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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This is always required for inline plot rendering in IPython Notebooks; might\n",
"# as well do it first, even before the markdown sections, just to be safe\n",
"%matplotlib inline\n",
"#%matplotlib notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SqDist Algorithm - Contents:\n",
"\n",
"- [Theoretical Basis](#Theoretical-Basis)\n",
" - [Motivation](#Motivation)\n",
" - [Simple Exponential Smoothing](#Simple-Exponential-Smoothing)\n",
" - [Holt's Linear Trend Forecast](#Holt's-Linear-Trend-Forecast)\n",
" - [Holt-Winters Seasonal Forecast](#Holt-Winters-Seasonal-Forecast)\n",
" - [Prediction Intervals](#Prediction-Intervals)\n",
" - [Spike Detection and Adaptive Baselines](#Spike-Detection-and-Adaptive-Baselines)\n",
"- [Putting it All Together](#Putting-it-All-Together)\n",
" - [Programming Interface](#Programming-Interface)\n",
"- [Functional Tests](#Functional-Tests)\n",
" - [Imports](#Imports)\n",
" - [Test Configuration](#Test-Configuration)\n",
" - [Test 1](#Test-1) | [Test 2](#Test-2) | [Test 3](#Test-3) | [Test 4](#Test-4) | [Test 5](#Test-5) \n",
"- [Synthetic Data Demonstration](#Synthetic-Data-Demonstration)\n",
" - [Construct synthetic time series](#Construct-synthetic-time-series)\n",
" - [Apply SqDist Algorithm](#Apply-SqDist-Algorithm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [Theoretical Basis](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Motivation](#SqDist-Algorithm---Contents:)\n",
"\n",
"Exponential smoothing of time series has been employed in countless research, engineering, economic, sociological, political, etc., applications. While its utility has been empirically demonstrated time and again over the last half century or more, it has only been in the last couple decades that it has normalized in form, stood up to rigorous mathematical scrutiny, and been tied directly to well-known statistical time series models. A major contributor to this recent maturation of this subdiscipline of applied mathematics is R. J. Hyndman. We largely follow notation used in his free Online textbook (http://www.otexts.org/fpp), and related literature, to provide a very brief overview of exponential smoothing that culminates in an algorithm that can be used to decompose a time series into a trend, a repeating “seasonal” pattern, and a residual."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Simple Exponential Smooting](#SqDist-Algorithm---Contents:)\n",
"\n",
"Exponential smoothing is a form of causal time series filtering; a way to estimate the most likely observation at time $t+1$, given observations up to time $t$. In its simplest form, it is a weighted average of the most recent observation, and the previous weighted average: \n",
"\n",
" <a name=\"eq01\">(1)</a> \n",
" $\\hat{y}_{t+1|t}=\\alpha y + \\left(1-\\alpha\\right)\\hat{y}_{t|t-1}$\n",
"\n",
"where\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"y_t & \\text{is} & \\text{observation at time } t \\\\\n",
"\\hat{y}_t & \\text{is} & \\text{predicted observation at time } t \\\\\n",
"\\alpha & \\text{is} & \\text{forgetting factor between 0 and 1} \\\\ \n",
"\\end{array}\n",
"$\n",
"\n",
"Equation [(1)](#eq01) is a recursive formulation, which is preferred for algorithmic implementation, but if expanded into an infinite series, it becomes clear why we refer to this as “exponential” smoothing:\n",
"\n",
"\n",
" <a name=\"eq02\">(2)</a> \n",
" $\\displaystyle\n",
" \\hat{y}_{t+1|t}=\\alpha y_t + \\alpha \\left(1 - \\alpha \\right)y_{t-1} +\n",
" \\alpha \\left(1 - \\alpha \\right)^2 y_{t-2} +\n",
" \\alpha \\left(1 - \\alpha \\right)^3 y_{t-3} + \\ldots\n",
" $\n",
"\n",
"\n",
"While $\\alpha$ is constant, the term $\\left(1-\\alpha\\right)$ changes exponentially for older and older observations. Since this term is by definition less than one, older observations are weighted exponentially less than newer observations. As $\\alpha$ approaches unity, the memory of the algorithm disappears.\n",
"\n",
"An alternative, but equivalent formulation decomposes [(1)](#eq01) into two steps:\n",
"\n",
"<a name=\"eq03\">(3)</a> \n",
" $\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+1|t} & = & l_t \\\\\n",
" l_t & = & \\alpha y_t + \\left(1-\\alpha\\right)l_{t-1}\n",
" \\end{array}\n",
" $\n",
"\n",
"where\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"l_t & \\text{is} & \\text{level, or instantaneous baseline at time } t\n",
"\\end{array}\n",
"$\n",
"\n",
"By rearranging [(3)](#eq03), we obtain the so-called error-equation formulation:\n",
"\n",
"<a name=\"eq04\">(4)</a> \n",
" $\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+1|t} & = & l_t \\\\\n",
" l_t & = & l_{t-1} + \\alpha\\left(y_t - l_{t-1}\\right) \\\\\n",
" l_t & = & l_{t-1} + \\alpha e_t\n",
" \\end{array}\n",
" $\n",
"\n",
"where\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"e_t & \\text{is} & \\text{1-step prediction error for time } t \\text{, or } y_t - \\hat{y}_{t|t-1}\n",
"\\end{array}\n",
"$\n",
"\n",
"The value of this formulation will become clearer when we present a numerical algorithm that includes not only simple exponential smoothing, but additional terms to model trend and seasonal variation in order to better match realistic observations. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Holt's Linear Trend Forecast](#SqDist-Algorithm---Contents:)\n",
"\n",
"Equation [(3)](#eq03) can be augmented to include a linear trend:\n",
"\n",
"<a name=\"eq05\">(5)</a> \n",
" $\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+h|t} & = & l_t + h b_t \\\\\n",
" l_t & = & \\alpha y_t + \\left(1-\\alpha\\right)\\left(l_{t-1}+b_{t-1}\\right) \\\\\n",
" b_t & = & \\beta^*\\left(l_t-l_{t-1}\\right) + \n",
" \\left(1-\\beta^*\\right)b_{t-1}\n",
" \\end{array}\n",
" $\n",
"\n",
"where\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"h & \\text{is} & \\text{forecast horizon } \\\\\n",
"b_t & \\text{is} & \\text{slope at time } t \\\\\n",
"\\beta^* & \\text{is} & \\text{slope forgetting factor between 0 and 1}\n",
"\\end{array}\n",
"$\n",
"\n",
"In words, a forecast is the level plus a slope multiplied by the number of discrete time steps. The level is still a weighted average of the observation at time $t$, and the 1-step prediction from time $t-1$ to $t$. The slope itself is the exponentially smoothed 1-step difference in the baseline from time $t-1$ to time $t$.\n",
"Just like with simple exponential smoothing, there is an error-correction formulation:\n",
"\n",
"<a name=\"eq06\">(6)</a> \n",
" $\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+h|t} & = & l_t + h b_t \\\\\n",
" l_t & = & l_{t-1} + b_{t-1} + \\alpha e_t \\\\\n",
" b_t & = & b_{t-1} + \\alpha \\beta^* e_t\n",
" \\end{array}\n",
" $\n",
"\n",
"A naïve implementation of Holt’s linear trend method tends to over-forecast, especially for larger forecast horizons $h$. One way to address this is to dampen the trend, or in other words, force the slope toward zero for larger forecast horizons. A minor tweak to [(5)](#eq05) gives:\n",
"\n",
"\n",
"<a name=\"eq07\">(7)</a> \n",
" $\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+h|t} & = & l_t + \\left(\\phi+\\phi^2+\\ldots+\\phi^h\\right) b_t \\\\\n",
" l_t & = & \\alpha y_t + \\left(1-\\alpha\\right)\\left(l_{t-1}+\\phi b_{t-1}\\right) \\\\\n",
" b_t & = & \\beta^*\\left(l_t-l_{t-1}\\right) + \n",
" \\left(1-\\beta^*\\right)\\phi b_{t-1}\n",
" \\end{array}\n",
" $\n",
"\n",
"where\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"\\phi & \\text{is} & \\text{dampening factor between 0 and 1} \n",
"\\end{array}\n",
"$\n",
"\n",
"if $\\phi=1$, [(7)](#eq07) is identical to the traditional Holt linear method; if $\\phi=0$, [(7)](#eq07) is identical to simple exponential smoothing. As before, rearranging terms leads to an error-corretion formulation:\n",
"\n",
"\n",
"<a name=\"eq08\">(8)</a> \n",
" $\\displaystyle\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+h|t} & = & l_t + \\left(\\phi+\\phi^2+\\ldots+\\phi^h\\right) b_t \\\\\n",
" l_t & = & l_{t-1} + \\phi b_{t-1} + \\alpha e_t \\\\\n",
" b_t & = & \\phi b_{t-1} + \\alpha \\beta^* e_t\n",
" \\end{array}\n",
" $\n",
"\n",
"\n",
"While we do not anticipate a need to forecast far into the future, data gaps are likely to occur, some of which may be long enough that if a constant slope is used to extrapolate across the gap, the algorithm may become unstable as the forecast increases/decreases without bound.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Holt-Winters Seasonal Forecast](#SqDist-Algorithm---Contents:)\n",
"\n",
"Finally, it is common for time series to exhibit a repeating pattern over a fixed interval. Seasonal variations are among the most familiar form of repetition, but a “seasonal” correction can be applied at any interval, even a 24-hour day. Holt and his student Winters share credit for formalizing a powerful seasonal correction tool that has stood the tests of time, and is still used frequently today. Augmenting Equation [(5)](#eq05) gives:\n",
"\n",
"\n",
"<a name=\"eq09\">(9)</a> \n",
" $\\displaystyle\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+h|t} & = & l_t + h b_t + s_{t-m+h_m^+} \\\\\n",
" l_t & = & \\alpha \\left(y_t-s_{t-m}\\right) + \n",
" \\left(1-\\alpha\\right)\\left(l_{t-1}+b_{t-1}\\right) \\\\\n",
" b_t & = & \\beta^*\\left(l_t-l_{t-1}\\right) + \n",
" \\left(1-\\beta^*\\right)b_{t-1} \\\\\n",
" s_t & = & \\gamma^* \\left(1-\\alpha\\right)\n",
" \\left(y_t-l_{t-1}-b_{t-1}\\right) +\n",
" \\left(1-\\gamma^*\\left(1-\\alpha\\right)\\right)s_{t-m}\n",
" \\end{array}\n",
" $\n",
"\n",
"where\n",
"\n",
"<!--- this is unnecessarily complex to get exactly the formatting desired\n",
"<table id=\"where\" style=\"width:100%; border-style: hidden\">\n",
"<tr><td style=\"width:5%; border-style: hidden\">\n",
" $m$\n",
" </td>\n",
" <td style=\"width:5%; border-style: hidden\">is</td>\n",
" <td style=\"text-align: left; border-style: hidden\"> \n",
" number of discrete time steps within a repeating interval\n",
" </td>\n",
"</tr>\n",
"<tr><td style=\"width:5%; border-style: hidden\">\n",
" $m$\n",
" </td>\n",
" <td style=\"width:5%; border-style: hidden\">is</td>\n",
" <td style=\"text-align: left; border-style: hidden\"> \n",
" modulo of $(h-1)$ with $m$ (i.e., $(h-1)\\mod{m}+1$)\n",
" </td>\n",
"</tr>\n",
"<tr><td style=\"width:5%; border-style: hidden\">\n",
" $m$\n",
" </td>\n",
" <td style=\"width:5%; border-style: hidden\">is</td>\n",
" <td style=\"text-align: left; border-style: hidden\"> \n",
" number of discrete time steps within a repeating interval\n",
" </td>\n",
"</tr>\n",
"<tr><td style=\"width:5%; border-style: hidden\">\n",
" $m$\n",
" </td>\n",
" <td style=\"width:5%; border-style: hidden\">is</td>\n",
" <td style=\"text-align: left; border-style: hidden\"> \n",
" number of discrete time steps within a repeating interval\n",
" </td>\n",
"</tr>\n",
"</table>\n",
"-->\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"m & \\text{is} & \\text{number of discrete time steps within repeating interval } \\\\\n",
"h_m^+ & \\text{is} & \\text{modulo of } (h-1) \\text{ with } m\n",
" \\text{ (i.e., } [(h-1)\\mod{m}]+1 \\text{)} \\\\\n",
"s_t & \\text{is} & \\text{seasonal correction for time } t \\\\\n",
"\\gamma^* & \\text{is} & \\text{seasonal correction forgetting factor between 0 and 1}\n",
"\\end{array}\n",
"$\n",
"\n",
"\n",
"In words, the appropriate seasonal correction is added to an $h$-step prediction. The seasonal correction for time $t$ is removed from the current observation before updating the level for time $t$, and the slope is updated using these seasonally corrected levels. Finally, the seasonal correction is updated using yet another exponential smoothing parameter, $\\gamma^*$. Rearranging terms, and including a trend dampening factor, leads to the error-correction formulation that will ultimately be implemented algorithmically:\n",
"\n",
"\n",
"<a name=\"eq10\">(10)</a> \n",
" $\n",
" \\begin{array}{r@{}l}\n",
" \\hat{y}_{t+h|t} & = & l_t + \\left(\\phi+\\phi^2+\\ldots+\\phi^h\\right) b_t + s_{t-m+h_m^+}\\\\\n",
" l_t & = & l_{t-1} + \\phi b_{t-1} + \\alpha e_t \\\\\n",
" b_t & = & \\phi b_{t-1} + \\alpha \\beta^* e_t \\\\\n",
" s_t & = & s_{t-m} + \\gamma^*\\left(1-\\alpha\\right)e_t\n",
" \\end{array}\n",
" $\n",
"\n",
"\n",
"A very astute and careful reader may have noticed that the 1-step prediction error will be the same if an arbitrary offset is added to the level and subtracted from the seasonal correction. This means that, while the final predictions will not be impacted, the separation of level from seasonal component is not unique, and may lead to equal/opposite drifts in both that are not desirable. This can be mitigated by assuming that the sum of seasonal corrections is zero over a repeating interval. To enforce this, a re-leveling adjustment can be calculated at each time step, added to the level, and subtracted from the seasonal correction:\n",
"\n",
"\n",
"<a name=\"eq11\">(11)</a> \n",
" $\n",
" \\displaystyle\n",
" r_t = \\frac{\\gamma^*\\left(1-\\alpha\\right)}{m}e_t\n",
" $\n",
"\n",
"Note that this adjustment can be applied with each iteration, or simply accumulated over all iterations, then applied at the end to obtain the same result. The latter may prove to be more efficient for certain interpreted programming languages that are known for slow loops, but which have vector-optimized functions for relatively simple mathematical operations.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Prediction Intervals](#SqDist-Algorithm---Contents:)\n",
"\n",
"Every forecast has an associated prediction error, or residual, $e_t$. Assuming a real measurement is available for time $t-1$ before time $t$, this residual allows forecasts to be made using the error-correction formulations described above. This formulation is convenient for causally smoothing a time series, but it also allows simulation beyond 1 step if $e_t$ is treated as a statistical value with its own distribution. By performing many simulations, each using $e_t$ drawn randomly from its distribution, so-called prediction intervals (PIs) can be constructed for any number of steps into the future. Useful percentiles can then be estimated from these PIs, providing a measure of confidence in the point forecast (i.e., the forecast that assumes $e_t = 0$).\n",
"\n",
"Any number of distributions for $e_t$ may be assumed. The most accurate might simply be to store every $e_t$, then draw a random sample from this distribution during a simulation. Such “bootstrapping” is not especially convenient in real-time processing, and drawing from a distribution that is not adequately populated can be problematic. For the formulations presented above however, analytic expressions exist if $e_t$ is assumed to be normally and independently distributed with a known variance $\\sigma^2$. For the damped Holt-Winters forecast given by [(10)](#eq10), the variance of the PI h steps into the future is:\n",
