{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# GolemFlavor Tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we will generate a fake measured flavor composition using a multivariate Gaussian distribution and sample from it using the [emcee](https://emcee.readthedocs.io/) MCMC algorithm." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function\n", "\n", "from functools import partial\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Astrophysical Neutrino Flavor Mixing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Source Flavor Composition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The origins and accelerations mechanisms of astrophysically produced neutrinos is still an active puzzle, and is part of a bigger question on the origins of ultra-high-energy cosmic rays. The new but very active field of neutrino flavor physics can be used as a powerful probe to help idenfify these sources. The most common hypothesis of the neutrino flavor composition at the source is one produced by the decay of a pion, which results in the following source composition:\n", "\n", "$$\\pi\\:\\text{decay}\\rightarrow\\left(f_e:f_\\mu:f_\\tau\\right)_\\text{S}=\\left(1:2:0\\right)_\\text{S}$$\n", "\n", "where $f_\\alpha$ is the flavor composition of a neutrino with flavor $\\alpha\\in\\{e,\\mu,\\tau\\}$ and the subscript S represents that this is the flavor composition at the source. In the code below we normalize this to 1 for later calculations." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source composition = (1.00 : 0.00 : 0.00)\n" ] } ], "source": [ "from golemflavor.fr import normalize_fr\n", "\n", "source_composition = normalize_fr((1, 0, 0))\n", "print('Source composition = ({:.2f} : {:.2f} : {:.2f})'.format(*source_composition))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neutrino Mixing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the three massive neutrinos, the flavor eigenstates of the neutrino $|\\nu_\\alpha>$, $\\alpha\\in\\{e,\\mu,\\tau\\}$, are related to the mass eigenstates $|\\nu_i>$, $i\\in\\{1,2,3\\}$ via a unitary mixing matrix $U_{\\alpha i}$ known as the PMNS matrix:\n", " \n", "$$ |\\nu_\\alpha>=\\sum^3_{i=1}U^*_{\\alpha i}|\\nu_i> $$\n", "\n", "The determination of the values of this mixing matrix is currently a world-wide effort. We can import values of this mixing from GolemFlavor which are taken from a [global fit to world neutrino data]():" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mixing Matrix =\n", "[[ 0.82327921+0.j 0.54796108+0.j -0.09913534+0.11010079j]\n", " [-0.30340559+0.06889398j 0.59033699+0.0458547j 0.74336952+0.j ]\n", " [ 0.47090947+0.06045075j -0.58950774+0.04023502j 0.65226662+0.j ]]\n" ] } ], "source": [ "from golemflavor.fr import NUFIT_U\n", "\n", "print('Mixing Matrix =\\n{}'.format(NUFIT_U))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This mixing matrix says that neutrinos can oscillation from one flavor state $\\alpha\\in\\{e,\\mu,\\tau\\}$ to another $\\beta\\in\\{e,\\mu,\\tau\\}$ as a function of the propagation distance. The oscillation probability gives the probability that a neutrino produced in a flavor state $\\alpha$ is then detected in a flavor state $\\beta$ after a propagation distance $L$:\n", "\n", "$$\n", "\\begin{align}\n", " P_{\\nu_\\alpha\\rightarrow\\nu_\\beta}\\left(L\\right) &= \\mid<\\nu_\\beta\\left(L\\right)|\\nu_\\alpha\\left(0\\right)>\\mid^2\\\\\n", " &=\\mid\\sum_{i=1}^3U_{\\beta i}U_{\\alpha i}^*e^{-i\\frac{m_i^2L}{2E}}\\mid^2\n", "\\end{align}\n", "$$\n", "\n", "where $\\Delta m_{ij}^2=m_i^2-m_j^2$ is the mass-squared differences and $E$ is the neutrino energy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Measured Flavor Composition" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once an astrophysical neutrino escapes the source it was produced from, they are free to propagate in the vacuum. Astrophysical neutrinos have $\\mathcal{O}(\\text{Mpc})$ or higher baselines, large enough that the mass eigenstates completely decouple ($L\\rightarrow\\infty$). This is useful for us, because the above oscillation probability simplifies so that:\n", "\n", "$$\n", "\\begin{align}\n", " \\phi_{i,\\oplus}&=\\sum_\\alpha\\phi_{\\alpha,\\text{S}}\\mid{U_{\\alpha i}}\\mid^2\\\\\n", " \\phi_{\\alpha,\\oplus}&=\\sum_{i,\\beta}\n", " \\mid{U_{\\alpha i}}\\mid^2\\mid{U_{\\beta i}}\\mid^2\\phi_{\\beta,\\text{S}}\n", "\\end{align}\n", "$$\n", "\n", "This is nice because all the complicated interference terms drop out, and the oscillation depends only on the **square of the mixing matrix**. From this, the measured flavor composition on Earth is defined as $f_{\\alpha,\\oplus}=\\phi_{\\alpha,\\oplus}/\\sum_\\alpha\\phi_{\\alpha,\\oplus}$, where the $\\oplus$ subscript denotes as measured on Earth. We can compute this using GolemFlavor:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Measured composition = (0.55 : 0.18 : 0.27)\n" ] } ], "source": [ "from golemflavor.fr import u_to_fr\n", "\n", "measured_composition = u_to_fr(source_composition, NUFIT_U)\n", "print('Measured composition = ({:.2f} : {:.2f} : {:.2f})'.format(*measured_composition))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The effect of neutrino mixing has modified the flavor composition from $\\left(1:0:0\\right)_\\text{S}\\rightarrow\\left(0.55:0.18:0.27\\right)_\\oplus$ at Earth!\n", "\n", "Here is listed the expected measured compositions from some other source composition models:\n", "\n", "$$\n", "\\begin{align}\n", " \\left(0:1:0\\right)_\\text{S}&\\rightarrow\\left(0.18:0.44:0.38\\right)_\\oplus\\\\\n", " \\left(1:2:0\\right)_\\text{S}&\\rightarrow\\left(0.31:0.35:0.34\\right)_\\oplus\n", "\\end{align}\n", "$$\n", "\n", "This can be more easily visualized using a [ternary plot](https://zenodo.org/badge/latestdoi/19505/marcharper/python-ternary), with axes being the fraction of each neutrino flavor:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import golemflavor.plot as plot_utils\n", "# getdist package requires `%matplotlib inline` to come after the import for inline notebook figures.\n", "%matplotlib inline\n", "\n", "nbins = 25\n", "fontsize = 23\n", "\n", "# Figure\n", "fig = plt.figure(figsize=(12, 12))\n", "\n", "# Axis\n", "ax = fig.add_subplot(111)\n", "tax = plot_utils.get_tax(ax, scale=nbins, rot_ax_labels=True)\n", "\n", "# Plot source composition\n", "tax.scatter([normalize_fr([1, 2, 0])*nbins], marker='o', s=350, facecolors='red',\n", " edgecolors='k', linewidth=2.3, label=r'$(1:2:0)_{\\rm S}$', zorder=3)\n", "tax.scatter([np.array([0, 1, 0])*nbins], marker='s', s=350, facecolors='green',\n", " edgecolors='k', linewidth=2.3, label=r'$(0:1:0)_{\\rm S}$', zorder=3)\n", "tax.scatter([np.array([1, 0, 0])*nbins], marker='^', s=350, facecolors='blue',\n", " edgecolors='k', linewidth=2.3, label=r'$(1:0:0)_{\\rm S}$', zorder=3)\n", "\n", "# Plot measured composition\n", "tax.scatter([u_to_fr([1, 2, 0], NUFIT_U)*nbins], marker='o', s=350,\n", " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n", "tax.scatter([u_to_fr([0, 1, 0], NUFIT_U)*nbins], marker='s', s=350,\n", " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n", "tax.scatter([u_to_fr([1, 0, 0], NUFIT_U)*nbins], marker='^', s=350,\n", " edgecolors='k', facecolors='white', linewidth=2.3, zorder=3)\n", "\n", "# Draw arrows\n", "ax.annotate(\"\", xy=np.array([0.415, 0.44])*nbins, xytext=np.array([0.499, 0.83])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "ax.annotate(\"\", xy=np.array([0.505, 0.335])*nbins, xytext=np.array([0.64, 0.55])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "ax.annotate(\"\", xy=np.array([0.67, 0.14])*nbins, xytext=np.array([0.975, 0.014])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "\n", "# Legend\n", "l_size = fontsize\n", "legend = plt.legend(loc=(0.7, 0.75), title=r'Source composition',\n", " fontsize=l_size, prop={'size': fontsize})\n", "plt.setp(legend.get_title(), fontsize=l_size)\n", "ax.add_artist(legend)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The coloured circle, square and triangle show the source flavor compositions. The arrows show the effect of neutrino mixing on the flavor composition. The unfilled circle, square and triangle show the corresponding measured flavor composition. Neutrino mixing during propagation has the effect of averaging out the flavor contributions, which is why the arrows point towards the center of the triangle." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Fake Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the things we have learned above, we can start to generate some data! Usually, this comes in the form of a likelihood fit comparing IceCube data to our models. GolemFlavor has built in hooks to the [`GolemFit` package](https://github.com/IceCubeOpenSource/GolemFit) for this, however `GolemFit` is only accessible to IceCube collaborators as it contains proprietary code/data. Instead, we can generate some fake data using a multivariate Gaussian likelihood. GolemFlavor has a convenient function to do such a task." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running without GolemFit\n", "Help on function multi_gaussian in module golemflavor.llh:\n", "\n", "multi_gaussian(fr, fr_bf, smearing, offset=-320)\n", " Multivariate Gaussian log likelihood.\n", " \n", " Parameters\n", " ----------\n", " fr : List[float], length 3\n", " The flavor composition to evaluate at.\n", " fr_bf : List[float], length 3\n", " The bestfit / injected flavor composition.\n", " smearing : float\n", " The amount of smearing.\n", " offset : float, optional\n", " An amount to offset the magnitude of the log likelihood.\n", " \n", " Returns\n", " ----------\n", " llh : float\n", " The log likelihood evaluated at `fr`.\n", "\n" ] } ], "source": [ "from golemflavor.llh import multi_gaussian\n", "help(multi_gaussian)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Smearing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In reality, a measurement does not have an arbitrary precision due to effects such as mis-reconstruction and model uncertainties. These effects are said to *smear* the data, and it can be described as in our Gaussian likelihood using the `smearing` keyword. Here we set the amount of smearing" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "smearing = 0.02" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Anarchic Sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have everything we need to do scan over our likelihood, from which we will be able to visualize the effect of this smearing. However, scanning directly in the space of the flavor composition would not be the correct way to do the scan. This particular parameterization has degeneracies, since the total flavor composition must add up to 1, $\\sum_{\\alpha}f_\\alpha=1$, which introduces an unwanted prior dependence." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The effective number of parameters of the flavor composition can be reduced from three to two due to the requirement $\\sum_\\alpha f_\\alpha=1$. Therefore, in order to make sure we have an unbiased prior, the parameters in which to sample in must be determined by the [*Haar measure*](https://doi.org/10.1016/j.physletb.2003.08.045) of the flavor composition volume element, $\\text{d}f_{e,\\oplus}\\wedge\\text{d} f_{\\mu,\\oplus}\\wedge\\text{d}f_{\\tau,\\oplus}$. The following *flavor angles* parameterization was created for this reason:\n", "\n", "$$\n", "\\begin{align}\n", " f_{\\alpha,\\oplus}=\n", " \\begin{pmatrix}\n", " f_{e,\\oplus} \\\\ f_{\\mu,\\oplus} \\\\ f_{\\tau,\\oplus}\n", " \\end{pmatrix}=\n", " \\begin{pmatrix}\n", " \\sin^2\\phi_\\oplus\\,\\cos^2\\psi_\\oplus \\\\\n", " \\sin^2\\phi_\\oplus\\,\\sin^2\\psi_\\oplus \\\\\n", " \\cos^2\\phi_\\oplus\n", " \\end{pmatrix}\n", "\\end{align}\n", "$$\n", "\n", "$$\n", "\\begin{align}\n", " \\text{d} f_{e,\\oplus}\\wedge\\text{d} f_{\\mu,\\oplus}\\wedge\\text{d} f_{\\tau,\\oplus}=\n", " \\text{d}\\left(\\sin^4\\phi_\\oplus\\right)\\wedge\n", " \\text{d}\\left(\\cos\\left(2\\psi_\\oplus\\right)\\right)\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This basically tells us that we should scan in the parameter space defined by $\\sin^4\\phi_\\oplus$ and $\\cos\\left(2\\psi_\\oplus\\right)$. GolemFlavor contains a convenient function `fr_to_angles` to convert from flavor compositions to flavor angles." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Measured composition = (0.55 : 0.18 : 0.27)\n", "Measured flavor angles = (0.54, 0.50)\n" ] } ], "source": [ "from golemflavor.fr import fr_to_angles\n", "\n", "measured_angles = fr_to_angles(measured_composition)\n", "print('Measured composition = ({:.2f} : {:.2f} : {:.2f})'.format(*measured_composition))\n", "print('Measured flavor angles = ({:.2f}, {:.2f})'.format(*measured_angles))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Markov Chain Monte Carlo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can define the wrappers to the [`emcee` package](https://emcee.readthedocs.io/en/stable/) which will sample over the flavor angles using an affine invariant MCMC algorithm. To do this, it is convenient to define our parameters using the GolemFlavor `ParamSet` class, as so:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from golemflavor.enums import ParamTag\n", "from golemflavor.param import Param, ParamSet\n", "\n", "# Convert from flavor composition to flavor angles\n", "measured_flavor_angles = fr_to_angles(measured_composition)\n", "\n", "# Params can be tagged for later convenience\n", "tag = ParamTag.BESTFIT\n", "\n", "# Define the asimov `ParamSet`, with `Param` objects containing information such as name, value and ranges.\n", "# Note: std defines the Prior standard deviation, however default behaviour is to use a flat prior.\n", "# The assignment of `std=smearing` is just a placeholder for later. See `measurement.ipynb` example for further details.\n", "asimov_paramset = [\n", " Param(name='measured_angle1', value=measured_flavor_angles[0], ranges=[ 0., 1.], std=smearing, tag=tag, tex=r'\\sin^4\\phi_\\oplus'),\n", " Param(name='measured_angle2', value=measured_flavor_angles[1], ranges=[-1., 1.], std=smearing, tag=tag, tex=r'\\cos(2\\psi_\\oplus)')\n", "]\n", "asimov_paramset = ParamSet(asimov_paramset)\n", "\n", "# Define the llh `ParamSet`, with `Param` objects containing information such as name, value and ranges.