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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "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": [
+ "# Adjusted Data Algorithm - Contents:\n",
+ "\n",
+ "- [Theoretical Basis](#Theoretical-Basis)\n",
+ " - [Motivation](#Motivation)\n",
+ " - [Affine Transformations](#Affine-Transformations)\n",
+ " - [Adaptive Coefficients](#Adaptive-Coefficients)\n",
+ " - [Anomaly Detection](#Anomaly-Detection)\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",
+ " - [Estimate Affine Transformation Matrix](#Estimate-Affine-Transformation-Matrix)\n",
+ "- [Comparison with (Quasi-)Definitive Data](#Comparison-with-(Quasi-)Definitive-Data)\n",
+ " - [Boulder (BOU) Observatory](#Boulder-(BOU)-Observatory)\n",
+ " - [Barrow (BRW) Observatory](#Barrow-(BRW)-Observatory)\n",
+ " - [Stennis (BSL) Observatory](#Stennis-(BSL)-Observatory)\n",
+ " - [College (CMO) Observatory](#College-(CMO)-Observatory)\n",
+ " - [Deadhorse (DED) Observatory](#Deadhorse-(DED)-Observatory)\n",
+ " - [Fredericksburgh (FRD) Observatory](#Fredericksburgh-(FRD)-Observatory)\n",
+ " - [Fresno (FRN) Observatory](#Fresno-(FRN)-Observatory)\n",
+ " - [Guam (GUA) Observatory](#Guam-(GUA)-Observatory)\n",
+ " - [Honolulu (HON) Observatory](#Honolulu-(HON)-Observatory)\n",
+ " - [Newport (NEW) Observatory](#Newport-(NEW)-Observatory)\n",
+ " - [San Juan (SJG) Observatory](#San-Juan-(SJG)-Observatory)\n",
+ " - [Shumagin (SHU) Observatory](#Shumagin-(SHU)-Observatory)\n",
+ " - [Sitka (SIT) Observatory](#Sitka-(SIT)-Observatory)\n",
+ " - [Tucson (TUC) Observatory](#Tucson-(TUC)-Observatory)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# [Theoretical Basis](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Motivation](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "The USGS employs 3-axis vector magnetometers, typically using fluxgate technology in the modern era. Alone, these magnetometers are high-quality variometers. That is, they record the relative variation of Earth's magnetic field over time very accurately, but are notoriously difficult to calibrate in an absolute sense. It is only when variometers are combined with frequent absolute calibration measurements that we get what is generally described as a **geomagnetic observatory**.\n",
+ "\n",
+ "Historically, the merging of these absolute calibrations and magnetic variation measurements has been a laborious process, ultimately resulting in the magnetic observatory community's standard \"definitive\" data product for each obseratory. Definitive data is processed in one year blocks, starting no less than one year after the first observation made that year, and so not nearly a real time product. More recently, some of the most stringent requirements for definitive data were relaxed so that a new community standard, \"quasi-definitive\" data, could be produced in a more timely (~1 month delay), but this was still not real time enough for many modern technological applications.\n",
+ "\n",
+ "There is a growing and still largely unmet demand for calibrated near real time data. Following INTERMAGNET terminology, we will refer to this as Adjusted Data, although it is also often referred to as \"provisional\" in the magnetic observatory community. The Adjusted Data standard is not strictly defined, but the goal stated in the newest version of INTERMAGNET's Technical Reference Manual is to match statistical specifications of quasi-definitive data, while acknowledging and accepting the fact that real time data is likely to be more noisy, and/or have more gaps.\n",
+ "\n",
+ "There was a previous attempt to produce Adjusted Data at the USGS that went largely undocumented. Given subsequent staff turnover and decommissioning of legacy computer systems, it is now impossible to asses the quality of this prototype data product, or fully understand why it was never deployed operationally. We speculate, with some anecdotal evidence, that the algorithm for generating these data was only a minor adaptation of the definitive processing software, which we already know is not particularly suited to real time processing.\n",
+ "\n",
+ "This report is an attempt to: 1) distill and document institutional knowledge associated with legacy procedures and software for generating (quasi-)definitive data, partly to 2) assess how or why these legacy procedures and software may have been inadequate for near real time adjusted data generation, and 3) desdcribe, demonstrate, and validate a new methodology that is specifically tailored for near real time processing."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Traditional Baseline Adjustments](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "Fluxgate sensors have a limited range over which their response functions can be considered linear. In order to maximize their sensitivity while still measuring the extremely wide range of magnetic environments encountered across Earth's surface, it is common practice to generate a bias magnetic field that opposes and (mostly) nullifies the Earth's main magnetic field along each of the instrument's axes. In doing so, the fluxgate can precisely capture minute relative variations in the magnetic field relative to the main field. One need only add the static oppositional field back to these variational measurements for an accurate and precise measure of the time-varying geomagnetic field.\n",
+ "\n",
+ "While this oppositional magnetic field strength might be estimated from first principals, significant static and time-dependent uncertainties in the fluxgate's mechanical geometries, plus electronic/electrical inefficiencies, conspire to require frequent and regular calibration to meet tolerances expected of magnetic observatories. So frequent, in fact, that removing the sensors to a lab to do so is not generally advisable, so the sensors are effectively calibrated in-place using absolute measurements, along with various physical and geometric assumptions, to calculate so-called baseline adjustments.\n",
+ "\n",
+ "Absolute measurements should not be made too closely to the fluxgate sensor, or there is risk of contaminating the data. Therefore, part of the baseline adjustment implicitly includes the quasi time-stationary vector difference between the fluxgate and absolute pier different locations. While this value could, in principal, be obtained through careful simultaneous absolute measurements at both locations, it is not really necessary unless one is genuinely interested in isolating the fluxgate's true response functions.\n",
+ "\n",
+ "Also, alignment of the fluxgate and absolute measurement coordinate frames is never perfect. But, if they are close, misalignment can be corrected using simple baseline adjustments and implicit small angle assumptions. When coordinate frames are not well aligned an initial rotation may be applied to obtain nominal alignment, and allow simple baseline corrections after that.\n",
+ "\n",
+ "The USGS, as well as many international magnetic observatories, install their fluxgate sensors so that the primary horizontal axis, $H$, aligns with the local magnetic meridian. This means that the secondary horizontal axis, $E$ (for eastward), need not nullify the Earth's main field in this direction since, on average, it will be zero. Also, it is assumed that the absolute-fluxgate pier difference is zero in the $E$ direction. There is undoubtedly additional uncertainty in the $E$ direction, but in practice, it is ignored in favor of rotating the $H$ axis by a declination baseline angle:\n",
+ "\n",
+ "$$\n",
+ "D_{base} = D_{abs} - \\arcsin(E/H_{abs})\n",
+ "$$\n",
+ "\n",
+ "With the absolute and fluxgate coordinate axes now roughly aligned, the $H$ baseline correction is estimated by projecting the absolute horizontal intensity onto the fluxgate's $H$ axis, and subtracting the fluxgate-measured magnetic field:\n",
+ "\n",
+ "$$\n",
+ "H_{base} = \\sqrt{H_{abs}^2 - E^2} - H\n",
+ "$$\n",
+ "\n",
+ "This is illustrated in the following figure, where the blue vector represents the absolute total horizontal magnetic vector in geographic coordinates, the red vector represents the horizontal vector measured by the fluxgate in its own Cartesian coordinates, the green items comprise baseline corrections, and the brown angle $D$ is the declination relative to the fluxgate's $H$ axis:\n",
+ "\n",
+ "\n",
+ "\n",
+ "Finally, the third axis, $Z$, points downward to complete a right-handed coordinate system. The downward absolute magnetic vector component, $Z_{abs}$, is assumed to align reasonably well with the downward fluxgate axis, $Z$, so a simple baseline correction is all that is needed:\n",
+ "\n",
+ "$$\n",
+ "Z_{base} = Z_{abs} - Z\n",
+ "$$\n",
+ "\n",
+ "We note, for completeness, that it is not uncommon for small angle approximations to be invoked when calculating $H_{base}$ or $D_{base}$, since for most observatories, the ratio of the $E$ to the $H_{abs}$ magnetic vector component is small. For example, even for Barrow, Alaska, small angle error is rarely more than a few parts in a million when the fluxgate in aligned with the local magnetic meridian. However, the local magnetic meridian drifts over time, more-so at higher latitudes than low. And at the magnetic pole, the concept of a magnetic meridian is undefined. If following traditional baseline correction procedures, it is best to avoid small angle approximations altogether."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Affine Transformations](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "One problem with traditional baseline adjustments is that they assume each axis is independent of the others when they are not in most real-world scenarios. Therefore, there are necessarily more than just 3 degrees of freedom to be adjusted. If one considers separately non-orthogonality of the instrument axes, scaling differences for each axis, rotation, and yes, actual baseline differences between the fluxgate sensor and the absolute measurement device, there are no fewer than 12 degrees of freedom that might be considered.\n",
+ "\n",
+ "A more rigorous approach to adjusting raw magnetic vector data is to generate a linear transformation that directly converts variometer data from its own local Cartesian $HEZ$ sensor coordinates into absolutely calibrated Cartesian $XYZ$ geographic coordinates. Standard linear transformations involve rotation, scaling, and even shear (that is, non-rigid rotation), while alignment of coordinate frame origins (that is, translation) can be easily included with a simple augmentation of the measurement vectors. This is known as an affine transformation:\n",
+ "\n",
+ "$$ \\left[\\begin{array}{cccc} X \\\\ \n",
+ " Y \\\\\n",
+ " Z \\\\\n",
+ " 1 \\end{array}\\right] = \n",
+ " [\\textbf{M}] \\left[ \\begin{array}{cccc} H \\\\ \n",
+ " E \\\\\n",
+ " Z \\\\\n",
+ " 1 \\end{array}\\right] $$\n",
+ " \n",
+ "...where $\\textbf{M}$ is the composition of potentially many separate affine transforms:\n",
+ "\n",
+ "$$\n",
+ "\\begin{array}{r@{}l}\n",
+ "[\\textbf{M}] & = & \\left[ \\begin{array}{cccc} 1 & 0 & 0 & t_1 \\\\ \n",
+ " 0 & 1 & 0 & t_2 \\\\\n",
+ " 0 & 0 & 1 & t_3 \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] \\cdotp \\text{ (i.e., } \\textbf{T}\\text{)}\\\\\n",
+ " & & \\left[ \\left[ \\begin{array}{cccc} 1 & 0 & 0 & 0 \\\\ \n",
+ " 0 & \\cos\\theta_x & -\\sin\\theta_x & 0 \\\\\n",
+ " 0 & \\sin\\theta_x & \\cos\\theta_x & 0 \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] \\cdotp\n",
+ " \\left[ \\begin{array}{cccc} \\cos\\theta_y & 0 & \\sin\\theta_y & 0 \\\\ \n",
+ " 0 & 1 & 0 & 0 \\\\\n",
+ " -\\sin\\theta_y & 0 & \\cos\\theta_y & 0 \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] \\cdotp\n",
+ " \\left[ \\begin{array}{cccc} \\cos\\theta_z & -\\sin\\theta_z & 0 & 0 \\\\ \n",
+ " \\sin\\theta_z & \\cos\\theta_z & 0 & 0 \\\\\n",
+ " 0 & 0 & 1 & 0 \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] \\right] \\cdotp \\text{ (i.e., } \\textbf{R}\\text{)} \\\\\n",
+ " & & \\left[ \\begin{array}{cccc} s_1 & 0 & 0 & 0 \\\\ \n",
+ " 0 & s_2 & 0 & 0 \\\\\n",
+ " 0 & 0 & s_3 & 0 \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] \\cdotp \\text{ (i.e., } \\textbf{S}\\text{)} \\\\\n",
+ " & & \\left[ \\begin{array}{cccc} 1 & h_{xy} & h_{xz} & 0 \\\\ \n",
+ " 0 & 1 & h_{yz} & 0 \\\\\n",
+ " 0 & 0 & 1 & 0 \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] \\text{ (i.e., } \\textbf{H}\\text{)}\n",
+ "\\end{array}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that order of operation is critical, but there is no single standard for composing a full affine transformation from its constituent matrices. The preceding example follows a norm used in the [Python Transforms3D](https://pypi.org/project/transforms3d/) package, that might better be represented algorithmically as $dot(\\textbf{T}, dot(\\textbf{R}, dot(\\textbf{S},\\textbf{H}) ) )$, where \"$dot$\" is a matrix dot product function. In words, a shear correction ($\\textbf{H}$ is first applied to the original $HEZ$ vector to orthogonalize it. This is followed by rescaling the orthogonalized $HEZ$ vectors with $\\textbf{S}$, which is followed by a rigid rotation $\\textbf{R}$ to align all axes with the desired $XYZ$ coordinate frame. Finally, the origin of the now rotated, scaled, and orthogonal vector is translated to make the coordinate frame origins coincide using $\\textbf{T}$.\n",
+ "\n",
+ "Looking more closely at $\\textbf{R} = dot(\\textbf{R}_x, dot(\\textbf{R}_y,\\textbf{R}_z))$, it should be even more evident that order of operation is important. The rotation angles ($\\theta_x$, $\\theta_y$, and $\\theta_z$) that combine to form $\\textbf{R}$ are presented here in a manner that resembles typical yaw-pitch-roll rotations. This is 1 of 6 possible so-called Tait-Bryan rotation sequences (re-orientation by rotating around each unique axis once). Proper Euler angles have 6 additional possible sequences that involve a rotation about one axis, then a second, then again about the first. If that were not enough, both the Tait-Bryan and proper Euler angles can be with respect to fixed axes (extrinsic; as above), or with respect to the new axes after each rotation (intrinsic). This leads to 24 possible valid rotations! It is highly recommended that one convention is chosen and used consistently.\n",
+ "\n",
+ "The astute reader may wonder at the particular form of $\\textbf{H}$. It is certainly possible to allow shear coefficients in all the off-diagonal elements of the upper-left 3x3 matrix, but we choose not to here for at least two reasons: \n",
+ "\n",
+ " 1. shear, by definition, must not alter the volume of the original data points, or in mathematical terms, its determinant is always 1, which is guaranteed by this triangular form; and \n",
+ " 2. this triangular form permits an efficient mechanism for separating rigid rotation from pure shear (see \"Decomposing a matrix into simple transformations\" by Spencer W. Thomas, pp 320-323 in *Graphics Gems II*, James Arvo (editor), Academic Press, 1991, ISBN: 0120644819).\n",
+ "\n",
+ "A nuance related to item 2 is that the solution is only unique in so far as this structure is chosen for the shear. If a different structure is chosen, the rotation matrix will be different, even though the composition of shear and rotation will always be the same. In more practical terms, this means that it is not correct to speak of \"rotation\" and \"shear\" separately, and certainly not appropriate to drop one without careful consideration. In particular, the structure chosen here (i.e., upper-triangular) means that the vector is rotated so that the $Z$ axis of both coordinate frames are aligned perfectly. Then the $E$ axis is sheared in the $Z$ direction, and the $H$ axis is sheared in both $Z$ and $E$ directions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Estimating Affine Transform Matrix](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "While the constituent affine matrices can, in theory, be created individually, through careful sensor design and construction, laboratory calibration, and/or surveying of the observatory site, in practice these are not always possible or adequate. Ultimately, if absolute $XYZ$ measurements are considered the \"truth\" to which variational measurements are to be corrected, the linear structure of the affine transformation allows us to invoke linear estimation theory to determine an optimal transform matrix $\\textbf{M}$ that encapsulates all the necessary corrections to the original $HEZ$ vector data.\n",
+ "\n",
+ "$$ \\left[\\begin{array}{cccc} X_1 & X_2 & \\ldots & X_n \\\\ \n",
+ " Y_1 & Y_2 & \\ldots & Y_n \\\\\n",
+ " Z_1 & Z_2 & \\ldots & Z_n \\\\\n",
+ " 1 & 1 & \\ldots & 1 \\end{array}\\right] = \n",
+ " \\left[ \\begin{array}{cccc} M_{11} & M_{12} & M_{13} & M_{14} \\\\\n",
+ " M_{21} & M_{22} & M_{23} & M_{24} \\\\\n",
+ " M_{31} & M_{32} & M_{33} & M_{34} \\\\\n",
+ " M_{41} & M_{42} & M_{43} & M_{44} \\end{array}\\right]\n",
+ " \\cdotp\n",
+ " \\left[ \\begin{array}{cccc} H_1 & H_2 & \\ldots & H_n \\\\ \n",
+ " E_1 & E_2 & \\ldots & E_n \\\\\n",
+ " Z_1 & Z_2 & \\ldots & Z_n \\\\\n",
+ " 1 & 1 & \\ldots & 1 \\end{array}\\right] $$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### [Least Squares](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "The most straight-forward estimation technique is least-squares. Many mathematical software libraries have efficient routines that, given measurement arrays constructed similarly to above, will use linear least squares to determine an optimal 4x4 $\\textbf{M}$ matrix to map $HEZ1$ into $XYZ1$ vectors. However, what these all inevitably do, at least under the hood, is rearrange the previous matrix equation algebraically such that:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$ \\left[\\begin{array}{c} X_1 \\\\ \n",
+ " Y_1 \\\\\n",
+ " Z_1 \\\\\n",
+ " 1 \\\\\n",
+ " X_2 \\\\ \n",
+ " Y_2 \\\\\n",
+ " Z_2 \\\\\n",
+ " 1 \\\\\n",
+ " \\vdots \\\\\n",
+ " X_n \\\\ \n",
+ " Y_n \\\\\n",
+ " Z_n \\\\ \n",
+ " 1 \\end{array}\\right] = \n",
+ " \\left[ \\begin{array}{cccccccccccccccc}\n",
+ " H_1 & E_1 & Z_1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & H_1 & E_1 & Z_1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_1 & E_1 & Z_1 & 1 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_1 & E_1 & Z_1 & 1 \\\\\n",
+ " H_2 & E_2 & Z_2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & H_2 & E_2 & Z_2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_2 & E_2 & Z_2 & 1 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_2 & E_2 & Z_2 & 1 \\\\\n",
+ " \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \n",
+ " \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\\\ \n",
+ " H_n & E_n & Z_n & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & H_n & E_n & Z_n & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_n & E_n & Z_n & 1 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_n & E_n & Z_n & 1 \\\\\n",
+ " \\end{array} \\right] \\cdotp\n",
+ " \\left[ \\begin{array}{c}\n",
+ " M_{11} \\\\\n",
+ " M_{12} \\\\\n",
+ " M_{13} \\\\\n",
+ " M_{14} \\\\\n",
+ " M_{21} \\\\\n",
+ " M_{22} \\\\\n",
+ " M_{23} \\\\\n",
+ " M_{24} \\\\\n",
+ " M_{31} \\\\\n",
+ " M_{32} \\\\\n",
+ " M_{33} \\\\\n",
+ " M_{34} \\\\\n",
+ " M_{41} \\\\\n",
+ " M_{42} \\\\\n",
+ " M_{43} \\\\\n",
+ " M_{44}\\end{array}\\right] $$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This system of equations is equivalent to the previous matrix equation with a 4x4 $\\textbf{M}$, but perhaps makes it more clear that we are solving a set of linear equations for 16 unknowns. Given that a single vector measurement comprises only three actual data points, plus the augmented \"1\", it should also be clear that no fewer than 4 sets of absolute and fluxgate measurements are required to obtain a solution. Preferably, there would be many more than 4 sets, thus reducing uncertainty associated with the solution."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is often desirable to constrain the system of equations, usually to reduce the degrees of freedom and reduce the uncertainty associated with the solution to a given system of equations. In fact, while one of the biggest advantages to using affine transformations might be that they include scale factors and shear corrections, in addition to rotation and translation, it has been our experience that allowing so many free parameters leads to over-fitting of the training data. When this happens, the solution, $\\textbf{M}$, does not generalize well when used to adjust raw data between absolute measurements. If the frequency of absolute measurements could be increased substantially, it might be sufficient to reduce uncertainty enough that realistic scale and shear calibrations could be obtained from absolute measurements. Until such time, limiting the number of degrees of freedom helps avoid over-fitting the limited absolute training data.\n",
+ "\n",
+ "We recommend doing so in a manner somewhat consistent with traditional (quasi)definitive processing. To start, if we look closely at all the transform matrices in the previous subsection, it is evident that the final row of $\\textbf{M}$ should always be $\\left[\\begin{array}{cccc} 0 & 0 & 0 & 1 \\end{array} \\right]$. We can exploit this knowledge to remove the $M_{4*}$ unknowns from our system of equations, and their corresponding columns in the measurement matrices, thus reducing our number of unknowns from 16 to 12, and slightly reducing uncertainty in the remaining estimated coefficients."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$ \\left[\\begin{array}{c} X_1 \\\\ \n",
+ " Y_1 \\\\\n",
+ " Z_1 \\\\\n",
+ " X_2 \\\\ \n",
+ " Y_2 \\\\\n",
+ " Z_2 \\\\\n",
+ " \\vdots \\\\\n",
+ " X_n \\\\ \n",
+ " Y_n \\\\\n",
+ " Z_n \\end{array}\\right] = \n",
+ " \\left[ \\begin{array}{cccccccccccccccc}\n",
+ " H_1 & E_1 & Z_1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & H_1 & E_1 & Z_1 & 1 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_1 & E_1 & Z_1 & 1 \\\\\n",
+ " H_2 & E_2 & Z_2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & H_2 & E_2 & Z_2 & 1 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_2 & E_2 & Z_2 & 1 \\\\\n",
+ " \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \n",
+ " \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\\\ \n",
+ " H_n & E_n & Z_n & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & H_n & E_n & Z_n & 1 & 0 & 0 & 0 & 0 \\\\\n",
+ " 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & H_n & E_n & Z_n & 1 \\\\\n",
+ " \\end{array} \\right] \\cdotp\n",
+ " \\left[ \\begin{array}{c}\n",
+ " M_{11} \\\\\n",
+ " M_{12} \\\\\n",
+ " M_{13} \\\\\n",
+ " M_{14} \\\\\n",
+ " M_{21} \\\\\n",
+ " M_{22} \\\\\n",
+ " M_{23} \\\\\n",
+ " M_{24} \\\\\n",
+ " M_{31} \\\\\n",
+ " M_{32} \\\\\n",
+ " M_{33} \\\\\n",
+ " M_{34} \\end{array}\\right] $$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This simple constraint does not buy us much. But now suppose we wish to reduce the the number of free parameters even further by imposing additional prior knowledge/assumptions on the structure of our matrix that are (mostly) consistent with traditional definitive processing techniques. For example, assume that 1) the vertical vector component $Z$ is perfectly aligned between the variometer and the absolute instrument, and 2) it is perfectly orthogonal to the horizontal components; also, 3) the transform of the horizontal vector component is a scaled rotation about the $Z$ axis (that is, no skew). Finally, 4) don't allow any translation of the horizontal components, and 5) only allow translation of the vertical component. This is accomplished by removing and/or moving elements of the previous matrix equation to give the following:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$ \\left[\\begin{array}{c} X_1 \\\\ \n",
+ " Y_1 \\\\\n",
+ " (Z-Z)_1 \\\\\n",
+ " X_2 \\\\ \n",
+ " Y_2 \\\\\n",
+ " (Z-Z)_2 \\\\\n",
+ " \\vdots \\\\\n",
+ " X_n \\\\ \n",
+ " Y_n \\\\\n",
+ " (Z-Z)_n \\end{array}\\right] = \n",
+ " \\left[ \\begin{array}{cccccccccc}\n",
+ " H_1 & E_1 & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 0 \\\\\n",
+ " \\color{green}{E_1} & \\color{green}{-H_1} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 0 \\\\\n",
+ " 0 & 0 & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 1 \\\\\n",
+ " H_2 & E_2 & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 0 \\\\\n",
+ " \\color{green}{E_2} & \\color{green}{-H_2} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 0 \\\\\n",
+ " 0 & 0 & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 1 \\\\\n",
+ " \\vdots & \\vdots & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\vdots \\\\ \n",
+ " H_n & E_n & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 0 \\\\\n",
+ " \\color{green}{E_n} & \\color{green}{-H_n} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 0 \\\\\n",
+ " 0 & 0 & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & \\color{red}{[]} & 1 \\\\\n",
+ " \\end{array} \\right] \\cdotp\n",
+ " \\left[ \\begin{array}{c}\n",
+ " M_{11} \\\\\n",
+ " M_{12} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " \\color{red}{[]} \\\\\n",
+ " M_{34} \\end{array}\\right] $$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, green indicates moved/edited elements, while red brackets indicate where whole columns were removed, which in turn corresponds to a removal of one of the $M$ coefficients. It is now clear that the degrees of freedom have been reduced from 16 to just 3. Once this set of equations is solved for its three unknowns, they can be inserted into the corresponding locations in the $\\textbf{M}$ matrix:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\\textbf{M} = \n",
+ " \\left[ \\begin{array}{cccc} M_{11} & M_{12} & 0 & 0 \\\\\n",
+ " -M_{12} & M_{11} & 0 & 0 \\\\\n",
+ " 0 & 0 & 1 & M_{34} \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] $$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### [Singular Value Decomposition](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "We note at this point that solving this reduced set of equations is not exactly equivalent to traditional (quasi)definitive processing as presented earlier. In fact, such an affine transform would look something like:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "$$\\textbf{M} = \n",
+ " \\left[ \\begin{array}{cccc} \\cos\\theta_z & -\\sin\\theta_z & 0 & T_X \\\\ \n",
+ " \\sin\\theta_z & \\cos\\theta_z & 0 & T_Y \\\\\n",
+ " 0 & 0 & 1 & T_Z \\\\\n",
+ " 0 & 0 & 0 & 1 \\end{array}\\right] $$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "...where $T_X$ and $T_Y$ are $H_{base}$ rotated into the absolute $XY$ frame through $D_{base}$, or as presented here, $\\theta_z$. But such a matrix cannot be solved for directly from the measurement matrices via least-squares. All is not lost, however. $\\theta_z$ can be found by invoking singular value decomposition (SVD) to obtain the eigenvectors that define an orthonormal rotation matrix between two vector spaces."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With modern numerical libraries, this is actually relatively simple. First, remove the means from both the $HE$ and $XY$ measurement matrices so that their origins coincide (ignore $Z$ for now):\n",
+ "\n",
+ "$$\n",
+ " H_i' = H_i - \\overline{H}, \\quad E_i' = E_i - \\overline{E} \\\\\n",
+ " X_i' = X_i - \\overline{X}, \\quad Y_i' = Y_i - \\overline{Y}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, create a cross-covariance matrix between $HE$ and $XY$:\n",
+ "\n",
+ "$$\n",
+ " \\textbf{C} = \\left[ \\begin{array}{cc} c_{11} & c_{12} \\\\\n",
+ " c_{21} & c_{22} \\end{array}\\right]\n",
+ "$$\n",
+ "\n",
+ "...where...\n",
+ "\n",
+ "$$\n",
+ " c_{11} = \\sum_i^n H_i' X_i', \\quad\n",
+ " c_{12} = \\sum_i^n H_i' Y_i', \\quad\n",
+ " c_{21} = \\sum_i^n E_i' X_i', \\quad\n",
+ " c_{22} = \\sum_i^n E_i' Y_i'\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, decompose $\\textbf{C}$ into its singular values, plus left and right eigenvectors using the SVD routine found in your favorite numerical linear algebra library:\n",
+ "\n",
+ "$$\n",
+ " \\textbf{C} = \\textbf{USV}^*\n",
+ "$$\n",
+ "\n",
+ "...where singular values $s_i$ comprise the diagonal elements of $\\textbf{S}$, and the orthonormal matrices $\\textbf{U}$ and $\\textbf{V}^*$ can be combined to give the unscaled rotation that aligns $HE$ with $XY$:\n",
+ "\n",
+ "$$\n",
+ " \\textbf{R} = \\textbf{V}^{*\\intercal} \\cdotp \\textbf{U}^\\intercal\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, with $\\textbf{R}$, simply rotate $\\overline{H}$ and $\\overline{E}$ into absolute coordinates to obtain $T_X$ and $T_Y$:\n",
+ "\n",
+ "$$\n",
+ " \\left[ \\begin{array}{c} T_X \\\\\n",
+ " T_Y \\end{array} \\right] = \n",
+ " \\textbf{R} \\cdotp \\left[ \\begin{array}{c} \\overline{H} \\\\\n",
+ " \\overline{E} \\end{array} \\right]\n",
+ "$$\n",
+ "\n",
+ "($T_Z$ is simply the difference in the averages of the vertical components, just as with traditional processing)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that there are now four degrees of freedom ($\\theta_z$, $T_X$, $T_Y$, and $T_Z$) instead of the three traditional (quasi)definitive baselines. This is because $T_X$ and $T_Y$ are allowed to float, rather than be trigonometrically locked to one another via $\\theta_z$. We might effectively do this with an affine transformation, and obtain a near perfect analog to (quasi)definitive processing, but one of the main problems with the traditional approach presented above is that it requires that the fluxgate sensor be aligned with the local magnetic meridian for the math/geometry to work out. Using affine transformations, this is no longer necessary. Of course by increasing the degrees of freedom, the uncertainty of our final solution is slightly higher; but then the uncertainty *should be* higher, since a potential offset in $E$ is now included in the calculations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Adaptive Coefficients](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "ddd"
+ ]
+ },
+ {
+ "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": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# standard Python libraries\n",
+ "import matplotlib as mpl\n",
+ "#import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy as sp\n",
+ "import scipy.linalg as spl\n",
+ "import glob\n",
+ "from datetime import datetime \n",
+ "import re\n",
+ "from matplotlib.gridspec import GridSpec\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "from openpyxl import load_workbook\n",
+ "\n",
+ "# 3rd-party libraries\n",
+ "import obspy\n",
+ "from obspy.core import UTCDateTime\n",
+ "import geomagio\n",
+ "from geomagio.edge import EdgeFactory\n",
+ "#from geomagio.Algorithm import DeltaFAlgorithm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Test Configuration](#SqDist-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define assert_almost_equal\n",
+ "assert_almost_equal = np.testing.assert_almost_equal\n",
+ "\n",
+ "# define factory for retrieving raw observations\n",
+ "# NOTE: need to make sure QD data is on cwbpub before public release\n",
+ "#factory = EdgeFactory(host='cwbpub.cr.usgs.gov', port=2060)\n",
+ "factory = EdgeFactory(host='igskcicgvmmage2.cr.usgs.gov', port=2060)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# [Synthetic Data Demonstration](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Construct synthetic time series](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "- define a regular series of times t, and synthetic \"truth\" vectors (x, y, z) that represent the desired results\n",
+ "- define an affine transformation matrix that converts the truth vectors into a different \"observed\" basis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
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\" width=\"800\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "t = np.linspace(0, np.pi, 5000)\n",
+ "\n",
+ "x1 = (2*t - np.pi)/np.pi\n",
+ "y1 = np.sqrt(1 - x1**2)*np.cos(24*t)\n",
+ "z1 = np.sqrt(1 - x1**2)*np.sin(24*t)\n",
+ "\n",
+ "# plot xy plane, xz plane, and yz plane\n",
+ "plt.figure(figsize=(8,3))\n",
+ "\n",
+ "plt.subplot(131)\n",
+ "plt.plot(x1,y1)\n",
+ "plt.xlabel('x1')\n",
+ "plt.ylabel('y1')\n",
+ "plt.gca().set_aspect('equal')\n",
+ "\n",
+ "plt.subplot(132)\n",
+ "plt.plot(x1,z1)\n",
+ "plt.xlabel('x1')\n",
+ "plt.ylabel('z1')\n",
+ "plt.gca().set_aspect('equal')\n",
+ "\n",
+ "plt.subplot(133)\n",
+ "plt.plot(y1,z1)\n",
+ "plt.xlabel('y1')\n",
+ "plt.ylabel('z1')\n",
+ "plt.gca().set_aspect('equal')\n",
+ "\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"800\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# first, create a pure rotation matrix\n",
+ "def make_axis_rotation_matrix(direction, angle):\n",
+ " \"\"\"\n",
+ " Create a rotation matrix corresponding to the rotation around a general\n",
+ " axis by a specified angle.\n",
+ " R = dd^T + cos(a) (I - dd^T) + sin(a) skew(d)\n",
+ " Parameters:\n",
+ " angle : float a\n",
+ " direction : array d\n",
+ " \"\"\"\n",
+ " d = np.array(direction, dtype=np.float64)\n",
+ " d /= np.linalg.norm(d)\n",
+ "\n",
+ " eye = np.eye(3, dtype=np.float64)\n",
+ " ddt = np.outer(d, d)\n",
+ " skew = np.array([[ 0, d[2], -d[1]],\n",
+ " [-d[2], 0, d[0]],\n",
+ " [d[1], -d[0], 0]], dtype=np.float64)\n",
+ "\n",
+ " mtx = ddt + np.cos(angle) * (eye - ddt) + np.sin(angle) * skew\n",
+ " return mtx\n",
+ "Mrot = make_axis_rotation_matrix(np.array([1,1,1]),45*np.pi/180)\n",
+ "\n",
+ "# second, create a pure scaling matrix\n",
+ "Mscale = np.array([[1.5, 0, 0], [0, 0.95, 0], [0, 0, 1.2]])\n",
+ "\n",
+ "# third, create a pure translation vector\n",
+ "Vtrans = np.array([[3],[4],[3]])\n",
+ "\n",
+ "# fourth, combine these into an affine transformation to our new basis\n",
+ "Maugmented = np.hstack([np.dot(Mscale,Mrot), Vtrans])\n",
+ "Maffine = np.vstack([Maugmented, np.array([0,0,0,1])])\n",
+ "\n",
+ "# finally, apply this affine transformation to our \"truth\" data, extended by\n",
+ "# a time series of 1s for the translation\n",
+ "x2, y2, z2, _ = np.dot(Maffine, np.vstack((x1, y1, z1, np.ones_like(x1))))\n",
+ "\n",
+ "\n",
+ "# plot xy plane, xz plane, and yz plane\n",
+ "plt.figure(figsize=(8,3))\n",
+ "\n",
+ "plt.subplot(131)\n",
+ "plt.plot(x2,y2*np.nan) # to force 2nd color\n",
+ "plt.plot(x2,y2)\n",
+ "plt.xlabel('x2')\n",
+ "plt.ylabel('y2')\n",
+ "plt.gca().set_aspect('equal')\n",
+ "\n",
+ "plt.subplot(132)\n",
+ "plt.plot(x2,z2*np.nan) # to force 2nd color\n",
+ "plt.plot(x2,z2)\n",
+ "plt.xlabel('x2')\n",
+ "plt.ylabel('z2')\n",
+ "plt.gca().set_aspect('equal')\n",
+ "\n",
+ "plt.subplot(133)\n",
+ "plt.plot(y2,z2*np.nan) # to force 2nd color\n",
+ "plt.plot(y2,z2)\n",
+ "plt.xlabel('y2')\n",
+ "plt.ylabel('z2')\n",
+ "plt.gca().set_aspect('equal')\n",
+ "\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"800\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,0,'z')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# plot 3D view of 'truth' and transformed vectors\n",
+ "plt.figure(figsize=(8,6))\n",
+ "\n",
+ "plt.subplot(111, projection='3d')\n",
+ "plt.plot(x1, y1, z1)\n",
+ "plt.plot(x2, y2, z2)\n",
+ "plt.gca().set_xlabel('x')\n",
+ "plt.gca().set_ylabel('y')\n",
+ "plt.gca().set_zlabel('z')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<img 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\" width=\"800\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot vs time\n",
+ "plt.figure(figsize=(8,3))\n",
+ "\n",
+ "plt.subplot(131)\n",
+ "plt.plot(t, x1)\n",
+ "plt.plot(t, x2)\n",
+ "plt.xlabel('t')\n",
+ "plt.ylabel('x')\n",
+ "\n",
+ "plt.subplot(132)\n",
+ "plt.plot(t, y1)\n",
+ "plt.plot(t, y2)\n",
+ "plt.xlabel('t')\n",
+ "plt.ylabel('y')\n",
+ "\n",
+ "plt.subplot(133)\n",
+ "plt.plot(t, z1)\n",
+ "plt.plot(t, z2)\n",
+ "plt.xlabel('t')\n",
+ "plt.ylabel('z')\n",
+ "\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Estimate Affine Transformation Matrix](#Adjusted-Data-Algorithm---Contents:)\n",
+ "\n",
+ "Define functions to solve for the affine transform matrix $[M]$, and perform the transformation once $[M]$ is calculated.\n",
+ "\n",
+ "$$ \\left[\\begin{array}{cccc} X_1 & X_2 & \\ldots & X_n \\\\ \n",
+ " Y_1 & Y_2 & \\ldots & Y_n \\\\\n",
+ " Z_1 & Z_2 & \\ldots & Z_n \\\\\n",
+ " 1 & 1 & \\ldots & 1 \\end{array}\\right] = [M] \\left[ \\begin{array}{cccc} h_1 & h_2 & \\ldots & h_n \\\\ \n",
+ " e_1 & e_2 & \\ldots & e_n \\\\\n",
+ " z_1 & z_2 & \\ldots & z_n \\\\\n",
+ " 1 & 1 & \\ldots & 1 \\end{array}\\right] $$\n",
+ "\n",
+ "Below, we:\n",
+ "- sample \"truth\" observations (similar to taking \"absolute\" measurements)\n",
+ "- sample transformed observations (similar to variation measurements)\n",
+ "- invert for optimal affine trasnformation matrix M\n",
+ "- use M to calculate adjusted data\n",
+ "- plot observed and adjusted data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_transform_from_abs_ords(xyz_abs, hez_ord):\n",
+ " '''\n",
+ " pass two 3-row arrays (or 3-lists of vectors), one for each of:\n",
+ " - absolute magnetic vectors in geographic frame (XYZ)\n",
+ " - observed magnetic vectors in instrument frame (HEZ)\n",
+ " \n",
+ " Returns:\n",
+ " - affine transform matrix to convert observed vectors to absolute basis\n",
+ " - sum of residues from linear fit\n",
+ " - effective rank of affine matrix\n",
+ " - singular values\n",
+ " '''\n",
+ " tol = 1e-15\n",
+ " \n",
+ " ones = np.ones(hez_ord[0].shape)\n",
+ "\n",
+ " ordp2 = np.vstack([hez_ord,ones])\n",
+ "\n",
+ " absp2 = np.vstack([xyz_abs,ones])\n",
+ "\n",
+ " M, res, rank, sigma = np.linalg.lstsq(ordp2.T, absp2.T)\n",
+ "\n",
+ " maskM = np.abs(M) > tol\n",
+ " M = maskM * M\n",
+ " \n",
+ " return M.T, res, rank, sigma\n",
+ "\n",
+ "\n",
+ "def make_adjusted_from_transform_and_raw(M, hez_ord):\n",
+ " '''\n",
+ " pass in:\n",
+ " - affine transform matrix M\n",
+ " - observed vectors as 3-row matrix (or 3-list of 1D arrays)\n",
+ " \n",
+ " Returns adjusted data in absolute geographic reference frame\n",
+ " '''\n",
+ "\n",
+ " adj = np.dot(M, np.vstack([hez_ord, np.ones_like(hez_ord[0])]))\n",
+ "\n",
+ " return adj[0:3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/erigler/anaconda3/envs/test_GIMP_py36/lib/python3.6/site-packages/ipykernel_launcher.py:21: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
+ "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"800\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# sub-sample indices used to solve for M\n",
+ "idx_abs = np.arange(x1.size)[::250]\n",
+ "\n",
+ "# solve for affine transforation matrix\n",
+ "M, resid, rank, lamda = get_transform_from_abs_ords([x1, y1, z1], [x2, y2, z2])\n",
+ "\n",
+ "# calculate adjusted data\n",
+ "xadj, yadj, zadj = make_adjusted_from_transform_and_raw(M, [x2, y2, z2])\n",
+ "\n",
+ "# plot sub-sampled observations and adjusted data (should look like plots above)\n",
+ "plt.figure(figsize=(8,3))\n",
+ "\n",
+ "plt.subplot(131)\n",
+ "plt.plot(t, xadj)\n",
+ "plt.plot(t, x2)\n",
+ "plt.plot(t[idx_abs], x1[idx_abs],'*k')\n",
+ "plt.plot(t[idx_abs], x2[idx_abs],'*k')\n",
+ "plt.xlabel('t')\n",
+ "plt.ylabel('x')\n",
+ "\n",
+ "plt.subplot(132)\n",
+ "plt.plot(t, yadj)\n",
+ "plt.plot(t, y2)\n",
+ "plt.plot(t[idx_abs], y1[idx_abs],'*k')\n",
+ "plt.plot(t[idx_abs], y2[idx_abs],'*k')\n",
+ "plt.xlabel('t')\n",
+ "plt.ylabel('y')\n",
+ "\n",
+ "plt.subplot(133)\n",
+ "plt.plot(t, zadj)\n",
+ "plt.plot(t, z2)\n",
+ "plt.plot(t[idx_abs], z1[idx_abs],'*k')\n",
+ "plt.plot(t[idx_abs], z2[idx_abs],'*k')\n",
+ "plt.xlabel('t')\n",