"\n",
"\n",
"<a name=\"eq12\">(12)</a> \n",
" $\n",
" \\begin{array}{r@{}l}\n",
" v_h & = & \\sigma^2\\left(1+\\sum_\\limits{j=1}^{h-1}c_j^2\\right) \\\\\n",
" c_j & = & \\alpha\\left(1+\\phi_{j-1}\\beta^*\\right) + \n",
" \\gamma^*\\left(1-\\alpha\\right)d_{j,m}\n",
" \\end{array}\n",
" $\n",
"\n",
"where\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"\\phi_{j-1} & \\text{is} & 1+\\phi+\\ldots+\\phi^{j-1} \\\\\n",
"d_{j,m} & \\text{is} & \\text{1 if } j\\bmod{m} \\text{ equals 0, otherwise 0; in words, bump } c_j \\\\\n",
" & & \\text{ each time seasonal corrections are recycled after } h \\\\\n",
" & & \\text{ grows longer than the number of seasons}\n",
"\\end{array}\n",
"$\n",
"\n",
"Here it is assumed that $\\sigma^2$ is known, which may not be the case. Since the variance is nothing more than the standard deviation squared, and the standard deviation may be thought of as a magnitude of an expected residual, one way to estimate and track this is using simple exponential smoothing. All that is required is to define a forgetting factor for the expected residual magnitude:\n",
"\n",
"\n",
"<a name=\"eq13\">(13)</a> \n",
" $\n",
" \\sigma_t = \\left(1-\\alpha\\right)\\sigma_{t-1} + \\alpha\\left|e_t\\right|\n",
" $\n",
"\n",
"where\n",
"\n",
"$\n",
"\\begin{array}{rcl}\n",
"\\alpha & \\text{is} & \\text{magnitude of expected residual forgetting factor} \\\\\n",
" & & \\text{(may be identical to baseline forgetting factor)} \\\\\n",
"\\left|e_t\\right| & \\text{is} & \\text{magnitude of residual}\n",
"\\end{array}\n",
"$\n",
"\n",
"In practice, $\\sigma$, and therefore $\\sigma^2$, should update via [(13)](#eq13) when 1-step prediction errors are available, and using [(12)](#eq12) for any forecast beyond 1-step. Note that [(13)](#eq13) is not an error-correction formulation, since $\\left|e_t\\right|$ is a random variable to be predicted, and linearly independent of the actual 1-step prediction error. It is possible to rearrange Equation [(13)](#eq13) into an error-correction formulation, but the notation might be confusing.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Spike Detection and Adaptive Baselines](#SqDist-Algorithm---Contents:)\n",
"\n",
"Imagine a threshold residual is defined as a multiple of the standard deviation. Any $e_t$ with an absolute value exceeding this threshold is treated as a bad data point. If this bad data point is treated as a gap (i.e., forecast is made, but level, slope, and seasonal correction parameters are not updated), $\\sigma^2$ will grow with the prediction interval simply due to the dynamical nature of the prediction equations. If the threshold residual is exceeded repeatedly, eventually the standard deviation will grow large enough that $e_t$ no longer exceeds the threshold residual, and the sequence of observations previously treated as bad data will once again be used to update the level, slope, and seasonal correction parameters. In effect, a large DC shift in observed data, while initially treated as bad data, will eventually be recognized as a new baseline, and the adaptive algorithm will converge on it according to [(10)](#eq10).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [Putting it All Together](#SqDist-Algorithm---Contents:)\n",
"\n",
"The purpose of all the preceeding mathematics is to specify a potentially time-varying baseline, and update a potentially time-varying set of seasonal corrections. In the geomagnetic time series context, these represent so-called secular variation (SV) of Earth's internal main field, and the solar-quiet (SQ) variation, or daily magnetic tides caused by quasi-stationary geospace electrical currents fixed spatially relative to the Sun. So-called magnetic disturbance (DIST) is what remains, or the $e_t$ term.\n",
"\n",
"We developed software to implement these mathematics, and decompose geomagnetic time series (or any time series really) into its SV, SQ, and DIST constituents. It is integrated with the USGS' [Geomag-algorithms](https://github.com/usgs/geomag-algorithms) software packageg as a new algorithm class [geomagio.algorithm.SqDistAlgorithm](https://github.com/usgs/geomag-algorithms/blob/master/geomagio/algorithm/SqDistAlgorithm.py). This is mostly a wrapper for the additive() method, so-named because the theory presented above assumes that the seasonal corrections are independent of the baseline level, and can simply be added.\n",
"\n",
"The additive method can be extracted and used in a stand-alone mode, but to encourage and facilitate interaction the USGS' robust geomagnetic data ingest and analysis framework, it is recommended that the full class be used as much as feasible. For those users not interested in working directly with Python, geomag-algorithms provides a command-line tool that can perform most geomagio.algorithm.SqDistAlgorithm actions, and generate output data in several standard formats. A users' guide for both is described in [SqDist_usage.md](SqDist_usage.md)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [Functional Tests](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The purpose of this section is to demonstrate that the algorithm works as expected, and to a lesser extent, demonstrate its utility with realistic usage examples. While some material here might be extracted to generate unit tests for the algorithm, these are primarily *functional* tests, and may be more complex than one might want to incorporate into an automated testing framework. Explanatory markdown, inline comments, or both, should tie different tests to the Algorithm Theoretical Basis above as much as possible."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Imports](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# standard imports\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "cannot import name ChannelConverter",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-d27d693f820e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# import SqDistAlgorithm class from geomag-algorithms package\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgeomagio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malgorithm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSqDistAlgorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# import some mid-level classes from ObsPy to construct test data objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mobspy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUTCDateTime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/arigdon/anaconda2/lib/python2.7/site-packages/geomagio/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mChannelConverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStreamConverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTimeseriesUtility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: cannot import name ChannelConverter"
]
}
],
"source": [
"# import SqDistAlgorithm class from geomag-algorithms package\n",
"from geomagio.algorithm import SqDistAlgorithm\n",
"\n",
"# import some mid-level classes from ObsPy to construct test data objects\n",
"from obspy.core import Stream, Trace, UTCDateTime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Test Configuration](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# define assert_almost_equal\n",
"assert_almost_equal = np.testing.assert_almost_equal\n",
"\n",
"# configure to test zero-step predictions of 4 \"season\" cycles\n",
"m=4\n",
"t=np.linspace(0,2*np.pi,m+1)[:-1]\n",
"hstep=0\n",
"\n",
"# initial slope is 0; average age is infinite\n",
"b0=0\n",
"beta=1/np.inf\n",
"\n",
"# initial trendline is 0; average age is 12 steps\n",
"l0=0\n",
"alpha=1/12.\n",
"\n",
"# initial seasonal correction is sinusoid; average age is 12 steps\n",
"s0=np.sin(t)[0:4]\n",
"gamma=1/12.*m\n",
"\n",
"# standard deviation of unit-amplitude sinusoid\n",
"sigma0=[np.sqrt(0.5)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Test 1](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASS\n"
]
}
],
"source": [
"# predict three cycles ahead given l0 and s0, no inputs,\n",
"# and assume PI only grows with trendline adjustments\n",
"\n",
"# instantiate SqDistAlgorithm object\n",
"SqDist_01 = SqDistAlgorithm(alpha=alpha, beta=0, gamma=0, m=m, hstep=hstep,\n",
" s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
"\n",
"# define input stream\n",
"yobs_01 = Stream()\n",
"yobs_01 += Trace(np.zeros(12) * np.nan)\n",
"\n",
"# process input stream\n",
"SvSqDistStream = SqDist_01.process(yobs_01)\n",
"\n",
"# test process outputs\n",
"assert_almost_equal(SvSqDistStream[0].data, \n",
" [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,\n",
" np.nan, np.nan, np.nan, np.nan, np.nan, np.nan])\n",
"assert_almost_equal(SvSqDistStream[1].data, \n",
" [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0, -1])\n",
"assert_almost_equal(SvSqDistStream[2].data, \n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
"\n",
"# also test certain states within SqDist_01\n",
"assert_almost_equal(SqDist_01.yhat0, [])\n",
"assert_almost_equal(SqDist_01.s0, [0, 1, 0, -1])\n",
"assert_almost_equal(SqDist_01.l0, 0)\n",
"assert_almost_equal(SqDist_01.b0, 0)\n",
"assert_almost_equal(SqDist_01.sigma0, 0.73361737)\n",
"\n",
"print 'PASS'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Test 2](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASS\n"
]
}
],
"source": [
"# predict three cycles ahead given l0 and s0, no inputs,\n",
"# and assume PI only grows with seasonal adjustments\n",
"\n",
"# instantiate SqDistAlgorithm object\n",
"SqDist_02 = SqDistAlgorithm(alpha=0, beta=0, gamma=gamma, m=m, hstep=hstep,\n",
" s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
"\n",
"# define input stream\n",
"yobs_02 = Stream()\n",
"yobs_02 += Trace(np.zeros(12) * np.nan)\n",
"\n",
"# process input stream\n",
"SvSqDistStream = SqDist_02.process(yobs_02)\n",
"\n",
"# test process outputs\n",
"assert_almost_equal(SvSqDistStream[0].data, \n",
" [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,\n",
" np.nan, np.nan, np.nan, np.nan, np.nan, np.nan])\n",
"assert_almost_equal(SvSqDistStream[1].data, \n",
" [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0, -1])\n",
"assert_almost_equal(SvSqDistStream[2].data, \n",
" [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
"\n",
"# also test certain states within SqDist_01\n",
"assert_almost_equal(SqDist_02.yhat0, [])\n",
"assert_almost_equal(SqDist_02.s0, [0, 1, 0, -1])\n",
"assert_almost_equal(SqDist_02.l0, 0)\n",
"assert_almost_equal(SqDist_02.b0, 0)\n",
"assert_almost_equal(SqDist_02.sigma0, 0.78173596)\n",
"\n",
"print 'PASS'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Test 3](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASS\n"
]
}
],
"source": [
"# smooth three cycles' worth of zero-value input observations,\n",
"# assuming only the trendline varies\n",
"\n",
"# instantiate SqDistAlgorithm object\n",
"SqDist_03 = SqDistAlgorithm(alpha=alpha, beta=0, gamma=0, m=m, hstep=hstep,\n",
" s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
"\n",
"# define input stream\n",
"yobs_03 = Stream()\n",
"yobs_03 += Trace(np.zeros(12))\n",
"\n",
"# process input stream\n",
"SvSqDistStream = SqDist_03.process(yobs_03)\n",
"\n",
"# test process outputs\n",
"assert_almost_equal(SvSqDistStream[0].data, \n",
" [ 0, -1, 0.08333333, 1.07638889, -0.01331019, -1.012201,\n",
" 0.07214908, 1.06613666, -0.02270806, -1.02081573, 0.06425225, 1.0588979])\n",
"assert_almost_equal(SvSqDistStream[1].data, \n",
" [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0, -1])\n",
"assert_almost_equal(SvSqDistStream[2].data, \n",
" [ 0, 0, -8.33333333e-02, -7.63888889e-02, \n",
" 1.33101852e-02, 1.22010031e-02, -7.21490805e-02, -6.61366571e-02,\n",
" 2.27080643e-02, 2.08157256e-02, -6.42522515e-02, -5.88978972e-02])\n",
"\n",
"# also test certain states within SqDist_01\n",
"assert_almost_equal(SqDist_03.yhat0, [])\n",
"assert_almost_equal(SqDist_03.s0, [0, 1, 0, -1])\n",
"assert_almost_equal(SqDist_03.l0, 0.0293435942031)\n",
"assert_almost_equal(SqDist_03.b0, 0)\n",
"assert_almost_equal(SqDist_03.sigma0, 0.61505552)\n",
"\n",
"print 'PASS'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Test 4](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASS\n"
]
}
],
"source": [
"# smooth three cycles' worth of zero-value input observations,\n",
"# assuming only the seasonal adjustments vary\n",
"\n",
"# instantiate SqDistAlgorithm object\n",
"SqDist_04 = SqDistAlgorithm(alpha=0, beta=0, gamma=gamma, m=m, hstep=hstep,\n",
" s0=s0, l0=l0, b0=b0, sigma0=sigma0)\n",
"\n",
"# define input stream\n",
"yobs_04 = Stream()\n",
"yobs_04 += Trace(np.zeros(12))\n",
"\n",
"# process input stream\n",
"SvSqDistStream = SqDist_04.process(yobs_04)\n",
"\n",
"# test process outputs\n",
"assert_almost_equal(SvSqDistStream[0].data, \n",
" [ 0, -1, 0, 1,\n",
" 0, -6.66666667e-01, 0, 6.66666667e-01,\n",
" 0, -4.44444444e-01, 0, 4.44444444e-01])\n",
"assert_almost_equal(SvSqDistStream[1].data, \n",
" [ 0, 1, 8.33333333e-02, -9.16666667e-01,\n",
" 0, 6.66666667e-01, 5.55555556e-02, -6.11111111e-01,\n",
" 0, 4.44444444e-01, 3.70370370e-02, -4.07407407e-01])\n",
"assert_almost_equal(SvSqDistStream[2].data, \n",
" [ 0, 0, -8.33333333e-02, -8.33333333e-02,\n",
" 0, 0, -5.55555556e-02, -5.55555556e-02,\n",
" 0, 0, -3.70370370e-02, -3.70370370e-02])\n",
"\n",
"# also test certain states within SqDist_01\n",
"assert_almost_equal(SqDist_04.yhat0, [])\n",
"assert_almost_equal(SqDist_04.s0, [0, 0.29629629629629639, 0, -0.29629629629629639])\n",
"assert_almost_equal(SqDist_04.l0, 0)\n",
"assert_almost_equal(SqDist_04.b0, 0)\n",
"assert_almost_equal(SqDist_04.sigma0, 0.70710678)\n",
"\n",
"print 'PASS'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Test 5](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASS\n"
]
}
],
"source": [
"# smooth three cycles' worth of sinusoid input observations,\n",
"# assuming only the seasonal adjustments vary, starting at zero\n",
"\n",
"# instantiate SqDistAlgorithm object\n",
"SqDist_05 = SqDistAlgorithm(alpha=0, beta=0, gamma=gamma, m=m, hstep=hstep,\n",
" s0=s0*0, l0=l0, b0=b0, sigma0=sigma0)\n",
"\n",
"# define input stream\n",
"yobs_05 = Stream()\n",
"yobs_05 += Trace(np.concatenate((s0,s0,s0)))\n",
"\n",
"# process input stream\n",
"SvSqDistStream = SqDist_05.process(yobs_05)\n",
"\n",
"# test process outputs\n",
"assert_almost_equal(SvSqDistStream[0].data, \n",
" [ 0, 1, 0, -1,\n",
" 0, 6.66666667e-01, 0, -6.66666667e-01,\n",
