diff --git a/.gitignore b/.gitignore
index 5e76d76..3cfaa81 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,4 @@ __pycache__/
 .DS_Store
 output/*
 !output/
+*.egg-info
diff --git a/README-normal-extension.md b/README-normal-extension.md
new file mode 100644
index 0000000..55cf2fd
--- /dev/null
+++ b/README-normal-extension.md
@@ -0,0 +1,99 @@
+# Normal extension of Bayesian analysis
+
+The folder `bayes_chime/normal` contains an extension of the Bayesian analysis of the SEIR model.
+In this extension, it is assumed that all data and model parameters are normally distributed.
+This simplifies the propagation of errors and allows to analytically compute approximated posterior distributions.
+Particularly, propagating parameter distributions through a 200-day simulation takes `20ms` while the estimation of the posterior distribution is at the order of `1s`.
+Tests indicated that, for a given parameter distribution, general Bayesian forecasts agree with their normal approximations within uncertainty.
+
+
+## Install the module
+You can locally install this module using pip
+
+```bash
+pip install [--user] [-e] .
+```
+
+## How to use the module
+
+### How to propagate uncertainties
+```python
+from gvar import gvar
+from bayes_chime.normal.models import SEIRModel
+
+# define fixed model parameters
+xx = {"market_share": 0.26, "region_pop": 1200000, ...}
+
+# define model parameter distributions
+pp = {"incubation_days": gvar(4.6, 1.5), "recovery_days": gvar(15.3, 3.5)}
+
+# set up the model
+seir = SEIRModel()
+df = seir.propagate_uncertainties(xx, pp)
+```
+The `gvar` variable represents a Gaussian random number characterized by it's mean and standard deviation.
+
+### How to compute posteriors
+```python
+... # after code above
+
+from lsqfit import nonlinear_fit
+
+yy = gvar(data_mean, data_sdev)
+
+fit = nonlinear_fit(data=(xx, yy), prior=pp, fcn=seir.fit_fcn)
+
+print(fit) # Fit statistics
+print("Posterior:", fit.p)
+```
+
+In the `notebooks/How-to-use-normal-approximations-module.ipynb`, the general usage of the module is explained.
+
+## Technical details
+
+### Uncertainty propagation and posteriors
+
+This module makes use of two libraries developed by [Peter Lepage](https://physics.cornell.edu/peter-lepage)
+
+* [`gvar`](https://gvar.readthedocs.io) and
+* [`lsqfit`](https://lsqfit.readthedocs.io)
+
+The random numbers represented by `gvar`s act like `floats` but automatically compute analytic derivatives in successive computations.
+Thus they allow to propagate errors through the whole computation.
+Because normal distributions are self-conjugate (if the likelihood is a Gaussian and the prior is Gaussian, so will be the posterior).
+
+To determine posterior distributions, the `nonlinear_fit` function from `lsqfit` uses the saddle-point approximation for the kernel of the posterior function (evolve the residuals up to the second order and evaluate at the point where the first derivative vanishes).
+Thus, computing the posterior effectively boils down to finding the parameters which cause the first derivative of the `chi**2` to vanish.
+For this it uses regular minimization routines.
+More details are specified in the [appendix A of the linked publication](https://arxiv.org/pdf/1406.2279.pdf).
+
+### Kernel functions
+
+To utilize the above modules, kernel operations must be written to utilize the syntax of `gvar`s and `lsqfit`.
+Model parameters are either categorized as independent / fixed parameters or distribution / variable parameters.
+Variable parameters follow normal distributions, fixed parameters are numbers.
+
+This module abstracts the compartment models like SIR or SEIR such that future implementations can easily be extended.
+E.g., after inheriting from the `bayes_chime.normal.models.base.CompartmentModel`, one only has to provide a `simulation_step` method and can use existing API to run simulations
+```python
+def simulation_step(data, **pars):
+    susceptible = data["susceptible"]
+    exposed = data["exposed"]
+    infected = data["infected"]
+    recovered = data["recovered"]
+
+    infected += pars["beta"] * infected * susceptible / pars["total"]
+    ...
+
+    return oupdated_data
+```
+
+```
+
+## Cross checks
+
+This module tests against the non-Bayesian `penn_chime` module. These tests can be run in the repo root with
+```bash
+pytest
+```
+Furthermore, the `How-to-use` notebook fits posterior distributions generated with the main module and compares the propagation of uncertainties.
diff --git a/bayes_chime/__init__.py b/bayes_chime/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bayes_chime/normal/__init__.py b/bayes_chime/normal/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/bayes_chime/normal/doc/.gitignore b/bayes_chime/normal/doc/.gitignore
new file mode 100644
index 0000000..ae34925
--- /dev/null
+++ b/bayes_chime/normal/doc/.gitignore
@@ -0,0 +1,9 @@
+notes.aux
+notes.log
+xetex_import.sty
+notes.bbl
+notes.blg
+notes.out
+notes.synctex.gz
+notesNotes.bib
+notes.pdf
diff --git a/bayes_chime/normal/doc/notes.bib b/bayes_chime/normal/doc/notes.bib
new file mode 100644
index 0000000..15acadc
--- /dev/null
+++ b/bayes_chime/normal/doc/notes.bib
@@ -0,0 +1,12 @@
+@article{Bouchard:2014ypa,
+    author = "Bouchard, C.M. and Lepage, G. Peter and Monahan, Christopher and Na, Heechang and Shigemitsu, Junko",
+    archivePrefix = "arXiv",
+    doi = "10.1103/PhysRevD.90.054506",
+    eprint = "1406.2279",
+    journal = "Phys. Rev. D",
+    pages = "054506",
+    primaryClass = "hep-lat",
+    title = "{$B_s \to K \ell \nu$ form factors from lattice QCD}",
+    volume = "90",
+    year = "2014"
+}
diff --git a/bayes_chime/normal/doc/notes.tex b/bayes_chime/normal/doc/notes.tex
new file mode 100755
index 0000000..edd927d
--- /dev/null
+++ b/bayes_chime/normal/doc/notes.tex
@@ -0,0 +1,242 @@
+\batchmode
+\documentclass[paper=a4, fontsize=12pt, prl, notitlepage]{revtex4-1}
+\usepackage[ssp]{xetex_import}
+\linespread{1.4}
+\errorstopmode
+
+\begin{document}
+
+\title{Math behind Bayesian uncertainty propagation using normal approximations}
+\author{Christopher Körber}
+\affiliation{%
+Department of Physics,
+University of California,
+Berkeley, CA 94720, USA\\
+Nuclear Science Division, %
+LBNL,
+Berkeley, CA 94720, USA
+}
+\date{\today}
+\begin{abstract}
+This document addresses the math used to propagate errors and to infer posterior distributions (Bayesian statistics) used in \href{https://github.com/pennsignals/chime_sims/pull/49}{the performance boost through normal approximations PR in the \texttt{pennsignals/chime\_sims} repo}.
+\end{abstract}
+
+\maketitle
+
+%==============================================================================
+% Content
+%==============================================================================
+
+\section{Assumptions}
+The general theme of this document is: \textit{casting everything to normal distribution simplifies computations}.
+
+In general, the techniques in PR \#49 make two approximations
+\begin{enumerate}
+    \item All model and data \textit{input} distributions can be sufficiently approximated with multivariate normal distributions
+    \item The posterior distributions can be sufficiently approximated by a multivariate normal distribution
+\end{enumerate}
+
+\section{Error propagation}
+The \texttt{gvar} module is used to analytically propagate errors.
+The eqution used to computed the standard deviation $\sigma_f$ of a function $f(\vec p)$ at a given point in the parameter space $\bar {\vec p}$ is
+\begin{equation}
+    \sigma_f^2(\bar {\vec p})
+    =
+    \left[
+        \sum_{i,j=1}^N
+        \frac{\partial f(\vec p)}{\partial p_i}
+        \left(\Sigma_p\right)_{ij}
+        \frac{\partial f(\vec p)}{\partial p_j}
+    \right]_{\vec p = \bar {\vec p}}
+    \, ,
+\end{equation}
+where $\Sigma_p$ is the covariance matrix of the normally distributed parameters $\vec p$.
+In the uncorrelated case, this simplifies to
+\begin{equation}
+    \sigma_f^2(\bar {\vec p})
+    =
+    \left[
+        \sum_{i=1}^N
+        \left(\frac{\partial f(\vec p)}{\partial p_i}\right)^2
+        \sigma_{p_i}^2
+    \right]_{\vec p = \bar {\vec p}}
+    \, .
+\end{equation}
+
+The module \texttt{gvar} is capable of tracing such derivatives since implemented functions are aware of their analytical derivatives.
+
+\subsubsection{Example}
+For example, suppose
+\begin{align}
+    f(x, y)
+    &=
+    x y^2
+    && \Rightarrow &
+    \sigma_f^2(\bar{\vec p})
+    &=
+    y^4 (\Sigma_p)_{xx}
+    + 2 x y^3  \left[ (\Sigma_p)_{xy} + (\Sigma_p)_{yx} \right]
+    + 4 x^2 y^2 (\Sigma_p)_{yy}
+\end{align}
+Thus, if $x, y$ follow a multivariate normal distribution of mean $\bar{\vec p}$ and covariance $\Sigma_p$, one finds
+\begin{align}
+    \bar{\vec p}
+    &=
+    \begin{pmatrix}
+        1 \\ -2
+    \end{pmatrix}
+    \, , &
+    \Sigma_p
+    &=
+    \begin{pmatrix}
+        2 & 1 \\ 1 & 1
+    \end{pmatrix}
+    &
+    \Rightarrow
+    \sigma_f(\bar{\vec p})
+    &=
+    \sqrt{32 - 32 + 16} = 4
+\end{align}
+The corresponding \texttt{gvar} code returns
+\begin{lstlisting}[style=python]
+from gvar import gvar
+
+mean = [1, -2]
+cov = [[2, 1], [1, 1]]
+x, y = gvar(mean, cov)
+f = x * y ** 2
+print(f)
+
+> 4.0(4.0)
+\end{lstlisting}
+
+
+\section{Computation of the posterior}
+
+This section explains how the \texttt{lsqfit} module approximates the posterior distribution $P(\vec p|D,M)$ given data $D$, an input model $M$ and it's corresponding priors $P(\vec p| M)$.
+
+\subsection{Defintions}
+
+The posterior distribution $P(\vec p|D, M)$ is proportional to the prior times the probability of the data given the model and parameters $P(D|\vec p, M)$ (the likelihood)
+\begin{align}
+    P(\vec p|D, M) &=
+    \frac{P(D|\vec p, M)P(\vec p | M)}{P(D|M)}
+    \propto
+    P(D|\vec p, M)P(\vec p | M)
+    \, .
+\end{align}
+The marginal likelihood of the data $D$ given the model $M$ is obtained by integrating over the whole parameter space
+\begin{equation}
+    P(D|M)
+    =
+    \int d \vec p P(D|\vec p, M)P(\vec p | M) \, .
+\end{equation}
+Because the posterior is normalized by the ratio of both distributions, one can neglect constant factors in the computation.
+
+The likelihood of the data given the model and parameters is described by a $\chi^2$ distribution
+\begin{equation}
+    P(D|\vec p, M)
+    \sim
+    \exp\left\{
+        - \frac{1}{2}
+        \sum_{i,j=1}^N
+        \left[y_i - \vec f_M(x_i, \vec p)\right]
+        \left(\Sigma_y^{-1}\right)_{ij}
+        \left[y_j - \vec f_M(x_j, \vec p)\right]
+    \right\}
+    =
+    \exp\left\{
+        - \frac{1}{2}
+        \chi^2_D(\vec p)
+    \right\}
+    \, ,
+\end{equation}
+where $\Sigma_y$ is the covariance matrix of the data and $f_M(x_j, \vec p)$ the model function evaluated at point $x_j$ which aims to describe the data point $y_j$.
+
+Maximizing the Likelihood as a function of $\vec p$ corresponds to minimizing the exponent--which is the standard $\chi^2$-minimization procedure.
+Computing the posterior distribution function for a given prior $P(\vec p| M)$ is called Bayesian inference.
+Normal approximations of the posterior distribution are somewhere in the middle of a full Bayesian treatment and regular $\chi^2$-minimization.
+
+\subsection{Normal approximation of the posterior}
+Including the multivariate normal prior distribution with mean $\vec p_0$ and covariance $\Sigma_{p_0}$, the posterior distribution is proportional to
+\begin{align}
+    P(\vec p|D, M)
+    &\sim
+    \exp\left\{
+        - \frac{1}{2}
+        \chi^2_D(\vec p)
+        - \frac{1}{2}
+        \chi^2_M(\vec p)
+    \right\}
+    =
+    \exp\left\{
+        - \frac{1}{2}
+        \chi^2_{DM}(\vec p)
+    \right\}
+    \, , &
+    \chi^2_M(\vec p)
+    &=
+    \left[\vec p - \vec p_0\right]^T \cdot
+    \Sigma_{p_0}^{-1}
+    \left[\vec p - \vec p_0\right]\, .
+\end{align}
+In short, the \texttt{lsqfit} module approximates the posterior by expressing the function $\chi^2_{DM}(\vec p)$ up to second order in $\vec p$ at the point $\bar{\vec p}$ where the first derivative vanishes (stationary or almost always minimal point)
+\begin{align}
+    \chi^2_{DM}(\vec p)
+    & \approx
+    \chi^2_{DM}(\bar{\vec p})
+    +
+    \left[\vec p - \bar{\vec p}\right]^T
+    \Sigma_{DM}^{-1}(\bar{\vec p})
+    \left[\vec p - \bar{\vec p}\right]^T
+    \, , & &
+    \left.\frac{\partial \chi^2_{DM}(\vec p)}{\partial p_\alpha}\right|_{\vec p = \bar{\vec p}} = 0 \, \quad \forall_\alpha \, .
+\end{align}
+In this approximation, the posterior is again a multivariate normal distribution of mean $\bar{\vec p}$ (same as maximal likelihood estimation) with covariance $\Sigma_{DM}(\bar{\vec p})$.
+The \texttt{nonlinear\_fit} method numerically computes the vector which minimizes the posterior $\bar{\vec p}$ (fitting) and analytically computes and evaluates the covariance matrix $\Sigma_{DM}(\bar{\vec p})$ at this point.
+The appendix A of \cite{Bouchard:2014ypa} describes how $\Sigma_{DM}(\bar{\vec p})$ is estimated using derivatives of the residuals with respect of prior parameters.
+%
+% \subsubsection{Example}
+%
+% Suppose the fit function is a linear function $f(x_i, \vec p) = p^{(0)} + p^{(1)} x_i$.
+% In this case, the normal approximation of the posterior is exact
+% \begin{align}
+%     \chi^2_{DM}(\vec p)
+%     & =
+%     \sum_{i,j=1}^N
+%     \left[y_i - p^{(0)} - p^{(1)} x_i \right]
+%     \left(\Sigma_y^{-1}\right)_{ij}
+%     \left[y_j  - p^{(0)} - p^{(1)} x_j\right]
+%     +
+%     \left[\begin{pmatrix} p^{(0)} \\ p^{(1)} \end{pmatrix} - \vec p_0 \right]^T
+%     \left(\Sigma_p^{-1}\right)_{ij}
+%     \left[\begin{pmatrix} p^{(0)} \\ p^{(1)} \end{pmatrix} - \vec p_0 \right]
+%     \\
+%     & =
+%     \left[
+%         \sum_{i,j=1}^N y_i \left(\Sigma_y^{-1}\right)_{ij} y_j
+%         + \vec p_0^T
+%         \left(\Sigma_p^{-1}\right)
+%         \vec p_0
+%     \right]
+%     +
+%     \left[\begin{pmatrix} p^{(0)} \\ p^{(1)} \end{pmatrix} - \bar{\vec p} \right]
+%     \left(\Sigma_{DM}^{-1}\right)_{ij}
+%     \left[\begin{pmatrix} p^{(0)} \\ p^{(1)} \end{pmatrix} - \bar{\vec p} \right]^T
+%     \, ,
+% \end{align}
+% with
+% \begin{align}
+%     \bar{\vec p} &= \ldots
+%     \\
+%     \Sigma_{DM}^{-1} &= \ldots
+% \end{align}
+
+%==============================================================================
+% End Content
+%==============================================================================
+\bibliography{notes.bib}{}
+\bibliographystyle{plainurl}
+
+\batchmode
+\end{document}
diff --git a/bayes_chime/normal/fitting.py b/bayes_chime/normal/fitting.py
new file mode 100644
index 0000000..d279cd4
--- /dev/null
+++ b/bayes_chime/normal/fitting.py
@@ -0,0 +1,82 @@
+"""Fitting routines for approximating distributions with normal distributions
+"""
+from typing import TypeVar, Dict, Any, Optional
+
+from numpy import linspace, sqrt, median, sum
+
+from scipy.optimize import curve_fit
+from scipy.stats import norm, beta, gamma
+
+from pandas import DataFrame
+
+from gvar import gvar
+
+from bayes_chime.normal.utilities import FloatLikeArray, NormalDistVar, FloatOrDistVar
+
+Dist = TypeVar("ScipyContinousDistribution")
+
+
+def fit_norm_dist_to_ens(
+    x: FloatLikeArray, thresh: Optional[float] = None
+) -> NormalDistVar:
+    """Approximates ensemble (random vector) by normal distribution
+
+    if thresh is specified, inferst how much results deviate from median and cuts out
+    outliers with modified z_score > thresh.
+    """
+    if thresh:
+        d = sqrt((x - median(x)) ** 2)
+        mod_z_score = 0.6745 * d / median(d)
+        x = x[mod_z_score < thresh]
+
+    return gvar(*norm.fit(x))
+
+
+def fit_norm_dist_to_dist(dist: Dist) -> NormalDistVar:
+    """Approximates distribution by normal distribution
+    """
+    x = linspace(dist.ppf(0.01), dist.ppf(0.99), 100)
+    y = dist.pdf(x)
+
+    mu, var = dist.stats(moments="mv")
+    mu, std = curve_fit(norm.pdf, xdata=x, ydata=y, p0=(mu, sqrt(var)))[0]
+
+    return gvar(mu, std)
+
+
+def parse_dist(data: Dict[str, Any]) -> Dist:
+    """Parses prior frame data to distribution
+    """
+    distribution = data["distribution"]
+    if distribution == "beta":
+        dist = beta(a=data["p1"], b=data["p2"])
+    elif distribution == "gamma":
+        dist = gamma(a=data["p1"], scale=data["p2"])
+    elif distribution == "constant":
+        dist = data["base"]
+    else:
+        raise KeyError(
+            "Distribution {distribution} not implemented.".format(
+                distribution=distribution
+            )
+        )
+    return dist
+
+
+def gv_to_dist(normal: NormalDistVar) -> Dist:
+    """Converts gvar to scipy dist
+    """
+    return norm(loc=normal.mean, scale=normal.sdev)
+
+
+def fit_norm_to_prior_df(prior_df: DataFrame) -> Dict[str, FloatOrDistVar]:
+    """Reads in prior data frame (`params.csv`) and returns fitted normal variables.
+    """
+    priors = {}
+    for _, row in prior_df.iterrows():
+        dist = parse_dist(row)
+        priors[row["param"]] = (  # account for constant dist
+            dist if isinstance(dist, float) else fit_norm_dist_to_dist(dist)
+        )
+
+    return priors
diff --git a/bayes_chime/normal/models/__init__.py b/bayes_chime/normal/models/__init__.py
new file mode 100644
index 0000000..592fa44
--- /dev/null
+++ b/bayes_chime/normal/models/__init__.py
@@ -0,0 +1,4 @@
+"""This module contains implementations of the compartment models
+"""
+from bayes_chime.normal.models.sir import SIRModel
+from bayes_chime.normal.models.seir import SEIRModel
diff --git a/bayes_chime/normal/models/base.py b/bayes_chime/normal/models/base.py
new file mode 100644
index 0000000..7d9004c
--- /dev/null
+++ b/bayes_chime/normal/models/base.py
@@ -0,0 +1,240 @@
+"""Helper functions to utilize SIR like models
+"""
+from typing import Dict, Generator, List, Callable, Optional
+
+from abc import ABC, abstractmethod
+
+from datetime import date as Date
+from datetime import timedelta
+
+from pandas import DataFrame, DatetimeIndex, infer_freq
+
+from bayes_chime.normal.utilities import (
+    FloatLike,
+    NormalDistVar,
+    FloatOrDistVar,
+    NormalDistArray,
+)
+
+
+class CompartmentModel(ABC):
+    """Abstract implementation of SIR like compartment model
+
+    Attributes:
+        model_parameters: A list of all parameters needed to run a simmulation
+            (used by `simulation_step` or `post_process_simulation`).
+            These parameters must be present after `parse_input` is run.
+        optional_parameters: Further parameters which extend model predictions but
+            are not required. E.g., to do conversions between parameters.
+        compartments: These are the compartments needed by the model.
+            E.g., susceptible, infected and recovered for standard SIR.
+    """
+
+    # ----------------------------------------
+    # Below you can find methods to overload
+    # ----------------------------------------
+
+    model_parameters: List[str] = ["dates"]
+    optional_parameters: List[str] = []
+    compartments: List[str] = []
+
+    def parse_input(  # pylint: disable=R0201
+        self, **pars: Dict[str, FloatOrDistVar]
+    ) -> Dict[str, FloatOrDistVar]:
+        """Parses parameters before fitting. This should include, e.g., type conversions
+
+        By default, checks dates and adds frequency.
+        """
+        dates = pars["dates"]
+        if not isinstance(dates, DatetimeIndex):
+            dates = DatetimeIndex(dates)
+
+        if not dates.freq:
+            dates.freq = infer_freq(dates)
+            pars["dates"] = dates
+
+        pars["days_per_step"] = pars["dates"].freq / timedelta(days=1)
+
+        return pars
+
+    def post_process_simulation(  # pylint: disable=R0201, W0613, C0103
+        self, df: DataFrame, **pars: Dict[str, FloatOrDistVar]
+    ) -> DataFrame:
+        """Processes the final simulation result. This can add, e.g., new columns
+        """
+        return df
+
+    @staticmethod
+    @abstractmethod
+    def simulation_step(
+        data: Dict[str, NormalDistVar], **pars: Dict[str, FloatOrDistVar]
+    ):
+        """This function implements the actual simulation
+
+        Arguments:
+            data: The compartments for each iteration
+            pars: Model parameters
+
+        Returns:
+            Updated compartments and optionally additional information like change
+            from last iteration.
+        """
+        return data
+
+    # ----------------------------------------------------------
+    # This part should be fixed unless you add functionality
+    # ----------------------------------------------------------
+
+    def __init__(
+        self,
+        fit_columns: Optional[List[str]] = None,
+        update_parameters: Callable[
+            [Date, Dict[str, FloatOrDistVar]], Dict[str, FloatOrDistVar]
+        ] = None,
+        fit_start_date: Optional[Date] = None,
+        debug: bool = False,
+    ):
+        """Initializes the compartment model
+        update function
+
+        Arguments:
+            fit_columns: When calling fit_fcn, this will only return specified column.
+                This should be used when only a subset of the simulation parameters
+                should be fit.
+            update_parameters: This function allows to update effective model parameters
+                based on the number of model iterations. It takes the number of iteration
+                and all model (initial) parameters as input. It should return updated
+                parameters and defaults to no parameter updates. This can be used to
+                implement social distancing.
+            fit_start_date: When calling fit_fcn, this will only return results after
+                this date. This should be used if only a subset of the dates are fitted.
+            debug: Print additional messages
+
+        Note:
+            If the update_parameters method requires additional arguments, they must be
+            passed to the respective model function calls.
+        """
+        self.fit_columns = fit_columns
+        self.update_parameters = (
+            update_parameters
+            if update_parameters is not None
+            else lambda date, **pars: pars
+        )
+        self.fit_start_date = fit_start_date
+        self.debug = debug
+
+    def propagate_uncertainties(
+        self, meta_pars: Dict[str, FloatLike], dist_pars: Dict[str, NormalDistVar]
+    ) -> DataFrame:
+        """Propagates uncertainties through simmulation
+
+        Arguments:
+            meta_pars: Fixed model meta parameters
+            dist_pars: Variable model prior parameters
+
+        Returns:
+            DataFrame containing simulation data
+        """
+        pars = self.parse_input(**meta_pars, **dist_pars)
+
+        df = DataFrame(data=self._iterate_simulation(**pars)).set_index("date")
+
+        return self.post_process_simulation(df, **pars)
+
+    def _iterate_simulation(
+        self, **pars: Dict[str, FloatOrDistVar],
+    ) -> Generator[Dict[str, NormalDistVar], None, None]:
+        """Iterates model to build up SIR data
+
+        Initial data is at day zero (no step).
+
+        Arguments:
+            n_iter: Number of iterations
+            pars: Model meta and flexible parameters
+        """
+        data = {
+            compartment: pars["initial_{compartment}".format(compartment=compartment)]
+            for compartment in self.compartments
+        }
+        for date in pars["dates"]:
+            data["date"] = date
+            yield data
+            inp_pars = self.update_parameters(date, **pars)
+            data = self.simulation_step(data, **inp_pars)
+
+    def fit_fcn(  # pylint: disable=C0103
+        self, xx: Dict[str, FloatLike], pp: Dict[str, NormalDistVar]
+    ) -> NormalDistArray:
+        """Wrapper for propagate_uncertainties used for lsqfit.nonlinear_fit function
+
+        Arguments:
+            xx: Fixed model meta parameters
+            pp: Variable model prior parameters
+
+        Returns:
+            Array of `fit_columns` columns without first row (inital data).
+        """
+        if self.debug:
+            print("fit_fcn call with")
+            print("xx:\n", xx)
+            print("pp:\n", pp)
+
+        df = self.propagate_uncertainties(xx, pp)
+        if self.fit_start_date:
+            df = df.loc[self.fit_start_date :]
+
+        if self.fit_columns:
+            df = df[self.fit_columns]
+
+        if self.debug:
+            print("result:\n", df)
+
+        return df.values if df.values.shape[0] > 1 else df.values.flatten()
+
+    def check_call(  # pylint: disable=C0103
+        self,
+        xx: Dict[str, FloatLike],
+        yy: NormalDistArray,
+        pp: Dict[str, NormalDistVar],
+    ):
+        """Checks if model meta parameters and priors are set as expected to use fitter.
+
+        Checks:
+            * Non-overlapping model parameters
+            * Data retrun shape
+            * Data retun types
+
+        Raises:
+            Specific error messages if not set up correctly
+        """
+        common_pars = set(xx.keys()).intersection(pp.keys())
+        if common_pars:
+            raise KeyError(
+                "Fixed and variable model paramers have shared variables:"
+                " {common_pars}. This is not allowed.".format(common_pars=common_pars)
+            )
+
+        yy_fit = self.fit_fcn(xx, pp)
+        if not yy_fit.shape == yy.shape:
+            raise ValueError(
+                "Fit function return has different shape as data"
+                " fit: {fit_shape} data: {data_shape}.".format(
+                    fit_shape=yy_fit.shape, data_shape=yy.shape
+                )
+            )
+
+        for n_el, (y_fit, y_data) in enumerate(zip(yy_fit, yy)):
+            if not isinstance(yy_fit, type(yy)):
+                raise TypeError(
+                    "Element {n_el} of fit has different type as data:".format(
+                        n_el=n_el
+                    )
+                    + "\t{y_fit} -> {y_fit_type}!= {y_data} -> {y_data_type}".format(
+                        y_fit_type=type(y_fit),
+                        y_fit=y_fit,
+                        y_data_type=type(y_data),
+                        y_data=y_data,
+                    )
+                )
+
+        print("Checks passed")
diff --git a/bayes_chime/normal/models/seir.py b/bayes_chime/normal/models/seir.py
new file mode 100644
index 0000000..0646240
--- /dev/null
+++ b/bayes_chime/normal/models/seir.py
@@ -0,0 +1,90 @@
+"""Implementation of SIR model
+"""
+from typing import Dict, List
+
+from bayes_chime.normal.utilities import FloatOrDistVar, NormalDistVar
+from bayes_chime.normal.models.sir import SIRModel
+
+
+class SEIRModel(SIRModel):
+    """Basic SEIR model
+    """
+
+    model_parameters: List[str] = SIRModel.model_parameters + [
+        "alpha",  # or incubation_days
+    ]
+    optional_parameters: List[str] = SIRModel.optional_parameters + [
+        "incubation_days",
+    ]
+    compartments: List[str] = SIRModel.compartments + [
+        "exposed",
+    ]
+
+    def parse_input(  # pylint: disable=R0201
+        self, **pars: Dict[str, FloatOrDistVar]
+    ) -> Dict[str, FloatOrDistVar]:
+        """Parses dates, gamma and beta parameters if applicable.
+        """
+        pars = super().parse_input(**pars)
+
+        if "alpha" not in pars:
+            pars["alpha"] = 1 / (pars["incubation_days"] / pars["days_per_step"])
+
+        return pars
+
+    @staticmethod
+    def simulation_step(
+        data: Dict[str, NormalDistVar], **pars: Dict[str, FloatOrDistVar]
+    ):
+        """Executes SIR step and patches results such that each component is larger zero.
+
+        Arguments:
+            data:
+                susceptible: Susceptible population
+                exposed: Exposed poplation
+                infected: Infected population
+                recovered: Recovered population
+            pars:
+                beta: Growth rate for infected
+                alpha: Incubation rate for infected
+                gamma: Recovery rate for infected
+                nu: changes effect of susceptible for exposed to `(S/N) ** nu`
+
+        Returns:
+            Updated compartments and optionally additional information like change
+            from last iteration.
+        """
+        susceptible = data["susceptible"]
+        exposed = data["exposed"]
+        infected = data["infected"]
+        recovered = data["recovered"]
+
+        total = susceptible + exposed + infected + recovered
+
+        d_se = pars["beta"] * (susceptible / total) ** pars["nu"] * infected
+        d_ei = pars["alpha"] * exposed
+        d_ir = pars["gamma"] * infected
+
+        susceptible -= d_se
+        exposed += d_se - d_ei
+        infected += d_ei - d_ir
+        recovered += d_ir
+
+        susceptible = max(susceptible, 0)
+        exposed = max(exposed, 0)
+        infected = max(infected, 0)
+        recovered = max(recovered, 0)
+
+        rescale = total / (susceptible + exposed + infected + recovered)
+
+        out = {
+            "susceptible": susceptible * rescale,
+            "exposed": exposed * rescale,
+            "infected": infected * rescale,
+            "recovered": recovered * rescale,
+            "exposed_new": d_se * rescale,
+            "infected_new": d_ei * rescale,
+            "recovered_new": d_ir * rescale,
+        }
+
+        return out
diff --git a/bayes_chime/normal/models/sir.py b/bayes_chime/normal/models/sir.py
new file mode 100644
index 0000000..babb742
--- /dev/null
+++ b/bayes_chime/normal/models/sir.py
@@ -0,0 +1,142 @@
+"""Implementation of SIR model
+"""
+from typing import Dict, List
+
+from numpy import log, NaN
+from pandas import DataFrame
+
+
+from bayes_chime.normal.utilities import FloatOrDistVar, NormalDistVar
+from bayes_chime.normal.models.base import CompartmentModel
+
+
+class SIRModel(CompartmentModel):
+    """Basic SIR model
+    """
+
+    model_parameters: List[str] = [
+        "dates",  # DatetimeIndex
+        "initial_susceptible",
+        "initial_infected",
+        "inital_recovered",
+        "beta",  # or inital_doubling_time
+        "gamma",  # or recovery_days
+    ]
+    optional_parameters: List[str] = [
+        "recovery_days",
+        "inital_doubling_time",
+        # all keywords below are used to compute hospital admissions and census
+        "market_share",
+        "initial_hospital",
+        "hospital_probability",
+        "hospital_length_of_stay",
+        "initial_icu",
+        "icu_probability",
+        "icu_length_of_stay",
+        "initial_vent",
+        "vent_probability",
+        "vent_length_of_stay",
+    ]
+    compartments: List[str] = ["susceptible", "infected", "recovered"]
+
+    def parse_input(  # pylint: disable=R0201
+        self, **pars: Dict[str, FloatOrDistVar]
+    ) -> Dict[str, FloatOrDistVar]:
+        """Parses dates, gamma and beta parameters if applicable.
+        """
+        pars = super().parse_input(**pars)
+
+        if "gamma" not in pars:
+            pars["gamma"] = 1 / (pars["recovery_days"] / pars["days_per_step"])
+
+        if "beta" not in pars:
+            total_population = 0
+            for comp in self.compartments:
+                total_population += pars["intial_" + comp]
+
+            beta = log(2) / (pars["inital_doubling_time"] / pars["days_per_step"])
+            beta += pars["gamma"]
+            beta *= total_population / pars["initial_susceptible"]
+            pars["beta"] = beta
+
+        return pars
+
+    def post_process_simulation(  # pylint: disable=R0201, W0613, C0103
+        self, df: DataFrame, **pars: Dict[str, FloatOrDistVar]
+    ) -> DataFrame:
+        """Compute admits and census based on exponential LOS distribution if parameters
+        present.
+        """
+        # fill initial hosp admits if present
+        df = df.fillna(0)
+
+        # Local infections
+        df["infected_new_local"] = df["infected_new"] * pars.get("market_share", NaN)
+
+        dependency = {
+            "hospital": "infected_new_local",
+            "icu": "hospital_admits",
+            "vent": "icu_admits",
+        }
+
+        for kind in ["hospital", "icu", "vent"]:
+            df[f"{kind}_admits"] = df[dependency[kind]].values * pars.get(
+                f"{kind}_probability", NaN
+            )
+
+            census = [pars.get(f"initial_{kind}", NaN)]
+            for base in df[f"{kind}_admits"].values[1:]:
+                census.append(
+                    base
+                    + (1 - 1 / pars.get(f"{kind}_length_of_stay", NaN)) * census[-1]
+                )
+            df[f"{kind}_census"] = census
+
+        return df
+
+    def simulation_step(
+        self, data: Dict[str, NormalDistVar], **pars: Dict[str, FloatOrDistVar]
+    ):
+        """Executes SIR step and patches results such that each component is larger zero.
+
+        Arguments:
+            data:
+                susceptible: Susceptible population
+                infected: Infected population
+                recovered: Recovered population
+            pars:
+                beta: Growth rate for infected
+                gamma: Recovery rate for infected
+
+        Returns:
+            Updated compartments and optionally additional information like change
+            from last iteration.
+        """
+        susceptible = data["susceptible"]
+        infected = data["infected"]
+        recovered = data["recovered"]
+
+        total = susceptible + infected + recovered
+
+        d_si = pars["beta"] * susceptible / total * infected
+        d_ir = pars["gamma"] * infected
+
+        susceptible -= d_si
+        infected += d_si - d_ir
+        recovered += d_ir
+
+        susceptible = max(susceptible, 0)
+        infected = max(infected, 0)
+        recovered = max(recovered, 0)
+
+        rescale = total / (susceptible + infected + recovered)
+
+        out = {
+            "susceptible": susceptible * rescale,
+            "infected": infected * rescale,
+            "recovered": recovered * rescale,
+            "infected_new": d_si * rescale,
+            "recovered_new": d_ir * rescale,
+        }
+
+        return out
diff --git a/bayes_chime/normal/plotting.py b/bayes_chime/normal/plotting.py
new file mode 100644
index 0000000..3ae2d5b
--- /dev/null
+++ b/bayes_chime/normal/plotting.py
@@ -0,0 +1,122 @@
+"""Plotting routines
+"""
+from typing import TypeVar, Dict, Any
+
+from numpy import linspace
+
+from matplotlib.pylab import gca as get_current_actor
+from seaborn import distplot
+
+from gvar import mean as gv_mean
+from gvar import sdev as gv_sdev
+
+from bayes_chime.normal.utilities import NormalDistVar
+from bayes_chime.normal.fitting import (
+    fit_norm_dist_to_dist,
+    parse_dist,
+    gv_to_dist,
+    fit_norm_dist_to_ens,
+)
+
+Axes = TypeVar("Axes")
+
+
+def plot_prior_fit(**kwargs):
+    """Parses distribution from meta parameters and plots exact distribution and
+    normal fit
+    """
+    data = kwargs["data"].iloc[0].to_dict()
+    dist = parse_dist(data)
+
+    ax = get_current_actor()
+
+    x = linspace(dist.ppf(0.001), dist.ppf(0.999), 100)
+    y = dist.pdf(x)
+    ax.fill_between(x, y, alpha=0.5)
+
+    plot_gv_dist(fit_norm_dist_to_dist(dist), color="black")
+
+
+def plot_posterior_fit(**kwargs):
+    """Fits normal distribution to ensemble and plots normal dist as well as hist
+    """
+    ax = get_current_actor()
+    x = kwargs["data"].x.values
+    distplot(a=x, kde=False, ax=ax, hist_kws={"density": True})
+    plot_gv_dist(
+        fit_norm_dist_to_ens(x, thresh=kwargs.get("thresh", None)), ax=ax, color="black"
+    )
+
+
+def plot_gv_dist(gvar: NormalDistVar, **kwargs):
+    """Plots pdf of gvar
+    """
+    ax = kwargs.pop("ax", get_current_actor())
+
+    normal = gv_to_dist(gvar)
+    x = linspace(normal.ppf(0.001), normal.ppf(0.999), 100)
+    y_fit = normal.pdf(x)
+    ax.plot(x, y_fit, **kwargs)
+
+    return ax
+
+
+def plot_gvar(
+    line_kws: Dict[str, Any] = None, fill_kws: Dict[str, Any] = None, **kwargs
+) -> Axes:
+    """Plots gvar as a band.
+
+    Arguments:
+        line_kws: Kwargs specific for line plot
+        fill_kws: Kwargs specific for fill between
+        kwargs:  Shared kwargs
+            Requires: x and y
+            Optional: z_factor to upadte band with (e.g., 0.674 for 0.5 CI)
+    """
+    y = kwargs.pop("y")
+    x = kwargs.pop("x")
+    yy_mean = gv_mean(y)
+    yy_sdev = gv_sdev(y)
+
+    z_factor = kwargs.pop("z_factor", 1)
+    yy_sdev *= z_factor
+
+    return plot_band(
+        x=x,
+        y1=yy_mean - yy_sdev,
+        ym=yy_mean,
+        y2=yy_mean + yy_sdev,
+        line_kws=line_kws,
+        fill_kws=fill_kws,
+        **kwargs,
+    )
+
+
+def plot_band(
+    line_kws: Dict[str, Any] = None, fill_kws: Dict[str, Any] = None, **kwargs
+) -> Axes:
+    """Plots gvar as a band.
+
+    Arguments:
+        line_kws: Kwargs specific for line plot
+        fill_kws: Kwargs specific for fill between
+        kwargs:  Shared kwargs
+            Requires: x and y1, ym, y2
+    """
+    line_kws = line_kws.copy() or {}
+    fill_kws = fill_kws.copy() or {}
+
+    y1 = kwargs.pop("y1")
+    ym = kwargs.pop("ym")
+    y2 = kwargs.pop("y2")
+    x = kwargs.pop("x")
+
+    ax = kwargs.pop("ax", get_current_actor())
+
+    line_kws.update(kwargs)
+    fill_kws.update(kwargs)
+
+    ax.plot(x, ym, **line_kws)
+    ax.fill_between(x, y1, y2, **fill_kws)
+
+    return ax
diff --git a/bayes_chime/normal/utilities.py b/bayes_chime/normal/utilities.py
new file mode 100644
index 0000000..aed34bd
--- /dev/null
+++ b/bayes_chime/normal/utilities.py
@@ -0,0 +1,30 @@
+"""Utilities for the normal CHIME Bayes module
+"""
+from typing import TypeVar, Union
+
+from numpy import exp
+
+FloatLike = TypeVar("FloatLike")  # Floats or integers
+FloatLikeArray = TypeVar("FloatLikeArray")  # Arrays of floats or integers
+
+NormalDistVar = TypeVar("NormalDistVar")  # Normally distributed random var
+NormalDistArray = TypeVar("NormalDistArray")  # Array of Normally dist random var
+
+FloatOrDistVar = Union[FloatLike, NormalDistVar]
+FloatOrDistArray = Union[FloatLikeArray, NormalDistArray]
+
+
+def logistic_fcn(  # pylint: disable=C0103
+    x: FloatOrDistArray, L: FloatOrDistVar, k: FloatOrDistVar, x0: FloatOrDistVar,
+) -> FloatOrDistArray:
+    """Computes `L / (1 + exp(-k(x-x0)))`.
+    """
+    return L / (1 + exp(-k * (x - x0)))
+
+
+def one_minus_logistic_fcn(  # pylint: disable=C0103
+    x: FloatOrDistArray, L: FloatOrDistVar, k: FloatOrDistVar, x0: FloatOrDistVar,
+) -> FloatOrDistArray:
+    """Computes `1 - L / (1 + exp(-k(x-x0)))`.