+ "plt.ylabel('z')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# [Comparison with (Quasi-)Definitive Data](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Notebook Functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Baseline and Absolute Data Retrieval\n",
+ "\n",
+ "Functions here should retrieve baseline and absolutes measurements:\n",
+ "\n",
+ "**Inputs** \n",
+ "```\n",
+ "obs_code - 3-character IAGA code for observatory\n",
+ "start_date - UTCDatetime for start of interval\n",
+ "end_date - UTCDatetime for end of interval \n",
+ "```\n",
+ "**Options** \n",
+ "```\n",
+ "path_or_url - string that holds a base path or url at which to\n",
+ " find baseline and absolute observations\n",
+ " (default = max(times))\n",
+ "```\n",
+ "**Output** \n",
+ "```\n",
+ "h_abs_bas_utc - array holding vectors of h_abs, h_bas, and h_utc\n",
+ "d_abs_bas_utc - array holding vectors of d_abs, d_bas, and d_utc\n",
+ "z_abs_bas_utc - array holding vectors of z_abs, z_bas, and z_utc\n",
+ "pc - array holding pier corrections\n",
+ "\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def retrieve_baselines_resid_summary_xlsm(obs_code, start_date, end_date,\n",
+ " path_or_url = './'):\n",
+ " '''\n",
+ " Retrieve baselines from USGS Geomag residual method summary Excel \n",
+ " spreadsheets on local file system. This is a very simple data reader\n",
+ " that assumes a fixed filename convention, and a fixed template for\n",
+ " the summary spreadsheet.\n",
+ " \n",
+ " Inputs:\n",
+ " obs_code - 3-character IAGA code for observatory\n",
+ " start_date - UTCDatetime for start of interval\n",
+ " end_date - UTCDatetime for end of interval\n",
+ " path_or_url - folder in which to find .xlsm files\n",
+ " \n",
+ " Outout:\n",
+ " h_abs_bas_utc - array holding vectors of h_abs, h_bas, and h_utc\n",
+ " d_abs_bas_utc - array holding vectors of d_abs, d_bas, and d_utc\n",
+ " z_abs_bas_utc - array holding vectors of z_abs, z_bas, and z_utc\n",
+ " pc - array holding pier corrections\n",
+ " '''\n",
+ " #from glob import glob\n",
+ " import os\n",
+ " import fnmatch\n",
+ " from openpyxl import load_workbook\n",
+ " from datetime import timedelta\n",
+ " from obspy.core import UTCDateTime\n",
+ " \n",
+ " # some default inputs\n",
+ " if end_date is None:\n",
+ " end_date = UTCDateTime.now()\n",
+ " \n",
+ " if start_date is None:\n",
+ " start_date = UTCDatetime(0)\n",
+ " \n",
+ " \n",
+ " # initialize outputs\n",
+ " h_abs = []\n",
+ " h_bas = []\n",
+ " h_dt = []\n",
+ " \n",
+ " d_abs = []\n",
+ " d_bas = []\n",
+ " d_dt = []\n",
+ " \n",
+ " z_abs = []\n",
+ " z_bas = []\n",
+ " z_dt = []\n",
+ " \n",
+ " pc = []\n",
+ " \n",
+ " # openpyxl uses Python datetime objects\n",
+ " start_dt = start_date.datetime\n",
+ " end_dt = end_date.datetime\n",
+ " last_dt = start_dt\n",
+ " \n",
+ " # loop over all [obs_code]??????????.xlsm files in all folders under path_or_url\n",
+ " for root, dirnames, filenames in os.walk(path_or_url + '/' + obs_code.upper()):\n",
+ " for filename in fnmatch.filter(filenames, obs_code.upper() + '???????????.xlsm'):\n",
+ " \n",
+ " # load workbook\n",
+ " # (data_only=True forces openpyxl to read in data saved in\n",
+ " # a cell, even if the cell is actually a formula; if False,\n",
+ " # openpyxl would return the formuala; openpyxl NEVER evaluates\n",
+ " # a formula, it relies on values generated and cached by the\n",
+ " # spreadsheet program itself)\n",
+ " wb = load_workbook(os.path.join(root,filename), data_only=True)\n",
+ "\n",
+ " # get worksheet 1\n",
+ " ws1 = wb[\"Sheet1\"]\n",
+ "\n",
+ " # get date (pyxl retrieves as datetime object)\n",
+ " date = ws1[\"I1\"].value\n",
+ " \n",
+ " # these spreadsheet files must have a particular layout; if there is\n",
+ " # any problem reading in data, just skip the whole file\n",
+ " try:\n",
+ " \n",
+ " # (re)initialize valid list\n",
+ " valid = [True, True, True, True]\n",
+ "\n",
+ " # get and convert declination times\n",
+ " d_time_str = ['%04i'%ws1[\"B10\"].value,\n",
+ " '%04i'%ws1[\"B11\"].value,\n",
+ " '%04i'%ws1[\"B12\"].value,\n",
+ " '%04i'%ws1[\"B13\"].value]\n",
+ " d_time_delta = [timedelta(hours=int(s[0:2])) +\n",
+ " timedelta(minutes=int(s[2:4]))\n",
+ " for s in d_time_str]\n",
+ " d_datetime = [date + td for td in d_time_delta]\n",
+ "\n",
+ " # get and convert declination absolute fractional angles\n",
+ " d_absolute = [[float(ws1[\"C10\"].value), float(ws1[\"D10\"].value)],\n",
+ " [float(ws1[\"C11\"].value), float(ws1[\"D11\"].value)],\n",
+ " [float(ws1[\"C12\"].value), float(ws1[\"D12\"].value)],\n",
+ " [float(ws1[\"C13\"].value), float(ws1[\"D13\"].value)]]\n",
+ " d_absolute = [d + m/60 for d,m in d_absolute if m is not None]\n",
+ "\n",
+ " # get and convert declination baseline fractional angles\n",
+ " d_baseline = [float(ws1[\"H10\"].value),\n",
+ " float(ws1[\"H11\"].value),\n",
+ " float(ws1[\"H12\"].value),\n",
+ " float(ws1[\"H13\"].value)]\n",
+ " d_baseline = [db/60 for db in d_baseline if db is not None]\n",
+ "\n",
+ " d_reject = [ws1[\"J10\"].value,\n",
+ " ws1[\"J11\"].value,\n",
+ " ws1[\"J12\"].value,\n",
+ " ws1[\"J13\"].value]\n",
+ "\n",
+ " # (relies on strings evaluating True, and Nones evaluating False)\n",
+ " valid = [v and da is not None and db is not None and not dr\n",
+ " for v,da,db,dr in zip(valid, d_absolute, d_baseline, d_reject)]\n",
+ " \n",
+ " # get horizontal field times (for consistency with WebAbsolutes, even\n",
+ " # if these spreadsheets always have the same times for D, H, and Z)\n",
+ " h_time_str = ['%04i'%ws1[\"B24\"].value,\n",
+ " '%04i'%ws1[\"B25\"].value,\n",
+ " '%04i'%ws1[\"B26\"].value,\n",
+ " '%04i'%ws1[\"B27\"].value]\n",
+ " h_time_delta = [timedelta(hours=int(s[0:2])) +\n",
+ " timedelta(minutes=int(s[2:4]))\n",
+ " for s in h_time_str]\n",
+ " h_datetime = [date + td for td in h_time_delta]\n",
+ "\n",
+ " # get absolute horizontal field magnitude in nT\n",
+ " h_absolute = [float(ws1[\"D24\"].value),\n",
+ " float(ws1[\"D25\"].value),\n",
+ " float(ws1[\"D26\"].value),\n",
+ " float(ws1[\"D27\"].value)]\n",
+ "\n",
+ " # get baseline horizontal field magnitude in nT\n",
+ " h_baseline = [float(ws1[\"H24\"].value),\n",
+ " float(ws1[\"H25\"].value),\n",
+ " float(ws1[\"H26\"].value),\n",
+ " float(ws1[\"H27\"].value)]\n",
+ "\n",
+ " h_reject = [ws1[\"J24\"].value,\n",
+ " ws1[\"J25\"].value,\n",
+ " ws1[\"J26\"].value,\n",
+ " ws1[\"J27\"].value]\n",
+ "\n",
+ " # (relies on strings evaluating True, and Nones evaluating False)\n",
+ " valid = [v and ha is not None and hb is not None and not hr\n",
+ " for v,ha,hb,hr in zip(valid, h_absolute, h_baseline, h_reject)] \n",
+ "\n",
+ "\n",
+ " # get vertical field times (for consistency with WebAbsolutes, even\n",
+ " # if these spreadsheets always have the same times for D, H, and Z)\n",
+ " z_time_str = ['%04i'%ws1[\"B38\"].value,\n",
+ " '%04i'%ws1[\"B39\"].value,\n",
+ " '%04i'%ws1[\"B40\"].value,\n",
+ " '%04i'%ws1[\"B41\"].value]\n",
+ " z_time_delta = [timedelta(hours=int(s[0:2])) +\n",
+ " timedelta(minutes=int(s[2:4]))\n",
+ " for s in z_time_str]\n",
+ " z_datetime = [date + td for td in z_time_delta]\n",
+ "\n",
+ " # get absolute vertical field component in nT\n",
+ " z_absolute = [float(ws1[\"D38\"].value),\n",
+ " float(ws1[\"D39\"].value),\n",
+ " float(ws1[\"D40\"].value),\n",
+ " float(ws1[\"D41\"].value)]\n",
+ "\n",
+ " # get baseline vertical field component in nT\n",
+ " z_baseline = [float(ws1[\"H38\"].value),\n",
+ " float(ws1[\"H39\"].value),\n",
+ " float(ws1[\"H40\"].value),\n",
+ " float(ws1[\"H41\"].value)]\n",
+ "\n",
+ " z_reject = [ws1[\"J38\"].value,\n",
+ " ws1[\"J39\"].value,\n",
+ " ws1[\"J40\"].value,\n",
+ " ws1[\"J41\"].value]\n",
+ "\n",
+ " # (relies on strings evaluating True, and Nones evaluating False)\n",
+ " valid = [v and za is not None and zb is not None and not zr\n",
+ " for v,za,zb,zr in zip(valid, z_absolute, z_baseline, z_reject)]\n",
+ " \n",
+ " except:\n",
+ " \n",
+ " print(\"There was a problem reading file %s...skipping!\"%\n",
+ " os.path.join(root,filename))\n",
+ " \n",
+ " else:\n",
+ " \n",
+ " # add to lists, filtering on start_dt and end_dt and valid\n",
+ " d_dt.extend([dtt for dtt,v in zip(d_datetime, valid) \n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ " d_abs.extend([abs for abs,dtt,v in zip(d_absolute, d_datetime, valid) \n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ " d_bas.extend([bas for bas,dtt,v in zip(d_baseline, d_datetime, valid)\n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ "\n",
+ " h_dt.extend([dtt for dtt,v in zip(h_datetime, valid) \n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ " h_abs.extend([abs for abs,dtt,v in zip(h_absolute, h_datetime, valid) \n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ " h_bas.extend([bas for bas,dtt,v in zip(h_baseline, h_datetime, valid)\n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ "\n",
+ " z_dt.extend([dtt for dtt,v in zip(z_datetime, valid) \n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ " z_abs.extend([abs for abs,dtt,v in zip(z_absolute, z_datetime, valid) \n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ " z_bas.extend([bas for bas,dtt,v in zip(z_baseline, z_datetime, valid)\n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ "\n",
+ "\n",
+ " # get pier corrections (one for each measurement, NOT one per file,\n",
+ " # even though that is all that is stored in these spreadsheets)\n",
+ " pc.extend([ws1[\"C5\"].value for dtt,v in zip(z_datetime, valid)\n",
+ " if dtt >= start_dt and dtt <= end_dt and v])\n",
+ "\n",
+ "\n",
+ " # the following is a kludge where we assume zero-amplitude horizontal field\n",
+ " # serves as a \"flag\" for when some change was made to the observatory that\n",
+ " # was significant enough to discard all previous absolute measurements\n",
+ " # (i.e., an observer set inclination to exactly 90, which should never\n",
+ " # happen for valid absolute measurements at USGS observatories);\n",
+ " flags = (np.equal(h_absolute, 0) & \n",
+ " (np.array(h_datetime) >= start_dt) & \n",
+ " (np.array(h_datetime) <= end_dt))\n",
+ " if (flags.any()):\n",
+ " last_dt = max(max(np.array(h_datetime)[flags]), last_dt)\n",
+ " \n",
+ " \n",
+ " # close workbook\n",
+ " wb.close()\n",
+ " \n",
+ " # convert output lists to NumPy arrays\n",
+ " h_abs = np.array(h_abs)\n",
+ " d_abs = np.array(d_abs)\n",
+ " z_abs = np.array(z_abs)\n",
+ " h_bas = np.array(h_bas)\n",
+ " d_bas = np.array(d_bas)\n",
+ " z_bas = np.array(z_bas)\n",
+ " pc = np.array(pc)\n",
+ " \n",
+ " # convert datetimes to UTCDateTimes\n",
+ " h_utc = np.array([UTCDateTime(dt) for dt in h_dt])\n",
+ " d_utc = np.array([UTCDateTime(dt) for dt in d_dt])\n",
+ " z_utc = np.array([UTCDateTime(dt) for dt in z_dt])\n",
+ " \n",
+ " \n",
+ " # print message about modified magnetometer\n",
+ " if last_dt != start_dt:\n",
+ " print('Magnetometer altered, discarding measurements prior to %s'%\n",
+ " last_dt)\n",
+ " \n",
+ " # only return data more recent than last_dt\n",
+ " good = (h_utc > last_dt) & (d_utc > last_dt) & (z_utc > last_dt)\n",
+ " h_abs = h_abs[good]\n",
+ " d_abs = d_abs[good]\n",
+ " z_abs = z_abs[good]\n",
+ " h_bas = h_bas[good]\n",
+ " d_bas = d_bas[good]\n",
+ " z_bas = z_bas[good]\n",
+ " pc = pc[good]\n",
+ " h_utc = h_utc[good]\n",
+ " d_utc = d_utc[good]\n",
+ " z_utc = z_utc[good]\n",
+ " \n",
+ " \n",
+ " # return \"good\" data points\n",
+ " return ((h_abs, h_bas, h_dt), \n",
+ " (d_abs, d_bas, d_dt), \n",
+ " (z_abs, z_bas, z_dt),\n",
+ " pc)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def retrieve_baselines_webabsolutes(obs_code, start_date=None, end_date=None,\n",
+ " path_or_url='https://geomag.usgs.gov/'):\n",
+ " '''\n",
+ " Retrieve baselines from USGS Geomag web-absolutes webservice\n",
+ " \n",
+ " Inputs:\n",
+ " obs_code - 3-character IAGA code for observatory\n",
+ " start_date - UTCDatetime for start of interval\n",
+ " end_date - UTCDatetime for end of interval\n",
+ " path_or_url - URL for web server hosting webabsolutes service\n",
+ " \n",
+ " Outout:\n",
+ " h_abs_bas_utc - array holding vectors of h_abs, h_bas, and h_utc\n",
+ " d_abs_bas_utc - array holding vectors of d_abs, d_bas, and d_utc\n",
+ " z_abs_bas_utc - array holding vectors of z_abs, z_bas, and z_utc\n",
+ " pc - array holding pier corrections\n",
+ " '''\n",
+ " \n",
+ " import json\n",
+ " import urllib \n",
+ " from obspy.core import UTCDateTime\n",
+ " \n",
+ " # some defaults\n",
+ " if end_date is None:\n",
+ " end_date = UTCDateTime.now()\n",
+ " \n",
+ " if start_date is None:\n",
+ " start_date = UTCDatetime(0)\n",
+ " \n",
+ " # convert to unix epoch time (seconds since 1/1/1970)\n",
+ " start_epoch = start_date.timestamp\n",
+ " end_epoch = end_date.timestamp\n",
+ " \n",
+ " # used to identify last epoch of obsolete observatory configuration\n",
+ " last_epoch = start_epoch\n",
+ " \n",
+ " \n",
+ " # open, read, and parse URL for WebAbsolutes webservice\n",
+ " baseline_url = path_or_url + '/baselines/observation.json.php'\n",
+ " full_url = (\n",
+ " baseline_url + \n",
+ " '?observatory=' + obs_code +\n",
+ " '&starttime=' + start_date.isoformat() + \n",
+ " '&endtime=' + end_date.isoformat() +\n",
+ " '&includemeasurements=true'\n",
+ " )\n",
+ " response = urllib.request.urlopen(full_url)\n",
+ " parsed_response = json.load(response)\n",
+ " \n",
+ " \n",
+ " # initialize observation lists\n",
+ " h_abs = []\n",
+ " d_abs = []\n",
+ " z_abs = []\n",
+ " h_bas = []\n",
+ " d_bas = []\n",
+ " z_bas = []\n",
+ " h_t = []\n",
+ " d_t = []\n",
+ " z_t = []\n",
+ " pc = []\n",
+ " \n",
+ " # loop over all sets, disregarding observation grouping\n",
+ " for datum in parsed_response['data']:\n",
+ " for reading in datum['readings']:\n",
+ " # extract only complete and validated baseline sets; also,\n",
+ " # filter on reading 'end' times to partially address issues \n",
+ " # with database time stamps\n",
+ " if (reading['H']['absolute'] is not None and\n",
+ " reading['D']['absolute'] is not None and\n",
+ " reading['Z']['absolute'] is not None and\n",
+ " reading['H']['baseline'] is not None and\n",
+ " reading['D']['baseline'] is not None and\n",
+ " reading['Z']['baseline'] is not None and\n",
+ " reading['H']['valid'] is True and\n",
+ " reading['D']['valid'] is True and\n",
+ " reading['Z']['valid'] is True and\n",
+ " reading['H']['end'] is not None and\n",
+ " reading['H']['end'] >= start_epoch and\n",
+ " reading['H']['end'] <= end_epoch and\n",
+ " reading['H']['end'] is not None and\n",
+ " reading['D']['end'] >= start_epoch and\n",
+ " reading['D']['end'] <= end_epoch and\n",
+ " reading['H']['end'] is not None and\n",
+ " reading['Z']['end'] >= start_epoch and\n",
+ " reading['Z']['end'] <= end_epoch):\n",
+ " \n",
+ " h_abs.append(reading['H']['absolute'])\n",
+ " d_abs.append(reading['D']['absolute'])\n",
+ " z_abs.append(reading['Z']['absolute'])\n",
+ " h_bas.append(reading['H']['baseline'])\n",
+ " d_bas.append(reading['D']['baseline'])\n",
+ " z_bas.append(reading['Z']['baseline'])\n",
+ " h_t.append(reading['H']['end'])\n",
+ " d_t.append(reading['D']['end'])\n",
+ " z_t.append(reading['Z']['end'])\n",
+ " pc.append(float(datum['pier']['correction'])) \n",
+ " \n",
+ " # the following is a kludge where zero-amplitude horizontal field\n",
+ " # serves as a \"flag\" for when observatory change was significant\n",
+ " # enough to discard all previous absolute measurements\n",
+ " if (reading['H']['absolute'] is 0):\n",
+ " last_epoch = max(reading['H']['end'], last_epoch)\n",
+ "\n",
+ " # print message about modified magnetometer\n",
+ " if last_epoch != start_epoch:\n",
+ " print('Magnetometer altered, discarding measurements prior to %s'%\n",
+ " datetime.utcfromtimestamp(last_epoch)) \n",
+ " \n",
+ " # convert lists to NumPy arrays\n",
+ " h_abs = np.array(h_abs)\n",
+ " d_abs = np.array(d_abs)\n",
+ " z_abs = np.array(z_abs)\n",
+ " h_bas = np.array(h_bas)\n",
+ " d_bas = np.array(d_bas)\n",
+ " z_bas = np.array(z_bas)\n",
+ " pc = np.array(pc)\n",
+ " \n",
+ " # convert epochs to UTCDateTimes\n",
+ " h_utc = np.array([UTCDateTime(t) for t in h_t])\n",
+ " d_utc = np.array([UTCDateTime(t) for t in d_t])\n",
+ " z_utc = np.array([UTCDateTime(t) for t in z_t])\n",
+ " \n",
+ " \n",
+ " # only return data more recent than last_epoch\n",
+ " good = (h_utc > last_epoch) & (d_utc > last_epoch) & (z_utc > last_epoch)\n",
+ " h_abs = h_abs[good]\n",
+ " d_abs = d_abs[good]\n",
+ " z_abs = z_abs[good]\n",
+ " h_bas = h_bas[good]\n",
+ " d_bas = d_bas[good]\n",
+ " z_bas = z_bas[good]\n",
+ " pc = pc[good]\n",
+ " h_utc = h_utc[good]\n",
+ " d_utc = d_utc[good]\n",
+ " z_utc = z_utc[good]\n",
+ " \n",
+ "\n",
+ " # return data arrays\n",
+ " return ((h_abs, h_bas, h_utc), \n",
+ " (d_abs, d_bas, d_utc), \n",
+ " (z_abs, z_bas, z_utc),\n",
+ " pc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Observation Time Weighting Functions\n",
+ "\n",
+ "Functions here should calculate time-dependent weights given:\n",
+ "\n",
+ "**Inputs** \n",
+ "```\n",
+ "times - 1D array of times, or any time-like index whose\n",
+ " relative values represent spacing between events\n",
+ "memory - time scale over which weights decrease by a \n",
+ " prescribed amount relative to the maximum weight \n",
+ " \n",
+ "```\n",
+ "**Options** \n",
+ "```\n",
+ "epoch - time at which weights maximize\n",
+ " (default = max(times))\n",
+ "```\n",
+ "**Output** \n",
+ "```\n",
+ "weights - 1D array of weights\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def time_weights_exponential(times, memory, epoch=None):\n",
+ " '''\n",
+ " Calculate time-dependent weights according to exponential decay.\n",
+ " \n",
+ " Inputs:\n",
+ " times - 1D array of times, or any time-like index whose\n",
+ " relative values represent spacing between events\n",
+ " memory - exp(-1) time scale; weights will be ~37% of max\n",
+ " weight when time difference equals memory, and ~5%\n",
+ " of max weight when time difference is 3X memory\n",
+ " \n",
+ " Options:\n",
+ " epoch - time at which weights maximize\n",
+ " (default = max(times))\n",
+ " \n",
+ " Outout:\n",
+ " dist - an M element array of vector distances/metrics\n",
+ "\n",
+ " NOTE: ObsPy UTCDateTime objects can be passed in times, but\n",
+ " memory must then be specified in seconds\n",
+ " FIXME: Python datetime objects not supported yet\n",
+ "\n",
+ " '''\n",
+ " \n",
+ " # convert to array of floats\n",
+ " # (allows UTCDateTimes, but not datetime.datetimes)\n",
+ " times = np.asarray(times).astype(float)\n",
+ " \n",
+ " # quick input check\n",
+ " if (times.ndim > 1):\n",
+ " raise ValueError('times must be 1D array')\n",
+ " \n",
+ " if not np.size(memory) == 1:\n",
+ " raise ValueError('memory must be a scalar')\n",
+ " \n",
+ " if epoch is None:\n",
+ " epoch = float(max(times))\n",
+ " else:\n",
+ " if not np.size(epoch) == 1:\n",
+ " raise ValueError('value must be a scalar')\n",
+ " epoch = float(epoch)\n",
+ " \n",
+ " # if memory is actually infinite, return equal weights\n",
+ " if np.isinf(memory):\n",
+ " return np.ones(times.shape)\n",
+ " \n",
+ " # initialize weights\n",
+ " weights = np.zeros(times.shape)\n",
+ " \n",
+ " # calculate exponential decay time-dependent weights\n",
+ " weights[times <= epoch] = np.exp((times[times <= epoch] - epoch) / memory)\n",
+ " weights[times >= epoch] = np.exp((epoch - times[times >= epoch]) / memory)\n",
+ " \n",
+ " return weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def time_weights_linear(times, memory, epoch=None):\n",
+ " '''\n",
+ " Calculate time-dependent weights according to linear decay.\n",
+ " \n",
+ " Inputs:\n",
+ " times - 1D array of times, or any time-like index whose\n",
+ " relative values represent spacing between events\n",
+ " memory - linear time scale interval; weights will be 0% of\n",
+ " max weight when time difference equals memory\n",
+ " \n",
+ " Options:\n",
+ " epoch - time at which weights maximize\n",
+ " (default = max(times))\n",
+ " \n",
+ " Outout:\n",
+ " dist - an M element array of vector distances/metrics\n",
+ " \n",
+ "\n",
+ " NOTE: ObsPy UTCDateTime objects can be passed in times, but\n",
+ " memory must then be specified in seconds\n",
+ " FIXME: Python datetime objects not supported yet\n",
+ "\n",
+ " '''\n",
+ " \n",
+ " # convert to array of floats\n",
+ " # (allows UTCDateTimes, but not datetime.datetimes)\n",
+ " times = np.asarray(times).astype(float)\n",
+ " times = np.asarray(times).astype(float)\n",
+ " \n",
+ " # quick input check\n",
+ " if (times.ndim > 1):\n",
+ " raise ValueError('times must be 1D array')\n",
+ " \n",
+ " if not np.size(memory) == 1:\n",
+ " raise ValueError('memory must be a scalar')\n",
+ " \n",
+ " if epoch is None:\n",
+ " epoch = float(max(times))\n",
+ " else:\n",
+ " if not np.size(epoch) == 1:\n",
+ " raise ValueError('value must be a scalar')\n",
+ " epoch = float(epoch)\n",
+ " \n",
+ " # if memory is actually infinite, return equal weights\n",
+ " if np.isinf(memory):\n",
+ " return np.ones(times.shape)\n",
+ " \n",
+ " # initialize weights\n",
+ " weights = np.zeros(times.shape)\n",
+ "\n",
+ " # calculate exponential decay time-dependent weights\n",
+ " weights[times <= epoch] = (times[times <= epoch] - epoch + memory) / memory\n",
+ " weights[times >= epoch] = (epoch - times[times >= epoch] + memory) / memory\n",
+ " \n",
+ " # set negative weights to zero\n",
+ " weights[weights < 0] = 0\n",
+ " \n",
+ " return weights"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Vector Distance Calculator\n",
+ "\n",
+ "Function here should calculate vector distances given:\n",
+ "\n",
+ "**Inputs** \n",
+ "```\n",
+ "vectors_A - NxM array where N is number of vector axes and M is number of observations\n",
+ "vectors_B - NxM array where N is number of vector axes and M is number of observations\n",
+ "```\n",
+ "**Options** \n",
+ "```\n",
+ "metric - string specifying a supported metric\n",
+ "VI - inverse (co)variance by which to scale distances\n",
+ " (if None, calculate scaling (co)variance appropriate\n",
+ " for metric from vectors_A and vectors_B, then apply)\n",
+ "```\n",
+ "**Output** \n",
+ "```\n",
+ "dist - an M element array of vector distances/metrics\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def vector_dist(vectors_A, vectors_B, metric=None, VI=None):\n",
+ " '''\n",
+ " Calculate a vector distance/metric between corresponding elements of\n",
+ " vectors_A and vectors_B. This is essentially the diagonal of the cdist\n",
+ " function, but without all the undesired extra calculations. SciPy \n",
+ " doesn't seem to offer an equivalent function.\n",
+ " \n",
+ " Inputs:\n",
+ " vectors_A - NxM array where M is observations, and N is vector order\n",
+ " vectors_B - NxM array where M is observations, and N is vector order\n",
+ " metric - string specifying an accepted metric. This does not include\n",
+ " all the options available to pdist and cdist, since we do\n",
+ " not use those; available options (for now) all involve the\n",
+ " L2 norm:\n",
+ " \n",
+ " euclidean - (default) euclidean distance between points\n",
+ " seuclidean - euclidean distance between points scaled by \n",
+ " the combined standard deviation of vector_A\n",
+ " and vectorB (this is what pdist/cdist do);\n",
+ " basically assumes the vector elements are \n",
+ " scaled orthogonal basis functions (e.g., \n",
+ " Cartesian XYZ coordinates, or EOFs)\n",
+ " mahalanobis - mahalanobis distance between points; similar to\n",
+ " seuclidean, except it doesn't assume orthogonal\n",
+ " vector components, while it does assume they\n",
+ " belong to a multivariate, and potentially\n",
+ " correlated Gaussian distribution.\n",
+ " VI - inverse (co)variance by which to scale distances (so it should be\n",
+ " symmetric with dimension N).\n",
+ " \n",
+ " Outout:\n",
+ " dist - an M element array of vector distances/metrics\n",
+ " '''\n",
+ " \n",
+ " # quick input check\n",
+ " if (vectors_A.ndim > 2 or\n",
+ " vectors_B.ndim > 2 or\n",
+ " vectors_A.shape != vectors_B.shape):\n",
+ " \n",
+ " raise ValueError('vectors_A and vectors_B must be 2D ',\n",
+ " 'arrays with identical dimensions')\n",
+ " \n",
+ " # transpose into MxN arrays for subsequent calculations\n",
+ " vectors_A = vectors_A.T\n",
+ " vectors_B = vectors_B.T\n",
+ " \n",
+ " # stack vectors_A and vectors_B, then transpose into MxN array for calculations\n",
+ " vectors_AB = np.vstack([vectors_A,vectors_B])\n",
+ " \n",
+ " # convert vectors_AB to masked array for caclulating variance and covariance\n",
+ " vectors_AB = np.ma.masked_invalid(vectors_AB)\n",
+ " \n",
+ " # default to euclidean\n",
+ " if not metric and not VI:\n",
+ " metric = 'euclidean'\n",
+ " \n",
+ " # generate inverse covariance matrix if not specified\n",
+ " if not VI:\n",
+ " if metric is 'euclidean':\n",
+ " VI = np.linalg.pinv(\n",
+ " np.eye(vectors_A.shape[1])\n",
+ " )\n",
+ " elif metric is 'seuclidean':\n",
+ " VI = np.linalg.pinv(\n",
+ " np.diag(np.ma.var(vectors_AB.T, axis=1, ddof=0))\n",
+ " )\n",
+ " elif metric is 'mahalanobis':\n",
+ " VI = np.linalg.pinv(\n",
+ " np.ma.cov(vectors_AB.T, ddof=0)\n",
+ " )\n",
+ " else:\n",
+ " raise ValueError('Unrecognized metric: ', metric)\n",
+ " else:\n",
+ " if np.isscalar(VI):\n",
+ " VI = np.eye(vectors_A.shape[1]) * VI\n",
+ " elif np.size(VI) == vectors_A.shape[1]:\n",
+ " VI = np.diag(VI)\n",
+ " elif (VI.ndim > 2 or\n",
+ " VI.shape[0] != VI.shape[1] or\n",
+ " VI.shape[0] != vectors_A.shape[1]):\n",
+ " raise ValueError('Invalid VI input: ', VI)\n",
+ " \n",
+ " # vector component differences\n",
+ " component_diffs = vectors_A - vectors_B\n",
+ " \n",
+ " # vector distances\n",
+ " return np.sqrt(np.sum(np.dot(component_diffs, VI) * component_diffs, axis=1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Statistical Time Series Filters\n",
+ "\n",
+ "Functions here should identify \"good\" elements in a univariate series:\n",
+ "\n",
+ "**Inputs** \n",
+ "```\n",
+ "series - univariate data to filter\n",
+ "```\n",
+ "**Options** \n",
+ "```\n",
+ "threshold - threshold value to be used for filter\n",
+ "weights - weights that can be applied to series\n",
+ " (defaults to uniform if None)\n",
+ "```\n",
+ "**Output** \n",
+ "```\n",
+ "good - a boolean array where True corresponds to \"good\" observations\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def filter_zscore(series, threshold=None, weights=None):\n",
+ " '''\n",
+ " Identify \"good\" elements in series by calculating a potentially weighted\n",
+ " mean and standard deviation, the number of standard deviations (z-score)\n",
+ " each value of series falls away from this mean, and finally, setting \n",
+ " elements of good to True that correspond to series values less than \n",
+ " threshold standard deviations from the mean.\n",
+ "\n",
+ " Inputs:\n",
+ " series - 1D NumPy array of observations to filter\n",
+ "\n",
+ " Options:\n",
+ " threshold - threshold in fractional number of standard deviations\n",
+ " each element of series may fall away from the mean and\n",
+ " still be considered \"good\" (default = 6)\n",
+ " weights - weights to assign to each element of series\n",
+ " (default = 1)\n",
+ " \n",
+ " Output:\n",
+ " good - Boolean array where True values correspond to \"good\" data\n",
+ "\n",
+ " '''\n",
+ " if series.ndim > 1:\n",
+ " raise ValueError('Invalid input series: ', series)\n",
+ " \n",
+ " if threshold is None:\n",
+ " threshold = 6\n",
+ " \n",
+ " if weights is None:\n",
+ " weights = np.ones_like(series)\n",
+ " \n",
+ " \n",
+ " # convert to NumPy arrays for convenience\n",
+ " series = np.asarray(series)\n",
+ " weights = np.asarray(weights)\n",
+ " \n",
+ " \n",
+ " # initialize good as all True for weights > 0\n",
+ " good = (weights > 0).astype(bool)\n",
+ " if np.size(good) <= 1:\n",
+ " # if a singleton is passed, assume it is \"good\"\n",
+ " return good\n",
+ " \n",
+ " # This should loop at least once\n",
+ " good_old = ~good\n",
+ " while not np.all(np.equal(good_old, good)):\n",
+ " # copy for comparison\n",
+ " good_old = good.copy()\n",
+ " \n",
+ " # weighted average of good values\n",
+ " wavg = np.average(series[good], weights=weights[good])\n",
+ " \n",
+ " # weighted standard deviation not available in NumPy, etc.\n",
+ " wstd = np.sqrt(np.average((series[good] - wavg)**2, weights=weights[good]))\n",
+ " \n",
+ " # NOTE: it is necessary to include good on the RHS here\n",
+ " # to prevent oscillation between two equally likely\n",
+ " # \"optimal\" solutions; this is a common problem with\n",
+ " # expectation maximization algorithms\n",
+ " good = good & np.abs(series - wavg) <= threshold * wstd\n",
+ " \n",
+ " return good"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def filter_iqr(series, threshold=None, weights=None):\n",
+ " '''\n",
+ " Identify \"good\" elements in series by calculating potentially weighted\n",
+ " 25%, 50% (median), and 75% quantiles of series, the number of 25%-50%\n",
+ " quantile ranges below, or 50%-75% quantile ranges above each value of \n",
+ " series falls from the median, and finally, setting elements of good to\n",
+ " True that fall within these multiples of quantile ranges.\n",
+ " \n",
+ " NOTE: NumPy has a percentile function, but it does not yet handle \n",
+ " weights. This algorithm was adapted shamelessly from the PyPI\n",
+ " package wquantiles (https://pypi.org/project/wquantiles/). If\n",
+ " NumPy should ever implement their own weighted algorithm, we\n",
+ " should use it instead.\n",
+ "\n",
+ " Inputs:\n",
+ " series - 1D NumPy array of observations to filter\n",
+ "\n",
+ " Options:\n",
+ " threshold - threshold in fractional number of 25%-50% (50%-75%)\n",
+ " quantile ranges below (above) the median each element of \n",
+ " series may fall and still be considered \"good\"\n",
+ " (default = 6)\n",
+ " weights - weights to assign to each element of series\n",
+ " (default = 1)\n",
+ " \n",
+ " Output:\n",
+ " good - Boolean array where True values correspond to \"good\" data\n",
+ "\n",
+ " '''\n",
+ " import numpy as np\n",
+ " \n",
+ " def wq(data, wgts, quant):\n",
+ " # sort data and weights\n",
+ " ind_sorted = np.argsort(data)\n",
+ " sorted_data = data[ind_sorted]\n",
+ " sorted_weights = wgts[ind_sorted]\n",
+ " # compute auxiliary arrays\n",
+ " Sn = np.cumsum(sorted_weights)\n",
+ " Pn = (Sn - 0.5 * sorted_weights) / Sn[-1]\n",
+ " # interpolate to weighted quantile\n",
+ " return np.interp(quant, Pn, sorted_data)\n",
+ " \n",
+ " if series.ndim > 1:\n",
+ " raise ValueError('Invalid input series: ', series)\n",
+ " \n",
+ " if threshold is None:\n",
+ " threshold = 6\n",
+ " \n",
+ " if weights is None:\n",
+ " weights = np.ones_like(series)\n",
+ " else:\n",
+ " weights = np.asarray(weights)\n",
+ "\n",
+ " \n",
+ " # convert to NumPy arrays for convenience\n",
+ " series = np.asarray(series)\n",
+ " weights = np.asarray(weights)\n",
+ " \n",
+ " \n",
+ " # initialize good as all True for weights > 0\n",
+ " good = (weights > 0).astype(bool)\n",
+ " if np.size(good) <= 1:\n",
+ " # if a singleton is passed, assume it is \"good\"\n",
+ " return good\n",
+ " \n",
+ " \n",
+ " # This should loop at least once\n",
+ " good_old = ~good\n",
+ " while not np.all(np.equal(good_old, good)):\n",
+ " # copy for comparison\n",
+ " good_old = good.copy()\n",
+ " \n",
+ " wq25 = wq(series[good], weights[good], .25)\n",
+ " wq50 = wq(series[good], weights[good], .50)\n",
+ " wq75 = wq(series[good], weights[good], .75)\n",
+ " \n",
+ " # NOTE: it is necessary to include good on the RHS here\n",
+ " # to prevent oscillation between two equally likely\n",
+ " # \"optimal\" solutions; this is a common problem with\n",
+ " # expectation maximization algorithms\n",
+ " good = (good &\n",
+ " (series >= (wq50 - threshold * (wq50 - wq25))) &\n",
+ " (series <= (wq50 + threshold * (wq75 - wq50))))\n",
+ " \n",
+ " return good"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Affine Transform Matrix Generators\n",
+ "\n",
+ "Functions here should generate a 4x4 affine transformation matrix given:\n",
+ "\n",
+ "**Inputs** \n",
+ "```\n",
+ "ord - 3xN array of training data where rows correspond to 3D\n",
+ " Cartesian vectors, and columns are observations; these\n",
+ " are the \"raw\" vector input to be transformed\n",
+ "abs - 3xN array of training data where rows correspond to 3D\n",
+ " Cartesian vectors, and columns are observations; these\n",
+ " are the desired \"absolute\" vector output\n",
+ "```\n",
+ "**Options** \n",
+ "```\n",
+ "weights - array of N weights that can be applied to observations\n",
+ " (defaults to uniform if None)\n",
+ "```\n",
+ "**Output** \n",
+ "```\n",
+ "M - a 4x4 affine transformation matrix that maps ord to abs\n",
+ " NOTE: functions should include some sort of condition\n",
+ " check (e.g., minimum rank of system), and return\n",
+ " a 4x4 matrix of NaNs if this check fails\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_0(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to a rotation about the z-axis, a uniform scaling in the\n",
+ " horizontal plane, and baseline shifts. This is closest to how (quasi-)\n",
+ " definitive data is processed at the USGS, and still be obtained directly\n",
+ " from a least-squares inversion.\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " else:\n",
+ " # Wikipedia indicates sqrt(weights) is appropriate for WLS\n",
+ " weights = np.sqrt(weights)\n",
+ " # same weight applies to all three vector components\n",
+ " weights = np.vstack((weights, weights, weights)).T.ravel()\n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # re-estimate cylindrical vectors from Cartesian\n",
+ " h_ord = np.sqrt(h_o**2 + e_o**2)\n",
+ " d_ord = np.arctan2(e_o, h_o)\n",
+ " z_ord = z_o\n",
+ " h_abs = np.sqrt(x_a**2 + y_a**2)\n",
+ " d_abs = np.arctan2(y_a, x_a)\n",
+ " z_abs = z_a\n",
+ " \n",
+ " # generate average rotation from ord to abs, then convert\n",
+ " # to rotation affine transform matrix\n",
+ " dRavg = (d_abs - d_ord).mean()\n",
+ " Rmtx = np.eye(4)\n",
+ " Rmtx[0,0] = np.cos(dRavg)\n",
+ " Rmtx[0,1] = -np.sin(dRavg)\n",
+ " Rmtx[1,0] = np.sin(dRavg)\n",
+ " Rmtx[1,1] = np.cos(dRavg)\n",
+ " \n",
+ " # generate average ratio of h_abs/h_ord, use this to\n",
+ " # define a scaling affine transform matrix\n",
+ " rHavg = (h_abs / h_ord).mean()\n",
+ " Smtx = np.eye(4)\n",
+ " Smtx[0,0] = rHavg\n",
+ " Smtx[1,1] = rHavg\n",
+ " \n",
+ " # apply average rotations and scales to HE data, determine the\n",
+ " # average translations, then generate affine transform matrix\n",
+ " dXavg = (x_a - (h_o * rHavg * np.cos(dRavg) - \n",
+ " e_o * rHavg * np.sin(dRavg))).mean()\n",
+ " dYavg = (y_a - (h_o * rHavg * np.sin(dRavg) + \n",
+ " e_o * rHavg * np.cos(dRavg))).mean()\n",
+ " dZavg = (z_a - z_o).mean()\n",
+ " Tmtx = np.eye(4)\n",
+ " Tmtx[0,3] = dXavg\n",
+ " Tmtx[1,3] = dYavg\n",
+ " Tmtx[2,3] = dZavg\n",
+ " \n",
+ " # combine rotation, scale, and translation matrices\n",
+ " M = np.dot(np.dot(Rmtx, Smtx), Tmtx)\n",
+ " \n",
+ " \n",
+ "# # NOTE: the preceding isn't quite how Definitive/Quasi-Definitive\n",
+ "# # processing works; the following is closer, but the two generate\n",
+ "# # very similar output, with most of the tiny discrepancy arising\n",
+ "# # due to the fact that the operation below *adds* an H baseline, \n",
+ "# # something that is not easy (or possible?) with an affine transform,\n",
+ "# # so instead, a scaling factor is used to adjust he to match xy.\n",
+ "# def_h = (h_o**2 + e_o**2)**0.5 + h_bas.mean()\n",
+ "# def_d = np.arctan2(e_o, h_o) * 180./np.pi + d_bas.mean()\n",
+ "# def_z = z_o + z_bas.mean()\n",
+ "# def_f = (def_h**2 + def_z**2)**0.5\n",
+ "# def_x = def_h * np.cos(def_d * np.pi/180.)\n",
+ "# def_y = def_h * np.sin(def_d * np.pi/180.)\n",
+ "\n",
+ " \n",
+ "# print(np.array_str(Rmtx, precision=3))\n",
+ "# print(np.array_str(Smtx, precision=3))\n",
+ "# print(np.array_str(Tmtx, precision=3))\n",
+ "# print(np.array_str(M, precision=3))\n",
+ " \n",
+ " \n",
+ " # ...or, solve for M directly\n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a,y_a,z_a])\n",
+ " abs_st_r = abs_st.T.ravel()\n",
+ " \n",
+ " # RHS, or independent variables\n",
+ " # (reduces degrees of freedom by 13:\n",
+ " # - 2 for making x,y independent of z;\n",
+ " # - 2 for making z independent of x,y;\n",
+ " # - 2 for not allowing shear in x,y; \n",
+ " # - 2 for not allowing translation in x,y;\n",
+ " # - 1 for not allowing scaling in z; and\n",
+ " # - 4 for the last row of zeros and a one)\n",
+ " ord_st = np.vstack([h_o,e_o,z_o])\n",
+ " ord_st_r = ord_st.T.ravel()\n",
+ " ord_st_m = np.zeros((3, ord_st_r.size))\n",
+ " ord_st_m[0,0::3] = ord_st_r[0::3]\n",
+ " ord_st_m[0,1::3] = ord_st_r[1::3]\n",
+ " ord_st_m[1,0::3] = ord_st_r[1::3]\n",
+ " ord_st_m[1,1::3] = -ord_st_r[0::3]\n",
+ " ord_st_m[2,2::3] = 1.\n",
+ " \n",
+ " # subtract z_o from z_a to force simple z translation\n",
+ " abs_st_r[2::3] = abs_st_r[2::3] - ord_st_r[2::3]\n",
+ " \n",
+ " # apply weights\n",
+ " ord_st_m = ord_st_m * weights\n",
+ " abs_st_r = abs_st_r * weights\n",
+ " \n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st_m.T, abs_st_r.T)\n",
+ " \n",
+ " if rank < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " M = np.zeros((4,4))\n",
+ " M[0,0] = M_r[0]\n",
+ " M[0,1] = M_r[1]\n",
+ " M[0,2] = 0.0\n",
+ " M[0,3] = 0.0\n",
+ " M[1,0] = -M_r[1]\n",
+ " M[1,1] = M_r[0]\n",
+ " M[1,2] = 0.0\n",
+ " M[1,3] = 0.0\n",
+ " M[2,0] = 0.0\n",
+ " M[2,1] = 0.0\n",
+ " M[2,2] = 1.0\n",
+ " M[2,3] = M_r[2]\n",
+ " M[3,:] = [0,0,0,1] \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ " \n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_1(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to rotate about z-axis, and a uniform horizontal scaling\n",
+ " factor.\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " else:\n",
+ " # Wikipedia indicates sqrt(weights) is appropriate for WLS\n",
+ " weights = np.sqrt(weights)\n",
+ " # same weight applies to all three vector components\n",
+ " weights = np.vstack((weights, weights, weights)).T.ravel()\n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a,y_a,z_a])\n",
+ " abs_st_r = abs_st.T.ravel()\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " # (reduces degrees of freedom by 10:\n",
+ " # - 2 for making x,y independent of z;\n",
+ " # - 2 for making z independent of x,y\n",
+ " # - 2 for not allowing shear in x,y; and\n",
+ " # - 4 for the last row of zeros and a one)\n",
+ " ord_st = np.vstack([h_o,e_o,z_o])\n",
+ " ord_st_r = ord_st.T.ravel()\n",
+ " ord_st_m = np.zeros((6, ord_st_r.size))\n",
+ " ord_st_m[0,0::3] = ord_st_r[0::3]\n",
+ " ord_st_m[0,1::3] = ord_st_r[1::3]\n",
+ " ord_st_m[1,0::3] = ord_st_r[1::3]\n",
+ " ord_st_m[1,1::3] = -ord_st_r[0::3]\n",
+ " ord_st_m[2,0::3] = 1.\n",
+ " ord_st_m[3,1::3] = 1.\n",
+ " ord_st_m[4,2::3] = ord_st_r[2::3]\n",
+ " ord_st_m[5,2::3] = 1.\n",
+ "\n",
+ " # apply weights\n",
+ " ord_st_m = ord_st_m * weights\n",
+ " abs_st_r = abs_st_r * weights\n",
+ "\n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st_m.T,abs_st_r.T)\n",
+ "\n",
+ " if rank < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " M = np.zeros((4,4))\n",
+ " M[0,0] = M_r[0]\n",
+ " M[0,1] = M_r[1]\n",
+ " M[0,2] = 0.0\n",
+ " M[0,3] = M_r[2]\n",
+ " M[1,0] = -M_r[1]\n",
+ " M[1,1] = M_r[0]\n",
+ " M[1,2] = 0.0\n",
+ " M[1,3] = M_r[3]\n",
+ " M[2,0] = 0.0\n",
+ " M[2,1] = 0.0\n",
+ " M[2,2] = M_r[4]\n",
+ " M[2,3] = M_r[5]\n",
+ " M[3,:] = [0,0,0,1] \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_2(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to rotate about z-axis.\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " else:\n",
+ " # Wikipedia indicates sqrt(weights) is appropriate for WLS\n",