" 0, 4.44444444e-01, 0, -4.44444444e-01])\n",
"assert_almost_equal(SvSqDistStream[1].data, \n",
" [ 0, 0, -8.33333333e-02, -8.33333333e-02,\n",
" 0, 3.33333333e-01, -5.55555556e-02, -3.88888889e-01,\n",
" 0, 5.55555556e-01, -3.70370370e-02, -5.92592593e-01])\n",
"assert_almost_equal(SvSqDistStream[2].data, \n",
" [ 0, 0, 8.33333333e-02, 8.33333333e-02,\n",
" 0, 0, 5.55555556e-02, 5.55555556e-02,\n",
" 0, 0, 3.70370370e-02, 3.70370370e-02])\n",
"\n",
"# also test certain states within SqDist_01\n",
"assert_almost_equal(SqDist_05.yhat0, [])\n",
"assert_almost_equal(SqDist_05.s0, [0, 0.70370370370370372, 0, -0.70370370370370372])\n",
"assert_almost_equal(SqDist_05.l0, 0)\n",
"assert_almost_equal(SqDist_05.b0, 0)\n",
"assert_almost_equal(SqDist_05.sigma0, 0.70710678)\n",
"\n",
"print 'PASS'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [Synthetic Data Demonstration](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [Construct synthetic time series](#SqDist-Algorithm---Contents:)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11365b390>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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rse4RodH3HtUTIvJHxphHReRjIvI71toPzX6T1mDGGHdzS+FsbOiIIKc5tG5s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\nMEPneXDGdDVvrytK25TKGdt53cIzStuUyhnT+by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33abj/Pnn3UAYyfnZszc6j+E89pg7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\nqPivlQT51Mfx73cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11358fed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# first 50 \"days\" at 100 samples per synthetic \"day\"\n",
"t000to050 = np.arange(5001)\n",
"syn000to050 = 10. * np.sin(t000to050 * (2*np.pi)/100.)\n",
"plt.figure(figsize=(16,4))\n",
"plt.plot(t000to050/100., syn000to050)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1159d2610>]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Ld3+TiJ6x1v6q/4Ix5mjh+3+biJ6u+mV0ef4Yc+QM0c5Ix1LHQXOFxsnRufad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\nqTsWInXe5Cpm3xmPZfaytHGOcr1Q5+5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K86QkJ8d7hC+AWJxYCHm3e67O0a4XVQE1KEVq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\noaGhoaGhoaGhoaExWPz/aRqqgW+kGgwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11367ee10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# next 50 \"days\"\n",
"t050to100 = np.arange(5001,10001)\n",
"syn050to100 = 20 + 10. * np.sin(t050to100 * (2*np.pi)/100.)\n",
"plt.figure(figsize=(16,4))\n",
"plt.plot(t050to100/100., syn050to100)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x115bb0250>]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Roa5F7euTVEodAZ0UR1cEKeI2Gk6cyB+PlBYn6m9mEyfqfJqb9UX01Knx7mah\n04NVnKzOb1oX4tZW2VAqLY3/f2pqRDynZWjoOK26jq1L0RqOZdqBGJB66ePHgXnz5Gs6rYQQQsj4\ngqI1ht27gZUr5fEVVwDPPWceIyo1zlUtqq47mhRDoTv2xkVNq0la72hwSJWAjnOxo+LEOZsmIrGu\nLnrTwlRsxo1OMo0zY0Z28VtSIgIuaoany/TgQtfGJr0ndOIUF8e/LuFYLua9DnUXYp37RJgzZ6Tp\n1aRJ8vW8ebIpaMujjwJvfWv0uCdCCCGEjDwoWmPYvTs3X3XVKvnalNOn8xdsruar1tbquS8qRtJC\nXUcAqzmlSd1tXY2qAcZfWu+JE2bpuLNnA01N0XFMxGZUHN+3E7/hzZi+PhEbOrWfirhrMUlIhXHZ\nhThO5J08qecgpzmTug67znvLVU1r0mutK3yTYtk4reGGTqou1raz+733Al//OvCTn9gdTwghhJDC\nQtEaw+7dwOWXy+OROF81acZmVAxX81WTutvW1EitaVL9r0nXX1cNlIayEZNp7WfcqCJTp9WVaJ0z\nJz9OR4ekq1ZWZjufkydF5JSU6MeJcmy7u6UzsW6DKZfpwXFxWlrcNGLSbeg0kua96orWgYHo91hV\nVW7Eky7hUovKSqC8PN19juLgQTmvT30KePhh8+MJIYQQUngoWmM4fFiafQAjU7QCbmtR0xaiOl1p\ni4rSnRjd7rauHNKhdmxtxWbYITJ1WlUzm+B8XRtnM0psmp5LUhyT10bFCb8/1PnojgNKyhxw1Riq\n0E5rWpyeHhH2SbXVhRatZ88CkyfLWK0gaTXrUTQ05OpZFfX1dl2In3oKuPFG4PrrgaefNj+eEEII\nIYVnzIrWU6fsU8eAwTv7wylak9JPXXX91XVaddMYk1L/Rkp6sO+nd20Nx4mr2TQReHEOkan4LSsT\ngRI8p1OnpJmRibM5c6bECKaZ24pNV6I1a5zqahFwYcf/wgUR+boOctL7SzddOe5cFK5G56j3Z5Kw\nd9GISTXLCm6WxJHkRsfVQMcRN+/VRrSqea+rVwP79wOdneYxCCGEEFJYxqRo3bZNFkX/9m92x3d2\nykJTibRZs6R+1GRx098vx4Rno9o4rXFi0ZXTqiN+dRsoJYlNE6E41Gm9bW0ygqi8XC9Oba38/cNj\neEzTg4GhE3g2MUpLJa07+BoNp2idNSt7HOX4h//uKo6uY5tW06ojNouK5B7goqFTVsfWRSMmlTbe\n3p4tjo2B3FkoAAAgAElEQVTTGiVajx/Xj6FQ817Ly4FFi9iFmBBCCBkNjEnR+o1vAHfdBXzxi3bH\nqwWSWtwWFUlq2pEj+jFaW8VlCXeVHc3pwVlrUTs6pAOojlB03UAp7LqbOpueF/23sxFmYYeop0dq\ngXVd32CcrKIVEMEZPB9bsXnixGAH7sQJiW16LkOZZmw6xifqfdHfr7+JA7gRv2miVcex1c3McNnQ\nKS6F2tRpjXq/RtVj66CcVoCjcwghhJDRwpgUrb/8JfChD4mbZrMTH7WrX19vtkBKmq/a3KyXuuz7\n6aJVd1SNi0ZMOqLKVVqvqziVlbLpcP784OdtHNKodEQXTqtKo9SZOxuOEzyf48fzRyzZnE/U9Z/G\nhAlSvxhMP3UpNk3Fb5xjaxInTpzpzFYNx4m7lnUbOrlwWnXTg9PcXxe1saaiNUoAm8YApO67oUEc\nVkD6FlC0EkIIISOfMSdau7rEEb3iCuCqq4Dt281jRC3aXTmkFRXivl64kB7j/HmpT5w4Mfr7uk5r\nWk1rZaW4R0npzyZOqyuH1EV6cNw52TQbciVaw3Gamtw4pIcOSf21KeE0y0OHcot60/MJCsVjx2Sz\nJ0sMwO71cREnTpyZjN9JiqNi6TR0ctGFePJkoLc3uWtvV5fU306eHP//1NZmr401TQ92Ne+1qUl+\nttpwoNNKCCGEjA7GnGjdu1d2z0tLRbjaiNYoR2Y4Gii5qEXViaPTzXM4RtUkzdo0SaWNimWaHgwM\nXS3q4cPAggVmMQA5Jtgg7PBhO9EadptsxW99PXD06OA4qgO3LiMpPTjOyTMVrYVID9ZxWj0vPY5u\nQ6dCOq19fZI1E37P28x7DXchnjdPnrPl6ac565UQQggpBGNOtO7eDaxYIY9XrQJ27TKPEZVOOxIb\nKOnE6emRf0nOiYqVtBB10YjJpA5wyhRxhcKNj3xfUip13ClFnGjN6pD29IgbHm62lUZ9/eCF8pEj\ndiJx6dLBYvPwYTuHNBjH9+3F75IlMgMTkNrWI0fMxfjkyXLsuXO5544fN3dsXaQHR50LoJ/Sq4jb\nXLp4Ueq8da4fFzWtunF00oyzilYTwXnmjDQLC/cHsEkPDnchtu1ADMi18YpXAK98pX0MQgghhOgx\npkXrokWcr6pipHVMdTWqxpXTGuf+njsni9dJk/TiANGLWxfpwQ0NIoJMa1HDKYm2InHpUhnZ4TLO\nyZOSjj5lil0c9Xs1N0vdp+54GYXniahQjq2tiI5yWk3Tg+Oab7lyWlU2hs71k3bPcDXvNa2UQMVw\nkR6sO+81bpPANMUYyC/9yCJan3pKrvG776bbSgghhAw1Y060HjwoogAY3vmqLkRrmuuhE8eFQwq4\nm6/qIq3XVGyq5lfhOFnTem0d0sWL5bpUs1FtxeaMGeJGnz0rI0i6uszEVNT57N9vntKrCIpx27pY\nFUc5ti0tInzTMgXCxKUZ23QzDr/vXTmtJnEmTRJnNpx5oHA1OkcnjovRObpurYoTlVlRWyvXfnDG\ncBrHjg1OD542TbIl4l7XJDZvBm6/HbjxRuAPfzA/nhBCCCH6ZBatnueVe573pOd5z3qet8PzvA9f\nen6B53lbPc/b63nedzzPK8l+uukEd9JnzpT0O52mR0FciNYkx8Kk629aTevp08mdiE26/qY5rYWc\nrxp3Tjaidd48Way6iBOs2bQVmxMnyu+mYtnG8TxxN/ftA55/XtLhdWeQBqmokL/bkSNSA752rXkM\nYLBoHW7xG5Ue3NjopgtxU5NZl+a497uJY6vqUeMcTt2U+bR7j8v04CRRbipao+KUlIjT2dqqFwfI\nd1o9L7+hmS47dgCrVwPr1kltKyGEEEKGjsyi1ff9HgA3+75/JYC1AO70PO9aAP8M4F98318OoA3A\nm9Ni9fYCr3oV8OCD9ucTXJQUFQHz55vNVwWiBedITA+eMAEoKxNhbhtDkSRaOzulfksn1bO6WjYJ\nenvtz0XhymkNi01AhIdNnJaWnCtjKzaBXEpuV5eci22cK6+UBfOzz8pjW665Bti6Fdi2zT7OggXy\n9zl/Ptv5LF6cc1ptRWtdnYiZnh75+uxZ2dypqTGLE+W0NjaaidakLsRZa7NNY6U1b0sbj6UTA5DX\nvatL7gdRVFXJfSXqPhHG5eicuHFmNqJ1504RratWyXv54kXzGIQQQgjRw0l6sO/7alhKOYASAD6A\nmwH84NLzXwfwqrQ4P/whsGUL8IEPiEgypb9fFpjBBWW4w6oOUQu3KVMkfnjep0kMhauuv0C6c+JC\ntKoUYx0Xr6go/vfTqZcLEjeqxjStd/78wU7ruXOyoNZJpwxSUiLiUgkqm0ZDissvlyZhO3cCy5fL\n5oMNL3oR8MQTkp64bp1dDAC4/npJd9y8Gbj2WrsYZWXSsfupp0T8XnWVXRwXjm1JiWwyqPf+wYMi\nfk2d6Cin1bQxlIv0YBUn6r1+4YLcm3TqvF01YkqraVWZGXGvd1GRmzRj07rWqLIAG6e1vx/YswdY\nuVKu+zlz8jfGCCGEEOIOJ6LV87wiz/OeBdAM4FEABwG0+b6vpOdxAKmJeb/+NfDBD8ri6/nnzc+j\nuVk6cZaX556bO9dsQdLdLbv/4QWg54mToburX4iaVp1YZ85kTw924ZAC5nWkrtKD58+XBaVKoz56\nVMSmTSqtSscFckLIhuuukw2aZ58VoWfLrbdKE5gf/hB4yUvs47zylcAXvyhukW16MCAi+uGH5f1r\nK1ovu0zEvO9ne31cOLZRTqupaFUbS+E0flfzXpXLqnM9u2rElCY2C9WF2MRpHRiIPq+6OvOGTseP\ni2uvaq0575UQQggZWpzUmV4Sp1d6njcFwI8AXB71v8Udv3HjRgDAj34EXHvtBlx33QY884z54jkq\n9cs0rTfJVVRiTGfxm1bT6spp1Un300k9dSla45oxmQrOmTPFzQjHWLpUPwYgLnlJiaSL1tZmS+td\ntkxEa3+/OKWrVtnFufFG4D3vkYX0XXfZxQBEkL/3vRInfO2bsHixvP/q6+3EvOK1rxWn9pWvtOtA\nDMiGQn+/CINt24DPfMYuzlDUxnZ0yLlVVenHmDBBNsHC2RcnT4rjrosLx9aF0zp1aq4BUngMjUkc\nk9rYuNRnE6e1rU3+DsFNTRXDZnROsKFTeNYxIYQQMt7ZtGkTNm3a5Cye0+ZIvu93eJ73WwDXAaj2\nPK/okqCtB9AUd9zGjRvR0wPcfz9wzz2yMNy+3fznR9WazZwJPPecfowksamb1tvVJY5VXMdTl6I1\nLdbp08DVV6f/rLT0YNOuv+FY58/LItekC+y8eYPnmQJ2TiuQSxGurc2W1rt6NfCLX4iDN2OGmXgJ\nsmCBCOAf/Qj4ylfsYigu7flk5pWvzB7jmmuAxx6T18kWz5N05f/8T6Cvz/5vFRQSBw/aOb/19SKe\nFY2NdsJepaAGhdyJE+Y1rVlrY3XuF2liUzVAamuLvy/o1sYW0mmNizNjBvDMM3oxFA0Ng0XrggXZ\n0oM7OmQDwHREFCGEEDJS2bBhAzZs2PDHr++7775M8Vx0D57meV7VpccTAdwGYDeA3wB4zaX/7R4A\niZPsDh0Sp6i0VNIBbdKDT5/OX5QMRwOltBpQV92DgfRRNbqCUwnNqE7ELpzWlhYRmyaL/aiuvzYd\nYIHBDbmyiNYbbwT++7+BJ5+0T39V/Pznct2bbAiMBm691W70TpDXvAb40IeA173O3vldvlzmNgOy\ncWUjpBcsEIHS1ydfHztm52hHzQM9ftwsVlJ6cCGdVhUn6b6jk2asex9Mq2nNKlptnVZX8177+uRa\nvemm5E7whBBCyHjGRU3rLAC/8TxvO4AnAfzK9/2HAXwAwL2e5+0DMBXAfyQF2b9fnCdA0voOHTI/\nEVfzVeMWba4c0qoqcWNVZ9Mo+vslBW/q1OSfpTOqRkdwVlTIhsG5c/YxFFGLQBuHVHX1VE25fN9e\ncF52WU7A7N2bm+VryqJF8jq9//3AHXfYxVBUVdmnKY91Xv964DvfAT76UfsYV18tnZW7u6VG1qZW\nt7xcXEzl+B84YHftzJkTPYLHtKFTlMgzSQ9WzY+ixFFfn8z71emw7Kqhk879NG3ea1pTKEXc65S2\n8RdF2GmdM2ewI2/C5s3yOXX6tFynhBBCCMnHxcibHb7vX+X7/lrf99f4vv9Pl54/7Pv+tb7vL/N9\n/3W+7ycOBNi3Lyda58yRhUqSqIvClWgd6lpUz0vvnHn2rIiakpQEbleiNSmW7oxWhatRNRMnSl2k\nOqfWVkmhixujkcTq1TJXERDXzba5j+cBH/uYXKtvfKNdDJJOURHw539uXxcLyHU4fTrwzW9KqrBO\nd90ogmnGWURr0Ik7d04avplcy3H3H5P04AkTRIhHbU6dOSOCNa5ONYgr0ZrmtF64IAI7Lm1W160F\n4l8nF06r7dgcAPjlL4FXvAK47TbJ4iCEEEJIPk66B7tg//5cg52SElnkhVND04gSaDNnyoJEd4SO\nq66/LmpRs46qAXKpyjrExVKpvbq46voLDK5rzVqL+txz8np0dNjHAYA3vAHYtEncaTKyuesu4C1v\nyVavG+xC7Eq02tTGxokr09E5cfce3dRgIF1w6tS06ghO5bLGvU66bm0wVhhbpzWcHtzUZJfe+/zz\nMtN4/XrJDCCEEEJIPiNGtB4/PjjdauFCu/mqYYFWXi7Nf3RTyNK6/rqqRS2EaO3uFrda12GKi2Uq\nOKNmW9qK1gUL3MxFXb1aXu/vfEeaBRWNmCufDCUf/CDwd38H/O3f2sdYulRSygFJ37zsMvMYYdFq\nOjYHiM8aMXFagXjBqSM002IoXDmtaXF0mzkB8aK1ulocXZPMnrBoragQF7u1VT+GYudOuT9ddZWM\neCKEEEJGKr4PfPWrubV5IRkxS/empsENdhYuzDXO0SVOcJqkCLtKD87qMui6HnV14rREkdYQKowr\n0RrV9ffECTvRumJFbuzN4cPSUMmG4mLgpS8F3vlON11yyeigtlbSuW27PAM5B+z0aRElqozBhCjR\nGu50nkZtLdDZKfXwQZqb3cx7NXFade5fWcd16ZxTba3c53QczjjR6nlmacY9PZJeHT4vm2ZM7e1y\nTalu4vv3sxkTIYSQkcvjj0sG2zveUfifPWJEa7grbFTn2DTixGKSsNONAeinohXSaVVxotKfbbr+\nuhCtM2dKTW53d+658FxDXVaskHmogDRSMplrGeZjHwP+/u+BN7/ZPgYZf6xfLw7Yli2Sxmnj0i9Y\nIJsuSpAcPChpxyZ4Xm50ThBXDZ10hGYwRlr3YFdOa9I5lZeLw9nRkRwHSE6jNpn3qn638GagyeeM\nYs8euacVFYnjO2GCeQxCCCGkUPz4xzLdYfNmGX1XSEaEaO3tlV88uMgxbaAExIs0kwVJoWpa0xZ9\nuoKzrEzSf6MuHNP5qlGi9cIF8/mqRUX53TRtU3tXrsyJ1iwNlABZ2P/TP8nCkBBdpkyR9M23vAW4\n8067GDU1ct2pe9r+/Xa1sWHR2tEhXX9dNHQy2ZxKEpyq83navUfFSHIWdcSvyeicuDRqk3mvLrsQ\nHz06+L64ZEmu6RchhBAy0vjNb6R54Lp1wFNPFfZnjwjR2twsi4lg10pT0drVBVy8GF2/6Uq0Tpki\nAjvoIJrGUKQtcEzqy5K6/po6reFdfpv5qoDUeymnfGBAHtuk9i5bJgu7s2fFlVi1yjwGIVn56EfF\nEfurv7KPsXy5dEkHRJioxnMmhEfn2DR0ihOcrkSrbufzigq551+4EP//FGp0jslnhOt5r8EMlEWL\n7Ma9EUIIIUPNxYuSKbZihYwR3L69sD9/RIjWcGowYC5ak+o3dRckvp883sXz9BZIuqI1aYFjkqrn\nSrS6bKAUTO9ubpZF7MSJ5nHKy4EXvQi47z5xXW3HlhCShdtuk47RJpkLYZYtk4ZOAwMiXm1Fa9aG\nTnGC88QJuQdkiQGYdyEuRJpxT4/UA8c50iaCM86xddGFOMvonN5e4GUvA770JbvjCSGEkCQOHpTP\nqYkTJfPxuecK+/NHhGgNN2EChme+6rlzkm6blD6qE0vHJY1yNcMxsorWkyf1F4+ALJ7CDZSUC27K\nokW5NLcsXX8B4LWvBT7zGeD1r7ePQchwc9llUpe9f7+I36lTzWO4EK1xAs2laDWpjc06OsekC3Gc\nIz19un6H+aF0Wm2aOSkeeURStT74QUkZJ4QQQlyya5e4rICsaVT2WKEYMaI13ElzxgxZROh++KaN\nqnHZQClpgZTm1irq6obeaTV1Sevr5W/R328fQxGsRT14ULpB2/JXfwU89JB0/iVktHL99cATT0gn\n4vXr7WKEa1ptROvs2fkZFYDZe72qSkoyosbEuO5C7EK0trQkb76ZzHt1WdMaNe/VVrT+5Ccy3mnW\nLGDbNrsYhBBCSByqeSBgN5o0KyNCtEaNQykpEeGnu3OdtCPvelRNUqyOjlxHyyR00oN1x1jExVL1\nqLqUl0vDmKAD3Nio774ECYrWnTuz1aIWFcm4mtJS+xiEDDfXXCO7kg8+CNx0k12MBQsGjwKzGQMV\nJYwGBuQeonu/8LzcuJkwpunBWUfn6DRiSss6cTHvdbid1m3bpJTi6quBZ56xi0EIIYTEEWweWFcn\nPSnOny/czx8RojVOoA3HfNVCjapRTkVUUyffT24aEsaV0wrkpwiHu1vqsnSpxOnuBp5/PlvXX0LG\nAmVlwD33AL/7HfC619nFWLZMhK/quLt/v3ltbF2d3KeCWSynT0ujubIy/ThxQs+kiZyrmtas815N\nRauLmtaurvx5r7aitb8feOEFSdtSc4UJIYSQIPv363/WRRHMDvK8/I30oWZEiNY4oVdo0epqVI3O\ngs3z4hc5qptmZWV6HGBoRattPWpZmeS7P/207PqvXWseg5Cxxmc+YybqwtTWShaKer/bdCEuKZGf\n39yce87mXpE079WF09rTI5teVVX2MRRpr7krp9VEtKosluDc31mzJIZpTeqhQyKkJ0+WLJcXXjA7\nnhBCyNjmzBnZ+H71q+1jhEtaCp0iPKJF68yZgxdWNjEAaXjS3p6+EHDhtLqoRTVJDY6L4/t2TZRc\niVZAUno//GHp2Bm8yAkZrxQV6W9GxaHc1o4O+RduYqdDeHSOSRMmRVIXYl0BnJTaq+6laeN8TBox\n2ZxHmDjRWl0tHYqj6nzj4oRfp9JSKdEwrY3duVPEKiCzXg8eNDueEELI2Oahh2S+6gsvSBalDWHR\nSqc1gKv5qsXFshBobbWPoXtOrrr+mojWurp8R7qjQxZApgvk4Kianh45F5tFMQDcfTfw+OPAX/+1\n3fGEkHyWL5cPnWefBdasGezU6RJu6NTQkN8ML42hHp2j60i7EK1Tp8rnw8BAchxVuhEVS41Eyyp+\nbRo6HTokYhWQ176jQ1KPCSGEEEA6zN91F3DzzTLCz5T2dvmMDI6Oi2vsOFSMCNEat6AwFa1pO+mu\nRtW4FK1RY29MR9Wo2X6qzg2w7/qrZkkCuYVsSYl5HEAW152dwDveYXc8ISSfa64BtmyR8SZXX20X\nI1w7eeSIeYfvuHuqidOaVG6hm2asK1qT7sulpTIDuq0tOU5bG1BREd9oL63BXpC4LsRZGzp5HrB4\nMd1WQgghOXbuBK68Uv7ZzFc9flxc1mD2k+l40qyMCNHa1iZOaBgT0Zq2KNEVrWmCs67O3XzVuLE3\npunBkyZJ599gJ0/b+aorV8qFDYh4Na2XCzNxYrbjCSGD2bBBMhh+/WvghhvsYoSdVhvRGrfp1tzs\nbt6rjmitqUkv/3A1OictC8bkM8tVbSyQn7I1d64sMGw4fx544xuBJ5+0O54QQsjIor9fyoouu0wa\no9qI1qjP9nEpWquqot08V+nBgH6HyTTBqTOqptDpwYDssgdrUU3cjiALF4r47eiQrr9r1pjHIIQM\nHZddJp1+N20CXvYyuxjh5gmHD5vXrkd1uj13TjI+Jk/Wi6FT05qGTvlHoUTrcKUHHzvmbt7rt78N\n/OAHwPveZ3c8IYSQkYVq1ldZKSMo9+wxjxGls8alaM3a9df35cWsrY3/f1w5rUmjagA381VN04MB\nWbCoWlRAiqxN5zcCUh93+eXitlK0EjLy8Dzg0UdlDvKkSXYxVDMnhY3TGlXLoupZ05onKaZOBc6e\nlV3gMK7nvboQrXEpvQrThk5R2TA26cENDe7mvf74x8BXviJd3zs67GIQQggZOezeLWt7QD67W1tF\ny5hA0XqJuMWErmg9d07SY+PqjHRj6YjWpFE1gNlCy1X3YMDdfFVABtRv2gRs3gxce61dDELI0DFz\nJrBokf3xS5fm5r12d0t2hWnDtShhZNqFuKREXOOoWlJXorW3V8aIBZtHRKEjOHWc1qzpwaZOa3e3\nvH5BAWwrWn0f+MMfgNtvB1avlmZfhBBCRjeHD0uvA0Cyk+bONe8gHKWRpk2Tz5/eXjfnmcaIEK1p\nTmuwwVAULrr+9vdLXdTUqclxgOQUYZPUXtdOa3hUjY3TCkhL7Pvuk02AZcvsYhBCRi7V1ZIm1NQk\nju2yZfJBZkJdXf5MUZuyhDihZzLPNq0LcW1tepdl3RISV/NeXTViOn5cRGrw97MVrapGua4OWL9e\n5mwTQggZXo4elRRfW1zMV43SWsXF8pmV1OvHJSNatFZWirN54ULy8TqLmzTRevaspP7qLNziGpAA\n5k5rVJzhdlpvuUXG1Hzuc/ppfoSQ0cWyZTI6x7YMoLRU7t3Be5jNvNc4hzMtFTccI+7+XsjROcNR\n09rQIB3kg9iK1p07pd7J8+zrngghhLijrw+47jr5Z+touhCtcX0mCpkiPKJFK6CX1qvTsCMtjkkD\npbiuv6rWtapKL45anIRnAzY1mbsVCxfmdmF8P5vTWlQEfPKTwB132B1PCBn5XHst8PvfSwroFVfY\nxQjXtQZHr+gy1PNeXY/OcSFa+/okpSqqD4Op09rSkv86hbtD67Jzp6QFAzL39cAB8xiEEELcsXWr\n6I5Fi4Df/c4uhiunNerzj6I1gKta1LQ4pvNVk2pRdd3J8nJJ0wvGunhR6stMx9WsWCGF1r4vi5iJ\nE6VWjBBCorjpJhmd8+ijMkbHhqh5r6YZHlFCz/fdilade3shRatKWY7K7DF1WqMc29paaaJ08aJ+\nHEBE6pIl8piilRBChp9HHwXuvFN6DWzaZBcjLFrr6/MbKaYRp5NsOt7bMiJEa9IiwJVoTXtRXYnW\nrF1/T5yQ+FEjgJKYOlXGTBw7BuzYIaldhBASx623Ak89JfX869bZxZgzZ/A80MOHzbsQR9WSdnSI\noNPtjlwopzUt1Vi3e3BS6nN1tcxK1U0DixKtRUUiXIOzu3UIdiGur5ffxbTDJCGEEHfs3AlceaX8\ns5mv2tcnnxPBZos27micTrKZLW7LiBCthXJaz5yJHq2gG0Phcr5quBa1sTG/PkmXVavk4t6xg6Nq\nCCHJVFQA27aJ25rWpCiOxYuBgwdzX9uMzokSeqa1sS5Eq47gTIulhGJa88Ckz4qiIje1sVlH5xQX\nyyInuClhwrPPSpnLE0/YHU8IIUSaJa5YIWU8NqL15EkxtkpLc8/NnGkmWpNGi1K0BnAlWktKpNY0\nbgC9SU1rUgOlrF1/GxvFvbDhiivEOXn6aWDtWrsYhJDxw9Kl9ptkgDRz2rtXHp89K/X5NTVmMaIE\nmqloTfqcMHFakz5r1Id20udEWZmUZrS3J/+stA1Ok3mvca6tTcrWsWODU8iyzHv9/OflM/dTn7I7\nnhBCxjs9PbIZvHSpbAifPi2ZOCZEZQiZOq0XLshGZkVF/vcoWgPoiladRUnSqBqTjr1xjZhsnNZ5\n89yJ1pe+FPiv/wIee0xS/wghZChZtkzmvQLiuC5aZN5xPEosDofTWlUlH8xxdaBtbfKBXVZmfy6K\nQsx7NXVaOztlMRR8rerr7ee9/uxnwIMPipMfbjZICCEknf37pU9Eeblk4cyfLyLWhKjN1poaaRyr\nW/6RtGE77kRr1ppWXZc0TbSajKoZqppWNXPPhhtvlF2Z9evtOwcTQoguixbJpltvr4zOUZ1nTYhK\nUzKd9+pCtBYVSQpVXB2oy9pYHdFqkh4c1bjP1Gk9flw+j4KbDrZOa3OzlOJcc428pmzoRAgh5hw+\nLJ+zClfzVT3PLEU46fPPJDMoK5lFq+d59Z7nPe553m7P83Z4nvfXl56v8TzvEc/z9nqe9yvP82IH\nwUyeHB9f58XQrUdNEq0mLqkS0uG6JRc1rYcODb5ATSgpkdz3n/3M7nhCCDGhrEx2gdW8V5vROeF7\nIGDutE6aJA5p1K6xbiYOULiGTidPDv28V1OnNZwaDLiZ97p+vZSsEELIeOP73wfe8570PgdxhLv+\nLljgRrQCZinCY8lp7QNwr+/7KwBcD+DtnuddBuADAB7zfX85gMcB/F1cgKR0Mlc1rUB8Wi9g5pKW\nl0uaWFvb4OdtRGv4AgyOHLChrCx6jAIhhAwFN94IbN4s9fRXXml+/PTpkpba2Zl7zrRMwvPkMyDK\nJW1u1h8hNtrmvXZ2iliP2vg1dVrDiyMgu2gFgMsuy6WQE0LIeKG/H3j724EvfEFmrdoQNV/VND04\nLhvVlWg1KWfJSmbR6vt+s+/72y89Pg9gD4B6AH8K4OuX/revA3ilTfw00drfL+JRp/lHXAMlwFxw\nzpyZP+OosXFwS2kdZs+WlF7l3B44IB05CSFkNHDTTVK7uGMH8KIXmR9fVORmdE7UZ0VPD3DuXHTH\nw7gYI6ELsa5oTZoNbuq0Njfnu9uzZ9uL1pUr5THnvRJCxiN/+INsmL773cDPf24XI0q02jitUZ83\nrkRrZaXolwsXzM7LBqc1rZ7nLQCwFsBWAHW+77cAImwBGFZ7Cmmi9exZaaChM9c0Lj3Y983rUaNS\n2myaKHme7Ejv2iXHT5okvw8hhIwG/uzP5EP0bW+Tzrk2hO+nhw9LFooJUUJPuay6I32SdoxddSHW\niWR4F/IAACAASURBVKUrWpPmvZo6rVEbt3V1djvohw/nMoYoWgkh45Hf/142dW+8UQSsDWHRapP9\nEic46+rijTzdGIDoGJ2sWDVpIAsaUk8Pz/MmAfgvAH/j+/55z/O0M7g3btz4x8cbNmzAhg0b/vj1\nlCmyW97dDUyYkH+sSb1SnGhtb5fY5eW6Z5y/yOrpkTim6cFATrS2twNXXWV+PCGEDBeVlZJ1kqUs\nIXg/7eyUe6FJTSvgZnROWnqwznigadOk42MSaaJVt7FFXBMmFcNUtK5blx/DdNYrMHihFZ7la8qR\nI8C//zvwkY/Yb4oQQkih2b1bavrXrJH5qr5v3l0/LFpNR9UA8YJz+nT9ua+nTyeX/6jPm/Bm86ZN\nm7Bp0yYAMtkkK05Eq+d5JRDB+g3f939y6ekWz/PqfN9v8TxvJoDYj76gaM2PnRObauh5ENP5qnFd\nf7OOqmlqkotJd0c/iJqv2twMXH21+fGEEDKcZK2jD9b2Hzki3c9N76VxTqupaI1LvUr70E46jyA9\nPSLMq6uTY+gIzqSylqTGg3GxwgK4qko2jOM2jaPw/VwnYnUe7e1mMYL8wz8A3/ymbBj8zd+YH08I\nIcPBrl3A3XdLmcXAgPnnEZB/zMyZ4o4ODOh/RiaJVt2NTZ2N1qhYQSOyqQl44on79H5gDK7Sgx8A\nsNv3/c8EnvspgDddenwPgJ+ED9Il6cP39Gn9eqWk+ao2o2pczVd9yUuAhx4CHn4YePGL7WIQQsho\nZcUK2ZUG7OpZgXintdCjc9JE65kz8pmVtODQTQ9OEq3V1dLgKm7urE4s3bSvIKdOifuuhtAXFcmi\nK9wDQoeLF+Vz8UtfAn76U/PjCSFkOPB9+UxbuVLuo8uWmZdJdHZKnMrK3HMTJkgZYdxotijiRKtJ\nA6W0hrc6nxMm5xyHi5E3NwB4I4BbPM971vO8bZ7nvQTAPwO43fO8vQBuA3C/7c9Im6+q65LGNWKy\ncVrD81WziNaFC6XD4qlTwM0328UghJDRSlC07toFXH65eYxp0/I/J1ynB7sQrTpxamslRtqYhCTR\nmjZ3NkxcfayLLsT19XYNnfbskZ//6lcDzzwj7gIhhIx0WlqA0tKcqZal6284pdgkRVj17MnqtI4Z\n0er7/hO+7xf7vr/W9/0rfd+/yvf9X/q+3+r7/m2+7y/3ff923/fb0qNFkyZadV3SyZNl5zY4WgGw\nc1rD6cFRM+5MeOwxmXWo01CKEELGEsuXy4zq3l6Z97pmjXmM2bPz3TxT0Zr0watbipJWj6rzmVVR\nISnXad0Y07re6zq2AwOyoIhb2JikGbsenbN6tZxXTU16rTAhhLhgyxbgHe8A+vrsjm9okDIXhe18\n1axdfy9ckM8SlfkSZFyK1kKQ1OHKRHCq+tjwC2vrtDY05HbCs85XLS2NvqgIIWSsM2GCfMDv2QNs\n3y51/qZEdXR35bSadJivrpYazrjFjst5r65Ea2urND0sLc3/no3TGu4/kUW0qtE5q1fL9UEIIUPN\nvfcCDzwA/OAHdse7GlUTN1+1uTlbDEBc4LY2GR2axMCAfEZMnRr//+g0Dxw3otVVejAQXdeaNDYg\njspKWZyo2YIHD2YTrYQQMp659VZZJDQ12TmtUaK1oUGv468iTuSdOye71cHaojiKi8UVbG2N/n4h\n572adCGO+wy0cVrDr7mtaN21S7rrA/L5SqeVEDLUtLZKucqnPmVfSx/OvlywwDw9OEm0upivWlws\nOiZNTLa1SaZq1KamIs1p9X29z6I0Rr1oNU3tnTkz/4/d2CipZaasXi07wUB2p5UQQsYzr30t8NnP\nAq95jV2ZxIwZQEcH0NUlX/u+eVOnigo5LlxCMly1sS7mvep2IY4bnWPqtLa05De/ikrd1mHfPkkd\nBzjvlRBSGDZvBq67TnrMPPmkXYxwxomNaI0rSXElWgG9tF6dz6y0OOfPJ4teXUa9aDV1WqN2422b\nKK1aJaK1vV0ujKiRPIQQQtK56Sbg178GPvEJu+OLiuQ+rrJfzp6V52pq9GN4XmHmvbpID05qsKEb\nQ5GUbWTqtEYJYFPhC8jvd+xY7nM1q2h98EHgmmvS64QJIeOb55+XmdVLl8pm27lz5jHi5qumNdcL\nEic4k0omdWModLJx0mIA6Rukqmt+VkaFaI0bVQOYO63hrr9ANtG6YwewbRuwdi2bKBFCSBZuuUVm\ng9oS3JR0PTpnpInWc+dk53riRPsYirR5ryaCMyqWqfAFZNOhpERqbYH85ocm+D7wkY/I3/Hb37aL\nQQgZH+zbJyNqSkqks/2OHeYxjh8fXCZRWQmUlUmqrS5xjZgKMV81fB5pojVtLvi4Eq1xL8bAQPwf\nNY6w09rfLzsWNunB114L/Pa3wNatwPr15scTQghxx+LFOTfu8GFJyTIl6kN8JIpWnTguRKuN0xqO\nZeO0hp0KVRdr4lQoXnhBJgd87GPAr35lfjwhZPygRCsg4yhtMjxOncrPODFJ6wXixWKhu/7qiNaq\nKqC7W/5FEded3pRRIVrVixqe0dbWltu90GXevMFO68mTkj5mEkOxYoUc96EPAS9/ufnxhBBC3BGc\n93rokDuntbnZ7eic0SZadRdIvh+d/TRtmixaTOashmvCJk8W58PEqVA88wxw/fVSp/bMM+bHE0JG\nBxcvAk88Ybe5Bchxe/fmRKvNqBogWuiZitak+aq6TY3SBKdO3wMd0Ro3nUUxrpzWsjIRp+EPqyyj\nahS2qcGA/JE+9zngXe+StDZCCCHDR1C07tiR6zxrQtSH+Gh1Wl11D9ZdILW3y/iiCRMGP19aKqIz\nrqNyFFGzz7OMzlm1Cli0SF63jg7zGISQkc8nPgHceKN911/VSVeJtIULzRsoXbwojYfCpS4mo2qA\neLGoPht0hHmhnFYgudb29OlxJFqB6BRh03pWQD70mptzc4miPhhNuOMO4JOflIYfhBBCho+VK3Oi\n9bnn7Oa9zpqV3+n2xIn8jrhJxAnO/n7ZfNX58E5bTOg6rTouaUtLfPfgKVMk5aunJz2OK8cWyE8P\nBrKL1uJi2dhQXf8JIWOLb38bePvbgW98w+54leHhefK1jdOqXMWwLrBJD466x5eXy8Zge7teDBei\nVUdruWyaG8eokVpRzZh0d6yDlJXJxaQuHI6qIYSQscHcudId9uBB+bdihV2McMMfV05ra6vMxSsu\nto+hcJ0eHBfL8+QzU2cwfFpDJ9N5ry5F68qV8njJErk2CCFji5Mn5f7w/vcDmzbZpQg3NQ3ucbNw\noblodTFfdWBAPi/iNjh1NwFdNGJK61KvcDmeNI5RI1rjnFYb5b5kiRRaAxSthBAyVigqAv7kT4B7\n75XmeEmddeMIi1bfl06QJmUkcWLR5IPbhWiNmztrGstVmrGJ03riRH6DRBvR2tsrC1FV37xgAXD0\nqFmMIB/7GPCKV9jXzBFChoannpKxVnPnysagzWzocMng3LlyL+rr04/hQrS2t0tZZNxsU937aSHT\ng+m0Bqiry88FD++I6KLmqwIUrYQQMpb4y7+Ueqa3vc3u+LBoVb0UTOa9xglOE8d26lTZaY9rXqQj\nWuPmzgbp6RFRW10d//+YOLZxacamTmvcvFfT0TlNTRJHjaRbsMC8Rk3R1QV8/OOyOP7Nb+xiEEKG\nhn37pNsvAKxZI/NWTWlqGixaS0vl3m9y34lzJk1Ea5q7qSM2lVs7dWq2OCaiNa6mddw5rfX1+Slb\ntk2UVq+WJh2+L/+1SSEjhBAy8rjjDvngfP3r7Y6fM0cWFqrvgZr3qmqcdIhbCJiIVtW8KK5brquG\nTmoUQdLvp1sb69JpHarROfPn24vWxx8H1q0D/vf/5ugcQkYawVE1QXPKhMbGfDPMpoFS1vmqLhzS\ntjb5DIlza4FcZ/ekzBGTRkx0Wi8RNVjcVrSuWiVi9dgx2X217R5MCCFk5JHlw7G8XHam1Y6xzbzX\nSZOkg2RX1+Dnh6MLsauGTjpOa0uLm5rW/n5xCMILJRunNSxaszitTz8N3HCDzGjn6BxC3HH6tGTJ\nBEdSmhIUrYsW2b3Pw04rMHzzVbOWbOiIzfJyKaOJ2xy9eFH6RIQ7IUfBmtYAc+fmX8y2onXtWukw\n+eijUvdECCGEKBYuzA2UV06rCSotN9y8yFS0jqV5ryaCs7VVFkkqpVcxfXp20ao2wE1mxip27ZKG\nTmvWyMY3IcQNn/60dP79yEfsY+zdCyxfLo9tN6finFYX81WnTUsu+QhS6K6/afNVdSakxN3jL14E\nzp1LTlPWZdSIVpdO6+TJwItfDLzlLcBdd7k5P0IIIWODFSuAPXvksY1oBaKFHue9psdIiuMiPXji\nRGlwcvasWRwg14V49mxpzBJXv0UIMeNXvwK++lXgZz+za3LW2yv3jfp6+TqLaA3ripkz3cxXLS2V\nLJw4VzNIWk2rTsmGi66/ujFUnKh74pkzIlhdjAYdNaI1XGfU1SXNI2yH1d5/P/COdwD33OPuHAkh\nhIx+VqzIzXvdtcuu70GUWGxudiNaOzvls3DSJPsYCp1FiQunVbcuNimOcirUOkCHqNE5potQQBbF\nhw6Jk+N50sDx0CGzGEH+7/8F/v3f7Y8nZKzQ0QG88ALwmtfIe8smRbi5We4ZapyYEq0mAvjiRRGU\n4U08V/NVgZHb9TduA043hopz6lS+k+wqNRgYRaK1tFReOHXhNDSIkDVpjhFk9Wr50Cgvd3eOhBBC\nRj8rV4po9X3gueeAK64wj1FXl7/QceW0KndU5/PPVXpw1kZMusIXiK+NLSkBpkwR4aqLK9F69Kg4\nrBMmyNdZamNPnQLe+175Z+ocEzLW2LNHNoPKy4Grr5bu3KaEHdLJkyWrwuT9FecIuqppBfTvpTo1\nra5Ea1oDJV3BWVYW7SS7asIEjCLRCkiKsNqBOXiQo2oIIYS4Z9UqYPt2cdImTrTbJZ47V+a7BnEt\nWrPEMImlIzj7+sQxiatbqq1N71KpSBudY7IQjXK3bUSry4ZODz0EvPzl0un6kUfsYhAyVti7N9dA\nafXqXGmGCVFpvVH34CTiRJ7p/SIpe2WkOq1JDZRMBGdUrHHptAJSV3TwoDw+cABYvHh4z4cQQsjY\no75ePmQ/9jHg5pvtYoTnvZ4/L8JuyhT9GC5Ea6G6B58+LfMM4+qWysuBigqgvT05DuCuodPAQPTv\nN9yidcsWYMMG6UT8hz/YxSBkJNDSIobSt75lH2PfvlwDpSVLgP37zWM0NeU3UJo5041DauK0+n6y\nWNQVrS7mtLoQrSafNSpWONXYNEYSo0q0BucuHThAp5UQQsjQ8MY3Ag88ANx9t93xYdF65IgIHZOS\nlrhUsuZmWZDpxnDltCa5pIVKMzbpINzWJulq4TKg4Ratu3bJembNGkk/J2S08sAD8h77x3+0jxF0\nWpcutROtUU6rzXzVKJFnslHW2SkbdxUV0d/XbUiXJjgrK+V+3NmZHEO3e3BcTauN0xqOZZphlMSo\nFa27d+d2ZgghhBCXvP/9Movzjjvsjg+LVpt5r3ELHJNFgAvROnGi1JNeuJAtjqvROVkbOrkQrfPn\nS52rKb6fG51zxRXA88/bdUtV/PKXuaZhhBSaX/8a+PjH5T3Z1GQXY/9+EauA/FeNGzMhTrS6cFon\nTZLmb0kCUeHCIU06F4Xn6WXRFLKmFZC/QWPj4OeOH7eb9BLFqBStvi+LiXXrhvuMCCGEjEVKSoCr\nrrI/Pkq0mo7OiRN5J07oO61ptaQmo3MKkWYMuEsPjotTV5ddtJq6OIqmJmlYMn26nFt/v934HUCc\n3jvvBF772mzClxAbBgaArVuB//E/gPXr7RooASJq1Htrxgypje/qMovhIj047h6mBKILh1Qn2+Ti\nRdkgrK5O/v/Ssk6Go6bV5XjSKEaVaF24UC7mzZvFetf90CaEEEIKybRpQHe3DFUH3IpWk9E5ZWXx\ntaT9/ZJCqzM6Lk1w6qSiuZr3mlW0unBaq6tlYW26uN67F7jsMnnsednSjH/6U+BNb5K/7d69djHI\n+KWlBejpsT++oUHeBzU1Ilqffto8Rk+PrOvVvaOoKNqtSyNKtLpKDwbMHFIXXX915pqmZZ0MR03r\n3Ln5I4saG3Pzc7MyqkRrUZF03Hv964E/+ZPhPhtCCCEkGs8TcfLCC/L1kSPmorWqSnbcL14c/Lyr\nLsRnzsiiU802TCJNcBZydM6ZM+kxkuLU1cXXcMURdIMA+fvOnGke59gxSS1WZBGtjz8OvOQl0tTp\niSfsYpDxycmTcv2+6U32MYK1qCtWSEMlU5qa5F4WFGj19WZdf4HosSquGjEBo6/rr3Jrq6rS49TU\nyOZqb2/+90yd1nCGke+PY6cVAN71Lpm/dO+9w30mhBBCSDwrVuTqDV94IVe3pUtRkey4h0WaqWiN\nWyi5Hp2TttDSSQ/u6pLFU1yXZd3FIxC/4KqtlZRc3ZTari6pZwuP87FxbI8dkxQ6xcKF2Ro6XXEF\nsHZt9oZOR49GL1rJ2OR73wNe9Srg4Yft09P37s3e9TdK0NTXmzmtfX2SbVBTM/j54ZivOlK6/uq6\ntYD8P1G/X9oYsyiCo0kBOd7zzLrmJ56rmzCFY906meG0atVwnwkhhBASz4oVIiw6O0UUqLRQE6ZP\nH+zm+b47p1W3u2RSDIWrmlYVJ67Lsm5dLBAvWktL9cfvqHOaMSP/nGzTjIOi1dZp7eoSN2rxYhGu\nWUTr/v1yHn/7t/YxyOhi82bgT/9U0nq3brWLERatBw6Y11bHiVYTp7W1VQRrOGNEOa2651QopzWt\nE7urtF4ThzSqGZOJ8FXMmiWbrGoDzKXLCoxC0UoIIYSMBlaskOaBO3fK4q6szDxGeAHX0SGLs0mT\n9GO4mPfqohGTTk1r2iB6F6LVVRwXTuv8+Xaide9eEQqlpcDll2eraf1//w943euAb3wjPxWdjDw2\nbQKefDJbjCefBK69VkTrM8/YxTh0SDZNABE3JSX67ylFY2N+LeqcOWaiNU7kVVbK+6OjQy9O0j3M\nVU1rRYWIwKRO7C5G1djMVw2LVpv5qsXFIlyVU37oELBokVmMJJyIVs/z/sPzvBbP854PPFfjed4j\nnuft9TzvV57naWRWE0IIIWODG26QWsNHHgGuu84uxty5gxdwNjPvkkSrzo5+UoxgLBc1rWkLNp2Z\nsYqRKlqDtbGzZ9t1Id69W8QqINdDW5t5UyjFL38JvPnNcl7bt9vFIIXhzBnglluAl77UfoPh3DkR\nO8uWSYf0bdvs4oQb7ITvVTo0NeU7cbNnm6UHu3BIdeK46B4M6G0AZh1VYzNfNRzLNIZi3rzcRtyB\nA7K55gpXTuvXAISn2X0AwGO+7y8H8DiAv3P0swghhJARz7RpwJo1wD/8A/DqV9vFCDe2CDt1uucR\n14VYtwt/2qJNxx0wSQ+OI6kbcphCiFaTmjlAFvXBhb6N8AVkUagcjKIiuSZs5sYODEhq8dVXi4DJ\nIlqff16clu99zz4GSeahh6QWdckS4L//2y7GwYPikBYVScmCTS0qkJ/6adNAKSp9NEmMReFCtHZ2\nykZYRUW2ODqCMy2Wq1E1pk5r2LVtabETrcFeDgcO5Nx4FzgRrb7vbwYQLuX+UwBfv/T46wBe6eJn\nEUIIIaOFL34R+MQngNtuszs+at7rggVmMVzMe00SeQMD4gC5aMTkqqETkCxa1fxaHVw5rarJVLCj\np+pkbFoLGB7BY1sbe+SIpHZWV2evjf3854EXvQj49KftY4xlNm4E7r4720zdrVuBG2+Uf7YpwkEh\nsXixiNiBAbMYapxX8L1qI1qjNs5MRWtaWq+JQ5pUS++iplWdUyFG1ZgIzlmzxPUOYluPumoVsGOH\nPN63b2Q6rVHM8H2/BQB8328GYJgZTQghhIxuVq4E3vve+MVQGlGi1XR0TtwiySTVOEkotrXl6seS\nUKMVktIaXTV06u2VnxXuKGoSQxEnWtPmJIaJaug0YYK4O6YdXMOOu61o3bkz19hy1aqcQ2LDb38L\nfPKT4riq+cQ2PP64TImwmfsZxPeTawd1aWoCHnssW4xTp4B/+Rfg5z+3ryEF3NSiHjyYExKTJkln\nV9OMgahRNaa1qEB0hkZSrWZcjJEwqkbF0antTxOtOi6pihO1CWLqtIY/ZwB70bp6tdxXfF9Sz6+8\n0jxGHCXuQtmzcePGPz7esGEDNmzYMGznQgghhIwUwsPaDx+Wrp8mJKUHm4jWuIWWbsMONcKntVXc\nlLhYaaLcZGxEXOdLU9G6enW2GCpO1OukHFuT0RJRTuvhw/rHKw4fzrluWcbvnDkji9z162Wj5vnn\npabbhk99StJWP/tZ4MEH7WIAwL/+q3RE3rPHrnO34g1vEEG+a5ekPtrwu98BN90k5/GrX8nrZIrv\ny+islStlM+ZDH7I7l4MHBwsJ5baaCJSoWtT6evk9TYgSi9XVkpXQ3S2bOjoxgu+FICajapLuYTr3\nnIEB2XxKex8nOaTqXHSc1vLy3IZX+GeaOq3z5kWL1uuv14+hUE7roUPAwMAmfPnLm8yDxDCUorXF\n87w63/dbPM+bCSD2TxQUrYQQQggRFi6UxUNPjyxSjhwxd1qT0oN1RevUqeKo9vfnj5aw6UIcJ1pd\n1camNRGZNk1foMX9fqaiNS6OEq0mgijstM6ZI4LGlKNHpYMxIAv/xkaZz1hiuDrcs0fEVEmJzI3d\nvt1OtPb1yTiWxx8H7rrL/HiF74voffnLgQcekBR9G1paJGX6ne8Evv994MMftouzdasIgCVLgO9+\n1y5Gc7OIlKoquRccOxb9fkzjyJHBNfamc1EBN6NqfF/eP7W1g5/3vFwWQ5wYDXLqlNRjRzF9ul6q\ncZrTWlMDnD8vWSJxGSVnz0qGQNp7R5UE2J5LONbJk/mi1cZpDW6OAvmNtnSZNk2u8/vuA269dQM2\nbtzwx+/dd9995gEDuEwP9i79U/wUwJsuPb4HwE8c/ixCCCFkzFNWJi7a/v2yyDtwwHyEQJS4GhiQ\nhY1uTWtJiaQRtrXlf89UtBZi3mvaoq22Nnt6cHW1jNPo68sWZ9Yss/TMjg5ZPAdTn20bOh09mhO/\n5eVyfqYCBpDataVL5fGqVZIeaMPOnSKG1q2TFG+b3wkQ16i7G/jAB0QA2/L730vn79tvl8e2bNsm\n7uq6dfZpvcFOrBMmyN/KNB0XyHdJXTVQmj07vy4yifZ2EeHl5fnfM0kRLkTXX5UlklQH76IWVQl5\nXdEaF8umpvX06dx8VUCuCdsZq//rf8kIrb/8S7vj43A18ubbALYAWOZ53jHP8/4CwP0Abvc8by+A\n2y59TQghhBADVDfGpiZxVUw7OkbVkp4+LSLUZHasi3mvaYtIF903VZw0pzWraC0ulte2tVUvTprT\nqotK6w7WxtqK1mPHck4rYF8bu3+/jFABsqUZ79ghTq3nyX+ffdYuzpYt4mxecYW8d2zHwzz5pIhW\nNRrGtonSgQPy+ixaJNeBTa1tsBYVkFiHDpnHUfWoChvR2tSUP181Le01TJI4M2nGVIiaViA91dhk\nvmrc73b+vAjkysr0OEmxTMfVlJTIPURtWA0MyP0k/DfW5a//WtLp77zT7vg4XHUPfoPv+7N93y/3\nfX+e7/tf833/rO/7t/m+v9z3/dt934/YnyWEEEJIEkq0bt+eW9CbUFQki8Cgm2fSOVgRt2jTXayp\nGIVwWtPEr65o9f1k19bEsY0T0qajc6JeI5vxO8BgpxUQwWlTGxt0Wm1jACJ+VZzVq2Xha8OuXfJe\nmTRJfr89e+ziqBpStXg3cRIV3d3iGs6dK+9F2/rj8PgQVYtqei4XLgxOyZ0zx9xdP3kyP8W/tlYy\nMXQzD5LeoyYCOElwmnT9zdpASbcWNa3rr+momnCsnh75G1dX68cBBjdjamqSv6fJpmaQoiL7+u/E\nuO5DEkIIIcQV69dLnd+TT0p6oQ3h7pAm9ayKJKdVN50taRF58aI4DXEdf9POI3xOaenBOiNvOjok\nfXHixPhzMRmdE3VOtl2Iw+fR1mbmKHZ1ye8XFB9z5tgJswMHcmJzwQIRw6ZjVIDBojWr+FWu5MqV\ndvW+wfPxPPmvqUgExA2dNy9X62jrkDY0DHbFFy40j6M2q4IbXzZOa9R7vrjYbBMnSSim1X2GzyVp\n5M1om69q4pBGpVGr19V0c3PevFxdazAVfSRB0UoIIYSMYG69VRq5/Md/AHfcYRcj3Gjj+HHzJhtx\nqb1RsxbjSBKcZ85I3Vhcx1+dGIo056S2VtJ604SVTkMnE6fVRUOnKPFbXKzfdEZx7JhcA8HX2zbN\nuKEh59hWVkrquU2ccJqxrWgNimjbOAMDIgqVu7lokb1DOlRpvaYOaVRar41ojXt/mTikLpzWzk5p\nRhWXTmtS0xpuCBUVy4VoVe/TqFRzU6c1Ko26pcW8hASQ993evfKYopUQQgghxkyaBLz73VKfd9NN\ndjFczHt10YU4aRGpu2DTrWlNilVaKgvd9vbkOK5Fa1Qs112IdYnqDmraFAoQx7azc/Ci3yYF1vfd\nOK2u4jQ1SYrlpEnyta3YPHRocPM02zgnTgwWnDZ/q3AMQK6blhYzZzxOcLpqoKQb58wZiRHnKk6e\nLM2FurvTz8VFerDO/WvCBMnciGpqZ+q0RpUFNDba1aIGG6iFU9FHChSthBBCyAjnox8FHn7YPOVL\nMVJEa5JA000zdlHTquKkpfa6FK1x6cEuRauJiIn63WycVuUAhlNOTdOMz50T4aTGd6iGTqbNj06f\nFudZxbEVieGFexaHNLg5YFvTGnZJZ882F61htxaQusVJk6JFVBxxgtO0gVJSerCLUTWep1fXqsRv\nEmlp/DajasKYdv0N39eB6M7OOqxeLY3QABnztGaNeYyhhqKVEEIIGeO4Eq1RizZT0Rq38NN1Kqqr\nczMT49CJpVN/lzY6Z7ic1rjROSaC01VDp6hFso0LGHaIJk2Sf6Yi+tgxEYYKW6f1+PH8JlW268ac\nHgAAIABJREFUojWr2Ozqym+gNGuW+cZAlNMKmDmkXV3SbEk50OE4LtKDTWpRszqkvi+iNS09OO13\nczGqxtV8VRvRumSJXE8XLshYJtv+CUMJRSshhBAyxlm8WJwjxZEjgxf2OkSl9l64IOl3VVV6MdKc\nVp0Fm+7MRJ0uxFlH5+g2nunsjF/oV1XJ93WbKCU1dNIVHkD0620qfIHoOklbxza82LZxbMPnY9sY\nKhxn3rx8V0sHF2m9qm486GbX1oo73dNjFifc9RewGzETlfVhmh6c1EDJhdOqYiW9Rzs6JGU3rVNu\nIUSrqdM6Y4ZcA11dueei0v51KCkBrr4a+PKXpXzCdtzNUELRSgghhIxxli8X0XrxogiktjbzRUmU\n4FQuq27actIC0tXonP5++f1UemhSjLT04DQhreuSqjhRr5Pn6XczTjonk07GcXEmTRLn6fx5/ThD\n5bQCds5vWGxOmCC/l8lrExVH1X6apitHxTl1Sq5T2xhAbpSVyeaAiwZKLtJ6AT2nNe211hWtQ931\nFzBzSV05rUVF8t4LbqbYOq0A8Gd/Btx7L/CqV9mXogwlFK2EEELIGGfiRNl9P3hQmm1cdll6l94w\nUc6k6eicigpZiHZ25n/PpHNmWhfi6mqpa0xCp7OozrxXHTGU5qCMlIZOnmcuFKMWybZOa1iY2Ti/\nUeLXRkRHid/Kyuzit7RUrk+TMUdRrw0gz5k40XHvsUJ3/QWSxeKECfIvrVGai4wKnXpWIP13M3FJ\nXTmtQH7pRxbR+ta3AvffD9x3n93xQw1FKyGEEDIOWLEC2L1bmmysXWt+vKqhC7ofpqI1qTGK6bzX\nrGnGOunBaYtiXaHoqjbW992OznFRGxsnNoerNtbV+biIo2qvw+nzpnHi3memceKEoiux6cqxVbF0\n0vddOK1p9ayAzI8+f17KIcIkpf9HEZdGbeq0AoPT1n1falznzjWLoZgwAXj/+/Vej+GAopUQQggZ\nB1xxBfDUU9Jkw0a0VlTIoiy4ADQVrUC8w2ky7iFpIaqbZuyiC7FJerCL2tj2dllYlpfbn0vwnKJe\nJ9OU06hrwJXT6qo21lWtro3YnD07P9XSNE7SBoMr0apbi5q0KaQbp7dX6uGTauF16lp156smvS90\n04OLiuLfY+r9rZtSGyXukzakkgg2Yzp1Spz8mhqzGKMFilZCCCFkHHDnncDPfgb88pfALbfYxQin\nojU0mDf9GG2jc5IWkVX/v71zD66yvPP49yEQIOESQLmEBNQEwk0LKgpqV3Zqi1TFUl1H1plu62h3\nde10ppettDtV2/2jt9121q3tTLvj2Is6tTvTum6XquMCbcc7oiC3cAtGSKAEEsBwS57945eneXPy\nvu95nud9zskJ+X5mGM95zzm/PDnnOfH9vt/fZXz+TsZAOKc1TfyGEq0+tbG5a5o8WWK41m0WqjbW\nNY7W8eNhXOMkpfX6iM2sab1GKFZV9X/MpxFTHDbuKCB7Y+LE9BIFm1i281WL0fXXJa13ypT+4v7Y\nMbkYNXq0fRxA5hHv3Cm3d+2SLsDnKxSthBBCyBDg6quly+TkyTJI3ofcEQv79oWd9zp1arYYgFt6\ncJrI0zr/Ca1NJ2ObNbk2dMoSA+htVhXnUvmI39z3aPhweV9shRAQRiSaOFlFa1ubiIeKimxxkrr1\nhnJIbUfDAL1jXZK6/oaoaa2slP+ePJkeI4TYBMLMV7WtaTWxkmpRXRzSuHFFvl1/FyyQPgWA9Cyg\naCWEEELIoKasDHjnHWD9ev/OkIWa92pG58S5QLYxDLbpwflO+E+ckPcsV7i4rMUQqhFTmmM7bhxw\n6pTdCJS0ZlWu4vfo0XjxG+cmJaF1vFvlmh585oyI8bhRPgNRQ5okZkLFsWkmZihWLaqN2LTJhrB1\nWkPMV7Wt4QzltNbWikiNjmHybaA0b544refOyd/3efPcYwwWKFoJIYSQIUJlpXv6WZQQojXuRLul\nJdzonFBOq0ucfE5ryPTgpDimyZVNam/aelyEUFubpEgPH54tzgcfyPqNU2cw4s52zIwRILlppz4i\nMVQNaZLYdOkeHMJpDdX1N5/gtBWbWRsomUyIEKI1RHqwi9M6ejQwdmzf389XtFZUyOsaG4E33pBZ\nq+crFK2EEEIIsaKuTuqmAGkKdPq0/QmfIW3ea5YYBtua1qoqoKNDHIo4XBxbm9rYEE5rMeK4jt8J\nka6c9JlVVEhjmePHs63HR7TGrcfV+U0Tm1lTsAE7lz+6lqTPqqpKLhzYuPShnNas6cHHj0sN6KhR\n6XHy/W4hRKvvqJpoqUWWUTVLlgAvvABs3AhccYVfjMEARSshhBBCrDBjcwBgzx7gkkvcU41Didak\nk3Vbh7SsTLpstrUlx7Ft6GSTHlxKo3PyiU0X9y6UaA0RJ83ZdBGJSXFCNbtycUi7u2WPxjmKLnHS\nxFnaKKpc8n0vbNZk65DmG1Vj8/1UKn+38WI7rUDY+aqrVgGf/7zUt5bquJoQULQSQgghxIpZs4Cm\nJqmd3LzZr6FT3FgMV9FqREhc2qitQ2riZBW/NkKmGGIzVJyBcloLGaeqqrdu2jZOCIc0SRC5XBg4\ndkxSSUeMiI/T1ta3NtJ1LQYbsZkmoA3F6vobooGSiZO1ptXHaY3OVwWA5mZ/0bpyJfClLwGPPur3\n+sECRSshhBBCrCgvlxrWnTuBt9+W2a+u1NT0PVkD3DoHA5I2qpSk/eXi4nqkCaJQ6cEnT8rJ/pgx\nfuuIEjI9uNBi09UFDBEnSWwqJcLEdpRP0nomTrQXiWY9aWOFbGp1097jESOkDvjYsWxxADuxefRo\nsoA22M5XzdqIKYRDqnUY0RrCaW1qAmbOdIthGD4c+O53gcsv93v9YIGilRBCCCHWLFwIvP468Oab\nctuVmhoZ9xCd4Rk37iQfceKqq0tOdOPGjNjGMIRKDzZiIS2NeswYmfXa2Zn+s4qRZlxZKe9j3AUB\nl/WUUnqwz3riPvsRI6RL89Gj9uuJi1NeLhdebMRmCIfUNk4IsWmzHhuX1HyvkoR9iK6/7e3yOZSX\n28eJ64jt2j0YEKfV1LR2d5//42pCQNFKCCGEEGtWrAB+/nMRrddd5/76kSOlljR68tfUBFx0kVuc\nOLF4+LDETnOCcmOUQhdi286/pdaFuNCNmHziFNr5tU3tzTfn13Y9NjWkNu9PCPFrk30QqnvwyJHp\nLnIIp9UlBiDZIK2tfZ327m455nrRbc4cYNs2uX3woHThTsvGIBSthBBCCHHglltktMLq1f4nWYUe\nneMSI8S813yi1caFySfOjAgKJVpDpRmXktgs1nps4nR0SGfbkSOzrcemW6+t+M0axyb7IJRjmy/W\nQIjWUaP6j6o5fFjc96TPOYk5c6QT+5kzMq6mrs7t9UMRilZCCCGEWGNc0h/9yD9GVLSePStOQ22t\nW4ykLsQutbHFSA+2rXfLJ2I6OvKP+BjqDZ1COKQmTpr4LVaX3XxrcVlPvji2Dmm+lNxQ3YPzxQoh\nWltb7UsJDKEaKI0aJbEaG6U/wGWXuccYalC0EkIIIcSJykoZGePLjBmSEgzICeC0afYpvYa4k/XQ\n815dxGZS7V0op9VG/IYSrbZuoo3YtGk2FLIWNatDapPWO9gc0ny/k4lj45Dapgcnfe4ffCAptZWV\n6XGiseII0T24pcXtIhcQP6qmpsYthmHBAuCdd6RHwJVX+sUYSlC0EkIIIaSoNDQAO3bIbZ/UYCBe\nPIQUrbbpwZWVyZ2MgXBOq42ItnHdurryjy0JIX5HjZIGN8ePZ4tT7PTgjg5Zd5KjHcrZdEkPzlrT\nevKkdJgdPTo9TohaVCNGT55Mj2Ez3zlfenDWRky+otU0UAKyzVddtgxYuxb44x+Bq6/2izGUoGgl\nhBBCSFGZPx949125vXOnX9fMuBNa15rWJAFy5oycdI8fny0OYO/YhqiNtXE329qkBi/N2bYRZmZc\nSKg046xi08wQzdr4yMYhDeVmFyvN2LYWNYRozRfL1SEtZHpwKKfVV7SuWgX87GdyMWHePL8YQwmK\nVkIIIYQUlXnzgK1bRfhkmffa3Nz3mGtNa5IAaWmRWrdhlmdJacLq0CG7urkQTmva/FqXODZCsb09\nvdGQbRxzgaCqyj8GIF1mx4xJFuMhBLSJU2ynNd+FgRA1pKG6/uaL5SI2QzViMnFyL+aEEK3vveef\nHlxTAzz/PPCb39g5z0MdilZCCCGEFJULLxRxceCAv2jNPXkE3Oe9JgkHny7ESQKktdW+prUYDZ1c\nHNs0bMSvjSt55IgIqqQLBBMmiEA+dy7/eoohEovttBZrVE1VlVw8OH3afy3RNYXq+hv3uxmX3zY9\nuKJC/t50dPQ9HiI9eNeubPNVP/pRKZcg+aFoJYQQQkjRWboUeO45YMsWYNEi99fnnjwCwL59bvNe\nKyulxjPXmfTpQpwkHEI6rSEaOoWqjS1WnLIyEa5tbelxitX4KKTTWqxGTDZC0cznTds7tqm9obr+\nJonf9nZJqS0vt4sDANXVksobxUe01tdLx1/j2mYVrcSegotWpdSNSqntSqmdSqmvFPrnEUIIIaT0\nWbkSeOAB4JprZPahK1VVUsfY3i73T52Sk+rqavsYSsXXu4Vq6KR1cRsxAflFjK3YPHIk/Tmh0oxt\n6i1DOL+TJtl1My6m2Mz3O506Jc5n2vfDJk6IWlSXOCHTg+PitLZmT+vV2m/kjXl+a6s4tydOuP2t\nIP4UVLQqpYYB+A8AywHMB7BaKTWnkD+TEEIIIaXPXXcB994LfPvbfq9Xqu+JaFOT3HcdxZPkwIRI\nD25vl5rPtK6thhAjb0LFCdXcJ1SacQjxO3q0pIieOJFtPS4jb7Km9RpnM63eMV+3Xpu1GNLE5tmz\n0gk6qfY4Ssj04EI1UDp+XDoq24zeiaKUjKrZskXq8hsaWI9aLArttF4FoFFr3aS1PgvgaQC3Fvhn\nEkIIIaTEGTUKeOwxYOFC/xgzZvSmCO/b5zc6Z/p0qYWN4pMeHCdkbFODgfxOoOvc2CRs4kyaJIIp\nzZW0qZO0bRIUQkSHjJMmqsaMkfratGZXQH4RPXasCMHOzvS1hHDXQ9SitrUBEyfaNScLlR5sHP/u\n7r7HfUTrjBl9RWtzs1tWRhQjWl9/HVi82C8GcafQonU6gGibhOaeY4QQQgghmZg9G9i+XW77znud\nPj2M0xp3kn7okF0dKpDfCSxmF+KRI+WiQm7jGtc4Nq6krWMbqsY2q/OrVBjxa2pI86VyFzOtN5TY\nTHJIAbcGSuXlcpHg2LG+x0M4ra7171EWLwY2bABeew248kq/GMSdQovWOMM8TzUBIYQQQkh+ovNe\nd+zwa4gSJ1pDOa22nYMNSSKvu9u+EVOIea8mToja2FJJDzZxQjQtyrees2fTx/gY8n1Wxa5FDdlA\nKUQcEytuHvNAitabbpImcr/+NbB8uV8M4s7wAsdvBjAjcr8GwIHcJz388MN/ub1s2TIsW7aswMsi\nhBBCyGBn/nzgiSfk9jvvADfe6B6julrG7kQJ1YjJJT04GifXMT52TGrvbLql2rh3LmnGSRcCbMRH\nyLTepqYwcUKN8kn7vfKN8bGNYyvw8n3mLjWtb76ZLYaJkyZabZ3WaKw5kY44LS3ArFn2MYD+orWp\nCZg50y2G4YILgG9+U2YMz5iR//lDlXXr1mHdunXB4hVatL4OoF4pNRPAQQB3Alid+6SoaCWEEEII\nsWHePHFatRbRetll7jFya1rPnhWx6VLvltbl1MVpTRIfLmnGoboQhxqdE8ppfeON9OeEFNE2cfK9\nN6XikLrGCdFAycTRum+TIq3tnf60NWWpae3ulosJ+/YBn/iEW4woX+E8lLzkGpGPPPJIpngFTQ/W\nWncBeADA8wDeBfC01npbIX8mIYQQQoYGkybJyevTT0sNpuuJLNA/Pfi99yTOiBFu62hvF8EbxUVs\nAmHSjNOEYne3m3uXVbROmCDvy7lzyc8JIRJt15PPkTxzxj6tt9TEZlKc7m5pomTjboZK662sFLGa\n29G4rQ2oqJDvqi1Tpsj+j+IjWseMkf0Ybdzm67SSgaHgc1q11mu11g1a61la628V+ucRQgghZOiw\nciVw993AzTf7jZ4wotV0yvWpdSsrE1F58GDf4z5Oa9Y04zSx2dYGjBtnl2YcQlCVlYlQaGtLfk6I\ndFzb9eQTvyatN98+CpWOGyqVO+39aW8XEWlzESZkLWpcirBrgzOgf1qvieNzgcrUwHd3A9u29U05\nJqVPwUUrIYQQQkih+MIXJM3vwQf9Xj9mjPxraZH7WUbn5DZ0OnBAjtuSJlptxe+kSSISc8eEACKi\nQ4jfzk5xlceOzRanu9uum2w+17ezU9zcMWPS4xTTIS32qJpQXX9LrYFSdKwVIG54W5vbxSCDGVXT\n1CQXbyZOdI9BBg6KVkIIIYQMWqqrgaeeypbqN3s2sHOn3N6716+raHV1/3mv778P1NTYxwhR0zpi\nRPyYENc4aULROIA2zraNC5jP+TUxkubGGkGV1SF1cTYHS02ri9isqpKRS2fOZIuTtKZQ81WnTQOG\ne3TlmT8f2LxZGq9deqn768nAQtFKCCGEkCFNQ0OvaN29O4zT2tXlng4ZV78HhE0zto0TSgjlE782\ncSoq4msko3FCiM2BqEUt9HpsXV9AGhRNmhS/JtvPypCUHpzVac0yqub664EXXwReegn48If9YpCB\ng6KVEEIIIUOa2bNlzisgToyPC5MrWltbJf3Qpn7UEOfWAv6jc+LihHRabeOEEFRpAq+UGh+FiqO1\n/XiY0HNR42K5XoAJlR48fbrUind1yf0sorWuTr5Hjz4KrFrlF4MMHBSthBBCCBnSNDQAW7cCp08D\nu3bJKB1Xqqv7itbmZrfUYEBEQW4zJyBcF+JQDZ1cRGua2Awlfm3jjB8vbm1c+qtLnFBjhdLE5okT\nkuo9enT+OOPHA6dOyf7NJUQDpe5u9z04eXKYrr/l5bJ+893K2vX3qaeAJ58E5s71j0EGBopWQggh\nhAxpli4FXn4Z2LQJqK93G8lhyJ336iNap04VwWBcJUNLi/tsy6y1saWUHmzWk9VpNemvR45kizNx\nYvooH5c4HR39RyUBboJeqWRRH6KB0pEj0nRr5Ej7OLW18h2I4tv1d84c6fYLZBet8+YBq1f7v54M\nHBSthBBCCBnSTJkiLufDDwMf+YhfjJqavg1j9u+XE3cXRoyQZjhR8aG1uEyuXYjjBIxLbWxVFXD8\neHZBFareMi1OqPXYxjGjfLKK32HDRLjGxQmV1nv4sF2KcVocH7FZW9u3FhWQizquI2+A3q6/gGRE\n0CUdmlC0EkIIIWTIc999wNq1wL33+r3+4otFtBqR19gorq0r1dV9U4Tb2sT5zTfOJcrUqfENnVyc\nViOo4masuojNUE5rWhxX5zeEg5wkEk0tatY4oUSry5ijpDghuv5qLaNmfFxSM1+1q0sc1wUL3GOQ\nwQ9FKyGEEEKGPA88IM7i/Pl+rx85UtzWPXvk/q5dwKxZ7nGmTeubZuw6NicuhsGnNjbJvRuIRkyh\nnNZQ4jcuzsmTIvgrKrLFGagGSkldf10d0mnTJI6pHz5yROpTx41ziwP0jqppbBQBbjMfmJx/ULQS\nQgghZMijlJubGUd03quv05rbjKm52S01GAjXhThJ4IWqRQ0pfrM6m67rKbRDGirOwYNuLmlcAyXX\nGIDMUZ06tbeBku/8YwC44gr5Xj3zDHDddX4xyOCHopUQQgghJAANDTI65/hxOfH3mfeaKzhDdSE+\ncUJSl12criShePCgvfNWjEZMIcSvy4gZs55Cis1Dh+x/J0AuRrS09D3W1SWxXS5UTJ8eroHSjBmS\nEgxkG1UzahSwfDnw9a8Dt93mF4MMfihaCSGEEEICsGAB8NZbwMaNwGWXSWMlV2bOlBN8Q5b0YK37\nx1HKLU6u+NXaTbRWVsp/T57s/5hrbWwopzVO/LqMmDFxCu2QuqTk5navNmupqnLbh7W1fWtRzVp8\nRGtDA7B9u9zOIloB4Ac/AH74Q+CWW/xjkMENRSshhBBCSACuvx5Yvx545RVg8WK/GHV1wO7dvff3\n7nVvXjN2rKRndnT0HnPtQAz0bwoFiIuslFtdYZzbqrVbN+MksdndDRw9mt0hdXFr09bj6pCGqkWd\nPr3vnGATw1VsTp0KHDsmc18N+/eLa+qKaaAESHqvT7q8oboauP9+qRcmQxN+9IQQQgghAairk2Yz\njzwC3Hyzf4yoaG1sDNPQyTfNONe9c3UAgfjZnx0dMjrGto44yWlta+sV6VniuKQqp8VxFZtTp/ZP\n6wXc3c0k0er6WQ0bJgIxmiLs65JGRevbbwMf+pB7DEIMFK2EEEIIIQFQSlIY778fuOEGvxg1NdJp\ntbNT7u/aFWZ0jq/TmitaDxyQ41nWAriL34kTRejmzo11jZPkkPrECdH4KKlplqtLGhfHN603Oq7m\n9Gl5v1z3DtA7X7WrS8TrpZe6xyDEQNFKCCGEEBKIFSuA733PP42xrEzSgffskbTXzk63RjqG6uq+\nzpuP0xpCbJo4WR3bYcPi3U0fsZnr+vrEmTYt2SHNmtZ79qx89i5pxuPHizg8frz3mG8Dpdra3gZK\n+/fLvikrc49TXS01wr/4hQhhn3E3hBgoWgkhhBBCSggzl3LTJkmpdGmeZLjoIqmHNfiMzgmVHhwq\nTggRPXmyCMJcx9bH2cwVmyaOj/jt7u49duiQCHQXoahUfwHs85kDfUc3ZWmgpBSwciVw773yX0Ky\nQNFKCCGEEFJCXHUV8NprwBtvAFde6Rejvr5vbeyePVIv68KkSdL116QqAyISfdKDQ6UZZxW/ZWXi\nYMbNInWJM2GCpM7mdkV2TckdOVJc0qiD7CPogf5Cet8+v7FL8+b11qLu2uUXw7BmDfCpTwFf/KJ/\nDEIAilZCCCGEkJJiyRLgD38ANmyQ2z7U1YngAMTF273bXbQq1T8N9sCBgUkPDh0n1yV1jaNUvPPr\n0/wod1yNb1pvTU3fBkp79/o3UNq6VW5v3pytFrW6GvjpT+27RBOSBEUrIYQQQkgJce214pL9/vfA\nTTf5xYh2IT54UNw82069UUJ0ki1F0VoI8fvBB8CZM/JeZ4njc2EAEHfdXKjQ2j+1t75ePvPOTnb9\nJaUDRSshhBBCSAkxfDiwbp04rb7Na6ZNk267x48D27f7jc0BRPzu2dN7f88e4JJL3GKUotjMjRPC\nITWpwa41yLm1qL5iM1qLevgwMGqU3/4ZMULc1Q0bxGmlaCWlgOVUK0IIIYQQUizmz8/2+mHDRHhs\n2gS8+SZw+eV+caJpxidPihB2TV298EJpfnTmjMyxBfy7GYcQrblis7tbmh+5dmnOdUh9a1GnT++f\n1uvjsM+aJXN9TQzfBkqA/Pz77pN9OGGCfxxCQkGnlRBCCCHkPOTqq4FXXxXRmqWhkxGte/dKUx7X\ncT5lZSLmjDDr6pJRKjNnusXJFa1ah3Fa//xncSSNoPaNs2+f++8E9E3lBnrfZ1dmzRKnVWuZj5rl\nwsfdd4vj/9Wv+scgJCQUrYQQQggh5yHXXQesXQu89BLw4Q/7xYh2Id692z01OC5Oc7M05hk1yi1G\nrrN56JDMAR071j1Ortj0cSXjGh9lEZtZ11NVJe9FU1P2WtTaWlnTLbf4xyAkJBSthBBCCCHnITff\nLE7rwoV+DiAgYtO4d+++C8yZ4x8n6tj6iN/Jk4ETJ+Rflji1tSLsDHv2+InNaOq0WY9PnNmzJa1X\na2nm1N7ul2YMANdcA/zpT8DGjaxFJecXFK2EEEIIIechFRWShvvss/4xLrhAUmd375Y04yuu8IsT\ndVp9mjkBkpYcTaX1FZuXXCJuZleX3M/ikBqxmSXOxImSinv4sDTNqqtzT8E2/NVfAc88I07r0qV+\nMQgpRShaCSGEEELOUyZMkBTaLCxZArz8MvD669lEq3Eld+3Klmac1bEdPVqaQ+3f3xvHR2xWVUmK\nc2trtjgA0NAgTvamTcCiRX4xAODOO4Hf/ha44w6gstI/DiGlBkUrIYQQQghJ5IYbgDVrZBRKfb1f\nDCPKAOCtt/xTV+vrezvk+jqtgLikuQ2mfOM0Nkpa76FDwIwZfnHMhYFNm7Kl9U6ZIunFP/6xfwxC\nSpFMolUpdbtSaotSqkspdXnOY2uUUo1KqW1KqY9lWyYhhBBCCBkI7rxTUla/8Q33GaSGOXNE1B05\nkj3N2IjWLVuAuXP94kTHw2zbJnWlPjQ0AFu3yjzTOXNE2Ptw3XXA+vXAiy9Kim8Wxo3zXwchpUpW\np3UzgFUA1kcPKqXmArgDwFwAKwA8ppTvnzlCBgfr1q0b6CUQkhnuY3K+wL0cjnHjRFDddZd/jLIy\nYPFi4PHHZbRMdbVfnIULpcnQuXMiWhcu9Iszd64IzcOHpbGTr9NqHNK33sqW1rt8ObBuHdDZ2Xc8\nEfcxIUIm0aq13qG1bgSQK0hvBfC01vqc1nofgEYAV2X5WYSUOvwfCzkf4D4m5wvcy6XHbbcBX/4y\ncPvt/o7tokXSzfjVV2XkjOu4G8PSpX3Fpu96rr0W2LBBxgpde61fDEDqT7dtE+EabcLEfUyIMLxA\ncacDeDly//2eY4QQQgghZAhyzz3iuN5xh3+M8nJxN++5B/hYhuKzRYukC/GTT/rPsAWA+fOlGdMz\nzwCPPeYfB/BvTkXIUCCvaFVKvQBgSvQQAA3ga1rr/056Wcwx7b48QgghhBByPjBiBPDZz2aPs2YN\n8JnPAJ/7nH+M8nJg9WrgJz8Bduzwj6MU8PzzkmZ8wQX+cQgh6Sits2tJpdT/Afii1npjz/0HAWit\n9bd77q8F8JDW+tWY11LMEkIIIYQQQsh5jNbau8dRyPTg6CKeBfBLpdT3IWnB9QBei3tRlsUTQggh\nhBBCCDm/yTry5hNKqfcALAHwnFLqfwFAa70VwK8AbAXwOwD36xCWLiGEEEIIIYSQIUVI/4lBAAAE\nW0lEQVSQ9GBCCCGEEEIIIaQQZJ3Tmhel1H8qpVqVUu9Ejk1QSj2vlNqhlPq9Ump85LF/V0o1KqU2\nKaU8p28REpaEfXy7UmqLUqpLKXV5zvPX9OzjbUqpDP0NCQlLwl7+Ts9e3aSU+i+l1LjIY9zLpCRJ\n2MvfUEq9rZR6Sym1Vik1NfIYzy9IyRG3jyOPfUkp1a2Umhg5xn1MSpKEv8kPKaWalVIbe/7dGHnM\n6fyi4KIVwOMAluccexDAi1rrBgAvAVgDAEqpFQDqtNazAPw9gB8XYX2E2BC3jzcDWAVgffSgUmou\ngDsAzAWwAsBjSvlOgCMkOHF7+XkA87XWCyFztc3f5HngXialS9xe/o7W+kNa60UA/gfAQwCglPo4\neH5BSpO4fQylVA2AGwA0RY7xPJmUMrF7GcC/aa0v7/m3FvA7Vy64aNVa/xHA0ZzDtwJ4ouf2Ez33\nzfGf9bzuVQDjlVJTQMgAE7ePtdY7tNaN6D/i6VYAT2utz2mt90FEwFVFWSgheUjYyy9qrbt77r4C\noKbn9kpwL5MSJWEvn4jcrQRg9vVK8PyClCAJ58kA8H0AX845xvNkUrKk7OU4Mep8rlwMpzWOyVrr\nVgDQWrcAmNxzfDqA9yLPe7/nGCGDCe5jMpi5G9JAD+BeJoMQpdS/KKX2A/hbAF/vOcy9TAYNSqlb\nALyntd6c8xD3MRmM/GNPOvtPIyWhznt5oERrEnFKnJ2iyGCD+5gMSpRSXwNwVmv9lDkU8zTuZVLS\naK3/WWs9A8AvAXyu5zD3MhkUKKVGA/gaelLbcx+OOcZ9TEqZxyAp7QsBtAD4157jznt5oERrq0ln\n6GmScKjneDOA2sjzagAcKPLaCMkK9zEZdCil/g7AxyHulIF7mQxmngLwyZ7b3MtksFAH4CIAbyul\n9kL26kal1GRwH5NBhtb6cGTs6U/QmwLsvJeLJVoV+irqZwF8uuf2pwH8NnL8UwCglFoC4JhJIyak\nBMjdx7mPGZ4FcKdSqlwpdTGAegCvFXpxhDjQZy/3dPP7JwArtdanI8/jXialTu5ero88diuA7T23\neX5BSpm/7GOt9Rat9VSt9SVa64shJ/eLtNaHwH1MSp/cv8lTI499EsCWntvO5xfDAy+0H0qpJwEs\nAzCpp8bkIQDfAvCMUupuAPsB/A0AaK1/p5T6uFJqF4CTAD5T6PURYkPCPj4K4FEAFwB4Tim1SWu9\nQmu9VSn1KwBbAZwFcH/kKhMhA0rCXv4qgHIAL/Q073tFa30/9zIpZRL28k1KqQYAXZCuq/8A8PyC\nlC5x+1hr/XjkKRq9gpb7mJQsCX+T/7pnNFM3gH2QrtfwOb9QPP8ghBBCCCGEEFKqlFojJkIIIYQQ\nQggh5C9QtBJCCCGEEEIIKVkoWgkhhBBCCCGElCwUrYQQQgghhBBCShaKVkIIIYQQQgghJQtFKyGE\nEEIIIYSQkoWilRBCCCGEEEJIyULRSgghhBBCCCGkZPl/rfsZMkHueusAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115a73f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# next 50 \"days\"\n",
"t100to150 = np.arange(10001,15001)\n",
"syn100to150 = 20 + 10. * np.sin(t100to150 * (2*np.pi)/100.) + 20*np.sin(t100to150 * (2*np.pi)/5000.)\n",
"plt.figure(figsize=(16,4))\n",
"plt.plot(t100to150/100., syn100to150)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x115e34b50>]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WRrnOdmCS6RTHRkymN40TvwfVe+IWOOPPhMlzBYzu+bYZRAYAP/85fc//8i/m\nMTw8Go3vfId+v+su8xhRUmoqdDTKvmuyT24kKTWJ00jhz6TV0gaVIaWDg+mbEm5PUaOUUtM4EmQy\naQNpet7psWP1805tlNLeXpq4BtCZbqYTST08qoCeHiKlNkppUjWY+/zFz2ED5CxKRVWVq0AmpeKY\nDEw6dUpuCGCjzimVUNmbmsw2SVL23SgpVT3fJpNEAeDJJ4Hf/33giSfMXu/h4QIbNtBRZ48/bh7j\n4ME6KTUVOpJIqcmgo3h+lBKBTIqAp07JtLaUyY1qi1xSGgTB4iAI7g2CYFMQBBuDIPjg6Z//WRAE\ne4Mg2HD6101pMdLeKCfpSm0CpEhpI5VSk+QtpZQOD1MBQH1GU6dSjJERXhxgdHVswQJPSj3GN1QR\nRlop5W6e4/YkwHxNaWQ1WMp2W2Vym9TDYxJHSimVPC4t6d6RKrZy48Q3tCbHQACjSWkQ2BVtt20D\nXvMaKmCZFI89PFxg61bg5puBzZvNXj8yQs9ItCXMhJTGc6NUT2mRSmnS+m+ybk8oUgpgGMAfhWG4\nGsA1AP4wCILzT/+3z4VhePnpX3enBZB4o2mbgKLsu5JKqUTyTlJHTJRS9cAqRbqpyfzh7+0dPXHN\nZAy4h0cVcPw4WQqnTq3f6yY91BJ9M0n2XdPEKzF9V0LFKxuZlIwj0eNapuPSgMYPTLIlpaYuoigp\nBWiDbdpXum0bHSG1fDnNX/DwKBsOHaJn7YYbaJAfZ4inwuHDlNPUSRVF2nfVaRfRNkBTB4eEiyit\nh9OE3NrOW0hrkSwdKQ3DsCsMw6dP/7kfwGYAi07/5yD1hRFIkFKpSnnZlNIkNULKvmsSJ74pBswr\nW+rAZICGQ9gMhajVfDXZo3EYGTHrv1ZQKmkQ0LCz9nazQo6EUppUoJJaU6QSLzdO2vo/HgcdScUp\nsqe0bA6gpImbJvkkPojMtK90ZIQGo61YASxa5KfTe5QTO3bQMWdTp9L60tfHjxE/jknKvmvidpA8\n5iytCMgh7mnTbotQOLNaJEtFSqMIgmAZgEsBrD/9ow8EQfB0EARfDYJgRtrrajX7I1gabZfiJvBG\nKqVSydukGpykspj230SV0tmzzRY0hT/6I1KgTGzEHh55eN/7aHKuSSUYqA85UjCdzCnRU5pm3y2K\nlEqcfVZGEphEbrn9jo3Ma0UqpY3uTS1KKY1vsE2V0gMHKM7kycDixcDevfwYHh6NRmcnzQMBqHhi\n0oJ19GjWvuH/AAAgAElEQVR9RglgLnL094/OjSY2/qTc2NpKRIzTG57k/mlpIWchN46twgnICX8S\nucgWLfn/hBAEwVQA3wfwP8Iw7A+C4EsAPhOGYRgEwf8H4HMA3pP02qam23D77fTndevWYd26dYXZ\nd7PsUkUNOkpL3mE4tmqRBqmKchIpNRmSAoxWSmfNMh8pHobAd79Ln8UjjwDXXWcWx8MjCbUa8K//\nSmvDhg3AS17Cj9HTU7/XgTopXb6cF6dR03dN7bvxNc6kYJbWN8O175ZJ4ZRK4I0k262t9Pvw8Nii\ncBqkjhZIy2vcFo5G5TWbIYDR59O0+KSGogG02fek1KOM6O6mGQlAXdFfs4YXI05KTUWOY8eSj3Kx\n3SerWCdOjC44ZSHLuXPy5FgLbBqS8lprK63ZIyN1y7PJ9bhqJbnvvvtw33336f+PcqCVqoIgaAER\n0m+FYfhDAAjDMJpe/hHAj9NeP3XqbbjtttE/k0zeEr1JXLtdUqVExbGdvtvURDd12v8jCZJKady+\na0JKw5BIqeq/sVFKe3roe3vHO2hSoSelHpJ48UXa6N58M91fJqQ0OmkasFNKowl8+nR+dVqKlCat\nP9z1bXiY1gJbp0wWCfS9qfn5Mb6mc+JwC7/qOBtFihW4BY2REbqepMKIreovRUpNVZ/u7jopXbzY\nT+D1KCe6u8mhBpjbzCWV0mhea26mtSqtlS4JaaRUrSlSpJQTJ2n6ulr/k641CY2c25MXRwmNCrcr\nBdIQuvbdrwHYFIbhF9QPgiA4K/Lf3wTgubQXS1WmJXqKGr0JkFBKAX4Cl+wplVBKjx4lYq2+MxtS\nunkzcMEFwMUXA88+axbDwyMNmzYBq1dTBXjjRrMYSb1mXFI6PEwb+ugm3GZYWRSSpJQTR1Vf45Vs\nfyRMdpwibMBhKKNqnzwp850rK3E8TtH2XSmlVG32zzrL/AgpD49GIkpKFy40myrfKFIK8C28WUqp\n1IwDCQeQBK9ROUS3JUkqp9lC50iYawH8DoAbgyB4KnL8y18HQfBsEARPA7gBwIfTYjQ6eUsppUXc\nlGmklLv5S/LKSymlJnGOHqXFR8Fm0JEipatW+SmFHvJQ99fKlcDu3WYxjhwZXR01GYCiCkLRTbhU\nX7jJRl6RiyjKNqDIDzrKjsP5voaHyaUTV7VNNlppLiKJDST3Xh4aItU1+vmo54rbQx53MkgopXPm\n0FRfD4+yoaurbt81vU8l7bvxvekZZ/DyY9qgU+6aIpmPbB1AYZi8/qse1+FhvThlIaW59t0wDB8C\nkORsTj0CJo40MjkwoO8HzyKlasyzDqRIqaRSGiVvCtyHpGw9pfFNuo1SumsXEYalS4GODrMYHh5p\n2LmTSKnN/XXkCNnwFEwUlKSka0pK1UZCwVQpbRQpHc+22yLO35ZQXCULtklxuO6fLBeRSW6M7jNa\nWugXp0UGSD4r0cTSGO0pnTvXk1KPciKqlM6eDTzzDD9G3F3Q3k5ForTckIY0pVSClBaV1yTI7dAQ\nWZmT+k9VHom3UiShLKSUNX3XFElvtKmJPihdQpmVdCU2ASZxJJTStKQoYd812dBK2nej1TEbUtrZ\nCSxYACxZQgMhTM5/9PBIg5owaEtK40qpBCk1ObpCamKpRE9p1rTzKg86KmOctGnAunlNam6DlItI\nSilN2swC5vkx+oyaHEsBjLbvzplD8xdM8eSTVNh+LrWBymMioquL7q27teWjsYiTUgmlNAjoGeLs\nKcNQRnhJW5sklVKpYqtunLQYUnG4hVZbOCGladUQiSpuUaS00ZstCauTychsqUFHaX0EJoSyq4tI\n6eTJtNn3/Tcekti/n0jprFlUdTQ5X7RRpNRk45w2NbdsSqkfdCQzxbdWI+UhqRrOyWtZ39XgoP7a\nLVWwzbLacZ4JKVJaq9E1RfMsd3Ot0N09ejL9kSPmx5390z/R+7jjDrPXe4xPfOc7tOf6ylfMY0QV\n/TlzzM8pje4FAf6e8sQJWsvirQVlU0qLyI95pFSKY5kel8dFYUopwPvA0r48KYVTarCE1E0pZd+V\n6Ecz7SmNLkQtLXQ9JlXlzs66HdGf6eYhDaXEBwHdXyZ2vDgpNTm/MG5zAsyevaTEK9VTynVwSBGU\nspHJMsUZGEg+9ByQUUqDgOLrupqy7LtcNUKi/ytNceU+W/399P9uiuyaTJXSvr76ZPqWFsqVJr2p\nAPD448BnPgM89JDZ6z3GJx59FLj9djpGz4RQ1GqjZ4NIKaUAn5T298u4Jlz0lLoedJRlg5bgWNz1\n3xaVIaWNVji5EnVWhUMijkT/jSKlnAVJ0r4bH4s9fbo5KV2wgP48bx7/rDsPjzTUanUlHjC/v8qk\nlEqR0jSltKiBEFUZdCRlS5awb0mQUm4cqQ1bUlEEkLPvcq3xSZtrU6X0yJHRsyRMLbzDwzQ9/B3v\noH4/39riofDYY8Cb30wKvIm77NgxetaUOlm0UipRWJLab0sqpY2270oprtyzqk1RKCmVSJhF9pRK\nkWQJ+27SRrS1lZqfORUOKfvukSPJCZxrjRwYoMVRVZXPPNOTUg85HDxI96V6lufNM9scxkmpyWY1\njZRy4yStBWo90S1QDQ8nnzUZPbBcB+PVdpvluJEYvMeJk7eZcE1Kpfp/pVxEUvbdtJ5vk0Lr4cOj\n1wvTYUc7dlDP36JFdG2+tcUDoPV7714aELlqFQ3z4+Lw4dGFE1Vo5RY+0kgp59mTGnqWZd8tk1Ja\nJvsuwOc1Nqg8KVWysu1ZPFJksqjEm2Z1MrEoJdl3bXtKATOlVDXaK8vUvHnU5+DhIQE15Ehh7lwZ\npXTaNP69LnUcU1LiVdNGOUQn6axJNWZ+aEgvTqN7b6SOBOPak7LiuC5uNlrhBPg2MKl+2zLZd5NI\nqaRSakJKOzqAZcvozytWmJEPj/GHvXtp39TWBixfbnaUXvzs7dZWema4wkL8GCWAL3SkkUmTntJG\nKqUS50IDcvZdqTwyYey73D6VpA++6lN8pWzAUlantGqwFCnlLmjR6W+AvVJ6xx3A//t/5q/3KBc2\nbwY++lHzISHx+0tSKeWS0qTN8+TJVPXWPWsMkOmbSVtPAB6hTKsEt7XRe9L93sq2bkvEUd9pfHgH\nN07Z7LtZ1X+p1hZOTksa/AUUp5QODdF7ixZ/TUnpvn31o6g8KfVQePFF4Oyz6c+m90VcKQXM+kql\n7LsSSmlaoasIpXRwkLhLUwIL4xRby7b+26LySqlUHKmBSUVMXwTkKklp55SaDDqK95