+    """
+    return 1 - logistic_fcn(x, L, k, x0)
diff --git a/notebooks/How-to-use-normal-approximations-module.ipynb b/notebooks/How-to-use-normal-approximations-module.ipynb
new file mode 100644
index 0000000..507e117
--- /dev/null
+++ b/notebooks/How-to-use-normal-approximations-module.ipynb
@@ -0,0 +1,2052 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to use the normal approximations module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This module explains how to approximate existing priors and posteriors with normal distributions and how to propagate errors.\n",
+    "It compares the propagation of errors to existing data.\n",
+    "For this reason, you first must generate a fit as specified in the (original) readme.\n",
+    "The fits here have been computed for the Downtown area.\n",
+    "\n",
+    "Make sure you have installed the module and dependencies before you run this module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import getcwd, path\n",
+    "\n",
+    "from datetime import timedelta\n",
+    "\n",
+    "from pandas import read_csv, read_json, DataFrame, Series, date_range, concat\n",
+    "\n",
+    "from gvar import gvar\n",
+    "from gvar import mean as gv_mean\n",
+    "from gvar import sdev as gv_sdev\n",
+    "from gvar.dataset import avg_data\n",
+    "\n",
+    "from lsqfit import nonlinear_fit\n",
+    "\n",
+    "from seaborn import FacetGrid, distplot, despine\n",
+    "from matplotlib.pylab import show as show_plot\n",
+    "from matplotlib.pylab import subplots\n",
+    "\n",
+    "from bayes_chime.normal.models import SEIRModel\n",
+    "from bayes_chime.normal import models as m\n",
+    "from bayes_chime.normal.utilities import one_minus_logistic_fcn\n",
+    "from bayes_chime.normal.fitting import fit_norm_to_prior_df, fit_norm_dist_to_ens\n",
+    "from bayes_chime.normal.plotting import (\n",
+    "    plot_prior_fit,\n",
+    "    plot_band,\n",
+    "    plot_gvar,\n",
+    "    plot_posterior_fit,\n",
+    "    plot_gv_dist,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Update the RUN directory to load in your data**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ROOT = path.dirname(getcwd())\n",
+    "RUN = \"2020_04_22_09_07_17\"\n",
+    "\n",
+    "OUTPUT = path.join(ROOT, \"output\", RUN)\n",
+    "DATA = path.join(OUTPUT, \"parameters\")\n",
+    "\n",
+    "if not path.exists(DATA):\n",
+    "    raise KeyError(\n",
+    "        \"You have to point to an existing run directory to run this notebook.\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read in data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hosp</th>\n",
+       "      <th>vent</th>\n",
+       "      <th>mort</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2020-03-06</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-07</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-08</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-09</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-10</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            hosp  vent  mort\n",
+       "date                        \n",
+       "2020-03-06     1     0     0\n",
+       "2020-03-07     1     0     0\n",
+       "2020-03-08     1     0     0\n",
+       "2020-03-09     2     0     0\n",
+       "2020-03-10     2     0     0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DATA_DF = (\n",
+    "    read_csv(path.join(DATA, \"census_ts.csv\"), parse_dates=[\"date\"])\n",
+    "    .dropna(how=\"all\", axis=1)\n",
+    "    .fillna(0)\n",
+    "    .set_index(\"date\")\n",
+    "    .astype(int)\n",
+    ")\n",
+    "DATA_DF.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit priors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, priors are loaded in from file. Because the prior distributions are known (but not normal), they are approximated by normal distributions for later use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>param</th>\n",
+       "      <th>base</th>\n",
+       "      <th>distribution</th>\n",
+       "      <th>p1</th>\n",
+       "      <th>p2</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>n_hosp</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>constant</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Number of hospitalized COVID-19 patients on day 1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>hosp_prop</td>\n",
+       "      <td>2.500000e-02</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>6.326833</td>\n",
+       "      <td>0.004169</td>\n",
+       "      <td>Prportion of infections requiring hospitalization</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ICU_prop</td>\n",
+       "      <td>4.500000e-01</td>\n",
+       "      <td>beta</td>\n",
+       "      <td>52.059311</td>\n",
+       "      <td>96.867420</td>\n",
+       "      <td>Proportion of hospitalizations admitted to ICU</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>vent_prop</td>\n",
+       "      <td>6.600000e-01</td>\n",
+       "      <td>beta</td>\n",
+       "      <td>5.224029</td>\n",
+       "      <td>3.078885</td>\n",
+       "      <td>Proportion of ICU patients requiring ventilation</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>hosp_LOS</td>\n",
+       "      <td>1.200000e+01</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>195.497640</td>\n",
+       "      <td>0.059681</td>\n",
+       "      <td>Hospital Length of Stay</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>ICU_LOS</td>\n",
+       "      <td>9.000000e+00</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>231.467053</td>\n",
+       "      <td>0.058324</td>\n",
+       "      <td>ICU Length of Stay</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>vent_LOS</td>\n",
+       "      <td>1.111111e+00</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>73.324820</td>\n",
+       "      <td>0.268274</td>\n",
+       "      <td>time on vent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>mkt_share</td>\n",
+       "      <td>2.600000e-01</td>\n",
+       "      <td>constant</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hospital Market Share (%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>region_pop</td>\n",
+       "      <td>1.200000e+06</td>\n",
+       "      <td>constant</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Regional Population</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>incubation_days</td>\n",
+       "      <td>5.000000e+00</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>9.514379</td>\n",
+       "      <td>0.513980</td>\n",
+       "      <td>Days from exposure to infectiousness</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>recovery_days</td>\n",
+       "      <td>1.400000e+01</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>9.833457</td>\n",
+       "      <td>1.642266</td>\n",
+       "      <td>Days from infection to recovery</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>logistic_k</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>4.018954</td>\n",
+       "      <td>0.227382</td>\n",
+       "      <td>logistic growth rate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>logistic_x0</td>\n",
+       "      <td>1.400000e+01</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>6.407435</td>\n",
+       "      <td>2.859728</td>\n",
+       "      <td>logistic days from beginning of time series to...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>logistic_L</td>\n",
+       "      <td>5.000000e-01</td>\n",
+       "      <td>beta</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>logistic depth of social distancing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>nu</td>\n",
+       "      <td>2.500000e+00</td>\n",
+       "      <td>gamma</td>\n",
+       "      <td>93.955217</td>\n",
+       "      <td>0.026343</td>\n",
+       "      <td>Networked contact structure power-law exponent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>beta</td>\n",
+       "      <td>2.500000e-01</td>\n",
+       "      <td>beta</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>SEIR beta parameter (force of infection)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>hosp_capacity</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>constant</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hospital Bed Capacity</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>vent_capacity</td>\n",
+       "      <td>3.830000e+02</td>\n",
+       "      <td>constant</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Ventilator Capacity</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              param          base distribution          p1         p2  \\\n",
+       "0            n_hosp  1.000000e+00     constant         NaN        NaN   \n",
+       "1         hosp_prop  2.500000e-02        gamma    6.326833   0.004169   \n",
+       "2          ICU_prop  4.500000e-01         beta   52.059311  96.867420   \n",
+       "3         vent_prop  6.600000e-01         beta    5.224029   3.078885   \n",
+       "4          hosp_LOS  1.200000e+01        gamma  195.497640   0.059681   \n",
+       "5           ICU_LOS  9.000000e+00        gamma  231.467053   0.058324   \n",
+       "6          vent_LOS  1.111111e+00        gamma   73.324820   0.268274   \n",
+       "7         mkt_share  2.600000e-01     constant         NaN        NaN   \n",
+       "8        region_pop  1.200000e+06     constant         NaN        NaN   \n",
+       "9   incubation_days  5.000000e+00        gamma    9.514379   0.513980   \n",
+       "10    recovery_days  1.400000e+01        gamma    9.833457   1.642266   \n",
+       "11       logistic_k  1.000000e+00        gamma    4.018954   0.227382   \n",
+       "12      logistic_x0  1.400000e+01        gamma    6.407435   2.859728   \n",
+       "13       logistic_L  5.000000e-01         beta    2.000000   3.000000   \n",
+       "14               nu  2.500000e+00        gamma   93.955217   0.026343   \n",
+       "15             beta  2.500000e-01         beta    5.000000  10.000000   \n",
+       "16    hosp_capacity           NaN     constant         NaN        NaN   \n",
+       "17    vent_capacity  3.830000e+02     constant         NaN        NaN   \n",
+       "\n",
+       "                                          description  \n",
+       "0   Number of hospitalized COVID-19 patients on day 1  \n",
+       "1   Prportion of infections requiring hospitalization  \n",
+       "2      Proportion of hospitalizations admitted to ICU  \n",
+       "3    Proportion of ICU patients requiring ventilation  \n",
+       "4                             Hospital Length of Stay  \n",
+       "5                                  ICU Length of Stay  \n",
+       "6                                        time on vent  \n",
+       "7                           Hospital Market Share (%)  \n",
+       "8                                 Regional Population  \n",
+       "9                Days from exposure to infectiousness  \n",
+       "10                    Days from infection to recovery  \n",
+       "11                               logistic growth rate  \n",
+       "12  logistic days from beginning of time series to...  \n",
+       "13                logistic depth of social distancing  \n",
+       "14     Networked contact structure power-law exponent  \n",
+       "15           SEIR beta parameter (force of infection)  \n",
+       "16                              Hospital Bed Capacity  \n",
+       "17                                Ventilator Capacity  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PRIOR_DF = read_csv(path.join(DATA, f\"params.csv\"))\n",
+    "PRIOR_DF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "PRIORS = fit_norm_to_prior_df(PRIOR_DF.query(\"distribution != 'constant'\"))\n",
+    "META_PARS = fit_norm_to_prior_df(PRIOR_DF.query(\"distribution == 'constant'\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PM2TMZB545l0em7+Nn7+8kMun3cEzzzzDlClTOH36tNOhthiiqv5vRKQvnjW/fw88AHwROA709BZ/Gg/8WlWvPd9xMjMzNS8vz+/xBhu3W3n3o0McKCr/3M/iYqP4+rgBtI6JdCAycwF+ezQoIvHAnDojMHYCU1T1iLfa+zJVTTnfMVrq+dQc3p81my9/6WaSR07gm7/4G63afvZJVk11FbOe+hWrFr7Piy++yLe+9S2HIg07Qf243c6pS1NbW8vtt9/OzJkzueeee7ju2w9RcOrTBMXH61bw7z89hLu2lh898W96xicRFxvFlzP60CXOnnw1QVCfT2DnVFM8+dzL/Ph73yJ1/JV87aeP0arNZ69PBZtyePWRe4mPH8DKlStp3769Q5GGpaA+p+x8ajxV5Ut3fIMP3nqdad/9GZO/ctfnRv5tW72Q137/IJdffjnz588nOtpGxzejBs+pQI3A+CvwEOD2vu4CnFZVl/f1QaBPQ28Uke+KSJ6I5B0/ftz/kQahbYfPNJi8qKooZ/n89/if3z7Opk2bHIjMBJEeqnrE+30h0KOhnex8unQ7d+7i9ttvp8+gIdz1qyc/l7wAiI6J5eYf/46MCZP53ve+R05OjgORGhNaHnjgAWbOnMljjz3GTT94+DPJC4Ahoy/nviffJjomluf+9zucPl5IaZWL9z46RFmV6xxHNablWLZqLQ/+8B4GDcvkm//vb59LXgAMShvPtx5+ku3bt/P1r3+dQDzINCZU/eoPf+aDt17n6q99nynT725w2mLqhGu58yd/YPHixTzwwAMORNny+D2BISK+ufrrm/J+VX1OVTNVNbNbt27NHF3wc9W6Wbv35Oe2b/pwIb/9+hW88cf/4e+/+ynp6enccsstlJWVORClCSbq6Y002CNp6efTpXK73dz29W8SERnFt37zd2Jbtz3nvpFR0Uz/yRN079mLu+++m4qKigBGakxomTFjBk8++ST3338/l335bj4+cqbB/br06sd//f55KsrO8MajP8HtdlNS6WLu5iO43XYjZlquisoqvnHXXbRp35G7Hn6KqJiYc+47YMR4vn7v/2P27Nm89tprAYzSmNCxZfsOHn3klwwdewXXfePe8+47bMo0bvr6f/H000+zbNmywATYggViBMZEYJqI7MNTtPNK4G9ARxHxFRHtCxwKQCwhZ8uhYkoqP/tkKS/7fV77/X107d2fHz7+Br94bTFf+vZ9vPvuu1x99dWWxGiZjnqnjuD97zGH4wlLjz7+NzblreHme/6Xjt16XnD/Vm3j+NK9j7Bz505+85vfBCBCY0LP0aNH+f73v8/o0aP5/kMPk7Pn80n7uvokDOFL3/8FezbnsvKDfwFw6HQFeZ9Y3WLTct33v7/hQP4Opt/7a+I6dL7g/sOvvYORo8dx//33U1hYGIAIjQkdqsrXvvktIqNjuOW+31xUwejxt/6A/vED+c53vkN5+edHzpvm4/cEhqr+XFX7qmo8cDuwRFW/BiwFpnt3+ybwgb9jCTWqyuaDxZ/ZdmjPDmb85ZckjhjD9x97jUHDM+ncsy+Tbvtv/vyPl1mzZg3333+/QxEbB83Ccx6BnU9+UVRUxO9/+xuSMyaSefXNF/2+hPQJTL3pNp544gn27t3rxwiNCU333nsvZWVl/POFl1iy68RFvWf0NV9m6NgrmPPi4xQd9Tz/WFNwktPltpiZaXl2F3zCy888Qfrk6xk2fupFvSciIoJpP3qE0tJSHn74YT9HaExoeeFfb7IlL4cbv/0/dOjS4Kzsz4lp1ZrpP36EPXv28NRTT/k5wpYtkKuQ1PdT4AERycdTE+NFB2MJSoeLKykq+7QzVuuq4fU/Pkjrdh2483//Qkyr1p/Zv+uIyTz00EM8//zzzJo1K9DhmgARkf8AOUCKiBwUkW8DfwSuFpHdwFXe16YZ/fzh31JeeoZp332o0Us3TrztB0RERvLLX/7ST9EZE5pycnKYMWMGP/vZzzga0YWyqtqLep+I8JUfeW665r38FwBq3cryXVbbx7Q8D/z8V7jdbr7wrQcb9b4OPQfwhVvv5MUXX2T37t1+is6Y0FJTU8OvH/4VPfonMPb66Rd+Qx19U8cw8cpr+OMf/8ipUzYq0F8CmsBQ1WWqeqP3+wJVHaOqiap6i6raeqD1bKk3+mL13Lc4+kk+t/74EeI6fn544ImSKr5z389ITU3lwQcfpLrankSFI1W9Q1V7qWq0d3TTi6p6UlWnqmqSql6lqkVOxxlODh48yMvP/YNRU6fRe9DgRr+/Y9ceXHfbt3jjjTes4K4xXqrKgw8+SM+ePbn1rv/m4yMljXp/p+69mPzlu9iwZDYHdm0FoOB4WYNFr40JVx9t/Zh5M99g/Bduo0uvfo1+f+bN36FVq1aWYDfG64m/v8Dhfflcf9f9REZGXfgN9Uz+6o8oLi7mT3/6kx+iM+DsCAxzHq5aN3uOl559XVlWyqLXnyYpfRxDx11xzvdtPVLGY489Rn5+Pv/85z8DEaoxYe/Xf/gTtS4X1975oyYfY8xNdxMX144//tEGxxgDMGfOHHJycvj1r3/DusNNSzpMve27xHXofHYUBkBOwUlbWcG0GL985A9EREVx1R33NOn9bTp04aavfZsZM2bYKAzT4rndbv76+GP0TUpl+MSrmnSMrv2TufKGm3jmmWcoLi6+8BtMo1kCI0gdOFVBtct99nXOvLcoKz7Fjd/5n/MOX993sowJU6YyZcoUHn30URuFYcwlOnXqFP9+9WXSJl/fpKdbPq3j2nPFzV/l7bfftloYpsVTVf7v//6P+Ph4Mq/5MidLm3atatU2jsnT72bn+pUc3L0NgEOnKjh4ylb9MeHvk4NHWPj+DDKvupn2nZu+stiQq24jOjqav/71r80YnTGh59U3Z1J4YC9X3PKdRk8Xrmv0tLsoKSnhhRdeaMbojI8lMIJU/rFPR1/Uumr48L3XSEofR7/k4ed9nypsP1zCQw89xKFDh5gxY4a/QzUmrP3pr09TUV7KFbd8+5KPNfL6ryIi/OUvf7nwzsaEsQ8//JCcnBzuu/9B8vZf2hOqCTfeQas2cSx+67mz29bbiiSmBfjdY3/FVVPNlK/cdUnHaduxK1dPm87LL7/MyZPnXwXImHD2+ONP0KlHH0ZMuuaSjtN94BAyx1/G3/72N2pqapopOuNjCYwg5HYrBXWmj2xcsYDTJwqZPP1bF/X+bYeLueaaaxk6dCh//vOfbSitMU3kcrl44Z//ICl9HH0Th17y8Tp268mk627mpZde4syZM80QoTGh6dFHH6Vbt25kXPMlyqsvrnDnubRu246J077G5g8XcvzQPgD2nijjZKmV1jLhq7Kyirdee5HUcVfQvd+gSz5exg1fp6Kigueff74ZojMm9Kxck8u2DWu5/EvfaFLti/om3PxNDhw4wHvvvdcM0Zm6LIERhI6WVH6mQ7dm3gy69h7A4MxJF/X+kkoXh4sr+dGPfsSmTZvIy8vzV6jGhLV3PpjNiaNHmDjta812zLRrb6OsrIw33nij2Y5pTCjJz89n3rx5fO+//5uthZXNcsxJN9+JRESyevZ/zm7beOB0sxzbmGD04hszKDl9kglf/GqzHK9T3wRGj7+MF154AbfbfeE3GBNmnnjqH0TFxDLmmi83y/H6j5hIn379LSnoB5bACEL7T35azOz4oU/YszmXMdd+hYiIi//ftaOwhDvuuINWrVrx0ksv+SNMY8Lek0//nfadu5F6nsK5jdUveThJQ4bxz3/+00ZHmRbp2WefJSoqiolfuJXKmksbfeHTvnM3Rlx2DbmL3qW60lP/YkdhyWdqSRkTTp5//nk69ehDSsbEZjvmuOtvZc+ePSxdurTZjmmaRkSuE5GdIpIvIj87z35fEREVkcxAxhduSkpKmP/+TEZOvoHWce2b5ZgRkZFMmXYb2dnZ7Nmzp1mOaTwsgRGE9tdZAm7doneRiAgyr765UcfYfayEtnHtmD59Ov/+978pL7dl5YxpjH379pGzfDFjr5tOZFR0sx1XRBhzw21s2rSJtWvXNttxjQkFFRUVvPTSS9x8880cqGzdrMee+MWvUlF6ho+WzQWg2uVmZ2HjlmY1JhRs2b6TTWs/ZOy1XyEiMrLZjts3YwqdOnXmueeeu/DOxm9EJBJ4BrgeGArcISKfm8cqIu2AHwPWmbhEz778OpXlpYy74ZZmPW7SZV8kIiKCF198sVmP29JZAiPI1NS6OVLsGVKrqqxfMpuUUZfRsWuPRh2nqsbNvpPl3H333Zw5c4a5c+f6I1xjwtZT/3wRVWXs9c17MQNInXQDbdq05eWXX272YxsTzGbMmMGpU6e44dZvUlrlatZjDxqeSc8BSaye8+k0kq2HbQk7E36e+ucLSEQEY679SrMeNzomlqlfnM57771HUVFRsx7bNMoYIF9VC1S1GngTuKmB/X4LPAo0z1y8FuzVV1+hR/8E4odmNOtx23fpwcQrruGVV16htrZ5RhwaS2AEncOnK6h1e4aV79+xmVNHDzFyyheadKz8YyVMnjyZnj172mokxjSCqvLmv98gIW0MnXv0afbjt2oTx5grrmXGjBlUVlq/w7Qcr7zyComJiUT0SW32Y4sI4264hQO7tlK4bzcAhcWVFJXZcuImfKgqH7wzg6T08XTs1rPZj5902Q3U1NQwc+bMZj+2uWh9gAN1Xh/0bjtLRDKAfqp63ieUIvJdEckTkbzjx483f6RhYFf+HrZtyGXU1JsuaenUcxk+5UaOHDnCsmXLmv3YLZUlMILMgaJP167fuHwekdHRDJswtUnH2nO8DEWYPn06c+fOpbS09MJvMsawclUOh/fvJXNqQw88msfgy27k9OnTNjrKtBh79+5l2bJlTJt+B0Vl/llWLuOKLxIRGcW6rE+rvu84Yiv+mPCxaOmHHDu0n1FXftEvx+86YDAJSclWaDqIiUgE8ATw4IX2VdXnVDVTVTO7devm/+BC0D9eeBWAjCtv9Mvx+6dPol379rz++ut+OX5LZAmMIHO42JPAcLvdbFqxgMGZk2jdtl2TjlXtcnPgVAW33norFRUVzJ49uzlDNSZs/ePFl4mKiSVt0nV+ayMxfRxdu/fkX//6l9/aMCaY/Otf/0JEGDzJP51EgLiOnRk6ZjJ5i2dRW+uZovJxYYkVzDVh47mXXiU6thXDJ17tl+OLCBOu/RIrVqzgk08+8Usb5oIOAf3qvO7r3ebTDhgGLBORfcA4YJYV8mw8VWXmjP8waFimX0bcAsTEtmLiVTfwzjvvWE3CZmIJjCBS61aOeutfHMrfxukThYyYeM0lHXP30RImTpxIz549+eCDD5ojTGPCmsvlYs777zBs/FRatY3zWzsRkZGMueqLzJs3j1OnTvmtHfN5ItJPRJaKyHYR2SYiP25gHxGRJ70V4Dd7h+uaJlJVXn31VSZdPpmymI5+bSvz6i9RUnScXetXAXCmoobCMzZVy4Q+l8tF1tz3SR13pV+vT4PGefqeb775pt/aMOe1DkgSkYEiEgPcDszy/VBVi1W1q6rGq2o8sAaYpqp5zoQbutauW8/Bvflk+GlEk0/KZV+gpKSEOXPm+LWdlsISGEHkWEklLm/9i605S5CICIaMnXxJx9x7ogwQbrzxRubPn091tc0FNuZ8FmZlU3L6VJNrzzRG8vhrqKmpseRi4LmAB1V1KJ4nVz9ooML79UCS9+u7wD8CG2J4yc3NpaCggAnXfRl/D4YYOnYyrdt1OLsaCcCuozaF0oS+OQuyKTldRPrkG/zaTofufRk+cpTVwXCIqrqAHwILgY+BGaq6TUQeEZFpzkYXXl5+/U0iIiIZMenSHhhfSL8ho+jarZudU83EEhhB5PDpT58QbctZwsChGcR16HxJxyyvrqXwTCXTpk3jzJkzrFix4lLDNCasvfz6f4ht3YbBoyf5va1+ycPp1be/FdkNMFU9oqobvN+X4Okg1h87ehPwmnqsATqKSK8Ahxo23nzzTWJiYug+3P/nVVR0DCMuu4Ytq7OprvJcV3cftWkkJvT96z9vEdMqMNenjMnXkZeXx759+/zelvk8VZ2nqsmqmqCqv/du+5Wqzmpg3yk2+qLxVJU5H7xLYpzecQsAACAASURBVNrYS77fupCIyEjGXnE9c+fOtWkkzcASGEHkiLf+RdHRQxwu2EHq+Cub5bgFx8uYOnUqrVu3tie9xpxHTU0Ni+bNJnX8VKJjYv3enoiQMfl6srKybBqJQ0QkHhgJrK33owtWgTcXp7a2lrfeeotJV1wNMW0C0ubIyTdQVV7GjnWepH1JpYujZ6oC0nZLJCLXichO75Srn51nv6+IiNpc/carra0le/4cho6dTExsK7+31z/jCgDeeecdv7dljBM2bNzE4f17GTHp2oC0lzDmKsrLy1mwYEFA2gtnlsAIIoXe+he+DtfQsVOa5bgFJ0pp06YNU6dOZf78+c1yTGPC0cJsz/SR9Mv9V7yzvuTxV+NyuXj//fcD1qbxEJE44B3gPlVt0lIVtkTdha1cuZIjR44w7PLrA9ZmQtoY4jp2+cw0kj3HbRqJP4hIJPAMnmlXQ4E7GpiShYi0A37M55OF5iIsXLKMM6dOBOxmq1PPfgwdnmZD3k3Yevn1/yAREX4riFtfv9RRdO7Shbfffjsg7YUzS2AEibIqFyWVnorpO/JW0qlHH7r3G9Qsxz5ZWk1xeQ3XXXcde/bsIT8/v1mOa0y4eePNmcS2bkNKpv+H5/r0TRpGr779effddwPWpgERicaTvHhDVRv68C9UBR6wJeouxowZM2jdujU9h00IWJuRkVGkTbqW7WuXUVXhGa5bYAkMfxkD5KtqgapWA2/imYJV32+BRwGrqNoEb7w5k6iYWIaMvjxgbWZMvo41a9Zw+PDhgLVpTKDMmfUBg4Zl0q5Tl4C0FxkZxZgp1zJ37lyqqmxE4KWwBEaQOOqtkF7rqmH3xhwGZ16GiDTb8feeLOPaaz1Z+4ULFzbbcY0JF263m4Xz5jB49OUBmT7iIyKMmHgVWVlZlJbaDVYgiOcf1xeBj1X1iXPsNgv4hnc1knFAsaoeCViQYcLtdvPee+8xZtKVxLZuG9C2R0y6lpqqSnauXwnAidJqTpdbIWs/uOB0K+8qPv1UdS4XYKOaPk9VyV4wl+SREwJ6HvVL9yRLZs+eHbA2jQmE3bvz+SR/J8MmTA1ou4NGTaGkpITly5cHtN1wYwmMIOGbm7tv+0aqyssYnNm8GfZ9J8pITEwkISHB5l4Z04CcNWs5deIYwyZcFfC2E0ZfQVVVlSUXA2cicCdwpYhs9H7dICL3iMg93n3mAQVAPvA88H2HYg1pa9eu5ciRIySOCWwnEWDQ8Ezatu/I5pWLzm4rOFEW8DhaOhGJAJ4AHryY/W1U0+dt2LiJY4cPNFtttIvVtV8C/QbEM2vW5+pGGhPS3njbM/By2PjAXpv6Dx9L6zZtrCbhJbIERpA4VuIZgbFz/UoiIqNISh/XrMc/UFROTa2ba6+9liVLlthyqsbU8+8Z7xARGcXQMZe2dHFTDEzNoH3HTnZBCxBVXamqoqojVDXd+zVPVZ9V1We9+6iq/sBbAX64VXhvmnfffZfo6GgGZQRuWpZPZGQUqeOnsn3NUlw1nmvePktg+MOFplu1A4YBy0RkH56li2dZIc+L9/pbnkKaqeOuCGi7IkLm5dewePFiGyFowsoHH3xAz/hkuvTqd+Gdm1FMbCtGTZjMrFmzbGWsS2AJjCBxzDsCY9eG1QwYPIJWbeOa9fgut3LwVAVXX3015eXlrF1rNbSMqWvO7FkkjhhD67j2AW87MjKK9IlTmTNnDjU1NQFv3xh/UFXee+89Roy5zJHzCmDEZddQWV7K7o1rADh4qoJql9uRWMLYOiBJRAaKSAxwO54pWACoarGqdlXVeFWNB9YA0ywpePHmzZ1D/8FptO8c+BEp/TMup6qqikWLFl14Z2NCQFFREZvXr2VYgEc0+SSOnsLBgwfZsGGDI+2HA0tgBIHSKhelVS4qSs9wYPdWEpt59IXPvpNlTJ48mYiICBYvXuyXNoyzROR+EdkmIltF5D8i4v+11sLArl272F+wO+DDc+saNGoKp06dYtWqVY7FYExz2rp1K3v27CFxdGCfGteVNHI8sa3bsGVlFgC1buXAqXLH4glHquoCfggsBD4GZqjqNhF5RESmORtd6Dt8+DC7tm507Gar7+CRdOjYyepgmLDxzgezcdfWkjrOmXNq0MjLERHmzJnjSPvhwBIYQeB4iWf0xZ7NuajbTdLI8X5p55MTZXTq1ImMjAxLYIQhEekD3AtkquowIBLPkzBzATPf9zwsbK6li5siOWM80TExdkEzYcM3b37IWOcSGNExsQzOnMT2tUtxuz0jLz45adNImpt3Clayd8rV773bfqWqnyueoKpTbPTFxZv5geeaMGTMFEfaj4yMImPCZObPn3/2HDImlL0/aw5xHTrTL2W4I+3HdexMavoo5s2b50j74cASGEHgRKl3+shHa4iObUX84HS/tHOqvIbi8hqmTp3KmjVrbD5jeIoCWotIFNAGsLXPLsIHs+bQc0BSwOdC1tWqTRypo8Yzd+4Fi/QbExJmzZpFYmo67bt0dzSO1PFXcqboOAd3bwXgk5M2AsOEjg9mzaFD1x70HpTiWAyDRk7i6NGjNuTdhLza2lo+XJrN4NGXExHh3G3w4NGXs27dOo4dO+ZYDKHMEhhBwDcCI3/TWgamjiIqJsZvbX1SVMbUqVNxuVysXLnSb+2YwFPVQ8Cfgf3AETzLPtqk1Qs4c+YM69esYsjYwBfvrC8hYxI7duwgPz/f6VCMuSSFhYXk5uaSNNr582rImMlIRATb1iwF4LQ3mW9MsKupqSHnw6UMGT0Zz+rPzhiQNgERsSfGJuStzllDSfFphjrc5+ufdhmqaitDNpElMILAidIqSouLKNy3i8S0sX5ta9/JciZMmEBUVJStQRxmRKQTcBMwEOgNtBWRr9fb57sikiciecePH3cizKAzf8FCamtdpDo4zN0nxXuzZ6MwTKjzTYVyao5xXW3bd2Lg0Ay25Sw5u21/kY3CMMFv6YoVVJSVMsSB1bHqiuvYmdS0DEtgmJA3470PiIiIJGXUZY7G0TthCF2797D+XhNZAsNhNbVuisqqKdjimQ6aMGK0X9s7UFRO69ZtGD16tCUwws9VwF5VPa6qNcC7wIS6O6jqc6qaqaqZ3boFvpp5MJr5/ixax7VnwFD/TN1qjK69+9NvUJJd0EzImz17Nl169qHXQOeGvdc1dNwVHC7Ywaljnll1lsAwoWDme7OJjIomOcM/tdEaY8iYyeTm5mIPP0woW7RgAfGpGY6tjOUTERFB+oQrWLhwIS6Xy9FYQpElMBxWVFaNKuRvyiU6thX9kof5tb1ql5vCM5VMnjyZdevWUVZmxczCyH5gnIi0Ec9Y06l4KsKbc1BVlmZnkTJqIpGRUU6HA0DyqMtYvny5nZsmZFVWVpKdnc1gh4e915U6zjPC6uNcT+J+f1E5qupkSMZc0OLsLAamZhDbuq3TodBn+HhUlezsbKdDMaZJjh49yq7tWxicOcnpUADoP2ICxcXF5ObmOh1KyLEEhsPOrkCyJZeBQzOIivZf/QufT06WM2XKFFwuF6tXr/Z7eyYwVHUtMBPYAGzBc34/52hQQW7z5s2cPH6UwaMvdzqUsxIzJlFdXW0jpEzIWr58OeXl5QxxcFWf+rr3G0Tnnn3PJjAqa2o57i2gbUwwOnz4MAU7tzN4dHDcbPVNTKVDp042Z9+ErDnzPH+7wXJODUobR0REBAsXLnQ6lJDj9wSGiLQSkVwR2SQi20TkN97tA0VkrYjki8hbIuL/O/cgdKK0irIzpyncu8vv00d89heVMXHiRCIjI+0mKcyo6sOqOlhVh6nqnapqPfTzeNe7PN1gh+dC1jVoeCatWrdh/vz5TodiTJPMnTuX6JhYEkeMcTqUs0SEIWMms/ujNdRUe/5ZPHiqwuGojDm3WXM914CUUcFxsxURGUna2MtZtGiRjV4yIen9OXOJ69iF3oMGOx0KAG3adWBIWoYlBZsgECMwqoArVTUNSAeuE5FxwKPAX1Q1ETgFfDsAsQSdk6XV7Nu2AVVl4PDMgLR5pLiSqNjWjBw5kg8//DAgbRoTjObOn0+fhCGOL/NYV3RMLEMyxtkFzYSsOXPnkZg+jphWrZ0O5TOGjplMdVUFezZ7husesDoYJojNmjufdp27Obp8an3xaRMoLCxk8+bNTodiTKO43W4+XLqYwZmXObp8an3Joyaxbt06Tpw44XQoIcXv/wfVo9T7Mtr7pcCVeIa7A7wK3OzvWIJRUVk1BdvWExkVTf+UEQFpU9Xz5GnSpEnk5uZSVWUP6U3Lc+bMGTbmrSUlSOZC1jUwfSL5+fm2nKoJObt27WJvwR7HV01oSELaWKJiYs9OIzl0ugK3254km+BTW1vLquVLSMmYGDR1ZAAGpnuKidqQdxNq1q9fT/GpIsdXH6mvv9WWaZKApKBEJFJENgLHgCxgD3BaVX1lVw8Cfc7x3rBd9rGyppbSKhd7t66nX/IwYmJbBaztA0XlTJo0icrKStavXx+wdo0JFosXL6HW5WJwZnBdzABSvDEtWrTI4UiMaRzfMotDgqiujE9MbCuS0sfxce4KAKpq3FYHwwSlDRs2cOb0KVJGTXQ6lM/o0KUHCSlD7dpkQs4sb/2L5IzgOqf6JQ+jfYeOdk41UkASGKpaq6rpQF9gDHDRk4/CednHE6VVVFdVcmDXVgYOGxXQtvcXlTNxoucktmkkpiV6f848Ylq1IX7oSKdD+ZyuvQfQs09/e8plQs7sufPp1jeeLr36OR1KgwZnTuLE4U84cXg/4BmFYUywmTU3OG+2AAaPmsjKlSupqLBzx4SOBQsX0XvQYNp16uJ0KJ8RERnJsNETycrKstoyjRDQSUCqehpYCowHOoqIb93CvsChQMYSDIrKqtm/czO1rhoGDQtM/Yu6bbdq34mUlBRLYJgWaXF2NolpYwOy8k9jiQhJGRNZsmQJNTU1TodjzEWpqKhg5YcrgmaJuob4qs/vzPNc9w5ZIU8ThBZmZQXlzRZA32Fjqaqqsr6jCRllZWVsXLc26EY0+fQfMY6DBw+yc+dOp0MJGYFYhaSbiHT0ft8auBr4GE8iY7p3t28CH/g7lmBzsrSavVs90zeceAq8/2Q5l112GatXr8btdge8fWOcsmfPHg7t3xu0FzOAhJETKC0tZc2aNU6HYsxFWb58OdVVlUGdwOjaewBdevVjhy+BcbrCnnqZoFJeXs5H69aSnDHB6VAaNHBYJtExMWRlZTkdijEXZfny5bhcNUE5ogkgIc1TW8amkVy8QIzA6AUsFZHNwDogS1XnAD8FHhCRfKAL8GIAYgkqJ8uq2bttAz0GJNK2fceAt3/AO43k1KlT7NixI+DtG+OU+Qs8UzNSgrD+hU9S+jgiIyPtgmZCxvuz5xEVHUNCEC2fWp+IMDhzErs3rsVVXU1FdS1FZdVOh2XMWcuWLcdVU01KkN5sxbRqTWr6aEtgmJAxa95CoqJjAj5d/2J16dWPvgMG2jnVCIFYhWSzqo5U1RGqOkxVH/FuL1DVMaqaqKq3qGqLq6R1oqSCTz7e6Ngc/P1F5UyY4Mnwr1q1ypEYjHHCrLnz6dS9N936xDsdyjm1jmtPQmq61cEwIWPhwoUkjBgTdMun1jc4cxLVleUUbPOMgLQ6GCaYzJq/wHOzNTywU4sbY2D6eDZt2sTRo0edDsWYC8rOzmLQsMyALpbQWCmjJrJ06VKqqy2hfjGCZyHcFqayppa9u3dRUXqGgakZjsRQXl1Lp14D6NatG6tXr3YkBmMCzeVysXrlCpJHBdfydA0ZmDaevLw8ioqKnA7FmPPav38/+/bsCrol6hqSmD6WyKhodq5fCcBhS2CYILIkezEDUzOC+mZrwPCxACxZssThSIw5v8LCQvbs/JikjPFOh3Je/YaNpaysjNzcXKdDCQmWwHBIkXf6COBYAgPgwKkKJkyYYCMwTIuRl5dHWckZUoJ0fnFdyRkTUFXrJJqg9+6suQBBuSxxfbGt2xKfOpKdeb4ERqXDERnjcezYMXbv2EbSyOC+PvVNTKVd+w4sXrzY6VCMOa+srGwAkoP8nEpMG4uI2Dl1kSyB4ZCismr2bf+IuA6d6dp7gGNx+Opg7N69m2PHjjkWhzGBMm/BQkSExPRxTodyQf1TRtCmbRzZ2dlOh2LMec2at4AOXXvQY0Ci06FclMGjJnG4YAdnio5TXFFDaZXL6ZCMITvbc/OSHORPiyMiIxk6arwt/ehHInKdiOwUkXwR+VkDP79HRLaIyEYRWSkiQ52IM9jNXbCINu060idhiNOhnFebdh1ISk2z/t5FsgSGQ3wJjAFD0x0dxn7odAVjxnpu5Gy1A9MSzF+YRZ+EocR16Ox0KBcUGRVNUvo4K+xkglptbS1rVy4jJQSmZfn4ViDatcEz+tCmkZhgMHfBIlrHtadvYqrToVxQ/+Fj2b9/P3v27HE6lLAjIpHAM8D1wFDgjgYSFP9W1eGqmg78CXgiwGEGPVVl2bIlJKWPJSIy0ulwLighbTxr1qyhpKTE6VCCniUwHLLv4BGOH9rnWAFPn2qXm96JQ4mOjrY6GCbslZaWsjEvl+RRwT2UsK5B6eMpKCigoKDA6VCMadCSD1dTXnKGlFHBu3xqfb0ThhDXoTM78qwOhgkOqsrSJYtJTAuVmy3Pwy8b8u4XY4B874IH1cCbwE11d1DVM3VetgVsKEw9u3fv5ujhQySNDO4RTT4DRozF5XKxYsUKp0MJepbAcMj6dZ4iLfFDnE1gAByvUEaOHElOTo7ToRjjVytWrPCsBR7kcyHr8tXqsFEYJljNnDUPEQn6Ye91RUREkDxqIrvWr8LtdnOk2OpgGGcVFBRw5NCBkLnZ6tZ3IN179rYh7/7RBzhQ5/VB77bPEJEfiMgePCMw7m3oQCLyXRHJE5G848eP+yXYYLXI228KlXNqYGoGsbGtLCl4ESyB4QBXrZvtG/OIiIyiX/Iwp8PhgHc51dzcXGpqapwOxxi/mbdgEVExsUG7FnhDuvUdSJceveyC1oxE5CUROSYiW8/x8ykiUuydW7xRRH4V6BhDyfLF2fRNHkbb9p2cDqVRUkZdRmlxEUf27uR4SRWuWrfTIZkWzFdsMCkE6jMB3qTlOJYuXYrbbeeOE1T1GVVNAH4K/OIc+zynqpmqmtmtW7fABuiw+Quz6NS9t6O1BhsjOiaWlLRM6+9dBEtgOOBUeQ17t39En4TBxLRq7XQ4FBZXkTlmLJWVlWzcuNHpcIzxm0XZWQxKHUV0TKzToVw0T8HR8SxevNg6ic3nFeC6C+zzoaqme78eCUBMIenQsZPs3voRKRkTnQ6l0XwjRnauX0WtWzlaUuVwRKYlW5CVTfsu3eneb5DToVy0fqljOXnyJJs3b3Y6lHBzCOhX53Vf77ZzeRO42a8RhZja2lpWrlhO0shxIVObCaD/sDFs3rzZFla4AEtgOODEmXIO7NzCgCCYPgLgVmWAtxaHTSMx4aqwsJDdH28P+rXAG5KYPp6ioiJLMDYTVV0BFDkdRziYMXshbnctyaNCL4HRoUsPesYns2u9p5BnYbHVwWgqWzHh0rjdblYsX0ZSemjdbPmG5tsT42a3DkgSkYEiEgPcDsyqu4OIJNV5+QVgdwDjC3obN27kTPFpktJDq8+X4I136dKlDkcS3CyB4YDc9R9RXVXBQIcLeNZV06oTffr0sQSGCVu+DlYo1b/w8Q0ptrnGATVeRDaJyHwRCf4lARyycOFCYlq1IX5IutOhNElKxgQKtuZRXVXJ4dNWB6MpbMWES7d161ZOnTwRMnP1fTp27UHfgQmWwGhmquoCfggsBD4GZqjqNhF5RESmeXf7oYhsE5GNwAPANx0KNyhlZYfWlCyfvklDaRvXzs6pC7AEhgNy164FYMDQ4OnwHSgqZ/z48ZbAMGFr3sKskFgLvCHtu3Sn76BkS2AEzgZggKqmAU8B759rx5ZcIK2iupYNq1eQmDaGqOgYp8NpkuRRE3HVVLN3Sx6FVsizqWzFhEvku1kJtZstgMS0caxYscJqqDUzVZ2nqsmqmqCqv/du+5WqzvJ+/2NVTfVOc7xCVbc5G3FwWbAomx4DEmnfpbvToTRKZGQUKSPHWgLjAiyB4YBNG3Jp17kbnbr3djqUs06WVpMxegyffPIJR44ccTocY5qVqrJkcXbILE/XkIFp41i5ciWVlXaT5W+qekZVS73fzwOiRaTrOfZtsQXSVmzYxvFD+0jOCL1RTT4Jw0cTGR3Nzg2rKK1yUVJpN2FN0GwrJnj3a3FJwfmLsunae0BQ9Qsv1oDhYykrKyM3N9fpUIwBoLq6mjWrV4Xc9BGf/sPGUlBQwL59+5wOJWhZAiPAVJWdm9cTPyQ96OY5DhjsmdKyZs0ahyMxpnnt3r2bwsOHQvpGK3nkBCoqKmyUVACISE/x/gMtImPwXCtPOhtV8Jk1bwHgWc0jVMW0as2g1FHs3OCrg2EJQn+5mBUTvPu1qKSgy+Vi9coVJI0MvdEXAIlpYxARlixZ4nQoxgCe+5iqygqSQ/ScSvZOJbNz6twsgRFgnxw6wonDBxgQhPOF4/omERMTYzdIJuxkhdha4A1JGD6aiMhIm0bSDETkP0AOkCIiB0Xk294ig/d4d5kObBWRTcCTwO2qakPe66h2uVm9fCkduvYIqVUTGpKcMZEjBTs5U3ScwjOWwGgCWzHhEuTl5VFWWhqS00cA2rbvxMCUVBvyboJGdnY2EhFBwogxTofSJD0GJNKpSzc7p87DEhgBtmSF5ylPfBDVv/A5WlrLyJEjbQRGCBORjiIyU0R2iMjHIhK6d+zNaP6ibDr16EPX3v2dDqXJWrWNY+CQNLugNQNVvUNVe6lqtKr2VdUXVfVZVX3W+/OnvXOL01R1nKqudjrmYLPn2Bl2fpRDyqiJQTeasLFSvCuo7NqwmiM2AqMpbMWES+D7Nz0xLTQTGAADR4wlJyeH8vJyp0MxhoXZi+mblErruPZOh9IkIkLSyHEsWbIEe3bSMEtgBNjq1TlEREbRN2mY06F8TmmVi/TMMeTl5VkxptD1N2CBqg4G0vBUr27RamtrWbFsKckjx4f8jVZC+njWrVvH6dOnnQ7FtHALl+dQUVJMckboLZ9aX++EIbTt0IldG1Zz7Ewlbrd1GBvDVky4NAsWZdNrUApxHTs7HUqTDRoxlurqalatWuV0KKaFKy0tZf263JCtf+ETP3wshYWFbN++3elQgpIlMAJsQ14ufRKHEBPbyulQGhQ/JJ2Kigo2bdrkdCimkUSkA3A58CKAqlaraou/0/3oo48oOVMc0tNHfJLSx+N2u1m2bJnToZgWzFXrZnG2d1pWiHcSASIiIkhKH8+uDauodrk5UVbldEghx1ZMaJrKykpy1+SQHOLn0aDhmURGRdkIQeO4lStXUutyna0jEap88ds51TBLYASQy+Vi+6YNDBgcfNNHfDoPTAWwOhihaSBwHHhZRD4SkRdEpG3dHVpidfez9S9CdH5xXQOGpBHbqrVd0IyjDpyq4OO8VfRJGEK7Tl2cDqdZpIya6KmBsW83x85YAsMExurVq6muriIxxK9Psa3bkjA03YoOGsdlZ2cTFR1N/NCRTodySTr37EuPPv3tnDoHS2AE0NatW6mqrAjK+hc+VbGd6N27t9XBCE1RQAbwD1UdCZQBP6u7Q0ur7g4wf1EWvQal0K5Tg6tghpSo6BgSRowmywp5Ggdt2XuUfds/InlU6E8f8fFNhdm1YbWtRGICJjs7m4iISBKGj3Y6lEsWP2Is69evtymOxlELsxYzYOhIYlq1djqUSzYobSzLli3D5XI5HUrQsQRGAK1a5akDFx+EK5D4uNyQNmqMjcAITQeBg6q61vt6Jp6ERotVUVHB2tWrSR4Zusun1peYPoGdO3Zw8OBBp0MxLZDbrSxavIRaV01IL59aX6fuvejebxA716+0lUhMwCzMyqb/4BG0ahvndCiXzKY4GqedPHmSbVs2hfyULJ/EtPEUFxezYcMGp0MJOpbACKAPV62mXedudOrRx+lQzmvQ0JHs3buXo0ePOh2KaQRVLQQOiEiKd9NUoEVX/1m1ahXV1VVhUf/CJ8nmRRoHHS6uYPPaFUTFxDIwNbzyo8kZE9mzZR2FRSXU1LqdDseEueLiYjZuWE9i2linQ2kWAwanEduqlQ15N45ZunQpqho2fb7EdM+/Ddbf+zxLYATQ2jVrGDA4LehXQuiS4FkhxaaRhKQfAW+IyGYgHfiDw/E4Kisri4jIKBKGZzodSrPpNTCZdh27kG3TSIwD8o+VsmvDahJGjCY6JtbpcJpVyqiJ1FRVUrB1PcdKrA6G8a8VK1bgdrvD5mYrKiaGhOGj7WbLOCY7O5vY1m3plxx8Kz02RbtOXembkGLnVAMsgREgx48fZ9/ePUFd/8Knbe8koqOjbRpJCFLVjd4aFyNU9WZVPeV0TE5asCib+CHpxLZue+GdQ0RERASJ6ePIys629cFNQKkq67bu5uj+PaSEwfKp9SWMGE1EZBQ71q/kqE0jMX6WnZ1NdExsyBcbrGtQ2li2b9/OkSNHnA7FtEBZ2YtJGDGayKhop0NpNgNHjGXVqlVUVto1qS5LYASIbzRD/JDgv1BFx8QyeNgIS2CYkHby5Em2bPqIpIzweLpVV9LI8RwtLGTbNluJ0ATO8ZIq1q9eDkByRvjUlfFp1SaO+KEj2bVhNccsgWH8LCt7MfGpGWE1ksm3rLJNIzGBtn//fgr25IfF0t51JY+cQGVlJatXr3Y6lKBiCYwAycnJITIqir5JqU6HclEGpY5k3bp11NTUOB2KMU2yZMkSVDUsnxSneFd/sGkkJpDyj5Wyc/0q2nfuRq+BKRd+QwhKGTWRQ/nb2bXPiuQa/yksLOTj7dvCLhHYJ2EIce072JB3E3C+v7lwe2jlGRkYaf29eiyBESA5OTn0TRgcMsv6dB00Pi79RQAAIABJREFUnIqKCjZv3ux0KMY0yaJFi2jVNo5+KcOdDqXZderem+79BrJoUZbToZgWZHdhMbs2rCZl1GVBX8upqQZnelZWyV25nMqaWoejMeHKN0IhOUzqX/hEREaSmDaObJviaAIsO3sx7Tp1oVd8stOhNKtWbeIYOCTNkoL1WAIjAFwuF7m5ufQPgekjPr2T0wBsGokJWYuyskkcMZbIyCinQ/GLpPTxLF+xnOrqaqdDMS1AUVk1Gzd+RHnJaZJHhd+oJp8+CUNp274jO9ev4rgV8jR+kp2dTZt2HeiTMNTpUJpdQvo4Dhw4QH5+vtOhmBZCVclevJjE9PFhmVxPSB9PXl4ep0+fdjqUoGEJjADYsmUL5eXlDBgS/AU8fTp170W3nr1szpUJSXv27GH/J/vC+kYrOWMi5WVllmQ0AbHnuGf6CBCW07J8IiIjSc6YyM4NqygsrnA6HBOGVJWs7GwS0sYSERnpdDjNzreqig15N4Gyfft2jh0tDLsRTT7JIyfgdrtZtmyZ06EEDUtgBIAvCRBqlaYHDR1pN0cmJGVleaZWhOvFDDzrg0dERp79XY3xp/xjpezMW0nfxFTiOnZ2Ohy/Shk1kZKi46xdv9HpUEwYys/P5+CBAySnj3M6FL/o1ieezj16WwLDBIzvby1cliSur//gEcS2bmP9vTr8nsAQkX4islREtovINhH5sXd7ZxHJEpHd3v928ncsTsnJyaFr95506t7b6VAapWdyGvv27bPlsEzIWbhoEZ2696Zb34FOh+I3rdu2o3/KCBYuWuR0KCbMnamsYd/hE+z7eGNYj2rySR7lqYOxYondgJnm57vZCtdzSURITB/HkiVLqK21OjLG/xYtyqJb33g69+jjdCh+ERUdQ8Lw0WRZUvCsQIzAcAEPqupQYBzwAxEZCvwMWKyqScBi7+uwlJOTw+C0USE3L8s35cWmkZhQ4nK5WLx4CcmjJobcOddYyRkTWZ+XR1FRkdOhmDCWf6yU3RvX4K51MWT05U6H43cdu/ag18BkNq6xQp6m+WVlZdG5Rx+69h7gdCh+k5wxkdOnT7N+/XqnQzFhrrq6mmXLl5E8MrxW9KkvKWMCu3ftYv/+/U6HEhT8nsBQ1SOqusH7fQnwMdAHuAl41bvbq8DN/o7FCYWFhRQUFJCQmuF0KI3WN2Eo0TGxlsAwISU3N5eSM8VnlxoNZymjJqKqVp3a+FX+0VJ25K0gtk1b4oeGTi2nS5Ey6jIKtuZRcPiE06GYMFJbW8viJUtIGhmexQZ9ktKtDoYJjDVr1lBeVkZyGNdmAs4maGwaiUdAa2CISDwwElgL9FBV39yEQqDHOd7zXfn/7N13WBRX38bx76EXwYK9NxRQUQRU7F1jjTEmmpieJ6abnjxp75PeqzEmRlM09opiAaKCihVLsCuKgoggRXpZds/7B2CIiqLAzpbzuS6uwMyA92Y5zMxvThEiWggRfenSJaPkrE5RUSWTnjX1Mr+LPjsHB1p17KIKGIpZCQsLQwhx5QLKkrX08sXJtRahoaFaR1EsVG5hMYmX8zgevR3PbkHY2tlrHckovAL7o9fp2PjXZq2jKBYkOjqarMxMi7/ZcqvrQfP23upmS6lx4eHhJcv3duupdZQa1bi1J7U9Gqo2VcpoBQwhRC1gBfCClDKr/D5Zslj0dReMllLOllIGSCkDGjRoYISk1WvHjh04OTlRt4V5rkvcwrsb+/btIz9fzcaumIeNoaG06NgFV/c6Wkepcba2dnh2CyI0LIySP6OKUr1iU3JISYgjIzkRr4C+Wscxmrad/HFwdGbzX+piUak+/0w2aJkTeJbXvlsQO3bsIDc3V+soigULCwunVUdfnF3dtI5So4QQePoF8ddfmzAYDFrH0ZxRChhCCHtKihcLpJQrSzcnCyGalO5vAqQYI4uxRUVF0a27P3b2DlpHuS1tOnVHp9OpcYyKWcjIyGDvnj0WPxayvI7+fTifkMCJEye0jqJYoNiUHI5HbwNKhlVYCzsHB9p368mebaoHhlJ9wsPDaeHpQ63alr2SD5T8vSgqKmLr1q1aR1EsVEZGBtHRe/Hsbh3XfB269yYtLZUDBw5oHUVzxliFRABzgWNSyq/L7VoDPFT6+UNAcE1nMbb8/Hz279+Pb/ceWke5ba29S5Z+VcNIFHOwaVNJZdorsJ/WUYym7KZSDSNRqlt+kZ7zGfkc27OVhi3a4tGkhdaRjMorsD+XEs9x6MhxraMoFiA7O5uoqCg8u1tHIbBNZ3/sHRwJUytlKTWk7JrPGuY8A64MPVNtyjg9MPoADwCDhRAHSz9GAZ8Cw4QQp4ChpV9blOjoaHQ6HR27Bmgd5bbVqlOPBs1bE6Eq6IoZ2LhxI8613K6soGMNPJq0oEHz1qzfsEHrKIqFOX0ph/y8XE7H7LGK1UeuVvaaV6xZq3ESxRJERERQXFxsNTdbDo5OtO0SyEZVXL8tQoiRQogTQohYIcQ1KzUKIV4SQhwVQsQIITYJISx3WZsKhIWF4VLLnZZeXbWOYhTu9RrQrJ0XoaGqgGGMVUi2SymFlNJXStmt9GO9lDJNSjlESukppRwqpbS4dQC3b98OQCsfP42TVE2bTv7s3LFTjblSTJqUko0bQ/Hs1htbWzut4xiVV0A/IiMj1Vw1SrU6lZJN7N+7KdYV4d1jgNZxjM6jSQsaNm9DmLoBU6pBaGgojk7OtPExv1XpbldH/z4cP3aMhIQEraOYFSGELTATuAPwAaYIIXyuOuwAECCl9AWWA58bN6W2pJSEhobSvlsvq7rm6+Dflx07osjJydE6iqaMugqJtdm+fTs+Pj7oHWppHaVK2nb253JGuhpjr5i0Y8eOkZh4no5WNNFgmY4B/SgsKGDbtm1aR1EsRIFOT3xaPsf3bsXByYW2nc23J2FVeAX2J3rndvLy8rSOopi5sLAw2nfriZ2Dec6JdjvKhjiqlRNuWQ8gVkp5RkpZBCwGxpc/QEq5RUpZ9odpF9DcyBk1deLECeLj4y1+RZ+rdfTvi06nY8uWLVpH0ZQqYNQQg8FAVFQUffr04XJekdZxqqRNZ38AtkSqYSSK6dq4cSOAVa2UUKZdl0Ds7B1Yv3691lHMghDiVyFEihDicAX7hRDi+9KuuzFCCOt5ZFoqNiUHvcHAsT2RePr1sqqbrvK8ewxAV1RIRESE1lEUMxYXF8epU6fw9LOum63GrT1x92h45fysVFozoHy3lfOl2yryGHDdcaRCiCeEENFCiOhLly5VY0Rtlc0DYW3XfG07+ePg5Gz1bUoVMGrIkSNHyMzMpGdQb3IL9VrHqZL6TVtRq44H4ZsjtY6iKBXasGEDTVq3p27DplpHMTpHZxfa+fZg3QbrPqHdgt+BkTfYfwfgWfrxBDDLCJlMSmxKDsnxp0lPTrTK+S/KtOsSiIOTC8FrQrSOYtLUeP0bs9YCuxCCjv59CQv/i+LiYq3jWCQhxFQgAPjievullLOllAFSyoAGDRoYN1wN2rBhA41btqVeY6vqeFKyQlbXnqy38us9VcCoIWXzX3TxM98VSMoIIWjT2Z9dO6K0jqIo15WTk0NkZCRegdY3Tr+Md4/+xJ48wZkzZ7SOYvKklFuBG827NB6YJ0vsAuqULfttDQp0es6l5XF0dwQA3j0GappHS3YODnTwC2Ld+nVIKbWOY5LUeP2b27BhAw2btqBB8zZaRzE678B+ZF7OYO/evVpHMSeJQPlln5qXbvsXIcRQ4C1gnJSy0EjZNJefn09ERASeVrS0d3kdA/pxNu4MsbGxWkfRjCpg1JCtW7fStGlT3BtZRmWwbWd/LibGcy5eTcSkmJ5Nmzah0+mscqLBMmWvfYNajaQ6VLr7riV2z41NycEgJUd3R9C0rRd1G1pN7ea6fHoNIjEhniNHjmgdxVSp8fo3UFhYyObNm/H074cQQus4Rufp1xthY6OGON6avYCnEKKNEMIBmAysKX+AEMIP+JmS4kWKBhk1ExERQUFBgdX2Dix73dZ8vacKGDVASsnWrVvp378/l/N1WsepFu26BAIQvPEvjZMoyrXWr1+Ps2st2nSyuqkKrmjQrDX1m7ZiTcg6raNYFUvsnnsyOZvcrMucPbIfn54DtY6jOe8eJReLISFqGEkFqm28viXavn07ubm5eAX00zqKJlzd69DKq6sa4ngLpJTFwLNAKHAMWCqlPCKEeF8IMa70sC+AWsAyIcRBIcSaCn6cxdm4cSOOTs608zX/Xu63o37TljRo1pqQddZbFFQFjBoQFxfHhQsXSgoYeZZRwGja1gtHF1fCN0doHUW5CSGErRDigBDCKq62pZSsX7+eDt37YGtnr3UcTXn36E9kRIRaTrXqKtV91xLlFRWTkJ7PiX3bMRj0+PQapHUkzdX2aESrjp1VAaMa3Gy8fukxFtWracOGDdjbO9C+Wy+to2jGK6AfB/fvwxLeT2ORUq6XUnaQUraTUn5Uuu1dKeWa0s+HSikbSSm7lX6Mu/FPtBwbNmzAs1sP7B0ctY6imY4B/dgaGWm113uqgFEDtm4tWa2jf//+ZJj5CiRlbGxtadPJn327d2AwqHHAJm46JRV7qxATE8P58+fpaKVPt8rz7jGAwoJ8q19eqxqsAR4sXY2kF5AppUzSOpQxXBk+smsLrrXr0rJDF60jmYSOgQPZuXMnqampWkcxRdU6Xt/SejWtX7+eDt164OjsonUUzXj36I+U0upXTlCqLjY2tmRFn+7Wfc3nHdiPgoJ8q10hSxUwasDWrVvx8PDAy8vLYgoYUDKMJOlsLDGx8VpHUSoghGgOjAbmaJ3FWMqeiqqu7tDOt0fpiglrtY5i0oQQi4CdQEchxHkhxGNCiCeFEE+WHrIeOAPEAr8AT2sU1ehOXMxGX6zj2N6t+PQchI2trdaRTEKnXoMwGAysW6eGaF2HGq9fgbi4OI4dO0Z7f+u+2WrWvhNudesTvFb1YlKqRl3zlWjfrRcOTs6stdKegaqAUQMiIiLo168fhXpJoc6gdZxq0863bB6MTRonUW7gW+A14Lq/eJbWNRdg7dq1tPHuins9839SV1X2Do509O/DmrUhasWEG5BSTpFSNpFS2kspm0sp50opf5JS/lS6X0opnyntuttFShmtdWZjyCksJvFyPqcPRZOfk0XnoCFaRzIZzT070bBxE4KDg7WOYnLUeP2KlRW8rP1my8bGBu8eAwgL3YhOZxlDqxVthISE0KJtBzyatLj5wRbM3sERT78gq73eUwWManbu3Dni4uIYNGgQGRYy/0WZFh064+DoTGREhFU2FlMnhBgDpEgp91V0jKV1zU1OTmbPnj14WfEyj1fr1GsQFy+c5++//9Y6imJmTiZnIyUc2bkJOwdHOnTvrXUkkyGEIKD/cEJDQykoKNA6jslR4/WvLyQkhGat2tKgWWuto2jOp+dAsrOy2LFjh9ZRFDOVlZXF1q1b6Wilq49czafHQBIT4jl69KjWUYxOFTCqWWRkJAADBw4kI9dyho8A2NrZ06azP8cO7CbxsnVOGmPi+gDjhBBnKVnGbrAQ4k9tI9WsdevWIaVUEw2W491jAEIIVgdbxQNOpRqduJiNlJLDOzfTwa+3VY/Zv56OPQaRl5fHpk2qF6Jyc7m5uUREROBlxct7l9ehe29s7exZvUadm5TbEx4ejk6no2PgQK2jmATv0p5da9da37BhVcCoZhEREdSrV4/OnTtbzAok5bXz7cHFsyfZcyxO6yjKVaSU/y3tDt+akjHIm6WUUzWOVaPWrl2LR6MmNG3rpXUUk+FWtz4tOvqycrXq6q5UXkZuERczC7hw5gQZyYl0ChqsdSST08TLHzc3N1avXq11FMUM/PXXXxQWFuIZoAoYAE4utWjXJZDgYOu72VKqx9q1a6nlXpvWnfy0jmIS6tRvRLP2PqxYbX1FQVXAqGYREREMGDAAGxsbi5rAs0z7riVrLm8M36JWI1E0lZeXR2hoKD69hiCE0DqOSenSeyiHDu7n/PnzWkdRzMTxi9kAHIoKRwih5r+4DjsHBwYNHUFwcDB6vV7rOIqJCw4OppabO+26BGgdxWR0ChpM3OlTnDx5UusoipnR6/WEhITg22sgtrZ2WscxGZ16DWbfnl1Wt0SxKmBUo7i4uCvzXwBctsACRosOnXF0duFI9A7i0/O0jqNUQEoZIaUco3WOmhQWFkZ+fj6deg/VOorJ6dy75OZzlXpSrFSClJITF7MAOBQVRpvOAbjV9dA4lWkKGjKKS5cuERUVpXUUxYSV3Wx17T0IWzt7reOYjLKeXaqHoHKrduzYQVpaGu3V8JF/6Rw0GCml1Q0jUQWMalQ2LnbIkCEYDNLiJvGEknkw2nYJ5NTBnVee2CmKFlavXk0t99rq6dZ1NGrZjobN27Bk+UqtoyhmIDmrkIw8HZcSz5IUdxLfvsO0jmSy2vv3w9HRkZUrVdtSKrZrV8kT0XYBA7WOYlLqNWpGs3beLF+5SusoipkJDg7Gzt4erwA1gWd5zdr7UKdBE5ausK42pQoY1WjTpk00adIEb29vsgp06C10iIVntyAunT9L9NFTFBVbzjKxivkoLi5m7dq1dOo5UD3dqkDnPkPZFbWNjIwMraMoJu5YUlnvi3AAuvRRBYyKZBXbMmLECFauXKlW41IqFBwcjL29PR0D+mkdxeR0ChrCgb27ra7Lu3L7pJQEBwfTJbAPTq61tI5jUoQQdAoaTMTmTeTlWU/PeFXAqCZSSjZv3szgwYMRQlhk74synn5BABzdu4MzqTkap1Gs0datW0lPT8erlxqnX5EuvYehLy4mWK1GotyA3iA5kVzSmy5mWxgtOnSmbsOmGqcyXZn5OsaOu5OEhAT27t2rdRzFBEkpWb16NZ0DgnB2ddM6jsnp3HsIBoOBVWoYiVJJR48eJTY2Fk81fOS6uvQeSmFBPmFhYVpHMRpVwKgmhw8fJiUlhSFDSm6o0i1sCdXymrTpQK3a9Th5YMeVJ3eKYkzLly/HydkFb7UWeIVadOxCnfqNWbBkmdZRFBN2Ni2X/CI96RfPE38ihq7979A6ksnrOXA49vb2LFum2pZyrSNHjnDq1Cm8g1RPputp1s6beo2asXDpcq2jKGZixYoVCCHwUQ+trqudbyDObrX5c7H1nJNUAaOalFW9hg0rOWFlWHABw8bGBk+/IE7u38HZ1FyyCyy3t4lievR6PStXrqRLrwE4ODlrHcdk2djY0KXfcCI3/0VWlio0KtdXVoQ+uHUDAN36j9QyjlkosHVm+PDhLF26VA0jUa5RdrPVoccgraOYJCEEXfoOJypyizo3KZWyYsUKOvkF4u7RUOsoJsnWzp7OvQazcX0IRUWWe/9ZnipgVJPQ0FB8fHxo3rw5AOkWuAJJeR0D+pKdkUri6RNqMk/FqKKiokhOTsYraLjWUUxe134j0BUVEmxls1MrlZNfpOfMpVwADkZuoGVHX+o1bq5xKtOXnFXAPffcQ3x8PLt379Y6jmJiVqxYga9/T9zrNdA6isny7TucYl0RK1erIY7KjcXGxhITE4NPb9Wj6UZ8+w4nNzuLv/7apHUUo1AFjGqQn5/P1q1bGT78nxsqS+6BAdChex8ATuzbzpHETPUUSjGa5cuX4+johE/PAVpHMXmtfbrjXq8B8xcu0TqKYoKOX8xCb5CkXojn/KkjdBugho9URnJWIePHj8fBwYGlS5dqHUcxIadOneLQoUN0UhPh3lAr726412vAgsWq/Sg3VrbiU4eeavjIjXTw74OjswvzFllHm1IFjGqwbds2CgsLGTFiBAAFOj15RXqNU9WsOvUb0biVJyf3RZGRpyPxcr7WkRQroNfrWbZsGb5BA3F0dtU6jsmzsbHBt99IIjeFq666yjWOXCj5nTgQsQ6Armr4SKVk5etwcK7FHXfcwZIlS9DrLft8r1Re2bwo7QIHa5zEtNnY2NCl73C2bg4nJ0dNBq9UbOnSpXj7+lGvUTOto5g0ewdHfHoNZv3aYHQ6yx/arwoY1WDjxo04OjrSv3/JhIKWPIFneR0D+nL68F4K8/M4nKhujpSaFxkZycWLF/Hpq260Kstv4CiKCgtYbmVrhCs3lpJdwKXsQqSU7N+8lradA9TqI7cgOauA++67jwsXLhAZGal1HMVELFmyBF//HqotVYLfgJJz01J1blIqEBsby759++g2YJTWUcyC38BRZGdmEBoWrnWUGqcKGNVg3bp1DBo0CBcXF8B6ChjePQag1+mIPbiLU8nZFOjUUyilZi1atAgX11p491STo1VWax8/6jZqxm/zF2gdRTEhhxMzAUg8fYzk+NN0HzxW40TmJTmrgLFjx+Lm5saCBaptKXDs2DFiYmLoPlDdbFVG607dqV2/EfMWLNI6imKiliwpGf7avqcaklUZXv79cHJ1Y+48yz8nqQJGFcXGxnLy5ElGjfrnhJVmJQWMtp38cXR24djeSIoNkqNqSVWlBhUVFbFixQoC+g/HwdFJ6zhmQwiB38BR7Ny6hdTUVK3jKCZApzdcmXx5/+a12NrZq+Ejtyg5uxBnZ2fuuusuli9fTkFBgdaRFI0tWbIEIQStA9VY/cqwsbGhW/87iIrYREZGhtZxFBO0ePFiugb0pG7DJlpHMQt2Dg506TOMsHVrKSws1DpOjVIFjCrasKFk6bnyBYz0XMv+pSlj5+CAp19vju6OREpJTMJlNZmnUmM2btxIRkYGXn3Ujdat8hs4Br2+mN//XKh1FMUEnErOoVBnwKDXs39LCF6B/XB1r6N1LLOSnFlSsLj//vvJysoiJCRE40SKlqSULF68mIBefXCvp5Z6rKxuA0ZRrCvij4XLtI6imJjDhw9z+PBh/NTk0rfEb+Ao8nKzWWHhK/yoAkYVrVu3jg4dOtCuXbsr29JyrKMHBoBPz4FcvpRE0tmTZOTpiE/P0zqSYqHmzZtHvfoN8OzeW+soZqdp2440adOB3/+Yr3UUxQQcSrwMwIn9UWSlpRAw9E6NE5mfnMJisgt0DB48mGbNmvH7779rHUnRUHR0NCdOnCBgyDito5iVll6+eDRtybw/1blJ+bf58+dja2tLmx7Db36wcoWnXxBudesz5/d5WkepUaqAUQVZWVls3ryZsWP/GTtcWKwnu6BYw1TG5d2jZOLSIzs3A3Aw4bKWcRQLlZ6eztq1a+k1bBy2dvZaxzE7QggCht7JkYPRHD9+Qus4ioYuZRdy4XJJ74G9YStxda9Dp15qTpnbkZxViK2tLQ8++CAbNmwgKSlJ60iKRubPn4+joyMt/NXqI7dCCIH/4HEc3B1F3Ll4reMoJsJgMLBgwQKCBgzBra6H1nHMiq2tHX4DR7NtUxjp6elax6kxNV7AEEL8KoRIEUIcLretnhAiXAhxqvS/dWs6R03YuHEjOp2O8ePHX9mWkWv5S9eUV9ujES07+nJ45yYA4lJzybCSOUAU41myZAlFRUV49x+jdRSz1X3wGISNDT/Mnqt1FEVDZb0v8rIzObTjL/wGjcXO3kHjVOYpOaukEPTwww9jMBj4888/NU6kaEGn07F48WL6Dx2Jk6ub1nHMjv+QcUgp+eGX37WOopiIiIgIEhMT6T5Y9Wi6Hf5DxlGsK+Ln3y13Mk9j9MD4Hbh60PobwCYppSewqfRrs7NmzRrq169P797/dGlPs5L5L8rr3HsICScOkZmWjJSqF4ZS/f744w/adfSmaVtvraOYrdoejejQvTdLFy9Er1crBlmjwmI9x5JKJu88sCUEvU5HjxF3aZzKfF0snQejQ4cO9O7dm99++03NA2WFQkNDuXTpkrrZuk0NmrWitY8fSxctUO1HAUqGDLu7u9O0az+to5il5p6daNSyHfPnW+4wkhovYEgptwJX92EZD/xR+vkfgNkNwNXpdKxbt44xY8Zga2t7Zbu1LKFaXuegkhm3D+8o6YVx5EKmWlJVqTaHDx9m9+7d9BoxESGE1nHMWo/hE7mUlMjaDaFaR1E0cCwpm6JiA1JKdq5fSrP2PjRv76N1LLOVnF1w5Ybr0Ucf5dixY0RFRWmcSjG2X3/9lfoNGlDfu4fWUcxWwNA7OX/mJOGRqv1Yu6ysLJYtW8bgUXeqFedukxCCwOETOHYwmoMxR7SOUyO0mgOjkZSybLDoRaBRRQcKIZ4QQkQLIaIvXbpknHSVsHnzZi5fvsyECRP+tT01x/p6YDRq1Z4GzVsTsz0MAJ1eql4YSrX55ZdfcHBwoGO/0VpHMXtdeg/F1b0O38/8SesoipFJKfm79O9y/PEYLpw5TtCoezVOZd4KdYYrDy0mT56Mu7s7P//8s8apFGNKTk5m7dq13HHnPdjaqaFYt8tv0GjsHZ2YMWu21lEUjS1evJi8vDx8h5jds22TEjD0TmxsbPl6pmWekzSfxFOWPL6osM+YlHK2lDJAShnQoEEDIya7seXLl+Pm5sbw4f+eHTc12/p6YAgh6NpvJKf/3kPO5ZLONgcTLlNUbNA4mWLuCgoKmD9/Pn2HjqJW7XpaxzF7dg4OBAybwNbwDSQlXdQ6jmJE59Lyrtxs71i3GEdnF7oPVnPKVNXF0nkwXF1deeCBB1i2bBlpaWkap1KMZf78+RQXF9N1yISbH6xUyNnVja79RvJXyErSM7O0jmMShBAjhRAnhBCxQohrhtoLIfoLIfYLIYqFEHdrkbEmzJ07Fy+fTtRuqXoHVoV7vQb49BrE6mULKSy0vHtTrQoYyUKIJgCl/03RKMdt0el0rFq1irFjx+Lk9E/3pvwiPTmF1rMCSXld+4/EYNBfmcwzv0jPocRMjVMp5m758uVkZGTQdagap19det0xCb2+mK9/tO4nXZW4OHxYCHFJCHGw9ONxLXJWlwMJGUDJ5J0HI9fjN2gsTi61NE5l/srmwQCYNm0ahYWFaklVKyGlZO7cuQT27IVNvRZaxzF7PUfeTUFeLjPmqCVVhRABYxcnAAAgAElEQVS2wEzgDsAHmCKEuPqOPh54GFho3HQ1JyYmhj179jB0/GQ1ZLga9Bx5N9kZacxduEzrKNVOqwLGGuCh0s8fAoI1ynFbIiIiSEtLY+LEif/abo3DR8o0beuFR9OWHIzccGXbvnPp6PSqF4Zy+2bOnEmbdp408wnUOorFaNSyHe269uCPub9Y7WSelbw4BFgipexW+jHHqCGrUWpOIWdT8wDYvXE5usIC+oy9T+NUliGpXAGjS5cu9O3bl5kzZ1pd27LGp8UREREcP36cYRNUW6oObbsE0KhlO+bN/UVN5gk9gFgp5RkpZRGwmJL5A6+QUp6VUsYAFnOhPWvWLJycnGjd6+q1H5Tb4RXYjzoNmvDzT5Y3jMQYy6guAnYCHYUQ54UQjwGfAsOEEKeAoaVfm42FCxfi7u7OqFGj/rXdmgsYQgj8Bozi1MGdZGekApBbqHphKLcvOjqaXbt2MXTiA9jYaD7azaL0HTeVS0nnWbBsldZRtHLTi0NLsv9cSe8Lvb6Y7WsW0M63B83aeWmcyjKk5RT9a7jk9OnTiYuLIyQkRMNUxmWtT4t//PFH6tWrRzP/IVpHsQhCCHqPmcKZY38T/Nc2reNorRmQUO7r86XbbpmpziV4taysLObPn8/oO+/Gxtld6zgWwdbWjt5jJhOzZxs79sdoHadaGWMVkilSyiZSSnspZXMp5VwpZZqUcoiU0lNKOVRKefUqJSYrPz+fFStWMHHixH8NHwFIzbG8MUa3wn/IOKTBwIGI9Ve27Y1LV3NhGIkQooUQYosQ4qgQ4ogQYrrWmapi5syZuLq60ra3mryzunXuPYQ69Rvz7fcztI6ilcpeHE4UQsQIIZYLIa7bR9zULw5zCos5frFk6dQjOzeTkZxIvzsf1DiV5TBISXLWP70w7rzzTlq2bMl3332nYSqjs7qnxRcuXGDVqlVMuu9BCqWd1nEsRsCwO3FwdOb7GT9qHcVimOpcglebN28eubm5BI2ZonUUi9Jz5N3Y2tnz+dffax2lWqnHmrdo3bp1ZGdnc99913YZtOYeGFDSNb1ZO2/2b157ZVtekZ4D8RkaprIqxcDLUkofoBfwTAXd4k1ecnIyixYtYsT4STiqcfrVztbWjqAxkzmwcys7ow9oHcdUrQVaSyl9gXD+Wfr7X0z94vBAfAZ6Q0l37MgVv1GvUTM6Bw3WOJVluViugGFnZ8czzzzDli1bOHDAatpWtT0tBtMvCkJJV3eDwUDAyHu0jmJRnF3d6D5kHNtCV3MoNl7rOFpKBMoXzZuXbrNIBoOBH374Af+AAGwbtdc6jkVxq1ufrv1GsHHVYs6nWM4E06qAcYvmzZtHkyZNGDRo0L+2GwyS1GzrLmBASS+M+BMxpCScubIt+lwG+UXWNR5YC1LKJCnl/tLPs4FjVOEiUkszZsygqKiI7qOnah3FYgWNvhd7Ryfe+/hzraNo4aYXh6U9Bcv+qM8B/I2UrdoU6PTEnC8ZxnfmcDRxR/YzYOIj2NjaapzMsly4nP+vr5944gnc3Nz4/HOrbFtVZupFwby8PGbNmsXosePIdayvdRyLM+CuhyguKuSTr622hyDAXsBTCNFGCOEATKZk/kCLtG7dOk6cOMG4+/+Dmv6k+g2Y+DCFebl8/PVMraNUG1XAuAVJSUmsX7+ehx56CNurLgDTcosoNqhW133wWGxsbNkTtvLKtqJiAzvPpGqYyvoIIVoDfsDuq7ab/JOt3NxcfvzxR4aOHI1LAzWze02pVbsePUZM5K+1Kzh91uqedN304rBspaxS4ygpCJqVA/H/LGe9eckcXGvXpedIi5g/0aQkZRb8a9LBOnXq8OSTT7J06VJOnz6tYTKjsaqnxfPmzSMtLY0x9/0Hg7rbqnaNWrbDu8cA1i76jfhLl7WOowkpZTHwLBBKyblnqZTyiBDifSHEOAAhRKAQ4jwwCfhZCHFEu8RV89VXX9GiRQsa+A7QOopFatGhC227BLL4t5/JzC24+TeYAVXAuAXz589Hr9fzyCOPXLMvJdsyfiGqyr1eA7x7DmBv+Gr0+n+WlD10Pos0Kx9iYyxCiFrACuAFKeW/FlQ39SdbULIGeEZGBv0nPqp1FIs3cOIjGAx63vnoC62jGFVlLg6B50vnkvkbeJ6SCQjNRoFOf2Xp1AtxJzi6ewt9x0/FwclZ42SWJ79Iz+U83b+2vfDCC9jZ2fHFF1bRtqzmabFer+frr78mMDAQ26beWsexWAMmPkLO5TS++MFsF3+qMinleillByllOynlR6Xb3pVSrin9fG/p3IKuUkoPKWUnbRPfnujoaCIjI7nv0Scp0KulU2vKwLsfISPlAl/9fN3RsGZHFTAqqWy97z59+tChQ4dr9qeo4SNX9Bwxkez0SxzbE3llm0FKtpy4pJbGqmFCCHtKihcLpJQrb3a8qSkoKOCzzz4jqE9fXFua5bnYrHg0aUG3AaNY8eevnEu8qHUco6rExeF/pZSdpJRdpZSDpJTHtU18aw4mXKZQV9L7IvzPmTi6uNJ3nBqSVVMuZP57GEnTpk155JFH+PXXX4mPt+weTtb0tHj58uWcOnWKB554luyC4pt/g3JbPLv1orlnJxbNmcGFjByt4yg16MMPP6ROnTp4DbxT6ygWzafnIBq1as+cH74mK9/8F51QBYxK2rx5MydPnmTatGnX3Z+SpXpglPHuORB3j4bsWLvoX9sT0vM4maxORDVFCCGAucAxKeXXWue5Hb/++isXLlxg3CNmvYCKWRl231PoCvN56wOzWs1auYECnZ79pZMnJ54+zt/bQuk/4SFc3etonMxyXbh87TXAm2++iRCCjz76SINExmUNT4sNBgMffvgh3t7eNOiiurrXJCEEw+57mrQL8Xzx429ax1FqSExMDMHBwTw27RkydGo1n5pkY2PD0MnTSIo7ybdzzH81a1XAqKQff/wRDw8PJk2adM0+g0FySfXAuMLW1o6g0fdyPHobqRf+/eQp8mQKBTo1oWcN6QM8AAwWQhws/RildajKKigo4NNPP6VXUG+cWvpqHcdqNG7Vnq79R7J83hzizidpHUepBtFnM670vgidPwMnVzcG3PWwtqEsXGJG3jXbWrZsyeOPP86vv/5KXFycBqmU6rRmzRoOHz7MMy++SlKWuuaraZ2CBtO4dQcWzf6Os5eytY6j1IAPP/wQNzc3AkZfu7KjUv26DRxF/aat+OX7L7ica969MFQBoxLi4+MJDg7m0UcfxcnJ6Zr9GXlF6PRqaER5ve6YhI2tHVFrFvxre26hnq0nTXPySHMnpdwupRRSSl8pZbfSj/Va56qsWbNmkZCQwN1PvIhqTcY1/P5nKCrM55W33tM6ilJF2QU6DpbOfRF3ZD+Hd/zFwImP4OJWW+Nkli0jT0du4bVDCt58803s7e15++23NUilVBe9Xs/bb79Nhw4daOE/VOs4VsHGxobhU58mOf40n/0wRw1BtjD79+9n2bJlTHvqGS4Wqt4XxmBra8ew+57ifOxRPvvJvOfCUAWMSvj+++8BeOaZZ667PylTDR+5Wm2PRnTrP5JdG5eRn/vvyvmRC1nEpeZqlEwxRZmZmXz44YcMGToMx5ZdtY5jdRq39iRw2J0EL/qVfYdPaB1HqYKdp9PQ6SVSStbM/gz3eg0YMPHaiaeV6pd41XKqAM2aNeOll15i4cKFREdHa5BKqQ4LFy7kyJEjvP72/3Em/dr3WakZvn1H0Lx9J5b+/BUHz6qHX5bkzTffpF69evS961G1dKoR+Q8ZR6NW7fn12084l2q+PZtUAeMmMjMzmT17Nvfccw+tWrW67jGqgHF9A+9+lMK8XHatX3rNvvCjF8kvUkNJlBKffPIJ6enp3PP066o3k0ZGPPA8AsELr/5XPekyUynZBRxNKll46GDkBs4dO8jIh6bj6OyicTLrcL0CBsBrr71GgwYNeOmll1TbMkMFBQW8++67dO/encZdB6qbLSOysbFh9KMvkZ6cyKffzKCwWF03WoItW7YQGhrKi6+8xlnzvYc2Sza2tox+5EVSzsfx/lc/YDCY5x80VcC4iZ9++ons7GxefvnlCo9JylTV+Otp7tmJ9l17Ernyd3RF/x4vmluoJ/xYsrqYUzh16hRff/01U+6fSoF7S63jWK26DZswYOIjbN+4igVrwrWOo9wiKSURJy4hJRTm57Jm9qc0betFj+F3aR3NapzPuP61gLu7Ox9++CHbtm1j4ULznzzN2nz99decPXuWd977iFMpqveosXXw70OH7n1Y+9v3bIg+qXUcpYqKi4uZPn06rVq1wm/EvRjUfYDRdQoaQpvO/iyd9SXbj5zTOs5tUQWMG8jJyeHLL79k5MiR+Pv7X/eYAp2etBzzngilJg277ymy0lLYs3H5NftOp+RwIOGyBqkUU/LCCy/g5OTE2P+8it5MK8GWYuiUadSu34g3X32R3AL1d82cnEjOJrH0Bjrsz5lkpiZz9/P/w8bWVuNk1iM1u5C8ousvrfnYY48RGBjIyy+/TGZmppGTKbcrMTGRjz/+mAkTJmDfsqu62dKAEII7n3qTwrxcvvz4A/XQ0MzNnj2bQ4cO8b+PPiM2Xad1HKskhGDC02+Tm5XB/95/n+wC83sfVAHjBmbNmkVqairvvvtuhceo4SM31r5bL9p06s6mxbOv6YUBsO1kKuevM3u7Yh1WrlzJ+vXreen1N0nWXTtBrmJcjs6ujPvP6yScOsor732mdRylkgp0/0yOnHj6GJEr/6DHiIm09vHTOJn1SahgfgRbW1t+/PFHUlJSeOONN4ycSrldL730EsXFxbzxfx9xMln1dddK41bt6TPuPnasW8Ls5WEU6w1aR1Juw8WLF3n77bcZNGgQdTv1VQVBDTVv70PPkZOIXDmPOcERZtcjXhUwKpCRkcEnn3zCiBEjCAoKqvC4pArGvColhBCMfPB5LqdevGZFEgCDlKyLSSLLDKt/StVkZGTw9NNP4+fnR4ch96hxxSai28BReAX049dvPyVy3xGt4yiVEBWbSm6hnmJdEYu+eAPX2nUZ+5/XtI5llRLSKy7IBwQE8MILL/DTTz+xefNmI6ZSbkdISAhLly7lrbfe4qyultZxrN7Ih6bjVrc+sz9+g63HL2odR7kN06dPJzc3l3c/+Zozl9RwLK2NfuwlXNzr8M27LxFTunqZuVAFjAp8+umnXL58mc8+u/FTyPgbXKwoJTz9gugY0JfwRT+Rl31t19m8Ij3BBxIp0KnJmazJ9OnTSU1N5a1PvyM5+/rdrhXjE0Jw9/T3EAKefOIJsvPVUBJTlpCeR8z5kr+r4QtnceHMcSZNfw9X9zoaJ7NON7sm+PDDD/H09OSxxx5TQ0lM2OXLl3nqqafo3Lkz4x966srwLEU7zq5u3PXMu1w4c5yvvvzihsVCxfSsXr26pCD49tskGNT5yRS4utdlwlNvkXDyMP/76DMycs3nek8VMK4jNjaWb7/9lgceeICuXSte0rFApyc569phEcq1xjz2KgU5WYTOn3Hd/ak5RYTEJKlugVZi8eLFzJ8/n9fe+C+XHJtqHUe5Sr1GzRj3xBsc37+D5976wOy6FlqLwmI94UeTAYj9ezd/LfqJgKF30jloiMbJrFdmvo7LeRVfBLq4uPDHH3+QkJDAtGnTVNsyUc888wxJSUnM+nk2u89maR1HKeXbdxjdBtzBxvk/8NOKcHIK1cMPc5CUlMTjjz+On58fw+79D6lq7kCT0W3gKDr3Hsra377hx5WbzOY+TBUwriKl5Pnnn8fR0ZFPP/30hscmXs5X47cqqVk7L4JGT2b7mgUknj5+3WMS0vNYdyhJTeRo4c6cOcOTTz5JUFAQfe5+Qi2na6J6jbqHLn2G8eeMz/gjeJPWcZTriDhxicx8HdkZafz56SvUb9KSic9VPGeTYhxn0278ZDgoKIgPPviAJUuW8MsvvxgplVJZf/75JwsXLuT//u//KKzbVt0km5iJz/0Pt7oe/PL+C6zcHauuGU2cwWDgkUceITc3l1lzfmPfeTWXjCkRQnDPix/g4laHGe88z/r9Z82isK4KGFdZsmQJGzZs4P3336dJkyY3PFYNH7k1dzz8Aq5udVj6zVvo9de/IDhzKVcVMSxYbm4uEyZMwMbGhg++/ZmTl1S3XFNVdlJzr9eAF594kL3H4rSOpJRz/GIWRy9kUawr4vcPniMvO5MH3voGR2dXraNZvbOpNx/b/frrrzNixAieffZZoqKijJBKqYxDhw4xbdo0+vXrx/1PTL8yPEsxHa7udbj/9S9IvRDP1++8yOZjyWZxw2Wt3n//fUJDQ/n66284q6+LTq/eK1NTq3Y97nv1M1LiT/Pe69M5EG/682GoAkY5Fy9e5JlnnqFXr14899xzNz0+/iZPWZR/c3Wvw13PvkPCycNsWTq3wuNOp+QQfDCRomLz6MakVE5ZFf7w4cPM/eNPTuQ5ax1JuQlX97o88n8/kJOZzn1TJpOQqi7mTUFaTiGbjqUgpWTFjPeIO7yPyS9/TPP2PlpHUyjpTai7STdcGxsbFi1aRKtWrbjrrruIi1MFQq2lpaVx1113Ubt2bf74cxGbT6ZqHUmpQPuuPRn96Ev8vXUj33/7FfvOmf4NlzVas2YN7733Hg8//DB+w+/mwmW1cqOp6ujfh5EPTWf/lhDe/+QLYlNytI50Q6qAUcpgMPDQQw+Rl5fHb7/9hq2t7Q2PT88tIt2MJjsxFV3730HXfiMInT+Dc8djKjzuXFoey/edV103Lchrr73GsmXL+PiTT8lr2IlCnSpQmYPmnp2Y/PLHxP69h4n3TiUjV837o6UCnZ61f1+gqNjAxj++Y/fG5Qyd8iTdB43ROppSqtggKzXBYN26dVmzZg1FRUWMGDGClJQUI6RTrqegoIDx48eTkJDAsmXLiEmH7AJ1/WHKBk16nG4D7iBkzpfMmDufw4mqwG5K9uzZw+TJkwkMDOS/H3zJrrh0rSMpNzFk8jS69B3Omtmf8cnM3zmfYboP6lUBo9RHH31EWFgY33//PV5eXjc9/pRaD/y2CCGY9MIHuHs0ZP5HL5CbVXHVPDmrgMV74knKVMMMzN3HH3/MV199xXPPPYfvHVNJUZPfmpXug8Yw5vFX2Ls5hLumPkamWplEE3qDZP2hJDLydGxe+gvhC2fRc+Td3PHwC1pHU65S2adX3t7ehISEcP78eUaMGEFaWloNJ1OuptPpmDx5MlFRUcybNw+bxl5qiUczIIRgyquf0aZTdxZ+9hoz/1ypihgm4vDhw4wePZrGjRuzYMlKNp++jBrlY/psbGy4//UvaOnVlXmfvMznvy432SKGKmAAK1eu5N133+X+++/n8ccfr9T3xF4y7a41pszFrTYPvvUtmekp/P7+8xTrKr4Zyi4oZln0efbHZ6gxjmZISsmHH37IW2+9xdSpU5n0zFscv6iKf+Zo0KTHGXTP40SsXsCd9z1Keo7qCmpMUkrCjyZzNjWXsD9nEjLnS7oNGFW65K3QOp5yldOXcis9l1OfPn1YtWoVx44dY9CgQSQnJ9dwOqVMUVERU6ZMITg4mB9++AHf/iPZdUYVkcyFvYMjj743i4Yt2jLn3aeY+ecqsxi/b8mOHj3KkCFDsLe3Z03IenYkFavJ2s2Ig6MTj70/iwbNW/PTW9P4/NcVlZrXydisvoCxbds2pk6dSs+ePZkzZ06lLgQz83TqCXIVtfLy5d4XP+J0zB4WfflfDPqK/7jpDZLIE5dYfTCR7AKdEVMqVaHX65k+fTrvvPMODzzwAM+99w3R8erpiLkSQjDmsVcYNOkxIlYvYPi4iZxLuax1LKsgpWTLiRQOn09nxYz/sXHe9wQMHc/9b3yBra2d1vGU6yjQ6UnMqHzvwREjRhASEsLp06fp1asXx44dq8F0CkBOTg5jx45lxYoVfPPNN4y692HCjqjikblxda/Dk5/+RoNmrfjlnSf47pf5bD15CYOaDN7oduzYQd++fbGxsWFjWDhHcl3UcHszVKt2PZ789Hc8Grdg1n8f5/0Zv5lc7yarLmBERUUxZswYWrVqxdq1a3FycqrU9x2+YFpvorkKGDqe0Y++zIEtISz99p0bFjEAzqbmMW/nOQ7EZ6gTk4lLT09n3LhxzJgxg5dffpln//c1UafV+EdzJ4RgzOOvMvY/r7Fvy3oGDhxExH51o1WTpJRsPp7C9pjTzP7vY+wIWczge59g8iufquKFiTt+MeuWjh86dCgRERHk5+fTq1cvVq9eXUPJlLi4OPr27ctff/3F3LlzmfDAfwj5+4JaAc1M1apTj6e/mE8Lz87M++gFPvv0E1YdOK+e/BvR/PnzGTJkCB4eHmyJ3MbxfHc1aacZc6vrwTNf/Ulzz078/sHzvP7Oe2w+lmwyfyOttoAREhLC8OHDady4MeHh4TRo0KBS36c3SJOrQpmzIZOfYPjUZ9kTuoJ5H71IUcGNn1gVFRuIOHGJBbvPEZeaq4aVmKDt27fj7+9PeHg4P/wwk9H/eU0VLyyIEIJBkx7jobe/I+nsScYO7ssHM+ffdNUF5dYV6w1sOHyRxSvX8NVTd3L22EGmvPIpYx57GRsbqz19m41TKTm3vJpWYGAgu3fvpkOHDkyYMIHnnnuO3FzT675rzlatWkVAQADnzp0jJCSE3qPuZvWBCxSbyIW5cnvKemJ0Gzia9b99w/89+xCzNu7nXJpqPzUpNzeXJ598kgcffJBevXoRtjmSven2JF5W89eZu6vb1POPTuHn0P2k5Wg/CsHqroAMBgMff/wx48aNw8vLi61bt9K8efNKf39sSg55qqJbrUY++Bzjp/2XQ1Fh/PDS/aQlJdz0e1Jzilh9IJGl0QmqkGEisrKyePHFF+nfvz9CCMI2baZxr3EcjFfDDCxR1/4jeWHGctw8GvDusw8yaOw9HDhxVutYFiOnsJi5Yfv5v5eeZs7bT+DiXpfp3y0hcPgEraMplVRUbODkbUz43apVK7Zv38706dP54Ycf6Nq1Kxs2bKiBhNYlJSWFBx98kLvuuovWrVuza9du3NoHsuHQRQzqGsIiODg5M/WNLxn/5H85vm87/3t4FO98/QsbDyep3hg1YNOmTfj5+TF79mxeffVVfl0STHhcPqnZ2t/gKtWjrE3d+dRbnNgXxev3jeClT35k5+lUijV8cGVVBYzY2FiGDh3KW2+9xb333su2bdto1KhRpb/fYJDsiVOTO9WEARMf5tH3ZpGaFM+XT41n14ZlGAw3bxgXLhew+kAif+46x98JlyksVicoYysqKmLWrFl07NiR7777jieffJKVf0VxUjYhvhJLCSrmq1HLdrz4fckynrvC19Db35eHp79JUqrqpVYVh+KSuO+pV5h+90AORKxj6JQneemHFTRte/MVshTTci7t9v4GOjo68u2337JlyxZsbW0ZNWoUd9xxB/v27avmhJavoKCAr776Ci8vLxYvXszbb7/Nhk2R/J3lzN6zqnegpRFCMOCuh3nxhxXUrt+I+R+/yHNTJ/C/X4OJPpuuegtWg5MnTzJp0iSGDh2KwWAgPPwvxj/xGsExF8ktVNfhlkYIQf8JD/LiDyuo27AJ8z5+iQfuGs3/5q7hZHK2Jg+RhTk9uQ4ICJDR0dG3/H0ZGRl8/vnnfPPNNzg6OvLNN9/wyCOP3PLM7YcTMwk/qiZ4qknpyYks+vINTv+9h1be3Rj7+Ku07RJQ6e+3txW0a1CLjo3daFnPBTtbTWt0Jr00wO22pzLp6en8/vvvfPvttyQkJNC3b1/e+/gzctxbc7qSSwgqliM5/jRrf/mco7sjcK1dl7umPsYbLz6LT7tW1fnPWHSbOn4ylv/77FvWLJlHQW42nYOGMOY/r9KweZtqTKkYk2ejWozxbVqln1FUVMR3333HJ598QkZGBsOGDeOFF15gxIgR2NraVuVHm3R7gqq1qdTUVObOncu3337LxYsXGTFiBF98+RV5Lo3ZezYdnd58rn+V22PQ69mxbjGh82eQm5mBV0A/Rk75D/eOv4OuLeri7FCl9nM9Jt2mqtKepJTs3buXb775hqVLl+Li4sIrr7zCpEeeZu/5XDVZp5Uw6PXsXL+E0HkzyMlMp2NAX8ZPfYIHJo7Fq4l7Tdx3XbdNWXQB4/jx4/z888/MmTOH3Nxc7r//fj777DOaNr29i4n5u86pblFGIKUkOnw16377mqy0FNp2DqDvnVPp3GsIdg4Olf45DnY2tPJwobWHKy3quuDubGfs5QYt7kSWm5tLWFgYS5YsYfXq1RQWFjJw4ECeev4lansGcPxijuqKa+Xijuxn0+LZHN29BRsbW7r3HcTkyZN56J4J1PeoV9Ufb3FtKikpiVWrV/Pb/EVE79yGsLHBt+9wBt/zOC06dKmhpIqxVEcBo0xWVhYzZ85kxowZJCUl0bx5cyZPnszEiRMJDAy8nWKGSbcnuPU2lZeXR3h4OIsWLWLVqlUUFRUxbNgwXn39Dep7+hF9NoOcwuIaTKyYooLcHLYFz2fbqnnkZKZTv2krAoeO486JdzO8d3da1HPF1qZamoNJt6nbOUedPXuW5cuXs2DBAg4ePIibmxvTpk3jnkeeIi7P/pZWW1IsR0FeDtuDF7Bt9TyyM1LxaNqSXsPGcffdk7ijfyAN3Zyq657L9AoYQoiRwHeALTBHSvnpjY6/WcPT6XTs37+fsLAwVq9ezf79+7Gzs2PSpEm88cYb+Pr6Vinv71FxZOSpZTyNpaggn10blhK58g8ykhNxruVOp16D8ek5gHa+PXGr63FLP8/NyY6mdZxp5O5EI3dH6tdyxMm+2qvv5Rn9RHYrbaoyJ7LMzEz27dvHjh07iIyMZNu2bRQWFlK/fn0mTrqHYROmYKjbivPqBKZc5VLiWXZtWMb+zWvJTE3GxsYW767+9BswgBGDB9Cvdy88PG6tDVONbepmbVeqp3UAACAASURBVEUI4QjMA/yBNOBeKeXZG/3Mm7UpKSXnzp1j7969REVFsXnLFg7FxADQoHlrAoaMJ2DYBOo2bFKVl6aYkOosYJQpKipi7dq1/Pbbb4SFhaHT6fDw8GDw4MH069ePHj160LVr18qsrGbS7Qlu3qZyc3PZv38/O3bsICIigoiICAoKCvDw8GDy5MlMmPIQNh6tOHYxi0KdGjpg7YoKC4jZFsrujcs5c2gvUko8mrTA278PQX36MmRAb4K6dcLdufIPy65i0m2qMueohIQEdu/ezbZt29i0aRNHjx4t+14mT30Qv0FjOZctuazuhxRAV1RIzPYwdm9YxumYPUgpqduoGZ0D+9C3X3+GDuhDkF8nnB3sb/efMK0ChhDCFjgJDAPOA3uBKVLKoxV9T0UN78KFC9x7773s27eP/Px8hBD06NGDe+65h/vuu4/GjRtXS2ZVwNCGQa/nxP4oDmwJ4cjuCPKzS8bXezRtSf8JD9Fv/NTb/tluTnbUdXGgrqs9tZ0dqO1sh5uTPbUc7XBxsK1q9dC43T1usU3d6EQ2Y8YMvvrqK86dO3dlW5cuXeg3cDAdA/vToL0fybk6VGcL5WYMBgPnjh3k2J5ITuzbzvnYo8jS+W08GjZmwr3383/vvU89Vwec7W/a5qqnnF+JtiKEeBrwlVI+KYSYDEyQUt57o59bUZsqLi5m4MCBHDp0iKyskqU1nZ2dad6xKx2698G75wCatO5g7B5iihHURAGjvIyMDDZu3MjGjRvZsmULCQklk2Db2Njg6enJJ598woQJFU78atLtCSpuU8uXL+fNN98kNjb2yvhrHx8fhg4dim/vQTTu6E9iVpEaj69U6HJqMkd2buL43q2cjtlLQV7J0FdHF1daeXbiqz9W0qi2M/VrOVLPxQE3Jztsbt5Lw6Tb1I2u+yZOnMiWLVvIyMgAwMXFhT59+tCz/xA69hyEoVZD0nLUMBGlYllpKRzZtYVjeyI5fWgv+Tkl1zsOTi60bN+R7+cH09TDDY9aDpW95oMK2pSWi8j3AGKllGcAhBCLgfFAhQWMitSvXx8bGxumTZtGUFAQgwYNqvSyqIrps7G1xTuwP96B/dHri0k4eZgzh/YSf/wQDo43fcJ0Q9kFxWQXFBN/nXm8bITAxcEWZwdbXBxscbK3xcneBkc7WxztbGhe14XGtav271ezam1TvXv3Ztq0aXTv3p3AwEDq1avHkQuZhB1J5mKOKuQplWNjY0ObTt1p06k7ox55kYLcHBJOHeb8qSMkxZ0kTe/MsujzADw1sF1N94oqU5m2Mh74X+nny4EfhBBC3kbV387OjmbNmtGtWze6dOlCQEAAPp0689O2czf/ZkW5gbp16zJlyhSmTJkCQEJCAnv37uXvv//m0KFD1KtX5WFblWHU9gTg4eGBr68vU6dOpXv37vTo0YOGDRsCMGfbGU5eUr0ClRurU78RfcbeR5+x96HXF3PxbCzxJ2K4cOY4hXk5nErJ5VTKP0uw2toIpvRoSQM3R2PEM3qbatasGffccw++vr4EBgbStWtXHBwcWBqdUDJMRBUvlJtw92hI0Oh7CRp9Lwa9novxscQfjyEp7gRZ6Zc4fimf4+X+NrfycOGu7pVfCbQ8LXtg3A2MlFI+Xvr1A0BPKeWzVx33BPAEQMuWLf3LPxE2touZBWr2YuUKd2d7ajvfsEuUsXtg3LRNVbU95RYWq4malBrTrI7zzZ5wVdfTrcq0lcOlx5wv/fp06TGpV/2s22pTUko19MoKODvYUr+WUW54bofJtafSfVU6TyVl5lOsJuhUakBDd0cc7W5YZDe5NlXV9pSSXaCGXyk1wtHehoZutzfUUcseGJUipZwNzIaSrk9aZjGxp+2Kcsuq2p5cHe1wdTT5PxuKYjS326aEELSo51JjuRTFXFX1PNWktnO1Z1IUc1XV9lSJG0xFMTot15hMBFqU+7p56TZFUW6PalOKUjmVaStXjhFC2AG1KZkoTVGUf1PtSVGql2pTinIDWhYw9gKeQog2QggHYDKwRsM8imLuVJtSlMqpTFtZAzxU+vndwObbHVusKBZOtSdFqV6qTSnKDWjWF1xKWSyEeBYIpWSJoF+llEe0yqMo5k61KUWpnIraihDifSBaSrkGmAvMF0LEAumUXEAqinIV1Z4UpXqpNqUoN6bpYHYp5XpgvZYZFMWSqDalKJVzvbYipXy33OcFwCRj51IUc6Tak6JUL9WmFKViWg4hURRFURRFURRFURRFqRRVwFAURVEURVEURVEUxeQJc5rvRQhxCbi1BYwrpz5wzVrkJkTlqzotMqZKKUca+d+stBpsT1ozh9/HmmTJr1+1qX+Y4vusMlWeKeQy6fYEJneeMoX3zBis5XVC9b9Wk25TJtaebsaSfw/Va6u867Ypsypg1BQhRLSUMkDrHBVR+arOHDIq1cPa32trf/3WwhTfZ5Wp8kw1l1Ixa3nPrOV1gnW9VnNjye+Nem1Vp4aQKIqiKIqiKIqiKIpi8lQBQ1EURVEURVEURVEUk6cKGCVmax3gJlS+qjOHjEr1sPb32tpfv7UwxfdZZao8U82lVMxa3jNreZ1gXa/V3Fjye6NeWxWpOTAURVEURVEURVEURTF5qgeGoiiKoiiKoiiKoigmTxUwFEVRFEVRFEVRFEUxeRZdwBBCjBRCnBBCxAoh3rjOfkchxJLS/buFEK3L7ftv6fYTQogRppZRCDFMCLFPCHGo9L+DTSlfuf0thRA5QohXTC2fEMJXCLFTCHGk9P+jU01kVGpGJd77J0vf14NCiO1CCB8tctaUm73+csdNFEJIIYRFLtllaSrxe/2SEOKoECJGCLFJCNGq3D596e/7QSHEGiNmelgIcancv/14uX0PCSFOlX48VF2ZKpnrm3KZTgohLpfbV1P/r34VQqQIIQ5XsF8IIb4vzRwjhOhebl+N/b9Sbs313kchRD0hRHjp+xMuhKirZcbqUMHr/EIIcbz093OVEKKOlhmry43aphDi5dLzZH0tslk7S/49tOTfu4pemxDiudL37ogQ4vMa+cellBb5AdgCp4G2gAPwN+Bz1TFPAz+Vfj4ZWFL6uU/p8Y5Am9KfY2tiGf2ApqWfdwYSTSlfuf3LgWXAK6aUD/6fvfsOk6o8Gz/+vXeXjnTEAkhHmlSpgoAFxIaJNfaYGPOa5H3fVJP4GmNiflGTmJiYIrFFjRJJVBQUqVKkLbCwNCkLS9/e6+zM/fvjnMFh3WVnd6fu3p/rmoszZ855zj2z83DOuecpJAE7gJHu867h+BvbIzyPIP/2HQKWbwA+inbckXz/7nbnAKuBDcC4aMdtj8b/XYEZQFt3+ZuB/+cCxVGK6T7gTzXs2wVIc//t7C53jlRc1bb/NvBSOD8rt9xpwBhgZy2vzwE+BASYCGwM92dlj9D8HYGngUfc5UeAp6IdZ5je59VAkrv8VFN4n7W9V3d9L2AJkA50i3aczfHRlL+HTfl7V8vfbQawDGjlPj83HMduyi0wxgMHVDVNVSuBt4Abq21zI/Cqu7wAuEJExF3/lqpWqOoh4IBbXszEqKrbVPWEu34X0EZEWsVKfAAiMhc45MYXDo2J72pgh6puB1DVHFX1hilOE3p1/u1VtTDgaTugKY1YHMx3H+AXOCf+8kgGZxosmO/1SlUtdZ9uAHpGO6azmAUsVdVcVc0DlgKzoxTXHcCbITp2rVR1NZB7lk1uBP6hjg1AJxE5n/B+Vqaeavk7Bl5PvArMjWhQYVDT+1TVj1W1yn0aif9jIuIsdfNZ4Ic0rWuEuNKUv4dN+XtXy3v7JvBrVa1wt8kMx7GbcgLjQuBowPNj7roat3ErSQHOL/HB7BvtGAN9Gdjq/7LEQnwi0h74EfDzEMcUkviAQYCKyBIR2SoiPwxjnCb0gqqjIvKwiBzE+eXsOxGKLRLqfP9u0/ReqrookoGZRqnvuecBnF/z/VqLSLKIbHATyJGM6ctuU98FItKrnvuGMy7cbjZ9gRUBq8PxWQWjtrgjdd1hGq6Hqp50l08BPaIZTIR8lTP/j2lSRORGnBbM26MdizmrJvU9bOLfu0HAVHG67X8iIpeG4yBJ4SjURI6IDMP5hfXqaMdSzePAs6pa7DbIiDVJwGXApUApsFxEtqjq8uiGZUJJVZ8HnheRrwCPAs2iX7mIJAC/w2nab5ogEbkLGAdcHrD6IlU9LiL9gBUikqqqByMQzvvAm6paISLfwPl1OizjMjXQ7cCCaq3sovVZmSZAVVVE4vaX02CIyE+BKuCNaMcSDiLSFvgJsXf9bAI0te9hM/jeJeF0hZyIc4/1LxHpp25/klBpyi0wjuP0L/Lr6a6rcRsRSQI6AjlB7hvtGBGRnsA7wD1huvBqTHwTgKdF5DDwP8BPRORbMRTfMWC1qma7zbEX4/TjMvGhvnX0LZpAc98Adb3/c3DGxlnl1sGJwEKxgTxjXVDfaxG5EvgpcENgyztVPe7+mwaswhkrKewxuV3w/HH8HRgb7L7hjCvA7VTrPhKmzyoYtcUdqesO03AZbncf3H/D0jQ6FojIfcB1wJ2hvvGIIf1xWmZtd8+TPYGtInJeVKMypzXR72FT/94dA/7jdpPcBPiAkA9S2pQTGJuBgSLSV0Ra4lzAVB9pfCGf/yJ7M7DCrSALgdvFmcGiLzAQ2BRLMbqj8S7CGVBqXRhia1R8qjpVVfuoah/g98CvVPVPsRIfzsA5I0SkrZvYuBzYHeL4TPjU+bcXkYEBT68F9kcwvnA76/tX1QJV7RZQBzfg3OwmRydcE6Rgvtejgb/h/D0zA9Z39o+D5I5oPoXQ/J8WTEznBzy9AdjjLi8BrnZj64zzi9OSEMQUVFxubBfjDIq5PmBduD6rYCwE7hHHRKDA7ZYQzs/KhEbg9cS9wHtRjCVsRGQ2Tt/8GwLG22lyVDVVVc8NOE8eA8ao6qkoh2Zout/DZvC9exdnIE9EZBDOINvZIT+KxsAopuF64Iz2vQ9npPKfuuuewKkMAK1xZsg4gJOg6Bew70/d/T4Drom1GHGaw5cAKQGPkI/02pjPMKCMxwnDLCQh+BvfhTPA6E7g6Wh/X+0R8r/9H9y/bwqwEhgW7Zgj+f6rbbsKm4UkLh5BfK+XARkB/+8vdNdPBlJxZuNIBR6IYEz/z61r2926dnHAvl91//89ANwfyc/Kff44zoBigfuF87N6EzgJeHAuTB8AHgIecl8X4Hk35tTAehnOz8oeIfk7dgWW4yTDlwFdoh1nmN7nAZzxWPz/x/w12nGG671We/0wcTobRLw/mvL3sCl/72r5u7UEXse5t9oKzAzHscUNwBhjjDHGGGOMMSZmNeUuJMYYY4wxxhhjjGkiLIFhjDHGGGOMMcaYmGcJDGOMMcYYY4wxxsQ8S2AYY4wxxhhjjDEm5lkCwxhjjDHGGGOMMTHPEhjGGGOMMcYYY4yJeZbAMMYYY4wxxhhjTMyzBIYxxhhjjDHGGGNiniUwjDHGGGOMMcYYE/MsgWGMMcYYY4wxxpiYZwkMY4wxxhhjjDHGxDxLYBhjjDHGGGOMMSbmWQLDGGOMMcYYY4wxMc8SGCakROR/RKRtHdscFpFu1da1FJHfi8gBEdkvIu+JSM+A138qIrtEZIeIpIjIhHC9B9O8icgFIrIgxGX2EZGd9dznPhG5IOD530VkaCjjqna84nCVbUxT1pD6bUw0NfY7KyKLRaTTWV4/41qwru1rKeMVEbm5oTEaE27xUI/OUnYXEVnq3nMtFZHOoSg3UiyBESNEJCnaMYTI/wBnTWDU4lfAOcBgVR0IvAv8RxyTgOuAMap6CXAlcDRUAZvYE836oKonVDUWLpruA04nMFT1a6q6O3rhmKYkVs45FodpruL5O6eqc1Q1/yybnHEtGMT2xjSI1aMGewRY7t5zLXefxw1LYISIm4XbKyJviMgeEVngz5qJyGMisllEdorICyIi7vpVbquDZOC/ReR6EdkoIttEZJmI9HC3e1xEXhWRNSKSLiJfEpGnRSRVRD4SkRaNjP0tEbk24PkrInKziCSKyDNu7DtE5Bvu69Pd2BcEvGcRke/g3HCtFJGV9Th+W+B+4H9V1Qugqi8DFcBM4HwgW1Ur3NeyVfVEY96zCa84rw+nM+puK4j/uOXuF5GnA7abLSJbRWS7iCwPiO37AdvsFJE+7tOkYD8PcX51Gge8IU6Lozbu5zPO3ecO9/3uFJGnAo5XLCJPujFt8H9mtbzPviKy3i3nlwHr24vIcve9pYrIje76J0TkfwK2e1JE/ltEzheR1W6cO0VkaiM+fhOkOK9j092yFwK7pZZzjbvtj9zjbheRX7vrRrnf7x0i8o6IdBaRi0VkU7XPJ9VdHisin4jIFhFZIiLn1/B5/FREDvnfm4h0CHxew3sY68a0HXi42nHXuPVnq4hMdtf/Q0TmBmz3hojcKCLDRGSTW392iMjAxny2JnLiuQ5Wex+tReRlt+xtIjLDXd9WRP4lIrvderZRPj8HHRaRbiLSTkQWuXVhp4jcJjVcC0pAy1sRucf9rm8XkdeCjPEX4lybJobqfZvYYPUovPVInBbt97jL3xCRN9yXbgRedZdfBebWtH/MUlV7hOAB9AEUmOI+fwn4vrvcJWC714Dr3eVVwJ8DXusMiLv8NeC37vLjwFqgBTASKAWucV97B5hbQzw/AFJqeDxXw7Y3Aa+6yy1xWje0AR4EHnXXtwKSgb7AdKAA6ImTBFsPXOZudxjoVsdndcY2wCXAthq2exb4DtDejX0f8Gfg8mj/ve3RpOtDH2Cnu3wfkAZ0BFoD6UAvoLtbT/oGvic3tu8HlLXTLa+hn8e4gNdW4SQ1LgCOuDEkASv879k9hn//p3Hrby1/o4XAPe7yw0Cxu5wEdHCXuwEHAHHfw1Z3fQJwEOgKfA/4qbs+ETgn2t+/5vCI8zo2HSgJqD+1nWuuAT4F2ga+L2AH7nkAeAL4vbucElDmj4BH3ffwKdDdXX8b8FItn8fLAXXpQf/nUcvnvwOY5i4/w+f/Z7QFWrvLA4Fkd/ly4F13uSNwyK1rfwTudNe3BNpE+7tlj2ZRB/sEfGe/F1AnLsY5v7QGvg/8zV0/HKjCPSfhXscBXwbmBZTbMfD1gPX+7YfhXMt1q/451RDjK8DNbv36q/9zskfTelg9Cns96oFzHTfV3cd/Hs0P2EYCn8fDw1pghNZRVV3nLr8OXOYuz3Azbqk4LQqGBewzP2C5J7DE3e4H1bb7UFU9QCrOTcJH7vpUnAp0BlV9RlVH1fD4Tg1xf+jG2ArngnG1qpYBVwP3iEgKsBHnZsX/69AmVT2mqj6civ2FGEJFVYuBsTgXlFnAfBG5L1zHMyETr/WhuuWqWqCq5cBu4CJgIk49OeSWnxtEOQ35PGpyKbBKVbNUtQp4A5jmvlYJfOAub+Hs9XIK8Ka7HJi9F+BXIrIDWAZcCPRQ1cNAjoiMxvm/YZuq5gCbgftF5HFghKoW1RG/CZ14rmOb/PWH2s81VwIvq2qpe4xcEekIdFLVT9x9X+Xz7/+/cBIUuP/OBwbjXDQudct/1H3fNX0ef8dpDYj778s1BS5OH+ROqrraXRVYf1oA89zP9G1gqBv7J8BAEekO3AH8262/64GfiMiPgIvcc6+JH/FcB/0uc2NHVffiJOoHuevfctfvxEnaVZcKXCUiT4nIVFUtqONYM4G3VTXbLbeuc+f/4dzMPaTunZZpkqwehakeqWoG8BiwEvheTdu6dSuu6pclMEKr+h9fRaQ1TquBm1V1BDAPJyPnVxKw/EfgT+5236i2nb/7hA/wBPxH7sP5FecMIvIDcZqkVn8894WgnRuzVcAsPr/oA+dG5tsBlbevqn4cGI/LW1MM9XAQ6C0i51RbPxbY5cboVdVVqvoz4Fs42UoT2+KyPtSgPt/1Ks78fzUw5oZ8HvUV+FkEUy9rOmHdidO6Y6yqjgIyAmL6O06rlPtxfiXBvYmbBhwHXvE3VTQREc91LDCOs51r6mM+cKuIDHJC1/1u2bsCyh6hqlfXFId7Ad1HRKYDie7FZn39L06dGYnTYqplwGv/AO7izPrzT+AGoAxYLCIzG3BMEz3xXAcbTVX3AWNwbsB+KSKPhfgQm4GxItIlxOWa2GL1KLz1aASQQ8C4akCGfN6d8nwgM8THDCtLYIRWb3EGnAT4Ck6zJX8lyhaR9jjN4WrTEecmAODexgTSgAzifJyLqql8np1cAnxTPu8TPEhE2tVx6CKcwTjrE2sJzq9ovxO3f6N7E9QWWCEig+XMfsGjcDKbJrbFc32oywZgmoj0BWc0Z3f9YZyTECIyBqcZvF99P4/a6tIm4HK332Qizq+5n9SwXV3WAbe7y3cGrO8IZKqqR5w+nBcFvPYOMBunFcgSABG5CMhQ1Xk4CY4xDYjFNExTqWO1nWuW4rTu8feH7uL+MpUnn4+1cjfu919VD+Ik7v6PzxPxnwHd/Z+TiLQQkbO1cvoH8E9qaX3hHicfyBcR/6+E1evPSfdi+W6cX/z8XsEZlA11B+QVkX5Amqo+B7yH06XSxI+mUAfX4H6H3eRfb5x6sw641V0/FOcm6AzizJRVqqqv43T18P//X9v5awVwi4h0dfevKzHxEfBrYFENP3KZpsPqUZjqkYiMx2ldPxr4vv+6Facbsf+zuhfn/BM3LIERWp8BD4vIHpz+WH9xL3Tm4fSFX4KTTa7N48DbIrIFyA5zrNV9jNNHd5mqVrrr/o7TZH6rOIMa/o26f9F9AfhI6h7Ec4eIHHMfvwN+DJQD+0RkP3ALcJObKW0PvCrOADg7cJrkPl7/t2giLJ7rw1mpahZOl6b/iDOIn/9m6d9AFxHZhdNSaF/AbvX9PF4B/upm/tsEHPskzmjRK4HtwBZVbciJ57/deFJxuon4vQGMc9ffA+wNOHale9x/qTvgLs54BttFZBtOC64/NCAW0zBNpY7VeK5R1Y9wLrKSxen+4R8g917gGfd8MApnHAy/+TitHP4Fp7+zNwNPuXU1BZh8lljewPks3zzLNuAk/J9345KA9X8G7nWPdTFntvDIAPZwZnLkVmCnW85wnASKiR9NoQ7+GUhw/8+fD9ynzqDpf8ZJ/u0GfonTIrZ60/YRwCb3+/szdzuo5VpQVXcBTwKfuHXkd3UFp6pv43yeCwPPhaZJsXoUhnokztAA84CvqjP5wfeAl0REcBKDV7n3XFe6z+OGf8AT00jizDTwgaoOj3IoxkSd1YemSUQSgK3ALW7zfBMlVsfCQ5wZgG5U1bvDUHZbnCbCY7TuPs4mxjX1Oui28GuhquUi0h9nTKTBAT9yGdNoVo9MQ8Tt3LnGGGMix236+AHwjiUvTFMkIn/EaWo7JwxlXwm8CDxryQsTJ9riTOHYAqeV0X/ZTZcx9Wb1KAysBYYJGxHZiDMlXqC7VTU1GvEY0xyJyE9xumQFeltVn4xGPMbEExF5HmfGnkB/UNVax8gwxgTPzlHGNF5zq0eWwDDGGGOMMcYYY0zMi6tBPGfPnu2fp9Ye9oiHR0yz+mSPOHzENKtT9oizR8yzOmWPOHvENKtP9ojDR43iKoGRnR1TExEYE9esPhkTWlanjAktq1PGhI7VJ9NUxFUCwxhjjDHGxBYReUlEMt1pcGt6XUTkORE5ICI7RGRMpGM0JhaJyGwR+cytG4/U8HorEZnvvr7RnbXD/9olIrJeRHaJSKqItI5k7MZEiyUwjDHGGGNMY7wCzD7L69cAA93Hg8BfIhCTMTHNnWLzeZz6MRS4w53xK9ADQJ6qDgCeBZ5y900CXgceUtVhwHTAE6HQjYmqoBIYQWQHp4nIVhGpcudQ96+fISIpAY9yEZnrvvaKiBwKeG1U6N6WMcYYY4yJBFVdDeSeZZMbgX+oYwPQSUTOj0x0xsSs8cABVU1zp9Z8C6euBLoReNVdXgBcISICXA3sUNXtAKqao6reCMVtTFTVmcAIMjt4BLgP+GfgSlVdqaqjVHUUMBMoBT4O2OQH/tdVNaXhb8MYY4wxxsSoC4GjAc+Pueu+QEQeFJFkEUnOysqKSHDGREkw9eL0NqpaBRQAXYFBgIrIEvdH5B/WdACrT6YpCqYFRp3ZQVU9rKo7AN9ZyrkZ+FBVSxscrTHGGFMPDe1fLCItRORVt1/xHhH5caRjN6Y5UtUXVHWcqo7r3r17tMMxJlYlAZcBd7r/3iQiV1TfyOqTaYqCSWAEnTWvw+3Am9XWPekO5vSsiLSqaSfLHDbe7gOHePC/f8DgocMZPPhivvSlL7F06dJoh2VMTFFVjueXsfN4AbklldEOx4RAY/oXA7cArVR1BDAW+Ebg4Gmmbl6fkpZVzO4ThRSUWdfsZu440CvgeU93nQlQWeVj94lCjuSUolrrDIKm6QimXpzexh33oiOQg3M/tlpVs90fhxcDNjhuHcoqvezLKOJgVjFen9WxeJUUiYO4/RxHAEsCVv8YOAW0BF4AfgQ8UX1fVX3BfZ1x48bZN60eKqt8PPPifJ78/kOUlxQxYNRE2nfoxKpP1vDOO+/w9a9/nT/+8Y+0alVj7siYZqPc42XJrlOkZZWcXjeqVyemDuxGUqKNdRzHTrcgBBARfwvC3QHb3Ag87i4vAP7k9i9WoJ17wdgGqAQKIxR33Cso9fBuyvHTycCkBGFi/66Mu6gzzsdrmpmFwLfcOjgBKFDVk1GOKabkl1byr+SjlFQ4wxgMOLc9s4adR8skOwc1YZuBgSLSFydRcTvwlWrbLATuBdbjtGZfoaoqIkuAH4pIW5zz0+U4SXhTi+P5ZSzaceJ0HevctgVzR19Ip7YtoxyZqa9gEhihyJrfCryjqqd/ggk4cVWIyMvA9+tZpjmLyiofP/ztPJ77yX9x3kUDuPe5f3Fuz74AVFVWsuXdF5g3748cPXqU999/n6SkiOSyjIk5Xp/y/vYTHMsrO72uylPJi6++xouFBm1t9gAAIABJREFUp5g8rC933nknHTt2jGKUpoFqakE4obZtVLVKRPz9ixfgJDdOAm2B/1XVLwxSKCIP4syqQO/evUMdf1wqrqhifvKR0xeJAKeOpfOTF3/HOVLO1HGX8NWvftXqVBMiIm/izILQTUSOAT8DWgCo6l9xfh2eAxzAGQ/t/uhEGpsqq3ws3H7ijDpzILOYpIQMZg8/z5J+TZR7zvkWzg+8icBLqrpLRJ4AklV1IfAi8JqIHMAZKPd2d988EfkdThJEgcWquigqbyQOFJR6eHfbcSqrPh/tIK/Uw/zNR/nKhN6c07pFFKMz9RXMXWsw2cG63IHT4uI0ETlfVU+6v3TNBWqcO9zUn8+nPL9gKX/52f/Sa9AIvvnUy7Rq0+7060ktWzLh1m/Rq/dF/Oax7/O9732PP/zhD1GM2JjoWX8w54zkxdF9qbz882+Tn+XkWN8AHnnkEf7+979z6623RilKEwXjAS9wAdAZWCMiy/ytOfysleCZVJWPd506fSOmqix/6wU+fOVZJCGRtu078M4/X+GJJ57g3//+NzNnzoxyxCYUVPWOOl5X4OEIhRN3Uo7mk1P8xa6Le08V0atLW4ZfaMm+pkpVF+Mk+ALXPRawXI7TpbGmfV/HmUrVnIXPp3y48+QZyQu/4nIP7yUf5rYJfWnRwn7MjRd1tktzR7z1Zwf3AP/yZwdF5AYAEbnUzbjfAvxNRHb593f7DPcCPqlW9BsikgqkAt2AXzb+7RiA9ftP8qvvfYO2HTrx1cefPyN5EeiCiddz/zce5rnnnuPdd9+NcJTGRF9eSSVb0vNOP09LTeb5799DQkICX39yHs98uIvv/fkdLh46jNtvv5158+ZFMVrTAI3pX/wV4CNV9ahqJrAOGBf2iOPcnpNFpOd8Plb3opd+x+KXf8fIadfwf6+v4Im31/PTee9ywQUXMnv2bD788MMoRmtM9JV7vCSn1z4D7boD2VRU2eyYxjTU/sxiThaUn7FOVVnz3uv88u6Z3D11EF27dePRRx+lvLy8llJMLAmqY52qLlbVQaraX1WfdNc95jZtQlU3q2pPVW2nql1VdVjAvodV9UJV9VUrc6aqjlDV4ap6l6oWh/KNNVcFpR5+/vOfk30inTt+8Gs6dDn7iMOX3votRoy4hIcffpiCgoIIRWlqE8SMCd8Vkd3u4LfLReSigNe8IpLiPhZGNvL4tOZANj53oLTC3Cxe/eV/07HbuXzn928x5NJpJCYmceGAoXznN/9g1qxZPPzww2zcuDHKUZt6ON2CUERa4rQgrF43/P2LIaB/Mc704DMBRKQdMBHYG5Go45TXp2xIyzn9fMfaj1kx/wUmzrmNu378Wzp27QFA14uG8OSL/2HYsGHceeedpKenRytkY6Iu9XgBFZ7aJ/ErrfSekWg3xgTPV+28BOD1VvHWb37MO8//gi7n9eTar36P/iMn8uSTTzJ79my7H4oDNjJQE/PaR+tYPv9Fxs/6EgNHTaxz+3JvAg89+mtOnjzJY489Vuf2JnyCnDFhGzBOVS/B6aP/dMBrZao6yn3cEJGg41hWUQUHM528qary1m9+THlpMff93x/p0PXcM7ctU5587gUuvPBCbrvtNkpLbTboeBBMC0Kc/sVd3f7F3wX8icPngfZui8LNwMvudOGmFoGzjRTlZfPWb39C78GX8KWHHyUh4czLjfRi4e+vvoHX6+Wee+6xGRdMs6Sq7DlZ+9jAPq+Xw3tSWLB4OYUldt4xpr4OZhV/YWa5D1/5PZuXvsOsu7/Nw795jStuf5B7/u85HvvdX1m3bh133XWXnZNinCUwmpCMwnLmPftrWrRuzXVf+0HQ+1V26c/d997PX/7yF/slLLpOz5igqpWAf8aE01R1pTtdFsAGnCbxpgG2H80/vbx7w0r2Jq/h2q9+l/P7Dqpx+z25Pl555RXS09P5zW9+E6kwTSMF0YKwXFVvUdUBqjreP8aFqha764ep6lBVfSaa7yPWqSrbjn7+K/Hil5+lsryMr/zoaZJafHGEd58qp+jMM888w+rVq3n77bcjGa4xMSGzqKLGsS8ADu7YzFNfm8Nz/30bz37ndgYNHMh7770X4QiNiW+7TpyZIPxsyzpWzJ/HxDm3Mevub50xQO65o67g108/wwcffMCf/vSnSIdq6sESGE3I6x+sYsfaJVz+pftp37FL0PupwsyvfBMR4Re/+EUYIzR1qGnGhAvPsv0DQGAH8tYikiwiG0Rkbk07iMiD7jbJWVlZjY84TpV7vOw95ZzUvFUeFr7wFD1692fK9bWPT5xZWEHPoWO55ZZb+PWvf82xY8ciFa4xMe94ftnpG7HjB/ewacm/mTr37tOzX9Vkf2YRX77jbkaOHMkPfvAD63tsmp3dtbS+2Lf1U/72kwcAuPNHz3Dvo3+gVfvOfPnLX+att96KZIjGxK2icg+Hc0pOP/dWefjPn56ge88+zH3ox1/YvrLKx2U33s0111zDT37yEzIyMiIZrqkHS2A0EZmF5bz8p2doc05HLv/yffXeP5cO3PfAg7zyyiukpaXVvYOJKhG5C2dAwcBfhS9S1XE4gw/+XkT6V99PVV9Q1XGqOq5797OPj9KU7TlZiMfrNA/cunIRWccPc90D3ycx6ezTaG1Nz+Opp57C4/Hw29/+NhKhGhMXUo993md42T//Qqs27bj6zv866z6qsON4EU8//TRHjhzhtddeC3eYxsQMVSUtq+QL6wtzMnntV/9L9wsv4tu/f5OxV9zAyGmz+eZvXmPMpRO4//772bvXhuMxpi57ThYR2BPk00XzyTp+mBu/8WNatm5T4z7bj+Xz2989S3l5OY8//nhkAjX1ZgmMJuKDtVvZvWElU2+8izbtzmlQGVO/fD8iwh//+McQR2eCFMyMCYjIlcBPgRtUtcK/XlWPu/+mAauA0eEMNp59dqoIAJ/Px4p/zeP8voMYOnFGnfsdyyujfbcLuOOOO3jhhRfIycmpcx9jmrpyj5f97ngyWccPs2Ptx0y54U7atO9Q5767ThQwdfpMxo4dyzPPPIPXa7MtmOYhr9RDoTtmTKC3//AYlRXl3PvoH85oTduqTTu+9cs/0a5dO+655x58vtoH/jTGwL6MotPLnsoKlr7xZwaMnMCQ8ZfXuk9JhZcWXS7goYceYt68eRw6dCgSoZp6sgRGE1BaWcUrL/yZhKQkJl931qnYzypHz+GmL9/Miy++SGFh7YNKmbCpc8YEERkN/A0neZEZsL6ziLRyl7sBU4DdEYs8jhSUek5Pp7Vn0ydkpB9g5q1fP6Mf5NmkHM3nhz/8IaWlpTz//PPhDNWYuJCWVYLX5/zMtWrBSyQmtWDaTfcEta/Hq+w5VcQjjzzC/v37bUpv02wENm3325+ygV0bVjLr7m9xbq9+X3g9n/Y89ZvfsnnzZubPnx+JMI2JSwVlHrKKTv/GR/Ky9yjOz+GqO/+rzuu9HccK+NGPfoSI8Ic//CHcoZoGsARGE7B+z1E2LnmHMdOvq3Pa1LPxqTLrtgcoKiripZdeCmGEJhhBzpjwDNAeeLvadKlDgGQR2Q6sBH6tqpbAqMFnARn5Tz94kw5dujPq8muC3n9fRhGDLh7KrFmzmDdvHlVVVeEI05i4sT/TqVPlpcVsWf4+Y2ZexzmduwW9/87jBcydO5fevXvz17/+NVxhGhNT0qslMFSVxS8/S8duPbjsxrtr3MfrU0ZNv45LLrmERx99FI/niy04jDGQllV8etnn8/HJgpfoOWAYA0ZOqHPf9JxSzunSgzvuuIO///3v5OXZNMaxxhIYcU5VefnVf1BZXsrUuTWf8OqjsnNfJk6cxN/+9jebQigKgpgx4UpV7VF9ulRV/VRVR6jqSPffF6P5PmKZ/2YrL/MEezevZsLsm+sc+yJQZZWPfRlFPPjggxw7doyPPvooXKEaE/PKPV7Sc5yJkbatXERleSmT5txerzJyiivJLPbwta99jWXLlnHw4MFwhGpMzKjy+jiWW3bGuoOpm0nfk8JVX/kmLVu1rnXfg1ml/OIXvyAtLY1///vf4Q7VmLh0MGB8mQMpG8g8dohpX7o36Na2e08V8t3vfpeSkhL+8Y9/hCtM00CWwIhzJwrKWbVwPhcOGErPgcMaXV65x8t1t97J3r17Wb9+fQgiNCZ2FFdUkVnoNCnc+JFz4Tdh9s31Lmf3yUKuv/56evTowQsvvBDSGI2JJ4dzPu8+sn7xfM7vN5jeF19S73J2Hi/gq1/9KgkJCcybNy/UYRoTUzKLKqjynfkj0Zp3X6PtOZ0Yd2WNk4iddjy/jGlXzmLAgAE899xz4QzTmLhUUeXleN7nCcKNHy2gzTkdGTltdtBl7D1VxMiRIxk3bhwvvvii/agbYyyBEefeW7aW4wf3NOgmrDa9x11J+/btefFF+xHfNC2Hs52MvKqSvPQdBo6eTJfzeta7nON5ZZR4lPvuu4/FixfTnKekNc2bv06dOryfY/t3MWHWzUH/whVof2YxPc47nzlz5vD666/bAIWmSTtZcGbri7zME+z8dBkT59xa6+wIgQ5mlfDtb3+b9evXk5ycHK4wjYlLx/PK8LkJh5LCfFLXLWXszOtp0bJV0GXkllSSWVTBAw88QGpqKlu2bAlXuKYBLIERxzxeH2+9/ipJLVoyZsZ1ISs3u1y46cu3MH/+fIqLi+vewZg4kebebKXvSSE34zhjr7ihjj1q99mpIu688068Xi9vv/12qEI0Jm6o6unuI1tXfoAkJDB6+pwGlVVZ5eNQdgl33nknx48fZ82aNaEM1ZiYciK//IznyUvfRX0+Jl97W1D7788o5t5776V169a8/PLL4QjRmLh1NKD1Rconi6nyVDJ+1pfrXc7eU0XcfvvttG7dmldeeSWEEZrGsgRGHDtwKp+tKxcxfPKVtD2nY0jLnjT7S5SUlPDee++FtFxjoqXK6+OIO2ja1pUfkNSyFSMmX9ng8vZlFDFixAiGDRvGP//5z1CFaUzcyCisoLTSi6qydeUHDBo9uV6Dd1a391QR119/PW3btuXNN98MYaTGxA5VPaMFhqqSvOw9+o8cH3SLwJMF5SS2bsdNN93EW2+9RUVFRd07GdNMHM0tPb2c8smH9Ojdnwv7D6l3OfsziujYsSPXXnstCxYssGm+Y4glMOLY2+99SElhPmNmhq71hV+LC4bQq1cvu4g0TcbJgnI8XsXrrSLlkw8ZNmE6rdu1b3B52cWVZBdX8JWvfIV169aRnp4ewmiNiX3+aSDT924n99QxRs+4tlHlHcouIalVa+bOncvbb79tMyyYJqmgzENJxec3Qkf27iDr+GHGXXFjvcpJyyrm7rvvJjc3l0WLFoU6TGPiUlml9/T0qYU5maSlbmbU5XMa1LWxqLyKU4Xl3HbbbWRkZLB69epQh2saKKgEhojMFpHPROSAiDxSw+vTRGSriFSJyM3VXvO60z0GTvmIiPQVkY1umfNFpGXj307z4fH6+OCdt2nTvgMXj50a8vKLKrxcN/dmlixZQk5OTsjLNybSjuY5GflDO7dQnJ/DyHpMnVqbfaeKuO02p8nvf/7zn0aXZ0w8OeJ2H0ld+zEJiUmNatEEzhSRh7JLuPXWW8nNzWXVqlUhiNKY2HKq8MzuI9tWLSKpRUtGTg1+gEGAg1nFXHXVVZx77rnMnz8/lCEaE7eO53/e+mL7miWoKqMacb23P6OYOXPm0LZtW6tnMaTOBIaIJALPA9cAQ4E7RGRotc2OAPcBNbWjLqs+5aPrKeBZVR0A5AEPNCD+ZuuzY9nsWLeUS6bOIqlleHI/o2ZcS1VVFQsWLAhL+cZEkn/KutRPl5HUoiVDLp3W6DIPZBXTv39/RowYwbvvvtvo8oyJF5VVPk4VlqOqpK5bxsBRE2nTvkOjy92fUczVV19N27Zteeedd0IQqTGxxT8TFuDWn6UMHjul3i0Cj+aW4UWYO3cuixYtory8vO6djGniAseX2bH2Y867aCA9evdvcHkHMotp27Yt1113He+8844NMB0jgmmBMR44oKppqloJvAWc0c5NVQ+r6g4gqL+qOO14ZgL+O+NXgbPPG2XOMP/dD6goK2X05Q0bMC0Y3s4XMXjwYBug0MS9yiofJwucm62dny5n0JgptGrTrtHl5hRXklNcwdy5c1m7dq3NRmKajVMF5Xh9yqnD+8k+kc6IKY1rfeF3OLuExBatmD17Nu+9955dLJomx9+8HeDY/p3kZZ5gxGVX17scr085mlvKl77kjFn28ccfhzJMEyFBtHJv5bZUP+C2XO/jru8jImUBrdz/GunYY5F/fJmSwjwO7dzC8MlXNKq8gjIP2cWVzJ07l8zMTDZt2hSKME0jBZPAuBA4GvD8mLsuWK1FJFlENoiIP0nRFchX1aq6yhSRB939k+3mwOHzKR8vep+253Si/8jxYTtOucfHlXOuZ9WqVdaNxMS1E/nOlFrHD+4hL+N4yG62wMnO33TTTfh8Pt5///2QlWtMLDvmdsna+ekyRIThkxp3kehX5VMO55Qwd+5cTpw4webNm0NSrjGxQFXJDEhg7Fi7lITEJIZNnNmg8g5llzJjxgw6depk3RjjUJCt3B8A8twW68/itGD3OxjQyv2hiAQdw6q8PjLcFk57Nq3G5/MyLATnprSsYmbPnk1SUhILFy6sewcTdpEYxPMiVR0HfAX4vYjUqx2Pqr6gquNUdVz37t3DE2GcOZxVQOr6FQyfPJPExKSwHmvopKvwer12Y2bi2vF8JyO/a8MKRKTBF4s1OZBVzKhRo+jdu7fVE9NsHMvz16mV9Bp8CR26nhuysg9mFnPdddeRmJhodco0KUUVVZR7Ph/Ac/fGlfQbPpZ2HTo1qLzD2SW0aNGCOXPmsHjxYmuxFH/qbOXuPn/VXV4AXCENGZGyGcgsqsDrU8C53junS3d6DRre6HIPZpXQuXNnpk2bZgmMGBFMAuM40CvgeU93XVBU9bj7bxqwChgN5ACdRMR/912vMpu7dxZ9TFlxISOm1L/JYX1J93707t3bMvsmrvkTGHs2rab3xSNp36lLyMrOLKyguKKKa6+9lmXLltl0dqbJ83id8S+K8nI4ui+VYROmh7T8QzkldOjYicmTJ7N48eKQlm1MNAWOf5GXeYKTh/YxtBH1p7iiiqyiCq699lqysrJITk4OQZQmgoJp5X56G7flegFOS3aAviKyTUQ+EZEaR/RvTi3Z/d1HvFUe9iavYeiE6SQkNP63+ozCcorKPdxwww3s2rWLtLS0RpdpGieYv+pmYKA7a0hL4HYgqPSTiHQWkVbucjdgCrBbVRVYCfhnLLkXeK++wTdXHyx8l1Zt2jJozOSwH6vc4+Oqa67n448/pqioKOzHMybUvD4lo6Cc4vxcjn62IySDd1aXllXCnDlzKC4uZu3atSEv35hY4h//Ym/yGlSVIeMvD2n5FR4fx/PKmDNnDtu2bePEiRMhLd+YaAkc/2LPJmdKxsbWn8M5pcyaNYuEhARL+DUvJ4Heqjoa+C7wTxH5wkjKzakl+8kCZwDPw7tTqCgtYciloTs3Hc4u5ZprnNlMlixZErJyTcPUmcBws33fApYAe4B/qeouEXlCRG4AEJFLReQYcAvwNxHZ5e4+BEgWke04CYtfq+pu97UfAd8VkQM4mcQXQ/nGmqq8kgq2rl3OxeOm0qJlq4gcc9jkK6ioqGDZsmUROZ4xoZRZVE5VGG+2ANKyi5kxYwatWrWyC0jT5PkvEvds+oRzunTngv5DQn6Mg9nO1HUAH330UcjLN6EVxECEvUVkpftr8Q4RCd8I5DEsuzgggbH5E7qc15Nze/VrVJmHc0ro2rUrEydOZNGiRY0N0URWMK3cT2/jtlzvCOSoaoWq5gCo6hbgIDAo7BHHMP/4F3uT15CQkMjA0RNDVvahnBIGDhxInz59LIERA4JqV6Oqi1V1kKr2V9Un3XWPqepCd3mzqvZU1Xaq2lVVh7nrP1XVEao60v33xYAy01R1vKoOUNVbVNXaXQfhw1XrKczJZGgI+/DXpU2voXTq1Mn6Ipu45J9Sa8/m1ZzTuRsXDqg+PlbjHc0to0WrNkyfPt0SGKbJO1lQhtdbxWdb1jLk0qkhaaJb3aGsEoYPH07Pnj2tTsW4IAcifBTnB7DROC15/xzZKGNDbkkl4DRxP5CygYvHXUZjhzM4mV9OucfLrFmz2LJliw26Hl+CaeW+EKelOjgt11eoqopId7fuISL9gIFAs+3bUFbppbDMAzgJjD5DR9Om3TkhK/9obilenzJ79myWL19OZWVlyMo29ReJQTxNCL3/wfuISFiawdemrEqYfsVVLFq0yAaIMnHnRH4ZPp+PfVvXMXjslLDcbHl9ypHcEmbNmsXevXs5evRo3TsZE4dUlZMF5Rzdt5Oy4kIGj62x23WjFZR5yCv1MGvWLJYvX47X6617JxMtwQxEqIC/eXtHoNn1C6ry+sgvdW6w0vdup6KslEFjpjS6XJ8qx/JKueqqq1BVVqxY0egyTWQE08odp4V6V7fF+ncBfwunacAOEUnBGdzzIVXNjew7iB2ZRc6PVUV52Rw/sJuLLw3tuamyysfx/DJmz55NcXEx69atC2n5pn4sgRFHqrw+1q34mIuGjArpIITBGDn5CjIzM21KOxN3ThWUcyJtLyUFeWEdN+ZQtnMBCbB06dKwHceYaMov9VBW6WXflnWISEib6FZ3OKeEq666ivz8fBucMLYFMxDh48BdbnfjxcC3ayusqQ46mFfqwafODAn7tq5HRBgwckJIyk7PKeXSSy+lQ4cOdv6JM0G0ci93W6oPcFuup7nr/62qw9wpVMeoarNuJu3vPrJv23qAsFzvHcouYcaMGSQmJlq3+iizBEYc2bbnEEf37WTohBkRP3aPYRNtSjsTd4rKPRRXVLFvq5MpHzQ6fAmMw9klDB06lPPPP98uIE2TdcId5X3f1k+5cMBQ2ncMXzI9LauEK664AhHh448/DttxTETcAbyiqj2BOcBrIlLjNWhTHXTQ330EYP+2T+k5aDhtz+kYkrLTc0pJSkpixowZdmNlmiV/C4z929bTpn0Heg4YFvJjpOeU0qFDB8aPH28tnaLMEhhxZMHCD4DGj1jdEOUJbRg/YSIffvhhxI9tTENlFDontM+2rOO8PoPo0PXcsB2ruKKK7JJKrrzySpYtW2bdrUyTlFFYTnlpMYf3pISk+fvZnMgvp33HzowZM8aSgrEtmIEIHwD+BaCq64HWQLeIRBcjckqcX4grykpI37sjpAn1gjIP+aXO+efQoUM2zaNpdjIKK1BV9qdsYMDICSQkJob8GLkllRSUeZg5cyabN2+msLAw5McwwbEERhxZtXwpHbqeywX9Bkfl+GOmzGDr1q1kZGRE5fjG1FdGYQWVFeUc2rmFwRGYdviw240kOzublJSUsB/PmEg7VVDBwR2b8Xmrwl6nAvv2r1+/3qbyjl3BDER4BLgCQESG4CQwmk7/kCDkFDstMA7t2orPWxWy7iN+R3JLmTHDaaH7ySefhLRsY2JZuccZwDPn5FHyMo4zcPSksB0rPcdpGej1eq2eRZElMOJEYWk5qZvWcPG4qY0esbqh+ox0LlZt+iATL04VlJO+extVnsqwntD8DrsnNoCVK1eG/XjGRFKV10d2cQUHUjaQ1KIlfYaNCfsxD2WXcsUVV1BVVcXatWvDfjxTf0EORPg94Osish14E7hP1R0QopnwdyE5uGMzCYlJ9Bk6OqTlp+eUMnToULp162Y3VqZZ8U9PvD9lAwADR4VzbKZSJk2aROvWrVm+fHnYjmPOzhIYcWLRsjWUFReGfFTd+kg6tx89evTgo48+iloMxgRLVTlVWM7+lA0kJCTSb/i4sB/zZH45Xbr3YPDgwdY/0jQ52cWVeH3Kge0b6TtsDC1atgr7MdNzSpg0aRItW7a0pGAMC2Igwt2qOkVVR7qDDjarQU18Pj09A8nBHZvoPXgErdq0DekxjuaVogrTp09n1apVIS3bmFiWVeQkMA5s30iHLt05t1e/sB3raG4pLVq2YsqUKVbPosgSGHHig0WLSUhIZHCY+xyfjSJMvnwmS5YssSntwkREZovIZyJyQEQeqeH174rIbhHZISLLReSigNfuFZH97uPe6vs2N3mlHiqrfBzYvpGeg4bTul37sB/T3+R9xowZrF69Go/HE/ZjGhMpGYXllBTmcfzgHgaE8ReuQEXlVZRpEhMnTrSkoIlbheXODCQVZSUc+Sw15N1HACo8PjKLKpg+fTrp6ekcOnQo5McwJhZlF1eiqhzcsYn+l4wPa0v1yiofpwrLufzyy9mxYwe5uc125tqosgRGnFi/ZgW9L76ENu071L1xGF08bhq5ubls3bo1qnE0RSKSCDwPXAMMBe4QkaHVNtsGjFPVS3Dm/X7a3bcL8DNgAjAe+JmIdI5U7LEos6j89MViOJsTVnc4u5SZM2dSXFzMli1bInZcY8LtVGE5B7ZvAsLbRLe6w9klzJw5k61bt5KXlxex4xoTKnlu64v0PdvxeavoNyI8LQKP5pUybdo0ANasWROWYxgTa7KLK8g+cYTCnEz6X3Jp2I+XnlPC9OnTUVWrZ1FiCYw4cOjYKQ7vTWXwuMuiHQrdLx4LYFPahcd44ICqpqlqJfAWcGPgBqq6UlVL3acbcEZ7B5gFLFXVXFXNA5YCsyMUd0zKLKwgbeeWsAyWdjaHc0q4/HJnpiD7xdg0JZmF5RzYvpGWrdvSa9DwiB03Pcdp1aSq1rffxKW8Umf8i7SdyUhCAn2GhHb8C78jOaUMGzaMTp062Y2VaRZ8PiWnuIKDO5zkev9Lxof9mEdyShk/fjytW7e2c1KUWAIjDvxn0RJUNardR05r3ZERI0fZlHbhcSFwNOD5MXddbR4A/PPaBrWviDwoIskikpyV1bQHgM8sck5o4Rgs7WyKyqthBYwVAAAgAElEQVRIateJYcOG2YktyoLoktVKROa7r28UkT4Br10iIutFZJeIpIpI60jGHms8Xh85JZUc3L6JvsPHkJjUImLHPpFfxphxl9rFoolb+W4C49DOLVzQ7+KwdWk8kV+GT2HKlCk26K1pFvLLPHi8TveR9p26hnX8C79TheVoQhKTJk2ycTCixBIYcWDZ0o9p3e4ceg0eEe1QABg9aRqffvqpTWkXRSJyFzAOeKY++6nqC6o6TlXHde/ePTzBxQBVJbOonLTU5LAMllaXdLcVxrp166iqqorosY0jyC5ZDwB5qjoAeBZ4yt03CXgdeEhVhwHTgWY9oEl2cQVF+bmcSt9P/xHh/4UrUJVPySr1MXHiREtgmLiUV+LBW+Uhfc92+g0fG7bjVPmUkwXlTJ06lb1799LUf6gwxj8DSdrOLfQfMS4iMzWqOoN5Tps2je3bt1NYWBj2Y5ozWQIjxvl8PjatXcXAURNJTEyKdjgA9B4xEY/HYxeSoXcc6BXwvKe77gwiciXwU+AGVa2oz77NRWFZFYWFxRz5LJV+I8LfH7K6I7mlXH755ZSUlNh4MdFTZ5cs9/mr7vIC4Apxrn6uBnao6nYAVc1R1WY9cnFWUQVpqckAEeljXJ0/KZiSkkJ+fn7Ej29MY+SXeTh2YA+VFWX0DWMCA5wbq8suc7ocWysM09TlFFeSl3mSvIzj9I3AbHN+R3JLmTp1Kj6fj08//TRixzWOoBIYQTTDnSYiW0WkSkRuDlg/KqAJ7g4RuS3gtVdE5JCIpLiPUaF5S03LttQ95GacYNCYydEO5bSOfUbQpk0b60YSepuBgSLSV0RaArcDCwM3EJHRwN9wkheZAS8tAa4Wkc7u4J1Xu+uapcyictL3pODzVkXlZutobimTpzgXkJboi5pgulWd3kZVq4ACoCswCFARWeKe235Y20GaS7eszEKnS1aLVq0jOv6Fnz8pqKqsW7cu4sc3pqE8Xh9F5R4O73KS2f2GhTeBcSS3lHHjxtGyZUu7sTJNXk5JBWk7neR6uAbHrUl6TikTJkwgMTHRxpuJgjoTGEE2wz0C3Af8s9r6UuAetwnubOD3ItIp4PUfuPOBj1LVlAa+hybt3cXOPeigWBj/wpXYoiVjJ0xi+fLl0Q6lSXFvoL6Fk3jYA/xLVXeJyBMicoO72TNAe+BtN/G30N03F/gFThJkM/CEu65Z8o9/IQkJ9B02JuLH93gVbdOJwYMHWwIjPiUBlwF3uv/eJCJX1LRhc+mW5dSpzfQZMoqkFi0jfvyc4kqGjRpLy5YtrU6ZuFJQ5kEVDu/eSpfzetKh67lhPV5GYQUkJjFu3DhLYJgmL6e4kkM7k2nVth0X9B0cseMWlHnwJrRizJgx1tIpCoJpgRHMzAiHVXUH4Ku2fp+q7neXTwCZQNO9wguDlStX0PncC+h2Qe9oh3KGIWMns2vXLk6ePBntUJoUVV2sqoNUtb+qPumue0xV/YmKK1W1R0Di74aAfV9S1QHu4+VovYdYkFVUwcHUZHoOGErrtuEZLK0uR9z+kWvXrsXrbda9D6IlmG5Vp7dxx73oCOTgtNZYrarZ7qw/i4HIZ8JihNenHD2VxclDn0WlS5ZfZqkyfvx4Vq9eHbUYjKkvJ4GhHNq9LSIDSvtUOZZXxuTJk0lOTqaioqLunYyJQ1VeH/mlHtJSt9Bn6GgSEhMjenx/N5KNGzdaPYuwYBIY9Z0ZoUYiMh5oCRwMWP2k27XkWRFpVd8ymzqv18u2DWsZOHpiRAalqY8eQ5yLWJsm0sSik7mFHNm7nX4R7A9ZXXqOc2IrKChg586dUYujGauzS5b7/F53+WZghaoqTiuoESLS1k1sXA7sjlDcMSe3pJIDqVtQ1Yg20a3uqHuxuGXLFkpKSqIWhzH1kV/qIS/zBIU5mfSN0IxYR3NLmTx5MpWVlTYOk2my8ko9FBXkcSp9f1Su9464481UVFSwZcuWiB+/OYvIIJ4icj7wGnC/qvpbafwYuBi4FOgC/KiWfZtF/+KarN2QTGlRAQNHx874F36dLhxI5y5dWLZsWbRDMeYMZZVe9qSmUOWpjOiATtVlFpVz6QSn7lr/yMgLskvWi0BXETkAfBd4xN03D/gdThIkBdiqqosi/R5iRVaR08c4MakFF108MmpxpLsXi1VVVWzcuDFqcRhTH4VlHg7v3gZAnwh1aTyaV8akSZMArBuJabJySypJ3+OMQBCN7sJHcktP1zMbmymygklgNGp2AxHpACwCfqqqG/zrVfWkOiqAl3G6qnxBc+lfXJP3P3TGvxg4amKUI/mihMRExkyYwrJly3B+sDQmNmQXV5CW6mTCwzldXV1UQTp0p2fPnpbAiJIgumSVq+otbrer8aqaFrDv66o6TFWHq2qtg3g2B06dSqbnwGG0bN0manGUVXoZNGIsImJ1ysSNAjeB0bJ1W87rMzAix8wuqqBDl27069fPEhimycopruDQrq0kJCbRe/AlET9+ucdLQttODBgwwBIYERZMAiOYZrg1crd/B/iHqi6o9tr57r8CzAWsjXU1q1au5LyLBtKhS2wmbgaMmsSxY8c4cOBAtEMx5rRM99fic3v1o32nLlGN5UhuGVOnTmXNmjWW6DNx63h2Pkf37YxqQtAv39uCkSNH2jgYJm4UlHlI35NC78EjSExMithxj+WVMXHiRNavX2/nnxgWxEyPrURkvvv6RhHpU+313iJSLCLfj1TMsSKnpJLDu7dx4YAhUUuuH8ktZcqUKXz66adWzyKozgRGMM1wReRSETkG3AL8TUR2ubvfCkwD7qthutQ3RCQVSAW6Ab8M6TuLc5WVlaRu2cDA0bHX+sLv3MHOxayNg2FiSWZhGYd3b6NvDNxs+ftHnjx5krS0tLp3MCYGbUlOxlvlieqYMn7+sWU2bNiAx+OJdjjGnJWqkpVfxPGDeyMygGego27z9pMnT3Ls2LGIHtsEJ8iZHh8A8lR1APAs8FS1138HfBjuWGNRVmEJRz7bQd+h0RtjOz3HSWBkZWWxf//+qMXR3AQ1BkYQzXA3q2pPVW2nql3daVP9TXBbBMyYcHq6VFWdqaoj3Oa5d6lqcbjeZDxateZTKivKY7L7iF+n8y/ivPMvYOXKldEOxZjTUranUlZcGBM3W4VlHkaOc+qwNS808aikooo9KZuByPXfP5sT+WVMnDyZ0tJSUlJs9nUT24orqkj/LBWftyri48cczS1l4kTn/LNhw4Y6tjZRUudMj+7zV93lBcAVbut1RGQucAjYRTPj9Sm7dmzHU1FOn2GRTQ4GOpFfxoSJNg5GpEVkEE9Tfx989DGSkED/S2ocGiQmiAgjx1/GihUrrNmUiQlen7It2RncLxaauwO07dGHjh072onNxCVnAM8t9LhoAO06dIp2OFT5lP5u3V67dm2UozHm7PJLPaTv2Q5A7wgnMPJKPfQZNITWrVtbAiN2BTPT4+lt3FbxBTiDT7fHmQDh5xGIM+YUlHk4uNOZYScaA3j6VfmUjuf3oVOnTjbeTARZAiNGfbJqFT0HDKVN+w7RDuWseo+4lKysLHbtanbJXxOD8ksrOZiazDldutPlvJ7RDgeAY/nlTJ482W62TFw63SVrWGwkBAEqW3Wib9++VqdMzHPGv9hOl/N6ck7nrhE/fmaxl7Fjx7J+/fqIH9uE3ePAs3W1YG+qszn6ZyDp3ONCOnbtEdVYjuWXM2nSJKtnEWQJjBhUWlrKrpRkBsRw9xG/8y52mukvX748ypEYA9nFlRzetZV+w8bgtrCMuqN5Tv/I3bt3k5ubG+1wjKmXLSk7KC8pot/w6Hcf8fOPLbNu3Tpr/WdiWmGZh/S9KVw0ZFTdG4fB0bxSJkyYwNatW6msrIxKDOasgpnp8fQ2IpIEdARygAnA0yJyGPgf4Cci8q3qB2iqsznmlVZyeNc2+kSpbgXyT6e6a9cu8vPzox1Os2AJjBi0cvVqvFUeBoycEO1Q6tT53Avo3aevjYNhYsKeA4fJzTgeE331/So8PoaMuhTAmheauLNxg/OdjaUWGJmFFVw6YSIZGRkcPHgw2uEYU6u09CMUZGdw0cWRn+IRnHEwxo8fT0VFBTt27IhKDOasgpnpcSFwr7t8M7BCHVNVtY+q9gF+D/xKVf8UqcCjbe+BQ+Rnn4r44Lg1ySgsZ9x4555t48aNUY6mebAERgxavGQ5CYlJMdOHvy5Dx07ik08+wev1RjsU08yt+9QZZyKa/SFr0rXvUJKSkqzJu4krXp+yc8ummOqS5dfXHaTX6pSJZdu2JAORH//Cr6i8iiGXOOdDu7GKPcHM9Ai8iDPmxQHgu8AXplptjpI3Od/nWEhgqML5A4eTkJBgP1RFiCUwYtAnq1bRe/AIWrVpF+1QgnLhkEvJz89n+/bt0Q7FNHPbNm+kZas2XNh/SLRDOUNGqTJmzBg7sZm4kldaSVqMdcnya9G1F506dbLBcU1M25WyhcSkFlE9J2n7bvTo0cMSGDEqiJkey1X1FlUdoKrjVfULc7Lr/2fvvuOjus7E/3+OKlWARG/qAtRASIiOsTE2xLGN47527Ow69ma9zjrx7i89Nl9nN8VxHKf45SSO4yS4N5oxFiAhCfUKEhIghGgSQr3XKef3x8zIaqBB7U4579dLL8S5d5Rngq/m3uec5zlS7pRSvjzesWtFSklBbhbunhOYH7BE63AAqO92JSIiQt3njROVwLAxLS0tnCrMt4vyEYtFYaaZsISEBI0jUZxZl97AmRPZLFoagaubu9bh9HG1qZM1a9eRnZ2t6pAVu1F89gINNlaSZXG5oYN169apBIZiswxGSWnRceYHLsXdw1OzOC43dLB69WqVwFAcRlu3gbKifBaF2M793qU6Ux+MzMxMtSJ9HKgEho1JSj6G0WCwqwSGl89sfAODVR8MRVOXrtZz5dxp/ENt72HLYJQsiYyhs7OTvLw8rcNRFKskmcszbK0kC0xL41euWsOpU6dUc1zFJjW1dXG55CSLl2jT/8KivKGD2NhYSkpKaGho0DQWRRkNlXXNVJw7hV+o9g08LRradSyPXkVLSwvFxcVah+PwVALDxhw8dARXN3ebqOm6EUuj1pCcnIxer9c6FMVJJaWmYzQabPJhC2BuiKkGWs0YK/YiKyPdJkuyLHzN17rauk57QohtQogzQohSIcSgNfpCiAeEEMVCiCIhxLvjHeN4yysopLuzXbMGnhYd3QZCIkz3lNnZ2ZrGoiijIT0rG4NeZxM7kPS2aIkpHvWZNPZUAsPGJCYm4rt0OR4TJmodyg2Zvyya1tZWcnNztQ5FcVJp6WkIIWw2+dfqOoWAgABVH6nYjZN5WTZZkmXhtXAJbm5uKimoMSGEK/AasB0IBR4WQoT2OycY+CGwXkoZhmnbR4eWlp4BaNfAs7fZ/qZ/jqysLI0jUZSRy8gwXVu+y2zsfm/aXGbOnNkTnzJ2VALDhjQ1NXH65AmCVthP+YiFX3gsgCojUTSTn53JnMVBTJzipXUog6pr7SZ2zVpSU1ORUmodjqJcV31TM5dKim2yJMuiphNWREWpBIb2YoFSKWWZlLIbeB+4u985TwKvSSkbAKSU1eMc47jLy81h4hQvZs731ToU6vVuLF26VPXBUBzC8ZwsfOYtYuoMH61D6eNyQwdr1qxRKzDGgUpg2JCkpCSMRqNd9b+wmDrDB//gpSqBoWjCaDRy5kSuTTYb7C04IpqqqirKygY0EVcUm5KQnIbRaLDpa6pbb2RFzGqysrJUc1xtLQAu9/p7uXmstxAgRAiRKoTIEEJsu9YPE0I8JYTIEULk1NTUjEG446PweB6Ll0Ti4qL9rXZFQwexsaZrRSXQFXsmpeTUiVx8bax8BEzlWhErV3H69GnVm2mMaf9bVenxxeF43Nw9bPKitEbQ8tWkpKSoG0ll3OWeKKCjrcVm+19YzA02LSVWZSSKrUs6ZmrgaWs1xv35hUbR2dnJ8ePHtQ5FuT43IBjYDDwMvCGEmD7YiVLKv0gpY6SUMbNmzRrHEEdPe3s7F86eYvGSCK1DAUBnkIRErKC6upqLFy9qHY6iDNuFi5dorK2y2XJhS8mYWu00tlQCw4YcPXoUv9AoTbfbGokFoTG0t7eTk5OjdSiKkzly9Bhgm7sl9Ca8F+Hl5aWWvCs2Lysjnbm+wUyaOk3rUK7LO8D0gKiuKU1VAIt6/X2heay3cmCflFInpTwPlGBKaDik/Px8jAaD5juQ9DYvyHStqEaeij2LTzLd7/naQG+ZwUxdtBQXFxfVB2OMWZXAGKq7tBBikxAiTwihF0Lc1+/Y40KIs+avx3uNRwshCs0/8/dCCDHyt2O/6uvrOVN8ksDlsVqHMmyBkasASEhI0DgS+zXCa80ghDhu/to3flFrLy0tjSnTffCZt2jokzXUqZdEr1qtVmAoNs1oNHIyP8dmZ7h66/KYhq+fn0pgaCsbCBZC+AshPICHgP6fQXswrb5ACDETU0mJw9bSWR5eFmu8A0lvHrN88fT0VI08FbuWkpqOu+cE5gcs0TqUQdV3uRAeEaH6YIyxIRMY1nSXBi4B3wDe7fdab+AFYDWmJk8vCCFmmA+/jqmpU7D565r1kM4gKSkJKSXBK9ZoHcqwTfaaQcCSMNUHY5hGcq2ZdUgpV5i/7hrTYG1MXlYG/mErsYc8aEhkDCdPnqSxsVHrUBRlUEVFRbS3Ntv8iiYAKWF5dKxqjqshKaUeeAaIA04BH0opi4QQLwohLJ9FcUCdEKIYOAr8f1LKOm0iHntpGZnMmLOAqTNmah1Kj9oOI8tXrFAJDMWu5WRlsCjEdnfH0hslEVExZGZmYjQatQ7HYVmzAmPI7tJSygtSygKg/7/U7cBhKWW9ufP0YWCbEGIe4CWlzJCmO45/AjtG+mbs2aHDR/DwnGhTyw2Hwz8ylrS0NLq6urQOxR6N5FpzWlVVVVy5fMEuZosB5gRHIqVUywsVm3XUvETXlht49uYXtpKrV69y/vx5rUNxWlLKz6WUIVLKQCnl/5nHnpdS7jN/L6WUz0kpQ6WUEVLK97WNeGzlZGfbTP8LCylhacRKcnJy0Ov1WoejKDess7OTM0WF+C6zzfIRi8VLl9Pc3ExxcbHWoTgsaxIY1nSXvtHXLjB/P+TPdJRu1EOJTziKX9hK3Nw9tA5lRHzDV9HZ2akezoZnJNcawATztZIhhBg0IeiI15Nl6bg9zBYDTFm0FFdXV1VGotispGMpTJnuw8z5i7UOxSoz/E0PiuqaUmxBTU0Nly5esMkJqQUh4bS3t3Pq1CmtQ1GUG5aXl4der8NvmW1PWE33DwdQz0JjyOabeDpCN+qhVFdXc/bMKbsuH7EIjFiFi4uLKiPRhq+UMgb4F+BVIURg/xMc8XpKPJaCm7sHC4PCtA7FKm6ek1gWFqFq9hWblZWRbjclWQBT5/ozVTXHVWyEpUnmIhtbgQHgtWgpoHZIUOyTpa+EX6ht747lNn0+3j4+qg/GGLImgWFNd+kbfW2F+fvh/EyHk5iYCJi2IbV3E6d44b80XDXyHJ6RXGtIKSvMf5YBiYBtp6hHSUpKKotCwnHzsJ/VS8GR0WRmZqplvIrNuXr1KuWXLtjNiiYAF1dXwqNiVAJDsQmZmZm4uLiwKNj2kuoe3guYNn266oOh2KXUtDS85y60qd4ygxFCELEiRiUwxpA1CQxruktfSxxwmxBihrl5521AnJSyEmgWQqwx7z7yGLB3GPE7hPiEBDwnTmZhiO192A2HX3gsGRkZtLe3ax2KvRn2tWa+xjzN388E1gMOX3zX0dFB4Yl8u6nVt5gTvJy2tjZOnDihdSiK0oclCWBv15TfsijVHFexCVlZWSwKDMFz4mStQxlACEFY5EqVwFDsjpSS9PR0/JbZ9uoLC99lKzh16hQNDQ1ah+KQhkxgWNNdWgixSghRDtwP/FkIUWR+bT3wM0wPZtnAi+YxgKeBvwKlwDng4Ki+MztyJD6BwMhVuLq6aR3KqAhYvhqdTqdmw27QSK41YBmQI4Q4ganD+y+llA6fwMjJyUGv09nVbDHArEBTbbS6RhRbk5KSgruHJwuD+m+AZNt8AiNUc1xFc1JKsrKy8Ftqu00GFy6JoLCwkLa2Nq1DURSrXb58mauVlfjaSQJjhr9pUlqVa40Nq3pgWNFdOltKuVBKOVlK6SOlDOv12r9JKYPMX2/1Gs+RUoabf+Yz0kn3P6uoqKCs9CxBy+2//4VFQHg0rm5uqg/GMAz3WpNSppk7uy83//mmlu9jvPTMFtvJDiQWM2bPY+78hSqBodiclNRUFi2JtLuG0gtCInF1dVXXlKKpsrIy6uvrWRBie/0vLLz9QzEajeTl5WkdiqJYzV76X1jMCQzDxcVFlZGMEZtv4unoLA/5wVGOk8DwnDiZgNAVqg+GMuZSU1OZsziAKdO8tQ7lhoVExpCSkoKT5m4VG9TW1sbx/Hy7W9EEps+doKXhpKSkaB2K4sQss63zgmw3gTEv0LRDgpoZVuxJeno6nhMmMD9gqdahWGXCpCkELglVCYwxohIYGouPj2ey13Tm+S/ROpRR5Re+iuzsbJqamrQORXFQRqORlNRU/ELt72ELYE7Icq5cucLFixe1DkVRAFPtvl6vxz88WutQhsU3bCWZmZnodDqtQ1GcVGZmJpMmTWKuX5DWoVzT1BkzmbtgkUpg2AghxDYhxBkhRKkQ4geDHPcUQnxgPp4phPAzj8cKIY6bv04IIe4Z79jHU1paGv7LluPq5q51KFbzW7aCjIwMDAaD1qE4HJXA0JCUkviEBAIiY3Fxcax/iqAVazAajSQnJ2sdiuKgTp8+TWNDg90+bC1eZkq8qCXviq1ITU1FCGE3TdL6mxeygo6ODvLz87UORXFSmZmZhEWusPmeZr5Ll6tGnjZACOEKvAZsB0KBh4UQ/RsQPQE0SCmDgN8CvzKPnwRipJQrgG2Y+qLZ9n94w2T5vb7Yzj6bZgVF0NLSQnGxw7ekG3eO9dRsZ8rKyrh86RLBKxynfMTCLzQKD88JqoxEGTOWpeL2uNwdYJ5fMJOnTFVL3sfYcGe3eh1fLIRoFUL8z3jFrJWUlBQWBoQwaeo0rUMZFsvvAnVNKVro6uoiPz+f0OW2n1SfExjOpUuXuHr1qtahOLtYoFRKWSal7AbeB+7ud87dwD/M338MbBFCCCllu7n5O8AEwGHrUXNzc9Hr9SxcYrvNcQfju8zUny0tLU3jSByPSmBoKD4+HsAhExjuHp74h63seY+KMtpSUlKY7j2TmfN9tQ5lWFxcXQkIi1IPW2NohLNbFq/gBLtkGQwG0tPTWWxnDXF78/KZzdyFvuqaUjRx4sQJuru7CQ6z/Vli32WmnbBUGYnmFgCXe/293Dw26DnmhEUT4AMghFht3o2uEPhWr4RGDyHEU0KIHCFETk1NzRi8hbFnSQD4LbOvz6eZ8xczzdtHJTDGgEpgaCg+Pp7pM+cwe1GA1qGMicDlaygsLKS6ulrrUBQHlJKSQnBkDEIIrUMZtoVLozh58qTaJ3zsDHt2C0AIsQM4DxTh4AoKCmhubsYv1PZnj69ncehK1RxX0YQlGeC7zPZniRcEheHq5qYSGHZOSplp3o1uFfBDIcSEQc75i5QyRkoZM2vWrPEPchSkpaWx2C+AKdPtq2G7EAK/0CiVwBgDKoGhEaPRSHxCAkEr1tj1A9j1BEetBVBlJMqoq6io4Pz58yxaZp/lIxaW/h2qD8aYGfbslhBiCvB94P9d73/AEWa34Muyi4CIGI0jGRm/sGhqamooKSnROhTFyWRmZjJ37lw8p8/WOpQheXhOYEHAUpXA0F4FsKjX3xeaxwY9x9zjYhpQ1/sEKeUpoBUIH7NINSKlJC0tjbCoVVqHMiyLlq6gtLRUTeaOMpXA0EhhYSF1tbUEr1irdShjZlFwGJOmTFVlJMqoszxs+dpp/wuLxUsicXNzV0vebdNO4LdSytbrneQIs1sAx44dY878BcyYPV/rUEYkwJwUPHbsmMaRKM4mMzOTtWvX0tY1YBW/TVq0JJKs7Gy1Q4K2soFgIYS/EMIDeAjY1++cfcDj5u/vAxKklNL8GjcAIYQvsBS4MD5hj5/S0lJqamoItNPkuqU3k1qFMbpUAkMjPf0vohw3geHi6kpARCxHjqgEhjK6jh07xqRJk5kfuEzrUEbEY8JEFgaHkZysHrbGyEhmt1YDLwkhLgDfAX4khHhmrAPWgpSSY8eOsWxFrNahjNishf5Mne6jEhjKuKqtraW0tJTY2FhaOu0jgeG7bDmtLS2cOnVK61CclnnV3zNAHHAK+FBKWSSEeFEIcZf5tDcxrQosBZ4DLM2oNwAnhBDHgd3A01LK2vF9B2PPskJ1gZ018LRYGByOm7uHWmk7ylQCQyNHjhxh3uIAps+aq3UoYyp45TouXDhPWVmZ1qEoDiQlJYXwqBib36rOGv7h0eTkZNPR0aF1KI5o2LNbUsqNUko/KaUf8CrwcynlH8cr8PF07tw5rl69ardbEvcmhMAvbCVJagtvZRxZSjGiYmLRG+2j/4qveUvKjIwMjSNxblLKz6WUIVLKQCnl/5nHnpdS7jN/3ymlvF9KGSSljJVSlpnHd0kpw6SUK6SUK6WUe7R8H2MlLS2N6dOnM3mOfTZsd/fwZGFwGCkpKoExmlQCQwNdXV0kJSURGLVO61DG3JKVpvd4+PBhjSNRHEVDQwMFBQWELLfPesj+AsJj0Ol0ZGVlaR2Kwxnh7JbTsKxWmBti+7snWCMgIoaLFy5QXl6udSiKk8jIyMDFxYWQMPuZJZ4535dJU6eTnKqWtiu2KzU1ldjVa9AbtY5k+PxCo8jNy6Wzs1PrUByGSmBoID09nfb2dkJWOn4CY9ZCf6bPmsuhQyqBoYyO1NRUpJT4htlnPWR//uHRCCFISkrSOhSHNNzZrX4/Y6eU8uXxjn28JD75jjEAACAASURBVCcn4+3jg8/CQK1DGRWBEabkpiojUcZLRkYGkZGRGFw9tQ7FakIIFi+NJC0tXetQFGVQdXV1FBcXsyJmjdahjEhAeDS67m5ycnK0DsVhqASGBg4fPoyLqytBkau1DmXMCSEIWbmO+IQE1ShKGRXJycl4eHjg7R+qdSijYtLUacz1DyE+USUwFG0kJycTvXodLi6OcUswP2ApnpMmc1RdU8o4MBgMZGZmsmbNGlo6dVqHc0N8ly2n7OwZmpqatA5FUQawNL5cusK+V9z6hZoaeap+Z6PHqrsVIcQ2IcQZIUSpEGLA8lohhKcQ4gPz8UwhhJ95/BEhxPFeX0YhxArzsUTzz7Qcs/19p0bJkSNHCAxdwYTJU7QOZVyErFxPU2MDeXl5WoeiOIDk5GRWxqxCuNnPTNdQAiNiycrIQKezr5tfxf6Vl5dTVlZG2ErHSai7uLriHxZNwtFErUNRnEBxcTEtLS2sW7eOVjvZgcTCP3SlqYmv2iFBsUEpKSm4u7uzMMS+d4edMt2b2YsCOJKoejONliETGEIIV+A1YDsQCjwshOg/9fkE0CClDAJ+C/wKQEr5jrm5zArg68B5KeXxXq97xHJcSukUG+TW19eTk5NDgANvn9qfZavYuLg4jSNR7F1rayu5ublErXKs6ycwIobOjnaV5FPGnaXMwi/cMUqyLAIjVnHu7Bmqq53i1kLRUHq6qQRj7dq1drMDicXiJZEIIYiLVw9Wiu1JSUlh1apVtBtdtQ5lxPzDo8nOTMdotONmHjbEmhUYsUCplLJMStkNvA/c3e+cu4F/mL//GNgihBD9znnY/FqnduTIEYxGI0uiN2gdyriZOsOHBUGhHDj4hdahKHYuPT0dvV7PEgfY7rG3APP+5nFHEjSORHE2SUlJeHl5MW1