\n", "tag = ParamTag.BESTFIT\n", "src_compositions = [\n", " Param(name='measured_angle1', value=0, ranges=[ 0., 1.], tag=tag, tex=r'\\sin^4\\phi_\\oplus'),\n", " Param(name='measured_angle2', value=0, ranges=[-1., 1.], tag=tag, tex=r'\\cos(2\\psi_\\oplus)')\n", "]\n", "llh_paramset = ParamSet(src_compositions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we have 2 `ParamSet` objects:\n", "* `asimov_paramset` contains the measured parameters\n", "* `llh_paramset` contains the model parameter values\n", "\n", "In this example, they contain the same parameters since we are doing a simple scan over the measured flavor angles to generate some fake data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we wrap our `multi_gaussian` likelihood into a function that accepts input parameters `theta` from the MCMC:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from golemflavor.fr import angles_to_fr\n", "\n", "def triangle_llh(theta, asimov_paramset, llh_paramset):\n", " \"\"\"Log likelihood function for a given theta.\"\"\"\n", " if len(theta) != len(llh_paramset):\n", " raise AssertionError(\n", " 'Length of MCMC scan is not the same as the input '\n", " 'params\\ntheta={0}\\nparamset]{1}'.format(theta, llh_paramset)\n", " )\n", "\n", " # Set llh_parameters values to the sampled parameters\n", " for idx, param in enumerate(llh_paramset):\n", " param.value = theta[idx]\n", "\n", " # Convert flavor angles to flavor compositions for the model parameters\n", " measured_angles = llh_paramset.from_tag(ParamTag.BESTFIT, values=True)\n", " measured_composition = angles_to_fr(measured_angles)\n", "\n", " # Convert flavor angles to flavor compositions for the injected parameters\n", " bestfit_measured_angles = asimov_paramset.from_tag(ParamTag.BESTFIT, values=True)\n", " bestfit_measured_comp = angles_to_fr(bestfit_measured_angles)\n", "\n", " # Get the value of `smearing`\n", " smearing = asimov_paramset['measured_angle1'].std\n", "\n", " # Calculate the log likelihood using `multi_gaussian`\n", " llh = multi_gaussian(measured_composition, bestfit_measured_comp, smearing)\n", " return llh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last thing we need to setup is our prior distribution, which in this case is simply the bounds on the flavor angles. As we have defined this already in the `ParamSet` object using the `ranges` keyword, we can use the GolemFlavor function `lnprior` to do the work for us:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from golemflavor.llh import lnprior\n", "\n", "def ln_prob(theta, asimov_paramset, llh_paramset):\n", " \"\"\"Posterior function for a given theta.\"\"\"\n", " # Get the value of the log prior (in this case it will be either 0 or -inf)\n", " lp = lnprior(theta, paramset=llh_paramset)\n", " if not np.isfinite(lp):\n", " return -np.inf\n", " \n", " # Return the log prior + log likelihood\n", " return lp + triangle_llh(theta, asimov_paramset, llh_paramset)\n", "\n", "# Evalaute the posterior using the defined `asimov_paramset` and `llh_paramset`\n", "ln_prob_eval = partial(\n", " ln_prob,\n", " asimov_paramset=asimov_paramset,\n", " llh_paramset=llh_paramset\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we simply define the number of walkers, burnin period and number of steps to run the MCMC and GolemFlavor takes care of the rest!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running burn-in\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c2a7fc6daaa34fd788670f73b21e0a70", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finished burn-in\n", "Running\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "76913b05a7e94f44922bdf4a43705758", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=10000.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finished\n", "acceptance fraction [0.7059 0.7071 0.7221 0.7087 0.7189 0.7099 0.7152 0.7069 0.7112 0.7217\n", " 0.7203 0.7234 0.709 0.7185 0.7138 0.7104 0.7067 0.715 0.7182 0.7177\n", " 0.7172 0.7156 0.7213 0.723 0.7152 0.7137 0.7165 0.7208 0.7159 0.714\n", " 0.7172 0.7138 0.7222 0.7161 0.7132 0.718 0.7244 0.7142 0.7124 0.7143\n", " 0.7097 0.7144 0.7167 0.7113 0.7074 0.7177 0.7107 0.7142 0.7047 0.7099\n", " 0.7077 0.7167 0.7238 0.7165 0.7083 0.7205 0.7172 0.7265 0.7189 0.7131]\n", "sum of acceptance fraction 42.905499999999996\n", "np.unique(samples[:,0]).shape (429072,)\n", "autocorrelation [30.41597538 31.01818003]\n" ] } ], "source": [ "import golemflavor.mcmc as mcmc_utils\n", "\n", "# Reduce these values for a quicker runtime\n", "nwalkers = 60\n", "burnin = 1000\n", "nsteps = 10000\n", "\n", "# Generate initial seed using a flat distribution\n", "p0 = mcmc_utils.flat_seed(\n", " llh_paramset, nwalkers=nwalkers\n", ")\n", "\n", "# Run the MCMC!\n", "# Progress bar provided by tqdm (this took about 5mins on my laptop)\n", "samples = mcmc_utils.mcmc(\n", " p0 = p0,\n", " ln_prob = ln_prob_eval,\n", " ndim = len(llh_paramset),\n", " nwalkers = nwalkers,\n", " burnin = burnin,\n", " nsteps = nsteps,\n", " threads = 4\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize Fake Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have generated the fake data, let's see if we can visualize it in a ternary plot. First we convert the data from flavor angles into flavor compositions:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "measured_angles = samples\n", "measured_compositions = np.array(\n", " list(map(angles_to_fr, measured_angles))\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can display the 90%/99% credibility regions on the ternary plot to show how our fake data is distributed:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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v4/o9Jwfeu5yk4llEZD+YmRUVFS1taWkp3rRp03fc/al0Xdvdu7u6us5+7rnnKC8vv9DMZqTr2pLXHDg9VWEZznX8hL966e73AUem+Ir+EYJ5m//Ua/vdBAMPk11N0CI9P0We0f1kPREY2ztr+Pw74dMjwnynpjjm1/1ceyj3GOp7ACmKXoLW91QFZzF9D4abn+K+t/W671DyfThFF43/5NWD+vrqivMY/bfSpzov0+9d3tEiKSIi+8HMTp4/f/5vduzYsbexsXGeu+/IwD1+fOSRR37oiSeeuNvd1f+5wIWtqR8Li8OTCAZnjQWqCBfxSHFOzyIkuwhaEucDt3ivBTiSjn8s6VgIitCfERQ/De5e02vxlC6Cr+/PTFVMJV3vuXDTr5OP65XvOYJC9Dt9FGZ9vS9DuUfK96DXa3LgeQ8WH3nNaw23nwpcn7T9CXc/Oul6uwgK1od59SIp3/VeC9wMMl/PLxSf4ZVFUiYAD7v7jSnyP56cJ7zGCoKBmA2E7/FA52XivSOPqXgWERkmMysF/jlp0qT527dvP9fdb87QfWaMGzduVUtLS1lHR8fb3P0vmbiPiKRXT/GcxYGPkgXqtiEiMnxfOuigg+bv3LlzJfCNTN3E3V9sbGy8bt68eZSUlNxiZsWZupeIiPRPxbOIyDCY2aSSkpLLm5ubSSQS57h7R4ZvecP69es319XVHQ6cnuF7iUh69NcfXPKUimcRkeG5etq0aWPXr1//gLs/kOmbuXtLS0vL+a2trVRVVV1rZtWZvqeIDI+ZHWNmGwkG4J2Y4YVMJMtUPIuIDJGZvX7q1KmfaW9v7wLOzeKtf9zY2PjonDlzJgKXZvG+IjIE4RzM093d3L0o3wfIyatpwKCIyBCYmRUXF/+5srLyrXv27LnZ3bNZPGNmR5aUlPy9vLy8a9++fQe7+6ps3l9EpNCp5VlEZGg+MG/evLcmEokG4Mps39zdn+jq6vpufX39qKKiopuyfX8RkUKnlmcRkUEys/KioqLnx44dO6OxsXGRu387ohx1lZWVqzs6Oio7OjpOcPffRZFDRKQQqeVZRGTwzj/kkENmNDU1PQPcHlUId9/a1NR0VX19PWVlZUvNbFRUWURECo2KZxGRQTCz6SUlJRfv3LmTrq6us929O+JIy9asWbNu/PjxBwBnRpxFRKRgqHgWERmc62bNmlW+adOmn7r7w1GHcff2tra2xYlEgurq6ivNbGLUmURECoGKZxGRAZjZW6ZOnXrqnj17OoALos6T5Nc7d+78/YwZM6qJYPCiiEgh0oBBEZF+mFlRSUnJ38eOHXtEQ0PD1e5+WdSZkpnZQcXFxU+PGzfOdu3a9Xp3fybqTCIiI5lankVE+vfx+vr6I9rb27cC10cdpjd3f7a7u/vWmTNnFhUVFS0zM4s6k4jISKaWZxGRPpjZ2NLS0tWjRo2a1Nzc/DF3vzPqTKmY2YTRo0evAca3tbW9z91/HnUmEZGRSi3PIiJ9u+Sggw6a1N7e/jfgh1GH6Yu7N7S1tV02b948ysvLbzaz0VFnEslFZnZM1Blymd6fwVHxLCKSgpnNKy4uPmfTpk10dXWd5e6JqDMN4FurVq1aMWbMmJnAOVGHEck1ZrYEmBZ1jhw3LXyfpB8qnkVEUjCzJQsWLCjdvn3799398ajzDMTduzo6Os4aNWoUEyZMuNTMpkadSSRXmNmpwIHunvIbJDO7wMw6s5jnPjNbEj7uy0aL72DuGb4/B4bvl/RBfZ5FRHoxs+MmT578YEdHR8vu3bvnu/vmqDMNVlFR0S/q6+vfvXLlyu+7+yejziOSC8xsl7vXpNi+IvzPdcBJ7p7xAbdmtgv4UnIhb2bN4f3/lAv37Ov9koBankVEkphZSWlp6TJ3Z/fu3VfnU+EM4O7nv/DCCx11dXWfMLOjos4jI4+ZfcPMEmb2jaizDIaZ3QHcn2qfuy9094XAXVnKsiS8b+8W8FuBn+XQPe8P3zdJQcWziMirLaqvr1+4b9++DcDNUYcZKndf09XVdfOUKVMoLi7+upnpc17SxswmEywHb8CZ4fNc9z7g0qhDhE4HVqfY/mtggpnNzJF7Xgp8OANZRgR9qIqIhMyspry8/OrVq1fT2tp6jru3RZ1pmK5Zvnz51vLy8qMA9V2UdPouQeFM+PN/IswyoLAwbHP3DVFnCY0nRSGb1HXig7lwz/D92qu+z6mpeBYRecUVBx544LjOzs4/Ar+IOsxwufu+rq6ui2fMmMGYMWNuNLPKqDNJ/gtbmU8InpX3bD4xx1ufrwYey+QNzKzTzAZ7DwO29bP/0By652PAaYO8R0FR8SwiQrDMdVFR0Znr1q1LJBKJsz3/R1P/YPXq1U+MGjWqDrgo6jAyIoStziUE3/iXQO63Ph8LPJzhe2wldbeIVxlkl4wJOXTPhxlkMV9oVDyLSMEzMysqKlp68MEHF+/evftb7r486kz7y90TnZ2dZ1VWVlJbW3uhmc2KOpPkr1e3On8CeGf4E8jt1ucJQEanmnT36e7+sUEcOmugSwGDmuEiS/d8nMEX8wVFxbOICJwyYcKE4zZs2LAHuDzqMOni7o++9NJLP6yqqioFbow6j+S1pFbnS8JNl5AHrc+lwPqoQ4TWD7DfgF05dM/1BO/fKwebzTSzx8zsjnCu6DvM7FQzu2D/o+YPFc8iUtDMrGz06NFLS0tL2bNnz+XuvjPqTGl20Ysvvtgyffr095vZsVGHkfzz2lbnOeGeOeRB63NxrgwWHGSOhly5Z3huSa/NjwAfcvePufvJYev3bUAu/tlnjIpnESl0Z8+dO3f2rl27nieY93REcfeXurq6rhs/fjzFxcVLzaw46kySd1K0OvfI/dbnDE3/NlxdwIH97H8mV+4Zvm+9x37UpSjIrxh+tPyk4llECpaZ1VVVVV22evVq2tvbF7t71pbnzbIly5cvf7G6uvow4NNRh5H80Xerc4+cb33uZuB+v9m0lRR9jHuWynb3THSv2p97tvZ6XtJ7+rrw/Cf3N2Q+UfEsIoXsmvr6+squrq7fuPvvog6TKe7e6u4X1NbWUlVVda2ZjYs6k+SNflqde+R06/NeYFrUIZLcDRySYvtnSHOXjTTc8y1A77nu/wbcZWYrzGxJT6t+itULRzQVzyJSkMzsDSUlJaevXLmyM5FInBd1niy4Z82aNY+Y2QRG0KBIyZyBW5175HTr8zbgiEEcN+wC28w2DnYpa3c/H2hLMcDufeEjl+55BL3mh3b3o4E7gZnAecB6M1sx2NwjhYpnESk4ZmbFxcW3HHLIIdbU1LTM3VdFnSnT3N27u7vPqqqq8rq6ui+Z2QFRZ5KcN4hW5x452/p8P3BSXzvDmSM2AteHzzuHUpiG6oD5gz3Y3WuA/wxnqrgjXOzkpKQV/3Llnm8l+DvQ+1ofc/cx7m7ATcCBQ3y/8p7l/zoAIiJDY2YfGT9+/A+7urp27tu3b56774k6U7aY2benT5/+2Y0bNz7g7n0WFVLYwtbjLYAF3eS/M4izPgPcDsEgsynu3t+qdlljZp3uPirqHPnGzJrdfUzS85nArN4Ft5ktAU4PC/SCoJZnESkoZlZRUVGxpKqqin379l1cSIVz6NJt27btnT179olmpuJZ+jKEVuceOdv6vLX3IDfpX/h+9Z5VYxbBb0i9/RpoyXSmXKLiWUQKzYXTp0+fumXLlqdI8ZXkSOfu2zs6Oq4sLy+nqKjoa2ZWOvBZUkgG39e5t5zt+/xR4NKoQ+SZS4EzU2w/rWeWjiTXA7dkPlLuULcNERkSMzPgdODQ8PFn4AZ3z/mWBzObMX78+FUtLS1l7e3tb3P3v0SdKQphwfzPurq6+Vu3bj3X3W+OOpPkDjO7HzgxaEVeyeCLZ4C1wAKCqYW5391PTn/CoQv7+N5SaLNCDEfY6nxWODgweftM4GqCQYSTeWXp7ufCQYkFQ8WziAyamVUBZwPtwP8Bi4ETCf6FPd3dl0cYb0Bm9uMjjzzyQ0888cTd7v7hqPNEycxOnj9//m+2b9++d8+ePfPdfXvUmSR6w+vr3FvO9n1e4e4Lo86R6/Q+DUzFs4gMipmVAxcBP3L3lUnb30TQ5+1B4OO5utCImf1reXn5nxOJRFt7e/sCd38x6kxRMjMrKip6oLKy8l179+79trsvijqTRO+VVmeAZQyt1bnHWoLfsYHcan2eCVwdLiktKYSD/76eK0ua5yoVzyIyKGZ2MLDQ3X8SPi929+7wv78AfA2Yn4tFqZkVl5SU/L+DDjro8KeffvpKd49FnSkXmNmBdXV1y4uLi4s2bdp0hLs/FXUmiZaZdQHpXMK9291L0ng9kchpwKCIDJYDT/R63uMPBF/1vjmriQbvk+Xl5YevXr16M3BD1GFyhbs/t3Xr1v/q7Ow0YGnYn10K2+Ycv55I5FQ8i8hgVQJLzOxsMxvj7omkfVuBciDnpn0zs+qqqqrramtraWlpOd/dm6POlGPie/bsaaivrz8G+M+ow0i03H0GQW2Qlkd4PZERRcWziAyKu/8N6CSYaWN6z3YzK3H3RuDvwAsRxevPpZMmTZq4YcOGR4EfRx0m17j77vb29q+4O2a2JOzbLgXM0yjq1yKSCerzLCID6unfbGajgVp335jimMeAj7r7mvB5rbvvyHbWXpnm19bWPtvU1FTS2tr6Rnd/YuCzCo+ZFQNPzpgx49AXX3zxMne/OupMIiK5Si3PIjKgsHA2d29z941m9vJnh5mNMrOxwEvApqTTJpnZcIbqp9NN06dPH9Xa2vo9Fc59Cwd+Li4pKWH8+PGXmNm0qDOJiOQqFc8iBc4CZcnPUx2X/BVscn/ncGq6MmA50J50fg0wPyOhB8HM3jV27NhTnnrqqSYGv75wwXL3P65du/Zn7l4OXBd1HhGRXKXiWaRAmVmxmX0QWAQ8GA4EnOvuPoxZF+qA3e6eCM8fBbwV+O905x4MMxtVVla2dNasWSQSiavcfWsUOfLQBaNGjeqYOXPmR8P5u0VEpBcVzyIFyMxmACcAT7r7N4FLgdnAQ2Y2vaeVeQhFdB2wLjxnCnBNeM3hLE+WDmeWlJQcsGrVqnUEKz3IILj72h07dizZu3cvZrYsuXuOiIgENGBQpACZ2eeBn7j7zqRtRcBfCLpgnOHuTw7iOha2NB8NjCKYqu5m4GDgTe6+NiMvoP9ME6urq9fU1tZWr1mz5t3u/qtsZ8hnZlZZXl6+etasWXXPPffcJ939+1FnEhHJJWpVECkgZlYUtgx/EGhO2mZhP+YTgXrgK2ZWH+7vs/U5qR90JfBJ4HfASmBqFIVzKF5dXV29du3aPxAsGy5D4O5Nra2tF7W0tABca2ZVUWcSEcklKp5FCkjYJ3kLsJNwQYykfsol7r4X+DTwH8AiM6vq3Qe6j2L6JOA04D3u/vlw9oasM7NDpkyZ8rldu3Z1JxKJxZpndtju3LBhw+Pz5s2bAlwcdRgRkVyi4lmkwJjZGKADONrManq2u3tX+PMe4NvAp4C3hNuSZ9pIVZD+BhjvwUIqkQiL+mWTJk0qam5uvtXd/xlVlnwXfgtxdmdnJ+PGjTsvB6YcFBHJGSqeRQqMB8tT/wX4CHBk8r6eAWLu/gWCbh1nmtm4XsdUm9k5ZvaupGs+5O6tGQ/fv/fU1ta+/emnn24AYhFnyXvu/tiGDRvuKCkpKTWzJVHnERHJFSqeRQqQu99K0Df5iuRWRXdPmFlJ+PSDwCnAVHilsCYYUHgx8PZcmY3BzEaXl5ffPHnyZIDL3b0h6kwjxMWJRKJl9uzZ7zWzd0QdRkQkF2i2DZECZWZzgRXAUuCasL9zz76isJD+MTDW3U/qtX2+u6+OJvlrmdmXx4wZc21HR8eKzs7Ow3q6oMj+M7OvVFZWXt3W1vbPrq6u1+u9FZFClxOtRiKSfe7+AhAHFgPv6GlFTpp5A+BWYFvv7TlWOE8dP378ZXV1dXR2dp6t4i7tvtbd3f3iAQcccDDw2ajDiIhETcWzSAFz968C/0uwHPM7wm2e1HWjCZjUsz2SkAP76ujRoyvWrl37S3f/fdRhRhp3b21tbT23oaEB4CozmxB1JhGRKKl4FpH3AnsJpqY7Fl6ZeQNIALdFlGtAZnbU9OnTP9HU1BxNF5oAACAASURBVNTh7udHnWcE+9nmzZsfXrhwYQ0ajCkiBU7Fs0iBCwvlzwKNwI1m9h4zm2xmxwFzgPsiDdiHnqnpKisr2bdv383uvibqTCNV+K3D4sbGxsTYsWO/YGYLo84kIhIVDRgUESCYgg44GTg83HS7u6+MMFK/zOy0qVOn3rl58+ZtwHx33xd1ppHOzL45ZcqURdu2bXswkUi8K4e78oiIZIyKZxF5jZ5ZNaLO0RczqxwzZszqmTNn1q1YseJT7v7dqDMVAjOrra6uXjNp0qSxq1evPsXdfxN1JhGRbFPxLCIvC2fUyPkPBTO7qqqq6tKWlpYnu7u735jLhf5IY2aLKyoqbu7q6nqho6Njobt3RJ1JRCSb1OdZRF6WJ4XzrNra2gtramro7u7+kgrnrPtGd3f3qgMOOGAucFbUYUREsk3Fs4jkmxsTiUTp+vXrf+juj0YdptC4e2d7e/vZmzdvBrjczCZHnUlEJJtUPItI3jCzY2bPnv3+rq6uNuDLUecpVO7+2507d95/2GGHVQFXR51HRCSbVDyLSF4ws2JgWVFREXv27LnW3TdGnanAnbt169auysrKT5vZEVGHERHJFhXPIpIvPj1r1qzDXnjhhReBJVGHKXTuvnLbtm23TJgwwYqLi28J590WERnxVDyLSM4zs3EVFRVfLSsrA7jA3VuiziQAXLV79+5dc+bMeQvwwajDiIhkg4pnEckYM3uLmX01DZe6vKysrGbVqlV/Ae5Jw/UkDdy9cd++fRdv2rSJ8vLyJWZWEXUmEZFMU/EsIpm0Avi0mR023AuY2YK6urovjRkzxt397HyYTq/A/E9nZ+cz9fX1rwPOjzqMiEimaZEUEckoMzuT4Cv9dwyn8C0uLr5v7NixJzU2Nn7H3T+b/oSyv8zsmLFjxz7c3Nzc2t3dvUCDOUVkJFPLs4hk2m1ADfC+oZ5oZifNmTPnJHffB1ya9mSSFu7+p717995z2GGHlQPXR51HRCSTVDyLSEa5exewGFhiZuWDPc/MSoGvtbe3s2fPnri7b8tYSEmHCzds2NBeUVHxETN7a9RhREQyRcWziGScuz8EPAmcO4TTvlBfX79g48aNq4GvZyaZpIu7r9+1a9cNdXV1lJSULDMz/fsiIiOSPtxEJFsuAM41s2kDHWhmteXl5VckEgmAc929I+PpJB2u37Zt25aZM2ceAXwi6jAiIpmgAYMikjXhtHWvc/ePD3DcNydPnrxo27ZtvwNO1Awb+cPMTqusrLwT2NHU1DTP3fdGnUlEJJ3U8iwi2XQt8E4zO7qvA8zs8GnTpp1RVFTUDZyjwjnv/LC1tfWx+fPn1wJfiTqMiEi6qeVZRLLKzD4OfAF4k7sneu2z4uLihysqKt62b9++Ze6+OJqUsj/M7I2jR49+3N0729vbD3L31VFnEhFJF7U8i0i23Rn+/GiKff85b968twG7gXj2Ikk6ufvf29ravnfwwQePMrMlUedJZmYXmtn7w8eFZjZniOePM7Prw3OvN7Nvmdn7M5VXRHKPWp5FJOvCbhv3AgeEczgTTmO3YvLkybO2bdv2eXe/NdKQsl/MbEp1dfXq9vb2MW1tbf/m7g/mQKYHgYvc/cmkbU8AH3D3tYO8xrfcfVGvbdcDL7j7t9MaWERyklqeRSTr3P0x4A/AxUmbzz344INnbd++fTnBwiqSx9x9y549e66eOXMmpaWly8ysJMo8Pa3DyYVz6FrgW4O8xoWpjnX3i4BFrz1DREYiFc8iEpWLgUVmNsfMppWVlV3S1NSEuy8OF1aR/Ld0w4YN66dMmXIg8LmIsywimGu8tyeB48xs3CCuMTe9kUQkH6l4FpFIuPsm4GvAjcB1kyZNqli/fv3PwgVVZARw97a2trZzGhsbGTt27FVmVhNhnDcAL/TemNRd4w2DuMYTwG29C+3w+aC6fYhI/lPxLCJR+hrwpkmTJn20vb29g2AhFRlZfrlv376H5s6dO45oB4GOAxr62T/gwMGwT/M4YJ2ZHZe06wzgs/sXT0TyhYpnEYlSe3FxcXNjYyPbt2+/abCDtiR/uLsnEonFGzZsSNTU1HzOzA7OdoZBdskYzDG4+1zg98CDZnaPmZ3h7je4e+N+hRSRvBHpAA4RKXgfra+vn1ddXb3vmGOO2RmPx2cBhwBVwG+BE4D1QBtwAPAn4ChgFPAwcBywJrzWPIKi5ligE3gcOAZ4HhgNzEq65j5gOfDm8OcEYFrS/obwukcB/wCmApOT9m8DNgOvD+8zL7xGz/5N4TUOAR4t9Nd0xRVXHLJ8+fJ/3HvvvUcCy82M/eXuQ7nIhAH2NwJD6VJyN0E3jTOA95sZmmlDpHBoqjoRiYSZVZnZynHjxk1pbm7+1CWXXPJgLBZ7Kepckn7xeLzqkUceqfvrX//6eFdX17j29vb3uPsvs3X/sOV5N8GUdD9Nsd8JprC7YRDXuT48tjF8fhvwfuCGcNYNERnh1G1DRKJy8WGHHTZlz549j3d0dHwfmBaPx4+IOpSkVzweLwU+8OCDD65ubm6+fO7cuYwePXqpmZVlK8Mgu1QM5pjbgOt7rufuje7+AYKZPC7s1Q9aREYoFc8iknVmNqe0tPS8HTt2kEgkzg6X6X4cfSaNRJOB34T//c01a9Y8V1NTMwvI9tLra+l/UGC//e3DVuZxqfrlh102LgKO36+EIpIX9A+ViGSdmd34ute9rnTTpk13hgumEIvFHFgVj8ffFXE8SZN4PD4RmB+LxbYDuHtnR0fH4tbWVsaPH3+ZmdVlMc6TpOjX3LM8t7v/foDzJ9B/6/RA54vICKHiWUSyyszeMX369Pc1Nja2Al9O3heLxfYC5fF4XJ9NI8NRBIMLX+bu/7t79+5fT58+fQzw1SxmuZtgMGZvxzGIwjdsce6vW9FxQORLkItI5ukfKBHJGjMrKS0tXbZnzx4aGhquCRdKeZVYLPYLBrdgheSweDw+H3gsFou19d7n7uf985//7KypqTndzN6YjTzhQMGGFP2SF5Fiae2eaeh6bb7IzO5JcexxQM0gWq9FZARQ8Swi2fTZ+vr6gzs6Ol4kWCClLzXhtHWSh+LxuAFvJ5jh4jXcfXUikVg6Y8YMiouLb7F0zF03CO5+PHC8mZ0RPq4nmIEjVX/nI+i1HHdYgF9rZt8KH9eH1xinmTZECoemqhORrDCz8SUlJavLyspqmpub3+/u9/Z1bDweLyaYo3hj2Bda8kg8Hq8D9sZisZa+jjGzsaNHj14D1La1tZ3m7j/MXkIRkeFTy7OIZEvs0EMPrWltbf0z8LN+D4zFuoHxwNFZSSZpE4/HK4B39Vc4A7j73ra2tovnzJlDRUXFjWY2JksRRUT2i4pnEck4M1tYUlLyxZdeeikRTk03YGtyLBZ7GtgddgGQ/FEF/HqQx35v1apV/6iqqpoKXJjBTCIiaaPiWUQyysysqKho6bx584q3b99+m7s/NYTTtwH/kalskl7xeHwK8PpYLNYwmOPdvburq+usRCLBxIkTLzKzmRmOKCKy31Q8i0imnTxlypTjt2zZshe4bCgnxmKx3UCLWp/zxgHAQ0M5wd0f2bFjx49ra2vLgH6XxxYRyQUqnkUkY8ysdPTo0Uvb2trYs2dPzN13DPUasVjsQYKZGySHxePxQ4BnY7FYxzBOv2jlypWtdXV1HzSzt6U7m4hIOql4FpFM+lJ9ff3cpqam1cA39uM6Fo/HZ6crlKRX+M3AG4Eh/3IE4O4vJhKJG+rq6hg1atQyMytOb0IRkfRR8SwiGWFmk8vLy2MrV66kvb39bHfv3I/LPQQUqftGzpoDfG8/pxW84dlnn91UWlp6OHB6mnKJiKSdimcRyZSrFy5cWNXZ2Xm/uz+wPxcKi7JS4Jj0RJN0icfjVcCbYrFYYn+u4+4tnZ2d50+fPp2qqqrrzKw6TRFFRNJKxbOIpJ2ZHVFSUvLptWvXdiUSiXPTcc1YLPYc8EI8Hh+VjutJ2owGfpmma929evXqR0eNGlXDEAeXiohki4pnEUkrM7Pi4uJbDjzwQNu9e/ct7r4yjZdvBt6dxuvJfojH4zOAN8RisX3puJ67e3d391mlpaU+efLks82sPh3XFRFJJxXPIpJuH6itrX3L+vXrG4Cr0nnhcP7gzer7nDOmAA+m84Lu/sTWrVu/W1VVVQLclM5ri4ikg4pnEUkbM6uoqKi4yd3Zt2/fxe7emO57xGKxR4FT0n1dGZp4PP5G4MVYLNaVgct/Zd26dfumT5/+72Z2QgauLyIybCqeRSSdzp87d+7rGhoangFuz+B9GsIuAxKBsOV/XiwW25KJ67v71u7u7qvGjx9PSUnJUjNTP3cRyRkqnkUkLcxselVV1cXPP/88nZ2dZ7l7d6buFYvFHgHGxeNxfYZF4zDgxxm+xy3Lly9fU1VVtQD4fIbvJSIyaPqHR0TS5fr6+vrRnZ2d97j7n7JwvybguCzcR5LE4/HxwAH7OafzgNy93d3PnThxItXV1XEzm5jJ+4mIDJaKZxHZb2b2lpKSko+sXLmyHbgwG/eMxWJrgafj8Xh5Nu4nLysDfpGle/3mhRde+D1QDVyZpXuKiPRLxbPkpb6W7zUz/Z3OMjMrKikpueWggw6iqanpRndfn8Xbt6Op67ImHo/PAw6JxWJt2bifu3sikVhcXl7ePXXq1EVmdmg27isi0h8VGpJ3zKwMGGdmB5rZSWZ2uJnVm9lcd9+vVc5kWD4xbty4I9asWbMFuC6bN47FYo3ACk1dlzXlwB+yeUN3f3br1q3/XVJSUgQsNTP9WYtIpFQ8S14xs6OBOLAYuB54I/B94CvAo2Z2n5ktMrOPm1lVhFELgpmNraysvL6srIzm5uYL3L05ghjLgY9EcN+CEo/H/xVo3N9luIfpis2bNzfMmTPn7cB7I7i/iMjLVDxLvtni7l9298uAd7t73N0PI1jK9yjg/4BDgdOA35nZ+RFmLQRfmTFjRu3WrVsfA34YRYBw4NrKeDw+NYr7F5BxsVhsYxQ3dveGrq6uy0aPHk1JSclNZjY6ihwiIgDmntEB0yIZYWYl7t7V87PXvmpgDrCQoKgeByx290xPrVVQzGze+PHjn21qairt7Ow8yt3/HmWeeDz+L8ATGVq0o6DF4/E3A3/N9Awb/TGzEuAfkydPPnjbtm2XuPu1UWURkcKmlmfJSz0Fc3LhbCF33+Pu/3D3u4B/BW4GbjezP5nZ/IgijzhmtmTOnDmlnZ2d34+6cA5tBv4t6hAjTTwerwGmRlk4w8v/ry8eM2YMEyZMuNTM9E2DiERCxbOMGB7qtW2Hu18PvB5YAfzEzL6sFcv2j5kdX1ZW9u5nn322Cbg46jwAYZeCx+Px+Nios4wwY8je1HT9cvc/rFu37hfd3d0VgFqeRSQSKp6lILj7KuAc4AbgncAXevpNavT+0JhZSWlp6bL6+nra2tqucfeMLNE8TG1o6rq0icfjBwGzcqkrjLufX1ZW1jljxoyPm9m/RJ1HRAqPimcpGO7eBvwKuIvg6/3Twu3q+D80n6uoqDhw1apV64GlUYdJFovFmoD/i8fjKecBl8ELp/9rA/4SdZZk7v7C9u3bb+ro6ABYprndRSTb9KEjBcXdm939e8C3gGvNLGZmRX0tuiKvZmY1Y8eOvWrs2LG0tbWdG/5CkmvWAZ/U3M/77Z1Ad9R9nfvw1Z07d25dsGDBvwCnRh1GRAqLimcpKD1dNNz9l8B/ALOBKnfvjjRY/rhi8uTJ41566aU/kiP9YHsLi72/AbVRZ8lz3bFYbH3UIVJx931dXV0XJxIJioqKrjezyqgziUjhUPEsBaVXF42ngC3AD8ysRl//9s/MDp40adKZGzZsSCQSibNzubtLLBb7J3BwPB4vjTpLPorH4/8GPBx1jgH8YPXq1f9v6tSpU4EvRx1GRAqHigUpGL0HBrp7m7tfDPwRWKClvftmZlZUVLT0da97XXFHR8c33X151JkGYQXwjqhD5Jt4PD4BKM/R7hovC/9/Pau4uJja2toLzGx21JlEpDCoeJYRo6c4NrNiMxtlZuPCn7VmNqanpTTpuJ6//48CH4gmdd74jzFjxrzz6aef3g1cHnWYwYjFYluBJ8NiUAZvMsHA2pzn7n/dsGHDXZ2dnaXAjVHnEZHCoOJZRoykbgTvBT4GnABcCrwJuNTMjks+rqel2d0fB5q06EJqZlY2evTom2fOnEl3d3fM3XdFnWkI9gKnRB0iX8Tj8dcDY3O91bmXLxcXF7fOmTPnP83s2KjDiMjIp+JZ8l5SS/JbzewTQDPwo3A57mvc/VfAI8DbzOwDZlae4jK/AjToKLWzR40aNXv16tXPA9+MOsxQxGKxNuCBeDw+JuosuS4ej5cAO4HHo84yFO7+0q5du65tbGzEzJZp5hwRyTQVz5L33N3DfzDfBtzt7g+4e6uZlbl7RzgSv55gme5twLzk88Piez1B4SBJzKxu/Pjxl0+cOJH29vbF7t4ZdaZh2AGcpqnrBvRvgOVZq3OPJU1NTRsXLlx4KPCZqMOIyMim4lnyWtIgwFOBg4G396wc6O7t4b4TgJXuvpugQH5V/+ZwVe8d7t6Qpdj55KvV1dVjNmzY8Gt3/13UYYYjLAb/AIyPOkuO2xGLxV6MOsRwuHtrR0fHec3NzRQVFV1jZvqzFpGMUfEseS1sdZ5MUDh/HnDgi2Z2DED4j+gM4H/D41cA28xsoZbl7p+ZvXHq1Kmnb926tTORSJwXdZ79EYvFXgCOjsfjqbrsFLx4PP5e4Imoc+ynn65fv/7Ps2bNqiFPBrUWiqSudfrMlRFBxbOMBHXAH9290d1/C/wAmGVmi4CzgN+5e5eZ9cz5+yhQksvzFEfNAktra2tpa2tb6u6ro86UBo8BR0cdItfE4/FqoCkWi+X1VI3h/89nd3R0+MSJE79oZgdEnUlentXoN2b2lrCxoyTqTCL7S8WzjAS7gIpwme0id9/u7t8Ptx8KHGpmNe7eER4/B3hjVGHzxIcnTJjw5qeffno7cHXUYdIhFos1AGvi8Xhd1FlyRdgPvD4Wiz0YdZZ0cPenXnrppe8kEomSoqKir0Wdp1AlLzgVzmr0MOFgY3fviiiWSNqoeJa8Fn4NOApoCz+kk1uTJwKLgTXAp8zsZDMbBawjaH2WFMxsTEVFxZK6ujqAS9x9b9SZ0mgbcHLUIXLI0UDHgEfll0sTicS+OXPmnGhmJ0UdphC5e8LMqs3sJDP7HHAscJCZfSjiaCJpYfrmWkYCM7sAuBvYGH41+G/ARHf/Ybh/NEHRNAPoBv7h7n+JLHAOM7N4ZWXl5W1tbf/o6up6o7t3R50pneLxeCXBCno7os4SpXDp8tpYLLYp6izpZmbnVlVV3dTa2rq6q6vr4KRvnSSDzOwgYDbwduBDQM/c+c8QDNb+kbvfHlE8kbRRy7OMFPcDdWHhXAYcBvwGgkU+wqW47wV+DXQBf40uau4ys5kTJ068aOLEiXR1dZ010grnUDPw3ng8XujzAZ8I5HU/5378V0dHx+oDDzxwPvDFqMOMVGZWbmYzzezDZvY7gvnyfw4sAlqBuwgK6VOAU1Q4y0ih4llGipeAo8zsfcAngP/r6W7QM2WdmZ0MjAV+Hw4g1Mjv17qhrKysbP369T9290eiDpMJ4dR19wEFu3BK2Nd5VSwW2xJ1lkxw94729vbFu3btwsxiZjYp6kwj1GLgWeCHwJsJWpc/RzA96Dvc/WPu/id33xjOvV+kz10ZCVQ8y4jg7nvc/b8IWjuOJVwt0MwOM7Pjw752s9z9SXdfFZ6jPktJzOxfZ86c+cGGhoZW4KKo82RS2FXh+LALRyE6DRgJM6j0yd3v37x5828XLFgwFrgq6jwjSVIB/ACwFvgScIK7/4u73+7uj7j7xvDYl7/hcfeEPndlJFDxLCNCz4e5uz9A0PIxxczeA8wl+Gr6N8CtycfKK8J/4JaNGTOG1tbWG9w9LxfLGKLfE8zGUlDCXxjWx2KxQpj14NzGxsaumpqaz5rZ4VGHGSnC7nHm7k8B5wNGrxmMercya9l0GUk0YFAiF34Ip+UvYjhVXWKgbfJqZvaZKVOm3LZly5aXgAXu3hJ1pmyIx+PzgPZYLLYx6izZEHbXOCYWiz0cdZZsMbObJ0+evHjnzp1/6e7uPkYtn+kXTk33dYIieh1wd1+/gJtZediFY7a7r8tmTpF0UcuzRMLMJpjZm82sdKj/mCXPIdpbOEVS75Zl/WPZDzOrrqqquq66uhrggkIpnEPrgeOiDpFFbyaYrq+QXNnS0tIwb968fwXeH3WYkcbMisPGiS8C3yEYrP07M7vLzH4Z9jn/cjio8DvAD8zsKeDPZnZilNlFhkstz5J1ZnYwQZ/a44AvhrNgDPbcorBAHgdcQjAPcVevY0YDtQSrCGa1ZSOdrejZYmZLqqqqzmtpaXm0u7v7rfmWf3/F4/FRwLRYLLY+6iyZFI/HRxNMTVcQrezJzGxRRUXFN7u7uze2t7cvcPfWqDONNMmffWY2EzgKeDfwBqCKYIBuKdAJ/IPgF9fvu/sfIwkssh/U8ixR+ALwO6ACeP0wr3EXcGgfq1WdAPwTuMjMTjezucO8x6CZ2cThtKJHzczqJ0+efPa4ceO8u7v7rHzLnw6xWKwTOC4ej4/0ZYP/nWBAbSH6TldX1/IDDjhgOnBe1GFGop5+0OHTTe5+j7t/1N0PIBh7MgeoJ5j7+UTgMyqcJV+N9H8sJMeY2Wyg3N3vNLMXgCfD7QO22IZfD3ab2VHAO4Dx4fZX9Wl291+Y2WXAdwn64B0NvJCZV/Syy4Cnwnvmk5sSiUTJxo0b/8fdn4g6TIR+QfDL3EhaTfFlYev632Kx2M6os0Qh/Nw4e/PmzQ8BF5vZ99z9pahzjTQ9n+E9jRpJn+td7t5gZruTBhu6xqNIvlLLs2TbS8CbzOxK4O/u3j7Yrg5JC3bcCvzA3dvC1t5UH75fd/d9QBMw18yOT9sr6CXsg/0W4L1mdmS4Ledn9DCzE+bNm/fvLS0tTcBXos4TpbCoPCEej4+LOkuGfAzYGnWIKLn7H3fs2HHvoYceWgFcF3WeQtC7mE563vNThbPkJRXPklXu3gncQjC45Cozm0rQOoyZvcvMXpfqPDMrCX9+GpgF3BmO2u4It7+qWO314fwdICOtqkmDZe4nmBLvk2Y2ttdXmDnHzEYBNwM0Nzdf6e4FXViF7iNYWnhEicfjFcDTYfeUQnfBli1bOsaNG3eamb056jCFIJc/B0WGS8WzROEe4O8EgwbPDgcA1hEMAGxMdUJS3+ZrgTMIWpS/YWZvDfenbLkOW7U73b0hza+hJ1dPa/gjwB8J+vZ9or9MOeLzM2fOPGDNmjVrCH6ZKXixWKwZaI/H4xnvI58t8Xi8CHh7LBYr5C45L3P3dTt27LhxzJgxjBo16pb+Zu6R4ek1t3PeDaAWGQx9cEjWuft2d38XwbygF5jZzwm6YvzQ3Zt6WpmTWWAiwfyh97r7PwiWhD3HzD5uZmV93CujH9xhrtHhrZYBy4F3mtk7evZn8v7DYWYTq6qq4mVlZQDnebh8uQDwPMEsASPFUQSvSV5xXWNj47a5c+ceSdCdRfZTz+dc2Ie5Z8aNivAbuDeZ2TVmNi3alCLpo+JZsi6ptWcZ8E1emc7oWXhVK/PLPLATSIRdPXD334fnHwsck/nkrxXmaiNcDhy4E2gBTjWzqTna6nJlcXFx9Zo1ax4Efh11mFwSi8USwD3xeHxh1Fn2VzweHwPsiMVimR4sm1fcvam5ufmC9evXU1FRcb2ZVUWdKd8kFcvF8PJMG8Xht4iTwgHb15nZ48CfCWbayPv/p0R6qHiWrOsZJOLua4GzgLXANIIP24Ph5RbdVH8/lwANSS26fyYYhPhzMzs62y29Yc4yoDT8x2M58DNgCnB6eMxYMxuTzVx9MbNDp06duqi8vLw7kUick6PFfaTCZauPjsfjKb/NyCOnALuiDpGj7urq6np8wYIFkwm6i8kQJI0p6QYws08Al5jZTwkGpr4DaCNoTCgHPunuD0YUVyTtVDxLJJKK3C8AjxK0HC8AfmFmbw9bdF+1WmBYTO8ESpM+vNvd/fLw/OXZLgbDnO3AZoKpzgDuJegD/QYziwNfJQe6ApiZFRUVLW1paSnasmXLf7v7s1FnymH3ECzskJfi8XgV8PtYLJZyDEGhc/dEV1fXWWvXrqWoqOjcbMwFP9KY2fFmttTMWggGgNcBBwKfAt4HXO7ut4TfJGpWDRlRVDxLJJJmo+gCrnT3vwDnE6xA9Qczu6Cnz1zSOQl3b3X3l+fiNbOisJ/d/3P35qy/EF7+RWAO0DNvqQM/AWYAHwV+7u5/zIH+z++dN2/e27u7u3cDV0ScJafFYrF9wLHxeHxi1FmG6UP0MfhWAu7+tz179vzg8MMPLzWzJVHnyUNPEjQYvJ+gceBLwH8RNGLsJlhJEHh5liXMbJaZHRT+d9SfhyLDpuJZIhMWmd929zXh8+8TtGBsIWitPTccJPjyVHUprpFI51yhSd0wXn4+0PHh63jR3ZvC1vI3Ap8DHgNeBN4cZnUzK01X1qEIBzUuaW9vZ9++fZdlavaREeY3BMu855VwGe7/C7ufSP8uXrduXUtlZeV7zOydUYfJJ+6+y93PcPf7gZ7FTn4MzLJg/v3usHHjXWb2bjO7F/gt8HkzG6MuY5LPVDxL2plZzWC/Bk1qkbDw+a8IWjEeAmLAheH2rky2VJhZsZl9EFgEPGhmZ5vZ3IHma076B6DazKab2SeBjwN3uvsXgD8A/25m55jZWcBJmXoNAzhn/vz5szds2PAs8K2IMuSVWCzWBpTG4/EDo84yWOES46fEYrHnos6SD9x98+7du6+pqamhtLR0WV+/pEvfwm/bugHCFucngWPN7CFgNXAucD1BS/SZwA8IBlWL5C0Vz5IJBhw0lBN69LjmvQAAIABJREFUitRw0N0mgq+dvw2cb2Z/NLP6TLVUmNkM4ATgSXf/JnApwWIZD5nZ9KSpl/or3tvC8yYQTP/2dLj9qwSDB28Cmtz9F5l4Df0xs6mVlZWXdnd3QzCvtlokB+8ZYH7UIYbgUIJvPGTwvrZt27YXZ82adRDBHPIyBL2/+XP3dQTf2BhwDXAl8E53/7C7/9Hd/6ZWZ8l3Kp4lrcICsxN4t5ktTNo2oHDwXU8LRiPBErq3EAwG/IGZHR5eL92tQ/8O/C2p+8ifCVpLembxOKInXz/XaAVud///7J13nJTV9f/fZyssSy8LCAjIgoKKLWoSey8x0Vh+akzMV40mUcFeo9dr7B1SjMaYpjGWqLH3EnvvioArSG+ywML2Pb8/7h18WJdlF2bmmXLfrxev2XnqWXbmueeee87n6A2q2hBRCvmhv85IVb09yXZ3lCvLysrKqqqqHlTVZ2OyISsxxijwhLX2O3Hbsi6stT2BBmPMrLhtySZUta62tvb0WbNm0b1798tEpE/cNuUA04C7cBO5N31AJCpTGghkNeGDHEgq3gFeBjyA02/ekGvNwhUR/gaXynGJ356UyKnPxxsEHAGsjGwTH03ZHxgNXCgio/3+NicCqvqiqr6ZOMbnPnfHRaR3UtUZybC5s4jIDsOGDfsZbkJzVhw2ZDvGmAZgrM8lzmR+AMyI24gs5YH6+voXKisrexOKaTcY/yxcBPQAthDfICWZ9SmBQJwE5zmQKl4FxovIFj4lo9OfNZ9L16SqV+Acg51E5AkR6ZeMCIYvNpyHk787NLJNRaTIq3ocj4senyQi3VvnQLflTCeOUdUVqvpEXAOGiBQUFhZOXrp0KQsXLrxBVUOzjPXnLqBv3EasDWttf+AhY0xN3LZkI6qqLS0tEz/77LOWkpKSXydWzQKdJ1K/8oCqvq6q7yYiz4FArhCc50BK8GoOdwHX+HzlTjuQPnpb4B3lZ4BzcbnI2ybLIRXXvKQB2FFEVjtHiei2qt6Ly70+Dvi+3xaVz9NW15O2tsfE0aNHj95eVRficg8D64mPPm9nrR0Uty2tsdYKcAihCGuDUNUPa2trb918880LCwoKJgUptfUjQ559gUBKCc5zIGWo6n+B94HfiUgldD5fWb/pRtigqn8BrsRVbCfLxpXAS8BRwLbRfYnotlfNWAn8SkR6tTqmp1fS2NcfmxEDh4iUA1dXV1dTU1NzrqquiNumHOBxIBNTN0qAJ40xzXEbkgNcPH369GVdu3bdC7faFchwwiQnEAfBeQ6khMgD7SpgAU73eHVEt6NpF5H84aEicq6q/g1YICLDk2Wrqt4MfA5cIiIjI9tbIs7+Ebh2x4Nb2V8KnA/snmHFMOeNGzdu8Lx5897GSUMFNhAffe5jrd0qblsSWGtLgCOMMTPjtiUXUNVFy5cvN4MGDaJLly43RTXfA5lJpgQsAvlFJg32gRwi8UDzxYO/xel+fploRKCtWm+v6zo49YsrReQsYEEKCvB+ios8nyQiPSL3b/K516/iugZeF7G/wKdEfF9Vz8uUYhgRGVFWVnbWihUrACZkil05wrtkVu5zJfB03EbkGH+cNWvW54MHDx4JTIjbmIBDRMqiQROvzd/Da/JfLSKHJgq7A4FUE5znQErxkeNpqrotLgf6WhG5UET6RfSTO/I5fBR4DRgL3NGJ8zqEL6azwGnAHolrR5Q3AG7GRdHX2K6q05JlR5K4pnfv3qVfffXVv1T1tbiNySW8dN3L1tpd4rbFWtsX6GKMmR+3LbmEqjbW19eftnDhQnr16nWxiFTEbVMAAAWsiAwRkZ7ARThJvItxbcL3BU5L5qpkILA2gvMcSCkJ5Qr/9hpcp6lxwB9E5AdRJ1RECtu5zjPAYap6nKpO9duSGlH1qh5P4VJN9mjD/hpgQGJ7Mu+dLERktxEjRhxWV1dXiyuwDCQZY0w9MMRaG/eS/t7AJzHbkJOo6hOrVq16dOTIkeWEYtuMQFVrgam4Au5XgTOBy1S1r6qeqqonAg8B343RzECeEJznQMqJKFdUA/fjIscNwCnAn0Vkb7+/ORpNjkR/E071fP8+lQUihwDLcekbu0XtB1qAP6fw3huEiBQWFxdPXrRoEUuWLLlSVWfHbVMOcxcwPK6bW2uHAQ/7FuKBFNDS0nLGhx9+2FReXn6ciGy77jMCqcKnbHwX2AnXDfZzVe2uqr9rdejpwIki0jvtRgbyiuA8B9KKqjaq6mO4Iru7cU1EjIi8JCK7+VziHv7YRFpEouugRl9TZF8T8AugGpdicrCIVIjIXsBIXPpIpnLC6NGjt2hpaZmNz80OpAafvrGJtXbjdN/bS9PtR5CmSymqOrWpqWlSZWWlFBYWTg6qDunH5zVvilu1fAHohqt/Wez3F0SCLJsAVcCfVXVpPBYH8gXJ0NXnQI7i0zQ08n4wMBQX8T0FJxs3DyjEPTBHAVNw+s7zgYHAU6kugvM5dQcCCWWFv6jq56m854bgJfSm9e3bt9+SJUuO8PrUgRRirS3Cqa/M8s50uu5bDnQ1xixK1z3zFRHp2a1bt+ktLS39amtrj1LVf8dtU66RGBN8AXZLZPsgnIToOUAzcI6q3un3/Qt4TlVva2NMKUj1+BAIBOc5EAutH3iR7cfgUjp2A/oAXwG9cA70DOCDdHfKy4aHsYjcsNVWW53+/vvvvwTsmqk52bmGl60rNca8kab7dcVJ0/09HfcLgIj8YsyYMbfOmjVrzqpVq0araoj4JxkvCThQVWeKSB/c8/83uALxa4FLfFpfoX/dFBilqo/489scTwKBVBHSNgKx0EZnviK//Q5VvUdVfw38TFXPAU5V1RtU9f50Os6RboGZ7jhv2qVLl1MXLFigwMQwiKQPY8z7ab7lRsAjab5nvnN7VVXVB/369dsIODtuY3KU7wEPich4XF3JfcAXOAf5okQ9TOR1ClAjIhf480NKTSCtBOc5kBFEivIQj6o2+Nf6xLY025QVTmhBQcENAwcOLJo3b95tqvpe3PbkIR9aa/dP9U2stQOBCmPMklTfK/ANqtrc2Ng4YdmyZfTt2/c8ERkWt025hqo+DywE3sOl8e2hqoer6mz5ptNrS/QV6A78QkQ2Sejux2F7ID8JH7ZAxqGexM+ttwW+QUQO2Hjjjfevrq5egVvmDKQZY0wt0MVaW5ziW+0IvJXiewTaQFX/t2zZsnuGDh3aBSe3GUgSETWlU3Epez9U1Rd8sWBUZ3/18SKyMU5OdGNcrUzGrxAGcovgPAcCWYqIlJSWlt60ePFiqqurL/HdDgMxYIx5ANgyVde31o4BnvUtwgPxcM4HH3xQ16dPnyNFZKe4jckVWqVi3A6ckNje+lgR6Q8ciZOKPBWn+Xx2YlUyKKIE0kVwngOB7OXk0aNHVzY0NEwDfh+3MQF6W2srk31Ra20BsCuuSU8gJlR1pqpeO2zYMIqKiiaHNIGUcBrwYiIaHelCWy4iuwN/Af6J09w/B/grcBBwTPT4QCDVhC9/IJCFiMiAoqIiM2PGDOrr609X1RCRjJ/ngHqvw5xM+gL/SqccXmCtXD1lypS5xcXFWwM/j9uYXCGSz9ygqi8Bo8GtronIlrguj4/h0jR2VtWdgBtV9Q1VfQAYmmi2FaLPgXQQnOdAUgnRmLTx2y233LJnTU3NE7hBJRAzxpgWXBOHnZN1Ta/pvJ8xJkSdMwBVXVlXV3f2xhtvTHl5+dWJhk6BpHOjiJwHHI97vh0DnKyq41X1lag2tIhsjWucspNP/wiTzEDKCY5OIKkkIghh9v9tRKSLbzG7odfZqrS09BezZs1qUtUzwmCRORhjPgOWJDH63At4KEnXCiSHu6ZNm/Za9+7d+xGKdJNKJPhyI3AFLh3t30A/Vb3dH1MUKSRvwXUVfAp4JxQNBtJFcJ4D602kLeoYETlIRG4Skf3A5Z4FB/pbbAQ87LsqrhciIoWFhZOHDRsmixYt+r2qfpZE+wLJYQauY+YGYa0dClQaY5ZtsEWBpKGq2tzcPKGuro4BAwacLiJJz3PPVyLpG08C9wO3qupZfjxJ9AJoanXOMuBYVQ2TzEDaCM5zYL3x2prlwLnA/wHbAo+JyIsislWk2CN8zgDf4OU2XERlfTl0yJAhO8+fP38pcGlyLAskE2PMSqDGt+/eEDbDtasPZBiq+vbSpUv/1r9//yLgurjtySUi0nVnA9uLSG/4ttPcihBxDqSV4NQENpR9gf+o6o+Bw3ARtwbgXRH5i4j0CUtpa3A5sI+IbN/ZE0Wka9euXW9YtmwZK1asuEBVl6bAvkASMMY8heuatl74tt9vGmPacxgC8XLBp59+WjNw4MAfisg+cRuTK0Sk677EydGtMy3Nn9NHRH4kIj1EpCeE9MFA6gjOc2C98ZqbI1T1UQBVXaCq/wUOx1Wi7wTMEJGzIuccKSLj4rA3E1DVFcCFwKT1eLCfOXr06KGrVq36CNfCNpDhWGs3XY9zCoDtjDHVKTApkCRUdZ6qXjZgwABKSkpuEpFUN8nJGyLpG6+q6urvQSQqvQYichSwGDgYVydwpD8/1IMEUkJwngPrhXf8lgFf+fclCWfQP+z+CewNXA/8RkQ+E5EJwMVAvkdM/w4UA0d39AQRGdKlS5fzP//8cxoaGia21UAgkHG8BDR4Z7gzDAP+lnxzAingps8++6yquLh4M+CXcRuTy3iFjWb/c2mr3W/gNKBvVtWvgBqvCx2iz4GUEJznQKdIFG0A2wEVwAIR6er1OVfP8n037a9w+b27Aa8BNwH3qurcfM6D9lGVCcDVPme8I1w1bty4srq6uv+o6vMpNC+QJLwucwGwZ0fPsdb2AnYM6RrZgarWNzY2nrHRRhvRs2fP34pI37htylV80eBgEbkVeEBEXhWR/ycig1S1CjhTVd/0hz8AHCwiPUP0OZAK8taBCXQeP/NPDOqPAzOBa4FTEpGA1k6xqjaq6vvAw8BXqmoSu9Jkdkaiqq8CL+KKLdtFRL5bXFz8k6qqqgZcEU0gSzDGTAemdqJ4sAvuuxLIHh6aPn36c8XFxT0BG7cxOc55uPbdGwFzgGOBS0WkUFWXA4hImaquAuqA4XEZGshtgvMc6DAR9YyLcI7zX3DpB7/FO3VegUPaWCobgXcUozqdec65wK9FZPjaDhCRguLi4smVlZUsXbr0Ol9EE8guvsYV07aLtXYkMM6rdQSyBFXVlpaWiaraMnjw4F+JyBZx25Rr+CGlFKfoNBnYTlUPV9UDgBeAY0VkZwBVXSUiFcCPgblx2RzIbYLzHFgfXgIOVNVf4Ao0bgDOFZEPRWQPn7KxWpcTQFVvUNW7/c9hSRpQ1dnAJOCadg776YABA7abOXPmAuDK9FgWSCbGmBXA7A7kPg8GQkpOFqKqHy9ZsuTm8vLyAuCmkGebXHywpQhXZ3OhqjaKSFe/+27gaWAsgFcyehqn+rQiOg4FAskiOM+BTqOqL6jqfP/zTNxS5R7A58AzIvKgiAxPOMki8rO1RKMDTiN2BxHZtfUOEeleXl5+TX19PStXrjxbVUOL5izFGPMysP/a9ltrvwt86lt8B7ITM23atKVDhw7dA/hR3MYkEJFzROQw/+8cERm5Adc5R0Su9q97JdvWdu5doKorcd0Gj/Cb63y6RpOqzgK+FpG7gX8A1bgc6LoQrAmkguA8BzpEe46vqtar6lu4XLRDgCHAVBExIvJH4LuJaHSazM0afG7e2TjputYyTBdssskmA6qrq98E7ky/dYEks8haO7r1Rt/Ke6Qx5usYbAokCVVdoqoX9+zZk9LS0hvaUIRIOyLyNPCMqt7n/10D3NsZB1pEeonIvcC7qnqNqp6Lc06fTpHZ3yIiXfcPb9NAP6Q0i0iRiPwSuADYHpfGcbaqPpEu+wL5R3CeAx0iku/8PRE5WETOEJEjfPVzT3/MMuAh4Ic4NYmzcXrPF/lzw+etbe4FlgPHJzaIyMju3buf8cknn9DU1DQhNJrJCd4CCtsoHhwH/CsGewLJ50+ffvrpp126dBkBnBanISJyGICqvttq15XALZ241J+Bt1T1mci2Z+hAsXMyiQRw3gd2EZFfevnT93Da+YuAy3AR5zf8OWHMCaSE8MEKtEvigSUi2/oH1enAScDOuGjoG8BVIrIDrJaom6uqfwI+A65Q1cV+eS04gG3gJyYTcVXjvQBE5LrRo0eXNDU1/TMxEASyGy9dtxJY3Y3OWtsP2MzvC2Q5qtrU0tJyWr9+/ejdu/dFIjIoRnNOAlo7zvhteyWeNe0hItsAe/mI9WpUtar1tlSTCOCo6nuqeg8wBRegKQf+CJygqn/x6R0JYo/+B3KT4DwH2sUX/hXgCgMfBI5Q1f35poPg07h0jRtFZJPEeSIyEPhSVS/zm4Lj3A6q+h5OouwiEdmzqKjokKlTp64Czo/ZtEASMcZ8BbxjrU0UOxUTpOlyClV9uqqq6iGgG07nPi62A75ovdFrIif2r4vzgbeTadSGEolA7wssAA5R1St9X4HEMRUicjXwkIi8LiJ/FpETRaRbHDYHco/gPAfWSuQhtRPwhn84FYNL0fAR0ROBH+AapjwpIv38/vk4DU581DlE1tbNb4Bji4qKbt50001ZsWLF5ao6J26jAklnFXCotXYzYIwxpi5ugwLJRVXPLCwsbBw2bNjPReQ7MZnRCyeTuDY6kve8F1AlItt45zNRdJi2YsHWRMYSAe5X1fdFZHMR2dLXpV8AzMNFpYcDbwIzcOlR1ySkBEMBe2BDCM5zYK34qHMv4ABVfcRva2h1TBPwFHAy7vP0fVhdHV3rjwmtpDuAqi4AXiwoKKicPn36VzgJwECO4aXrPsYtKb8YszmBFKCq0xcvXnyj988mpdtR60hKBs657ugxfVT11kjR4bmJnOp0E8ljvgynuHEg0Afoh6sduAx4FfgFrlh9gqperqoTgVuB78AaTngg0GkkfH4Ca0NExDvQl6jqJSLSRVXXGiUTkV8B31HV49JoZs4gIn26d+8+vX///r3Hjx9/8/jx468BtgC6A08A++EiKHXApjjHa3vcasALuCjRdH+5Ubiint2ARlz0ZVdcnmAXXEQmcc0VwEfA9/xrH1wHr8T+r/11t8cV5wzGrTQk9i/ANSPY2t9nlL9GYv8cf40tcINa+J2gJ06V5pMc+p1y8e+03r9TTU3Nnu+8885Nzz//fDlJQlU75IR7NY0vgMNV9b429i8FbvXKGWu7Ri9gKU5lY9tW+/YC7lXV3p2xP1lExqaSREBHRHYHngXuA36jqlP99kJc2qCoa+L1S2Caqj4bh+2B3CA4z4FEWkWb0WH/4Pk7Tph+pt8m0Vl7JKoyCjgOl36goUCwc4jIpC222GJCVVXVy2eeeeYZ1tq34rYpkHy8NN2PgC+BFcaYqnWcEshCrLVljz322IU1NTUXTJ8+fW5DQ8PoVsVsKSPi+K7NeVbg3PaK/iLXuKa1kx3Zt3crFY7YEJFngQWqenRkW8LJXj1michQ3CTnthB9DqwvIW0j0G5ahd9XBbzmK6+/tdwV0XCeAzTjZ/gpNDnnEJGxffr0OfnTTz9tWbly5SkFBQUrrLXD4rYrkBJ2MMY8aIz5AOhtrS2J26BAcvETpO+/+eabF3366afv9OzZczBplHZT1eoOHNbuMZFrLGln3zadNC0l+AL1bYHf+/eJ2hyNvorI/rhVhv8lnOpW1wl50IEOEZznPCQiP7e/iDwuImPXccqluAKMa0XkQBHpHrlWtLHHYThB/qbwEOo4IiIFBQWThg8fXtjc3Hyrqn6AWy7+VtfBQHZjrR2ISztIMId2Og8GspYdgVk+iDCxW7du9O/f/xwRGZ5GG6povyiwIyse1UDfdezPBLriIsmvAqhqY+sDxHVxvQ+4Hpe6A1AS2V8UItGBjhKc5zwk8oA4ECf301Zr6ILEqy8KvAbYAfgL8BsR2VlEyhJRaxHZDOitqi+0ukdg3fyguLh4r88++2wZcDGAMaYJuMtaWxmvaYEkUww8mnhjjJkPvGSt7RmfSYFkYq3tAswzxkwBUNVXZsyYcVdDQ0Mp7jmaLt6lDcfX50PTwXSLZ2jDAY8UJGZKylED0FVESqDtCLKqvgj8DThDVd8WkdHARBEZ7Pc3ichGIjJRRLZtfX4gECU4z/nNROAoVb0Zvokii0hxJO1CAFT1bmAz4DWcBNB/gY9E5E4ReR3YA3jOnx+izh1EREq6dOly0+jRo6mtrb1EVRcl9nkHeldrbXGMJgaShLV2K2CI/7tGqcXpqAdygx/iigajnFtUVFQ3YsSIw0VklzTZcTeu6LE1e+Gc4o5wSzvXqMqEfGefzzwH13PgQPh28CYxtqnqyQmbfUHheOAZEfmviDyCi0xXqeo76fwdAtlHcJ7zFB9RbvZOMb5quVlENgfmiMhx4HKeRaTQO9SzgCOASuAu3Cz+Y+B0Vf2Dqn7kzwlR544zoVu3biOnTp06FfhDG/vvB8rSbFMgyfgc2FXA6633GWNqgVetteF5nOX4qPNLxpg18oRVddaSJUuuqqurAyddV9jmBZKILxT8ug1N5pP8vzUQkXtF5MRW13gG51yeEzmuF3B1W9eIg8h48wgwSkS2EJHuIlKZiES3ruuJBHheweVAH4RLQTlAVUPTosA6CQ/rPKV1QV9Ev/lCnPTSH0TkfRHZxTvZjf6B06yqX/gZ/G/VdXZ6Ld325wIiUtGrVy9TWlpKfX39xLby9IwxXwP7WWtjkYQKJI39cMoabU4sjTHTgGO9kx3IXo6mjQI7z7ULFy6cNWbMmK1wqkQpR1X3BvYW1+DkRHFd9w7Xb7oMRtkG2KT1RlU9HEBEbhGRW3CO8+GZEHVO4KPPTcAduNXSH+BWAEaISGmrY0uA8SLyB+BG3N/rElU9RlWXpmNiE8h+iuI2IJA5iMgQoAlX7NIDOAt4QUTuB05T1dmA+ih0Y0Lizkexg7pG57m8oqKifNq0aY+q6hPtHPcIMAInDRXIMrxD3GiMmbeOQ1/FaT/PSr1VgWTjo87vGGMa2tqvqqtE5OyWlpZ/FxUVXS4i93ZQFWODaE/LudVx33KcI/vSmavdaSKqGvOAeSIyVVv1JPB1PMNwhe1n4vyfn+CafN0hIr1VdSlOEzoQaJcQeQ6sxjvHP1PVT1X1dVx05FCc4/aFiFzmK5ITEdIu/rzwsOkkIrJNRUXFcV988UVTS0vLGe0da4xZCRRYa0elybxActkT17yhXYwxnwMbeycskEX4lJv9vPxge9wzbdq0lysqKvoDF6XBtLwk4ThHlKX648ay/wBXAX9X1f7qOiYuB94CzvDnhrTDwDoJznOe07q4L/rgUNXlqvoArgjjPOB44CsROcYfcoWIbJ02Y3MEEZHCwsLJgwcPlqampsm+cGVdfAxslWrbAsnFS9OVrC1dow2m4BRwAtnF1sCH6zrIP18niohWVFRMEJExqTctP4k0SNkMV/h4N64b5TBVPc8fk5CquwroKSJ92rpOumwOZA/Bec5TRKTAp12oiPQQkSPX9pBQ1fnAZGB3nMrGzSJSjWvF/V4azc4Vjujatev3P/roo0XAbztygjGmBfivtXbL1JoWSBY+XaMH8HhHzzHGLMZJ1/VPmWGBpGKtLQdWdrRTpKq+O3v27Nvr6uqKCgoKrk+xeXmLH9vG4lQ4xgK7qupBqjrXF8GLqjb4tMNG4AJV/Xot1xkvIkem+3cIZC7Bec4jRGSciBzmneaWSAXy+UBFe8tVvmhwCvBrXFOHHjjJutaNUgLtICJlZWVl12+88cY0NTVd2JmcR2NMI7CttbZ0nQcHMoHtgfJORJ0T1OCq/wPZwUG4JlKd4UIRqRk5cuSB4rreBVLDAcA7wFaq+hKsjkg3R/KkW/xrTfTESMpHKc4B/5dXowoEgvOcZ1QB/YGnReQ0ABEZhtO6/JN/3267Uv/A+R7wX1V9JfEgSofxOcLZJSUlG02dOvUD4Pb1OP9uIDTUyHCstUXAXGPMu5091xecPR5ynzMfr4LzuDFmWWfOU9UF1dXVtrq6GuBG8e2kA0lnB+DuVsWDg9o6MOIs/xhWR5yLVbUeuAGnMvJJiu0NZAnBec4jVLXWN0QxwGUi8grOab5OVetbS/p4NhORsSJSFHGk7+MbqaXwGeogIjK0X79+53Xv3p3GxsYJ6zPpMMasAnYJy/oZzwHAhkwq5wPHBO3nzMWn5RyKWylYHyZXV1dP33zzzccAJyfPsoBX1gD4PdDLbxstIjsD24nIgNbnRFZe/09Ezmi17ypVPT8UEwYShAdzHiGOQr989W/gu7jipIkispGq1vvZdpE/vhgYAAwFBkWWuaq8pM+3xOcD7XJ1jx49usyaNeteVf3fBlznYUL0OdNZaIyZu74n+1SPZ3Dfv0BmUgy82EbHyA6hqg1NTU1n1NTUUFhYeIlXhAgkgUgqxovAEhE5GpduuBKYxrc7QCbGx41xz9brRKRva+39iFMeyHPCByGPUEfC2X0CV9l/BlCBU9G4zh/X5F8bVfUFXIXykSKyQ/qtzg1EZKchQ4YcNWvWrDrgnHWe0A7GmHqgp7V2XHKsCyQTa+3BwBsbeh1jzAxgC2tttw02KpBUfFrO4b65zYbwyIwZM54aNmxYT+DSJJgW8CRWStV10X1QVSep6ruq+pmq1rY6tgduFeF+XBrjZbShqx9kWQMJgvOcv5TitC4nAUfhiv+OEpHFIvLzxEE+p/kjXOvoEBlZD0SkoKCgYFLfvn1pbGy8VlVnJOGy7+JWBAIZhLW2H+10ElwP3gR2TtK1AsljLPDChl7Er+adXldX1zxw4MATRWT8BlsWAL6l15zQfV6jMZyIlIrIjsA/gHuAL4DNVPXi4CgH2kNCCk9+IiKDgK99MURC73JT4Bf+32fAiar6lt+/JbCLqv4+JpOzFhE5rnfv3n+prq6eo6pjVHVlMq7rVTe2McaE9uhXA58bAAAgAElEQVQZgM+B3coYk1T5RmttBVBkjJmTzOsG1g9rbU9ghDHm/WRdU0Qm9erVa8KKFStebG5u3j3k1qYG3+SryY93w4Bf+X9TgAmq+rI/LnTNDbRLiDznL9VE/v6q2qCqH+Jk6/bHpWq8ISKvisjVuErjxyCIxncGEenRrVu3qyoqKlDVc5LlOMPq9I3KIF2XMewM1Kfguktw38lAZnAALm82mVzS0tKydJNNNtkV+HGSrx0ARGRfvlE4OhF4HvgpcKqqbqOqL3v95yJVbQnjXKA9gvOcJ0RkeDYWkV2B/YAXReRXfnsBOK1LVX0ep6axFy4CvQKnyFGV6NoUyy+RnVxYUFDQf9q0aa8Dd6Xg+v9iLdJLgfThJzDTjTGfJvvaviDtHmtt32RfO9A5rLWDgIeNMUmbBAOo6tLly5f/Zv78+RQWFl4nIkGmMMmo6pPA90VkJnAtLk1jmKr+BVZHm5t9ZDqMc4F2Cc5znhB5EBwD1AILcU7XPn5/S6vjF6jqc6p6vKpepqrPptXgHEBEKgcMGHBGr169aG5unpCKh7F3rLa01g5O9rUDneIgoHGdR60nxpjlwCG+UC0QAz4t5yBgVYpucWttbe0n48aNG44r5A4kiUiu81m4WpFtVfVMVa1LSLT6aHOpiJwI3C0it4nI+SLynch1QjQ6AATnOS9IRJVFZFtgtqq+qaqv4JYfJ/h9q+XpEhqYbXUODLPxTnFdaWlp0axZs/6WyB1PEY8DwamKCe9UTTHGLErxrR4jSBTGSSnwqDEmJbmwqtrU2Ng4cfHixRQUFFwgIhul4j75SERB6gGcosZIWB1tTtT9nAx8jut9UAH0w626niAiF4hI/zD+BRIE5zkP8DPqIuBYnHZs4qHxkarO8scktEqLgR39tqDhvJ6IyD7Dhw//4YIFC2qAC1J5L9+2e4C1dptU3iewVo4mDZ3HvG7093zBWiCNWGtLgCNTXbSpqs/OnTv3gcrKym7Alam8V74RCQadCkyF1WNjdxG5E/gdMB03Th6rqger6u9V9STgOVy3wkAACM5zXuCXmpqBlao6B9auV6mqq4C+IrJ3Gk3MKXw3xhu7detGQ0PDZao6Lw23fQcIesBpxlrbB/gyidJ06+J5YOs03SvwDSNwKzzp4KylS5c2VlRU/NTLqAWSQCIYpKrzVHVqZNdOwMHA9cApqvrPhJxopG36W8BIEdncbw/pG3lOcJ7zgMhS044i8mtY7eCt7QFwP7BeXbMCAPyyoqJi7CeffFIF3JSOG3rn7W1r7Z7puF8AfOvsscaYV9N1T2NMDfCFtXZEuu6Z73jt7t7GmAXpuJ+qVi1cuPD65uZmioqKJoWudqlDRLoBlwP/BayqTvHbEw1WGn3xYDPwLPB9vz2kb+Q54UuZJ/gv+yPAaSJSoapNvhV3W5+BFbhZdqj47iQi0rdHjx6/7dGjB8CZiXy6dGCMqQX6+iXmQOrZFZgfw33nAGGSlD52A5Km6dxBrqitrV1YWVm5PfCTNN87nxiAGx6PVtWaiNMcdY4TQabtgRoIkedAcJ7zjclAC/CyiBwB36RvtOq89ENgqarWpd/ErMcCvaZPn/4cLpqRbu4FxsRw37zCt8yeYoyZnu57+4K1v1trh6X73vmGj/A/YYxJ67NQVVesXLnynJkzZ1JaWnqNiJSn8/55xAqc3CciUtxWRDmS4rgP3mcKkedAcJ7zBBEpVNVG4EbcbPsWEZkkIrvBNwWDIjIG2AZ4OC5bsxUR2WLw4MG/Ki8vb2lpaZkYxwPWp28MsdYOT/e984yD8C1/48AXie4VVhlSh1dR2RtIqqZzJ/hnY2Pj25ttttlAXPOqQPIZCJSBS9Fo6wAR6SoiPwX6Ag+l0bZABhOc5xwmUV0cydlCVW/BLfnOwFUdPyYiU0Tk3yLyGfBr4M1ErldMpmcd/v/qxubm5oK5c+ferKofx2jOU0CzH/wDScZrLb9ujFkasykP4Qf+QEroAdybxmLQNVDVlsbGxglz5syhoKDgTBEJee5Jxj+np/vGYfi23asRkQrgEuDvwNvA8oj0aw//+i1J10DuE5znHEZVm321cO+EELzf/raqbo1Lz7gHeBFYClymqhNV9RF/XFia6jg/rKys3HPZsmXVgInTEGNMM9AHLzkYSDrHAl/FbYQxZjGwuy9oCyQRa20ZcHDcEyRVfW3RokV3jh07thTXFS+QJCLBoWeAfURkhKo2iEi5iGwpIsfhigQnAheq6gXqaBGRg4B/QpB0zVeC85xjRNpwjxeRy3CdBF8BjIjs0urwx3E5shNV9Veqemf0GoGO4btS3SAi1NXVXayqS+K2yRjzATGmFeQqXmP5nVQ1ylgPHgc2iduIHGQAmZO6dt68efNq+/Xrd6iI7B63MbmCL5gXVV0EPA38n4g8iysOfQm4DViGayT2R1hjbGzAtfo+pNX2QJ4QnOccwj8IVESGAycBi3HtuN8EDgVuFJHJIpLQiS30x6wh/h4izp3mtKFDh46cOnXqp7juVJnCVGvtQXEbkSv4dI0djDHpVl5YK76QbZG1dtO4bckVrLWDgI2NMV/HbQuAqs5esmTJFcXFxZSUlExqVdwd2AASY52qvqCqFwMXAsfhcsy3BA4HblPVZf6UUhHZCpfy2AcYF71OIH8IznMOEfkC7wr8TVVvUtVHVfVY4KdAPXAIcLOInAP0VdU3gC4islc8Vmc3IjKoV69eFxUXFwOctraikzgwxqwE1Dt9gQ1nJ2BK3Ea0wZfAd+I2IofYGngtbiNacX11dfWsysrKLYAT4jYm10jkMavq66r6P1X9o6p+rKpzEx16RWQUcCmu2+DewDmqelliMhOiz/lFcJ5zDBEZC5Sp6pv+fVe/qwg3qz4Jt5x/HvAfEZkI7AcMjcHcXOAKoFtVVdVDqvp03Ma0xhjzCMGx2mCstT2AL4wxsec6t8YXtN1prd0sbluyHWvtWOAVY0xD3LZEUdXa2traM6ZPn05ZWdnlItI7bptyibY67kZSIAfgJizPAscDt+BWdJ8WkUOB40SkLESf84vgPOcejURy9VS1VkR64qLRL6vqYzhn+XZgOPADYGPgH+k3NbsRke8MHTr056WlpU2qelbc9rRDmbW2Mm4jspwfAtVxG7E2fA729tbarus8ONAmvmPkTsDyuG1ZC/9pamp6acyYMX2Ai+M2JhfxEebj/NtiETkAeAD4Pa7z7lBVPV9V71XVD1T1P7ji4QNiMjkQE8F5zj3mACeJyKMicqRX26gFZuHytUpUtc47ezfjcrqO8socQXKng4hjUk1NDQsWLLhRVafFbVM7PAc0BOm69cNaWw48aYxZEbct6+B+gnTdhjAQ+Edc0nTrQlW1ubl5wpdffqkFBQWniEhYaUgyvt/BPiLyH1z9ysO4osHNVPV0VV0ViUgX+3OeAHYSkVC4m0cE5zmHEJECVV0F/A/XDWkzn4M7BDhYVWu8FE+xP+UVoCDRQjpI7nSKo8aMGfPd+vr6RcBlcRvTHt4ZKMOtPgQ6gZ9wHA1kRPFYe3jnfmdr7cC4bck2rLXdgT3S3Umws6jq+9XV1X/ecsstiwoKCm4KebbJI5H3DJyFqw3aF9hHVQ9Q1WmRvOhEkWGjP68cN8YOT7vRgdgIznMOkcjb8rm33XHFDQBb4CqDE4ociaK2cbhGAIFOICLdgGvq6upYtWrVeaqaqcu8qzHGfAbMCdHnTtMNeNFrZ2cDj+Fk1gKdo5zs6R73my+//HJ5z5499yGkCyQNXxhYqKqzgd/i0hyfFZECP262lRc9DDgXGAG8kWaTAzESnOcsJtLpaICInOKF2xOtuOsikeRPgN1E5FeJWbOI/AiYqaoz4rA9yzl31KhRG82YMeMd4G9xG9MJFuAkCwMdwLe+3tsY83nctnQUX+hWb60dH7ct2YK1dhiwuTEm4yfBAKq6aNmyZZeUl5dTWlp6U+uueIENIhGAMkCjiAxQ1ZbWxYC+ZffuwB24Qvx7VLUmEr0O5DjhD53FRGbCv8SpPhwUbcUdOW46rhvSdSLygIgcj1PdeCmtBucAIrJxz549z2lqagInTZcpzTLWiXcOllhrQ257x9gWeD1uI9aDqcDIuI3IIjYBno/biE7yh4ULF04bOXLkKOCUuI3JFXyfhIRfdLyqLozu9wWF44DJwFM4Z3srVb3an58140FgwwjOc5bji0Zm4arEL29HLucm4B1cy+Y9gUmqujTMlDvNNcXFxaUzZsz4t6q+HLcxncUY8zywR9x2ZDrW2j7AYmPMvLht6Sw+x/0ha22QKFwH1tptgQ+MMU1x29IZVLWhvr7+tKqqKnr06HGJl1MLJIFI+mO9iIwTkZ0BRGQoLh/6RZzO81GqupuqfphI7YjP6kC6CY5TBiMiJSJyoIjs1mp79Es6A6hW1Q9xkjltoqrvqeouwHaqerSqPum3h5lyBxGRXUaMGHGEqtbh8tyylVpr7ei4jchwDgJmx23E+uJztDf1SiGBNvD5/1tmSifBzqKqjzU2Nj4+atSo7mR40XIWUwLcKSInA4/gJAJvUtXhqnofrK4j+lZqRyC3Cc5zZrMHcCVwq4gMjmxP5DpPxIm1rxSR3n7Jqc3Zb0KGTlXn+PdhltwJRKSwsLBw8uLFi1myZMlVqppxzTI6wSu4zoPh+98G1tr+wP3GmNq4bdlA7iUUBLdHJdlVs/AtWlpazpgyZUpTcXHxCSKyddz25BLeKX4Pl9LzO9zK7UhVvczvX0N9I5BfhMEzQxGR4cBewBE4LeYVfnuB12QeAhicePs/gPO9hrOKSGFr57iNPOjwhe8cx2266abjm5ub5wDXxm3MhuCX9Ztxn69ABB+NPASoiduWDcXLrm1trQ3dQ1thre0NbJOpms4dRVWnrFq16nebb765FBYWTg5BkaSS+L88A7eqe6mqzk+04w6rtvlNcJ4zlwqgRFWnqOoH+ME88oU9Hdf84iTgaf/+vyJSoarN3okuisPwXMN3aLx86dKlrFq16iyvpZ3VGGOqgE+8okTgG7oCT2S7UxXhSaBLkCj8FiVkjzTdurh0+vTpX3fr1m0n4LC4jckVItJ1S3A1Qz/027MqPz6QGoLznLl8ClSKyF6wugq4FMCncNyEK1j4G85x/inO4Z4jIlf4c5oicnZ90/8r5AwXjR07tv/cuXNfAe6O25gkUgP8OG4jMgVrbRfgR8aYbE7JWQNfCFcCbBe3LZmCtXYUsIUxJusnwQCqWr1ixYoL+vbtS1lZ2Q0iElq0J49E8eBNqjo5bmMCmUNwnjMQv/S2EudAPyUiJ4Cr/vWH/APYJNHsRFUXA/fgHKELgONEZIGIHONnzwOByZHOgoEOIiJjunfvPnH58uUKTMyldBdjzDKgKkQlVzMWt4qTUxhjPgF6xW1HBtEHt2qXS9w2e/bsjzbaaKMhOEWIQBJoQ985+EwBIDjPGYk6WlT1TOBU4CYReUdEhovIPsA0VX0Bvin888fPACYB+wMPALeLyKu45ckPVLUx5MR1joKCguvLy8uLZs+efbuqvhO3PcnGGPMmcHDcdsSNtXYA0GiMWRy3LSnieWtt3rdnt9buBMwwxuRUvqqqNjc2Nk6YPXs2vXv3vsDLqgWSTMhzDiQIznOGElHH+AOwD/AFUAX8Fbgvemj0PFWt9xXC5wO7A8OAnqp6jd+fM5HTVCMi+40cOfLA2traGlwXqVxltrU235tq7ANkTSfBzuLTNwZZa7vFbUtc+BWWwcaYhes8OAtR1Rdqa2vvGzlyZBfgqrjtCaxJJIWyXUWsQHYQnOcMpZU6xuuqegTwL0CBx0TkKhEpTsyEE1/ISCR6qaq+AtTil/FCAWHHEZHikpKSm+bNm0d1dfWlqrogbptShTHmLaDcWpuXnw/fnvk+39o6l7kHGBS3ETGytTHmnriNSDFnf/jhh/XdunU7WkS+F7cxgW/wKZQFQE/fw2EvEektIruISC9CalVWEZznDCchwO6LBIfiosmnA8cCM0Xkp7C6oFCi54lIH+B3qvqwPyZUCXecX48ZM2ZMc3NzFa4Va66zFNgvbiPSjde63g+oX9ex2Y5PVRhhrd0kblvSjdfuzvnVFVWd0djYeF1lZSXFxcWTQ45u+mkdWRaRriJS7hutPAX8ASd9dzRwvX89G/iliBwbUiuzg/DFynAiaRbdgXNVdRpwG7AbLn3jzyLyhojs4HOlE8cXAk2JCuHwhew4ItIfsPPmzaOuru60SKFmzmKMmQW8kYfL+mXAgzkkTbcungWa87BItJjckaZbF1dNmzZtfklJybbAz+I2Jp/wwS71P1eIyJ64brTv44Jet+JWkPdS1eOAX6vqL3Ea0pcDD4bUyuwgOM9Zgqp+rqqv+58bVPVz3Gx1T2AR8JqI3OGVNcA1UNk7cn74QnacS8ePH99z8eLFT+FasuYLteSRdJ2fKBycqzmwbeGjz2XATnHbki6steOAUXmQlgOAqtasXLny7EGDBlFeXn6NiIQuk2nCrwB39SvCV+IK/jcDLlTVUap6j6o+qqof+1WBeu9w1/vzl8VofqATBOc5i/HFga8AP8Et/WwNTBWRfwMnAI/GaV82IiJbduvW7cQFCxY0A6fn06TDGFMDvJdHUcnhwMNxG5FujDGf4jpM5jz+syzAS3Hbkmb+NWPGjDf69evXHydfGkgDIjIGV2N0NE4j+m5V/X+qerffv7quxCtkaT6NMblEcJ5zAD9bvRvYF/egPAK4RFXrQgVvxxERKSgomNS3b9+C+fPn/1FVP43bpnRjjPkYODrXHWhr7RCgi9e6zkfettYeELcRaWAPYEkepeUAzjFramqasGjRIvr373+GiORdnns68LVFCRWNSlykeXvgGeAsVb3L7yuAUHeUSwTnOUfwE9jZuMKvj1X1Fr897REmEekpIjv7WXg2ccjIkSN3q66urgYuiduYGPkQGBK3ESlmR+CDuI2IC5/CUGKtzdludH4CWGqMmRe3LXGgqm+uXLnyH0OGDCkGrovbnlwk0ZNBRLYHHselaFykqterarV3riXoQ+cewXnOPWqBkyAe3UjfTnwiLgJ+b7rvv76ISJcuXbrcMHfuXJYvX36hqn4dt01xYYz5CNjIWlsSty2pwFq7KfCI1z7OW4wxDwLZNsHtDDvjHJp85vz3339/Ze/evQ/2z+ZAkhCRQu8bnwQ8CNymqnuq6vt+v4S0jNwlOM85hqrer6qv+Z/TFnX2D5JTgWJVvRT4JXCeiGRLZOuMMWPGbNzU1PQJriI63/mSHJSu81rWO5EH0nQdpKe1dmzcRiQba20F0Cvf0jVao6pzVfXyoUOHUlJSMilo/ScPP772xsnHTlDVq2CNBmd5/dnLdYLznEPELEfXFShS1USkZ7aqPqaqtTHa1CFEZHBhYeEFX375JQ0NDRNDXhoYYxYAL1tre8dtS5LpCdyd705VhP8BNTmY496VUDCd4MapU6fOKCoqGotflQxsGD7ivCnwKtCEk4BMRJvzohg33wnOcw4R80x3J1y+1xoPkCyJdFy51VZbdVu+fPmDqvps3MZkEKuAH8VtRLKw1vYE9jXGrIjblkzBTyJKcYV1OYG1dhugwhgTnBhAVevq6urO3GijjejRo8dlvnlWYAPwY+04YJKqHqOqSyPbA3lAcJ4DyWIToA5Wa10WishRwMkicqmI/L94zWsbEdmhrKzsZzNnzmzEtzEPOIwxdcALOdS2uy/50yijwxhjpuG04rMea20hrmj6zbhtyTAe+OKLL17o3r17L/K7GDopiMiOwPnAKz4KndT6oqCSlfkE5zmQLD4HDvdtSPsARwHTgTuAlcAfReSWTIp6iEhBUVHR5EGDBrF48eLrVfWLuG3KQGYCP8/2ZX1r7UhgoNeyDnybz621h8VtRBLYF2gIaTlroqra0tIycfny5S2DBg36tYiMi9umbCTi1G6Bk4P90NcErvcqh4hUisjG0W2Rldusfu7mMsF5DiSLhcAS4ESgEnhVVd9S1SWqejVwGPAL4HIRyRQVh58MGzZs+wULFiwErojbmEzEOyEvAgPXdWyGsynwRtxGZCrGmHpgmbW2NG5bNpAaY8ycuI3IRFT1wxUrVtzSr1+/QuDG4Jh1noiTvAnw9oZcK/L/vwXwZ7+tQkQ2F5GfiMhTwEMicpeI5ExaVa4QnOdAspgGFOJyZMcBMxKzdBEpUNXngQuB/wO+H5uVHhEp79at27WLFi2ipqbmHFUNebBrwS/rb5qtmsA+B/bFkAPbPsaYp4HvxG3H+uKbvuRbJ8HOcvFHH31UXVFRsTdwUNzGZCMiMhZ4WVXn+/edmoRE1Th8fdD9QIGI/BP4N6748J/AKKABeAE4RkRyYWUoZwjOc2CDEZFCr6pxLk5b9VSgn6o2+weLAqjqlcDHOBm7uDmvsrKyoq6u7m3cgyrQPh+ShUVlPl97K2PMyrhtyRIarbXj4zais1hr+wMtIV2jfVR1MXDJgAED6NKly40iku0rDXEwRVUfSbxJFAmKSHlbB7d2riMpGbsAZ4nIHbhn609w4+eduJXaH6vqob7h2d+ArUSkOPm/TmB9CM5zYK34QojS6Pu2jks4yar6EHA7MB7Yy+/TRAGhP/xcYISIlKXY/LUiIiNKS0vP+vzzz2lsbJwQuj+tG2PMEuBNa+2AuG3pJIMIk6PO8CYufSNrCpZ8Pn4F8GTctmQJf5wyZcrnhYWFI4EJcRuTbbQ1XojIKcCDInKsf1+QaKISda5FpLeI/FxE3sA18LkaN17+1b+/RlXPAB6INFv5Gy76PAbIlJTHvCc4z4Fv4b/0R+A0QZ8WkYkisklimWkdp58FzAZ+JSLbRLYnHjg9cV0Q49R/vnbzzTcvra2tvTPRUCbQIZYBP4jbiI5ire0L7GiMaYzblmzBR24VV3iXLewAFIWoc8dQ1cbGxsaJgwYNonfv3heLSLbXM8SGDzB1xX1ftgAOBudgq2qzHzNH+S6Ek3AreLfjcqZfBw4EdlPV44EjgANEZJg/LxFl/jsuAPAC8Y6bgQjBec4SRGRYGu+zH/Cuqv4J+A0wAnhORIZGZtGtl6LU5zZX4x4C2wNn+PywqP5lFXBHXHqYIrJ7aWnpoV988UUtcF4cNmQrxpgG4CFrbfe4bekgXYGH4zYi2zDGzASmZEP02VpbDMwyxrwfty3ZhKo+WVVV9UhJSUk5cHnc9mQrfhyrBz4F9lfVQ0SkVES2FZF9fR7zm8DNuHHxRVza4j6+lffjqrrEX2slcCOwtX/f6F+fB05Q1T+EVdLMITjPGUyiwYiIHAc8l6bb/gB4Q1WnA6jq/4AzcNHkBxLR5LacX1Vt8Q7068DJOIWGe3z1cBcROQQ4AfhPmn6XNRCRwpKSkknDhw+nurr6ClWdHYcdWc4S4KhMd6ystZsCI7xWdaDzzAOOjNuIDnAA36xqBTpBS0vLmXV1dY1Dhw79PxHZNm57shE/3rXgOnX+WkR+i3OQ78OlYewKfIArlh8A/FxVb1XVd/35iTFeAFT176r639b3STjSIhJ8tgwh/CFiZm1fBp8rlWgTfSVe2D5V4uk+R2sQbna8MrJN/MNhf2A0cKGIjE7Y2MalEsWBtwE/B54HzsFJwZUDE1T161T8Dh3ghIEDB27x1VdfzQKuj8mGrMYvjT8BZHrb7grg5biNyFaMMbXATB/ZzWRmG2PmxW1ENqKqU5ctWza5rKxMgElBuq7zJCLBqvooboz+Epee8Sdgc+C7wEGqeqUvql9DvzkxxrcORrVTXxQmihmChG6S8SEiXf0Xqq19RaraJCKXAT9Q1a3SZNN9wIOqekcbthwO/AuYjBOIX9GqIEIi8jutHwZr/V3TgYj07t69+/TCwsI+1dXVR6jqvXHZkgskZMEysdW1tfb7wIeZaFu2Ya3dD3gyE/OJrbWHA/dlom3Zgoj0BKYNHTq0/6xZs45S1X/HbVMu4oNkGtp35w4h8hwTPv3hPhH5nn9fENlX4J3VvsDZwK1+e2Eql21EpBtOV3JHf29gjdnxvd6W4/BazdGHQeLniDO92t44HWfPxaNGjeqzYsWKl3BLaoEN42VcXntG4dNJRgTHOWnMxxVCZRTW2j7AouA4bxiqugy4oEePHpSVlV0fpwpSrpAY86JjtS8gDJ/VHCI4z/ExElehe5GIdG+1HJP4kv0DeBR4TUT29dW7KVu28QULL+Faa6+RAxdxgk/GpXX8SkR6tTqmp4icLiL7+mNTam9HEZHNysvLT/noo4+0ubl5QniIbTjGmOW4orKhcdvSitE4ndRAEvCFeLWZlL7hpekqjTEvxG1LjvDXKVOmvFdUVDQYl2IX2AAiqRyxj32B1BGc55hQ1ftwGpv7An/xFboJB1W96sVI4HBgDvATEflzqlU3VPVm4HPgEhEZGdnekihuwOVFHwQMhjVm2KXA+cDumVTYUFBQcOPo0aOLmpqa/pzQzgwkhQVkkKSZtXYgsGmIRiadGlxhXqawM7A8biNyBVVtbm5untinTx/69et3XrqUnfIVL2+XMeNjYP0If8AYiBQD3As8gusmND46U1XVr3CtOotVdaGq/gzXAvv4xMMthQUeP8VFnk8SkR4Rm5p8SsmrwD3AdX57QmVjIfB9VT0vU2bdInJAUVHRvtOmTVsBXBS3PbmEMaYJuNM7rZlAIW6lJpBEfEHeG5nQnt1a2wWYZoz5LG5bcglVfWnGjBn3tLS0lOIadwSSSCTwlGgc1iIig0Vkc78/FGtmGcF5joFIbvAinOP8MnADrJ6VJr5IM4Bog4dHga2AS6PXSYF9XwAWOA3YI5LDJRGn+GZc5HGN7ao6LRU2rQ8iUlJaWnpTZWUlK1asuMQ794Ek4lUZfuDbYMeGbym9sdeiDiSf5bgVp7j5AWs+EwPJ4xxVrR8+fPiRIrJz3MZkM1FnGb4JPInIQSJyhIg8ALwDnCgipSGVMBPP0dIAACAASURBVPsIznOaSci/+Z+LVLUB+APwPREZ72eliS/SwzjJLQBU9RPgdGCkiOyWSjtV9QrgKeAqYA+/TSMPhRqcbmXKnPgkcErfvn0rp0+fPg34fdzG5DAP42QI46QECN0iU4QxZhXwYZyTJJ/r/JExZnFcNuQyqjpz6dKlV/vhaVKqZFHzgUSRvYhsJyL7iMjVuOLbCcAvgDrcZPQ2oGmtFwpkLMF5TiMiUhGtuo2oWNwNvA0cEz1eVb9W1bmR8wtUtUpVd1HVF9Jg8iG4iNNJCWc9oj3dAvw5DTasFyIyoFevXpfU19dTX19/mp+kBFKAMWYBsLu1ttc6D04B1tq9cUv5mTqJywmMMe8BB3snNg4SqWuB1HHNjBkzZo8cOXJrnE5/oBOISFcR+a6IHCsiH+G6N16I++xeiasJOlZVj1LVl1T1Q1VtjtPmwPoRnOcUI990EPohMEVEdm9rP05Z4/D2ruXzpNIWDfCO8i+AauBaETlYRCpEZC9cMWMm55deNmzYsO5Lly59XFUfi9uYPOApYFy6b2qtLQDKjTHV6b53nvIJsFm6b2qt7QlMMcZkRC1FrqKqK1X1nJKSErp06XJltOYl0CHqcJOO84G7gLOAO4A/qeqNqvp2IiAWHctFJPZ6gkDnCE1SUkirBiJfAUNw+c2nqOqHPn1DvFPcBXgT50BPy5SCO1gtpH8gLt8a4C+q+nmMJrWLiGzVp0+fd6urq5tbWlq2UNUpcduUD1hrhwOFxpgv0njP7xhj3krX/QJgrd0SmJqu1ud+grSrMeb5dNwv3/Hj0st9+/b93pIlS65V1SBf1wlEpLeqLm217SDgFfXddf3/cQWwCpeKWQncoL5tdyDzCZHnFBJxnC8B5gJ7AmOA/4rIbpGqW1HVOuBjoDSxLTbDW6Gqy1T1X/4hel6GO85SWFg4efjw4dLS0vL74Dinla9wMmJpwWtMD0jX/QKrmYsr3EsXuwAz03i/vMaPWxPLysqoqKg4XUQq47Ypm0g4ziJSFBnHPwLGiciBInI8rlPvLbjv0kE4edhY0t4C60dwntPDs8Dhqvo8bjmnL/CYiBwjItHmA83AXpB5RXiJh0AmRcTXwmFFRUU7f/bZZ0vwqiSB9OCX1P9prd0k1feK5N0+mep7BdbEF+w951MpUoq1thsun70q1fcKfIOqvj1r1qy/1tXVFRUUFFwftz3ZiKo2ReqbZgCfAufiivCrgf8Be6nqdsDlqvpcXLYGOk9wntOALwyY5X++HTgVEOC3wM4RR/kpXE5hxuk+Zpoz3xYi0rWsrOyGUaNGUVtbe2HrpbNA6jHGNAO7WmtLU3yr7YABXms6kH5WAoem4T4H+XsF0s8FIrJyxIgRByW6xgY2iAZcIG1bYKKqXq+qr3shgEwPSgVaEZznNBKJ3v4d2BX3ZbpfRI72h1QAm/hjMt5ZzUDOKi8vHzJt2rSPcBJAgXh4AChL1cV91LnGGPNOqu4RaB9jTD3wirU2ZQXMfgL2UigGjQdVnV9dXX1pXV0dwI3/n737DrOzqhY//l2nTMvMpAyZhDQSkkkCKXRFEZAmFkQvitd21Xs1YrkCAoIC9263HVSaGtv1Z71YAL2CCFLEigVpgUB6QiCTZJLMJJNpp67fH++eOExmMu2c855zZn+eZx7IKe+7JpPkrHe/a6/V7y6pN0Kquh94BLgAOEFETu03O8ErIT55LqA+NdARgtZ0lxH0fvyBiHyYoHfyn9xrimrludiJyKyGhoZPiAjJZPJi3/4nPMaYNuBMa+1heTrFebgBPV54jDFrgbe7DX358G/4n3PYbm5ubt64aNGio4APhB1MGbgH+Laq/gV4xC+SlS6fPIfA9XrOqurdwIeBFuDjwCuBevca/5dqZL4wbdq06p07d95RoB7Y3qHdDczJ9UHdqnPSGNOa62N7o/Iw7m5ZLllra4G/+7KccKlqQlUvz2QyVFRUfEpE8nVBPC64JgGd7v8H7FbTZ4iaX0ArYj55DpmqPggsB7YDy1X1TyGHVHJE5OXTpk17x5o1axLAx8KOxwPXxqzDWpvrnsCvNMb4TYJFwrUlnOI29uWEm2L4SmPMqlwd0xuTOzds2PBAQ0PDJMCGHUw5cJMHv+9mJvQ+FoEDk3wjfgGtuPnkuQio6m7g3wluU/YdnFLWcnFlLSKReDx+y+GHH042m/2Sqm7ORWxeTqwnuDDMCWvtEcC4+LtRYtYCr8nh8V4KPJnD43lj4JK4jwKZGTNmfEBEloUdUxmoJGjrua33gd7aZxE5hWCq71ki8jEROU1EFoQUpzcIPyTFC4WINAD3AqeratcYjvOeCRMmfDeZTG5PpVILVbUjd1F6Y+U2lC0Z6yqiK9c4Etjkx3AXH9e2rtIY05KD4zT41nTFR0S+Ul9f/58dHR0PZbPZs/zK6Mi4BPhYggvD4wmmEf6qd3+OiCwkKONcD3yXIMFWYDqwjKDrzIOq2tN3AJsXDr/y7IVCVfcAG4FRT68Skbra2trr5syZQyqVusonzsXHta47zlo71u4brwCqfeJctDqB83NwnPMJ9oB4xeeTwN4FCxacAbwh5FhKUS3wPDAFWKWqv+yTOL8KOBd4Ftju6qLbVLVNVZ9V1Z8BdcDbQord68cnz16YrgQ+IiKj3Vh2dUVFReP69ev/DvxvDuPycut2gg+OUXE1sM8ZY57OXUheLrmNffeMpb+3tXYS8CtjjL8ILkKquqe9vf2/2traAL4sIlVhx1RKVPUJVf2b+3q239MVqvoVVf0GMFtEFrja575lar8AThORue45v6EwRD559kKjqluBrwDXj/S9IjK/sbHx8srKStLp9MW+V2bxMsZ0Ai+z1h4+ykOcD4y6tMcrDGPMNuCd7mJnRFxZzluA9pwH5uXSN/bs2bN62bJlRwKXhh1MORCR44C+/zY+BLwKgimF7jUvBf4D2MwophD36eAxLTdRez559sJ2PfByETl1hO/7Un19fXz79u0/UNW/5SMwL6d+DYx4nLNLqna6kdBe8bsPmDGK91UBD7kyH69IqWo6m81e2t7eTjwev1ZERvOz9l6sEjdZGEBVnwQ6RGSZiDSIyAcJNl7fqaqfAipFZETtIfsk2q8QkbwNNhpPfPLshcptFrwKuHm4f6lF5OxZs2a9cfPmzZ3AJ/IaoJcTxpgUELXWHjvCt55njPlzPmLycs8Y8zww11pbP9z3WGsrgNcbY9bnLzIvV1T1geeee+6XM2bMmAB8Lux4ysByglrovv4GXA28B/iTqn5bVXs7c+xU1Y0jPYnbsHicHyCWGz559orBTwhuy79nqBeKSCwWi908ZcoUMpnMZ1W1Oe/RebnyDDBruC+21s7E38YvRY8TDHwarmOAP+QnFC9Pruju7k7NmjXr3SLykrCDKUV9apbX0edujYicA5wDbALWqepT7vHexaX5IjLsErje/tHAWwG/EJEjPnn2QuduKV0CfEZEhrq1//7a2tqjn3766c3AjfmPzssV1ynjXmvty4Z6rSvXaDDG/D7/kXm5ZIzZD/zJWjt7qNdaaxuATmPMjvxH5uWKqm5oaWm5sb29nWg0eovfvDZyfUop2oC1IrJQRC4FGoHvqOo1QJOINLrXZ1yL17WMoCONqmZdrfORwO/8zyo3fPLsFQVVfZSgLvbawV4jIlPq6+s/O23aNLLZ7BWDjTf1ipfrynDkMCbSnQl0FyAkLz/2Aa92F0GH8lqCFTav9Hw2m83uWrhw4UuBt4cdTAlLAO8nWG2+U1X/V1V7/+17FDeASEQqgXcTlG0Mu/RCRGYCPwI2ueP65DkHfPLsFZNrgH93zeIH8kkRmbR+/frfEbTt8UrTz4CGwZ601lYBz/ga2NLlNv7dxiE2ibruK3e4Ue5eiVHV9o6Ojquam5uJRCLXi8io21GOZ6q6BthJUNbWAdDbBlBVfw/Uich0oALYqKp/GeqYfUo1IOgN/biqfsYd03emygGfPHtFQ1V3EHTf+HL/50RkyeGHH/6hmpqabDabvcRPVypdbvPgUjdueyBvIFiN8UqYMWYv8Ca3IfBF3Ir06/F3F0rd9zs6Oh5btmzZDIKN30VBRK4UkTe7rytF5MgxHu9IEflmruLrc9zeVeD/A/YAV4jIbNzqsKtz/j1wMcHm+OZ+7xtQn1HfnyIYrf4b92uf8+WI/430is3NwFEicm7vA+4fihvj8Xh0+/bt31TVMY169orCfUCk/2199+s1xpjWcMLycuxXDHyXoQq420+MLG2qms1kMh/ZtWsXsVjsYyIyN+yYROR+4AFVvd19XQ/cNsYE+rYchfcivYtAqrrPfa79L7AU+JiIWOBC9+tngJtU9ZG+7xuMiMwSkU8STGY9X1UfdO/zq8454pNnr6ioagK4DLhRROLu4fPmzZt3TnNz817gv8OLzssVV/tcC/Tfqf9WwF8clQljzE5gudsYCBwoy3mTG6rilThVfbi5ufnH8+bNq2QUA69ySUTe7GJ6rN9TnwdGtXLsjlmQi3lVfVJV73H9nL9GUOL2G1X9kaq2DGezn4gcRjBQ5TjgC6r6qF9xzj3/G+oVo7uAF4APikhlNBq9oaamhnQ6/UlV9cMyyoQx5imguvfX1trpwEa/Gll2/gAc3+fXTcA9IcXi5cdVra2tPTNnzrxQRE4PMY6LgP6JM+6xs0Vk0kgO5lar91LATa29CbKqtqhqVlVbex8fxorzacD3gFcDP1LV+9yx/IpzjokvHfWKkYgsAX4HfLWxsfGTLS0ta4DlqpoKNzIvl6y1ceA0gp/1icYYPy2yDFlrGwk2D+4HDjfGPB5ySF6Oich/T5482XZ2dq5KJpPHhzGMQ0TagKtU9VsDPKfAOar6wAiO935V/VZvvbOqXpS7aEdnoCRaROYBVwAfBG4FPqiq+8OIb7zwK89eUVLV1cAvKyoqrqmvrwe41CfO5cdtHmwAXgf4Xr/laxdwBsHwlNWHfqlXor6USCReaGpqWg68N6QYJnHoEoth1z27co2fjTmiURCRSG+phQR6/z/SmziLSKOILBGRjwAPA6cDJ6vqO33inH9+5dkrWiLyw1gs9s7p06c//L73ve9rBP9ALAPqgHsJbk1tAXqAxQS7kl8CxAlWMs8GNrjDLQAeIPjwTgF/J/jHZg3B5qW5fY65H3gKeLn77xRgZp/nW91xX0IwTW0GMK3P8zsJdkUf586zwB2j9/lt7hjL/PfE7wgS5yhBrXO5fE/l+HMa6/d0AcHGp+4y+p7K8ec06u/p5z//+fKpU6fe8OCDD5IrqjqsvsSuJKMNuFBVbx/geSVYlR6yLtuVaxzZu0pdyJVnEakBEr0r9y5hzopIHUEru3kE7edmEGwmzABfU9U78h2b908+efaKkoicMHv27EdisVjmtNNO+8/vfe97OW8T5BUHa+07CMZwr/G9ncuTtTZKsBqZAH7qezuXp2uuuWbqvffe+3tVPerxxx+/QVUvL9S5XcK7kcGT5zbgW6o6ZEu93nKNPr8uZPI8l+Ci6gSCYUO9d+eOAzoJFht+SnAhuk1Vf5LvmLyD+bINr+i4DRM3p1Ip2bx5883z5s27w1pbF3ZcXu5ZayPAXwlamiWHMZHOK021wO0Em4H9MI0yVVFRUbl27dr3vPDCCxqNRi8WkUUFPP1QHTEmEfRSPqQwyzUAVHWLqv6CoEPIo8AfCe5UXEGwGXCOqr6XoHWdT5xD4pNnrxj968KFC0/Zs2fPLuDTBLd53xhyTF6OuUT534FNrsNGDXBquFF5uWatrQXON8a0uv7dJ1trp4Udl5db1tqlwLyOjo6/79q16zuLFi2KRSKRGwp1flXdO4yXHfI1vb2gh3msvFLVTlX9rao+qqpfVdU1ro1dqyvlKPiGTO+ffPLsFRURqYlEIl8ESKVSV6vqPmNMJ/A3vypZdhqAR3pb0xljnsVPnCtHM4A7+/z6PoJ2dV6ZcP82J4E/uYeubW5u3t/Y2PhaEXltAUPZxKE3BQ7Vcu5s4BwR+WbfL/f42e7X1+Uq2OHobV3Xt8ezbz0XPp88e8XmyhkzZsxat27d48B3ex80xqwD3uMT6PLgWtQtN8b0H4iyylp7fhgxeblnrZ0NTDHG7Ot9zBiTBDa7lUqvPJwNdPVeCKvqzr1799psNktlZeVNfQZe5dtjDDDRss+K8iHb1Knqt1T1ov5f7rgPuF8XdAx5nymEfoNaEfHJs1c0RGROQ0PDVfF4HOCSAW5L/R04vPCReXlwJvBs/weNMQkga62tLHxIXh4cD/xjgMebgRP9xXD5MMa80O+hr7S3t29cuHBhE/DhAoXxU4JEvr+zCbqOeF5O+OTZKybXZTKZqs2bN/9MVf/Y/0ljzGpgvk+sSpu1djLwpDFm+0DPG2N+BSwvbFRerrmV5QfcKPYXcSuU3wcWFjwwL6estWcxQGKqqsmenp5L161bR01NjRWRqfmOxXXZaBWR/gn0Re7rRUTkNhF5/zAOfSQj6BHtlT+fPHtFQUROnTt37ltjsVgCuPIQL10LvKpAYXn5cT5Ba7pDqbLWLilEMF7uWWtjwMluv8KAXAL9Ereh0CtB1tqpQE1vucYA7k6lUvctXry4nmDzd96p6jkEdcvvd1/XEbSvG6je+Xhg/mDHEpErReR+97qzReT+YSbbXpnzybMXOhGJAje3t7eze/fu61T1ucFea4xpAf5irT2ors0rftbaGuBeY0zXEC/9E7DftbLzSk8DwZjgofwfwbAOrzRNImgzOSBV1Ww2e+mmTZsy0Wj0/SJybCGCUtWrXP3yt9z/D7hRUFXnH6qGWVWvV9VzVFXc1zkDjf72xh//weQVg/csWbLkuM7Ozm3AkNOfCKYsvT7PMXk55upb3wm0DPVat5JVAZyV77i83LLWTgLOGMYFEsaY/QSt62bnPzIvl6y1JwCTDrHqDICqPrt3796vLlmyRKLR6M19u0Z4XqnyybMXKhGpF5HPdXZ2kkgkrlTVQW/z9nLTye631voVq9IyCXhoqA/bXsaYDcDzflNZyakjGIYyXPcCjXmKxcsDV5azi4E3gw74ls2bN7dNmTLlNIIx7Z5X0nzy7IXt2nnz5jVu2bLlYeDHI3hfM/Aun1iVBneh8/JRjN9+DnhLHkLy8sBaOx+Yd6ha5/7chsLd1toT8xeZl2OvBrLDvRBW1bb9+/dfE4/Hqa6uvkFEqvMcn+fllU+evdCISFNDQ8OlqVQKgtZ0w+5j6f7RfhDI+w5uLydOJmg1OCLGmG5gl7W2IvcheXlwJP8clDFsxpjngHm5D8fLkz0DtKYbyrd379799Pz58+cAl+UjKM8rFJ88e6GJRCJfVtX4888//11VHe7tvwOMMRuBY90mNK9IWWsbgc3GmF2jeb8x5rfAK3IblZdr1tqTgL8aY0Y7/ex2V0frFTFr7RuAv470faqaTiaTl6xdu5b6+vprRGRmHsLzvILwybMXChF51bx5817vapyvHsOh/oFPrIrd64AdYzxGh7XW934uUtbaKLDUbQAcFXc3aaG1dmLuIvNyyVo7BegebrlGf6r623Q6/fOmpqZq4Au5jc7zCscnz17BiUg8Go3e1NLSQltb26dVddSJlTGmFXjKWusnDxYha+1hwO1ucuBYPEKQQMdyEJaXe3MJhp6M1S8AfyepCLn9JXOMMfeN5Tiq+rE1a9Yk4/H4O0Xk5ByF53kF5ZNnLwwfOOqoo45KpVKbgJtycLw9wGtzcBwvh1yP5gsIWguOiVvpSgHnjvVYXm65C6TjxlCucYDrpLPcWuunuRWflwFj/hmr6qbOzs4vL1myhFgsdouI+DzEKzn+D61XUCJymIh8qq2tjZ6enstUdawrkhhjkgT1klNyEKKXOxOAe0Z7i7c/Y8zzwCq/ebDoVHCIQRmjcD9Q4TvpFA/3d+45Y8yqHB3y8+vXr99ZV1d3EkHvd88rKT559grNLlq0aNK2bdseAO7M4XHbgTe72ksvZNbaCcC5LuHNpVZ867qiYa09CljgVoxzwq1gJ4FTcnVMb8xeB2RydTBV3d/Z2XllXV0dtbW114tIXa6O7XmF4JNnr2BEZNnkyZM/0N7engE+OpLWdENxq5u/Bvxmo+KwHHgg1wd1/YPX+9rnojEZ+GOuD2qM2QTU5vq43si5OwAbjDFj3fTb34+am5sfmTlz5jTgEzk+tufllU+evYIQEYlEIjdXVFREmpubv6GqT+f6HK7v6KnWWr+KESJr7UyCPrB783F8Y8zfgNf62/rhstaeDqzOVVnOAO6z1p6Wp2N7w/c2YHWuD6qq2XQ6ffGWLVtoaGi4XER8nbtXMnzy7BXKGxYsWHBGd3f3XsDk8Ty/BY7L4/G9oZ0DbM7zObYAR+f5HN4g3GbQw40x+/J1Dle+Mc1aOylf5/AOzbUN3JqLzaADUdW/JhKJH82dO7dCRL6Yj3N4Xj745NnLOxGpqqiouOGFF16gvb39v1R1T77O5frMPmetnZOvc3iDc7/vtxpjUvk8j9u4lPWbB0OzzBjzkwKc5+cEpSFegbk7O8uMMSOeGDlCH3/qqae6qqurLxCRM/N8Ls/LCZ88e4Vw6eLFi+el0+lngG8U4HzbCFY/vQJydcjnErSUK4S9+BaFBed6qs8txLmMMRngCLcx0SusU4Hd+T6Jqm5LJpOfa2pqoqKi4mYR8fsZvKLnk2cvr0TkcODaHTt2kEwmL1HVdL7PaYxJAz+01s7O97m8F6kGfpnHGtgXMcZsB/5srfUbyworAtxTwPP9Huj2Ne6FY62tAjYaY9YU6JQ3rFu3bmtVVdVSYEWBzul5o+aTZy/fPrd8+fIJLS0td6pqzrsvDMb1fj7XWhsv1DnHM2ttPfA6Y0xLgU/dAbypwOcct6y1xwKz3d+vgnAXY1HgjEKd0+N8IGftB4eiqt3d3d2XNzQ0UFdX91kR8aU6XlHzybOXNyJy0sSJE9+zY8eONHB5CCH8Ej/qt1COBO4u9EmNMd3AY76/d8Eo8LdCn9QYs5Gg97OXZ6786hFjTN72pgzijq1bt/6hsbFxMvndVO55Y+aTZy8vRESi0ehXamtraWlpuUFVNxQ6BmPMLuAca61fxcgja+1cIO02axacMeYp4C3+tn5+WWtfDTxfqLKcAfzFWutr3PPvXUCuhxsNSVU1k8lcsm3btmxjY+N/iojvpuMVLZ88e/ny9gULFrx03759u4DPhhjHr4EFIZ5/PDgZeDbkGB4HmkKOoWy5C5O4MaY1rBjc5sGYKxHy8sDtH1jl9o0UnKo+0dPT8+1Zs2ZFgRtFxF8Qe0XJJ89ezonIhJqamuu3bt1KR0fHVaraHlYsxpguYK+11idWeeC6INzhEpvQuI1NdW6jk5d7LzfG3BV2EMaYO4Ejwo6jHLnSp1OMMf8IOZT/euKJJ/ZNnDjxVQRjwT2v6Pjk2cuHqxYuXDgjlUo9Bnw/7GCAjQSro14OuR7LL893T+cReA44L+wgyo3rWlNMY+9r3cZFL7deBqwPOwhV3ZXNZu2cOXOoqqq6SUR8L3ev6Pjk2cspEZkbiUQ+tmXLFtLp9MWqmpfJVCPhpmPdaq1dHHYsZaYWuC3sIHoZY3YDD1hrp4QdS7lw5RpR4Ddhx9LHX4E9fpNo7rhyjeeNMZvCjsX52rp169ZHo9H5wEfCDsbz+vPJs5dr1x977LFVe/fu/bGq/jnsYHq5soKX+dv6ueES1LONMaGV5AyiC3hj2EGUkZcAk8Muy+mrT+s6Pwgpd84nGDpUFFQ1mUgkLpk2bRqTJk0yIjIt7Jg8ry+fPHs5IyKn19XVXfjcc8/1AFeFHc8Afo5vXZcrjUDoNbD9uf7DD7l2W94YWGsjwG5jzONhx9KfMWYLsCPsOMqBtXYC8IAxZl/YsfSlqvds2bLl1/X19XXAZ8KOx/P68smzlxMiEo3H47c0NDSwZ8+ez6tqwVsdDcV9OJxhrZ0adiylzFq7EKhxPZaLjjFmM/Aul/x5o/d6oNjuLPT1tLX2zWEHUcpcWc7bgNC6qBxKNpu9bPfu3emZM2e+V0SOCzsez+vlP1y8XPmPuXPnLm9padkGfCnsYA7hV8D0sIMocUcRtIYrZr8D5oQdRKlySVW765VelFw7tX1u5dQbnWrg4bBa0w1FVdd2dXV9paGhQYCbfes6r1j45NkbMxGZVFtb+7lt27bR1dV1uap2hR3TYIwxCSBjrV0SdiylyFp7AnBPiIMyhsVtfJrlNkJ5I/cqY8xDYQcxFGPM/cByPyBn5Fxp07nGmGfCjmUIn1q1atXuqVOnngpcGHYwngc+efZy47+ampoOSyQSfwZ+FnYww/AssDDsIEqNtbYSWObqikvBU/hNZSNmrZ0FFM0GwWFIACeFHUQJOgF4JOwghqKqe4FrGhsbqamp+bKI+H0rXuh88uyNiYgsqqiouHjt2rWayWQuVtWiXpGEA7v173KrqN7wHQb8OOwghsvVuD9krT087FhKhVvBrQUeDDuW4TLGPAZst9bGw46lVFhrJwJ7jTEvhB3LMH1n7dq1qyKRyCzgirCD8TyfPHtjEolEbli6dGmsq6vrO6r6WNjxDJer8TvaWutXMYbBWjsNeIkreyklHfgpZSNxKhAp9rKcAWSB14QdRAk5HyiVxBlVzaTT6YunTp1KQ0PDJ0RkdtgxeeObT569UROR11RVVb1248aNHcC1YcczCrdRXJPTilk1cHfYQYyUu0i6028qG5qbGLmxBGpgD2KM2QY86zusDM1aexjwf8aYzrBjGQlV/f3mzZtvj8fjVcAXwo7HG9/8PzTeqIhIvLKy8uYZM2awb98+q6o7w45ppIwxPcBJ1tqZYcdSzKy1y4GpJVTr/CLGmBbgbb7385DOB0ryZ+w8B7wj7CCKmSvLuQAoqcS5j4/t378/ccQRR7xdRE4JOxhv/PLJszdaH54xY0bTCy+8sBG4JexgxuAeglVVb3BTZg6oTQAAIABJREFUgX+EHcQY3UMw2MUbgEuqnivm1nRDcRd3m6y1/u/z4CqB+4wx2bADGQ1V3dLZ2fnF6upqCFrX+RzGC4X/g+eNmIhMnThxot29ezc9PT2XqmrJrlYZY1JAjd88ODBr7anAn0uwBvZF3G39o91GKe9gF1D6F0gYY/4MvMK3rjuY65bzJjedsZR9Yc2aNdtmzpx5AvDusIPxxiefPHuj8en58+fXd3Z23kcJ1sEO4CmgIewgio2rgZ3jylvKwV+Al4cdRLGx1k4Hdpb6BVIfzcBLwg6iCB0F3B92EGOlqp3AVfX19dTW1l4nIvVhx+SNPz559kZERI6pra1dsWrVqkw2m/1oKbSmG4pLGh5yq6zeP80Dbg07iFxxG6T+Ya2dG3IoRcNtsJtpjPlT2LHkijFmNbDTrbR6HNgkqK7+vxzcum7dur9FIpGpwNVhB+ONPz559oZNRCQajd68cOHCSDqd/pqqltyu/MG48o05vl4y4AZlLCyj1chee4BX+dv6B5wF7As7iDzoBM4LO4gici6wNuwgckVVNZPJXDxp0iQaGxsvE5EFYcfkjS8+efZG4oJ4PH76unXrWoFPhh1MHvwYmBF2EEVCCDbZlRW3Uep/CTZBjmuux/kqY8yGsGPJNbfx8WFrbVXYsYTNWjsb+HkZlV8BoKp/37p16/czmUwc+FLY8Xjji0+evWERkerq6uob5s6dS0dHx7Wq2hZ2TLnmEqum8X5b31p7IjDN9UguO6584zxX0z2elXpruqG0Am8LO4gwuTssrwHKKnHu4xOpVKpz/vz5bxCRc8IOxhs/fPLsDddlhx122JxNmzY9DXw77GDy6H5Ax/lt/agxpuQ7LwzhV8C43Wjk/nw/aYwpu4vgXm4a5hPjvPa5BvhlGZZfAaCq29vb2z/jtt7cKCK+l7tXED559oYkIjOnTJly9f79+0kmk5eoalmuSAIYYzIEUwdPDjuWMFhrzwWeDjuOfHMbp17qNlKNR+8C1oQdRL4ZYx4HXj0eJw+6/RsXGGNKboDVCN20adOmzfPmzVsCXBR2MN74MO7+QfFG5fNz5syp2bdv3y9U9bdhB5NvxphVBDW/44qbwDeh1Mb2jsGDwNKwgyg0a+1UYHW5rkYOYDVwXNhBhGAu5dFK9JBUtQe4PB6PM2HChM+IiG876uWdT569QxKRkydPnvxvTz75ZFJVrwg7ngJ61Fr76rCDKLBjjDE/DzuIQnEbqNZaaxeFHUuhuAukxeOgLOcAtyGyy22QHBestYcDdcaY1rBjKZD/W79+/W8rKysnUZ6b2b0i45Nnb1AiEonFYrfMnTsXVf2yqm4KO6ZCcfWSNeNlt7619khgPJYw7ABOHUc17mcCz4UdRAh2EGyQHC9OAZ4IO4hC0cClVVVV2RkzZnxQRMbdHSWvsHzyXCAiUoofzu+MxWInrVmzZgfw+bCDKTS3Clv2q5IucUxTBtPHRsqVLnyPYCBMWbPW1hNsEtwadiyF5jZG3jcexrNbaxcCvzbGlHMnlYOo6lPNzc3f6OzsjEYikZtK9DPXKxE+ec4zEelth1VSv9ciUjthwoTr582bR3d398dVdX/YMYXkMPdhVM5OASa6Vn3jjmvJd+o4GJDzeqAr7CBCtB+4MOwg8slaGwVeCXSHHEpYjKruW7BgwVkEf949Ly9KKqErNSLyL8C1IvJ5Sm+E6Cfq6+unbdiw4R/AD8MOJkS/BRLlelvffV97jTFPhR1LyH4JlG3ybK2NA38wxozXi+DeKaJ/dL8X5WoS8JNxtBn0RVR1d3t7+393d3cD3CAi47lNoZdHPnnOAxGpE5EVQJWq/jfwNeBwEbkx5NCGRUSOnDp16hWJRIJUKnWxqo7LFUk4cFu/Bjg97Fjy5A2MzxrYFzHG7AVeYa2dHnYsueYukN4DvBByKKEzxqwFLnQbJ8uKtbYOeI0xpj3sWEL29eeff/7ZRYsWzQcuCTsYrzz55DnHXJ3V5cADqvpjAFV9AbgUOEZEjg8zvmH64rRp0ypaW1t/pKp/CTuYsBljniWYVlZW3C3envG8GtnPvcARYQeRB5OBh8frauQA/gwsDjuIPDgMuDPsIMKmqingo6lUiurq6v8SkbK7IPbC55PnHBKRKEFvzf2qurnP4zFVTQLfAdaGFN6wiMgZ06ZNu2D16tVdwMfDjqeIrLPW/kvYQeTYKcaYe8MOoli4DVbbrbXLw44lV9wI8pOMMavDjqVYGGOeA+JuA2VZsNbOAWb6VeeAqv5m06ZNd02cOLEW+FzY8XjlxyfPOaSqGWA3QVskIFiJ7jORLwP09D5e+AgPTURiFRUVN0+fPh1V/byqbgs7pmLhegJ3u2Sk5Flrm4Bo2HEUoeeBZWEHkUOnAqvCDqIIbQBeG3YQObQU+GvYQRSZy1U1PXv27PeIyIlhB+OVF588514TcLyInO46bdSLSExEjiL4/T5SROYS3EotNu+Lx+PLnn322a3Al8MOpti4VdqXhB3HWPVpTfe7kEMpOq604VZrbckn0NbaKcBaY8z2sGMpNq5U6W5r7bSwYxkra+0xwB9d1xjPUdX1O3fuvGnv3r0SjUZvKcYFK690+eQ5x1T1MeBp4DKgE9gIrCMYBfwjgsb1TwFPiMjFIlIUU69EZHJdXd3nZs2aRTKZvFxVx2uroyFZa5eEHcMYnQHEfQ3swNzvyzFuA1Ypez2wJ+wgilgX8IZS7qTj9i2c5PctDOozqrq7qanpZcBbww7GKx8+ec6h3itbVf2uqr4B+HfgYuARgkEMbwUuAt4N/BiYD1xVJFfEprKycvKGDRv+ANwRdjBF7M9Ap7W2JP/uuC4Dm4wx68KOpcj9ghJuXefqee8yxviL4EEYYzLAr4FSLsWaAfwg7CCKlaru6+jo+HhbWxvA9SIyIeyYvPJQkglAsVJVhQMbB1HVH6nqrcDNqnq1qv7MPfZzVb0K+AxBjfSc8KIGETlq+vTp/yki2Uwmc0nv9+EdzK1KRoCzw45llC4A2sIOotgZYzqBE621s8OOZaTcSupb8T/nIRljXgDeVop7Gay1kwk2/Y6rSYKj8L2WlpbHli5dOgu4MuxgvPLgk+dhkkBl318P9lq3cbD3dYtwk45EJOL+25tc7wIagNBuD7vv48b6+vrorl27/kdVnwgrllJhjNkEbHC3TEuGWy3fbozZF3YsJeI+gqETpaYOeMCX5Qzbbwi6JJWaGuCusIModqqaUdVL29vbqaqqulJEQl2s8sqDT56HICJREXkLQbnF/SJyiYjMV1UdZrnFFIKa5wNUNdObQBN+TeJrZ8+efe6GDRv2AdeGHEsp2Qm8JewgRujVxpg/hh1EqXAbsDqttSWzSdRaWwWc6S7wvGFwGyob3AbLkuC65TS5OyTeEFT1j1u3bv1pY2NjFXB92PF4pc8nz4fgrlBfDTymqt8gSC7nAb8Vkdl9yjQOlUR3A+8UkQZVzYpIFRxIoOcBCeCZvH4jgxCRioqKipsmTZpENpu1biXcGwb3obWtVCaVWWvnA3vDjqPUuCT08LDjGIETgD+FHUQJehI4M+wgRmAm8IewgygxV3Z1dSXmzp37ryJyatjBeKXNJ8+Hdh7wN1XdAKCqfyDoovEC8IveaYGD1Qi7Hs9PAPcA17jX9vZ5vgB4F3BHiOOvP1JVVbVg9erVawlGiHsjYIz5AyVQ++zKSyqAcT8tcpTutNaeHHYQQ3Ft13YYY3aHHUupMcZ0Ab+x1hb9hEn3Z/FxY0xYnxslSVW37t69+wu7d+8mHo/f0ufur+eNmE+eByAiERE5nOC2fGefx8Qluq8BFgLXiMhC9/xBq899kuqvEozm/n8iYkTkM8AE4NOqGkrZhog0Tpo0yTQ2NpLNZj/qJiB6I7enBFrXnQN0+xrY0XG/b3OttRPDjmUIryUY8uKNTgdwbjG3rnP7Fpr8voVRuz6bzTYvXLjwWIJuWJ43Kj55HoCqZlV1O0EnjDf1eUzdqO124L3A+cBFIlLXvwa69/9FJOJWm88DPg38EPiqqv4wxBVngM+ISN2mTZvuUdV7Qoyj1P0DSBZr+YargX3CGLMl7FhK3M8JcWPvUKy104Gf+s4Lo+cukm6niH/OwCKCeQHeKKhqV1dX1xXNzc2IyGdFpNgviL0i5ZPnQbh+kEngZBFp6H28d9S2qt4GfAv4D+AU95j2eZ26//YmyAlV3ayqm1T1wPjuMIjIcbNmzXpfPB5PZ7PZy8KMpdS5D9xugtr4YvQmgmEQ3hi4pPQoa+2CsGPpz61Gnk/w59AbA2NMK3CBu+gsKtbaqcASfwdpzH6yd+/ePx977LGN+E3y3ij55HkQqtoJ/BF4G8EmnAN6W86p6ocJyjo+KCKT+r1mooh8VETOda8tivo0tyJ+cywWk5aWlq+o6pqwYyp1rlfso8X2getuPz9jjGkPO5Yy8SAc+H0tJjXA3T6pyplfAY1hBzGAGEFs3hho4JIdO3ZoRUXFJb2ll543Ej55PgRV/TqwFvikiBzZ5/GsiPTepn8LQR/nGfDPxBqoBD4BnNHnsWJw4bx5807dunXrbuBTYQdTRtqBC8MOopdL8C40xjwedizlwm3QigCvCDuWXtbaCcB5xphtYcdSLtyGy/nW2qJJoN2+iiZjTE/YsZQDVX10+/bt3501a1Y8Eol8Oex4vNJTTEldsfo3gpXni0SkvvdBVU27euaHgZ8BX3KPZ93jLcApqvrxIlp1ro7H41+qqakhm81eo6q+dVmOuNZ1zxTR4JQjgA1hB1Fu3FjzYppGdxRBNx8vtx4GTgw7iD5qCO6EerlzTVtbW8fs2bPP671D7HnD5ZPnIajqRsAClwJn9pkSKH2S4q8TDM140eOquj6EkA/likmTJs1evXr1k8B3wg6m3BhjHgX+Jezb+tbaONBgjHkszDjK2O+stWeFHYS1dibQ5Tsv5J4xJgE8bK1dFHYs1tozgY2+LCe3VHVHW1vbp/fu3UtlZeXNIhIPOyavdPjkeRhU9XMEo3q/gGuk39t5w72kA1cjN1jP57CJyOyGhoara2trAS7tO0Lcy6l1BG0Mw/RqINRNqeXMGJMBJltrw+7KcCb9ppd6ObUPOD3Mi2F37sluI6OXezcnEonNCxcuXAR8MOxgvNLhk+fh+xeCutaLROSV8M/OG0AW+HZIcQ3XF9LpdNXmzZtvV9XfhR1MuTLGrAKqrLWh3Np3Cd1ffA1s3t2B2+cQBmvtkQSt6dJDvtgbFbfS+wNgeohhnGiMuSPE85c1VU309PRcumnTJuLxuBWRw8KOySsNPnkeJpcoryAYcfxFEXmjiEwTkbOBI4G7Qw3wEETklLlz5769oqIiCXws7HjGgR3A60I697/gW5blnUuspllrjy70uV1P8TN9T+f8cxv0znYbMwvKWjuD0hoNX6ru6u7ufmDZsmWT8JvovWHyyfMIqOqTwBXAjcDLgcuB51X1DlVNhRrcIFyN9s3JZJJdu3Zdr6pbwo6p3BljdgK/t9bWD/niHHK3eP/qNi96+fdHoMP1WS6kGoKhLV5h3Ek4g1MiwK9DOO+4oqqazWYv3bp1ayYej18kIsvDjskrfj55HiFV3aeqt6rqlcDHVXVt2DEN4V2LFi06YefOnc3AdWEHM450ARcU6mQucX6P6wbhFYBbfa7A7YMoBDci/HW+BrZw3IbMZW6DZkFYa48HZvqynMJQ1dW7d+/++vz58yORSOTmvtOCPW8gPnkehd6/WMXSgm4wIlIfi8W+oKpkMpmrVLUj7JjGC3e7968FXJWcAfy9QOfyHGPMBmB/AU85G7irgOfzAg9RoI3A7kK4G//3udBMc3Pz3hkzZrwSeGPYwXjFzSfPo1CsHTUGcHVjY+O0devW/RW4NexgxhtjzBrgHfnerW+trQTmGWNW5/M83qAetdaen++TuE2CMWOMvwguMLcC/JS19tgCnO41wB7fmq6wVLW1vb392u7ubqqrq28UkaKaGOsVF588lykRWdDY2HhZLBYDuKTYV8nL2N+BuXk+x6vwLctC4xKrdAE2lR0PrMrzObzB7QGOz+fFsDt21hjTkq9zeIf0zf379z/T1NR0BPDRsIPxipdPnsuUiHwpkUjEt27d+n1V9bf/QmKMWUvQlaE6H8e31jYQtKbzH7YhMsb8Glicr+Nba5cBd7oR4V4I3Erw94AFeTzN6caYe/N4fO8QVDWdTCYvXrNmDTU1NdeISGjtKL3iFhv6JV6pEZGzjzzyyDe0tbV1AVeHHY/HeoLBJb/Iw7HPB36ch+N6Ixez1h5njHk8lwd1PcNPMsY8lcvjloMVK1srCGqRm4A5BD2ZG4B6oBKIAhkgAXQCbQQryM3AC8BG4Plvf2jKsIZGGWOy1tqTrLUtuZ7s6DYk5uUi2xs+VX0wEon8cunSpW947LHHPge8J+yYvOLjk+cy46Ye3tTe3k5bW9tnVLU57JjGO2PMHmvtQ9baw4wxu3N1XFfr/Bu3OdEL39+BWdbaqJtCmCuTgJ/m8HglbcXK1inAKcDJwNEEn2PV7quKoANKjCBxFkAJEugMkAKSQA/BprwU0LNiZeszwOPA3779oSnbhwjhl0BOS3RcuUYV8JtcHtcbHVW9YtOmTa+JxWLvFpGV/u6t158v2yg/Fy1dunTJ3r17txD0o/aKQxfwhlwdzH3YvhsY6oPeKxB3Wz9CUIOeE64s55XjvXf3ipWtsmJl64krVrYagtKJ/wTOIUieTwCWAkcAEwmS5iTBSvN+998kQSI9gWB1eiFwrPtaSrBJ70PAt1asbL1hxcrWV69Y2Vo5UCzuZ3GctXZeDr/Fk4FaX5ZTHFR1w969e29cvHgxsVjsK25egucd4P9AFAERiYnIf4z1L6iINESj0U93dHSQTqcvU1W/Ilkk3DS43+RwbPdU4EG/I7+4GGOeA7bmcFPZRIIhHeOSS5pfAXwV+CRwLnAUcAxBEpwANgFPEKwcP0tQirGVoCxjm/vvVve6tQSbLh8FnnbPZdyxlhMk0qcClwD/b8XK1jesWNk60B3aB4DGXHyPbmLkNmPMk7k4npczn92yZUvLYYcd9hLg7WEH4xUXnzwXhwzwPoKVxLH45Jw5cyZv2bLlt8D/jT0sL5eMMS8A77LWRsdyHGttDXCsMWZjbiLzcmwd8K9jPYi1djFw2Hgty1mxsrUJ+CLwceAkgoR5LkG5xTPAk8BzBHXMI53wqu44u4HN7lhPu2NN458r0hcBN65Y2Tq375tdWc4Wa+3LRv6dHeR1BBcBXhFR1f0dHR1XqSoTJkz4oojUhh2TVzx88lwEXN/oS4DPisioRjqLyJJp06Z9MJFIZIFLS6gX9XjzAHD4GI9xKvBIDmLx8sAYkwJ25qDDyjzG4c95xcrWihUrW98LfBl4GcGK8AxgJ8EK83MEpRi51k2wUv0kwSr1RHfu44Avr1jZ+tK+LzbG7ASOGMtdBvfePe5YXvH5QWtr66Pz58+fDlwVdjBe8fDJc5FQ1UeA+xhFdwwRkUgkclMymYw2Nzd/U1X9rvwiZYzZAjRZa+tG835r7eHAKmNMW04D83LKGPMQcNJoEytr7UuBh8ZbWc6Kla2zgBuAC4FFwJEE3TFWEdT353Ij5qG0EqxEtxC0ppsHfGLFytbj+r3upwQr4qN1HvDnMbzfyyNVzaZSqYufeeYZ6uvrPyYic8OOySsOPnkuLp8A3iciI+0j+voFCxacnc1m9wH/nYe4vNx6DHjlKN/7WoJbzV7xawdOHOmbXF384vFWrrFiZetJBInzMcAygo4ZfeuSD0lVJZvRSA5vuinBSvQ6grtFM4GrVqxsndr7Andxs9Bt7BwRdyHcOd4ukEqNqj6cTqdvbWpqqiQoI/I8xN/dLy4i8nHgZFV94zBfXxmNRldPmDBhfnt7+yWqekueQ/RywH3YVhljto3gPVOATK77y3r5Y62dA2x3pRzDfc9c4AU3uXBcWLGy9VUEHTRmuq9mgsR1UNmsStf+zJSerszkVCJbm81qvPc5iUg6EiEtIlkFQTWiWSKqRFVVREQlQioWj3RXVkfaa2qjrbF45FA/oynAfIINib8H7Lc/NEUBrLVVwGRjzLA737g7Ekt97+7SICKzJkyYsDaZTNakUqkzVPV3YcfkhcuvPBefm4BlInL2MF9/yZIlS+b39PSsAb6ex7i83NoHvGa4t/Xdjvw3EaxmeqUjRXC3YFistdOB48ZZ4vw64CMEpREzgA0cInHOZlX2taam79jac+ze3cn5XR3paclEdmIqqXXuqzaZyE5MdGen9HRlGhJdmSmJ7uzkRCI7KZnI1qWSWpdMZOuSPdnJ3R3pae2tqbk7n+85pnVnYm4mrYNt5m0luOMzj+Buwkt6n3B3CBZbaxeN4Ns+HRg3P+NSp6ovdHZ2fuGoo46ioqLiZhEZ06Zvr/T55LnIuPZylwM3uYEngxKR6ZFI5No9e/aQTCYvVdWR7jj3QuKSozsIJqENRz1wj7/FW1rcauST7uJnOOLA3XkMqaisWNl6FvABgtrmKQQru4PW83d3Zupanu9Ztr8tdUQqka1LJbMTM2mtBiQSIRWJkJQIaYEswZ3ViKpGABFQEbISIS0SPJ/JaGUqka1PJbWuc39mesu2nqXJnsxgGz23Evx8GoH/6NfC7nfuuSG54UbrjDHPDuf1XtH40oYNG16YOHHicuC9YQfjhcsnz8XplwQ7yy8a4nWfXbBgQd22bdt+pap+MlWJcZv+LhwqsXKbC1/hWt15pWc78I6hXmStXQ7McD3By96Kla3LCVacjyCYoriGQbpoqKq07UrO2rMzsTiZzNanktk6zWo8GpPOeIXsi8WlMxqTnmhMErGY9MTi0h2LS2e8ItIZr4h0xuLSGYtLVywu3S9+XtpjFbIflHQyW5dKZmt370guTiWzAw1IyRCsiM8EZtPnjoK7qG2z1p4xjG/99QRDW7wSoqrdXV1dl8fjcerq6j4vIpPCjskLj0+ei5BrM3cpYERkwI0oInJiY2Pjv+/bty9NsFLtlaa7CVbcDuV44KECxOLlgTEmAaxxK46HMolgxHfZW7Gy9TCC1l8zCAb+rCOYwnmQdDob27UtsaizPT0zndTaTFqro1HpiVVIezQqKZHRz6MRESIRycTi0hGNSXcmpTWZtFa37kwuUNWBDtxCUG4xE3jHipWtk3ufcPsXJgyjFGuDMcZv+i1Nt+3cufNPs2fPnoLfnD+u+eS5SLl2c7cRTNV6ERGRaDR6SzablZ07d96kqusKHqCXE+62/gnW2gFXMay1RwCbjTH7CxuZl0vGmL8BZw2WWLkVy0fHQ1nOipWtAlxGkDjPIRhS0jHQa5OJbNWubYkliZ7s5FQyWwcq8bi0R2OSGEvS3J+IEI1JIhKVRDqVrUkls7XtrenpA7xUCfpMTwMOIxhu1dfdwCmDncda+1aCPtJeCVJVzWQyF69bt04bGho+IiKLw46pl4hcKSJvdl9XisiRI3z/kSJynfu6TUTuF5Hj8xVvqfPJc3H7b+BfRWRJv8ff2tTU9LJEIrEb+EwIcXm59QcGb2l2NkN0HfBKxmaCuwgv4sp2phlj8jH4oxi9lqAd3XxgF0Ef54MkejI1u7cnjkolsxPSqWxtJCKpWFz2S0Sy+QosGqNbQDNprepsT89Ip7MDlVTtI6jLnguctmJl67G9T7iLn6nW2oNGd1trpwJbx8MFUjlT1cfT6fT/HHHEEbFIJHJD2PEAiMj9wAOqerv7uh64bbgJtHvdm1X1Kvd1IXAd8KiIvD+PoZcsnzwXMVXdA3wauFHcMouI1FRUVHxx69at7N+//xOq6tuWlTiXNK221r7oHzpr7Wzgh24UsFfi3AaxdtfarK/FBMM2yt6Kla0TgXcBs9xDWwd6XTKRrdqzI7koncrWZFI6IRqVnmiMrlyuNg/ErUB3ZTNakclovH1PauYgL90KVBNsHryo3+bBu9xzB1hrI8B8Y8zDeQncK7RrV69e3V5ZWfkaERl2N518EJE3A6jqY/2e+jzwzWEe5s0u4T5AVR8Arge+6eu7D+aT5+L3DYIPmte7X1959NFHz0wmk08A3w0vLC/HdtLntr61Ng6cO142j40jbQRT5YADF0hHjKPVyH8lqPGfBmwh6IrxIpm0xvbsSCxKp7LVmZTWRGPSHY1JT74T516RqKQlQjqT1uruzkxjMpHtf7EDwYa/3s2DRxDcIQIOdNKZ5TaA9jqLoF7aKwOq2pJIJD61YMECKisrbxKRihDDuYhg8FZ/jwFnDzPxvWiQFebei/rhts4dN3zyXORc+7mPAjeIyAIRuaq5uZl0On2xqvoVyTJhjMkC3yeoAwWoIei64pURt1HsD9bavhvLxkWnnBUrWycBryZYDGgnKH94EVVlz87E/HRKqzNpndDbQaPQsUZj0q1ZjWUzGt+7Ozl3kGFiOwmS/+nAhStWtvbt/fsw0GGtjVhrJwCrjDGb8h+5V0BfWbdu3Yaampom4MMhxnEisLH/g6q6qc/zQ9lLUEY10OMw9Kb2cccnzyXAtaF7Brh9yZIlVS0tLT9V1T+GHZeXW26V+Vw3KOMcY8yusGPy8mIfwQrsiUDjOBqI8mqgFpjMIHX8+9vS05M92YnpVHZCJCLJSJRQRpRHIpKJRCWRSWt1sidb39meGajrkRK0IWwkWEk/ofcJdychBZxDcNew4BcAXn6pajKRSFxaW1vLpEmTPikiB9W5F8gkgiE+gxmy7llVT1DVqw7x3n+MJrBy5pPn0vFjETmmubk5AVwZdjBe3twJLAV+HXYgXn641nWPAGKMeTTseArBddg4hyDR7GKA7hrpVLZi/770zExaqwUoRI3zoURj9KgbpNLelpqTSetAmwd3E3yOTgZO6/uEMeZ5gvKOfxhj9g7wXq/0/Xrbtm2/mT59ej3B/qSCGmZJxljqlS8CHhugnnrck0FuR3naJZ+LAAAgAElEQVRFRESisVjsH4cffvixc+bMufucc865leC24DKgDriXYFVnC9BDsAHp9wQjZOME06/OJhh7C7AAeAB4JcHqyN8JxsWuAaoIdpH3HnM/8BTwcvffKQR1fr3Pt7rjvgR4nKDsYFqf53cCzcBx7jwL3DF6n9/mjrHMf0/8jmCYRiPwW4L+t+XwPZXjz2ms39MHCW6J/sXFXQ7f06A/p/aaEzNZqb6up2LW4bFM+75Ypr2rq7KppSaxvjETmZDIROt6Eq07FuzunDYhnt1XWxXvSezLzumaFN1amdQJmbRWaU1kT2x/ZnqqJtoai5KUvZk5iUnRrZUJrctkNabVkbZYe2ZGsjbaEhey0p6ZkZwYfaGiJzsxA1AV2Rfdl5mVrI82VygR7cg0puqjzRXd2cnpiKSlUvZHe4+ZoUK7MlPStbKjqj0xpaKyItldX9OxP3pY0+qaxPrGbKQqmYo1dFYmt01OVMycGM10RGKZtm31XX8/P6Kpvj+nS4G/EqxQF/3PqRz/7OX7e1q1atWcVCp161133ZWzK71BeowfxHXJ2AhcqKq3D/B8G/CtQVaVhzr28cCDwFk+eT6YT55LgIi89+ijj/6fLVu2NL/3ve996y233OJLNsqUtfbfCPp7n2CM+XPY8Xi5Z62NAq8DVgMTjDGrQg4p71asbH0HwUjjo4EnCJKiA9KpbMXO53uWp5JaB6rxikhRtO1TVdIpnQAisbh0TJ1R+UxldbR/bNMILnifAt797Q9NaQWw1k4h2Ew4A/irMWbAlnxeabPWLrjhhhsuX7BgwQeeeOKJP2Sz2VdqgRIrt/LcxuDJswJX9e+kMcxjbwQucl03vH582UaRE5GJ1dXVn9+0aRNdXV1XNDQ0PGKtPW/od3qlxlq7GLjVGNMDbLXWHhV2TF5enGSMudMYsxHAbSgrd8uBeqCbfokzQMe+9GHZLDHNaiwak1DqnAciIsR6Nw9mNd7eNmDrugTQ222hAQ707j7WGPM4QQnWtGFMHvRKjLV2BtDY3t5+9ZNPPrmntrb2NOBNhTq/qg6nHGjEJUMichtB0u0T50H45Ln4Xbt48eKpqVTqYeAnLrGKWGvDbI3j5Zjr/fuyPj2dXyC41eiVEdfLu67PQ1v5ZxvKsuR6IC8g2Cw44KTMnq5MQzajFRIhE4lIUXURkohkI1FJZDNUJXuyEwdoXZfhn5+lvc+dDqyHA5sHqxle1wOvtJwEPKKqbZlM5trZs2dTXV19g4hUD/nO3NnEoTcFjqjLi4hcB/x0oJVs75988lzERKQpEolcsmXLFs1kMhf33goyxtxJMKHLKx81wB29v3AfuD+w1i4KLyQvl9zKY4agjhAAt5HsHneLv1zNIFiZnQAcVI6RTmUrMmmtymaJRyJy0Kp0MYhGSWhWo9ks0e6OdP+fVd8V5ay1diLwrNswCIDbGLrdrUh7ZcDdGfytMab3z+y3169f/1QsFpsNXF7AUB7D3fHoq3e64EhWj12v50f6J85+TPfBfPJcxCKRyJePOeaYeFtb2/dUtf+u/FqfWJUHN8r3DGNMe9/HXQJ9srW2JpzIvBx7BVDnenr31QlcEEI8hTILiBJsDuvu/2SiO1uriqAakcjBJR3FQCKSFSGrWY0lE9nafk9HCS6KIPj+zifoY92fAq/JY5hegbh9C6+gT9cYVc0kk8lLpkyZQkNDw9UiMth0ylz7KQMPMTmbYOPlsIjI2UDrIIlzOV/cj4pPnouUiJxbX1//+i1btnQCVw/wkt8BPb6OrixMB341yHO/oN+oX6/0uL+nO40xT/d/zvV5/q2bKlmOpgGV7v8PqmdOJbPVqkQBRCiqko0XEbIKolmi/Z45kDxXJbZkgPuNMQe14jPGbCPouuGVvsOA/+0/GVRVH3ruued+XldXVw18oRCBuGS31SW/fV3kvl5ERG7rP03QJcgXAntF5Gz39WY3+vsTjLD0YzzwyXMREpF4ZWXlTfX19bS1tX1KVXf0f437S1tF0M7HK1HW2qVAyvX+PYhbjX6F25jila4LCNpiDchNn3tbmd7WbyAo28jCwclxJq2VqhpByIbZ1/lQVBVVoiJkI1FJ9ns6BqRRZcr+37yeQ4/hfsxa++b8Rerlm7W2DjjbGNM1yEs+tn379sSsWbPeKSIvK0RMqnoOcI6IvN99XUfQgWOgpPd4Dp4m+CDwfuD+Pl+3ua83D3Kccc0nz8Xpg/PmzVvc0tKyGbh5sBcZY9YCO/zqc0lbSNC/9FB+TbA67ZUga20E2GuMOWgcdT8PMfCI3FI3kd4EcwDZjMZRIiIUc99UQRERyVRUHtRGLwpkItoTQ7MPDlCWc4B7rs1aO5bBFV64JhIMsxqQqm5KJBJfbmhoQERuFpGC5FmqepWqfst9XTVYwquq8/v3fVbVyaoqg30VIv5S45PnIiMih9XV1X1qy5Yt9PT0XKqqQ4113UoBW+N4uWOtPQG4s/+tv/7chpS91tpjCxOZl2NnGmMeHOpFboPZRLfhrJxM4FDJsxJVRaB4k+feUg0RsvHKSP8VxyiazlQlNtVZc82zQx3L/Vlo8osepcdaOxdoMsYM2DWmj88/+eST2xsaGk4C/i3vgXkF55Pn4mObmpomJpPJB4G7hnqxMaaTYCWjHG/3li3X23eJq3cdjs1AUx5D8vLAWjufAfoaH8IzwLl5CicsVQSfNQPXM+uBbhXFmzwrUYlIRgStGCB5rkxtq0xUzNw2gkN2A6fkMESvMJqAPwz1IlXtAD4+depUamtrrxORuqHe45UWnzwXERFZFo/HP7B27dpMNpu9dLhTitxKxql5Ds/LrXrgZ8N9sVudvt1a61sGlQi3shhhGB+2vdxGs3vKrMY9TvD7MNi/Z72PF+1KbDarMREykagkozF50QVvJNtVkYnU92Sik3YP93hu4+gma23lkC/2ioK7U/j3Pr34h/Kj9evXPyIi0wg23XllxCfPRUJEJBKJ3LJs2bJIZ2fn11X1oF35Q0hYa4/OS3BeTllrZwInuoE3w+YS6KPchhWv+J0FB35uI9EFnFdGt/V7P2cG/H2QCNlirndWVdEs8UiEVEVV5KAWdNWJjRMz0dpuRj7JLU0wpt0rcm7fwjHD2LdwgKpm0+n0xZMnT6axsfGK3r7LXnnwyXPxeOOECRNeuWHDhr2AGcX7/0LQuq5/GyWv+FQD94zyvXcQ1JB6RcxNAH3WmP/P3nnHyV1V/f99Zmdbkt0km977QoAAUhQEpQhIUUAFfFQUseETpCnS4XIBpSrF8kOjPiKKKAhSFZGm9N4S0lkSNr1udrPZNuf3x72TLGGzdWa+U+779coryZR7z+zd+X7PPfeczzELevpeH9m6nwKRKIzFpAVB0ey8HyXaKEVISExa+g2IfyC6HGurL95cWt2iUrwF6HbkGcAYswqYnUebpHxmInBbT9+kqs8vWbLk9ng8XgzckHKrApGRlRerQkNEysrKyn46dOhQ6urqLlHVdT0dw0e3FDg89RYGUoU/+uvXg1znD+Cj1XtZayek1rJAivkcLoLcK4wxK4D/8U54rpPAXZs6dBKL4tIkIglVYt3MVMsYqiptbVpaFJem4pLY5rJ+sU3tnqT/lnfGqJTEcfrVy3oxxULg5BSZG0gDvvvnvj1I19ieC9etW9cwceLEz4nIoam0LRAdwXnODs4eO3bsxNra2jnAr3o7iDHmXVwkI1+bLeQDQ4C3+jjGI0BZiFhlJ35d3jPGrO/jUA8DmepSlk6SG8UOf19LSmObRdxrks1SsoW2VsoFNBaT5orB8ffb61CLNhc1lk5pQERxBYA91sL1DtmikIqV1ZTSjeL9HaGqtVu2bLm6vLwc4CYRCcX9eUBwniNGREYPGjTo0mXLltHc3HyWqvYqItmODbhOQYEsw1r7SeCJXuTAfgB/w1XgoykxLJBqjjPGPN/XQXz0eayPfOUyW3BKGx06xqXlsU0iqMSkLdFG1kTaEwktSrRpSVFxbHNpeWxDvwHxrfmuoi2xfk3zhrfGq8pw7dXb6FqvvUOMMc8C+4TNcPZhrZ0GTO+kIUp3+ek777xTM3r06BnAt1NgWiBigvMcPT+eMmVKv8bGxvtUtdt96HeE1598N+Q+ZxfW2n7AWK/Z3GeMMfNxUexAFuF1YD/UEbQPvEzuK+msB5rZ1qL7A8SLYy0lZbGNsSKaEm1aqgmN/L6kqrS1ar9YkbTEYrQMGlryXvvnS1qWVTaWTF4DVOE+39xZM6u60v7tjPfJ/XXOR4bhmhf1CVVtBM6tqKigsrLyRyKS6xvigifyi1QhIyIf7d+//ylvvvlms6qem6pxjTHPAUeHSEZWMQq4M8VjPmKtDTfcLMFvWCtTEXVOYoxpBJ601uZypf5CoB4Xee7X0QsqBhUvi8WkWWLS2tqq/aLOfU4kKFGlqCgujf0r48uLS2Jbm1UVtW0sTUhZa6JoQAVOhm8NfXSwfGHpYr/JDmQB1toDgbl9PSlsxz0LFix4ChhM70QBAllEcJ4jQkQkHo/fUl1dTUtLy42qujDFUyzDtX4ORIy1dhKuK9UO2/b2Bp++MdpaW5nKcQO95gig21JWPaAOOCyHN8Mv4SLP9bhI3oco61dU368ivipeLJtVKWpt0f5ROdCqSqJVy2IxaSoqkqbKquLl7Z8vb1o0rCU+ZDMwFliFizz3OTqJlyhMwTiBPuK/a+OMMT0u3t8RqqqJROKsioqKxKhRo04XkSAtm8ME5zk6vlxSUvKx+fPnrwJ+lOrBjTGvAEWh82C0+IuwAo+maYq/4Y6OAxHiO0a+Yox5r8sX9xAf+fozObrOs2ZWrQZeAVbinOcOC5oHDS1eUlIaq4sXx+pVibe26ABVzfiGIZGgRCFWFJem/gPjy0U08fbjt45+898/H/vGQ1dOffTBO/v9945z95n7zB8qgFrgzlkzq3qk2d4R3lF7Nk8UVnKdGcaYP6d6UFV9o7a2dlZLS0sRcKO0r0AN5BTBeY4AERnQv3//G8aMGUNDQ8MFqtqXXLnO2AgclaaxA91jf9xRfm9ljjrFS95N9YUtgeg4Hqe4kBZ8LcNnrbVl6ZojzdwFrMMVD3aoIBKLiQ4ZVTq/tCy2sbgktglEWpq1MtGmGQsAqKq0tWp5rEi2FBVJ04CB8dXP/uX8ac//7eK9XrzX7PnSwzftPPup31bPe/b24c/+5YeDNJFYADyUQhNWEKTrIsVaOxyYmsYpLt28eXPdtGnTjiA0yclZgvMcDecPGzZs5LvvvvsqvRBe7y7GmFrgBR8VC0SEMebNNE/xGNCaw8f6OY3/ub/hHdx08gC5G32ejYs+L8FFnwd09LqiImkbOrp0bnn/otXxYqmPxaS5tUUHtLZov3RHoRMJLWpt1gEikigqkqaKwfH3YzFJTNzzMytFYh/KIakYOml9w4ZlN8+aWZWyjbHfDL8artmREgceTNfgqrp68+bNxqcl3Sgi4aQhBwnOc4YRkYlDhw49b82aNbS2tp6hqinNg+2ABuDzaZ4j0AHW2iNxaglpxR/rlxCq9TOOd5y/Yox5O91zGWPWArtaa0eke640MQtYiyuwm8wOpOtiMdGqESWLBw0pXlxcEmuIl8TqNaFxH4VOqYa9b4JS0tKcGNDarBXEJBEvlvry/kVr+le6boJjdj6o7vBv//YFiX3Q3I0rF1Tdeeke/xGRy0RkdKpsMsa8DhwcFJMyj7V2N2CyMaY5zVP9YuHChXMnTpw4FTgzzXMF0kBwnjPP9ePHjy+pr6+/Q1WfTfdkxpgG4A1rbVjrDOKP1/tl4CIMgDFmHi63OpBZRgNpd5zb8RSwewbnSxmzZlbVArfjos/gHOgOEREGDCpeM2xM6Vtl5bG18ZLYJh+F7t/XKHQ7h7l/S5MObGvVchFJxEtim4qLpX7AwHht1YiSxe3TUadO37v1E1+6fmv77aJ4aR0wD5eCYoH3ROQuETkkRXmss4GPpWCcQM+IAc+kexJVbQHOicfjDBw40IhIrm6IC5bgUGUQETlo0KBBJ7z22muNwAWZmtenDZwUjvUzys7GmHsyPOdzPtodyAC+k+d4HynMCH4z9pq1Nlcr9f8OvADMByqATtvMF5fEmoeOLp0/aGjJouKSWH37KHRbmxZ3V5HD5zKXtjQnBrRzmDVeLA3FJbG64tJY3YCB8drhY8veHDS0pLa9/xtvXV+uEp9Q/fFTGmYc9r25gLa1Np8FTAc+hSvaFeAE4HFgjoicISIDe/7jcRhjaoAVofNg5rDWHgYsT6E0Xaeo6j8XLVr0cCwWGwBclYk5A6kjOM8ZQkSKSkpKbpkwYQKqeo2qLs2wCW8DkzI8Z0Fird0Jp+WZUXy+ZHnIl8wYRwI1Ecy7FjggFzfDs2ZWKXADznmeDwylCwdaRBgwML52+NjSt8r6Fa2Jl8Q2xYqkqc1FoQckEhrvyIlWVRIJjbe2qIswt2mZiLTFS2L1xSWxjcWlsbp+lfFlQ0aWzh01oey1wcNKlhaXxD50UlTSsnx6S3wYwNyPfc7eNnj09AGqid+r43FVPcF/hstxEqE7A7cAy0Tk1yKyZy9/XKuBY3v53kAP8N+lfsaY1ZmcV1W/X1JS0jp27NhvishemZw70DeC85w5vllUVLT7/Pnzl+JuHhnF52RWWWs77PIVSA3+ItwIPBmRCX8nbJLSjrV2IPCcMWZ5ly9OMT4y9n/AuEzPnQpmzaxqAC7DNU9JdsqcShf3o3hxrGXoqJIFySh0cUmsDhFtbdYBrc1a2dqi5W2tWtLWqiWtLVre2qyVrc06QBV8hHljSVls44DKeG3SYR4yorSmvH9R3Y4yLYpb10zeXLaTILH5uILHn6yrnfOhVs2qWquqFpjItgh0P1wr5tdE5FkR+aqIdFstxReg/sP/rgXSy/7GmPszPamqzlu5cuUt9fX1EovFbg7SdblDcJ4zgIgMqqiouHrcuHE0Njaeq6ofuvhmiCXA0RHNXSgcjItgRJJ/7Ocd7AtfAunjeFxTi0jwpwwHWWs7VK3IdmbNrFoLXAjMAd7BOZq7AuWdvS8ZhR4xruzN/gPjy4pLYvXFpbG6WJE0q1KUaNOyRJuWqVIUK5Lm4tJYXXGJ1PeriC8fOqr0nZHjy94YPLxkSXn/ok1d+inaVlXSsmwsUrQI5+hfPWtmVUunb1FtUdW/qeqncGkdN+MkQ/cH/gC8LyLXikh3O0bWEVLu0oq1djRuAxcVV7a0tKyprq4+EDgpQjsCPSA4z5nhssrKyqpFixY9jdM7jQRjzCrgKWvtoKhsKADWGmPmRmzD00BdKBJND77x0FPGmMicZ8/97KDddS4wa2bVGuCHwLO4ArlGnAM9qqv3FhVJ2+BhJUtHjit7vXJw8Xtl/YpWF5fIpuJSl79cXCL1peVFaysGF783cnz560NGlr5b1q+ovgeBvYqYtkxpLK1ejEvFuGrWzKr6nnw+VZ2rqmfjigq/DbyGc9LOAxaKyEMi8hkR2aGqht8kPQWEE8P0EQf+EdXkqrqhoaHhos2bNwNcLyI5+50uJMLNNc2IyE4jRow4Y9OmTdrW1namRtVzdhubcVGzQIqx1n4edwwdKe2k6z4VtS35ho8Afh1IeSfBnmKM2QjsZa3NyfQN2JrCcTkuKjsfeBfnPE/H/Q53SlFcWiurilcMH1s2d/Sk8pdHjCt7fcS4stf9v+cMrCpeURSX1h6aNUgSW3Yqa1rUlIiVLQGunTWz6v0ejrEVVW1Q1d8AewP74T5rM+4U8AFgkYhcICLDO3q/MWY+cKwvUA2kEGvt3sAIv0mJkt8tWbLkjerq6nHAuRHbEugGwXlOPz8dPnx4vK6u7req+lrUxhhjtgD/DRqiqcW31N3sf76RY4xZiCssC6SW4cDTUaXldMC/6aLgLtuZNbMqMWtm1Z24KPSbuOJmBXYDun1KJiLEi2Mt8eJYi4j0Zn3iuJzlafFE/brG0qlvAzfNmln1ai/G+hC+wPAFVT0FGIuLQC/Grd/VuJSOP4nIAR3kvj5NjkoUZiv+ZG4jGdDi7wpVbQPOam5upqKi4kIRydkNcaEQnOc0IiJHDx8+/Oi33nqrDrgkanuSGGMWAaeEPLqUsq8x5p9RG7Edb/loeCAFeO3uXbMgLWcrPmK2wEfQcppZM6vmA2cB9wJzgZXANJxDm84ubCXAeGAPoCLeuqYmlmhYrrHSK2fNrHoiHROq6hpVvR73+Y7CRaCLgC/jHOXXReS7IlIBYIxZBjRaazOu4pPHHAXUZ8tGWFWfqqmpuau8vLwMuDZqewKdE5znNCEiJaWlpTeNHDkS4ApVXRm1TdvxX1z0I9BHsrU4zxjTAjRYazstwgp0m0OBt6I2YnuMMStxnQdzfjM8a2ZV06yZVbcCPwYW4JzoCpxjOwPXWGUE0B+nrdwX+gNT/NiVONnBt8qb33u3JT78q7NmVj3fx/G7RFUTqvpPVT0W99l+jJOo2x34f0CtiPxcRHb19n0m3TYVAklVJGPMiqht2Y7zEolE08SJE78kIgdGbUxgxwTnOX2cHo/Hp82dO3ch8LOojdkeY8wCYKK1NhQn9AGf/lKPK3rKOowxj+Ccg0AfsNYOA17OtA5sD7gdyNXGKR9i1syq53BR6P/iNixzcE4luNSZXXA5xNNxQYDBdC86XQwM8+/fBZeqMQ+XKvLEwPr//GFgw7On/up7YzIuQaiq76nqxTgJwmQEugI4HXj78ssv/8e1114r3/nOd0LQo+8cDqTlVKEvqGrNmjVrrluzZg3xePxmEQk+WpYSFiYNiMiwgQMHXj569Giam5vPUdWMtGjuBbNxF5FA7zkMiGXL0d8OSFhrgwB/3/gsLj8yK/G/f7vnkybwrJlVK2bNrLoUOAf4LU4R4XWcM/0qLjK9CRiAi9ruAeyFc4qn4tI9xvu/p+GiuXvinO16P847wIPA2WNX/8xWNL5REXXdgqo2qeqfVfUTbItA1wOfbGxsvO33v//9bBG5MuTF9g5r7Siy+5p9raour66u3gs4JWpjAh0TnOf0cGVZWVnlokWL/gU8FLUxO8IYsw5XPNhhlXegc3zUeYExZnHUtnTBS8AaL7EW6CFeS/l+Y0xT1LZ0wX3koaTZrJlVC2fNrPrTrJlVlwEnA98CrgHuAP6Dc4BfwTnDNcB6oBWXQ1zq/27C5VDPwUnGvYZTvfjmrJlV18+aWbUI1+3wjsx9sq5R1bdUdSYwGpgJzG5paanE1dDUiMjfReSIEKHsHj5doxJ4JGpbdoSqNjQ0NPxw3bp1AFeLSGXUNgU+TPjCpRgR2XPMmDHfaW5ubkskEmdngTRdV9TjomqBnvNF3A05q/ERFgU+HbUtuYavyP8yOaBc4nWn97TWdrcBR84xa2aVzppZtXLWzKr/zJpZdeusmVVn4RpLXIBLj/szzjF6Bhedft3//TSuKO/XOJWLU2fNrLrda01jrR0KfDzqqPOOUNVNqvr/cHnfnxw6dOjzQBtwHO7zzhORH4hIVZR25gD7A6VZHHVOcseKFSue22233UYAF0dtTODDSPb7drmDlxd6Yqeddjpo3rx5N3uB/KzHWjsCqDPGNEZtS67go7j7G2P+G7Ut3cVaOw1YmAM3jqzBWjsEqDTGvBu1Ld3BO/t7GmNSIq9WKFhrJwArs9V53h5r7bA33nhjl3vvvfcA4Ltsa9W+BbgT+KWqvhSZgVmI18keYYzptWZ3JhGRfceNG/fiunXrWhoaGnZV1QVR2xTYRog8p5YvjB079qB58+atBWzUxnQXX61/ctB+7h7+6O+wXHKcPUtwUdRAN7DW9gf2yxXHGcAYkwBWWGsPiNqWXMFaOx0YnSuOM4AxZvUee+xRf/nll/8GmMS2CHQZronPiyLykoh8I3Ss28pncOk8OYGqvrR06dLfDxo0qFhEbojansAHCc5zihCR8rKyshsGDhwIcKmqro/aph7yCK6KPdA1u7Kt8j9n8Dm7S6y1eZcXmyY+hjv+zym8JvCIfJCuyxCjgbTL0qWBt4FDVbVNVe9X1SNxhZE3AOuAfXCFlrUi8lMRqY7Q1mygNgul6brios2bNzdMmjTpWBEJxf1ZRHCeU8f3S0pKJsydO/dtYFbUxvQUY8wSYDdrbUXUtmQzPl2jyRjzStS29AYfLf9EcKw6x1o7GphvjNkQtS295F5g36iNyHZ8hP75XExl8pvh+6y1E5OPqepCVf0hTlHk68ALuC6N5+Dyoh8Vkc+JSEEVD/tmUTmXxqKqy9evX3/VypUrKSkpubnQ1i2bCc5zChCRMUOGDLl46NChtLW1naWqOXM0tB3PA+G4t3OOAnI9N3w1TtIrsGOOAnItSrUV7wyO9znbgQ7wG+HJxpiGqG3pA1uAI3yu+1ZUtVFVb1PV/dgWgW7ESWveg1PquFRERmXc4gzjNdrX5eIGyXNTW1tbTXV19XRcfnsgCwjOc2q4RlXLFy9efI+qPh61Mb3FGLMJeNVaG/RDO8C3Z345VwpOdoQx5g1gvbU2nS2PcxZfQHunb32dy9yPy4ENdMxk4I9RG9EXvEN4D65bYoeo6iuq+i1gDHA2MN//+wpgiYj8VUQO9gXveYU/YRtvjHkyalt6i6pu2bJlyznLli1DRK4QkbAhzgKC89xHRGT/CRMmnCwizcAPo7YnBawDPh2O9TvkRKAuaiNSRCNwdNRGZBs+GnkssDlqW/qKMaYZmOYL4gLt8I0ypudwNHIrxpg1wPFddYtV1fWqejOwM9si0IK7rj0BzBaRM0QkbxrtAAfj5FhznfvWrVv32J577jmYHBIjyGeC89wHvDD9zbFYjLVr196gqtneLKNLfLTtTlzDgIDHK5HMyfEj3q0YY5YDL/poemAb/YEH88Gp8jyF6zAZNsMfpAjXsTBfeBAXTe4SdTymql8AJuCcseW4Vue34AoMfyUie6bN2gzgr21zjTHzoralr/h+EWcvX8wfAR8AACAASURBVL48UV5e/r8islvUNhU6wXnuGydPmjRp35qamuXA1VEbkyqMMfXAcV4Xs+DxjsfxuVok2AnrcI1eAoBvbX2Y31jkBX4T0AgcGrUt2YK1dk9grI/M5wXGmPXAUF/o2m1UtVZVL8c50ScCj+M2kN8BXhORZ0XkZBHJxU32sUDerLGqvr1ixYpbR44cGYvFYjfnY5pNLhGc514iIhWlpaXXlpWVoaoXqGo+HA2150FcG9MAVOPyBPMKr2v7dsh93squwL+iNiLVeCWdoOG+jTKcCkW+8TK9VFhR1RZVvVtVPwXsgotA1+E68t0OLBWRa0RkUsqsTSM+4PG2MSbrO4P2kMvWrVu3ceLEiYfiNgeBiAjOc++5cMCAASPfeeedF8nxopOO8HqYH7fWDo7alijxjmWpMeatqG1JBz6aflShH+t7ua+Vvmg2H3nUWlvw0Wdr7WHA7DxKy9mKMaYF+Hdfc9xV9R1VPQunf/1tXIvzocD5wCIReUhEjhGRbN6QnQy8E7URqUZV127cuPHSVatWUVZWdpOIBM3+iAjOcy8QkcnDhw8/d8CAAQBnqWoiapvSxGPA7lEbETHHkMOSZd1kLmGdDwZqIrYhbXhnsdJaW7C1DL5uYVAeb5DAFboe6Atf+4SqNqjqb3CylskIdAuu0PhBYKGIXCAiw/o6Vyqx1lbhNNrzboPkubWlpWVudXX1RJx6SiACgvPcC0TkhqampuL33nvvdlXNxc5U3cIYsxlYZK2dGrUtUeBzYJ8yxqyK2pZ04gtqmgu1eNBaOxn4ozGmLWpb0sz9QD4pKfSUPYwxd0dtRDrxDuMdQFWqxvQFhs+r6tdwRYnnAe8CE3G1Pu+LyB9F5ONR5+H6DdJuxph8TMsBXIpNU1PTmYsXLyYej19SCFrd2UhwnnuIiBw6adKkzxUVFTUCF0ZtTwZYBhwUtRER8TnyQLKsm6wGPhu1EZnGp+Uckgeazl1ijEkAw621e0RtS6ax1o4HRkZtRybwikCHW2tTXrOiqmtU9XpgKi4C/QBQDHwF18r+NRE5TUQGpHrubnIwkNM6/N1BVR9taGh4YI899hgA/ChqewqR4Dz3AN8a86ampibWrVv3I1WtjdqmdONvuLe1bwFbCPiOXc/5orq8x2vFPpaOG26WU45rZV0oPA9s8BG6gsDn8yt5WAzaCWk9ZVDVhKr+Q1WPxTWbuRq3Ad8DuBVYJiI/E5Fd0mXD9lhr++PkRHNeMrY7qOoPampqWkpLS08VkV4VigZ6T3Cee8a3d9pppxnLly+vAX4arSmZw0flDrHWFkRxgr/ZnpwP+qA9pB44IWojMoXPjTzCGLMualsyhT/WV+DwqG3JIB8FhhTC6UISn9c9ORNBD1WtUdWLgHHAl4GngQrge7jGK0+KyEkikm5Vn7xobtRdVHXB2rVrbxo/fjxFRUW3RJ0yU2gE57mbiEhVSUnJlYlEgkQica6qNkZtU4a5j05awOYZk3ARuoLC694+l4pioxxhAq7wqaDw0nX50imzU/xGuMEY83rUtkTA08C0TE2mqk2q+mdV/QTbItD1uLS/vwDv+fbSY1M9t+9J8IwxZmOqx85yrlq+fPmasWPH7gd8KWpjCongPHcfM2TIkCELFix4EtfWtKDw0bmDrLXDo7YlnVhry4Fhxpi803XuDsaYd4Av+rSVvMVauxPQaIwptE1wkuestYWQ4/4ZYGnURkSBL4B9wVq7d6bnVtU3VfV/cQWGpwOzcTnnl+Kc6HtF5HDfpbdP+A3SKRRArvP2qGpdfX39+Rs3bqR///43iEihBLgiJ69vkKlCRHYZNWrU6bFYLAGc7VtlFiIP4yqs85mjgAVRGxExzwE7RW1EmvkIUGhpOVvx6Rst+azj7jeArQUYjdyKMaYO2COqzbCq1qnqL4EZuAj0nUACOB6Xgz5XRM4Rkb78HlYCL/r6nELk9/X19a9NmzZtFE4JJZABMvaFEpGBItKj1qHZgIhILBa7qaGhoai2tnaWqr4RtU1RYYxpAlZba3eN2pZ0YK0dBjxeSDmwHeELbsp9AU7eYa2dAdydxzqw3cIY809gQh43yDnQGPOPqI3IAm4j4qCHl7v7j6p+CZcbfQnuRGAarn5omYj8TkT26cm4PsVsf2PMmyk3OkdQ1URra+uZ77zzDuXl5eeJyISobSoE0u48i0ipiHwROBK4T0RuFpGT0j1vCjlmypQph4tIHe7IqdCpAfaM2og0UVAFJ12wCNcgJq/wWtZ7F1LxWBfEcAV1eYXX7g5t59mavrGPL5CNHFVdoao/wql0HA88gmuZfirwkoi8JCKniki/bgx3IJCX3V97gqo+3dTUdOcuu+xSBlwXtT2FQFqdZxHZFXcD/q+q/gX4PK7V5x0i8plsrw711cE3bty4kY0bNxpVXR21TVGTFOG31u4WtS2pxOv9/ssXzRU8/qj7n3mY414B3BW1EdmCMeZV4H1fcJUXtJOmezxqW7KIB4CsUktS1VZVvU9Vj8RFoH8CrAP2AX6Ha77yExHpsOjRN7FaZIzJe8nYbnL+okWLtpSUlJwkIp+M2ph8J92R548DT6rqMgBVXaqq/4f7kvwRl1+azZy52267TV2zZs1c4BdRG5MteAd6r3w51veat18zxhRkYVEnbMZF4/MCa+0I3FF+Q9S2ZCHZfi3uCZ8Aygs4B/ZD+MLYna211VHb0hGqulBVzwXGAl8HXgQGA98H5ovIv0TkeN9rIcmxOGc7AKjqkg0bNlwzdepUiouLbxGRgtFyj4K0Oc8iUgZ8G9/VqX1VraqejyvK+omIzEiXDX1BREbE4/FL6+vrSSQS31fVlqhtyjLuwTWYyAdGA/+O2ohsw6c2/MtH5fOBwcBDURuRbfjIXU3UdqQCnwO7xBgzJ2pbspAngWFRG9EZqtqoqrep6seAfXER6EacLvm9wLsicun06dOnAg+HjfCHuL6mpqZ2+PDhe+DSYAJpIi3Os4jEVHULbvf4OXBJ7f655M7x88Bw4KoIW3l2xlVjxoyprKmpeVhVQ9HJdhhj6oGPW2vHRG1LX7DWVgBTjTE1UduSjXhN4K/kuvazLxIsCWk5O2S2tfZ/ojYiBRxPgWhY9xR/YjjHWntg1LZ0B1V9WVW/iZO7OweYj4tMXzFv3ry5l19++S9F5KBsT//MJKq6efPmzedu2bKFioqKa0QkbV0mC520OM9JR9nzURGZ0u65VhEpUtWluCOZzwJfzKYvgIjsNXr06G9u2bKlFWdjoGP+AWRFEUofOBh4JWojspxHcQ1FcpmJhMKiHeKLypb7zWRO4nOdVxW6Wk5nGGPWA+NzSWFFVder6k3AzsBhsVjsPq8WexIumv62iHwvOIpb+cuGDRuenTx58hCCyEHaSFfkOfnFvB04AjiyfWtOVW3zf9+G0w4+DxeFjhwRkaKiolsaGhpk5cqVt6hqwWrBdoUxpgXYYq3dK2pbeoOPmj/rtVADO8AY8z4w0hfo5BzW2o8B/yx0abquMMY8BeyeS47VdhxljPlP1EZkO8aYO3C6yzmFqurll1/+38suuywpvXcFsALYBfgZUCsit4rIHhGaGTmqqm1tbWe+/fbbWllZeZaI5LtmfySkK/KsPnXjBVyLToOroN1Ku2T2E3DHMp9Ohy294KRp06YdkEgk1gJXRm1MDrAQp9uZU3gH4WigYBso9JA3gE9FbURPsdb2A6r9Ri/QNRuAA6I2oqdYayfiWkEHusfUHFXS2Rt4RlXfV1UDjAdOBJ4A+gOnAa+LyDMi8hURySqFkUyhqq+0tbX9btq0afFYLPaTqO3JR9Ku86yq5+By0C4RkXHtHm8TkbjPjb4MuLB9dDoKRKRfLBa7bvny5WzatOkiVd0QpT25gI/mPeCje7lEJa5RRtD77QY+x/0xa22ubZSGAH+J2ohcwRgzG3jX62HnBL57Xjnw36htySEeBHKqjsFaOxRYY4xZlXxMVVtU9W5VPZRtEeg6nNLXH3Fyd1eLyMQITI6ai+fNm1cfj8ePEZEjozYm3+iR85xUzNhOLqZDVDXRLn3jdFxk+atJ4XMREVVNOi6PA6+ratTFPOfOmDFjfENDw5vAbyO2JWfwklBTcyVf0mvanujz/wLdZxNwVK4c63tHf49QJNhjmoHPRG1ED/gU0BLScrqP/05MzjG9/mNwXQk7RFXfUdUzcSfZ38H1lBgKXAAsFpEHReToQpFwU9WV9fX1V1RXV1NaWnqziOSNlns20G3nWUTGAK+KSFmy6K+r96jP6lfVR4CLgYuAL/sxtF2keT4wIMojFhEZF4/HL1izZg2tra1nJvOyA93mLlw0NxeoIkiW9Ri/SfobkI3qOB1RBPwzaiNyDWPMauBVr3+e1fgI+dvGmIVR25KDPEOORJ+9RvvdxpgtXb1WVetVdRawFy4CfTvQgnO+HwIWiMj5IpLVsn0p4paFCxcuHjx4cDUwM2pj8omeRJ4n4bpz/RW2Ff11F1W9BudgfRk42T+WjAjtDfxFVZt6MmaKuXbSpEnltbW1d6nqUxHakZP4SMYMn3uYtVhrBwMfMcYsj9qWXMQYsxY4Mdu1n621+wCDQlpOr1kCfDVqI7rB8UCXDlXgw/hI/fvW2sOjtqUz/EnXcbimTd1GHc+p6tdwEnfnA+/ifJlrcCkdt4vI/tmk9pVKVLVpy5YtZ6sqAwcOvKJANgwZoVvOs4icgSuu+i5wiIh81z/erchEu9d9D5ei8S0R+bGI7CkixwEC3NFT41OFiBwwevToL61fv74Jp/wR6B2PAvEsP9bfh5Ab2VceIkvUcTqhwhjzetRG5Cp+0zEnm7uI+uvMgpB+1XuMMWuA8iy/ZpfjGqL0Oi1HVVer6nW4NuDH4HK+i3GBvGeB10TkO1nac6KvPLh69epHx40bV4lTKAmkgO5GnjcCV6rqo7j8oVtEZKwv+utO+kabV99oAH4MfA1YDhwKvKqq/9lOGzpjiEgsHo/fsmXLFtasWXO9qtZEYUc+4LVii4H9oralI6y1k4G3QleqvmGMWQlU+wKerMNaeyjuSDrQB4wxLwIH+IK8bOQEY0zQaO8jxpj7gf2jtqMjvFrOcV4us8+oapuqPqyqnwUm4yLQq4E9gF/h5O5uEZHpqZgvG1BVTSQSZ7/zzjttQ4YM+Y6I7B61TflAty6KqvoHVW30RxuzcNrM9/nn2jo78kg+1845FlWdr6o/U9Wf+mYpUXJKdXX1Xk1NTctxX6RAHzDGvIOTDMoqfGTlMGBVV68NdItnce1zswqfAzssFAmmjMVkoWNlrR0LvBe1HXnEkCztFrsz8Eg6BlbVGlW9ECe1+hXchrsSOAOYIyJPiMiJ+VBop6pz2trafjlhwoRYLBa7OV/TVDJJTwoGxecQNQNnA2NEJKmDvMNxfGHgQBEZ6R+KJMLcESJSWVJScs17771HQ0PDD31kPNB3nvTRv2xiNHCbL3oL9BFfuPOCtXZa1LZsxyR8XUag7/hCvPd9BDAr8K3iR/rIeCA1/ANXYJs1WGtHAc3p7hipqk2qeoeqHoiLQN8KNOC6z/4VWCIiV4jI2HTakQEunzNnzvqSkpKDgc9FbUyu023nOamc4f9dg5Ofu1hE9klqNnf0PhEZAvwep/M8oP04WcDFu+666/AtW7Y8T4Q51/mGz5ccaq3Nivwxa20p8GljTJQFqfnIeuCQbMmX9Gk544NkWcqpAz4btRHtOIJwgpRS/DV7hC+0zRY+hVPiyhiq+qaq/i8u2PI9YA4wEtfmukZE7hGRw5KyvbmEqq7bsmXLJVOnTqW8vPxGEckZLfdspC+/APcAv8ZJV+Hl6z40nqquBWqBF1Q1azpAicjUoqKic2pra2lrazszy5z6fOAu3AUoG+gP3B+1EfmGd1Jvx91cIsU78K24otVACvEFeU9mQ/TZa8m/ZIxZErUtecjLwMZsyHH3qk13RZV+pap1qvoLYDfgIFyjJcVFbB8F5orIOSIyOAr7+sCv58+fP7tfv37jge9HbUwu0+sviXc2LwU2i8jvko+3z6VJFhOq6vdU9fa+GJpqROSGnXbaqXjVqlW3qepLUduTb3jHaqy1tjpKO6y1w4ADfVV5IMUYYxqBI6y15RGbcgBQGdJy0sY6nMxo1BxHkKZLC/6aXYdT1ooMry9+BK5ZT6T4VNX/qOr/4HKjLwXex6l2/BRXYPhbEdk7Sju7i6q2Njc3n1VSUkJVVdXFIpItAa6co087TFVdjZOv+5qIHK+qCZ/jPMA/n5WNRkTk8OHDhx+3fPnyBuDCqO3JY54AmiI+1t8J+FeE8xcCD+A04KOkyRjzdsQ25C3GmBbg+Sg3Sf468pIxZlNUNuQ7XkmnMeJrdj/gnmxLv1LVFap6Fa6u4njcfaUc+Abwsoi8KCJfF5GoAwmdoqqPrVix4u/Dhg3rB1wdtT25Sp+PZ3xDkR8BvwEQkWOAC0Qk6ptph4hIvKSk5GZVZf369VepamiWkSb8xa8Md+yVcay104H3utOVKtB7fEHP3tbaSNI3rLXHAG9FMXch4Tcnn/YFexnFO3NfJ8M5sAXK4zhloozj03KOzeaTQlVtVdX7VPXTQDXwE1z9x77A/+Gi0T8RkWwrpt6Kqp67aNGi5pEjR35NRD4WtT25SJ+c53YydAZYKSJNwBeAK1Q1W6MD362urp5eV1dXA9wUtTH5jjFmHtAY0fQfxR2xBdLPY0DGtVF9p8OisEHKGK8CH4lg3pHAK9kWjcxH/M84Zq2N4kh/HO4kKydQ1QWqei4wBjgVeAkYjMsnni8ij4jIcTsSVIgKVV3U2tr601GjRlFUVHRLLhZARk1f0zYUQEQOAkYAX1DVb7Rru51ViMiQ8vLyKxcsWMCWLVvOUdVww80Mr1prP5PJCa21OwF/CDfbzOALe2Zba2dkeOo9fJOHQAbwhXp11trKTM3pN0jTjDFvZmrOAP8CKjKZvmGtHQ+UG2PqMjVnqlDVRlX9vap+FBeB/h0uN/8I4O/AYhG5pJ1kbzbw47feemtFeXn5R3E614Ee0OfdhogchWt3OVVVH+y7SWnl8unTpw9qaWl5At/kJZB+fL6kZKpa38+zX3CcM85qYN9M3XCttTuThQ15CoBlZFa67jBgQQbnK3j8tbMU+HgGp90XeCOD86UFVX1ZVb+Ji0Z/H/e7Ow64ElgqIneKyEFRNypR1U2tra0Xjh8/nv79+1+Xp63J00YqQvVPqep5qrohBWOlDRHZLRaL/e+7776bSCQSZwVpusxijHmAzB3rlwP3ZmiugMffcH8PTEn3XN5B3ww8le65Ah/EF+w9aK2tSvdc1trBuCLBUJuSYXyk/z1rbdo77FlrdwUe8nrTeYGqrlPVG3FdEg/H3ZNiwBeBJ4G3ReR0EcnYKU4H/GHBggWvFBcXjwQuiNCOnCMVBYObU2FIOhERicViN8+YMaNo/fr1t6pqKC6KhvJ0H+v7FrMH5OLRXz7gpeL284U/6eQQ3BFv2ARHQz3whQycMhzr5wpEQzOQ1pQ775zvn691C16F7N+q+nlgAnAFsALYBfg5sExEbhWR3aOwraWl5Yz+/fszbNiwH4rIpEzbkKsUSpL4sVVVVYfW1NRsBC6L2pgC5hlgQ5pF+EfiWs0GouM+nMpKWvAO23JfjBqIAGNMG65ItCRdc/jOoP/2WuKBCDDGrAKWpXmTNAj4cxrHzxpU9X0vsDAeOAkXge4PnAa8ISJPi8iXRaQ0gzY9V1tb+6eKiooS4PpMzZvr5L3zLCKlZWVlN5aUlLBx48ZLfcfDQAT4KGExaZJBstZ+BFjnc6wDEeGP9ff1BUDp4EQgdJiLGGPMYlz0OeUOtHfWTsHlVwei5UWcrnHK8Wk5nzLGNKRj/GxFVVtU9S5VPQTYFfgZrkHNAcCfcLnRV4vIxAyZdMGSJUs2jxs37gsicnCG5sxp8t55Bs6aMmXKpLVr184Fbo3amELH33DTFcmYCtSkYdxAz/kXMCrVg3qN4bWFdrPNYp4gPbUMQ4AnQ1pO9Pg1WGetHZ6G4QeTQ9J06UBV56jqmbgCw9NwRZPDcDnIi0XkARE5Kp1ycqr6fmtr6zWDBw+mqKjo5mR36MCOyWvnWURGVlZWXrZgwQKamprOVtUQkcwO5uOihynDWrsXcHe42WYHvvBnqbV23xQP/QljzGMpHjPQS5KFfKksHrTWlgF7G2NCQ5QswRjzFDA2lUEPa+00YETYCDtUtV5Vf43TUf848EegBZdz/jCwUETOE5GhaTLhhjfffHPJwIEDdwe+laY58oasdJ5FJFX5kj+eNm1a/5aWlgdU9ZEUjRnoI14TeK2/SfYZX5w2PTjO2YUxZhkwJVU3XGvtLkBTKsYKpJT5wFEpHO8TwCspHC+QGhqAg1M43hTghRSOlxeo4zlV/SowFheBrsG1Bb8WeF9EbheR/VMpd6eqjcC5w4YNo6Ki4moRGZyqsfORrHSegcdE5JC+DCAi+xYXF586b968FlX9QaoMC6QGHz38aIqG6w/8LUVjBVLLX4A9+jqItbYI13TguT5bFEgpvqDvXq900yd8asCcbG7PXKj4At13UhH0sNbuA/zHq/MEdoCqrlbVa3EpiccAD+GKdE8GngVeFZFvi0iq9O7vXrhw4X+94xzEFTohW53nnwI39balpYhIUVHRzbvtthv19fU3qWoQ2M9ONvsiv15jrZ0I7JWvMke5jj8N2NlaO6iPQx0OJMLpQtbSCByTglOGz+Ca7QSykxb62CDH1y3MMMZkvcxttqCqbar6sKp+BhexvwZYA+wJ/Bond3eLiPSp/kBVta2t7cyKigodOXLk90Rk575bn59kq/N8D7CO3ufd/E9lZeX+CxYsWANclTqzAqnEGPMysN5fTHuMv1GXASElJ7v5O32QrvM6sLONMTUpsyiQUvym5gFcg6Je4ZUX/ubTugJZiDFmLTCnj5ukscDtKTKp4FDVd1X1QtzPMRmBrgTOAOaIyOMicoKI9Kq5jaq+Xltb+5t4PB7HBTIDHZCVzrPv/nc2YHuadyMi/fv163dDRUUF9fX1F6hqaJaR3bTS+3zJ/YA2rzkbyFL8qcAMa21vOw+eBGR1B9PA1uLBE3tzrO+130/EyXUFsps5OKetx1hrh+GKQfOmk2BUqGqTqv5JVQ/ARaB/hctLPwS4C3hPRKyIjO3F8JesWrWqbvLkyUeJyNEpNDtvyErnGUBV38C1szQ9fOt548aNG71s2bLXcK2CA1mMMeZ94K1eRp8HGWNCSk5u8Bi9iEr6XOfFXjs6kP08BEzsxfsqgX+GtJzsx6/RPGvtkF68vRR4MMUmFTyq+oaqfhcnd3cGboMzCpe3XCMifxORw7pbYKiqq5qbm21ZWRmxWOxGEUlbM6RcJWudZ8+lwFe6m8cjIhMGDx58fk1NDa2trWeqaohI5gYrgC/15A3W2k8Q0jVyBl8YtNGvW7fwR8NHG2NCkWCO4Av9BvZEE9ha2x842BgTGt/kCMaYF4HpfnPbLay1uwITjDFBMSdNqOpGVf05sBtOGeWvgAKfBx4F5orI2d080f/5nDlz5g8fPrwa+F66bM5Vstp5VtXVwI+AG7u5Y7pu8uTJpU1NTXeq6tNpNi+QIvyx/sLudiqz1g4ERodK7dzCGLMUGNiDt+xE6DCXi7wOfLIHr98L12wlkFvUAof24PVDcPm5gTTj5e6eUtUv4lqBXwq8D1QDN+Lk7j7WxRjNwDkDBgxg0KBBl4vIBzbEIvIVEVkjIl9J08fIarLaefb8ApgAdJp3IyKfKC8vP+mtt95qBM7PiGWBlOGji4d1sxBlMEGaLld5yFp7YFcv8mk8MWNM0PvNMXxk8R/dyXG31o4FlhpjNqbfskAqMca8i0u5G9DVa621nwReC2k5mUdVl6vqVTid6M/hur/GcBKvXb334cWLF/8zkUhUAFcmHxeRAcDNuA3RTf7/BUXWO8++K+D3cdHnDiOTIlJUXFx8S3V1Nc3Nzdepajj+y03epwtNYGttNTAlFJzkJv7mObIb+ZJHA8Ghyl02A5/yhYCdcQTuex/ITRqBYzt7gd8Ijw11C9Giqq2q+ndV/bSqlqvq4915XyKR+H6/fv1ax44d+20R2dM/fDrOcQYYCsxMh83ZTNY7zwCq+g9gAS4RviNOLSsr23PBggXLgOsyZ1kglRhj3gQadpS+4aPSLUC3vvSBrOXvdBL18DmwzxtjajNnUiCV+E3SX4Ad6nv7qPOfwkY4d/EnBs90UfC9M/DnDJkUSDGq+s6KFSt+3tzcLMDNPsr8Q/fs1hrwHxZa9DknnGfP94ELO8i7GVhRUXHNsGHD2Lx58w9UNQiv5zZ1uE5KHXEQUBKO/nIb7yxN9AVEHfEFnORSIIfxjtUxfjP0AbyzdWQoHssLlgJf7yjlzlo7GndSGK7Zuc0VGzduXLvTTjt9ErgVGAJxnLR7HCKKPovIeV7T+gT/78mZGiNnnGdVnQfcxoebnlw6fPjwIe+9996zuEhHIIcxxqzERTI6ikwmfIvYQO7zX6Bx+xuuP+Z/3RgTnOf84H5gWAeP9/PPBXIcX7j9Ih0XAwvwcGYtCqQaVV3f1NR0cUtLC2xVxjoF+JT/G8hw9FlEHgX+rap3+z/XAXf1xIHuyxg54zx7rgSOFZGPAIhI9bBhw85atmyZtrW1nembqwRynzrghPYPWGuPJFRq5w0+EpXAXX2BrWk5J/n0nUAe4KPP4621Y5KPebWcI40xq6KzLJBK/Hf2o+3TN6y1H8HlOrdEZ1kghfxm8eLFy4GYizZf5B++iExHn0XkBABVfXW7p67GNYtJ+xg55Tyr6gac6PfNXrruJ+PGjYs3Njb+n6qGqvw8wUvXvZa8EPtq7vKQG5lf+Hbb7bXYpwBvRWNNII08ywcLgasJ0ch8ZDbQXsc9hotIB/KDcrbWqpwCJIOzk4kg+nwasL3Ti3/sfKMcgQAAGtBJREFUMBHZYa1FqsaQXAvWikgR8Arw0MCBAy/atGnTpkQiUa2qK6K2LZBarLUnAncDuwKzQ95c/uGjzUcATwK7BWm6/MRaW4mTHG0AikP6VX5irR0HbAI+hiv6DYo5eYKInA9c46LM89jmPAMsxsnytwKc79Mf0mnLej/Przt4ToHDVfXf6Rwj55xnABE5FHhs1113Zfbs2VGbE0gTQ4YMobKykra2NpYsCeqD+Up1dTUA77//Pps3h3rffGX33Xenra2NOXPmkIv3nUDXFBcXM2XKFBKJBPPnz4/anEBa+Cbwmw4e/xbwW4A1wCRVrU+XBd65PVFV797Bc6d15BSncozO5GWymeEAS5cuZcaMGSxdupThw4dTWlrKwoULmTp1Khs2bKC1tZWhQ4dSU1PDmDFjKCoqoqamhsmTJ7Nu3ToAqqqqWLx4MRMnTqStrY3a2lomTpzImjVriMfjDBo0aOuYTU1NrFq1inHjxrFy5UrKy8uprKzc+nxjYyPr1q1jzJgxLF++nIqKCgYMGLD1+fr6ejZt2sSoUaOora2lqqqK8vLyrc/X1dXR2NjIiBEjwmeqqWHIkCEsXLiQ4cOHM2PGjLz4TPm4Tn39TJs3b2blypWMHj06bz5TPq5TXz/T0qVLaWxsZMqUKXnzmfJxnfrymVpaWnj33XeJxWLsvPPOefGZ8nGdevqZVq1axerVq/lgrvP2XITTdGgdCmzqXlPobahqt97QzZSMzlMuUjBGrjrPI4DX6urqznvzzTc7Dc0HchcR+Tvw/PLly6+J2pZAehCRARs2bJgHnFBTU/Nc1PYE0oOI7AvcB+y8YMGCuqjtCaQHETkXOFhVPxO1LYHU4HOYa4AhH8x13p5k7nPao89VXTy/gW0NXNI2Rk4VDAKIyFDgYuDkrnJaArmLiBwGzABuitqWQFq5EHhcVYPjnKf44u6bgEtUNTjOeYqIjAAuAH4QtS2BlOK7CXYWdU6SEeWNdV08PwhYm+4xcs55Bq4A7lTVOVEbEkgPIhLH3WzPVdUtUdsTSA8iMgn4Lu6GG8hf/gcoBX4fsR2B9HIVcJvvyRDIAz7YTbCzqHOS9CtveNW1ruj0NakYI6ecZxHZHdd97PKITQmkl9OAlbg2zoH85XrgRlUNbbjzFBHpD1wHnKWqiajtCaQHEdkL+CyuF0Mgf+hB1DlJRqLPi+nck1+c7jFyxnlud/RnVbWrkHsgRxGRKsAAZ4emN/mLiBwC7AP8JGpbAmnlPOC/qvpM1IYE0oO/N98MXNrNiF4gB+h51DlJRnSfX6WDnORkZ8BupvT2aYyccZ6B43FtXjuVHwnkPJcDd6lqaJaRp2yXltMYtT2B9CAiE4DvAedHbUsgrZwIVAC/i9qQQErpRdQ5Sdqjz38BDuvg8cOA7tbC9WmMnHCeRaQMF6E6W1VDl7k8RUR2xeVHXha1LYG08i1gPfC3qA0JpJXrgFtUdWnUhgTSg4j0w6VfnaWqbV29PpAb9D7qnCS90WevzbzOCwu05zT/5wOIyF0i8p2+jLE9uSJVdw7wuqo+FrUhgfTgj/5uBK5U1a4qZQM5iogMBizw6ZCWk7+IyCeB/YBTo7YlkFbOBV5Q1aeiNiSQUnzUGWB34MFeDLF78h/J6HNKuw6q6uEicm0yzQKYgmt60lGu8l50kMPcwzE+QNZ3GBSR0cCbwEe784ECuYmIHAtcA+yhqi1R2xNIDyJyE1Cuql3u7AO5iYgUAS8D16jqX6K2J5AeRGQc8Dqwt6rWRGxOIIWIyBJgXAqHXKqq41M4XuTkQuT5amBWcJzzFxEpBX4KnB4c5/xFRKYDXwF2idqWQFo5FagH/hq1IYG0ci3wy+A45yXPkVrnOe90/HMh8nw88JiqboralkB68M7z8SFKld/4Jgp7qOq/orYlkD5E5CNAi6q+HbUtgfQhIp8HHlHVhqhtCaQen0pZAhwH3NWXsfIxRS/rnedAIBAIBAKBQCBbyAm1jUAgEAgEAoFAIBvImPMsIsFRLwDCOhcGYZ3zn7DGhUFY5/xFRIqjtiFfSXvahs+LGglUAvcCi4JWc/4R1rkwCOuc/4Q1LgzCOuc/InIwTibuD8CewHxVDYW8KSBtO04RiYvId4HvA/OBG/xTp4rInumaN5BZwjoXBmGd85+wxoVBWOfCQVWfxPXJENx6T4CtxYCBPpDO45rTcZ3E/qSq/1bVVlWdp6qzgO+mqd95IPOEdS4MwjrnP2GNC4OwzgVA0kFW1VpgAFCqqtf7x4JSRB9Jp/N8KfAkXuvTC+cnO4wNBSrSOHcgc4R1LgzCOuc/YY0Lg7DOBUDSQRaR/sCFwC/9/3Ohv0fWkxbnWUQuAhS4PdlquV3f+03AWOCAdMwdyBxhnQuDsM75T1jjwiCsc0FyGDAdMP7/bZ28NtBNUl4wKCJDgXnAbcDFqtroHy9S1TafU/UqMDA0PsldwjoXBmGd85+wxoVBWOfCw6ttPA4sUNVviEi8u0WhIiIhvWPHpCN8f6If96/JL+d2XAQ8CzR3Nki7fJ2weNlJSta5PeHLmpWkfJ0DWUdK11hEYqqaSKF9gdSQqnvz1vUN1+ys5wD/54v+/936Xm63xpVAffhOf5B0pG38ANfKcXbyAb8QbSIyEjgBuJ8ujg7UIyKfE5HTRGSvZG5WICtIyTqLY4yInAicLyJH+nbdgewgJescyGpSusbtbrpTg85sVpGqe3NyfU8CzhORQ8M6Zx9ev/sHwAOqusyfMHTqALcLWiZEZLKI3AN8GfiSiFSHdd5GSiPPIrI/0B+3WO2PfZKyKOcAK4FndnR0kNzxiMgM4GvAVJxO4UDgWBHZoqr/SKXdgZ6RonUu8rl2JwH1wFxVvUtEjgR+JyIvAreEqEZ0pGKduzGHAGU7iIQF0kyq11hE9gEOxWnKPgH0F5GlwEOquiWlxge6TYrvzcfh9KEbVfVaEfkkYERkPvBnVW1J3ycJ9IA9gaOB3f3/u3MvFUBF5GTgOGCOqt4KICK7AKeLyC9VteBPGlMdef448AKwCLZGFcXvbPsDZwAPA3OSz7d/s39twkeYbwbGAD9Q1eWqOldV7wXGesc6EB19WmdwRSp+na8GBqnqW/7xfwKnsO24KRAdfV7n7Wn/GhH5BvAQcHEabA90j5StsYjsC9wNFANXqeosVb0JaMSdKg1J70cJdEIqrtkJ//ihwIuq+gf/+H9U9RJcMO7IjHyaQHc4B7cZmt2dVKp2m6MRwFVADXCdf05UdQ4upeeMNNudE6TaeV6P27nMhq35yslUi4uAzcBtqrq+3fMd2XMeLuL8e1VdnPyi++cmAJ9Isd2BntHXdU6yK7AWKE8+4L/ArbiuV+eHY6JISdU6tycmIsNF5Eu4Y+IE8FsIwv0R0ac1Fi97JSJfw63j06r6I1WdI9vaPs8Ffgh8Ks2fJbBj+vxd9qeFitMMPsA/1v7e/BzwCREJUncRIiIxEdkZd6p7UfLhHgxxJdCAU2Sp846z+nV+DydnWPCk2nmuAlb6H3QJgKq2ikg5cBbwC+AV6DQaGcc5z38Enu5gjgU46ZVAdPRpnZOo6pvAfmzTG22/O34NOCAcAUZKStY5+ZyIjAWOAsYBW3DpOs+o6rt+7JCik3n6es1OHvFfDazGnRgmn0tGKuuBizS0BY6SPn+XdZuk3XDgq/7fMbY54Vtw1+yg1BEBIrIbbM1JPw3Xivvp5AlDV+/339eJwKnAr/CnEP53Jrlx2pngfwGpd55/CbwnIuXJnBh/BHATLnfGqGoDdHqjvAQXjbpbVTe3e23S1pnA4hTbHegZqVjnrXnPqlrnX9v+WOkA3DFjIDpSss5AuYhMAw4EnlTVV4BdcMf7//TjhqhzNPR6jdttir4DjMJJYg0QkW+LyIEiMlQdq4CfZfAzBT5Mn7/L7U4BLwWmicjh/vqd3EBdgA+EBDKL/y5OFZE/icjvcH7Saf7pLk9v250SXYKLLj+iH8x9T96bv4VL7yl4Uu08N+KixZ8XV4FbBfwZKAG+A9u6GXXCacAdOD1K/HuSFcETgX2BB1Jsd6BnpGKd20cy2t+Ii0TkeJyj9VAabA90nz6vs4+GHI1Lw/mXqtaLyB7A3sDbqvoahKhzhPR6jdut2fm4qOOrwFO49I064GsiMny71waioc/fZVVtEacV3QYsBX4pTgVroojMxJ08/DydHyLQMX6T+ndc1PgVnG93pIhUaTeK+3zUOQ58CfgDLt8Z2JauIyIHAjsBj6XjM+QaKW+SAlsdob1wIf51ugN1jHa5NEmR9pNwC3eoqj7b7nXJ5/+Ic54/qaorU254oEf0dJ27GCdZLHomTl7neeAbyWhIIDp6ss6w9ZhvODARGKCqj2/3uvNwm6NrVPXZrn4/AumnF9fsuD/2PwEXbZypviq/3WuvA95S1dvTbX+ge/RinZN/7wfsj6s5uh53MjgLmA8cCzSp6rqMfIhAl/iN0KeBg4A/qOrszl7r/aszgMuAg9QVByafTxYSPoYLghyhPje+kEmL87zDybYt0iBV3dDB84/gIhbfTB7lt/vyDgLex+VmXRButtlLV+vsXxODD2rCAqcD38DlTV4bHOfsprN1FpHDgM/hqvJva/f4NODHQI2q/jCzFgd6Sjeu2c/iIpFfVdWa7TZQ3wAOAU7R0GAhq+niu1yCq0H6o6re3+7x7+LScY5U1RCNzFLabXSL/enBfgCq+vx2r3sUqAXOUtWN/rHk78VOuJOlq1X1qkx/hmwkHU1Sdki7Y/pfi4gVkQHJ50RkMtAP+E/ScfYkj5IuwVUE3xMc5+yms3Vu95qE383uLyK/Ai4HVgB7qeplwXHOfjpbZ1X9N/ATYKSI/EJEvuKfOgB3VPxvCLnO2U4X1+zxQBnwhKrW+Ne3r085BBgeHOfsp6N1bpcH+2lclPp+2KrmEMOdOMwDzhWR4vBdzk6Sucu6rfh+Fq65zbDka8SlxG4Gnk06ztvxY2A5LtUnQHrac3eK38U24G6g2ztIjfhc53YRjFafi/M9XAXoy5mzNtBbOlrndqcIA3FHh7vjhPvvAF7Q0EQh5+js+6yqi4FrRWQMcIyI3A5MB95Q1Uf8a8JGOMvpZI034YIb21+zk6pJX8DJXgVygO3Xud138+vA/2/vfkIrO8s4jv+eUehO0xn/LKSbBF3VIpkENypdJAVx0YqTVnSjQid0IbiaYRZCd5LZuqiZgiAuSpp0o4hCUrSgGzsT7M6Fc4VCu9A2jVIYWquPi/c59745Offk3OTknntvvh8Y0t6/7/2T8Dvved7n3Yrb5B2RDszseaVZ6c8VB1CYeN+Q9Ji7/1Pq/96+pdSC7u9x2SWlP8/F7pPfVFrbQLOGMPbwHMXr349TCZ79Mh4o1UgW4fhSdjT8Y6V+wC94g5Yr6F75c46LP2Wp/+RjSgsStt397a7GiLOr+H3u1y9nYeotpRmty0qtCR+xtMDoJWrnJl/N3+yPlHr+vhY3/ZjSqnxX6rzwH7G4aGrUfM6/kfTZuNklDTovSKmd2WsarY8wOuTub0p6M/t/j4PdBxps3W7ZQdJPlXLZL5jsGBhr2UYuO5Xwv6irOZT0c0lX4vL/xumhRyXdkvRDd/9rV+PF6fjRdjdfU/pD+5ykDyT9O35p+4xdyKZS9vvcD875KXxLGycsKbU5+rakt5V2q8KUKP3NvuSpn+8dpbNIcveP4rrPKPXqf0GpThJTJP+c46LfSfpkfl3mQ6VFg/8Y2wDROnd/oHRmf8UG3c0eMrPvKJXtfMtTy0mEsS4YPImZLSqtDv2zu//JzL6n1ObqfXf/QaeDQyvM7CGl8Hxb0htKB0y/VjoL8mlJH7r7G92NEG0ys2eUSnM+L+lJSc87m2XMjFgA+iOlnuwvKS34fVLp1P/XnU2OZoKZPS7pu0qTH6+4+wMze0LSo5L+li8kxHSKiazHlErr/iXpcaX+7b9y960OhzaRJio8S/1TvV9Q+kV9T9IflPrB8kd4hlhqpfO0pJ8pzVocKi0a3PUGfSkxHaJ2blWpZvITkr7iWRtKTL/4jL+sdEbhfaUg/Xtnp7mZEp/zlyQ9pbTT7yOSfivpL5zOnx3xOX9R0juS3nH3Dzoe0kSauPCMiyUOlp6Q9Jy7P9X1eHA+4nP+qtJB8S/d/Y8dDwnAKVnaqfBB1+MAukJ4BjBWRe/QrscBAMBpEJ4BAACAhjrrtgEAAABMG8IzAAAA0BDhGQAAAGiI8AwAAAA0RHgGAAAAGiI8AwAAAA0RngEAAICGCM8AAABAQ4RnAAAAoCHCMwAAANAQ4RkAAABoiPAMAAAANER4BgAAABoiPAMAAAANEZ4BAACAhgjPAAAAQEOEZwAAAKAhwjMAAADQEOEZAAAAaIjwDAAAADREeAYAAAAaIjwDAAAADRGeAQAAgIYIzwAAAEBDhGcAQOvM7HrXYxiVmV0zs/muxwFgspm7dz0GAGhdhLerku5LWnD39Y6HdGGY2aakDXfvjXCfRUkbkvbjos1R7t+W04wdwMVCeAYwcyI4r7n7qpldk7QtadXd9zoeWmNmtivpsrtf7XosoyhmnN39zgj3WZT0qtLBzlz898tdHPCY2ZykV6ftfQcwPpRtAJhFm0qzmHL3HUnrkxSczexGg5vNx7+pEcFzfZTgHDYk9WK2d0kpQO+2Pb4m3P1Q0qaZbXTx/AAmHzPPAGZK1Kzel/RwBKGJY2abTWZVzWxuUl9DlSh52B7lQCUC93uS7kxSaY2Z3Zd0dZrefwDjwcwzgFkzL/VnECfVSpMbTfhrOCJC8NOnmOFfip/3Wh7SWe1IutX1IABMHsIzAIxR1GBPVTlGQ9clnaY0ZjV+TkxZTdhSek0AcAThGQDGwMzmssWLs+gZpVrzUS1K0qR1t3D3fUkHsZgRAPo+3vUAAMwGM7unCEJKi78WhtxuW5Lcfa3l51+RdFPS5fj/fMHZs5JeUVqIdtndH85mgJcl7ZYXuZVmiBck3atbCBe11jeV6q0l6Uo8bjGjel1plrUnab40vn13v5k91q4GCwYra7fj+dbj+eYqnq/fOSKun3d3izBYlI0sSDrMn/sMFpuWbMQYXizuJ+kwvj+StOXut1sYTxv2lN6r/ZNuCODiYMEggNZki/UqF3+ddH1LY1hR6tRwJHRm4fZpSWuS5O57RWjLW5NFu7WXS/ffVgqgx1qYxXNuKrXD62WXL8Z9drLLriktqrOa1zCnVG97o/w6ssdYd/fV0uWbKoXheKwNDcL7XGk8u0oHO6f+PIpWc+7+8Ij3m8jFgoXoirJafp8BXGyUbQBoUzHzPKzNWBGQxl66EKF2V4NZ2GKWdC3+5TaVQnbuWUmL5Z3zIgBuq3pjjVuKlnkjjvVQQ97DOAjYrhizIoCuRLjOH6t4v1fz4By2dfba3nmlGfVRFTPgnbSla2BfcSYDAAqEZwBtKmbohp3mvq40y9n14rB+0HP3Xmm2eE7SoVLIVna7w7hfuRyl6Cc9rKSj7Y4Zm5L2ajpxbGpQElH2esVlB1L/dZ/WZZ0uPC/Hz0ktizjQbC7uBHAG1DwDaNOKUtnAsSAVs6Fzqpgx7cDdYVdEKB1WfnAsVCu95srg2HZdd/Z8dTXBPUlzZrYYi95yVSG1jXBfWd/ewEQuFiw5y0EFgBlEeAbQipi5nFfqj1t13YuSdiZg1rlx/+Sid7FSgCqCc/k0fuVrPg9RsiFJ79bc7CB+Lul4WD7Q+Zg75WNXjbE1UYv9TPzvvAYz73cafgemps82gPEhPANoS1396rakg3OaiW1d1g3irqTNYgbXzLpe1DbKLOg4Z0wPNWJ5QxyYzKnmLMBZxGK/fhcTM7tRdPEwsxtm1quo/y5j1hnAMdQ8A2hLUb96ZGY5FtgtaVAPPdEiON9Tapm2XlH6UNbTGetizWylST/hbCxXam5WzIyPs464biZ8mHPbWTCC851hZzmyEN2kh/Mkl5QA6AAzzwDacqzeOULMuqSrw+pas37KPaUAvtlxDewtpddRVVfcn4mMEop5pYOFod0qilKLFl/Tvuq39y4OUs5lRneIngblEU0VwbV2nKN+PyIQ759UluHuO2a2ofqDjMuidANACTPPANqyqGzWOfoir7r7Qk1wLnon33b3nTjF3nVpxKIqZhuzmu4iQBc/+2UBQx5vvfT6e3H7fLZ6lFZvRcu8YbPdRQ/ocYa+08y+L0tHZtOPOeX3Y2WEuvqtvK1fhUWdX504gClFeAZwZrFJiCS9HvWku0ozhENLNSJsLuYzvHHZVkvDqurPW+w+WFfLuqMUTsu3ua7U5aIIifOS7kZIXZV0K3sfFM9TbNjSF2GxpxRyC3M1YffI64j7r0vaLo8xNknZq2ibV9yu6j2pu66RGNOo9cGVBymFMXw/inEv19xkQZPbgxpAR9hhEMCZRai5Jellpd3zTpz5M7P7SqfMtzQIbntnKW+I0L6kQXeMu0ozwz2lRYvFdb34V54Vzl/Paoyv2P66CKTFhiPbeUjNdjA81KAGeK9qZjVuuxmP/65SF5K83CV/Hb24/mbFY6xnz3VF0uul3QOLDVzyx9p397W668rjbSJ2arw5whbdrpqdBU/7/YjZ6qp+1quqDsJXhm1PHmNYa1D3DuACITwDGLtsW+arBJPZEAccy03CdzYjv1oVts/y/ci7apx0edGKsGqDmzg4uTfqluMAZh9lGwDGLitROFaqcMad7tCdO6pZyFj6XFeVFmUO64Yxru/HioYvWFzX4GwDAPQRngF0ZU+DjguS+p0VlqpvjkkWgXevXPct9T/X96K7hZRC60nB9MTvh5nNmdl2vnjS3W/XLN4sW66Z2V6R9JOGjwPgAqFVHYCurEnaMLN8odqZap7RuWclvSrpaunyZaVZ5K2sdV9lnXGmyfdjXinkXtPRLcv3zex6VTmG1J+93lB0Sqm4/lo8F23qABxDzTMAoDUx63tYWkxZhNViJ8KbbR4kVQXlbEHlrrvvxbh2lML2Ql14N7Pduk4xAC42wjMAoFXRMm9jXGcRTphlnlcq/5hXCu+1ZzeitKTrjXoATDDCMwCgdXWBtu3nUUvlPlGusU9wBlCH8AwAmFrjCukAUCA8AwAAAA3Rqg4AAABoiPAMAAAANER4BgAAABoiPAMAAAANEZ4BAACAhgjPAAAAQEOEZwAAAKCh/wNl6zvFnP+x/QAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nbins = 25\n", "fontsize = 23\n", "\n", "# Figure\n", "fig = plt.figure(figsize=(12, 12))\n", "\n", "# Axis\n", "ax = fig.add_subplot(111)\n", "tax = plot_utils.get_tax(ax, scale=nbins, rot_ax_labels=True)\n", "\n", "# Plot source composition\n", "tax.scatter([np.array([1, 0, 0])*nbins], marker='^', s=350, facecolors='blue',\n", " edgecolors='k', linewidth=2.3, label=r'$(1:0:0)_{\\rm S}$', zorder=3)\n", "\n", "# Plot measured composition posteriors\n", "coverages = [(99, 'cornflowerblue'), (90, 'royalblue')]\n", "for cov, color in coverages:\n", " plot_utils.flavor_contour(\n", " frs=measured_compositions,\n", " fill=True,\n", " ax=ax,\n", " nbins=nbins,\n", " coverage=cov,\n", " linewidth=2.5,\n", " color=color,\n", " alpha=0.7,\n", " oversample=8\n", " )\n", "\n", "# Draw arrow\n", "ax.annotate(\"\", xy=np.array([0.67, 0.14])*nbins, xytext=np.array([0.975, 0.014])*nbins,\n", " arrowprops=dict(arrowstyle=\"-|>\",facecolor='k',lw=2), zorder=3)\n", "\n", "# Legend\n", "l_size = fontsize\n", "handles, labels = ax.get_legend_handles_labels()\n", "legend = plt.legend(handles=[handles[-1]], labels=[labels[-1]], loc=(0.7, 0.85),\n", " title=r'Source composition', fontsize=l_size, prop={'size': fontsize})\n", "plt.setp(legend.get_title(), fontsize=l_size)\n", "ax.add_artist(legend)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What a measurement! In truth, the accuracy shown here has not been reached thus far, however the field of neutrino flavor physics is developing quickly. If you are interested in comparing this to a real flavor contour then you can checkout [this paper](https://doi.org/10.1088/0004-637X/809/1/98) by the IceCube collaboration.\n", "\n", "Thanks for reading! In the next example, `inference.ipynb`, we will see if we can make an inference of the source flavor composition using this fake data." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }