3FFIpJeVdWEr7LaziiCwBByvw3Up+KAYQh5rvwl17Rm5fXPBBbBoUS4VRERERERERERERERERERERA47ClUTERERERERERERERERERGRw4LLYWNkwHOop9GtMn8ZVeVVrAmuAaDIW0TAHSAcDUM8Ts3O96lwWCFpPakIQ9BPexJHXhRq/OlOA6J2yOuUz3ZMPfjS24YJM6sPUjwSgfnz4a67ensTRUREpDccLigYfahn0S821W3aL1AtZaayGje2YCw1a5ZBytq/KApnTwW3E0yg1Q7lTqgcDY70J15t6fAzw4SUYYWetUmakOcGrxPqGyFmQjwGBT4o9kOoHpoaweUCWzqUze8Cp8OaQsoEe6dP1hpAoADGjgRPEiJBSOaDzQ2pkBVka09BDGiug/IEmCZE6sG/HPyPA9GOWhPrerhDPB545BEIBLrfxzBgyhQF4IqIiIiIiIiIiIiIiIiIiIhkoFA1ERERERERERERERERERERkUEyo2IGa4NrrdCR5mYqk3msju+DHdsxUymqAzCptuc6/jiUN0Ewz9qOOsBuQjIdKuJOduzricMJezu2q4JQ2tzDARYsgHnzFNYhIiIiWXHanPttr96z2gqO7Uk8zr66HTQVWpunHQ2FRRBtW9PkwegR1hqnbXljt0PUhGgCEqYVwOY0rKyxVDrXzW6DkgLY1wjxODS2QMAL+R5oaoFQIxQVdASy2QzwusFMQdwB/jxwGOByWO2xFnDGrKC3ZBg27IS8OnB5IeGFpUFIrYCddRCpg1QcvrSC9kC1rLW2wooVCrcVERERERERERERERERERER6SOFqomIiIiIiIiIiIiIiIiIiIj0Qbg1zOb6zdkFhhxg6wuLYcMGSKWIl0CTE/BA2A0G+4eidcdmQsQJ9hREXOBJQJPL2namOvY7ph58ceu6YcLM6iwmGInA/PkK9BAREZGsjAuMw8CwgmOBysJKVtes7nngju0EWkxibhhTBBdOA4cTYgYkUnBsGWCDdFmaY7AnBCMLre2kAV4nOOzgsHWEpAFgQIkfasLQ2Ap5DvD7oLkVEjErcK0kzwpgs1u7Y9jACfi8VmAt6UN7EunjxaF2K4yuhbAHok3QuB3WvQ7j66A1fegp2YTYdue+++DOO8Fm62MBERERERERERERERERERERkSOXQtVEREREREREREREREREREREemldcB2PbniUlJnqeecDNL22hK071kGBtZ0wIOTp6F9TDqUt2dVqccJeHzhS4EpZgWptoR8AnjicsLdju6o3AR8LFsC8eVBamuUAEREROVIFPAGqyqtYE1wDQJG3iIA7cNDwWVcqyjHOHUyYAe5SKC2GyjKrL2FAsR/8bognoCUKO2th4x7Ic3eEqtlMiCWtULVEEpIpiCbB60r326DQB6EINDRCSQC8boikg9X2hK3+fBdWepoBSdOq2y4BjiRE6qFuJ5Q2QEUIVoyBeBQ2vAzjGzp2zzrEtjuhEGzcCJMn51BERERERERERERERERERERE5Mik09iJiIiIiIiIiIiIiIiIiIiI9EJjtJHF6xf3KVCN5mb8ryylvKmjyZsAd6cgtIgL4ll+osMfg5gdmp2QAgKt4Ow0rWPqwRe3rvc64CMSgfnzezFAREREjmQzKmZgYLRvVxZWQjwOTU0Qj+N1ejmm6BgmFI3nwgl+/mPsFj5+jMkEF4yKwGi/FRTrSIHXhDyXFW7mtlvXi9xQmQ+pJrAnrbWNAcTS6ygDcNqgKQrxJO2NPhe4gWQS6hvA5bL2daTANK3AttYoxOLWuHgcUikrpK01CuGtsGM97NsGqSRUhsAfh7JaePd5aO4UqAYwraYXIbaZGAYUF+dQQEREREREREREREREREREROTI5TjUExAREREREREREREREREREREZTjbVbcLEbN/uVbja0tchEWdsA9TkdTQHWjttGxByQ2lLz+WanVaIWtxuXUZFIOSBqAM8cThhb8e+VcE+BHwsWADz5kFpaS8HioiIyOEg3Bpmc/1mwtFwdgMM2Fq/1Uore/tt4nvW0+S00s/CpWWMPdnDmRV7KHQ0wq4gZjqDzWFAvhPastD86eCzNvXN4AQqiqHMB6k4mB6rryUKhd6O/Yu8sK8FRnZaW/ld0NQC8QSEm6ywNK8b7CYUe63pJuKQSEBtLbQt7+LN0LKzYx7lTenA2t0w8T5YehQ0FnT0H9UAF23K8s7tzuzZMGJEjkVEREREREREREREREREREREjkwKVRMRERERERERERERERERERHpBafNud/26j2rswsaicdh1zKosDb3+aDJ1dEdcUHSZl0Pu6HBYwV9dMeRhIQNPAmI28CdAG8CSAe0HVOfDv0ADBNmVmd/GzsmFYH58+Guu/owWERERIazdcF1PLrh0V4FyDbFmtga2grbtsHOLSQcVuArwAjbXib5nwdKCe2tB5e10HE4YHQ5FAbAcIJhgM8FNhskTGiOQXMKnHZwJ8HnAbsbYoYVkmamoLXVagdw2KHACYmkdR3A4QRaIAUYSdixB/L9MKkCXPaO+SeaOwLVABLB/W9fRRBYArwOrhR8NggfFFu3sbAVJtTtHwbXaxdcAIsW5VJBRERERERERERERERERERE5IimUDURERERERERERERERERERGRXhgXGIeBgYkVBFJZWMnqmtU9D9yxHVIdKR2BVmhy0p684Ul0ClkzIGoHX6L7cjbT2s9ugjMJpc1Wuzdh1T5hb8e+VcGO/l677z64804r2URERESOCI3RRhavX9y+3smW3+Wn3BEgWG2luXoTVvCrLwD/djLYkk0kY/nYGxqw22FMORQVgNcDXhckDTANK0DNZrM+5GoDin0QaoFkygqLjaZgRAHsi0AyAc2N4HZ1LFf8LnA6rKVXygR7p0/LGkCgAAp8kAhBcxycPrAZkAqB3W4F3RoJiO8F04RIPfiXg/9xILp/rYl1B7lDPB545BEIBA5+xxkGTJkCpaW9ur9FREREREREREREREREREREZH8KVRMRERERERERERERERERERHphYAnQFV5FWuCawAo8hYRcAcIR8PdD4rHYcfO/ZpcKfDHO4LUnCmwp6wQD4CYHdxJKzTtQI6k1d9mTCPkxWgPaDtuH/ji1nXDhJnVfbihbUIh2LgRJk/OoYiIiIgMJ5vqNu0XqJYyUwfZe39jN+6mplOQbFEUzp4KbqcVUNYa2ku5FypHg6PTp1hNrNDYpAEYtB/dBPLc4HVCqBESMatOUxSK/RCqt3YKNUJhgRWOZmD99LrBTEHcAf48cBjgcljtsRZwxCDZCHtbYc8WKGqGhBfq/dC4EoLLIVIHqTh8aQX7BaplpbUVVqyAu+7q5UARERERERERERERERERERER6QuFqomIiIiIiIiIiIiIiIiIiIj00oyKGawNrm0PG6ksrGR1zer2fq/Ty+i80R0D3loB+7qGkYxugPdKwUyHofliUOft6HcmIRCFRhckOoWo2UzaA9Qw4bSd0OCBPXnWdmWnfLdpNVDanMONNQwoLs6hgIiIiAw3Tptzv+3Ve1YfPEC2TTwOu95mn68jOPa0o6GwCKLptYvbk2T0CCs8LZkeZrdD1IRYAuKA02GFn7UFpAHYbVCcD7WNkExAYyv4nJDvgaYWK2xtbwTK/Na+dqyxhg2cgM/bEVZrAp5EunAMdm8HVz1Ek0AT1G6Hbf+wAtkApgRzWE/ddx/ceSfYbH0sICIiIiIiIiIiIiIiIiIiIiLZUqiaiIiIiIiIiIiIiIiIiIiISC+V+cuoKq9iTXANAEXeIgLuQHvYSGu8lVJfKV6nF5qb4c33rISQDBI2COZ1bCdtEE1/oiNlWMFqiU4ZHI4kxDoFrJU1w+gIjGiCZid44uBLH+uoBrhoU443dvZsGDEixyIiIiIynIwLjMPA6DZAtls7tkMqRUkcAmVQkg8XnwiGHSIpaE3ApBGADdKlaY7Btn0wrtTaNrFC0XwucBpWwFobw7CC1WpCYJoQboTSfGhutdZNZgz2JKDQB/mudDEDkmY6lLZNwlpTReqhbieMboKwx+qKR2Hvsx2BaoYJM6v7eEcChEKwcSNMnpxDERERERERERERERERERERERHJhkLVRERERERERERERERERERERPpgRsUM1gbXZgwbMTGpDlczqXQSLH0D4t0kqgEVYQj6AcPaLmyFmraQNQP25Fm5I21sZse+mDB9t3XVbsJJu+DCD6x9ClthQl3Hrn1ywQWwaFEuFURERGQYCngCBw2QzcSVinKMcwcTZkFxwAp7LSmGyjKrP2FAsR9cdojEINoKtY2wqcZar4wtsULTDCCWAJ/TCkNLxiCeAqcNMMBmWKFpoQiQtILVnE5rjCMJMQNaotDqArvDCnSLxyGVsoLYEgkIb4VYEJLpJdpxtbAtANud8O7z0NzQcbum1UBpcw53pmFAcXEOBUREREREREREREREREREREQkWwpVExEREREREREREREREREREemDMn/ZQcNGaiI1VLjK8L65/KB1/HEob4JgOkjNmwB3AqIOSBrQ6IL8mBWa5khCzN5pDs0wOtKxPSUIp+884AAeDzzyCAQC2d84w4ApU6C0NPsxIiIicljpNkA2HodYDK8vwOjiCgxSTCwJMqHlPRxea19aoCAKZWPBkbKanDbIc1mhaW4vxB1QUwOeOJhYQWg+T3p4FIq8HXMxTIgnwZn+1KvXDS3pNVA8AU0pMGOQ57HWTMXedIBa3ApRq60FMz2PeDO0dFovlTeBLw4T1sGK96Ex1dF3VANctCnHO3L2bBgxIsciIiIiIiIiIiIiIiIiIiIiIpINhaqJiIiIiIiIiIiIiIiIiIiI9FG3YSOAiUn1Wy8wKR7vsU5FGIJ+rJQRoLAVavIgarfaonbwJcBmduyDCdN3d9QwTJhZnaF4ayusWAF33dXXmykiIiKHkXBrmM31m9uDYA/KgK31W63rpkl88yaa9u0CE8IYjB17NGdeWEahsxF2BTGNjqEOA/KdkExv+10dyxiAUAscVQylPtiyG4KNUJkOVSNpLWG86W23DaLm/lPzuaC5BVJYYWsbP4CyYphUAa5OIbSJ5o5ANYBEcP86FUFgCTheh+tS8EExhDzWemxC3f5z7rULLoBFi3KpICIiIiIiIiIiIiIiIiIiIiK9oFA1ERERERERERERERERERERkT4q85dRVV7FmuAaAIq8RQTcASukJB6nZuf7VDjAmzh4HX8cypsgmGdtexPgSELMbW3H7OCPWT/bj90MoyMd21VBKG3u5gALFsC8eVBa2rcbKiIiIoeFdcF1PLrhUVKdU8YOoinWxNbQVmtj2zYSu3cRSq9XRgRMJk3+EBpqCaVS4LJSzxwOKA5AcYEVcpa0g2mC2wl2G0STEE9CcwqcdvB54OhK2LAXkilrH0cKmhrB5bK2AZw28DohloRkEuwuoAWS6f1LCqEwHxIhaI6D0wc2A1IhsNut/YwExPda84nUg385+B8HotYxDGBiXQ93iscDjzwCgUD3+xgGTJmitZeIiIiIiIiIiIiIiIiIiIjIIFOomoiIiIiIiIiIiIiIiIiIiEgOZlTMYG1wLSYmNDdTmcxjdXwf7NiOmUpRHYBJtT3XqQhD0I+V5gHYzI7rGBC3g8NMb5swfXfHWMOEmdUHKR6JwPz5cNddvb59IiIicnhojDayeP1ia82SJb/LT7m/nGBoJ1RX402BOwG+APzbyWBzQjIcwm5awWVjyqGowMoU87jBYbeum+lANJsNXE5obLUCz+J2KxDNcMCxI2BfBEbmgz09xVAjFOcDto5lkd8FZsoKm00lwbCB1w4+N8RbwBGDZCPsbYU9W6CoGRJeqPdD40oILodIHaTi8KUVtAeqZa21FVas0LpKREREREREREREREREREREZAhSqJqIiIiIiIiIiIiIiIiIiIgcFjbvjfCDp9Znte+3Lzmeo8vyurSHW8Nsrt9MOBru9fG3vrAYNmyAVIp4CTQ5AQ+E3VYIiDvZcw0DaHCBI2kFhdhTkLRZPx2d8k/KmmF0pGO7KgilzT0UX7AA5s2D0tJe3zYREREZAPs+gOduz27fC34EpRNyOtymuk37BaqlzFRW48YWjKVmzTJIWfsXReHsqeB2ggm02qHcCZWjwZH+VGqnHFhsJiQNwLC2DcDtgFEFsK8Joqa1j8MOeelgNLfLGp+MQi1QlA92G5jpwm3hbF6ftU5Kl8eTSB84Bru3g6seokmgCWq3w7Z/WIFsAFOyWT9157774M47rYmIiIiIiIiIiIiIiIiIiIiIyJChUDURERERERERERERERERERE5LDS2JliycW9W+37lvESXtnXBdTy64dGsA0Y6a3ptCVt3rIMCazthQMjT0b+mHEpbeq4Ts0GtF5qd4EhZwSBNrk4BIQAmTN/dsWmYMLM6i0lGIjB/Ptx1VzY3SURERAZaNAyb/pndvrO+mfPhnDbnftur96zOLkg2Hmdf3Q6aCq3N046GwiKIGta2Ow9Gj7CC0zpnyCZNsMUhZQI28LjAabf6DKyQtLI8CEYgGbPa8lzQuA88ditkzZWCljjsCUOhD7xOa+2DAZjW9fQ0sKWsYNpIPdTthNFNEE6vx+JR2PtsR6Ba1uun7oRCsHEjTJ6cQxERERERERERERERERERERER6W8KVRMRERERERERkW41NDTw9ttvs3LlShobG7v0FxYW8pWvfGXwJ9ZP9u3bx5IlS1i+fDlvvfUWu3btIhQKEQ6HMQyDwsJCCgsLGTt2LKeeeiqnnXYa55xzDvn5+QM2p1QqxerVq1mxYgXr1q1j3bp1bNu2rX1eTqeToqIiioqKGDt2LGeccQZnnHEGZ555Jn6/f8DmdSg0NTXx5ptvsnz5ctasWUN1dTXbt2+noaGB5uZmEokEHo8Hn89HeXk5o0aNYuLEiUyePJlTTz2Vk046CbfbfahvRr9avnw5//jHP7q02+12br/9dhyOwXvLd/PmzTz44IMZ+7785S9TVFQ0aHPp7He/+x0LFy4c8ONcdtll3HbbbQN+HBEREREZPI3RRhavX4yJ2fvBzc34X1lKeQCCeVaTNwHuBETTy/SICwJRcPaQ1+ZKB6mFPBCzQ0EUPPH9x5U1w+hIx3ZVEEqbs5zrggUwbx6UlmZ980REROTwMC4wDgOjfb1TWVjJ6prVPQ/csZ1Ai0nMDWOK4MJp4HBCzIBECo4tA2zQtoxqjkH1XitM7YSjrDYTwIACD3gd4LBZ7YYBZX7YkwBSVqhsUQC27IDKMeAwwJ6CpA3qm8DvANMFhh1SKTCTkDAhkYDGHdC0A5Jxq/ZxtbAtANud8O7z0NzQcZOm1fRi/ZSJYUBxcQ4FRERERERERERERERERERERGQgKFRNREREROQAlZWVVFf3/pTULpeLQCBAQUEBhYWFHH/88UyfPp3p06fz0Y9+FKfT2XMRERGRQ6i5uZlVq1axYsUK3nrrLVasWMH777+PaXb/ZfKKiophGar26quv8qtf/Yq//e1vxGKxbvfbs2cPe/bs4b333uP5558HIC8vj6uuuopbbrmFadOm9ct8ampqePzxx3n++edZsmQJdXV13e4bi8Voampix44drFmzpj1gq6CggBtuuIEvfelLTJo0qV/mdSjE43H++te/8uc//5nnn3+e1tbWg+7f3NxMc3Mz+/btY/369bz44ovtfR6PhwsuuIBPf/rTXHrppRQUFAz09AdccXEx3//+9zP2TZ8+nUsuuWTQ5vKrX/2K+fPnd2k/9thj+d73vjdo8zjQ008/zeuvvz7gx7nmmmsG/BgiIiIiMrg21W3aL1AtZfaQftbZ0tchEWdsA9TkdTQHWjttGxByQ2lLFvXapmFA3A4jIxDydvRN392xq2HCzN68pR2JwPz5cNddvRgkIiIih4OAJ0BVeRVrgmsAKPIWEXAHCEfD3Y5xpaIc49zBhBngLoXSYqgss/oSBhT7we+GeAJaorCzFjbuAdMEm2H9NAwwgFgCEkloSoLXZQWmxU2w2yDgg8ZGsJuADTxu2PYhjBwHPg/YUuBKj0nE0yFqzRBtsuZiT0FjNbQt4cqbwBeHCetgxfvQ2Glpd1QDXLQpxztz9mwYMSLHIiIiIiIiIiIiIiIiIiIiIiLS3xSqJiIiIiLST2KxGHv37mXv3r0AvP322zz00EMAlJWVcd1113HzzTcP65ATERHp3ksvvcTZZ5/dpf3+++/nxhtvHPwJZaGlpYUHHnigPURt3bp1JJPJQz2tAVVXV8dXvvKV9v+j+yISifC73/2OP/zhD3z1q1/lBz/4AV6vt+eBB0gmk/zf//0fixYt4pVXXsn5vm9oaOCXv/wlv/rVr7jlllv48Y9/jM/ny6nmYIpGo9x7773cc8897N69u+cBWWhtbeXvf/87f//733G73Vx88cV861vf4uSTT+6X+ofChAkTmDFjBq+88kqXvoULFw5aqFoikeDhhx/O2DdnzpxBmUN3Vq5cOSjHOe200wblODJ8Dce1gYiIyJHOadv/xBir96w+aMBIu3gcdi2DCmtznw+aXB3dERckbdb1sBsaPOmwkG44kpCwgTNlBaq5E1AYhRYnRB1Q1gyjIx37VwWhtDnLG9lmwQKYNw9KS3s5UERERIa7GRUzWBtc2x4mW1lYyeqa1e39XqeX0XmjMUgxsSTIhJb3cHitfRMRKBsLjnRAmdMGeS4r5NVtB7sLitxQmQ976qyc2JaoFYpG+npR+u30lhi4nVYYG4DfBc02IP1WeXEAtu2FTVuhvATKSqG401vxNhNincJqjcaOQDWAiiCwBByvw3Up+KAYQh4obIUJdVbIW59dcAEsWpRLBREREREREREREREREREREREZILZDPQERERERkSPB3r17ueeee5gyZQr/8z//QzQaPdRTEhERoaamhi984Qv84Q9/4N133z3sA9U2btzIlClTcgpU6yyVSvGzn/2MU089lWAw2OvxjY2NfOELX2DJkiX9et+bpsmCBQuYNm0aq1at6re6A+mf//wnkydP5rbbbuu3QLUDRaNRHnvsMU455RQuvfRS3n777QE5zmCYO3duxvYnn3yS2traQZnDP/7xj4yPe7vdzg033DAoc8iktraWbdu2DfhxPB4P06ZNG/DjiIiIiMjgGhcYh9EpXqOysDK7gTu2Q6ojwSPQCnQKTfMkOu1rQNR+8HI209rPk7DqtAWmFabrTu/0sskwYWZ1dtPcTyQC8+f3YaCIiIgMd2X+MqrKq9q3i7xFBNwBKyi2qYnW5kbGBXxcPmkvp48IUbJnL4EoBKJQGoUSmxWq5khBgdNauxhYl1AzOIGKYphyFOTbIdjY6eBJaG21rppAPAn+dK6tAXjc0LaqyndCzGntF6yDWAMkmqwQNtOERDPY0ussewqi+6z2xjpIPQv+/we8CqSs2hPr4NRd1s+MgWoeDzz2GLz4YveXf/0L9u6FZ5+FgoL++QcRERERERERERERERERERERkX7lONQTEBERERE5kiQSCX74wx/y1FNP8a9//Yvi4uJDPSUREZEjwqZNmzj77LN7DOxyOp2MHTuW4uJikskkdXV1bNu2DdM0ux2zbt06zjvvPJYsWUJJSUl/Tx2AvLw8xo4dS35+PrFYjNraWnbs2HHQeX3wwQfMmjWLF154gVNOOWVA5pWrZDLJ7bffzk9/+tOD3pbOXC4XY8aMoaCgAK/XSywWo6mpiV27dtHY2NhzAeCpp57iqaeeIhwOUzAMv/j26U9/mltvvbXL7Y3FYjzyyCPceuutAz6H+++/P2P7BRdcwOjRowf8+N1ZuXLloBznxBNPxOl0DsqxRERERGTwBDwBqsqrWBNcA3QEjISj4e4HxeOwY+d+Ta4U+OPQ5LK2nSkr6COZPu1dzA7uJNgzvAxyJK1+sPpLmyEvbm17E1ARgtGRjv2rgh2ha712331w551g0/n4REREDgfh1jCb6zcffO2SZrfZqQ5VY2KCaeJYv4FQ7SYwobwQRlWsJZmqILRrt7WYcYLDAWUjIRCAlBsMA7wuaykRN6ElDi1JcNis9ZDPA0dXwoa9kEyBPR3G1tQILhcYto72PDe0xsDtgBYb2FLp+h6wGTBmBJQYYDZCbQwSSWjdBKYD6v0QCMPqv0I4CKk4fGkF0NvznLW2wooVcNddfbj3RURERERERERERERERERERGSoUKiaiIiIiEiWqqqqCAQC3fa3trZSX1/Prl27aG07vXY3Vq9ezcUXX8wLL7yA3+/v76mKiIhIJ6lUimuuuabbQDW73c6nP/1pbr75Zs444wy8Xu9+/eFwmJdeeolf/vKXvPDCCxlrrFmzhi9+8Yv85S9/6Zc5jx07losvvpjzzjuP0047jbFjx3bZp66ujiVLlrBgwQJefvnljHXC4TCXXHIJq1atYuTIkf0yt/7S2trKVVddxd///veD7ldeXs5ll13G+eefz6mnnkplZSWGYWTcd9++faxcuZLXX3+dp59+mrfffvugtVOpVJ/nfyj5fD6uvPJKfve733Xpu//++wc8VG3v3r08/fTTGfvmzp07oMfuyTvvvJOx/T/+4z/43//930GejYiIiIgMRzMqZrA2uNYKGAEqCytZXbO6vd/r9DI6r1OQ8FsrYF/X1xajG+C9UjDTL198Majr9HLTmYRAFBpdkLB3tNtMoO0ljwkf2Q7bA+k2E07f1bGvYcLM6hxubCgEGzfC5Mk5FBEREZGhYF1wHY9ueJSUmf17nk3xJoJNQdi2DXZsoSUPfAE453RoToWprdmCvaERux/GlENRgRVy5vBC0rDWOQ6HFapmxwo/K/RDqAVsMWtdYzjh2BGwLwIj8ztCZesbrXo2A1JJq06eGzwOME1wxgE7THKkg9dS4IxZY+1x2FkN/pC1nYzA5qVQl14nTVHorIiIiIiIiIiIiIiIiIiIiMgRTaFqIiIiIiJZWrBgAbNmzepxv3g8zrvvvstf/vIXfv/731NXV5dxv2XLlvHNb36TBQsW9PNMRUREcmez2Zg0aRKnnHIKp5xyCqlUiq985SuHelp98pvf/Ia33norY9/YsWP5y1/+wkc+8pFuxwcCAS6//HIuv/xyFi1axE033URjY2OX/RYtWsTNN9/Mueee26d5ut1uPv3pT/O5z32Os846q9vgsDbFxcV86lOf4lOf+hRPPvkkc+bMoba2tst+NTU1fPGLX+Sxxx7r07wGQiKR4BOf+ATPPfdct/tMnjyZ//mf/+GKK67A4cjubczS0lJmz57N7Nmz+f73v8/WrVv57W9/y29/+9uM981wNnfu3Iyhau+88w7vvvsuU6dOHbBj//GPfyQej3dpLy0t5bLLLhuw42Zj5cqVGdtPPPHEwZ2IiIiIiAxbZf4yqsqrWBNcA0CRt4iAO0A4GgagNd5Kqa8Ur9MLzc3w5nvQdXkMQMIGwbyO7aQNoumXNynDClZLdMrrcCQh1ilgrawZjglB3AF78qC8CXydjjWtJofAEADDgOLiHAqIiIjIUNAYbWTx+sXtobDZqghUEAzthGorpbU4CmdPBY/T6o82NVLmhcrR4EyvYdretbaZVrAand7GNrGC0bxOqI1AImq1O+xWe7wF3C6rLRaH2kYoybPG2UwrpM1mA58b7E6rdCwGLVHwJKxxyTiEPgRXU8dxHbtg29r0/BQ6KyIiIiIiIiIiIiIiIiIiInLE0+n0RERERET6mdPp5OSTT+YnP/kJ77///kGDJX7zm9/w4YcfDuLsREREujIMg+OOO45rr72We+65h1deeYWGhgbWrVvHAw88wK233sq0adMO9TT77Be/+EXG9uLiYl544YWDBqod6IorruDxxx/H6XRm7L/nnnt6Pb/8/Hxuu+02qqureeihh5gxY0aPgWoHuvTSS1m6dCllZWUZ+//2t7+xbNmyXs9toHzhC1/oNlDN4XDwox/9iHfffZdrrrkm60C1TCorK7nrrrvYtm0bP/zhDykoKOhzraHmIx/5CJO7+WLf/fffP6DH7q7+tdde2+3vxmDpLlRtOD+HiYiIiMjgm1ExA6NTQkhlYWX7dROT6nA6qWPpG5AhcLhNRRg6Z5sUtnbqNKygtM5BJDaz07YJ03dbVyfWQkErVIY79j2qAS7a1Isblcns2TBiRI5FRERE5FDbVLdpv0C1lJnK6uJ1einbXouZSmECp4yHUemQMxNw+2H8WHA4OtpSQCIJrVFoaoFYAlIp67htyxi7DUrzwe7umGOeC1qbrLGp9L6JGOwJWwFr7QcwrWC0Tksi7CkrfDZSBzvfg5I9MLbB6o9HYcPLYKbnoNBZEREREREREREREREREREREen7NxJFRERERKRHJSUlPPbYY1x++eU8/fTTXfrj8Tg/+9nP+PWvf30IZiciIke6srIylixZwsknn0x+fv6hns6AWLduHRs3bszYd+edd3Lsscf2uuY555zDl770JX7+85936XvxxReJRCLk5eX1WMdut3Prrbfy7W9/u9swtN6YOHEijz76KDNmzMjY/7//+7+cccYZOR8nV3/84x/53e9+l7EvPz+fJ598kpkzZ/brMX0+H9/61re46aab+OIXv8ijjz7ar/UPlTlz5nDbbbd1aX/kkUe4++67cwqk687KlSt59913M/bNnTu334/XG42NjXzwwQdd2g3DYMqUKYdgRiIiIiJyKOR7HJx9XHavsfI9mdfMZf4yqsqrWBNcA0CRt4iAO0A4aqWa1URqqHCV4X1z+UHr++NQ3gTB9EtEbwLcCYg6IGlAowvyY2A3raCQmL3THJphdMS6bjfhhtVWSEjIY4WzTajbL4+t9y64ABYtyqWCiIiI9Ad3ACbOzn7fDJy2/U90sHrP6vZ1y0HF48R2v8+uALidcPFUaz0St4PDBuPHQNRmrUUAmmOwrwbGlVrbJmA6wOey9nd1WssYBhTnQTAEpgmOFBQFYMsOGDMW3OkQtJgNkgloNsHugLgJZsJaK5kmhBqAPRDaB8l0lm1lCNxJaEjBq69AczpgTaGzIiIiIiIiIiIiIiIiIiIiIgIKVRMRERERGXB2u52FCxcyfvx4IpFIl/5nn332EMxKREQE/H4/s2bNOtTTGFCvvvpqxvaCggJuuummPtf9+te/njFUrbW1laVLl3L++ef3WCM/P5977723z3PI5KyzzuKKK65gUYYvxj/99NOYpolh5PS1+5zs2bOHW265JWOf2+3mmWee4aMf/eiAHb+8vJzFixfz0EMP8fnPf37AjjNYbrjhBr71rW+RSCT2aw8Ggzz99NNcfvnl/X7MhQsXZmw/+eSTmTp1ar8frzdWrVqFaZpd2o855pisgg5FRERE5PBwdFke9885Lec6MypmsDa4FhNrjVlZWMnqmtUAmJhUv/UCk+LxHutUhCHopz0BrbAVavIgarfaonbwJcBmduyDCdN3d9QwTJhVDaXNGQ5w9dXQm9e3hgFTpkBpafZjREREZOCUToBr/5pTiXGBcRgYGdctB7VjO66ESWEKTjkORhdaS4WoAaMC4HZYwWamCfUR2FRjLVfGllj7GUAsYYWqJVKQTEI8BY50uJrNBoU+CEXSwWw2cHlgw3aYVA4+D/ht4LRZx0jEIdwMrek1jwlUfwgjGjqmXN4EvjiwG069F4pMhc6KiIiIiIiIiIiIiIiIiIiIyP4UqiYiIiIiMghKS0uZO3duxuCULVu28MEHHzBhwoRDMDMREZHD265duzK2z5w5E6fT2ee6Y8aM4YQTTmDdunVZH3Ow3HjjjRlD1fbt28emTZs49thjD8GsLN/73vcIhUIZ+375y18OaKBaZ9dffz3Tpk3D4/EMyvEGyogRI7jooot44oknuvTdf//9/R6qFovFeOSRRzL2zZkzp1+P1RcrV67M2H7iiScO7kRERERE5LBQ5i+jqryKNcE10NxMUSROwPAQNlshHqdm5/tUOMCbOHgdf9wK/wimc369CXAkIea2tmN28Mesn+3HbobRnc7PURXsJlAN4Mkn4d57FZImIiJyBAt4Ah3rFqDIW0TAHSAcDXc7xpWKcoxzBxNmQaAQxk8Af3p9YthgZCFgQDwBdU2weW86YA1ojnXs2xKFgNcKMzOwwmDjSXDarW2vG1o6rWsKimDvZti0FUaUwrTKjj7TBHsjkF4XtbRCwQFroIogsAR4HYwUTOzpzskmgFahsyIiIiIiIiIiIiIiIiIiIiKHFYWqiYiIyBEp3Bpmc/3mg36IeKAF3AGOLjqagCdwyOYgg+uCCy7IGKoGsGHDhgEJVXvvvfd47rnnWLp0Ke+//z7bt28nEokQj8fx+/2MGjWKY489ljPPPJNLL72UE044od/nAFa4zFNPPcU777zDqlWr2L17Nw0NDTQ2NmK32/H5fO3zqaio4Oijj+bkk0/mjDPOYNy4cf0+n+3bt/PUU0/x2muv8d5777Ft2zYikQiJRAK/38+IESM45phjOP300zn//PP5yEc+gmHkdF7zPlm3bh1/+9vfeO2119iwYQO1tbXEYjFKS0spKyujqqqKj3/841x44YWUDtCH/JPJJO+88w5Lly5l/fr1bNiwgW3bttHQ0EBDQwNOp5Pi4mKKi4sZM2YMH/3oR5kxYwannXYabrd7QObUk6Fwvx1ox44dPPfccyxbtowNGzZQXV1NKBSipaUFt9tNQUEBpaWlTJo0ieOPP56zzjqLs846q9/vw6HynNDfhuPjVAZPMBjM2D5+/Pica48fPz5jqFpNTU3OtXMxa9asbvs+/PDDQxaqtmXLFn7/+99n7DvvvPO4qacvtvWzqVOnDkjdwX6unTt3bsZQtX/84x/s3buXsrKynOp39sQTT1BbW9ul3ePxcM011/Tbcfqqu1C1adOmDfJMpM1QXnsMlfXZQBsur33eeustnnnmGZYtW8amTZsIBoM0NTXh9/uprKzkpptu4pZbbhn0eYmIyOGjr38TsBs2ql9YjLlhA6RSODwGoYmlVkKIK8WK0TCuoec6NhMiTvAkwGFa27T9l2tA3G61A2DC9N0dYw0TZlYfpHgkAvPnw1139eq2iYiIyOFlRsUM1gbXYmItKioLK1lds7q93+v0MjpvNAYpJpYEmdDyHg6vta/RCiU2SKSsffNcYAMwwW0HuwGnjIWaOthTB+F6yBsBpgFmygpZy3NZY512iKb2n5vPBc0tkAJ8bnAaVjhbvgNitZDMB6cPzBg449bxkjaI1FmBtKYJkXrwLwf/40C0F3eMAmhFREREREREREREREREREREjjiGaZpmz7uJ9I/8/Hwikch+bXl5eTQ2Nh6iGYmIHH7ee+89Mv33bhgGkyZNOgQzGnrWBdfx6IZHSZmpnnceYDbDxqcmf4oTyodHaM2RorKykurqrt9SW7JkyUFDSnqyY8cOxo4dm7HvD3/4A3PmzOlz7c4SiQR/+tOfmD9/PqtWrerV2FNOOYVvf/vbXHbZZf0yl3/961/8+Mc/5sUXXySV6tvv3Pjx47niiiu4/vrrcw5ZWLJkCXfddRcvvvhixufK7lRWVjJv3jxuvvnmnEIUFi5cmPHf+cDH1sqVK/nGN77BCy+8kFVdr9fLvHnz+OY3v0leXl6f59cmFArx+OOP89hjj/HKK68QDvc+gLKkpIQvf/nL3HrrrRQWFuY0n+Fyvx0olUrx6KOPcu+99/L666/36jHXNr8LL7yQz33uc8yePRubzdaneQyl54T+NBQep909NnM1VN+qeemllzj77LO7tFdUVLB169bBn1CW/vM//5MFCxZ0af/Wt77FD3/4w5xqX3vttTzyyCNd2n/605/yta99LafauSouLqa+vr5L+yOPPMLVV199CGYEt912G3fffXfGvtWrVw9YyNlgOJTPtYlEgqOOOipjmN/8+fP56le/2qe6mVxyySU8/fTTXdqvuuoq/vSnP/Xbcfpq6tSprFmzpkv7E088waWXXnoIZtR3sViMUaNGUVdXt197Xl4ee/bswe/399uxGhoaGDVqFM3Nzfu1l5WVsXPnTpxOZ6/qDeW1x0Cvz4bS2mC4vPb529/+xh133NHjY+Uzn/kMCxcu7PN8sqH31EREDl85/U3g9ddZv+oFgp3eOtqdB9G209eZMKYRnFmU3uuDJicUtUC9FxrdVliIPQX5sY79yprg4k0d21Nq4FMbeiielwdbthxWYSHvv/3hoZ6CSI+iY/v/fWXpvSnlIw71FESGjEfXP8qaYMf7Y+/sfodwZB/EYhguN7MmTuekUVvIt9XDsmWQtBYxbj+UH2utTwAK88HlhpQNEknYE7bWLM4UNLdCdQ2MGw/29FsjNmBEobXdtizyuSCWhGQSYlFoDEM8vfbZE4TiAJQYVs2kAXV2aFwLXjskvLDXAx/+HRxBK1wtFYcvrYDS/d/Cys7ttyuAVkREREREREREREREREQkA2XAiMjhqm/fyBYREREZphqjjSxev3hIBKoBpMwUi9cvpjGqF5dHgpKSkm779u7d2y/HWLp0KSeddBI33HBDrwMMAN566y0uv/xyLrnkkpzm1NDQwDXXXMO5557L888/3+dANYAtW7bw//7f/+O73/1un2vs27ePK6+8knPOOYcXXnih16EEW7du5T//8z+ZOnUqr7/+ep/n0RPTNLnjjjs47bTTsg4GA2hpaeGHP/wh06ZNY8uWLTnN4Wtf+xojRoxgzpw5PPnkk30KqgKora3lO9/5DhUVFfz1r3/NaU49GQr324GWLVvGKaecwhVXXMFrr73WpyCMlpYWHnvsMS688EJuv/32Ps1jqDwn9Lfh+DiVQ6e8vDxje6bAsd6qra3N2F5WVpZz7VzlEoQzEBKJRLdhOB//+MeHdaDaoX6udTgcXH/99Rn7+jOAaPfu3Tz77LMZ+wYiRKq3Wltb2bAhc9LEtGnTBnk2uXO5XFxzzTVd2iORCIsXL+7XYy1atKhLoBpYwZG9DVQ71L8PBzNU1mcDbbi89mlsbOSTn/wkn/zkJ/v0WBEREclWTn8TaG6Gl1+mIgx0+i+1sLXTPgaEs3z5FUiP21kAKQM8CWu77SdYx5m+u1N5E2Z2Pf9HV5EIzJ+f3URERERkWAm3hnln9zu8tPWlHi92m53qUDVbQ1vZWr8Fx/oNhN55g9Dat3Bufp1RsYdIprYT2rGJkDNFyAORPHCNA08xlBVBSQkECsDjBpcDYilr7ZJIB6b5PFA5DuqaOuZoT0KoEVImYICRbve7IN8N+T7w50FRHowogqPHgt/ZEUxrJKHlfbC1QrQJkvtgwxJoWgvhICQTUBXsY6AawH33QQ5/rxYRERERERERERERERERERGR4UWhaiIiInJE2VS3CbPTt59SZuqQXdqYmGyq23Qo7g4ZZAf7MntfvtB/oJ///OfMmDGDNWvW9LxzD55++mlOOeUU3nvvvV6Pra+v57zzzuNPf/pTzvPoD+vXr+fUU09l0aJFOdd6//33Ofvss/m///u/fpjZ/lKpFDfccAPf/e53SSaTfaqxefNmzjrrLDZv3tznebz99tvEYrE+jz9QQ0MDV155Jd/5znf6rWZnQ+V+6+zuu+/mrLPO4p133umXegDRaLTXY4bKc8JAGG6PUzm0Tj311Izt69aty7n2+vXrM7afdNJJOdfORSwWY9++fRn7ioqKBnk2lldffbXbgKQbb7xxcCfTj4bKc+3cuXMztr/77rusXLky16kB8NBDD2X8v3bcuHGcd955/XKMXLz77rskEoku7UVFRYwbN+4QzCh33f1u9GdY3sHq9TYsb6j8PmQyVNZnA224vPYJhULMmDGDv/3tb/1eW0RE5EA5/U1g6eukEnG8CShrtnLVTKwQNHeiYzvigpitY7u7iytljY3ZIW63QkQ88Y4wEbCOM7rTST97FR6yYAF081pQREREhqd1wXX84s1f8PeNf88qVG3VnlU0xZusULU1r1K7YxMtdjBK4JwzoTkVprZmC6GGvTT6oWA8jDkB8srB7gWbHYz0xeEAtxMCPij0g5lew6QAwwlFPogmrBBYuwnJKAQjkExZoWptf/622cDlBK/PClmz263+tmDZZByCH8LxH3Tc7sY6SCzr2M46aLY7oRBs3JhDAREREREREREREREREREREREZThyHegIiIiIig8lpc+63vXrPasLR8KDPI+AOMH3U9PbtA+clh6fa2tpu+woLC3Oq/d///d/cddddB93HMAxGjhxJSUkJXq+XYDDI7t27uw0m2rZtG+eccw5Lly6loqIi67lce+21rFix4qD7eDwexo4dS15eHm63m8bGRsLhMMFgsF+DkjZu3MjZZ59NMBg86H4FBQWMHj0an8/Hrl27CAaDpLo5W3k8Hufmm28mmUzy+c9/vt/messtt/Dwww93abfZbIwYMYLy8nJsNhvBYJCdO3d2W2fnzp3ceOONvPzyyxiG0e1+fVVQUEBRURGFhYV4PB7C4TD79u3rNrwHrNDAH/zgB5SXl3PLLbf063yG2v325S9/mXvvvbfH/VwuF2VlZYwYMQLTNKmrq2Pv3r00N2f7LdmDG0rPCYfCUHucyqH1sY99jPz8fBobG/drX7p0KXV1dRQXF/ep7qpVq9i+fXuX9lGjRnHCCSf0qWZ/efvttzOGSwFMmDBhkGdjefrppzO2u91uLr300kGeTf8YSs+1kydP5owzzmDZsmVd+hYuXNgvQX/dBW995jOfwWY79Ofu6C48btq0aYM8k/5z8sknM2XKlC4hZS+//DJbt26lsrIy52N88MEHvP76613ap0+fztSpU7OuM5R+Hw40VNZnA224vPZJJpP827/9G6tWrerSZ7PZKCsro7y8HK/XS21tLVu3bu1zeLKIiAjk8DeBeBx2LYP0MiRmg135WAkgQNwGTa6O3Zud4Mv8MgywQthGRaAt381I/xwZgZA3vZMJ03d3jOl1eEgkAvPnQw/rMhERERkeGqONLF6/eL+A2GxUBCoIhnZCtbWQKI7C2VPBk14WRZsaKfNC5Whwpj892nYEg/Q6xbQ2DKyz9ua5weuEfU0QT+fMO+yQSoARp/3UvtEEBENQ6LP2N9J1MK3rbX+Bs6XAkYRIPdTthNIGK0h2ZAS2O2HDy9DpPHVMq+lF0GwmhgF9/FuAiIiIiIiIiIiIiIiIiIiIiAw/ClUTERGRI8q4wDgMjPYPHlcWVrK6ZvWgz6OysLL9uoHBuMC4QZ+DDL5MXxpvM378+D7X/cUvftFtgIFhGFx00UXMmTOHmTNnUlpaul9/c3MzL774Ir/+9a959tlnu4zfvXs3119/PS+99FJWYRmLFy/mmWeeydh33HHH8cUvfpGPf/zjTJw4MWNwVSKR4P333+fdd9/ln//8J08//XSPoQDdaWho4LLLLut2vN/v55ZbbuGaa67pEhhRV1fHX//6V375y1+ydu3ajONvueUWjj32WM4555w+za+zBx98kPvvv3+/tpkzZ/L5z3+e2bNndwn82bZtG4888gg/+tGPaGho6FLv1Vdf5be//S3/8R//kfPcTj75ZC677DJOOeUUpk2bxpgxYzLut3PnTl577TX+8Ic/8Pzzz2OaXb/g8fWvf52PfvSjTJ8+PUOF3htq99v3v//9gwZ2jBgxgptuuokLL7yQM844A7vdvl9/KpVi/fr1LF++nCeffJJnnnmGaDTa63kMpeeEwXKoHqcjRozgox/9aPt2OBzO+JwxceJEysvLe3GLpD/5/X7mzp3LL37xi/3a4/E4P/7xj/nJT37Sp7rf+973MrZ//vOfH5BQy9549NFHM7aXlZVxzDHHDPJsLK+99lrG9tNPPx2fzzfIs8ndUHyunTNnTsZQtUceeYSf/vSnuFyuDKOy8+abb7Jhw4Yu7YZhcOONN/a5bn965513MrYP51A1gBtvvJGvfe1r+7WZpskDDzzAd7/73ZzrP/DAAxnb58yZk3WNofj70Gaw12eHam0wnF773H333bz00kv7tX3kIx/h85//PBdddFGXx0hTUxPPPPMMu3btyvnYIiJyZOrz3wR2bIdOwaOuFPjjHUFqzhTYU5BML1FidnAnwd5N5klhqxXE1uoEZxIcKStorTAKLU6IOqCsGUZHOsZUBfsQHrJgAcybBwf8nyoiIiLDz6a6TfsFqqXMzKHoB/I6vZRtryWYXsucMh5G5UFb/qvbD+NHgN3oCFNLJK1LPAHJFNhs4HWBYQNHer1jt0FZHuw1IRlP17JDJAwFeWA6rTWOaUB9E/gdYLrAsFvLKjMJCRMSCWjcAU07OupUhqyfE3bCypXQ3CmD/6gGuGhT7++//cyeDSNG5FhERERERERERERERERERERERIYLw8z0DWKRAZKfn08kEtmvLS8vj8bGxkM0IxGRw897772XMSDEMAwmTZp0CGY09Dy6/lHWBNe0b7+z+x3C0fCgHT/gDjB9VEdQy5TyKXzq+E8N2vGlZ5WVlVSnz9zd2ZIlS5g1a1af637pS1/i17/+dZd2wzDYt29flwCobCxdupSzzjqLZDLZpe/444/nj3/8IyeeeGJWtZ566imuvfbajGFTP/zhD/nWt77VY42zzz67yxfkAb71rW/xgx/8oNdBCKlUipdffpmf//zneDwe/vKXv2Q99qabbuL3v/99xr4ZM2bwwAMPUFlZedAasViMO+64g7vuuivjc+uoUaN47733KCgoyGpOCxcu7DGgIhAI8MADD3D55Zf3WG/Hjh2cc845bNrU9ZsExx57LBs3bsxqXp3NmjWLDRs28J//+Z/ccMMNjB07ttc1XnvtNebOnZtxXmeddRavvPJKr+oNh/vt2Wef5cILL8zY53A4uP3227ntttvIy8vLumZDQwMPP/wwP/nJT/jEJz7Bz3/+8x7HDLXnhIEyFB+nAC+99BJnn312l/b7779/yIQO5aK721dRUcHWrVsHf0K9UFtbywknnEBNTc1+7Xa7nb///e9cfPHFvap3zz33MG/evC7tY8aMYd26dQQCgZzmm4tIJMK4ceOor6/v0jdnzhz+8Ic/DPqckskk+fn5tLS0dOn7xje+wY9//ONBn1MuhupzbUNDA6NGjaK5uWvqwuLFi/nUp/r+muPzn/88v/nNb7q0z5o1iyVLlvS5bn869dRTeeutt7q0/+EPf+hVQNhQEwwGGTNmDIlEYr/2o48+mg8++CCnEEfTNKmsrGTbtm37tbtcLnbt2kVJSUmPNYbq7wMMjfXZYK0NhutrH6/Xy29/+1uuu+66rGoONL2nJiJy+Or13wTicVi2bL9QNYCYDXblA+klWNzWEbIG4EqAb/9lG2CFp42KwD4vRNzgjlshaiOawJuAFgfU+GH2hx2haoYJX1rRh1A1gNtvh25Cb4eT99/+8FBPQaRH0bHZv56QgTOlXEFJcnhaU7OGRzd0nEAj6881xOPEli9ll9/E7YTPzgK7A+J2KyDtpLHgtnWEwTbHYPs+OHYUtL3V5PNCoc/a3+OA1rgViGZiha7tDYFpWmGxJOH97VB+FBTZIWEDlwtGpp8iEwY0NkO0ydq2p6DuLWjLiCtvguP3AruBR8GshQ+KIeSxgmkn1LUvv/rmggtg0SLI8j0NEREREREREREREREREZEjiTJgRORw1btEAxEREZHDwIyKGRidPnZbWVi5X7/X6eWYomP67eJ1ever3/l4BgYzK2cO5M2VISIYDLJw4cKMfWeddVafAtXi8Tif/exnMwYYXHjhhSxfvjzrAAOASy65hFdeeSVjEMwdd9xBMBg86Pja2lpeffXVLu1XXXUVP/zhD3sdqAZgs9k4++yz+fvf/95tSEAmK1as6DY05qyzzuLZZ5/tMVQArECJO++8k5/+9KcZ+3fv3s13v/vdrOfVk/Lycl599dWsgsEAjjrqKP75z3/i8/m69L3//vsZ/z168p3vfIfq6mr++7//u09BVQAf+9jHeOONN6iqqurS9+qrr7Jq1ao+1e3Oob7fmpqauOmmmzL2eb1e/va3v3HHHXf0KrADoKCggC9+8Yt88MEHfOELX+hx/6H2nDCQhuPjVA6tkpISHn74Ydxu937tyWSST33qU/z85z8ndcCX5TNpaWnhK1/5SsZANafTyQMPPHBIA9UAfvzjH2cMVAO6fa4aaJs3b84YqAZwwgknDPJscjOUn2sLCgq6DU67//77s65zoNbW1m6DbefOndvnuv0pkUiwZs2ajH3Tpk0b5Nn0r/Ly8ozBj5s3b+5TAGhn//rXv7oEqgFceumlWQWqDeXfh6GyPhsMw/W1j9/v55lnnhkygWoiInJ46/XfBLY1csy+FMfUsd9l8j44ph6KWqxLeRMUtIInbl3sJuS3guOA5VFhqxXAFnEBJpS2QFmzFagG1s+KUEegGkBVsI+BagD33dclEE5ERESGn3GBcQddw3Rrx3ZcCZPCFJx1HIwuhJH5UO6HE0aC2wGmYQWk1UVgzXaob7bC1dq0RK3QtEQKmqJgt1n7g3W9wG+FwNpNa9uZBxu3Qs1esCWhuNNHJGwmxDq9PWw0dgSqAVQEgX8BvwNqrQC1iXVw6i7rZ5dAtauvhhdf7Pnyr3/B3r3w7LMKVBMRERERERERERERERERERE5wihUTURERI44Zf4yqso7AlSKvEUE3B1f2m6Nt1LqK2VsYGzOl1JfKa3x1vbaAXeAIm9R+3ZVeRWlvtLBueFyyCQSCW644QaamzN/C+7mm2/uU93f//73bNiwoUv79OnT+etf/4rf7+91zWnTpvHb3/62S3s0GuW+++476Nj33nsvY6DCV7/61V7PI5PeBB784Ac/wDTNLu1HHXUUTz75JF6vN8Oo7s2bN6/bf6ff/OY31NTU9KpeJoZh8OCDDzJlypRejausrOS2227L2Pe3v/2t1/M455xz8Hg8vR53oNLSUhYvXozT6ezS96tf/Srn+m2Gwv3285//nJ07d2ac22OPPcYll1zSq3oHcjgcHHfccT3uN9SeEwbScHucytBw3nnn8eijj1JwwJenotEoX/3qV5kwYQJ33nknr7zyCjU1NcRiMVpaWti5cyfPPvss//Vf/8XYsWP5xS9+0aW22+3mz3/+M+eee+5g3ZyM1qxZw913352xb9asWZx55pmDPCNLptCmNsccc8wgziR3Q/25truQs+eee449e/b0em5g/b8YCoW6tB8sxG2wrV+/nmg0mrHvC1/4Ah/72Mdyupx11lndBgMOhhtvvDFje3ehzdnqbnx3xzvQUP59GCrrs8EwHF/7APzsZz9j5kyFzIuIyODo1d8EnCWMffM9xjaQ8XLSbgi0QiBqXUZFwJO0Lu4kJOyQ6PQpDHfCCk0LuwED/HFwpuDE3XQkk5hw+q6OMYYJM6tzuMGhEGzcmEMBERERGQoCnsBB1zCZuFJRJjt3cOksuOVcuPojMGEEHFMOJ4yA40fCUYVWyJrdAZv3WuFpAHsbOuqYqXTImmmFmsUT4LZbfQbgc4ErvW8KKCq01j3BOog1QKLJGmOakGgGWzpM1p6C6D6rvbEOUs+C//8Br6YLZePJJ2HqVDjnnINfzj4bSvVZDBEREREREREREREREREREZEjkULVRERE5Ig0o2JGt2d1NjGpDufyjaUO1eFqTDq+3Nz5OAYGMyv1BeLD3b59+/jEJz7Bc889l7H/lFNO4Zprrul1XdM0+clPftKl3WazsXDhwj4FGLS54oorOP/887u033fffcRisQwjLN0FdVRVVWVsHyjbt2/nqaeeytj3s5/9jEDg4F826M6Pf/xjSjN88L6lpSXnQAuA66+/ngsuuKBPY+fOnYthdDlPO2+//Xau08rJcccdx2c+85ku7c8//3y/HeNQ32+xWCxjwBLALbfcwsc//vE+za23huJzwnAxGI9TGTouvvhiVq1axezZs7v0bdmyhW9/+9vMnDmTkSNH4na78fl8HHXUUVx44YX89Kc/pba2tsu4KVOm8Oabb/LJT35yMG5Ct1paWrjuuusy/l7abDbmz59/CGZlyRRs1GbkyJGDOJPcDIfn2pkzZ2YMqkskEjz00EN9mtv999+fsf3KK6/E5/P1qWZ/W7lyZbd9y5cv5/XXX8/psmfPnl4HU/Wniy++mLKysi7tixcvpqmpqU81Gxsbeeyxx7q0jxw5Mqv1y1D+fRgq67PBMFxf+5x55pl9DhcXERHpq6z/JrD0DYjHu63jj0N5pyWYN2EFiLSp9UKy06cwClshboOICzCt7UArjInAyHSd8ibwdTrktBoozXx+juwYBhQX51BAREREhoqDrWEAvE4vxxQdw4Si8Vw4wc9/jN3Cx48xmeCC8lYosYEjZV0CDuvDojbTCkizG3DKWBhfYK1pGush1en8WQ1NkEoHnZlA0oQ8NzhsVrBa27lvkjbId0KRH46rhDIvmI1QG4KdQdi2EkKbYdtuqHsfXn8EXnsEVj4BU58EMp8roXuRCBzC97tFRERERERERERERERERESGok9/+tMYhjGglxNPPPFQ30yRrClUTURERI5IZf6yg57VuSZSQ0u8JadjtMRbqInUtG8H3AGKvEXt21XlVZT6dGbkw1EikeDtt9/mtttu47jjjuPpp5/OuF9RUREPP/xwxlCnnrz44ots2bKlS/t1113H1KlTe13vQF//+te7tNXU1PDGG290OyaRSGRsH+zQpT//+c+Yptml/cQTT+SKK67oc92ioiK+8Y1vZOx7+OGH+1y3Tab7PFtjx45l+vTpXdpXrVqV8b4YTJlChqqrq7sN4eutQ32/Pf744+zdu7dLe2FhIT/60Y/6PLfeGorPCcPJQD9OZWgZP348zz33HEuXLuVzn/scI0aM6HUNu93OrFmz+POf/8yqVauYNm3aAMy0d774xS/y7rvvZuybN29exue7wRIKhbrtyxTaM1QNh+dawzC48cYbM/b1JQhpx44dvPjiixn75s6d2+t6A+VgoWr94dRTTx3Q+j1xOp1ce+21XdojkQiPPvpon2r+9a9/pbm5a1LHddddh8Ph6HH8UP59GCrrs8EwXF/73HrrrX16HSwiIpKLrP4m0FALby7vsVZFGDqdS4XCVutn0oCYHaJ2a9udsAJKwm7AsALZnCmoDFn9E2uhoBUqwx21jmqAizb17Ta2mz0b+vBaV0RERIaentYwrfFWxgV8XD5pL6ePCFGyZy+BKASiUGaD/FhHqJrHDvaUFYiWTFo5snYbVBTDlKPAZ8DOUKeDpyDcCCkzHaqWtD5s6ndBvhsK88CfB4F8GBWwAtX8Tmu9YzfBHoeaLZAIQXIfJGtg8xKo2wXJBFQFcwiSve++jsQ3ERERERERERERERERERERYfnynj//mKvTTz99wI8h0l8UqiYiIiJHrIOd1dnEpDpcnVP96nA1ZqdvVnWub2Aws3JmTvVl8N1666187GMf6/Zy6qmnMnHiRAoKCjjllFO4++67qaury1irvLycp59+muOOO65Pc3nssccyts+ZM6dP9Q507rnnkpeX16X9YCEGJSUlGdufffbZfplTtro73mc+85mca1933XXY7fYu7WvXrmXXrl19rjt16lSmTJmSy9Qyhlc0NDSwb9++nOrmqrvb9eabb+Zceyjcb48//njG9jlz5uD3+3OZWq8MxeeE4WQgH6cydDU0NJBMJvsUPnn66adz3XXXcf7552OzHfq31372s591G5g1depU7rzzzsGd0AFaWroPK/Z4PDnXv+SSSw66Ruvp8s4772R1nOHyXHvjjTdmfFyuX7++138geeCBB0hl+HLi5MmTOeOMM3pVayANdKjaaaedNqD1s9Hd46wvYXkHG5ft43ko/z4MlfXZYBiOr30CgQD/9m//lsvURERE+qzHvwm89YKVLtIDfxzKmzq2vQkrQC1qB9LBaknDCluL2yDiAkxrO9AKRekQNrsJN6yGz70NF78P174Ln10JrmQON/KCC2DRohwKiIiIyFCTcQ0Tj0NTEyXORiaWvEK+OwLbt0PSei/P7oTCkVAQgBEBKCuGwgLwucBth3j6Lb+kDVKAzwNHV0K4BZqiVp8jBcko7GuEpJkOY0uPs9vA6wKvzwpZc9rBbgdP+hxcyTiE3gdXpz+VO3bBtrXWdcOEmbl8HCMUgo0bcyggIiIiIiIiIiIiIiIiIiJy+KipqWHbtm0DfhyFqslw4jjUExARERE5VNrO6rwmuAboOKtzOBoGoCZSQ0WgAq/T2+vaLfEWaiI17dsBd4Aib1H7dlV5FaW+0hxvgQy2tWvX9kudSy65hF//+teMHTu2zzX++c9/dmkrLS1lxowZuUytnd1u54wzzuCFF17Yr/1gIQZVVVUZ2//rv/6L008/nfHjx/fL3A4mmUyydOnSjH1XXnllzvVHjhzJrFmzePHFF7v0vfrqq30+xsc+9rFcp8aECRMytjc0NFBWVpZz/b4aMWJExvatW7fmXHso3G+ZHgsAc+fOzWlevTUUnxOGk4F8nMrQ8/zzz3PbbbexatWqPtd44403eOONN/jyl7/MzTffzLe+9S1KSw/N2u7xxx/ntttuy9hXUFDA4sWLcbvdgzyr/UWj0W77nE5nzvWXLVtGbW1tn8eHw+Gs9hsuz7VHHXUU559/Ps8991yXvoULF/YqICzX4K3BYJomq1evHtBjDIVQtalTp3LSSSd1CZB76aWXqK6upqKiIutamzdv5rXXXuvSfuqpp3L88cdnVWMo/z4MlfXZQBuur31OP/30Q/7/koiIHLm6/E3AdBOIGYSNGAA1O9+nwmGFpPWkIgxBP7Tlm+RFoaYtv9WAlGHV2ee1tv0xcKagMtRRwzBhVjWUNmc4wNVXw003ZX/jDAOmTIFD9NpUREREeifcGmZz/eb2zyb0yICt9VvBNOHtt4nvWY+z0ORjH4WGfT5c5lgc+3Zg88OIkVBQCB4vGD7rA6JxOxg2sBnWWXgDNisgLdxsBauRAsMJk0bBB0GYWAZtp8NoScCeMBR7weOw6mDQvg6ypc9b4jDBnoRIPdTthNIGq29PHsSjsOFlMNOhbNNqulkDZcswoLg4hwIiIiIiIiIiIiIiIiIiIiKHj+XLlw/KcRSqJsOJQtVERETkiDajYgZrg2sxsT7pW1lYyeoa6wv5JibV4WomlU7qdd3qcHV7zba6bQwMZlbOzG3iMux4vV6uuuoqbrrpJs4888ycatXX1/Phhx92aZ82bRo2my2n2p1lCmZ49913u91/5MiRTJs2rUuoxY4dO5g+fTrf/OY3+dznPkdJSUm/zfFAH3zwAS0tLV3ax44dy6hRo/rlGKeffnrGYIHVq1f3OVhg2rRpuU6LgoKCjO3ZBsX0pKmpibVr17JmzRrWr19PXV0djY2NNDQ00NraimmaPRfpJBQK5TynQ32/bd++nWAw2KW9sLCQE044Iee5ZWuoPiccCkPxcSpDRyKRYN68efzyl7886GPB7XYzevRoSkpKSCQS1NXVsWvXLhKJrt+qb2pq4p577uHPf/4zDzzwAOeff/5A3oQuXn75Za6++mpSqVSXPrvdzh//+EcmTpw4qHPK5GDhOfF4HJfLNYiz6Zvh9lw7d+7cjKFqf/rTn7jnnnuyCjR67bXX+OCDD7q0OxwObrjhhl7PaaC8//77RCKRLu1+v5+GhoZ+/fc51G688cYuoWqmafLAAw/wne98J+s6DzzwQMbnwWzD8oby78NQWZ8NhuH62ufkk0/OdVoiIiLteh1GAthtdqpDWzHfegs2bMDhSBEqAbxecKVYMRrGNWRXywAaXOCNQ9QBdhOS6XARw4QWB0RcgAmFrRBohaLWjvFVwYOEiTz5JNx7r0LSREREDkPrgut4dMOjpMyu7+t2pynWxNbQVti2DXZuIeWEM08DvNAabSa450PKCk0qR4PTAVHAsIM3/VaN2XZpW6sAPg+4XVAXgUT6vBgOO4wthurtcOxosNnTE0hBfRPYDSj0WrUBUkmrL5GApt3QsBWScauvMgTuJDSk4NVXoDm9xjqqAS7a1Ke7rsPs2dDNCXNERERERERERERERERERESONJdeemn790R27NjB2LFj9+v/05/+xFVXXdVjnUmTJrFx48b27RtvvJH777+/fycrMkgUqiYiIiJHtDJ/GVXlVawJrgGgyFtEwB1o/xJWTaSGikAFXqc365ot8RZqIjXt2wF3gCJvUft2VXkVpT59EepI09LSwvbt2wkEAjnXWrduXcb2SZN6HwB4MJnCz2praw865mtf+1rGkI1wOMztt9/Od7/7Xc4991wuvfRSzj777H6fc6bgD4Dp06f32zG6q5UpWCJb/RE0l5eXl7E9U8hItlpaWnj88cd55JFHeO6554jH432udaD+CKs61Pdb5zeHOjvttNMwDCOnefXGUH5OGAxD/XEqQ0MikeCaa67hr3/9a8Z+n8/H3Llzueqqqzj11FO7hHw1NTWxbNkyFi5cyF/+8pcuj7Pdu3dz0UUX8ac//Yl///d/H7Db0dlbb73FZZddRmtra5c+wzC4//77ueSSSwZlLj3xertfS7e2tg6LULXh9lx7+eWXU1xcTF1d3X7toVCIxx9/PKswpO7+6HHhhRcyYgh9YfHAkLE2/R3wNRRcc801fP3rXycWi+3X/uCDD/Ltb387q/WHaZo8+OCDXdrdbjdXX311VvMYyr8PQ2V9NhiG62ufCRMm9HmsiIhIZwcLI0mmDFpj3X8Uon71B+zbvQcKi7CnwrTYE0RTLeCBkNsKSHNmkXESs0GtF1I+SNrAk4AmF9hT4DAh6AcM8MesepWhjrGGCTOrD1I8EoH58+Guu3qeiIiIiAwbjdFGFq9fvN8J2uypJL541+D0zvKB4/DwYXU1CeDU8TAqD5LpfrcrwfgR4Oj09kcyaV1iCUimwGYDr8v66Ui/bWa3QUk+7ANS6be981wQ88Cmahg1FnzpfVM2cNsgEbdC1Fri0BgCG9b6p3lXR6BaeRP44sBuOPVeKDIh5LGCZifUWaFufXbBBbBoUS4VREREREREREREREREREREDlvvvPNOl7YTTzyxx3FNTU1s2rT/WdKyGScyVClUTURERI54MypmsDa4tv2Dy5WFlayuWQ2AiUl1uJpJpdl/Obw6XL3fh6ArCyvbrxsYzKyc2T8Tl2HnhRde4JRTTuHBBx/k05/+dJ/rbN++PWP7r371K371q1/1uW42mpubicVi3YafXHvttTz88MP885//zNgfi8V45plneOaZZwAoLy/njDPO4Mwzz2TWrFmcfPLJOBx9f5mye/fujO1HH310n2seqLsQgF27dvW5ZmFhYZ/HtukuJCKVyv4s950tXryYefPmdft4y1VDQ0PONQ71/dbdfTNx4sSc5tRbQ/k5YaANh8epDA3f/OY3uw1UO//883nooYcOGhLl9/s599xzOffcc/nOd77DVVdd1SXIKZFIcPXVVzNq1Cg++tGP9uv8D7RmzRouuOCCbh+jv/rVr7j++usHdA69cbBQ2draWgoKCgZxNn0z3J5r3W431157LQsWLOjSt3Dhwh5D1Zqbm7v9nZk7d27W8xgM3YWq9WewVDY+9rGP5TR+7ty5Pd63JSUlXHrppTz66KP7tX/44Ye8+uqrzJgxo8fjvPTSS2zdurVL+yc+8Yms11ZD+fdhqKzPBsNwfe3TH0HjIiIimcJIOqtv9PHU0uMPUuFEKLOujar9KoWtm6hpy903IOyG0oPnmgDgSoE/Dnt9ELNDQdQKFPEkrH7TAEwrPCTQCkWdMrGrglDa3MMBFiyAefOgVCdpEREROVxsqtu03xomZaYY2VjD59/5S1bj/8MBm1MwZSKQgIgLHHaYONIKPWvLm22Nwofb4eijaE8wiwMJvxWo5nFaAWwJEwwDivNgbwhMExwpKC+BmhCs3gkT8mBEMbh94EwHrNlMK1yt7ZQGnjjUdVo/VQSBJcDrYKSgx3dmrr4abrrp4PsYBkyZorWRiIiIiIiIiIiIiIiIiIjIQaxatWq/bZ/Px7HHHtvjuNWrV3f5Xq1C1WQ4U6iaiIiIHPHK/GVUlVexJrgGgCJvEQF3gHA0DEBNpIaKQAVep7fHWi3xFmoiNe3bAXeAIm9R+3ZVeRWlPn3Id7hasmQJs2bNytjX2tpKQ0MDZAyY2AABAABJREFUGzdu5I033uCPf/wja9asybjf1VdfjWEY/Pu//3uf5hEMBvs0rr/U1dUxcuTIjH02m42//OUvXHbZZbz66qs91goGgzzxxBM88cQTAOTn53P++edz7bXXcvHFF+N2u3s1t/r6+ozt/RnW0l2tUCjU55p2u73PY/tbPB7n+uuv5y9/ye7LG32VTCZzrnGo77e6urqM7UVFRRnbB8pQfk4YKMPpcSqH3muvvcb8+fMz9l155ZX86U9/6jZgMZOJEyfyxhtvcN555/Haa6/t15dIJLjhhhtYs2YNPp8vp3l3Z+PGjZx//vndPgf99Kc/5Qtf+MKAHLuvxowZ023fnj17GD9+fE719+3bl9V+lZWVVFdX9+kYw/G5du7cuRlD1f75z3+yc+fOg/67LF68mMbGxi7t5eXlXHLJJb2ax0DLdAYdgJNOOmlQ5/H666/nNP68887Lar85c+Z0CVUDKywvm1C1hQsXZmy/8cYbszo+DO3fh6GyPhsMw/W1z3AI0hQRkaEvUxhJZwduH4yJFYLmTkBr+tMTEZcVkObMoow/BjvzAQPidis8LWXr1B+36lSGOtoME2Zm89IkEoH58+Guu7K+PSIiIjK0OW3O/bZX71lNTUP24eUhD/groSkdCBuzw7hysLkgBiRTUB+BTTVWQFphCvzpP7fG4uAD4ibEY+C2Qzz9ZxC7DQr80NAIdhOwQV4xNIVg7z6oq4eTj4W4xwpxM4BkOiDWnoJUM6SSEKkH/3LwPw5Ee3HHPPkk3HuvAtNERERERERERERERERERERydGCo2pQpU7DZbJl37uTA7+cYhqFQNRnWen7Ui4iIiBwBZlTMwKAjTKOysLL9uolJdTi78IXqcPV+X+bqXMfAYGblzJznKkOTx+OhvLycs846i2984xusXr2aBx98EL/f32XfZDLJDTfcwOrVq/t0rObm5lynm5PW1taD9hcWFvLiiy/y7W9/u9eBMo2NjTz22GN86lOfYsyYMfz4xz+mqakp6/HRaOZP5wcCgV7N42C6q9XT/TIcxONxrrjiigEPqjpctLS0ZGwvLCwc1HkM9eeE/qbHqfTWHXfcgWmaXdqPP/54HnjggV4FqrVxu908/vjjlGb4gtfmzZv5wx/+0Ke59mTz5s2ce+651NTUZOz/wQ9+wNe+9rUBOXYuxo0b123fBx98MIgz6bvh+Fx74oknMn369C7tqVSKhx566KBj77///ozt1113HQ7H0DpPR3ehaplu++Hg4x//OKNGjerSvnjx4h4fp5FIJGMg2+jRozn//POznsNQ/n0YKuuzwTBcX/s4nc6edxIREelBpjCSV6pfab+8szvzGjGTPflQXWgFqYU91iXkhQ+KrfaDXfbkQbOzI3zNMKGs2QpoA8CEwlYraK2o03+fVUEozXZJtWABZBkkLSIiIkPfuMC4bj+XkI1AAj4yAUrzoawAxubD2EJoe5u7Kd4RqAawt6FjrJmC5ph13TAhnrCC1dr4XOBKX08BRYVW+KxhwNFl4G6F2hAEa2HHVqjfBNt2Q2gzvP0UvPYIrHwCpj5J7wLVoCNMVkRERERERERERERERERERHJyYKhatsFoB34/p7Kysl+/nyAy2BSqJiIiIgKU+cuoKq9q3y7yFhFwdyz0ayI1tMQzfzm7TUu8hZpIR8BFwB2gyFvUvl1VXkWpT2dWPlIYhsH111/Pyy+/TH5+fpf+lpYWrr766j59GT2RSPS80yHmdDq54447+PDDD/mf//mfg4apdKe2tpbbb7+dyZMns3Tp0pzmkylMZyjUGmruueceHn/88W77R48ezWc+8xkWLFjAc889x9q1a9mzZw9NTU3E43FM08x4OVx1F8Q02Ld5ODwn9Cc9TqU3PvzwQ55//vmMfXfeeSdut7vPtUtKSrj99tsz9i1YsKDPdbtTXV3N2Wefzc6dOzP2//d//zf/8z//0+/H7Q9HH300Ho8nY9/69esHeTZ9M1yfa+fOnZuxfeHChd2O2bJlCy+//HKv6h0qW7dupa6urku7y+Wiqqoqw4jhz263c91113Vpb2xsZPHixQcdu3jx4oyBxTfccAN2uz3DiMyG8u/DUFmfHUp67SMiIkeCXMNIMnGmwJ7q2I7ZIdlDBnde1Apj8yQAE/xx8CasIDWwtp0pqAx1jDFMmJndOVwsChgRERE5rAQ8gS6fS8hz5mU9/rKPwtnHwYQRcEw5TDsKRubDUYXWz4YYGJ0+Ebq3EZKd1jgNTda2AZhA0oQ8NzhtVlvb27hJG+Q7ocgPx1VCuQ/sJjiTEEtAwyZI7oNkDcT2wOYVkEz0Mjz2QPfdB6lUz/uJiIiIiIiIiIiIiIiIiIhIRg0NDWzZsmW/tr6GqmU7TmSochzqCYiIiIgMFTMqZrA2uBYT60vDlYWVrK5ZDYCJSXW4mkmlk7odXx2ubh/bNr6NgcHMypkDM3EZ0k4++WQeeeQRLrvssi5fSN+wYQPf//73+dGPftSrmt2FkowdO7ZP4WW91d3xMxk5ciQ/+MEPuOOOO3jrrbd48cUXeemll1i6dCkNDQ09FwC2b9/OjBkzeOKJJ7jwwgsPum934TjZHisb3dXqzf0yFO3atYsf/OAHGfvGjx/PL37xCy6++GJstt5lc8fj8f6Y3pDU3b95KBQaEvMYis8JudLjVHrrxRdfzNheWFjIZZddlnP96667jq9//etd/o9///332bZtW7/9Du7YsYNzzjmHbdu2Zez/2te+xp133tkvxxoIDoeDqVOnsnz58i59y5YtOwQz6r3h+lx77bXX8vWvf51oNLpf+8aNG1m6dCkf+chHuoxZuHBhxiCl0047jRNOOKFP8xgoK1euzNh+wgkn4HQ6B3k2g2fOnDncfffdXdoXLlzIDTfc0O247sL0brzxxl4dfyj/PgyV9dlg0GsfERE5krWFkawJrgE6TpISjoZ7X6zT0teTgCZXesOAqB183eTJuhMQdVj72U3Ij0Fx+rws3gS441a4WqAVijqdV6NPQSMLFsC8eVCqk7aIiIgcDg78XMLo/NFA5ve5DlQK5MWg2QkY4LFba5EUYDdgZB6M9MLefbCnzlrq7K6Do9LLCNOEUAOUFFjjkynrrLx+l5Vn5jQgYoBpB68N/JWQioEzZo13xWDrTvCnt4taYcsaSMT7EB57oFAINm6EyZNzKCIiIiIiIiIiIiIiIiIiInLkWrVqVZfvBGUTjhaPx1m7dm2vx4kMZQpVExEREUkr85cd9ItYNZEaKgIVeJ3eLmNb4i3URGratwPuAEXeovbtqvIqSn36wtOR6pJLLuHmm2/mN7/5TZe++fPnM2fOHI499tis6wUCgYztn/70p/nZz37W53kOJMMwOPXUUzn11FP55je/SSqV4t133+XVV1/l1Vdf5YUXXqC+vr7b8YlEgiuvvJLVq1czfvz4bvcrKirK2D4YwQLdHXu4eOCBB4hEIl3ap0+fzgsvvEBxcXGf6tbV1eU6tSGrpKQkY/tgh3YMx+eEvtLjVHrrrbfeytg+Y8YM7HZ7zvXLy8upqqpizZo1XfrefPPNfgkX2rNnD+eccw6bN2/O2H/LLbfw05/+NOfjDLSzzjorY6ja0qVLaWlpwevtusYeSobrc21RURGXX345ixYt6tJ3//33dwlVM02TBx98MGOtOXPmDMgcc3HgWXDanHTSSYM8EzIG0Q2UyZMnc9ppp3X5nXrppZeorq6moqKiy5gtW7bwyiuvdGn/yEc+wnHHHder4w/l34ehsj4bDHrtIyIiR7qDnSSlN8Y0QnOnT03UpiCWfrloAPmt4DCh0QWJTi8j86JQ60tvmHBsHSSN9CBgQr0V0FYZ6hjT56CRSATmz4e77urDYBERERlqDvxcQr47P+uxBVihr6YBcTu4bVaomgnE42AzARuMLYYyH2zZDTtDUJQPfre1HjGjsK/RarOTDlazg90GXhckTWs/uwl2O7jSIbPJONRuhaP2QX367dzGOtiW/kz1tJo+hMd2ZhjQx7/ziIiIiIiIiIiIiIiIiIiIiBWq1pnNZmPq1Kk9jlu/fj2xWGy/tunTp/fn1EQGne1QT0BERERkKJlRMQOj7VtPWF/EamNiUh3O/I2n6nB1+5e3DhxnYDCzcma/z1WGl7vvvpuRI0d2aY/FYtx+++29qjV27NiM7bW1tX2a26Fgs9k48cQTufXWW1m0aBHBYJAlS5Zwww034PF4Mo5pbGzku9/97kHrjh49OmN7d0E0ffHBBx9kbB81alS/HeNQeOKJJ7q02e12/vjHP/Y5qAoO77Cq7n4XN23aNCTmMZyeE7Klx6n01t69ezO2d/d70xfd1aqpqcnY3ht79+7lnHPO6fZ55eabb+bee+/N+TiD4eKLL87YHo1GeeqppwZ5Nr03nJ9r586dm7F90aJFtLS07Ne2ZMkStm7d2mVfr9fL1VdfPRDTy8nKlSszth8Jf7jJFHJ3sFC8Bx98MGPw24033tjrYw/l34ehsj4bDHrtIyIiR7q2MJI2bSdJ6a1J+yDQCoGodRkVAU/SuriTVmCJLw6JTp+scCcg6qA9QM0fh6oglDeldzDhhL0wLgRFrR3jqoI5BI3cdx+kUn0cLCIiIkPNgZ9LyJYvAP5CGFEIZcWQ7wOPCzwO6PzOT9IGPg8cXWn93LQHEklwpKwPjEYTsLsBmqNgpqwQNaB9fWNLbztMcCQhUgc734OCIFTthYIoxKOw4WVr/FENcFGub7/Mng0jRuRYRERERERERERERERERERE5Mh1YKjaxIkT8fl8mXfu5J133unSduKJJ/bTrEQODYWqiYiIiHTS0xexaiI1tMT3Dx5oibdQE+kIzQi4AxR5i9q3q8qrKPWVDuCsZTjIz8/ne9/7Xsa+xx57jLfeeivrWscdd1zG9h07dvRlakOCw+Fg1qxZPPDAA6xfv54zzzwz436LFi2iubn7bx5OmDAhY3umF/R9deCbCm0mTpzYb8cYbK2trSxfvrxL+6xZs5g8eXJOtdesWZPT+KFs0qRJGduXL1+eMbRkoByOzwmZ6HEqfdHU1JSxPRDo/Rftu1NYWJixvaGhIae6tbW1nHvuuWzYsCFj/5w5c/jf//1fDKP3X7w7FD72sY9RWpp5Tbxw4cLBnUwfDOfn2vPPPz9j0FQ4HOaxxx7br+3+++/PWOOTn/xkv/7e9JfuQtVOOumkQZ7J4LvqqqsyhhE/8MADXdq6C1vzer1ceeWVvT72UP59GCrrs8Gg1z4iIiIHP0lKtryJTmFo6W13omM74oJaD3TOPMmLWu0AmDAubIWnVYSt7fImK4jt6FDHGMOEmZnP2ZKdUAg2bsyhgIiIiAwl7Z9LME1Y/mbW43yF4PSAww5OwDDA4QCPE0rzoMhvtSVtkDTAcMLkUYAB7+8AMwFtMa1mCkJNEG6GWBziSUimIJWEZBJao9C0G3ash33brPbKENhTMH0zHHMvnP0WXPsufHYluJI53CEXXACLFuVQQERERERERERERERERERERA78DkC2wWgHfg+hpKSk25PeiwwXClUTEREROcDBvohlYlId3v+bT9XhasxO533uvL+BwczKmQM2VxlePvvZz3Lsscdm7Pv2t7+ddZ2jjz6akpKSLu3Lli0jHo/3eX5Dxfjx43nmmWcYM2ZMl75oNMprr73W7dgJEyZkTE3fvn07e/bs6Zf5vflm5i82TJs2rV/qHwo1NTWkUqku7WeddVbOtd94442cawxVY8aMYeTIkV3aQ6EQa9euHbR5HO7PCW30OJW+KCoqytgeDof77Rj19fUZ27sLW8tGKBRi9uzZ3Qb+XXvttfzud78bNoFqAE6nkxtvvDFj3zPPPDPkww2H83OtzWbjM5/5zP9n787j4yoL/Y9/zuyTfU8XmqYrLXShpSyKtAWhZZHKcmUREYqIoqD+ioJwlUWxwkWqEhT14qVcEK+lBQTLvksXWrqk+0aXdEmTZs8kmcks5/fHmTRJM2n2rXzfr9e8Zs6znedMZ6bPTGa+J2Zd00C76urqFiFrDebOndsTU+uSw4cPx1zf2Ww2Jk2a1Acz6l0pKSlcfvnlLco/++wz/v3vfzcr++ijj9i9e3eLtldccUWnwvL68/Ohv6zPeoPe+4iIiLR9kpT2aghDa5Dib7wdtkFZk/9y3SEIODgashYfhLGljbcH+SA3xlvOyUWQ0fq5ItpmGJCW1oUBREREpL+ZPnw6xooVsGFDu/uYTf5M4ohA0z+bOAyId8OgZHA5oN4eLbfDmGyI1MLOfVAVtJYyzmhftw1CQagLQJkPSkqhpByqK6H2EISjH/c0BMdSCMZfYeQWOOMQjClrlj9r8XjgxRfh3XePf3nvPThyBN54A5KSOngPioiIiIiIiIiIiIiIiIiI9I2ZM2diGEaLS18KBoNs2bKlWVlnQ9X0mwE5EShUTUREROQYbf0Qq8hXRF2wDoC6YB1FvqKjdcnuZFK9jeEdE7ImkBGX0QuzloHA4XBw3333xax74403OhTqc8EFF7Qoq6mpYcWKFZ2eX3+SlJTEd7/73Zh1e/bsabWfzWbji1/8Ysy6Rd1wdvPi4mI++OCDmHXdEezUV4qLi2OWxwqk6AjTNFm6dGmXxujvLrzwwpjlTz/9dK/O40R/TYCB8Th1u90xy+vr67tlfOm4zMzMmOX79+/vtn20NlZWVlanxquqqmL27NmsXbs2Zv0111zDM888g8028D7Wu+2223A4HC3KTdPkxz/+cR/MqGMG8mvt3LlzY/5x5r333jv6GP7HP/5BbW3LlIfc3FzOP//8Hp9jR7X2HBk7dizx8fG9PJu+0VrYXdOwvFjbbfVvj/78fOgv6zPo2bWB3vuIiIhYjneSlPaKD1ohIQ28ISs8DSBgtwJJwtFdJATA54o2NCGnElKbhLBdvq0xZK3BSVVwyc4OT6u5WbMgO7uLg4iIiEh/kllnMGHxRx3qEw5DOAT1dRCsgUBdY7Ca3QZ2w7rOSASHqzE3NsEFQ1OgNgj5B+HQEbAFwesAZ/SjZptphas1fPLsCVr7aTC8GHgPeAo4Zr3Tgt8Pq1fD+ecf/3LeeZCh71OIiIiIiIiIiIiIiIiIiIh01datWwkEAs3K2hOqZpom+fn5zcqmTJnSnVMT6RMD79eXIiIiIr3geD/EMjHZV7kPgH2V+zCPfhW5eTsDgxm5M3p8rjKwXHfddYwfPz5mXWuBa7FcddVVMct/+9vfdmpe/VFrb9bLysqO2++iiy6KWf7ss892dUr87W9/IxQKtSifPHlyl4Od+pJpmjHL/X5/zPL2+te//sWuXbu6NEZ/d/nll8csX7hwYcxgmp7yeXhNGAiP06SkpJjlNTU1Mcul55100kkxyz/66CPC4XCXxy8qKmLz5s0x6wYPHtzh8WpqarjkkktYtWpVzPorr7yS5557Drvd3uGx+4ORI0fyrW99K2bdW2+91SeBRx0xkF9rR44cyYwZLd+bRCIRnnnmGaD1wKmbbrqpz8+WE0troWpTp07t5Zn0nQsuuCDm69wLL7xwdB1SU1PD4sWLW7QZNmxYl8Ly+vPzob+sz6Dn1wZ67yMiIhLjJCmmmzQqyBy0mMxBi8mq/BXn7HqI83bEviT6DwEwvBKafNRPit8KUqu3A4YVruYOQcBhbYMVxtY0QM0w4YI98K21cP0GuHSHdf2tteDqylvQ2bOhG0JTRUREpJ957DGmbw9QXhth0bZa8tNrWTOolvdya/lgVC07v1DL/i/WUnymdTk4pZaDxRFKCqC2EuqqoaYYauqgzm+Fq3md0bENyIjn6LdDHRHISodQNGitvBi2fgbOAARDYJrWWiYc/ejEHoFILUTCUF0GkTcg/hHg30CknceXlwclJd15j4mIiIiIiIiIiIiIiIiIiEgr1q1b16KsPaFqu3fvpqqqqsP9RPo7haqJiIiIxNDih1jeVJLdyUe3i3xFlNWVUeQrOlqW7E4m1Zt6dHtC1gQy4nRWZWnOZrO1Gp727rvv8uGHH7ZrnMsvvzzmD9n/+c9/snr16i7Nsb+IRGJ/Iz8lJeW4/a699lpstpZvdT799FNeeumlTs+nsrKShx9+OGbd9ddf3+lx+4OMVs4Av3379k6PGQwGeeCBBzrdf6C47LLLyM7OblFeXl7Ovffe22vz+Dy8JgyEx2lrwSn79+/vtn1Ix5x33nkxyysqKnjllVe6PP4zzzwTM/AvPj6eM844o0Nj1dXVcdlll7Fs2bKY9XPmzOH//u//cDgcnZprf/HAAw+0+n/59773PVauXNm7E+qAgf5ae/PNN8csX7hwITt27GD58uUt6gzD4KabburhmXVOa6Fqn6ez4dhsNr75zW+2KK+urmbJkiUALFmyBJ/P16LNjTfeGHPN3F79+fnQX9Zn0PNrA733ERGRE1Glv5J1hev4YO8H7b7YbXb2Vexl7zuL2fvXx/B88CbBHQsJ7v8H9aygMH4lphn7UphYw94UOBJvZaVVeKDaBc4wRAyOBqjV28ETBJ8rOlETciohtUnW+4RiyKi1uowpgzMOWdcGgMcDL74I777b/st778GRI/DGG9DKukJEREQGqJISyMsjsxZGH4LKtBDliSGqPSGKEkJExoaoGR6iNCdE9dAQR+JC7KoIUVMLvibnn7KFIVgH4QjU+oGGYDUD7DZIjrPC0uwmGDZITrHWOQ5gZCa4/VBaAcWlcGAvlO+EgkKo2A1r/gUfPw9rX4FJrwKBGMdxPD4fLFjQPfeXiIiIiIiIiIiIiIiIiIiIHNf69eubbWdnZ7frhOqdDWMT6e8UqiYiIiLSiunDp2M0/GIKyE3JPXrbxGRT8SZMzJj1BgYzcmf0xjRlALr66qs59dRTY9bdf//97RrD6XTy4x//uEW5aZp87Wtfo6ioKEavgWXNmjUxy4cOHXrcfkOHDmXOnDkx6+bNmxczVKI9fvazn1FcXNyiPC4urt+GjbTX4MGDcTqdLcpfeeUV6uvrOzXmL37xi1bDTk4kTqeTH/3oRzHrHn/8cd5+++1em8eJ/powEB6nQ4YMwev1tij/9NNPu20fnzcLFy7EMIyYl71797bZ/4wzziAtLS1m3c9+9jMCgY7+CqxRcXExjzzySMy68847D5fLFbMulvr6eq688kref//9mPWXXHIJL7zwQsznwEAzaNAg8vLyYtb5/X4uuugiPv74416eVfsM9Nfa//iP/4gZ8PTZZ59x6623xuxz/vnnM3z48J6eWqfE+qMNwNSpU3t5Jn2rtXXowoULm123t1979efnQ39Zn0HPrw303kdERE40m4s38/tPfs8/t/+zQ6Fq6w+vp2bjWvYe3MzepAilcVBnh4pIHRUe2J0KO9Ngb8rxLwE7VLihNA4KkiHU+OcB7CZUeTgashYfhLGljfWGCTP2Hefg/H5YvRrOP7/9l/POg1ZC5kVERGSAe+wxqKkBJ8wMw9izICEN4lPhJDekNZ7vjaowlOyDhvN71JRDJNxYH6q0QtUA/PVWmFqCGxw2iHdZ2wBhG2QmQaYLTs6FrDhrjeMMQ30IqnZCuATCRVB/GHavhnCoMTi2U558Elo5kZaIiIiIiIiIiIiIiIiIiIh0n2ND1dobjHbs73M8Hg/jxo3rplmJ9B2FqomIiIi0IjM+kwlZE45up3pTSXY3fns5YjZ++TfZnUyqN/Xo9oSsCWTE6cdOEpvNZms1PO3DDz/k3Xffbdc4d9xxB2PGjGlRvm/fPi6++OJ2hc2010cffcS999573DZ33303Dz30EGVlZcdt1x7FxcX8+c9/blHucDj44he/2Gb/n//85xiG0aJ87969XH755R0O0fnDH/7AE088EbPue9/7HpmZmR0ar7/xer2cffbZLcoPHjzIf/3Xf3V4vD/+8Y/86le/6o6pDQg/+MEPGDZsWIty0zS54ooreOONN7o0figUYseOHW2262+vCd1tIDxO7XY7kyZNalG+fPlytm7d2q37kvax2Wxcf/31Meu2bNnCjTfeiGmaMeuPp7a2ljlz5rT6f15r+4wlFApx9dVXt/paMXv2bF588cUOhbT1d9/4xjf41re+FbOusrKS888/n0cffZRQKNRt+wyFQgSDwS6PM5Bfa71eL9dee23Mug8//DBm+c0339xt++9O5eXlrd7XU6ZM6d3J9LExY8ZwzjnntCh///33+eijj/jggw9a1J177rmMGjWqy/vuz8+H/rI+6421gd77iIjIiaI6UM3iLYubfe7ebrW1DF+xlSbnQCHF36TegEp328O4IlZYGoDfAQEH2KLT8YTAbPgv14ScSkhtso92hY3k5UFJSdsTERERkRNbyQFY8Tu4FbgH0r8B48dDRg5kDofR42BwOqSnQJwH9pZBfZO3/pEwVDbJQreFoabCCl0zgbog2A0rUC3JDVkpEJ8AyYkwOhNOzoF4Jzij6xxXPRTvBXv0/DWpfijYCKFgO4Jj21JRAdu3d2EAERERERERERERERERERERaY/8/Pxm250NVZswYQIOh6O7piXSZxSqJiIiInIc04dPx6DxG8q5Kbkx2zUtNzCYkTujh2cmA91//Md/MHHixJh19913X7vGcLlcPPfcczHfnK5bt44pU6bw9NNPdzpApLi4mP/+7/9m2rRpzJgxg9dee+247YuKivj5z3/OsGHD+O53v8uHH35IpBNnHt+7dy8XXXQRRUVFLepmzZpFRkbbgYVTp07l1ltvjVn37rvvcskll1BQUNDmOMFgkF/84hf84Ac/iFk/dOjQdv979Xdz5syJWX7ffffxpz/9qV1j1NTU8JOf/ITvf//7R0OKYgU8nGji4uJ46qmnYh5rTU0Nc+bM4cEHH6SmpqZD4/p8Pv785z8zduxY/vjHP7bZvr+9JvSEgfA4nTlzZouycDjMxRdfzAsvvEBdXV237Uva52c/+xkJCQkx6/7xj3+0+n9Oa7Zt28YXvvAFPvnkk5j1U6dO5ZprrmnXWOFwmG984xv885//jFl/wQUX8PLLL+N2t+PX/wPMn/70J2bPnh2zLhgMctdddzFlyhT+8Y9/dClcLRgM8swzzzB+/HgOHTrU6XEaDPTX2o6EpCUnJ3PFFVd06/67y9q1a2OW5+bmkpqaGrPuRDZ37twWZaZpct1118UMjrzpppu6Zb/9+fnQX9Zn0PNrA733ERGRE8XOsp2YTVLRImak/ZcVy/D6Q2TWWkEiJlYImjvUuO1zQb2tcbu1S5IfIkC9HTDAEQF7pDF0BKzgtbGljdvtDhvx+WDBgq7cTSIiIjKghYGPYM31MNMPgwEDSGgeCOtwWmsQpwPivZAzBJwnRdtGVRZDoEmgq70OyiohZEI4ApHosspmg3g3eOMgzg0uGzjs1loJIByE0l1w0v7GsarLoGCTdXtyUTuCY4/HMCAtrQsDiIiIiIiIiIiIiIiIiIiISFv27dtHeXl5s7L2hqpt2LChU/1E+jtFA4qIiIgcR2Z8JhOyJrCxeCMAqd5Ukt3JVAYqj7ZJdieT6m384f6ErAlkxLUd+iSfb4Zh8MADD3DVVVe1qFu+fDlvvPEGF110UZvjnHnmmfzpT3/illtuaVFXUVHBzTffzH333cctt9zCzJkzOfPMM/F6vTHH2r9/P/n5+axbt44333yTFStWdCoUrba2lj//+c/8+c9/ZtCgQXz1q1/l7LPP5vTTT+eUU07Bbre36OP3+1m5ciUvvPAC//M//4Pf72/RxuFw8Mtf/rLd83j00Uf58MMP2bZtW4u69957jwkTJnD77bfz9a9/nQkTJjSrLy8vZ8mSJeTl5bX4QKCB3W7nueeeIzExsd1z6s++853v8PDDD1NaWtqs3DRNbrvtNl566SXmzZvHjBkz8Hg8zdps376dl19+mby8PA4ePNis7gc/+AG///3ve3z+fW3WrFncf//9PPDAAy3qgsEgDzzwwNHn6iWXXMJZZ52FzdY859w0TbZv386qVat49dVXWbp0aYdDNvrja0J3GgiP029961v813/9V4sQm3379nH11Vdjt9sZMWIEKSkprQZlffzxx90yl/Zat24dd9xxR5vtKisrY5YfPnyYL33pS+3aV28fG0BWVhb3338/P/nJT2LWv/XWW4waNYqbb76Za6+9lmnTpuFyuZq1qampYfny5SxcuJBFixa1GvJlt9tZsGBBu4P6Fi1axD/+8Y9W68vKyrjgggvaNVZ73XzzzR0K1uopDoeDl19+mWuuuYZXXnklZptNmzZx7bXXkp2dzeWXX86FF17ItGnTyMnJafU+DgQC7Nixg40bN7J06VJee+01KioqunXuA/m19qyzzuLUU09l8+bNbba97rrrWp13X2stVG3KlCm9PJP+4eqrr+YHP/gBtbXNf+EaK0gwPj6eq6++utv23Z+fD/1lfdYbawO99xERkROB0+Zstp1/OL/Z5/CtCgbh0EoYboWmHUrkaOBI0AY1Td7e1Tohro3MZndDfXQMRwQS6iHSsEwwIacSUpt8fDmhuANhI3l5MG8etOPEESIiInIiKQGWQO1nsGZF8yoPuCIQX2+tXWwOsJtWQFp9COw2SBkC7jgo2wPBAGBCyT4YNAbs0fZmPRyqgpQ48DitcQzAHl3XGKa17TLAEQZfOZQdhIwqOLkU8rOhFNj6IZgROKkKLtnZxcOeNQuys7s4iIiIiIiIiIiIiIiIiIiIiBzP+vXrW5S1Jxyturq6xW8tR4wY0U2zEulbClUTERERacP04dPZVLwJE+sH0LkpueQX5R+tz03JPXrbwGBG7ozenqIMUFdccQWTJ08mPz+/Rd19993XrlA1sH6kX1tbyw9/+MMWP9QHOHDgwNEgAYfDQVZWFqmpqSQmJuLz+aioqKC8vJyampouHU8shw8fPhqwBuByuUhPTyc1NZWkpCQCgQDl5eUUFBS0GZhw3333MXXq1HbvOzExkVdeeYVzzz2XoqKiFvXV1dX8+te/5te//jXJyckMGTKEuLg4CgsLOXz4cJvz+eMf/8jMmTPbPZ/+LjExkfnz5/Od73wnZv1bb73FW2+9hcvlYuTIkSQnJ1NVVcXhw4dbJNg3OOOMM3j44Yc/F6FqAPfffz+lpaXk5eXFrD98+DAPPfQQDz30EG63m6ysLLKysjBNk/LycoqLi7vledifXxO6aiA8TseMGcONN97IwoULY9aHw2F27drVLfvqLpWVlSxbtqzT/QOBQJf694Yf//jH7Ny5k7/85S8x62tqasjLyyMvLw+Px8OQIUNIS0sjHA5TVlbGwYMHWw1Sa+ovf/kLM2a0fy0YCASOW99acFRXdHdIW1d4PB6WLFnC3XffzW9/+9uYr1kARUVFzdYTbrebk046iaSkJDweD4FAAJ/Ph8/na9f/4U3dcMMNnTqDykB+rZ07dy4//vGP22zXH8L3WrNu3bqY5R1ZK55IEhMTueqqq3j22WfbbHvVVVeRkJDQrfvvz8+H/rA+6421gd77iIjIiSAnOQcDo9XP4Vt1YD9EIjjskJUM2WlQ74ZwBKrqYHcA/NElSr0d3GErdKQ1CQGodUB0GsQHIa0OihIat8c2yXo3TJixrwMH6vPBggUwf34HOomIiMiAVFIChw/DEBPSXgPqYPlyqA9a9S5gEJADDIU0O9hd4IoD0wRnGJq+ZTcTrRC1ot1QX2uFqxXthuyRVrAaptWvvMYKT0uLA8Nu3Q5HwB4GfwjqiqFyN4Sj08itAHsEpuyGPf+ClApI8cPosqM5s50zezYsWtSVEURERERERERERERERERERKQdjv2dTVxcHGPHjm2z3/79+1uUeTyebpuXSF9SqJqIiIhIGzLjM5mQNYGNxRsBSPWmkuxOpjJQSbI7mVRv6tG2E7ImkBGX0VdTlQHGMAweeOABrrjiihZ1q1ev5tVXX+Wyyy5r11h33HEHJ510EjfffDMVFRWttguFQhw6dIhDhw51dtpdUl9fT2FhIYWFhR3q97Of/Yyf//znHd7fmDFjeO+99/jKV77Cnj17Wm1XWVlJZWVlu8Z0Op388Y9/5JZbbunwfPq7W2+9lXXr1vGnP/2p1Tb19fVs27atzbFOPfVUli5d+rn7AOXxxx8nOzub++6777jhFIFAgP3798f80Kk7DJTXhM4YCI/TP/zhD2zZsoVVq1Z167jSNU8++SSRSISnnnrquO38fj+7d+9m9+7d7R7b6XSyYMGCfh1C1V85HA4ee+wxLrzwQm677Tb27t3bZp9AIMBnn33Wpf1+6UtfYv78+Zx77rmdHmOgvtbecMMN3HPPPQSDwVbbnHrqqZxxxhm9OKuOaS1wcMqUKb08k/7jpptualeo2ty5c3tk//35+dAf1me9sTbQex8RERnokj3JrX4O3xpXJMAo5wFGz4S05GhgiAHV7sY2IQMO1MLGQthUCIEwxLWSme0OQcABdsAZXTak1YE3FK2zQ04lpPob+0wohozaDh5sXh7MmwcZ+luCiIjICck04e674fHHrcTW79hh5lnwxS/CqlXWNzZHA9lYC5h4wGmtQdwGGA6w2cBhQtgGvlpryHo7uJ1WiNrhnVaoWn2tdTs1F0IesEUgYrOGDQUhFIJQBIrLwRWxwtMCxY2Balk1EBcECsFYAiNLYWRrx+XxwPPPQ3Ly8Y/fMGDiRK11REREREREREREREREREREesn69eubbU+cOBGbzdZmv1i/LXj55Zf5/ve/j9vtjtFDZOBo+xkgIiIiIkwfPh2jyXmYc1Nym10DGBjMyJ3RyzOTge7yyy9n6tSpMevuv/9+TNNs91hXXHEF+fn5XHnlld01vaNGjx7Nt771reO2SUxM7Pb9DhkyhL///e/88pe/7PQYp5xyCqtXr+ZrX/tal+czduxY3n///RM6VOCJJ57gpz/9KYbR+XPPz5kzh+XLl5OZmdmNMxs4/vM//5P333+f8ePHd9uYSUlJHe7T168JPam/P07j4uL4+OOPue+++0hNTW27g/QKm83Gf//3f/PSSy9167/7xIkTWb16Nbfffnu3jfl5dNFFF7Ft2zYefvhhBg0a1CP7sNvtXH755bz77rv8+9//7lKgWoOB+FqblZXFpZdeetw2/Tkg0OfzsXPnzph1ra2rPw/OO+88cnNzj9tmxIgRzJjRc+9Z+/Pzoa/XZ721NtB7HxERGeha+xy+gdfpZVTqKEanjuDi0fF8Z9geLhplMtplhZ+l1lkBZ6l14AlZl4QgDPfCxaPhh1+Ac0ZAcsBq4wg3339CAHwu67YnCCl+K1ANrNvxQRhb2tjeMGHGvk4cqM8HCxZ0oqOIiIgMCL/5DTz6qJV6dhXgDMOy5bB4MXiDcCYwCI4ue5oEvnpCYEbDXQ0D4tyQkQpOp1UWcIDdARm5jf2DAdhyAAqPgC06lsduXdtMCIbBEWkcP1TfuL/hxcB7wFNAk3VOTH4/rF4N559//Mt55ylQTUREREREREREREREREREpBcdG6p22mmntatfXFxci7J///vfDB06lDPOOIPzzjuvG2Yn0jcUqiYiIiLSDpnxmUzImnB0O9WbSk5yDqnexh9DT8iaQEacvhwsHffAAw/ELF+3bh0vvfRSh8bKyclhyZIlrFq1iuuvvx6v19vpeY0fP5477riDjz/+mJ07d3LHHXcct31eXh5btmzh0Ucf5fzzz4/5Zrq9Ro8ezS9/+Uu2b9/Otdde2+lxGqSnp7No0SLee+89Lrjggg4HMeXm5vL73/+eDRs2cM4553R5Pv2Z3W7n17/+NW+88QZnnHFGh/pOmjSJF154gX/+85+dCgE7kUyfPp38/Hyefvrpdn8AdazU1FRuuukmli9fzi9+8YtOjdGXrwk9aSA8Tp1OJw8++CCFhYUsWbKEO++8kwsvvJBRo0aRmZmJx+PpsX3L8V1++eXs2rWLJ554ggkTJrTdIQbDMDj//PNZtGgRa9euZfLkyd08y88nt9vN3Xffzb59+3juuee49NJLu3xWE7fbzfnnn09eXh4FBQW89NJLnH/++d00Y8tAfK09Xmia0+nkhhtu6JH9dof8/HwikUiL8uzsbAYPHtwHM+ofDMPgxhtvPG6bb37zm10KJG2P/vx86Ov1WW+tDfTeR0REBrJYn8Mnu5OPbvuDfnKS4/jquCOclV1B+uEjJAdocRnsawxV84Qgod4KEomzwYyRcOWXYLAbQk2+LeEOWSElDeEkSfXNA9S8QZhQDKn+xrIJxVaIW6c8+STEWNeKiIjIAFdSAg8+aN0+B2j6cVXZXpgEuI7pUwX4rGu7D4wm6w27AW47pCVZwWr1dggb4PZCcpbVJmgDnxOKSmHrLqgqByJgmtaXQ8P11rU9YoXK1tdCdRlE3oD4R4B/W+3bJS/POkYRERERERERERERERERERHpFyoqKti3r/kZYtv7m4mTTz6Z+Pj4FuWlpaV8+umnZOikajKAGaZpmn09Cfn8SExMxOfzNStLSEigurq6j2YkInLi2bZtG7H+ezcMg3HjxvXBjE4cR2qO8MfVf8Qkxv2LwffP/L5C1aTfqa2t5Z133mH58uWsX7+evXv3UlRURE1NDaZpkpCQQGJiImlpaYwdO5Zx48Zx6qmnMn369C6HUoRCIfLz8/nkk0/YunUru3btYs+ePZSXl+Pz+QgEAsTHx5OcnEx6ejqnnnoqU6ZM4dxzz+XMM8/spnsgtoKCAl599VWWLVvG1q1b2b9/P9XV1YTDYeLi4sjOzmb06NGceeaZzJo1iy9+8Ys9HkDRX3388ce8+eabfPjhhxQUFFBaWkpdXR3x8fFkZmZy8sknc+aZZ3LxxRe3+u92+PDhFmVer5fk5OQYrU9M27Zt44033mDlypXs2LHj6GMuFAoRHx9PQkICgwcPZty4cZxyyinMnDmTs846C7vd3q3z6MvXhJ6kx6l0xcaNG1m5ciWffPIJmzZtorS0lIqKCiorK7Hb7aSkpJCcnEx2djZTp07lzDPP5JxzziEnJ6evp/654PP5WLlyJatXr2bjxo3s3buXAwcOUFVVRV1dHaFQCLfbTVxcHJmZmQwZMoRRo0YdXVecddZZXQ5m66gT9bVWpDP68/Ohv6zPepre+7SfPlMTEekfjv0cvryunPyifAAy4gJcOb6WIYmpsHs3FOwHwO6EhFRwx4PTCzYbVNmhKgQ1fiirhHIbhJuEqIX88MJ6KKq0ttNroDQOK1TNhPElVmja6iFWWZYPTmmSH2KY8P3VXQhVA9iyBcaP78IAJ7Ydaz7r6ymItCkwLKGvpyDAxKzsvp6CSKN77oGHHwY3MI/GADUHcCbNA9VqgF1AOXA6kGgV1zuhbgw4Pda6JhQBXz2EI1BSDs4QxAWtfNYDm6HYDb7ouIYBU4ZAJNFa1tjsULEbyl2QWgOBQlixCCLBLqxl7rkH5s/vREcREREREREREREREREREelvYmXA2Gy2HvmN74oVK7p9zL4yc+ZMPvzwwxblfRHh9MEHH3Deeec1K1uxYgVnn312u/r/9a9/5bbbbiMYDLaoe/TRR/nxj3/cLfMU6W0KVZNepVA1EZGepx+A9qyXt73M+sPrW5SfNug0Lh93ea/PR0REREREREREuk6fqYmI9B9LtixhY/HGo9vrCtcBJVwyphC3I8JQbxbOVWuwESFtKMSnWgEiTYUNqI7mK5smHKmG7ZUQDkPEsOrDAfjHJ1BbCc4w+KLt4+vhgt2Q6odtGXA4Hs48ZIWXNDjtMFy+rQsHaRhQWAjZCgJqjULVZCBQqFr/oFA16TdKSiA3F2pqrAC1i5vUjQMGNdk+AmwFItHtocCYxurKoWAmWsFqdgf4I+Cvhzo/VPogMQB2E4oPwZZ6q49hwOhsGOqBGicE7VBTCsGdUO6x1jY7V8HBrTCxCK7a2snjTEmB0lIr8U1ERERERERERERERERERAa0WBkwPeVEijfqT6Fq3WH//v288847FBYW4na7yczMZNq0aYwfP/5ze9J2GfgcfT0BERERkYHkkjGXUFJbwoGqA0fLTko6iUvGXNKHsxIREREREREREREROTFMHz6dTcWbMLG+XDTCncmwtHzcRhiwU3loN0O9EbKGg90Zewy7aQWlBe1WwEhWEsQlwvZiKAtA2AYuF1w8Cd5+F454ox1NyKm0QkcAxpRCal3zQLWTquCSnV08yFmzFKgmIiJyonnsMStQzQmcD2QDBtY3NHOAIGACNTQPVAMoAkYCdmvTWwS+OAj6wYgDlw1sHrAZUF0LAYe1PrFnAQcgMQ6GZkO6YY3rCUGtCf591pCpfgiHoGgXGCbM2NeF46yogO3bYfz4LgwiIiIiIiIiIiIiIiIiIiIi/cWwYcOYO3duX09DpFspVE1ERESkA1x2F9+a8i12le2iwl9BiieF0WmjlbIsIiIiIiIiIiIiIhJDpb+S3eW7qQxUtr+TAXvL9sCaNYw1tpAyzcRfA3i9OKkj8WQIGlY2SYNgPdTVQigMDju44iEUH600wWvAuCGQXwRVfggbMDgBTjkZPtxvNYsPwtjSxjEdEfjF+1DhsS4pfhhdZuWjdNrs2bBoUVdGEBERkf6m5ACs+B3cCgwFzqVxwZAMpGIFqtUD6wAbzUPVQkABMMLadIXALAEzC0IBcHmtQLU4LwzOAF8dOOshzQmnesDpAHvEKgMwwuDfBfb6xl0UbIRQEE4rgozaLhyrYUBaWhcGEBERERERERERERERERERERHpWQpVExEREekgwzAYkz6mr6chIiIiIiIiIiIiItKvbS7ezJKtS4iYkbYbN1FTX8PeTR/jKtrNObMhYoDfDvZwHcOGQZ0NnNEh/QE4WATVx4aDlIA9GYZmQ5wrWmbAuGxYu9/KLnFE4NSxsLIQAkHIqYRUf+MQE4ohs9a6tODxwPPPQ3Jy+w7KMGDiRMjI6NB9ISIiIv1ZGFgGa34OM6OLiESaJ7C6o9cGYAcGAZlYIWoFWGFrRG9nRPsDiZVQ5ID4FHC6wWaz1kROB3jd1rheIM4DwRB4QtEZBeHIXjhtJ+xMhyo3VJdBwSY4qQou2dnFQ541C7KzuziIiIiIiIiIiIiIiIiIiIiIiEjPUaiaiIiIiIiIiIiIiIiIiIiIdKvqQDWLtyzGPJoU0n7xIYOsLftIP8UKDiEC9gjkZoHLYcWX2CNQVQ37DoHZyi7qq2BjPYzJhtSEaBCJHUakw65iCDjABYwfAjt3wdjSxr6GCTP2HWeSfj+sXg3z53f4+EREROREUAIsgdrPYM2KxmLPMc2cTW4Hotd2YARWiNpWoBYrXG0rMMXq44qApwaqQ2B3gCvOamOzNR/ebodIPTjC4CuHsoOQUQWJ9TClEEoN2P8CjCuE0WXN8946bPZsWLSoKyOIiIiIiIiIiIiIiIiIiEg/Z7PZOPPMM/t6GiIiXaJQNREREREREREREREREREREelWO8t2NgtUi5iR9ndesYxhZWEG53B0hEQgPbFxuzoEe48TqAZgM8ERgp1FcKobPG6rf2YCHCqBcNhqd8pQCKyBVH9j3wnFkFHbxjzz8mDePMjIaP+xiYiIyAngIPAcUAfLl0N90Cp2AYOBLKwwNRuQjLUACQG+Y4ZJxApR2wBUY4WrbQAmWf2T/VDjAr/PGsJwQtgOoTAYhrUO8tVC6ABUlEA4Oo3cCuvaqIOMZyGjsJXDuO46uOWWtg/XMGDiRK15REREREREREREREREREQ+B+Li4lixYkXbDUVE+jGFqomIiIiIiIiIiIiIiIiIiEi3ctqczbbzD+dTGahsu2MwCIdW4hgGIzKg0m4VD0mFiA0wrO2tlVDrsYLTmvIF0tl4aPbRbQMrhGTVQchKaNyuroXc1DdJcJcyJB78TaZmmDBjXzsO0ueDBQtg/vx2NBYREZEBr6QEjmyFnLch3ga1tbBqlfUtzNFANpAAxDXpY2DVO4HMaFkZ0JA3GzEgxQV1WMFr1cAnVluXCzKdQDzYHRAYX48/waSkonH4I4fA2yQ0LasG4oJAIbAEKD3O8bz6Kjz+uMLSRERERERERERERERERETkhLd27VoKCgq6ZaySkpKY5S+//HK3jB8XF8esWbO6ZSyRzyuFqomIiIiIiIiIiIiIiIiIiEi3yknOwcDAxEo9y03JJb8ov+2OB/ZDJEJWJmQ4IRQHhg0Gp4DXDn4T6uqhog7sRqxQtTQ+2ff1ds3x2qmrSXCXkhKAYXFQ6bPKJxRDRm07DzQvD+bNUxiJiIjIicw04e674Ynfww31MMwOZ50FhgHeIJwKuKJtQ8f0jQDRkFicWKFrXqAYCAABA/a7m/cJYYWiAfFNioPDg9hczRc/Xl/zrsOLgfeBZTQGt7VGAbEiIiIiIiIiIiIiIiIiIvI58fjjj/PMM8/06D6uuOKKbhln+PDh7N27t1vGEvm8svX1BEREREREREREREREREREROTEkuxJZkLWhKPbqd5Ukt3Jx+3jigQY7zzAZTPh6otgXA6MzYJRWTAsDbKTYFgypMfD4GTAARGja/N0hiG9FtzRxBLDhBn7OjBAQxiJiIiInLh+8xt49FGYVg+DgVAYli2HLStgEo2BagD1QBCoAaoAH43hZjas0+DagWzgmCy1tpgmmAGwR8ezR8CotsqryyDyBsQ/AvybtgPVGuTlQStn0BYRERERERERERERERERERERGYgcfT0BEREREREREREREREREREROfFMHz6dTcWbMDEByE3JJb8o/2i91+llSMIQDCKMSS9mdN02HF6rbXI9eILgMqDWaWWP2EwwALcdxqTByBQ4XAZlR8AE6u0dn2NyALwhsEX7TiiGjNoODpKXB/PmQUZGxycgIiIi/VtJCTz4oBWA9qUm5Q5gXMRapDSoAXZhhaklRssqo9dOwBu9VGMFrGUBpe2fSnUZHNwNASeUx4PrEGxdCr4yiATh+6uBQAePryEgdv78DnYUEREREREREREREREREREREemfbH09ARERERERERERERERERERETnxZMZnMiFrwtHtVG8qye7ko9v+oJ+c5Di+Ou4IZ2VXkH74CMkBK+gsPgCeMMSFrNAzI2KFqtlNK5PEZoLTgGHpMGEYJNohYnRsfo4IpEcD1CJhMEyYsa8TB9oQRiIiIiInnsceg5oamAy4mpSPPmb7CLAGKAcONykPY4WthbDC1MJAXLTODiS1fyr+KqithHAJhItg/zKoLIZwqJPBsA2efBIikU52FhERERERERERERERERERERHpXxSqJiIiIiIiIiIiIiIiIiIiIj1i+vDpGDSmneWm5B69nR7nZ0z6RyS6fbB/P4StMA+7E7yJEJ8CiZkwKBW8bvC4wOmAOEfzfbi8MDIX4j0dm1tanRXYBhCoURiJiIiIHKOkBPLyrNunNSl3AdlNtmuArUDDUqAIKzytQVmT7VqsMLVEwAF42z+dyuLG20k+KNpl3e50MGyDigrYvr0LA4iIiIiIiIiIiIiIiIiIiIiI9B8KVRMREREREREREREREREREZEekRmfyYSsCUe3U72pJLuTSXYHuXBkEfXhSoKBWjh4EJsdMnLgpFMgIR0cbrDbwW4DmwkOB7hdkBQH2YlgRLPaQjbACeMHg9vZ/rml+K1r0wRfqcJIRERE5BiPPQY1NRAHnIwVpDYIGA944Ghu7C4aA9UAQkBBk+0IUNykTR1WsFpC9NJOab7G2wUbIRS0bk8u6kIwLFiLqrS0LgwgIiIiIiIiIiIiIiIiIiLSvy1cuBDTNLvlMmPGjJj76K7x9+7d27t3jsgJyNF2ExEREREREREREREREREREZHOmT58OpuKN2FiQm0tIyJxDMs5jNthJYtUHtrNUG+ErOFgbwhFMyEcAkfDdj2YLivzwzAgNR5sdij1QV3YauKww7BO5IH4ymHiQYWRiIiISBMlB2DF7+BWrEC1qU3qsrCC1kygJnq7GitMrUEBkAEkRrcDQFG0L1gBazYag9naYVgVlMXBwRoo2GSVnVQFl+zswHHFMmsWZGd3cRARERERERERERERERERERHpDYbRgS8b9BDTNPt6CiLHpVA1ERERERERERERERERERERaZdKfyW7y3dTGajscN+97yyGrVsZOy5CSiL4Q3EQH4/TLCXxZAgaEGzSPlQD3jiIRMBmA48bXC6rzm6DBLeVZVZUA4EQmCZ4nTF3fVyRfIWRiIiISIMwsAzW/Bxm+q0i7zFNGtYbRvQyBhiJFaRWgBW2ZgJbgSlN2geAg0AaVhCbq2Mzs5swZTckvwiDaiHFD6PLOpTL1tLs2bBoUVdGEBERERERERERERERERERERHpVxSqJiIiIiIiIiIiIiIiIiIiIm3aXLyZJVuXEDEjHe5b8/H77D2wGVcanDMFIoA/UIvd9DNsCNTZwBkd1h+Ag0VQ64dTRlsBagDUQWYmxLmtMqcdEjxg2GB/BUQMcHRgajmVMKEU0v8BRrjDh9RIYSQiIiIniBJgCdR+BmtWNBYfm1pma3K7YQ1hB0YAGVhharXRywZgEo3BapHobgDSgVAHpucH41kYVQijYtVfdx3cckv7xjIMmDgRMjI6MAERERERERERERERERERERERkf5PoWoiIiIiIiIiIiIiIiIiIiJyXNWBahZvWYyJ2fHOtbXEf7SCrGRInwROBxABewRyUyK4HFYeiT0CVdWw7xCY0d0Ul8LgzOg4JpRUQlqSFazmsIPdgCQPjEyHilqoig+T7K3ENMEEDPOYHJQQ2KPhJ4Nqw2R8AASidR4PPP88JCe377gURiIiInICOQg8B9TB8uVQH7SKXUA2kIUVjGYDkqJdQjSGpTVIBKZghalVRy/rgPHRugYG1hokALgjVthagxC0WHKFgZeBwuMcwquvwuOPa20iIiIiIiIiIiIiIiIiIiIiIp9rClUTERERERERERERERERERGR49pZtrNZoFrEjByn9TFWLINQkGFVMDinMSMkEUhPbNyuDsHeJoFqAEWlkJwIXk90vwE4Ug3pJiR5wWazyj1OSHDDxOG7WXrH9dSHoKIMEuvBGYFwEI7shUk7IC6akUIhsKzJPP1+WL0a5s9v/7GJiIjICaCEo4FqtbWwapX1zcrRWIFqHiDumC7O6MWFFXhWRmMwmhOYhBWmVhu9rAVyohc74I62TY/AV31Q3GTsdUBl9HY98DHWmqWt5ZfPBwsWaC0jIiIiIiIiIiIiIiIiIiJyAjPNTpwcV+RzRqFqIiIiIiIiIiIiIiIiIiIiclxOm7PZdv7hfCoDla20biIYhEMrYTg47DAiAyrtVtWQVIjYAMPa3loJtR6wHfN9nw2lMOEkq79hglEPhyshaEJmvNXfAFx2qA9Zfdx2cJngCIOvHMoOQkZVk0C1OmAJLcNJ8vJg3jzIyGjvXSMiIiIDWUkRBP4AqX6Ii4Ply8EbhFOxAtPACjYzObpmIYQVnAZWQFoK4MUKRgtEy53AeKwwNTN62QccxAprGxztawD+JvMxgWqs8Nf1QH6TMdtDaxkRERERERERERERERERERER+ZxTqJqIiIiIiIiIiIiIiIiIiIgcV05yDgYGJlbiWW5KLvlF+W13PLAfIhEcdhgzHIbEQZ3LygvJTLS+tBAC/EGoqAO70TJUzR+ErYdg/BArr8Qwoc4Oh6vBaYDHBXablUkSjoBpghmE+mIoPwDhaJBabkV0wDrgWaA0xnx9PliwAObP7/B9JCIiIgOIacLdd8Pa38GXglZ669SpsHsdTMIKPDvaFqiN3g5jBaa5mrTxYi1osoEiGkPQEoEcrDC1BpFoeSHW4sUBbImOGwEKgAVAkM7RWkZEREREREREREREREREREREPucUqiYiIiIiIiIiIiIiIiIiIiLHlexJZkLWBDYWbwQg1ZtKsjuZykBlq31ckQCjnAcYPRPSkiEtCYYPhnobRGwwKBnsdgiE4UClFYwWBuxmy2C1mgBsLYBTM8HjscoMEwJBK4gtCIQicLgSbBHwhiBc0RiollUDcUGsAJMlxA5Ua5CXB/PmQUZGZ+6qE96ONZ/19RRERI7aWFzUo+NPzMru0fGlD/3mN/D4ozAvuh0Kw9rVcCbNA9VqgF1AHDCmSXkYSMEKVLNH62uBLOAgVkAaWKFqB7FC12zAeCA+WmcC+6P1Dd6n84FqDZ58Eh56CGy2Lg4kIiIiIiIiIiIiIiIiIiIiIjLwKFRNRERERERERERERERERERE2jR9+HQ2FW/CxEo8y03JJb8o/2i91+llSMIQDCKMSS9mdN02HN5oOlodJHvAEwSXAbVOK3/EboLXBlnxcPYwOFgBxaXgCkG9HSJGkwnUwc69kJQNmelWCFukSQCbEW3riIAnBIEmfYcXY4WULKMx5KQ1Ph8sWADz53f2rhIREZH+rKQEHnwQJgOuJuWjj9k+AmzFWjtUAyNpDFwrwwpUC2EFqrmidXVAGlASbWcHsrEC10bTGKgGUI8V2NZ0O5+uq6iA7dth/PhuGExEREREREREREREREREREQGmmAwyI4dO9iyZQtHjhyhoqICt9tNamoqgwcP5owzziBDJ56VE5hC1URERERERERERERERERERKRNmfGZTMiawMbijQCkelNJdidTGagEwB/0k5Mcx9TBe0i0VcDKIxBu7B8fAE902zTAiIAtGnzmBJwG5KZCZhwc3A/+Jvu2R8AZgZAB+yqhqAIykiBhGBjOaKCaCbZoO2cEIhGoLoP4VRD/MhDowMHm5cG8eaAvDYmIiJx4HnsMamrgtCZlLqzwswY1NAaqgRWeVgCMiG5HgGIaA9Ps0UsCVtiaHQhGrzNoDFlrEAY2R8dt8DEdW6+0xjAgLa0bBhIREREREREREREREREREZGBwDRNPvnkE958803eeecdVq1aRX19/XH7nHzyydx44418+9vfVsCanHBsfT0BERERERERERERERERERERGRimD5+OgXF0Ozcl9+jt9Dg/Y9I/ItHtg/37IWylkNidkJwFydmQmAlJWZCdAm4nOB1W7ofH3riPeDeMGAHxnsYyTzRwJBA9dZyzHipLwFcFxaVQWgGHi+HQIajYDYU7YcUiWPsKTHqVjgeU+HywYEEHO4mIiEi/V1Jihac6gUFNygdBkyUO7KIxUK1BAVDdZDsAFGEFpNU1KbcDKUASEB+9NB27HtgAVDYpKwSWdfBYWjNrFmRnt91OREREREREREREREREREREBrTi4mJ+8pOfkJubyxe+8AUeeOABPv744zYD1QC2b9/OvffeS05ODr/73e8wTbMXZizSOxSqJiIiIiIiIiIiIiIiIiIiIu2SGZ/JhKwJR7dTvakkGx6SwxVcmHuI+nAlwUAtHDyIzQ4ZOXDSKZA6BFxesNmsi8MODgPcLojzQIIH4qKBaSZgOGHcEPC4wB4BZwTCBtTbwRkGuwlZLkgyrPbBENQUgXkYwiXg90HVEZhQDBm1nTzYJ5+EyLFpKiIiIjKgPfYY1NTAMKzgs2ysQLVhgBsr/MwPlMfoawJbgWCTsgBwEKjACldr4Ghy2wBcWCFth4FVNA9UqwOW0DLErTNmz4ZFi7phIBEREREREREREREREREREenvNmzYwG9+8xsKCgo6PUZdXR3/7//9Py699FICgY6ewVakf3K03UREREREREREREREREREREROVJX+SnaX76YyUNl2Y8Bus7OvYh+mGYE1a3Dt3sykS8E8Av64OA6VlzMoJcLQYeBwWFkjDTwRcDijGyGwu8EwrHbD0mFPKdRFA0lsdhiTDXt2W9uB6DccPCGrz8h063bQDhETKILU6M585RAJwox9XbhjKipg+3YYP74Lg4iIiEi/UXIAVvwObgUmA6c0qRsKOLGC00qj20VA6JgxaoENwKRoe7DC0EoAO1ZQW8O3MsPRuiBQCGwDjj0RdB3wbHSfsVx3HdxyS9vHZhgwcSJkZLTdVkRERERERERERERERERERE5oTqeT008/nS996UucdNJJZGdnEwwGOXDgAB988AHvvPMOkWNOOvv6669z9dVX8+KLL2K32/to5iLdQ6FqIiIiIiIiIiIiIiIiIiIin1ObizezZOsSImak7cZN1ARrKN76KRzcw6SJkJIKfhsQqMXphfTREDKa55DUB6GmAlKSwGYDlwOMEDijgSR2B+Skw55yCITANCDBBUNT4FAZ1NvBGba+6DA6G+JdgAmuMJRXgK1JetvhXTC5CDJqu3DnGAakpXVhABEREekfwsAyWPNzmOm3imzHNGnYNgAXMAYYCRREL2aTttXAOmA8kNikPIiVJhuI7nJ/k7piWgaqFQJLaD1QDeDVV+HxxxWWJiIiIiIiIiIiIiIiIiIiIm2aMWMGt9xyC1dccQXx8fEx29xzzz3s2LGDb3/723z00UfN6l555RX+9Kc/8f3vf783pivSY479apCIiIiIiIiIiIiIiIiIiIh8DlQHqlm8ZXGHA9UAhruyYN8+XA6YOAY80fQ0ux3GZIPZ5CSF/gB8VgBbPrMuFdVQWQ1HyqGoBILhxrZOB4xIA6+zsSwrHUIu63amC07Ohay4xnpXLTh2Nm6HQ+BYB5c0KeuUWbMgO7uLg4iIiEjfKgGegtp/wZoVjcXmMc2aLofsTa5HAFOBuGPa1wJrgT1YAWpN+x073rHb9cB71rSOG6gG4PPBggVtNBIREREREREREREREREREZHPK8MwuOaaa9iyZQsffPAB3/jGN1oNVGswduxY3nvvPa666qoWdffddx/V1dU9NV2RXuHo6wmIiIiIiIiIiIiIiIiIiIhI79tZthOzSaJIR8LVvJ+uI7M6QvpEcDiACNgjMDzLCkYLY21XVcPeQ2BGdxOKQFEpDM60tgP1cOAIDM2w+gE47JCTCtV+CAXBCMOpI8EMQaLdGtdZH51zBMytcOZeKPOC3wGR9+H8lWB05c6ZPRsWLerKCCIiItLnDgLPAXWwfDnUB61iF5AGZAFOrFPTxmOFooVoDElrkAhMATYATb8zbAL7orvJBjKjZQYQPGYMP1AIrAfygUAHDiMvD+bNg4yMDnQSERERERERERERERERERGRE92oUaPIz89n4sSJHe5rt9t59tlnWbVqFfv37z9aXlZWxhtvvMHXvva17pyqSK9SqJqIiIiIiIiIiIiIiIiIiMjnkNPmbLadfzifykBl2x2DQTi0kvpEGHMyVHqsYrsTUpMhHE0z85mwuQxMd/PuVTXgToP4hvIQ7C+FnAwrUM0AnHZw2cFls5oMcUFFmXXbE7Kuw0E4shcm7bf6pNdhhZW8fsx8r7sObrml7eMCMAyYOFGhJSIiIgNeCUcD1WprYdUq69uSo7EC0GyAl8YUVhMrYM0JeLCC1Q4DDZmzTmASsA6oPWZXoWjZkeh4Lqw1SXG0vx/4eYx+7eXzwYIFMH9+JwcQERERERERERERERERERGRE9GIESO61N/r9XLnnXfyox/9qFn5a6+9plA1GdAUqiYiIiIiIiIiIiIiIiIiIvI5lJOcg4GBiQlAbkou+UX5bXc8sB8iEZJccHI62D1WFllGAngcUB8B04RdZVYeie2Y7iaw8zBMOMkKUXNGrJy2veWQk2oFqRmGFapWHw1Qc9vBboAtDI4w+Mqh7CBkVEFcMDpwHbCExvCTBq++Co8/rqA0ERGRz4OSEjh8EHLfgYQ6q2z5cvAG4VSswDOwFiT1QEPIaz1WmJoRvaQDdqxgtEC0jRMYD6yN9m9gwwpraxi3DtgWHROsgLXOBqo1ePJJeOghsB27shIRERERERERERERERERERHpvIsuuqhF2Z49e/pgJiLdR9+wERERERERERERERERERER+RxK9iQzIWvC0e1UbyrJ7uTj9nFFAox3HuCymXDzlTBxKIzOhlFZMDILBiXCSSmQHgdeF1YYSQz+IGw9BOEQ2E3AhLogVPvB54dgPYQjEApb12YI6vxQsx8ObIGSAoiEIbciOmAd8CxQGmNnPh8sWNDBe0dEREQGFNOEu+6Ck06C750Gv7sb3n4bampg2ycwicZAtQZlQA1QBfgAf5M6O5AIZNMYvEa0LKfJtg0raC2+SVkxjYFqAOs7f1hHVVTA9u3dMJCIiIiIiIiIiIiIiIiIiIh0xQcffIBpmi0uA1VOTk6LsqKioj6YiUj3cfT1BERERERERERERERERERERKRvTB8+nU3FmzCxvtCTm5JLflH+0Xqv08uQhCEYRBiTXszoum04vFbbpAjEBcFmQsgObls0IA0rX2RMGoxMgdIjUFQGJlBvh4hhtakJwGd7YeQgcCSAYUIwBC4nhIJQb8LhSrBFwBWB8mLwFjbOPavG2j+FwBJiB6o1yMuDefMgI6Nb7jcRERHpZ37zG3j0USsA7UtYyazLlkPRARgbah70WgPsAqqBL9BYVwoMBxKiZS4gOXp7HxCJtssBDmIFrI2meaBafXTsptv5dJ1hQFpaNwwkIiIiIiIiIiIiIiIiIiIi0qimpqZFmdfr7YOZiHQfW19PQERERERERERERERERERERPpGZnwmE7ImHN1O9aaS7E4+uu0P+slJjuOr445wVnYF6YePkByA5AAkBsEThsR6cESsvBGbCfYImBHrttOAoRkw8SRIbBKoBla7iB82HoT9pWALNfaDxoA2RzTAJCnYfO7Di4H3gKc4fqAagM8HCxZ04h4SERGRfq+kBB580Lo9GSsMrYGjoPn2EWANUA6EgIImdRHgEFCFFYYG1gInGRgDZAJpwCDgy9F9NQ1UCwObo+M2+BgIdPrIGs2aBdnZ3TCQiIiIiIiIiIiIiIiIiIiISKPPPvusRdmgQYP6YCYi3cfR1xMQERERERERERERERERERGRvjN9+HQ2FW/CxEoxy03JJb8oH4D0OD9j0j8i0Z0Ku/dD2Eo4szshPhXiU8DmhHg7eLyAaTVxNgkTCdvA44GRuVBbBDV+q9wTgrABATscLIeaYhg/2hrXsFtjOcPW2eLsEXAFod4EXznEr4L4l2kRUrI5awQ3/8cDsQ+0yoCH3gKbdf65/7npDE4dkhy7rYiIiAwcjz0GDWdNPq1JuQtomkNWA2zFCk9rUABkAInR7QBwGMgCUrBC1QA8QAJEl0vWNy8Lm4xTjxWoVtmkrBBY1sbcs23w9bjjt/G4IXU3FG6AwZPaGFBERERERERERERERERERESk/V566aUWZdOmTeuDmYh0H4WqiYiIiIiIiIiIiIiIiIiIfI5lxmcyIWsCG4s3ApDqTSXZ8EDoMBfmllMfNggG3DgPHsRmh7ShVvCZwwVOjzWGAWCCw2F9ESHDASETKmrBNCFkB8OA8YNh00EI+sEZgVqn1d8ZBsIQXwulhjVeXQD8B6AqDlJrYPubULgTIkH4/mpaBKoBhGwOihLTWz9YX7CxbdhsvZ2IiIgMDCUlkJdn3XYCTU+UPIjoIiVqF80D1cAKSdsKTIn2B2uNcRAIYYWrGdGLi8b1hytaFgaKo2M3CZWlDlgSY38eDzz/PCRHg12rd8K6u9o4yCDUFFmLIBEREREREREREREREREREZFuEggEePbZZ1uUz5kzpw9mI9J9FKomIiIiIiIiIiIiIiIiIiJygqn0V7K7fDeVgcp2tbfb7Oyr2IdpRmDNGly7NzPpUjCPgD8ujkPl5QxKiTB0mBWcFsC6eAwrLA3AjIA9Op7NBole8Dih1Ad1YbCZ4LDDmGzYsxvCBtRHO3hCkO6GBDvUhCFoh9pSsB2BsAdCdVagWjgEE4sho7a77zEREREZkB57DGpqrEC0qTQGqZlAdpPbfqC8lTFqgQ3AJBqD1SJAIdY3LOOi1w0hahEgCBwGPgPqjxmvDngWKI2xL78fVq+G+fOt7YPJsK5DRywiIiIiIiIiIiIiIiIiIiLSLX73u99x6NChZmUTJ07k9NNP76MZiXQPhaqJiIiIiIiIiIiIiIiIiIicQDYXb2bJ1iVEzEiH+tUEayje+ikc3MOkiZCSCn4bEKjF6YX00RAyINSkjy+6EYmAPQQpTitQDcDrhJAJ6YlQXAP1QbCbkOCCoSmwp8pq5wxbX14YmW5te0JQbwOKrO1UP1SXW4Fqhgkz9nXufhEREZETTMkBWPE7uBUrTG0QML5J/VCsRUY9sD96O3TsIFHVWOFm44HEJuV+rNTYAFaQ2sEmdaW0DFQrBJYQO1CtQV4ezJsHGRnHaSQiIiIiIiIiIiIiIiIiIiLSc7Zt28aDDz7Yovyhhx7CaDjbrsgAZevrCYiIiIiIiIiIiIiIiIiIiEj3qA5Us3jL4g4HqgEMd2XBvn24HDBxjBVuBmC3w5hsMO2Nbf0B+KwANu+CymqoroGKKjhSCTUBCEfAboM4J2CDjHgwbGACjghkpkPYaY3lDcPobIh3RfdngnEYbIHG/R3eZV1PLoKM2g4fmoiIiJxQwsBHsOZ6mOmHwYARvTRli5a5gSzgC8DwGO0a1AJrgT3RXdDkumG8Y8dvUA+8BzzF8QPVAHw+WLCgjUYiIiIiIiIiIiIiIiIiIiIiPaO2tparr76aurq6ZuVz5sxhzpw5fTQrke7j6OsJiIiIiIiIiIiIiIiIiIiISPfYWbYTE/PodkfC1byfriOzOkL6RHA4gAjYIzA8C5wOK1PEHoGqath7CEwT7H4YnAm2aKiIrxpcLitUzesCpx0SbOAPQooXKnxWaFrQAZmJEPTDmMGQblj7AwgHYdRq2JkMVW4Ih6BoF5xUBZfs7La7SkRERAakEmAJ1H4Ga1Y0rzKPaRoBGkJh7dHLCCAD2IoVonYsE9gHHASygSnRsiZrlaPCQCGwHsgHArRfXh7Mm9eBDiIiIiIiIiIiIiIiIiIiIiJdZ5omN954Ixs3bmxWnpWVxZ///Oc+mpVI91KomoiIiIiIiIiIiIiIiIiIyAnCaXM2284/nE9loLLtjsEgHFpJfSKMORkqPVax3QmpyRA2rG2fCZvLwHQ3dt1dDcPSG7eP1FiBaXX1EO8Guw3iXeB1gNMAgmDaYWoi+CrBGQFnvdU3EoHIVkishSm1UOaF4k/gmjUwuszKMxEREZHPq4PAc0AdLF8O9UGr2AUMwgpBGwrYsALQvFhBavVAk7ULiVhhaRuA6lZ2FYrubhhQE91HNbA7OnYAeCK63Rk+HyxYAN+/qpMDiIiIiIiIiIiIiIiIiIiIiHTcvffey+LFi5uV2e12nn/+eQYNGtRHsxLpXgpVExEREREREREREREREREROUHkJOdgYGBiApCbkkt+UX7bHQ/sh0iEJBecnA52DxgGZCSAxwH1ETBN2FUGYayskgaHyiEtwQpQAyufraQK0hOgLgRxLqvcboNkL9RHc98ME+oM8ISs7XAQjuyFSfuj9UD6bkj/J1Z4SYPrroNbbol9HJUR+CTQ9vGKiIjIAFPC0UC12lpYtcr69uNorDA1I3pxRK/t0YsX8ABBIA6ojQ7nBCYB65qUHSs12tfEClHbDxyO1pnR7a548km47couDiIiIiIiIiIiIiIiIiIiIiLSPr/73e94+OGHW5T/+c9/5stf/nIfzEikZyhUTURERERERERERERERERE5ASR7ElmQtYENhZvBCDVm0qyO5nKQGWrfVyRAKOcBxg9E4YNgtFDoT76bYL0BPC4IGJAbQC8Lqi1A6HG/iaw8zBMOAkcdrCZEK6HwipIjgOvE2yG1dbeJI3NZoLbBo4w+Mqh7CBkVEFcMNqgDlhC80A1gFdfhccfh4yMlgezvwI+Wdbeu0tEREQGhDDWoqDO2ly+HLxBOBVwNWlmAvVANOiVeqxQNCPabgSwCysgDaxgtfHA2mjfpmxYgW0NIkBRk+3DWEFtXVFRAfv2dXEQERERERERERERERERERERkbYtXLiQefPmtSh/5JFH+Na3vtUHMxLpOba2m4iIiIiIiIiIiIiIiIiIiMhAMX34dAyMo9u5KbnN6r1OL6NSRzE6dQQXj47nO8P2cNEok9EuyIxYoWaeIDgiVuiZ3bQC0GzAmDQ4YzjkJoE3BJ6QVecPwtZDEApb/eymFX9SVgsl1RCot+oAwhEIhyAQgEAxHNgCJQUQCUNuRXSSdcCzQGmMA/T5YMGC7r7bREREpL+qWArF+VBba122fQKTaB6oBuAH9gNVQA1W6FnT4DMnMJLG0DWARCDnmHFsWGFr8U3KirFC2hqs7+SxNGUYkJzUDQOJiIiIiIiIiIiIiIiIiIiItG7x4sXccsstmGbzs87de++93HXXXX00K5Ge4+jrCYiIiIiIiIiIiIiIiIiIiEj3yYzPZELWBDYWbwQg1ZtKsjuZykAlAP6gn5zkOKYO3kOirQJWHrES0ICEIHjC4IxAtRvsWKFpmGBGouFqBgzNgKw42FMIR6L5bTUB2FoAp2aCMw6M6HdvwmEIBaE+BGETCivAFbL2ESyFcDTsJKvGCnSjEFhC7EC1Bnl5MG8eZGQ0K85KcvP980a1637KSnK33UhERET6jmnCf94JjsfBFgaHHYZmw9iQtUhpUAPsAsqxvhH5BRrr44ARWIFqYAWqjQb2ALXRshzgIBACUqP1TQPV6qPjN93O74bjmzULck6Bc+9sX/uEQd2wUxEREREREREREREREREREfk8ee2117j++usJh8PNym+//XZ+9atf9dGsRHqWQtVERERERERERKRVVVVVrFmzhrVr11JdXd2iPiUlhR/96Ee9P7Futm3bNpYuXcrKlSvZtm0bBw8exOfzYRgGiYmJDB06lPHjxzNjxgwuvfRScnJyemwukUiE/Px8Vq9ezebNm9m8eTMFBQVUVFRQWVmJ0+kkNTWV1NRUhg0bxtlnn83ZZ5/NF7/4ReLj49vewQBTU1PDJ598wqpVq9i4cSP79u1j//79VFVVUVtbSygUwuPxEBcXR1ZWFoMHD2bMmDGMHz+eM844g6lTp+J2n1hBCatWreK1115rUW6327nnnntwOHrvY9/du3fzv//7vzHrfvjDH5Kamtprc2nw1FNPsXDhwh7fz5w5c3Q2HhEREenXpg+fzqbiTZhYyWa5KbnkF1npH+lxfsakf0SiOxV274dwBAC7E+JTIT4FbE6It4PHC5hWE2eocfywDTweGJkLtUVQ449W1MG2fZA4CAangt0GdgNsWOOETLBFrG1PCPxNTno4vBh4H1gGRNo4QJ8PFiyA+fObFQ9O9vKT2eM6cY+JiHRMwdYDPb6PnPEn9fg+RPq13/wG3v0tXBzdDoXBewhcTdocAbbSuHYIAQVYQWpgBad9Ft1u+JjQCeQCVUAAK1z27GhfzzFzCAObo3UNPo7264rZs2HRIkhKgi/f18XBRERERERERERERERERERERFp67733uOqqq6ivr29WPnfuXB5//PE+mpVIzzNM0zTbbibSPRITE/H5fM3KEhISYv4oW0REOmfbtm3E+u/dMAzGjdOPiETaIzc3l3379nW4n8vlIjk5maSkJFJSUjjllFOYMmUKU6ZM4ZxzzsHpdLY9iIiISB+qra1l/fr1rF69mk8//ZTVq1ezY8eOmOvLBsOHD2fv3r29N8lutmTJEh555BFWr17d7j42m41LLrmE+++/n2nTpnXLPIqKinj55Zd5++23ef/99ykrK+vwGElJSXzzm9/k+9///oBf+weDQV544QX+7//+j7fffhu/3992p1Z4PB5mz57N1772NS677DKSkpK6caZ9Y9euXYwZMyZm3auvvspXvvKVXpvLnXfeyYIFC1qUjx07lu3bt/faPJq64oorePnll3t8P3/4wx/43ve+1+P7EZHeoc/UROREtWTLEjYWbzy6vW7fSggd5pKJ5bhdBkO9WThXrcFGhLShVqCawwWJ6Vb7CGCLg4bc3nAEDldDRS2YJthMiBhWvsmmgxD0Q2I91Dqh3g4eEzKSYEouxLnAMMAXhKpKcEastoU7oWQ/xK+C81+mYwElCQmwZw9kZHTPHSbtsmPNZ309BZF22emr6OspdJlC1frexKzsvp7C51dJCeTmwvU1MDha5gK+ABjR7RpgDS3DWA1gKpDYpMwDjMIKVAMwsULVGt4KBoDCY8apxwpUq2xSVgg8dcw+r7sObrmlfcdlGDBxotYvIiIiIiIiIiIiIiIiIiLS55QBc2Jbvnw5s2bNoqampln5tddey9/+9jdsNlsfzUyk5ylUTXqV/kMVEel5+gGoSNd1NlTteDIzM/nGN77BrbfequeiiMgJ6oMPPuC8885rUf70009z00039f6E2qGuro5nnnnmaIja5s2bCYfDHRpjoIaq7dmzh5tuuomPPvqo02PYbDZuu+02HnvsMdxud4f7h8Nh/vu//5tFixbx0Ucfdfi+b41hGNx+++08/PDDxMXFdcuYvSUQCPD444/z29/+lsLCY3/B2HVut5tLL72Ue++9l9NPP73bx+9NM2bMiPn4veqqq1i8eHGvzCEUCjF06FCKi4tb1P3617/mpz/9aa/M41jDhw+noKCgx/ezevXqbgtWlBPXQFwffF7pMzURGSgq/ZXsLt9NZaCy7cZAhb+Cf277J6YZgTVrqNm9mUmXQmYKEBeHFweDfFUMHdYYnIYB6RlW3geA4QKXq3HM6noIhqDUB3VhK1jNAGoCsGc3GBGojr5FSgxAugdyh1ttHA6oLIcSH6TWgL0W3n8aQnXw/dWQUduJO+Wee2D+/E50lM5SqJoMFApVk+6gULU+dM898NjDcA+NIWo5wMgmbfKB8lb6xwFTaAxRayjLjZYZQB2Nga4mUBC9jgDFwC4g1KR/HfBXoPSYfSnoVUREREREREREREREREREBiBlwJy41qxZw5e//GUqK5t/3/SrX/0qixcvxnH0S6MiJyY9wkVEREREesGRI0f47W9/S15eHnfffTc///nPOxW+IiIi0p2Kioq47bbb+noave7999/niiuuaPGhcEdFIhH+8Ic/sGrVKt544w3S0tI61L+6urpH7n/TNMnLy+P111/nhRde4LTTTuv2ffSEt956i+9+97vs2bOnx/YRCAR48cUXefHFF/nKV77CAw88MGDD1W6++eaYoWqvvvoqpaWlpKen9/gcXnvttZiBana7nW9+85s9vv9YSktLeyVQzePxMHny5B7fj4iIiEhTm4s3s2TrEiJmpEP9aoI1FG/9FA7uYdJESEkFvw0I1OL0QvpoCBnN80J80Y1IBOwhSHFCwwkJvU4ImZCeCMU1UB8EuwkJLhiaAnuqrHbOsPWFhDGpQBiCdggEwb8LwgaE/VBRBkE/TCzuZKAawJNPwkMPNU5QREREBr6SEsjLg0E0BqoBJDW57af1QDWAWmADMInGYLVaoCq67QLCQBCwYQWp+YDDQBFQf8x4dcCztAxUA/D5YMECBb2KiIiIiIiIiIiIiIiIiIhIn9u0aROzZ89u8du52bNns2jRIgWqyeeCvlUsIiIiItKLQqEQv/rVrzjrrLMoKyvr6+mIiIh87rz//vtcfPHFbQaqZWVlMXHiRKZMmcLgwYMxDKPVtqtXr+aCCy7o0bOwJCQkMH78eM4880xOO+00hg0bdtw5AezatYuZM2fy6aef9ti8ukM4HOauu+7ioosuanegmsvlYsSIEUyePJmzzz6bqVOncvLJJ5OYmNju/f7rX/9i2rRpVFVVdXbqfeprX/tazOOtr6/n+eef75U5PP300zHLZ8+ezZAhQ3plDsdau3Ztr+zntNNOw+l0tt1QREREpJtUB6pZvGVxhwPVAIa7smDfPlwOmDgGPNHANLsdxmSDaW9s6w/AZwWweRdUVkN1DVRUwZFKqAlAOAJ2G8Q5ARtkxINhAxNwRCAzHcLRZZI3DKOzId7VuM+6MrAFINVvbR/eBYYJM/Z1+q6BigrYvr0LA4iIiEi/89hjUFMDmUAykI0VsDYIcGMFrbXnY71qYF30ukEAa/ESwApKOwjsj17vi94+NlCtEPhr9Lo1eXlWGJyIiIiIiIiIiIiIiIiIiIhIH9m5cycXXnghpaXNzxo3c+ZMXnrpJVwuVx/NTKR3KTpQRERERKSdJkyYQHJycqv1fr+f8vJyDh06hN/vP+5Y+fn5XHrppbzzzjvEx8d391RFREQkht27d3PFFVcQCARi1qenp3PXXXdx1VVXMWrUqGZ1hw8f5p///CcPP/wwe/fubdF33bp1zJ07l8WLF3fLXIcNG8all17KBRdcwJlnnsmwYcNatCkrK+P9998nLy+PDz/8MOY4lZWVfOUrX2H9+vUMGjSoW+bWnfx+P9deey3//Oc/j9suKyuLOXPmcOGFF3LGGWeQm5vbaqhcSUkJa9euZdmyZSxdupQ1a9Ycd+xIpOOhFP1BXFwc11xzDU899VSLuqeffpo77rijR/d/5MgRli5dGrPu5ptv7tF9H8+6detiln/nO9/hT3/6Uy/PRkRERKT77CzbiYl5dLsj4WreT9eRWR0hfSI4HEAE7BEYngVOB4SxtquqYe8hME2w+2FwJtiip2nzVYPLZYWqeV3gtEOCDfxBSPFChQ/sJgQdkJkIQT+MGQzpRnR/phWm5t8HDRlu4RAU7YLJRZBR24U7xzAgLa0LA4iIiEi/UnIAVvwObgVOA8Y3qRuMtZgwAQ9QCRQBoeOMVwusBXKil3CTumNPSXvsdj3wMbAMaGv55fPBggUwf34bDUVERERERERERERERERERES63759+/jyl7/M4cOHm5V/8Ytf5NVXX8Xr9fbRzER6n0LVRERERETaKS8vj5kzZ7bZLhgMsmHDBv7xj3/w17/+lbKyspjtVq5cyU9/+lPy8vK6eaYiIiJdZ7PZGDduHNOmTWPatGlEIhF+9KMf9fW0Os00TW644QYqKytj1s+ZM4enn36atFZ+iD9o0CC+853vcOONN3LnnXfyxz/+sUWbJUuW8PTTTzN37txOzdHtdvO1r32Nb3/725x77rmthoY1SEtL46qrruKqq67i1VdfZe7cuS3OIgJQVFTE9773PV588cVOzaunhEIhLr/8ct58881W24wfP56f/exnXH311Tgc7fsoMyMjg1mzZjFr1iwefPBB9u7dy1/+8hf+8pe/xLx/BrKbb745ZqjaunXr2LBhA5MmTeqxff/tb38jGAy2KM/IyGDOnDk9tt+2rF27Nmb5aaed1rsTEREREelmTpuz2Xb+4XwqA7Hf3zQTDMKhldQnwpiTodJjFdudkJoM4ejbDp8Jm8vAdDd23V0Nw9Ibt4/UWIFpdfUQ7wa7DeJd4HWA0wCCYNphaiL4KsEZAWe91TcSAfcmSK2Dqug+CjbCoFK4ZGfn7pOjZs2C7OwuDiIiIiJ9LwwsgzU/h5mtnMAqghWqZgAJwBhgJFAQvZixu2EC+4CDwNmAG3DRMigtEm17GFgP5AOxz9ERW14ezJsHGRkd6CQiIiIiIiIiIiIiIiIiIiLSNYWFhVxwwQXs37+/Wfnpp5/Oa6+9RkJCQh/NTKRvHHtuRRERERER6SKn08npp5/Of/3Xf7Fjx47jhkr8+c9/5rPPPuvF2YmIiLRkGAYnn3wy119/Pb/97W/56KOPqKqqYvPmzTzzzDPccccdTJ48ua+n2SV///vfWb58ecy6//iP/2DJkiWtBqo15fF4+MMf/sCdd94Zs/7uu+9uNbitNYmJidx1113s27ePZ599lunTp7cZqHasyy67jBUrVpCZmRmz/qWXXmLlypUdGrOn3Xbbba0GqjkcDn7961+zYcMGvv71r7c7UC2W3Nxc5s+fT0FBAb/61a9ISkrq9Fj9zRe+8AXGjx8fs+7pp5/u0X23Nv7111+P0+mMWdcbWgtVG+ivYSIiIiI5yTkYNL5PyE3JbV/HA/shEiHJASenQ0YiZCbB2EzwOKDhrceuMivGpKlD5VDTJEQkGISSKghFoC7UWG63QbIXvHEQ57aC1uwGeKJtwkEo/gyG7YcphTCxCHK3wpeeh2+tBdexO+6I2bNh0aIuDCAiIiL9QwnwFNT+C9asaCw+NlutacZ/QxisHRgBTAXi2thNKHopxAph2wFsAbZFr/8X+DXwF2AVHQtUA/D5YMGCDnYSERERERERERERERERERER6bzS0lIuvPBCdu3a1ax80qRJvPXWWyQnJ/fRzET6jkLVRERERER6UHp6Oi+++CKXXnppzPpgMMhjjz3Wy7MSERGxZGZm8v7771NZWcm2bdt47rnn+NGPfsS5555LfHx8X0+vWz388MMxy3NycvjrX//a4dCuRx55hLPOOqtF+ZEjR8jLy2vXGHa7nTvuuIPPPvuMRx55hOzs7A7N4VhjxoxhyZIlrdb/6U9/6tL43elvf/sbTz31VMy6xMRE3nnnHX760592KUztWHFxcdx7773s3LmTq666qtvG7Wtz586NWf78888TCoVi1nXV2rVr2bBhQ8y6m2++uUf22R7V1dUt/gAEVnDkxIkT+2BGIiIiIt0n2ZPMhKwJR7dTvakku4//JRdXJMB45wEumwk3XwkTh8LobBiVBSOzYFAinJQC6XHgdWEFkjRhAjsPQygaemYzIVwPhVVQUQemydGYN3uTbx7YTHDbwBEGXxkc3AZJxRAXtNqnl0HuX2FMCTSLk/Z44MUX4d1327689x4cOQJvvAEnUHCyiIjI59NB4K9AISxfDvXR5DQXkApkAkOBYUASEI8VqOYEPE2GSQSmRK9bk9qkjwkcBoqj10XAWpoHt3XGk09CJNLFQURERERERERERERERERERETaVlVVxezZs9m8eXOz8nHjxvH222+TlpbWRzMT6Vvd96tEERERERGJyW63s3DhQkaMGIHP52tR/8Ybb/TBrERERCA+Pp6ZM2f29TR63OrVq9m4cWPMukceeYSkTvwA326388QTT3DGGWe0qPv973/PXXfdhcvlOu4YiYmJPP744x3e9/Gce+65XH311SxatKhF3dKlSzFNE8MwYvTsPYcPH+b222+PWed2u3n99dc555xzemz/WVlZLF68mGeffZbvfve7Pbaf3vLNb36Te++9t0WAWnFxMUuXLuWrX/1qt+9z4cKFMctPP/10Jk2a1O37a6/169djmmaL8lGjRpGQkNAHMxIRERHpXtOHT2dT8SZMrDVPbkou+UX5R+u9Ti9DEoZgEGFMejGj67bh8FptkyJWqJnNhJDdCj2zR5dONmBMGoxMgdIjUFRmZYzU28EfhK2HYPwQ8Eb3Uw+U1YLTgGQP2KNhbOEIEIFQEALFcGQvhKOhJLkV0c51wLNAaYwD9Pth9WqYP79b7i8REREZCEqA54A6qK2FVausbzSOBrKxElgdWAFqAOEm257o9S6gIcPMCUwC1gG1x+zKFh23QQQrSK3BYboeqAZQUQHbt8P48d0wmIiIiIiIiIiIiIiIiIiIiEjr8vLyWLNmTYvy2tpaZs2a1aWx169f36X+In1JoWoiIiIiIr0gIyODm2++OWZwyp49e9i1axejR4+O0VNERES66pVXXolZPmjQIK666qpOjztt2jTOPvtsVq5c2ay8pKSEf/3rX1x55ZWdHrsrbrrpppihaiUlJezcuZOxY8f2wawaPfDAA1RUVMSse+KJJ3o0UK2pG264gcmTJ+PxeHplfz0lOzubSy65JObj/Omnn+72ULX6+nqef/75mHVz587t1n111Nq1a2OWn3baab07EREREZEekhmfyYSsCWwstkKjU72pJLuTqQxUAuAP+slJjmPq4D0k2ipg5REreARICIInDM4IVLvBjhWwhglmxLptM2BoBmTFwZ5COBLNY64JwNYCODUTnHFgRMPYwmErQK0+BGETCivAFbL2ESxtDFTLqrEC3SgElhA7UK1BXh7MmwcZGd1514mIiEi/FMZaHNRZm8uXgzcIpwJNz1fhA9zR2yZW8JkLK3AtDhgL7AEC0TZOYDywNtoerEC18UB8k3GLsdJiG6zv+hEBYBigszyLiIiIiIiIiIiIiIiIiIhILwgGY59BrqCggIKCgl6ejUj/oVA1EREREZFeMnv27JihagBbt27tkVC1bdu28eabb7JixQp27NjB/v378fl8BINB4uPjGTx4MGPHjuWLX/wil112Gaeeemq3zwHg0KFD/Otf/2LdunWsX7+ewsJCqqqqqK6uxm63ExcXd3Q+w4cPZ+TIkZx++umcffbZ5OTkdPt89u/fz7/+9S8+/vhjtm3bRkFBAT6fj1AoRHx8PNnZ2YwaNYqzzjqLCy+8kC984QsYhtHt82jL5s2beemll/j444/ZunUrpaWl1NfXk5GRQWZmJhMmTOCiiy7i4osvJqOHfmgaDodZt24dK1asYMuWLWzdupWCggKqqqqoqqrC6XSSlpZGWloaQ4cO5ZxzzmH69OmceeaZuN3utnfQA/rD/XasAwcO8Oabb7Jy5Uq2bt3Kvn37qKiooK6uDrfbTVJSEhkZGYwbN45TTjmFc889l3PPPbfb78P+8prQ3Qbi41R617Jly2KWX3LJJTidzi6Nffnll7cIVQN4/vnn+yxUbebMma3WffbZZ30aqrZnzx7++te/xqy74IILuOWWW3p1PpMmTer2Mfvitfbmm2+OGar22muvceTIETIzM7u8jwavvPIKpaUtUzA8Hg9f//rXu20/ndFaqNrkyZN7eSbSVH9df/SX9VlPGyjvfQA+/fRTXn/9dVauXMnOnTspLi6mpqaG+Ph4cnNzueWWW7j99tv7ZG4iIv3J9OHT2VS8CTOaEJKbkkt+UT4A6XF+xqR/RKI7FXbvh3AEALsT4lMhPgVsToi3g8cLmFYTZ6hx/LANPB4YmQu1RVDjj1bUwbZ9kDgIBqeC3QZ2w8onwYSQCbaIte0Jgd9sHHN4MfA+sAyItHGAPh8sWADz53ftjhIREZH+r2Ip1OdDQoK1ve0TmISV/tpUKVaImhmtCwOpRBcigAcYAxQAVdGyRCAH2BdtO5rmgWr1wK5jtvO746CAWbMgO7ubBhMRERERERERERERERERERERkY4yTNM0224m0j0SExPx+XzNyhISEqiuru6jGYmInHi2bdtGrP/eDcNg3LhxfTAjkYEnNzeXffv2tSh///33jxtS0pYDBw4wbNiwmHX/8z//w9y5czs9dlOhUIi///3vLFiwgPXr13eo77Rp0/j5z3/OnDlzumUu7733Hg8//DDvvvsukUhbv5iMbcSIEVx99dXccMMNXQ5YeP/995k/fz7vvvtuzNfK1uTm5jJv3jxuvfXWLoUoLFy4MOa/87GPrbVr13L33XfzzjvvtGtcr9fLvHnz+OlPf0pCww9PuqCiooKXX36ZF198kY8++ojKysoOj5Gens4Pf/hD7rjjDlJSUro0n4Fyvx0rEomwZMkSHn/8cZYtW9ahx1zD/C6++GK+/e1vM2vWLGw2W9udYuhPrwndqT88Tlt7bHZVf/2o5oMPPuC8885rUT58+HD27t3b+xPqgKysLI4cOdKi/C9/+Qvf/va3uzT2hx9+GHN9kJycTElJCQ5H35zTIC0tjfLy8hblzz//PNddd10fzMhy11138eijj8asy8/P75GQs97Q16+1oVCIk046iaKiohZ1CxYs4P/9v//X6bGP9ZWvfIWlS5e2KL/22mv5+9//3m376YxJkyaxcePGFuWvvPIKl112WR/MqGvq6+sZPHgwZWVlzcoTEhI4fPgw8fHxrfTsuKqqKgYPHkxtbW2z8szMTA4ePNjhAMq+fk60pjfWZ/1lfdDX732g/ev4l156iV/84hdtPlZuvPFGFi5c2KU5HY8+UxORgWTJliVsLI6ue2prWXdgNXhquOSUEtyOCEO9WThXrcFGhLShVqCawwWJ6VaXCGCLg4a3K+EIHK6GilowTbCZEDEgFIZNByHoh8R6qHVCvR08JmQkwZRciHOBYYAvCFWV4IxYbQt3Qsl+iF8F578MBDpwgAkJsGcP9FIYvnTcjjWf9fUURNplp6+ir6fQZTnjT+rrKXzuTcxSOFa3M034zzvB8TjYwuCww9BsGHrICk9rUIMVfFYODAdGNKlLBobR/JSykWhbP1bwmgEU0nxMonUbgKZ/VngP+HfXD43Zs2HRIkhK6obBREREREREREREREREREREepYyYAa+Bx54gAcffLBHxu6vv3MUaY/O/SJbREREREQ6LD09vdW6WEEvnbFixQqmTp3KN7/5zQ6HFwB8+umnfPWrX+UrX/lKl+ZUVVXF17/+db785S/z9ttvdzpQDWDPnj088sgj3H///Z0eo6SkhGuuuYbzzz+fd955p8Nv5Pfu3csPfvADJk2axLJlyzo9j7aYpskvfvELzjzzzHYHgwHU1dXxq1/9ismTJ7Nnz54uzeHOO+8kOzubuXPn8uqrr3YqqAqgtLSU++67j+HDh/PCCy90aU5t6Q/327FWrlzJtGnTuPrqq/n444879eFRXV0dL774IhdffDH33HNPp+bRX14TuttAfJxK36mrq2v18XvyySd3efzWxqisrOTTTz/t8vid1dUgnJ4QCoVaDcO56KKLBmygWn94rXU4HNxwww0x67ozgKiwsJA33ngjZl1PhEh1hN/vZ+vWrTHrJk+e3Muz6R4ul4uvf/3rLcp9Ph+LFy/u1n0tWrSoRaAawPXXX9/hQLX+8JyIpb+sz3raQHnvA1BdXc2VV17JlVde2anHiojIiaLSX8m6wnV8sPeDdl/sNjv7Kvay953F7P3rY7je+4BpjtWY1UX4g3UcOrSdQEqEtIngyIKA08okCRoQskHEZgWpmVgXmw2SPDAoGVwOCNqscocdxmSDNwxhwwpUA3DWg8MH1dVQXAqlFVB8AA4UQsVuOLQD3vsfWPsKTHqVjgWqAfh8sGBBN97LIiIi0q/85jfw7m+tQDWwkly9xwSqHQHWYIWkARQATb+vXYkVuOZvUmYDEoF4ICl6+9g/z9bTMlCtEDj2LfB118G777b/8t57cOQIvPGGAtVERERERERERERERERERESk1zzwwAOYptkjF5GBzNF2ExERERER6Q7HewPZHW8uf/e73/GTn/yEUCjU5bGWLl3KtGnTePPNNxk3blyH+paXlzN79mxWr17d5Xl0hy1btnDppZeyd+/eLo+1Y8cOzjvvPP7whz/w7W9/u+uTayISiXDjjTfy3HPPdXqM3bt3c+655/LRRx8xcuTITo2xZs0a6uvrOz2HY1VVVXHNNdewceNGfvGLX3TbuA36y/3W1KOPPsq9997bLc/FBoFAR3993H9eE3rCQHucSt86XuheSkpKl8dPTU1ttW716tWcffbZXd5HR9XX11NSUhKz7njz7Wn//ve/Ww1Iuummm3p3Mt2kP73W3nzzzfzmN79pUb5hwwbWrl3L1KlTuzzHZ599lnA43KI8JyeHCy64oMvjd8WGDRti/jukpqaSk5PTBzPqHjfddBNPPPFEi/KFCxdy4403dtt+Wgvf62hYXn96TjTVX9ZnPW2gvPcBqKio4LzzzlOYmoh87m0u3sySrUuImB0/IUHNxrUUH9wMSTBpLKSkgD9QC0YYpy1A+mgIGdD0fz9fdCMSAXsIUpxWoBqAxwWhAGQkQlEN1AfBbkKCC4amwJ4qq50zbH3JYEwqEIagHQJB8O+ygtfCfqgog6AfJhZDRsvc1vZ58kl46KHGCYqIiMiJoaQEHnwQrm9S5gKym2zXAFuBpkskM1o2BWjIf/cDO4FRgBcwonVGtD1AAlYYWwQoxgpia7pAqgOWHLMvgFdfhccfh4yMThykiIiIiIiIiIiIiIiIiIiIiIj0JYWqiYiIiHRIPXAYqADCgB1IAQbR/NTZIi2Vlpa2WtfVUJf//M//ZP78+cdtYxgGgwYNIj09Ha/XS3FxMYWFha0GExUUFHD++eezYsUKhg8f3u65XH/99W0Gqnk8HoYNG0ZCQgJut5vq6moqKyspLi7u1qCk7du3c95551FcXHzcdklJSQwZMoS4uDgOHTpEcXExkUjsH7MGg0FuvfVWwuEw3/3ud7ttrrfffnvMYDCbzUZ2djZZWVnYbDaKi4s5ePBgq+McPHiQm266iQ8//BDDMLptfg2SkpJITU0lJSUFj8dDZWUlJSUlrYb3gBUa+Mtf/pKsrCxuv/32bp1Pf7vffvjDH/L444+32c7lcpGZmUl2djamaVJWVsaRI0eore3sL42b60+vCX2hvz1OpW8dL0AnPj6+y+O73W7sdnvMoKm1a9d2efzOWLNmTavHPXr06F6eTaOlS5fGLHe73Vx22WW9PJuu62+vtePHj+fss89m5cqVLeoWLlzYLaFqrQVv3Xjjjdj6OOyitefb5MmTe3km3ev0009n4sSJbNy4sVn5hx9+yN69e8nNze3yPnbt2sWyZctalE+ZMoVJkya1e5z+9pxo0F/WZz1tIL33CYfDXHHFFTED1Ww2G5mZmWRlZeH1eiktLWXv3r0x/58VERnoqgPVLN6yGJNOnGyhtpbhK7ZSnAUuJ0wcA4SgxgX2YIAxw8C0cTQcxB+Ag0XgdsNJTQJLgkBCHHicYLeB1wW19ZARD4VVYIbBGYHMdNhVZ43nDcPobIh3QThkharVlYEtAA0Rzod3gWHCjH1duIMqKmD7dhg/vguDiIiISL/z2GNQX2P9eb3BIKwgtAa7aBlyBlALbAAm0RisFgYKgMFYf653YP0JPxAdIwTsAQqx/tTfVB3wLBDrT7g+HyxYAG181iEiIiIiIiIiIiIiIiIiIiIiIv2PQtVERERE2uQH8oH1WIFqsX7kZmB92/s0YDLg6aW5yUAS6wfjDUaMGNHpcX//+9+3Gl5gGAaXXHIJc+fOZcaMGWQcczb12tpa3n33Xf74xz/yxhtvtOhfWFjIDTfcwAcffNCuoIzFixfz+uuvx6w7+eST+d73vsdFF13EmDFjYgZXhUIhduzYwYYNG3jrrbdYunRpm6EAramqqmLOnDmt9o+Pj+f222/n61//eouwiLKyMl544QWeeOIJNm3aFLP/7bffztixYzn//PM7Nb+m/vd//5enn366WdmMGTP47ne/y6xZs0hLS2tWV1BQwPPPP8+vf/1rqqqqWoz373//m7/85S985zvf6fLcTj/9dObMmcO0adOYPHkyQ4cOjdnu4MGDfPzxx/zP//wPb7/9NqbZ8rXyxz/+Meeccw5Tpkzp8ryg/91vDz744HEDO7Kzs7nlllu4+OKLOfvss7Hb7c3qI5EIW7ZsYdWqVbz66qu8/vrrBAKBDs+jP70m9Ja+epxmZ2dzzjnnHN2urKyM+ZoxZswYsrKyOnBE0p2OF1wa67Wgo2pqaloNetmxY0eXx++MJUuWxCzPzMxk1KhRvTybRh9//HHM8rPOOou4uLhenk3X9NfX2rlz58YMVXv++ef5zW9+g8vV+SDoTz75hK1bt7YoNwyDm266qdPjdpd169bFLB/ooWoAN910E3feeWezMtM0eeaZZ7j//vu7PP4zzzwTs3zu3LntHqO/Pif6Yn3WF+uDgfTeB+DRRx/lgw8+aFb2hS98ge9+97tccsklLR4jNTU1vP766xw6dKhb9i8i0l/sLNvZLFAtYsYOuYxpxTK8/hCZtZA+ERwOIAL2CAzPAqcjekqQCFRVw95DYJpg98PgTGj4L9VXDS4XhCNWoJrTDvEeCAQhxQsVPrCbEHRAZiIE/TBmMKQb0f2ZVpiaf5+VXQJW0FrRLphcBBldySY1DDjms532qqwLsmZfWbvanj48jWSvs+2GIiIi0nUlJZCX1zJELanJbT9QfpwxqoF1wHggsUmfINaf8QNY4WtN3yJX0DJQrRBYQuxAtQZ5eTBvHhzzPrXb1VX8f/buPD6q+tD//+vMksxkD1nZA4KKAgpurRVEW7e6a69LXaro9Xaz39+lvfXa26pVL/XWSheutfbaiuu9RVC09Va97iIgqBhBFkEgbFkIWWeS2c/vjzMhE2ZCJpksE3g/H495ZM5nO585zBw+s70Hdn2QXNuxp4G7YCBnIyIiIiIiIiIiIiIiIiIiIiIy7Blmom8QiwyQ3NxcPB5Pl7KcnBxaW1uHaEYiIoefTZs2JQwIMQyDY489dghmNJyFgfeB5cR/yvpQMoAzgK/Q+VUyGU4qKiqoqqqKK3/rrbeYM2dOn8f93ve+x+9///u4csMwqK+vjwuASsbKlSuZNWtWwjCX4447jmeeeYYTTzwxqbH+9re/cd111yUMmPn3f/93fvKTn/Q4xllnnRX35XiAn/zkJ9x33329DkGIRCK88847/OY3v8HlcvGXv/wl6b633norf/rTnxLWzZ49myeeeIKKiopDjhEIBLj33nuZP39+wnPryJEj2bRpE3l5eQl6x1u0aFGP4RT5+fk88cQTXHrppT2Ot3v3bs4++2y2bNkSV3f00UezefPmpOYVa86cOWzcuJEf/OAH3HjjjYwdO7bXYyxfvpy5c+cmnNesWbN49913ezXecDhur7zyChdccEHCOofDwZ133smPf/xjcnJykh6zpaWFp59+ml/+8pdcdtll/OY3v+mxT7qdEwZKOt5PAd5++23OOuusuPLHH388LQKHUtXd7Rs/fjw7duwY/An1QmZmJoFA/Hru9ddf56tf/WpKY+/evbvb+2B5eTnV1dUpjd9bHo+HcePG0dgY/83Dm2++mT//+c+DOp8O4XCY3Nxc2tvb4+ruuOMOHnjggSGYVd+k87m2paWFkSNH0tYWn16xZMkSrrzyyl6NF+vb3/42jz76aFz5nDlzeOutt/o8bn855ZRT+PDDD+PK//znP/cqHCwd1dXVMXr0aEKhUJfyiRMnsnXr1oRhxckyTZOKigp27tzZpTwjI4O9e/dSVFTU4xjp+phIl/XZYKwP0vG5DyS3jne73fzxj3/k+uuvT3rcgaLX1ERksK2rXcfSjZ2ByGur19Lsb+65YzAIq1ZBJELABnPOg9Lo6dnuhBMrrDwyAJ8fPttpBap1GF0IY2P+i3dGA9NsBmRndgauhSOw3wMEwYy+zO1pBmcEcqNPryIRCK4Hbzu0ZFpl29dCeDncWAkZifOnk3PeeZAgcDUZlbuauPTh95Nq++L3vsIJYwv6tJ8j2ecffTHUUxBJyhZP01BPIWXjpowZ6ikc8aaVlg31FA4fd94JDzwAM4HrsX6nzACmYr2tHgBqgQ1JjGUA46IXO1ACZEfrgsCemLYb6AxZC2C9/f8+kEym7Z13QjdB8v1mz0fwX0kGmf/jmzD6pIGdj4iIiIiIiIiIiIiIiIiIHDGUASMih6veJRqIiIiIHDHqgceAN+ldoBrR9m9G+9f387xkuKqrq2PRokUJ62bNmtWnQLVgMMgtt9ySMLzgggsuYPXq1UmHFwBcdNFFvPvuu+Tn58fV3XvvvdTV1SXo1Wn//v289957ceXXXHMN//7v/97rQDUAm83GWWedxYsvvthtSEAia9as6TY0ZtasWbzyyis9hgqAFSZx//3386tf/SphfXV1NXfffXfS8+pJaWkp7733XlLBYABjxozhtddeIysrK67u888/T/jv0ZO77rqLqqoq/u3f/q1PQVUAZ5xxBitWrGDq1Klxde+99x6ffPJJn8btzlAfN6/Xy6233pqwzu1288ILL3Dvvff2KrADIC8vj+9+97ts3bqV73znOz22T7dzwkAajvdTGVrFxcUJy9etW5fy2J9++mm3dTU1NXEhSAPtgQceSBioBnR7rhoM27ZtSxioBnD88ccP8mz6Lt3PtXl5ed0Gpz3++OO9GiuWz+frNtx27ty5fR63v4RCoW4fzyeccMIgz6b/lZaWcuGFF8aVb9u2rU8hoLHefPPNuEA1gIsvvjipQLV0fUyky/psMAzX5z4A2dnZ/P3vf0+LQDURkaEwLn8cBp3hqBUFFcl13L0LIhEcdhhfBNPKoSQPinNhwgiwx+Stbm2wfjok1t5G8Po7t00/1LdCKALtMU9f7DbId4M7C7IyITvDGtsVbRMOQt0XMHYXzKiGabVQsRHOeBZu+bgfAtUWL05hABEREUk79bth5W/gNuBWYAYwBTgWGBm9jAPGA6MBRw/jmUAVsBLYAniiZRD/iUgDqAb+DiwA3iO5QDWAhQuhXu/5i4iIiIiIiIiIiIiIiIiIiIgMJwpVExEREYmzB/gT1ierY/mBncB6YBXWz1evim7vjNbHqo6Oswc5soVCIW688Uba2toS1t922219GvdPf/oTGzdujCufMWMGzz33HNnZ2Ql6HdoJJ5zAH//4x7hyv9/PI488csi+mzZtShim8M///M+9nkcivQk8uO+++zBNM658zJgx/PWvf8Xtdvdq3/Pmzev23+nRRx+ltra2V+MlYhgGTz75JNOmTetVv4qKCn784x8nrHvhhRd6PY+zzz4bl8vV634HKy4uZsmSJTidzri6hx9+OOXxO6TDcfvNb37Dnj3x53rDMHj++ee56KKLejXewRwOB8ccc0yP7dLtnDCQhtv9VIbejBkzEpavXLky5bFXrVp1yPqGhoaU95GsdevW8eCDDyasmzNnDqeffvqgzeVgiUKbOhx11FGDOJPUDIdzbXchZ6+++io1NTW9Hg+s/xubmpriyg8V4jaYNmzYgN9/8PMxy3e+8x3OOOOMlC6zZs3qNhRwsNx0000Jy7sLbk5Wd/2729/B0vUxkS7rs8EwHJ/7dHjooYc488wz+208EZHhJt+Vz9TSzqDvQnch+ZnxwaKxMiJ+pjh3c/EcuO4iuOKrcEwpHFUKk8rgmJEwtgDKcyHDAa1+CBtdxzCBLTUQCoNhgsOEUACqW6C5HcyYgBF7zKcJbCZk2sARBk8D7NkEeXWQFbRySooaoOJPMLkeuuzS5YLnn4c33uj58uabsG8fvPIK5OX17cCKiIhImgkD78JH18EcnxWedrCO9YcB5ACTgS9jBawZCdrHCmG9HV+F9ZZ9NbAX2ARsANYCf4xeVhP/ln5PPB5YsKCXnUREREREREREREREREREREREZCgpVE1ERESki3rgaSD2C/NBrE9drwK2Rdv4ouW+6Pa2aP2maHmH9uh4+vXqI1V9fT2XXXYZr776asL6k08+mW9+85u9Htc0TX75y1/GldtsNhYtWtSn8IIOV111Feecc05c+SOPPEIgEOi2X3chHVOnTk1YPlB27drF3/72t4R1Dz30EPn5h/5yanceeOABiouL48rb29tTDrMAuOGGGzjvvPP61Hfu3LkYRvy3Sj766KNUp5WSY445hm9961tx5f/3f//Xb/sY6uMWCAT47W9/m7Du+9//Pueff36f5tZb6XhOGC4G434qQ6+7wJaXX36ZlpaWPo9rmib//d//fcg2+/fv7/P4vdHe3s7111+f8HFps9lYMMRf/EsUbtShvLx8EGfSd8PlXHvmmWcmDKoLhUI89dRTfZrf448/nrD86quvJisrq09j9qePP/6427rVq1fz/vvvp3SpqanpdTBVf7vwwgspKSmJK1+yZAler7dPY7a2tvL888/HlZeXlye1hknXx0S6rM8Gw3B97gNw+umn9zlgXETkcDJ7/GyMmKSQioKKLvVup5ujCo9iUuEELpiUzT+N3c75R5lMyoAR7VAcsULNXEFwRKzQM7sJmXbIz4AvjYWxhZAZBlfICkYD8AVh414woi9l200r7qTJA/tawR+wQtcAwhEIh8DvB38d7N4A9TshEoaKpuhE24GngERPf3w+WLMGzj6758tZZ0GC/4NERERkuKoHHoO2v8FHMT9w4TuoWezb65nRv3ZgAjATSOblpzys9Fg/UAvUAHVAM4nXKL3xyCMQifTcTkRERERERERERERERERERERE0oJC1UREREQOCANL6Rqo1gSswfrUtdlDfzPabk20X4f26LjhfpqnpLtQKMRHH33Ej3/8Y4455hhefvnlhO0KCwt5+umnE4Y69eSNN95g+/btceXXX38906dP7/V4B/vRj34UV1ZbW8uKFSu67RMKhRKWD3bo0v/8z/9gmvGP1xNPPJGrrrqqz+MWFhZyxx13JKx7+umn+zxuh0THPFljx45lxowZceWffPJJwmMxmK644oq4sqqqqm5D+HprqI/bsmXL2LdvX1x5QUEBv/jFL/o8t95Kx3PCcDLQ91MZemeddVbCcq/Xy5/+9Kc+j/u3v/2NrVu3HrJNW1tbn8fvje9+97t8+umnCevmzZuX8Hw3mJqamrqtSxTck46Gy7nWMAxuuummhHV9CUPavXs3b7zxRsK6uXPn9nq8gXCoULX+cMoppwzo+MlwOp1cd911ceUej4elS5f2acznnnsu4Tnq+uuvx+Fw9Ng/XR8T6bI+GwzD9bkPwO23396n58IiIoebkuwSppZ2/iBBobuQ/MzOUExf0Me4/CwuPXYfp5U1UVSzj3w/By65QXCFITdgharZsYLT7BEwI+A0oKIQpoyDXDtEYk69vnb4Yge0+axyewQMrBC1UBDa/eDxQXUT7G+A1mYI7odwNPSk1GsFulEN/Cn6tzsLF0K9fnhERETkyLKHA4uEFSsgEF1EZACFQAkwGhiLFYiWjRWo5gRcMcPkAjOif7tTeFCf5pjrHW/hp6KpCTZvTnEQEREREREREREREREREREREREZLApVExERETngfbp+86sJ+BQ4OBDKBZRifcq7lK6f0Cba/lO6BqtVR8eX4ez222/njDPO6PZyyimnMHnyZPLy8jj55JN58MEHaWhoSDhWaWkpL7/8Msccc0yf5vL8888nLL/55pv7NN7BvvrVr5KTkxNXfqgAg6KiooTlr7zySr/MKVnd7e9b3/pWymNff/312O32uPL169ezd+/ePo87ffp0pk2blsrUEgZXtLS0UD/EX1jt7nZ98MEHKY+dDsdt2bJlCctvvvlmsrOzU5lar6TjOWE4Gcj7qaSHk08+udtQsbvvvpsdO3b0eszW1lZuv/32HtsNRrjoQw891G1Y1vTp07n//vsHfA49aW9v77bO5Tp4Pd17F1100SHXaT1d1q5d2+M+htO59qabbsJmi3/pd8OGDaxevbpXYz3xxBNEIpG48ilTpvClL32p13MbCAMdqnbqqacO6PjJ6u6+1pewvEP1S/Y+na6PiXRZnw2G4fjcByA/P5/LL788pTFERA4ns8fPxqAz7ayioAKCQfB6KXK2MrnoXXIzPbBrl5V4BtidkF8KhaMgtwQKS6AsH9yZ4HSA3bAuHVxumDABsmOW3q4Q+AOwqQp2NIIjmnNiN6wPEdhMME2wRaxtV8ja7jC+DngTeAzY38ON9HhgwYI+HyMREREZbuqBp4F2aGuD1avBARwLfBmowNp2YqXChqPbbqyAtXF0/VSjE5gOZCXYlQ2YFLMdAWpjtmuAYIo3xzBgxIgUBxERERERERERERERERERERERkcHiGOoJiIiIiKQHH7A8ZjsIbMD61HWHLKxPZBdCzJfcrJ+3bgS2Am3Rski0/ylYn/ImOv6pxIewyXCxfv36fhnnoosu4ve//z1jx47t8xivvfZaXFlxcTGzZ89OZWoH2O12vvSlL/H66693KT9UgMHUqVMTlv/Lv/wLp512GhMmTOiXuR1KOBxm5cqVCeuuvvrqlMcvLy9nzpw5vPHGG3F17733Xp/3ccYZZ6Q6NSZNmpSwvKWlhZKSkpTH76uysrKE5X0JMDpYOhy3RPcFgLlz56Y0r95Kx3PCcDKQ91NJH3fccQfXXHNNXHlraytXXHEFr732GsXFxUmN5fP5uOaaa6iqquqxbTCY6jf2Dm3ZsmX8+Mc/TliXl5fHkiVLyMzMHNA5JMPv93db53Q6u61L1qpVq9i/v6ckh+41Nzf32GY4nWvHjBnDOeecw6uvvhpXt2jRol6FhKUavDXQTNOksrJyQPeRLqFq06dPZ+bMmXEhcm+//TZVVVWMHz8+6bG2bdvG8uXL48pPOeUUjjvuuKTGSNfHRLqszwbacH3uA3Daaaelxf9NIiIDqdnXzLbGbTT7e15nAmDAjsYdVmrZRx8RrNmAs8DkjK9AS30WGeZYHPW7sWVDWTnkFVjZHqYzeiH6xr8NMuzWK9I2BzS0WUOaWO2mjIT1eyDoA2f0pW+fA9rrwVcL7iIoqYiGqRkQCoEjAvaI1T4UgNYGyF4N2cuA7pf58RYuhHnzIMnnXSIiIjJchYGlQPRHFlasAHcQjgcyYpp5gI6nhibW2/MZWG/DZwFHA9vpXG84gSnAx9H2YAWqTQFiM9Tr6PqbaZ+kfos491zo5n0EERERERERERERERERERERERFJPwpVExEREQGgkq6frv7ioO1i4Di6/iR2BwMYAZyMFaRWHy0PRMc5Nma7Ejit32Ytw4fb7eaaa67h1ltv5fTTT09prMbGRr744ou48hNOOAGbLdF9tG8ShTJ8+umn3bYvLy/nhBNOiAu12L17NzNmzOBf//Vf+cd//EeKior6bY4H27p1K+3t7XHlY8eOZeTIkf2yj9NOOy1hsEBlZWWfgwVOOOGEVKdFXl5ewvJkQmKS4fV6Wb9+PevWrWPDhg00NDTQ2tpKS0sLPp8P0zR7HiRGU1NTynMa6uO2a9cu6urq4soLCgo4/vjjU55bstL1nDAU0vF+KunjG9/4BjNmzGDt2rVxdWvXrmXWrFksWrSI00479Frt888/55ZbbkkYRpRIRkZGz4366J133uHaa68lEonE1dntdp555hkmT548YPvvjUOF5wSDwQE9Tv1hOJ5r586dmzBU7b//+7/59a9/nVSg0fLly9m6dWtcucPh4MYbb+zTvPrb559/jsfjiSvPzs6mpaWlX/990sFNN90UF6pmmiZPPPEEd911V9LjPPHEEwn/X0w2LC9dHxPpsj4bDMP1uQ/ASSedlMq0RETS3md1n7F041IiZvw6vTvegJcdTTtg507Ys52IE04/FXCDz99GXc0XlBSYVIwCp6MzW8QwIduwAtYAiIAj+gmAzAwYNQLqWsAXAtMAux0ml8H2bVabsAEBO+T6IWyCwwOtrVbGicMBzY1Q74FCL7S2wVuPQ6gdvreG3gWqAXg8sGABzJ/fy44iIiIyvLwPVFtX29pg0wcwHbAf1Gw/VoiaGa0LY/22WcfLCi5gMrATaImW5QLjgKpo20l0DVQLYP0OWux2qln8550HixenOIiIiIiIiIiIiIiIiIiIiIiIiAwmhaqJiIiIAF1/otoP1MZsZ9F9oFosW7Tdh0BbtKwWmEDnz2x/gkLVjkzt7e3s2rWL/Pz8lMf67LPPEpYfe+yxCcv7KlH42f79+w/Z54c//GHCgI3m5mbuvPNO7r77br761a9y8cUXc9ZZZ/X7nBOFfgDMmDGj3/bR3ViJQiWS1R9Bczk5OQnLE4WMJKu9vZ1ly5bx7LPP8uqrrxIMBvs81sH6I6xqqI/b5s2bE5afeuqpGAe+zTzw0vmcMBjS/X4q6cNut/Pcc89x0kknJQxO3LRpE1/+8pe55JJLuPLKK/nyl79MeXk5drud2tpa1q5dy7Jly/jv//7vuPvZUUcd1e3/Ay6Xa0Buz4cffsgll1yCz+eLqzMMg8cff5yLLrpoQPbdF263u9s6n8+X9qFqw/Fce+mllzJixAgaGhq6lDc1NbFs2bKkApEef/zxhOUXXHABZWVlfZpXfzs4YKxDf4d7pYtvfvOb/OhHPyIQCHQpf/LJJ/nZz36W1BrENE2efPLJuPLMzEyuvfbapOaRro+JdFmfDYbh+twHYNKkSSn1FxFJZ63+VpZsWIJJ70K9szOyKXXkU1dVBcApE2BkjpUtApCZEWJCGTgO+u/MH4QWjxWAZrNBRgQynJ0hazkZEM6FfR7whyBiWGWjC6B2P/gd4AyD3bT6TC4EwhC0W2P7tlrBa2EfNDVA0AfT6qC4jb555BG4/35rsiIiInIY8gExP0axajkcHeoaqObFCj5rBMZjvZXeoQUYS+cnGh1ARbStD2txVASMxgpkixUGPgNCMWXL6QyCdbng2Wch2fdqDQOmTYPi4uTai4iIiIiIiIiIiIiIiIiIiIhI2lComoiIiAgBoCZmuxa6fOltEj0HqnWwRdt/Gt02o+ONi27XRPeX3qERMjBef/11Tj75ZJ588kn+4R/+oc/j7Nq1K2H5ww8/zMMPP9zncZPR1tZGIBDoNvjkuuuu4+mnn+a1115LWB8IBPj73//O3//+dwBKS0v50pe+xOmnn86cOXM46aSTcDj6/jSluro6YfnEiRP7PObBugsA2Lt3b5/HLCgo6HPfDt2FREQikT6Nt2TJEubNm9ft/S1VLS0tKY8x1Metu2MzefLklObUW+l8Thhow+F+KunlqKOO4sknn+SKK64gHA7H1ZumyYsvvsiLL76Y9JgjR47k0Ucf5Wtf+1rC+v4IVD3YunXrOO+887q9jz788MPccMMN/b7fVBzqOOzfv5+8vLxBnE3vDcdzbWZmJtdddx0LFy6Mq1u0aFGPoWptbW0899xzCevmzp3bq7kMpO5C1fozWCoZZ5xxRkr9586dm9RxLSoq4uKLL2bp0qVdyr/44gvee+89Zs+e3eMYb7/9Njt27Igrv+yyy5JeX6XrYyJd1meDYbg+94GB+b9RRCRdbGnY0iVQLWIm/7rM2M3V1EYiZDhg2mQgBJ4McNhhcjlEbNAxnM8Pe2qhtQ2KC2FMTN5t0IT8HLDbrEt2BkSyoaYFIoA9AqVFUNsEATvk+q3MkEllVttwyApVa28Amx8Ko+PWbAXDhDOrUjhATU2weTNMmZLCICIiIpK+KqGtCTweK0S1YTXEZpLtAzZiLUoAdmLV50a3m7FC0CqAjt+qsEXrY4PZAkDrQdufRft3qAbej9n2+WDNGpg/v4+3TUREREREREREREREREREREREhguFqomIiIhQQ9cQtdhwChedXxtLVmG0ny/BeGZ0f+MO7iTDwFtvvcWcOXMS1vl8PlpaWti8eTMrVqzgmWeeYd26dQnbXXvttRiGwTe+8Y0+zaOurq5P/fpLQ0MD5eXlCetsNht/+ctfuOSSS3jvvfd6HKuuro6XXnqJl156CYDc3FzOOeccrrvuOi688EIyMzN7NbfGxsaE5f0Z1NLdWE1NTX0e026399xokASDQW644Qb+8pe/DOh+EoUZ9dZQH7eGhoaE5YWFvf1/IzXpfE4YKMPpfirp55JLLmHJkiVcf/31eL3elMYaNWoUr7/+OoFAoNs2I0eOTGkfB9u8eTPnnHNOt+egX/3qV3znO9/p1332h9GjR3dbV1NTw4QJE1Iav76+Pql2FRUVVFX1PgViuJ5r586dmzBU7bXXXmPPnj2H/HdZsmQJra2tceWlpaVcdNFFvZ7LQFm7dm3C8pkzZw7qPN5///2eGx1Cd8GMidx8881xoWpgheUlE6q2aNGihOU33XRT0nNI18dEuqzPBsNwfe5zqHFFRA4HTpuzy3ZlTSXN/uZuWscIBmHvR9RnweRJ4M2xigN2GFcKtgwrKyQcgUYPbKkF0wRc4PHDiGiAGkCtB3BAbqYVyOa0Q77b6lvbAqYBDhvkjABvExS4YXQZFBlABOymFabmq+rMLgmHoHYrnFALxW0pHCDDgBEjUhggOceOzOW9H5+VVNvSvN69/igiIiLdME14/mew8U0Iha23y0+LqffSNVANrLfONwIzgI5llA/YAhwFuAEjWmfQ+ZZ+DlaoWgSoA7YCoZhx24GlB+0LYOFCmDcPiotJK2VT4f992nM7gNzBfT9GRERERERERERERERERERERGQ4sg31BERERESGXtNB256Y63lYn9DuDSPaL9F40PUnsuVw4XK5KC0tZdasWdxxxx1UVlby5JNPkp2dHdc2HA5z4403UllZ2ad9tbWl8s3F1Pl8vkPWFxQU8MYbb/Czn/2MrKysXo3d2trK888/z5VXXsno0aN54IEHehV44/f7E5bn5+f3ah6H0t1YPR2X4SAYDHLVVVcNeFDV4aK9vT1heUFBwaDOI93PCf1N91PpD5dddhlr1qzh9NNP7/MY55xzDh999BFTpkzpNtCrsLAQl8vV530cbNu2bXz1q1+ltrY2Yf19993HD3/4w37bX38aN677UOGtW7cO4kz6Zriea0888URmzJgRVx6JRHjqqacO2ffxxx9PWH799dfjcKTPb3V0F6qW6HYfLs4///yEgY1Llizp8b7q8XgSBrKNGjWKc845J+k5pOtjIl3WZ4NhOD/3cTqdPTcSERmmxuWPw4h5PbmioCK5jrt3QSRCURC+PAmKc6EkD8bmwtgCK4sMwBuMCVSLCkdgb0zWpmlCYwu0+SESsV6tdhhQkgNj8yE/F7Jz4JhxcNI4OGocZDvBGQ0diUQgsxIKY/J1d66D8v3w9S19OSoxzj0XyspSHKRnmQ47Y0dkJXXJdKTPjx2IiIgMawsegE//zwpUAyil69vsW4kPOQNoAz4FgjFlYWAn1m+XtWMFptmj5cHo9nZgFbCJ+EC1p4D9Cfbl8cCCBb28YYPAkQmF45O7OBQIKyIiIiIiIiIiIiIiIiIiIiLSE4WqiYiIiBA+xHZfv+gb2+/gT4eHkMOfYRjccMMNvPPOO+Tm5sbVt7e3c+211/bpy+ihUPrfh5xOJ/feey9ffPEFP/3pTw8ZpNKd/fv3c+eddzJlyhRWrlyZ0nzM2G+apqg/x0o3v/71r1m2bFm39aNGjeJb3/oWCxcu5NVXX2X9+vXU1NTg9XoJBoOYppnwcrgyjMShm4N9m4fDOaE/6X4q/WXKlCksX76cF154gbPOOgubLbmXyU455RSWLl3Ka6+9Rnl5OQB1dXUJ2x577LH9Nt+qqirOOuss9uzZk7D+3/7t3/jpT3/ab/vrbxMnTuw2YG7Dhg2DPJveG87n2rlz5yYsX7RoUbd9tm/fzjvvvNOr8YbCjh07aGhoiCvPyMhg6tSpQzCjwWG327n++uvjyltbW1myZMkh+y5ZsiRhaPGNN96I3Z58oEi6PibSZX02lPTcR0RkaOW78pla2rkOKXQXkp956MDLjIifKc7dXDwHbrkQzjoGJpXBUaVwwhgoz4UxBdbflgAYCZ667G0EbzRv0zDB9MO+VvAEOtsYgDsDsjLBnQUjsqxtAFf0v/ZwEOq+gLG7YEY1TKuFio1wxrNwy8eQcfBL6b1x3nmweHEKA4iIiEjaqq+H/7qva4ha7G+Q+YBGutcKrI3+je0TBPyAFytgbRewB9iH9btpAbqqBv4U/dudhQut+YqIiIiIiIiIiIiIiIiIiIiIyGHLMdQTEBERERl6B39x3E7nT2EH6ZvYfgd/y01LsCPJSSedxLPPPssll1wS94X0jRs38vOf/5xf/OIXvRqzu0CSsWPH9im8rLe6238i5eXl3Hfffdx77718+OGHvPHGG7z99tusXLmSlpaWpMbYtWsXs2fP5qWXXuKCCy44ZNvMzMS/zp7svpLR3Vi9OS7paO/evdx3330J6yZMmMBvf/tbLrzwwqRDhzoEg309j6a/7v7Nm5qa0mIe6XhOSJXup9LfDMPgsssu47LLLqO2tpa3336btWvXsn37dhoaGggEAmRlZTF69GimT5/OeeedxzHHHBM3ztatWxOO31+hTrt37+bss89m586dCet/+MMfcv/99/fLvgaKw+Fg+vTprF69Oq5u1apVQzCj3hnO59rrrruOH/3oR/j9/i7lmzdvZuXKlXz5y1+O67No0aKEYUqnnnoqxx9/fJ/n0t8+/vjjhOXHH388TmdfA7KHh5tvvpkHH3wwrnzRokXceOON3fbrLkzvpptu6tX+0/UxkS7rs8Gg5z4iIulr9vjZrK9bj4m1nqooqKCytvJAvdvpZlTOKAwiTC6qY1L7Jhxuq60byAlAmxMwwGUHu2n9bIfdgPIcKHfDvnqoaQATCNghYsCWGpg6xhrDBrSHYFcTHFUELoeVcWKPebpsM8Fhh0gAHGHwNELDHihugazo0+SiBij6E7D/oBvpcsGzz0L+oQPjADAMmDYNiov7cDRFRERkWHjoIcho71qWHXM9maeqbcDHwLjoxY4VqNbx1vrBL/XEPi0OAMuB94n/vbODeTywYAHMn5/EpEREREREREREREREREREREREZDhSooeIiIgIBQdt52D99DVYn/A26fqz2j0x6frJ8JyD6pP4opkcVi666CJuu+02Hn300bi6BQsWcPPNN3P00UcnPV5+N19W/Id/+AceeuihPs9zIBmGwSmnnMIpp5zCv/7rvxKJRPj000957733eO+993j99ddpbOz+J+pDoRBXX301lZWVTJgwodt2hYWFCcsHI1igu30PF0888QQejyeufMaMGbz++uuMGDGiT+M2NDSkOrW0VVRUlLB8sEM7huM5oa90P5WBVFZWxtVXX83VV1/d674bNmxIWH7aaaelOi1qamo4++yz2bZtW8L673//+/zqV79KeT+DYdasWQlD1VauXEl7eztut3sIZpWc4XyuLSws5NJLL2Xx4sVxdY8//nhcqJppmjz55JMJx7r55psHZI59tXbt2oTlM2fOHOSZkDCEbiBNmTKFU089Ne4x9fbbb1NVVcX48ePj+mzfvp133303rvzLX/5ywsDIQ0nXx0S6rM8Gg577iIikr5LsEqaWTmVd3ToACt2F5Gfm0+xvBsAX9DEuP4uZI7eTa2uCVfsgbPXNzoKsEJgGBO2QabNC1UwgGLSC0LDB2BFQkgXbq2Ff9KVrXxA+3w3TyyES/R0RMwLNXghlQqbdGjcUjv4MSATa/BDeDU31EI4GqVU0RW9IO/AU8YFqAD4frFmjMBIRERGB+npYuBAmH1Qe+7tmgSTHMoEqYA9QhpUWm4X1Vv3Bv6ViANXAJ0AlVgBbshYuhHnzFPoqIiIiIiIiIiIiIiIiIiIiInKYOvjjRiIiIiJHoHK6hqblxVz3Ad0HPSXWSGco28HjGdH9yZHmwQcfpLw8/t8+EAhw55139mqssWPHJizfvz/RNxzTk81m48QTT+T2229n8eLF1NXV8dZbb3HjjTficrkS9mltbeXuu+8+5LijRo1KWN5dEE1fbN26NWH5yJEj+20fQ+Gll16KK7Pb7TzzzDN9DqqCwzusqrvH4pYtW9JiHsPpnJAs3U8lXX3wwQcJy+fMmZPSuPv27ePss8/u9rxy22238bvf/S6lfQymCy+8MGG53+/nb3/72yDPpneG+7l27ty5CcsXL15Me3t7l7K33nqLHTt2xLV1u91ce+21AzG9Pvv4448Tls+YMWOQZzI0EoXcHSoU78knn0wY/nbTTTf1et/p+phIl/XZYNBzHxGR9DZ7/GyMmNecKwoqDlwvyvIxuehdcjM9sGsXhCMA2J2QWwTZBVBWACUjIDcLXBngclgZIx3CNshywcQKyI55KS3shS1V0BK0Xol2RiBighGBUBDa/VDTDPX7ob4R9leDr7ozUK3UC1lBrICSP0X/dmfhQitERURERI5sDz0EXq/1KcR8rDC0cqwwtEysRUlGL8cMYQWrVQE7sdYke4FNwAZgLfDH6GU1vQtUA/B4YMGCXnYSEREREREREREREREREREREZHhQqFqIiIiImTQNeisjK4ha1uBSJJjRaLtOxjR8TqU0/tPjcvhIDc3l3vuuSdh3fPPP8+HH36Y9FjHHHNMwvLdu3f3ZWppweFwMGfOHJ544gk2bNjA6aefnrDd4sWLaWtr63acSZMmJSxfu3Ztv8wT4JNPPklYPnny5H7bx2Dz+XysXr06rnzOnDlMmTIlpbHXrVuXUv90duyxxyYsX716dcLAkoFyOJ4TEtH9VNLV1q1b2b59e1z50UcfzVFHHdXncffv389Xv/pVNm7cmLD+5ptv5g9/+AOGYSSsT0dnnHEGxcXFCesWLVo0uJPppeF+rj3nnHMShk01Nzfz/PPPdyl7/PHHE45xxRVXkJ+fPyDz66vuQtVmzpw5yDMZGtdcc03CQOInnngirqy7sDW3283VV1/d632n62MiXdZng0HPfURE0ltJdglTS6ce2C50F5JvuMgPN3FOxV4C4WaC/jbYswebHYrHwZjjILcYnC5w2MEJGAY4HOByQnEOFGZbZWEbhA0wnDBlpBW8Zo9YIWptQajcA3v3gS0IdqPzQwGGCbZI53aOr+u8x9cBbwKPAT1lpSqMREREROp3w8rfwG3AdcAMYApwLDACGAmMA44DRgOOXo6fh5Us6wdqgRqgDmim57VKTx55BCLJfgZARERERERERERERERERERERESGE4WqiYiIiABwYsz1TLoGobVh/eR1Tx+qjkTbxQY+lUXHS7QfOdLccsstHH300QnrfvaznyU9zsSJEykqKoorX7VqFcFgsM/zSxcTJkzg73//O6NHj46r8/v9LF++vNu+kyZNIisrK658165d1NTU9Mv8Pvjgg4TlJ5xwQr+MPxRqa2uJJPjiyKxZs1Iee8WKFSmPka5Gjx5NeXl5XHlTUxPr168ftHkc7ueEDrqfSrpaunRpwvJrr722z2M2NTVx7rnndhv4d9111/HYY48Nq0A1AKfTyU033ZSw7u9//3taBxwO93OtzWbjW9/6VsK62EC71tbWuJC1DjfffPNATK3PampqEq7vbDYb06dPH4IZDb6CggIuu+yyuPIvvviC9957r0vZu+++y7Zt2+LaXn755X0Ky0vXx0S6rM8Gg577iIgMvmZfM2ur1/L2jreTuthtdqqaqtjRuJ0dry8h49VXOdlViblvJ77Gfezduxl/QYQR08BRCn4ntBsQslkXAwiZVo6ICdhtkJUJ5fmQ4YCA3ZqXww6Ty8Adtrb9Dqt9Yx1s3AohDwRDYJoQDoIr2s4eAXu7Vd7aAJFXIPs/gPdI/jdGFi6E+vp+PMoiIiIyPISBd+Gj62COzwpP82ItQjr4o38NIAeYBnwZGE/X3zfrTiEQmyXfHHPdxApYS0VTE2zenOIgIiIiIiIiIiIiIiIiIiIiIiKSjhSqJiIiIgLACUBGzPZRB23XAx8CDXT9NDjR7YZofewXyDKi48Ru64vHRzKHw8Fdd92VsO6VV17pVajP1772tbgyr9fLypUr+zy/dJKXl8e3v/3thHXbt2/vtp/NZuP0009PWLd48eKU51VXV8fbb7+dsK4/gp2GSl1dXcLyRIEUvWGaJi+//HJKY6S7c845J2H5448/PqjzONzPCTA87qeZmZkJywOBQL+ML+knEonw2GOPxZXbbDZuvPHGPo3Z0tLCeeedx8cff5yw/uqrr+aJJ57AZhueL+t95zvfweFwxJWbpsmPfvSjIZhR8ob7ufbmm29OGMT35ptvsmvXLgD+8pe/0NbWFtemoqKCs88+e8Dn2BvdPUaOPvposrOzB3k2Q6e7sLvYsLxE2z31T0a6PibSZX3WYaDWB3ruIyIyuD6r+4zffvBbXtz8YtKhap/UfII36GXHuvfYseczRk6DgkLw2cHnbyNka6FoEoRc4HNYF68JPhNag9DihzY/hE0wDbDZrGA1ww5FueDI6HylOicDRhdA2LDC1pxhsJtQ4ISMNtjfBHX7Yc8OaP4CdlZD0xew+gVY/ix8/BJM/yud4SfJ8nhgwYL+PNQiIiKS9uqBx6Dtb/BRzGsAYcAT08xD17fVRwAOYAIwE4jPCe9kAybFbEeA2pjtGiDVTHfDgBEjUhxERERERERERERERERERERERETS0fD89qWIiIhIv3MBZ8RsO4Hj6LpcagM+BT4ANgBbon8/iJbHhg/Yov2dMWVn0PXntOVIdO211zJlypSEdd0FriVy5ZVXJiz/9a9/3ad5paMTTzwxYXlDQ8Mh+51//vkJy5966qlUp8QzzzxDKBSKKz/hhBNSDnYaSqZ5cFikxefzpTTu3/72N7Zu3ZrSGOnusssuS1i+aNGihKE0A+VIOCcMh/tpXl5ewnKv19sv40v6eeaZZxLefy6//HImTpzY6/G8Xi9f//rXWb16dcL6K664gqeffhq73d7rsdPFxIkTueWWWxLWvfbaa0MWepSM4X6unThxImeeeWZceSQS4YknngC6D5266aabEgayDaXuQtVmzpw5yDMZWl/72tcYM2ZMXPlzzz13YC3i9XpZsmRJXJuxY8emFJaXro+JdFmfdRjI9YGe+4iIDI5WfytLNiwhYkZ63Xd8RilUVZHhgGmTwRU9tdrtMLkMzJilvc8PW3dagWfNrdDqhf0N0OoDrx/CEXB3vNxsg+JsDrx87YhAaRGEor8T4gpZOSETi6xwNWfYCmfz74RwPYRroWkLNFZDOART66C4r/9NPvIIRHp/bERERGQ42gP8CaiGFSsgEE02ywDGAdnAaGAsUI4VopYJGNE2xdHrucCM6N+D2YAp0bE61AGx2eSf9MNNOfdcKCvrh4FERERERERERERERERERERERCTdKFRNRERE5ICvACNjtguA6Vif8I7lw/rk9p7o34PDXDKi/QpiykZGx5cjnc1m6zY87Y033uCdd95JapzLLrss4RfZX3zxRdasWZPSHNNFpJsvYxYUFByy3zXXXIPNFv9U58MPP+SFF17o83yam5t54IEHEtZdd911fR43HRQXFycs37x5c5/HDAaD3HPPPX3uP1xcfPHFlCX40k1jYyM/+clPBm0eR8I5YTjcT7sLTdm1a1e/7UPSx/79+/nxj38cV26z2fjpT3/a6/Ha29u5+OKLef/99xPWX3LJJfzP//wPDoej12Onm3vuuafb/8+/+93vsmrVqsGdUJIOh3Pt3LlzE5YvWrSIzz//nBUrVsTVGYbBTTfdNMAz673uQtVmzJgxyDMZWjabjRtvvDGuvLW1laVLlwKwdOlSPB5PXJtvfetbCdfNyUrXx0S6rM86DOT6QM99REQGx5aGLZh0Bn1HzEjSF/eHaylpjTBxHDgcVviZPQLji8DpgLANIkBTK2zaAS1tsL8ZTKxLJGKFrYUiVrBaxISsaLCazQb5WWCYVnCaYYP8AitAzQFMKoPsmJC19gaw+a3tQh/URPOhDRPOrErhADU1QQrPzUVERGS4qAeeBtqhrQ1Wr7YWHccCXwYmYm3boxcn1kInE8gDsoAcrLfNXdH66dHyDoXASUBJTFkA2HrQdmWKN+W882Dx4hQHERERERERERERERERERERERGRdKVQNREREZED7MCVgDumrAA4BeuntI0e+hvRdqfQNVDNHR3X3k/zlOHuqquu4vjjj09Yd/fddyc1htPp5Ec/+lFcuWma/MM//AO1tbUpzTEdfPTRRwnLR48efch+o0eP5pJLLklYN2/evISBEsn46U9/Sl1dXVx5VlZWWgaN9MbIkSNxOp1x5S+99BKBQKBPY957773dhp0cTpxOJ//f//f/Jaz73e9+x//93/8N2jwO93PCcLifjho1CrfbHVf+4Ycf9ts+jjSLFi3CMIyElx07dgzZvEKhEN/85jepqamJq7vllls48cQTezVeIBDgiiuu4K233kpY//Wvf53nnnsu4WNgOCovL2fhwoUJ63w+H+effz7Lly8f5Fn17HA4137jG99IGPD0xRdfcNtttyXsc/bZZzN+/PiBnlqvrV27NmH5zJkzB3kmQ6+7teiiRYu6/E22X7LS9TGRLuuzDgO5PtBzHxGRweG0dV2HV9ZU8m7Vuz1ftr7Bu3tXsScXRhwDzS7rQiYU5luBaiEbNJhQ2QBNmVb9Dj+02cDvsC4N7RAyrUyS9gDYDMjJBIfNCk2zR9/xD9ugJA9KMuCYCiiNDSgJQO6Gzs1wCGqj4SQn1EJxWwoHyDBgxIgUBhAREZH0FwaWAu3W5ooV4A7CqXR9Gz0CNMd0MwFv9HoGkIv11nk5MAYYBZwJHAd8CTgByD5ot58BoZiy5UA0KBaXC55/Ht54I7nLm2/Cvn3wyivQTQi6iIiIiIiIiIiIiIiIiIiIiIgMfwpVExEREemiGLiersFqTqyf2P4S1k9sF2P9fHZG9G9xtPxL0XaxX7JzAzdE24hYbDZbt+Fp77zzDm+88UZS49x+++1Mnjw5rryqqooLLrigX8Nm3n33XX7yk58css0dd9zB/fffT0NDQ8r7q6ur49FHH40rdzgcnH766T32/9nPfoZhxAch7tixg8suuwy/35+gV/cefvhh/vM//zNh3Xe/+11KSkp6NV66cbvdfOlLX4or37NnD7/85S97Pd7vf/97/v3f/70/pjYs/OAHP2Ds2LFx5aZpcvnll/PKK6+kNH4oFOLzzz/vsV26nRP623C4n9rtdqZPnx5XvmLFCjZu3Niv+5K+iUQiKY/R3t7ON77xDV577bW4urFjx/b6/hgKhbjqqqu6PVecd955PP/882RkZPRpvunq+uuv55ZbbklY19zczNlnn82DDz5IKBRK2KYvQqEQwWAwpTGG+7nW7XZzzTXXJKx75513EpbPnTu3X+fQHxobG7s91jNmzBjcyaSByZMn85WvfCWu/K233uLdd9/l7bffjqubNWsWRx11VMr7TtfHRLqsz2Dg1wd67iMiMvDG5Y/DiPnBjYqCiuQ67t4FkQh5DjimCIpzrdCzo0vA5bCyyAC2Nlh5IR3CEdjb2LltmrDfA5hWLokvBHbDClTLy4TSAsjOgfxcmFQCx4yDbCc4o09/IhGIbITjqyEv+t/CznUQCsKYFvj6lr4dlwPOPRfKylIcRERERNLb+0C1dbWtDTZ9ANOx3i6P5QO2ArVYYWohrIWOBytwzQbkYP0WmQMrQK0MmIz1tnusAPApXUPaqqNTObA/H6xZA2efndzlrLOgWO/bi4iIiIiIiIiIiIiIiIiIiIgc7hSqJiIiIhJnNHALMPKg8kxgHDAVK0Dt9OjfqdHyzIPaj4yOM2ogJyvD1De+8Q2mTZuWsO6uu+5KaoyMjAyefvppHA5HXN3atWuZMWMGjz/+eJ/DQ+rq6viv//ovTj75ZM4880z+93//95Dta2tr+dnPfsbYsWP59re/zTvvvNOn8JodO3Zw/vnnU1tbG1d37rnnUpzElx1mzpzJbbfdlrDujTfe4Otf/zo7d+7scZxgMMi9997LD37wg4T1o0ePTvrfK91dcsklCcvvuusu/vCHPyQ1htfr5V/+5V/43ve+h2maAAkDHg43WVlZPPbYYwlvq9fr5ZJLLuHnP/85Xq+3V+N6PB4effRRjj76aH7/+9/32D7dzgkDYTjcT+fMmRNXFg6HueCCC3juuedob2/vt31J723YsIGZM2fy+OOP9+nf4r333uOkk07ixRdfjKtzOp0888wzFBQUJD1eOBzm+uuvTzgewNe+9jWWLVtGZubB68zDwx/+8AfOO++8hHXBYJAf//jHzJgxg7/85S8phasFg0GeeOIJpkyZwt69e/s8Dhwe59rehKTl5+dz+eWX9/scUvXxxx8nLK+oqKCwsHCQZ5Mebr755rgy0zS59tprD/x/F+umm27ql/2m62MiXdZnHQZyfaDnPiIiAy/flc/U0qkHtgvdheRn5h+yT0bEzxTnbi6eA3OvgGmjYVIZHFUKE0uhPBfGFEBRFrgzsIJFYuxtBG9MLmYoAPWtVuBaKGIFrRmAzQbZmeDOgqxMyLCBww6u6PI5HIS6L2D0LrBHYEY1TPkUpj4P130Kt3wMGWH67rzzYPHiFAYQERGR9OcDlndurloOR4e6rl+8QCWwCvgMeAfYC+wGaoB2oBUrKA2sMLXYp+z5dH6KMRLts5qugWrtwNJofayFC6G+vm83TUREREREREREREREREREREREDkvx33QSEREREaAYuBXrp66X0/kJ72RkAGcAXyHu23AiUYZhcM8993DllVfG1a1YsYJXXnmF888/v8dxTj31VP7whz9w6623xtU1NTUxd+5c7rrrLm699VbmzJnDqaeeitvtTjjWrl27qKysZO3atbz66qusXLmyT6FobW1tPProozz66KOUl5dz6aWX8qUvfYmTTjqJ4447Drs9/nHh8/lYtWoVzz33HH/+85/x+XxxbRwOB/fdd1/S83jwwQd555132LRpU1zdm2++ydSpU/n+97/PN7/5TaZOndqlvrGxkaVLl7Jw4UI+/fTThOPb7XaefvppcnNzk55TOvunf/onHnjgAfbv39+l3DRNvvOd7/DCCy8wb948zjzzTFwuV5c2mzdvZtmyZSxcuJA9e/Z0qfvBD37Ab3/72wGf/1A799xzufvuu7nnnnvi6oLBIPfcc8+Bx+rXv/51TjvtNGy2rjnnpmmyefNmVq9ezV//+ldefvnlXgdspOM5oT8Nh/vpLbfcwi9/+cu4AJuqqiquuuoq7HY7EyZMoKCgoNugrOXLlycsHyhr167l9ttv77Fdc3NzwvKamhrOOOOMpPY12LctkbVr1zJ37ly+//3vc+6553LxxRdz8sknM2XKFJxOZ1z73bt38/rrr/PEE0/w9ttvdzvuY489xqxZs3o1l8WLF/OXv/yl2/qGhga+9rWv9WrMnsydO7dXoVoDyeFwsGzZMq6++mpeeumlhG3Wr1/PNddcQ1lZGZdddhnnnHMOJ598MuPGjes2ENHv9/P555+zbt06Xn75Zf73f/+Xpqamfpv3cD/XnnbaaRx//PF89tlnPba99tpru537UOouVG3GjBmDPJP0cdVVV/GDH/yAtra2LuWJggSzs7O56qqr+m3f6fqYSJf1GQz8+kDPfUREBt7s8bNZX7ceE+tcXlFQQWVt5YF6t9PNqJxRGESYXFTHpPZNONxW27wIZAXBZkLIDpk2sEf/S7ABk0fAxALYvw9qG8AEAnbYUgNTx1ghac4IhIKwtxUK3JDlAJvDyiKxR5fFRjRoLcMARxg8jdCwB4pbrP0DGO1Q9iSUdX1aDS4XPPss5B86LO4Aw4Bp0yCJHz8QERGRYa7pHQjshpwca7thtfVWeod9wEa6hp21AZ8C06Pb1Vh9TKILFqyFkC/6NwKEgO1ALfFvzbcDTwEHr2EAPB5YsADmz+/jDRQRERERERERERERERERERERkcONQtVEREREumUHZgOnYv209idYP4ttJmhrAOXAicAJgCtBG5GuLr/8ck444QQqKyvj6u66666kQtXA+oJ+W1sb/+///b+4L+mDFQbTESTgcDgoLS2lsLCQ3NxcPB4PTU1NNDY24vV6U7o9idTU1BwIWAPIyMigqKiIwsJC8vLy8Pv9NDY2snPnzh7DEu666y5mzpyZ9L5zc3N56aWXmDVrFrW1tXH1ra2t/OIXv+AXv/gF+fn5jBo1iqysLKqrq6mpqelxPr///e+ZM2dO0vNJd7m5ucyfP59/+qd/Slj/2muv8dprr5GRkcHEiRPJz8+npaWFmpoaGhsbE/Y55ZRTeOCBB46IUDWAu+++m/3797Nw4cKE9TU1Ndx///3cf//9ZGZmUlpaSmlpKaZp0tjYSF1dXb88DtP5nJCq4XA/nTx5Mt/61rdYtGhRwvpwOMzWrVv7ZV/9pbm5mffff7/P/f1+f0r9h0pbWxvLli1j2bJlgPV/1MiRIykoKMDtduP1etm9e3e3950Odrudhx9+mBtvvLHXc/D7/Yes7y44KhX9HdKWKpfLxdKlS7njjjv49a9/nfC8BVBbW9tlTZGZmcmYMWPIy8vD5XLh9/vxeDx4PJ6k/h+PdcMNN3DiiSf2at7D/Vx7880386Mf/ajHdukSwHewtWvXJizvzVrxcJObm8uVV17JU0891WPbK6+8kpyOL0L3k3R9TKTL+myg1wd67iMiMvBKskuYWjqVdXXrACh0F5KfmU+z3wqf9gV9jMvPYubI7eTammDVPghbfXOC4ApbwWitmdarzjYTMMGMWNdtBowuhtIs2F4N+wzwBWHjXjhupPVqc9iEiAmNXnCYMCILDLv1ynQ4AvYw+ELQXgfN2yAcDVKraIreiEOFkfh8sGaNwkhERESkk2nCHXdA26+hOGQlvY4thXHhzjZe4gPVOrQCa4EpQC5QDzixAtX8WG+513BgzYQX2JVgnGpgKYnXMB0WLoR58xT4KiIiIiIiIiIiIiIiIiIiIiIigELVRERERJLgAk6LXgJYn+5uxvq5bAeQjxWoljFUE5RhyjAM7rnnHi6//PK4ujVr1vDXv/6Viy++OKmxbr/9dsaMGcPcuXNpamrqtl0oFGLv3r3s3bu3r9NOSSAQoLq6murq6l71++lPf8rPfvazXu9v8uTJvPnmm1x00UVs376923bNzc00NzcnNabT6eT3v/89t956a6/nk+5uu+021q5dyx/+8Idu2wQCATZt2tTjWMcffzwvv/wyLteRFTL5u9/9jrKyMu66665DhlP4/X527drFrl2JviGUuuFyTuiL4XA/ffjhh9mwYQOrV6/u13FlYAUCAaqqqqiqqkq6z4gRI1i0aFHS/19LYg6Hg4ceeohzzjmH73znO+zYsaPHPn6/ny+++CKl/Z5xxhnMnz+fWbNm9an/cD7X3nDDDdx5550Eg8Fu2xx//PGccsopgzir5HUXODhjxoxBnkl6uemmm5IKVbv55psHZP/p+phIl/XZQK8P9NxHRGTgzR4/m/V16zGjP7pRUVBB5e4PIRCgqMBkctG75GYWwrZdVsoZYHdCdiFkF4DNCdl2cLkB02riDHWOH7aBywUTK6CtFrw+8Prhix0wsRwcOWCYVgibAYSCEApBKAJ1jZARAXsE/HWdgWqlXsgKojASERER6b1f/Qp+8yDcGd0OhSFcbS1EOmwlcaBahzbgY2Bc9NKA9bY6WONkAy3R7Rys9NmOkLUAsBx4v4d9AHg8sGCBAmJFRERERERERERERERERERERARQqJqIiIhIL2VgfeJbpH9cdtllzJw5M2EwxN13381FF12EYRgJesa7/PLLOemkk/jnf/5nnn/++X6d56RJk7jlllsO2SY3N7df9wkwatQoHnroIa655po+j3HcccexZs0avvOd7/Dcc8+lNJ+jjz6aP//5z3zlK19JaZx09p//+Z8UFBTwH//xH5im2acxLrnkEp566iny8vL6eXbDw7/9278xa9Ysvv3tb7Nx48Z+GbMvx3KozwkDKd3vp1lZWSxfvpz777+fhQsX0tjY2O/7kKF36aWX8vDDDzN69Oihnsph4/zzz2fTpk385je/4Te/+Q01NTX9vg+73c7FF1/M7bffztlnn53yeMP1XFtaWsqFF17IsmXLum0zd+7cAdl3qjweD1u2bElYN3PmzEGeTXo566yzqKioOGQw4YQJEzjzzDMHbA7p+phIh/XZYKwP9NxHRKT3mn3NbGvcRrM/ucBJDNjRuANMEz76iGDNBpwFJmd8BVrqs8gwx+Ko340tG8rKIa8AnBlgZIGJlRMSAZwO63qRE4ImNLdZQ4bsYBgwZSSs3wNBH0QCsG6PlXU2pgAMO7js1nRsJgTD4IgGjbhC4Al0Tnd8HfAWR1wYyfZ6L/P/N7n/83/y9SlMKM4e4BmJiIgMM/X18POfWwFosW9Txj4V9gHJPLU1gSpgD1AG5AJZ0XFddIaqdYSsbQY+ASoBfy/mPJwDYvd/Aa8l+eNS594HRUcN7HxERERERERERERERERERERERIY521BPQERERETkSHfPPfckLF+7di0vvPBCr8YaN24cS5cuZfXq1Vx33XW43e4+z2vKlCncfvvtLF++nC1btnD77bcfsv3ChQvZsGEDDz74IGeffTZZWVl93vekSZO477772Lx5c0qBah2KiopYvHgxb775Jl/72teSDqrrUFFRwW9/+1s+/fTTwz5UwG6384tf/IJXXnmFU045pVd9p0+fznPPPceLL754xAaqdZg9ezaVlZU8/vjjnHjiiX0ao7CwkJtuuokVK1Zw77339mmMoTwnDKThcD91Op38/Oc/p7q6mqVLl/LDH/6Qc845h6OOOoqSkhJcLteA7Vu6N27cOH76058yY8aMXv9fANa/6xVXXMH777/PsmXLFKg2ADIzM7njjjuoqqri6aef5sILLyQzMzPlMc8++2wWLlzIzp07eeGFF/olUK3DcD3XHio0zel0csMNNwzYvlNRWVlJJBKfCFJWVsbIkSOHYEbpwzAMvvWtbx2yzY033tin819vpOtjIh3WZ4OxPtBzHxGR5H1W9xm//eC3vLj5Rd7e8XZSF4/fw46mHexY9x479nxGxGZy+qmAG3z+NupqviBYYDJyCmSWgN8JXhOCBoRsELZBOAKmYV1sNshzQ2kBOJ0QtFm5Iw47TC4DdxjCBvjtsKcRNn8OLY1AxAphswHhgPXXHgFHGAJt0NoAkVcg+z+A9+g5UK3DwoVWiMow19Ie5P821CZ1aWkPDvV0RURE0s9DD4HXCwUHlcfmkLbQOyGsYLXPgJ1ANbAf2ARsANYCTwF/BFbTu0A16AyIHY58TbD55eQuvqahnq2IiIiIiIiIiIiIiIiIiIiISNozTNM0h3oScuTIzc3F4/F0KcvJyaG1tXWIZiQicvjZtGkTif57NwyDY489dghmJCJDqa2tjddff50VK1bwySefsGPHDmpra/F6vZimSU5ODrm5uYwYMYKjjz6aY489luOPP57Zs2enHEoRCoWorKzkgw8+YOPGjWzdupXt27fT2NiIx+PB7/eTnZ1Nfn4+RUVFHH/88cyYMYNZs2Zx6qmn9tMRSGznzp389a9/5f3332fjxo3s2rWL1tZWwuEwWVlZlJWVMWnSJE499VTOPfdcTj/99AEPn0hXy5cv59VXX+Wdd95h586d7N+/n/b2drKzsykpKeGYY47h1FNP5YILLuj2362mpiauzO12k5+fP9DTTxubNm3ilVdeYdWqVXz++ecH7nOhUIjs7GxycnIYOXIkxx57LMcddxxz5szhtNNOw2639+s8hvKcMJB0P5W+2LdvHytXrmT16tVs2LCBbdu2UV1djcfjwefzHfj3P+qoozjuuOM488wzOeeccyguLh7qqR9xPB4Pq1atYs2aNaxbt44dO3awe/duWlpaaG9vJxQKkZmZSVZWFiUlJYwaNYqjjjrqwNritNNOSzmYrTcO13OtSF+l62MiXdZnA03PfZKn19REjiyt/lYWrFyASe/fKt+wey11b/0NIhGmHw2TT7DC0gByXXBCGTgOOpXm51p/IxGw26Eg3wpUAytkrTUAERP2eSAQBLsJhgkNNbC9BQJ2cIYhJwQzRkEkFwzAZoembdCYAYVe8FfDysUQCcL31kBxWx8Ozp13wvz5feiYPip3NXHpw+8n1fbF732FE8YWDOyEDkOff/TFUE9BJClbPE1DPYWUjZsyZqincMSbVlo21FMYXPX1UFFhhaqdCFwaU3c6kBG9vhvY2ofxJwEdd+sAsCKm7kXgkz6M2aGgAPbv71xoDRd7PoL/SvJHIP7xTRh90sDOR0REREREREREREREREREjhjKgBGRw5VC1WRQ6T9UEZGBpy+AioiIiIiIiIiI9I5eUxM5snxc/TEvbX7pwHbEjCTd1/vmK3y4ew0ZDrjqPCADPBngsMMJY8FlA2d0OJ8f9tRCZiaMicljycmDnCxwOcFug0AYvEErYK2mBYwwZITBDMPK3VZ5XgCOKYXRLvA6IWgH734IboFGFxT6YMtq2LMRptXClRv7eHCGaxhJDIWqDTyFqslwoVA16Q9HXKjanXfCAw+AE5gDXI6V5moCU2Ku1wEb+jD+cUBp9Ho78EFM3VJgfd+mfcCGDTBlSoqDDDKFqomIiIiIiIiIiIiIiIiIyBBRBoyIHK4cQz0BERERERERERERERERERGRweK0ObtsV9ZU0uxv7rljMAh7P6I+CyZPAm+OVRyww7hSsGVAACsErdEDW2rBNMHuhxE2K0ANINAOjkwImpDtBKcdcmzgC0KBG5o8YDch6ICSXAj6YPJIKDKACLhC0GaCrwrsWIFq4RDUbgXDhDOrUjg4TU2wefPwCyMRERGR1NXvhpW/gduAcqAAK0itQyngxlrwZAKfA6Fe7iMv5rr3oLoklmOHZBgwYkSKg4iIiIiIiIiIiIiIiIiIiIiIyHA3fH9eWkREREREREREREREREREpJfG5Y/DwDiwXVFQkVzH3bsgEqEoCF+eBMW5UJIHY3NhbIGV4wHgDXYGqoEVsra3sXMY04T9HiAC7QGIRMBuQHYGlOdaQWrZOZCfCzMr4PiR0fC1iNXfCINvM9gDnWPuXAehIJxQC8VtfTwwoDASERGRI1IYeBc+ug7m+GAkYAAewIxp5o+WZ2K1ORsYHy1LRiHgitmODVEzgZq+zf6Ac8+FsrIUBxERERERERERERERERERERERkeHOMdQTEBERERERERERERERERERGSz5rnymlk5lXd06AArdheRn5tPsb+62T0bEz1HO3UyaA8UFMOUYiER/wizXBblZYBoQDMGnrWDYwAx39t/bCCNyIDvT2g4FoL7VKmsPWYFqAHYb5Lsh4LS2DRPaDXCFrO1wEPbtgBO3wJYiaMmE1gbYuR7GtMDXt6R4cBRGIiIicoSpB5ZC2xfw0cquVWGsYLXc6LYHKKAzRK0YCEb/bgQOFexqAybFbEeA2pjtmuhYfXXeebB4cQoDiIiIiIiIiIiIiIiIiIiIiIjI4UKhaiIiIiIiIiIiIiIiIiIickSZPX426+vWY2ICUFFQQWVt5YF6t9PNqJxRGESYXFTHpPZNONxWWzeQE4A2J2CAyw5208oGsRtQngPlbthXDzUNYAIBO2ypgaljwGEHZ8QKVqtugfwscDvAFg1ps9s652kzIdMGjjB4GqFhDxS3QG4AZlTDfgN2PQfHVsOkhs6Mkz5RGImIiMgRZg/wNNAOK1ZAIJpqlgGUA3lAEVCGtdAJYi1sbNG/GViBagAzgE+B1gS7sQFTgOyYsjogELP9CeBywbPPQn5+8jfBMGDaNCgu7rmtiIiIiIiIiIiIiIiIiIiIiIgcERSqJiIiIiIiIiIiIiIiIiIiR5SS7BKmlk5lXd06AArdheRn5tPsqYdAAF9GiHGjs5g5cju5tiZYtQ/CVt/sLMgKgWlA0G6FntlNK1skGLSC0LDB2BFQkgXbq2GfAb4gbNwLx40EV3QeAaDRC04g3w12u1UejgARCAXBXwf7dkA4mnNS0WT9Ndqh+Ckoru7mRl57Ldx6a88HQ2EkIiIiR6B6DgSqtbXB6tXWJwknYYWodSS1hqLXndGLAeRGy9uxgtKcQAMwHVgLtMXspjA6ZmygWgDYetB2JeD3wZo1MH9+P95OERERERERERERERERERERERE50ihUTUREREREREREREREREREDgvNvma2NW6j2d/cY1u7zU5VUxUmJpgmjg0badq/BUwoLYCR49cTjoynaW81OCPgBIcDcsvBWQRFDgg6IMcNERPsEfAEIWKAYULYBlkumFgBbbXg9YHXD1/sgEnl4Myy2tlMK0QtFIRACMImVDdBRsjabXB/Z6BaqReygkA1sBTYf4gb+Ne/wu9+p7A0EREROUgYayHRbm2uWAHuIBwPZBzUNAI0Y4WjgZUi6wVysD556I32KQeKo+02YwWt5dGZJBu768+wQtk6LAf80esLF8K8eVq/iIiIiIiIiIiIiIiIiIiIiIhInylUTUREREREREREREREREREhr3P6j5j6calRMxI0n28QS913jrYuRN2b6c9B7Ly4ezToC3SzP7a7dhbWrFnw+hSKMwDtwvsbqu/zQAMcNitN9+LbFb2SFM7BLEC0wwHTBkJ6/dA0AeRAGyqssLZxhSAYQe7ATYAE0Im2CLWtisEPrNzvuPrgLeA97F2dCgeDyxYAPPnJ308Dgc5LgdnTEouiCXHpY9MiIjIkeh9rIRWoK0NNn0A0wH7Qc18QAuwBzgGKMBa8IQBD5CNFa7miZY5sILV7FhBbAcLYAWqxdZVR6fT4Uhcv2TmwcQ5ybcVEREREREREREREREREREREZFD0ieERURERERERERERERERERkWGv1t7JkwxJMzJ4bxxifP566pj1QVQXACD+cNR1cTqve722lxA0Vo8AZfXc9HO7sbzMhbEYD0QCHDXIywe2Eei8EAmBgha5NLoPt26x2Pge0NIK3DnJGQEkFmKYV0hYKgSMC9gg4IxAKQGsDZK+G7GWAvxc3cOFCmDcPipMLGTscHFWSw9O3njbU0xAREUlTPmB55+aq5XB0qGugmhfYCjTGlO0BZgC5wAggA2gF3Fjhaq1wYBmWH93uCICNAHXRMUMxY7YDS4kPij3S1i/Fk+HGF4d6FiIiIiIiIiIiIiIiIiIiIiIihw1bz01ERERERERERERERERERETS15aGLV0C1SJmJKmL2+mmZNd+zEgEEzh5AozMsTJBTCAzGyaMBYejsywYglAY2v3Q1g7tPohErDqbDRwG2G1QkgP2jM58kZwMGF0AYQMCdnCGgTA4PNDaCnX7YX8T1O2G3dXQtA32fg5v/hk+fgmm/5XeBaoBeDywYEGKR1dEREQOH5VAwLra1gYNq62AtA77gI/oGqgG0AZ8ihWWVo0VvGZGy1uBcPR6MFruBOqBL4BVwCbiA9WeAvYnmKLWLyIiIiIiIiIiIiIiIiIiIiIikgLHUE9AREREREREREREREREREQkFU6bs8t2ZU0lzf7mnjsGgwSqP2dvPmQ64cLpVuBZ0A4OG0wYDX4b2KPJaG0BqNoH48KQnWmVGUEot4HTDi4HuJ3QGgDDgOJsqA0BEXBEoLQIdnqsfq6Q1WZyIRC29ukPgm+rFbwW9kFTAwR9MK0Oitv6eHAeeQTuv99KfBMREZEj3CedVz94D4rCndteYCMQ6aZrK7AWmBLddmIFspmAgRWQFo5pu76bcaqBpSQOVOuwcCHMmwfFxYdoJCIiIiIiIiIiIiIiIiIiIiIiEk+fmhYRERERERERERERERERkWFtXP44DIwD2xUFFcl13L2LjJBJQQRmHQOjCqA8F0qz4fhyyHSAaVhZIQ0eWLcLmtthX0vnEGbEClsLRcDrh4gJWdGMN7sN8rPAMK1gNsMG+QXgDFu/gDapDLIzrIA1gPYGsPmh0Gdt12y1+p5ZlcLBaWqCzZtTGEBEREQODwGgBtraoKoKdq0hZvkEW+k+UK1DG/AxsA2ojyk3gOyY7RzAnmD3bwKPcehANQCPBxYs6KGRiIiIiIiIiIiIiIiIiIiIiIhIPMdQT0BERERERERERERERERERCQV+a58ppZOZV3dOgAK3YXkZ+bT7G/utk9GxM9Rzt1MmmMFnU2YBNmZVp1hg/ICwIBgCBq8sG0fmKZVv68VxhZZoWkALV5wOcFpQHsAsjIhJxP8QSs0rc0GhCFsg5I8sHlg3BgoMoCIFbhm84OvqjN/JByC2q1wQi0Ut6VwcAwDRoxIYQARERE5LJjV8Ppr8MEHEArD1Jg6H9CY7DhAFbAH+CowAsgAXEBH8KyBFazWBNQAnwCVgL8X8124EObNg+LiXnQSEREREREREREREREREREREZEjnULVRERERERERERERERERERk2Js9fjbr69ZjYiWfVRRUUFlbeaDe7XQzKmcUBhEmF9UxqX0TDrfV1vBBkQ1CEattTgbYAEzItIPdgJPHQm0D1DRYWSLVDTAmmvFhmtDUAkV5VoaZL2SFqTkyIBIBowD87WDaodQOxQ6whcEZsPpHIpC5HgrboSUa7LZzHZTvh69vSfHAnHsulJWlOIiIiIgMe0/8Fnas6NzOjqlriWvdsxCwCysoLRoUy0asRVQE+F/gHSDYt+ni8cCCBTB/fh8HEBERERERERERERERERERERGRI5FtqCcgIiIiIiIiIiIiIiIiIiKSqpLsEqaWTj2wXeguJD8z/8C2L+hjXH4Wlx67j9PKmiiq2Ue+H/L9UGKD3AA4ItbFZQd7xMoHCYchGAS7DcaPgGljINcOe5rA67fGNkww/VDfCuEIhMIQsfLasNkgOxPcWZCVCRk2cNjBFbLqw0Go+wLG7oIZ1TCtFio2whnPwi0fQ0Y4hYNy3nmweHEKA4iIiMhhob4e/vhI1zJ7zPVAH8ft6GdihafVATXRv/X0PVCtwyOPWOmzIiIiIiIiIiIiIiIiIiIiIiIiSXIM9QRERERERERERERERERERET6w+zxs1lftx4TK9GsIrOMyoa9kJFBUX6EyUXvkptZCNt2WelngN0JBeWQl2+FnkXskO0GTAia0B4NAwnbrKC1LBdMrIC2WthSA1PHgBvrF80CQdjbCiNckOUEm8MKZrMb1hiGaW1nGOAIg6cRGvZAcQtkRfdT1ABFfwL2H3TjXC549lnIz6dHhgHTpkFxcSqHU0RERA4XDz0EHl/Xstjg1ow+jhvb7+Ag2FAfx4zV1ASbN8OUKf0wmIiIiIiIiIiIiIiIiIiIiIiIHAkUqiYiIiIiIiIiIiIiIiIiImmp2dfMtsZtNPubk+9kwI6G7fDRR7BxI8GCCM4RcMY52bT4c8gw/Tjqd2PLhrJyyCsAlxuMLOsN9KAdDBvYDCsoLd8GkQg0tVnBakTAcMKUkbB+D3y+G6aXA3bABDMCTV6r74gsMOxWkFo4AvYw+ELQXgfN2yAcDVKraIrOvR14ivhANQCfD9asgfnz+3QsRURE5AhVXw8LF8KIg8q9WMmwAHl9HDu2n/egul4s37plGDDi4ImLiIiIiIiIiIiIiIiIiIiIiIh0T6FqIiIiIiIiIiIiIiIiIiKSdj6r+4ylG5cSMSO96ucNeNmxfjns2QZ5ELHD6acAES8+T4g6s4WSApOKUeB0gB8r+Mxts/qbHRfD2jYAtwsyndDggVDAKnfYYXIZbN8GW6pg7Chw5IAzAhHD6hcKQigEoQjUNUJGBOwR8Nd1BqqVeiErCFQDS0kcqNZh4UKYNw+Ki3t1TEREROQI9tBD4PVaC5w8rCA1AysQNhMIAC6gEGjsxbiF0X4dYkPUTKAmlUlHnXsulJX1w0AiIiIiIiIiIiIiIiIiIiIiInKksA31BERERERERERERERERERERGK1+ltZsmFJrwPVALJDBqUbqg5snzIBRuZEN/x+Mm3tTBhrBap1CIetS7sf2tugPQCR6K4d0XfVbXYozgVbZme/nAwYXQD+AKzbA3v3gS0aluayR/uZEAyDIzqeK9QZzAYwvg54E3iMQweqAXg8sGBB0sdCREREjnD1u2Hlb+A24EfAZGAKcCxQDJQD44CRwMmAM8lxbcCkmO0IUBuzXQMEU5o5nHceLF6c4iAiIiIiIiIiIiIiIiIiIiIiInKkcfTcREREREREREREREREREREZPBsadiCiXlgu1fhaivfZ2xDmNpRkOGAaZOBEHgyrIC0yWUQsUHHkD4/fLELJo4BDKssCISyrfYuJzgMCJmADYpyoK4JTNMKSistgtom8NuhsQ68+6BsJIzOAdMAmwHhgJU9Yo+AIwyBNmhtgOzVkL0M8Pfi4CxcCPPmQXFxLzqJiIjIkSUMvA8f/Qzm+DqLa4DcmCZeIAfIBEYDlwAfAVUQsxTryoYVzJYdU1YHxITG8gngcsGzz0J+fu+mbhgwbZrWOiIiIiIiIiIiIiIiIiIiIiIi0icKVRMRERERERERERERERERkbTitDm7bFfWVNLsb+65YzAIe1fBKKjPgsmTwJtjVQXsMK4UbBlW5kc4Ao0e2FJrBaQVRCA7M9o2CFlAMAJhPzgdEAxbdXYb5GVDSyvYo0FrOSPA22RtR4ByYH+TldFms0PTNmjMgEIv1FbDysUQCcL31tC7QDUAjwcWLID583vZUY4Un3/0xVBPQUREhlQ9sBTavoCPVnatqgUmAvbodgPgjtnOBU4HxmKFq7UdNHQhMImugWoBYOtB25WA3wdr1mjNIiIiIiIiIiIiIiIiIiIiIiIig8o21BMQERERERERERERERERERGJNS5/HAbGge2KgorkOu7eBZEIDjscnQVnT4GSPCjOhVI3lOV2NvUGOwPVAPa1dNaZEWgLcGAGwRBk2jvrszIgI3o9AhQWgCsEhgGTyiDPCc4wmEBLHYSrIVwL4XrYvhbCIZhaB8UHB5Uk65FHIBLpY2cRERE5fO0B/gRUw4oVVlIsWAuXccCxWIFoY4HRQDHQftAQGcBRwKXATKwQteOALwEn0DVQLQx8BoRiypbTGRq7cCHU1/fPTRMREREREREREREREREREREREUmCY6gnICIiIiIiIiIiIiIiIiIiEivflc/U0qmsq1sHQKG7kPzMfJr9zd32yYj4Ocq5m0lzYEQ+2A04diJEoj81luuC3CwIhMETgI21Vg5Ixy+R7WuFsUVgjxa0eiCrwNo2gYgJuZngC0IoDC4X+L0QtkGuDQqzYVQJFBlAxApZazPBVwV2oNBnhanVbgXDhDOrUjhATU2weTNMmZLCICIiInJ4qQeeBtqhrQ1Wr7Y+HTgJKKMzLdaHFYKWCTijZXastgGshQ/R+uOBGiCYYHcBrEC12OVZNfB+zLbHAwsWwPz5Kd42ERERERERERERERERERERERGR5ChUTURERERERERERERERERE0s7s8bNZX7ceM5rsUVFQQWVt5YF6t9PNqJxRGESYXFTHpPZNONzRFJB2cGdBTgDanIABLjvYTci0gz0Tji2GGhs07LOyQwJ22NtoBasB2MPQ1AqFeVb/cBgMJ2RnQCQCTgM8Bph2cNsguwIiAXAGrP5GGHxbwR7ovE0710EoCCfWQnFbCgfHMGDEiBQGEBERkcNLGFgKtFubK1aAO2iFomUkaF4PlGOFqXV0j0TbhqLbROuLscLSOkSAOmBrtG2H9ugUIgfta+FCmDcPiov7csNERERERERERERERERERERERER6RaFqIiIiRwjTNDFNE8Mwem4sIiIiIiIiIiJyhOh43UxE0k9JdglTS6eyrm4dAIXuQvIz82n21EMggC8jxLjRWcwcuZ1cWxOs2tcZAAJkZ0FWCEwDgnbItFmhaiYQDFqhaGOLYJQLduyFfYYVqjYiB3IyrLZhP9S3WmUOG4QjYLOD3QbuDAibYJhWW7sdMqLBIuEg7NsBJ26BLUXQkgmtDbBzPYxpga9vSfHgnHsulJWlOMjhKRiO0NIeTKptntuJ024b4BmJiIgMhvc5kHzW1gabPoDpdIamdfABLUAAaACOBjKjdSbgj173ADbAifUJQzewC2gGaqP9Y7UDTwH7E0zN44EFC2D+/D7etsNcOAi+5uTauvLB7hzY+YiIiIiIiIiIiIiIiIiIiIiIDHMKVRMRETnM2Gw2wuFwwjqFqomIiIiIiIiIiHR1qEA1m01BOyIDodnXzLbGbTT7ew6PsNvsVDVVYWKCaeLYsJGm/VvAhNICGDl+PeHIeJr2VoMzAk5wOCCvELKKwV0KbgP8NnC7wDQhEoYQVm6IAWS4YWIFtNWC1wdbamDmSMBmZYm0h6C2GQqyINMBhi3aMfpyuy16GnGYYA+DpxEa9kBxC+QGYEY17Ddg13NwbDVMajjQtW/OOw8WL05lhMPahr0tXPrw+0m1ffF7X+GEsQUDOyEREZEB5wOWd26uWg5Hh7oGqnmBrUDjQV03AicBY4GMmHI3sAeIRLfDwCasRdTBqoGlJA5U67BwIcybB8XFPd2YI0/Np/BfZyfX9h/fhNEnDex8RERERERERERERERERERERESGOYWqiYiIHGbsdnu3oWrhcFhfBBUREREREREREYnR3WtpYL3WJiL967O6z1i6cSkRM9Jz4yhv0Eudtw527oTd22nPgax8OPs0aIs0s792O/aWVuzZMLoUCvPAMMCeCabTGsNugN0ONhs4TYgY0OQDMwymDbDBlJGwfg8EfbBjJ0wcCzY72CMQsUGj1xqnwA1G9PQQCQMRCIXAWw0tOyActOoqmqy/RjsUPwXF1d3cwGuvhVtv7flAGAZMm6YwEhERETlIJRCwrra1QcNqiF0u7MMKT0u0/GrDymMbjxWulhsttwMjgPqY7TKsoLUOgWjf97sZO5bHAwsWwPz5yd0kERERERERERERERERERERERGRPlKomoiIyGHmUKFpPp8Pp9M5iLMRERERERERERFJbz6fr9s6haqJ9K9WfytLNizBxOxVv/H546lr2gNVVQCM8MNZ08EVfbnb722lxA0Vo8AZ8w54bGaizYSwCTasbLLsDBiZB/VeCERD0Bx2mFwG27dBWwC2VMHIsZDnhBBWsFqmDUJBK0StPQitTdaY9gi07e0MVCv1QlYQqAaWAvsPcQP/+lf43e8UliYiIiJ99Enn1Q/eg6KYRZCX7gPVOpjADqzAtDlYgWwZQDbQCHQMVw7sBmqiu6wE/L2Y5sKFMG+e1jwiIiIiIiIiIiIiIiIiIiIiIjKguk9dERERkWEpMzOz2zqv1zuIMxEREREREREREUl/h3rNLCMjYxBnInL429KwpUugWsSMJHVxO92U7NqPGYlgAidPgJE5Vv6HCWRmw4Sx4HB0lpmA1wdtPvC2g88PwWBnnd0GmXYoyQGnE0zDKs/JgNEF1vzagrBxJ9TuA1sI3A5wRt9ht5lWuFrHG+6uIATaO2/r+DrgTeAxDh2oBuDxwIIFqRxaEREROWIFsFLOgLY22LUGjJjqrRw6UC1WEPgQKxR2Z3TYfcAmYAOwBfgV8EdgNb0LVAOteUREREREREREREREREREREREZFA4em4iIiIiw0l2djbNzc0J6zweD6ZpYhhGwnoREREREREREZEjiWmaeDyebuuzs7MHcTYihz+nzdllu7KmkmZ/4tezuwgGCVR/zt58yHTChdMhYIegHRw2mDAa/DawR/Pa2gJQtQ+a22GaAdnR3yIx/DCmyApHc9jA7YRgAIqzoaYFIgZkhKG0COoboc0OmSGo3Q/1TXDS0RB0gcNuZZWE26xx7RGItEEkDJ5GyF4N2cvoXdDIwoUwbx4UF/eik4iIiEgNYFqBaq+/Dlnhziof0NjL4Rqj/VxYa5kIBzLbACgC2lKY7iOPwP33g02/BSsiIiIiIiIiIiIiIiIiIiIiIgNDoWoiIiKHmUN90TMYDOLxeMjNzR3EGYmIiIiIiIiIiKQnj8dDMBjstl6haiL9a1z+OAwMTKz0s4qCCiprK3vuuHsXGSGTggicfAyMKgDDAL8BI/Mh0wGmaV0aPbCl1roOsK8Fskus62YEWtohkgFZGVY4WpYT2oJQ4IZGLximlfGRXwCtrVZQm2HAxBLI9MF+0wpUC7SCrxYas6HQC7tWwherIRKE762hd4FqAB4PLFgA8+f3sqOIiIgc0cxGeP3/YNUqCEfgtJi6lj6O2YIVqgZw8FOifGBXH8cFaGqCzZthypQUBhEREREREREREREREREREREREemeQtVEREQOMw6HA5fLhc/nS1hfU1NDZmYmGRkZgzwzERERERERERGR9BEIBKipqem23uVy4XDorTSR/pTvymdq6VTW1a0DoNBdSH5mPs3+5m77ZET8HOXczaQ5VtDZhEmQnWnVGTYoLwAMCIagwQvb9nUGqgHsa4WxRWC3WdutHsgqgPaANU6G3apzGNDaBmEb2CKQVwiZjZCbBaPLoMgAewScYWgzoWUL2AMQ9kDAA9vWQDgE0+qguK2PB+iRR+D++61UNxEREZFkPPk4bF/RuW2PqQv0cczYfvaD6lJ9imQYMGJEioOIiIiIiIiIiIiIiIiIiIiIiIh0T98EEREROQzl5OR0G6oWCoWoqqqivLycnJwcDMMY5NmJiIiIiIiIiIgMHdM08Xg81NTUEAqFum2Xk5MziLMSOXLMHj+b9XXrMbGSzyoKKqisrTxQ73a6GZUzCoMIk4vqmNS+CYfbamv4oMgGoYjVNicDbAAmZNrBbsDJY6G2AWoawAQCdtjbaAWrAdjD0NQKhXnQHoLsDKtfTiaMGwGt7WCEIdsG2QY4HdEwtWi4SEYAduyB7Oh2oQ+2r4NQEAwTzqxK4eA0NcHmzTBlSgqDiIiIyBGjvh5+/0e4IKYsHHO9r7+zFtsvfFBd90+hknPuuVBWluIgIiIiIiIiIiIiIiIiIiIiIiIi3VOomoiIyGFoxIgRNDY2Eg4f/AlnSygUYvfu3TidTnJycsjOzsblcmG32zEMQ0FrIiIiIiIiIiJyWDBNE9M0CYfD+Hw+vF4vHo+HYDB4yH52u50RI0YM0ixFjiwl2SVMLZ3Kurp1ABS6C8nPzKfZ3wyAL+hjXH4WM0duJ9fWBKv2HQjzyMyG3AC0ZlrbLrsVeBaxQSgMwSDYbTB+BJRkwfZq2GdYoWojcqwQNrsJYT/UeaAoB7IcYNis8TIckBUd2x6BgAuCIXBFw0PCQdi/A8bUQ6PbKmttgJ3rresn1EJxWwoHxzBA5x4RERFJ1kMPQXV71zIvEF2nkNfHcWP7eQ+qa+7jmADnnQeLF6cwgIiIiIiIiIiIiIiIiIiIiIiISM8UqiYiInIYstvtlJSUUFNTc8h2wWCQxsZGGhsbu5QrVE1ERERERERERA4Hpmn2qV9paSl2u72fZyMiHWaPn836uvWYWI/RioIKKnd/CIEARQUmk4veJTezELbtgnAEALsTCsohL98KPovYIdsNmBA0oT2alRi2WYFoWS6YWAFtteD1wZYamDkSiAao+UNQ1wQZIyC3I0gt+tK4YYIB2O0QCYAjDJ5GaNgDxS1wzH6oLIP9wMZ3wIzAmBb4+pYUD8y550JZWYqDiIiIyBGhvh4WLoQAYGItXgBagOLodRdQCDQm6N+dwmi/DrEhanYH/O4ZyCumVwwDpk2D4l72ExERERERERERERERERERERER6QOFqomIiBymCgoKaGlpoa2trdd9+/plUxERERERERERkeEuKyuL/Pz8oZ6GyLDT7GtmW+M2mv3NPTcGMGBH4w4wTfjoI4I1G3AWmJzxFWipzyLDHIujfje2bCgrh7wCcLnByLLe5A7awbCBzbBy0vJtVv5ac5sVrEYEDCdMGQnr90DQBzt2wsSxYDrBEQHTgNZ2sEXA6bC2Q2FwmBAywdMGod3QVA/haGhbRZMV2jZjG2z/GxQ0QYEPJjV0Zpn0yXnnweLFqYwgUeOLsnj4mzOTbisiIjIsPfQQeL3gxApWG4e1GIkAGUAQK2xtEvBRtLwntmj7DhGgNmZ7dwjaP4H581Ofv3QqnAD/sCj5tiIiIiIiIiIiIiIiIiIiIiIickiGqdQUGUS5ubl4PJ4uZTk5ObS2tg7RjEREDm+RSISdO3fS3t4+1FMRERERERERERFJe263m3HjxmGz2YZ6KiLDymd1n7F041IiZjJpHRZvwMuavWtg507Yvh2/E876GpTmWfUZNgclhKgYZQWeAbgyoSDXuh42INsFHQ9XfwjaQ1awWoMHwn6IROu8fti+DRxhcGTCyHFQaIeQDQpzIdcZHcOE2kbIiN6M6l3g3tM551IvHLcPqAaWAvsPcQNdLnj2WegppNEwYNo0KC5O7sCJJOHzj74Y6imIpIUtnqahnkLKxk0ZM9RTOOJNKy0b6inEq98N35gMx/igHBgDTI6pLwayscLWPEAVsJ5DB6vZgClASUxZDbApZvvvwIYc2L5daxcREREREREREREREREREZHDgDJgRORw5RjqCYiIiMjAsdlsjBs3jl27dtHW1jbU0xEREREREREREUlbWVlZjB07VoFqIr3U6m9lyYYlmPTut7yyM7IpdeRTV1UFwCkTYGQOhKP1mRkhJpSBw+jsEw5bl0A0PM1mA3eG9dcRfejabVCUC/VAJGiV5WTA6AKo3Q8tEWjZARPyoKQIXPbO8SNBcMSEjbi7fk6I8XXAW8D7HDqUBMDngzVrYP78XhwVERERkUMJA+/DRz+DOb7O4lpgItCxrmkA3EBm9FIIjATWROsOVghMwgpi6xAAth60XQn4PbBggdY4IiIiIiIiIiIiIiIiIiIiIiKStvStEBERkcNcR7BaeXk5dru95w4iIiIiIiIiIiJHELvdzsiRIxk3bpwC1UT6YEvDli6BahEzkvRl7OZqzEgEpwOmTQZXCEzAbofJ5RCxW9sm0O6HTduhrgGaWqHVC00eaPWD12/tuyOAzTBgRI71F6ygtNIiwA4BO2SGrIC12l0Q9EAwBKYJ+MEZDUuzR8BotcpbGyDyCmT/B/AePQeqdVi4EOrrUzzCIiIiImBFxj4GbX+Dj1Z2rQoBO2O2I0AdnWsWG1ao2nnALOA4rBC144AvASfQNVAtDHwWHbfDciC65tIaR0RERERERERERERERERERERE0pljqCcgIiIiA88wDAoLC8nLy6OhoQGPx4PP5+u5o4iIiIiIiIiIyGHK5XKRk5PDiBEj9GMEIilw2pxdtitrKmn2N/fcMRiEvR9RnwWTJ4E3xyoO2GFcKdgyIACEI9DogS21VsBZQQSyM6Ntg5AFBE0IBiDTDsGwVWe3QV42tLSC3QRskDMCvE3WtmFARSGYrbA/YI3d9rkV5NaYDRl7YePL4GmASBC+t4bOIJFkeTywYAHMn9/LjiIiIiKx9gBPA+2wYoW1CALIAMqBPKxQtFLACQSx1i31QDRYFoBMoAJwYy20EglgBarFLueqgfdjtrXGERERERERERERERERERERERGRNKZQNRERkSOI3W6npKSEkpISQqEQXq8Xr9dLIBAgHA4TDoeJRCI9DyQiIiIiIiIiIjJM2Gw27HY7drudjIwMsrOzyc7OxuHQ22Qi/WFc/jgMDExMACoKKqisrey54+5dEIlQFIQvT4LiXCvozLDB2AIIRqygM2+wM1ANYF8LZJdY180ItAWskDXDhGAIMh3gjwarZWVAx8+LRIDCAmiut/YzqQyyMwATnGFoaoJgo9U27IFd70NznbU9rQ6K2/p4gB55BO6/H2y2Pg4gIiIiR7Z6DgSqtbXB6tXWJ/4mAWWAEdO0EStkLSt6MYGONUxWtK092q8GK3ytQwSoA7YCoZjydmBptD7WwoUwbx4UF6d8C0VERERERERERERERERERERERPqTvi0iIiJyhHI4HOTn55Ofnz/UUxERERERERERERGRYSrflc/U0qmsq1sHQKG7kPzMfJr9zd32yYj4Ocq5m0lzoLgAphwDkWjmWK4LcrPANKyQtE9braA1MxqUtq8VxhaBPdq+xQsuJzgNKzckbEJOJviDEAqDywV+L4RtkGuDwmwYVQJFBgfCQZzt4NthZYwA5Hngs63WdcOEM6tSOEBNTbB5M0yZksIgIiIicmQKYyWatVubK1aAOwjHAxkJmgeBWqzQNDtWiFp2dJh9WJ8UdAFOoBDYCXiA5mi/wEHjtQNPAfsT7MvjgQULYP78vt88ERERERERERERERERERERERGRAaBQNRERERERERERERERERER6bPZ42ezvm49JiYAFQUVVNZWHqh3O92MyhmFQYTJRXVMat+Ew221dQM5AWhzAga47GA3rbwzuwHlOVDuhn31UNNgBadVN8CYYmts04SmFijKs/qHI2ADsjMgErHC1jwGmHZw2yC7AiIBcEZDQyIRMDbA6BaoybHKdq6DUNC6fkItFLelcHAMA0aMSGEAEREROXK9D1RbV9vaYNMHMJ3OJNgOPqAFKxQtAysMbQyQGa23A8VYwWktMf2qge7CY6ux8twSBap1WLgQ5s2D4uIkb4+IiIiIiIiIiIiIiIiIiIiIiMjAsw31BEREREREREREREREREREZPgqyS5haunUA9uF7kLyM/MhGASvF19bK+Pys7j02H2cVtZEUc0+8v2Q74dCICsE7hA4IpBps0LVDNPqbjPBZoOxI2DaGMi1w54m8PqtfRkmmH6ob4WwCZhWsBqA3QbuDHBnWSFrTjvY7eAKWfXhINR9AaN3weQGyPNDawPsXG/Vj2mBr29J8eCcey6UlaU4iIiIiBx5fMDyzs1Vy+HoUNdANS9QCawCNgBbo3/fA17FCkaLrouwAaV0/bTgOOJ/kjUAvAk8xqED1QA8HliwIOlbJCIiIiIiIiIiIiIiIiIiIiIiMhgO/liUiIiIiIiIiIiIiIiIiIgIzb5mtjVuo9nf3GNbu81OVVMVJiaYJo4NG/n/2fv3GDvvO8/zez/Pc+6nWKcup6pISiRLEm2LNim5bU83tmdGbnfPSC0PkExjECQOJvkj652exFkEEIIA6cEgWUAgMskOkTUHS8+MvYvdnjiAWgx64pnFZHt3jB7bbbduFq0LTVOieCerWJdTl3Oqzu158sfvkFWUJbFYRbUufL+Awzq/y/f7/J7nrweowoeN+bOQweQI7DnwOv30AI2r1yCfQh5yOdi1G/LjMJ6Dbg6GypBmkKSw2oU0CsFp/RgqJXh4GlozcPY6HH4QyoRskLUeXFuGsRKUcxDFQDT4EMLZAHIZJH1YXYSFK1Bfhko3rP3GOaj9f2B3C0bW4eDCrfLteeopeO65nXSQJEn3rVOEhDOg1YKFF6C+afkGcJqN0LR3WwD+DPgCMA0MAQVgDJgb7EmAKeAycB14dXDZ9l0c8/hxeOYZqNfvvFeSJEmSJEmSJEmSJEmS/goYqiZJkiRJkiRJkiRJus0bs29w8vRJ0uz9kjp+XbPbZLY5CxcvwuV3WBuCSg1+97eglS4xP/MOyfIKSRUemITRYSiXICmH+ngQgpZLwi+yx+OQE9JYgy4hGC3KwaE98PoV+NVleGw3pEmoz1JoNCEXwUgZosF82gdS6PWgeQ2Wz0N/EKQ23Rgcfg2iP4ZHrsEj73Vz3/gGfPObW3sQUQRHjhguIkmSduDVja9/+SMY72+Mm3xwoNpNKfAGUAKqhLTYHHAWGLwfcRE4RnjZ2o7VVTh2DI4e3WYDSZIkSZIkSZIkSZIkSbq3DFWTJEmSJEmSJEmSJN2y0l7h+TefJyO7q7oDtQPMNq7AhQsAjLXha49BKR/W280VJsowvRfyg99U9zflg8QZ9DOIB+NcDENFKOdhrgmdziALJIHPTME75+DsBdizD0o5yKeQRlCModcNIWprXVhphJ5JCq2rG4Fqk02odIFrwElg/gNu7gc/gG9/26A0SZL0V6ADXA9fWy249CLs37T8FncOVLspHex/HMgYJNUCVwbrxXtw3BMn4NlnIY7vvFeSJEmSJEmSJEmSJEmSPmT+JZMkSZIkSZIkSZIk6ZazC2dvC1RLs3RLn3K+zMSlebI0JQO+8hDsGQr5HRlQrMJD+yCX25jr9qDXh7U2tNZgbR3SNKzFMeQiSGKYGIKkwK1TDRXggRFodeHUFbh6A+IulHOQH/wWPM5CuNrNX4qXutBZ27jPA7PAvwe+ywcHqgGsrsKxYzt7sJIkSVtynVtvPX/xF1DZlEK7DizeZbvFQd1NtU3fI2D3No64WaMBZ87ssIkkSZIkSZIkSZIkSZIk3Ru5j/oAkiRJkiRJkiRJkqSPj3ycv2186vopltpLdy7sdulc+xVXa1DMw995DDoJdBPIxfDQA9COIRlkhLQ6cOEG7O9DtRjmoi7sjiGfQCkH5TysdCCKoF6FmR6QQi6FyXG4uBoiRxZnoTkHX/4sdEuQS0JGSL8V+iYppC1I+7C6CNUXoPqnQPsuHszx4/DMM1Cv30WRJEnS3WqEH3Nz8Jd/CV/atLS8zZbLQGnwvfqutRpwaZt9IbyojY3toIEkSZIkSZIkSZIkSZIk3TuGqkmSJEmSJEmSJEmSbtlf209EREZIP5semebUzKk7F16+RKGXMZLCVz4He0dCxkY7gj01KOYgy8JncRXOzoTvN5ahOhFaZGkIW6sWoZmy4IEAAQAASURBVNmGSgEqeWh2IYmhVoGVlRDMlsZQG4HeQvjF98MTUFyH+SwEqnVWYH0GFqsw2oRLP4W3X4C0C996kbsLVANYXYVjx+Do0bsslCRJugtZD/6HP4Of/hTSDJJNa51t9txcl7xrbad/QfjkkzA1tcMmkiRJkiRJkiRJkiRJknRvGKomSZIkSZIkSZIkSbqlVqpxePIwr82+BsBoeZRascZSe+l9awppm0fylzn4OyHo7KGDIRgNIIph9wgQQbcHC004dyMEqgHcWIF94yE0DWC5CaU85CNY60ClCENFaHehWoBWDPShH8PEMMSrsP9BGI8gSSHfh1YGy2ch6UB/FTqrcO5F6PfgyCzUW9t8OCdOwLPPQhxvs4EkSdId/Df/Cs7/xca4v2mtsM2em+v671rrbbMnwFNPwXPP7aCBJEmSJEmSJEmSJEmSJN1bhqpJkiRJkiRJkiRJkm7zxIEneH32dTJC8tn0yDSnLr8EnQ4UCpQrw+wd2ktEymfGZzm49kty5bA3WofxGHpp6DVUgBggg2ICSQRf2QczC3B9ATLg2gI8WA/7swwayzA+DFEE670QppYrQJpCNALtNcgSmEygnoO4D/lOqC904PwVqA7Go+vwzmvQ60KUwVcv7ODBNBpw5gwcOrSDJpIkSe9jbg7+b9+B/8WmuSZQHnwf3mbfzXXNTd+PHIa//UfQnrq7flEER45Avb7NA0mSJEmSJEmSJEmSJEnSh8NQNUmSJEmSJEmSJEm6DyytL3Fu8RxL7aWtFURwfvF8SDl7+WW619+kmc8ggqX6BPu+XOK3D1xnJLcCV2fJolBWKkMhhV4Sxvk8xBmkUQha63QhF8OBMZiowDvX4EoDRndBtRiCz7I2zK3A2FAIXUsziCOI47AnHlwrSSGXQKEdxv0uzJ+HB+dgcRA+srIAF18P3x+fgXprBw8ximBsbAcN9GGbWV7nT166tKW9/7Ov7GNquPQhn0iSpLvwT/8pnFsLL0CD9x2WgZvZZSVgFFi8i56jg7qbNr8K/vJX8NWvQn3vdk+snVq+Bq/+q63t/eLfh+E9H+55JEmSJEmSJEmSJEmSpE84Q9UkSZIkSZIkSZIk6VPujdk3OHn6JGmWbrmm2WlyvnEeLl6EK+/Qy0FjEMgxFd/g0eqfAXUaNxahkAGQy8H4HqiMQDUPxDA0qOml0FqDfhxyQpIUKiV4eBpaM3D2Ohx+EMqhjE4Xrq7AWAkqeYhzIVskGQSMRCHfjUIEuT6sLsLCFagvw+fm4dQUzAOn/xyyFB5chq+f3eGDfPJJmJraYRN9mK4vrfOf//e/2tLev/mZCUPVJEkfH3NzcPw4dIHrwM3srOvAQ2yErB0EXga28loXD/bflAIzm8YXO3Dsn8HRozs6unZg5Sr8+2e3tveR3zVUTZIkSZIkSZIkSZIkSboDQ9UkSZIkSZIkSZIk6VNspb3C828+T0Z2V3XVQpXJXI3ZCxcAKPeg2INKDf7gyxD3m/Q7u0iWl0kSeGASRodDUFqpGILT+jEkMcQR5BIYiaCfQWMthKzlUojycGgPvH4FfnUZHtsNJEAWwtAazZAHMlaBKAl5Iv0Ukj6s92BtFpbOQb8bzj3dCIFtv3EO3vk3MNKAkXU4uLCRRbItTz0Fzz23kw6SJEnv75/+U2g2IQ9cA75IeHnJgBWgNvheBQ4Bp/ngYLV4sK+6aW4W6Gwavwq8eRyeeQbq9XtzH5IkSZIkSZIkSZIkSZL0ETJUTZIkSZIkSZIkSZI+xc4unL0tUC3NPih943b7zlxjJt3YP9qGrz0GxTxkGaw3bjBZhum9kBv89jmFW1eLsrAvG6SZxcBQEcp5WFiFXjvM5xL4zBS8cw7OXoB9eyE3BPkU0ijkifS60OuFMLbZRSikITytPbsRqDbZhEoXuAbRSXh4Hh5+v5srleD734da7YMfQhTBkSMGjUiSpA/P3GX46f8T/gGwmxCs9llC0CyEl6i9QB9YHYwrwFvA4nv0GwUOcnugWmewf/P4FNBehWPH4OjRe3Y7kiRJkiRJkiRJkiRJkvRRMVRNkiRJkiRJkiRJkj7F8nH+tvGp66dYai/dubDbhasvM1eBZiFM/ebDMDIK7UFIWrHUZ+8U9KOQ8QEhQK2cQacH/QxIoFKAOIJcHPYkMdSH4AaQDgLRhgrwwAjMzMNrV2BqDPbXIC1AaRAoEmfQ7UNukPNW6sFqZ+PIB2aBHwI/IaS7fZD1dXjxRQNEJEnSR6gP/ARe/sfwO+sb0z3gIvDQYJwSXpymgHFCaNoSITRtHVgmhKQVgGGg9B6XeWPQ96YfA4OAW44fh2eeMURWkiRJkiRJkiRJkiRJ0ide/FEfQJIkSZIkSZIkSZL04dlf209EdGs8PTK9tcLLlyBNGe/C3hoceRD+zhdhTxWGS1DIwaNT3PZb51YHXrsI1xqw0ISlFsw3YaUNrUFoR25wlCiC8aHwE0JQ2uQ4kEA7gcVZOP0WLC8CKWRZuFS/E34mKeT60GnBygKk/w6q/wT4EXcOVLvp+HGYm9viZkmSpHtpDvgutP4NvPzTX1++CKxsGreBGUJAWkwIVtsD7AImgQcHP98dqNYBfkEIYbvpGiGE9qbVVTh2bAf3IkmSJEmSJEmSJEmSJEkfD7mP+gCSJEmSJEmSJEmSpA9PrVTj8ORhXpt9DYDR8ii1Yo2l9tL71hTSNo/kL3Pwd2CsBq08jI/B9ERY70UwVoVCAqsdaK/D/AqcnQnhZ60OVIth71obRsrQz6DZhnwOej0ggiSC4Sosr0CSATEMjUGzEcYpsBuYb0AExAk0zsFiAUabMHMNfvocpF341ouEsJG7cTNA5OjRuyyUJEnaiSvAvwLW4C/+AjrdMF0gvPwMA1VCQNoeQpBaG1gdlI4N1ouD/TOE8LTNUmAWeAvobZpfA07y6yG0x4/DM89AvX5P7lCSJEmSJEmSJEmSJEmSPgrxnbdIkiRJkiRJkiRJkj7JnjjwBBHRrfH0yPRt6+V8mUdGH+Hg6EM8fbDKH+57h99/JONgAcbWYPcq7K1CLg2fcgZDBSgmMF6GiSHotaDUhVIP5jbltWUprK9vjLs9KG76778qhZAfAiHbY3Qk9IgiODgFw3nI9yEDlmehfw36M9Cfg3d+Dv0eHJ6FemubD+fECUjfnSoiSZL0YZnjVqBaqwUvvBD+a9RHgf8IeBioA2VCquw8ITxtFHiQEKi2AFwe/GwD44SXpbVB+7eBnwG/5NcD1f540PPdbobNSpIkSZIkSZIkSZIkSdInmKFqkiRJkiRJkiRJkvQpN1Gd4PDk4Vvj0fIotWINul1oNllvrbC/VuF/+ugNfmuqwfj1G9Ta3PrU2zAeb4SqDechzkLORwQsrcGDY3DkQdiVwOwq9Ac5ZVEGrZXbx2kKQ0XIxaG+VApr/Rh25WG0Cp+bhslKmC/1Qhjb+oXB+ddDmNrMW6HfVy/s4OE0GnDmzA4aSJIkbVUfOElINwP+4i+g3IXfBHbDpgzcDR3gOiE8LQKGgAcIQWzLwCxwDVgFXgBeBy4N6ja7Bnxv8PP9HD8Oc3PbuC9JkiRJkiRJkiRJkiRJ+njI3XmLJEmSJEmSJEmSJOnjaGl9iXOL51hqL91xbxInXGhcICODLCP35mka82chg8kR2HPgdfrpARpXr0E+hTzkcjA8CrUajI9DNwdZBsU8xDG0U+j2Yb0XAtIqJXh4GlozcHUR9o2HEDYyaKzA6DAkhIC1CKgWQsBaPoLVCLIEyjFUpyHtQH4QBhL1Yf0tSDaFg1x8DXpd+OIM1Fs7eIhRBGNjO2ggSZK0VT/hVqpZqwW//Et4jPCCtNk6ITCtAxSAYaAL1AafBJgCZghhawC7gP3Au8NmO8CPB5dO73C81VU4dgyOHr3bG5MkSZIkSZIkSZIkSZKkjwVD1SRJkiRJkiRJkiTpE+iN2Tc4efokaXandIwNzW6T2eYsXLwIl99hbQgqNfjd34JWusT8zDskyyskVXhgMoSgRREUi0Ae4giyCIqFEKqWB1bWQxhaF4gziHJwaA+8cQXGKzCWD9fut2FhGUZ3QRKFYLU4gSSGcgH6GUQZJBkkCRR6g7ou3DgPXzwLZ8dhuQgrC3DxdXhwGb5+docP8sknYWpqh030cZHEEbtKW/tTiCSOPuTTSJK02Toh3WzgZz+Gz/ZuD1RrAm8Bi+9RPgocBFaAKjAETABX2QhL2w9cIbyYXQdeBU6xEby2FcePwzPPQL1+F0XatiiBYm3reyVJkiRJkiRJkiRJkiR9IEPVJEmSJEmSJEmSJOkTZqW9wvNvPk9Gdld1B2oHmG1cgQsXABhrw9ceg9Ig+KzdXGGiDNN7Ib/pt8k3o6fiDPrRxkQEFHOwZxjmmtDphLlcAgen4MplGH4wjGOg34HrSzBSCXVRPGgSbfQHyGWQ9GF1ERauQH0ZdnXgN67BfASX/gQevQYHFzbOti1PPQXPPbeTDvqYOfxAjdf+r0991MeQJOk9nAI64WurBQsvwObcshvAaTYC0t5tEXgZOEQIU1shvAgtAZcJL1sp8ALw7wjBatuxugrHjsHRo9tsoLuy94vwf774UZ9CkiRJkiRJkiRJkiRJ+tQwVE2SJEmSJEmSJEmSPmHOLpy9LVAtzd4vfeN25XyZiUvzzKZh/1cegj1D0BusF6vw0BQkEbfFtbV7sNaGNIUsgXIBoiSsRUASw8QQzK6G4LQIGCpAvQLnLsHD+6AUQRpDlsFiM1xjpLzRJ+0DKfR60LwGy+ehPwgDmW4MrrUG9T+G+rX3ucFvfAO++c07P4gogiNHoF6/815JkqR74tWNr3/5Ixjvb4ybfHCg2k3pYF8FqBJe2IaABrfy2tjD9gPVbjpxAp59FuJ4h40kSZIkSZIkSZIkSZIk6a+WoWqSJEmSJEmSJEmS9AmTj/O3jU9dP8VSe+nOhd0unWu/4moNinn4O49BJ4FuArkYHnoA2jEkg0S1Vgcu3IA0gy88GOYyoJfAcAlKuVAHIaesXoWZHpBCLoXJcZhbhLMX4OAU5KsQpyFcrRhDrxtC1Na6sNKAGEhSaF3dCFSbbEKlC1wDTgLzH3B/P/gBfPvbhqVJkqR74rXZmXvSJ6LDROUtiDLi1hrjl14k3r9pw1vcOVDtpnSw//HBOAamgEuD8W4gz86C1RoNOHMGDh3aQRNJkiRJkiRJkiRJkiRJ+qvnfyUpSZIkSZIkSZIkSZ8w+2v7iYhujadHprdWePkShV7GSAp/83OwdwR274LJKnxhNxRzkEUhOG1hFV67BEtr0GxDNghai4BOD3ppmE8zyA2OksRQq0CUhWC2JIbRGkQtePMSXLsBcQ/KOcgPflsdZyFc7eYvr0td6KxtHPnALPDvge/ywYFqAKurcOzY1p6FJEnSX5FcPBtekIDKSy8RV/obi+vA4l02XBzU3VTb9D0iBKvtRBTB2NgOm0iSJEmSJEmSJEmSJEnSX73cR30ASZIkSZIkSZIkSdLdqZVqHJ48zGuzrwEwWh6lVqyx1F5635pC2uaR/GUO/g7URuChg1AthrUoht0jQATdHiw04dyNjSC1NINWZ2P/WhtGyiGzY60DxXyoi4BqAVoxMMgKGavB6o0Q1DYzD3MN+PJnoVuCXBJq+q2wN0khbUHah9VFqL4A1T8F2nfxcI4fh2eegXr9LookSZI+PEm0DEDcWqP8yivwpU2Ly9tsugyUBt+r71qrAZe22RfgySdhamoHDSRJkiRJkiRJkiRJkiTpo2GomiRJkiRJkiRJkiR9Aj1x4Alen32djJB8Nj0yzamZU9DtQqdDuVJj79gBIlI+Mz7LwbVfkiuHvdE6jMfQS0OvoQLEABkUE0gi+Mo+mFmA6wshEG1uCaqTYX+Wwvo6VEphrZtCNQ+tbghJKxWh0w09d+Whkw+9owgenoDiOsxnYW9nBdZnYLEKo0249FN4+wVIu/CtF7m7QDWA1VU4dgyOHt3ZA5YkSbpXopA2W/3pT4m7PUg2rXW22XNzXfKutZ38VeBTT8Fzz+2ggSRJkiRJkiRJkiRJkiR9dAxVkyRJkiRJkiRJkqSPkaX1Jc4tnmOpvXTnzRGcXzwfvmcZ3XNnac5dhQyWiNi372F+++kJRvIrcHWWLApbS2UopNAbBHDk8xBnkEYhaK3dg1wMB8ZgogLvXIPZVXiwDkkMUQatFSgWIIqh14dyHoaKsN6BYg7WYojTEKRWLkEcwQNTMB5BkkK+D60Mls9C0oH+KnRW4dyL0O/BkVmot7b5EE+cgGefhTjeZgNJkqR7KIvZ9T/8j5R//moY9zetFbbZc3PdzX65BP7e34On/zY0H767flEER45Avb7NA0mSJEmSJEmSJEmSJEnSR89QNUmSJEmSJEmSJEn6mHhj9g1Onj5JmqVb2t/sNDnfOB8GFy/Su3aVxlAYTtUyHj30NizP00hTKGQA5HIwvgcqI1DNAzEMlUJNL4XWWghX6+Qg7kKlBA9PQ2sGri7CvnHIpUAGiyswUoMESPuh91ARSjnIMsh3gQQezUE/HYSpdcK1Ch04fwWqg/HoOrzzGvS6IbTtqxd28CAbDThzBg4d2kETSZKke2P4v/nXVJZf3ZhoAuWbi9ttuul7c/Cz14erV+H3ngT2b7OxJEmSJEmSJEmSJEmSJH1y+d9yS5IkSZIkSZIkSdLHwEp7hefffH7LgWoA1UKVyeokdLtw4QLlHhR7MFqFP/gyxGXoLzVgeZkkgf174AuPwOQYlIpQiCEZfPIJlPIwUoGxCsQRtHMhYC3Kw6E9sNAMoWtJyGej04W5FUhTyIB4MB/HUClCqQLlIhTyYb7UCz/7XZh/Cx68tOn+F+Di6+H74zNQb+3gYUYRjI3toIEkSdK9kczPU//P/kV4WbppedP3EjB6l01HB3U3LW36/rMXYM7/a1WSJEmSJEmSJEmSJEnS/clQNUmSJEmSJEmSJEn6GDi7cJZsU9pGmqVb+uwb3kd26RJZmpIBo234/cegmA/ZHesJVMvw6EMwWgMiSNnI9YgyyLKNcQwMFWHPcAhD6yRhPpfAwSm4chV6/dAjjSHtwPWlELBGtvGJMogGPTMgSSHXh9UFuPJLGJ6FwzdguA3dNpz+c8hSeHAZvn52hw/zySdhamqHTSRJknaufuI7JMvrcH3T5HVuD1k7yNb/ki8e7L8pBWY2jS924Ng/29ZZJUmSJEmSJEmSJEmSJOmTzv+SUpIkSZIkSZIkSZI+BvJx/rbxqeunWGov3bmw22Vu4TLNkTD8zYdhZBTag0Sz4hDsnYJ+BP1BSRZBOYNOD/oZkEClAHEUwtPoQRLDxBDcWIF+N9QNFaBegXOXYN+BQVBaCp0I+j1oZZDkoJtB1gvXzDJoLAPXoTG30Wu6Eep/4xy8829gpAEj63BwYSOMbVueegqee24nHSRJku6JZH6e8e9+NwxeBfYMFjqEILTdg3EVOAScJoSkvZ94sK+6aW520O+mV4E3j8Mzz0C9vqPzS5IkSZIkSZIkSZIkSdInjaFqkiRJkiRJkiRJkvQxsL+2n4iIjAyA6ZFpTs2cunPh5UvU1jI6RXhgFJ5+HHL5EHTWS+GzE4QAjtCWVgfOXIXP7oFokF7WzUK4Wi6Gcg5yEfSysF4fgtlGCEfLpTA5DjMNOHUVDo9DoQTVGPJx2NPrwlIL1luhdwZceBumljeOPNmEShe4BtFJeHgeHn6/+yuV4Pvfh1rtg59DFMGRI4aHSJKkj436ie+QrK1BHlgAxoAC4QVpFhgnrAFMABXgLWDxPZqNAge5PVCtM9i/eXwKaK/CsWNw9Og9vBtJkiRJkiRJkiRJkiRJ+vgzVE2SJEmSJEmSJEmSPgZqpRqHJw/z2uxrAIyWR6kVayy1l963ppC2eSR/mYNPQLEO9TGYnghrvQjGqlAtQrcHa224Mg9nrofws1YnrEFYq5Wh14dmH/I56PbDWhLDcBWWVyDJgBiGxqDZgLfPw1QdHp/eOFOWQbICJIPe6zDcuv3cB2aBHwI/AdI7PJj1dXjxRUNBJEnSJ0pu4Srjp/4F/ANgNxABu4CHNm0qEsLSmoNPFXgcWAeWCSFpBWAYKL3rAn3gDaC3ae7HQHvw/fhxeOYZA2clSZIkSZIkSZIkSZIk3Vfij/oAkiRJkiRJkiRJkqTgiQNPEBHdGk+PTN+2Xs6XeWT0EQ6OPsTTB6v84b53+P1HMg4WYM8q7K1CLg2fcgZDBYgzKCbh+2gRpndBuQdLixBloW+WhpC1m1fu9kLNTZVCyPOAkIE2OgKlHhDBrhx05qG1HOq6a5DvQjIIS1tdCNfLMlhZgPTfQfWfAD/izoFqNx0/DnNzd/EkJUmSPip9Kvmfsu/s/47ka23Yw8ZL1kVgZdPWNrAIjAAPArXBfAmYHMxN8uuBah3gF8Dm7N1rhMDam1ZX4dixnd+OJEmSJEmSJEmSJEmSJH2C5D7qA0iSJEmSJEmSJEmSgonqBIcnD/Pa7GsAjJZHqRVrLK3OQafDeqHH/gcqfGnPO+yKG/CzG9APtVEM4zF0B0Fl1WIIVLsZ4tFoQR44MAYTFbgwA70MksH6yipURiCJIQPSDHYVYb0LvT6UStBuQj+GXTGMVmHvBIxHIZQtW4HrLVh5HcoJ9MpwowRv/2vIzYZwtbQL33qRECByN26Gghw9upPHK0mS9KFKonlqxX9LoX2eyi9e+vUNGXAa+A3CixmE96IrwBghXK0CzAHd97hACswCbwG9TfNrwEl+PbD2+HF45hmo17d5R5IkSZIkSZIkSZIkSZL0yWKomiRJkiRJkiRJkiR9yJbWlzi3eI6l9tId9yZxwoXGBTIyyDJyb56mMX8WMpgcgT0HXqefHqBx9RrkU8hDLgcTu6FWg7QIUQTlAsQxdDNY68JaH3IxFFKolGB6P8w1YWLX4Lp9aKzA6DAQQb8PUR6qBUhTyEewGkGWQDmG6jSkHch3Qn3Uh7W3IV4fZKY14fRZ6L6+cW9HZqHe2uZDPHECnn023JQkSdLHTC6+xkjpeeJoncpLLxF3B6lnBWA3MAxUgWTwGScEo60SQnLngMXBnmE2gtX6QBNYAmaAzrsuvAb8MTD/HocymFaSJEmSJEmSJEmSJEnSfcZQNUmSJEmSJEmSJEn6EL0x+wYnT58kzdIt1zS7TWabs3DxIlx+h7UhqNTgd38LWukS8zPvkCyvkFThgckQhFYuQa4M/QiyKAStxXHI7IgjGKlCYw3iDsRZCEwbjaHdg1ICSQb9NsytwNhQCGDrpxAnkMQhpK2fQZSFvUkChUFWSL8LN87D59+B1yfD3MoC9H62cU9RBl+9sIMH2WjAmTNw6NAOmkiSJN17STR/K1Atbq1RfuWV8Jd5B4EpIHqPomWgDowQQtMWCAFqy4P1HvAq8EGBtNeAk7x3oNpNx4/DM89AvX4XdyRJkiRJkiRJkiRJkiRJn0yGqkmSJEmSJEmSJEnSh2SlvcLzbz5PRnZXdQdqB5htXIELIYVsrA1fewxK+bDebq4wUYbpvZAf/Nb3ZlZHnIVgtc3hHRkwVIRyHuZXodcO87kE0h5EXSCGGFjrwcwSjFSgmIMoHvSKNvoD5DJI+rC6CAtXoL4M9RbsXoVLeTj957A5R+7xmbC+bVEEY2M7aKD7wZtXl/lP/tuXtrT3X/6vv8Ln9w5/yCeSJH369akV/y1xtA5A5aWXiCs9+AJQ+ICyLiEUrTb4lIFZYPCeRh44BLwCv/Yq2QF+DPwEuFNu7+oqHDsGR49u/Zb0V+f6a/D//l9ube83vg+7j3y455EkSZIkSZIkSZIkSZI+4QxVkyRJkiRJkiRJkqQPydmFs7cFqqXZnVIvgnK+zMSleWbTsP8rD8GeIegN1otVeGgKkmgjYyMFen3o9kLORiEHhSSEot3MV0tiqO+CeSDtDnolsLoEw0MQJ5CkkMaw2Az9R8oQJYNr9MOFej1oXoPl89Af9JluhJ8Hr8Arr0Crs3E/Dy7D189u9am9jyefhKmpHTbRp123n3KlsbblvZIk7VQl/wK5ZAaAuLVG+a2X4TEgedfGdWCZwYsaMAyUgCVgBagCo8AiG8Fqu4D9wAXCS9914FXg1KY9W3H8ODzzDNTrd3t7+rD1O7B0cet7JUmSJEmSJEmSJEmSJH0gQ9UkSZIkSZIkSZIk6UOSj/O3jU9dP8VSe+nOhd0unWu/4moNinn4O49BJ4FuArkYHnoA2jEkg0S1VgfmZmD/ICcjA7IcVAphf2FTqEcUwdgQzDYgyyCXQm0XvH0RJh+A4XwIb0tjKMbQ64YQtbUurDQgJgSvta5uBKpNNqHSBa5B7iR8Yx7eGoNGCUbW4eDCRrDbtjz1FDz33E46SJIk3XMR61TzL9waV155gfhz/dsD1ZrAW4SwtHcbBQ4SAtVWBp8ucJrwl30xITzt/0UIVutu86Crq3DsGBw9us0GkiRJkiRJkiRJkiRJkvTJYKiaJEmSJEmSJEmSJH1I9tf2ExGREdLPpkemOTVz6s6Fly9R6GWMpPCVz8HekRCG1o5gTw2KuRCIlmWwuApnZ0Jo2b7xsC8COr0QqtZLod+Hbgq5QcBHHMNIBRqrg2C2GEoVOH0RpodhYhyKFcjHg/1ZCFcbDCl1YWFt47gHZoEfAj8B0nD9zyx8wP194xvwzW/e+TlEERw5AvX6nfdKkiT9FSvl3iSKOgDErTXKy6/AxKYNNwgBaen7NFgEXgYOsVGXByaBX27aN0YIZtuJEyfg2WfDi6AkSZIkSZIkSZIkSZIkfUoZqiZJkiRJkiRJkiRJH5JaqcbhycO8NvsaAKPlUWrFGkvtpfetKaRtHslf5uDvQG0EHjoI1WJYi2LYPQJE0O3BQhPO3RgErAGtzsbetTbUyiHgLAKiDLp9yCdhXC7C2urGdYdH4cYizMzDXAO+/FnolkIQWwT0W2FfkkLagrQPq4tQfQGqfwq07+LB/OAH8O1vG5YmSZI+0cq51299r/z8L4nrm9LTmnxwoNpN6WBfBagO5iaBc0BnMP4i8MIOD9towJkzcOjQDhtJkiRJkiRJkiRJkiRJ0seXoWqSJEmSJEmSJEmS9CF64sATvD77OhkZANMj05y6/BJ0OlAoUK4Ms3doLxEpnxmf5eDaL8mVw95oHcZj6A3COIYKEANkUEwgieAr+2BmAa4vwNIiDE1BFkGWhpC1oUKozSfQfleoR6UArbWQ5VEpQj4K4WwPT0BxHeazEKjWWYH1GViswmgTLv0U3n4B0i5860XuLlANYHUVjh2Do0e39UwlSZI+ahEdcvEsAHFrjfL1V2H/pg1vcedAtZvSwf7HB+MYmAIuDca7gTzQ3cmBIxgb20EDSZIkSZIkSZIkSZIkSfr4M1RNkiRJkiRJkiRJku7S0voS5xbPsdRe2lpBBOcXz0OWwcsv073+Js18SCxbqk+w78slfvvAdUZyK3B1liwKZaUyFFLoJWGcz0OcQRqFoLVOF3IxHBiDiQpcmIFeFsLWAFZWoTICSRzC0goxVPLQ6UO/D0kBWIN+DLkU6iMwVoPxCJIU8n1oZbB8FpIO9FehswrnXoR+D47MQr21zYd44gQ8+yzE8TYbSJIkfXRy8SxEIQi38tJLxJX+xuI6sHiXDRcHdaXBuMZGqFpECFa79B51W/XkkzA1tYMGkiRJkiRJkiRJkiRJkvTxZ6iaJEmSJEmSJEmSJN2FN2bf4OTpk6RZuuWaZqfJ+cZ5uHgRrrxDLweNQWDGVHyDR6t/BtRp3FiEQgjnyOVgfE8IRavmgRiGBjW9FFqDMLSMEIBWKcH0fphrwsSusC/pQ2MFRodD/SD3g2oBshQ6CaR9iGIoJ1ApQncN8p2wr9CB81egOhiPrsM7r0GvG3p99cL2nyONBpw5A4cO7aCJJEnSRyOJlgGIW2uUX3kFvrRpcXmbTZfZCFWrvmttc8ja3XrqKXjuuW0WS5IkSZIkSZIkSZIkSdInh6FqkiRJkiRJkiRJkrRFK+0Vnn/zeTKyu6qrFqpM5mrMXggpZOUeFHtQqcEffBnifpN+ZxfJ8jJJAg9MhiC0SglKxRCc1o8hiSGOIJfASAT9DBprIWQtl0KUh9EY2j0oJZBk0G/D7CqMD0E+gmxw9DiGQh7KlRDKFhE+pV5Y73dh/jw8OAeL5cH9L8DF18P3x2eg3trBw4wiGBvbQQNJkqSPUNQHoPLSS8TdHiSb1jrb7Lm57ma/I4fhS1+Cr/82rH7mLs8YwZEjUK9v80CSJEmSJEmSJEmSJEmS9MliqJokSZIkSZIkSZIkbdHZhbO3BaqlWbrl2n1nrjGTbuwfbcPXHoNiPgSdrTduMFmG6b2QG/wmN4VbV4uysC+LwjgGhopQzsPCKvTaYT6XQNqDqDvYRAhZm23ASCXsjzJCgloWvg9aEqeQ68PqIixcgfoyfG4eTk3BPHD6zyFL4cFl+PrZu3ly7+HJJ2FqaodNJEmSPiJZQtxao/zKK2Hc37RW2GbPzXU3+505A08/DQ/9NnBkm40lSZIkSZIkSZIkSZIk6f5gqJokSZIkSZIkSZIkbVE+zt82PnX9FEvtpTsXdrtw9WXmKtAchGX85sMwMgrtQaJZsdRn7xT0o40MjSyCcgadHvQzIIFKAeIIcoPAtCSG+hDcANLuoFcCq0swPARZHnJp6LXYhGoOsgJECaQpZH3oZdDrwcplaF6G/qDPdAOSFH7jHLzzb2CkASPrcHBhI4htW556Cp57bicdpPc0savIP3ji4S3vlSRpu/rZMNWf/pS42wsTTaA8WBzeZtPNdc3Bz04XfvpT+L3//Tab6mNtaAp++z/d+l5JkiRJkiRJkiRJkiRJH8hQNUmSJEmSJEmSJEnaov21/UREZGQATI9Mc2rm1J0LL1+CNGW8C7UJGN8Ff+eLIdhsNYX1Hjw6BcQwaE2rA2euwmf3QDRIMOtmIVwtH0EpD7koBKJFEYwPwWwDsiyEqNV2wa8uweSDMJpAL4ZCYVDTHYSotaA9COxIUli5AFkaxpNNqHSBaxCdhIfn4X2jqkol+P73oVb74OcQRXDkCNTrd35m0jbsHSnzR18/9FEfQ5L0aZdl1P/o25T3/HwjaXYZuPmKUwJGgcW76Dk6qLtpc27vz16Ax3Mb/fXpUXsQnnz2oz6FJEmSJEmSJEmSJEmS9KlhqJokSZIkSZIkSZIkbVGtVOPw5GFem30NgNHyKLVijaX20vvWFNI2j+Qvc/B3YKwGrTyMj8H0RFjvRTBWhUICqx1or8P8CpydCQFprQ5Ui2HvWhtGyiFYrdmGfC6EoxFBEsFwFZZXIMmAGPJDcOY8PDQME3UYK2+cK86gs7YxjlY2AtUADswCPwR+Amyaf0/r6/Dii3D06FYeoyRJ0ida/b/8L6n/y/8W/gGwZzB5HXiIjZC1g8DL3Pk9CkKw7sFN4xSY2TS+2IFj/8x3LUmSJEmSJEmSJEmSJEm6g/ijPoAkSZIkSZIkSZIkfZI8ceAJoltpGTA9Mh2+dLvQbFLOcjwy+ggHRx/i6YNV/nDfO/z+IxkHCzC2BrtXYW8Vcmn4lDMYKkAxgfEyTAxBrwWlLpR6MLcpry1LQ37ZTd0eFDf9V1qVAhQG31NgdASKPZhdgM4y9JqhJsvCNeJe2Juk0J4L8ysLkP47qP4T4EdsLQgE4PhxmJu7m0cpSZL0iZPMzzP5//jPw+DVTQsdbg9CqwKHuPNf6MWDfdVNc7ODfje9iu9akiRJkiRJkiRJkiRJkrQFuTtvkSRJkiRJkiRJkqRPv6X1Jc4tnmOpvXTnzRGcXzwfvmcZ3XNnac5dhQyWiNi372F+++kJRvIrcHWWbCODjVwEu/LQH4yrBTZFtEFjDR4cg3oF3rkGs6vwYB2SGKIMWitQLGyM0xSGirDehV4fSiVoN6Efw64YRquwdwLGI8hWYL4T9q2fhSwHi1WoLcGpP4GlWUi78K0XgfZdPsDVVTh2DI4evctCSZKkT476ie+QrK2FwSng99hItX0LGNs0ngAqg/nF92g2Chzk9kC1zmD/5vEpoO27liRJkiRJkiRJkiRJkiTdiaFqkiRJkiRJkiRJku57b8y+wcnTJ0mzdEv7m50m5xvnw+DiRXrXrtIYCsOpWsajh96G5XkaaQqFDIBcDsZqMDYME2PQTyDLoJgPAWntPnT70Eohn0ClBA9PQ2sGri7CvnHIpUAGjRUYHYYE6KchlK1aCAFr+QhWI8gSKMdQnYa0A/lOOF/ShSsXoNoI4/4qnPspLFwN4yOzUG9t80GeOAHPPgtxvM0GkiRJH1/J/Dzj3/3uxkQb+DHwu4NxD3gDeIzwogYhMO1xYB1YJoSkFYBhoPSuC/QH9b1Ncz9mI+z2+HF45hmo1+/NDUmSJEmSJEmSJEmSJEnSp4x/yS5JkiRJkiRJkiTpvrbSXuH5N5/fcqAaQLVQZbI6Cd0uXLhAuQfFHoxW4Q++DHEZ+ksNWF4mSWD/HvjCI7B3AkaGIZdALgo/y3ko5WFXKYSpxRF0E+hHEOXh0B5YaEJrDZKQz0a/DQvL0Ls5Hhw9iaFcgHIlhKzlE0gSKA2COfpdaPwKCgsb95K7ChdfD9+jDL56YQcPs9GAM2d20ECSJOnjq37iOyRra5AH9gFHgCbhr/BqhCC1JeAXhPC0zUrAJPDg4Oe7A9U6g7qlTXPXgJ9sGq+uwrFj9+ZmJEmSJEmSJEmSJEmSJOlTKPdRH0CSJEmSJEmSJEmSPkpnF86Skd0abzVcbd/wPmZe+xmkYf9oG772GBTzkAHrCUzmYXov5Aa/mb15lQyIsxCcRhTGEVDMwZ5hmGtCOwt7cgkcnIIrl2H4wTCOgX4Hri/BSCXURfGgSRSuEQ8ulssg6cPqIixcgfpyWLs+BN02nP5zuHnLj89AvbXdJwlEEYyN7aCBJEnSx1Nu4Srjp/4F/ANgN7feuYAQkHaY8Nd4q8B14BVgGph61953S4FZ4C2gt2l+DTg5WN/s+HF45hmo17d/M5IkSZIkSZIkSZIkSZL0KWWomiRJkiRJkiRJkqT7Wj7O3zY+df0US+2lOxd2u8wtXKY5Eoa/+TCMjEJ7EJpRHIK9UyE4rb+prJ9B3IU0A2IoFSCfhLUISGKYGILZ1RCcFgFDBahX4NwleHgflCJIY8gyWGxCEsFIGaJBn7QPpNDrQfMaLJ+HfjesTTeg2IflFH70H6C1HOYfXIavn73Lh/duTz4JU1M7bCJJkvRx0qeSf4Gps/+E5Gvt997SAn4BPAbsGnweBi4CPwMmgRpQBRLCy2ETWAJmgM67+q0BfwzMv8e1Vlfh2DE4enRntyVJkiRJkiRJkiRJkiRJn0KGqkmSJEmSJEmSJEm6r+2v7SciIiMDYHpkmlMzp+5cePkStbWMThEeGIWnH4dcHjoR9FL47AQQw6AtrQ5cuBHC1L7wYJjLACIYLkE5B7k4zEcRTFTheg9IIZfC5DjMLcLZC3BwCvJViNMQrlaModcNIWprXVhphEsnKbSubgSqTTah0gWuwV/7Noxm0CjByDocXAgBbtv21FPw3HM76SBJkvSxkkTz1Ir/lkL7PJVfvPTBm1eAnwOHCKFqCfAQUAdOA5e2eNFrwEneO1DtpuPH4ZlnoF7fYlNJkiRJkiRJkiRJkiRJuj8YqiZJkiRJkiRJkiTpvlYr1Tg8eZjXZl8DYLQ8Sq1YY6m99L41hbTNI/nLHHwCinWoj8H0RFjrRTBWhWoRuj1Ya8OVeThzHbIM4ij8jKIQYtbpQa8PzT6UC5CLoJtBEkOtAisrkGRADKM1WL0Bb14KIWsT41CsQH4QxhZnIVxtMKTUhYW1jXMfmAV+CPwEohQ+c6eH841vwDe/+cF7ogiOHDHUQ5Ikfark4muMlJ4njtapvPQScbcXFgrAbmAYqBLC0/pAE1gGXh+s7x+s7QJ+A/gFIXjt/XSAHwM/AdI7HG51FY4dg6NHt3dzkiRJkiRJkiRJkiRJkvQpZaiaJEmSJEmSJEmSpPveEwee4PXZ18nIAJgemebUzCnodqHToVypsXfsABEpnxmf5eDaL8mVw97eKkzsg9wg/CIfw1ABogyKCSQFGC3C9C64vgAZIWitUgr719owWh5870AxH8LYAKoFaMWEoA5gbBCqlgEz8zDXgC9/FrolyCUhpK3fCnuTFNIWpH1YXYTqC1D9U6B9Fw/mBz+Ab3/bwDRJknRfSaL5W4FqcWuN8iuvhL+0OwhMEV663q0M1IGHgBnghcF4NzAEPAb8HGhtqsmA68CrwCnu7j3t+HF45hnf0yRJkiRJkiRJkiRJkiRpE0PVJEmSJEmSJEmSJH1qLa0vcW7xHEvtpTtvjuD84vnwPcvonjtLc+4qZLBExL59D/PbT08wkl+Bq7NkgzCNXAS78rdyz6gWbs/ZWGiFPQfGYKIC71yD2RWYHoSq0Yf1dSiXQq5Gtw/VPLS6oU+pCJ0uxITrdPJABlEED09AcR3ms7C3swLrM7BYhdEmXPopvP0CpF341ovcXVAHwOoqHDsGR4/eZaEkSdInVZ9a8d8SR+sAVF56ibjSgy8AhS2UR4QgtTHgDeBlICEEq60BPwA6wBIhUK27zWP6niZJkiRJkiRJkiRJkiRJv8ZQNUmSJEmSJEmSJEmfSm/MvsHJ0ydJs3RL+5udJucb58Pg4kV6167SGArDqVrGo4fehuV5GmkKhQyAXA72TsJIDaJ8CDqrFCCOoZdBqwNrKUQ5KPWgUoKHp+H0DeinkMSQS6G5AoUCRPHG/FAR1jtQzMFaDHEa+pdLEEfwwBSMR5CkkO9DK4Pls5B0oL8KnVU49yL0e3BkFuqtbT7IEyfg2WfDTUmSJH3KVfIvkEtmAIhba5TfehkeIwSjbbYOLBMC0grAMFDatF4g1P2CEKB2M+O3Crxyjw7re5okSZIkSZIkSZIkSZIk3cZQNUmSJEmSJEmSJEmfOivtFZ5/83kysi3XVAtVJquTzDauwIULlFMo9qBSgz/4MsR56C81SDJIEnhgEkaHQ8hZuQD9CLII8knItcgBMTBWgcYadBIo9kPA2menYG4Vdu+CZHDExZXQL44g7YfAtqEilHKQZZDvAgk8mhsEr6WQ74TaQgfOX4HqYDy6Du+8Br0uRBl89cIOHmajAWfOwKFDO2giSZL08RexTjX/wq1x5ZUXiD/Xvz1QrQm8BSy+R4NR4CAhOA1C3ReAF4DeYO5vDMbte3Bg39MkSZIkSZIkSZIkSZIk6Tb+F5WSJEmSJEmSJEmSPnXOLpy9LVAtzdItffYN7yO7dIksTcmA0Tb8/mNQzEMGrCdQLcOjD8FoDYhC/wyIb14u4taVU0Iw2p5hyOehM/gNbS4J8921jTN3ujC/EgLTNveLY6gUoVSBchEK+TBfGgRz9Lsw/xY8eGmj18oCXHw9fH98BuqtHTzMKIKxsR00kCRJ+mQo5d4kikJKbdxao7z8ChQ2bbgBvMx7B6oxmH95sO+mAiFobfP48Xt0YN/TJEmSJEmSJEmSJEmSJOk2uY/6AJIkSZIkSZIkSZJ0r+Xj/G3jU9dPsdReunNht8vcwmWaI2H4mw/DyCi0B+FpxSHYOwX9CPqDkiSBdgadHnSBfA4KOYij8AFIYqjvCqFp3W6YGyrAyhyUEoiTkM/W68D1Jdg3CsWb/0VWBlF2K7+NDEhSyPVhdREWrkB9GT43D6emYB44/eeQpfDgMnz97DYe4GZPPglTUztsIkmS9PFXzr1+63vl539JXE83FpvAaUJq7gdJB/sqQHUwNwmcAzqD8ReBF+7BgX1PkyRJkiRJkiRJkiRJkqTbGKomSZIkSZIkSZIk6VNnf20/EREZGQDTI9Ocmjl158LLl6itZXSK8MAoPP045PLQiaCXwmcngBgGbWl14OIc7K+HcUYIUKsUIBdDIdloHUUwNgSzDcgyyKUwWoN3LsMD+6A4COjoxNDvQSuDJAfdDLJeCHLLMmgsA9ehMQf9QUDbdCMErf3GOXjn38BIA0bW4eDCRhjbtjz1FDz33E46SH+llte7nLrU2NLex/eNMFzK33mjJOm+ENEhF88CELfWKF9/FfZv2vAWdw5Uuykd7H98MI6BKeDSYLwbyBMSebfL97RPh/UluPLy1vY+8GUo1T7c80iSJEmSJEmSJEmSJEmfcIaqSZIkSZIkSZIkSfrUqZVqHJ48zGuzrwEwWh6lVqyx1F6Cbhc6HSgUIL8RqFRI2zySv8zBJ6BYh/oYTE+EtV4EY1WoFqHbg7U2XJmHM9dDaNm+8RCaFgGdXghV6/Uh7UM3hdwgXC2JYaQCjVVIMiCGQglOX4JHJ6FSgmoM+TgEqPW6sNSC9Vaoz4ALb8PU8sa9Tjah0gWuQXQSHp6Hh9/vwZRK8P3vQ+0OYQxRBEeOQL1+V89d+qi9c6PJ/+p7L2xp77/+1l/n8X0jH+6BJEmfGLl4FqKQnFt56SXiSn9jcR1YvMuGi4O60mBcg7WhR1k/cgSA5d/6XfprEzw8Onp3fX1P+3SZfwv++A+2tvc/+fchWE2SJEmSJEmSJEmSJEnS+zJUTZIkSZIkSZIkSdInytL6EucWz4WAtA+QxAkXGhfICOEYuTihce5NmLsBGRT7EeNT08SPfo7PTy7y+ehX5Gthb68Dk0O3cjUoRFAthHEhgbgIo0WY3gXXF6CzBqUyZFEIXKuVIR6cI8qg24f8IFitXIS11Y1zDo/CjXNw9jxM1eHx6Y21LINkBRjUrq3DcOv2+zwwC/wQ+AmQ3uHhra/Diy/C0aN32ChJknR/SaKQWhu31ii/8gp8adPi8nvX3NEyG6FqVSj+/G1Wf/d3SStl2lOP0u4fgsmpHZxakiRJkiRJkiRJkiRJkrSZoWqSJEmSJEmSJEmSPjHemH2Dk6dPkmZ3Sg8Lmt0ms83ZMLh4kbXFG7SLg8UsY3z5Hb5WW2Z8rAyXF+kOwsviCIaKkEZhPFSECAb/wMIapDl4cBwmKrC8CsXKoG0agtWGBtfJJ9BJb5UCUClAay1koFWKkI8gA3bloDMP/V2Qr0DWgXwXkgj6MawuQLkXwtZWF6H6AlT/FGjfxUM8fhyeeQbq9bsokiRJ+pSL+gBUXnqJuNu7FWoLQGebPTfXJRB3ulReeonVJ/4mUdTb7kklSZIkSZIkSZIkSZIkSe/DUDVJkiRJkiRJkiRJnwgr7RWef/N5MrIt1xyoHQihat0uXLjASAwzQ2FtagT+1uMwls7D0hA32+ZysHcSJoYgykMUhRC0OIZeBq0OrPfCfDeBcgniEiQx9AdZb60VKOfDXAYU4jDu9KHfh6QArIWgtFwK9REYq8F4FELZshW43oKV16GcQK8MN0rw9r+G3GwIV0u78K0XubtANYDVVTh2DI4evctCSZKkT7EsIW6tUX7llTDub1orbLPn5rpBv/LPf07rK18hi/3TPUmSJEmSJEmSJEmSJEm61+KP+gCSJEmSJEmSJEmStBVnF87eFqiWZukdP+V8mYnKBNnli2RpSqkHxR6MVOHvfhniMvQzyFZWiRPYtwe+8AjsmYBSAfJxCFPLJeFTysFQAcYqEA0C03oxkINiLuyJMohTaKxAOjhuNDhztQC7irCrAtUhGB2CqVF4eB9U85AfhLJFfVj7FcTr0G5Cfw5O/xCar8PSLPR7cHgW6q1tPswTJyBNt1ksSZL06dPPhqm89BJxtxcmmpsWh7fZdHPdoF/c6VJ56SXSbNc2m0qSJEmSJEmSJEmSJEmS3o//3aUkSZIkSZIkSZKkT4R8nL9tfOr6KZbaS3es66y3uLp8BUbCuJ/A01+GqAzrQDeGsSI8MgWFHLSBJIH+IDQti7iVipYBKTBUhHIe5leh3wlrrQ6MVMJPgH4bZlehXg3BbNkgYC2OoZCHcgWSNLSOgNIgv6PfhRvn4fPvwOuTYW5lAXo/27inKIOvXtj6s/s1jQacOQOHDu2giSRJ0qdHeiOh/PLPNyaWgfrgewkYBRbvouHooO6mTa+t5Vd+TjqdwPg2DytJkiRJkiRJkiRJkiRJek/xR30ASZIkSZIkSZIkSdqK/bX9RDfTzYDpkekt1RWuXafaycgnsHcE/udfgi/ugYkhGC7Brio8ujcEqm3W68NaG1bWodODNAvhZ/HgCEkM9V2Q25T11lyDamFj3O3CzBI02+GXs9EgWI0sfL95N3EKuT6sLsCVX8LwLNRbsHsVum04/eeQpRt9H58J69sWRTA2toMGkiRJny71E/8V8aXuxsR1QqLuTQfZ+l/bxYP9N6XAzKbli13q3/mvt3lSSZIkSZIkSZIkSZIkSdL7yd15iyRJkiRJkiRJkiR99GqlGocnD/Pa7GsAjJZHqRVrLLWX3remkLZ5JH+Zp38LChOQS+BL0xDFkMYhIG33cAgsW1uHZicEoF2cg/310CMjBKhVCpCLoZBs9I8iGN8FMw3IMqAH7QjSFPo5KPShm8BiE6o5yAoQJWE960Mvg14PVi5D8zL0Bzke043w8+AVeOUVaHU2rvngMnz97A4f5pNPwtTUDptIkiR9OiTz84x/73twGNgzmOwQgtB2D8ZV4BBwmhCS9n7iwb7qprnZQb+bXoXxN77H3D/8Q5j0nUySJEmSJEmSJEmSJEmS7hVD1SRJkiRJkiRJkiR9Yjxx4Alen32djAyA6ZFpTs2cgm4XOh3KlRp7xw4QkfKZ8VkOrv2SXDnsbazDrgkoAqTQBUarkI+ABEoVKMYwMwNLi5CMhuA1Iuj0Qqhavw/rfeimIaANII5hpAKNVUgySDOYbUBpF4zkoZ9CoQC5CHrdQYhaC9rNUJ+ksHIhBLsBTDah0gWuQe4kfGMe3hqDRglG1uHgAkQ7eYhPPQXPPbeTDpIkSZ8q9RPfIWm14BTwe0BhsPAWMLZpPAFUBvOL79FoFDjI7YFqncH+zeNTkLSb1L/zz+G/+C/u3Y1IkiRJkiRJkiRJkiRJ0n3OUDVJkiRJkiRJkiRJH7ml9SXOLZ5jqb10580RnF88H75nGd1zZ2nOXYUMlojYt+9hfvvpCUbyK3B1lmyQQFbtwvjwRps8UClujDt9WF2B/WNQr0DahawU1tbaMFLeCDOLMuj2oTAIVisXYW11o1dpF5w7B3vGYKIOY+WNtTiDztqm21nZCFQDODAL/BD4CZCGa35m4QOexze+Ad/85gc/M4AogiNHoF6/815JkqT7RDI/T/273w2DNvBj4HcHiz3gDeAxYPDeRxV4HFgHlgkhaQVgGCi9q3l/UN/bNPfjwXWA8e99D/7xP/b9TJIkSZIkSZIkSZIkSZLuEUPVJEmSJEmSJEmSJH2k3ph9g5OnT5JuThb7AM1Ok/ON82Fw8SK9a1dpDIXhVC3j0UNvw/I8jTSFQgZALgfjI/DARPgeRVDIQT4H/Qw6PZhfh24OCn2olCApQieCbi+Enq2vh3mAfAKddx23UoDWGqSEsLZcBLMLUCtArwhRBXIJ9NYg7kE/hiSF9hxkGawuQvUFqP4pt4I2tuQHP4Bvf9swDgn43O5d/PD/+Dtb2run9u7kG0nS/ah+4jvEa5sSb38CHAL2DMZLwC+ALxDC024q8eshapt1CIFqmzODrw36DyTNJhw7BkePbvv8+oSb/AL8p69sbe/wAx/uWSRJkiRJkiRJkiRJkqRPAUPVJEmSJEmSJEmSJH1kVtorPP/m82RkW66pFqpMVieZbVyBCxcop1DsQaUGf/BliPPQX2qQZJAk8MAkjA5DIQ/VfAgzgxCqlksgD+TjEIq21IMuEPWhncLUMMytQr8HrRUoFiC5WR9DOQ+dPvT7kBSAtdA/l0J9BMZqMB5BtgLzHej1Yf0sZDlYrEJtCU79CSzNQtqFb73I3QWqAayuGsYhDZTyCQ/Vqx/1MSRJnxDJ/Dz173739skUOAn8x0B5MLcEvAAcBKaA6AOapsAs8BbQ2zS/Nuj77hzh48fhmWcMyL1f5Usw/shHfQpJkiRJkiRJkiRJkiTpUyP+qA8gSZIkSZIkSZIk6f51duHsbYFqaZZu6bNveB/ZpUtkaUoGjLbh9x+DYh4yYD2BahkefQhGa0AEcRKuEWVhT7wpDKOfwVARdtdC2FonhiyDZhvGqpBPgQwaK5AOjnuzvFqAXUXYVYHqEIwOwdQoPLwvhLjlU0LAWxdm3oFeA/pz0J+Bcz+EhashtO3wLNRb23yQJ05A+u6EDkmSJH2Q+onvEK+thUEe2AccGfx8GSgCg3dIesAvgZ8CbwNzhKC0zuDn3GD+Z4N97w5U+2Ng/j0OcTMgV5IkSZIkSZIkSZIkSZK0Y7mP+gCSJEmSJEmSJEmS7l/5OH/b+NT1Uyy1l+5c2O0yt3CZ5kgY/ubDMDIK7UHSWXEI9k5BP4L+oCRJoJ1Buwe9LASw5SOIoo2gtCSG8WGYW4FuF1bWoZKHXSVorkG/HdbGh8LebFAXx1DIQ7kCSRoC1yKgNAjT6Heh8TYUmhu3kLsKF18P36MMvnrhLh/eZo0GnDkDhw7toIkkSdL9I5mfp/7H/xJ+E/gisJuN1Nxbm4DDg+/XgRlCiNqlwWcrrgEnee9AtZuOH4dnnoF6fYtNJUmSJEmSJEmSJEmSJEnvJf6oDyBJkiRJkiRJkiTp/rW/tp9oU3rF9Mj01govX6K2lpGPYXocnn4c9g7BxBCMVuDQFLf9NrTVgXOzcH0JFleh0YLVNqy0w9rNcDQAIhivhrA1gKUVKJc2xr1u6NNsh0tEN2uz8P3m3cQp5PqwugBXfgnj12HfcljrtuH0n0OWhvHjM1Bvbfmx/boogrGxHTSQJEm6n/TZ89//Y+L/7To8Dezh1wPVAFrAK8Ac8DDwHwEH3mfvu3WAfw98lw8OVANYXYVjx7Z6eEmSJEmSJEmSJEmSJEnS+8h91AeQJEmSJEmSJEmSdP+qlWocnjzMa7OvATBaHqVWrLG0OgedDhQKkM/fVlNI2zySv8zBJ6BYh/oYTE+EtV4EY1WoFqHbg7U2XJmHM9dhqAi7R8K+OINOH3IJ9PrQT6Hdh3JhsB7DSAUaq0A/BKsV8rDWCUFpnQgWm1DNQVaAKIE0hawPvQx6PVi5DM3L0O+GntMNKPZhOYUf/QdoDQLWHlyGr5/d4YN88kmYmtphE0mSpE+/JJpnpPccI6v/XyhsoSADLgBXgClgN1AHThNC19699zrwKnAKaN/FwY4fh2eegXr9LookSZIkSZIkSZIkSZIkSZsZqiZJkiRJkiRJkiTpnltaX+Lc4jmW2kt33JvECRcaF8jIIMvIvXmaxvzZEEoRQbG+m/HP/gZxnPH5iUU+H/2KfC0DoNeBySGIwpBCBNVCGBcSSIowWoTpXTC7CEkf0jj07fSgUoAIyMew0Aoha/kkrFcKsDY4Y7cH7W4IYKuWIE5D3lsugl53EKLWgnZzcE8prFyALA3jySZUusA1+GvfhtEMGiUYWYeDC+EM2/bUU/DcczvpIEmSdF/IxdcYKT3P8I/+jKjb21goEMLShoEqkAB9oAksE4LSOoRgtSuD9RzwI2AB6AFLg33dbR5udRWOHYOjR7fZQJIkSZIkSZIkSZIkSZJkqJokSZIkSZIkSZKke+qN2Tc4efok6c1EsS1odpvMNmfh4kW4/A5rQ9C++dvM5euMX/0FX/sbFcaLa3B5kW4SluIIhoqQDlLJhoqDgLLBeH4NkggOjMFEBdIuZKWwttaGkfJGoNloGebWYPfQYGIQ0NYcJKvl8/Crd6A+ChN1GCtvnD/OoLO2MY5WNgLVAA7MAj8EfgJRCp+50wP5xjfgm9/84D1RBEeOQL1+p26SJEn3vSSaZ6T0PDFrlF99NUzmgIPAFO+dclsG6sBDwAzwFiFArT/4fAX4HjB/jw554gQ8+yzE8T1qKEmSJEmSJEmSJEmSJEn3F0PVJEmSJEmSJEmSJN0zK+0Vnn/zeTKyu6o7UDvAbOMKXLgAwMg6zAzCzaZG4G999gpjxQdheZmbrXM52DsJE0MQ5UPGWKUQMih6GbQ6sN6DKAdJFyolSIrQiaDbC6Fn6+thHiCXwHAeev3wHSCXB9YgJeRslIowuwC1AvSKEFXC3t4axD3ox5Ck0J6DLIPVRai+ANU/Bdp38UB+8AP49rcNTJMkSbon+tSK/5Y4Wic3v0Dc7kAN+AJQ2EJ5BOwGxoA3gKXBfBn4e8B3CS+MO9VowJkzcOjQPWgmSZIkSZIkSZIkSZIkSfcfQ9UkSZIkSZIkSZIk3TNnF87eFqiWZltLlyjny0xcmmc2DftLPSj2oFyDv/tliPPQX1okXm2SJPDAJIwNQ6kEpQKkUfjkkhCqlgNiYKwCjTXo3Qw7S2FqGOZWod+D1goUB0FsANUC5HOQppBmkGz6jWoE1IZh324YjyBbgflOCGFbPwtZDharUFuCU38CS7OQduFbL3J3gWoAq6tw7BgcPXqXhZIkSXq3Sv4FcskMAFmWkdUgegxI3rVxHVgGOoSwtWGgtGm9ADwG/IKNYLU9wF8HfnQPDhpFMDZ2DxpJkiRJkiRJkiRJkiRJ0v3JUDVJkiRJkiRJkiRJ90w+zt82PnX9FEvtpffZvUm3S+far7haI6SXAf0Env4yROWQb9HtNRmrwSNTUMiFnLIkgX4MGZBFG7UQ5oaKUM5DYxl6XcgyaLZhrAqNxbCpsQIjwxBHoTyOoFyELIVuDqpDkIvCNctF6KxBvhOukXThygWoNgZnXoVzP4WFq2F8ZBbqre09S06cgGef3Uh8kyRJ0l2LWKeaf+HWuPzLN4i+wO2Bak3gLWDxPRqMAgeB6mCcAF8AXgB6g7m/MRjfbZDuuz35JExN7bCJJEmSJEmSJEmSJEmSJN2//Ot7SZIkSZIkSZIkSffM/tp+ok3JZtMj01srvHyJQi9jJIW9I/DoHvjGV+Cx3TBcCrli1Qo8ujeEm23W68NaG1bWodODNAvzN0+RxDA+DEkhjFfWIcpgV2lQ34Ebq5Cmg/2D2jiGfAKVcghTS5IQ1FYahGf0u9D4FRQWNs6SuwoXXx9cP4OvXtja7b+nRgPOnNlBA0mSJJVybxJFIRE3bq1RXn4FCps23ABe5r0D1RjMvzzYd1OBELS2efz4Dg/61FPw3HM7bCJJkiRJkiRJkiRJkiRJ97fcnbdIkiRJkiRJkiRJ0tbUSjUOTx7mtdnXABgtj1Ir1lhqL0G3C50OFAqQz9+qKaRtHslf5uDvQG0EVoshCO1L0xDFkMYQRTBegU43BKhlGbQ6cHEO9tdDn4xQV8lDPob8zd+GZkAEY7tgphFql1agvgta65BGkHXgeg9GKrCrsFHTzyDONt1gD3J9WF2EhStQXw7r14eg24bTfw7ZIJzt8Rmot3bwMKMIxsZ20ECSJEnl3Ou3vld+/pfE9XRjsQmcBtJfK7tdOthXAaqDuUlYy3+G9UNfBKD3Pxll5Z0n7/p8D4+NwZEjUK/fda0kSZIkSZIkSZIkSZIk6XaGqkmSJEmSJEmSJEnakqX1Jc4tngsBaR8giRMuNC6QEdLIcnFC49ybMHcDMij2I8anpokf/Ryfn1zk89GvyNcGyWU96McwMg65CMhCCNpYJYSlVfIwVoJr8/DWO6Fk/zgQQQR0elDNQz+Ffge6KRRiyCKIoxCa1lgF+iFYLZ8PNbk+dKIQ2LZegCQHURJy4NI0nKHXg6Xz0JmFfjdce7oBxT4sp/Cj/wCt5TD/4DJ8/ewOH/iTT8LU1A6bSJIk3b8iOuTiWQDi1hrl66/C/k0b3uLOgWo3pYP9jw/GMRSb51id+NuklTJkEa0Hf5OMwt0dctL3PUmSJEmSJEmSJEmSJEm6VwxVkyRJkiRJkiRJknRHb8y+wcnTJ0mzraVONLtNZpshwIKLF1lbvEG7OFjMMsaX3+FrtWXGx8pweZFuslGbS6FeC0FoEMLVKgVCahrQziAGDj8A71yDZgeqg97tdaC0sTdJoZtBbvCb0XIR1lbD924PmilkHRgqQZLBWHkQoNYNIWrz83DzlrstWLuycc7JJlS6wDX4a9+G0QwaJRhZh4MLt46wPU89Bc89t5MOkiRJ971cPAtRCO+tvPQScaW/sbgOLN5lw8VBXSkM40qfyksvsfrE34QoIxfP0k0fvAcnlyRJkiRJkiRJkiRJkiRth6FqkiRJkiRJkiRJkj7QSnuF5998noxsyzUHagdCqFq3CxcuMBLDzFBYmxqBv/U4jKXzsDTEzba5HIzVYKgMByaAOAScJQkUctDph62La9BNoFyCh6dhprURqhb3YL0d1gAKcci92KxSgNYapEA+B2fegokxePQAFDaFu/VaG4FqAL3Zd93jLPBD4CcQpfCZOz2Ub3wDvvnND94TRXDkCNTrd+omSZKkO0iiZQDi1hrlV16BL21aXN5m02VuhapRhfLPf07rK18hrZSJo5UdnFaSJEmSJEmSJEmSJEmStFOGqkmSJEmSJEmSJEn6QGcXzt4WqJZuThp7H+V8mYnKBLNv/AzSlFIKxR6Ua/B3vwxxHvptiFdWSRJ4YBLGhoEoBJ0VYujHIUStUoBcAsU8rHcH4WoR9GKIYpjYFbLISCHJoLkChQLkorAvH0E5H+r6fUgKwFron0thfARGdkGvAa0u5CsQR5A2QqBbP4aoB90bIeRtdRGqL0D1T4H2XTzIH/wAvv1tA9MkSZL+qkR9ACovvUTc7cGmAF062+y5uS6BuNOl8tJLrD7xN4mi3nZPKkmSJEmSJEmSJEmSJEm6BwxVkyRJkiRJkiRJkvSB8nH+tvGp66dYai/dsa6z3uLq8hUYCeN+Ak9/GaIyrAPdGMaK8MgUFHIb+WRJIQSZpQARxPFGzySGPcMw14R+ujGXGwSfAZBBYwXqQ5AlEA2mqwXIUugkkPZDIFs5gUoRumuQ60B/BW6sw/V3YLQFvTIsVmHlFZh9AVYXIO3Ct17k7gLVAFZX4dgxOHr0LgslbdX5uSb/9//fL7e09//01KNM16sf8okkSR+pLCFurVF+5ZUw7m9aK2yz5+a6Qb/yz39O6ytfIYv9czzdpfm34X/8z7a29/f+LzD+yId7HkmSJEmSJEmSJEmSJOkTzr/ikiRJkiRJkiRJkvSB9tf2ExGRkQEwPTLNqZlTd6wrXLtOtZPRKcPELvjtz8DhPSF7ot2DOIJHJyGJbq/r9qDVhn4GJCEMLY5COFqWhRC1iSGYy6DXDTW9HlRj6A2CLeI1mANGdoX9WTaYj6GQh3IFkjT0jIDSzUC2Dly7BIVFaPeBJsxfgov/XQhkAzgyC/XWNh/miRPw7LO3J8VJumeW1rr8d69d39LeP3zCUBJJ+rTrZ8NUXnqJuDt42WsC5cHi8Dabbq5rhh9xp0vlpZeY/2v/m2021X1rvQFv/uut7f3r/4cP9SiSJEmSJEmSJEmSJEnSp4GhapIkSZIkSZIkSZI+UK1U4/DkYV6bfQ2A0fIotWKNpfYSdLvQ6UChAPn8rZpC2uaR/GWe/i0oTEAugS9NQxRDGoeQtN3DIahsbR2aHWi24cINyCdwcHfo04+gmEApD+VcCEADiCIYH4LZRghMy6XQA9I05JWVetDowvUlGKlAOQ9RxiCZLXy/2StOIdeH1UVYuAJ7m7BUCmvdNtz4dxuBalEGX72wg4fZaMCZM3Do0A6aSJIkaSvSGwnll3++MbEM1AffS8AosHgXDUcHdTctbXwtv/Jz0ukExrd5WEmSJEmSJEmSJEmSJEnSjhmqJkmSJEmSJEmSJN3HltaXOLd4LgSkfYAkTrjQuEBGBkAuTmicexPmbkAGxX7E+NQ08aOf4/OTi3w++hX5Wti72oORGuQGgWZZBqOVwTiBYgWKMczMQGcF8qUQXpZFEGfQ6UM+hmYfCjkGJ4AkhuEqLK9AMphcWILJKnQTSFLox7DYhGoOsgJESQhey/rQy6DXg5XL0LwM/W7o8bl5uFiDS3n4xZ9Ba3njOTw+A/XWDh54FMHY2A4aSJIkaavqJ/4r4nYX9gwmrgMPsZGuexB4GUi30Cwe7L8pBWY2LV/sUv/Of83MP/qjnR5bkiRJkiRJkiRJkiRJkrRNhqpJkiRJkiRJkiRJ96k3Zt/g5OmTpNlWUiSg2W0y25wNg4sXWVu8Qbs4WMwyxpff4Wu1ZcbHynB5kW4SlnIp1GshJA1CmFqlwK0wi3Yf5tfggXGoV+DC9ZBREQ0+vS5E+bC30wMyiOIwrhRgfdMZK2WY+RVUD0IlgTiFQiFcs9cdhKi1oN0M+5MUVi7AzUcw2YRKFw6+AS/+ClY2PZoHl+HrZ7f0qN7fk0/C1NQOm0iSJOlOkvl5xr/3PTjMRqhahxCEtnswrgKHgNN8cLBaPNhX3TQ3O+h306sw/sb3mPuHf0h/fPwe3IEkSZIkSZIkSZIkSZIk6W4ZqiZJkiRJkiRJkiTdh1baKzz/5vNkZFuuOVA7EELVul24cIGRGGaGwtrUCPytx2EsnYelIW62zeXC2oGx8D2KoJCDfA76WQhJW1wL27sJlEswfQB6KeQHoWzdNcjKoTbjVhYbDL6XShshabUcXAOunofJcZiow1h5Y3+cQWdtU/3KRqAawIFZ4IeQ+wn8/RTeGoNGCUbW4eDC7de+a089Bc89t5MOkiRJ2qL6ie+QtFpwCvg9oDBYeAsY2zSeACqD+cX3aDQKHOT2QLXOYP/m8SlI2k3q3/nnzPyjP7p3NyJJkiRJkiRJkiRJkiRJ2jJD1SRJkiRJkiRJkqT70NmFs7cFqqWbk8XeRzlfZqIywewbP4M0pZRCsQflGvzdL0Och34b4pVVkgQemISxYcjnoZqHfhxC0Qo5yCXhl5X5GCoFWOoBKfRiiGLYlQ/Bav0eRH1Yb4fANYA4CoFr7X4IOcvlYQ2IgUIK6S7I1mB2AWoF6BUhqoRr9tYg7oWzJCm05yDLYHURqi9A9U+BdrhOBHxm4Q4PpVSC738farX33xNFcOQI1Ot3fMaSJEnauWR+nvHvfS8M2sCPgd8dLPaAN4DHgEGIL1XgcWAdWCaEpBWAYaD0rub9QX1v09yPufUOOf697zH3D/+Q/vj4PbwjSZIkSZIkSZIkSZIkSdJWGKomSZIkSZIkSZIk3Yfycf628anrp1hqL92xrrPe4uryFRgJ434CT38ZonLIoOjGMFaER6ZCeFobSAohxCwFiCCON/r1MxgqQjkPiyvQ7Yb5dg/Gh2C5EcbNFSgUNmqTGIYSaHdDwFo/hmIXukkIcdsVwQNTMB5BtgLzHej1Yf0sZDlYrEJtCU79CSzNQtqFb73IrTCMLVtfhxdfhKNH77JQkiRJH5b6ie+QtFobEz8BDgF7BuMl4BfAFwjhaTeV+PUQtc06hEC1za/N1wb9B5Jmk/p3/jkz/+iPtn1+SZIkSZIkSZIkSZIkSdL2GKomSZIkSZIkSZIk3Yf21/YTEZGRATA9Ms2pmVN3rCtcu061k9Epw8Qu+O3PwOE90CcEocURPDoJSXR7Xa8P7T70MijlQhBaFEEaLk8Sw/gumF+BTg+yDDod2FWC5hqQhdC1seFQRwa5GHJFKOZCTWUdugU4WAjXS1LIdwb9u3DlAlQbYdxfhXM/hYWrYXxkFuottufECXj22dvT4iTpPfzq5bc/6iNI0qdeMj/P+Pe+d/tkCpwE/mOgPJhbAl4ADgJTwLveX3+tfhZ4C+html8b9E1v3z7+ve8x9w//kP74+DbvQpIkSZIkSZIkSZIkSZK0HYaqSZIkSZIkSZIkSfehWqnG4cnDvDb7GgCj5VFqxRpL7SXodkOiWaEA+fytmkLa5pH8ZZ7+LShMQC6BL01DFEMah0C13cOQpbC2Ds0ONNtwvQG7R0KPfgTlfKjNxXBbDFkM40NwfSmEqq21oLoLWuvQzyDrwNxK2BMRPhnhutUCJPnwC9B2J4SqlQaBF/0uNN6GQnPjUrmrcPH18D3K4KsXdvAwGw04cwYOHdpBE0mSJN0L9RPfIWm9R1ruPPCvgL/PRrBaD/glcI4QrFYDqkBCSA1uEsLXZoDOu/qtAX886PsuSbNJ/Tv/nJl/9Ec7vR1JkiRJkiRJkiRJkiRJ0l0wVE2SJEmSJEmSJEn6lFlaX+Lc4rkQkPYBkjjhQuMCGRkAuTihce5NmLsBGRT7EeNT08SPfo7PTy7y+ehX5Gth72oPRmqQGySbZRmMVgbjBIoVKMYwMwPNFuSGQ6BaDHT6IVSt34d2Cu0+lAvhTFEMIxVorEKSwXITCnlY64RxpxNC1x4Z51YiWz+DONu4r34fkhRyfVhdhIUrUF8Oe64PQbcNp/88hL8BPD7D/5+9f4+R7L7zu7/3Oafu1ffu6WGLlxntUpRG0oiURHvtvelqMlIQx4kDG3I2fzwrOdITJf8GtgTEhiPI/z0IHiAh5d2xsWtFwMOQsIInXi1WgOUnK2Z3SYrkcHfI5c4sNTOcW9+7urqqq+rUOSd//GqmuyVypudCznD4fgGNrt/5fX/f8zu/hoBqVc+HzL1F7sa+RRHMzNxEA0m3SrNa4tc+uL//PTar/smEJN1tktVVZo8de/uCC8Ax4B8DC7uuD4A3R1/7cRF4hrcMVLts9tgxVr7xdbLZ2X021ftSZRwO/eb+ayVJkiRJkiRJkiRJkiRdlX8hLEmSJEmSJEmSJN1FTiyd4JnXniG/nBh2DZ20w1JnKQzOnmV7fZl+dTRZFMxu/pzPTW4yO1OHc+ukSZgq5TA3CUU0GkfQqACjcT+D1W24dxYGTahE0I3C9GAIjXJ4XY5hrRtC1ipJWN+owPZoC1kGvUF4HSfhvkkFhsNReFoJ+ikUWcg2KwpY3YDKKmysQJaGtYc3oJrBZg5/+v+F7ma4ft8mfPnkDRz0bo89BgcP3mQTSbfCg/Nj/A9f//u3exuSpNtk7oknSbq70nLLwD3AFJAAGbAB/AHwd4HfBCrXcYMB8FPgWeAab7eTToe5J7/H4re/dR030PvOgYfgv/nPt3sXkiRJkiRJkiRJkiRJ0l3DUDVJkiRJkiRJkiTpLtHut3n61acpKPa95tDkoRCqlqZw5gxTMSyOhbmDU/DFh2EmX4XWGJfblkph7tBMeB1FUClBuQRZEULT1rdDeZpArQrNMUj7kA5huw/T9Z09TNdhtQsHx0cXImhWoDNKVksSOHka7r8HGjWYG4W+FQWkA1hfg+Fob90enD8NC1s7/ec70EiBi/B3/nuYLmCjBlM9eHDtSg7cjXn8cXjqqZvpIEmSpFsgWV1l9tgxqAIPA48QAtXe6s1eAVwC/nQ0/ug+al8GjgP9/e9p9tgxVr7xdbLZ2f0vkiRJkiRJkiRJkiRJkiTdMEPVJEmSJEmSJEmSpLvEybWTewLV8iK/5pp6uc6BxgGWTvw55Dm1HKpDqE/CP/o0xGXI+hC3t0gSuHceZiagXIZmGbI45ExUSlBKwgeQ5RgaFWgNgRyGMWwP4cAYLLUhG0KvB/Va2EM5gfEKDLPQA6BUBrYhJ2RbxBGcOgOffBBqQDraf7oN1T4MK2G81gphabsdWgJ+AjwLUQ4futahfOUr8LWvXb0miuDoUZibu+YZS5Ik6Z039+T/g+TTXfhNoHKN4ghYGH0NgJ8CfwDMA5OEN7VDoEUIVEvfus21JJ0Oc09+j8Vvf+vGGkiSJEmSJEmSJEmSJEmSrouhapIkSZIkSZIkSdJdohyX94yPXzpOq9+65rpBr8uFzfMwFcZZAl/6NER16AFpDDNV+NWDITytDySVEKiWA0QQxzv9sgLGqlAvw3ob0hQ6A5howEwTNtah04ZKZWddswLlEuQ55AUkuz7JjIDJCbj/HqhlsLUE2TjEVcg3oJxDkoc8jO4azA+hKGBrHZrPQfOHhE3v1//4P8J//98bmCZJkvQekRTLzEXH4PM3sLhCWHcEeAZ485Zujdk/+AMW/+W/2PuGWZIkSZIkSZIkSZIkSZL0jjBUTZIkSZIkSZIkSbpLPDD5ABERBQUAh6cOc3zx+DXXVS5eojkoGNThwDj8+ofg4wuQAf0hxBF8ZB6SaO+6YQb9DIYF1EpQTiCKQigaQBLD7DistmEwhPY2TNZhvAadbdhow/RE6A/he70KRQ5pCZpjUIpCkFu9CoNtKA+gALIWvHYextagUodhHf5sCfLn4fwabK1BnsI3n+f6AtUAtrbgv/vv4Lvfvc6FkiRJereV4ovMtZ8knurtnagA9wATQBNICG9wO8AmcImQynvZAvBV4PvAhVu3v6TVonrqFP2HHrp1TSVJkiRJkiRJkiRJkiRJb8lQNUmSJEmSJEmSJOkuMVmb5OPzH+cvl/4SgOn6NJPVSVpbKzAYQKUC5fKeNZW8z6+Wz/GlX4PKASgl8KnDEMWQxyHo7J6JEHS23YPOADp9uLQB90yFHlkE9XJYW4oh3n2DGGbH4FIL2j0YK0GzAd0eDAew0g7zSRxyLiLCvctAow7JKKCtAGrD0f1SWD0NH1iFVg36HWi/CSeehQ+uweU4jaNLMNe9wcN84gn4zncgjq9dK0mSpNsiiVaZqj0NeUQRQVQQ/iLuQeAg4c3lL6oDc8AHgUXgFDDcNfc7wDFg9dbssYgisqnpW9NMkiRJkiRJkiRJkiRJknRVhqpJkiRJkiRJkiRJd7hWr8Ub62/Q6reuWZvECWc2zlBQQFFQevU1NlZPhlSyCKpz9zD70CeJ44KPHljno9HfUJ4MyWVbQ5iahFIEo+VMN0bjBGoNqMawuAidLpQmQqBaDAyyEKqWZdDPoZ9BvRL2FMUw1YCNLdhsw+wk1KuwNQpWu9QK8+MVruwzKyAudj3YEEoZbK3D2nmY24RDG/D8vZD24bX/CT64uVMeFfCZMzdx6Bsb8PrrcOTITTSRJEnSOydjsvqfiaMeebNJNjFBiU34GFDZx/IIuAeYAU4Al99q14F/DPw+kN/8Lrc++1mG8wduvpEkSZIkSZIkSZIkSZIk6ZoMVZMkSZIkSZIkSZLuYCeWTvDMa8+QF/tPdOikHZY6S3D2LJz7Odtj0L/8yeDmJWYvvMLnfrPBbHUbzq2TJmGqlMPcJBTRaBxBo0IInAB6GWxuwQMzsN2ESgTdKEwPhtAoh9flGNa6IWStkoT1jQpsE0LX1jeh2YCoF+45iGG7D70KJCWIEkhTyPMQ7DYcQus0DJYgS8NeDm9AI4UDq/DHfwbdXYFqAA8vwlz3Bg78siiCmZmbaCBJkqR3UqP8HKVkEYC4u01c6sBHgeQXCnvAJjAghK1NALVd8xXgE8Ar7ASrLQC/Afzpze2x/bnPcfb3/t3NNZEkSZIkSZIkSZIkSZIk7ZuhapIkSZIkSZIkSdIdqt1v8/SrT1NQXNe6Q5OHWNo4D2fOADDVg8WxMHdwCr740HlmqvfB5iaXW5dKYe7QTHgdRVApQbkEWRFC09a3IU2gNoR6FZpjkPYhHYZQtOn6zh6m67DahYPjowsRNCvQ2Q71rQ70+qFPUsBMfRSgloYQtdVVuJwjl3Zh+/xO7/lOCFTjInzoCfiz+6A9sTN/3yZ8+eR1Hdkve+wxOHjwJptIkiTpnRDRo1l+7sq48eJzxB/O9gaqdYBTwPpbNJgGHgSao3ECfAy2Nx+i99GHASj+NyVaJ/8XFHnlRjZI78gRstnZ618rSZIkSZIkSZIkSZIkSbphhqpJkiRJkiRJkiRJd6iTayf3BKrll1PGrqFernPgzVWW8lBfG0J1CPVJ+EefhrgMWWudeKtDksC98zAzAeUyNMuQxSFrrVKCUhI+VCzH0KhAawhZBHEB20M4MAZLbciG0OtBvRb2UE5gvALDLPQAKJWBbciBKINzl2C8CR85BJVdARjD7k6gGsBwae/zHVoCfgI8C5UcvroEp2ZgoxYC5B5cg+g6zvmXPP44PPXUzXSQJEnSO6hWepUoGgAQd7epb74IB3YVLAOvEd54vpV14GfAEXbWVaDK37J14IvkjZAWnH1giu3hp96JR5AkSZIkSZIkSZIkSZIkvQMMVZMkSZIkSZIkSZLuUOW4vGd8/NJxWv3WtRemKYOLf8OFSa6ki2UJfOnTENWhB6TDDjOT8KsHQ3haH0gqIVAtJ6yL452WWQFjVaiXod2C/hA6A5howEwTNtah04ZKZWddswLlEuQ55AUkuz6djIDJibB+uAHdFMoNiCPINyBJwl6iIaTLUBSwtQ7N56D5Q8KGd/X60NpVzqNWgx/8ACYnr35uUQRHj8Lc3LXPWJIkSbdNvfRXV143XvoL4rld6Wkdrh6odlk+qmsAzXApnslovPwXbP36Z6/cx1A1SZIkSZIkSZIkSZIkSXrvMFRNkiRJkiRJkiRJukM9MPkAEREFBQCHpw5zfPH4tReee5PKsGAqh8YsTNThEw/AR+6BXgZbA2jW4CMHIYn2Lh1m0M9gWECtBOUkBJ0VYQskMUxPwPIWZENob8NkHcZr0NmGjXaYj0d94wjqVShySEvQHINSFILc6lUYbENpAFkblntw6ecw3YVhHdab0H4Rlp6DrTXIU/jm8+wJVNuXXg+efx6++93rXChJkqQ7TcSAUrwEQNzdpn7pZXhgV8Eprh2odlk+qn94NI6hfu5lut1fI2/UKcVLRAwoqNyq7UuSJEmSJEmSJEmSJEmS3kGGqkmSJEmSJEmSJEl3qMnaJB+f/zh/ufSXAEzXp5msTtLqtyBNYTCASgXK5StrKnmfXy2f48HPwuQUbFVDENqnDkMUQx5DFMFsAwYpbPdDYFp3AJc24J6p0CeLoF6GUgzl0RoACihimB2DSy1o92CsBM0GdHswHMBKO8wnMSRARLh3GWjUISmutKI2HPUdwMU3obIeQt3owOqbcPaPQiAbwNElmOve4GE+8QR85zsQxzfYQJIkSXeCUrwEUXhD2XjhBeJGtjPZA9avs+H6aF0tDONGRuOFF9j67d+CqKAUL5Hm992CnUuSJEmSJEmSJEmSJEmS3mmGqkmSJEmSJEmSJEm3QavX4o31N0JA2lUkccKZjTMUhOCIUpyw8carsLIMBVSziNmDh4k/8mE+Or/OR6O/oTw5Si0bQhbD1CyUIkIgWgEzDWiUw9dMDS6uwqmfQ6MK0SQUEcQFDLIQqJblkOYh7KxZDvNRDFMN2NiCzTbMTkK9ClujYLVLrTA/Xgn3JYKsCH2vGEIpg611WDsPH+hAaxRmkfZh+Y93AtWiAj5z5iYOfGMDXn8djhy5iSaSJEm63ZJoE4C4u039xRfhU7smN2+w6SZXQtVoQv2ll+g++ih5o04ctW9it5IkSZIkSZIkSZIkSZKkd5OhapIkSZIkSZIkSdK77MTSCZ557Rnyy4lh19BJOyx1lsLg7Fm215fpV0eTRcHs5s/53OQmszN1OLdOmuysLeUwNwpKgxCu1qgAo3G/gBj4+L1w5hLko6kIGKYQlUNdOYb1DlRiKJVCQaMC20CWwfomNBsQ9cI9BzFs96FXgaQEUQJpCnkegt2GQ2idhsESZGm4x4dX4ewkvFmGV34M3V2hGA8vwlz3+s55jyiCmZmbaCBJkqQ7QpQB0HjhBeJ0CLve+zK4wZ671yUQD1IaL7zA1m//FlE0vNGdSpIkSZIkSZIkSZIkSZLeZYaqSZIkSZIkSZIkSe+idr/N068+TUGx7zWHJg+FULU0hTNnmIphcSzMHZyCLz4MM/kqtMa43LZUgplJGKvDoQNAHMLMkgQqJRhkoXR9G9IE6jU4fAiGOZRHwRTpNhT1kEcGMFuD5W2YHx9tLIJmBTrbkA6h1YFeH+pVSAqYqY8C1NIQora6Cpdz5NIubJ/fecb5DjRSePAEPP830N6VN3ffJnz55HUe9C967DE4ePAmm0iSJOm2KxLi7jb1F18M42zXXOUGe+5eN+pXf+kluo8+ShH7J3aSJEmSJEmSJEmSJEmS9F7hX3xJkiRJkiRJkiRJ76KTayf3BKrlRX6V6qBernOgcYClE38OeU4th+oQ6pPwjz4NcRmyPsTtLZIE7p2HmQkggnIJKjFkcQhRa1SglEC1DL10FK4WwTCGKIbxcghWy4YQZaOQtBpERQhqGytgmIUeAKUysA05of7cJRhvwkcOQSXZeYZhdydQDWC4tPcZDy0BP4HSs/A7OZyagY0aTPXgwTWIbvC8AXj8cXjqqZvpIOk9YpjldAbZtQuBZiWhlMTv8I4kSbdaVkzQeOEF4nQYLnSA+mhy4gab7l7XCd/iQUrjhRdY/Tu/e4NNpX3IhjDY2l9tZQwS/+RTkiRJkiRJkiRJkiRJuhr/wkaSJEmSJEmSJEl6F5Xj8p7x8UvHafVb11w36HW5sHkepsI4S+BLn4aoDj0gjWGmCr96ECol6I/WJZUQqJYDRBDvyg9KYliYgJUOZKPAs/4QZsdgcyOMO22oVKAM5DE0KyGoLc8hL/b+m/4ImJyAiQYMN6CbQrkBcQT5BiRJ2Es0hHQZigK21qH5HDR/yJVNR8CH1q5xILUa/OAHMDn59jVRBEePwtzcNZpJulucuLDJ//L//uy+av/f3/wNHr5/6p3dkCTplsuXE+o/e2nnwiZw+e1eDZgG1q+j4fRo3WW73prXX3yJ/HACsze4WelaLh2H3/v8/mr/+X+Bez/9zu5HkiRJkiRJkiRJkiRJeo8zVE2SJEmSJEmSJEl6Fz0w+QAREQUFAIenDnN88XiYTFMYDEYpZnvD1yoXL9EcFAzqcGAcfv1D8PEFyAhBaHEEH5mHJNp7v3QI3T5kBZCEULQ4CsFlRRGC1Q6MwUoBwzRcGwxgvAadbaCAdgumRtllEWF9vQpFDmkJmmNQikKYW70Kg20oDSBrw3IPLv0cprswrMN6E9ovwtJzsLUGeQrffJ6dFLj96vXg+efhu9+9zoWSJEl6L5t74t8T91NYGF24BHyQ8EYV4EHgZ4xSha8hHtVflgOLu6bPpsw9+R9Y/Pa3bnbbkiRJkiRJkiRJkiRJkqR3gaFqkiRJkiRJkiRJ0i3S6rV4Y/0NWv3W1QsjOL1++sowzVI6Z07CyjIUYZ65A7CwAFFEpRjw0NibfO7D0JiFUgKPHoI4hjwKIWcLE5DnIUBtuw+dPpxZhnICD94T7pNFUE2gVoZ6aSd3IopgdgyWNkKo2nYXmuPQ7YVxuQtLSahJYkhGW4xiKAONOiQhI44CqA1HjQdw8U2orEM/Azqw+iac/aMQyAZwdAnmujd44E88Ad/5TjgISZIk3fWS1VVmjx2Dj7MTqjYgBKGN3vPSBI4Ar3H1YLV4VNfcdW1p1O+yl2H2xDFWvvF1stnZW/AEkiRJkiRJkiRJkiRJkqR3kqFqkiRJkiRJkiRJ0i1wYukEz7z2DHlxteSGoDPocHrj9JXx8PxZNtrLUA3jcgZjS8vEpYRHHhnjkcZFyr0w1ytgbjyEm102XR+FncUwlYQgtQtLMGhDuQZRAUUEcQGDDMoxdDKolEIIGoS1E03YbIeAtM0OVMqQb0MWw3AAl1ow1YDxClfC37Ii9N15GChlsLUOa+fhAx1o1cJU2oflP94JVIsK+MyZGzjsyzY24PXX4ciRm2giSZKk94q5J54k6XbhOPAFoDKaOAXM7BofABqj6+tv0WgaeJC9gWqDUf3u8XFI+h3mnvwei9/+1q17EEmSJEmSJEmSJEmSJEnSO8L/ZLskSZIkSZIkSZJ0k9r9Nk+/+vS+AtUAmpUm8835MEhT6mcuUB3uzKcxLFThnx1e5LcWuox3OtSGUBvCeB8WxkKQWVyE3IhmBSLCV5pBqwMLB+Dj90EyhMu7ioBhGr4DDIZAvjNuVHZyKLIMOtuQDGCQQCmHooDtPvT6MEjDvdIU8hyGWbjeOg3nXoWVs5Bn8OFVuGcrBKq98mPobu4858OLMNe97uPeEUUwM3MTDSRJkvRekayuMnvsWBj0gZ/umhwCJ4Bs17Um8DDw94CPEkLUPjoaP8zeQLVstH7Xe3J+OroPMHvsGMnq6q16FEmSJEmSJEmSJEmSJEnSO8RQNUmSJEmSJEmSJOkmnVw7SUFxZZwX+TW/7p+4n4KC4txZijxnsgcF4Wt+Cr74WZiZKChWViiKcL1Ugg/MwqEZuGcSFqbggZkQqlYphXC09W2u7KRah8OHYLgr6y3dDuFojO4V7UwRAbXazrhewMk3odsLHywmBczUw/phGgLWFpdheQ1W1mF5EbbPQ5aG9fMdaKTw4Am49BS0d+VQ3LcJXz55kwf/2GNw8OBNNpEkSdJ7wdwTT5J0dyXyPgtc3FXQAl4BBr+wsAbMA/eNvtd+YX4wWtfade3iqP9I0ukw9+T3bmb7kiRJkiRJkiRJkiRJkqR3Qel2b0CSJEmSJEmSJEl6ryvH5T3j45eO0+q33qZ6x8rmIp32OZgK460KTI7Blx6FQRnWM4iLAUkTDs/C3DhUy1CtQjFKQyuXoJSED/4qCTT60BoCORBBVIbxcghWy4YQZdDrQ30UJhFHUE6gn4VQtVIZtgkhapUcelU4eRoOzsFHDoV7XDbsQrErsG24tPf5Di0BP4HSs/A7OZyagY0aTPXgwbW9gW7X7fHH4amnbqaDJEmS3iOS1VVmjx3bezEHngG+CtRH11rAc8CDwEGu/oYzB5aAU8Bw1/XtUd98b/nssWOsfOPrZLOzN/gUkiRJkiRJkiRJkiRJkqR3mqFqkiRJkiRJkiRJ0k16YPIBIiIKCgAOTx3m+OLxa66bXG3TyUMw2oFxGBuDxz4J0w3oD2G7FwLRPnQQKqNP9kpxyIYoCiCC5PIYyHIYq0K9DOttGA7Cmv4QZsdgcyOMO22oVCCOwziJYSyBfhoC1rIYqimkCZTLMBbB1DgMN6CbQrkRwtjyDUhG9dEQ0uWwr611aD4HzR8C/XCPCPjQ2jUOpFaDH/wAJiffviaK4OhRmJu75vlKkiTp7jD3xJMk3e4vT6wC3wd+h51gtSHw18AbhGC1SaAJJEAGdAjha4vA4Bf6bQP/cdT3FySdDnNPfo/Fb3/rZh9HkiRJkiRJkiRJkiRJkvQOMVRNkiRJkiRJkiRJukmTtUk+Pv9x/nLpLwGYrk8zWZ2k1W9BmsJgEFLMyuUrayp5nyPlizz2azA5HULH7p2GhTnCAKgkMFOD3gA6g1GQGjDMoJ/BsIBaKQShxdHOfBLD3DistGEwDNcHAxivQWcbKELo2sxEyCijCGFtpSpUS2FNowdpBR6shPslOZQGkLVhuQeXfg7TXRjWYb0J7Rdh6TnYWoM8hW8+z5VAtX3r9eD55+G7373xH4YkSZLuKsnqKrPHjr19wQXgGPCPgYVd1wfAm6Ov/bgIPMNbBqpdNnvsGCvf+DrZ7Ow+m0qSJEmSJEmSJEmSJEmS3k2GqkmSJEmSJEmSJElX0eq1eGP9jRCQdhVJnHBm4wwFIdmsFCdsvPEqrCxDAdUsYvbgYeKPfJiPzq/z0ehvKE8WDGNo5ZAkcO8UxAUMCSFpB8agFEG9BFN1WNuE8xdhMA1ZBMMY6mUoJVCORgFpl0UwOwaXWiFUbbsLzXHo9iAroBiE0LXZsZDhNspWI46gWYGkHD5M7A9CqFptOOo7gItvQmU9BLvRgdU34ewfQZGHkqNLMNe9wQN/4gn4zncgjm+wgSRJku4mc088SdK9xpvLVeD3gd8AfhOoXMcNBsBPgWeB/OqlSafD3JPfY/Hb37qOG0iSJEmSJEmSJEmSJEmS3i2GqkmSJEmSJEmSJElv48TSCZ557Rny4hrpCiOdtMNSZykMzp5le32ZfnU0WRTMbv6cz01uMjtTh3PrpEmYSgqYnYA42ek11YAkDkFnOdBLIU1D0Folgm4UAtgGGZTiEJSW5iHorFEGIoji0GdjK9xjswOVMmwPwngwCKFrvzoLjDLMsiL0vSzLIMmhlMHWOqydhw90oFUL82kflv94J1AtKuAzZ67/rK/Y2IDXX4cjR26iiaT3qwdmGvzf/ukj+66VJN3ZktVVZo8d219xDvwp8BzwMPAIcA8hPfgXFcAl4GXgONDf/55mjx1j5RtfJ5ud3f8i6e1MfxD+17+3/1pJkiRJkiRJkiRJkiRJV2WomiRJkiRJkiRJkvQW2v02T7/6NAXFtYtHDk0eCqFqaQpnzjAVw+JYmDs4BV98GGbyVWiNcbltqQSHJuDQfTBegygCImhUIS9gMAzBaatbQAK1AqanIO1AOgzzzXLoVY5hrRu+l0ujPhXYHu0vy6A3CK/jBEo5JBUYDkfhaSXop1BkYR9FAasbUFmFjRXI0rD2w6twdhLeLMMrP4bu5s4ZPLwIc90bOfGRKIKZmZtoIOn9bLpZ4R998t7bvQ1J0i0y98STJN1feHNZJoSlTQEJkAEbhJC0lBCQ9tzo63LtJOEv5YZAa1ftDUg6Heae/B6L3/7WjTWQdmvMwCf+ye3ehSRJkiRJkiRJkiRJknTXMFRNkiRJkiRJkiRJegsn107uCVTLi/yaa+rlOgcaB1g68eeQ59RyqA6hPgn/6NMQlyHrQ9zeIkng3nmYmQg5YtN1KOLQp1qCShJy16olWN7aCTlLY0gLODAGS23o94AaEIW1MzVY3Yb58dGmImhWoDNKVksSOHka7r8HGjWYq4brRQHpANbXYDh67G4Pzp+Gha2dZ5zvQCOFB0/A838D7V3Hct8mfPnk9Z3zL3nsMTh48CabSJIk6T0vz5n9wz8Mr6vAw8AjhJC06C3qC0JY2svAcUK4Wgq8Ofq6hWb/4A9Y/Jf/AuL41jaWJEmSJEmSJEmSJEmSJN0UQ9UkSZIkSZIkSZKkt1COy3vGxy8dp9VvXXPdoNflwuZ5mArjLIEvfRqiOvQIoWgzVfjVg1AphayHSikEqhWjcIg45kqcW16EYLWDU7DWhjSFbh+mmjDThI116PWhXgv1lQTGKjDMoJSEa6UysA05IX8ijuDUGfjkgyGPLR3dK92Gah+GlTBea8FUb+/zHVoCfgKlZ+F3cjg1Axu1UPfg2lvnW+zb44/DU0/dTAdJkiTdJaqnTpG0W/BbwG8ClWssiICF0dcXgJ8CzxLeBN9iSatF9dQp+g89dOubS5IkSZIkSZIkSZIkSZJumKFqkiRJkiRJkiRJ0lt4YPIBIiKKUbzZ4anDHF88HlLNBgOoVKBc/qV1lYuXaA4KBnU4MA6//iH4+AJkQH8YAs0+Mg/JrvSxUgxZBr0MsgKqCSQxRBFkoxCIOIa5cVhpQ2cAE40QoDZeg047bKcUhWC2ZgXKJcjzEMqW7PpUMAImJ+D+e6CWwdYSZOMQVyHfgHIOSQ4DoLsG80MoCthah+Zz0PwhIQlu1OtDa9c4yFoNfvADmJx8+5oogqNHYW7uGs0kSZL0vjFbUPxziO65gbUV4PPAEeAZYPWW7owiisimpm9tU0mSJEmSJEmSJEmSJEnSTTNUTZIkSZIkSZIkSe8rrV6LN9bfoNVvXbs4gtPrp8ProiB94ySdlQtQhDnmDsDCQggFAyrFgIfG3uRzH4bGLJQSePRQCETLoxCotjARws62e9AdQKcPiy04OBnaZjE0KtDPQtha9Av7mR2DSy1ob8N0DZoN6PZgow0z42ErEeFe9SoUOaQlaI6F0LVKKVwfbEN5MLpnC147D2NrUKnDsA5/tgT583B+DbbWIE/hm89zJVBt33o9eP55+O53r3OhJEmS3q9K8UXG7/8TBn/vENXTZ3YmKsA9wATQBBJCenEH2AQuEdKBL1sAvgp8H7hw6/a39dnPMpw/cOsaSpIkSZIkSZIkSZIkSZJuCUPVJEmSJEmSJEmS9L5xYukEz7z2DHmR76u+M+hweuN0GJw9y/DiBTbGdubLG8uMJSXihXkeuafDI42LlHthrlfA3Dgk8U79dD2MkxgqTagmsLgIgxRK45BFkBdQFKE+y6CThtf1ChCFgLapBmxswUQJyqOQtF4Xlkeha0kc8iUiIIqhDDTqkIz6FkBtOLpHCqun4QOr0KpBvwPtN+HEs/DBNRg9DkeXYK57HYe92xNPwHe+EzYvSZIkXUUSrTJVe5o46tH+B/+Ayu//PlECPAgc5BdSh0fqwBzwQWAROAUMd839DnAMWL35/bU/9znO/t6/u/lGkiRJkiRJkiRJkiRJkqRbzlA1SZIkSZIkSZIkvS+0+22efvVpCop9r2lWmsw351naOA9nzlDPoTqE/uhTtjSGhdZFvvD5OrONAZzrcLl9OYOFMYhH4ySCZmUnAyLNYK0L98+E8LRKBN0o1A8yKMWhthrD+Tbck0AlCe0bFdgGNtowOwn1Ggy6MBzApVYIXRuvEIojyIqdfQAwhFIGW+uwdh7mNuHQBjx/L6R9eO1/gg9u7pRHBXzmzA0c+mUbG/D663DkyE00kSRJ0t0vY7L6n4mjEO1bf+UVogngY0BlH8sj4B5gBjgB2x/4CL2jRwEY/tNp2j//AiF++AZE0DtyhGx29sbWS5IkSZIkSZIkSZIkSZLecYaqSZIkSZIkSZIk6X3h5NrJPYFqeZHva939E/ez+Jd/Dnmon+zB4liYOzgFX3wYpocrFJvFTqBaKcwdmoFSCaIIKqVwfVhAOoT17VCeRVBOYHoK0k6YGwyhWR71SqCWwHoXDo4DUQg5a1agsw3rmzAzDlkMpRwGMWz3oVeBpARRAmkatl8UMBxC6zQMliBLwz0Ob0AjhQOr8Md/Bt1dgWoADy/CXPf6z/yKKIKZmZtoIEmSpPeDRvk5SskiAHF3m/rf/gw+wS/noPWATWBACFubAGq75ivAJ6D62im2DnyevFEHoPhghW7699/hp5AkSZIkSZIkSZIkSZIk3S6GqkmSJEmSJEmSJOl9oRyX94yPXzpOq9+69sI0ZWXtHJ2pnUtbFZgcgy89CoMyrA82iYGkCYdnYW4cqmWoVqGIRvcvQSkJH9BVYmhUoDWEKIMMSAs4MAZLbej3CKEQo7UzDbjQhmEWelzuByGEbWMjzCUJJAXM1EcBamkIUVtdhcsZcmkXts/vPMt8JwSqcRE+9AT82X3QntiZv28TvnxyHwd8NY89BgcP3mQTSZIk3c0iejTLz10ZN158jvihbG+gWgc4Bay/RYNp4EGgORonED80pPHSc2z9xmcAaJafYzv9JMWeBDZJkiRJkiRJkiRJkiRJ0t3CUDVJkiRJkiRJkiS9Lzww+QAREQUFAIenDnN88XiYTFMYDKBSgfLe8DXOvcnkdsGgCgcmYKIOlOHzn4CxKmwNIMthsgofOgiV0SdwpThkohUFEEESX8lIIyvC2noZtjagl0G3D1NNmGnCxjr0+lCvQQGUE5hrQD8LfYmgdPk+OfRjeHMRahX4yCGo7AqeGHZ3AtUAhkt7H+/QEvAT4Fmo5PDVJTg1Axs1mOrBg2s7+74hjz8OTz11Mx0kSZL0PlArvUoUDQCIu9vUN1+EA7sKloHXgPytVhOC1n4GHGFnXQXqKy/R7f5d8kadKBpQK73K9vBT79BTSJIkSZIkSZIkSZIkSZJuJ0PVJEmSJEmSJEmS9J7X6rV4Y/0NWv3W1QsjOL1++sowzVI6Z07CynJIL4uAuQOwsABRRKUY8NDYm3z48zA2A9korOy+abh3NrwuCKFq01XoD0YhaiPDLAShDQuolUI4Whzt1CQRTE/C0hZ0BjDRCIFo4zXotEPGWzwKY2tWoJSEtXkORbz30cbqMNaA4QZ0Uyg3wr3yDUgSyGKIhpAuhx5b69B8Dpo/BPp7jogPrV3lDGs1+MEPYHLyGmcdwdGjMDd39TpJkiQJqJf+6srrxkt/QTy3Kz2tw9UD1S7LR3UNoBkuxTMZjZf/gq1f/+yV+xiqJkmSJEmSJEmSJEmSJEl3J0PVJEmSJEmSJEmS9J52YukEz7z2DHlxrYQF6Aw6nN44fWU8PH+WjfYyVMO4nMHY0jJxKeGRR8Z4pHGRci/M5Tl0E0hiuHcK4gLyKASj3TcNpRiKHLpdWN2Ac6swOwNFBFkE9TKU41AXRaFnBOQxzI7BpRa0t2G6Bs0GdHuw3oaZiVAfEULZKknYy2AIkw0ggUoJalVIt6E0gKwNyz249HOY7sKwDutNaL8IS8/B1hrkKXzzefYEqu1LrwfPPw/f/e51LpQkSZLeWsSAUrwEQNzdpn7pZXhgV8Eprh2odlk+qn94NI6hfu5lut1fI2/UKcVLRAwoqNyq7UuSJEmSJEmSJEmSJEmS7hDxtUskSZIkSZIkSZKkO1O73+bpV5/eV6AaQLPSZL45HwZpSv3MBarDnfk0hoUq/LPDi/zWQpfxTofaEGpDaKRQT2GhCeUIkgKiAqbrISgNII6gVIYog343BKpBCGBLsxCMluWwncL2YHTTCKIYphqwtQ3ZMPSpVyEbwEo7rIFwX4A4hiSBWiPUJQlkWdgnAAO4+AaU16DfgWwFVv8aTv0RtJbCPT6+BHPdGzz4J54IyW6SJEnSLVCKl8Kba6DxwgvEjWxnsgesX2fD9dG6kbiR0XjhhTCIiisBbpIkSZIkSZIkSZIkSZKku4uhapIkSZIkSZIkSXrPOrl2koLiyjgv8mt+3T9xPwUFxbmzFHnOZA8Kwtf8FHzxszAzUVCsrFAU4XqpBPOzcOQgfPIwLEzBPZNwcAxmGiEsDWCQweoWVOuwcA+Uk3A9Aobpzr7LMWxvh6C1ywWNClSBjXbIK6vVQhjbcACXWpAORx/ujR43z3buWwBFCqUMttbg/F/DB85BedQ/7cPyH8Pl7LmogM+cuYmD39iA11+/iQaSJEnSjiTaBCDublN/8UVo7prcvMGmu9c1of7SS8Td7XCfqH2DTSVJkiRJkiRJkiRJkiRJd7LS7d6AJEmSJEmSJEmSdKPKcXnP+Pil47T6rWuuW9lcpNM+B1NhvFWByTH40qMwKMN6BnExIGnC4VmYG4coCiFm9Sowej2RQLUMtXIISFveGmWeRUAZJuuwsQ3ZEIZdKOqjPgVM12F5G+bHR5uKoFmBzjasb8LUBMQRlHIYxJAU0O1BUgJiGPTDsqKA7jZsnYX1FchG4W0fXoWzk/BmGV75MXR3hUo8vAhz3Rs8dAgPMTNzEw0kSZKkXaKQBtx44QXidAjJrrnBDfbcvS6BeJDSeOEFtn77t4ii4Y3uVJIkSZIkSZIkSZIkSZJ0BzNUTZIkSZIkSZIkSe9ZD0w+QEREEaLMODx1mOOLx8NkmsJgAJUKlPeGr02utunkUC7BgXEYG4PHPgnTDegPYbsH42X40EGo7PpErVwKeWnFaJzEO3OlGGYasJpDPgo866Uw04SN9RB+1utDvQZxAaUSjBUwzKA0Co0olYHtsIfVFgyHkCQw34BKEnoMU+j0od3euffSBahc3BnPd6CRwoMn4Pm/gXa+M3ffJnz55A0fefDYY3Dw4E02kSRJkkaKhLi7Tf3FF8M42zVXucGeu9eN+tVfeonuo49SxP7ZnCRJkiRJkiRJkiRJkiTdjfzrMEmSJEmSJEmSJN2RWr0Wb6y/QavfunphBKfXT18ZpllK58xJWFkO6WcRMHcAFhYgiqgUAx4au8BnPwbTU2H6vmm4d3anZaUEMzXYHsB2P4SZAeR5GKdAHEOjHNYDpHkIWTswDqsFDIbQGcBkHcZr0NmGThuqZUgiKCJoVkJQW55DXkCy69O7PIPVTbj3AIzHO7kSWQ7ZOlc+6ctyKBb3HsmhJeAnUHoWfieHUzOwUYOpHjy4trPnG/L44/DUUzfTQZLeMUubPf7TS+f3Vfu/+uS9zE/U3uEdSZL2IysmaLzwAnE6DBc6QH00OXGDTXev64Rv8SCl8cILrP6d373BptIt1r4Er/wP+6v9xD+F8Xve2f1IkiRJkiRJkiRJkiRJ73GGqkmSJEmSJEmSJOmOc2LpBM+89gx5kV+ztjPocHrj9JXx8PxZNtrLUA3jcgZjS8vEpYRHHhnjkcZFyj3II+gSgtDunYK4CNfiCA40oRRDrRRC0dZb8NppKJXggYVQl43mawnUyjvBaxEwMw6LG+FatwvjDej2RuMNGJveqY0jqFehyCEtQXMMSlEIdqtXYbAN3UXIxqFUg14bKkPox2EP60tQHQW/ba1D8zlo/hDo79zjQ2vXOMRaDX7wA5icfPuaKIKjR2Fu7po/E0m6XS62evzbH/31vmr/3q/MGqomSXeIfDmh/rOXdi5sApffdtaAaWD9OhpOj9Zdtiunuf7iS+SHE5j9xUXSbbB5Hn78f9lf7eHfNFRNkiRJkiRJkiRJkiRJugZD1SRJkiRJkiRJknRHaffbPP3q0xQU+6pvVprMN+dZ6ixBmlI/c4FqA/qjT8LSGBaq8IXDi8wuxHChw+XWBTA7A+UoDApguhEC1S4bpNDL4YEZ2NiCIgpBZVERgsyyHDp9iOJRw1Ew21Qj1G93oVEJAWlpB5IBLJVhdiwEuiWM+sVQBhp1SHbtrzYM9xmuw4srMLUCzQoM63C6gAs/gsoqbK1BnsI3n+dKoNq+9Xrw/PPw3e9e50JJkiTp5s098e+J+yksjC5cAj5IeKMM8CDwM+DamcsQj+ovy4HFXdNnU+ae/A8sfvtbN7ttSZIkSZIkSZIkSZIkSdIdJr52iSRJkiRJkiRJkvTuObl2ck+gWl7k1/y6f+J+CgqKc2cp8pzJXggkK4D5KfjiZ2FmoqBYWaEYhaeVSvDABHzsPrhnEham4P4pODAGlVLIbxhksLoV9lGtwz0HoTwKa4sLGA539j0YQprtjOtVqI5eb7ShWgkBaWkCwwFcaoUwttIo0A0gK0LfK4ZQykJg2qVXYWIJSin0O9A7D3/2/4Lt16C1BNkQPr4Ec90bPPgnnoB8PykVkiRJ0q2TrK4ye+wYvLzr4oA9QWg0gSNc+6/d4lFdc9e1pVG/y16G2WPHSFZXb3jPkiRJkiRJkiRJkiRJkqQ7U+l2b+BW+Df/5t/c7i28Z/zDf/gPeeSRR273NiRJkiRJkiRJkt5WOS7vGR+/dJxWv3XNdSubi3Ta52AqjLcqMDkGX3oUBmVYzyAuBiRNODwLc+MQRzDZJCSoAdUEygmUgVoJlrcgiqAoQs0wgnvGYLkdQsyyFKiEtZUI1gYwuesTuEYFutuQZdDbhCSDQRVKOQxi2O5DrwJJCaIE0jTkmhWjwLbWaRgsje4DfGoJ/moe0j688mOY35UDERXwmTPXf95XbGzA66/DkSM30USSJEm6PnNPPEnS7cJx4AtceX/NKWBm1/gA0BhdX3+LRtPAg+wNVBuM6nePj0PS7zD35PdY/Pa3bt2DSJIkSZIkSZIkSZIkSZJuu7siVO1f/+t/TRRFt3sb7wn33XefoWqSJEmSJEmSJOmO9sDkA0REFBQAHJ46zPHF42EyTWEwgEoFynvD1yZX23RyKJfgwDiMjcFjn4TpBvSHsN2D8TJ86CBURp+SlUshT60Y9UjinX55AdUSHJyCtTYMB6Euy2GmCRvrMNiGohGC1wCaZRhmUEpC36QCbIe5Uh/+dhnGDkKjBkkBM/VRgFoaQtRWV6HIR4/ahe3zO/uZ78BcF6Z+Dn/yMnQ3957bw4th/oZFEczM3EQDSZIk6fokq6vMHjsWBn3gp8DnR5ND4ATwCSAZXWsCDwM9YJMQklYBJoDaLzTPRuuHu679dHQfYPbYMVa+8XWy2dlb+ESSJEmSJEmSJEmSJEmSpNvprghVu6woimsXvY8ZPCdJkiRJkiRJkm63Vq/FG+tv0Oq3rl4Ywen101eGaZbSOXMSVpZDslkEzB2AhQWIIirFgIfGLvDZj8H0VJi+bxru3ZWPUCnBTA22B7DdD2FmpRiyDHoZZAVUkxCsFkUhPA0gjuHAGCxvwWAI3T5MNWC8Bp1t6PWhUYUigkoCURzWRkBSghyoZqHXdhkunoaDc/CRQ6H+smF3J1ANYLi090gOLQE/gY/+ObxwFLoTO3P3bcKXT177/K/qscfg4MGbbCJJkiTt39wTT5J0dyUDPwscARZG4xbwCvAxQnjaZTV+OURttwEhUG33rx0XR/1Hkk6HuSe/x+K3v3XD+5ckSZIkSZIkSZIkSZIk3VnuqlA1Q8PenoFzkiRJkiRJkiTpdjuxdIJnXnuGfHdy2NvoDDqc3jh9ZTw8f5aN9jJUw7icwdjSMnEp4ZFHxnikcZFyD/IIuoRgtHunIC7CtTiCA80QolYrwWQd1ltw4RIMpmAYwTCBRgX6GZSiEKx2RQyzY3CpBZ0BTNeg2YBuDzptqJYhHgWkVRIolaGXhnC1LIZaH/ql0HOsAVPjMNyAbgrlRthfvgFJEuqjIaTLIfhtax2az0Hzh0A/ZEl89UU4NQMbNZjqwYNrIcTthj3+ODz11M10kCRJkq5LsrrK7LFjey/mwDPAV4H66FoLeA54EDjI1d/45sAScAoY7rq+Per7C7+KzB47xso3vk42O4skSZIkSZIkSZIkSZIk6b3vrgpVMzjsrRk2J0mSJEmSJEmSbrd2v83Trz5Nwf4+z2lWmsw351nqLEGaUj9zgWojBJMBpDEsVOELhxeZXYjhQofLrQtgdgbKURgUwHQjBKpdNkihl8PBCUiiEKoWFSHEjALyArbSUNsoQxFBHMNUAza2oNuF8SbUqzDowvoWTE+EcLSY0LNZhWoJ0jGolSGuwAfLIWgtyaE0gKwNyz249HOY7sKwDutNaL8IS8/B1hrkKXzzeaC/s/8I+NDaPg7yK1+Br33t7eejCI4ehbm5ff1cJEmSpFtl7oknSbrdX55YBb4P/A47wWpD4K+BNwjBapNAE0iADOgQwtcWgcEv9NsG/uOo7y9IOh3mnvwei9/+1s0+jiRJkiRJkiRJkiRJkiTpDnBXhapJkiRJkiRJkiTpznRy7eSeQLW8yK+55v6J+1nsLMK5s5DnTPZgcSzMHZyCLz4M01FBsbJyJVCtXIIHJuDwfTBeC5lhRNCoQlZAOoRBBqtboT4phzC0tAPFaK48Cl+rxnC+DQvjoW8BNCohk6GzDbUq1GshVC3rw0obZsdCeNsoz40CaFYgqoQ8iO52CFWrDUcPOYCLb0JlHfqjMIjVN+HsH8HlIzq6BHNvkTWxLz/6EXz/+yERTpIkSbpDJKurzB479vYFF4BjwD8GFnZdHwBvjr724yLwDG8ZqHbZ7LFjrHzj62Szs/tsKkmSJEmSJEmSJEmSJEm6U93VoWpRFN3uLdw2RVFcu0iSJEmSJEmSJOldUo7Le8bHLx2n1W9dc93K5iKd9jmYCuOtCkyOwZcehUEZ1jOIiwFJEw7Pwtw4xBFMNgnJZkA1gXICZaBWguWtELZWFJBmQAwHxmCpDcMUotFWywnUE1jfhvnx0C8qQkhaZxs22jA3AVkMMTAYQKsDExWIk9Ajz65sgwIYZpDkUMpgax3WzsMHOtCqhZq0D8t/vBOoFhXwmTM3cuIjGxvw+utw5MhNNJGk9444iqiXk33XSpJuj7knniTpXiM5eBX4feA3gN8EKtdxgwHwU+BZ4Bp5zkmnw9yT32Px29+6jhtIt0gUQ7mx/1pJkiRJkiRJkiRJkiRJV3VXh6oZLCZJkiRJkiRJknRneGDyASIiCsLnN4enDnN88XiYTNOQSFapQHlv+NrkaptODuUSHBiHsTF47JMw3YD+ELZ7MF6GDx2EyuiTr3IpBJld/qQo2fXvzvMCqiU4OAVrbRgOQrBapQQzTWi1oKiH0DWAmTqc3wphaKVkpz9AloX6KA4haOUCpmrQH8AggiSB7na4fxTBIIWNTaiuwcYKZGno8+FVODsJb5bhlR9Dd3Nnvw8vwtw1siauKopgZuYmGkjSe8vR+yZ57f/6P7vd25AkXUWyusrssWP7K86BPwWeAx4GHgHuYSe5eLcCuAS8DBwH+vvf0+yxY6x84+tks7P7XyTdCh/4JHz74u3ehSRJkiRJkiRJkiRJknTXuOtC1aIooigKoiji937v9/jd3/3d272l2yKO4ytnIUmSJEmSJEmS9E5q9Vq8sf4GrX7r6oURnF4/fWWYZimdMydhZTkEIETA3AFYWIAoolIMeGjsAp/9GExPhen7puHeXTkHlRLM1GB7ANt9KAooxSHwrJdBVkA12Qk2y/KwLo7hwBgsb0G3H/pUEhirQK8P9RpERQhQm2tAPwt9iaA0+oStlEMPuLAM0+Nw7xzUopD7UBQhHG1zC7JRqNu5RWgvw8LWzv7nO9BI4cET8PzfQDvfmbtvE7588gZ+ILs99hgcPHiTTSRJkqRbZ+6JJ0m615kc3CcEqz0HlAnBapOEv34bAi1CoFp6Y3tKOh3mnvwei9/+1o01kCRJkiRJkiRJkiRJkiTdEe66UDVJkiRJkiRJkiS9e04sneCZ154hL/Jr1nYGHU5vnL4yHp4/y0Z7GaphXM5gbGmZuJTwyCNjPNK4SLkHeQRdQjDavVMQF+FaHMGBZgg7q5Vgsg7rLbhwCQZTMIxgmECjMgpFi0Kw2hUxzI7B4iZMFFAqoNmA5XWoVEI9QLMCpSQEpeU5FPHe56qUQt+oD0kCeRzC24YtqBXQqYTxegvmenvXHloCfgKlZ+F3cjg1Axs1mOrBg2shSO6GPf44PPXUzXSQJEmSbq08Z/YP//DmeqTAm6OvW2j2D/6AxX/5L0ICsyRJkiRJkiRJkiRJkiTpPclQNUmSJEmSJEmSJN2Qdr/N068+TUGxr/pmpcl8c56lzhKkKfUzF6g2oD/6xCqNYaEKXzi8yOxCDBc6XG5dALMzUI7CoACmGyFQ7bJBCr0cDk5AEoVQtagIYWgUkBewlYbaRhmKKOQlTNZhqwszdSCCegU2WzA5BXG4RDmBShJC1QZDmGwASQhUq1Uh3YasDVtbkDZhuwtjWdhvksPFVSgNoD4M+9lah+Zz0Pwh0A97ioAPrV3jEGs1+MEPYHLy7WuiCI4ehbm5ff1cJEmSpHdL9dQpklZr78UycA8wBSRABmwAlwgBau+SpNWieuoU/YceevduKkmSJEmSJEmSJEmSJEm6pQxVkyRJkiRJkiRJ0g05uXZyT6BaXuTXXHP/xP0sdhbh3FnIcyZ7sDgW5g5OwRcfhumooFhZuRKoVi7BAxNw+D4Yr4XMMCJoVCErIB3CIIPVrVCflGF6AtIOFKO58ih8rRrD+TYsjIe+BdCohLVpOVyr16C3BittmB2DJB6FuRFC2JIEao0QggaQZVAbhtfDAVw8Bb0OVIBKHVoD+E8n4VcW4dIabK1BnsI3n+dKoNq+9Xrw/PPw3e9e50JJkiTp9sumpiiiiKhSwMPAI4RAtegtigtCsNrLwHGu/73zdSqiiGxq+p29iSRJkiRJkiRJkiRJkiTpHWWomiRJkiRJkiRJkm5IOS7vGR+/dJxWv3XNdSubi3Ta52AqjLcqMDkGX3oUBmVYzyAuBiRNODwLc+MQRzDZ5ErYQjWBcgJloFaC5a0QtlYUkGZADAfGYKkNwxSi0VbLCdQTWN+G+fHQLyqgWYGNNsxOQiWGIg4BaZdaoS6uht5EkGc7mQ8FUKRQymBrHdbOw9xmmFtqhgC1V/4IejH0NqA3Wnd0Cea6N3buPPEEfOc7IeFNkiRJeg8Zzs+y/X96mEbz5ZBCfDURsDD6+gLwU+BZ4NpZzjdk67OfZTh/4J1pLkmSJEmSJEmSJEmSJEl6V/gvLSRJkiRJkiRJknRDHph8gOhKvBgcnjocXqQpdDrh+1uYXG1DHgLOPjAFn56H/8NvwAPTMFGDIoHxOjx8PxyYCGFp5RK77gTJrk+58gKqJTg4BZUSZIRgtTiGmSak26NAtJGZOvSGMMx2rpVLkGWwvhlqSyUo5eF1UkC3B4MUBkMY9CHLw/r2FmydhXOvwsrZELh2eAMObcCwD6/8GLqb8CsbO/eKCvjMmRs58ZGNDXj99ZtoIEmSJL37kmiVmdr/k+7/+VGK6nUurgCfB74GzN76vbU/9znO/t6/u/WNJUmSJEmSJEmSJEmSJEnvqtLt3oAkSZIkSZIkSZLuLK1eizfW36DVb127OILT66fD66IgfeMknZULUIQ55g7AwkJIRgMqxYCHxi7w2Y/B9FQouW8a7t0VjFApwUwNen3oDkYhZ3EIPetlkBVQTUKwWhSFgDMIIWoHxmB5C7r90KeSwFgl9KrXQqBZuQRzDehnoS9RCFEDKAawvBnuFQPzjdCjKGCYQqcP7fbOXpcuQOXizni+A40UuAiD/w+0a798ZA8vwlx3Hz+Itz3zCGZmbqKBJEmS9O4qxReZqj1NHPWov/IK0eXQ4wpwDzABNIGEkJLcATaBS8AglG4f+Qi9f3KU/P9YYevsb5P1bkG6WgS9I0fIZt+BpDZJkiRJkiRJkiRJkiRJ0rvurgpVi0b/GEc7PBNJkiRJkiRJknQ9Tiyd4JnXniEv8n3VdwYdTm+cDoOzZxlevMDG2M58eWOZsaREvDDPI/d0eKRxkXIP8gi6hGC0e6cgLsK1OIIDzRB2VivBVB3WN+HCJRhMwTCCYQKNyigULbqS1xbEMDsGi5swUUCpgGYDltehUgn1AM0KlEZhaXkORbzTIstgZR0WDsB4HDIdIIS3Zetc+YQty6FY3Hseh5aAnwDPwhcjuPAwnJvYmb9vE758cl9H+/YeewwOHrzJJpIkSdK7I4lWrwSqxd1t6i++GN5TPwgcJCQt/6I6MAd8EFgETkH1b/+Wrc9/nrxRp/LBs6xv/yZZYRiaJEmSJEmSJEmSJEmSJGnHXRWqVhTFtYveZzwTSZIkSZIkSZK0X+1+m6dffZqC/X++0Kw0mW/Os7RxHs6coZ5DdQj90adQaQwLrYt84fN1ZhsDONfhcvsCmJ2BchQGBTDdCIFql/WGkA3gnokQuDaMICpCGBoF5AVspaG2UYYigjiGyTpsdWGmDkRQr8BmCyanIA6XKCdQSUKo2mAIkw0ggUoJalVIt6G7CNk4lGrQa0NlCP0YshjWl6DaD3vZWofmc9D8IdAP+6kAX30RTs3ARg2mevDg2ltnRuzb44/DU0/dTAdJkiTpXZQxWf3PxFEPgMYLLxA3hvAxwhvma4mAe4AZiE+kNF54ga3f/i3iqMdk9T+z1vvfAsk7t31JkiRJkiRJkiRJkiRJ0nvKXRGq9tu//dtE0S//85OFhYXbsJs7g2ciSZIkSZIkSZKu18m1k3sC1fIi39e6+yfuZ/Ev/zykkwGTPVgcC3MHp+CLD8P0cIVis7gSqFYuwQMTcPg+GK9BFAERNKqQFZAOYZDB6haUIqgnMDEJaQeK0Vx5FL5WjeF8GxbGQ98CaFTC2rQcrtVr0FuDlTbMjkESj8LcCCFsSQK1xk7gWZZBbRgC04br8OIKTK1AswLDOpwu4MKPoLIKW2uQp/DN57kSqHZZBHxo7RoH+JWvwNe+dvWaKIKjR2Fu7lo/DkmSJOmO0Sg/RylZBCDublP/25/BJ/jlHLQesAkMCGFrE0Bt13wF+ATUX3uR7qOPkjfqlJJFGuXn6KZ//51/EEmSJEmSJEmSJEmSJEnSe8JdEar2X//rf73dW7jjeCaSJEmSJEmSJOl6lePynvHxS8dp9VthkKYwGEClAuW9daQpK2vn6EztXNqqwOQYfOlRGJRhfbBJDCRNODwLc+MQRzDZ5EqSWTWBcgJloFaC5a2QJRYBgyEQw4ExWGrDMIVotI1yEkLX1rdhfjz0i4oQgLbRhtlJqMRQxDAcwKVWqIurITSNCPJsJ1CtAIoUShlsrcPa+ZDpUMqgn0K6Bn/2LMy/CdujNUeXYK57gwf/ox/B978f0t0kSZKku0REj2b5uSvjxovPET+U7Q1U6wCngPW3aDANPAg0R+ME4oeGNF56jq3f+AwAzfJzbKefpNiTwCZJkiRJkiRJkiRJkiRJer+6K0LVJEmSJEmSJEmSdHWtXos31t/YCUl7C1uDLc5snKGgAKAUl9jYXoeLF2FlOaSNRcDcAVhYCIlnABcvEOUFWQXmJ2CiDnEC/+AojFVhawADYKYCH5mHyugTqnIptCtG9092ZYoVBVRLcHAKtjZgO4M0C2tnmtBqQVHf2cJMHc5vwTCDUrLTv7MN65swOw6lEmRDGMSQFNDtQVICYhj0d+7b3Yats7C+Alkarn9qCf5qHtI+vPJjmF/d2WtUwGfO3MhPZWRjA15/HY4cuYkmkiRJ0p2lVnqVKBoAEHe3qW++CAd2FSwDrwH52zRYB34GHGFnXQXqKy/R7f5d8kadKBpQK73K9vBT79BTSJIkSZIkSZIkSZIkSZLeSwxVkyRJkiRJkiRJusudWDrBM689Q168XVrBjk7aYamzdGW8vXSBfnsFqruK2suQpDA5SZUhR6aX+ejHYHIKilEw2v1TcP9seF0AWQ5TVdhOIc8gBkoxZBn0MsgKqCYhWC2OIB1tNY5gegIGHej2Q6haJYGxCvT6UK+FULNyCeYa0M9CX6IQogZQDGB5M9wrBuYboUdRwDCFTh/a7Z3HW7oAlYs74/kOzHVh6ufwJy9Dd3PvmT28GOZvWBTBzMxNNJAkSZLuPPXSX1153XjpL4jndv0+0uHqgWqX5aO6BtAMl+KZjMbLf8HWr3/2yn0MVZMkSZIkSZIkSZIkSZIkgaFqkiRJkiRJkiRJd7V2v83Trz5NQbGv+kOTh3ZC1dKUqQurLDZ25ssZjA0gHmzwyNEZHplYodwLc3kK3XIIRntgEuIc8giSKISslWIocmj3YH0dzq/B9nSoyWJoVGAw3AlFY/QtT2B2DBY3YaKAUgHNBiyvQ6UCpVFtswKlUVhanu8EvEEIVFtZh4UDMB5Ddvl6Dtk6Vz41y3IoFn/hTJaAn8BH/xxeOArdiZ25+zbhyyf3dbRv77HH4ODBm2wiSZIk3TkiBpTi8HtF3N2mfulleGBXwSmuHah2WT6qf3g0jqF+7mW63V8jb9QpxUtEDCio3KrtS5IkSZIkSZIkSZIkSZLeowxVkyRJkiRJkiRJuoudXDu5J1AtL66eXFAv1znQOBCC1c6dpTYoqFagN/pUKY3hnip84VMwO7UEW509YQgFMDMzCjorwni6EYLWAOIIqkkIV8sHUEQhOC0qQhhaFIVgs600XG+UQ00cw2QdtrowUwciqFdgswWTU3A5h62cQCUJoWqDIUw2gAQqJahVId2G7iJk41CqQa8NlSH04xDstr4E1X7Yy9Y6NJ+D5g+BPlSAr74Ip2ZgowZTPXhw7Ur+2415/HF46qmb6SBJkiTdcUrxUniTDzReeIG4ke1M9oD162y4PlpXC8O4kdF44QW2fvu3ICooxUuk+X23YOeSJEmSJEmSJEmSJEmSpPcyQ9UkSZIkSZIkSZLuYuW4vGd8/NJxWv3WVdcMsgEXWuegfQ6mQpBapxLmFibg1z8BpQRag62QNAaUS3BgHOZr8CsHoVYOwWTDAsZrkGZAAYMMVregWYXmQaAE6RDiAoZDqIz6VWO4tAnlCSiVQjhboxLWpuVwv3oNemuw0obZsRDcVh4lnMUxJAnUGjuhZ1kGteFoX+vw4gpMrUCzAsM6nC7gwo+gsgpba5Cn8M3ngf7O2UTAh9b2cfBf+Qp87WtvPx9FcPQozM3to5kkSZL03pJEmwDE3W3qL74In9o1uXmDTTe5EqpGE+ovvUT30UfJG3XiqH0Tu5UkSZIkSZIkSZIkSZIk3S0MVZMkSZIkSZIkSbqLPTD5ABERBQUAh6cOc3zxeJhMUxgMQpJZeSd8rZJUaLa6dAooJ/CBcUgmYKoJX/ooUEC6DUUegssOz8LceMgJiwghZZeTzJoJVEtQL4VgteUtRjuBYQT3jMFyG7IhZClXQtoqMdQTWNuG+fHQLypC7402zE6GmiKG4QAutUJdXA2haUSQZzuBagVQpFDKYGsd1s7DBGHcTyFdgz97FubfhO3RmqNLMNe9wYP/0Y/g+98P6W6SpHfNaxc3+W+//7N91T7xO5/myMLEO7wjSXqfijIAGi+8QJwOIdk1N7jBnrvXJRAPUhovvMDWb/8WUTS80Z1Kt9elv4Kn/nf7q/0n/xHu+fg7ux9JkiRJkiRJkiRJkiTpPc5QNUmSJEmSJEmSpPeoVq/FG+tv0Oq3rl4Ywen101eGaZbSOXMSVpZD2lgEzB2AhQWIIirFgE+MrXLfUZifCNNFBB+/H8ZGoWcUIVRtrALpcBRkBpRLo/pRWRLvBJuVYphpwHoGWR5qshxmmrCxDoNtKBohnC0uYKoB57ZgmEEp2enf2Yb1TZgdh1IpBLINYkgK6PYgKQExDPqjrRbQ3Yats7C+MgpvAz61BH81D2kfXvkxzK/uOrICPnPmun8kOzY24PXX4ciRm2giSbpeg2HO6dX9JWIOhvk7vBtJeh8rEuLuNvUXXwzjbNdc5S1XXNvudaN+9ZdeovvooxSxfwan96isD2tv7L9WkiRJkiRJkiRJkiRJ0lX512SSJEmSJEmSJEnvQSeWTvDMa8+QF9cOhOkMOpzeOH1lPDx/lo32MlTDuJzB2NIycSnhkUfGeKRxkXIPeiUYjtbcP7UrUA2oluFgE+II8gK63RB2Fg2h14cBEMfQKO+sGeYhZG1uHFbaIQyt2w/haeO1sL7Xh8ZoX6USzDWgn4VANqJwDaAYwPImZBnEwHwDKkkIUBum0OlDu71z76ULULm4M57vwFwXpn4Of/IydDf3ntnDi2H+hkURzMzcRANJkiTpvSsrJmi88AJxOvqNogPUR5MTN9h097pO+BYPUhovvMDq3/ndG2wqSZIkSZIkSZIkSZIkSbqbxLd7A5IkSZIkSZIkSbo+7X6bp199el+BagDNSpP55nwYpCn1MxeoDnfm0xgWqvDPDi/yWwtdxjsdakMY70MpD9lr909CkkMEJBEcaIbcsMvjWi0Eql1chNUObHbC93Y/BKfleQg8owAimBkP6zsDiHJoNkbjNhQZ5NHlvcNUbRSqBhS7Pt3KMlhZh3IJxndfzyFb3zsuFveeyaEl4L/AR/8tzJzbO3ffJnz55L6O9u099hgcPHiTTSRJkqT3pnw5of6zl3Yu7A4xrgHT19lwerTustbOy/qLL5EvJ9e9R0mSJEmSJEmSJEmSJEnS3ad0uzcgSZIkSZIkSZKk63Ny7SQFxZXxfsLV7p+4n8XOIpw7C3nOZA8Wx8LcwSn44sMwHRUUKytcbl1NYHoSDs7BvVMh9CwDKiUoJZAOQ1BabwCrW+HavQswjMNcVAA5ZBF0+hDFIYStiCCOYKoBG1vQ7cJ4E+pVSDvQaUF9JvzXgSKgnEAlCcFsgyFMNoAk7KNWhXQbuouQjUOpBr02VIbQjyGLYX0Jqv2w1611aD4HzR8CfagAX30RTs3ARg2mevDgWrjvDXv8cXjqqZvpIEmSJL2nzT3x74n7KSyMLlwCPsjOG+0HgZ8B+8mJjkf1l+XArtDk+GzK3JP/gcVvf+tmty1JkiRJkiRJkiRJkiRJeo8zVE2SJEmSJEmSJOk9phyX94yPXzpOq98KgzSFwQAqFSjvrVvZXKTTPgdTYbxVgckx+NKjMCjDegZxMSBpwuFZmBsHIjgwCfHoU6UyMFYNr2sl6KVwYX10gyiEmB0Yg6U2MIRhFkLRAIZpCGUrjXrVq7C9BZ3tEI5Wr0HSgm2g04bZMUhiKI+CF+IYkgRqjZ0shiyD2ijcbbgOL67A1Ao0KzCsw+kCLvwIKquwtQZ5Ct98HujvnEsEfGhtHwf/la/A17729vNRBEePwtzcPppJkiRJd6dkdZXZY8fg4+yEqg0IQWj3jMZN4AjwGlcPVotHdc1d15ZG/S57GWZPHGPlG18nm529BU8gSZIkSZIkSZIkSZIkSXqvMlRNkiRJkiRJkiTpDtLqtXhj/Y2dkLS3sDXY4szGGQoKAEpxiY3tdbh4EVaWoSAkhc0dgIWFEPYFRKsr9GIol2B+HA434EuPwEwD+kPo9mCqDA8dhGoSWkRAJdm59+7XAHkB90zC6hYMB5ANw+1mmrCxHoLUqITacgSbA5jc9QlVowLdbdhohz2lEQyT0OtSK1yLqyE0jQjybCdQrQCKFEoZbK3D2nmYIIz7KaRr8GfPwvybIagN4OgSzHWv/+cCwI9+BN//fkh3kyRJkvSW5p54kqTbhePAF7jy+wCngJld4wNAY3R9/ZfawDTwIHsD1Qaj+t3j45D0O8w9+T0Wv/2tW/cgkiRJkiRJkiRJkiRJkqT3HEPVJEmSJEmSJEmS7hAnlk7wzGvPkBf5NWs7aYelztKV8fbSBfrtFajuKmovQ5LC5CRVhhyZXuXxT8DcZAgmu38K7p/dKa8mMF2DbgqdAUQZ1EqQ5dDLIM8hqUO5gDgKQWfDHKIYDozB8hYMhpBmIXxtvBYC04oGxEARQbMMwwxKo9C2pAJsQ5bB9ir0y2EvpRwGMSRFCHtLSkAMg36YL4rQe+ssrK9Alobrn1qCv5qHtA+v/BjmV3eeLyrgM2eu+8eyY2MDXn8djhy5iSaSJEnS3StZXWX22LEw6AM/BT4/mhwCJ4BPAJfDmpvAw0AP2CSEpFUIacm1X2iejdYPd1376eg+wOyxY6x84+tks7NIkiRJkiRJkiRJkiRJkt6fDFWTJEmSJEmSJEm6A7T7bZ5+9WkKin3VH5o8tBOqlqZMXVhlsbEzX85gbADxYINHjs7wyMQK5R7kEXRzSGJ4YBLiPFyLIzjQhFIM9VIIV1vegkuLkA9gkIS6ehmyItTF0ehmEUQRzI7BpRZ0+1CrQ7MRAtF6fWhWII9D2FoUh6C2iBCWlgPVDLIhnN6EyXFo1GC+EeqLAoYpdPrQbu8849IFqFzcGc93YK4LUz+HP3kZupt7z+zhxTB/w6IIZmZuooEkSZJ0d5t74kmS7q433c8CR4CF0bgFvAJ8jBCedlmNXw5R221ACFRr7bp2cdR/JOl0mHvyeyx++1s3vH9JkiRJkiRJkiRJkiRJ0ntbfLs3IEmSJEmSJEmSJDi5dnJPoFpe5Ff9qpfrHGgcoCgKinNnqQ0KqkMoCF9pDPdU4St/F35zaomxbofqEOpp+LqnCaUI4gKiAqbrIWjtskEagswOTkElCjUQcsUoIMtgtQPdQRjnUVg/1QihahQhdK1ehUErBLFdVklgvBrqowiyGGpD6JcgHcLFJSiXYHzXfrIcsvW942Jx7xkeWgL+C3z038LMub1z923Cl0/e6E9n5LHH4ODBm2wiSZIk3Z2S1VVmjx3bezEHngG2d11rAc8Bl+CamdL5qO459gaqbY/65nvLZ48dI1ldve69S5IkSZIkSZIkSZIkSZLuDqXbvQG9vbNnz+6r7oEHHniHdyJJkiRJkiRJkt5p5bi8Z3z80nFa/RakKQwGUKlAeW/NIBtwoXUO2udgKgSpdSphbmECfv0TUEqgNdiC0fVyCebG4cP3wUR1FJIGjNVCUNkgg8EQVrfC9XoFxpuQdqCXQVGENREhNG2lAwvjoW9RQKMC21vQ6YZ19RpEm7C2BdMTYU0MJBE0q1AtQToGtTLEFfhgOewjH0B3EbJxKNWg14bKEPpxCGFbX4JqP9xzax2az0Hzh0A/POpXX4RTM7BRg6kePLgW9nzDHn8cnnrqZjpIkt4Fc+NV/pvfOLzvWknSrTP3xJMk3e4vT6wC3wd+B6iPrg2BvwbeAA4Ck0ATSIAM6BBC1BaBwS/02wb+46jvL0g6Heae/B6L3/7WzT6O9O5ozsOv/bf7r5UkSZIkSZIkSZIkSZJ0VVFRFNf6733qNonjmCi6+j/viaKI4XD4Lu3o5o2Pj7O1tbXn2tjYGO12+zbtSJIkSZIkSZKkd1ar1+KN9TdCQNpVbA22eObVZygIH920+5v87YmfwsoyFIREsLkDsLCwk4QGbFz8Ob3uJuUE5sehMgZTTfifHwEK6PegyKESwQdnQ6BaHMHCDFdSxqoJVHfltS1vwdrmKCQtgplpyHNYbMNEA5qj2jSDC5th7fx4uF9cjELVtmF2CqoxrKxALwZqMDsGU/WwhyIKPQa9ncCz1Q1Ih9AcQHkU8vbKCkytQLMCwzqcLuDCj6CyCltrkKfwzedh7i3yG67pK1+Br33t7eejCI4ehbm5G2guSdJef/Ozv73dW5DeN05ubdzuLdy0B47cd7u3sG/J6ioffvTvvHWo2mWzwD8GFm7iRheBZ3jLQLXLsmaT159/jmx29iZu9O45On/wdm9BkiRJkiRJkiRJkiS9D5kBI+luVbrdG9DVmXknSZIkSZIkSdJ714mlEzzz2jPkRb6v+k7aYamzFAZnz7K9vky/uqugvQxJCpOTAFQZcnRmk199GA5OhGCyIoKH74Wxy+uKEIg2VoFhGsbl0qh2VJLEO7coCqiW4OAUtDZhkIZrcQyzTegNgFGoWjmBWgm2hzDMoJRAnIf+ABttWBiDogLxEAYDaHVgogJxEmrybCdQrSD0SXIoZbC1DmvnYYIw7qeQrsGfPQvzb8L2aN3RpRsMVAP40Y/g+98PDyhJkiTphsw98eTVA9UgBKH9PvAbwG8Cleu4wQD4KfAscI1fr5JOh7knv8fit791HTeQJEmSJEmSJEmSJEmSJN0NDFW7w0VR9LZzBq5JkiRJkiRJknTnavfbPP3q0xTs///PPzR5KISqpSmcOcNUDItjO/PlDMYWW8QH7uOR+3o80lih3INeCYajYIH7pmG8wpXEtGoJDjYhjiAvYLsLWQbFELZzGBZQTUKwWhxBOuoTxzA3DotbkGZQKUElCT2KAqIIogKmG7Ddhn4GpTiEupVGn0DFA1jbBEYBauUCpmrQH8AggiSB7na4dxSFALeNTaiuwcYKZGlY96kl+Kt5SPvwyo9hfnXnTKICPnPmun88OzY24PXX4ciRm2giSZIkvX8lq6vMHju2v+Ic+FPgOeBh4BHgHnaSlncrgEvAy8BxoL//Pc0eO8bKN75ONju7/0WSJEmSJEmSJEmSJEmSpPc8Q9XeA94qPO1qYWuSJEmSJEmSJOn2O7l2ck+gWl7k11xTL9c50DjA0ok/hzynlkN1GELTANIY7ikXfOH+88x+oAnnO5BDKYOtaghCu28S4hyyUUjaXHMUgAYkEdQr0OnARgs6UahrVKA/hHLMTphBAXkMs2PQ7oVQtbiARg26fajVQlklgbkGZHlYWkThfgCVDDoJXLoEk2Nw7xzUopCjUBTQ3YTNrbAHgHOL0F6Gha2dM5nvwFwXpn4Of/JyWLPbw4th/oZFEczM3EQDSZIk6f1t7oknSbrX+aa8TwhWew4oE4LVJgl/zTYEWoRAtfTG9pR0Osw9+T0Wv/2tG2sgSZIkSZIkSZIkSZIkSXpPMlRNkiRJkiRJkiTpHVCOy3vGxy8dp9VvXXPdoNflwuZ5mArjNIZOJbxemIBf/wSUSuu0NrdgdL1cgvlJuG8aPjAVcsJyQhBaOYZ0GELMegPYbEGzAtNTkHagl4W5KArBaFuj0IJmOQSkxXEITityiAEiyIeQZxAno9pK2EORhx4ZUM4gHc3HySjYrQ9JEsLashyGLagV4fmyHNZbMNfbex6HloCfwEf/HF44Ct2Jnbn7NuHLJ/fz07iKxx6DgwdvsokkSZL0PpXnzP7hH95cjxR4c/R1C83+wR+w+C//Rfil5g72l0uL72j/o/P+viNJkiRJkiRJkiRJkiTp/cNQNUmSJEmSJEmSpHfAA5MPEBFRUABweOowxxePh8k0hcEAKhUo7w1fq1y8RHNQMKjDgXGYqEOvCo0afPEhGOYh1CzupyQJHJ6FuXEgggOTUBoFmZWA8VHoWq0EvRQurEOlgDSDKIEDY7DYDuPKKGegGsOFNlTGoVSCAqhXoNeFcj3U1CuwsQmTUxBHEAG1BOIy5DlsRzDZgLQCk6UQuJYPIGvD1hakTdjuwlgG5QiSHC6uQmkA9VEA3NY6NJ+D5g+BfsiP++qLcGoGNmow1YMH18K9b9jjj8NTT91MB0mSJOl9rXrqFEnr2uHRt0PSalE9dYr+Qw/d7q1IkiRJkiRJkiRJkiRJkt4lhqq9B0TRTf1zIEmSJEmSJEmSdIu1ei3eWH+DVv8a4QERnF4/fWWYZimdMydhZTmklUXA3AFYWIAoolIMeGjsTT73YWjMwuWPCIoIPn4/jFXCsm4fej2Ya0IpvnIrKsnOrS+/vvwpQ17AwgS0N2E7C8FlcQyzTegNgFG2WzmBRgxr2zA/CmuLCiCHdBgC0ioxDIew0obZMaiWIInC3uI47CkaDx9EFcAwg8Yw9B8O4OIp6HVCUFqlDq0B/KeT8CuLcGkNttYgT+GbzwP9PcfJh9b28QP6ylfga1+7ys8lgqNHYW5uH80kSZIkvZ1saooiioiK4nZv5ZcUUUQ2NX27tyFJkiRJkiRJkiRJkiRJehcZqnaHK+7APziUJEmSJEmSJOn97MTSCZ557RnyIr9mbWfQ4fTG6Svj4fmzbLSXoRrG5QzGlpaJSwmPPDLGI42LlHthrlfAcJSIdv9UCFSDECw204SD82Hc7UJnGyolyDPYziHPIalDpQj5YXkBwxySGKYnoN+BNAtrKkmYL0a1UQHTDTi3FcLQSqNwtnIJNtowOwmlKKzrDGCxBR+cJaSnjb5lw50zGA4hzqCUwdY6rJ2Huc0wt9QMAWqv/BH0YuhtwOjxOboEc919/1j2+tGP4PvfDwlvkiRJkt4xw/l5tj7zGcb/63/dO1EG7gGmgATIgA3gEpC+O3vb+uxnGc4feHduJkmSJEmSJEmSJEmSJEm6Ixiqdgf7V//qX93uLUiSJEmSJEmSpF3a/TZPv/o0Bfv7j6I0K03mm/MsdZYgTamfuUC1Af3RJzRpDAtV+MLhRWYXYrjQuRJOVs6gXQ1BaPdPQpRDHkMcwYFmCECLgfEmVCI4ewm6OWQRFBHUy1DkECdhDUW4TgyzY9DuhVC1uIBGDbp9qNVCqFqpBHMN6GdQioEoXMsy2NyAiSlIkrDH8UYIUetmkJRCWFs2DPsrClheh8GbsLEC2Sg84fBGeMwLJXjlx9DdhF/ZdW5RAZ85cxM/qI0NeP11OHLkJppIkiRJ2o+zv/97PPDP//eM//9+Ag8DjxAC1aK3KC4IwWovA8eB/juzp/bnPsfZ3/t370xzSZIkSZIkSdL/n71/D7brPgz73u967Pd54bwAiAAhypQsyAFJ24pzG1uiFSVkTV83t63vzLXTae9ETqxWdzpz77RNI6Vp6sm9dzKZyT99mIpNdZJ62g4v2VLTOJqJ2tJ14nFMQaQoG6QlUAhJUQTOwXnsffZ77/W4f/w2cA5IEAABkiCJ72cGg7PW+q3f+q21zRnvs7G/kiRJkiRJet8yqvY+ZlRNkiRJkiRJkqT3l7M7Z68IqhVlcd1zji8cZ6O/Aa+9CkXB4gg25sKxw0vwF++HQ1FJubW1H1RLYX0Rjs3B6hKszodA2biAWgWSKAwtSxhNoNOH5WXIYiizEFaLZ9fPc9gdQyWCRhWKKJxfTWbRNYAIigyKHKLZia0qpEm4RlFAOdufZbDdCbG1tUWoJPtrGY9hZwfyS9cu4Affg/XO/vNY70NzCpyHyT+Bbv3Nz+z+DVgd3MAL8laiKDwQSZIkSe+6Yr7J5pP/IfHvdWk9f/ragyPgaPgz/A8/xe6x/xujrR8HkndmMRGMTp4kX1l5Z+aTJEmSJEmSJEmSJEmSJH2gGFWTJEmSJEmSJEm6QZW4csX28xeepzOeFcOmU5hMoFqFypXjtvY26Hdfg6Ww3avC4hz8wqdhUoHdHOJyQtKCj66EiFo0C6ctzkGUhPZAE5ifRcimOXRHsN2DNIIohbUmbHahzLkcaIsIHwjt9EKoLU1DAK1RhdEAKo0wrlGFXhtayyG0FhGCadUkRNUmGSw2gQSqKXQHMB5BowbTSpiz34b6BPrVMOfmNsz3r3yGJzaBp4E/gL8Ywev3w2sL+8eP7cEjZ9/+a3OFhx6Cw4dvcRJJkiRJ15NE2yzWfpfq+GUaL3znbZ1be+UslYcuUNbqdMa/SF4aQpMkSZIkSZIkSZIkSZIk3RqjapIkSZIkSZIkSUBn1OHc7rn9SNpV9CY9Xmm/QjkrlqVxSnu4C+fPw9bFEDKLgNU1OHo0lNGAaHuLUQyVFNbn4aNN+IUHYLkJ4wwGI1iqwCcOQ20WUIMQN6sm+9d/48+VWeAsm0BWQBzDcgt22yG6ls7GNxLYAXaG4frlbG4KmGZhXbUY9qaw1YWVOUhiqMwWEseQJFBv7q8tzyEZQK+ESRP2JrCUQVJCUkB3Ap1NOJKF4FpvF1rPQOspYDy7B+ALz8JLy9Cuw9II7t3Zv8ZNefhhePzxW5lBkiRJ0g1I4/Ms1Z8gjkY0T58mnmbhQBU4AiwALSABcqAP7AEXgAnEkynN06cpPtvgUOO/pz36ZbLi6G25F0mSJEmSJEmSJEmSJEnSh4NRNUmSJEmSJEmSdMc7s3mGJ198kqIsrju2P+2z2d+8vD3cfJ1xdwtqBwZ1L0IyhcVFamScPLTNw/fB6mIIhh1fguMr+8NrCRyqw2AK/QlEeYielSUMJ5CVIWxWOxA1K8oQR1udDyG06SxeVk1goQbZFKhCVEI1DvG1YQbZLLYWFyGm1u7CyiJUyjA+m8CFToivxbUwJxEU+f61S6CcQppDdxcunIHdFNZHUG3AKIWvfx+OvAznd6C3A8UUvvQtLgfVLomAj+9c56H/yq/Ar/3atcdEEZw6Baur15lMkiRJ0q1Kou3LQbV4MKTx7LPhX6LdCxzm6qXkBrAK3ANsAC9B47nnGHz609CEpfoT7A5/lbxcucrJkiRJkiRJkiRJkiRJkiRdn1E1SZIkSZIkSZJ0R+uOuzzxwhOUlDc0/sTiif2o2nTK0uvbbDT3j1dymJtAPGnzwKllHljYojKCIoJBAUkMdy+GqFkRQRzBWgvSGBppiKtd7MH3z4V90Ucgi6GMQnytyKFWgfzSciNYmQshtGkO9RhaTdhqQ9mEZBZfO9SCYRfGeZi3jCBNIZ+F0ZIlSBJIM5jE4bzBCJIUiGEyi6GVJQyG0HsVdrcgn87aCXvQqYeA2ne/CTs5LLVhNFvmqU1YHdzki/SNb8Dv/E4oy0mSJEm6zXIWa79LHIX/b795+jRxM4OfAKo3cHoEHAGWIT4zpXn6NL3PfoY4GrFY+112Rn8FSN695UuSJEmSJEmSJEmSJEmSPrT85okkSZIkSZIkSbqjnd05e0VQrSiLa/5pVBqsNdcoy5LytVepT0pqWQiXlcA0hiM1+JWfgZ9b2mRu0KeWQWMa/hxpQRpBXEJUwqFGCK1dMplCNoVjKzCXhvhZPFteFEFeQH8Mo+nsghFEMSw1QwQtJoTa6hWYDkOvoIigmsBqE4oi7CujMB9APIbNPRhPwvnrzTC+LMNaOntwcSf82dqFl8/B6HwIqgGs9+ET29Dbhuf+KXS34WPt/XuKSnjwlVt4kdpt+N73bmECSZIkSe+UZuUZ0mQDgHgwpPGDb8N9vDmoNgI2gddmf4/ecLwK3AeNHzxLPBgCkCYbNCvPvJvLlyRJkiRJkiRJkiRJkiR9iKW3ewGSJEmSJEmSJEm3UyWuXLH9/IXn6Yw7YWM6hckEqlWo7I+b5BNe77wG3ddgKYTU+rOAwNEF+PP3QZpAZ9K7HBaopLA6Dz9+DBZq+0GzuXoIpU1ymGSw3Qv7WzVYnIMshmkWwmQUQBKiaL0JVOIwLxE0q7DTh6IRomqNOoy3IF+HcvaJUKsaxpdFuGYOVHKYJpDnsLkLd63BfByOwWzcLpc/VcoLKDeufIYnNqH1NGTbMFh/8zO+fwNWB2/nVXmDKILl5VuYQJIkSdI7IWJE60D0rPnsM8SfyCE5MKgPvATsXmWCQ8C9QGu2nUD8iYzmc8/Q+9kHAWhVnmE4/UlK6u/KPUiSJEmSJEmSJEmSJEmSPryMqkmSJEmSJEmSpA+tzqjDud1z+5G0q+hNerzSfoWSEoA0TmkPd+H8edi6CCWhYra6BkeP7tfQOh1GCVQSODIP1TlYasEvngRKGI9CvCyN4WPLsDYfYmfLjdl8QC2ZhdFiqKdwcRqmL8NSyGNYm4PNLpBBlofrAdRj2BrC+vzsRiJoVqA/gPkW1OIQStvtwsJSuHYE1BOIK1AUMIxgsQnTKiymIbhWTGCwAfk8pHUYdaGawTgO69ndhNo4rLG3C61noPUUMIZHEtiqw2sL+8/32B48cvYWX8iHHoLDh29xEknSnaA7mvInP9q7obF/5q4F5uuV6w+UJF1WT18giiYAxIMhjb1nYe3AgIvAi4Qg9NXsAt8GTrJ/XhUaW88xGPwMRbNBFE2opy8wzH7qXboL6X1ktAfnn7+xsUfvh/rC9cdJkiRJkiRJkiRJkiRJdzCjapIkSZIkSZIk6UPpzOYZnnzxSYryrb7Nv68/7bPZ37y8Pdx8nXF3C2oHBnUvQjKFxUVqZJxa3uPH7ofDCyFWVkZw/10wd+mcMoTL5mowzqDMQ7QsCocASOL96csSaikcXoLOHkwKKLIQWVtuQXsXsilQDeMrcZgoyyGdhdbSSji3XoMWEFVgMoWtLqzMhfmTKFw/jkPwLZoPHxiVhLmaWVhLtgvPbsHSFrSqkDXg5RJe/wZUt6G3A8UUvvQtYByuX83hC8/CS8vQrsPSCO7dudyQuzkPPwyPP34rM0iS7iDnLvb5ld/6lzc09utf+lnuP7707i5Ikj5kGumfXP65+dwfEa8eeL/V59pBtUuK2bgm4Y0LEC/nNL/zR/T+/M9fvo5RNd0Rts/CP/o/39jYv/a/w10//e6uR5IkSZIkSZIkSZIkSfqAM6omSZIkSZIkSZI+dLrjLk+88ATl5XzZtZ1YPLEfVZtOWXp9m43m/vFKDnMTiCdtHji1zAMLW1RGMEohmwUDjh2C+SqXi2m1FA63II6gKKE7gmgKZQbDArISakkIq8URTGfzxDGszsNGD/IMpjlUE5ivw2AIZRNiQsRtuQnjfBZHA5LZJz/9NrQWIKtCnMFkAhsduGdlf30lYf5LsgziHNIceruw8yNYIGyPpzDdgT/8A1j/IQxn55zahNXBlc8yAj6+cwMPvdWCp54KN3w1UQSnTsHq6g1MJkmSJOndFjEhjcP7pngwpHHhO3D3gQEvcf2g2iXFbPz9s+0YGq99h8Hgz1E0G6TxJhETyktVaUmSJEmSJEmSJEmSJEmSboBRNUmSJEmSJEmS9KFzdufsFUG1opx9s38wgF4P5uaguV9Na1QarDXXQljttVepT0pq1RBNA5jGcKQGn/8pWFnahF4filmArBa6YMcWIS4gn0XSVluhCxYBSQStKvQn0O5APwrjmlUYZ1CJCQMBSihiWJmDCx0YjKHegFYTBiMYjaFZC0OrCVRS9kNpcWgTVMawtwvjethfKWG+GSJqgzzE16Z52I4iKEu4uAuTH0J7C/JpOO+nNuFP1mE6hu9+E9a3959xVMKDr9zCi9Tvw113wcmTtzCJJEmSpPdKGm+GNwJA8/Rp4ma+f3AE7L7NCXdn583et8TNnObp0/Q++xmIStJ4k2lx7B1YuSRJkiRJkiRJkiRJkiTpTmFUTZIkSZIkSZIkfWB0Rh3O7Z6jM+5cc9y53XO83H758vbZ7bP0XzkLWxdDgCwCVtfg6NFQFQOyImOrtwnDi7AKWQSjSjj/6AL8zH1AAttZj6QapqiksL4Ixw7BR5bCVAVQTUMobZJDWcBoAts9WEhhcQmmfRjlIWYWRZAX0JuFzFoVKKMQaltqQqcPy/UQamvUYNSB6loItQE00zB2NIVs1jRIShgAL78OR1ZgfRkqSbj1soTxGHZ24FICIS/gB9+D9QOPdb0PqwNY+lfwz74Dg70rn/H9G+H4TYsiWF6+hQkkSZIkvZeSKLwpiAdDGs8+Cz914ODe1c+5rj0uR9VoQeO55xh8+tMUzQZx1L2F1UqSJEmSJEmSJEmSJEmS7kRG1SRJkiRJkiRJ0gfCmc0zPPnikxRlcd2xo2x0RVQt+9GrtLsXoXZgUPciJFNYXLy8Kx/0mCYhQHZkDpotWGrBL30yxMiG4xBJI4WPH4L1OSCCtUVIkzBHCsxXw8/1NMTOXt+drSOHKIG1OdjowjSHahyO1WJ4vQvVeUjTEEBrVmHYg/4A5lvQqMNkAO0uHFoIobU0Dn2yVi38XM5DpQaNKnxktqbxKATZppVwH/021CfQn61zcxvm+1c+wxObwNPwqX8Jp0/BYGH/2LE9eOTsdV+Ga3voITh8+BYnkSRJkvSeiUKWuXn6NPE0g+TAsclNznnwvATiyZTm6dP0PvsZoii72ZVKkiRJkiRJkiRJkiRJku5QRtUkSZIkSZIkSdL7Xnfc5YkXnqCkvKHx9bTOemudzf4mTKc0XnmdWhPGb/xkpNOGuTlIEmpknDo05MeOw+EFiIAygvvvgrlLMbYSiiJsjzMghxioHogJHPw5AooSjiyGENpkGqJmcQwrLRhNgEoYW0mgGcPOENbnw8lRCa0q9IdQr4UAWx5DPoatLhxZgCQKAbZytr5GDahBHdjrhesVe9ArYdKEvQksZZCUkBTQnUBnE45kYWxvF1rPQOspYAxV4AvPwkvL0K7D0gju3Qn3dtMefhgef/xWZpAkSZL0XisT4sGQxrPPhu38wLHqTc558LzZfI3nnmPw6U9Txv7TNkmSJEmSJEmSJEmSJEnS2+O/PJMkSZIkSZIkSe97Z3fOXhFUK8riuuccXzjORn8DXnsVioLFEWzM7R+v5DA/gbg75oGfXOSB1kUqIxilkM2mP3YI5qtw6dK1FA63II5CLG0wgOkUyGBQQF5C0oBKuT8mKyCKYXUONvowzaGahvhaUYaQWTQLqB1qwms9yHJIZ3G2yuzTnHYX1hYgTaHMwnXzKUzT/bF5tn9/2SySlhSQ5tDdhQtnYDeF9RFUG+Fev/59OPIynN+B3g4UU/jSt4Dx/lwR8PGdG3ihfuVX4Nd+7a2PRxGcOgWrqzcwmSRJkqT3k7xcoHn6NPF09sajDzRmBxductKD5/XDX/FkSvP0abb/7F+9yUklSZIkSZIkSZIkSZIkSXcqo2qSJEmSJEmSJOl9rxJXrth+/sLzdMadsDGdwmQC1SpUrhy3tbdBv/saLIXtXhXyeHawhOYi/MJPXGR9dUjeG5CnUESQxZDEcPTQbGgUImmrrVkADUgiaFWhN4F2B7pxGNeoQF6E0FkcHVhMDCtz0B2FqFpcQrMOgzHU6yGqlqaw2oRxDmkMRGEfQDSF7e7+dOsNqCQwnsAkgiKH8TSMjyLY60GnC7UdaG+FAFsK3LsHnXoIqH33m7CTw1IbRrN5T23C6uAmX6hvfAN+53cgjq8/VpIkSdIHSnExofHt5/Z37AGXesl14BCw+zYmPDQ775LO/o+NZ5+j+GgCKze5WEmSJEmSJEmSJEmSJEnSHcmo2gFJktzuJbxtURSRZdntXoYkSZIkSZIkSTetM+pwbvfcfiTtKnqTHq+0X6GkBCCNU9rDXTh/HrYuQkkona2uwdGjoSoGRNtbjGKopLA+D3c3IKuG6Fkthb/4MWjGkA37JGWYopbAkUU4dgiOLYWpCkIIrRLDNIOyhNEEem1o1mB+AaZ9GBb70bU8h91xiLO1KpBHIcRWTaAsICasuchCEC2adcha1RBkK0soCigP9MmmWQilHT8Cc7N4WxFDlsN4A/pJiMblBbzwEqQTONrbP3+9Dyfa8HQdXvh9GOzBxw4856iEB1+5hRez3YbvfQ9OnryFSSRJkiS9H63+5teIx1M4OttxAbiH8AYI4F7g24Q3UNcTz8ZfUgAbBw6/OmX10f+Wja98+VaXLUmSJEmSJEmSJEmSJEm6gxhVO6Asy9u9BEmSJEmSJEmS7ihnNs/w5ItPUpTX/9Z9f9pns795eXu4+Trj7hbUDgzqXoRkCouL1Mg4eWibh++D1cX97/nnMdQr8MBRSGMYjGGzV7LTh3sWYX0OiGBtMcTNIHygMl8Nc9RTGE1hYyfsL4AogbU5ON8LMbRZ0404gu0+VOchTcOxRhVGA6g0wphGNcTZWsuhKxABlSTE14oCJhksNoEkhN3qNeh3oVGB6lyItfXbUM2hXkK/CpvbIay2OrryGZ7YhNbTkG3DYP3Nz/j+DVgdXPeleGtRBMvLtzCBJEmSpPejZHublccegz/DflRtQgihHZltt4CTwItcO6wWz8a1DuzbnM13yXdg5cxjbH3x18lXVt6BO5AkSZIkSZIkSZIkSZIk3QmMqr1BdOlbTh8ARuAkSZIkSZIkSR9k3XGXJ154gpIb+333icUT+1G16ZSl17fZaO4fr+QwN4F40uaBU8s8sLBFZQRFBIMDX+hPSvjUR6Aah+35Gqw34VATBhMY9SEqQ9TskmqyH2WLgKKEIwvQ6cJktvw4hrUmZBlUq2FfPYGogJ0hrM9DSegHUMA0g0oKtRj2prDVhZU5SGKoRPtzJgnUmweicDnUMxhPYNiB8yM4lEKlBpUCpgPY3IFaBo0shNx6u9B6BlpPAWN4JIGtOry2sH+Px/bgkbM39FK8tYcegsOHb3ESSZJu3o8fmeef/T8/e0Nj715uXn+QJAmA1d98lGQwgOeBzwOz9zy8BCwf2F4DmrP9u1eZ6BBwL1cG1Saz8Qe3n4dk3Gf10a+y8ZUvv3M3Ir3frH8K/oN/eWNjD330XV2KJEmSJEmSJEmSJEmS9GFgVO0qPgixsg9S/E2SJEmSJEmSpKs5u3P2iqBaURbXGA2NSoO15loIq732KvVJSa0Ko9mnHdMYjtTg8z8FK0ub0OvDbMoSmMwiaXcdgvkKl69cS2GtBXEE1QbkKQwGIV42KqAoIGmEkFochb+zPMTODi3ARj+Ey6IoxNcOtNioxmH+YRbOSROIixBTa3dhZREqJVCFbAIXOiG+FtfCnERQ5PtBtRIop5DmIZS28yOY78NmHaIY4gSefQ7+uAX3bsKFHejtQDGFL30LGM/WlcMXnoWXlqFdh6UR3Luzf52b8vDD8PjjtzKDJEm3rF5J+MTh+du9DEn6UEm2t1l57LGwMQb+BfAXZgcz4AxwH/tvhlrA/cAI2CNE0qrAAlB/w+T57PzswL5/weX3LiuPPcbWF3+dfGXlHbwj6X2k0oD1k7d7FZIkSZIkSZIkSZIkSdKHhlE1SZIkSZIkSZJ0W1TiyhXbz194ns64EzamU5hMoFqFyv64ST7h9c5r0H0NlkJIrV8Nx44uwJ+/L4TLOpNe+NI+IWC2Og/1JszX4MQyEMEkh2ke9h0UASSw04dRDmUEzRTKIkTL4iiMKSMghuU5yAqoJBCXkMShCwAQlXCoBcMujHNI43BemoZoW3cXkqUQaEszmMSQlDAYQZKG+SezmEBZwmAIvVdhdwvyadj/49vw8hL8KIXvfhO62zBZglE7NAwATm3C6uDN9/nxnRt4oVoteOopiOOrH48iOHUKVldvYDJJkiRJHzSrv/koyeDAG4o/AE4CR2fbHeC7wE9w+X0YEAJqb4yoHTQhBNU6B/adn80/k/T7rD76VTa+8uWbXr8kSZIkSZIkSZIkSZIk6c5hVO0qoih6z69ZluU1j9+ONUmSJEmSJEmSdLM6ow7nds/tR9Kuojfp8Ur7FUrC78jTOKU93IXz52HrIpSE8tfqGhw9GuJdAJ0OoyREzI7MQ3UOllrwiyeBEsajEEBLY/jYMqzNh1PzCFr1cF64HjSaUE1CXG08geEE9jpQacDaHGx0YVKEkBqEENruGKoR1KtQRJBEQBGufSk7VuahQVZGUI1htQl5sR9ju3Qr8Rg296Aowrnrs/WUJWRT6I+h291/ZpuvQ/X8/vZ6H5pTOP6n8E9ehcFe2P+x9v6YqIQHX3mbL+BB/T7cdRecPHkLk0iSJEn6IEq2t1l57LErdxbAk8AXgMZsXwd4BrgXOMysVv0WCmATeAnIDuwfzuYtrhy+8thjbH3x18lXVm7yLiRJkiRJkiRJkiRJkiRJdwqjam9wvbjZuyGKoiuiaVdbw+1YlyRJkiRJkiRJN+PM5hmefPFJirK47tj+tM9mf/Py9nDzdcbdLagdGNS9CMkUFhepkXFqeY8fux8OL+xHyu6/C+YunVOGSNlcDcZZCJxFQFzCXHV/2pgQMItmf8dV2O6EUBqEKNpKCy52QuQMZmOB7R6sL0KahmNxFOa/LJ+dM/skplWFShpib3kBOVDJYZqEUNvmLty1BvNxOAazcbv7c+QFlBtXPr8Tm8DTMP8H8IlPwHeOvPkZ378Bq4PrvhRvLYpgefkWJpAkSZL0QbX6m4+SDK7yhmIb+B3g32E/rJYBfwqcI4TVFoEWkBDe6PQJ8bUNYPKG+YbAfzeb9w2Sfp/VR7/Kxle+fKu3I0mSJEmSJEmSJEmSJEn6kDOqdsB//p//5+/JdcbjMdvb2+zs7PDaa6/x3HPPMZmEfyl4tcBaFEX8u//uv8tHP/rR92R9kiRJkiRJkiTdrO64yxMvPEHJjf2PhZxYPLEfVZtOWXp9m43m/vFKDnMTiCdtHji1zAMLW1RGMEohmzXbjh2C+SpcumQthcOtEDorSuiOYNSHSQZZHkJrcQwL9RBJu2Scw+o8bPT2564mcKgG0xzSBKIS6km41M4Q1ufDzzGQZxDPPnmpxXCxC/NLYR0R4by4EoJvwwgWmzCtwmIagmvFBAYbkM9DWodRF6oZjGPIY9jdhNo4xNp6u9B6BlpPAeNwzUfOwlYTXlvYv6dje2H/LXnoITh8+BYnkSRJkvRBk2xvs/LYY2894HXgMeDfBo4e2D8Bfjj7cyPOA09y1aDaJSuPPcbWF3+dfGXlBieVJEmSJEmSJEmSJEmSJN2JjKod8F5F1d5oNBrxrW99i//pf/qf+NrXvka3270cVouiiLIs+frXv85v//Zv82/9W//WbVmjJEmSJEmSJEk34uzO2SuCakVZ7B8cDKDXg7k5aIZyWqPSYK25FsJqr71KfVJSq4ZoGsA0hiM1+PxPwcrSJvT6UECaQ68W4mjHFiEuQngsjmC1BdEsZJZE0KrCoAsbGxBVwzgIIbS4hFo1jM1yiGJYmYONToiXJSXMNWBvDFTD+CSGagrDWaQtTcL1J3mYt5KG80pgqxvmq6VhLSVhzWkM0Xz4oKacXbuZhWtmu/DsFixthbVnDXi5hNe/AdVt6O1AMYUvfYvLQTWAag5feBZeWoZ2HZZGcO/OleG4t+3hh+Hxx29lBkmSJEkfUKu/+SjJYHDtQdvAbwM/C/wcUH0bF5gA/wL4A6C49tCk32f10a+y8ZUvv40LSJIkSZIkSZIkSZIkSZLuNFFZluX1h+m90u12+a/+q/+K3/iN32AymVzeX5YlURTx9/7e3+M/+o/+o9u4wlszPz9Pr9e7Yt/c3Bzdbvc2rUiSJEmSJEmSdKM6ow7nds/RGXfecsy53XP8/iu/f3n77PZZ+pMenD8PWxdDQSwCVtfg6FGIIrIiY6u3efl4FsGoEs4/ugC/eh/MJ/vXSIoQNTu0CMcOwbGlEFErCPsrcQicRQWMJrDdg0oOaQWyGEY5lBHcNQ+VBIooBM+KIuyPShiMoRpDY3bdvUHowF2KoA1LeL0LKy1oVWYRtQx292B9IcTdtvuwNwUSuGcF6pUwfwkMh7OJgGkG7R2YG0N/F3Z+BCNCJA1gOob/+Q9g/Yf7z+DUBvzbL97kC9lqwVNPhbrbW4kiOHUKVldv8iKSJOlqvv/tH9zuJUh3jLO99u1ewi27++Sx23btZHubH//0n71+VO2gGnA/8ABwhKsXnkvgAvAd4HmuCEVfT95q8b1vPUO+snLjJ4lT64dv9xIkSZIkSZIkSZIkSdL7kA0YSR9W6e1egK40Pz/P3/ybf5Nf/MVf5Fd+5Vd48cUXiaKIKIooy5K/8Tf+BoPBgL/9t//27V6qJEmSJEmSJOkOcmbzDE+++CRFWVxz3Cgb8XL75cvbWZHR3nwVuu3wBftLuhchmcLiIgD5oMc0CZGzI3PQbMFSC37pk1CWMBxDWUAaw90rcKQFMbC2COksfJYC89Xwcz2F0RRe392/ZJTCWhM2ujApQvgMQkStPYJGCmkavuPfrMKwD41mGNOoQj6EtBEibNUYVpuQF6ETUEahQ5bn0N+G+mrYXylhvgl5BoMckhSmediOonBvF3dh8kNob0E+Ddf7qU34k/UQVPvuN2F9+8B9lPDgKzf80r1Zvw933QUnT97CJJIkSZI+zFZ/89G3F1SDEEh7ZvanQgirLRLerGVAhxBUm97cmpJ+n9VHv8rGV758cxNIkiRJkiRJkiRJkiRJkj70jKq9T91333380R/9ET/3cz/Hd7/73SvCav/Ff/FfcOLECf69f+/fu93LlCRJkiRJkiTdAbrjLk+88AQl5XXH1tM66611NvubADRIqW13GCdXGdxpw9wctaTk1KEhP3YcDi/sR8ruvwvmLoXYSiiKsD3OQowsKqB6YN6DP0dAUcKRRWjPImoAcQwrLbjYCXNcGlsBdoawPh92RCXEZZgjjqAWQ78DeQ3K2acrrSpU0hB7ywvIgUoOI8LYJA7Rt0oSQm1lCeMx7OyEsRDO+8H3YL2zv/b1PqwOYOlfwT/7Dgz2rnxs92+E4zctimB5+RYmkCRJkvShVhSs/ON/fGtzTIEfzv68g1b+0T9i42/+p+HNnSRJkiRJkiRJkiRJkiRJb+C/Lnsfm5ub4xvf+AZ333335X2Xwmpf/OIXefHFF2/j6iRJkiRJkiRJd4qzO2evCKoVZXHNP8cXjlNSUpYl5WuvsjgMZ1/6k+ZwaAgrQ/j8oR7/jz+zyy/+GHy0CZUC0gLuWYD5aoibRSXUU7jnEBxuwbEFWKpBPQEyGIyhO4KsCBG0sMawHcWwOgdJNWxDiK8dqsF0VjaLSqilMMogy/fvO02hP4uXpQWkEex2968REdbQqoXYW6UCi01oLMHywiyiNoI0C+PLEvptqE/2r7G5DfP9K5/3iU3gf4dP/X9h+bUrjx3bg0fO3sSLeNBDD8Hhw7c4iSRJkqQPq9pLL5F0OtcfeBsknQ61l1663cuQJEmSJEmSJEmSJEmSJL1Ppbd7Abq2o0eP8uijj/LII48QRREQwmrj8Zhf+7Vf4w/+4A9u8wolSZIkSZIkSR92lbhyxfbzF56nM559wX46hckEqtVQFZvZ6m/RH+1B9zWYg14V8kv/Uy8lNBfhF07B+mKbPIc8hSKCLIYkhqOHZkMjiCNYbQFxCKAlEbSq0J9AuwPdOIxrVCAvIE3COZfFsDIHG50QNktKmGvA3hiYhduSJATSxjmkMRCFqFq7C60KxDGQwmQKW90wXy0NaykJx9MYovnw4UtJCLQVA+iVMGnC3gSWsnD9pIDuBDqbcCQL6+rtQusZaD0FjKEKfOFZeGkZ2nVYGsG9OyHmdtMefhgef/xWZpAkSZL0IZcvLVFGEVFZXn/we6yMIvKlQ7d7GZIkSZIkSZIkSZIkSZKk9ymjah8A//q//q/zuc99jqeffvpyWA3gX/7Lf8nXv/51/vJf/su3cXWSJEmSJEmSpA+yzqjDud1z+5G0q+hNerzSfoWS8IX6NE5pD3fh/HnYuhgKYhGwugZHj0IUEUURo70dSKCSwLEFqM+FYNpyA/7Cx4AcsiJcIymhlsCRRTh2CI4tQRRBAVRTqMQwzcKlRhPY7sFSAq0lmPZhWITxEZDnsDsO12pVII9C/GyxCVkW1kMEcQEUIcgGsNyEYbYfLStnEbjxDtSXYZpCPGvIbXTgnhWYPRJKIM/2n1mWQZxDmkN3Fy6cgd0U1kdQbcAoha9/H468DOd3oLcDxRS+9C1gvD9PBHx85wZeyF/5Ffi1X3vr41EEp07B6uoNTCZJkiTpTpatr9N78EHmf+/3bvdS3qT38z9Ptr52u5chSZIkSZIkSZIkSZIkSXqfMqr2AfEf/8f/MU8//fQV+8qy5O///b9vVE2SJEmSJEmSdFPObJ7hyRefpCiL647tT/ts9jcvbw83X2fc3YLagUHdi5BMYXGRGhn3rw44eQwOz89iZzHUK/DA0RA2i0roj2GrD3NVWGtCDKwtQpqEKVNgvhp+rqcwmsLruyGIViYQJbA2B+d7UJahHQYQR7Ddh+o8pGk41qzCsA+NZhjTqEI2hLQVtqtJWF8xexw5kBYQZbC3C8PZvVZKmG+GiNoghySFaR62oyhc6+IuTH4I7S3Ip+E+7t2DTj0E1L77TdjJYakNo9njO7UJq4O38woe8I1vwO/8DsTxTU4gSZIkSfte/e3f4u6/9teZf8O/V7qdup/7HK/+1j+83cuQJEmSJEmSJEmSJEmSJL2PGVX7gPjc5z5Hs9lkOBwCEEURZVnyh3/4h7z66qvcfffdt3mFkiRJkiRJkqQPku64yxMvPEFJeUPjTyye2I+qTacsvb7NRnP/eCWHuQnEkzYPnFrmgYUtkjEMKsAsUpaU8KmPQDUGyhBaW2vCJ9dCjKw7gnEfanEIkxGF0NklEVCUcGQRuh2YzJYex2GeLIPqpQBbAlEBO0NYn4eSEGyLyzBHHIXrbPWg1YBk1iJrVcJyR1MYTvavPQBeehWOH4H15RCFKwnrHI9hZydE2ADyAn7wPVjv7J+/3ocTbXi6Di/8Pgz24GMHnm9UwoOv3NBLcXXtNnzve3Dy5C1MIknvolc71x9zq+5efPevIUnSHaKYn+fl//F/INna4ujf+s849D//z1ABjgBLQEJ4E9QGLgBT2P03/012/8pfeecXE8Ho5EnylZV3fm5JkiRJkiRJkiRJkiRJ0oeKUbUPiFqtxoMPPsg3vvENoii64tj/8r/8L3zpS1+6TSuTJEmSJEmSJH0Qnd05e0VQrShn5bPBAHo9mJuD5n41rVFpsNZcC2G1116lPimpVWE0+6RhGsORGnz+p2BlaRN6fShCeGwyC6PddQjmK1y+ai2FtVYInAEsNYBqiKHtFVAUkDSgOougFSVkRYioLS/ARj9EzaJZfO1Af41qHOYfZpDlkCYQF5Cm0B/AfCtE3soKtLtwaGE2RxTiba1aCJ2li1CZQKMCHynDtccjaNRgWgnX77ehPoH+LOi2uQ3z/Suf94lNaD0N2TYM1t/8ety/AauDt/86XhZFsLx8CxNIkiRJ0ptF8YSF7X8Kf50QVIuuMqgELsDC977BhZ/4z8iWP/LeLlKSJEmSJEmSJEmSJEmSpBmjah8gH//4x/nGN77xpv3PPPOMUTVJkiRJkiRJEgCdUYdzu+fojDvXHHdu9xwvt1++vH12+yz9V87C1sXwhfgIWF2Do0dDsAvIioyt3iYML8IqZBGMKuH8owvwM/cBCWxnPZJqmCJNYW0RFhtw8jAkMeSEONpCDfICmPXcpjlkY6jXoDeCcR5iZkUBSbIfXwMoY1ieC/NUEojL/bkhBNEOtWDYDfOkMZRRWE+7C60KFGmYdzyG7S58dGV2LiHgRhHiadQgzaAy+1Sl2INeCZMm7E1gKQuBtqSA7gQ6m3AkC8G13i60noHWU8AYHklgqw6vLezfy7E9eOTs23mVr+Khh+Dw4VucRJIkSZIuyWlWnuHw2b9H8rnxtYdGwFFIjo44/tKX2PjZ/4TB9Ge4Mn0tSZIkSZIkSZIkSZIkSdK7z6jaB8ja2tqb9pVlyZkzZ27DaiRJkiRJkiRJ7zdnNs/w5ItPUpTFdceOstEVUbXsR6/S7l6E2oFB3YuQTGFx8fKufNBjmoSQ2ZE5aLZgqQW/9MkQERuOoSyAFD5+CNbnoIihVYfq7FOJmBBUq86+X59lsNuH7R4spNBohvPOd0PPLQLyHHbHIY7WrEAeQRIRgmxlmBOgzCGeBdSqMaw2Q7gtIuyb9eEY7gJrYX8MLFRDXI0qpEm4HuX+oxiOwt9JAWkO3V24cAZ2U1gfQbUBoxS+/n048jKc34HeDhRT+NK3gFmDoJrDF56Fl5ahXYelEdy7E9Zx0x5+GB5//FZmkCTpQ+OV7T7/4Jvfv6Gx/6+/9AlOrLTe5RVJ0gdPEm2zWPtdquOXaX739Ns6t/nd0yz8uW9Sr5+lM/5F8nLlXVql9CGxcw6e/v/c2NjPfRmWP/burkeSJEmSJEmSJEmSJEn6gDOq9gGysnLlPzKMooiyLPnRj350m1YkSZIkSZIkSXq/6I67PPHCE5QHS2DXUE/rrLfW2exvwnRK45XXqTVh/MZPDjptmJuDJKFGxqlDQ37sOBxe2A+V3X8XzF2KsZVQFGF7nAE5xCXMVfenjNkPqkWECFo1hUYSGmkQgmmrrTAfs7BaHMFODyoLkKYh4hZHYf7L8rD/0icgrSpU0hB6ywvIgUoeGmejPahUYHUJ6hFkwHgCwxLyLuS1cB2ATg8GQ6jtQHsL8mm4xL170KmHgNp3vwk7OSy1YdZg49QmrA6ufKQR8PGdG3iRWi146qnwgK4miuDUKVhdvYHJJEm6M7QHU77+nddvaOxf/dl7OGHrR5KukMbnWao/QRyNaJ4+TTzNwoEqcARYAFpAQniD1Qf2gAvABOLJlObp0xSfbXCo8d/THv0yWXH0ttyL9IEw3IU//v/d2Nj/07//7q5FkiRJkiRJkiRJkiRJ+hAwqvYB0u12r7p/b2/vPV6JJEmSJEmSJOn95uzO2SuCakVZXGN0cHzhOBv9DXjtVSgKFkewMbd/vJLD/ATi7pgHfnKRB1oXqYxglEI2m/7YIZivwqVL11I43Aqxs6KEwSDEyFJgMIa8DMG1YhZEAxhloRu2Ogc7o/3r19/QEmsm0C5hZwjr8+GSMZBnEM8+8ajFcLEL80th/gioJxBXQuxtGMFiE6bVEHLr9iEqII3D8TyC3i5UB9CtQh7D+YuwsQ21DI729tez3ocTbXi6Di/8Pgz24GMH1huV8OAr130Z3lq/D3fdBSdP3sIkkiRJknRjkmj7clAtHgxpPPvsrCYNHCa8wXqjBrAK3ANsAC9B47nnGHz609CEpfoT7A5/lby0YilJkiRJkiRJkiRJkiRJevcZVfsA2dzcvOr+yWTyHq9EkiRJkiRJkvR+U4krV2w/f+F5OuNO2JhOYTKBahUqV47b2tug330NlsJ2bxYSA6CE5iL8wk9cZH11SN4bkKdQRJDFkMRw9NBsaBQiZqstiGYxsySCVhWmQ+h0oDubN4nDmDSGahICbRFADAuN/bXFJaQVmORhuxqFENowgyyHNIG42D9eSSEpQ2xtqwsrcyHylkSzAFscrhnNhw9ISmA8haQHcQMqTdgaQm0Y5qtncLGAzZ2wvXQg+AZwYhNaT0O2DYP1N78m92/A6uAaL9r1RBEsL9/CBJIkSZJ0o3IWa79LHIU3Ps3Tp4mbGfwEUL2B0yPgCLAM8ZkpzdOn6X32M8TRiMXa77Iz+itA8u4tX5IkSZIkSZIkSZIkSZIkjKp9oDz33HNX3d9oNK66X5IkSZIkSZL04dAZdTi3e24/knYVvUmPV9qvUFICkMYp7eEunD8PWxdDQSwCVtfg6NEQ7AKi7S1GcQiSrc/D3Q3IqpAXIUj2Fz8GzRiyYZ+kDFPUEjiyCMcOwbGlMFVBCJ5VYphmUJYwmkCvDY0atOZgMoBxHkJqURnCaJ1RmLNVCbG2JAqxtCyDGCiysLYsC/d5qAXDbpgnjUPMLU1hdw/WF8IcaQyDCWx04J4VmD0SSiDP9p9ZlkGcQ5rD9g9h9zxUlyFZDJG1eAIXzod7qWXQmN1Xbxdaz0DrKWAMjySwVYfXFvbnPrYHj5y9xRf+oYfg8OFbnESSJEmSrq9ZeYY02QAgHgxp/ODbcB9v7qCNgD1gQoitLQD1A8erwH3QePFZBp/+NEWzQZps0Kw8w2D6r737NyJJkiRJkiRJkiRJkiRJuqMZVfuA2Nvb45//839ONPuS20Grq6u3YUWSJEmSJEmSpPfCmc0zPPnikxRlcd2x/Wmfzf7m5e3h5uuMu1tQOzCoexGSKSwuUiPj5KFtHr4PVhdD3Awgj6FegQeOzgJlY9jslez04Z5FWJ8DIlhbDAE0CB84zFfDHPUURlPY2An7SyBKwnnnuyHCdulaRQHtIVTnw1xRmHr/A4wyBNjiMgTTqjGsNkP0LSJE1aII8hz621BfDfsrJcw3Q0RtkEOSwjQP21EU4mgXd2HyQ2hvQT4Nl/uxP4Y/WYdsCue+DTsRvLQO927ChR3o7UAxhS99CxiHc6o5fOFZeGkZ2nVYGsG9O/v3eFMefhgef/xWZpAkSZKkGxIxolV55vJ289lniD+RXxlU6wMvAbtXmeAQcC/Qmm0nEH8io/ncM/R+9kEAWpVnGE5/kvKKApskSZIkSZIkSZIkSZIkSe8so2ofEF/96leZTqdEUURZllf8/dGPfvR2L0+SJEmSJEmS9C7ojrs88cITlJQ3NP7E4on9qNp0ytLr22w0949XcpibQDxp88CpZR5Y2KIyCrGywYFmW1LCpz4SAmYA8zVYb8KhJgwmMOqH0Fn1wBfsq8l+RCwCihKOLECnC/nsQBLBaotZZS2MiyOICtgZwvp8iJ1NM6jHkOWzCadhvnL2qUarCpUUyiLE1fLZvY2AfgeSOATfKkm4VFnCeAw7O2EshPN+8D1Y7+zfw3ofVgew8DL8r8/BYC/sn1yAUTvMD3BqM4w7KAI+vnMDL1KrBU89BXF89eNRBKdOgf+DKpIkSZLeI/X0BaJoAkA8GNLYexbWDgy4CLwIvFXrexf4NnCS/fOq0Nh6jsHgZyiaDaJoQj19gWH2U+/SXUiSJEmSJEmSJEmSJEmSZFTtA+FHP/oRf/fv/l2iKLrq8QceeOC9XZAkSZIkSZIk6T1xdufsFUG1ojzwDfbBAHo9mJuDZiinNSoN1pprIaz22qvUJyW1KoxmnwZMYzhSg8//FKwsbUKvf/lL8SUwmUXS7joE8xUuX7mWwlorBNCqDcjTcPk8h1EBRQFJI4TP4ij8neWQJHBoAbZH+8uuv6El1kxgJ4JRFs5J4xB1m2ahLwaQ5rA7hsbcLMIG1BOIK+HawwgWmzCtQjWFdhfGI2jUYFoJUbV+G+oT6FfDnJvbMN+/ci0nNoGn4fhzMPxpLlfiPtbeHxOV8OArN/TyXV2/D3fdBSdP3sIkkiRJkvTOaaR/cvnn5nN/RLx64L1nn2sH1S4pZuOaQCvsipdzmt/5I3p//ucvX8eomiRJkiRJkiRJkiRJkqR3w2/8xm/c7iV8YPwb/8a/8aFuVhlVe5/b3Nzk4YcfptvtEkURZVm+acxnPvOZ27AySZIkSZIkSdKt6Iw6nNs9R2fcecsx53bP8XL75cvbZ7fP0p/04Px52LoYqmcRsLoGR49CFJEVGVu9TRhehFXIIhhVwvlHF+Bn7gMS2M56UIekCCGy1XmoN2G+BieWw7yTHKZ52HdQRJhjpw+jHMoImimUBcTJfvisjIAYFhr758YlpJUwN0AlgnoKwxzGs6haBORFiKHVZhG2ooCtLqzMhchbEoXbj+PZOfPhQ4+SEGcrBtArYdKEvQksZSHWlhTQnUBnE45k4Rq9XWg9A62ngDGsAfdvwHeOvPk1uX8DVgc38gq/hSiC5eVbmECSJEmS3jkRE9J4E4B4MKRx4Ttw94EBL3H9oNolxWz8/bPtGBqvfYfB4M9RNBuk8SYRE0qq79TyJUmSJEmSJEmSJEmSJAmAv/N3/g5RFN3uZXwgHDt2zKiabo//7X/73/jiF7/ID37wgyuCagf/4221WvzCL/zC7VqiJEmSJEmSJOkmnNk8w5MvPklRXvub6aNsdEVULSsy2puvQrcNB0Nn3YuQTGFxEYB80GOaQCWBI3PQbMFSC37pkyEiNhyHAFoaw90rcKQFCZBH0KqH8yAcbzShmoS42ngCwwnsdaDSgLU52OjCpAghNYA8h90xVCOoV6GIQgAtTSDLIAaKDCpp2C4jONSEYTeE0yLCOVEEu3vwkRbkaTgvm8BGB+5ZIdTTZn/l2f6jyDKIc0hz6O7ChTOwm8L6CKoNGKXw9e/DkZfh/A70dqCYwpe+BYz353nkLGw14bWF/X3H9sL+W/LQQ3D48C1OIkmSJEnvjDTehCi8wWqePk3czPcPjoDdtznh7uy8etiMmznN06fpffYzEJWk8SbT4tg7sHJJkiRJkiRJkiRJkiRJerNLjSZd3Z0QnjOq9j4znU75p//0n/K1r32Nf/JP/slb/kdaliVRFPGrv/qr1Ov193iVkiRJkiRJkqSb1R13eeKFJyi5/oc09bTOemudzf4mAA1SatsdxslVBnfaMDdHLSk5dWjIjx2HwwshUlZGcP9dMHcpxFaGgNlcDcZZCK2VBcQlzFX3p4wJQbVo9ndche1OiJ4BxDGstOBiJ8wBs7HAdg/WFyFNw7GIAx9KlOE7+1EJRRzmXm3un19GYUw8ge4EqmshnlYpYb4ZImqDHJI0xN7yLETYyhIu7sLkh9DegnwarnnvHnTqIaD23W/CTg5L7fA9f4BTm7A6uPJxVnP4wrPw0jK067A0gnt3wvpu2sMPw+OP38oMkiTpOlq1hJ8+ceiGx0rSnS6J9gCIB0Mazz4LP3Xg4N5NTrrH5agaLWg89xyDT3+aotkgjrq3sFrpQ6g6B8f/3I2PlSRJkiRJkiRJkiRJ0jXdCdGwm3WnBOeMqh3w+7//++/ZtcqyZDAYsLe3R6fT4U//9E/57ne/y7e//W329vYuj7n0H+ml/4M8+B9ttVrlb/yNv/GerVmSJEmSJEmSdOvO7py9IqhWlMX+wcEAej2Ym4NmqIwdXzjORn8jVMVee5XFYcnGge/QVnKYn0AcwQOHejxw94TKCEYpZLOpjx2C+SpcumwthcOtcE5RQncULp1NIctDaC2OYaF+ZURsnMPqPGz09ueuJnCoFuJmaRJCafUkXGpnCOvz4edpBvU4zA/AFHL2529VoVEN4/ICxgXUMxik0O2EMWuLUJnNXZYwHsPOTpgHwnk/+B6sd/bXvN6HE214ug4v/D4M9uBjB+4pKuHBV67+WkXAx3eufuwKX/sanDjx1sejCE6dgtXVG5hMkiTdinvX53ny3//zt3sZkvTBEYV3VM3Tp4mnGRzsTU5ucs6D5yUQT6Y0T5+m99nPEEXZza5U+nBa+3H4wj+73auQJEmSJEmSJEmSJEn60LhTwmFv150UmzOqdsDP//zP3/YX/+B/lG8Mqh0cE0URX/7yl7nnnnve0/VJkiRJkiRJkt5aZ9Th3O45OuPOW445t3uOl9svX94+u32W/qQH58/D1sVQDIuA1TU4ehSiiPaozWgygOFFWAqhsSKeTVBCWoFfug8OL7QZF5ClUEaQxZDEcPTQbGgUQmqrLSAOQbEkCkGzYRc2NiCqQj6bOwbiEmrVsKQshyiGlTnY6ISwWVLCXAP2xkA1jE9iqKYwzMI5aQJxEYJpl34NnxTQHcPcLO5WiaEaQ7UGRQEUEC1AXIVKCtttGI+gUYNpJVy734b6BPrVMOfmNsz3r3zeJzah9TRk2zBYf/Prcf8GrA5u9BW+iiiCRx6Bw4dvYRJJkiRJuk3KhHgwpPHss2E7P3CsepNzHjxvNl/juecYfPrTlLH/XE2SJEmSJEmSJEmSJEmS9O7xX6ldxe2sDR6Mur1xHQeP/aW/9Jf4W3/rb71n65IkSZIkSZIkXduZzTM8+eKTFGVxzXGjbHRFVC0rMtqbr0K3DbUDA7sXIZnC4iJ5kTMa7EEClQTumodWK8TLlhvw+XtCiCwrgFlMrRnBkQU4dgiOLYX2V0GInVXiEDgrgdEEtnvh0h87Ec4d5SHAVo0hL6A3DvG1iFmYLYbFJmRZWA9RiKZRzMYAh1oh1DbOIY3DeUUeYmi1GIoIohy2uiHSNl/dPzeKII2AeWgwW2sJxR70Spg0YW8CS1mIuiUFdCfQ2YQjs7G9XWg9A62ngDE8ksBWHV5b2H/Ex/bgkbO39LLDQw8ZVJMkSZL0gZWXCzRPnyaeZmFHn/BGDGDhrc66joPnzcLX8WRK8/Rptv/sX73JSSVJkiRJkiRJkiRJkiTp7TvYbLrT3M6O1u1kVO0qbvd/CFf7P8ZLayrLks985jM88cQTt32dkiRJkiRJkqSgO+7yxAtPUHL9DxvqaZ311jqb/U0AGqTUtjuMk6sM7rRhbo5mUvKplTEnj8Hh+RAfy2OoV+CBoyFsFpXQH8NWH+ZqsNIMwbH1RUhnc6eEeFlYB4ym8PpuCKKVQJTCWhM2ujApIJ79GjoqoT2CRgppGsY2qzDsQ6MZxjSqkA8hbYRgWjWG1WaIsl2KsUUR7O7B0TnIKhAD+TTE2YoD18tyOPgoh6MQTktz6O7ChTOwm8L6CKoNGKXw9e/DkZfh/A70dqCYwpe+BYzDHNUcvvAsvLQM7TosjeDenbC2m/bww/D447cygyRJkiTdVsXFhMa3n9vfsQeszn6uA4eA3bcx4aHZeZd09n9sPPscxUcTWLnJxUqSJEmSJEmSJEmSJEnS23SnhsXuZEbVruL98h/CwWjapTV94Qtf4L/8L/9L6vX6W50mSZIkSZIkSXqPnd05e0VQrSiL8MNgAL0ezM1Bs3n5+PGF42z0N0I47LVXWRyWbMztz1fJYX4SImMPHOrxwN0TkjEMKsBs6qSET30kxMsoQxxsrQmfXAvxsu4IhgOoxVCWQATVA+G2CChKOLII3Q4MCR8axDGstOBiZ3bebGwF2BnC+nzYEZUQl2GOOArX6Xcgr0E5+/ShVYVKCmUR4mo5EE9gZw/mDoVA25FmiL6NJzCJIIphMII0DvdRFLCxDZUtaG+FCFsK3LsHnXoIqH33m7CTw1IbRrP7O7UJq4MrX6cI+PjODbygrRY89VR4GFcTRXDqFKyuXv24JEmSJH1ArP7m14jHUzg623EBuIf9AvW9wLe5/F70muLZ+EsKYOPA4VenrD7637LxlS/f6rIlSZIkSZIkSZIkSZIk6S1FUURZlkRRxG/91m/xV//qX73dS7ot4ji+/CzuJEbV3kcORtQuufR/kB//+Mf5B//gH/CLv/iL7/WyJEmSJEmSJOmO1Rl1OLd7js64c81x53bP8XL75cvbZ7fP0n/lLGxdDOG0CFhdg6NHQ5ALaI/ajCYDGF6EJRikUFxqeJWQVuCX7oPD822yHPIEiij8ATi+BK0al1Nu1TTE0C794n+pAUtViArYK0KcLGlAdRZBK0rIitANW16AC/2wDSG+dqgG0zwEz6ISaimMhpDN9kGIovUHMN+CtIA0gt0uLCyFa0RAPYG4Eq4/jGCxCdNqONaoQRKFNRZxGNPdhnEG+exZnL8I7Q042tt/3ut9ONGGp+vwwu/DYA8+duD1iEp48JUbeYXfQr8Pd90FJ0/ewiSSJEmS9P6WbG+z8thj8GfYj6pNCCG0I7PtFnASeJFrh9Xi2bjWgX2bs/ku+Q6snHmMrS/+OvnKyjtwB5IkSZIkSZIkSZIkSZIkXcmo2lVcLW72XjlY9YvjmAcffJAvfvGL/PIv/zJxHF/jTEmSJEmSJEnSO+nM5hmefPFJivJa3xoPRtnoiqha9qNXaXcvQu3AoO5FSKawuAhAXuSMBnuQQCWBu+ah1YIkhuUGfP6eEBnLCsiASg7NCJYWYbEBJw+HsTlhzEIN8pzLlbVpDvkIanXojWCcQ2MWN0uSED27pIxhZQ42OlCWkJQw14C9MVANkbIkCYG0cQ5pDEQhqtbuQqsS4mykMJnCVjfMV0tDNK0kHE9jiObDhxMlcHEbllNoNmEKtPegPoR6BP0qDEawuQProyuf94lNaD0N2TYM1t/8ety/AauD675sby2KYHn5FiaQJEmSpPe/1d98lGQwgOeBzwPV2YGXgOUD22tAc7Z/9yoTHQLu5cqg2mQ2/uD285CM+6w++lU2vvLld+5GJEmSJEmSJEmSJEmSJEmaMar2BgejZu+1hYUFfvzHf5wHHniAz3zmMzz00EOsr1/l22CSJEmSJEmSpHdVd9zliReeoOTGfmdcT+ust9bZ7G/CdErjldepNWH8xt/Cd9owNwdJQjMu+dTKmJPH4PA8REAeQ70CDxwNobWohP4YtvowV4X1RgigtepQnc0dE4Jq1QSoQJbBbh9292C+EkJo63NwvhtaYREhvrY7DpGzZgXyKMTPFpvh/EoCRBAXQAHlLMC23IRhFuaAsBaA8Q7Ul2GaQjyFySQE2u5Z4XLkrQTybP9RZBnEOWQDuPBD2ChgcQGaSVgLU3jldahOoZGF2FtvF1rPQOspYAyPJLBVh9cW9uc9tgePnL2hl+2tPfQQHD58i5NIkiRJ0vtXsr3NymOPhY0x8C+AvzA7mAFngPuAZLavBdwPjIA9QiStCiwA9TdMns/OP/AekH8xuw6w8thjbH3x18lXVt7BO5IkSZIkSZIkSZIkSZIkyajaFZ5++un37FpRFJGmKbVajcXFRdbX11lYWLj+iZIkSZIkSZKkd93ZnbNXBNWKstg/OBhArxfiaM3m5d3HF46z0d+A116FomBxBBtz+6dVcpifQNwd88BPLvJA6yLJGAYVYDZ9UsKnPgLVGChDvGytCZ9cC0G0wQB6Q5ivhuNEIapWnX3JPQLiOATXmkmIpUGIlK229s+JgDiCnR5UFiBNQ7SsWYVhHxqz22pUIRtC2grb1SRE34rZenMgLSDKYG8XhrXZvZYw3wwRtUEOSQrTPGxHUbjWxV2Y/BDaW5BPQ1BuB+gBcQLP/zF8pwb3bsKFHejtQDGFL32Ly1/Er+bwhWfhpWVo12FpBPfu7EffbsrDD8Pjj9/KDJIkSZL0vrf6m4+SDAb7O/4AOAkcnW13gO8CP0GIp11S580RtYMmhKBa58C+87P5Z5J+n9VHv8rGV7580+uXJEmSJEmSJEmSJEmSJOlqjKod8OCDD97uJUiSJEmSJEmS3mWdUYdzu+fojDtvOebc7jlebr98efvs9ln6kx6cPw9bFy/HyVhdg6NHQykMaPd3GA1fg9Vw3iCFIp5NUkJagV86dZHDKz2y/pA8gSIKfwCOL0GrxuWcWzWBtVYIoAEsNmCuEq7dm4a42VwNijKE0wBGGaQRLC/A9mj/nuoxV2gm0C5hZwjr8+GaMRCXYb44gloMWz1oNSCZnd+qhAbcaArDyf58A+ClV+H4EVhfhkoS5ixLGI9hZydE2ADyAn7wPVg/8BLc1Q1//2AML/4fMNiDyRKM2nDpNk5twuqB7/wzexk+vsP1tVrw1FOhOnc1UQSnTsHq6g1MJkmSJEkfXMn2NiuPPXblzgJ4EvgC0Jjt6wDPAPcCh7l2wboANoGXgOzA/uFs3uLK4SuPPcbWF3+dfGXlJu9CkiRJkiRJkiRJkiRJkqQ3M6omSZIkSZIkSbpjnNk8w5MvPklRFtccN8pGV0TVsiKjvfkqdNtQOzCwexGSKSwuApB3dxnFUEnh8BzMNaFMQ0SsnsLnPwaNGLLJkCyBSg7NCJYWQzDt5OEQL8uBrICFaginXaqs5RkMx9BIoTuCSR7GR0CahAhbVkAClDEsNPaXGs+ibpNZ2awaQTWFYQZZHs6PC0hT6A9gvgVJCWUF2l04tBDmT6JwvVYNohLSRahMoFGBj5ShWTYeQaMG00qIqvXbUJ9AvxquvbkN8/0rn/mJTZj+ITyRhagbwMfa+8ejEh585Tov8LX0+3DXXXDy5C1MIkmSJEkffKu/+SjJYPDmA9vA7wD/DvthtQz4U+AcIay2CLQIbzxzoE+Ir20AkzfMNwT+u9m8b5D0+6w++lU2vvLlW70dSZIkSZIkSZIkSZIkSZIuM6omSZIkSZIkSbojdMddnnjhCcpLhbJrqKd11lvrbPY3AWiQUtvuME6uMrjThrk5aknJyaUeP3YcVhdDeAwgj6Hf8iU7AAEAAElEQVRegQeOQhrDYAybPdjpw91rcLQZAmiteoicAcTAQg1qSeipZRl0+rOmWw2iGNbn4EI3hM5iQhitMwo/NythniQKsbQsC/uLLATfsiwcP9SCYRfGeVhbGYWoWrsLrQoUKSQJjMew3YWProTzImbhsyLE06hBOpsboNiDXgmTJuxNYCkLgbakgO4EOptwJAvBtd4utJ6B1lPAGO77JHznyJsf8/0bsHqV7/zfsCiC5eVbmECSJEmSPviS7W1WHnvsrQe8DjwG/NvA0QP7J8APZ39uxHngSa4aVLtk5bHH2Prir5OvrNzgpJIkSZIkSZIkSZIkSZJ0bVEUXX/QHeZOeyZG1SRJkiRJkiRJd4SzO2evCKoVZbF/cDCAXg/m5qDZBOD4wnE2+huhavbaqywOSzbm9k+p5DA/gTiCBw71eODuCZURFBEMDkydlPCpj0A1DtvzNVhvwqEmDCbh0lEOc9X9c2JCLA1CwCyOoR7DqALFbJ4kgtVW+PvSuKKA9gAqCyGmFs32X/4woISohLgM66zGsNqEvAjjyii0xwCGu8Da7PrAQjXE1aiGufM8zHfJcDRbVwFpDt1duHAGdlNYH0G1AaMUvv59OPIynN+B3g4UU/jSt4BxOP+Rs7DVhNcW9uc+thf235KHHoLDh29xEkmSJEn6YFv9zUdJBtcpVm8Dvw38LPBzQPXaw68wAf4F8AdAce2hSb/P6qNfZeMrX34bF5AkSZIkSZIkSZIkSZKkt1aW5fUH3WHutGdiVE2SJEmSJEmS9IHXGXU4t3uOzrjzlmPO7Z7j5fbLl7fPbp+lP+nB+fOwdTEEwiJgdQ2OHoUooj1qM5oMYHgRlmCQ7kfNKCGtwC/dB4cX2owLyNJZ3CwKfwCOL0Grtt8fqyaw1goxtmoD5lPY2wvhsv4khNHmalCW+4Gz0TSE1Q4tQGcS9kVALYE03o+ipbN17QxhfT7MMc1CkC3LZwuYQlFCOfuEoFWFSgplEebJCcG4MTDag0oFVpegHkEGjCcwLCHvQl6DdDZPpweDIdR2oL0F+TSs59496NRDQO2734SdHJbaMGuwcWoTVg98n7+awxeehZeWoV2HpRHcuxPu76Y9/DA8/vitzCBJkiRJH3jJ9jYrjz12Y4ML4J8DzwD3Aw8AR7j6m7MSuAB8B3iey9HsG7Hy2GNsffHXyVdWbvwkSZIkSZIkSZIkSZIkSXqDz372s0TRm/+B09GjR2/Dat4f7tRnYlRNkiRJkiRJkvSBdmbzDE+++CRFWVxz3CgbXRFVy4qM9uar0G1D7cDA7kVIprC4SF7kjAZ7kEAlgbvmodWCJIblBnz+nhBBywoggiyGZgQn5qDehPkanFgOxyY5THNYqF25rrgEKrDbh+HsFioR9IAkCRG2vAgRNiKYb+yfmxQhepbPzmskIeY2ykJELY0hmYXVLn0GkuawO4bGXJgzAuoJxJVwL8MIFpswrUI1hW4foiLMVRSQR9DbheoAulXIYzh/ETa2oZbB0d7++tb7cKINT9fhhd+HwR587MC9RyU8+MqbX6sI+PjONV/OfV/7Gpw4cfVjUQSnTsHq6g1OJkmSJEkfXqu/+SjJYHD9gQeNCWG1Z4AKIay2SPhXZxnQIQTVpje3pqTfZ/XRr7LxlS/f3ASSJEmSJEmSJEmSJEmSBPze7/3e7V7C+86d+kyMqkmSJEmSJEmSPrC64y5PvPAEJeV1x9bTOuutdTb7mwA0SKltdxgnVxncacPcHM2k5FMrY04eg8PzIfaVx1CvwANHQ2gtKqE/hq0+zNVgpQlpGeJmrXoYAyFK1miGSNo0h/EERiPY24O0CWtNON8Lx6pJuFaWw94ohNHq1TBPRLj+aAIxUBazOTOoxFBPYZjDeBZViwjRtbKEWhzmKArY6sLKHNRSSCIogTienTMfPkAogfEUkh7EDag0YWsIteHsmWZwsYDNWQBtaXTlYzyxCa2nIduGwfqbH/P9G7D6Nr/Pf4UogkcegcOHb2ESSZIkSboDFAUr//gf39ocU+CHsz/voJV/9I/Y+Jv/aXhTKkmSJEmSJEmSJEmSJEnSLTCqJkmSJEmSJEn6wDq7c/aKoFpRFvsHBwPo9WBuDppNAI4vHGejvxFqYa+9yuKwZGNu/5RKDvMTiCN44FCPB+6ekIxhUAFmUyclfOojUI2BMkTL1prwybXQ+OqOwqXTAuaq+3PH7MfSqgnEVWjvhkhbSvju+FoTNvbCPBDGljlsD2B9EdLZ+XG0/wv+PINaHfJpCLkdasKwG8JpEWFfFMHuHnykBXka1pJNYKMD96yE+2D2V57trznLIM4hzWH7h7B7HqrLkCyGyFo8gQvnZ8G2DBpZ+Lm3C61noPUUMIZHEtiqw2sL+3Mf24NHzr7dV/wNHnrIoJokSbpCXpSMs/yGxtbShCSO3uUVSdL7Q+2ll0g6ndu9jKtKOh1qL73E+BOfuN1Lkd57RQ7Z6PrjANI6xFf7XweQJEmSJEmSJEmSJEmSdIlRNUmSJEmSJEnS+1Jn1OHc7jk647f+0ve53XO83H758vbZ7bP0Jz04fx62LoZKWASsrsHRoxBFtEdtRpMBDC/CEgxSKOLZBCWkFfil++DwQptxAUkSwmTFrLdxfAlatcsdMqoprLT2f+G+1ID5FLZ2YJrDOAvBtMV6WMol4wwOLcCkv7+vmsBSLYTJAOIS0ihca2cI6/PhWJZDJYFpFtZczrohZRTmWA0NuRBli8KYeALdCVTXwnyVEuabIaI2yCFJw3rzLETYyhIu7sLkh9DeCtE2gB/7Y/iTdcimcO7bsBPBS+tw7yZc2IHeDhRT+NK3gPHsvnL4wrPw0jK067A0gnt3rnweb9vDD8Pjj9/KDJIk6UPoT37U4S//139wQ2O//qWf5f7jS+/ugiTpfSJfWqKMIqKyvP7g91gZReRLh273MqTb4/x34Lf+wo2N/Wv/O9z10+/qciRJkiRJkiRJkiRJkqQPOqNqkiRJkiRJkqT3nTObZ3jyxScpyuKa40bZ6IqoWlZktDdfhW4bagcGdi9CMoXFRfIiZzTYgySEye6ah1YLkhiWG/D5e6AoICuACLIYGhGsLMBiA04eDmNzwpiFGuQ5lytr0xwGPSCFwQiGs1tISohKqFVDTCwvgARW5mBnFlaLyxBfGxSQJmG7Pou6jbIQU0vjEDSrVA7c3xDKA7/xb1WhUQ3RtbyAcQH1LATkurNG3dpiuP+SEFAbj2FnJ9wXs/X94HuwfqBpt96H1QEsvAz/63Mw2Av7Jxdg1IbRbNypzTDuoAj4+M41X87Z4lvw1FOhRHc1UQSnTsHq6g1MJkmSJEkCyNbX6T34IPO/93u3eylv0vv5nydbX7vdy5AkSZIkSZIkSZIkSZIkfQgYVZMkSZIkSZIkva90x12eeOEJykuVsmuop3XWW+ts9jcBaJBS2+4wTq4yuNOGuTmaScmnVsacPAaH52eBsxjqFXjgaAiNRSX0x7DVh7karDQhBlp1qM5+sx4TgmrVBKhAlsFuH3b3IC2g0oSlJpzvhdBaNQmhst4YkijMUUahHdasAmWYkwgSwhoiQkStnsIwh/EsqhYR5gSIi3BudwiNBsQRVGKoxlCthUAcBUQLEFehksJ2G8YjaNRgWglRtX4b6hPoV8O8m9sw37/yEZ7YBJ6G48/B8KfDWgE+1t4fE5Xw4CvXfeneWr8Pd90FJ0/ewiSSJEmSpDd69bd/i7v/2l9n/umnb/dSLut+7nO8+lv/8HYvQ5IkSZIkSZIkSZIkSZL0IWFUTZIkSZIkSZL0vnJ25+wVQbWiLMIPgwH0ejA3B83m5ePHF46z0d+AEnjtVRaHJRtz+/NVcpifhNjYA4d6PHD3hGQMgwowmzop4VMfCSEyZjGztSZ8cg2iCLoj6A9gfhY/IwoBtOos3hYR4mjVFJoJDAm/gI/jMM/GXpgHQnRsbwS1FNI0TNesQpkRamqzc6MiTFxEcKgJw24IpEWEfUUWtluEKFyZwVYXVubCOqNLS40gjYB5aADTLETUij3olTBpwt4ElrLwHJICuhPobMKR2djeLrSegdZTwBjWgPs34DtH3vz63b8Bq4O3/bLviyJYXr6FCSRJkiRJV1PMz/Py//g/kGxtUf/TPyXudDj+7/8HJOPxm8bm9To//G/+a4qFxXdnMRGMTp4kX1l5d+aXJEmSJEmSJEmSJEmSJN2RjKpJkiRJkiRJkt4TnVGHc7vn6Iw71xx3bvccL7dfvrx9dvss/VfOwtbFy0EzVtfg6NHLpbL2qM1oMoDhRViCQQpFPJughLQCv3QfHJ5vk+WQJ7Mw2Sx0dnwJWjUup9yqKay09n+JvtSA+TSE2XrTEDObq0FRQjKbY5SFeNnyAlzo799PNYGlWoiTAcRlmHdnCOvz4X6iMsx5KapWi2A8gqgBZRTmWJ115CLCPkrot6G5AHkatvMMsllsLZ6tK8vhQKOO4SiE09Icurtw4QzsprA+gmoDRil8/ftw5GU4vwO9HSim8KVvAQe+Z//IWdhqwmsL+/uO7YX9t+Shh+Dw4VucRJIkSZL0VvLVVfo/93Mc/rv/76sG1QCS0Yjmd55n4ytffo9XJ0mSJEmSJEmSJEmSJEnSzTOqJkmSJEmSJEl6153ZPMOTLz5JURbXHTvKRldE1bIfvUq7exFqBwZ1L0IyhcVFAPIiZzTYgwQqCdw1D60WJDEsN+Dz94TQWFZABlRyaEawtAiLDTh5OIzNCWMWapAfiJFNc5iOoNGC7ggmeRgfAWkSomdZEZpoZQwrc7AzC6vFJSzWYVCEsXEZom2jYQiepbOQWlmESFscQVrAqAdZPVwDoFWFRhWmGeQFjAuojGG7C3OHwnqOLIT5xhOYRBDFMBhBGof+XFHAxjZUtqC9Bfk0fFBw7x506iGg9t1vwk4OS20Yza59ahNWB1e+TtUcvvAsvLQM7TosjeDenf313pSHH4bHH7+VGSRJ0rW8eu247S27e/HdnV+S9I5JtrdZeeyxa45Zeewxtr746+QrK+/RqiRJkiRJkiRJkiRJkiRJujVG1SRJkiRJkiRJ76ruuMsTLzxBealQdh31tM56a53N/iZMpzReeZ1aE8Zv/I12pw1zc5AkNOOST62MOXkMDs+HsFceQ70CDxwNobWohP4YtvowV4X1RgigteohcgYQE4Jq1QSoQJbBbh9292C+EiJl63NwoRvGxIQwWmcUfm5WwjxJBM0qUIb9RCG4Fs0eQZJAPYFxHoJnRJCk0B+EyFtMuNZODw4thNBaJYZqDNVaiKNRQLQAk+rsudXC8aiAIg5jutswzsKzADh/EdobcLS3/xjX+3CiDU/X4YXfh8EefOzAY45KePCVq79WEfDxnRt4Ub/2NThx4q2PRxGcOgWrqzcwmSRJkiTpVq3+5qMkgwFUgCPAEuGNaw60gQuQ9PusPvpVNr7y5du3UEmSJEmSJEmSJEmSJEmS3gajapIkSZIkSZKkd9XZnbNXBNWKstg/OBhArxfiaM3m5d3HF46z0d+A116FomBxBBtz+6dVcpifQNwd88BPLvJA6yLJGAYVYDZ9UsKnPhJCY5QhALbWhE+uhYbXYAC9IczP4mdEIWZWTcL5ERDHIbjWTEKoDEIwbbUV/r40riigPYDKAqRJ2NesQpkRvpRO+IX8peAZwHIThlkYCyHw1h/CUgxUYJpCPoatLqzMhXVGl5YaQRoB89AAphlst2GlEh7jFGjvQX0I9Qj6VRiMYHMH1kdXvj4nNqH1NGTbMFh/8+t3/wasDt7y5b2+KIJHHoHDh29hEkmSJEnSOyXdeZ2V5/8h/HVCUC26yqASuAArz/9Dtnf+72TLH3lvFylJkiRJkiRJkiRJkiRJ0k0wqiZJkiRJkiRJummdUYdzu+fojDtvOebc7jlebr98efvs9ln6kx6cPw9bFy8HzVhdg6NHQ4QLaPd3GA1fg9Vw3iDdD5JRQlqBXzp1kcMrPbL+kDyBIgp/AI4vQavG5ZxbNYG1FsSz44sNmKuEa/emIYw2V4Oi3A+mjbIQL1tegM4k7IuAWgJpDHkRttPZmnaGsD4PZbkfW7sUVatFMBpB3NpfT70yGwPkQFrAsA3xCkziEHmbTiHLwrhLa89yONCpYziCpIBsDy78EDYKWFwIMbgkAqbwyutQnUIjC+vr7ULrGWg9BYzhkQS26vDawv68x/bgkbNv+dLemIceMqgmSZIkSe8LOc3KMxw++/dIPje+9tAIOArJ0RHHX/oSGz/7nzCY/gyX3+RKkiRJkiRJkiRJkiRJkvQ+ZFRNkiRJkiRJknRTzmye4ckXn6Qoi2uOG2WjK6JqWZHR3nwVum2oHRjYvQjJFBYXAci7u4xiqKRweA7mmlCmIWRWT+HzH4NGDNlkSJZAJYdmBEuLIZh28jAkcYiVZQUsVGcBs1mMLM9gOIZGCt0RTPIwPgLSJETPsiJ8XbyMYb6xv9SkgEolrAWgMQu6jbIQPEtjSMpwvChDDC0tQlSt1gjXAWhVoABGUxjOom1FDttdmERQrcDKXFjPeBL2RTEMRuEaURTuaWMbKlvQ3oJ8CnkMO0APiBN4/o/hOzW4dxMu7EBvB4opfOlbwOx79NUcvvAsvLQM7TosjeDenfA8btrDD8Pjj9/KDJIkSZKkd0ASbbNY+12q45dpfvf02zq3+d3TLPy5b1Kvn6Uz/kXycuVdWqUkSZIkSZIkSZIkSZIkSbfGqJokSZIkSZIk6W3rjrs88cITlJcKZQcNBtDrwdwcNJvU0zrrrXU2+5sANEipbXcYJ1eZuNOGuTlqScnJpR4/dhxWF/fDXnkM9Qo8cDRExQZj2OzBTh/uXoOjzRBAa9WhOvsNeAws1KCWhJ5alkGnP2u61UKkbH0OLnRDSC0mhNE6o/BzsxLmiQjXHk3C/rII46cZVOIQehvmMJ5F1SIgSaE/CJE3gCKFdhcOLYRzkyiMa9UgKiFdhMoEygr0L42LISqgiENArbsN4yw8C4DzF6G9AUd7+4/xrm74+wdjePH/gMEeTJZg1IbRbMypTVgdXPn4I+DjO9d58QFaLXjqKYjjqx+PIjh1ClZXb2AySZI+IF7tvPvXuHvx3b/Gh9zx5SZ//5fvu+GxknQnSOPzLNWfII5GNE+fJp5m4UAVOAIsAC1CVTwH+sAecAGYQDyZ0jx9muKzDQ41/nvao18mK47elnuRPnSWPgp/+b+58bGSJEmSJEmSJEmSJEmSrsmomiRJkiRJkiTpCp1Rh3O75+iM3zoc8v3t7/Ov2v/q8nZRFqFY9uy34U9fhLwMxbBPnoSf+mkAdke7YcyF80RFybC2P19aQGMKcQQ/fajNn707ozGCMoJBsT8uKeFTHwmhMYD5Gqw34VATBpPQc4tymKvunxMTAmYQomFxDPUYRpUQKoOw1NVW+PvSuKKA9gAqC5Ams3Oj/V+s5xnU6pBPoYjCGobdcF5E2BdF0B/CUgx5LezPx7DdhY+uHLhWCRTQqAE1SLNw7nYbVirQbMIUaO9BfQj1CPpVGIxgcwfWL5XSZk5swvQP4YlsNjfwsfb+8aiEB195y5f3+vp9uOsuOHnyFiaRJEl65y23qvxfP338di9Dkt43kmj7clAtHgxpPPtseGN7L3CY/Yr5QQ1gFbgH2ABegsZzzzH49KehCUv1J9gd/ip5ufLe3Yj0YdVagZ/8K7d7FZIkSZIkSZIkSZIkSdKHhlE1SZIkSZIkSdJlZzbP8OSLT4ZI2jVs9DZ4uf3y5e3z3fOMdzah34bLDYsS+i/AH78Oi4v0p32m0zFMujAPkySExy4NPbIE/5c/A0fme0yASR0qeRhzadzxJWjVQpsNQixtrRViZ9UGzKewtxdCav1JCJzN1aAsQ6QMYDQNYbVDC9CZhH0RUEsgjSGfRdHS2bp2hrA+H+bIcqgkMM3CsTKfLT8Ka1lt7s9XRmFMJYdeB2rrYd0xsFCF8RiohmBbnrN/U8BwFklLCsj24MIPYaOAxQVoJrP42xReeR2qU2hkYX29XWg9A62ngDHc90n4zpE3v373b8Dq4Jov8bVFESwv38IEkiRJkqR3X85i7XeJo/Ams3n6NHEzg58Aqtc+Ewhvbo8AyxCfmdI8fZreZz9DHI1YrP0uO6O/AiTv3vIlSZIkSZIkSZIkSZIk6Qb9xm/8xu1ewm31t//2377dS3jfMKomSZIkSZIkSQKgO+7yxAtPUB6se72FxfriFdtL6Rwbne9ffXCnDXNz1JM6034XCGGyo/PQakESw3IDPn9PiKAVB3puURXubkKjCfM1OLEMRDDJYZrDQu3KS8UlUIHdPgxn81Qi6AFJEsJneREibEQw39g/NymgUgnHARqz6NsoCzG1NIZsGsZcNoTywG/aW1VoVEN0LS9gXEA9g2EKg71wr6tLUI8gA8YTGJaQdyGvQTqbq9ODwRBqO9DegnwKeQw7hHuJE3j+j+E7Nbh3Ey7sQG8Hiil86VvAOMzzyFnYasJrC/trPLYX9t+Shx6Cw4dvcRJJkiRJ0rupWXmGNNkAIB4Mafzg23Afb+6gjYA9YEKIrS0A9QPHq8B90HjxWQaf/jRFs0GabNCsPMNg+q+9+zciSZIkSZIkSZIkSZIkSdfxd/7O3yGKotu9jNvGqNo+o2qSJEmSJEmSJADO7py9IqhWlAfqZoMB9HowNwfNJtWkylpzjc3+JgD1C5vUpjC62m+dS6C7R2Npnk+tjDl5DNbnISKEwuoV+MmjIbQWldAfw1Yf5mqw0gzf9S6BVj2MgRA4azRDJG2ahzjZaAR7e5A2Ya0J53vhWDUJ18py2BtBUkK9GuaJCNcfTSAGymI2ZwaVGOopDHMYz6JqEWFOgLgIC+sOodEIobZKDNUYqrVZHK6AaAHiKlRSaHchKsJcRQF5BL1dqA6gWw3P4/xF2NiGWgZHe/uP8a7Qo+MHY3jx/wiRtskSjNrh++8ApzZhdbB/TjWHLzwLLy1Duw5LI7h3J9zHTXv4YXj88VuZQZIkSZL0LosY0ao8c3m7+ewzxJ/Irwyq9YGXgN2rTHAIuBdozbYTiD+R0XzuGXo/+yAArcozDKc/SXlFgU2SJEmSJEmSJEmSJEmSbp+yLK8/6EPmTo7JXY1RNUmSJEmSJEm6A3RGHc7tnqMz7rzlmHO753i5/fLl7bPbZ+lPenD+PGxdDGWzCFhdg6NHycqcrcFWqIMNL5I1YFTZny8uIc1DbOzPHd3jX/uxPtUCstkXuMvZmJNrkCZhmwiWW/DJNYgi6I5Czy0qYa56YG72Y2nVJETL2rshSpYCcRzCaht7YZ7Z1JQ5bA9gfTFcMyKs79Ivy/MManXIp1BEcKgJw264xYiwr8jCdotwvTKDrS6szMF8dXYdwnXTCJiHBiHUlmUw2IK4AZUmbA2hNgzXrmdwsYDNnbC9dKmUNnNiE6Z/CE9kUMw+3/lYe/94VMKDr7z5dY2Aj++85ct+pa99DU6cuPqxKIJTp2B19QYnkyRJkiTdLvX0BaJoAkA8GNLYexbWDgy4CLwIFFc7mxBa+zZw8v/P3r8HyXXfdf7/83NO37vnPhqNfJOjyIllIsuxY4ISloQA1q5gISRbBYSlilpncfgaKKgKkEsRllxgYRcW+LKJkyDDfgnf/H6O9MNLFshuljWBOBBfZBlHlpEUXRyj0Yxnerqn731uvz/eZ6ZnrJEsS5Yl269HRTVzzvmcz/mc7kkqOlI/xeC8HBTnH6Pd/nbiUhHn+hQyT9IJb71EdyEiIiIiIiIiIiIiIiIiIiIiIvLCvNoCY6/GiNzzUVRNRERERERERERE5BXu4NxB9h3aR5yc7ZPSpht210TVwjikNvc0NGqQXzWw8Sz4AYyMECURQXsJfMj6cNUIDBfB9yCKIBfDzuthQxkgIvbAi7HSF3DtKAytmjufgY0l8NPjo0UY9uHZRQgi6IUWTBsprEwB2P6xYei3BvtyPozmYfnPBrzEImcJUO3A1JAdCyNbexACiYXXABJnc0yWbNul+0igVYPSMEQZ247SYFocW6QNbF5W/blEpwt+bKG5hW/B4gzkxsEfscia14fTM7amfAjF0L5vLkL5ISjfD/Tg5hvhwPSZ79+OWZhsn/XtfX7Owe7dsHHjRUwiIiIiIiIiV4Ji5hsr35ce+zre5KpnAi3OHVRbFqfjSlhZHPDGI0oHvk7zLW9fuY6iaiIiIiIiIiIiIiIiIiIiIiIicqV4NUXGXm0BufOlqJqIiIiIiIiIiIjIK1ij12Dvk3tJeP4/EChkCkyVp5hrzQFQJEN+oU7PX2dwvQaVCiOZDJun+mzfBBuHLDoWp8/jh3KwfRqyHrR6MNuEuYZFx6YqFkzbtjENsAFhDMN5iFfFyMIA2i0gA+0udNIPfPsJuATyOYudRTHgw0QFqmlYzUssvtaOIePbdsG39XVDi55lPLtGNrvq3jqQrHp6Xs5BMWfRtSiGXgzZHiw0oDJm658etmv0+tB34Dxbb8azVlkcw+wCZOehNg9RYHO/9gn4xpSt4dijUHVwdAq2zsHpKjSrEAdw98NAz87ZfQTmS/DM8GCN1yzZ/otyxx0KqomIiIiIiLwCOPpkPPu9vdfuUDx9AK5bNeAozx9UWxan43ek2x4UnzlAu/1m4lKRjDeHo09C7sVavoiIiIiIiIiIiIiIiIiIiIiIiMgFU1RNRERERERERERE5BXsSPXImqBanKz61HS7Dc0mVCpQKgFw7fC1zLZmLWr2zNOMdBJmK4NTshEM9S2MdstYk1uu6xOFEHqsfCA78KCwKqjmJTCcg9EJuP1qiBJodqBcgJwPOPCwoFrOB7IQhrDYgkYtvW4JRksw04QgsnFRDM0e+GnELXHgeVDKAYnNiQMfC7A5LHJWyEAngl4aVXPYnABebOc2OlAs2n1mPch5kMtbHI0Y3DD008+LF/J23MUQezamsQC9ECLPxsw8C7VZ2NQcvJZTLZhsw/AJ+N+PQXvJ9vdPQ7cG3XTc9jkbtywXwZ374eg41Aow2oWtVbuPC7ZrF9x338XMICIiIiIiIleIjDdnvxEGSo88gleKBge7wOILnHAxPa9gm14povTIIzS/61+AS8h4cwTxNS/CykVERERERERERERERERERERERC6Ocxf1CZvnlSTJWY9d7LXPNfeLMf+rhaJqIiIiIiIiIiIiIi9T9W6dY4vHqPfqZx1zbPEYJ2onVraPLByh1W/CzAzMP2vxNAdMboBNm8A5at0a3X4bOs/CKLQzFgsDIIFMFv71zbBxuEYvBufSqNoyB6+fgoxv00dA0YepMvjpuKEMae3Mtj0slLb8aN/zIO9DPwvtyB5mex5sKMHskl0T7DPiS13IZyCTseuVcpCE6fzYuS62a8UOxkrQaVj8zGH74tC2y1gILQlhvgETFRjK2bgEu27GAUNQBIIQFmowkbUuXQDUlqDQgYKDVg7aXZirwtRyJS21eQ54AK59DDq3DV6LLbVVL2UCbzt55vvqgBuq67/nZ7j3Xti8ef1jzsH27TA5eZ6TiYiIiIiIyJXOd1bt9todivv3w62rDi5d4KRLrETVKEPxscdov+lNxKUinmtcxGpFREREREREREREREREREREREReHM8XJbsUnhs6u9A1OOfWzLXePJfj/l6OFFUTEREREREREREReRk6OHeQfYf2ESfxOcd1w+6aqFoYh9TmnoZGDfKrBjaeBT+AkRGiOKLbXgIfsj5cPQTlNIg2XoTveY0FyMI0VAaQ82BqGIbycPUIjFcgTiCIIElgKDsIpoEF0/IFWOhAP4RK3sYtP/vvhhYvGxuGXmtwXs6H0XQsgJfYg+5qB6aGbD0usfUtR9XyDnpdcEVInM0xWbJjDttHAq0alIYhyth2FEKYxta8dF1hZMeWdbrgxxAuwelvwWwMI8NQ8sF3QAAnT0EugGJo624uQvkhKN8P9GADsGMWDkyf+f7tmIXJ9jnf4nNzDnbvho0bL2ISEREREREReVlxEQClRx7BC8KV3x8D0L/AOVef54PXDyg98gjN7/oXOBde6EpFREREREREREREREREREREREReFHF87s9YXYxWq8UHP/hBPvnJT54zdjY2Nsbtt9/Ojh072LFjB6973esYGRlheHiY4eFhstksS0tLK79mZmZ4/PHHefzxx9m/fz//9E//BKwfWHPOccstt7Bnzx5uueWWS3avrwSKqomIiIiIiIiIiIi8zDR6DfY+uZeE5zyEb7eh2YRKBUpWDStkCkyVp5hrzQFQJEN+oU7Pf+6sQL0GlQolP+GmiR7broGNQxYeizwoZOGWTRZacwm0ejDfsiDaeMn6XZ6DyYp976cBs6Gc7Q9C6AYQBtDqQsmDjAcLPfvqAb5v54RRGiVzMFGBahpW8xIYKUA7hoxv27kMdDt2Tia9ryS2qJvnIBNDtwlhYRB2K+egmLM1RTH0Ysj2YKEBlTELyE0P23y9PvQdOA/aXVurcxZbm12A7DzU5iEK7HWqAk3A8+HxJ+BAHrbOwekqNKsQB3D3w0Bv8NLvPgLzJXhmeLDvmiXbf1HuuENBNRERERERkVebxMdrdyju32/b0apjuQucc/V56XzFxx6j/aY3kXj6K2giIiIiIiIiIiIiIiIiIiIiIvLK9MQTT/DOd76TEydOAIPA2fL3xWKRH/zBH+Q973kP//Jf/kuy2ew55xsfH2d8fByAm2++mV27dq0ce+qpp/jc5z7H5z//eY4fP75yneWvBw4c4M1vfjOf+MQneP/73/9i3+orhv5Gm4iIiIiIiIiIiMgVot6tc2zxGPVe/ZzjDi8c5njt+Mp2HMew/1F46hBEidXIbtwGt962UhFb7C5CApyewcUJnfxgvkwMxcACZLeP1XjzdTGZHrSzQPqPtPgJ3HQV5DwgsWk3lODGDRYYa3Sh0YFyweJo2DAKGTsOkM1AkkBtEfBtDt/BZNm+OiyMVu/a9YrpB7Z9B6WcTegBOPCxsBtYiK3gQy+y4BkO/Ay02jBStHOcB9UmjA3bfWY9u5dc3uJoxOCGoZ9es5C34y6G2LMxjQXohRZOA5h5FmqzsKk5eC2vbtjXb/bg0FegvQT9UejWoJuO2T4Hk+2172kugjv3w9FxqBVgtAtbq4MI3AXZtQvuu+9iZhAREREREZGXoSgZpvTII3hBaDtaQDE9OHy2s57H6vOWw+f9gNIjj7Bw+7+7wElFRERERERERERERERERERERESuXF/60pf4kR/5ERqNxso+59xKWO3Hf/zH+c3f/E2uuuqqF+V6N954Ix//+Mf56Ec/ymc+8xk+8pGPMD8/v3JNgCAI+OVf/mWeeuop/vAP//BFue4rjaJqIiIiIiIiIiIiIleAg3MH2XdoH3ESP+/Y2eYsJ2onVrZnTh6k15qHa5f3JNB6Ep44BSMjALSCFkHQg34DhqDvQ+wGw6dH4Z1vgOmhJm0sgBa7wZhrR6Gct1AaQC4DE+XBQ+bRIlQyEAKtwCJkGR+G8oMwWBRDP4DxYagHts8BOd9+hZFtxzHUWzA1YnM4LKqWhFhNDbvucvAMYLwEnXBwrcSDVgdGPSALQQaiHsw3YKICQzkbm2DRt4wDhuwz5kEICzWYyEKpBAFQW4JCBwoOWjlod2GuClPLpbTU5jkI/h72hhCnL9aW2uC4S+BtJ9d/Xx1wQ3X9Y2vcey9s3nz2487B9u0wOXkek4mIiIiIiMgrTfysT/HRxwY7loDl3yIWgDFg8QVMOJaet2xVC764/zHi632YuMDFioiIiIiIiIiIiIiIiIiIiIiIXIG+9rWv8a53vYtut4tzbmV/kiRs2bKFz33uc3zHd3zHJbm253m8733v48d+7Mf4hV/4Bf74j//4jDX80R/9EYVCgT/4gz+4JGt4OVNUTUREREREREREROQya/Qa7H1yL8lKsmyVdhuaTahUrPAFjBRGBseDgNFTC8yW1pm4XrPzfJ+CXyBo2b+KkvVh0xCUy+B7MF6E73mNxczitOkW+FAGxkZgpAjbNtrYCAhjGM5DFLFSWQsi6DShPALVFvQjG9PsQcaDQha6oUXFEs/mjBMLibkE8plBVC2XTlvtwNQQJMkgtrYcVcs76HbBK9t2zrdrLK8/AjIxdGrgTUDfA89eLsLQxnnpnyWEq+4DoNMFP4ZwCU5/C2ZjGBmGkg++AwI4eQpyARRDW19zEcoPQfl+oAc33wgHps98S3bMwmR7nffqfDkHu3fDxo0XMYmIiIiIiIi8kk1+6l68XgCb0h2ngdcwKJFvBR4Fnr/rbr+Z3rpqOwZmVx1+OmDynj9i9sMfuthli4iIiIiIiIiIiIiIiIiIiIiIXBFOnz7ND/7gD64JqiVJgnOON7/5zfzlX/4lY2Njl3wdIyMj3Hvvvdx000380i/90spanHMkScKnPvUp3vCGN/C+973vkq/l5URRNREREREREREREZFLqN6tc2zxGPVe/axjDi8c5njt+Mp2nMQW+dr/KDx1CKLEal43boNbbwMHCQm1Tg1Oz4CXEDno++tM3qjiVYYp+46bJnpsu8ZCZQ6I0tjZGzdZaM0l0OrBfAsqedhQBByUC5BLnyZ7WCwt5wNZC5QttmBxCYoeeB5sqMBsw0JpLrEIW61t4bVKBhJnQbNCBrp98BMLrOV8CEIo+BA7i7CFkUXZ/ASi2MZ5zoJp3S7kixZ7Ayhn7bPd3QA6/fS1jGChAX0HuSxMVCDjQ69v+5wH7a5dwzmLrc0uQHYeavMQBfY6VYEm4Pnw+BNwIA9b5+B0FZpViAO4+2GgZ9fdfQTmS/DM8OCtuGbJ9l+UO+5QUE1ERERERETOyl9YYGLPHngDg6haHwuhLce/y8A24BDnDqt56bjyqn1z6XzLDsDEwT3Mv+8uoomJF+EORERERERERERERERERERERERELq+f/umfplqtnhFUe/3rX8//+l//i6GhoZd0Pe9///tpNpt89KMfPSOs9ku/9Evs3r2b66677iVd05VMUTURERERERERERGRS+Tg3EH2HdpnkbRzmG3OcqJ2YmV7pjFDrzoHrRpcu7w3gdaT8MQpGBkhiiMavSXoL8EoRA6CVVE1l4Afgxe02TkVsfPqgGJsgbB0NrwEtm2wyFiSnrehBDdusMBYuw3NDgzl0hOcfZ46l17HYRG1XAZKPuDbPt/BZNm+OiBJoBdBrQnZYQu4RTGUchCm140jyGYsYuZ5FlzrRHZexkvnzUCrDSPF9JwM1BowNmxrWr5eOW/3nxmBbB+SLLSWx3ngYog9C6g1FqAXDl6XmWehNgubmoPX8uqGff1mDw59BdpL0B+Fbg266ZjtczDZHpyTi+DO/XB0HGoFGO3C1qqt74Lt2gX33XcxM4iIiIiIiMgr3OSn7sFvt+Fx4HuAXHrgKDC+ansDUEr3L64z0RiwlbVBtX46fvX24+D3Wkze82lmP/yhF+9GRERERERERERERERERERERERELoO/+7u/47//9/++Ei9blslk+MIXvvCSB9WW/Yf/8B/4m7/5G/72b/92zdparRYf+chH+OM//uPLsq4rkaJqIiIiIiIiIiIiIpdAo9dg75N7SVZyZWc3UhhZsz2aqTBbP7z+4HoNKhV83yfbCwiwSNk1FSiVwaXBsnoHCOD7t8H0aABRTOIgE0OcPje/dhSG8oOpCxnYUAYvPT5ahEoWcNAMLEJWyUOcWMAMoBtCxsH4MNQD2+ewyFnOhzCy7TgGEqh2YKoy2Jf1IQghCiGftf2Rg7ESdBo2xmFrdg5aHRj1IMqnY3uw0IDrJwbXjhMghmIeyEMmtHMXajCRhVIJAqC2BIUOFBy0ctDuwlwVppZLaanNcxD8PewN07mBLbXBcZfA206e+VY54Ibq+m/jGe69FzZvXv+Yc7B9O0xOnudkIiIiIq9ec40uX3x85rzG/usdm5gaKlziFYmIvHT8hQUm9uyxjR7wVeAd6cEQOAjcDCxH2cvADqwYvoRF0nLAMPDc/3mM0vPDVfu+ml4HmNizh/n33UU0MfEi3pHIq0BjFr6x7/zGvuHdMLTx0q5HRERERERERERERERERERE5FXut37rt9ZsJ0mCc4677rqLb/u2b7tMqzK/93u/xxvf+MaVbeccSZLw+c9/nk984hNcffXVl3F1Vw5F1UREREREREREREQugSPVI2uCanESDw6229BsQqUCpRI5P8eG0gbmWnMAFE7PkQ+gu94T3ARoLJEfHWL7WIfXXgtTwxbwSpwFyQCGcrB9GoIAZpsxcw2LreV9mBiCkSJs2wi+Z5+LDiMYzg/iZy6x0FmnB8UMNLrQj2y8AzLL0bTYPoudeDZnnNhxl0A+M4iq5bAwWje0c7IehKF9DdL7igPwPPt8ds6HyZLdy/K9kUA2gmYd8lP2UnjAcA56PbtIxocoSl+nVCeNpPkxhEtw+lswG8PIMJT8NBAXwMlTkAugGEKSQHMRyg9B+X6gBzffCAemz3xLdszCZPv8fi7W5Rzs3g0b9aFYERERkYs1U+vysf/x5HmNfdPmMUXVROQVZfJT9+C3V/0G9UFgG7Ap3a4D/wh8G/Yb9WUFzoyordbHgmr1Vftm0vlTfqvF5D2fZvbDH7rg9Yu8Ki09A//zg+c39ro3K6omIiIiIiIiIiIiIiIiIiIicgktLCzwpS99CefcGcd+/ud//qVf0HPs2LGD7/7u7+aBBx5Ys8YwDPn85z/P+9///su4uiuHomoiIiIiIiIiIiIiL1C9W+fY4jHqvfpZxxxbPMaJ2omV7SMLR2j1mzAzA/PPWvTLAZMbYNMmwiRivj1vVbPOs4RF6GYH83kJZCLwHLx50xI7X9siH0PkWaxsVUOMYtaCahkfch7ckIOtYxY8yyUWQCsUIJc+IfaAkbyFzBIsdtZsQG0J8nlwHkxV4HTDxnhYLK3ete9L6TozDgoZ6PbBT9Lr+RCEFnNL0mf1vchialEIuXy6hhiSLkQ5Vm6mnINizs6PYujFUAihk4H2kgXeJkeh4CzE1utDJ4GoAVEeMun91ZvQ7kC+CrV5iAJ73apAE/B8ePwJOJCHrXNwugrNqkXe7n4Y6Nk8u4/AfAmeGR681tcs2f6LcscdCqqJiIiIiIjIRfEXFpjYs2ftzhjYB9wJFNN9deAhYCuwEXs2cTYxMAccxX7jvayTzhuvHT6xZw/z77uLaGLiAu9CRERERERERERERERERERERETk8vnLv/xLoihaCZYliX3I6eabb2bLli2Xc2kr3vWud/HAAw+csf+LX/yiomopRdVEREREREREREREXoCDcwfZd2gfcRKfc1w37K6JqoVxSG3uaWjUIL9qYONZ8AMYGSFKIoL2EviQ9eGqERguWjwsiiAXw87rYUMZICLywI+xslnKATdtsPMBXAIFHyYqNk/ch8UmlFetwWPVeGxcNgPZLMTp3L6DybJ9XR4Xx1BrQ3bYAm5RDKXc4HPWcWTzRAHg2zq6kZ0H1k7rp4MzsW032hZ885yF13KehdfiGIjBDYOXs3lrDXAxZDw7HjloLkKuDY2chdNmnoXZBciHsKk5uOerG/b1mz049BWLtPVHoVuDbjpm+xxMtgfn5CK4cz8cHYdaAUa7sLV67s+fP69du+C++y5mBhEREREREREmP3UPfrt95oEF4HPAv2UQVguBp4BjWFhtBCgDPhABLSy+Ngv0nzNfB/iTdN7n8FstJu/5NLMf/tDF3o6IiIiIiIiIiIiIiIiIiIiIiMhL7mtf+9oZ+5xzvPnNb74Mq1nfd3zHd6zZds6RJAkPP/wwSZKsBOFezRRVExERERERERERETlPjV6DvU/uJSE582C7Dc0mVCpQKlHIFJgqTzHXmgOgSIb8Qp2ev87E9RpUKoxkMmye6rN9E2wcgsRBnD7HHsrB9mkLjbV6MNuEuQb4PlxVgUoByjkYL8FICfoR9EKLsU2WLFLmEshlYHwEYh9afYuRDeUgSWD5mXknAM+DsWGopx+edkDet4BZFNt2BiCBagemhmyOOLZAWxBCFEI+a+cnztZ2qmFhNofdW68PXmTXCzyIOjAfWARuKGfjEmxtGQcM2WfAgxDCENrz4BUhW4L5DuQ7dr1CCM/GMFe17dHlUlpq8xwEfw97Q4jTt3NLbXDcJfC2k2e+VQ64obrOe7iee++FzZvXP+YcbN8Ok5PnOZmIiIiIiIjI+vyFBSb27Dn7gFPAHuDdwKZV+/vAt9Jf52MG2Me6QbVlE3v2MP++u4gmJs5zUhERERERERERERERERERERERkSvDk08+ue7+rVu3vsQrObvXvva1K9+vjqj1ej2OHj3KDTfccLmWdsVQVE1EREREREREREQEqHfrHFs8Rr1XP+uYwwuHOV47vrIdJ7EVv/Y/Ck8dgiixYtiN2+DW2wBY7C7amNMzuDihkx/Ml4mhGFjw7LaxGrdfF+JCiDwgtjGBB4VVQTUvgeEcjE7A7VfbJdttOydxMF5OA2QelLMwlIcwTuNlCfRDWApgqAT1DoQR5Jyd4/uQ8y2M5hzgYKg4WK8fQzZrUTWAom9htG5o82Q8C51lPQgAEogDcL59n/Mt8JZPn0wnDoIIqEJ+HKIMxJHdUxjaOrw09BZGNseyTtfWk4lg4VuwOAO5cfBHLLLm9eH0jIXe8iEUQ/u+uQjlh6B8P9CDm2+EA9Nnvtc7ZmGyfe6fmXNyDnbvho0bL2ISERERERERkec3+al78NvP85vYBeAPgbcC3wnkXsAF+sBXgQdZeV5xNn6rxeQ9n2b2wx96ARcQERERERERERERERERERERERG5/I4fP74SKVtteHj4Mqxmfeday4kTJxRVQ1G1K9rTTz99XuOuu+66S7wSERERERERERGRV7aDcwfZd2ifRdLOYbY5y4naiZXtmcYMveoctGpw7fLeBFpPwhOnYGSEVtAiCHrQb8AQ9NMQ2fLQ6VF45xtgeqhJH0gy1g7zlqdz8PopyPi2P8JiZlNl8NNBwxlYaIKXH0TIEqCYte2cD7k8LDVgdgnyBWu/TZbh2SXI+uCwcFmjY9cu5FYuTyEL3b7tT2KbL0jjaYUMdCLorYqq5dJwnBdD3IV41Qe1RwuQz0I3sDhbEEAuhIUGlMfA82C6bPfb60PfgfOg3bX5nbPY2uwCZOehNg9RYHO/9gn4xhSEARx7FKoOjk7B1jk4XYVm1SJvdz8M9Oyc3UdgvgTPrPrzhGuWbP9FueMOBdVERERERETkkvMXFpjYs+f8BsfA3wEPATuAW4Bp7Df/z5UAp4EDwOOs/D76fEzs2cP8++4impg4/5NEREREREREREREREREREREREQus0ajse7+fr//Eq/k7IIgOOuxpaWll3AlVy5F1a5g119//brlwtWcc4Rh+BKtSERERERERERE5JWn0Wuw98m9JCRrD7Tb0GxCpQKlEgAjhZE1Q0YzFWbrh9efuF6DSoWCXyBo2QP1rA+bhqCcBtHGi/A9r7FIWJz23ByQycB0BYbzcPUIjFcgTiCIIElgKLv28845H8Yq0Iqh0bUwWTFj11uWJOD74K8KnBU8u8byY0gvsWjaQhumRixs5rAw2/LD5Ci0KFsUWBxurASdhq3fYfv66SPLTAyRB402FAs2TzFj1yvnLaoWdiE7AknOom6VEuQ8cDHEns3bWIBeaHMBzDwLtVnY1Bzcy1QLJtswfAL+92PQTv8MoH8aujXopuO2z9m4ldcugjv3w9FxqBVgtAtbq+t/nvy87doF9913MTOIiIiIiIiInJfJT92D324//8DVelhY7SEgi4XVRrDf/IdAHQuqnf3v3p2T32oxec+nmf3why5sAhERERERERERERERERERERERkcug2Wyuu//ZZ599iVdydnNzc2c91mq1XsKVXLkUVbvCJUny/INERERERERERETkDPVunWOLx6j36uccd3jhMMdrx1e24ziG/Y/CU4cgSsB3cOM2uPU2cJCQUOvUbPDpGSKgf5YnrV5jkfLIMDdN9Nh2DUwNWawr8qCQhTdusvCZS6DVg/kWVPIwXrLwmOdgsmLf+87iaUM52x+E0A0gDKDWhFYCG8fgdMPiZGN5yGXsOr6DTgiJBxMVqHfS9SUwUoAoXa+f3m4CVDu23iSx+bK+XZMEkvSEJF3TpDXncOm+Xh+8CPDsXuM+LARw7bjF5BKbhjiGQg7IQRFYakK7CxNZ69gFQG0JCh0oOGjl7PhcFaaWK2mpzXPAA3DtY9C5jZUq2pbaYIxL4G0nz3yfHHBD9ew/I2vcey9s3rz+Medg+3aYnDzPyUREREREREQuQhwz8f/8Pxc3RwB8K/31Ipr4b/+N2Q9+wMrvIiIiIiIiIiIiIiIiIiIiIiIiLwPFYnHdMNk3vvGNy7Ca9Z1rLfl8/iVcyZVLUbUrnHPurMcUXBMREREREREREVnfwbmD7Du0jziJn3fsbHOWE7UTK9szJw/Sa83Dtct7Emg9CU+cgpERojii0W9YEazfICpA4A/mcwn4sYXPdl7VYueWDsXE4mLpbHgJbNsAGd+2cTBehhs3WJer0YVGB8oF8AaroJCx4wDZjAXPaovQiSCbsyDaZBmeXbKgWhRBM4aCD2FswTPfQTlnE3oMQmguse8LPsQOuqHF1DKehduy2VUvWgeSVU+Xyzko5iy6FsXQi8Grgr/Bjmc8GCtCEIBPet8JBP3BHHECnZ69duESnP4WzMYwMgwl39ZNACdPQS6AYmhzNBeh/BCU7wd6sAHYMQsHps98r3fMwmT7eX8kzs452L0bNm68iElEREREREREXhz5o0fx6+eOyV8ufr1O/uhReq973eVeioiIiIiIiIiIiIiIiIiIiIiIyHkZGRlZE1VzzpEkCQ888ABxHONdAf/I5Je//OWzHhsZGXkJV3LlUlTtZWC9eNq5YmsiIiIiIiIiIiKvZo1eg71P7iXh/P5RgpHCqofFQcDoqQVmS+sMrNegUsH3fbJ+lqC7BEDBg03DMFwE37OQWS6GndfDhjJATOIgE1usDODaURha9Q9/5DOwsZSGw4DRIgz70HfQCqzflvHtnOUng1EM/QDGh+F0y8JlDsj7MJyHrJ+OTaDWgkzGfiWJBdeWO3AOyDgg7c9lPIu3dSLoRYN5g8iOe7HN2ehAsWjxuKwHOQ9yeVsrMbiyxdVG0tfSJXbtXh96QK8DiWdrcg7qDagtQb4KtXmIAgvRVYEm4Pnw+BNwIA9b5+B0FZpViAO4+2Fs0tTuIzBfgmeGB/uuWbL9F+WOOxRUExEREbnCOQcZ7/z+PF1/7C4iL3fR6CiJc7gr8B9mTJwjGh273MsQeRlw4J3vX+PU/3kRERERERERERERERERERERuZS2bt3KP//zP6/E1JYbT/V6nfvvv593vetdl3V9QRDw+c9//qztqa1bt77EK7oyKaomIiIiIiIiIiIiryhHqkfWBNXiJB4cbLeh2YRKBUpW+8r5OTaUNjDXmoNnnqbQT8jnoPvcp6cJ0FiCkVGGMxmun+qzfRNMDQEOovRZ9FAObp620FirB7NNmGtADpgYgpEibNuYBtiAMLYIWhyl1wDCANotKI9AtQX9yMY0e2n0LAvdMA2VeTBRgaWOnevHMFQcLNuPIUrs+NSQXcJLfy2/Sn6UNtWchd/GStBpWCDNYfvi0LbLWOwsCWG+Ydceytm4hDRi4YAhC7c12xAnUEpfowCLpxVa0M7aXO0uHDkJuQA2NQdrv7phX7/Zg0NfgfYS9EehW4NuOmb7HEy2175VuQju3A9Hx6FWgNEubK1e5MdOd+2C++67mBlERERE5CVw8zWjHP313Zd7GSIiL4lwaorm297G0N/8zeVeyhmab3874dSGy70MkSvf1bfCRxYu9ypEREREREREREREREREREREBNi+fTtf+cpXztifJAkf+9jH+OEf/uGzBs1eCvfccw+zs7NnRN8ASqUSr33tay/b2q4kiqqJiIiIiIiIiIjIy0a9W+fY4jHqvfpZxxxbPMaJ2omV7SMLR2j1mzAzA/PPpuUvYHIDbNoEzhHGIfPNOeg8C5MQOuhmB3N6CWQi8HpLfMeGmLds6uElFhtbrZiF7dPg+0ACwzkYnYDbrwYiWOpBqQA539bgYbG0nA9kIQxhsQWNGmQz4HmwoQKzDchnLKIWxlBrQxBBJQOJA9/BUMGu6WExM8+zCJoHeM4ibGEEGR+8GKLYxoDdWycCL2/z5XyYtOacxdLSYlqrBqVhiDK2HYW25ji2a4BdY1XTjlYHem3IZCDJwHwAmRD8BAoh1H04eQqSxOJnq22eg+DvYW9oYTaALbXBcZfA206u/3PggBuq6x9b4957YfPmsx93DrZvh8nJ85hMRERE5CI9ffb/nysiIrKep//ws1z373+KoQceuNxLWdH47u/m6c9+5nIvQ0RERERERERERERERERERERE5AX53u/9Xv7gD/5gZXs5XJYkCf/4j//Ib/7mb/KBD3zgsqzt2LFjfOQjHzkj6ra8xre//e2XNfh2JVFU7WVAP6wiIiIiIiIiIiJwcO4g+w7tI07ic47rht01UbUwDqnNPW2lsvyqgY1nwQ9gZASAqN0k8CHrw3QFSmVwvsXH6h0ggO/fBtOjbYhi/BgrlqUccNMGOx8s+FXwYaICvgd+DJUsxJl0cHp6zl/ZxPMg70M/C0m633cwWbavDouP9SKoNSE7bNdzQC5jsTSwCJzvAZH1zXLpk+BeBBnPImlJAiSQSUNsSQvC3GAt5RwUcxCE9hr0Ysj2YKEBlTGbf3rYIm29PvQdOA/aXbuGcxZbm12A7DzU5iGJIdgEmyqQ5MHrwzOz0AsgH0IxtHU1F6H8EJTvB3pw841wYPrM93rHLEy2z/njcG7Owe7dsHHjRUwiIiIiIiIicvnEQ0Oc+P98Hn9+nsJTT6Wh84TxP/kThv/yr/CCgDibZWn3v6L6Ez/B4Hf+l4CD7rZtRBMTl+4aIiIiIiIiIiIiIiIiIiIiIiIil8j3fu/3UqlUaLVaKzE1YOX7X/mVX+H1r389P/zDP/ySrmthYYEf+qEfol6vr1nXau985ztf0jVdyRRVu8Kt9wMsIiIiIiIiIiLyatPoNdj75F4S1nle1m5DswmVCpRKFDIFpspTzLXmACiSIb9Qp+evM3G9BpUKeT9h+1iH114LG4ft48Wxs18AQzl4wzQEAcw2Y+YaEAEFDyaHYSgPV4/AeAXiBIIIiKGSRspcMgif5Qqw0IF+CJW8RcSW/12FbggZB2PDUA9sn8PCazkfwihdWwwkUO3AVGWwL+9bBM1hYbUYiBxkPQu8xXEaZnN2zVYbJlaF5qpNu7aXnpPzIJdPrxeDG4Z+zsYW8nbcxRB7NqaxAL0QojQ2N/Ms1GZhU3NwjfGjMFeAZg2+9QTUc3B0CrbOwekqNKsQB3D3w0DPztl9BOZL8MzwYJ5rlmz/RbnjDgXVRERERERE5BUhmpyk9Z3fCcDkf/2vjP73P1855gUBo//9z+ns2MH83XdfriWKiIiIiIiIiIiIiIiIiIiIiIhc0UqlEv/23/5b7rnnHlz6ga8kSXDO4ZwjiiJ+9Ed/lE9+8pPceeedL8majh07xg/90A9x8ODBM0Jvy0ZHR3nPe97zkqzn5UBRtSvYr/7qr17uJYiIiIiIiIiIiFxy9W6dY4vHqPfqZx1zeOEwx2vHV7bjJIYE2P8oPHUIogR8Bzdug1tvA2Cxu2hjTs/g4oTOqnhYJoZiYPGw28Zq3H5dSLELvQyE8WBc4EEhB9un0zBZDobHYeuYxdOyWFAscTBZsVCZ7yw2NpS3+YMQwj6EIbR6UPIg48FCz756gL8qmuY7wMFI0a6xHGXLZwZRtRwWfOuGtt6sB35igbZlXh9chpUM3XgJ+un5YFG4ftPW72Wh50HUg/kGTFQsJOew852z2BtDUMTuaaEGE1kolSAAaktQ6EDBQSsH7S7MVWGqu/a9fN0MPHIMHj0FSfpa909DtwbLQ7fPwWR7cE4ugjv3w9FxqBVgtAtbq4N7uSC7dsF9913MDCIiIiIiIiJXHH9hgan//NvrHpv67d9h8Ud/lGhi4iVelYiIiIiIiIiIiIiIiIiIiIiIyMvDL/7iL/JHf/RH9Pv9lYjZ6pBZEAT81E/9FH/1V3/Ff/pP/4nXvOY1l2QdURTx+7//+3z0ox9laWlp3THLwbef//mfp1gsXpJ1vBwpqnYFU1RNRERERERERERe6Q7OHWTfoX0WSTuH2eYsJ2onVrZnGjP0qnPQqsG1y3sTaD0JT5yCkRFaQYsg6EG/AUPQ9y1Etjx0ehTe+QaYHmrSB/oF8GOIvFUXdvD6Kcj4FheLgbJn0THfgyiAhSZ4aUAtnZpidrCd88HLWIQs8S0E5juYLNtXh8XS6l0LoxVzdl7GQSED3b7tjxObKwgh71vIDaAXWVTNA+L0ZfRi+9UPwGUH66jkoR+m58UWK2vXoDxhQTMPCAILwMXx4B7CiEGdDeh07bUKl+D0t2A2hpFhKPlpFC6Ak6cgF0AxtNhbcxHKD0H5frjJh7+5nZUq2pbaqpc8gbedPPNnwAE3VM/cv65774XNm9c/5hxs3w6Tk+c5mYiIiIiIiMjLx+Sn7sFvt9c95rdaTN7zaWY//KGXeFUiIiIiIiIiIiIiIiIiIiIiIiIvD695zWv4hV/4Bf7jf/yPOOfOOL4cWvuzP/sz/uIv/oKf+7mf46677mLLli0vyvW73S5/9md/xq/92q9x5MiRNUG31d8vu+666/jFX/zFF+XarxQuWX6lRF4CQ0NDNJvNNfsqlQqNRuMyrUhERERERERERC6XRq/B7/z975CwziPKdhuaTahUoFSiG3b5h2f+YeVwp9Ng9sh+1jsVB1x9DZGDxtKz0OuR9WFyCMpli6GNF+F7XmPhsHhVzy3nw8YKVApQzsF4CUZK0I+gF0IUwYbSIDbmxxAC3QTCdK6sZ9fylyNrEXS7FkWrBzBUsGUn6fXCyL5f6kGjBVMjFnHzsLGdjg32s5DJQLdjcbiZJnQjGCvCcB4y6fWdg0Jk1362B4X0njOe3VOYQDeAWgOSJSgF0CtAM4ZiHoYqdn2HzeU8aHftfOfsGif+GbLzEMxbWC5IQ3Q5wPPh8SfgQB62zkG+Cs0qxAHc/TBMpp/rvv9GODB95tt3y2l451Pn9SO0PudgZgY2bryISURERORF93T90l/jupFLO/9LcQ8vd5f6PXgFOPzoNy/3EkReNY40a5d7CRftum3XvKDx/sICr3/T7WeNqgFE5TL/9PBDRBMTF7s8eZnYPqVnJCIiIiIiIiIiIiIiIiIiInImNWBEzi4IAnbu3Mljjz0GwOpE13LQ7LmBs9tvv50f/dEf5Tu/8zt5wxveQKFQOO/rHTt2jMcee4w/+7M/48///M9ptVpnzL/ediaT4YEHHuCtb33rxdzuK07mci9AREREREREREREXnnq3TrHFo9R7509PHF44TDHa8dXtuMktrrY/kfhqUMQJVYmu3Eb3HobCQm1Ts0Gn54hAvpne8LZqFIcKvPtkz1uuhqmhiwSFnlQyMIbN0HWB5dAqwfzLajkYaJkMbPEWZtrvGxfl4NkQzmLp/X6dm4QQrULo0Ow0LJA2lgeml0LkxUyFmMDSDwYKVpczWHn5zN2jsOCZAlQ7dh6kySNtPl2nSiEfDady1nw7VTDXiKHhdaSBPotKBUtdJbEFk8bH4Zixsb5Dko5iPPQH4VsH3qA68BQGXIeuBhiz67fWEiDcmk4beZZqM3CplV/bnZ1+udl3+zBoa9Ae8nm7tagm47ZPjcIqgHsPgLzJXhmeLDvmiXbf1HuuENBNRERkVcrRc9ERORVbvJT91hQLQtMA6OAD0RADTgNfqvF5D2fZvbDH7p8CxUREREREREREREREREREREREbmCZbNZvvCFL/DWt76V2dlZnHMrUbOzxc4eeughHn74YQA8z2Pr1q287nWvY2RkhKGhIYaHh8lmszQaDZaWllhaWuLUqVN84xvfWBM4fO78q/et3nbO8V/+y39RUG0diqqJiIiIiIiIiIjIi+rg3EH2HdpnkbRzmG3OcqJ2YmV7pjFDrzoHrRpcu7w3gdaT8MQpoqEKjX7DSl/9BlEBAn8wn0vAj8FzsPOqNju3dMgOnh2TAF4C2zZY8CwBSMNpN26weFqjC+22BcoKBZtr+dxixrZzPmQLsLQEs0vg5yxUNlmGZ5cgl8bLwhjqHQhDKGUthOY5C611++AnFljLpdG0gm/X7YYWWst4dm7WgyBdRByA8+37nA+TJQuzgc0fRBAvQZCBqAhRBHFkX5efoztsnO9BMQ/kwTXB82ChBhNZKJXsmrUlKHSg4KCVg3YX5qow1WWNzXMQ/D3sDe2eALbU1r43bzu59pxcBHfuh6PjUCvAaBe2Vm19F2zXLrjvvouZQURERERERORlKVM9xcTjn4GfwoJq6/0GOwFOw8Tjn2Gh+pOE41e9tIsUERERERERERERERERERERERF5mXjNa17Dl7/8Zd7xjncwPz+/JqwGg7DZ6rja8vEoivinf/onDh8+/LzXeW4w7VwxtdXHPvaxj3H33Xe/8Bt7FVBUTURERERERERERF40jV6DvU/uJWHtA1vabWg2oVKxYhcwUhhZM2Q0U2G2fpYHxfUafqVC1s8SdJcAKHiwaRiGixYIiyLIxbDzethQBkgggchZ1MsB147CUH4wbT4DG0sWRQMYLcKwD7OLkBQssuZ5FkLLrgq4JYmF2fycxc8ckPdhOG/jXHr5IIBqC/wR2x/FUMpBmM4TR5DNQBQMrtOJoLcqqpZL1+vFEHchzq16zQqQz0I3sLmDAHKJXbOStzk3VCDroN2BfM5eq7A3mCNOoJNu+zGES3D6WzAbw8gwlPz09Qng5CnIBVAM7TVoLkL5ISjfD/Tg5hvhwPSZb9+OWZhsn7nfATdU13/Lz7B3L4yNrX/MOdi+HSYnz3MyERERERERkVeKiFL2ITYe+U387+6de6gDNoG/qcu1R+9m9q2/RDv4dsA/93kiIiIiIiIiIiIiIiIiIiIiIiKvQt/2bd/G1772Nb7/+7+fw4cPrwmorf4Ka4Nny54bRVvP+Zy3+rqZTIZPfvKTvPe97z3/G3mVUVRNREREREREREREnle9W+fY4jHqvfo5xx1eOMzx2vGV7TiOYf+j8NQhiBKrc924DW69DRwkJNQ6NRt8eoYI6J/lqaXXWKQyXGbLVJ/tm2BqCHAWTQMYysHN05D1oNWD2SbMNSwoNjVkAbJtG9MAGxDGFkGLI1huwIUBtFt2PAmh1bf9ozkLsBWydgudABIPJiqw1LExfgxDxcF6/diCZQlQ7cBUxT67HMcWWAtCiEKLojnsPsZK0GnYGAfEDvppgS0TQ+RBow3FAngOihnriZXzFlULu5AdgSRnkbmRiq03G9nr2u1BqwZ5B1ERMhlotqDbh24X8lWozVvkLfKgCjQBz4fHn4ADedg6B6er0KxCHMDdDwPpZ7Z3H4H5EjwzPHgdrlmy/Rdl1y5497svchIRERERERGRVxbfLTCS/wtyvROU/vGRF3Ru6R8fYfjNX6ZQOEK99/1EycQlWqWIiIiIiIiIiIiIiIiIiIiIiMjL12tf+1oeeughfvqnf5rPf/7zOOfOiKs99/vl4+sF09Zztvja6vOTJOE1r3kNn/vc59i5c+cLvo9XE0XVRERERERERERE5JwOzh1k36F9xEn8vGNnm7OcqJ1Y2Z45eZBeax6uXd6TQOtJeOIUjIwQxRGNfloR6zeIChD4g/lcYnEyz8HOq1rs3NIhB1YcW6WYhe3T4PuQJBYZe30ebr/aWm7NDpQLkPPtXA8LquV8IAthCIstaNRsvlwRJoZgpgFhZEG1KIJmDIWMBcwSZ8GyoYLdlgf4gOfZ7XjY8dhBN7SIW9aza2U9CNKXIw7snBBbz2QpvXfsGr0+eBHgWegs7sNCANeOWyAusWmIYyjkgBwUgaUm9AKYKoKXAUKoNqHQttek0bdfR07aa5YPYVNz8Jpe3bCv3+zBoa9Aewn6o9CtQTcds30OJtuDc3IR3Lkfjo5DrQCjXdhaPePtemF27YL77ruYGURERERERERecTLeDKOFvXiuS+mRR/CCtMqeA6aBYaCMPayIgBawBJwG+uD1A0qPPEL8XUXGiv8vte6/IYw3XZZ7ERERERERERERERERERERERERuZINDw/zp3/6p7z73e/mF3/xFzl+/PiauBqcPbB2IZ47bzab5Wd+5mf4tV/7NSqVykXN/WqgqJqIiIiIiIiIiIicVaPXYO+Te0lY50Fuuw3NJlQqULIS2EhhZHA8CBg9tcBsaZ2J6zWoVPB9n6yfJeguAVDwYNMwDBctGBZFkIth5/WwoQwQ20qSQajLATdtgOyqGFvJg8mKzQEw4kOcYeUkDwuYLc/heZD3oZ+FdgR5z4Jok2V4dsnmdlh8bLFl25mMbecy4KW9OS9JrxnZdt63MBpAL7KYWhRCLp+OjyHpQpSzewIo56CYgyC0eFsvBq8K/gY7nvFgrAhBYJ+LzqQhuaA/uP84gU7PYmztJrQSiD0gC356nVwPTs7YuWABtNU2z0Hw97A3tPkAttQGx10Cbzt55lvrgBuqZ+5f1733wubN6x9zDrZvh8nJ85xMRERERERE5NXBdwsrQTWv3aG4f7/9LbCtwEbWr5sXgUngNcAscBSKjz1G+01vghKMFvay2HkPUTLx0t2IiIiIiIiIiIiIiIiIiIiIiIjIy8i73vUufuAHfoB77rmH3/3d3+XEiRMAZwTWlj1fXG29c1afl8/n+fEf/3E++MEP8trXvvbiFv8qoqiaiIiIiIiIiIjIq1S9W+fY4jHqvfpZxxxeOMzx2vGV7TiJLf61/1F46hBEidXHbtwGt94GDhISap0anJ4BLyFy0PfXmbxRxasMU/E9tkz12b4JpoYAB1H6PHgoBzdPW4ys1YPZJsw17JJTQzCUh6tHYLxi4a8gskjYUHZVdC0Nn+UKsNCBfgiVvI1bfu7cDSHjYGwYeq3BEgseDOcH47wE4hiqfbt+gm3nfYugueUx6fm+DwUfupGNAzunH9r3mfTlbLShUADP2b3mPAuvxTEQgytbXG2kNLinJIFeH3pArwOJZ6E356DegNoS5KtQm4coAM+HjWXoTUMmBwvHsLpcBvIhFEObs7kI5YegfD/Qg5tvhAPTZ759O2Zhsn3WH53n5xzs3g0bN17EJCIiIq8iT5/9/7O9KK4bef4xIiIicgWIGMn/BZ6zOnrpkUfwSiF8G5A7j9MdMA2Mg3cwoPTIIzS/61/guS4j+b+g2v1xLOMuIiIiIiIiIiIiIiIiIiIiIiIiz5XL5fi5n/s5fuZnfoa//Mu/5POf/zxf/OIXaTabK2OWY2lni6Y913Pja29+85v5kR/5Ed7znvcwNTX14i3+VUJRNRERERERERERkVehg3MH2Xdon0XSzmG2OcuJ2omV7ZnGDL3qHLRqcO3y3gRaT8ITp2BkhCiOaPSWoL8EoxZIC1Z9Ftcl4MfgBW12TkXsvDoglzCooKWKWdg+bWEyEhjOwegE3H41JBHU+9blmizbV99BzrcQm+csctYNLCjW7EHJg4wHCz376mFz53wIIzsfBxMVqHdsDV4CIwWI0jX5ic3dDe2cjA9ebDGyZV4fvAxEMSQOxktwKg3BOSB2FkPzIvA8CDyIOjAf2LWHcjYuwe4r44Ah+zhzs23xuFIanguweFqhBe0sRB60u3DkJOQC2DR4Fs/kElzzNDxwHJ76GvgZ6I3D0SnYOgenq9CsQhzA3Q9jtTZg9xGYL8Ezw4O5rlmy/RfljjsUVBMRERERERF5gUrZh8j4swB47Q7Fbz4KN3NmB60LLAF9LLY2DBRWHc8BN0Px0H7ab3oTcalIxp+llH2IdrDz0t+IiIiIiIiIiIiIiIiIiIiIiIjIy5jnefzAD/wAP/ADP0AYhnz961/nK1/5CgcOHODgwYMcO3aMXq93zjmcc0xPT3PTTTfxhje8gbe85S28/e1vZ8OGDS/RXbwyKaomIiIiIiIiIiLyKtPoNdj75F4SkjMPttvQbEKlAqUSI4WRNYdHMxVm64fXn7heg0oF3/fJ9gICIOvDNRUolcH5Fhqrd4AAvn8bTI8GEMW2kmTQVXPATRvsfLAQW8G36JjvWZStnIN2GiZLT6eQsegZQDZjO2cXIfFtTj+NsC0HzsII6l2LpRVzdp7vbG4SC685LI7m0pcrlz5V7UUWZ0scxGmbzovtV9SDOJ0v58NkCfLpeYmDIAKqkB+HKANxBFECYWhzLd9DGKU3lmp1oNeGTAaSjIXYMqGtvxBC3YeTpyzyNtpd+/ZsnoPyA9CrQnP5ufoc9E9Dt2aftQbYPgeT7cF5uQju3A9Hx6FWsHm3Vs9o4L0wu3bBffddzAwiIiIiIiIirzqOLuXsQyvbpf0P4b0uWhtUawFHgcV1JhgDtgLldNsH73UhpcceovnWtwFQzj5EJ3gjyZoCm4iIiIiIiIiIiIiIiIiIiIiIiJxNJpPhrW99K29961vX7F9cXGR2dpZ2u0232yWOY/L5PIVCgfHxcTZu3EgmowTYi02vqIiIiIiIiIiIyCtIvVvn2OIx6r36WcccXjjM8drxle04iS3ctf9ReOqQ1b18Bzdug1tvIyGh1qnZ4NMzRED/bE8WG1WKQ2W+fbzDDdfA1PAgShalFa6hHGyfhiCA2WbMXMNiazkfpoZgKA9Xj8B4BeIkDZDFUMnZXC6Nr+V9KBWh3oN+CBnfzl0WxRD0YWwY6oHtc9h1cr4FyxwWMau3YGrE5nBYOG3588gOyDhbQ+wg61ngLY4H9xaE4Bzk0rha2II4ay8jwGgB8lnoBum6AsiFsNCA8piF4abLdv1eH/oOnAftroXbXBpum12A7DzU5iGJIdgEmyqQ5MHrwzOz0AsgH0IxtLhacxHKD0H5fqAH31WCb0zaugG21AavmUvgbSfPfFsdcEP1bD9Rz3HvvbB58/rHnIPt22Fy8jwnExEREREREZFlhcyTONcHwGt3KC7th9X/IOmzwCEgPssEi8CjwDYG5+WgOP8Y7fa3E5eKONenkHmSTnjrJboLERERERERERERERERERERERGRV4exsTHGxsYu9zJedRRVExEREREREREReYU4OHeQfYf2WSTtHGabs5yonVjZnmnM0KvOQasG1y7vTaD1JDxximioQqPfsKpXv0FUgMAfzOcS8GPwHOy8qs3OLR1yzqbA2ZdlxawF1TI+5Dy4IQdbxyyelgViz2JfkxXrb/nOxg3lbf4ghLAPYWghtKExi44t9KCUhWbX5i6kATMHJB6MFO0ay1G2fGYQVculS612LOqWpOM8Bmv3I4vCLYfIxkvQT88HiICoaZE3gD7QbFjQzXNQzNj9lPMWVQu7kB2BJGfrqJTsPl1sr0EcQ2MBeiFEXvo+PQu1WdjUHLye40dhrgDNGnzrCajn4OgUbJ2D01VoViEO4O6HgZ6ds6ENO2bhwPSZPxs7ZmGyfc4fn3NzDnbvho0bL2ISEREREZEL99TpJX72/33svMb+3+95IzdOD1/iFYmIvHiKmW+sfF967Ot4k6ueAbU4d1BtWZyOKwFl2+WNR5QOfJ3mW96+ch1F1UReIrMHYe+/O7+x/+Ze2Phtl3Y9IiIiIiIiIiIiIiIiIiIiIiIvc4qqiYiIiIiIiIiIvAI0eg32PrmXZE3CLNVuQ7MJlQqUSowURtYcHs1UmK0fXn/ieg2/UiHrZwm6SwAUPNg0DMNF8D2IIsjFsPN62FAGSJb/A8kgPOaAmzZANg2yuQQKPkxUbJ64D/Mt8NKAWno6xexgO+eDl4GFGoTO5vQ9mCxbiMwBYQy1toXXylkLoXkOChno9sFPLLCW8y3SVvAhdtANLXCW8WxMFIOXBs0yEXQTW9vyOip56Ie23YuBBoQZ8LLQBaIeLDTg2nFbY5LeTxxDIQfkoAgsNaHdhYkslEoQALUlKHSg4KCVs+NzVZjqrn17XjcDjxyDR0/Bckuvfxq6NVsDwPa5M0Npu4/AfAmeWdWPuGbJ9l+UO+5QUE1ERERELqteEHNkrvn8A9OxIiIvF44+GW8OAK/doXj6AFy3asBRnj+otixOx+9Itz0oPnOAdvvNxKUiGW8OR5+E3Iu1fBE5m7ALzz51/mNFREREREREREREREREREREROScFFUTERERERERERG5wtW7dY4tHqPeq591zOGFwxyvHV/ZjpPYCl77H4WnDkGUgO/gxm1w620kJNQ6NRt8eoYI6J/taWGjSrFSZPtUn5s3wdQQ4CBKQ2dDObh5GrIetHow24S5hl1uqgJDBSjnYLwEIyXoR9ALLcY2WbLgmUsgl4ENIxYva/UtPpb1YCg/WEocQz+AsWGo9y2ilqSBtGzWQmhJYrGzWhMyIxZxi2Io5SBcnieCbAaiwMJphQx0IuilUTWHvWQkkEk7de2eBd58z8YUMzZ3N4AwgMSHdg3yGyzOVliOpAXgAxnf1hb0V91PAp0e+DGES3D6WzAbw8gwlHx7DQng5CnIBVAMbY7mIpQfgvL9cJMPf3M7K/W6LbXB/C6Bt5088y3NRXDnfjg6DrUCjHZha3UQwLsgu3bBffddzAwiIiIiIiIichYZb85+ow+UHnkErxQNDnaBxRc44WJ6XsE2vVJE6ZFHaH7XvwCXkPHmCOJrXoSVi4iIiIiIiIiIiIiIiIiIiIiIiLx0FFUTERERERERERG5gh2cO8i+Q/ssknYOs81ZTtROrGzPNGboVeegVYNrl/cm0HoSnjhFNFSh0W+klbIGUQECfzCfSyz05TnYeVWbnVs6FhtL1l63mIXt0+CnwbByHl6fh9uvtihZuw2RB4mD8TI4Z0GyctZiaWEMvT54aQit2oXRIVhoWZhsLA/NrgXJChmLsQEkngXa4mQQAitmodlLQ2tpU67asbCbI420+RCEEIWQz6bxNAdjJeikL4cDYmdrbbVhIm/34BKoNWB82IJqDouelXIQ56E/Cn4fmn0bk83aOUli99gDeh1beyZj89cbUFuCfBVq8xZ5izyoAk3A8+HxJ+BAHrbOwekqNKsQB3D3w0APNgA7ZuHA9Jk/FztmYbK9/s+MA26onvPHyuzdC2NjZz/uHGzfDpOT5zGZiIiIiIiIiFwI3y0B4LU7FPfvh1tXHVy6wEmXWImqUYbiY4/RftObiEtFPNe4iNWKiIiIiIiIiIiIiIiIiIiIiIiIXB6Kql2gXq/Hgw8+yGOPPcY3vvENTpw4wczMDAsLC3Q6HXq9HnF87g+6vhje9KY38fWvf/2SX0dERERERERERF56jV6DvU/uJSE582C7Dc0mVCpQKjFSGFlzeDRTYbZ+eP2J6zX8SoWsnyXo2qduCx5sGobhIvgeRBHkYth5PWwoAyQksQW/vDQ85oCbNliobFnJg8mKzQEw4sN8C7y8BdpsJgugeQ5yPuTysNSA2SXwcxYqmyzDs0uQS+NlYQz1DoQhlLIWact4kPOg2x+E1XJpNC3jLIzWDe3crGfnZj0I0kXEAXgehOl5kyWbw2Hzk0C/afccFW3dcd9eG+cGY4PI7reYB/IQNaHWtDhcyVm0LcDiaYUWtLM2Z7sLR05CLoBNzcFreHX6meVv9uDQV6C9ZMG2bg266Zjtc2tjabuPwHwJnhke7LtmyfZflF274N3vvshJREREREREROSiuQiA0iOP4AUhrHoeQ/8C51x9ng9eP6D0yCM0v+tf4Fx4oSsVERERERERERERERERERERERERuWwUVXsBut0u+/bt40/+5E/427/9W3q93prjSbLOh1svsctxTRERERERERERuXj1bp1ji8eo9+pnHXN44TDHa8dXtuMktrLX/kfhqUMQJVYgu3Eb3HobCQm1Ts0Gn54hAvpnewLYqFKsFNk+1efmTTA1BKQBMIChHNw8bRGyVg9mmzDXsJDZxmEYycPVIzBegTixsFiSwFB2EDhziUXRxirQiqHRtYhZMbM2xJYk4PsWVMt4dn7eh+G8jXMACQQBVFvgj9j+OIGCb1G0JLGXI5uBKLDzk3QhvcjuIwot4AYWhku6EOVsboByDoo5i7JFMfRiKIR2zaG8hdM2jEDWQbsD+ZztC1c9JowT6PRsPbUqZDKQZGA+gEwIfmJz1n04ecrGjXZZY/McBH8Pe0ObD2BLbXDcJfC2k2vPyUVw5344Og61gs25tTp4Ly7Irl1w330XM4OIiIiIiIiIvFgSH6/dobh/v21Hq47lLnDO1eel8xUfe4z2m95E4umvlYmIiIiIiIiIiIiIiIiIiIiIiMjLj/7223no9Xr83u/9Hr/927/N/Pw8sH7MzLmL+oiiiIiIiIiIiIi8ShycO8i+Q/sskras3YZmEyoVKJUAmG3OcqJ2YmXITGOGXnUOWjW4dnlvAq0n4YlTREMVGv0GxDH0G0QFCFbFy1wCfgyeg51Xtdm5pWMRs+c86ipmYfu0hc6SBMp5eH0ebr8awiSNozmYLINz1nXL+RZi85xFybqBxc0WW9BKYOMYnG5AGMFY3mJrhayd2wkh8WCiAksdW4Mfw1BxsCY/tsBYAlQ7MFWx8BlYYC0IIY4gm0bdfN+Ca93IXo70laIf2veZtE/XaEOhYOvOepDzLLwWx0AMbhj6OcBBuQQ+kI0sVtft2VuRdxAVLaDWbEG3D90u5KtQm4ckhmATbKpAkgevD8/MQi+AfAjF0F7n5iKUH4Ly/UAPbr4RDkyf+fOzYxYm22fud8AN1TP3r2vvXhgbW/+Yc7B9O0xOnudkIiIiIiIiInKpRckwpUcewQvShxstYPnZyfAFTrr6vJZ98foBpUceYeH2f3eBk4qIiIiIiIiIiIiIiIiIiIiIiIhcPoqqPY8HH3yQO++8kyNHjqwJqZ1PQG298NqLwTl3yeYWEREREREREZELU+/WObZ4jHqvfs5x7aDNFw5+gYT0+U6SwKOPwqFDVvLyPNi2DW67jX4cUOvWVs714oRuq7b+U71WDVfMkfEyhN0GAAUPNg3DcBF8D6IIcjHsvB42lAESkhgiD7zYolwOuGmDhcqWlTyYrNgcAEMZ6ES2VJsFChkLkwFkM7ZzdtHGZXMWT5ssw7NLFlSLImjGFj4LY0jSONtQwc71sICZ56UvC3Y8dtAN7ZysZ8Ey34MAiEJwGTsnBMZLcKph5zns3F4fvHTtgQdRB+YDC7oN5WxcgnXFMg4Yss8nByHUGjBVBC8DhFBtQqENUQKNvv06ctLe0nwIm5qD13D8KMwVoFmDbz0B9RwcnYKtc3C6Cs0qxAHc/TDQs3N2H4H5Ejyz6gPO1yzZ/ouyaxe8+90XOYmIiIiIiIiIvJTiZ32Kjz422LEELPfQC8AYsPgCJhxLz1u26pFWcf9jxNf7MHGBixURERERERERERERERERERERERG5TBRVO4fPfOYz/OzP/ixhGJIkyRkhNYXNREREREREREQE4ODcQfYd2kecxM87dqYxw/Ha8ZXt5OmT8M8nYCWcFcM/H4RcC669jn7Up9Vv2aH6IoGD0H/urKl2jWwhwxun+uzYBFNDgIMofaw1lIObpy1G1urBbBPmGpDxYOMwjOTh6hEYr0CcQBBZIGwoa7ExAJdYCK1chHoP+iFkfBjKD5YRxRD0YWwYei2b3wF5H4bzFmxzAAnUWpDJ2K8kseCal76MXpKG3CLbzvsWXwPoRXYfUZhG3LBYWhxiBbYYcj5MliCfHk+c3RNVyI9DlIE4sihaGKbxtnT+MLL1Let0gQDaTWglEHtAFvx0TK4HJ2fsHgBGu2vfmtfNwCPH4NFTFoID6J+Gbg2Wh26fg8n24JxcBHfuh6PjUCvYnFurg/figuzaBffddzEziIiIiIiIiMhlMPmpe/F6AWxKd5wGXsPgQcFW4FHg+R9P2bOTrau2Y2B21eGnAybv+SNmP/yhi122iIiIiIiIiIiIiIiIiIiIiIiIyEtKUbWz+K//9b/ycz/3cysxteWg2uqQ2nMja889vtp6Y5/vnHOde675RERERERERETkpdPoNdj75F4S1nnG025DswmVCpRKAHjOGxwPAk7Pn6Q3us7EtRNQDokcNPoNK371GkQ+BKuiai4BP42B7byqz84tgUXMnrOcYha2T4PvW/irnIfX5+H2qyFMoNG1OSbL4Bz4zqJkQznbH4TQDSAKLIQ2NGaxtIUelLLQ7FpcrZC1cQ5IPJiowFLH1uDHMFQcrMmPLWi21LEAXILdZt636zksrLb8WWDft6BbN7JxkEbd0qecmRiiLkS5wTVGC5BP1xTFEASQC2GhAeUx8DyYLtvae33oO3AetLtpDM7ZtWYXIDsPtXl7DTwfNpahNw2ZHCwcwz6QnIF8CMXQXufmIpQfgvL9cJMPf3M7Kx923lJb+z6+7eSZPwYOuKG6zs/Heu69FzZvXv+Yc7B9O0xOnudkIiIi8qrzdP1yr0BERETOwl9YYGLPHngDg6haHwuhTafbZWAbcIhzh9W8dFx51b65dL5lB2Di4B7m33cX0cTEi3AHIiIiIiIiIiIiIiIiIiIiIiIiIi8NRdXW8aUvfWlNUA3OHlN7viDaucYtz/N8853vNURERERERERE5MVV79Y5tniMeu/sgYnDC4c5Xju+sh0nsdXB9j8KTx2yapjv4MZtcOtt9OM+i91FG3N6BhcndPJnmbxRxasM4zufsG9lsrwHm4YtTuZ7EEeQi2Hn9bChDJCQxJA4C5KBhblu2gDZVTG2kgeTFZsDYCgDncgiYzYLFDIWVAPIZmzn7CKEzub0PYuwxbFthzHU2hCGUM7aGnwHQwU71wN87BpxbNueg24IYWRhMy+2GNkyrw9exoJoiYPxEpxq2LwOiJ3F0Lx0vsDBUguKRZu7mLGeWDlvc4RdyI5AkrNrVkqQ88DFEKfraixAL4QofS1mnoXaLGxqDtY1uQTXPA0PHIenvgZ+BnrjcHQKts7B6So0qxAHcPfDQA82ADtm4cA0Z9gxC5Pts/wcnA/nYPdu2LjxIiYREREREXn5m6jk+InvOEtseJ2xInL5HGnWLvcSXjYmP3UPfrsNjwPfAyz/z9dRYHzV9gaglO5fXGeiMWAra4Nq/XT86u3Hwe+1mLzn08x++EMv3o2IyJnKG+D2957/WBERERERERERERERERERERG5Ip08eZITJ04wMzPDwsICnU6HXq9HFEWX/NpXXXUV733vef4dlFcBRdWeY2FhgZ/8yZ983qBakiRks1ne8pa3sHnzZqanpzl06BBf/OIXcc6tnL/89SMf+QhJklCv11lcXKRarXLs2DEOHTq0Mr9zbk1gbfk6mUyG9773vWxc5wORV1111aV6KUREREREREREXtUOzh1k36F9Fkk7h9nmLCdqJ1a2Zxoz9Kpz0KrBtct7E2g9CU+cgpERWkGLIOhBvwFD0PctDnaGqA2dkJwXc/OmkFumYeNQOmM6figH26ch66DTg9kmzDUsXjY1AkN5uHoExisQJxBEFi0bylqUDMAlUPChXIR6D/qhBc6GVsXeohiCPowNQ71v5yYJ5HzIZtPoWWLn1pqQGbGImwNyGYuegYXefA+ILNyWS59Q9iLIeHZf8fLY2H5FPYjTDwfnfJgsQT4zeB1aXahEEGcthBb0oB3CteN2rcTeAeIYCjkgB0VgqQntLkxkoVSCAKgtQaEDBQetnB2fq8JUd+1bs3kOyg9ArwrN5c8yzkH/NHRrsDx8+9zaWNruIzBfgmeGB/uuWbL9F+WOOxRUExEREREBrhkr8bF3vuFyL0NE5EXjLywwsWePbfSArwLvSA+GwEHgZuxhEFgwbQf2cGIJi6TlgGGg8JzJo/T8cNW+r6bXASb27GH+fXcRTUy8iHckImuMXgff/9uXexUiIiIiIiIiIiIiIiIiIiIi8gL9wz/8A1/60pf4P//n//D444/TbDYv21puu+02RdVWUVTtOT760Y8yNze3EkRbtjqmdt111/Gxj32Md77znQwNDa2M2bNnD1/84hfXnfdXf/VX191fq9X42te+xv/4H/+DP/mTP6HVaq0JqznnCMOQffv28ZnPfIYf+qEfejFuU0REREREREREzqHRa7D3yb0kJGcebLeh2YRKBUolRgojaw6PZirM1g+vP3G9BpUKBb9A0GoAFh7bNATlsgXAohiWOvDskn2/85qIndclZJzFz1y6pAgoZuHmjRYj82MYzsHoBNx+tQ2o9cE5mCzbV99ZlGwoB56DIIRuAFEAtRYMjdlcCz0oZaHZtbhaIWvjHJB4MFKyQNvyU6xiFpq99HhsAbNqB6Yqti+OIe/b9RwWVouByEHWs6BbHKfnp+tyDnJpXC1sWTDNTy84WoB8uqYohiCAzhLkp+zavoNKyfb72D0kiUXhlsWJRej8GMIlOP0tmI1hZBhKfnqtAE6eglwAxdDmaC5C+SEo3w/04LtK8I3JQeRuS21wDZfA206u/RHIRXDnfjg6DrUCjHZha3XwWl6QXbvgvvsuZgYREREREUkdfvSbl3sJIiJrTH7qHvz2qmL7g8A2YFO6XQf+Efg2LJ62rMCZEbXV+lhQrb5q30w6f8pvtZi859PMfvhDF7x+ERERERERERERERERERERERGRV4p2u80nP/lJPvOZz/DNbw7+zunqTpVcfoqqrTIzM8M999yzJmoGrAms3XXXXfzO7/wOxWLxRbnm6Ogou3fvZvfu3fzGb/wGn/3sZ/n1X/91arXaynWdczz77LO8613v4hOf+AQf+MAHXpRri4iIiIiIiIi8GtW7dY4tHqPeq591zOGFwxyvHV/ZjpdLYfsfhacOQZRYdevGbXDrbSQk1Do1G3x6hgjon+3JW6NKcajMt0/2uOlqmBqyoFbksZJwc0DZh6uHoRNGzDUgjC3c5SWDMTdtsCjb8nbBh4kKZBz4CZRy0I7A82xMAhQyFlQDyGZs5+wihM7m8D2LsC1HzsIYam0IQyhnbQ0ZD3IedPuDGFgujaZlHMQOuqGdm/VsLaufC3t9cJnB/Y6XoB8N5oqAqAml9BFcH2g2YGzY1l7MWHStnLeoWtgFvwK9GCoFG+fSa/b60AN6HQvCZdJz6w2oLUG+CrV5C8tFHlSBJuD58PgTcCAPW+fgdBWaVYgDuPthbFJgQxt2zMKB6TPf6h2zMNk+c78Dbqie5efjufbuhbGx9Y85B9u3w+TkeU4mIiIiIiIiIi8n/sICE3v2rN0ZA/uAO4Hlv75UBx4CtgIbOXe9PQbmgKNAuGp/J503Xjt8Ys8e5t93F9HExAXehYiIiIiIiIiIiIiIiIiIiIiIyMvfvffeywc+8AEWFhbOiKg9t1cll5eiaqt89rOfJQiCNRG11WGzX/iFX+A//+f/fMmuPzIywvvf/35+5Ed+hJ/8yZ/kgQceWPkvzPI6PvzhD1Ov1/mN3/iNS7YOEREREREREZFXqoNzB9l3aJ9F0lZrt6HZhEoFSiVmm7OcqJ1YOTzTmKFXnYNWDa5d3ptA60l44hTRUIVGv2Elsn6DqACBP5jeJeDHFgTbeVWbnVs6ZFc9J03SMUm6r5KD7dMWLvMT2DIK/1yD+SWYGIZyEa4agfEKxAmEESSxnecAL729vG9hsnoP+iFkfBjKD64bxRD0LUJWTwNpSWKBtGzWjieJnVtrQmbEIm5xYgG3MB0fJRZoiwK75vJ99CKLqnnYS0O6Ni+GfgAua/tyPlTydh2wOBoNCDPgZaELRD1YaMC14xZ+S9LXLY6hkANy4APVOuRzUHIQOQiweFqhBe2shdPaXThyEnIBbGoOXo+rG/b1mz049BVoL0F/FLo1WwPA9rkzQ2m7j8B8CZ4ZHuy7Zsn2X5Rdu+Dd777ISURERERERETk5WryU/fgt9cpti8AnwP+LYOwWgg8BRzDwmojQBl7YBIBLSy+NosV7FfrAH+SzvscfqvF5D2fZvbDH7rY2xEREREREREREREREREREREREXnZWVpa4sd+7Mf40pe+tKZJ9VzPDa1daqsbWbKWomqr/Omf/umaH9jVQbXv+q7vuqRBtdWuvfZa/vqv/5pf+ZVf4ROf+MQZYbXf+q3fYsuWLfz7f//vX5L1iIiIiIiIiIhc6erdOscWj1Hv1c86ph20+cLBL5Cw6kFhksCjj8KhQ1bn8jzYto3+LdupdWsrw7w4oduqrf80rVWDYhaHI+73cEDBg03DMFy0AFgUQS6GndfDhjJAAolFv1xiMTMAHBSyFlTL+uBHFiTzPXjjNVBwMNexz71OVsA58B3kPYuSeQ7CEIKefa23YGgMMh4s9KCUhWbX4mqFLHSDNKTmwUjJYmnLaylmodlLj8cWMKt2YKpisTWwNQYhxJFF2Bzg+xZc60aDkFoCBJGtNxdDjIXNCr7dW8aDYsbm6wYQBpD40K5BfoNF4wpZKJUgCOyzwBnf3r5g1YeA4wRaHeg0IZOBJAPzAWRCi9MVQqj7cPKUnTvaZY3NcxD8PewNbS6ALYMfA1wCbzt55o9ALoI798PRcagVbN6t1VXv64XYtQvuu+9iZhARERERERGRl7FsrcbEnj1nH3AK2AO8G9i0an8f+Fb663zMAPtYN6i2bGLPHubfdxfRxMR5TioiIiIiIiIiIiIiIiIiIiIiIvLyNzs7yzve8Q6eeuqplQ7VMgXNrlyKqqW++c1vcuTIkTUhtWW5XI7PfvazL/maPvaxj5EkCb/+679+RljtZ3/2Z/nO7/xOtm3b9pKvS0RERERERETkSnJw7iD7Du0jTuJzjptpzHC8dnxlO0kS+NbT8M8nYHh5bwz/fBByLfoTRVr9lu2uLxI4CP2zTN6uky1keMN0nzdOw3QFcBZNAxjKwc3TkPWg1YPZJsw1IOtgagiG81DOWdNt47DF0XohdHuQczBRgYyzMNjUEDQDG7OskB1s53xwWVioQ+jS0JkHk2WLnDkgjKHWtvBaOQuJs7BZzoNufxADy6XRtIyD2EE3tHOznoXWfA8CIArBZWz9ITBeglMNC7457NwkgX4LSkUI0vNrDRgftqCaw8aXchDnoT8Kfh+afRuTzVrULEmg14ce0OtYEC6TsWBbvQG1JchXoTZv1wg2waYKJHnw+vDMLPQCyIdQDG2+5iKUH4Ly/UAPbr4RDkyf+TbvmIXJ9vo/Ag64oXqWn4/V9u6FsbGzH3cOtm+HycnzmExEREREREREXqm23Pc5/PZZHkQsWwD+EHgr8J1A7gVcoA98FXgQK+Cfg99qMXnPp5n98IdewAVERERERERERERERERERERERERevhqNBrt27eLQoUMAK/2n1TG11Y2q1c4WXDvb+As993zmezVSVC31d3/3d2fsW46r/fAP/zBbt269DKuCj3/84zz22GP81V/91Zof4n6/z/ve9z6+8pWvXJZ1iYiIiIiIiIhcCRq9Bnuf3EvCcx4UttvQbEKlAqUSAJ7z1gw5Xf9nerUTMLrOxLUTRKVNNMK2lch6DSIfglVRNZeAH1vMbOdVfXZuCch4kGChruUnOcUsbJ8G37f95Ty8Pg+3Xw1RAs2ORc1iYLgIlfTDr6UsZEoWNusF4MU2t+fBZAXqHQgiC5xV8oN1RTH0AxgbhnoaSEsSmyebteNJAv0Qak3IjEDWhziBgm9RtCSxtWUzEAWQ922NAL30mlFoxwG8COIQ8IDYrjVZgnx6PHG21ngJggxERYgiiCP7uvzYy2HjfA+KeSAPUdPWOToEpTRUF2DxtEIL2lmIPGh34chJyAWwqTl4PcaPwlwBmjX41hNQz8HRKdg6B6er0KxCHMDdD2OlNmD3EZgvwTPDg3muWbL9F2XXLnj3uy9yEhERERERERF5pcvWalz///v/nt/gGPg74CFgB3ALMM3g4dRqCXAaOAA8zsqzkPMxsWcP8++7i2hi4vxPEhEREREREREREREREREREREReZn6v/6v/4t//Md/fN6Y2tkiaOt5vrHOuXWv90Kv82qnqFrqscceO+ux9773vS/hSs70mc98hte97nV0u13AfviTJOGrX/0qX/7yl/m+7/u+y7o+EREREREREZEXW71b59jiMeq9+jnHHV44zPHa8ZXtOI5h/6Pw1CGrgvkObtwGt95GP+6z2F1kub/mFhfp+GeZGPCaS/jFPGG/A0Deg03DMFS06FccQS6GndfDZBkcCUlsgS8HeGlY7aYNFi1bVkqjaH7aeBvxYbYDfWC4sOr6QCln0bacB70edAJYbML4sO1fasNwHppdyPhQzEA3tPMTD0ZKFktbHXhr9tLQWhppq3ZgqmKxNbC1BqHdXzZrY33fgmvdyBpzYFG2TPp0MRND1IUoN1j/aAHyWegGNncQQC6BassicJ4HGyqQddDuQD5nr0m46sO8cQKdnkXealW7XpKB+QAyIfgJFEKo+3DylI0b7a59H183A48cg0dP2T0D9E9DtwbLQ7fPwWR7cE4ugjv3w9FxqBVszq3V9T+LfN527YL77ruYGURERERERETkVWLLfZ8j0+28sJN6WFjtISCLhdVGsL8dFgJ1LKgWXNia/FaLyXs+zeyHP3RhE4iIiIiIiIiIiIiIiIiIiIiIiLxM/MVf/AV/+qd/es6gWpIkbN26lXe96138q3/1r9i8eTPT09N87nOf46d+6qdWGlGrv0ZRBEC9XmdxcZFqtcqxY8d48MEHefDBBzlw4ABhGK6Jqy1fK5PJ8MEPfpCPfOQj+P45Phgpiqot+6d/+qeV71f/QBWLRb77u7/7oudf/sG+EFdffTV33XUXv/u7v3vGHL//+7+vqJqIiIiIiIiIvKIcnDvIvkP7iJcLWOcw25zlRO3EyvbMyYP0WvNw7fKeBFpPwhOnYGSEVtAiiAIrg/Ub9PMQn+2RTdQiF3S5eVPELdOwcSidMR0/lIPt05D1oNWDZxsw14CMB9MVi51dPQLjFYuDBZFFv4aygziXSyCXgfEyzLZsHs+DvA+lgoXTwL5mMrCwlJ4H5Hy7Ri5j22EE9b5dp5S1dWY8C7J1+4Nr5tJoWsbZvXdDCGO7jyS2sFkARCG4jK0nBMZLcKphnTqHndvrgxdbAC5wsNSCYtHWW8yAc1DOW1Qt7EJ2BJIcRBGMVGyubAT9DHR70KpB3kFUtPtttmzt3S7kq1CbtzUGm2BTBZI8eH14ZhZ6AeRDKIb2OjcXofwQlO+Hm3z4m9tZeRG21AZvs0vgbSfPfPsdcEP13D9/K+69FzZvXv+Yc7B9O0xOnudkIiIiIiIiIvKqFsds/vN9FzdHAHwr/fUimvhv/43ZD37AHhiJiIiIiIiIiIiIiIiIiIiIiIi8AiVJwi//8i+v2Ya1TaqRkRE+9rGP8dM//dN4z/n7VOfTmBoZGWFkZITrr7+eW2+9lX/zb/4NAKdOneIP/uAP+MM//EPm5+dX5nLOEYYhH//4x/nSl77En//5n7Nx48aLvtdXKkXVUs8888wZdT7nHLfccssFx9BWC8OQbDZ7wecvR9WWLRcI/+f//J8sLS0xPDx80WsUEREREREREbncGr0Ge5/cS0Jy5sF2G5pNqFSgVAJgpDAyOB4EjJ5aYLa0zsT1GlQqFPyCRdX6PQAKDiZGYKRgMbE4gkYbnm3Bm66Bna+JyPgW3nLpkiKgmIWbN1q0zCUwkoXxCbj9aggTaHatpTVZtq++s5jZUM6CY0EI3QCiABZb0EhgYggWOxZHyzrIZ21NvrMoWSeAiQrUOxb8cgkMFS0gtrzdDaHaAn8Esr7F3Aq+RdGSBKIEshm7bt4fBOJ6kUXVotCOA3gRxCFWTItt/ZMlyKfHEwetLlQiiLMQeRD0oB3CteO29gT7FcdQyAE5KAJLTYugTRXBywAhVJtQaNsaG337deSkrTsfwqbm4O0cPwpzBWjW4FtPQD0HR6dg6xycrkKzCnEAdz8M9GADsGMWDkyf+aOxYxYm2+f6qXwezsHu3aCH0CIiIiIiIiLyIqh86yTZZuNyL2Ndfr1O/uhReq973eVeioiIiIiIiIiIiIiIiIiIiIiIyCXxV3/1Vzz55JMrfScYhNKSJGHTpk389V//NTfeeOOLfu2rrrqKX//1X+cjH/kIH/7wh9ftTT388MO85S1v4Utf+hI33HDDi76GVwJF1VKzs7Pr7r/llltelPn7/f5FRdVe//rXs3XrVr75zW+u2R9FEf/7f/9v3vWud13sEkVERERERERELql6t86xxWPUe/Wzjjm8cJjjteMr23ESW5Vr/6Pw1CErbvkObtwGt94GDhISap0anJ4BLyFy0PfXmbxRxasMU/IdN071uXkTTA1BnMa/wOJkFR9eP2XfzzZhtmFRMi8ZjLlpg0XLlreLvgXP/DSyNpyBdgTL/8hEAhQyFlSDNFyWwOwidNJxxTSi9uwSVHIQRdCMoZyFXghxGmcbLti5fgJ4kPFtrEssopYA1Q5MVSzGBrbWILRoXDZra/Z9C651I4ueAfRDyKRPDDMxRF2IcoOXcLRgsbduYHMHAXSWID9l1/UdVEq238fWliQQ9AdzxAl0ehZtazehldh7QDa9JyDXg5Mzdi7AaHftW/m6GXjkGDx6CpLltZ+Gbg2Wh26fWxtL230E5kvwzKp/m+CaJdt/Ue64Q0E1EREREREREXnRBJUhEudwyw9GriCJc0SjY5d7GSIiIiIiIiIiIiIiIiIiIiIiIpfMZz7zmTXbq4NqlUqFBx54gNdd4n+YslAo8Nu//dv863/9r/mJn/gJTp06tbKWJEk4fvw43/d938fXv/51NuqzbWdQVC3V6XTW3T85OXnec2QyZ385G40G5XL5Ba9rtZ07d3L06NGV/6Ite/DBBxVVExEREREREZEr2sG5g+w7tM8iaau129BsQqUCpRKzzVlO1E6sHJ5pzNCrzkGrBtcu702g9SQ8cQpGRojiiEZvCfpLMAqRg2BVVM0l4MfgBW12TkV8x9V9clhYbPl4km5UcrB9GnKeBb6Gx2HLKMzVIYihXIBNozBesThYENlyKtnBfF4CeR9KRaj30lCZD0P5wZqi2EJjY8PQa1mAzAE5H4bzkMvYdpJAowMxNocDClkgGqw960MSpqE0Z/G1bghhDFnPomO+BwEQheAyFnELgfESnGrYeQ47t9cHLwYPCBwstaBYtCBcMQPOQTlv9xB2wa9AL4ZKwe7HJbbuXh96QK8DiWexNueg3oDaEuSrUJuHKADPh41l6E1DJgcLx7AFZCAfQjG0OZuLUH4IyvfDTT78ze2DN3JLbe17/raTa3/UchHcuR+OjkOtYKG2rdXB+3ZBdu2C++67mBlERERERERERNboTUwy/6Y3s+Hhf7jcSzlD8+1vJ5zacLmXISIiIiIiIiIiIiIiIiIiIiIickn0ej2+/OUvn9F3SpIE5xwf//jHL3lQbbW3v/3tfPWrX+Ud73gHJ06cAAZhtaeffpp3vvOdPPjgg3ie95Kt6eVAUbVUt9tdd//o6Oh5z5HL5c56rFarMT09/UKXtcYNN9yw7v6nnnrqouYVEREREREREblQ9W6dY4vHqPfqZx3TDtp84eAXSEgGO5MEHn0UDh2COLbK17Zt9G/ZTq1bWxnmxQndVm39p1itGhSzOM8n0+sTYoGxaypQKoPzLfxV7wABfP82mB4NIEqInIW3Vh5tOouVbZ+2OfzIml7OwUQFXjsG3RiqbZis2H7fWQRtKGfBsSCEfmDhsnoLhsYg48FCD0pZaHYtjFbIQjdIo2mezV/v2LZLYKhoL8/ydieERgBTQ7Y/Sey6QWgBN8fgPvL+IBDXiyyqFoWQTV8/L4I4xG4utnkmS5BPjycOWl2oRBBnIfIg6EE7hGvHLc6WYL/iGAo5IGdRuGod8jkouTRsh8XTCi1op3O1u3DkJOQC2NQcvJWTS3DN0/DAcXjqa+BnoDcOR6dg6xycrkKzCnEAdz8M9GADsGMWDqzzyG3HLEy2z9zvgBuq6/wsrWfvXhgbW/+Yc7B9O7yAf5BBREREREREROR87f8P/5E3/of/P3v/HmXXfdf3/899Oftc5sxVM7pZdmxFdiLTiRwsJ7FD4yYt6Ff/KC0YViklpa0JgWVIWaR0/Qp8oV+6Vvmm39IVrk2ApEDS9otkBQMpC1YCpJBvoLblOHHGSiJFF9u6zEgzc86c+9mXz++P9z5zZjwjRZZsy5Ffj7W8NGfvz/7sz95nJ2vpLJ3n/Fu2Pv7X13spqxrvfCfP/uZvfP2BIiIiIiIiIiIiIiIiIiIiIiIi36D+6q/+ik6nsxouWxtXu+OOO3jf+973iq/pda97HX/8x3/M2972NlZWVoBhWO2xxx7jP//n/8y//tf/+hVf16uZomq5crlMu73xm5aVSuVFzXEp8/PzvPGNb7yqtQ1MTU2tez14uI8fP35N84qIiIiIiIiIXI25hTkOHz1M5rLLjjvXOMfJ2snV1845eO5ZOHMKxgZbMzgzB1GL/pYyrX7LNteXiT1IgktM3q5TLEfMbu0yuwO2j+axsjzsBRY9m90OcQzzzYyFBrjMQmPbqzBahEoRdk1YUC1OIYkhS2GiAqFn8bJiADsnLWqWOguLlUMLqoHN52dwrgWJZ+sIfJgesQCZByQZ1NqQJDBSsHUGHoyVAAeBA3yLr6WpRdUSoJtAklqkzaXg521/DyCxJl2WQRBAKYBuaq8B+gmE+aeAYQZpF9I1vxtgogTFPPSWZnafOitQ3GrXGHhQrdj2AFubcxD3h3NkDlod6DTtXC6EizGEiV1TKYF6AKfP2rETL/j9Bq9bgJG/gN4SNGfyjQvQPw/dGgyGzy6sj6U9cAwuVuD5seG2XSu2/ZocOAAPPniNk4iIiIiIiIiIXJ1kpMrj//evcNtMidKXv8za31XwivOgu3cv6ZYt13ERIiIiIiIiIiIiIiIiIiIiIiIiL78nnnhiw7ZBXO2hhx66Disyb3jDG/jgBz/Iv/gX/2I19DZoT/2f/+f/yQ/8wA8wMzPzdWZ57VBULTc2NrZpVK3ZbF7xHFsu848Hn3vuuata11pBsPm3h5eXl695bhERERERERGRF6PRa/DIM4/gNvtGZ7sNzSZUq1Cp4Hv+ut3n62fo1U7BxCYT106RVnbQSNpWBes1SAOI13ws4jkIMouZ3buzz727YyIPMs+CXWuVCxZUCwOIfLg9gj2TFgELPMCDDBgrQ6VgxxR8CIsWPUtSSPrD8xbyoFijDZ5nQbaBLIN+DJNjUO/ncTcHUQCFggXLnLPIWa0J4bjN5wGlApC+4DyJvQ7yaFsvj6oNf7eFRdy8FLzIvlvrPJiqwNmGHedh96XXt7E+EHuw0oJy2e5hObRrGSnaGpMuBFXoZVAt2fV4ztbe60MP6HXA+RZQ8zyoN6C2AsUlqF20aF28A3ZUwRXB78Pz89CLoZhAObH5mssw8hiMPAr04B0V+NK0XQfA7tr69/3+0+vf3yiFh56E41NQK1msbc/S+nv0oh04AAcPXssMIiIiIiIiIiIviXR6mta3fMvq62BxkXBhgWTrVkXOREREREREREREREREREREREREXmJf+MIXLrnv3e9+9yu4ko1+4Ad+gF/91V/lyJEjq2E1gHa7zYc//GF+5md+5jqu7tVFUbXc2NgY58+f37C9Vqtd8RyXq/WdOHHiapZ1RWt5MeE3EREREREREZGvp96tc2L5BPVe/ZJjvrr4VU7WTq6+zlxmVa8nj8CXj0KaV8veuJf+vlmWu8sM+mve8jKdzdvxplnHK0Wk/S4eUPRhxxiMliHwIUshyuDeW2F6BDwcuDz85YE/OA9w54wFysD2lwLYUs3n6cN8B/rAWGl4eh8LrHmeBdGiErTasNiEcd8mDn3o9qHZtWBbqQDdOA+p+TBesXDb4KPJcgGavXx/fquWOrC1uj6+Fie2/rz3RupBMV9/lg0jaXEeXAvzbWkXssi2RQFMV6CYf/LnPGh1oZpCVoDUh7gH7QRunrJ74bD/sgxKERBBACzVoRhBxbO1xFg8rdSCdj5XuwvHTkMUw441H1NNHYeFEjRr8NzTUI/g+FbYswDnl6C5BFkMDz+OldqAmTbsm4entm98LPbNw/TG34mAB9y+dJnnaa1HHoHJyc33eR7MzsL09BVOJiIiIiIiIiLyCnGO7f/+37Pltz6C3+uRFYss/uBDnP8//g/7TENERERERERERERERERERERERESu2enTp1d/Xhsue93rXse2bduuef40TQmCy3258vLe//73833f932rrz3PwzmnqNoLKKqW27JlC865dQ8zQL1+6S8Pv9DrXve61eNfOM9TTz11zWs8d+7cptuzLLvmuUVEREREREREAOYW5jh89LBF0gbabWg2oVqFSgWA+eY8p2qnVoeca5yjt7QArRrcPNjqoPUMPHOWViUkTmOrdvUb9IsWB9tU2iZKevytnSl3bYMdo/ls+fjRCGa3Q8GHVg8uNGChYQ23rVUYLcFIBFMVi5v1U+glkKYWG/M9C6xFIWwdheUudPKl+T5Mlm0M5MGyNO/BhXkADQuXlUr2c5JBrQ1pYjE2l0fXojy8NrjMQTQt9Ozau4kdW/DBpeBHw3OS2FoSLApXCuz6vPw+9GMoYAE4gH4T3KSNAZgoQTEPvaUZxDF0VqC41dYceFCt2PYAC8M5B3F/+DZkDlod6DQhDMGFcDGGMIHAQSmBegCnz9qxE931b+Md5+CJE3DkrIXkAPrnoVuDwdDZhY2htAeOwcUKPD823LZrxbZfkwMH4MEHr3ESERERERF5tWv2Er58buWKxr5xxxjVov7ZhIi8+k3/+q8z82u/vvra7/WY+bVfJ9myhYsPP3wdVyYi16zXgPm5Kxu77ZugOPryrkdERERERERERERERERERETkNezMmTPrulGDHtX+/ftfkvmTJLmmqNp3fud3UqlU6HQ667afPXuWL3zhC+zbt+9al3hD0L8Ozt1xxx187nOf27B9fn7+iucol8vs3LmTs2fPrm4b1Pz+5m/+5prXeKk5pqamrnluEREREREREblx1bt1TiyfoN67fDy+Hbc5NHcIZwkxK2UdOQJHjw6LY3v3wt13089iat3a6rF+5ui2apt/2tSq4ZemcTi8fg+AkgdbxmG8BIEPWQqNNlxowf5dcO9tqYW+AN/lcTOgXIA3bbNomedgvABTW+CemyB10G1BHFh4bGoEvDxwNlKA0aJFzHp9m7OfwGIXRsqw3IEkhYJnYbTMQSm0W9Dtg/NhS9W2u3w9IyWLujlncbLlFgTjFkHLnIXQkvw2pg4KIaQxFINhIK6XWlTNX3O7/Ay8FFwRyGNkUxW7loFeBl4LilV73XXQacDkmAXhyqGNHylaVC3pQlC146olG+fl19LrQw/odew6w/zYegNqK1BcgtpFC6PFO2BH1dbm9+H5eejFUEygnNh8zWUYeQxGHoU7A/jMPayW5XYPHxk8B/cPf3HHqiiFh56E41NQK1msbc/SME53VQ4cgIMHr2UGERERkct79sp/UZOIvLy+ttDkuz/011c09g8efjv7bp54eRckInKNgsVFtv6nX9x039Zf/M8sf+/3km7Z8gqvSkReMhe/Ch89cGVj3/PncNPdL+96RERERERERERERERERERERF7D6vXN/13461//+iueY22U7YVarRbFYvFFr2ugWCxy33338elPf3rDeT796U8rqpZTVC33xje+cd3rQQzti1/84oua56677lotDg5Kg2Bxtqeeeoq77rrrqtZ34cIFPv/5z28oGQJs0T+MFBEREREREZFLmFuY4/DRw2Qu+7pjzzXOcbJ2cvW1e/Y0nDkFY4MtGZyZg6gFN99CP+3T6rdsV32Z2IPkUr8kobOCX/S4a2uffTtg6yhkPoN8Gx5QDeANW+3n+SbMN6Cf7xuE1e6csWjZ4JhyYLGzII+sZYGF2fyixcXAzlEu2OsogKgIKw2YX7E1lAt2/IUVqEY2b5JZMC1NIfIsghb6EPkWWcOz8FgUWJzNOTvPUge2Vi1kBrbWOLFoXKFgcweBBde6qbXqBmuME/s5zOx1LwY/v9ZKwebqxPa6nwAtKBbBL0Ang7QHiw24ecqux+XzZhmUIiCCAFiqQzGCigepBzEWTyu1oF2A1Id2F46dhiiGHc3h2zh1HBZK0KzBc09DPYLjW2HPApxfguYSZDE8/DjQgxlg3zw8tX3jI7FvHqbbmz8uHnD70iWepbUeeQQmJy+93/Ngdhamp69gMhERERERERGRV5/p//IhgvbmH6IErRbTH/ow8z/9U6/wqkRERERERERERERERERERERERG483W530+3j4+NXPEcURZfc12w2mZqaetHrWmt2dpZPf/rTG7a/2E7WjUxRtdwLo2oDx44do9vtUiqVrmiet771rfzP//k/N933O7/zO1cdVfvwhz9MHMcbYm2e57Ft27armlNEREREREREbmyNXoNHnnkEt5ouW6PdhmYTqlWoVADwPX+4P445f/E0vYlNJq6dgpGE1INGv2HVrl6DNIB4TVTNcxBkFjO7d2eft+32CH0LpA32uzx8Vo1gdrtFywBuj2D3BJxtQprAaBF2TMBUFTIHcQo4qOahMrB5gxAmq9DKoNEF34dyOAyxgQXQggCCPDLmYXG0sSJEob12zoJq5+uwbRwK+bGFABIgzYtlhRDSGAIPMg+6iQXZCj64zOJmMXYNXmjrSYCpCpxt2HEedmwa23l8IPah3YJyHowr58f6PnT60O+AC6FdA3/S4mmlgr2VcWzXFQZ2HXF/eO2Zg1YHOk0IQ5vjYgxhAoGDUgL1AE6ftWMnXvAZ8B3n4IkTcOSsXR9A/zx0azAYOruwPpb2wDG4WIHnx4bbdq3Y9mty4AA8+OA1TiIiIiIiIiIi8uoVLC6y5SMfueyYLR/5CBd/+L2k+qWMIiIiIiIiIiIiIiIiIiIiIiIi18S5Tb6LyYuLqhWLxUvuu3DhArfccsuLXtdau3bt2rDNOcdXvvKVa5r3RqKoWm5t7GxttCzLMr70pS+xf//+K5rn7/7dv8vP/uzPrts2CKH91m/9Fu9///s3fTAv5/Tp0/yn//SfVtf0Qvfdd9+Lmk9EREREREREvvHVu3VOLJ+g3qtfcsxXF7/KydrJ1deZy6y+9eQR+PJRK4MFHrxxL3zz3fSzPsvdZRtz/hxe5uhc6vO7xhJ+dYzAC0j6HQCKPuwYg9GyhcCyFKIM7r0VZkYAHKmziNggrIZnMbDZ7Xn4LA+xBR5sqcKtUxZQW2rDdBU8z/ZFAYxGFmyLE+jHFi6rtaDlYNsknG9AksJkHksrFezYTgLOt/nrHVuP52zdzg1fdxO7FYsd2JbH3DxsnS6x21fwbVsxGAbieqltTxOLrgH4KWQJVkzLbP3TFSjm+51n1+m1oVCFND++1oBd03Y/wdZfDGF6HPoJBH1YbMGWcSgU8lCdg14fekCvY9cahnbv6g2orUBxCWoXLYwW74AdVXBF8Pvw/Dz0YigmUE5svuYyjDwGI4/CnQF85h5Wa3a7a8PHwnNw/+n1j0qUwkNPwvEpqJUs1LZnaRjDuyoHDsDBg9cyg4iIiIiIiIjIq970f/kQQbttJf7twARW00+BGnAeglaL6Q99mPmf/qnrt1AREREREREREREREREREREREZEbwOjoKMvLyxu2Z1l2xXOMjY1dct/58+eval1rjYyMrHs9aFudOXPmmue+USiqlrvlllu47bbbOHXq1IZ42V/+5V9ecVTtbW97G1u3buXChQurD9xgvlarxXd913fx2c9+liiKrmi+RqPBP/7H/5iVlZXV+V7one985xXNJSIiIiIiIiI3hrmFOQ4fPWyRtLXabWg2oVqFSoX55jynaqdWd59rnKO3tACtGtw82Oqg9Qw8fRbGx2nFLeK4B/0GjEI/gGyz+lXahk5C5Ge8aUfCXdth22g+Yz5+NMpjaR50ejDfhPmGhdC2j8JoESpF2DVhobI4tVAYCUxULCTmOQuW7Zy0uFfqLHRWDm0esHCZn8G5FnRSKEQWH5segQsrFlRLU2hmUAogyWyNgQdjJbsFgQN8CAMb6zmLqGWexdWSLI+AuXxdWDTO88H3ba5SAN0UBp+P9hOLmQGEGaRdSNd8JDRRgmIBujGkGcQxeE3wKnl4zoepCqQxZH5+vQ7iPgQBlANwEbglqDVhYhQqHqQexFg8rdSCdsEibe0uHDsNUQw7msN1TB2HhRI0a/Dc01CP4PhW2LMA55eguQRZDA8/DvRgBtg3D09t3/hY7JuH6fbG7R5w+9Imz9FmHnkEJic33+d5MDsL09NXOJmIiIiIiIiIyDem4soCW77wG/BDWFBts8/oHHAetnzhN1hc+uckUztf2UWKiIiIiIiIiIiIiIiIiIiIiIjcQMbGxjaNqtXr9SueY/oy3307ceLEVa1rrU6ns+n2RqNxzXPfKBRVW+Od73wnH/3oRzdE1X7/93+fn/iJn7iiOTzP493vfje/+Iu/uDrPIKzmnOPIkSO8613v4mMf+xi33XbbZed65pln+Kf/9J/yhS98YV1Qbe36RkdHue+++17MZYqIiIiIiIjIq1S9W+fE8gnqvUt/wNaO2xyaO4RjTXjdOThyBI4etaKX78PevfTvmqXWra0O8zNHt1Xb/BOhVg3KBXzPx/V7eFjobMcojIxYSCzNYKVjobI0g3t3pdx7iyP0LELm5UtKgXIB3rQNQh+CDMYimNgC99xkS1zp2dhqCSoF+7ngw1gRRgqQpBYb8/OIWiGwy2y0ras1WhwuPcugH8PkGPRadk4Pi7GNFe3YQRCt1rLQWRjafKVCvmBs/YUAXGLjA28YiOumFodzzm4vgIuB0NbjsADa2YYd52FBtl7fgm8+EHuw0oJy2QJp5fzYkaLdz6QLhXEbN1qw6/EckEA7g6hgcTPPDdfVbOexuRRqS/l1hXAxhjCxWFwpgXoAp8/a2Inu+rf+jnPwxAk4chYGnb7+eejWYDB0dmF9LO2BY3CxAs+v+aUZu1Zs+zU5cAAefPAaJxERERERERER+UaWMjX+Be44/yGCd/YuP9QDdkCwo8vNxx9m/u3/hnb8FiB4JRYqIiIiIiIiIiIiIiIiIiIiIiJyQxkbG1ttRa31YqJqO3bsuOS+r3zlK1e9toHNom8A7XZ70+2vRYqqrTGIqg0MQmZ/8zd/w8WLFy9bAVzrR37kR/jgBz9IlmWrc6wNq33uc5/jTW96E9/1Xd/Fd3/3d3PnnXeyfft2giBgYWGBxx9/nN///d/n4MGDpGm66TkG8733ve+lWCxuOkZEREREREREvnHMLcxx+OhhskHVaqDdhmYTqlWoVDjXOMfJ2snV3c45eO5ZOHMKVgNXGZyZg6hFf0uZVr9lm+vLxB4kl/pOZbtOsRxx17Y++3bA9qp9LzP1WU24ecBIADeNQSdJWWhAklnky3fDMXfOWKBs8LoUwJYqhPm4SgRLbRgrD0/veRZY8zyIQoustduw2IRx3yYKfej2odmFMLAoWje2czjfzrGS/6KFIIPRNfMHGaTO9m8dtWtyDqIA4sTW5eXrBVvD6h3N35YkgUIegQsTyJI8cJbPM12BYn6c86DVhWoKWcHuY9yDdgI3T1moztmhZBmUIiCyr7zWVqBUhGIeq+sA5y5ApQNhGdKyHVdv2DV1u1BcgtpFC6PFO2BHFVwR/D48Pw+9GIoJlBO77uYyjDwGI4/CnQF85p7hxe+urXlfHNx/ev2jEqXw0JNwfApqJQu17Vka3rurcuAAHDx4LTOIiIiIiIiIiHxDK4Q1dsz8GZXseSaPffFFHVv54hOMvfVTlErHqPf+v6Ruy8u0ShERERERERERERERERERERERkRvTLbfcwhe/uPHfbtVqtSueY+fOnVQqFTqdzro4m3OOJ5544prXODc3t+n2SqVyzXPfKBRVW+Pbvu3bCIKALFv/5eUsy/iDP/gDHnrooSuaZ/fu3fzAD/wAH/3oRzc82IOwWqvV4uMf/zgf//jHLzmPc/ZN5MExg58HSqUS73//+6/4+kRERERERETklVfv1jmxfIJ679K/iaAdtzk0dwi3mi7DqldHjsDRo1bc8n3Yu5flO3dT69ZWhy02LxA3z8JmLfjmKbLiFjppDx/wek3SAOI1UTXPWWzM9+DenX3u3R1T8CDzbAmDMS7/SKIawex2iHwIHOyegDM1uLgCW8ZgpAw7x2GqCpmDJLXIVzWy4Jaf2Z/FALaNQTeFfgKeD1NlWwfkobE0j7mFeTQNC5eVSvZzkkGtDWliMTbnQeDBaMkG+1igzPfzW4jN301sXWEALgU/yq8TILHxaQZBYDG4bmrz4tlaw8CuwwfSLiTR8H5OlKCYh97SDOIYOitQ3GprDjyoVmx7QL4GB3F/OEfmoNGGbhu2liFNod6xa/JSSJtQ68DR56CXH1dMYEdzOMfUcVgoQbMGzz0N9QiOb4U9C3B+CZpLkMXw8ONAD2aAffPw1PaNj9G+eZje5JdUeMDtS5s8d5t55BGYnNx8n+fB7Cxc4S80EBERERERERG5ERWjBW7a9scEfo/JI1/ETxLbEQHbsV+oMIJ9qJQCLWAFOA/0we/HVJ54guwdZSbL/51a97tJskv/xlMRERERERERERERERERERERERFZ741vfCOf/OQnN2w/duzYi5rnDW94A5///OdXW1GDftSTTz5Jo9FgdHT0qtf4uc99bl2DamDLFv0izgFF1daYmZnhXe96F5/61Kc2xNB+5Vd+5YqjagC/8Au/wB/+4R+yuLi4Loo2CKsNfr6cS40bzPFzP/dzbN269YrXJCIiIiIiIiKvrLmFOQ4fPUzmssuOO9c4x8naydXXzjl47lk4c8q+LAlABmfmSAoNauPDzxqyZp1OsGHKoU6DtBDQj3tEHhR92DEGo2UIfAuXRRnceytMj4CHY9B28wB/8LGEB6WCBdUKAQRpHi3z4c27oOTBQgf6wHTVOllBfr5q0WJmSQJxz/5cbkN5FEIfFnsWFhspWFCsVLCQW68PzocteaDNOVvTSAla+TFxDMstCMZtXR4QhRY9I19/4AN5oC3KPw3rpXZut+azQz+zaJkXWVQOYKoCZxtQzI/LPGh3oZjPn3pQa1oozfegHNq1jxQtqpZ0IahCL4NqCSbH8khdfn09oNex6wzzY+sNqK1AcQkWF6E6CdVt4Ofvc5rA0tegt+Z9n+iuf9vvOAdPnIAjZy1qB9A/D90aDIbOLqyPpT1wDC5W4Pmx4bZdK7b9mhw4AA8+eI2TiIiIiIiIiIjcuAphbTWoFnS7TByds3/VtQfYRv7bAF6gjP2ihduAeeA4lD//edr790MFJkqPsNz5PlKnfygnIiIiIiIiIiIiIiIiIiIiIiJyJd74xjeuez3oRn3xi198UfPcc889fP7znwfW96bSNOXRRx/l3e9+91Wt71Of+hTnzp1bXdfarpWiakOKqr3A937v9/KpT31q9fXgwXn66af59Kc/zd/7e3/viuaZmZnhox/9KP/oH/2jDQ/g4PVmxb8XWhtUW1se/I7v+A7+zb/5Ny/m0kRERERERETkFdToNXjkmUdwbBJVb7eh2YRqFSoVfM9ft/t8/Qy92imY2GTi+rO0SlPELoUsg16LOBhGwDZI+0RhwF07M/Zthx1V+w7mICY2GuWhNN9CZRcasNCAggdbR2GsCCMR+D5sG7NwWC+Bbg8iz4JnoQeBs/HN2MYMlArD11EAXgEW6xY1Gw8Az2Ju3b6tK8mg2bNwWNGzdYY+RL6NwbPwWBRAP7E4mQOWOrA1v7Ysg2IAcTIMw2VYAK3gQymwMYP7ECe2vjDflvTARcM17xy148jHd5pQCIECJB702nY9N09ZwM1h/2UZlCIgggBYqkMxgopna4mxeFqpBe0CpL4F246dhiiGHc38LZ+H0mkoTcPKOKxcAL8PxSr0QigmUM7vRXMZRh6DkUfhzgA+cw+rX7rdXRu+L56D+0+vf1SiFB56Eo5PQa1kobY9S5t/Z/eKHTgABw9eywwiIiIiIiIiIje4lB0zf0bg9wCYnPsi/kgC3wREV3C4B2wHpsCfi6k88QTNd/xtfK/LePF/stT9p9inUyIiIiIiIiIiIiIiIiIiIiIiInI5e/fuXf15bQxteXmZ5557jptvvvmK5rnvvvv4jd/4jQ3bnXP86q/+6lVH1T7wgQ9sut3zPN7whjdc1Zw3IkXVXuC7vuu7+OEf/mHiOF633TnHf/yP//GKo2oA3/7t386v/Mqv8KM/+qMAG8JqL8bgf2DOOe6++25+93d/90UdLyIiIiIiIiIvnXq3zonlE9R79UuO+eriVzlZO7n6OnOZlbaePAJfPgqpg8CDN+6lv2+W5e4yg/6at7xM53Lfc+x0SKMAr9/DA0oeTI3DeCmPeqWw0oYLLdi/C+67LSUMbHrPgQ+kQLkAb9pm0TLPwXgBprbAPTfZ8lodi7VlwFgZqvmXOCsFCCsWHOvF4OeX5vswXYV6B+LUQmTV4nDZaQb9GCbHIOvmUTNsnlJpGCNLUliow7ZxKOTHFgJIsHXhLGqWxnYLMw+6iQXZCr4F3tZ+9OL3wQtXby9TFeinw1hYN4Fifl8AWm2bP8g3TJUtptaNbW3dHoRNKG6118UQKhWIY/t6ahjY+eP+cA1Zfj87TQhDcCFcjCFMbL2lBOoBnD5rx05017/lt5yD+BPwsQS8AKpT0JuC41thzwKcX4LmEmQxPPw40IMZYN88PLV94yO0bx6m2xu3e8DtSxu3b+qRR2BycvN9ngezszA9fYWTiYiIiIiIvDTu2DbK/3zft1zR2N3T1Zd5NSIiX9/U+BcoRhcBCLpdJs5+Cd7Exg5aF1gB+lhsbQwordkfAW+C8tEnae/fT1YpEwbzVAqP0Y7vffkvRESuzsxeeO9fXdnYLXte3rWIiIiIiIiIiIiIiIiIiIiIvMbdfffdlEoler3eau9p4Mknn7ziqNoDDzyA7/urYba1fz7xxBP88i//Mu973/te1Np+7dd+jT//8z9f17Ba653vfOeLmu9GpqjaC4yPj/OVr3yFXq+3Yd8LH/Qr8SM/8iNMTk7ynve8h1artW6OrxdWe+H5nHP8g3/wD/j4xz/O6Ojoi16LiIiIiIiIiFy7uYU5Dh89bJG0gXYbmk2oVq2uBcw35zlVO7U65FzjHL2lBWjVYPVzMwetZ+CZs7QqIXEaQ5ZBv0G/aLGwTaUdwsxndmfGXdtgx6hFv9aqhLB3xiJZFxow37DvW/qA72z7nTMWKwN7XQ5gS9ViYp6D8QDmO3bc2JovaPpAJQLfg8iHXg86MSw3YWrMtq+0YawIza5FxsqhxcsAnG/nyfL4mQeMlKDVy2NksQXQFjuwLR/nYWt1iYXVCr5tKwbDa+/lITcfu41gwTc/j7l5eaEtCiz21s/X08vAa0FxxAJyvQxaDbuWkciicxkwUoReH5JRCPo2rliwoJ2XX0uvDz2g17HrDEPri9UbUFuB4hLULoLLIN4BO6rgihZ+e37eInXFBMqJzddchpHHYORRoAfftBee3gb1BWAB+uehW7Pv1ALMLqyPpT1wDC5W4Pmx4bZdK7b9mhw4AA8+eI2TiIiIiIiIvPTKUcA37Ry/3ssQEbkivtdjcvyp1deTX34K/w3p+qBaCzgOLG8ywSSwBxjJXwfg35FQ+fxjNN9+PwAjhcfoxG/GrSuwicirRlSBHW+63qsQERERERERERERERERERERESCKIu67777VeNlan/zkJ/mH//AfXtE809PT3H///fzFX/zFunkGQbSf/Mmf5NZbb+U7vuM7rmi+3/7t3+bHf/zHL9u/UlRtSFG1Tdx6660v6Xzf+73fy1ve8hbe97738cd//MeAPeBXEmkbhNe2bt3Kf/gP/4F/+S//5Uu6NhERERERERGBerfOieUT1Hv1y45rx20OzR3CkYfSnYMjR+DoUat4+T7s3Qt3300/i6l1a6vH+pmj26pt/mlMq4Zfmsbh8PoWei95sGUcxksWOctSaLThQgv274K33ZYR5l+u9B3ghhG20Qhmt1vwzHcwMQW7J+BsE7LEgmI7JmAqD5bFqR1fLViobDBnEMLUCMy3LHjm+xYxq5QsnAb2ZxjC4oq99rBo2VgRotBeJynU+3aeSsEiaKFv6+v27SDPs+P6eUws8yzClmT5mlwee8PuhefbegIPSgF002FIzWHn8jyIMguitbs2LvDt3OXQIm3dGJIYGl0oR5AVbb1hfn2hP7yu1EGaQLkIFC2kdqEG41WoeJB6EGPxtFIL2gVIfTv3sdMQxbCjOXzbp47DQgmaNXjuaahHcHwr7FmA80vQXIIshocfx0ptwDtOw5e2DkNyu4ePGJ6D+0+vf7SiFB56Eo5PQa0EE13YszR8n6/KgQNw8OC1zCAiIiIiIiIiIsBY9Ri+FwMQdLtMxHMQrRlwATiKfcC1mWXgCLAXmMm3RVC++Hna7beQVcp4Xp9S+Ayd5JtfpqsQERERERERERERERERERERERG5cbzzne/kz//8z1dfD0Jon/zkJ1/UPO95z3v4i7/4i9XXg46U53nEccx3fud38kM/9EP85E/+JLt37950juPHj/MzP/MzHDp0COfc6lrWrsvzPN72trdx2223vdhLvWF5bnCX5BXx5JNP8lu/9Vv80R/9EWfOnLns2CAIePvb3873f//38/3f//2USt/4vzF2dHSUZrO5blu1WqXRaFynFYmIiIiIiMhr3dzCHIePHiZzL/hmYrsNzSZUq1CpAHCucY6vLH5ldYh79jScPLVx0ttuhZtv4UL7Aq1+y7bVl2l2GiT+JRZSjPCLHneN99i3A7aOQuYzyLfhAdUA3rDVfp5vwvkmxM4CaL6zgFepAHftsGCYn0HoLC42UYGRCPopLLVhesy2g/05GlkgLU6gH0PWh1oLGg6mRmG5Y7Gxgge7tkC5YEEzl1pwLXFQ71iEDWff83R5CA0H7T4stWDruK0tCmytnQ6kwEjJrrXbgWYM8x1b25YRGA1htGjX3e6CD5TLFmTrObumsw2YrkA1sriYSyBuw2QZYh8WW9Ylmxqz4Fvg52vE3uZ+DKUEwlH7OQhsv29vDWFg1+DS4VvWaEGrA0EG0yG4EC7EECYwEtt564EF1Xp92NaEcjI8/p6T8MQJ+MxZGDx+JybWh9Jm5+HBo+sflUffCE9t3/gI3XUe/tGXL/F8XalHHoHJyc33eR7MzsL09DWeREREROQG9OzlA80irxm3jF/vFVyzrx752vVegshrxrFm7Xov4bq7ZcdhitFFAKa/8DdMjT09rOG3sGDapYJqa/nA3cBI/jqDVno3zfv+DgBJuo2l7j97KZcu30Bmt2673ksQERERERERERERERERERGRVyE1YEQ299d//de8/e1vXxctG/z5l3/5l7z97W+/onnSNGXv3r187Wv2b1PXZr7Wzglw1113ceedd7J9+3aCIGBhYYHHH3+cZ555ZvXYtUG1F87xB3/wB3z7t3/7S3ULvuGF13sBrzXf/M3fzK//+q/z67/+65w6dYpnnnmG06dP02g06Pf7lMtlZmZmeP3rX8+b3/xmKvmXtkVERERERETkxal365xYPkG9d+m4QTtuc2juEI41zXnn4MgROHoUsgx8H/buhbvvZrlbo9at2bgkYbF+inizvlTzFFxo4TyPbtrFyxz0GqQBxMFwmOcsyOV7cO/OPm/b7RH6Fkgb7Hf5lyirEcxuhyiPst0ewW0TcLYJWWLRsVIRdk1atCxOIY4tAjZRsYhYkEExgJ2Tdpmps6hYObQ1ABRCi7Gda0EntcsvF+z4Cyu2jjSFZgYjBeglkHkWWBsrAQ4CB/gWIktTu44sP9dSB7ZW7bWHrdUltpaCb9uiNZ9YZdnq7aZQsJ/DxK7ZeXa+KLCgWjE/znl2/dkKxCGkZVtHlubrGcTksHGBD+UiULRQWqlo+6MUksBCbitNGHGQlSAMLTC3vAKdLhSXoHbRwmjxDthRBVcEvw/Pz0MvhmJiQTXnoLkMI4/ByKNwZwCfuYfVL8uuDap5Du4/vfHxeuAYXKzA82PDbbtWbPs1OXAAHnzwGicREREREREREZEr4XkxxWgRgKDbZaI2B2vblMe5sqAa+bjjwL78tQ/l55+i3X4rWaVM6C/g0ccRvWTrFxERERERERERERERERERERERuRHde++93HTTTZw9e3Y1ejbw3/7bf7viqFoQBPzCL/wC3/M937NhHmBdJO3zn/88Tz311Lr9LwyobXas53ncfffdCqq9gKJq19Gtt97Krbfeer2XISIiIiIiInLDmVuY4/DRw2Tu8t86PNc4x8naydXXzjl47lk4cwpWg1UZnJmDqEVy005q3ZqNqy+TAZ1LfbrSrkOxRJzFBHGCDxR92DEGo2ULeWUpRBnceyvMjAA40jw2Ngir4UGpYEG1QoBFyzKLmG2pwq1bLDi22IJiESp5eKzgQ1i08FmSQj+2eT2Xh8wcNNoWGBstDpedZTZ2cgx6LQjIQ2cBjBUteOaRH9+x72uGgW0rFYA0X7YbBtM8bL2ZB90EkixviDm7Dx52LzzfIm4BUAqgm9pxeNBP7Dx+Bj6QdiFZ8x3QiRIUC9CNIc0sKBc5WGpBtWjzzlSh4EG7A8XIzp301lx7fk9aXZgagaIH7R4sd/KQWwyNHvQcHDsNvb7F0nas+YUsU8dhoQTNGjz3NNQjOL4V9izA+SVoLtk8Dz8O9GAG2DcPT23f+Ajtm4fp9sbtUQoPPQnHp6BWgoku7Fla7bJdnQMH4ODBa5lBREREREREREReBAuq2YeAk3NfxB9Z81lmF1h+kRMu58eV7KVfSak88QTNd/xt8Byhv0Cc7br2hYuIiIiIiIiIiIiIiIiIiIiIiNzgvud7vocPfvCDqzGzQcTsd37nd/j3//7fs2XLliua58EHH+S7v/u7eeSRR9ZF1AZ/DuZ3zq2LqA2s3f/CbQDj4+P8j//xP67iCm9siqqJiIiIiIiIyA2l0WvwyDOP4Nj4ARLtNjSbUK1CpYLv+et2n6+foVc7BRObTFw7BSMJraxLHPeg14AI4sBiYRukfUgg8jP+1s6EN2+D7aO2y+XjR6M8luZBpwfzTZhvgO/Z2NEiVIqwa8ICZXFqcTESmKhYFAxnobOtYxYOG3z10sMCa55n+yMfWm1YbMK4bwNCH7p9aHYtWFbKo2Qe4HyLttU7wxjbaNliaoPXnQQaMWwdte3OWXwtTiwKl/fQACgGw+vupRZ9c87WDOBi7JMqH8hgqgJnG1DMP73KPGh3oZhfYOpBrQnVit2vcmjXOlK0qFrShcI4uAjSFMarFmgrpNAPoduDVs3CaWkZwhCaLbsf3S60TkDZh8o0uDKUEjtv1INn5i2oBhY0W+uOc/DECThyFgZNv/556NbsO60AswvrY2kPHIOLFXh+bLht14ptvxQPuH3p0vsB2L8fPvCBy4/xPJidhenprzOZiIiIiIiIiIi8lAphA4Cg22Xi6BzcvWbnylVOusJqVI0RKH/+87T37yerlPG9xjWsVkRERERERERERERERERERERE5LXj+77v+/jgBz+4IXTW7Xb51V/9VX7u537uiuf66Ec/yle/+lW++MUvrgurwca42gu98PxrI2u+7/Pbv/3bvP71r7/itbxWKKomIiIiIiIiIt8w6t06J5ZPUO/VLznmq4tf5WTt5OrrzGXggCePwJePQuqsrvXGvfT3zbLcXWbQX/OWl+kEl1lAYwl/pIrr9wALne0YhcqIBc7SDBodWFixn+/blXLvLY7QA5yFxgBSoFyAN22zsFmQwVgEE1vgnpsgy2DFTkG1ZHE0sBDZWBFGCpCkFkALnIXUwsDWs9S2y5ksW2wM7NxZml9mmEfTsABaqWQ/JxnU2pAmdj7n2W0aK9ngwAG+nSdNLaqWAN3E1hL64FLwo/xeAiR56C2DIIBSAN3UXoPF16L82sLEzj0Ir0UB7By1awbb3mlCIQQKkHjQa1ug7eYpu/8uv64sg1IERFAGVprQi2FrGfzQ1rXUhFLbHodG3/47dtpCb8UEdjQtglY/D8VpKI/b9V48DVnB7mMxgXJixzSXYeQxGHkU7gzgM/ewWpTbXRs+Qp6D+0+vf6yiFB56Eo5PQa1kobY9S8Mg3VU5cAAOHoSxsa8/VkREREREREREXnGelwIwOfdF/CSBtZ9L9q9y0rXHBeD3YypPPEHzHX8bz0uudqkiIiIiIiIiIiIiIiIiIiIiIiKvKfv37+cHf/AHaTQ2/jLLpaWlFzVXtVrlU5/6FA888ABHjhxZF1AbRNNeGE/bzNqgWhiGfPSjH+U7vuM7XtRaXisUVRMRERERERGRbwhzC3McPnrYImlrtdvQbEK1CpUK8815TtVOre4+1zhHb2kBWjW4ebDVQesZeOYsrUpInMZW4uo36Bchu1TNKm1T7Pf5WzsT9m2zoJrHxvHlEHaNQj9OWWhYsCxvkgF2zJ0zFkEbvC4FsKUKoWfxtUpkgbSx8nBez7PgmedBFFpwrN2Giw0YqVogLPTtdaUAmYNSAfwMun1wvp0jcxYC84CRErR69jqOYbkFwbitzcOOJ83P72y7y79/GeTX3cujamtvg5+Bl4IX5bEzD6YqcLZhx3n5cYXAxvpAvw/pmk+rpsp2XDfOI3I9CJtQ3GqviyFUKrbuAAu+OQfxmi+PZg46PfBTaDeh5SDzgUIeigOiHpw+Z8eCRc1Wj09h9xH45AI8/xWoTkBvCo5vhT0LcH4JmkuQxfDw40APZoB98/DU9o2P0L55mG5v3O4Bt1/pZ6mPPAKTk5vv8zyYnYXp6SucTERERERERERErgfnAoJul4mjc7YhXbMzuspJ1x6Xz1f+/Odp79+P8/XPxERERERERERERERERERERERERK7Ub/zGb7xkc83MzPC//tf/4id+4idW5/U8b11g7Uo459izZw8f+9jHeOtb3/qSre9Go38tJyIiIiIiIiLXVb1b58TyCeq9+iXHtOM2h+YO4VhT23cOjhyBo0ctiOb7sHcv/btmqXVrq8P8zNFt1Tb/FKRVwyttIc1S/H7fImIebBmH8RIEvkW1Gm240IL9u+De2xIKvoXU/DxM5rlhWG00gtntNo8H7J6AMzVYqsPkGIyUYec4TOVxsyQFl0E1svF+Zn8WA9g2Bt0U+gl4vkXG/Pw8Dlubw84d5YG2KIBtVZsjyaDZgyyByLNAWehD5FtkDS8PtAV2DudsvqUObM3ncM72x8ma6wVSz9YIdvsHcbk4D66F+ba0C1k0XNt0xWJoYOtp96CYQZYfX2vCaAVGIltrBowUodeHZBSCPvQyKBZgatzuvXO2vwf0OhaPC0O7tnoDaitQXILaRUhj8APYNgK97RBGsHgCq7qFUEygnN+L5jKMPAYzj8LYbki3QX0BWID+eejWYNBfm11YH0t74BhcrMDzY8Ntu1Zs+zU5cAAefPAaJxERERERERERkestTkaZnPsifpJ/oNYCBr9gYexSR30da49r2R9+P6byxBMs3vMvr3JSERERERERERERERERERERERERuVaVSoUPfehDPPTQQ/z8z/88f/Inf0KaDn8b52aBNeeG36m9+eab+Vf/6l/xoz/6o0TR1f7mztcGRdVERERERERE5LqZW5jj8NHDZC4bbmy3odmEahUqFQDONc5xsnZydYhzDp57Fs6cWvNFwQzOzEHUor+lTKuff2uwvkzsQRJcYhGdBmHR45t29PnmbbC9CpnPar7NA6oBvGGr/TzfhIUG9PPAmL9mYKlgQbWCP4yjBT7cfRNEN8NCB/rAdNWCX4EHRR+qRYulJQnEPftzuQ3lUQuLLfYs8jVSsBBbqWAxsV7fAmIzY6t3AA8YLUM3ttdpAvN12DYOhXyphQASIHWAg0JosbHAs7BZN7EgW8EHl4IfDe8FifXrknyeUmDHeVgkrR/befz8XP0muEkbAzBRsiBaN4Y0g0YrP08xv+exjQv94TlTZ9dRLgJFu/YLNRivQsWzwFuMxdNKLWgXIPWh3YVjpyGKYUdz+JZPr8CuZ+EvTsKXPwdBCL0pOL4V9izA+SVoLkEWw8OPAz14x2n40la7RoDdteF8noP7T69/rKIUHnoSjk9BrQQTXdizlN/Dq3XgABw8eC0ziIiIiIiIvLyevXQw/SVzy/jLfw4RkVdAthAw/szccMMKMJ3/XAImgeUXMeFkftzAmv9LLj/5ebJbA9hylYsVERERERERERERERERERERERGRl8Q999zDH/3RH3H+/Hk++clP8tnPfpZnnnmG06dP02g06Pf7lMtlZmZmeP3rX88999zDt33bt/GOd7wD3/e//glEUTUREREREREReWnVu3VOLJ+g3rv8F6nbcZtDc4dwg3yZc3DkCBw9Cllm5a69e+Huu1nu1qh1a6vHLjYvEDfPDr9kuFbzFK44Tdf18TIHvQZpAPGaqJrnIMgsZHbvzj5v2+2tRrycs/2DeFY1slBalO+/PYLdE3B+BeIMRosW+/J92DZmc/YT6PQg8mBLFSIsprV1FJqxjRkoFYavowC8AizWoZfCeGAHTo9At29zJBk0exYZK3q2zkJg8bZObGNC3+bqpRAnFitb7MC2qkXZPOwYl1iwrODbtmIwvO5emsfh1txaPwMvtQAaeQdvqmKBuIFeBl4LilV73XXQacBkfm/KoY0fKVpULelCHEOxCKEHU2O2xnYHihGEgcXjGP5CBVptC8/VliAMwYVwMYYwgcBBKYF6AKfP2vs50V3/iLxuAUb+AnpL0JzJNy5A/zx0azAYPrsA0237eaYN++bhqe0bH7l988Nxa3nA7Usbt2/qkUdgcnLzfZ4Hs7MwvdkDLyIiIiIi8hrzSoTbREReAbf93u8RVBPYkW84D9zGsMq/BzjC6udwl+Xn4wcyYH7N7mdjpj/0X5n/6Z+61mWLiIiIiIiIiIiIiIiIiIiIiIjIS2D79u384A/+ID/4gz94vZdyw1FUTUREREREREReMnMLcxw+epjMveCbfu02NJtQrUKlAsC5xjlO1k6uDnHPnoYzp2BssCWDM3MQtUhu2kmtW8M5K2tlzTqdgEvrrBAXAoI4wQeKPuwYg9GyxceyFKIM7r3VgmUejjQPqXmA7wDPgmez2y1ARh5iC/JQ2q1TFva62Iaeg7GSBdgARgowXbGwWRyDl1kTzPdhugr1DsR5tKxaHC47zaAfW4As69paHDZPqWQ/OyBJYaEO28ahkB9bCCDuQ+rZ2gshJLEFyjIPuokF2Tzya8lDalkKnm9rCzwoBdBNrWuXDyVO7Ocwv45eDH5+/ysFO3cnttf9BGhZJM0vQCeDtAeLDbh5ys47uI4sg1IERHZNg++LRikkAbS7sNKEEQdZyQJqSQrLK9DpQnEJahfBZRDvgB1VC775fXh+3tZZTKCcWFytuQwjj8HIo0AP3lGBL00PQ3K7a8P3wnNw/+n1j9UDx+BiBZ4fG27btWLbr8mBA/Dgg9c4iYiIiIiIiIiIfKMo1Grc+onfgzcxjKr1sRDaIOo/AuwFjnL5sJqfjxtZs20hn2/gKdgy9xEu/vB7SbdseQmuQEREREREREREREREREREREREROTVSVE1EREREREREfm66t06J5ZPUO/VLzmmHbc5NHcIhxtudA6OHIGjR62g5fuwdy/cfTfL3Rq1bs3GJQmL9VPE05tM3DwFiy16WUzmMjznoNciDiwWtqm0TxQG/K2dKW/eBttH8+Xk40ejPJbmQ6sHFxow3wAC2DECY0WoFGHXRB4rS/NYWAITlTxI5ixANjMGF5oWVRvwgEoEvmdBt27PomPLTZgas+0rbTtPswthAOXQwmcAzrdwW+bsFnrASMnW6pyF2hyw2IFt1eE5ozyIhrNAmo/9ObjubgoFz+bw/fxcMRCC59mcUxU427DjPOwep7HF23wg9qHdgnLV7kM5tLl8Hzp96HfAhdCugT9pc5YK1tKLYwiw63XOInADSQpLdZisQNGDdg+WOxAmkMXQ6Fm87thp6PUtlrajOTx+6jgslKBZg+eehnoEx7fCngU4vwTNJZvn4ceBnh0z04Z98/DUdjbYNw/T7fXbohQeehKOT0GtBBNd2LM0jMFdlQMH4ODBa5lBRERERERERES+wew++HHCbge+APxdIP9lDRwHpta8ngEq+fblTSaaBPawPqjWz8evff0FCHotpj/0YeZ/+qdeugsREREREREREREREREREREREREReZVRVE1ERERERERELmtuYY7DRw+TuWz9jnYbmk2oVqFS4VzjHCdrJ1d3O+fguWfhzCkYG2zN4MwcRC2Sm3ZS69ZsXH2ZDOhc6pOKdp0sioizmEKS4QMlD6bGYbxkcS+XWqjsQgvu2QX33pYSBoADP++8pUC5AG/aBmEeRhsvwNQWuOcmiB20uja2WoJKwX4u+BZAGylY/KsbD+fMHEyN2DYcRCFUShZOA4uVhSEsruSvsfjZWNHGetic9b7F2yoFi6CFPkQ+dPt2kOfZcf3EgmSZZxG2JMuvBbsPfgLpIKrm2zlW737+FiYJFPJrCxPIkjy85uwc0xUo5sc5z9bltaFQhdSHNIFaA3ZN2znBzlcMYXrc1hj0YbEFW8btXF4eiOv1rWnW61g8LsyDbvUGLC5D82tQ9qEyDa4MpTw0F/XgmXk7HixottYd5+CJE3DkLAwe1f556NZgMHR2YWMo7YFjcLECz48Nt+1ase2b8YDblzbft87+/fCBD1x6v+fB7CxMb1YSFBERERERERGRG1WhVuPWT/yevegBnwXele9MgDngTdhvJwALpu3DPuRawSJpEfaZ65pf9ADYB6Bz+TwDn2X1lwxs+chHuPjD7yXdsuUlvCIRERERERERERERERERERERERGRVw9F1UREREREREReo+rdOieWT1Dv1S85ph23OTR3CIcbbnQOjhyBo0et0uX7sHcvy3fuptatrQ5bbF4gbp6FzZpRzVOw2KKXxWRZgtdrQgRxYLGwDdI+pBD6KbM7U968DbaP5iGwNSoh7J2BwMF8E+YbkDjIu194wJ0zUAiGr8sBbKnmcTAHowVYasNYeTiv51nszPMsUlbwrSl3oQmtFGbGLWx2YcViasWCzRd4kGbQie0c9Y6d03MwWrZbOXjdTWCpBcH4cH2FwL7/mDpbWyGENLZ5B9feTaGQB9H8PK7mEvAiW28QQCmwcYFnF91PIAzAz+zepF1IouH1TpTsGrqxrT+OwWuCV7H5fR+mKraWzM8Dcg7ivp2vHICLwC1BrQkTo1DxIPUgBmorUGpBu2CRtnYXjp2GKIYdTft+aP08FKehPG734OJpyApACMUEynlcrrkMI4/ByKNwZwCfuSd/Y4Hdw8cRz8H9pzc+WlEKDz0Jx6egVrJY256l1SmuzoEDcPAgjI19/bEiIiIiIiIiIvKasvvgxwm7neGG/xfYC+zIX9eBLwLfhMXTBkpsjKit1ceCams/7j2Xz58LWi2mP/Rh5n/6p656/SIiIiIiIiIiIiIiIiIiIiIiIiKvZoqqiYiIiIiIiLwGzS3McfjoYTKXrd/RbkOzCdUqVCqca5zjZO3k6m7nHDz3LJw5Bau9qAzOzJEUGtTGPRsDZM06neAyi2jXyaKIuN+h4EHRhx2jUBmxIFmaQaMDCyv28327Ut52S0boWSDLdxbVGkTYRiOY3Q4lz4JYY1OwewIu1KDvYKQEOyZgqgqZgzgFHFQLw4BW4KAYwLYxi5D1E/B8mCrn4TA7hCy1PxPPomRRfp1jRfszTaGZwUgBeomtMfBgrGQTBA7wLWyWpnY9mbM5lzqwNV+jh4XVXGJhtUIeTYvWfKKT5W9h5iyiBuD3wRXy8JqzANrZBhTz4zLPQmbF/NjUs/hZtWLXWQ5trpGi3fukC4VxiD2Lzk2O2ZpJoJ1BVIAstm2D2Fuzbe9PmkJtCcIQXAgXYwgTuwelBOoBnD5rYye6a64rhd1H4JML8PxXoDoBvSk4vhX2LMD5JWgu2XkffhzowQywbx6e2r7xcds3D9PtzR9FD7h9afN96zzyCExOXnq/58HsLExvVhIUEREREREREZHXukKtxq2f+L31GzPgMPAQMPhFD3XgMWAPsI3L/waADFgAjmO/nWCgk8/7go+At3zkI1z84feSbtlylVchIiIiIiIiIiIiIiIiIiIiIiJyY/r+7/9+/viP/3jTfe9+97v5pV/6pVd4RXI1boio2u/+7u9uuv3tb387r3/961/h1bw66J6IiIiIiIi8NtW7dU4sn6Deq19yTDtuc2juEA433OgcHDkCR49apcv3Ye9elu/cTa1bWx222LxA3DwLmzWjWs/SjcZIXEqAh9drEQfD6NkGaZ+iy7h7Z8qbtsOOqgXKXji+HMKuUejHKQsNC6R5WFQNAA9KBQuqFXzwM9vveTAzAq+fgK6DpTZMV2174FkIbTSyiFicQD+GNIHlNpRHIfRhsWe3ZqRg0bJSwcJhvT44H7ZUYak1/E7jaDkPsQE4i8JlWDzNw44nzZfthsE0D1tT5kE3gSQbzhHkIbUstcCb70MAlAILvwWe3YM4sdian9l/aTK8l1EAO0ft/oCFzzpNKIRAweJwvTb0Urh5ys7pyANyGZQiILLz1lagVISis2voAOcuQKUDYRnSsh1Xb1gIrtuF4hLULoLLIN5h77UrWvzt+XnoxVBMoJzY/W4uw8hjMPMojO2GdBvUF4AF6J+Hbg0G/bXZhfWxtAeOwcUKPD823LZrxbZfkwMH4MEHr3ESERERERERebk9W+/yS088e0Vj/9X+W7hlvPQyr0hEZGj3wY8TdjsbdywCHwe+n2FYLQG+DJzAwmrjwAj2IV0KtLD42jzQf8F8HeBj+bwvELRaTH/ow8z/9E9d6+WIyEth6ST8r/94ZWPv/zcwddvLux4RERERERERERERERERERGR17AvfelL1Gq1Dds9z+O9733vK78guSo3RFTtn//zf47nbfyG9m/+5m++ZgNiuiciIiIiIiKvPXMLcxw+epjMZet3tNvQbEK1CpUK5xrnOFk7ubrbOQfPPQtnTsFqiCqDM3MkhQa1cc/GAFmzTie49BqyXock9ImTmMiDkgdT4zBeykNdKay04UIL9u+C+25LKPgW//KcRdU8N4yBjUYWSyt5FhbbPQHP1+BCA2ZGYaQM5SLsmrBAWZJCHFuAbKICEXZc0YedkxbsSp1Fv8qhBdXA4mJ+BudaFhYbD+zA6RHo9m2OJINmz2JlRc/CZIEHkxVbs4dtG4ksROaAXgKNGLaO2rmds8BZnFgULu+hAVAM7HiwNRR8G+8PQmgx9kmOb2/PVAXONqAYDo8JPAjzt7/fh2TNezVVtvm7sd2nbg/CJhS32utiCJWK3b8AC8E5B/GaL2RmDhpt6LZhaxnSFOodwIGXQtqEWgeOPmfhObBY2o7mmnUch4USNGvw3NNQj+D4VtizAOeXoLkEWQwPPw704B2n4Utbh/dmd204l+fg/tPrn8EohYeehONTUCvBRBf2LA3v81U5cAAOHryWGUREREREROQVstyNOfyVhSsa+8/+1g5F1UTkFVOo1bj1E7936QFngY8ADwI71mzvA8/l/12Jc8BhNg2qDWz5yEe4+MPvJd2y5QonFZGXTWcJvvDfr2zsW34QUFRNRERERERERERERERERERE5OVy5syZDc0m5xx33HEHd95553ValbxYN0RUbWDwBW9g06DYa5HuiYiIiIiIyDe+erfOieUT1Hv1S45px20OzR3CMfx7IM7BkSNw9KhVvnwf9u5l+c7d1Lq11WGLzQvEzbMwvcnErWfpRmMkLiXAw+u1iINh9GyDNCYMfd60M2PfdthRZUPNqhLC3hnbfKEBCw3o54Exf7B8D0oFC6oVfAueeViYbf9NFvy62Iaeg7ESVAp2WORDUISRggW/4l4+nbPomsujYJ4Ho8XhmrIM+jFMjkHWzQNpWACtVLKfHRYfW6jDtnHIT0mpAKR5sM2DqAD9bj4e6CZ2XJhH5fxo9RIhsbclyyAIoBRAN7XXYPG1KD9RmFjQbRAXiwLYOWr3B2x7pw0VDzJbEkt1u6aRyM6fASNFC54loxD0oZdBsWDxOy8Pv/X60AN6HXA+hKHds3oDaitQXILFRahOQnUb+Hm8LU1g6WvQWxNzm+iuf//vOAdPnIAjZ2HQ/+ufh24NBkNnF2C6bT/PtGHfPDy1fcPTxr754bi1POD2pY3bN/XIIzA5ufk+z4PZWZje7H8cIiIiIiIicj189elTl93/bKN/2f3rxn7tHOWFF1SHtlevYlUiIl/f7oMfJ+x2Lj9oEfgt4O3At2C/NeJK9YHPAv8v9kHgZQStFtMf+jDzP/1TL+IEIiIiIiIiIiIiIiIiIiIiIiIiN7aVlZV1r51zeJ7HW9/61uu0IrkaN1RUbRANWxsSe63TPREREREREfnGNrcwx+Gjh8ncmm/BtdvQbEK1CpUKAOca5zhZO7k6xDkHzz0LZ07B2GBrBmfmSAoNauPe6t8Vs2adzpoI1gtlvQ5J6BMnMZEHJc8CXOMli5y5FFbacKEF+3fBvbdlhPl8nrP/BhG20chCaZFvAbWJKdg9AedXLDxWLUG5aKGxbWPge9BPoNODyIMt1eH3CGfG4ELTomoDHhZY8zyLjhXK0GrDYhPGfRsQ+tDtQ7NrcbZSAbpxHlLz7RxZHhfzgJEStHr2Oo4tlrbYgW1V2+9h88SJXWvgDTtyQf5DL4+qre3L+Rl4KXhRHm3zYKoCZxvDOXqpxeD8DHyg34d0zac5U2U7rhvb/avVIRoBV7TXQf7YhP7w/qTO4mflIlC0NV+owXjVgmypBzEWTyu1oF2A1Id2F46dhiiGHU2brz4PpdNQmoaVcVi5AH4filXohVBMoJzYvWsuw8hjMPIo3BnAZ+5h9Ybsrq15Dx3cf3r9M/jAMbhYgefHhtt2rdj2a3LgADz44DVOIiIiIiIiIiIicnmFWo1bP/F7VzY4A/4KeAzYB9wFbGfDL68A7IPF88BTwBew35RwhaZ+87e4+MPvJd2y5coPEhERERERERERERERERERERERuYH5vr/p9je84Q2v8ErkWtxQUbVB2U+GdE9EREREREReferdOieWT1Dv1S87rh23OTR3CEceynYOjhyBo0chy6w8tncv3H03y90atW5t9djF5gXi5lmY3mTi1rP0ojEywHMOei3iYBg+2yCNCUOfN+3M2LcddlTZ8AW+Sgh7Z2zzhQacb0LsLATmOxtfKlhQrRCAl0HgLH62pQq3TVoE7EIbes5CadW8njZSgOmKRdLi2I51WHBt25gFxeLUQmQTFds+kKU2ljCPpmHzlEr2c5JBrW2RsUrBAmWhb9G3bt/WPQi09fM4WOZBN7FjCz6QQRBaiMx3Fpnzfbv+Yh6XyzI7f+ZZfA0gzLelXcjya40Cu9Zi/omN86Ddg2Jm36XMPKg1YbQCI5GtNQNGitDrQzJq8xeLUExhS2iBuHYHipHF33r9/EYMHoc2JAnUliAMwYVwMYYwsfeolEA9gNNn7fonuuvf+1vOQfwJ+FgCXgDVKehNwfGtsGcBzi9BcwmyGB5+HOjBDLBvHp7avvFx2zcP0+3126IUHnoSjk9BrWRr2LO0+fdIr9iBA3Dw4LXMICIiIiIiIiIickV2H/w4Ybfz4g7qYWG1x4ACFlYbx/61VwLUsaBafHVrCjttpj/0YeZ/+qeubgIREREREREREREREREREREREZEbzOjoKIuLixu2j4+PX4fVyNW6oaJqIiIiIiIiIq92cwtzHD56mMxl63e029BsQrUKlQoA5xrnOFk7uTrEPXsazpyCscGWDM7MQdQiuWkntW4N56yWlTXrdIJLryPrdYhDn0KS4QMlD6bGYbwEgW9hsJU2XGjB/l1w720ZYT6f5+y/QYRtNLJYWuRbVGxiCm6bgLNNcAlUi1Aqwq5JC6rFqcXRXGoRtMCHIIMggJkxuNC0qNqAB1Qii6UVfej2oBPDYhPGRmz7Stv+LEcWESuHFgDr9sH5Fm7LnG3zgJEStHr2Oo5huQXBuK0P7M8ESB3goBBCGlu4zeXX3U2h4OVz5r98IMiA1EJsLp+nFNhxHnZsP7bvQA5+X0G/CW7SxgBMlKBYsFhcmkGjZfE2V8xbaPmXJEN/eH9SZ2G4chEoWgAuy+yeRCkkAbS7sNKEEQdZyQJqSQrLK9DpQnEJahfBZRDvsHieK4Lfh+fnoRdDMYFyHpdrLsPIYzDyKNCDb9oLT2+D+gKwAP3z0K3BoME2u7A+lvbAMbhYgefHhtt2rdj2zXjA7UuXfKSH9u+HD3zg0vs9D2ZnYXqz4qCIiIiIiIiIiMhLLMt43R8evrY5YuC5/L+X0Jbf+R3m/+3/z35LhIiIiIiIiIiIiIiIiIiIiIiIyGvcxMTEplG1MFSm6xuJ3i0RERERERGRl0C9W+fE8gnqvfolx7TjNofmDuEsjWWcgyNH4OjRvILlw969cPfdLHdr1Lo1G5ckLNZPEW/WgWqegsUWvSwmcxmec9BrEQfD8NkGaUwY+szuzHjzNtg+OoyFDVRC2DtjMasLDTjfhNhZDMx3gAelggXVCgF4GQTOmlVbqnDbFogTWGxBsQiVgs1b8CEswkjBol79OA+OYeGzqRELiuEgCqFSsjgY2NxhCIsrNneUR9DGivl+bM5Wan9GeQQt9C361u3bIM+zY/t5IMwBSx3YmsfXPOyaXGLBsoJv26I1n6Rk2cafwwzoW4xs8DZPVex8A70MvBYUq/a666DTgMmxPAwX2viRokXVkq6F34pFCD2YGrM1tjtQjCAMoNcfng+g0bRrnRqBogftHix3IEwgi6HRg56DY6ft2GICO5rD46eOw0IJmjV47mmoR3B8K+xZgPNL0FyyeR5+HOjZMe84DV/aOnyOdteG83kO7j+9/vmKUnjoSTg+BbUSTHRhz5Ld56t24AAcPAhjY19/rIiIiIiIiIiIyCug+txpCs3G9V7GpoJ6neLx4/TuuON6L0VEREREREREREREREREREREROS627NnD8ePH8fz1n/TsdF4df4bMNmcomoiIiIiIiIi12huYY7DRw+TuWz9jnYbmk2oVqFS4VzjHCdrJ1d3O+fguWfhzClYbUBlcGYOohbJTTupdWs2rr5MBnQu9Tf5dp0sioizmEKS4QMlD6bGYbwEgQ8uhZU2XGjBPbvgbbdlhIEFr3xncbFBhG00slha5Nu+iSm4bQLONi00Vi1CqQi7Ji0+FqcW/nIpTFTsfH4GXghbx6wVN7g7HhZY8zyLlEU+tNpwoWkxtJlxi6BdWLHIWLFg8wWeRcY6sUXbllrDANdo2QJpg9dJCufrsG0c8pYbhQASLJKGg0IIaWzzZh50E0iyfA5n5/SALAXPt2sIgFIA3dSOw7P7Fsd2vT4WPOv0Icjfq0rBzt2J7XU/AfLQnF+ATgZpDxYbcPNU/l6RR+YyKEVAZNc0uL4ohSSAdhdWmjDiICtZcC5JYXkFOl1onYCyD5VpcGUoJfnxPXhmPo+xYUGzte44B0+cgCNnYfBY989DtwaDobMLMN0eHjPThn3z8NT2jY/nvvn1Ywc84Paljds39cgjMDm5+T7Pg9lZmN6sOigiIiIiIiIiInL9xNVRnOfZL8N4lXGeRzpxic/cREREREREREREREREREREREREXmPe8IY38Cd/8icbtj/77LPXYTVytRRVExEREREREbmEerfOieUT1Hv1S45px20OzR3CseYLcc7BkSNw9KhVsXwf9u5l+c7d1Lq11WGLzQvEzbOwWQeqeQoWW/SymCxL8HpNiCAOhuGzddI+pBD6KbM7U968DbaPgnvB2EoIe2cgcDDfhPNNi4z5g+V7UCpYUK0QgJfZWM+zkNltWyBOYDGPglXyYlnBh7AIIwWLevVjC4w5B2Fgcy21LRQ2WbZYmt0ri5Y5IPHsmCiwXWPF/NJSaGY2dy+x6w88mKxYEM7DrnMkstvtsHCZAxY7sK1qoTMPW4dL7JoLeTStGAzvUy+17c7Z2wbgYuwTFB/IYKoCZxtQzD9VyTzod6Hq2/1L/bylN2aBtHJoc/m+xdb6HXAhtGvgT9o6SwWoVCzOFmD3zDmI+8P3LklhqW7XXfSg3YPlDoQJZDE0etBzcOy0xdKKCexoWgStfh6K01Aet7DcxdOQFey6igmUEztfcxlGHoORR+HOAD5zD6slt93DRxfPwf2nNz6GDxyDixV4fmy4bdeKbb8mBw7Agw9e4yQiIiIiIiIiIiKvvN6WaS7ufyszj//N9V7KBs2/83dIts5c72WIiIiIiIiIiIiIiIiIiIiIiIi8Ktx333380i/90obtTz/99HVYjVwtRdVERERERERENjG3MMfho4fJXLZ+R7udF7OqUKlwrnGOk7WTq7udc/Dcs3DmFKyGpTI4M0dSaFAb92wMkDXrdILLLKJdJ4si4n6HggdFH3aMQmXEYl1pBo0OLKzYz/ftSnnbLRmhZ9Er31koaxBhG40sllbyrJM1NgW3TdjxSQojJSgVYdekxcfi1CJfLoWJip3Tz8ALYeuYRcIGd8fDAmueB1EIkQ+tNlxswEjVImahb68rBYuclQo2X7cPzrdo21JrteHFaNniaB6As2vNsOCYhx1PateYehAVLG7msPkzD7oJJNlwjiAPqWUpeHnoLPCgFEA3tSgbWDguyoNxYQJpMgyvRQHsHLX4Gtj2tG9rLY5aHC6JodaAXdN2TrDzFEOYHrexQd/idFvGoVCw98w5C6L1gF7H7ksY2n2tN2BxGZpfg7IPlWlwZSgl+bp68My8HQ8w0R0+SlkKu4/AJxfg+a9AdQJ6U3B8K+xZgPNL0FyyONvDjwM9mAH2zcNT2zc+mvvmYbq9cXuUwkNPwvEpqJVsDXuWhu/pVTlwAA4evJYZRERERERERERErqsn/93/xZv/3b9l6+N/fb2Xsmrhnnu58Ju/cb2XISIiIiIiIiIiIiIiIiIiIiIi8qrxrd/6rfi+v/o9YM+z7wT/7//9v+l0OpTL5eu8QrkSN3RU7b/+1//KZz/72eu9DBEREREREXkVqXfrnFg+Qb1Xv+SYdtzm0NwhHG640Tk4cgSOHrXylu/D3r0s37mbWre2OmyxeYG4eRamN5m49SzdaIzEpQR4eL0WcTCMnm2Q9im6jLt3prxpO+yogs/G8eUQdo1CP05ZaEDfWcTKHyzfswDZ7HYLgfl5ZMzzYGYEdk9CP7PAV7Fo0TOwsWERRgoWXevHdn7nLGxWCGCpbRGzyTL4g3U5i3i5fK1RHo6LAthWtXMnGTR7kCUQeRYmCzyYrFhczMO2jUR2ux3QS6ARw9ZRW4OHrSNO7Jggj8V52M+DCFo3hYJnx/iDEFoM5LEyB0xV4GxjOEcvtevzM7vmfh/SNZ+iTJVt/m5s96bbA78LbgRSoBDCaAXSGDI/vzcO4j4EAZQDcBG4Jag1YWIUKp7F4WKgtgKlFrQLkPrQ7sKx0xDFsKMJXaB+HorTUB6HBLh4GrKCXVcxgXJi19xchpHHYOZRGNsN6TaoLwAL0D8P3ZrNBzC7sD6W9sAxuFiB58eG23at2PZL8YDbly69H4D9++EDH7j8GM+D2VmY3ux/TCIiIiIiIiKbK/oed1QLVzxWROSVkIxUefz//hWi2jKjJ79mH9xdL57Hym17iCcmuGV09PqtQ0RMoQI733zlY0VERERERERERERERERERETkZTMxMcG3fuu38qd/+qd43vDfmXa7XX7/93+f7/u+77uOq5MrdcNF1QaVP+ccn/vc5/jc5z53nVd0/bjr+Q8wRUREREREXoXmFuY4fPQwmcuGG9ttaDahWoWKfRnlXOMcJ2snV4c45+C5Z+HMKViNS2VwZo6k0KA27q3+HSxr1ukEl15D1uuQhD5xEhN5UPJgahzGSxD44FJYacOFFuzfBffdllDwLU7mOQt8eW4YVhuNLJZWymNguyfg+RpcaMDMKIyUoVyEXRMWCUtSiGOLnk1UIMrXFYWwdcyiY4O742GBNc+z/ZEPrTZcbMBIFVIHoW+vKwXInMXb/Ay6fXA+zIyt3i08YLRsIbIMSBOYr8O2cRh81blUAFL7TmHqQVSAftfCZwnQTewaQt8mDUKLkPnO7p3vW4QtWvOJR5ZfUJJAIT9RmFjQzeWxsyiA6QoU8+OcB+0eFDNba+ZZ/Gy0YqG3ML9PI0Xo9SEZhUIRYg9Kob2nXr7odmbXkcW2bRB7a7bz60yhtgRhCC6Ei7GtL3BQSqAewOmzNnaiu+a6Uth9BD65AM9/BaoT0JuC41thzwKcX4Lmkp334ceBHrzjNHxp63ANu2vD+TwH959e/7xGKTz0JByfglrJzr9nyd7Lq3bgABw8CGNjX3+siIiIiIiIyIt060iBX33ztuu9DBGRTfUnJll88/512wq1GsWli/SmpoknJq7PwkTk+tm6F37oM9d7FSIiIiIiIiIiIiIiIiIiIiKS+7Ef+zH+9E//dN025xwf+MAHFFX7BnHDRdXWUlRMRERERETkxlfv1jmxfIJ6r37Zce24zaG5Qzjyvys6B0eOwNGjVt3yfdi7F+6+m+VujVq3tnrsYvMCcfMsTG8ycetZetGYRcOcg16LOBhGzzZIY8LQ5007M/Zthx1VNhSqKiHsnbHNFxqw0IC+s9f+4K+6ngXIZrdDwbeQmYeF2fbfBGEAF9vQczBWsugZWBgtKMJIwWJecS+fztkxhQCW2hYxmyyDP1ibs4iXw64tysNxUQDbqnbuJINmz2JlkWfhrkJga+rENib07ZheCnFi8y12hnN42DrixNYUeMPbE+Q/9PKoWpaB5+f7MiC19aZAEEApgG6aH+dBP7G5/czidGkXkkFVDpgoQbFg0bc0g0bL7q0r2jqJbVzor74FpM7icOUiULQ1X6zD5CgUnb3uAOcuQKUDYRnSss1Xb9jx3S4Ul6B2EVwG8Q57LlwR/D48Pw+9GIoJlBN7dJvLMPIYzDwKY7sh3Qb1BWAB+uehW4NBf212Aabb9vNMG/bNw1PbNz6a++aH49bygNuXNm7f1COPwOTk5vs8D2ZnYXqz/yGJiIiIiIiIiIi8xjjHGz/8y9x6+PcI4j5pIeLUg/+YL7/3ffZZmoiIiIiIiIiIiIiIiIiIiIiIiLziHnjgAd7ylrfw+OOPA+B5Hs45vvSlL/Frv/ZrPPzww9d5hfL13NBRNe81/A8MFZQTEREREZHXgrmFOQ4fPUzmsvU72m1oNqFahUoFgHONc5ysnVwd4p49DWdOwdhgSwZn5iBqkdy0k1q3tvp3q6xZpxNceh1Zr0Mc+hSSDB8oeTA1DuMlC4q5FFbacKEF+3fBvbdlhPl8Xh7eGkTYRiMLpUW+BdQmpmD3BJxfgSSFaskCXr4P28YsItZPoNOzkNmWKgw6YTNjcKFpUbUBDwuseXkYrVCGVhsuNmCkapGv0LfXlQJkzuJtfgbdPjjf5s3vGB4wWrYQWYYFxubrsG0c8o4bhQDiPqR53KwQQhLb3JkH3cSCbAXfJg1C65f5zu6d70PsoJjfsywPyGWe/QwQZkAfiIb3cqoCZxtQzD/9yDxod6GYH5N6UGtCtWL3sRzafRkpWlQt6UIcQ7EIoQdTY7bmdgeKkQXaen1gzV/BW207prYMW8sWrqt3bIyXQtqEWgeOPpcfi8XSdjSHc0wdh4USNGvw3NNQj+D4VtizAOeXoLkEWQwPPw704B2n4UtbLWIHsLu25v12cP/p9c/rA8fgYgWeHxtu27Vi26/JgQPw4IPXOImIiIiIiIiIiMhrw+7f+xiv/38+tvo6iPu8/v/5GP2JSU587z+7jisTERERERERERERERERERERERF5bfsv/+W/8Ja3vIUs/xLrIKz2kz/5k+zfv5+3vvWt13mFcjk3dFRNYTEREREREZFvTPVunRPLJ6j36pcc047bHJo7hFtbtHIOjhyBo0ettuX7sHcv3H03y90atW7NxiUJi/VTxNObTNw8BYstellM5jI856DXIg6Gsa4N0pgw9JndmfHmbbB9dBi4GqiEsHfGYmAXGnC+aaEwH4uH4Vm8bHa7Rci8DAJnka8tVbht0qJqF9rQcxZKq+b1tJECTFcskhbHdqzDQmHbxix4FqcQeDCRB8QGstTGZnlkDezPbVVba5JBswdZYtE259n6Ah86sY0JfTuml0Kc2HyLHZsDuzSiALop4GwdPvbn4D51Uyh49hZ6vm0LMiC1e+Cw85YCO87Djo1jC7752Nz9Lrji8Dp2juaxtnx8p2lRNwqQeNBr27pvnsoDeOT3I4NSBER23we3LEohCSzOttKEEQdZCcLQxi2vQKcLxSVYXITqJFS3gZ/f2zSBpa9Bb02kb6K7/lm54xw8cQKOnIVBL7B/Hro1GAydXYDptv0804Z98/DU9o2P5r754biBKIWHnoTjU1Ar2fn3LA2v8aocOAAHD17LDCIiIiIiIiIiIq8ZhVqN23/7Nzfdd/vv/BbP/X++g3hi4pVdlIiIiIiIiIiIiIiIiIiIiIiIiADw5je/mV/8xV/kx3/8x/E8+/al53l0u13+/t//+/zhH/4h3/It33KdVymXckNF1QYPoIiIiIiIiHzjmluY4/DRw2SDmtRAuw3NJlSrUKlwrnGOk7WTq7udc/Dcs3DmFIwNtmZwZg6iFslNO6l1azauvkwGdC71t+J2nSyKiLOYQpLhAyUPpsZhvJTHt1JYacOFFtyzC952W0YYgOcskubcMMI2GlksLfJt38QU3DYBZ5vgEqgWoVSEXZMWDotTi4W51CJogW+BsSCAmTG40LSo2oAHVCKLpRV96PYseLbYhLER277Stj/LEWQOyqGtsdsH59u8+R3DA0bLFmPLsBDYfB22jUMhP2chgLgPqWcHFEJIYps786CbWJAt9G2+wAc/gXQQVfMhWnP/s2zjz2EG9PNIWt7Om6pYZG2g2YfxwU3AHpFiwc4HMFW2mFo3tuhZtwdhE4pb7XUxhErF7ncAhIHdl7g/PEeSwlIdJitQ9KDdg+UOhAlkMTR6Fro7dhp6fSgmsKNpx9bnoXQaStOwMg4rF8DvQ7EKvdDGlhM7Z3MZRh6DkUfhzgA+c8/wunbX1rzfDu4/vf6RfeAYXKzA82PDbbtWbPtmPOD2pc33rbN/P3zgA5fe73kwOwvTmxUKRUREREREREREZDO7D36csNvZdF/YabP70H/jK+95+BVelYiIiIiIiIiIiIiIiIiIiIiIiAy8733v48SJE/zyL//yurBarVbjXe96Fz/+4z/Oz//8z1Mqlb7OTPJKu6GiaiIiIiIiIvLqVe/WObF8gnqvfskx7bjNoblDuEFBC6w2deQIHD1qtS3fh717Wb5zN7VubXXYYvMCcfMsbNZ2ap6CxRa9LCbLErxeEyKIg2H4bJ20DymEfsrszpQ3b4PtoxbnWqsSwt4ZCBzMN+F806Jh/mD5HpQKFlQrBOBlNtbzYEsVbtsCcQKLLSgWoZIXywo+hEUYKVjQqx9bBMth0bKpEYuE4SxMVilZMA1s7jCExRWbOwps+1hxdUkkKbRS+zPy7LoKgYXIOvm5Qt+O7aU2jwMWO7CtOpwnCqCb2joCD3zsz8F96qZQyBfu53E1l4AX2TqDAEr5HEEeZ3POAmd+ZvNlDjp9CPJPMCoFW2sntte9xEJpI1ULwHVS6DRgagxGIruODBgpWvAsGYWgD73M4mtT4xYpc87294Bex0JzYWjrrDdgcRmaX4OyD5VpcGUoJbaGqAfPzNvxABPd9c/JLecg/gR8LAEvgOoU9Kbg+FbYswDnl6C5ZIG2hx8HejAD7JuHp7ZvfDz3zcN0e/22KIWHnoTjU1Ar2Rr2LK022a7OgQNw8CCMjX39sSIiIiIiIiIiInJFCrUat37i9y475tZP/B4nvuefEk9MvDKLEhERERERERERERERERERERERkQ0++MEPMjU1xb/7d/9uXVgtSRJ+8Rd/kcOHD/P+97+ff/JP/gmTk5PXebUycENE1W655ZbVh04ub3R09HovQUREREREXoPmFuY4fPQwmcvW72i3odmEahUqFc41znGydnJ1t3MOnnsWzpyC1a5TBmfmSAoNauOejQGyZp1OcJlFtOtkUUTc71DwoOjDjlGojFhMLM2g0YGFFfv5vl0pb7slI/QsuuXn4a1BhG00slhaybNw1dgU3DZhxycpjJSgVIRdkxYBi1OLhbkUJip2Tj8DL4StYxYdG9wdD4uHeZ5F0yIfWm240LQY2sy4xcIurFhMrViw+QLP1t6JLdq21BpGtUbL0E+Gr5MUztdh2zjkLTdbZx/SPHBWCCGJLWyWedBNIMns3B75NSQWkgs8u4ZozScNWX5BWR6SA/D74Ap5eM3BVAXONqCYH5d50O9C1bc1pH7+iIzZ+cqhncf3LbbW70A9tihdP7D4WyE/b+gP72fqIE2gXASK9p5eqMF4FSqeXXMM1Fag1IJ2wc7d7sKx0xDFsKMJXaB+HorTUB6HBLh4GrICEEIxgXJiz0pzGUYeg5FHgR580154ehvUF4AF6J+Hbs3mBJhdWB9Le+AYXKzA82uaZrtWbPtmPOD2pc33rbN/P3zgA5fe73kwOwvTmxUKRURERERERERE5FrsPvhxwm7HPpjdDkwAAZACNeA8hJ02uw/9N77ynoev30JFRERERERERERERERERERERESEn/3Zn+Wee+7hPe95D+fOnQMsrOac4+TJk/zYj/0Y73//+/n2b/923vGOd/DN3/zN3HXXXYyMjFznlb923RBRtVOnTl3vJYiIiIiIiLwm1bt1TiyfoN6rX3JMO25zaO4QDjfc6BwcOQJHj1p5y/dh716W79xNrVtbHbbYvEDcPAubtZ1az9KNxkhcSoCH12sRB8Po2QZpn6LLuHtnypu2w44q+GwcXw5h1yj045SFBvSdxar8wfI9KBUsqFYYhNGwDtXMCOyehH4Giy0LfVXyYlnBh7AIIwULmvVjO79zEAYWNFtqWxRssmyxNLtXkKW2PfHsmCiPx40V80tLoZnZ3L3ErinwYLJi8TAPi5iNRHa7HRZYc8BiB7ZVVy+NKIBuaucN8vMFXh5Bw/YV8iCan8fVXAJeZPcgCKCUzxHkcbY4sdian9l/aTK871EAO0ft/pCvM+3b+oqjds1JDLUG7Jq2sBr5moohTI/b2DiBLIKZSVtD5qDdgWJk97fXtzWvPj5tSBKoLUEYggvhYgxhAoGDUgL1AE6ftfdoojs8Nkth9xH45AI8/xWoTkBvCo5vhT0LcH4JmkuQxfDw40DPjnvHafjS1uG93F0bzuk5uP/0+mcxSuGhJ+H4FNRKtoY9S8Mw3lU5cAAOHoSxsa8/VkRERERERERERF5SxZUFbn3uf8APYUG1zT7sc8B5uPXL/51TK99Db2zrK7tIERERERERERERERERERERERGR17B3vetdm26fmZnh7NmzeJ79w6/Bn845er0en/jEJ/jEJz6xum9ycpLx8XHGxsYYGxvD9/2Xbc2e5/Fnf/ZnL9v832huiKiaiIiIiIiIvPLmFuY4fPQwmcuGG9ttaDahWoVKBYBzjXOcrJ1cHeKcg+eehTOnYLXrlMGZOZJCg9q41dkBsmadTnDpNWS9DknoEycxkQclD6bGYbxk8S2XwkobLrRg/y6477aEgm9BL89ZMMxzw8DXaGSxtJJn32XbPQHP1+BCA2ZGYaQM5SLsmrAIWpJCHFtka6ICUb6uKIStYxYdG9wdDwuseZ7tj3wLe11swEgVUgehb68rBYuClQoWIev2wfmwpQpLreH37EbLFhTzABw0Ona+MLBtpQKQWhQs9SAqQL9r38nL8uvuJpBkdm4Pu29+YusJPLuGaM2nB1l+QZmzawHw++AKeSzMwVQFzjYsegbQywNrYX5svw/Jmvd1qmzHdmO7p90e+F1wI5AChRBGK5DGkPl5cM5B3LeAWjkAF0F9BSZH8/cgtXO0u7DShBEHWckCakkKyyvQ6UJxCWoXwWUQ77DYnivaNT0/D70YigmUE7uPzWUYeQxmHoWx3ZBug/oCsAD989CtwaC/NrsA0+3hdc60Yd88PLV947O8b3792AEPuH1p4/ZNPfIITE5uvs/zYHYWpjcrFIqIiIiIiIiIiMjLK2Vq/Avccf5DhO/qXX6oB+yAcEePN3d+hq/e/MMs1fcBl/mwXERERERERERERERERERERERERF4Sn/nMZ1aDaZsZfAcaLGa2Nq62dszi4iKLi4ur414uzrmXdf5vRIqqiYiIiIiIyKp6t86J5RPUe/XLjmvHbQ7NHcKR/wXfOThyBI4eteqW78PevXD33Sx3a9S6tdVjF5sXiJtnYbO2U+tZetEYGeA5B70WcTCMnm2QxoShz5t2ZuzbbkEsXjC2EsLeGdt8oQELDeg7e+0PPp/wLEA2ux0KvoXMBoGx/TdZpOxiG3oOxkoWPQMLowVFGClAmkKcfxfOc3ZMIYCltkXMJst5DAzAWYjNYdcW5d+FiwLYVrVzJxk0e5AlEHkWHQs8mKzY/B62bSSyW+6AXgKNGLaO2lviYeuIEzsmyGNxHvazy9fTTaGQh8r8PK7mEvAia3EFAZQCGxfkE8SJxdb8zP5Lk+H7FAWwc9TuJfk6O22oeBZ9S4GlOkyO2frDPD43UoReH5JRKBQh9qAUWijPc0AC7czicFls2wbX0Gxb4G6xDlMjUPSg3YPlDoSJjW/07D08dtrOU0xgR3P4rEwdh4USNGvw3NNQj+D4VtizAOeXoLlk8zz8ONCDd5yGL20drmF3bTiX5+D+0xsf2QeOwcUKPD823LZrxbZfkwMH4MEHr3ESEREREREREREReakVwho7Zv6MSvY8k8e++KKOnTz2Rba+6a+oVk5y7sLfJU4mXp5FioiIiIiIiIiIiIiIiIiIiIiIyDprI2lfb8zauNpmY65krquhmNrmFFUTERERERERAOYW5jh89DCZy9bvaLeh2YRqFSoVAM41znGydnJ1iHv2NJw5BauhqAzOzEHUIrlpJ7VubfUv/FmzTie49DqyXoc49CkkGT5Q8iyqNV6yyJlLYaUNF1qwfxfce1tGmM/nOftvEPcajSyUFvkWUJuYgt0TcH4FkhSqJSgXLSS2bcyiZ/0EOj0LmW2pQpSva2YMLjQtqjbgYYE1Lw+jFcrQasPFBoxUIXUWDLvYsHGZs3ibn0G3D863efM7hgeMlqEb5/GxBObrsG0c8o4bpQKQWjQt9Sww1u9aVC0BuoldW+jbpEEIMXb9LrVrzTILoq3e8/wtz5xdC4DfB1fIg2EOpipwtgHF/LheHlgL82P7fUjWvK9TZTu2G9t6anWIRsAV7XWQHxf6w3uZOrvmchEo2nt5sQ6To1DM39sOcO4CVDoQliEt27XXG3Z8twGtE1D2oTINrgylxM4R9eCZeQuqAUx01z97d5yDJ07AkbMw+J9B/zx0azAYOrsA0+38mWjDvnl4ajsb7JsfjlsrSuGhJ+H4FNRKtoY9SxtagC/OgQNw8OC1zCAiIiIiIiIiIiIvg2K0wE3b/pjA7zF55Iv4yeDDSmA79pn6CBBgv42iBawA54E++HHC5DNPk35ziZt3PMqZ+Qfo9bdel2sRERERERERERERERERERERERF5LdksWHapONoLt689VuGzV56iaiIiIiIiIje4erfOieUT1Hv1NRvrsLQEU1MwPk47bnNo7hCONX9pdw6O/P/Z+/sYue78vvd8n4c69dhPxX6ihh5LNBWbM6YoWxx7kptoAt8ANAQ4cew4WcBJHEDQZBfKBkGQBImxCAz/sysk/+ze/WPj68Ree+z1UNFANzsJkgBeGEoudCGBupqRWhyZNDmkKKm7yO6u6q6uOs+//eN7qqtbbFIPMxJnpM8LILrr1O/8zu/8qnpAUlNvXoRLl6y65ftw+jQ89hjbcZ9+3Ldxec7m4Ltki0dcfPhd2NwjKTNKV+I5B8keWTANn92hyAhDnzMPlPzUCqzOVGGvA1ohnF6yENWtXVgfQubAx+JheBYfO7MKtQC8EoIqGHasAw8tWNjr1ggSZ6G0TlVPa9dgsWWRtCyzcx0WXFuZtUhYVgXF5lt2fKIsbGxZRdbAvq50bK15CcMEytyibc6z9QU+jDMbE/p2TlJAltt8m+PpHB4QBvac52wdkyUE1TdJFVUrS/CqaFlQAoWttwCCABoBxNW94NmcUWjRN7+0wNnkdYoCeGAGatV8zoPxCFpeFYADtgawMAvtqLo+0K5bxCyfsfnrdagXcCy0iNtoDPXI7ilJ4eBbcG9kr0F/G5abUBQwGNsYr4BiCP0xXHprGkqr53B8aBG0wTrUF6E5Z8G529ehrAGhjWvm9jYfbkP7JWg/D18I4E++xP6mnuxP1+M5+Mr1w+/FJy7D7RbcnJ0eO7Fjx+/GAx7euvvzAJw7B888c+8xngdnzsDiUT98IiIiIiIiIiIicj/Vwv5+UC2IY+Yvrdn/U+sUsMLR/9JCE1gEHgI2gCswf+l1tr9wBhrwuZX/xFvv/iJZPv+J3YeIiIiIiIiIiIiIiIiIiIiIiMhn0d0Cah/3uR+Ggm1HU1RNRERERETkh9CRobQjXNu+xgs3Xpj+4fsuobTN0w/y1u7N/fOcc9DrweY6LE+OlrC5Bt/epFg8xma8SeiF+DsDSmB8tz9hjgaUUURWZtTyEh9oeNCdg7mGBcVcATsjuLUHXzoBX36oJAwsYuU7W/Yk7jUTWSwt8u25+S48NA/vDMHl0KlDow4nFixYlhUW5nKFRdAC3wJjQQBLs3BraFG1CQ9oRRYfq/sQJxY82xzCbNuO74zsazOyMFgztDXGKTjf5q12DA+YaVqMrcRCZRsDWJmDWnXNWgBZCkUVN6uFkGc2d+lBnFuQrebbpEEIGdXeFPYyZg7qVcitLO26pWffA4QlkALRdC+7LXhnF+rVa5dUgbWwOidNIQ+me9NtWkwtzixK1x9A1AZXt8fB5Fr+dC8LZ/fcrAN1SPPqredBVNj8oxh2htB2UDYgDG2+7R0Yx1Dfgs1N6CxAZwX8ak1FDlt/BsmBNc7H0+/LAk5ehG/24Oab0JmHpAtXluFUD9a3YLgFZQZPvwwksASc3YBXV+98K5/dgMXR4WNRAU++Ale60G/Y9U9tHf15yA/s/Hm4cAFmZ99/rIiIiIiIiIiIiPwAKji+9McEfgLAwtq38ds5fBGIPsDpHrAKdMFfy1l44zVu//SXCPyE40t/zI13fxEI7j2HiIiIiIiIiIiIiIiIiIiIiIiIfGQKlv3wUlRNRERERETkh8xab43nLj1H6crDT4xGMBxCpwOtFkme8OLNFw+PuXED3r4G+62mEt5eY1jr05+b/hFxN96hiLdh/ogFxOsw2CNzBUWZU0tTggiyYBrrOqRIoYDQLzjzQMFPrcDqjMW5DmqFcHoJAgcbQ1gfWpDLn8TYPWjULKhWC8ArbaznwbEOPHQMshw296Beh1ZVLKv5ENahXbNQV5rZ59EcFi3rti0ShoMohFbDgl9gc4chbO7Y3FH1GbXZ+v6SyAvYK+xr5Nl91QKLt42ra4W+nZsUNo8DNsew0pnOEwUQF7aOwAMf+zrZp7iAmmfxNq+KlgUlUNg6HXbdRmDnedi5WQZ+afPhII0tgkZ1zQdmqlhbNX48gpZXBeCArQEszEI7svsogXYdkhTyGbufeh3qBRwLbU9HY6hHEAY2jgNB/d2hxee6bah7MEpgewxhbnGz3QQSB5ev27n1HI4P7dzBBjSuQ2MRduZg5xb4KdQ7kIQ2tpnbHg23of0SLD0PsyehWIFBD+hBug5xHyb9tTO9w7G0Jy7D7RbcPNA0O7Fjx4/iAQ9vHf3cIefOwTPP3P15z4MzZ2Bx8QNMJiIiIiIiIiI/lNaHH/81Vjsf/zVE5J66c9+iHt0GIIhj5t95HR7hzg5aDOyw/w9iMAsc+EdAiIBHYP6N19j+whmKRoN6dJvu3LfYGvz0x38jIiIiIiIiIiIiIiIiIiIiIiIin1HOufcfJD+QFFUTERERERH5ATGIB1zdvsogGdx1zCgb8ezas7iDlSrn4OJFuHQJyhJ8H06fZvP0g/Tj/nRYlsHGtcMfyKoUt94mbi7h+QGhH9KIU+758c40wY8iiqIgDaDj4EdnoNW2mFhRwu4Yejv2/V84UfDlz5eEHnhVKM25aYRtJrJYWqOKgc124aF5Oz8voN2ARh1OLFg4LCssFuYKmG/ZNf0SvBCWZ20LJsk5DwuseZ5F0yIf9kZwa2gxtKU5i4Xd2rGYWr1m8wWerX2cWbRta8/mAphpQppPH+cFrA9gZQ6qlputM4XCs0XUQsgzi46VHsQ55KVd26O6h9xCcoFn9xAd+FN7Wd75fVgCaRVJq94S3Zbd68QwhbnJRmDdvck9AnSbFlOLM7uP/gCits2ZF1W4DVvnZJrCQZFDsw7UbS/K0vYvKiAPYBTDzhDaDsqGxenyArZ3YBzD3lVo+tBaBNeERm7zRwm8sVHF2ID5Sfms8vl3IfsG/H4OXgCdLiRduLIMp3qwvgXDLQu0Pf0ykMDj1+H15Wmg7mR/Op/n4CvXD18jKuDJV+BKF/oNW8Oprenr/ZGcPw8XLsDs7PuPFRERERERERERkR9avpewMPfq/uOF77yK/+PF4aDaHnAF2D5iggXgFNCuHgfg/3jBwne+xe1Hf9aGzL1Kf+eLlJN/QUNERERERERERERERERERERERES+Lx5//HE873v6RKncZ4qqiYiIiIiI/ABY663x3KXnKF15+InRyCpYnQ60Wry7+y7X+tf2n3bOwVs34O3vwn6rqYS319irbbM9O/1j33DQI79HzykbbVNUpa0oTcmDafTsDkVKw5U89kDBI8fheBt87hzfDOHEDKRZQW8XUmdhKn/ShPOgUbOgWm0SRsOiYEttOLkAaQmbe1CvWxwNbGxYh3bNQl1pZtd3DsLAgmZbI+uMLTQt9mUbBmVhx3PPzomqD7LNVp89KwoYljZ3kts9BR4stCzA5WFxrnZkITGHRcUcsDmGlc7+rREFEBd23aC6XuBN415xATXPnveruJrLwYtsD4IAGtUcQRVnc86Ccn5Z7bmDcQpB9VK3anb/48weJznECbQ7FpobFzDehe6s3UNYBejadQuZ5TOQ5bbf9QKOhXaN0Rjqke1vksLBrt/uEOIUum2oezBKYHsMYW5xs90EEgeXr9u59RyODyEGButQX4TmHOTA7etQ1oDQxjVzu+fhNrRfgvbzQAJfPA2vrcCgB/QgXYe4b3MCnOnB4si+XxrB2Q14dfXOt/LZjem4gzzg4a07jx/p3/97WFg4+jnPgzNnYHHxA04mIiIiIiIiIiIiP8xmO5fxPfsL2iCOmc/WIDow4BZwiem/DPJe28BF4DSwVB2LYH7wOtvxWYpGA9/LmO1cpr/7kx/TXYiIiIiIiIiIiIiIiIiIiIiIiHw2/cmf/Mn9XoJ8jxRVTdBO2wABAABJREFUExERERER+RgN4gFXt68ySAZ3HTPKRjy79izuYKXKObh4ES5dsnKX78Pp02x/4ST9uL8/bHN4i2z4DhzRayr33mEUtsDzCPAo05jsHn8KdEVO4QdQFoxDqPuwOgvzDQt6lSUMxtDbg5/5HPylH82p+RYd86vYmOemYbWZyGJpDc+eOzkPN/twaxeWZqDdhGYdTsxbBCwvLBZWFjDfmn7GLApheda2YPIZMw+Lh3mePR/5sDeC27sWDyucxcJu79q40lm8zS8t/uV8ONaBrT2bC2CmaXE0D8DB7tiuFwZ2rFEDCntpCg+iGqSxtcXK6r7jHPLSru0BgQ9+busJPLuH6MBrUFY3VDq7FwA/BVerwmsOui14Zxfq1XmlZ9ft+LYRhV9192btes3QruP7FltLxzDILJKWBrbeWnXd0J/uZ+GgyO01oW57UZYWpIsKyAMYxbAzhLaDsgFhaK/b9g6MY9i7Ck0fWovgmtDIq9cwgTc2qhgbMD8pn2Gv98mL8M0e3HwTOvOQdOHKMpzqwfoWDLcszvb0y0Bi5z1+HV5fngbqTvanc3oOvnL98Pv7ictwuwU3D4QFT+zY8e/J+fPwy7/8PU4iIiIiIiIiIt8vpXOU7v3Hgf29h69/SVBEvs9mO2/uf7/w5qv4iwfqaXvcO6g2UVbjWkDbDvnHShb+9FVuP/Ll/esoqibyKVCW4IoPNtYL7D8AiYiIiIiIiIiIiIiIiIiIiIjIXSmqJiIiIiIi8jFZ663x3KXnKN2BT0eNRlUBqwOtFgDv7r7Ltf61/SHOOXjrBrz9XdgPQJXw9hp5bZf+nGdjgHI4YBzcfQ1lllIEPlmRE2HxseUZmGtYgKsoYRDD+hCyAhp+wSOrcPY4HG8zLY5VWiGcXgIf6O3ar9RNo2pg5zRqFlSr+RYymwTGzn3OImW3R5A4mG1Y9IxqbUEd2jUoCsiqeJbn7JxaAFsji4ItNO1Dr7ZhFuZyWHAsqvYjCmClY9fOSxgmUOYQeRbiCjxYaNn8HnasHVWfXQGSHHYz2y9XjQkDyHI7J6hicR72/STuFRdQq4JofhVXczl4kYXTggAagY0Lqgmy3GJrfmm/inwap4sCeGDG9pJqnUVq0bP6DOQe5Bn0d+HEou0z1ZrqISzO2dgshzKCpQVbQ+lgNIZ6ZPeVpLbmid2hBei6bah7MEpgewxhboGz3cRew8vX7dx6DseHEAODdagvQnMOcuD2dShrQGjjmrnt6XAb2i/B0vMwexKKFRj0gB6k6xD3bT6AMz1YHE3XtzSCsxvw6uqd7/uzG4fHgoXhnnwFrnSh37Cw26mtO97iH87583Dhwvcyg4iIiIiIiIh8n10eZvyfX+19oLH/06PL/PhM9P4DRUQ+IM/LqEebAARxzHx/DeYODLjC+wfVJspq/NnqsQ/z22+wHT9K0WhQjzbxvAznat+39YvIffDu/w7/8899sLFP/f/gc499vOsREREREREREREREREREREREfkhp6iaiIiIiIjIhzCIB1zdvsogGRw4OICtLeh2Yc4+HTXKRjy79ixuUqlyDi5ehEuXrNrl+3D6NDz2GNtxn37c359uc3iLbPgOLB6xgL0bJNEsJeA5B8keWTANcL2XK3LC0OPsA46zq3C8wx0VKa9aYieCTgi3Yout+Q4LllXjZyILpUU+BA7munByHm71LazWbkCzbre2MmvRszSHcWIhs2MdmHxEdWkWbg0tqra/Diyw5lVhtFoT9kZwexfaHSgchL49btUsCtaoWYQsTsH5Ni/YZ808YKYJcWaPixw2BrAyB5OPmDVqQGEvT+FBVIM0trZYDsQ55IVdlxKCEDJsb1xh91qWFkSbKKsPxJXO7gXAT8HVqvCag24L3tm16BlAUgXWwurcNIX8QCyv27Rz48zWEyfgx+DaUAC1EGZaUGRQ+lVwzkGWWkCtGYCLYLADCzM2Z1TYNUYx7Ayh7aBsQBjaNbZ3YBzD3lVo+tBaBNeERl6dn8AbG1WMDYuU7e9BAScvwjd7cPNN6MxD0oUry3CqB+tbMNyyONvTLwMJPH4dXl+exulO9g+/R79ynTs8cRlut+Dm7PTYiR07fhQPeHjr6OcOOXcOnnnm7s97Hpw5A4tH/ZCKiIiIiIiIiIjIZ5UF1ewv3RfWvo3fPlBQi4HtDznhdnVe9Xfpfrtg4Y3XuP3TXwIc9WiTODniX54QERERERERERERERERERERERER+YxSVE1ERERERD7zjgylHeHa9jVeuPECzr1/KG1zvMVbO2/tn+s2NmBzHZYnR0rYXINvb1IsHmMz3iT0QnzPpxwOGAd3XH5fmYzJQp9aXuIDDQ+6czDXgMC32NfOCG7twbkT8OWTjppv53pu8nGuqXYIp5dhtmbRqczBjQFsbAEOkgAaVVCtFkBQgI91pY514MfmIS3h1ggSZ6G0TlVPa9dgsWWRtCwDr7Tr+56F1+IMsiooNt+qYmCT+yxsbFlF1sC+rnRsnXkJwwTK3KJtzqvW58M4szGhb+ckBWS5zbc5ns7hAWFgz3nO1jFZQlB9k1RRtbIEr9rHoAQKW2+BhcsaAcTVveDZnFFo0Te/tKjbJFAXBfDADPuvi/NgPIKWVwXggK0BLMxCO6quD7TrFjHLZ6BWh8yDRmivv1eV4EalxeHKrHq9q2sOR/YabA6g24a6B6MEtscQ5jZ+N7HX8PJ1u049h+ND+8zeYB3qi9Ccs+Dc7etQ1oDQxjVz+5EYbkP7JVh6HmZPQrECgx7Qg3Qd4r7NB3CmB4sj+35pBGc34NUjPv93dmM67qCogCdfgStd6Dcs7HZq645u4Idz/jxcuACzs+8/VkREREREREREROSAWrgLQBDHzF9ag8cOPLnzESfdYT+qRhvmX3md7S+coWg0qIVD4uR7WLCIiIiIiIiIiIiIiIiIiIiIiIjIp4yiaiIiIiIi8pm21lvjuUvPUbry8BOjEQyH0OlAq0WSJ7x488XDY27cgLevwX57qYS31yAaMVzt0o/7drgo2B2tU8wfsYB4HQZ7ZK6gcAU1LyRI9siCaYDrDkVGGPqceaDkp1ZgdWYazppohXB6yeJSt3bhnT0oSouh4aZzz1SxtMiDoKqtRcDDC3C6C9sjIIQTCxYsywrIMwuezbcg9MB3UA9gaRZuDS2qNuEBrcjiY3Uf4sSCZ5tDmG3b8Z2RfW1GUDpohhbnilNwvs1b7S4eMNO0GFuJhco2BrAyB7XqmrUAshSKKm5WC6s1V/cd5xZkq/k2aRBCht2HK6yNl1X3BFVMDTu3rN4mYQmktlmTvey24J1dqFd/0k6qwFpYnZOmkB+I5XWb9rrFGeQF9AcQtcHV7XEwudYkiAcUzu65WQfqFk27PYCFGag7ezwG3r0FrTGETSiaFpMb7Nr58S7sXYWmD61FcE1o5NVrn8AbGxZUA4uUTZQFnLwI3+zBzTehMw9JF64sw6kerG/BcMvibE+/DCTw+HV4fXn6/jzZP/DecPCV64fft09chtstuHmgZ3Zix47fjQc8vHX35wE4dw6eeebeYzwPzpyBxcX3mUxERERERERERETkaJ5XALCw9m38PIeD/4BK+hEnPXheAH6Ws/DGa9z+6S/heflHXaqIiIiIiIiIiIiIiIiIiIiIiIjIp5KiaiIiIiIi8qk0iAdc3b7KIBncdcwoG/Hs2rM43PSgc3DxIly6ZAUt34fTp9k8/eA0kga4LIONa9C4c142rlHMRSRFQuiF+LsDGjkMo7ssJE3wo4jCFaTZmDrQ8KA7B3MNCHyLfe2M4NYefOkEfPmhkjCwMJXvbNl3hNJ8e26+Cw8uwLu74HLo1KHesPjX8RkLbeUZjBObZ6EF7QjwYHHWImitqlhW8yGsQ7tm4a/8wIe5SgfdtkXCcBCF0GpYMA2sWRWGsLkDWQ5R9WGy2Xr1PDbnXmFfI89iXLXA9mCc2ZjQt3OTwuZxwOYYVjrTeaIA4sLWEXgWkwu8adwrLqDm2f16VbQsKIHC1umw6zYCO8/Dzs0y8A/E6dLYImhU13xgpoq1VePHI2h5VQAO2BrAwqztb+jb8XbdImb5jN1PvQ71Ao6FtqejMdQjCIMqdnbg7bo3sjX1t2G5CUUBg7GN8QoohtAfw6W3pqG0eg7HhxADg3WoL0JzDnLg9nUoa0Bo45q57dFwG9ovwdLzMHsSihUY9IAepOsQ920+gDM9WBzZ90sjOLsBr67e+bY/uzEdNxEV8OQrcKUL/YZF3U5t2f5/ZOfPw4ULMDv7/mNFREREREREREREvgfOBQRxzPylNTtQHHjybv+N4P0cPK+ab/7S62x/4QzO6f/6JSIiIiIiIiIiIiIiIiIiIiIiInKQ/p91IiIiIiLyqbPWW+O5S89RuvLwE6MRDIfQ6UCrxbu773Ktf23/aeccvHUD3v4u7PeXSnh7jb3aNtuz0z9CDQc98ns1mravkdUCirIgSlP8CNJgGj47pEjxCgj9gkceKHl0xWJn7j1jWyGcXoLAwcYQ1ocWRPMnkS0PGjULqtUC8Eob63kWO3uoC3kOm3sQO1idnYbNvBCOtSzcled2bu5ZU25lFkZpdS0ssOZ5dm7UsLDXraHF0JbmLBZ2a8diavWaBdECD4rSwmjHOrC1Nw1lzTQhzaeP8wLWB7AyB1XLjVoAWQqFZ/dZCy0EV1YxuTiHvLRre9g1/dzWHFT3ER34E3BZ3vl9WAJpFUmr9rTbsnvdf91TmKv2GuztNLlHgG7TXrc4s/voDyBq25x5UYXbsHVOpikcFDk060Dd9qIsbf+iAvIARjHsDKHtoGxYnC4vYHsHxjHUt2BzEzoL0FkBv3pdixy2/gySYHoP8/H0+7KAkxfhmz24+SZ05iHpwpVlONWD9S0YbkGZwdMvAwk8fh1eX56+P0/2p/N5Dr5y/fD79onLcLsFNw/8vJzYseNH8YCHt45+7pBz5+CZZ+7+vOfBmTOwuPgBJhMRERERERERERH53mX5DAtr38bPczuwBzSrJz/qv/tw8Lw9++JnOQtvvMZ3H/hbH3FSERERERERERERERERERERERERkU8nRdVEREREROSHxiAecHX7KoNkUB0YwNYWdLswNwfAKBvx7NqzuEkVC8A5uHgRLl2qalU+nD7N9hdO0o/7+8M2h7fIhu/AEQ2mcu8dRqEVtgI8yjQmu9efqIoU59coypxxCA0fPteBmVYVGHMwGMPGri3p8RMFX/58SehZJM1ztuxJhG0mslhaw7Po1GwXHpqH3o7FtdoNaNThxEIVICsgy8AVMN+y6JfnwA9hadbiZtGB0FYQQLuKpRFAnEDONIbmezBMYL5p30MVBCusP5Z7FlybzDlbr7ahgGFpcye53U/gwULL1uNhca52ZPvgsKiYAzbHsNKZXisKIC6ASSitWt8k7hUXUPPseb+Kq7kcvMjuKwigUc0RVHE252yf/NLmKx2MUwiq17ZVs/0cZ/Y4yW1v2h0ogXEB413ozto9hL4db9chSSGfgSyHeh3qBRwL7RqjMdQji9glKRx8u+4OIU4thFf3YJTA9hjC3OJmuwkkDi5ft3PrORwf2rmDDWhch8Yi7MzBzi3wU6h3IAltbDO3+x5uQ/slWHoeZk9CsQKDHtCDdB3iPkz6a2d6sDiy75dGcHYDXl29821/dmM6biIq4MlX4EoX+g2Lup3amkb0PpLz5+HCBZj9qJ9CFBEREREREREREfn+K3sBc2+sTQ/sMP1vDg1gAdj+EBMuVOdNDKbfzr2xRhkG1b8EIiIiIiIiIiIiIiIiIiIiIiIiIiKgqJqIiIiIiNxHd0TS7uHa9jVeuPECzrm7RtJ47DE2x1u8tfPW/nnOOej1YHMdlidHS9hco7h8i82OT+iF+J5PORwwDo68vJ2VpRSBT1bkREDkw/IMzDUsWlaUMIhhfWhRs4af85OrjkeOw/F2Fe16T0mqGcKPzECSFfR2IXVVVG0ywINGzYJqNd/iXx4WCVtqw8kFSEvY3LNwV6tmp9V8COsWM8sLSDOLmOVVSKzbslBXWa2hVaeqkFk0LapBb2ChtsLZ8bKowmgOGqHNE6fgfDjWga296bpnmhZH87Bzd6trhYEda9SAwuYoquulsbXFyiomF+eQlxYq87A99nNbT+DZyx4d+FNtWVZfXRWHw4JirlaF16r7fmcX6tV5pWfX7fh2/4UPwyF0Zu16zdCu4/sWW0vHMMhsr9PA1lurrhv6+y8ZhYMih2YdqNtelFWcLiogD2AUw84Q2g7KBoShvVbbOzCOYe8qNH1oLYJrQiO3+aME3tioYmxYpOygz78L2Tfg93PwAuh0IenClWU41YP1LRhuWaDt6ZeBBB6/Dq8vTwN1J/vT+TwHX7l++BpPXIbbLbh5oGl2YseOH8UDHt46+rlDzp2DZ565+/OeB2fOwOIR5UMRERERERERERGR++yhr3+doJPD8erAOvAQ0788PwVcxP7C/P341fiJEtiYPgzeynjoOxd486mnv9dli4iIiIiIiIiIiIiIiIiIiIiIiHxqKKomIiIiIiL3xVpvjecuPUfp3vPJodGoqlp1oNUCIMkTXrz54nTMjRvw9jXYDzqV8PYaRCOGq136cX9/6G68QxFvw/wRi4h7ZEGDAkfNCwmSPbLgzvDZhCtywtDj7AOOs6twvMOB+pnxnH3tRNAJHVvjw6E0z03nn4ngkVVoTEJW8/DuFmzswWIH2k2Lcp2Yh1pgwa0ss7jZfAui6ppRCMuzFv6a7KaHBdY8z56PfNgbwa0d8GsQBBYBW9+10Fg9tNBZDchz2MtgpmFzumq+Y20LuOUl7CVQFBB5FuIKPFho2f152LF2ZCExh8XYdjOL0LlqTBhAlts5gVftD/b9JO4VF1CbxN6quJrLwYvs3oIAGoGNC6oJstzu2S/tV5FP9zwK4IEZi85RrbNILXpWn7HoXJ5BfxdOLFpYjWpN9RAW52xslkMZwdKCraF0MBpDPbL7SlJb8/77cGgBum4b6h6MEovahbnFzXYTSBxcvm7n1nM4PoQYGKxDfRGac5ADt69DWQNCG9fMbU+H29B+CdrPAwl88TS8tgKDHtCDdB3ivs0JcKYHiyP7fmkEZzfg1dU73/dnN6bjJqICnnwFrnSh37Cw26mtO34cPpzz5+HCBZidff+xIiIiIiIiIiIiIj9gav0+D37j6/AI06haioXQJn/32gZOA5e4d1jNr8a1DxzrVfNNvAoPvvZ1rv7Kr5LNz3/vNyAiIiIiIiIiIiIiIiIiIiIiIiLyKaComoiIiIiIfF8N4gFXt68ySAZ3HTPKRjy79izuYHXKObh4ES5dshKX78Pp0/DYY2yOt6ahtDzHbVyDxhETb1yjmItIioTQC/E9n0acMrzHev28oAh90mxMHQucdedgrmFBLVfAzghu7cG5E/Dlk24/yOW5Q90sANohnF6G2ZoFpooSbgzg3W3AQeFBHljA7Myqxb28soqJ+XDuc+DV4PbIIluzDYujgYXRgjq0axY0y5LpOsLAwmtbI1vTQhP8SeHKWYhtstZOVWOLAug2LH6WlzBMoOlbVMz59nwtgFE2jaDhWSCtKGB9ACtzFmIDuycKeykLD6IapLFdNwfi3MJwoQ+UEISQYcE5V1RRuNKCaBNl9aGy0llEDcBPwdWq8JqzKNw7uxY9A0iqwFpYnZumtucT3aadG2e2njgBPwbXhgKohTDTgiKD0q/20UGWWkCtGYCLYLADCzPVXhZ2jVEMO0NoOygbEIZ2je0dGMewd9X2uLUIrgmNvDo/gTc2qhgbFinb34MCTl6Eb/bg5pvQmYekC1eW4VQP1rdguGVxtqdfBqr3xePX4fXlaaDuZH86p+fgK9cPv3efuAy3W3DzQNPsxI4dP4oHPLx19HOHnDsHzzxz9+c9D86cgcXFDzCZiIiIiIiIiIiIyA+mkxe+RhiP4VvA/8j0X0a5AnQPPF4CWtXx7SMmWgBOcTiollbjDz7+FoTJiJPP/gFvPvX09+9GRERERERERERERERERERERERERH6IKaomIiIiIiLfN2u9NZ679BylKw8/MRrBcAidDrRavLv7Ltf61/afds7BWzfg7e/CftCphLfXINpjb+UY2+Pqk0WDbYYdyP27LGL7GlktoCgLIr+Gn+yRBlB6Rw/3ioww9HnkgZJHV+D4zDRCNdEK4fSSRaRu7cI7exZL88GCZdX4mchCaZEHQVUw8z348Xn48WOwPQIvtHjYyqw9l+UwSuycxbZ9dcDSLNwaWlRtf61YYM3zquBZE/ZGcHsX2h0onAXLbu/auNJZ6MwvIU4tlHasY9d31XzH2pCW1WMHN7dhoWXz+J5F1cIMSixU5geQZRZIc8DmGFY6NtckvJblNlfg2TGqc8GCZ2EVT/Oq1zAogcKuV2DhskYAcRVHo9qnKLR78Uso8um+RwE8MMN+7M55MB5By7N1F8DWABZmLSAX+na8XbeIWT4DtTpkHjRCi+p5VQluVFocrsyqiF51zeHI9mFzAN021D17HbfHEOY2fjexMN7l63adeg7HhxADg3WoL0JzzoJzt69DWQNCG9fMLU433Ib2S7D0PMyehGIFBj2gB+k6xH2bD+BMDxZH0/fL0gjObsCrq3e+789uHB4LFoZ78hW40oV+w8Jup7amr+FHcv48XLgAs7PvP1ZERERERERE5PtgpR7wj07Nf+CxIiLfD7V+nwe/8XV7kAD/Hfi56skcWAMeASb/s9MGzmJ/wbuDRdIi7L+RvPcflSmq8/MDx/47+//AxoPf+DpXf+VXyebnv383JCKfnPkfhV/4v3/wsSIiIiIiIiIiIiIiIiIiIiIick+KqomIiIiIyPsaxAOubl9lkAwOHBzA1hZ0uzA3xygb8ezaszjcdIxzcPEiXLpkFS3fh9On2f7CSfpxf3/Y5vAW2fAdWDzi4sPvUtZ3GRcJPuAlQ4oAsrt93rFIcX6NgpJxlhKEFgZbnYX5hkW6yhIGY7i1B3/+OHz5oZIwAN9V4ayjQmm+PT/fhQcX4N1dcDl06lBvQD20IFvhIM9gnNg88y2YCS1otjgLvaGFzjqRze/V4FjLwmBFChyIsa3MQpxBVsXF5lt2fKIsbHhZRdbAvk4iZ3kJwwTKvIq1TWJsAYyyaQQND5IcisJiaZtjWJmp1ofde1zagwD7VVZ7FOd2nZoPlBCEkGF75Qp7yTMHk8+nlqXNWVavA0BYsv+Bscm+d1vwzq7tK1iMLfCqsUCaQn7gPdBt2v3FGeQF9AcQtcHV7XEwuZY/va/CWZytWQfq9trfHsDCDNSr98IYePcWtMYQNqFo2p4Pdu38eBf2rkLTh9YiuCY0qg+2RQm8sWFBNbBI2cHX7uRF+GYPbr4JnXlIunBlGU71YH0LhlsWZ3v6ZSCBx6/D68vTsNvJ/nQ+z8FXrt/x08ATl+F2C24eaJqd2LHjR/GAh7eOfu6Qc+fgmWfu/rznwZkzsHjUD7WIiIiIiIiIyMdnPgp44njnfi9DRD5jTl74GmE8nh74X4HTwPHq8QD4NvBFLJ420eDOiNpBKRZUO/CfZ3i3mr8SjkecfPYPePOppz/y+kXkPmovwmN/736vQkRERERERERERERERERERETkU0NRNRERERGRz6gjQ2lHuLZ9jRduvIBzVe3rLqG0zdMP8tbuzf3znHPQ68HmOixPjpawuUZx+Ra32x4eHqEfUg4HjO8WSQMY71LUAtIsIfKg7sPxWZhpQuBDUcIghvWhBci8sqBR83hkFR5dsdiZ8w5P2QrhC4vgAxtDO3cS/ALAs/jZmVWLkHklBM5aUd02PNSFPIfNPYidRdsmYTMvtFBaGNgYv7QImR/Acse27MBlaEUWS/N8iBMYZ7A5hNm2Hd8Z2ddmZPM0Q3sZ4tRibUuz+7uLh+1LnNnjIoeNAazMQQ2bpxZAWD0feLauLLNAWunBuICsrO4bC5F5BVDdf1D9muxpXEDNszV51b0FJVDYeIfN1QjsPA87N8tsb3xs7jS2CBrYXj4wU8XaqvHjEbS86r6ArQEszEI7sjWWQLtuEbN8BrIc6nWoF3AstL0bjaEe2WuTHIjYAeyNbE39bVhuWmRuMK7uu4BiCP0xXHprGkqr53B8CDEwWIf6IjTnIAduX4eyBoQ2rpnbHg23of0SLD0PsyehWIFBD+hBug5x3+YDONODxZF9vzSCsxvw6uqdPyJnN6bjDooKePIVuNKFfsPCbqe2DrzPP4rz5+HCBZidff+xIiIiIiIiIiIiIp9ytX6fB7/x9cMHS+A54EmgWR0bAC8Bp4AV7v0XtSXQA65gf+E8Ma7mLQ8Pf/AbX+fqr/wq2fz8R7sJERERERERERERERERERERERERkU8JRdVERERERD6D1nprPHfpOUp34FM3oxEMh9DpQKsFQJInvHjzxcMn37gBb1+D/Z5SCW+vMaz16c9N/4ixG+9QxNswf8QC4h5Z0KDAgXPU0pQisKDXkYqUKAx49IGSs6twvDMNcx3ioLcLflmyNGeHfAeeXWZ//pkIHlmFRvV4tgs/Ngfru5AX0G5Aow4nFiwGlhUW23IFzLcs5OY58EMLmo2zaVANIAigXbOgGIGF0gpnc3ueBcB2x1ALYaZhoTOongthc8eCYJM5Z6vQmIfNsVfY16gKm9UCW9M4m0bQogCSwuZxwOYYVjrTeSIf4qrCFtgyKdx0T5PcImgeFoHznUXnwsDWGx3402RZ3vl9WAJpFUmrwmXdVrUnlWEKc5MFYW+/es3uBaDbtPXEmd1vfwBR2+bMiyrcVt3vZJrCWUiuWQfqkOZV+8+zwFgewCiGnSG0HZQN2/O8gO0dGMdQ34LNTegsQGfFonNg8279GSQHXuv5ePp9WcDJi/DNHtx8EzrzkHThyjKc6sH6Fgy3oMzg6ZeBBB6/Dq8vT/f9ZH86n+fgK9c55InLcLsFNw/0zE7s2PG78YCHt+7+PADnzsEzz9x7jOfBmTOwuPg+k4mIiIiIiIiIiIh8dpy88DXCeHznE5vA14C/zTSslgPfAa5iYbU5oE31l/TAHhZf2wDS98w3Bn6/mvc9wvGIk8/+AW8+9fT3ejsiIiIiIiIiIiIiIiIiIiIiIiI/lH7zN3/zyON/9a/+VR599NFPdjE/ID6re6KomoiIiIjIp8QgHnB1+yqDZHDg4AC2tqDbhTmrjI2yEc+uPYublK6cg4sX4dKlqj7lw+nT8NhjbI636Mf9/elclsHGNWjcef3i1tskrWXCIML3fBpxyvAe6/XzgiL0cUVOGkALODYHcw2LarkCdkZwaw/OnYC/8FBBGFify3PgY58vOqgdws8+DDN1KEq4MYB3twEHhWdBrUYNzqxCzQevtOCU58FqCx7sQlrC5h7U69Cq2bw1H8K6hdLyAtLM1pB7tn3dFmyPoQSaIbTqVNW3KuZVg94AdmNYmrM58hz6oyoiVoXWitLCaMc6sLW33xpjpmlxsMnjvID1AazMQbVEi7+ldp94FmzLMyirmFycQ15abG0SXvMKW6Pn2RomcTeoAmuF3R9ede0c+2CXZ+G4RgBxYedS7UWWgV/a61M6GKcQVH/ybNVsnePMHie5BefaHdu7cQHjXejOQjuyNZZAuw5JCvmMReLqdagXcCy0a4zGUI8s+Jak7EfcAHaHEKfQbUPdg1Fir1WYW9xsN4HEweXrdm49h+PVG3ewAY3r0FiEnTnYuQV+CvUOJKGNbeZ238NtaL8ES8/D7EkoVmDQA3qQrkPch0l/7UwPFkf2/dIIzm7Aq6t3/oyc3ZiOm4gKePIVuNKFfsOibqe2pu+Nj+T8ebhwAWZn33+siIiIiIiIiIiIiOyr9fs8+I2v333AO8C/BX4ZOH7geAq8Vf36IN4FnuPIoNrEg9/4Old/5VfJ5uc/4KQiIiIiIiIiIiIiIiIiIiIiIiKfHr/xG7+B5935idsTJ058qgNi9/JZ3RNF1UREREREfoAdGUo7wrXta7xw4wWc+2ChtLd2pp/ScRsbsLkOy5MjJWyuwbc3GXdn2E12Cf0Q3/MZDnrk9+guZXtbFIFP5Nfwkz3SwIJeRyoyaqHPIw+UnD0Ox9vcUYZqhXB6yQ7f2oWNXfuckQ/41a1O5p+JLJbWgv2Y2ak5+FwbtkfgRxY7OzFvYbEst19lAQtNiDxrcUUhLM/alpXVOjwsCOZ59nzkw94Ibu2AX7PAWOjD+q4F1uqhxdtqWDxtL4OZhgW9JlGzmabNVxSwV0ArsshYWQXOFloWbvMA51lkrCxtjWluXzfHsNKZrjGqIme4KpKGfXXVHsWFhdA87P58Z3OGwfTeJibnldUa/NIibC6a7ku3Be/s2v1OXos0ho5vCyp8GA6hM2uhvGZYXde32Fo6hkFmkbS0CubVqslDf3pfhYMih2YdqNv9l2UVrCssljeKYWcIbQdlA8LQ4nPbOzCOYe8qNH1oLYJrQiO3+aME3tioYmxYpOygz78L2Tfg93PwAuh0IenClWU41YP1LRhuWaDt6ZeBBB6/Dq8vT/f9ZH86n+fgK9cPX+OJy3C7BTcP/Gyd2LHjR/GAh7eOfu6Qc+fgmWfu/rznwZkzsLj4ASYTERERERERERERkfc6eeFrhPH43oM2gd8G/gfgLwLRh7hACvx34H9l+pfzdxGOR5x89g9486mnP8QFREREREREREREREREREREREREPl32P28PRwbFPos+a3uiqJqIiIiIyA+otd4az116jtK951Myo1FVqupAq0WSJ7x488XDY27cgLevwX6kqYS31yAaMVzt0o/7drgo2B2tU8wfsYB4HbczJHEZADUvhDQmu8efIlyRU/gB4ywlCKEWwOoszDcs0lWWMBhDbw9+5nPwF3+0JAzsXN8B7s5IWlTFv+a7cHIe3hlCmUOnbpEtr4qELc9YoCyJ7TqeB0sNiBqQe9AbWuisVbP5axEEDWjXwOUW1fKw6FUY2Nq3Rhb6WmhawMtu0kJskz86dqoPP0UBdBsWP8tLGCYW8cozcL7d/7FOdZ+erW+mYdfDg92xfR4qrO6nUQMK6+MVHkQ1C5Y5LHRWehDndq3Qt3MCH/zcImSBZ/Gyg6G0soSitDnxqoZdDgQWAKsFFl2LCwulOc/Cc6EPYVkFznIomN7zAzNQqwJozoMitehZfcb2Pc+gvwsnFm19YGurh7A4Z2OzHMoIlhYsUFc6GI2hHtl+JCnTDQd2hxCn0G1D3bNY3fYYwtziZrsJJA4uX7dz6zkcH0IMDNahvgjNObv129ehrAGhjWvmtj/DbWi/BO3ngQS+eBpeW4FBD+hBug5x3+YEONODxZF9vzSCsxvw6uqdPyNnN6bjJqICnnwFrnSh37Cw26mtOxqDH87583DhAszeo4IoIiIiIiIiIiIiIh9Zrd/nwW98/YMNLoH/BrwEnAUeBVY5+i+CHbAOvAp8C0g++Joe/MbXuforv0o2P//BTxIREREREREREREREREREREREfkUmUTDDobEPus+a3uiqJqIiIiIyCdsEA+4un2VQTI4cHAAW1vQ7cLcHKNsxLNrz+IOlqScg4sX4dIlq2P5Ppw+zebpB6eRNMBlGWxcg8YRF9+4RjEXkRQJoRfi7w5o5DCM7rLYLMWvBRRlQZqPqTuLnC3PwFzDIllFCYMY1oeQF9DwCx5ZhUdX4PiMhbYOaoVwegl8oLdr52XOYmMegGdBsTOrFvnySgicRciOdeChLqQFbO5ZOMth4baoipHRgDy30FcN+5yS78Nyx75OeFhgzfMgDC3mtTeC27vQ7liYLPTtcatmoa9GDfzSgl7OryJpvq3BA461IS2rxw5ubsNCy+YBaNeBwuZ2HrQiSBOLlCU57Ga2t67aizCw4JhXRdK86jqBN93XuICaBzhbi4dF4rzI7i04EEoLqgnKan6/BK8AF01jdt0WbI2nobSksHGTrUtTSL1pIK3btLXEmb3+cQJ+DK5t91ULYaYFRQalX8XpHGSpra0Z2PUHO7AwY3NGBeQBjGLYGULbQdmw1ykvYHsHxjHsXbVwXWsRXBMaeXV+Am9sVDE2LFI2URZw8iJ8swc334TOPCRduLIMp3qwvgXDLYuzPf0y+x9We/w6vL483feT/QPvJQdfuX74ff7EZbjdgpsHmmYnduz4UTzg4a2jnzvk3Dl45pm7P+95cOYMLC5+gMlERERERERERERE5KM6eeFrhPH4w52UYGG1l7D/iLEKzGH/D64cGGBBteyjrSkcjzj57B/w5lNPf7QJREREREREREREREREREREREREfsg55/YjYmI+a3uiqJqIiIiIyPfBkaG0I1zbvsYLN16YVpzvEUp7a/fm/nnOOej1YHMdlidHS9hcY3xlnUErwPd8Qj9ktHObfPaOS09tXyOrQmlRmuJHkAbTqNYhRQp+jTAoOLtS8ugKrM5Q1cumvOp2ZmsWILsVgyvsuHPTuWcii6VFvoXS5rrw0Dys70KZQ7sBjTqcWLCgWlZAltlc8y0LeQUl1ANYmoXe0EJnUTBdSxBY8M1zFvgqHOBsPt+H/p51p+ZbVeDLnqYsqq/edL4ogJWO3W5ewjCxdUZV1CwKbN5RNo2g4VkgrSggzmFzDCtVLGwyxuVQeBZby7CgXI6Nz4sqwlZCEE6fd4WtvywhOvAnubKsvlbhOQA/BVerAmDOQmnv7EK9Oi8p7B7C0taUFfY6Te7583MWrqveZYz70G7Z90kB/TF0Z6Ed2VpLLBiXpJDPQK0OmQeNELpz1fsjh1EJUc2CZZ6bBsqGI3udNwfQbUPdg1EC22MIcxu/m1hE7/J1u049h+NDiIHBOtQXoTln+3j7OpQ1ILRxzdzub7gN7Zdg6XmYPQnFCgx6QA/SdYj7Nh/AmR4sjqb7vDSCsxvw6uqdPyZnNw6PBQvDPfkKXOlCv2Fht1Nbd/zofDjnz8OFCzB7rx9wEREREREREREREfnYlSU/+h+e+97myIC3ql/fRz/6v/x73nzy/3T4X5oRERERERERERERERERERERERER+YxQVE1ERERE5Hu01lvjuUvPUbry8BOjEQyH0OlAq0WSJ7x488XDY27cgLevwX4jqYS31xjW+vTnpr9d3413KOJtmL/z+i7eJPEjK2o5R5ClFPf6nX6R4vwaRZkzDqHhw+c6MNOCwLMI2WAMG7sW7Hr8R0p+9vOOmmdxL89B8Z4p2yF8cRFm6vY4c/DOtv1yQBJAowqq1QIICvCxJR/rwMmuRbw296BetzAbQM2HsA7tmsXG0ipe5rkq5FXd9ii10FotqM71bP5mHeIYbu/AbNuCYWUBwxSakYXImqEFt+IUnG+xtuqVwANmmhBn9rjIYWMAK3NQw6JstQDC6vnAAz+wQFicW6BtXEBW2r14zqJvk3vA2WeaXGnngkXLwiqe5lWfdwpKoLDrFdgcjQDiojrPgyy32Jpf2q8in8bsogAemLE1gO11iO1RiUXKUixa16rZPdVCu+/hCLIx1Gvg6jAeWYwNqvhbtU+Fs2s260Dd7u/2ABZmoF69b8bAu7egNYawCUXT3h+DXTs/3oW9q9D0obUIrgmNvLqHBN7YsLWCRcomygJOXoRv9uDmm9CZh6QLV5bhVA/Wt2C4ZXG2p18GEnj8Ory+PA27nexP5/McfOX6nT86T1yG2y24eaBpdmLHjh/FAx7eOvq5Q86dg2eeufvzngdnzsDi4geYTEREREREREREREQ+bp23rlMb7t7vZRypNtyl89Z1hj/60P1eioiIiIiIiIiIiIiIiIiIiIiIiMgnTlE1EREREZG7GMQDrm5fZZAMqgMD2NqCbhfm5gAYZSOeXXsWh5ue6BxcvAiXLlkZy/fh9Gk2Tz9IP+5Ph2UZbFyDxp3XLm69TdxcwvMDQj+kEacM77FWvygpAh+KnNyHug/LMzDXsFBWUcIghvUhZAU0/JyfXHU8chyOt6u4lnd4zmYIPzIDSVawsWvRLd9ZKIoD42eqWFoLmGxDw8GPdeHhRdgeASGcWLBYV1ZAnlkIa74FYRVri0JYnrXtmuTpPCzy5Xn2fOTD3ghuD2G3hHYDOhHc2rHY2OostKuwm8NiZs5ZkC0KppE0z6tCYAXsFdXzngW2aoHt2bgKuIW+nZsUFi5zwOYYVjrTNUY+xFWFLcB+FW4a7EpyqFWhOL8KkYWlxdR8DzLPonBQxdSq/S3L6VhSIJrue7cF7+xCvfpTXVIF1ibBszSFPJi+nt2mrSfO7H53hjBfh8KHPIdRBitdqFV7E3hVeC6FdL6KtkXQxvandDAaQz2CMKhiZwd+DPZGFpfrb8Ny0/Z6MLYxXgHFEPpjuPTWNJRWz+H4EGJgsA71RWjOQQ7cvg5lzd5L9Ryaub22w21ovwRLz8PsSShWYNADepCuQ9y3+QDO9GBxZN8vjeDsBry6yh3ObkzHHRQV8OQrcKUL/YaF3U5tTX8mPpLz5+HCBZidff+xIiIiIiIiIiIiIvIDIevM4DwPz7n3H/wJc55HOqO/cxYREREREREREREREREREREREZHPJkXVREREROQz445I2j1c277GCzdewDl310gajz3G5niLt3be2j/POQe9Hmyuw/LkaAmba4yvrLPbrhH6Ib7nMxz0yO/xmZZstG2hNCBKU/LgzvDZ/nWLnFrocfYBx9njsNq2cNhBXvW5nk4EndCxNYbUTUNpnjscSntkFRrV45PzcLMPt3ZhqQPtJjTqFvQ6PmPbkmcwSm27lhoQNQAfFmctUNaqomI1H8I6tGsW98rT6frCwKJmWyPrcy00LTpmN2khNgfkVYitE1nwbLYOw9TOHSY2d4CFwgIfjnVsPJ7ty0yj2g/P1rA+gJU5qJZo8bcUCs/G1MIqBFftUZxDXk5DbaFvoTCczR94B9aNBdZ8Z+dP1Apwhc0/ibk1AjvXw45lGfilRe9wkMbgqmhcFMADM7afVOPHI2h5FqUrgK0BLMxCO7I1llh0LklhGIILIYwsUhaGFmILo+q94yBObf+adaAOgyE0our6hUXbRrEF2toOyobNkxewvQPjGOpbsLkJnQXorIBfhd6KHLb+DJID4bf5ePp9WcDJi/DNHtx8EzrzkHThyjKc6sH6Fgy3oMzg6ZeBBB6/Dq8vT2N2J/uH3/9fuX74Z+KJy3C7BTcP/Bye2LHjd+MBD2/d/XkAzp2DZ5659xjPgzNnYHHxfSYTERERERERERERkR80ybFFbp/7WZZe/t/u91LucOvcl0m7x+73MkRERERERERERERERERERERERETuC0XVREREROQzYa23xnOXnqN05eEnRiMYDqHTgVYLgCRPePHmi9MxN27A29dgP7xUwttrEI0Yrnbpx/39obvxDkW8DfN3rsHFmyS+FatqXghpTHaP35G7IqfwAygLxiHUfVidhfmGhbfKEgZj6O3Bz3wO/ocH3X5gy3cW0DqoHcLpZZitWRiqKOHGAN7dBpwFxPIAGjU4s2qxLq+0sYEPP/MAeDXY3IPEWdxsdXYaFqMG3badV2RV/Axr0K3MWnCtcBYIa9WsKRUFFl/bG8HtXWh3bEzo2+NWzUJkjZrFxeIUXBVJ2xhMrz3TtPkmcbhxaudNjrXrQGFzOw9aEaSJhcfS3Na6OYaVju2Vh80dV6G0wLN1B9402BUXFkHzqnv0nb0mYVDd24HXdv88Z/s+CaW5FIr6dFy3dTiGN0xhbrIg7K1ar9nrAdBt2rxxZiGz/gCitoXX8gKC6k0Q+tP7KpwFzZp1SD1o+pBUr7OXw6iwiFxRWihucr9ZbvG0vcBe57oHowS2xxDmFjfbTey9cfm6hdvqORwf2vmDDWhch8Yi7MzBzi3wU6h3IAltbDO3KN9wG9ovwdLzMHsSihUY9IAepOsQ92HSXzvTg8WRfb80grMb8OrqnT9PZzem4yaiAp58Ba50od+wqNuprf3t/mjOn4cLF2D2HsVEERERERER+eTdeP/IvoiIiMiH8cpv/N/4qd/4Fyy//OL7D/6E9L705/nff+P/er+XISIiIiIiIiIiIiIiIiIiIiIiInLfKKomIiIiIj/UBvGAq9tXGSQHPhg7GMDWFnS7MDfHKBvx7NqzONx0jHNw8SJcumQlLN+H06fhscfYHG9NQ2l5jtu4Bo0jLr5xjWIuIikSQi/E93waccrwHuv1i5Ii8EnzMXUHkQ/LMzDXsFBWUcIghvUhZAU0/IJHVuHscTje5o7aUyuE00sW6Ortwjt7VbTL2dCyGj8TWSgt8iCotsH34Mfn4cePwfYIvHAaQPM9i2iNEjtnsW1fCw+WZqE3tNBZFEzXEgTQrlWxsBDixKJqgVetyYNhAvNN+57qdorComalN50vCixw5gF5aeeVua3BeTbnsY7dJ56FyGYaFlTDs73bHMNyNYeHxc5cbvcQ+pBRhdCcXTvO7VqhPw3J+blFyALP9uZgKK0s7b7cZA1Q3bCtsRZYdC0uoB7aMedgHEOjnL4NRzHUour1rNl548weJ7ntY7tjkbxxAeNd6M5CO7K1llg0Lkkhn7HXrV6HegHHQru/0Rjqke1BksLkR8E5uN2HRmT3WCtgkMPGHkQldGpQNO3eN/sWoIt3Ye+qxdhai+Ca0Mir1y2BNzaqa2CRsoM+/y5k34Dfz8ELoNOFpAtXluFUD9a3YLhlgbanXwYSePw6vL48jbud7E/n8xx85frhazxxGW634OaBptmJHTt+FA94eOvo5w45dw6eeebuz3senDkDi4sfYDIRERERERERERER+WGXtzu8/K/+J6L+NjPX/qz6Dwb3ieex89Apsvn5+7cGERERERERERERERERERERERGRH0D/6l/9K772ta/d72XIJ0hRNRERERH5gXNkKO0I17av8cKNF3DuQCHqiFDa5ukHeWv35v55zjno9WBzHZYnR0vYXINvbzLuzrCb7BL6If7OgGEHcv8ui9i+RlYLKMqCyK/hJ3ukwTRmdociJww9zj7geHQFVme4I5TmVbfTiaATwq14GkrDHRFK8y2UNteFBxdgfRdcBu0G1BsW9Do+Y3GwPINxYls134KZEJwPiwdCaZ0q8OXV4FjLAmdFFeLyHPgBdFtV7Cy2bW6EMFO3e/Gc9aWiGvQG0KjbtXFQFhYKK52d4xzEqa1haXb/lcADZpoQZ/a4yGFjACtzUKv2qV0HCpvbedCKIE2gAJJiGkmr+bamIKgCa9VafN8uFnjTYFdcQM2bPu9hITYvsnsKDoTSgqrWVlYBO78ErwAXTV+jbgu2xrYGsOPZCJoR4Nv7aqcP812LuDWrsJ3vwziFdAyDzCJpaWAttFoVZAurOT1sD4ocmnWgbvGzsgrZRQXkgcXbdobQdlA2IAwhL+D2toXYjkcQFDAKwAUQpVAk0B/Dm29bDA6gnsPxIcTAYB3qi9Ccs57c7etQ1oDQxjVze42H29B+CdrPAwl88TS8tgKDHtCDdB3ivs0JcKYHiyP7fmkEZzfg1dU7f5zObkzHTUQFPPkKXOlCv2Fht1Nbd/yYfTjnz8OFCzA7+/5jRUREREREREREROQzJZ1fYPOnzt3vZYiIiIiIiIiIiIiIiIiIiIiIiEhl0h9wzvGnf/qn/Omf/ul9XtH94+7nPxh6nyiqJiIiIiI/UNZ6azx36TlKVx5+YjSC4RA6HWi1SPKEF2++eHjMjRvw9jXY7x6V8PYaw1qf/tz0t7678Q5FvA3zRywgXsftDElcBs5RS1MIIAvusuAixfk1CkrGWUoQQi2A1VmYb1h4qyxhMIZbe/Dnj8PPnnTUfIukec4iYAe1Qzi9DLM1C0FlDm4MYGMLcJAE0KiCarXAQlg+Fv3qtuHkAmQFbO5B7GwtUbV+L7RQWhhAnlsIrKxCacudKjRW8bBQme+B50OcQJLB7T0IQouvbQ9t/d22xduaoY0vctjLYKZhc7pqvmNtSEuLne0lUBQQVVGzWmBhsXFmY0Pf1p0UFv1ywOYYVjpVHA27D5dD4dn4DNvXovqzXVJUQTM3vbewBFcFx0ogOvCnorJ625VVGA7AT8HVqvCas1DaO7t2v5NrRJ7N62F7P/mzZRTA5+cgLfbfkRRDiOtQn4GkWkt/F04s2v2DvW/qISzOWSAty6GMYGnBwm6lg9EY6pHtQVJF7yZ2hxar67ah7sEoge0xhDmUGewmkDi4fN3OreeQ7cHcMnRWgchCfckYNm/CuDadez6efl8WcPIifLMHN9+EzjwkXbiyDKd6sL4Fwy275tMvA4md9/h1eH15GrM72T/wvnPwleuHfyaeuAy3W3DzQNPsxI4dP4oHPLx19HOHnDsHzzxz9+c9D86cgcXFDzCZiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIj8IPksRsU+6xRVq/zrf/2v+c53vsPf//t/ny996Uv3ezkiIiIinzqDeMDV7asMkkF1YABbW9DtwtwcAKNsxLNrz+IO1qGcg4sX4dIlK175Ppw+zebpB+nH/emwLIONa9C489rFrbeJm0t4fkDohzTilOG9Fpul+LWAIk9IA+g4OD4LM02LXhUlDGJYH1pAyysLGjWPR1bh0RU4PjONRU20QvjCosXPNobwzh6QW/wJoKy+maliaZFnUSuACHh4AU53YXsEhHBiwSJkWQF5ZnGr+RaE1TxRCEuzFiiLDgThggDatSoYFlgorXCQF3Ys9GF3DLWwCqJV83kehCFs7ljka6ll887WYZhCO7I59gqoB7YmV0XRagGMsmkEDQ+S3IJq6wNYmYNJs6sWQJZaJA3P1pFnFhErPYhzC7LVfItvBUEVWHPsh9MKN133JJJWHGj01QpwVeTMVXvSCCAuLGaGZxGzKLTonF9aJG7yGkUBPDBTxdqwYFqIvbYlFilLsfdKq2b3VAshzmA4gsyDYA9cG8YpRDWYaUGRQelXa3e2D0EAzQBcBIMdWJip1lBAHsAohp0htB2UDXuN8gK2d2Acw95VaPrQWgTXhEZenZ/AGxtVjI0qlOZgsAG7t2GpDu84SMcQAPUOJKHF15q5/VgOt6H9Eiw9D7MnoViBQQ/oQboOcR8m/bUzPVgcTV+DpRGc3YBXV7nD2Y3DYyf3++QrcKUL/Yat99TW9OfnIzl/Hi5cgNnZ9x8rIiIiIiIiIiIiIvIh1Pp96lu3SbqLZPPz93s5IiIiIiIiIiIiIiIiIiIiIiIin1me9z19IvmH2mc1KKeoWmVvb49/9+/+Hb/zO7/DI488wle/+lV+9Vd/lVl9wF5ERETkSHdE0u7h2vY1Xrjxgv2m+y6RNB57jM3xFm/tvLV/nnMOej3YXIflydESNtcYX1lnt10j9EN8z2c46JHf47du2WibIrAKVpSm5ME0knWHIqUeBjz6QMnZ43C8bfGm94bScNDbBb8sWbIuHL6zwJdzh0Npj6xCo3o824WH5qG3YwGsdgPqDaiHFmQrnIXExonNs9CyaBkeLFahtFZVIav5ENYtlJYXkKd2Tu7Z124LtscW+2qG0KpT3YzFu6Ia9AawG8PSXDVHDv0R1GsWGQurkNw4g2Md2BhYWMzDQnOeV4XNqm25uW1rDqtAWC2AMLM1BB74AWSZBdIcsDmGlY6d62Fzx4WtMfAsVhZ40/2PC6hV3/tV2CwswZW2FodF0qjO87DXoqgCaT7gUguVTV6jbgve2bXXACCpAmthFWNLU4uYTXSbtp44sz3bGcJ8HQrf9m+UwUrX1ulV62hGUKaQzkMthdRBs2HBNc8BOYxKe03KrHofVesbjmzPNgfQbUPdg1Fir22Y2/jdBBIHl69bLK2ew/Ghhc0G61BfhOYc5MDt61DWgLuE0hb/K1x4BPwadLqQdOHKMpzqwfoWDLfsmk+/DCTw+HV4fXm63pP96V55Dr5y/b0/ZPDEZbjdgpsHfm5P7Njxo3jAw1tHP3fIuXPwzDN3f97z4MwZWFz8AJOJiIiIiIiIiMgPm+204L/dHn+gsX9pscnCwX+VQkTke+UcP/Fv/h88+NzXCbKUohbx3V/+W3zn7//D6l+8ERF5j2EP3vhfPtjYL/w16Cy//zgRERERERERERERERERERER2fdZDYt9limq9h7OOb71rW/xD/7BP+Cf/tN/yt/6W3+Lp556ii9/+cv3e2kiIiIiPzDWems8d+k5SlcefmI0guEQOh1otQBI8oQXb744HXPjBrx9DfZDSiW8vQbRiOFql37c3x+6G+9QxNswf+caXLxJ4kfgedS8ENKY7B6/u3VFTuEHUBaMQ6j7sDoL8w0LXpUlDMbQ24Of+Rz8pR8tCAMLdPnOgk7Fe+Zsh/CzD8NM3YJdNwbw7jbgoPAswtWowZlVi595ZRUf82ClBQ91IStgcw9iZ+uZfH7PC+FYC8LAIl1eaaE034eVWRilFl/zscCa59m5UQP2RnBrx4JYQWBxs/VdC4fVQ1tTDZt3L4OZhgW63htKKwrYK6AVQZJbgCzwLKzmO8CzcTONKgrmWWAszqtQ2kx1L0DkQ1zagwD7VVbhuTiHvLR1ekDgg5/b/QXVPUcHXtuyPPwVoFaAK6qol7OQWyOYRtKcZ5G6RnWO5yAdg2vY4yiAB2bsddofP4KWZzG4AtgawMKsBe5C34636xYwG4bgQggji5SFoYXYwqj6jJSDOLV7a9aBOoz2YG9sc9arGN8YePcWtMYQNqFo2ntwsGv7Ee/C3lVo+tBaBNeERl7dQwJvbNh6AObjA3tWwMmL8M0e3HwTOvP3DqUtjuAne/DaCgx6QA/SdYj7FmkDONOzcQBLIzi7Aa+u3vmzd3ZjOu6gqIAnX4ErXeg3bL2ntqZxvo/k/Hm4cAEUxxYRERERERER+czqJQX/zz/rf6CxPz4TKaomIt9XJ7/++/zYH/3+/uMgS/mxP/p90vkFrv4f/u59XJmI/MAavAX/6Z98sLGf+2lF1URERERERERERERERERERETeh6d/APMzT1G19/A8D+cczjlGoxG/+7u/y+/+7u/ykz/5k3z1q1/lb//tv83c3Nz9XqaIiIjIx2IQD7i6fZVBMjhwcABbW9Dtwtwco2zEs2vP4jhQZHYOLl6ES5esdOX7cPo0PPYYm+OtaSgtz3Eb16BxxMU3rlHMRSRFQuiF+J5PI04Z3mO9flFSBD5pPqbuLNy1PANzDYtXFSUMYlgfWrys4Rc8sgpnj8PxNnfUm1ohnF6yUFlvFzZ2IWUaVQOLgAHMRBZLawE48D04NQefa8P2CPwIWnU4MW/Briy3X2UBC02IPAt1RSEszcI4mwbVwGJo7SqWRgBxAjkWGStKu94wgfmmfU91O0XB/ivTiexrFEC3YTGwvLTzmj7kGTj//UNpu2MLiIVVdK1dBwqLfDnPomtpYveTVvG1cQFZaXEzjyqYVtheeVWcLfCqCBoQF1Cr4mN+FVdzOXhVlCyoImlxYefh2duuKMEv7TUrMyij6R6udg6/xnsjaNYA384dDGxPgiqk1m3aeuLM4nD9AURtcHV7HFRBttA/sN8OitxCaaln+5pM4nk5jAqohbZOr5jeb5ZbUA2gvw3LTXvtBuNqjwoohtAfw6W3pqG0eg7HhxY2G6xDfRGac/beuH0dyhoQ2rhmbvc53Ib2S7D0PMyehOIDhtIevw6vL0/XfLI/3UvPwVeuc8gTl+F2C24e6Jmd2LHjd+MBD2/d/XkAzp2DZ5659xjPgzNnYHHxfSYTEREREREREREREfl41Pp9Hv7d//nI5x7+f/82b/38XyWbn/9kFyUiIiIiIiIiIiIiIiIiIiIiIvIZ45x7/0Hyqaao2hEO1gYnPySvvfYa//Af/kP+2T/7Z/zNv/k3eeqpp/gLf+Ev3K8lioiIiHxgHySUBnBt+xov3Hhh+oeEu4TSNk8/yFu7N/encs5Brweb67D/j2KXsLkG395k3J1hN9kl9EP8nQHDDuT+XRa7fY2sFlCUBZFfw0/2SINpyOwORU4Yepx9wPHoCqzOcEcozatupxNBJ4RbcRXicoC7M5IW+RA4mOvCyXlY37HYVbth4SyvioQtz9jxJLbt8TxYakDUgNyD3hAaNWjVbP5aBEHDQmkut0iW72ysc9BtwXYVL2uGFmRjEhnzIKpBbwCNuoW8cBZoS3IoHTRCmydOLZR2rGMvWdVF41gb0rJ67ODmNiy0poGwe4XSkhx2M7tnVwXmwsDuo/Bsjgy7n8n5VOc1qv3yfXu+LO1c37Og3P47pgqWlVV0DcBPwdWq+ao9emcX6tV5pQfjGBrVuQUwTqBW7flMHXJnwTrnYJTAKIN2xyJpqQfZLnRnLa4W+rb/7bq9PvmMxc/qdagXcCy09Y3GUI/sPpKU/Yqdc3C7D43Iwm+1AgY5bOxBVEKnBkXT9mKzbwG6OIb6FmxuQmcBOivgB/tvb7b+DJIDsb35ePp9WcDJi/DNHtx8EzrzkHThyjKc6sH6Fgy3LDb39MtA8uFCaUsjOLsBr65yh7Mb0/jaRFTAk6/AlS70G7bWU1t3/Eh+OOfPw4ULMDv7/mNFRERERERERERERO6jkxe+RhiPj3wuHI84+ewf8OZTT3/CqxIREREREREREREREREREREREflsePzxxw91o+Tujh8/fr+X8LFSVO0Ik5CI53n7PyjOOZxzjMdjfu/3fo/f+73f4wtf+AJf/epX+Tt/5+8wr39JVkRERD5BR4bSjvBBQ2nZo4+wdvuNwyf3erD57h2htPGVDQbtAA+PWlBjLxlSxNswf8QC4nXczpDEWdWqlqYQQBYcMRagSHF+jYKScZYShFALYHUW5hsWqipLGIzh1h78+ePwsycdtSrY5TkLax3UDuH0MszWLO6UObgxgI0twFmwqlEF1WoBBAX4WNjrWAd+bAHiEjb3IHHWz1qdhaiKhdGAPIeaDzXbJXwflquo2YSHBdY8D8IQggD2RnBrB/yaPQ59WN+1eFg9tChbDZt/L4OZxtGhtLyEvcQib5FnwawosPsZZdMIGp6FzooC4hw2x7AyM13f3UJpOTY+L+y452y9HlW0zlX3Wlosbf/ldOAKe9vhVfuVA9VagsCia3Fhry2eRcyiEPzSfhX5NHwXBfDAjO012H2mI2hGUPoWqNvpw3zXgmqBb6+l78NgCK6EQWaRtGFmc4RVkG0Sl/OqdRe5RfSoW/ysrO4tKiAPYBTDzhDaDsqGvaZ5Abe37R6OR/ZeGgXgAohSKBLoj+HNty0GB1DP4fjQvh9sQOM6NBZhZw52bllYrt6BJLSxzdz2c7gN7Zdg6XmYPQnFCgx6QA/SdYj7MOmvnelNA2gfNpT2xGW43YKbB5pmJ3bs+FE84OGto5875Nw5eOaZuz/veXDmDCwufoDJRERERERERERERETur1q/z4Pf+Po9xzz4ja9z9Vd+lUz/HyMREREREREREREREREREREREZHvuz/5kz+530uQHxCKqh3B87z9iNrBYxOT42tra/yjf/SP+Of//J/zN/7G3+Cpp57iL/7Fv/iJr1dEREQ+W9Z6azx36TlKV04PjkYwHEKnA60WAEme8OLNFw+ffOMGvH0N9gNJJby9xm5tm/5cbX+YKwrovwuNO69fDm4TR7M4gAzqSUZ+rwVnKX4toMgT0gA6Do7PwkzToldFCYMY1oeQFeCVBY2axyOr8OgKHJ+xeNZBrRC+sGjBrI0hvLMH5FW0i2mEa6aKpUUeBNVv7SLg4QU43YXtERDCiQULkGUF5BmUBcy3IPQsKlYPYGkWekMLnUUHonBBAHMNi4vFiQW5cDaf70N/z/pQ861pcMxh15j8brMTVWsLoNuAdmShtGECTd/W5Pz3D6WtD2BlzkJsvmdjw8xCb4EHfgBZZoG00oNxAVlpkbK7hdJcWQXPgKSKqjk3DcaFpY3xPQvaRQf+hBFUgbfS2bx+CV4BLrI1gQXk3tm1iNzkGoE3jZ2lqUXMJrpNmzPOLGI2jqGRQThnj2uBNdsORtJ8oBHC0oIF0tIUXA2W2nbPpYPRGOqR7WmSMn1xgN0hxCl021D3YJTA9hjCHMoMdhML7l2+bufWc8j2YG4ZOqv2pgscJGPYvAnj6Y8a8zGHfP5dyL4Bv5+DF0CnC0kXrizDqR6sb8Fwy6779MtAAo9fh9eXpz8nJ/vT+TwHX7l++BofJpQWFfDkK3ClC/2GrffU1vRn7SM5fx4uXIDZ2fcfKyIiIiIiIiIiIiLyQ+Dkha8RxmP7jzSr2D9GFGD/8aQPrEM4HnHy2T/gzaeevn8LFREREREREREREREREREREREREfmUU1St4ldVCOccnuftR9QmAbXJ1/c+55xjPB7zta99ja997Wv8xE/8BF/96lf5u3/377KwsHAf7kRERER+GA3iAVe3rzJIBgcODmBrC7pdmJsDYJSNeHbtWdyk+OQcXLwIly5BWVrp6vRpeOwxNsdb9OP+/nQuy2Dj2pGhtPHmOyTNZcIwwvd8hru3yet3X2+WjCgC+/1TXKT4wTRkdocipR4GPPpAydnjcLxtQab3htJw0NsFvyxZstvFdxaFcu5wKO2RVWhUj2e78NA89HYsqtVuQL1hka7jMxY5yzMYJzbPQsuiZXiwOAvjDFpV5KrmQ1iHds3mylM77jmLgLUjC6SNUgut1YLqXM/CXc06xDHc3oHZtkXQygKGKTQji3c1Q1tHnFoo7VinipfZNBxrQ1pWjx3c3LY1h/77h9IcsDmGlU61biDyIS7tQUD1+R033f8kh1p1/0eF0jLP7hXsLeYB+YHgWK0AV1TPe7aHjQDiwl4D50GW2/rD6vwit88Qge3RAzN2HtX7YjyClmf3WABbA1iYtf0P/eq1qFvALJ+BMgWvBnUHtXAauGvU7XpZbO+vIIBmAMMckup5sHBYHsAohp0htB2UDQhDex9s71i8be+qRe5ai+Ca0KhqglECb2xUMTaqUJqDwQbs3oalOrzjIB3b/tc7kIQWX2vm9n4YbkP7JWg/DyTwxdPw2goMekAP0nWI+zBpsJ3pweLIvl8awdkNeHWVO5zdmI6b+LChNA94eOsuT06cOwf/8l9Cu333MZ4HZ87A4uL7TCYiIiIiIiIiIiIi8sOjvtPjwbf+P/BVLKh21F+4O2AdHvzOH/LdnV8hmV3+ZBcpIiIiIiIiIiIiIiIiIiIiIiIi8hmhqFrl13/91/niF7/Ib//2b/Nf/+t/pSzLQwE1mEbUJt77HMClS5f4x//4H/Mv/sW/4Jd/+Zd56qmnePzxxz+5GxEREZEfGEeG0o5wbfsaL9x4Yfr7jPcJpb2189b+uW5jAzbXYf9zFyVsrsG3Nxl3Z9hNdgn90EJpgx757NFrcEA62sQFAZFfgzQmvcfvFF2RU/pVMcy3uNcDszDfsO/LEgZj6O3Bz3wO/tKPFoSBXcd3VVjrPXO2Q/jZh2GmDkUJNwbw7rYtrvAsetWowZlVC3B5VaDL82ClBQ91IStgcw9iB6uzFuwC8EI41oIwgDy3c3PPtnZl1iJphbMwWqtmc0YBRA3YG8HtIeyWFmzrRHBrx9a/Omtxr8keZqW9fHlh53vATNPm84CigL2iet6zgFhUxdlGmY0JA8Cz2FlRWCxtcwwrM9W9cHQorazCc3EOeTm9fuiDV9gCPa8KsR34ME/h7DUpjwqlVWusBRZKC7xpEK8owS9tz1wKLpqG2rot2BpPQ2lJMR0LkKaQelB1+eg27dw4s73pDyBqg6vb46C0caE/3YPCWZytWQeqcaWzewtLe36Y2/GOB14wDbwNxzbP5gC6bah7MEpgewxhDmUGuwkkDi5ft1haPYfjQ4uaDdahvgjNOciB29ehrAF3CaUt/le48Aj4Neh0IenClWU41YP1LRhu2TWffhlIbG2PX4fXl6d7erI/fX08B1+5fvjn54nLcLsFNw/8jJ/YseNH+UChNLBY2jPP3P15hdJERERERERERERE5DOroDv3Lf7c+v+L8OeSew/1gOMQHk/4qfH/hT/9kf8jW4Oz2H/lEREREREREREREREREREREREREZHvF0XVKkEQ8Eu/9Ev80i/9Ejdu3OC3f/u3+Z3f+R3efvttgEOBtUnwZPL1vc8554jjmD/8wz/kD//wD/lzf+7P8dWvfpVf+7Vfo9vt3oe7ExERkU/aWm+N5y49R+nKw0+MRjAcQqcDrRZJnvDizRcPj7lxA96+BvtxpBLeXoNoxHC1Sz/u2+GiYHe0TjF/xALiddzOkMRlANS8ENKY7B6/+yvKAuc5xkVKEEAQWshrEkorHPRjWB9amKrhFzyyCmePw/E29mGQA1ohnF6ykFZvFzZ2IWUaVQOLgAHMRBZLawFUYaxTc/C5NmyPwI+gVYcT8xYLy3L7VRaw0LRAWQFEISzNwjibBtUAggDaVSyNAOLEYljBJA7mwTCB+eY0ODaJoDmqAJuzoFoUwGwdhqnFxoaJzR1gYbTAh2MdG49n15xpWAgLzyJf6wNYmYMadr1aAGEGZbUmP4Ass0Ba6cG4sGBb7R6htMCbBrjiwiJoHhaO852F7sKgCsYdeB/sn+fuDKUV9em4bqvav8peCq3q7e05GMfgV+OjAD4/B2lVziuBcR/aLfs+KaA/hu4stCO7nxIL1CUp5DP2+tbrUC/gWGjBtNEY6pHdR5Lamid29yDNoDtj95AlcCuxSJorwG9B1oDtHduL0kG8C3tXoelDaxFcExp5dQ8JvLFRXQeYj6fXKgs4eRG+2YObb0Jn/t6htMUR/GQPXluBQQ/oQboOcd8ibQBnejZuYmkEZzfg1VXucHbj8FiAqIAnX4ErXeg3bL2ntu74sfxwzp+HCxdg9i41RhERERERERERERGRz6ha2Of40h/TKm+ycPnbH+rchcvfZvmR/0andY13b/2PZPn8x7NIERERERERERERERERERERERERkc8gRdWO8PnPf57f/M3f5Dd+4zf4j//xP/Jbv/Vb/Of//J8piuJQQA2mEbWJ9z4H8Oabb/JP/sk/4dd//df5pV/6JZ566in+8l/+y5/Y/YiIiMj3zyAecHX7KoNkcODgALa2oNuFuTlG2Yhn157FHSw+OQcXL8KlS1Z08n04fZrN0w9OI2mAyzLYuAaNIy6+cY1iLiIpEkIvxN8d0MhhGN1lsVmKXwsoyoI0H1N3EPmwPANzDYt/FSUMqlCaKyAMSs4uw6MrsDrDHUUmr7qlmZqFxG7FVYjLAe7OSFrkQ+Bgrgsn52F9x0Jl7QY06+BV4a/lGTuexLY9ngdLDYgaFjTrDaFRg1bN5q9FEDRsDS638JXvbKxzFgDbHluoqxlakI0qHOZ7ENWgN4BG3WJxOAtlJbnFthqhzROn4KpI2sbAYmEeMNO0NXrVnoxTO29yrF0HCpvbedCKIE0s/Jbm1gLbHMNKp9pXbK/i0h4E2K/J+WBru1sozX9PKK0s7XVxk7AbVBU5m68W2FxxAfXQjrkqjNYop2/ZUWx7Dbb3tcCCdQDDse0tnu3z9i7M1+x9NRlbCyHOYDiCbAz1Grg6jEcQVtcJ/ekeFA6K3N4b1G2vyip6FxWQB7amnSG0HZQNCEML1W3v2PrHN2CuDbU6hDWLpDkHaR9e24B+Nt2neg7HhxY2G6xDfRGac7ZVt69DWQNCG9es5hluQ/slWHoeZk9C8QFDaY9fh9eXp6/nyf7hn6uvXOcOT1yG2y24eaBpdmLHjh/FAx7eOvq5Q86dg2eeufvzngdnzsDi4geYTERERERERERERETks6Ue9fjcyn8i8BMWLn4bP5/8iy3AKvYPJ7Wp/mMPsAfsAOtACn6Ws/DGaxQ/3eBHjj/P2xtPkKTL9+VeRERERERERERERERERERERERERD5tFFW7B9/3+YVf+AV+4Rd+gXfeeYff/u3f5nd+53e4ft2KBwcDa5OA2uTrUfG1JEn4oz/6I/7oj/6Ihx9+mKeeeopf+7VfY1GxAhERkfvqg4TSAK5tX+OFGy9Mg6r3CKW9tXtzfyrnHPR6sLkO+5+HKGFzjfGVdQatAN/zCf2Q0c5t8gMBpTtsXyOrQmlRmuJHkAbTmNkhRQp+jTAoOLtSvm8obbYGzQhujy2w5jn7nMdB7RBOL8FcFdrKHNzsw/qWhcKSABpVUK0WQFCAjzWajnXgxxYsHLa5B4mzc1Znp7EyGpDnUPOhZruE78Nyx77urxkLd3meRbWCAPZGcGsH/Jo9Dn1Y37XAWj20KFsNm38vg5mGzVl1xzjWhrSEvIS9xCJvkWcRrKBavz+JlHl2vlc9zgqLpC13qtAaFjtzORSerSWjCqFV8bk4t2tN7j30wSuAKs4WeBYT2385nb0uR4XS8OyeJ6G0oFpE6aoIW2lzu2j6Xum2YGtsew12PBvZewAfch92+jDftVBaM6xibr5F5JIx7NZgvmmvqSthNIaVLtSq9QWezVemkM5DlkMUVZ8j8m19ozHUI9uvJLX7n9gdWtiu24a6B6PEYnlhDmUGu4m9jy5ft3MnkbShB3PLtpa0bmu9/V2oF0BnOv98PP2+LODkRfhmD26+CZ15SLpwZRlO9ew9Ptyy6z79MpB8uFDa0gjObsCrq9zh7MY0vnZQVMCTr8CVLvQbtt5TW3f8CH8458/DhQswe6//oRERERERERERERERkaPUwv5+UC2IY+Yvrdn/++oUsMLRf4nfBBaBh4AN4ArMX3qd7S+cgQZ8buU/8da7v0iWz39i9yEiIiIiIiIiIiIiIiIiIiIiIiLyaeW5/SqIfBDOOf7Lf/kv/NZv/Rbf/OY3yfP8UDxtMuag98bVDh6v1Wr89b/+13nqqaf4uZ/7uY938T8AZmZmGA6Hh451Oh12d3fv04pEROTT6shQ2hE+aCgte/QR1m6/cfjkXg/effeOOceriwzaAR4etaDGXjKkGGwfeX0HJK3IKlrOEcQphX/k0Ok5tRpFmUPpaPiw2oGZlgWsCgeDMWzs2vIffyjgZz9fUvMcfhXYKt7zYY52CF9chJm6rSdz8O42vLNtY9PAxs1UsbQGFkqbrD/wbJu2R0AIJxYsqJYVkGcWq5pvQehB4GzODOgNLXQ217S5PCy01alZkCpObC2FZ8d9H/p7tlXzLWjW7DzfQZFbcGt9D2bbEIW2uGEM7YadA9D0bU2Fb3PWAhhldj/NyCJnSW5rXh/AypyNAYueUVhoznl2/TSx9Q0zuD2CB2YtUlb3LJYWp/Z8p27Rr7KE3Qw2xjbnsTbMR7b3ZQl7MdTrdu44gWEOb1e/TVpswUzN5vKAeAQ1B2EHxpkF4dIC3tmFz1XriAILw5Uju8eyBUkJRWlzRoGdA/bWT9+FVh3qM7AHbPbBq8OJRahX+1Bir208gjSH+TqMSzvfD8ALoBVZEK50MEo5VOcbxRbe830IC8gDW8/OENoOyoaF8vICepswjqG+Za9daxH2mjCT2HspcfDqho0BWBlCM59e60tvwZsFvDMH7Xl7H7zbgSScBticg+E2tF+Cn3senjsJr61M57g6fziWdmYDfvnS9PHzP3F0KO3RdfjF7xw+lgbwe2fh5oGe2Ykd+LvfsoDaR3buHDzzzL3HeB6cOQMKOouIiIiIiMhn1Y17/z2hiPzw+NPXvvvxXmC18/5jvhfrw/cf8736uO9B5Pvk8rB/v5fwIRV8/vjz1KPbACxefInujW/BF4HoQ0yTAmuw9aOPcvunvwRAki5y491fxP41nU/O50+f+ESvJ/fHmeWV9x8kIiIiIiIiIiIiIiIiIiIinzlqwIjIp1V4vxfww8bzPH7+53+en//5n2djY4N/9+/+Hf/23/5brl69uv/8JKI2ibNMvh58bnI8TVMuXLjAhQsX+LEf+zGeeuop/t7f+3ssLS19wncmIiLy6bHWW+O5S89RunJ6cDSC4RA6HWi1AEjyhBdvvnj45Bs34O1rsB89KuHtNXZr2/TnavvDXFFA/10rjL1HObhNHM3iADKoJxn5ncP2+UVJEfhQ5OQ+1H1YnoG5hoW/ihIGsX3OKiug4ef85KrjkeNwvF3Fst4TSmuG8CMzkGQFG7v22YxJVI0D4yehtBZYIQ2b78e68PCihdJcDeohHJ+xaFuRWijLOTjWtBgZPizOWtyrVW1TzYewDu2aBbLy1I57zsJc7aolN0ot2FULqnO9KnJWhziG2ztVKC2w2NkwtWuWzu7TOYuXOR+OdSzW5WwajrUtNuaq697choWWBdJ8z64ZZraewLMoWJZBnNs5m2NY6dhcHhAG4HILpYW+xeF8Z/sCkBR23zhbB0BYgiuryBhV8G3yOlSBM1ct2APIgcDCbbUAGgHEhb0GzoMst2uHZRXJy6fNsiiAB2aqNWDBtLB6TUss7JZi76tWzeavhRBnMBxB5kGwB64N4xSimgX7igzKas9wkKUQBNAMIMHWHlTX9HIYFTZvUYJX2LrB1r4zhL0Aum2Lz40S2B5DmEOZwW5isbTL1229k/hZDAzWob4IzTnbptvXoawBVSStmR+OpLWfhx8J4P/7JfBr0OlC0oUry3CqB+tbMNyy6z79MpDA49fh9eXpmg8G1TwHX7l++Gftictwu3VnKO2Jy9whKuDJV+BKF/oNmI/h1Nb05/IjOX8eLlyA2dn3HysiIiIiIiIiIiIiIh9Zd+5b+0G1II6Zf+d1eIQ7O2gxsIP9R5kI+++OB/+bYgQ8AvNvvMb2F85QNBrUo9t0577F1uCnP/4bEREREREREREREREREREREREREfkU89yk+CXfkz/+4z/m3/ybf8N/+A//gTRND8XTYBpWm3hvXO3g8Vqtxl/7a3+Np556ir/yV/7Kx7vwT5gqpSIi8lEN4gFXt68ySAYHDg5gawu6XZibA2CUjXh27VncpBDmHFy8CJcuWcHK9+H0aXjsMTbHW7y189b+dC7L4DtvWH3qPcY12D2xTBhG+J7PcNAjT+K7rjerhxZKA0hTfHdn+Gz/ukCt7nF22XH2OKy2LTZ2kFfdTieCTghbY0jdNJTmOBxKe2QVGtXjooSbfbi1C0sdaDehUZ+G0soS8mwaSltsQNSwNeQONvag27Jg12QtoWeBsTKzc3LfIlSdus1TOAt5deoW4vKcLXJvBLeGsFtCu2H3c2vHomCrsxbaCjy7r6ywwNbGAFYXqvt0MIyh05yGzvICIs+iX4FvobBRVoXZIshLSHILsr07sFDYyoyNa9YgTSEuodOwc/bGMEgsqOY8+NysRcoiz/YgTi2q1qlb9CtzsJPB5ggWmjBbt+BZuwGjGKIcvLrdY1ZA4cP6roXSFlu2BzOR3Us8gpqDsAN7ud1bWtjrvdqZ7n/TA7+K1KUBjA9EzTqRrTvO7PxiCPN1yHy7z2EGK12oVRG3svq1N4Q0gyiFLLJoWi2063nO9jeq2WteuGl0bHfPzluYtdeuVsAgh509iEro1KBo2o/eZh/S3GJ59S1o+tBahL0mzCQQOAuqvboB4+rHa2VosbSJL96Eb/bg5pvQmT8cSau/J5K2OLJznjsNr61M57g6fziWdmYDfvnS9PHzPwGvrt75s/roOvzid+487vg+h9LOnYN/+S+h3b77GM+DM2dgcfF7uZKIiIiIiIjIZ8eNwfuPEZEfCn/62nc/3gtM/jL247I+fP8x36uP+x5Evk8uD/v3ewkfmO8lPPQjf4DvZQAsvvq/0W29ZoG0iT3gCrB9xAQLwCng4F/9p7A1eoTbj/4sAKWrce2tX6V09Y/lHo7y+dMnPrFryf1zZnnl/QeJiIiIiIiIiIiI/P/Z+/Moue76zv9/3qX26kWt3mTLwpblgI1lOUYmAYI9SUgUHJJAWBJCQtiMzTiZCZNzMknOxMNkTiZxtu9MkkkGY7ZAIAhwnMDAhLD/AIONjY1swNjYlixL6pZ6rb3qLr8/3re6utW71K3ull+Pc3zUVfdT937uUrcF6n6WiIiIiIg846gBIyLnK3+jJ3C++Mmf/El+8id/klOnTvHe976Xd7/73Xz/+98HLJTWjqi1A2rtP2cvaz/fbDb52Mc+xsc+9jEuueQSbrzxRt74xjcyODh4jvdKRERkfa00lPbExBN8+ciXOyHS1YTSRkZg7ATMfBuNYOxh+PYYtb4uSo0Svut3QmndC881BprVMWLPI+2moFmnucTfpOIwIHI9K3m5Fpu6oBt6s/Z1FMFUDUYr8PwL4UUXx6SSKJYbz++6FXy4fBC6UxZrCiP7PdDjEza50IHAg2wK9g5bBMyJbKznwvMvACcFYxWLVsVYxCztJfGnlAXNUi6ELQuARdih3dVjcbUoCbh5HhSSeeBBvQEBtl9hZBG1cgN6c/Y1JHMObbtBEk0rpm373RkoNy1yVm7Yuj0sjOa5sL1o43GsI9WVTSJtjkXDTkzBUA+ksO2lPPBbNn/PAdeDVgvqgYXnaiG0IhvnAL4LTmjH0XHsNZ7TiYbVQwuQkRwPsGhaHNn4GMgmwTmvHSprH4vIQm1xE+J0J3zXl4djJQvbATSSMJyfnLNWaJc52DHa1WNxteQKpjYJhbx93QhhsgZ93VBI2/5EQCGJvpV9iH3w05AJwPctJOenk3hfbKE4z4VcBshAtWJxuW3dkEmiajXg+EnI18DPWSgtBqZKFlmbLMGONHghVD2IPQu0hQ2b3yNPd0JpmQB2lKEOTJ2ATD/keuw6OnUYohTg27hckMT0JqBwDwzcBd27IRyCqVFgFJonoD5p6wPYO9oJqgFcdxgeGuyc09lBNSeG6w/Pfb/d8CicysPRWfeDndP2/EIc4LLxhZfNsX8/3Hbb4ssVShMRERERERERERER2ZS6i4/OBNW8ep3e1sNzg2onge+y4Ic3ARZauw+4HBhInktD79RDTNT3EWazuE6L7uKjTJauXKe9EBERERERERERERERERERERERETn/OfFMnUTW2pe+9CVuv/127rzzThqNxpx4GnTCam2nx9VmP+/7Pj//8z/PjTfeyE//9E+v78TXkSqlIiLnv7UOpbWuvoqHT31n7kZGR+H48fkb37GD2rYuphvTpLwUbhRTGj1K6C4817hYpBHbLz+kHB8qFVre4vsWOhB7LsQRXgieD0NdnVBaGMNkHU6UoRVANg1XDcK+HbCjQFIh68j7cPmARbdGS3CsYiEuL7L4UzvA1ZW2UFraAX/WXx98AA8mquD4dsiGui0s1gqg2rDX9Bfsz9CBFjBatvhaT87W0w6vFVNJACy2UFodm08hYxGy6Tpk/U4szU0ia0EIR6cgm4G0D8RQrkMha+GyrG9j6/Uk/hbDyBQMb7PXx8n4Ys4eOyQBN6cTPSO00FvsQC4FzYatq9q0UFwqDUNFC5DlUhYNq0dQzNo+VWow2YCxpLrVn4eeDHRl7HKr1CGTsdfXG1AJ4KnkryfbctCTtmOfz0K1DukAnAy0HGgmvxxzogTbkzAdgBtC1pp6AFQiiLOd89cMbXsz1wPgNpNQmg/N5LzkU3YcgxjqLShXoTUBg90QZ+z8t0Lo7oWerJ2b2A4Z9RozsbhcxuYOFhKLHUj5do7jsBMcawUwNmlfpx0YzFkb8FTNzlXB3jJMevDdpyzcBkkorQI9g1AchmbaxjZqcPQoPJXq7OtQ2WJpbc89Cp8chaOPQLEXGn3w2CDsGYXMOJTHIWrBLfdaLO1kHv722s6cZ3PizrjZ7noOPDA8f/zVJ+Dl35v/fAw81geTWeitw57xeW/h1TlwAA4ehO5Fyo0iIiIiIiIisr6OTC0/RkS2hO8fenJ9NzBcXN/1nygvP+Zsrfc+iKyRR8uTGz2FFdu14+Nk0qcA6H/w6/R1H+r8w0EFC6YtFlSbzQWeBxSSxxGMl/dy6qofBaDR7OfI8Veu6dyXsuvynedsW7Jx9g4ObfQUREREREREREREREREREREZBNSA0ZEzlf+Rk/gfHb99ddz/fXXMzExwfvf/37uuOMOvvMdi8I4jjMTUWvHZNp/zl7Wfr7VanHnnXdy5513cvHFF/OWt7yFN77xjQwPL1CGEBERWWMLhtIWcNahtLHjMNh+IoKxh6k+PMJ0wcPBIeWlcKIYJo9DlvkmjxPlHWpBjWqrSqERkg2gnF5gLECriZvyCKOQZlAjE0PahcEui1N5rgWnppJQWhyC70XsG4Srh2C4i3mVJSfZ9a4UFFJwsm7rcGMgXiCU5oIXQ08fXLzNwlxxy4JkmSxkfNjRZcG2oAW1hh3W3jx0+RC70N/dCaUVk311UrA9b9GusGnbdmJwPejLQxBZyMx1LXrWlbF9cZIAVzoFI9MW5vI8e30QQi22OeV8i3cFAVRa0JW1dcXJIdlesNhYEEGlAWFoga7YsQDd9mJyTBzbXlc2OXaOBcLGajBY7ETWfA/iwEJqvmtxODe2+FrkQD2wbaW9TojNCZkJinmOzbctjJOQWHsOAAHg2WPPg6wH9dBei2NjwwjcyH7fJWpBNOvaGi7OvR4qVQu84dprp6agkLbrCqAvZ8ej3rJjO12G3gyErh3XaguG+iCVzM9zIJeGqAnNXoufpdP2Ozeea8eiWoNM2o5XIznvYNs/NWmhP8+BVAhTAYxUIB1ZUC/M2Tkcm4RmYBG8zDiMjUFxGxSH7PoBCAMY/wE0ZkXheuu2vakRKJ2CgQwci6FZs8OaKVosLhNYUC2OoTwBhXtg4C7o3g3hEEyNAqPQPAH1SYv7Aewd7YTSBqqwb2ThSNq+kflBNYAbHoVTeTg6q2m2c9qeX4gDXDa+8LI59u+H225bfLnjwN690N+/gpWJiIiIiIiIiIiIiMhm4zgtMukxALx6nd7Jh6Fn1oDHWFlQjWTcY8C+5LELvRPfYaJ+NWE2SyY9huO0iOPUEisRERERERERERERERERERERERERkcU48Uz1RM6Fr371q9x+++187GMfo1arAcwLqM222DLHcfB9n5e97GW89a1v5cCBA+s887WhSqmIyNbz8OjDfPy7HyeKZ/0mQLUK5TIUi5DPA9AIGtx99O65Lz5yBJ54Yt46SxdfwFhP5xcB4jCEp4/OBKBma3pQ7euyMBHgNOrEjebiE86kaSWhNJpNMgG0vE7MbJ5UCt8L2TcQLRtK605Z1OpUzYJcDhb5mq3gw+UD0JPEtloxHJ2EE+O2ew3PwlZX74CUB35ogS7HgZ68RbdaIYxVoB7DcLeFwtrz8B0LZgWBxb0iIPA6zbqZOQPFjIXEnAjqDai14FQdPN/iayeTaFpfAXpzFkrzHQtmVVoQxNYZS3mdYxA7nblkPQu9ha5FvVKehcBc7DgFETQCiEI4MQVDPTYGLHpGaNuPHYuPNRt2PMstOFWFC7oh5ULGsWNQb9ryYsaCYVEEpRaM2F+p2F6A3rQF4qIIKnXIZOy1tQaUA3g6+StHf97Cd8WMHat6FVIx+EU7Ts0ImiEcK8GFyTyc2EJt2SSq1nKhHEMquZS70nbMai0Lho2PQb8LhaIF0J4ug5OBvm47z2kv+f0ax/annETVSNv4MALHg3zazmMUQ7Vpx62tWrfryXXtWgo8e910GQoxRFnwfYu2jY7ZenekwQuh7Nl7o6thYb9JDx55GmpJxSwTwI5Zf20bqkO2H0o9MH0SgiYcnxVK21GeG0rb+xn426vATUGxDxp98Ngg7Bm1WFt53MJ0t9xrEbSTefjbazvX2Onvwfa4tqYHf79vfiTt9Q9COpy/DrD34GN9MJm1CNye8Xlv99U5cAAOHoTu7uXHioiIiIiIiMjGOrL0B0aIyNbx/UNPru8Ghovru/4T5eXHnK313geRNfJoeXKjp7Ai2cwJLhr+ZwD677uHvvhBaH+WSh34+hms9EfpfIjUKRh3r+bUNdcC8NSJX6DeODcfsrjr8p3nZDuysfYODm30FERERERERERERERERERERGQTUgNGRM5X/kZP4JnmRS96ES960Yv4q7/6Kz7wgQ9wxx138O1vfxuwUFo7otYOqLX/nL2s/Xyr1eKuu+7irrvuYteuXbzlLW/hTW96Ezt27DjHeyUiIlvNVH2KxyceZ6ox6xcJp6ZgfBz6+qDHPlq92qry0Yc/StyuncUx3HcffPe7nYrX5ZfD857HWG2cyfrkzOriVgtGnuj8MsAstbFjNHKD+H4a13Epl04RZBaeaww0m1Viz2pccdjEWepvMGET3BRhFIALYQYuLEJXHjwHwhimajBSsl247qKIH9kVk3LAjS3edHqPqeDDc/st1hVjobTjE3BswiJfzSQU1pWGvcO2y25yyNLAZdvg2dthogr4sHObxcVaoUXJohB68xY0A0j7MNBtca52UA3A86CQSvpynoXSwtiCWY5jobJSDVI+dGUtxAXJMh/GpqEZwEDe1tudgXLTAl9BCJUQMkkoLXbteOVTNg8nmVeErcMBjk7Atrxt13WSSFzLxngOuB60WlAP7LiN1WCoaK91sDhcHNgx9F1oJcctTI5dI7SYGXEnGOdHEEdJZCyZU1sUWVAsjm0DDlgVzrNQV8qzEFw9hIxvz7UC27YfJZG8oHP+0x5c0JXMARvfrFowLnIhcGB6Enr77NrwXIutuS5MlW2eUy0Lu5VbnflDEpVLjkMY23ZzGWg6kHOhkczHCaAa2jkNI4u6taNjrcDiaRXPwngZB6oNmKiBH1iwrNSARgyPHrZwWyaAVgV6BqE4bBeoF0OjBmNHodZpHdJbn/s+2HUcWnfCBwKLvZ0eSjuxQCjtylE4NARTo8AoNE9AfdJ+vwhg72gnlDZQhX0j8MACvyO0b2RuUA0snPbm+1cXSXOAy8aXGLB/P9x6KxQKSwzC3lR790J//9LjRERERERERERERERky0v59kOiXr1O73cfhufNWjh9hiudpvPvqAXovf8hJq7YS5jNkvLL1BtnMWGR0xwaHVnX9SvaJiIiIiIiIiIiIiIiIiIiIuebp556iqeeeopSqUQYhnR1dTE4OMill16K7yvXJLLZ6V26QXp6eviN3/gNfuM3foN77rmHd77znRw8eJBKpQJYRK0dVAPmfL1QfO3w4cPceuut/Lf/9t942ctexk033cSBAwfO4R6JiMhGW2ko7YmJJ/jykS93vrcsE0p7avqpmdXFIyMwdgIG289EMPYwfHuMWl8XpUYJ3/UtlDY1StC98FxjoFkdI/Y80m4KmnWaS/ytJIpCYidOaloWpNrRBb3ZTihtsg4nyhaaKrgBVw7HXLUDdhQsdBWdVlrK+XBRFzRaISMlaJJE1drbTL5oh9Ly7Ylj67u0Dy7rt1BanLJI146uJJDVhGrTDu32nEW4cKE/CaXlk3BVygU/Y6G0IIQgeU3g2J99eQtkRcl88xlsgrFFxdIpGJ2CUh0GepJ1BDBZhUzKjo3vWoyr1oLtRRiZsliYA3TlrA3VjpzB3FCa59rYRmDb9FwLv7UC21Y9SEJpXfZaB0i7UI/sgYf9F8V2POsBBJHttxNbJM7Bvm6H08K4E4OLkgBZGHXOWyqEOOxcR96sUJqX7EiUnEe3HSFLd85nXx7Ga51QWiO0cclDmk0Lm3nJE305i5jVW7bPtTpkW+Anxzvl2T7OjqS5QNaHgW0WoGs27RoZKNh8oxiqNcikLSzXaHaurTiGU5OQTdv+pEKYCmCkAukIiikIc3asxiZt/fUSVB63GFu+H+IcZANbX7oB3xlJtkESSothagRKp2AgA8diaNZsPzJFaPgWX8sFNp/yBBTugcJdQAOee/nKQ2nXHYaHBjshuN2TnXPpxHD9Yea44VE4lYejs+4dO6ft+YUsG0lr278fbrtt8eWKpImIiIiIiIiIiIiIyCIcx/5xatvD38YNAvtHlbbmGa509us8cFsB275ziFPXXIvjBGc6VREREREREREREREREREREREROQOTk5N86EMf4pOf/CRf+cpXKJVKC45Lp9P88A//MDfccAO/+qu/yu7du8/xTEVkJZx4dq1LNlS5XOaDH/wgd9xxB/fff/+8sNrp2mG1tvbY9vM/9EM/xNvf/nbe8IY3kE6n12/iq9DV1UW5XJ7zXLFYXPSbiYjIM92CobQFrDSU1rr6Kh4+9Z25Lx4dhePH5690xw5q27qYbkyT8lK4UUxp9CihO38oQFws0ohbAKQcHyoVWt7CYwFCB2LPhTjCC8HzLc61UCitGVhkat8g7NsBwwVrIM3mJLvelbJI2XgNmnEnlBYzN5R21TBkk8dhBEcn4WQJBopQyEE20wmlRREErU4orT8L6azNIYgteNWXtwBZey6+Y8GsqJWE0lwLSxUztp4wtvhWMWMRsXZYrFKF4xVwU7bOZgAnSvZ1bw6yKUgBYQCVVrL9KRjeluxnDOU6FHP2OJ+2KFoY2eNGy/arXVGL2yE5xyJhx6cs1jbUZcGwXMpe0wg7cy3VodKAk1Xbpwu7LRSXT3XmVUjOY60OUw0Yqdn2thegy4e0Y+e0Wrd9z2SgFUIjtj+fLkF/HoppW55PQatuYbHYgSANzeTrZgjHSjaPVBKCSzsQVZOgXh4aUSfOlvbsNe39d6egkLd4XQW7dvq6oZC2sVFyfBpNKJch3YRMbxKLi2x+jmfXjAM06nZe2soVO4bbuu2t6IcQeDaf6TIUYoiy4Pt2DkbHLFy3Iw1eCGUPWh50NcCLYdKDR562YwsWQNuR/PXK9SDTD73WUOTUYTiS6oTSdpTnhtL2fgb+9iq73op90OiDxwZhzyhkxqE8btfwLfd2Qmkn8/C313ZCaae/D2ePBbjrOfDA8PyxV5+Al39v/vMx8FgfTGYtArdnvBP9OyMHDsDBg9C9SOVRRERERERERJ4Zjiz9/7GKyNbx/UNPru8Ghovru/4T5eXHnK313geRNfJoeXKjp7AiXYVHubD4KS752IctqvYjQC5ZOAp8Z4kXL+YKOh9kVQO+AVHK54lXvpanyz9LqbJnDWa+vF2X7zwn25Hz297BoY2egoiIiIiIiIiIiIiIiIiIiJwBNWBgamqK//bf/hu33347lUplVa91XZdf+IVf4M/+7M+49NJL12mGInIm/I2egHQUi0Ve/epXU6lUOHz4MOPj40uG1WY/7zjOTEyt/fwjjzzC2972Nv7wD/+Q3//93+emm27C85ao22xWp07BiRMwPAz9/RszdrPMY7VzFpEt7eHRh/n4dz9OFEdzF1SrVnYqFiGfpxE0uPvo3XPHHDkCTz8BMw2hCJ5+mFJqgsme1MywOAxh8jhkF5jA5HGivEMtqFFtVSk0QrIBlBfrdLaauCmPMAppBjUyMaRdGOyCnix4rkWkppJQWhyC70XsG4Srh2C4i3nlpNmhtHwaTtVsHW6chK5mKfhw+QD0JPNrh9KOTVikKXQsZJVNwd5hi285SWjMc+H5F4CTgrGKhb1iYLjboloOQAr6Cva6sAWhHVVcF3b1WEQrSgJlnmdhNwfAg3oDApJYXGRhsnLDImluss8OEIa2XbCYGNj2+7IW+Aoie13Otchb7No6txftmOBY6K0rmxw7B0o1m6ef7EchA4QWdYsdO67Nhu1PM7DwXC2EVmRRNQfwXYuq4SSP6bweLNqWTca6bnJ+Itum60B61t84o2junwCp0K6HOKnfpTxbXyZ5XexArQHZqHNdNGsQZzvH6IIuOzdgwTSfJKiGxdCayXnOp2z9KR/qLShXoVWDTAriDNSq4Cfb8d1Z5ya2YFwuA2QsfhbFtn9+ZMvLgT1fdCyyFjsWRysnQbmxKbuGMg5UGzBRAz+waFmpYdfdo4dtvpkAWhXoGYTiMJC2oFqjBmNHodZ5G9Nbn3V8Q9h9H3xyFI4+AsXeuaG0EwuE0q4chUNDMDUKjELzBNQnob3avaNzI2kDVdg3snAobd/I3LEANzwKp/JwdFbTbOe0Pb8QB7hsfOFlc+zfD7fdtvhyx4G9e/V3JhEREREREREREVmdcxE9E5EtpRV0se3hb1tQDexTetpRtTP9TJfZr0t+FtNtBWz7ziGevOCXznClIiIiIiIiIiIiIiIiIiIiIiKyUp/97Gf51V/9VUZGRs7o9VEU8U//9E98+tOf5o//+I/5rd/6rbWdoIicMUXVNonPf/7zvOtd7+Kuu+6i2WwCLBpTa5sdXGv/OTuu1n7+2LFj/OZv/iZ//dd/zf/+3/+bn/iJn1invTgzWa/K/7z7/wPHodaqMVYbox7UIY7ha1+Db3/byiuuC1ddBS98ITgOTmxll5h42bFZL0shXaDSqlBv1VY+dpl5ZP3ciseeyZy357eTS+Vs7Cc+AV/+shV3PA+uuw5+7ufAcSikCuzo2kExfdonjk9Nwfg49PVBTw8APZkedm/bTU+2Z+7YzRKC2wzz2Ir7J1vKVH2KxyceZ6oxlTwx/71abVX56MMftftFWxzDfffBd7/buW9cfjljl1/MZH2yM6zVgpEnFgyl1caOUcv243o+vutTLZ0iyCwx2fIpWkkorR42yaSh5Vl4a56wCW4K3wvZNxQtG0rrTkEuCaXFoT0fnrbK2aG0GGjF8PSkhaFCB5pJK7QrbaG0LElcDItdXd4Dl/XDRBUc3w7ZULctawUWtko70F+wP0MHBrphtGzxtfSsFmk7lOYCsW+htDp2W876dkrKDfu6K2P77cTWdUqnYHQKshmLbxFb/KoRWJgr69vprTctlLa9aHNNumhsL0AzSh7HcHQCtuU70a+lQmmNAEoti9vFSfTN9yAObH99F1rJcTs9lJb3k0hcsi8k++M5nRgcJK8Lbf3t8JpV5Oyxl0TS6qG9FsfGhhG4URI/a0E0K9g3XJx77VSqkEsBrr12aspCc15yDPpyNvd6y8Jm02XozUDoQhBAtQVDfZByOvuUS0PUhGavXQ/pNBSwdUYxVGuQSdvxajRh9tuxVIFmC/q6bB9aDTjZsEhaHIKbh1YWJqbt2ohiqJeg8rhF8fL9EOcgm/zuTboB3xlJtkMSSothagRKp2AgA8eSmJwHZIrQ8C2+lgvsmJQnoHAPDNwF3bshXGEo7brD8NBg59zvnuzspxPD9YeZZzWhtHQIb74fHuuDyazt257xebeG1TlwAA4ehO4z/U0lERERERERERERERGRlYlGPXq+83DniWmg/U/4WWAbMLGKFW5j7r/lTnW+7PnOw0S+Bz2nv0hERERERERERERERERERERERNbK3/3d3/Gbv/mbhOHphYPVq9frvP3tb+fBBx/k3e9+N67rrsEMReRsOPFy5S5ZN6Ojo7z3ve/ljjvu4PHHHweWD6kBc6Jps53+2tPjau3n/v2///f85V/+JalU6kynfsa6urool+d+snfXAFzzG5dQGd7OyerJzoKpKZicnL+S3l6axRzVlpVA8qk86XJt0bH09NAMm1Rb1dWP9dIrmsdqxq50zgBRHNGV7iJ1atziWqcbHoahIQDCKGR7bjvZVNbKKt//Phw+nBR0HHjWs+CHfggch1bY4qLuiyhmis/4eN1m2b+ZgB6cWURvgSgXLBLR2ywhuK0Yr1tk7LxIGix6Tp6YeIIvH/my3ZcXiaTxvOcxVhvnqemnZl4XxzGMji54L6gNb6dUSOG7Pq7jUp4aJWjU540Da0I1Mx6xZ7Uwv94kWO7v5KkUYWTVpowDO4rQlbcoVRjDVA1GSrYL113i8SO7IlJOjJsEvMLTvm0VfHhuv4XH2qG04xNwbGKJUFry2tABP4l6TVQhTkHGhx1dNpewCdWmHdq+HBRTECXRsHYorSd5q7XDWmnPXkcMERB40AwgiJLol2vRs96chcS85NttEMJT0xYu68vb66fqFuTqzUHOt7mGAVRaEMRJKC0JlKW9uaG0MLSwW+TaOlKehcBcLP4VREmELYTjUxZqG+pKTpFrpdxmYBGxrgzUarY/Ey2YqMEF3RZQyyb7XG/a8SxmLOQVRTDVgpM1W+e2HPRnoJC1IFilBtk0pHx7bakFT5dsbH8eulK2LgeoVyEVg1+EWsv2sxnCsRJc2G3zdWJwQsgmUbWWC+UY2n896UrbMau17HyOj0G/C4WiBdCeLoOTgb5ui6ulPdtfHNufchJVI23jwwgcz4JzrpNE05rMKflV67aPrgt+aNdCGFmgrRBDlAXft3M/Oga1OhRK0FOAVAYqKYukebGd90MjMNnqrD8TwI7kr0GuB5l+6E1uD6cOw5FUJ5S2ozw3lLb3M/C3V4GbgmIfNPrgsUHYMwqZcSiPW5julnstlnYyD397bSeUNpsTd8a13fUceGB4/tirT8DLvzf/eeySX9tQ2v79cOutUCgsPsZxYO9exUZFREREREREZPWOTC0/RkS2hO8fenKjp7D5DReXHyOyCTxantzoKazIs2//G/YU3wc7kifSwAvo/MNIBbiP5B+rluECz8M+6YfkNV8Hkg/e4Tg8Vnkjj9x4y1pMfVm7Lt95TrYj57e9g0MbPQURERERERERERERERERERE5Aws1YIrFIqVSaYNmdG7ccccdvPWtb11R42e13vjGN/Ke97xnzdcrIqujqNoG+MxnPsPtt9/OJz7xCYIgmHOTXSiEttjznufNFC8Xe91CyxzH4brrruP//t//Sz6fX6O9WpnFompX/YLD0ct3WsUErCzz9FGrdZwmcmB6oNuiGgBRRPdYCXehH051ILrgAqaDyurHAt1+EffY0yuax2rGLjcPLtxJk9AibKsZC3RnunGnSyuKu61m7PkYr9ss+0dPTyeg56VgZGTJiF4YhTTCBgP5AbrSxSUDekAnopcubInQ3VaL843VxzkyeYSsn7Xzt0TUMIhDnpp6irSXxnM9i6QdPz7/XO/YQW1bF9ONaVJeCtdxKdWnCacW/njzGGjk0+A4pBwfKhVa3oJDAQtpxZ4LcQSRxbuGu6A32wmlTdbhRNliVIW0w5WDMVftgB0F+zn/6LRyUs6Hi7qg0bLAWhPwIrtNRU5nfDuUNvu7T4xt13WXD6Vty0EuY4c1iGGkYkGzdLK/TmwhM9+zwNTsUFr71LU5WATMdcCJoN6wgNepOng+FNNwsh1NKyweSguwY+gk9/92xMqJIetB0LLQ2XKhtBNTMNRjY1KeBeAqdZt/MQnQVep2LE5WbTvtQFl6kVBaGMFUAKeqsL0AhRRkkm1X6zbHTMYiYa0Q6vH8UFp30uqsVy14lsrYHJoRnChBPezMI+tauC2qJiG8HNRimwdYWC0969psTUNvGmIXGh6MTkBvn0Xh0p7tcxDDVBkmJyAdwmC3Bd2mmjaf7l7oydp5jLHzVa/ZuXccu16q9c45iR0Lw4URxGHnfLUCGJu0jmVfAfKObWeiBn4AhRaU0tCI4dHDdnxnImkO9AzCUB80M9CowaknYTqEkVm/tzZUhlzQefzco/DJUTj6CBR7lw+lffxyODTr9xEe74Xdk53He0fgld/tPF5NKK3pwd/vg6Pdned2TsPrH7Tjfsb274fbblt6jEJpIiIiIiIiIgKKnonIiimqtgKKqskWsRWiaqnJSX7il38O/6oavHTWgucAs/8d5iTwXZYOq7nA5cDArOdOALP/3ebTEBzK8/kP/wut3t6zm/wKKKoma0FRNRERERERERERERERERERka3pmRhV+8pXvsKP//iPEwTBomN+7Md+jDe84Q382I/9GBdddBG+7zMyMsI3v/lNPvKRj/Cxj31spvezkD//8z/nt3/7t9dj+iKyQoqqnSPHjx/nPe95D+9+97s5fPgw0ImfzY6ezX7+9GXt56+88kpuuukmfu3Xfo2HHnqId73rXXzsYx+jUqks+prZz7fDar/wC7/AnXfeuZa7uazFomp7XgFjO7qgZ5vNcWoCphb+Jtv0oFZMQyZrTzRq5MqtRYMfzZ48tYyfjK2TKzdXNhbINQLSU9UVzWM1Y5ebc9RTpJz17Pwlc84sMbbUHgvkvSzpE6OLxt1Ks+JuOTdDZomx53O8brPsHw40h4eoRvVVjwXoboA7ucAvmp0Wd1vN2I0M3W21OF/U28N0pvN4uTk3illqrZqN9bKkjo8seq6jCy6gFFSI45hCuoBTmqbcqCww2LQyPqHnQhiQaUSQhsEui015bhLXSkJpzdDCWPsGYd8OGC503gozU0jm1ZWyENd4DZoxuLHFyGLmhtKuGoZs8jiM4OgknCzBQBEKOchmOqG0KLLQWDuU1p+FdHblobQw2XbsWMgriCFK5uW5UEwlHwofWyitFVsIy3FsDqWaRbW6sp1tuLFFtZ4ct8jZQI8tK1Wh3IThXlunC2RmhdIAMimLsblA2rffk2gG9vjkNGzLg+8vH0obq0AqDUNF23YuBc0m1CMoZm19lRpMNmAsuQ30521/0w5kFwilNSKLnz1dsiBdd8b2tSsJjaUDcDJ2TBuRHdPTQ2ld6aS/V7Vj6Beh3LLz3Azt2mj/bpYTQ84Bt2mPmx7UHDsvYKG62IF6y+ZXOgnbPPB7bD9LFejuge6cRdLAwnq1uoXsmgFkmlD3IZO2AFrUPi9pu0aqTYultVXrdmw8B1KhReamK5CO7NiFOYvtjU3a+ut1C5rlXMj3QyUHXQ3wkuvogRGbD8yPpF37FDwSwrEeKPTa8TpehIbfCbDFMZQnoHAP/MRd8PHdKw+lnczD317bCcGd/p5tx9faVhtKi4HH+mAyC7112DOevJfO1IEDcPAgdHcvP1ZERERERERERFE1EVkhRdVWQFE12SK2QlTt2bf/DXs+9D775KL/BKSTBT7w/FmPASrAY8BCn5W1DdgDFGY91wTuwT7Fqf34L4EGPPa6N/LIjbes1W4sSlE1WQuKqomIiIiIiIiIiIiIiIiIiGxNz7SoWqlU4oorruDo0aMLLu/r6+Pd7343L3/5y5dcz0MPPcRrX/taHnrooQWX+77PN77xDa655pqznbKInCF/+SFypuI45tOf/jS33347n/rUpwjDcNlg2unPt5dlMhle+cpX8ra3vY0XvehFM8te+MIX8sIXvpC//uu/5h/+4R945zvfyQMPPDCzHsdxZtY9O+IWxzH//M//zIc+9CF+5Vd+Zc33fTWqKXh0O7TCEpQbAIRRk3iR/kYMxHETt2k/VRrFEeXC4tGPOKriNN32Ayp5Fh08M9ZxII6pRBEsNo8YnLhpBRZYeuyq51zGaXTmXMstETUJytDq/JRu2KjiZRYeGsfgNOozcbe4WiZaJKvYdCGeNZZmnaYz9+eBZ+9gszxJ3A7SNRsrHws0yxOkVziP1Yxdas6bZf+iGKrVCeJZ+5dZ4VjimFa5tPA8piaJCnmqQVLXiSJa5RILXhqnja22qvixizs1ufA8piappiNwnFWNBag2yvjTJdyVzGM1Yzdozq3SFKS6rMgEVBplvCXmTG5o5l5cqUzgpTphrnnKp2ilPMIopB7UyTSbtLxOyGyeMMD3HfZdEHP1EAx3Me/G0Q6lFdMWSjtVszCWG8//wPSCD5cPQE9ygbVDaccm7J4WOhAkkbC9wxbfcqJO2Oz5F4CTslBYI7bXDHdbLMwBSEFfwV4XtkNp2KHc1XNaKM2z+ToAnoXS6kAYQta3SFu5YV93ZWy/nSSilk7B6BSU6hZKC0IIApisWgzNc8BPonO1FmwvwshUZ55dOVuPM+twHp1IQmmu7WvasxAbsT32HQu0BSHUAxirwVBXcg6AtGsBMRzwbJeIYju39QCCqLN93wUntHU7js3XnXVewyR0N/tengqTqFg7POdB1rPXOthzYQRuZDGyuAlxuhPq6stbKC2VXJuNEFLJWGJohfZ6sHnu6rG4Gsk5rE1CIW9fN0KYrEFfNxTStj8RUMhAowlBF0RNu1YyscXunCSGl83YfFv15Nh6kPOgHEAjWQ4WBws8i6dNl6EQQ5S1kF0QwqkJOx870uCFUPUg9iDdhLBh83vk6U4orR0/qwNTJyDTD7ke+12WU4chSgFJJC0XzI2kFe6Cizz4xLXgpqDYB40+eGwQ9ozCiXEoj1sc8JZ7gQZcdxgeGuwc/9lBNSeG6w93Hg9UYd8IPDDMPPtG5gbV2sfmzfevPJTmAJeNL7Kwbf9+uPVWKBQWH+M4sHcv9PcvszIREREREREREREREZHNKzU5ycV3fsQeNICvAD+RLAyAh4GrsH/wAwum7cP+oWkai6SlsZ8lyZ628jB5/ewPvP1Ksh3g4js/wuOvfh2t3t612yERERERERERERERERERERERkWewd7zjHYsG1QYHB/niF7/I5Zdfvux6rrzySu6++25++qd/mrvvvnve8iAIuOWWW/ja1742ryEkIueGE8+uecmaOHr0KO9+97t5z3veM3MznR00m225yNqll17KTTfdxBvf+Ea2b9++ou3ffffd/Mmf/Amf/OQnieN4Tlht9nbiOGbPnj18//vfP4O9PDMLVUpzF0H+9VAGq60AcRjhpuwDtHuzFoIJY5isw/GyxVGcTnOMlA87Fhh7ogxBC2LPAawalHFhsHuZsa4LUYgbgrfIPEamoRlBu5601Ngzm3NygCIL4gx3LTPW9azsEkaL1lKcCGKXmQDUUmPj5D83GRtF0Zyo0LzxMThest4oshcvtu722CRet+Q8kpDQWs95s+6fG0SLziHCojteql25Ckg1I7xF7uJxJoWTydmDRp1cqUn69HJWotmTpzYrBJdrBKSnqguP9aBWTM8E6VYzlkaNXLlFOlzBPBp1cuXmysZu0JybLtS6OmObjQpeM1zynDR91+7JzSZetERUDSCVIiSauR+lPRiYdf+KIpiqwckKvGAH/MhuC2G5SYwsPO1iKvhw+SB0p+xxK4anpmBk3K6vZnLf6UpbKC0LcwJx2QiCFExUwfHtsh3qtshXK4BqA9IO9Bfsz9CBFjBatvhaT3I5tsNrxVQS9YpnhdIiC261IpiuWyitN2fbaO9XEMJT0/Z7Bn15IIapuq2zNwc538JmYQCVlgXaRqZgeFsSFYuhXIdiEqzMpy2K1n6rNlqQ8Zl538bJdnFs28enLNY21GX351zKXtMIoZixuZbqUGnAyaq9by/shnzK/mvPq5Ccx1odphowUrPtbS9Ab9oCcVEElTpkMnb+aw2Lij2dxL3789CVAi+GTNoCZOnIttlMjiNY9MxxOqE0IsgHnfM7HYM7q7qY9jqhtDi2sV4S36sAkW/HO5+yYxDEUG9BuQqtCfs+G2fsWmmF0N0LPVk7NjF27uo1O3dtUWzLU2ES7UvOddGBlmf71ApgbNLGe56F+fIOlFowUQM/gEILSmmL+T162OJtmQB2VKBnEIrD0EzbuEYNjh6Fp1KdeQyVLZbW9tyj8MlROPoIFHvnRtIyp0XS2lGzj18Oh2Z9GPvjvXNjaXtH4JXf7Ty+6zkLh9KuPgEv/97c55oe/P0+ODor5LpzGl7/IIveL1dk/3647bbFlyuUJiIiIiIiIiLr7cjURs9ARLaI7x96cqOnsPkNFzd6BiIr8mh5cqOnsKRn3/437PnQ+zpPuMBbgB2zBvUAz2WRT2VbRBMLqs3+689x4A7mfBrWY697I4/ceMvqJr1Kuy7fua7rl2eGvYNDyw8SERERERERERERERERERGRTWehBkyxWKRUKm3QjNbPkSNH2LNnD61Wa94yz/P4whe+wItf/OJVrfPkyZNcffXVHDt2bMHlBw8e5NWvfvUZzVdEzo6//BBZiSiK+MQnPsG73vUu/vVf/5UoipYNpp3+fHuZ7/u87GUv421vexs/9VM/teq5vOAFL+Cf//mfeeCBB7jlllu4++6754TV2qE1gB/84Ad89rOf5SUvecmqt7NW+ovwtn8Hx8rwnVMRMXDFAAx0JbGp08QxjJThOyetxfLcARgqLj72ZAkePhUTJWN3FC3MstjYb43EfHcs5PLt8MNDi8/DiS2W9p2T9vjK/rWb82gJHj7VGTtcZMEoV3vst0bgwdGIZmDRIddfIO5Ws/2rR0Ac4eAsPnZWCI44mtnfZUNwcZIqi1cQr2vXxOIINwZvkXnMxOuSeSw1djVz3lT7F3fmETk2j0X3L4QwbILjEsURQWrxEJwTtohnqkgRlcLC15EtruI0OyG4ShTZp2UvNDYGJ25C06pDS44F4riJm4yN4ohyYYl4XXse7TnnN/ecTx8bxxEscU6IWjiRTxxZzcr34MIui42dfh21AvCjkGzK4aphuHoIdnTNv3/lfbii335+f6QMxypA0JlDlHzRDqWlHTrRNwcu64PL+yx+FacsJrajy+YSNqHatOPXl7MIWuRCf3cnlFZMfjnAScH2vMW4wqYdHCe23mNfHoLIQmaua6G0roxt30neKumUvR9CLJZFbEGtWmxzyvkW3AqSIFl3pvOh7Q72fomdZDsNyHrJe9G1Y7u9aPcPHNteV9a2jQOlmv1ugu/ZugoZILRjEDsWXWs2bG7NwI5pLbRgWSp5je9aVA0neUzn9WDRtmwy1k3Cd1Fk23QdSM/6G1kUWeAtjjvrIwCSsFjKs3XVQzs2cdJvrNUtfGfXIlTr0G4wtuNnteR/45VrdkxxbN8nStCbmhtKS/mdUFrYsGBd6EKtCqEHQ32QSubnOZBLQ9SEZq9dv+k0FLB1RjFUaxZ+8z0Lnc0OqpUq0GxBXxe4EbQacLJhkbQ4BDcPrSxMTNvxiWKol6DyOORcyPdDnINsclGkG/CdkWQ7QG/dtjc1AqVTMJCBYzE0a+ABmSI0fIuv5YIkvDcBhXtg4C7o3g3hEEyNAqPQPAH1SQsBAuwd7QTVAK47DA8Nds7/7KCaE8P1h5njhkfhVH5+KO2GR5knHcKb74fH+mAya/u2Z3yJ+85KHDgABw9C9yI3RhEREREREREREZEt5IlKi9seGV/R2P/87D4uKaSWHygizzipyUkuvvMjc5+MgI8DbwaSD5RiCrgH2AMMsfQ/2kTAKPAYnX/sBKgl6z3tQ9IuvvMjPP7q19Hq7T2znRCRrWHkO3DnW1c29hdvh6Er1nc+IiIiIiIiIiIiIiIiIiIi56G/+Iu/WDCoBvAbv/Ebqw6qAQwMDPA3f/M3/OIv/uKCy2+77TZF1UQ2iKJqZ+nJJ5/kjjvu4L3vfS8nTpwAOtG0hYJpbQtF1i688EJuvPFG3vKWt3DBBRec9dyuvvpqvvrVr/KHf/iHvOMd75gTVpvtM5/5zIZG1bwYuppw3TC86rkWuzk6CU9NQsud01yx8FUML9oBr74CcODYhI1fbOwLLrCxkWtjn560n0NtuXPnkY5gVx5e8MMw1AsjU8nYaP7YVGRBnPY8XGzsWs35hRfAq59rMZb2nFtOJ440+9hdWLQA0oHLYsam7Odzt3dhkzpt3dAJwblx/IyP122G/SPZv+8m+/fcfou1LTQ0Sub84AmL6DXCxQNsJ0pJmCkJwTlJCG5gE4futmKc78RCYz0Y7lp4bNiCrBtw5TDsG7Z1OixwLcVwatrWO9DDzJyd2K6x2aG0q4Yhmzzu7oNLemF02oJkhSxksnNDaUELag1bz/Y8FNJ2CPu7YaRiAbS0Z+tzfOgrWAQrajEnlDZYtDhYm4PFx1wHHBfqDWi04FTFjnUxDRNlC5P1FeaG0sIzDKXlUxYJc7AoWYRFzxzg6ARsy1vsDJYOpTUCKLVgsCsJ8GH7HAcQOraOVnIOTg+l5f1OVMxJLl3HscfurPMaxhYHWyiUhmMhuXYozXPsuSiZixuBE0Kc7pz7vjyM1yCV7F/kQKtqYTNcCFyYnoTePoua5fwk5uZCrQmNGpRSFkqrR3YJV2tLhNJiiH3wUxZKc11oNsFPrh9iqDeTbWWADEyVIZtE3dIhBJ6F3qbLUIghyoLv27U6MW1RuNoR6ClAKmPbyiaBs+YkHBqByVn/OzUTwI6yhc2mTkCmH3I9dlhPHYYoBSwSSuv/DBy8CtwUFPug0QePDcKeUTgxDuVxu+ZvuRdorD6SNlCFfSPwwDDz7BuZG2BrH5/VhNIc4LLlfh9w/3649VYoFBYf4ziwdy/09y+zMhEREREREREREZGtoxnFPF5Z+AefFhorIrKQ3Qc/iF+vzV8wBnwQ+FU6YbUA+B7wOBZW6yH59CHsHyQrWHxtBGietr4a8IFkvafxa1V2f/QfeOTGW852d0RkMwtqMHJo5WNFRERERERERERERERERERkVarVKu95z3sWXFYoFLj11lvPeN2veMUreMELXsDdd989b9l9993H1772NV74whee8fpF5MwoqnYGwjDkrrvu4vbbb+dzn/sccRwvG0xbbJnjOBw4cICbb76Zn/u5n8N1TythrYFbb72ViYkJ/tf/+l8LhtW+8Y1vrPk2V8MHrrkA8lmLtkQO7O6FoRw8fBKmZ30Sb5cHVw5YHCdMDuWlvTC8zFiwmEp77PdHYHxWGMeJoduFZw/Z+JjVjXVY+znHWCCoPfZ7ozAx69Q5MWRC2/YF2+y/1C4LDz0yCpOz1usAqWTsi3bAa5IQ3DM9XrdZ9u+FF3TOydOT8OSUvQ9O/xWOVBLRu+iH4Nf3WcBoOvl9kIWiXKOzQnBX9lvoa7OH7rZinM+Oc2fscJF5JaR21LA7ZSGwU3UIk/MP9nP0s3V58KM/BMWMjTs6adddjN1HAg+yKdg7bFEtJ7JNOg4M5eGSPmiFMFaBegzD3XNDadvzFg0LktBUhEWydvVAEHdiXp4HhVSyO56F0sLYIlhOEhsr1Sw615XtRMQcx2JZY9MWORtIQm3dGSg3LeQWhFAJIXOGoTTPtXU2AiC2x75jkbsghHoAYzUY6krmxOKhtAAbH4T2vBPbvjvtcxcnAblofiht5tPiHVtXHGLf2Bybf5vn2LW8YCgtGdOXh2MlC84BNEJIO+An57fVjrJh+76rB5rJxRMBYRnqGch0QSMJpU2WYGe/HZ/2PDI+9PfYcY19e/HANovlLRVKazrgRZ11OQFUk7hj2N6f5Pi0AounVbwkoudAtQETNfADC5aVGtCI4dHD0Gh2ImllB3oGLfDWzFgA7tST9j2PYueY9tY7X0ch7L4PPjkKRx+BYu/SobT+Klw5CoeGYGoUGIXmCahPWqQNYO9oJ3622kgawA2Pwqk8HO3uPLdz2p5fyIpCaWCxtNtuW3y5QmkiIiIiIiIiIiIiIiJnJTU5ycV3fmTxAceAdwOvBHbMer4JPJX8txLHgY+zYFCt7eI7P8Ljr34drd7eFa5URERERERERERERERERERERERmu+uuuyiXywsue8Mb3kBfX99Zrf/tb3/7glE1gA9+8IOKqolsACc+vbAli/rBD37Au971Lt7//vczOjoKdKJpzmm1neUia/39/bzpTW/ipptu4pJLLlnvqVMqlbjwwgupVCrz5nnRRRdx+PDhdZ8DQFdX17xvNFdfAwfvhFoDggiavkVrChlwPDhZg1ZkoZvBnAVrKk0LuIAFYpYbG0UWFQt9G+smY5uRrSeOYKgArVZnbMuzeaTTcKICTjvi40J/e90N8ILO2LWesxsDbmfOo8nYMLLjFTctMJRN2dgoudQaETw4CtPJJxx7EfTMCsHNHltpLBB3c+eH4Lx4+bHQicxVGgsH6bY584N0qxm71nPeDPuXz9j58JLbRqlhka7yrNKWF1mw7YJtsGObXS9ubGO/cwrKQWesG1uw7cJe2Nlr2zw2aWGuhUJw7bjbhb2nheCWGXvBtpWF7nb22NiVxOt29ticl4vXbbY5X7gtOc7JnFtO5z0GUPDhuf3QlVxHQWTj2qG0lgthMu+uNOwbguys16dCKEUwUQU3bdfMzl4La7UC+y8KYVvOIlxhEvAKYguU5TOddfkuFFNJNCuyUFodu68UMnaPma5D1ofenN3b3CQEFoRwdApKdRjose2XqhZK27kdcr6tP0ruj0Fs52Z4m70+jqFch2IuCZYBJ6eTUJpv99h82q5rYsglca9KA8IQjk9BOmWhtJQHuRQ0WhYfK2ZsrqW6jT9ZtWNwYbeF59KOzbfetONTzFjIK4xgKoBTVdhesIhcBtt2tZ4ELDO2763QInVPl+xY9uehKwXdWdu3ehVSKUhloNq07zEnSlAPO/PIuskxqtq1SA5qcef7QzPsBPAA8oDbtGu74dvvYXiuxedSnh3jegvKVWhNQL4F6UE7J0EIXUXIpCCbHJ8otv2afTFH8awgXnKfaofS4gVCadu67ftYKrRjN12x+1MxBWHOAnRjkxZtq9chMw45F/L9UMlBV8Pud40YHhiBWlIxGypDbta97Nqn4JEQjvVAodeuheNFOw7tAFscQ3kCCvfAT9wFH99tobS2x3th92Tn8d4ReOV37euTefjbaxcONzpxJ77W1vTg7/fNj6S9/kFIn15GTMTAY30wmbUI3J7xec3F1TlwAA4ehO7u5ceKiIiIiIiIiGw1R6Y2egYiskV8/9CTSy5/pNTkNx8YXdG6/vrqQZ7dlV6DWW0yw8Xlx4hsAo+WJzd6Cgt69u1/w54PvW/5gS7wIuDHgNXcSprAV4Cv0vkEpiU89ro38siNt6xiAyu36/Kd67JeeWbZOzi0/CBZ3NP3wbt+YmVjb/w8XPi89Z2PiIiIiIiIiIiIiIiIiIg8YyzUgCkWi5RKpQ2a0fp4xStewV133bXgsm984xs8//nPP6v1NxoNhoaGmJqa//PQAwMDjIyMzOsSicj68jd6Aptdq9Xi4x//OO9617v44he/CCwfTFtq2Ytf/GJuvvlmXvWqV5FKpdZx5nN1dXVx4MABPv7xj8+70U5OTp6zeSzEdaFYgGLeAjeBC5k0kESBchk4WYH+AviOhU66c9Bo2JiVjvVj8DMQuzY2m4YTZQue7ChapMaJLQBTL0N3HrJZG9uTX3hsbw5a9fWbsxeDn+2M3ZWB4yULMm3LwGARPCyKVClD3bH427Yc/OQeGK1YLCftwPa8xXHqdXDCTrxuuAd29C0ed4uTH+BdydgosrBM6NvYC/qWjtelk3hdVxZecNnK43VrOedNs3+RRbhcBy7ogQu2W+SpHdGjacGnKIZWDG5kYZ6eNFy9A749moSogF4HnnNBJwTnxrC7F4ZyC8fdut25IbhLe2F4hWMdOuueF6Tz5sfr2uteaixYKGk189joOcfYz7q3x35vFCaSbwldadg7bHGsdsgqG8MPbYPL+i2U5vgWWXSw93UU2vs6iuze1VeAoSwEDoyWLaSYT76FpNLgZS0GFgd2H3VjGxvH0JeHiZrNL+cngTXH5uI6FikbmYYQ8Dx7Pggt9JXx7TWuA0EAlZZdz9WGhb8coCtnsaswhEpoUbRGkIQCHdheTAKRjo3rytp5wbHt1AMYq1kozXVsnRnXQmle8p5wgVqyzlpo74tUsn0/GUvyWh8IZ10DjcBCZ2DfbwD85P3qOhbAyyQRsyh5XwWzvp2nQnufgm0/5ULWs1BaxrftRLGtz0/eq3HajifY8R+v2evA5upGSVANaDah6dh9AaAvZ+ust+z4TJehN2PRvSCAaguG+iCV7K/nWAAuakKzF1JNaMaQy1oYzYmBAKqRneuo1QmnAZQq0GzND6WNLBFKmyzBjjR4IVQ9iD1INyFswGQNHnm6E0prx8/qwNQJyPRDrgcC4NRhiFJ20jKBBdVmR9IKd8FFHnziWnBTUOyDRh88Ngh7RuHEOJTHbZ9uuRdowHWH4aHBzv7NDqo5MVx/uPN4oAr7RuCBYebZNzI3qAZ2T3/z/auLpDnAZeNLDNi/H269FQqFJQZhb569e6G/f+lxIiIiIiIiIiIiIiIiclZSk5NcfOdHVjY4Av5/wD3APuBqYJiF/wEpBk4ADwAPAo2Vz+niOz/C469+Ha3e3pW/SERERERERERERERERERERERECIKAz3/+8wsuu/DCC886qAaQyWS44YYb+PCHPzxv2cmTJ/nWt77FNddcc9bbEZGVU1RtEY888gi33347H/jABxgbGwM6YbTTo2TLRda6u7t5/etfz80338wVV1yx3lNf1NVXX83HP/7xmbm153p6NXQjtEM1fb32uNKyaA5Y9OZZ2ywwA/ZzpikPenpWP7besLALWMCmv2CD2jGbGOjJwmAXNFrLj0050LXOc64mgRyw4M1AAU6VYaCYBIJiKKah0AuNGLwUMz+gm/Zt7HBXJwTXl7Vgm+J1m2v/mnVouRZHwrH3RC4NJ0pJ8G27zSWOodawSJaDBbJ6U3DhNgvuEcPArBAcgYWegtTWCd1txThfFEEqAtzOnEdr9vodXbY8aNn7OY6hPw/ZDEQe9HdbKK0RwnB3J1ZGziJamSROFmHv+cFiJw5G8nbPp6y55PsWRqtU4eS0hag8z+Z7omSBr4xvUbYUnVBad8YiV+319SbXbBBBuWERsaBl1/1yobRSEnDzk/0oZICwEzrLp6HZsOhY8yxCaXm/ExVzYrv2HacTYmsLY5trtFAoLXnPpzzbRy/ZXuzYtdaOn8VJKK29/YVCaal2KC2GVpjEELHzuasHmu0wG1CbhELevm6EFiLr64ZC2vY9So5bowllH2If/LSFx3zfQmx+2vaXGOpNu95zGSAD1QpUahZKy8R2fGrA8ZOQr4Gfs1BaDEyV7BitNpTWqkDPIBSHgbRFQBs1GDsKtVnN2N565+sohN33wSdH4egjUOxdPpI2AFw5CoeGYGoUGIXmCahPdr5H7x3tBNBWG0q74VE4lYej3Z3ndk7b8wtZNpLWtn8/3Hbb4ssVSRMREREREREREREREdmUdh/8IH69troXNbCw2j3YP8QOAz3YP3QGwBQWVGud2Zz8WpXdH/0HHrnxljNbgYiIiIiIiIiIiIiIiIiIiIjIM9Q3v/lNpqenF1z2kpe8ZM2285KXvGTBqBrAZz/7WUXVRM4xRdVmaTQafPSjH+X222/nq1/9KrB8MG2pZc973vO4+eabee1rX0s+n1/Pqa/I9u3bN3oKC4pjcD3IJVEgsOhOuWVhM9ftBHOCCDIpCwi1m0KrGZvLWBioHljIJuvba4LQAkC+25lHzrWxQWzLU0l8p9KEjGevPRdzzqchDGxsI7Q4U3/Bjlub50I2B0WgFHRiPhkPLuqdGxPyFK/btPsXA9VkHu1Y1FDRrgXXtXPuONCVhm0FaCXXDI69dme3jW3H2rpzENSTKNUWC91txTifF4Of7YzdlbH3nu8mkbQU9BUsxBW2r63Yzu1A0YJrM0E1LIbWk8TK6g0LXxHbtei6MFmx66E334mIxVi4qv2WL6btz7RnQcVCuhNKa9/j2qG0fApqLdt+2rewVzOwx0cnYFve9gWWDqU1Aii17PqP4+T68yAOIHRsHS0sdHY2oTSS9yuOrSsOsb/VODb/Ni85H8TzQ2lhpjOuL9+5n4Pd6/PJNpzYgmJupnM8Z4fSGoEF+7xkWpUqRL5de/mUnbOUb/eUchVaNbvXxxmoVS18CJ3j6yT7GAZ2zTWT70mN5N7gBFANbZ1hBE7YOY6twIJqAJMTFv8LQ5iqJcczhLBsobTvPmXhNjiDUFoMUyNQOgUDGTgWQ7NmxyBThIZv68wFdh2UJ6BwDwzcBd27IVxhJA3gusPw0GBnH3dPdpY5MVx/mDlWE0pLh/Dm++GxPpjM2r7tGe+8D8/IgQNw8CB0dy8/VkRERERERERERERERDaPKOJZ//Lxs1tHC3gq+W8NPeufP8Yjb37b3E/gEhERERERERERERERERERERGRJd1zzz2LLnvBC16wZttZal1LzUFE1oeiaomPfvSj3HzzzUxOTgKdMNpKQ2rt5fl8nl/+5V/m5ptvZv/+/es76VVKpVLLD9oArSQU1u7hAMSRPTdShh3dFqKJscc7e5KgmLP6sWFs40+UOhEqJ4bjJYukXbzNoi0O9mfGh+MTFmFrB6vGq7Z8V++5mXMU29gTJfu6Nw3pDBydsmjaUAFyWYsBRVjAp9yw4+o7kErDRNUiQOcqBLdV43UbvX/xafNohNCdtdcFoa035UA+Z+PT2Nh2RK83a/NtBLautAs9vbZsK4butuKcq01oJt8q8im7HsqNZF4eFJLrKPY7obQgtDhXTw4qdQtlpTx7vZNsM5exgNypaegu2Ps5CqHchFza7g053+4d9aaF0rYXkxgftp7tBWhGyeN4bijNc22djaBz3HzHAl1BaNf6WA2GumxflgqlBdj49jXrxLbvDp33lesC0dmF0oI4uVfG4EXM3JCj5JxkPaiHdh+PHRtbq0M22UYcQ7Vu98j2+Up5FpYDKNfsmJLcWydK0JtaPJQWNizcF7oWSgs9GOqz664disulIWpCs9eObToNheSajWKo1izi53tJ7CzuzPXUpMUFPQdSIUwFMFKBdATFFIQ5O65jkxbDq9chMw5jY1DcBsUhC5iCxdrGfwANr3NMzyaU1v8ZOHgVuCko9kGjDx4bhD2jcGIcyuMQteCWe4HG6iNpA1XYNwIPDDPPvpG5ATZYfSjNAS4bX2Rh2/79cOutUCgsPsZxYO9e6O9fZmUiIiIiIiIiIiIiIiKyGRWfOkyqXNroaSwoVS5RfOow5WddstFTERERERERERERERERERERERHZMr71rW8tuuyaa65Zs+08+9nPplgsUi6X5y27//7712w7IrIyiqolvvOd7zAxMTHzeKUxtfayK664gptuuonXv/719LTrOrIinmfdmHIDCskVWUmiUMNdFiBqx6yGu5KQy1mMBdiWs3iQl5zKYtqCQrUmkLYYVSuyx/mUjTt9LGc5j9XOuTdncbTunAVgCmmoNSzE04os8IMDRBY0Gi3BRT3QTOJfE5VzF4LbivG6zbB/DnMjesWMBdNibN1dGdjVPStE5dj1OVmzlk8Y2TrCyAJUeb8zdiuG7rbinPNpC1Y1W+D4EIY2Nozt/OHY6x0H0ikYnYKpBhSytp4ggJMVGO629ziuXXutyP4MQrv/OEBXEtdzsO1UkvheOtn3tGf3hWqrE0HDsXtfeFoozU3Wk3HtWHuOPecCtQAiB2phcq9ZIpQWR517ZSOJqsXtiBrgRzbGdey6P9tQWpS8F/0IaEKctpcA9OVhvGbzBduHVtXCZrgQuDA9Cb19FjXLJdeX69q9v1GDUsruvfVk3tXaEqG02GJ5fspCaa4LzSb46eS6S4J3nmvXNhmYKlsoDSwEFngWepsuQyGGKAu+b+f11ISF2HakwQuh6kHsQbppQbfJGjzytIXjwOJnO5L/vTU1AtnDkO2H6R6YPgluc+1Caf1VuHIUDg3B1CgwCs0TUJ/sxA73jnbiZ6uNpAHc8CicysPR7s5zO6ft+YWsKJQGFku77bbFlyuUJiIiIiIiIiIiIiIi8ozRKnYROw7OaT8ntBnEjkOzq3v5gSIiIiIiIiIiIiIiIiIiIiIiMuPhhx9edNlznvOcNduO67pcdtllC0bcnnjiCarVKvl8fs22JyJLU1TtNO1g2lIhtfbydDrNL/7iL3LzzTdz3XXXnbM5roXT92cjebOmMp3UVzzfgijd2SR0Flvcp5CFauPsxgIUUxbnaetOJ8EhbB1xyqJay409V3NuR9SisBPJKqYh61qgp9qComsxrVrTYko9mfljFa/b/PtXDiwWtS2XRMBiO49dGfuv0oBCyq6LKLLgVb1lYxste22pAX2uhaja+7EVQ3dbcc7tKF6pmUTHkm8l9QBqrSTc5Vh4rdKCrqyd02K6E0qrtiwgVmlYpM3DnvNc2F4EN9me49jrneRxEMKJKRjqgRS2nZQHfsuuBc8B14NWEqqbE0pLAml+ElUjOa5++zy0Q2kBpFL29UKhtJZjETuw4+BgobS2VAhxUj2LHAuenU0orRFCKkqu8xhaod0Hwd5fu3qg2d4eEJahnoFMFzSSeU+WYGe/HV+S45Txob8HmoGF0ohgYJsdv6VCaU3HYnDtdTkBVENI+Un0MOwcy1Zg8bSKB30FyDh2/5+ogR9YsKzUgEYMjx6GRtPiZ60K9AxCcRhIgxdbAG7sKNRSnWPdW2eOXcehdSd8IADHW/tQ2nWH4aHBzv7tnuxs24nh+sNz57PaSFo6hDffD4/1wWTW9m/P+Mxb78wcOAAHD0K3fvlEREREREREREREREREoLG9n1P7f4SBe7++0VOZ5+T+H6XZt32jpyEiIiIiIiIiIiIiIiIiIiIisqU8/vjjCz4/NDREV1fXmm5rz549C0bVAJ588kmuuOKKNd2eiCxOUbUFzA6qzY6PtZ+/5JJLeOtb38qb3vQmBgYGzvn8ztbpwbjNZCyJswx2J0Efx8I4zQDSvgV/nLMcC/Z8MZ3Eg5Kvm6GFkmIs2tSbtXFLjT2Xc/Zi2JZPAk5AzoM4iRfFWIStHVFbaiwoXrfZ9y+OoTeXxKFiezxQsGhWnKzbj+2ajFh87PkQutuqc+5PzoGDRdF6cxbRqoQWHQtaELs2z9687YODhbq2F2wbTnKuo9ied4BCBgg7obN8GpoNi441k3mO1WCoaPNwsHXXI3vgJfM5PZSWT65/z0kibck2PadzXyF5nRvbnNpmQmmOrTPl2bXuzQrehRG4SfwsTkJp7e2fTSitEYAX2D5FQKUKkW9Rs3zK5pLy7X1Yrlr0zatAXEgClCnoykPYgshN9jWGVhLFy3nQSA7kSkJpzZaF0rZ12/6nQpgKYKQC6cjuA2HOgnRjk3bO6iWoPA45F/L9EOcgm1xH6QZ8Z8SCapCE0mKYGoHSKRjIwLEYmjU7BpkiNHyLr+WC5PqcgMI9ULgLaMBzL1+fUNpAFfaNwAPDzLNvpLPOtjOJpDnAZeNLDNi/H269FQqFJQZhF/fevdDfv/Q4EREREREREREREdmSulMuLx1e5v8rnjX2vHSivL7rHy6u7/pFNtD97/gTfvgdv8fgvXdv9FRmjF77Ar71jj/e6GmIyHrJb4drXr/ysSIiIiIiIiIiIiIiIiIiIrIipVKJsbGxBZddeOGFa769pdb5xBNPKKomcg4pqraA2SE1sAiZ53n87M/+LG9729s4cODABs3s7Pzsz/4sX/jCFzZ6GosKQqiFna8zKYvqtEM2nmsxH8+FRuvMx/oepBwLmLVDab5jQZ4WFmeaqluMKeUuPfZcztmNIetDNYl0ZVwIYvsvBkYrsD1vc15qLChetxX2L+PbuW8Gdp3k0lCu2ZhGBCcmYGAFY7d66G4rztnBgl7lJHSX9m0OQRLKOjph0UM/ee93ZcGJLIwGUMxYdC1yLSg2VoPBYuc68j2IAwiTe1OLTugscqAeQBDZdeyQXMMhS4bSSKJlOLauOMT+huDY/Nu8JJxGPD+UFmY64/rytq22ShPyyTacGGp1cJPxZxtKCxtJWNCFWhVCD4b67N7dDsXl0hA1odkLqSY0Y8hlbT1ODARQjSyyFrXsuXZIrFSxWNpqQmmTJdiRBi+EahK1TDdtrpM1eORpOwZgAbQdZYuaTZ2ATD/keiAATh2GKGXnYqFQWv9n4OBV4Kag2AeNPnhsEPaMwolxKI/b/txyL0kdbn1DaTc8CqfycLS789zOaXt+IctG0tr274fbblt8uSJpIiIiIiIiIiIiIjLLjqzP2y/bttHTEJEtKigUuffP/pr05ARdT/yg82l2G8FxmL5kD63e3o2bg4isv20Xw8//9UbPQkRERERERERERERERERE5Lxz4sSJRZcNDy/wS/RnaceOHWc0FxFZe4qqLSJOfijyggsu4M1vfjM33ngjO3fu3OBZnZ3h4eF1uamfLQcgtpBOO/LSCCGX6gRxSP50sIDP2Yz1XYvzeE4S06HzuBV1olf10KI8S449h3P2XQt+gc3FdyGOoBVbQKke2OtSqcXHBrHidVth/3wviVcl80j7nXmEkUW2msEKx27x0N1WnLPn2nXUHusl70cnOb/1wM7hUJe9xnPsPNZDe9+nfQthRbHtRzuSlnLt/ewlsTQnOQCuC0SzgmfYurLJODe5jqPItuMsEEoL4uR3AWLwIqzw5ts1mvJsXfXQAn6xY2NrdcgmobQ4hmodUml73I6f1ZLYXbkGuSTSFgETJehNrVEoLYbYBz8FhWR/m03w00nYLYZ607aVywAZqFagUrNQWiZOQm/A8ZOQr4Gfs1BaDEyVLDy32lBaqwI9g1AcBtLgxdCowdhRqKU6x7+33vk6CmH3ffDJUTj6CBR7lw6l9VfhylE4NARTo8AoNE9AfdIibQB7R+fGz9YzlJYO4c33w2N9MJm1fdsz3nmfnJEDB+DgQejuXn6siIiIiIiIiIiIiIiIyBpp9m5j7If3b/Q0RERERERERERERERERERERETOqWq1ygte8II1X+/dd9+95utcztjY2KLLtm/fvubb6+vrO6O5iMjaU1TtNHEc4zgOL3nJS3jb297Gz//8z+N53kZP67znxhDMehzGnYiZy/w/z2as264rOXO/bouiuX8uNvZcz9l1bdvOrMdOEo5qB6SWHat43ZbYv4yfxN2S59pBLseBVhJgcxwLdi01dqFY21YL3W3FOae8ToStOes68h2oBhA5Nr51WijNDSB0bDuuC25k7+n2dZFy6UTUAD+yWKKb7OPsUFoUWVQvjrF7AdiNxbNrbaFQWhTb+vwIaEKctrYaQF8exmvJHLB9aFUtbIYLgQvTk9Dbl8TL/GQfXKg1LSZWSlkorZ7Mu1pbu1Ba07EYXPv8OAFUQwu1hZHd/9rvsVZgQTWAyQkYzEEYwlTN1uuEEJYtlPbdp6DRtLGrDqXFMDUCpVMwkIFjMTRr4AGZIjR8W2cusPNUnoDCPTBwF3TvhnCFobTrDsNDg5392z3ZmYcTw/WHmWc9Q2kOcNn4Igvb9u+HW2+FQmHxMY4De/dCf/8yKxMREREREREREREREREREREREREREREREREREREREZG1EEURX//61zd6GmtiYmJi0WXd3d2LLjtTS61zfHy5X8KX81Ecx5TLZWq1Go1GgziOZ5bt2rVrA2d2/lNUbZbt27fzhje8gZtvvplLL710o6fzjDI7ZAUWCZp5GFsshyQORLzGY5Ov42Qi7qygFk7y/GljN3LOfpTEc5yZqcwE4DynE9Y5fSwoXrdV9s9LFrdXNyfullSuYmcFY8+D0N1WnHMu7kTU2qe6/downjU2gFTK3uOum4xJ7gVu8l5u34/a11E463pKhRC3rwcszNYOpbXvX1EyF7cdFktbEA3mh9IaIaSi5LqPLeDX3l7ag1090Ey2F2HhsXoGMl3QSEJpkyXY2T8rPudYtK2/xyKAsW8vHtgGrrd2obRmC6bLsK3btpkKYSqAkQqkIyimIMzZcR6btLnU65AZh7ExKG6D4pDNCSAMYPwH0JjVVD2bUFr/Z+DgVeCmoNgHjT54bBD2jMKJcSiPQ9SCW+4FGqsLpQ1UYd8IPDDMPPtGOvG12RRKExERERERERERERERERERERERERERERERERERERERkfNZtbrAL9snCkv9XvwZWmqdS81Fzg8PP/wwX/rSl/jWt77FoUOHOHr0KCMjI0RzwjPGcRyCIFhgLbJWFFVL/Pqv/zq/93u/Rzqd3uipPCNFDmRmxWsyPoTtZZEFYKLIgjghZzc2SkJDUWTRsfbr2uvIep2xYM+fPjaKz/2c22NTIQQuENtYh054KeNbhCdeYGw7rqR43dbZPycJvTmzQnlhPGus2wm/nT7WWSDWthVDd1txzs6sbTsxM9eRE3eui9n7F7WvMzr3ApKoWvt+1I64RY4FxdwkfhY354fSjpU6969GCGnH7l8OFkprh2tPD6U1AvACC4RFQKUKkW9Rs3wKUp4FzeotKFeh5YBXgbgAtSakU9CVh7AFkZvsawytpgXfch40kuOyXqG0yRLsSIMXQtWD2IN0E8IGTNbgkaehVrf1ZwLYUbavp0Ygexiy/TDdA9MnwW2uXSitvwpXjsKhIZgaBUaheQLqk5BMh72jnQDaakNpNzwKp/JwdFa4eue0Pb+YFYfSbrtt6TEKpYmIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMgm02w2F13m+2ufXEqlUmc0F9m6HnroId7znvdw8OBBjh8/PvN83A57rJFDhw5x7733Lrhs7969XHvttWu6vfOBomqJiy++eKOn8IwWOxbsyXqAAykXgtgCNnEIXvJn7NvzZzM2xuI9cdgJI0WhjQUb35OxsWDPR7PGxmHyevcczzm2UJCLBZLCqDPnrAdZvzPnEPBPGxtFitdtlf2bHdHzZ8XdotOiXGl/ps02b2wcnaehuy0w55m/28R2TuIISN7H6Vnfdb0knBZF9ho36twL2tdA+17Qvo5iB2oNyCb758TQrEGctcdpDy7o6twLmqF9o3exUFqjCU0WD6WFDejNQehCrQqhB0N9kHI6xzCXhqgJzV5INaEZQy5r63FiIIBqZJG1qGXPtUNppYrF0tYzlNaqQM8gFIeBtB3TRg3GjkJt1v8G6a0zx67j0LoTPhCA4619KO26w/DQYOdY7J7sbNuJ4frDc+ezmlBaOoQ33w+P9cFk1vZtz/ic/uDqHTgABw9Cd/fyY0VEREREREREREREREREREREREREREREREREREREREQ2kVarteiycx1VW2ousvV84xvf4L/+1//Kv/3bvwELR9QcZ+Hiw5kE1/L5PDfddBNRFM1btm/fPu6///5Vr/N8p6iabLjZb/W+/NxlQYwVgLA/g9TajA2BsAp+ErOq1yFOQ/t+dEEX1IJkfjHUm9BeRViFKHPu59xKQl9ggaRGi5liTsqDocJpYwNb7AJB046z4nVbY//aza44tOhUFIGTzK8da2tH9MJZ6zt97PkQutuKc25fR1Fk5yQOgWQd7XNdD21snETYWq3OnOPW3HvBcJE5daxKFXIpLNQWw9QUFNIWSgPoy9l66y0IQpguQ2/GQmlBANXWEqG02K55PwUFbB+bTfDb98fkfui5kMsAGahWoFKzUFomtkBYDTh+EvI18HMWSouBqZIFItc1lBbD1AiUTsFABo4l4TkPyBSh4ds6c4Edv/IEFO6Bwl1AA557+fqE0gaqsG8EHhhmnn0jnXW2rTaU5gCXjS+ysG3/frj1VigUFh/jOLB3L/T3L7MyEREREREREREREREREREREREREREREREREREREREROZ+4rsvzn//8jZ7GmlgsagVnFrZazkLBq5XMRbaOqakpfvu3f5v3vve9QOc6Wum1dqbXwaWXXsprXvMaPvzhD89b9uCDD/Ltb3+bq6666ozWfb5SVE023OzvCb1ZC+/UksBmqQbF2GIxcWzhHsc7+7G1JlCFYpcFrMoNC+DkspBPQToJXNVaUG9A3IRsNomZVaHq2NhzPedMCKmUzXmqArmC9W/yKXAdKCeBtnrDAkL5JLLUmAJ6OsdZ8brNvX9hEgKjaddcHFoQCyzKlfNhW64z39PHRq5FxM6H0N1WnXMUd+YcN+26aM+5Lw/jtc6cI8eiX+33awBUa3bPAejK2GtrLZtDtWFhtELRomlNB1ol6Ou2uJqfhPkKGWg0oewnobS0xcR8f+lQWtMBL+pE2pwAqiGkfDuWTtiJiLUCu28BTE7AYA7CEKZqtl4nhLBsobTvPmXzgXMXSuv/DBy8CtwUFPug0QePDcKeUTgxDuVxiFpwy71Aw7a3nqG0Gx6FU3k42t15bue0Pb+QFYXSwGJpt922+HKF0kRERERERERERERERERERERERERERERERERERERERGQZ+Xyeu+++e6OnsSbS6fSiy4IgWPPtLbXOpeYiW8MDDzzAK17xCo4cObJgTG09Qn2z/dZv/RYf/vCHF9zm3//93/Pnf/7n67r9rUZRtVmOHDmy4PP9/f3k8/kFl50r1WqVU6dOLbhs165d53g2aysMLRyTz4CfvG9d18JBE5OQ7YZ0DIED4xPWhDnbsZUyBAEUIgg8qDUgFVmUqB06aofVpqeg1YQgZ2OmW9AoQT577ufs+7AtZWNrNUhnoKdoYx0gl7JAXKVsEads1iJAjQqEuY0JwW21eN1m2b96C1LtebSg2a6EARf1QDPsPG40bSwATWjMGns+hO624pwbAaSSsU4yp7a0B7tmncMIaE1AbpvF3xoRlEuQSltQzXPtmnFdmCpDHMFUCzIZKCfXi5/EKf12CA0IYwiDTigtl6zbYelQWrMF02XY1g2eA6kQpgIYqUA6gmLK7ieuC2OT0AzsnGTGYWwMitugOARucr7CAMZ/AA2vcwzOVSitvwpXjsKhIZgaBUaheQLqk1BP5rJ3dG78bD1DaekQ3nw/PNYHk1k7DnvG7ZycsQMH4OBB6O5efqyIiIiIiIiIiIiIiIiIrJrjtMikx0j5JRwnJI49WkEXjeZ24ji1/ApEREREREREREREREREREREROScWypk1mw2F112ppZap6JqW9unPvUpXv3qV1Ov14njeCZsdnpIbXbwrG2tYmvXXnstz3ve87jvvvtmtuM4DnEc8w//8A/82Z/92YLbf6ZSVG2Wiy++eMGL413vehdvetObNmBGHR/+8Id561vfOu95x3HWpX55LqV8C/e4Tics4zr2XH83hC64voV5+nvWZuxAj4WAAsde098LngdB00JlVrKCoAV9XRZ+C11wI8j2QPcGzbkVgJO2+fT3WrTIpTM25VqQKJ2E4oIchCWoe9DcoBDcVovXbZb9m5qGbVm75oIYShUo5CGbsrGeawG2MIKJKvSn7VqII5iuWLwNzo/Q3Vac83QVtiXv3QiYKEEmb+ctn4KUZ/e+egvKVWgB9Qr4PVCrQtq39/LsSJoLZH0Y2Gb3r2YT4hQMFOz+FcVQrUEmbQG6RhObVDLfU5OQTa88lDZZgh1p8EKoehB7kG5C2IDJGjzyNNSSMlkmgB1l+3pqBLKHIdsP0z0wfRLc5saF0q47DA8NdqJxuyeZ4cRw/WHmWc9QmgNcNr7Iwrb9++HWW6FQWHyM48DevXaDEhEREREREREREREREZE15ToNuouP0l18hEx6jJl/fJ3DodHcznT52UyXLyOKMwuMERERERERERERERERERERERGRjVAsFhddVi6X13x7pVJp0WVdXV1rvj05N/71X/+VX/zFX6TZbOI4zkzIrO30VtVaRdQW8vrXv5777rtvZjvtbY+OjvLNb36Ta6+9dt22vdUoqnaalRQAN8p6vmk2kuNAHEKjDtnk50vrDYtDeUmwKHBtnOet3dicB/UACC1uBOC1oAZkUtBo2ePAs/GtJMKWS9a1EXP2PGjEnbEAzboFpOJkbCqGvh4Lr9Uj8HzI5aFH8bottX+FDHgZGxsGkHUgl+5E9NKeXQcjE9CsQZCxENx0HcrNJDh2noTutuKcy2XwU9Cbs7G1GoQODPVBKjnfXnJOoyY0e+1PfIvnea4Fv+oNuxc4QKtu11P7/lUOoNHo3CvSoR2fah2my1CIIcqC70MQwqkJu4+tJpTWqkDPIBSHgTR4MTRqMHYUarM+cL23zhy7jkPrTvhAYOG5jQylDVRh3wg8MMw8+0Y665xNoTQRERERERERERERERGRZ6qQvp4H2dbzAK7TWmZsTCZ9ioG+U2zfdg8TU1czPrUP+wgtERERERERERERERERERERERHZSNu3b1902fT09Jpvb6l1LjUX2bweeeQRfumXfmkmqAYsGFRrP3fBBRdw3XXX8axnPYvt27dz6NAhPvCBD8wLsZ2pX/7lX+btb3/7nKBa22c/+1lF1WZRVO00sy+YzRgx2+zzO1NuDAQwXrPH+ZTFpWIHoghOnILeLgsWreXYySTyuT0JZ3kRVMswUoeeLHSlLIIVxnPHrvU8VjP25JSNTfkWD/ICmGxaaMkH/BhaSXBrqmxjc8k7XfG6rbV/cQxxCpzQol31BuTTgGPLmi2LdkVpu06ttgYD+fMrdLeV5xz7dv/o7wXXg2YT/LRdW8RQb1pALZcBMjBd6VwbfmSvLQcWRSs6FiiLHbuOysk9YmwK+gqQcZL4Ww38wIJlpYaFGB89DI3mGYTSYpgagdIpGMjAsdgifh6QKULDt3XmArsmyxNQuAcKdwENeO7lmyOUdsOjcCoPR7s7z+2ctucXs+JQ2m23LT1GoTQRERERERERERERERGRLSHlT7Jj4HNk0qdW/VrXabG9916K+Sc4fvInaQW9az9BERERERERERERERERERERERFZsaVCZqOjo2u+vaXW2dfXt+bbk/UVBAGvec1rmJ6enhdPm/24q6uLm266ibe+9a3s2bNnzjre/e5384EPfGDN5jQwMMCP/MiPcPfddy8YVfu93/u9NdvWVqeo2gIWqvFtJpt9fqsVRdCaglYNppKgT9wCPwetLEyVLChUb0JvEdKNtR0bxVCuwIV5qNchiCHMQGsCph1wsvB0LQmeOXBibH3msZqxoy3Y1mVj61UoexD4MJSBVBJkGi9ZXGqytPEhuK0Yr9sM+9doWkitHXpzAqhHnTibEwNJ+C2KoBJ14mznW+huq865GnbGts9hNdmHMLJgXjsi1gqgVLF19HVZFK7VgJMNi6TFIbh5uxdMTNs5j2Kol6DyOORcyPdDnINsYOtMN+A7I3YtwdmF0vo/AwevAjcFxT5o9MFjg7BnFE6MQ3ncQm633As0bHubJZSWDuHN98NjfTCZteOwZ7wTwzsjBw7AwYPQ3b38WBERERERERERERERERHZ1DLpUS4c+hSe25jzvOu2yKTG8P0KnlfDISTGIwxzBEGBRms7UZSatZ5TXLTjLp4euYFGc/Bc74aIiIiIiIiIiIiIiIiIiIiIiCR27NiB67pEUTRv2YkTJ9Z8eyMjI4su27lz55pvT9bXX/zFX3Do0KElg2o33ngjf/qnf0pPT885m9dLX/pS7r777pnHjuMQxzF33303jUaDTCZzzuaymSmqJhuuUoHKKGTykHLtOSeE6kl4+BRMh52xXS5c2b/2YzOBxYD6n2XjuxrgxRYsO/UwHElZaGi957Gasb0+7B0Gz4dsDHWgXoNSA6aqUClCNgtusDlCcFsxXrcZ9g9ge68FtPwalFrQdKG7AD0+tDyLtY1PWwBrpTG6rRi624pzHp+2x9u6bc6pEKYCGKlAOoJiCsKcrWtsEpqBnffaEegpWCTRT1kkLY6hOQmHRmCy1bkXZALYUbZ7wNQJyPRDrgcC4NRhiFLAGoTS+qtw5SgcGoKpUWAUmiegPmnbBtg7Ojd+tplCaQ5w2fgiC9v274dbb4VCYfExjgN790J//zIrExEREREREREREREREZGtIOVPzguqOU5AIXeUdHoch/i0VwR4boN0apJc7hjNZh+V2k7i2H6wxHMbXDj0KZ46/nJaQe+52xEREREREREREREREREREREREZmRTqe58MILeeqpp+YtO3LkyJpv7/Dhw4su271795pvT9bP9PQ0f/InfzInoDb760wmwx133MHrXve6cz63F7/4xTNfz55Xo9Hg0KFD7N+//5zPaTNSVE02nBvC8UehZxCKQ9armThh0Z6cB9PFztj8NByfWPuxvXVoBZ15/FAWvl9PwkEx9IYwcg7msZqxmUk4UeqE4LaVYWS8M+fjxc0XgtuK8brNsH/5LPzQhXYdph1oZKA6Ze+d0IMTTYvrrTZGtxVDd1t1zpMl2JEGL4SqB7EH6SaEDZiswSNPQ63euY52lKHs2L1gqA+aGWjU4NSTkAmB0+5fbVEIu++DT47C0Ueg2Lu2obTrDsNDgxaNA9g92dm2E8P1C/wdX6E0EREREREREREREREREdm8QnYMfG5OUC3llynmH8d1W0u8zjjEZNJjpPxpytXdtAL7x1zPbbBj4HMcOf5ywFunuYuIiIiIiIiIiIiIiIiIiIiIyFIuvfTSBaNqpVKJkZERhoaG1mxbjz322ILPp1Ipdu7cuWbbkfX3zne+k6mpKRzHmRdUc12X97///bzmNa/ZkLlde+21uK47Z15t3/ve9xRVSyiqJhvOjSGO4Oh3oetDQAyla6C4DXKxBYYavv2ZCyCO129seRyiT0Ph0zDxUqg8PxkfnNt5rHRsswXf/wZcOAXP9uD7F3WOa29984XgtmK8bjPsX7UOp74HuQuTAFtoQa7xZOzxwpnH6LZi6G6rzrlVSc71MJC2OTdqMHYUaqnO2JlIWgxTI/BD34RHQjjWA4VeyDmL3L8moHAPDNwF3bshXIdQ2kAV9o3AA8PMs2+ks87ZFEoTERERERERERERERERkc2qr+dBMulTM49TfpmuwqM4TjRnXBilCYICUZzCdVr4fgXPbc4sd90WXYVHKVUumwmrZdKn6Ot5kPGpa87NzoiIiIiIiIiIiIiIiIiIiIiIyBxXXXUVX/ziFxdcdujQoTWLqtVqtUWjas997nPxfSWetpK///u/nxcsa0fM/st/+S8bFlQDyOfzXHLJJTz++OPzln3ve9/bgBltTnrHyYarTMJXPmSRpFvuhRj426PgpqDYB40+eGwQ9ozCifEkfLbOY6nC3k/A357Y2HmsdOwL7oVCCNHLN38IbivG6zbD/vkVOPZ9SOegz4HjDYiSaNfZBPS2Yuhuq865HUkrnYKBDByLoVmzzyTPFBePpBXugos8+MS1K7x/NdY3lHbDo3AqD0e7O8/tnLbnF7PiUNptty09RqE0ERERERERERERERFZRi2MOFINVjR2V94n57nrPCMR2axcp8G2ngdmHjtOQDH/+JygWhBmqdYuohV0Mfejo2JSfol87il8r568PqKYf5zJ0hXEsf1I1raeB5icfi5RnDkHeyQiW1KzAidX+AOtA8+B9BIfQCciIiIiIiIiIiIiIiIiIiJzXHPN4h+IeN999/GSl7xkTbbz4IMPEobhqucgm8/3vvc9Hn74YRzHmQmptV100UX8/u///gbOzjznOc/hBz/4wbzwm6JqHYqqbRHVaqdqM/uCPh9KlGELwgD2jnbiPVeOwqGhJJI0Cs0TUJ+EevKaczF2oLo55rGasVspBLcV43WbZv++Dcd/em1jdFsxdLcl55yE0vo/AwevWnkkbeAM7l/rFUpLh/Dm++GxPpjMWjBuz/jcXx9YtQMH4OBB6O5efqyIiIiIiIiIiIiIiMgSjlQDfvOB0RWN/eurB3l2V3qdZyQim1V38VFcpzXzuJA7iut2HjeavZSrlwALxRcdWkE3U6XLKeafIJOeBMB1WxRyRylXL7bHTovu4qNMlq5cvx0Rka3t5PfgXT+xsrE3fh4ufN76zkdEREREREREREREREREROQ88qIXvWjRZV/+8pf5z//5P6/Jdr70pS+d0Rxk81noXLbjan/wB39AOr3xP3d60UUXzXsujmOeeuqpDZjN5rT1i1zPEOVyecHnM5nz45NsnRiuP9x5fN1heGgQ4qTSs3vy3I/dLPNYzditGILbinPe8P2bWL8Y3VYM3W3FOfev8rqA1d+/1jOU5gCXjS+ysG3/frj1Vigs8QnJjgN790J//zIrExERERERERERERGRreD7h57c6CmIiKxYd/GRma9dt0U63flH0CDMLhFUm82lXL0Ez/suvmf/2ptOjeO6FxJFqZntKKomIiIiIiIiIiIiIiIiIiIiInLu7dmzh2c961kcPnx43rIvf/nLNBqNNWn3fOYzn1l02U/91E+d9frl3Pn6178+87XjdKobnufxqle9aiOmNM/w8PCcx47jEMcx09PTGzSjzUdRtS3i8ccfX/D5bdu2neOZrI99I3PjQQNVe+6B4Y0bu1nmsdo5b4a42/ker9sM+7eeMbqtGLrbinNe7XW02nuBQmkiIiIiIiIiIiIiIiIiIgtznBaZ9NjM40xqDId45nG1dhHLB9XaXKq1i+guPpqsOyaTGqPWsH/czaTHcJwWcZxaq+mLiIiIiIiIiIiIiIiIiIiIiMgKvfSlL+X//J//M+/5crnMpz71KV7xilec1fpHRkb40pe+tOCy5z73uVx00UVntX45tx577LE5j+M4xnEcnv/859PT07NBs5prsXmUSqVzPJPNa6U//Scb7IEHHpjzOI7tBzkHBwc3YDZrKx3CDY/Of/6GR2HnaQHEndPnduxmmcdqxrbDSwtZLNi2kWM3yzy24v5dd9jiW23LxdpWOnY91605dx6v9jqC1d+/2qG0a4/Zn4sF1di/Hz73ucX/+/zn4eRJuPde+Lmfg5/4icX/+/EfV1BNRERERERERERERERERDY1C6p1/nHX9yszX4dRmlbQtar1tYIuwii94PognhNwExERERERERERERERERERERGRc+e1r33tosvuuOOOs17/+973PsIwXHDZr/zKr5z1+uXcOnz4MI4zv85xzTXXbMBsFpbNZhd8XlG1Dn+jJyDLe/zxx7nvvvtwHGemXgjgOA6XXHLJBs/u7A1WIL3A8+kQ3nw/PNYHk1norcOeRaJA6zV2s8xjtXO+4VE4lYej3Z3nlgq2bfTYzTKPrbZ/7SjXA8Pz17FYrG0lY9dz3Zrz3OdWex2t9l6wIgcOwMGD0N29/FgREREREREREREREdnSvn/oyY2egojIppDy5/7wmOfVZr4OggKr/1dYhyAo4KWb89Zn2ytTb5zJTEVERERERERERERERERERERE5Gy8+MUvZvfu3Tz++OPzln3605/mwQcfZN++fWe07lqtxl/91V8tuMzzPH71V3/1jNYrG2d6enrB5wcGBs7xTBYXx/GCz1er1QWffyZSVG2Ti+OY//gf/+Oiy6+44opzOJv1cSKdhv/xP+xBuQwnTkDy5nWAyxZ7Ya0GrguZzPJjAXp6YHoa4nhVY5dd92rGrnTOzSb827/B0aMzTznAZeNLTbpjM8Tdzvd43WbZv60WgtOc5zqTSNqy94L9++HWW6FQWGIQ4Diwdy/09y89TkRERERERERERERERETkPOM4cz8Z1qHzOIpTZ7TO2a9ziE7bXnBG6xQRERERERERERERERERERERkbPjOA6/9Vu/xX/4D/9h3rI4jnn729/O5z73ORxntR/ECLfddhvHjh1bcNmrXvUqdu3atep1ysaqVCoLPr+Zomrj4wtHRzJJz0gUVdu0arUa/+///T/+9E//lHvuuQfHcRasBP7oj/7oBsxubZ1Kp+G3f3ujp7E5nTwJDz00E2xb0uQkPPHEzMMl426K153Z2NPmsSb7t0BAb2bsCiJ6WzEEpznPtaJzvZJQmiJpIiIiIiIiIiIiIiIiIiIrEsfe3Md4gIXPXKd1Ruuc/boY97Tt6Ue0REREREREREREREREREREREQ2ypvf/Gb+6I/+iJGRkXnLvvCFL/Bnf/Zn/M7v/M6q1vnVr36VP/qjP1pwmeu6/O7v/u4ZzVU2ViqVotFozHu+VqttwGwWtlhULZfLneOZbF7n7U/svelNb1qzdb33ve/lK1/5ypqtbyGtVotarcb09DSPP/44R44cIQztU3Bnx9RmVy2z2SzXX3/9us5LNtjAAPz4j2/0LOR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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "\n", "fig_phase, ax_phase = plt.subplots(figsize=(10, 6), dpi=500)\n", "ax_phase.set_xlim(2, 5)\n", "ax_phase.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\", fontsize=12)\n", @@ -1363,14 +916,20 @@ "\n", "ax_phase1 = ax_phase.twinx()\n", "ax_phase1.set_ylabel(R\"Angle [a. u.]\", fontsize=12)\n", - "ax_phase1.set_yticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", - "ax_phase1.set_yticklabels([R\"$-\\pi$\", R\"$-\\frac{\\pi}{2}$\", R\"0\", R\"$+\\frac{\\pi}{2}$\", R\"$+\\pi$\"])\n", + "ax_phase1.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])\n", + "ax_phase1.set_yticklabels([\n", + " R\"$-\\pi$\",\n", + " R\"$-\\frac{\\pi}{2}$\",\n", + " R\"0\",\n", + " R\"$+\\frac{\\pi}{2}$\",\n", + " R\"$+\\pi$\",\n", + "])\n", "\n", "# Plot total phases\n", - "ax_phase1.scatter(x, total_phase, s=22, color=\"red\", marker=\"^\",label=\"Total Phase\")\n", + "ax_phase1.scatter(x, total_phase, s=22, color=\"red\", marker=\"^\", label=\"Total Phase\")\n", "\n", "colors = [\"green\", \"yellow\"]\n", - "point_styles = [\"v\",\"o\"]\n", + "point_styles = [\"v\", \"o\"]\n", "marker_size = [20, 9]\n", "\n", "for i, (k, v) in enumerate(sub_phase.items()):\n", @@ -1396,56 +955,43 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total_dynamics = dynamics_func_fvector(data)\n", "sub_dynamics = {\n", - " p:compute_sub_intensity(\n", - " dynamics_func_fvector,\n", - " data,\n", - " resonances=[p.latex],\n", - " coupling_pattern=r\"\\\\beta\",\n", - " )\n", - " \n", + " p: compute_sub_intensity(\n", + " dynamics_func_fvector,\n", + " data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", + " )\n", " for p, _ in resonances\n", "}\n", "\n", "sub_dynamics_bw = {\n", - " p:compute_sub_intensity(\n", - " dynamics_func_bw,\n", - " data,\n", - " resonances=[p.latex],\n", - " coupling_pattern=r\"Dummy_\",\n", - " )\n", - " \n", + " p: compute_sub_intensity(\n", + " dynamics_func_bw,\n", + " data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"Dummy_\",\n", + " )\n", " for p, _ in resonances\n", "}" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "x_1 = np.linspace(2, (m_res1**2+m_res2**2)/2, num=500)\n", - "x_2 = np.linspace((m_res1**2+m_res2**2)/2,5, num=500)\n", + "x_1 = np.linspace(2, (m_res1**2 + m_res2**2) / 2, num=500)\n", + "x_2 = np.linspace((m_res1**2 + m_res2**2) / 2, 5, num=500)\n", "\n", - "#x_1 = np.linspace(2, 5, num=500)\n", - "#x_2 = np.linspace(2,5, num=500)\n", + "# x_1 = np.linspace(2, 5, num=500)\n", + "# x_2 = np.linspace(2,5, num=500)\n", "\n", "data_1 = {\"m_01\": np.sqrt(x_1 + epsilon * 1j)}\n", "data_2 = {\"m_01\": np.sqrt(x_2 + epsilon * 1j)}\n", @@ -1454,9 +1000,9 @@ "y_real_1 = dynamics_func_fvector(data_1).real\n", "y_imag_2 = dynamics_func_fvector(data_2).imag\n", "y_real_2 = dynamics_func_fvector(data_2).real\n", - "fig_A, axs = plt.subplots(1,2, figsize=(10, 5))\n", + "fig_A, axs = plt.subplots(1, 2, figsize=(10, 5))\n", "colorsA = [\"red\", \"blue\"]\n", - "axA,axA1 =axs\n", + "axA, axA1 = axs\n", "for i, (k, v) in enumerate(sub_dynamics.items()):\n", " axA1.plot(\n", " v.real,\n", @@ -1465,37 +1011,36 @@ " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", " )\n", "\n", - "axA.plot(y_real_1, y_imag_1, label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \", color=\"red\")\n", - "axA.plot(y_real_2, y_imag_2, label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \", color=\"blue\")\n", + "axA.plot(\n", + " y_real_1,\n", + " y_imag_1,\n", + " label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \",\n", + " color=\"red\",\n", + ")\n", + "axA.plot(\n", + " y_real_2,\n", + " y_imag_2,\n", + " label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \",\n", + " color=\"blue\",\n", + ")\n", "axA.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", "axA.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", - "axA.axhline(0, color='black')\n", - "axA.axvline(0, color='black')\n", - "axA1.axhline(0, color='black')\n", - "axA1.axvline(0, color='black')\n", + "axA.axhline(0, color=\"black\")\n", + "axA.axvline(0, color=\"black\")\n", + "axA1.axhline(0, color=\"black\")\n", + "axA1.axvline(0, color=\"black\")\n", "plt.tight_layout()\n", - "axA.legend(loc='upper left')\n", + "axA.legend(loc=\"upper left\")\n", "# Save the plot as PDF\n", - "#plt.savefig(\"_func_plots.pdf\", dpi=750)\n", + "# plt.savefig(\"_func_plots.pdf\", dpi=750)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig_phase_bw, ax_phase_bw = plt.subplots(figsize=(8, 5), dpi=500)\n", "ax_phase_bw.set_xlim(2, 5)\n", @@ -1515,25 +1060,39 @@ ")\n", "\n", "\n", - "ax_phase1_bw= ax_phase_bw.twinx()\n", + "ax_phase1_bw = ax_phase_bw.twinx()\n", "ax_phase1_bw.set_ylabel(R\"Angle [a. u.]\")\n", - "ax_phase1_bw.set_yticks([-np.pi,-np.pi/2,0,np.pi/2, +np.pi])\n", - "ax_phase1_bw.set_yticklabels([R\"$-\\pi$\",R\"$-\\frac{\\pi}{2}$\",R\"0\",R\"$+\\frac{\\pi}{2}$\", R\"$+\\pi$\"])\n", + "ax_phase1_bw.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, +np.pi])\n", + "ax_phase1_bw.set_yticklabels([\n", + " R\"$-\\pi$\",\n", + " R\"$-\\frac{\\pi}{2}$\",\n", + " R\"0\",\n", + " R\"$+\\frac{\\pi}{2}$\",\n", + " R\"$+\\pi$\",\n", + "])\n", "colors_bw = [\"magenta\", \"cyan\"]\n", "\n", "# Plot total phases\n", "ax_phase1_bw\n", - "ax_phase1_bw.plot(x, total_phase_1, color=\"blue\", label=\"Total Phase Breit-Wigner\",linestyle=\"--\",)\n", + "ax_phase1_bw.plot(\n", + " x,\n", + " total_phase_1,\n", + " color=\"blue\",\n", + " label=\"Total Phase Breit-Wigner\",\n", + " linestyle=\"--\",\n", + ")\n", "\n", "\n", "for i, (k, v) in enumerate(sub_phase_bw.items()):\n", " ax_phase1_bw.plot(\n", " x,\n", " v,\n", - " color=colors_bw[i % len(colors_bw)],zorder=999, linestyle=\"--\",\n", + " color=colors_bw[i % len(colors_bw)],\n", + " zorder=999,\n", + " linestyle=\"--\",\n", " label=f\"Resonance at {k.mass} GeV Breit-Wigner\",\n", " )\n", - " ax_phase1_bw.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\"),\n", + " (ax_phase1_bw.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\"),)\n", "# Set labels for twin axes\n", "ax_phase1_bw.set_ylabel(\"Angle [rad]\")\n", "\n", @@ -1545,29 +1104,17 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "\n", "y_imag_1_bw = dynamics_func_bw(data_1).imag\n", "y_real_1_bw = dynamics_func_bw(data_1).real\n", "y_imag_2_bw = dynamics_func_bw(data_2).imag\n", "y_real_2_bw = dynamics_func_bw(data_2).real\n", - "fig_A, axs = plt.subplots(1,2, figsize=(10, 5))\n", + "fig_A, axs = plt.subplots(1, 2, figsize=(10, 5))\n", "colorsA_bw = [\"red\", \"blue\"]\n", - "axA_bw,axA1_bw =axs\n", + "axA_bw, axA1_bw = axs\n", "for i, (k, v) in enumerate(sub_dynamics_bw.items()):\n", " axA1_bw.plot(\n", " v.real,\n", @@ -1576,18 +1123,28 @@ " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", " )\n", "\n", - "axA_bw.plot(y_real_1_bw, y_imag_1_bw, label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \", color=\"red\")\n", - "axA_bw.plot(y_real_2_bw, y_imag_2_bw, label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \", color=\"blue\")\n", + "axA_bw.plot(\n", + " y_real_1_bw,\n", + " y_imag_1_bw,\n", + " label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \",\n", + " color=\"red\",\n", + ")\n", + "axA_bw.plot(\n", + " y_real_2_bw,\n", + " y_imag_2_bw,\n", + " label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \",\n", + " color=\"blue\",\n", + ")\n", "axA_bw.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", "axA_bw.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", - "axA_bw.axhline(0, color='black')\n", - "axA_bw.axvline(0, color='black')\n", - "axA1_bw.axhline(0, color='black')\n", - "axA1_bw.axvline(0, color='black')\n", + "axA_bw.axhline(0, color=\"black\")\n", + "axA_bw.axvline(0, color=\"black\")\n", + "axA1_bw.axhline(0, color=\"black\")\n", + "axA1_bw.axvline(0, color=\"black\")\n", "plt.tight_layout()\n", - "axA_bw.legend(loc='upper left')\n", + "axA_bw.legend(loc=\"upper left\")\n", "# Save the plot as PDF\n", - "#plt.savefig(\"_func_plots.pdf\", dpi=750)\n", + "# plt.savefig(\"_func_plots.pdf\", dpi=750)\n", "plt.show()" ] }, @@ -1614,6 +1171,9 @@ } ], "metadata": { + "colab": { + "toc_visible": true + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", diff --git a/Toyfits_DataFVector_MoreChannel.ipynb b/Toyfits_DataFVector_MoreChannel.ipynb index 7fb9df09..8dc9ad7c 100644 --- a/Toyfits_DataFVector_MoreChannel.ipynb +++ b/Toyfits_DataFVector_MoreChannel.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -40,6 +40,7 @@ "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", "from IPython.display import Math, display\n", + "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", "from matplotlib import cm\n", "from qrules.particle import Particle, ParticleCollection\n", "from qrules.transition import ReactionInfo\n", @@ -58,8 +59,6 @@ "from tensorwaves.optimizer import Minuit2\n", "from tensorwaves.optimizer.callbacks import CSVSummary\n", "\n", - "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", - "\n", "_ = np.seterr(invalid=\"ignore\")" ] }, @@ -93,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -117,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -125,36 +124,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "680eabb7bba743b09c1cbe16ad28890d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Propagating quantum numbers: 0%| | 0/36 [00:00" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "@unevaluated(real=False)\n", "class PhaseSpaceCM(sp.Expr):\n", @@ -419,7 +313,7 @@ " s: Any\n", " m1: Any\n", " m2: Any\n", - " _latex_repr_ = R\"\\Sigma\\left({s}\\right)\" # noqa: RUF027\n", + " _latex_repr_ = R\"\\Sigma\\left({s}\\right)\"\n", "\n", " def evaluate(self) -> sp.Expr:\n", " s, m1, m2 = self.args\n", @@ -440,7 +334,7 @@ " s: Any\n", " m1: Any\n", " m2: Any\n", - " _latex_repr_ = R\"q\\left({s}\\right)\" # noqa: RUF027\n", + " _latex_repr_ = R\"q\\left({s}\\right)\"\n", "\n", " def evaluate(self) -> sp.Expr:\n", " s, m1, m2 = self.args\n", @@ -458,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -468,23 +362,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\begin{array}{rcl}\n", - " \\Gamma_s\\left(s\\right) &=& \\gamma_{0} \\rho^\\mathrm{CM}_{m_{1},m_{2}}\\left(s\\right) \\\\\n", - "\\end{array}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "@unevaluated(real=False)\n", "class EnergyDecaywidth(sp.Expr):\n", @@ -492,7 +370,7 @@ " m1: Any\n", " m2: Any\n", " width: Any\n", - " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\" # noqa: RUF027\n", + " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", "\n", " def evaluate(self) -> sp.Expr:\n", " s, m1, m2, width = self.args\n", @@ -506,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -516,30 +394,14 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\begin{array}{rcl}\n", - " CM_{m_{1},m_{2}}\\left(s\\right) &=& - \\frac{2 \\left(- \\frac{\\left(m_{1}^{2} + m_{2}^{2}\\right) \\log{\\left(\\frac{m_{1}}{m_{2}} \\right)}}{2 m_{1}^{2} - 2 m_{2}^{2}} - 0.5 - \\frac{\\sqrt{\\left(- s + \\left(m_{1} - m_{2}\\right)^{2}\\right) \\left(- s + \\left(m_{1} + m_{2}\\right)^{2}\\right)} \\log{\\left(\\frac{\\sqrt{- s + \\left(m_{1} - m_{2}\\right)^{2}} + \\sqrt{- s + \\left(m_{1} + m_{2}\\right)^{2}}}{2 \\sqrt{m_{1} m_{2}}} \\right)}}{s} + \\frac{\\left(m_{1}^{2} - m_{2}^{2}\\right) \\log{\\left(\\frac{m_{1}}{m_{2}} \\right)}}{2 s}\\right)}{\\pi} \\\\\n", - "\\end{array}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "@unevaluated(real=False)\n", "class CM(sp.Expr):\n", " s: Any\n", " m1: Any\n", " m2: Any\n", - " _latex_repr_ = R\"CM_{{{m1},{m2}}}\\left({s}\\right)\" # noqa: RUF027\n", + " _latex_repr_ = R\"CM_{{{m1},{m2}}}\\left({s}\\right)\"\n", "\n", " def evaluate(self) -> sp.Expr:\n", " s, m1, m2 = self.args\n", @@ -583,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -634,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -669,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -680,23 +542,7 @@ "full-width" ] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left[\\begin{matrix}\\frac{\\left(g_{N(Fakestar)^+,0}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}} & \\frac{g_{N(Fakestar)^+,0} g_{N(Fakestar)^+,1}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\\\\\frac{g_{N(Fakestar)^+,0} g_{N(Fakestar)^+,1}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}} & \\frac{\\left(g_{N(Fakestar)^+,1}\\right)^{2}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\end{matrix}\\right]$" - ], - "text/plain": [ - "Matrix([\n", - "[ g_{N(Fakestar)^+,0}**2/(-m_01**2 + m_{N(Fakestar)^+}**2), g_{N(Fakestar)^+,0}*g_{N(Fakestar)^+,1}/(-m_01**2 + m_{N(Fakestar)^+}**2)],\n", - "[g_{N(Fakestar)^+,0}*g_{N(Fakestar)^+,1}/(-m_01**2 + m_{N(Fakestar)^+}**2), g_{N(Fakestar)^+,1}**2/(-m_01**2 + m_{N(Fakestar)^+}**2)]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def formulate_k_matrix(\n", " resonances: list[tuple[Particle, int]], n_channels: int\n", @@ -746,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -756,23 +602,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left[\\begin{matrix}\\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+,0}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\\\\\frac{\\beta_{N(Fakestar)^+} g_{N(Fakestar)^+,1}}{- m_{01}^{2} + \\left(m_{N(Fakestar)^+}\\right)^{2}}\\end{matrix}\\right]$" - ], - "text/plain": [ - "Matrix([\n", - "[\\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+,0}/(-m_01**2 + m_{N(Fakestar)^+}**2)],\n", - "[\\beta_{N(Fakestar)^+}*g_{N(Fakestar)^+,1}/(-m_01**2 + m_{N(Fakestar)^+}**2)]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def formulate_p_vector(\n", " resonances: list[tuple[Particle, int]], n_channels: int\n", @@ -818,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -828,23 +658,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left[\\begin{matrix}\\rho^\\mathrm{CM}_{m_{0,0},m_{1,0}}\\left(m_{01}^{2}\\right) & 0\\\\0 & \\rho^\\mathrm{CM}_{m_{0,1},m_{1,1}}\\left(m_{01}^{2}\\right)\\end{matrix}\\right]$" - ], - "text/plain": [ - "Matrix([\n", - "[PhaseSpaceCM(m_01**2, m_{0,0}, m_{1,0}), 0],\n", - "[ 0, PhaseSpaceCM(m_01**2, m_{0,1}, m_{1,1})]])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def formulate_phsp_factor_matrix(n_channels: int) -> dict[sp.MatrixElement, sp.Expr]:\n", " matrix_expressions = {}\n", @@ -896,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -904,29 +718,15 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/latex": [ - "$\\displaystyle \\left(\\mathbb{I} + - i K \\rho\\right)^{-1} P$" - ], - "text/plain": [ - "(I - I*K*rho)**(-1)*P" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "F = (I -sp.I* K * rho).inv() * P\n", + "outputs": [], + "source": [ + "F = (I - sp.I * K * rho).inv() * P\n", "F" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -943,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -977,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1009,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1037,7 +837,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1069,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1083,7 +883,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1094,7 +894,7 @@ "outputs": [], "source": [ "for i in range(n_channels):\n", - " INTENSITY_FUNCS_FVECTOR[i].update_parameters(new_parameters_fvector)\n" + " INTENSITY_FUNCS_FVECTOR[i].update_parameters(new_parameters_fvector)" ] }, { @@ -1113,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "tags": [] }, @@ -1130,28 +930,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import re \n", - "re.match(r\"^m_\\d\\d$\",\"m_01\")" + "import re\n", + "\n", + "re.match(r\"^m_\\d\\d$\", \"m_01\")" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1159,67 +949,12 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-05-18 22:07:37.952367: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-05-18 22:07:37.952389: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-05-18 22:07:37.953198: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2024-05-18 22:07:38.561912: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b1a52872669e4e21a1a11a21ef4fe83f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Generating phase space sample: 0%| | 0/100000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for i in range(n_channels):\n", " fig, ax = plt.subplots(figsize=(6, 5))\n", @@ -1446,7 +1082,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1454,43 +1090,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "339f1c4dc3444581b81c002bd8e0adec", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Generating intensity-based sample: 0%| | 0/50000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for i in range(n_channels):\n", " resonances = sorted(\n", @@ -1608,37 +1187,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to K^{0}_{0} \\\\Sigma^{+}_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to K^{0}_{0} \\\\Sigma^{+}_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to K^{0}_{0} \\\\Sigma^{+}_{+1/2}}': (1+0j),\n", - " 'm_0': 0.547862,\n", - " 'm_1': 0.93827208816,\n", - " 'm_2': 0.93827208816,\n", - " 'm_012': 3.0969,\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+3/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{+1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'C_{J/\\\\psi(1S) \\\\to N(Fakestar)^+_{+1/2} \\\\overline{p}_{-1/2}; N(Fakestar)^+ \\\\to \\\\eta_{0} p_{+1/2}}': (1+0j),\n", - " 'm_{0,0}': 0.49761099999999997,\n", - " 'm_{1,0}': 1.1893699999999998,\n", - " 'm_{N(Fakestar)^+}': 1.71,\n", - " 'g_{N(Fakestar)^+,0}': 0.8,\n", - " 'm_{0,1}': 0.547862,\n", - " 'm_{1,1}': 0.93827208816,\n", - " 'g_{N(Fakestar)^+,1}': 0.9,\n", - " '\\\\beta_{N(Fakestar)^+}': (1+0j)}" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "initial_parameters = {\n", " R\"m_{N(Fakestar)^+}\": 1.9,\n", @@ -1646,12 +1197,12 @@ " R\"g_{N(Fakestar)^+,0}\": 0.8,\n", " R\"g_{N(Fakestar)^+,1}\": 0.6,\n", "}\n", - "INTENSITY_FUNCS_FVECTOR[0].parameters\n" + "INTENSITY_FUNCS_FVECTOR[0].parameters" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1701,15 +1252,25 @@ " density=True,\n", " )\n", " indicate_masses(ax, function)\n", - " ax.axvline(DECAYS[0].child1.mass+DECAYS[0].child2.mass, color='grey', linestyle='dotted', label=rf'${DECAYS[0].child1.latex} \\, {DECAYS[0].child2.latex}$ threshhold')\n", - " ax.axvline(DECAYS[1].child1.mass+DECAYS[1].child2.mass, color='grey', linestyle='dotted', label=rf'${DECAYS[1].child1.latex} \\, {DECAYS[1].child2.latex}$ threshhold')\n", + " ax.axvline(\n", + " DECAYS[0].child1.mass + DECAYS[0].child2.mass,\n", + " color=\"grey\",\n", + " linestyle=\"dotted\",\n", + " label=rf\"${DECAYS[0].child1.latex} \\, {DECAYS[0].child2.latex}$ threshhold\",\n", + " )\n", + " ax.axvline(\n", + " DECAYS[1].child1.mass + DECAYS[1].child2.mass,\n", + " color=\"grey\",\n", + " linestyle=\"dotted\",\n", + " label=rf\"${DECAYS[1].child1.latex} \\, {DECAYS[1].child2.latex}$ threshhold\",\n", + " )\n", " ax.legend()\n", " fig.show()" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1719,28 +1280,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ORIGINAL_PARAMETERS_F = []\n", "for i in range(n_channels):\n", @@ -1773,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1796,7 +1336,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1823,7 +1363,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1851,7 +1391,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1859,49 +1399,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a1cf71730e9844c8befd2d7ccf3282a4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "FitResult(\n", - " minimum_valid=True,\n", - " execution_time=3.351858377456665,\n", - " function_calls=162,\n", - " estimator_value=-18694.71978168161,\n", - " parameter_values={\n", - " 'm_{N(Fakestar)^+}': 1.7102099446837458,\n", - " 'g_{N(Fakestar)^+,0}': 0.8145358423603044,\n", - " 'g_{N(Fakestar)^+,1}': 0.8963220998212011,\n", - " '\\\\beta_{N(Fakestar)^+}': (-36.888094928163476-4.678879465472437j),\n", - " },\n", - " parameter_errors={\n", - " 'm_{N(Fakestar)^+}': 0.000913074977897192,\n", - " 'g_{N(Fakestar)^+,0}': 0.010543626222578407,\n", - " 'g_{N(Fakestar)^+,1}': 0.0036131105694627934,\n", - " '\\\\beta_{N(Fakestar)^+}': (13106208.4666789+13641374.272541337j),\n", - " },\n", - ")" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "minuit2 = Minuit2(\n", " callback=CSVSummary(\"fit_traceback.csv\"),\n", @@ -1913,7 +1411,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": { "editable": true, "slideshow": { @@ -1923,28 +1421,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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initialfit resultoriginal
