{ "cells": [ { "cell_type": "markdown", "id": "5add9362-edc2-4c55-a0ff-1fc5cf1d4ed7", "metadata": {}, "source": [ "# Calibration of a line\n", "\n", "Simple demo demonstrating the workflow of `rxmc`." ] }, { "cell_type": "code", "execution_count": 2, "id": "69d96b52-427c-4345-8622-d2726544a77c", "metadata": {}, "outputs": [], "source": [ "from collections import OrderedDict\n", "\n", "import corner\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from scipy import stats\n", "\n", "import rxmc" ] }, { "cell_type": "code", "execution_count": 3, "id": "315006bb-c255-4555-b458-e00bfef26ef9", "metadata": {}, "outputs": [], "source": [ "rng = np.random.default_rng(49)" ] }, { "cell_type": "markdown", "id": "4443b51f-20b9-42f4-8292-1bd5098c8f17", "metadata": {}, "source": [ "## define parameters" ] }, { "cell_type": "code", "execution_count": 4, "id": "ab500ce6-552f-4c9e-9b0f-648d456e4a53", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class Parameter in module rxmc.params:\n", "\n", "class Parameter(builtins.object)\n", " | Parameter(\n", " | name,\n", " | dtype=,\n", " | unit='',\n", " | latex_name=None,\n", " | bounds=(-inf, inf)\n", " | )\n", " |\n", " | Methods defined here:\n", " |\n", " | __eq__(self, other)\n", " | Return self==value.\n", " |\n", " | __init__(\n", " | self,\n", " | name,\n", " | dtype=,\n", " | unit='',\n", " | latex_name=None,\n", " | bounds=(-inf, inf)\n", " | )\n", " | Parameters:\n", " | name (str): Name of the parameter\n", " | dtype (np.dtype): Data type of the parameter\n", " | unit (str): Unit of the parameter\n", " | latex_name (str): LaTeX representation of the parameter\n", " | bounds (tuple, optional): Bounds for the parameter as a tuple (min, max)\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " |\n", " | __dict__\n", " | dictionary for instance variables\n", " |\n", " | __weakref__\n", " | list of weak references to the object\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " |\n", " | __hash__ = None\n", "\n" ] } ], "source": [ "help(rxmc.params.Parameter)" ] }, { "cell_type": "markdown", "id": "fcf6b674-c76d-47a4-852c-11e57538e625", "metadata": {}, "source": [ "## make the model" ] }, { "cell_type": "code", "execution_count": 5, "id": "0669a9b0-3941-429b-887f-edcfeb909453", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class PhysicalModel in module rxmc.physical_model:\n", "\n", "class PhysicalModel(builtins.object)\n", " | PhysicalModel(params: list[rxmc.params.Parameter])\n", " |\n", " | Represents an arbitrary parameteric model $y_{model}(x;params)$, for\n", " | comparison to some experimental measurement $\\{x_i, y(x_i)\\}$ contained\n", " | in an Observation object\n", " |\n", " | Methods defined here:\n", " |\n", " | __call__(self, observation: rxmc.observation.Observation, *params) -> numpy.ndarray\n", " | Call self as a function.\n", " |\n", " | __init__(self, params: list[rxmc.params.Parameter])\n", " | Initialize the PhysicalModel with a list of parameters.\n", " | Parameters:\n", " | ----------\n", " | params: list[Parameter]\n", " | A list of Parameter objects that define the model's parameters.\n", " | Each Parameter should have a name and a dtype.\n", " |\n", " | evaluate(self, observation: rxmc.observation.Observation, *params) -> numpy.ndarray\n", " | Evaluate the model at the given parameter values.\n", " | Should be overridden by subclasses.\n", " |\n", " | Parameters:\n", " | ----------\n", " | observation: Observation object containing x and y data.\n", " | params: Parameters for the model, should match the model's parameters.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " |\n", " | __dict__\n", " | dictionary for instance variables\n", " |\n", " | __weakref__\n", " | list of weak references to the object\n", "\n" ] } ], "source": [ "help(rxmc.physical_model.PhysicalModel)" ] }, { "cell_type": "markdown", "id": "b497f926-7772-4b57-a8e4-707e69efdee1", "metadata": {}, "source": [ "# Clearly to make a model, we need to understand these things called `Observation`s. \n", "\n", "This is an important detail, the whole point of `rxmc` is to compare predictions of a `PhysicalModel` to data contained in an `Observation`, to calibrate the parameters of the `PhysicalModel`." ] }, { "cell_type": "code", "execution_count": 6, "id": "3c64a7e3-c4a4-41e4-a7de-34eb7ab4728a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class Observation in module rxmc.observation:\n", "\n", "class Observation(builtins.object)\n", " | Observation(\n", " | x: numpy.ndarray,\n", " | y: numpy.ndarray,\n", " | y_stat_err=None,\n", " | y_sys_err_normalization=None,\n", " | y_sys_err_normalization_mask=None,\n", " | y_sys_err_offset=None,\n", " | y_sys_err_offset_mask=None\n", " | )\n", " |\n", " | A class to represent an observation with statistical errors,\n", " | as well as systematic errors associated with a common normalization\n", " | and offset of all or some of the data points of the the dependent\n", " | variable y.\n", " |\n", " | Attributes:\n", " | ----------\n", " | x : np.ndarray\n", " | The independent variable data.\n", " | y : np.ndarray\n", " | The dependent variable data.\n", " | statistical_covariance : np.ndarray\n", " | The covariance matrix representing the statistical errors of y.\n", " | systematic_offset_covariance : np.ndarray\n", " | The covariance matrix representing systematic errors associated with\n", " | the offset of y.\n", " | systematic_normalization_covariance : np.ndarray\n", " | The fractional covariance matrix representing systematic errors\n", " | associated with the normalization of y.\n", " | n_data_pts : int\n", " | The number of data points in the observation.\n", " |\n", " | Methods defined here:\n", " |\n", " | __init__(\n", " | self,\n", " | x: numpy.ndarray,\n", " | y: numpy.ndarray,\n", " | y_stat_err=None,\n", " | y_sys_err_normalization=None,\n", " | y_sys_err_normalization_mask=None,\n", " | y_sys_err_offset=None,\n", " | y_sys_err_offset_mask=None\n", " | )\n", " | x : np.ndarray\n", " | The independent variable data.\n", " | y : np.ndarray\n", " | The dependent variable data.\n", " | y_stat_err : np.ndarray, optional\n", " | The statistical error associated with y. Defaults to an array of\n", " | zeros with the same shape as y.\n", " | y_sys_err_normalization : float or array-like, optional\n", " | The fractional systematic error associated with normalization of y.\n", " | Defaults to 0.0. If array-like object is passed in, that implies\n", " | that there are multiple systematic errors associated with\n", " | normalization, each corresponding to an entry in\n", " | `y_sys_err_normalization_mask`.\n", " | y_sys_err_normalization_mask : list of np.ndarray, optional\n", " | Masks for the systematic errors associated with normalization of y.\n", " | Each mask should have the same shape as y, and the systematic error\n", " | associated with normalization will only apply to the points where\n", " | the mask is True. Defaults to None, meaning no systematic errors\n", " | associated with normalization, or equivalently, a single\n", " | systematic error for all points.\n", " | y_sys_err_offset : float or array-like, optional\n", " | The systematic error associated with the offset of y. Defaults to\n", " | 0.0. If array-like object is passed in, that implies that there\n", " | a multiple systematic errors associated with normalization, each\n", " | corresponding to an entry in `y_sys_err_normalization_mask`.\n", " | y_sys_err_offset_mask : list of np.ndarray, optional\n", " | Masks for the systematic errors associated with the offset of y.\n", " | Each mask should have the same shape as y, and the systematic error\n", " | associated with the offset will only apply to the points where\n", " | the mask is True. Defaults to None, meaning no systematic errors\n", " | associated with the offset, or equivalently, a single systematic\n", " | error for all points.\n", " |\n", " | covariance(self, y)\n", " | Returns the default covariance matrix for the observation,\n", " | which is the sum of the statistical and systematic offset covariance\n", " | matrices, and the fractional normalization covariance matrix\n", " | multiplied by the outer product of y with itself.\n", " |\n", " | Parameters\n", " | ----------\n", " | y : np.ndarray\n", " | The dependent variable data for which to compute the covariance.\n", " |\n", " | num_pts_within_interval(\n", " | self,\n", " | ylow: numpy.ndarray,\n", " | yhigh: numpy.ndarray,\n", " | xlim=None\n", " | )\n", " | Returns the number of points in y that fall between ylow and yhigh,\n", " | useful for calculating emperical coverages\n", " |\n", " | Parameters\n", " | ----------\n", " | ylow : np.ndarray, same shape as self.y\n", " | yhigh : np.ndarray, same shape as self.y\n", " | xlim : tuple, optional\n", " | If provided, only consider points where self.x is within\n", " | this range. Defaults to None, meaning all points are\n", " | considered.\n", " |\n", " | Returns\n", " | -------\n", " | int\n", " | The number of points in self.y (within xlim) that fall\n", " | within the specified interval defined by ylow and yhigh.\n", " |\n", " | residual(self, ym: numpy.ndarray)\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " |\n", " | __dict__\n", " | dictionary for instance variables\n", " |\n", " | __weakref__\n", " | list of weak references to the object\n", "\n" ] } ], "source": [ "help(rxmc.observation.Observation)" ] }, { "cell_type": "markdown", "id": "09fdde46-187c-4849-947e-15d98bcef3ae", "metadata": {}, "source": [ "Ok, so basically it's just some `x` and `y`, ans some information about the errors of `y`. We can think of `Observation`s like experimental measurements of an observable, and we want to build a model to make uncertainty-quantified predictions of that observable." ] }, { "cell_type": "code", "execution_count": 7, "id": "9f859e53-c6f9-4d88-b7bf-bbc5bc9ab686", "metadata": {}, "outputs": [], "source": [ "class LinearModel(rxmc.physical_model.PhysicalModel):\n", " def __init__(self):\n", " params = [\n", " rxmc.params.Parameter(\"m\", float, \"no-units\"),\n", " rxmc.params.Parameter(\"b\", float, \"y-units\"),\n", " ]\n", " super().