Sa2Hf7+mgh\nVLDpKT16FHj3u6n/xpU/3qOx+OQngb/9W+CnPzV7fXTgCGCmlA4MELmKPn8mm9WkxBsEchMGOQk8\nq/rKSbxZwxO409cbbZeVmE0gZbuVjFOE4iqllEqQUqncmKT4mBSf1CY96kKYMYMKW1zs3VsnpaaK\nmMf4w+7ddQV92TL6OxdppNRkgF/8ueG67ySV0rIMOkrLaUBxbReelFaYlGZtSqpsA05SaySVUm4C\n7+sbbSGxse8+9hjw0pcCV10FPPigWQyP8mBkBPj1r4E//mPgF78wi5FU9OAqpUoljW4yTTaradVg\nybH3HFKaFEPFsSWlAC/xNnq9lVJKOfbdPLtUWRROQG5SPrfQKpEbJUlpXCmdNInWIU5hJGmzL0FK\nFy2idgQPjygpPesss17juH0XMHO7JU2VL1IpbWQ7CTc3VoEbARPIvitVDZZI4NyEKTUwQ4qUSp3p\nlvTwm/SUSg06OnRoNGmwse+uXw+sXUuk9KmnzGJ4lAfPP0/3wxveQAUHExw8OPr+MlFK49ZdgJ5f\n7mZVasKgC6WUE6cKMwWkekrLtpmQUDhNrifp3lE2ZV0rutTEZSlSmrS5DgJ+X2nSeiFBSufP94OO\nPAh79gBLltCf58+nNhUukoonXFIahvSM2Z7oUBWlVKoIKDULQOIoL+712KLwnlKJD75M/S5F2oAb\nlcCLPBImXq2zse8+8wxw2WXARRcBzz1nFsOjPNiyhb7L888Htm41O4strpTOnWuulEZhsllttFIq\n2VPqet2u0kyBqtpuXZBbrjqedB9zJzdnbWg5z2eSUgrwn/NGKaWmipjH+ENPT31WgikpPXQomZRy\n9nCnTtVPgYiiKKU0La9JHVclMRAOKNcsAG4cWxSulEp98GXZlJRNKeU+bEkPv2lPqZRSGiWls2eb\nnZUFUL/NypXAhRd6UjoesH07jbufPZueRZMNWaNIKcC38GaRUomqspRSyh0z3+jJgK7bN0ZG6FiE\npAFFkvbdqpLbvEKErQ24KPvuiRPp551ynnNJpbSri8go4EmpRx29veQiAuj3nh5+0fbwYXv7btrk\n66KUUqmJ3hKilIupub6nNAVlGuaQVp1oaaGNhu40SCmlVLKnVIKUJj38kyaR5Ur3GAhAbvpunJRO\nm0aLB2caqYLqszA9t8ujXFCkFADOO4/UUi7ipNRkc5iloEiRUt3EOzJCz0Zb29j/xrHyZ/WUShAL\nFacsCdwkRvy4HG6cslXKXRQiJHpTpey73GMgjh9Pfj65z7mELRIgknHwIBXSgLoiZuIYUfEOHzZ7\nrYcsDh3i7bfi6OkhVxlAz9AZZ/AHFEncp2nPTJFKaZr7QnedzDrKpYgBRf5IGAOUbZhDUhw1DdI2\nThFKqdqIxg+6B3ikNG1DqyaAchaRNKXU1r7b1ERxucShv59+zZ9PJGRoiL8J8CgXoqT07LOpj4aL\nOClV9iTOAeFJ54sCfAUlK4HrkkmVdJMIk9TZZxIDilQc1wd7p72vopTJ8Vgpl7LvZlnkJOy7Ei4i\ngL/Bjp9RCpgVw44epc9C5eszzqA9gGleu+02yrW+YFssDh4kdfN97zOPEVVKATMLb5p9t+pKqe1+\ne2iIRKy4JRkopmBbtvXfFoUrpWWqBkhsKDgxwlCG3GZtRKXiTJmiX5FSze3xxcikGhwnDQAtlNyK\n7osvkkoaBPRryRIzEuNRHuzYQXZsAFi4ENi/nx8jfn+1tNDmjpMwkwY5AMUopWnJG+CR0qyeUgm1\nCygXgWtupnVBx4GRdS1Vtt1ONPuuFCk1UUol7LtRlVTB1MJbqwH/9/8Cr3898M1v8l/vIYfvfx94\nxSuA736X3zYF0LMxMDC6UGpCSiXsu2nD+7gtYVlKqcS8hfGY04qKY4vKkNKyffASZ7FlHZ4r9ZBw\nEm/agw/wrE6nTtHmLF5JklBKAfo7l5Qq667C0qWelFYZIyNkUVq4kP6+aBEdJM9FUtFjxgze/dXf\nn5x4y0ZKOQqTFLGQnDBYlvXfRS4qG7ktyr6bRial7Lu2x6UBxSmlBw+OPmMZIHXMZKjN9u30Gf3h\nHwL33cd/vYcc7rsPeOtbgUsvBR55hP/63l6y7kbFhfnz+UfpJRVPyqaUci34UgOKpNa3RjtupPLI\nhLLvlinxuu5NkrK25akjug9tVhxOAk9biCR6SgFK6Nz+iP376wQG8Epp1dHTQ2RSDZsxUUrDcOw5\nuADdX5wNYn9/ulLqevpuHim1JQQqThUdLlnOFE6cquQibhzJ/l+JvJZ33qlu72TZlNKkQUcmLSkH\nDowlpXPmmA0CfOwxOi7t8svpuDRO+4KHLDZsAK64gk4KePZZ/ut7ekZbdwHKcdw9U9KshKJIadqz\nJzV7pcpKaZkKthKYkEqpbZWjViNfedIwESlPedmUUs7ZhGkLkZRSamLfjY5IB7xSWnV0dQELFtT/\nvmgRn5QeP05Ohfizw1UtskiphFLKPcpFwr7rYm1yvREYHiZXSlIvEKBfJK2SwlnGHGtLkpuaePMo\nGk1KuYpr0mZ/6lR6v5zhNkn23Tlz6OdcPPsscMklFG/mTN9XWhSOHaNjfs4/H7j4YjNSqpTSKExO\nLZAipRJ92FLPsAultKpkUops22JCklKpSrnE9EWJDZuUOnLiRLmU0qEhem/xhdGk6hevHpraPT3K\ngc7O+lEIACml3O8zSSUF+EUPqSJM2qZXyu3Ase9m9ZRyC29lWbez1ltOHFe5qEzkVnKzJdHXzO2P\nbiQp5Q5bSSpiBQH/DMgk+67pkWk7d9aHxp1/PvDCC/wYHvbYvp3mJLS00NF1mzbxY1RFKXWdGwE3\nSul4HHTEKQLaojL23TJ9gVmV4JYWmYEZUlY77oY2q6fUtVKqRpLHyb+JUtrdPXqhPvNMqiiaIAyB\nb38b2LLF7PUedI984Qt85VwhrpQuWEBElXMcQtIgB6BYpTTpuZFSSouqBjfa6qSbMLPWfs71ZMVR\ndnLd9V9iE+BC4SybA4hbYGlkTyk3Ttp6wV13pEnpihX055UrvVJaFHburA/vW7aMBjRykaSUcknp\n4CDl0vi6IjXoSOooF4kjD4FicqOkfdcFN5oQPaVlGlDEiZMVA9C/wSWVUin7bpZSqhsnK3mfPKnf\nr3L06NjeG8DcvhsnpdzGf4X77wduvRW45RbzM+EmOv7qr4CPfpSOITBBXCmdPJnuXc6mLunYIkCO\nlHKqwUND9FzYHuskad+VILeu+mZsbbec68lb/yVswFXNjep6Gl1slXASlUkpBfh9pQcOyNh3w5Am\nmStSumKFJ6VFIfo9zJ9P9wOHvAHJxQouKVUqaVwQkFJK1bOnu3+S6CkdHqYcqwqHpnEkldIyrf++\npxTupWVVvU66KTnXk5V0OXFc2He5NqdGKqVNTbze1KNHk89/NLXvRntKbUjpd79LpOroUeC558xi\nTGQopfmf/xm4804zYh9XSgHaoB04oB8jjZSa2HdtBx2pZy/tfFHX9t3xaFHKI6USZFLqeqq8KZHa\nbOXNSigLKa26UtrXR+uOmkLuSWlxiCrWTU00kLGjgxcjyQHEvS+SrLuAHCltbqb11vYZVkVcnV5s\nVeSyPTqxbIOOyjbjwBaFk1KXH5irSrlrpVTK5tToQUcAr6qctjAWrZTecw/wqlcBN94I/OY3ZjEm\nMnbupHvyv/5XSry7dvFjxJVSgK8SSCqlthalvGdPipRKnVPqsm8+DGWIjgsyKRWnbAOTyjbcSsK+\nW0alVKKnlKuU7t5dP8Mb8KS0SERJKQCcfTbfwqvanqIwUUqT7tHJk0nc0R3IlbUX5LjvJPJj3pyE\ngQG9ArmrQUdlKvxOqCNhpHzTZalwc+K4UkpdHwmTtkkHiiGlIyO0IEcT+JQpZOXgHk599ChNx7v4\nYuC664CHHuK93gN44gngqqtoE3TNNcDDD/NjSCilSUczAHJHwnDu9bQphUD1p+/aEiY1NdfW4eJi\n0BGgn8DLZt9yoQBIFkaklFKdjWgYNl4p5Q5/SZpMb3IkTGfn6OPSli0jourhHh0ddDKAQpGkNGnv\nFQT0c1211FVe09njZrUDNDWR6qq7bpfpDO+yOWVsUbhSqvNGXZwvp+LYKpxAuZRSqSNhJOy7AG8U\neNrCyF1gDx6kRTq6qQ0Cs2FHmzfTdMKmJvNzxCY6nnwSeMlL6M+XXQZs3MiP0dMzdpiDlH13xgxe\n0UOClLpSSjn23bQ4rqu4Lsmky/aNvE2JGjaiE6dMm5K8c0pd2ndHRuhzTIrT2kp5QEf1GRigf590\nnBDnOa/Vss875RyTkVRUM7HvdnaOLvDNmEGfCbdg62GP7u7RbUZLllARnAMJUtrfn7z3AijXcYqt\nWUqplJNIZy3Iyo2A3L7d9TpZtqKkLSpBSoeHKRk0pVytVC+o7o3gwgbMsRNI9pQ2ctARwFdKk0gD\nt68haUQ6YGbh3bQJWL2a/nzeeTSYQGfSpkcdmzcDa9bQn88/H9i6lR8jybomZd/lKqUSUznLZt8t\n04TBsjlcXMRR56nqEqaq2He5BQ3bAou6lqQ+MkD/2ZIq2Cr1N4nccs9uTCOlXPtuV9foVoggIOW0\ns5MXx8MOg4P0/aveXoAIanc3L04SKZ0+nfKU7l4lTRAAePdp2vRdQHaqvK19V8WxJaWqsGlrAy6b\nUuqPhImhjJuJRt/cUnaCKiulaYOOuKS0t3fslEKAlDYbUtreThVm33/Dw7Zto8/E4x6tU6vRhixu\nXZNSSrlTMKuilHILVC76AnXiSCqTeeu/VD6SiFPVKb4S9t2REdpAJ02j5sTJU0ckSCnnOU9bKwDe\nEVJhmExKp0yhz47TKxsnpQCR0v379WNE8X/+D/DpT0/MyfQ27125f6LiiwkpPXRoLCltauI5gLJI\nKbctJW0vyJlTIpEfs4pcgNy+nbNuN/pIGMmjxSZET2nZ7FtSg46k3pdE4pUYCAGUa9ARl5QeOjS6\n+qhgopS+8AIppAoXXEDKn4cehoepX0mdxbZqFQ06GhnRj3H4MN0X8R5DE1Ka1FPKsdFl2fGklFLX\nFWWgXGPvpdZbV8XNMp2bWoR9K8+WxilEZCmcEqRU1wEkZUPMIqWcgu2pU7QBjn/Oaooux8IrSUo3\nbwb+8i9psvovf8l/fZXx/PPAX/wF8M1v0jBELpIcXVJKKcCz8EoppRLPjXKLpBWoJAYdATKkFOC1\nAbooJvrpuwlw0Qs63pRSbhyJQUculFIJUqpIg+55p0kDIQAzUtrRQcMHFM47z8x+OlHR0UGJVt33\nkyaR7ZZjFevrSy4yzJ3Ls64dOZKslHIGjqhNr22vWRntu1lxXBIdF4PlgHKRSU6csg1MklAAdDaQ\nuhvRPKVUJ6+VTSlNK6gB/EGASUPjFiwwI6V33gm8853Ahz9Mx35NJNxxB/AHfwB86ENm7z3eTwrw\nSempU1TgTbrnZ83SL1a4UEp195RZzx6gX6ByoZQCcjMFXK7btRrdN2nE39t3YyijwlkmcpuVwDmW\nvRMnGt9TKjHoqLmZrlN3YUyyswBmB413dNDwAYVly/jT8SYytm0Dzjln9M+WLuWdxZbUTwrQzyTs\nuxylVGrSdFbvTRH2XQnb7fAwKTdVmZo7Hm3ArntKpWy3LlxEgPueUimlNG1yOMDviU9TSk16Su+5\nB/it3wLe8AbgJz+ZWBbeu+8GXvc6Oi7u17/mv15CKT1yhL7/JIfBzJn6DjOp+zRv+q5EYalMPaUq\nji2PaGsjhVhHeJEq/La1pTtTxp19t60t+edl3ExIxClT4uVuaMumlCaRBoBXVU5TSrk2pxMn6Nqj\nU19NRrZPZGzfXu8nVeB+hllKqRQp1b23spK3emZ0NmZ5m17X03d1znTLw0Qlk1KKq0Qc9Z5cTfFV\nMdI2NxLuH0DOvqubH/M218eP633GUkppmssD4CmlYShn3x0YAJ55ho77WrKEPtsdO3gxqoojR2i2\nxJVX0syJY8f4U3OTlNKpU+k70iWBhw8n73UAXttTnlLq0r6ro5RKkVvX9t20OEHgdqaAVI6VgBNS\nKjE1t0ykVGqz5epoGY59t9E9pZxx4mmDjgDeAitFSvfsoWQb3XB5pZSHXbuA5ctH/0xSKeVO301S\nGyZN0j8gPGuT2dxMiUXnGdax79pO4nY9oMjF+gZUN4+4iMOZ4itVcW/0hg2Qs+/qFnyynk91VIzO\nBtKFUsodaBMEY6/JxL67cSMVHFXuv/pqYP16Xoyq4umn6ezylhb6PC+5hH/UWRIpDQKeWprWTwrI\nkVLOHk5i+q5UYcmVfZdzikej85pr96cEnJDSNBQxfVdik6RzPWVRSpWNSmdT4koptbXvArwFNq16\nyD1oPG7dBUjl2717YtmUbLBvH7B48eifcUlpmlLK7aVKU0rVAeE692nWvQ7IVIPVJG7dXjwp+64t\nuXU1oKhMyiQga98tC7mV+s451jZX9l3d5zPrOdd1AEkqpRL23d7esec9A6ScdnXpxVDYsqU+mR4A\n1q6dOKT0qaeAyy+v/33NGj4pTTu6rmyktIxKqW2bG1CuQUcqji2vkZgErOJMCFKq+6EPDqZbgDlx\nXJLbsiilgP5Dm9dTWpZBR4CcUspR1vbsIQIVxcyZRBo4B1Mr/PCHlLx+/nP+a4tCby9w3XXARz9q\n9vq9e4FFi0b/bOlS+mx1kaaUqntXl3hlWeB0hx1lbTIB/fs9yx4IyFiUlLqkq7iWyb7rwpkiVVWW\nJMmuyO3wMPUvNbr/V2JOAuDevpu3MdbNj656SnWLc2lr6Zln0lrPwZYtdMSXwpo1NJF2ImDDBuCy\ny+p/NyGlSUopwCOlafMzAPdK6cgIrRm27jupntLxqJSGocygO6lCqwQqQUpdVrilpO6yKKUAb0Mr\n0dcmZd91QUptlVLAzMI7MAC8//3ArbcCv//77qpQtviTP6Fq+A9+ADzyCP/1SUoptyp/8GCyUgro\nb8iGh+nZSrtPdVULKVKqs+m1JaUtLVRA0TlAvUzni+qst64HC7l6X642NypG1qALl24kV/ZdiZ5S\ngKeUZq05UvZdXaW0ry+ZlM6eTeuozlqhECelq1dPnOPSnn56NCm96CI+IU9TSjkFAimltL/fXilV\nOS1tTSmip7RM7SQS6//wcL1FIwktLfXJulmYcD2laSjTlEIVx7VSKjUwSaKS5EoplbDvSg460rXe\n7t8/VuUDzIYd/exnlMD/9E9pGu1PfsJ7fRE4dAi46y46h+6DHwT+4R94rw/D5M+QS0rTNlKAPilV\nQ7TSEibHvptFSiUTr1RVOW9NUapZWSaoVsnmyo1Tll5ZneF9Lom2lKrhoqdUxZFQSqXsu7ZKaXMz\n30m0efNoUrp4Mb0fExdRlVCr0VT56PnlK1fSkCdOW0+aUsqZlSBp3826TyXcP657Sss26EiqmJi1\n3gaBTJwJY98dj+fCqTiuldI8q5OtUlrUoKM0e6WEUtreTlUmXQU4LWksXkwKIAd33w28/vX057e8\nhche2fGrX5F1d+5cGvn/85/rnxUL0GTcKVPGJgZlT9JN4GkbKUB/Q5Z1bwE8+65Er5nLCYN5caRU\nM0mC4oK8VdF2KxVH97PJe0bL1pIiad910VOqW7DNOqeUM+goay2dN0//HO/hYSJh0eO+ggC44ILx\nr5bu2UMEPnp/KCePLiGv1Sg/JvX3cooDeaRUV0GX6CnNe2bG6zmlLgt4edeiG8fbd09DV1qW6il1\nNb32uuEAACAASURBVHijjMMcbCeA6h5vEYYySqmafpr2vnRJaRhm91lwFvw0UrpwIZ+U/vKXwKtf\nTX/+z/+ZlNO856Bo3H03cNNN9OcVKyhpbdqk//p9+5KV5smT6f7STeBpg44AOVIqZd8t29h7nbUg\nr8ile4aan5qbHycrr+mQ2zCkf9NoUqqGbeVdTxXtuxLPp4RSqu4FnXtHatBRFinl2Eb37aNiZfwz\nuuACXo6oIpLO3g4CmjK/a5dejL4+yjlJ6wFnIGOZpu/qDAEcj+eUSolSEuu2VJwJY9+VkpaLUEpd\nyPeSVWVb+25rK31feVN8BwfrG5gk6FaUlX0kTa3RXWCPH6drSfveOX2laT0fixbxxud3dtK1q0mF\nixZRhbTMQyHCEPjFL4DXvKb+s7Vrgcce04+RRkoBnoVXQinNGnIE6KsWVeopBfQSeN56UtV1W6oa\nLEECpeIMDZHVMu3YNd04UhV3KReR1AayjD2lWeuF7rojdU5pVn8+Rynt6Bg7BBAgS+vWrXoxqook\nUgpQ4XbnTr0YaQVvoDj7rq1S6mIyPVDMkTASopQLhwugV9z09t0IJD4wyU2JRI9T2ZRSTiXJdmOc\ntxDpVtmyFkVAf4FNs+4qcEiplFL61FM0FCFKuK+7DnjwQf0YrrF7NxHTVavqP7vqKh4p3bt37JAj\nBQ4pzVNKdRTXLPsboG/fzbvfOaQ0K47LntK85A3IEJ0yklJXCqfU9biqlAN6947kvAUX9t0y9ZQC\n+uuO1KAjKaU0aTI9QL2VusQsDtfHrJn+/9JIKUcpTSt4A3xSmrbfmTFDb8+kns+09UBSKZXqKXV9\nJEzW9eist2FIBUVbPuJq/W9tJScfp13LFIWTUtcVd1ejnMt0uLxOJWloiB6UNIUT0FtEdDbpOlU2\nV6RU1xrT30+fT9KGYuFCnlIan9QHAC97GfDAA/oxXOPxx4mERon0lVe6V0qHh+m7sB3yMR7tu2o8\nvG0C11HNpAhKmQYdlXHGgW3BVvd6pOKUbXhflXpKAZ5SKjHoKGtonIRSqgb+cPHFL9Lz+Hu/52Yj\n/KlP0X37P/8n/7WNVko5hXMJpTRr8i7AU0olhve5nLcgpZTq5EblPkyDy3U7r0UyCPSLrbaoBCkt\n2zmlVTwgXKePLOvBV3FsSalule3o0eyFUZc06CilOlXInh5KGkmLyKJFfKX00ktH/+zaa4GHHtKP\n4RqPP04kNIqLLqJjAHR7YZPOKFXQJaWqPzjNrjhrlhwprZp9VyW6LCunzmY+r6Ks4uiQW52BQDrD\nc8qmlJaFJJeRlJapYFulnlJARimVmL4L8JTSPFLKUSG3bQNuvx149lngmWeAO+7Qf60J7r4b+Pa3\ngeeeA771Lb5jKY2ULltGDiMdqP1FEjhKqcQ5pXmCQBmVUl37rlSRtCq9oK7jSKBwUirld9bZ3JTN\nBiyplNo+tFn9pApSSqmufTeLNLi273Z3p9trZs2ie0J3qvDTT48lpatW0es5R6O4RBIpnTaNhlvo\nHoeTdEapgi4pzdpEAe6n77o8EkaHTOY9w7pkUmqiX1YczvAcqR7OvOKmbk+prX23ViPVP8uZUiYy\nCegVW/O+c2UDyytklW36bhl7StNI6RlnkPNJ516Wmr67Z0/yGd6zZtF3rktuAeALX6AzvC+4gBTT\nT386f5aFDT75SeBv/gY491zg4x8HPvc5/dcODxPxXLly7H/jTOXP2l9wjq7LUkqnTaO8lxcn6zgY\noHzTd6VcEy4HHem2S5SJlLrqKy2clLqeDOhi0JFLpVSdKdjSkh1HZ0Obp5RKkNK2tvrEyCxI2Xez\neiwAHilNq2QGgb6Fd3CQEnjStL6rrgLWr8+P4RojI8CGDcAVV4z9b6tX609XlLDvSpHSrE0dwLPv\nujoSJu/Z0yGluvZdKXJbpsmAZYqjiG1Z7Fu6cWy/8yDQy486/V8SG9Hx2FMaBPp9pY1WSgGehXd4\nGPje94B3vIP+vnYt9Wb+6Ed6r+fi6afpKJabb6a///ZvA//+7/rnxXZ00L4g6R7jOKiy9hdtbXS/\n5+13wjCblLa2Uqy8+11HKXU56Eiyp7Qsg44k1+2sAiknjs71ePvuabj64ItQSm296epBy9rc6Dy0\nukqp7aAjQM/+IdlTmrZIA/o9pVlJA9AnpTt3UgU16X7mTrN1ha1b6b0nDRfikNI8+25nZ36MrCFH\nQPl6SnVJqU7/jZRSajt9V8WRqgbbxqliT6luZbqKPaVS1u8yTd8tU0/p0BCRt6z3pUNKBwfpvaet\ngxI9pQCPlD76KOWI6EC9970P+PKX9V7PxR13AG9/e73lYfp0mu/w05/qvX7bttHXGsWcOXTf6BCv\nrEFHKlbePuXUqXrRJw06Z5Xm7b3a2kgIyVubpI6EkZy+Wxb7rpQbKW+CO6CXR/LcP7rXI4HCSanu\nB1aminuZlFKdm1t3QyuhlOYpR4Ce/cPl9F1OT2kadKuiaf0nQHmV0sceG2vdVVi9Wu9w9P5+eibS\nCOX8+XobIJf2Xd1qsK19t1bLT3Q6CVzSvuuKoEjYgF0rpRI2YKlNgEsyKfFdqTi2aj1HHZHY0Eoo\npWGop5TmrTtq7coqROusg319lBvT4ugqpceO0f2ctrZzSem1147+2ZveRHMYdNtEdDE0BNx5Z12V\nVfhP/wn41a/0Ymzfnk5Kg0B/X5BX9NbpK81SSRV09k15g46CQE9YKFtPqcT03eFh+j3PmViWqbmS\ncbx9NwKpD8zlwAxXPaW6lj2JnlIdC2FeRRnQqypLDTo6ciR7oZboKQX0ldI8Uvr447xpg7t3A3fd\npUesh4cp2T7yCG/wxBNPpJNS3XPolHU3awPU3Z0fp4xKqa2CcvIkrRlZA4qklFKX9l1XCbO1lTaY\nec+NzrotcZRLFQcUSanaOgUNnaKtTm6Umr6rszE+fjw7js5zPjhYn2KZBp11J2/tAmgdzFPEsibv\nAkRYjx3L7+dU/aRpazuHlK5fT46hKCZPBm65BfjmN/Vi6OIXv6BrO/fc0T+/4Qbg/vv1YuzYkdxP\nqqDbV5pX9HZJSvMEAUDvftd1/+TtRfL2prprgcQ5pa5aUqoaRwK5pDQIgsVBENwbBMGmIAg2BkHw\n30//fFYQBL8MgmBrEAS/CIIgo0MrHS79znlVXJfy/fAwPYy2FRcppTSvEgzI9JQCMvbd9na9YQ55\nC7VETynAU0rjSVBh7lz6tWVLfhyAkuoVVwD/8A+kWN5zT/q/PXYMuPFG4BOfAG69FfjIR/T+HwCR\n0qR+UkB/w5HVTwqQVezgwXxiUbZBR3mkVOeZ0SnkSPWUurbvSqlvEjMFJKa4h6E7+24V47iy77a1\nUQ7NG5gk1VN68qT9c67jIuIopVmYMSN/HcxbS5ua6L8fOJAdp6MjeciRAues0sceG0tKAeBd7wK+\n8Q3Z42HuuAN45zvH/nz1avrsdPJ5HildtIjaVrIQhvlFb519iiQpzVLzAb37NC+vNTXRc2x7MoSu\na0KClOrktDIVbKXjlKWndBjAH4VhuBrANQA+EATB+QA+BuDfwzA8D8C9AD5ucgG6ZNKF1UkRRCXR\np0HqnKLJk7MtOFJKqVQcnQSuq5TmLWg6FpLp0/OJQ95AG91x63mVTF2l9IUX0pVSQL+v9MAB6oX5\n4Q+Bn/8c+O53gbe9DfjZz8b+28OHgVe/mqYZrl9PQ4t+9CN6bR6GhoCNG8eeq6qwYAF9B3nfQ9bk\nXYBIxfTp+d+FjlJ66FB+9VVn0JGEfVenoqxbEKra9F3XVWWJKe46xcTm5mxVW9e+Ox57SqXsu3lW\nO87AJFvrXxjmqzW6ypHOZj9vLdVRsnSKc3mkFNDrK923L5+U6hQuu7rovSXZYV/yEvr8pc7y7usj\n19Att4z9b01N+ueGSyil/f11S2wadPYpefMzAPdKad5eUKeY48q+q9tW4KLQ6jqOTjtJaey7YRh2\nhWH49Ok/9wPYDGAxgJsBqBOk7gDwBpML0HmjrnpKpeK4rihL2XellNK8OBJKKaC3wOoqpXkkJq+S\nKdFTChAp1ekr/V//ixKq6r+54QYimr/7u6OHNPT0AK98JVmDv/xlSrgzZgBf+hLwsY/lKw3PP0/n\nraUlzCDQOyQ8a8iRwvz5+RbevI3UpElUXMq73yXsuyMj+VVcXfuuzhnBEueL6tp3qzToCHC3bksl\nb9dTE13mIykbsM712BZqdJ6rwUFaU7JcTVIuIp1imJR9V4eU6vSV7ttHRdk0LFxI15K3Dq5fT3kq\nqVAfBHW1VALf/jZw003pewPVSpOFMKS8Z6uU5g05AvTtu1nzMwCZnlJATynV2QvqOonKMuiojPMW\nysSNJMDqKQ2CYBmASwE8CmB+GIbdABFXAPNMLqCKH7yrAUW6CqfUoKMy9ZTmDYQA9ElpliLW3k5E\nLe99SUzfPXmSElDalEJAb9jR4CD113zoQ6N/fvXVwI9/TMn7058GvvIVIrmvfS3w+c+PTvavehUl\n5B//OPv/9eSTVKXOgk4lPE8pBfSGHeX1QQF6KoGEfVclyyzVTCLpAnrPsG4vqCv7btUSr4TaKnUt\nVY4jcd6pVF+zRE+pzrwFqZyms+5I2nezXCeAnlK6f392wbGpiQqX27dnx0mz7irceivwb/+WTYYO\nHgTuvReZk9zDEPjHfwR+7/fS/80VV1DbShY6O+n7yiJwOkpp3t4CkLPvzpjhTinV2QtKKKWtrflW\nfuV+zDoXerzadyUdN3lxHn44+7/rIKP2NxpBEEwF8H0A/yMMw/4gCLRHpdx2223/8ed169Zh3bp1\n//H3qiVenZ4iyYqyxAZSatCRbjU4j3zo2nd1SKmOfTdvoVZVyLQFdHCQrierCqlIaRimW7J37KAz\n17Iq7pddRoODshbin/yE+l6SKrRr19IEw89/Hti1i9TR17xm7L8LAhq1/4//CLwhw+OQ1U+qoENK\n9+4FXv7y7H+jM+xIZyOlLLxZ1fu8jd2UKfScj4yQXTMJOsqHlH1XsqdUZ23Ku56qrds6cZTCmfUM\nu7ISS8fR+Wzy7mVde5tUXsuLI+EeUM6BrO/c5bwFSaU0z72iq5Tq2Hdf+9rsf3POOeQSuuSS9H+z\nfj3w4Q+n//f588lW+/3vkyMojjvvBD74wfoxZR/7GPDRj479Xp94gj7DG29M/39dfjlN/K3V0ouO\nedZdQM9BldcaBND3lEeS8wrwgNueUl37rq1gEgT15zjt/6cj3ozHQquLOPfddx/uu+8+AMDXvpYd\nQwdapDQIghYQIf1WGIaqE607CIL5YRh2B0FwFoDUpStKSuPQZfFlsTqpnqK0jaruteiqEbY9M4Ds\nkTB5FhKd6piUfXfaNHv7LlA/FiZNweztpQFEeYpYe3u2kpd1ppnC5MmUVDdsGDsaX+HrXyc1NA0r\nVgB/93fZ/x8AeMtbaBOgpicm4YknqHc1CytXUt9pFvIGHQF6SqnORirPuqZst1n3aRDUiydpiV6n\ncCKllEr1lOrad/OIf5kGHenGybPeql7R4eH0irqUfVfSBiy1KdH5zl3lNQmlNAzz46hhK1n3qs7z\nWTalVPdImDxSpbMm5ymlQJ2UpqFWI7vsVVdlx3nXu4C/+RsaUBQlm3fcQUP87r8fuOgiymmvex1d\n2+c+N/rf/vVfAx/4QHY+nzOHcv4LLwDnn5/8b3RJaZ59N681SF2Pjn03LzfqzG3Qse9KzkqQcCqo\nPW5aTi9be0IZyGQUNvkoKjT+7GfAnj23ZwfKga5992sANoVh+IXIz34E4HdP//mdADTGpoyF7gfm\n6gvMu6F0bsqWFkqIWQOTpHpmJM8plRh0pNNH4Mq+W6vpJfC8g6l7e/OTBpBfFd2xI5+UAtkW3q4u\nGsDwX/5Lfpw8nHFG9qj9gQHqKc2qbgNy9l1dpdTWvqsKHllDxoD8DaKkUipVWHJp35UYdOQqgddq\nVIzIsm/pxCnbZqKKcVxNlR8aIuKR5UwB8jfGUmd4SymlOgVbiem7gN6anNdTCuST0i1biATOy2kC\ne/3rKaf/4Af1n33968AnP0nT5y+6iH62ZAlw331kJ/zAB+pTex98EHjoIXIJ5eGKK6h9JQ06+XzB\nAto/ZO0FdZRS19N3pXpKpey7tu0tkseclanQWsY4ttA5EuZaAL8D4MYgCJ4KgmBDEAQ3AfgsgFcF\nQbAVwCsB/JXJBVTtg9eJEQR6cco26EjCvqurlEok3rwFtr+frjlvU5JXhezpyU+WACXmPFKaV1kF\nsocdfetbwBvfmE/YdfHOd1KVOWnQ0+OPk2qb933mkdKhIUrMZ52VHSevKn/qFMXKu548UqpTqADy\nhx3pKqXHj2cP0pLsKXVpUapSNVi5bfIKEXmW2TK9J06cspwvyrke23tZp0gD5BdbpZRS3ePSynJO\nKZA/fG5oiOLkkapzzyXVMQ15/aQKLS3UcvLf/hvlwj/+Y+C224iQxhXNWbNowu6zz1I/6ne/C7z1\nrTRrIe97AGiWQpZlVieft7QQ2e7qSv83kkqpDinNuy907LtS93teW8rwMBUU8oqJeflRx76r1qWs\nXF3GeQsuXaQ6rlZb6EzffSgMw+YwDC8Nw/CyMAwvD8Pw7jAM+8IwfGUYhueFYfiqMAw1Tgcci6rZ\nd3ViADKKq47VTnLQkc7G2PbAZEC2pzSLlOos0kD+gq+rlC5ZQrahNHBIadKxMGFI0wezrLtcrF1L\nG/VHHx37337zG+D66/NjnH02kfG0Q9a7uujzyysO5G2A1CYqj1jkkdIjR/RJadZ9qpN0W1vJFpq1\nprjsKXU5fbdM/Te663aeZVYnF+nkNJdHwuiQSV0FwNXEZQmlVJeU5j1bOgXbSZNoA52liOnkNJ2p\n3y4HHeWtyV1dVLDNamcC8pVSNXlXB9dcQwTzO9+hNXj9euC885L/7fTpwN13k4vpG98A/v7vSW3V\nQd6wI918njfsyOWgI505HLrTd3UHAWZBFW3ToJ7hvJyvs9/OW0+am/PPu3Z9zJmEi1SqDUS3LcUW\nrOm7jUDZqsp5CVzn5paK4yp5A27PKXV1JEzeWZQKUkrp0qUypPSccyjBxFXDxx+ne+plL8uPoYsg\nqKulcfzmN3TUTB7a2kglfvHF5P+ucxwMkG8V07GbAfVBR2nQVUrzEq/OJhOg5yFv0+vynFJX9t0y\nqYq6pDQvjmQrSVkKrZJxXOc1240okO8A0nk+gyA/jq5916VSqkNKs9wrOv2kANlYjx9Pz9fr1+sp\npQrr1tHAvy99Kd+BM3Uq9aH+7GfAzTfr/z9e8hIadpRUaAhDvRkRQH5fqY59d+ZM+uyyJsweOiRz\nJIzuoCOpc0ptXQqAjH0XyF+bXNt3pdxIOkVSqXxki0qQUomNwMgILSR5ak3ejcDZ3Ejd3KofIglS\n9l2dDW3e5hrQP6c0S4EaGqJEkPf55A060lVK1aCjNOgqpUuXAh0dyf9taIgS07Jl+XGamoArrxxr\n4f3a18YOeJDA298OfO97o+/XU6eARx4BrrtOL8bKlekj/3WGHAH5GyCdTRRAidmFfVcn6QL5VifJ\nc0qlpu9KqWZVU0ol4qgjCrLWbZcDKlz2gkoUfnUm3Ks4UkqpxMY4zwF0/LieLfLUqWzyoUtKswpz\ngN56qgqFaZZGnX5SgHLWqlXJaunJk9RTetll+XFcYuZMem+bN4/9bz09lKd1itV5syZ07LvNzfSd\nZ+U1l0fC5JFS5RrIe4Z1+rklnmEdRyGQv6a4PMO7ikXJcUFKJc/QKcumBJCxE+j0pkrZd3V7SiXO\npsojpUqByiNfkkppljWGo5SmkdKODqoW520gFV7xCqoEKxw7Rnald79b7/UcLFlCG4If/aj+s1/9\nin6mo0wCpO6mkdK9e/OHHAH5GyCOUppHSnXuC4meUiCflEpNKZRyTZRpUmGtRgUdW+KlQ9504ugo\nnEGQn9fKuJkoi1I6OEjEPms6KiBn35UYdATkP+f9/fm5MQjy8+OxY3r23f7+dHKrPjeddae1NT3P\n6iqlQHpf6YYNNLtAhzS4xlVXkUMpjk2b6Jp1CsQ69t08tRfId3RJ9ZTqTt/NukdVTsv7fPKEDilS\nquuaaG/PdziWab11GUe3LcUWhZNSV1YnSVIqZd+VIrcuj4RxoZTqbvZnzMheYMvUU6pr3VV461tp\nwqB6yL/5TTrnU3cDwMU73wn80z/V//6d7wBvepP+67N6hnSV0ilTqCKcdm9IklJXPaVAfjFHh5S2\nteUfEO66p9RFwlRkUmdAkVRPaVZi1XHt6FyP7gh+l+eUulRKJWy3UoOOJJXSrOdcd73IK4YdPZpP\nGpqa6N+k5UedflKFrL5SXaUUoByRREq51l2XuPLKdFJ6wQV6MbLsu6dO0f2VZ7sFsh1dYShzTqly\nqeU9w3l7OE5udEVKXdl3pdxILltkJNtSbFEJUirxgXEUTgkymWdv41Ru8hKvy55SF9N3dewjgDtS\nqquULllCySfJtsclpWefTQd4f+Mb9Hn8xV8AH/+4/uu5eMtbgK1baYz+7t3Uf3PrrfqvzyOlOkop\nkN1XqruRKtP0XUBGKVUHhNsqQ1I9pVITVF0OKHLplJGIU7ZKuSullJMbJSx7Oj2lOhtaHaVUZ73I\na0vRXb9mzUq38Oq2QgC0Jqe1VXCU0jVraBJuHFUlpatX68XIsu+qIUc6imuWo+vECVK0855hdW+l\nuZE4x6XZ5jRApp8bKJd9Vx0JmVVALtu6LSH8heE4UkrLZt+V6CmVqLjoxNF9SIIgfToqINNTGobl\nUkp17btSPaXt7bRZSErgXFIKAH/1V3T+2g030DEwV17Jez0HkycDX/wi8La3ATfdBPzpn+pvWoBs\nUvrii0TYdZDVV6pzhAHgjpRyekpdJV4J10SZ+maqSCaB8WnflarcS5JSWxeRilM1pVRn/crqK+WQ\nUiml9LLLaHBQHI88Alx9tV4M17j0UiKg8fueo5QuXpyulHZ16Vl3gex9im4BftIkciOlPTc61l2g\nmkqp1H67akVJScdNnosob2aPDgonpXkVbtVT5OLQc8k4klN8JWwJOonXtqdU3ZR5N6YOKa2iUgqk\nT+DdsQNYsUIvhsLllwO//CURxL/7O95rTfC61wH/9m/AZz8L/Mmf8F67YgW976TCx65d+u89awNU\nhH3XlVIq0fvmcvKp1DEjOsU73XXblgSqOLY9pTpxOEfCZJ2b55rcSgy3krTvuugplSSlukpp2roT\nhvpOoqyBb1KklKOUnnMOFXij19TRQffMOefoxXCNM86gXthnnqn/rFYjcn355XoxlFKa9BxzSGmW\nUnr4sJ4FGMgedqQzeRfQG+ol0dri2r6bV+iSEpOqWJTM42q6rS15KJyUlq2nKC+ObsLUse9KKK66\ntgQdlUW3pzRtk6SjkgJ69l0ppVSHlM6aRf82yXZ76hT90lFcAVIEk4YdmSilAFWXb7kl/xw4Kaxd\nS2PzuRN+1bEwu3eP/vnJk5RIdavpefZdiSNhdM8plTwSRsLqVDalNCvO8LD+tHMX67buoCNXPaU6\n5La5mXoDs86+lCKTEgM8dL9zl/ZdqcmdrgYdAdmk9Phxul6dfJBl39VdSwE5pbSpCbj44tEE78EH\nacK79ER5SaxdCzz8cP3vL7xAhH7uXL3XT5lC93PSd+FaKQWyhx3pFjzy9nCS9l3X03cl1iYJx6VL\nUuqyRTIPlSClZVI4pey7khYlVwm8uTm7WqLTTwrIKqV5R8LokMmWFvr/JVWVe3tJJdVNmkkTeGs1\nYOdOvTPNqowkC+/u3fSZ6JLqPPuubk/pkSPpxRPXg46kSKmE1WnSJFKz846ZslVKXbdLlM0pI2Hf\nlboel6q2y42fVMHWpVKqs15Mn55OSnVJA9D4ntL+flpLdMkQQGd/Ro86e+AB/WPHisKrXkWOJYXH\nHqOpvBykDTuSVEo5pDRt38Sx70rc61KTryXtu1lxXDolq9ZTOm5IaRkHVEj1lOZdj1QCl6ok2Vqd\ndJVSdfOnbdp0ldL2dqrOp8XhLNRpFl7dflKFs88eqxZ2dNB16BChKiNpuuLOnTzbsoRSqgY+pCVN\n1/Zdiem7gMwzrI6ZsiUFksRCarCcqzwidbRM1XqTdFxEEjlWqtDKyY1ls++mkQbdtQuQI6VpSun+\n/aSSclTOV76SjhsDqGj4058Cr361/uuLwCteQeRZ3Wv33w9cey0vRtqwIyml9NAhGVKqu/fS6Sl1\nVWgF3E3flcprnPU2r31DakCRrX1XNzfmoXBSqvPllWkTUIR9V8KWINFTCmRvsHWVUiB7UdOt1gVB\ntoVX174LpC/4nH5SADjvPJpiG8XmzfqT+qqMJKV01y5g+XL9GFlKKcdyltVXWsSgo7y+GakEbrsW\nhKHM+aJF9PC7uB6XR8IAeoqr7aZExZH4zqUKES6PhDnjDJncmKceSdh3y0RKOdZdhXXrgEcfpc/p\nySfpOyx7bpw1C7jkEuDee8lh8vOfA699LS9G2rCjIpTSvJ5Snb1XWxtNl00bnqm7F3RFSl3bdyXy\nUVMTFdht20ny1v+hoXqriE2cCdNTWrYBRa7tuy76b9TiYmt10t2kA9mkVLdaB2STUl37LiCnlJ5/\nPrBly+ifcSb1VRkSSmnaBigMeRspF6SU01PqYvquRDV4eJiSk21foMuBEED58ojLXtm8OOq7lOhN\nlVBKy9hTWjaltNGklHNO6VlnAZ2dY3/e0UHOIA6mTydieuedwJe/DPz2b5e7n1ThHe8A/v7v6ai0\nhQv5rTiNVkql7Lu6e68gyC7ClG0yveSgo6q1pUgVSKW4UR5KT0pd95S6JLdlse+qTYBOcshaRHQX\nEEBGKQXklNK0KiRXKV22jEhV9DPinGlWZaxZQ0MsonaTzZtJPdZFWv/SsWP03OkuelVSSl32lAJ6\na0EepEbnS/amSiReSftuWTYleXF07Vsuc5pLUqqjlNqS0pER/VztSinVdZ0sXkyEKt6H/uKLJmqF\nSwAAIABJREFUfFIKAJ/4BPCRjxDB++//nf/6IvD2t9P7veUW4FOf4r8+i5TOn68XI+uUAM703axB\nR5y9V9Yerkw5DXAvAkkVW6VswGXKRXkoPSktY0+plH1XYtCRhGVP98EHsu27XiklG8SqVaMVw+ef\nnxhK6eLFtMHdv7/+s40biazqIk0pVYeM6yKPlOrcF1Wavqtst7ZKqWTSdTWlUF1PmWzAedcjSZJt\n8yPHvuVionwRLiKJYStZz7l6xnUKv1mktIhBR2nnb5uS0muuoWFBGzbwzsIuEu3t1Ff68MM0nZ6L\nJPturUYK9IIFejFcDDri3F9Zx8IUcU6pRGuLzhGMVZviW7bCbx4qQUrLkrwB2cRbFquTbs8MkL2I\nlEkpPXWKNuo6nw1AC/6BA2N/3t3NI6UAWXg3b6Y/Dw0RMbv0Ul6MKiIIRh+QfugQJT/OxmXmTEpo\n8WeQ+z2kHQtTq/FsdFWZvjswQP0necQCkFFK887QlFRKXZLbsjl3XEzxlSz8eqU0+b/prjlA+QYd\nAcnnb5uSUoBypK5ttSyYPZt6S02QpJR2d9N3qbtnmj6d7tOk9UCyp1T3Ps3aw1X1nNIyKZyAjMOl\nbMJfHipBSstmu5WIIzWiWsK+q1sJBtz0lOoqUEC6FUVZd3X7Vc46K1mh6+zkD3NYs6ZOzJ59lnoq\nx/vkXYXLL6cKOAA89xxw4YV6REmhqYns0r29o38upZQeO0b3qM41TZlCz9fIyNj/FoYypHRkRJ/o\nZFWDddcTFcdWKVVTfCWITpmsReM5TtrnzCHIWccJlU0plZgoD+gXbbOec90hR0D2kTBF9JQCyedv\n795tTkonGpYsIRIfLeJxe3KDIP077evj2Xdtj4QBsntKJc8plZpM7/IIRkkHUFocXYdLayvtM9LW\nbdeunTyUnpRWtadUygYsdRablH3XRU8px0KSppRyrLsAkdKurrE/379f316j8NKXAo88Qn9+9FHg\n6qt5r68yrryS3jNAn8GVV/JjJB0LwyWls2Ylk1LOpi4I0i1KJ0/SM65z/mpWNVg9ezrFkyxFh/MM\nZxW6OORWygZcpmJi1Zw7Ep+PZCGiikqpzmR610qpBClNc4ucPEkbVN1cDYxVSms1sqMuXaofYyJj\n9mwiD1E3VkcH//NLG3Z04AAwd65ejKyeUs7eS0IpzZviO9F7SiUKv0GQfZyLV0pjKFvyluoFlUy8\naXHCUP99VamnlKOUpllROEOOgPQJg+osNg6uuooG/vT3A7/4BXDDDbzXVxk33gg8+CDds/feC7z8\n5fwYScfCSCmlR4/qJ10gfYPIuUd1es10kKWUcp7hrLVAl1gA2WtcEUppley7Zeop5Wwm8sit65YU\niThSak0eKdXNjZKk9MiRsQqJsu5ypt7GldKuLoqvu+ZMdAQBTaffvr3+MxNSmtZXevAgj5RK2Hfz\nekp1npkgkCm2Stp3s+KUaTYBZ9120b4x7npK03qTxmNlGpBJ4OpabPvIOBtjFz2lRSilCxaMVUoH\nBmjx1l3sFaZPB66/HvjKV4D77gNe9zre66uMWbOIlH/pS6SY3ngjP0bSsCMpUnrokL7NCcgmpbqb\nTCl3gaRSaqtw5sWRUs1cJ8wy5pGsoqTE++Jubmy/cxUjqx9ZorVFYkMbhvpxpOy7UqS0pYX+n/FY\nvb28ifIAkdKoUrp9O7ByJS/GREf8HG+TI3WSlNIwJKVUd5pyVk+ppH1X937P21NW9ZxSifY9ibYL\ndT0SBeQJoZQ2N9OvNPm+bGSyTIqr7oMGyKksZespTSOlnJH3QP0okmhVWY1r5/REKnz0o/Tr/e/n\nKbbjAZ/6FI38/4M/4BUGFJKOhSmKlKbdpxw7XtZmldPPLdlT6sK+W9W2i6rYdwcH9YdblUkpbW4m\nwpR2bmoRg47Snquhofoh9nlwMeiIU7AFknsQe3r4w/tWrhyt8m3ePDEmykti1arRn+GOHcDy5bwY\nSacE9PfT/am7bktN35Ww7wLZRVvd/Chl35VyX+SdBQ7knwUOyBYTs/JImXpKNT6WxkO92aQPhmOX\nyqu465ADnQ9eqlfKthqsa0kA6N+lHZFRVE9p0rRbQOZIGO50wUmTaDHu66sroybWXYV162jh130f\n4wk33GD33ufPH2ulLqNS6tq+K6WUSgw6AuSqr4ODVAxKIldlKyZy4mRtkqQUTl27VJmUUqB+7ySR\nPdeDjrKKPZzJ9NJKaRiOtdhylFKActmBA3R+toIJKT3/fDrmbGSEigqelPJxzjnAT39a/7vJZzh7\n9lj77sGDvAK8lH1X4pxSILsoVNXpu64UTpfrf9TVmmT951xPFgpXSgGZxOuyp1Sqb8Y2DlcprUJP\naRjKKKXc6YIAWXijZMiGlAK0ueD07Ywn2Lz3RiulnPsijZRyK8Fl6ikt06AjNTwn7dgT12RS53zR\nsii3uteSF0eq4q6b01Qc23unrY1Uh6Tp2Jw4WcUezvMppZS2ttJ7S7ombk980iRzE1I6bRoR3Bdf\npL9v3kxE1UMf554LbN1Kfz51io6IkVBKOUOOgOxBR5yjZSTOKQXckNIynVMqqXC6XP+VqzXN4cK5\nnixUgpROZPuulKpRlZ7SkycpIetYGwA5pRQYO4HXlpR6mCHeUxqGVCzgnGuXNnmSMzofkOkpVc9w\n0kj2opTSRpNSSfWtLCRQxXFZJC3ToAt1PZJKaRI4BY28oq2uOjIwYP98quc8iSRzNulA+rpz+DBv\n/Uoq8JmQUgBYvRrYtInW4+eeo7976OOii4AtW+he27KFjorTsYVHMXfu2CIDZ8gRUFdK4z3dYUg/\n1225yeop5ZJS26KtymkSfepp60mtRnZ+nfVfcp0s0/ovFScL44aUqrN40qqmZSS3tg8J177b6CNh\nJJRSTqM9kK6IcXtKgbETePfsoUOvPdxi4cLRB4339tL9KXFfSNl3jx3Tt9E1NdFznPTccOyBeUqp\nS0KQF8d14tVROMvkuJFoSynbpoSjlErdgxJ5LYvcckhpU1P6M8opYgHp686hQ7wZBUmktLfXjJSq\n87c7OkgtiVqCPfJxxhlk4d24kYYArl3Lj5F0njpnyBFQL/rH7/cTJ+oqvQ7KZN9VfeppjhvdvXKW\nCKRyiI4DTKL3HnCz/nOm5kpdTxZKT0p1k3feGWqcwRsuGp1rNT01MMtOUJR9t5FKKaenAUjusQDM\n7LtLl44ee79zp58wWASWLwd27apXPV98kT+lUE0YjKsfJqQ06T7l9nalWfuklFKpApXrQUdAtZRS\n3XyUtSlRU3N1VBIXvaBcMpl1Pa4LI1J5LY1McgaRAenPOce+CyQPO6rVeEoWIKuU3nADHfP14IPA\ntddO3NYUG1x3HXDPPcDDDwPXXMN/fdLRdVz7LpDcV8o9sUBq0JEEKQXS14IwlGmX46xLZSsmurIT\n+55SZhzXm5u8m0C34lKm5N3ontIjR3gLoyKlaWexcbBiBRFRhZ076WcebjF1Km3KlJXahJS2tNC9\nGr/HpHpKTUhpmlJaRE9pWQYdScWRHFA00abmcsikJLlt9D3IKdqmFXw4zyeQTUptldJjx+hadFtb\nAFlSev31pJR+9rMT65gzSbzpTcBXvwr85CfATTfxX590dB130BGQTEq5Z7un3evDw2Rz5Tx7SXmN\ncxwTkL7HHRykZ6a5OT+Gi3kLZSOT3r6bgDJ9YFI9pXnyPYdMNtq+W6aeUi4pbW2lWPG+UhP7bpSU\nhiGNbPektBhEvwsTUgqQIhpX0U2OhJEipY1USsvWU8qtKtuu221ttPnIOu+6LD2lVd5MVEUpHR6m\ne0G3Zy+t4MOx1wPpzzlneB+QrmRxjxeTJKXTptFxX2eeCbztbfzXe9BU/osuAt79bnJmcTF//tij\n60yV0vieibv3ymrBmjpVX0lP21MODNDzq0MmgfS1gLNP9mTSTZwslIaUluULbG2t22uTIHXoretK\ncFV6Srn2JCB5Ip2JfXfFCiKiQH1IDjeGhwwkSGnSUAipQUecnlJAhpRKKaVlG3QkEaepqU5MbeK4\nUkp13T9l3NxUQSlVzwNnY9xIpZTbU5rUE180KQXo/Ol//3ceUfeoo7kZuOsu4H//b7PXT5pEOSm6\n39m/nxRUDpIGRHLvr6wWLM78h7Q9JSenAel5rah5CxKilItBR9ye0glBSrM+eNdjj4OAviAJxbXR\n9qSy2XellFLOZh8YS0prNb4iBgBLlhCJOXWKDrleudL3zRSFlSuBbdvozy+8QAePc3HmmWNJqdSg\nIymllKOg5PWUTuRBR0A2YdJNvHlHeUkcCVPlCreLHieJvGayobW11wPpzzm3iDVr1tjp4SY5LU5K\nlfrKIcge5YLEKQGzZ4+9v6SUUi4pTVNKpUippDOxiJ7SshUlfU+p455SqThS07fyPO6up+9m2Xe5\nSmnSZp+7MAJjSanqveGOW29upiE7W7cCTz8NXHIJ7/UecrjkEuCZZ+jPzz5r9l3Mmzd6QxaGxZHS\ntGIOZwBKlZTSsk0Y9PbdcpFJyfYWCVKatTGWIKXc9SKJlJoopWoNVLb2PXuo+OqLrdVFfNjRvn38\nUwKSBkRy915ZBRiOVT3t2eNa5yXsuyoXJbWBVNm+W7bryUIlSGlZ+mbCkNfjNDCQfnOXyb4r0VM6\nNEQKpW4Bob2dXhO325mS0gMH6n83afxXuPJK4PHHgQ0bgMsvN4vhYY/LL6fvoLeX7tslS/gx4krp\nyZP1Yxt00ejpuxyltK2NjrxKOry66oOOXPQ72pJS9bnrDJrJqyhL5bQqFmyBxt+DnBhAen6UVEo5\n6pEUKW1vp89Bxdqzx6yX0aM8iA47GhmhHMc5wxtIJqVS9l3usX5ZBVuOoi9h321poT3C0NDY/1bV\n1hagfOt/FjwpZcQZHqYKo86mRPU4JcUpwk7Q6J5SbnN7ECT3NUgopb29/MZ/hbVraVz7Aw+YnSPm\nIYNly2jz/q1vUaHApLI/b95oUmpyXyQNhACKIaVBIJN4yzjoqCyJt2xkMuszdt3aoq5HqleqTPZd\nKbVGSilNslea2HcB6sdXR50ppdSjuliwgCy7AJ1ZOns23xUmpZQ20r7LPUZJwr4LpK9NUmKSZE6T\nKEpyZxxMeFJaRBNu2iaJ+6GnfYFca1ua4lqEfVdZCOPXw12IAKrKJZFSbk/p3LmjSWl3N79yqPD6\n1wNf/zptAK64wiyGhz2CAHjjG2mwxhvfaBYjbt/t7qbphRzMmjV24AggdyQMdypn1kAW3aqyVKEr\nq7WgCKVUgty2tdVdH0kxqqpMlrHHycWgI11IDTqaOjWdlBahlAJU4Nu1i/7c0eGV0qpDneMNmPWT\nAjJKaVsbiS9xt5skKZVQSjn7ZBUnzX0hsW5LtTkUUZT0PaUol32XS0rTEi/npmxqoipY0vVw1JFJ\nk2izNTKSHEf3oW1uphsv/r64fQQAVeXiG36J6btdXeakdOlS4Ac/AL73Pb1zBD0ahz//c+Av/xJ4\nz3vMXh+375qQ0pkzx24OgWKUUiB7IItuAnd1FptrpVQigatBd0lTfMvk2uHGkZxx0EillHPQPdD4\nQUfc5zOpBz0M+ZbGtEFHpqR09276s1dKq49Vq2gQIwDs3cvvJwVklFIgWS0tGynl7JMBmZYASaW0\nLC4iyThZKMW2u2wfWFqcImwAWXE4FaAgSI/DtSglLSLcpAukK6W2PaU2pBSgA66vvdb89R4ymDcP\n+PjHzatvEkrptGl0r8f7OCVJKSfxpik6x4/zjpYp06AjyYFJWbMAbFVOqc1EUe6frDhFFBDScqNS\nYHSQpWpwcmzacyVxvujx43QtuuctAslnLB8+bGbfXb68Tkp37/ZKadURJaXbtgHnnMOPkUZKuUWP\npIGVZSOlXLdDWtG2jPZdKYXTk9IYpD6wRp/pI2nflSCl3ApQ1kPLefiTNtgmSmnSWWwmpDROPrq6\n+OTDY/wh3lNqQkqbmsZuNNWALoleM0mlVDfx5g1Pq/IQhqTrGR6m71GXFKTFKSIXlfEzbqRSapIb\nG6mUcjfGM2aMJaXc42CAximlmzYBq1fzY3iUB0uX0h5nYIBOCjj3XH6M2bPHnu3e18e/v5LmgnBz\nWlprS5Gk1Na+W7ZBR3lxOEVSiWJrFkpDSqU+sDL1lGbZdzlxpKrBaQ8t9+FPqo6ZKKVpg464CXzx\nYhqLrmCrlHqMD8TP6DMhpcBYC6/aZHKGL0kcCaPi2CbwvP5yCXJblNWpkcXEqveUlokkN3pqrlRP\nqYlSGs9p3H5SIJmU9vZSoY2L884DnnuO1sKhIRqU41FdtLTQ8Krt2+kM7/PO48dIUuIPHODfX0l7\nuKorpRL77fFqu50wSmmjFc4wlJkwJWm7dW3fBeRIaVLfTJFK6aJFRErV8CVPSj0AuqdbWur3mCkp\njQ874lp31bVIKaW2iTeLWEjFKWLQkeQsAAn7btqAurINqKi6UprWksLdiEr0lCbZd03WizPOoGFb\n0fdmQ0oPHADuuQe46CJ/Rul4wJVXAg8+SGd4X3QR//VJ9l2T+2s8klKJ4WmtrTS/JWmGi9Sgo7Kt\n/+OKlDb6AxscpJtEt08lq6dUquIuZVGyrSqPjND1cMjt9On2fQSA3JEwU6bQ9atFtrPT23c9aPMV\nta5JKaUmm8ykZ0YNQOH2lNom8Lb/v72zD+7rKu/891iW3+RXWbJkW36PHdvBdghJJpAJcdpk02QG\nQg2FLttJgB2WKSxsu9OWsoXyMvtHoF2GZToMMwWygSFkS5MNtGVoCJBJSUMIcUKc5oVgO3Fsy7Jl\nW5aV+EWSz/7x/E5/V1f3J/s+57m+Vz99PzMeyYr15Oqn8zvP+T5vZ4aUtMYOPSv6SpgQTLzYmdKJ\n/MiF2mlpEX+TdafsZM64Fh1AKOt+UatKhixRqvGNzo3PlmpFaUsLcPXVwCc/CdxwQ/7vJ9Xj+uuB\nz39eJu9q1kQIeoT3zvCwrFOL8l0rUWoVsM1T/QPYtASEGS5FziZgT2mBFP2ClREpD3YsyneL7CkN\nA1LyRE+zMqV5NxBgfKZ0dFQ2p7wOHJAS3v37xcbBg5wwSISkKN23T7cu0ut0YEAnStOH1bNn6/cZ\nXygWh/CJ7ju17E3NI26z9tvhYTlQW/SC5nmNJ9r/rexM1gj3RIekqmVK81b/WAwiy+op1QSxgLGi\n9PRp+ZM3YBv40IeAPXuA22/XfT+pFjt2yDnnwx/Wfb9zY7OlR4/K3/PeONCopzSvKG0UEJqsmdKJ\n7FRx/7/Y951OxPR4E/EU/YJZiUlNb5JV34yF420kSvPYABqX78ZmSsNhX3MVSxClCxdK5DDP60ua\nl1WrRJSOjEiJt2byZLp899gxmfichyxRqgnkWJU6hb0p/f/PM4nbSpRa7rdVC0qeOTP+NS5rQEWV\nxK3FMBFg4paUvPeLpn0aYNNTqvGNQH2A3+bN9SyptvT2Xe+SPTDPBGBSXTo6ZI3H/D47OmRdLV+u\n6ycF7DKlRYvSjo58dhqdt/NUWjXya1bVSGUNzMu6tz2vnYmofKa0SoMuNFHcIntT80aAsvrarESp\n9kqY5AI/ejT/YT/Q0yN3sO3ZA6xdq7NBmo/Vq4FXXpG10dWl2zTT5buadZpVvqsRpVmOd3hYSl1b\nW+PseG/jMIeH5eOFPo9Vz3wVK2UalQFbRaatMpyxh5vQPxX7O89bamc1UX7u3PH3LQLl9ZQCMheh\nt1c+15buJqEgbS5if5/Llo1dX3mEW6CRKM1bdjs8PL7VoRkypVl7d96AbdWCiVO+fLcMUVp0xjXP\ntQtA4xr3skRpo76ZvAfs9IZ29KhuYwRkmMNzz4koXbNGZ4M0H6tXy5qICVZkZUrb2/PZKDJTGt7D\nebIoWY73zBkRFRd62LFy3o3227ztCRPt2xZ2LMuALQYmWR4m8vT/TvQzXegatBKljezkzZTOmzde\nlHqvHwKY/H1ppu8CMiX30CH53EKUEpJk2TIpAQbKzZQ6l12pULV7Sq3Kd60GHeUJ2hZdjZp3mOxE\nTApRauUwY5/HqnzXyvHmfbNlRYPLLN9NZ0r7+/WZ0m3bgF/9Sv5s2aKzQZqPbduAnTvlSoRNm3Q2\nrDKlFqI0a0po3n0g2Ek7Xqt9qQznDUwcBCxj2rmFSD7fwKQqld1a/K7ylI8DjcvZNZnStE87e1YO\nzHkOWq2t8rMln0lzTykgojRksg4fliuuCLEiub4sM6WaYZVZZ8oqZkovtn+06uE/3/4fK26DINW0\n3qWptCgdGZEJYdMvsPO1aDFpVb5rdWjTiNIiy3djBx3FlO8GUfroo8CVV+pskOZj3Tp579x/v35d\npEWpJlM6e7Zs3KG0Fcg/2ROwew9n9c3kFQRWA9gmmnZuUS6lEUxWdooc5lOlgRllXeUyUT9abMBW\n49OA8QGogQHdgKKkaDh0iBPliS3JTGlvr+4avbQo9V7We94pvpNFlFoFWy2ulsnjZyfa/y3Ebd5Z\nABNRGVHayOnOmnXhJUFVy5RaNDoDdm+2trZqZUqXLJEIcCCmfLezU8ozd+4Err1WZ4M0H84BN98M\nPPIIcOutOhsdHbI2A5rgiXPj+0rzTvYEsssMNZnSrBIlq0iwRkwWLW6t7Fj4EavnqVJPkdWBLe8a\nbHT/r2b6rqUoTR7Ujx/PH8QCxvaUvvKKDG0jxIpk0OPVV3VDANOi9ORJ2QfyzDcAGovSi32Hd7Bj\n1ZYS69ecs/EjF0OUWg0XrYQoLTrCbSVKy8pwZpUo5R1KAhRbvqtx4G1t9b4dIK58FwC++13gySfz\nb4ikufnSl4CHHxYnrKGrS+44DWgypcD4DIrmPWPRewNkO/C8mdIZMyTze+7c2K9bDYTQlMsWWXZr\nVSkzmUWpVaB1It+Yx04jUZo3U9rWNr4XVPO+AsZfC6PdL5KiYd8+ilJiSzJTum+fjSg9flxmMOTF\nIlM6USm/RU+pppLIQkdYVBI12m+9zxdsrYQodc593TnX55x7JvG1Tzvn9jvndtb+/E7MQ1QpojyR\nnTKmbwHZjvf0aTkU5pnAZlX6lzVJVHtB+JIl9QN/TPkuIKWaV1yh/37SnHR0yGXjWrq66gNHAP06\nTYtSTe9NVmBJmylNO6m8WapGUdwyIspANct3q+LXQgtMVm+qxTTIMqt/LDKlra3yJ7kOrcp3taI0\nTJT3XjKlGtFASCN6ekSMAnpRmlUVUJYozTrfAtUs373Yfm2iFslp0/K1SE5U1WrBhWRK7wJwc8bX\nv+i9v6L254cxD1G1Q4BlOZnF4s6KAGkOoo3Kd/M63nnzssfeayYMdnXVS3hjyncJKYrOTjlYhr6O\ncNF4XrJ6zTS9N1mitIxMKdBY3E72XtAip/iW0VPa6HlGR+VgcqHDfKwObHPmNJ5waSFK82ZKgfHv\nLavy3WPHdAf19nb5nfX2yl3LFKXEkp4eWePHjskd3j09+W1YZUobXZmWx69lnW8B21kJZdixCNpW\nTWNNxHlFqff+ZwCOZ/wn5TXO4yn6BSsjXQ40jgZbOF6NKC1q0JH3+s0o2Vd6+DBFKake06eLeOzv\nl1JV7fh8C1HaqHw3716QNSAm774E2IhSq+Bd1Rxv0b2pFq9z+Jnyzm1IX1FjEWgF8h8gwwCx9CAQ\nTaAm/d7Slu+2t8tBP6DNlALA5s3AQw/J74i+kVjiHLBxI/CP/yj9y3n3fkB82tBQ/f1nlSkdHc03\nFRZofNdwFXtKL7Zfq5pvnIiYntKPOOeeds59zTmnmC1Xp2ovmNUhoFE0WNM3Y5EpLaqn9NQpKSPW\nLMpk+a42WkdI0XR3yzrt75f1r1nr6QoDzVROq/LdRoGusjKlRV4zUqadKvm1rIx03t9VS4v8SU6R\n1tiZOTNbTGpKyLMCLGVmSjs7ZZ8A5OcbHMwffAps3gzcfTewdavu+wmZiMsui1tfLS2ytkMQxkqU\nau7enjGjfl9mkjKvhCmqUia0YVzo/JSJgolVE6UXWEk8jq8A+Jz33jvn/ieALwL4z43+8Wc+85l/\n/3z79u3Yvn37mP9eNTFpWaKUFQ3W2LHIlDYq3807Cjy9gRw/rne6IVPqvTTdL1+us0NIkYRhR6Oj\nMiBCQ1ZPqUX5riawlFU1YdmbWkZliuUh4HhGbVAVRWns66w5TIQKoGTJb97fuXN1O0nhFxNgSd4F\nqs2UpkWpJlPa0SEBVkDe4/Pn55v9kOSmm4CvfhX4q7/SfT8hE3HTTcAf/AHwN