BkNahjKrASNOS40TVW0YZY+np6cycOZPAwEBa7SyBMWHyFOb6BZOWrvpgKLals7OTnJwcNmzYQGO7/a9ODQiLprW5iZMnT2odikOwJoGxALjc6+/l5rFBzzF3fW8CfPqd8yDwXr+xt8zlIz8dJOEBgBDiKSFEjhAip6amxopwbVtcXBxTvKaxaEmE1qGMqyXRG8jOyqS5uVnrUBQ7lpSUhKurK4uWRWkdyqiaOmMmcxYHcjjhqNahKE4mMTGRjRs30tTpWLNCi0LC8fCcyIG4eK1DcQpDlRr3Ou9eIYQUQjjMkp+0tDTWrVuHEIJmO+uBAaYdEopP5KLTqz5liu3Iysqiu7ubdevW09Rhf9dVf5Ztyg8cVvd5o2FcOnYJIVYD7VLK3mmnR6SUEcBG89fXB3utlPIvUsoYKWXMrFmzxiHasSOlJC4ujtCY9bi6umkdzrhaGrMBg15PQoKaYVaGLzExkVWrVtEm3bUOZdQFRsaSm2laYaIo46GyspIzZ86wat1GjFJqHc6ocnVzxz88muRktQJjrFlZaowQYirwLJA5vhGOndraWkpKSli7di1Go6Sty/6SAH6hK+lsayUhXe2QoNiO5ORkhBCsiFmDwWj/n08+8xbh5TObI0fVZ9JosCaBUQEs6vX3heaxQc8RQrgB04C6Xscfot/qCyllhfnPFuBdTKUqDq24uJiKigr8lzv+9qn9+YVG4TlxEp99flDrUBQ71dbWRlZWFhs3baKhrVvrcEZdYGQsHW2tZGSrm0hlfFi27g2Ptu8t6q4lMDKWC2dPU1Wl+mCMMWtKjQF+hqlHWud4BjeWLLskrF+/nnadwS4TgX6hphWNB4+oByvFdiQnJxMREYH0nKx1KKNCCEFgxCryMtNUH4xRYE0CIxsIFkL4CyE8MCUj9vU7Zx/wuPn7+4AEKU2/xYUQLsAD9Op/IYRwE0LMNH/vDnwVcPiiIEsTy5Do9RpHMv7c3D0IWr6aL+LikHb4Aa9oLz09HZ1OR/SaDQ6Rje/PUrO/+/PDGkeiOIujR4/i5eXFLL+lWocyJoKWm+ZF9nxxRONIHN6QpcZCiJXAIinlgaF+mD2VDqempuLu7k5MTIzdbaFqMXP+YqZM9yEjPQ2jA362KvZHp9ORlpbGpk2baGh3nAmrgIgYGmuryDl5RutQ7N6QCQxzT4tngDjgFPChlLJICPGiEOIu82lvAj5CiFLgOaB3/eMm4LKUsqzXmCcQJ4QoAI5jWsHxxojfjY07ePAgfkFLmDF7vtahaGJpzEYqLl2kpKRE61AUO5SYmIirqytBEdFahzImvLxnMXtRAIlHVX2kMj4s/S8aOhyzbGlRSDgeEybxxSGVwNCSeSLrFeC/rTnfnkqHU1NTiYmJYeLEiXbXwNNCCIF/eDRnC3OpaOzQOhxFIT8/n7a2NjZt2uQQDTwtAiNME1V7VVJ9xKzqgSGl/FxKGSKlDJRS/p957Hkp5T7z951SyvullEFSytjeyQopZaKUck2/n9cmpYyWUkZKKcOklM9KKe2vcPAGtLa2kpycTGjsJq1D0czSVab3vm//kBMwTmuoRmhCiE1CiDwhhF4IcV+/Y48LIc6avx7v/1p7Fx8fT2xsLJ14aB3KmAlesYaivCxaOxxmhbVio8rLyykpKeGWW26hpqVL63DGhKubOwHh0WSmHlMr/8bWUKXGU4FwIFEIcQFYA+yz90aenZ2dZGdns369aVVts50mMAD8Q1dSf7WczJPntA5FUXrKGzdu3Ei9A5UMz14cyGSv6SSr3bFGbFyaeCqQkJBAd3c3fiucr3zEwmfeImYvCmDvZ59pHYpNsrIR2iXgG5j6xvR+rTfwArAaUz3yC0KIGWMd83hpbm4mOzubW265hdpWx3zYAghasYaujjb2HzmmdSiKg7M0VN6waTMtdvzgNZTgqLVUXizlxJnzWofiyK5baiylbJJSzpRS+kkp/YAM4C4ppV03/MnJyaG7u7sngdHaZb/XkX/YSgAOH01UZSSK5hITE1m6dClz5851qBISFxcXAiJjKc7NcOh72fGgEhjj5PPPP2fy5Cn4hTnm8ndrLYu9icy0VFpbW7UOxRYN2QhNSnlBSlkA9O8AdDtwWEpZL6VsAA4D28Yj6PGQnJyMwWBgy5Yt1LY6zodZf0HLYxFC8NkXh7QORXFw8fHxzJw5k7n+IVqHMqaCV5gWgH7yWZzGkTguK0uNHc6xY6ZE84YNGwDstgcGwMLgUDw8J1Kcn63KSBRN6fV6jh07xubNm9EZjA6XYA+KjKW+qoKk3CKtQ7FrKoExDqSUHDx4kKi1G3Fzd9zl79ZYFnsTel03h4+o+q9BDNkIbaSvtafmaL0lJCQwYcIEVq6KpdkB9gO/lsleM5gfuIys1GN06R26qk7RkJSS+Ph4br75ZhraHevmsL/5gcuYOHUaR48mqDKSMTRUqXG/czfb++oLMCUwli1bxsyZMwHs+kHL1c0d32XLOX8yh5KqFq3DUZxYfn4+LS0tbN682aH6X1gELV8NwMFD6jNpJFQCYxwUFhZy6dIlgmOct/+FRUB4NJ4TJ/HBp3u1DsUp2VNztN7i4+NZt24dbXrH/5UVvGIN54vzOHnRfhJMin0pKSmhoqLCvKLJsZexuri4ELR8NcU5aVQ2qZllZXQYDAZSU1PZtOnL+zp7beJpERCxiitlpykoq1RlJIpmEhMTAbjppptodKDyEYs5vkFMnjaDguw0ahz883csOf7TgA3Yv38/AL7LN2gcifbc3D1YEr2Rw18cVJnHgYZqhDZWr7VpVVVVFBQUcOuttzr8wxaYavYNOh374+K1DkVxUIcPm7bqdZZrKiRqLQ3VV4jPOKF1KIqDKCgooLm5mY0bNwJgMErauu08gREejZSS4uPZXKpv1zocxUklJCT09L9wpAaeFi4uLgRFxnL2eAZnKtVqp+FSCYxxsH//fsKXR+HlM1vrUGxC2Jqbqa+pIivb7leQjrbrNkIbQhxwmxBihrl5523mMbt3xFxutHXrVqd42AqMWIWruzvJRxPo6FZlJMroO3z4MP7+/gQEBDh0TxmLJdGmyYPP4+LUzLIyKpKTkwF6EhitnXrsfU7Gd9kKXN3cKSvM5vTVZq3DcRpW7D7nKYT4wHw8UwjhZx7fKoTIFUIUmv+8ZbxjH23d3d0kJyezZcsWABocsIQETA3bG2sqSc4pUJO5w6QSGGOsqqqKrKwsojbeqnUoNmNZ7E0IIXj3o91ah2JTrGmEJoRYJYQoB+4H/iyEKDK/th74GaYkSDbwonnM7h0+fBgfHx+ioqKobXH8hy2PCRMJCIvmdF4qZ6tVdl4ZXTqdjqNHj3LbbbfR1KGjW9+/H7DjmTl/Md5zF1KYeUw1KFRGRWJiIgEBASxevBiAli77f9DymDCRxUsiKD2RxbmaNqf43aA1K3efewJokFIGAb8FfmUerwXulFJGAI8Du8Yn6rGTkZFBe3s7t95qemZypB1IegtZuQ6A45kp6jNpmFQCY4wdOHAAKSV+Uar/hcWU6d74LlvB5wfUdqr9DdUITUqZLaVcKKWcLKX0kVKG9Xrt36SUQeavt7R6D6NJSsmhQ4fYsmULLi4uTlMvGLJyPZVlZ8goPKd1KIqDyczMpKWlha1bt1LT4hzXE8CS6PWUnsik8FKd1qEods5oNJKUlMTmzZt7xuy5gWdvgZGxlJecpLm5mXM1are4cTDk7nPmv//D/P3HwBYhhJBS5kspr5jHi4CJQgjPcYl6jMTHx+Pi4sLmzZuRUjpkCQnAzPm+TJ81j7P56ZxWZSTDohIYY2zv3r0sXLSI6QuDtQ7FpoStvYXSU4WcO39B61AUG1ZcXExlZSVbt26luUPvNDNClux8UmI8TQ6864oy/g4dOoSLiwu33HKLkyUwNtDV3sbhpBR0Buf4PaKMjcLCQhoaGhwygRG0fDVGo4HzRXmcqlRlJOPAmh3kes4xr9RtAnz6nXMvkCelHPBL3Z52n4uPjyc6Oprp06fT1m1w2Hs+IQTBUWs5ezyT01eb0KvPpBumEhhjqK2tjUOHDrHx1u0IIbQOx6ZErN8KwN/f/VDjSBRbdvDgQQC2bdvmNKsvABYEhTJlug+ns4+pm0hlVB08eJDVq1czY8YMp7qmgleswcXFlZOZyZTVtGkdjmLHeu+SYNHqACUk8GUfjNITmVyqb6e50zHelyMTQoRhKiv598GO28vuc83NzWRmZn7Z/8JBV19YhEStpb2lkbJTJymrVZ9JN0olMMbQF198QWdnJ8vWqv4X/c1e6M8c3yD27lXbqSrXdvDgQcLDw1m4cKFTNPC0cHFxYWnMBs7kplBU3qCaPCmjorq6mpycHLZv3w7gVCswJk7xwi80ilPZySopqIxIQkJCn/4X4DgrMDwnTjL3wchESjh1RV0rY8yaHeR6zhFCuAHTgDrz3xcCu4HHpJR2XXN69OhR9Ho9t99+O4DDlo9YhKxcD8CZ3BSK1XV2w1QCYwzt2bMHHx8fpvqGax2KTYpYdytFuZlUXK3WOhTFBrW0tHDs2DG2bdsGQLUTPWwBLInZSFtzI4XH87nS1Kl1OIoDiIszbUy0fft2OnUGh3nostbSVZuoKC2m4OwFWruc670ro0Ov15OYmNgzS2zR7EDXUnDUWsrPFtHe0kTRlWaVQB9b1uw+tw9Tk06A+4AEKaUUQkwHDgA/kFKmjlvEYyQuLo4pU6awbp2phLbeQRt4Wkyd4cOCoFDO5KZyoa5NfSbdIJXAGCPd3d189tln3Lx1O1K4ah2OTYpYfytGo4G/vfuR1qEoNighIQGdTueUs8VgajoohOBUdjJFFU1ah6M4gIMHDzJ79mxWrlzpdNcTmHbAAjilSrOUYcrJyaG5ublnlwSLFgcqtQiJWoc0Gik9kUVTh47yBrVLwlixZvc54E3ARwhRCjwHWLZafQYIAp4XQhw3f80e57cwag4dOsTNN9+Mh4cH4PglJGC6zztflEdHW6tahXGDrEpgjGCPYj8hREevC+tPvV4Tbd67uFQI8XvhYE0ijhw5QmNjIys3b9M6FJu1MDgc7zkL+PSTT7QORbFBBw4cYMqUKWzYsIFOnYFmJ2tmOWWaN4uXLudUViJnq1vp0hu0DkmxY3q9nri4OLZt24aLiwtVzc63qmd+wBK8fGZzKjORooomNbOs3LD4+HgAbr755p6xLr2BLp3jNOFbvDQSD8+JnD2eDkChSqCPKSt2n+uUUt5v3mEuVkpZZh7/X/OOdCt6fdnlkuZz585x7tw5brvttp4xRy8hAViycj1Gg57SE5mcVJ9JN2TIBMYI9ygGONfrwvpWr/HXgSeBYPOXQz3pf/TRR0ybNo1ZS2K0DsVmCSGI3Hg7hVkpXKywy9+5yhiRUvLZZ59x++234+Hh4ZSzxQChqzdzueQktVVXKbmqtrRThi8tLY36+nruvPNOwPlKssD0mRO6+mZO56ZQ09RGRaOaWVZuTHx8PMuXL6d3M0RHK8Vyc/cgICKGs3lpAJRWt9Le7VjvUbEtvRu2A3TrjQ53XQ3GPywaz4mTOJ2dTFOHjsv16jPJWtaswBj2HsXX+oFCiHmAl5QyQ5rSTf8Edtxw9Daqu7ubPXv2sP2OO2nROdTCklG34qbtGPQ6/vqO2o1E+VJeXh6VlZW9Hracb7YYIGyNaZavOCuJk1fULJgyfPv378fd3b1nhqvaCVdggOma6mpv41xBNifVzLJyA1pbW0lJSekzSww45OrAJdEbqC4/T31VBQajpEgtb1fG0IEDBwgJCSEoKAhwjtUXAG4eHgRHraM4MwkppVrtdAOsSWCMdI9ifyFEvhAiSQixsdf55UP8TMC+9i+2iI+Pp7GxkdVbvqJ1KDZvUUgE3nMWsHf3x2rplNJj//79CCH4yldM15CzrsCY57+EGbPnU5SRwNWmTqd96FRGbv/+/WzevBkvLy+69AYa2h3vocsawVFrcfecQFHGUc5WtdLRrUqzFOskJiai0+l6dkmwcMSZ4iUxGwA4k5MCQEF5E0ajukdTRl97eztHjx7ljjvu6Bmra3Oee77Q1ZtprKnk6oWzlFa3qmaeVhrrJp6VwGIpZRSmxjPvCiG8buQH2Mv+xb299957TJ8+ndlLV2kdis0TQrD8pu0UZady4uzloV+gOIV9+/axdu3anmW6zrjcHcxL3tfcTEleGt2dHZwoV9l55caVlJRw5syZnhVNzpoQBPDwnEBI1FqKMhLQGYwUV6prSrFOXFwckyZNYsOGDX3Gmx2ogafFnMWBTJ85l9O5pgRGc4eO83VtGkelOKKEhAS6urp6JqwA6lqdYwUGmHbHAijOSsQoJYXqPs8q1iQwhr1HsZSyS0pZByClzAXOASHm8xcO8TPtUnt7O7t37+aer91LTbvKVltj5c13YjToeeOf72odimIDLly4QH5+Pjt2mKrKuvVGp1lOOJiIdbei6+rkTG4qZ64206lTM8bKjdm9ezcAd99tqv501oSgRfi6W2moqqDi3Ck1s6xYLS4ujs2bN+Pp6dln3BFXYAghWBKzgbP56RgMpveXf6lR46gUR7R//36mTJnCxo0be8ac6Z5v+sw5LAgKpTjjKAAnK5owqM+kIVmTwBjJHsWzzE1AEUIEYGrWWSalrASahRBrzL0yHgP2jsL70dz+/ftpbW1l8x33YFQlEVaZH7CEOb5BfLH3Y7WcV2HPnj0A3HPPPYCp/4UzX0qBkauYOHUahamH0RlULfKNGMEOWluFELnmnbJyhRC3jHfso+nTTz8lJiaGxYsXA87b/8IibO0tCBcXClMO0diu44KaWVaGcPbsWc6ePdvTZLA3R+yBAaaZ4c62Fi4U5QNwub7daftRKWPDaDSyb98+tm/f3icxWOdECQyAsDW3cKE4n5aGOlq79JytbtE6JJs3ZAJjhHsUbwIKhBDHMTX3/JaUst587Gngr0ApppUZB0fpPWnqnXfeYcGCBcwIWKF1KHZDCEH0LXdSdjKXI1mFWoejaOzTTz8lIiKip5lTVbNzzxa7urkTtvpmijISMOh1HL/cqGaMrTDCHbRqgTullBGYkvO7xifq0Xf58mWysrL42te+1jN2tcm5H0KmTPMmMGIVBSmHADWzrAztwIEDAHz1q18dcMwRV2CAaYtHVzd3ijOP9ozlXVTXijJ6srKyuHr1as/qQDCtunXUpOC1hK/bgpSy51rLu9io+gIOwaoeGCPYo/gTKWWYeQvVlVLK/b1+Zo6UMtz8M5+RDvAvVVNTw8GDB3nwwYe41KC2wrkRUZtNzXve+ufb6qJ1YlVVVaSkpPR52Kpy8tligMgNW+lobab0eCbNHTrKatWWqlYY9g5aUsp8KeUV83gRMFEI4YkdspSPWK6pTp3zNvDsLWLDbVRdOkfVpXNcqm+nttW5E6XK9R04cIDQ0FD8/f37jOsNRodtujdh8hQCImIozkrqGTtztcUhe34o2ti7dy+urq59+l84U/mIxYLAZcyYPZ+T6fGA6b73ipNPNAxlrJt4OpV33nkHvV7PrXc/SLfeqHU4dsVn3iICI2M5duBjLtSq5bzO6uOPTbvR3HvvvT1jKoEBIdEb8Jw4iePJpoVqahbMKiPdQcviXiBPSjnoE66t75T14YcfEh4ezpIlSwB1PVlEbtiKEILjSaZrKvdig8YRKbaqubmZpKSkQVdfOGrywiJ09WaqLpZSV2n6VWqUUq1YUkaFlJJPP/2UzZs3M2PGjJ5xZ0wmCyEIW7uFM7mpdHWYnoFyLtQP8SrnphIYo0RKyVtvvcWqVasQ3ouGfoEyQOxtX6P2ykXe239I61AUjXzwwQeEhoYSHh4OmGaLG9VsMR6eEwhbu4WC1MPodd1UNHZQ0ahWeY01IUQYprKSf7/WOba8U9bly5dJTU3loYce6hlz9vIRi2k+c/APj+F40udIKTlztcXhH0aV4Tl48CA6nc6pykcswtaY2v9YZobB1GRQ9StTRurkyZOUlJRw33339Rl3xgQGwPKNt6Pv7qI407TiqaymzWn/v7CGSmCMkvz8fAoKCnjs8cfV8u5hitx4O54TJ7H3w3docMIlZM6uoqKClJQUHnzwQUy9fdVscW9RN32FjpYmSvLTAZWdt8Kwd9Ay/30hsBt4TEp5bsyjHQMffvghAA8++GDP2FV1TfWI2vwVqi6do/JCCQajJP+SWoWhDLRnzx5mzZrFunXrBhxrcvBa/ZnzFzPPP4TC1CM9Y916I3nqWlFG6OOPP8bFxaWnYbtFrRNtodqbf9hKpnrP4kTyFz1j6j7v2lQCY5T89a9/ZcKECay77S66dKp8ZDg8J05ixU1f4XjSF6QUX9I6HGWcffjhh0gp+zxsXWlUD1sWS6I3MGHyVI4nmprJldW0UePk22EOYSQ7aE0HDgA/kFKmjlvEo+z9999n5cqVPQ1xpZRqBUYvkRtux8XFlfyjpmuqoFzNLCt9dXV1ceDAAe6++25cXV0HHHf0BAaYth0+X5RLa+OXD1PHLzeqLb2VYZNS8tFHH7Fp0ybmzJnTZ9xZVx24uLoSuX4rp7KT6OpoB+D01RY1oXsNKoExClpaWti1axcPPvggVZ1uWodj19be8SDdne3sevtt9eHoZHbt2kV0dHRPrT7A1WZVJmHh5uHB8k3bKEg53PPhlnVeZeevZYQ7aD0DBAHPCyGOm79mj/NbGJFTp06Rk5PDo48+2jPW1KGjXT2g95g6w4eQlevIjd+H0WikW28k/7KaWR4OK7Ysfk4IUSyEKBBCxAshfLWI80YlJCTQ0tLCjh07Bj3uDAmMiPVbkUZjnzKSbr2RnAvqWlGGp7CwkFOnTnH//ff3GW/rNjh1Enn5TdvRdXVSlJEAgJSQpVZhDEolMEbBe++9R2trK0988ynO1ajykZFYFBLBwqAwUva/x3G1RNFpnDx5kvz8fB577LGeMSkllWq2uI9Vt+6gu7OdwtTDAJytbnHa2QprjGAHrf+VUk4276Bl+arW8r3cqF27duHq6srDDz/cM6b6pgwUs3UHjTWVnCvIAtTM8nBYuWVxPhAjpYzEtOPPS+Mb5fB8+OGHeHl5ceuttw563Bm2e1wQuAyfeYs4ceyLPuPHLzfQpvrGKMPwzjvv4ObmxgMPPNBnvNbJV5UGhMcwfeZc8hI+6xk7XdnilDuzDEUlMEZISsnrr79OZGQkswLD0RnUFqAjIYRg7VcfovJ8CZ98cdTpdnOxYhbLUwjxgfl4phDCzzzuJ4To6DVb/Kfxjn0kLA9bvZsN1rV1q3KsfvzCVuI9ZwE5R/YCpux8RlmdxlEptsZoNPL2229z2223MXfu3J7xSlWSNUD42i14Tprcc0116YzkqR1JbtSQWxZLKY9KKdvNf83A1JPGpnV1dbF792527NiBp+fguyg7wwoMIQTLN23nbF46rU1fzgbrDJLM8+rzR7kxRqOR9957j9tvv52ZM2f2OebsEzIuLi5E3XwHp3OO9VxrRilJP6eus/5UAmOEUlJSOH78OE8//TSnrjZrHY5DWHnzV5k4xYsjH73FyStNWoczbqycxXoCaJBSBgG/xbRDgsW5XrPF3xqXoEeBXq9n165dbN++ndmzv1ylr2r1B3JxcSFm6w7O5qfRUH0FgLNVrarZqdJHfHw8ly9f7rOiCeBKk1qB0Z/HhIks37iNE8lf0NluWkGZf7mR9m41s3wDrNmyuLcngIPXOmgrWxMfPnyYpqamPn2ZeuvWG52mJGvFTdsxGg0UphzuM15Y3qxmh5UbkpyczOXLl/mXf/mXAcecPYEBsPKWuzAa9JxI+vJXZElVC9XqPq8PlcAYoVdffRVvb2923P8QF+vah36BMiTPiZNYs/1+Co4d4lDmSXQGp5mFH3IWy/z3f5i//xjYIixbdtipAwcOUFlZyTe/+c0+41fUcvdBxd72NQAyv/ikZyzlbC1SqtVfiskbb7xh+lzqVbffqTNQ56Td3YeyZvv9dHe2k5/4OWB6MM0sU3XHY0EI8SgQA/z6WufYytbE7777LjNmzLhmSJ62SwAAIABJREFU+YgzrL6wWBC4jFkL/cg7+lmfcaOUHDurXZJJsT9vvfUWXl5eg/aVqXbyEhKA+QFLmB+wlKxDn/YZT1b3eX2oBMYInD9/nj179vDUU09xqdmA+u9q9Ky/6xEkkkMf/YOCcqdZhWHNLFbPOeYmhU2Aj/mYvxAiXwiRJITYONj/gK3MbPX2xhtvMG/ePO64444+4+UNKoExGO+5CwlZuZ6suE8wGkyzf5fq27mgEqgKUF1dzZ49e3j88ceZMGFCz7hKCF6b77IVzPULIePghz1jBeVNqvu79azZshghxK3Aj4G7pJQ2/aTS3NzM7t27eeihh/Dw8Bj0HGdKYAghiL7lLs4VZFFf1feftqymjfO1bRpFZv9GUDrsI4Q4KoRoFUL8cbzjHo7m5mY++ugjHnroISZNmtTnWLfeqFbzYLrWYm+/l8slJ7lSdrpn/HJ9O2XqOuuhEhgj8Morr+Di4sLTTz9N8RVVPjKavOcsIHLDbaQf+IBjJy86XS+MYagEFkspozDtpvCuEMKr/0m2MrNlcenSJQ4ePMi//uu/4ub25Q4+LZ06p7o5vFFrtt9PY00lp7KTe8aOna3BYFRZVGf397//HZ1ON2BFk0oIXpsQgjXb7+fymULKzxYB5pnl0lqNI7MbQ25ZLISIAv6MKXlh8w1xP/nkEzo7O/n6179+zXOaO53rMyp6i2nzpryE/QOOJZ2pRu88q2VHzQhLhzuBnwL/M07hjtgHH3xAR0cH//Zv/zbgWHVLp5oINovecieu7u5kxn3SZzy5pEZdZ2YqgTFM1dXV/PWvfzV9uE32UVnDMXDLA0/S2d7KkU/fJs85diSxZhar5xwhhBswDaiTUnZJKesApJS5wDkgZMwjHqHXX38dgKeeeqrPuHrYur7wdVvw8plNyr63e8bqWrs5Ud6oYVSK1vR6Pa+99ho333wzoaF974EvN6gVOtezausOPDwn9rmmzlW3ckHNeA3Jyi2Lfw1MAT4yN5red40fZxP+8Y9/EBQUxJo1a655TlO7cyUwfOYtIiBiFdmHdg9Yyt7QriNbbas6HMMuHZZStkkpUzAlMmyeZdOD8PBwYmNjBxxX5SNfmuw1g4h1W8k5vIfuzi/vhxvbdeSqJtOASmAM2+9+9zu6urr4/ve/70wlDuNqUUg4S6I3kPTp30kvqXSG7bqGnMUy//1x8/f3AQlSSimEmGXO5COECACCgbJxintYOjo6+Mtf/sLdd9+Nr69vn2MVKoFxXa5u7qy74yHO5KRQffnLf+b0c3W0Ov51olzD/v37uXTpEt/+9rf7jHfqDNSom8PrmjjFi5itO8hL+IzWxi/7XySqmWWrWLFl8a1Syjm9Gk3fdf2fqJ2SkhKSkpJ44oknuF6LqcYO55u4Wr3tPmoqLnCuMHvAsewL9dSpJow3aqSlw0OyldLhrKws8vPzefrppwe9rlSTyr7W3/kvdLQ2k594oM941vl6Gtud73dPf1YlMEZQn7VVCJErhCg0/3lLr9ckmn+mZdvH2f1/rq2qq6vjD3/4A/feey8L/AIprW7VOiSHteXhf6e1sY7kfe85/HaRVs5ivQn4CCFKMZWKWK7HTUCBEOI4pgz9t6SUNt2F7p133qG+vp7/+q//GnCsXM0WD2ntHQ/i6u7OsT27esa69UaSS2yjt4ky/l599VV8fX258847+4yXN7SrpblW2HD3o+h13aQfeL9nrKFdR46a8XIqb7zxBm5ubnzjG9+47nkNbc61AgNg+cbbmTB5KhmffzjgmMEoOVxchVGVMtoUWykdfu2115g6dSqPPvrooMermlXyq7eAiBjm+gaTuu/dPiue9EbJkVPVTt/Qc8gExgjrs2qBO6WUEZhmjXf1e90jvbLxNl8TafHrX/+a1tZWXnjhBQrLmzA6+X9EYykoMpbgFWs48t6fySmtdPgMrRWzWJ1SyvullEFSylgpZZl5/BMpZZj5WloppRxYpGpDDAYDL7/8MlFRUdx00019jjV36mhwsqW5wzF1xkyib7mLzLhPaGn4Mrl35moL52pUUtXZpKenk5yczLPPPtunnwzA5Xq1oskac32DWLpqE8f27KK768vPmqzzambZWXR2dvL3v/+dO++8k7lz517zPL3B6HQ9MMC07XD0lrsoOBbXZ6WSRWVTp0r43Zhhlw6PS3Sj5MqVK7z//vs8/vjjTJ06dcDxTp2BBrWqoA8hBBvufoTy0iLKCnP6HLtc387JCufuvWjNCoyR1GflSymvmMeLgIlCCM/RCFwrVVVV/OEPf+Dhhx9mybJQClTN+Zjb9viztDbWcWzP2xw9o7KOjmDv3r2cOXOG73//+wOWEl5Su2lY7eb7n8Cg6yZlb9/c8NHT1XTqDBpFpWjhV7/6FTNmzODJJ58ccOxinerjYK1bHnyS1qZ6sns1T1Mzy87jvffeo7a2dkAZVn+NHTqnXdW0/s5/Qa/r7rNrT2/p5+qocvDJplE07NLhcYxxxP74xz9iMBj4zne+M+jxqmbVwHMwMbfuYLLXdBI/+duAY8lna5yuD09v1iQwRqs+614gr9/WWW+Zy0d+Kq5RaGgrtVsWL7zwAt3d3ezcuZPTlS20d6uHhLHmH7aS0NWbif/gL5RcvEKR2vHFrkkp+cUvfkFAQAD33nvvgOMX1MOW1eYsDiRs7RZS9r1DR1tLz3hLp56jp+1mUZsyQoWFhezdu5dnnnmGKVOm9DnW0NatVjTdgMCIVfguW0HCR2+i1305I6hmlh2flJLf//73hIeHs3nz5uue68w16HN9gwhZuZ7U/e9i0A/83WKUkoOFlXTp1f3xUEZYOowQ4gLwCvANIUT5ICvkNdfS0sLrr7/OPffcQ2Bg4KDnVKhtvgflMWEi6+96hKL0BKounetzrFtv5IuiSqdNrI9LE08hRBimspJ/7zX8iLm0ZKP5a9C9qmyldgtMN4lvvPEGTz/9NIGBQeRetOkWAw7lzie/R3dHO3G7/kjy2RrVqNCO7d+/n5ycHH784x8PWOpuNEou1asVGDfitkeepqO1meRP/9Fn/PTVFk5VqmSfM9i5cydeXl6Dzm6phOCNEUJw26P/SUNVBVn9trBLP1fH1SY1s+yo4uPjOX78OM8+++x1m3cCTp8U3HTPYzTVVg1oMGjR0K7jcHGVWjFrheGWDpuP+UkpvaWUU6SUC6WUxVq9j2t57bXXaGxs5Pvf//41z6lsVL9Xr2XDXY/i4TmRI+/9acCxK42dDt8f8FqsSWCMqD5LCLEQ2A08JqXsSR9JKSvMf7YA72IqVbFZUkqee+45pk2bxgsvvMCZqhan/wAbT3MWB7L2qw+R/tn7XCg5Tfwp9cFoj4xGI88//zxBQUE89thjA45XNnfSpVMd/2/EwuAwItZvJemTt2hr7lvSlnC6Wm3x7ODy8/P59NNP+e53v4u3t/eA4yqBceOWxmzELzSKw+++jq77y0WjRin5vLBSlWc5qF/84hfMmzePr3990Pm0Phqc/PfqstibmOcfQvwHb2A0Dv6ZfbaqVW356ORaW1t5+eWX2b59O6tWrRr0HKNRclWVHF3TlOnerLvzYfKOfkZ1+fkBx7Mu1HPeCbf7tiaBMZKtHacDB4AfSClTLScLIdyEEDPN37sDXwVOjuytjK3333+fI0eO8OKLLzJ9+gwynTTjpaVtj32biVOm8vHvX6C0qkWVktihd999lxMnTrBz584Bqy8AylTzyWHZ9th/0dXZPiBD3603cqDgilrK66CklPzP//wP3t7efPe73x1wvFtvpFw18LxhQgi+8q/fpam2iuTdfVc2NXXoiCu6qhLoDiYjI4OEhASee+45PD2HbtXW6OQTWEIIbnnwKaoulnIyLf6a56WU1qrPdSf26quvUldXx/PPP3/Nc2pbu+jWq4mr67n5/idwd/ck7p9/GHBMSjh4stLpkqpDJjBGWJ/1DBAEPN9vu1RPIE4IUQAcx7SC443RfGOjqaGhge9+97usWrWK//iP/+DU1Wa1+kIDk71mcOdT3+d8UR6ZX3xM4hk1u2xP2tvb+eEPf0hMTAwPP/zwgONSSrUl8TDN8w8h9vZ7Sdn7NjUVF/scq23t5lCRWrHkiD777DMSEhLYuXMn06ZNG3D8fG0beietjx2poOWrCVtzM0fe+1OfXX4AymraSFeTGA7lJz/5CbNmzeJb3/rWkOdKKalT9x6suGk7sxf688U/f4/RMHiSXEr4vLBSNfV0QtXV1bz00kvs2LGDNWvWXPO8ctX/YkhTZ8xk09ceJz/xAJdLCgcc79IZ2Xu8wqlWB1rVA2MEWzv+r5Rycq+tUldIKaullG1SymgpZaR568dnpZQ2+//6s88+S21tLX/+858xIkg/p25ctLJq6z0ELo9l/19+RXVlBQcKrqjMrZ345S9/SXl5Oa+88gouLgN/9dS2djv9rNZIbH/8Wdzc3dn7p58PSFaUVreSUlqrUWTKWOjs7OS5555jyZIl13zoOlvdMui4Yp07n/weuq4uDvzt5QHHMsvqVY8ZB5GQkEB8fDw//OEPBzTBHUxrl96pHhSuxdXVjdsf+y+uXii5Zi8MAJ1Bsie/Qk04OZmdO3fS3t7OL37xi+ued1n1PbPKLQ88yeRpM9j3l5cGnZBqaNex78QVdAbneCYalyae9uyTTz5h165d/PSnPyUqKorciw20dKoGkloRQvDQcz9HSiPv/foHVDd3ckT1w7B5p0+f5pe//CWPPPIIGzduHPQctfpiZLy8Z3H7179NcWYihamHBxzPudBA/iVVj+wofv7zn1NaWsof//hH3N3dBxzv1hs5X+N8dbGjafaiADbf969kxX1KaUHWgOOHi6vUFrV2zmAw8Nxzz7F48WKrVl+AKdmumCzftI0FQaEc+NsrdHdeeya9vdvAp3nlTr17izPJzc3lT3/6E08//TRLly695nkGo6S8Qa3AsMaEyVPY/vh3OFeQRd7RzwY9p6Khg88LKzE4wcpLlcC4jgsXLvDkk08SExPDj370I5o6dORcUDuPaM1n3iLu/taPKD2RScIHb3DmaguZ59W/i60yGAw8+eSTTJ48md/85jeDniOlpKRKzRaP1MZ7HmN+wFI+/eOLAxp6AiSeqaHoSpMGkSmjqaCggF/+8pc8+uij3HrrrYOeo8pHRsdtj/wn3nMW8NGrPx3wgGYwSvafuMIVtQTabr355pucOHGCl156iYkTJ1r1mtrWrqFPchIuLi7s+NaPaKyp5OhHb1733JZOPR/nlquVGA7OYDDw9NNPM3v2bF588cXrnlvZ1KFWUd+ANdvvZ1FIOPv+8ivaWwa/lyuraeOLk1cdPomhEhjX0NXVxX333YfRaOT999/Hzc2NhNNV6AyO/R+EvVi97T6iNt/BwX+8ytnjGaSfq6NYNfW0Sa+88gopKSn87ne/Y86cOYOec7W5U93UjAJXVzce+u+f09rUwKev/WzQcw4XV6kkhh3r6uri0Ucfxdvbm1deeeWa56l/49HhMWEiDz73f9SUX+CzNwcmYHUGye78CipUEsPuVFVV8YMf/IBNmzbxwAMPWP262haVwOgtMHIVK276CvHv/5nqy2XXPbelU89HOZdVTwwH9pvf/IasrCx++9vfMn369Ouee6lOlY/cCBdXV+5/9kXaGuvZ8/rPr3leSVULBwor0TtwOYlKYAxCSsk3v/lNcnNz+fvf/05gYCCnr7ZwoVZdaLZCCMED3/0Zsxf688//fZaaioscLq5SZQg2Jjs7m5/85Cd87Wtfu+7WdCr5NHoWBodx+6P/Sf7Rz8g+tHvAcSnhUFGVKiexU9/73vcoLCzkzTffZNasWYOe09Sh45KqKx41wVFr2XjPY6Ts3UVResKA4916I7vzyrnghFvZ2SspJd/+9rdpa2vjz3/+M0IIq19bo1ZgDLDjP36Eu+cEPvztT6/Z0NOivdvAx7nl6n7NAZ04cYLnn3+ee+65h4ceemjI88+rErwbtjA4jC0PPUXOkT0UpAwsF7Y4V93KnuNXHLZfj0pgDGLnzp28/fbb/OxnP2PHjh00tetIOF2tdVhKP54TJ/Nv/+91AP7606dobqzj88JKSlXjOptQW1vLfffdx7x583jjjTeueYOoMxg5o8pHRtWWh/6d4BVr+Oj3L1Bx7vSg5ySeqSG5pAajgy8zdCQffPABv//97/nOd77DHXfccc3ziq40odoCja6vPvE/LAwK452XvkftlUsDjusMkr3Hr3CyQq18sQe7du3io48+YufOndet0e9PbzDS0KaaTffn5T2LHd/6EWUnczj68fVLScCU9Pus4Arp5+pUDzMH0dLSwgMPPIC3tzd/+tOfhkwKNrXrqG5WycDh2PrI0yxaEsEHr/yIusrL1zzvcn07H+ZcdsgtVlUCo59XXnmFF198kW984xv8+Mc/xmCUfFFUqWq0bNSsBb78687XaKiu5M8/fILW5iYOFFxVM/oa6+zs5J577qGqqoqPP/4Yb2/va557qrKZLp26vkaTi6srj/7wN0yaOo03n/8WTXVVg56Xe7GB/QWOm6F3JJmZmXzjG99g/fr1vPTSS9c8T28wUlShfv+NNncPTx7/6e9wcXHlrz95atAeM0YpOVxcReKZaoevP7ZnxcXF/Od//iebNm3ie9/73g29tra1G6N64B5UzNYdLN+0jYN//x1lJ3OGPF9KyCirY3d+Ba1dqjm+PTMajXz961+ntLSU9957j9mzZw/5GrVL1vC5uXvw2I9+i5SSt/7fM3R1XHslS11rN+9lX3K4FU8qgdHLyy+/zH//939z33338cYbbwCQcLqaK42qVu//Z+/Ow6Mqz8aPf++shLBDWGSRfYewhB0EpCq4VsWtat14qa1Lra1d3vanVlvbvvqitdat6itWwAVFLYiAgLIvCftOgLCEQAJJSMiezP37YyYaQvbMZM4k9+e65srMmeecc8/y5Jy5z7M4WfeBMdz35D84lXCQ1397HxnpZ1my+xQbDltm3x8KCwu5++67WbNmDbNnzyYmJqbcsi6XsuWodWXwhaYt2zDj2TfIzjzHv34/s8IBn+ZsPEbSOevD71R79+7luuuu45JLLmHBggVlzjryXdmkTPsx4COtO3Tmvqde4ezpE7zz1E/Jyym7m87WY+nMjzvOuRy7Uu80qamp/PCHPyQyMpK5c+cSHBxcrfVtrJPyiQi3PvYsrdt3YvazPyct+WSV1jt6Npv3Nxxl/6lMO2cLQKrKr371Kz7//HNefPFFJk6cWKX1DtazH9R1rXWHzvz49y+SlHCAfz/3OEWF5R9v8gpc/Gf7SVbuS643F+QtgYF7xNwnnniCJ554gltuuYU5c+YQEhLClmNp1hw0QPQbOZF7n/wHSQkHeeXxOzmbdJz1h86ycEeSXV2uQwUFBdxzzz3Mnz+fWbNmcdttt1VY/vCZ86Rl20m+r3Tq2Z97n3yZ08cP8cZ/P1BuEiMjp4APNx9nXfyZej3oUyDat28fl19+OUFBQSxevLjccS/AnRCMPWozMvlSj8EjuPM3z5OwdxtvP/lguVe+Tqbn8v6Go+xKPGc/yhwiOzub6667jqNHjzJ//nw6duxY7W1YAqNiEU2acd/T/6QgL5c3/3sGWRlVu0CRk1/ElzuT+GL7Sc7ZOUFAefbZZ3nxxRd55JFHeOSRR6q0TlpWPqfO2cXh2uobM4GbH36SPRu/Ye7zv6WoqOKLF9uOpzNn41GO14Mxshp8AiMtLY0bbriBF154gZ/+9KfMmzePsLAwdp44x6oDZ/wdnqmGAaMn85Pn3iYz/SwvPjydA1vWEZ98njkbj9lJRx3IysrixhtvZO7cufzlL3/hF7/4RYXlXS5l/aGzdRRdw9U3ZgL3/uFlTh7exyuP30l6yqkyy6nCxiOpzNl4jKM2sJYjbNiwgfHjx+NyuVixYgW9e/eusPyepAzS7eTf54ZcNo07fvVXDu3czKu/vofMtLL/j+UXuli25zSfbEnkrA386FfFyYv169czZ84cxo8fX+1tqKpNmVsF7S/tyf1/fJUzScd4/bf3VzmJAe4WgbPXJ7D6YIpdfHI4VeV3v/sdTz31FD/+8Y956aWXqjwY7vYTF3fBMzUz9to7uOb+X7J15UL+/efHKcyveLyL9OwC5sedYPHOJDJyA/d8oUEnML755huio6NZsmQJr776Kq+++irBwcHEHU3j671l9xk3ztZj8Agee/ljmrZqwxu/u5//vPU8Z8+d5+PY46zcl2wHRB85dOgQY8eOZfHixbz++uv89re/rXSdXSfPceZ8/RtYyIkGjLmcmX9+i7SUk7z48M0c2rG53LKpWfl8uiWRz7clkpxpV0j8QVV56623mDhxIi1atGDdunX079+/wnVyC4pYG29J97oS84MbuO+pV0g6coAXH76ZY/t3lFv2eGo27284xvK9p617jx8kJyczZcoUvvnmG9577z2mT59eo+2kZuWTk2/nEFXRM3oU9z/9KqePxn/XKraqilxKbEIab685wrr4M2TnW51xmuzsbH70ox/x17/+lQcffJB33nmHoKCq/aTML3SxJ8nGafKmKbfP5IYHf8eONUt49dc/JjOt8nOBfacymb02gW8PpJAVgMelBpnASE5OZsaMGUyePJnw8HDWrVvHT3/6U4pcysr9yaw6kOLvEE0tRHW8lMde/phRU29h5Udv8cKD17M/bh3bjqcze10C24+n2wBrXlL8Q2vIkCEcP36cxYsX85Of/KTS9bLyCllnrS/qVK8ho/n53z+iUWQTXv31j/ny/16kIL/8q8KHU7KYs+EYX2w/ycn0HGsGX0dSUlK49dZb+a//+i8uu+wyNm7cSI8ePSpdb92hM2Tbj6s6NXDMFB59cR4iwsuP3cGyua+V2w/ZpcqOE+f4vzVHWLkvmfRsS97WhXXr1hETE8P27dv55JNPuOuuu2q8LRsPrXr6jbiMmX95m4y0M7z0yHT2bPq2WuvnF7rYeCSVt1cfYdme0yRn2PvvBNu2bWPEiBF8+OGH/PWvf/3u4m9V7T55zgZu94GJN93Lj//wEomH9vLCgzewd/OqStcp9IxD986aIyzfezqgWgpKIJ2UxsTEaGxs5SMbl+fs2bO8/PLLzJo1i5ycHH75y1/y1FNP0bhxY9Kz81my+5QdoOqZ/bFrmP+PpzmbdJw+MeO58s6H6DZgGM0iQhl+aUv6d2hGWIjP8nhVn1jeD2pbnzZv3szjjz/OmjVrmDRpErNnz6ZLly6VrqeqfLYtkYQzgd8HLxDlZp/ns9f+zKYln9Lmkku5fuavGTBmSqVNP9s1a8Sgjs3p3b4J4SHVG/jOi+ptncrPz+eNN97gqaee4vz58zzzzDM88cQTVToxjE/O5D/bk2q0X1N7WRnpfPKPP7Lt2y9pd2lPrp/5G/rGTKiwTolAtzaRDOzYnK6tIwkO8stX29H1CWpep86fP88f//hHZs2aRdeuXfn4448ZNmxYrWLZnJDKmoPWyqm6UhITmP3szzl5eB+jpt3CtQ/8kshmLWu0raim4fTr0JTe7ZrStFH5gxn7kaPrVG2OUefOneNPf/oTL774IlFRUbz33ntcccUV1dpGbkER765LsJZMPnTyyH7ef+6XnDp6kGGXX8e1D/yKFlHtq7x+p5YR9L+kGT3b+vVcr6Qy61S9T2CoKrGxsfzrX/9izpw5ZGdnc9NNN/Hcc8/Rp08fCopcbD2WzqYjZykoCpz3wlRdYX4+a76Yw/IPXicrI51L+w1h7LV3MHj8FTRr1pR+HZox4JJmRDUJr3L/vSqqdwcyVeWbb75h1qxZLFy4kDZt2vC3v/2Ne++9t8rNB9cdOsPGwzbQoL/tj1vLgn8+S/KJI3TuM4jJ0+9n4NgfEBIaVuF6IUFCt6hIerZtQtfWkTQKrdMDXL2rU+np6cyePZtZs2Zx7NgxLr/8cl555RX69etXpfWTM3OZH3fCrmg5wO71K1jw2p9JPXWCrv2HMmn6AwwYM5ng4JAK14sIC6ZX2yb0bNuEji0iCAmus8axjq5PUP06lZ2dzTvvvMNzzz1HUlISM2bM4IUXXqB58+a1jsUSGDVXkJ/HV++9zDfz36FRZFMmT3+Acdf/iIjIpjXeZvvmjejWJpJLWzemXdNGBPknCViaI4IoT02OUWfPnuW1117jpZde4uzZs9x33308//zztG7dutr7Xxt/hk1H7PzP1wry8/h63uus+OhfiAQx5upbmXjTvbRq36nK2wgJEi5tE0n3NpF0axNJZHjFxzEfqnkCQ0SmAn8HgoG3VPWvpZ4PB94DhgNngdtUNcHz3O+AB4Ai4FFVXVKVbZalqhUvJyeHdevWsXjxYj7//HPi4+OJiIjg9ttv5/HHH2fgwIHkFRax+2QGW46mkZkbeH1/TPXl5WSz8auPWfufuaScSCA0vBF9R1zGgFGT6DV0LD26XUqPqCZ0bdOYDs0jvHFFrNob8EVdK09V65PL5WLbtm189tlnzJs3j/j4eKKionj00Uf5+c9/TtOmVT8B2ZV4jmV7bHwZpygqKmTz0gWs/OgtUhITaNKiNUMmTmPQuCvoPnA4wSEVX+EKEqFD80Z0ahVBpxaNadc83NcZ+3pRpzIyMli2bBnz58/n888/JycnhwkTJvCHP/yBK664osqJ1IzcAuZuPGZXsxyksCCfjYs/ZuXHb5N6OpGmraIYOvFqBo27gq79h1Rap8JCgujUMoJOLRvTqWUEbZqE+7J1htc2XJt6VpGq1CmXy0VcXBxz585l9uzZpKWlMX78eJ5//nlGjx5dw1d0MUtg1F7SkQMsfPsF9m76lvCIxgydfB3DL7+ObgOGEVTNKW1LCg8NomOLCDo0j6B9s0a0bRZe18n1YvXiGJWVlcXSpUv58MMPWbBgAfn5+VxzzTU888wztWrJ9Pm2RA6n2CDhdSX11AmW/PsV4lb8B3XeyOXMAAAgAElEQVQV0Xv4OIZNupb+oydVuxVUm6bhdG4ZQccWEbRr3oim4SHevuhbnpolMEQkGDgAXAGcADYDd6jqnhJlfgYMVtUHReR24EZVvU1E+gPzgJHAJcDXQPEw6hVusyzlVbyMjAw+/PBDtm3bRlxcHFu3biU/P5/Q0FAmT57Mbbfdxk033USTps04lprNweTzxCefrzdz4ZrqUVWO7Ipjy8qF7Fq/nIyzyQC0bNeRLn0G0bFHPwaOuoyRMTF0aNGI9s0a0aZpeE0qa/UK+6CuqWq5v2wqOpBt3bqVpUuXsmnTJlavXk1KSgpBQUFMnDiR++67j+nTpxMREVGdlwfAyv3JbDtmo087jauoiH1xa9j01Sfs2fQNhfl5hEc0pmv/YVzaL5rug0bQe+iYSrcjAq0iw2jbNJw2TcJpFRlGq8gwmjYK9daPsICsUwUFBcyfP5/Nmzezbt064uLiKCwspHXr1txyyy3MmDGD4cOHV+elAe7BIefHnaj2esb3iooK2bvxGzYt+ZS9sasoKiggvHEk3foPo0vfaHpGj6Rn9KhKtxMSJEQ1DSeqRJ1qGRlGZFiwN04evVMpa1HPKtt2eXUqISGBr776irVr17J8+XKSkpIICwvjhhtu4OGHH2bChIq78NSEJTC85/iBXaz5Yg7bv11Mfl4Okc1a0HPIaLoNGE6f4eNo16XycX8q07RRCFFNPXWmcRjNI0Jp3jiUJmEhvmytEZDHKICFCxeyfv161q5dy/r168nPz6d169bccccdPPjggwwYMKA6L61MlsDwj7TkJDYs/ojYrz8n7XQiEhREp5796TZgOJf2jWbQuCsICau49W1pjcOCLzoutYgIpbF3jk0l1TiBMQZ4WlWv8jz+HYCq/qVEmSWeMutFJAQ4BUQBvy1ZtricZ7UKt1mW8ipeamoqrVu3pmnTpgwZMoTRo0czadIkJkyYcMHV4SNnsvhsa2KFr9c0LKrKycP7id++gaN7t3Fs3w5STydy1Y8f4aq7Hr6gbFhIEK08FbRZRCjdoyLp0LzCH/HVPZB5va6p6vry9lfRgezJJ5/k2WefpXv37owfP54pU6YwdepU2rZtW52XdBFLYDhfXk4WB7as58CWtRzeFcupo/H0GzGRGc++XuNtBonQtFGI5xZKk/AQGocH0zgsmIhQ9y08NJhmjSpNEgZknXK5XLRs2ZL8/HyGDx/OZZddxlVXXcXYsWMJDa15P25LYASG3KzzHNi6zl2ndsZx+lg8g8Zdyb1PvlzjbYYGC80iQt11KjyUyPAQIj11qlGo+9YkPKSyq9DeSmDUuJ5pJSeh5dWpt99+mxkzZtCuXTsmTZrE1VdfzXXXXUfLljUbW6EqLIHhfXk5Wezd9C17Nn5L/PaNpKckMe2en3PFnT/z2T6DRIgMD6ZpoxB3vQkLIaL4WBQWTKOQYMJDgwgPCSIsJIiw4KDqdOsKyGMUQHR0NHv27CE6OppJkyYxbdo0Lrvsslodo0qzBIZ/qSrH9u9k7yZ3fSuePesvn8VV2jqwqkKDhaaNQr+rX03C3fWrsaduRYQFEx4SRHhIMGEhQVW5uFVmgap0aOkIlJz/6ARQ+rLBd2VUtVBEzgGtPcs3lFq3o+d+ZdusslatWnHkyBG6dOlSYT/8yPBg+raveX87Uz/16zCCKeNGfPc4KzMDl6uIps3L/65k5BRwPrcQat+ttiRf1bVqe+SRR3jsscdo1apVTTdRpnZNG1kddLymRHe7EW6+EYDc7GzOZ6TTxkufm6qSmVtAZhnzj0/p146wEK9m7h1Rp4KCgti6dStdunQhJMR7/UgjwuyYFhiaMqTHzTD9ZgBysrPIysygTTvvfHYFRS7Ss/NJLzUucseWEQzu1MIr+6hEbepZjTICN910E5MnT6Zbt2511YyZVpFhVt+8rinR3W6D29yNcc6cOklwaCgtW9fd+5xbUERuQRFpFZQJChLCgoOI6drS24OHOuIYBbBgwQI6dOhQo9a1VdWpZQRhdTfGjylDvw7juGrSOAAKCwo4deIonTp591y/mMulZOQUkJFT9uxcLSPDGN29+mOpQNUSGH4lIjOBmUCFMxx07dq10m21bdqIaYM6eCs0U2/V3+9IVetTVFSUT/bf/5Jm9L+kmU+2bYw/VLVOde/e3ev7btMk3I5ppt6pSp1q2bKlT1tblKVHVBN6RDWp0302OPb/zOv8eYwqbfilvvmhbGphWOWzBzpRVdJgiUDnEo87eZaVWcbTvKk57oFmylu3KtsEQFXfVNUYVY3x1Y8qYxzCF3XtAlafTANjdcoY36tNPbuI1SnTgNgxypgaqEoCYzPQS0S6iUgYcDvwRakyXwD3eO5PB1Z4+jV+AdwuIuEi0g3oBWyq4jaNaWh8UdeMacisThnje7WpZ8Y0ZHaMMqYGKu1C4ulv9TCwBPcUP++o6m4ReQaIVdUvgLeBf4tIPJCKuwLiKfcRsAcoBB4qHh23rG16/+UZEzh8VdeMaaisThnje7WpZ8Y0ZHaMMqZmKp2FxEmqOn+xMQ5RNyOL1ZDVJxOArE4Z4z2Ork9gdcoEHEfXKatPJgCVWadsKFhjjDHGGGOMMcY4niUwjDHGGGOMMcYY43gB1YVERFKAo/6OA2hDDecudyh7Pb5xRlWn+juI8lSjPjnl/XQSe08uVhfvSX2oU4Hy3bE4vc9psTq6PoHfz/uc9nlVhcXsexXF6+g65cP6FGifYU01lNcJznmtZdapgEpgOIWIxKpqjL/j8BZ7PaYi9n5ezN6Ti9l7UjWB8j5ZnN4XSLGawPy8LGbfC7R460JDeU8ayusE579W60JijDHGGGOMMcYYx7MEhjHGGGOMMcYYYxzPEhg186a/A/Ayez2mIvZ+Xszek4vZe1I1gfI+WZzeF0ixmsD8vCxm3wu0eOtCQ3lPGsrrBIe/VhsDwxhjjDHGGGOMMY5nLTCMMcYYY4wxxhjjeJbAMMYYY4wxxhhjjONZAqMSIvKOiCSLyK4Sy54WkUQR2ea5Xe3PGKtDRDqLyEoR2SMiu0Xk557lrURkmYgc9Pxt6e9YK1PBawnYz8dpRGSqiOwXkXgR+a2/43ECEUkQkZ2e71asv+Opa+X8Twy4/x91zcl1KVA+00A5folIIxHZJCLbPXH+0bO8m4hs9HwHPhSRMH/Gacrn5PpalrLqsJOVV5edrLx63ZAFWj2pjkA53niLiASLyFYRWeh57OjjlSUwKvcuMLWM5S+q6hDP7cs6jqk2CoFfqmp/YDTwkIj0B34LLFfVXsByz2OnK++1QOB+Po4hIsHAP4FpQH/gjhLvb0M32fPdcuwc2T70Lhf/TwzE/x91JgDq0rsExmcaKMevPOByVY0GhgBTRWQ08Dfcx6aeQBrwgB9jNOUIgPpalncp+1zVqSo6f3Oq8up1gxSg9aQ6AuV44y0/B/aWeOzo45UlMCqhqquAVH/H4S2qmqSqWzz3M3F/WTsCNwCzPcVmAz/0T4RVV8FrMd4xEohX1cOqmg98gPt7Yhqwcv4nBtz/jzrm6LoUKJ9poBy/1O2852Go56bA5cB8z3K/x2nK5ej6WpZAO1cNxPO3Cup1QxVw9aQ6AuV44w0i0gm4BnjL81hw+PHKEhg197CI7PA02wvI5kMi0hUYCmwE2qlqkuepU0A7P4VVI6VeC9SDz8cBOgLHSzw+gcNPMOqIAktFJE5EZvo7GIcI6P8fdSAQ65KjP1OnH788zXG3AcnAMuAQkK6qhZ4igfAdaKgCsb4GrDLO3xyrdL1WVcfH7EMNpp44/XjjBS8BvwZcnsetcfjxyhIYNfMa0AN3E7Ik4H/9G071iUgT4BPgMVXNKPmcuufWDZischmvJeA/H+No41V1GO5mkw+JyGX+DshJAu3/h6mc0z7TQDh+qWqRqg4BOuG+UtnXzyEZ4zgV1WUnKl2vRWSgv2MyvhUIx5vaEJFrgWRVjfN3LNVhCYwaUNXTnn9iLuBfuE9OAoaIhOKujHNU9VPP4tMi0sHzfAfc2WXHK+u1BPrn4yCJQOcSjzt5ljVoqpro+ZsMLMC+XxCg/z/qUCDWJUd+poF2/FLVdGAlMAZoISIhnqcC4TvQUAVifQ045dTlgFCiXgfSuCPeVu/rSaAdb2poHHC9iCTg7gZ0OfB3HH68sgRGDRR/cT1uBAJi1Gf4rl/T28BeVZ1V4qkvgHs89+8BPq/r2KqrvNcSyJ+Pw2wGenlGIg4Dbsf9PWmwRCRSRJoW3weuxL5fEID/P+pYINYlx32mgXL8EpEoEWnhuR8BXIG7//RKYLqnmN/jNOUKxPoaUCqoy45VTr3e59+o/Kpe15NAOd7Ulqr+TlU7qWpX3J/hClW9E4cfr8Td+sWUR0TmAZOANsBp4CnP4yG4mw0lAD8p0R/K0URkPLAa2Mn3fZ3+G3e/ro+ALsBR4FZVdfSAUBW8ljsI0M/HacQ9Be1LQDDwjqr+2c8h+ZWIdMfd6gIgBJjb0N6Tcv4nfkaA/f+oa06uS4HymQbK8UtEBuMe9CwY94Wij1T1Gc//jw+AVsBW4C5VzfNXnKZ8Tq6vZSmrDqvq234NqgLl1WUnzxpXXr32b1T+FWj1pDoC5XjjTSIyCfiVql7r9OOVJTCMMcYYY4wxxhjjeNaFxBhjjDHGGGOMMY5nCQxjjDHGGGOMMcY4niUwjDHGGGOMMcYY43iWwDDGGGOMMcYYY4zjWQLDGGOMMcYYY4wxjmcJDGOMMcYYY4wxxjieJTCMMcYYY4wxxhjjeJbAMMYYY4wxxhhjjONZAsMYY4wxxhhjjDGOZwkMY4wxxhhjjDHGOJ4lMIwxxhhjjDHGGON4lsAwxhhjjDHGGGOM41kCwxhjjDH1iohMEpGF1VznMRFp7KuYjDHGGFN7lsAwAIhIVxHZVYv1vxSRFhU8f8GJYWXly9nG0yLyq5rGaEx1iUiIv2MwJlAFYP15DLAEhjHGGONglsBwkAA82fuOql6tqukVFLngxLAK5Y2pNU9ibp+IzBGRvSIyvziRJiJPishmEdklIm+KiHiWfyMiL4lILPBzEblORDaKyFYR+VpE2nnKPS0is0VktYgcFZGbROR/RGSniHwlIqG1jH2SJ5b5JV5DcYwJItLGcz9GRL6pzb6MKUsg1x+PZiKySET2i8jrIhLk2feVIrJeRLaIyMci0kREHgUuAVaKyEpPuddEJFZEdovIH70QjzHGGGNqyRIYXlQPTvaKX0cjEfk/z7a3ishkz/LGIvKRiOwRkQWeOGM8zyWISBsRifScMG73vNbbyjkxLPkD7McissOzzr+99TqM8egDvKqq/YAM4Gee5a+o6ghVHQhEANeWWCdMVWNU9X+BNcBoVR0KfAD8ukS5HsDlwPXA+8BKVR0E5ADXlA5ERJ4QkW1l3F4uJ/ahuJN//YHuwLgavQPG1Fwg15+RwCO4608P4CbPcecPwA9UdRgQCzyuqi8DJ4HJqjrZs/7vVTUGGAxMFJHBVX3TjDHGGOMbAXvF38H6AA+o6loReQf3yd4LuE/2ngHw/Ei/FviPZ50wz0kSItIS98meisgM3Cd7v/SU6wFMxn0yth64WVV/LSILcJ/sfVYyEBF5ArizjBhXqeqjFbyGhwBV1UEi0hdYKiK9Pa8lTVX7i8hAYFsZ604FTqrqNZ4YmqvqORF5HPeJ4ZlSMQ7AfTI5VlXPiEirCuIypiaOq+paz/33gUdx18nJIvJr3C2DWgG7+b5Oflhi/U7AhyLSAQgDjpR4brGqFojITiAY+MqzfCfQtXQgqvo88Hw1Yt+kqicARGSbZ5trqrG+MbUV6PXnMICIzAPGA7m4j6FrPdcRwnAfT8tyq4jMxH2u1MGz3o5q7N8YY4wxXmYJDO8L5JO9YuOBf3i2sU9EjgK9Pcv/7lm+S0TKOpHbCfyviPwNWKiqqyvZ1+XAx8WJDVVNrUG8xlRESz8WkUbAq0CMqh4XkaeBRiXKZJW4/w9glqp+ISKTgKdLPJcHoKouESlQ1eJ9uSjj/2sNkop5Je4XldhmId+3oGuEMb4TyPXnotgBAZap6h1llC+5r27Ar4ARqpomIu9idc0YY4zxO+tC4n0VnexN9zSP/RcVn+y94in3k1LlvjvZA6p0slfN5ra1pqoHgGG4Exl/EpEnfbUvY6qoi4iM8dz/Ee4WDMX16oyINAGmV7B+cyDRc/+e2gSiqs+r6pAybhW1iCpLAjDcc//m2sRkTCUCuf6MFJFu4h774jZP7BuAcSLSE8DT7bG3p3wm0NRzvxnuY/M5T1fOabWJ3RhjjDHeYQkM7wvkk71iq/Fc5fKc2HUB9gNrgVs9y/sDg0qvKCKXANmq+j7u1h/DPE+VPDEsaQVwi4i09qxvXUiMt+0HHhKRvUBL4DXPALL/AnYBS4DNFaz/NPCxiMQBZyooV5f+CPxd3GPnFPk7GFOvBXL92Qy8AuzF3ZpxgaqmAPcC8zytCNcDfT3l3wS+EpGVqrod2ArsA+biPv4ZY4wxxs/k+4v4prZEpCvubh2xuK+O7gHuVtVsEfkTcAdwCjgAHFXVp8U9e8CvVDXWs40bgBeBNNw/7keo6iRPE93zqvqCp9x5VW3iuX/Bc7WIfaGqDvS0GHkNiMHdVP1xVV0pIpHAbNz9gPfhHlTwFlU9KCIJnvLDcScuXEAB8FNVjRWRR4CHcY+PMbm4vGfci3uAJ3D/ENuqqveWE+PTuAc0PF+8TFU71fQ1m/qv5Pfaz6EYE3Cs/hhjjDHGaSyB4UX1/WRPRIKBUFXNFZEewNdAH1XN93NoxpSpvtdJY3zJ6o8xxhhjnMYG8TTV0Rj3VKihuAdC+5klL4yTqWoCYD++jKkBqz/GGGOMcRprgWEcR0R+D9xSavHHqvpnf8RjjDHGGGOMMcb/LIFhjDHGGGOMMcYYxwuoWUimTp2quKcptZvdAuHmaFaf7BaAN0ezOmW3ALsZY4wxASegEhhnzjhlBkNjAp/VJ2O8y+qUMcYYY4xvBVQCwxhjjDHGGGOMMQ2TJTCMMcYYY4wxxhjjeJbAMMYYY4wxxhhjjONZAsMYY4wxxhhjjDGOZwkMY4wxxhhjjDHGOF5IbVYWkXeAa4FkVR1YxvNPAHeW2Fc/IEpVU0UkAcgEioBCVY2pTSymaopciqoSEmy5K2Nq48iRI3zyySfk5ORw6aWXcvPNNxMZGenvsIwJaEUuxVVUyNatWykoKKBHjx60b9/e32EZY4wxxiFqlcAA3gVeAd4r60lVfR54HkBErgN+oaqpJYpMVlWbd87H8gtd7DiRzt5