__init__(params)\n", "\n", " def evaluate(self, observation, m, b):\n", " return self.y(observation.x, m, b)\n", "\n", " def y(self, x, m, b):\n", " # useful to have a function hat takes in an array-like x\n", " # rather than an Observation, e.g. for plotting\n", " return m * x + b" ] }, { "cell_type": "markdown", "id": "74fd0a56-9c1e-4a99-a20a-286d373ced62", "metadata": {}, "source": [ "Well that's not too complicated." ] }, { "cell_type": "code", "execution_count": 8, "id": "b83c6308-935b-4c8a-b1ef-0d65677ec809", "metadata": {}, "outputs": [], "source": [ "my_model = LinearModel()" ] }, { "cell_type": "markdown", "id": "a841a40b-cddf-4921-a660-2ee3bfa3de71", "metadata": {}, "source": [ "Let's test it out:" ] }, { "cell_type": "code", "execution_count": 9, "id": "27f09315-06dc-44ee-8190-f5d393974b68", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# true params are obviously m = 1, b = 0\n", "observation = rxmc.observation.Observation(x=np.array([1, 2, 3]), y=np.array([1, 2, 3]))\n", "my_model(observation, 1, 0)" ] }, { "cell_type": "code", "execution_count": 10, "id": "f5de0b99-d85b-4acf-bfb4-eabdc4580688", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 5, 7])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_model.y(observation.x, 2, 1)" ] }, { "cell_type": "code", "execution_count": 11, "id": "bf846a07-d5d2-4f34-838c-06b1deca0739", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 5, 7])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# now with parameters that are obviously wrong\n", "my_model(observation, 2, 1)" ] }, { "cell_type": "code", "execution_count": 12, "id": "fb8188e6-7e47-479f-8b72-a8825ae5dacd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 5 7]\n", "[3 5 7]\n" ] } ], "source": [ "# just to show some ways that may be convenient to pass around params\n", "# note they must be in the same order as in my_model.evaluate,\n", "# which should be in the same order as my_model.params\n", "\n", "p = [2, 1]\n", "print(my_model(observation, *p))\n", "p = np.array([2, 1])\n", "print(my_model(observation, *p))" ] }, { "cell_type": "markdown", "id": "0f73f0a7-667b-485f-9753-591d17464409", "metadata": {}, "source": [ "## define a prior\n", "\n", "Let's imagine this line corresponds to some physics, and we have some backround knowledge to inform us what we expect $m$ and $b$ to be. We can encode that into a prior distribution:" ] }, { "cell_type": "code", "execution_count": 13, "id": "d251f717-09be-4280-b59d-de137fd7cfc2", "metadata": {}, "outputs": [], "source": [ "prior_mean = OrderedDict(\n", " [\n", " (\"m\", 1),\n", " (\"b\", 1),\n", " ]\n", ")\n", "prior_std_dev = OrderedDict(\n", " [\n", " (\"m\", 1),\n", " (\"b\", 1),\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "id": "016fc2de-c206-44c1-916a-a3b663a8fd25", "metadata": {}, "outputs": [], "source": [ "covariance = np.diag(list(prior_std_dev.values())) ** 2\n", "mean = np.array(list(prior_mean.values()))" ] }, { "cell_type": "code", "execution_count": 15, "id": "d28fcb22-9ca7-4846-9cba-24b7b4808e2c", "metadata": {}, "outputs": [], "source": [ "n_prior_samples = 1000\n", "prior_distribution = stats.multivariate_normal(mean, covariance)\n", "prior_samples = prior_distribution.rvs(size=n_prior_samples, random_state=rng)" ] }, { "cell_type": "markdown", "id": "a5f9acb2-90e3-4e7e-bcd1-6c12ef2baa44", "metadata": {}, "source": [ "Let's plot some samples of lines from this prior" ] }, { "cell_type": "code", "execution_count": 16, "id": "70a97727-68ec-4eea-880f-203a3f0817cc", "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0, 1, 10)\n", "\n", "# array to hold the lines\n", "y = np.zeros((n_prior_samples, len(x)))\n", "\n", "# propagate the prior through to the observation\n", "for i in range(n_prior_samples):\n", " sample = prior_samples[i, :]\n", " y[i, :] = my_model.y(x, *sample)\n", "\n", "# grab confidence intervals for plotting\n", "upper, lower = np.percentile(y, [5, 95], axis=0)" ] }, { "cell_type": "code", "execution_count": 17, "id": "e8476136-5ab0-42c7-a3fe-88539ee7a019", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'prior')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in np.random.choice(n_prior_samples, 1000):\n", " plt.plot(x, y[i, :], zorder=1, alpha=0.2)\n", " # pass\n", "\n", "plt.fill_between(\n", " x, lower, upper, alpha=0.5, zorder=2, label=r\"inner 90$^\\text{th}$ pctl\"\n", ")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.legend()\n", "plt.title(\"prior\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "30ed8fc8-d5e9-4e58-85e8-aba6b8683d00", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'prior')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = corner.corner(prior_samples, labels=[p.name for p in my_model.params])\n", "fig.suptitle(\"prior\")" ] }, { "cell_type": "markdown", "id": "278a52b1-4326-43a4-8157-0b9e7e24a352", "metadata": {}, "source": [ "## Now let's update our prior by comparing to some data\n", "\n", "This will require learning about `LikelihoodModel`s in `rxmc`. These encode our assumptions about the error on an experimental `Observation`, and are a necessary ingredient for comparing to the predictions of a `PhysicalModel`.\n", "\n", "In our case we will mock experimental data by synthetically generate some data with noise about a \"true\" $m$ and $b$. Our calibration posterior should converge to be centered about this true point.\n", "\n", "Let us assume that the experimentalists made a perfect estimate of the experimental noise in their setup. That is, the error bars they report will correspond exactly to the true distribution from which we sample.\n", "\n", "This noise will correspond to statistical noise. Later on we will look at systematic experimental error." ] }, { "cell_type": "code", "execution_count": 19, "id": "d07c4b8c-fb40-4af3-9a76-7c798943a213", "metadata": {}, "outputs": [], "source": [ "true_params = OrderedDict(\n", " [\n", " (\"m\", 0.6),\n", " (\"b\", 2),\n", " ]\n", ")\n", "\n", "x = np.linspace(0, 1, 10)\n", "noise = 0.1\n", "y_exp = my_model.y(x, *list(true_params.values())) + rng.normal(\n", " scale=noise, size=len(x)\n", ")\n", "y_stat_err = noise * np.ones_like(y_exp) # noise is just a constant fraction of y\n", "obs1 = rxmc.observation.Observation(x=x, y=y_exp, y_stat_err=y_stat_err)" ] }, { "cell_type": "code", "execution_count": 20, "id": "664d9b5e-d7d2-4d08-91f6-763e75b40ab4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'experimental constraint')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.errorbar(\n", " x,\n", " y_exp,\n", " y_stat_err,\n", " color=\"k\",\n", " marker=\".\",\n", " linestyle=\"none\",\n", " label=\"obs1\",\n", ")\n", "plt.plot(x, my_model.y(x, *list(true_params.values())), \"k--\", label=\"truth\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "\n", "plt.fill_between(\n", " x,\n", " lower,\n", " upper,\n", " alpha=0.5,\n", " zorder=2,\n", " label=r\"prior inner 90$^\\text{th}$ pctl\",\n", ")\n", "\n", "plt.legend()\n", "plt.title(\"experimental constraint\")" ] }, { "cell_type": "markdown", "id": "766bb583-3be2-469a-85bb-4f364610f175", "metadata": {}, "source": [ "Clearly, our prior is at odds with our observation. We will now determine a posterior distribution of $m$ and $b$ that takes `obs1` into account. To do this, we will need to think about a `LikelihoodModel` for `obs1`." ] }, { "cell_type": "markdown", "id": "4818c696-b984-4145-b911-5307782e0dd5", "metadata": {}, "source": [ "## set up `LikelihoodModel` and `Constraint`.\n", "\n", "We will use the simplest assumption about the error on y: that the experimentalists exactly reported the statistical error, and there is no systematic error at all. This implies that each data point in `obs1.y`, say `obs1.y[i]` can be modeled as being an random variate, each sampled independently from normal distributions with mean `obs1.y[i]` and with standard deviation `obs1.y_stat_err[i]`.\n", "\n", "This behavior is handled by the default `LikelihoodModel`:" ] }, { "cell_type": "code", "execution_count": 21, "id": "1ca25e05-9b9d-4c22-94bf-0caf31854835", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class LikelihoodModel in module rxmc.likelihood_model:\n", "\n", "class LikelihoodModel(builtins.object)\n", " | A class to represent a likelihood model for comparing an Observation\n", " | to a PhysicalModel.\n", " |\n", " | The default behavior uses the following covariance matrix:\n", " | \\[\n", " | \\Sigma_{ij} = \\sigma^2_{i}^{stat} \\delta_{ij}\n", " | + \\Sigma_{ij}^{sys}\n", " | + \\gamma^2 y_m^2(x_i, \\alpha)\n", " | \\]\n", " | where $sigma^2_{i}^{stat}$ is the statistical variance of the i-th\n", " | observation, (`observation.statistical_covariance`) and $\\gamma$ is the\n", " | fractional uncorrelated error (`self.frac_err`).\n", " |\n", " | Here, $Sigma_{ij}^{sys}$ is the systematic covariance matrix:\n", " | \\[\n", " | \\Sigma_{ij}^{sys} = \\eta**2 y_m(x_i, \\alpha) y_m(x_j, \\alpha) + \\omega,\n", " | \\]\n", " | where $\\eta$ is the uncertainty in the overall normalization of the\n", " | observation (`observation.y_sys_err_normalization`) and $\\omega$ is the\n", " | uncertainty in the additive normalization to the observation\n", " | (`observation.y_sys_err_offset`).\n", " |\n", " | Here, also, $y_m(x_i, \\alpha)$ is the model prediction for the i-th\n", " | observation. Thus the covariance matrix is dependent on the values\n", " | of the PhysicalModel and its parameters, as is the case when systematic\n", " | errors are present in the observation, following D'Agostini, G. (1993) 'On\n", " | the use of the covariance matrix to fit correlated data'\n", " |\n", " | Note that if there is no systematic uncertainty encoded in the `observation`\n", " | and `self.frac_err` takes the default value of 0, the\n", " | covariance matrix becomes diaginal, and the `chi2` function reduces to the\n", " | simple and familiar $\\chi^2$ form.