+ " weights = np.sqrt(weights)\n",
+ " # same weight applies to all three vector components\n",
+ " weights = np.vstack((weights, weights, weights)).T.ravel()\n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a,y_a,z_a])\n",
+ " abs_st_r = abs_st.T.ravel()\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " # (reduces degrees of freedom by 8:\n",
+ " # - 2 for making x,y independent of z;\n",
+ " # - 2 for making z independent of x,y\n",
+ " # - 4 for the last row of zeros and a one)\n",
+ " ord_st = np.vstack([h_o,e_o,z_o])\n",
+ " ord_st_r = ord_st.T.ravel()\n",
+ " ord_st_m = np.zeros((8, ord_st_r.size))\n",
+ " ord_st_m[0,0::3] = ord_st_r[0::3]\n",
+ " ord_st_m[1,0::3] = ord_st_r[1::3]\n",
+ " ord_st_m[2,0::3] = 1.\n",
+ " ord_st_m[3,1::3] = ord_st_r[0::3]\n",
+ " ord_st_m[4,1::3] = ord_st_r[1::3]\n",
+ " ord_st_m[5,1::3] = 1.\n",
+ " ord_st_m[6,2::3] = ord_st_r[2::3]\n",
+ " ord_st_m[7,2::3] = 1.\n",
+ "\n",
+ " \n",
+ " # apply weights\n",
+ " ord_st_m = ord_st_m * weights\n",
+ " abs_st_r = abs_st_r * weights\n",
+ "\n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st_m.T,abs_st_r.T)\n",
+ "\n",
+ " if rank < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " M = np.zeros((4,4))\n",
+ " M[0,0] = M_r[0]\n",
+ " M[0,1] = M_r[1]\n",
+ " M[0,2] = 0.0\n",
+ " M[0,3] = M_r[2]\n",
+ " M[1,0] = M_r[3]\n",
+ " M[1,1] = M_r[4]\n",
+ " M[1,2] = 0.0\n",
+ " M[1,3] = M_r[5]\n",
+ " M[2,0] = 0.0\n",
+ " M[2,1] = 0.0\n",
+ " M[2,2] = M_r[6]\n",
+ " M[2,3] = M_r[7]\n",
+ " M[3,:] = [0,0,0,1] \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_3(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " with no constraints.\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " else:\n",
+ " # Wikipedia indicates sqrt(weights) is appropriate for WLS\n",
+ " weights = np.sqrt(weights)\n",
+ " # same weight applies to all three vector components\n",
+ " weights = np.vstack((weights, weights, weights)).T.ravel()\n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a, y_a, z_a, np.ones_like(x_a)])\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " ord_st = np.vstack([h_o, e_o, z_o, np.ones_like(h_o)])\n",
+ "\n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a,y_a,z_a])\n",
+ " abs_st_r = abs_st.T.ravel()\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " # (reduces degrees of freedom by 4:\n",
+ " # - 4 for the last row of zeros and a one)\n",
+ " ord_st = np.vstack([h_o,e_o,z_o])\n",
+ " ord_st_r = ord_st.T.ravel()\n",
+ " ord_st_m = np.zeros((12, ord_st_r.size))\n",
+ " ord_st_m[0,0::3] = ord_st_r[0::3]\n",
+ " ord_st_m[1,0::3] = ord_st_r[1::3]\n",
+ " ord_st_m[2,0::3] = ord_st_r[2::3]\n",
+ " ord_st_m[3,0::3] = 1.\n",
+ " ord_st_m[4,1::3] = ord_st_r[0::3]\n",
+ " ord_st_m[5,1::3] = ord_st_r[1::3]\n",
+ " ord_st_m[6,1::3] = ord_st_r[2::3]\n",
+ " ord_st_m[7,1::3] = 1.\n",
+ " ord_st_m[8,2::3] = ord_st_r[0::3]\n",
+ " ord_st_m[9,2::3] = ord_st_r[1::3]\n",
+ " ord_st_m[10,2::3] = ord_st_r[2::3]\n",
+ " ord_st_m[11,2::3] = 1.\n",
+ "\n",
+ "\n",
+ " # apply weights\n",
+ " ord_st_m = ord_st_m * weights\n",
+ " abs_st_r = abs_st_r * weights\n",
+ "\n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st_m.T, abs_st_r.T)\n",
+ "\n",
+ " if rank < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " M = np.zeros((4,4))\n",
+ " M[0,0] = M_r[0]\n",
+ " M[0,1] = M_r[1]\n",
+ " M[0,2] = M_r[2]\n",
+ " M[0,3] = M_r[3]\n",
+ " M[1,0] = M_r[4]\n",
+ " M[1,1] = M_r[5]\n",
+ " M[1,2] = M_r[6]\n",
+ " M[1,3] = M_r[7]\n",
+ " M[2,0] = M_r[8]\n",
+ " M[2,1] = M_r[9]\n",
+ " M[2,2] = M_r[10]\n",
+ " M[2,3] = M_r[11]\n",
+ " M[3,:] = [0,0,0,1] \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_4(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to 3D scaled rigid rotation+translation (that is, no shear). \n",
+ " \n",
+ " References: \n",
+ " https://igl.ethz.ch/projects/ARAP/svd_rot.pdf\n",
+ " http://graphics.stanford.edu/~smr/ICP/comparison/eggert_comparison_mva97.pdf\n",
+ " http://graphics.stanford.edu/~smr/ICP/comparison/horn-hilden-orientation-josa88.pdf\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = np.ones_like(ord_hez[0])\n",
+ " \n",
+ " # NOTE: do not sqrt(weights) as with weighted least-squares (WLS);\n",
+ " # NumPy's average and cov functions handle weights properly\n",
+ " \n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " \n",
+ " # weighted centroids\n",
+ " h_o_cent = np.average(h_o, weights=weights)\n",
+ " e_o_cent = np.average(e_o, weights=weights)\n",
+ " z_o_cent = np.average(z_o, weights=weights)\n",
+ " x_a_cent = np.average(x_a, weights=weights)\n",
+ " y_a_cent = np.average(y_a, weights=weights)\n",
+ " z_a_cent = np.average(z_a, weights=weights)\n",
+ " \n",
+ " # generate weighted \"covariance\" matrix\n",
+ " H = np.dot(np.vstack([h_o - h_o_cent, e_o - e_o_cent, z_o - z_o_cent]),\n",
+ " np.dot(np.diag(weights),\n",
+ " np.vstack([x_a - x_a_cent, y_a - y_a_cent, z_a - z_a_cent]).T))\n",
+ " \n",
+ " # Singular value decomposition, then rotation matrix from L&R eigenvectors\n",
+ " # (the determinant guarantees a rotation, and not a reflection)\n",
+ " U, S, Vh = np.linalg.svd(H)\n",
+ " \n",
+ " if np.sum(S) < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " R = np.dot(Vh.T, np.dot(np.diag([1, 1, np.linalg.det(np.dot(Vh.T, U.T))]), U.T))\n",
+ " \n",
+ " # symmetric scale factor\n",
+ " s = np.sqrt(np.sum(np.vstack([(x_a - x_a_cent)**2, \n",
+ " (y_a - y_a_cent)**2, \n",
+ " (z_a - z_a_cent)**2])) / \n",
+ " np.sum(np.vstack([(h_o - h_o_cent)**2, \n",
+ " (e_o - e_o_cent)**2, \n",
+ " (z_o - z_o_cent)**2])) )\n",
+ " \n",
+ " # re-scale the rotation (must be done prior to estimating T)\n",
+ " R *= s\n",
+ " \n",
+ " # now get translation using weighted centroids and R\n",
+ " T = (np.array([x_a_cent, y_a_cent, z_a_cent]) - \n",
+ " np.dot(R, [h_o_cent, e_o_cent, z_o_cent]))\n",
+ " \n",
+ " \n",
+ " M = np.eye(4)\n",
+ " M[:3,:3] = R\n",
+ " M[:3,3] = T\n",
+ " \n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_5(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to re-scale each axis.\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " else:\n",
+ " # Wikipedia indicates sqrt(weights) is appropriate for WLS\n",
+ " weights = np.sqrt(weights)\n",
+ " # same weight applies to all three vector components\n",
+ " weights = np.vstack((weights, weights, weights)).T.ravel()\n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a,y_a,z_a])\n",
+ " abs_st_r = abs_st.T.ravel()\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " # (reduces degrees of freedom by 13:\n",
+ " # - 2 for making x independent of y,z;\n",
+ " # - 2 for making y,z independent of x;\n",
+ " # - 1 for making y independent of z;\n",
+ " # - 1 for making z independent of y;\n",
+ " # - 3 for not translating xyz\n",
+ " # - 4 for the last row of zeros and a one)\n",
+ " ord_st = np.vstack([h_o,e_o,z_o])\n",
+ " ord_st_r = ord_st.T.ravel()\n",
+ " ord_st_m = np.zeros((3, ord_st_r.size))\n",
+ " ord_st_m[0,0::3] = ord_st_r[0::3]\n",
+ " ord_st_m[1,1::3] = ord_st_r[1::3]\n",
+ " ord_st_m[2,2::3] = ord_st_r[2::3]\n",
+ "\n",
+ " # apply weights\n",
+ " ord_st_m = ord_st_m * weights\n",
+ " abs_st_r = abs_st_r * weights\n",
+ " \n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st_m.T, abs_st_r.T)\n",
+ "\n",
+ " if rank < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " M = np.zeros((4,4))\n",
+ " M[0,0] = M_r[0]\n",
+ " M[0,1] = 0.0\n",
+ " M[0,2] = 0.0\n",
+ " M[0,3] = 0.0\n",
+ " M[1,0] = 0.0\n",
+ " M[1,1] = M_r[1]\n",
+ " M[1,2] = 0.0\n",
+ " M[1,3] = 0.0\n",
+ " M[2,0] = 0.0\n",
+ " M[2,1] = 0.0\n",
+ " M[2,2] = M_r[2]\n",
+ " M[2,3] = 0.0\n",
+ " M[3,:] = [0,0,0,1] \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_6(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to translate origins.\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " else:\n",
+ " # Wikipedia indicates sqrt(weights) is appropriate for WLS\n",
+ " weights = np.sqrt(weights)\n",
+ " # same weight applies to all three vector components\n",
+ " weights = np.vstack((weights, weights, weights)).T.ravel()\n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a,y_a,z_a])\n",
+ " abs_st_r = abs_st.T.ravel()\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " # (reduces degrees of freedom by 10:\n",
+ " # - 2 for making x independent of y,z;\n",
+ " # - 2 for making y,z independent of x;\n",
+ " # - 1 for making y independent of z;\n",
+ " # - 1 for making z independent of y;\n",
+ " # - 3 for not scaling each axis\n",
+ " # - 4 for the last row of zeros and a one)\n",
+ " ord_st = np.vstack([h_o,e_o,z_o])\n",
+ " ord_st_r = ord_st.T.ravel()\n",
+ " ord_st_m = np.zeros((3, ord_st_r.size))\n",
+ " ord_st_m[0,0::3] = 1.\n",
+ " ord_st_m[1,1::3] = 1.\n",
+ " ord_st_m[2,2::3] = 1.\n",
+ " \n",
+ " # subtract ords from abs to force simple translation\n",
+ " abs_st_r[0::3] = abs_st_r[0::3] - ord_st_r[0::3]\n",
+ " abs_st_r[1::3] = abs_st_r[1::3] - ord_st_r[1::3]\n",
+ " abs_st_r[2::3] = abs_st_r[2::3] - ord_st_r[2::3]\n",
+ "\n",
+ " # apply weights\n",
+ " ord_st_m = ord_st_m * weights\n",
+ " abs_st_r = abs_st_r * weights\n",
+ " \n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st_m.T, abs_st_r.T)\n",
+ "\n",
+ " if rank < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " M = np.zeros((4,4))\n",
+ " M[0,0] = 1.\n",
+ " M[0,1] = 0.0\n",
+ " M[0,2] = 0.0\n",
+ " M[0,3] = M_r[0]\n",
+ " M[1,0] = 0.0\n",
+ " M[1,1] = 1.\n",
+ " M[1,2] = 0.0\n",
+ " M[1,3] = M_r[1]\n",
+ " M[2,0] = 0.0\n",
+ " M[2,1] = 0.0\n",
+ " M[2,2] = 1.\n",
+ " M[2,3] = M_r[2]\n",
+ " M[3,:] = [0,0,0,1] \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_7(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to shear y and z, but not x.\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " else:\n",
+ " # Wikipedia indicates sqrt(weights) is appropriate for WLS\n",
+ " weights = np.sqrt(weights)\n",
+ " # same weight applies to all three vector components\n",
+ " weights = np.vstack((weights, weights, weights)).T.ravel()\n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a,y_a,z_a])\n",
+ " abs_st_r = abs_st.T.ravel()\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " # (reduces degrees of freedom by 13:\n",
+ " # - 2 for making x independent of y,z;\n",
+ " # - 1 for making y independent of z;\n",
+ " # - 3 for not scaling each axis\n",
+ " # - 4 for the last row of zeros and a one)\n",
+ " ord_st = np.vstack([h_o,e_o,z_o])\n",
+ " ord_st_r = ord_st.T.ravel()\n",
+ " ord_st_m = np.zeros((3, ord_st_r.size))\n",
+ " ord_st_m[0,0::3] = 1.\n",
+ " ord_st_m[1,0::3] = ord_st_r[0::3]\n",
+ " ord_st_m[1,1::3] = 1.\n",
+ " ord_st_m[2,0::3] = ord_st_r[0::3]\n",
+ " ord_st_m[2,1::3] = ord_st_r[1::3]\n",
+ " ord_st_m[2,2::3] = 1.\n",
+ " \n",
+ "\n",
+ " # apply weights\n",
+ " ord_st_m = ord_st_m * weights\n",
+ " abs_st_r = abs_st_r * weights\n",
+ " \n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st_m.T, abs_st_r.T)\n",
+ "\n",
+ " if rank < 3:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " M = np.zeros((4,4))\n",
+ " M[0,0] = 1.\n",
+ " M[0,1] = 0.0\n",
+ " M[0,2] = 0.0\n",
+ " M[0,3] = 0.0\n",
+ " M[1,0] = M_r[0]\n",
+ " M[1,1] = 1.\n",
+ " M[1,2] = 0.0\n",
+ " M[1,3] = 0.0\n",
+ " M[2,0] = M_r[1]\n",
+ " M[2,1] = M_r[2]\n",
+ " M[2,2] = 1.\n",
+ " M[2,3] = 0.0\n",
+ " M[3,:] = [0,0,0,1] \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_8(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matrix from ordinate to absolute coordinates,\n",
+ " constrained to rigid rotation+translation (that is, no scale or shear)\n",
+ " in xy, and translation only in z. \n",
+ " \n",
+ " References: \n",
+ " https://igl.ethz.ch/projects/ARAP/svd_rot.pdf\n",
+ " http://graphics.stanford.edu/~smr/ICP/comparison/eggert_comparison_mva97.pdf\n",
+ " http://graphics.stanford.edu/~smr/ICP/comparison/horn-hilden-orientation-josa88.pdf\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = np.ones_like(ord_hez[0])\n",
+ " \n",
+ " # NOTE: do not sqrt(weights) as with weighted least-squares (WLS);\n",
+ " # NumPy's average and cov functions handle weights properly\n",
+ " \n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " \n",
+ " # weighted centroids\n",
+ " h_o_cent = np.average(h_o, weights=weights)\n",
+ " e_o_cent = np.average(e_o, weights=weights)\n",
+ " z_o_cent = np.average(z_o, weights=weights)\n",
+ " x_a_cent = np.average(x_a, weights=weights)\n",
+ " y_a_cent = np.average(y_a, weights=weights)\n",
+ " z_a_cent = np.average(z_a, weights=weights)\n",
+ " \n",
+ " # generate weighted \"covariance\" matrix\n",
+ " H = np.dot(np.vstack([h_o - h_o_cent, e_o - e_o_cent]),\n",
+ " np.dot(np.diag(weights),\n",
+ " np.vstack([x_a - x_a_cent, y_a - y_a_cent]).T))\n",
+ " \n",
+ " # Singular value decomposition, then rotation matrix from L&R eigenvectors\n",
+ " # (the determinant guarantees a rotation, and not a reflection)\n",
+ " U, S, Vh = np.linalg.svd(H)\n",
+ " \n",
+ " if np.sum(S) < 2:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " R = np.dot(Vh.T, np.dot(np.diag([1, np.linalg.det(np.dot(Vh.T, U.T))]), U.T))\n",
+ " \n",
+ " # now get translation using weighted centroids and R\n",
+ " T = (np.array([x_a_cent, y_a_cent]) - \n",
+ " np.dot(R, [h_o_cent, e_o_cent]))\n",
+ " \n",
+ " \n",
+ " M = np.eye(4)\n",
+ " M[:2,:2] = R\n",
+ " M[:2,3] = T\n",
+ " \n",
+ " M[2,3] = np.array(z_a_cent) - np.array(z_o_cent)\n",
+ " \n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def generate_affine_9(ord_hez, abs_xyz, weights=None):\n",
+ " '''\n",
+ " Generate affine transform matix from ordinate to absolute coordinates,\n",
+ " constrained to rotate about z-axis, with only rotation and shear in the\n",
+ " horizontal plane (no scaling), using QR factorization.\n",
+ " \n",
+ " References:\n",
+ " https://math.stackexchange.com/questions/1120209/decomposition-of-4x4-or-larger-affine-transformation-matrix-to-individual-variab\n",
+ " https://math.stackexchange.com/questions/2237262/is-there-a-correct-qr-factorization-result\n",
+ " \n",
+ " Inputs:\n",
+ " ord_hez - 3xN array holding HEZ vectors of Cartesian ordinate measurements\n",
+ " abs_xyz - 3xN array holding XYZ vectors of Cartesian absolute measurements\n",
+ " \n",
+ " Options:\n",
+ " weights - array of N weights that can be applied to observations\n",
+ " \n",
+ " Outout:\n",
+ " M - a 4x4 affine transformation matrix to convert ord_hez into abs_xy\n",
+ " '''\n",
+ " \n",
+ " if weights is None:\n",
+ " # equal weighting\n",
+ " weights = 1\n",
+ " \n",
+ " \n",
+ " # extract measurements\n",
+ " h_o = ord_hez[0]\n",
+ " e_o = ord_hez[1]\n",
+ " z_o = ord_hez[2]\n",
+ " x_a = abs_xyz[0]\n",
+ " y_a = abs_xyz[1]\n",
+ " z_a = abs_xyz[2]\n",
+ " \n",
+ " # weighted centroids\n",
+ " h_o_cent = np.average(h_o, weights=weights)\n",
+ " e_o_cent = np.average(e_o, weights=weights)\n",
+ " z_o_cent = np.average(z_o, weights=weights)\n",
+ " x_a_cent = np.average(x_a, weights=weights)\n",
+ " y_a_cent = np.average(y_a, weights=weights)\n",
+ " z_a_cent = np.average(z_a, weights=weights)\n",
+ " \n",
+ " \n",
+ " # LHS, or dependent variables\n",
+ " abs_st = np.vstack([x_a - x_a_cent, y_a - y_a_cent])\n",
+ "\n",
+ " # RHS, or independent variables\n",
+ " ord_st = np.vstack([h_o - h_o_cent, e_o - e_o_cent])\n",
+ " \n",
+ " \n",
+ " # apply weights\n",
+ " ord_st = ord_st * np.sqrt(weights)\n",
+ " abs_st = abs_st * np.sqrt(weights)\n",
+ "\n",
+ " # regression matrix M that minimizes L2 norm\n",
+ " M_r, res, rank, sigma = spl.lstsq(ord_st.T, abs_st.T)\n",
+ " \n",
+ " if rank < 2:\n",
+ " print('Poorly conditioned or singular matrix, returning NaNs')\n",
+ " return np.nan * np.ones((4,4))\n",
+ " \n",
+ " # QR fatorization\n",
+ " # NOTE: forcing the diagonal elements of Q to be positive\n",
+ " # ensures that the determinant is 1, not -1, and is\n",
+ " # therefore a rotation, not a reflection\n",
+ " Q, R = np.linalg.qr(M_r.T)\n",
+ " neg = np.diag(Q) < 0\n",
+ " Q[:,neg] = -1 * Q[:,neg]\n",
+ " R[neg,:] = -1 * R[neg,:]\n",
+ " \n",
+ " # isolate scales from shear\n",
+ " S = np.diag(np.diag(R))\n",
+ " H = np.dot(np.linalg.inv(S), R)\n",
+ " \n",
+ " # combine shear and rotation\n",
+ " QH = np.dot(Q, H)\n",
+ " \n",
+ " # now get translation using weighted centroids and R\n",
+ " T = (np.array([x_a_cent, y_a_cent]) - \n",
+ " np.dot(QH, [h_o_cent, e_o_cent]))\n",
+ " \n",
+ " \n",
+ " M = np.eye(4)\n",
+ " M[:2,:2] = QH\n",
+ " M[:2,3] = T\n",
+ " \n",
+ " M[2,3] = np.array(z_a_cent) - np.array(z_o_cent)\n",
+ " \n",
+ "\n",
+ "# print(np.array_str(M, precision=3))\n",
+ "\n",
+ " return M"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Affine Transform Matrix Interpolators\n",
+ "\n",
+ "Functions here should interpolate between 4x4 affine transformation matrices:\n",
+ "\n",
+ "**Inputs** \n",
+ "```\n",
+ "utc_target - list of UTCs at which to interpolate affine matrices\n",
+ "utc_list - list of UTCs that correspond to a list of known affine matrices\n",
+ "affine_list - list of known affine matrices\n",
+ "```\n",
+ "**Options** \n",
+ "```\n",
+ "fill_value - if None, disallow extrapolation; if not None, use this\n",
+ " value when utc_target falls outside utc_list range; if\n",
+ " \"extrapolate\", extrapolate based on first/last two in\n",
+ " affine_list.\n",
+ "```\n",
+ "**Output** \n",
+ "```\n",
+ "affine_target - list of interpolated affine matrices```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 115,
+ "metadata": {
+ "code_folding": [
+ 0
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "def interpolate_affine_polar(utc_target, utc_list, affine_list, fill_value=None):\n",
+ " '''\n",
+ " Interpolate between affine transform matices. Interpolating linear/affine\n",
+ " transform matrices is problematic because the rotation component cannot\n",
+ " be directly interpolated in a way that maintains a valid rotation matrix\n",
+ " at intermediate points. Here we first use Polar decomposition to decompose\n",
+ " the transform into an orthogonal matrix Q and a \"stretch\" matrix S (M=QS).\n",
+ " The Qs are interpolated between Slerp, while the Ss are interpolated using\n",
+ " using standard linear interpolation.\n",
+ " \n",
+ " \n",
+ " References:\n",
+ " http://research.cs.wisc.edu/graphics/Courses/838-s2002/Papers/polar-decomp.pdf\n",
+ " https://en.wikipedia.org/wiki/Slerp\n",
+ " http://run.usc.edu/cs520-s15/assign2/p245-shoemake.pdf\n",
+ " \n",
+ " Inputs:\n",
+ " utc_target - list of UTCs at which to interpolate affine matrices\n",
+ " utc_list - list of UTCs that correspond to a list of known affine matrices\n",
+ " affine_list - list of known affine matrices\n",
+ " \n",
+ " Options:\n",
+ " fill_value - if None, disallow extrapolation; if not None, use this\n",
+ " value when utc_target falls outside utc_list range; if\n",
+ " \"extrapolate\", extrapolate based on first/last two in\n",
+ " affine_list.\n",
+ " NOTE: SciPy's Slerp cannot presently extrapolate, so \n",
+ " this function will simply extend the first/last\n",
+ " affine_list matrices to all times outside utc_list.\n",
+ " \n",
+ " Outout:\n",
+ " affine_target - list of interpolated affine matrices\n",
+ " '''\n",
+ " import numpy as np\n",
+ " from scipy.spatial.transform import Rotation\n",
+ " from scipy.spatial.transform import Slerp\n",
+ " from scipy.interpolate import interp1d\n",
+ " \n",
+ " if fill_value is not None:\n",
+ " raise ValueError('fill_value extrapolation not implemented')\n",
+ " \n",
+ " # decompose affine_list\n",
+ " Ts_in = [] # translations\n",
+ " Rs_in = [] # rotations\n",
+ " Ns_in = [] # +/- I (accomodates reflections)\n",
+ " Ss_in = [] # stretches\n",
+ " for M in affine_list:\n",
+ " # polar decomposition\n",
+ " Q, S = spl.polar(M[:3,:3])\n",
+ " if np.linalg.det(Q) < 0:\n",
+ " # factor out -I if det(Q) is -1\n",
+ " R = np.dot(Q, np.linalg.inv(-np.eye(3)))\n",
+ " N = -np.eye(3)\n",
+ " else:\n",
+ " R = Q\n",
+ " N = np.eye(3)\n",
+ " Ts_in.append(M[:3,3, None])\n",
+ " Rs_in.append(R)\n",
+ " Ns_in.append(N)\n",
+ " Ss_in.append(S)\n",
+ " \n",
+ " # interp1d Ts\n",
+ " Ts_out = []\n",
+ " Rs_out = []\n",
+ " Ns_out = []\n",
+ " Ss_out = []\n",
+ " for T in np.reshape(Ts_in, (-1,3)).T:\n",
+ " int1d = interp1d(np.asarray(utc_list).astype(float), T, fill_value=\"extrapolate\")\n",
+ " Ts_out.append(int1d(np.asarray(utc_target).astype(float)))\n",
+ " Ts_out = np.array(Ts_out).T.reshape(-1,3,1)\n",
+ " \n",
+ " # SLERP Rs\n",
+ " Rs = Rotation.from_dcm(Rs_in)\n",
+ " Rslerp = Slerp(np.asarray(utc_list).astype(float), Rs)\n",
+ " Rs_out = Rslerp(np.asarray(utc_target).astype(float))\n",
+ " Rs_out = Rs_out.as_dcm()\n",
+ " #Rs_out = [R.as_dcm() for R in Rs_out]\n",
+ " \n",
+ " # interp1d Ns\n",
+ " for N in np.reshape(Ns_in, (-1,9)).T:\n",
+ " int1d = interp1d(np.asarray(utc_list).astype(float), N, fill_value=\"extrapolate\")\n",
+ " Ns_out.append(int1d(np.asarray(utc_target).astype(float)))\n",
+ " Ns_out = np.array(Ns_out).T.reshape(-1,3,3)\n",
+ " \n",
+ " # interp1d Ss\n",
+ " for S in np.reshape(Ss_in, (-1,9)).T:\n",
+ " int1d = interp1d(np.asarray(utc_list).astype(float), S, fill_value=\"extrapolate\")\n",
+ " Ss_out.append(int1d(np.asarray(utc_target).astype(float)))\n",
+ " Ss_out = np.array(Ss_out).T.reshape(-1,3,3)\n",
+ " \n",
+ " # recombine components into M_target list\n",
+ " affine_target = []\n",
+ " for t in np.arange(len(utc_target)):\n",
+ " \n",
+ " affine_target.append(np.vstack(\n",
+ " (np.hstack((np.dot(Rs_out[t],\n",
+ " np.dot(Ns_out[t], Ss_out[t])),\n",
+ " Ts_out[t])),\n",
+ " [0,0,0,1])) )\n",
+ " \n",
+ " return affine_target"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[[ 1.00e+00 0.00e+00 0.00e+00 2.00e+01]\n",
+ " [ 0.00e+00 1.00e+00 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 4.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[ 9.24e-01 3.83e-01 0.00e+00 2.25e+01]\n",
+ " [-3.83e-01 9.24e-01 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 3.75e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[ 7.07e-01 7.07e-01 0.00e+00 2.50e+01]\n",
+ " [-7.07e-01 7.07e-01 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 3.50e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[ 3.83e-01 9.24e-01 0.00e+00 2.75e+01]\n",
+ " [-9.24e-01 3.83e-01 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 3.25e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[ 2.22e-16 1.00e+00 0.00e+00 3.00e+01]\n",
+ " [-1.00e+00 2.22e-16 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[-3.83e-01 9.24e-01 0.00e+00 3.25e+01]\n",
+ " [-9.24e-01 -3.83e-01 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 2.75e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[-7.07e-01 7.07e-01 0.00e+00 3.50e+01]\n",
+ " [-7.07e-01 -7.07e-01 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 2.50e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[-9.24e-01 3.83e-01 0.00e+00 3.75e+01]\n",
+ " [-3.83e-01 -9.24e-01 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 2.25e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]\n",
+ "\n",
+ " [[-1.00e+00 1.22e-16 0.00e+00 4.00e+01]\n",
+ " [-1.22e-16 -1.00e+00 0.00e+00 3.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 1.00e+00 2.00e+01]\n",
+ " [ 0.00e+00 0.00e+00 0.00e+00 1.00e+00]]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "t1 = UTCDateTime(2019,6,1, 0,0,0)\n",
+ "aff_1 = np.array([[1,0,0,20], [0, 1, 0, 30],[0, 0, 1, 40],[0, 0, 0, 1]]) # 0 rot/ref\n",
+ "t2 = UTCDateTime(2019,6,2, 0,0,0)\n",
+ "#aff_2 = np.array([[ 0,-1, 0, 40], [1, 0, 0, 30],[ 0, 0, 1, 20],[ 0, 0, 0, 1]]) # 90 rot\n",
+ "aff_2 = np.array([[-1, 0, 0, 40], [ 0,-1, 0, 30],[ 0, 0, 1, 20],[ 0, 0, 0, 1]]) # 180 rot\n",
+ "#aff_2 = np.array([[-1, 0, 0, 40], [ 0, 1, 0, 30],[ 0, 0, 1, 20],[ 0, 0, 0, 1]]) # 180 ref\n",
+ "\n",
+ "tn = [t1 + h for h in np.linspace(0*3*3600, 8*3*3600, 9)]\n",
+ "aff_n = interpolate_affine_polar(tn, [t1, t2], [aff_1, aff_2])\n",
+ "\n",
+ "print(np.array_str(np.array(aff_n), precision=2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "code_folding": [
+ 0
+ ],
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "def old_do_it_all(obs_code, start_date, end_date, validate=False):\n",
+ " '''\n",
+ " This function will do the following for a given observatory\n",
+ " between start_date and end_date:\n",
+ "\n",
+ " - Read in absolutes data from Webabsolutes DB\n",
+ " - Convert cylindrical to Cartesian coordinates\n",
+ " - Generate different Adjusted Data transform matrices\n",
+ " - Apply each Adjusted Data transforms to real HEZF data\n",
+ " - Compare to (quasi-)definitive data by generating plots\n",
+ " - Return transform matrices to be saved\n",
+ " '''\n",
+ "\n",
+ " # retrieve absolute calibrations and baseline data\n",
+ " if obs_code == 'DED':\n",
+ " ((h_abs, h_bas, h_dt), \n",
+ " (d_abs, d_bas, d_dt), \n",
+ " (z_abs, z_bas, z_dt),\n",
+ " pc, weights) = retrieve_baselines_resid_summary_xlsm(\n",
+ " obs_code,\n",
+ " start_date,\n",
+ " end_date,\n",
+ " xlsm_dir = '/Volumes/geomag/pub/Caldata/Checked Baseline Data/DED/'\n",
+ " ) \n",
+ " elif obs_code == 'CMO':\n",
+ " ((h_abs, h_bas, h_dt), \n",
+ " (d_abs, d_bas, d_dt), \n",
+ " (z_abs, z_bas, z_dt),\n",
+ " pc, weights) = retrieve_baselines_resid_summary_xlsm(\n",
+ " obs_code,\n",
+ " start_date,\n",
+ " end_date,\n",
+ " xlsm_dir = '/Volumes/geomag/pub/Caldata/Checked Baseline Data/CMO/'\n",
+ " ) \n",
+ " else:\n",
+ " ((h_abs, h_bas, h_dt), \n",
+ " (d_abs, d_bas, d_dt), \n",
+ " (z_abs, z_bas, z_dt),\n",
+ " pc, weights) = retrieve_baselines_webabsolutes(\n",
+ " obs_code,\n",
+ " start_date,\n",
+ " end_date\n",
+ " )\n",
+ "\n",
+ " # recreate ordinate variometer measurements from absolutes and baselines\n",
+ " h_ord = h_abs - h_bas\n",
+ " d_ord = d_abs - d_bas\n",
+ " z_ord = z_abs - z_bas\n",
+ "\n",
+ " # convert vector components from cylindrical to Cartesian coordinates\n",
+ " x_a = h_abs*np.cos(d_abs*np.pi/180)\n",
+ " y_a = h_abs*np.sin(d_abs*np.pi/180)\n",
+ " z_a = z_abs\n",
+ " h_o = h_ord*np.cos(d_ord*np.pi/180)\n",
+ " e_o = h_ord*np.sin(d_ord*np.pi/180)\n",
+ " z_o = z_ord\n",
+ "\n",
+ "\n",
+ " # generate static affine transform matrix equivalent to traditional\n",
+ " # definitive/quasi-definitive processing\n",
+ " M0 = generate_affine_0((h_o, e_o, z_o), (x_a, y_a, z_a))\n",
+ "\n",
+ " # generate static affine transform matrix type 1\n",
+ " M1 = generate_affine_1((h_o, e_o, z_o), (x_a, y_a, z_a))\n",
+ "\n",
+ " # generate static affine transform matrix that allows all free parameters\n",
+ " # to vary in optimal fit to data, but enforce a uniform scaling factor\n",
+ " M2 = generate_affine_2((h_o, e_o, z_o), (x_a, y_a, z_a))\n",
+ "\n",
+ " # generate static affine transform matrix that allows all free parameters\n",
+ " # to vary in optimal fit to data\n",
+ " M3 = generate_affine_3((h_o, e_o, z_o), (x_a, y_a, z_a))\n",
+ "\n",
+ "\n",
+ " # retrieve raw HEZ variometer data from Edge server\n",
+ " factory = EdgeFactory(host='mage2.cr.usgs.gov')\n",
+ " hezf = factory.get_timeseries(\n",
+ " observatory = obs_code,\n",
+ " interval = 'minute',\n",
+ " type = 'variation',\n",
+ " channels = ('H', 'E', 'Z', 'F'),\n",
+ " starttime = UTCDateTime(start_date),\n",
+ " endtime = UTCDateTime(end_date)\n",
+ " )\n",
+ " hezf_dt = np.array([(hezf[0].stats.starttime + second).datetime for second in hezf[0].times()])\n",
+ "\n",
+ "\n",
+ " # multiply M0 by hez1, plot results against XYZ\n",
+ " XYZ1_0 = np.dot(M0, np.vstack((hezf[0].data, \n",
+ " hezf[1].data, \n",
+ " hezf[2].data, \n",
+ " np.ones(hezf[0].data.shape))))\n",
+ "\n",
+ " # multiply M1 by hez1, plot results against XYZ\n",
+ " XYZ1_1 = np.dot(M1, np.vstack((hezf[0].data, \n",
+ " hezf[1].data, \n",
+ " hezf[2].data, \n",
+ " np.ones(hezf[0].data.shape))))\n",
+ "\n",
+ " # multiply M2 by hez1, plot results against XYZ\n",
+ " XYZ1_2 = np.dot(M2, np.vstack((hezf[0].data, \n",
+ " hezf[1].data, \n",
+ " hezf[2].data, \n",
+ " np.ones(hezf[0].data.shape))))\n",
+ "\n",
+ " # multiply M3 by HEZ1, plot results against XYZ\n",
+ " XYZ1_3 = np.dot(M3, np.vstack((hezf[0].data, \n",
+ " hezf[1].data, \n",
+ " hezf[2].data, \n",
+ " np.ones(hezf[0].data.shape))))\n",
+ "\n",
+ " #\n",
+ " # plot Absolutes, Adjusted, and (quasi-)definitive XYZ\n",
+ " #\n",
+ " plt.figure(figsize=(9,6))\n",
+ "\n",
+ " plt.subplot(3,1,1)\n",
+ " plt.plot(hezf_dt, XYZ1_0[0,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_1[0,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_2[0,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_3[0,:], '-')\n",
+ " plt.ylabel('X (nT)')\n",
+ " plt.plot(h_dt, x_a, '*')\n",
+ " plt.xlim(hezf_dt[0], hezf_dt[-1])\n",
+ "\n",
+ " plt.subplot(3,1,2)\n",
+ " plt.plot(hezf_dt, XYZ1_0[1,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_1[1,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_2[1,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_3[1,:], '-')\n",
+ " plt.ylabel('Y (nT)')\n",
+ " plt.plot(d_dt, y_a, '*')\n",
+ " plt.xlim(hezf_dt[0], hezf_dt[-1])\n",
+ "\n",
+ " plt.subplot(3,1,3)\n",
+ " plt.plot(hezf_dt, XYZ1_0[2,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_1[2,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_2[2,:], '-')\n",
+ " plt.plot(hezf_dt, XYZ1_3[2,:], '-')\n",
+ " plt.plot(z_dt, z_a, '*')\n",
+ " plt.ylabel('Z (nT)')\n",
+ " plt.legend(('$Adj_0$','$Adj_1$','$Adj_2$','$Adj_3$','Abs'), loc='right')\n",
+ " plt.xlim(hezf_dt[0], hezf_dt[-1])\n",
+ "\n",
+ " \n",
+ " if validate:\n",
+ " \n",
+ " #\n",
+ " # retrieve QD XYZ data\n",
+ " #\n",
+ " #factory = EdgeFactory(host='localhost', port=11111)\n",
+ " factory = EdgeFactory(host='mage2.cr.usgs.gov')\n",
+ " xyzf = factory.get_timeseries(\n",
+ " observatory = obs_code,\n",
+ " interval = 'minute',\n",
+ " type = 'quasi-definitive',\n",
+ " channels = ('X', 'Y', 'Z', 'F'),\n",
+ " starttime = UTCDateTime(start_date),\n",
+ " endtime = UTCDateTime(end_date)\n",
+ " )\n",
+ " xyzf_dt = np.array([(xyzf[0].stats.starttime + second).datetime for second in xyzf[0].times()])\n",
+ " \n",
+ " #\n",
+ " # Plot QD data over Adjusted\n",
+ " #\n",
+ " plt.subplot(3,1,1)\n",
+ " plt.plot(xyzf_dt, xyzf[0].data, '-k', linewidth=1)\n",
+ " plt.subplot(3,1,2)\n",
+ " plt.plot(xyzf_dt, xyzf[1].data, '-k', linewidth=1)\n",
+ " plt.subplot(3,1,3)\n",
+ " plt.plot(xyzf_dt, xyzf[2].data, '-k', linewidth=1)\n",
+ " plt.legend(('$Adj_0$','$Adj_1$','$Adj_2$','$Adj_3$','Abs', 'QD'), loc='right')\n",
+ "\n",
+ "\n",
+ " \n",
+ " #\n",
+ " # Calculate and plot delta-F metrics\n",
+ " #\n",
+ " dFlist = []\n",
+ " plt.figure(figsize=(9,3))\n",
+ "\n",
+ " dFlist.append(np.sqrt(XYZ1_0[0,:]**2 + XYZ1_0[1,:]**2 + XYZ1_0[2,:]**2) - xyzf[3].data)\n",
+ " plt.plot(hezf_dt, dFlist[-1], alpha=0.75)\n",
+ "\n",
+ " dFlist.append(np.sqrt(XYZ1_1[0,:]**2 + XYZ1_1[1,:]**2 + XYZ1_1[2,:]**2) - xyzf[3].data)\n",
+ " plt.plot(hezf_dt, dFlist[-1], alpha=0.75)\n",
+ "\n",
+ " dFlist.append(np.sqrt(XYZ1_2[0,:]**2 + XYZ1_2[1,:]**2 + XYZ1_2[2,:]**2) - xyzf[3].data)\n",
+ " plt.plot(hezf_dt, dFlist[-1], alpha=0.75)\n",
+ "\n",
+ " dFlist.append(np.sqrt(XYZ1_3[0,:]**2 + XYZ1_3[1,:]**2 + XYZ1_3[2,:]**2) - xyzf[3].data)\n",
+ " plt.plot(hezf_dt, dFlist[-1], alpha=0.75)\n",
+ "\n",
+ " dFlist.append(np.sqrt(xyzf[0].data**2 + xyzf[1].data**2 + xyzf[2].data**2) - xyzf[3].data)\n",
+ " plt.plot(xyzf_dt, dFlist[-1], 'k', linewidth=1)\n",
+ "\n",
+ "\n",
+ " plt.xlim(xyzf_dt[0], xyzf_dt[-1])\n",
+ "\n",
+ " plt.ylabel('$\\Delta F$ (nT)')\n",
+ "\n",
+ " plt.legend(('$Adj_0$ ($\\sigma=%5.2f$ nT)'%np.nanstd(dFlist[0]), \n",
+ " '$Adj_1$ ($\\sigma=%5.2f$ nT)'%np.nanstd(dFlist[1]),\n",
+ " '$Adj_2$ ($\\sigma=%5.2f$ nT)'%np.nanstd(dFlist[2]),\n",
+ " '$Adj_3$ ($\\sigma=%5.2f$ nT)'%np.nanstd(dFlist[3]), \n",
+ " '$QD$ ($\\sigma=%5.2f$ nT)'%np.nanstd(dFlist[4])),\n",
+ " loc='upper left')\n",
+ "\n",
+ "\n",
+ " #\n",
+ " # Calculate and plot vector distance metrics relative to (quasi-)definitive data\n",
+ " #\n",
+ " plt.figure(figsize=(9,3))\n",
+ " plt.plot(\n",
+ " xyzf_dt,\n",
+ " vector_dist(\n",
+ " np.vstack([xyzf[0].data, xyzf[1].data, xyzf[2].data]).T,\n",
+ " XYZ1_0.T[:,:3],\n",
+ " 'euclidean'\n",
+ " ),\n",
+ " zorder=4\n",
+ " )\n",
+ "\n",
+ " plt.plot(\n",
+ " xyzf_dt,\n",
+ " vector_dist(\n",
+ " np.vstack([xyzf[0].data, xyzf[1].data, xyzf[2].data]).T,\n",
+ " XYZ1_1.T[:,:3],\n",
+ " 'euclidean'\n",
+ " ),\n",
+ " zorder=3\n",
+ " )\n",
+ "\n",
+ " plt.plot(\n",
+ " xyzf_dt,\n",
+ " vector_dist(\n",
+ " np.vstack([xyzf[0].data, xyzf[1].data, xyzf[2].data]).T,\n",
+ " XYZ1_2.T[:,:3],\n",
+ " 'euclidean'\n",
+ " ),\n",
+ " zorder=2\n",
+ " )\n",
+ "\n",
+ " plt.plot(\n",
+ " xyzf_dt,\n",
+ " vector_dist(\n",
+ " np.vstack([xyzf[0].data, xyzf[1].data, xyzf[2].data]).T,\n",
+ " XYZ1_3.T[:,:3],\n",
+ " 'euclidean'\n",
+ " ),\n",
+ " zorder=1\n",
+ " )\n",
+ "\n",
+ " plt.xlim(xyzf_dt[0], xyzf_dt[-1])\n",
+ "\n",
+ " plt.ylabel('Euclidian Distance (nT)')\n",
+ "\n",
+ " plt.legend(('$Adj_0}$', \n",
+ " '$Adj_1}$',\n",
+ " '$Adj_2}$',\n",
+ " '$Adj_3}$'), \n",
+ " loc='upper left')\n",
+ "\n",
+ " # end if validate:\n",
+ "\n",
+ "\n",
+ " return M0, M1, M2, M3, pc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "metadata": {
+ "code_folding": []
+ },
+ "outputs": [],
+ "source": [
+ "def do_it_all(obs_code, start_UTC, end_UTC,\n",
+ " update_interval=None, acausal=False, interpolate=False,\n",
+ " first_UTC=None, last_UTC=None,\n",
+ " M_funcs=None, memories=None,\n",
+ " path_or_url='https://geomag.usgs.gov',\n",
+ " validate=False, edge_host='cwbpub.cr.usgs.gov'):\n",
+ " ''' \n",
+ " This function will do the following for a specified obs_code between\n",
+ " start_UTC and end_UTC, incrementing by update_interval:\n",
+ "\n",
+ " - Read in absolute and baseline data, removing outliers\n",
+ " - Convert absolutes+baselines back to fluxgate measurements\n",
+ " - Convert all cylindrical to Cartesian coordinates\n",
+ " - Estimate memory-weighted Adjusted Data transform matrice(s)\n",
+ " - Estimate memory-weighted average pier corrections\n",
+ " - If validate is True, also:\n",
+ " - Apply Adjusted Data transforms to raw HEZF data\n",
+ " - Retrieve (quasi-)definitive data for comparisons\n",
+ " - Generate common time stamps for each update_interval\n",
+ " \n",
+ " \n",
+ " INPUTS:\n",
+ " obs_code - 3-character IAGA code for observatory\n",
+ " start_UTC - beginning date to do stuff (UTCDatetime)\n",
+ " end_UTC - final date to do stuff (UTCDatetime)\n",
+ " \n",
+ " OPTIONS:\n",
+ " update_interval - how often (in seconds) to update the Adjusted matrices\n",
+ " (default = end_UTC - start_UTC)\n",
+ " acausal - use absolute/ordinate pairs from the future if True\n",
+ " (default = False)\n",
+ " interpolate - interpolate between key transforms\n",
+ " (default = False)\n",
+ " first_UTC - earliest observation date to retrieve\n",
+ " (default = start_UTC)\n",
+ " last_UTC - latest observation date to retrieve\n",
+ " (default = end_UTC)\n",
+ " M_funcs - list of function objects used to generate affine matrices\n",
+ " given 3D Cartesian vector inputs; compose final Adjusted\n",
+ " affine matrix by:\n",
+ " 1) calculate 1st matrix from inputs->outputs;\n",
+ " 2) transform initial inputs to intermediate inputs;\n",
+ " 3) calculate 2nd matrix from intermediate inputs to outputs;\n",
+ " 4) repeat until all M_funcs used;\n",
+ " 5) final Adjusted matrix is composition of all in reverse\n",
+ " (default = [generate_affine_0])\n",
+ " memories - time constant(s) used to calculate weights; memories may be\n",
+ " a scalar, or a list with same length as M_funcs\n",
+ " (default = np.inf)\n",
+ " path_or_url - url for absolutes web service, or path to summary xlsm files \n",
+ " (default = 'https://geomag.usgs.gov/')\n",
+ " validate - if True, pull and process raw data, then compare with QD\n",
+ " (default = False)\n",
+ " edge_host - edge host for raw and QD magnetometer time series\n",
+ " (default = 'cwbpub.cr.usgs.gov')\n",
+ " \n",
+ " OUTPUTS:\n",
+ " utc_list - list of first UTCDateTimes for each update_interval\n",
+ " M_composed_list - list of composed Adjusted Data matrices for each update_interval\n",
+ " pc_list - list of pier corrections for each update_interval\n",
+ " \n",
+ " (if validate is True)\n",
+ " utc_xyzf_list - list of UTCDateTime arrays for each observation\n",
+ " xyzf_trad_list - list of static baseline adjusted data arrays for each update_interval\n",
+ " xyzf_adj_list - list of Adjusted Data arrays for each update_interval\n",
+ " xyzf_def_list - list of Definitive Data arrays for each update_interval\n",
+ " utc_bas - UTCDateTimes for absolute measurements\n",
+ " abs_xyz - absolute XYZ values used to train affine transforms\n",
+ " ord_hez - ordinate HEZ values used to train affine transforms\n",
+ " Ms_list - list of lists of Adjusted Data matrices for each M_func,\n",
+ " for each update_interval\n",
+ " weights_list - list of lists of weights used to estimate Adjusted Data\n",
+ " matrices for each M_func, for each update_interval\n",
+ " \n",
+ " '''\n",
+ " \n",
+ " from functools import reduce\n",
+ " \n",
+ " \n",
+ " # set start_UTC and end_UTC if not passed\n",
+ " if first_UTC is None:\n",
+ " first_UTC = start_UTC\n",
+ " if last_UTC is None:\n",
+ " last_UTC = end_UTC\n",
+ " \n",
+ " # default update_interval\n",
+ " if update_interval is None:\n",
+ " # only one interval from start_UTC to end_UTC\n",
+ " update_interval = end_UTC - start_UTC\n",
+ " \n",
+ " # default M_func\n",
+ " if M_funcs is None:\n",
+ " M_funcs = [generate_affine_0]\n",
+ " \n",
+ " # default memory\n",
+ " if memories is None:\n",
+ " memories = [np.inf]\n",
+ " \n",
+ " # make sure memory is compatible with M_funcs\n",
+ " if np.isscalar(memories):\n",
+ " memories = [memories for func in M_funcs]\n",
+ " elif len(memories) is 1:\n",
+ " memories = [memories[0] for func in M_funcs]\n",
+ " elif len(memories) != len(M_funcs):\n",
+ " raise ValueError('Memories must be a scalar or list with same length as M_funcs')\n",
+ " \n",
+ " \n",
+ " # retrieve all absolute calibrations and baselines from start_UTC to end_UTC\n",
+ " if obs_code == 'DED' or obs_code == 'CMO':\n",
+ "\n",
+ " # if a file: URL is passed, just trim off the front for now\n",
+ " if baseline_url.startswith('file:'):\n",
+ " baseline_url = baseline_url[len('file:'):]\n",
+ " while baseline_url.startswith('//'):\n",
+ " baseline_url = baseline_url[2:]\n",
+ "\n",
+ " ((h_abs, h_bas, h_utc), \n",
+ " (d_abs, d_bas, d_utc), \n",
+ " (z_abs, z_bas, z_utc),\n",
+ " pc) = retrieve_baselines_resid_summary_xlsm(\n",
+ " obs_code,\n",
+ " start_date = first_UTC,\n",
+ " end_date = last_UTC,\n",
+ " path_or_url = path_or_url\n",
+ " ) \n",
+ " else:\n",
+ " ((h_abs, h_bas, h_utc), \n",
+ " (d_abs, d_bas, d_utc), \n",
+ " (z_abs, z_bas, z_utc),\n",
+ " pc) = retrieve_baselines_webabsolutes(\n",
+ " obs_code,\n",
+ " start_date = first_UTC,\n",
+ " end_date = last_UTC,\n",
+ " path_or_url = path_or_url\n",
+ " )\n",
+ " \n",
+ " # recreate ordinate variometer measurements from absolutes and baselines\n",
+ " h_ord = h_abs - h_bas\n",
+ " d_ord = d_abs - d_bas\n",
+ " z_ord = z_abs - z_bas\n",
+ "\n",
+ " # convert from cylindrical to Cartesian coordinates\n",
+ " x_a = h_abs * np.cos(d_abs * np.pi/180)\n",
+ " y_a = h_abs * np.sin(d_abs * np.pi/180)\n",
+ " z_a = z_abs\n",
+ " #h_o = h_ord * np.cos(d_ord * np.pi/180)\n",
+ " #e_o = h_ord * np.sin(d_ord * np.pi/180)\n",
+ " #z_o = z_ord\n",
+ " \n",
+ " # WebAbsolutes does not generate h or d \"baselines\" in the most obvious\n",
+ " # way, and not just because it makes small-angle approximations; so, if\n",
+ " # we want exactly what was measured by the 3-axis fluxgate...\n",
+ " h_o = h_ord\n",
+ " e_o = h_abs * d_ord * 60 / 3437.7468\n",
+ " z_o = z_ord\n",
+ " \n",
+ " # use h_utc as common time stamp for vectors\n",
+ " utc_bas = h_utc\n",
+ " \n",