TmZw9n4fLpRzYsIzNiz9i15aNqCrR0dHcf//9zJgxg9DQUH+HbIxjpaRncvcDD7J0wTxU9bvljz76KK+88gp33XWXH6MzJvAUFrnYejydHcdS+fTd11jz+b/JSE0BICgoiGuuuYZZs2bRs2dPP0dqjDHGGH+rVQJDVVeJSNcqFr8DmFeb/ZnqS0zPYfHOJDJzCwHIzTrPhy/+nu2rvqJV+06MufZHdGzdhN2bVvOzn/2M999/nw8//JBOnTr5OXJjnGfn4RNcO20axw7sYuLN9zHhh3fTvE07ju7ZxtoPX+Huu+9m9+7dPPfcc4iIv8M1xvEycwv4bNtJjiae5r0/P8bBrevpN3IiP7zih0Q2aUZB4m4+mfN/DB8+nDlz5nDttdf6O2RjjDHG+JGUvIJYow24ExgLy+pCUqJMY+AE0LO4BYaIHAHSAAXeUNU3y1l3JjAToEuXLsOPHj1aq3gbkoOnM1m86xRFLvdnnJeTzWu/vocTB3cz7b7HmHzLDIKC3F1JurRqTNbeVTz005/QokUL1q9fb0mM2nP0L9iYmBiNjY31dxgB48SZDKZccRWHdsdxzx/+zsAxUy54vqiwgLX/fp7P5s3m5Zdf5pFHHvFTpPWa1al6JCuvkA82H+fsufO89ut7OH5wF9MfeZpRU6dfUK5X4xyeevR+du7cyZIlS5g8ebKfIq53HF2fjDHGmLLU1UAI1wFrS3UfGa+qw4BpwEMicllZK6rqm6oao6oxUVFRdRFrvXD0bBZf7vw+eVFUVMh7f36M4wd3cc//e5kpt838LnkBcCw1m5Be41m2fCXnzp1j2rRppKen+yt8Yxwlr7CIex98lAPbNnDbL/50UfICIDgklPE//g0/mHoNjz32GN9++60fIjUmMBQWuVi44yTnsvP54IXfkbBnK3f+5vmLkhcAB7Mj+NcHn9GrVy9uvPFG9u/f74eIjTHGGOMEdZXAuJ1S3UdUNdHzNxlYAIyso1jqvfTsfBbtTMJVonXNig/eZO+mb7n54acYNO4HZa53OiOXY0HtmD//E/bt28dDDz1UVyEb3IPiikiyiOwq5/lJInJORLZ5bk+WeG6qiOwXkXgR+W3dRd0wvPbBlyz/ZDbjb7ibmB/8sNxyQcHBXP3oc3Tt2o0ZM2aQk5NTh1EaEzg2HUnlZHouccu/YOs3i7j6vscZctm0csuvP5HHvPmfERISwr333ktRUVEdRmuMMcYYp/B5AkNEmgMTgc9LLIsUkabF94ErgTJ/tJnqcbmUxbtOkVfg+m5Z4qG9LHn/nwyddA1jr729wvUT03LQjoP4wx/+wNy5c5k/f76vQzbfexeYWkmZ1ao6xHN7BkBEgoF/4m7N1B+4Q0T6+zTSBuTw6XT+8t+P0bJdR665/xeVrxDSiJm//yvx8fE888wzvg/QmACTnJnL5oQ0MlJTWPDan+nafyiX3zqjwnXyC13sPR/OSy/9nQ0bNvDSSy/VUbTGGGOMcZJaJTBEZB6wHugjIidE5AEReVBEHixR7EZgqapmlVjWDlgjItuBTcAiVf2qNrEYt7hjaZw6l/vdY5fLxQf/+99ENm/JTQ//vyptY8/JDK778UMMHz6cn/3sZ5w7d85X4ZoSVHUVkFppwYuNBOJV9bCq5gMfADd4NbgGSlX50//+g+Tjh7n54ScJj6jaNKlhnQcz/fY7mTVrFsePH/dxlMYEDlXlm/0puFRZ9M4sCnJzuO3xPxMUHFzpuifTc+k7fhrXX389Tz75JElJSXUQsTHGGGOcpFYJDFW9Q1U7qGqoqnZS1bdV9XVVfb1EmXdV9fZS6x1W1WjPbYCq/rk2cRi3jNwCNh4+e8GyuOVfkBi/h+tn/obIZi2rvK11R9L4n5deISUlhb/+9a/eDtXU3BgR2S4ii0VkgGdZR6Dkr+QTnmUXEZGZIhIrIrEpKSm+jjXg7Tqawvy3/0H3QSPoN3Jitdad/KOHAXj22Wd9EZoxAenImSwS03I4eWQ/scsWMP6Gu2jXpUeV1994JJU//eV/yM/PtxZOxhhjTANUV2NgmDqw9uAZCoq+H/ciPy+Xxe++ROfeAxk66ZpqbaugSEkOu4Q777yLF198kWPHjnk7XFN9W4BLVTUa+AfwWXU3YIPiVp2q8twLL5KZmsLV9z1W7WlR8xq14kf3PMA777xDfHy8j6I0JnCoKmsPuZPsX77zIuGNmzDl9p9Uaxv5hS5O0YIHH3yQf/3rXxw4cMAXoRpjjDHGoSyBUU+kZOax71TmBcvW/Wce6SlJXDfzNxfMOFJVSedyueNnvwawK10OoKoZqnrec/9LIFRE2gCJQOcSRTt5lplaOHQqnUXz3qb30LF0HxhTo22MvvF+goODmTVrlpejMybwJJzN5kxmHscP7GLPxpVcfut/EdmsRbW3s/vkOR55/DeEh4fz3HPP+SBSY4wxxjiVJTDqiQ2luo4U5ufzzSfv0DN6FD0H13yCl4S8CH587/289957JCbab2J/EpH24mkGICIjcdffs8BmoJeIdBORMNyz/nzhv0jrh1feeo/M1BQm3XJ/jbeRHdKMm269nXfffZfU1JoMb2J8QUQaicgmT3es3SLyxzLKhIvIh56ZfTaKSNe6j7R+iU1w14GVH79NeONIxl3/oxptRxUOnQ/mgQceYM6cOZw4ccKbYRpjjDHGwSyBUQ+cPZ9HfPL5C5bFLv+cjLPJTLltZq22nV/oYuwP78XlctlVZB+rwqC404FdnsFvXwZuV7dC4GFgCbAX+EhVd/vjNdQXaVl5zJ/9Bu0u7Umf4eNrta3xP7yHnJwc3njjDS9FZ7wgD7jc0x1rCDBVREaXKvMAkKaqPYEXgb/VcYz1SnJGLifScjibdJztq79i7DW3ExHZtMbb2386k/sffBhV5e9//7sXIzXGGGOMk1kCox6IO5p2wWOXy8XKj96iU88B9B4+rtbbTwtuwQ0338obb7xBWlpa5SuYGqlsUFxVfcUz6G20qo5W1XUl1v1SVXurag8bFLf25v1nGYmH9nLZjfdUe+yL0gqbd2bS5VP45z//SVFRkZciNLXhSfwVZ31DPTctVewGYLbn/nxgitT2y9CA7Tjhns1q9Wf/JigomAk33lOr7alCCs255ZZbeOONN8jMzKx8JWOMMcYEPEtgBLic/CL2lxr7Yn/cGlISE5g4/b5a//gqNur6e8jKyuLdd9/1yvaMcSqXS3l/9v8R3jiSYZOrN/htmdtTZcqNd5KYmMiSJUu8EKHxBhEJFpFtQDKwTFU3liry3ew+nlZO54DWZWzHZvapRF5hEftPZ5Kfl8vmZQsYPP5KWrRpV+vt7k3KYMaDPyMzM5N58+Z5IVJjjDHGOJ0lMALcrpPnKHRdeOFw3cJ5NGnRmujxV3ltP6FtuzFsxChee+01XC6X17ZrjNPsPHKSuJWLGTrpWsIjIr2yzdb9xhAVFcXbb7/tle2Z2lPVIlUdgnvQ25EiMrCG27GZfSqxLymT/EIX279dTM75DMZcc5tXtlvkUsI69GXQoEHWRcsYY4xpICyBEcBU9btmucVSTyeyZ+M3jJ52CyFhYV7d38hr7uDgwYOsWLHCq9s1xknefPd98vNyGD1tute2mesK4oZb7uCLL77g9OnTXtuuqT1VTQdWAlNLPfXd7D4iEgI0xz1oruCEArQAACAASURBVKmmvUkZAKz/8kPadupGj1oMLF3a7qQMZvzXTLZs2UJsbKzXtmuMMcYYZ7IERgA7nppDRk7BBcs2fPkRgNeucJXUPWYKrVq34dVXX/X6to1xgrzCIr789EPad+1N596DvLrtIT+4icLCQubOnevV7ZrqE5EoEWnhuR8BXAHsK1XsC6B4oIbpwApVLT1OhqlEWlY+SedyOXU0noQ9Wxl99a1e69oI7m6UI35wPREREbz11lte264xxhhjnMkSGAFsT9KFrS9cRUXELvuMPsPH07LtJV7fX0hYGKOu/CELFy7kzJkzXt++Mf72bewuEvZsZfiU67z6IwugsOklDBk61PrqO0MHYKWI7MA9DfEyVV0oIs+IyPWeMm8DrUUkHngc+K2fYg1o+zxjNMV+/TlBQcEMn3J9JWtU35EMuPHGG/noo4/Iy8vz+vaNMcYY4xyWwAhQeYVFF02dGr99I+lnTjHyyht9tt8BE6+joKDAfoSZeum9Oe7v9dBJtR+8szSXKpOvuYnNmzdz8OBBr2/fVJ2q7lDVoao6WFUHquoznuVPquoXnvu5qnqLqvZU1ZGqeti/UQceVWXfqQxcLhdbVvyHPjHjadqyjdf3czojl+tvvp20tDQWLVrk9e0bY4wxxjksgRGgDqdkUVB0YWvmTUs/JaJJMwaMmeKz/V7SvS9d+wyw2UhMvZOTX8jy/3xCt4HDadWuo0/20XPUFYiIJQBNg5ByPo/07AIO7dhEekoSMVNu8Nm+WvYaRvv27fn3v//ts30YY4wxxv8sgRGgSk+dmpt1np1rlzF00tWEhoX7dN9DLv8hW7ZsYefOnT7djzF1aemaWE4djWeYD1pfFMsLb8nY8RMsgWEahPjT7laCccu/ILxxJAPGXO6zfR08k8Ntt9/BokWLSE1N9dl+jDHGGONftUpgiMg7IpIsIrvKeX6SiJwTkW2e25MlnpsqIvtFJF5ErG9xNeQWFHH0bPYFy3at/5qCvFyf9C8ubdjkawkKDrYfYaZe+fDj+YgIg8Zf6dP9jJlyDfv27WPv3r0+3Y8x/hafcp6iwgJ2rvuaQWN/QFijCJ/tK7/Qxdgrr6egoIDPP//cZ/sxxhhjjH/VtgXGu1w89Vxpq1V1iOf2DICIBAP/BKYB/YE7RKR/LWNpMA6nZOEqNRj+tm8X07LtJVzab6jP99+kRSt6DRnDvA8+wAblN/VBQZGLb5cspNuA4TRrFeXTfXUZNgmATz75xKf7Mcafzp7P4+z5fA5sXU9O5jmiJ1R2qlB7QVE96Nq1Kx9//LHP92WMMcYY/6hVAkNVVwE1aas5EohX1cOqmg98APiuc2w9E59y4eCdWRnp7ItdQ/RlUwkKqpteQUMnXU3CkSPExsbWyf6M8aVVsTs4eWQ/g8Zf4fN9FTZqwYhRo/n00099vi9j/OXwmSwAtq9aTKPGTegzfLzP93kiPYfrb7yJZcuWWTcSY4wxpp6qi1+7Y0Rku4gsFpEBnmUdgeMlypzwLLuIiMwUkVgRiU1JSfF1rI6XX+jiqOfEsNjOtctwFRX6ZOaE8gwadwXBIaHMmTu3zvZpjK/M/cB9xXawj7uPFBs75Wq2bt3K4cM2sYWpnw6nnKewIJ+d65YzcOwUQsLCfL5PVRh62TQKCwutG4kxxhhTT/k6gbEFuFRVo4F/AJ9VdwOq+qaqxqhqTFSUb5t2B4JjqdkUukp1H/nmS1pf0oVOvQaUs5b3RTRpRt8RE/jgw49wuVx1tl9jvE1VWbn0Szr1GkDLtpfUyT67xkwG4IsvvqiT/RlTl7LzC0k6l0v89o3kZJ5j8ISr6mzf0tbdjWT+/Pl1tk9jjDHG1B2fJjBUNUNVz3vufwmEikgbIBHoXKJoJ88yU4kjpVpfZGWkE799I9ETpiIidRpL9ISrOJ10ks2bN9fpfo3xpt2HjpGwd7tPZ0goTZu0pV///pbAMPXSkTNZqMKudcsJa9SY3sPG1dm+U7MKuOqa61i+fDmZmZmVr2CMMcaYgOLTBIaItBfPr2oRGenZ31lgM9BLRLqJSBhwO2Bn8pVQVRJKJTD2bPwGl6uIwXXQd7+0/qMmExQcwuy5H9X5vo3xlg8++RxVZcDouktgAIyedCWrV68mPT29TvdrjK8lnMnG5XKxa/1y+sZMICy8UZ3uf8CYH5CXl8eSJUvqdL/GGGOM8b3aTqM6D1gP9BGREyLygIg8KCIPeopMB3aJyHbgZeB2dSsEHgaWAHuBj1R1d21iaQhSMvM4n1d4wbKda5bSok17OvceVOfxNG7anJ7Ro/jss09tNhIvqMK0xHeKyA4R2Ski60QkusRzCZ7l20TERlathqWLv6RFm/Z07NGvTvfbY/hECgsL+eqrr+p0v8b4ksulHEvN5viBXWScTWbg2Cl1HkPYJX1p06YNn31W7V6rxhhjjHG42s5CcoeqdlDVUFXtpKpvq+rrqvq65/lXVHWAqkar6mhVXVdi3S9Vtbeq9lDVP9f2hTQER1OzL3icl5PFvrg1DBp/RZ13Hyk2ePwVJB1LYPPW7X7Zfz3zLhVPS3wEmKiqg4BngTdLPT/ZM11xjI/iq3fSM7PYvnE1/UdPrvM61LhjX6KioqwbialXTmfmkltQxO71ywkKCqbfyIl1HkN2IVx+5VQWLVpEQUFBne/fGGOMMb5TN3NuGq8o3X1kX+waCvPzGDSu7ruPFBs49geICG+//6HfYqgvKpuWWFXXqWqa5+EG3GPHmFr4ZNFS8nOz6T96ct3vPCiIcZOv4KuvvqKwsLDy8sYEgIQz7kT7rnXL6T4ohshmLfwSx6CxPyA9PZ3Vq1f7Zf/GGGOM8Q1LYASI/EIXSedyL1i2e/0KGjdtQbeBw/0UFTRrFUWXvtEsXbzIupHUrQeAxSUeK7BUROJEZKafYgo4ixYtJiQ0jJ6DR/pl/31iJpCWlsamTZv8sn9jvO3o2SxST53g1NGDdT6uTEnNegwlPDycRYsW+S0GY4wxxnifJTACRGJ6DkUlpk91FRWxd9M39Bt5GcHBIX6MDAaMnkzCvp3E7jns1zgaChGZjDuB8ZsSi8er6jBgGvCQiFxWzrozRSRWRGJTUlLqIFrnUlXWffs1PaJHEtYowi8xtO4TQ1BQkI2DYeqF3IIiTmXksmfTtwD0G1X33UeKFQU3YvS4CSxcuNBvMRhjjDHG+yyBESCOlxr/ImHvVrIy0ut06sfyFF9l+/dHn/o5kvpPRAYDbwE3qOrZ4uWqmuj5mwwsAMpsUqCqb6pqjKrGREVF1UXIjrVt935OHz9CvxH++5EVEtGMYSNGsXjx4soLG+NwJ9JyUHXPjhXVsSttO3XzazyDx17OgQMHOHjwoF/jMMYYY4z3WAIjQBxPuzCBsXv9CoJDQuk7fIKfIvpe+669aNW+EyuWfEluQZG/w6m3RKQL8Clwt6oeKLE8UkSaFt8HrgTKnMnEfO+jz/4DQL+RZTZWqTNDxkwiNjaW5ORkv8bRkIhIZxFZKSJ7RGS3iPy8jDKTROScZ2afbSLypD9iDSTHU7PJy8kmftsGvwzeWVqHgWMBrBuJMcYYU49YAiMA5BYUkZKZd8Gy3etX0GPwSBpFNvFTVN8TEQaMnsz+revZkWA/wmqqCtMSPwm0Bl4tNV1qO2CNZ7riTcAiVbU+CZVYvmwprTt0JqpjV7/G0Wmw+0fWkiVL/BpHA1MI/FJV+wOjcXe76l9GudWemX2GqOozdRti4DmW6k5eFBbk03/UJH+HQ6NWHejVp68lMIwxxph6xBIYASAx3d0st9iZk8dIPnHEESeIxfqPmkxhfh7z/2O/m2uqCtMSz1DVliV+UMV4lh/2TFUc7Zm22KYlrkRubh7bN62jb4z/WzA1uaQnbdpEsWzZMn+H0mCoapKqbvHczwT2Ah39G1Vgy8wtIDUrn32xqwlr1JjuA50xm/PQsZNZtWoVWVlZlRc2xhhjjONZAiMAJKblXPB4b/EAaQ5oolusx6ARhDVqzNqVy0jLyvd3OMZUaPGKVeTnZtNn+Dh/h0JQUBAx4y5j6dKlNpOPH4hIV2AosLGMp8eIyHYRWSwiA8pZ3wbGpXj8C2Xv5lX0GjKKkLAwf4cEQKfBY8jPz2flypX+DsUYY4wxXmAJjACQmH5xAiOqU1eiOl7qp4guFhIWRu+hY9i7eRV7ks75OxxjKvTFosUEBQXTM3q0v0MBoNewcZw+fZqdO3f6O5QGRUSaAJ8Aj6lqRqmntwCXqmo08A/gs7K2YQPjup1IyyElMYHUUycc0bKpWPteQ4lo3Nhm+jHGGGPqCUtgOFxeYRHJGd+Pf5GXk0389o30HznJf0GVo9+oSaSdTmT5+i12Jdk42upvlnNp/yGOGEMGoG0fd3N7Gwej7ohIKO7kxRxVvWgKJVXNUNXznvtfAqEi0qaOwwwYJ9Ky2bd5FQB9R/h3YNySQsLCGDZqPIsXL7bjkjHGGFMPWALD4U6dy8VV4qQrfvtGCgvyHdV9pFg/z0nr5lXLL2o1YoxTJKec4fDenfQZ5v/uI8UiW7Wjd99+LF261N+hNAgiIsDbwF5VnVVOmfaecojISNzHy7NllW3oMnMLSM8uYN/m1UR16krrDp39HdIFug8bx+HDh206VWOMMaYesASGw51Mz73g8b7Nqxw1QFpJLaLa06F7H/Zu/pZ9SZn+DseYMi1YtARVpfewsf4O5QKDR01g9erV5ORY8q8OjAPuBi4vMU3q1aVm/ZkO7PLM7vMycLvaJfwynUjLoSA/j/gdmxzVfaRY50Huum4JQmOMMSbwWQLD4ZLOff9jxokDpJXWN2YCCbu3siMhicIil7/DMeYiXy1ZSqPGTejcZ5C/Q7lA54GjyMvLY926df4Opd5T1TWqKqo6uMSsPl+WmvXnFc+sPtGqOlpV7YMpR2JaDkd2xVGYn0cfByYw2lzShU6XdrMEhjHGGFMP1CqBISLviEiyiOwq5/k7RWSHiOwUkXUiEl3iuQTP8m0iElubOOorl0tJOvd9C4wzJ486boC00vrGTKCosIDdsetJOJvt73CMucj61d/QM3oUwcEh/g7lAlG9hhASEsLXX3/t71CMqZbE9Bz2x60hOCSUHoNG+DucMvWLGcfKlSvJz7dZsowxxphAVtsWGO8CUyt4/ggwUVUHAc8Cb5Z6frLnypfz+kM4wNmsfPILv2/FsHeT8wZIK63bgGGERzRm3+ZV7DtVelB/Y/zrYPwhTiceo5fDuo8ANGrchEFDR1gCwwSU7PxCUrPy2R+39rv//07UedAYzp8/z4YNG/wdijHGGGNqoVYJDFVdBaRW8Pw6VU3zPNwAdKrN/hqaU+dKjX8R68wB0koKCQ2j15Ax7Nu8msPJ58ktKPJ3SMZ857NF7qkUew8d4+dIytYvZixxcXGkppb7b9UYRzmZnkNGagonD++jz/Dx/g6nXD2iRxEcHGzdSIwxxpgAV5djYDwALC7xWIGlIhInIjPLW0lEZopIrIjEpqSk+DxIJyk5/kVBfh6HHDpAWml9R0wg9XQiJ48e4lDKeX+HY8x3li77muZt2tG2c3d/h1Km9v1GoKqsXLnS36EYUyUn0nI4sMU9PEif4c6Z2ae0iMim9B40zBIYxhhjTICrkwSGiEzGncD4TYnF41V1GDANeEhEyuwXoapvqmqMqsZERUXVQbTOcTrj+xYYh3fGUpCX6+grXMWKY9wft5YDp202EuMMLpeLTetW02vIGDyzYzpOh54DaRwZyYoVK/wdijFVcjI9lwNb1hHZvCWX9Ojn73Aq1HXwaGJjY62FkzHGGBPAfJ7AEJHBwFvADap6tni5qiZ6/iYDC4CRvo4lkOQVFnE26/vBxvbHrSE4NJQeg53/NrXu0Jk2l1zK/rg1HDubQ3Z+ob9DMoYdO3aSkXaWXkNG+zuUcgWHhBIdM4bly5f7OxRjKpVf6CI5I5cDW9bSa8gYgoKcPbFZz6FjUVVLEBpjjDEBzKdnGyLSBfgUuFtVD5RYHikiTYvvA1cCZc5k0lAlZ+Sh+v3j/XFr6D5guGMHSCutT8x4Dm3fRH5eHvHJ1o3E+N8Xi91Nx3s6OIEB0GPIKPbv309iYqK/QzGmQqfO5ZKUcJCM1BR6O3Bg3NK69B1M48gmLFu2zN+hGGOMMaaGajuN6jxgPdBHRE6IyAMi8qCIPOgp8iTQGni11HSp7YA1IrId2AQsUtWvahNLfVOy+0jG2WSSjhygt4P7F5fWZ/g48vNyOLI7jv2nrBuJ8b+vV6wgqmNXWrbt4O9QKtSur3tSJhsHwzjdyXM5HNjqHv8iEBIYwcEh9Bwyymb6McYYYwJYSG1WVtU7Knl+BjCjjOWHgeja7Lu+O52R9939/XFrAQJiAM9iPaNHERQcwv4ta+k9bAzn8wppEl6rr5sxNVZYWEjchrVET7za36FUqkO3vrRo2ZLly5dz1113+TscY8p1Mt09gGdUx660atfR3+FUSY8hY1mwdjmHDx+me3dnDuZrjDHGmPI5u8NqA5ac+X0LjP1xa2jSojUduvXxY0TV06hxE7r2H8r+2DWowkEbzNP4UWxsLNnnMx09/kWxoKAgBsWMZcWKFWjJfmTGOIjLpZw4m8mhHZvo5dBpicvSx9NSxLqRGGOMMYHJEhgOlFtQRHp2AeCeOeHAlnX0GT7O8QOkldY3ZjyJh/aSmXaWg6dtHIzKiMg7IpIsImWOByNuL4tIvIjsEJFhJZ67R0QOem731F3UgWHhV+4fKz2jnZ/AAOgyaCTHjh3jyJEj/g7FmDKdycrj4K6t5OVk03tY4HRvjOrUjVZtO9hAucYYY0yACqxfxA1EconuI0lH9nP+XGpAnSAWK+4TfWDrOhLTc8jMLfBzRI73LjC1guenAb08t5nAawAi0gp4ChiFezafp0SkpU8jDTDLV6ykQ7feNGnRyt+hVEmn/iMAbLYE41hJ6bkc2LIeCQqiZ7TzZ8cqJiL0GDKGr5cvx+Vy+TscY4wxxlSTJTAcqHT3Efi+2Wsg6dRzAI2btuCAZwyPgzYbSYVUdRWQWkGRG4D31G0D0EJEOgBXActUNVVV04BlVJwIaVDy8vLYunm942cfKalt5+60iWpnCQzjWEnncjm4dR2deg2gcdPm/g6nWnoNGU1aairbtm3zdyjGGGOMqSZLYDhQcuaFA3h26NabZq3b+jGimgkKDqb3sDHs37IWVbVxMGqvI3C8xOMTnmXlLb+IiMwUkVgRiU1JSfFZoE6yYcMG8nJz6RU9yt+hVJmI0H/4GFauXGnjYBhHOpyUwtF9O+g9NPCS6709Y3bYbCTGGGNM4LEEhgOleBIY+bk5HN4VS5/h4/0cUc31HjaOjLPJnD4az8n0XDKsG4lfqeqbqhqjqjFRUVH+DqdOLF76NRIURI/BgdPMHaDTgBGcOnWKffv2+TuUekdEOovIShHZIyK7ReTnZZQpd8yZhi47v5CtG9fjKir8LhkQSJq1bkv7rr1YZgkMY4wxJuBYAsNh8gtdpGXnA3B4VyxFBf+/vfuOj7LM+j/+OQlJqEqVjtQAoZMQQECKiMAquBZEXbGu+lPXx1332dVnn9VHXXfdqq6ua8WKoALSQ++99xqaJARI733O748M2YCUQJK5p5z36zUvMtd9T/hO4E4yZ67rXIWlvSR8UcfIkt4dZ7eCjbVlJBURD7Qsc7+Fe+xi44aS/hfN23WmRu1rnI5yRdp0L5kxsmzZMoeT+KUi4HlVjQD6AU+LSMR551yw54wpWT5ycOtaqoWG0bqLb9Z1OvTqz6pVq8jLy7v8ycYYY4zxGlbA8DJJWfmcnTF+YMsaqoWE0rZrlLOhKqDedc24rkUbDmx198GwZSQVMQuY4H5nuB+QrqoJwAJghIjUczfvHOEeC3g5OTls37zRJ7ZPPV/9Ji1o3KyF9cGoAqqaoKpb3R9nAvv48bKri/WcCXgJaXkc3LaOtl0iCQkNczrOVQnv2Z/8vDzWrVvndBRjjDHGXAErYHiZxDL9Lw5uXUObrpGEVq/hYKKKC48cwJGdmygqKOBkWp7tRnIRIjIZWAd0FJE4EXlURJ4UkSfdp8wDjgCxwEfAUwCqmgK8Bmxy3151jwW8tWvXUlRUSHsf6n9xlojQqXd/li9fbrslVCERaQ30Ajacd6hcvWUCsa/MvsPHOXXsIB16+97ykbPadY8mKCjYlpEYY4wxPsYKGF7mbAEjI/kMCUcP+vTykbPCe99AQX4uR/duBWwZycWo6r2q2lRVQ1S1hap+oqrvq+r77uOqqk+rajtV7aaqm8s8dqKqtnffPnXuWXiXhYuXEBQUTJuukU5HuSrNI6JITk5m165dTkfxSyJSG5gGPKeqGVfzOQKtr4zLpaxZtQLAJ/tfnFW9Vm1aderO/AWLnI5ijDHGmCtgBQwvk5RVUsA4uG0tgE838Dyrffe+BAVX4+DWkudk26kaT1m0eAmtOnWnes3aTke5Ku2sD0aVEZEQSooXk1R1+gVOsd4yF5CUnc++LWupUedamrc7v22Ib+nQqz87tm0hPT3d6SjGGGOMKScrYHgRVSU5u6SB54Eta6h9bX2ate3kcKqKq16rNtd37sGBLasBOJmWS3Z+kcOpjL/LzMxk5/atPrl85Kx61zWleas21gejkomIAJ8A+1T1Hxc57WI9ZwLaydRcDm1bR/sefQkKDnY6ToV06NUfl8tlBUJjjDHGh1gBw4uk5xZSUORCVTm4dS0devUnKMg//onCe99AfOxestJTULVlJKbqrV69GldxsU8XMADa9+zHihUrKCqyol8lGgA8AAwTke3u2+jy9JwJdFt37yf1zEnCfbAx7vlad+pJaFgN5tkyEmOMMcZnVOjVsYhMFJEzIrL7IsdFRP4pIrEislNEepc59qCIHHLfHqxIDn9xtv9FwtEDZKYmlW5B6g86Rg5EVTm0bT1gy0hM1Vu0eAnBISE+u83jWa269CEjI4Nt27Y5HcVvqOpqVRVV7a6qPd23eeXtORPIVixbAkAHP+jPVC00lDbdIllsjTyNMcYYn1HRt/c/A0Ze4vgooIP79jjwbwARqQ+8DPQFooGX3ds/BrSkrJLlI2d7RYT39p8CRsvwrtSofQ0H3dupxqfmkltQ7HAq488WLV5C6869CA2r7nSUCmnfs2QGiS0jMU7LKyxm+/rV1G3UlEbNWzsdp1KE97qBo7EHiY8P+PYmxhhjjE+oUAFDVVcCl9qucSzwhfudrPVAXRFpCtwCLFLVFFVNBRZx6UJIQEjOLpmBcWDLGhpf3566jZo4nKjyBAdXo0PP/hzYsgZVxaXK4USbhWGqRmpqKnt27fD55SMAdeo1pGXbcFunbxwXn5JN7I4NhPfuT0kbEd93dqevmIW2jMQYY4zxBVXdYKE5cKLM/Tj32MXGf0REHheRzSKyOTExscqCeoPkrAIKC/I5vGuTX2yfer7w3jeQlpjAmRNHAOuDYarOihUrUFU6+ME6fYA23fuyatUqCgoKnI5iAtjK9ZvIyUyjQy//+fnUtE1Hal1bj9nzFjgdxRhjjDHl4PUdIlX1Q1WNUtWoRo0aOR2nyhQVu0jNKeDo7i0UFeT7xfap5zvb0+PAlpJlJD+k5JBXaMtITOVbunQpIWHVadWxu9NRKkXb7n3Jyclh48aNTkcxAWzpUnf/Cz8pDAIEBQXRoWc/Vq9Yjqo6HccYY4wxl1HVBYx4oGWZ+y3cYxcbD1gpOQWowoEtqwmuFkK7bn2cjlTpGjRtScNm15f2+Ch2KUcSsx1OZfzRwsVLaNs1imqhoU5HqRTtuvdBRKwPhnGMqrJ5zUqatA7nmvr+9WZCh143kJJ4it179jodxRhjjDGXUdUFjFnABPduJP2AdFVNABYAI0Sknrt55wj3WMBKdjfw3L9lNW269CasRk2HE1WNjlEDid2xgaLCkucba30wTCU7ffo0B/btLW1+6Q9qXVOX68O7WAHDOCYhOYPYnZvo6IfLG88+p6mz5jmcxBhjjDGXU9FtVCcD64COIhInIo+KyJMi8qT7lHnAESAW+Ah4CkBVU4DXgE3u26vusYCVnFVARvIZEo4coGOU/y0fOatj5AAK8nI4umcrAMeTsikocjmcyviTs80u/WmaO0Cb7tGsW7eOnJwcp6OYABSzeDlFhQV+2Z+pfpMWNGx2PQsXWSNPY4wxxttVdBeSe1W1qaqGqGoLVf1EVd9X1ffdx1VVn1bVdqraTVU3l3nsRFVt7759WtEn4uuSs/M5uK1kaYU/9r84q333vgQFVyvtg1HkUo4l2zISU3kWL15MjVp1aNG+i9NRKlX7Hv0oKChgzZo1TkcxAWjR4kUEVwuhrR8ubwQI792fbRvWWqNcY4wxxst5fRPPQJGSXcD+zaupfW19mrXt5HScKlO9Vm1aR/TiwJbVpWO2G4mpTEuWLqVd92iCgoOdjlKp2naLIrhaNVtGYhyxYfUKWkf08tvljeG9B5Cfm03M0lVORzHGGGPMJVgBwwsUFrtIzc7n4Na1hEcOICjIv/9ZOkYOID52L5mpSQAcTcqmqNiWkZiKO3bsGMeOHqW9ny0fAQirUYvWnXuwZMkSp6OYAJNw+gzHD+4hvFd/p6NUmQ49+yFBQcyYE+N0FGOMMcZcgn+/UvYRqTkFxMfuIyst2a+Xj5zVKWoQ8J/tVAuKXPyQYuv6TcWdfXHvj+v0Adr16MeWLVtITU11OooJIN/PmY+qEu7HP59q1L6GluHdWLXcZjgZY4wx3swKGF6gZPlIybTVTn7cwPOs5u0jqH1t/XOWkRyyZSSmEixevJhrG1xH41btnI5SJcJ73YDL5WL58uVORzEBZMHChdSocy0tO/hXX5nzdYwcwJF9OzgWf8bpKMYYY4y5CCtgeIGUrJL+F83bR1CnXkOn41S5oKAgwiMHcGDzalyukqUjRxKzKXapw8mcJyIjReSAiMSKyAsXOP6mIHj83gAAIABJREFUiGx33w6KSFqZY8Vljs3ybHLnuVwuFi9ZQvue/RARp+NUiVaduhNWvYYtIzEeo6qsXbGU8J79/K6vzPk69h6Aulx8Z8tIjDHGGK9lBQwvcOJ0Esf2bitdWhEIOkUNIis9hfjYvQDkFRYTn5rrcCpniUgw8C9gFBAB3CsiEWXPUdVfqmpPVe0JvANML3M49+wxVR3jseBeYvfu3SQlJtLBj9fpVwsJpW23PixatNjpKCZA7N+/n6TTCX69fOSs6zv3IKxGTRYutOvLGGOM8VZWwPACa1etwFVcFFAFjLO9Ps5dRpLpVBxvEQ3EquoRVS0ApgBjL3H+vcBkjyTzAWdnJXTo6b8FDIAOvW7g4MEDxMXFOR3FBIDZ80pmI3T0074yZQVXC6F9z35sW7eC7Pwip+MYY4wx5gKsgOEwl0vZtGopYTVr0Tqip9NxPKZOvQa06NCFfZtWlo4dTszCFdjLSJoDJ8rcj3OP/YiIXA+0Acp2nKsuIptFZL2I3H6Rxz3uPmdzYmJiZeX2CosWLaJxyzbUu66p01GqVHjvkgLN4sX2LvHVEpGJInJGRHZf5PgQEUkvsyTrJU9n9Bbz5i+gUfPW1G/SwukoHtExciDJCSdYvnGX01GMMcYYcwFWwHBYem4B+zauJLz3AIKrhTgdx6M6RQ3i+N7t5GSmA5CdX0xCRp7DqXzGeGCqqhaXGbteVaOA+4C3RORHnSxV9UNVjVLVqEaNGnkqa5XLz89nxYoVdOg9wOkoVa5J63Dq1GvAokWLnI7iyz4DRl7mnFVllmS96oFMXicvL4/1q1fRMQBnB86cO9fhJMYYY4y5ECtgOGz95u2kJZ2ic58bnY7icZ2jB+NyFXNw65rSsUOnA3oZSTzQssz9Fu6xCxnPectHVDXe/ecRYDnQq/Ijeqd169aRk5Pjt9unlhUUFESHnv1ZuGhxaRNcc2VUdSWQ4nQOb7d69Wry83Lp1CdwChiNml9Pg2atWLdyGXmFxZd/gDHGGGM8ygoYDouZX7K+uFMAFjCu79SDGnWuZd+mVaVjsWeyUA3YZSSbgA4i0kZEQikpUvxoNxER6QTUA9aVGasnImHujxsCA4C9HkntBRYuXEhQcDDtu/d1OopHdIwcSFLiGXbu3Ol0FH/WX0R2iEiMiFx0/1B/XpYVExNDtZAQ2nXr43QUj+oUOZCD29ZzIN5qXMYYY4y3sQKGw1YsWUjTth2p27Cx01E8Lig4mI69B7B/08rSd5Iz84o4nZHvcDJnqGoR8AywANgHfKuqe0TkVREpu6vIeGCKnlvp6QxsFpEdwDLgDVUNmALGokWLaN+lF9Vr1XY6ikd0cM80sWUkVWYrJUuyelCy28+Mi53or8uyAObFzKdt1z6E1ajpdBSP6hQ1iIK8HGYvWuZ0FGOMMcacp0IFDBEZKSIHRCRWRF64wPE3yzRBOygiaWWOFZc59qN3mQNBRkYGe7ZuonOfwU5HcUzn6MFkpiaVbqcKJbMwApWqzlPVcFVtp6qvu8deUtVZZc75P1V94bzHrVXVbqraw/3nJ57O7pTk5GS2bNlCGz/ffaSsug0b0/j69sTMX+B0FL+kqhmqmuX+eB4Q4p7ZFDDi4uLYv28vHaP8f/vU87Xv2ZfgaiEsX7yIgiJbpmWMMcZ4k6suYIhIMPAvYBQQAdwrIhFlz1HVX55tgkbJu1jTyxzOLdMgrey7ywFj4cKFFBcXBWT/i7M6RQ1CRNi3cUXp2KEzmYG8jMRcocWLF6Oqpc33AkXH3gNYs2Y1OTk5TkfxOyLSRETE/XE0JT8rk51N5Vnz588HCKjtvc8Kq1GLtl0j2btpJceTs52OY4wxxpgyKjIDIxqIVdUjqloATAHGXuL8ezmv6WCgmzlrNjXqXEvrLgHTa/FH6tRrQMuO3dm7YXnpWFpOIUlZBc6FMj5l/vz51LmmLq3CuzkdxaM69RlEgXv3FXNlRGQyJT1kOopInIg8KiJPisiT7lPuAna7l2T9ExivAVZVnTdvHg0aN6NJ6w5OR3FE5+jBJBw9yNqdB5yOYowxxpgyKlLAaA6cKHM/zj32IyJyPdAGWFpmuLq78dl6Ebm9Ajl8ksvlIiYmhs5RgwgOruZ0HEd16TuEHw7sJDM1qXTs0JmA3o3ElJOqsmDBAjpFDSAoONjpOB7VtlsfQkLDiHG/U27KT1XvVdWmqhqiqi1U9RNVfV9V33cff1dVu7iXZPVT1bVOZ/akgoICFi1aRMc+N+KeiBJwOkeXLO2cP38+hcW2jMQYY4zxFp5q4jkemKqqZfcku15Vo4D7gLdEpN2FHuivHd43bdpEclIinfsOcTqK4yL6DQVg36aVpWOB3AfDlN+uXbtISEigbU//3z71fKFh1WnbvQ9z58U4HcX4mdWrV5OVlRWQu2OddV3LttRr3Jzd65ZzLMmWkRhjjDHeoiIFjHigZZn7LdxjFzKe85aPqGq8+88jwHLgguso/LXD+5w5cwgKCgrI9cXna9a2E9c2bMze9ctLx5KzCkjOCszdSEz5LVhQ0sQy0PpfnNUpchBHYg9x7Ngxp6MYPzJv3jxCQkPp0LOf01EcIyJ07nMjB7etY29cQLU/McYYY7xaRQoYm4AOItJGREIpKVL8aDcREekE1KNkvfHZsXoiEub+uCEwAAiYLR8BZs+eTeceUdS6pq7TURwnIkT0HcKBLasoKvhP7wubhWEuJyYmhtYdOlG3UROnoziiU5+SAmhMjM3CMJVn7ty5RPTuR1iNWk5HcVTn6MEU5OWwaMly243EGGOM8RJXXcBQ1SLgGWABsA/4VlX3iMirIlJ2V5HxwJTzGqB1Bja7G6QtA95Q1YApYBw7dowdO3bQY+Bwp6N4jS79byI/N4dDO9aXjh20Aoa5hIyMDFatWkXHqMCe5l6/cXO+nzXH6SjGT8TGxrJ//37C+wxxOorjOvTqT0hYdXasXcox243EGGOM8QoV6h6pqvOAeeeNvXTe/f+7wOPWAoG1ZUAZs2aVTFTpED3U4STeo0PPfoRWr8medUtKt5VNyswnNbuAerVCHU5nvNHixYspKiqifWTgFjBEhM59h7Bq0ffk5eVRvXp1pyMZHzdnTkkxLDyAC4NnhYZVp0PP/uzdsIz9CRmEN67jdCRjjDEm4HmqiacpY+bMmXTq1JlrG7dyOorXCAkNo1PUIHavW4rL9Z+puodsFoa5iLlz51LnmmtoHdHT6SiO6hw9mLzcHNtO1VSK2bNn0y68Ew2atrz8yQEgou8QUk7FsX7LDvKLii//AGOMMcZUKStgeFhqaiorVqzgppGjnY7idbr0H0ZG8hniY/eUjtl2quZCVJV58+bRo99ggquFOB3HUe27R1MtNIzpM20ZiamY9PR0Vq5cSdQgW9541tldsnauXcrhM7aMxBhjjHGaFTA8bN68eRQXFzNg2Eino3idiL6DCQoKZteaxaVjZzLyScspuMSjTCDaunUrp06dom2vwNx9pKzQ6jVo36Mvc+fO5dxWQ8Zcmfnz55csy+oz2OkoXqNuw8Y0bx/BnvVLOXA6w+k4xhhjTMCzAoaHTZ8+naZNm9KqUw+no3idWtfUo133Puxas+iccVtGYs43e/ZsRIR2vW0bYoAu/YYS/8NR9u/f73QU48NmzJhBo0aNqN+6i9NRvErXG27i+L7t7D70AzkFRU7HMcYYYwKaFTA8KCcnh5iYGH7605+SmW+/BF1It4EjOP3DYU7/cLh07OBpW0ZizjV79my69e5DnXoNnI7iFbr0GwbAlKnfO5zE+Kr8/Hzmzp3LsBGjkaBgp+N4lW43DEdV2bVuKQdPW0HdGGOMcZIVMDxowYIF5Obmcscdd5CWU+h0HK/U9YaStddlZ2HYMhJTVlxcHFu3bqVr/2FOR/EadRs1oXn7CGbMnOV0FOOjli9fTmZmJlGDRzgdxes0bdOR+k1asHvtYvYn2DISY4wxxklWwPCgadOmUb9+fQYPHkxarhUwLqRuw8Zc37knO1ctPGfc3vUyZ53d5vH6XrbNY1ld+g1j19aNJCYmOh3F+KAZM2ZQq1Ytmnfp43QUryMidLthOAe3reVoQhKp2VZQN8YYY5xiBQwPyc/PZ/bs2YwdO5ZihNwC247tYroNuJm42D0kJ5woHTsQIMtIRGSkiBwQkVgReeECxx8SkUQR2e6+PVbm2IMicsh9e9CzyT1n5syZtGrdhoYt2jodxat07T8MVbVlJOaKuVwuZs6cyS23jCQlX5yO45W63jCc4sJC9m1cyT6bhWGMMcY4xgoYHrJw4UIyMjIYN24c6bZ85JJ63liyQ8uOVfNLx5Iy80nx83e9RCQY+BcwCogA7hWRiAuc+o2q9nTfPnY/tj7wMtAXiAZeFpF6HoruMenp6SxZsoS+Q0ciYi+0ymrePoJ61zXj26nTnY5ifMzatWtJSEhgxK1jKChyOR3HK7Xp0ps69Rqyc/UC9p3KtB1/jDHGGIdYAcNDvvnmG+rVq8dNN91Eui0fuaT6TVrQqmN3ti+POWf8wCm/n4URDcSq6hFVLQCmAGPL+dhbgEWqmqKqqcAiwO/26p03bx6FhYV0iLb+F+cTEboOGM761cvIzPT7a8VUoqlTpxIWFkbXfkOdjuK1goKD6TbgZvZtWEFSagZxqblORzLGGGMCkhUwPCA3N5eZM2dyxx13EBISYv0vyqHn4NHExe4hMf546dihM37/rldz4ESZ+3HusfPdKSI7RWSqiLS8wsf6tOnTp9O4cRPqt+nqdBSv1G3AzRQVFDBlujXzvBQRmSgiZ0Rk90WOi4j8072Ua6eI9PZ0Rk9xuVxMmzaNkSNHkl5Uzek4Xq3HoFsoyM9l/+ZV7Dlpy0iMMcYYJ1gBwwMWLFhAVlYW48aNA7AlJOXQ48ZbANix8j+zMJKzCkjK8u9lJOUwG2itqt0pmWXx+ZU8WEQeF5HNIrLZ15o95ubmEhMTw8DhIwkKsm9dF9K2SyS1rq3HlO+mOR3F233GpWcojQI6uG+PA//2QCZHbNy4kbi4OO68807ibVbBJbXt3oda19Zjx8r5xJ7JJL/IelkZY4wxnmavAjzg66+/pmHDhgwdWjI915aQXF6965rROqIXW5fNPWfcz5eRxAMty9xv4R4rparJqprvvvsxEFnex7of/6GqRqlqVKNGjSotuCcsWLCA7Oxsugy42ekoXisoOJiuNwxnzdKFZGfnOB3Ha6nqSiDlEqeMBb7QEuuBuiLS1DPpPOubb74hNDSUwTePIiu/yOk4Xi04uBrdBoxgz/plZGfncPCU7Y5ljDHGeFqFChi2Y8LlZWRkMHv2bO655x5CQkIAK2CUV+Sw2zh17CAnj+wvHTtw2q+XkWwCOohIGxEJBcYD56wFOO9F1Bhgn/vjBcAIEannbt45wj3mN7799lsaNGhA3Xa9nI7i1XreOIr83Gy+mmbLSCqg3EuyfHlWU3FxMd988w2jR48myxXqdByf0HvoTyjIy2HvhmXsPpnudBxjjDEm4Fx1AcN2TCif6dOnk5eXx/333w+Ay6Vk5tm7XOXRY/AogoKrsWXp7NKxjNxCEtLzHExVdVS1CHiGksLDPuBbVd0jIq+KyBj3ac+KyB4R2QE8CzzkfmwK8BolRZBNwKvuMb+Qm5vLrFmzGD56DEiw03G8Wvuefal1TV0mT/nG6SgBwZdnNa1cuZKEhATuvfde4tNs+Uh5tO0axTX1G7F12VxOpedxJtM/fx4ZY4wx3qoiMzBsx4RymDRpEm3btqVfv34AZOYV4fLfGQSVqva19ekUNZBtS+fgcv1naz9/XkaiqvNUNVxV26nq6+6xl1R1lvvjF1W1i6r2UNWhqrq/zGMnqmp79+1Tp55DVYiJiSE7O5teg0c5HcXrBQdXo9vAW1i3bCGpGTbF/SqVa0mWr5s8eTK1atXi1ltvJT7VlhyVR1BwMD0Hj2bfphXkZmWwO95mYRhjjDGeVJEChkd2TPDl6bnx8fEsWbKE+++/HxEBICPPlo9cid7DbiMt6RSHd24sHTt4OhOXy4pAgWTKlCk0atSIOq27Ox3FJ/QcPIqCvBw++dqaeV6lWcAE924k/YB0VU1wOlRlys/PZ+rUqYwdOxZXcCip1ly63HoN+QnFhYXsWLWAfQmZFBS5Lv8gY4wxxlSKqm7iWaEdE8C3p+d++eWXqCoTJkwoHbP+F1ema/+bqF6zNpsWfl86llNQzA8p9m5hoEhPT2fWrFncevud5LvE6Tg+oX33aOrUb8SUKV87HcUrichkYB3QUUTiRORREXlSRJ50nzIPOALEAh8BTzkUtcrMnTuX1NRUHnjgAdt95Aq16tSdRs1bs2XJLAqKXH49K9AYY4zxNhUpYFT5jgm+TFX59NNPGThwIO3bty8dtwLGlQmtXoOeg0ezc9UC8nL+Mx1+/6kMB1MZT5o+fTr5+fn0GT7m8icboGSae+8hP2H72mUcPO5XEwcqhareq6pNVTVEVVuo6ieq+r6qvu8+rqr6tHspVzdV3ex05sr2xRdf0KRJE4YPH058mhWEr4SIEDV8LId3biTlVBw74tL8ubm0McYY41UqUsCwHRMuYd26dRw8eJCHH374nPEMK2Bcsehb7qAgP5ftK2JKx2LPZNm03QAxadIk2rVrR2jTjk5H8Sm9h91GcWEh739uszDMuZKSkpg7dy73338/1apVI85mYFyxyJtKCqpblswiMTOfk37aXNoYY4zxNlddwLAdEy7t008/pWbNmtx9993njNsMjCt3feeeXNeiDRsX/Gc9f2GxEnvGGhT6u/j4eJYuXcrYO8eRYbv3XJEWHbpwXcu2zJo6xYp95hyTJ0+mqKiIBx54gKz8IpKzCpyO5HPqN2lBu+7RbFo0A1Vl+w9pTkcyxhhjAkKFemDYjgkXlpGRweTJk7nnnnuoU6fOucesiecVExH6jrqbY3u3cerYodLxfQm2jMTfffHFF6gqfUfc7nQUn1Myzf12Du/azMJ1252OY7yEqvLRRx8RGRlJjx49iLPdR65a9C13knTyOId3bSL2TJb9fDfGGGM8oKqbeAakSZMmkZ2dzZNPPnnOeGGxi+z8YodS+bY+I35KcEgIa+d+Uzp2IjXHfmH0Y2f7yNx4443k1rjO6Tg+Kerm25GgID76ZKKt0TcAbNq0iV27dvHYY48BcCLFlo9crR6DbqF6rTqsn/ctLpuFYYwxxniEFTAqmarywQcf0KtXL/r06XPOsUybAn/Val9bnx4Db2Hz4hkU5JX8wq0K+xOs+7u/WrNmDYcOHeLOe39GSrZNcb8adRs2pmPkQFbOncbxJFtyZeCjjz6iZs2a3HfffQCcsB2drlpo9RpEDruNnasWkJ2Rxq74dPKL7E0KY4wxpipZAaOSbdiwgR07dvDEE08gcu6Wj9bAs2L6/2Q8edmZbF02p3Rs78l0e2fZT02cOJFatWrRqd/NTkfxadG33EFa0ik+/Xam01GMwzIyMpgyZQrjxo3jmmuuIT2n0PoyVVC/0eMoKixgy5KZFBS52B2f7nQkY4wxxq9ZAaOSvf3221x77bXcf//9Pzpmyx0qpm23KJq0DmfVzC9LixapOYUkWPd3v5OamsqUKVO47777OJFlBaqK6Nr/JmrXbcD0rz8j1WayBLQvvviCrKwsnnrqKQB+sNkXFda8XWeu79yTNbO/xuVyse2HNIqKrWmuMcYYU1WsgFGJ4uLimDp1Ko8++ii1a9f+0fGMXFtCUhEiwo0/nUDCkQMc3rmxdHzPSWvm6W8+//xzcnNzufNnD9vMpQqqFhJK35F3sWf9MhZs3O10HOMQVeXdd98lOjq6dHnj8ZRsh1P5h4Fj7icx7hiHtq4lM6+I/adsaaMxxhhTVayAUYnee+89XC4XzzzzzAWP2wyMius97DZqXVOXld9/UTp28HSmbRPpR1SV999/n759+xLcsK3TcfxC/9HjQJXPJk4kp8AKqYFoyZIlHDhwgKeffhoAl0ttBkYl6TFoJLXrNmD1rK8A2HQsBZfLZo4ZY4wxVcEKGJUkKyuLDz74gDFjxtCmTZsLnmPvJFdcaFh1+v9kPHvWLSEx/hgABUUuDp62d7z8xeLFizlw4AA/f+IJDti/a6Wo36QFnaMHs3buN2w4dNrpOMYBb731Fo0aNWLcuHEAnM7MI7/QCr+VoVpoKP1/cg97NywnMf4YaTmFNgvDGGOMqSJWwKgkH3/8MSkpKfz2t7+96Dk2A6NyDBz7M4KrhbDsu4mlY9Y4zX+8+eabNG7cmOhht9nMmko06PYJZKYmMfHLSeQV2k4JgWTv3r3MnTuXZ555hurVqwNwNMmWj1SmgWPuJ6haNVZM+xSADUeTbRaGMcYYUwWsgFEJCgoK+Pvf/87gwYPp16/fBc8pKnaRnW8vGirDNfUbEX3LHWxaNJ2M5DMAJKTnkZiZ73AyU1H79u0jJiaGp556ikPJ1py1MoX3voEmrcNZ+t2nbD2e6nQc40F///vfqVGjRmnzToBjSbZ8pDLVqdeQqOG3s3Hh92SlpZCWU8jeBOvPZIwxxlQ2K2BUgkmTJhEXF3fJ2ReZebbuvDINuetRXMXFLHe/2wU2C8MfvPnmm4SFhXHfhEc5nmwvsCrT2Sa4J4/sZ8rMeeQWWEE1EMTHx/PVV1/x8MMP07BhQwCy8os4nWEFwso25M6HKSrIZ9XMLwFYfyTZdiQxxhhjKpkVMCqosLCQ1157jcjISEaOHHnR86yAUbkaNmtF76G3snb2ZDJTkwDYm5Dh80sORGSkiBwQkVgReeECx38lIntFZKeILBGR68scKxaR7e7bLM8mr7j4+Hg+//xzHnnkEU4Vhjkdxy9F3jSGa+o3Yv5X/2bz8RSn4xgP+Mtf/oLL5eLXv/516dgxWz5SJRq3ake3gSNYNeNLcrMyyMwrYkecFdaNMcaYymQFjAr6/PPPOXr0KK+88goictHzrP9F5Rvxs6cpKixg6bcfAyXNPPef8t0puyISDPwLGAVEAPeKSMR5p20DolS1OzAV+EuZY7mq2tN9G+OR0JXoH//4B8XFxfzyV8+z56T90l8VQkLDGHLXIxzavp4ZC5aTGcDfl8pRLHxIRBLLFAUfcyJnRSQkJPDhhx8yYcKEc5pLW/+LqjPi/qfIy85k5YySnbI2Hk2xnjPGGGNMJbICRgXk5eXx2muvER0dzejRoy95rhUwKl+j5q2JHD6WNbO/Ji2pZGeFHXHpqPps47RoIFZVj6hqATAFGFv2BFVdpqpn11asB1p4OGOVSExM5IMPPuC+++6jsGYjcmx5Q5Xp/5N7qFmnLvO/+jdrDyc7HccR5SwWAnxTpij4sUdDVoI///nPFBYW8j//8z+lY4XFLo4nWwGjqjRv15ku/Yexcvrn5GZlkFdYzPojgXmdGWOMMVWhQgWMQJ7uDvDuu+/yww8/8Prrr19y9gVARq4tIakKI+5/ClUX8z9/G4CkzHziUnMdTnXVmgMnytyPc49dzKNATJn71UVks4isF5HbL/QAEXncfc7mxMTEiieuJG+88Qa5ubm8+OKLbP3BGkxWpbAatRhy18Ps3bCMeUtWBmovhMsWC33dsWPH+Pe//82DDz5Iu3btSsePJ+dQWOyzRV6fMHLCs+RmZZTODtxxIp3kLGsybYwxxlSGqy5gBPp095SUFF5//XVGjhzJ8OHDL3t+IE/VrkoNmrZk4JifsWnhdE4ePQDA9hNpDqeqeiLyMyAK+GuZ4etVNQq4D3hLRNqd/zhV/VBVo1Q1qlGjRh5Ke2lxcXH861//YsKECdRufL3tJuMBg25/gNp1GxDz6ZssP3DGl2ctXa3yFgvvdBfgp4pIywt9Im8tCr700ksEBQXxyiuvnDN+ODHLoUSBo3m7zvQeeisrv/+c9OTTuFRZdiAxEK8zY4wxptJVZAZGwE53B3jllVfIyMjgr3/96+VPxpp4VqXh9z5J9drXMOv9N1BVDidmkZ7jkwWjeKDsi6QW7rFziMhw4HfAGFUtfbWvqvHuP48Ay4FeVRm2srzyyiu4XC5efvllm33hIWE1anHT+Mc5tH09yxYvZs9J3+0dU4VmA63dBfhFwOcXOskbi4Lbtm3jq6++4tlnn6VFi//82C12KUcSbfmIJ4x66DlcxcXEfFYyO/BESg4HTmc6nMoYY4zxfRUpYFT5dHfwzne3duzYwbvvvsvjjz9O165dL3u+qpKVbwWMqlLrmrqMnPAsB7etZeeqBajCthM++UJ4E9BBRNqISCgwHjhneZWI9AI+oKR4cabMeD0RCXN/3BAYAOz1WPKrtH37dj755BOefvppatZvYi+uPGjArffRoGlLZn74Z1bsP0VOQUB9j7pssVBVk8sUCD8GIj2UrUJUlV/84hc0bNiQF1988ZxjP6TkWENJD2nQtCWDbn+ATQun88OBnQCsOJBo2xcbY4wxFeSRJp5XO90dvO/dLZfLxVNPPUX9+vV5/fXXy/WY7IJiil02dbQq3XDreJq368yM9/9Ifm42e05m+Nwv6qpaBDwDLAD2Ad+q6h4ReVVEzi6z+itQG/juvP4xnYHNIrIDWAa8oapeXcBQVX71q19Rv359XnrpJdYftW09PalaaCi3/fw3nDp2kGUzJ7Nsv3cUiD2kPMXCpmXujqHkmvR6kyZNYs2aNfzpT3+ibt265xw7cMpmAHjSiPufpna9hkx/9zVcLhc5BcWsOHjm8g80xhhjzEVVpIARkNPd33//fdauXctf/vIX6tevX67HWP+LqhccXI07f/EyGclnmDvxHxQUudgZ53tbcarqPFUNV9V2qvq6e+wlVZ3l/ni4qjY+v3+Mqq5V1W6q2sP95ydOPo/ymDx5MsuWLePVV1+lMLgGh8/Y2nxP6zbgZtr1iGbeZ2+xZf8xDgbIFPdyFgufFZE97qLgs8BDzqQtv+TkZJ5//nn69OnDww8/fM6xwmKX9b828iDtAAAXKUlEQVTwsOq1anPbY7/mhwM7WTd3CgD7EjI5FCDXmTHGGFMVKlLACLjp7kePHuU3v/kNI0aM4KGHHir347Ks/4VHtI7oxcDbH2D1zK+I3bmRbT+kUljscjqWuYCUlBR++ctfEh0dzRNPPMHq2CSnIwUkEeGuX/wfBbk5zPzgTyzZdyZgtnwuR7HwRVXt4i4KDlXV/c4mvrxf/epXpKSk8NFHHxEUdO6P96NJ2RQU2fdDT4u8aSzhvW5gzsd/JfVMAgBL9p+xZaXGGGPMVbrqAkagTXcvLi7moYceIigoiI8++uiy26aWlWEFDI8Z/dAvadCsFVP+9iLJqWnsjve9WRiB4Pnnnyc5OZkPP/yQuLQ8jifnXP5Bpko0btWOYff8nK1LZ7N1zVLm7z6Fy5a8+Zw5c+bwxRdf8MILL9CjR48fHd9rjVodISLc/dyrqEv59s3/xeVykVtQTMyuBLvOjDHGmKtQoR4YgTTd/bXXXmPlypW88847tGrV6ooea0tIPCesRk3u/81fSDuTwHdv/Z5NR1MoslkYXuX777/ns88+44UXXqBrt+6sPBhQvRe80vB7n6RJ63C++cfvOHAsnjWHbUaML0lISODhhx+mR48e/O///u+PjmfkFXIs2RrkOqVB05bc9vhvOLBlNatmfAFAXGou644kO5zMGGOM8T0eaeLp65YsWcJrr73GAw88wIMPPnjFj7ctVD2rdUQvRj30HNtXxLBw6hfsslkYXiM+Pp7HH3+c3r178/LLL7P9RCpJWQVOxwp4IaFh3P/bv5KTkca3//hfNh1NsYaPPqK4uJgJEyaQlZXF119/TVhY2I/O2XsyA7U3+x11w6330qX/MOZ88jdOHNwFwMajKdYPwxhjjLlCVsC4jMOHD3P33XfTuXNn3nvvvav6HLbW1fOGjnuMztGD+f7ff2TSjBhb++0FCgsLGTduHLm5uUyaNIncYlh/xHYe8RbN23Xi1kd/ze51S1g+dSIL95wiPi3X6VjmMn73u9+xePFi3nnnHSIiIn503OVSW0rnBUSEe371OtfUa8Snr/yCrLSS730L9pzidEaew+mMMcYY32EFjEtISUlhzJgxiAgzZ86kdu3aV/V5bAmJ5wUFBfGzF/9Ow6Ytef+lZ5i5YpPTkQKaqvLcc8+xdu1aPvnkEzp27MjCPaetsORlbrzjQboPvIW5n/ydvZvXMGv7SZKz8i//QOOIr7/+mj//+c888cQTPPbYYxc8JzYxy2YBeona19bnoZfeISstmc9ee5aiggIKi5WZ2+NJz7HfE4wxxpjysALGReTk5HDrrbcSGxvLtGnTaNeu3VV/riJr1OWIGrXq8Ohr7yMIT95/J7FHf3A6UsB66623eO+99/jv//5v7rnnHradSONEijXu9DYiwvjn/8h1rdrx2au/4Nih/UzbGkdqti3z8TZLlizhoYceYvDgwbz99tsXPW/bD6keTGUup2V4V8b/+k8c2bWJr//6G1wuF9n5xUzbGmezNY0xxphysALGBWRnZ3PbbbexYcMGJk+ezJAhQ5yOZK5So+at+fnrH5Kdkcqwm27i5MmTTkcKOF9++SXPP/88d955J2+88QYJ6bmsPmRNIr1V9Vq1+fkfPiCkeg0++J9H+eHYMaZuiSPJZmJ4jTVr1nD77bfTsWNHZsyYccG+FwBxqTmcTLPlCd6m99BbufWx/2b7ihi+e/slXC4X6bmFTN18wmZsGmOMMZdhBYzzpKWlMWrUKJYvX85nn33GHXfc4XQkU0Etw7vx8z98xJlTJxk46EaOHj3qdKSAMXnyZB566CGGDRvGl19+SW6hizk7Eii2WUlerd51zXjijx9TmJ/Pv3/zIMePHeW7zXHEpdqsGaetXLmSkSNH0qxZMxYsWEDdunUveq71mPFeQ+9+lOH3PsmGmO/47q3fU1xcRGpOId9tjiMtx2Y8GWOMMRdjBYwyjh07xoABA1i/fj1ff/01DzzwgNORTCVp2y2KJ/40kdOJifTr14+NGzc6Hcnvvffee9x///0MGjSImTNnEhQSyozt8TZN2kc0a9uJJ/70Mbk5Wbzzy3s5cnAv07fGW0NIB3333XfcfPPNNG/enGXLltGsWbOLnnsiJceWaXkxEWHUQ89x8/1PsWH+VD575RkK8nJJzy3km00nOGkNdI0xxpgLsgKGW0xMDJGRkZw8eZKFCxdyzz33OB3JVLLWEb34xZtTCAmrwaBBg/jggw9Q21uw0hUWFvKLX/yCp59+mttuu42YmBhCq9dg9o4EzmTYMgRf0jK8G7/4+yQQ4Z1f3suONYtZtPc0C/ecsgasHlRcXMzvf/97xo0bR1RUFGvWrLlk8cLlUlYeSvRgQnM1RIRRD/4XP3369+zdsJx/Pjee5IQT5BQUM3VLHLvj0+1nlDHGGHOegC9g5OTk8NxzzzF69GhatGjBxo0breeFH2vcqh1Pv/UNA24czJNPPsldd93FmTNnnI7lN44ePcqQIUN49913ef7555k2bRpBIaF8vy3e3g32UU1ad+C5f37HdS3aMPHlp5j14Z/ZcSyJSRuO25ISDzh+/DjDhw/nD3/4A4888ghLliyhQYMGl3zMrvh0Kxb6kEFjf8Zjr31AamICf3/qp2xePJOiYheL9p5m/u5T5BUWOx3RGGOM8RoBW8BQVWbNmkW3bt14++