\n", " |\n", " | Note also that this is equivalent to the alternative method to handle systematic\n", " | errors described by Barlow, R (2021) 'Combining experiments with systematic\n", " | errors', in which nuisance parameters are introduced corresponding to the\n", " | normalization and additive offset bias of the observation.\n", " |\n", " | The advantage of this approach is that it does not require introducing\n", " | nuisance parameters, but instead encodes the correlation between the data\n", " | points in the observation in the covariance matrix directly.\n", " |\n", " | Methods defined here:\n", " |\n", " | __init__(self)\n", " | Initializes the LikelihoodModel, optionally with a fractional\n", " | uncorrelated error.\n", " |\n", " | chi2(self, observation: rxmc.observation.Observation, ym: numpy.ndarray)\n", " | Calculate the generalised chi-squared statistic. This is the\n", " | square of the Mahalanobis distance between y and ym\n", " |\n", " | Parameters\n", " | ----------\n", " | observation : Observation\n", " | The observation object containing the observed data.\n", " | ym : np.ndarray\n", " | Model prediction for the observation.\n", " |\n", " | Returns\n", " | -------\n", " | float\n", " | Chi-squared statistic.\n", " |\n", " | covariance(self, observation: rxmc.observation.Observation, ym: numpy.ndarray)\n", " | Default covariance model. Derived classes of `LikelihoodModel` will\n", " | override this.\n", " |\n", " | Returns the following covariance matrix:\n", " | \\[\n", " | \\Sigma_{ij} = \\sigma^2_{i}^{stat} \\delta_{ij}\n", " | + \\Sigma_{ij}^{sys}\n", " | + \\gamma^2 y_m^2(x_i, \\alpha)\n", " | \\]\n", " | where $sigma^2_{i}^{stat}$ is the statistical variance of the i-th\n", " | observation, (`observation.statistical_covariance`) and $\\gamma$ is the\n", " | fractional uncorrelated error (`self.frac_err`).\n", " |\n", " | Here, $Sigma_{ij}^{sys}$ is the systematic covariance matrix:\n", " | \\[\n", " | \\Sigma_{ij}^{sys} = \\eta**2 y_m(x_i, \\alpha) y_m(x_j, \\alpha) + \\omega,\n", " | \\]\n", " | where $\\eta$ is the uncertainty in the overall normalization of the\n", " | observation (`observation.y_sys_err_normalization`) and $\\omega$ is the\n", " | uncertainty in the additive normalization to the observation\n", " | (`observation.y_sys_err_offset`).\n", " |\n", " | Here, also, $y_m(x_i, \\alpha)$ is the model prediction for the i-th\n", " | observation.\n", " |\n", " | Parameters\n", " | ----------\n", " | ym : np.ndarray\n", " | Model prediction for the observation.\n", " | observation : Observation\n", " | The observation object containing the observed data.\n", " |\n", " | Returns\n", " | -------\n", " | np.ndarray\n", " | Covariance matrix of the observation.\n", " |\n", " | log_likelihood(\n", " | self,\n", " | observation: rxmc.observation.Observation,\n", " | ym: numpy.ndarray\n", " | )\n", " | Returns the log_likelihood that ym reproduces y, given the covariance\n", " |\n", " | Parameters\n", " | ----------\n", " | ym : np.ndarray\n", " | Model prediction for the observation.\n", " | observation : Observation\n", " | The observation object containing the observed data.\n", " |\n", " | Returns\n", " | -------\n", " | float\n", " |\n", " | residual(self, observation: rxmc.observation.Observation, ym: numpy.ndarray)\n", " | Returns the residual between the model prediction ym and\n", " | observation.y\n", " |\n", " | Parameters:\n", " | ----------\n", " | observation : Observation\n", " | The observation object containing the observed data.\n", " | ym : np.ndarray\n", " | Model prediction for the observation.\n", " |\n", " | Returns\n", " | -------\n", " | np.ndarray\n", " | Residual vector.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " |\n", " | __dict__\n", " | dictionary for instance variables\n", " |\n", " | __weakref__\n", " | list of weak references to the object\n", "\n" ] } ], "source": [ "help(rxmc.likelihood_model.LikelihoodModel)" ] }, { "cell_type": "markdown", "id": "685dec98-6cf8-49e4-b1aa-3bad227ffe8b", "metadata": {}, "source": [ "Sorry, I know that's a lot to read. The important piece is right here:\n", "\n", " | Note that if there is no systematic uncertainty encoded in the `observation`\n", " | and `self.fractional_uncorrelated_error` takes the default value of 0, the\n", " | covariance matrix becomes diaginal, and the `chi2` function reduces to the\n", " | simple and familiar $\\chi^2$ form.\n", "\n", "This means that, in our case in which the errors are only statistical, the likelihood will be porportional to the familiar form:\n", "\n", "\\begin{equation}\n", " \\mathcal{L}(\\alpha|y) \\propto e^{ - \\chi^2(\\alpha,y) }\n", "\\end{equation}\n", "\n", "where the Chi-squared is simply\n", "\n", "\\begin{equation}\n", "\\chi^2(\\alpha,y) = \\sum_i \\frac{(y(x_i) - y_m(x_i;\\alpha) )^2}{\\sigma^2_i}\n", "\\end{equation}\n", "\n", "In a later tutorial we will use more complicated `Observation`s that include systematic error, and look at other `LikelihoodModel`s." ] }, { "cell_type": "code", "execution_count": 22, "id": "8b698c69-7a23-4955-ab7a-15107774bec9", "metadata": {}, "outputs": [], "source": [ "likelihood_model = rxmc.likelihood_model.LikelihoodModel()" ] }, { "cell_type": "code", "execution_count": 23, "id": "211990b6-85ba-47b1-89c4-796c5b396168", "metadata": {}, "outputs": [], "source": [ "constraint = rxmc.constraint.Constraint(\n", " [obs1],\n", " my_model,\n", " likelihood_model,\n", ")" ] }, { "cell_type": "markdown", "id": "6e59d72f-7175-4ac8-9b08-bdeb1119379f", "metadata": {}, "source": [ "Let's test this `constraint` thing out. What is the reduced $\\chi^2$ for the prior mean?" ] }, { "cell_type": "code", "execution_count": 24, "id": "51b72666-3986-42f1-8262-8de23eb87ee3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(64.18631209285807)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "constraint.chi2(prior_distribution.mean) / constraint.n_data_pts" ] }, { "cell_type": "markdown", "id": "5cc86910-5ded-4339-ad1b-0ff82fabd5f6", "metadata": {}, "source": [ "Here is proof that, in this case, we reduce to the form described above:" ] }, { "cell_type": "code", "execution_count": 25, "id": "a845e4ff-1d51-455b-97c8-e7ad561a89ce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.float64(64.18631209285807)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = my_model(obs1, *prior_distribution.mean)\n", "np.sum((y - obs1.y) ** 2 / y_stat_err**2) / constraint.n_data_pts" ] }, { "cell_type": "markdown", "id": "fbb7e0d9-8a86-4228-9372-ee63dfad5ee3", "metadata": {}, "source": [ "## running the calibration" ] }, { "cell_type": "code", "execution_count": 26, "id": "dd9fa5b9-10ab-4649-96c0-58f9cb6896f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class Walker in module rxmc.walker:\n", "\n", "class Walker(builtins.object)\n", " | Walker(\n", " | model_sampler: rxmc.param_sampling.Sampler,\n", " | evidence: rxmc.evidence.Evidence,\n", " | likelihood_samplers: list[rxmc.param_sampling.Sampler] = [],\n", " | rng: numpy.random._generator.Generator = Generator(PCG64) at 0x14FA84891460\n", " | )\n", " |\n", " | A class that encapsulates the sampling configuration for a Bayesian\n", " | inference problem, including the physical model and likelihood model\n", " | configurations. It manages the sampling process for both the physical\n", " | model and the likelihood model parameters, alternating between each\n", " | model in a Gibbs sampling framework.\n", " |\n", " | Methods defined here:\n", " |\n", " | __init__(\n", " | self,\n", " | model_sampler: rxmc.param_sampling.Sampler,\n", " | evidence: rxmc.evidence.Evidence,\n", " | likelihood_samplers: list[rxmc.param_sampling.Sampler] = [],\n", " | rng: numpy.random._generator.Generator = Generator(PCG64) at 0x14FA84891460\n", " | )\n", " | Initialize the Sampler with a list of samplers.\n", " |\n", " | Parameters:\n", " | ----------\n", " | model_sampler: Sampler\n", " | A Sampler object for physical model parameters.\n", " | evidence: Evidence\n", " | A Evidence object containing the data for which the likelihood\n", " | model is evaluated.\n", " | likelihood_model_samplers: list[Sampler]\n", " | A list of Sampler objects for likelihood model parameters.\n", " | Corresponds to the order of `evidence.parametric_constraints`.\n", " | rng: np.random.Generator, optional\n", " | A random number generator for reproducibility. Defaults to a new\n", " | default_rng with a fixed seed.\n", " |\n", " | log_likelihood(self, model_params, likelihood_params)\n", " |\n", " | log_posterior(self, model_params, likelihood_params)\n", " |\n", " | log_prior(self, model_params, likelihood_params)\n", " | Returns the log-prior probability of the model parameters and\n", " | likelihood parameters.\n", " |\n", " | Parameters:\n", " | ----------\n", " | model_params: tuple\n", " | The parameters of the physical model.\n", " | likelihood_params: list[tuple]\n", " | A list of tuples containing additional parameters for the\n", " | likelihood model for each constraint.\n", " |\n", " | Returns:\n", " | -------\n", " | float\n", " | The log-prior probability.\n", " |\n", " | run_likelihood_batches(\n", " | self,\n", " | n_steps,\n", " | starting_locations,\n", " | model_params,\n", " | burn=False\n", " | )\n", " | Walks each of the likelihood parameter spacers one by one, for a\n", " | fixed value of `model_params`\n", " |\n", " | Parameters:\n", " | ----------\n", " | n_steps: int\n", " | The number of steps to run for each likelihood model.\n", " | starting_locations: list[np.ndarray]\n", " | A list of starting locations for each likelihood model.