$m_{N(Fakestar)^+}$1.9+0.0j1.710+ 0.000j1.71+0.00j
$g_{N(Fakestar)^+,0}$0.8+0.0j0.815+ 0.000j0.80+0.00j
$g_{N(Fakestar)^+,1}$0.6+0.0j0.896+ 0.000j0.90+0.00j
$\\beta_{N(Fakestar)^+}$1.0+0.0j-36.888- 4.679j1.00+0.00j
\n", - "
" - ], - "text/plain": [ - " initial fit result original\n", - "$m_{N(Fakestar)^+}$ 1.9+0.0j 1.710+ 0.000j 1.71+0.00j\n", - "$g_{N(Fakestar)^+,0}$ 0.8+0.0j 0.815+ 0.000j 0.80+0.00j\n", - "$g_{N(Fakestar)^+,1}$ 0.6+0.0j 0.896+ 0.000j 0.90+0.00j\n", - "$\\beta_{N(Fakestar)^+}$ 1.0+0.0j -36.888- 4.679j 1.00+0.00j" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -2053,62 +1462,23 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FitResult(\n", - " minimum_valid=True,\n", - " execution_time=3.351858377456665,\n", - " function_calls=162,\n", - " estimator_value=-18694.71978168161,\n", - " parameter_values={\n", - " 'm_{N(Fakestar)^+}': 1.7102099446837458,\n", - " 'g_{N(Fakestar)^+,0}': 0.8145358423603044,\n", - " 'g_{N(Fakestar)^+,1}': 0.8963220998212011,\n", - " '\\\\beta_{N(Fakestar)^+}': (-36.888094928163476-4.678879465472437j),\n", - " },\n", - " parameter_errors={\n", - " 'm_{N(Fakestar)^+}': 0.000913074977897192,\n", - " 'g_{N(Fakestar)^+,0}': 0.010543626222578407,\n", - " 'g_{N(Fakestar)^+,1}': 0.0036131105694627934,\n", - " '\\\\beta_{N(Fakestar)^+}': (13106208.4666789+13641374.272541337j),\n", - " },\n", - ")" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "fit_result" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-37379.43956336322" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "n_real_par = fit_result.count_number_of_parameters(complex_twice=True)\n", "n_events = len(next(iter(data.values())))\n", "log_likelihood = -fit_result.estimator_value\n", - " \n", + "\n", "aic = 2 * n_real_par - 2 * log_likelihood\n", "bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", "aic" @@ -2116,20 +1486,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-37335.34067194117" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "bic" ] @@ -2143,6 +1502,9 @@ } ], "metadata": { + "colab": { + "toc_visible": true + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", From e018711a95e02dc0610cfa95f9bf5fbd57a45232 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 15:16:51 +0200 Subject: [PATCH 03/92] MAINT: update Python version in kernel --- Mulitpleqn_Toyfit.ipynb | 2 +- SubintensityPlots_mitAgrand.ipynb | 2 +- Toyfits_DataFVector_MoreChannel.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Mulitpleqn_Toyfit.ipynb b/Mulitpleqn_Toyfit.ipynb index c2bb241d..a7f1162d 100644 --- a/Mulitpleqn_Toyfit.ipynb +++ b/Mulitpleqn_Toyfit.ipynb @@ -1214,7 +1214,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/SubintensityPlots_mitAgrand.ipynb b/SubintensityPlots_mitAgrand.ipynb index 15403775..48a7ed7f 100644 --- a/SubintensityPlots_mitAgrand.ipynb +++ b/SubintensityPlots_mitAgrand.ipynb @@ -1189,7 +1189,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/Toyfits_DataFVector_MoreChannel.ipynb b/Toyfits_DataFVector_MoreChannel.ipynb index 8dc9ad7c..db339903 100644 --- a/Toyfits_DataFVector_MoreChannel.ipynb +++ b/Toyfits_DataFVector_MoreChannel.ipynb @@ -1520,7 +1520,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, From 2005d2b57f00d5866b3431a4d979e391bae9af95 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 15:18:26 +0200 Subject: [PATCH 04/92] MAINT: address Ruff linting issues --- Mulitpleqn_Toyfit.ipynb | 72 +++++++++++----------- SubintensityPlots_mitAgrand.ipynb | 87 +++++++++++---------------- Toyfits_DataFVector_MoreChannel.ipynb | 9 ++- 3 files changed, 79 insertions(+), 89 deletions(-) diff --git a/Mulitpleqn_Toyfit.ipynb b/Mulitpleqn_Toyfit.ipynb index a7f1162d..23552ba0 100644 --- a/Mulitpleqn_Toyfit.ipynb +++ b/Mulitpleqn_Toyfit.ipynb @@ -12,18 +12,23 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], "source": [ "from __future__ import annotations\n", "\n", + "from typing import Any\n", + "\n", "import graphviz\n", "import numpy as np\n", "import qrules\n", "import sympy as sp\n", "from ampform.io import aslatex\n", - "from IPython.display import Latex\n", + "from IPython.display import Latex, display\n", "from qrules.particle import Particle, ParticleCollection" ] }, @@ -86,7 +91,6 @@ " ],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", - " # mass_conservation_factor=5.0,\n", " particle_db=PARTICLE_DB,\n", ")\n", "dot = qrules.io.asdot(reaction, collapse_graphs=True)\n", @@ -266,11 +270,10 @@ "def formulate_rel_bw(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", " m_a = variables.outgoing_state_mass1\n", " m_b = variables.outgoing_state_mass2\n", - " q = BreakupMomentum(s, m_a, m_b)\n", " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", @@ -278,9 +281,9 @@ " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", - " for i, (p, va) in enumerate(resonances):\n", - " PARAMETERS_BW[w[i]] = p.width\n", - " PARAMETERS_BW[m[i]] = p.mass\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_BW[w[i]] = resonance.width\n", + " PARAMETERS_BW[m[i]] = resonance.mass\n", " PARAMETERS_BW[b[i]] = 1\n", " PARAMETERS_BW[d[i]] = 1\n", " PARAMETERS_BW[L[i]] = 0\n", @@ -309,16 +312,14 @@ "def formulate_K_matrix(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", "\n", " kmatrix = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", - " for i, (p, va) in enumerate(resonances):\n", - " PARAMETERS_F[m[i]] = p.mass\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = resonance.mass\n", " PARAMETERS_F[g[i]] = 1\n", " return kmatrix" ] @@ -339,16 +340,14 @@ "def formulate_P_vector(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " beta = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", " P_vector = sum((g_ * beta_) / (m_**2 - s) for m_, g_, beta_ in zip(m, g, beta))\n", - " for i, (p, va) in enumerate(resonances):\n", - " PARAMETERS_F[m[i]] = p.mass\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = resonance.mass\n", " PARAMETERS_F[beta[i]] = 1 + 0j\n", " PARAMETERS_F[g[i]] = 1\n", " return P_vector" @@ -370,15 +369,14 @@ "def formulate_F_vector(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", " m_a = variables.outgoing_state_mass1\n", " m_b = variables.outgoing_state_mass2\n", " rho = PhaseSpaceCM(s, m_a, m_b)\n", " K = formulate_K_matrix(resonances)\n", " P = formulate_P_vector(resonances)\n", - " Fvector = (1 / (1 - rho * K)) * P\n", - " return Fvector" + " return (1 / (1 - rho * K)) * P" ] }, { @@ -405,10 +403,12 @@ "}\n", "model_rel_bw = attrs.evolve(\n", " model,\n", - " parameter_defaults=ParameterValues({\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_BW,\n", - " }),\n", + " parameter_defaults=ParameterValues(\n", + " {\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_BW,\n", + " }\n", + " ),\n", ")" ] }, @@ -444,10 +444,12 @@ "}\n", "model_fvector = attrs.evolve(\n", " model,\n", - " parameter_defaults=ParameterValues({\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_F,\n", - " }),\n", + " parameter_defaults=ParameterValues(\n", + " {\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_F,\n", + " }\n", + " ),\n", ")\n", "Latex(aslatex(dynamics_expressions_fvector))" ] @@ -858,11 +860,13 @@ " domain_transformer=helicity_transformer,\n", ")\n", "data_momenta = data_generator.generate(50_000, rng)\n", - "pd.DataFrame({\n", - " (k, label): np.transpose(v)[i]\n", - " for k, v in data_momenta.items()\n", - " for i, label in enumerate([\"E\", \"px\", \"py\", \"pz\"])\n", - "})\n", + "pd.DataFrame(\n", + " {\n", + " (k, label): np.transpose(v)[i]\n", + " for k, v in data_momenta.items()\n", + " for i, label in enumerate([\"E\", \"px\", \"py\", \"pz\"])\n", + " }\n", + ")\n", "phsp = helicity_transformer(phsp_momenta)\n", "data = helicity_transformer(data_momenta)\n", "data_frame = pd.DataFrame(data)\n", @@ -1175,7 +1179,7 @@ "source": [ "for p in optimized_parameters_BW:\n", " print(p)\n", - " print(f\" initial: {initial_parameters_bw[p]:.3}\")\n", + " print(f\" initial: {initial_parameters_relbw[p]:.3}\")\n", " print(f\" optimized Breit-Wigner: {optimized_parameters_BW[p]:.3}\")\n", " print(f\" original: {original_parameters[p]:.3}\")\n", "latest_parameters_BW = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", diff --git a/SubintensityPlots_mitAgrand.ipynb b/SubintensityPlots_mitAgrand.ipynb index 48a7ed7f..aa80b3ff 100644 --- a/SubintensityPlots_mitAgrand.ipynb +++ b/SubintensityPlots_mitAgrand.ipynb @@ -12,19 +12,25 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], "source": [ "from __future__ import annotations\n", "\n", + "from typing import Any\n", + "\n", "import graphviz\n", "import numpy as np\n", "import qrules\n", "import sympy as sp\n", "from ampform.io import aslatex\n", - "from IPython.display import Latex\n", - "from qrules.particle import Particle, ParticleCollection" + "from IPython.display import Latex, display\n", + "from qrules.particle import Particle, ParticleCollection\n", + "from tensorwaves.interface import DataSample" ] }, { @@ -81,7 +87,6 @@ " allowed_intermediate_particles=[\"N(Fakestar)+\", \"N(1900)+\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", - " # mass_conservation_factor=5.0,\n", " particle_db=PARTICLE_DB,\n", ")\n", "dot = qrules.io.asdot(reaction, collapse_graphs=True)\n", @@ -271,11 +276,10 @@ "def formulate_rel_bw(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", " m_a = variables.outgoing_state_mass1\n", " m_b = variables.outgoing_state_mass2\n", - " q = BreakupMomentum(s, m_a, m_b)\n", " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", @@ -287,9 +291,9 @@ " (w_ * m_ * dummy_) / (m_**2 - s - m_ * w_s_)\n", " for m_, w_, w_s_, dummy_ in zip(m, w, w_s, dummy)\n", " )\n", - " for i, (p, va) in enumerate(resonances):\n", - " PARAMETERS_BW[w[i]] = p.width\n", - " PARAMETERS_BW[m[i]] = p.mass\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_BW[w[i]] = resonance.width\n", + " PARAMETERS_BW[m[i]] = resonance.mass\n", " PARAMETERS_BW[b[i]] = 1\n", " PARAMETERS_BW[d[i]] = 1\n", " PARAMETERS_BW[L[i]] = 0\n", @@ -318,16 +322,14 @@ "def formulate_K_matrix(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", "\n", " kmatrix = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", - " for i, (p, va) in enumerate(resonances):\n", - " PARAMETERS_K[m[i]] = p.mass\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_K[m[i]] = resonance.mass\n", " PARAMETERS_K[g[i]] = 1\n", " return kmatrix" ] @@ -351,16 +353,14 @@ "def formulate_P_vector(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " beta = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", " P_vector = sum((g_ * beta_) / (m_**2 - s) for m_, g_, beta_ in zip(m, g, beta))\n", - " for i, (p, va) in enumerate(resonances):\n", - " PARAMETERS_F[m[i]] = p.mass\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = resonance.mass\n", " PARAMETERS_F[beta[i]] = 1 + 0j\n", " PARAMETERS_F[g[i]] = 1\n", " return P_vector" @@ -382,15 +382,14 @@ "def formulate_F_vector(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", - " (p1, variables), *_ = resonances\n", + " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", " m_a = variables.outgoing_state_mass1\n", " m_b = variables.outgoing_state_mass2\n", " rho = PhaseSpaceCM(s, m_a, m_b)\n", " K = formulate_K_matrix(resonances)\n", " P = formulate_P_vector(resonances)\n", - " Fvector = (1 / (1 - rho * K)) * P\n", - " return Fvector" + " return (1 / (1 - rho * K)) * P" ] }, { @@ -884,7 +883,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "outputs": [], "source": [ "fig_phase, ax_phase = plt.subplots(figsize=(10, 6), dpi=500)\n", @@ -990,9 +993,6 @@ "x_1 = np.linspace(2, (m_res1**2 + m_res2**2) / 2, num=500)\n", "x_2 = np.linspace((m_res1**2 + m_res2**2) / 2, 5, num=500)\n", "\n", - "# x_1 = np.linspace(2, 5, num=500)\n", - "# x_2 = np.linspace(2,5, num=500)\n", - "\n", "data_1 = {\"m_01\": np.sqrt(x_1 + epsilon * 1j)}\n", "data_2 = {\"m_01\": np.sqrt(x_2 + epsilon * 1j)}\n", "\n", @@ -1031,15 +1031,17 @@ "axA1.axvline(0, color=\"black\")\n", "plt.tight_layout()\n", "axA.legend(loc=\"upper left\")\n", - "# Save the plot as PDF\n", - "# plt.savefig(\"_func_plots.pdf\", dpi=750)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "outputs": [], "source": [ "fig_phase_bw, ax_phase_bw = plt.subplots(figsize=(8, 5), dpi=500)\n", @@ -1105,7 +1107,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "outputs": [], "source": [ "y_imag_1_bw = dynamics_func_bw(data_1).imag\n", @@ -1143,31 +1149,8 @@ "axA1_bw.axvline(0, color=\"black\")\n", "plt.tight_layout()\n", "axA_bw.legend(loc=\"upper left\")\n", - "# Save the plot as PDF\n", - "# plt.savefig(\"_func_plots.pdf\", dpi=750)\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/Toyfits_DataFVector_MoreChannel.ipynb b/Toyfits_DataFVector_MoreChannel.ipynb index db339903..a194875d 100644 --- a/Toyfits_DataFVector_MoreChannel.ipynb +++ b/Toyfits_DataFVector_MoreChannel.ipynb @@ -13,6 +13,9 @@ "execution_count": null, "metadata": { "editable": true, + "jupyter": { + "source_hidden": true + }, "slideshow": { "slide_type": "" }, @@ -472,9 +475,9 @@ " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", - " for i, (p, _) in enumerate(resonances):\n", - " PARAMETERS_BW[w[i]] = p.width\n", - " PARAMETERS_BW[m[i]] = p.mass\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_BW[w[i]] = resonance.width\n", + " PARAMETERS_BW[m[i]] = resonance.mass\n", " PARAMETERS_BW[b[i]] = 1\n", " PARAMETERS_BW[d[i]] = 1\n", " PARAMETERS_BW[L[i]] = 0\n", From b5df13ada03404a8439ba8a2298c4e1c15857c21 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 15:33:16 +0200 Subject: [PATCH 05/92] MAINT: move `import` statements to top --- Mulitpleqn_Toyfit.ipynb | 130 ++++++++------------------ SubintensityPlots_mitAgrand.ipynb | 46 +++------ Toyfits_DataFVector_MoreChannel.ipynb | 7 +- 3 files changed, 57 insertions(+), 126 deletions(-) diff --git a/Mulitpleqn_Toyfit.ipynb b/Mulitpleqn_Toyfit.ipynb index 23552ba0..d38ef3d7 100644 --- a/Mulitpleqn_Toyfit.ipynb +++ b/Mulitpleqn_Toyfit.ipynb @@ -21,15 +21,42 @@ "source": [ "from __future__ import annotations\n", "\n", + "import os\n", + "import re\n", "from typing import Any\n", "\n", + "import ampform\n", + "import attrs\n", "import graphviz\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "import pandas as pd\n", "import qrules\n", "import sympy as sp\n", + "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", + "from ampform.helicity import ParameterValues\n", "from ampform.io import aslatex\n", + "from ampform.kinematics.phasespace import Kallen\n", + "from ampform.sympy import unevaluated\n", "from IPython.display import Latex, display\n", - "from qrules.particle import Particle, ParticleCollection" + "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", + "from matplotlib import cm\n", + "from qrules.particle import Particle, ParticleCollection\n", + "from sympy import Abs\n", + "from tensorwaves.data import (\n", + " IntensityDistributionGenerator,\n", + " SympyDataTransformer,\n", + " TFPhaseSpaceGenerator,\n", + " TFUniformRealNumberGenerator,\n", + " TFWeightedPhaseSpaceGenerator,\n", + ")\n", + "from tensorwaves.estimator import UnbinnedNLL\n", + "from tensorwaves.function.sympy import create_parametrized_function\n", + "from tensorwaves.interface import DataSample, ParametrizedFunction\n", + "from tensorwaves.optimizer import Minuit2\n", + "from tensorwaves.optimizer.callbacks import CSVSummary\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"" ] }, { @@ -105,19 +132,6 @@ }, "outputs": [], "source": [ - "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import ampform\n", - "\n", "model_builder = ampform.get_builder(reaction)\n", "model_builder.adapter.permutate_registered_topologies()\n", "model_builder.scalar_initial_state_mass = True\n", @@ -185,11 +199,6 @@ "metadata": {}, "outputs": [], "source": [ - "from ampform.kinematics.phasespace import Kallen\n", - "from ampform.sympy import unevaluated\n", - "from sympy import Abs\n", - "\n", - "\n", "@unevaluated(real=False)\n", "class PhaseSpaceCM(sp.Expr):\n", " s: Any\n", @@ -261,8 +270,6 @@ "metadata": {}, "outputs": [], "source": [ - "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", - "\n", "PARAMETERS_BW = {}\n", "PARAMETERS_BW.update(model.parameter_defaults)\n", "\n", @@ -394,21 +401,16 @@ }, "outputs": [], "source": [ - "import attrs\n", - "from ampform.helicity import ParameterValues\n", - "\n", "dynamics_expressions_rel_bw = {\n", " symbol: formulate_rel_bw(resonances)\n", " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", "}\n", "model_rel_bw = attrs.evolve(\n", " model,\n", - " parameter_defaults=ParameterValues(\n", - " {\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_BW,\n", - " }\n", - " ),\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_BW,\n", + " }),\n", ")" ] }, @@ -444,12 +446,10 @@ "}\n", "model_fvector = attrs.evolve(\n", " model,\n", - " parameter_defaults=ParameterValues(\n", - " {\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_F,\n", - " }\n", - " ),\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_F,\n", + " }),\n", ")\n", "Latex(aslatex(dynamics_expressions_fvector))" ] @@ -490,8 +490,6 @@ }, "outputs": [], "source": [ - "from tensorwaves.function.sympy import create_parametrized_function\n", - "\n", "unfolded_expression_rel_bw = full_expression_rel_bw.doit()\n", "\n", "intensity_func_rel_bw = create_parametrized_function(\n", @@ -583,21 +581,6 @@ }, "outputs": [], "source": [ - "import os\n", - "\n", - "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from tensorwaves.data import SympyDataTransformer\n", - "\n", "helicity_transformer = SympyDataTransformer.from_sympy(\n", " model.kinematic_variables, backend=\"numpy\"\n", ")" @@ -611,15 +594,6 @@ }, "outputs": [], "source": [ - "import pandas as pd\n", - "from tensorwaves.data import (\n", - " IntensityDistributionGenerator,\n", - " SympyDataTransformer,\n", - " TFPhaseSpaceGenerator,\n", - " TFUniformRealNumberGenerator,\n", - " TFWeightedPhaseSpaceGenerator,\n", - ")\n", - "\n", "rng = TFUniformRealNumberGenerator(seed=0)\n", "phsp_generator = TFPhaseSpaceGenerator(\n", " initial_state_mass=reaction.initial_state[-1].mass,\n", @@ -644,11 +618,6 @@ "metadata": {}, "outputs": [], "source": [ - "import re\n", - "\n", - "from tensorwaves.interface import ParametrizedFunction\n", - "\n", - "\n", "def compute_sub_intensity(\n", " func: ParametrizedFunction,\n", " input_data: DataSample,\n", @@ -701,9 +670,6 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "from matplotlib import cm\n", - "\n", "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", "ax.set_xlim(2, 5)\n", "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV^{2}]\")\n", @@ -826,8 +792,6 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", "figD, axD = plt.subplots(figsize=(6, 5))\n", "c = axD.hist(\n", " np.real(phsp[\"m_01\"]) ** 2,\n", @@ -860,13 +824,11 @@ " domain_transformer=helicity_transformer,\n", ")\n", "data_momenta = data_generator.generate(50_000, rng)\n", - "pd.DataFrame(\n", - " {\n", - " (k, label): np.transpose(v)[i]\n", - " for k, v in data_momenta.items()\n", - " for i, label in enumerate([\"E\", \"px\", \"py\", \"pz\"])\n", - " }\n", - ")\n", + "pd.DataFrame({\n", + " (k, label): np.transpose(v)[i]\n", + " for k, v in data_momenta.items()\n", + " for i, label in enumerate([\"E\", \"px\", \"py\", \"pz\"])\n", + "})\n", "phsp = helicity_transformer(phsp_momenta)\n", "data = helicity_transformer(data_momenta)\n", "data_frame = pd.DataFrame(data)\n", @@ -879,8 +841,6 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", "resonances = sorted(\n", " model.reaction_info.get_intermediate_particles(),\n", " key=lambda p: p.mass,\n", @@ -930,9 +890,6 @@ "metadata": {}, "outputs": [], "source": [ - "from tensorwaves.interface import DataSample\n", - "\n", - "\n", "def safe_downcast_to_real(data: DataSample) -> DataSample:\n", " return {\n", " key: array.real if np.isrealobj(array) else array for key, array in data.items()\n", @@ -949,8 +906,6 @@ "metadata": {}, "outputs": [], "source": [ - "from tensorwaves.estimator import UnbinnedNLL\n", - "\n", "estimator_bw = UnbinnedNLL(\n", " intensity_func_rel_bw,\n", " data=data_real,\n", @@ -1120,9 +1075,6 @@ "metadata": {}, "outputs": [], "source": [ - "from tensorwaves.optimizer import Minuit2\n", - "from tensorwaves.optimizer.callbacks import CSVSummary\n", - "\n", "minuit2 = Minuit2(\n", " callback=CSVSummary(\"fit_traceback.csv\"),\n", " use_analytic_gradient=False,\n", diff --git a/SubintensityPlots_mitAgrand.ipynb b/SubintensityPlots_mitAgrand.ipynb index aa80b3ff..1ccfd938 100644 --- a/SubintensityPlots_mitAgrand.ipynb +++ b/SubintensityPlots_mitAgrand.ipynb @@ -21,16 +21,29 @@ "source": [ "from __future__ import annotations\n", "\n", + "import os\n", + "import re\n", "from typing import Any\n", "\n", + "import ampform\n", + "import attrs\n", "import graphviz\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import qrules\n", "import sympy as sp\n", + "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", + "from ampform.helicity import ParameterValues\n", "from ampform.io import aslatex\n", + "from ampform.kinematics.phasespace import Kallen\n", + "from ampform.sympy import unevaluated\n", "from IPython.display import Latex, display\n", + "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", "from qrules.particle import Particle, ParticleCollection\n", - "from tensorwaves.interface import DataSample" + "from sympy import Abs\n", + "from tensorwaves.data import SympyDataTransformer\n", + "from tensorwaves.function.sympy import create_parametrized_function\n", + "from tensorwaves.interface import DataSample, ParametrizedFunction" ] }, { @@ -100,9 +113,7 @@ "tags": [] }, "outputs": [], - "source": [ - "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol" - ] + "source": [] }, { "cell_type": "code", @@ -112,8 +123,6 @@ }, "outputs": [], "source": [ - "import ampform\n", - "\n", "model_builder = ampform.get_builder(reaction)\n", "model_builder.adapter.permutate_registered_topologies()\n", "model_builder.scalar_initial_state_mass = True\n", @@ -192,11 +201,6 @@ "metadata": {}, "outputs": [], "source": [ - "from ampform.kinematics.phasespace import Kallen\n", - "from ampform.sympy import unevaluated\n", - "from sympy import Abs\n", - "\n", - "\n", "@unevaluated(real=False)\n", "class PhaseSpaceCM(sp.Expr):\n", " s: Any\n", @@ -268,8 +272,6 @@ "metadata": {}, "outputs": [], "source": [ - "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", - "\n", "PARAMETERS_BW = {}\n", "\n", "\n", @@ -407,9 +409,6 @@ }, "outputs": [], "source": [ - "import attrs\n", - "from ampform.helicity import ParameterValues\n", - "\n", "dynamics_expressions_rel_bw = {\n", " symbol: formulate_rel_bw(resonances)\n", " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", @@ -512,8 +511,6 @@ }, "outputs": [], "source": [ - "from tensorwaves.function.sympy import create_parametrized_function\n", - "\n", "unfolded_expression_rel_bw = full_expression_rel_bw.doit()\n", "\n", "intensity_func_rel_bw = create_parametrized_function(\n", @@ -659,8 +656,6 @@ }, "outputs": [], "source": [ - "import os\n", - "\n", "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"" ] }, @@ -672,8 +667,6 @@ }, "outputs": [], "source": [ - "from tensorwaves.data import SympyDataTransformer\n", - "\n", "helicity_transformer = SympyDataTransformer.from_sympy(\n", " model.kinematic_variables, backend=\"numpy\"\n", ")" @@ -687,8 +680,6 @@ }, "outputs": [], "source": [ - "import re\n", - "\n", "epsilon = 1e-8\n", "from tensorwaves.data import (\n", " SympyDataTransformer,\n", @@ -720,11 +711,6 @@ "metadata": {}, "outputs": [], "source": [ - "import re\n", - "\n", - "from tensorwaves.interface import ParametrizedFunction\n", - "\n", - "\n", "def compute_sub_intensity(\n", " func: ParametrizedFunction,\n", " input_data: DataSample,\n", @@ -777,8 +763,6 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", "ax.set_xlim(2, 5)\n", "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", diff --git a/Toyfits_DataFVector_MoreChannel.ipynb b/Toyfits_DataFVector_MoreChannel.ipynb index a194875d..8f988e67 100644 --- a/Toyfits_DataFVector_MoreChannel.ipynb +++ b/Toyfits_DataFVector_MoreChannel.ipynb @@ -27,6 +27,7 @@ "source": [ "from __future__ import annotations\n", "\n", + "import re\n", "from dataclasses import dataclass\n", "from typing import Any, Iterable, Mapping\n", "\n", @@ -937,8 +938,6 @@ "metadata": {}, "outputs": [], "source": [ - "import re\n", - "\n", "re.match(r\"^m_\\d\\d$\", \"m_01\")" ] }, @@ -954,8 +953,6 @@ }, "outputs": [], "source": [ - "import re\n", - "\n", "PHSP = []\n", "epsilon = 1e-8\n", "for i in range(n_channels):\n", @@ -1445,8 +1442,6 @@ }, "outputs": [], "source": [ - "import pandas as pd\n", - "\n", "original_parameters = {\n", " **ORIGINAL_PARAMETERS_F[0],\n", " **ORIGINAL_PARAMETERS_F[1],\n", From 683b2393b261859b902ca7bee144a7874e0f3e53 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 15:51:30 +0200 Subject: [PATCH 06/92] DOC: integrate notebooks into TR infrastructure --- .../report/030.ipynb | 60 +++++++++++++- .../report/031.ipynb | 79 ++++++++++++++++--- .../report/032.ipynb | 64 ++++++++++++++- 3 files changed, 183 insertions(+), 20 deletions(-) rename Mulitpleqn_Toyfit.ipynb => docs/report/030.ipynb (97%) rename SubintensityPlots_mitAgrand.ipynb => docs/report/031.ipynb (96%) rename Toyfits_DataFVector_MoreChannel.ipynb => docs/report/032.ipynb (97%) diff --git a/Mulitpleqn_Toyfit.ipynb b/docs/report/030.ipynb similarity index 97% rename from Mulitpleqn_Toyfit.ipynb rename to docs/report/030.ipynb index d38ef3d7..b475f2c3 100644 --- a/Mulitpleqn_Toyfit.ipynb +++ b/docs/report/030.ipynb @@ -1,21 +1,75 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "```{autolink-concat}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "::::{margin}\n", + ":::{card} Amplitude building with K-matrix dynamics\n", + "TR-030\n", + ":::\n", + "::::" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Genarate data BW $F$ vector and fit with Breit-Wigner for 2 poles and 1 channel\n", - "## Do not mind tho Sub-Intensity plots \n" + "# Fit amplitude model with P-vector dynamics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "%pip install -q 'qrules[viz]==0.10.2' ampform==0.15.4 pandas==2.2.2 sympy==1.12" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": true, "jupyter": { "source_hidden": true }, - "tags": [] + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input", + "scroll-input" + ] }, "outputs": [], "source": [ diff --git a/SubintensityPlots_mitAgrand.ipynb b/docs/report/031.ipynb similarity index 96% rename from SubintensityPlots_mitAgrand.ipynb rename to docs/report/031.ipynb index 1ccfd938..0791c09c 100644 --- a/SubintensityPlots_mitAgrand.ipynb +++ b/docs/report/031.ipynb @@ -2,20 +2,82 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ - "\n", - "# Sub-Intensity plots " + "```{autolink-concat}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "::::{margin}\n", + ":::{card} Amplitude building with K-matrix dynamics\n", + "TR-031\n", + "^^^\n", + "Sub-intensity plots for a model with $K$-matrix ($P$-vector) dynamics.\n", + ":::\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Sub-intensity plots " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "%pip install -q 'qrules[viz]==0.10.2' ampform==0.15.4 sympy==1.12 tensorwaves==0.4.12" ] }, { "cell_type": "code", "execution_count": null, "metadata": { + "editable": true, "jupyter": { "source_hidden": true }, - "tags": [] + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input", + "scroll-input" + ] }, "outputs": [], "source": [ @@ -106,15 +168,6 @@ "graphviz.Source(dot)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, diff --git a/Toyfits_DataFVector_MoreChannel.ipynb b/docs/report/032.ipynb similarity index 97% rename from Toyfits_DataFVector_MoreChannel.ipynb rename to docs/report/032.ipynb index 8f988e67..307e1c4e 100644 --- a/Toyfits_DataFVector_MoreChannel.ipynb +++ b/docs/report/032.ipynb @@ -2,10 +2,65 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ - "# [R990] Genarate data with $F$ vector $F$ vector for n poles and n channels \n", - "## Working plots Remco" + "```{autolink-concat}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "::::{margin}\n", + ":::{card} Amplitude building with K-matrix dynamics\n", + "TR-031\n", + "^^^\n", + "Illustration of how to formulate an amplitude model with P-vector dynamics.\n", + ":::\n", + "::::" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# P-vector fit comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "%pip install -q 'qrules[viz]==0.10.2' ampform==0.15.4 pandas==2.2.2 sympy==1.12 tensorwaves==0.4.12" ] }, { @@ -20,7 +75,8 @@ "slide_type": "" }, "tags": [ - "hide-cell" + "hide-input", + "scroll-input" ] }, "outputs": [], From 526c4f47e9fddceedf1f64baef135813e8c87c9f Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 15:58:33 +0200 Subject: [PATCH 07/92] FIX: import `create_dynamics_symbol()` from K-matrix-research This makes the notebooks standalone, as the `kmatrix` package has not been published --- docs/report/030.ipynb | 27 ++++++++++++++++++++++++++- docs/report/031.ipynb | 27 ++++++++++++++++++++++++++- docs/report/032.ipynb | 27 ++++++++++++++++++++++++++- 3 files changed, 78 insertions(+), 3 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index b475f2c3..44cf13f6 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -77,6 +77,7 @@ "\n", "import os\n", "import re\n", + "from collections import defaultdict\n", "from typing import Any\n", "\n", "import ampform\n", @@ -93,7 +94,6 @@ "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import unevaluated\n", "from IPython.display import Latex, display\n", - "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", "from matplotlib import cm\n", "from qrules.particle import Particle, ParticleCollection\n", "from sympy import Abs\n", @@ -135,6 +135,31 @@ "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_dynamics_symbol(\n", + " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", + ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", + " J = sp.Rational(resonance.spin)\n", + " Q = resonance.charge\n", + " P = sp.Rational(resonance.parity)\n", + " if variable_pool.angular_momentum is not None:\n", + " L = sp.Rational(variable_pool.angular_momentum)\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", + " else:\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", + " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", + " parameter_defaults = {}\n", + " return X, parameter_defaults\n", + "\n", + "\n", + "COLLECTED_X_SYMBOLS = defaultdict(set)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 0791c09c..07a8081a 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -85,6 +85,7 @@ "\n", "import os\n", "import re\n", + "from collections import defaultdict\n", "from typing import Any\n", "\n", "import ampform\n", @@ -100,7 +101,6 @@ "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import unevaluated\n", "from IPython.display import Latex, display\n", - "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", "from qrules.particle import Particle, ParticleCollection\n", "from sympy import Abs\n", "from tensorwaves.data import SympyDataTransformer\n", @@ -130,6 +130,31 @@ "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_dynamics_symbol(\n", + " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", + ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", + " J = sp.Rational(resonance.spin)\n", + " Q = resonance.charge\n", + " P = sp.Rational(resonance.parity)\n", + " if variable_pool.angular_momentum is not None:\n", + " L = sp.Rational(variable_pool.angular_momentum)\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", + " else:\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", + " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", + " parameter_defaults = {}\n", + " return X, parameter_defaults\n", + "\n", + "\n", + "COLLECTED_X_SYMBOLS = defaultdict(set)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 307e1c4e..39e4cab4 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -84,6 +84,7 @@ "from __future__ import annotations\n", "\n", "import re\n", + "from collections import defaultdict\n", "from dataclasses import dataclass\n", "from typing import Any, Iterable, Mapping\n", "\n", @@ -100,7 +101,6 @@ "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", "from IPython.display import Math, display\n", - "from kmatrix import COLLECTED_X_SYMBOLS, create_dynamics_symbol\n", "from matplotlib import cm\n", "from qrules.particle import Particle, ParticleCollection\n", "from qrules.transition import ReactionInfo\n", @@ -150,6 +150,31 @@ "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_dynamics_symbol(\n", + " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", + ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", + " J = sp.Rational(resonance.spin)\n", + " Q = resonance.charge\n", + " P = sp.Rational(resonance.parity)\n", + " if variable_pool.angular_momentum is not None:\n", + " L = sp.Rational(variable_pool.angular_momentum)\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", + " else:\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", + " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", + " parameter_defaults = {}\n", + " return X, parameter_defaults\n", + "\n", + "\n", + "COLLECTED_X_SYMBOLS = defaultdict(set)" + ] + }, { "cell_type": "code", "execution_count": null, From 3718df9034af1611b951ce19ae1fe5b0a57c7075 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 16:23:03 +0200 Subject: [PATCH 08/92] FIX: import additional nstar definitions --- docs/report/.gitignore | 2 + docs/report/030.ipynb | 2 +- .../additional-nstar-sigma-definitions.yml | 458 ++++++++++++++++++ docs/report/031.ipynb | 2 +- docs/report/032.ipynb | 2 +- 5 files changed, 463 insertions(+), 3 deletions(-) create mode 100644 docs/report/030/additional-nstar-sigma-definitions.yml diff --git a/docs/report/.gitignore b/docs/report/.gitignore index c2728bc9..98a133c7 100644 --- a/docs/report/.gitignore +++ b/docs/report/.gitignore @@ -6,3 +6,5 @@ 002-*-graph 013-graph? 018-graph + +!030/*.yml diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 44cf13f6..77565582 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -169,7 +169,7 @@ "def load_particle_database() -> ParticleCollection:\n", " particle_database = qrules.load_default_particles()\n", " additional_definitions = qrules.io.load(\n", - " \"../../../additional-nstar-sigma-definitions.yml\"\n", + " \"030/additional-nstar-sigma-definitions.yml\"\n", " )\n", " particle_database.update(additional_definitions)\n", " return particle_database\n", diff --git a/docs/report/030/additional-nstar-sigma-definitions.yml b/docs/report/030/additional-nstar-sigma-definitions.yml new file mode 100644 index 00000000..db180d29 --- /dev/null +++ b/docs/report/030/additional-nstar-sigma-definitions.yml @@ -0,0 +1,458 @@ +# Imported from ComPWA/PWA-JPsi2pbarSigmaKS@a0aba08 +particles: + - name: N(1875)+ + pid: 200002 + latex: N(1875)^+ + spin: 1.5 + mass: 1.875 + width: 0.2 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: N(1880)+ + pid: 200000 + latex: N(1880)^+ + spin: 0.5 + mass: 1.88 + width: 0.3 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: 1 + + - name: N(1895)+ + pid: 200001 + latex: N(1895)^+ + spin: 0.5 + mass: 1.895 + width: 0.12 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: N(Fakestar)+ + pid: 20047545456003 + latex: N(Fakestar)^+ + spin: 1.5 + mass: 1.82 + width: 0.6 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: 1 + + - name: N(Fakestar1)+ + pid: 2004754545864656786003 + latex: N(Fakestar1)^+ + spin: 0.5 + mass: 1.65 + width: 0.6 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: N(Fakestar2)+ + pid: 20047545458356789764656786003 + latex: N(Fakestar2)^+ + spin: 0.5 + mass: 1.75 + width: 0.6 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: N(1900)+ + pid: 200003 + latex: N(1900)^+ + spin: 1.5 + mass: 1.92 + width: 0.2 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: 1 + + - name: N(2060)+ + pid: 200004 + latex: N(2060)^+ + spin: 2.5 + mass: 2.1 + width: 0.4 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: N(J05Pm)+ + pid: 100001 + latex: N(1/2^+)^+ + spin: 0.5 + mass: 1.99 + width: 0.15 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: N(J05Pp)+ + pid: 100000 + latex: N(1/2^+)^+ + spin: 0.5 + mass: 1.99 + width: 0.15 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: 1 + + - name: N(J15Pm)+ + pid: 100003 + latex: N(3/2^-)^+ + spin: 1.5 + mass: 1.99 + width: 0.15 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: N(J15Pp)+ + pid: 100002 + latex: N(3/2^+)^+ + spin: 1.5 + mass: 1.99 + width: 0.15 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: 1 + + - name: NonResonantN12M + pid: 20000016 + latex: NR(\Sigma^+K_S^0)(J^P=\frac{1}{2}^-) + spin: 0.5 + mass: 1.99 + width: 0.5 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: NonResonantN12P + pid: 20000006 + latex: NR(\Sigma^+K_S^0)(J^P=\frac{1}{2}^+) + spin: 0.5 + mass: 1.99 + width: 0.5 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: 1 + + - name: NonResonantN32M + pid: 20000017 + latex: NR(\Sigma^+K_S^0)(J^P=\frac{3}{2}^-) + spin: 1.5 + mass: 1.99 + width: 0.5 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: -1 + + - name: NonResonantN32P + pid: 20000007 + latex: NR(\Sigma^+K_S^0)(J^P=\frac{3}{2}^+) + spin: 1.5 + mass: 1.99 + width: 0.5 + charge: 1 + isospin: + magnitude: 0.5 + projection: 0.5 + baryon_number: 1 + parity: + value: 1 + + - name: N(1875)~- + pid: -9999993 + latex: \overline{N}(1875)^{+} + spin: 1.5 + mass: 1.875 + width: 0.12 + charge: -1 + isospin: + magnitude: 0.5 + projection: -0.5 + baryon_number: -1 + parity: + value: -1 + + - name: N(1880)~- + pid: -9999994 + latex: \overline{N}(1880)^{+} + spin: 0.5 + mass: 1.88 + width: 0.3 + charge: -1 + isospin: + magnitude: 0.5 + projection: -0.5 + baryon_number: -1 + parity: + value: -1 + + - name: N(1895)~- + pid: -9999995 + latex: \overline{N}(1895)^{+} + spin: 0.5 + mass: 1.895 + width: 0.12 + charge: -1 + isospin: + magnitude: 0.5 + projection: -0.5 + baryon_number: -1 + parity: + value: 1 + + - name: N(1900)~- + pid: -9999996 + latex: \overline{N}(1900)^{+} + spin: 1.5 + mass: 1.92 + width: 0.2 + charge: -1 + isospin: + magnitude: 0.5 + projection: -0.5 + baryon_number: -1 + parity: + value: -1 + + - name: N(2060)~- + pid: -9999997 + latex: \overline{N}(2060)^{+} + spin: 1.5 + mass: 2.07 + width: 0.4 + charge: -1 + isospin: + magnitude: 0.5 + projection: -0.5 + baryon_number: -1 + parity: + value: -1 + + - name: N(2100)~- + pid: -9999998 + latex: \overline{N}(2100)^{+} + spin: 0.5 + mass: 2.1 + width: 0.26 + charge: -1 + isospin: + magnitude: 0.5 + projection: -0.5 + baryon_number: -1 + parity: + value: -1 + + - name: N(2120)~- + pid: -9999999 + latex: \overline{N}(2120)^{+} + spin: 1.5 + mass: 2.12 + width: 0.3 + charge: -1 + isospin: + magnitude: 0.5 + projection: -0.5 + baryon_number: -1 + parity: + value: 1 + + - name: Sigma(1580)~- + pid: 300000 + latex: \bar{\Sigma}(1580)^- + spin: 1.5 + mass: 1.58 + width: 0.015 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: 1 + + - name: Sigma(1620)~- + pid: 300001 + latex: \bar{\Sigma}(1620)^- + spin: 0.5 + mass: 1.62 + width: 0.07 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: 1 + + - name: Sigma(1880)~- + pid: 300004 + latex: \bar{\Sigma}(1880)^- + spin: 0.5 + mass: 1.88 + width: 0.2 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: -1 + + - name: Sigma(1900)~- + pid: 300005 + latex: \bar{\Sigma}(1900)^- + spin: 0.5 + mass: 1.925 + width: 0.165 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: 1 + + - name: Sigma(1940)~- + pid: 300007 + latex: \bar{\Sigma}(1940)^- + spin: 1.5 + mass: 1.94 + width: 0.25 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: -1 + + - name: NonResonantSigma12M + pid: 30000019 + latex: NR(\bar{p}K_S^0)(J^P=\frac{1}{2}^-) + spin: 0.5 + mass: 1.7 + width: 0.5 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: -1 + + - name: NonResonantSigma12P + pid: 30000009 + latex: NR(\bar{p}K_S^0)(J^P=\frac{1}{2}^+) + spin: 0.5 + mass: 1.7 + width: 0.5 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: 1 + + - name: NonResonantSigma32M + pid: 30000222220 + latex: NR(\bar{p}K_S^0)(J^P=\frac{3}{2}^-) + spin: 1.5 + mass: 1.72 + width: 0.52 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: -1 + + - name: NonResonantSigma32P + pid: 30000010 + latex: NR(\bar{p}K_S^0)(J^P=\frac{3}{2}^+) + spin: 1.5 + mass: 1.7 + width: 0.5 + charge: -1 + isospin: + magnitude: 1.0 + projection: -1.0 + strangeness: 1 + baryon_number: -1 + parity: + value: 1 diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 07a8081a..9c79a711 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -164,7 +164,7 @@ "def load_particle_database() -> ParticleCollection:\n", " particle_database = qrules.load_default_particles()\n", " additional_definitions = qrules.io.load(\n", - " \"../../../additional-nstar-sigma-definitions.yml\"\n", + " \"030/additional-nstar-sigma-definitions.yml\"\n", " )\n", " particle_database.update(additional_definitions)\n", " return particle_database\n", diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 39e4cab4..eba0a384 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -190,7 +190,7 @@ "def load_particle_database() -> ParticleCollection:\n", " particle_database = qrules.load_default_particles()\n", " additional_definitions = qrules.io.load(\n", - " \"../../../additional-nstar-sigma-definitions.yml\"\n", + " \"030/additional-nstar-sigma-definitions.yml\"\n", " )\n", " particle_database.update(additional_definitions)\n", " return particle_database\n", From 8f0529a57eec7398e49a38f8bfd5511b2732f4ad Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 16:37:03 +0200 Subject: [PATCH 09/92] MAINT: rename `additional-definitions.yml` --- docs/report/030.ipynb | 4 +--- ...nstar-sigma-definitions.yml => additional-definitions.yml} | 0 docs/report/031.ipynb | 4 +--- docs/report/032.ipynb | 4 +--- 4 files changed, 3 insertions(+), 9 deletions(-) rename docs/report/030/{additional-nstar-sigma-definitions.yml => additional-definitions.yml} (100%) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 77565582..7d0cf711 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -168,9 +168,7 @@ "source": [ "def load_particle_database() -> ParticleCollection:\n", " particle_database = qrules.load_default_particles()\n", - " additional_definitions = qrules.io.load(\n", - " \"030/additional-nstar-sigma-definitions.yml\"\n", - " )\n", + " additional_definitions = qrules.io.load(\"030/additional-definitions.yml\")\n", " particle_database.update(additional_definitions)\n", " return particle_database\n", "\n", diff --git a/docs/report/030/additional-nstar-sigma-definitions.yml b/docs/report/030/additional-definitions.yml similarity index 100% rename from docs/report/030/additional-nstar-sigma-definitions.yml rename to docs/report/030/additional-definitions.yml diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 9c79a711..bc11a0c4 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -163,9 +163,7 @@ "source": [ "def load_particle_database() -> ParticleCollection:\n", " particle_database = qrules.load_default_particles()\n", - " additional_definitions = qrules.io.load(\n", - " \"030/additional-nstar-sigma-definitions.yml\"\n", - " )\n", + " additional_definitions = qrules.io.load(\"030/additional-definitions.yml\")\n", " particle_database.update(additional_definitions)\n", " return particle_database\n", "\n", diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index eba0a384..627d7d0d 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -189,9 +189,7 @@ "source": [ "def load_particle_database() -> ParticleCollection:\n", " particle_database = qrules.load_default_particles()\n", - " additional_definitions = qrules.io.load(\n", - " \"030/additional-nstar-sigma-definitions.yml\"\n", - " )\n", + " additional_definitions = qrules.io.load(\"030/additional-definitions.yml\")\n", " particle_database.update(additional_definitions)\n", " return particle_database\n", "\n", From b6e7c20de87ebb4ef36038755b2e00d46a6393f9 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 16:42:21 +0200 Subject: [PATCH 10/92] DOC: fix cspell errors --- .cspell.json | 7 +++++++ docs/report/030.ipynb | 18 +++++++++--------- docs/report/031.ipynb | 10 +++++----- docs/report/032.ipynb | 10 +++++----- 4 files changed, 26 insertions(+), 19 deletions(-) diff --git a/.cspell.json b/.cspell.json index 2dbbd97b..5e52fdc7 100644 --- a/.cspell.json +++ b/.cspell.json @@ -139,6 +139,7 @@ "Colab", "Danilkin", "Deineka", + "Fakestar", "MAINT", "Tiator", "absl", @@ -159,6 +160,7 @@ "axhline", "axvline", "azim", + "bbox", "bdist", "bgcolor", "boldsymbol", @@ -214,6 +216,7 @@ "forall", "framealpha", "funcs", + "fvector", "getitem", "getsource", "graphviz", @@ -225,6 +228,7 @@ "heli", "hepstats", "histtype", + "hotpink", "hoverinfo", "hspace", "hypotests", @@ -242,12 +246,14 @@ "isinstance", "isnan", "isort", + "isrealobj", "jaxlib", "joinpath", "jpsi", "juliaup", "jupyterlab", "kernelspec", + "kmatrix", "kutschke", "lambdifier", "lambdifygenerated", @@ -371,6 +377,7 @@ "toprettyxml", "tqdm", "treewise", + "twinx", "unevaluatable", "unsrt", "venv", diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 7d0cf711..717b0f3c 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -322,7 +322,7 @@ "\n", "\n", "@unevaluated(real=False)\n", - "class EnergyDecaywidth(sp.Expr):\n", + "class ChannelWidth(sp.Expr):\n", " s: Any\n", " m1: Any\n", " m2: Any\n", @@ -363,7 +363,7 @@ " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", - " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", + " w_s = (ChannelWidth(s, m_a, m_b, w_) for w_ in w)\n", " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", " for i, (resonance, _) in enumerate(resonances):\n", " PARAMETERS_BW[w[i]] = resonance.width\n", @@ -619,7 +619,7 @@ " R\"g_{N(Fakestar2)^+}\": 1,\n", "}\n", "\n", - "new_parameters_relbw = {\n", + "new_parameters_bw = {\n", " R\"m_{N(Fakestar)^+}\": 1.85,\n", " R\"w_{N(Fakestar)^+}\": 1 / 1.85,\n", " R\"m_{N(1900)^+}\": 1.9,\n", @@ -638,7 +638,7 @@ "outputs": [], "source": [ "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", - "intensity_func_rel_bw.update_parameters(new_parameters_relbw)\n", + "intensity_func_rel_bw.update_parameters(new_parameters_bw)\n", "intensity_func_fvector.parameters" ] }, @@ -934,7 +934,7 @@ " density=True,\n", ")\n", "ax.set_xlabel(\"$m$ [GeV]\")\n", - "for (k, v), color in zip(new_parameters_relbw.items(), colors):\n", + "for (k, v), color in zip(new_parameters_bw.items(), colors):\n", " if k.startswith(\"m_{\"):\n", " ax.axvline(\n", " x=v,\n", @@ -1120,7 +1120,7 @@ " R\"g_{N(Fakestar2)^+}\": 1,\n", "}\n", "\n", - "initial_parameters_relbw = {\n", + "initial_parameters_bw = {\n", " R\"m_{N(Fakestar)^+}\": 1.8,\n", " R\"w_{N(Fakestar)^+}\": 1 / 1.85,\n", " R\"m_{N(1900)^+}\": 1.93,\n", @@ -1140,7 +1140,7 @@ "source": [ "original_parameters = intensity_func_fvector.parameters\n", "intensity_func_fvector.update_parameters(initial_parameters_fvector)\n", - "intensity_func_rel_bw.update_parameters(initial_parameters_relbw)\n", + "intensity_func_rel_bw.update_parameters(initial_parameters_bw)\n", "compare_model(\n", " \"m_01\", data_real, phsp_real, intensity_func_fvector, intensity_func_rel_bw\n", ")" @@ -1157,7 +1157,7 @@ " use_analytic_gradient=False,\n", ")\n", "\n", - "fit_result_BW = minuit2.optimize(estimator_bw, initial_parameters_relbw)\n", + "fit_result_BW = minuit2.optimize(estimator_bw, initial_parameters_bw)\n", "display(\"Fit Breit-Wigner:\", fit_result_BW)\n", "fit_result_F = minuit2.optimize(estimator_fvector, initial_parameters_fvector)\n", "display(\"Fit F vector:\", fit_result_F)" @@ -1208,7 +1208,7 @@ "source": [ "for p in optimized_parameters_BW:\n", " print(p)\n", - " print(f\" initial: {initial_parameters_relbw[p]:.3}\")\n", + " print(f\" initial: {initial_parameters_bw[p]:.3}\")\n", " print(f\" optimized Breit-Wigner: {optimized_parameters_BW[p]:.3}\")\n", " print(f\" original: {original_parameters[p]:.3}\")\n", "latest_parameters_BW = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index bc11a0c4..e5f78979 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -323,7 +323,7 @@ "\n", "\n", "@unevaluated(real=False)\n", - "class EnergyDecaywidth(sp.Expr):\n", + "class ChannelWidth(sp.Expr):\n", " s: Any\n", " m1: Any\n", " m2: Any\n", @@ -364,7 +364,7 @@ " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", " dummy = [sp.Symbol(Rf\"Dummy_{{{p.latex}}}\") for p, _ in resonances]\n", - " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", + " w_s = (ChannelWidth(s, m_a, m_b, w_) for w_ in w)\n", " rel_bw = sum(\n", " (w_ * m_ * dummy_) / (m_**2 - s - m_ * w_s_)\n", " for m_, w_, w_s_, dummy_ in zip(m, w, w_s, dummy)\n", @@ -695,7 +695,7 @@ " R\"g_{N(Fakestar)^+}\": g_res1,\n", "}\n", "\n", - "new_parameters_relbw = {\n", + "new_parameters_bw = {\n", " R\"m_{N(Fakestar)^+}\": m_res1,\n", " R\"w_{N(Fakestar)^+}\": g_res1 / m_res1,\n", " R\"m_{N(1900)^+}\": m_res2,\n", @@ -710,9 +710,9 @@ "outputs": [], "source": [ "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", - "intensity_func_rel_bw.update_parameters(new_parameters_relbw)\n", + "intensity_func_rel_bw.update_parameters(new_parameters_bw)\n", "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", - "dynamics_func_bw.update_parameters(new_parameters_relbw)\n", + "dynamics_func_bw.update_parameters(new_parameters_bw)\n", "dynamics_func_fvector.parameters" ] }, diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 627d7d0d..5eb81caf 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -448,7 +448,7 @@ "outputs": [], "source": [ "@unevaluated(real=False)\n", - "class EnergyDecaywidth(sp.Expr):\n", + "class ChannelWidth(sp.Expr):\n", " s: Any\n", " m1: Any\n", " m2: Any\n", @@ -461,7 +461,7 @@ "\n", "\n", "s, m1, m2, width = sp.symbols(\"s m1 m2 gamma0\")\n", - "expr = EnergyDecaywidth(s, m1, m2, width)\n", + "expr = ChannelWidth(s, m1, m2, width)\n", "Math(aslatex({expr: expr.doit(deep=False)}))" ] }, @@ -553,7 +553,7 @@ " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", - " w_s = (EnergyDecaywidth(s, m_a, m_b, w_) for w_ in w)\n", + " w_s = (ChannelWidth(s, m_a, m_b, w_) for w_ in w)\n", " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", " for i, (resonance, _) in enumerate(resonances):\n", " PARAMETERS_BW[w[i]] = resonance.width\n", @@ -1335,13 +1335,13 @@ " DECAYS[0].child1.mass + DECAYS[0].child2.mass,\n", " color=\"grey\",\n", " linestyle=\"dotted\",\n", - " label=rf\"${DECAYS[0].child1.latex} \\, {DECAYS[0].child2.latex}$ threshhold\",\n", + " label=rf\"${DECAYS[0].child1.latex} \\, {DECAYS[0].child2.latex}$ threshold\",\n", " )\n", " ax.axvline(\n", " DECAYS[1].child1.mass + DECAYS[1].child2.mass,\n", " color=\"grey\",\n", " linestyle=\"dotted\",\n", - " label=rf\"${DECAYS[1].child1.latex} \\, {DECAYS[1].child2.latex}$ threshhold\",\n", + " label=rf\"${DECAYS[1].child1.latex} \\, {DECAYS[1].child2.latex}$ threshold\",\n", " )\n", " ax.legend()\n", " fig.show()" From 0a561e12517be3525a76ebfbd4c70ee57431d4ad Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 16:46:32 +0200 Subject: [PATCH 11/92] FIX: install `jax` and `phasespace` --- docs/report/030.ipynb | 2 +- docs/report/031.ipynb | 2 +- docs/report/032.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 717b0f3c..b921b0fa 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -52,7 +52,7 @@ }, "outputs": [], "source": [ - "%pip install -q 'qrules[viz]==0.10.2' ampform==0.15.4 pandas==2.2.2 sympy==1.12" + "%pip install -q 'qrules[viz]==0.10.2' 'tensorwaves[jax,phsp]==0.4.12' ampform==0.15.4 pandas==2.2.2 sympy==1.12" ] }, { diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index e5f78979..e5f923fd 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -60,7 +60,7 @@ }, "outputs": [], "source": [ - "%pip install -q 'qrules[viz]==0.10.2' ampform==0.15.4 sympy==1.12 tensorwaves==0.4.12" + "%pip install -q 'qrules[viz]==0.10.2' 'tensorwaves[jax]==0.4.12' ampform==0.15.4 sympy==1.12" ] }, { diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 5eb81caf..546ed068 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -60,7 +60,7 @@ }, "outputs": [], "source": [ - "%pip install -q 'qrules[viz]==0.10.2' ampform==0.15.4 pandas==2.2.2 sympy==1.12 tensorwaves==0.4.12" + "%pip install -q 'qrules[viz]==0.10.2' 'tensorwaves[jax,phsp]==0.4.12' ampform==0.15.4 pandas==2.2.2 sympy==1.12" ] }, { From eb98b66310f2f060306fa8448c904b34c1971622 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Wed, 22 May 2024 17:39:58 +0200 Subject: [PATCH 12/92] FIX: make notebooks runnable --- docs/report/030.ipynb | 24 +++++--------------- docs/report/031.ipynb | 35 ++++++++++++++-------------- docs/report/032.ipynb | 53 +++++++++++++++++++++++-------------------- 3 files changed, 52 insertions(+), 60 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index b921b0fa..3fdbaf0a 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -1193,9 +1193,9 @@ "source": [ "for p in optimized_parameters_F:\n", " print(p)\n", - " print(f\" initial: {initial_parameters_fvector[p]:.3}\")\n", - " print(f\" optimized F vector: {optimized_parameters_F[p]:.3}\")\n", - " print(f\" original: {original_parameters[p]:.3}\")\n", + " print(f\" initial: {initial_parameters_fvector[p]:.3f}\")\n", + " print(f\" optimized F vector: {optimized_parameters_F[p]:.3f}\")\n", + " print(f\" original: {original_parameters[p]:.3f}\")\n", "latest_parameters_F = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", "latest_parameters_F" ] @@ -1208,24 +1208,12 @@ "source": [ "for p in optimized_parameters_BW:\n", " print(p)\n", - " print(f\" initial: {initial_parameters_bw[p]:.3}\")\n", - " print(f\" optimized Breit-Wigner: {optimized_parameters_BW[p]:.3}\")\n", - " print(f\" original: {original_parameters[p]:.3}\")\n", + " print(f\" initial: {initial_parameters_bw[p]:.3f}\")\n", + " print(f\" optimized Breit-Wigner: {optimized_parameters_BW.get(p, -9999):3f}\")\n", + " print(f\" original: {original_parameters.get(p, -9999):.3f}\")\n", "latest_parameters_BW = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", "latest_parameters_BW" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index e5f923fd..e400258c 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -100,7 +100,7 @@ "from ampform.io import aslatex\n", "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import unevaluated\n", - "from IPython.display import Latex, display\n", + "from IPython.display import Latex, Math, display\n", "from qrules.particle import Particle, ParticleCollection\n", "from sympy import Abs\n", "from tensorwaves.data import SympyDataTransformer\n", @@ -201,36 +201,28 @@ "source": [ "model_builder = ampform.get_builder(reaction)\n", "model_builder.adapter.permutate_registered_topologies()\n", - "model_builder.scalar_initial_state_mass = True\n", - "model_builder.stable_final_state_ids = [0, 1, 2]\n", + "model_builder.config.scalar_initial_state_mass = True\n", + "model_builder.config.stable_final_state_ids = [0, 1, 2]\n", "for name in reaction.get_intermediate_particles().names:\n", " model_builder.set_dynamics(name, create_dynamics_symbol)\n", "model = model_builder.formulate()\n", "model.intensity.cleanup()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "amp, *_ = model.amplitudes.values()\n", - "amp" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + } }, "outputs": [], "source": [ "selected_amplitudes = {\n", - " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i < 3\n", + " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i < 2\n", "}\n", - "src = aslatex(selected_amplitudes)" + "Math(aslatex(selected_amplitudes, terms_per_line=1))" ] }, { @@ -274,7 +266,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + } + }, "outputs": [], "source": [ "@unevaluated(real=False)\n", @@ -933,7 +929,10 @@ "sub_phase_bw = {\n", " p: np.angle(\n", " compute_sub_intensity(\n", - " dynamics_func_bw, data, resonances=[p.latex], coupling_pattern=r\"Dummy_\"\n", + " dynamics_func_bw,\n", + " data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"Dummy_\",\n", " )\n", " )\n", " for p, _ in resonances\n", diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 546ed068..051f09dd 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -827,22 +827,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "full-width" - ] - }, + "metadata": {}, "outputs": [], "source": [ "combined_expressions = {**K_expressions, **rho_expressions, **P_expressions}\n", - "F_expressions = np.array([\n", - " perform_cached_doit(F_vector[i].xreplace(combined_expressions))\n", - " for i in range(n_channels)\n", - "])" + "F_exprs = F_vector.xreplace(combined_expressions)\n", + "F_exprs[0].simplify(doit=False)" ] }, { @@ -858,6 +848,15 @@ "### Model $F$ vector" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "F_unfolded_exprs = np.array([perform_cached_doit(expr) for expr in F_exprs])" + ] + }, { "cell_type": "code", "execution_count": null, @@ -873,7 +872,8 @@ "DYNAMICS_EXPRESSIONS_FVECTOR = []\n", "for i in range(n_channels):\n", " exprs = {\n", - " symbol: F_expressions[i] for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " symbol: F_unfolded_exprs[i]\n", + " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", " }\n", " DYNAMICS_EXPRESSIONS_FVECTOR.append(exprs)\n", "\n", @@ -956,11 +956,15 @@ "metadata": {}, "outputs": [], "source": [ + "m_res = 1.82\n", + "g_res_ch0 = 1.8\n", + "g_res_ch1 = 2.5\n", + "\n", "new_parameters_fvector = {\n", " R\"m_{N(Fakestar)^+}\": 1.71,\n", " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", - " R\"g_{N(Fakestar)^+,0}\": 0.8,\n", - " R\"g_{N(Fakestar)^+,1}\": 0.9,\n", + " R\"g_{N(Fakestar)^+,0}\": g_res_ch0,\n", + " R\"g_{N(Fakestar)^+,1}\": g_res_ch1,\n", "}" ] }, @@ -1273,8 +1277,8 @@ "initial_parameters = {\n", " R\"m_{N(Fakestar)^+}\": 1.9,\n", " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", - " R\"g_{N(Fakestar)^+,0}\": 0.8,\n", - " R\"g_{N(Fakestar)^+,1}\": 0.6,\n", + " R\"g_{N(Fakestar)^+,0}\": 2.8,\n", + " R\"g_{N(Fakestar)^+,1}\": 1.6,\n", "}\n", "INTENSITY_FUNCS_FVECTOR[0].parameters" ] @@ -1555,10 +1559,7 @@ "n_real_par = fit_result.count_number_of_parameters(complex_twice=True)\n", "n_events = len(next(iter(data.values())))\n", "log_likelihood = -fit_result.estimator_value\n", - "\n", - "aic = 2 * n_real_par - 2 * log_likelihood\n", - "bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", - "aic" + "log_likelihood" ] }, { @@ -1567,7 +1568,8 @@ "metadata": {}, "outputs": [], "source": [ - "bic" + "aic = 2 * n_real_par - 2 * log_likelihood\n", + "aic" ] }, { @@ -1575,7 +1577,10 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", + "bic" + ] } ], "metadata": { From e34a211b83481f74b96981c48dc5d1a7a7dec8e8 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 00:14:57 +0200 Subject: [PATCH 13/92] DOC: improve fake Nstar names --- docs/report/030.ipynb | 44 +++++++------- docs/report/030/additional-definitions.yml | 68 +++++++++++----------- docs/report/031.ipynb | 12 ++-- docs/report/032.ipynb | 18 +++--- 4 files changed, 71 insertions(+), 71 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 3fdbaf0a..3abc0595 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -188,10 +188,10 @@ " initial_state=\"J/psi(1S)\",\n", " final_state=[\"eta\", \"p\", \"p~\"],\n", " allowed_intermediate_particles=[\n", - " \"N(Fakestar2)+\",\n", + " \"N**3\",\n", " \"N(1650)+\",\n", " \"N(1900)+\",\n", - " \"N(Fakestar)+\",\n", + " \"N**1\",\n", " ],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", @@ -605,27 +605,27 @@ "outputs": [], "source": [ "new_parameters_fvector = {\n", - " R\"m_{N(Fakestar)^+}\": 1.95,\n", - " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", + " R\"m_{N^{**}_1}\": 1.95,\n", + " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", " R\"m_{N(1900)^+}\": 1.9,\n", " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", " R\"g_{N(1900)^+}\": 1,\n", - " R\"g_{N(Fakestar)^+}\": 1,\n", - " R\"m_{N(Fakestar2)^+}\": 1.75,\n", - " R\"\\beta_{N(Fakestar2)^+}\": 1 + 0j,\n", + " R\"g_{N^{**}_1}\": 1,\n", + " R\"m_{N^{**}_3}\": 1.75,\n", + " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", " R\"m_{N(1650)^{+}}\": 1.65,\n", " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", " R\"g_{N(1650)^{+}}\": 1.65,\n", - " R\"g_{N(Fakestar2)^+}\": 1,\n", + " R\"g_{N^{**}_3}\": 1,\n", "}\n", "\n", "new_parameters_bw = {\n", - " R\"m_{N(Fakestar)^+}\": 1.85,\n", - " R\"w_{N(Fakestar)^+}\": 1 / 1.85,\n", + " R\"m_{N^{**}_1}\": 1.85,\n", + " R\"w_{N^{**}_1}\": 1 / 1.85,\n", " R\"m_{N(1900)^+}\": 1.9,\n", " R\"w_{N(1900)^+}\": 1 / 1.9,\n", - " R\"m_{N(Fakestar2)^+}\": 1.75,\n", - " R\"w_{N(Fakestar2)^+}\": 1 / 1.75,\n", + " R\"m_{N^{**}_3}\": 1.75,\n", + " R\"w_{N^{**}_3}\": 1 / 1.75,\n", " R\"m_{N(1650)^{+}}\": 1.65,\n", " R\"w_{N(1650)^{+}}\": 1 / 1.65,\n", "}" @@ -1106,27 +1106,27 @@ "g_Fakestar2 = 1.0\n", "m_Fakestar1 = 1.94\n", "initial_parameters_fvector = {\n", - " R\"m_{N(Fakestar)^+}\": 1.95,\n", - " R\"\\beta_{N(Fakestar)^+}\": 0.9 + 0j,\n", + " R\"m_{N^{**}_1}\": 1.95,\n", + " R\"\\beta_{N^{**}_1}\": 0.9 + 0j,\n", " R\"m_{N(1900)^+}\": 1.91,\n", " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", " R\"g_{N(1900)^+}\": 1.0,\n", - " R\"g_{N(Fakestar)^+}\": 1.0,\n", - " R\"m_{N(Fakestar2)^+}\": 1.7,\n", - " R\"\\beta_{N(Fakestar2)^+}\": 1 + 0j,\n", + " R\"g_{N^{**}_1}\": 1.0,\n", + " R\"m_{N^{**}_3}\": 1.7,\n", + " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", " R\"m_{N(1650)^{+}}\": 1.67,\n", " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", " R\"g_{N(1650)^{+}}\": 1.6,\n", - " R\"g_{N(Fakestar2)^+}\": 1,\n", + " R\"g_{N^{**}_3}\": 1,\n", "}\n", "\n", "initial_parameters_bw = {\n", - " R\"m_{N(Fakestar)^+}\": 1.8,\n", - " R\"w_{N(Fakestar)^+}\": 1 / 1.85,\n", + " R\"m_{N^{**}_1}\": 1.8,\n", + " R\"w_{N^{**}_1}\": 1 / 1.85,\n", " R\"m_{N(1900)^+}\": 1.93,\n", " R\"w_{N(1900)^+}\": 1 / 1.93,\n", - " R\"m_{N(Fakestar2)^+}\": 1.7,\n", - " R\"w_{N(Fakestar2)^+}\": 1 / 1.65,\n", + " R\"m_{N^{**}_3}\": 1.7,\n", + " R\"w_{N^{**}_3}\": 1 / 1.65,\n", " R\"m_{N(1650)^{+}}\": 1.6,\n", " R\"w_{N(1650)^{+}}\": 1 / 1.6,\n", "}" diff --git a/docs/report/030/additional-definitions.yml b/docs/report/030/additional-definitions.yml index db180d29..47fdd623 100644 --- a/docs/report/030/additional-definitions.yml +++ b/docs/report/030/additional-definitions.yml @@ -1,39 +1,39 @@ # Imported from ComPWA/PWA-JPsi2pbarSigmaKS@a0aba08 particles: - - name: N(1875)+ - pid: 200002 - latex: N(1875)^+ + - name: N**1 + pid: 20240522001 + latex: N^{**}_1 spin: 1.5 - mass: 1.875 - width: 0.2 + mass: 1.82 + width: 0.6 charge: 1 isospin: magnitude: 0.5 projection: 0.5 baryon_number: 1 parity: - value: -1 + value: 1 - - name: N(1880)+ - pid: 200000 - latex: N(1880)^+ + - name: N**2 + pid: 20240522002 + latex: N^{**}_2 spin: 0.5 - mass: 1.88 - width: 0.3 + mass: 1.65 + width: 0.6 charge: 1 isospin: magnitude: 0.5 projection: 0.5 baryon_number: 1 parity: - value: 1 + value: -1 - - name: N(1895)+ - pid: 200001 - latex: N(1895)^+ + - name: N**3 + pid: 20240522003 + latex: N^{**}_3 spin: 0.5 - mass: 1.895 - width: 0.12 + mass: 1.75 + width: 0.6 charge: 1 isospin: magnitude: 0.5 @@ -42,40 +42,40 @@ particles: parity: value: -1 - - name: N(Fakestar)+ - pid: 20047545456003 - latex: N(Fakestar)^+ + - name: N(1875)+ + pid: 200002 + latex: N(1875)^+ spin: 1.5 - mass: 1.82 - width: 0.6 + mass: 1.875 + width: 0.2 charge: 1 isospin: magnitude: 0.5 projection: 0.5 baryon_number: 1 parity: - value: 1 + value: -1 - - name: N(Fakestar1)+ - pid: 2004754545864656786003 - latex: N(Fakestar1)^+ + - name: N(1880)+ + pid: 200000 + latex: N(1880)^+ spin: 0.5 - mass: 1.65 - width: 0.6 + mass: 1.88 + width: 0.3 charge: 1 isospin: magnitude: 0.5 projection: 0.5 baryon_number: 1 parity: - value: -1 + value: 1 - - name: N(Fakestar2)+ - pid: 20047545458356789764656786003 - latex: N(Fakestar2)^+ + - name: N(1895)+ + pid: 200001 + latex: N(1895)^+ spin: 0.5 - mass: 1.75 - width: 0.6 + mass: 1.895 + width: 0.12 charge: 1 isospin: magnitude: 0.5 diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index e400258c..bc782e1c 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -182,7 +182,7 @@ "reaction = qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", " final_state=[\"eta\", \"p\", \"p~\"],\n", - " allowed_intermediate_particles=[\"N(Fakestar)+\", \"N(1900)+\"],\n", + " allowed_intermediate_particles=[\"N**1\", \"N(1900)+\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " particle_db=PARTICLE_DB,\n", @@ -683,17 +683,17 @@ "g_res2 = 1\n", "\n", "new_parameters_fvector = {\n", - " R\"m_{N(Fakestar)^+}\": m_res1,\n", - " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", + " R\"m_{N^{**}_1}\": m_res1,\n", + " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", " R\"m_{N(1900)^+}\": m_res2,\n", " R\"\\beta_{N(1900)^+}\": 1 + 0j, # 0.5l\n", " R\"g_{N(1900)^+}\": g_res2,\n", - " R\"g_{N(Fakestar)^+}\": g_res1,\n", + " R\"g_{N^{**}_1}\": g_res1,\n", "}\n", "\n", "new_parameters_bw = {\n", - " R\"m_{N(Fakestar)^+}\": m_res1,\n", - " R\"w_{N(Fakestar)^+}\": g_res1 / m_res1,\n", + " R\"m_{N^{**}_1}\": m_res1,\n", + " R\"w_{N^{**}_1}\": g_res1 / m_res1,\n", " R\"m_{N(1900)^+}\": m_res2,\n", " R\"w_{N(1900)^+}\": g_res2 / m_res2,\n", "}" diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 051f09dd..a75d1e70 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -217,7 +217,7 @@ " qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", " final_state=final_state,\n", - " allowed_intermediate_particles=[\"N(Fakestar)+\"],\n", + " allowed_intermediate_particles=[\"N**1\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " particle_db=PARTICLE_DB,\n", @@ -961,10 +961,10 @@ "g_res_ch1 = 2.5\n", "\n", "new_parameters_fvector = {\n", - " R\"m_{N(Fakestar)^+}\": 1.71,\n", - " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", - " R\"g_{N(Fakestar)^+,0}\": g_res_ch0,\n", - " R\"g_{N(Fakestar)^+,1}\": g_res_ch1,\n", + " R\"m_{N^{**}_1}\": 1.71,\n", + " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", + " R\"g_{N^{**}_1,0}\": g_res_ch0,\n", + " R\"g_{N^{**}_1,1}\": g_res_ch1,\n", "}" ] }, @@ -1275,10 +1275,10 @@ "outputs": [], "source": [ "initial_parameters = {\n", - " R\"m_{N(Fakestar)^+}\": 1.9,\n", - " R\"\\beta_{N(Fakestar)^+}\": 1 + 0j,\n", - " R\"g_{N(Fakestar)^+,0}\": 2.8,\n", - " R\"g_{N(Fakestar)^+,1}\": 1.6,\n", + " R\"m_{N^{**}_1}\": 1.9,\n", + " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", + " R\"g_{N^{**}_1,0}\": 2.8,\n", + " R\"g_{N^{**}_1,1}\": 1.6,\n", "}\n", "INTENSITY_FUNCS_FVECTOR[0].parameters" ] From bd0cedfd9bde613ca90e1b1497b825cdfe70ed83 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 00:14:58 +0200 Subject: [PATCH 14/92] ENH: use JAX as backend --- docs/report/030.ipynb | 6 +++--- docs/report/031.ipynb | 6 +++--- docs/report/032.ipynb | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 3abc0595..150c9a00 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -659,7 +659,7 @@ "outputs": [], "source": [ "helicity_transformer = SympyDataTransformer.from_sympy(\n", - " model.kinematic_variables, backend=\"numpy\"\n", + " model.kinematic_variables, backend=\"jax\"\n", ")" ] }, @@ -836,7 +836,7 @@ "source": [ "dynamics_func_bw = create_parametrized_function(\n", " expression=dynamics_expr_rel_bw.doit(),\n", - " backend=\"numpy\",\n", + " backend=\"jax\",\n", " parameters=model_rel_bw.parameter_defaults,\n", " use_cse=False,\n", ")" @@ -850,7 +850,7 @@ "source": [ "dynamics_func_fvector = create_parametrized_function(\n", " expression=dynamics_expr_fvector.doit(),\n", - " backend=\"numpy\",\n", + " backend=\"jax\",\n", " parameters=model_fvector.parameter_defaults,\n", " use_cse=False,\n", ")" diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index bc782e1c..3e89ac87 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -610,7 +610,7 @@ "source": [ "dynamics_func_bw = create_parametrized_function(\n", " expression=dynamics_expr_rel_bw.doit(),\n", - " backend=\"numpy\",\n", + " backend=\"jax\",\n", " parameters=model_rel_bw.parameter_defaults,\n", " use_cse=False,\n", ")" @@ -658,7 +658,7 @@ "source": [ "dynamics_func_fvector = create_parametrized_function(\n", " expression=dynamics_expr_fvector.doit(),\n", - " backend=\"numpy\",\n", + " backend=\"jax\",\n", " parameters=model_fvector.parameter_defaults,\n", " use_cse=False,\n", ")" @@ -740,7 +740,7 @@ "outputs": [], "source": [ "helicity_transformer = SympyDataTransformer.from_sympy(\n", - " model.kinematic_variables, backend=\"numpy\"\n", + " model.kinematic_variables, backend=\"jax\"\n", ")" ] }, diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index a75d1e70..3ac9ec69 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -1010,7 +1010,7 @@ "for i in range(n_channels):\n", " HELICITY_TRANSFORMERS.append(\n", " SympyDataTransformer.from_sympy(\n", - " MODELS_FVECTOR[i].kinematic_variables, backend=\"numpy\"\n", + " MODELS_FVECTOR[i].kinematic_variables, backend=\"jax\"\n", " )\n", " )" ] @@ -1115,7 +1115,7 @@ "for i in range(n_channels):\n", " func = create_parametrized_function(\n", " expression=DYNAMICS_EXPR_FVECTOR[i].doit(),\n", - " backend=\"numpy\",\n", + " backend=\"jax\",\n", " parameters=MODELS_FVECTOR[i].parameter_defaults,\n", " use_cse=False,\n", " )\n", From 80c04669959d21fb173b5b7ea64e930767ee4f25 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 00:14:59 +0200 Subject: [PATCH 15/92] BEHAVIOR: generate data with F-vector instead of BW --- docs/report/030.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 150c9a00..fcb17eb1 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -897,7 +897,7 @@ ")\n", "data_generator = IntensityDistributionGenerator(\n", " domain_generator=weighted_phsp_generator,\n", - " function=intensity_func_rel_bw,\n", + " function=intensity_func_fvector,\n", " domain_transformer=helicity_transformer,\n", ")\n", "data_momenta = data_generator.generate(50_000, rng)\n", From 49d349f37956565a1b6536075d94fb180bb38f8b Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 00:14:59 +0200 Subject: [PATCH 16/92] DOC: remove N* tables --- docs/report/030.ipynb | 15 --------------- docs/report/031.ipynb | 15 --------------- docs/report/032.ipynb | 21 --------------------- 3 files changed, 51 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index fcb17eb1..a865f5ef 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -120,21 +120,6 @@ "# Collect dynamics symbols" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "| Resonance | $m$ [MeV] | $\\Gamma$ [MeV] | $J^P$ |\n", - "|-----------|-----------|----------------|-------|\n", - "| $N^*(1440)$ | 1398 | 167 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1535)$ | 1530 | 210 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1650)$ | 1668 | 194 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1710)$ | 1749 | 263 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1880)$ | 1876 | 261 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1895)$ | 2045 | 240 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 3e89ac87..fb8d5dbd 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -115,21 +115,6 @@ "# Collect dynamics symbols" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "| Resonance | $m$ [MeV] | $\\Gamma$ [MeV] | $J^P$ |\n", - "|-----------|-----------|----------------|-------|\n", - "| $N^*(1440)$ | 1398 | 167 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1535)$ | 1530 | 210 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1650)$ | 1668 | 194 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1710)$ | 1749 | 263 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1880)$ | 1876 | 261 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1895)$ | 2045 | 240 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 3ac9ec69..c62c5482 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -129,27 +129,6 @@ "## Collect dynamics symbols" ] }, - { - "cell_type": "markdown", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "| Resonance | $m$ [MeV] | $\\Gamma$ [MeV] | $J^P$ |\n", - "|-----------|-----------|----------------|-------|\n", - "| $N^*(1440)$ | 1398 | 167 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1535)$ | 1530 | 210 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1650)$ | 1668 | 194 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1710)$ | 1749 | 263 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1880)$ | 1876 | 261 | $\\frac{1}{2}^{+}$ |\n", - "| $N^*(1895)$ | 2045 | 240 | $\\frac{1}{2}^{-}$ |\n", - "| $N^*(1900)$ | 1970 | 255 | $\\frac{3}{2}^{+}$ |" - ] - }, { "cell_type": "code", "execution_count": null, From 0437e1b5fe01b2a510637c7f49aabfe3241a9b91 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 00:15:00 +0200 Subject: [PATCH 17/92] MAINT: remove `slideshow` and `editable` notebook metadata --- docs/report/030.ipynb | 16 ---- docs/report/031.ipynb | 20 ----- docs/report/032.ipynb | 196 ------------------------------------------ 3 files changed, 232 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index a865f5ef..037f9a4d 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -3,10 +3,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -17,10 +13,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -42,10 +34,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "remove-cell" ] @@ -59,13 +47,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, "jupyter": { "source_hidden": true }, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input", "scroll-input" diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index fb8d5dbd..094fcab4 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -3,10 +3,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -17,10 +13,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -36,10 +28,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -50,10 +38,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "remove-cell" ] @@ -67,13 +51,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, "jupyter": { "source_hidden": true }, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input", "scroll-input" diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index c62c5482..1a4ba945 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -3,10 +3,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -17,10 +13,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -36,10 +28,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -50,10 +38,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "remove-cell" ] @@ -67,13 +51,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, "jupyter": { "source_hidden": true }, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input", "scroll-input" @@ -158,10 +138,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -180,10 +156,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -241,10 +213,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -261,10 +229,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -295,10 +259,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -309,10 +269,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -327,10 +283,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -348,10 +300,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -416,10 +364,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -448,10 +392,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -493,10 +433,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -509,10 +445,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -546,10 +478,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -560,10 +488,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -579,10 +503,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -595,10 +515,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input", "full-width" @@ -656,10 +572,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -712,10 +624,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -751,10 +659,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -774,10 +678,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -790,10 +690,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "full-width" ] @@ -817,10 +713,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -840,10 +732,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -873,10 +761,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -901,10 +785,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -951,10 +831,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -1007,10 +883,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -1082,10 +954,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -1104,10 +972,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -1118,10 +982,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -1146,10 +1006,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -1182,10 +1038,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -1224,10 +1076,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -1237,10 +1085,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -1266,10 +1110,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-cell" ] @@ -1334,10 +1174,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -1363,10 +1199,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -1377,10 +1209,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -1400,10 +1228,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-cell" ] @@ -1427,10 +1251,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -1441,10 +1261,6 @@ { "cell_type": "markdown", "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "source": [ @@ -1455,10 +1271,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [] }, "outputs": [], @@ -1475,10 +1287,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] @@ -1494,10 +1302,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, "tags": [ "hide-input" ] From 953cbb2da043cb65c11f3c324f9688d1eed2219a Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 00:15:01 +0200 Subject: [PATCH 18/92] DOC: improve notebook rendering on website --- docs/report/030.ipynb | 1077 +++++++++++++++++++++++++---------------- docs/report/031.ipynb | 1074 +++++++++++++++++++++++----------------- docs/report/032.ipynb | 2 +- 3 files changed, 1292 insertions(+), 861 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 037f9a4d..7b4b735d 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -17,17 +17,21 @@ }, "source": [ "::::{margin}\n", - ":::{card} Amplitude building with K-matrix dynamics\n", + ":::{card} Amplitude model fit with P-vector dynamics\n", "TR-030\n", + "^^^\n", + "Comparison between fit performance for a model with Breit–Wigner and $P$-vector dynamics. In both cases, data is generated with $P$-vector dynamics.