23eeaZZ1i/fj0dOnRwOpqpYtXr1OPBVz7g9T/+iTlz5hAREcGHH35IcbH9kni1iouLeeedd+jRowe7d+9m8uTJ/O1vfyO7QPl20wniU206tC+r26gJT/99Ejfcei/Lp07kH8/cyfbNG/lucxwL9pwi25YFVbqioiLeeustunXrxubNm5k4cSIff/wx1atXv+Tj0nMLWXPYmuT6ms7Rg/nVu9No2roDX//lN3zy0pMkJ5xg/6lMvlx3nNgzWTYbwxhjjCEACxiqysqVKxk6dChjx44lLCyMZcuW8c4771CjRg2n4xkPSckuJHLMQ2zZsoUuXbrwxBNP0KNHD6ZNm4bLZVPjy0tViYmJoVevXjz77LPccMMN7Nixg/HjxxN7JpOvN/5AUpY1pPMHoWHVuevZ/+Ox1z4gLzuTfz43nq/+9Dyrtuzh0zVHWRObRG6BFQEryuVyMXXqVLp27covf/lLBgwYwM6dO3n44YcRkcs8Vlmw+xT5hfY9zBc1aNqSp//2FWMef4HDOzfyxmOjmPn+n0g4dZrZO04yY3s8iZk2s8YYY0xgC5gCRnZ2Nl988QV9+/Zl8ODBHDhwgHfeeYcdO3bYkpEAtfdkBnm1m7F8+XK+/fZbiouLueuuu+jUqRNvv/02ycnJTkf0Wvn5+UyaNIk+ffowevRosrOz+fbbb4mJiaFh0xbE7Epg9o4Em/rshyL6DuG3H8/lpvFPsGvtYv708C18+odf8e28pXy86jBL95+2LVevQnp6Ou+99x6dO3fm7rvvJjg4mBkzZjBv3jzatGlz2cerKssPniHemj/6tKDgYIbc9TC//TiGyGFjWDnjC157YCjfvvUSazZsYdKG48zblcCZTNse1xhjTGASX5qSGBUVpZs3by73+WlpaSxevJjvv/+eWbNmkZWVRXh4OM899xwPPvggNWvWrMK0//He8lh7R8xL1QgN5snB7YCSZRBTp07lzTffZMOGDYSGhjJixAjuvPNORo4cSZMmTa7001/67VKHXen1VFBQwMqVK5k6dSrfffcdKSkpdOjQgd/85jdMmDCBIoLYejyNHXFp1uAxQKQnn2bFtM9YN3cK+bk5NGkdTu+hP6Fr/5vo0b0rHZtcQ/vranNtjZDK+iv96ppKS0tjwYIFTJs2jTlz5pCbm0tUVBS//vWvueuuuwgODi7359p/KoOYXaeuJrbxYmfijrJ86qdsXjyDooJ8mrXtRM/Bo+jSbxiRvbrTrXld2l9Xm9BqV/V+lFdfT8YYY8yFVKiAISIjgbeBYOBjVX3jvONhwBdAJJAM3KOqx9zHXgQeBYqBZ1V1weX+vkv9clhYWMihQ4fYvn07mzZtYvXq1Wzbto3i4mLq16/PT3/6UyZMmMCgQYMuOw23slkBw3uVLWCUtWPHDj7//HOmTp3KiRMnAIiIiGDgwIFER0fTq1cvOnfufLllR1f8H82T19TlXmwlJSWxfft2Nm7cyOrVq1m1ahVZWVnUrFmTMWPG8MgjjzB06DDi0/PYl5DJwdOZFLt8pyBqKk9eThZbl85hy5KZHN2zFYC6jZrSvkdfWkf0olvPXvSL7EHbJvVpVrc6NUOrXe1fVWnfvCtyrV3Mpa6p4uJiDh8+zPbt29mwYQOrVq1i69atFBcXc91113HXXXfxyCOPEBkZeVXPZ8vxFFYetN4X/ionM50tS2ezbdkcju3dBsA1Da6jXfdo2nXpSXSfKG7s25surZtSO6zc15cVMIwxxvicqy5giEgwcBC4GYgDNgH3qureMuc8BXRX1SdFZDzwU1W9R0QigMlANNAMWAyEq+ol55tf7JfDEydO0L59ewoKStbaV69enejoaG688UZuueUW+vXrR7VqV/0Lc4VZAcN7XayAcZaqsmPHDubPn8+KFStYu3YtGRkZAAQFBbFgwQKGDx9+sYdf0S+Hnr6mLvVi68UXX+SNN/7zeq5Tp04MHTqUkSNHctNNN1GrVi02Hk1hy/FUWyZizpGWeIq9G5ZzaNtaYnduJDs9FYCgoGCefXsKrTp2p071ajSqE0bD2mHUqxlK3ZohXFsjhJqhwZcrMFfKC66KXGuX+rwXu6YKCwtp0KABmZmZAISFhdGnTx+GDBnCqFGj6Nu37xXNtrgQK2AEjrSk0+zbuILY7es5vGsTGcn/2Unr7v96lVF3/4ym19YgvHFt2jaqfalPZQUMY4wxPqcir+qjgVhVPQIgIlOAscDeMueMBf7P/fFU4F0p+e10LDBFVfOBoyIS6/58664mSPPmzXn++eeJiIigW7dudOnSxdGChfEfIkLPnj3p2bMnL7zwAi6Xi0OHDrFz5052795NREREZf51XnNN3XLLLTRo0IAePXoQGRlJ/fr1f3TOqYw8K16YH6nbqAk33DqeG24dj6qSnHCC+Ni9xB/eR6MWJb0cMvOKyMwr4khi9jmP/X9D2lE9pGIv5Mvpqq81vYqqf0hICC+++CJNmzale/fudO3aldDQ0Io9AxOw6jZsTP/R4+g/ehxQUjSMj91LwrGDXN+5B2k5haTlFAJcroBhjDHG+JyKvMpvDpwocz8O6Huxc1S1SETSgQbu8fXnPbb5hf4SEXkceBygVatWFwwSFBTEH//4xyt/Bh7y2MC2TkcwlSQoKIiOHTvSsWNH7r777sr+9FV+TZXnegIYMmTIZZvbjuraBB9qoWMc0wEYVq4zQ4I99oZwRa61c6Y5lPeaevHFFysc+lJ6tqxHt+Z1q/TvMN6qPTDwR6NBNr/CGGOMH/L6aQqq+iHwIZRMz3U4zlW5yuZaxlS6yryeQoLt/7Ux3vIzKjhICLZXrMYYY4zxcxV5BRIPtCxzv4V77ILniEg14FpKmqGV57HGBBq7pozxjIpca8YYY4wxxiEVKWBsAjqISBsRCQXGA7POO2cW8KD747uApe71w7OA8SISJiJtKJljvLECWYzxB3ZNGeMZFbnWjDHGGGOMQ656CYl7TfAzwAJKtqGbqKp7RORVYLOqzgI+Ab50NxRMoeSXRNznfUtJw7Qi4OnL7UBijL+za8oYz6jItWaMMcYYY5xz1duoOuFS2z4a44W8ekG6XU/GB9k1ZUzl8erryRhjjLkQ68JnjDHGGGOMMcYYr2cFDGOMMcYYY4wxxng9n1pCIiKJwHGnc1yhhkCS0yGqkD2/i0tS1ZGVGaYyefn15Mv/ryx71Qmka8rb/y3A+zN6ez5wNqNXX0/GGGPMhfhUAcMXichmVY1yOkdVsednqoIvf90tu6kMvvBv4e0ZvT0f+EZGY4wxxpvYEhJjjDHGGGOMMcZ4PStgGGOMMcYYY4wxxutZAaPqfeh0gCpmz89UBV/+ult2Uxl84d/C2zN6ez7wjYzGGGOM17AeGMYYY4wxxhhjjPF6NgPDGGOMMcYYY4wxXs8KGMYYY4wxxhhjjPF6VsCoBCIyUkQOiEisiLxwgeNhIvKN+/gGEWnt+ZQVU47n+JCIJIrIdvftMSdyXg0RmSgiZ0Rk90WOi4j80/3cd4pIb09n9Eci0lJElonIXhHZIyL/dYFzvPJrX87sQ0Qkvcw18ZITWc8nItVFZKOI7HBnf+UC5/j89yxf4Qs/P8qR8Vfua2GniCwRkeu9KV+Z8+4UERURj29bWp6MIjKuzPeUrz2d0RhjjPEFVsCoIBEJBv4FjAIigHtFJOK80x4FUlW1PfAm8GfPpqyYcj5HgG9Utaf79rFHQ1bMZ8DISxwfBXRw3x4H/u2BTIGgCHheVSOAfsDTF/h/5a1f+/JkB1hV5pp41bMRLyofGKaqPYCewEgR6XfeOT79PctX+MLPj3Jm3AZEqWp3YCrwFy/Lh4jUAf4L2OCpbGX+7stmFJEOwIvAAFXtAjzn6ZzGGGOML7ACRsVFA7GqekRVC4ApwNjzzhkLfO7+eCpwk4iIBzNWVHmeo89S1ZVAyiVOGQt8oSXWA3VFpKln0vkvVU1Q1a3ujzOBfUDz807zyq99ObN7JffXMst9N8R9O7+bs69/z/IVvvDz47IZVXWZqua4764HWnhTPrfXKCn+5Hkw21nlyfhz4F+qmgqgqmc8nNEYY4zxCVbAqLjmwIky9+P48QuZ0nNUtQhIBxp4JF3lKM9zBLjTPYV4qoi09Ew0jyjv8zdXyT0tvhc/fnfU67/2l8gO0N+9VCNGRLp4NNgliEiwiGwHzgCLVPWiX3cf/Z7lK3zh58eVXoOPAjFVmuhcl83nXnrWUlXnejBXWeX5GoYD4SKyRkTWi8ilZgUaY4wxAcsKGKayzAZau6cQL+I/7xgac0kiUhuYBjynqhlO57kSl8m+FbjevVTjHWCGp/NdjKoWq2pPSt4pjxaRrk5nMr5PRH4GRAF/dTrLWSISBPwDeN7pLJdRjZKlckOAe4GPRKSuo4mMMcYYL2QFjIqLB8rONmjhHrvgOSJSDbgWSPZIuspx2eeoqsmqmu+++zEQ6aFsnlCef2NzFUQkhJICwCRVnX6BU7z2a3+57KqacXaphqrOA0JEpKGHY16SqqYBy/hxDxhf/57lK3zh50e5rkERGQ78DhhT5meBJ1wuXx2gK7BcRI5R0rNmlocbeZbnaxgHzFLVQlU9ChykpKBhjDHGmDKsgFFxm4AOItJGREKB8cCs886ZBTzo/vguYKmqnr/m3Jtd9jme15dgDCU9AfzFLGCCe0eMfkC6qiY4HcrXudfxfwLsU9V/XOQ0r/zalye7iDQ526tARKIp+X7reBFARBqdfWdXRGoANwP7zzvN179n+Qpf+PlRnu//vYAPKCleeLp3wyXzqWq6qjZU1daq2pqSHh1jVHWzt2R0m0HJ7Avchc5w4IgHMxpjjDE+oZrTAXydqhaJyDPAAiAYmKiqe0TkVWCzqs6i5IXOlyISS0mzyPHOJb5y5XyOz4rIGEp2Z0gBHnIs8BUSkcmU/OLYUETigJcpaWyIqr4PzANGA7FADvCwM0n9zgDgAWCXux8DwP8ArcDrv/blyX4X8P9EpAjIBcZ7SRGgKfC5e2eEIOBbVZ3jT9+zfIUv/PwoZ8a/ArWB79w1ux9UdYwX5XNUOTMuAEaIyF6gGPhvVXW84GmMMcZ4G/GO36eNMcYYY4wxxhhjLs6WkBhjjDHGGGOMMcbrWQHDGGOMMcYYY4wxXs8KGMYYY4wxxhhjjPF6VsAwxhhjjDHGGGOM17MChjHGGGOMMcYYY7yeFTCMMcYYY4wxxhjj9ayAYYwxxhhjjDHGGK/3/wF9B0p3PiwUmAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x648 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>hosp_prop</th>\n",
+       "      <td>0.024(10)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICU_prop</th>\n",
+       "      <td>0.349(39)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vent_prop</th>\n",
+       "      <td>0.65(17)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hosp_LOS</th>\n",
+       "      <td>11.64(83)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICU_LOS</th>\n",
+       "      <td>13.47(89)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vent_LOS</th>\n",
+       "      <td>19.5(2.3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>incubation_days</th>\n",
+       "      <td>4.6(1.5)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recovery_days</th>\n",
+       "      <td>15.3(5.0)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_k</th>\n",
+       "      <td>0.78(43)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_x0</th>\n",
+       "      <td>16.7(6.9)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_L</th>\n",
+       "      <td>0.37(23)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nu</th>\n",
+       "      <td>2.46(25)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta</th>\n",
+       "      <td>0.32(12)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       val\n",
+       "hosp_prop        0.024(10)\n",
+       "ICU_prop         0.349(39)\n",
+       "vent_prop         0.65(17)\n",
+       "hosp_LOS         11.64(83)\n",
+       "ICU_LOS          13.47(89)\n",
+       "vent_LOS         19.5(2.3)\n",
+       "incubation_days   4.6(1.5)\n",
+       "recovery_days    15.3(5.0)\n",
+       "logistic_k        0.78(43)\n",
+       "logistic_x0      16.7(6.9)\n",
+       "logistic_L        0.37(23)\n",
+       "nu                2.46(25)\n",
+       "beta              0.32(12)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g = FacetGrid(\n",
+    "    PRIOR_DF.query(\"distribution != 'constant'\"),\n",
+    "    col=\"param\",\n",
+    "    col_wrap=5,\n",
+    "    sharex=False,\n",
+    "    sharey=False,\n",
+    ")\n",
+    "g.map_dataframe(plot_prior_fit)\n",
+    "show_plot(g)\n",
+    "DataFrame(data=PRIORS, index=[\"val\"]).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit posteriors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, posterior distributions are fitted using normal distributions as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The line below which reads in json may take a while. Maybe exporting to `HDF5` might be faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "POSTERIOR_DF = read_json(\n",
+    "    path.join(OUTPUT, \"output\", \"chains.json.bz2\"), orient=\"records\", lines=True\n",
+    ")\n",
+    "drop_cols = [\n",
+    "    col for col in POSTERIOR_DF.columns if col not in PRIORS and col != \"offset\"\n",
+    "]\n",
+    "POSTERIOR_DF = POSTERIOR_DF.drop(columns=drop_cols)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The below fit removes outliers to stabilize the fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x648 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>val</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>hosp_prop</th>\n",
+       "      <td>0.0235(30)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICU_prop</th>\n",
+       "      <td>0.368(11)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vent_prop</th>\n",
+       "      <td>0.764(40)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hosp_LOS</th>\n",
+       "      <td>11.59(29)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICU_LOS</th>\n",
+       "      <td>13.45(26)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vent_LOS</th>\n",
+       "      <td>19.62(61)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>incubation_days</th>\n",
+       "      <td>3.25(29)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recovery_days</th>\n",
+       "      <td>16.6(1.7)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_k</th>\n",
+       "      <td>0.94(14)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_x0</th>\n",
+       "      <td>25.41(42)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_L</th>\n",
+       "      <td>0.787(19)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nu</th>\n",
+       "      <td>2.440(73)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta</th>\n",
+       "      <td>0.467(26)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        val\n",
+       "hosp_prop        0.0235(30)\n",
+       "ICU_prop          0.368(11)\n",
+       "vent_prop         0.764(40)\n",
+       "hosp_LOS          11.59(29)\n",
+       "ICU_LOS           13.45(26)\n",
+       "vent_LOS          19.62(61)\n",
+       "incubation_days    3.25(29)\n",
+       "recovery_days     16.6(1.7)\n",
+       "logistic_k         0.94(14)\n",
+       "logistic_x0       25.41(42)\n",
+       "logistic_L        0.787(19)\n",
+       "nu                2.440(73)\n",
+       "beta              0.467(26)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "keys = PRIORS.keys()\n",
+    "POSTERIORS = avg_data(POSTERIOR_DF.T.loc[keys].T.values, median=True, spread=True)\n",
+    "POSTERIORS = {key: val for key, val in zip(keys, POSTERIORS)}\n",
+    "\n",
+    "stacked = (\n",
+    "    POSTERIOR_DF.T.loc[PRIORS.keys()]\n",
+    "    .T.stack()\n",
+    "    .reset_index()\n",
+    "    .drop(columns=[\"level_0\"])\n",
+    "    .rename(columns={\"level_1\": \"param\", 0: \"x\"})\n",
+    ")\n",
+    "g = FacetGrid(stacked, col=\"param\", col_wrap=5, sharex=False, sharey=False,)\n",
+    "\n",
+    "plot_dist = lambda **kwargs: distplot(\n",
+    "    a=kwargs[\"data\"].x.values, kde=False, hist_kws={\"density\": True}\n",
+    ")\n",
+    "\n",
+    "g.map_dataframe(plot_dist)\n",
+    "\n",
+    "for ax, gv in zip(g.axes, POSTERIORS.values()):\n",
+    "    plot_gv_dist(gv, ax=ax, color=\"black\")\n",
+    "\n",
+    "show_plot(g)\n",
+    "DataFrame(data=[POSTERIORS], index=[\"val\"]).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Propagate normal posteriors to SEIR and compare to original prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This section takes the fitted posterior distributions as input and propagates the parameter uncertainties through the SEIR model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FORECAST_DF = read_csv(\n",
+    "    path.join(OUTPUT, \"output\", \"forecast.csv\"), parse_dates=[\"date\"]\n",
+    ").set_index(\"date\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, the model is initialized.\n",
+    "To eventually run the simulation, the following parameters must be provided"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'bayes_chime.normal.models.seir' from '/Users/christopherkorber/gdrive/git/bochum/covid/chime_sims/bayes_chime/normal/models/seir.py'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from bayes_chime.normal.models import seir as ms\n",
+    "from bayes_chime.normal.models import sir as msi\n",
+    "from importlib import reload\n",
+    "\n",
+    "reload(msi)\n",
+    "reload(ms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['dates', 'initial_susceptible', 'initial_infected', 'inital_recovered', 'beta', 'gamma', 'alpha']\n",
+      "['recovery_days', 'inital_doubling_time', 'market_share', 'initial_hospital', 'hospital_probability', 'hospital_length_of_stay', 'initial_icu', 'icu_probability', 'icu_length_of_stay', 'initial_vent', 'vent_probability', 'vent_length_of_stay', 'incubation_days']\n"
+     ]
+    }
+   ],
+   "source": [
+    "seir = ms.SEIRModel()\n",
+    "print(seir.model_parameters)\n",
+    "print(seir.optional_parameters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `model_parameters` must be provided before running a simulation.\n",
+    "The `optional_parameters` are pre and post-processing parameters which simplify the interface.\n",
+    "For example, if you specify `recovery_days`, this is used to compute `gamma`.\n",
+    "\n",
+    "On the other hand, `hospitalization_probability` and `market_share` will add hospitalization information to the simulation in the post-processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_infections = (\n",
+    "    META_PARS[\"n_hosp\"] / META_PARS[\"mkt_share\"] / POSTERIORS[\"hosp_prop\"]\n",
+    ")\n",
+    "\n",
+    "## Fixed paramters (no distributions)\n",
+    "XX = {\n",
+    "    \"dates\": FORECAST_DF.index,\n",
+    "    \"market_share\": META_PARS[\"mkt_share\"],\n",
+    "    \"initial_susceptible\": META_PARS[\"region_pop\"],\n",
+    "    \"initial_infected\": 0,\n",
+    "    \"initial_recovered\": 0,\n",
+    "    \"initial_hospital\": 0,\n",
+    "    \"initial_icu\": 0,\n",
+    "    \"initial_vent\": 0,\n",
+    "}\n",
+    "## Variable parameters (distributions)\n",
+    "PP = {\n",
+    "    \"initial_exposed\": total_infections,\n",
+    "    \"incubation_days\": POSTERIORS[\"incubation_days\"],\n",
+    "    \"beta\": POSTERIORS[\"beta\"],\n",
+    "    \"recovery_days\": POSTERIORS[\"recovery_days\"],\n",
+    "    \"nu\": POSTERIORS[\"nu\"],\n",
+    "    \"hospital_probability\": POSTERIORS[\"hosp_prop\"],\n",
+    "    \"hospital_length_of_stay\": POSTERIORS[\"hosp_LOS\"],\n",
+    "    \"icu_probability\": POSTERIORS[\"ICU_prop\"],\n",
+    "    \"icu_length_of_stay\": POSTERIORS[\"ICU_LOS\"],\n",
+    "    \"vent_probability\": POSTERIORS[\"vent_prop\"],\n",
+    "    \"vent_length_of_stay\": POSTERIORS[\"vent_LOS\"],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to make parameters time-dependent. This is done by providing a `update_parameters` method. The arguments of this method are the simulation date and initial parameters (combined `XX` and `PP`). This method should return the updated parameters.\n",
+    "In the below example, a social distancing measure is implemented using a logistic function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_parameters(ddate, **kwargs):\n",
+    "    xx = (ddate - kwargs[\"dates\"][0]).days\n",
+    "    ppars = kwargs.copy()\n",
+    "    ppars[\"beta\"] = kwargs[\"beta\"] * one_minus_logistic_fcn(\n",
+    "        xx, L=kwargs[\"L\"], k=kwargs[\"k\"], x0=kwargs[\"x0\"],\n",
+    "    )\n",
+    "    return ppars\n",
+    "\n",
+    "\n",
+    "OFFSET = POSTERIOR_DF.offset.mean()\n",
+    "\n",
+    "PP[\"L\"] = POSTERIORS[\"logistic_L\"]\n",
+    "PP[\"x0\"] = POSTERIORS[\"logistic_x0\"] + OFFSET\n",
+    "PP[\"k\"] = POSTERIORS[\"logistic_k\"]\n",
+    "\n",
+    "seir.update_parameters = update_parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21 ms ± 155 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "seir.propagate_uncertainties(XX, PP)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>susceptible</th>\n",
+       "      <th>infected</th>\n",
+       "      <th>recovered</th>\n",
+       "      <th>exposed</th>\n",
+       "      <th>exposed_new</th>\n",
+       "      <th>infected_new</th>\n",
+       "      <th>recovered_new</th>\n",
+       "      <th>infected_new_local</th>\n",
+       "      <th>hospital_admits</th>\n",
+       "      <th>hospital_census</th>\n",
+       "      <th>icu_admits</th>\n",
+       "      <th>icu_census</th>\n",
+       "      <th>vent_admits</th>\n",
+       "      <th>vent_census</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2020-03-06</th>\n",
+       "      <td>1.2e+06</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>163(21)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0(0)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0(0)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0(0)</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-07</th>\n",
+       "      <td>1.1999999999999997335465(32)e+06</td>\n",
+       "      <td>50.3(8.7)</td>\n",
+       "      <td>0(0)</td>\n",
+       "      <td>113(14)</td>\n",
+       "      <td>0(0)</td>\n",
+       "      <td>50.3(8.7)</td>\n",
+       "      <td>0(0)</td>\n",
+       "      <td>13.1(2.3)</td>\n",
+       "      <td>0.307(28)</td>\n",
+       "      <td>0.307(28)</td>\n",
+       "      <td>0.113(11)</td>\n",
+       "      <td>0.113(11)</td>\n",
+       "      <td>0.086(10)</td>\n",
+       "      <td>0.086(10)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-08</th>\n",
+       "      <td>1.1999765(42)e+06</td>\n",
+       "      <td>82(13)</td>\n",
+       "      <td>3.03(70)</td>\n",
+       "      <td>102(13)</td>\n",
+       "      <td>23.5(4.2)</td>\n",
+       "      <td>34.8(5.2)</td>\n",
+       "      <td>3.03(70)</td>\n",
+       "      <td>9.0(1.3)</td>\n",
+       "      <td>0.213(11)</td>\n",
+       "      <td>0.494(36)</td>\n",
+       "      <td>0.0783(45)</td>\n",
+       "      <td>0.183(14)</td>\n",
+       "      <td>0.0598(51)</td>\n",
+       "      <td>0.142(15)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-09</th>\n",
+       "      <td>1.199938(11)e+06</td>\n",
+       "      <td>108(17)</td>\n",
+       "      <td>8.0(1.8)</td>\n",
+       "      <td>109(15)</td>\n",
+       "      <td>38.3(6.4)</td>\n",
+       "      <td>31.3(4.7)</td>\n",
+       "      <td>4.9(1.1)</td>\n",
+       "      <td>8.1(1.2)</td>\n",
+       "      <td>0.1916(89)</td>\n",
+       "      <td>0.643(42)</td>\n",
+       "      <td>0.0705(40)</td>\n",
+       "      <td>0.240(17)</td>\n",
+       "      <td>0.0538(45)</td>\n",
+       "      <td>0.188(19)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-10</th>\n",
+       "      <td>1.199888(19)e+06</td>\n",
+       "      <td>135(21)</td>\n",
+       "      <td>14.5(3.2)</td>\n",
+       "      <td>126(18)</td>\n",
+       "      <td>50.6(8.4)</td>\n",
+       "      <td>33.5(5.3)</td>\n",
+       "      <td>6.5(1.4)</td>\n",
+       "      <td>8.7(1.4)</td>\n",
+       "      <td>0.205(12)</td>\n",
+       "      <td>0.792(49)</td>\n",
+       "      <td>0.0753(53)</td>\n",
+       "      <td>0.297(21)</td>\n",
+       "      <td>0.0575(55)</td>\n",
+       "      <td>0.236(23)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 susceptible   infected  recovered  exposed  \\\n",
+       "date                                                                          \n",
+       "2020-03-06                           1.2e+06          0          0  163(21)   \n",
+       "2020-03-07  1.1999999999999997335465(32)e+06  50.3(8.7)       0(0)  113(14)   \n",
+       "2020-03-08                 1.1999765(42)e+06     82(13)   3.03(70)  102(13)   \n",
+       "2020-03-09                  1.199938(11)e+06    108(17)   8.0(1.8)  109(15)   \n",
+       "2020-03-10                  1.199888(19)e+06    135(21)  14.5(3.2)  126(18)   \n",
+       "\n",
+       "           exposed_new infected_new recovered_new infected_new_local  \\\n",
+       "date                                                                   \n",
+       "2020-03-06           0            0             0                  0   \n",
+       "2020-03-07        0(0)    50.3(8.7)          0(0)          13.1(2.3)   \n",
+       "2020-03-08   23.5(4.2)    34.8(5.2)      3.03(70)           9.0(1.3)   \n",
+       "2020-03-09   38.3(6.4)    31.3(4.7)      4.9(1.1)           8.1(1.2)   \n",
+       "2020-03-10   50.6(8.4)    33.5(5.3)      6.5(1.4)           8.7(1.4)   \n",
+       "\n",
+       "           hospital_admits hospital_census  icu_admits icu_census vent_admits  \\\n",
+       "date                                                                            \n",
+       "2020-03-06            0(0)               0        0(0)          0        0(0)   \n",
+       "2020-03-07       0.307(28)       0.307(28)   0.113(11)  0.113(11)   0.086(10)   \n",
+       "2020-03-08       0.213(11)       0.494(36)  0.0783(45)  0.183(14)  0.0598(51)   \n",
+       "2020-03-09      0.1916(89)       0.643(42)  0.0705(40)  0.240(17)  0.0538(45)   \n",
+       "2020-03-10       0.205(12)       0.792(49)  0.0753(53)  0.297(21)  0.0575(55)   \n",
+       "\n",
+       "           vent_census  \n",
+       "date                    \n",
+       "2020-03-06           0  \n",
+       "2020-03-07   0.086(10)  \n",
+       "2020-03-08   0.142(15)  \n",
+       "2020-03-09   0.188(19)  \n",
+       "2020-03-10   0.236(23)  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FORCAST_DF_NORMAL = seir.propagate_uncertainties(XX, PP)\n",
+    "FORCAST_DF_NORMAL.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x648 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = subplots(ncols=2, nrows=2, figsize=(12, 9))\n",
+    "\n",
+    "gv_kws = dict(color=\"black\", zorder=10, lw=3, z_factor=0.674)\n",
+    "gv_line_kws = {\"ls\": \"--\", \"label\": \"Normal posteriors\"}\n",
+    "gv_fill_kws = {\"alpha\": 0.2}\n",
+    "\n",
+    "fill_kws = {\"alpha\": 0.3, \"edgecolor\": \"k\", \"lw\": 2}\n",
+    "line_kws = {\"ls\": \"-\", \"label\": \"General posteriors\", \"lw\": 2}\n",
+    "\n",
+    "t_range_shifted = FORECAST_DF.index - timedelta(days=OFFSET)\n",
+    "\n",
+    "# Census hospital\n",
+    "ax = axs[0, 0]\n",
+    "ax.set_ylabel(f\"COVID-19 Hospital Census\", fontsize=12, fontweight=\"bold\")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## General posteriors\n",
+    "plot_band(\n",
+    "    x=FORECAST_DF.index,\n",
+    "    y1=FORECAST_DF[\"Hospitalized Census 25%\"],\n",
+    "    ym=FORECAST_DF[\"Hospitalized Census Median\"],\n",
+    "    y2=FORECAST_DF[\"Hospitalized Census 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "## Normal posteriors\n",
+    "plot_gvar(\n",
+    "    x=t_range_shifted,\n",
+    "    y=FORCAST_DF_NORMAL[\"hospital_census\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "\n",
+    "ax.legend(bbox_to_anchor=(1.0, 1.0))\n",
+    "\n",
+    "# Census vent\n",
+    "ax = axs[0, 1]\n",
+    "ax.set_ylabel(f\"COVID-19 Vent Census\", fontsize=12, fontweight=\"bold\")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## General posteriors\n",
+    "plot_band(\n",
+    "    x=FORECAST_DF.index,\n",
+    "    y1=FORECAST_DF[\"Vent Census 25%\"],\n",
+    "    ym=FORECAST_DF[\"Vent Census Median\"],\n",
+    "    y2=FORECAST_DF[\"Vent Census 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "## Normal posteriors\n",
+    "plot_gvar(\n",
+    "    x=t_range_shifted,\n",
+    "    y=FORCAST_DF_NORMAL[\"vent_census\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Admits hosp\n",
+    "ax = axs[1, 0]\n",
+    "ax.set_ylabel(f\"COVID-19 Hospital Admits\", fontsize=12, fontweight=\"bold\")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## General posteriors\n",
+    "plot_band(\n",
+    "    x=FORECAST_DF.index,\n",
+    "    y1=FORECAST_DF[\"Hospitalized Admits 25%\"],\n",
+    "    ym=FORECAST_DF[\"Hospitalized Admits Median\"],\n",
+    "    y2=FORECAST_DF[\"Hospitalized Admits 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "\n",
+    "## Normal posteriors\n",
+    "plot_gvar(\n",
+    "    x=t_range_shifted,\n",
+    "    y=FORCAST_DF_NORMAL[\"hospital_admits\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "\n",
+    "# Admits vent\n",
+    "ax = axs[1, 1]\n",
+    "ax.set_ylabel(f\"COVID-19 Vent Admits\", fontsize=12, fontweight=\"bold\")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## General posteriors\n",
+    "plot_band(\n",
+    "    x=FORECAST_DF.index,\n",
+    "    y1=FORECAST_DF[\"Vent Admits 25%\"],\n",
+    "    ym=FORECAST_DF[\"Vent Admits Median\"],\n",
+    "    y2=FORECAST_DF[\"Vent Admits 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "\n",
+    "## Normal posteriors\n",
+    "plot_gvar(\n",
+    "    x=t_range_shifted,\n",
+    "    y=FORCAST_DF_NORMAL[\"vent_admits\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "fig.suptitle(\n",
+    "    \"General vs normal posteriors @ 50% C.I.\", y=1.02, fontsize=12, fontweight=\"bold\"\n",
+    ")\n",
+    "fig.autofmt_xdate()\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "despine()\n",
+    "show_plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the size of the census uncertainties differs by a few standard deviations while the mean agrees. This is interesting as they are computed using admission data (which agrees) and use the same function (see `bayes_chime/normal/models/sir.py` line 70). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute normal posteriors given normal priors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, original priors are approximated by normal distributions and `lsqfit` is used to compute posteriors.\n",
+    "\n",
+    "To account for the offset, the data is extended with zeros before the first date."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hosp</th>\n",
+       "      <th>vent</th>\n",
+       "      <th>mort</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2020-03-02</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-03</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-04</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-05</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-06</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            hosp  vent  mort\n",
+       "2020-03-02     0     0     0\n",
+       "2020-03-03     0     0     0\n",
+       "2020-03-04     0     0     0\n",
+       "2020-03-05     0     0     0\n",
+       "2020-03-06     1     0     0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extended_range = date_range(\n",
+    "    DATA_DF.index[0] - timedelta(int(OFFSET)), freq=\"D\", periods=OFFSET\n",
+    ")\n",
+    "tmp = DataFrame(index=extended_range, columns=DATA_DF.columns).fillna(0)\n",
+    "extended_data = concat([tmp, DATA_DF])\n",
+    "extended_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the same as above with the difference that now the priors are used instead of the posteriors and the date range is set by the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_infections = META_PARS[\"n_hosp\"] / META_PARS[\"mkt_share\"] / PRIORS[\"hosp_prop\"]\n",
+    "\n",
+    "## Fixed paramters (no distributions)\n",
+    "xx = {\n",
+    "    \"dates\": extended_data.index,\n",
+    "    \"market_share\": META_PARS[\"mkt_share\"],\n",
+    "    \"initial_susceptible\": META_PARS[\"region_pop\"],\n",
+    "    \"initial_infected\": 0,\n",
+    "    \"initial_recovered\": 0,\n",
+    "    \"initial_infected\": 0,\n",
+    "    \"initial_recovered\": 0,\n",
+    "    \"initial_icu\": 0,\n",
+    "    \"initial_vent\": 0,\n",
+    "    \"initial_hospital\": META_PARS[\"n_hosp\"] / META_PARS[\"mkt_share\"],\n",
+    "}\n",
+    "## Variable parameters (distributions)\n",
+    "pp = {\n",
+    "    \"initial_exposed\": total_infections,\n",
+    "    \"incubation_days\": PRIORS[\"incubation_days\"],\n",
+    "    \"beta\": PRIORS[\"beta\"],\n",
+    "    \"recovery_days\": PRIORS[\"recovery_days\"],\n",
+    "    \"nu\": PRIORS[\"nu\"],\n",
+    "    \"hospital_probability\": PRIORS[\"hosp_prop\"],\n",
+    "    \"hospital_length_of_stay\": PRIORS[\"hosp_LOS\"],\n",