\n", " | model_params: tuple\n", " | A fixed value of the parameters for the physical model\n", " | burn: bool\n", " | If True, the batch is considered a burn-in batch and\n", " | the acceptance rate, log probabilities, and parameter\n", " | chain will not be recorded.\n", " |\n", " | Returns:\n", " | -------\n", " | list[np.ndarray]\n", " | A list of parameter chains for each likelihood model.\n", " | logp : list[np.ndarray]\n", " | A list of log probabilities for each likelihood model.\n", " | accepted : list[float]\n", " | A list of acceptance rates for each likelihood model.\n", " |\n", " | run_model_batch(self, n_steps, x0, likelihood_params=[], burn=False)\n", " | Walks the model parameter space for fixed values of the\n", " | `likelihood_params`\n", " |\n", " | Parameters:\n", " | ----------\n", " | n_steps: int\n", " | The number of steps to run for the model sampling.\n", " | x0: np.ndarray\n", " | The starting location for the model sampling.\n", " | likelihood_params: list[tuple]\n", " | A list of fixed values of the parameters for each of the\n", " | parametric likelihood models, corresponding to the order\n", " | of `self.likelihood_samplers`\n", " | burn: bool\n", " | If True, the batch is considered a burn-in batch and\n", " | the acceptance rate, log probabilities, and parameter\n", " | chain will not be recorded.\n", " |\n", " | Returns:\n", " | -------\n", " | batch_chain: np.ndarray\n", " | The parameter chain generated by the model sampling algorithm.\n", " | logp: np.ndarray\n", " | The log probabilities of the parameter chain.\n", " | accepted: float\n", " | The acceptance rate of the model sampling algorithm.\n", " |\n", " | walk(\n", " | self,\n", " | n_steps: int,\n", " | burnin: int = 0,\n", " | batch_size: int = None,\n", " | verbose: bool = True\n", " | )\n", " | Runs the MCMC chain with the specified parameters.\n", " | Updates the internal state of the `model_sampler` and\n", " | `likelihood_samplers` with records of the walk and relevant\n", " | statistics.\n", " |\n", " | Parameters:\n", " | -----------\n", " | n_steps : int\n", " | Total number of active steps for the MCMC chain.\n", " | batch_size : int\n", " | Number of steps per batch.\n", " | burnin : int\n", " | Number of extra burn-in steps to do before active steps\n", " | verbose : bool\n", " | Flag to print extra logging information.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " |\n", " | __dict__\n", " | dictionary for instance variables\n", " |\n", " | __weakref__\n", " | list of weak references to the object\n", "\n" ] } ], "source": [ "help(rxmc.walker.Walker)" ] }, { "cell_type": "markdown", "id": "cc7f9eff-e473-4d7d-b081-4ba1fa6a94b5", "metadata": {}, "source": [ "First we need to put together our `Evidence`. With one constraint this seems trivial, but this will be useful down the road when we may want to combine multiple constraints together." ] }, { "cell_type": "code", "execution_count": 27, "id": "1ff7c728-b0ac-4c48-8779-19da58277cc9", "metadata": {}, "outputs": [], "source": [ "evidence = rxmc.evidence.Evidence([constraint])" ] }, { "cell_type": "markdown", "id": "bf1e4ad1-a94a-42c2-b084-c29b9945c29e", "metadata": {}, "source": [ "Another chore we have to do is configure how we will sample our parameter space." ] }, { "cell_type": "code", "execution_count": 28, "id": "5470a662-4880-418d-85aa-deadeca0cfe7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class MetropolisHastingsSampler in module rxmc.param_sampling:\n", "\n", "class MetropolisHastingsSampler(Sampler)\n", " | MetropolisHastingsSampler(\n", " | params: list[rxmc.params.Parameter],\n", " | prior,\n", " | starting_location: numpy.ndarray,\n", " | proposal: rxmc.proposal.ProposalDistribution\n", " | )\n", " |\n", " | Metropolis-Hastings sampler. The ol' reliable.\n", " |\n", " | Method resolution order:\n", " | MetropolisHastingsSampler\n", " | Sampler\n", " | builtins.object\n", " |\n", " | Methods defined here:\n", " |\n", " | __init__(\n", " | self,\n", " | params: list[rxmc.params.Parameter],\n", " | prior,\n", " | starting_location: numpy.ndarray,\n", " | proposal: rxmc.proposal.ProposalDistribution\n", " | )\n", " | Parameters:\n", " | ----------\n", " | params: list[params.Parameter]\n", " | List of parameters to sample.\n", " | prior: object\n", " | Prior distribution object that has a method `logpdf`.\n", " | starting_location: np.ndarray\n", " | Initial parameter values for the chain.\n", " | proposal: proposal.ProposalDistribution\n", " | Proposal distribution object that has a method `__call__` which\n", " | takes in a parameter vector and an rng, returning a proposed\n", " | parameter vector.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Methods inherited from Sampler:\n", " |\n", " | batch_acceptance_fractions(self) -> numpy.ndarray\n", " | Returns the acceptance fraction of the sampler in each batch run.\n", " |\n", " | most_recent_batch_acceptance_fraction(self) -> float\n", " | Returns the acceptance fraction of the most recent batch run.\n", " |\n", " | overall_acceptance_fraction(self) -> float\n", " | Returns the overall acceptance fraction of the sampler.\n", " |\n", " | record_batch(\n", " | self,\n", " | n_steps: int,\n", " | n_accepted: int,\n", " | chain: numpy.ndarray,\n", " | logp_chain: numpy.ndarray\n", " | )\n", " | Records the batch of samples and acceptance statistics.\n", " |\n", " | Parameters:\n", " | ----------\n", " | n_steps: int\n", " | Number of steps in the batch.\n", " | n_accepted: int\n", " | Number of accepted samples in the batch.\n", " | chain: np.ndarray\n", " | Array of sampled parameter vectors.\n", " | logp_chain: np.ndarray\n", " | Array of log posterior values corresponding to the sampled parameter vectors.\n", " |\n", " | sample(\n", " | self,\n", " | n_steps: int,\n", " | starting_location: numpy.ndarray,\n", " | rng: numpy.random._generator.Generator,\n", " | log_posterior: Callable[[numpy.ndarray], float],\n", " | burn: bool = False\n", " | )\n", " | Samples from the posterior distribution using the specified\n", " | sampling algorithm, updating the state and recording the chain,\n", " | log posterior values, and acceptance statistics.\n", " |\n", " | Parameters:\n", " | ----------\n", " | n_steps: int\n", " | Number of steps to sample.\n", " | starting_location: np.ndarray\n", " | Initial parameter values for the chain.\n", " | rng: np.random.Generator\n", " | Random number generator for reproducibility.\n", " | log_posterior: Callable[[np.ndarray], float]\n", " | Function that computes the log posterior probability of\n", " | a parameter vector.\n", " | burn: bool\n", " | If True, the samples are considered burn-in and will not\n", " | be recorded in the chain, only the current state will be updated.\n", " |\n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from Sampler:\n", " |\n", " | __dict__\n", " | dictionary for instance variables\n", " |\n", " | __weakref__\n", " | list of weak references to the object\n", "\n" ] } ], "source": [ "help(rxmc.param_sampling.MetropolisHastingsSampler)" ] }, { "cell_type": "markdown", "id": "628ad075-8062-4420-8e1f-76a5ed69f3b4", "metadata": {}, "source": [ "This means we have to decide on a proposal distribution. We will use a simple form: just sampling from a scaled down version of the prior about the previos point:" ] }, { "cell_type": "code", "execution_count": 29, "id": "9c0ac067-047a-4357-a462-415e32d0481c", "metadata": {}, "outputs": [], "source": [ "def proposal_distribution(x, rng):\n", " return stats.multivariate_normal.rvs(\n", " mean=x, cov=prior_distribution.cov / 100, random_state=rng\n", " )" ] }, { "cell_type": "code", "execution_count": 30, "id": "5362bf0b-76d4-419b-af34-2ee94e3b2dda", "metadata": {}, "outputs": [], "source": [ "sampling_config = rxmc.param_sampling.MetropolisHastingsSampler(\n", " params=my_model.params,\n", " starting_location=prior_distribution.mean,\n", " proposal=proposal_distribution,\n", " prior=prior_distribution,\n", ")" ] }, { "cell_type": "code", "execution_count": 31, "id": "784ebdb9-b5f2-4a2f-9882-a65d2f107c05", "metadata": {}, "outputs": [], "source": [ "walker = rxmc.walker.Walker(\n", " sampling_config,\n", " evidence,\n", ")" ] }, { "cell_type": "code", "execution_count": 32, "id": "194af035-fafd-4c64-8b58-0f7af34bb685", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Burn-in batch 1/1 completed, 1000 steps.\n", "Batch: 1/1 completed, 20000 steps. \n", " Model parameter acceptance fraction: 0.273\n", "CPU times: user 6.51 s, sys: 151 ms, total: 6.66 s\n", "Wall time: 7.97 s\n" ] } ], "source": [ "%%time\n", "walker.walk(\n", " n_steps=20000,\n", " burnin=1000,\n", ")" ] }, { "cell_type": "code", "execution_count": 33, "id": "b57ce430-2014-4999-9e4f-3f16f0c3c8fc", "metadata": {}, "outputs": [], "source": [ "chain = walker.model_sampler.chain\n", "logp = walker.model_sampler.logp_chain" ] }, { "cell_type": "code", "execution_count": 34, "id": "8bd838cf-8ed2-43b9-a545-416b9f5b802c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20000, 2)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chain.shape" ] }, { "cell_type": "code", "execution_count": 35, "id": "122e8c8e-4975-42b0-96ef-611cb9187c85", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, '$i$')" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(chain.shape[1] + 1, 1, figsize=(8, 8), sharex=True)\n", "for i in range(chain.shape[1]):\n", " axes[i].plot(chain[:, i])\n", " axes[i].set_ylabel(f\"${my_model.params[i].latex_name}$ [{my_model.params[i].unit}]\")\n", " true_value = true_params[my_model.params[i].name]\n", " axes[i].hlines(true_value, 0, len(chain), \"r\", linestyle=\"--\")\n", "\n", "\n", "axes[-1].plot(logp)\n", "axes[-1].set_ylabel(r\"$\\log{\\mathcal{L}(\\alpha_i | \\mathcal{O})}$\")\n", "\n", "axes[-1].set_xlabel(r\"$i$\")\n", "# plt.legend(title=\"chains\", ncol=3,)" ] }, { "cell_type": "code", "execution_count": 