\n", ":::\n", "::::" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "# Fit amplitude model with P-vector dynamics" + "# P-vector model fit" ] }, { @@ -59,6 +63,7 @@ "source": [ "from __future__ import annotations\n", "\n", + "import logging\n", "import os\n", "import re\n", "from collections import defaultdict\n", @@ -74,12 +79,13 @@ "import sympy as sp\n", "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", "from ampform.helicity import ParameterValues\n", - "from ampform.io import aslatex\n", + "from ampform.io import aslatex, improve_latex_rendering\n", "from ampform.kinematics.phasespace import Kallen\n", - "from ampform.sympy import unevaluated\n", - "from IPython.display import Latex, display\n", + "from ampform.sympy import perform_cached_doit, unevaluated\n", + "from attrs import define, field\n", + "from IPython.display import Markdown, Math, display\n", "from matplotlib import cm\n", - "from qrules.particle import Particle, ParticleCollection\n", + "from qrules.particle import Particle\n", "from sympy import Abs\n", "from tensorwaves.data import (\n", " IntensityDistributionGenerator,\n", @@ -90,59 +96,34 @@ ")\n", "from tensorwaves.estimator import UnbinnedNLL\n", "from tensorwaves.function.sympy import create_parametrized_function\n", - "from tensorwaves.interface import DataSample, ParametrizedFunction\n", + "from tensorwaves.interface import DataSample, FitResult, ParametrizedFunction\n", "from tensorwaves.optimizer import Minuit2\n", - "from tensorwaves.optimizer.callbacks import CSVSummary\n", "\n", - "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"" + "logging.getLogger(\"absl\").setLevel(logging.ERROR)\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", + "improve_latex_rendering()" ] }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Collect dynamics symbols" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "def create_dynamics_symbol(\n", - " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", - ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", - " J = sp.Rational(resonance.spin)\n", - " Q = resonance.charge\n", - " P = sp.Rational(resonance.parity)\n", - " if variable_pool.angular_momentum is not None:\n", - " L = sp.Rational(variable_pool.angular_momentum)\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", - " else:\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", - " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", - " parameter_defaults = {}\n", - " return X, parameter_defaults\n", - "\n", - "\n", - "COLLECTED_X_SYMBOLS = defaultdict(set)" + "## Studied decay" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "def load_particle_database() -> ParticleCollection:\n", - " particle_database = qrules.load_default_particles()\n", - " additional_definitions = qrules.io.load(\"030/additional-definitions.yml\")\n", - " particle_database.update(additional_definitions)\n", - " return particle_database\n", - "\n", - "\n", - "PARTICLE_DB = load_particle_database()" + "PARTICLE_DB = qrules.load_default_particles()\n", + "PARTICLE_DB.update(qrules.io.load(\"030/additional-definitions.yml\"))" ] }, { @@ -157,19 +138,92 @@ " initial_state=\"J/psi(1S)\",\n", " final_state=[\"eta\", \"p\", \"p~\"],\n", " allowed_intermediate_particles=[\n", + " \"N**1\",\n", " \"N**3\",\n", " \"N(1650)+\",\n", " \"N(1900)+\",\n", - " \"N**1\",\n", " ],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " particle_db=PARTICLE_DB,\n", - ")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ "dot = qrules.io.asdot(reaction, collapse_graphs=True)\n", "graphviz.Source(dot)" ] }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Amplitude builder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, + "outputs": [], + "source": [ + "@define\n", + "class DynamicsSymbolBuilder:\n", + " collected_symbols: set[sp.Symbol, tuple[Particle, TwoBodyKinematicVariableSet]] = (\n", + " field(factory=lambda: defaultdict(set))\n", + " )\n", + "\n", + " def __call__(\n", + " self, resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", + " ) -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", + " jp = render_jp(resonance)\n", + " charge = resonance.charge\n", + " if variable_pool.angular_momentum is not None:\n", + " L = sp.Rational(variable_pool.angular_momentum)\n", + " X = sp.Symbol(Rf\"X_{{{jp}, Q={charge:+d}}}^{{l={L}}}\")\n", + " else:\n", + " X = sp.Symbol(Rf\"X_{{{jp}, Q={charge:+d}}}\")\n", + " self.collected_symbols[X].add((resonance, variable_pool))\n", + " parameter_defaults = {}\n", + " return X, parameter_defaults\n", + "\n", + "\n", + "def render_jp(particle: Particle) -> str:\n", + " spin = sp.Rational(particle.spin)\n", + " j = (\n", + " str(spin)\n", + " if spin.denominator == 1\n", + " else Rf\"\\frac{{{spin.numerator}}}{{{spin.denominator}}}\"\n", + " )\n", + " if particle.parity is None:\n", + " return f\"J={j}\"\n", + " p = \"-\" if particle.parity < 0 else \"+\"\n", + " return f\"J^P={{{j}}}^{{{p}}}\"" + ] + }, { "cell_type": "code", "execution_count": null, @@ -180,47 +234,55 @@ "source": [ "model_builder = ampform.get_builder(reaction)\n", "model_builder.adapter.permutate_registered_topologies()\n", - "model_builder.scalar_initial_state_mass = True\n", - "model_builder.stable_final_state_ids = [0, 1, 2]\n", + "model_builder.config.scalar_initial_state_mass = True\n", + "model_builder.config.stable_final_state_ids = [0, 1, 2]\n", + "create_dynamics_symbol = DynamicsSymbolBuilder()\n", "for name in reaction.get_intermediate_particles().names:\n", " model_builder.set_dynamics(name, create_dynamics_symbol)\n", - "model = model_builder.formulate()" + "model = model_builder.formulate()\n", + "model.intensity.cleanup()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "full-width" + ] }, "outputs": [], "source": [ "selected_amplitudes = {\n", - " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i < 3\n", + " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i == 0\n", "}\n", - "src = aslatex(selected_amplitudes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Formulate dynamics expression" + "Math(aslatex(selected_amplitudes, terms_per_line=1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ - "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", + "for symbol, resonances in create_dynamics_symbol.collected_symbols.items():\n", " display(symbol)\n", + " src = \"| resonance | mass | width |\\n\"\n", + " src += \"|:---|---:|--:|\\n\"\n", " for p, _ in resonances:\n", - " print(f\" {p.name:<20s} {p.mass:>8g} GeV {p.width:>8g} GeV \")\n", - "model.parameter_defaults" + " src += f\"| ${p.latex}$ | {p.mass:g} GeV | {p.width:g} GeV |\\n\"\n", + " display(Markdown(src))" ] }, { @@ -229,12 +291,15 @@ "tags": [] }, "source": [ - "## Formulate Dynamics" + "## Dynamics parametrization" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "### Phasespace factor" ] @@ -242,7 +307,15 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ "@unevaluated(real=False)\n", @@ -287,67 +360,134 @@ "\n", " def evaluate(self) -> sp.Expr:\n", " s, m1, m2 = self.args\n", - " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))\n", - "\n", - "\n", + " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "s, m1, m2 = sp.symbols(\"s m1 m2\", nonnegative=True)\n", + "exprs = [\n", + " PhaseSpaceCM(s, m1, m2),\n", + " ChewMandelstam(s, m1, m2),\n", + " BreakupMomentum(s, m1, m2),\n", + "]\n", + "Math(aslatex({e: e.doit(deep=False) for e in exprs}))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "### Relativistic Breit-Wigner" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ "@unevaluated(real=False)\n", "class ChannelWidth(sp.Expr):\n", " s: Any\n", " m1: Any\n", " m2: Any\n", - " gamma_R: Any\n", + " width: Any\n", " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", "\n", " def evaluate(self) -> sp.Expr:\n", - " s, m1, m2, gamma_R = self.args\n", - " return gamma_R * PhaseSpaceCM(s, m1, m2)" + " s, m1, m2, width = self.args\n", + " return width * PhaseSpaceCM(s, m1, m2)\n", + "\n", + "\n", + "width = sp.Symbol(\"Gamma0\", nonnegative=True)\n", + "expr = ChannelWidth(s, m1, m2, width)\n", + "Math(aslatex({expr: expr.doit(deep=False)}))" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "### Relativistic Breit-Wigner" + "PARAMETERS_BW = dict(model.parameter_defaults)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "PARAMETERS_BW = {}\n", - "PARAMETERS_BW.update(model.parameter_defaults)\n", - "\n", - "\n", - "def formulate_rel_bw(\n", + "def formulate_breit_wigner(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", - " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", + " m1 = variables.outgoing_state_mass1\n", + " m2 = variables.outgoing_state_mass2\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", - " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", - " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", - " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", - " w_s = (ChannelWidth(s, m_a, m_b, w_) for w_ in w)\n", - " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", + " Γ0 = [sp.Symbol(Rf\"\\Gamma_{{{p.latex}}}\") for p, _ in resonances]\n", + " Γ = [ChannelWidth(s, m1, m2, _w) for _w in Γ0]\n", + " β = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", + " expr = sum(\n", + " (β_ * m_ * Γ_) / (m_**2 - s - m_ * Γ0_) for m_, Γ_, Γ0_, β_ in zip(m, Γ0, Γ, β)\n", + " )\n", " for i, (resonance, _) in enumerate(resonances):\n", - " PARAMETERS_BW[w[i]] = resonance.width\n", + " PARAMETERS_BW[β[i]] = 1 + 0j\n", " PARAMETERS_BW[m[i]] = resonance.mass\n", - " PARAMETERS_BW[b[i]] = 1\n", - " PARAMETERS_BW[d[i]] = 1\n", - " PARAMETERS_BW[L[i]] = 0\n", - " return rel_bw" + " PARAMETERS_BW[Γ0[i]] = resonance.width\n", + " return expr" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], "source": [ - "### $K$ matrix " + "dynamics_expressions_bw = {\n", + " symbol: formulate_breit_wigner(resonances)\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", + "}\n", + "model_bw = attrs.evolve(\n", + " model,\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_BW,\n", + " }),\n", + ")\n", + "Math(aslatex(dynamics_expressions_bw))" ] }, { @@ -358,28 +498,18 @@ }, "outputs": [], "source": [ - "PARAMETERS_F = {}\n", - "PARAMETERS_F.update(model.parameter_defaults)\n", - "\n", - "\n", - "def formulate_K_matrix(\n", - " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", - ") -> sp.Expr:\n", - " (_, variables), *_ = resonances\n", - " s = variables.incoming_state_mass**2\n", - " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", - " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", - "\n", - " kmatrix = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", - " for i, (resonance, _) in enumerate(resonances):\n", - " PARAMETERS_F[m[i]] = resonance.mass\n", - " PARAMETERS_F[g[i]] = 1\n", - " return kmatrix" + "full_expression_bw = perform_cached_doit(model_bw.expression).xreplace(\n", + " dynamics_expressions_bw\n", + ")\n", + "sp.count_ops(full_expression_bw)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "### $P$ vector" ] @@ -387,56 +517,35 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "def formulate_P_vector(\n", - " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", - ") -> sp.Expr:\n", - " (_, variables), *_ = resonances\n", - " s = variables.incoming_state_mass**2\n", - " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", - " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", - " beta = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", - " P_vector = sum((g_ * beta_) / (m_**2 - s) for m_, g_, beta_ in zip(m, g, beta))\n", - " for i, (resonance, _) in enumerate(resonances):\n", - " PARAMETERS_F[m[i]] = resonance.mass\n", - " PARAMETERS_F[beta[i]] = 1 + 0j\n", - " PARAMETERS_F[g[i]] = 1\n", - " return P_vector" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### $F$ vector" + "PARAMETERS_F = dict(model.parameter_defaults)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "def formulate_F_vector(\n", + "def formulate_k_matrix(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", - " rho = PhaseSpaceCM(s, m_a, m_b)\n", - " K = formulate_K_matrix(resonances)\n", - " P = formulate_P_vector(resonances)\n", - " return (1 / (1 - rho * K)) * P" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model relativistic Breit-Wigner" + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + "\n", + " expr = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = resonance.mass\n", + " PARAMETERS_F[g[i]] = 1\n", + " return expr" ] }, { @@ -447,17 +556,20 @@ }, "outputs": [], "source": [ - "dynamics_expressions_rel_bw = {\n", - " symbol: formulate_rel_bw(resonances)\n", - " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", - "}\n", - "model_rel_bw = attrs.evolve(\n", - " model,\n", - " parameter_defaults=ParameterValues({\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_BW,\n", - " }),\n", - ")" + "def formulate_p_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (_, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " β = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", + " expr = sum((g_ * β_) / (m_**2 - s) for m_, g_, β_ in zip(m, g, β))\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_F[β[i]] = 1 + 0j\n", + " PARAMETERS_F[m[i]] = resonance.mass\n", + " PARAMETERS_F[g[i]] = 1\n", + " return expr" ] }, { @@ -468,27 +580,32 @@ }, "outputs": [], "source": [ - "full_expression_rel_bw = model_rel_bw.expression.doit().xreplace(\n", - " dynamics_expressions_rel_bw\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Model $F$ vector" + "def formulate_f_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (_, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m1 = variables.outgoing_state_mass1\n", + " m2 = variables.outgoing_state_mass2\n", + " rho = PhaseSpaceCM(s, m1, m2)\n", + " K = formulate_k_matrix(resonances)\n", + " P = formulate_p_vector(resonances)\n", + " return (1 / (1 - rho * K)) * P" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ "dynamics_expressions_fvector = {\n", - " symbol: formulate_F_vector(resonances)\n", - " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " symbol: formulate_f_vector(resonances)\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", "}\n", "model_fvector = attrs.evolve(\n", " model,\n", @@ -497,25 +614,18 @@ " **PARAMETERS_F,\n", " }),\n", ")\n", - "Latex(aslatex(dynamics_expressions_fvector))" + "Math(aslatex(dynamics_expressions_fvector))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_fvector.parameter_defaults" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "full_expression_fvector = model_fvector.expression.doit().xreplace(\n", + "full_expression_fvector = perform_cached_doit(model_fvector.expression).xreplace(\n", " dynamics_expressions_fvector\n", ")\n", "sp.count_ops(full_expression_fvector)" @@ -523,9 +633,12 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "### Create Parametrized Function\n" + "### Create numerical functions" ] }, { @@ -536,10 +649,9 @@ }, "outputs": [], "source": [ - "unfolded_expression_rel_bw = full_expression_rel_bw.doit()\n", - "\n", - "intensity_func_rel_bw = create_parametrized_function(\n", - " expression=unfolded_expression_rel_bw,\n", + "intensity_expr_bw = perform_cached_doit(full_expression_bw)\n", + "intensity_func_bw = create_parametrized_function(\n", + " expression=intensity_expr_bw,\n", " backend=\"jax\",\n", " parameters=PARAMETERS_BW,\n", ")" @@ -548,13 +660,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "unfolded_expression_fvector = full_expression_fvector.doit()\n", - "\n", + "intensity_expr_fvector = perform_cached_doit(full_expression_fvector)\n", "intensity_func_fvector = create_parametrized_function(\n", - " expression=unfolded_expression_fvector,\n", + " expression=intensity_expr_fvector,\n", " backend=\"jax\",\n", " parameters=PARAMETERS_F,\n", ")" @@ -562,61 +675,52 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Update parameters" + "## Generate data" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "new_parameters_fvector = {\n", - " R\"m_{N^{**}_1}\": 1.95,\n", - " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", - " R\"m_{N(1900)^+}\": 1.9,\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"g_{N(1900)^+}\": 1,\n", - " R\"g_{N^{**}_1}\": 1,\n", - " R\"m_{N^{**}_3}\": 1.75,\n", - " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", - " R\"m_{N(1650)^{+}}\": 1.65,\n", - " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", - " R\"g_{N(1650)^{+}}\": 1.65,\n", - " R\"g_{N^{**}_3}\": 1,\n", - "}\n", - "\n", - "new_parameters_bw = {\n", - " R\"m_{N^{**}_1}\": 1.85,\n", - " R\"w_{N^{**}_1}\": 1 / 1.85,\n", - " R\"m_{N(1900)^+}\": 1.9,\n", - " R\"w_{N(1900)^+}\": 1 / 1.9,\n", - " R\"m_{N^{**}_3}\": 1.75,\n", - " R\"w_{N^{**}_3}\": 1 / 1.75,\n", - " R\"m_{N(1650)^{+}}\": 1.65,\n", - " R\"w_{N(1650)^{+}}\": 1 / 1.65,\n", - "}" + "### Generate phase space sample" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", - "intensity_func_rel_bw.update_parameters(new_parameters_bw)\n", - "intensity_func_fvector.parameters" + "rng = TFUniformRealNumberGenerator(seed=0)\n", + "phsp_generator = TFPhaseSpaceGenerator(\n", + " initial_state_mass=reaction.initial_state[-1].mass,\n", + " final_state_masses={i: p.mass for i, p in reaction.final_state.items()},\n", + ")\n", + "phsp_momenta = phsp_generator.generate(100_000, rng)\n", + "\n", + "epsilon = 1e-8\n", + "transformer = SympyDataTransformer.from_sympy(model.kinematic_variables, backend=\"jax\")\n", + "phsp = transformer(phsp_momenta)\n", + "phsp = {k: v + epsilon * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "## Generate data with $F$ vector\n", - "### Generate phase space sample" + "### Update function parameters" ] }, { @@ -627,9 +731,17 @@ }, "outputs": [], "source": [ - "helicity_transformer = SympyDataTransformer.from_sympy(\n", - " model.kinematic_variables, backend=\"jax\"\n", - ")" + "new_parameters_bw = {\n", + " R\"\\Gamma_{N(1650)^{+}}\": 1 / 1.65,\n", + " R\"\\Gamma_{N(1900)^+}\": 1 / 1.9,\n", + " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", + " R\"\\Gamma_{N^{**}_3}\": 1 / 1.75,\n", + " R\"m_{N(1650)^{+}}\": 1.65,\n", + " R\"m_{N(1900)^+}\": 1.9,\n", + " R\"m_{N^{**}_1}\": 1.85,\n", + " R\"m_{N^{**}_3}\": 1.75,\n", + "}\n", + "intensity_func_bw.update_parameters(new_parameters_bw)" ] }, { @@ -640,28 +752,44 @@ }, "outputs": [], "source": [ - "rng = TFUniformRealNumberGenerator(seed=0)\n", - "phsp_generator = TFPhaseSpaceGenerator(\n", - " initial_state_mass=reaction.initial_state[-1].mass,\n", - " final_state_masses={i: p.mass for i, p in reaction.final_state.items()},\n", - ")\n", - "phsp_momenta = phsp_generator.generate(100_000, rng)\n", - "phsp = helicity_transformer(phsp_momenta)\n", - "phsp = {k: v.real for k, v in phsp.items()}\n", - "phsp" + "new_parameters_fvector = {\n", + " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", + " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", + " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", + " R\"g_{N(1650)^{+}}\": 1.65,\n", + " R\"g_{N(1900)^+}\": 1,\n", + " R\"g_{N^{**}_1}\": 1,\n", + " R\"g_{N^{**}_3}\": 1,\n", + " R\"m_{N(1650)^{+}}\": 1.65,\n", + " R\"m_{N(1900)^+}\": 1.9,\n", + " R\"m_{N^{**}_1}\": 1.95,\n", + " R\"m_{N^{**}_3}\": 1.75,\n", + "}\n", + "intensity_func_fvector.update_parameters(new_parameters_fvector)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Plot Sub-Intensities" + "## Plot sub-intensities" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ "def compute_sub_intensity(\n", @@ -691,21 +819,35 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "total_intensities = intensity_func_fvector(phsp)\n", - "total_intensities_1 = intensity_func_rel_bw(phsp)\n", - "sub_intensities = {\n", + "total_intensities_bw = intensity_func_bw(phsp)\n", + "sub_intensities_bw = {\n", " p: compute_sub_intensity(\n", - " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " intensity_func_bw, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", " )\n", - " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", - "}\n", - "sub_intensities_bw = {\n", - " p: compute_sub_intensity(intensity_func_fvector, phsp, resonances=[p.latex])\n", - " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_intensities_fvector = intensity_func_fvector(phsp)\n", + "sub_intensities_fvector = {\n", + " p: compute_sub_intensity(\n", + " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " )\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", "}" ] @@ -713,55 +855,63 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ - "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", "ax.set_xlim(2, 5)\n", - "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV^{2}]\")\n", - "ax.set_xlabel(R\"Intensity [a. u.]\")\n", + "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^2$]\")\n", + "ax.set_ylabel(R\"Intensity [a. u.]\")\n", "ax.set_yticks([])\n", "\n", "bins = 150\n", "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", "ax.hist(\n", " phsp_projection,\n", - " weights=total_intensities,\n", - " bins=bins,\n", + " weights=total_intensities_fvector,\n", " alpha=0.2,\n", + " bins=bins,\n", " color=\"hotpink\",\n", - " label=\"Full intensity\",\n", + " label=\"Full intensity $F$ vector\",\n", ")\n", "ax.hist(\n", " phsp_projection,\n", - " weights=total_intensities_1,\n", - " bins=bins,\n", + " weights=total_intensities_bw,\n", " alpha=0.2,\n", + " bins=bins,\n", " color=\"grey\",\n", - " label=\"Full intensity\",\n", + " label=\"Full intensity Breit-Wigner\",\n", ")\n", "ax.hist(\n", - " len(sub_intensities) * [phsp_projection],\n", - " weights=list(sub_intensities.values()),\n", - " bins=bins,\n", + " len(sub_intensities_fvector) * [phsp_projection],\n", + " weights=list(sub_intensities_fvector.values()),\n", " alpha=0.6,\n", + " bins=bins,\n", + " histtype=\"step\",\n", " label=[\n", - " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ $F$ vector\"\n", - " for p in sub_intensities\n", + " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\"\n", + " for p in sub_intensities_fvector\n", " ],\n", - " histtype=\"step\",\n", ")\n", "\n", "ax.hist(\n", " len(sub_intensities_bw) * [phsp_projection],\n", " weights=list(sub_intensities_bw.values()),\n", - " bins=bins,\n", " alpha=0.6,\n", + " bins=bins,\n", + " histtype=\"step\",\n", " label=[\n", " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\"\n", - " for p in sub_intensities\n", + " for p in sub_intensities_fvector\n", " ],\n", - " histtype=\"step\",\n", " ls=\"dotted\",\n", ")\n", "\n", @@ -772,7 +922,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Dynamics expressions" ] @@ -780,11 +932,13 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "dynamics_expr_rel_bw, *_ = dynamics_expressions_rel_bw.values()\n", - "dynamics_expr_rel_bw" + "dynamics_expr_bw, *_ = dynamics_expressions_bw.values()\n", + "dynamics_expr_bw" ] }, { @@ -804,9 +958,9 @@ "outputs": [], "source": [ "dynamics_func_bw = create_parametrized_function(\n", - " expression=dynamics_expr_rel_bw.doit(),\n", + " expression=dynamics_expr_bw.doit(),\n", " backend=\"jax\",\n", - " parameters=model_rel_bw.parameter_defaults,\n", + " parameters=model_bw.parameter_defaults,\n", " use_cse=False,\n", ")" ] @@ -827,7 +981,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Weighted data with $F$ vector " ] @@ -835,21 +991,23 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ - "figD, axD = plt.subplots(figsize=(6, 5))\n", - "c = axD.hist(\n", - " np.real(phsp[\"m_01\"]) ** 2,\n", + "fig, ax = plt.subplots(figsize=(6, 5))\n", + "ax.hist(\n", + " phsp[\"m_01\"].real,\n", " bins=100,\n", - " weights=np.real(intensity_func_rel_bw(phsp)),\n", + " weights=np.real(intensity_func_fvector(phsp)),\n", ")\n", - "\n", - "axD.set_xlabel(R\"$M^2\\left(\\eta p\\right)\\, \\mathrm{[(GeV/c)^2]}$\")\n", - "axD.set_ylabel(R\"Intensity [a.u.]\")\n", - "figD.tight_layout()\n", - "plt.show()\n", - "phsp[\"m_01\"]" + "ax.set_xlabel(R\"$M^2\\left(\\eta p\\right)\\, \\mathrm{[(GeV/c)^2]}$\")\n", + "ax.set_ylabel(R\"Intensity [a.u.]\")\n", + "fig.tight_layout()\n", + "fig.show()" ] }, { @@ -867,24 +1025,21 @@ "data_generator = IntensityDistributionGenerator(\n", " domain_generator=weighted_phsp_generator,\n", " function=intensity_func_fvector,\n", - " domain_transformer=helicity_transformer,\n", + " domain_transformer=transformer,\n", ")\n", "data_momenta = data_generator.generate(50_000, rng)\n", - "pd.DataFrame({\n", - " (k, label): np.transpose(v)[i]\n", - " for k, v in data_momenta.items()\n", - " for i, label in enumerate([\"E\", \"px\", \"py\", \"pz\"])\n", - "})\n", - "phsp = helicity_transformer(phsp_momenta)\n", - "data = helicity_transformer(data_momenta)\n", - "data_frame = pd.DataFrame(data)\n", - "phsp_frame = pd.DataFrame(phsp)" + "data = transformer(data_momenta)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ "resonances = sorted(\n", @@ -897,7 +1052,7 @@ "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", "fig, ax = plt.subplots(figsize=(9, 4))\n", "ax.hist(\n", - " np.real(data_frame[\"m_01\"]),\n", + " np.real(data[\"m_01\"]),\n", " bins=200,\n", " alpha=0.5,\n", " density=True,\n", @@ -912,57 +1067,45 @@ " color=color,\n", " )\n", "ax.legend()\n", - "plt.show()\n", - "# Multiply" + "plt.show()" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### Perform fit" + "## Perform fit" ] }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define estimator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [] + }, "source": [ - "def safe_downcast_to_real(data: DataSample) -> DataSample:\n", - " return {\n", - " key: array.real if np.isrealobj(array) else array for key, array in data.items()\n", - " }\n", - "\n", - "\n", - "data_real = safe_downcast_to_real(data)\n", - "phsp_real = safe_downcast_to_real(phsp)" + "### Estimator definition" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "estimator_bw = UnbinnedNLL(\n", - " intensity_func_rel_bw,\n", - " data=data_real,\n", - " phsp=phsp_real,\n", + " intensity_func_bw,\n", + " data=data,\n", + " phsp=phsp,\n", " backend=\"jax\",\n", ")\n", - "\n", "estimator_fvector = UnbinnedNLL(\n", " intensity_func_fvector,\n", - " data=data_real,\n", - " phsp=phsp_real,\n", + " data=data,\n", + " phsp=phsp,\n", " backend=\"jax\",\n", ")" ] @@ -970,7 +1113,12 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, "outputs": [], "source": [ "reaction_info = model.reaction_info\n", @@ -982,9 +1130,7 @@ " 0, 1, len(intensity_func_fvector.parameters.items())\n", ")\n", "colors_F = [cm.rainbow(x) for x in evenly_spaced_interval_F]\n", - "evenly_spaced_interval_BW = np.linspace(\n", - " 0, 1, len(intensity_func_rel_bw.parameters.items())\n", - ")\n", + "evenly_spaced_interval_BW = np.linspace(0, 1, len(intensity_func_bw.parameters.items()))\n", "colors_BW = [cm.gist_rainbow(x) for x in evenly_spaced_interval_BW]\n", "\n", "\n", @@ -998,7 +1144,7 @@ " label=r\"$\" + k + \"$\" \"(F vector)\",\n", " color=color_F,\n", " )\n", - " for (k, v), color_BW in zip(intensity_func_rel_bw.parameters.items(), colors_BW):\n", + " for (k, v), color_BW in zip(intensity_func_bw.parameters.items(), colors_BW):\n", " if k.startswith(\"m_{\"):\n", " ax.axvline(\n", " x=v,\n", @@ -1053,135 +1199,228 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### Set initial parameters" + "### Initial parameters" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "m_1900 = 1.93\n", - "beta_1900 = 0.9 + 0j\n", - "g_1900 = 1.0\n", - "m_1650 = 1.65\n", - "beta_1650 = 1 + 0j\n", - "g_1900 = 1.0\n", - "m_Fakestar2 = 1.5\n", - "beta_Fakestar2 = 1 + 0j\n", - "g_Fakestar2 = 1.0\n", - "m_Fakestar1 = 1.94\n", + "initial_parameters_bw = {\n", + " R\"m_{N^{**}_1}\": 1.8,\n", + " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", + " R\"m_{N(1900)^+}\": 1.93,\n", + " R\"\\Gamma_{N(1900)^+}\": 1 / 1.93,\n", + " R\"m_{N^{**}_3}\": 1.7,\n", + " R\"\\Gamma_{N^{**}_3}\": 1 / 1.65,\n", + " R\"m_{N(1650)^{+}}\": 1.6,\n", + " R\"\\Gamma_{N(1650)^{+}}\": 1 / 1.6,\n", + "}\n", "initial_parameters_fvector = {\n", " R\"m_{N^{**}_1}\": 1.95,\n", - " R\"\\beta_{N^{**}_1}\": 0.9 + 0j,\n", - " R\"m_{N(1900)^+}\": 1.91,\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"g_{N(1900)^+}\": 1.0,\n", - " R\"g_{N^{**}_1}\": 1.0,\n", " R\"m_{N^{**}_3}\": 1.7,\n", - " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", " R\"m_{N(1650)^{+}}\": 1.67,\n", + " R\"m_{N(1900)^+}\": 1.91,\n", + " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", - " R\"g_{N(1650)^{+}}\": 1.6,\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", + " R\"g_{N^{**}_1}\": 1.0,\n", " R\"g_{N^{**}_3}\": 1,\n", - "}\n", - "\n", - "initial_parameters_bw = {\n", - " R\"m_{N^{**}_1}\": 1.8,\n", - " R\"w_{N^{**}_1}\": 1 / 1.85,\n", - " R\"m_{N(1900)^+}\": 1.93,\n", - " R\"w_{N(1900)^+}\": 1 / 1.93,\n", - " R\"m_{N^{**}_3}\": 1.7,\n", - " R\"w_{N^{**}_3}\": 1 / 1.65,\n", - " R\"m_{N(1650)^{+}}\": 1.6,\n", - " R\"w_{N(1650)^{+}}\": 1 / 1.6,\n", + " R\"g_{N(1650)^{+}}\": 1.6,\n", + " R\"g_{N(1900)^+}\": 1.0,\n", "}" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "original_parameters = intensity_func_fvector.parameters\n", + "original_parameters_bw = dict(intensity_func_bw.parameters)\n", + "intensity_func_bw.update_parameters(initial_parameters_bw)\n", + "original_parameters_fvector = dict(intensity_func_fvector.parameters)\n", "intensity_func_fvector.update_parameters(initial_parameters_fvector)\n", - "intensity_func_rel_bw.update_parameters(initial_parameters_bw)\n", - "compare_model(\n", - " \"m_01\", data_real, phsp_real, intensity_func_fvector, intensity_func_rel_bw\n", - ")" + "compare_model(\"m_01\", data, phsp, intensity_func_fvector, intensity_func_bw)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Optimize parameters" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "minuit2 = Minuit2(\n", - " callback=CSVSummary(\"fit_traceback.csv\"),\n", - " use_analytic_gradient=False,\n", - ")\n", - "\n", - "fit_result_BW = minuit2.optimize(estimator_bw, initial_parameters_bw)\n", - "display(\"Fit Breit-Wigner:\", fit_result_BW)\n", - "fit_result_F = minuit2.optimize(estimator_fvector, initial_parameters_fvector)\n", - "display(\"Fit F vector:\", fit_result_F)" + "minuit2 = Minuit2()" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "optimized_parameters_BW = fit_result_BW.parameter_values\n", - "optimized_parameters_F = fit_result_F.parameter_values\n", - "intensity_func_fvector.update_parameters(optimized_parameters_F)\n", - "intensity_func_rel_bw.update_parameters(optimized_parameters_BW)\n", - "compare_model(\n", - " \"m_01\", data_real, phsp_real, intensity_func_fvector, intensity_func_rel_bw\n", - ")" + "fit_result_bw = minuit2.optimize(estimator_bw, initial_parameters_bw)\n", + "fit_result_bw" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fit_result_fvector = minuit2.optimize(estimator_fvector, initial_parameters_fvector)\n", + "fit_result_fvector" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "intensity_func_fvector.update_parameters(fit_result_fvector.parameter_values)\n", + "intensity_func_bw.update_parameters(fit_result_bw.parameter_values)\n", + "compare_model(\"m_01\", data, phsp, intensity_func_fvector, intensity_func_bw)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### Parameters for $F$ vector v.s. sum of Breit-Wigners" + "### Fit result comparison" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ - "for p in optimized_parameters_F:\n", - " print(p)\n", - " print(f\" initial: {initial_parameters_fvector[p]:.3f}\")\n", - " print(f\" optimized F vector: {optimized_parameters_F[p]:.3f}\")\n", - " print(f\" original: {original_parameters[p]:.3f}\")\n", - "latest_parameters_F = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", - "latest_parameters_F" + "def compute_aic_bic(fit_result: FitResult) -> tuple[float, float]:\n", + " n_real_par = fit_result.count_number_of_parameters(complex_twice=True)\n", + " n_events = len(next(iter(data.values())))\n", + " log_likelihood = -fit_result.estimator_value\n", + " aic = 2 * n_real_par - 2 * log_likelihood\n", + " bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", + " return aic, bic" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def compare_parameters(original: dict, initial: dict, optimized: dict) -> pd.DataFrame:\n", + " parameters = sorted(set(initial) | set(optimized))\n", + " df = pd.DataFrame(\n", + " {\n", + " f\"${p}$\": (\n", + " initial.get(p, \"NaN\"),\n", + " optimized.get(p, \"NaN\"),\n", + " original.get(p, \"NaN\"),\n", + " )\n", + " for p in parameters\n", + " },\n", + " ).T\n", + " df.columns = (\"initial\", \"fit result\", \"original\")\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "for p in optimized_parameters_BW:\n", - " print(p)\n", - " print(f\" initial: {initial_parameters_bw[p]:.3f}\")\n", - " print(f\" optimized Breit-Wigner: {optimized_parameters_BW.get(p, -9999):3f}\")\n", - " print(f\" original: {original_parameters.get(p, -9999):.3f}\")\n", - "latest_parameters_BW = CSVSummary.load_latest_parameters(\"fit_traceback.csv\")\n", - "latest_parameters_BW" + "compute_aic_bic(fit_result_fvector)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "compare_parameters(\n", + " original=original_parameters_fvector,\n", + " initial=initial_parameters_fvector,\n", + " optimized=fit_result_fvector.parameter_values,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "compute_aic_bic(fit_result_bw)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "compare_parameters(\n", + " original=original_parameters_bw,\n", + " initial=initial_parameters_bw,\n", + " optimized=fit_result_bw.parameter_values,\n", + ")" ] } ], diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 094fcab4..b187eca1 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -17,10 +17,10 @@ }, "source": [ "::::{margin}\n", - ":::{card} Amplitude building with K-matrix dynamics\n", + ":::{card} Sub-intensities of P-vector amplitude model\n", "TR-031\n", "^^^\n", - "Sub-intensity plots for a model with $K$-matrix ($P$-vector) dynamics.\n", + "Sub-intensity plots for a model with $K$-matrix ($P$-vector) dynamics. Also includes an investigation of phases in a $P$-vector lineshape.\n", ":::\n", "::::" ] @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "%pip install -q 'qrules[viz]==0.10.2' 'tensorwaves[jax]==0.4.12' ampform==0.15.4 sympy==1.12" + "%pip install -q 'qrules[viz]==0.10.2' 'tensorwaves[jax,phsp]==0.4.12' ampform==0.15.4 sympy==1.12" ] }, { @@ -63,6 +63,7 @@ "source": [ "from __future__ import annotations\n", "\n", + "import logging\n", "import os\n", "import re\n", "from collections import defaultdict\n", @@ -77,63 +78,46 @@ "import sympy as sp\n", "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", "from ampform.helicity import ParameterValues\n", - "from ampform.io import aslatex\n", + "from ampform.io import aslatex, improve_latex_rendering\n", "from ampform.kinematics.phasespace import Kallen\n", - "from ampform.sympy import unevaluated\n", - "from IPython.display import Latex, Math, display\n", - "from qrules.particle import Particle, ParticleCollection\n", + "from ampform.sympy import perform_cached_doit, unevaluated\n", + "from attrs import define, field\n", + "from IPython.display import Markdown, Math, display\n", + "from qrules.particle import Particle\n", "from sympy import Abs\n", - "from tensorwaves.data import SympyDataTransformer\n", + "from tensorwaves.data import (\n", + " SympyDataTransformer,\n", + " TFPhaseSpaceGenerator,\n", + " TFUniformRealNumberGenerator,\n", + ")\n", "from tensorwaves.function.sympy import create_parametrized_function\n", - "from tensorwaves.interface import DataSample, ParametrizedFunction" + "from tensorwaves.interface import DataSample, ParametrizedFunction\n", + "\n", + "logging.getLogger(\"absl\").setLevel(logging.ERROR)\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", + "improve_latex_rendering()" ] }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Collect dynamics symbols" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "def create_dynamics_symbol(\n", - " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", - ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", - " J = sp.Rational(resonance.spin)\n", - " Q = resonance.charge\n", - " P = sp.Rational(resonance.parity)\n", - " if variable_pool.angular_momentum is not None:\n", - " L = sp.Rational(variable_pool.angular_momentum)\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", - " else:\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", - " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", - " parameter_defaults = {}\n", - " return X, parameter_defaults\n", - "\n", - "\n", - "COLLECTED_X_SYMBOLS = defaultdict(set)" + "## Studied decay" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "def load_particle_database() -> ParticleCollection:\n", - " particle_database = qrules.load_default_particles()\n", - " additional_definitions = qrules.io.load(\"030/additional-definitions.yml\")\n", - " particle_database.update(additional_definitions)\n", - " return particle_database\n", - "\n", - "\n", - "PARTICLE_DB = load_particle_database()" + "PARTICLE_DB = qrules.load_default_particles()\n", + "PARTICLE_DB.update(qrules.io.load(\"030/additional-definitions.yml\"))" ] }, { @@ -147,15 +131,91 @@ "reaction = qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", " final_state=[\"eta\", \"p\", \"p~\"],\n", - " allowed_intermediate_particles=[\"N**1\", \"N(1900)+\"],\n", + " allowed_intermediate_particles=[\n", + " \"N**1\",\n", + " \"N(1900)+\",\n", + " ],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " particle_db=PARTICLE_DB,\n", - ")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ "dot = qrules.io.asdot(reaction, collapse_graphs=True)\n", "graphviz.Source(dot)" ] }, + { + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Amplitude builder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, + "outputs": [], + "source": [ + "@define\n", + "class DynamicsSymbolBuilder:\n", + " collected_symbols: set[sp.Symbol, tuple[Particle, TwoBodyKinematicVariableSet]] = (\n", + " field(factory=lambda: defaultdict(set))\n", + " )\n", + "\n", + " def __call__(\n", + " self, resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", + " ) -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", + " jp = render_jp(resonance)\n", + " charge = resonance.charge\n", + " if variable_pool.angular_momentum is not None:\n", + " L = sp.Rational(variable_pool.angular_momentum)\n", + " X = sp.Symbol(Rf\"X_{{{jp}, Q={charge:+d}}}^{{l={L}}}\")\n", + " else:\n", + " X = sp.Symbol(Rf\"X_{{{jp}, Q={charge:+d}}}\")\n", + " self.collected_symbols[X].add((resonance, variable_pool))\n", + " parameter_defaults = {}\n", + " return X, parameter_defaults\n", + "\n", + "\n", + "def render_jp(particle: Particle) -> str:\n", + " spin = sp.Rational(particle.spin)\n", + " j = (\n", + " str(spin)\n", + " if spin.denominator == 1\n", + " else Rf\"\\frac{{{spin.numerator}}}{{{spin.denominator}}}\"\n", + " )\n", + " if particle.parity is None:\n", + " return f\"J={j}\"\n", + " p = \"-\" if particle.parity < 0 else \"+\"\n", + " return f\"J^P={{{j}}}^{{{p}}}\"" + ] + }, { "cell_type": "code", "execution_count": null, @@ -168,6 +228,7 @@ "model_builder.adapter.permutate_registered_topologies()\n", "model_builder.config.scalar_initial_state_mass = True\n", "model_builder.config.stable_final_state_ids = [0, 1, 2]\n", + "create_dynamics_symbol = DynamicsSymbolBuilder()\n", "for name in reaction.get_intermediate_particles().names:\n", " model_builder.set_dynamics(name, create_dynamics_symbol)\n", "model = model_builder.formulate()\n", @@ -180,36 +241,40 @@ "metadata": { "jupyter": { "source_hidden": true - } + }, + "tags": [ + "hide-input", + "full-width" + ] }, "outputs": [], "source": [ "selected_amplitudes = {\n", - " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i < 2\n", + " k: v for i, (k, v) in enumerate(model.amplitudes.items()) if i == 0\n", "}\n", "Math(aslatex(selected_amplitudes, terms_per_line=1))" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Formulate dynamics expression" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ - "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", + "for symbol, resonances in create_dynamics_symbol.collected_symbols.items():\n", " display(symbol)\n", + " src = \"| resonance | mass | width |\\n\"\n", + " src += \"|:---|---:|--:|\\n\"\n", " for p, _ in resonances:\n", - " print(f\" {p.name:<20s} {p.mass:>8g} GeV {p.width:>8g} GeV \")\n", - "model.parameter_defaults" + " src += f\"| ${p.latex}$ | {p.mass:g} GeV | {p.width:g} GeV |\\n\"\n", + " display(Markdown(src))" ] }, { @@ -218,12 +283,15 @@ "tags": [] }, "source": [ - "## Formulate Dynamics" + "## Dynamics parametrization" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ "### Phasespace factor" ] @@ -234,7 +302,11 @@ "metadata": { "jupyter": { "source_hidden": true - } + }, + "tags": [ + "hide-input", + "scroll-input" + ] }, "outputs": [], "source": [ @@ -280,162 +352,158 @@ "\n", " def evaluate(self) -> sp.Expr:\n", " s, m1, m2 = self.args\n", - " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))\n", - "\n", - "\n", - "@unevaluated(real=False)\n", - "class ChannelWidth(sp.Expr):\n", - " s: Any\n", - " m1: Any\n", - " m2: Any\n", - " gamma_R: Any\n", - " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", - "\n", - " def evaluate(self) -> sp.Expr:\n", - " s, m1, m2, gamma_R = self.args\n", - " return gamma_R * PhaseSpaceCM(s, m1, m2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Relativistic Breit-Wigner" + " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ - "PARAMETERS_BW = {}\n", - "\n", - "\n", - "def formulate_rel_bw(\n", - " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", - ") -> sp.Expr:\n", - " (_, variables), *_ = resonances\n", - " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", - " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", - " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", - " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", - " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", - " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", - " dummy = [sp.Symbol(Rf\"Dummy_{{{p.latex}}}\") for p, _ in resonances]\n", - " w_s = (ChannelWidth(s, m_a, m_b, w_) for w_ in w)\n", - " rel_bw = sum(\n", - " (w_ * m_ * dummy_) / (m_**2 - s - m_ * w_s_)\n", - " for m_, w_, w_s_, dummy_ in zip(m, w, w_s, dummy)\n", - " )\n", - " for i, (resonance, _) in enumerate(resonances):\n", - " PARAMETERS_BW[w[i]] = resonance.width\n", - " PARAMETERS_BW[m[i]] = resonance.mass\n", - " PARAMETERS_BW[b[i]] = 1\n", - " PARAMETERS_BW[d[i]] = 1\n", - " PARAMETERS_BW[L[i]] = 0\n", - " PARAMETERS_BW[dummy[i]] = 1\n", - " return rel_bw" + "s, m1, m2 = sp.symbols(\"s m1 m2\", nonnegative=True)\n", + "exprs = [\n", + " PhaseSpaceCM(s, m1, m2),\n", + " ChewMandelstam(s, m1, m2),\n", + " BreakupMomentum(s, m1, m2),\n", + "]\n", + "Math(aslatex({e: e.doit(deep=False) for e in exprs}))" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "### $K$ matrix " + "### Relativistic Breit-Wigner" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ - "PARAMETERS_K = {}\n", + "@unevaluated(real=False)\n", + "class ChannelWidth(sp.Expr):\n", + " s: Any\n", + " m1: Any\n", + " m2: Any\n", + " width: Any\n", + " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", "\n", + " def evaluate(self) -> sp.Expr:\n", + " s, m1, m2, width = self.args\n", + " return width * PhaseSpaceCM(s, m1, m2)\n", "\n", - "def formulate_K_matrix(\n", - " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", - ") -> sp.Expr:\n", - " (_, variables), *_ = resonances\n", - " s = variables.incoming_state_mass**2\n", - " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", - " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", "\n", - " kmatrix = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", - " for i, (resonance, _) in enumerate(resonances):\n", - " PARAMETERS_K[m[i]] = resonance.mass\n", - " PARAMETERS_K[g[i]] = 1\n", - " return kmatrix" + "width = sp.Symbol(\"Gamma0\", nonnegative=True)\n", + "expr = ChannelWidth(s, m1, m2, width)\n", + "Math(aslatex({expr: expr.doit(deep=False)}))" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "### $P$ vector" + "PARAMETERS_BW = dict(model.parameter_defaults)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "PARAMETERS_F = {}\n", - "\n", - "\n", - "def formulate_P_vector(\n", + "def formulate_breit_wigner(\n", " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", ") -> sp.Expr:\n", " (_, variables), *_ = resonances\n", " s = variables.incoming_state_mass**2\n", - " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " m1 = variables.outgoing_state_mass1\n", + " m2 = variables.outgoing_state_mass2\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", - " beta = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", - " P_vector = sum((g_ * beta_) / (m_**2 - s) for m_, g_, beta_ in zip(m, g, beta))\n", + " Γ0 = [sp.Symbol(Rf\"\\Gamma_{{{p.latex}}}\") for p, _ in resonances]\n", + " Γ = [ChannelWidth(s, m1, m2, _w) for _w in Γ0]\n", + " β = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", + " expr = sum(\n", + " (β_ * m_ * Γ_) / (m_**2 - s - m_ * Γ0_) for m_, Γ_, Γ0_, β_ in zip(m, Γ0, Γ, β)\n", + " )\n", " for i, (resonance, _) in enumerate(resonances):\n", - " PARAMETERS_F[m[i]] = resonance.mass\n", - " PARAMETERS_F[beta[i]] = 1 + 0j\n", - " PARAMETERS_F[g[i]] = 1\n", - " return P_vector" + " PARAMETERS_BW[β[i]] = 1 + 0j\n", + " PARAMETERS_BW[m[i]] = resonance.mass\n", + " PARAMETERS_BW[Γ0[i]] = resonance.width\n", + " return expr" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], "source": [ - "### $F$ vector" + "dynamics_expressions_bw = {\n", + " symbol: formulate_breit_wigner(resonances)\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", + "}\n", + "model_bw = attrs.evolve(\n", + " model,\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_BW,\n", + " }),\n", + ")\n", + "Math(aslatex(dynamics_expressions_bw))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "def formulate_F_vector(\n", - " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", - ") -> sp.Expr:\n", - " (_, variables), *_ = resonances\n", - " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", - " rho = PhaseSpaceCM(s, m_a, m_b)\n", - " K = formulate_K_matrix(resonances)\n", - " P = formulate_P_vector(resonances)\n", - " return (1 / (1 - rho * K)) * P" + "full_expression_bw = perform_cached_doit(model_bw.expression).xreplace(\n", + " dynamics_expressions_bw\n", + ")\n", + "sp.count_ops(full_expression_bw)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "### Model relativistic Breit-Wigner" + "### $P$ vector" ] }, { @@ -446,18 +514,7 @@ }, "outputs": [], "source": [ - "dynamics_expressions_rel_bw = {\n", - " symbol: formulate_rel_bw(resonances)\n", - " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", - "}\n", - "model_rel_bw = attrs.evolve(\n", - " model,\n", - " parameter_defaults=ParameterValues({\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_BW,\n", - " }),\n", - ")\n", - "Latex(aslatex(dynamics_expressions_rel_bw))" + "PARAMETERS_F = dict(model.parameter_defaults)" ] }, { @@ -468,7 +525,19 @@ }, "outputs": [], "source": [ - "model_rel_bw.parameter_defaults" + "def formulate_k_matrix(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (_, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + "\n", + " expr = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_F[m[i]] = resonance.mass\n", + " PARAMETERS_F[g[i]] = 1\n", + " return expr" ] }, { @@ -479,28 +548,56 @@ }, "outputs": [], "source": [ - "full_expression_rel_bw = model_rel_bw.expression.doit().xreplace(\n", - " dynamics_expressions_rel_bw\n", - ")\n", - "sp.count_ops(full_expression_rel_bw)" + "def formulate_p_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (_, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", + " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", + " β = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", + " expr = sum((g_ * β_) / (m_**2 - s) for m_, g_, β_ in zip(m, g, β))\n", + " for i, (resonance, _) in enumerate(resonances):\n", + " PARAMETERS_F[β[i]] = 1 + 0j\n", + " PARAMETERS_F[m[i]] = resonance.mass\n", + " PARAMETERS_F[g[i]] = 1\n", + " return expr" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "### Model $F$ vector" + "def formulate_f_vector(\n", + " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", + ") -> sp.Expr:\n", + " (_, variables), *_ = resonances\n", + " s = variables.incoming_state_mass**2\n", + " m1 = variables.outgoing_state_mass1\n", + " m2 = variables.outgoing_state_mass2\n", + " rho = PhaseSpaceCM(s, m1, m2)\n", + " K = formulate_k_matrix(resonances)\n", + " P = formulate_p_vector(resonances)\n", + " return (1 / (1 - rho * K)) * P" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ "dynamics_expressions_fvector = {\n", - " symbol: formulate_F_vector(resonances)\n", - " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " symbol: formulate_f_vector(resonances)\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", "}\n", "model_fvector = attrs.evolve(\n", " model,\n", @@ -509,25 +606,18 @@ " **PARAMETERS_F,\n", " }),\n", ")\n", - "Latex(aslatex(dynamics_expressions_fvector))" + "Math(aslatex(dynamics_expressions_fvector))" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_fvector.parameter_defaults" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "full_expression_fvector = model_fvector.expression.doit().xreplace(\n", + "full_expression_fvector = perform_cached_doit(model_fvector.expression).xreplace(\n", " dynamics_expressions_fvector\n", ")\n", "sp.count_ops(full_expression_fvector)" @@ -535,71 +625,84 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "### Create Parametrized Function\n" + "### Create numerical functions" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "tags": [] }, - "outputs": [], "source": [ - "unfolded_expression_rel_bw = full_expression_rel_bw.doit()\n", - "\n", - "intensity_func_rel_bw = create_parametrized_function(\n", - " expression=unfolded_expression_rel_bw,\n", - " backend=\"jax\",\n", - " parameters=model_rel_bw.parameter_defaults,\n", - ")" + "#### Amplitude model function" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "dynamics_expr_rel_bw, *_ = dynamics_expressions_rel_bw.values()\n", - "dynamics_expr_rel_bw" + "intensity_expr_bw = perform_cached_doit(full_expression_bw)\n", + "intensity_func_bw = create_parametrized_function(\n", + " expression=intensity_expr_bw,\n", + " backend=\"jax\",\n", + " parameters=PARAMETERS_BW,\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "dynamics_func_bw = create_parametrized_function(\n", - " expression=dynamics_expr_rel_bw.doit(),\n", + "intensity_expr_fvector = perform_cached_doit(full_expression_fvector)\n", + "intensity_func_fvector = create_parametrized_function(\n", + " expression=intensity_expr_fvector,\n", " backend=\"jax\",\n", - " parameters=model_rel_bw.parameter_defaults,\n", - " use_cse=False,\n", + " parameters=PARAMETERS_F,\n", ")" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "#### Dynamics function" + ] + }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "unfolded_expression_fvector = full_expression_fvector.doit()\n", - "\n", - "intensity_func_fvector = create_parametrized_function(\n", - " expression=unfolded_expression_fvector,\n", - " backend=\"jax\",\n", - " parameters=model_fvector.parameter_defaults,\n", - ")" + "dynamics_expr_bw, *_ = dynamics_expressions_bw.values()\n", + "dynamics_expr_bw" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "full-width" + ] + }, "outputs": [], "source": [ "dynamics_expr_fvector, *_ = dynamics_expressions_fvector.values()\n", @@ -609,20 +712,29 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "dynamics_expr_fvector" + "dynamics_func_bw = create_parametrized_function(\n", + " expression=perform_cached_doit(dynamics_expr_bw),\n", + " backend=\"jax\",\n", + " parameters=model_bw.parameter_defaults,\n", + " use_cse=False,\n", + ")" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "dynamics_func_fvector = create_parametrized_function(\n", - " expression=dynamics_expr_fvector.doit(),\n", + " expression=perform_cached_doit(dynamics_expr_fvector),\n", " backend=\"jax\",\n", " parameters=model_fvector.parameter_defaults,\n", " use_cse=False,\n", @@ -631,58 +743,52 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Update parameters" + "## Generate data" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "m_res1 = 1.82\n", - "m_res2 = 1.92\n", - "g_res1 = 1\n", - "g_res2 = 1\n", - "\n", - "new_parameters_fvector = {\n", - " R\"m_{N^{**}_1}\": m_res1,\n", - " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", - " R\"m_{N(1900)^+}\": m_res2,\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j, # 0.5l\n", - " R\"g_{N(1900)^+}\": g_res2,\n", - " R\"g_{N^{**}_1}\": g_res1,\n", - "}\n", - "\n", - "new_parameters_bw = {\n", - " R\"m_{N^{**}_1}\": m_res1,\n", - " R\"w_{N^{**}_1}\": g_res1 / m_res1,\n", - " R\"m_{N(1900)^+}\": m_res2,\n", - " R\"w_{N(1900)^+}\": g_res2 / m_res2,\n", - "}" + "### Generate phase space sample" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "intensity_func_fvector.update_parameters(new_parameters_fvector)\n", - "intensity_func_rel_bw.update_parameters(new_parameters_bw)\n", - "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", - "dynamics_func_bw.update_parameters(new_parameters_bw)\n", - "dynamics_func_fvector.parameters" + "rng = TFUniformRealNumberGenerator(seed=0)\n", + "phsp_generator = TFPhaseSpaceGenerator(\n", + " initial_state_mass=reaction.initial_state[-1].mass,\n", + " final_state_masses={i: p.mass for i, p in reaction.final_state.items()},\n", + ")\n", + "phsp_momenta = phsp_generator.generate(100_000, rng)\n", + "\n", + "epsilon = 1e-8\n", + "transformer = SympyDataTransformer.from_sympy(model.kinematic_variables, backend=\"jax\")\n", + "phsp = transformer(phsp_momenta)\n", + "phsp = {k: v + epsilon * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, "source": [ - "## Generate data with $F$ vector\n", - "### Generate phase space sample" + "### Update function parameters" ] }, { @@ -693,7 +799,10 @@ }, "outputs": [], "source": [ - "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"" + "m_res1 = 1.82\n", + "m_res2 = 1.92\n", + "g_res1 = 1\n", + "g_res2 = 1" ] }, { @@ -704,9 +813,14 @@ }, "outputs": [], "source": [ - "helicity_transformer = SympyDataTransformer.from_sympy(\n", - " model.kinematic_variables, backend=\"jax\"\n", - ")" + "new_parameters_bw = {\n", + " R\"\\Gamma_{N(1900)^+}\": g_res2 / m_res2,\n", + " R\"\\Gamma_{N^{**}_1}\": g_res1 / m_res1,\n", + " R\"m_{N(1900)^+}\": m_res2,\n", + " R\"m_{N^{**}_1}\": m_res1,\n", + "}\n", + "dynamics_func_bw.update_parameters(new_parameters_bw)\n", + "intensity_func_bw.update_parameters(new_parameters_bw)" ] }, { @@ -717,42 +831,55 @@ }, "outputs": [], "source": [ - "epsilon = 1e-8\n", - "from tensorwaves.data import (\n", - " SympyDataTransformer,\n", - " TFPhaseSpaceGenerator,\n", - " TFUniformRealNumberGenerator,\n", - ")\n", - "\n", - "rng = TFUniformRealNumberGenerator(seed=0)\n", - "phsp_generator = TFPhaseSpaceGenerator(\n", - " initial_state_mass=reaction.initial_state[-1].mass,\n", - " final_state_masses={i: p.mass for i, p in reaction.final_state.items()},\n", - ")\n", - "phsp_momenta = phsp_generator.generate(100_000, rng)\n", - "phsp = helicity_transformer(phsp_momenta)\n", - "phsp = {k: v + epsilon * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}\n", - "phsp" + "new_parameters_fvector = {\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", + " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", + " R\"g_{N(1900)^+}\": g_res2,\n", + " R\"g_{N^{**}_1}\": g_res1,\n", + " R\"m_{N(1900)^+}\": m_res2,\n", + " R\"m_{N^{**}_1}\": m_res1,\n", + "}\n", + "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", + "intensity_func_fvector.update_parameters(new_parameters_fvector)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Plots" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Plot Sub-Intensities" + "### Sub-intensities" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ "def compute_sub_intensity(\n", " func: ParametrizedFunction,\n", " input_data: DataSample,\n", " resonances: list[str],\n", - " coupling_pattern: str = r\"(\\\\beta|g|Dummy_)\",\n", + " coupling_pattern: str = r\"(\\\\beta|g)\",\n", "):\n", " original_parameters = dict(func.parameters)\n", " negative_lookahead = f\"(?!{'|'.join(map(re.escape, resonances))})\"\n", @@ -775,21 +902,35 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "total_intensities = intensity_func_fvector(phsp)\n", - "total_intensities_1 = intensity_func_rel_bw(phsp)\n", - "sub_intensities = {\n", + "total_intensities_bw = intensity_func_bw(phsp)\n", + "sub_intensities_bw = {\n", " p: compute_sub_intensity(\n", - " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " intensity_func_bw, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", " )\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", - "}\n", - "sub_intensities_bw = {\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_intensities_fvector = intensity_func_fvector(phsp)\n", + "sub_intensities_fvector = {\n", " p: compute_sub_intensity(\n", - " intensity_func_rel_bw, phsp, resonances=[p.latex], coupling_pattern=r\"Dummy_\"\n", + " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", " )\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", "}" ] @@ -797,12 +938,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ - "fig, ax = plt.subplots(figsize=(8, 5), dpi=300)\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", "ax.set_xlim(2, 5)\n", - "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", + "ax.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^2$]\")\n", "ax.set_ylabel(R\"Intensity [a. u.]\")\n", "ax.set_yticks([])\n", "\n", @@ -810,94 +959,110 @@ "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", "ax.hist(\n", " phsp_projection,\n", - " weights=total_intensities,\n", - " bins=bins,\n", + " weights=total_intensities_fvector,\n", " alpha=0.2,\n", + " bins=bins,\n", " color=\"hotpink\",\n", - " label=R\"Full intensity $F$ vector\",\n", + " label=\"Full intensity $F$ vector\",\n", ")\n", - "\n", "ax.hist(\n", " phsp_projection,\n", - " weights=total_intensities_1,\n", - " bins=bins,\n", + " weights=total_intensities_bw,\n", " alpha=0.2,\n", + " bins=bins,\n", " color=\"grey\",\n", " label=\"Full intensity Breit-Wigner\",\n", ")\n", "ax.hist(\n", - " len(sub_intensities) * [phsp_projection],\n", - " weights=list(sub_intensities.values()),\n", - " bins=bins,\n", + " len(sub_intensities_fvector) * [phsp_projection],\n", + " weights=list(sub_intensities_fvector.values()),\n", " alpha=0.6,\n", + " bins=bins,\n", + " histtype=\"step\",\n", " label=[\n", - " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\" for p in sub_intensities\n", + " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\"\n", + " for p in sub_intensities_fvector\n", " ],\n", - " histtype=\"step\",\n", ")\n", "\n", "ax.hist(\n", " len(sub_intensities_bw) * [phsp_projection],\n", " weights=list(sub_intensities_bw.values()),\n", - " bins=bins,\n", " alpha=0.6,\n", + " bins=bins,\n", + " histtype=\"step\",\n", " label=[\n", - " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ Breit-Wigner\"\n", - " for p in sub_intensities\n", + " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\"\n", + " for p in sub_intensities_fvector\n", " ],\n", - " histtype=\"step\",\n", " ls=\"dotted\",\n", ")\n", "\n", - "fig.legend(fontsize=\"9\")\n", + "fig.legend(loc=\"upper right\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Plot Phase" + "### Phase" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "epsilon = 1e-8\n", "x = np.linspace(2, 5, num=400)\n", - "data = {\"m_01\": np.sqrt(x + epsilon * 1j)}" + "plot_data = {\"m_01\": np.sqrt(x + epsilon * 1j)}" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "total_phase = np.angle(dynamics_func_fvector(data))\n", - "total_phase_1 = np.angle(dynamics_func_bw(data))\n", - "sub_phase = {\n", + "total_phase_bw = np.angle(dynamics_func_bw(plot_data))\n", + "sub_phase_bw = {\n", " p: np.angle(\n", " compute_sub_intensity(\n", - " dynamics_func_fvector,\n", - " data,\n", + " dynamics_func_bw,\n", + " plot_data,\n", " resonances=[p.latex],\n", - " coupling_pattern=r\"\\\\beta\",\n", + " coupling_pattern=r\"Dummy_\",\n", " )\n", " )\n", " for p, _ in resonances\n", - "}\n", - "sub_phase_bw = {\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_phase_fvector = np.angle(dynamics_func_fvector(plot_data))\n", + "sub_phase_fvector = {\n", " p: np.angle(\n", " compute_sub_intensity(\n", - " dynamics_func_bw,\n", - " data,\n", + " dynamics_func_fvector,\n", + " plot_data,\n", " resonances=[p.latex],\n", - " coupling_pattern=r\"Dummy_\",\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " )\n", " for p, _ in resonances\n", @@ -910,41 +1075,45 @@ "metadata": { "jupyter": { "source_hidden": true - } + }, + "tags": [ + "hide-input", + "scroll-input" + ] }, "outputs": [], "source": [ - "fig_phase, ax_phase = plt.subplots(figsize=(10, 6), dpi=500)\n", - "ax_phase.set_xlim(2, 5)\n", - "ax_phase.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\", fontsize=12)\n", - "ax_phase.set_ylabel(R\"Intensity [a. u.]\", fontsize=12)\n", - "ax_phase.set_yticks([])\n", + "fig, ax1 = plt.subplots(figsize=(10, 6), dpi=500)\n", + "ax1.set_xlim(2, 5)\n", + "ax1.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\", fontsize=12)\n", + "ax1.set_ylabel(R\"Intensity [a. u.]\", fontsize=12)\n", + "ax1.set_yticks([])\n", "\n", "# Plot histogram\n", "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", - "ax_phase.hist(\n", + "ax1.hist(\n", " phsp_projection,\n", - " weights=total_intensities,\n", + " weights=total_intensities_fvector,\n", " bins=bins,\n", " alpha=0.2,\n", " color=\"hotpink\",\n", " label=\"Full intensity $F$ vector\",\n", ")\n", "\n", - "for i, (k, v) in enumerate(sub_intensities.items()):\n", - " ax_phase.hist(\n", + "for i, (k, v) in enumerate(sub_intensities_fvector.items()):\n", + " ax1.hist(\n", " phsp_projection,\n", " weights=v,\n", " bins=bins,\n", " alpha=0.2,\n", - " color=plt.cm.viridis(i / len(sub_intensities)),\n", + " color=plt.cm.viridis(i / len(sub_intensities_fvector)),\n", " label=Rf\"Resonance at ${k.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", " )\n", "\n", - "ax_phase1 = ax_phase.twinx()\n", - "ax_phase1.set_ylabel(R\"Angle [a. u.]\", fontsize=12)\n", - "ax_phase1.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])\n", - "ax_phase1.set_yticklabels([\n", + "ax2 = ax1.twinx()\n", + "ax2.set_ylabel(R\"Angle [a. u.]\", fontsize=12)\n", + "ax2.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])\n", + "ax2.set_yticklabels([\n", " R\"$-\\pi$\",\n", " R\"$-\\frac{\\pi}{2}$\",\n", " R\"0\",\n", @@ -953,14 +1122,14 @@ "])\n", "\n", "# Plot total phases\n", - "ax_phase1.scatter(x, total_phase, s=22, color=\"red\", marker=\"^\", label=\"Total Phase\")\n", + "ax2.scatter(x, total_phase_fvector, s=22, color=\"red\", marker=\"^\", label=\"Total Phase\")\n", "\n", "colors = [\"green\", \"yellow\"]\n", "point_styles = [\"v\", \"o\"]\n", "marker_size = [20, 9]\n", "\n", - "for i, (k, v) in enumerate(sub_phase.items()):\n", - " ax_phase1.scatter(\n", + "for i, (k, v) in enumerate(sub_phase_fvector.items()):\n", + " ax2.scatter(\n", " x,\n", " v,\n", " color=colors[i % len(colors)],\n", @@ -969,40 +1138,51 @@ " marker=point_styles[i % len(point_styles)],\n", " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", " )\n", - " ax_phase1.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\")\n", + " ax2.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\")\n", "\n", "# Set labels for twin axes\n", - "ax_phase1.set_ylabel(\"Angle [rad]\", fontsize=12)\n", + "ax2.set_ylabel(\"Angle [rad]\", fontsize=12)\n", "\n", "# Add legends\n", - "fig_phase.legend(loc=\"upper left\", fontsize=\"9\", bbox_to_anchor=(0.1, 0.9))\n", - "plt.tight_layout()\n", - "plt.show()" + "fig.legend(loc=\"upper left\", fontsize=\"9\", bbox_to_anchor=(0.1, 0.9))\n", + "fig.tight_layout()\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Dynamics" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "total_dynamics = dynamics_func_fvector(data)\n", - "sub_dynamics = {\n", + "total_dynamics_bw = dynamics_func_fvector(plot_data)\n", + "sub_dynamics_bw = {\n", " p: compute_sub_intensity(\n", - " dynamics_func_fvector,\n", - " data,\n", + " dynamics_func_bw,\n", + " plot_data,\n", " resonances=[p.latex],\n", - " coupling_pattern=r\"\\\\beta\",\n", + " coupling_pattern=r\"Dummy_\",\n", " )\n", " for p, _ in resonances\n", "}\n", - "\n", - "sub_dynamics_bw = {\n", + "total_dynamics_fvector = dynamics_func_fvector(plot_data)\n", + "sub_dynamics_fvector = {\n", " p: compute_sub_intensity(\n", - " dynamics_func_bw,\n", - " data,\n", + " dynamics_func_fvector,\n", + " plot_data,\n", " resonances=[p.latex],\n", - " coupling_pattern=r\"Dummy_\",\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " for p, _ in resonances\n", "}" @@ -1011,51 +1191,57 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ - "x_1 = np.linspace(2, (m_res1**2 + m_res2**2) / 2, num=500)\n", - "x_2 = np.linspace((m_res1**2 + m_res2**2) / 2, 5, num=500)\n", + "x1 = np.linspace(2.0, (m_res1**2 + m_res2**2) / 2, num=500)\n", + "x2 = np.linspace((m_res1**2 + m_res2**2) / 2, 5.0, num=500)\n", "\n", - "data_1 = {\"m_01\": np.sqrt(x_1 + epsilon * 1j)}\n", - "data_2 = {\"m_01\": np.sqrt(x_2 + epsilon * 1j)}\n", + "plot_data1 = {\"m_01\": np.sqrt(x1 + epsilon * 1j)}\n", + "plot_data2 = {\"m_01\": np.sqrt(x2 + epsilon * 1j)}\n", "\n", - "y_imag_1 = dynamics_func_fvector(data_1).imag\n", - "y_real_1 = dynamics_func_fvector(data_1).real\n", - "y_imag_2 = dynamics_func_fvector(data_2).imag\n", - "y_real_2 = dynamics_func_fvector(data_2).real\n", - "fig_A, axs = plt.subplots(1, 2, figsize=(10, 5))\n", - "colorsA = [\"red\", \"blue\"]\n", - "axA, axA1 = axs\n", - "for i, (k, v) in enumerate(sub_dynamics.items()):\n", - " axA1.plot(\n", + "y1 = dynamics_func_fvector(plot_data1).imag\n", + "y2 = dynamics_func_fvector(plot_data2).imag\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "colors = [\"red\", \"blue\"]\n", + "ax1, ax2 = axes\n", + "for i, (k, v) in enumerate(sub_dynamics_fvector.items()):\n", + " ax2.plot(\n", " v.real,\n", " v.imag,\n", - " color=colorsA[i % len(colorsA)],\n", + " color=colors[i % len(colors)],\n", " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", " )\n", "\n", - "axA.plot(\n", - " y_real_1,\n", - " y_imag_1,\n", + "ax1.plot(\n", + " y1.real,\n", + " y1.imag,\n", " label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \",\n", " color=\"red\",\n", ")\n", - "axA.plot(\n", - " y_real_2,\n", - " y_imag_2,\n", + "ax1.plot(\n", + " y2.real,\n", + " y2.imag,\n", " label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \",\n", " color=\"blue\",\n", ")\n", - "axA.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", - "axA.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", - "axA.axhline(0, color=\"black\")\n", - "axA.axvline(0, color=\"black\")\n", - "axA1.axhline(0, color=\"black\")\n", - "axA1.axvline(0, color=\"black\")\n", - "plt.tight_layout()\n", - "axA.legend(loc=\"upper left\")\n", - "plt.show()" + "ax1.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", + "ax1.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", + "ax1.axhline(0, color=\"black\")\n", + "ax1.axvline(0, color=\"black\")\n", + "ax2.axhline(0, color=\"black\")\n", + "ax2.axvline(0, color=\"black\")\n", + "fig.tight_layout()\n", + "ax1.legend(loc=\"upper left\")\n", + "fig.show()" ] }, { @@ -1064,21 +1250,25 @@ "metadata": { "jupyter": { "source_hidden": true - } + }, + "tags": [ + "hide-input", + "scroll-input" + ] }, "outputs": [], "source": [ - "fig_phase_bw, ax_phase_bw = plt.subplots(figsize=(8, 5), dpi=500)\n", - "ax_phase_bw.set_xlim(2, 5)\n", - "ax_phase_bw.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", - "ax_phase_bw.set_ylabel(R\"Intensity [a. u.]\")\n", - "ax_phase_bw.set_yticks([])\n", + "fig, ax1 = plt.subplots(figsize=(8, 5), dpi=500)\n", + "ax1.set_xlim(2, 5)\n", + "ax1.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", + "ax1.set_ylabel(R\"Intensity [a. u.]\")\n", + "ax1.set_yticks([])\n", "\n", "# Plot histogram\n", "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", - "ax_phase_bw.hist(\n", + "ax1.hist(\n", " phsp_projection,\n", - " weights=total_intensities_1,\n", + " weights=total_intensities_bw,\n", " bins=bins,\n", " alpha=0.2,\n", " color=\"grey\",\n", @@ -1086,10 +1276,10 @@ ")\n", "\n", "\n", - "ax_phase1_bw = ax_phase_bw.twinx()\n", - "ax_phase1_bw.set_ylabel(R\"Angle [a. u.]\")\n", - "ax_phase1_bw.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, +np.pi])\n", - "ax_phase1_bw.set_yticklabels([\n", + "ax2 = ax1.twinx()\n", + "ax2.set_ylabel(R\"Angle [a. u.]\")\n", + "ax2.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, +np.pi])\n", + "ax2.set_yticklabels([\n", " R\"$-\\pi$\",\n", " R\"$-\\frac{\\pi}{2}$\",\n", " R\"0\",\n", @@ -1099,10 +1289,10 @@ "colors_bw = [\"magenta\", \"cyan\"]\n", "\n", "# Plot total phases\n", - "ax_phase1_bw\n", - "ax_phase1_bw.plot(\n", + "ax2\n", + "ax2.plot(\n", " x,\n", - " total_phase_1,\n", + " total_phase_bw,\n", " color=\"blue\",\n", " label=\"Total Phase Breit-Wigner\",\n", " linestyle=\"--\",\n", @@ -1110,7 +1300,7 @@ "\n", "\n", "for i, (k, v) in enumerate(sub_phase_bw.items()):\n", - " ax_phase1_bw.plot(\n", + " ax2.plot(\n", " x,\n", " v,\n", " color=colors_bw[i % len(colors_bw)],\n", @@ -1118,12 +1308,12 @@ " linestyle=\"--\",\n", " label=f\"Resonance at {k.mass} GeV Breit-Wigner\",\n", " )\n", - " (ax_phase1_bw.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\"),)\n", + " (ax2.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\"),)\n", "# Set labels for twin axes\n", - "ax_phase1_bw.set_ylabel(\"Angle [rad]\")\n", + "ax2.set_ylabel(\"Angle [rad]\")\n", "\n", "# Add legends\n", - "fig_phase_bw.legend(loc=\"upper left\", fontsize=\"7\")\n", + "fig.legend(loc=\"upper left\", fontsize=\"7\")\n", "plt.tight_layout()\n", "plt.show()" ] @@ -1134,46 +1324,48 @@ "metadata": { "jupyter": { "source_hidden": true - } + }, + "tags": [ + "hide-input", + "scroll-input" + ] }, "outputs": [], "source": [ - "y_imag_1_bw = dynamics_func_bw(data_1).imag\n", - "y_real_1_bw = dynamics_func_bw(data_1).real\n", - "y_imag_2_bw = dynamics_func_bw(data_2).imag\n", - "y_real_2_bw = dynamics_func_bw(data_2).real\n", - "fig_A, axs = plt.subplots(1, 2, figsize=(10, 5))\n", - "colorsA_bw = [\"red\", \"blue\"]\n", - "axA_bw, axA1_bw = axs\n", + "y1_bw = dynamics_func_bw(plot_data1)\n", + "y2_bw = dynamics_func_bw(plot_data2)\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "colors = [\"red\", \"blue\"]\n", + "ax1, ax2 = axes\n", "for i, (k, v) in enumerate(sub_dynamics_bw.items()):\n", - " axA1_bw.plot(\n", + " ax2.plot(\n", " v.real,\n", " v.imag,\n", - " color=colorsA_bw[i % len(colorsA_bw)],\n", + " color=colors[i % len(colors)],\n", " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", " )\n", "\n", - "axA_bw.plot(\n", - " y_real_1_bw,\n", - " y_imag_1_bw,\n", - " label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \",\n", + "ax1.plot(\n", + " y1_bw.real,\n", + " y1_bw.imag,\n", + " label=rf\"$Im(F)$ $s$ in domain of {m_res1}-GeV resonance \",\n", " color=\"red\",\n", ")\n", - "axA_bw.plot(\n", - " y_real_2_bw,\n", - " y_imag_2_bw,\n", - " label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \",\n", + "ax1.plot(\n", + " y2_bw.real,\n", + " y2_bw.imag,\n", + " label=rf\"$Im(F)$ $s$ in domain of {m_res2}-GeV resonance \",\n", " color=\"blue\",\n", ")\n", - "axA_bw.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", - "axA_bw.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", - "axA_bw.axhline(0, color=\"black\")\n", - "axA_bw.axvline(0, color=\"black\")\n", - "axA1_bw.axhline(0, color=\"black\")\n", - "axA1_bw.axvline(0, color=\"black\")\n", + "ax1.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", + "ax1.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", + "ax1.axhline(0, color=\"black\")\n", + "ax1.axvline(0, color=\"black\")\n", + "ax2.axhline(0, color=\"black\")\n", + "ax2.axvline(0, color=\"black\")\n", "plt.tight_layout()\n", - "axA_bw.legend(loc=\"upper left\")\n", - "plt.show()" + "ax1.legend(loc=\"upper left\")\n", + "fig.show()" ] } ], diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 1a4ba945..02e023dd 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -20,7 +20,7 @@ ":::{card} Amplitude building with K-matrix dynamics\n", "TR-031\n", "^^^\n", - "Illustration of how to formulate an amplitude model with P-vector dynamics.\n", + "Illustration of how to formulate an amplitude model for two channels with P-vector dynamics. A combined fit is performed over the sum of the likelihood over both distributions.\n", ":::\n", "::::" ] From 271abc0fa183f725ef798a008cce297e83b29f72 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 15:55:11 +0200 Subject: [PATCH 19/92] FIX: do not project to `imag` before Argand --- docs/report/031.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index b187eca1..2a4ceaff 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1208,8 +1208,8 @@ "plot_data1 = {\"m_01\": np.sqrt(x1 + epsilon * 1j)}\n", "plot_data2 = {\"m_01\": np.sqrt(x2 + epsilon * 1j)}\n", "\n", - "y1 = dynamics_func_fvector(plot_data1).imag\n", - "y2 = dynamics_func_fvector(plot_data2).imag\n", + "y1 = dynamics_func_fvector(plot_data1)\n", + "y2 = dynamics_func_fvector(plot_data2)\n", "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", "colors = [\"red\", \"blue\"]\n", "ax1, ax2 = axes\n", From 1a3ad54a742978e44f4523884648703ce40bd998 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 00:15:02 +0200 Subject: [PATCH 20/92] ENH: render plots as SVG --- docs/report/030.ipynb | 13 +++++++++++++ docs/report/031.ipynb | 13 +++++++++++++ docs/report/032.ipynb | 13 +++++++++++++ 3 files changed, 39 insertions(+) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 7b4b735d..961767e9 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -104,6 +104,19 @@ "improve_latex_rendering()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_formats = ['svg']" + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 2a4ceaff..f6174b19 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -98,6 +98,19 @@ "improve_latex_rendering()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_formats = ['svg']" + ] + }, { "cell_type": "markdown", "metadata": { diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 02e023dd..76cd7af2 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -102,6 +102,19 @@ "_ = np.seterr(invalid=\"ignore\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "remove-input" + ] + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_formats = ['svg']" + ] + }, { "cell_type": "markdown", "metadata": {}, From 8546e61a907eec5e6923c14fddadb4c73ad97d68 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 11:23:42 +0200 Subject: [PATCH 21/92] MAINT: remove redundant cspell ignore --- .cspell.json | 1 - 1 file changed, 1 deletion(-) diff --git a/.cspell.json b/.cspell.json index 5e52fdc7..40168b41 100644 --- a/.cspell.json +++ b/.cspell.json @@ -139,7 +139,6 @@ "Colab", "Danilkin", "Deineka", - "Fakestar", "MAINT", "Tiator", "absl", From af5aca68d52f8f2969e0d84ef83354facbeeaf24 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 11:27:08 +0200 Subject: [PATCH 22/92] MAINT: clean up additional definitions file --- docs/report/030/additional-definitions.yml | 401 --------------------- 1 file changed, 401 deletions(-) diff --git a/docs/report/030/additional-definitions.yml b/docs/report/030/additional-definitions.yml index 47fdd623..45c947b9 100644 --- a/docs/report/030/additional-definitions.yml +++ b/docs/report/030/additional-definitions.yml @@ -42,48 +42,6 @@ particles: parity: value: -1 - - name: N(1875)+ - pid: 200002 - latex: N(1875)^+ - spin: 1.5 - mass: 1.875 - width: 0.2 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: N(1880)+ - pid: 200000 - latex: N(1880)^+ - spin: 0.5 - mass: 1.88 - width: 0.3 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: 1 - - - name: N(1895)+ - pid: 200001 - latex: N(1895)^+ - spin: 0.5 - mass: 1.895 - width: 0.12 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - name: N(1900)+ pid: 200003 latex: N(1900)^+ @@ -97,362 +55,3 @@ particles: baryon_number: 1 parity: value: 1 - - - name: N(2060)+ - pid: 200004 - latex: N(2060)^+ - spin: 2.5 - mass: 2.1 - width: 0.4 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: N(J05Pm)+ - pid: 100001 - latex: N(1/2^+)^+ - spin: 0.5 - mass: 1.99 - width: 0.15 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: N(J05Pp)+ - pid: 100000 - latex: N(1/2^+)^+ - spin: 0.5 - mass: 1.99 - width: 0.15 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: 1 - - - name: N(J15Pm)+ - pid: 100003 - latex: N(3/2^-)^+ - spin: 1.5 - mass: 1.99 - width: 0.15 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: N(J15Pp)+ - pid: 100002 - latex: N(3/2^+)^+ - spin: 1.5 - mass: 1.99 - width: 0.15 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: 1 - - - name: NonResonantN12M - pid: 20000016 - latex: NR(\Sigma^+K_S^0)(J^P=\frac{1}{2}^-) - spin: 0.5 - mass: 1.99 - width: 0.5 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: NonResonantN12P - pid: 20000006 - latex: NR(\Sigma^+K_S^0)(J^P=\frac{1}{2}^+) - spin: 0.5 - mass: 1.99 - width: 0.5 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: 1 - - - name: NonResonantN32M - pid: 20000017 - latex: NR(\Sigma^+K_S^0)(J^P=\frac{3}{2}^-) - spin: 1.5 - mass: 1.99 - width: 0.5 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: NonResonantN32P - pid: 20000007 - latex: NR(\Sigma^+K_S^0)(J^P=\frac{3}{2}^+) - spin: 1.5 - mass: 1.99 - width: 0.5 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: 1 - - - name: N(1875)~- - pid: -9999993 - latex: \overline{N}(1875)^{+} - spin: 1.5 - mass: 1.875 - width: 0.12 - charge: -1 - isospin: - magnitude: 0.5 - projection: -0.5 - baryon_number: -1 - parity: - value: -1 - - - name: N(1880)~- - pid: -9999994 - latex: \overline{N}(1880)^{+} - spin: 0.5 - mass: 1.88 - width: 0.3 - charge: -1 - isospin: - magnitude: 0.5 - projection: -0.5 - baryon_number: -1 - parity: - value: -1 - - - name: N(1895)~- - pid: -9999995 - latex: \overline{N}(1895)^{+} - spin: 0.5 - mass: 1.895 - width: 0.12 - charge: -1 - isospin: - magnitude: 0.5 - projection: -0.5 - baryon_number: -1 - parity: - value: 1 - - - name: N(1900)~- - pid: -9999996 - latex: \overline{N}(1900)^{+} - spin: 1.5 - mass: 1.92 - width: 0.2 - charge: -1 - isospin: - magnitude: 0.5 - projection: -0.5 - baryon_number: -1 - parity: - value: -1 - - - name: N(2060)~- - pid: -9999997 - latex: \overline{N}(2060)^{+} - spin: 1.5 - mass: 2.07 - width: 0.4 - charge: -1 - isospin: - magnitude: 0.5 - projection: -0.5 - baryon_number: -1 - parity: - value: -1 - - - name: N(2100)~- - pid: -9999998 - latex: \overline{N}(2100)^{+} - spin: 0.5 - mass: 2.1 - width: 0.26 - charge: -1 - isospin: - magnitude: 0.5 - projection: -0.5 - baryon_number: -1 - parity: - value: -1 - - - name: N(2120)~- - pid: -9999999 - latex: \overline{N}(2120)^{+} - spin: 1.5 - mass: 2.12 - width: 0.3 - charge: -1 - isospin: - magnitude: 0.5 - projection: -0.5 - baryon_number: -1 - parity: - value: 1 - - - name: Sigma(1580)~- - pid: 300000 - latex: \bar{\Sigma}(1580)^- - spin: 1.5 - mass: 1.58 - width: 0.015 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: 1 - - - name: Sigma(1620)~- - pid: 300001 - latex: \bar{\Sigma}(1620)^- - spin: 0.5 - mass: 1.62 - width: 0.07 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: 1 - - - name: Sigma(1880)~- - pid: 300004 - latex: \bar{\Sigma}(1880)^- - spin: 0.5 - mass: 1.88 - width: 0.2 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: -1 - - - name: Sigma(1900)~- - pid: 300005 - latex: \bar{\Sigma}(1900)^- - spin: 0.5 - mass: 1.925 - width: 0.165 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: 1 - - - name: Sigma(1940)~- - pid: 300007 - latex: \bar{\Sigma}(1940)^- - spin: 1.5 - mass: 1.94 - width: 0.25 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: -1 - - - name: NonResonantSigma12M - pid: 30000019 - latex: NR(\bar{p}K_S^0)(J^P=\frac{1}{2}^-) - spin: 0.5 - mass: 1.7 - width: 0.5 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: -1 - - - name: NonResonantSigma12P - pid: 30000009 - latex: NR(\bar{p}K_S^0)(J^P=\frac{1}{2}^+) - spin: 0.5 - mass: 1.7 - width: 0.5 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: 1 - - - name: NonResonantSigma32M - pid: 30000222220 - latex: NR(\bar{p}K_S^0)(J^P=\frac{3}{2}^-) - spin: 1.5 - mass: 1.72 - width: 0.52 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: -1 - - - name: NonResonantSigma32P - pid: 30000010 - latex: NR(\bar{p}K_S^0)(J^P=\frac{3}{2}^+) - spin: 1.5 - mass: 1.7 - width: 0.5 - charge: -1 - isospin: - magnitude: 1.0 - projection: -1.0 - strangeness: 1 - baryon_number: -1 - parity: - value: 1 From 0d87c86568f54bd54dd2b72fec588f36e36684ad Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 15:35:30 +0200 Subject: [PATCH 23/92] =?UTF-8?q?MAINT:=20rename=20`epsilon`=20to=20`?= =?UTF-8?q?=CE=B5`?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/report/031.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index f6174b19..5f170b1f 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -788,10 +788,10 @@ ")\n", "phsp_momenta = phsp_generator.generate(100_000, rng)\n", "\n", - "epsilon = 1e-8\n", + "ε = 1e-8\n", "transformer = SympyDataTransformer.from_sympy(model.kinematic_variables, backend=\"jax\")\n", "phsp = transformer(phsp_momenta)\n", - "phsp = {k: v + epsilon * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}" + "phsp = {k: v + ε * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}" ] }, { @@ -1033,9 +1033,9 @@ }, "outputs": [], "source": [ - "epsilon = 1e-8\n", + "ε = 1e-8\n", "x = np.linspace(2, 5, num=400)\n", - "plot_data = {\"m_01\": np.sqrt(x + epsilon * 1j)}" + "plot_data = {\"m_01\": np.sqrt(x + ε * 1j)}" ] }, { @@ -1218,8 +1218,8 @@ "x1 = np.linspace(2.0, (m_res1**2 + m_res2**2) / 2, num=500)\n", "x2 = np.linspace((m_res1**2 + m_res2**2) / 2, 5.0, num=500)\n", "\n", - "plot_data1 = {\"m_01\": np.sqrt(x1 + epsilon * 1j)}\n", - "plot_data2 = {\"m_01\": np.sqrt(x2 + epsilon * 1j)}\n", + "plot_data1 = {\"m_01\": np.sqrt(x1 + ε * 1j)}\n", + "plot_data2 = {\"m_01\": np.sqrt(x2 + ε * 1j)}\n", "\n", "y1 = dynamics_func_fvector(plot_data1)\n", "y2 = dynamics_func_fvector(plot_data2)\n", From 1762a39d5afdb4d6c4f9d0ba315acf1462315822 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 16:33:30 +0200 Subject: [PATCH 24/92] FIX: get resonances from double loop --- docs/report/031.ipynb | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 5f170b1f..92e93431 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1056,6 +1056,7 @@ " coupling_pattern=r\"Dummy_\",\n", " )\n", " )\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", "}" ] @@ -1078,6 +1079,7 @@ " coupling_pattern=r\"\\\\beta\",\n", " )\n", " )\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", "}" ] @@ -1197,6 +1199,7 @@ " resonances=[p.latex],\n", " coupling_pattern=r\"\\\\beta\",\n", " )\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", "}" ] From 05da2afd722ec56926b9517b687278085924142a Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 13:42:41 +0200 Subject: [PATCH 25/92] FIX: filter with `\\beta` instead of `Dummy` * DOC: reorganize plotting code --- docs/report/031.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 92e93431..b2a42d32 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1053,7 +1053,7 @@ " dynamics_func_bw,\n", " plot_data,\n", " resonances=[p.latex],\n", - " coupling_pattern=r\"Dummy_\",\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " )\n", " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", @@ -1187,7 +1187,7 @@ " dynamics_func_bw,\n", " plot_data,\n", " resonances=[p.latex],\n", - " coupling_pattern=r\"Dummy_\",\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " for p, _ in resonances\n", "}\n", From c60758b31df4dfc0674aecca60aaef200f89bb6e Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 16:48:21 +0200 Subject: [PATCH 26/92] ENH: make `coupling_pattern` argument oblicatory --- docs/report/030.ipynb | 12 +++++++++--- docs/report/031.ipynb | 12 +++++++++--- 2 files changed, 18 insertions(+), 6 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 961767e9..66e0f29a 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -809,7 +809,7 @@ " func: ParametrizedFunction,\n", " input_data: DataSample,\n", " resonances: list[str],\n", - " coupling_pattern: str = r\"(\\\\beta|g)\",\n", + " coupling_pattern: str,\n", "):\n", " original_parameters = dict(func.parameters)\n", " negative_lookahead = f\"(?!{'|'.join(map(re.escape, resonances))})\"\n", @@ -840,7 +840,10 @@ "total_intensities_bw = intensity_func_bw(phsp)\n", "sub_intensities_bw = {\n", " p: compute_sub_intensity(\n", - " intensity_func_bw, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " intensity_func_bw,\n", + " phsp,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", @@ -858,7 +861,10 @@ "total_intensities_fvector = intensity_func_fvector(phsp)\n", "sub_intensities_fvector = {\n", " p: compute_sub_intensity(\n", - " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " intensity_func_fvector,\n", + " phsp,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index b2a42d32..c3d3fabc 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -892,7 +892,7 @@ " func: ParametrizedFunction,\n", " input_data: DataSample,\n", " resonances: list[str],\n", - " coupling_pattern: str = r\"(\\\\beta|g)\",\n", + " coupling_pattern: str,\n", "):\n", " original_parameters = dict(func.parameters)\n", " negative_lookahead = f\"(?!{'|'.join(map(re.escape, resonances))})\"\n", @@ -923,7 +923,10 @@ "total_intensities_bw = intensity_func_bw(phsp)\n", "sub_intensities_bw = {\n", " p: compute_sub_intensity(\n", - " intensity_func_bw, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " intensity_func_bw,\n", + " phsp,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", @@ -941,7 +944,10 @@ "total_intensities_fvector = intensity_func_fvector(phsp)\n", "sub_intensities_fvector = {\n", " p: compute_sub_intensity(\n", - " intensity_func_fvector, phsp, resonances=[p.latex], coupling_pattern=r\"\\\\beta\"\n", + " intensity_func_fvector,\n", + " phsp,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", From 779159e5a47240256f50c54f08c48c89bfd6efdb Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 15:38:44 +0200 Subject: [PATCH 27/92] =?UTF-8?q?BEHAVIOR:=20define=20=CE=B5=20offset=20ou?= =?UTF-8?q?tside=20sqrt?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/report/031.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index c3d3fabc..2d4e4be5 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1041,7 +1041,7 @@ "source": [ "ε = 1e-8\n", "x = np.linspace(2, 5, num=400)\n", - "plot_data = {\"m_01\": np.sqrt(x + ε * 1j)}" + "plot_data = {\"m_01\": np.sqrt(x) + ε * 1j}" ] }, { From b57bc5b8ac590122b376ccacb8ee0ec148500eca Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 13:42:41 +0200 Subject: [PATCH 28/92] ENH: switch Argand and phase plots * DOC: reorganize plotting code --- docs/report/031.ipynb | 314 ++++++++++++++++++++---------------------- 1 file changed, 146 insertions(+), 168 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 2d4e4be5..eb315e4e 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1028,7 +1028,7 @@ "tags": [] }, "source": [ - "### Phase" + "### Argand plots" ] }, { @@ -1052,15 +1052,24 @@ }, "outputs": [], "source": [ - "total_phase_bw = np.angle(dynamics_func_bw(plot_data))\n", - "sub_phase_bw = {\n", - " p: np.angle(\n", - " compute_sub_intensity(\n", - " dynamics_func_bw,\n", - " plot_data,\n", - " resonances=[p.latex],\n", - " coupling_pattern=r\"\\\\beta\",\n", - " )\n", + "total_dynamics_bw = dynamics_func_bw(plot_data)\n", + "sub_dynamics_bw = {\n", + " p: compute_sub_intensity(\n", + " dynamics_func_bw,\n", + " plot_data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", + " )\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", + " for p, _ in resonances\n", + "}\n", + "total_dynamics_fvector = dynamics_func_fvector(plot_data)\n", + "sub_dynamics_fvector = {\n", + " p: compute_sub_intensity(\n", + " dynamics_func_fvector,\n", + " plot_data,\n", + " resonances=[p.latex],\n", + " coupling_pattern=r\"\\\\beta\",\n", " )\n", " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " for p, _ in resonances\n", @@ -1075,19 +1084,134 @@ }, "outputs": [], "source": [ - "total_phase_fvector = np.angle(dynamics_func_fvector(plot_data))\n", - "sub_phase_fvector = {\n", - " p: np.angle(\n", - " compute_sub_intensity(\n", - " dynamics_func_fvector,\n", - " plot_data,\n", - " resonances=[p.latex],\n", - " coupling_pattern=r\"\\\\beta\",\n", - " )\n", + "x1 = np.linspace(2.0, (m_res1**2 + m_res2**2) / 2, num=500)\n", + "x2 = np.linspace((m_res1**2 + m_res2**2) / 2, 5.0, num=500)\n", + "plot_data1 = {\"m_01\": np.sqrt(x1) + ε * 1j}\n", + "plot_data2 = {\"m_01\": np.sqrt(x2) + ε * 1j}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, + "outputs": [], + "source": [ + "y1 = dynamics_func_fvector(plot_data1)\n", + "y2 = dynamics_func_fvector(plot_data2)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "ax1, ax2 = axes\n", + "for ax in axes:\n", + " ax.axhline(0, color=\"black\", linewidth=0.5)\n", + " ax.axvline(0, color=\"black\", linewidth=0.5)\n", + "\n", + "colors = [\"red\", \"blue\"]\n", + "for i, (k, v) in enumerate(sub_dynamics_fvector.items()):\n", + " ax2.plot(\n", + " v.real,\n", + " v.imag,\n", + " color=colors[i % len(colors)],\n", + " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", " )\n", - " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", - " for p, _ in resonances\n", - "}" + "\n", + "ax1.plot(\n", + " y1.real,\n", + " y1.imag,\n", + " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res1}-GeV resonance \",\n", + " color=\"red\",\n", + ")\n", + "ax1.plot(\n", + " y2.real,\n", + " y2.imag,\n", + " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res2}-GeV resonance \",\n", + " color=\"blue\",\n", + ")\n", + "ax1.set_xlabel(R\"$\\text{Re}\\,F$\", fontsize=14)\n", + "ax1.set_ylabel(R\"$\\text{Im}\\,F$\", fontsize=14)\n", + "fig.tight_layout()\n", + "ax1.legend(loc=\"upper left\")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, + "outputs": [], + "source": [ + "y1 = dynamics_func_bw(plot_data1)\n", + "y2 = dynamics_func_bw(plot_data2)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", + "fig.suptitle(\"Breit-Wigner\")\n", + "ax1, ax2 = axes\n", + "for ax in axes:\n", + " ax.axhline(0, color=\"black\", linewidth=0.5)\n", + " ax.axvline(0, color=\"black\", linewidth=0.5)\n", + "\n", + "colors = [\"red\", \"blue\"]\n", + "for i, (k, v) in enumerate(sub_dynamics_bw.items()):\n", + " ax2.plot(\n", + " v.real,\n", + " v.imag,\n", + " color=colors[i % len(colors)],\n", + " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", + " )\n", + "\n", + "ax1.plot(\n", + " y1.real,\n", + " y1.imag,\n", + " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res1}-GeV resonance \",\n", + " color=\"red\",\n", + ")\n", + "ax1.plot(\n", + " y2.real,\n", + " y2.imag,\n", + " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res2}-GeV resonance \",\n", + " color=\"blue\",\n", + ")\n", + "ax1.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", + "ax1.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", + "plt.tight_layout()\n", + "ax1.legend(loc=\"upper left\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Phase" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_phase_bw = np.angle(total_dynamics_bw)\n", + "total_phase_fvector = np.angle(total_dynamics_fvector)\n", + "sub_phase_bw = {p: np.angle(v) for p, v in sub_dynamics_bw.items()}\n", + "sub_phase_fvector = {p: np.angle(v) for p, v in sub_dynamics_fvector.items()}" ] }, { @@ -1170,102 +1294,6 @@ "fig.show()" ] }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "total_dynamics_bw = dynamics_func_fvector(plot_data)\n", - "sub_dynamics_bw = {\n", - " p: compute_sub_intensity(\n", - " dynamics_func_bw,\n", - " plot_data,\n", - " resonances=[p.latex],\n", - " coupling_pattern=r\"\\\\beta\",\n", - " )\n", - " for p, _ in resonances\n", - "}\n", - "total_dynamics_fvector = dynamics_func_fvector(plot_data)\n", - "sub_dynamics_fvector = {\n", - " p: compute_sub_intensity(\n", - " dynamics_func_fvector,\n", - " plot_data,\n", - " resonances=[p.latex],\n", - " coupling_pattern=r\"\\\\beta\",\n", - " )\n", - " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", - " for p, _ in resonances\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "hide-input", - "scroll-input" - ] - }, - "outputs": [], - "source": [ - "x1 = np.linspace(2.0, (m_res1**2 + m_res2**2) / 2, num=500)\n", - "x2 = np.linspace((m_res1**2 + m_res2**2) / 2, 5.0, num=500)\n", - "\n", - "plot_data1 = {\"m_01\": np.sqrt(x1 + ε * 1j)}\n", - "plot_data2 = {\"m_01\": np.sqrt(x2 + ε * 1j)}\n", - "\n", - "y1 = dynamics_func_fvector(plot_data1)\n", - "y2 = dynamics_func_fvector(plot_data2)\n", - "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", - "colors = [\"red\", \"blue\"]\n", - "ax1, ax2 = axes\n", - "for i, (k, v) in enumerate(sub_dynamics_fvector.items()):\n", - " ax2.plot(\n", - " v.real,\n", - " v.imag,\n", - " color=colors[i % len(colors)],\n", - " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", - " )\n", - "\n", - "ax1.plot(\n", - " y1.real,\n", - " y1.imag,\n", - " label=rf\"$Im(F)$ $s$ in domain of {{{m_res1}}} [GeV] resonance \",\n", - " color=\"red\",\n", - ")\n", - "ax1.plot(\n", - " y2.real,\n", - " y2.imag,\n", - " label=rf\"$Im(F)$ $s$ in domain of {{{m_res2}}} [GeV] resonance \",\n", - " color=\"blue\",\n", - ")\n", - "ax1.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", - "ax1.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", - "ax1.axhline(0, color=\"black\")\n", - "ax1.axvline(0, color=\"black\")\n", - "ax2.axhline(0, color=\"black\")\n", - "ax2.axvline(0, color=\"black\")\n", - "fig.tight_layout()\n", - "ax1.legend(loc=\"upper left\")\n", - "fig.show()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1339,56 +1367,6 @@ "plt.tight_layout()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "hide-input", - "scroll-input" - ] - }, - "outputs": [], - "source": [ - "y1_bw = dynamics_func_bw(plot_data1)\n", - "y2_bw = dynamics_func_bw(plot_data2)\n", - "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", - "colors = [\"red\", \"blue\"]\n", - "ax1, ax2 = axes\n", - "for i, (k, v) in enumerate(sub_dynamics_bw.items()):\n", - " ax2.plot(\n", - " v.real,\n", - " v.imag,\n", - " color=colors[i % len(colors)],\n", - " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", - " )\n", - "\n", - "ax1.plot(\n", - " y1_bw.real,\n", - " y1_bw.imag,\n", - " label=rf\"$Im(F)$ $s$ in domain of {m_res1}-GeV resonance \",\n", - " color=\"red\",\n", - ")\n", - "ax1.plot(\n", - " y2_bw.real,\n", - " y2_bw.imag,\n", - " label=rf\"$Im(F)$ $s$ in domain of {m_res2}-GeV resonance \",\n", - " color=\"blue\",\n", - ")\n", - "ax1.