+ " # stack absolute and ordinate vectors for output\n",
+ " abs_xyz = np.vstack((x_a, y_a, z_a))\n",
+ " ord_hez = np.vstack((h_o, e_o, z_o))\n",
+ " \n",
+ " # initialize outputs\n",
+ " utc_list = []\n",
+ " M_composed_list = []\n",
+ " Ms_list = []\n",
+ " pcwa_list = []\n",
+ " weights_list = []\n",
+ " utc_xyzf_list = []\n",
+ " xyzf_trad_list = []\n",
+ " xyzf_adj_list = []\n",
+ " xyzf_def_list = []\n",
+ " \n",
+ " # process each update_interval from start_UTC to end_UTC\n",
+ " while ((start_UTC < end_UTC) or \n",
+ " (start_UTC <= end_UTC and interpolate is True)):\n",
+ " \n",
+ " print('Generating key transform for ', start_UTC)\n",
+ " \n",
+ " # reset intermediate input values\n",
+ " h_tmp = h_o\n",
+ " e_tmp = e_o\n",
+ " z_tmp = z_o\n",
+ " \n",
+ " # reinitialize weights, Ms and pcwa lists\n",
+ " weights = []\n",
+ " Ms = []\n",
+ " pcwa = []\n",
+ " \n",
+ " # loop over M_funcs and memories to compose affine matrix\n",
+ " for M_func, memory in zip(M_funcs, memories):\n",
+ " \n",
+ " # Calculate time-dependent weights using h_utc\n",
+ " weights.append(time_weights_exponential(h_utc, memory, start_UTC))\n",
+ "\n",
+ " # set weights for future observations to zero if not acausal\n",
+ " if not acausal:\n",
+ " weights[-1][h_utc > start_UTC] = 0.\n",
+ " \n",
+ " # return NaNs if no valid observations\n",
+ " if np.sum(weights[-1]) == 0:\n",
+ " Ms.append(np.nan * np.zeros((4,4)) )\n",
+ " pcwa.append(np.nan)\n",
+ " print('No valid observations for interval')\n",
+ " continue\n",
+ " \n",
+ " # identify 'good' data indices based on baseline stats\n",
+ " good = (filter_iqr(h_bas, threshold=3, weights=weights[-1]) &\n",
+ " filter_iqr(d_bas, threshold=3, weights=weights[-1]) &\n",
+ " filter_iqr(z_bas, threshold=3, weights=weights[-1]))\n",
+ " \n",
+ " # zero out any 'bad' weights\n",
+ " weights[-1] = good * weights[-1]\n",
+ " \n",
+ " # generate affine transform matrix\n",
+ " Ms.append(M_func((h_tmp, e_tmp, z_tmp), (x_a, y_a, z_a), weights=weights[-1]))\n",
+ " \n",
+ " # calculate weighted average of pier corrections\n",
+ " pcwa.append(np.average(pc, weights=weights[-1]))\n",
+ "\n",
+ " \n",
+ " # apply latest M matrix to inputs to get intermediate inputs\n",
+ " h_tmp, e_tmp, z_tmp = np.dot(Ms[-1],\n",
+ " np.vstack([h_tmp, e_tmp, z_tmp, np.ones_like(h_o)]))[:3]\n",
+ " \n",
+ " # end for M_func, memory in zip(M_funcs, memories)\n",
+ " \n",
+ " \n",
+ " # append Ms, pcwa, and weights used to generate them to lists \n",
+ " # of outputs for each update_interval\n",
+ " Ms_list.append(Ms)\n",
+ " pcwa_list.append(pcwa)\n",
+ " weights_list.append(weights)\n",
+ " \n",
+ " # compose affine transform matrices\n",
+ " M_composed = reduce(np.dot, Ms[::-1])\n",
+ " \n",
+ " # append to list of outputs for each update_interval\n",
+ " M_composed_list.append(M_composed)\n",
+ " \n",
+ " # append to list of outputs for each update_interval\n",
+ " utc_list.append(start_UTC)\n",
+ " \n",
+ " # generate/pull data for validation if requested\n",
+ " if validate:\n",
+ " \n",
+ " if interpolate is True:\n",
+ " if len(utc_list) == 1:\n",
+ " # can't interpolate with only 1 transform\n",
+ " start_UTC = start_UTC + update_interval\n",
+ " continue\n",
+ " else:\n",
+ " valid_start = start_UTC - update_interval\n",
+ " valid_end = start_UTC\n",
+ " else:\n",
+ " valid_start = start_UTC\n",
+ " valid_end = start_UTC + update_interval\n",
+ "\n",
+ " print('Validating interval ', valid_start, ' to ', valid_end)\n",
+ " \n",
+ " # retrieve raw HEZF variometer data from Edge server\n",
+ " factory = EdgeFactory(host=edge_host)\n",
+ " hezf = factory.get_timeseries(\n",
+ " observatory = obs_code,\n",
+ " interval = 'minute',\n",
+ " type = 'variation',\n",
+ " channels = ('H', 'E', 'Z', 'F'),\n",
+ " starttime = valid_start,\n",
+ " endtime = valid_end\n",
+ " )\n",
+ " \n",
+ " # place hez traces into hez1 matrix required for regression\n",
+ " hez1_arr = np.vstack((hezf[0].data,\n",
+ " hezf[1].data, \n",
+ " hezf[2].data,\n",
+ " np.ones_like(hezf[3])))\n",
+ " \n",
+ " # generate list of UTCDateTimes\n",
+ " utc_arr = np.array([(hezf[0].stats.starttime + second) \n",
+ " for second in hezf[0].times()])\n",
+ " \n",
+ " if interpolate is True:\n",
+ " # interpolate transform matrices\n",
+ " M_each = interpolate_affine_polar(\n",
+ " utc_arr,\n",
+ " utc_list[-2:],\n",
+ " M_composed_list[-2:])\n",
+ " else:\n",
+ " # generate list of identical matrices\n",
+ " M_each = [M_composed for each in utc_arr]\n",
+ " \n",
+ "\n",
+ " \n",
+ " # generate adjusted data using composed affine transform matrix\n",
+ " xyz1 = np.transpose([np.dot(M_each[obs], hez1_arr[:,obs]) \n",
+ " for obs in np.arange(len(utc_arr))]).squeeze()\n",
+ " xyzf = np.vstack((xyz1[:-1], hezf[3].data + np.mean(pcwa_list[-1])))\n",
+ " \n",
+ " # append xyzf to list of outputs for each update interval\n",
+ " xyzf_adj_list.append(xyzf)\n",
+ " \n",
+ " \n",
+ " # apply average traditional baseline adjustment to cylindrical \n",
+ " # ordinates, then convert to XYZ (this may not be exactly how\n",
+ " # MagProc does things, but it is how BGS documented it)\n",
+ " h_bas_avg = np.mean(h_bas[filter_iqr(h_bas, threshold=3)])\n",
+ " d_bas_avg = np.mean(d_bas[filter_iqr(d_bas, threshold=3)])\n",
+ " z_bas_avg = np.mean(z_bas[filter_iqr(z_bas, threshold=3)])\n",
+ " \n",
+ " h_trad = np.sqrt((h_bas_avg + hez1_arr[0])**2 + hez1_arr[1]**2)\n",
+ " d_trad = d_bas_avg * np.pi/180 + np.arcsin(hez1_arr[1] / h_trad)\n",
+ " z_trad = z_bas_avg + hez1_arr[2]\n",
+ " x_trad = h_trad * np.cos(d_trad)\n",
+ " y_trad = h_trad * np.sin(d_trad)\n",
+ " \n",
+ " xyzf_trad_list.append(np.vstack((x_trad, y_trad, z_trad, xyzf[3])))\n",
+ " \n",
+ " # retrieve (Quasi)Definitive xyzf data from Edge server\n",
+ " factory = EdgeFactory(host=edge_host)\n",
+ " xyzf = factory.get_timeseries(\n",
+ " observatory = obs_code,\n",
+ " interval = 'minute',\n",
+ " type = 'quasi-definitive',\n",
+ " channels = ('X', 'Y', 'Z', 'F'),\n",
+ " starttime = valid_start,\n",
+ " endtime = valid_end\n",
+ " )\n",
+ " \n",
+ " # place xyzf traces into xyzf matrix\n",
+ " xyzf = np.vstack((xyzf[0].data,\n",
+ " xyzf[1].data, \n",
+ " xyzf[2].data,\n",
+ " xyzf[3].data))\n",
+ " \n",
+ " # append xyzf to list of outputs for each update interval\n",
+ " xyzf_def_list.append(xyzf)\n",
+ " \n",
+ "\n",
+ " # finally, return array of common times for plotting, etc.\n",
+ " utc_xyzf_list.append(utc_arr)\n",
+ " \n",
+ " \n",
+ " # increment start_UTC\n",
+ " start_UTC += update_interval\n",
+ " \n",
+ " if validate:\n",
+ " return (utc_list, M_composed_list, pcwa_list,\n",
+ " utc_xyzf_list, xyzf_trad_list, xyzf_adj_list, xyzf_def_list, \n",
+ " utc_bas, abs_xyz, ord_hez,\n",
+ " Ms_list, weights_list)\n",
+ " else:\n",
+ " return utc_list, M_composed_list, pcwa_list"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Boulder (BOU) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 124,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#\n",
+ "# default configuration parameters for BOU\n",
+ "#\n",
+ "\n",
+ "# INPUTS\n",
+ "obs_code = 'BOU'\n",
+ "start_UTC = UTCDateTime('2018-12-01T00:00:00Z')\n",
+ "end_UTC = UTCDateTime('2019-05-31T23:59:00Z')\n",
+ "\n",
+ "# OPTIONS\n",
+ "update_interval = 86400 * 7\n",
+ "acausal = False\n",
+ "first_UTC = UTCDateTime('2018-11-01T00:00:00Z')\n",
+ "last_UTC = UTCDateTime('2019-06-30T23:59:00Z')\n",
+ "\n",
+ "M_funcs = [generate_affine_9, generate_affine_6]\n",
+ "memories = [86400 * 100, 86400 * 10]\n",
+ "\n",
+ "#path_or_url = '/Volumes/geomag/pub/Caldata/Checked Baseline Data/'\n",
+ "path_or_url = 'https://geomag.usgs.gov'\n",
+ "\n",
+ "validate = True\n",
+ "edge_host = 'cwbpub.cr.usgs.gov'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[20540.08888174, 20539.97689157, 20539.79632613, ...,\n",
+ " 20533.31383075, 20533.60335062, 20534.0016337 ],\n",
+ " [ 3049.64347206, 3049.630928 , 3049.55800175, ...,\n",
+ " 3044.91292622, 3045.42813888, 3045.55756173],\n",
+ " [47577.05220498, 47577.05120498, 47577.05320498, ...,\n",
+ " 47577.36320498, 47577.37220498, 47577.45020498],\n",
+ " [51910.911 , 51910.861 , 51910.791 , ...,\n",
+ " 51908.912 , 51909.054 , 51909.283 ]])"
+ ]
+ },
+ "execution_count": 136,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xyzf_adj_weekly_007_causal[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "metadata": {
+ "code_folding": [],
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-01T00:00:00.000000Z\n",
+ "Generating key transform for 2018-12-08T00:00:00.000000Z\n",
+ "Validating interval 2018-12-01T00:00:00.000000Z to 2018-12-08T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-15T00:00:00.000000Z\n",
+ "Validating interval 2018-12-08T00:00:00.000000Z to 2018-12-15T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-22T00:00:00.000000Z\n",
+ "Validating interval 2018-12-15T00:00:00.000000Z to 2018-12-22T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-29T00:00:00.000000Z\n",
+ "Validating interval 2018-12-22T00:00:00.000000Z to 2018-12-29T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-05T00:00:00.000000Z\n",
+ "Validating interval 2018-12-29T00:00:00.000000Z to 2019-01-05T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-12T00:00:00.000000Z\n",
+ "Validating interval 2019-01-05T00:00:00.000000Z to 2019-01-12T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-19T00:00:00.000000Z\n",
+ "Validating interval 2019-01-12T00:00:00.000000Z to 2019-01-19T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-26T00:00:00.000000Z\n",
+ "Validating interval 2019-01-19T00:00:00.000000Z to 2019-01-26T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-02T00:00:00.000000Z\n",
+ "Validating interval 2019-01-26T00:00:00.000000Z to 2019-02-02T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-09T00:00:00.000000Z\n",
+ "Validating interval 2019-02-02T00:00:00.000000Z to 2019-02-09T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-16T00:00:00.000000Z\n",
+ "Validating interval 2019-02-09T00:00:00.000000Z to 2019-02-16T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-23T00:00:00.000000Z\n",
+ "Validating interval 2019-02-16T00:00:00.000000Z to 2019-02-23T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-02T00:00:00.000000Z\n",
+ "Validating interval 2019-02-23T00:00:00.000000Z to 2019-03-02T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-09T00:00:00.000000Z\n",
+ "Validating interval 2019-03-02T00:00:00.000000Z to 2019-03-09T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-16T00:00:00.000000Z\n",
+ "Validating interval 2019-03-09T00:00:00.000000Z to 2019-03-16T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/erigler/anaconda3/envs/test_GIMP_py36/lib/python3.6/site-packages/ipykernel_launcher.py:312: RuntimeWarning: invalid value encountered in arcsin\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-23T00:00:00.000000Z\n",
+ "Validating interval 2019-03-16T00:00:00.000000Z to 2019-03-23T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-30T00:00:00.000000Z\n",
+ "Validating interval 2019-03-23T00:00:00.000000Z to 2019-03-30T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-06T00:00:00.000000Z\n",
+ "Validating interval 2019-03-30T00:00:00.000000Z to 2019-04-06T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-13T00:00:00.000000Z\n",
+ "Validating interval 2019-04-06T00:00:00.000000Z to 2019-04-13T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-20T00:00:00.000000Z\n",
+ "Validating interval 2019-04-13T00:00:00.000000Z to 2019-04-20T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-27T00:00:00.000000Z\n",
+ "Validating interval 2019-04-20T00:00:00.000000Z to 2019-04-27T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-04T00:00:00.000000Z\n",
+ "Validating interval 2019-04-27T00:00:00.000000Z to 2019-05-04T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-11T00:00:00.000000Z\n",
+ "Validating interval 2019-05-04T00:00:00.000000Z to 2019-05-11T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-18T00:00:00.000000Z\n",
+ "Validating interval 2019-05-11T00:00:00.000000Z to 2019-05-18T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-25T00:00:00.000000Z\n",
+ "Validating interval 2019-05-18T00:00:00.000000Z to 2019-05-25T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ }
+ ],
+ "source": [
+ "#\n",
+ "# Run do_it_all() with a \"short\" memory in causal mode\n",
+ "#\n",
+ "(utc_weekly_007_causal, M_weekly_007_causal, pc_weekly_007_causal,\n",
+ " utc_xyzf_weekly_007_causal, xyzf_trad_weekly_007_causal, \n",
+ " xyzf_adj_weekly_007_causal, xyzf_def_weekly_007_causal,\n",
+ " utc_bas_weekly_007_causal, abs_xyz_weekly_007_causal, ord_hez_weekly_007_causal,\n",
+ " Ms_weekly_007_causal, weights_weekly_007_causal) = do_it_all(\n",
+ " obs_code, start_UTC, end_UTC,\n",
+ " update_interval=update_interval, acausal=False, interpolate=True,\n",
+ " first_UTC=first_UTC, last_UTC=last_UTC,\n",
+ " M_funcs=M_funcs, memories=memories,\n",
+ " path_or_url=path_or_url,\n",
+ " validate=validate,\n",
+ " edge_host=edge_host)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "metadata": {
+ "code_folding": [],
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-01T00:00:00.000000Z\n",
+ "Generating key transform for 2018-12-08T00:00:00.000000Z\n",
+ "Validating interval 2018-12-01T00:00:00.000000Z to 2018-12-08T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-15T00:00:00.000000Z\n",
+ "Validating interval 2018-12-08T00:00:00.000000Z to 2018-12-15T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-22T00:00:00.000000Z\n",
+ "Validating interval 2018-12-15T00:00:00.000000Z to 2018-12-22T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-29T00:00:00.000000Z\n",
+ "Validating interval 2018-12-22T00:00:00.000000Z to 2018-12-29T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-05T00:00:00.000000Z\n",
+ "Validating interval 2018-12-29T00:00:00.000000Z to 2019-01-05T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-12T00:00:00.000000Z\n",
+ "Validating interval 2019-01-05T00:00:00.000000Z to 2019-01-12T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-19T00:00:00.000000Z\n",
+ "Validating interval 2019-01-12T00:00:00.000000Z to 2019-01-19T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-26T00:00:00.000000Z\n",
+ "Validating interval 2019-01-19T00:00:00.000000Z to 2019-01-26T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-02T00:00:00.000000Z\n",
+ "Validating interval 2019-01-26T00:00:00.000000Z to 2019-02-02T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-09T00:00:00.000000Z\n",
+ "Validating interval 2019-02-02T00:00:00.000000Z to 2019-02-09T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-16T00:00:00.000000Z\n",
+ "Validating interval 2019-02-09T00:00:00.000000Z to 2019-02-16T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-23T00:00:00.000000Z\n",
+ "Validating interval 2019-02-16T00:00:00.000000Z to 2019-02-23T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-02T00:00:00.000000Z\n",
+ "Validating interval 2019-02-23T00:00:00.000000Z to 2019-03-02T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-09T00:00:00.000000Z\n",
+ "Validating interval 2019-03-02T00:00:00.000000Z to 2019-03-09T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-16T00:00:00.000000Z\n",
+ "Validating interval 2019-03-09T00:00:00.000000Z to 2019-03-16T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/erigler/anaconda3/envs/test_GIMP_py36/lib/python3.6/site-packages/ipykernel_launcher.py:312: RuntimeWarning: invalid value encountered in arcsin\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-23T00:00:00.000000Z\n",
+ "Validating interval 2019-03-16T00:00:00.000000Z to 2019-03-23T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-30T00:00:00.000000Z\n",
+ "Validating interval 2019-03-23T00:00:00.000000Z to 2019-03-30T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-06T00:00:00.000000Z\n",
+ "Validating interval 2019-03-30T00:00:00.000000Z to 2019-04-06T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-13T00:00:00.000000Z\n",
+ "Validating interval 2019-04-06T00:00:00.000000Z to 2019-04-13T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-20T00:00:00.000000Z\n",
+ "Validating interval 2019-04-13T00:00:00.000000Z to 2019-04-20T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-27T00:00:00.000000Z\n",
+ "Validating interval 2019-04-20T00:00:00.000000Z to 2019-04-27T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-04T00:00:00.000000Z\n",
+ "Validating interval 2019-04-27T00:00:00.000000Z to 2019-05-04T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-11T00:00:00.000000Z\n",
+ "Validating interval 2019-05-04T00:00:00.000000Z to 2019-05-11T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-18T00:00:00.000000Z\n",
+ "Validating interval 2019-05-11T00:00:00.000000Z to 2019-05-18T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-25T00:00:00.000000Z\n",
+ "Validating interval 2019-05-18T00:00:00.000000Z to 2019-05-25T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ }
+ ],
+ "source": [
+ "#\n",
+ "# Run do_it_all() with a \"short\" memory in *a*causal mode\n",
+ "#\n",
+ "(utc_weekly_007_acausal, M_weekly_007_acausal, pc_weekly_007_acausal,\n",
+ " utc_xyzf_weekly_007_acausal, xyzf_trad_weekly_007_acausal, \n",
+ " xyzf_adj_weekly_007_acausal, xyzf_def_weekly_007_acausal,\n",
+ " utc_bas_weekly_007_acausal, abs_xyz_weekly_007_acausal, ord_hez_weekly_007_acausal,\n",
+ " Ms_weekly_007_acausal, weights_weekly_007_acausal) = do_it_all(\n",
+ " obs_code, start_UTC, end_UTC,\n",
+ " update_interval=update_interval, acausal=True, interpolate=True,\n",
+ " first_UTC=first_UTC, last_UTC=last_UTC,\n",
+ " M_funcs=M_funcs, memories=memories,\n",
+ " path_or_url=path_or_url,\n",
+ " validate=validate,\n",
+ " edge_host=edge_host)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-01T00:00:00.000000Z\n",
+ "Generating key transform for 2018-12-08T00:00:00.000000Z\n",
+ "Validating interval 2018-12-01T00:00:00.000000Z to 2018-12-08T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-15T00:00:00.000000Z\n",
+ "Validating interval 2018-12-08T00:00:00.000000Z to 2018-12-15T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-22T00:00:00.000000Z\n",
+ "Validating interval 2018-12-15T00:00:00.000000Z to 2018-12-22T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-29T00:00:00.000000Z\n",
+ "Validating interval 2018-12-22T00:00:00.000000Z to 2018-12-29T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-05T00:00:00.000000Z\n",
+ "Validating interval 2018-12-29T00:00:00.000000Z to 2019-01-05T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-12T00:00:00.000000Z\n",
+ "Validating interval 2019-01-05T00:00:00.000000Z to 2019-01-12T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-19T00:00:00.000000Z\n",
+ "Validating interval 2019-01-12T00:00:00.000000Z to 2019-01-19T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-01-26T00:00:00.000000Z\n",
+ "Validating interval 2019-01-19T00:00:00.000000Z to 2019-01-26T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-02T00:00:00.000000Z\n",
+ "Validating interval 2019-01-26T00:00:00.000000Z to 2019-02-02T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-09T00:00:00.000000Z\n",
+ "Validating interval 2019-02-02T00:00:00.000000Z to 2019-02-09T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-16T00:00:00.000000Z\n",
+ "Validating interval 2019-02-09T00:00:00.000000Z to 2019-02-16T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-02-23T00:00:00.000000Z\n",
+ "Validating interval 2019-02-16T00:00:00.000000Z to 2019-02-23T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-02T00:00:00.000000Z\n",
+ "Validating interval 2019-02-23T00:00:00.000000Z to 2019-03-02T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-09T00:00:00.000000Z\n",
+ "Validating interval 2019-03-02T00:00:00.000000Z to 2019-03-09T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-16T00:00:00.000000Z\n",
+ "Validating interval 2019-03-09T00:00:00.000000Z to 2019-03-16T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/erigler/anaconda3/envs/test_GIMP_py36/lib/python3.6/site-packages/ipykernel_launcher.py:312: RuntimeWarning: invalid value encountered in arcsin\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-23T00:00:00.000000Z\n",
+ "Validating interval 2019-03-16T00:00:00.000000Z to 2019-03-23T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-03-30T00:00:00.000000Z\n",
+ "Validating interval 2019-03-23T00:00:00.000000Z to 2019-03-30T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-06T00:00:00.000000Z\n",
+ "Validating interval 2019-03-30T00:00:00.000000Z to 2019-04-06T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-13T00:00:00.000000Z\n",
+ "Validating interval 2019-04-06T00:00:00.000000Z to 2019-04-13T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-20T00:00:00.000000Z\n",
+ "Validating interval 2019-04-13T00:00:00.000000Z to 2019-04-20T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-04-27T00:00:00.000000Z\n",
+ "Validating interval 2019-04-20T00:00:00.000000Z to 2019-04-27T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-04T00:00:00.000000Z\n",
+ "Validating interval 2019-04-27T00:00:00.000000Z to 2019-05-04T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-11T00:00:00.000000Z\n",
+ "Validating interval 2019-05-04T00:00:00.000000Z to 2019-05-11T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-18T00:00:00.000000Z\n",
+ "Validating interval 2019-05-11T00:00:00.000000Z to 2019-05-18T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2019-05-25T00:00:00.000000Z\n",
+ "Validating interval 2019-05-18T00:00:00.000000Z to 2019-05-25T00:00:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ }
+ ],
+ "source": [
+ "#\n",
+ "# Run do_it_all() with infinite memory in *a*causal mode\n",
+ "#\n",
+ "(utc_weekly_inf_acausal, M_weekly_inf_acausal, pc_weekly_inf_acausal,\n",
+ " utc_xyzf_weekly_inf_acausal, xyzf_trad_weekly_inf_acausal, \n",
+ " xyzf_adj_weekly_inf_acausal, xyzf_def_weekly_inf_acausal,\n",
+ " utc_bas_weekly_inf_acausal, abs_xyz_weekly_inf_acausal, ord_hez_weekly_inf_acausal,\n",
+ " Ms_weekly_inf_acausal, weights_weekly_inf_acausal) = do_it_all(\n",
+ " obs_code, start_UTC, end_UTC,\n",
+ " update_interval=update_interval, acausal=True, interpolate=True,\n",
+ " first_UTC=first_UTC, last_UTC=last_UTC,\n",
+ " M_funcs=M_funcs, memories=[np.inf, np.inf],\n",
+ " path_or_url=path_or_url,\n",
+ " validate=validate,\n",
+ " edge_host=edge_host)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Generating key transform for 2018-12-01T00:00:00.000000Z\n",
+ "Validating interval 2018-12-01T00:00:00.000000Z to 2019-05-31T23:59:00.000000Z\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n",
+ "read_wave_server_v returned flag FR - requested data right (later) than tank contents\n"
+ ]
+ }
+ ],
+ "source": [
+ "#\n",
+ "# Run do_it_all() with infinite memory in *a*causal mode for the entire\n",
+ "# interval\n",
+ "#\n",
+ "(utc_all_inf_acausal, M_all_inf_acausal, pc_all_inf_acausal,\n",
+ " utc_xyzf_all_inf_acausal, xyzf_trad_all_inf_acausal, \n",
+ " xyzf_adj_all_inf_acausal, xyzf_def_all_inf_acausal,\n",
+ " utc_bas_all_inf_acausal, abs_xyz_all_inf_acausal, ord_hez_all_inf_acausal,\n",
+ " Ms_all_inf_acausal, weights_all_inf_acausal) = do_it_all(\n",
+ " obs_code, start_UTC, end_UTC,\n",
+ " update_interval=None, acausal=True, interpolate=False,\n",
+ " first_UTC=first_UTC, last_UTC=last_UTC,\n",
+ " M_funcs=M_funcs, memories=[np.inf, np.inf],\n",
+ " path_or_url=path_or_url,\n",
+ " validate=validate,\n",
+ " edge_host=edge_host)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
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+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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Tah9ZMHFC+LoEUe3pQj3EiBAgACBVAl4l5uq7k5NsBKEqelqgeaagEkl13pEewgQIECAAAECBAgQIJAOgVm/mxXTH51eG+ys02bFZcdelo7gRUmAAAECBNoh4F1uO9A8kvMCEoQ530UamK8CJpV87VlxESBAgAABAgQIECBAILcFpvxoSjzw8gO1jZxy2JSYc+6c3G601hEgQIAAgS4U8C63C/FV3WECEoQdRqtgAnsXMKkYIQQIECBAgAABAgQIECDQ2QLJ+YNDZwyNsh1ltVUP7jM41ly1xjmEnd0Z6iNAgACBbiPgXW636SoNbYOABGEbsNxKIJsCJpVsaiqLAAECBAgQIECAAAECBFoj0Pj8wZpnnEPYGj33ECBAgEBaBbzLTWvP53fcEoT53b+iy2EBk0oOd46mESBAgAABAgQIECBAIE8F6p8/eOjgiFfXVwfqHMI87XBhESBAgEBWBLzLzQqjQnJMQIIwxzpEc9IjYFJJT1+LlAABAgQIECBAgAABAp0lcMWjV8S9L97bYnXJ1qLbdm3LfH7H5Ijz9xxFWNKzJAb2Htjic+ceeW7MPHVmZ4WhHgIECBAgkFMC3uXmVHdoTJYEJAizBKkYAm0VMKm0Vcz9BAgQIECAAAECBAgQILA3gaqqqiiddXAs3bh8n1DDDoh448qIUTMj1lTnC/d6lQ48OBZdvmRft/mcAAECBAjkpYB3uXnZrakPSoIw9UMAQFcJmFS6Sl69BAgQIECAAAECBAgQyE+BtdvWxugbR0d5RXmDAIsKIkb2rfvRgf2qVw8ePizipTXVqwjf3Fz3+cotERVVDY2Ki4pjxRUrYugBQ/MTT1QECBAgQGAvAt7lGh75KCBBmI+9KqZuIWBS6RbdpJEECBAgQIAAAQIECBDoVgKLyhbF1XOvjvteuq9Bu88+PGLGKRHjBrUczqKyiKvmRsx5qeE95xxxTsyYNCNKB5V2KwuNJUCAAAEC2RLwLjdbksrJJQEJwlzqDf3O3pkAACAASURBVG1JlYBJJVXdLVgCBAgQIECAAAECBAh0qsC8JfNi+qPT408r/1Rbb3FRxOPnR5wwtmlT5i+LeP8dEeUVdZ9NGDkhbjr1pjjxkBM7te0qI0CAAAECuSbgXW6u9Yj2ZENAgjAbisog0A4Bk0o70DxCgAABAgQIECBAgAABAq0WqKisiNl/mh2ff+LzsXrr6sxzF7wtYvbkpkVc8EDEHQuqfz6iZETccPINceGEC6OosKjV9bmRAAECBAjkq4B3ufnas+mOS4Iw3f0v+i4UMKl0Ib6qCRAgQIAAAQIECBAgkCKBJEl40U8vykQ87e0Rt5zRNPhpD0bc9nz1z28/8/ZMctBFgAABAgQIVAt4l2sk5KOABGE+9qqYuoWASaVbdJNGEiBAgAABAgQIECBAoNsLTLpjUjy++PFMHPMvijj2oIgbn454cU3EkcMirjgu4nevR0y8vTrUSeMmxdyPze32cQuAAAECBAhkS8C73GxJKieXBCQIc6k3tCVVAiaVVHW3YAkQIECAAAECBAgQINAlAks3LI3SWaVRFVUxfnDEY1Mjpt4f8dTyuuYcPybizikRp9wZsbAsoiAKYsn0JTF2QDOHFXZJFColQIAAAQJdK+Bdbtf6q71jBCQIO8ZVqQT2KWBS2SeRGwgQIECAAAECBAgQIEBgPwWu/831ce2vrs2UcsyoiFfWRWza2bTQ/r0iDh0c8Yc3qz+7/qTr45r3XrOftXucAAECBAjkh4B3ufnRj6JoKCBBaEQQ6CIBk0oXwauWAAECBAgQIECAAAECKRGoqqqKQ791aCwsW9gk4tKBpXHNxGviht/eEIs3LG7y+fjB4+OVS1+JgoKClGgJkwABAgQItCzgXa7RkY8CEoT52Kti6hYCJpVu0U0aSYAAAQIECBAgQIAAgW4rMH/Z/Jh4+8Qm7f/Y0R+Lb3/w29G/V//YtHNTXPLIJXHXn+9qct/8i+bH8WOP77bxazgBAgQIEMiWgHe52ZJUTi4JSBDmUm9oS6oETCqp6m7BEiBAgAABAgQIECBAoNMFpj04LW57/rbaepOE4M2n3xznHXVek7bc/cLd8amHP5VJGNZc094+LW4545ZOb7cKCRAgQIBArgl4l5trPaI92RCQIMyGojIItEPApNIONI8QIECAAAECBAgQIECAQKsFnlj8RJx337mxeuvaOH7M8XHXWXfFIQMPafH5JRuWxNQ5U+PJ5U/G8JKh8cOzfxQnl57c6vrcSIAAAQIE8lXAu9x87dl0xyVBmO7+F30XCphUuhBf1QQIECBAgAABAgQIEEiJwBub34g5L82Ji4+5OHoU9thn1Lsrd8fNz90cZx1+VozqN2qf97uBAAECBAikQcC73DT0cvpilCBMX5+LOEcETCo50hGaQYAAAQIECBAgQIAAAQIECBAgQIAAgb0IeJdreOSjgARhPvaqmLqFgEmlW3STRhIgQIAAAQIECBAgQIAAAQIECBAgkHIB73JTPgDyNHwJwjztWGHlvoBJJff7SAsJECBAgAABAgQIECBAgAABAgQIECDgXa4xkI8CEoT52Kti6hYCJpVu0U0aSYAAAQIECBAgQIAAAQIECBAgQIBAygW8y035AMjT8CUI87RjhZX7AiaV3O8jLSRAgAABAgQIECBAgAABAgQIECBAgIB3ucZAPgpIEOZjr4qpWwiYVLpFN2kkAQIECBAgQIAAAQIECBAgQIAAAQIpF/AuN+UDIE/DlyDM044VVu4LmFRyv4+0kAABAgQIECBAgAABAgQIECBAgAABAt7lGgP5KCBBmI+9KqZuIWBS6RbdpJEECBAgQIAAAQIECBAgQIAAAQIECKRcwLvclA+APA1fgjBPO1ZYuS9gUsn9PtJCAgQIECBAgAABAgQIECBAgAABAgQIeJdrDOSjgARhPvaqmLqFgEmlW3STRhIgQIAAAQIECBAgQIAAAQIECBAgkHIB73JTPgDyNHwJwjztWGHlvoBJJff7SAsJECBAgAABAgQIECBAgAABAgQIECDgXa4xkI8CEoT52Kti6hYCJpVu0U0aSYAAAQIECBAgQIAAAQIECBAgQIBAygW8y035AMjT8CUI87RjhZX7AiaV3O8jLSRAgAABAgQIECBAgAABAgQIECBAgIB3ucZAPgpIEOZjr4qpWwiYVLpFN2kkAQIECBAgQIAAAQIECBAgQIAAAQIpF/AuN+UDIE/DlyDM044VVu4LmFRyv4+0kAABAgQIECBAgAABAgQIECBAgAABAt7lGgP5KCBBmI+9KqZuIWBS6RbdpJEECBAgQIAAAQLtFKisqoxjbz02tpZvjpLifvHMtGeisKCwnaV5jAABAgQIECBAgEDXCXiX23X2au44AQnCjrNVMoG9CphUDBACBAgQIECAAIF8FliwckFM+N6E2hAXXLwgjh5xdD6HLDYCBAgQIECAAIE8FfAuN087NuVhSRCmfADkWvh//OMf4xe/+EX89re/jb/85S+xevXq6NmzZ4waNSre8573xMc//vGYOHHifjf7uuuui//8z/9sVTm/+tWv4n3ve1+r7m3LTSaVtmi5lwABAgQIECBAoLsJzPrdrJj+6PTaZs86bVZcduxl3S0M7SVAgAABAgQIECAQ3uUaBPkoIEGYj73aTWM68cQT4ze/+c0+W/+xj30sbr311iguLt7nvS3dIEHYbjoPEiBAgAABAgQIEGiVwJQfTYkHXn6g9t4ph02JOefOadWzbiJAgAABAgQIECCQSwIShLnUG9qSLQEJwmxJKme/BcaPHx8LFy7MrBb8yEc+klkpOHbs2KioqIinn346Zs6cGStWrMjUc95558Xdd9/d7jrrJwhfeOGFvZZTWloaJSUl7a6rpQdNKlknVSABAgQIECBAgECOCCTnDw6dMTTKdpTVtmhwn8Gx5qo1ziHMkT7SDAIECBAgQIAAgdYLeJfbeit3dh8BCcLu01d539IPfehDcf7558fZZ58dRUVFTeJdu3ZtHH/88fHKK69kPktWG7Z3u9H6CcKqqqousTWpdAm7SgkQIECAAAECBDpBoPH5gzVVOoewE/BVQYAAAQIECBAgkHUB73KzTqrAHBCQIMyBTtCE1gs89NBD8eEPfzjzwGWXXRazZs1q/cP17pQgbBebhwgQIECAAAECBAi0SqD++YOHDo54dX31Y84hbBWfmwgQIECAAAECBHJMQIIwxzpEc7IiIEGYFUaFdJbAli1bol+/fpnqTj/99EgShu25JAjbo+YZAgQIECBAgAABAtUCVzx6Rdz74r0tciRbi27btS3z+R2TI87fcxRhSc+SGNh7YIvPnXvkuTHz1JmYCRAgQIAAAQIECOSUgARhTnWHxmRJQIIwS5CK6RyB9evXx5AhQzKVJSsJH3zwwXZVLEHYLjYPESBAgAABAgQIEIhki/7SWQfH0o3L96kx7ICIN66MGDUzYk11vnCvV+nAg2PR5Uv2dZvPCRAgQIAAAQIECHSqgARhp3KrrJMEJAg7CVo12RG4//7746yzzsoUdtVVV8WMGTPaVXD9BOGkSZPij3/8Y2zevDkGDhwYRxxxRJx22mnxyU9+MgYNGtSu8lvzkEmlNUruIUCAAAECBAgQyDWBtdvWxugbR0d5RXmDphUVRIzsW/ejA/tVrx48fFjES2uqVxG+ubnu85VbIioaHQdeXFQcK65YEUMPGJprYWsPAQIECBAgQIBAigW8y01x5+dx6BKEedy5+RZaZWVlHHfccfH73/8+E9qzzz4bxxxzTLvCrJ8gbKmAJFk4e/bsOPPMM9tVRzJp7O168803413velfmluXLl8dBBx3Urno8RIAAAQIECBAgQKCzBZ5d83L8y8+nx2uLH21Q9dmHR8w4JWLcXr5nt6gs4qq5EXNeatjqc444J2ZMmhGlg0o7Oxz1ESBAgAABAgQIENirgAShAZKPAhKE+direRrTzJkz4zOf+UwmuilTpsScOXPaHWmSIEyenzx5ciZJN2rUqNi1a1f87W9/ix/84Afx2GOPZcouKiqKn/3sZ/GBD3ygzXUVFBS0+hkJwlZTuZEAAQIECBAgQCAHBB7evC5+ubUsXn/jmZj31A2xZl1dtq+4KOLx8yNOGNu0ofOXRbz/jojyirrPJoycEDedelOceMiJORCZJhAgQIAAAQIECBBoKiBBaFTko4AEYT72ah7GNG/evEi2At29e3cMHz48/vznP8eIESPaHemGDRsy24m2dH3ve9+Liy++OPNxkjx87bXXok+fPm2qT4KwTVxuJkCAAAECBAgQ6EYC39+wMl7YsSXT4srKivjrK3Pi+Wf/O9ZtW5P52QVvi5g9uWlAFzwQcceC6p+PKBkRN5x8Q1w44cIoKizqRtFrKgECBAgQIECAQNoEJAjT1uPpiFeCMB393K2jfPHFF2PixIlRVlYWvXr1ikcffTROPLHjv138iU98Im699daM3V133RUf/ehH2+Roi9E2cbmZAAECBAgQIECgGwl8be2yWLm74RmEL/7tvpj7689lopj29ohbzmga0LQHI257vvrnt595eyY56CJAgAABAgQIECCQ6wIShLneQ9rXHgEJwvaoeabTBBYvXhwnnHBCvPHGG5ntPn/84x9nthftjOu5556Ld77znZmqkmTh//7v/2a1WpNKVjkVRoAAAQIECBAg0IkCP9+yPh7bsr5Bjfc9dEEsX/FU5mfzL4o49qCIG5+OeHFNxJHDIq44LuJ3r0dMvL36sUnjJsXcj83txFarigABAgQIECBAgED7BLzLbZ+bp3JbQIIwt/sn1a1LkoLJysFFixZFsl3n7Nmz4/zzz+80k23btkVJSUmmvg9+8IPx8MMPZ7Vuk0pWORVGgAABAgQIECDQiQJPbC2Ln21eV1vjps0r4vt3nxQRVTF+cMRjUyOm3h/x1PK6Rh0/JuLOKRGn3BmxsCyiIApiyfQlMXZAM4cVdmIsqiJAgAABAgQIECCwLwHvcvcl5PPuKCBB2B17LQVtXrt2bWYb0b/+9a+ZaL/97W/HJZdc0qmRb926Nfr27ZupU4KwU+lVRoAAAQIECBAgkOMCjROEz/zxO/H0szdlWn3MqIhX1kVs2tk0iP69Ig4dHPGHN6s/u/6k6+Oa916T49FqHgECBAgQIECAQNoFJAjTPgLyM34Jwvzs124d1caNG+Pkk0+OP/7xj5k4vvrVr8ZnP/vZTo/p2WefjXe9612ZeqdNmxa33HJLVttgUskqp8IIECBAgAABAgQ6UeDxrWXx0J4VhFVVVTH7nkmxcdOyJi0oHVga10y8Jm747Q2xeMPiJp+PHzw+Xrn0lcyOIS4CBAgQIECAAAECuSrgXW6u9ox27Y+ABOH+6Hk26wLJtp7/+I//GE8++WSm7GuuuSauv/76rNfTmgKTpOBtt92WufXOO++MqVOntuaxVt9jUmk1lRsJECBAgAABAgRyTOCXW8vi4T0JwhVvPhc/fvC8Ji382NEfi29/8NvRv1f/2LRzU1zyyCVx15/vanLf/Ivmx/Fjj8+xCDWHAAECBAgQIECAQJ2Ad7lGQz4KSBDmY69205jKy8vjwx/+cDz22GOZCC6//PK46abqbYraciVnFV500UWZR774xS/Gdddd1+DxF154Ifr06RPjx49vsdjvfe97cfHFF2c+HzlyZLz22mu15xG2pS17u9ekki3JdJRTWVUZx956bGwt3xwlxf3imWnPRGFBYTqCFyUBAgQIECCQcwL1E4Rz530+Xnz5x7VtTBKCN59+c5x3VNOk4d0v3B2fevhTmYRhzTXt7dPiljOyu1tHzoFpEAECBAgQIECAQLcW8C63W3efxrcgIEFoaOSMwNlnnx1z5szJtCfZYjRJDu5tq6Hi4uJ461vf2qT9+0oQJp8nqwNPOumk+MAHPhBHHXVUDBkyJHbv3h0vv/xy3HXXXTF37txMuUVFRZk2nXHGGVl3MqlknTSvC1ywckFM+N6E2hgXXLwgjh5xdF7HLDgCBAgQIEAgdwXqJwiXr3g6fvH45bF1e1mMGvmOePLcn8QhAw9psfFLNiyJqXOmxpPLn4zhJUPjh2f/KE4uPTl3g9UyAgQIECBAgACB1At4l5v6IZCXABKEedmt3TOotp47cvDBB8eSJUvalSCsWWG4N6kkaZhsMXrmmWd2CKhJpUNY87bQWb+bFdMfnV4b36zTZsVlx16Wt/EKjAABAgQINCeQnHX3zI7NsbR8RxzVuySO6FUCqosE5m4pi0e2rKutfcvWVfHa4sfi6CPOi1mjDttnq3ZX7o6bn7s5zjr8rBjVb9Q+73cDAQIECBAgQIAAga4U8C63K/XV3VECEoQdJavcNgt0VoJw9erV8dBDD8XTTz8dzz//fKxatSrWrVsXyQunwYMHx9ve9rY47bTT4sILL4z+/fu3OY7WPmBSaa2U+xKBKT+aEg+8/EAtxpTDpsScc6tX3LoIECBAgEBaBJ7dvjnu3riqNtx/G3JQjO3ZOy3h51ScjROE9Rv33yNb3so/p4LQGAIECBAgQIAAAQKtFPAut5VQbutWAhKE3aq7NDafBEwq+dSbHRtLcv7g0BlDo2xHWW1Fg/sMjjVXrXEOYcfSK50AAQIEckzg31a+1qBFY3r2jiuGHJRjrUxHcyQI09HPoiRAgAABAgQIEKgW8C7XSMhHAQnCfOxVMXULAZNKt+imnGhk4/MHaxrlHMKc6B6NIECAAIFOFGicIEyqtlqtEzugXlUShF3jrlYCBAgQIECAAIGuEfAut2vc1dqxAhKEHeurdAItCphUDI7WCtQ/f/DQwRGvrq9+0jmErRV0HwECBAjki4AEYcf2ZHlVZbxWvj0GF/WMkT2K91qZBGHH9oXSCRAgQIAAAQIEckvAu9zc6g+tyY6ABGF2HJVCoM0CJpU2k+XtA1c8ekXc++K9LcaXbC26bde2zOd3TI44f89RhCU9S2Jg74EtPnfukefGzFNn5q2bwAgQIEAgfQIShB3X57uqKuMb616P1bvLo7CgIC4cMCKO6t23tsKKqqr4/fbNsbOqMt7dp3/MWLc8yip2Ndsgqzo7rp+UTIAAAQIECBAg0DUC3uV2jbtaO1ZAgrBjfZVOoEUBk4rBkQhUVVVF6ayDY+nG5fsEGXZAxBtXRoyaGbGmOl+416t04MGx6PIl+7rN5wQIECBAoNsISBB2XFc9s31T3LNxdW0FQ4p6xheGHVz738lnyT2tuSQIW6PkHgIECBAgQIAAge4k4F1ud+otbW2tgARha6XcRyDLAiaVLIN20+LWblsbo28cHeUV5Q0iKCqIGFn3pf04sF/16sHDh0W8tKZ6FeGbm+seWbkloqKqIUJxUXGsuGJFDD1gaDfV0WwCBAgQINBQIB8ThJVVlXHsrcfG1vLNUVLcL56Z9kwUFhR2etfftO71WLprR4N6axJ9lVVVceWqha1ukwRhq6ncSIAAAQI5IJArc3EOUGgCAQJ7EfAu1/DIRwEJwnzsVTF1CwGTSrfopk5p5KKyRXH13Kvjvpfua1Df2YdHzDglYtyglpuxqCziqrkRc15qeM85R5wTMybNiNJBpZ0Sg0oIECBAgEA2BZJE1c+3rI9eBYVxZr8hmTPxkisfE4QLVi6ICd+bUMu34OIFcfSIo7PJ2aqyZqxdHm/u3tng3ppE310bV8Ufttf7ZtI+SpQgbBW5mwgQIEAgRwRyZS7OEQ7NIECgBQHvcg2NfBSQIMzHXhVTtxAwqXSLburURs5bMi+mPzo9/rTyT7X1FhdFPH5+xAljmzZl/rKI998RUV5R99mEkRPiplNvihMPObFT264yAgQIECCQLYHkrLv/WLMktlVWT3BvKe4Tlw4enfn/+ZggnPW7WZn5v+aaddqsuOzYy9rNmWxf/uT2TXHfpjWZMwSnDhgexXtWJO6uqoo5m9fGizu3xrieveOf+g+LPoVFmbq+vHZZrNndcEeDJNG3o7Iy/n31oja1R4KwTVxuJkCAAIEuFsj2XNzF4aieAIEOEvAut4NgFdulAhKEXcqv8jQLmFTS3Pstx15RWRGz/zQ7Pv/E52P11upzgC54W8TsyU2fueCBiDsWVP98RMmIuOHkG+LCCRdG0Z4XfYQJECBAgEB3FPjLjq1x24Y3GzS9JuGUjwnCKT+aEg+8/EBtvFMOmxJzzp3T7q57rXx7fGf9itrnxxX3iX/dk2B9fseWuGPDytrPPth3SJzSt3qrgi+tWRrrK3Y1cd9QsTv+c03bzjSunyAsq9gVr5XviNE9imNUz17tjsuDBAgQIECgowSyPRd3VDuVS4BA1wp4l9u1/mrvGAEJwo5xVSqBfQqYVPZJlOobkiThRT+9KGMw7e0Rt5zRlGPagxG3PV/989vPvD2THHQRIECAAIHuLpBsZZlsaVn/unHEW6KgoCDvVhAmZx4NnTE0ynaU1YY7uM/gWHPVmn2eQ5gkAh/Zsi7+vldJnHTAwIxPcn1m1cJIVmHWv2oSdtevWRrrmkkCJve2lCBMEnz/tWZpm4ZVTX1JwvEb616P7ZUVUVRQEJ8cdGAcWnxAm8pyMwECBAgQ6EiB/ZmLO7JdyiZAIPcEvMvNvT7Rov0XkCDcf0MlEGiXgEmlXWypeWjSHZPi8cWPZ+Kdf1HEsQdF3Ph0xItrIo4cFnHFcRG/ez1i4u3VJJPGTYq5H5ubGh+BEiBAgED+CjSXIPzGiLdkEkz5toKw8ZlHNb26r3MIl+3aEf+97vXaQfCuPv3jvAHDM/+9N6OrVy2KXVWVDQZPTTIvSQImycD614wR4+L/NqzKbEnalqumzB9uXB2/376p9tFhPYrj80Ob2Te9LYW7lwCBrAgkSZFjbz02tpZvjpLifvHMtGf2+cWErFSsEAI5JtDeuTjHwtAcAgQ6QcC73E5AVkWnC0gQdjq5CglUC5hUjISWBJZuWBqls0qjKqpi/OCIx6ZGTL0/4qnldU8cPybizikRp9wZsbAsoiAKYsn0JTF2gJduRhYBAgQIdG+B5hKEXxsxLnOOXr4lCOufeXTo4IhX11f33b7OIdybQ0tnCSbl7u25ZBvRZDvR+tc/9R8e926q3vK8LVc+bwnbFgf3EqgRyMVkXOOkyL6+mKA3CeSrQHvn4nz1EBcBAi0LeJdrdOSjgARhPvaqmLqFgEmlW3RTlzTy+t9cH9f+6tpM3ceMinhlXcSmnU2b0r9XRPIy8Q97jmm6/qTr45r3XtMlbVYpAQIECBDIlkBzCcKvDB8XvQu7X4LwikeviHtfvDeSNXvJtp/JKsjCelDJ1qLbdm3L/OSOyRHn7zmKsKRnSQzsPbBF0hGlp8Z7j/v3Bp/XJOW+unZZrNpd3uxn16xeHNsqKxp8VrN9a3MJwvb2qQRhe+U8l68CuZiMq58USdz39cWEfO0bceW/QM1c3FKk7Z2Lzz3y3Jh56sz8BxQhAQK1At7lGgz5KCBBmI+9KqZuIWBS6Rbd1OmNrKqqikO/dWgsLFvY5rrHDx4fr1z6Su0ZRG0uwAMECBAgQCAHBJpLEN4wvDQOKCzqVisIkzm9dNbBsXRjvS0AWvAddkDEG1dGjJoZsaY6X7jXa2C/UXHhv8xrcE9NUu7r65bHG7safrOo5rNvrn89FpfvaPBcso1oz4LCkCDcl7rPu0ogF1fftdUiF5NxU340JR54ec+3EiJiymFTYs65c9oamvsJ5LRAR87FpQMPjkWXL8np+DWOAIHsCniXm11PpeWGgARhbvSDVqRQwKTSMZ1eXlUZBRGZF13d8Zq/bH5MvH1ik6Yf1O+geH3z61FYEFFZFVHz341vnH/R/Dh+7PHdMXRtJkCAAAECGYHmEoTJz8f27B3J2XuNr0sHj47hRT2jX1GPVgluqayIGeuWx+aK3XFgj14xfcjozPal2b7Wblsbo28cHeUVDVfzFRVEjOxbV9uB/apXDx4+LOKlNdWrCN/cXPf5yi3J6sOGrSsq7BnTps6PPn0G135QkwS8cd3rsbyRU80Zjt9avyIWlW9vUNiXh5dGn8Ki+NKapbG+0RmE7TWxgrC9cp5rTiAXV9+1tadyLRmXJF2Hzhgaycqpmmtwn8Gx5qo1ziFsa+e6P6cFOnIuLi4qjhVXrIihBwzNaQONI0AgewLe5WbPUkm5IyBBmDt9oSUpEzCpZL/Df7m1LB7ZvD6KCwvio/2Hx1G96719y351HVLitAenxW3P31Zbdv9e/eO7p383PvXQp2Jzed3bwuTn3/ngd+KSRy6JTTs31d4/7e3T4pYzbumQtimUAAECBAh0hkBzSazW1HvVkDExqmevzK1JEvCHG1fH8l07Y0Lvkjiz39DM9p7J9d31b8Sr5XXL9E4sGRiT+3XMy71FZYvi1J99Ol5b/GiDEM4+PGLGKRHjBrUc2aKyiKvmRsx5qeE95xxxTvR726djQP8xDT6oScrNWv96LGm0SvD64aVRUlgU31m/Il5rlCC8esiYOLBnr7hh7dJYu3tXa6j3eY8E4T6J3NAGgVxcfdeG5kcuJuMaJ11r4knTOYT5sDK1LeMwzfcmc/HVc6+O+166L6tz8YxJM6J0UGmaacVOIHUC3uWmrstTEbAEYSq6WZC5KGBSyW6vJC8Cr129uLbQvsk34Yd3v3+sP7H4iTjvvnNj9da1cfyY4+Ous+6KjTs2xoTvTWgClvwCnyQKp86ZGk8ufzKGlwyNH579ozi59OTs4iqNAAECBAh0kkByTt9nVrV9m+2keQUFBZGcp5dcD29eF8kXh2qujw88MP6+d0nmP/9t5WtNoqlJaHVEmEl9r7/xTMx76oZYs64u21dcFPH4+REnjG1a6/xlEe+/I6K83nGBE0ZOiJtOvSlOPOTEvcbQXIL1C0MPjiE9ejabIExWUV49dEzqVxAm/5Z8aPO6THJ5UsmgOKS4d0cMB2W2QyDXVt+1NYRcTMbVT7omZ5q/ur46qjSdQ5gPV6jFgwAAIABJREFUK1PbOhbTfv+8JfNi+qPT408r/1RLsT9zcdo9xU8gjQLe5aax1/M/ZgnC/O9jEeaogEklux3z7PbNcffGVQ0K7ciXfdltfcPS3tj8Rsx5aU5cfMzF0aOwRzT+1nTN3TW/wO+u3B03P3dznHX4WTGq36iObJqyCRAgQIBAhwqs270rrl+7tN11tLRyrSAK4saR1cnDrkgQJvVWVlbEX1+ZE0/9fmZs274u05YL3hYxe3LTcC94IOKOBdU/H1EyIm44+Ya4cMKFUVRYtM8YmksQfmbImBjds1fcXPZG/G1nw0MOkx0X/r+BI+O6NUtiY8XudtvXf7CmH65YuTCqouH+qLn677PbN6yMP+/Ykgkj2XL2S8MP6ZCtZ7MCnKJCcnH1XVv5uyIZd8WjV8S9L97bYlOTrUW37ar+uyDZ4jjZ2ji5SnqWxMDeA1t87twjz42Zp85sK0FO3t/dV6bmJGo3aFRFZUXM/tPs+PwTn4/VW1fv11zcDcLVRAIEsizgXW6WQRWXEwIShDnRDRqRRgGTSnZ7vTsnCPf1C/z67etj++6G5wUlegf0OCAG9Wl5b7J8+gU+u6NFaQQIECCQqwJJgipJVLX32tfWlruqKuPqVYuaFN+apFXj3Qq+NmJcqxJIjROSL/7tvpj7689l2jDt7RG3nNE02mkPRtz2fPXPbz/z9kxysP7VXJIzWT2ZrKL873WvNzmrMTmn8S3FfZpdQdi/qEf857BD4j9WL4nNlelNEDY2/Uj/YfGeAwa0dyh6LksCubj6rnFo+/q3fGcn46qqqqJ01sGxdOPyffbCsAMi3rgyYtTMiDUNvzvQ7LOlAw+ORZe3/+/ofTaoE2/o7itTO5EqL6tKkoQX/fSi/ZqL8xJGUAQI7FXAu1wDJB8FJAjzsVfF1C0ETCrZ7abntm+OH3TDFYRt+QW+rWL59At8W2N3PwECBAh0T4GOThD+aceW+L8NK5vg1CQIky1O7920Jv64Y3Mc1LNXXDhgZAwo6pG5/8trl8Wa3eW1z769d784f+CIfUI3Tjzd99AFsXzFU5nn5l8UcexBETc+HfHimogjh0VccVzE716PmHh7ddGTxk2KuR+b26Ce5hKEXx/xluhRUBBfW7ssVtZrZ/LgJwYdGEf0Kolvrn89Fjc6n3BEj+L43NCxcc3qxbGtst6epvuMrOUbkmTl+ordza4GrZ+MTf4dtGjXjkyidcye8yP3o9r9erSx6bF9+sc/DxieKTNp59qK3dGnsDCSbezrX7/fvike3VKW2Zb0uAP671cbOuLh7n7OWlesvmtLP7Tl3/KdlYxbu21tjL5xdJRX1P19lcRUVBAxst4R7Qf2q149ePiwiJfWVK8ifLPuyPNYuSWiouEC4CguKo4VV6yIoQd0zLmtbbHfn3vzYWXq/sTv2YhJd0yKxxc/vl9zMUcCBNIn4F1u+vo8DRFLEKahl8WYkwImlex2yx+2b467umGCsDW/wG8pj9i4s9qr/hkhA3pF9C2u/nk+/wKf3ZGiNAIECBDIZYENFbvjP7OwgnD6m6/EPfefE7t3b4kePfrGP0/5Sdx04Fvjrzu3xi1lbzYg6FlQGDNGjMv87C87tsZtG+o+r58kauvWpDWrijbWW5WXbDO6bfvaTF3jB0c8NjVi6v0RT9Vb6HP8mIg7p0SccmfEwj3HKCbbjCbbjidXskNA5ds+1aQbvzy8NPoUFjWb6Ltg4MiY0Ltvs6sLBxf1jGuHHRyfW70odlZWZmV4JEm0ZMVlc1f9BOGdG1ZlkrHJ9cG+Q+KUvi3vjJCVhu2lkL31b/JvzOTfmslYmTpgeBzduzrL8vS2TXHvpuot6pLrjH5D46SSlrdn7OgYmis/189Za8vqu9H9IlbsSWDlylaYrfm3fNIvnZ2MW1S2KK6ee3Xc99J9DYbF2YdHzDglYtxe/qgtKou4am7EnLojUzNlnHPEOTFj0owoHdT9znlv/GejO6xM7Yq/L9JS59INS6N0VmlmC+zWzsXJVuVLpi+JsQOaOTg4LXDiJEAgvMs1CPJRQIIwH3tVTN1CwKSS3W7qrgnCROHjP/14/PAvP2yyjWifHhFJEnBTecS2XdVeDc8IiehXHLFhZ8SORjuC9enRJ/7lqH+JW8+4NbvQSiNAgAABAh0gUFlVFUkirbyqKr66dlmLNezcVBjPfmtQrH+lOEYduz3e/okNUdSz7vaa5NPUF38WP/hJ3d6dHz3nZ3HXkR+KV3Zui/8pe6NB+TUr6JIf7i1J1JYEYWtWFR0zKuKVdRGb9nwJqH6j+veq/lLQHxrmMjO3FBUUxduOuiDee9y/N4gj2SY02S60uXaeN2B4vKtP//j6uuXxxq6mFSZuydaryRasHX3V9FFz503WbJPa0W1oXH6SGE0SpI2vpK2v79oZM9fVZXBbO146O4aW6svlc9Za8+ekvY6duZNGLifj5i2ZF9MfnR5/WvmnWsrioojHz484oZk8x/xlEe+/I6K8Xn5/wsgJcdOpN8WJh5zY3u7IuedyfWVqzoHlWYOu/831ce2vrs1E1Za5+PqTro9r3ntNnmkIhwCBtgh4l9sWLfd2FwEJwu7SU9qZdwImlex2aXMJwq56ydSWyNryYqSztiVqS/vdS4AAAQIE9legvKoys6rvtfKm5+02Lvuv9/aLv/6oX+2P3/2Z9XHQcTtq/ztJ6CRz60m/vDbmPXVD7c9PfM8X4tenfKnJCsHkhmQL0euGHZK5N1sJwmRV0aiZo6Kicle0JuVWWBDxP6dHfOrhiMpGW/o159u/78j4/z762wYfJasAk9WAzcWQrM47qndJxnl9xZ5vHdV7OjFItnftjKsmQdjcdq9fHT4uehUW7rMZSR+/VL4tehYUxPiefTJnL7bmSup8dMv6zDahyRmDw3tUb8Xw660b4qebq1d21r+Stt6/aW38ZtuGJj/f13hpTXs6455cPmettavvkj8fyzfVaY3p3/DPSa7spJGrybiKyopIzlv7/BOfj9Vbq1e8XvC2iNmTm47ACx6IuGNB9c+Tlcs3nHxD5gzUokZb63bG2N2fOtqyMrXhFzBLYmDvllcBO+N9f3olN55N5o9Dv3VoLCxb2KRBpQNL45qJ18QNv70hFm9Y3OTz8YPHxyuXvtLqOSc3ItYKAgSyKeBdbjY1lZUrAhKEudIT2pE6AZNKdru8MxOEL+zYEmsqdsU/9O4XA/ecS9TeaFr7YqSztyVqbzyeI0CAAAECbRV4dvvmuLvRNuFJGTU7c+7ZWTNT7E/OHtWg+MKeEWfdU7ciMEkwFRUUxGF3nBoLl9Sd2/eWQ06J1y54LGqSUlVVEZW7IpLnexcVRvJccmUrQZiU9dwbz8Wxtx4byVlXe7uG9In4+Ucj3jk64tkVEaf9IGL9PnKlRYU9Y9rU+dGnz+DaopNzBJPVbc3F0NY+6cj7axKED29eF7/cumcP1T0Vfml4aZMz/ppryx0bVsXze7YmfW/JwJjSr+48tGQb2Se2bogBhT3ijH5Das+Q3FpZEV9Y3fCFb01bfrJpTTy5bWOTqpLP/7fsjXhp57YGn9V8Ca0tq0o70rSlsrvDOWutWX035UcRD7xcF+WUwyLmnBuRi1thtpSM+/6ZEcfeGrG1PKKkOOKZaREX/bRzk3FJkvCin16UgZz29ohb6hZZ1+JOezDituer//P2M2/PJAe72+ULmN2txzq3vfOXzY+Jt09sUunUo6bGi2tejB27t0XvHgfEEcOOiB+88IMm982/aH4cP/b4zm202ggQyBkB73Jzpis0JIsCEoRZxFQUgbYImFTaorXve5tLEM4c8ZYobOU3yvddQ/UdyTfIk2+SJ9cBhUXxhaFjM+f97M/VmhcjLZWfiy9G9sfCswQIECCQnwLLdu3InH+XXOOK+8Slg0bVfgP/mtWLY1uj8+peebBvvHBn/+jRuyreeVlZjHpn9SrBxgnC5Gfn3FeXIPz80LFRUlAYg2cMjZ3ldQmf3r0GxtbProvndmyJ//lTWfzyM8NqoSffuTK+WVqaaU9LCZ/khfMVq5quNmi8W0FFVVUk699qVrQlc/x7f/zPseLNZ5t0bHKO8P9+KOK8o5r2+d0vRHzyoYjkHOLG16l/NyVG/cO/xoD+Yxp8dOWQMXFQz16Zdibt7Yxr3d96xu9uHBy9+lfG0edvjMFvLY8evfZec01S7sHNa+NXWxuuzKtZBbm3EsoqdsV/rVna4Javj3hL9CgoyIyj/1izJJJ+SK7k3MXk/MXkun3Dyvjzji0Nnqvpvy+tWdrsysqkrc2Nz/8YdnAMamG1Zi7tYNGec9YSu3nbNsS6it3xnj79Y3TPfXRolgZaS6vv5n4sYvI9EWV1C4VjcJ+I+8+tPqczV7fCbJyMu/RdERO+V4e14OKIbz7Tucm4SXdMiscXP55pxPyLIo49KOLGpyNeXBNx5LCIK46L+N3rERNvr27npHGTYu7H6r5okaWu7vBifAGzw4m7dQXTHpwWtz1/W20M/Xv1j5tPvzmTEJzwvQm1P19w8YL4y+q/xKce/lRs2lm3hHna26fFLWfc0q0NNJ4AgfYLeJfbfjtP5q6ABGHu9o2W5bmASSW7HdxRCcLkBdt9m9fG09s3xfCinrFyd8M3dcmWXaf0HZSVYHJ1W6KsBKcQAgQIEEi1QOPE2yWDR8f44j4Zk8aflW8piIc+fmDtCsK+IyvitO+sytzbXILw5K+uif5jd2USU18YenC8uvqFOO6WdzTxTl72bel3cJx7Sr1DC/fc9dpve2e2tkzasmtbQWxY3DP6jdodvQdVxj/2HRx/V9wnvrV+RZMyvzJ8XPTesyVm/TiS2JIYa+JbtvzpeOTx6VFevr52C9GehRFPXNDyOWAn/1/Erj2LD5MtFvv1Gxs/nXJH9B3xD3FXMysuLxs8Og7u2TuubCaR2RGDb/f2gnhg6oENiu574O5435fWZtxaur48vDTz5ar6X7qqufffh46t3fazpef/tnNb3NzoHMmaMh/fWhYPbV7X4NGahGRzyd+a51padZk829xnNYnML65ZEpsabc06Y8S46Fmw721SO6JPGpfZnnPW6q/sTJKuydmWyZfisn0lichktW/9q7nVdx9+a8TPXmlae/2f5+JWmI2Tcc+siLjysbo4bjw14p2jOi8Zt3TD0iidVRpVURXjB0c8NjVi6v0RT9UdrxnHj4m4c0p14nVhWURBFMSS6Uti7IBmDivM9oDIcnn58AXMZAVwsgp9a/nmKCnuF89MeyYKc+Tvlix3V6cW98TiJ+K8+86N1VvXxvFjjo+7zrorDhl4SLR0XuuSDUti6pyp8eTyJ2N4ydD44dk/ipNLT+7UNndkZcZZR+oqOx8FvMvNx14VkwShMUCgiwRMKtmF/+P2zXFno5dl3xjxliYvHtpa6+LyHfHN9dUrHpq7Du91QPy/QQ23O2trHY1fjMx8amZ89vHP1v64NWeEJDd/bdLX4srjruyyM0L8crE/Pe9ZAgQI5K9AS1uItpS4WfbbPvH7mxp++WbE23bGoLfsipfn9G0RKkki3vD3o+P7z347/v2xKzP39S6K2FFR/chNp94Ug3tdFJ+/tEeTMv7ym+LMVpSf/MuieOST1SvOkmvitetixISdmf9fvrUgHjy/LiE2+e434mtjSjPJk1fLt8V319etZEzur9nJ4NOvLoyfXXhgVEVFrDvqothx4H1tmuN79iyJd7/jX+PtR10Ys0b9Xfx++6b44cbqc8TqXxcPGhXjinvH1asWdcpgevGefvHSj+vOg6yp9PCPbI4j/3lzi204/oABcU7/YfG5VYtiZ6PtVz8zZMw+V6wlW4gmZynWv24YXt0P925aHU9vq3dYXUTsLUG4t5WASfnJs9/fsDKS7eXrX8lK1WE9ijOrYpPVsfWv64eXRkkHJNSaA+2Ic9Y2Vu6Ot77l9Hjvcf+eqfL9JYPiQ/2GZHVMzUm2dN2+KYYU9Yz/N/DAGNqjYdK+/uq7d4+O+N2e3PyhgyNeXV/dlPo/z7WtMJtLxh1zS8Otg5NVkM99onOScUlS/VtPfjW+89svZeyOGRXxyrqITdV/tTW4+veKSJz/sOeP2PUnXR/XvPearPZ/ZxbWnb+A2XgFcPIll6NHHN2ZfHlb1xub34g5L82Ji4+5OHrs2cd8b+e17q7cHTc/d3OcdfhZMapf9n73zwVg4ywXekEbupOAd7ndqbe0tbUCEoStlXIfgSwLmFSyC/r8ji1xx4aVDQrNRoJw1vrXY0l5wxc/9Ss5oldJfGJQw2/P729k1//m+rj2V9fWFtOaM0KSm7v6F3i/XOxvz3ueAAEC+SeQbPl47ZolUdnMlpctJW4emz48Ni1vmsSr0dnw1s/G9pFzmsUaNqwgNu0oi227Gp4Zl9zcu7AkesSA2N70oxg+rCDOO/LcuPe/ql+g17/ee93a2LW1MJ7+et15fzWf//k3PVvcarIm+TTm+LoK17zj9Ng55NdtmuNPed9X48i/OzvzTGJWf7vW+u2cOmBE/HXntvjjnrP5Ono0/fn/+keyFWxzV/1tX3fvKIiNS3tEyciK6D2gemVhSyvzki9e/VP/4ZkznjdW7M6c+Ty6R3GD7dybW0G4t0RfTaK2uZWAVw0ZE6N69oprVy+OLY22ua1p5wOb18a8Rluh1jz3zfUrYnF5wwMjW7NNajb6piPPWRvYb1Rc+C/zMs08sldJTGvHv3XLqyrj8a0b4qAeveKo3iW1ITcevwuemRGvLHykAUmyReTOiursVe8eETt2V398x+SI8x+o/v99ekRs3/PzXkW9YugBdedQJp+fe+S5MfPUma2iTpLOyS4hb+vVN4Y0Sla2qoBGN9X/t3ySjPvb2ojNzWwX3K844q1DWp+MS/5M9CwoaNOKzmTcJ2Nl9j2TYuOmZU3CKS4qjkF9BkXZ9rIor2jayKEDDomVly2Moj0rpdvj0dXPtHQu5OzJTVt2wQOdey7k3mxaWtHW1Z75WH93OK+1o9yNs46SVW6+CniXm689m+64JAjT3f+i70IBk0p28JOXD7/dtjF+v31zrG60/Wc2EoTNfTO8fsvb+9KkpeiTX+AP/dahsbCs7pyj1pwRkpQ3fvD4eOXSV2rPPcqOcOtL8ctF663cSYAAgbQIPLG1LH7WaMvHmthrzmqrn7ip3B0x59yWv52fbI+3+r2Hxa7eLa/ub6/tqD4HR+FP/9qmx5+d1yNG9ijOnIn3y++UxOK5B2Sef9/1a+PrE0fGiKKeMfaE6gTS7t7LYuXEIyIK6s4HbM0cP2rkO+KfzrwnU0aSWFu+a2fcuG55PPyJkbF9ffVWlh+e/WaMHtQj1u7e1ab2t/XmpH+SK1lw8ey3B8bSX1XH2/iqSRDu2FgYv/78sNiysiiK+1XGe7+4LgaW7moxQZiU07ewKD4+8MBIvqRVc1037JDMCs/keq18e3yn0XavZ/cfFiccMKDZ7UD3to3oh/sNiZNLBsUvtqyPR7fsWZpWL5jE+yfJardtdedZJh8n27mWFvfJtCNpT/3rs0PHZsZER18dec5aUWHPmDZ1fvTpM7hdCcLdVVVxVb2tbpMVgtcMPThDMndLWTyypXob2Ezi6u4TY+OWhitCm7MbdkDEG1dGjJoZsaaZJH/jZ0oHHhyLLl9S++MVu3bGqt3lcVivAxok2J7Zvinuqbci90vDSzNjsL1Xc/+Wb21Ze/u3/L2b1sTT2zZmtq+dOmB4HN275dXUNfXVJGNXvPlc/PjB85o047hx/xhPL6rb9/T940+Lx1/7RZP7pp99f/z33zfMpiW/g5VXVWXOfK05c7W1cXbVfY3PhbzljKYtmfZg554LuTeLyfdMjp/+7aeRbDFdWRUx+e8mx/3/fH9X8eV1ve05rzVfQPa2cjJfYhQHgWwKeJebTU1l5YqABGGu9IR2pE7ApJKdLm9u26eakr8+4i2RnJ2yP9e+EoR/36skPt6Ob1W31Kb5y+bHxNsn1n7c2jNCah6Yf9H8OH7s8fsTcruf9ctFu+k8SIAAgbwVSLbCTLbEbO6qn7hJVgwun98nk0RacPuAFj0qeq6NN088NKKw4UqXooKIkfXel28pj9i4M2pfrA7oFdG3Xs5m5ZaIiro8Xaa+gsriGDnv1Sja1XAl0t4657GHC+Pwgb3jCw+tj//7Su8Gtz7yy6r4u9594i0nVO9EsKn0a7Hp0P9q+xxfFTHyty/HuXcVxU0Hjs/sbDDtm5vib/fXBTzu1K3xD/+vLon1l7v7xZInSmLwoeVxzKVlUVxSF2yymHP7uqLoM6QiGv8z6bWHS2LhL0qi/5jd8fZPbqhd8ZckBp+5aXCseLo6xvGnb43k3paumgThX+/tF3/9Ud02pKOP3RHHXb1+rwnClsqsWXHa3ArCZLvPZNvP5lYJfnHYIZkViZ9ZtTCSc+/qX8kZeMkXylpKZCd1NjeGk3MpP9B3cOZcykWNEoT/NuSgGNuz4VjoqD/gHXHO2qHjTosT/n/2rgM8imoL/9tLeieEQELvxYb0IlUUpCgWqqIiFhSxgYoFEFBUfKJIkaooAgLSQToIqPTeCSG9b5Ltu+87M5nd2dnZ7KZQhD3f53tk59479557p93//P9p+TZCguOZbpcnGI4Ds/jj5gICpmdfQ7KZZQfq9TmYu6QtrDZXYFt4PccGsezBBlHA6UyWRZjKU7EVu56JGXd9zHWGWXjcUIT5+WkMIBksk+PdiHgHK1W4ZigX6ZTomkz/qPx+vQ4ku9pKE+wAqekYH+h8PDgKrbXsfUv4Li+ce75Mqti6EHuXz7SYMTnrqqM4yemSrC5ndE/4pSADersNvQLD8YAmmDk0LesaUi1GbNk5DifP/OYoH6wKxqxeszAr9QR2/TXZ8XuH1uPxQpWGGLF2JPQmp4Mb138C/z6xFMqS/HeL89IdTGW69t6LiHeAhMRy3F6cBxkkeCggtExsxxt1nXDtCvNCtqwGfPkXcDITaBQFjGkF7E++eXkhSxsvMdpCp4RCx5sHmrfcd3L9eQhvwEIpT77WG9CNm96kJ+bk4deTsbQgk2HWdw8MZwJw/Ob3gN8DrAf8e7n+lXAnesAPEN6Js+of03/CA/6HSsWniTZ5aLPHk02LqclE2VbEvAGETdSBeDbUmauoIueiuiPWjMC8w/MczZQlRwhTv8UIzOk9p6LdKHP9u1mWpczO8lfwe8DvAb8HbgMP3Ky8sbNyU3C6uBhXtmlxaFYoM3IOzPokKgFBMjk+PZOC2c+xx3wxi+Yy8uu+D31MidZgSaX+DYBpXYGEUCB0iqukH+XVyn0HuJIHvLUFWHna9UyhGf1R99I0pBSU7Zkep1BBI5EhxWIEyany7dV3rRjVS4sG7Ywg5mNa26awap35AcvyjA8+/yGCL7+Da3u1DCDVe5iZke3kLLi6Bd2+YvMSpv6rwt7JzpxxBOY1f5YFDy16CTaPiUZxBsuQoryNgVXYfqf8rca+KU4Z1YTOxbjv5TwQC3Dz6GiYdL6/U3EA4fL+7mxQOsZJjBJel3NOAakCCKtZOvuRZD13FufjklnvxpTsEhCGXkERogAhx+gTAw9p3NSXzYU52OCBQcgHQ/jzS/W+zk7GVUEOwlHhVVFHyTIraXN1YuZVJtfijZQerYw8a1ERDUAAUbWqLV3WcXkAwtLkXIXHSPYy8/C3WHvGlRnFXc81XdORuvTtUq749dyzfl/M7DYdiWEsiDY+47LL9cmtFzom1lcOjP4sK8lFoYT7ttDbrBiXcdnRl537JiPjCsvEy3WTOJZAJpHCamevM75Mqkwih81uZe4PnGkVWoSp2UFzMqn79QX4VZB3dGpMTQdg90X2NRBDkkwCCSZE1WDAzHczLsFos+Ha9b+w8c/RKNLnolncg1g1YCmqhdRA4tzWSE7Z7zh3fNVWSHp+HxZdP4L31o5AStq/CNCEocdDMzC1yQDUUmqYNU2SvHyj/Kf1VFoGUB0j+DbjfOlS4Rb8IZYXctDvwL5rzs60iQcW9705eSG9ueBuZrR58015jt+IfK38a7Q8fbod6nhaZ+89tRn6YPb+SfeUT6ITKsSsvh3G6u+D3wOV5QH/Xm5ledLfzu3kAT9AeDvNhr8vd5UH/A+Vik+3UL5I2CL/w7m8Z/MGEJK8z/BKBAj/vPQnui/pBqudzdHDt8TQRIxvNx6Tdk/C5TzXD3MqR5sPmwZtxkM1HyrvcMtdz/8RW27X+Sv6PeD3QIkHUlPBsJmqlA2f8ftPxAMMQ0wPKJWA3EMqv4rkjT12DLh+HWjbFghyksNE52KjLgfj35Ig47jK5Xj9foVY8FoICGDr9WYhju33HXziGjKG7UZevbdhDj7maFspA354BBi+2r078/sAL64FTDwcT1HQFKFnp0GV2w5SSGDjbdT7srhi5SoESGW4YHLXPCRmDcnvXTEbYAzdh8wHunpsUlacgMArr6Kg7oewy4vcyskMcYjZeRbX9wUwkpadOgnojwD6L09hrqEd70ci67SrxCUH2J37IwDHFrgyAbhjez8LR+o/rsw3AhC3vBENq6lsigyt383BqV+CkHdF4TaWl1dn4d3I6vg08ypmPxblOB6aYEaX6ZkefaSSSBmgTcxI7pNkP6nNHKsr0PhKeBxqKtRuwAXXDgEYJHlJjDCh0TFPahV0jM+G4+py7EL6Wwg+cSw6OkZAy7aiPNDK71wJTCudxYR+e6Zj38HpKNazEp5DmwHe8qxFB8Sg6X2j0bBuP0hFpDVLU8sgttiXOcmgd/K3I+IdDDux3Iw0DwQwiQFyBDClpf6NZ9eMwKXcCy7X859DgLbV3Wd9TxLw0CLX6zk0JAFd2k9kQE5i2BHTTmwe6DdPOVBLO8b54pihEPNLcp8TKDZrQQsYTe7XrbDXZZVJDVUHIfedAuwrzsdvBa7XBt07x0bEM6xYYcAkyeaSfC7f14VF6bhweTOX0+AEAAAgAElEQVQ+ePBVmCHFBl0WZv7YDBaLM9e6XK6BcVwhNhXlYWNBBo6dWoraid0QGBCDTgGh6B0UiZ/y0/GPnkffLBkk+fOEoQjz8lzlYj+Lrgl1Sf7ClQWZTGoIsns1QaC8qTfLhHkhz2UDBSym6mIUTEIsz39LhnGrcrwLUzdwnZzRYwZea/nazXKb23n+Ki7AUWMhegVGIF7h+ly/ZZ3ycuIbma9VKGV8u/rAU788MSc7tH4fLZoMdVR7ODACXQNLidb4rw3c31+/ByrgAf9ebgWc569623rADxDetlPj79id7gH/Q6XiM3w7AITN1YEYWokAoSdZosFNB+Pbh78FScsUGAvw8vqXseTYEjcn3iqJ0btVlqXiq9jfgt8Dfg+QB+YvsGPmfFbu8PUXZHjm6bKBEX4vOj1gNAIffggcPAjUqAF89hkQG+vuofLmjX3pJeDMGWd7GzcCqlL2CGcfzMenb7iDRNTCtPWFeCokGvXaubPvfJ1TO6wojluC/NoTYFOxG+j3VwX+TnFvgf+71BiNkAsfQXt9ECQof74xyjUXKJUj2WKEQcAgpB4kKNQMQJjTcBSKqy0UHZYm5SmYA05BqiiGzayF1KaBKczJ6uEqaVbtgO6f9jhVpMfDPd2b6jkrHQFRVqwaFMswBfn2yNw0qMNs2D0xAumHnRNGIMO9864wEqRrhsbCVOhaL76NHtf2anydDrdydtiQ0bIDJAod7OYgRB/cgScWZKNZpBq//ASc+JmVQuSs0+QshNc1uUmfChu2GIGkXVoQ/lO9fTGTE5EAio8zryDPWpIosaQSMeAssIOkScWM6q3RZWN7US6MBVJYjBLGj2R0bE5uKk4ZXcEfDoDhJByF7VK9FLMRn2fz6EkAOKYVlf8yOxnXStiHNZUavBoeV24/U0UCRgkgPXl2BbbseJdpa0QLwFuetZe6zYAq8WGP526iDsCzoe43ETG2GAeAcv7kN0oydT0Cw/FDbgrOCObiyZBotNQEY8DvQ7Di2GKXvnAgJ+VgazkXKDIBAUqgQSSw2BkbwNRpVG8Aunb8jPn3I0EReCggDEK2H9c4zZHBZsN7GU5WL/8YMe+IgSc0ArwIJDtRsiYIjJ296EG3cpUhk0qNHnk9GcWKICwrYBnCfKMxEEj7UaYz1yIdr63U4OXwOFEwlqufmX0aPy13T8I3d+huBIfXY0BJvnGypgvz0nDEUCjaFzHJ3Tcj4lFNoRJlF/LBwwotfi+VS8sL6S0AsyI53gm8zbFaECSVOUBSrqveGG05+hzoLa75TamuVq5FmMYzUMOxTm+EP/kAL7VPADU9Q253u5H5WvlSxrejH7ytMz7jmc9uVsi1UKmcEWAUoKPmKTPdyHV2O/rR3ye/B/ge8O/l+tfDnegBP0B4J86qf0z/CQ/4Hyru00Qf8AbKtyCV+5Ts/k4ECIUSo1yOkKeaPOXmsJ+P/4yX1r3EAIac3SiJ0fJ+XAQoAhCq9iwb5/+4+E/crvyd9HvghnqAmG4tuhlRVAKuBEnlOLxVWSrodEM7dIMbt1gAq5UFRddssKNWIjBhvBSRXtLeHT4MFBQAbdqIswKJNUgA67ZtwKefig9i61ZAVoKFPfZLX6w+65To7FO3L1Y9tdJR0WwGLlwAoqOBCKdaJTp1cm17xAjgmWc8O+2t2YX4ZaE4O7DX7DR8XasmmnY1Mpv4vlpe3Xegr+LsK9WzyYphV+QxTdDGvDC/oPB3iTkUUisrA8mZJq0/Qs9N8bUbTDliCQZLZUxONTF2GwGIaRYTDOE7kH1PP9ilTsqKxByMsNMzIC+sj4zWrRznjd63H8awXciv/x4gcfpFevw51Dw2B+GxNkbStDKMmHeXTXrU7VOIc6t5SRwro3EApsBjLmNL0G1DVN1aCDcpcXKpKzjInTKmmREPvJGDP4Y5QalH56dBFexkD64dUQWGXHZdVbnHiLbjsxkwj6QPDyzV4vSyIGijrWj/YRYCY50+1KXIsOnVGETUM6HDJ1kOYHFC5hUc26rEP9+y7ywSGfDUb2kgNYrvc1NwTgBoETuLQEJiaBWmynB1pxZVWhgQUY9lL1Jf+CwzbmwEtBHglms145NMZ045Os7lSqR/83MiEsuK2FZ8I/YhWSCP8UcSkyQ1uWLtUFy7vo85vmc44C3PWs1qbdG713yPM075G1uoA0Fs2fvUgY539CSzAaS0wTeurwRmEdNIaOSX73JScN5UDHOxBAoty4TlwMPZZ9dhzOrBjBQmZxzIeTQNaP6Ds8W+9YHfecEKnBRmfBx7LbXRhmBAcBQjAUtSsGJ9ybaYMZGX248rQ/3MtJgwOSvJ65VAeRTnLG4Dm90VmKaKFZFJpfpSiRzPD94LjcYp/cvvUP/gKMTJlSDGJt84pqMnWV0qe+jYfEf+QX5eRJKZbdFkmOi4yS9iDEJajxQ0SexmoXGgOIGKBC7yjQJEuHyJ9LvNbmfuowRESCqYR55/nlsRgEnPtFm5qaDrhOReyQ/0PCArC6PN6wIUFLiRjLbS5HjL2s+bXf5G5Gsd0HAApnWZ5pAyvtlj8na+sqyzsrKbb+Q68zYu/3G/B261B/x7ubd6BvznvxEe8AOEN8Kr/jb9HvDBA/6HiquTThuLMTuXDfenD8whITFePwxvB4CwhToIQ0IrTx5n2+VteGrFQGQUZaFNfBss6bcECaEJHlfUlbwrGLRyEPZe24vogEgs7f8rOid29mEF+l7E/3Hhu6/8Jf0e8Hug7B6Y8qUVM1e4gh1bf1KhXkL5WV1l70XZa5w6BSxfzgJow4YBaldlRtEGSZpz9Ggw+aauM4wzGyNr+ezjckx4XQEC+Qg8FMqCfvEFsG4d22R8PLBwIQsGkk2fDqxd6zydVgsUixOlGBDyt99oE9YG9YeRMMudm/AqWziKP8qElGQcjcCrrwLnzwMaDTBpEtCiBZCXBzzWl2VEmO02ZtOT8u9t386en4737evsy5YtwFvzdFi+yPNchsoUyBNIQpY2G+S7jPb1YVa7b/iXfRZdaygM1RGzS5Cc0IdGKT8PP4cYvwqxK7jcYMUxK5HfZCSs0iIoc1sh/Pg8yA01oKs+E/n133ZUCznzOYKSRsEgv4ise/oAoZchM0RA+sdSqJO7oH4zG1IrCSAk5lq21cywkG6ECcemOTUNijMvI7gcWGTNbkVI/ksjmgvx0QWp+K5OLQxYkI4Dc1zBNE5ClXI2bhkT7TJMLh/i6ykXsOJx13yJzYcV4I/nq4BYUdMedTJ22ozPxnPtAxm5tef+ucLkZ+QspIYF3b7MxJdVamFrUS7W6VipT85Ilp7k6Qk0nioAnzg2jhhz7YuYWpCVXPR7ivOxokRuki/7dtGkx+SrB/Hjz4Ti21E7HNg8CPAlz9rwp3cgOIgdPy2t078FozBNjlrdCxHdxOToPwFSbbWsRC2xKoldybfHgiLRISCUAYI8scy+ykrGr5O1uH5AzQCEnSZnomtdLYhFuLc4HwtST2D5H4ORl8/K6XMgZ79fgT/OOc82uiUw4wD7d1xYbfTstYCRwuSsrkqLl8KqMsxRyoUqNGI7plhMDKgqNGK9EVD1rQB483SNUB7FQwen4+jF9W5FSPa4LDKpXAN1avZA25ZvIyQ4vsyXJgFR6f9+jbnHl3q8N+kNebBa2Wcvnzkkl6mhVrvKEDvWt1SOmMTuaH3Pe1j1NLte2k7KgK12MaqWgF/CzjpB40z8JWAlhssUTG5OMj4Tku5Lz4fGurHuqBzN5wpdFlOnf1Akk/vQm92KAEz+dUr946el8IXRVmgC8ktejfgAbogKCCxRkE4rdA+GuZGMtv8yQMitkcrI19q8SnN83f1rdEjo4G3p3dLjvqwz6mBsEHsPaBAFnM4EhqwCUnkqwjd7nd1Sp/lP7veADx7w7+X64CR/kf+cB/wA4X9uyvwdvlM84H+ouM6k8IODy1NS2nx7AwinRNeEqiTnRXnXjbcchPeogzC4EgFC6meKLgUrT6/EyPtGQk6aWV7MYrNg1j+z0K9BP1Qt2dzxVqcsx/0fF2Xxlr+s3wN+D/jiAQKgsrMBukX3e9LmkNnj6q5aJMe9tVxzqPnS7s0qU1gI9O5rQ47RCrlEgicflWHsWBaxy8gABg5ke9K7NwsIco+idh1tyLZaoLO5gjFqqQybF6nw7LPOEfz5J1uPQMPOgriPiRNZJiGZkNFHv+ntVia/GUlCEbsNcMpGEpjnLW/shg3AtGnOviQmAj/+CFy8CPQeZkIBr//3x6mxchnL5BLrS5MnC/D7Iu/PMl/nzqrIQmqHOoDUCVpQXb6cX3k3VmFTInbnecjMXuicPnZWKZEiRq50Wd9WVSr00asRkDwCEjvrl6xmT8IQ84ejVXV6b0QeXcr8rcuwo6jqPEjP94WkqCqz3ho0szEAU2UY5YTL8gAQkqRgcRmYnWL9EY5Nfr03gg8thbaSVelqP1yM7eMjUb2N3g0QeeTHNBSmyJncjEKjY0PiI/HZsiIcnuMOiiTt0eCVNVlYMy3ApersTQb0DAzHPS/mIfOE672Kk879W6/DnPNZ+PPtKAbQf+jzTDxXK4JhTJH86KSryfjn2zDYrBK0fCMHr8bFMmAHB64RoCnX2hm5U+692GS34Z10V9nLT6MTGSYhSUKOXDoRx5O/YPp6X1XA1zxrre9/AzVkb0CXIkf2aaVDVpZeQ3t+nwZNuJO9yeXu+1evw5L8dDef0rs3gZxb34pC3iVWXviB0bmo3l6PJ4KjsXh3Mf74xMkeJTYnScu+H1kD+/QFmPHrJfyZ3BKQeAY5qc1utYCLOcBFJs5Bgmef3gGJjgXTgqpaESyT462IeAZQEusnAbJ0nxQDD6mNwSExWCwyvtKuu8cM1/Hc+ldwMfOESzFfckFyFSLD66Njm/eZPIrlNQru+/3Xh5CU7w5+CtssK3MoJKgqglacdzQT0iMXtZ/KR2IJakXPrJQDauaxE9eSvU4oL+dnWUnIELlv0XoSy6PIB6O5k9G4Psq6ioKSgAYKUpkQyQKMpTEOb0UAJsnT0nXAN+7aod+eW/0clp5Y6iYjqpEDBAIWmIDiknSqfAA3QAEEKYE8I2AQxHVo5Bo83eRpzO091+vSoWeI3mZDotI1uolYnJRXlAJbSBKY2L2c3QkAIY3FarNiwZEFGLdtHDKKWOleX67RmIAYTOo8CcOaD4NMJF+rV6ffggJ3I3PyFrjZf8q7zAP+vdy7bMLvkuH6AcK7ZKL9w7z9POB/qDjnhHLFUM4YvjVRB+JZL7n9vAGElZHXgp8fRmwVkeQTRcbe6eb/uLjTZ7j84yMJpZW6LFw3m3CvJhCdtaFe2b/lP5u/5p3ggb17gfffd46EpEWFbKiVC+W4v/btCxD+uMCOT+YamE1NMmJBHN3FboQLQbJPPgHatWOBvobtjcyGtJjVVrqzIAjMW7QImDffzrC8KAcXyf0R8EfHsrKAxx93bY1YL9fMRDtg+xbNSGA6ATqq5y1vrBjQR/WOHAEefdmVnkhtxwcoERgIZGbbmXMTu5DApVg6dx0jDp903SSt6DrOafgSimOXATKDS1MV2ViVy1Sok9Abuu8XMG3GNDci/Yg7iqWNsqLhE2xo/T8zPUtoB1W1QJ+qYCTlSH7Uk1GOvpRO1WFXONmcUlM4YndchQRSZt1knSzphwQIrmZBUKgdBBT5YjJIUEOpZgApknEXGuUqSzIbRduLkCmYdVdeExsbjOEI33IVWqW47Gx5z0Xrbd18FToPdQcIS2vz3lF5SHyoGMv7u7IHuTrv/pGLn6ao3fIwTlpgwJA64Yhv407XfX+2Hi82isDBYh36d3Vlzy7aYEan4BBcNRnQtpPrfCzebELHgFAmj+Dsx6Jcur1qq5WRGf2tIBMbThc5mJDd/5eOD5vEorpCjc+zkvDe551g1brnzaM8a7WaPY+Dh75Dgc6dfatBIsI3HwexYd3WSK8iNH/WmY+OYzNuL8rDGl0WKF6AJFn5ipBZp5VugGz/5SlMmXUvxkCf5eqX3gtToQy0M+zFReMXo6DOJ0w3SgM5qactqgCHSlQra2vehWH1B0y9wFgLuv8vw9GnogwZNrzEvq8TUHn/q7mgdFoksXs+34DVQ2NhtwKJXYpx70usVDEBuQf17jKp3taozWbFqXMrsWvfJJjMbO5KX3JBapRBaNPqPTSs2w/SCoIPJHs6e3Er2AXXfOXkRZQidvtlSE2RDPAePTAbUNogOxWMgBgL0o+63jenrNfhmZAYj/kQick5Ky/VTcaX/MYH1AgU/DEvjZGM9WQDQ6LxoEZcuvhmBGBSH7/KSXbLg8r1lxvPrVZnEQJ9fD+PSb/ISKByRqB+Ky3r0zsFIOTGRiDh8NXDfb5G5/eZz4CD/0W7m5iT/8X58ff5v+UB/17uf2u+/L31zQN+gNA3P/lL+T1Q6R7wP1ScLhXLYZKgVGN0eLVS/U4bkG8Loqj5FSoDICTZIU4aTKwzzdUB6BoQjki5AsQUuNPN/3Fxp89w2ce3oTAHmwtzHBV9Yf+W/Sz+GneKB0wmoHt352hIlpFk8YS2fL4cLeveWoCQZDhbzm2JIpMOAcogHBhxgJHfJHt2vAFbdrhu7l/bq2VYkf0H2HHJZGBYTJEyBQPobd8uYWQ/63X1oP0JgABCi90Gk90OLUM5lDAg4OuvAxv/deZoJElSivhf+4eEYfX9/rur9yg3XUrtsS45+jhpQioZFQnk6nNRbGH7wmcmaOUBCNOEIpNVb3Mxqtcm9AnsmzfR7RgHbtK5+YwzAk5pE78iIJPwZGWRGC0rMyY0qCqGPb2TOeXJpUE4vVxcqpKOW/QSHPg6DKn/iGvLknRl7hkVznxUtdT3CGGOPm68lIdQWdiE/TPSiPzLKigD2DWnKYM6AjEOExQaZFpNojKiNHfXzAbR/IkEOtE7WnnN09iCd+xHsKlkbOVtXFBPLZWimlwlmgOtIqfQRFihz3aCWQSGEd7eshWw/AstarTRw1YCxnPnIVbTxp+V2JGuw3uvuQJhXXsAh7cqkGuxwCqo99yLdnw0JAAfr8nH3KlswAFnP/0EtE/QYuSpS1j3fBWXYxs2A40DtBi0bjV++ucxt+EObPIMhnf6DBv0Rpw8qcPxqxOQft3JWOUqRB3cClWeMxcm93vsfQYGUCM5ULoFkrz+fRo239yM722O3JVcLkiqt//LMCTv1bj05aFpmQirZca6F2JcfEqFHhybg2qtDMg4qcCvmzuLgpze5lFeVAsxe486QM5Wb+Ug7kF2/QoB4IYDdQ6gX3isbu9CNB3qBAb12VLoc2QIq212AUG99WfRrz2Rk3eBKeZLLsjwsNoY8sQGb836dJwAnh+XtIGuONOtfEXzIsqNVVFl53kmEKS40PntE6ARz4/7wtw8fNCgqgNcKsqUwWKQICSepb+RtOwv+SyLS2gccCXGnPXkCD7Y5ZOzKrFQaTkf6TRc326WOguxBM12O6rJlY7gvUyLGZMFeTcfCYrAQwGsjHJpIOCdBhB2WdQFf17+0+drtEvNLtgyeEslrpib21R5mZORRgWG3zsGn/Wd9J9hTt5cz/rPdrd5wL+Xe7fN+N0xXj9AeHfMs3+Ut6EH/A8V56SI5WGhiPt3IquXOnPePhYnRydCU8EI3NIAQjp/sd2GUKmcYWi8Gh7HSDzd6ebp42JubyBkCuW2AqQSIP9d4Lk1wKKjrEfKI8tCGxzbi/Nw3qRHA5UW7TQhfnbabbbAhJsFVRUqRtLLb3enB/79l81b16oVUINV/XIxoXRlvs2CTBHJsdsBIBTKcB4deRRNY5qCQM5mD5lQKJAJ3b9Mi0GDgAsmJwhIQfgBOhUe7S7D1at27E9xB0M5BwnZWiT9uGO7hGEk8ttk7qdyJXq0kyMlBbh02Q7yI917Q6QyXDEbkNa+nk85+soKoGmNNRC+85TbvHIAobCfVLCiLDThyXyRGKU65cmpI5MqMGLQHmg04dDnSLHuhSocERPRTY1oP8E1nxzXt1PLgnDqVyeY2O/XFAZEMhdLsHpwbKk3A36OPpc8UyV5CGPfTIFEzQKDUY2MyP1Xi6TppbfJP6FCIkUNhdptDXFlaO4um/RuYBUdp2Nic+rr3c3T2FRHP0dU+ihfm/G5HF0zYgEHPjfgpaA+V4rC6yxwFxwKNKopwyWTO0BIx+MVagYsLws4TsFmh3cq0PPzLJxY5coofvlFKV7pp0a3T7NxbY8r8DbtSzueahmA4BeGQxfHMmDJJOZghJ2egac+b4eEECWWfqaBvDMb0JOV/QeuXv0IVmuho3xA8jCEnZpZqhd6fJuOwFiWBW3Il2Lts65gZbsPs2HIleLYwhAYC1wD54h9G9nQhKSdrv3nn9AYug+ZD3R164Mi/x6YQw4xv/OvE2FBIchJzEQCNcWuQy43pRh7lDv2x/AqLuPgWJB03pS/1dg3hZVf7DgxC5ENnLK/Bbrr+PHnjswxX3NBUtlnn97pyAUpHBs9cuicqhAbohq6SgybCiXYOjYaxZky0BwE103F3CVtYbWJM4DLmxeR6ZNNiZjt51Cc58py1agBpSuuzRQn9uncoRGYkpWEc38E4NgCVspXprKj78+uOSyFY6ZroqZCg8tmg+i7gthivVUAoTdlG+orv283Wp2FcpzypajHRVZHlFyJv4oLsKzAFZCloIaPotic955AQDEZWCrPMYrpm43Yh2T0zBkdHndbf7NdzbuKxBmJTDCXr9coMayvvH4F1UNK36Mo9SZ6GxwsK3Nyyoma6N7lTTR/sfKf27eBO/xd8HugzB7w7+WW2WX+Cv8BD/gBwv/AJPm7eGd6wP9Qcc4rbQ5PzkpymWj6gKEPmdLsVgOEqRYTE3GfoGDZA10CwtArKOLOXLAioxJ+XHStBQxc7iz42+PAxgvAvMPsb77KstAHNm1wE/BKwCD/I1YsH8ld4/DbdKDCjYQgqRyfRLObDH67uzywaxcwYQI7ZpUKmD8fiBXgGJTXjkBCYsoZ7XbobJQrT5BEB8Cvc+Vo3cDJIKS8fpcvA/XrAyHuacJuiKP5Mpx0ghk9ZuC1lq9h2TLgzRnuTMCn22qx/yCxB1kQkMBBAojyLyvR6h4ZswlVFvCCgJ1/1qjxWD8bsznKNwpGqSJnZdyoTWqbM7siG9c71C41Rx+VLQ+AJrEpGeYIP0cfbSxGydi5uhkAIZ3HormM/LrvQx+zysUvFWHG1KnZA21bvo2QYGeAw4V1ATi1LBiqUCuTQy2spme5Tdqgp1xyqmCbC8to5fwvcL3Ana3FddymyINd5s7mlFgCILWEQBrEgjEkzSiV22G3SmDJl0GT1h+h56Y4xj91vY6Roz35iyvrkdQNLuxW4+mp+djzh/vuPTFfRy7PxLqvXHPsUcO9m2ux5ohn1mte3XdcmKrCC9HT2GAJgMzi+UIWjs3XCzxMpkBuBSRRSzuPuUiCvMvOexKxchvXkyJVLi5pShvJJOdI7zS+GjFumyUocCkoH1ePuM4VJxUsdo11bi/F7I/UqPf4Jlxv+gwsimwoc1sh/Pg8yA1spEbnqZn4Z4sCNo0V2TuCQcqThsIUmB54DqawvyA3RyD06CKoc1hQqzSjNd54UAHkGjsOTGdZRxU1bi3Z5Hmwy3lrzi6BxBwKyIyi1wlz6+OpokosWkgtTunf0tZSaIIZLd/MwaZX3VMF9F2awrT7+5Pu0rMJnYthNUncgNq+v6RAVjJtWzZPxcnLbB64suSCvKfpCLRv9Y6bO2kZbRkTjYIkp1w0B2JSYSHI+cjcNBhlV7By3gzka9eITo8vOdeEFTVpfRFyfiIkqqoovObK8tcGAAoPYiqKADvufyUX+6eHM5K0nHH5Jyu6fvj1P4lKQJCM9ROBVskWE9QSCQOO+WrpFhMDaJJ5+vagtvl5D0kaOMfL/YeCe2jJ9guKZPKNkn1/dj0mbBmLzOzTju6VFcBtXqU5vu7+NTokdGDa8ATmEUBJsrlLBYxNUiV4Lbwa4y8O5OP7iupRwMP4jMtuLqSgQAoOnJebihNGVlKXrI02BAOCXUFkX/1/M8pN3DURH2xnJYnLco1O7DQR49uPvxldvGHnKCtzsnV2MN6rOwGPvDLmhvXJl4ZLU/bwpb6/jN8DleUB/15uZXnS387t5AE/QHg7zYa/L3eVB/wPFed0iwGExDh4P0qEfsJbJWUBCOlDaVNRDlLNJtyvCUJTdaBP6600BiFJK5FR/h7OblXUqk+DqeRCwo+Lz/cBq886T/JYfeDNVkC7+exvvsiy0DzNyLnOyJ3R5hv9TdHSmSeViGlmRJUWRpfo20oekr+5cnjADSCUyUGbM36rmAfOnAGT061a6UrLpZ5kxw5g8WIgPBwYM8YdrKtYD91rd+sGmHn4SdeuwLhxruV69wbS8t1zDgpbW/KdHB2asZt5xEgkmU2S6AwNBX74AYiOLlvvaXPVahWXP/PUUv2P+uKsxAlA9a3fFysHrhRl9HHPApLyovxIFqME+VcVsJnYnWsCCEmGkAMPfe09MbgKbBZkiLAsWdaeXVRS8UYAaMoLA9Aocwoyda6oLz2vCZghu1kAIQcmyAIMMNvyYafkYSVW1o1ViUSGEHUoLFI56tbqhfat3vN1eryWo83WBT93QH5h6SwZaqisbE6FoTpidrEbynXusWLb/4LwQ1YqJvZxBd56vlmI2f2ise58IUYOc9+9J4Bwf3EBHu/qBB64gdGxXw8UY6zInuCNlHrlj82rk29CAUobmn2aywNph0RlB+EPjRPkuA4C730HAUvrbqhMwcgSX7cYQfl9+cZdZxRARQEWfCNgkf5LsxiRr0iGPno1ApJHQGIvAUlgQ0bLDpAF5cOUHwLttp1Mfkuy4GALiqrNhSajD2RG35mplen223UtEQtYJH7F49Ap72GPbzNgs9nx7fctYZM784r66i+VIhSR65JZmVQJ0PL1XJAkZy0jYtsAACAASURBVEC0FQe+FAdj6/crxJnfAz0uQ2PYbuTVexvm4GMu3fAlLyJXQVHQFKFnp0GV2475SRJoQV4q754hAYICUbKqfB0tW673olQoA+y4/KcW/37HgrtNhhSgXh8nu7VsLbJMPSHY1Vobgsd9AKzEvi+JXUfBMGT0/Tcz57qjSw8HRiBKrsDCvJJkmD50loJ8PoyqAQoE2lSYg/UFmTh0bB72HPjcUdtXAHdql6l4s9WbLtKPlOv4AxEwj/yyoygPq3WuOuL0LftyeByTI9lTPQI/CQQVGpej8L8kP0pro87/6uBiLst45FtwUDU8cM8oj/laa4fXxrlXzrmxI+meTQEhHDjtwzK4JUXKxZy0A1+HvIuXX5/MfJ/fKvOk7HGr+uM/793rAf9e7t0793fyyP0A4Z08u/6x3dYe8D9UnNMjBhDSRscHFQQIJ0UnQlsi+bmlMBfrC53SYG9HxCNWUbLZU8pK+Tz7GlLMRtESdzNAKPZxUe9bwMzbs6Io4rOvAF0XAxdzaZ/DuyzLMUMh5vM+sFMPqbB3kpOV2f7jLPzU5b8t63Jb35jK0bn/0oZAOYZ3S6qQpCRnJNO5wKkY53N/CgqAfv1YUIysdWtg0iSfq/tUcO9e4P33gZ49gTffBLp0ca1GAOcfAuJUx06+sejmfyNHl3uVMBqBESOA5GRn2716AWPH+tRFptCHHwK7d7Plhw4Fhg3zXpeilFUfRMKicG7wBsnDcXp4JgY9IxUFwihfG2250wa+LkUOQ45TcpoAQsrXROBhWYxAwCKbOKBKx6x2d3Yhv32xjeGyAmiyjObQ7PwakUUdEFvDxgRw8E0lkTJyimRiACE9z72xKsriE1/ABF83VoXn5ecgLEufPJXV63NEZf6IWVaFF6dUHjYnyfzFlrA531yVi9ej4rBKl4Wf/zTgr89Z2cPQRDOenaHDGxHVcNxQhIcfct3Yo5xwC9rUYHITtnqqGLrrzg1/YpwtbFuDAarqtnN/D7qRUq/8sVXGPFRGG3RNWxQ2SEvkXkm2MtiddFmhU3FsXGEuT2YuS8BDAgFpE59vpdWjcsJckNot+yEraMIwHOk+favtTltLhtC9yHqgm6hb5eZ4BJ17D0UtJsJkShEtE3lwC9R5rcs9LVXlKoZxzM+hbocVxXFLkNtgNCBlI3l8yYsIm4KRqdVeH0ScWJc+5evYPxVaGxRSCSjfaWVCByEJZnSd7p5D0RfHfBlTC+dMeszKdfXxx1EJCC4B+rh26B5HED/3zfhrfgb26505KKnco0ERuE8dhCCpTJRh50ufhGWIsUfMveUFmdhbnI8Dh2bir7+/dhTzFcB9ud2HUDcagkcDI9Bey6ZioG9X+oYVGgGE63TZ2FrkCl5z6h9HDIVuQCe1Nz26Jkg5R6zNBzTBeCokutTcheXxj7c6FETKKc5EyEW0bUtpYE/SHrSbz4LdfGtQ5zF0bPshVMogGE06bN/zMc6cX+1Wbs/wPWhTvY3jd/736+3GnByzaQyWnVzm6GuBsQA6E3vxloU5qYEKwUERkJdc5QMbDcT07tO9TVOlHvek7FGpJ/E35pMH7nY2p38v16dl4i/0H/OAHyD8j02Yv7t3jgf8DxXnXPJlXLhfiY1AkZWlmTcGIR8gFIIYlM/uhTB32SA6H7VLgCLlF/xHr2P+FrO7GSAUyrKcyQQKRZTXAhVAvUjg3xLyhDdZlh/z0nDc4IwYFssLc3a3yvERf+fcEf67I/EDhJU7dwSoffmla5vbt3s+B0lvTp0KXLsGEDvvmWdYKcKffwbmzHGtN3MmEBXF/ie0U6fAgHHNm7P1vRkxHF96yVsp9vh33wENGrD/vq+DBXTP92YEOPXpKcXJk+zYhLZlC8sGvH4duHoVaNoUIF9oNK5MybQ04KmnXGtv3QrIePucxEykvILETuRsw+GjeHhNc7fz9s84iqxTTUSlQmlTljb2yTJPuAag3N9CgmyrBbqy0FFKcsBRDjMx2UQCCA12K5I9BLFwnec2hvNrT4BNxW62cgAa5S1sORcoMgEBSqBBJLC4hGQiKYqBet8kKE8Ng8QuQ1wcEBQpfj5iPUklElGmo9CJUY1MqN2rEEd+DGFyg93zYh6qNGf9dv2AGn9NY8EtoTEsmZWB8AQm8Mv7urEqPAc/B6G3Nerr8fyCJOzePw0XLm9yqVIROdTaCT2g2DcF5uRa6DQ5E83ilQz7g+TahTk9E5UavBYeh1PGIvyQmcrkKSu4JkfnKZkIr2NmmDYGmw3vZVwCBeUUpshRs3sRI5VIx4w2G2q3Ewe2bwRTlZMwlOtvPBOd8ultfMVdXlJsbsP6ZePKQjZgSSqzQ6a2Qyv1oKfo6+IQlKNNepIgFGMQBpfku061GJmgAb5RvQiZnNksFzN+Lkg6rjryOVSXRjEBDQEBvt3zyzkkn6vdiLUUlTEAsrMf42asJf5As5r3hyF6o9vYNSlPwhJ4GhJ5MSRaJSxXG8Na41e3cuqMnog8wtPs99mLbEEKVlFKJG7scos6CWntGgIS33OuwS5Bld2nITe455YOfzYdl2fEQBNoZ853IyyupQEEFNbtwwIaZ5YHw1QkQVGanGFYNh2Sj5Aa7jLllMteb7cxwQ9C46u9jE49j1ybhWF+hUjlGBsZjyX56cgTqcdcO1Ipc0+sLKO+0Pn+KS7Agl+6gJ4XnPkE4FLwQHANDH1yCwMM0n8Ejp4wFGFenjtznfIF/q7LYgBJoX0anYjfC7JwyFCC/PIKTI5OZADC//GYk9xhume9G1ldFCCcGJ2IAKmMUYRZU5iNKJkCbbUV14nnK85QP4aHVnFTB6LvdwKz6N1EaCPWjMC8w/McPwergjGk01TIawgi3QDcn3kQL617CQSscTaixQjM6e18yRZ+C42LrMGwSm+l0TqlkJ9a3yTgar7Ii7Sgc1IJ8H0v4KV1YHJae7PE0Bq4NPqKt2KVerzvr32x6oy7skelnIQ+tr76CnjrLaCdO3hcKee4gxq529mc/r3cO2gx+4fi8IAfIPQvBr8HbpEH/A8Vp+MpgTolUucbP1m6pymiF993My55nEHuo4QKCF/cS8txuDgvXfTjSHiiuxUgLE2WxdvlFBWSgG0jjzN5N1brspno3rpKDXoHRYDyJBF7cPdZAy5sCERIvJnZQBbaiV1Kh8yPt/P5j994D9wpACGBY6u3WNDmXhniYitno+viRZb91qQJMGOGb5uwxB4khlSO1cJE49OG1ebNgIK3z5CbCxgMLBA2fTqwdq1znmvWBO67D6B9619+cZ9/yg346afA/fc7j/EZi/RraYAkV+ujj4CdO4XtszkFKTcrbQhpJE4U7scfWQDvhbfMPrHJCCDMTpWiShV2LGJG3/HvvMOCe0LjxrByJfC//7kenT0bOHgQWL0ayBSQE6geyZE+N3sG5qe9zlSsEw6cz2HbqH1+BsKvvewAAvkt0yY9BwA6AEKJHYpoCwLDbAygWRaj+U9QaJBhNaFAZLOyhkIN4j14Awi5cxZVXYzcxiOZPzkA7Wga0PwHZ6/61gd+P1My1hPzkbWVpVs2bAgolYDebnVhpZRlPCTX1/BJVjaOgEExK86SYf2LroBN9/+lQx3K5vVbNYiVQPQEJnBt+rqxyu9D53p9UOOe0S45CMsyPm9lk1MOYOe+SRXKMxUV0QAdWo9Htaot3U5HG80kDSdkl1VTqPBmRDzOm4rxXY47Y4nqEbv17XT39yk6Rpus7d/MY8BbMeuZvQp1tDMxt81RnIt05p8qK1OVL2HY4vl8BFW1IPWwCnV6FUEbaXXLscb1hdZGfPti5F9WIOO4d2UIrl67D7IR09zIgKWbX3fVLCaAtPmz+Ti2MAQX1rM0warjWRozqdnaUlS4siACmkoGCElukK7rZLMBBkFwmhQSRMsVyLdZ3eRH1VIpA/J6sqxmT8IQ46Rzy6/3hvbAUgRVMjhIwFSSB5CytPyQdO8kCWYCPiuD9Uxrac2LMyC72hHPfuSeQ5PAVoWEgjbcI9vo2UWsaDHWMzHMKA+bN8ts0Rfm6M3OTXZzIMJP/w/ywvrIaN3KUZ2YnPaQU9C3eA1QsMFxtEmvzOiOyMMrvZ3G4/FaSg2j2iFkdBckTkVBnU+YemVhDgWfn4Dgy2+7nY+uiXMfxkGjEnILy911jxUTuxbj8hY2Zx/fSNa1y/QMnF0ZhNj7DEzQgy9G7D2a4+nZ15j3Ftb3EowKi8XGwrJLw3o6Z0BWNqodP4XshOrISnQPfH0uNBYH9AXYdGUnflvjjGaqHQ5sHgQM+h3Yx8N22sQDi/s61Vm48z7y6M/QRjdjgM5vYmohyWJyyeHOlRsXWR2bi3KZANiy2EthVWGBHXNyU6HNzUNwRiYyaiXCVvJiQzlSxaTQx1J+QrnSjXXpa0oOUl04aihCNbkSLdSBDllP6v9P+emOIdD70ucxtZi/yQfTsq85gmWGhVZBM0FqkXUXt2D4708hsygbbeLbYEm/JZhhcAeTqT3q65W8Kxi0chD2XtuL6IBILO3/KzondmbOl20xY2KWq/Rqt8Bw9AwUD3Yqi9/LU9ZG0ub56UzArdZciC8WtoTJWnpgXoQG2PAMcH8c8Pd1oMdPQA6bScWjKWVKXB9zHZHaSI9liMVMbFW6rz4cGF6hAF9irEVOi0SuwXl9hmvCkflWJqSeXip9dSBFUC7nBWWQLMojj/ha+64sd7ezOf17uXflsr/jB+0HCO/4KfYP8Hb1gP+h4pwZMYCQ5F9IBqY08wYQUiQk5XggE4IYkXIFxkeKMxTFAA+xftypACFtLtKmSZxcxQAUfKONwk9OrcWk5X08Tg1/M12s0BN9fsHQ2l0YliZnT4ZEo6UmGDMvpWPK4KBS5/2fbQrEqG5tVObtel+5Ff166ewlHPo+FLmXFajeTo/GTxfg66q1b0VXyn3O1GwbHujtZF689gbw1gD3jaiynIDAsIEDXWv4BrzZMW+L61f5lnla1K/PtvXnn8CUKYDFwn678sFB/tlSU4GIKjbk2SxMBHWYjP6XvZ6bNQO+LlGxIobeK6+49nPpUjDAHGckU8pn3NHvnTuzQBrfXHPlSVBdoWKAf74Re9AXFh0BhKePsgAhvy++zkHHjqzkKeUvXLjQtdbDDwPfnB2DjGin5JKwXYs8FzYZu7G86DFgSEnAstQaAInZc/S7Jq0/Qs9NcTAIpYFWyCMsUMgBrTNdrU/DIECgplLDsAfFNrJpPukZ54s/6YSZ9/aCMWIHc24OQOv3K/DHOWd3RrcEZhxg/w7MfgiWhZsQ39iCGIUCcomU2RznWJLeBvHo/DQUZ8oYudXoJkYG5PPFhMzxAStYUMuilzgAQq6dOq+uw+HUt5BX4NycK+vGarXQmljS50dEVLlPlHHhS599LWOzWbH49W0QY3MK2xi6Clh0lP1VpYlA/XtfQ4cGAyEtea8RlqdNzAmZV0TBZGaD02TAjByeVm9JA2K5uri26Rhttr567jJOLAmGsUCK1H+cQGGEKQPfnRuKyPpG5GfIsLJqJn5ocwW5AewmvS9Sr1JjNEIufIS2jzyC3PMahNY0o1aPIjcmM+UjPjgjjFkHfOu/PMVRlvb5938RjrRDanT7Oh0bX40RzcvWYIAOjZ5ybpCfXxuAo/NDQCB216/SEVTVCQJx65EDCMNqmhlJRbVJjv1D3d8j6RmYe0mJ6/vdAdU+i1ORfkSF/dM9bxwTQEjvxRRoURlmhw0pnarDzpNLhjEc4ZuvQqsqOwMyVs6CsMRkFFrSHg0+/sqKeSvcN6NXzlTjj3V2zF/vXm/FV1oUFgKjvtWhKF0GB+u5zgTYlK6s59KuE24tkRxm8l72XTK+jTtASIwnAiXFJJGJeRbo4ZivcsmG8B3IbTYIVkUuFLkPIuL4j5AbakDI5FT+/QU0l0bBJkmGvssQWML3Q2YOQ9jRJVDndCx1+un5QKCqmLH5aV0lnynwiNiDVo1rQKYva0ymr4Equ0863iEe/iGNecYVNc2D7owayTNiYRfHVkCMccojfrOsbp9CNB3iKg/q6dx0bxOybgeFxLjJi5a374FZ2Rj45jioCotgl8mwdtxYXGveRLS5LTvH4eSZ3xzHygLgMuu8bj80a/MRU5+CMLsHhLuk1ijvGLh6JCFKb5EHVy/HQ/9zRhXNWTwH5lJebojZTu8vv+RnuHSB0ojQ9VSaEeA4Jfsak0uSrFdQBLoEsHk4xb7ViTlJDMqjhkIsEOSDJOYklzuPk1EtLErHhcubsaXzB5BL5Y42NXn5CL+WzAC6xsBANFYFoG9wJJMnct6/P6BxrZ54pWpTxs9kZ43FmH/tHDrO+hFqXSH2DXkK9Zs0Rz8f8l16m5ddxXkMq5NsQlQCQgXyuGL1dxfnY2WBM/qtuikHe/dPw4rTK9yKByqB2Y8AT4ksy5+PAy+uBQpFsMUBDQdgWpdpSAxL9DgECjz6MPOKI3iFQFoCaznLz8nGv++/g/ijJ3CtWWN0/HY2pKUE3QgZa1w7R5/Zg6a1S+Re6YPl++8BSsJOydcpv4Iv1qkTE4yXb7UiWCpjmPw+RUv60jbJoAQFucqU+FLvNi9zQ9mct/nYqXv+vdz/wCT5u1hmD/gBwjK7zF/B74HK8YD/oeL0Y3kBQk4Sy9OMjAyr6nh59xUgFCa0L22270SAkJicNB+cxSlUTET37GWPIjX7FPOz3cumFX8z3ZP/JCWgAbEhnu7P7rzTRmT/9wtxcHvpG1bb1spQJ4zdoErWm7DvkgEtqqlQJ8R35kDlXMUVb4XyniSZjThv0iNBoUZjdSUnNKp4F7220OrtbCTvdSIf7T7Mxs/d3aWohA3Rtba+MAd/G3SgDbmng6Pd8sJ4PXklFej3eS7+XuW6fq7t9Q0gJIBu1SqgWjXgtdeA8JJ93/79gZwS1hnXTb605ccfs9+vZBMmAARokb30VSHWLne9BmizjwMXCXQk8NGbpabboA8zOjYQOXk6rh7XnpA9SMeJldejB6DTsbKlnH3xBXDvvexf48cD+/a59kK42UpsC5Ld5JuYdJ7YWIj1dOaojGHdNW7sbbRlO04bpQdb1YBe5V1yKUoLpLwJVJ0OZLrvM7udWGGojphdp1mAUGaHshp7Py1vrq/aSo2bTBx3UhkkDIOQbwRwiKmY+iItR+10qwVczGHzxpK0nHLuRUTXiGFY21EypVs+xPi2euizZUjoXIx/Zjo1WhUBdvRZ5C5v5stM0V4gATZ0/jqPFjrAH4tBglXPsAxCztpPyMaRnDE4ddYZ+V3WjdVHmgzGH/0WMRKcxI6oDCPgtn9wlFsuJ2p772fhuJDymxubU3jeEWuAeYfZX9u0n4yI2o+iZkmuR7E+0jPUU4ATHTtsKMQiwaYptcMxOYR1iflB7wAk5TY2/aLjlHsmRSDtEHtdD06bgyewDAFVrEz+QkOuDOsapmFyDxZ19kXqNezELNSNfxyt3hbcMEUGSWvbZpYgabcGpiIpanYtgjLQsxba6d+CcPIXZ9BRlXuMaDvemY/al7nOPqvE9nGRUNcvRszgLBBASEyjeLkKqwfEuTXBAdpiEuncMQLBNoxyMmW7fpmBLWNYFiPdq0kakTZXK8OE+Qe5NsP37Ie2WBysKO2853aroJZIUb2tO72Ee26KgXIEHtJM1SilHjFc543TMExQdZgN0UNmYd/pt8q0lgJSBqPZ8HysHcHeK8T68t0sOx5pHIDqbd1v6gP6StGprhovT3U/RnK9BLTTM8SbWVWp0EevRkDyCEjsLH1cyOSUnn8MgWtXQqmQQFarAMaEudBk9IHM6Hqf45+LgEG6Lin80ZOkbNIeLXPf7LMoBYd+YO/LxtB9yHygq7duezzeo8VyVI29F8HxTiSQ2OvEdI3JDEJRhgyRDUzM9Xb+j0BUfcDAXNNcnKHY9VDuznip2P2bDATFeUAseXXZgBdX9IPm2DXTYvl72X72fDTe9KejgaLwMCycI5A0KDmalLwPv68fLvqNFRxUDQ/cMwoHD32HAp17kAd9Uz3QdRai4h5kWiMgLLGUZ0X5RwSM6j/IpXphZAQW/TDDY5PE1NxYlINzRvfriXv2UBDUJ5lskE9TApFCYhig77eCTOwTSKESA5JUgMSedY1UARgRFsvIZQsZ1UNDq6B5CYtQWLdPUCQ6BoQybUZcTcLAMeMc46Gx0RiFlqBUY3R4NeZnYk0bO3YEvaMQw1yhtSNt3Wq0C3Pe47n7ObHEfTW6vojhyjdfmJeelF3WXPwTQ9Y8C50uycFuVkiBbUOBttXde7UnCei8EDCXPIqI3RwXnIjFj81Hh4QOXofxt16HEz8txIM//wZ9cBC2vPEy3unQE8f0OlwqyIV99mw03rjV0U5Kx/boMGmax3b5jDV+MPKM87Xx2pLzbL3XXweOlkRW0d8UUdnSXXFBeJJrbVu7sPaJRV59D+8jZ9cuVgqmQwdg5EhWUsMHuzJ5Iszr1jISMjHvf4jgHj19qHX7F7mhbM7bf/hMD/17uf+RifJ3s0we8AOEZXKXv7DfA5XnAf9DxelLsUTqQTI5PhFhENLHHOWFIF1/qx3MR4AnI6mjNyLYl3fhy3KETIH3RXIcioGVntq/kQAhsYNI6SI9HXjsMaBG6ekYy7UwSVpnrS4b+/QFTFQofVCKfbbYbDbMnNcAVh9ybpR1M10uleGV50v07IjVwtt49DSotSulaBajxql0E3r0o+1x2qiR4NvpEvR5UFwCrVwOusGV1umyGdkVvonl0LjB3ahw82Kbb//sZPMolWZ0/czk5TKhD3f6gL8VJjYGPkBIgMWRI0BICEASnpxdugQ895zzb2KmUeoKMgLeiNnF5dujzZpPP5Yy35bZ2cCAAa4jHTUKiIgAPt6TiUubXIFiAom2b5cwUprdu/vmoTSFHvkFdhfmH8cq4FqIiWHvMUJr0YLNgygGHnLA4ujRwLGSXHVcfSFAyDHg+O17AggT9BcwKH0edLYA/KAahezCONitLFOIJFrNUiuz4UOgo5CV6JtHnKVMiiz81SoOdqnrxiCBeFUCneXo30YLmGcNHVPJgTRnilTm33TMxWxKxO48j9xTsVBEm8HdVAM0bM5EX639x1kouKrAi43D8e4MIyIbGKGNtuL4omBHEzXiJbh6zdmBDp9kIbKhCccWBjMbtHzzVVqOPN6iCnAoja2t/PtjROeOZf5N66fQZnEEkbQZl43Ye51sIAJvaGNfFWxDWC3fZN589QdXjkCU/Kty1Co+i/o4g+jZ1XBSctnjxmpiKDC+HTBpN3A5z/1stLG65ukNeKR2N4YFMCvXXYKzrH2k8lweZbGNOpK0/Pmn4TCEubI5v/wLOJkJNIoCxrQC9icD7eazZ68e1xrPPLoY46Jq4F+9Dqt0LKOAb94AQjEZNKrPbTjyAdJ22hAH+0EYOEXMuL++CGeYeS/kzcTAoBUgNWFzkQR5l5UY3f8Y/qnBOtsXqdf4uNbo/4iA5lsep4vUIcbjjvcjGfCS1mW7CVkITfAOHIidnvxwsUQ+M1GpBgH0HPOQK8/JltLf+mwp1r3gfKY1eEKHRgM9y/qtHhLL+JCM2LqWEoDwkR/TkHtBAYtRCmO+FEfm+pa/67GfU0DxGXu3Lcbf51lZSf6m6j33jkPGZ+NFPS2zW/BY5q+IMaViS3gvnNeySWQpEGDXJxHM5n2vOak4tsDZl9bv5uDXR9l37sQuRS5Mz3tH5WHVM2ze78Hbk5g54azX7DTMasQ+WCl/JuXRpHsJBTusWDsU166zm7S+rKXY8Lbo33s+M25uXQ/bkII/JzqDF2h/fsN6oFGQVhQ8/Ge7HDFKJRo/k4/8K64Mp6u7tXh6pBl7T5bt/kZMcGJy7mkT5crk1Iej9pp0VIsRZzMKJ4d7hlNAUmqaHeeLDW4swgED7fjqNfYdgu5pb/+ax4CExk4vIFPxk/h8FyegdasXkXx1Ni7nXRYt06j+4+jaYbLLMZK+zrCaQe8oQlMU62GTy9AkOBzE2DIXS7B6sGfgsyyXfJu87Xgh5Rumyg9xo7EvxJ1tSWuuevtiGHJkTN7VyIZGPDg2h8mtyjfuW477TWwsZekbv+zzg0ZAoXfNC/rdiiWizalXfocpmV+5HWtQ5zF0bPshVMogGE06bN/zMc6cX+1WrvXDCxEe04L5nRQYVDcoJ6QQIKTzeRoTHbtPE8QwBTcXugeAeApO4Z4/pTHiSwuG+SjzilvuSY1UBsqjSPsI4zNc13gDlRYvhFVlWI73PsyLigOQEx+HX76eKjpnXP+PZ6bCdO/jMOaz0LJEasfRmcPw4lPDmb/363UOuVdiYj6gcb7Hlba2fshNwRkBsDo1pqbXd2BP7ErKITk18woOH1+A/f9+A7OZBW19YfoHmKV45VIcnh+5ALXas9Kq3mz3tcuo8qRTNjetfl1ETZqM7HfeRvQF9/2byXWvYksTz/A8SYsWl/SZH4wcYJUiNLTk3pIlyBsgV2CgoTamH41hZUXeeEP0Zfx8GxZc51udvfvZP4VSKwQ4EvDIGUmyUM4Cylv4CfusJTOeO4ek4UOcfwcGsIAoXZt2O4o7dUSq2Qham7FTpkHSpoQFCcC8Zzf048dDef/9UH/+hW85KrxNSCUe98jmHHkUTWOaVuKZbt+m/Hu5t+/c+HtWfg/4AcLy+85f8wZ7ICkpCd988w3WrVsH+rdKpULt2rXxxBNPYNSoUdBqfWN3eOvmxo0bMXv2bBw8eBCZmZmIiorCAw88gBdeeAE9iEJxg8z/UHE6VgwgpOh7kgjlG+nY0+YdSWBWVagwIrSKI+LQ0zR5+vjwBBBeNOnxrUgCdrH2byRAyJfC12iAFSsA+v/KMtro+jTrKii6jzPadCdJQKHl5F7ComXuqIRwMz02iJXiaxAFnM5kJflSeXthopvpAIY8sQnhYezmkC8A4W+/Ag9W06Jxe5ICcW70qaUyhvjouQAAIABJREFUnN/tG4uQwNF/9YVoog5g2Gs32wjk/jhTPLG7L9GhN7u/pZ1PCK5JFDYMX5YFnd3KRDBTJDAB/mS07o4ZixiAYWMpmwU3e3yJDxUxkb98o41AUrohcJDkNDnjf/8J8/9RGQ5Aa9vRBsqdwjeOCUiSlwsWiI9STPKMNhd3bpcwcqJ0Tr5RVDL502y3MwzMSMbXEqSF6JB2WYZAHk4kBAjL42diOxL7Ufye6BohLgYQJlsM7rmy7HbMPDcMkeYMZNnCMc44CUna+pCVRFpHVrPCEExgnp2ROIsXkS71bSxsMAGZXn0JF2u9jawoV8ml/g2AaV2BmmGAMD/f0ZFA0xjgUi7w1hZg5WnXs2rS+iLk/ETI9QnIzZRCqi4Ju5YAIa54XandTXioGPeNYgEWkl6m/ERkpkIJdnwQhYIkFuzo92k+THF6GHKkUAbbHBuftGZXDGA345nrjnJadm8MvV38nlNaZ7TSRIRtPM74ndbPM+OLcbJx5YBovs2Za6ncSwqYpl7C6AMTEFTFDEmYAp+PG4j5+wQ6uQCeaQLUjWDZkLXCgLPZAElmCW3P8D1oU70Nk2vvHZE8fOXpJ5fnmN5ZaJOebwW66/jx507MzPguhyrB6MG78XXNNthTnI8VPPkwrm16dnyfm+KRqUF5id7kMQH59byNUbjhSPNAoNvAE7PRYusmR/XjV+0YMOIgkT/LNLZnn96B4CDnmvXWn7IcJyVMXbIC2mgLVEGuiD7JzvHZkd7apSCFZIsRfJYRtV9wTcEwC4XkEGJVXd+vQXB1M6o0d5fW5J/v9PJAnFzKbhzTvbP7rDQERLnnu+NAcipHgQSHrnyCy9fXQyq3M6Ca1GaBuriYeXiZtBoU2fWwWFi2H39TVS7XQiUPhiHPGRqmsJmgsJshgR0Pnw7Cq7tqwSqRYmS9n9BmjhEB0VYXximN/crWAFTvUMwwObn3l1HnLmHHB5HQJcvRcKAODR7X4etYVnqcW0vhV5LQYMduZgP+TMRJLDvFyitSnjNSrpDYbNDp2c1e368TknKORFgJc31go4Ho2vJ9jB0lYwILyAjk/OnDQFAQ4atXLrqxko/vUjIyfusLcvBiT2fQ2dOLMzG1Zg1G3juxg+s1Xb2DHkk7Pb+kU07AooBj+Of+5m5LrM3Jw/hkZHMM/cA7RX3DD1pGpaBqyaWyJT0fk+eaHHkyH/o8E3NaxYPPUjpuKGIAugNH5uH7XeOdeRFLelK7Wl90bv8BtEFB+DgsGp1+H4ZDZ3936Scxh9q3HIfmzVjAg7PQK0k4EahGtfBIl2TBTdduRNv5LBBWq+8ASMeOdcx73PGT6PPRZwzT6oPCD0XBvdKuQ6nditlnn0aQhX0uFshDMLLeEkSYM1EgC4FBVvr+QL9lKeCrNPMBQjZ3Y8VMajbjvhWrEZKSBg5gIKyfwesknsG05A9aY3k1J7ihkajRofNk1K/9qFuHzpxfg217JsBkckYscRKjFFyQIPItV7FROWuXFSCkmj0Cwz2+85tsVlSf1QIWSyHk8kA82Xc5KHCHC3ghoDn27DnkxlWFLjqK6Qgd+yL7GmJ+WYb7lq9m8jtuffVF5FeNZY6RlDYxfYVGx4jt91W2KwvzHnUQBofG4IsdG9BnPPuCy8xZya2RD4CqdDpYFUpY1CpwkqbT165Eq2dcWZRnXuoBvDucyQMoNL7caWnzQjKp+bt3ofGmrSiIjsKBpx/H2IRGoDQpFCi0pCQHo0oqxWdRiUzghs5qYWQ9m6zfjAbbdiGjVgJ2PzsYk+IbIN1qwjepl9Dj82/wp+Q4pkSwwRe+MP2nnKiJfilRUEyZioR2ruxBTrKV2iJ2M6kPkJ2d/T2kAo3/w0OeQYtF7oEK9J7auf0RXFeXniuR2i1rMHKiUYVLf5Xkfp06FXjgATe3iwKEe/5iL9yZM6H/bRkT9KmElFHUkHAfewQ4UgQpZyQfU5K34czCHyGjpOc8q7V2A6RhYTC+NBJJx5z1aKnVKgEkdevXIW3Sp45ajOTp7r2VdQlXSjse2Zw9ZuC1lq9Vyjlu90b8e7m3+wz5+1ceD/gBwvJ4zV/nhnuAQMFnnnkG+fn5oueqV68e1q9fj5p8KkcZe0Ub1SNHjmTAQU9GIOGsWbMcCbHLeIpSi/sfKk73EPBHL/p8EwMIhZF0lIdAyMASOt0TQEjRjJT/QGhCVlNpk3gjAcJOHe1okr8HYeYM/Bv6EAa9Eoonn6zMFQgMSznDJFbnjJUDFAfL1m1+BecvbwHgKnnF30z31DtPm+nEV6yd2AO9ujk/qnwBCH9aIkH7RI1o5DfH+qJNvJHbU5B8WInE5hbM6BzjSIwulGyhfCP3akrPe1i5ngd2FOVhtQgDhM5Da5akRwtsVkTKFI6cGXSMoutpu/BWgJqefND0gSSMTp6CBP0l7AnthKU9huHBD3IRRjuVAFppg/FEMCudtrc4H8t5G9vEsEg9pGYkqyLrm/B8WCzSLWY0Uwe45SbhQEVibTwSFM4wdCpiubnAnDmAXg/M36x3k2r8/j0tk+Nv7lzgJ8G37LZt7Dcjsei2HzYzOeKIgUuSc3NmSfHvv8DHs4zMPPKtfqAGWzZJRJl5XLkrZj2T84tvJD+7e4cUgweTpInrqIU5/UiaUy2R4aq0CLlZkkoHCPlnz7dZmDVJRhtrFGDBNzGAUAwAHZo6Cw9nsxHxNPRXi7/G1YAGsEnZzVl1TQPkHNgGMNeyULq0tLXAZy3SuqEAEc7yQnbiQu3XURjk/FBXyoA/hwD/pgCvO7EPzOgB3BMLPLQIMPGmVlHQFKFnp0GV287Rbn5JcIRcY4dUaoemlLwq/L5zEoSexmM1gcnpp420liqtSDmfDs0KhcUgRdUntmDnpX5uTcZENkF6FouYlZY7tkujFSg+1AGLnotAVtUCt1xCFbkOS6urLCpCo83bYNZocKpLR9hKKJiDR76OoEyWQaeVSjG6UyFWyJzIH21mf9kNWHYK2Md7tWgTDwxoCLy5yfVJNqLFCMzpPYdpb2FeGrOZXlHj3jHEIvkPHJqJv/5mk4CWRQ618wNj8WfPz/FXcYGDjcDvJz07NhXmlBp8IewP5YfyhTHjia3RZsFPaPbHBkc3volNwbdNWKeXZWyt73+DkdC72VYa69JTXyj/tUIqdVNckJlMsCoU5Y70pw3pixsCUJAsR42OxYio552lRt8yC37ugPxC79K4Zd1UrapT4bc5rDTbhRe74vAY9iXU03t1WY5pc3Ix5KU3ILVYmPy493U/jasWzxKzZVlL/HlLDK2BBc8fwS/Xs3F1h5a5Z9boUIz3oqszKgf0HrYtPw/pR5Vod34jqhZcxWP9n4akUSMmoOk3XSZzvVHexbciqjm+B0n9YNV8Bc6sDESrHpfQ7UlgSO2GmHuwAJvOFjtkPQOsOmi0Zhz6Mw7fHPgGr2963e1+O737Vxjz4OuiwKNwDV5bns8yVcLCWHkRlYp5z7hsNsBkt6OeUsOwUdzsxRcxQrIG82JLqOHEDlcGonO7TxgAKjg9A/FHjmNEt97IjIrAkL++xbbdE2AyO++FjydHIe5Tp+Rey5+W4d6Va/DV+2OgUiiBRo2Yta8qLMRzQ0c6ulCH2IWbN+ON7KvMA37UgMEu3ZuzZC5Obo1wsFGjTGmoX3wSxwLvQb6czTfHt+qGy/j8gud7xd6Qjvgm/h2vtxLKo6iNtCHJaER+ngRxgXLIspQIqcEGHtKz9siPoQybtdmw/FKft/yTPTf4BUgz9bDbJEyu0oJkBUw6KeRqO0KqmzHrj8VufaOcd/XfGoQxTS8gW2nBfblBmHa8Ftb8xILmBEp1+2omNAWF+POV55GdUAP5umRs2jYWKWn/IkAThibtpyGyakuf7ufUJs35oFFjmPZ//XIysms4NSYDsnPQ7atvEXXxCi60fRDbXxoBe8k7TGkAIV3P/d/7CFGXriClUQP8MX4srCoV2geEYleRO4Wf7iWrk/bjsfkl4A2BVV0WI7DWg8x95t0LR1zW0q7nh+FEjy7MsR93bkK7cRMcvsypXg2/fDWFOfZzfjqSjhxC+3mLkJlYAztffJbpPx0jwO5HgdQ2p2BCIJHNDGSfdQabknrD4rmszGjHWfPQcMt2GAMDsHHsa3ity6MMm++deVvRZ4wT0KFO/Tb8BeBd9v7ZeuHPaL5mPfPvvwf2Q/3nX8I9Pnx3rr5wEg2HjXAk/D79UAc0/+Bj5ltjjCDYp3tgOAPEUtD18vW/o+e0kkTjAI707omsF55nAgl7Ptqf6cfQe0/jrwgWZPeFnd0yNQSLj9fHwQnjkNi2A1prnexx4fsBqTdRAMa18e/BsGO712uRCuQozGjX4TDMUtfvn8oIRlbaJLj+VytEmpVsUnNKtM43ux3n2zrXIHeozsYtTO5AU+9HkZSd6RCWpm+I8C1bmfuvvVMnRuqZ+26j9ykOPDz6/f+gXeL6AVl1zjwENGyE0gDJUtmMPnmz4oXGbBqDZSc952n3yOZUBCBU7WTsC3tCQTvTuwsiXSve3VvSgn8v95a43X/SG+wBP0B4gx3sb77sHjh69Chat26N4uJiBAYG4r333kOnTp2g1+vxyy+/YA7t6AKoX78+/v77b6ZMeWz8+PGYPJmVSmnRogXefvtt1KpVCxcvXsS0adNw+DCb+IXKTZw4sTynKLWO/6HidI+Yxj4dFTKpPG1QlWdyaPPuieAoJkqZWGRctC3lSKAIfF+sogCh6ehp5D49CqTcGfT1RAT+n73rgI6ier93W3pvkEpCSGjSm4CAoPTeO6iAgIACIiJFqYLSBekdRJoUaSK9SO8dAumN9F62ZP/ne7OzO7O7STaK+jt/+c7xSHZm3rx5U993v3tvW4O0xNxqO9ExgbvWMxWeuDXxJ3w+peyACGnEN9rQCLnKbNhbOeLa8GuQ6koiye/hho6ZQvshllc5M6CLlUSC7TtbICErzuyw8Mn04vwLjJPpwkZcHH3wwYDz+p8sAQg3bwHeDzEvDcUDhJ/9moz93xlkGkd8U4Cv23DmcFPjIrC9j8EXouP6RKypJtCNtOTk/8V1zudm6CXichJlTJaPJPmIgUCVl2vTE1ilIk2wRrv6wEYqxdncDPyqAxWF8m9/sSv6zSkZNiM5kt0TFDM9A1mVZGkxreIydEw1VJt/vWMxPOrYwVsANptLKBakS3FkuEF+re6oDFRszVXQ0/FO86gAKhSgoL5NeRaLa/tsGEuiZT8lenm7MUCb+njxhgYHTqlRu4oUg7sp9J43JfV90iQwII/CHCi3aqotOneU6EE5knsj3ykCvXbuBLy9gY+/UOH4ZUMSl8BLYpZQmAPCWJJxrAyrVnGsLhprAhZtSZ9PF/HqQiaBJAwhIEbbEQOVoHpnqczEg4iSFc751ohIVQPOKtg78Jw5jpWikEgYwCxkGFCbBMBSm3TeaL3SIl+rARV3lBTEOgu2IpDP0J5+XHQgqKs6FWueiROGY3OXIcauCtQybixtQvNM5DktZUOSCHGEEXDJASKGPmmhQWL5LQivOBUqK87gkSSXMguBgwYFZHSvAjhaA9t09ibSQi84v5gJu7hBJNIsGoocTRGKCqSQ2xaxPVkCEJJEn08D06r30s5FccvPX/4WYeHHUFCYpWcQ0bp0XpxtXJCjLjDLLDJuTy63hY21E5ylcpaE9q/YDs0bf/Vnu1Xidl5yKyQR6KzVou/nU+EexYFNz1q8g9OfcklnYYKSrtfrblno0OAZCnWM3RpeQFQmkGXm8nSyBgKcgIc6ooaDlR0O9TuMVkGmklkHslNMkpp0L9L9Sc/gi0beSMIDs5fKMNcryETenAE6u95HZla0yTiU5jPl5hyIlM/CmW/rz5mmRqT0nCWFhRlG8mkt7F3QzZGTdLyan4Xdum35PlpyIkXfX4IChhGDR+gl9Og58n6Te4hxMB348kp7hIYOwp2EA8guNO27i1MFDO138m8pyCvp+IwBQnlBIervOwib7Gzc69Qe6f6mHoPUnoGLDBDY9cGIcfrd/LxsAdL9OanNvzvy89OwYcc70FBWWxCvI6mqUEtwcN3bqOivRUzTOjj21UQ0tnNm384UxpL8QrCZv16abP0JtX89zooy3Pbs5ZKzAK7/vA2uK1exd1GhtQa+ja9AaZQYLm7sSDb4o9rAnIviQg1z61vJrHDyk2c4oBTLypLUIAFpvJd57UNH0WTbz+xZzd4Pu3YBpMHNR0EBmMa3E8fwJFBualIE+k78ij2jqIjny3op2OObwRiQORoNpLkq2FGxgUQLua0d0uUq8/J4sIKLIzemwkjJKWIyrbaJPeHyfAGqdkrH79fHAhk6oIUMgom9UlpkZQFdu+KMSzr6VH+EVIUafu410brtcjg7+mHgmM/hnMhpjTMwb8kSTPB2hMfJA9h/azZuu+bAXSXHknuVcH/lPljb2qG3wgGunbuw75eF0yZyYDnpnnp4MKCx8xyDJCNrc8cOTFMooYiLw8CxnGQ1H4dmfoXYt6rj1ioX+O6/hk9jDR5k40I3IclKIEuq1aJ2zk18FfV1iUfd9y1D0UJJK0rkGsTXehdS+ywU5TvB+co59N39CiCf057xmBo1nW1+1L07Un7tgrxkOZwrqKBRSvD7Z+UYq5/Y/MTcJDljl0Al+tUcxnxSzYWtuwarD25n59UpQK3/VvWIiELLdrORbF+I8yEp6HvfFzZD62DLFI4ZT++8/BQZpAotrByKsG73Rga8FRWpcf/xz6gU1AbZ1q4g3zQ3o+925/gEWOUXIDnYoMpD7NzRvQ3Sh3Qfzvxpnb4Ip+nmHah15Df9IRyfPB4RjeqDiiBG9v/I5NB4hl2neQsRcNvg/0agC/nOsdBqUfHqDeS5uoCkJino+dv59EwcuWSQpZj+tAIUE7fi6wYtsPe72Xpgjd/pTysWYmbdZoh8pzEDvISxeu82fOddCQcf30Xtj8UgMvWRGH9X8rNw4vlDDBrzOdv0RdNGiPrqS4xy9WGgzauHVhgx4A4KrDWwKZRh6+FacK2gxtOWzRB89BLyU+TsPGQ2C0b+j0vR3sENKyctR931+0V92bT2E1QvKkLg4SsIeXCTsczp3Dn6qLH20FYs8eHGgIK+Ucnuo6qVnQg4PLZjE0JWiwvZVWdOs+fmHJ1no3CnNJ7UltqMPwAdP/+uirMpRKtmd8vE9JdogdNn6uDuV9MQXacWK9Sb5xXElBdmP7qBoSM/Y105N+ojVO7ZB83tXJDRqSOS0y33/I22LcAPA8vj12hBZR6Av1KM3P25HN+m1oVfvjX7Dqd5GlxcODkWnmhQWIiwVqaeiiG797Hn2supX6LovCFXQccZsnM3839Jbv4Om5PxIfQufLroO8gOiNnYLvMXwLNZC7OAZKU16yGpUaNE8LCk59nrWkbfqUHLKyAq8/X7tFPRTvhnZVczeV3H9jrbeZPLfZ2j+aat/5UReAMQ/q+ciTf90I8AgYHnzp2DXC7HhQsX0LixuKJn4cKFDMyjmDVrFr7+uuRJgrmhffHiBapWrQq1Wo369euz/dgK9BsJnGzRogVu3rzJ+vH06VMGHr7OePNSMYzmvwEQCs8lJdQXeHHSHDGqQiwxYjOaO+9UXfnISgo1Sd8KvDfKIg8ZEdgSGTqSrI0NUCXmFCRyLsl8x5Xkxwzxstsk9NrcscyXoLFG/D2BNvy29ERWHc2Hi0yhkycEA2Yo0Ujgy2g7ewQs9YOSymmLCUv8C8xtKpMqMHzQJdjacuCdCUCo1aJd2q9ok3YUp13bsUn6uo0StK1iA5vPG0CiyIZW5Qiva+dB0A0PEJbkKUe+WcZSUEK/OY1WC2JG0RgIvc7o999z00E+TZTw6ODgDrmFHh+8dxGlDCoSGyw/E5vuZuLmjy4ij5vmM1PQor4cTwSSdCR908HBDXNTokRD2NLelQG81A9i6f3VOJ6TZuIRUtr1TBPDmy7ia3XtxvFQvl25WJZj+ktOmu7GSlcmMSWMhuPTmSSZ/zv56FHFER0d3dniKGUB2g9Ssu2Mo/MADY79LAedHwI+mlSTQ5mqYMBe9+5c4W1UFECq1F4ckZEFzZ8LtUUMqCPWIkl1CqNLaxl+nGnNvOh/e2BgGFKic9o4BSg31/KzLMQ8EPeJB67MSWmSDK4fkz7TQihrJQQAkzVi6Vy+T3y7BJ5kUUk786mSiNiGGhWQpqt8tnLVQOasZtLEMiNCgaNU7BFpDGZaAr6ZA0CLuwYJ9HHSMUppu3pZVzE5uhidUgAcQFiZAwgdNLD3V5qAvpb0kfqTolGKJu/0GwHv5nwMvdtvwc/5nITasNrA/qdAugCvc7MFulUGNunIhq4P18A+Xgxu8mOgDc5D/nMbfb9JAsqcx6twzHrui7cI3LbkXv87mUVU2PG95zfwffgEfwwdgDw3U5ZHaX2k5DzPsCVp3K89KrBkMy85Xv5ZGHpMFV8jq/ZtZwwVY4DQXiqFc6cwZOeYFvcEuQRhWrNpmHdxnllvrUCXQIR/Gm4WmCKfPyrmEAb5/9CzhvpfUtESPYuWlA/G/JRoDvDURVzCTez91eDJw/9uqc8UyaHaeNbUS4sJ+1Ycs2uqRwXm2/xXgj/WgDv38P7y1bDOy8eLxg0RQhJcurjpko0BDR+b7KZrrAc+KGyG4/36QanJw6WoTXiRetFkvRENlsK+bqe/0s0yb2sMEAoT3PS+Xr1rM4qIFUgsnpwcvLt2M5KCg3Cnm6GfA8Z9AZd4MYOvJE8u406ShJ5EWwSlvdh7ln7vOW0Wsjw98dvkz/TJe357m6xsaKUSJBWl4eLV7/Ei4vUlVVs+98AnF4Lgk2ULz7cKEVm/LtwWfGeitkD3QrJGxRQD6BnHB7F0Djy4if7jp7D3FMnXs8QsL8tGhZenT+vXD7fJx+QP/fDLE7Hks/FYDa4JrOwAENBPBQBjjgE7jLxw+W16VeuF79//HuWcA5ikvvxVEtouXgHfmDgEdekOfPope57Quz/33RawkUjZfyyoAogqgSiuXgW+0hVE1K8PLFzIfi5KSUFRr16scIgkWYMaX0WUdclFM7RdWZmcioIAVAp/gCHdTmHKuhXiIeHHk34lIJC0y3Nzgb59DcnviRMBXdHrJUkmfvdKg1/bL/CkPWcdwD9PCXRw1BVlTfh5PTrOWwTfO3fws98rtElyQ4jKFk++moz67TrBYcVK4OBBtv2EL3UAEP1RuzZarNmE6ifPsGX0Hc2KxZYvR2K1Kth56gg6z+EAQCpWIoAnJbAC9iyeB4lGg9F9hrJlVECW+0qOC/5tcW/hYFyY6cE+5kha1FltxkzW6EKxFCDMt7mL1OZNQazzIi3gcOoyrHJrwc5Gi92POohaHVl5BzIU7kzGV/cJxpaT31+MrjjFS5mAFc8NABp/jMKG+L6RdPy+39T4Nj0KHpceo+UwA6uGrsO4pt1wZmNHWKVno1erT6HK465NZ3sJTi0agchWHDuNj5h7chQWSlCpoaFYoPek6bA6F8ck9F0CVdi0dz1UtjYo//Q5enw1G/npMlZ0F+imwIU503GyMgci6t+xRWB+nGHuztixaimI1f9hv1FIe86pzZDXHvker96zFVqZTPRu5vvFPwuNmYf0+/flKqLqtrZ4GXlKfxytX7nix3uh8LpwCWktmpkoatCK7hcuIrNFc5Nlj9q8hxbTZyJszky4njQ8X2ibncu/x9jajXG7IAeBbduLxu7E5+NQ0KI5WjQbiseFmfhgyG398q3b6qJaoR3sPNTIjLRicwoKG1cNtl/dggHO5fDi/Smo8PChqM1Dk3qh+/K9ZsHiP/ZOxBdturL1I5QF+CHNIA3S1dED79pzDKznzZowqWWq/6BHE9USpu3/BT5uHjizeS1jJlKorayw7udNrKBUo1RCbsYih76fiNnZe/IMrAqKw7IQbp9lYWePuRuAiiOXIqY25zFHgCup3SQ1f0d07NE7d+C9CpWQ1aY1XuUW77sr2kj3h3LRIqR4axjb+m6iqbKHpcXIVbPsMO1ZBbS/mYK04IqsqIfeRWRRIOcLBPlCkIwMhHU0tRXiAbsrk8bD44rOj1DXz5CVq4lhYB7MO3WW+cI8+Xoa5IL3HG1q98Vk+LRuixdt3jM5fCo0UZw7pz/vwhUqHf8dEl2Birlxe52/peSlwHeJr0ne53UUHlHRTtzEOHjYGXyIX2ff/8m23uRy/8nRfrOvf2oE3gCE/9RIv9mPRSNAjEDy/6MYOXIkk/c0jqKiIrz11lt48uQJXF1d8erVKyh0k3eLdgJgzJgxWEUUDgBXrlzB22+bGhNfvXpVD06OHTsWK1YYTcos3Vkx6715qRgGpjhQ7u9kEBqfFpI2rGZtz8yivy8FIKx45TraLfoBi6ZNRIGTA9wqGaoASwNU+P1qMzJxN6ibqBvuCyYjYCQ3aTEGCDMbt8e7xzhgvCwh1Iin7ZYLtOFJIolACpLFIPygm5OnflJC65JfESU+KMLTwzH55ORikzeW+BcY99td0wX2D+bBr7I36o7MAEnxEUCYcUODLin78MKuMjxUyRgW/6N+UwIIGx4agQCP56i7rq7+d6/LV2CVU9MigNAceBh9yZYliI3N66d6BIC8pNg5KcjBNoEsTS8nTzQVSKyUdF72ZCUxmSoKYp64aa3wUS85VHniKmMag247SpcKM95XdycPVq3JnzehzxTvdfGwIBfhqgJUs7YzK0FkLtld2vVME9sETy7JlOXhhNvdGuF5g6q4+/AdpJ51hFeNQrz9RRqs7LkZdewVG1xd7MbRL0oImZUWIzZkYFowx944+TgfH40oZSNde5z/CidNGRgIhIYydSsGkpE1xB6dYgrJgqYSoiYIN1Uyk7TSSOR4v7kUP86wQasOpkw5HpyiSl1iqAmDX1YcgEbLc4rUjH1hbjseIHRSZ0AlUSBfxiWNS2uX1slJkCM/lUMDZS5qWLlozAJH+brcAAAgAElEQVSEwvYIJOWTW3x/yENEyGo0d6qKOz7/gkiQBNhDh9oieTDO20eCmII0bHvcvcTzn6u1Q2/tXmgcbBg4aO4Vz3sFZRdpGNBLiUj6d722SmyZag+NRsLyumOnmp5nPzsr9O4sxc5fihhbMoGMtCh50KUjIgvOsX9v7gp8yKmeikL4u9erOli0vx5SFR7Y6P0JSx7