+    "    \"icu_probability\": PRIORS[\"ICU_prop\"],\n",
+    "    \"icu_length_of_stay\": PRIORS[\"ICU_LOS\"],\n",
+    "    \"vent_probability\": PRIORS[\"vent_prop\"],\n",
+    "    \"vent_length_of_stay\": PRIORS[\"vent_LOS\"],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pp[\"L\"] = PRIORS[\"logistic_L\"]\n",
+    "pp[\"x0\"] = PRIORS[\"logistic_x0\"] + OFFSET\n",
+    "pp[\"k\"] = PRIORS[\"logistic_k\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One has to specify the time range and data (columns) to fit. For now, this only fits the hospitalized census data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seir.fit_start_date = \"2020-03-06\"\n",
+    "seir.fit_columns = [\"hospital_census\", \"vent_census\"]\n",
+    "seir.debug = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Furthermore, one needs to know the uncertainty of the data.\n",
+    "Since I have no experience with how reliable this data is (one also must account for temporal fluctuations), I estimate the uncertainty to be 10% of the mean plus an additional 10 patients to not emphasize early points too much."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yy = gvar(\n",
+    "    [DATA_DF.hosp.values, DATA_DF.vent.values],\n",
+    "    [DATA_DF.hosp.values * 0.1 + 10, DATA_DF.vent.values * 0.1 + 2,],\n",
+    ").T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The below method checks if the function call does not raise any problems (e.g., too few prior parameters or data is of the wrong shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checks passed\n"
+     ]
+    }
+   ],
+   "source": [
+    "seir.check_call(xx, yy, pp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This runs the fit and computes posteriors (stored in `fit.p`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "fit = nonlinear_fit(data=(xx, yy), prior=pp, fcn=seir.fit_fcn, debug=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.54 s ± 255 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "nonlinear_fit(data=(xx, yy), prior=pp, fcn=seir.fit_fcn, debug=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x648 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_prediction = xx.copy()\n",
+    "tf = FORECAST_DF.iloc[:100]\n",
+    "x_prediction[\"dates\"] = tf.index\n",
+    "df = seir.propagate_uncertainties(x_prediction, fit.p)\n",
+    "df.index -= timedelta(days=OFFSET)\n",
+    "\n",
+    "fig, axs = subplots(ncols=2, nrows=2, figsize=(12, 9), sharex=False)\n",
+    "\n",
+    "gv_kws = dict(color=\"black\", zorder=10, lw=3, z_factor=0.674)\n",
+    "gv_line_kws = {\"ls\": \"--\", \"label\": \"New normal fit\"}\n",
+    "gv_fill_kws = {\"alpha\": 0.2}\n",
+    "\n",
+    "fill_kws = {\"alpha\": 0.3, \"edgecolor\": \"k\", \"lw\": 2}\n",
+    "line_kws = {\"ls\": \"-\", \"label\": \"Original fit\", \"lw\": 2}\n",
+    "\n",
+    "\n",
+    "# Census hospitalized\n",
+    "ax = axs[0, 0]\n",
+    "ax.set_ylabel(f\"COVID-19 Hospital Census\", fontsize=12, fontweight=\"bold\")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## Fit hospitalized\n",
+    "plot_gvar(\n",
+    "    x=df.index,\n",
+    "    y=df[\"hospital_census\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "\n",
+    "## Original hospitalized\n",
+    "ax.set_ylabel(f\"COVID-19 Hospital Census\", fontsize=12, fontweight=\"bold\")\n",
+    "plot_band(\n",
+    "    x=tf.index,\n",
+    "    y1=tf[\"Hospitalized Census 25%\"],\n",
+    "    ym=tf[\"Hospitalized Census Median\"],\n",
+    "    y2=tf[\"Hospitalized Census 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "\n",
+    "## Data hospitalized\n",
+    "plot_gvar(\n",
+    "    x=DATA_DF.index,\n",
+    "    y=yy.T[0],\n",
+    "    ax=ax,\n",
+    "    z_factor=0.674,\n",
+    "    color=\"red\",\n",
+    "    line_kws={**line_kws, \"label\": \"Data\"},\n",
+    "    fill_kws={**fill_kws, \"alpha\": 0.5, \"zorder\": 5},\n",
+    ")\n",
+    "ax.legend(loc=\"upper left\")\n",
+    "\n",
+    "\n",
+    "# Admits hospitalized\n",
+    "ax = axs[1, 0]\n",
+    "ax.set_ylabel(f\"COVID-19 Hospital Admits\", fontsize=12, fontweight=\"bold\")\n",
+    "\n",
+    "\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## Prediction hospitalized\n",
+    "plot_gvar(\n",
+    "    x=df.index,\n",
+    "    y=df[\"hospital_admits\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "\n",
+    "## Original hospitalized\n",
+    "plot_band(\n",
+    "    x=tf.index,\n",
+    "    y1=tf[\"Hospitalized Admits 25%\"],\n",
+    "    ym=tf[\"Hospitalized Admits Median\"],\n",
+    "    y2=tf[\"Hospitalized Admits 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "\n",
+    "# Census vent\n",
+    "ax = axs[0, 1]\n",
+    "ax.set_ylabel(f\"COVID-19 Vent Census\", fontsize=12, fontweight=\"bold\")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## Fit vent\n",
+    "plot_gvar(\n",
+    "    x=df.index,\n",
+    "    y=df[\"vent_census\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "\n",
+    "## Original vent\n",
+    "plot_band(\n",
+    "    x=tf.index,\n",
+    "    y1=tf[\"Vent Census 25%\"],\n",
+    "    ym=tf[\"Vent Census Median\"],\n",
+    "    y2=tf[\"Vent Census 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "\n",
+    "## Data vent\n",
+    "plot_gvar(\n",
+    "    x=DATA_DF.index,\n",
+    "    y=yy.T[1],\n",
+    "    ax=ax,\n",
+    "    z_factor=0.674,\n",
+    "    color=\"red\",\n",
+    "    line_kws={**line_kws, \"label\": \"Data\"},\n",
+    "    fill_kws={**fill_kws, \"alpha\": 0.5, \"zorder\": 5},\n",
+    ")\n",
+    "ax.legend(loc=\"upper left\")\n",
+    "\n",
+    "\n",
+    "# Admits vent\n",
+    "ax = axs[1, 1]\n",
+    "ax.set_ylabel(f\"COVID-19 Vent Admits\", fontsize=12, fontweight=\"bold\")\n",
+    "\n",
+    "ax.grid(True)\n",
+    "\n",
+    "## Prediction vent\n",
+    "plot_gvar(\n",
+    "    x=df.index,\n",
+    "    y=df[\"vent_admits\"].values,\n",
+    "    ax=ax,\n",
+    "    **gv_kws,\n",
+    "    line_kws=gv_line_kws,\n",
+    "    fill_kws=gv_fill_kws,\n",
+    ")\n",
+    "ax.grid(True)\n",
+    "\n",
+    "\n",
+    "## Original hospitalized\n",
+    "plot_band(\n",
+    "    x=tf.index,\n",
+    "    y1=tf[\"Vent Admits 25%\"],\n",
+    "    ym=tf[\"Vent Admits Median\"],\n",
+    "    y2=tf[\"Vent Admits 75%\"],\n",
+    "    fill_kws=fill_kws,\n",
+    "    line_kws=line_kws,\n",
+    "    ax=ax,\n",
+    "    zorder=20,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "fig.suptitle(\n",
+    "    \"General PDF vs normal PDF @ 50% C.I.\", y=1.02, fontsize=12, fontweight=\"bold\"\n",
+    ")\n",
+    "fig.autofmt_xdate()\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "despine()\n",
+    "show_plot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Least Square Fit:\n",
+      "  chi2/dof [dof] = 0.3 [94]    Q = 1    logGBF = -318.38\n",
+      "\n",
+      "Parameters:\n",
+      "        initial_exposed       21 (13)     [   160 (67) ]  **\n",
+      "        incubation_days     2.0 (1.0)     [  4.6 (1.5) ]  *\n",
+      "                   beta    0.497 (92)     [  0.32 (12) ]  *\n",
+      "          recovery_days    16.4 (4.9)     [ 15.3 (5.0) ]  \n",
+      "                     nu     2.46 (25)     [  2.46 (25) ]  \n",
+      "   hospital_probability   0.0450 (20)     [ 0.024 (10) ]  **\n",
+      "hospital_length_of_stay    11.78 (80)     [ 11.64 (83) ]  \n",
+      "        icu_probability    0.370 (35)     [ 0.349 (39) ]  \n",
+      "     icu_length_of_stay    13.47 (89)     [ 13.47 (89) ]  \n",
+      "       vent_probability    0.830 (89)     [  0.65 (17) ]  *\n",
+      "    vent_length_of_stay    18.5 (2.1)     [ 19.5 (2.3) ]  \n",
+      "                      L    0.829 (39)     [  0.37 (23) ]  **\n",
+      "                     x0    29.73 (79)     [ 20.7 (6.9) ]  *\n",
+      "                      k     0.95 (37)     [  0.78 (43) ]  \n",
+      "\n",
+      "Settings:\n",
+      "  svdcut/n = 1e-12/1    tol = (1e-08,1e-10,1e-10*)    (itns/time = 63/1.5)\n",
+      "  fitter = scipy_least_squares    method = trf\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Priors</th>\n",
+       "      <th>PDF from normal approx</th>\n",
+       "      <th>PDF from general dists</th>\n",
+       "      <th>diff</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>hosp_prop</th>\n",
+       "      <td>0.024(10)</td>\n",
+       "      <td>0.0450(20)</td>\n",
+       "      <td>0.0235(30)</td>\n",
+       "      <td>0.0214(36)</td>\n",
+       "      <td>5.879241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>incubation_days</th>\n",
+       "      <td>4.6(1.5)</td>\n",
+       "      <td>2.0(1.0)</td>\n",
+       "      <td>3.25(29)</td>\n",
+       "      <td>-1.3(1.1)</td>\n",
+       "      <td>1.181243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_L</th>\n",
+       "      <td>0.37(23)</td>\n",
+       "      <td>0.829(39)</td>\n",
+       "      <td>0.787(19)</td>\n",
+       "      <td>0.042(44)</td>\n",
+       "      <td>0.960861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vent_prop</th>\n",
+       "      <td>0.65(17)</td>\n",
+       "      <td>0.830(89)</td>\n",
+       "      <td>0.764(40)</td>\n",
+       "      <td>0.067(98)</td>\n",
+       "      <td>0.686208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vent_LOS</th>\n",
+       "      <td>19.5(2.3)</td>\n",
+       "      <td>18.5(2.1)</td>\n",
+       "      <td>19.62(61)</td>\n",
+       "      <td>-1.2(2.2)</td>\n",
+       "      <td>0.540339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_x0</th>\n",
+       "      <td>20.7(6.9)</td>\n",
+       "      <td>25.73(79)</td>\n",
+       "      <td>25.41(42)</td>\n",
+       "      <td>0.32(89)</td>\n",
+       "      <td>0.357845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta</th>\n",
+       "      <td>0.32(12)</td>\n",
+       "      <td>0.497(92)</td>\n",
+       "      <td>0.467(26)</td>\n",
+       "      <td>0.030(95)</td>\n",
+       "      <td>0.318960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hosp_LOS</th>\n",
+       "      <td>11.64(83)</td>\n",
+       "      <td>11.78(80)</td>\n",
+       "      <td>11.59(29)</td>\n",
+       "      <td>0.19(85)</td>\n",
+       "      <td>0.227801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nu</th>\n",
+       "      <td>2.46(25)</td>\n",
+       "      <td>2.46(25)</td>\n",
+       "      <td>2.440(73)</td>\n",
+       "      <td>0.02(26)</td>\n",
+       "      <td>0.082891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICU_prop</th>\n",
+       "      <td>0.349(39)</td>\n",
+       "      <td>0.370(35)</td>\n",
+       "      <td>0.368(11)</td>\n",
+       "      <td>0.002(37)</td>\n",
+       "      <td>0.061055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recovery_days</th>\n",
+       "      <td>15.3(5.0)</td>\n",
+       "      <td>16.4(4.9)</td>\n",
+       "      <td>16.6(1.7)</td>\n",
+       "      <td>-0.2(5.1)</td>\n",
+       "      <td>0.031884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ICU_LOS</th>\n",
+       "      <td>13.47(89)</td>\n",
+       "      <td>13.47(89)</td>\n",
+       "      <td>13.45(26)</td>\n",
+       "      <td>0.02(92)</td>\n",
+       "      <td>0.022986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>logistic_k</th>\n",
+       "      <td>0.78(43)</td>\n",
+       "      <td>0.95(37)</td>\n",
+       "      <td>0.94(14)</td>\n",
+       "      <td>0.007(399)</td>\n",
+       "      <td>0.018485</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>initial_exposed</th>\n",
+       "      <td>160(67)</td>\n",
+       "      <td>21(13)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>nan +- 13.3117</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Priors PDF from normal approx PDF from general dists  \\\n",
+       "hosp_prop        0.024(10)             0.0450(20)             0.0235(30)   \n",
+       "incubation_days   4.6(1.5)               2.0(1.0)               3.25(29)   \n",
+       "logistic_L        0.37(23)              0.829(39)              0.787(19)   \n",
+       "vent_prop         0.65(17)              0.830(89)              0.764(40)   \n",
+       "vent_LOS         19.5(2.3)              18.5(2.1)              19.62(61)   \n",
+       "logistic_x0      20.7(6.9)              25.73(79)              25.41(42)   \n",
+       "beta              0.32(12)              0.497(92)              0.467(26)   \n",
+       "hosp_LOS         11.64(83)              11.78(80)              11.59(29)   \n",
+       "nu                2.46(25)               2.46(25)              2.440(73)   \n",
+       "ICU_prop         0.349(39)              0.370(35)              0.368(11)   \n",
+       "recovery_days    15.3(5.0)              16.4(4.9)              16.6(1.7)   \n",
+       "ICU_LOS          13.47(89)              13.47(89)              13.45(26)   \n",
+       "logistic_k        0.78(43)               0.95(37)               0.94(14)   \n",
+       "initial_exposed    160(67)                 21(13)                    NaN   \n",
+       "\n",
+       "                           diff         z  \n",
+       "hosp_prop            0.0214(36)  5.879241  \n",
+       "incubation_days       -1.3(1.1)  1.181243  \n",
+       "logistic_L            0.042(44)  0.960861  \n",
+       "vent_prop             0.067(98)  0.686208  \n",
+       "vent_LOS              -1.2(2.2)  0.540339  \n",
+       "logistic_x0            0.32(89)  0.357845  \n",
+       "beta                  0.030(95)  0.318960  \n",
+       "hosp_LOS               0.19(85)  0.227801  \n",
+       "nu                     0.02(26)  0.082891  \n",
+       "ICU_prop              0.002(37)  0.061055  \n",
+       "recovery_days         -0.2(5.1)  0.031884  \n",
+       "ICU_LOS                0.02(92)  0.022986  \n",
+       "logistic_k           0.007(399)  0.018485  \n",
+       "initial_exposed  nan +- 13.3117       NaN  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(fit)\n",
+    "\n",
+    "# Convert names back to original\n",
+    "name_map = {\n",
+    "    \"L\": \"logistic_L\",\n",
+    "    \"x0\": \"logistic_x0\",\n",
+    "    \"k\": \"logistic_k\",\n",
+    "    \"hospital_probability\": \"hosp_prop\",\n",
+    "    \"hospital_length_of_stay\": \"hosp_LOS\",\n",
+    "    \"icu_probability\": \"ICU_prop\",\n",
+    "    \"icu_length_of_stay\": \"ICU_LOS\",\n",
+    "    \"vent_probability\": \"vent_prop\",\n",
+    "    \"vent_length_of_stay\": \"vent_LOS\",\n",
+    "}\n",
+    "\n",
+    "new_posterior = {name_map.get(key, key): val for key, val in fit.p.items()}\n",
+    "new_posterior[\"logistic_x0\"] -= OFFSET\n",
+    "new_prior = {name_map.get(key, key): val for key, val in pp.items()}\n",
+    "\n",
+    "# Create comparison frame\n",
+    "comparison = DataFrame(\n",
+    "    [new_prior, new_posterior, POSTERIORS],\n",
+    "    index=[\"Priors\", \"PDF from normal approx\", \"PDF from general dists\",],\n",
+    ").T  # .dropna()\n",
+    "\n",
+    "# Compute difference in standard deviations\n",
+    "comparison[\"diff\"] = (\n",
+    "    comparison[\"PDF from normal approx\"] - comparison[\"PDF from general dists\"]\n",
+    ").dropna()\n",
+    "comparison[\"z\"] = comparison[\"diff\"].apply(lambda x: abs(x.mean) / x.sdev)\n",
+    "\n",
+    "# Present\n",
+    "comparison.sort_values(\"z\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Besides the `hosp_prop`, fitted parameters seem to agree. This might be related to optimization criteria or the estimated data uncertainty which I have not analyzed in detail."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chime",
+   "language": "python",
+   "name": "chime"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..2c2dad0
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,2 @@
+[pytest]
+addopts = -v
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 0000000..cec21c3
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,2 @@
+pytest
+penn_chime
diff --git a/requirements.txt b/requirements.txt
index abd3461..f4c6613 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,7 @@
 ConfigArgParse
 gitpython
 seaborn
+numpy
+pandas
+gvar
+lsqfit
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..629c531
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,36 @@
+"""Setup file for bayes_chime
+"""
+__version__ = "0.1.0"
+__author__ = "Predictive Healthcare @ Penn Medicine"
+
+from setuptools import setup, find_packages
+
+with open("requirements.txt", "r") as INP:
+    REQUIREMENTS = INP.read()
+
+with open("README.md", "r") as INP:
+    LONE_DESCRIPTION = INP.read()
+
+setup(
+    name="bayes_chime",
+    version=__version__,
+    author=__author__,
+    author_email="",
+    description="Bayesian fit to SEIR model."
+    " An extension to Penn Medicine's CHIME tool.",
+    long_description=LONE_DESCRIPTION,
+    url="https://github.com/pennsignals/chime_sims",
+    project_urls={
+        "Bug Reports": "https://github.com/pennsignals/chime_sims/issues",
+        "Source": "https://github.com/pennsignals/chime_sims",
+    },
+    packages=["bayes_chime"],
+    install_requires=REQUIREMENTS,
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires=">=3.7",
+    keywords=[],
+)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/normal/__init__.py b/tests/normal/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/normal/models/__init__.py b/tests/normal/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/normal/models/seir_test.py b/tests/normal/models/seir_test.py
new file mode 100644
index 0000000..6ecd038
--- /dev/null
+++ b/tests/normal/models/seir_test.py
@@ -0,0 +1,87 @@
+"""Tests for SEIR model in this repo
+* Compares conserved quantities
+* Compares model against SEIR wo social policies in limit to SIR
+"""
+from pytest import fixture
+
+from pandas import Series
+from pandas.testing import assert_frame_equal, assert_series_equal
+
+from bayes_chime.normal.models import SIRModel, SEIRModel
+
+from tests.normal.models.sir_test import (  # pylint: disable=W0611
+    fixture_sir_data_wo_policy,
+    fixture_penn_chime_setup,
+    fixture_penn_chime_raw_df_no_policy,
+)
+
+COLS_TO_COMPARE = [
+    "susceptible",
+    "infected",
+    "recovered",
+    # Does not compare census as this repo uses the exponential distribution
+]
+
+PENN_CHIME_COMMIT = "188c35be9561164bedded4a8071a320cbde0d2bc"
+
+
+@fixture(name="seir_data")
+def fixture_seir_data(sir_data_wo_policy):
+    """Returns data for the SIHR model
+    """
+    x, p = sir_data_wo_policy
+    pp = p.copy()
+    xx = x.copy()
+    pp["alpha"] = 0.5
+    pp["nu"] = 1
+    pp["initial_exposed"] = 0
+
+    return xx, pp
+
+
+def test_conserved_n(seir_data):
+    """Checks if S + E + I + R is conserved for SEIR
+    """
+    x, pars = seir_data
+
+    n_total = 0
+    for key in SEIRModel.compartments:
+        n_total += pars[f"initial_{key}"]
+
+    seir_model = SEIRModel()
+    predictions = seir_model.propagate_uncertainties(x, pars)
+
+    n_computed = predictions[SEIRModel.compartments].sum(axis=1)
+    n_expected = Series(data=[n_total] * len(n_computed), index=n_computed.index)
+
+    assert_series_equal(n_expected, n_computed)
+
+
+def test_compare_sir_vs_seir(sir_data_wo_policy, seir_data, monkeypatch):
+    """Checks if SEIR and SIR return same results if the code enforces
+
+    * alpha = gamma
+    * E = 0
+    * dI = dE
+    """
+    x_sir, pars_sir = sir_data_wo_policy
+    x_seir, pars_seir = seir_data
+
+    pars_seir["alpha"] = pars_sir["gamma"]  # will be done by hand
+
+    def mocked_seir_step(data, **pars):
+        data["exposed"] = 0
+        new_data = SEIRModel.simulation_step(data, **pars)
+        new_data["infected"] += new_data["exposed_new"]
+        return new_data
+
+    seir_model = SEIRModel()
+    monkeypatch.setattr(seir_model, "simulation_step", mocked_seir_step)
+
+    sir_model = SIRModel()
+    predictions_sir = sir_model.propagate_uncertainties(x_sir, pars_sir)
+    predictions_seir = seir_model.propagate_uncertainties(x_seir, pars_seir)
+
+    assert_frame_equal(
+        predictions_sir[COLS_TO_COMPARE], predictions_seir[COLS_TO_COMPARE],
+    )
diff --git a/tests/normal/models/sir_test.py b/tests/normal/models/sir_test.py
new file mode 100644
index 0000000..0750a8e
--- /dev/null
+++ b/tests/normal/models/sir_test.py
@@ -0,0 +1,246 @@
+"""Tests for SIR model in this repo
+* Compares conserved quantities
+* Compares model against Penn CHIME w/wo social policies
+* Checks logistic policies in extreme limit
+"""
+from typing import Tuple
+from datetime import date, timedelta
+
+from pytest import fixture
+
+from pandas import DataFrame, Series, DatetimeIndex
+from pandas.testing import assert_frame_equal, assert_series_equal
+
+from penn_chime.model.parameters import Parameters, Disposition
+from penn_chime.model.sir import (
+    Sir,
+    sim_sir,
+    calculate_dispositions,
+    calculate_admits,
+    calculate_census,
+)
+
+from bayes_chime.normal.models import SIRModel
+from bayes_chime.normal.utilities import one_minus_logistic_fcn
+
+PENN_CHIME_COMMIT = "188c35be9561164bedded4a8071a320cbde0d2bc"
+
+COLS_TO_COMPARE = [
+    "susceptible",
+    "infected",
+    "recovered",
+    "hospital_admits",
+    # Does not compare census as this repo uses the exponential distribution
+]
+COLUMN_MAP = {
+    "hospitalized": "hospital_admits",
+}
+
+
+@fixture(name="penn_chime_setup")
+def fixture_penn_chime_setup() -> Tuple[Parameters, Sir]:
+    """Initializes penn_chime parameters and SIR model
+    """
+    p = Parameters(
+        current_hospitalized=69,
+        date_first_hospitalized=date(2020, 3, 7),
+        doubling_time=None,
+        hospitalized=Disposition.create(days=7, rate=0.025),
+        icu=Disposition.create(days=9, rate=0.0075),
+        infectious_days=14,
+        market_share=0.15,
+        n_days=100,
+        population=3600000,
+        recovered=0,
+        relative_contact_rate=0.3,
+        ventilated=Disposition.create(days=10, rate=0.005),
+    )
+    return p, Sir(p)
+
+
+@fixture(name="penn_chime_raw_df_no_policy")
+def fixture_penn_chime_raw_df_no_policy(penn_chime_setup) -> DataFrame:
+    """Runs penn_chime SIR model for no social policies
+    """
+    p, simsir = penn_chime_setup
+
+    n_days = simsir.raw_df.day.max() - simsir.raw_df.day.min()
+    policies = [(simsir.beta, n_days)]
+    raw = sim_sir(
+        simsir.susceptible,
+        simsir.infected,
+        p.recovered,
+        simsir.gamma,
+        -simsir.i_day,
+        policies,
+    )
+    calculate_dispositions(raw, simsir.rates, market_share=p.market_share)
+    calculate_admits(raw, simsir.rates)
+    calculate_census(raw, simsir.days)
+
+    raw_df = DataFrame(raw)
+    raw_df.index = simsir.raw_df.date
+
+    return raw_df.fillna(0)
+
+
+@fixture(name="sir_data_wo_policy")
+def fixture_sir_data_wo_policy(penn_chime_setup, penn_chime_raw_df_no_policy):
+    """Provides data for local sir module
+    """
+    p, simsir = penn_chime_setup
+    raw_df = penn_chime_raw_df_no_policy
+    day0 = raw_df.iloc[0].fillna(0)
+
+    total = day0.susceptible + day0.infected + day0.recovered
+
+    pars = {
+        "beta": simsir.beta * total,  # This repo uses S/total in sir
+        "gamma": simsir.gamma,
+        "initial_susceptible": day0.susceptible,
+        "initial_infected": day0.infected,
+        "initial_hospital": day0.hospitalized,
+        "initial_recovered": day0.recovered,
+        "hospital_probability": simsir.rates["hospitalized"],
+    }
+    x = {
+        "dates": DatetimeIndex(raw_df.index),
+        "hospital_length_of_stay": p.dispositions["hospitalized"].days,
+        "market_share": p.market_share,
+    }
+    return x, pars
+
+
+@fixture(name="sir_data_w_policy")
+def fixture_sir_data_w_policy(penn_chime_setup):
+    """Provides data for local sir module with implemented policies
+    """
+    p, simsir = penn_chime_setup
+    raw_df = simsir.raw_df.set_index("date")
+    day0 = raw_df.iloc[0].fillna(0)
+
+    total = day0.susceptible + day0.infected + day0.recovered
+
+    pars = {
+        "beta": simsir.beta * total,  # This repo uses S/total in sir
+        "gamma": simsir.gamma,
+        "initial_susceptible": day0.susceptible,
+        "initial_infected": day0.infected,
+        "initial_hospital": day0.hospitalized,
+        "initial_recovered": day0.recovered,
+        "hospital_probability": simsir.rates["hospitalized"],
+    }
+    x = {
+        "dates": DatetimeIndex(raw_df.index),
+        "hospital_length_of_stay": p.dispositions["hospitalized"].days,
+        "market_share": p.market_share,
+    }
+    return x, pars
+
+
+def test_conserved_n(sir_data_wo_policy):
+    """Checks if S + I + R is conserved for local SIR
+    """
+    x, pars = sir_data_wo_policy
+    sir_model = SIRModel()
+
+    n_total = 0
+    for key in sir_model.compartments:
+        n_total += pars[f"initial_{key}"]
+
+    predictions = sir_model.propagate_uncertainties(x, pars)
+
+    n_computed = predictions[sir_model.compartments].sum(axis=1)
+    n_expected = Series(data=[n_total] * len(n_computed), index=n_computed.index)
+
+    assert_series_equal(n_expected, n_computed)
+
+
+def test_sir_vs_penn_chime_no_policies(penn_chime_raw_df_no_policy, sir_data_wo_policy):
+    """Compares local SIR against penn_chime SIR for no social policies
+    """
+    x, pars = sir_data_wo_policy
+
+    sir_model = SIRModel()
+    predictions = sir_model.propagate_uncertainties(x, pars)
+
+    assert_frame_equal(
+        penn_chime_raw_df_no_policy.rename(columns=COLUMN_MAP)[COLS_TO_COMPARE],
+        predictions[COLS_TO_COMPARE],
+    )
+
+
+def test_sir_vs_penn_chime_w_policies(penn_chime_setup, sir_data_w_policy):
+    """Compares local SIR against penn_chime SIR for with social policies
+    """
+    p, sir = penn_chime_setup
+    x, pars = sir_data_w_policy
+
+    policies = sir.gen_policy(p)
+    new_policy_date = x["dates"][0] + timedelta(days=policies[0][1])
+    beta0, beta1 = policies[0][0], policies[1][0]
+
+    def update_parameters(ddate, **pars):  # pylint: disable=W0613
+        pars["beta"] = (beta0 if ddate < new_policy_date else beta1) * p.population
+        return pars
+
+    sir_model = SIRModel(update_parameters=update_parameters)
+    predictions = sir_model.propagate_uncertainties(x, pars)
+
+    assert_frame_equal(
+        sir.raw_df.set_index("date")
+        .fillna(0)
+        .rename(columns=COLUMN_MAP)[COLS_TO_COMPARE],
+        predictions[COLS_TO_COMPARE],
+    )
+
+
+def test_sir_logistic_policy(penn_chime_setup, sir_data_w_policy):
+    """Compares local SIR against penn_chime SIR for implemented social policies
+    where policies are implemented as a logistic function
+    """
+    p, sir = penn_chime_setup
+    x, pars = sir_data_w_policy
+
+    policies = sir.gen_policy(p)
+
+    # Set up logistic function to match policies (Sharp decay)
+    pars["beta"] = policies[0][0] * p.population
+    ## This are new parameters needed by one_minus_logistic_fcn
+    pars["L"] = 1 - policies[1][0] / policies[0][0]
+    pars["x0"] = policies[0][1] - 0.5
+    pars["k"] = 1.0e7
+
+    def update_parameters(ddate, **kwargs):
+        xx = (ddate - x["dates"][0]).days
+        ppars = kwargs.copy()
+        ppars["beta"] = kwargs["beta"] * one_minus_logistic_fcn(
+            xx, L=kwargs["L"], k=kwargs["k"], x0=kwargs["x0"],
+        )
+        return ppars
+
+    sir_model = SIRModel(update_parameters=update_parameters)
+    predictions = sir_model.propagate_uncertainties(x, pars)
+
+    assert_frame_equal(
+        sir.raw_df.set_index("date")
+        .rename(columns=COLUMN_MAP)[COLS_TO_COMPARE]
+        .fillna(0),
+        predictions[COLS_TO_COMPARE],
+    )
+
+
+def test_sir_type_conversion(sir_data_w_policy):
+    """Compares local SIR run with set gamma vs set with recovery_days
+    """
+    x, pars = sir_data_w_policy
+
+    sir_model = SIRModel()
+    predictions = sir_model.propagate_uncertainties(x, pars)
+
+    pars["recovery_days"] = 1 / pars.pop("gamma")
+    new_predictions = sir_model.propagate_uncertainties(x, pars)
+
+    assert_frame_equal(
+        predictions, new_predictions,
+    )