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", - "ax1.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", - "ax1.axhline(0, color=\"black\")\n", - "ax1.axvline(0, color=\"black\")\n", - "ax2.axhline(0, color=\"black\")\n", - "ax2.axvline(0, color=\"black\")\n", - "plt.tight_layout()\n", - "ax1.legend(loc=\"upper left\")\n", - "fig.show()" - ] } ], "metadata": { From ed66683d1243752870bc7adc0c64fbde8b1dc2e4 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 15:18:51 +0200 Subject: [PATCH 29/92] MAINT: bundle plotting code in functions --- docs/report/031.ipynb | 362 ++++++++++++++++++++---------------------- 1 file changed, 171 insertions(+), 191 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index eb315e4e..700ced56 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -91,11 +91,12 @@ " TFUniformRealNumberGenerator,\n", ")\n", "from tensorwaves.function.sympy import create_parametrized_function\n", - "from tensorwaves.interface import DataSample, ParametrizedFunction\n", + "from tensorwaves.interface import DataSample, Function, ParametrizedFunction\n", "\n", + "improve_latex_rendering()\n", "logging.getLogger(\"absl\").setLevel(logging.ERROR)\n", "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", - "improve_latex_rendering()" + "plt.rc(\"font\", size=12)" ] }, { @@ -797,7 +798,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -881,6 +881,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Function for computing sub-intensities" + }, "tags": [ "hide-input", "scroll-input" @@ -997,11 +1000,13 @@ " weights=list(sub_intensities_fvector.values()),\n", " alpha=0.6,\n", " bins=bins,\n", + " color=[f\"C{i}\" for i, _ in enumerate(sub_intensities_bw)],\n", " histtype=\"step\",\n", " label=[\n", " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\"\n", " for p in sub_intensities_fvector\n", " ],\n", + " linewidth=2,\n", ")\n", "\n", "ax.hist(\n", @@ -1009,12 +1014,13 @@ " weights=list(sub_intensities_bw.values()),\n", " alpha=0.6,\n", " bins=bins,\n", + " color=[f\"C{i}\" for i, _ in enumerate(sub_intensities_bw)],\n", " histtype=\"step\",\n", " label=[\n", " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\"\n", " for p in sub_intensities_fvector\n", " ],\n", - " ls=\"dotted\",\n", + " linestyle=\"dashed\",\n", ")\n", "\n", "fig.legend(loc=\"upper right\")\n", @@ -1097,6 +1103,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Function definition for plotting Argand plots" + }, "tags": [ "hide-input", "scroll-input" @@ -1104,98 +1113,86 @@ }, "outputs": [], "source": [ - "y1 = dynamics_func_fvector(plot_data1)\n", - "y2 = dynamics_func_fvector(plot_data2)\n", + "def plot_argand(\n", + " total_func: Function, sub_funcs: dict[Particle, Function], title: str\n", + ") -> None:\n", + " fig, axes = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n", + " fig.subplots_adjust(wspace=0.05)\n", + " fig.suptitle(title, y=0.99)\n", + " ax1, ax2 = axes\n", + " ax1.set_title(\"Total amplitude\")\n", + " ax2.set_title(\"Amplitude for resonance only\")\n", + " ax1.set_ylabel(R\"$\\text{Im}\\,F$\")\n", + " for ax in axes:\n", + " ax.axhline(0, color=\"black\", linewidth=0.5)\n", + " ax.axvline(0, color=\"black\", linewidth=0.5)\n", + " ax.set_xlabel(R\"$\\text{Re}\\,F$\")\n", "\n", - "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", - "ax1, ax2 = axes\n", - "for ax in axes:\n", - " ax.axhline(0, color=\"black\", linewidth=0.5)\n", - " ax.axvline(0, color=\"black\", linewidth=0.5)\n", - "\n", - "colors = [\"red\", \"blue\"]\n", - "for i, (k, v) in enumerate(sub_dynamics_fvector.items()):\n", - " ax2.plot(\n", - " v.real,\n", - " v.imag,\n", - " color=colors[i % len(colors)],\n", - " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", + " y1 = total_func(plot_data1)\n", + " ax1.plot(\n", + " y1.real,\n", + " y1.imag,\n", + " label=f\"Domain of {m_res1}-GeV resonance \",\n", + " color=\"C0\",\n", + " )\n", + " y2 = total_func(plot_data2)\n", + " ax1.plot(\n", + " y2.real,\n", + " y2.imag,\n", + " label=f\"Domain of {m_res2}-GeV resonance \",\n", + " color=\"C1\",\n", " )\n", + " for i, (k, v) in enumerate(sub_funcs.items()):\n", + " ax2.plot(\n", + " v.real,\n", + " v.imag,\n", + " color=f\"C{i}\",\n", + " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", + " )\n", "\n", - "ax1.plot(\n", - " y1.real,\n", - " y1.imag,\n", - " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res1}-GeV resonance \",\n", - " color=\"red\",\n", - ")\n", - "ax1.plot(\n", - " y2.real,\n", - " y2.imag,\n", - " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res2}-GeV resonance \",\n", - " color=\"blue\",\n", - ")\n", - "ax1.set_xlabel(R\"$\\text{Re}\\,F$\", fontsize=14)\n", - "ax1.set_ylabel(R\"$\\text{Im}\\,F$\", fontsize=14)\n", - "fig.tight_layout()\n", - "ax1.legend(loc=\"upper left\")\n", - "fig.show()" + " ax1.legend(loc=\"upper left\")\n", + " fig.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [ - "hide-input", - "scroll-input" + "hide-input" ] }, "outputs": [], "source": [ - "y1 = dynamics_func_bw(plot_data1)\n", - "y2 = dynamics_func_bw(plot_data2)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n", - "fig.suptitle(\"Breit-Wigner\")\n", - "ax1, ax2 = axes\n", - "for ax in axes:\n", - " ax.axhline(0, color=\"black\", linewidth=0.5)\n", - " ax.axvline(0, color=\"black\", linewidth=0.5)\n", - "\n", - "colors = [\"red\", \"blue\"]\n", - "for i, (k, v) in enumerate(sub_dynamics_bw.items()):\n", - " ax2.plot(\n", - " v.real,\n", - " v.imag,\n", - " color=colors[i % len(colors)],\n", - " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", - " )\n", - "\n", - "ax1.plot(\n", - " y1.real,\n", - " y1.imag,\n", - " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res1}-GeV resonance \",\n", - " color=\"red\",\n", - ")\n", - "ax1.plot(\n", - " y2.real,\n", - " y2.imag,\n", - " label=Rf\"$\\text{{Im}}\\,F(s)$ in domain of {m_res2}-GeV resonance \",\n", - " color=\"blue\",\n", - ")\n", - "ax1.set_xlabel(r\"$Re(F)$\", fontsize=14)\n", - "ax1.set_ylabel(r\"$Im(F)$\", fontsize=14)\n", - "plt.tight_layout()\n", - "ax1.legend(loc=\"upper left\")\n", - "fig.show()" + "plot_argand(\n", + " dynamics_func_fvector,\n", + " sub_dynamics_fvector,\n", + " title=\"F vector\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "plot_argand(\n", + " dynamics_func_bw,\n", + " sub_dynamics_bw,\n", + " title=\"Breit-Wigner\",\n", + ")" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Phase" ] @@ -1221,6 +1218,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Function definition for plotting phases" + }, "tags": [ "hide-input", "scroll-input" @@ -1228,70 +1228,80 @@ }, "outputs": [], "source": [ - "fig, ax1 = plt.subplots(figsize=(10, 6), dpi=500)\n", - "ax1.set_xlim(2, 5)\n", - "ax1.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\", fontsize=12)\n", - "ax1.set_ylabel(R\"Intensity [a. u.]\", fontsize=12)\n", - "ax1.set_yticks([])\n", + "def plot_phases(\n", + " total_intensity_array: np.ndarray,\n", + " sub_intensity_arrays: dict[Particle, np.ndarray],\n", + " total_phase_array: np.ndarray,\n", + " sub_phase_arrays: dict[Particle, np.ndarray],\n", + " title: str,\n", + ") -> None:\n", + " fig, ax1 = plt.subplots(figsize=(10, 6))\n", + " ax1.set_title(title)\n", + " ax2 = ax1.twinx()\n", + " ax1.set_xlim(2.0, 5.0)\n", + " ax1.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", + " ax1.set_ylabel(\"Intensity [a. u.]\")\n", + " ax2.set_ylabel(\"Angle [rad]\")\n", + " ax1.set_yticks([])\n", + " ax2.set_ylim([-np.pi, +np.pi])\n", + " ax2.set_yticks([\n", + " -np.pi,\n", + " -np.pi / 2,\n", + " 0,\n", + " +np.pi / 2,\n", + " +np.pi,\n", + " ])\n", + " ax2.set_yticklabels([\n", + " R\"$-\\pi$\",\n", + " R\"$-\\frac{\\pi}{2}$\",\n", + " \"0\",\n", + " R\"$+\\frac{\\pi}{2}$\",\n", + " R\"$+\\pi$\",\n", + " ])\n", "\n", - "# Plot histogram\n", - "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", - "ax1.hist(\n", - " phsp_projection,\n", - " weights=total_intensities_fvector,\n", - " bins=bins,\n", - " alpha=0.2,\n", - " color=\"hotpink\",\n", - " label=\"Full intensity $F$ vector\",\n", - ")\n", - "\n", - "for i, (k, v) in enumerate(sub_intensities_fvector.items()):\n", + " # Plot background histograms\n", + " phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", " ax1.hist(\n", " phsp_projection,\n", - " weights=v,\n", + " weights=total_intensity_array,\n", " bins=bins,\n", " alpha=0.2,\n", - " color=plt.cm.viridis(i / len(sub_intensities_fvector)),\n", - " label=Rf\"Resonance at ${k.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", + " color=\"gray\",\n", + " label=\"Full intensity\",\n", " )\n", + " for i, (k, v) in enumerate(sub_intensity_arrays.items()):\n", + " ax1.hist(\n", + " phsp_projection,\n", + " weights=v,\n", + " bins=bins,\n", + " alpha=0.2,\n", + " color=f\"C{i}\",\n", + " label=Rf\"Resonance at ${k.mass}\\,\\mathrm{{GeV}}$\",\n", + " )\n", "\n", - "ax2 = ax1.twinx()\n", - "ax2.set_ylabel(R\"Angle [a. u.]\", fontsize=12)\n", - "ax2.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi])\n", - "ax2.set_yticklabels([\n", - " R\"$-\\pi$\",\n", - " R\"$-\\frac{\\pi}{2}$\",\n", - " R\"0\",\n", - " R\"$+\\frac{\\pi}{2}$\",\n", - " R\"$+\\pi$\",\n", - "])\n", - "\n", - "# Plot total phases\n", - "ax2.scatter(x, total_phase_fvector, s=22, color=\"red\", marker=\"^\", label=\"Total Phase\")\n", - "\n", - "colors = [\"green\", \"yellow\"]\n", - "point_styles = [\"v\", \"o\"]\n", - "marker_size = [20, 9]\n", - "\n", - "for i, (k, v) in enumerate(sub_phase_fvector.items()):\n", + " # Plot phases\n", " ax2.scatter(\n", " x,\n", - " v,\n", - " color=colors[i % len(colors)],\n", - " alpha=0.5,\n", - " s=marker_size[i % len(marker_size)],\n", - " marker=point_styles[i % len(point_styles)],\n", - " label=f\"Resonance at {k.mass} GeV $F$-vector\",\n", + " total_phase_array,\n", + " color=\"gray\",\n", + " label=\"Total Phase\",\n", + " s=18,\n", " )\n", - " ax2.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\")\n", - "\n", - "# Set labels for twin axes\n", - "ax2.set_ylabel(\"Angle [rad]\", fontsize=12)\n", + " for i, (k, v) in enumerate(sub_phase_arrays.items()):\n", + " ax2.scatter(\n", + " x,\n", + " v,\n", + " alpha=0.5,\n", + " color=f\"C{i}\",\n", + " label=f\"Resonance at {k.mass} GeV\",\n", + " s=8,\n", + " )\n", + " ax2.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\")\n", "\n", - "# Add legends\n", - "fig.legend(loc=\"upper left\", fontsize=\"9\", bbox_to_anchor=(0.1, 0.9))\n", - "fig.tight_layout()\n", - "fig.show()" + " # Add legends\n", + " fig.legend(bbox_to_anchor=(0.1, 0.9), loc=\"upper left\")\n", + " fig.tight_layout()\n", + " fig.show()" ] }, { @@ -1302,70 +1312,40 @@ "source_hidden": true }, "tags": [ - "hide-input", - "scroll-input" + "hide-input" ] }, "outputs": [], "source": [ - "fig, ax1 = plt.subplots(figsize=(8, 5), dpi=500)\n", - "ax1.set_xlim(2, 5)\n", - "ax1.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", - "ax1.set_ylabel(R\"Intensity [a. u.]\")\n", - "ax1.set_yticks([])\n", - "\n", - "# Plot histogram\n", - "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", - "ax1.hist(\n", - " phsp_projection,\n", - " weights=total_intensities_bw,\n", - " bins=bins,\n", - " alpha=0.2,\n", - " color=\"grey\",\n", - " label=\"Full intensity BW\",\n", - ")\n", - "\n", - "\n", - "ax2 = ax1.twinx()\n", - "ax2.set_ylabel(R\"Angle [a. u.]\")\n", - "ax2.set_yticks([-np.pi, -np.pi / 2, 0, np.pi / 2, +np.pi])\n", - "ax2.set_yticklabels([\n", - " R\"$-\\pi$\",\n", - " R\"$-\\frac{\\pi}{2}$\",\n", - " R\"0\",\n", - " R\"$+\\frac{\\pi}{2}$\",\n", - " R\"$+\\pi$\",\n", - "])\n", - "colors_bw = [\"magenta\", \"cyan\"]\n", - "\n", - "# Plot total phases\n", - "ax2\n", - "ax2.plot(\n", - " x,\n", - " total_phase_bw,\n", - " color=\"blue\",\n", - " label=\"Total Phase Breit-Wigner\",\n", - " linestyle=\"--\",\n", - ")\n", - "\n", - "\n", - "for i, (k, v) in enumerate(sub_phase_bw.items()):\n", - " ax2.plot(\n", - " x,\n", - " v,\n", - " color=colors_bw[i % len(colors_bw)],\n", - " zorder=999,\n", - " linestyle=\"--\",\n", - " label=f\"Resonance at {k.mass} GeV Breit-Wigner\",\n", - " )\n", - " (ax2.axvline(k.mass**2, linestyle=\"dotted\", color=f\"C{i}\"),)\n", - "# Set labels for twin axes\n", - "ax2.set_ylabel(\"Angle [rad]\")\n", - "\n", - "# Add legends\n", - "fig.legend(loc=\"upper left\", fontsize=\"7\")\n", - "plt.tight_layout()\n", - "plt.show()" + "plot_phases(\n", + " total_intensity_array=total_intensities_fvector,\n", + " sub_intensity_arrays=sub_intensities_fvector,\n", + " total_phase_array=total_phase_fvector,\n", + " sub_phase_arrays=sub_phase_fvector,\n", + " title=\"F vector\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "plot_phases(\n", + " total_intensity_array=total_intensities_bw,\n", + " sub_intensity_arrays=sub_intensities_bw,\n", + " total_phase_array=total_phase_bw,\n", + " sub_phase_arrays=sub_phase_bw,\n", + " title=\"Breit-Wigner\",\n", + ")" ] } ], From e4d7b54df013ede127fea749cd5fc97c06d74f18 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 13:40:44 +0200 Subject: [PATCH 30/92] ENH: increase phase space size to 500,000 for plotting --- docs/report/031.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 700ced56..2583235e 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -787,7 +787,7 @@ " initial_state_mass=reaction.initial_state[-1].mass,\n", " final_state_masses={i: p.mass for i, p in reaction.final_state.items()},\n", ")\n", - "phsp_momenta = phsp_generator.generate(100_000, rng)\n", + "phsp_momenta = phsp_generator.generate(500_000, rng)\n", "\n", "ε = 1e-8\n", "transformer = SympyDataTransformer.from_sympy(model.kinematic_variables, backend=\"jax\")\n", From 391201f2305d367a46b6d0adb10bc52d87c6e1e0 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 18:35:30 +0200 Subject: [PATCH 31/92] DOC: improve TR description cards --- docs/report/030.ipynb | 6 ++++-- docs/report/031.ipynb | 4 +++- docs/report/032.ipynb | 8 +++++--- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 66e0f29a..11782b4b 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -17,10 +17,12 @@ }, "source": [ "::::{margin}\n", - ":::{card} Amplitude model fit with P-vector dynamics\n", + ":::{card} Single-channel amplitude model fit with $P$-vector dynamics\n", "TR-030\n", "^^^\n", - "Comparison between fit performance for a model with Breit–Wigner and $P$-vector dynamics. In both cases, data is generated with $P$-vector dynamics.\n", + "Comparison between fit performance for an amplitude model with Breit–Wigner and $P$-vector dynamics. In both cases, data is generated with $P$-vector dynamics.\n", + "+++\n", + "🚧 [compwa.github.io#278](https://github.com/ComPWA/compwa.github.io/pull/278)\n", ":::\n", "::::" ] diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 2583235e..bc8e36d3 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -21,6 +21,8 @@ "TR-031\n", "^^^\n", "Sub-intensity plots for a model with $K$-matrix ($P$-vector) dynamics. Also includes an investigation of phases in a $P$-vector lineshape.\n", + "+++\n", + "🚧 [compwa.github.io#278](https://github.com/ComPWA/compwa.github.io/pull/278)\n", ":::\n", "::::" ] @@ -31,7 +33,7 @@ "tags": [] }, "source": [ - "# Sub-intensity plots " + "# Sub-intensities of P vector" ] }, { diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 76cd7af2..068724cd 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -17,10 +17,12 @@ }, "source": [ "::::{margin}\n", - ":::{card} Amplitude building with K-matrix dynamics\n", - "TR-031\n", + ":::{card} Coupled-channel fit with $P$-vector dynamics for one single pole\n", + "TR-032\n", "^^^\n", - "Illustration of how to formulate an amplitude model for two channels with P-vector dynamics. A combined fit is performed over the sum of the likelihood over both distributions.\n", + "Illustration of how to formulate an amplitude model for two channels with P-vector dynamics. A combined fit is performed over the sum of the likelihood over both distributions. The example uses a single pole, but can easily be extended to multiple poles.\n", + "+++\n", + "🚧 [compwa.github.io#278](https://github.com/ComPWA/compwa.github.io/pull/278)\n", ":::\n", "::::" ] From 6cde93356ae83bfb2f548680c9ca701c551899d7 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 18:39:33 +0200 Subject: [PATCH 32/92] DOC: add TR tags to K-matrix reports --- docs/report/005.ipynb | 1 + docs/report/009.ipynb | 1 + docs/report/010.ipynb | 1 + docs/report/025.ipynb | 4 +++- docs/report/026.ipynb | 4 +++- docs/report/027.ipynb | 4 +++- docs/report/030.ipynb | 4 +++- docs/report/031.ipynb | 4 +++- docs/report/032.ipynb | 4 +++- 9 files changed, 21 insertions(+), 6 deletions(-) diff --git a/docs/report/005.ipynb b/docs/report/005.ipynb index b3520591..b9029fc8 100644 --- a/docs/report/005.ipynb +++ b/docs/report/005.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "K-matrix", "physics" ] }, diff --git a/docs/report/009.ipynb b/docs/report/009.ipynb index 99b0000c..e402dd94 100644 --- a/docs/report/009.ipynb +++ b/docs/report/009.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "K-matrix", "physics", "sympy" ] diff --git a/docs/report/010.ipynb b/docs/report/010.ipynb index b96335a8..e679e32b 100644 --- a/docs/report/010.ipynb +++ b/docs/report/010.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "K-matrix", "physics", "sympy" ] diff --git a/docs/report/025.ipynb b/docs/report/025.ipynb index 2bda35b4..58edec15 100644 --- a/docs/report/025.ipynb +++ b/docs/report/025.ipynb @@ -11,7 +11,9 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [ + "K-matrix" + ] }, "source": [ "::::{margin}\n", diff --git a/docs/report/026.ipynb b/docs/report/026.ipynb index 9fa8229d..3950765e 100755 --- a/docs/report/026.ipynb +++ b/docs/report/026.ipynb @@ -11,7 +11,9 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [ + "K-matrix" + ] }, "source": [ "::::{margin}\n", diff --git a/docs/report/027.ipynb b/docs/report/027.ipynb index 95b40f11..f42c734c 100755 --- a/docs/report/027.ipynb +++ b/docs/report/027.ipynb @@ -11,7 +11,9 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [ + "K-matrix" + ] }, "source": [ "::::{margin}\n", diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 11782b4b..92ed37ce 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -13,7 +13,9 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [ + "K-matrix" + ] }, "source": [ "::::{margin}\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index bc8e36d3..688249ae 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -13,7 +13,9 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [ + "K-matrix" + ] }, "source": [ "::::{margin}\n", diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 068724cd..cac4b80f 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -13,7 +13,9 @@ { "cell_type": "markdown", "metadata": { - "tags": [] + "tags": [ + "K-matrix" + ] }, "source": [ "::::{margin}\n", From aa1da59c4f20076253a90db16a00252af62553cf Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 18:45:32 +0200 Subject: [PATCH 33/92] DOC: add TR tag `dynamics` to reports --- docs/report/000.ipynb | 1 + docs/report/004.ipynb | 1 + docs/report/005.ipynb | 1 + docs/report/009.ipynb | 1 + docs/report/010.ipynb | 1 + docs/report/026.ipynb | 1 + docs/report/027.ipynb | 1 + docs/report/029.ipynb | 3 ++- docs/report/030.ipynb | 1 + docs/report/031.ipynb | 1 + docs/report/032.ipynb | 1 + 11 files changed, 12 insertions(+), 1 deletion(-) diff --git a/docs/report/000.ipynb b/docs/report/000.ipynb index f326c088..7cb450e6 100644 --- a/docs/report/000.ipynb +++ b/docs/report/000.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "lambdification", "sympy" ] diff --git a/docs/report/004.ipynb b/docs/report/004.ipynb index efea7e57..bb6546a3 100644 --- a/docs/report/004.ipynb +++ b/docs/report/004.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "physics" ] }, diff --git a/docs/report/005.ipynb b/docs/report/005.ipynb index b9029fc8..c40485fe 100644 --- a/docs/report/005.ipynb +++ b/docs/report/005.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "K-matrix", "physics" ] diff --git a/docs/report/009.ipynb b/docs/report/009.ipynb index e402dd94..6208f40e 100644 --- a/docs/report/009.ipynb +++ b/docs/report/009.ipynb @@ -13,6 +13,7 @@ "metadata": { "tags": [ "K-matrix", + "dynamics", "physics", "sympy" ] diff --git a/docs/report/010.ipynb b/docs/report/010.ipynb index e679e32b..e4c67457 100644 --- a/docs/report/010.ipynb +++ b/docs/report/010.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "K-matrix", "physics", "sympy" diff --git a/docs/report/026.ipynb b/docs/report/026.ipynb index 3950765e..e2942936 100755 --- a/docs/report/026.ipynb +++ b/docs/report/026.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "K-matrix" ] }, diff --git a/docs/report/027.ipynb b/docs/report/027.ipynb index f42c734c..688f228f 100755 --- a/docs/report/027.ipynb +++ b/docs/report/027.ipynb @@ -12,6 +12,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "K-matrix" ] }, diff --git a/docs/report/029.ipynb b/docs/report/029.ipynb index 67b17c2c..de528a8c 100644 --- a/docs/report/029.ipynb +++ b/docs/report/029.ipynb @@ -14,7 +14,8 @@ "cell_type": "markdown", "metadata": { "tags": [ - "PDG" + "dynamics", + "sympy" ] }, "source": [ diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 92ed37ce..6de1aafb 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -14,6 +14,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "K-matrix" ] }, diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 688249ae..2128d2d6 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -14,6 +14,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "K-matrix" ] }, diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index cac4b80f..427d4f74 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -14,6 +14,7 @@ "cell_type": "markdown", "metadata": { "tags": [ + "dynamics", "K-matrix" ] }, From e2002b77f67c8f14b0370f6ba7a408f7a00f20cb Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 20:49:38 +0200 Subject: [PATCH 34/92] DX: order parameters by quantum number and mass --- docs/report/030.ipynb | 52 +++++++++++++++++++++---------------------- docs/report/031.ipynb | 14 ++++++------ 2 files changed, 33 insertions(+), 33 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 6de1aafb..08fe353a 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -750,14 +750,14 @@ "outputs": [], "source": [ "new_parameters_bw = {\n", - " R\"\\Gamma_{N(1650)^{+}}\": 1 / 1.65,\n", - " R\"\\Gamma_{N(1900)^+}\": 1 / 1.9,\n", - " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", - " R\"\\Gamma_{N^{**}_3}\": 1 / 1.75,\n", " R\"m_{N(1650)^{+}}\": 1.65,\n", - " R\"m_{N(1900)^+}\": 1.9,\n", - " R\"m_{N^{**}_1}\": 1.85,\n", " R\"m_{N^{**}_3}\": 1.75,\n", + " R\"m_{N^{**}_1}\": 1.85,\n", + " R\"m_{N(1900)^+}\": 1.9,\n", + " R\"\\Gamma_{N(1650)^{+}}\": 1 / 1.65,\n", + " R\"\\Gamma_{N^{**}_3}\": 1 / 1.75,\n", + " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", + " R\"\\Gamma_{N(1900)^+}\": 1 / 1.9,\n", "}\n", "intensity_func_bw.update_parameters(new_parameters_bw)" ] @@ -772,17 +772,17 @@ "source": [ "new_parameters_fvector = {\n", " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", - " R\"g_{N(1650)^{+}}\": 1.65,\n", - " R\"g_{N(1900)^+}\": 1,\n", - " R\"g_{N^{**}_1}\": 1,\n", - " R\"g_{N^{**}_3}\": 1,\n", + " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", " R\"m_{N(1650)^{+}}\": 1.65,\n", - " R\"m_{N(1900)^+}\": 1.9,\n", - " R\"m_{N^{**}_1}\": 1.95,\n", " R\"m_{N^{**}_3}\": 1.75,\n", + " R\"m_{N^{**}_1}\": 1.95,\n", + " R\"m_{N(1900)^+}\": 1.9,\n", + " R\"g_{N(1650)^{+}}\": 1.65,\n", + " R\"g_{N^{**}_3}\": 1,\n", + " R\"g_{N^{**}_1}\": 1,\n", + " R\"g_{N(1900)^+}\": 1,\n", "}\n", "intensity_func_fvector.update_parameters(new_parameters_fvector)" ] @@ -1239,26 +1239,26 @@ "outputs": [], "source": [ "initial_parameters_bw = {\n", + " R\"m_{N(1650)^{+}}\": 1.6,\n", + " R\"m_{N^{**}_3}\": 1.7,\n", " R\"m_{N^{**}_1}\": 1.8,\n", - " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", " R\"m_{N(1900)^+}\": 1.93,\n", - " R\"\\Gamma_{N(1900)^+}\": 1 / 1.93,\n", - " R\"m_{N^{**}_3}\": 1.7,\n", - " R\"\\Gamma_{N^{**}_3}\": 1 / 1.65,\n", - " R\"m_{N(1650)^{+}}\": 1.6,\n", " R\"\\Gamma_{N(1650)^{+}}\": 1 / 1.6,\n", + " R\"\\Gamma_{N^{**}_3}\": 1 / 1.65,\n", + " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", + " R\"\\Gamma_{N(1900)^+}\": 1 / 1.93,\n", "}\n", "initial_parameters_fvector = {\n", - " R\"m_{N^{**}_1}\": 1.95,\n", - " R\"m_{N^{**}_3}\": 1.7,\n", - " R\"m_{N(1650)^{+}}\": 1.67,\n", - " R\"m_{N(1900)^+}\": 1.91,\n", - " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", + " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"g_{N^{**}_1}\": 1.0,\n", - " R\"g_{N^{**}_3}\": 1,\n", + " R\"m_{N(1650)^{+}}\": 1.67,\n", + " R\"m_{N^{**}_3}\": 1.7,\n", + " R\"m_{N^{**}_1}\": 1.95,\n", + " R\"m_{N(1900)^+}\": 1.91,\n", " R\"g_{N(1650)^{+}}\": 1.6,\n", + " R\"g_{N^{**}_3}\": 1,\n", + " R\"g_{N^{**}_1}\": 1.0,\n", " R\"g_{N(1900)^+}\": 1.0,\n", "}" ] diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 2128d2d6..a366bc07 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -832,10 +832,10 @@ "outputs": [], "source": [ "new_parameters_bw = {\n", - " R\"\\Gamma_{N(1900)^+}\": g_res2 / m_res2,\n", - " R\"\\Gamma_{N^{**}_1}\": g_res1 / m_res1,\n", - " R\"m_{N(1900)^+}\": m_res2,\n", " R\"m_{N^{**}_1}\": m_res1,\n", + " R\"m_{N(1900)^+}\": m_res2,\n", + " R\"\\Gamma_{N^{**}_1}\": g_res1 / m_res1,\n", + " R\"\\Gamma_{N(1900)^+}\": g_res2 / m_res2,\n", "}\n", "dynamics_func_bw.update_parameters(new_parameters_bw)\n", "intensity_func_bw.update_parameters(new_parameters_bw)" @@ -850,12 +850,12 @@ "outputs": [], "source": [ "new_parameters_fvector = {\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", - " R\"g_{N(1900)^+}\": g_res2,\n", - " R\"g_{N^{**}_1}\": g_res1,\n", - " R\"m_{N(1900)^+}\": m_res2,\n", + " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", " R\"m_{N^{**}_1}\": m_res1,\n", + " R\"m_{N(1900)^+}\": m_res2,\n", + " R\"g_{N^{**}_1}\": g_res1,\n", + " R\"g_{N(1900)^+}\": g_res2,\n", "}\n", "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", "intensity_func_fvector.update_parameters(new_parameters_fvector)" From 7c90e19d1500c908ea7871007f858b68747e7326 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 21:21:08 +0200 Subject: [PATCH 35/92] BREAK: define fake N* inline with Python --- .cspell.json | 2 + docs/report/030.ipynb | 134 +++++++++++++-------- docs/report/030/additional-definitions.yml | 57 --------- docs/report/031.ipynb | 72 ++++++++--- docs/report/032.ipynb | 74 +++++++++--- 5 files changed, 200 insertions(+), 139 deletions(-) delete mode 100644 docs/report/030/additional-definitions.yml diff --git a/.cspell.json b/.cspell.json index 40168b41..ae092f87 100644 --- a/.cspell.json +++ b/.cspell.json @@ -245,6 +245,7 @@ "isinstance", "isnan", "isort", + "isospin", "isrealobj", "jaxlib", "joinpath", @@ -300,6 +301,7 @@ "noreply", "nrows", "nsimplify", + "nstar", "numpycode", "operatorname", "pandoc", diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 08fe353a..a4033815 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -136,12 +136,57 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Define N* resonances" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def create_nstar(\n", + " mass: float, width: float, parity: int, spin: float, idx: int\n", + ") -> Particle:\n", + " spin = sp.Rational(spin)\n", + " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", + " return Particle(\n", + " name=f\"N({idx})({spin}{parity_symbol})\",\n", + " latex=Rf\"N^{{\\frac{{{spin.numerator}}}{{{spin.denominator}}}^-}}_{{{idx}}}\",\n", + " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", + " mass=mass,\n", + " width=width,\n", + " baryon_number=1,\n", + " charge=+1,\n", + " isospin=(0.5, +0.5),\n", + " parity=parity,\n", + " spin=1.5,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "mystnb": { + "code_prompt_show": "Define N* resonances" + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ "PARTICLE_DB = qrules.load_default_particles()\n", - "PARTICLE_DB.update(qrules.io.load(\"030/additional-definitions.yml\"))" + "for nstar in PARTICLE_DB.filter(lambda p: p.name.startswith(\"N\")):\n", + " PARTICLE_DB.remove(nstar)\n", + "PARTICLE_DB += create_nstar(mass=1.65, width=0.6, parity=-1, spin=0.5, idx=1)\n", + "PARTICLE_DB += create_nstar(mass=1.75, width=0.6, parity=-1, spin=0.5, idx=2)\n", + "PARTICLE_DB += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)\n", + "PARTICLE_DB += create_nstar(mass=1.92, width=0.6, parity=+1, spin=1.5, idx=2)" ] }, { @@ -155,12 +200,7 @@ "reaction = qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", " final_state=[\"eta\", \"p\", \"p~\"],\n", - " allowed_intermediate_particles=[\n", - " \"N**1\",\n", - " \"N**3\",\n", - " \"N(1650)+\",\n", - " \"N(1900)+\",\n", - " ],\n", + " allowed_intermediate_particles=[\"N\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " particle_db=PARTICLE_DB,\n", @@ -750,14 +790,14 @@ "outputs": [], "source": [ "new_parameters_bw = {\n", - " R\"m_{N(1650)^{+}}\": 1.65,\n", - " R\"m_{N^{**}_3}\": 1.75,\n", - " R\"m_{N^{**}_1}\": 1.85,\n", - " R\"m_{N(1900)^+}\": 1.9,\n", - " R\"\\Gamma_{N(1650)^{+}}\": 1 / 1.65,\n", - " R\"\\Gamma_{N^{**}_3}\": 1 / 1.75,\n", - " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", - " R\"\\Gamma_{N(1900)^+}\": 1 / 1.9,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.65,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.75,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.85,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.9,\n", + " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{1}}\": 1 / 1.65,\n", + " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{2}}\": 1 / 1.75,\n", + " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{1}}\": 1 / 1.85,\n", + " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{2}}\": 1 / 1.9,\n", "}\n", "intensity_func_bw.update_parameters(new_parameters_bw)" ] @@ -771,18 +811,18 @@ "outputs": [], "source": [ "new_parameters_fvector = {\n", - " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", - " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", - " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"m_{N(1650)^{+}}\": 1.65,\n", - " R\"m_{N^{**}_3}\": 1.75,\n", - " R\"m_{N^{**}_1}\": 1.95,\n", - " R\"m_{N(1900)^+}\": 1.9,\n", - " R\"g_{N(1650)^{+}}\": 1.65,\n", - " R\"g_{N^{**}_3}\": 1,\n", - " R\"g_{N^{**}_1}\": 1,\n", - " R\"g_{N(1900)^+}\": 1,\n", + " R\"\\beta_{N^{\\frac{1}{2}^-}_{1}}\": 1 + 0j,\n", + " R\"\\beta_{N^{\\frac{1}{2}^-}_{2}}\": 1 + 0j,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.65,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.75,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.95,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.9,\n", + " R\"g_{N^{\\frac{1}{2}^-}_{1}}\": 1.65,\n", + " R\"g_{N^{\\frac{1}{2}^-}_{2}}\": 1,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{1}}\": 1,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{2}}\": 1,\n", "}\n", "intensity_func_fvector.update_parameters(new_parameters_fvector)" ] @@ -1239,27 +1279,27 @@ "outputs": [], "source": [ "initial_parameters_bw = {\n", - " R\"m_{N(1650)^{+}}\": 1.6,\n", - " R\"m_{N^{**}_3}\": 1.7,\n", - " R\"m_{N^{**}_1}\": 1.8,\n", - " R\"m_{N(1900)^+}\": 1.93,\n", - " R\"\\Gamma_{N(1650)^{+}}\": 1 / 1.6,\n", - " R\"\\Gamma_{N^{**}_3}\": 1 / 1.65,\n", - " R\"\\Gamma_{N^{**}_1}\": 1 / 1.85,\n", - " R\"\\Gamma_{N(1900)^+}\": 1 / 1.93,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.6,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.7,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.8,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.93,\n", + " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{1}}\": 1 / 1.6,\n", + " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{2}}\": 1 / 1.65,\n", + " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{1}}\": 1 / 1.85,\n", + " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{2}}\": 1 / 1.93,\n", "}\n", "initial_parameters_fvector = {\n", - " R\"\\beta_{N(1650)^{+}}\": 1 + 0j,\n", - " R\"\\beta_{N^{**}_3}\": 1 + 0j,\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"m_{N(1650)^{+}}\": 1.67,\n", - " R\"m_{N^{**}_3}\": 1.7,\n", - " R\"m_{N^{**}_1}\": 1.95,\n", - " R\"m_{N(1900)^+}\": 1.91,\n", - " R\"g_{N(1650)^{+}}\": 1.6,\n", - " R\"g_{N^{**}_3}\": 1,\n", - " R\"g_{N^{**}_1}\": 1.0,\n", - " R\"g_{N(1900)^+}\": 1.0,\n", + " R\"\\beta_{N^{\\frac{1}{2}^-}_{1}}\": 1 + 0j,\n", + " R\"\\beta_{N^{\\frac{1}{2}^-}_{2}}\": 1 + 0j,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.67,\n", + " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.7,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.95,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.91,\n", + " R\"g_{N^{\\frac{1}{2}^-}_{1}}\": 1.6,\n", + " R\"g_{N^{\\frac{1}{2}^-}_{2}}\": 1,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{1}}\": 1.0,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{2}}\": 1.0,\n", "}" ] }, diff --git a/docs/report/030/additional-definitions.yml b/docs/report/030/additional-definitions.yml deleted file mode 100644 index 45c947b9..00000000 --- a/docs/report/030/additional-definitions.yml +++ /dev/null @@ -1,57 +0,0 @@ -# Imported from ComPWA/PWA-JPsi2pbarSigmaKS@a0aba08 -particles: - - name: N**1 - pid: 20240522001 - latex: N^{**}_1 - spin: 1.5 - mass: 1.82 - width: 0.6 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: 1 - - - name: N**2 - pid: 20240522002 - latex: N^{**}_2 - spin: 0.5 - mass: 1.65 - width: 0.6 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: N**3 - pid: 20240522003 - latex: N^{**}_3 - spin: 0.5 - mass: 1.75 - width: 0.6 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: -1 - - - name: N(1900)+ - pid: 200003 - latex: N(1900)^+ - spin: 1.5 - mass: 1.92 - width: 0.2 - charge: 1 - isospin: - magnitude: 0.5 - projection: 0.5 - baryon_number: 1 - parity: - value: 1 diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index a366bc07..b2550d28 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -131,12 +131,55 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Define N* resonances" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def create_nstar(\n", + " mass: float, width: float, parity: int, spin: float, idx: int\n", + ") -> Particle:\n", + " spin = sp.Rational(spin)\n", + " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", + " return Particle(\n", + " name=f\"N({idx})({spin}{parity_symbol})\",\n", + " latex=Rf\"N^{{\\frac{{{spin.numerator}}}{{{spin.denominator}}}^-}}_{{{idx}}}\",\n", + " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", + " mass=mass,\n", + " width=width,\n", + " baryon_number=1,\n", + " charge=+1,\n", + " isospin=(0.5, +0.5),\n", + " parity=parity,\n", + " spin=1.5,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "mystnb": { + "code_prompt_show": "Define N* resonances" + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ "PARTICLE_DB = qrules.load_default_particles()\n", - "PARTICLE_DB.update(qrules.io.load(\"030/additional-definitions.yml\"))" + "for nstar in PARTICLE_DB.filter(lambda p: p.name.startswith(\"N\")):\n", + " PARTICLE_DB.remove(nstar)\n", + "PARTICLE_DB += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)\n", + "PARTICLE_DB += create_nstar(mass=1.92, width=0.6, parity=+1, spin=1.5, idx=2)" ] }, { @@ -150,10 +193,7 @@ "reaction = qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", " final_state=[\"eta\", \"p\", \"p~\"],\n", - " allowed_intermediate_particles=[\n", - " \"N**1\",\n", - " \"N(1900)+\",\n", - " ],\n", + " allowed_intermediate_particles=[\"N\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " particle_db=PARTICLE_DB,\n", @@ -832,10 +872,10 @@ "outputs": [], "source": [ "new_parameters_bw = {\n", - " R\"m_{N^{**}_1}\": m_res1,\n", - " R\"m_{N(1900)^+}\": m_res2,\n", - " R\"\\Gamma_{N^{**}_1}\": g_res1 / m_res1,\n", - " R\"\\Gamma_{N(1900)^+}\": g_res2 / m_res2,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": m_res1,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": m_res2,\n", + " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{1}}\": g_res1 / m_res1,\n", + " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{2}}\": g_res2 / m_res2,\n", "}\n", "dynamics_func_bw.update_parameters(new_parameters_bw)\n", "intensity_func_bw.update_parameters(new_parameters_bw)" @@ -850,12 +890,12 @@ "outputs": [], "source": [ "new_parameters_fvector = {\n", - " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", - " R\"\\beta_{N(1900)^+}\": 1 + 0j,\n", - " R\"m_{N^{**}_1}\": m_res1,\n", - " R\"m_{N(1900)^+}\": m_res2,\n", - " R\"g_{N^{**}_1}\": g_res1,\n", - " R\"g_{N(1900)^+}\": g_res2,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": m_res1,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": m_res2,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{1}}\": g_res1,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{2}}\": g_res2,\n", "}\n", "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", "intensity_func_fvector.update_parameters(new_parameters_fvector)" diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 427d4f74..dd305d21 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -87,7 +87,7 @@ "from ampform.sympy import perform_cached_doit, unevaluated\n", "from IPython.display import Math, display\n", "from matplotlib import cm\n", - "from qrules.particle import Particle, ParticleCollection\n", + "from qrules.particle import Particle\n", "from qrules.transition import ReactionInfo\n", "from sympy import Abs\n", "from sympy.matrices.expressions.matexpr import MatrixElement\n", @@ -156,18 +156,54 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Define N* resonances" + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ - "def load_particle_database() -> ParticleCollection:\n", - " particle_database = qrules.load_default_particles()\n", - " additional_definitions = qrules.io.load(\"030/additional-definitions.yml\")\n", - " particle_database.update(additional_definitions)\n", - " return particle_database\n", - "\n", - "\n", - "PARTICLE_DB = load_particle_database()" + "def create_nstar(\n", + " mass: float, width: float, parity: int, spin: float, idx: int\n", + ") -> Particle:\n", + " spin = sp.Rational(spin)\n", + " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", + " return Particle(\n", + " name=f\"N({idx})({spin}{parity_symbol})\",\n", + " latex=Rf\"N^{{\\frac{{{spin.numerator}}}{{{spin.denominator}}}^-}}_{{{idx}}}\",\n", + " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", + " mass=mass,\n", + " width=width,\n", + " baryon_number=1,\n", + " charge=+1,\n", + " isospin=(0.5, +0.5),\n", + " parity=parity,\n", + " spin=1.5,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "mystnb": { + "code_prompt_show": "Define N* resonances" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "PARTICLE_DB = qrules.load_default_particles()\n", + "for nstar in PARTICLE_DB.filter(lambda p: p.name.startswith(\"N\")):\n", + " PARTICLE_DB.remove(nstar)\n", + "PARTICLE_DB += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)" ] }, { @@ -186,7 +222,7 @@ " qrules.generate_transitions(\n", " initial_state=\"J/psi(1S)\",\n", " final_state=final_state,\n", - " allowed_intermediate_particles=[\"N**1\"],\n", + " allowed_intermediate_particles=[\"N\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", " particle_db=PARTICLE_DB,\n", @@ -838,10 +874,10 @@ "g_res_ch1 = 2.5\n", "\n", "new_parameters_fvector = {\n", - " R\"m_{N^{**}_1}\": 1.71,\n", - " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", - " R\"g_{N^{**}_1,0}\": g_res_ch0,\n", - " R\"g_{N^{**}_1,1}\": g_res_ch1,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.71,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{1},0}\": g_res_ch0,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{1},1}\": g_res_ch1,\n", "}" ] }, @@ -1116,10 +1152,10 @@ "outputs": [], "source": [ "initial_parameters = {\n", - " R\"m_{N^{**}_1}\": 1.9,\n", - " R\"\\beta_{N^{**}_1}\": 1 + 0j,\n", - " R\"g_{N^{**}_1,0}\": 2.8,\n", - " R\"g_{N^{**}_1,1}\": 1.6,\n", + " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.9,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{1},0}\": 2.8,\n", + " R\"g_{N^{\\frac{3}{2}^-}_{1},1}\": 1.6,\n", "}\n", "INTENSITY_FUNCS_FVECTOR[0].parameters" ] From b5944828d4b90f1f2fe87daca0b487fca01e5acf Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 21:29:25 +0200 Subject: [PATCH 36/92] BREAK: rename TR-030 to TR-033 --- docs/report/{030.ipynb => 033.ipynb} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename docs/report/{030.ipynb => 033.ipynb} (99%) diff --git a/docs/report/030.ipynb b/docs/report/033.ipynb similarity index 99% rename from docs/report/030.ipynb rename to docs/report/033.ipynb index a4033815..544b5e2f 100644 --- a/docs/report/030.ipynb +++ b/docs/report/033.ipynb @@ -21,7 +21,7 @@ "source": [ "::::{margin}\n", ":::{card} Single-channel amplitude model fit with $P$-vector dynamics\n", - "TR-030\n", + "TR-033\n", "^^^\n", "Comparison between fit performance for an amplitude model with Breit–Wigner and $P$-vector dynamics. In both cases, data is generated with $P$-vector dynamics.\n", "+++\n", From 0be0d29a591330ccd415cbac72e7615e9a935582 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 21:31:19 +0200 Subject: [PATCH 37/92] BREAK: rename TR-031 to TR-030 --- docs/report/{031.ipynb => 030.ipynb} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename docs/report/{031.ipynb => 030.ipynb} (99%) diff --git a/docs/report/031.ipynb b/docs/report/030.ipynb similarity index 99% rename from docs/report/031.ipynb rename to docs/report/030.ipynb index b2550d28..92472fc0 100644 --- a/docs/report/031.ipynb +++ b/docs/report/030.ipynb @@ -21,7 +21,7 @@ "source": [ "::::{margin}\n", ":::{card} Sub-intensities of P-vector amplitude model\n", - "TR-031\n", + "TR-030\n", "^^^\n", "Sub-intensity plots for a model with $K$-matrix ($P$-vector) dynamics. Also includes an investigation of phases in a $P$-vector lineshape.\n", "+++\n", From 11300bcee66db64de0f03d0f9b1b9f2295238355 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 21:32:17 +0200 Subject: [PATCH 38/92] BREAK: rename TR-033 to TR-031 --- docs/report/{033.ipynb => 031.ipynb} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename docs/report/{033.ipynb => 031.ipynb} (99%) diff --git a/docs/report/033.ipynb b/docs/report/031.ipynb similarity index 99% rename from docs/report/033.ipynb rename to docs/report/031.ipynb index 544b5e2f..93a2b156 100644 --- a/docs/report/033.ipynb +++ b/docs/report/031.ipynb @@ -21,7 +21,7 @@ "source": [ "::::{margin}\n", ":::{card} Single-channel amplitude model fit with $P$-vector dynamics\n", - "TR-033\n", + "TR-031\n", "^^^\n", "Comparison between fit performance for an amplitude model with Breit–Wigner and $P$-vector dynamics. In both cases, data is generated with $P$-vector dynamics.\n", "+++\n", From ac369751d3b39a1dffc4a9af7294afb58045ebb0 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 22:02:32 +0200 Subject: [PATCH 39/92] MAINT: plot sub-intensity histograms with loop --- docs/report/030.ipynb | 49 +++++++++++++++++++------------------------ docs/report/031.ipynb | 45 +++++++++++++++++++-------------------- 2 files changed, 43 insertions(+), 51 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 92472fc0..5705b799 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -1040,33 +1040,28 @@ " color=\"grey\",\n", " label=\"Full intensity Breit-Wigner\",\n", ")\n", - "ax.hist(\n", - " len(sub_intensities_fvector) * [phsp_projection],\n", - " weights=list(sub_intensities_fvector.values()),\n", - " alpha=0.6,\n", - " bins=bins,\n", - " color=[f\"C{i}\" for i, _ in enumerate(sub_intensities_bw)],\n", - " histtype=\"step\",\n", - " label=[\n", - " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\"\n", - " for p in sub_intensities_fvector\n", - " ],\n", - " linewidth=2,\n", - ")\n", - "\n", - "ax.hist(\n", - " len(sub_intensities_bw) * [phsp_projection],\n", - " weights=list(sub_intensities_bw.values()),\n", - " alpha=0.6,\n", - " bins=bins,\n", - " color=[f\"C{i}\" for i, _ in enumerate(sub_intensities_bw)],\n", - " histtype=\"step\",\n", - " label=[\n", - " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\"\n", - " for p in sub_intensities_fvector\n", - " ],\n", - " linestyle=\"dashed\",\n", - ")\n", + "for i, (p, v) in enumerate(sub_intensities_fvector.items()):\n", + " ax.hist(\n", + " phsp_projection,\n", + " weights=v,\n", + " alpha=0.6,\n", + " bins=bins,\n", + " color=f\"C{i}\",\n", + " histtype=\"step\",\n", + " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", + " linewidth=2,\n", + " )\n", + "for i, (p, v) in enumerate(sub_intensities_fvector.items()):\n", + " ax.hist(\n", + " phsp_projection,\n", + " weights=v,\n", + " alpha=0.6,\n", + " bins=bins,\n", + " color=f\"C{i}\",\n", + " histtype=\"step\",\n", + " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\",\n", + " linestyle=\"dashed\",\n", + " )\n", "\n", "fig.legend(loc=\"upper right\")\n", "plt.tight_layout()\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 93a2b156..475c0e47 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -954,30 +954,27 @@ " color=\"grey\",\n", " label=\"Full intensity Breit-Wigner\",\n", ")\n", - "ax.hist(\n", - " len(sub_intensities_fvector) * [phsp_projection],\n", - " weights=list(sub_intensities_fvector.values()),\n", - " alpha=0.6,\n", - " bins=bins,\n", - " histtype=\"step\",\n", - " label=[\n", - " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\"\n", - " for p in sub_intensities_fvector\n", - " ],\n", - ")\n", - "\n", - "ax.hist(\n", - " len(sub_intensities_bw) * [phsp_projection],\n", - " weights=list(sub_intensities_bw.values()),\n", - " alpha=0.6,\n", - " bins=bins,\n", - " histtype=\"step\",\n", - " label=[\n", - " Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\"\n", - " for p in sub_intensities_fvector\n", - " ],\n", - " ls=\"dotted\",\n", - ")\n", + "for i, (p, v) in enumerate(sub_intensities_fvector.items()):\n", + " ax.hist(\n", + " phsp_projection,\n", + " weights=v,\n", + " alpha=0.6,\n", + " bins=bins,\n", + " color=f\"C{i}\",\n", + " histtype=\"step\",\n", + " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", + " )\n", + "for i, (p, v) in enumerate(sub_intensities_bw.items()):\n", + " ax.hist(\n", + " phsp_projection,\n", + " weights=v,\n", + " alpha=0.6,\n", + " bins=bins,\n", + " color=f\"C{i}\",\n", + " histtype=\"step\",\n", + " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\",\n", + " ls=\"dotted\",\n", + " )\n", "\n", "fig.legend(loc=\"upper right\")\n", "plt.tight_layout()\n", From 50018d0dc8e938d6bec0cc5a5d875c12e467506d Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 22:03:41 +0200 Subject: [PATCH 40/92] ENH: plot histograms with JAX --- docs/report/030.ipynb | 66 +++++++++++++++++++++++++++++----- docs/report/031.ipynb | 84 +++++++++++++++++++++++++++++++++++-------- 2 files changed, 127 insertions(+), 23 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 5705b799..fe1ad5b0 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -77,6 +77,7 @@ "import ampform\n", "import attrs\n", "import graphviz\n", + "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import qrules\n", @@ -1002,6 +1003,45 @@ "}" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Function for plotting histograms with JAX" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def fast_histogram(\n", + " data: jnp.ndarray,\n", + " weights: jnp.ndarray | None = None,\n", + " bins: int = 100,\n", + " density: bool | None = None,\n", + " fill: bool = True,\n", + " ax=plt,\n", + " **plot_kwargs,\n", + ") -> None:\n", + " bin_values, bin_edges = jnp.histogram(\n", + " data,\n", + " bins=bins,\n", + " density=density,\n", + " weights=weights,\n", + " )\n", + " if fill:\n", + " bin_rights = bin_edges[1:]\n", + " ax.fill_between(bin_rights, bin_values, step=\"pre\", **plot_kwargs)\n", + " else:\n", + " bin_mids = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " ax.step(bin_mids, bin_values, **plot_kwargs)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1024,45 +1064,50 @@ "\n", "bins = 150\n", "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", - "ax.hist(\n", + "fast_histogram(\n", " phsp_projection,\n", " weights=total_intensities_fvector,\n", " alpha=0.2,\n", " bins=bins,\n", " color=\"hotpink\",\n", " label=\"Full intensity $F$ vector\",\n", + " ax=ax,\n", ")\n", - "ax.hist(\n", + "fast_histogram(\n", " phsp_projection,\n", " weights=total_intensities_bw,\n", " alpha=0.2,\n", " bins=bins,\n", " color=\"grey\",\n", " label=\"Full intensity Breit-Wigner\",\n", + " ax=ax,\n", ")\n", "for i, (p, v) in enumerate(sub_intensities_fvector.items()):\n", - " ax.hist(\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=v,\n", " alpha=0.6,\n", " bins=bins,\n", " color=f\"C{i}\",\n", - " histtype=\"step\",\n", + " fill=False,\n", " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", " linewidth=2,\n", + " ax=ax,\n", " )\n", - "for i, (p, v) in enumerate(sub_intensities_fvector.items()):\n", - " ax.hist(\n", + "for i, (p, v) in enumerate(sub_intensities_bw.items()):\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=v,\n", " alpha=0.6,\n", " bins=bins,\n", " color=f\"C{i}\",\n", - " histtype=\"step\",\n", + " fill=False,\n", " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\",\n", " linestyle=\"dashed\",\n", + " ax=ax,\n", " )\n", "\n", + "ax.set_ylim(0, None)\n", "fig.legend(loc=\"upper right\")\n", "plt.tight_layout()\n", "plt.show()" @@ -1301,23 +1346,26 @@ "\n", " # Plot background histograms\n", " phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", - " ax1.hist(\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=total_intensity_array,\n", " bins=bins,\n", " alpha=0.2,\n", " color=\"gray\",\n", " label=\"Full intensity\",\n", + " ax=ax1,\n", " )\n", " for i, (k, v) in enumerate(sub_intensity_arrays.items()):\n", - " ax1.hist(\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=v,\n", " bins=bins,\n", " alpha=0.2,\n", " color=f\"C{i}\",\n", " label=Rf\"Resonance at ${k.mass}\\,\\mathrm{{GeV}}$\",\n", + " ax=ax1,\n", " )\n", + " ax1.set_ylim(0, None)\n", "\n", " # Plot phases\n", " ax2.scatter(\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 475c0e47..0b323cd2 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -77,6 +77,7 @@ "import ampform\n", "import attrs\n", "import graphviz\n", + "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", @@ -916,6 +917,42 @@ "}" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "mystnb": { + "code_prompt_show": "Function for plotting histograms with JAX" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def fast_histogram(\n", + " data: jnp.ndarray,\n", + " weights: jnp.ndarray | None = None,\n", + " bins: int = 100,\n", + " density: bool | None = None,\n", + " fill: bool = True,\n", + " ax=plt,\n", + " **plot_kwargs,\n", + ") -> None:\n", + " bin_values, bin_edges = jnp.histogram(\n", + " data,\n", + " bins=bins,\n", + " density=density,\n", + " weights=weights,\n", + " )\n", + " if fill:\n", + " bin_rights = bin_edges[1:]\n", + " ax.fill_between(bin_rights, bin_values, step=\"pre\", **plot_kwargs)\n", + " else:\n", + " bin_mids = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " ax.step(bin_mids, bin_values, **plot_kwargs)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -938,44 +975,49 @@ "\n", "bins = 150\n", "phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", - "ax.hist(\n", + "fast_histogram(\n", " phsp_projection,\n", " weights=total_intensities_fvector,\n", " alpha=0.2,\n", " bins=bins,\n", " color=\"hotpink\",\n", " label=\"Full intensity $F$ vector\",\n", + " ax=ax,\n", ")\n", - "ax.hist(\n", + "fast_histogram(\n", " phsp_projection,\n", " weights=total_intensities_bw,\n", " alpha=0.2,\n", " bins=bins,\n", " color=\"grey\",\n", " label=\"Full intensity Breit-Wigner\",\n", + " ax=ax,\n", ")\n", "for i, (p, v) in enumerate(sub_intensities_fvector.items()):\n", - " ax.hist(\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=v,\n", " alpha=0.6,\n", " bins=bins,\n", " color=f\"C{i}\",\n", - " histtype=\"step\",\n", + " fill=False,\n", " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", + " ax=ax,\n", " )\n", "for i, (p, v) in enumerate(sub_intensities_bw.items()):\n", - " ax.hist(\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=v,\n", " alpha=0.6,\n", " bins=bins,\n", " color=f\"C{i}\",\n", - " histtype=\"step\",\n", + " fill=False,\n", " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\",\n", " ls=\"dotted\",\n", + " ax=ax,\n", " )\n", "\n", + "ax.set_ylim(0, None)\n", "fig.legend(loc=\"upper right\")\n", "plt.tight_layout()\n", "plt.show()" @@ -1053,6 +1095,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -1060,13 +1105,15 @@ "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(6, 5))\n", - "ax.hist(\n", + "fast_histogram(\n", " phsp[\"m_01\"].real,\n", " bins=100,\n", " weights=np.real(intensity_func_fvector(phsp)),\n", + " ax=ax,\n", ")\n", "ax.set_xlabel(R\"$M^2\\left(\\eta p\\right)\\, \\mathrm{[(GeV/c)^2]}$\")\n", "ax.set_ylabel(R\"Intensity [a.u.]\")\n", + "ax.set_ylim(0, None)\n", "fig.tight_layout()\n", "fig.show()" ] @@ -1096,6 +1143,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input", "scroll-input" @@ -1112,11 +1162,12 @@ ")\n", "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", "fig, ax = plt.subplots(figsize=(9, 4))\n", - "ax.hist(\n", + "fast_histogram(\n", " np.real(data[\"m_01\"]),\n", " bins=200,\n", " alpha=0.5,\n", " density=True,\n", + " ax=ax,\n", ")\n", "ax.set_xlabel(\"$m$ [GeV]\")\n", "for (k, v), color in zip(new_parameters_bw.items(), colors):\n", @@ -1127,6 +1178,7 @@ " label=r\"$\" + k + \"$\",\n", " color=color,\n", " )\n", + "ax.set_ylim(0, None)\n", "ax.legend()\n", "plt.show()" ] @@ -1222,38 +1274,42 @@ " function1,\n", " function2,\n", " bins=100,\n", - "):\n", + ") -> None:\n", " intensities1 = function1(phsp)\n", " intensities2 = function2(phsp)\n", " _, ax = plt.subplots(figsize=(9, 4))\n", " data_projection = np.real(data[variable_name])\n", " ax = plt.gca()\n", - " ax.hist(\n", + " fast_histogram(\n", " data_projection,\n", " bins=bins,\n", " alpha=0.5,\n", " label=\"data\",\n", " density=True,\n", + " ax=ax,\n", " )\n", " phsp_projection = np.real(phsp[variable_name])\n", - " ax.hist(\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=np.array(intensities1),\n", " bins=bins,\n", - " histtype=\"step\",\n", + " fill=False,\n", " color=\"red\",\n", " label=\"Fit model with K matrix\",\n", " density=True,\n", + " ax=ax,\n", " )\n", - " ax.hist(\n", + " fast_histogram(\n", " phsp_projection,\n", " weights=np.array(intensities2),\n", " bins=bins,\n", - " histtype=\"step\",\n", + " fill=False,\n", " color=\"blue\",\n", " label=\"Fit model with Breit Wigner\",\n", " density=True,\n", + " ax=ax,\n", " )\n", + " ax.set_ylim(0, None)\n", " indicate_masses(ax)\n", " ax.legend()" ] From d5bfba94a478984edd59d0580264ec76a35cb6f8 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 22:20:43 +0200 Subject: [PATCH 41/92] FIX: correctly color resonance mass lines --- docs/report/031.ipynb | 61 ++++++++++++++----------------------------- 1 file changed, 20 insertions(+), 41 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 0b323cd2..504b66e7 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1157,10 +1157,6 @@ " model.reaction_info.get_intermediate_particles(),\n", " key=lambda p: p.mass,\n", ")\n", - "evenly_spaced_interval = np.linspace(\n", - " 0, 1, len(intensity_func_fvector.parameters.items())\n", - ")\n", - "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", "fig, ax = plt.subplots(figsize=(9, 4))\n", "fast_histogram(\n", " np.real(data[\"m_01\"]),\n", @@ -1169,15 +1165,12 @@ " density=True,\n", " ax=ax,\n", ")\n", + "mass_parameters = {p: v for p, v in new_parameters_bw.items() if p.startswith(\"m_{\")}\n", + "evenly_spaced_interval = np.linspace(0, 1, num=len(mass_parameters))\n", + "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", "ax.set_xlabel(\"$m$ [GeV]\")\n", - "for (k, v), color in zip(new_parameters_bw.items(), colors):\n", - " if k.startswith(\"m_{\"):\n", - " ax.axvline(\n", - " x=v,\n", - " linestyle=\"dotted\",\n", - " label=r\"$\" + k + \"$\",\n", - " color=color,\n", - " )\n", + "for (k, v), color in zip(mass_parameters.items(), colors):\n", + " ax.axvline(v, c=color, label=f\"${k}$\", ls=\"dotted\")\n", "ax.set_ylim(0, None)\n", "ax.legend()\n", "plt.show()" @@ -1239,32 +1232,16 @@ " reaction_info.get_intermediate_particles(),\n", " key=lambda p: p.mass,\n", ")\n", - "evenly_spaced_interval_F = np.linspace(\n", - " 0, 1, len(intensity_func_fvector.parameters.items())\n", - ")\n", - "colors_F = [cm.rainbow(x) for x in evenly_spaced_interval_F]\n", - "evenly_spaced_interval_BW = np.linspace(0, 1, len(intensity_func_bw.parameters.items()))\n", - "colors_BW = [cm.gist_rainbow(x) for x in evenly_spaced_interval_BW]\n", "\n", "\n", - "def indicate_masses(ax):\n", - " ax.set_xlabel(\"$m$ [GeV]\")\n", - " for (k, v), color_F in zip(intensity_func_fvector.parameters.items(), colors_F):\n", - " if k.startswith(\"m_{\"):\n", - " ax.axvline(\n", - " x=v,\n", - " linestyle=\"dotted\",\n", - " label=r\"$\" + k + \"$\" \"(F vector)\",\n", - " color=color_F,\n", - " )\n", - " for (k, v), color_BW in zip(intensity_func_bw.parameters.items(), colors_BW):\n", - " if k.startswith(\"m_{\"):\n", - " ax.axvline(\n", - " x=v,\n", - " linestyle=\"dotted\",\n", - " label=r\"$\" + k + \"$\" \"(Breit-Wigner)\",\n", - " color=color_BW,\n", - " )\n", + "def indicate_masses(ax, intensity_func, ls: str, lw: float, typ: str):\n", + " mass_pars = {\n", + " k: v for k, v in intensity_func.parameters.items() if k.startswith(\"m_{\")\n", + " }\n", + " evenly_spaced_interval = np.linspace(0, 1, len(mass_pars.items()))\n", + " colors = [cm.gist_rainbow(x) for x in evenly_spaced_interval]\n", + " for (k, v), color in zip(mass_pars.items(), colors):\n", + " ax.axvline(v, c=color, label=f\"${k}$ ({typ})\", ls=ls, lw=lw)\n", "\n", "\n", "def compare_model(\n", @@ -1278,8 +1255,8 @@ " intensities1 = function1(phsp)\n", " intensities2 = function2(phsp)\n", " _, ax = plt.subplots(figsize=(9, 4))\n", + " ax.set_xlabel(\"$m$ [GeV]\")\n", " data_projection = np.real(data[variable_name])\n", - " ax = plt.gca()\n", " fast_histogram(\n", " data_projection,\n", " bins=bins,\n", @@ -1295,7 +1272,7 @@ " bins=bins,\n", " fill=False,\n", " color=\"red\",\n", - " label=\"Fit model with K matrix\",\n", + " label=\"Fit model with F vector\",\n", " density=True,\n", " ax=ax,\n", " )\n", @@ -1305,13 +1282,15 @@ " bins=bins,\n", " fill=False,\n", " color=\"blue\",\n", - " label=\"Fit model with Breit Wigner\",\n", + " label=\"Fit model with Breit-Wigner\",\n", " density=True,\n", " ax=ax,\n", " )\n", " ax.set_ylim(0, None)\n", - " indicate_masses(ax)\n", - " ax.legend()" + " indicate_masses(ax, function1, ls=\"dashed\", lw=1, typ=\"F vector\")\n", + " indicate_masses(ax, function2, ls=\"dotted\", lw=1, typ=\"Breit-Wigner\")\n", + " ax.legend()\n", + " fig.show()" ] }, { From 6782e6145c3f4240552159c9d8ac52482c6d139a Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 22:36:58 +0200 Subject: [PATCH 42/92] ENH: render x-axis in phase plot --- docs/report/030.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index fe1ad5b0..1171a6d4 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -1326,7 +1326,7 @@ " ax1.set_xlim(2.0, 5.0)\n", " ax1.set_xlabel(R\"$m_{p\\eta}^{2}$ [GeV$^{2}$]\")\n", " ax1.set_ylabel(\"Intensity [a. u.]\")\n", - " ax2.set_ylabel(\"Angle [rad]\")\n", + " ax2.set_ylabel(\"Angle\")\n", " ax1.set_yticks([])\n", " ax2.set_ylim([-np.pi, +np.pi])\n", " ax2.set_yticks([\n", @@ -1343,6 +1343,7 @@ " R\"$+\\frac{\\pi}{2}$\",\n", " R\"$+\\pi$\",\n", " ])\n", + " ax2.axhline(0, c=\"black\", lw=0.5)\n", "\n", " # Plot background histograms\n", " phsp_projection = np.real(phsp[\"m_01\"]) ** 2\n", From f41842ea21b604e1bfe887d72d640a568e643d1b Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 22:38:00 +0200 Subject: [PATCH 43/92] MAINT: remove redundant `resonances` definition --- docs/report/031.ipynb | 4 ---- 1 file changed, 4 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 504b66e7..5465c7da 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1153,10 +1153,6 @@ }, "outputs": [], "source": [ - "resonances = sorted(\n", - " model.reaction_info.get_intermediate_particles(),\n", - " key=lambda p: p.mass,\n", - ")\n", "fig, ax = plt.subplots(figsize=(9, 4))\n", "fast_histogram(\n", " np.real(data[\"m_01\"]),\n", From ce0a9ec452c70d2d130dfba70ba2d278ce31251e Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 23:05:45 +0200 Subject: [PATCH 44/92] DOC: improve HTML rendering of TR-030 and TR-031 --- docs/report/030.ipynb | 45 ++++++++++++++----------- docs/report/031.ipynb | 78 +++++++++++++++++++++++++++---------------- 2 files changed, 76 insertions(+), 47 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 1171a6d4..589299dc 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -59,6 +59,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Import Python libraries" + }, "tags": [ "hide-input", "scroll-input" @@ -88,7 +91,7 @@ "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", "from attrs import define, field\n", - "from IPython.display import Markdown, Math, display\n", + "from IPython.display import Math\n", "from qrules.particle import Particle\n", "from sympy import Abs\n", "from tensorwaves.data import (\n", @@ -121,7 +124,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -167,12 +169,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "mystnb": { - "code_prompt_show": "Define N* resonances" - }, - "tags": [ - "hide-input" - ] + "tags": [] }, "outputs": [], "source": [ @@ -221,7 +218,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -235,6 +231,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Dynamics builder with X symbols of J^PC channels" + }, "tags": [ "hide-input", "scroll-input" @@ -328,13 +327,14 @@ }, "outputs": [], "source": [ + "src = R\"\\begin{array}{cll}\" \"\\n\"\n", "for symbol, resonances in create_dynamics_symbol.collected_symbols.items():\n", - " display(symbol)\n", - " src = \"| resonance | mass | width |\\n\"\n", - " src += \"|:---|---:|--:|\\n\"\n", + " src += Rf\" {symbol} \\\\\" \"\\n\"\n", " for p, _ in resonances:\n", - " src += f\"| ${p.latex}$ | {p.mass:g} GeV | {p.width:g} GeV |\\n\"\n", - " display(Markdown(src))" + " src += Rf\" {p.latex} & m={p.mass:g}\\text{{ GeV}} & \\Gamma={p.width:g}\\text{{ GeV}} \\\\\"\n", + " src += \"\\n\"\n", + "src += R\"\\end{array}\"\n", + "Math(src)" ] }, { @@ -349,7 +349,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -363,6 +362,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Expression classes for phase space factors" + }, "tags": [ "hide-input", "scroll-input" @@ -440,7 +442,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -454,6 +455,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Expression class for energy-dependent width" + }, "tags": [ "hide-input" ] @@ -522,6 +526,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -559,7 +566,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -649,6 +655,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -686,7 +695,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -813,7 +821,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 5465c7da..8268955d 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -59,6 +59,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Import Python libraries" + }, "tags": [ "hide-input", "scroll-input" @@ -89,7 +92,7 @@ "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", "from attrs import define, field\n", - "from IPython.display import Markdown, Math, display\n", + "from IPython.display import Math\n", "from matplotlib import cm\n", "from qrules.particle import Particle\n", "from sympy import Abs\n", @@ -105,9 +108,10 @@ "from tensorwaves.interface import DataSample, FitResult, ParametrizedFunction\n", "from tensorwaves.optimizer import Minuit2\n", "\n", + "improve_latex_rendering()\n", "logging.getLogger(\"absl\").setLevel(logging.ERROR)\n", "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", - "improve_latex_rendering()" + "plt.rc(\"font\", size=12)" ] }, { @@ -126,7 +130,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -172,12 +175,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "mystnb": { - "code_prompt_show": "Define N* resonances" - }, - "tags": [ - "hide-input" - ] + "tags": [] }, "outputs": [], "source": [ @@ -228,7 +226,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -242,6 +239,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Dynamics builder with X symbols of J^PC channels" + }, "tags": [ "hide-input", "scroll-input" @@ -335,13 +335,14 @@ }, "outputs": [], "source": [ + "src = R\"\\begin{array}{cll}\" \"\\n\"\n", "for symbol, resonances in create_dynamics_symbol.collected_symbols.items():\n", - " display(symbol)\n", - " src = \"| resonance | mass | width |\\n\"\n", - " src += \"|:---|---:|--:|\\n\"\n", + " src += Rf\" {symbol} \\\\\" \"\\n\"\n", " for p, _ in resonances:\n", - " src += f\"| ${p.latex}$ | {p.mass:g} GeV | {p.width:g} GeV |\\n\"\n", - " display(Markdown(src))" + " src += Rf\" {p.latex} & m={p.mass:g}\\text{{ GeV}} & \\Gamma={p.width:g}\\text{{ GeV}} \\\\\"\n", + " src += \"\\n\"\n", + "src += R\"\\end{array}\"\n", + "Math(src)" ] }, { @@ -356,7 +357,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -370,6 +370,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Expression classes for phase space factors" + }, "tags": [ "hide-input", "scroll-input" @@ -447,7 +450,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -461,6 +463,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Expression class for energy-dependent width" + }, "tags": [ "hide-input" ] @@ -529,6 +534,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -566,7 +574,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -656,6 +663,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -693,7 +703,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -744,7 +753,6 @@ { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -766,16 +774,15 @@ ")\n", "phsp_momenta = phsp_generator.generate(100_000, rng)\n", "\n", - "epsilon = 1e-8\n", + "ε = 1e-8\n", "transformer = SympyDataTransformer.from_sympy(model.kinematic_variables, backend=\"jax\")\n", "phsp = transformer(phsp_momenta)\n", - "phsp = {k: v + epsilon * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}" + "phsp = {k: v + ε * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}" ] }, { "cell_type": "markdown", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ @@ -844,6 +851,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Function for computing sub-intensities" + }, "tags": [ "hide-input", "scroll-input" @@ -921,6 +931,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "mystnb": { "code_prompt_show": "Function for plotting histograms with JAX" }, @@ -1002,6 +1015,7 @@ " color=f\"C{i}\",\n", " fill=False,\n", " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV}}$ $F$ vector\",\n", + " linewidth=2,\n", " ax=ax,\n", " )\n", "for i, (p, v) in enumerate(sub_intensities_bw.items()):\n", @@ -1013,7 +1027,7 @@ " color=f\"C{i}\",\n", " fill=False,\n", " label=Rf\"Resonance at ${p.mass}\\,\\mathrm{{GeV^2}}$ Breit-Wigner\",\n", - " ls=\"dotted\",\n", + " linestyle=\"dashed\",\n", " ax=ax,\n", " )\n", "\n", @@ -1047,17 +1061,23 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "full-width" + ] + }, "outputs": [], "source": [ "dynamics_expr_fvector, *_ = dynamics_expressions_fvector.values()\n", - "dynamics_expr_fvector" + "dynamics_expr_fvector.simplify(doit=False)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "dynamics_func_bw = create_parametrized_function(\n", @@ -1071,7 +1091,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "dynamics_func_fvector = create_parametrized_function(\n", From b13dfe58ac1e0e22341fb1ba8bc12ad7bc9d39c6 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 23:22:59 +0200 Subject: [PATCH 45/92] MAINT: remove redundant dynamics functions TR-031 --- docs/report/031.ipynb | 67 ------------------------------------------- 1 file changed, 67 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 8268955d..01ce0d38 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1037,73 +1037,6 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Dynamics expressions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "dynamics_expr_bw, *_ = dynamics_expressions_bw.values()\n", - "dynamics_expr_bw" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "full-width" - ] - }, - "outputs": [], - "source": [ - "dynamics_expr_fvector, *_ = dynamics_expressions_fvector.values()\n", - "dynamics_expr_fvector.simplify(doit=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "dynamics_func_bw = create_parametrized_function(\n", - " expression=dynamics_expr_bw.doit(),\n", - " backend=\"jax\",\n", - " parameters=model_bw.parameter_defaults,\n", - " use_cse=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "dynamics_func_fvector = create_parametrized_function(\n", - " expression=dynamics_expr_fvector.doit(),\n", - " backend=\"jax\",\n", - " parameters=model_fvector.parameter_defaults,\n", - " use_cse=False,\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": { From 6c498528aa73a3f17cec3d50da7b0c62a9ee14e6 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 23:23:37 +0200 Subject: [PATCH 46/92] ENH: set `use_cse=True` --- docs/report/030.ipynb | 2 -- docs/report/032.ipynb | 1 - 2 files changed, 3 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 589299dc..13a475dd 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -789,7 +789,6 @@ " expression=perform_cached_doit(dynamics_expr_bw),\n", " backend=\"jax\",\n", " parameters=model_bw.parameter_defaults,\n", - " use_cse=False,\n", ")" ] }, @@ -805,7 +804,6 @@ " expression=perform_cached_doit(dynamics_expr_fvector),\n", " backend=\"jax\",\n", " parameters=model_fvector.parameter_defaults,\n", - " use_cse=False,\n", ")" ] }, diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index dd305d21..9d9a1b91 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -1018,7 +1018,6 @@ " expression=DYNAMICS_EXPR_FVECTOR[i].doit(),\n", " backend=\"jax\",\n", " parameters=MODELS_FVECTOR[i].parameter_defaults,\n", - " use_cse=False,\n", " )\n", " DYNAMICS_FUNCS_FVECTOR.append(func)" ] From 7d8c9f311b0e319fe4f280afd75db62102a7ccf3 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 23:51:19 +0200 Subject: [PATCH 47/92] ENH: improve fit result printing --- docs/report/031.ipynb | 87 ++++++++++++++++++++++++++++--------------- 1 file changed, 56 insertions(+), 31 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 01ce0d38..769aba75 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1325,24 +1325,34 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "tags": [ + "hide-output", + "full-width", + "scroll-input" + ] }, "outputs": [], "source": [ "fit_result_bw = minuit2.optimize(estimator_bw, initial_parameters_bw)\n", - "fit_result_bw" + "assert fit_result_bw.minimum_valid\n", + "fit_result_bw.specifics" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "tags": [ + "hide-output", + "full-width", + "scroll-input" + ] }, "outputs": [], "source": [ "fit_result_fvector = minuit2.optimize(estimator_fvector, initial_parameters_fvector)\n", - "fit_result_fvector" + "assert fit_result_fvector.minimum_valid\n", + "fit_result_fvector.specifics" ] }, { @@ -1371,8 +1381,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "source_hidden": true + "mystnb": { + "code_prompt_show": "Functions for inspecting fit result" }, "tags": [ "hide-input" @@ -1386,38 +1396,34 @@ " log_likelihood = -fit_result.estimator_value\n", " aic = 2 * n_real_par - 2 * log_likelihood\n", " bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", - " return aic, bic" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "def compare_parameters(original: dict, initial: dict, optimized: dict) -> pd.DataFrame:\n", + " return aic, bic\n", + "\n", + "\n", + "def compare_parameters(initial: dict, optimized: dict, expected: dict) -> pd.DataFrame:\n", " parameters = sorted(set(initial) | set(optimized))\n", " df = pd.DataFrame(\n", " {\n", " f\"${p}$\": (\n", - " initial.get(p, \"NaN\"),\n", - " optimized.get(p, \"NaN\"),\n", - " original.get(p, \"NaN\"),\n", + " f\"{initial[p]:.3g}\",\n", + " f\"{optimized[p]:.3g}\",\n", + " f\"{expected[p]:.3g}\",\n", " )\n", " for p in parameters\n", " },\n", " ).T\n", - " df.columns = (\"initial\", \"fit result\", \"original\")\n", + " df.columns = (\"initial\", \"fit result\", \"expected\")\n", " return df" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "#### P vector" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1433,17 +1439,31 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ "compare_parameters(\n", - " original=original_parameters_fvector,\n", " initial=initial_parameters_fvector,\n", " optimized=fit_result_fvector.parameter_values,\n", + " expected=original_parameters_fvector,\n", ")" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "#### Breit–Wigner" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1459,14 +1479,19 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ "compare_parameters(\n", - " original=original_parameters_bw,\n", " initial=initial_parameters_bw,\n", " optimized=fit_result_bw.parameter_values,\n", + " expected=original_parameters_bw,\n", ")" ] } From 787e268339514b613fec0ba3ce0c7f58616a58de Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Thu, 23 May 2024 23:51:41 +0200 Subject: [PATCH 48/92] ENH: use same masses and betas as starting paramters --- docs/report/031.ipynb | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 769aba75..96f4a054 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1261,24 +1261,27 @@ }, "outputs": [], "source": [ - "initial_parameters_bw = {\n", + "initial_parameters_beta = {\n", + " R\"\\beta_{N^{\\frac{1}{2}^-}_{2}}\": 1 + 0j,\n", + " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", + "}\n", + "initial_parameters_masses = {\n", " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.6,\n", " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.7,\n", " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.8,\n", " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.93,\n", + "}\n", + "initial_parameters_bw = {\n", + " **initial_parameters_beta,\n", + " **initial_parameters_masses,\n", " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{1}}\": 1 / 1.6,\n", " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{2}}\": 1 / 1.65,\n", " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{1}}\": 1 / 1.85,\n", " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{2}}\": 1 / 1.93,\n", "}\n", "initial_parameters_fvector = {\n", - " R\"\\beta_{N^{\\frac{1}{2}^-}_{1}}\": 1 + 0j,\n", - " R\"\\beta_{N^{\\frac{1}{2}^-}_{2}}\": 1 + 0j,\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", - " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.67,\n", - " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.7,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.95,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.91,\n", + " **initial_parameters_beta,\n", + " **initial_parameters_masses,\n", " R\"g_{N^{\\frac{1}{2}^-}_{1}}\": 1.6,\n", " R\"g_{N^{\\frac{1}{2}^-}_{2}}\": 1,\n", " R\"g_{N^{\\frac{3}{2}^-}_{1}}\": 1.0,\n", From a3820e74f179b045a1634bde27217ce701a7425e Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 00:13:03 +0200 Subject: [PATCH 49/92] ENH: print simple fit results, not full `iminuit` output --- docs/report/031.ipynb | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 96f4a054..82adc414 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1329,16 +1329,14 @@ "execution_count": null, "metadata": { "tags": [ - "hide-output", - "full-width", - "scroll-input" + "scroll-output" ] }, "outputs": [], "source": [ "fit_result_bw = minuit2.optimize(estimator_bw, initial_parameters_bw)\n", "assert fit_result_bw.minimum_valid\n", - "fit_result_bw.specifics" + "fit_result_bw" ] }, { @@ -1346,16 +1344,14 @@ "execution_count": null, "metadata": { "tags": [ - "hide-output", - "full-width", - "scroll-input" + "scroll-output" ] }, "outputs": [], "source": [ "fit_result_fvector = minuit2.optimize(estimator_fvector, initial_parameters_fvector)\n", "assert fit_result_fvector.minimum_valid\n", - "fit_result_fvector.specifics" + "fit_result_fvector" ] }, { From c6b730815a86cf99d2d46115ce32f471db423e65 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 00:13:22 +0200 Subject: [PATCH 50/92] ENH: print parameter deviation --- docs/report/031.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 82adc414..c74fe2de 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1406,11 +1406,12 @@ " f\"{initial[p]:.3g}\",\n", " f\"{optimized[p]:.3g}\",\n", " f\"{expected[p]:.3g}\",\n", + " f\"{100 * abs((optimized[p] - expected[p]) / expected[p]):.1f}%\",\n", " )\n", " for p in parameters\n", " },\n", " ).T\n", - " df.columns = (\"initial\", \"fit result\", \"expected\")\n", + " df.columns = (\"initial\", \"fit result\", \"expected\", \"deviation\")\n", " return df" ] }, From 1acf3ffbf61481ebdb10c80150abc9ea830246b9 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 00:13:22 +0200 Subject: [PATCH 51/92] ENH: print parameter deviation --- docs/report/031.ipynb | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index c74fe2de..766acbcd 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1380,6 +1380,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "mystnb": { "code_prompt_show": "Functions for inspecting fit result" }, From 7fc149c82e21414a47a30c4016f279f0c25b7d79 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 00:29:11 +0200 Subject: [PATCH 52/92] ENH: improve legend position in comparison plot --- docs/report/031.ipynb | 27 +++++++++++++++------------ 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 766acbcd..2b420446 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1167,6 +1167,15 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Initial parameters" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1205,8 +1214,11 @@ ") -> None:\n", " intensities1 = function1(phsp)\n", " intensities2 = function2(phsp)\n", - " _, ax = plt.subplots(figsize=(9, 4))\n", - " ax.set_xlabel(\"$m$ [GeV]\")\n", + " fig, ax = plt.subplots(figsize=(11, 4))\n", + " fig.subplots_adjust(right=0.85, top=0.95)\n", + " ax.set_xlabel(R\"$m_{p\\eta}$ [GeV]\")\n", + " ax.set_ylabel(\"Intensity [a. u.]\")\n", + " ax.set_yticks([])\n", " data_projection = np.real(data[variable_name])\n", " fast_histogram(\n", " data_projection,\n", @@ -1240,19 +1252,10 @@ " ax.set_ylim(0, None)\n", " indicate_masses(ax, function1, ls=\"dashed\", lw=1, typ=\"F vector\")\n", " indicate_masses(ax, function2, ls=\"dotted\", lw=1, typ=\"Breit-Wigner\")\n", - " ax.legend()\n", + " fig.legend(loc=\"outside upper right\")\n", " fig.show()" ] }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Initial parameters" - ] - }, { "cell_type": "code", "execution_count": null, From eba8a13dfbb043551b11f1ad5b66aa0f78801f78 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 00:34:08 +0200 Subject: [PATCH 53/92] ENH: use color cycler for resonance indicators --- docs/report/031.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 2b420446..ae16bd41 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1198,10 +1198,8 @@ " mass_pars = {\n", " k: v for k, v in intensity_func.parameters.items() if k.startswith(\"m_{\")\n", " }\n", - " evenly_spaced_interval = np.linspace(0, 1, len(mass_pars.items()))\n", - " colors = [cm.gist_rainbow(x) for x in evenly_spaced_interval]\n", - " for (k, v), color in zip(mass_pars.items(), colors):\n", - " ax.axvline(v, c=color, label=f\"${k}$ ({typ})\", ls=ls, lw=lw)\n", + " for i, (k, v) in enumerate(mass_pars.items()):\n", + " ax.axvline(v, c=f\"C{i}\", label=f\"${k}$ ({typ})\", ls=ls, lw=lw)\n", "\n", "\n", "def compare_model(\n", From cbd427a7966d0dad9a89364b5cfa7ea3d4d7263d Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 00:40:06 +0200 Subject: [PATCH 54/92] DOC: hide `compare_model()` definition cell --- docs/report/031.ipynb | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index ae16bd41..3e90b865 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1183,7 +1183,13 @@ "jupyter": { "source_hidden": true }, - "tags": [] + "mystnb": { + "code_prompt_show": "Functions for comparing model to data" + }, + "tags": [ + "hide-input", + "scroll-input" + ] }, "outputs": [], "source": [ From 8d3b9a49a8326770fee0cb2a25830e0e3bea9f07 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 00:40:57 +0200 Subject: [PATCH 55/92] FIX: remove redundant `resonances` and `reaction_info` definitions --- docs/report/031.ipynb | 7 ------- 1 file changed, 7 deletions(-) diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 3e90b865..249913e5 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -1193,13 +1193,6 @@ }, "outputs": [], "source": [ - "reaction_info = model.reaction_info\n", - "resonances = sorted(\n", - " reaction_info.get_intermediate_particles(),\n", - " key=lambda p: p.mass,\n", - ")\n", - "\n", - "\n", "def indicate_masses(ax, intensity_func, ls: str, lw: float, typ: str):\n", " mass_pars = {\n", " k: v for k, v in intensity_func.parameters.items() if k.startswith(\"m_{\")\n", From 25bd0f7e58e9ded69718cc8b9c07ce4e99c81fe9 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 09:42:56 +0200 Subject: [PATCH 56/92] ENH: remove `PARTICLE_DB` global --- docs/report/030.ipynb | 33 +++++++++++++++------------------ docs/report/031.ipynb | 37 +++++++++++++++++-------------------- 2 files changed, 32 insertions(+), 38 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 13a475dd..621bd822 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -75,6 +75,7 @@ "import os\n", "import re\n", "from collections import defaultdict\n", + "from functools import lru_cache\n", "from typing import Any\n", "\n", "import ampform\n", @@ -92,7 +93,7 @@ "from ampform.sympy import perform_cached_doit, unevaluated\n", "from attrs import define, field\n", "from IPython.display import Math\n", - "from qrules.particle import Particle\n", + "from qrules.particle import Particle, ParticleCollection\n", "from sympy import Abs\n", "from tensorwaves.data import (\n", " SympyDataTransformer,\n", @@ -141,11 +142,22 @@ "code_prompt_show": "Define N* resonances" }, "tags": [ - "hide-input" + "hide-input", + "scroll-input" ] }, "outputs": [], "source": [ + "@lru_cache(maxsize=1)\n", + "def create_particle_database() -> ParticleCollection:\n", + " particles = qrules.load_default_particles()\n", + " for nstar in particles.filter(lambda p: p.name.startswith(\"N\")):\n", + " particles.remove(nstar)\n", + " particles += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)\n", + " particles += create_nstar(mass=1.92, width=0.6, parity=+1, spin=1.5, idx=2)\n", + " return particles\n", + "\n", + "\n", "def create_nstar(\n", " mass: float, width: float, parity: int, spin: float, idx: int\n", ") -> Particle:\n", @@ -165,21 +177,6 @@ " )" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "PARTICLE_DB = qrules.load_default_particles()\n", - "for nstar in PARTICLE_DB.filter(lambda p: p.name.startswith(\"N\")):\n", - " PARTICLE_DB.remove(nstar)\n", - "PARTICLE_DB += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)\n", - "PARTICLE_DB += create_nstar(mass=1.92, width=0.6, parity=+1, spin=1.5, idx=2)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -194,7 +191,7 @@ " allowed_intermediate_particles=[\"N\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", - " particle_db=PARTICLE_DB,\n", + " particle_db=create_particle_database(),\n", ")" ] }, diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 249913e5..f9592990 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -75,6 +75,7 @@ "import os\n", "import re\n", "from collections import defaultdict\n", + "from functools import lru_cache\n", "from typing import Any\n", "\n", "import ampform\n", @@ -94,7 +95,7 @@ "from attrs import define, field\n", "from IPython.display import Math\n", "from matplotlib import cm\n", - "from qrules.particle import Particle\n", + "from qrules.particle import Particle, ParticleCollection\n", "from sympy import Abs\n", "from tensorwaves.data import (\n", " IntensityDistributionGenerator,\n", @@ -147,11 +148,24 @@ "code_prompt_show": "Define N* resonances" }, "tags": [ - "hide-input" + "hide-input", + "scroll-input" ] }, "outputs": [], "source": [ + "@lru_cache(maxsize=1)\n", + "def create_particle_database() -> ParticleCollection:\n", + " particles = qrules.load_default_particles()\n", + " for nstar in particles.filter(lambda p: p.name.startswith(\"N\")):\n", + " particles.remove(nstar)\n", + " particles += create_nstar(mass=1.65, width=0.6, parity=-1, spin=0.5, idx=1)\n", + " particles += create_nstar(mass=1.75, width=0.6, parity=-1, spin=0.5, idx=2)\n", + " particles += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)\n", + " particles += create_nstar(mass=1.92, width=0.6, parity=+1, spin=1.5, idx=2)\n", + " return particles\n", + "\n", + "\n", "def create_nstar(\n", " mass: float, width: float, parity: int, spin: float, idx: int\n", ") -> Particle:\n", @@ -171,23 +185,6 @@ " )" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "PARTICLE_DB = qrules.load_default_particles()\n", - "for nstar in PARTICLE_DB.filter(lambda p: p.name.startswith(\"N\")):\n", - " PARTICLE_DB.remove(nstar)\n", - "PARTICLE_DB += create_nstar(mass=1.65, width=0.6, parity=-1, spin=0.5, idx=1)\n", - "PARTICLE_DB += create_nstar(mass=1.75, width=0.6, parity=-1, spin=0.5, idx=2)\n", - "PARTICLE_DB += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)\n", - "PARTICLE_DB += create_nstar(mass=1.92, width=0.6, parity=+1, spin=1.5, idx=2)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -202,7 +199,7 @@ " allowed_intermediate_particles=[\"N\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", - " particle_db=PARTICLE_DB,\n", + " particle_db=create_particle_database(),\n", ")" ] }, From 93217d98b3f98c3476f99a5b3466fbc7b55bf7c1 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 09:48:55 +0200 Subject: [PATCH 57/92] MAINT(TR-032): move decay definition to top --- docs/report/032.ipynb | 124 ++++++++++++++++++++++++------------------ 1 file changed, 72 insertions(+), 52 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 9d9a1b91..eabe66eb 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -71,10 +71,12 @@ "import re\n", "from collections import defaultdict\n", "from dataclasses import dataclass\n", + "from functools import lru_cache\n", "from typing import Any, Iterable, Mapping\n", "\n", "import ampform\n", "import attrs\n", + "import graphviz\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", @@ -87,7 +89,7 @@ "from ampform.sympy import perform_cached_doit, unevaluated\n", "from IPython.display import Math, display\n", "from matplotlib import cm\n", - "from qrules.particle import Particle\n", + "from qrules.particle import Particle, ParticleCollection\n", "from qrules.transition import ReactionInfo\n", "from sympy import Abs\n", "from sympy.matrices.expressions.matexpr import MatrixElement\n", @@ -122,34 +124,11 @@ }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Collect dynamics symbols" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [] + }, "source": [ - "def create_dynamics_symbol(\n", - " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", - ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", - " J = sp.Rational(resonance.spin)\n", - " Q = resonance.charge\n", - " P = sp.Rational(resonance.parity)\n", - " if variable_pool.angular_momentum is not None:\n", - " L = sp.Rational(variable_pool.angular_momentum)\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", - " else:\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", - " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", - " parameter_defaults = {}\n", - " return X, parameter_defaults\n", - "\n", - "\n", - "COLLECTED_X_SYMBOLS = defaultdict(set)" + "## Studied decay" ] }, { @@ -163,11 +142,21 @@ "code_prompt_show": "Define N* resonances" }, "tags": [ - "hide-input" + "hide-input", + "scroll-input" ] }, "outputs": [], "source": [ + "@lru_cache(maxsize=1)\n", + "def create_particle_database() -> ParticleCollection:\n", + " particles = qrules.load_default_particles()\n", + " for nstar in particles.filter(lambda p: p.name.startswith(\"N\")):\n", + " particles.remove(nstar)\n", + " particles += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)\n", + " return particles\n", + "\n", + "\n", "def create_nstar(\n", " mass: float, width: float, parity: int, spin: float, idx: int\n", ") -> Particle:\n", @@ -187,25 +176,6 @@ " )" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "mystnb": { - "code_prompt_show": "Define N* resonances" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "PARTICLE_DB = qrules.load_default_particles()\n", - "for nstar in PARTICLE_DB.filter(lambda p: p.name.startswith(\"N\")):\n", - " PARTICLE_DB.remove(nstar)\n", - "PARTICLE_DB += create_nstar(mass=1.82, width=0.6, parity=+1, spin=1.5, idx=1)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -225,12 +195,64 @@ " allowed_intermediate_particles=[\"N\"],\n", " allowed_interaction_types=[\"strong\"],\n", " formalism=\"helicity\",\n", - " particle_db=PARTICLE_DB,\n", + " particle_db=create_particle_database(),\n", " )\n", " for final_state in FINAL_STATES\n", "]" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "for reaction in REACTIONS:\n", + " src = qrules.io.asdot(reaction, collapse_graphs=True)\n", + " graph = graphviz.Source(src)\n", + " display(graph)\n", + " del reaction, src, graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect dynamics symbols" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_dynamics_symbol(\n", + " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", + ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", + " J = sp.Rational(resonance.spin)\n", + " Q = resonance.charge\n", + " P = sp.Rational(resonance.parity)\n", + " if variable_pool.angular_momentum is not None:\n", + " L = sp.Rational(variable_pool.angular_momentum)\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", + " else:\n", + " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", + " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", + " parameter_defaults = {}\n", + " return X, parameter_defaults\n", + "\n", + "\n", + "COLLECTED_X_SYMBOLS = defaultdict(set)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -490,9 +512,7 @@ "tags": [] }, "source": [ - "### \n", - "\n", - "Relativistic Breit-Wigner" + "### Relativistic Breit-Wigner" ] }, { From f5807fde96ddc054bbbfdd7b7c43cadf070660a6 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 09:55:05 +0200 Subject: [PATCH 58/92] MAINT(TR-032): define `DynamicsSymbolBuilder` --- docs/report/032.ipynb | 140 +++++++++++++++++++++++++----------------- 1 file changed, 84 insertions(+), 56 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index eabe66eb..b50c4727 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -87,6 +87,7 @@ "from ampform.io import aslatex\n", "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", + "from attrs import define, field\n", "from IPython.display import Math, display\n", "from matplotlib import cm\n", "from qrules.particle import Particle, ParticleCollection\n", @@ -223,41 +224,71 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "## Collect dynamics symbols" + "## Amplitude builder" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Dynamics builder with X symbols of J^PC channels" + }, + "tags": [ + "hide-input", + "scroll-input" + ] + }, "outputs": [], "source": [ - "def create_dynamics_symbol(\n", - " resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", - ") -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", - " J = sp.Rational(resonance.spin)\n", - " Q = resonance.charge\n", - " P = sp.Rational(resonance.parity)\n", - " if variable_pool.angular_momentum is not None:\n", - " L = sp.Rational(variable_pool.angular_momentum)\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}^{{l={L}}}\")\n", - " else:\n", - " X = sp.Symbol(Rf\"X_{{Q={Q:+d}, S={J}, P ={P}}}\")\n", - " COLLECTED_X_SYMBOLS[X].add((resonance, variable_pool))\n", - " parameter_defaults = {}\n", - " return X, parameter_defaults\n", + "@define\n", + "class DynamicsSymbolBuilder:\n", + " collected_symbols: set[sp.Symbol, tuple[Particle, TwoBodyKinematicVariableSet]] = (\n", + " field(factory=lambda: defaultdict(set))\n", + " )\n", + "\n", + " def __call__(\n", + " self, resonance: Particle, variable_pool: TwoBodyKinematicVariableSet\n", + " ) -> tuple[sp.Expr, dict[sp.Symbol, float]]:\n", + " jp = render_jp(resonance)\n", + " charge = resonance.charge\n", + " if variable_pool.angular_momentum is not None:\n", + " L = sp.Rational(variable_pool.angular_momentum)\n", + " X = sp.Symbol(Rf\"X_{{{jp}, Q={charge:+d}}}^{{l={L}}}\")\n", + " else:\n", + " X = sp.Symbol(Rf\"X_{{{jp}, Q={charge:+d}}}\")\n", + " self.collected_symbols[X].add((resonance, variable_pool))\n", + " parameter_defaults = {}\n", + " return X, parameter_defaults\n", "\n", "\n", - "COLLECTED_X_SYMBOLS = defaultdict(set)" + "def render_jp(particle: Particle) -> str:\n", + " spin = sp.Rational(particle.spin)\n", + " j = (\n", + " str(spin)\n", + " if spin.denominator == 1\n", + " else Rf\"\\frac{{{spin.numerator}}}{{{spin.denominator}}}\"\n", + " )\n", + " if particle.parity is None:\n", + " return f\"J={j}\"\n", + " p = \"-\" if particle.parity < 0 else \"+\"\n", + " return f\"J^P={{{j}}}^{{{p}}}\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ @@ -265,47 +296,68 @@ "for reaction in REACTIONS:\n", " builder = ampform.get_builder(reaction)\n", " builder.adapter.permutate_registered_topologies()\n", - " builder.scalar_initial_state_mass = True\n", - " builder.stable_final_state_ids = [0, 1, 2]\n", + " builder.config.scalar_initial_state_mass = True\n", + " builder.config.stable_final_state_ids = [0, 1, 2]\n", + " create_dynamics_symbol = DynamicsSymbolBuilder()\n", " for resonance in reaction.get_intermediate_particles():\n", " builder.set_dynamics(resonance.name, create_dynamics_symbol)\n", - " MODELS.append(builder.formulate())" + " MODELS.append(builder.formulate())\n", + " del builder, reaction, resonance" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input", + "full-width" + ] }, "outputs": [], "source": [ "selected_amplitudes = {\n", - " k: v for i, (k, v) in enumerate(MODELS[0].amplitudes.items()) if i < 3\n", - "}" + " k: v for i, (k, v) in enumerate(MODELS[0].amplitudes.items()) if i == 0\n", + "}\n", + "Math(aslatex(selected_amplitudes, terms_per_line=1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ - "for X, resonance_info in COLLECTED_X_SYMBOLS.items():\n", - " for res, _ in sorted(resonance_info):\n", - " display(X)\n", - " print(f\" {res.name:<20s} {res.mass:>8g} GeV {res.width:>8g} GeV\")" + "src = R\"\\begin{array}{cll}\" \"\\n\"\n", + "for symbol, resonances in create_dynamics_symbol.collected_symbols.items():\n", + " src += Rf\" {symbol} \\\\\" \"\\n\"\n", + " for p, _ in resonances:\n", + " src += Rf\" {p.latex} & m={p.mass:g}\\text{{ GeV}} & \\Gamma={p.width:g}\\text{{ GeV}} \\\\\"\n", + " src += \"\\n\"\n", + "src += R\"\\end{array}\"\n", + "Math(src)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ @@ -329,31 +381,7 @@ " PARAMETERS_DEFAULTS.update(model.parameter_defaults)\n", " del model\n", "\n", - "resonances, *_ = COLLECTED_X_SYMBOLS.values()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Formulate dynamics expression" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for symbol, resonances in COLLECTED_X_SYMBOLS.items():\n", - " display(symbol)\n", - " for p, _ in resonances:\n", - " print(f\" {p.name:<20s} {p.mass:>8g} GeV {p.width:>8g} GeV \")\n", - "MODELS[0].parameter_defaults" + "resonances, *_ = create_dynamics_symbol.collected_symbols.values()" ] }, { @@ -814,7 +842,7 @@ "for i in range(n_channels):\n", " exprs = {\n", " symbol: F_unfolded_exprs[i]\n", - " for symbol, resonances in COLLECTED_X_SYMBOLS.items()\n", + " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " }\n", " DYNAMICS_EXPRESSIONS_FVECTOR.append(exprs)\n", "\n", From 28d35cd07f5570d080bc585954ea7f854d240a8a Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:00:13 +0200 Subject: [PATCH 59/92] MAINT(TR-032): split `DECAYS` cell and move to relevant locations --- docs/report/032.ipynb | 75 +++++++++++++++++++++---------------------- 1 file changed, 37 insertions(+), 38 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index b50c4727..3e6532d1 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -348,42 +348,6 @@ "Math(src)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class TwoBodyDecay: # specific to the channel\n", - " child1: Particle\n", - " child2: Particle\n", - "\n", - "\n", - "DECAYS = tuple(\n", - " TwoBodyDecay(\n", - " child1=reaction.final_state[0],\n", - " child2=reaction.final_state[1],\n", - " )\n", - " for reaction in REACTIONS\n", - ")\n", - "s = sp.Symbol(\"m_01\", real=True) ** 2\n", - "\n", - "PARAMETERS_DEFAULTS = {}\n", - "for model in MODELS:\n", - " PARAMETERS_DEFAULTS.update(model.parameter_defaults)\n", - " del model\n", - "\n", - "resonances, *_ = create_dynamics_symbol.collected_symbols.values()" - ] - }, { "cell_type": "markdown", "metadata": { @@ -613,6 +577,39 @@ "### $K$ matrix " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@dataclass\n", + "class TwoBodyDecay: # specific to the channel\n", + " child1: Particle\n", + " child2: Particle\n", + "\n", + "\n", + "DECAYS = tuple(\n", + " TwoBodyDecay(\n", + " child1=reaction.final_state[0],\n", + " child2=reaction.final_state[1],\n", + " )\n", + " for reaction in REACTIONS\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PARAMETERS_DEFAULTS = {}\n", + "for model in MODELS:\n", + " PARAMETERS_DEFAULTS.update(model.parameter_defaults)\n", + " del model" + ] + }, { "cell_type": "code", "execution_count": null, @@ -834,7 +831,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "tags": [ + "hide-input" + ] }, "outputs": [], "source": [ @@ -1212,7 +1211,7 @@ "execution_count": null, "metadata": { "tags": [ - "hide-cell" + "hide-input" ] }, "outputs": [], From 2f934044733a7eafcfcc75b48926387f2a5ddb2a Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:33:49 +0200 Subject: [PATCH 60/92] ENH(TR-032): collect `DECAYS` with function --- docs/report/032.ipynb | 41 +++++++++++++++++++++++++++++------------ 1 file changed, 29 insertions(+), 12 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 3e6532d1..d68d55e0 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -70,7 +70,6 @@ "\n", "import re\n", "from collections import defaultdict\n", - "from dataclasses import dataclass\n", "from functools import lru_cache\n", "from typing import Any, Iterable, Mapping\n", "\n", @@ -87,7 +86,7 @@ "from ampform.io import aslatex\n", "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", - "from attrs import define, field\n", + "from attrs import define, field, frozen\n", "from IPython.display import Math, display\n", "from matplotlib import cm\n", "from qrules.particle import Particle, ParticleCollection\n", @@ -580,22 +579,40 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Find decay products per channel" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ - "@dataclass\n", - "class TwoBodyDecay: # specific to the channel\n", + "def get_decay_products(reaction: ReactionInfo) -> DecayProducts:\n", + " some_transition, *_ = reaction.transitions\n", + " decay_product_ids = some_transition.topology.get_edge_ids_outgoing_from_node(1)\n", + " for transition in reaction.transitions:\n", + " if decay_product_ids != transition.topology.get_edge_ids_outgoing_from_node(1):\n", + " msg = \"Reaction contains multiple sub-systems\"\n", + " raise ValueError(msg)\n", + " child1_id, child2_id = sorted(decay_product_ids)\n", + " return DecayProducts(\n", + " child1=reaction.final_state[child1_id],\n", + " child2=reaction.final_state[child2_id],\n", + " )\n", + "\n", + "\n", + "@frozen\n", + "class DecayProducts:\n", " child1: Particle\n", " child2: Particle\n", "\n", "\n", - "DECAYS = tuple(\n", - " TwoBodyDecay(\n", - " child1=reaction.final_state[0],\n", - " child2=reaction.final_state[1],\n", - " )\n", - " for reaction in REACTIONS\n", - ")" + "DECAYS = tuple(get_decay_products(m.reaction_info) for m in MODELS)" ] }, { From 1870a9196919ff826fa9ef6aad088d8aa4145451 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:04:30 +0200 Subject: [PATCH 61/92] DOC(TR-032): improve rendering Phase space factor section --- docs/report/032.ipynb | 102 +++++++++++++++++++----------------------- 1 file changed, 45 insertions(+), 57 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index d68d55e0..8b35f0bf 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -353,12 +353,14 @@ "tags": [] }, "source": [ - "## Formulate Dynamics" + "## Dynamics parametrization" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Phasespace factor" ] @@ -367,8 +369,15 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Expression classes for phase space factors" + }, "tags": [ - "hide-input" + "hide-input", + "scroll-input" ] }, "outputs": [], @@ -415,10 +424,23 @@ "\n", " def evaluate(self) -> sp.Expr:\n", " s, m1, m2 = self.args\n", - " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))\n", - "\n", - "\n", - "s, m1, m2 = sp.symbols(\"s m1 m2\")\n", + " return sp.sqrt(Kallen(s, m1**2, m2**2)) / (2 * sp.sqrt(s))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "s, m1, m2 = sp.symbols(\"s m1 m2\", nonnegative=True)\n", "exprs = [\n", " PhaseSpaceCM(s, m1, m2),\n", " ChewMandelstam(s, m1, m2),\n", @@ -427,10 +449,25 @@ "Math(aslatex({e: e.doit(deep=False) for e in exprs}))" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Relativistic Breit-Wigner" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Expression class for energy-dependent width" + }, "tags": [ "hide-input" ] @@ -450,7 +487,7 @@ " return width * PhaseSpaceCM(s, m1, m2)\n", "\n", "\n", - "s, m1, m2, width = sp.symbols(\"s m1 m2 gamma0\")\n", + "width = sp.Symbol(\"Gamma0\", nonnegative=True)\n", "expr = ChannelWidth(s, m1, m2, width)\n", "Math(aslatex({expr: expr.doit(deep=False)}))" ] @@ -464,55 +501,6 @@ ] }, "outputs": [], - "source": [ - "@unevaluated(real=False)\n", - "class CM(sp.Expr):\n", - " s: Any\n", - " m1: Any\n", - " m2: Any\n", - " _latex_repr_ = R\"CM_{{{m1},{m2}}}\\left({s}\\right)\"\n", - "\n", - " def evaluate(self) -> sp.Expr:\n", - " s, m1, m2 = self.args\n", - " return (\n", - " -2\n", - " / sp.pi\n", - " * (\n", - " -1\n", - " / s\n", - " * sp.sqrt(((m1 + m2) ** 2 - s) * ((m1 - m2) ** 2 - s))\n", - " * sp.log(\n", - " (sp.sqrt((m1 + m2) ** 2 - s) + sp.sqrt((m1 - m2) ** 2 - s))\n", - " / (2 * sp.sqrt(m1 * m2))\n", - " )\n", - " + (m1**2 - m2**2) / (2 * s) * sp.log(m1 / m2)\n", - " - (m1**2 + m2**2) / (2 * (m1**2 - m2**2)) * sp.log(m1 / m2)\n", - " - 1 / 2\n", - " )\n", - " )\n", - "\n", - "\n", - "s, m1, m2 = sp.symbols(\"s m1 m2\")\n", - "CM_expr = CM(s, m1, m2)\n", - "Math(aslatex({CM_expr: CM_expr.doit(deep=False)}))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Relativistic Breit-Wigner" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], "source": [ "PARAMETERS_BW = {}\n", "\n", From 92573b2ff22213a9093caecfcb871054cd12330b Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:36:13 +0200 Subject: [PATCH 62/92] MAINT(TR-032): remove redundant Breit-Wigner definition --- docs/report/032.ipynb | 79 ------------------------------------------- 1 file changed, 79 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 8b35f0bf..55e81231 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -449,85 +449,6 @@ "Math(aslatex({e: e.doit(deep=False) for e in exprs}))" ] }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Relativistic Breit-Wigner" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "mystnb": { - "code_prompt_show": "Expression class for energy-dependent width" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "@unevaluated(real=False)\n", - "class ChannelWidth(sp.Expr):\n", - " s: Any\n", - " m1: Any\n", - " m2: Any\n", - " width: Any\n", - " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", - "\n", - " def evaluate(self) -> sp.Expr:\n", - " s, m1, m2, width = self.args\n", - " return width * PhaseSpaceCM(s, m1, m2)\n", - "\n", - "\n", - "width = sp.Symbol(\"Gamma0\", nonnegative=True)\n", - "expr = ChannelWidth(s, m1, m2, width)\n", - "Math(aslatex({expr: expr.doit(deep=False)}))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "PARAMETERS_BW = {}\n", - "\n", - "\n", - "def formulate_breit_wigner(\n", - " resonances: list[tuple[Particle, TwoBodyKinematicVariableSet]],\n", - ") -> sp.Expr:\n", - " (_, variables), *_ = resonances\n", - " s = variables.incoming_state_mass**2\n", - " m_a = variables.outgoing_state_mass1\n", - " m_b = variables.outgoing_state_mass2\n", - " w = [sp.Symbol(Rf\"w_{{{p.latex}}}\") for p, _ in resonances]\n", - " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", - " b = [sp.Symbol(Rf\"b_{{{p.latex}}}\") for p, _ in resonances]\n", - " d = [sp.Symbol(Rf\"d_{{{p.latex}}}\") for p, _ in resonances]\n", - " L = [sp.Symbol(Rf\"L_{{{p.latex}}}\") for p, _ in resonances]\n", - " w_s = (ChannelWidth(s, m_a, m_b, w_) for w_ in w)\n", - " rel_bw = sum((w_ * m_) / (m_**2 - s - m_ * w_s_) for m_, w_, w_s_ in zip(m, w, w_s))\n", - " for i, (resonance, _) in enumerate(resonances):\n", - " PARAMETERS_BW[w[i]] = resonance.width\n", - " PARAMETERS_BW[m[i]] = resonance.mass\n", - " PARAMETERS_BW[b[i]] = 1\n", - " PARAMETERS_BW[d[i]] = 1\n", - " PARAMETERS_BW[L[i]] = 0\n", - " return rel_bw" - ] - }, { "cell_type": "markdown", "metadata": { From ea3ca02e7fb8d0416404f009753f6522a5a705a1 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:51:01 +0200 Subject: [PATCH 63/92] ENH(TR-032): remove redundant decay product mass definitions --- docs/report/032.ipynb | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 55e81231..585456de 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -556,18 +556,10 @@ " resonance_contributions = []\n", " for res, _ in resonances:\n", " s = sp.Symbol(\"m_01\", real=True) ** 2\n", - " m_a_i = sp.Symbol(Rf\"m_{{0,{i}}}\")\n", - " m_b_i = sp.Symbol(Rf\"m_{{1,{i}}}\")\n", - " m_a_j = sp.Symbol(Rf\"m_{{0,{j}}}\")\n", - " m_b_j = sp.Symbol(Rf\"m_{{1,{j}}}\")\n", " g_Ri = sp.Symbol(Rf\"g_{{{res.latex},{i}}}\")\n", " g_Rj = sp.Symbol(Rf\"g_{{{res.latex},{j}}}\")\n", " m_R = sp.Symbol(Rf\"m_{{{res.latex}}}\")\n", " parameter_defaults = {\n", - " m_a_i: DECAYS[i].child1.mass,\n", - " m_b_i: DECAYS[i].child2.mass,\n", - " m_a_j: DECAYS[j].child1.mass,\n", - " m_b_j: DECAYS[j].child2.mass,\n", " m_R: res.mass,\n", " g_Ri: 1,\n", " g_Rj: 0.1,\n", @@ -611,15 +603,11 @@ " resonance_contributions = []\n", " for res, _ in resonances:\n", " s = sp.Symbol(\"m_01\", real=True) ** 2\n", - " m_a = sp.Symbol(Rf\"m_{{0,{i}}}\")\n", - " m_b = sp.Symbol(Rf\"m_{{1,{i}}}\")\n", " g_Ri = sp.Symbol(Rf\"g_{{{res.latex},{i}}}\")\n", " beta_R = sp.Symbol(Rf\"\\beta_{{{res.latex}}}\")\n", " m_R = sp.Symbol(Rf\"m_{{{res.latex}}}\")\n", "\n", " parameter_defaults = {\n", - " m_a: DECAYS[i].child1.mass,\n", - " m_b: DECAYS[i].child2.mass,\n", " m_R: res.mass,\n", " beta_R: 1 + 0j,\n", " g_Ri: 1,\n", From c4cbfbed15eb6efcc722adda718e9b285125f24f Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:55:13 +0200 Subject: [PATCH 64/92] MAINT(TR-032): make variable naming in builders --- docs/report/032.ipynb | 27 ++++++++++----------------- 1 file changed, 10 insertions(+), 17 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 585456de..2b8d8b2a 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -550,7 +550,7 @@ "def formulate_k_matrix(\n", " resonances: list[tuple[Particle, int]], n_channels: int\n", ") -> dict[MatrixElement, sp.Expr]:\n", - " Kmatrix_expressions = {}\n", + " expressions = {}\n", " for i in range(n_channels):\n", " for j in range(n_channels):\n", " resonance_contributions = []\n", @@ -567,13 +567,11 @@ " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", " expr = (g_Ri * g_Rj) / (m_R**2 - s)\n", " resonance_contributions.append(expr)\n", - " Kmatrix_expressions[K[i, j]] = sum(resonance_contributions)\n", - "\n", - " return Kmatrix_expressions\n", + " expressions[K[i, j]] = sum(resonance_contributions)\n", + " return expressions\n", "\n", "\n", "K_expressions = formulate_k_matrix(resonances, n_channels=len(REACTIONS))\n", - "Math(aslatex(K_expressions))\n", "K_matrix = K.as_explicit()\n", "K.as_explicit().xreplace(K_expressions)" ] @@ -598,7 +596,7 @@ "def formulate_p_vector(\n", " resonances: list[tuple[Particle, int]], n_channels: int\n", ") -> dict[MatrixElement, sp.Expr]:\n", - " P_expressions = {}\n", + " expressions = {}\n", " for i in range(n_channels):\n", " resonance_contributions = []\n", " for res, _ in resonances:\n", @@ -606,7 +604,6 @@ " g_Ri = sp.Symbol(Rf\"g_{{{res.latex},{i}}}\")\n", " beta_R = sp.Symbol(Rf\"\\beta_{{{res.latex}}}\")\n", " m_R = sp.Symbol(Rf\"m_{{{res.latex}}}\")\n", - "\n", " parameter_defaults = {\n", " m_R: res.mass,\n", " beta_R: 1 + 0j,\n", @@ -615,13 +612,11 @@ " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", " expr = (beta_R * g_Ri) / (m_R**2 - s)\n", " resonance_contributions.append(expr)\n", - " P_expressions[P[i, 0]] = sum(resonance_contributions)\n", - "\n", - " return P_expressions\n", + " expressions[P[i, 0]] = sum(resonance_contributions)\n", + " return expressions\n", "\n", "\n", "P_expressions = formulate_p_vector(resonances, n_channels=len(REACTIONS))\n", - "Math(aslatex(P_expressions))\n", "P_vector = P.as_explicit()\n", "P.as_explicit().xreplace(P_expressions)" ] @@ -644,8 +639,7 @@ "outputs": [], "source": [ "def formulate_phsp_factor_matrix(n_channels: int) -> dict[sp.MatrixElement, sp.Expr]:\n", - " matrix_expressions = {}\n", - "\n", + " expressions = {}\n", " for i in range(n_channels):\n", " for j in range(n_channels):\n", " if i == j:\n", @@ -653,16 +647,15 @@ " m_b_i = sp.Symbol(Rf\"m_{{1,{i}}}\")\n", " s = sp.Symbol(\"m_01\", real=True) ** 2\n", " rho_i = PhaseSpaceCM(s, m_a_i, m_b_i)\n", - " matrix_expressions[rho[i, j]] = rho_i\n", + " expressions[rho[i, j]] = rho_i\n", " parameter_defaults = {\n", " m_a_i: DECAYS[i].child1.mass,\n", " m_b_i: DECAYS[i].child2.mass,\n", " }\n", " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", " else:\n", - " matrix_expressions[rho[i, j]] = 0\n", - "\n", - " return matrix_expressions\n", + " expressions[rho[i, j]] = 0\n", + " return expressions\n", "\n", "\n", "rho_expressions = formulate_phsp_factor_matrix(n_channels=len(REACTIONS))\n", From 60e6ba177a1a744eb13dcf8f8f8c8f2721ebb40f Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:58:24 +0200 Subject: [PATCH 65/92] ENH(TR-032): use `itertools.product` in `formulate_k_matrix()` --- docs/report/032.ipynb | 39 +++++++++++++++++++++------------------ 1 file changed, 21 insertions(+), 18 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 2b8d8b2a..d30f5342 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -71,6 +71,7 @@ "import re\n", "from collections import defaultdict\n", "from functools import lru_cache\n", + "from itertools import product\n", "from typing import Any, Iterable, Mapping\n", "\n", "import ampform\n", @@ -540,9 +541,12 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input", - "full-width" + "scroll-input" ] }, "outputs": [], @@ -551,23 +555,22 @@ " resonances: list[tuple[Particle, int]], n_channels: int\n", ") -> dict[MatrixElement, sp.Expr]:\n", " expressions = {}\n", - " for i in range(n_channels):\n", - " for j in range(n_channels):\n", - " resonance_contributions = []\n", - " for res, _ in resonances:\n", - " s = sp.Symbol(\"m_01\", real=True) ** 2\n", - " g_Ri = sp.Symbol(Rf\"g_{{{res.latex},{i}}}\")\n", - " g_Rj = sp.Symbol(Rf\"g_{{{res.latex},{j}}}\")\n", - " m_R = sp.Symbol(Rf\"m_{{{res.latex}}}\")\n", - " parameter_defaults = {\n", - " m_R: res.mass,\n", - " g_Ri: 1,\n", - " g_Rj: 0.1,\n", - " }\n", - " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", - " expr = (g_Ri * g_Rj) / (m_R**2 - s)\n", - " resonance_contributions.append(expr)\n", - " expressions[K[i, j]] = sum(resonance_contributions)\n", + " for i, j in product(range(n_channels), range(n_channels)):\n", + " resonance_contributions = []\n", + " for res, _ in resonances:\n", + " s = sp.Symbol(\"m_01\", real=True) ** 2\n", + " g_Ri = sp.Symbol(Rf\"g_{{{res.latex},{i}}}\")\n", + " g_Rj = sp.Symbol(Rf\"g_{{{res.latex},{j}}}\")\n", + " m_R = sp.Symbol(Rf\"m_{{{res.latex}}}\")\n", + " parameter_defaults = {\n", + " m_R: res.mass,\n", + " g_Ri: 1,\n", + " g_Rj: 0.1,\n", + " }\n", + " PARAMETERS_DEFAULTS.update(parameter_defaults)\n", + " expr = (g_Ri * g_Rj) / (m_R**2 - s)\n", + " resonance_contributions.append(expr)\n", + " expressions[K[i, j]] = sum(resonance_contributions)\n", " return expressions\n", "\n", "\n", From 19a0f73feacbb9a756950488bc0eb89bd6c64bd7 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 10:47:49 +0200 Subject: [PATCH 66/92] DOC(TR-032): improve section organization Amplitude model formulation --- docs/report/032.ipynb | 55 +++++++++++++++++++++---------------------- 1 file changed, 27 insertions(+), 28 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index d30f5342..f6f09fa1 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -456,7 +456,7 @@ "tags": [] }, "source": [ - "### Define matrix symbols" + "### $K$-matrix formalism" ] }, { @@ -475,17 +475,6 @@ "rho = sp.MatrixSymbol(\"rho\", n_channels, n_channels)" ] }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "\n", - "\n", - "### $K$ matrix " - ] - }, { "cell_type": "code", "execution_count": null, @@ -528,7 +517,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "PARAMETERS_DEFAULTS = {}\n", @@ -537,6 +528,15 @@ " del model" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "#### $K$-matrix parametrization" + ] + }, { "cell_type": "code", "execution_count": null, @@ -581,9 +581,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### $P$ vector" + "#### $P$-vector parametrization" ] }, { @@ -626,9 +628,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### Phase space" + "#### Phase space factor parametrization" ] }, { @@ -671,12 +675,14 @@ "tags": [] }, "source": [ - "### $F$ vector" + "### $F$-vector construction" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ ":::{note}\n", "For some reason one has to leave out the multiplication of $\\rho$ by $i$ within the calculation of the $F$ vector\n", @@ -720,18 +726,11 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": { "tags": [] }, - "source": [ - "### Model $F$ vector" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, "outputs": [], "source": [ "F_unfolded_exprs = np.array([perform_cached_doit(expr) for expr in F_exprs])" From 89d0c0146553b2f05bc32810837a8a6e7c945e63 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 11:31:30 +0200 Subject: [PATCH 67/92] MAINT: remove `ChannelWidth` definition --- docs/report/030.ipynb | 39 ++------------------------------------- docs/report/031.ipynb | 39 ++------------------------------------- 2 files changed, 4 insertions(+), 74 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 621bd822..92791c36 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -445,40 +445,6 @@ "### Relativistic Breit-Wigner" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "mystnb": { - "code_prompt_show": "Expression class for energy-dependent width" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "@unevaluated(real=False)\n", - "class ChannelWidth(sp.Expr):\n", - " s: Any\n", - " m1: Any\n", - " m2: Any\n", - " width: Any\n", - " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", - "\n", - " def evaluate(self) -> sp.Expr:\n", - " s, m1, m2, width = self.args\n", - " return width * PhaseSpaceCM(s, m1, m2)\n", - "\n", - "\n", - "width = sp.Symbol(\"Gamma0\", nonnegative=True)\n", - "expr = ChannelWidth(s, m1, m2, width)\n", - "Math(aslatex({expr: expr.doit(deep=False)}))" - ] - }, { "cell_type": "code", "execution_count": null, @@ -505,12 +471,12 @@ " s = variables.incoming_state_mass**2\n", " m1 = variables.outgoing_state_mass1\n", " m2 = variables.outgoing_state_mass2\n", + " ρ = PhaseSpaceCM(s, m1, m2)\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " Γ0 = [sp.Symbol(Rf\"\\Gamma_{{{p.latex}}}\") for p, _ in resonances]\n", - " Γ = [ChannelWidth(s, m1, m2, _w) for _w in Γ0]\n", " β = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", " expr = sum(\n", - " (β_ * m_ * Γ_) / (m_**2 - s - m_ * Γ0_) for m_, Γ_, Γ0_, β_ in zip(m, Γ0, Γ, β)\n", + " (β_ * m_ * Γ0_) / (m_**2 - s - m_ * Γ0_ * ρ) for m_, Γ0_, β_ in zip(m, Γ0, β)\n", " )\n", " for i, (resonance, _) in enumerate(resonances):\n", " PARAMETERS_BW[β[i]] = 1 + 0j\n", @@ -595,7 +561,6 @@ " s = variables.incoming_state_mass**2\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", - "\n", " expr = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", " for i, (resonance, _) in enumerate(resonances):\n", " PARAMETERS_F[m[i]] = resonance.mass\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index f9592990..950a870c 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -453,40 +453,6 @@ "### Relativistic Breit-Wigner" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "jupyter": { - "source_hidden": true - }, - "mystnb": { - "code_prompt_show": "Expression class for energy-dependent width" - }, - "tags": [ - "hide-input" - ] - }, - "outputs": [], - "source": [ - "@unevaluated(real=False)\n", - "class ChannelWidth(sp.Expr):\n", - " s: Any\n", - " m1: Any\n", - " m2: Any\n", - " width: Any\n", - " _latex_repr_ = R\"\\Gamma_s\\left({s}\\right)\"\n", - "\n", - " def evaluate(self) -> sp.Expr:\n", - " s, m1, m2, width = self.args\n", - " return width * PhaseSpaceCM(s, m1, m2)\n", - "\n", - "\n", - "width = sp.Symbol(\"Gamma0\", nonnegative=True)\n", - "expr = ChannelWidth(s, m1, m2, width)\n", - "Math(aslatex({expr: expr.doit(deep=False)}))" - ] - }, { "cell_type": "code", "execution_count": null, @@ -513,12 +479,12 @@ " s = variables.incoming_state_mass**2\n", " m1 = variables.outgoing_state_mass1\n", " m2 = variables.outgoing_state_mass2\n", + " ρ = PhaseSpaceCM(s, m1, m2)\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " Γ0 = [sp.Symbol(Rf\"\\Gamma_{{{p.latex}}}\") for p, _ in resonances]\n", - " Γ = [ChannelWidth(s, m1, m2, _w) for _w in Γ0]\n", " β = [sp.Symbol(Rf\"\\beta_{{{p.latex}}}\") for p, _ in resonances]\n", " expr = sum(\n", - " (β_ * m_ * Γ_) / (m_**2 - s - m_ * Γ0_) for m_, Γ_, Γ0_, β_ in zip(m, Γ0, Γ, β)\n", + " (β_ * m_ * Γ0_) / (m_**2 - s - m_ * Γ0_ * ρ) for m_, Γ0_, β_ in zip(m, Γ0, β)\n", " )\n", " for i, (resonance, _) in enumerate(resonances):\n", " PARAMETERS_BW[β[i]] = 1 + 0j\n", @@ -603,7 +569,6 @@ " s = variables.incoming_state_mass**2\n", " m = [sp.Symbol(Rf\"m_{{{p.latex}}}\") for p, _ in resonances]\n", " g = [sp.Symbol(Rf\"g_{{{p.latex}}}\") for p, _ in resonances]\n", - "\n", " expr = sum((g_**2) / (m_**2 - s) for m_, g_ in zip(m, g))\n", " for i, (resonance, _) in enumerate(resonances):\n", " PARAMETERS_F[m[i]] = resonance.mass\n", From bc3a2ea4da771cc6fc60f574325be7e46123badd Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 11:38:25 +0200 Subject: [PATCH 68/92] MAINT: simplify `evaluate()` of `ChewMandelstam` --- docs/report/030.ipynb | 12 ++++-------- docs/report/031.ipynb | 12 ++++-------- docs/report/032.ipynb | 12 ++++-------- 3 files changed, 12 insertions(+), 24 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 92791c36..87bd518c 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -392,14 +392,10 @@ " s, m1, m2 = self.args\n", " q = BreakupMomentum(s, m1, m2)\n", " return (\n", - " 1\n", - " / (16 * sp.pi**2)\n", - " * (\n", - " (2 * q / sp.sqrt(s))\n", - " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", - " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", - " )\n", - " )\n", + " (2 * q / sp.sqrt(s))\n", + " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", + " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", + " ) / (16 * sp.pi**2)\n", "\n", "\n", "@unevaluated(real=False)\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 950a870c..0c6e198f 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -400,14 +400,10 @@ " s, m1, m2 = self.args\n", " q = BreakupMomentum(s, m1, m2)\n", " return (\n", - " 1\n", - " / (16 * sp.pi**2)\n", - " * (\n", - " (2 * q / sp.sqrt(s))\n", - " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", - " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", - " )\n", - " )\n", + " (2 * q / sp.sqrt(s))\n", + " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", + " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", + " ) / (16 * sp.pi**2)\n", "\n", "\n", "@unevaluated(real=False)\n", diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index f6f09fa1..13ce8461 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -406,14 +406,10 @@ " s, m1, m2 = self.args\n", " q = BreakupMomentum(s, m1, m2)\n", " return (\n", - " 1\n", - " / (16 * sp.pi**2)\n", - " * (\n", - " (2 * q / sp.sqrt(s))\n", - " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", - " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", - " )\n", - " )\n", + " (2 * q / sp.sqrt(s))\n", + " * sp.log(Abs((m1**2 + m2**2 - s + 2 * sp.sqrt(s) * q) / (2 * m1 * m2)))\n", + " - (m1**2 - m2**2) * (1 / s - 1 / (m1 + m2) ** 2) * sp.log(m1 / m2)\n", + " ) / (16 * sp.pi**2)\n", "\n", "\n", "@unevaluated(real=False)\n", From 0117fc8407624119c4dbe86b41e549688052cdc6 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 11:54:17 +0200 Subject: [PATCH 69/92] ENH: simplify LaTeX name of fake N* resonances --- docs/report/030.ipynb | 22 +++++++------- docs/report/031.ipynb | 70 +++++++++++++++++++++---------------------- docs/report/032.ipynb | 21 +++++++------ 3 files changed, 58 insertions(+), 55 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 87bd518c..3c475906 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -165,7 +165,7 @@ " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", " return Particle(\n", " name=f\"N({idx})({spin}{parity_symbol})\",\n", - " latex=Rf\"N^{{\\frac{{{spin.numerator}}}{{{spin.denominator}}}^-}}_{{{idx}}}\",\n", + " latex=Rf\"N_{idx}({spin.numerator}/{spin.denominator}^-)\",\n", " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", " mass=mass,\n", " width=width,\n", @@ -836,10 +836,10 @@ "outputs": [], "source": [ "new_parameters_bw = {\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": m_res1,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": m_res2,\n", - " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{1}}\": g_res1 / m_res1,\n", - " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{2}}\": g_res2 / m_res2,\n", + " R\"m_{N_1(3/2^-)}\": m_res1,\n", + " R\"m_{N_2(3/2^-)}\": m_res2,\n", + " R\"\\Gamma_{N_1(3/2^-)}\": g_res1 / m_res1,\n", + " R\"\\Gamma_{N_2(3/2^-)}\": g_res2 / m_res2,\n", "}\n", "dynamics_func_bw.update_parameters(new_parameters_bw)\n", "intensity_func_bw.update_parameters(new_parameters_bw)" @@ -854,12 +854,12 @@ "outputs": [], "source": [ "new_parameters_fvector = {\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": m_res1,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": m_res2,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{1}}\": g_res1,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{2}}\": g_res2,\n", + " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", + " R\"\\beta_{N_2(3/2^-)}\": 1 + 0j,\n", + " R\"m_{N_1(3/2^-)}\": m_res1,\n", + " R\"m_{N_2(3/2^-)}\": m_res2,\n", + " R\"g_{N_1(3/2^-)}\": g_res1,\n", + " R\"g_{N_2(3/2^-)}\": g_res2,\n", "}\n", "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", "intensity_func_fvector.update_parameters(new_parameters_fvector)" diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 0c6e198f..efc69613 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -173,7 +173,7 @@ " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", " return Particle(\n", " name=f\"N({idx})({spin}{parity_symbol})\",\n", - " latex=Rf\"N^{{\\frac{{{spin.numerator}}}{{{spin.denominator}}}^-}}_{{{idx}}}\",\n", + " latex=Rf\"N_{idx}({spin.numerator}/{spin.denominator}^-)\",\n", " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", " mass=mass,\n", " width=width,\n", @@ -756,14 +756,14 @@ "outputs": [], "source": [ "new_parameters_bw = {\n", - " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.65,\n", - " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.75,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.85,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.9,\n", - " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{1}}\": 1 / 1.65,\n", - " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{2}}\": 1 / 1.75,\n", - " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{1}}\": 1 / 1.85,\n", - " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{2}}\": 1 / 1.9,\n", + " R\"m_{N_1(1/2^-)}\": 1.65,\n", + " R\"m_{N_2(1/2^-)}\": 1.75,\n", + " R\"m_{N_1(3/2^-)}\": 1.85,\n", + " R\"m_{N_2(3/2^-)}\": 1.9,\n", + " R\"\\Gamma_{N_1(1/2^-)}\": 1 / 1.65,\n", + " R\"\\Gamma_{N_2(1/2^-)}\": 1 / 1.75,\n", + " R\"\\Gamma_{N_1(3/2^-)}\": 1 / 1.85,\n", + " R\"\\Gamma_{N_2(3/2^-)}\": 1 / 1.9,\n", "}\n", "intensity_func_bw.update_parameters(new_parameters_bw)" ] @@ -777,18 +777,18 @@ "outputs": [], "source": [ "new_parameters_fvector = {\n", - " R\"\\beta_{N^{\\frac{1}{2}^-}_{1}}\": 1 + 0j,\n", - " R\"\\beta_{N^{\\frac{1}{2}^-}_{2}}\": 1 + 0j,\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", - " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.65,\n", - " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.75,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.95,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.9,\n", - " R\"g_{N^{\\frac{1}{2}^-}_{1}}\": 1.65,\n", - " R\"g_{N^{\\frac{1}{2}^-}_{2}}\": 1,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{1}}\": 1,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{2}}\": 1,\n", + " R\"\\beta_{N_1(1/2^-)}\": 1 + 0j,\n", + " R\"\\beta_{N_2(1/2^-)}\": 1 + 0j,\n", + " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", + " R\"\\beta_{N_2(3/2^-)}\": 1 + 0j,\n", + " R\"m_{N_1(1/2^-)}\": 1.65,\n", + " R\"m_{N_2(1/2^-)}\": 1.75,\n", + " R\"m_{N_1(3/2^-)}\": 1.95,\n", + " R\"m_{N_2(3/2^-)}\": 1.9,\n", + " R\"g_{N_1(1/2^-)}\": 1.65,\n", + " R\"g_{N_2(1/2^-)}\": 1,\n", + " R\"g_{N_1(3/2^-)}\": 1,\n", + " R\"g_{N_2(3/2^-)}\": 1,\n", "}\n", "intensity_func_fvector.update_parameters(new_parameters_fvector)" ] @@ -1220,30 +1220,30 @@ "outputs": [], "source": [ "initial_parameters_beta = {\n", - " R\"\\beta_{N^{\\frac{1}{2}^-}_{2}}\": 1 + 0j,\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{2}}\": 1 + 0j,\n", + " R\"\\beta_{N_2(1/2^-)}\": 1 + 0j,\n", + " R\"\\beta_{N_2(3/2^-)}\": 1 + 0j,\n", "}\n", "initial_parameters_masses = {\n", - " R\"m_{N^{\\frac{1}{2}^-}_{1}}\": 1.6,\n", - " R\"m_{N^{\\frac{1}{2}^-}_{2}}\": 1.7,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.8,\n", - " R\"m_{N^{\\frac{3}{2}^-}_{2}}\": 1.93,\n", + " R\"m_{N_1(1/2^-)}\": 1.6,\n", + " R\"m_{N_2(1/2^-)}\": 1.7,\n", + " R\"m_{N_1(3/2^-)}\": 1.8,\n", + " R\"m_{N_2(3/2^-)}\": 1.93,\n", "}\n", "initial_parameters_bw = {\n", " **initial_parameters_beta,\n", " **initial_parameters_masses,\n", - " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{1}}\": 1 / 1.6,\n", - " R\"\\Gamma_{N^{\\frac{1}{2}^-}_{2}}\": 1 / 1.65,\n", - " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{1}}\": 1 / 1.85,\n", - " R\"\\Gamma_{N^{\\frac{3}{2}^-}_{2}}\": 1 / 1.93,\n", + " R\"\\Gamma_{N_1(1/2^-)}\": 1 / 1.6,\n", + " R\"\\Gamma_{N_2(1/2^-)}\": 1 / 1.65,\n", + " R\"\\Gamma_{N_1(3/2^-)}\": 1 / 1.85,\n", + " R\"\\Gamma_{N_2(3/2^-)}\": 1 / 1.93,\n", "}\n", "initial_parameters_fvector = {\n", " **initial_parameters_beta,\n", " **initial_parameters_masses,\n", - " R\"g_{N^{\\frac{1}{2}^-}_{1}}\": 1.6,\n", - " R\"g_{N^{\\frac{1}{2}^-}_{2}}\": 1,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{1}}\": 1.0,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{2}}\": 1.0,\n", + " R\"g_{N_1(1/2^-)}\": 1.6,\n", + " R\"g_{N_2(1/2^-)}\": 1,\n", + " R\"g_{N_1(3/2^-)}\": 1.0,\n", + " R\"g_{N_2(3/2^-)}\": 1.0,\n", "}" ] }, diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 13ce8461..3106976d 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -59,6 +59,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Import Python libraries" + }, "tags": [ "hide-input", "scroll-input" @@ -165,7 +168,7 @@ " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", " return Particle(\n", " name=f\"N({idx})({spin}{parity_symbol})\",\n", - " latex=Rf\"N^{{\\frac{{{spin.numerator}}}{{{spin.denominator}}}^-}}_{{{idx}}}\",\n", + " latex=Rf\"N_{idx}({spin.numerator}/{spin.denominator}^-)\",\n", " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", " mass=mass,\n", " width=width,\n", @@ -826,10 +829,10 @@ "g_res_ch1 = 2.5\n", "\n", "new_parameters_fvector = {\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.71,\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{1},0}\": g_res_ch0,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{1},1}\": g_res_ch1,\n", + " R\"m_{N_1(3/2^-)}\": 1.71,\n", + " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", + " R\"g_{N_1(3/2^-),0}\": g_res_ch0,\n", + " R\"g_{N_1(3/2^-),1}\": g_res_ch1,\n", "}" ] }, @@ -1103,10 +1106,10 @@ "outputs": [], "source": [ "initial_parameters = {\n", - " R\"m_{N^{\\frac{3}{2}^-}_{1}}\": 1.9,\n", - " R\"\\beta_{N^{\\frac{3}{2}^-}_{1}}\": 1 + 0j,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{1},0}\": 2.8,\n", - " R\"g_{N^{\\frac{3}{2}^-}_{1},1}\": 1.6,\n", + " R\"m_{N_1(3/2^-)}\": 1.9,\n", + " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", + " R\"g_{N_1(3/2^-),0}\": 2.8,\n", + " R\"g_{N_1(3/2^-),1}\": 1.6,\n", "}\n", "INTENSITY_FUNCS_FVECTOR[0].parameters" ] From b818a0a7de05023974859870e03e2c6043876970 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 11:59:58 +0200 Subject: [PATCH 70/92] ENH: render particle name with unicode subscript --- docs/report/030.ipynb | 3 ++- docs/report/031.ipynb | 3 ++- docs/report/032.ipynb | 3 ++- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 3c475906..8373d6e3 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -163,8 +163,9 @@ ") -> Particle:\n", " spin = sp.Rational(spin)\n", " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", + " unicode_subscripts = list(\"₀₁₂₃₄₅₆₇₈₉\")\n", " return Particle(\n", - " name=f\"N({idx})({spin}{parity_symbol})\",\n", + " name=f\"N{unicode_subscripts[idx]}({spin}{parity_symbol})\",\n", " latex=Rf\"N_{idx}({spin.numerator}/{spin.denominator}^-)\",\n", " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", " mass=mass,\n", diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index efc69613..93b2fed8 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -171,8 +171,9 @@ ") -> Particle:\n", " spin = sp.Rational(spin)\n", " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", + " unicode_subscripts = list(\"₀₁₂₃₄₅₆₇₈₉\")\n", " return Particle(\n", - " name=f\"N({idx})({spin}{parity_symbol})\",\n", + " name=f\"N{unicode_subscripts[idx]}({spin}{parity_symbol})\",\n", " latex=Rf\"N_{idx}({spin.numerator}/{spin.denominator}^-)\",\n", " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", " mass=mass,\n", diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 3106976d..66a610f6 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -166,8 +166,9 @@ ") -> Particle:\n", " spin = sp.Rational(spin)\n", " parity_symbol = \"⁺\" if parity > 0 else \"⁻\"\n", + " unicode_subscripts = list(\"₀₁₂₃₄₅₆₇₈₉\")\n", " return Particle(\n", - " name=f\"N({idx})({spin}{parity_symbol})\",\n", + " name=f\"N{unicode_subscripts[idx]}({spin}{parity_symbol})\",\n", " latex=Rf\"N_{idx}({spin.numerator}/{spin.denominator}^-)\",\n", " pid=2024_05_00_00 + 100 * bool(parity + 1) + idx,\n", " mass=mass,\n", From 3bff6b48acfa8395a38098d68a699b01754b2bf1 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 12:15:43 +0200 Subject: [PATCH 71/92] MAINT: move expression unfolding to numerical function generation --- docs/report/030.ipynb | 40 ++++++++-------------------------------- docs/report/031.ipynb | 40 ++++++++-------------------------------- docs/report/032.ipynb | 26 +++++++++++++++++++++----- 3 files changed, 37 insertions(+), 69 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 8373d6e3..63a58085 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -509,20 +509,6 @@ "Math(aslatex(dynamics_expressions_bw))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "full_expression_bw = perform_cached_doit(model_bw.expression).xreplace(\n", - " dynamics_expressions_bw\n", - ")\n", - "sp.count_ops(full_expression_bw)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -637,20 +623,6 @@ "Math(aslatex(dynamics_expressions_fvector))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "full_expression_fvector = perform_cached_doit(model_fvector.expression).xreplace(\n", - " dynamics_expressions_fvector\n", - ")\n", - "sp.count_ops(full_expression_fvector)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -677,9 +649,11 @@ }, "outputs": [], "source": [ - "intensity_expr_bw = perform_cached_doit(full_expression_bw)\n", + "full_expression_bw = perform_cached_doit(model_bw.expression).xreplace(\n", + " dynamics_expressions_bw\n", + ")\n", "intensity_func_bw = create_parametrized_function(\n", - " expression=intensity_expr_bw,\n", + " expression=perform_cached_doit(full_expression_bw),\n", " backend=\"jax\",\n", " parameters=PARAMETERS_BW,\n", ")" @@ -693,9 +667,11 @@ }, "outputs": [], "source": [ - "intensity_expr_fvector = perform_cached_doit(full_expression_fvector)\n", + "full_expression_fvector = perform_cached_doit(model_fvector.expression).xreplace(\n", + " dynamics_expressions_fvector\n", + ")\n", "intensity_func_fvector = create_parametrized_function(\n", - " expression=intensity_expr_fvector,\n", + " expression=perform_cached_doit(full_expression_fvector),\n", " backend=\"jax\",\n", " parameters=PARAMETERS_F,\n", ")" diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 93b2fed8..e86459f3 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -517,20 +517,6 @@ "Math(aslatex(dynamics_expressions_bw))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "full_expression_bw = perform_cached_doit(model_bw.expression).xreplace(\n", - " dynamics_expressions_bw\n", - ")\n", - "sp.count_ops(full_expression_bw)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -645,20 +631,6 @@ "Math(aslatex(dynamics_expressions_fvector))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "full_expression_fvector = perform_cached_doit(model_fvector.expression).xreplace(\n", - " dynamics_expressions_fvector\n", - ")\n", - "sp.count_ops(full_expression_fvector)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -676,9 +648,11 @@ }, "outputs": [], "source": [ - "intensity_expr_bw = perform_cached_doit(full_expression_bw)\n", + "full_expression_bw = perform_cached_doit(model_bw.expression).xreplace(\n", + " dynamics_expressions_bw\n", + ")\n", "intensity_func_bw = create_parametrized_function(\n", - " expression=intensity_expr_bw,\n", + " expression=perform_cached_doit(full_expression_bw),\n", " backend=\"jax\",\n", " parameters=PARAMETERS_BW,\n", ")" @@ -692,9 +666,11 @@ }, "outputs": [], "source": [ - "intensity_expr_fvector = perform_cached_doit(full_expression_fvector)\n", + "full_expression_fvector = perform_cached_doit(model_fvector.expression).xreplace(\n", + " dynamics_expressions_fvector\n", + ")\n", "intensity_func_fvector = create_parametrized_function(\n", - " expression=intensity_expr_fvector,\n", + " expression=perform_cached_doit(full_expression_fvector),\n", " backend=\"jax\",\n", " parameters=PARAMETERS_F,\n", ")" diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 66a610f6..0abc276a 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -592,6 +592,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -639,6 +642,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -720,8 +726,8 @@ "metadata": {}, "outputs": [], "source": [ - "combined_expressions = {**K_expressions, **rho_expressions, **P_expressions}\n", - "F_exprs = F_vector.xreplace(combined_expressions)\n", + "parametrizations = {**K_expressions, **rho_expressions, **P_expressions}\n", + "F_exprs = F_vector.xreplace(parametrizations)\n", "F_exprs[0].simplify(doit=False)" ] }, @@ -740,6 +746,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -771,6 +780,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], @@ -786,9 +798,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ - "### Create Parametrized Function\n" + "### Create numerical functions" ] }, { @@ -802,7 +816,9 @@ "UNFOLDED_EXPRESSIONS_FVECTOR = []\n", "INTENSITY_FUNCS_FVECTOR = []\n", "for i in range(n_channels):\n", - " UNFOLDED_EXPRESSIONS_FVECTOR.append(FULL_EXPRESSIONS_FVECTOR[i].doit())\n", + " UNFOLDED_EXPRESSIONS_FVECTOR.append(\n", + " perform_cached_doit(FULL_EXPRESSIONS_FVECTOR[i])\n", + " )\n", " INTENSITY_FUNCS_FVECTOR.append(\n", " create_parametrized_function(\n", " expression=UNFOLDED_EXPRESSIONS_FVECTOR[i],\n", From 99af9f54931cc8f446e37cfd2ce77d29c3b4184b Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 12:21:00 +0200 Subject: [PATCH 72/92] ENH: define estimator sum with comprehension --- docs/report/032.ipynb | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 0abc276a..969f9021 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -1280,7 +1280,15 @@ }, "outputs": [], "source": [ - "combined_estimators = EstimatorSum(ESTIMATORS_F)" + "combined_estimators = EstimatorSum(\n", + " UnbinnedNLL(\n", + " INTENSITY_FUNCS_FVECTOR[i],\n", + " data=DATA[i],\n", + " phsp=PHSP[i],\n", + " backend=\"jax\",\n", + " )\n", + " for i in range(n_channels)\n", + ")" ] }, { From 290cdedd54f534ea40c4613ec2a4dd347e21c6ab Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 12:21:28 +0200 Subject: [PATCH 73/92] ENH: remove redundant `display` statements --- docs/report/032.ipynb | 84 ++++++++----------------------------------- 1 file changed, 15 insertions(+), 69 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 969f9021..16518fae 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -896,15 +896,6 @@ " )" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "re.match(r\"^m_\\d\\d$\", \"m_01\")" - ] - }, { "cell_type": "code", "execution_count": null, @@ -930,33 +921,6 @@ " PHSP.append(phsp)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "PHSP[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "INTENSITY_FUNCS_FVECTOR[0](PHSP[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "INTENSITY_FUNCS_FVECTOR[0].parameters" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1007,6 +971,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -1063,6 +1030,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -1127,16 +1097,19 @@ " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", " R\"g_{N_1(3/2^-),0}\": 2.8,\n", " R\"g_{N_1(3/2^-),1}\": 1.6,\n", - "}\n", - "INTENSITY_FUNCS_FVECTOR[0].parameters" + "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ - "hide-input" + "hide-input", + "scroll-input" ] }, "outputs": [], @@ -1199,6 +1172,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -1237,27 +1213,6 @@ "tags": [] }, "outputs": [], - "source": [ - "ESTIMATORS_F = []\n", - "for i in range(n_channels):\n", - " estimator_fvector = UnbinnedNLL(\n", - " INTENSITY_FUNCS_FVECTOR[i],\n", - " data=DATA[i],\n", - " phsp=PHSP[i],\n", - " backend=\"jax\",\n", - " )\n", - " ESTIMATORS_F.append(estimator_fvector)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], "source": [ "class EstimatorSum(Estimator):\n", " def __init__(self, estimators: Iterable[Estimator]) -> None:\n", @@ -1357,15 +1312,6 @@ "df.round(decimals=3)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fit_result" - ] - }, { "cell_type": "code", "execution_count": null, From 8e8960caad9c13cab4dcb9a3d15934b917ad2a8a Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 13:44:38 +0200 Subject: [PATCH 74/92] ENH(TR-032): compute histograms with JAX --- docs/report/032.ipynb | 130 +++++++++++++++++++++++++++--------------- 1 file changed, 83 insertions(+), 47 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 16518fae..4487cc9e 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -80,6 +80,7 @@ "import ampform\n", "import attrs\n", "import graphviz\n", + "import jax.numpy as jnp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", @@ -967,6 +968,45 @@ "### Weighted data with $F$ vector " ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Function for plotting histograms with JAX" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def fast_histogram(\n", + " data: jnp.ndarray,\n", + " weights: jnp.ndarray | None = None,\n", + " bins: int = 100,\n", + " density: bool | None = None,\n", + " fill: bool = True,\n", + " ax=plt,\n", + " **plot_kwargs,\n", + ") -> None:\n", + " bin_values, bin_edges = jnp.histogram(\n", + " data,\n", + " bins=bins,\n", + " density=density,\n", + " weights=weights,\n", + " )\n", + " if fill:\n", + " bin_rights = bin_edges[1:]\n", + " ax.fill_between(bin_rights, bin_values, step=\"pre\", **plot_kwargs)\n", + " else:\n", + " bin_mids = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + " ax.step(bin_mids, bin_values, **plot_kwargs)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -980,18 +1020,25 @@ }, "outputs": [], "source": [ + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.set_xlabel(R\"$m_{p\\eta/K\\Sigma}$ [GeV]\")\n", "for i in range(n_channels):\n", - " fig, ax = plt.subplots(figsize=(6, 5))\n", " intensity = np.real(INTENSITY_FUNCS_FVECTOR[i](PHSP[i]))\n", - " c = ax.hist(\n", - " np.real(PHSP[i][\"m_01\"]) ** 2,\n", - " bins=100,\n", + " fast_histogram(\n", + " np.real(PHSP[i][\"m_01\"]),\n", " weights=intensity,\n", + " alpha=0.5,\n", + " bins=200,\n", + " density=True,\n", + " label=f\"${DECAYS[i].child1.latex} {DECAYS[i].child2.latex}$\",\n", + " ax=ax,\n", " )\n", - " ax.set_xlabel(R\"$M^2\\left(\\eta p\\right)\\, \\mathrm{[(GeV/c)^2]}$\")\n", - " ax.set_ylabel(R\"Intensity [a.u.]\")\n", - " fig.tight_layout()\n", - " plt.show()" + "mass_pars = {k: v for k, v in new_parameters_fvector.items() if k.startswith(\"m_{\")}\n", + "for i, (k, v) in enumerate(mass_pars.items()):\n", + " ax.axvline(v, c=f\"C{i}\", label=f\"${k}$\", ls=\"dashed\")\n", + "ax.legend()\n", + "ax.set_ylim(0, None)\n", + "fig.show()" ] }, { @@ -1039,33 +1086,23 @@ }, "outputs": [], "source": [ + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.set_xlabel(R\"$m_{p\\eta/K\\Sigma}$ [GeV]\")\n", "for i in range(n_channels):\n", - " resonances = sorted(\n", - " MODELS[i].reaction_info.get_intermediate_particles(),\n", - " key=lambda p: p.mass,\n", - " )\n", - " evenly_spaced_interval = np.linspace(\n", - " 0, 1, len(INTENSITY_FUNCS_FVECTOR[i].parameters.items())\n", - " )\n", - " colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", - " fig, ax = plt.subplots(figsize=(9, 4))\n", - " ax.hist(\n", + " fast_histogram(\n", " np.real(DATA[i][\"m_01\"]),\n", - " bins=200,\n", " alpha=0.5,\n", + " bins=200,\n", " density=True,\n", + " label=f\"${DECAYS[i].child1.latex} {DECAYS[i].child2.latex}$\",\n", + " ax=ax,\n", " )\n", - " ax.set_xlabel(\"$m$ [GeV]\")\n", - " for (k, v), color in zip(new_parameters_fvector.items(), colors):\n", - " if k.startswith(\"m_{\"):\n", - " ax.axvline(\n", - " x=v,\n", - " linestyle=\"dotted\",\n", - " label=r\"$\" + k + \"$\",\n", - " color=color,\n", - " )\n", - " ax.legend()\n", - " plt.show()" + "mass_pars = {k: v for k, v in new_parameters_fvector.items() if k.startswith(\"m_{\")}\n", + "for i, (k, v) in enumerate(mass_pars.items()):\n", + " ax.axvline(v, c=f\"C{i}\", label=f\"${k}$\", ls=\"dashed\")\n", + "ax.legend()\n", + "ax.set_ylim(0, None)\n", + "fig.show()" ] }, { @@ -1114,16 +1151,12 @@ }, "outputs": [], "source": [ - "def indicate_masses(ax, function):\n", - " ax.set_xlabel(\"$m$ [GeV]\")\n", - " for (k, v), color_F in zip(function.parameters.items(), colors_F):\n", - " if k.startswith(\"m_{N\"):\n", - " ax.axvline(\n", - " x=v,\n", - " linestyle=\"dotted\",\n", - " label=r\"$\" + k + \"$\" \"(F vector)\",\n", - " color=color_F,\n", - " )\n", + "def indicate_masses(ax, intensity_func):\n", + " mass_pars = {\n", + " k: v for k, v in intensity_func.parameters.items() if k.startswith(\"m_{N\")\n", + " }\n", + " for i, (k, v) in enumerate(mass_pars.items()):\n", + " ax.axvline(v, c=f\"C{i}\", label=f\"${k}$\", ls=\"dashed\")\n", "\n", "\n", "def compare_model(\n", @@ -1133,23 +1166,25 @@ " function: Function[DataSample, np.ndarray],\n", " bins: int = 100,\n", "):\n", - " fig, ax = plt.subplots(figsize=(9, 4))\n", - " ax.hist(\n", + " fig, ax = plt.subplots(figsize=(9, 4), sharex=True)\n", + " fast_histogram(\n", " data[variable_name].real,\n", - " bins=bins,\n", " alpha=0.5,\n", - " label=\"data\",\n", + " bins=bins,\n", " density=True,\n", + " label=\"data\",\n", + " ax=ax,\n", " )\n", " intensities = function(phsp)\n", - " ax.hist(\n", + " fast_histogram(\n", " phsp[variable_name].real,\n", " weights=intensities,\n", " bins=bins,\n", - " histtype=\"step\",\n", " color=\"red\",\n", - " label=\"Fit model with $F$ vector\",\n", " density=True,\n", + " fill=False,\n", + " label=\"Fit model with $F$ vector\",\n", + " ax=ax,\n", " )\n", " indicate_masses(ax, function)\n", " ax.axvline(\n", @@ -1164,6 +1199,7 @@ " linestyle=\"dotted\",\n", " label=rf\"${DECAYS[1].child1.latex} \\, {DECAYS[1].child2.latex}$ threshold\",\n", " )\n", + " ax.set_ylim(0, None)\n", " ax.legend()\n", " fig.show()" ] From 14daa273f269352ca92cd1ba2f49b02e84a1c451 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 14:07:37 +0200 Subject: [PATCH 75/92] MAINT(TR-032): equalize notebook style with TR-031 --- docs/report/032.ipynb | 22 +++++++++++++++------- 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 4487cc9e..933f7566 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -71,6 +71,8 @@ "source": [ "from __future__ import annotations\n", "\n", + "import logging\n", + "import os\n", "import re\n", "from collections import defaultdict\n", "from functools import lru_cache\n", @@ -88,7 +90,7 @@ "import sympy as sp\n", "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", "from ampform.helicity import HelicityModel, ParameterValues\n", - "from ampform.io import aslatex\n", + "from ampform.io import aslatex, improve_latex_rendering\n", "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", "from attrs import define, field, frozen\n", @@ -111,7 +113,10 @@ "from tensorwaves.optimizer import Minuit2\n", "from tensorwaves.optimizer.callbacks import CSVSummary\n", "\n", - "_ = np.seterr(invalid=\"ignore\")" + "improve_latex_rendering()\n", + "logging.getLogger(\"absl\").setLevel(logging.ERROR)\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", + "plt.rc(\"font\", size=12)" ] }, { @@ -291,9 +296,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [ - "hide-input" - ] + "tags": [] }, "outputs": [], "source": [ @@ -597,7 +600,8 @@ "source_hidden": true }, "tags": [ - "hide-input" + "hide-input", + "scroll-input" ] }, "outputs": [], @@ -647,7 +651,8 @@ "source_hidden": true }, "tags": [ - "hide-input" + "hide-input", + "scroll-input" ] }, "outputs": [], @@ -1144,6 +1149,9 @@ "jupyter": { "source_hidden": true }, + "mystnb": { + "code_prompt_show": "Functions for comparing model to data" + }, "tags": [ "hide-input", "scroll-input" From ee34514ab6cd0e79e8548148f251c0a88c3b6288 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 14:16:52 +0200 Subject: [PATCH 76/92] ENH: improve sub-section orderin in fit section --- docs/report/032.ipynb | 213 ++++++++++++++++++++++++------------------ 1 file changed, 123 insertions(+), 90 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 933f7566..4202a253 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -109,7 +109,13 @@ ")\n", "from tensorwaves.estimator import UnbinnedNLL\n", "from tensorwaves.function.sympy import create_parametrized_function\n", - "from tensorwaves.interface import DataSample, Estimator, Function, ParameterValue\n", + "from tensorwaves.interface import (\n", + " DataSample,\n", + " Estimator,\n", + " FitResult,\n", + " Function,\n", + " ParameterValue,\n", + ")\n", "from tensorwaves.optimizer import Minuit2\n", "from tensorwaves.optimizer.callbacks import CSVSummary\n", "\n", @@ -1125,21 +1131,56 @@ "tags": [] }, "source": [ - "### Set initial parameters " + "### Estimator definition" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "initial_parameters = {\n", - " R\"m_{N_1(3/2^-)}\": 1.9,\n", - " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", - " R\"g_{N_1(3/2^-),0}\": 2.8,\n", - " R\"g_{N_1(3/2^-),1}\": 1.6,\n", - "}" + "class EstimatorSum(Estimator):\n", + " def __init__(self, estimators: Iterable[Estimator]) -> None:\n", + " self.__estimators = tuple(estimators)\n", + "\n", + " def __call__(self, parameters: Mapping[str, ParameterValue]) -> float:\n", + " return sum(estimator(parameters) for estimator in self.__estimators)\n", + "\n", + " def gradient(\n", + " self, parameters: Mapping[str, ParameterValue]\n", + " ) -> dict[str, ParameterValue]:\n", + " raise NotImplementedError" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "combined_estimators = EstimatorSum(\n", + " UnbinnedNLL(\n", + " INTENSITY_FUNCS_FVECTOR[i],\n", + " data=DATA[i],\n", + " phsp=PHSP[i],\n", + " backend=\"jax\",\n", + " )\n", + " for i in range(n_channels)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Initial parameters " ] }, { @@ -1212,6 +1253,20 @@ " fig.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "initial_parameters = {\n", + " R\"m_{N_1(3/2^-)}\": 1.9,\n", + " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", + " R\"g_{N_1(3/2^-),0}\": 2.8,\n", + " R\"g_{N_1(3/2^-),1}\": 1.6,\n", + "}" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1247,56 +1302,7 @@ "tags": [] }, "source": [ - "### Define estimator" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "class EstimatorSum(Estimator):\n", - " def __init__(self, estimators: Iterable[Estimator]) -> None:\n", - " self.__estimators = tuple(estimators)\n", - "\n", - " def __call__(self, parameters: Mapping[str, ParameterValue]) -> float:\n", - " return sum(estimator(parameters) for estimator in self.__estimators)\n", - "\n", - " def gradient(\n", - " self, parameters: Mapping[str, ParameterValue]\n", - " ) -> dict[str, ParameterValue]:\n", - " raise NotImplementedError" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "combined_estimators = EstimatorSum(\n", - " UnbinnedNLL(\n", - " INTENSITY_FUNCS_FVECTOR[i],\n", - " data=DATA[i],\n", - " phsp=PHSP[i],\n", - " backend=\"jax\",\n", - " )\n", - " for i in range(n_channels)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Optimized fit" + "### Optimize parameters" ] }, { @@ -1331,61 +1337,88 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { - "tags": [ - "hide-input" - ] + "tags": [] }, - "outputs": [], "source": [ - "original_parameters = {\n", - " **ORIGINAL_PARAMETERS_F[0],\n", - " **ORIGINAL_PARAMETERS_F[1],\n", - "}\n", - "df = pd.DataFrame({\n", - " f\"${p}$\": (\n", - " initial_parameters[p],\n", - " fit_result.parameter_values[p],\n", - " original_parameters[p],\n", - " )\n", - " for p in fit_result.parameter_values\n", - "}).T\n", - "df.columns = (\"initial\", \"fit result\", \"original\")\n", - "df.round(decimals=3)" + "### Fit quality check" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Functions for inspecting fit result" + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ - "n_real_par = fit_result.count_number_of_parameters(complex_twice=True)\n", - "n_events = len(next(iter(data.values())))\n", - "log_likelihood = -fit_result.estimator_value\n", - "log_likelihood" + "def compute_aic_bic(fit_result: FitResult) -> tuple[float, float]:\n", + " n_real_par = fit_result.count_number_of_parameters(complex_twice=True)\n", + " n_events = len(next(iter(data.values())))\n", + " log_likelihood = -fit_result.estimator_value\n", + " aic = 2 * n_real_par - 2 * log_likelihood\n", + " bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", + " return aic, bic\n", + "\n", + "\n", + "def compare_parameters(initial: dict, optimized: dict, expected: dict) -> pd.DataFrame:\n", + " parameters = sorted(set(initial) | set(optimized))\n", + " df = pd.DataFrame(\n", + " {\n", + " f\"${p}$\": (\n", + " f\"{initial[p]:.3g}\",\n", + " f\"{optimized[p]:.3g}\",\n", + " f\"{expected[p]:.3g}\",\n", + " f\"{100 * abs((optimized[p] - expected[p]) / expected[p]):.1f}%\",\n", + " )\n", + " for p in parameters\n", + " },\n", + " ).T\n", + " df.columns = (\"initial\", \"fit result\", \"expected\", \"deviation\")\n", + " return df" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "aic = 2 * n_real_par - 2 * log_likelihood\n", - "aic" + "compute_aic_bic(fit_result)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide-input" + ] + }, "outputs": [], "source": [ - "bic = n_real_par * np.log(n_events) - 2 * log_likelihood\n", - "bic" + "compare_parameters(\n", + " initial=initial_parameters,\n", + " optimized=fit_result.parameter_values,\n", + " expected={\n", + " **ORIGINAL_PARAMETERS_F[0],\n", + " **ORIGINAL_PARAMETERS_F[1],\n", + " },\n", + ")" ] } ], From 42eefdf5f3ef253457876e7015e55da3334f1cb4 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 14:24:28 +0200 Subject: [PATCH 77/92] ENH(TR-032): remove `CSVSummary` callback --- docs/report/032.ipynb | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 4202a253..c466d625 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -117,7 +117,6 @@ " ParameterValue,\n", ")\n", "from tensorwaves.optimizer import Minuit2\n", - "from tensorwaves.optimizer.callbacks import CSVSummary\n", "\n", "improve_latex_rendering()\n", "logging.getLogger(\"absl\").setLevel(logging.ERROR)\n", @@ -1313,10 +1312,7 @@ }, "outputs": [], "source": [ - "minuit2 = Minuit2(\n", - " callback=CSVSummary(\"fit_traceback.csv\"),\n", - " use_analytic_gradient=False,\n", - ")\n", + "minuit2 = Minuit2()\n", "fit_result = minuit2.optimize(combined_estimators, initial_parameters)\n", "fit_result" ] From 1b0a15dd6ed0d540b8ae68b774d5a5137d79d7e6 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 14:26:00 +0200 Subject: [PATCH 78/92] DX(TR-032): assert if fit succeeded --- docs/report/032.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index c466d625..a7bd55a9 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -1314,6 +1314,7 @@ "source": [ "minuit2 = Minuit2()\n", "fit_result = minuit2.optimize(combined_estimators, initial_parameters)\n", + "assert fit_result.minimum_valid\n", "fit_result" ] }, From 4dfb58f27a7d0790426d579a1d2cc56342cc973a Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 14:39:42 +0200 Subject: [PATCH 79/92] MAINT(TR-032): use list comprehensions --- docs/report/032.ipynb | 95 ++++++++++++++++--------------------------- 1 file changed, 34 insertions(+), 61 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index a7bd55a9..4854e6aa 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -743,77 +743,58 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "tags": [] }, - "outputs": [], "source": [ - "F_unfolded_exprs = np.array([perform_cached_doit(expr) for expr in F_exprs])" + "### Create numerical functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [ "hide-input" ] }, "outputs": [], "source": [ - "DYNAMICS_EXPRESSIONS_FVECTOR = []\n", - "for i in range(n_channels):\n", - " exprs = {\n", + "F_unfolded_exprs = np.array([perform_cached_doit(expr) for expr in F_exprs])\n", + "DYNAMICS_EXPRESSIONS_FVECTOR = [\n", + " {\n", " symbol: F_unfolded_exprs[i]\n", " for symbol, resonances in create_dynamics_symbol.collected_symbols.items()\n", " }\n", - " DYNAMICS_EXPRESSIONS_FVECTOR.append(exprs)\n", - "\n", - "MODELS_FVECTOR = []\n", - "for i in range(n_channels):\n", - " MODELS_FVECTOR.append(\n", - " attrs.evolve(\n", - " MODELS[i],\n", - " parameter_defaults=ParameterValues({\n", - " **MODELS[i].parameter_defaults,\n", - " **PARAMETERS_DEFAULTS,\n", - " }),\n", - " )\n", - " )" + " for i in range(n_channels)\n", + "]\n", + "MODELS_FVECTOR = [\n", + " attrs.evolve(\n", + " model,\n", + " parameter_defaults=ParameterValues({\n", + " **model.parameter_defaults,\n", + " **PARAMETERS_DEFAULTS,\n", + " }),\n", + " )\n", + " for model in MODELS\n", + "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [] }, "outputs": [], "source": [ - "FULL_EXPRESSIONS_FVECTOR = []\n", - "for i in range(n_channels):\n", - " FULL_EXPRESSIONS_FVECTOR.append(\n", - " perform_cached_doit(MODELS_FVECTOR[i].expression).xreplace(\n", - " DYNAMICS_EXPRESSIONS_FVECTOR[i]\n", - " )\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "### Create numerical functions" + "FULL_EXPRESSIONS_FVECTOR = [\n", + " perform_cached_doit(MODELS_FVECTOR[i].expression).xreplace(\n", + " DYNAMICS_EXPRESSIONS_FVECTOR[i]\n", + " )\n", + " for i in range(n_channels)\n", + "]" ] }, { @@ -824,19 +805,14 @@ }, "outputs": [], "source": [ - "UNFOLDED_EXPRESSIONS_FVECTOR = []\n", - "INTENSITY_FUNCS_FVECTOR = []\n", - "for i in range(n_channels):\n", - " UNFOLDED_EXPRESSIONS_FVECTOR.append(\n", - " perform_cached_doit(FULL_EXPRESSIONS_FVECTOR[i])\n", + "INTENSITY_FUNCS_FVECTOR = [\n", + " create_parametrized_function(\n", + " expression=perform_cached_doit(FULL_EXPRESSIONS_FVECTOR[i]),\n", + " backend=\"jax\",\n", + " parameters=MODELS_FVECTOR[i].parameter_defaults,\n", " )\n", - " INTENSITY_FUNCS_FVECTOR.append(\n", - " create_parametrized_function(\n", - " expression=UNFOLDED_EXPRESSIONS_FVECTOR[i],\n", - " backend=\"jax\",\n", - " parameters=MODELS_FVECTOR[i].parameter_defaults,\n", - " )\n", - " )" + " for i in range(n_channels)\n", + "]" ] }, { @@ -898,13 +874,10 @@ }, "outputs": [], "source": [ - "HELICITY_TRANSFORMERS = []\n", - "for i in range(n_channels):\n", - " HELICITY_TRANSFORMERS.append(\n", - " SympyDataTransformer.from_sympy(\n", - " MODELS_FVECTOR[i].kinematic_variables, backend=\"jax\"\n", - " )\n", - " )" + "HELICITY_TRANSFORMERS = [\n", + " SympyDataTransformer.from_sympy(model.kinematic_variables, backend=\"jax\")\n", + " for model in MODELS_FVECTOR\n", + "]" ] }, { From 6afb6eaa1ac355ee29ff148b3a2b7ffaeda347b7 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 14:43:37 +0200 Subject: [PATCH 80/92] FIX(TR-032): remove redundant `DataFrame` call --- docs/report/032.ipynb | 6 ------ 1 file changed, 6 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 4854e6aa..91cc4df5 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -1046,12 +1046,6 @@ " domain_transformer=HELICITY_TRANSFORMERS[i],\n", " )\n", " data_momenta = data_generator.generate(50_000, rng)\n", - " pd.DataFrame({\n", - " (k, label): np.transpose(v)[i]\n", - " for k, v in data_momenta.items()\n", - " for i, label in enumerate([\"E\", \"px\", \"py\", \"pz\"])\n", - " })\n", - " phsp = HELICITY_TRANSFORMERS[i](phsp_momenta)\n", " data = HELICITY_TRANSFORMERS[i](data_momenta)\n", " DATA.append(data)" ] From eaad1d2a837725585cb8d1ad474e5ae2f7328c97 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 14:54:00 +0200 Subject: [PATCH 81/92] FIX(TR-032): remove `m_0` etc from default parameters --- docs/report/032.ipynb | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 91cc4df5..3c9b27b2 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -89,7 +89,7 @@ "import qrules\n", "import sympy as sp\n", "from ampform.dynamics.builder import TwoBodyKinematicVariableSet\n", - "from ampform.helicity import HelicityModel, ParameterValues\n", + "from ampform.helicity import HelicityModel\n", "from ampform.io import aslatex, improve_latex_rendering\n", "from ampform.kinematics.phasespace import Kallen\n", "from ampform.sympy import perform_cached_doit, unevaluated\n", @@ -527,6 +527,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [] }, "outputs": [], @@ -534,7 +537,12 @@ "PARAMETERS_DEFAULTS = {}\n", "for model in MODELS:\n", " PARAMETERS_DEFAULTS.update(model.parameter_defaults)\n", - " del model" + " del model\n", + "PARAMETERS_DEFAULTS = {\n", + " par: value\n", + " for par, value in PARAMETERS_DEFAULTS.items()\n", + " if not re.match(r\"^m_\\d+$\", par.name)\n", + "}" ] }, { @@ -772,10 +780,7 @@ "MODELS_FVECTOR = [\n", " attrs.evolve(\n", " model,\n", - " parameter_defaults=ParameterValues({\n", - " **model.parameter_defaults,\n", - " **PARAMETERS_DEFAULTS,\n", - " }),\n", + " parameter_defaults=PARAMETERS_DEFAULTS,\n", " )\n", " for model in MODELS\n", "]" From 00d2280f200a55495c442ef38027aa9515c08056 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 15:01:33 +0200 Subject: [PATCH 82/92] MAINT(TR-032): remove redundant `DYNAMICS_FUNCS_FVECTOR` --- docs/report/032.ipynb | 37 ------------------------------------- 1 file changed, 37 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 3c9b27b2..46b3781d 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -910,43 +910,6 @@ " PHSP.append(phsp)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dynamics expressions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "DYNAMICS_EXPR_FVECTOR = []\n", - "for i in range(n_channels):\n", - " values, *_ = DYNAMICS_EXPRESSIONS_FVECTOR[i].values()\n", - " DYNAMICS_EXPR_FVECTOR.append(values)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "DYNAMICS_FUNCS_FVECTOR = []\n", - "for i in range(n_channels):\n", - " func = create_parametrized_function(\n", - " expression=DYNAMICS_EXPR_FVECTOR[i].doit(),\n", - " backend=\"jax\",\n", - " parameters=MODELS_FVECTOR[i].parameter_defaults,\n", - " )\n", - " DYNAMICS_FUNCS_FVECTOR.append(func)" - ] - }, { "cell_type": "markdown", "metadata": { From 665493dce7b51a3d3a0d899fd2a3ff2d48fa9104 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 15:05:28 +0200 Subject: [PATCH 83/92] DOC(TR-032): improve section organization "Generate data" --- docs/report/032.ipynb | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 46b3781d..ff49a8ec 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -861,14 +861,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generate data with $F$ vector" + "## Generate data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Generate phase space sample" + "### Phase space sample" ] }, { @@ -916,7 +916,7 @@ "tags": [] }, "source": [ - "### Weighted data with $F$ vector " + "### Toy data sample" ] }, { @@ -962,9 +962,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "jupyter": { - "source_hidden": true - }, "tags": [ "hide-input" ] From 56495fbedbcce403a32c9c950066408cdc00a483 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 15:06:33 +0200 Subject: [PATCH 84/92] =?UTF-8?q?MAINT(TR-032):=20rename=20`epsilon`=20to?= =?UTF-8?q?=20`=CE=B5`?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/report/032.ipynb | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index ff49a8ec..3e33bfba 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -894,7 +894,7 @@ "outputs": [], "source": [ "PHSP = []\n", - "epsilon = 1e-8\n", + "ε = 1e-8\n", "for i in range(n_channels):\n", " rng = TFUniformRealNumberGenerator(seed=0)\n", " phsp_generator = TFPhaseSpaceGenerator(\n", @@ -904,9 +904,7 @@ " phsp_momenta = phsp_generator.generate(100_000, rng)\n", " phsp = HELICITY_TRANSFORMERS[i](phsp_momenta)\n", " phsp = {k: v.real for k, v in phsp.items()}\n", - " phsp = {\n", - " k: v + epsilon * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()\n", - " }\n", + " phsp = {k: v + ε * 1j if re.match(r\"^m_\\d\\d$\", k) else v for k, v in phsp.items()}\n", " PHSP.append(phsp)" ] }, From 77df570d1952ca076cd8218f0e5cdfca154769e0 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 15:12:47 +0200 Subject: [PATCH 85/92] ENH(TR-032): set original parameters just before data generation --- docs/report/032.ipynb | 71 ++++++++++++++++++------------------------- 1 file changed, 30 insertions(+), 41 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 3e33bfba..1b3c98c5 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -820,43 +820,6 @@ "]" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Update parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "m_res = 1.82\n", - "g_res_ch0 = 1.8\n", - "g_res_ch1 = 2.5\n", - "\n", - "new_parameters_fvector = {\n", - " R\"m_{N_1(3/2^-)}\": 1.71,\n", - " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", - " R\"g_{N_1(3/2^-),0}\": g_res_ch0,\n", - " R\"g_{N_1(3/2^-),1}\": g_res_ch1,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for i in range(n_channels):\n", - " INTENSITY_FUNCS_FVECTOR[i].update_parameters(new_parameters_fvector)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -910,11 +873,9 @@ }, { "cell_type": "markdown", - "metadata": { - "tags": [] - }, + "metadata": {}, "source": [ - "### Toy data sample" + "## Set parameters for toy model" ] }, { @@ -956,10 +917,29 @@ " ax.step(bin_mids, bin_values, **plot_kwargs)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_parameters_fvector = {\n", + " R\"m_{N_1(3/2^-)}\": 1.71,\n", + " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", + " R\"g_{N_1(3/2^-),0}\": 1.8,\n", + " R\"g_{N_1(3/2^-),1}\": 2.5,\n", + "}\n", + "for func in INTENSITY_FUNCS_FVECTOR:\n", + " func.update_parameters(new_parameters_fvector)" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -987,6 +967,15 @@ "fig.show()" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Toy data sample" + ] + }, { "cell_type": "code", "execution_count": null, From 6bbc48bf65d198c1b69568f8911e2e2c4b8c2449 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 15:25:49 +0200 Subject: [PATCH 86/92] DOC(TR-032): indicate thresholds in toy model parameter plot --- docs/report/032.ipynb | 72 ++++++++++++++++++++++++++----------------- 1 file changed, 44 insertions(+), 28 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 1b3c98c5..ee48b089 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -519,6 +519,10 @@ " child1: Particle\n", " child2: Particle\n", "\n", + " @property\n", + " def children(self) -> tuple[Particle, Particle]:\n", + " return self.child1, self.child2\n", + "\n", "\n", "DECAYS = tuple(get_decay_products(m.reaction_info) for m in MODELS)" ] @@ -917,6 +921,37 @@ " ax.step(bin_mids, bin_values, **plot_kwargs)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "mystnb": { + "code_prompt_show": "Functions for indicated resonances and thresholds" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [], + "source": [ + "def indicate_masses(ax, intensity_func):\n", + " mass_pars = {\n", + " k: v for k, v in intensity_func.parameters.items() if k.startswith(\"m_{N\")\n", + " }\n", + " for i, (k, v) in enumerate(mass_pars.items()):\n", + " ax.axvline(v, c=f\"C{i + n_channels}\", label=f\"${k}$\", ls=\"dashed\")\n", + "\n", + "\n", + "def indicate_thresholds(ax) -> None:\n", + " for i, decay in enumerate(DECAYS):\n", + " m_thr = sum(p.mass for p in decay.children)\n", + " label = f\"${'+'.join(f'm_{{{p.latex}}}' for p in decay.children)}$\"\n", + " ax.axvline(m_thr, c=f\"C{i}\", label=label, ls=\"dotted\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -947,6 +982,7 @@ "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.set_title(\"Model rendering from phase space\")\n", "ax.set_xlabel(R\"$m_{p\\eta/K\\Sigma}$ [GeV]\")\n", "for i in range(n_channels):\n", " intensity = np.real(INTENSITY_FUNCS_FVECTOR[i](PHSP[i]))\n", @@ -956,12 +992,11 @@ " alpha=0.5,\n", " bins=200,\n", " density=True,\n", - " label=f\"${DECAYS[i].child1.latex} {DECAYS[i].child2.latex}$\",\n", + " label=f\"${' '.join(p.latex for p in DECAYS[i].children)}$\",\n", " ax=ax,\n", " )\n", - "mass_pars = {k: v for k, v in new_parameters_fvector.items() if k.startswith(\"m_{\")}\n", - "for i, (k, v) in enumerate(mass_pars.items()):\n", - " ax.axvline(v, c=f\"C{i}\", label=f\"${k}$\", ls=\"dashed\")\n", + "indicate_thresholds(ax)\n", + "indicate_masses(ax, INTENSITY_FUNCS_FVECTOR[i])\n", "ax.legend()\n", "ax.set_ylim(0, None)\n", "fig.show()" @@ -1016,6 +1051,7 @@ "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(9, 4))\n", + "ax.set_title(\"Toy data sample\")\n", "ax.set_xlabel(R\"$m_{p\\eta/K\\Sigma}$ [GeV]\")\n", "for i in range(n_channels):\n", " fast_histogram(\n", @@ -1023,12 +1059,11 @@ " alpha=0.5,\n", " bins=200,\n", " density=True,\n", - " label=f\"${DECAYS[i].child1.latex} {DECAYS[i].child2.latex}$\",\n", + " label=f\"${' '.join(p.latex for p in DECAYS[i].children)}$\",\n", " ax=ax,\n", " )\n", - "mass_pars = {k: v for k, v in new_parameters_fvector.items() if k.startswith(\"m_{\")}\n", - "for i, (k, v) in enumerate(mass_pars.items()):\n", - " ax.axvline(v, c=f\"C{i}\", label=f\"${k}$\", ls=\"dashed\")\n", + "indicate_thresholds(ax)\n", + "indicate_masses(ax, INTENSITY_FUNCS_FVECTOR[i])\n", "ax.legend()\n", "ax.set_ylim(0, None)\n", "fig.show()" @@ -1118,14 +1153,6 @@ }, "outputs": [], "source": [ - "def indicate_masses(ax, intensity_func):\n", - " mass_pars = {\n", - " k: v for k, v in intensity_func.parameters.items() if k.startswith(\"m_{N\")\n", - " }\n", - " for i, (k, v) in enumerate(mass_pars.items()):\n", - " ax.axvline(v, c=f\"C{i}\", label=f\"${k}$\", ls=\"dashed\")\n", - "\n", - "\n", "def compare_model(\n", " variable_name: str,\n", " data: DataSample,\n", @@ -1153,19 +1180,8 @@ " label=\"Fit model with $F$ vector\",\n", " ax=ax,\n", " )\n", + " indicate_thresholds(ax)\n", " indicate_masses(ax, function)\n", - " ax.axvline(\n", - " DECAYS[0].child1.mass + DECAYS[0].child2.mass,\n", - " color=\"grey\",\n", - " linestyle=\"dotted\",\n", - " label=rf\"${DECAYS[0].child1.latex} \\, {DECAYS[0].child2.latex}$ threshold\",\n", - " )\n", - " ax.axvline(\n", - " DECAYS[1].child1.mass + DECAYS[1].child2.mass,\n", - " color=\"grey\",\n", - " linestyle=\"dotted\",\n", - " label=rf\"${DECAYS[1].child1.latex} \\, {DECAYS[1].child2.latex}$ threshold\",\n", - " )\n", " ax.set_ylim(0, None)\n", " ax.legend()\n", " fig.show()" From 85c63582a8d459286bf55fb87b5eb4be9074e115 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 15:26:24 +0200 Subject: [PATCH 87/92] =?UTF-8?q?ENH(TR-032):=20improve=20coupling=20value?= =?UTF-8?q?s=20for=20clearer=20Flatt=C3=A9=20effect?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/report/032.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index ee48b089..5a7a2c03 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -961,8 +961,8 @@ "new_parameters_fvector = {\n", " R\"m_{N_1(3/2^-)}\": 1.71,\n", " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", - " R\"g_{N_1(3/2^-),0}\": 1.8,\n", - " R\"g_{N_1(3/2^-),1}\": 2.5,\n", + " R\"g_{N_1(3/2^-),0}\": 3.2,\n", + " R\"g_{N_1(3/2^-),1}\": 1.5,\n", "}\n", "for func in INTENSITY_FUNCS_FVECTOR:\n", " func.update_parameters(new_parameters_fvector)" From 35c75543283015ed699467234b05d43788dc1221 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 16:13:16 +0200 Subject: [PATCH 88/92] ENH(TR-032): combine parameter comparison plot in one figure --- docs/report/032.ipynb | 80 ++++++++++++++++++++++++++----------------- 1 file changed, 48 insertions(+), 32 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 5a7a2c03..3dff60d0 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -95,7 +95,6 @@ "from ampform.sympy import perform_cached_doit, unevaluated\n", "from attrs import define, field, frozen\n", "from IPython.display import Math, display\n", - "from matplotlib import cm\n", "from qrules.particle import Particle, ParticleCollection\n", "from qrules.transition import ReactionInfo\n", "from sympy import Abs\n", @@ -937,18 +936,21 @@ }, "outputs": [], "source": [ - "def indicate_masses(ax, intensity_func):\n", + "def indicate_masses(ax, intensity_func, set_labels: bool = True):\n", " mass_pars = {\n", " k: v for k, v in intensity_func.parameters.items() if k.startswith(\"m_{N\")\n", " }\n", " for i, (k, v) in enumerate(mass_pars.items()):\n", - " ax.axvline(v, c=f\"C{i + n_channels}\", label=f\"${k}$\", ls=\"dashed\")\n", + " label = f\"${k}$\" if set_labels else None\n", + " ax.axvline(v, c=f\"C{i + n_channels}\", label=label, ls=\"dashed\")\n", "\n", "\n", - "def indicate_thresholds(ax) -> None:\n", + "def indicate_thresholds(ax, set_labels: bool = True) -> None:\n", " for i, decay in enumerate(DECAYS):\n", " m_thr = sum(p.mass for p in decay.children)\n", - " label = f\"${'+'.join(f'm_{{{p.latex}}}' for p in decay.children)}$\"\n", + " label = None\n", + " if set_labels:\n", + " label = f\"${'+'.join(f'm_{{{p.latex}}}' for p in decay.children)}$\"\n", " ax.axvline(m_thr, c=f\"C{i}\", label=label, ls=\"dotted\")" ] }, @@ -1153,38 +1155,58 @@ }, "outputs": [], "source": [ - "def compare_model(\n", + "def compare_models(functions: list[Function], title: str, bins: int = 100):\n", + " fig, axes = plt.subplots(figsize=(8.5, 4.5), nrows=2, sharex=True)\n", + " axes[0].set_title(title)\n", + " for ax in axes:\n", + " ax.set_yticks([])\n", + " for i in range(n_channels):\n", + " _plot_comparison(\n", + " axes[i],\n", + " decay_id=i,\n", + " variable_name=\"m_01\",\n", + " function=functions[i],\n", + " bins=bins,\n", + " color=f\"C{i}\",\n", + " legend=(i == 1),\n", + " )\n", + " fig.legend()\n", + " fig.tight_layout()\n", + " fig.show()\n", + "\n", + "\n", + "def _plot_comparison(\n", + " ax,\n", + " decay_id: int,\n", " variable_name: str,\n", - " data: DataSample,\n", - " phsp: DataSample,\n", " function: Function[DataSample, np.ndarray],\n", - " bins: int = 100,\n", + " bins: int,\n", + " color: str,\n", + " legend: bool,\n", "):\n", - " fig, ax = plt.subplots(figsize=(9, 4), sharex=True)\n", + " phsp = PHSP[decay_id]\n", " fast_histogram(\n", - " data[variable_name].real,\n", + " DATA[decay_id][variable_name].real,\n", " alpha=0.5,\n", " bins=bins,\n", + " color=color,\n", " density=True,\n", - " label=\"data\",\n", + " label=f\"Data ${' '.join(p.latex for p in DECAYS[decay_id].children)}$\",\n", " ax=ax,\n", " )\n", - " intensities = function(phsp)\n", " fast_histogram(\n", " phsp[variable_name].real,\n", - " weights=intensities,\n", + " weights=function(phsp),\n", " bins=bins,\n", " color=\"red\",\n", " density=True,\n", " fill=False,\n", - " label=\"Fit model with $F$ vector\",\n", + " label=\"Fit model\" if legend else None,\n", " ax=ax,\n", " )\n", - " indicate_thresholds(ax)\n", - " indicate_masses(ax, function)\n", - " ax.set_ylim(0, None)\n", - " ax.legend()\n", - " fig.show()" + " indicate_thresholds(ax, set_labels=legend)\n", + " indicate_masses(ax, function, set_labels=legend)\n", + " ax.set_ylim(0, None)" ] }, { @@ -1216,18 +1238,9 @@ "source": [ "ORIGINAL_PARAMETERS_F = []\n", "for i in range(n_channels):\n", - " resonances = sorted(\n", - " MODELS[i].reaction_info.get_intermediate_particles(),\n", - " key=lambda p: p.mass,\n", - " )\n", - " evenly_spaced_interval = np.linspace(\n", - " 0, 1, len(INTENSITY_FUNCS_FVECTOR[i].parameters.items())\n", - " )\n", - " colors_F = [cm.rainbow(x) for x in evenly_spaced_interval]\n", - " original_parameters = INTENSITY_FUNCS_FVECTOR[i].parameters\n", - " ORIGINAL_PARAMETERS_F.append(original_parameters)\n", + " ORIGINAL_PARAMETERS_F.append(dict(INTENSITY_FUNCS_FVECTOR[i].parameters))\n", " INTENSITY_FUNCS_FVECTOR[i].update_parameters(initial_parameters)\n", - " compare_model(\"m_01\", DATA[i], PHSP[i], INTENSITY_FUNCS_FVECTOR[i])" + "compare_models(INTENSITY_FUNCS_FVECTOR, title=\"Model with starting parameters\")" ] }, { @@ -1257,6 +1270,9 @@ "cell_type": "code", "execution_count": null, "metadata": { + "jupyter": { + "source_hidden": true + }, "tags": [ "hide-input" ] @@ -1265,7 +1281,7 @@ "source": [ "for i in range(n_channels):\n", " INTENSITY_FUNCS_FVECTOR[i].update_parameters(fit_result.parameter_values)\n", - " compare_model(\"m_01\", DATA[i], PHSP[i], INTENSITY_FUNCS_FVECTOR[i])" + "compare_models(INTENSITY_FUNCS_FVECTOR, title=\"Model with optimized parameters\")" ] }, { From dd579578e1127d9eccd3fb5267c3be2589486b0d Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 16:36:25 +0200 Subject: [PATCH 89/92] DOC: improve HTML rendering --- docs/report/030.ipynb | 34 ++++++++++++++++++++++++++++------ docs/report/031.ipynb | 20 ++++++++++++++++---- docs/report/032.ipynb | 17 ++++++++++++++++- 3 files changed, 60 insertions(+), 11 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 63a58085..256af61a 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -23,7 +23,7 @@ ":::{card} Sub-intensities of P-vector amplitude model\n", "TR-030\n", "^^^\n", - "Sub-intensity plots for a model with $K$-matrix ($P$-vector) dynamics. Also includes an investigation of phases in a $P$-vector lineshape.\n", + "Sub-intensity plots for a model with $P$-vector dynamics. Also includes an investigation of phases in a $P$-vector lineshape.\n", "+++\n", "🚧 [compwa.github.io#278](https://github.com/ComPWA/compwa.github.io/pull/278)\n", ":::\n", @@ -286,8 +286,8 @@ "model_builder.config.scalar_initial_state_mass = True\n", "model_builder.config.stable_final_state_ids = [0, 1, 2]\n", "create_dynamics_symbol = DynamicsSymbolBuilder()\n", - "for name in reaction.get_intermediate_particles().names:\n", - " model_builder.set_dynamics(name, create_dynamics_symbol)\n", + "for resonance in reaction.get_intermediate_particles():\n", + " model_builder.set_dynamics(resonance.name, create_dynamics_symbol)\n", "model = model_builder.formulate()\n", "model.intensity.cleanup()" ] @@ -353,6 +353,17 @@ "### Phasespace factor" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + ":::{seealso}\n", + "**[TR-026](./026.ipynb)** and **[TR-027](./027.ipynb)** on analyticity and Riemann sheets.\n", + ":::" + ] + }, { "cell_type": "code", "execution_count": null, @@ -490,7 +501,8 @@ "source_hidden": true }, "tags": [ - "hide-input" + "hide-input", + "full-width" ] }, "outputs": [], @@ -690,7 +702,13 @@ "cell_type": "code", "execution_count": null, "metadata": { - "tags": [] + "mystnb": { + "code_prompt_show": "Breit–Wigner parametrization" + }, + "tags": [ + "full-width", + "hide-input" + ] }, "outputs": [], "source": [ @@ -702,8 +720,12 @@ "cell_type": "code", "execution_count": null, "metadata": { + "mystnb": { + "code_prompt_show": "F-vector parametrization" + }, "tags": [ - "full-width" + "full-width", + "hide-input" ] }, "outputs": [], diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index e86459f3..083d1f7e 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -36,7 +36,7 @@ "tags": [] }, "source": [ - "# P-vector model fit" + "# P-vector model fit, single channel" ] }, { @@ -294,8 +294,8 @@ "model_builder.config.scalar_initial_state_mass = True\n", "model_builder.config.stable_final_state_ids = [0, 1, 2]\n", "create_dynamics_symbol = DynamicsSymbolBuilder()\n", - "for name in reaction.get_intermediate_particles().names:\n", - " model_builder.set_dynamics(name, create_dynamics_symbol)\n", + "for resonance in reaction.get_intermediate_particles():\n", + " model_builder.set_dynamics(resonance.name, create_dynamics_symbol)\n", "model = model_builder.formulate()\n", "model.intensity.cleanup()" ] @@ -361,6 +361,17 @@ "### Phasespace factor" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + ":::{seealso}\n", + "**[TR-026](./026.ipynb)** and **[TR-027](./027.ipynb)** on analyticity and Riemann sheets.\n", + ":::" + ] + }, { "cell_type": "code", "execution_count": null, @@ -498,7 +509,8 @@ "source_hidden": true }, "tags": [ - "hide-input" + "hide-input", + "full-width" ] }, "outputs": [], diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 3dff60d0..c8b4de52 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -378,6 +378,17 @@ "### Phasespace factor" ] }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + ":::{seealso}\n", + "**[TR-026](./026.ipynb)** and **[TR-027](./027.ipynb)** on analyticity and Riemann sheets.\n", + ":::" + ] + }, { "cell_type": "code", "execution_count": null, @@ -745,7 +756,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [ + "full-width" + ] + }, "outputs": [], "source": [ "parametrizations = {**K_expressions, **rho_expressions, **P_expressions}\n", From 3e2649a5fd79dfb9af579926958ce0af1a30cba2 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 16:40:54 +0200 Subject: [PATCH 90/92] MAINT: rename `new_parameters` to `toy_parameters` --- docs/report/030.ipynb | 18 +++++++++--------- docs/report/031.ipynb | 16 ++++++++-------- docs/report/032.ipynb | 4 ++-- 3 files changed, 19 insertions(+), 19 deletions(-) diff --git a/docs/report/030.ipynb b/docs/report/030.ipynb index 256af61a..bb0bdc35 100644 --- a/docs/report/030.ipynb +++ b/docs/report/030.ipynb @@ -834,14 +834,14 @@ }, "outputs": [], "source": [ - "new_parameters_bw = {\n", + "toy_parameters_bw = {\n", " R\"m_{N_1(3/2^-)}\": m_res1,\n", " R\"m_{N_2(3/2^-)}\": m_res2,\n", " R\"\\Gamma_{N_1(3/2^-)}\": g_res1 / m_res1,\n", " R\"\\Gamma_{N_2(3/2^-)}\": g_res2 / m_res2,\n", "}\n", - "dynamics_func_bw.update_parameters(new_parameters_bw)\n", - "intensity_func_bw.update_parameters(new_parameters_bw)" + "dynamics_func_bw.update_parameters(toy_parameters_bw)\n", + "intensity_func_bw.update_parameters(toy_parameters_bw)" ] }, { @@ -852,7 +852,7 @@ }, "outputs": [], "source": [ - "new_parameters_fvector = {\n", + "toy_parameters_fvector = {\n", " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", " R\"\\beta_{N_2(3/2^-)}\": 1 + 0j,\n", " R\"m_{N_1(3/2^-)}\": m_res1,\n", @@ -860,8 +860,8 @@ " R\"g_{N_1(3/2^-)}\": g_res1,\n", " R\"g_{N_2(3/2^-)}\": g_res2,\n", "}\n", - "dynamics_func_fvector.update_parameters(new_parameters_fvector)\n", - "intensity_func_fvector.update_parameters(new_parameters_fvector)" + "dynamics_func_fvector.update_parameters(toy_parameters_fvector)\n", + "intensity_func_fvector.update_parameters(toy_parameters_fvector)" ] }, { @@ -916,11 +916,11 @@ "\n", "\n", "def set_parameters_to_zero(func: ParametrizedFunction, name_pattern: str) -> None:\n", - " new_parameters = dict(func.parameters)\n", + " toy_parameters = dict(func.parameters)\n", " for par_name in func.parameters:\n", " if re.match(name_pattern, par_name) is not None:\n", - " new_parameters[par_name] = 0\n", - " func.update_parameters(new_parameters)" + " toy_parameters[par_name] = 0\n", + " func.update_parameters(toy_parameters)" ] }, { diff --git a/docs/report/031.ipynb b/docs/report/031.ipynb index 083d1f7e..57cb8c89 100644 --- a/docs/report/031.ipynb +++ b/docs/report/031.ipynb @@ -744,7 +744,7 @@ }, "outputs": [], "source": [ - "new_parameters_bw = {\n", + "toy_parameters_bw = {\n", " R\"m_{N_1(1/2^-)}\": 1.65,\n", " R\"m_{N_2(1/2^-)}\": 1.75,\n", " R\"m_{N_1(3/2^-)}\": 1.85,\n", @@ -754,7 +754,7 @@ " R\"\\Gamma_{N_1(3/2^-)}\": 1 / 1.85,\n", " R\"\\Gamma_{N_2(3/2^-)}\": 1 / 1.9,\n", "}\n", - "intensity_func_bw.update_parameters(new_parameters_bw)" + "intensity_func_bw.update_parameters(toy_parameters_bw)" ] }, { @@ -765,7 +765,7 @@ }, "outputs": [], "source": [ - "new_parameters_fvector = {\n", + "toy_parameters_fvector = {\n", " R\"\\beta_{N_1(1/2^-)}\": 1 + 0j,\n", " R\"\\beta_{N_2(1/2^-)}\": 1 + 0j,\n", " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", @@ -779,7 +779,7 @@ " R\"g_{N_1(3/2^-)}\": 1,\n", " R\"g_{N_2(3/2^-)}\": 1,\n", "}\n", - "intensity_func_fvector.update_parameters(new_parameters_fvector)" + "intensity_func_fvector.update_parameters(toy_parameters_fvector)" ] }, { @@ -825,11 +825,11 @@ "\n", "\n", "def set_parameters_to_zero(func: ParametrizedFunction, name_pattern: str) -> None:\n", - " new_parameters = dict(func.parameters)\n", + " toy_parameters = dict(func.parameters)\n", " for par_name in func.parameters:\n", " if re.match(name_pattern, par_name) is not None:\n", - " new_parameters[par_name] = 0\n", - " func.update_parameters(new_parameters)" + " toy_parameters[par_name] = 0\n", + " func.update_parameters(toy_parameters)" ] }, { @@ -1063,7 +1063,7 @@ " density=True,\n", " ax=ax,\n", ")\n", - "mass_parameters = {p: v for p, v in new_parameters_bw.items() if p.startswith(\"m_{\")}\n", + "mass_parameters = {p: v for p, v in toy_parameters_bw.items() if p.startswith(\"m_{\")}\n", "evenly_spaced_interval = np.linspace(0, 1, num=len(mass_parameters))\n", "colors = [cm.rainbow(x) for x in evenly_spaced_interval]\n", "ax.set_xlabel(\"$m$ [GeV]\")\n", diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index c8b4de52..1715a63d 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -975,14 +975,14 @@ "metadata": {}, "outputs": [], "source": [ - "new_parameters_fvector = {\n", + "toy_parameters = {\n", " R\"m_{N_1(3/2^-)}\": 1.71,\n", " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", " R\"g_{N_1(3/2^-),0}\": 3.2,\n", " R\"g_{N_1(3/2^-),1}\": 1.5,\n", "}\n", "for func in INTENSITY_FUNCS_FVECTOR:\n", - " func.update_parameters(new_parameters_fvector)" + " func.update_parameters(toy_parameters)" ] }, { From d8f368482dfdec60b573f0d10ef897c9c0be1f58 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 16:44:27 +0200 Subject: [PATCH 91/92] MAINT: update notebook kernels of affected notebooks --- docs/report/000.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/report/000.ipynb b/docs/report/000.ipynb index 7cb450e6..ccf08615 100644 --- a/docs/report/000.ipynb +++ b/docs/report/000.ipynb @@ -918,7 +918,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" }, "orphan": true }, From 1bf0249ac06db3ede6bb3dee5b3cacaeb9f74a31 Mon Sep 17 00:00:00 2001 From: Remco de Boer <29308176+redeboer@users.noreply.github.com> Date: Fri, 24 May 2024 16:44:51 +0200 Subject: [PATCH 92/92] FIX: do not fit `beta` in coupled fit --- docs/report/032.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/report/032.ipynb b/docs/report/032.ipynb index 1715a63d..1bdf2969 100644 --- a/docs/report/032.ipynb +++ b/docs/report/032.ipynb @@ -976,8 +976,8 @@ "outputs": [], "source": [ "toy_parameters = {\n", - " R\"m_{N_1(3/2^-)}\": 1.71,\n", " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", + " R\"m_{N_1(3/2^-)}\": 1.71,\n", " R\"g_{N_1(3/2^-),0}\": 3.2,\n", " R\"g_{N_1(3/2^-),1}\": 1.5,\n", "}\n", @@ -1232,7 +1232,6 @@ "source": [ "initial_parameters = {\n", " R\"m_{N_1(3/2^-)}\": 1.9,\n", - " R\"\\beta_{N_1(3/2^-)}\": 1 + 0j,\n", " R\"g_{N_1(3/2^-),0}\": 2.8,\n", " R\"g_{N_1(3/2^-),1}\": 1.6,\n", "}"