y4TclDmHfGpgPVqxymZJKRejf5wJefqdGtdz7eDvzEmJtKuDIyHHwG2XwuypxgEpZSAmlyru2Y1zmt1BJxcD365jgK4okuHi+DtxUHHCycesaFJZRzWGgcznszUpmgGh/Jy/UsuHUIHhWUuj5S3j/B/E34JrdWxhIYgwQ3nXNRrM6Ag8Y3fgMrjkYKzushJO1E7IKszDm2BjsuM/5YgmD9yA0/p0Y28TcFobwmViaqgGtuysziXlIkvRasw3b8FPcBpHPlFDmj98PD1qZ85kiOdQxbZYyKVQKApGCbtxijIxZtZqw38KUediS8Yq9z0jarbuOPWjRNUXZz4EDgQTde4g0loODGRjqkJKKITqWgLm2plYLFx2bROUE1yfLcSl2Nbq+9wKpLtaQSWzRrdo83LSLxKOr80QeWiRh2L36RFzv18uirtJKrjGx0EplyPAVsIzIw0+tZgB2nquzSDqPtiFpvX7jpyC6bi20/XYRJqRzVfJCgIL+JqWJG43qgpLH5Cs2qp/Bf23VnKlAtWpsu9I8uQhYbP/dMki0WhyZOgkqO4NXXa0jx9F0y04UyWS4MHwIHrfWMVnNSDEKQcc6B4+g8fZdbP9XBvdjgGVs/DVcPjMD8bkR+vErq8JDSJI9PjsXjDqxBokwHiBsvZybN1kaOWPHQPvgAZMBZuAgBYFKzs7AnDkAaXULY+9enM95ZJIYplXsFcD6zkD/GuJNLkUD724BNEafBx2ta+DI0BMc0AfgfkEOUr//DiEnT8NTpmBMK2LKUYKXhRnGjR7MNF5GSjaVKgGjRwNPOYp5ikJplgX5Op63UqkVPup3Ec1PXsLgvYfFA0AG5WRKSDFlCnDtGvdvd3dg+3bOvLxXLyCVY/HEq5VMueDMmI/xtFVzvCe3Rd3ufdg5IoY4/0VKAGGDPfvRYDfHiiJPRho3BjxS1dSYMcBjrhhg2mfDkWdjjZqXrqLWo6dwTOLkn+mck+Q5qZCAinlbtoTqxG/A/AX64joqRKQ31Mzdm9Dh28UIvMWp9wiDrnt6LHndeYbec+cgL1mG3CTz6hYEWJG0Z9/qx/RSv31ebUPPZE5ScI/XIPziNVDffEbINOQEGaQYrZ9NgOvLueiQdUQ096ANqEhxnS/HkqLgC51O+Y7Getcu7Ldh8StZQSMfvHJAPknF6EIptUK63A37K85BrF0oU5t4O/MCJsTM169DAOFNt9bY3HMkQl7cwagzBvlKWnbWqxf2dOiPxl+m4eQEL+avSaCd3F4LKzc1Bh6Ng0xShF5VR+jbpPNgV0GJ9Se2IfiPa2g0do0edHRxkKBCtxqYPoM7vlFdBiEzyooBix4uEpSrJMfMn9bAMSkZ7ZpNEZ0fYkXu+W0FCpwc9c9CKv7jhTHove395BnznRQGPVff7dAFjZb4IU/FzY8oXJRyXD1XF5mnTsKrdVsTliCtIz17FtKWraDSaNkx0JxBZq1lMqLWZ86gqHt3KJPSkf7SimPt+amZ16n7th14lJ2Oqp16IOUxJyPq6KtCYVVP/PTjYlSvuwwPQ3/D8pYv9f0ZfzYYve+YZ5LvfbqBXWeeY37Du6c4OVg+Xvl6olycoQhXuMwmVI66NzgAk96txOwj5vrTd5shKbSSXkHpedPGSHlosP6wcixC3pEluOVXHp269hbt7+KwIXjQoQ2G52hgNZQD2oWxadMquMYnoNv02Wj9zj1E25kWM/jlWePTx274oWoKYu1NJa79s2xw6mpN9i1/eMaXGNu2KyvAI7BWGAlVK6P5hq1Ib94MKUbzLJOOGf0QM/lztOraG5q2bbDFLRpTK4YjyYrriyXFyB6FCkx44YcecZ7sGSTJyICWgDVdAQsxbPU2AzRBXLuWfe+E9TKdlwTM/w7WzVvgzicfw+GeuBIl+JNxkA4ciPvt28CWijMEwTMPn074FLLr10XLrIcPh1f7TojpKc4B0UqsoOLiH3jeuhUk5CMsCP/Va2FTs1Zpw/falheX9/krbE6+aCfI1cBmfm0d/hcaepPL/RcG/c0u//YReAMQ/u1D/GYHZRkBoewnAXSNGomr4/i2FixYwKRHKX7//Xe0bt3a4t1QVb2/vz/i4uKYTCkBjcUFLX/27Bn8/PwQHR39WqWP/usvFWIe/ZKVwthYJHVHyXoKkneiD61cN1e84+zBKvVb27syJltJyTj7lFQ4JSWzym6SXSlr8NX+5vwQjdvik0EEEFLYVa4M6Cq/SwNU+LZUyRl4GCr+GJV99y1qfswxZl8XQNh9d3ccfMpV+VJ0r9Id+/tyE/4FKdEg/zI+LPHim7r2PFaEif26aHtL/Av4/Xi6V0WI1RxEbzVUcBJAWLFNHq7PscfCHSWbLcaeOYYbqTOx6Moifd8dwifA5cXcPw0Qhl+yYdfh4tQYvExVIS1MweR/7D01+onaktRYPI1VIeWJFTyqKmFfzrCspOuNrm2ScxWG781yWD5fBgd1FtQSObQSKZQSK/b/nnvj8fxXB2SEK+DfLF8vOZj7SoZ7W5yZtFH1vllwCxVP3qZ4BOB8XoYeiBTu7yMXb2zKMACPnRzdUd3aXsTyo/uL9nF/uzP8m+TBr0mBicyv8XESwJzkyVVd/jJ7ANJ93BF7pzzunKwLG92c1vftAriFKpkXy/0tzizhwAcdf43cO4i39keUjVjmNaRLDs58yVH+Dl3Px9gJlgGEfNt0TwdZ2RQrlykEuOw0Odj0uDdStW6IKArCaqdxiM8PRL06ErNSoVT5TcCSOTlQHggrCSA0x2oj+VGqbs3UqLH+vuGdtq38CBz16KEDCMXMQ+PzQcqkqU+45x8BhG1kJzA4eycD6tb4jmfjzAcPOIYr80E8SmEIGX/FXdtRqgIT1mWNnDuYEjUDcq0G2XInTApepQfNyDOQqukbx65B9+TdJd0ycK9ciNY5x5HraAVbOZ1J88HLUbZf9Yrdj8Lgn8UXcjOx8U4GkwGz89QgJ0GGoT6eOCtNxfruBlk3tU00EptTwl+LSm5A/7eAORe4FoX+fF83B356ALxMZ8px2LehIcpn20Doe9Q1eQ8KF9nj6HROskhpb8ueL3R+v++2DPUP/orkh+L31N2WrRG2puRnn3tUNKxy80DJl5KohrzEG8k1LQyNxoly6aKx+SsT/JCK7bBmawL88w1gJnnzbFtr8JIt6eSSxFiHmFcIHj6SS14bBb2T6N0klL3Tr0IMw2pV4PPI8N1G7JTxVcNMvLW+aL0Uc+sPN2l/54OdGH10NAMM+Rge2APrh5oyl37NTsXZXPHYCd/xt/KzzTL5+HZpXZLzJFlPHtC54paJibVeMJk/n3J18aH3xyivcUC2pwcqXb6G2BrVMKFDL/33jtBnytHWFQd774OndyP2PHeNiUP/8QavrZC58/VAB31j4eQpWFH1epUqwKBBECHtr14B9+5xIBdJA/JBAAMBDcI4e5b1p8+k6fCIKF4aih2bzkOrZpwTMsPOodmrSPTy+RrdmhnYFT2rf4/Chu3gWZiKM0fHICrzsV7CsHGas56NUuJDAoDQ//DqgN643ZNjZBCaMPiTCXqggiTPeODNnD8agSEUxqA0FUqQ3+DsPZsZm5VYrXxc794ZNwf1ZX+aAwgpGVvg7GTWc41nwtL37scDh4kOc91PG6G2sRZ5VvEr7Fk4FykVA0GSgQPHfSHabsuGlUy6r9K5C8jcPxdLKsUg1ZpjmluSVHUtssWkJ+XR/LQvpGox15kAwogGddFmWTEA4e3bwOecXB/IuoFXfmnfHiBpTmEsXw7UrAksWACcEDMe8cMPQI0a0KSn4ctJtbA8IBa8cBs9s/b1Mb0ihh40SD4rpAqoilTwUsvx88PqaGVXHdi0iflEsSgLCEjrEztPpQLatDHdMS0zao+xIDvb4JdksRTdX3netkt0Q702s5HbuC1qHD2Bj3bsE/flvfeA6dM5qYRWRjLJJD/KpA4MKg+FWlJMUOLixx+iuos7ai81X/x66ehBZM2ayRjClGQPZN9SuqBjJw+vdZz04cNKgTjwTkP0W/IjrBwd4Su3hhJaWEtIeFv39iZAkYDKvXvBNNaNgu5Bc/cRrXbq01HI8PFGrykGvzlNIXE2Abk19+2i0EqYTYKzRI6XsUXY5jQM2+x7wVmdjnVPB+j3RoDtiOrH4KCwZYVRiW83hNrpkX55QJITvC9fxoLI8bAqMsyP6FgOlxuE7Z6DmKTikrAR8C2MBbXHqxF87T8TfZJ2IDj/ub49HiCk01MgUKmgbwGNzAHTavyKMFUBWqcdxfD4laLtHji/g6m+X0JZrybspYlM7nL9zjogaehnjvXxhT/HsKfvvoIwDRzyMpGvcIVKZg9JYB7KWWVh41PuGUVBwCIVtPWofgy9HQ6g11WDdCUt8/QEvj2yEbZ2SvR6a6R+Ozp2nwA5vv99DQJu3UWjAabXzKlTs5Dh64OR3YYgI9IKdH4oyL9v66YVrNjH78EjQ3Gg7rKYtW4aftrHgavCoHdE51YD0bxLTxTkAQWZUhA+TWAkxfVjhxA0YixwwiBXbeumgYOPGgQo9x82EepLOokeXcP0HKNlbyWmoGoLsUelpoEH9u/4Dr0rD8NXXR7hQiWDLGbzMHfMP1zdpI/0w45dKxAZ5YUGO/ah7QMxQKjWADpRILPbNsnknhPzL57grm2dbvUfHwzE0OFjmd/84yrvICdRDIYX7JyAh0H+aD9CwNzV7eHi0YOo/zwcthO4/AQxD5l4h+Ajujimfy2f1ti8PQ3SeAVUDhmYXTcBhyuIx5Da/Pl6NdTLcOT2ePYMUhMS4D7AALrTz0o7W1T7/QzC32nMCgDKEpqPP0aVIR9C26oVU/PZUf4VxlXj3r2WFCMveFgRPeIN3/ZF2dmQkoSMTkqF7r0gXREpdwxnUfTyJV4OER8DLSr/xWQ4duuBJ0MGQv7SABrTsoDuPWE96QuzAGHQilWQ162L58OGQvL0mejw5b17w71jZ7z6wCDxK1wh5MIfCG/RDBojVZsKpHxyjitg/CfjfOT5v8zmrF2+Npa1XYYWgaYyrv/ksbzuff3Xc7mvezzftPe/MQJvAML/jfPwphe6EWjevDkuXrwIe3t7ZGRkMHlPc0GsP/IppCCJUZIatTTCw8P1cqHFsRT5tmj5Ot0EiLYLCnp9FS//9ZfKzfxs/JTJeV7wYZzs2rruB+S6u4GSwFPc/U0MufntvB8/Rad5i6AoKEC6nw/2zZ8lqtC29NqgZJ4lEqMmACElOatWZbuxFCDMj03F0xriKnmrhd+i+vDiAMJ2ePeYeEJT2nGR/6DzPHfkaDL08jmuNq5ImZzCfAinJUWIvM5ImiTU2g7Z2VxOgaJ2ba7ImlfSpDwD+XWFB01HTMACNumgZPrvg4BBB4DLArn6pv7A9u5A6+1cMp0mPw4Rk1Av5HO8PG4qidnrl3jU7rQCIWGmTBDhsa7fvR4Hn7RBRpYBdHPPdIf91QiEX3KATCoBzxKUF6mglnIsF6H8qEyrhm9hDJIVXoyhde+0NdxsZBj5MALHRhp8Z5rNSMXOdhyoMuxGJM585cm8Q2jC1XJ+MrY0qVDaadAnesmrhMaL2HRhR+xRY/5edE4xJKXzZHaYFLwafiOtcW+zwfy99keZ8G5YgKsL3UDynBTEQgpolseAxBqDs1C5Ww7zg+C96yhhkJsoh305NeurcJmww20c3Jh/BsWIuxH4bYzh2IUAXXEHSYzbCI82yCzngr3zOKnF47OawwfJaKi6jTDbKnhmL55YE0vPU5mEV1blsfbZIH3T633G4ZSbWNLprWoSJgVDxPJ1W8o6zeSaJjZhBnn96argebhJCOC1TT2M0Oi72KXkMpBKqTWi7Kqgfh2pWYCQJmoE+BDzjPds5A+E9kcsxlhVAbY+4NiVFLu9BuOU9xB4yayQoVEVW1XbNOOsyIOHtiVJKgL0zPnpUWKweeZp+BTG4rzL+7gfxslhuzmlYFdmX8gJxaKiA8f6WFDBUIVeEiORwDy6Zrggr8Qi5gupT/YxplchkyEWxrKwYfAuNMg8Hnfvgi3eo0XrrHj+AbyU4me/8fVFiZx2eUeRacOBpgQseihfIaAwkjGLg0dJULFtLrsXqXJcp14qaoZknxrYOuK3nDScyEkTLSMwdtGu9khJfaKXi2Jonw4oVUgBleBy29YNGGKos4BwOW2mG2IUSaRQZNXEhS2O2LxjLA5PeZftV2NtBa2CWCsSnAGXxE19YoUijSFr41pJifWHtzIfrFqHj+PItEmIrltb3+9GP+1Bvf2/sr9fhQTjlwWG7x6q6h88mvOiOvXZaDTYcwDOCRzDjOK6axbmVY7CEydDRXJZmUUVla6o2WMFKjtXx7ChnBegMEqTVKx89iLeW7kWrjI5Y2axZPNvvxmS9+xS0yIlLQXfZyaCvO0sCd6DkPfW8vWsgzatl2CQTy20Utgz7ytcvgx07sy92CQSRGZEYtDCxvjDKhFeagV+flgNrSb9CDQ1+ABTX45HvcApuUYk7bjUI5Bj0Lx8CU3Dhtj91QTcK8xjSWPjoO+BG/nZ2Jn5SpT8fmWtZDJ/lRsMR8P9R0y2Iwnrnw/uxvX8LMZqa7TtJzy6tw2hzYZj0JjprAiEWOkd5i9B4E1OCo3G1YMyqLzkoFAWkVYYMQIYoEuUx8VxgCEfQibVZ58B9400G8+exbqfNqDlqg2lnpJ4rQqHFJnods8HBVInOGiycbRdFL6tZnhfNwn4AG6tJqCStR0+7jVQL2FYrpCrKrnVowvudmnPJDdbrt6AKmcu4Ml7LXD2E8M1YQz00XZrdHKgdX85hLd3ipO1xcnc0Vh/vn01O8etflzH9sUHPefpvM7YvckEvCjv4oav1y1lScehI8bBPk0MJF/6cBDud2oHr7CXImCD2uY9Csl3rM+kaaIxJT+2uBrVMXzwCFjl5YuWnRz/CcKaNcFHH4yCTXaO2WV1DhxG4x27sd8nGVPeCmfrWJJUnZ7YAEPvS1kBlPC5RNvT8zjbywN123fhZCsJlOI/CtVqwLhI89QpLhm7erWBss/3dv58gFRbZswALl0SX08zZwItWnD367RpWOoXg4mVuKSsJcewoel3yN+yHj2SPeGj1IGC8+YBNF/U+fCZXMB0v+TnAx3E3x5sPVpGCVlzc0xinYw0gCj6dseOxfl6Hhh/eAzuphmAp7I+b2tnO2DSU380TOe+lW/26grr3Dz0OG00ZsSApPuXZEU7Gcnz0jjTeJsDRgn0PHmSAxbNRbVqyH34ECpw7Hw9t5CY4ocPA1u3cnKmushJToKksBC2fv7mpbT79ePGa+NG7plsFA9WLoPNx2bGk1+Prrdi+kosRVteGlbQ7pDxO5E/7Z6ImUeA3Q8hKxFpXx1x6nyEtSJmkcE/TK6RoMUvF/Blwrew1+Sy1ui9TWoq8TbBuO7eHtetQzHjxVgmic3YkboQsgT534Tew9R9FTjFFtqW4rsqW3BLXh7tX+1Av1fb9G3Rdi8c6mBstT5IamKwWiG5yxppTki29sOw4LVsfe+EcKijuYJBeq/myZ2RWqM8qqmfYl64waOS70uXakfRO2k7eiXpJHR14CF1iZa9l3YcH8f/oO8Lk92VSDF/41aM2jcDtke554owHKvSbWQFdaSpFcWVvROZpGn15QeRl8LldeQ2WtA3z4iZ7+P85XnsN2EhVkvPvnBtOQHzh4xHik7OlN8fgY4bft6A1j1nwf6l2M/Wo0oh83isOmAL3rotvldcKiqx/vh2hB46g1qTfxL1/5pTU0Tf+AgDGg7FewOvI9vGcE045ctxdHXjYosNE6x9RN+91PAN1ENtzS3IZZz1gXEQi7RJ2gkGLh+b8Bm89tyGMoeD4N0rK+Fx+QK77h77toQ6X1yw8Wh6TxSEeKLdwuVMhUarkYCYhRT7FsxCzTwlQmfNQ0akAqpcKfNLJHlZYlhSGDP9HVQyvNN2IXo/t0KjZT8xNqprAPfNvMtWjcWtXyDX2vCtT0z/eY+5gs61OzeiyvlLaLF2s8kxxv3+G5NyNfaJNFnR6AfmV3voV0R17sA8r4fWe4Ir7lxBlyXFyE1SnbDlFpeToWAAIRWJWBmYmPTdSEA7OzNnzkD58CGiRpl+c3rXrc/klF/27IaiRMM3NW3m1X8gnMeOw7NWzSEtFF/35b+eCce27fCiX29oY8Q+frI2beDWuSuSx5kCvOw+OHIcLzu1NwFWqfDCzsgHsbSxfF3LNUUabLm7BVPPTEVSrsGnfYspCRLCop1y9uUwr9U8fFD7A8j0c8rX1at/v53/ei733z8Db3rwd4zAG4Dw7xjVN23+6RHw9PRESkoKatWqhbt3iwcJ0tPT4aaTVOnduzf28JpxFuz56NGj6KSbRC1duhTjx48vditaPpG8G8ig/OhRdDA3gbRgn+ZW+a+/VMyxAUuSaZpk5YLHs76GZ0QkTn42GvHVDR9/fT6fBo9IQ/KJl1sq66mhZF60qgBLU2OZEXujn/ehXNhLZiT9sN37+uZMAEKSrSAkzRKAkBInGzYgv1VHPP1yk6iLNgvnoupwLkFpzCCky71CALgEAF+pXcoB7jp5E/0vNzBZi3wIq3vVwKRX4mq4Lz0CGKOMrAsKBcojfBEyNUR5hqpZ13C1Thfc8ec+EsviX2CX2gp1759EpM6vQ9i59xYmY0yvQWxCU1JsXbsE6583hFaQkCUVvWUbJ2DI80VwtpMi5O0UbH3SU9/MkGoH8OIKB4TVaBiLtc8MlYLfB3yDbw42Q51ghR5YFO6fBxbbLHuFJ3t1VZOUNKuuxO11Bhmu4vpM1/of892QcJNj3FTpkYNyslf45JuxJpv8Vr8zNuWPRrPMs6iY/wKXnZvjhV0Vtp6NJo9Jryq0Khzx6CGSb6zaOxvV+2UjL0XGJnd/fGuQOmz34ys2ebz1oytL/nk3KEDtDzMhs9GyHB8PareYnQzZgWh8mLCGSSldrNAWD47ZCoAi0yMMSytEclAbpPp7YMuIEXhyIhhOL9KxKGESpGTqAmCV70Scd+UYcdVz7uLL6JmwLjLv00OTZqra3uc1AHu9xN5utbNvYGjiWqglCibnmCtzZABvgcwOBPiWL4xHmsJdL8nJ91bo00c+eFRwQCEECNc/7YeMQkd8XzBJf5DPHGqhXk0pYooKoNYWwacwBnlSO8aIo4SUjUSGRFUBesSvQdu0X5Fo5YvFAdORbhvIZHsHxv+I9qkcoMPHqFqnmLQNVfBna1QIzX+CHJmjiNm3+6HYG4W2/SR0K1wdAhmoSOAiyVGVUybiuHtXtE07jEGJG9kuaPxGV9qGiOde+Mh9CwalGhJOtPyAZ1/EW/vhgvN78LeyZTJr/DhUzn2E4LdS8KxyPdRpCAz388KoMUWie5X67ipTsHl251XRaNWrL2QKLQiD399wOJpv2cbkvYRB916h1MA2+/blpwjONzBxjK8qB281qxLf/MkcrFvZmCXSKhe8wFJMhL00D/lOTti/fBZyPEv3r/jWKwgnctNNfORIqvzHjVWhEch+FXf/ltUvSg4pHh5piCWzJugBQnZu7O1gJwFOS7h3SWGWlPlt0niRl42jrxrExCMZRz6OTx6PiEb1zbKQSN4ppjant1cc80J4TAQu7/dNLjOzyFbujAZ+/RDq8S4kdevCNy4RAz4Ty11nepfHTysNjG7jsbTOzsawD0aDVUAb+7bygBYxdcaPh/LRIxA71dKgRA8lsHlvrZZ2TfHrt7Mw2d0f3gcOcSAFH8S0IXCDPHtatcAan3gxmMD3RakE2rZl91u6Rg2euUXbLV24GiDGFB8EOk6YYFbhgJ6tvGSqJeeIb5JAq6i9u7FMnYVWK9eiylnOr48SvH49++DXYYMZs1HYJnkMuhBSzh+DsYQjyTrqPMPMJun57QhIzRGDTwRAhI0VA/0lnR+SuiOmO4XctgjTejzCKQGLNdClARq2XAIfr/LFXrt0L8TWrC4C7Eg+bvXebcwHq1xYODrP+U7UjR0/LkZW+XJm29y88UfkuzibLKOx/m7mZCRWDkHzdZvx1glO9o1nGVAByPTdm9B34ldwjzIk+mi7qyuWYreHgwm7kO8UgZLBl68x3zth3OjbgyXM/e+JPatoHZKII6m4EYOGQ5Evvg9Ojx2JZy2bmT0+HpBstnEbahz7vcxJ1ffTXHHyfi1cvacxCxCWYz6yuqIRmjd11bE1iYVqPI8ihRcCoBYvBo4YAeD0Oy2nD0vjeR6B09266UG592vdxWlXTuLXksTw+y71cPKg4RuNbcgDU48eAWNNv7nY/UKJ3/6mvqBsGSVkCbi0NEjHfOxYaB49xJa5vcosj1fO2h3z7nngg8TyCFeaPgfpuhNFSAjnpUiSonwBAL8CfbDrpD0t7X6p69FzhEDcmzeBeIHna0QEB8LWKkYCj4ovyHeS7Dr2cyomwtB6eyM+JhJ5Zt7JNCMgn788rYax4KiQUxgmY6JbqN6/Hys2HsQ7izfByqkIweVlUKhkTGkwastZ9Bt/B7caGKwK+DZ7H1qBryJ+EdkslDouuhWEICEVNpXmU76jwnTccm2FplHL0TH1AGuFBx4JBOzTsiYyqxjetyR3OeSeP3JlzuhfmQP4dj9oh3OFzRGn9kYFRMNXG4dp9Vegbv4tTIo2FIXR+4Pk87tXOYiPElahbZrh3tSDh1WP4PuXY1ChwCBTTGNOr+2eVQ5gx2PdfW/hgPzQ4iWONiyEXK2CIkdc8MD89JxkUKu534WFWLZqKaxldlDkKhZhl58AACAASURBVM1aEjRwb4gxK7zgksvJ2QrjyLUl8O60D/WSL4t+p++sXWfXoMmXq+F1wvTZ+7hKQ9hknsbg/qYytwTMVkrhZNBLi89slyDWwx+7IvtALi0qVn3KNvEo6tg64GHVpsiKFxfEH7i3CgOcPJEfJJYRpX27BCmh8nVC7iXx88GzWiHy3JyZ5HTNkQbwnrYhBQ2nAA5EPifLwZSmz5Bmo0adZEcsfhKM8wt/QMcFS6E+lcIAR0fvDKhz5MjJcECSawFmt3+K+75ZcCuQY+nDSiCmP0VSpYrwemEKGNMyKirJqF8P/seMmOLGA8jXKOimDiRn7F7tLbx8cBdxNoVo1ewutGUoRqaCvbMXa8OngCsSYQAhTVrMqEtVtLKBbPce5EVGIG6CQT6Y7yKpCNht2oyo0R9DafRd5NqxEzymTEV4s6YmVhN+Ti6wnTkLEXNmQp0uLh6SNWoE18ZNkbJsidlLqeJnExG+3HQZ+/67KL6mS7sWX/dyAgk/PMRJrVtStLO562YGDv5/jf96Lvf/63n9rx/XG4Dwv34F/A8df0FBAWx1clMdO3bEEeNJpVFfHRwckJuby/wDiVFoaZCv4WjyjQApnexFL5I8KSb27dsHAiApaDtiFFoa9NIoKRISEvR+izExMUzG9L8UlgKEh2dMRsCd++jw21nE6XyiaJyEnkfGiTdiHRL70JIgXwQCAaPq1MLXDVrozcL5Kmy+jf3ffsMSSBQlAoQx6cBkQfL00CGAN5WeNo2rjAagUgNGfuYIHzobPZc1Y8uLBQhp4YcfAkME0hRUlkoJDkIRBR/A/Ud8jF1+nHSWMMiHcEj9TzArWSwVNtcriFXUtW2ej/kPOkGCItx1eRfbAr/R5xwn1j6DgVGz0GjcRWhkppXHbrZAZXfgWSqQJp4Lsi5IihRodqGAMW6qJvyEmjm3mVfZYfeeTF7THDhi3P8VC4ZgV5KpdNzmbXXx/o1r8HOT45h7N7ioDR/l9x3qYFTcMpDE8BUXjtUjjMSDJ9GjpRUDCKvkPkDfpO1MMpAYbTxAWLFxBj5K+BGt0n9nmw6tuh9hVw1AXHHX2+izcXg68RUmR89ChtyVAWZNvG6j6xVxxWWRRIKdSz5C1iIHfBJnKtf30L4W3sq9x3ZDFauO6mzGzkhReGFc6CYEtilE+Ek7tpw84Ag4OubWDXE2AYxlGLlZy1hmUTZBDFSjaPR5Ovb14Lw1GjYJx7JHBo8lYn/1ezKKSZFSkNzvU2UeKipsmPQNeWDdClNheJuhSPVxx2duyyFLU2JzzFBWMSuRGYBe8luhxISHSlztW9yY0e/fBC3EU/u32CoEAJJMFB2vcVA/Q/OeMNCJxvfbwLkmcqXm9mOryWW+NDZF+WiZfgJUrjk+b7F+1Rf2NVC9mhzp8kJsum+QGHtkXxPrQ5axe8Up4zq+DDdIvd1waoxFAV+zNsxdy4sCZiLdoyVeFObiy+hvUDf7BuOsrff5FKfdOGDQ3HYvbUOxqep6vFTm47sXo0XJG3PHRo+EJ+81QNUzN0S1y+TFQkmCM50G4nanTki8Y4M765yw+1EHBsoROEdBiW0CN/odi0HCDRtW0Uxxa7UrgmV2+PhjwD1tOQJ/4nx9+FDnSZAebqjSpd9JLnRvUmes9eUq2Uu7x538VbB25qqhR36zCTX23sOIqytYgoOPx61b4twosTRfSdeSTWYWS/x7REXjzCfDkah8hW17DOxOftvX4RdFJ/S3P2pi/4RpHECo1aJr5kG8l3+OgddvBxsSU+TTQwAhVXgXF3QuPF9GoPfkGSar0DJzTKqSxqKszKIWQaNR2YN7ZubUqoFaz8PRfboh6Ui/pwQFYs+iuWwd8n7rMvs7JgP6+P13Yf3FZLisWYcah4/DXucBI+ofD0zpmDpUbW6ugIS2cdT5TAq35wHCrCINktQkbMfJj5HHoQlzhnzDyD+MvhmnTjUdJr4v5N8THa0HCGnF1Xu2QqrRYNFQM1XfOglOWm/wqPFwTE5hYEq5UZ8goU8v5mtcVoDw1XfzsSDAQ7QdPXN85FY4fvgX/J6TZh4gJF83ls3tCaSJmbPgmV30/WBU1c784Gg7KkIiNpIwHBwQlmbeS8nctUbycrmv5Ixaa1dOjcZtbyJTYWAgWMscMF/yGSqqNKh28mxJl6vJMvINDLjNvQeN43b3zrg6qK/ZsSZGLrHujH0tCVw4MagPfuvYWiRZWol5pgJ52iJM27UR/SZMgVu04buetisc0B9TOrdCv/Ffwi0mzuz9yTwGNxvYKuT1Zg4E4Te+37EtLn00GO0XLEHQDQEQTdd135642ac7Puk12IRNda9ze5A0XdtFP8DmzsU/lVSNvPo2Em4p9OAu36egmmoQQCgq3eLvFWIBEhtQGM2aAbNnm2eukXUEyYsScEcyt8IgqYDvvgN+/RVRq+Yj6O2rZUsMQ4LIK40QUCjwciX2ILEI//iDk+I0DjqO58/NswFJDpOKAYiFV5age4mkVA8dwpbyCfiwCicxZ1FC1fVDfHCAA2eSWUGQgclEVgtexBI2F76+ADGDhVGjBphsK4G5Rh5ZZTmcUtelisKwMIDYpLpiSbPb0FjT+SXmuJmgZzcxhuQSMH/oTI2G3YN07dGznw9aj8aFvgjIT6w4CXJan9algi0C6ozXG9fRGStzTY2G6z7pgw0C6UryQLa3AwoKTRVzaR90yDzWTd9eVIxD+yKgj4SHCDstLiLs38KKkBXoHzUfddNOsG9nqq2jglB6DIe2cUBBOQOQR3KXy47WYHvoV/0oCoo02POwHarmPcRzTQjbll6zq32motBei5Hx3FyCfme+mwCGhGzDgFeb0SKDK4ag4AHCn917oVuyzqjbaNn4oKVolnEGHVLNmDObPZ9a9B5xHQmO5gsChZuUtRDLr8AKu1e9bXZYYx0CkeflitBwU6DvyPjxaHjuKLzumi9S21MnTu8/KGQ0luRDKOyEWivD9HKzMT9ep/gjkxULEH75/mZ8vdUV1jUHQJ4q9psLn9oezzq2RPum4mIs2pejjwpZdQIgOWrqV09s7z/69UXodIHkha6DtIxuiORH1ki2L8T5kBTG9C9fVUkEURYkfZ+tdYAT0lmhSJGEAy61ci0O1opHizAPhJaXQG5rueVDQbqUfQ9Q/RL5Peq3pXtFKUFamGHOQOxQAo4JmCPLl1VBcVgWwr13y1KM/MUDD3x5zQq2WdmIqeBfLEBI7dK7vLjvTiqapmdPjLoQBUbFC/wympcZ60fw36Wx6kIYM4sJAGVtGhU68NcRgZbmikPY99/FP4p/mPwDS97f9j5OR3DPDYuKdiq+j5ODT/4DPft3dvEGIPx3xv3NXv/eEXgDEP694/um9TKMQHJyMry8OL+pvn37YteuXSVuXa5cOSQlJeGtt97CgwcPLN7TwoULMVkH4Bw/fhztiC5VTNBynjW4aNEifG4hc4uakxhXyZfQwzcAITc4JSXQSFpBCBCmVvDH7iXcpL0k5mFJFwZ5IZFEBx8hW7bjZQU/rEyLYwlQAg/5IN+jg3M4nw+WnAFAHoSUUGaTLp5B2N+MLBqfSBHI/ChVABU0C+Np58noP74ck3C6EyT2J3RzBSoI1SzPnsXEExOx5+FuIFVQQenopAcJU9MSUKAwlT2zV9jD0cYFOUWGxAP1w1mn09f0gi2mnDcA1rv8J+O7+xx4QcDlnpqxWNDqhcnQ0gQvWTDHMf6b36DXtT7wjR+N/pEzGehEscx/Cq44tygVPKB1x4wvh7tSTj5MOIEbcN0PM9aHo4qPApedTXXuye9BXaTFdVdTgPDKst/x+YfWeLfOHXwbbmAVn3JrjxnhX7L7eXC1Axgdt0x/3Gdc22BaxNRi73U6NkoU1HknB+sfdhaNV6KVN8orxZO7V8HlsaHHKExdOLPEhIe5a/q8y3u451APPZN3Ml8UYWz2Hg0CteaHfwoFGVLo4opzM/zoOwlhl51ZImNGsy1of98AWpIEKF7+iO5OHswjVOijSB58T1IKkf9bBkbMmICnXlVwuaAhPkjfDAmdU6kUEnNgQEk3pJllUysug1yrZmD1rAjTiXJpzX1UdQ/ypXaolXOLMfXC7AzM48lRM1Ev+5qoibG5hvOb5uwP3yoe6JiwEO+milkQn9Y6BUeZAr1fTkHtTHGBSt/qx0DuOLsedTTpXoHUBt/UOg5kPcJ3L8VsBrrWCOAUyj4JG5hW9xyilAXY8dDwziJ2jpV9kV62SbTDJgq4ZmQjN8aKyRA5eqth7WJ4HvCSewG37zKJZmG8nD8P7Zq/x7GitFr43X/EZC3zYIcFXsEglbHod5qg0IysYkYEJ2tEQRK3dh4alpAgVqi7Khkt00ueLAoBwr19uqLr7gN6jx9hH3kJTpKY9gyPRGSDuii0twOxjJTkN8KHVou+n08VsX9+3LcdG7a/jdx8IwAFwF/xi6JdehbK8cf5elg0dQIKxijRIfMo/FQxkMi4RAtL0lgQJBdJiYTNB3bCac06JjtqHARa0Xuq2zffmm3x29AoHC9vYCTSSukKNZS64g5LJvgkLudWKIPKSoECeztYq9ToEWaFr54bXkgpgRWYBB5JQrZYuwnOiYbEf0i3nlz2VMg2Efb2wAHAxUUPJphL1FAihqS4KHFsnDjhEzHUJG1LV7jV2bPcc7k4zzGSBzT3jWn0ruYZhMLummWrnD6NtZmJ0Jw6hdY6nzbyIqW7IO7EcSxKi7UYICQpX3+5NcK/+By7HRUieUpqj9o98us+nM1Mxqi+hspsPXhALEFi+Zg7dpIFDAgAQ/gpoS8MAnQI2OnRAzCqeKfVEtRKE0lh44uO2KGU4BfGE4dcdG1iytY4cKYSqqtLL7Cx4FZhqxAgS/J9i/duw7ChI2GdYwRyFtMQnc9r3TriUNuW+HCYAfzlz3O+tgirvpqALrMXiFqg5ZoqVTDpm89NJEYJXEzWKHGlZTPkOzuj3i+GhDqxzwnSKA4EfxVaCb/Mn4nu02fD+4nBy4zfOSlaeD99LmIa07LIenVwZswIfPTRJ386qTo3PAg97pVHeoQBhLLzUqO2n7jog/WFlwemIrhlhvcmW0ae3MQyO3bMdNSpGJJYvcRUNQ56bh89yiQo556agRlBXBFbWRLDdAzTogUfy4TQkA8hFZ0So9E4CMy7dQv4QuzpqF+NCkPpefEno8wsyLzyOHmdU40gYIvmPXRPERuN5kH0/zKFGa/EMm1f0srkL/n0qXgNKsKgDwRzQcWSZQAq6XlOh1syBFh8BycGv8Aer+KL0pIUKqik3PNKOJew0krhUSBneCe9to0/ZTtHeuHj45VQvjzgXd6wf3rtJQg+64MrGupDaS1S/aZaTlLfJeyWP5OvxszGyc9/Q/UsroC0nBfg40M1a1rYN76OAmtDtSXJXaZfa4rcHAkOJb2Nh3IfpixSXVc8KLezxfN8P+Y3eyBgNDolbmTvIV7SlNovSQq1tGUHPfuYAIjFnYFMGxW6fnwVKrn4nfA6CrEUagkOrnsbLgXFAObFdIoYjacqp0FK1VlmgqRF+XmziNGolMKh0LztDTXz3jNPjD4fip+1fTGoSKDcUQJASNvNqLgYU6K+gb3GiLUPwM5Tjbxk8/ukQr+saNNjJ793+ixPf2H6zHb0U7GCtBwjtiLv3Uj9IYBwr7IHekk5kJgHCHX2few35woqvaRpac8SrRpIeWrw25bKtHALUSIzWgFVnqlaEBXL8QWJWmjR+p17iLYz/Wb2y7PG4EsB2NowBvEupkzrwEw57q2yhUwhQWyQH6SE8tsICkcEHWfFPlotopUFyIqVozBTBqlcC/cqSvDKDKRqYfxtQ8sob0IAoTFcSko1zlIZe893bvgA+QoNbFUy7LtWnSk9OMnkiC0GICSbCnPfB0xG/ux5vZdiaWP/updHZUQhaHkQ6LxYai1DBQ+R4yMR4EwSWP//4g1A+P/vnL45IqrtJDrFm3gzAv8DI0AgWQAlLqgCe/BgbNsmlkYz7iKtS9sEBwfjxQtTsKK4Q5ozZw7zLaQ4ffo0Whmbugs2PHPmDN7TyVHRdtPNVZ8Ws6M3AGHJF5WlDEK+FWOAkH6nBLdUpcKofgbWE78+n/yWFxTi44EGpsnanzdBo9OhNwYWqcI0avIkZN28jqqnOfNwYbD9qdUY3uVDZMUqsHbVOCgUWoxevAz2Gi7xbjZ5aAYgJLn6x4/F7QtBwDvFKOzS/JosYKjaP2h5BURlirXtX8et7JttjcMbDZ4X1GaddK7K/757S3QZdA1RboYJK032yF8lX4w3svVt5UChBigSvGkqpNniwLZGbLm5yShN4M6EFs9WSLHXQCvlJnfCCRx5h7g5eEMhl0AZb7r9oNZjMKflItzzbGkyTL+MPYTF81zMAou+KSeYNJ450LFRxjnRxDtTo2aAMSVTN2cmIlujRv3a3yIoXyznau48xQf7Yk3hCMyM+Yb5nZQ11BIZ5MVMeonB56o2BUMibSqiVdxaJi1726c5CnLF+912aQsGBLvibn4uNk6wg/xJGjIdPfDOnAwkjk9E28RDaJT1B+4F1EBNQbUugTTS1wAQ8mNAYCVJav6ZiLf2hU8hV1n/i2d/PLOrjnyZLeaEf27S3GNNFawqGMVAt8IAf7h4eGHeHTLeFAPtn4VsRKK1D/Y8bG8yMSS2X3C+aWKX3xn5Cc6ImKJng1p6TFPrnoM85wVmPzewZ3mwqTBTiqwYQ7LANViJ9HIOcE1Ng5wmxmaCvOrI95X3tROu4uTgiHInTmLu1dPo+7nBI+v8xx9iyJARDKyJatYESnMSnVqgMFsKSgQo7LX6imVh+zYuGjj6qVkywjgo8cGzBbOhhZVKBWuFmQQ15agnfYp2i8yzxfl3gM/DxyYA2qqVc7DiSEdoBIC5sB9l9YsSbqsokuDi+Tq40/Rd1Fh+1QD2lxEg5BlMjL6tA3uMK5WJUVTx+i14RIjZ4NQfmsS3ak4STaaeQLTc0gm+uWvHv8AKpy/UKfXS1XtD1a/PSdKZiz59AFJ10B2jOYDQn0n6SlnBRUkAob55Ai8oo2XsjUYZ3507OUCAGD7CoGfu8eNcgY2uLxYDhLt3I8XNFVk9ukGRls5YLVTtTRH/4woscZBiZP+PSh0vWoEqy+l5TP6e9B4RhVaLEA1w4tRxPL50Fp3mLtQvJnCO3lMg32ySHTQHEBJzkJQGdu8WZ7KpFfr+Jm9Fc8xKYpBDizSNmhX1ZBt5j9LmJD9Mfn3Gy7YGJGJeFU4CXpiE/+qBDz5M4Px9LQlyC6I+FBf07UVLJ+5chyGjxpsAaCVtR2wlOjZh8N9yBdois5X+bH+DB2Nih+YYMXA488Dmg5ZRMtGcVC5dywR4RJQgo7tz+fdot/gHEWPRkjGidUpKqpbWRqU8Wxy/VAM5r7gkKfmEEeMjVKfuYrI9yVq++y7wi8FLubR9WLJcW6c2QqzW4KWtaeI3CK6YdlmGedXTEKFjmgvbpGN4fr2hwfeLvOxXrQIuXgS2bzfdPYGHRPEiSV5zQZKnvDSvJZ0XrBNlXVB2FqQWICYnz4KkLw+eAVey+H4ZO/c6VieGcnS0uCVCtnTFvq9jF3+2DboPnN+5hGy5eSDIuF1jn+GS9uukliHzEqf2Yhz0DFIpOWBRIKJh0WFoNAC9Nviv8Hv2OajdwPS9ee9GfdTM5UDYx0+A3AItQnMfwMme5Cy5XWkqV8X9Z4bvq5BKgLUNZx9x/5lp4Wj1YCnCI0oGDyljSM9DYfCS4cbgCa1DyyKc8rCqWQTOhYqlQP9KIVbL5x745EIQfLKM5HZLGWW6Jv4uRqNPtjXWrW2LqZiDVUUGOW6tlCRtyz6fs+iCMbMSPbPty6uRGVk24JTmEvQ5TGDeuLxlWGE9lqn6aHW8cRFAGKgCqZGIogjIS5WhSC0BAY6832HuK5kJyEn9y00sHmzl5zU3XbIxoKFRogRArwRPTLwcCG2ULXKt1Fjc6gVOVDMtBPhtsz3ejpUjsZovQExIazsG/BlX4NL7mq7rpzFqUbGlrYcaFXyl7HuOADtjL0X6nSwX6LvcOJiPokyO49ap6NDYQGL49XIN1MtzZCChsOhduD0xpsmGwjiYKsXipQAx7f+FmHthLmac5dQCylS003IupjUXey3/C93/W3b5BiD8W4b1TaP/8gi8AQj/5RPwZveGEfj/xiB8IzFa8tVdVoDQR26NeIHEKLVOyWGnpGQ02SaWuaNl5PFik5WNGsdN2Sp84rgskl/UJi/n1qvFOGYmvmHDODS4egXvnzwBOHKTtWIBQppEk2yZLkiq5skT8Ri5u3E5OoriAEJaRlWpyjM74bsiEEqN+CNSWJmp0gBJYtUS1raXHaDQqfUk5pB0pLgfVJn524bGcC0wJOVtAspBmp+DvORcbK0TjRXvhotAP74F30wbDLteARsbRiHO2TS5Q/OkcecqYugd7kAp8SH86KYJXLtxl5EjkCMr7koqqySNo5UjfvjgBUKrmHo6bGi3FRt3VTArP2ofdgI1PK1x1Yw0aZ30M7DVJYIXpcYgKTMDKlsbxqLzW38BNnHpqLPnCJGwSo1b5eqgYuILOGmzIPsHJ5SyhMNoZOeEQ9WHwTNWXGyxdOxKID4fhXJbTNljYLxt9BmDYfE/6o/pWkhDNHxi8EagiaW0rNmRUkfon1khV2uHlTafIr2yP1IQig33Of9EYdx2bIC9XoMwNnYRfAvLBtKbq8Amb5SCdIOElnBflKAloG1cv0Nov2w53s66xBYLPUXMjUyEb3lUeBoGqaORJ5OFw0jPO3PPyBcnjuF6fhbenTaLyUhaEsZAIO8zSNJCJEcoOt4aWjypVxUZrq5osf8wimRSaG3tysyceNmkER61bgXfh49FDB7aFwGdgTvXYVFwBE4IvNGE/RhaC9jSzfTohh4EtplXOES7RDd8EeYP/3wb5nuSEu5SZoCQmGDEHtQng8lXikAt5psprlQ29iwU9jZNoUKzFnf0DAnjIynLBN94Wx4EdVOVnITSvw+JLXT4sCWXillgJUBhzQovLAYIhXsiEJu8Y4jBSCAOeWRZWIBhMUBI+6PqdAFIxHchrXEjrOzeDv3HT7Ho+HmAUAgwUSGU9zOO8WcnlSFtype4mJ8pkufkPfMs2slfXClMkBAjlupv5dOYmgIVAhHrRRhZCg3yZVwy0dhjihLtxUWHRHc9S5WeEFRVXxKoxl9rwr6VdphuMgXcZXKooUWkskDfc5aI03nVkhyoKJGn1cL30RPOm9fTE2GenLexMEqSLKNrmQos6F4uKfJcXWCXzvnvlSWKS6pa2sbu69VQJ0P8zijO483SNsu63iXnDDSrY1olNzixHFaGhcApOQtZiVEY0zIPO6oaVBH4/Vy6XQdNsziPrH8z5laIfD0syH/zIEraNz1b798Xr1G9OmOv/duRrChE+aZXTKT/zPWrrHMJemol/tEEHirzhUuv69iX+8ZifAj3TS4srlgeVgmfxhnZkkRGAhm654W/P+dJWUw8jFQjK00MNzepy/19+bYpePh2bSnS0jlV6iKtlrGtKHhZUvp3ccxDfo53xy8Dy999iTAvA7O7rIVYIUn2+OxcMOrElu79bu7QM2xU6PY3Mhr3bn0Hx6sNweCLBiWWIokMMno5/osh9GEvrhsEytG3eNJzWywumIDJigXFAoR8G0ImYVYMV1RCQSAcsQSZx6TAj9jSIeBlRqdWC8c+P0Oxr4NKhtlPAtEp0cOkuPD3KklY3DpMlDsYcluBH47aIaZiAHKSHFCkVUBhX8QYkLykKvWJivEIILxjpEhLbMbAqhpWsEWFaWRNXZghhcxGy+ZfzjIZK4wqTg6UvuPnekdijq5AivY1/WkFjIz1ga1EalKUVNL4PGrzHjJatsAHzVpb/P1q6Xhbsh7xiUJWhOBlummxc5BLEKY1m4Z5F+chIsNUT7mSWyU8H/u8TMpqlvTpf2GdNwDh/8JZeNOH1z0CbwDC1z2ib9r70yPw/82DsLSB+K+/VIwBQmKy9B+v0+w3M3jkuxNvpqKqtHE2t/zPAoR8W3yymwDCybNmMm8HCRlUyKTFA4RGkmb5+cBTg4Ipa9rDHaB5HUVJACEtrzO5DcL9HfD5H7NxsLz5yszDz4DxOm9w0eSyHdApFPjiJLDfKL/fq1ovDJ0UA98SKjPv+GRgWB/T5E3Hx+Xw5bkQOCjlyLFSY0HLMByrauQxA2DT7jqonWCavKEJZrqNEp1GXzWp8HsdkjSU/Tu6urFZSZq1leZh0blaCPPrZHLJZPy8GS3bVMAd91YmyyrGnYCzrAgnVXkIbtaFVUXSBERjbQVlvGWVy3yj4S6BCEp5wSZl/yS4FnZvFxpoJAhvPg5uOZZ7BAoH46FXNVSPFyeKJHKaLP67E+OyPB+SK3jiZavKcMrPhGd+MqrcfYjUKI+yNFHiujTpJZYfFRcIQ8iYM1dty1fTEjk0I1IBdb6UVee6BCohLSYPR+mbaC8P+L+M+NMA4Ws7cJ1skbA9HiDMCDeVGXKpqcKtOjVQ7+IVyKikniry7e1h/RoZqcK+XHfNwrzKUXjiJK6msMQvim+napYdpj2rgIbpTvqmTQBCRg2QlCoxWlIynnz2hJXFBCYRaEYgBlXrUyENSYnxFc3RtgVYGBpdLAjKdzbIBZjWDJh3EYgoBZMQgqAlXSOUPPSTmzJES7uuzDGvAhQ2sJZwDDKLGIT8TogxSJU4uutIv++aNU1148x0rEwAYTEHZq7PJY2Bg1QGkk2lILBLotHARyB3TsCU1sYGLyoKNce5ZO2fGe/Szoe55TwIVxpLVbhtWZPwxFINv9IY5CpJ4DA9NUsC1f4MQKhn6ZLXIH1/FKlYIQIl/YixSEEJbh6YNAZq2TmqGmqSsKO+CLcTjgN/LZcFyCzLOZpdPRI7fA3fXQTCdk32wHZv02+xwZFOOOSXiywBy6pPWkerAAAAIABJREFUrCfmPiaZCkP80wDh8MpPsdHboBhAx7DmeSj6J5XjOkUAxT2uSmNnZSVGt8pDluBRMzzeG+ufVy7LsL32deneCGl0zTwLMt8G06ICMM/7hWUsyNfeu9fYIG++R02SVHRg4Gts/M83laJQwrfxFSh1EqJ8S69jLmFVJEHclcZ/GSAsTQI1Xa5GnpniCnuNFC5qIwYWoy5yYHnfDF8sflmp2MGjZ9PDOBXykrg2qtXSwkVHEYtP0iIyVlzkwYOHySlArNjBQL8PcwBhaIAUMbEG8FAj0eJY9USsbRqJdHuur5YUYrnlKvDxH4Ho8Kg8bCA1YXKV5SqJc87/2xiNARlyuHqlIj3TBap83fl5TXYLZTlG43XpnWbMfjNex8ZNA7m1FjkJchRorWGtydUDhPTpXVxNFc1PiFSa8sSaM/zUhZOfilkamFMJKe1YeJuBK26ZmFjzBVKt1Kib7oBFDyrBr4B70Jtrt7B+FqbUeInrrtlwK5Bj815r/B971wEdRfV+7/b03isJSYDQEem9KKCCNFEBRUC6itgpKgoKIiDqnx8ICggIKGChCQhKR5BeAoSQQEIS0nvZ+j/fzM7u7O7sZjckgJLvHI9h5703773p73733q4pUlz2jYe2gnj77DOdpFadeHYLpLxA83NOQLmJxkdMwDylGjlX5YxEKwV9w/j5g/GEpfeD8hwJSjKlFm1Pan4Nf/CSEHule2PpxbiqpsBk+/WObbFn2ivMb+QNfz/i8K3D6LzKkjU9stlIfN3va3goPFBUWYTJOydj3fl1Fl08/NJhdIzoeD+6Xqv7fNjXcmt1cusav28zUAcQ3repr9ux0Az4+/sjJycHzZs3x1n+R4dZ4fz8fPiQTBKAoUOH4scfTQ21bc3u9u3b8ZTe+2Lx4sWYOtXoN2Zej7ZPmzaN+XnHjh0GP8KaOHoP+0PFABDqdOi9+P8Qe+S4zWm1JrlQE8fC0TboxVSurcD2mYPx7NrvmRdnsbOCyZqtaiGFvAeLiwFKvDX/0PL3A8iehaJKgLAF8Mgj/+CsawnzTq6jd1YzLMZJClToVbNMMvelAnKgZBtHA9Hp0CDLDes3tLY6LR/1uoJfmhgXb9yVEnx0NA5dz+oXb3g1dzW4g097XEOJwgiWDbwQjFn7LBdvbAGE1OTdSNIwXbIBENJmryglCpItM4LpdzcXEdIuWaIxvnFKiOU6qMtEyL9R/Wzisggn+OTnoDyf9UgQkSzWvygSvBqgYY6pqea/bQxiXw08XVh0RKxfVFIq3VCcYfStIGCOfEHIrlNZJEZ5nikDhuR9RGKdoLcG33uOPAGpvpO31iDFQ/slW9C8qwoD41SIJUgf48zHugD2ShLIzgWFKFYYz0Ux+UHVErhm7ylqjUEoBBAGNiyFmjIoeKFTKCDRS0Pbu09HyhGIszU0GzPjk9l7KQB7/PlEOmDO5SgMuu1vABS4/Ury8pCZ6m/sBvOgkNwVQEh30Qx1JZOtT4BgsN6TinL+CUDhTglz8IFA0BnxN3DT1dLLZWQz4Ot+gIcCKKoEJu8E1pmRQmgQkcVyzL0QgTYllswE8s3zEEsZUIT8SgmkpKxrypJ2NOh5RnJO1BYFZb8Te4zGRuO/Yca84nsQCu7r6lWAfz7R9UASnHaEubwk9YFAJUeDAxqpPoFP9G9bYQ52+dxKg7Peu4thrhEwFW/6DI2SO1Xbq8vR8XDnlzWWak0swhNLNZ23CE9ngz0AIR9gJoZgkVZtOJe4cdI5QxKw9qavMOPVahF6mfVbI8CS8/SyBhBaA4ZpMZKuXUcBQvI0ItApW2157ixumIqfA3KY8ajEWhTI1aBXPwIyvFVSFEnVKNf7jvKPtYtaBHeNlPUlFetAbx30PifXstctx+Ks6r3W0fOnqvL7vfLxXJPLyJKq0LHQA+sSGqFehdl1RwCh3u84pXkERrS4gSOeRQhQy7DhYjx6FHhXtZta3V4lC1IjRdHlMw88C7LKSeK+1TljvSor3LsCN5zK8Xb9JGzxrzl5yyFZ/vjsRjSizM9HB4dF13JU++O4qajak9jR5IqoSgWTXGEr6D5JctCUeENJKfzIK9Hi6jXAJ1CHuFDT+2RZOUCPVHodi48HSkvZ99FzVyyZh+1biZGTAyTeMt22Iz4Tn/RhZfjtScSa/nscnrjMmj3GhItxPdVyXw5OP2qL0egaUAK5ohJ5KfokWJEIIrP5tdVXuvNWd3SUrMy38+D2I+QNXNV86dRqBiAkL3lbggsKTw2TtKhRGp+mHGPRv3El4z/uaDj7ahgAjr4bsl0rcSA2B0+fC0FwvNLwkisEEFISpkoLfOucg66Jfggt1sEL+UjXBTOWB2KBJ74hAZNAzsuWfeXYjPStx/dupO88P+oPAL7vOv2bS4AkNYW23U6hUG5c/yAP0ROHWhn6wh8H+UdK5KbgPLE6Lz7VG0fGv8Ds634BhGN/G4tvz3xrOJQECC57Yhmea/qcxeH94cIPmLhjIgMYcjG25Vis6L/C0VPhgS//sK/lPvAHqK6D1ZqBOoCwWtNWV6m2ZqBLly44dOgQXF1dUVBQAKmVRfJjx46hQ4cOTDfIT3D27Nl2d+nGjRuMbyHF+PHjsWzZMqt1afs35OdCi1I3biCKzO5rKB72h8qsC8fRZeX3qPfPabtmlMyghRZG7Kpcw4XohS608hbKYlwNr5tM8qWbm02AsLwCuMKuLwmGvz8QFspuqgogbN5CC3m3g8yCaVXh6MclDeXkF92sNnsiLB/Tn7yMPCcVWtz2wJzdjWx6QaR7lGPm4wk4G1oEnwoZPtkejzZplos3tOhNEjADxh2DeXIs1xlHJWn4g5CqgV+/EWYQVjWHUmct8xFUG6GWieCkK4eqwggK/tvAtTKtHM5aUwbWv20McNbC2y+fOcQcQEjgmrJMirJsCeOroTDzPMpJkEOnYT+MuQ9J+lvo45UPENo6jyoJeMyRMCCkW7DKKktQqA1xRQXU+ixybrvIxQUivoFIbZzEVbRpDSDksm4NfdV/dBP7jh9i8oWrRYCQ9nXKsxjPkd+JyAF/Ph3ASPIVWsq4SvPykMEHCGknEqlNgNAemUhaPiDwgRY7rN2RyE+NfNX48V78DWzhyTXRts4RwEFLC190XgUcNrOWGpLihU+uWrKlaPkwWm4E0bnlDXvBF6FTp0ynRY4eCPGTyuCiBxqrBRCSvDd5ZXFB7392yu7SWDLVSsYPkMZDiUrE2qxO0IIstUHZ/FWBQ+YAoaK4BH432QPCAIRSKUriGxk8CvmSmNXpm6N1+P0nlurncan4PdDU4/ZuEno4lmoPjel7grV5o+ugvhXglhin5t6FjgJehVoNstRKBF+5Bqlaw7CZDWC8GVBLc8n5IQoBmgR2c4wOAsK5IIA3WWkpy85tt8aQJKDhsa7n7AIaHD3OnNeoo/Pl6H6EyqfLK7HVPxsT0kMg1Qnc6TgWYVAQEBQEtUiLZSHpGJTtjxCl44vSNdFnfhtVsiCpsB5c+yFejYk9yk2YnA8CC9KuOeEAwgeIPWje7wOeBZgacx1n3UsMmxz9lmhR7IYvrsega2H15C3N+/TAMRwJ7bts6fdm1zlAihWqSqRdMCZREtjR0I19LzCXLX1t8Hn8E8km49mTiNX6pheWbGnGlO8w4wmc+vo3g5ylvf0jD2MCbPheiRyj8btuKciSV4/RKOEyyvQdUWjK4eeZjbTCEP0zQsT6IfOCYd1p9Kw7swFQcg1x3TgZV3vHlyMLQLgmR1Dq9eewVzAw7St7m2LKcQBhddRsUt2bI7zYig6/jV78FDAcT+T+DBdNGSNRShYE5sF4JSpFyL1m3Par31Ds9B2An7TPMPNKybonB7fHub6tkS3zx1sj3odEQu9fpm+lPwaMwKV+vbD0n+egVYmQe9Vyf+TnLnXWofCmDMpi0+cQ983H35ahCEFwZTrznp/gVooBHS5ajOHXA03RqNLF5nciJYvmXmGfY5sCRuLyhP5oMrzovgGE+5P347ktw5BVmoOO4R2xbtA61POqB61Oi7Yr26JUWQxXuTv+Hvs3xCIxUgpSMGLrCBxJPYIAVz9sGLwJPaIsVZgcOikfwMIP+1ruA3hI6rpUAzNQBxDWwCTWNVFzMzB9+nR8+umnTIPHjx9H27ZtBRufN28e3nvvPWbb7t278dhjj9ndCdLRDgsLQ3p6Oho2bIgEcyM4XkuNGjXClStXEBoaitTU1BrVz35oHyok+bV5MxK/+sLuY0YFQ6UKq4bODjVUA4VpsTukMg3lMc7M6yZDDKH3xioAwqpAv4AAIDSE7WBVZZ3alyC+raV5fU1k7tP+t65+FPUKXK3OFmX17YvJxpDzVhZvzGrS4s3mZunoed0f/qXCizecRI01CRiuSXskacw7Xl1T+xo4XapswsWlGKVmkq7/NnlOyuTXmcn43W+AkNh+5v52/IMhd9OayH3Ss8EnIo+5nkUiHft/t+r59+UnsVKg/LAXIKzyhLFRYF+vruj+83bTEi4uED9oAGGImgFcmY/5JDmbfSwCGGkhDy3j38cPkbNzrbNqX2yVgGN+bMarI/58HXI8sPp0I4ujIiOAMM3f6D8qljBgr29DU99YfkUOPLibc4DqCskbMnJNza8jV6anlduZvU/t+SolWHQ+Fu3zLKWhCTQjgOpeRLUAQnrnuKhfpImMBLwdYxYRSEiLinQ1E7BTE2ELIORLhfLLkZcmw1wjgJP8DuXEKGOjZnpl/8jM+0/A9i2/CkyJScRF9+p7TJlL9ZoDU9bmzV8ig5dEmHVPcrvmjIzqAF6VOi00ag2cL10yzndcHBKllrNvS+40SubEsGKFwtZ5wbVpzmolFmeXrmdsSimWKIFCPVGJLzfvqQDc9Guign7UWhEOHWiJtiKjdLL9Z8k9KjlkCPNNIRhduwIHDtyjjpjuxi4W5M2bQH4+EBiIlCgvjGiUUHMsyNdfBxYvrv2xcwChHqit/R1Wbw+UJLA6KBPTYq4bgFh7viVI3nbR9RiMygyyUAmoXk+MtWqF4XjLDZ+lN3ac4UjnyurVBulek7EtWgScOgWsX291yOaMaW+9lzJVuFJcYQB7Mt0rMGTsCUapIcYH2DMCGPEzcJRn5d0xHFg7EOi9FkjKB0ipYfPKNohSSNEy6QhudepgAkbSPtyC1JAp2Cdi/k1LtZfWLUTMd/qJ05aG8Jc2PIZxJ2cydR1lNApNiFxbCSVkJMsj+HTusGIyNGfO4O+lRt92rh2qEvHhS0j5wOhjaM95RvU2Rn2EZ268b1F8bb33MTLlI3uaMZS5G4Dw25iFGHP9DYf2R4V/9h+GTQEvYOOlJ2zWJaD35efPoEKhgVOlBL3/WIif/Ydj06V+jL8gAXZnnnoUpwa0wx15EIKnXMOowtWsShIv1gSNw06/gdiPHlBXiJB/3RIg5ORO+WonpRJXFEs8EBd0i/le4SdA3nCOwcf1PsV2cX+siczEXL3/IP+5++7ZehidFSgIEPo2qGS+E/htrgh5BWf9B+CnP9TVUrBw+EBYqZBenI6tCVsxofUESCmDFcC5zHNosbyFoca5CefQLJAF89VaNZb9swyDGg1CiLt+caumOvOAtPPQruU+IPNf143amYE6gLB25rWu1WrOwIkTJwygoDV2n1arRZMmTRhgz8vLC1lZWZA5aIg+adIk/O9//2N6SWzEdu3aWfSYAMr27VmJDir/f//3f9UclXC1h/ahQhmKkydXmUFvPmuhMgVuq6qWYqnRg2SlMXpxC69IQUmsOwsQEuOI7KXc3JiXt1upQG4uEBEB+LJKuExUBfr5eANyWrBxBa5b+kCb9CbkESXCOx6FSiCx+m4y92knEg2wZ0UHeFdUXzKzOsfB3MOCJGC+6HYd1wOq7w0Wk+WCqX/FVNvUvjrjcLROiUgBV5VxUZXq329wzdExUHn6oOTHPRmDCJC7ahlZFjKUL73DfrRInbTwilJBpMcthBh9JB1LH5KcpwTV8wnPZSRCmQ9JF5dqS3Mynhw8uRpzz4vqzK89dSqcFJCTUQwvxHaypexpv7plLBiEeoCQOW80YIBaAnRJopUiXyaFZx7L5qQQu7lZN0ER6JSjfmRaaNG410loqkESJougS388CtYlzRjut9ORI/VFUaYL+6NECs96KhAw7SmRolBjer24iMVMIkxNBAEinA8h1x4x/SqcNXii1QWkuLJMJXuy96OK5Pj+TGMEVgo/D/g+bjXRd1ttCAGE5E9McpE2g/S8SeushXEho7b7aqt9W0AQAV0EeFGQL95tNfve46FUIiAzG6KY++NBwx+PEEBIwFe6uhKrgzOxKCYVuQr2/LZnEd6vUobXr4eZSPUS4yNcZno9WAUIpTJ46ReszOedWHzmS8IGgDA0FJgxg17y7T8dSK6WNPaaN2fuSUJ9snX/IaajtduMtfGZg4p8hjCxd8c1uIoNAVko1/uVcYNxlgIEAhYpgTI9oZgvN+8qA9zlQEGlUY6eq6vQiPBUhh8+uRxtqYxBCi5JScAdS19DbNgAPGcpO2b/BAuUbNwYuGQqYW4oRcmidCyefday4rp1AEkKDxx4V7uvbuUqWZAVFSRPw3oLtGkD9dXLNceC/OADgO4VI0c61n36Np440f46HEBop6+r/Q3XTsn4R/9GgisrYW4PGBRf4oJL/7SpfmcoIYVAYKF4/HEgIwMHbh6sOYbjs++wjLWlSy33SOfC2rXCfVm+HCDrlqFDLbeT0lJ0NFBFMja9d5CXGyVAME/kESOAdesM90h6L/5f/dv4IpY1MnQkEWvSqQhMzQ1m7Eh03bszctPF6VJU5EngHa2E1EVnuE+duq4GKXHwo00r6pMIf5/XQKs2BYpmLdBgf8YRprg970R8RqPVE4MYzlYSQToUHmDkkY96WSr1uIWo0OTQbzhef7BD51xSqwE4/8jrGLjCss0VMZ/h5etvC7ZHScUyiEGJJ/xgAEJKamNATssgadoimR8UymyLjTNa/onut5eiV9ZPDo1BK1HguUa/YNPFvhb1dvn2x98eHfFh8jtI9CvBqBeM6lODdn2KE5JXTeod7NMdh5t1weXTcchJ9MGOlD4GWXCu8f8LewMHvXrhD2VPkOCGkMUIJyWad02fyKivPKzJLvh2eh6Z4p0M+5ALlUiGYqkn/HQ5KJKTtDc7ryY2LyoxPDQSk3pqkQwSnRoi6PBkrg/GbjX6FC6PeA+3/Ppg715GPOKBiiXHl2DqbqNV05I+S/Bq21cfqD7WZmce2rXc2pzUurbv+wzUAYT3/RDUdcB8BjiZUZIXPXjwoAGk48otWLAAb7/Nvuh88MEH+PDDD02a+Ouvv9C9e3fmtxdffBGrKSPOLK5du4bGjRtDrVajdevWzH6cnY3+FuXl5aB+/PPPP4zM6eXLlxFrp2eNvUf0oX6odO/uMEDImESbLajaO9c1XY4WuxuXnkN6wzCjxKhcAmK5lF11Bp9IFRwMBOmt+aoCCB3pZ5PGQKp7Ocb5JmFfrCkgQO04Kp/D7bvXNX+8djgaoWaMNkf6Vt2yQib3nATMZ70SobfFsesDTqwF3v4jljG1N5eAqW7/aqveHN93MePOHJPm7wm4VsMDuh8AIXlcmNBntIBWA0aek/+7NclPknGhjzsGmBIBS6eOx4SPP0dNgGoG4EuuYyRq7lUkBwUgMpHNMFC7uEB+n9mD1I/cBDm0eilW+rdvAyUD6FqL5HrhiLxwGTqxGCqFHE5Sy4xwa3UjZAqGacUFSV8R+4fYX55iKUp1GkbCicpwXnDrwu7go/iUah+iDy/Xw/Npph6sYQlXkRYeiopKN6jKpZC7axmJWpKoJMlskj7kBz3jyJ+upiJN71XItcckrygqENXuuMPZ+38eaoGQCkvwkgBNAjbvVQgBn3YBhNRBAjT08vIW/Z0wAejTB/jqK2DfvpobDjEWiSVkFrYAwgiZE+MLxQUxM8hXiBZdRaSqMW8e8MsvwJIllv38809A//5rsZHk8seNEx4b1XvrLeAfS1UC/PorMGCAST1rAOEdtRJFWg22hmTj3SY3mDr2LMLPuxiNQelGv046o0JkCgsPy2yNCgUC74F0PVULIKQO2pqzjRuBsWOBEqM0oWEi5s5lwEVHAcLY9RuBw4eBb41+PlybQm1V5S1Zm15mViVG9+9nu9xDQDKM5vO114DzAkamjzzCMpEcjTVrAPLgfOUVy5qffAJQMqfQef/bb4yyh2A/bfXB15c1WDt0yLJU587Cvzs6Jq48AQnE0GrZ0vF+2tonsSppHNbuB9bqEoDkCKjIAYS2ki/o/kFqPddY37n7FTcVFajX7rjh3dAeMIiyC24eb4eISqOMtkX/CYy1ZnVCv9N2oaAVf3p+9uzJyCATw3F69A2H5C4DlTLMvRFtZDj+8ANAH55Cx33HDpZN/847lr35/nuAEiZ69hTeFh7u+Ln088/MuZ24bw/TJt2renc6h1sulsm+YWUKTEwOwf+i0pEmsD2yVIE9R5oj7sjfJDGFxDeMoATXYS4x42pZuUGekbZ5RSsRunMLPPMLcPWl0SbbKloUoVePc0zfHGE07tndEmEqBeM/Zw440j5JhURnJj/K9ZMBCAFcbdMOuVdN36tIaST28DFB8NDWdZM3axb6TO2FE75dLYoVLlgGz7cmWK1OqgX0/W3CzKsQY/n6lpAKvN/R8/n9FnugEskx5fJIBFfy6J8AZrQ6gNGJU1G/+IxDl3q2UyRmN/wOkxNfRaNS04SQRJeGmBm9mAEBf2x5G0u6G7OoW58eg8ycL00Awnkh7+CkpDUqRQoQ+LYjuQ8kZmDnwoiZOOHRERPTFqJbwR9291UjEuO5xtuR3zkaZc5ZVdZz1OaFnrsblxqJC5uDx+FM8HDm1c+KsFqVfaitAgM3DcQvV34xND+w4UBsHba1tnb3wLX7UK/lPnBHo65DNTUDdQBhTc1kXTs1NgNnzpxBx44dQSCdm5sbSHaUAD/698aNGw2egHFxcQyA527GjrAHIKTOkkQpSZVStGzZEu+88w7jTZiUlIT58+eD+sGV+4Q+QGs4HuqHCkmMLv68hme0ZpujxX3KTlRXiEEm3K7+GhNz7Ojya8iP82UwCKXYCc7utOQMFF82As1cj1rqSQs1CRA2asRacp07B5wKLcBnXRORGGDKQrMnc5/rY2yGE97eF41HcgJqdiIdaI1k8Sj71DxIkmbw2BMOe4NtWdkGQcU2Puod6FttFp3ecx7m7n7TZBf3EiBcHzQawzO/u+sh3iuAUCzVMcxA91AVZC72AW8kNcr3s/BtWMmCiGahEYux+L2piHZ1h1YkwpD3TBNQ7nqS7kEDiQ3q44/HeuClL5eDWDjmcWpQf8T/8RecacHVLI4/PxTOxSW42aoFlE4Km+M/PfAptPp5GxJ6dkV6o4bo+fVyq6OrLBSj+LaUYWu6+KnhGmTbPXXplnV4aeDzTHtOPL8vazvInzkdbT5bzDIxaKGLA1hoYf+ppwSrqfr3R8rmTcy2xzqeRYqrMEM9qtwJM25GYm7kTSQ7C3uERZUosPuoKTst8lICbtaLYBksvLDGLjIBCGmRbiYrecVolNJiO5nU0oKiQNKToXla4Hv3XWYhne6n5ENIV4iPRAa5SIQ5kSmYFcUCoY5k77+eGIaJyXqDXEpAEYlAQNa9lra8K4Bw1Cjg4EGAPAnN2M6ghUzy0FIqAWJ12BN+fkCDBsARlnkgGASW/P47MH++yWZrACExSwOatQD69QNKS4VZINQmLf4LqVrQtjffFAZhaBstDguBLLQtM1OY+UXbvvwSzBzpoyqA8MVHEnDMl72/2LMI3yHXA6tPNYKfRMZc7zKIBGU46Vwu1WoM3ovUPp2DBKIRO0QobDIIqQKNj4DhrWaLWpR8SBKV9C0wbZpl01QvLQ2Jw4ZYbIs9cJgBpBNHv2i5bdcewMNDcLHdFtho7RSrTS8zmTWJURo7hRAIQduIZUnnsHkQsJiezjKLzOOzz9hzXghooWNDTCyh/dGxa9JEeBuB/bTALVSPWKPEiqLrxTyoD126CNcjwHjlSuvXfHW20D2dkgkcBfNs7YujmzjaJs2ZEEhkbV8FBUBKim12Nnd/dbQv1ZlLG3XerH8dC8NZ9pq9YBCVffNmOBYk17feMs11797C2wncpudyr17C9xCz62h1UAZeaniVKWtPcsWqKw0wKjPY2Pa2bSwoLjTXdP0VFgozam0BytUFm/XnYGJHFuz4x6sYz5PPs1kMSPfDBwmRcNNIUSJRY8ErcdhweYNFuQ0n4vHsDhY44trkCoVRQsn6H9htzwwmNBLqShGjLkKvwZEbf4Q8IBCJPVgAjQhz9PvSqOoxGrl3Ip0aKLpt6k9H+3QJVKE41VJ1wTVIjeZX2XcGZgw6oDxfwiqhKHTQfvghGvTug6OelkCfrctB9dVcdH2hk2A9t72/Q/VYX0FfQ3pqTm91APPOdMUVX1Nm3urVrRCb52ax27NeXfBT9MfM7yKtCnPOGs/t3f5DcTB8CipKk7Hw6iiHruAvoz9BplcHDLz+Hh4tOmZRl1h7BBC+1/8SDsbkGraHpHWA+PJefJvwDNw0rD3BZvVAbIh6HpViJ2hFUmy70RcSs8+hWdELcc0lHsPurMGg7I0W+yuWesBdbfmddMCrJ76KGoXMLjHQSUy9vmvC5kWmEeGX5e3gVcEmDH7QYCXUrrEgG5pN7CfLAxHkP+j3mR/yK4wMaR9nH2S/lc34ED4M8VCv5T4MB/ghHWMdQPiQHvgHfdjbtm3DiBEjUCSwgEl9J3Bwx44diBGQWrIXICSp0pdffhnffWd9YXzMmDEMICmuhQz5h/2hYv5y/6Cdk2VZEpRmGREEzwgV5B6sVASxkRTacrhEkvSlCBUSV+Z7zBZASAwAAvNqKqKiAJkUuJbItkgef4+/fAz5LsaXVXs+Lqmud4kUW75oDw/orEKyAAAgAElEQVRnCSOVej+DmD40V5ToR/+n+LpLEja0vs387cii9nMnwzDlUPT9HI5d+05o0Qie53IRrErXlxeB9SCs/Vh/+ju4uAED40Zb7EwpluOt+v/DksQxdnWEDxBqRRJI6EupFoLv5aeTSCDSapEfGgzvNG7+bOyUzqkqukVFsvbuZpgqDfrY9sKoheE51OTqFV9BVlGB4a+8ZVLvr15dcWHiy/DJyMTwKcYF0G/XLEMl3az0oSgpQezh45CXliK5TWvkh7MgEMngkURl+NkLqHfqDDIaxEJOzPqVayDWaHFi2GD8M/Rpk33KysrReO9+dPjedGEnt14EfFNuMVKihHVZUQI0tHW2f1/0eGM6tm5YhW7LjM9ntVwOlbMTlM7O2D9lHOIbN8fthEvwCA/HgHoN4GLr5sWXe8rIYBfQ3NyQ2ImVEZ/dMBkbIrMM9xyuMyMzA/F1Yiw8vINQtO5bTJ7RAuvck03GTfeq524G4IMrUSa/h91KQ1pYiIVMrU2AMDrGNgBIi7Ek/0ZgDkngLlwIkPoBrRp06gSQ9B9Fairwwgsm/aEM+dh+SUgqYxdI+RHlFYVhzr2wLul7m9n7Iv3FY2A1EVOI+pGczIJr5iALLcCThJ55EPuKFvZpHA5EtQFCYhjRPjnguG9fgOT9KGj+du409mLVKhZkthakYEEa4gSAEAA9fryxLfM6BJaQHCUxFHkhyBTjvOmIlTVrFgtiCi06U5skB0fsNn7ExbG/Z2UBw4aZblMoWKCSZFaFJOSoTbpGzBlhXF+0WhPQwBpAmKlW4oq8DD06n60WS7WB0gVBUivS5nS+6MHNcp0WtC+tTgdfjj04fDiQlwfs2mUydpsAIUlQ0uJ9WRnwBO9eP2iQEUitrGTZpfx46SXD9SX0LksyeBQpndpbJD3FHjrK3hMIVKF2uPjhByQOsZTCNMihEoBN55s5uA2gNrzM+mT64K3EcISXO1lKjFYFENKYrIGHQucZlbfVpi2wyxa4ZqvNjz9m75lC/XzjDeDJJ4W3bd8ODB4M0HnhSND1R9eh0P5+/JFNALEXQKPkhAULAEqQJQ9GobA1dlv9tsWoFapHx5M+bmwxCAmYonuvveNzZF7tLEvPP7+OR5Cn9+B15FvCRylFzlOHIKLkEr09icluac6sJRJwY6fzlMBCLkJCjJ5+vHnp1fws9nkXMKXsSa7oleeNveebG9v94w9WYpTY4on6D0Ruq61zgtiF9CwXOkZ0TyXfW6FtDRuy5+HJk5ZHQj/2ym5dQUoO0+NvYHOYUZLSQ+GB+aeC0DPd6AtMfsZ+Bw5hydA4fNAoBSUyYzLZ0DR//LiCZWyRzCgx1ou1GjCqEbPeNzyjhO7JMb/vhcjdHRmdO6KEZEaqYDSGFDhhzMUwfNs0DemelolhHKOReyeqyBOjJEMGhZcGbsFqhkGoLJGgJEMKUiqh917XQDWjImF4PuzaDtUcU/UYbpsQQEh+d+bSqdykS9YsRtunW+Fvr67QmOVNti84gM8bfYuOGZbvNccCnsH2sMmYfa4vtjZLNGHmvbY/Gs+cDbM4rkf9B2FH+GvG3zUViM7/EznOUShybYD160UY8rwWn59h1bzsjen1v4TIszkmXRqJ0MpbFtUIINxwsQ/6TTqGYiejRL9c6YGmh7PROGs7xqezygpn1c3wjWYsUuPqQSuS4bekvsxlwY83G21EqsQTfXJ/xUsZy0y2vRGzDGlOkYJyp5sCRmJrwPPwFR+Bd8Tz+CvOVMXpbmxePDL64dsd2QjhqThNaP6HQV2Eu4TtndPaLGfuP8jti+9DWJv7fxDaftjXch+EY1DXh5qfgTqAsObntK7FGpqBmzdvYsmSJQwQSDdguVzOAIJDhw7FlClT4EIvsgJhL0DIVd25cycDAp48eRI5OTnw8/PDo48+CvJA7EuLSLUUD/tDpSYBQpIILEoltp8ITl5a5uW8KhCgqsPKN6SmspTZ5xOrZLL9si8poFLIEBCQyeymXOLGrDfTtpIEYQYhJUZzYF5V+7Zne4EsAF4qo7TFmZACjHnmrElVez4uuQpumw5DmtER+2H5Qj+tBZstvuis8LbxSW+hQbGANJmNgbzbdCfmXRDIMOfVIckT+qjvM+koSpxsM46EduVWIcXvS9uDPuDow4L0/X+4ZMkmom0UQr4H9Ps1l0aIK0uw2MWrcd/hy2uWwJo9x49fRiMRY5+8O3oX72Z+rk1wzbxvrfP/hFwsxmMtjjPeDlwkuDbGJ00XoMf8LFwbXYhPk4wfg8c8O+OWoh7aFR1GZIURLGEBQhG0lDkogoWci6PzwpX3iFAxPiOUlcu/tnOi6uHHz40f12K1GhOGsRmrSlcXrPz+G8MuJw22ZCxk14+Cf5Ip2MNV4D7S3008gzGjjH48mxbOhby8AhVurog6cQrtfjD12CAQS+niDJeCQovhrli3Eg33H0DkmXOIOGOUX9s+401k1Y9Gm01b0GS3pbzh8eHP4OYjLRB56izarf/R0G5K61bY+R7LbHHLyUXX5asQefos7sTWx9aPZmCgXyi2Fln6gzzjEYAMdSVuqCosfF1f9ApCvMKFYfAcLCvEL8WmH76K4mIGICz38gS1k6QqRztnD2ah/n/56QxYO3GoEZgqCA3Bps/nYPxz9l8ne6ZNweTBI/BG2hV0+m4tYo7+jazY+vhjynhUeHow4w2UyvGuX0R1TylDPdXRI8h65y1Ma3QdP4aaLlwte2IZnmtq5qVVXIwfxrbFxLhrKJKaLlzNvWxMRqDnQvjjfXFrtylYQTu2CRD+/CsrC2crCMwgqTZiS9JCsbUgb7WjRw1bD8+fjM67LP2FRjYbia/7fY1ikRxJzw3AQr9T+C3EmJ3NNUDZ+6p+o3F68AAsDhLwwTNf+CdZdgKsiJFCYAjR3inpi6T7iC1z/TqwYgW7mEmgFYE1BCY2bWpVws8hgJCYPgQaWQvqC4GatuZQCIgzX6Ehf0NaHCZQkQA4LghIIPaoACvRJlOMgEeS+6QwX5DlQA0Cwgik4Af5Q5Efm1A9btFYaBsByRxQRSAqleWCY2CZ1TPvPzFKI2VODGg3P/JWtTymiJExNSWcWew1BElEEuBJ8nYkmWANYCDwgZO9PHHCKKEXEoLL2RmQlZr6GDPX4JQppnNIUrAk3Upp+nQ+8s1+zPfLLfoLMFmYa1wPEN7q1IGRN+YHt03otBQEG6mve/YAnNf6F1+AkX3lB11DGg0OeBZgarN0nJUY3w0dlZuPL3LB9KuRaJPP3muZ8VAfuKB9EwNS6Fwilu6LetakNYBQqB79xl1X5J32k5l3FbeNrilKiuCHLXahLUDk888BkjwV6icxftu0sQ5y0v2XEgMcCVsgJyUoUKKCPQAa//5D9zAz+V9Dl7hyBCBSQoO94ShASO2SzKgtgJDrC/XVSuKvvd2zq5xAYsohz3x0aSmcpRlZrsCspFB8XP82bjoLA7+HRh1Cp8hOLGOcY/dTZzgwuarzmrbT/ZvuHXQt0zHn7jEkC7piBUgCtToS4Cl8CVRurs2ThAhQbt1a+LqlX22xbbn7HUnuvv++6SGwM1lADR12eubi5aZXkCVVo2N4R6wbtA4+xy4jd+7HjOIBscB93n4bXgMGMuy6NKdKvNn0Ok57l8BXJcXk1nMx+2m9lx4l+JDXKSVNEQuYl+Bxs1N7KM3UaGIPHmHeNVTduiFFxQJ+1hiNT6b4YdaFevBWSBhG44eNUgTfiX472QQN800VIgyTY8ODkP8MSCF234cfQhMSggabNkOkT0i/2radiRQqtRvRTAWdWoTMm2KoSk0ZWgF7lyKmTWPc+GgtMhcaGc43PB7BiNRFeOdtHYZ+091kXqiFi/P+QGg9Gc5M/AS7Oy8yYeZ1SfTFp9samxxvqvNT1Cyc9u5lyLcyF/6h04VeY2YddYwF+cUzXyD7ekt8crqrhW9wnlMYPm74PUbceQyvPqeXuOb1rNXJs4hOycDrqZ8yv6p0Urxe9jmKZd4obByAn6/1tbCEnNhsD7ylcnS49SWezDFVEJjQYC3yZX6C6wKn3Nvis8gPUa/8OuYnvYIzYQVY0i3JRMXJ0eeuvKgpPK8ugCK/M9oVHjSM45W476BxjoC3hNacHiwGId9/MNYHSMxjD8jD5EP4sK/l2vU8riv0r5uBOoDwX3fI6jr8X5mBh/2hcnTks/C/Yb/n09LNazHszRkME4WLS4/1ROM9+5gMvfJcY2qYVz0VZG6mizL2njdXu3ZCgwOHGZageTDMJT1AWOmiQJBvOvPvomJP6EoljA8Baflz8X/1F8O/Mg2fPnEYJfv+RuJ1e3thvdzncSuQ7hIDqVaJz84bpdA+6nUFvzQxsjEckc+hvckvjIXLvhVY6foaokuN4MX3ke/jrDcLDLbN3YlhqQsMnZvT6AfkKYLhpsrHR5cGWXT6q5gvkaUIxyvXX0MALxuQq0eMhUXnBDxs9C3Rwtrp4AJMflbAy8bOqVy6sRmap3sxACGFEAhobdv3QS9jhx87LvN6t5zq4a2Y/0GqVWH95f529sZ6sZOBrdD69gloSDuT5NXuDYEQ7Qv+gkgkYoDA0DVHMPH2Fzjt3gbzIz7EkK0Z6O7qjcl9nOGqKUbj0vNIcopFrjwAHhFquCTewZLEsYZBEUBI/WdIOmJYlXwTmoWUx9ui4Ymj0GlETJYtyYhqyBtQwXoDcnHm6SdxeuCTzD/5TLiqDsCzU9+BTyrLQuWCgLl+8xdDrDYFnw+NeQGjR09iir2eybtozT/4dTr0Xvx/zGJwVkw0fv54JjS0iA1ACJAk2Uwu3LOyEXLpClIebWkyjnonT6PfvEUm/eTqORUWYcDsT+F7MxWFQYH47YN30T66AfaU6L/KzCaBQJxZWcmGbGnaPMIzEI84uxtK3lJVYHEuyyhr7+KBIe7+EOtZViTlR6DfbVUlvCRSC++vD/3rgSQRuaDye0vzcTI1GU1//wMV7m640JeV3eKAW34XMxo1QHACK6XFDw4gNJl7fQHyYSPZVBpHA4VwklBV54LQ9v3J+/HclmHIKs0xLFzV86on3JROh5RD2zDizCwcKTiPALEr1pyKRvNcd0b+UC4Sw1sihfvGTcKMoNlzmEx3jr3I7YT85WIWCfjKVWdAVIcYLiTtRv8fNAhj/3wd354x+p5R9j4fBM1QVWLtob3o/N067HFLwbywC6jQGIGVJg2fQa+uc5neCAKEtIGYZhcuAJ6eQD0r81fVeAgM+ZRd5DEPIYBQ8eabiKgfx/qe8YMW8J99tqq92d6+fr2lnKC1FG66P9AiK0m4kUwpj6nLADzcmN56C4lzP7LYrwGE2bIF8PFht1+5wjJGKWgBfvFiYz1zMIEPAlKpv/5imaTPPMMyl7ig1TtaqObCfDwEyNEYzH23eftLVyuZc50LAuyJ+UGJB+06nBb0mCKp3rGXffC/RrlWWapHj7VCiFTfV2JqmifpkZcXycSax4YNQFCQ4LG80qMLJJVKk222QDqrJwSxZAloIVCJ5xFZ0a0rUlVGUKGezAkymnvyXp02FXl/s2xCCpqjwEPWJWnNAULySY04bAT5mUbM2JwM8P7qqyxw0bAhNO3bYeHRhXhnnzHhxy65eR0w/0Y0BiYZvSC5fhvOTfNzhfry8ssssE/HiqRYOdCjugChOWOTfx4IAXO7d7PgMbHwzNldtsCLr78GGjcWBuU4HzeSmT3A+oVZXC9C4yPbCpJ4FgoOZCF/P0oq4Ae3zRq7Umj/3G8EdF8y9etiZKgpCYNCiFFs9STXA7VVsajN69++zTJxrQV3HIiBxwHIVJZkkgkMpmtLwKvVVjcttrVqxTLoif1LQWbwPAb2k03OY4ef5bvSyPAn8XXr9+HR7FEUFWZh8k+jsO62ZWLPk7FPYtvz29i2L19m2fLUPt8UzNY5b2swdNxffBFzxIeqJQE+50YUZtyKZL0Ou3Uz7unOHfZ5TFLYlGTBRXWvTarPl8zmWLH0u51tpssrsdU/GxO+T4CUaHUqFSqWLUWH9I9Q7iqBm38E/h53Aj+sXo62K9dALdJhQ9gdeHcfi1fHzYCEd++1NqUcY5HbzsiP/qW/jrt3Z8An8nwc3+Aqvgs2fje7qST4KKEensy0TLzaFpSD2fEpJolhL0Q8jQ9XZUFN55oDYc/zh54FfGsEv/hKVL73Dpr6BeLGm28g85Ip0z7q1AoEx8QxSgB/P/ER5Mf+QqpzHB4//zkUfuw7/7jWpzDu+hvM2MUQ4Zj/IEy+8iqTo7XN92kMH7fDhJnnUS7Fjv+1Z8rSyga9f+crQrGq2/dYv5H91jPP2aD8LlIkJ4vdRtMcAwhXd1mGb7Y1wnGvrhZqHt9FzkKSby9UuL+A44+sZcbDB6XeiF+CW7NjMTWVtQ6iKNa5YbLfJlQGi/FK2mfoVKCXxtZvn9T8DwZ4Q8FpzL3xuskRHB7/G9RimeCawW3nKHzVaBWKlKVYepFVHtCIdNjZOBPLOt1Cgd5D057nrlO5Fzyvvw9pxliIIPzh7y+VM/7ptLxEjzt+7pIDp53DRaftnoYfLxmTUs0bIGnRMhX7jfD908ALeitCV5krvJy8rO5vWONhWPi4WaKPw717MCo87Gu5D8ZRqOtFTc9AHUBY0zNa117dDNg5Aw/7Q+XNW5erZJUUBfpDXlaOHz+fixI/X4RcvIz+H38GYgqVeXli6ycfYMSkaRZgnoHtV8WxINmP4jQZVGUivDVpK1pNKIBEqsOkISOtAoQkk5eToEC5uzNCvNKgrpSgIMPLIF3BBwg55t2fm3NR2ndItRiEV90fRYNiVrrlhE8fbIwwLvrwGX0nwvIxadAlaMWs7IYj8jkijRQuP++GLK0HHm1WiRE356Bp4WHsChqNvUEjTWZRpqmApyoHefIgaHlagebswoP+Q/BL6GSmrpOmFH0zvoOXKhsH/IfghlszY5s6Hbpmb4Zf5W10zDXNiieJ0Vf7n8PR+kZ9e35nggoUGHUiAl92zEeZqynTiSvXMckHkadW4aAX65FgDvT97vsUVgVPgodEilJ1BSanLUTHwr+wPOQ17PcxyooFVd42AcOIPXhHzvp+CIGOu3z7I00RiZfTv7LrjnC8fTu0PXQYGpGUkaYxN1O3qxGBQlWxHDsUsh/N4y/ewM7xxgVWuhb+96wvQmQKTL6ehN9eNHqc9F6UBc9INf6a5QfpmWy0LDnJjPXdpOkQiXXMRyNhepz0DrWvFkkhJbMOXmS5BuPP18ZB09oXssaucM3JhVSpRGFIMKQVlfBKz0BuZDjDSgu8fgPF/n7MfcA8QmUKxmPtQkWJ1WnyyLyDEZPfMGz/ccEc5ETXg2d6BrwyMlHm6Ymok6eQHxbKADeLg2OZskIglT3H4uURYyErN5Ul4gOEttoY+tZMQ/LE7jdeQVKHtobidO9zy81DqZcnNAoFZvlHMgAgB/Lx2yUQp0yrwU9F2bipqkRXF090cfFkAGF7Q6PToVCrhptYgr9KC7BLD0a2dyEGoeVC8qGyQgvWYphEhv5Pm0oekjzpiWeHCAKpSR99gD49++Kb/HQkVJoyf6wCU/YOyEa59OJ0bE3YigmtJ7ALV1WEWqvGsn+WYVD0EwiZ/C67EMuFnpVlS37wRsf2zEINF74vvAif8Ua2alX7d3R7VSBonkaFj7NvGpotLE7DpYMzcCLtKFydvdGn5xKEh7bHFJ9QRoK21kKIpaDfmRBAKJ0zB1Hde7FAGgFqFLSSQkAbyTLeTRQXs35N3AIgSRLywTVH29YnGVhlihHbZzL73KwyyOdtzBiAFoGJiUmsmdoM3gJwhU6LdJWSOX/pnSdUqoAoIABr6oswSmFpkjM8MxBLE2NxJzsfJZ5OVhkZm4sHY3C9buQjwC6+m9+rCDwiOV1i+uXr3wuIlTR1qtWRU9IX3Tf5Yc8Crd1TWVIC7VNPMSxqWrwW8RbNNSoVsnt0Y2TwiGXpPmcufLpZT4pK6tgO/NQ20eOPI+b92cJdIUCFfPk4Nh+v1JyDczDrz1mGX+yVm58T/TIGbLoFRT4rcciFVYDQ1iTRPZAAai44Xz/6t54xZdi2di3LiOaCrhMCk0h2k68WQ78TIJmUxJYkQIhYrxR0blBCADGKKDjZTvp72TJL6gWtYNM1IwRscJ555tKz/DbpmqNxcEH7pqQEui4psYD+4wcHkpGtBY1XaJv5fNoDONF4aW6JxUdBAORoM8Y+STn/8w9AthxcwoG1Y0f9pASBp00lxE2KEzOaQHlaraZ7LHcMhNokf0iSB+ZCpQLovkr1+PYd9rAnrfWZQDECx8xjyRL2XgGgX9Pz2OVrBAjd5e5Y/uRyS5UAOj0v/IAJ2yegWMn6mTH1Y/phx/Ad1nrA/m4OYNMcmieuWGlBp9Mh9qtYJOXrz21euap8kGO8onFt/CWISAbUnrATzDM0ZY+uIcnMmt+H7WEXAjCXKiSJQplXDH46fwKK0jJ4NWuO8T42AGjzMS9dCuWPP6Jcp2ESyhQNGrJqBhQ89v1+r3w81/G2ITFs/g9lCCi3InFNyiRz3sH4Sx/jSO4ZBLj6YcPgTehUHoSbL1sqZJDyB1/xg+vin++8jnH9zSTABY5Z0v690JLUOC/CDhyCs1SGxCMHoZ36DorSpIwfu0e4CvUOH4acl7AndBoQ6fLd9gfQIv9P5jt84p6BCA5hvwfWxLbDqBF/W1Rb830rxOS4oVjuh0NBL+CUV0+8/5kbSLWdCyJjk0ItBUfopsu8R9vzDMPO3rgz7yscil6NdXv/z6JKmcQNLt7OyC/NhlrKJvyYg1LqIhd4qE3VWyq9/aAqF8EltR9++8WYkEgswJLF7+O1J9zQoHeZyXf8Jddm+CiK9Y0emL0Bz94xlWbdFDIe54OeZ86vRWdMn+e74jMxp881pq49z13vi8vgmj4SzmIJKrVakG6SeVAC1uA+Uibng/If7L3M7Z13oXJ0P4paEombhbxnuJUG/V2A9DeAkIVAtuknm2CNKK9I3HjNfoLA3Yyjtus+7Gu5tT2/de3fnxmoAwjvz7zX7bVuBhjZ1HB9Rl9qairC+B/HD8H88BfeSR7vhfGvMR5V+TfkTNbc3w16IuXn5y088bxuZ8AnNQ3pjRowMoLETDFn+xH7yLehEt+s/xYuhYUMwED+ZH0/+4L5m8DFDUvm4/RGPyTucGeyxCgIFInuXYb4vfvReIrZxz0AYhASxpFzRYGCIG9Eya4jL9lLLwnJHjRBgPBPoPjR7oyimj2xM3gsGhadQJpzLHaEvAyVSP/BYrZY1jpvD56/xTIt/vHqjnEvzibHcHt2YVpGK4bHlyo4K8RMMnp1onHhUYxJnmGo+k6z36ESW7IwbbUtJGH64jOncCm02CKbsM/lAEzbHwNXpRSlcjVeHOiGjNA9Js2TN1i9jECoL17H1b1uWLK1HBfn7MbE20YWxojG27FhsTtatdIhulu5zaG7qwsRVnkTqYp6KJF64I2pIiz8QofXUueBA9qogddiVyJTwX7MWpMtNd/RtoB+eDL9N2jJU09EBDzbIM5O3wHIl/lieKaph2qScxzql7MfJ4vD38Nxzy6YcHsRuufvFRwb12+6HisKxci9IgcxcF0DNQaWEG2jdTkC0mUuLADoLpYiJUGEP6cbQaL15/pCJKHMVCM4SHKkxEikj7HvEp4x6cOBuCeQtc2SeerI+fdJQBTzYUVxoLTARBLzHb8Ig58VjYF89nzS0hm/wqrYhxwQNS/nFuNzwg+S1nQRi7G6wLp/GvnxvTzyZUO1488PRYPRL+Ov0kKGZWMexIjbVJQNlV6OziUvH0oXF6idFAx7r6erN7YIyIVSP+lD7kBZIX7lyYH2d/dDd9e7BEcEDkS6qhIkFRVOgIAA0Gh+DKiJ+QFRuNm5o0lrh18agfNP9sEzb86EX7Lph2Lxrp1o5eHDyBV+lpvKjI/CnP3oyHlyv8raAghLunZhzi26Y7uLJQhasBBo165Wu2oLBKV5np+bajjf/aQyvO0dguWnliMu6nHkyT3RycUTlI1fq0FeRsQaEgjlhAm4+SWPRUdZ7XPnIqpbT+D8eRa8Iwk7khcV8tqrTscJsCT5U2L10QJoYGB1WjGpY+u8uOvGa6sBmgMCVfRB4CB5HckIFKPffvsNAxd1wS8wSp1zjIwpuWFMueP1I+GbwIK4xMgw95h6vtkorB+4yr4R0EIvgULBxuQVoYrHPp8Hv5/1qe0A8qMi0WadJYhp305tlLImKafTQZ2XB7GrK8RVrOzd/PYbqL77jlkiJLZG8M7dcBIAAG31VQhosFduPsYnBr/sioK8sMhkFwxASBJ+JOVnb9ALL0nekuwusazIa4tPfSDwjVasCeR0IGmFub5pFZpYgySzy2fHWusb+XVyAAFXhmMJkgxxupl/sT2ACB1vYiFSX4gxTOxJvYKAhZ/nU0+x2ykI0KN7Ewdac8CiUN/tAQjtPR5cuaqAOFugDn2n0nyTrCb/Y4GfnMHvD3nTEVBK/68qquqXrfoEupIxu3lQYkcvNjmQwKBhbZORU1mEDuEdsH7QelhVCSD/0IIUDN86HEdTj8LPxQebhvyEHlHWwX3DroktTqAkzRWxO+28fg/fOozOqzpbDGGkZ2d8vV0HD40URRI1JscmYl3QHYtyh186jI4Rpu9ZVqdMSKaYO+7EgjfzxTPI/1Z1DElG95NPWPUAOibcdU0MV5oLLigJhuedyZcqpCKcRCHJjhJgQqoMDgcx9ulYUKIJ3Yes3GP470S3Ro+GJpH9fjIP5h1txy6o3VzYxLBGgxDiHsIUO5Z+E35DjaDfP9NeRVzXHrizYD5iDx9jypx/4nEk9OyKUa062v0O9eeEMQi7wLKDT78yEcOeZeWbtVotrvTuwfiPU6S0a4PeC7+0a4rotkXEb8Lm+VPSf9Kb2BbIsrr4zLypf9bH0DOhOOj3FHZHvEkkdZAKtKB1we4AACAASURBVD237PCOZZia+gnaFx5i2qVvUk4CVKiz4n0/4dnDrWsFlJJXROCZX7/A0zk/Il/qi+UhUzFufghe7umC3m8X4soRGeqXXUWx1ANZ+uRf6qOvMgtLr+lls/WdXhs2FVcCBqJCp8FCM4Bw0nPXcE7PSqXn7qOhQP0vgVIl4CoHkl4FTtwGOutfcxS53eF/ajvofZtYguT9bh4TRosxY4yd4L9dZ0HVhXLKchC6KBRKjen3r0QEBBlt7BHszgK1jfyBhGyWRZhhzKtAZgksPDHlEjluT7sNPxcbFglVd/GBKPGwr+U+EAehrhM1PgN1AGGNT2ldg3UzYN8MPOwPFXPpPmLtFSTLDLr6L8T/jIDOOrSdxmaJU6bcqaVeKM+XoNHgYkT1YtOUBs78CNKfjKwHbvYJzFu2aTW0vAUJv+Sb8E67jdTmTVHh4Y7Ng9mXe34M2ZLOACG/jmQXncbdXoKe+b8zf1ObWhWQe1WB0/3bQOqrRpOV/zBiHXzza1os+zX8Vfztz3oEkXrQrHeUiN/4Ppy0pchwikaH3N9M9psjD4WLpgi7gsfgiN8A+04ikqxS5kCurcCZqF+Q1EA4U09eHo6omx8gOXI2lM7C2WAhp/8PcUWsrKIjQR8JnP+AWKuGTKdEpdi5yq8HkiV5klWKNMQria8gqvSi6W8DzuFoVL4hp85N7oYlPb/BioktsPD6BEPZMT1+w6yPdmDctvEoVbFMMjoOiuzHEXJ9M5J+d8MfZ5R4aYoaQZXp8FLnItGlEcPYSz3CShXSx4x5fD5dhshQMYZOtgR1qB7V8VdmYlz6lwhQZmKX79P43dcoOWovQDjf902Mz1kGD1Exw7yriuP1ZdjbOOLV3QKA5Gc+0liCW1eg9HgFXshcgc4Flr4NHEC4uyQPv5tJVXIgGTHH+OBTlNwZr/qEMuy69H+ckHHSCT5xSkye8hKcteUm4Oaq4Ik41bUP6vcpgcf0oxiTzmaGKsVyzOi1Am2+MmV2OHLuPeXuix6u3iZVCMAitlwjhQsDrHHhKBOQGzvJ6M3MMvoUfhoQDSd91vvs7BQL2U0CzZxFYoa5R3590SdOoSA4CBnxDQ2AK/kCEtOOi6Ee/ujg4gmlTot37tywmAKuL0Jj4DPqiO13S1XJyNj5Stmkh3sd2WolPskxykCTXOmnBBB26mDSlUOjR+LCE49j0PTZCLqaaLKt+I+9aKWXQb2pqsDFilKEyxRo5sT7Kr3XA6vm/mwCQRs3Qr18GXP/lMbGQvTNN1XeN6vZDburESi7vZj1q3rS3dcAsNvdQE0U5C3qGprTy2dWFBcjtQ8rW8uFASCsiX3fozb+lQAhzQ15LRK7iBhL/CDQdONGfDN7JKarNiJXpkarfDd8fiEGYRUK+H+9FF4xsbiTn4vMaVPhkpGJ4vrRuPbmGHy05UUkiG/DS+GFLcO22LcI78BxyslMR8bY0XDKL2AkoKWLFiKm5aMOtHBvi2b8fRRlN5IR3Ks3XPwDHN65OdDgqNz82kuPou1t0wX52OAwFgyzIuNqtZME4JaUsOC6PSvKDo/WzgpCoAf5WxJwRYAdsYS5IMYiR4exs3nBYsQkJICC2iOQij9+kqik/ZMsJwGEnL+keUP3GiAk0I9W/ykc2ffFi8D06SwzkCR4SWqV+yiy97gLHSP+fHBypH36AAREUXmKZs0AYgraCj0iUm2VAB4YdDenhK26Y38bKywBHjfIlBk7YwZ+CMzCxB0TUVRpBPLHthyLFf1X2Ne90lL2nCeaFwUxQImNywX56ZLMLPkhE1PU2vlp397YUgTCb9sGdOwIEKuUFwM3DcQvV4xJHAMbDsTWYaaecI7sqrplS3/5BRkL5hm+N/0kMob5TeEy9BmI+CAnbyeUlLEmIxnFJ/6GOjgIQ1t1QKhUjmPlRYyCB33RdXH1ZFQ8GElLO4Pe6cnv1wki1FOYqjaUKitx6eCfkHt6oVnrtgZrAGtNV1cu0lkphlulFCqpM2ReboYcCG4/tuQiF61RYvE3xm88L1Uell+17gud9+cGDN4ZVSugFLRyBB9IhERlBKW+XaPDYzGuGLWgGPt+seLrodNh06V+JtP65wuzMOOrXvjtoBK3np+BVsUnmO2Z7hUYPPYkINKBe+4O3ASc4+H5LQKBrcOA3muBJFre0okQdCgBPsooBEjluK60XId4YZIGc4fbkWRh53llb7Eb+Tfw9t63sSVhi0mVwY2Az3oD0aaf3yZlbuQDb+0FtiaY7m1I/BB81uszRHkLJHTY27EHqNzDvpb7AB2Kuq7U4AzUAYQ1OJl1TdXNgCMz8LA/VMwXu4lF2LPzTMi1SsyMXsSANxQE2FEcnuuLzNN65oIIeOKbTDj7aOGamwe3frvQxQz8IDCPfAttLUoIAYTmx9BPeQdfXxvFADbUplotRv4VGVaufAWyykpEvnEZ8ZUJOOPVDt8Hj4ebphgVBJDJXBEmZTO+yA6EEibpm4ziifQV6JnFkyYCwMmROnIO8cseaxeBSidL8C8wcyRiE7+GVOMBtaQICbGTkBtkyY50qgzHukduMWtA5kGJpWS9w0v4NBShbdVJ/KV6pLxDpA8uwsuu4rVrkyFmhMtE+DH8TcidVmFJr99QpFDCvbADzr+/HsHO9RDTw/QlmhhvV7a643pOMuLnPA+V93FIVN7wPrcOTnndGBDwdq4G7foLA33UByGA0BZ4SNtWXcrH++OsM2rc1EX49krVcjKLmkzDldwYPKHaj2KpJ5Kd6mNWynSrp8OS8Hdw1LObBUCY4NoYH0Z9bqj39LoM/DKCBbvnJU1BVLlRvuiaSyOMyljGbCNpk3ezjOAUn33HXH9lhYy/XCdnT/Ry9WLYY1Se6nGx7Sk51lwbaQJu7hs5Cnkz2azoE0u8ELLtH4RX3sRhz+6I/cQZAc0sj0dV18B7fhEMCEkZl/ZGdQFCW+2nqSqxMNd4zRGzsI2zO8OgnHbHUiaKA/M4xt+J8mL0dfNBUydXw25sgYBHygqxmcci7ObqhQHuD14G5obCLJwoL2Lk9p71CEBzJ1ekWAEIey/6mvFw5EfJvj/Q8l8IBgqdK1UCQSQLR1KJtDhnDxPG3hP+316OW6CkcejlWunPipISpD7OMkK4qAMI78PB5tgZ3K71C8xnP3ofJX9tx56APDyXFgipjl1c9dv0I7zDIpi/S8rLkJubg4CAQDjLFchSlWPFP8sxqvFQhHo4ICPnwLDv5GTh9rkz8Kkfg3r16jtQ899X1BxocERunkY7+E4oPj3Hk/skRsnve+1jgT2o00UsxmHDWOoMBQFzJPPJgVfERqV7DoFbnS1ZXPdtWI6AdPZ20toLO3mmkhQo5x1rXo5YguRnai0IHKTkAZpbe0FBflt0bF5/3fSjgNvu58d6FXJBSSQEviqVAAGG/4Fnp00JcGLbErM+JMTgDUsMxxFbR+BI6hGD3KVdDEduDul4kexsRARQ//7dE7U6Lfw+8wP5mXHh4+yD7LeyIa4Oc9De60CoXGUlSieMR1nSdcgUTvD85FOIKLmBzk1zT16B+hVaLcOUt8cn8W666Wjd+yUXqdXqENOl0qCMMqRLIYYuFfaFXh80GmP/HAZP53QMfL0fzkeZ+pPfLSh1ePlsSMtNfbG379WiuYsbPtxShG8XmdoKPDKpAGEeUvw6zw3vpcxEi5JTzLQXST2xb/5qLBjrg1PXVHj6JRVcNcXwVeXgYqNNKIpl/Z0dee56JH6AOI+peL+vFybNswQIv14kxoC295ZByD/HDqQcwNTdU3E206gOIZcA+14AOrGvdSZx+BbQ83tAybPmbBHUAl88/gW61nPMl9LRc/1el3/Y13Lv9XzX7e/ezEAdQHhv5rluL3UzYDEDD/tDRWghXAiw6/pxDrLOK5Dwk2n2VMPBxWjyfDHDvtgyJAQKbQVWXhnGAIxM9A/GTwvmMH/e2OuCMyu80HRkIeKe0qN0gCCDUOhUjS89h575uxE01Q37Gg+E5LXTyP00APlSb5ydHgOSXHB2ASRmKigxcpaZZh6RpZfxWqLRZ4gYiG+00GfDVvNaOdWyLUq9ThikOMVqVzS4tgKBWc+ZtFisVSPZby3y4qcAUvZFlKQ4Wwe3wf+1/BtvvWXZgapsJKytN5AtSHy8MSGZ3zK1Scmrjz1mur+wsmuILT6NZLemSHFtzGyslKcj238rQtIn4MB+KXPMIzoJA4RUPqxTEUrDVsI5awAklSw4RmAefaxEdraU77AGAo6eoMXskSxryRp4qNLqEC3QptBhXJQ4DqGVwgzOBZ3extGbbSH3NJ4zJG0SoMrETadorEoYatLktrGv4Hb/NpjQ39Qj8nizx3Bg3EsoyZCiwcBiKNx1OL/GA9d+c0NM2RV8nDwNYpLtEYnwYb0F+OOskUlBH5HlOi0UIrFdH7hnK0qwhiezuXZgEBYlv44YvcSpWiTBV3O/QRhLpEVRqhRH5/swfYvoWo5Hp+SD+/5vrHDFpUr22iTwkZOVpH/LRGLDB+azngFo6+zh8FUidL8Z7OEvKNvZxdULA+0E3ohxRf0OkyoQJ3c2yG5WxfazNoAV+Rm4rJ8HrgwHLJZrNfiuIBPXleWMRNFYr2B4VuE54vBE1UAFOnY5GhUjC0X9I5mo5E48wxIAB8e+iDEvTcSHpw9h+CvGm87pgU8hbsprDw9AWAPz/TA1oSsrw83HekKlp63TIzdiwULIOtgprfaATFaVwPED0k+r3UhJAd59l/U/7NqV9f8SibB393bU+4h97+KH30+b4R1iCjo96EP8t/Zv3419eHzdY9Do5ar544jyisKMzjMw99BcJBcYmfFcGYlIjOWXWqBLmmnyTY16Nt6viSWZ0ZUrWaBzxgygdev71RP791sbAOHChQBJeFCQwgp5KRK7iwAQ8nzkgljbJBdJ91oyv6K540vE2j8K+0sSEEP+kgTKkNcogYLEwJw5EyamZ/a3+K8q+SAzHGtrIs39B7n9kA9hs0CeX31tdcC8XQKdycuYWPE1ICd+r7ptaz/3Uy6Sbh8JKRr4eoogK83DlSb6D0Jeh7mE1+N/SUCe8o3aZmJKyeNY0i0JiQHGNRtHQSlZUTMsGfcpJjboh+F7UnFwtqmH/ZkDMibRdHduAcb2N3pPUvL59h8liHN2Zr79PdQFePbOGjhry/Cz/7PoMyscs572QE6ZBi17s0muOuiQ2akZNC6WKjBVnQPS0vqYN/wo3mgegD8zi/HCYFM2Y/JhZ0Zy/H6GRqvB6rOrMX3/dGSVsn7rLzYHVgtY1b74C/D9Oba3ga6BmNtjLka1GAWJ3g7kfo6jpvf9sK/l1vR81rX3YMxAHUD4YByHul48hDPwMD9UCjVqfJhtaVBsD6OPO1WiHy9Fq3GFIHn0n59jpULbFR7EiDvfolzsguTNzyMrtj7+WeqFlH1G0KVezzK0nlTAlHdkf+anaMiMNOan5C8CobojY0yjzb+drQGE9ME9JnkmGhcdhRYSbIh4B6d8TGXTHL0k8r32I6HJYCilBXAvbI/4hB/gXGHMliPfAALjjt8pww9rAbXTTeQ2fQkq778hV3th12hW2qs63vVk+0QyquZhDVgke6m+fdnSEyYAV00TBW0OnWsztnMlKrTG9LSgYODkZutSoVUxAWmnp1OUGDCclUNp0FqN3YvdISH0lMzGN2dh92KjxOHwGeWY18+XAbIiOpmCjsRm7Lv0DnwkMnzztHHBxRZAuLTfZOy+2BtOHgJsRAGJk1/feBPKcY0g6rwPQ/RsVPIt+mDwSjT9xPRDQqsGEre74cJaD8ZjoXHZeSS4NGFYuty8OHq+UXkCfkg+51xFCSNFuHG+M9z23MCU2wvgqinBD4GjkfNWR8T0M37g0Zol/SeWkl+nBE+5+TLykQR4kTylUqdDiFSOCp0WeRo1yJydPEgIFKP5pP1UJ4QAu0WB9XGmogSZGiUKNRokqyqY9p/x8IfbXX7IVBcgvK2qxOc8VqKHRIrZ/sbrmM43FXSQQlSlpFB15qk26pBM0g0zgPDUpHF4dvhoRqY2+vhJxP/xJ/JDQ0BejcMDI+sAwto4EP+FNsvLUdrncdxR01Wgg79UDo/5n9W6d2NNT92/HiCkCSEWD3kguRoZ0IdL8hH4uP7hzps0v81b4B1cO+zAmj42//b2rHqZNRuJr/t9DQ+FByNNOHnnZKw7v85iuGsvPoq26WYSo2Ys73/tHHEGXHxPgAd5MLUBEBIbjeQjs7JYWZCmTa3PAPlIUhIAgan3g6VHfpP0YUUgYV38J2eA7z/I977jfAj/k4O+D4N6EOQi89JycaWxpe/8tJjlCH7DA6uHBTBJhY8tysSHs4dBI9JhZ+NMLO+YgnxXVg7XHlBKXBkAz+sfomXrpzFwrBZT9JYYfy/yRuoRZyYxtcN7uVjVJ4T5Dj1WVoQv9hXh3CpPiCU6tHy5EAu6ByBEpsCwbWk4Os/H5IiNf12LmUPcGFuI+p1YH8hKr6PIbiOwjqOVA2Kjjx/575n7+lH9H0fuw9DoHsyawvDdaTj0MQtmvr6qGNPi7t73uqZOOQIJX/r1Jaa5sS2BFUZHFcMuxv4GfHuG/eeqAasYcPC/Gg/zWu5/9ZjWjYtUkvlp+nUzUjcDdTNwz2bgYX6okEweyeWZhyOAXXTvMrSaUICSTAl+n2z58sRJk1rzGaR9O7I/874aAMIvA6DKkDOGd27GdTKmuFWAkDbqdAgvv4ZSiQfyFCzL7W6Dz7QT60zlMgYMAKZOBTZn5+L1p1kvA51IDXX4d4jOewZHd7Mgq631CHOLkDVrWHUaSrYk9SHz4MC8rVtZ5SIKKk/e9dz6DCUMU6Iy2bV88YXtGWjRAli8mC3Tuqsad9TcS7cI0a4KHNjDLmqZs/2IyZR0mJXnsCUjyh4WgtlYFhs/MlRKTFqXh9tn5YhoqcLS4d4IkLFglXmb0V0rsHuOFyM1U48HHi5KfBmhlSywbDFXw3tj4elX4WqFEfbxjWmIK2PF/MlEfcWXXyO8pwo/DwrAkzlbEVKZiv3efZDfPgZdPmA9xChe8grCKj3LT+h8vxuA0HwMU9OvY8tQU1/PFmMLEdO3FAR0FWlM/QYpU/RN33DbB72GtpKP3bcFGYbWXvcNQ4Ss9iRbqgsQUgd/Ls7BwdICuIglGOUViFgrTOQamppab4YAwmMvjUBgIiu7qpVKkPrjRvQKDGcAQvN40SsILR4WidFan/3/2A5ocfuJJ0wHRX5Xbdv+qwb6nwAIBWY8V63CgXkfoemuvSZb/bdshVeQpefzv+qg/Us6a9XLrKmpmgQN54cLP1h4mQ3PDsMHZ4xgLikKRBw++i8Z/X+sm7UBEP7HpqhuOA/2DFTX+85V5govJy+rg7Plffdgz8j97d39lIu0BhBuXvUpdB0CDD7tr92+jjaPfGKwxPi1SS4+e+wSM3H2gFKPRi5EbOgQ+DdRYrhXIB51dseMrGTGG74sRwypsw5yV51hf5wtAv/IvO8fyfhFTr+TjLWDTNeYFn+rxpCGHiYJwnnxk1AWtsbQBCXiRIdMxNnk+RYH/NOen2LOoU9RqjR6iI5pOQYr+69kytI6RKq6EhKIGEblgxS9vu+Ffcms4tXhl4C2YcCiY8ClbKCxPzCtPXA8Dei8iu11r+he2DvS9H3wQRrP3fblYV7Lvdu5q6v/4M5AHUD44B6bup79x2fgYX6oHCgtwC/FORZH2BHALrRtBdq/nYecBDn+mmnpw+XbUInKQjEjZ2geBB5y0qTVPc3E8SUoOOkKnZb8BthWyMKDHzYBwuruuJr1SO6TVMC+z7+DGU8a5Vq9JFL4SeTgwDyy86jk2cKNHw88y7MMoKTjW7eAuDjAQ6/0KLRmS93k2qS/z50DsrNZf3pnU691w4iq8jIcOxYYrvc3795dh2KthmGauYokDJjC7e/5d8px6DAL9FH8tFyGdk1YySxzMG/sRB0+GGGG7FqZYwK4MtRKhuHmzgPy2ryah4xTRrDplS/L8PYj7DkZ/0wRim+z5+AXiWMQXMl6avLjrNsjWP/8GJQ1dYZHijucvLRQeGhxZoWnoVhQZTpezFzOSOluDHwRUTPD0a6HFrOfNJahwvRB1HW28dr6PLA+3tT74e2aFIjSO0bpEp9YFc6tNq1fzdOLqUZgj/k1TEzLp7vL0FThiplZppJm9xIgpP7R4vU1ZTniFS61Ls35Zd5tJCtNmaWcVKg9c0xyogRsP2heJvb03byMVqfD/MN70PPr5VCUlOL48GH/z959gElV3X0c/21vbAG2UKQsJXbFiBUbqNGIEsHeMWKCxtcWRYWgqKgJVmKJDStiSUSjYiRG0NgLlliw0Du7sL2Xmfc5d51ld2dmd2b2zuyU732ePEl27j3lc87hztz/Pedo1xMn6bD0HI8Bwt/m9NOeBAgDoY7+a5qapKM7vKW9YIHU356XbEIF+K9pl2nE+x+3Zrdhrz009m8tD4ci+ShpbtSj7yzWxJntlxnNW/iScgoiq40itR063cvMQ6U67mU2/4uR+sVapzWb37xyZWZRpL39TqRyRHa5zfKf5t8312Fm/JkNzTkQiACBntr7LgJoerSIPbVcZOmmUi3f1X1Nypeeu02HHPKL1j3VzW/J704p029//KtSHbX63SkrVFTQsv+dL0GpwQMP1qTjW4J1t+UPU2p8vF6r3K63qnfsc2k+c/0mK2pq0G3b1rVrE/Pb2fz+uql4rd59Kk3fL2x5wGN+Yz90T4IVdDTHZRtWaOFpA1TX521V7X+O6hwlGjNojOZPmq/Jnz6qdz64xTqv4+zYCTtP0BkLz9JH6z8IbA/RHuhBa8vWqnBuobV6x4g+0r/Pls5+Sfrg551TzIJL6YnSl1OlY+ZLK0vNu/NxWnP5Gg3O9rBZYQ/Uwe4sY/lZrt2WpBc+AgQIw6ctKEmMCcTyTcXTrBHT/P4ECM35R91ZpO3fp7QLpPjSjcY/skU1xYlaOt09sOjL9eacOBMg/LjlC2M4BQhNkMws2WmW7mx77L67dN990n0lG/WXE3q3fpSbkKSchKTW4JoJ/p13XsvHO+0kPfSQb6v72PGis38BQveWcgUIKyqcmnZ3vVascmrScQm66NRksz2SdTywqEa33dryv5NTpa9fT1N6SvfW9p/8/lp9MKeParclyCx9e80VCTous2VJkhOf2aRlD7S8BXvvj5OV37DVreDXDv+rth2br+wjKnTgXjsCeBs+SFX52iT1H12nJde22RtG0tXTHbp0fC+3gOfIPRx69W9pqnI0Ky8hyVqG0jXeytcm6s0r81vyj5OOnFOsJw4e4muX7/I8k89Hd/bWhg9aIsAm0Pnwc00am9lS/47jPtQBwi4rYOMJxn9mm4Dor3v10a96tV+mxsbswjop85Doyp+D1K6CTszKtQKEf9m2TmYfx7ZHsGd3hhIrWmeKhdLQLa8HHmjZm8ochx0m3XhjjxYnkMzN3psnXzfLCpg3pqXqpZtnasZBRwaSVFhdY2YL37l0kXuA8OV/KicvfJbJCiu0IBSmW3uZnX2RHBUVqnc6lKS4lr2P2r7tFYTykqQXge3bpauuksyen4MGSXfcIeX//B0ONATCXKAn974Lc5qwKF6ol4ss21Km73b+jVvdt77+N5148K6tK/f8eds6/fCTU58/nK2apg1aMcLsRek5KGUSGzNIenqidPTTLUEp8wP3t2e+razMATJbSZgVgcwLvrO2rW1dJci8hGheRnQd1xevUeXPq9yMTsvUWdkt31eWVpfplcpt2vZ9shpr4tRvVL3O79NPe6f2kpkd+8jXO17gcDgdamqqU15Ky9vTW2tL1NTU8qLoUydK577cklvb2bHVDdVKTUpVQtyO3/7hOjt29n9na+bSmVYdRg+QftwuVbR5odxlmZEk7ZIrLft50Z7ZY2drxmEzwqLP212IWH6Wa7cl6YWPAAHC8GkLShJjArF8U7ErQNiTXSZ+zyqVfhBeAUKzTceiRdKqVdIFF7TXGTlSevhhaXbxWj3UZl88s+dar/jEds9/zAzCqiopJ2fHUqBdWV92mfS//+04yzzD3XXXrq5q/3lXAUJTp7PPbrmm47lm+VSzjKovxwff12vNVofG75ei7PT2e+34cn3Hc8xs2Lcry6y99ZKS4jQrb2jrHnaXb16hTR+navsPybr89ovVt7HYLYvrhs9V0RH91ef4Mu2/W/vNyc3JZg9B84Zi22PaDKf+77gMtwDhr86v07wp7QNRbZfXrNqcYJWlzy8alDmgufUNykDq3fEaM66bauP0/cJMNVTF6Re/qdI5v+htBYLM0XHc75aSoQt7R++skk2N9fq4rtIK1I5Jy3JbttYO80hIo7MA4dySDVrT0LKHh+tw/aCPhLp1VcYnHr1XYx5/pvW0t6f+Vhee87uuLuPzzgTM9P8vv2yZ6r7//lJ89/8NDzW4+bcwvaRUBStWqbhwiKrycm39tzjU9Wmb38vvLdHu10xvV4Q8AoQ92ST+5W2+SJm939oeBAj9M7Tz7IYGyQQK+/aVkgPbg9nO4pAWAv4IhMPed/6UN5bODfVykeVby/TtL9x/qGcsfkp7H9jysqoJui349nlVO5qt/9/QUKWGxpZ97DsLSmWltMzScwWlkpMylJ6cqYyf95M3QbczD5ult6rLlJWQoFMy89Q3sWVlIddhliCNV5w149B1bGis151t9oU3fz8pK8/6TVc4d4jWlv88fa6TjpOXLm36ozTgTqm4puseVpgzRKsuW9P1iSE8w/yOG3nvSK0sbdkqou3RJ62PSmpLvJZmRJ8R+vGSH6PyN3AsP8sNYfcjqxALECAMMTjZIdD6pWPDBg0yb4RKWr9+vXYy07Vi5Li/ZKNWdFh+z1Td3xmEPcmVsFeVSt7vmQDhSy9JK1e2vFjc9hgwQHrmGc8BQnPekiVOTStapecm7QjK7JSUooN+b7enagAAIABJREFUmaC77uqepnlma15uXrJEmjFDOuQQ/9PrKkD4299K55zTkm5ZmTRxYsv/3mUXyQQkO2wb6H8BAryi0enQf6rLZJZXOyQ9W0Pa7G3XNii2/yF/1pDin9xymT7sHq3qM1KDrtuo/fdwDxCawGPHvf2um+nUxcdm6KQXNuqTuS0zQlN7O3TJQ5W6bKB70K3jjDZXIfxZ9rIrHk+Bf9dMMXPtK5XbtbTNEi/X5A6WCVBzRL9Ax75xYmauDs/IkacAoZ19sqdlr13xpY6a+6AGfvOd1u2zl9665PeaM2zPni4W+fewQHf2KO3honeZ/fpln6ju0kvbnZf/8ivKzmPmU5d44XACAcJwaAXKgEBUCfTk3ndRBWlTZbpaLtJk03FmXneXiywvKte3Iye41SD3nSf0i1GF1uw+X4NuZjnLv42XLlokOXbsKOJVJ9CgW4PToWu2rmqX7unZ+RrubNDAuwaqobn9CihmRal+bbab6Z/ZMntw1zxpeXHLLMLNlTuS21IlNXcof3JCsjZeuVG56YGvcmVTN2lN5r117+nQxw91S/acvc5RaW2pXvvptU6zfO/89zRm8Bi7i9Xj6REg7PEmoABBECBAGARUkkTAF4FYvqncWLxGZT8v5eCy8jRLyhfHnjqnMa9eNataNo82XwhTU6XEDtsdBmsPQvMy9+bN0plnutfefGaCdePGtf/syCOly6e3LHv43QuZ+u75lvXzhyal6snH4jVsWE9J7si3swChCf6ZVeXMS9SRdNxcvNYKHJpj5bnbde3H09yKf9WIv2l96lDlHlGpM69sUGZ8glY21KrJNKS1Ybn04sntZxDePtep00dnWLPyzIzAyk2J6r9vnfbITdXFfdqf68ow2A+lf6iv0YOl7fdYbBsgrHM49ErVNm1patSBaZnaP+3nTSwjqUEpa0ACsRogDPaYC6gxuKjHBaK5XxAg7PHu1b0CECDsnh9XI4CAR4Ge2vuO5nAX8HW5yI4z87qzXGRFUbm+8RAgHPbRw+q3687ytiRtx9L3TZP+dZa030Dp043Ssc9IJe23fHercKBBN08roJiVb8wKOGZ27MWL/6jFP/y8dujPuZ60qzTnaGnYjt1c3MqzqlS6+k1p4fL2H52828mac9QcFfYuDKtuO+WVKZr3xbzWMmWlZOnB8Q/qtD1OU+6cXJXW7djfsVdyL2vvwcqGHZHQKftM0SMTHgmrOtlRmFh+lmuHH2mEpwABwvBsF0oVAwKxfFPp+HDMBEB+ei1D/3siO2JavnRLvOIzHFZ5k1KcSk9238cuGAFCEyD7xz8kh0MyQb+2x377SXPmtPylY7Dt3/+WVjlaAjhN9dK3z2WpYm2Sbp2Uo18d1b09+OxqtEsukb79tn1qffq0zBY0swfPOsuunEKbzjPlW/VZbaUWX5KnQ75ZpMmbH2otwNbkfrp05GPW9McD/1iiv09qmUlsZtg+UrpZ5u3F7IREvfHnLG38KNX6LHNgk556Ik6j0zPdlu0ckZymP/QZ6LGCoXgo3TEPsxSLmVXJEdsCHfuFK3Ac7TMIQzHmYrtnRWbto7lfeAwQ/vNVZee230c3MlsuBko9YYJU2WaKg6kyS4zGQMNTRQRCIxDqve9CU6vIyaWz5SILcwo149AZuv7pS7Qpp/3y/6aG3VkusnJbhb4efoIb1MD//V1DhrSsMOBtSVrzWa9k6eHjpTM8LMKx4Gvp969JVe0n9FlpdjfoZn6Lf1ffssxpZkKibsgdooQ2SxbFwuzYJauX6IwXT1NR9TaNGTRG8yfN19Ccofpqy1ca9dAoy8bM6nTN5nz9zNd1y7u36P317ys/I1fPnvS8xhV2eHM9coaM15LG8rPcKGg+quBFgAAhXQOBHhKI5ZvKtVtXqd6sm/jzYYKDXz0eWUGEko0JSshqWSM/OcOh5Lh4ayZh2yMYAcI//lE6/viWXDoGAf/v/6RJk1o+M0HX11+XNmxoWZbT7E/4RlWJFle1Xyc+nJbz++kn6eKLpaamlm2l7r5b2svsTR4lxz6nVmvbxjgVNGzWmVseU7wcerZgsjalDFLBPvU66U9Vuq5fy7LD5jAzD7c1NWpAUoquW79aK17rpab6OI0YX60pg/O0b5p/AcK2m7Cb9LMSEnVj3lBbdQkQ2soZNYl5CxDes32D1ja2fwgRTv8mdbcBojkQ1F2bWL4+mvsFAcII79kECCO8ASk+AuEtEOq97/zVcDgdOuDRA1TdUKmM5Ex9POVjxcdF3l7H3urd2XKR9x13n8zssDfzxujOcSu0eLcit2QCXS6yurRSXw39+QFGm1Rzly/WLwa0vADrOv658i1d+MoF2l65tjXolBQvLTlPOmSwe83eWyeNe1Jq/PnRkglWFeYM17wJ83T40MP97QLtzjd7E5rnJ/VOp47KyFGeh60xYmF27KbKTVq4fKGmjp6qxPiWJbPmfjRXly++3M137rFzdfF+F+vBzx7UpF0naUCm55WNutUwYXBxLD/LDQN+ihAkAQKEQYIlWQS6Eojlm8qntZVaUL7VIqori9drF/TriivsPm/MaFTNlpYNrk2AUHFSWocfEHYHCM0Spq+9JqW0rGzqFiCcP18a6HnymHX+m1Wler1qezvLcHsYv2KF9MUX0p57tuwtGE2HK0DYsU4HX1ui/qPrrEDgtNwdAULXeWap0au3tt8Y/JzsAv3SQ4BwWHKa/s/LDMLv62v0UJslQF3LpNhp3PHB91nZBRqd1rKcLUfsCnTsF5Oy8nRoerY6Lku7Z2ov/TYn8u4H3lo2mgNBsdubu1/zaO4XngKEBS+/oiz2IOx+xwlFCiwxGgpl8kAgJgV6Yu87f6Hbzooy13419SvtVRA9b6t6Wy7yjD3PaKX6ILslqPbvXYp0x5E/qTql5YVocwS6XGRNWaW+HOIeIBzw/SIN7d9m476f8zFBt3s+ukez3pmlqoYq66/n7S09caJ7i573svTUVy1/z0zO1A2H36DLD7xcCfEJ/jZ/t86PttmxVy6+Ui98+4JXE7O0aE1jjdvnGUkZyknN8XrdabufpjuPubNb1uFwcSw/yw0Hf8oQHAEChMFxJVUEuhSI5ZuKWd7is7oq3ftJiZZOj8xlp9IPqNDm/2QpLkFKSg1ugPCmm6QtW1qWFDVLbrqORYuku+7asdzon/7Uebd7q7pUr1XuCBD2T/QckOqy83JCQALeAoTj/lKsPiNaZgpe3dc9QNjsdOoqHwOEO6eka2pv72/qfVVXpR8aavWL5DSNSnX/QRZQxdpcZJawNUEfc6TEx2t2XqES2yzF0t30uT4yBbzNLHU4nZpfXqQv6irVOyFJU3L6WeMgWo5oDgRFSxv1RD2iuV+s/ekHNUw+rx1r/utvKDvb+8OinmgD8vQiQICQroEAAjYJdAwwVNRXtO5NNnqA9ON2qaLePbOOe99lJWcpM2XHy4bBDDB0nBVlZkNdesClNon0fDLelotsW7Jlgw9RfXlLcG1zVp1u+u0W/S9+XbeWi6wpr9KXg8e7AYxc+7Lycrxv2BdJQbdwnx3rT+8zz+oK5w7R2vL1/lzm07mFOUO06rI1Pp0bzifF8rPccG4XytY9AQKE3fPjagQCFojlm4pZ/vLxx6WHn2rWxkYPvwwCVg3dhVlHlmn9yzmKT3IqMdnpcQbhor+la9Mm6dFHpe3tJ+75XNApUzrfe2/zZqm6Who+3NrGrtNjRtFqmaUyXIfZ226WzUtM+lyxGDxx1MnV2r7ZvZECCRCem9NP+6T20gsVRfqwpqJV88q+gzSoBwMspc2N+mfldtU4HDq2V2+ZGY0cCHS29Kz5EWqW7kmKi2u3r0c0qD1ZtkVf1rW8+WyOA9KydHp2y14rHLErEM0BwpUNtSo6f7Jy16y1GnjjHrtpnwcetvbS5YgAAQKEEdBIFBGB8BfwJ8BgloT823jpokU79jHrrIbBDDBMfH6iXv7+5dbsJ+4yUQtPWxj+4H6U0NNykW0v/2G/g1SxMVGOxjil5zWp/qyj9dHRed1aLtJTgLDXgEYVvPuyCnK9fy+OlKBbJMyO9aOLaFvNNg28a6Aamttv7Gi20+n38/vF5nne1mqp2bkjZfN5QcaOZ1Jbqtp/bs5MTkjWxis3Kjc9158ihd25sfwsN+wagwLZJkCA0DZKEkLAP4FYvak0N7fsLWdmv0lOrWio9Q8uTM7OPqZM6/6Ro/hEpxJTWr4ZpZmN89oc699Pt/6f2Tfw88/9K/juu0sjR0q//72U2n5pfv8SanN2ND+UDBglhBfuPalaJVu9Bwi9LQ9qfmRf2WEG4Xk5/awZgCYg90x5kYqaGnVQepaOzeituK4ixSGsM1khYARidW/KoqYG3VeySZWOJitA8n+9B6pvYsvS1ByxKxDN92ITIHx09XL98uXX1JyYqC9OHK/pQ3cnQBgp3f2886R169qXdunSSCk95UQAgTAR8BZg6Fi8vmnSv86S9hsofbpROvYZqaSLRwPBCjCY/Qdz5+TKLJ3oOvqk9VHx1cVRtQ9hV13kpzEHtjul+rhfa9SMG7q6rNPPayur9cVOx7U7J3OnRuW9+YL6DdjJ47WRFHSb/d/Zmrl0plUPf2bHzh47WzMOm9Et22BdvKp0laa9OU0vLn+xXRYn7SrNOVqqrJdGPeSe+1dTpV7J0tVvSguXt//85N1O1pyj5qiwd2Gwih2ydGP1WW7IgMmoRwQIEPYIO5kiIMXqTeXTT6Vp03b0gBUN7muXR0L/yD62VOv+3rIkhrUHYScBwrfekmbP9r1Wl1winXSS7+f7emY0P5T01aAnz9vrxGqVFnsIEP65WH1GNuqKvjtpcJJ7NNhTgNAsRRpNSzH2ZLuQd/AFOv7b462vB78koc+hytEsEyjsl5is9BDvhxL62pKjLwLRfC82AcL7Sja2YzArFTCD0JeeEQbnfPWVdPnlOwpy/vnSueeGQcEoAgIIRJqAtwCDqYcJIDx8vHTGnu61WvC19PvXpKr2k5esE4MZYOi4/6CrZNG2D2FX/SgYAcK66hp9PuDX7bI2AcLd/vuqEvr29VikSAm6md/pI+8dqZWlK7uidft8RJ8R+vGSH8P65d531ryjyxdfri+3fNla/uQEaeq+0l8/afnTyD7STyUt//uyA6S/fSY17Fi0SqP6jdI9x9yjw4e27G8ZDUesPsuNhrajDt4FCBDSOxDoIYFYvqmMHbsDPRYChDU10nj3Zfc99jwzCfGll6SsLHs7pqcg0xEZOfpNZmQv72CvUnBT23NCjco8LDU77s/FOnufLB2VkeP1B8LCimK9W1NuFXBEcpr+0GdgcAtL6gjYKLC4qkRvVLX8cqT/2ghLUhEpEGsBwhvzhiqLJUYjo6+aNcOefVYyb7aZZSwuvVRKb1kNgwMBBBAIRMAEGC545QKtLlspx8/LESbFS0vOkw4Z7J7ie+ukcU9KjS3v38osQVqYM1zzJswLaoCh7f6DbQMe0bYPYVdtGIwAYX1NjZb19xAg/OZ9j9sLdBZ0MzNI+6b11vbaUrclMK3fGSEOur237j0d+vihbqzJ8clqcOyIcptyd1yy01z03vnvaczgMV01S8g/77iHaE1jjcrry2Vm2pojMV5q+nmMPnWidO7PK/O2/Xt8XLyyU7KVnrTje0Qw9xANJVIsP8sNpTN5hVaAAGFovckNgVaBWL6pmGcPDz/cQhFuAcLBh9Vq3X+73jct+9elWveCbzMITT3bBkU9DYP+/aWKCumCC6SJE+0fKGYWy8yi1e0Snpk3RH0SWO7Ofm3PKV70xha9drN75PepBxM1ds/kTothfih9U1+tBqfTWlo0gWVEQ9Vs5GOTgFlOutbh0G4p6fRfm0xJJjIFCBBGZrtRagQQQACBwASaHc2656N7NOudWapqaNmb+by9pSdOdE/vvJelp75q+XtmcqZuOPwGXX7g5Uro5ioMHQMeHXM2S4uaIIg52gY8MpIylJOa47Xi0RLwcFUwGAHChtpafdbv2HaG7//2TF1214VKjmu/RYs5yVvQ7fiRx+u1n15rTWf8yPFa9JO1b027I5RBtymvTNG8L+a15p+VkqXrDrlO1711nVu5bjvyNt323m2qqK9o/WzKPlP0yIRHAhtYQbrKnz1E89KlTX+UBtwpFfuwMFgw9xANEofHZGP5WW4onckrtAIECEPrTW4ItArE+k3FFTALlwBh5oAm/WpukbZ9n6x3ZnY9qy5rbLnWv5JttWdXS4yaczZvbllatbxc2m036eOP2w+GYG/xsq6xTndv39Caqdmn7vb8YTyoD+G/STdvWaeXZ2Wq6OuUdrm+OD9B+xe2/1sIi0VWCCCAAAIhFCBAGEJsskIAAQQQCBuBJ758Quf/83yrPFP2kR6Z4F60Ka9I875o+fvjv3lck0dN7nb5CXj4Trhs0gRlbS1qvWDlXXN07AGH+Z6AhzM9BQgX/9/Fuvamk5XmIfDrKej24PgHVVRdZC136TrM7M7c9FxdtOiiHgu6LVm9RGe8eJqKqrdpzKAxmj9pvv75/T9by9lxNuqEnSfo7IVn6/317ys/I1fPnvS8xhWO65av3Rd720M0IU7q12tHbv0zW4Lpu+ZJy4tbZhFurtzx+ZYqqfnnWcOuvwZrD1G7DbpKL9af5Xblw+eRKUCAMDLbjVJHgUCs31RcAcI6Z7M2NNZ32aI5wxpVtip4s90mPrdJZjJdU12cXj6rf5flMScUf5us5MENPgUI2yY4b540f377LIIdIPyyrkpPlm1pzdTMHDQzCDlCJ3DrtnUqamzQ0um5KvmxZcZg9tBGvf5Eise9B0NXMnJCAAEEEAiVwKe1lVpQvrU1u5Oy8nRIessLR5F+eNqDkCVGI71VKT8CCCBgj8BRTx2lt1a/ZSX23vnSATtJd30ofVss7Z4nXXmQ9NEG6dDHW/I7athRevOcN7udOQEP3wnveuMlHXvHXCXV1umnQw5S1fRrdWp2ge8JeDizqb5en+T/qt0n/770Yl1148nq5SFA6CnoNjRnqCY+P1Evf//zWpaSJu4yUQtPW6g1ZWtCGnTrOBvVzJCtbaqVmW1qXsLuajaqCVhXN1YrLTGt3czYcJqN6m0P0ZN2leYcLQ1rWUjL47GqVLr6TWnh8vYfB3MP0W510AAujvVnuQGQcUkECBAgjIBGoojRKRDrNxV/9yE8+u4ivXlFvm2dIbW3Q2aVxtqSeGvmYNZOTa1p/+OkAT7lU/xNilKG1Skp1SnFSWltlsgYOaFKS67xXN5HH5WeeaZ9FsEOEC6tLtMrldtaM2UfMJ+a2NaTbi5eq5LmRtVXxOv7hb3kbI7TLidV6rrhAwgQ2ipNYggggED4CjQ4HXq5cpu+q6+x9uQ8OTNPqWYD4ig4CBBGQSNSBQQQQCAIAmvL1qpwbqGccmpEH+nfZ0tnvyR9sH5HZmMGSU9PlI5+WlpZan5ex2nN5Ws0ONvDZoV+lpGAh29gZpWD5OpqJdfWqapvHx2Uka1Ts7r3DKapoUGf5B3drgAmQHjFrJOU7WWP4k2Vm7Rw+UJNHT1VifGJ1t53uXNyreCb6+iT1kfFVxfL7HXX5GjSg589qEm7TtKATN+e5fgm0v6sWJuNavYQNbM2v9zyZStEcoL01rne9xA98impoXmH26h+o3TPMfcEdQ/RQNqyO9fE+rPc7thxbfgKECAM37ahZFEuEOs3FX8ChNlDmnTUnUVacm2eSlfsmEU4Yny1VizK8NpTBh9aq3Xvet5PcOTx1dr7/HKP1zqd0srXM9RQHa8NH6apYl2ix/NMgDB1eJ0SU5xKiotTYlycdplUpbS+zRr2q2rdM2CEx+t6IkD4YkWx3qvZUd/907J0Rnb3vuxH+RC1vXquAGHHhK/ouxMBQtu1SRABBBBAINQCBAhDLU5+CCCAQGQIzP7vbM1cOtMq7OgB0o/bpQoPiwhlpUhmWcZlm1vqNXvsbM04bIZtlSTg0Tllx2XQzfMC89ygO0dzQ4M+7hAgNEuMXn7jJPU2Szj5cHy15SuNemiU25lfTf1KexXs5UMK9pwSi7NRm5qbNOCuASquKW5F9GUPUXNyfka+Nl6xUYleAsH2tEroU4n1Z7mhFyfHUAgQIAyFMnkg4EEg1m8q//mPdMstLTDe9iHc9ZRKORrjZGbjpWY7tP2HZL17U19rGdD0/GYdfuM2/esi70teHP/YFus6c6x/P00f39WyFkJShlPH/HWrUnNaPuvsMMtBmnw9HTXbEtSc0WQFCM0G22OuLtFOB9W1nnp3P88BwkcekRYsaJ9isGcQPlK6Wd/VV7dmekyvPjq2V5+uqs/nNgrcWLxGZc07Zqq6kiZAaCMySSGAAAII9JgAAcIeoydjBBBAIGwFzKyrkfeO1MrSlW5lLMwp1IxDZ+iWd2/R6rLVbp+P6DNCP17yo7V0o12HWRLS7Ic4fcl0a187c/gS8CjIKNAt426x9kVM8LA0pl3l68l02q46ZLYkmZ47WAndtG9ubNTHuUe1q5YJEF59k+clRj3Vf+5Hc73u63fpAZeGlCzWZqO+t+49Hfr4oe2MfdlD1HXBe+e/pzGDx4S0jYKdWaw/yw22L+n3jAABwp5xJ1cEFOs3laYmycyke/55aVNOpWqKEtx6xckvbnL7mwnKVW1KVO8RDYpLcOrlM70vIdE2QGgSKvomWRXrkjR1XIYqe9fp49qKLnvikut27BfX8WRHs5S8e43qixI1ZP967X9+peLbTDb0FiBctUq64IIdqfXpI734YpdF6dYJwXgbsFsFisGLCRDGYKNTZQQQQCCGBAgQxlBjU1UEEEDARwFPAQZz6Tl7naP7jrtPWSlZqqiv0B9e/4Pm/2++W6rBCjCYIOH5/zzfys+XgMfjv3ncCg5G+7G8vsZ6qXVUaobSbAiEmgDhJwVHWttruI5P5k7R5ZPPaf3/Hff162jc1b5+3tokmPv6xcps1CmvTNG8L+a1I/ZlD1HXBVP2maJHJjwSVcMm1p/lRlVjUplWAQKEdAYEekiAm8oO+As+XaN/X95+ucv4JGnSc+4BwrbN1VSvTgOEJy7YpMQU9wY2gbsqR7NmFrm/pdjx7A0fpOqjO73MtIuTDnhsnepTG9UvMdltk21vAUKTx5w50r/+JaWmSjNnSgcfHLyOWO9w6NqiVe0y+H3vAdolJT14mZKym8Bd2zdofeOOGaauE5hBSGdBAAEEEIgGAQKE0dCK1AEBBBCwV6BjgMEEBB8c/6DO2PMMt4wWfL1AFy26yAoYuo5gBRiOeuoovbX6LSsbXwIeRw07Sm+e86a9ODGQWnNTk5aPOkyVGxPldMQpI79J8fdcoz3HT7BqH8n7+sXCbNQlq5fo1L+frO21Lfs/+rqHqDm3b1pvvXDKPzSucFxU9XSe5UZVc1KZnwUIENIVEOghAW4qO+Av+GyN/n1Z+wDhwAPqdNC0kk5bx+wVaK4zXzY9HZ5mIJrzTOCuxtGsGT4ECB1N0rs356r4m2QlZzp0wBWl2vJliqo3J2rYsdXSHpVWsLF/Yooy4uPbFaOzAKE5sahISkuTMjOD2wnfqCrR4qr2ljfmDVVWlK0FH1zF7qe+uqFOfy3Z4JYQAcLu25ICAggggEDPCxAg7Pk2oAQIIIBAuAmYAMMZL56mouptGjNojOZPmq+hOUO9FnNN2RqdvfBsvb/+feVn5OrZk563PcCwtmytCucWyimnzwGPOMVpzeVrNDh7cLgRh3V5mpubteqwMXKa3V2cUlyC1DxjhnY57gSr3NGwr1+0z0ad9uY03f7B7VZ7+bOH6LQx0/SXo/4S1v0zkMLxLDcQNa4JdwEChOHeQpQvagW4qexo2oVl23TZ+Paz2Y5/dItSe3e9R6BZNnTZA71VvdW3JUpNriZwV+dw6LoOs+q8dTbzZbZqS4JSsh1KznC2O21LU0NLgDApRRlx/gUIQ9W5zUxJU8a2R1fBy1CVLZbyMW9HvlS5Te/WlLerNgHCWOoF1BUBBBCIXgEChNHbttQMAQQQ6I7ApspNWrh8oaaOnqrEtntyeEm0ydGkBz97UJN2naQBmd63FAm0TLP/O1szl860Lvcn4DF77GzNOGxGoNnG5HWuAGHbyrcNEJq/R/q+ftE8GzXc9hANh0HEs9xwaAXKYLcAAUK7RUkPAR8FuKnsgDJr3P/+n1v1wZ29rT/uclKl9jiz0kfJltNWv5WuZQ/ktLumsxmE/gQIOytIJAQIpxetVi0BQr/6U7BOdjidml68WmbZV3OYTd//nD9Mid3c/D1Y5SVdBBBAAAEEfBUgQOirFOchgAACCPSUAAGP0Mo3OxxadWj7/VQ6BghdJYrEff2ifTZquO4hGtpe3D43nuX2pD55B0uAAGGwZEkXgS4EuKm0BypuatSt29YG3G+2LU/W23/KbXd9ZwFC88Pgyq0r/cpv/7QsFTU3aE3Djn3kXAHCAUkpSg/TGYRXbFnRrp55icmansvSKH41vo0nL6ut1PMVxdaSNidn5emAtCwbUycpBBBAAAEEekaAAGHPuJMrAggggIDvAgQ8fLey40yHw6GVPgYITX6Rtq9ftM9GDdc9RO3om4GmwbPcQOW4LpwFCBCGc+tQtqgW4KbSvnnLm5s0q3hNwG1u9iN866o8la1JstIYOq5Go/9Q5jE9s7xmIAHCm/MLVetwtAtkRmKAcHBSqsyylhw9J9BsOuzPMwh7rhTkjAACCCCAgH0CBAjtsyQlBBBAAIHgCBDwCI6rt1TNCjorDzmo3ceO6dO18/gJnRYkEvb1i4XZqOG4h2hoe7B7bjzL7ekWIP9gCBAgDIYqaSLggwA3lfZINY5mzSha7YOc91Maa+KspUaT0pwaMrZG8e7bEloXu/bf6zizrqvMb80vVLXDoVvazHR0BQgHJqUorcMMwrsKhiuuh5eONF/I/9hhpuThGTk6MbP9bMuu6s7nCCCAAAIIIICcByhRAAAgAElEQVRAZwIECOkfCCCAAALhLkDAI7QtZIJoKwIIEEbCvn6xMhs13PYQDW0PJkDY097kHxoBAoShcSYXBNwECBC2J7FrT0BfulqgAcK/FAxTZXOzZrcJEG5ualC1o1k7JaUotUOA8M6C4Yrv4QBhZXOTru8wM/Oa3MHql5jsCxXnIIAAAggggAACPgl4ChDelDdUmQmJPl3PSQgggAACCIRCgIBHKJRb8ggkQBgp+/oxGzV0/SiccuJZbji1BmWxS4AAoV2SpIOAnwLcVNqDNTgdumbrKr8UE+Li5Fqq0Z8LAw0Q3lEwXOWOJt1cvGOvxFWNdTKz9DwFCM35pow9eWxurNec7evbFSEcytWTJuSNAAIIIIAAAvYLECC035QUEUAAAQQQiGSBQAKEkbKvH7NRI7lnBl52nuUGbseV4StAgDB824aSRbkAN5X2DWwCfVd1WAqzqy5gAl3f1dfosbLNXZ3a7vNAA4RmydDSDgHCFQ21VtrhGiD8ob5GD5Zuaq1/enyCbskv9MuLkxFAAAEEEEAAga4ECBB2JcTnCCCAAAIIxJaApwBh45w52m3MYR4hIm1fP2ajxlZ/NrXlWW7stXks1JgAYSy0MnUMSwFuKu2bJZAAoQn0dQyAuVL9Xe8B1tKfz5RvdWv/QAOE5rrS5kbd1GYGoStAOCgpVSkdZgtOzx2ivMSkHu1/H9VW6PnyotYymL0Sr+o7qEfLROYIIIAAAgggEH0CqxpqdW/JxnYVY4nR6GtnaoQAAggggICvAibg9+YVf1Dhp59bl1Tm5cqxYIFGp2d5TCJW9vXz1Y/zwk+AZ7nh1yaUqPsCBAi7b0gKCAQkwE2lPZv54nilnzMITcDO09vqJuXOgoChChAWJqfp0j4DA+ofdl20pLpUr1Zub01u55R0Te09wK7kSQcBBBBAAAEEELAEzMteZt/jGkez9f/7JCTpT7mDFdfDy63TPAgggAACCCDQcwLTVn2tX770qpJra/XFicfrN8N30+i0TI8FYl+/nmsncvZNgGe5vjlxVmQJECCMrPaitFEkwE3FvTGv2LLCrxY2gb41DXWaW7LB7bpgBQjLm5s0q3hNa36uGYSDk1KV7OEBmKscflXMxpMXVW7Xf6pLW1McldpL5+X0szEHkkIAAQQQQAABBFoEvqyr0sKKbYqPk07LyteuKenQIIAAAggggEAMC3R8znNWdoHXACH7+sVwR4mQqvMsN0IaimL6JUCA0C8uTkbAPgFuKu6WgQQI1zXW6e7tBAi99cy/VxTrg5ry1o8PTs/WKVl59nVkUkIAAQQQQAABBBBAAAEEEEAAAQQ8CPgTIDSXs68f3SicBXiWG86tQ9kCFSBAGKgc1yHQTQFuKvYECDc21uuO7evdEgvVDMKVDXVyyqmdklKUGhfvtRzd7C4BX/5k2RbrbX7XcVRGb43P7BtwelyIAAIIIIAAAggggAACCCCAAAII+CLgb4DQlzQ5B4GeEuBZbk/Jk28wBQgQBlOXtBHoRICbijuOPzMI8xKTNT13sDY31muOjQHCzIREVTY3eWw5E3Q0n5n9dVzH+sZ61TsdCtclRh8s3aQf6mtay3tCZl+Ny+jN2EQAAQQQQAABBBBAAAEEEEAAAQSCKkCAMKi8JB5iAZ7lhhic7EIiQIAwJMxkgoC7ADeV7gUIzdUmYLe1qUF/3rbOLTHXDML/1pTppYptrZ+33YPPU0Cyq5mHVY5mzSxa3ZqeK0A4JClVSWG0B2Gto1mPlW2Ra49EV4FPy87XgWlZDEkEEEAAAQQQQAABBBBAAAEEEEAgqAIECIPKS+IhFuBZbojByS4kAgQIQ8JMJgi4C3BTcTfxZwZhany8bssfpuKmRt26ba3XAKHD6dTT5VutZTbNrMMLcvqpIDHZOt/OAOGgpFSlhFGA0Jvl+Tn9tFdqL4YkAggggAACCCCAAAIIIIAAAgggEFQBAoRB5SXxEAvwLDfE4GQXEgEChCFhJhMECBD60gf8CRC6ZgKWNDfq5mLvAUJXviZQGCcprk0Qz84A4dDkVCVaObQ/XDMSfam/ned4s7y4zwCNTE63MyvSQgABBBBAAAEEEEAAAQQQQAABBNwEOj6bODu7QPumZSKFQEQKECCMyGaj0F0IECCki4SVwLp167Ro0SK9/fbb+vLLL2X+4W1ublZubq723XdfnX766TrllFOUmJjYrXKb9MeOHetTGjfccINmzZrl07n+nMRNxV3LnwCh+UJpvliWNTfpxjZ7ArpS9SUwF0iAsMbRrBltlhhd11ivBqdDhclpSvDQAXwphz/9xpdznU6nrty60uOpV/UdpIFJKb4kwzkIIIAAAggggAACCCCAAAIIIIBAwALmeY15buM6bskvVHq8p6cnAWfBhQiETIBnuSGjJqMQChAgDCE2WXUucP3112v27NkywY3OjtGjR+vFF1/U4MGDAyYlQBgwXVAv9CdAODotU2dlF6iiuUk3hEGAcERymkebzgKEJrCYoDgleFiatDvQJt1rtq7ymMTMvCHqk5DUneS5FgEEEEAAAQQQQAABBBBAAAEEEOhS4Nv6as0v36p6p1MTevXVERk5XV7DCQiEqwABwnBtGcrVHQEChN3R41pbBaZMmaJ58+YpIyNDEydO1JFHHqmRI0cqNTVVy5cv11//+ld9+umnVp7m759//rl69QpsL7W2AcLHHntM++23n9e65Ofny/zH7oObirtoIAHCKkezZraZ0edK1ZeZe4HMIKx1NGu6hxmE3gKE52QXaLeUDJk9E9ser1Ru19LqUuvNuck5BbYu+1nZ3KTrPQRNTf6z8wuVwdt6dg9n0kMAAQQQQAABBBBAAAEEEEAAAQ8CzT9PBLD75WiwEQi1AM9yQy1OfqEQIEAYCmXy8EngmmuuUd++fXXRRRcpM9N9PXKz1OiZZ56pF154wUrvpptu0syZM31Ku+NJbQOES5cu1RFHHBFQOt25iJuKu54/AcL90jJ1ZnaBOgbsXKmGS4DQlKdvQpKm5Q7St/U11nKkQ5JS9Zdt61oB+iemWJ/bdRQ3NerWbe77Mpr07ygYbvuMRbvKTToIIIAAAggggAACCCCAAAIIIIAAAgiEowDPcsOxVShTdwUIEHZXkOtDKrB9+3YNGDBADQ0N2muvvfTVV18FlD8BwoDYgn6RPwHC/dOydEZ2vuocDl1X5L6cZrAChB3zc+1B6G0Goa9ovpTX17Q2NNbrzu3r3U5PjIvT7QXDfU2G8xBAAAEEEEAAAQQQQAABBBBAAAEEEEBAEgFCukE0ChAgjMZWjfI6meVAP/vsM2sp0qqqqoBqS4AwILagXxRIgNDbfnu+BNwCWWK03uHQtW0CkuEYIFzZUKv7Sja6tVev+ATdnF8Y9HYkAwQQQAABBBBAAAEEEEAAAQQQQAABBKJJgABhNLUmdXEJECCkL0ScgJk5+PXXX1vLkFZUVARUfgKEAbEF/aJAAoRmLfurtq50K1uwAoQdA5IrGmqtvMNpBuF39dV6pHSzm4lZ6vRPeUOC3o5kgAACCCCAAAIIIIAAAggggAACCCCAQDQJECCMptakLgQI6QMRKVBUVKSBAweqqalJZibhJ598ElA92gYIDzroIK1bt04mbTMrcejQodaehGYvxF/84hcBpe/LRdxU3JX8CRAemJal07Lz5XQ6daVNAcI4xemufi1LcHqbXRgJAcLPayv1dPlWN+ABSSm6uq99ex360s85BwEEEEAAAQQQQAABBBBAAAEEEEAAgUgX4FlupLcg5fckwAxC+kVECVx99dW64447rDLffvvtuuqqqwIqf9sAobcE4uPjNXPmTN1www2Ki4vzOx9z0+js2Lx5s/bff3/rlPXr12unnXbyO49ou8CfAOFB6Vk6NSvfIuhsqdDOjJ4u26rP6ypbTzkhs6/GZfTuNM1Gp0PTtu7Y8zAcZxAurS7TK5Xb3Ko+KClVV/aln0XbuKE+CCCAAAIIIIAAAggggAACCCCAAALBFSBAGFxfUu8ZAQKEPeNOrgEIfPzxxzrkkEOs2YMmmPbDDz8oPT09gJQkEyA888wzNWnSJCvNYcOGKTEx0ZpJ+Oqrr+rpp59WY2OjlfZ1112nW2+91e98/AkqEiBs4Q11gHBLU4MeKt2ksuYmDUxK0cW9Byg9PsFrWcyypU1Op65uM2MxHAOEs4vXantzS/9te6TEx+vP+cP87stcgAACCCCAAAIIIIAAAggggAACCCCAQCwLECCM5daP3roTIIzeto2qmm3dulWjR4+W+YfYBN7+85//aNy4cQHXsbq6WsnJyUpKSvKYhlm69Fe/+pXKy8ut/D7//HONGjXKr/wIEPrFZZ3sX4AwW6dm5Xm9zpc9CM3F9Q6HKhzN6pOQqIQ2M0W9zUrsuOdhOAYIO3P01cX/1uMKBBBAAAEEEEAAAQQQQAABBBBAAAEEolOAAGF0tmus14oAYaz3gADqb2bweQus+ZPc448/rsmTJ3d5SWVlpcaOHatly5ZZ595222269tpru7yuuyc888wzOvvss61kpkyZokceecSvJFli1C8u62R/AoQHp2frFBsChN5KSYDQ//bjCgQQQAABBBBAAAEEEEAAAQQQQAABBKJRgABhNLYqdSJASB/wWyCUAcK6ujodd9xxWrp0qVXOK6+8UnfeeaffZQ7kAlPP3NxcaxbhyJEj9eOPPwaSjNdruKm403gKyo1ITpNrll7bK8akZ+vkHggQOpxO/THMlxj1Fmg9PCNHJ2bm2tqPSQwBBBBAAAEEEEAAAQQQQAABBBBAAIFoF+BZbrS3cGzWjwBhbLZ7t2v9/fffdzuN/v37Kzs722s6JkBn9gg0ewKaI5BZfN0t5P77769PP/3U2uvQLEtq58FNxV3TU2DrroLhapJT07auanfBoenZmuQlQJgaH6/burnXnrcZhE6nU1dGYIBwUFKqrugz0FoylwMBBBBAAAEEEEAAAQQQQAABBBBAAAEEfBfgWa7vVpwZOQIECCOnrWKqpA6HQ2eddZaee+45q96nnXaaFixYoPj4+JA67Lfffvrss88IEIZI3VtQzmTf8bPD0nM0MatlNlzHz7ITEjUrb2i3Su1rgHBNY52anE6ZmY7dOezaG7BjANOU6aiM3hqf2bc7xeNaBBBAAAEEEEAAAQQQQAABBBBAAAEEYlaAAGHMNn1UV5wAYVQ3b+RW7sILL9Sjjz5qVeD444/XwoULbdn30B8RM4Oxb9++qqio0IgRI/TTTz/5c3mX53JTcSfyJ0DYdrnMUAYITanb5re+sV71TkfYBAhrHM2aUbS6He703MHKS0zusk9yAgIIIIAAAggggAACCCCAAAIIIIAAAgi4C/Asl14RjQIECKOxVSO8TmafwbvvvtuqxZFHHqlFixYpJSUl5LWaP3++zjnnHCvfCy64oDVgaVdBuKm4S/oTILy4zwCNTE63EglFgDA9PkG35Be65RduAcLPaiv1TPnWdriz8wuVEZ9gV9clHQQQQAABBBBAAAEEEEAAAQQQQAABBGJKgGe5MdXcMVNZAoQx09SRUdFZs2bpxhtvtAp78MEH69///rcyMjL8KvyaNWtUWNgSyDn88MP19ttvt7u+tLRUX331lY444giv6X7yySc65phjVFZWZu3ZZv7/6NGj/SpHVydzU3EX6ixA+GltpZ6tKJJZQnPnlHT9Pqd/6356wQgQLq4q0RtVJa2FnNp7gJWvOdrmV+twaGNTfdjMILy3ZKNWNdS2w7Vr+dKu+jSfI4AAAggggAACCCCAAAIIIIAAAgggEI0CPMuNxlalTgQI6QNhI3Dvvffq0ksvtcozcOBAPf/888rOzu60fDvvvLPb0qNdBQhdn++111468cQTte+++6p///5KSEjQunXr9Oqrr+rpp59WY2OjlffVV1+tOXPm2O7ETcWdtLMAoTm7uKlBVQ6HhiSlKD4urjWBjtflJCTqhm7uQVjraNaLldu0rrFev0ztpV9l9G7Ns21+JmBZ3NykgqRkK3gZ6GFXEK8rw0DLx3UIIIAAAggggAACCCCAAAIIIIAAAgjEqgDPcmO15aO73gQIo7t9I6p2ZkbfO++841eZV69eraFDh7a7xtcAYVcZmYDhzJkzdf3117fOVOvqGn8+56birhVocCsYAcLO2tJbOT+urdBz5UX+dIPWcwkQBsTGRQgggAACCCCAAAIIIIAAAggggAACCARdgGe5QScmgx4QIEDYA+hk6VkgVAHChoYGvfLKK/rwww+tpUM3btyobdu2qa6uzpqxaGYlmrJMmTLFLfhoZ9txU4meAGFWQqJuzBuqT2or9CwBQjuHCWkhgAACCCCAAAIIIIAAAggggAACCCDQ4wI8y+3xJqAAQRAgQBgEVJJEwBcBbirREyA8MC1Lp2Xny+yTuKB8qy/N73aOHTMIvc1gtCPtgCrFRQgggAACCCCAAAIIIIAAAggggAACCESBAM9yo6ARqYKbAAFCOgUCPSTATSV4AcLeCUm6Pm9I0Fq24xKjB6Vn6dSsfC2rrdT8HgoQ1jkcuq5olVudL+2zkwqTU4NmQcIIIIAAAggggAACCCCAAAIIIIAAAghEuwDPcqO9hWOzfgQIY7PdqXUYCHBTiaYAYbZOzcrrVoDwroLh3drr8s2qUr1etd0NtbvphsFQoQgIIIAAAggggAACCCCAAAIIIIAAAgj0qADPcnuUn8yDJECAMEiwJItAVwLcVKInQHhwerZOycqTtyU+u+oL5vM7C4YrPi7Ol1M9ntNxVqPrJJYXDZiUCxFAAAEEEEAAAQQQQAABBBBAAAEEELAEeJZLR4hGAQKE0diq1CkiBLipBC9AuE9qps7NKQhaP+gYjBuanKrL+uyk+0s2akVDbUD53lEwXAkBBAhrHc1qcjp1ffEat3xNeiZdDgQQQAABBBBAAAEEEEAAAQQQQAABBBAIXIBnuYHbcWX4ChAgDN+2oWRRLsBNxb2Bb922TsVNDa0fuGbmddUVPqgp198riq3TTFDsur6D1TcxqavLAv68Y4AwJT5ef84fpj9vW6etbcrvTwa3FwxXop8Bwv/VVenxsi1es7klv1Dp8Qn+FINzEUAAAQQQQAABBBBAAAEEEEAAAQQQQKCDAM9y6RLRKECAMBpblTpFhAA3FfdmWt9YrwdKN6rO4VDfhCRd3ncn9fIhwOV0OrWsrkqbmuo1KrWXBielBrUPdAwQHpCWpdOz8/Vy5Ta9U10WUN45CYlqcDo1vlcfmcCoL4e3ZUVd17K8qC+KnIMAAggggAACCCCAAAIIIIAAAggggEDnAjzLpYdEowABwmhsVeoUEQLcVDw3U2Vzk7Y3N6l/YrLMzLxwPDoG5g5Jz9ZJWXn6pLZCz5YXdavIZh/Cm/OG+jTzr7MA4W8yc3VERk63ysLFCCCAAAIIIIAAAggggAACCCCAAAIIIMAehPSB6BQgQBid7UqtIkCAAGEENJKXInYMzB2anq1JWXn6tLZSC8q3drtiJ2bm6nAfgnudBQj/lDskqMusdruSJIAAAggggAACCCCAAAIIIIAAAggggECECPAsN0IaimL6JUCA0C8uTkbAPgFuKvZZhjolbwHCz2or9YwNAUJfZv+ZZVWv3LrSa9X/UjBMyXHhOQMz1O1FfggggAACCCCAAAIIIIAAAggggAACCHRHgGe53dHj2nAVIEAYri1DuaJegJtK5DaxtwDhstpKzbchQOjLDEKzFOv1xWu8IrL/YOT2L0qOAAIIIIAAAggggAACCCCAAAIIIBBeAjzLDa/2oDT2CBAgtMeRVBDwW4Cbit9kYXNBsAOEE7NydVh65/sHljQ36ubitQQIw6ZXUBAEEEAAAQQQQAABBBBAAAEEEEAAgWgV4FlutLZsbNeLAGFstz+170EBbio9iN/NrL0FCD+vrdTTNswgNMFBEyTs7ChuatCt29YRIOxmW3I5AggggAACCCCAAAIIIIAAAggggAACXQnwLLcrIT6PRAEChJHYapQ5KgS4qURuM3YMELoCenYtMWpkuloidENjve7cvp4AYeR2I0qOAAIIIIAAAggggAACCCCAAAIIIBAhAjzLjZCGoph+CRAg9IuLkxGwT4Cbin2WoU4pHAKEqxtq9deSjQQIQ9345IcAAggggAACCCCAAAIIIIAAAgggEHMCPMuNuSaPiQoTIIyJZqaS4SjATSUcW8W3MoVDgPCH+ho9WLqJAKFvTcZZCCCAAAIIIIAAAggggAACCCCAAAIIBCzAs9yA6bgwjAUIEIZx41C06BbgphK57fvHrSvlcDpbK/CHPgM1IjlNX9ZV6cmyLbZUrKslRr+pq9a8ss0e80qKi9ecgmG2lINEEEAAAQQQQAABBBBAAAEEEEAAAQQQiHUBnuXGeg+IzvoTIIzOdqVWESDATSUCGslLET+vrdSCiiI1O53aNSVdF+b0V1xcnBqcDl2zdZXbVaNSe1nBQ3+OvMRkDUxM1ilZeUqPT3C71JTh6fKtHpPsn5iiabmD/MmOcxFAAAEEEEAAAQQQQAABBBBAAAEEEEDAiwDPcuka0ShAgDAaW5U6RYQAN5WIaCavhSxpblS1w6GdEpOt4KDr6Lj8qPm7mQ3o6e++CPyqVx/9ulcft1M/rq3Qc+VFHpPYNy1TZ2cX+JI85yCAAAIIIIAAAggggAACCCCAAAIIIIBAFwI8y6WLRKMAAcJobFXqFBEC3FQiopn8LqTdAUJXgLFjQd6tKdfCimKP5Tsnu0C/TMv0u+xcgAACCCCAAAIIIIAAAggggAACCCCAAALuAjzLpVdEowABwmhsVeoUEQLcVCKimfwuZDAChGYWoZmxeGh6tgYnpVplWlJdqlcrt3ss34W9+2u3lAy/y84FCCCAAAIIIIAAAggggAACCCCAAAIIIECAkD4QGwIECGOjnallGAoQIAzDRrGhSMEIELqKlRAXp5vyhlp7Ei6q3K7/VJd6LPHU3gO0c0q6DbUhCQQQQAABBBBAAAEEEEAAAQQQQAABBBDgWS59IBoFCBBGY6tSp4gQ4KYSEc3kdyGDGSA0hXHtSXh90RpVOpo8lu+SPgM1PDnN77JzAQIIIIAAAggggAACCCCAAAIIIIAAAgi4C/Asl14RjQIECKOxValTRAhwU4mIZvK7kMEOEI5K7aVTsvI0o2i117Kdl9NP5jwOBBBAAAEEEEAAAQQQQAABBBBAAAEEEOi+AM9yu29ICuEnQIAw/NqEEsWIADeV6GzoYAcIfVG7su8gDUpK8eVUzkEAAQQQQAABBBBAAAEEEEAAAQQQQACBLgR4lksXiUYBAoTR2KrUKSIEuKlERDP5XchwCBDekl9o7VPIgQACCCCAAAIIIIAAAggggAACCCCAAALdF+BZbvcNSSH8BAgQhl+bUKIYEeCmEp0NHQ4BwjsKhishLi46gakVAggggAACCCCAAAIIIIAAAggggAACIRbgWW6IwckuJAIECEPCTCYIuAtwU4nOXuEtQPho6WZ9W18dkkrf3W9ESPIhEwQQQAABBBBAAAEEEEAAAQQQQAABBGJBgGe5sdDKsVdHAoSx1+bUOEwEuKmESUPYXIyOAcLMhETdlDdUngKHNmfdmhwBwmDJki4CCCCAAAIIIIAAAggggAACCCCAQCwK8Cw3Fls9+utMgDD625gahqkAN5UwbZhuFqusuUk3Fq9pTeXm/EL1ik8gQNhNVy5HAAEEEEAAAQQQQAABBBBAAAEEEECgpwR4lttT8uQbTAEChMHUJW0EOhHgphK93aPZ6VSt06G0uPjWvQCZQRi97U3NEEAAAQQQQAABBBBAAAEEEEAAAQSiW4BnudHdvrFaOwKEsdry1LvHBbip9HgThLQAoQoQXpM7WP0Sk0NaNzJDAAEEEEAAAQQQQAABBBBAAAEEEEAgmgV4lhvNrRu7dSNAGLttT817WICbSg83QIizD3aA8OI+A9Q/McVazpQDAQQQQAABBBBAAAEEEEAAAQQQQAABBOwT4FmufZakFD4CBAjDpy0oSYwJcFOJrQa/eutKNTmdQal0enyCbskvDEraJIoAAggggAACCCCAAAIIIIAAAggggECsC/AsN9Z7QHTWnwBhdLYrtYoAAW4qEdBINhZxXulmfVNfbWOKO5KakTtEuYlJQUmbRBFAAAEEEEAAAQQQQAABBBBAAAEEEIh1AZ7lxnoPiM76EyCMznalVhEgwE0lAhrJxiI+WbZFX9ZV2ZhiS1Kj0zJ1VnaB7emSIAIIIIAAAggggAACCCCAAAIIIIAAAgi0CPAsl54QjQIECKOxValTRAhwU4mIZrKtkPPLt2pZbaVt6ZmEpucOUR4zB201JTEEEEAAAQQQQAABBBBAAAEEEEAAAQQ6CvAslz4RjQIECKOxValTRAhwU4mIZrKtkI+VbdHXNs8gvLvfCNvKR0IIIIAAAggggAACCCCAAAIIIIAAAggg4FmAZ7n0jGgUIEAYja1KnSJCgJtKRDSTbYW8uXitSpobbUvvoPRsnZqVZ1t6JIQAAggggAACCCCAAAIIIIAAAggggAACBAjpA7EjQIAwdtqamoaZAAHCMGuQIBfnnu0btLaxzrZcxmbkaEJmrm3pkRACCCCAAAIIIIAAAggggAACCCCAAAIIECCkD8SOAAHC2GlrahpmAgQIw6xBglyclyu36Z3qMttyOSqjt8Zn9rUtPRJCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQKE9IHYESBAGDttTU3DTIAAYZg1SJCLQ4AwyMAkjwACCCCAAAIIIIAAAggggAACCCCAQJAEeJYbJFiS7VEBAoQ9yk/msSzATSW2Wn9xVYneqCqxrdIFicm6NnewbemREAIIIIAAAggggAACCCCAAAIIIIAAAgh4FuBZLj0jGgUIEEZjq1KniBDgphIRzWRbIVc21Oq+ko22pWcSurvfCFvTIzEEEEAAAbVmyu8AACAASURBVAQQQAABBBBAAAEEEEAAAQQQcBfgWS69IhoFCBBGY6tSp4gQ4KYSEc1kWyHXNtbpnu0bbEuPAKGtlCSGAAIIIIAAAggggAACCCCAAAIIIICAVwGe5dI5olGAAGE0tip1iggBbioR0Uy2FXJdY53utjFAeGBalk7LzretfCSEAAIIIIAAAggggAACCCCAAAIIIIAAAp4FeJZLz4hGAQKE0diq1CkiBLipREQz2VbI4qYG3bptnW3pTc8dorzEJNvSIyEEEEAAAQQQQAABBBBAAAEEEEAAAQQQIEBIH4gdAQKEsdPWEVHTI444Qu+8845PZXU6nT6d19VJH374oR544AG9++672rJli3r37q29995bkydP1umnn97V5QF/ToAwYLqIvND01yu3rrSt7NfnDVHvBAKEtoGSEAIIIIAAAggggAACCCCAAAIIIIAAAl4EeJZL14hGAQKE0diqEVynUAcIb7rpJt14441yOBwe1U444QS98MILSk1NtV2Vm4rtpGGf4BVbVthWxll5Q5WdkGhbeiSEAAIIIIAAAggggAACCCCAAAIIIIAAAp4FeJZLz4hGAQKE0diqEVwnV4Bw9OjRevzxxzutyR577NGtmj766KO68MILrTSGDx+u6dOna88999SmTZs0d+5cLV261PrsrLPO0vz587uVl6eLuanYThr2CdoZILw5v1C94hPCvs4UEAEEEEAAAQQQQAABBBBAAAEEEEAAgUgX4FlupLcg5fckQICQfhFWAq4A4eGHH6633347aGUrKytTYWGhzH8PHjxYy5YtU25ubmt+zc3Nmjhxol599VXrb2bZ08MOO8zW8nBTsZUzIhKzM0B4a36h0ggQRkS7U0gEEEAAAQQQQAABBBBAAAEEEEAAgcgW4FluZLcfpfcsQICQnhFWAqEKEN5+++2aNm2aVfdnn33W416D5h/9oUOHygQLjz/++NZgoV1g3FTskoycdOwMEP6lYJiS4+Ijp/KUFAEEEEAAAQQQQAABBBBAAAEEEEAAgQgV4FluhDYcxe5UgAAhHSSsBEIVIBwzZow++OADZWVlqbi4WMnJyR4djj32WC1evFgpKSnatm2bevXqZZsXNxXbKCMmITsDhHcUDFdCXFzE1J2CIoAAAggggAACCCCAAAIIIIAAAgggEKkCPMuN1Jaj3J0JECCkf4SVQCgChA0NDcrIyFBTU5OOOeYYvfHGG14NbrvtNmtvQnMsWbJEY8eOtc2Lm4ptlBGTkJ0BwrsKhiuOAGHEtD0FRQABBBBAAAEEEEAAAQQQQAABBBCIXAGe5UZu21Fy7wIECOkdYSXgChAWFBRo0KBB+v7779XY2GjtD7jvvvvqpJNO0hlnnKGkpKSAy/3tt99qjz32sK6/7LLLdM8993hN66WXXtKkSZOsz++//35dfPHFAefb8UJuKrZRRkxCdgYI7+43ImLqTUERQAABBBBAAAEEEEAAAQQQQAABBBCIZAGe5UZy61F2bwIECOkbYSXgChB2VqjddttN//jHP7TrrrsGVHYzY/DXv/61da3Zi/Cqq67yms5nn32m/fbbz/r82muvlZlR6OthbhqdHevXr9fBBx9snfLJJ5+of//+vibNeREqMKtotW0ln5VfaFtaJIQAAggggAACCCCAAAIIIIAAAggggAAC3gU2b96s/fff3zph9erVGjp0KFwIRLwAAcKIb8LoqsC4ceMUHx+v4447Tnvvvbf69u2ryspKff7553rooYe0fPlyq8JmhqEJqg0ePNhvgL///e869dRTrev+9re/aerUqV7TMPmZgKQ5LrnkEt17770+58fyjz5TcSICCCCAAAIIIIAAAggggAACCCCAAAIIIBARAua5tGtSSUQUmEIi4EWAACFdI6wEysrKlJOT47FMZqnRCy+8UE8++aT1+cSJE7Vw4UK/y//000/r3HPPta6bN2+efvvb33pNY9WqVRo+fLj1+QUXXKBHH33U5/wIEPpMxYkIIIAAAggggAACCCCAAAIIIIAAAggggEBECBAgjIhmopA+CBAg9AGJU9oLNDU1dWsPQFdqjz/+uCZPnuwXr8l7zz33tPYmNIdZxnPgwIF+pRGqGYRdLTFaV1dn1cPMhszLy1NiYqJf9Yjkk9tOyWd51UhuScoezgKMs3BuHcoWDQKMsWhoRepgpwBjwk5N0kLAXYAxRq9AoPsCjKPuG5ICAl0JRPM4M8+li4uLLQLzfDo1NbUrDj5HIOwFCBCGfROFXwF7MkBoNO644w5dffXVFswzzzyjM8880y+kUO1B6FehYuxkNvWNsQanuj0iwDjrEXYyjSEBxlgMNTZV9UmAMeETEychELAAYyxgOi5EoFWAcURnQCD4Aoyz4BuTAwJ2ChAgtFMzhtJyzeDrTpX79++v7Oxsv5N4/fXXNX78eOu6OXPmtAYLfU3om2++sd7yMMdll12me+65x+ulL730kiZNmmR9fv/99+viiy/2NRvO60SALwt0DwSCL8A4C74xOcS2AGMsttuf2rsLMCboFQgEV4AxFlxfUo8NAcZRbLQztexZAcZZz/qTOwL+ChAg9FeM83tcYNGiRTr++OOtcgQSIGxoaFB6erqam5t1zDHHyMwo9Hbcdtttmj59uvXxkiVLNHbs2B6vfzQUgC8L0dCK1CHcBRhn4d5ClC/SBRhjkd6ClN9uAcaE3aKkh0B7AcYYPQKB7gswjrpvSAoIdCXAOOtKiM8RCC8BAoTh1R6UxgeB22+/XdOmTbPOnD9/vs466ywfrmp/ysEHH6wPP/xQWVlZ1trRycnJHtM49thjtXjxYqWkpFjnZWZm+p0XF7gL8GWBXoFA8AUYZ8E3JofYFmCMxXb7U3u+39EHEAi1APedUIuTXzQKMI6isVWpU7gJMM7CrUUoDwKdCxAgpIdElIDZ/9AsD+pa4nTdunUaNGiQ33UwMw+vueYa67pnn31Wp59+ulsa5oY2dOhQa6bhcccdJzNzkcMeAb4s2ONIKgh0JsA4o38gEFwBxlhwfUk98gQYE5HXZpQ4sgQYY5HVXpQ2PAUYR+HZLpQqugQYZ9HVntQm+gUIEEZ/G0dMDZcuXap99tlHOTk5Hsvc2NioCy+8UE8++aT1+QknnKBXXnnF7dw1a9aosLDQ+vvhhx+ut99+2+2ckpISDRs2TOXl5RoyZIiWLVumvn37tp5ngoITJ07Uq6++av2N5UXt7UZ8WbDXk9QQ8CTAOKNfIBBcAcZYcH1JPfIEGBOR12aUOLIEGGOR1V6UNjwFGEfh2S6UKroEGGfR1Z7UJvoFCBBGfxtHTA0nT56sF198URMmTNARRxyhnXfe