3+jt7Fkidxz2tlp\nJ0o18xacq+8FYW/yXld9YVHFYVm+G7tvJ2cKJIWsRWDz4Ycfxte//jAOHAASUk+NSpR6748k/vq3\nAP5hon//mfM8aRUjylZXuTQSpZO9fHdoSN7wzuk3IkAO+7t3S2S5rS1/poaQi0EYdnT2rD5wku6b\n0ZbvFpUptSzfLauHv1FQMk+/exX9UaOfK/Z58vo0oF4BlDw0au28/vp4UZrXTnotj45KJjfvvXnp\n8t3BQd39oh0dcmc2EFe6CwBvexvw5S8Dt9+ut0FII971LpmRcMcdehudnZJY2LxZn6CYN2/sMEHt\ney/4x3AePXtWAkJ5bmTI8o1hSm2sKB0ZEVsXOlgo2Mnat/MGE4OdGFGa5UO2b9+OwcHtOHFCROln\nP/vZfA+W4kLLdx0SPaTOuWRubQeAZ2Meomq9NxP1gpbRN2M16MiqfHf6dHn+4MA1h+vAmjXAnj3A\n/v0s3SXVZfVqYO9eyYDEiFKL8t3YgRCAXfluVuCtrAxn1cSk5cAkqymOsVltIPt3HiNK03Zig63B\nN+Yp/QPGVyFo3p+AHNKP1ML22iFHgdZW4KMf1T0HIedj5kzgYx/TlakHQqYUsM2UJisfLpRGe0Ee\nsnxjEHPTcjQ8Zu3bYZ/MszdNNAsgDxYZ10r0lDrn7gHwrwA2OOf2OefeD+ALzrlnnHNPA7gewB/H\nPER40dP1zlYZzrx2Jspw5rGT1VOquV/UKlPaqHxXU6K0eLFE2IC4TOn69cBLL3G6IKnrt22NAAAg\nAElEQVQ2GzYAv/513DpdtGjsABRN+W4I0iX7Zk6ezO/ArTKljYRFnv2ttVUiyBb3nTZjL2ijqbl5\ns4FZwVbNYaJRkNRClFocIrU+LR201bw/AcmUhp7S2Lu3Cak6ySDM4cNSVZQXi/JdYPwZV7OfWPg0\nwC4I2EhMWtmZdKLUe/9e7/0y7/1M7/1K7/1d3vvbvfdbvfeXe+/f4b3vO5+diWhpkexbeniCRkxa\nZTiz7ORdCFmL++xZOYTl6TGxzJSmD6Lavpn29rooHRjQi9K1a+WOrF27pPyDkCoSROnzzwObNuls\ndHTU3zOAvsIgfXgeHLQRpVbCQtNfWLTDLGuKb1EiOVwHkyfinuUftZlSCztWh7/0Wtb4RmB8FYJF\nprS3F1i6NL8NQiYLyRsUDh3KP6MEsBOlad9o2dpisU9qxFuWuJ3SmdKLhcUL1kiUliVurcqcLHtK\n05lSbe1+OlOqLd+dNQtYsQL4zneAN7xBZ4OQotm4EXjhBeCJJ4AtW3Q2Fi+uZ1AA/aG3KFGqyZRm\nTQbXRoOzHHizXgkTa0cTKW90SKqKmATKzZRmHYxjM6W9vfrBaIRMBtJBGAtRqvFpwPjEi2YKt5Uo\ntdonG80CoCgtmCJFaZV6Ssssc8oama2t3U+K0phMKQDceKMc+G+8UW+DkCJZtEh6SXt7gUsv1dlI\nHlbPnJESTM1gr6yBLHnfw40GJmn2JisHnpV9K6N9w7KHs6jBe5pDSaMgqXb6btpOVcp3tZnSBQvE\nHwZigkYjI/IcBw9SlJLmJnmDQtmZ0nT57smT+YbcBRsW+1LW/qYJAlplSi3stLRIdU64kiaQd07C\nRFRalOb9QWfNEsGV7k3KuxAm6imNLQPWZiMsIsrA+EiS9s1vlSkFgM9+FvjBD1jmRKrNffcBjz2W\nf4BKIClKQx+2xlZ6IIvW8Valp7SRnbIypZNh8FLMVS5JyswAFJVx1WRHAGlJie35BuQ93dMDvPoq\ny3dJ87N0qQwAPHlS1n5eXwSMDwhZDTrS+MZw/k/OuNHuS1ZBwCwxWUZPqaWdRlRalOb9QUNvUmxU\nuVFPqUWmNMZ5J98kMaI0XfqnFaXBgR89GjfMYckS4JZb9N9PyMVg82bg6qv1358UpYcPy7rXUFT5\nrmWpk2Z4TpUypUWJyXC1QOxUeatstKW4tQhoWGRKNYO/ABGlyftptZlSQGYl7Nkjh3VmSkkzs26d\nXOt34IA+AJMOCMVeCRPQiNKWlvHtJGUG3RpNuC9LTDayk/cKrkZURpRapagt+mZmzJD09Ojo2K/n\nPSRlRVw0JXItLfJMydfHYphDmHisWUzJTKm2ZIOQqcScOfXLvPv6dFMKAZvy3SwxqXHgRWdK8wYT\nsypcqjSgKAy6y3O1QJG+sWqZUgtRqu1Hs5iOHVi7Vq6Q+s1v5NBOSLOydKn4jqeeAi65RGejrU3O\n3GGfi8mUxorS8DzJvaksn9bIjlXbRZmDlxoxJURp3l+gc/LvY8VtuLQ3eTDRRJSB8QtTK0oXLqyX\nSYTSXU0JIUUpIflwrp4tjcmUpst3rUTp0FD+++qySpSshufktdPaKqI/PcVdE0zMEqV520ksr2Cx\nGFBU9em7586JaM8bJLUSpclM6blzejuA3L/9+OOyHjs7dTYImQw4J0GYH/5QrvjT2kieKWN6SmPL\nd4HxsxLKzpQWVeFCUToBVlEFqwmDaTvBYcZGOTSLEsh+s2kuPF64sO54tSUSwNhJon19FKWEXAhB\nlPb16UXp/Pn1wNK5c7Iv5N0Lsq6H0mZKrcbnW0SDLUpUrTKuloeJKkXKLTOlWQe/vEFSq/LdZKZ0\naEieb/r0/HYA4IorgG9+Uz5qe9AJmSxcfjnwrW8BV16pt5Es4bW6Eka7F6T3FE3AtkpVRMFOUf6o\nKUVpkdEAi8NNqJnO62DSdrSiNL0wYxzvwIB8ri2RAOqDHEZGZCNhNJiQ89PVJcNPYsp3Fy2qB5Ze\ne032kzz3HgP1fSk5FK6KjreMqHKRV4tpphQWFWgFqpUp1VYRWWZKw6E4pp8UAK69Vj6+8516G4RM\nFnbskI833aS3kcyU9vdLADcvluW7RYjSsitlqiRuG6GMA9pTdIo61vFqxWR6IWjLbq3KEqwypatW\nAa+8IvdTtbfnPxQTMhVZvRp4+eW4q2Xa2+WqCUB/AJ82rT7MIdzpaDXFV3NPpFXfjIW4zfIho6Py\nJ0/mrMjyXcurXPL2Tc6ZU7+XMGlHI0qTdjTzFgBZ/4OD9b8PDuqGC82fLz/H8LA8V0ygNfzOrA5q\nhFSZHTvi13tSlGrff1nn5LVrdXaSfkQrSouqIrLURnmHlFr5tUZUJlNa9GTA2BS1VYbTys7goE6U\npjOlWlE6f770cP3yl3LQJoScnzVrZADK3r06ZwmMzejE9L0VFQ3WitKq9M1MlOHMUyljeZgo8lBi\nJZJjr0yIaUkJPg3QvyecE384MBBXyRCgICVTidj13t4uovTcOfmozZQmhwBa9ZRaVhGVNeioStVI\nE1EZUVq047XIlGpe9CJ7SjWON5kpjSnfBSRb+uMf6yeuETLVCKJ0zx69KE32vsW8h5N7ysiICC/N\n5NP0vl3FMmDNPaXJ0uaye3iKKt+y7P+N/V1pD5DpOw5jAjUh4BMziIwQkp9wzeDAgPiVGTPy28ja\nC8oq3w37dvr2jckcsA122FMaaceqp1Sb4Uza0V7sbRUBSvajxThvANi0CbjnHmDjRr0NQqYSGzYA\njzwiTm75cp2NZKb02DH9HcFJxxuym5qe+azy3bL6b9L7v/f5J7qG0ubkhMGyM5NFDTqyuu/UQpQO\nDelbUiwypYBkZw4flj+xmVJCyIUTyndjSueTlYCA3ZUwGlGadZWjpoqokQgsK8OZtf9rZiU0ojKi\ntFE0IO94eIurXIDiyne1jtcqApR04EeP6g+0AHDddbKBbN+ut0HIVGLLFhnisG2bvg87eXWFdiAE\nMHYCb0yZU3J/Gx21y75ZOPAzZ+RgkFdspx142ZHpogYdWU4DLitTailKe3qAAweYKSXkYtPdLbMW\nYkRpshIQKHfQETB+j7Oct5BXGxV9w0lTDjoq4gXT3n1W1KCjoSHdGy4pSr3XZSOAsZnSw4eByy7L\nbyNwxx3y84VJg4SQiWlpAX70o7grlJLlu/39+sBSsvpC63TTzjsMcpuWM9w5Z4708QXOnpWPeUu4\nsipcYtouwmurCZA2ct6aw0QR1T/BTqyY9N7mSpiY8t2BAXkO5+JE6YoVMlW+rw/YulVngxCSn5Ur\nZXhmb6/ePybPt0C5PaXBTtKPDA3ZlO9qJpVPlvLdSonSdBmYhSg9c0Z3lYuVKM2KBmsWd1KUvvaa\nvC6aO9SS5Q1HjsRFg+fOBT7wAf33EzIVufHGuO9Plu/GZEqTGSarTKkmEmxpJ2vauTaYaJEpff31\nuliKtZPk9On8Q+qKuhLmzBnxRa2t+ezMmze+JUXjG2fNkiBIyB7HZkr37pUJ2StX6mwQQvITbnR4\n+WX98Mw5c+rzEWbO1O8FVpnSdBmwJplkVUXUKFNqFWxtuvJdy6m5VRpQlFW+GytKtaW7gIjS2LHb\nhJDymD1bRM6pU/ophcDYAJVVeVKZZU7BTnL/1wyWAGxEaWuriKXh4Tg7VSvftfqdp69y0QZGgLEB\nlhj/GDKlu3cD69bpbBBC8tPTAxw6JO+9Vat0NpyrZ0u9189csOgpBYor39W2tqT3f80VlVatlo2o\njCjN+kE1B4qsw4RFpDxmUVqVKAVRGuO8u7ulNMl7ilJCJivt7ZIlrUKm9NSp+qRaKzFplSnVDpZL\n7/8xz5MWyRaHgDIHHVn5xvSchBi/Fkp4T52SvmbN7xyQu4MffVSeSzuIjBCSn9ZWec898IAM0dQS\nROnQkLR/aMTS3Lny/YGyRWkR2ijYsSgD1gZ/s6iMKM1KUVu88NpMadahRBsNTpYoWThw7XUwgPxc\nbW1ymKUoJWRy0tMD7N8v72ErUarZl9KTapslU5p+Ho3zBrJFaZWmL2pFskUWYd48yWqGKxO0QwAB\nWcsnTkjlQGdn/nadwObN8p7ati1/XzQhJI43v1mSJlddpbcRhh0dPSrBWw3pgJllJZF2/09eLaMJ\ntmZlXK38kdY/ZlGpntK0w9S88LNnjx2YETOgyCIaPH++1MnH2rEq3wWAZcukbv/Eibjpu4SQclix\nAti3T4RpT4/OxsKFwAsvyOcDA/J3DaHUac4c/f6W1QuqFYFFZEq1z5MltvPaaW2VA8nISH2OgFWm\n1OJOWe3vfOZMGfwVBLZF+e7MmfogDSDP8+1v6+8QJoTo+Yu/AK6/XrKdWkJbysyZ+vNteqK3ZbA1\nr53WVgmyDQ/XB/9Zle9qNVbSzujo2GeLpTKiNB3hHh6WHzbvD2pVdjVnztgpXpoIB2DXN5MWpTH3\niy5fDjz2mIhTzbAkQki5hEmFBw7EidLgeI8d00eV29pkX+voKH/Q0Zw59SFQgF1PqWX5bt7937m6\nnfC9VqJU83NZlbYBdb9mIUqPHRNRGSNKAeC97437fkKIjssui7sRAqiX77a26kWpZaY02b4X60eC\nHrIs3421EwK22uqUNJUpUMkqu9X8oLNnj72stuwBHukJgxaZUm3zdqCnB3jkEWDNGr0NQkh5rFgB\nPP64HMa1U++SovT4cZtSp7LLdy0zpUWV71plgPO+zrNmyVTK0VH5u/f6Q8nwcH2AU4woTQZttSXk\nQH1WQkyPNSFk8hOm0x89apMpDQkyTW9qkdPprcp3Y2ccWPaTAhUTpelDQJkR7nQ0OKan1DpTGnM3\nIQBs2QLcd19cMzkhpDwuvRS4//64uxSTF43HZErT11WVfSWMVU+plZgs4nk0r49zIkxD0DZE3lta\n8ttJBlstRak2U7p0KXDwIEUpIVOdpUvlrtPYntIgSgcG5O+aTGDWfadl+ce0Lwp3gee9yssq8NuI\nyojSLKdb5tREqwxn0umeO6e79BaoH/y8j4sAAcA118jHt75Vb4MQUh5veYt8vPpqvY10+a62j8cq\nU2rVdtFsPaVZz6M93CTv39MGWoH6kKLwLLHlu0C8KO3tjRv8RQiZ/IQhgAcPSouahjA4DZDArdY3\nJgednj0r53dN72Vy/x8dFVuxV3lZJf4shxwBFRKlRR0CLMtuY3tKw0Xumql+s2ZJ/+drr8XdTQjI\nQfaBB4B3vlNvgxBSHgsXAj/6EfCnfxpnw6KnNC1KtWLSsvcmUHbFTZHiVuvXQgWQ9ncFjD1sWWVK\nY4ZtLVsmonTfPiltJ4RMTXp65K7hmCGAc+fKvj0yEidK01VEc+fqM65h/9f2cGb5tDIDto2olCi1\nUPFZZbfaTGmy7NaifDcmEgyM7ZuJyZQ6B9x2W/60PSGkOtx4o/4QD4ijPXZMordWPaUnTuieqa2t\nfsckUI1MadV6Sq0rgKqQKZ0/v752YiqAQvnuK68Aq1frbBBCJj8hUxozBNC5+tk9NlMa9kntng2M\nrSTS2pk5s94fC9hel9aUPaVW5bvpDGdMRNm6fDfGeQMiSg8dis+UEkJIW5sEpk6ckECXhSg9flwn\nSqdNk+cJAcWyM6VV6ynN6uOJDdpWIVO6YIH4x9OnJSuhfZ61a0WQvvACRSkhU5meHhGku3YB69fr\n7YS+UstMaYwoDZVEWh+SnOIe7FgFSJs2U2pxCMgaV19mT2l4Hu/jypOAuig9coT3ixJC4lm+XA7z\nx44BS5bobCQd78BAuVHlogYmWWVctXaSInl0tH63Z14sM6VWonRgoF4+rr1WYM4caW85dIhT5QmZ\nyrS1yZ/Dh4FVq/R2Ql+pNtAK2FVKpvdtrbhN+rWqlu9W5pbK8IN6L45J+4NmZUq7u23saBbC9OnS\nD/raa3F9W4D8HL29UprAvhlCSCzLlwNPPimCVHtn8YIFwN698nlM4C3df68d5JMMSpad4Swi4xoz\nmyAtJrWHG6vy3c5O4De/iR/eBwCf+pRk/DWvCyGkefj4x6XVLebuTOtMqZVvjMlMpntTqzjoqDKi\ndNo0mUp15kxdxMWUJwVxq40Gz50r33vunDybxTAHC1H63HNyeJw/X2+HEEIAEaW/+IV+SiFgU74b\n7MTeWZksKwXsMq5l95Qmy7esMpwxdpKvc8z9ol1dwKOPxl3fEIgZ+kUIaR7+5E/ibXR0SFXi8eMS\nPNNQhCiN6eFMlwFbDN1r2p5SwOYFa20V0RbuYtNGg1ta5EARnidWlA4MxDvedeuABx+MK0kghJDA\nihUyxTe298aqfDd2YFL6XmitH0lenQLEld1aOPDk1NyY8i2rntJkpjQmk9DVJRmNY8fYkkIIqQ5h\novfhw/rWFqvp4lYDk5LBRK2dWbPkSprkUMKm7CkFxr5gMSnhpOON+QWGqHK4X1TTwwNIlKW/Pz5T\nunUrsHs3cMUVehuEEBJ44xuBPXuALVv0NqwypWkHvmBBfhtz54rvOHdO/q7d/63EbVYbiMaO1aGk\niExpzOTmIEr7+vTZCEIIsWbZMpno3durryQqIlMaU5liobGcG6uxmvZKGMAuRW09zGFwUD7X9qos\nWSLRllhRummT2Hr72/U2CCEk8Na3SnXJLbfobbS3y942MiL7tnaYQ7J898QJnSidNk0cZDIoaSEm\ntf4o6dO0l56n7cRmOC0GFCUzpceOxWdKX32VcxIIIdXBQpSGCpdz5+xEqbaKKDxPUmNpxWTaTtOW\n76bFpDYzaVXqFJ4npjwJEMdrIUqnTxcH/ru/q7dBCCGBzk4RSpdfrrexdGn9qqr2dn3wzsrxpu1o\nxG06U2ohbk+d0l16nn6e2ECrhW9cuFAOWUCcX1u0SF6XF18EVq7U2SCEEGuWLZOrZfbv14vSlpZ6\nkFRb/QPY+LS0Hcsy4CmRKT15Uj/MZ+5cuwmDJ0/Gi8mQKe3vZ98MIaS5WLhQBtTt3i0CVUuyp7Rs\nB54WpVo/YjU10epKgKRvjHmNOztlCEiYmK8NIE+bJv3M//RPvMqFEFIdVq2SIWytrXHn9lABFJsp\ntdi3k3ZOnNBrLCu/lkVlRengoP4FS0aDLabmxmZKgyg9cECmXRJCSLPgnEwGf+op3fVbgbDfnj4t\nQkdT5pq0A8hHjQNPisDhYckmx953XYUId/J5YvxaEKXBRszVCxs2SFCDsxIIIVVh3TrZ97VDjgLt\n7ZKQsizf1fqRZNmt1jcGO8GPxNjJojJXwgDjf1CLTGnMNMiODllMZ8/Gi9If/5j3ixJCmpNly4Cd\nO+NE6YIFwL/9W93paoVOUtw6B8ycmd/G7NkiRoeH674otuw2xqcV0VMaI0qt5iQAwB/9EXDJJfrg\nMSGEWDNtGnDnncDGjXF2urulveXwYf0wN8vy3ePHbexY+LUsKiVKLaMBQ0P1wRsxF3sfOSIDKmJE\n6bp1wNNPS6kTy3cJIc3GsmXAT38KvO99ehvhsvKYflKg7kdiypOcGzvoLqZqxyIyXcTVAjGTkjs6\nRJAeORLv0667Tv4QQkiV+PjH420sXSrDkvr69EFbS1G6b1/dToxfsygDzqIpy3dDpjTUXmsHbwRR\nGjNdEAAuvRTYuxfYvDmuzIkQQqrI1q2yx23apLexZIk47qNH4/bb4DBjnDcwVtxWqYcntvrn6NF4\nO62t8jrv2sWWFEIIaURSlHZ16WxUrXzXokWmEZUSpVbqe9EicbgxTheo3y8a21Pa1iYL89pr9TYI\nIaSq/NZvyce3vEVvI5Q5xThvwEZMAnUHHhMgbWuTCpnR0fiMa7LsNibDeeRI3U5sW8ovfwn09Oht\nEEJIM7N0KfD888CMGXGD7sLVMlUIkqbF7ZTJlGpf+NALGuO8gXqm9NChuF4pQCZTfvGLcTYIIaSK\nXHutOLuYrFm4WqYqojQ48Bg706aJMB0asuvhiQm2LlggIvnMmbgyYED6QH/yE4pSQghpRGhtWb9e\nb6OlRfbq/v64Pn7LMuApVb7rfZz6TorS2EzpkSM2U3Nnz5aFRQghzUjsoJq5c2Xv3707TpQuXCh7\nv1X5bmx5UtKO1qeFYU1nzsQFW52THtCXXxaf1NqqswPIAJADB4Bt2/Q2CCGkmdm6VfbJSy+Ns9Pd\nDRw8KJpEOxHYcvru4KBUAJ06ZTukrlKiNJQonToFTJ8u6W4NVqJ0+XLg1Vdlai6jwYQQUhzhapnH\nH4/bb7u6JNva36+fdgjUHW9sJDhZBhwrbk+ciG9L6egAnn02/qqDULL9pjfF2SGEkGYlZEi3b4+z\n090NvPCCVN5otdGCBeJDQuJv3jydnZApDTYsZ+VUSpQmpy/GHAKsyneXLZPnefFFYOVKvR1CCCHn\nZ/Vq4JFH4qLKyRH8McIr6Y+sBiZVwa91dgK/+lV89c+tt8p0e+3BhhBCmp1p02Sf/OAH4+x0dwPP\nPBPn0zo7xS8ODkp/q1bchgCpdT8pUDFRGspljxyJi3AH593bK31KWpyrl9zyDjVCCCmWN75RPsZM\n8e3ulkxprCjt6hIb/f3iU7QsXiwTb48ejbvTM4jtWFG6dKlkoy2m5s6eHW+DEEKamdmz47OJ3d3A\nE0/E7dsLFgBnz0r7RozGsvJpWVRKlAanayFKrXpBv/1t4JvfjLNBCCHk/Lz//cCnPhU3WK6rqz4w\nKcaPhDJgC3Hb1xfv17q7pZVkYCDubtD164EHHwRWrdLbIIQQcvHo6QEeeihuYJJz4o9i2zeSPi22\nDSTNdFtzccybB4yMyH13Mc67rU0iE08/DbzjHXHPFPv9hBBCLozLLgM+97k4G4sXS2nRSy/FCa/u\nbuBf/kV6Z2LFpEWwtbtbym47OuIGFIXS6JCVJoQQUm2uuEI+WgxM2rWrGj4ti/NmSp1zX3fO9Tnn\nnkl8bZFz7kHn3IvOuX92zplcnRoGXTz7bPwPun498OSTwLp1Fk9GCCFkMtDSIjMAnn46LqocMq6x\njtcyU7pzp8w6iOGmm4BrrgFuuCHODiGEkIvDlVfK1Wu33RZnp6srvgw4TMqPLQPO4kLKd+8CcHPq\na38O4CHv/aUAfgLgE1YPFKYvxpYWhe/fsCH+mQgBgIcffrjsRyAkmqmwjlevlo8xZa5BTL7yStyg\nu+5uYN8+GQoR8zzLlgE/+xmwYoXeBiCZ1scesy+7KoOpsJZJ88N1TM7HnDmy/8cm2pYvl/ulY8uA\nu7uBp56Ka7XJ4ryi1Hv/MwDHU1++DcDdtc/vBmBW5NrTA/z85/Ev/F/+JfCNb/BuUGIHHQdpBqbC\nOr7zTuDee+OGS/T0SAnw66/HZzgfe0zE5PSIhpnLL5f2Fpbd1pkKa5k0P1zH5GIR/Mcll8TZ6e62\nEclptIOOlnjv+wDAe38IgFkCN7xgsRnOyy6ToRmEEEKmFlddBbznPXE2wsT14eE4cbtpkwxLWrMm\n7nk2bwY2bpSrWAghhJC83HijVJJed12cneDXrEVppQYdAcCOHcBzz4moJIQQQsriS1+Ku6MUkDJg\nID7D2dICPP98nA1CCCFTl7VrpRc0lmuukWrUzZvjbSVx3vvz/yPnVgH4B+/91trfnwew3Xvf55zr\nBvBT733mzXLOufP/DwghhBBCCCGETFq89+raogvNlLran8D3AbwPwOcB3AHge0U8HCGEEEIIIYSQ\n5ua8mVLn3D0AtgNYDKAPwKcBPADguwBWANgH4Pe89wOFPikhhBBCCCGEkKbjgsp3CSGEEEIIIYSQ\nItBO3/13nHNfd871OeeeSXzt0865/c65nbU/v5P4b59wzr3knHveOfcfYv//hFiQtY5rX/+oc+4F\n59wu59ydia9zHZNK0mBPvjexH+91zu1M/DeuZVI5Gqzjbc65x5xzTznnfuGcuyrx375cW8dPO+cu\nL+epCRlPg7W81Tn3r865Xznnvuecm5v4b9yTSeVwzvU4537inHuudib+WO3ri5xzDzrnXnTO/bNz\nbkHie3Lty9GiFMBdAG7O+PoXvfdX1P78sPZwmwC8G8AmALcA+IpzMcP2CTFj3Dp2zm0H8DYAb/De\nbwHw17Wvcx2TKjNuLXvvfz/sxwDuA3A/wLVMKk3W2eILAD7tvX8jpJXoCwDgnLsVwDrv/XoAHwLw\n1Yv5oISch6y1/DUAf+a93wbg/wH4MwBwzm0G92RSTUYA/Hfv/WYAbwbwEefcRgB/DuAh7/2lAH4C\n4BMA4Jy7BTn35WhR6r3/GYDjGf8p6010G4B7vfcj3vuXAbwE4OrYZyAklgbr+A8B3Om9H6n9m/7a\n17mOSWWZYE8OvBvAPbXPuZZJJWmwjs8BCFH4hQAO1D5/O4Bv1r7vcQALnHNdF+M5CTkfDdbyhtrX\nAeAhAO+sff52cE8mFcR7f8h7/3Tt8yEAzwPogZwj7q79s7trf0ftY6592SJT2oiP1NK1X0ukcpcD\neDXxbw7UvkZIFdkA4K3OuZ87537qnHtT7etcx2RS4py7DsAh7/2e2pe4lslk4o8B/LVzbh8kS/qJ\n2te5jslk41nn3Ntqn78bcrgHuJbJJMA5txrA5QB+DqDLe98HiHAFsKT2z3Kv5aJE6VcgKdvLARwC\n8L9qX8/KnnLSEqkq0wEs9N5fAymt+W7t61zHZLLyHwF8J/F3rmUymfhDAP/Ne78SIlC/Ufs61zGZ\nbHwAwH91zj0BoA3A2drXuZZJpan1P/89ZC8eQuP1mXstFyJKvfdHfH2s79+iXnqwH3KNTKAHwMEi\nnoEQA15FrffOe/8EgFHn3GLIOl6Z+Hdcx6TyOOdaAOwA8H8TX+aeTCYTd3jvHwAA7/3fAwiDjriO\nyaTCe/9r7/3N3vurANwLYHftP3Etk8rinJsOEaTf8t5/r/blvlCW65zrBnC49vXca9lKlDokFHHt\noQI7ADxb+/z7AH7fOTfDObcGwCUAfmH0DITEMmYdQ+7j/W0AcM5tADDDe38Uso7fw3VMKkx6LQPA\nTQCe994nnQL3ZFJl0uv4gHPuegBwzv02pN8OkHV8e+3r1wAYCOVkhFSE9Dm5s6vR61EAAAE/SURB\nVPZxGoBPoj4EhnsyqTLfAPCc9/5/J772fQDvq33+PgDfS3w91748PfbpnHP3ANgOYHGtz+PTAG6o\njf49B+BlyNQleO+fc879HYDnAAwD+HAio0pIaTRYx98AcJdzbheAM6i9ubiOSZXJWsve+7sAvAdj\nS3e5lkllabAnfxDAl2tZ/9MA/gsAeO9/4Jy71Tn3GwCvAXh/OU9NyHgarOV5zrmPQMoZ7/fe/x+A\nezKpLs65awH8JwC7nHNPQdbu/wDweQB/55z7AIB9AH4P0O3LjmudEEIIIYQQQkhZFDl9lxBCCCGE\nEEIImRCKUkIIIYQQQgghpUFRSgghhBBCCCGkNChKCSGEEEIIIYSUBkUpIYQQQgghhJDSoCglhBBC\nCCGEEFIaFKWEEEIIIYQQQkqDopQQQgghhBBCSGn8f2IvURSfI61uAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115ae9a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# next 50 \"days\"\n",
"t150to200 = np.arange(15001,20001)\n",
"syn150to200 = 20 + (10. * np.sin(t150to200 * (2*np.pi)/100.)) * (1*np.cos(t150to200 * (2*np.pi)/5000.))\n",
"plt.figure(figsize=(16,4))\n",
"plt.plot(t150to200/100., syn150to200)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1160f79d0>]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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G4B5XUViYOc/RhKDumjYLuiCnVbsiu3dHx0giPThqRqsmbH6sqcsKpMaJeN93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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115d91c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# next 50 \"days\"\n",
"t200to250 = np.arange(20001,25001)\n",
"syn200to250 = 20 + ((10. * np.sin(t200to250 * (2*np.pi)/100.)) * (1*np.cos(t200to250 * (2*np.pi)/5000.)) + \n",
" 20*np.sin(t200to250 * (2*np.pi)/5000.) )\n",
"plt.figure(figsize=(16,4))\n",
"plt.plot(t200to250/100., syn200to250)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x116345310>]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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uoGleXkrRevnlfmVIF9C8epoTO+wQt9/229vXhQhF05znPBZe2H/bGPd113bq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\nREREVFoMWomIiIiIiKi0/h+wfkK3QHBrCgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11367e5d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# next 50 \"days\"\n",
"t250to300 = np.arange(25001,30001)\n",
"np.random.seed(123456789)\n",
"syn250to300 = 20 + ((10. * np.sin(t250to300 * (2*np.pi)/100.)) * (1*np.cos(t250to300 * (2*np.pi)/5000.)) + \n",
" 20*np.sin(t250to300 * (2*np.pi)/5000.) ) + 5 * np.random.randn(5000)\n",
"plt.figure(figsize=(16,4))\n",
"plt.plot(t250to300/100., syn250to300)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x116895fd0>]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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GeGGMUE6rmxsb8rpKO6koE++roijZ41OfAk46iewk0drV5RetLBY5XBewlX2/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CGEEEIIIbmFopUQQgghhBBCSG6haCWEEEIIIYQQklsoWgkhhBBCCCGE5Jb/D+7n5/Es2k4lAAAA\nAElFTkSuQmCC\n",
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"<matplotlib.figure.Figure at 0x115a82a10>"