36, "id": "6b20814a-1595-4881-9190-6f03ee4811e3", "metadata": {}, "outputs": [], "source": [ "posterior_range = np.vstack([np.min(chain, axis=0), np.max(chain, axis=0)]).T" ] }, { "cell_type": "code", "execution_count": 37, "id": "21ca84ce-5d9a-44db-8651-f9c80e9b1f68", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'posterior')" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = corner.corner(\n", " chain,\n", " labels=[p.name for p in my_model.params],\n", " label=\"posterior\",\n", " truths=[true_params[\"m\"], true_params[\"b\"]],\n", ")\n", "fig.suptitle(\"posterior\")" ] }, { "cell_type": "code", "execution_count": 38, "id": "4aaec946-4f48-47e0-ad46-f6d71e866341", "metadata": {}, "outputs": [], "source": [ "x_full = np.linspace(-1, 2, 10)" ] }, { "cell_type": "code", "execution_count": 39, "id": "2aab97fb-db36-4a05-aef0-8774173188df", "metadata": {}, "outputs": [], "source": [ "n_posterior_samples = chain.shape[0]\n", "y = np.zeros((n_posterior_samples, len(x_full)))\n", "for i in range(n_posterior_samples):\n", " sample = chain[i, :]\n", " y[i, :] = my_model.y(x_full, *sample)\n", "\n", "upper, median, lower = np.percentile(y, [5, 50, 95], axis=0)" ] }, { "cell_type": "code", "execution_count": 40, "id": "92fc68f8-9be2-4e5f-9bf0-65eb796f5e0a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CVWPJk18rnf3vi6gjZBZXYuX+bBgrzS7/e1y7bi2+22WfyBAaGoK5c+ephUDbKiLIgFvc0BW8uet3Q8zcEBEReRir1aZ6Qn17shBWN+Qgdu/eXRvYXDbiMkyfNt2r/ulgcENERORBSqvMqn3CuaJKFz+zrXYl4dGjR+FcxjmMHjUKXbu6brjbUzC48SKyojARXytEndfx3DKsPZij6mxcxWazqVXP9+7dq2rlpMZOq9Xh2muuhbdicENEROQBa9dsPpaPPenFLn3e/Px8LF26tLbp7p49e9RSE96OwQ0REVEHr12zYl8W8kqrXfacFotFTe2WGbDyuZ+fAVOnTsPw4fVXj/dWDG6IiIg6yIHMEmw4kgdTTf0V09vi3LlzKlvjaB/Tq3cvzJ83D+HhEfAVDG6IiIjaWXWNBesP5+FQltHlz71+/XoV2AQHB2H27DlISxtYW0jsKxjcEBERtaNcY5UahiqqcN3aNTabFRqNfZXwefPmYcvXWzBj+nQEBgahPQTvrUbYrmpk3dL8+jPthcENERFRO5BZS1IwLIXDFqtr1q6RBryrVq1SveDmzp2rtkVFRWHhgoVoL4ZcC3r/sgAaK2Ac4Q+Mrt+EuCMwuCEiInIzmdq95mAOTuSWuegZbdiz53usWbNGtUCR3m7SA05W8G0X0t9KZx/qMsfpkD8/CBoLUJ2iR8eHNgxuiIiI3CpDWijsy0JpVY1Lnq+oqBBLly3DqZOn1P2EhAQsXLigfQIbiw2xS8oR+3k5jv4tBjWR9m7h5+4NBzSeU9fDzA0REZGbhqF2nC7CNycKXNJCQRoLf/vtt9iwYT3M5hrV2Fb6xI0dO0YtytcuNEDUukr451gQs6wC2TeGnt/uOYGNsFcfEbWz3/3ud6pxY2en0WjwxRdfqM9Pnz6t7ssiWUTk28qra/D5dxn4+ni+y3pDVVVVYsuWLSqwSe2eirvvvlsNRbk7sAk4abYPQwmtBufuCcPZB8OR/aMQeCpmbsjp1g5TpkxBUVERIiLavlbCI488gvvvv9+rzn6XLl2QlZWFmJiYjj4UIupAZwrKVW+oClPbWyhYLDXQ6eyX6qCgYFU0bDabMWyY/HPo/mxJ8msliP2iHGcfikDhbPvMq/KB/urmyRjcULunaWW1zJCQEHVrC/kDNxg8oXTNTvq1yNg3EfkmmQElQ1DSzdsVTp06iaVLl2HmzJno16+f2paWlob2ZI7VQWMDAs+4btq61w9Lvfbaaxg8eDDCwsLUbezYsVi5cmWzX7Nx40bVF0Nas/fo0QP//Oc/2+14PZmMu953333qJpmV6OhoPPHEEyqYcJCsy09+8hNERkYiKCgIc+bMwbFjx2ofP3PmDBYsWKAeDw4OxsCBA7FixQo13CJZGyGPydDLzTffrO7L8//lL39RvwuZijhkyBB8+umn9TI+sv/q1asxYsQI+Pv7Y/PmzRcNS8lY8u9//3ukpKSofeQxmd7o4Bjy+b//+z/1s8rv/4MPPmj0XMh+r7/+OubPn69+zv79++Obb77B8ePH1dfKzyavtRMnTtT7OlnRs+5r66mnnkJNzYUCQDlXEydOVI8PGDAAa9eurff1DYelJIi77bbb0L17d3Vu+vbti5dffrne18h5XLRoEZ577jkkJiaq39u9996rAjci6jxKKs34dFe6SwIbmf30vyX/w3vvva/et2Uoyt7R280sNkStLLcPQ52XtzAYh/8Rg4w722kWljdkbuRC9uyzz6JXr17q/rvvvosrrrgCu3fvVhfWhk6dOqVScnfccYe6sEnfjHvuuQexsbG4+uqr3XqslnJ7elEbpFUXMGE1WWEz26DRa6D11168b6AWGu35fc1W2EwydQ7QBeguua/W0PK4U86fXEy3bduGnTt34qc//Sm6deumzpfjQioX6CVLlqhg8le/+pU6nwcPHlQZELmomkwm1YtEAgDZLtkVGW757LPP1Dk+cuSI+lq5WAsJoD7//HMVqPbu3Vt97Q033KB+J5MmTao9tl/+8pfqAi5BgwRfEqTWJRf9559/XgUlw4YNw7///W8sXLgQBw4cUM/rIMcs+7399tsqCGrKH/7wB7zwwgvqJl/zox/9SH3vRx99FF27dsWtt96qAkFHMC3Blxz33/72N0yYMEEFPnL+xJNPPqmCr6uuukoNOUlBn9FoxIMPPtjs70O+Rl7jEpDJ123dulU9pwQxP/jBD+qtJirb5KMEYNddd50K7hy/NyLybMdzS9U072pzW1so2HDgwEH1viTr18ilZsTIkZg2dVq7DEElvV2K+P8rQ+kwPxx/NloVCdv8NKjs44fOpkODG8kS1PX000+ri6RcPBoLbiRLIxeml156Sd2X/8jlIi4XzaaCm+rqanVzkItSa2wO2aw+jssdB79Y+y86/a/pOPXEKSTenoi+b/at3ffruK9hrbBi9KnRCEy1BwEZr2TgxEMnEPejOAz4cEDtvt+mfgtzvhkj949E8MBgtS37nWwk3ZHU4mOUIOTFF19UwZdkCfbt26fuy0XSEdRIQDhu3Di1/4cffqi+Rgpir732WtU1Vs7joEGD1OMSDDjIolAiLi6utuZG/vgkePjqq69UJsTxNfJfhgQpdYMbycrMmDGjyWOX36EEIT/84Q/V/T//+c/qYi+/61deeaV2PwkoJMi4lFtuuaU2gJDnleP7zW9+g1mzZqltP/vZz9Q+dV97v/71r3HTTTfV/hwSIElQJsHNunXrcOjQIZWdkYBF/OlPf1LZr6ZIwCjZHwfJ4EiAI8FO3eBGsmH/+Mc/1LCWpJ5lddEvv/ySwQ1RJ+jkvelYHr5PL2nzcxmNJVi+YgWOHjmq7sfExqiF+OQ9ur3kzwtC1NoKlIwMsCeKPGsCVOesuZEU/n//+191wXRcKBuSoQUZe6xLLlZvvfVWk/UXzzzzTL0LjDcbM2ZMbVZJyHmULIecW7kwy7TB0aNH1z4uQyASBMlj4oEHHlDV97Io1PTp01WgI8OGTZHMTlVV1UVBi2R/JPtSlwxJNUUCzszMTFX1X5fc//77751+nrrqHnd8fLz66AjaHNvk2OV7SyZq165d2LFjhwpyHOS8yT4VFRXqHElg7QhsRFOv04YB+b/+9S815CepZjk3DWeJSSAvgY2DZHEkMCUiz+7kvXxfFvJd1Mk7NzdPBTY6nRYTJkzE+PGX1xYSu4O+2IKE90tRE6ZF9k32lgmmRD0OfBAPm6ETRzWeEtzIm7hcJOQiIkMgixcvVvUMjcnOzq69UDnIfamLyM/PVxeFhmQY4uGHH669Lxez1kTCE8om1A5LOXT5RRekPJiihqXqujz38tqhJofke5Pt2ZgGM/bGnB5z0b4JN7u+KLVu7U3D7Y6A6Pbbb1fB4vLly1WAI4GhBEdNzWqSYRch+ycnJ9d7rOGQkQxzXUrdwKzhsbXkeUTdQNfxHI1tc/wM8lGC4MayQlJj09j5a3hsDUmG5qGHHlLnUF7joaGh+Otf/6qGDZs6VsfzOo6LiDyzk/f6w7kwO6ZHt5L8s+PnZx8JkPKMadOmqX84ZVjf3YIOmRG7tAJWA5C/MLh2MT5vCGw8IriRX6QUYBYXF6u6DhkWkHqMpgKcxi6AjW2ve5FtrjbDWbrgi9cR0PppAT8n95UaGkML9m0FGc5reF/qVSQrIOdTgkC5sDqGpQoKCnD06FE1vOcggd9dd92lbhIYvvnmmyq4cfwBSjbDQZ5Tzq0MZ9UdgmopyZwkJSWp4Swp2HWQIZxRo0ahPQwfPlzVEznqvxqSn1V+TskwybE6MonNkcJpOddSF+bQsIiZiHyvk7dM7968eYvKFt91150IDbVnTsaPHw+3sdmgL7SiJtp+zTGO8UfeFcEoHh9QG9h4kw4PbuSi6bigyJCD/LKluFRqNhqSabaSvalL2rrLcIsMsfi69PR0laW688478d133+Hvf/+7yhoICXKkWFvqb+TcShZBakwk4yLbHfUsUkPSp08fVaEvtTSOwEcKkyWAXLZsmSpCloJieQ5Zr0ayE5JpkD9MyYxJUCJZOEf9ijN+8YtfqNqWnj17qmEbKRiWoFfqgtrDb3/7WzW7SoI7qT+SPi179+5VmcU//vGPaphOAnGZbSbnVH7Oxx9/vNnnlNf1e++9p4qVpd7m/fffV69v+ZyIfLOTd3r6WSxZuhT5efnq/t69+y4aknc1v6wadPtzEQzFVhx6M86endFo7C0TvFSHBzcNSSambgFwXZLal+m6dcnwiQRFnrTeSUeRC6/UdUi2Q7I1knFxzPgREjBIIa1cxCUdKlkSmertOHeSlZEZU+fOnVPZlNmzZ6uCZCFBkAzbSEAkhbjyvd555x1VdCtFxjKEdfLkSVVsLFmQxx57rEXHLvU+EjD8/Oc/VwGrZEqkALruTCl3kuE4Cdyk8Fmmtss5keJeGaoTEuzIkKnMRpPzm5qaqmZWyTlqimS/JECT2U8SGF5//fUqi3Op5Q6IyPs6ect1bd2X67Bzx051PyQkGHPmzMWAARcy5+5SE66Ff5YF2gobAo+bUdG/881+aimNralijHYgF0DJFMh/y6WlpfjPf/6jpobL+iZSpCrDIhkZGeq/X8dUcFnASDITkoGQYQG5gHz88cdOTwWXC6g0FyspKVEX8Lqk7ke+h/xnLXUWnYms3yIZD8dMMiJP05n/vsg3uaqTtwx5r1ixHEZjqbo/bPgwzJwxAwEB9tm0rqYttyL8myoUTbevKCyC91ajOllfOyzlThFBBtxyuesz1M1dvz0qc5OTk4Mbb7xRLVkvBywzXByBjZDtUufgIG+KkmmQYRCZHiy1D/Lfs7vXuCEiIt/iyk7eMltSAhtZUmPBgvlITXXf0LS2wooBt+XCUGiFKUGH8jR7zWn5YM9ul+BqHRrcyBTu5siwR0NSuCr1JERERK5mtUon70J8e7KwDQ0vbaisrKpd7HTKlMnqc+nerde7t4TCGqRFyZgAhOw1AT486dLjam6odaTNARERta2TtzS8PFtY0ernKCwsUP2gpIZR6hOl3s5g8FMrn7uDtEpIfL8U6T8LR02Efcgp444wWP01QINlSnwJgxsiIvJ5p/PLsfpA6zt5W60WbN36DTZu3ICaGgsMBr0qvXBrM12bDV1fLEbwETPM0Vqcu8++erw1uEPbRnoEBjdEROSzXNHJOzMzA0uWLFXBjOjRswfmz5uvWqu4mqbKap/KrbNP5868PQwxS8uRe3WIy79XZ8bghoiIfLaTtxQNZ5VUterrZWFUWQ9MFkyVicdSVyPLSgwZIu1fXD8kFLGxEsmvlyDr5jAUzrTPhCob4q9uVB+DGyIi8jnHckqx9lDbOnnL+lenTp9SgY30rpPAxtkWMa3hl2OBX74V0SvKUTgjUGVuqHEMboiIyGe0tZO3NNL19/dTTS0luLli4UKUlpa5ZcFR/wz7NHRZn0bkLQqG1Q8omBvMwOYSWHXkJtLdXKrk5SafExFRx3fy/nhHeisDG5tqxyJrrElfKIeEhES3BDaR6yrQ745cpPytWBUOqyPw0yB/UYj6SM1jcOMmdRtMbtq0qd59X3P69GkV5EkrAiKi9ibDRtLJ+6NtZ5Bf2nh7n+ZIY+cPP/wIn3/+ucrcHD12VPXTc6fygX6qbMem10Bb1WGNBDotBjduIH8AdbuaS6NJ6UUk232RtNeQ1aaldUZH+93vfqfaVBCR73Tylineaw7kwGxpWZBgs1lVsfCrr76K48ePQ6/XYerUqbj9ttvUkJTL2GwI/7oSMf+7kOU3Jepx6I04nHw6GtZAXqpbijU3LiYBzDXXXKP+U6hLemTJ9k8//RRXXXUVfIU06JTO725d64GIyMWdvAsK8vH54sXIzMhU97t166ZaJ0RHx7j8XMtqwj2eKlL1NCVj/WGOs1+aTedrbajlGA66kAw9SdftxnqROrY9+OCDbhuiku8hHa179OihpiQOGTJEBVOyffr06aqDteM4JM3atWtXPP7447UrHMvQ0fLly9XXSWPD0aNHqzHmurZu3aq6icvzS0ZGunnXrSmSDNUf//hH3HzzzapfmDQ4bTgs5fheq1evxrBhw9RzyX9D0g1cOmb3799fNUWTLtqSAr7Uz+fgeN4vv/xSdYoPCgrCuHHjVNM6RzsP6Wz+/fff19ZDNdbig4g6N3mv2H22CP/Zkd6qwEZotTrk5eYiIMAfCxYswE033eTawKZOFqlssB+MI/zVWjWWEF6WXYFn0YU2b96Mc+fONfsHl56ervZzhyeeeAJvv/02XnvtNRw4cEA1GL3hhhtUzc+7776L7du3q0ajQrqpx8fHq2Gaun7xi1/gueeew44dOxAXF4eFCxfCbLa/OUigI1MdJfO0d+9efPLJJ9iyZQvuu+++es/x17/+VQ1B7dq1C7/5zW+aPF753v/4xz9UwCTn5Qc/+IHqav7RRx+pIGvt2rX4+9//fsmfb+PGjfWeVwK2559/Hjt37oRer8ett96qtl933XX4+c9/joEDB6phMrnJNiLyrk7eS/dmYcORPLVAX0sUFl5YyE8W4Lvmmmtxzz33Yvjw4eqfIVctwpfwnhF9782Dxnz++DQanHg6Clm3hKneUNR2zHm5kFwsXblfS0j25IUXXlALSo0dO1ZtkwyHBB+vv/66Chjko3Rhl1U0ly5dit27d8NgqN/E7cknn6ztyi4BUUpKChYvXqwCDwlafvSjH6nsk5AZAhIsSTNTCTgk2yMkC/PII4/UPqdkbhojGZ7LL79cfX7bbbfh0UcfxYkTJ9RxCxnGW79+PX71q19d8ueTY3B4+umna+//+te/xrx581BVZW9iFxISogIeDpMReZ/WdvKW94e169Zi93ff4aabblZDUKJPnz4uP0aNFYhZVgFDsRURmytRNNW+GB/XrHEtBjculJiY6NL9WuLgwYPqD9QRmNSteZGhH3HttdeqQOWZZ55RwUhjf7iOwEFERUWhb9++OHTokLovmRgpqvvwww/rZaNk1sCpU6fUcJKQISFnDB4sq3jaSRZJhpEcgY1jm2SbnP35Gntex7mWIS8ZhiMi79OWTt7y/rZy5Qq1Vo04c+ZMbXDjKgGnzahKtf8jKZmZc/eFq8+LJ9j/ISTXY3DjQtL1VTIdUjzcWN2NpDXlcXd0h3VMS5ThnOTk5HqP+fvbl+aW+hUJUHQ6HY4dO+b0czvSsfI97rzzTlVn01DdwMHZFTrrZo3snXPrZ5Fkm+Pncubna+p56349EXmXsvOdvNNb2Mm7tLQUK1auwOFDh9X96OhoVVvj0sDGYkP3p4sQsaUKR1+MsU/vlqBmYqDrvgc1isGNC0nQ8PLLL6vhFLmo1g1wHBdZqSmR/VxNpp7LRf7s2bP1hmjqknoTmb4oRbsyPV2Ga2QIqS6Z9ugIVIqKinD06FH069dP3ZdxZ6l16dWrF9qbMz+fM2Tmli+vOUTkTU7mlWHNwRxUtrCTt9QMrlixAtXV1eo9cfz48eqfThmydimdBjWhWti0QNARU21wQ+7H4MbFpNhWZvBIdkMyOA6SsZHAxl3TwENDQ1WdixTZSpZC/liNRqMq1pU6k5iYGPz73//GN998o4IUqUWR6n/5I6/bufb3v/+9+g9GhoSkMFe+btGiReoxqX0ZM2YM7r33XjULSjI0ktJtWPjbET+f/CzOkNlcMoQmM7fkdyLP2zDzQ0Se30Jhy/F87D5b3OrnkMBGssALFy5AXFy8S45LCoSjl5ejeHIgaiLs/8Rm3RyK3KuDUd21fmaa3IvBjRtIACNTr2UqtJD/EGbOnOmWjE1df/jDH9QMJ6mpOXnyJCIiIlQgI4W6MitIZifJfUfh8Jo1a9SsKZn15PDss8+q6ewybCVTrZcsWaKyHY5aFpmZJEGP/JcjmamePXu224yjpn6+xx57zOnnuPrqq9VaRFOmTFHT4WX2lUxbJ6LO00JB1q7Ja8FKw5KtlZlQsbGx6v7gwYPU8HW/fn2h0bhudlK3Z4sQubkKAedqcO6+CLWtJlKnbtS+NLbGikO8mPy3L0FHSUmJWkulLilYlf/qu3fvXjvzp7Vkdo9kFERZWZlbO8W6gqwRIxd8GYqSoIHI1Vz590W+Ry5VB7OMaoq3qcb5GjpZnmPJ0iWorKjEvffeg4AA99W7hOypRuozRci8JQyFs8/PgvJBEUEG3HJ593a9fjfEzI2bSDDjY3EjEZHbWih8dSgXh7NLnf4ak6kaX371FXZs3676TspszPz8fKSkdHHJMRnyLUh8x4jSof4omm4PZMqG+uPAe/Gw+bOxZUdjcENERB4rq0TWrslGSaXzKw3LsPry5ctQUmJU92WIXUoDJMBxlcivKhG9phKh31WjeFIgbAZ7QMPAxjMwuCFl8uTJzDQRkceQzPfOM0XYerzA6bVrZLLB4i8WY/++/ep+RGQE5s+br2oD28xqg67MBkuYvUYnb1Ewgo6ZkHtVSG1gQ56DwQ0REXnc2jWr92fjbAvXrpFp3TqtTi29MWbsGEyZPBkGQ9unXweeMKPr88WoidDixJ+i1TabnwanH49q83N7E40G6B4TjCEpHV+3yeCmEayVIXI9/l2RM07ll2PNgWxUOLl2jUyC0Ot1CA21F5jOmjUTo0aNQlJSkstOuCVQg4AzZtgyNTDk1MAcz0tnXWGBBqQlhWFAUhhCAzxjyjt/Q42sbCsr+UofIiJyHUeH94YrURM51q75+kQBvjtT5PQQlCw6umHDenTv3gPXX/9DyR0gMDBI3dpCV2pF8CETjKPss/pMSXqcfjQS5Wl+tevX+DqtRoOeccFISwpHt+gglzUWdRUGN3XIOjQyDVr6EAkpPvO0XxhRZ8zYSGAjf1fy9+Xu9Z6o8ymStWv2ZyHX6NzaNdnZWViyZGltE2KT2QSTyVy7Jldb+GXVoO99edBW23Dw3/Ewx9lfryXj+Q+vY5r3oORw9E8MQ7C/54YQnntkHcTRLdoR4BCRa0hgw27s1NDBTCPWH8l1au0as9msFhL95putqlmmrJcks6CGDRuqsjauYErQoTLVAH2pFXqjtTa48WU6rQa940KQlhyOlMjATvFPP4ObBuSXJp2kZSVc+UMioraToShmbKjh2jXrD+fiUJZza9cUFOTjw48+QlGhfdhq4MCBmD17du1iqa0VeNyM2C/KkP6zCPusJ40Gp5+IRI3MitJ5/kXcnaJD/DAwKRwDEsMQ6Ne5gjwGN02QN2K+GRMRuV6OsUq1UCiucP4fyLAwezsbWZlWmv726dPHJb2gej5RAEOhFZU9Dci70h4o+XK7BINOg97xoSpLkxQe0CmyNI1hcENERO1Wf/Xd2SJsOebM2jU2HD9+Aj179lD9nyT7d/0Pf6iCnLY0u5WApnbBPYMGWTeGIvR7E4rH+XZLkNhQfxXQ9EsIRYCh8wd3DG6IiMjtyqtrsOZgNk7nX3rtGqOxBMtXrMDRI0cxZ84cNbVbxMbGtekYItdVIOnfRpx6IgoVA+zFxwXzgtXNF/npteh7PksTH+bfabM0jWFwQ0REbnU6vxyrnVi7Rq1KvHMn1q1bB5PJBJ1O69Lax5C9JvjlWxH3WRlOD/DdBfgSwgPUFO4+CSHw13f+LE1jGNwQEZFbWKw2fH08H7ucWLsmLy8PS5cuRXp6urqf0iUFCxcsRGxsbKu/v19GDSyh2tqWCVk3haI6RYe8RW0rQu6M/A1a9E8Iw8DkMMSFev8QHIMbIiJyueIKE1bsy1bFw5eye/du1ejSYrHC398P06ZNx4gRI9o0TBLzRRmS3zAif14wMu61FyPXROuQ+4NQ+JLkiEAV0PSJD4VBZw/yfAGDGyIicqlDWUZ8ddi5tWuELL8hQ1J9+vbBvLlza2dGtUVVNwO0NYB/To1qegmt99STXEqAQYf+iaFqsb3okNYXX3dmGpuPNXwxGo0IDw9HSUmJmlJIRESuXLsmTwU3ze5XXY0zZ87Um84tC6fGxckQVCuCEJsNYd/aVzc2jr0w5BJ41ITKPm1ftbiz6BIVhLTkMPSKDYHeC7M0Lbl+M3NDRERtJsNPK/dloegSa9ccOXIEK1YsR1lZOX76058iPj5ebZeFU1sr8stKpP6lGKYYLQ4Oi4MtwH5h94XAJshPpxpWSoFwZLD3/7zOYnBDRERtXLumWBUOSwFxU8rKyrBy5UocPHhQ3Y+MinTZTKjiiYGo+rhMrVWjsckKOd5NSpGkWaUEND1iQ1R7BKqPwQ0REbVKhakGaw7k4FR+eTN72bB79x6sWbMGVVVV0Go1GDtuHCZNnNSqDvGaahviPi9D4AkzTj9hn85t89Pg0OuxgN67L/Ih/noMTJIZT+EID2z5ufMlDG6IiKjFzhZUYNWBLJRXN7d2jQ0f/+c/ajE+R+HwwoULkJCQ2Oozbsi3IOH9UlUsnLe3GuWDzxfMemlgI1ma7jHBaqG97tHBKjikS2NwQ0RETpOhp29OFGDnmUKp470EDbqndsepkycxZcpUjB49Glptywtd9YUW1ETZF5szJevVejXmGB3K07y3xiQs0GDP0iSFITSAWZqW4mwpIiJySkmFGSv2ZyG7pOm1azIzM2G1WpCS0kXdt9ms52e5RLT4LGsrrejyUgnCt1bh0L/jYI71ztV0HbQaDXrEBqsp3F2jgpilaYCzpYiIyKUOZxvx5aGm164xm01Yv34Dvv32W0RGRuLuu++CXm9QTS9bE9gIa4AGfnkWaE02hO6sQuEc7+wBFRFkUMNOAxLDEOzPARVX4FkkIupkysvLERISUjsLKTjYfRd9CWY2HMnFgcym1645ceIEli1bhuLiYnU/OTkZNTUWFdy0iMWGyE2VKJoQaK+h0WiQfn84NBagspd3Dc3IDKdecSEqS5MSGehVTSs9AYMbIiJqVK6sXbM/G4XlpkYfr6iowOo1q7H3+73qviywNn/+PPTq1btVZ7Tn44UI+64aOqMN+VfYA7aq7t4V1ESH+GFgkj1LE+jn3cNsHYnBDRERXbR2ze70Ymw51vTaNUZjCV5//Q0V4EjSYdTo0Zg6ZSr8/Fpf5Fs8PgBBR0ywedmVSa/VoHd8KAalhCMpPIBZmnbgZS8hIiJq69o1aw/m4GRec2vXQC1/L8NPJcYSLFywAMnJKS36PrpSKxI+LEXRpEBU9LcHRAVzgtSCfI4u3p1dTKi/GnbqlxCq+j1R+2FwQ0RESnphBVbtz0ZZdc1FZ0RmPe3cuQtpaWkIDAxU07yvvHIR/Pz8odO1/MKd+K4RsUsqEHzIhKMvxdgXdNFpYAnr3LUnfnqt6sAtQU18mD+zNB2EwQ0RkY+ToadvTxZgx+nG167Jzc3BkiVLkZGRgcysTFyx8Aq1PTAwyPlvIk9subDYXvb1oQg6ZkbWjaH2wKaTiw8LUAFNn4QQ+OuZpeloDG6IiHxYSaVZNbzMamTtmpqaGmzatAlff/01rFYr/P39kdLC4ScRcMaM5NeMqOqqR8Y94fbnjtbh6MvSBbxzZ2n6J4aqadxxoRe6kVPHY3BDROSjjmSX4svDOag2X7x2zZkzZ7B06VIUFBSo+/3698PcOXMRGhra4u9jKLCqWVDBB03IvjEUltDOXVOTFBGgAprecaEqwCHPw+CGiMjHVJosWH8kVwU3jdm7dy8WL16sPg8NDcGcOXPRv39/p59fY7LBP7MGVan2adylw/2RcXsoiicEdtrARgqCHVmamJDz/azIYzG4ISLyIdLBe+3B7GYbXvbu3VstEti3b19Mnz4dAQEBLRqC6vFEITRW4OBbsbAF2IOZ3B+0POPjCWSBPZnC3Ss2BHpd5wzMfBGDGyIiH1BdY8Gmo/nYn1Fy0WOlpaXYs2cPJkwYr2ZByWyoe++9t0VBTe33SdBDI0XJVhsCMiyo7Nn5AoIgPx0GJIUhLSkckcHe25zTmzG4ISLqZCyWC1kXKfidOXNms9OxZYr3moM5MFaaL1qs77vvvsPatWtRXV2tekLJVG/hbGBjyLMgcmMlcq+xt4Ow+Wtw4g9RMCXoYA3sPIGNTNiSZpUy46lHbIhqj0CdF4MbIqJO5PPPP8cDDzxQe3/u3LlISUnByy+/jKuuuqrevmaLFV8fz8fus/aeT3Xl5+dj6bKlOHvmrLovC/LFxbVs9pK23Ip+P82FvtyGylQ9SkcEdLqWCSH+egxMClMtEcKDOs9xU/MY3BARdaLA5pprrlEZl7pk/RnZ/umnn9YGONklVVh94OK+UJL1kandkvGRz/38DJg6dRpGjRqpOni3hDVYi8KZQWq9mppIXafK0nSPCVbFwd2jg6FllsbraGwN/0q8nNFoVM3dSkpK1PLhRESdgQQiqampOHfuXKOPS1dpyeAcP3ESO88WY8epIlgbeXv/5P8+weFDh+tte+yxR2EwXLq2JHhvNZLeKcXpxyJhjtHVzoyyScKjEyzEFxqgVwGNZGpCA5il8ebrd+cZECUi8gLl5eUqEJGbfO6szZs3NxnYCPk/NT09HU+9+Sm2nSxsNLARo0eNRnBwEBYuXNiyA7fZVGATst+EhPcvTCG3+Wk8OrDRajToFReCK4cl49bLu2NMj2gGNj6Aw1JERJ1AVlaWU/udPZeJmN7Da+8fO3ZMBVFDhw5V9yX787OfPShhCZYsWdLsc+nKrLAEaOwtEzQaZNwZhqjVFWohPk8XEWRQWZoBiWEI9uelztfwN05E1AkkJiY6tV9YlL0oWAKaVatXYf++/TAYDEhN7YaIiEj1mNw3m+vX4jQUtbYCSW8YVSCTvzBYbavo66dunkpmOEmWRqZwd4kKZNNKH8bghoioE5gwYYKqqZHi4cZLJTWIiI1Hj7TL8P3332P16tWorKxUF/gRI0cgONgeoDhLW2mDocSKyA2VyF8Q5NFDT1HBfrVZmkC/zlPYTO7D4IaIqBOQdWxkurfMipKApX6AYw88pt/4M3z40cc4efKkuh+fEI+FCxYiKSnJqZWFYb0wjTt/XhCsARoUTg30yMBGsjS9JUuTHK5WEZZzQuTA4IaIqJO48sor8dKb7+G3v34EJfk5tdslYzPv9l9g3fb9qKqqhl6vx+TJkzF27BhotZfOZER/VYUeL5ahvJ8Bx16MsQczOo2a5u1pIoMMqh3CgMRwZmmoSQxuiIg6gQpTDb48lAtL11H49b9X44+P/RxWUyVuvOV29Bk6BlqdDkYE42z6WSyYvwBRUVFOP7dxsAFWg0atVSPDUdYgjcdlaXrGhmBwCrM05BwGN0REHu54bhm+PJSDCpO97YIEMvrwePV5cFxXdV9MmjQRWq2s8NFMcGKzIfzrKujTq2s3yZo1h/4dB3OszuNmPEk7BOnzFOTHyxU5j68WIiIPVWW2YMORPBzKMtbbfurU6drPl69Yjjt/+lO1urAzQ1BBh83o8fsiWHVA0pBwZAbaG2l6SmAj69L0jAvG4OQIzniiVmNwQ0Tkgc4WSLPLbJRW1dRuk9lPa9auwZ7de2q3TRg/4dJtEyw2VUMjKvr7oWhCACqSNSg87fwigu4WFmjP0sjqwVyXhtqKwQ0RkQcx1dibXe5Jr9vs0oYDBw5i5cqVF61q3KdP7yafS1NtQ/wnpYjYVIUjr8Sqjt3i9BORqK6uQtljGapu58TeHbV1O+2dpekRG6yCmm7RQZzxRC7Toe0XnnnmGYwcORKhoaGIi4vDokWLcOTIkWa/ZsOGDbVLl9e9HT5cv1cKEZGn9ohycDSvdMgsrsSH2840CGyAI0eOqqaYEtjExMbgxhtvdPr7Ra+uRODZGkSur6zdtvfrtXj21lko3/8lKo9uxRuP3oo/3DgVe7esQXv1eBrXMxq3TeiOBUOSkBoTzMCGvCdzs3HjRtx7770qwKmpqcHjjz+OmTNn4uDBg5dccEqCoLqNs2Jj7atyEhF5clfvBx54oPb+3Llz1cJ8L7z4IhKHTMbOM4VS73sRyc50S+2G7qndMX785bBarU1+j4BT5tq1aiRTk35fOLRmG4onBKhtEsC883s5hvrfSKaWy/abf/s3DB4/E+7qxD04JQLdooLYiZu8N7hZtWpVvftvv/22yuDs2rULEydObPZrZb+IiIhLfo/q6mp1q9tVlIh8k2Q+QkJC1OdlZWUtXrW3rYGNLMDXcHVhWXH4B9f+oF5QkZeXp7I6CxcuUN26pabmpp/cVJvdsFobaZ1gsyH1mWK1ovDxZ6NROtxfbTaOtQc16ussFix+9emLApvzT6BmWX3x2tNIGzvNZUNUkqUZmBSOtGR24qb241FdwaWNuXBmfYZhw4apXivTpk3D+vXrmx36khbpjluXLl1cesxERJciQ08/+9nPGm2b4NgmQYXZVK2G3l9//Z/Yv38/Nm3aXLvfJVfg1WhgjtTCpgUCjzXeN+rk/p0oyc9u5klsKM7LVvu1hRyq1NJcMTRJdeIe25OduMlHC4rlD/zhhx/G+PHjkZaW1uR+EtC88cYbuOyyy1RG5v3331cBjrwhNJbtefTRR9Xz1s3cMMAhova0efNmnDt37pJBxcvP/A7lsGda+vTtg5EjRzT5FVqbBrOy+8GQbwHO99TM/nEoCmYH1Q5LNWQszHPqeJ3dr6EQfz0GJoeplghhAY0fA5FPBTf33Xcf9u7diy1btjS7X9++fdXNYezYsUhPT8dzzz3XaHDj7++vbkREHSUrK8up/YryshHVfSDmzJmLAQP6N7sY333HJmJqXh/kvV+Bc78MVNssYVp1u1TH8Etxdj9HliY1OlgFND1igllLQx7BI4Kb+++/H0uWLFFjzFJc11JjxozBBx984JZjIyJqK8k4O6PPwMG47pa7EBBgD1aaInU4yU+Mg/m3hajqf6Gm5lJ6pI1AeEzC+b5UzXUWbzpj5BDsr0NaUjgGJocjPJBZGvIs+o4eipLAZvHixWpYqXv37q16nt27dzv95kFE1N7vc5E9BiMiNgHFeU0FFUBIZCxu/OkDjRby6ossSHi/FJU9DSiYF1y7GN+BD+Jr165xhjz3lfc8fn62lHzdxZ3FF939eLPFxLIejfR46h4Tono+EXmiDg1uZBr4Rx99hP/9739qrZvsbHuhmxT+BgYG1tbMyGyC9957T91/6aWXkJqaioEDB8JkMqmMzWeffaZuRESepLy6BusO5eBkXrkKGuxBRUP2AOGa+3/bZFARsbkKscsqYA7XonBaIGwB9qGnlgQ2DjIjS2ZmLX71jxd1FpdjbGwaeJCfTs14ksX2woOYpSHP16HBzWuvvaY+Tp48+aIp4TfffHPtWPXZs2drH5OA5pFHHlEBjwRAEuQsX75crRdBROQpjuaU4qvDuag83+wyZcAIdJt4Jc5+uwo2U0XzQYXNBl2prbZ+Jn9uEEL2m5A3P6g2sGkL+V59R0xotLN4XV2jgjAoJVx15GaWhjoTja2xuYleTGZLSWZIpp3XXQSQiLxfe6xzI80u1x/OxeHsUnXfarVg69Zv1KKlslipTqdFdWFWk0GFf3oNur5gX6H42AvR9opdNzCbTfjTn55Rnz/22KOqjkcEqixNmMrSRATZtxF1tuu3RxQUExF5g1P55Vh3MAdl1fZml5mZmViydAlysu3DPz169sCsmbNqs9Y9B4+8KFtiDdAg6LhZVcNIoFPdtX2GgbpIliZZsjTB0Os8agk0ohZjcENE1EbVNRZsPpqPfRn2hUgdC/d98skn6r9NGUKfNWsWhgwZDLPZXO9rtZVWhOwzwTjKPuvJHKvD6V9HoKKvH8wx7m1kaauphinnJH48MgXJsZde8Z2os2BwQ0Q+27RSetnp2thm4FxRBdYcyEFJZf2gRZ539uzZOHTokApsGhsCMxRaMeBnudAZrTj8Zhyqk+1vySWXNz8VvK1iQv3RPyYMv9rxhfRkQASLhMnLMPdIRD5BejsNGDCg9r5MQpCZl7K9NWosVmw8modPd51TgU1FRQUWf7FYLUbq0L9/f1x11VVN1vaYo7So7GFQ2Rp9cdPNMF1BSnd6xoXgmstScMPoruifGKoCGyJvxMwNEXm95ppWyvZPP/1UBSHOyi6pwpqD2Sgokx5ONuzbt181ApYA58Tx4yqoMRgurpXxz6hB8n9K4WfRwaSzBxZnfhEBS7AWNj/3FA77G7Rqsb0hKRGcxk0+g8ENEfl000ppSPnggw/iiiuuuOQQVWmVGVtPFOBQllFma6OkpBjLli3H8ePH1eNxcXFYsEA6eTdSBGyxoedjBfDPsmBBtzR8lvK92lwT6Z66mqhgPwztEoH+iWHw0zNJT76FwQ0R+XTTSglwpD+d7Ndwza26BcO7Thfhu7NFMFtssNms2L59B7766kuYTGYVFElvu8svv7x+gGSxAbrzGRmdBlk3hiLiq3Jss5yBu3SPCVZBjawkfMlO4kReisENEXk1Z5tWNraf1WrDgUwjvjmZj/LqC/UpWVnZahhKdO3WFQvmL0BMTEy9rw3bXoXkfxpx7r5wlA63N+8tmhaI3El6nPuTfR0bV5HMzIDEMAzpEqEyNkS+jsENEXk1Z/vO1d1PsjmnCyqw+Vje+boatbW2VUJSUhLGXT4OUZFRGD58eKMZktAd1Qg4V4P4T0prgxtXL8gnDSsloJFF9wIM7p02TtSZMLghIq82YcIEpKSkqOLhxupuJDCRx2U/kVtapdasOVt4oUXCmTOnsWrVavzgB9ciMjJKbZsxfUa959EXWlTs46ihyb4xFNYgDXJ+YF8R2dUL7snQU4+YYGjZvJLoImy/QERe0fbAmdlSom6A48i4yGypGXMX1CsWFlVVVVi3bh127dql7qcNSsPVV1190fNHflmBLn8rQfHEQJz9uXsWw9NrNeiXGKaCmtjQ85kgIh9iZPsFIqILZJq3BDAPPPCAyuA4SMbmr8+/gIQhE/Hu1tOqWNhBFt9buXIFSkvL1P3LRlyG6dOmN3paq5P00FXaEHDWDI3ZBpvBdcNPoQF6DE6JUK0RpO8TEV0aMzdE5PWZm4b/+YnlK1YgacBobD9TVK9YuLS0FCtWrsDhQ4fV/ejoaDW9u1u3brX7BB0ywVBkQcm4CysJB++rRvlAP8BFw0RJEQEY2iUSveLYkZtIMHNDRNQIxzRtfWQSMoN64sjR/Iv22b17twpstFotxo8fr2px9PoL5YmhO6rQ6/FCmMO1KB3iD2uwfQ2Z8kFtHyrSaTXoEx+igpqEcHuvKSJqORYUE5HPyCurRvDAqdBHJKCw3ASDwT5tWtat0WjsQcrll49DQUGB+hgXF3/Rc5QO80dlVz0q+vtB46LuBUF+OgxKCVfDTyH+fFsmaiv+FRGR15OVhb85UYDvzxSowKbu6sVbt27F0aNHccstN0Or1UGn0+PKK688v4MNUWsrEbarCqcfi7RP5dZrcOSVWNj82z78FBfmj2FdIlW2Rq/jKsJErsLghoi8llpZ+EwRvjtzfmVhtVaNXUZGJlauXInc3Fx1/+DBQ0hLS6v39YYiK7q8UgJttQ2FU6thHGsfKmpLYKPVaNAzLhjDukYiKTyAqwgTuQGDGyLyOk2tLFzXu+++qz4GBQVh9uzZSEsbqO7ryqywhNizKOYYnWqZYNMCpSPaVlMji+zJjKfBXcIRFtBI7ykichkGN0TkNRwrC285lof82pWFLzhx4mS9+4OHDMasmbNUgCNDUMlvliB6ZQUOvxYLU5L97TG3jYvwxYRIA8tI9EsMhYFDT0TtgsENEXmFxlYWrs+Gr7d+XXvvhz/8Ifr27XvhYZ0GAWdr1Ho1kRsrkXN9aKuPRUpzpIGl1NN0iQrk0BNRO2NwQ0ReUSx8sM7KwhfYYLFYz08B12DunLl444031CM9uqcidGcVygb7w+Znr6E5d1cY/HItKB0R0OoGltLnSVYRjghiA0uijsLghojcTmYlOWzatAkzZ86sXXOmtUw1Vuw8U1hbLNxQUVERli1bhrj4ODX0JGJiomsf7/5yGWLXViPjtlDkXmfP0lR3NahbS0UEGVRAMyApDP56riJM1NE495CI3N7XacCAAbX3586di9TUVLW9tcXC+86V4J2tp7DtZOFFgY3ValXTu1977VWcPHkS3+3ahYqK8ouexzjIAKse0F5cmuO0btFBuGJoEm4el6pmPzGwIfIMbL9ARG5vWNmwG3fdhpXS98kZ8hxnCiqwuYliYZGdnYUlS5YiKytL3ZcgasGC+YgOiULsF+Wo6GdQw1CK1aaGoEwJLUtgy6EPTArH8K4RiA5hA0siT2y/wOCGiNw2FCXBxblz5xp/89FoVOPKU6dOXXKISoqFtxzLV8FNY8xmMzZu3IhvvtmqMjsBAQFq6GvYsKGq1ibxHSMSPipDRQ+9WoBPiodbm6mZ2CcWMQxqiNode0sRUYfbvHlzk4GNIxOTnp6u9ps8eXKj+5RV12Dr8fwmioUvqKqqwq5dO1VgM2DgAMyZPQchQdKc0x7E5F4ZjPCvq5B3VYhjU4tEBhlUUCMzoBxZJyLyXCwoJiK3cAwNtWa/SxULC7P5Qm+o0NBQzJs3HwaDAQNjeiHpVSNsfiVIfzBCPW4J1+HwG7H2MaUW8DdoMaZHNIakRKimlkTkpQXFN998s5rtQETUnMTExBbvd6liYTsbDhw4gL/97W84duxY7VZpnSDr1vhnWxC9rhLRqypgyK258GUtCGykRYLMfrplXHcM7xpZG9iUl5erzI3c5HMi8pLMTWlpqRrL7tKlC2655RbcdNNNSE5Ods/REVGnNWHCBFVTk5GRcVFBcd2aG9lPnM4vb7ZYWDI1f/rTM/W2bdu+Db179oJflgWmZPvbWflAP2T+JFS1SzDHtTw5zboaIh/M3Hz22Wfqzeq+++7Df//7X1UwOGfOHDXrQYr6iIiEFAm//PLL6vOGdSqO+y+99BIKK2rw+XfnsHh3RpOBjQRHO3fuuvDGpdVi0qRJ+Mm0a9HnZ/no81A+tOXW2sdzbghFRb+WLaIXFeyHRcOSceWwZBYME/niOjfR0dH42c9+ht27d2P79u3o1asXbrzxRiQlJeGhhx6qlyomIt8l07zlHx95b6hLMjbvf/wJQvqOw4fbzjQ5C0rk5eXh7bffxpo1a2q33XbbraoI2Rbtpxpdak02BB03t7qh5eS+sbhhTDcWDBN5iTYVFEshoLzhyE3+S5PFuWQsXBbs+stf/qICHSLybRLgTJ8+Xa1PIZYsW46I3pfh+3NG5GYaL/n1hYWFalaVn58BugpgUl4vxMbEqMdsBg1OPxapunfXRLZsZWCpq5EO3WO6RyPQj6sKE/l0cCNDT0uWLKn9T2rw4MEqiPnxj3+sZiyI//znP7j77rsZ3BC1kRSthoTYu1KXlZUhOFimN3c+ah0bjRZ+cd1xxr87jp8paXb/qqpKBAQEqs+lSHjGjBno17M3Bt1eiLjqUBz91oTyifYF9Cp7t7yHk0zpntA7hovwEXmpFgc3MrNBlje//vrr1ZDU0KGySFZ9s2bNQkSEfQomEfk2aWy5/VQRwkYugsYQgAqTBQZD45mS6upqfPnVlziwfz/uueceBAfbA7tx48apguINsdtxeX53WAObX520KdEhfpjQ275eDRF5rxYHNy+++CKuvfZatQJoUyIjI9Wqo0Tkm6QAOKukCnvSi3EspwzVpmoV2DTn6NGjWL58GYzGUnU/a/NxzNjWFRl3hMGUZH+r+jRlNz7psgu/GvprGFpYVzO2ZzQGJYdzvRoiH9Di4EYKh4mIGlNjseJITim+Ty9BjrHKqZMkw22rVq1S9XoiMioSC+YvwLS3whC+vQo2HXD6iSj1mEl3obu4s3U1Q6Supke0CnCIyDdwhWIicsnQkyy+ty+jRA07OWvPnj1YvXq1ap9ggBZjxo7FxKmToNcbkHmbGTZdKbJuat0QVI9YqauJVVO8ici3MLghIpcMPVmba/7UhMysTBXYTNENwm1Hx6Ckfyhy9fYBp6ruBpx6yp6xaWldzcTesUhlXQ2Rz2JwQ0QtHno6mlOmghpnh54azgCLiLBnU6ZNnYaY6BjMLBqIkE0l0K+sQO7VIa3q2i3Tucf2sNfVaN3UB0o6nTtIGxpZrf1SHc2JqJMs4kdEvsfRofutLaew+kB2qwIbsfiLL6AvqkHAaTP8/f0xatQoFM8Iwrm7wnDkH7FNBjZWiwU1JTkw5Z3Gib071H1HXc3wbpG4eVwqhnSJcFtg8/nnn6s1vBxkXS9ZoV22E5FnYeaGiNw69CRTuNd9+WXt/YRDOvT/PBemFAOO/D3GHsxoNci7yj7tuzF7t6zB4lf/iPL8HHX/jUe3IjwmAXf9+vf41V03IdLNdTUSwFxzzTUX9ciSVjSyXVZhlsUKicgzMLghIpcPPTmcOHECy5YtQ3Fxce22iffOB35WDZsW0JdYURPV/LCOBDbv/P4B1Q28LmNBDv7yizsxqnu0WwMLGYqSdjONNf+UbdIn68EHH8QVV1zBISoiD8HghojqDT3tPVesZj61ZNZTY4vxrVy5Et9//z16l8ZipmkE9Pf3Qe/evdXjR182ozpFrzI2zZGhp8WvPn1RYNOegcXmzZtx7ty5Jh+X45D2ELKf9Lsioo7H4IbIg7VHAasrhp4a0uv1qvdcclU4/rL3ChXEHNbFwpEDqu7q3BJ8J/fvREl+docGFvJzuHI/InI/BjdEHkrqPB54QIZjLhSwSjftl19+2SXDMK4aenIoKSlBaGgItBqtCsAWLboCFosVxe8GwuqnQU14y+cvGAvzOjywkJYzrtyPiNxPY2tsINmLGY1G1Z1Y3ojDwlq3OBiRuzVVwCrDMKItBayuGnpysNms2L59B9Z/+SVuC5uL8QeTcey5aFiDzgczFlurpnbHhPrDL+cQfrhoziX3Xb9+vdsyN5I9k1lRUjzc2Nul/E4k6JSWM5wWTuQZ128GN0QexnExbarOozUXU7koZxursOdsscrWtHToSWY8/elPz6jPH3vsURgM9tlJubk5WLJkqbrw661avLr/esSVBiHzllDkXB+K1gjy02FczxgMTApTgZMnBBaOYFPUPQ5XBJtE5PrghuvcEHmYlhSwOjP0dDDTiI+3p+M/29NxOLvUJTU1NTU1+Oqrr/Dfv32AjHMZar2a2QvnwvjLZGTcHmpfiK+FdFoNLusWiZvGpWJQin0hPglYZBiubiDh4Lj/0ksvuT1jIoGLBDBJSUn1tktgxcCGyPOw5obIw7iigNXVQ0/1v282lixZghm7u+GBcz/Ax9MPI/WuMQgNDYX08y4d3fLn7BkXgom9YxAR5NdkYCH1R5LBqRtYSGDTXhkT+T7Tp09X/zmKFStWcIViIg/F4IbIw7S2gLWtQ0/O8vMzoKSkGEGGvjDYdJhrGIn00NYNQcWG+mNSn1h0iQrqFIFF3e83ceJE1tgQeSgGN0QeZsKECSorcak6E9nPMfR0LNc+6ym7pO2znhqTm5uLIcXJyAooQXR0NK677jrER6XgxHENjCP9W1VXc3mvGAxIDHO6XQIDCyJyFoMbIg/jqDORAlYJZBorYJXhmMoaG/aeznfL0JNDaWmpWoxvyOpAPJU+F19Hn1Tbe/WyL8ZnHNXyuprhXSMxsnsk/PVsOElE7sGCYiIP1FwB65vvfojA3mPx1uZT2Hay0C2BjQRUu3btwquvvoJDhw5hW+wZmDQ1KPArB6ytG+5KigjAjWO6YXzvGAY2RORWzNwQeaj6dSYavP7hpwjuPgy5ZWYYs6V01z0Kc/JQ8uI+mIvzUZVYjeTkZEydPRu3//NDGA1VeEw7pcXZmrE9o3FZ10i3dewmIqqLwQ2RBys1WRHQbQj84nugOKIPysvMbv1+27ZtQ+VHx/HQoSmo0JkQ+IPuGDzlMjX1WwKblooJ8cOstATEhQa45XiJiBrD4IbIw1isNpzIK1O1NCdyiuGfMtDt31NjtsFm0MDPzw+rI49jTvJgYF4ihkzqJoU+LX8+DdSaNWN7REOv4+g3EbUvBjdEHqKo3IR9GSU4lGV0W4FwQ9b8KsS9WYioTAOOvhyDYcOGqvVqKnv1VENhrREWaMCsgfFIiWx+ejcRkbswuCHqQDKN+/j5LM25osp2/d7Hjx/Dxs/W4S+b5yCoBgj53oSyYf7o1avXRftaLRbUlOTAaqrEib070GfoGGgbWWMmLTkcE/uwYJiIOhaDG6IOUFBWfT5LU4oqc/tkaRSZVr6vFJ8fX4d9+/apTe+mfYfh88fBb1jj69Xs3bIGi1/9I8rzc9T9Nx7divCYBFx5z+MYPH5m7bo10wfEo2dsy9suEBG5GoMbonZilsX2csqwP6MEGcXtm6URmmorEh9IR+wpHd4Zeg6aEA3GjBmDy6ZMrm2E2Vhg887vH5CoqN72kvwctf3m3/4NV8qsrv5xCPLz/reT4ODgRhdWJCLP4v3vRkQdLK+0WgU0h7KNqDZbO+QYZLbTx//9GNcYuyNC2xWDDN0x8/bhSEpKbvJrZChq8atPXxTY2Mk2DVb961m8+Zu7oNe7/62EgQUROYvBDZEbmGqsOJpTqoKaLDe1RLgUbaUVsf8rR94VwdAH6hEYGIh3em/H4TE6DJs9F1pt8ysEn9y/EyX52c3sYUN2Zga2bNmCyZMnu/z4iYhai8ENkQvlGqtULc3h7FIV4HSklF9lI/owoK2yIevmMMyZPQfVU6oQFRXt1NcbC/Nc2sWciKi9MLghaqPqGguOZEuWxogcY8dkaeqqqTFjw8aNsGoycUfIeFT0iqwd1pGbs8KiYl3axZyIqL0wuCFqBSkqzTHaZzzJ8JO7sjRS6Pvkk09ecj+/rBokvWXE4bRSvJm+BIWFhUAkUDYuCFeMXYTWtKjskTZCzYqS4uHG6m4adicnIvIUDG6IWkCmbcuQk9TSSKGwpwhfakTkpiqk7ChD0bBChIWHYu7ceejbty/MZhP++Men1H6PPfZokzOjGpJ1bG546Ld49Yl7VfFwU93JpYs5EZEnYXBDdAlyUZeiYMnSHMsphdniAVOBLTZVS2MN1qqal1eLPsFN0Zfhv132YMSokZg2bRr8/Rtft8ZZQ7qE494pd2Jqv3g88MADyMjIqH1MMjYS2EhzTyIiT8PghqiZLM3BLCMOZJQgv8zkMecp6LAJXV8sRlVXPU4/HoXo6GiY/IF3JuzFwgVXoEuXrm16/hB/PWYMiEdqTHAj3cmBFStWYObMmczYEJHHYnBD1CBLIwvsybCTLLhXY/WALE0DVj0QcNoMQ74F+mILEOGHG2+8AREREdDp2vYn3TchFFP7xSHAUH+oqe7Q08SJExnYEJFH69B2vc888wxGjhypGvXFxcVh0aJFOHLkyCW/buPGjbjssssQEBCAHj164J///Ge7HC95r0qTBbvOFOK9b87gvzvPqbYInhLY6IxWhHxvr+/Jy8vDq5s+wPO9v8LHD2ahJsIedERHx7QpsPE3aDF3UKK6NQxsiIg6mw7N3EiQcu+996oAR1ZQffzxx1W6++DBg01OWT116hTmzp2LO+64Ax988AG+/vpr3HPPPYiNjcXVV1/d7j8Dde4sjTSrlFqa47llsHhIMFOX/1kz+jyUD1iBf/30ONbu2gCLxYqcZD/MDklzyffoFh2khqFCAwwueT4iIp8OblatWlXv/ttvv60yOLt27VKp78ZIlqZr166qmFH0798fO3fuxHPPPcfghpxSXl2jamlk6Km4wuzRZ606WY/yCCtKS4w4uH43LMFW9OnTB/PmzUVYmL0GprUMOg0m9I7F4JTw2tlPRETewKNqbkpKStTHqKioJvf55ptvVHanrlmzZuGtt96C2WyGwVD/v8/q6mp1czAajS4/buocWZqzhRUqS3MitxxWD21+GHDKjOiVFci4KwzQarB12zd4JWErCrtUIDAkCNfMuQYDBw5QU7PbIiE8ALMHJiAy2Llp4UREnYneky4+Dz/8MMaPH4+0tKbT7dnZ2YiPj6+3Te7LsFZ+fv5Fq6VKXc9TT9nX+CDfU1Zdo2Y77c80wljp2Vka6QXV++f50JfZUNHHgKLpQejWrRvW+a/DkKFDMHPGTNUfqi0kQzO2ZzRGpUZBq2W2hoi8k8cEN/fddx/27t2rmvBdSsMUumNxscZS648++qgKmupmbrp06eKSYybPZLXacLqgXGVpTudXeGyWRpE6n/NBhjVQi/RFftDtK0P5QHtGJTk5Gffffx8iI5vOZjrLUlGCa4YlontC25+LiMiTeURwc//992PJkiXYtGmTWhysOQkJCSp7U1dubi70er1a76MhWcisrYuZUedQUmnGgcwSHMw0orSqBp4ufHMlkt424tRvo1CVqsfu3XuwJnMNzOFm3GW4EzGw93ZyRWBTnXkYVWe+R1wo/xaIyPt1aHAjGRcJbBYvXowNGzage/ful/yasWPHYunSpfW2rVmzBiNGjLio3oa8n8xwOplXhv2ZJThTUAFPTtI0FPVVJQLOWRDxXgH+nLoap0+dVttlaLVuq4O2CA3QY1yfcPzq1HfqvvwDwQX4iMjbdeg6NzINXKZzf/TRR2qtG8nIyK2ysrLesNJPfvKT2vt33XUXzpw5o4aaDh06hH//+9+qmPiRRx7poJ+COkJRuQmbj+XhX5tPYtneLDX85OmBjb7AoupqHNJvC8b2iUV4BO+qwMZg0GPGjBm4/fbbERsb1+bv1z8xDCFZ32Hm5ZfVbpNlFFJTU/H555+3+fmJiDyVxuaqfxFb882bmH4qU8Jvvvlm9bl8PH36tMrs1F0f56GHHsKBAweQlJSEX/3qVyrocYbU3Mgy8jIzKywszEU/CbUHs8Wq1qORKdyyPk1nEr2sHMmvG5F7TTCybwpTmRl5naenp6vHZTHK+fPnIzIyss3fK9BPh2n94rDv67W45pprLsoCOf7uPv30U/aGIqJOoyXX7w4NbjoCg5vOR7pvy7DT4axS1e+pMwrfUokevy9C6TA/HH82WiIMtQDl119vwaxZszFkyOA2T+8WPWKDMb1/PAL0GpWhOXfuXKP7SYAj9W2yKCa7ehORt12/PaKgmKghU40VR3NKVZZGOnJ3NsEHTGomVPkgewFvyeUB2PxANUqGWpF8PnMyduwYDB06tMnVuFvCT6/FpD6xGJgUpgIXyXQ2FdgI+Z9GskabN2/G5MmT2/z9iYg8CYMb8hhywc0xVquA5khOqQpwOqPILyuQ+udiVKXoceiNWFSaK7F69Rp8//33iDkTg7vuulP1gdJqdS4JbJIjAjFrYALCgy4U1GdlZTn1tc7uR0TUmTC4oQ4nQ02Hs+1ZGhmC6uxKxgTAHKVFWZoBh3cfwLKNK1FeXiEjUejZs6dah6dOk+1W02k1GNczGsO7Rl60IF/DxSyb4ux+RESdCWtuqMOyNBnFldifYcSxHM/pwN1iFhui1lQg8FQNMu650OupLKsI/1u/AsePHVf3pWfaggULLrmOk7NiQv1V+4TYJtatsVgsquYmIyOj0WnlrLkhos6GNTfksSpMNTikmlYaUVhuQmcXcLYGXV8qgcYGFE0JREV/P9UG5M133oDJZFbFutIE9vLLL3dJ4a5kf0Z0i8KYHlHQ65peyUG+18svv6xmS0kgUzfAccyWkuazLCYmIm/EYSlqt6aVEtCcyCtTC+91ZpoqK2wB9sCiqrsBeVcGwxSjQ2Uve81LTEw0UlK6oMZSgwXzFyAmJsYl3zc80IBZaQmqxsYZV111lZru/cADD6gMjoNkjySwkceJiLwRh6XIbUqrzKoVwoFMo2qN0Nlpqm1IeseIyK8qcejNOFjC7AGONG2VbvWjRo2Ev3+A2lZVVaXafjS1llNLpSWHY2KfGPjrda1O5YoVK1ZwhWIi6pQ4LEUdRoplTxWUq+LgU/nlHr9qcEvY9EDo7moYiqyI3FCJ/IXBOHv2DJYsXYqC/AL1hzdv3jy1b0CAPchpqwCDDjMGxKFXXGirn6Pu0JMMkXEoioi8HYelyHVNKzNKVJamrNrzm1Y6K3hvNcrT/Oydu3UapN8fAW2FFfmDgbXLl2HXzl1qv5CQELXKsCulRAaqYaiwAPZMIyJqCQY31GpSOyM1NJKlkZoab8rSiNSnCxG5sQqnfxmBoulBalv5QD8cPnwYK15ZjtLSMrXtsssuw/Tp012WrdFqNKpgeGRq1EVTvImI6NIY3FCLySwnCWhk1lOFqXO2Q3BGZQ8Dwr+ugl/+hZ9x27ZtWLVqlfo8KjoKCxcsQLduqS77nmGBBsxJS0CSk0XDRER0MQY35HTTymM5ZarHU0Yna1rpDI3Zhtj/lcM40h9V3ezDQLlXh6BoYiBMyRf+TAYNSsOWLVswbNgwVb+i17vuT6hfQiim9ItTdTZERNR6DG7o0k0rJUuTbUS1uXO2Q3BG8msliF1WgZDd/jj5dLTaZvPTICugGPs27MfkyZNUY8ugoGA88MD9MBj8XPa9pS/U5L6xGJBo7wtFRERtw+CGmmxauS+jBNmdsGml06RI6HwwkXd1CMK3V6N4QqDabrFasXXrVmzatBE1NRbEx8ejf//+al9XBjbxYQFqGCoy2HXPSUTk6xjcUL0szb6MYhzK6rxNK52hL7Ag6Z1SVMfrkHODfYp1dbIeB96NUzOiMjLOYcmSpcjNzVWP9ezV0y09mEakRmJczxjVI4qIiFyHwY2Pq7FIlqZMBTWZxV6cpakj5IAJ0asrYPHXIP+KYFhC7YvxmSxmfLXuK2zftk0ldYKCgjB79mxVZyNDUq4S7K/D7IGJ6Bptn4FFRESuxeDGRxWVm7A3o0StICxdub2azQZ9oRU10fZC3eIJAchbGITCqUG1gY34+OOPcfr0afX54CGDMWvmLBXguFKP2GDMGBCPID/+6RERuQvfYX1sXZqTeWXYe86+Lo0v8MuqQbc/F0FfasOh12MBvUbV2Zy7L+KifcePH4/i4mLMnz8PPXv2culx6LUaTOgTiyEp4e1eNBwcHNxoZ3AiIm/F4MaHVg+Wadzl1V6epWmgJkwL/wwLtFU2BJ0wo6Kvo3DXhr1796mL/pAhQ9SWnj174r777nN5e4LoED/MSUtEbKi/S5+XiIgax+DGi3s8nS4oVzOevK3HU3O05VaEb69C0RT7cJI1WIvTj0eiqov+wrBUcRGWLluGkydOwt/fDz16dEdoaJh6zNWBzeAUaXgZC4PuwvAXERG5F4MbLyN9nSRLI0FNaZX39HhyNrAZcGuuamxZnaBHRX97lqZsqD1jYrVa1QrD69evh9lsVgvwTZgwUQ3buJq94WU8esWFuPy5iYioeQxuvIAMraQXVmJvRjFO5JbD6itpmgYkS2Mc4Y/gQya14nBd2dnZWLJ0CbIys9T91NRULFgwH1FR9gX7XN3wcnZaAkLZ8JKIqEMwuOnEKk0WHMwqwb5zJSiqMMPXBJ4wI+GDUpx9KAKWMPuwz7l7wmH119gLh88rLy/DW2/9Sy3GJ80tZ86ciWHDhrp0erej4eXYntEY0S2SDS+JiDoQg5tOmKXJKqnC3nPFqtdTjdU3szRSRNT1uSIEnaiBKa4UGXeH12ZvGgoODsGo0aPVTKg5s+cgJMT1Q0Xh0vByUAISw9nwkoioozG46SSqayw4nFWq1qbJL62GL9JWWu1ZGVnRV6NB5u1hiF5VgbxF9WtmqqoqsXbdOowZPRqxsXFq2/Rp06DRuKeot3+iveGlv54NL4mIPAGDGw+Xa5QsTQmO5Hh3S4RLifyqAslvGJFxRxiKptlnQpVeFqBuF9hw8OAhrFy5AmVl5cjLzcOtt96ihp/cEdhIw8spfeMwIMk+04qIiDwDgxsPZLZYcSTbBxpXtoBftgWGQqvK1DiCm7qMRiNWrFiBI0eOqPsxMTGYPn26y+tqHBLC7Q0vI4LY8JKIyNMwuPEgBWXVatjpUJYR1WbfzdKIgDNmWPUamJLtL9Hcq0NgCdGiYHbQRTVIO3fuxJdfrkN1tQk6nRbjx0/AhAnjodO5/uUtiwuP6BalCofZ8JKIyDMxuPGAxpXHz7dEyCiq7OjD8QjRK8rR5W8lalr3yT/ap2rbpMnlwovXo9m3b5/K2IiUlBQsXLigts7G1UL89Zg1MIENL4mIPByDmw5SUmFW69IcyDSqKd10Qdlgf9i0gM2ggcZkg82v6aGltLQ07N69G/3798fIkSPcVjQsDS9nDkhAoB+LhomIPJ3G5mMd9aQ2Izw8HCUlJQgLC2v3lggn86UlQjFO5/tG48pLstoQub4SujIb8q8Irtfw0pR4ceydnp6OrVu34uqrr1YrDNvZ3FZbIw0vpX3C4A5oeElERK27fjNz0w5Kq8zYn2HE/owS1R6BLgjbWY3UPxfD4q9B8fiA2v5PDQOb6upqfPnVl9i5Y4fqkyUBzsSJE88/6p6gI0YaXg5KREwIG14SEXUmDG7cRBJiZwoqVIHwybwyn2lc6ZQaW+0KwsaR/qq2pmywnyoYbszRo0exfPkyGI2l6v7QYUMxcuRItx7i0C4RGN87hg0viYg6IQY3LlZhqlF1NNISoaTS91oiNEdXZkXCe6UI3VuNw6/EAjr7Ynwnno6yT0NqQNomrFy5CgcOHFD3IyMjMX/+fPTo0cNtxyg1NdLwsmcsG14SEXVWDG5cSBbZ+9fmU7D4aksEJ0R9VQm90Yqw7dUwjj2/AF8TtSyOwEar1WDs2HGYNGkSDAaD246tS1QQZg2MZ8NLIqJOjsGNC9lgY2BT74TYEHTYjIr+9oXuZNgp/f5wWEK1KB1+6ToWWYSvtKwUs2fNRmJiItxFGl6O6xWNy7qy4SURkTdgcENuoTHb0OtXBQjZb8KRv8Wgop89wCme1HhjSavVim+++QbGUqNqbikiIiJwy83SPsF9pOHl3EGJasVhIiLyDgxuyC1kjZrqBB0Cj2kQkF5TG9w0JisrE0uWLEV2dra6P3TIECQmJrn9N9M/MQxT+sWy4SURkZdhcEMuKxaO+2+ZvU1CmH3Wk3Ttzro1DOaYxhe+M5vN2LBhg8rYyOyywMBAzJo1y61DUI6Gl1P7xanghoiIvA+DG3KJ7k8VIvR7E7TVNmTcFa621UQ1vZrvyZMnsHTZMhQXFav7aYPSVG1NcPDFLRZcSYaf5qYlIjzIfYXJRETUsRjcUOs4Fu45P9Mp57oQ6IuMThUKm80mfP755ygvr0B4eBjmzZuP3r17u/U3IYc5MjUKY3qw4SURkbdjcEMtFnjMhOQ3jCiYG4SiKfYu3aUjAnD4dX/72jWNckyP18Bg8MPs2XNw7tw5TJ06BX5+7l0BODTA3vBSpnoTEZH3Y3BDLRa2o1oNQRkKrCiS2U/a8wFNE4GN9AFZvnw5hg4digEDBtQ2vJSbu/WMC8GM/vFseElE5EMY3NAlaSus0Fbaavs+5V4VDEOhBTnXhlwIbBphs1mxY8dOfPnlOphMZuTm5qJfv77Qat3fWZsNL4mIfBeDG2pW6M4qdPtrMSr6GHDyD9Fqmy1Ai3P3RTT7dRLILFm6BBnnMtT9rl27YsGCBe0S2EQFS8PLBMSFcu0aIiJfxOCGmmVK0Kt2Cf7pNdCVWtXqws2pqanB5s2bsWXLFrUwn7+/P2bMmIHhw4dD00SbBVcamBSGyX3j1HRvIiLyTQxuqJ6AU2YEnK5B8RT7SsLVKXocfzYa5QP81MJ8l5KRkYFNmzapz/v264t5c+ciNNT968lIMDOtfxz6JXDtGiIiX8fghmoFHjWh7wP5sBo0KBvsV1tjUzak+dlMsgCfIyvTrVs3XH755UhOTkb//v3U7Ch3iw8LwNxBCYgIanoVZCIi8h0MbqhWZW8DyvsZYI7SQWN17sQcPnwY69atw4033ojw8PDahpftZXi3SIzvFQNdM4XNRETkWxjc+CqrDZHrKxH1ZSVO/CHKPo1bo1FDUFIwfCmlpaVYuXIlDh06pO5v3rIZ8+fNR3sJ9NNh5oB49IgNabfvSUREnQODGx8lU7tTXjOqYuGotZUonG1f4O5SgY0MQX333XdYt24tqqqqodVq1TDUxIkT2unIgZTIQMxOS0BoAFsoEBHRxRjc+BBdiQWWcHsdjTVYi8zbQqEvtqLofPHwpRQU5GPp0mU4c+aMup+UnISFCxYgPj4B7UHKesb2iFZtFLQchiIioiYwuPEFNhuS3jQi9n/lOPZCDCr62gtvC+a0rEnlnj3fq8DGYDBg6tSpGD16FDSa9plyLS0UJFuTEskWCkRE1DwGN75Ao4Gh2AqtGYjYUlUb3DjDarXULrw3adJEVFRUYMKECYiIaH4RP1e3UJD6mgCD+xcAJCKizo/BjTey2RD2bTXK0/xqF93LvCUMhVMCUTrSuVV7TSYT1q9fr5pb3nrrLSpDo9cb1CrD7UVmQE3sE4shKeHtsgAgERF5BwY3Xijl7yWIXVaBnGuCkflT+/Rsc6xO3Zxx/PhxLF++DMXFJer+iRMn0atXL7QntlAgIqLWYnDjhUrGBiB6dQWsAS3LdlRUlGPV6tXYt3efui9DT/Pnz0fPnj3RngYkhWEKWygQEVErMbjp5LSVVsR9Wo7KVD1KJthnPcnQ04H341ET5WyNig179+7D6tWrUFFRqYaARo8ZjalTpsBgaL9Vf6WFwtR+ceifyBYKRETUegxuOrmYZRVIfL8U1fE6GEcHwOZnz9Y4H9jY167Zvn27Cmzi4+OxcOECJCUloz3FhfljbloiIoPZQoGIiNqGwU0npDHZaoOYvAVBCPu2CvlXBMPWgjXtbDar6tqt0+lVsbAUCh87dgzjxo2tnR3VXoZ1jVAtFPQ6dvImIqK2Y3DTifhl1SD5n0b1+amnompXFD7+fEyLnicnJxtLlixFjx49MG3aNLVNMjZya09soUBERO7A4KYT0ZhtCN9WpT73y6iBKbllv76aGjM2btyErVu/htVqQ3FxMcaPHw9//+a7frsDWygQEZG7MLjx8OGnwBNmVPS316FUdzUg/WfhKBvo1+LA5vTpU6p1QmFhobo/YMAAzJkzu90DG1muZkyPaIxiCwUiInITBjceypBnQe9H8qEvsuLg23Goida1qmVCVVUl1q5dp5pditDQUMybNw99+/ZFe2MLBSIiag8MbjyUOUaLmnAttNU2+GfW1AY3LVVdbcL+/fZ1a0aMHIHp06bB39+5VYpdqUdsMGYOSFB1NkRERO7E4MZDGHJrELukApm3hAI6jRq/Of1oJGoitLAGtmwWUVVVFQIC7AFMeHg4FixYiLCwMHTt2hXtTVooTOgdg6FdIthCgYiI2gWDG09QY0OfB/Phl29FdaIOBfPsQ0+mxJb9emS9mp07d+LLL7/Eddf9AN2791Db09LS0BEigwyYOygRcWHtnykiIiLfxeCmo9hs9upa9VvQIPfaEIR/3bKO3XXl5eVh6dKlSE9PV/d3795TG9x0BLZQICKijsLgpgOE7qxC8r+MOPNwBCr72IOZvCuCkbco+ELA4ySLpQZbtnyNzZs3w2KxwM/PT61dM3LkCHQEaaEgfaEkuCEiIuoIHbok7KZNm9TKuElJSaoe44svvmh2/w0bNqj9Gt4OHz6MziRqXSUCT9Yg4cOyCxu19jqbljh37hxef/0NdV4ksOndpzfuvfcejBo1Sq063N6khcKPRnVlYENERL6buSkvL8eQIUNwyy234Oqrr3b6644cOaIKZB1iY2PhyfRFFlj9NLAG2wOOzFtDYY7SIueHoW163pKSEjUcFRwchDlz5mLgwAGykgw6AlsoEBGRp+jQ4GbOnDnq1lJxcXGIiIhAZxC1pgIpr5Ygf24QMn8arraZ4/S1n7dUeXkZgoND1OcSzJSVzcbgwYMQGBiEjhBg0GHmwHj0jLUfExERUUfrlJ0Khw0bhsTERFVbsn79+mb3ra6uhtForHdrTzKVW1dhQ/AhM2Cxtfp5JKj59LNP8dprr6GiouL8Vg1Gjx7dYYFNcmQgbhjTlYENERF5lE5VUCwBzRtvvIHLLrtMBS3vv/++CnCk5mTixImNfs0zzzyDp556qt2OMfiASbVNKBtmb2tgHOmP43+KQulwf3tdTYvZsGfP91izZjUqK6ug1Wpw6tQpDBw4EB1FSoNGd4/G6O5R6niIiIg8icYmi6N4ACkMXrx4MRYtWtSir5OCZPnaJUuWNPq4BEFyc5DMTZcuXVS9St26HVfI/E82jl5/GNUJOhz6Vxxsfm278BcVFap+UBLMiITEBCxcsFAFeR1FWijMGpiALlEdky0iIiLfZDQa1cK0zly/O1XmpjFjxozBBx980OTj0hiyvZpDRsyJgilWh9Jh/tCabLC0OrixYevWb9SQW01NDQwGPSZPnqJ+Vq2240YS2UKBiIg6g04f3OzevbtDMxl16YIlYxPb4nYJF9OgoKBABTY9evTA/PnzEBkZhY7CFgpERNSZdGhwU1ZWhuPHj9fel+GXPXv2ICoqSvVBevTRR5GRkYH33ntPPf7SSy8hNTVV1ZuYTCaVsfnss8/UzVO0NrAxm80wmaprZ0LNmDEd3bp1UzOhOmp6t2ALBSIi6mw6NLiRPkhTpkypvf/www+rjzfddBPeeecdZGVl4ezZs7WPS0DzyCOPqIAnMDBQBTnLly/H3Llz0ZmdPHkSy5YtQ1R0FG748Y9VMBMQEIjBgwd36HH1TwzDlH6x8NezkzcREXUeHlNQ7IkFSS1VXWPBq+tPOL1/ZWUF1qxZq7JVIjw8DLfddjtCQ9u2uJ8rWihM7huLgUmtW4uHiIjI1XyqoLhzsmH//gNYtWolyssr1NTqkaNGYdrUqfDza5/i56bEhPpj3qBERAW3roEnERFRR2Nw085kAb4v/vcFjh09pu7HxsWq6d0pKSnoaINTwjGxTywMuk65tiMREZHC4KadSdfuwsJC6HQ6tfDg5Zdfrj7v6GGo6f3j0TehY4fDiIiIXIHBTTvIz89XM8BkjRq9Xo+rrrwKfn4GxMR0fMPP+LAAzB2UgIggDkMREZF3YHDjRrJOzebNm7FlyxZMnz4dY8eOVduTkpLgCdjJm4iIvBGDGzc5e/YMlixdioL8AnU/MytTFRJ35Jo1Dv4GLWYOSECvOHbyJiIi78PgxsWqq6uwdt067Nq5S90PCQlR6/D079/PIwKbxPAAzBmUiPBAQ0cfChERkVswuHGhTZs24ZVXPkFpaam6P3z4cLXSsCzI5wlGpEZiXM8Y1U6BiIjIWzG4caHYmFhUVJSrlYYXzF+gWkV4gkA/nerk3T0muKMPhYiIyO0Y3LhQ/wH98aMf/Rhdu3aBXu8Zwz7JkYGYk5aA0ADPOB4iIiJ3Y3DjYtLF2xPIqsejukdhTPdoaDkMRUREPoTBjRcK9tdh9sBEdI0O6uhDISIiancMbrxM16ggzE5LQLA/f7VEROSbeAX0EjIMNbZHtBqK0sgdIiIiH8XgxguEBuhVtiYlksNQREREDG46OZneLdO8Zbo3ERERMXPTaWk1GozvHY3hXSM5DEVERFQHMzedUFigQXXyTgz3jJWPiYiIPAmDm05Gml3OGBCPAAOHoYiIiBrD4KaTkH5QE/vEYkhKOIehiIiImsHgphOICDJg3qBExIUFdPShEBEReTwGNx6ub0IopvWPg7+ew1BERETOYHDjofRaDSb3jUNachiHoYiIiFqAwY0Higr2w9xBiYgN9e/oQyEiIup0GNx4mAFJYZjSNw5+em1HHwoREVGnxODGQxh0GkztF6+CGyIiImo9BjceICbUX82GkuEoIiIiahsGNx1scEq4Wr/GoOMwFBERkSswuOkgUlMzvX+8mupNRERErsPgpgPEhdmHoSKCOAxFRETkagxu2tnQrhGY0CsGeg5DERERuQWDm3bib9Bi5oAE1fiSiIiI3IfBTTtIDA/AnEGJCA80tMe3IyIi8mkMbtxsRGokxvWMUV29iYiIyP0Y3LhJoJ8OswYmoHtMsLu+BRERETWCwY0bJEcGYk5aAkIDOAxFRETU3hjcuJAGGozuHoUxPaKh5TAUERFRh2Bw4+KF+cb1inHlUxIREVELcc1/IiIi8ioMboiIiMirMLghIiIir8LghoiIiLwKgxsiIiLyKgxuiIiIyKswuCEiIiKvwuCGiIiIvAqDGyIiIvIqDG6IiIjIqzC4ISIiIq/C4IaIiIi8CoMbIiIi8ioMboiIiMirMLghIiIir6KHj7HZbOqj0Wjs6EMhIiIiJzmu247reHN8LrgpLS1VH7t06dLRh0JEREStuI6Hh4c3u4/G5kwI5EWsVisyMzMRGhoKjUbj8qhSgqb09HSEhYW59Lm9Dc8VzxVfVx2Pf4c8V53pdSXhigQ2SUlJ0Gqbr6rxucyNnJCUlBS3fg/5ZTK44bni66rj8G+Q54uvLe/8O7xUxsaBBcVERETkVRjcEBERkVdhcONC/v7+ePLJJ9VH4rni66r98W+Q54uvrY7nCX+HPldQTERERN6NmRsiIiLyKgxuiIiIyKswuCEiIiKvwuCGiIiIvAqDmzZ6+umnMW7cOAQFBSEiIsKpr5Ea7t/97ndqlcXAwEBMnjwZBw4cgLcrKirCjTfeqBZhkpt8Xlxc3OzX3HzzzWol6bq3MWPGwNu8+uqr6N69OwICAnDZZZdh8+bNze6/ceNGtZ/s36NHD/zzn/+Er2jJudqwYcNFrx+5HT58GN5u06ZNWLBggXqfkZ/5iy++uOTX+OrrqqXnypdfV8888wxGjhypVvmPi4vDokWLcOTIEY97bTG4aSOTyYRrr70Wd999t9Nf85e//AUvvPAC/vGPf2DHjh1ISEjAjBkzavteeasf/ehH2LNnD1atWqVu8rkEOJcye/ZsZGVl1d5WrFgBb/LJJ5/gwQcfxOOPP47du3djwoQJmDNnDs6ePdvo/qdOncLcuXPVfrL/Y489hgceeACfffYZvF1Lz5WDvPnWfQ317t0b3q68vBxDhgxR7zPO8OXXVUvPlS+/rjZu3Ih7770X3377LdauXYuamhrMnDlTnUOPem3JVHBqu7ffftsWHh5+yf2sVqstISHB9uyzz9Zuq6qqUl/7z3/+02t/FQcPHpQlB2zffvtt7bZvvvlGbTt8+HCTX3fTTTfZrrjiCps3GzVqlO2uu+6qt61fv362X//6143u/8tf/lI9Xtedd95pGzNmjM3btfRcrV+/Xr3GioqKbL5MzsHixYub3ceXX1ctPVd8XV2Qm5urztnGjRttnvTaYuamnUkEm52drSJdB1noaNKkSdi6dSu81TfffKOGokaPHl27TYaXZNulfm5JAUv6s0+fPrjjjjuQm5sLb8r87dq1q97rQcj9ps6LnMuG+8+aNQs7d+6E2WyGt2rNuXIYNmwYEhMTMW3aNKxfv97NR9o5+errqi34ugJKSkrUuYiKivKo1xaDm3YmgY2Ij4+vt13uOx7zRvKzSYDSkGxr7ueWIYcPP/wQX331FZ5//nk1jDd16lRUV1fDG+Tn58NisbTo9SDbG9tf0sPyfN6qNedKApo33nhDpb8///xz9O3bVwU4UmNB9fnq66o1+Lqyk0TXww8/jPHjxyMtLQ2e9Nryua7gzpBi36eeeqrZfeQiO2LEiFZ/Dyk+a/giabjNm86VaOznu9TPfd1119V+Ln88cs67deuG5cuX46qrroK3aOnrobH9G9vujVpyriSYkZvD2LFjkZ6ejueeew4TJ050+7F2Nr78umoJvq7s7rvvPuzduxdbtmyBp722GNw08Qv74Q9/2OyJS01NbdUJl+JhRyQr0b+DDLU0jGy96VzJH0BOTs5Fj+Xl5bXo55ZzJsHNsWPH4A1iYmKg0+kuyjw093qQ11Bj++v1ekRHR8NbteZcNUaGQz/44AM3HGHn5quvK1fxtdfV/fffjyVLlqgsaEpKise9thjcNPEmKjd3kCms8ouWKnMZr3XUEkgF+p///Gd467mS/5hlbHb79u0YNWqU2rZt2za1TabSO6ugoED95103MOzM/Pz81PRIeT1ceeWVtdvl/hVXXNHkuVy6dGm9bWvWrFFZLYPBAG/VmnPVGJmt4S2vH1fy1deVq/jK68pms6nAZvHixaoeUq5pHvnaclupso84c+aMbffu3bannnrKFhISoj6XW2lpae0+ffv2tX3++ee192WmlMyOkm379u2zXX/99bbExESb0Wi0ebPZs2fbBg8erGZJyW3QoEG2+fPn19un7rmSc/jzn//ctnXrVtupU6fUDIWxY8fakpOTvepc/ec//7EZDAbbW2+9pWaVPfjgg7bg4GDb6dOn1eMyE+jGG2+s3f/kyZO2oKAg20MPPaT2l6+Tr//0009t3q6l5+rFF19UM1+OHj1q279/v3pc3vY+++wzm7eTvx/H+5H8zC+88IL6XN6zBF9XrT9Xvvy6uvvuu9X1a8OGDbasrKzaW0VFRe0+nvDaYnDTRjJVWV7UDW9yIa49yYCaKl53OviTTz6ppoT7+/vbJk6cqIIcb1dQUGD78Y9/bAsNDVU3+bzhFN2650r+WGbOnGmLjY1Vfwhdu3ZV5/vs2bM2b/PKK6/YunXrZvPz87MNHz683rRK+ZknTZpUb395Yxk2bJjaPzU11fbaa6/ZfEVLztWf//xnW8+ePW0BAQG2yMhI2/jx423Lly+3+QLHdOWGNzlHgq+r1p8rX35doZHz1PAa5wmvLc35gyUiIiLyCpwKTkRERF6FwQ0RERF5FQY3RERE5FUY3BAREZFXYXBDREREXoXBDREREXkVBjdERETkVRjcEBERkVdhcENERERehcENEREReRUGN0RERORVGNwQUaeXl5eHhIQE/OlPf6rdtm3bNvj5+WHNmjUdemxE1P7YOJOIvMKKFSuwaNEibN26Ff369cOwYcMwb948vPTSSx19aETUzhjcEJHXuPfee7Fu3TqMHDkS33//PXbs2IGAgICOPiwiamcMbojIa1RWViItLQ3p6enYuXMnBg8e3NGHREQdgDU3ROQ1Tp48iczMTFitVpw5c6ajD4eIOggzN0TkFUwmE0aNGoWhQ4eqmpsXXngB+/btQ3x8fEcfGhG1MwY3ROQVfvGLX+DTTz9VtTYhISGYMmUKQkNDsWzZso4+NCJqZxyWIqJOb8OGDWpW1Pvvv4+wsDBotVr1+ZYtW/Daa6919OERUTtj5oaIiIi8CjM3RERE5FUY3BAREZFXYXBDREREXoXBDREREXkVBjdERETkVRjcEBERkVdhcENERERehcENEREReRUGN0RERORVGNwQERGRV2FwQ0RERPAm/w84miaGanx2aAAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x_full, my_model.y(x_full, *list(true_params.values())), \"k--\", label=\"truth\")\n", "plt.xlabel(\"x\")\n", "plt.ylabel(\"y\")\n", "plt.errorbar(\n", " x,\n", " obs1.y,\n", " y_stat_err,\n", " color=\"k\",\n", " marker=\"o\",\n", " linestyle=\"none\",\n", " label=\"experiment\",\n", ")\n", "plt.fill_between(\n", " x_full,\n", " lower,\n", " upper,\n", " alpha=0.5,\n", " zorder=2,\n", " label=r\"posterior inner 90$^\\text{th}$ pctl\",\n", ")\n", "plt.plot(x_full, median, \"m:\", label=\"posterior median\")\n", "\n", "plt.legend()\n", "# plt.title(\"predictive posterior\")" ] }, { "cell_type": "markdown", "id": "cf93c216-e2e7-44f0-91d5-09e0725a9fdf", "metadata": {}, "source": [ "# Nice!\n", "\n", "Hopefully this simple example served to illustrate the basic function of the working pieces of `rxmc`. The true power is the ability to compose different `Constraint`s, and easily manage and test different model forms for both the `PhysicalModel` and `LikelihoodModel`. \n", "\n", "\n", "Check out the other demos to see how `rxmc` helps us handle more realistic problems." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 5 }