2VoCtKqqygrgPfTQQ1q+fLlVn/z8fH300UetgcC2lfQlQGjON+lNnTrVunT48OGaMWOGNTtx06ZNuueee2QCluY444wztGDBgohxjISC8mUhElqJMka6AOMs0luQ8oe7AGMs3FuI8oVagDERanHyizUBxlistTj1DYYA4ygYqqSJQHsBxhk9AoHIEiBAGFntFdWlNQFC1+zAzipqgnjPPfecdtttN4+n+RogNBffcMMNuvnmm+V0Oj2mZZYWNUHL1NTUqLYPdeX4shBqcfKLRQHGWSy2OnUOpQBjLJTa5BUJAoyJSGglyhjJAoyxSG49yh4uAoyjcGkJyhHNAoyzaG5d6haNAgQIo7FVI7ROZnbg4sWL9eGHH+q7775TcXGxzFKgKSkpKigo0OjRo3XyySdbS38mJCR4raU/AUKTyAcffKD7779f7777rrZu3Wotcbr33nvr/PPPt2YPciCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC0SRAgDCaWpO6IIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINCFAAFCuggCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACMSRAgDCGGpuqIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIECAkD6AAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQAwJECCMocamqggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggQIKQPIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBDAgQIY6ixqSoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACBAjpAwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEkAABwhhqbKqKAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAFC+gACYS7w+eef64033tC7776rb775RkVFRUpKStKAAQN08MEH64ILLtChhx7qcy1MWg8//LA++eQTFRcXKy8vT/vvv79+97vf6dhjj+00nfr6en3xxRf69NNPrevNf3766Sc5nU7rOtd/+1KYhoYGPf300/r73/+ur776SiUlJVa9Bg4cqDFjxljlOfDAA31JqtNzHA6Hvv/++9bymrL/73//k8nfHEuXLtURRxzRZT7r1q3TokWL9Pbbb+vLL7/Uhg0b1NzcrNzcXO277746/fTTdcoppygxMbHLtDgh/ASidZzV1dXpscce04svvmj1+/LycmvMjxo1Sueee65OO+002xvjww8/1AMPPGD9m7Vlyxb17t1be++9tyZPnmyNk86OpqYmff3119Z4df07891331ljzRyrV6/W0KFDbS8zCQZfgDFmn3F3xlhFRYVef/11vfXWW1q2bJlWrVqlmpoaZWdna/fdd9fxxx+vKVOmKCcnx74Ck5KbAOPBvk7RnfHw2Wef6Z133rHuN+ZeY74Xm++jycnJ1vds8z3U3LvGjh1rX4FJKWQCjDP7qLszzryVYvPmzdp1112t76bmOPzww63fWRzhJcA4sq89ujOOZs2apRtvvNGnwvj6fMOnxDgpJAKMM/uYuzPOOpbiP//5j+bPn6/33ntP5p5lnvUVFBRor7320pFHHqlzzjlHvXr1sq/wpIRADwoQIOxBfLJGoCsB80Ppv//9b1enWTemRx991Hqg4e0wwbupU6dawUFvhwnKPfjgg4qLi/N4yvnnn68nnnii0zy6LKyk9evXa/z48VYgoLPjiiuu0J133um1PL7k9eSTT1oPd7wdvnyBvv766zV79uwuA6CjR4+2AjGDBw/2pWicEyYC0TrOfvjhB/3mN7+R+W9vh3kp4B//+IcyMjJsaY2bbrrJ+vFqAvOejhNOOEEvvPCCUlNTPX5urjU/gL0dBAhtaaaQJ8IYC48x9q9//UsTJ06Uedmns8P88H322WcJigRppDAewmM8mOY95JBD9P7773fZ0uYFsKeeesrrvavLBDgh5AKMs/AZZ94a/+STT7Z+N7kOAoQhHyZdZsg4Cp9xRICwy+4asScwzsJnnLk6UWlpqcyzz3/+85+d9iszecK8eM2BQDQIECCMhlakDlErMGLECK1cudJ6i9k8nDAzBU3wycymMW/GmODZxo0brfqfccYZWrBggVeLGTNm6NZbb7U+32effTRt2jQNHz7cSn/OnDnWzEBzmPNMMMzTYQJtJuBmjszMTP3yl7+0gg9mlpA5fJlBaGYImetcwUHz9s2VV16pnXfeWZWVldbbOaZe1dXVVpqmbFdffXXAbWwCmubmbg4zQ3GPPfaQa5aS+ZsvAUIzm2LevHlWEMU8XDVvC40cOdJ6ULR8+XL99a9/td4+N4f5u3kDjDeJAm6ykF8YjePMzIIwM1tNMN4c5t+P8847z/q3ZNOmTdY4NrN3zWGCdq+88kq33c1LChdeeKGVjvm3Zfr06dpzzz2t/ObOnWuNNXOcddZZ1pt4no62P37N+DJfuE1dzL9T5iBA2O1m6pEEGGPhMcbMuDMvFMXHx+voo4+2Vg0ws3vNbEEzK/6ZZ57R888/b/WR9PR0K3DCj177hwzjITzGg2lZ833OfKc2K3KYmUz9+vVT3759rfuOWd3CvDRn7jvmMDPun3vuOfs7BCkGRYBxFj7jzFMDv/rqq5owYYLy8/Ot1XHMQYAwKEOhW4kyjsJnHLX9jdTVS9aFhYW2vfzZrQ7ExT4JMM7CZ5yZBjOz2s33Q7PSijnMxAazCpJpJ/Odce3atdazP/OStbmX8VvJp27OSREgQIAwAhqJIsaugFnqyywDeNJJJykhIcENYtu2bdZynD/++KP1mZlt6Gm50RUrVlgPPkxgzMxyM+elpaW1pmeWFzM/ysxSS2bavFmS0zzg73iYB4e1tbXab7/9rPTMQ0azPKdZnskcvgQIzZui5o1Rcxx00EHWMoQd62ZuxuazxsZGa3lC88Mx0KU7zVKFH330kbWMqrl5m6BD2y/YvgQIr7nmGuuB0UUXXWQFRjse5ovCmWeeac2MMoeZRTVz5szY7bgRVvNoHGeXXHKJ7r//fqslbrjhBo+z8szfTV81hxmXkyZNCrjlysrKZH6Mmv82LzGYMWyW33UdZoyY4Lr5Em0O82/GYYcd5pbf4sWLtWbNGuvfGPPygBn3bV9MIEAYcBP16IWMsfAYY+Yebu55Jnjvbab7vffeq0svvdTqL+PGjbOWIuWwV4DxEB7jwbSq+V7c2fdL853XPCQyL+WZwyzVbV584Qh/AcZZ+Iyzjr2lqqpKu+22m/USm5mZa37rmoMAYfiNK8ZR+Iyjts8vfHnmEn69iRJ5E2Cchc84M21k7klmKyTz/dC8XOltSxYzDs0zjkCfUzIiEAg3AQKE4dYilAcBPwVee+01awaQOcxDPTNTp+Pxhz/8wdoTzBzmIYenvf1MEM0E5cxhggvmIaEvh78BQjNb8O6777aSNrOWXGXvmJcJVrz00kvWn81bcmbmn12HvwFCX/Ldvn27NTvL7G1oAhvmzXOO6BGIpHHm2hvTBOuGDBlizb7z9IKBOW/YsGEy+2uaFwdcs2ADabXbb7/dmpVsDrM0oae9Bs0MJbN/oMnX/BByBQu7yo8AYVdC0fE5Y6zzdgzmGOuYswnQmxeGzEtA5gUd84IMR2gFGA/hMx5MYN11T7vvvvtkvlNzRIcA46xnxpn5vWp+Z5q9PZcsWdK6lQQBwsgcV4yj0IwjAoSROT7sKjXjLDTjzKxm5ppwYcaceaGaA4FYESBAGCstTT2jVsC8hema1Wamv5svD20P82bLoEGDrKVId9llF2tJTG+H+dwsGbrTTjtZQQNvexG2vd7fAGHbmU3ffPONdt99d4/FMcuK3nHHHdZn5kGlWS7RriMYAUJTNtdDVbMUqWkXjugRiKRxZmYAmxm+5jDL4z7yyCNeG8K1fK45oTuz88xM5g8++EBZWVnW0mze9kM1SxqaWYIpKSkyM6B9WYqXAGH0jKPOasIY67ydgznGOubc9v5rXhwwLxBwhFaA8RA+46HtQznzvfSPf/xjaDsDuQVNgHEW+nFmVnYxL6SaGRdmRq7ZYsL1e5MAYdC6elATZhyFZhwRIAxqNw77xBlnoRln5oUw82KYeUaxefNmn55VhH3noYAI+ChAgNBHKE5DIFwFSkpKWt/u97SX2KpVq1qXC/39739v7afi7TCfP/zww9bH5jqzZGBXh78BQrNf32WXXWYl68sMQvOj0cyEMoEHu45gBQjNzEEz29EEbCsqKuwqLumEgUAkjTMTqDPBBHN0tqeo63PX3qRmX0LXMk/+kJtZsyYobpZqO+aYY/TGG294vfy2226zljc0h3lr3Lw93tVBgLAroej4nDHmvR2DPcY65uya3WH+bpYLNvsGc4RWgPEQPuPB7Jvr2uPbBAvNy3gc0SHAOAvtOHNtdWFWWTFbMbiWuSdAGNnjiXEUmnFEgDCyx0l3S884C/44M7+3sv+fvTMBj7I6+/4/CQlLCCQhIRDWULAsZatoKosohIKoQADlpbLW9BNEgaLQVuqr/QpVY+NHfLGigPAC4kpAXCEFi2zihpEiFAyEfQmQkJAAIct33c8wk0kmk1kyM8/2P9fldeE85znnvn/3OTkz5z7nvps2xbVr1/DAAw/Y0gfJ2iWXLWStklzVzg5C19XGfJ8E1CZAB6HaFmD/JFBHAhKG05o7TE79p6amVmnx448/VsL5SZHQnrNnz3baozyXEKBS5L3hw4e7lM5TB6HcLpIEv+JAEyeG5CKrHv5w7969ShhUWaTHjx9v25hxKYybFfzhIJQwbK1atVKcJHKTUE7IshiHgJ7mmWy8WJNluwoXbH+jV3JtPv/88x4bbf/+/bYQwOL8X7RokdM27DlKjsRHH33UZX90ELpEZIgKnGPOzejvOVa95549eyo3O+SGh4TP9uUBHUMM1gAowfmg3nwoLy9XbsLLvJMwiBs2bFCEkZtOEvmCuWYCMAEC1AXnWWDnmXzH/NOf/qQcXJW5JHnhpdBBGKAB76duOI8CM4/s9y+SkpLw3XffobCwEJGRkUpOT4nSIoe9o6Ki/GRpNqsmAc4z/88ziZpy++23Kx299NJLyj6krFnvvfceioqKlM9l3ZIDzn/+85/Rt29fNYcE+yYBnxOgg9DnSNkgCQSOgGxiSJgWqzOqplBgcmNw+vTpilCyuI0dO9apgO+//75yWkaKvCdfMl0VTx2E0p58wZET2VevXkXv3r0Vp+Utt9yihOXcuXMn0tLSlC+84uT49NNPlZM6viz+cBDah2STXFFPPvmkL0VmWyoS0Ns8ky+w8uPwxo0bLvNhWm+9Cl5vnfFyY/Cee+5RLORq7Eu4YHGgS/njH/8IuVHoqtBB6IqQ/p9zjtVuQ3/PMfve7Q8V1RS2XP+jTfsacD6oMx8kR+6xY8dq7Fzy+crccBYWX/ujihJWJ8B5Fth5JpFpJJ+8/PaTUPO//vWvbQLQQajf+cl5FLh5ZL9/4axXcRauXLkSI0eO1O+gouQOBDjPAjPPJJqS7DtIkQhL4iSUlCg1FcnTLnuWtV2+4FAmAb0RoINQbxajvCRgR0AWJasjKjk5GRkZGQ58ZMN+3rx5yufibJPTZc6KPLfeGnQ3z4o3DkLp/8cff1QW3TfeeAOSJ9G+xMXFQW4z/Z//83+U0IW+Lr52EO7Zswf9+/dXbg9K/kbJ49ioUSNfi832VCKgx3kmDjtrqE8JjSbOv+rlrbfewm9+8xvbx3LT+MMPP/SYshw8ePDBB5X3Xn31VUybNs1pG5IDVU65SnF1u9HaCB2EHptEdy9wjtVuMn/PMWvvEr5IwomKk0Ru9suhIznEwxJYApwP6syHmhyEclvwv//7v5XQ+LxJG9h54O/eOM8CO8+GDBmCf/7znxg3bhzefvvtKp3TQejv0e6/9jmPAjePZP9C9npGjRql3HKKj49XDoPKvsObb76JzZs3K8LI9zf5PWc9vOk/67PlQBHgPAvMPLOPpla/fn1cv35dicQmc08OuFy+fBnr1q1TDjlLNDRZu+TwGOdaoGYC+/E3AToI/U2Y7ZOAnwhIaE4JLyFOqebNmyvhwMSxVr389a9/VTY3pGzZsgWDBg1yKpHkBBs8eLDyXN6Tq/OuijcOQvky+5e//AVLly6FhOasqcgto2eeecYvuV586SA8d+4c+vTpg5MnTypfEuTHb22MXfHkc20R0Os8k1vFEsJX/j6EhoYqfwMkv2DLli2VhNurVq1Scr/ImJVQvlJk7sv49bSsXr3alrtw+fLl+O1vf+u0CfucqA8//DCWLVvmsjs6CF0i0nUFzjHX5vP3HBMJysrKlB/B1oMFsv7KWskSWAKcD655+2s+HDp0SFkP5aS+hNaViBZy6EVOj4tT4x//+AcaN27sWkDW0DwBzjPXJvLlPJPvnJMnT1ac7AcPHlS+i9oXOghd20OLNTiPXFvFl/MoPz9fCSfqrLz22mu2Q5riPPzpp5/QsGFD10KyhqYJcJ65No+v5tmCBQuU/LjWcv/99yth5uW2oH3ZsWMHBg4cqHxfFMeh7MNa1zHX0rIGCWiXAB2E2rUNJSMBpwQkL8qAAQOQl5cHOd0ioVpkkaqpaO0GoYQ/lFuKX3zxhXLC7YknnsDUqVPRoUMHJSGw3MYTx4UsvLLQykkeObltXyRvhbOSkJDg8tahrxyEEgZVYpB/++23ijgSLlFOFLEYg4Ce55lYQDZkfve739kcgNWtIvNP8gU+/vjjyiM5kSrhf63FullakzXlpqz1R6q/bzfRQWiM+VSTFpxjFoeE2nNM+peQ4q+//roiioQW/eCDDxzyAxt3JGpDM84H7cwH64iQ79lDhw5VbtNKSG75bhoREaGNAUMpvCLAeRbYeSYO9i5duiiOdsnpKdEjqhc6CL0ayqq+xHkU2HnkrrHld5/18OWaNWuUlC4s+iXAeRbYeSYR1CRtkLXIgRbJP11TkbRMkp5JijgIu3fvrt+BRslJ4CYBOgg5FEhAZwSOHj2qhLM8ffq0snknm/MSXtRZ0VoOQgmJKmESpEiMfDlRWr3IrSfJTfH5558rJ3b27t2rbMxYS20ndOQdudVYW/GFg1CcmeLolP6kzJkzx6aXzoYUxa2BgN7nmVWlrKwsLFy4ULkVJA5tKTKnxLEtn0sIX+sXWnHUS8hfa6ktH9OKFStsMfr9nR+NDkJjTlHOMUArc+xPf/oTnn/+eWWgyfcLCVPFU+eBnXecD9qZD9Utbx8aW+aK5KVh0ScBzrPAzzOJXiG3OyTaihwCrX4TQ0YSHYT6mk+cR4GfR+6OEPtc7+IstB78cvd91tMOAc6zwM8z+1u4culAIh85K+KIlzkmxVUEJe2MKkpCArUToIOQI4QEdERAnIJyc1AWK/kxJQ42+eFVW/noo48g1+OlyG282hLp2sfdlnja1nyEtbXvSYhRyTUYExMDyXN0yy23KDHznRUJ7SQblVJEZpHNWtR2EIoDc/To0bZ8bSkpKUq4VBZjEND7PKvJChI+UEKLimNbws5Yc2RKfkLr6VK5bWzNaSptJ5JOgAAAIABJREFUuOu8kBu9Viej3PaVW4nOitxQlLkj5ZVXXsGjjz7qctDQQegSke4qcI5ZTKaFOfbCCy/Ybr5L/kEJNd60aVPdjSk9C8z5oJ354GwcyXfWw4cPo1OnTpDb9Sz6I8B5Fvh5JsxbtWqldDxv3jynOW2tebLlpqE1LYZsziYmJupvoBlcYs6jwM8jT4ZUcXGxLZKS7OPIfg6L/ghwnqkzzz755BNbeiPZc5WIZ86KRHAbNmyY8phRxPQ3xyhxzQToIOTIIAGdEJDQLBJG9Mcff1QkXrx4MWbMmOFSevucXxJCTG4UOiv2IcbkPflx5qp44iA8e/asLe9ETUnq7fsSR4b1BoMsvp9++qkrUdx+XpcbhBJrXBwqb7/9ttKf6CFOlppOxLotECtqhoAR5pknMMVBJzmWpMiXYPky7GmREInicBQnpIRis+Ywq6kd+QL91FNPKY/EESE3GV0VOghdEdLXc84x7cwxyalm/R4hG7PyN0AO8bAEjgDng3bmQ21Wl5y+u3btQlhYGK5fvx64AcKefEKA80ydeZaTk+PWb8majCwRZuQgLIt2CHAeqTOPPBkBksrFmiuXDkJPyGmnLueZevPs2LFjyuFNKX379lVyUTsr9s7E6oestTOaKAkJeEaADkLPeLE2CahC4PLlyxg0aBC+++47pX8JBfaHP/zBLVnk1p7kC5OTSJ07d4aESnJWZINQYm3Lac8TJ064lWzXEwehfOGJjY1Vuh8zZowtbndN8kg4RElmL+W+++6z3dZzS2kXleriILSP7S9yZWRkIDQ01BdisQ2VCRhlnrmLURx78rchNzdXmfPypVjCFntT5Ev07t27lTkr7ckmak1FnP1y4k5yp0o9d3I50UHojUW0+Q7nmHbmmIR8kw1Y+Y4gOYC3b9+u3C5mCRwBzgftzAdXVrfe9o2OjsbFixddVedzDRHgPFNvntFBqKGJUEdROI/Um0eemE7y5d5+++3KK4xw5Ak5bdTlPFN/nrVr1w7Hjx9HXFwc5HKDsyI5dWfOnKk8lssC1pvw2hhJlIIEvCNAB6F33PgWCQSMgISKkHx81hMs8+fPx4IFCzzq3/6WkGzi/+pXv3J4/8svv8Qdd9yhfC71JfyfO8UTB6HcvouKikJBQYGyESkOiXr16tXYjX1o1Mcffxwvv/yyO+K4VcdbB6HkGbSGOh08eLAStkMcHSz6J2CkeeauNeS0m4R8kvLXv/4Vf/7zn9191aFeamqq7dDCW2+9hf/6r/9yqHPy5EnlVJ7cNPTkVCsdhF6bRVMvco5pZ47JwZYHH3xQmYtySECcg9YTs5oaNAYWhvNBO/PB1TCz33CVSB7/+te/XL3C5xohwHmmj3nGHIQamTBOxOA80sc8EvOJU1DyoUmRg2ATJkzQ9uCidDYCnGfamGf2+32y/yqHoGsqEgXJ+n1QLlbI7ykWEtA7AToI9W5Bym9oAnLDR/IHbt68WdHTVX4vZzAkX0q3bt0gufMkSbyEEbOG75R3rl69ijvvvBOS2FocdhLGVPKsuFM8cRBKe7/5zW8gDgQp4qh75plnHLrJy8tT8g9aw6nKjSNxkvqqeOMgtH9HviiITcLDw30lEttRkYAR55nglNNvbdu2rZHshx9+qNzivXHjhjLXf/jhBzRo0MBrK0heUbmFJCcf5eTdt99+i2bNmtnaE0dEcnKy7Sawu+FFpQE6CL02i2Ze5BzTzhyTtUu+V4hNmjdvrnwf+PnPf66ZsWIGQTgftDEfvvrqK+U7r+TedFZOnTqFIUOG2KJvLFu2DA8//LAZhqnudeQ808Y8c2cg0UHoDiV16nAeaWMe7du3T9m76dixo9OB8Nprr2HatGnK8xYtWuCnn37iXoU608bjXjnPtDHPrPsn8rtI0h3deuut2LZtm8M8WrNmDSZOnKjY+d5774VcbGAhASMQoIPQCFakDoYlIBv4ctJfioQYXbRoUa1hPyWs3y233FIjjz/96U9KaFIpvXv3Vm77/OxnP0N2djZeeOEF7N27V3km9f72t7/V2IZcs6+eX0za/M9//qPUX7FiRZX3xMlX/YushDCVxVZOSUmRjUoJcybOBVmI5Saj6CnODSlyU++f//xnnWxcPYfFhg0b8MEHHyhtCgcJvWotIq/IbV/sQwhIKMZ33nkHTZs2rVUm+WLB0KN1MlvAXjbiPBN4Eu5TbgU/8MADygEB+fsg4Z7ee+89ZQxLkRu9W7ZsUf4m1LXY/zCVvy1y27l79+5KeGOZ059//rnShYTgkFAcNZUrV644hB6WDVnrDWq59WifI61Xr16Q/1i0TYBzTBtzTNZXWVNl/ZX1SdbGHj161Dp45ERsZGSktgeYzqTjfNDGfJDxP3XqVOV0uHwXlbXEGgZfHIOyZsn3Wjn4IiUpKUkJkc2c0/qYcJxn2phn7owWOgjdoaROHc4jbcwjWa/kdqDcWrrnnnuU31dyEFMOf8veijgsMjMzlUEi6SJk/2jEiBHqDBr26jEBzjNtzDOr4eyjLHXt2lWJuPSLX/xC+T4oc2vJkiVKFBbZa5ELFu5erPB4YPAFEggwAToIAwyc3ZGAJwSsP5jcfUdu7ogDoKYi4T0lf94bb7zhtDk5Ff3666873fyQa/TyxdTdIhsrcvunehGHnzgJJCdhbUWcou+//77ixKhL8YSjOCurOxTtb0m6K8fRo0cZss1dWCrX82R8iKh6mWeSpF6S1Tsr8oVXflD6wjlo7UNuBEu4UslrVlOR0KLr1q1zelvR03w10p/c7mXRNgHOsbo74H0xx+xvwrs7Ypyt4+6+z3qOBDgftDEfrA5Cd8aofJeV0PuNGjVypzrraIAA55k25pk7Q4EOQncoqVOH80gb88jd9UqchhJidOTIkeoMGPbqFQHOM23MM3vjyaUJuUThbE9DorDIpQNriiavDM+XSEBjBOgg1JhBKA4J2BPw5ZcFa7uffPKJ4gSUnCrioJPbOLfddhseeeQR5URabcVXDkLp4+LFi8oX2E8//RT79+9Hfn6+EupJQmKIPBKKVE6+ecqgJvk9aYMOQvPNQU/Gh9CpzUGopXn29ttvK6FwJYzamTNnILfz5HaE3BgaO3asEhrDH7dcd+3apWykSl6zc+fOKbePevbsqdzUcJXAmw5CY84/zjHf2tXbOUYHoW/t4G1rnA/ekqv5PW/ng4TXl3VKQl5LG3Jr8Pz580r4XTkVLifC+/Xrp6yVrm7a+lYjtuYLApxnvqBY2Ya388wdKeggdIeSOnU4j3zL3dt5JGuThDHcvXu3EvVJfl/JXoo4L6Kjo5XfWcOGDVMOZsv6xaIvApxnvrWXt/OsuhQy31599VXlu6LspUg6FonWJnuUjz/+uMuIYr7Viq2RgP8J0EHof8bsgQRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQ0Q4AOQs2YgoKQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgP8J0EHof8bsgQRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQ0Q4AOQs2YgoIIAXfjbw8cOBCSD6+uRXJ0rVixAj/88APy8vKU/HcDBgzAjBkz8Ktf/aquzfN9EiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEtAcAToINWcScwsUKAfhtWvX8MADDyjJnmsqwcHBePbZZ/H000+b2yDUngRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwHAE6CA0nEn1rZDVQTh9+nQ8+uijTpUJDw9HQkKC18o+9NBDWLt2rfL+3XffjVmzZiE+Ph779u3D3/72N2RnZyvPli5dipSUFK/74YskQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkoDUCdBBqzSIml8fqIHzmmWeUG3z+KNu2bcNdd92lNH3//fdj/fr1CAkJsXV14cIF3HrrrTh+/DiioqJw5MgRREZG+kMUtkkCJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACASdAB2HAkbPD2ggEwkF477334pNPPlGcgjk5OWjdurWDSJKbcPz48crnf//73/HEE0/QcCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgCAJ0EBrCjMZRwt8OwitXriAmJgbXr1/HsGHD8Omnn9YIr6SkBLGxsSgoKEDfvn2xc+dO40CmJiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAqYmQAehqc2vPeX97SDcunUrBg8erCj+3HPP4Y9//KNTCEOHDsXmzZtRr149FBcXIzQ0VHvAKBEJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJeEiADkIPgbG6fwlYHYRdu3ZFaWmpkgdQHHQtWrRQbvJNmTIFd999t9dCvPLKK3jssceU9yX34KhRo5y2NWvWLLz88svK8/3790NkYiEBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABvROgg1DvFjSY/FYHYW1qiVNv5cqVaNq0qcfay43BF154QXnv66+/Rp8+fZy2IbkH586dqzz/7LPPIDcKPSknT56stfq1a9dw8OBBxMXFKeFMxRHKQgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkoC0CcpklNzdXEap79+5o0KCBtgSkNCTgBQE6CL2Axlf8RyA8PBwjRoxQwoB27twZjRs3Vv7wbtu2DUuWLMHFixeVzgcOHIjMzEyPw37OmDED//jHP5Q2Dhw4oPThrLz66qt49NFHlcfvv/8+xowZ45Hi7jg7PWqQlUmABEiABEiABEiABEiABEiABEiABEiABEiABEiABFQl8NVXX+G2225TVQZ2TgK+IEAHoS8osg2fEcjPz0dkZGSN7Z07dw733HMP9u7dqzxPT0/HzJkzPer74YcfxhtvvKG8k52djQ4dOjh9X+pJfSmrV6/GhAkTPOqLDkKPcLEyCZAACZAACZAACZAACZAACZAACZAACZAACZAACWieAB2EmjcRBXSTAB2EboJiNW0QOHLkCLp06YKSkhJ07NgRhw8f9kiwQN4gdBVi9MSJE0peRSmyqLRs2dIjXViZBEiABEiABEiABEiABLRMoLyiHPe/dT+KS66gUVhjfDj+QwQHBWtZZMpGAiRAAiRgYAJzN8/F2/9+26Zh4/qN8dyg5zCqyygHrdcfWI+ntj6FK9ev2J6N/8V4pP461cCEqBoJkEBtBM6cOYPbb79dqXL06FG0b9+ewEhA9wToINS9Cc2nwH333YePP/5YUfzUqVOIj493G0IgcxC6EkociG3atFGqibOwdevWrl7hcxIgARIgARIgARIgARLQDYGss1no9Vovm7xZ07LQI66HbuSnoCRAAiRAAsYisPXoVoxfNw7niy6gX5t+WDN6DdpHOt/gz8nPwYSMCdh5Yieah8fgrTHvYFDCIGNBoTYkQAJuE+BertuoWFFHBOgg1JGxKKqFwLx58/Diiy8q//b0OvfixYvx+OOPK++uX78eo0Y5nhKzcp41axZefvll5X/379+Prl27+tQEXFR8ipONkQAJkAAJkAAJkAAJaIxA+pfpmL1ptk2q9GHpmJnoWYoAjalEcUiABEiABHRO4HThaWQcyMC0PtNQL7ieS21Ky0ux5JslGN1lNOIj3D+g7rJhViABEtAdAe7l6s5kFNgNAnQQugGJVbRFYO7cufj73/+uCOWpg3Dr1q0YPHiw8u5zzz0HuVHorAwdOhSbN29GvXr1UFRUhLCwMJ+C4KLiU5xsjARIgARIgARIgARIQGMEkt9JxoaDG2xSJXdORsa4DI1JSXFIgARIgARIgARIgARIwDUB7uW6ZsQa+iNAB6H+bGZ6ie+991588sknCgf5w9yqVSu3mRQWFiImJkbJYThs2DB8+umnNb4rz2NjY1FQUIA77rgDu3btcrsPdytyUXGXFOuRAAmQAAmQAAmQAAnojYDkH4xJjUHetTyb6NENo5E7N5d5CANoTLFD4rJEFJUUIjwsAntS9pB/APmzKxIgARIgARIgAeMQ4F6ucWxJTSoJ0EHI0aArAkeOHEHnzp1x48YNdOjQAdnZ2R7LP3z4cMUxKDcDJaFsTbn/3n77bYwfP15pOzU1FXJr0deFi4qvibI9EiABEiABEiABEiABrRConn/QKhfzEAbWQswDGVje7I0ESIAESIAESMC4BLiXa1zbmlkzOgjNbH2N6f7hhx/innvuURx3NZVz584pz/fu3as8TktLw5w5c6pUXblyJaZOnap89swzz+DZZ591aMo+zOiIESOQkZGBkJAQW70LFy7g1ltvxfHjxxEZGQlxSkZFRfmcFhcVnyNlgyRAAiRAAiRAAiRAAhohYJ9/sFM0cPiSRTDmIQysgZgHMrC82RsJkAAJkAAJkIBxCXAv17i2NbNmdBCa2foa0719+/bKzcAxY8YoYT3l/xs2bAhx2P3rX//CkiVLcPHiRUXq/v3745///Cfq169fRQt3HITygtwOlFuCUu6++27Mnj0b8fHx2LdvHxYuXGi7mSh9PvLII34hxUXFL1jZKAmQAAmQAAmQAAmQQAAIzNk0B+/uf9dpTxJatPhGsfJ81Shg0s1UhOGh4YhsEOn0vXHdxiFtaFoANDBHF8wDaQ47U0sSIAESIAESIAH/E+Berv8Zs4fAE6CDMPDM2aMTAuIQPHbsmEs+4kBctmyZcruvenHXQXj16lWMHTvWlsuwejvBwcF4+umna7yB6FJANytwUXETFKuRAAmQAAmQAAmQAAloikBFRQUS0tvh2OUTLuWKbQScfgKITwNyLf7CWktCZDscmZXjqhqfu0GAeSDdgMQqJEACJEACJEACJOAmAe7lugmK1XRFgA5CXZnL2MJu27YN8t/u3buVsJ5yc7CgoACNGzdGmzZt0LdvX0yePFm5XeisuOsgtL6/du1ayDtZWVnIz89HXFwcBgwYgMcee6zWfnxhCS4qvqDINkiABEiABEiABEiABAJN4ELxBbR6qRVKykqqdB0SBLRoXPlRywjL7cEuscCBXMstwjOFlc/PXgHKKqpKHxYShlNzTiGmUUyg1TJcf8wDaTiTUiESIAESIAESIAEVCXAvV0X47NpvBOgg9BtaNkwCtRPgosIRQgIkQAIkQAIkQAIkoFcCR/KOYF7mPKw7sK6KCmO6AKlDgA61pPA+kgfMzQQyDlTVfmzXsUhNSkVCVIJesWhKbuaB1JQ5KAwJkAAJkAAJkIDOCXAvV+cGpPg1EqCDkAODBFQiwEVFJfDslgRIgARIgARIgARIwGcEtuVsw+xNs/H92e9tbYaFAFsmAf3bOnaz4zgweBVQUlb5rFeLXlg0dBEGth/oM7nM0BDzQJrBytSRBEiABEiABEhAKwS4l6sVS1AOXxKgg9CXNNkWCXhAgIuKB7BYlQRIgARIgARIgARIQLMEysrLsPL7lXhq61M4X3RekXNyT2DlKEeRJ28AVmVZPo8Lj8PCQQsxpdcUhASHaFY/LQrGPJBatAplIgESIAESIAESMDIB7uUa2brm1Y0OQvPanpqrTICLisoGYPckQAIkQAIkQAIkQAI+JSBOwqkfTFXaTOkNLB3h2HzKRmD5XsvnK0auUJyDLJ4TYB5Iz5nxDRIgARIgARIgARKoCwHu5daFHt/VKgE6CLVqGcpleAJcVAxvYipIAiRAAiRAAiRAAqYikLQqCVuOblF03jEVSGwNvLQb2J8LdIsF5twBfHkSGLDCgiWpQxIyJ2aaipEvlWUeSF/SZFskQAIkQAIkQAIkUDsB7uVyhBiRAB2ERrQqddIFAS4qujAThSQBEiABEiABEiABEnCDwLH8Y0hIT0AFKtAxGtg8AZiwHth1ovLlfm2A1cnAkNVAdh4QhCDkzM5B26Y1JCt0o09WsRBgHkiOBBIgARIgARIgARLwPwHu5fqfMXsIPAE6CAPPnD2SgEKAiwoHAgmQAAmQAAmQAAmQgFEILPhiAZ7+/GlFnT7xwKGLQMF1R+2a1Ac6RQPfnrE8W3D3Asy/c75RMKimB/NAqoaeHZMACZAACZAACZiEAPdyTWJok6lJB6HJDE51tUOAi4p2bEFJSIAESIAESIAESIAEvCdQUVGBTv/TCdl52Q6NJEQmYP6A+Vi4fSGO5h91eN4xuiMOPXYIQUFB3gvAN20EmAeSg4EESIAESIAESIAE/EOAe7n+4cpW1SVAB6G6/Nm7iQlwUTGx8ak6CZAACZAACZAACRiIwI7jOzBgxQAHjSb2mIjFwxejSf0mKLhegBmfzMCaH9Y41NsxdQf6te1nICLqqcI8kOqxZ88kQAIkQAIkQALGJsC9XGPb16za0UFoVstTb9UJcFFR3QQUgARIgARIgARIgARIwAcEUjamYPne5baWxCG45N4lGN99vEPra/etxfSPpysOQ2tJ6Z2CpSOW+kASczfBPJDmtj+1JwESIAESIAES8C8B7uX6ly9bV4cAHYTqcGevJMAchBwDJEACJEACJEACJEAChiCw9ehWjF83DueLLqBfm35YM3oN2ke2d6pbTn4OJmRMwM4TO9E8PAZvjXkHgxIGGYKFmkowD6Sa9Nk3CZAACZAACZCA0QnQQWh0C5tTPzoIzWl3aq0BAlxUNGAEikACJEACJEACJEACJOATAqcLTyPjQAam9ZmGesH1XLZZWl6KJd8sweguoxEfEe+yPivUToB5IDlCSIAESIAESIAESMC/BLiX61++bF0dAnQQqsOdvZIAbxByDJAACZAACZAACZAACZAACfiEAPNA+gQjGyEBEiABEiABEiABpwToIOTgMCIBOgiNaFXqpAsCXFR0YSYKSQIkQAIkQAIkQAIkQAKaJ8A8kJo3EQUkARIgARIgARLQOQHu5ercgBS/RgJ0EHJgkIBKBLioqASe3ZIACZAACZAACZAACZCAwQgwD6TBDEp1SIAEAkqgvKIcicsSUVRSiPCwCOxJ2YPgoOCAysDOSIAEtE+Ae7natxEl9JwAHYSeM+MbJOATAlxUfIKRjZAACZAACZAACZAACZAACQBgHkgOAxIgARLwjkDW2Sz0eq2X7eWsaVnoEdfDu8b4FgmQgGEJcC/XsKY1tWJ0EJra/FReTQJcVNSkz75JgARIgARIgARIgARIgARIgARIgARIAEj/Mh2zN822oUgflo6ZiTOJhgRIgASqEOBeLgeEEQnQQWhEq1InXRDgoqILM1FIEiABEiABEiABEiABEiABEiABEiABAxNIficZGw5usGmY3DkZGeMyDKwxVSMBEvCGAPdyvaHGd7ROgA5CrVuI8hmWABcVw5qWipEACZAACZAACZAACZAACZAACZAACeiAgOQfjEmNQd61PJu00Q2jkTs3l3kIdWA/ikgCgSTAvdxA0mZfgSJAB2GgSLMfEqhGgIsKhwQJkAAJkAAJkAAJkAAJkAAJkAAJkAAJqEegev5BqyTMQ6ieTdgzCWiVAPdytWoZylUXAnQQ1oUe3yWBOhDgolIHeHyVBEiABEiABEiABEiABEiABEiABEiABOpIwD7/YKdo4PAlS4PMQ1hHsCZ9XW6kJi5LRFFJIcLDIrAnZQ9vohpoLHAv10DGpCo2AnQQcjCQgEoEuKioBJ7dkgAJkAAJkAAJkAAJkAAJkAAJkAAJmILAnE1z8O7+d53qKqFFi28UK89XjQIm3UxFGB4ajsgGkU7fG9dtHNKGppmCIZV0n8DeM3vxy9d/ieAgoLwC2PvIXvRq0cv9BlhT0wS4l6tp81A4LwnQQeglOL5GAnUlwEWlrgT5PgmQAAmQAAmQAAmQAAmQAAmQAAmQAAnUTKCiogIJ6e1w7PIJl4hiGwGnnwDi04Bci7+w1pIQ2Q5HZuW4qsbnJiMwb/M8vLj7RZvW8/rNwwtJL5iMgnHV5V6ucW1rZs3oIDSz9am7qgS4qKiKn52TAAmQAAmQAAmQAAmQAAmQAAmQAAkYmMCF4gto9VIrlJSVVNEyJAho0bjyo5YRltuDXWKBA7mWW4RnCiufn70ClFVUBRUWEoZTc04hplGMgQlSNU8J9Hi1B/ad32d7rUfzHsianuVpM6yvUQLcy9WoYShWnQjQQVgnfHyZBLwnwEXFe3Z8kwRIgARIgARIgARIgARIgARIgARIgARcETiSdwTzMudh3YF1VaqO6QKkDgE6RDlv4UgeMDcTyDhQtc7YrmORmpSKhKgEV93zuYkISP7BBgsa4Eb5DZvWocGhuPbna8xDaJBxwL1cgxiSalQhQAchBwQJqESAi4pK4NktCZAACZAACZAACZAACZAACZAACZCAqQhsy9mG2Ztm4/uz39v0DgsBtkwC+rd1RLHjODB4FVBSVvlMcsktGroIA9sPNBU7KuseAWv+weq1mYfQPX56qMW9XD1YiTJ6SoAOQk+JsT4J+IgAFxUfgWQzJEACJEACJEACJEACJEACJEACJEACJOCCQFl5GVZ+vxJPbX0K54vOK7Un9wRWjnJ8cfIGYNXNyJBx4XFYOGghpvSagpDgEHImgRoJVM8/aK3EPITGGTDcyzWOLalJJQE6CDkaSEAlAlxUVALPbkmABEiABEiABEiABAJCQEJtJS5LRFFJIcLDIrAnZQ9DbAWEPDshARIgARKojYA4Cad+MFWpktIbWDrCsXbKRmD5XsvnK0auUJyDLOYmMGfTHLy7/12nEMTpbA0v2ikaOHzJUjUsOAyx4bFO3xvXbRzShqaZG65OtOderk4MRTE9IkAHoUe4WJkEfEeAi4rvWLIlEiABEiABEiABEiAB7RHIOpuFXq/1sgmWNS0LPeJ6aE9QSkQCJEACJGAqAkmrkrDl6BZF5x1TgcTWwEu7gf2mFnr5AAAgAElEQVS5QLdYYM4dwJcngQErLFiSOiQhc2KmqRhR2aoEKioq0PT5JigsueIWmlWjgEkb3KqKJvUjcPmPBe5VZi1VCXAvV1X87NxPBOgg9BNYNksCrghwUXFFiM/1RoC3BPRmMcpLAiRAAiRAAv4lkP5lupLvyVrSh6VjZuJM/3bK1kmABEiABEigFgLH8o8hIT0BFahAx2hg8wRgwnpg14nKl/q1AVYnA0NWA9l5QBCCkDM7B22b1pCskLRNQSC3KBct0pqjvMK1urGNgNNPAPFpQG6x6/ohQcDZJ3MR0yjGdWXWUJUA93JVxc/O/USADkI/gWWzJOCKABcVV4T4XG8EeEtAbxajvCRAAiRAAiTgXwLJ7yRjw8HK4/PJnZORMS7Dv52ydRIgARIgARKohcCCLxbg6c+fVmr0iQcOXQQKrju+0KQ+IGEivz1jebbg7gWYf+d8sjUpgQvFF9AqrRVKykuqEBDnXovGlR+1jADk9mCXWOBAruUW4ZnCyudnrwBl1ZyMEoL01BOn6CDUwdjiXq4OjEQRPSZAB6HHyPgCCfiGABcV33BkK9ohwFsC2rEFJSEBEiABEiABtQlIZIGY1BjkXcuziRLdMBq5c3OZh1Bt47B/EiABEjApAQkT2el/OiE7L9uBQEJkAuYPmI+F2xfiaP5Rh+cdozvi0GOHEBQUZFJ6VPtI3hHMy5yHdQfWVYExpguQOgToEOWc0ZE8YG4mkHGgap2xXcciNSkVCVEJBKwDAtzL1YGRKKLHBOgg9BgZXyAB3xDgouIbjmZpRQ/hO3lLwCyjkXqSAAmQAAmQgGsC1SMLWN9gHkLX7FiDBEiABEjAPwR2HN+BASsGODQ+scdELB6+GE3qN0HB9QLM+GQG1vywxqHejqk70K9tP/8Ix1Z1Q2BbzjbM/mw2vj/3vU3msBBgyySgbxsgcRlQVAKEhwF7UizhawevAkrKKlXsFdcLi4YtwsD2A3WjNwUFuJfLUWBEAnQQGtGq1EkXBLio6MJMmhFS6+E7eUtAM0OFgpAACZAACZCAJgjYRxaQEG2HL1nEYh5CTZiHQpAACZCAKQmkbEzB8r3LbbqLQ3DJvUswvvt4Bx5r963F9I+nKw5Da0npnYKlI5aakh2VrkqgrLwMK79fiTmb59jGyOSewO9/BfR6rbJu1jQgbTewKsvymYy5l379Eqb0moKQ4BBi1RkB7uXqzGAU1y0CdBC6hYmVSMD3BLio+J6pkVvUevhO3hIw8uijbiRAAiRAAiTgSGDOpjl4d/+7TtFIaNHiG8XKc8nFIzl4pISHhiOyQaTT98Z1G4e0oWlE7iMCeohC4SNV2QwJkAAJuCSw9ehWjF83DueLLqBfm35YM3oN2ke2d/peTn4OJmRMwM4TO9E8PAZvjXkHgxIGueyHFcxDoOsrXXHggiVuaEpv4BfNgdmbKvVPHwb8cA5YvtfyWdfYrtj/6H7zADKYptzLNZhBqY5CgA5CDgQSUIkAFxWVwOu0W62H7+QtAZ0OLIpNAiRAAiRAAl4QkBxOCentcOzyCZdvxzYCTj8BxKcBuRZ/Ya0lIbIdjszKcVWNz90koPUoFG6qwWokQAIk4DMCpwtPI+NABqb1mYZ6wfVctltaXool3yzB6C6jER8R77I+K5iHwLH8Y2ifXulg3jEVeHEX8MF/KhmM+jnwRF9gwIrKz47NPoa2TduaB5SBNOVeroGMSVVsBOgg5GAgAZUIcFFRCbwOu9VC+E7eEtDhwKHIJEACJEACJOAnAheKL6DVS61QUlZSpYeQIKBF48qPWkZYbg92iQUO5FpuEZ4prHx+9gpQVlFVyLCQMJyacwoxjWL8JL25mtV6FApzWYPakgAJkAAJGInAk5ufRNpuS9SDjtHAZw8BnV8BSssrtawXDBycAQxdA2TnWT5/8o4n8eKvXzQSCtPowr1c05jaVIrSQWgqc1NZLRHgoqIla2hbFrXDd/KWgLbHB6UjARIgARIgATUIHMk7gnmZ87DuwLoq3Y/pAqQOATpEOZfqSB4wNxPIsETkspWxXcciNSkVCVEJaqhkyD61HoXCkNCpFAmQAAmQgOEJyD5JTGoMLl2zJFnuE285DFV0w1H18FCgcwzw7RnLs+iG0bgw9wKCgoIMz8loCnIv12gWpT5CgA5CjgMSUIkAFxWVwOuwW7XDd/KWgA4HDUUmARIgARIggQAR2JazDbM3zcb3Z7+39RgWAmyZBPSvIXrWjuPA4FVASVmlgL1a9MKioYswsP3AAEltjm60EIXCHKSpJQn4lgBzh/qWJ1sjAX8Q2H5sO+5ceafTpjtFA4ctvsMay/Yp29G/XX9/iMY2/UiAe7l+hMumVSNAB6Fq6Nmx2QlwUTH7CKjUXw/hO3lLgOOVBEiABEhATwS4uRpYa5WVl2Hl9yvx1NancL7ovNL55J7AylGOckzeAKzKsnweFx6HhYMWYkqvKQgJDgms0CboTe0oFCZATBVJwC8EmDvUL1jZKAnUmYD93s3FqxdxrfSa0zYlxLqEVndWGtRrgGYNmymPx3Ubh7ShllClLNomwL1cbduH0nlHgA5C77jxLRKoMwEuKnVGaIgG9Ba+Uyu3BLjxa4jhTyVIgARIwG8E9p7Zi1++/ktb+3sf2Qu5pcbiXwLiJJz6wVSlk5TewNIRjv2lbASW77V8vmLkCsU5yOIfAmpHofCPVmyVBIxPgLlDjW9jaqg/Anrbu9EfYX1IzL1cfdiJUnpGgA5Cz3ixNgn4jAAXFZ+h1HVDegzfqYVbAjxVq+thT+FJgARIwO8EJDfei7tetPUzr988vJD0gt/7NXsHSauSsOXoFgXDjqlAYmvgpd3A/lygWyww5w7gy5PAgBUWUkkdkpA5MdPs2LzWXw9RKLxWji+SgIkJMHeoiY1P1TVLQI97N5qFqWPBuJerY+NRdKcE6CDk4CABlQhwUVEJvAa71Wv4TjVvCfBUrQYHMkUiARIgAQ0R6PFqD+w7v88mUY+4HsiadjOupYbkNJIox/KPISE9ARWoQMdoYPMEYMJ6YNeJSi37tQFWJwNDVgPZeUAQgpAzOwdtm9aQrNBIcPygC28y+AEqmyQBDRBg7lANGIEikIATAnrdu6FBfUeAe7m+Y8mWtEOADkLt2IKSmIwAFxWTGdwNdbUSvtMNUZUqat4S4Klad63EeiRAAiRgPgKyudpgQQPcKL9hUz40OBTX/nwNwUHB5gMSII0XfLEAT3/+tNJbn3jg0EWg4Lpj503qA52igW/PWJ4tuHsB5t85P0BSGqcb3mQwji2pCQnYE2DuUI4HEtA+Ab3t3WifqH4k5F6ufmxFSd0nQAeh+6xYkwR8SoCLik9xGqYxLYTvdAdmTbcEHsoAdp8EgoOA8gqgb2tgzWjf3xLgqVp3LMQ6JEACJGBeAtXzD1pJMA+h/8aE3Gbr9D+dkJ2X7dBJQmQC5g+Yj4XbF+Jo/lGH5x2jO+LQY4cQFBTkPwEN2jJvMhjUsFTL1ASYO9TU5qfyOiKgl70bHSHVhajcy9WFmSikhwToIPQQGKuTgK8IcFHxFUljtqNm+E53iKp5S4Cnat2xEOuQAAmQgHkJ2OcflJtqhy9ZWDAPof/GxI7jOzBgxQCHDib2mIjFwxejSf0mKLhegBmfzMCaH9Y41NsxdQf6te3nPwEN3jJvMhjcwFTPUASYO9RQ5qQyJACt793QRL4lwL1c3/Jka9ogQAehNuxAKUxIgIuKCY3ugcpqhu90JWZttwRcveuLWwI8VeuKMp+TAAmQgLEJuNpczS3ORUlZiQJh1Shg0gYLj7CQMMQ2inUKZ1y3cUgbmmZseH7SLmVjCpbvXW5rXRyCS+5dgvHdxzv0uHbfWkz/eLriMLSWlN4pWDpiqZ+kM0ezvMlgDjtTS30TYO5QfduP0pNATQS0vHdDi/meAPdyfc+ULapPgA5C9W1ACewIfPfdd/jss8+wfft2/Pvf/8b58+cRGhqK+Ph49O3bFw8//DAGDHA8newpxGeffRZ/+ctf3Hrt888/x1133eVWXU8qcVHxhJa56tYUvnPCemDXiUoO/doAq5N9H77THdLObgm0adIGJwoqhaz+/9a2Xd0ScLXxm3ctD8U3ipXm7Dd+w0PDEdkg0qkK3Ph1x7qsQwIkQALaJiCbq1EvNMXl64UuBY1tBJx+AohPA3Ity0atJbJBBPL+UOm0clWfzysJbD26FePXjcP5ogvo16Yf1oxeg/aR7Z0iysnPwYSMCdh5Yieah8fgrTHvYFDCICL1AQHeZPABRDZBAn4iwNyhfgLLZklAJQJa37tRCYuhu+VerqHNa1rl6CA0rem1p/jAgQPxxRdfuBRs4sSJWLZsGcLCwlzWdVaBDkKv0fHFABBQM3ynO+rVdEvgH8P/gcc/fRzivLOW6IbRSB+WroQTc/eWAE/VumMB1iEBEiAB8xLILcpF8783dwAQEgS0aFz5ccsIyyGSLrHAgVzLLcIzdj7Fs1eAsgpHjrlzcxHTKMa8gOug+enC08g4kIFpfaahXnA9ly2VlpdiyTdLMLrLaMRHxLuszwruEeBNBvc4BbqW5NBOXJaIopJChIdFYE/KHgQHBQdaDPanAQLMHaoBI1AEEvARAa3v3fhITTZjR4AOQg4HIxKgg9CIVtWpTh07dkR2drZyW/CBBx5Qbgq2bdsWZWVl2L17N9LS0nDq1ClFu/Hjx2Pt2rVea2rvINy3b1+t7SQkJCA8PNzrvpy9yEXF50gN0WBt4TsTIhMwf8B8LNy+EEfzjzro64vwne5ArOmWwOVrl9HrtV4Or2dNy1LyDrl7S4Cnat2xAOuQAAmQgHkJyDoRnxaPG+U3HCCM6QKkDgE6RDnncyQPmJsJZBxwrBMaHIrTT5ymg9C8w0v3mvMmg3ZNWD2HtnxH7hHXQ7sCUzK/E2DuUL8jZgck4FcCeti78SsAkzbOvVyTGt7gatNBaHAD60m9++67D5MmTcKYMWMQEhLiIPqFCxfQr18/HDp0SHkmtw29DTdq7yCURV2NwkVFDera79NZ+M6JPSZi8fDFirNNbuPJrbw1P6xxUMhV+E5fEah+S8BVXkBPbgnwVK2vrMR2SIAESMCYBJytE6JtWAiwZRLQv62j7juOA4NXASVljs/Gdh2L1KRUJEQlGBMatTIFAd5k0K6Z7b8ri5QSZWNm4kztCkzJAkKAuUMDgpmdkIBfCOhl78Yvypu4Ue7lmtj4BladDkIDG9eIqn300Ue4//77FdVmzpyJ9PR0r9Skg9ArbHwpAARqCt+55N4lGN99vEPva/etxfSPp7sdvrMu4quRF5CnautiMb5LAiRAAsYnIOvErM9mIetcVhVlJ/cEVo5y1H/yBmBV1aroGddT2agf2H6g8YFRQ0MT4E0GbZs3+Z1kbDi4wSZkcudkZIzL0LbQlC5gBJg7NGCo2REJ+IyAVvdufKYgG6qRAB2EHBhGJEAHoRGtamCdrly5goiICEXDe++9F+Iw9KbQQegNNb4TCAI1he9sH9neadc5+Tluh+/0Vn418wLyVK23VuN7JEACJGAOAtZ14vebfo/CEkuSwZTewNIRjvqnbASW77V83iSsCV4a+hKm9JqCkGDHyBXmoEctjUSANxm0a03JPxiTGuOQq1tynjIPoXbtFkjJmDs0kLTZFwn4hoAW9258oxlbqY0AHYQcH0YkQAehEa1qYJ0uXbqEZs2aKRrKTcKNGzd6pS0dhF5h40sBIlA9fKerbj0J3+mqrZqeayEvIE/VemM5vkMCJEAC5iHQ7ZVu+PHCj4rCO6YCia2Bl3YD+3OBbrHAnDuAL08CA1ZYmHSN7Yr9j+43DyBqangCvMmgXRNXzz9olZR5CLVrs0BKxtyhgaTNvkjAtwS0tnfjW+3YWk0E6CDkuDAiAToIjWhVA+u0fv16jB49WtFw7ty5SE1N9UpbewdhUlISvvvuOxQWFiIyMhJdu3bFsGHD8MgjjyAqKsqr9t15iYuKO5RYRysE1M4LyFO1WhkJlIMESIAEtEdANlfbp1tu23eMBjZPACasB3adqJS1XxtgdTIwZDWQnWf5/NjsY2jbtIZkhdpTkRKRgEsCzm4yyO21xGWJKCopRHhYBPak7FFurQUiCoVLoU1SwVWubpNgoJpOCDB3KIcGCZAACeiHAPdy9WMrSuo+AToI3WfFmioTKC8vxx133IGvvvpKkeTrr79Gnz59vJLK3kHorAFxFq5cuRIjR470qg9ZNGorZ86cwe23365UOXHiBFq3bu1VP3yJBAJJQI28gDxVG0gLsy8SIAES0B+BJzc/ibTdaYrgfeKBQxeBguuOejSpD3SKBr49Y3n2ZN8n8eKQF/WnMCUmAScEarrJUP32mv2tNX9HoTCLodTI1W0WtkbXk7lDjW5h6kcCJGA0AnQQGs2i1EcI0EHIcaAbAmlpaXjyyScVeZOTk5GR4X1Sd3EQyvujRo1SnHTx8fG4ceMG/vOf/+DNN9/E5s2blX5CQkLw4Ycf4p577vGYU1BQkNvv0EHoNipW1ACBQOcF5KlaDRidIpAACZCARgnI5mrMizG4dPWSxxI2a9gMkgPMk+9sHnfCF0hAZQL2t9dElPRh6ZiZOFNlqYzTvZq5uo1D0byaMHeoeW1PzUmABPRJgA5CfdqNUtdOgA5CjhBdENi2bRskFGhpaSmaN2+OH374AXFxcV7Lnp+fr4QTdVZee+01TJs2TXkszsOffvoJDRs29Kg/Tzab6CD0CC0ra4RAIPIC8lStRoxNMUjAhAScheUzIQpNq7z92HbcufLOGmVs17Qdnr7zafz1i7/i2OVjNdbZPmU7+rfrr2kdKRwJ1IVA8jvJ2HBwg62J5M7JyBjn/UHLushixHe1kKvbiFzNohNzh5rF0tSTBEjAKAToIDSKJamHPQE6CDkeNE9g//79GDBgAPLy8lC/fn1s2rQJAwcO9Lvcv/vd77Bs2TKlnzVr1uChhx7yqE+GGPUIFyvrkEAg8gLyVK0OBwZFJgGDEKgtLJ9BVDSEGvetvQ8fH/7YQZeJPSZi8fDFaFK/CQquF2DGJzOw5oc1DvXuv+V+bBy/0RAsqAQJVCcgBx1iUmOQd+1m4k0A0Q2jlZuzkoeQxTcE1M7V7Rst2IoaBJzlDnUmC3OHqmEl9kkCJEAClQToIORoMCIBOgiNaFUD6XT06FH0798fp0+fVsJ9vvfee0p40UCUb775BrfddpvSlTgLX3/9dZ92y0XFpzjZWIAJBCovIE/VBtiw7I4ESMBGgGH59DEYhr85HJuyP0V5hUXexmGN8fp9r2N89/EOCqzdtxaPfPQIrpRcUZ4FBwHDfjYcHz/k6GDUh/aUkgRqJ1D9oIO1tn0eQj0w1MuNbjVydevBfpSxdgI15Q6t7Q3mDuWIIgESIAH1CHAvVz327Nl/BOgg9B9btlxHAuIUlJuDR44cUXLDrFy5EpMmTapjq+6/XlxcjPDwcOWF4cOH4+OPfbt5xEXFfVuwpvYIBCovIE/Vas/2lIgEzEKAYfn0YWlZJx58bywuXs1D39Z98eaYN9E+sr1T4eX2xUPrHsKuk7vQrGEU3n3gfQxKGKQPZSklCXhIwP6gQ6do4PDNVJ16y0Oopxvdgc7V7eGQYHUSIAESIAESIIE6EOBebh3g8VXNEqCDULOmMbdgFy5cUMKI/vjjjwqIxYsXY8aMGQGFUlRUhMaNG9NBGFDq7EwPBAKdF5CnavUwKigjCRiLAMPy6cueXCf0ZS9K6zsCczbNwbv733XaoIQWLb5RrDxfNQqYdDMVYXhoOCIbOM/HPq7bOKQNTfOdoHVsSY83ugORq7uOWPk6CZAACZAACZCAhwToIPQQGKvrggAdhLowk7mEvHz5MgYNGoTvvvtOUfz555/HH/7wh4BD+Prrr3H77bcr/aakpGDp0qU+lYGLik9xsrEAEmBewADCZlckQAKqEDBKWD5V4LFTEiCBgBCQA1sJ6e1w7PIJl/3FNgJOPwHEpwG5Fn9hrSUhsh2OzMpxVS1gz/V4ozsQuboDZgB2RAIkQAIkQAIkoBDgXi4HghEJ0EFoRKvqWCcJ6/nrX/8aO3fuVLSYP38+FixYoIpG4hRcvny50vfq1asxYcIEn8rBRcWnONlYAAkwL2AAYbMrEiABVQgYJSyfKvDYKQmQQEAIXCi+gFYvtUJJWUmV/kKCgBaWIChKaRlhuT3YJRY4kGu5RXimsPL52StA2c0cntZPw0LCcGrOKcQ0igmILrV1oscb3YHK1a26cSgACZAACZAACZiMAPdyTWZwk6hLB6FJDK0HNUtKSnD//fdj8+bNirizZs3CokWLPBZdchVOnTpVee+ZZ57Bs88+W6WNffv2oWHDhujYsaPTtl977TVMmzZNed6iRQv89NNPtnyEHgvk5AUuKr4iyXYCTYB5AQNNnP2RAAn4moBZwvL5mhvbIwES0BaBI3lHMC9zHtYdWFdFsDFdgNQhQIco5/IeyQPmZgIZB6rWGdt1LFKTUpEQlaAJZfV4oztQubo1YSAKQQIkQAIkQAImIsC9XBMZ20Sq0kFoImNrXdUxY8YgIyNDEVNCjIpzMCgoyKnYYWFhuOWWWxyeu3IQynO5HXj33XfjnnvuQffu3dGsWTOUlpbi4MGDWLNmDTIzM5V2Q0JCFJlGjBjhc3xcVHyOlA0GkADzPQUQNrsiARLwKQEzheXzKTg2RgIkoFkC23K2Yfam2fj+7Pc2GcNCgC2TgP5tHcXecRwYvAooKat81qtFLywauggD2w/UlJ56u9Ed6FzdmjIWhSEBEiABEiABgxPgXq7BDWxS9eggNKnhtah2bc7AmuRt164dcnIcc2O44yC03jCsjYM4DSXE6MiRI/2Ci4uKX7CyURIgARIgARKolYBZwvJxGJAACZiLQFl5GVZ+vxJPbX0K54vOK8pP7gmsHOXIYfIGYFWW5fO48DgsHLQQU3pNQUhwSMChGe1GN3N1B3wIsUMSIAESIAESCBgB7uUGDDU7CiABOggDCJtd1U4gUA7C8+fP46OPPsLu3buxd+9enDt3DhcvXoSc9oyOjkbPnj0xbNgwTJkyBU2aNPGb2bio+A0tGyYBEiABEiCBWgmYISwfhwAJkIA5CYiTcOoHlnQLKb2BpTUEQknZCCzfa+GzYuQKxTmoRjHijW7m6lZjJLFPEiABEiABEggMAe7lBoYzewksAToIA8ubvZGAjQAXFQ4GEiABEiABElCXgJHD8qlLlr2TAAmoRSBpVRK2HN2idL9jKpDYGnhpN7A/F+gWC8y5A/jyJDBghUXCpA5JyJxoSa8Q6GLEG93M1R3oUcT+SIAESIAESCBwBLiXGzjW7ClwBOggDBxr9kQCVQhwUeGAIAESIAESIAH1Ceg1LJ/65CgBCZCA1ggcyz+GhPQEVKACHaOBzROACeuBXScqJe3XBlidDAxZDWTnAUEIQs7sHLRtWkOywgAoaMQb3czVHYCBwy5IgARIgARIQAUC3MtVATq79DsBOgj9jpgdkEDNBLiocGSQAAmQAAmQgHYI6Cksn3aoURISIAEtEVjwxQI8/fnTikh94oFDF4GC644SNqkPdIoGvj1jebbg7gWYf+d8VVXhjW5V8bNzEiABEiABEiABNwhwL9cNSKyiOwJ0EOrOZBTYKAS4qBjFktSDBEiABEjACAT0FJbPCLypAwmQgG8JSD6/Tv/TCdl52Q4NJ0QmYP6A+Vi4fSGO5h91eN4xuiMOPXYInuaE960GAG90+5oo2yMBEiABEiABEvAlAe7l+pIm29IKAToItWIJymE6AlxUTGdyKkwCJEACJKBRAnoMy6dRlBSLBEhAJQI7ju/AgBUDHHqf2GMiFg9fjCb1m6DgegFmfDIDa35Y41Bvx9Qd6Ne2n0rSV+2WN7o1YQYKQQIkYAIC5RXlSFyWiKKSQoSHRWBPyh4EBwWbQHOqSALeEeBernfc+Ja2CdBBqG37UDoDE+CiYmDjUjUSIAESIAFdEdBzWD5dgQ6gsNzwCiBsdqUJAikbU7B873KbLOIQXHLvEozvPt5BvrX71mL6x9MVh6G1pPROwdIRSzWhC290a8IMFIIESMAEBLLOZqHXa71smmZNy0KPuB4m0JwqkoB3BLiX6x03vqVtAnQQats+lM7ABLioGNi4VI0ESIAESEA3BIwQlk83sAMoKDe8AgibXWmCwNajWzF+3TicL7qAfm36Yc3oNWgf2d6pbDn5OZiQMQE7T+xE8/AYvDXmHQxKGKS6LrzRrboJKAAJkICJCKR/mY7Zm2bbNE4flo6ZiTNNRICqkoBnBLiX6xkv1tYHAToI9WEnSmlAAlxUDGhUqkQCJEACJKA7AkYKy6c7+H4UmBtefoTLpjVL4HThaWQcyMC0PtNQL7ieSzlLy0ux5JslGN1lNOIj4l3WD0QF3ugOBGX2QQIkQAIWAsnvJGPDwQ02HMmdk5ExLoN4SIAEnBDgXi6HhhEJ0EFoRKtSJ10Q4KKiCzNRSBIgARIgAYMTMFJYPk9NZeQwnNzw8nQ0sD4JqE+AN7rVtwElIAESMA8B+R4YkxqDvGt5NqWjG0Yjd24u8xCaZxhQUw8JcC/XQ2CsrgsCdBDqwkwU0ogEuKgY0arUKVAEjLypHSiG7IcESMBCwChh+byxp1HDcHLDy5vRwHdIQH0CvNGtvg0oAQmQgHkIVP8eaNWceQjNMwaoqecEuJfrOTO+oX0CdBBq30aU0KAEuKgY1LBUKyAEjLqpHRB47IQESMCBgBHC8nljVqOG4eSGlzejge+QgPoEzHyjWw36PHCnBnX2SQLaIWD/PbBTNHD4kkU25iHUjo0oifYIcC9XezahRHUnQAdh3RmyBRLwigAXFa+w8SUSsPxoYTJ1jgQSIAESqDMBo4bhtF8jGoQA18q44VXnwcIGSCAABMx8ozsAeB264IE7NaizTxIIHIE5m+bg3WrUQ2wAACAASURBVP3vOu1QQosW3yhWnq8aBUy6mYowPDQckQ0inb43rts4pA1NC5wi7IkENESAe7kaMgZF8RkBOgh9hpINkYBnBLioeMaLtUnAnoBRN7VpZRIgARIIFAE9h+H0ZMPLnic3vAI1utgPCXhPwKw3ur0n5v2bZj5wx9uT3o8bvqkPApLTNSG9HY5dPuFS4NhGwOkngPg0INfiL6y1JES2w5FZOa6q8TkJGJIA93INaVbTK0UHoemHAAGoRYCLilrk2a/eCeh5U1vv7Ck/CZCAcQjoNQynJxtenlqLG16eEmN9EiABPRMw84E73p7U88il7O4QuFB8Aa1eaoWSspIq1UOCgBaNKz9qGWG5PdglFjiQa7lFeKaw8vnZK0BZRdUew0LCcGrOKcQ0inFHFNYhAUMR4F6uocxJZW4SoIOQQ4EEVCLARUUl8OxW9wT0uqmte/BUgARMRsDotwv0mnfG3Q2vohtA/rXKQRvZAAgP5YaXyaYx1SUBEnBCwOwH7sx8e5KTwjwEjuQdwbzMeVh3YF0Vpcd0AVKHAB2inLM4kgfMzQQyDlStM7brWKQmpSIhKsE8IKkpCdgR4F4uh4MRCdBBaESrUiddEOCiogszUUgNEtDrprYGUVIkEiCBWgjo/XaBJ2E49ZZ3xtWGV/tIICYVyLNzEEY3BHLnAjn53PDixCcBEiABsx+4M/PtSY5+8xHYlrMNszfNxvdnv7cpHxYCbJkE9G/ryGPHcWDwKqDkZg5nqdGrRS8sGroIA9sPNB9AakwCdBByDBicAB2EBjcw1dMuAToItWsbSqYuASNvaqtLlr2TAAl4QkDPtws8CcOp57wzzja8XrsPmPqBo7VXjAQe+YgbXp7MA9YlARIwJgEzH7gz++1JY45oauWKQFl5GVZ+vxJPbX0K54vOK9Un9wRWjnJ8c/IGYFWW5fO48DgsHLQQU3pNQUhwiKtu+JwEDE+Ae7mGN7EpFaSD0JRmp9JaIMBFRQtW0I8MRg91Z7WEWTa19TPyKCkJmJeAnm8XuBuG0wh5Z2ra8LotHvj6tGXsdooGDl+y/Nv+c254mXduU3MSMAMBHrhzbmWz3540w/injs4JiJNw6gdTlQopvYGlIxzrpmwElu+1fL5i5ArFOchCAiRgIcC9XI4EIxKgg9CIVqVOuiDARUUXZtKMkHoPdecuSDNtarvLhPVIgAQCT8AItwtcheHUc96Zmja+i28UI+9anjJY6gUDpeWWcWMfPtX+86gGUWgU2qjK4BrXbRzShqYFfsCxRxIgARLwIQFPDtzJ30XrQQrr383aREmIbIcjs3J8KG3gmzLz7cnA02aPWiOQtCoJW45uUcTaMRVIbA28tBvYnwt0iwXm3AF8eRIYsMIieVKHJGROzNSaGpSHBFQjwL1c1dCzYz8SoIPQj3DZNAnURoCLCseHJwT0HOrOEz2lrpE3tT1lwfokQALqEDDS7QKj5Z3xZONbz+FT1Rn57JUESMAIBNw9cNe0AfBjbqXGXWOBy3a5W89eAcoqqhIJCwnDqTmnENMoRrOoeHtSs6ahYCoTOJZ/DAnpCahABTpGA5snABPWA7tOVArWrw2wOhkYshrIzgOCEISc2Tlo27SGZIUq68PuSUANAtzLVYM6+/Q3AToI/U2Y7ZOAEwJcVDg0PCGg51B3nuhpX9dom9recuB7JEACgSdgtNsFRso74+7GtxHCpwZ+5LNHEiABoxBw58Bd+pfA7E2VGqcPA2YmymE9YG4mkHGgKo2xXcciNSkVCVEJmsXEQySaNQ0F0wCBBV8swNOfP61I0iceOHQRKLjuKFiT+pabxd+esTxbcPcCzL9zvgY0oAgkoD4B7uWqbwNK4HsCdBD6nilbJAG3CHBRcQsTKwEwQqg7bw1ppE1tbxnwPRIgAd8TMOvtAqPknXFn49vZqNHzxrfvZwJbJAESMDqB2g7cpe0GNhysJJDc2RJecPAqoKSs8vNeLXph0dBFGNh+oOZx8RCJ5k1EAVUiIM7zTv/TCdl52Q4SJEQmYP6A+Vi4fSGO5h91eN4xuiMOPXYIQUFBKknPbklAOwS4l6sdW1AS3xGgg9B3LNkSCXhEgIuKR7hMXdlIoe68NaRRNrW91Z/vkQAJ+I6AmW8XGC3vDG+a+25esCUSIAHjEqjpwN2kHsCHh4A8u5Ci0Q2BezsBq3+wsIgLj8PCQQsxpdcUhASH6AYQD5HoxlQUNIAEdhzfgQErBjj0OLHHRCwevhhN6jdBwfUCzPhkBtb8sMah3o6pO9Cvbb8ASsyutEpADrAnLktEUUkhwsMisCdlD4KDgrUqrs/l4l6uz5GyQQ0QoINQA0agCOYkwEXFnHb3RmujhbrzhoHRNrW9YcB3SIAEfEPArLcLjJp3RpyEd/3vXVUGx+SewMpRjuNl8gZgVVbVz7dN3oY729/pm8HFVkiABPxGwOwbkr4Aa3/gLvnnwPr/OLYqtwjX37xVuGLkCsU5qNfCQyR6tRzl9geBlI0pWL53ua1pcQguuXcJxncf79Dd2n1rMf3j6YrD0FpSeqdg6Yil/hCNbeqMQPUD7FnTstAjrofOtPBeXO7les+Ob2qXAB2E2rUNJTM4AS4qBjewB+qZNdSdu4iMuqntrv6sRwIk4HsCZrxdYNS8M9U3vGS0pPQGlo5wHDcpG4Hle6t+zg0v388vtkgC/iBglA3JK+Vl+LTwIvZev4LhjaPRv1GkP3DV2Kb9gbuZtwMvf2WpJrnGDl+y/HtWIpC+x/LvpA5JyJyYGTD5/NER0xX4gyrb1COBrUe3Yvy6cThfdAH92vTDmtFr0D6yvVNVcvJzMCFjAnae2Inm4TF4a8w7GJQwSI+qU2YfE7A/wC5Npw9Lx8zEmT7uRbvNcS9Xu7ahZN4ToIPQe3Z8kwTqRICLSp3wGeZlT0Ld1Quu/AFfWu4aQUJkOxyZleO6osZrGHVTW+PYKR4JmIKAWW4XGDnvjP2Gl3XQ7pgKJLYGXtoN7M8FusVa8mp9eRIYsKJyaHPDyxTTnEoahIDeNiRLKspRWlGBRjdDc5ZVVCj//2reaWRdu4ILZTcUyzSvF4ZRETEY1jgKoT4M0Vb9AGJZRRnOXjmr9BkSBISFAFdLLYNj1Shg0gbLvxuFAtdLgbIKy/+3aNwCIUGV4UXHdRuHtKFpuhtVTFegO5NRYD8QOF14GhkHMjCtzzTUC67nsofS8lIs+WYJRncZjfiIeJf1WcEcBJLfScaGgzcXDQDJnZORMS7DHMoD4F6uaUxtKkXpIDSVuamslghwUdGSNdSTxd1Qd00bAD/mVsrZNRa4bJcz5OyVyh/y1lphIWE4NecUYhrFqKdgHXs28qZ2HdHwdRIgAR8RMMPtAqPnnfnq1Ff41bJfoQIV6BgNbJ4ATFgP7DpROUj6tQFWJwNDVgPZeUAQgpScKbe1us1HI4nNkAAJ+JOAnjYkNxRewLaifBuOgeGRtv+/Vl6Ok6XXbc+CgoLQPrQBasrwN7xxM4ij8VcNm6BZvVC38XpyADG2EXD6CSA+Dcgtdt2FXg8gMl2Ba9uyBgmQAAm4IiDhvmNSY5B3Lc9WNbphNHLn5pomDyH3cl2NEj7XIwE6CPVoNcpsCAJcVAxhRp8o4U6ou/QvgdmbKrtLHwbMTASO5AFzM4GMA1VFGdt1LFKTUpEQleATGdVqxOib2mpxZb8kQAKOBIx8u8BZ3plxvxiHxGWJKCopRHhYhOIwe/vfb+su7wxvmnNGk4CxCWh1Q1LChYYHBUOcfNYinz19/qhTg5wtLYHUsS9RIaFoFlL7bZ7uDRpjdESM4kiMcFHX3QOILSMstwe7xAIHci23CM8UVkpmlAOITFdg7L8P1K5uBJjftW78zPZ29XDfVv3NlIeQe7lmG/Xm0JcOQnPYmVpqkAAXFQ0aRWWRagt1l7Yb2HCwUsDkzpZwaYNXASV2ewy9WvTCoqGLMLD9wIBr448fF0ymHnAzskMSMC0BI98ucJZ3xllOLz3lneFNc9NOWSpuIgJa25CUcKGv5J3G0ZKrihUebNIcl8tLsenKzUR+tdhG/mZl36gMA9I4OATiIKxv52R0x7QpUS3RrX6406ruHEB09rLRDiDyEIk7I4p1zErAKPldzWq/QOttH+7bPn+tmfIQci830KOO/QWCAB2EgaDMPkigBgJcVDgsaiJQU6i7ST2ADw8BeXYhRaMbAvd2Alb/YGklLjwOCwctxJReUxByM9dJoAn748cFk6kH2orsjwTMScAMtwtqyjtTW04vveSd4U1zc85Zam0uAlrbkNxdXIB3C857bQRxMB69cQ1tQht47Bis3qk4GJ+KaYuGwSEQ56P9bUapKwcQR787GpeuVjovJf/glklA/7aOKuw47ngAsVnDZlj34DpVDiB6DfnmizxEUleCfN/oBPSW39Xo9lBbv+r5a6vLI6FFi29Y4lHb568NDw1HZINIp+LrNX9tTQpxL1ftUcr+/UGADkJ/UGWbJOAGAS4qbkAycRX7UHfJPwfW/8cRhtwiXH/zVuGKkSsU56CaxV8/LphMXU2rsm8SMAcBs94u0FNOL2cjkTfNzTFHqaWxCWh1Q/L3Z3+qAv6Z2PZ48eIJFFcLEeqNde4KjsbI5tEQZ+GT57K9acLpOw9HtsQvGlTeLszMzsSYd0ejsOSK7Z3JPYGVoxybmLwBWJVV+XlEWGOsezADQ342xKcyBqoxHiIJFGn2o1cCRvguqFf2WpOb+Wvdswj3ct3jxFr6IkAHob7sRWkNRICLioGM6QdV7EPdzbwdePkrSyf2YRxmJQLpeyyfJ3VIQubETD9I4n6T/HHhPivWJAES0A4Bs94u0GpOL09HBm+ae0qM9UlAWwS0uiH54/UiLM0741NYj0W3wv5rxdicGoG3Xg1F+/ZB+OwzIDYWqO6M9EXHYUHBeDQ6HosunsSVonPY/mUq/vPTRqXplN7A0hGOvaRsBJbvtXz+UPeHkDokFfER8b4QR5U2eIhEFezsVCcEjPJdUCe4NS8m89e6ZyLu5brHibX0RYAOQn3Zi9IaiAAXFQMZs46qVD81XVZRhrNXziqthgQBEgboaqmlE/swDo1CgeulQFmF5VmLxi0QEhRikyaQYRz446KOg4CvkwAJqEbArLcLtJbTqy4DgDfN60KP75KAugTU3pAUB+VzF08gt7QEv2wQgUbBwUgKj8JreWdwpvS613DKS4FLP4Xh9n1tkZFR2UxFBfDjj8CNG5Wf3XILsGED0LKl5bOtRXn4sPCi1307e3HdR5Nx4tQu5fGOqUBia+Cl3cD+XKBbrCW/+ZcngQErLC1o4QBiXSHwEEldCfJ9IxMw0ndBI9spkLoxf61r2tzLdc2INfRHgA5C/dmMEhuEABcVgxiyjmqofWr6clkpfrxejNsbRiAkKEjR5v2CXOwsvqz8OzWuA0KDgl1qyR8XLhGxAgmQgEYJOLtdMO4X45C4LBFFJYUID4vAnpQ9ePvfb2P6x9NRcL3Apk1K7xQsHbFUo9o5F0trOb10B5ACkwAJ+IyAmhuSdb25J47AkivByP13fez5f1E2JrH1wtA0uJ4Do8uXgaNHq37cvj0QFQUMGwZMnw6EhJfhjfyzyC65io5hDTEqIgav559BeFCI107LgsJTeGPt3QAq0DEa2DwBmLAe2HWiUpZ+bYDVycCQ1UB2HhCEIOTMzkHbpjUkK/SZ9f3fEA+R+J+xNz3IAdPq37OC3fjd6U1ffKdmAvwuyJHhjIDkr529aTa+P/u9rYqn+Wt7teiFRUMX6TJ/bW0jg3u5nDdGJEAHoRGtSp10QYCLii7M5Hch1Tw1XV5ejilnDuFKeRkaBgcjNiQUFhdh1fL/WnR0yYE/LlwiYgUSIAGNEnB2u6D6wYesaVnoEdcDOfk5mJAxATtP7ETz8Bi8NeYdDEoYpDnttJrTS3OgKJBfCXAD2K94Dde4vzYkJWfgFzcPvw0Nj0LQzUNxnoYRzcsOVZyAV87UQ1zP6ziXVR9x9cIQEVwPReVlKKkoR0RwCOrV4uQ4cwY4d66q6Xr2BG6KpDyIiwMaNKjAfz8DJLSXZ1W/ocsBv2dzczyy/57vXsHurxcp7/SJBw5dBApquCDZpL4lpcG3N6OrLrh7AebfOd+jvliZBNwh4Ox7ljvvso57BPhd0D1OrFUzgbLyMqz8fiWe2voUzhedVyq5k782LjwOCwctxJReUxASXBnhyiicuZdrFEtSD3sCdBByPJCASgS4qKgEXoPdqnVqenneGWwovGAj0jSknuIkrF7kBPQXuxbi28MfKrcMQ5TzxFVL3rU8FN8oVj60D4MaHhqOyAaRTqkHMgyqBk1PkUiABDRCoKbbBfYHH0TM9GHpmJk4U5G4tLwUS75ZgtFdRmsyN5Pat9M1YlaKoQEC3ADWgBF0JkJdNiRnDXwGv/9lChrYfZ8Vp90fzh3xmIKEAi06G4LPHotz+m5USCia1fDd2VVnly4Bx49basXHA82b1/7G448Do0dX++5ddgP/N/eYq66U57ImrHw7CZcLbnZq91aTiNa4/ZeP4qvv/oGCwpMO7XWM7ohDjx1ycFK61TErkUAtBGr7nkVwdSfA74J1Z8gWLATESTj1g6nKv93JX7ti5ArFOWjUwr1co1rW3HrRQWhu+1N7FQlwUVERvka7llPTo98djUtXL9kk9DSMQ7OGzbDuwXW1hnGQHwv/uHQanxVV9mPtsG1oA4RVO6WsbCqsHYjLV24eJa6FX2wj4PQTQHwakGvxF9ZaEiLb4cgsz05Au2qTz0mABEjAFwSS30nGhoMbbE0ld05Gxji7RFK+6MRPbah5O91PKrFZnRLgBrA+DXeh9Abyym+gQ2hDWwh6X2oi+f6O37iOn4U1RGSIYxjOa+XleCvrf/Hbjb9VunVnQ3LIXc+j28/HKPX/ENMWLeqFKf/+V1E+PrA7EFddjxvFQfhg4s3kf06UbFmvPsKDJeR+TbE2PCfTpAkwezaQnQ2sXg0oTbtR7r8fmDOnasWr5WVIv3QK50pLqqQGWJB7DBfLLIkOT535Bu9tHO/QQ5dOo3BX//9G/bAIXC8pxOc7/oKDhz9wqPfgyLcR3+JWTGgah1sbRtiey2+ErcX52FqUj/saN8MdjZq4oQWrkICFgJ6/Z+nBhvwuqAcr6UPGpFVJ2HJ0iyKsWfLX1mYZ7uXqY9xSSs8I0EHoGS/WJgGfEeCi4jOUhmooMzsTY94djcKSKza93AnjIJUjwhpj3YMZGPKzIbUy+aI4H+sLLiihRc+WltjqBgcFoV1oA1QPAnH16iUsW9MfZeWWTQZrCQkCWjSu/P+WEZbbg11igQO5wKQNwJnCyudnrwBlFVVFCwsJw6k5pxDTKMZQdqQyJEAC+iYgYRFjUmMgt6OtJbphNHLn5kIv+XHUup2ub8tTel8T4Aawr4n6tj1xxIUGBUH8Ux9duYTL5aVICG2AjMILKK+oQOvQ+pgd3bpWJ6E4qCTCRFi1sJr2Oa1nRrdGQlgDRfjdxQV4t8ASqkzK76Jaomv9cOXfZRUVePJctvLvdR9NxolTu5R/u7Mh2bZVX4y+73+V+s3rheGuRpHIuXENX10tgNwG3Pm3Zjj7XX1bv03bleLyMUfnZHXCckNQbgp6W9auBVrW7n/ETz8Bv/ud+z3IbcKpU4HGdt/Dq7/9U8lVvHLplPJx5ransP/ge7YqYWGNMWjA/0Xnjvc7dHrw8EZs3fEMSux+i/yi84NIGrjQVvcvse3RJKQe3rl8Xgndan+4cH5MO8TU856X+xTqVpPhj+vGr65vG+F7Vl0ZBOJ9fhcMBGVj93Es/xgS0hNQYcL8tc4sy71cY495s2pHB6FZLU+9VSfARUV1E2hWAAl1Ny9zHt7c96Yiozunph/q/hBSh6S6FeruuQvHcf6mY1A2YvLLS5FXVqqc4u7doLHy/zkl12x8ZNPn0uVj2P5lKn46uqkKtzFdgNQhQIco5ziP5AFzM4GMA1XrjO06Fi8MfgEJUQkMW6TZ0UjBSMAzAtfLy3G45KqyOWi9veFZC9qoXT0solUqax5CbUjpnhT+yunlXu+s5Q4Bo25UcwPYHesHrs6ZG9eVvHV55aVKsPjYeqGoOft0VZlahdZHXEgYokPqYUjjKNs7cuZLHIm7igsQHASMiGiGOxtZwsofLilWolXYl+ead0CD4GD8/uxPDkpb811bD7EVFJ7CG2vvluCY6BgNbJ4ATFgP7DpR+Wq/NsDqZGDIaiBbOcsRhN/+5l9oEhGvOASv5QWj4HgoTn/TANmfWhyQrorcZhSHoGMw/drf/OtfgZdf/v/sXQd4FFXXfre39E5CSAIJvVcFEURQ7IIo8iMKiooVPlRU0M8GohQFK4qKop9drNgQpPfea4CE9LYpm+27/3PuZLKzm93sbrKBBPb48Ag7t557Z+bOec95D2C1Au+8A8hkgEIBhDqC7bx1DYsF2LuXiyjcv99r8doC1F+XLu7LP1FwigG92Tlb8OfqqdDpy1gk4LXDFiA8tLXHTsorz+GvNU8iN38XNKpIjLx6MZKTLq8tb7HbGfgqlGSZEgqRCL2VoZgQ4ZmW1feZNW3JIP1x0+rXW+sX0znL21ybw/X6zoIDk4EBHwE6E6CRA9smc8/aq5cDJqtj9D0TemLRtYvqZSpqDnMNjiGwGpi9fjae//d51mgwfy2n26AtN7B7LNha89BAECBsHusQHMUlqIHgS+USXHQ/ptxUNA715WHhjTP8MMm4U2G1skjDYquZUUFtzVqPfzfNRlGJA+3zlwY1NroT7hzyMjKSLkemSY9OCjUmhMfDZLcjRCxhXuyFVjMiJdI63uh+qLBZFSWj3B+6Upa/caA6DCtPV6HKYMf/dQxDW4WqWY01OBj/NEBRuK8XO3L6vBbXFgpfucL866rZl6bny4KScyDqOJFIhInh8eiurCe8oRnPSEiLmBEFnKhhZBbmIWzGw68ztMbk9JozbA7LIyIRu8aXtyQNNO+xXqyG6qAB+PzvO6J8zLOYoLfb0FamrHXAIoewx/JOINtihFUvgkhmh1QmYmXcSXWxBPQnsq0JEo6ps8mlpzIEZrsdh4w6bNv9LrbsWMT69McgmaibBdWBZ2CqEsNu82/IURIZ6I8v0q8fsGMHcMstwNSpgAs7vy9N1FvmxAnggQf8a+bXX91HFOaYjVCLxdBXF2HR/i8haXcLDD5QpdpsFuw//BXS065BiMYZ8CPQMdMFIHTQsAKu3xT+zeT8lA7SH58fPXvq5WI7Z11YbfrWu6ez4H8uA3p+4Ghj3xRg4RZg+T7ut3hNPIJnQd90fLGVojNFxtsZOFXGRfYLJS0iDbMGz8KcDXNwWnu6zvWLOX9t0JZ7se304HxIA0GAMLgPghq4QBoIvlQukOJbQLdNSePwR1Up/q6qm3vw9fi2PoFxZrsN2SY9vtm/HPPXvoCy6iKmUV9oUNWqGAzsPx2d24+G2EdD88SIBPRooQADv9XoYD3pl3ysnhfOfkrsZ0DeLiUzXKVfrceql6IgDbRlqQXs84tliPVFQlwsc/R1Hpury/FdBfdMICHa4oXx7dxW19msoD8UqbFNX4HVOi3LQ3VnWCxia/JG+dpvQ8pN/2s6vj30rceqRC1abeYSqRJ1MlEmk2hkGkQouQgZdzK2y1gsvHZhQ4Z0Xup8uvdTTPp5EuvLl+j0ZbcsY+BgUJpWAxeroTpoAG7afeOu9T/KS/HhSgMor15ifwPkITZ0jlDhuFGHMyYjsrcpkbU0FhKZHYk3VsC0JRzpI3UoOyVD5l/uo+yunl8E7WkZiEFUE2dBVb4UIa0siGxngt0qwtl/1cjbrYQ61orTq9S1wwprY0FFFkfhmXJVNStHQnU73laFne84nqXxvYwIS7Kg4pwUke2M2FR1OfT2ujmqpYZUKI49BUPnebDKztZRgVTXDvGb9vkUAUiRkUSxSnn6iJI0TOxMN3r55RQlAFC+QAIBMzLO73pWV3P9nz4NvPaa732PHw+MHVt/9KK7s4vvPQB0tj3lAhCGS6SIrQFYX4hNrc0tSWXJaUgoREv7e1Upc0IcrokErcX5liD9cdNq/FI9ZzWtVgPTuutZsGscME1AErR4JLC/APh4D9df8CwYGL23xFY2Zm3E4GWD6wx9QvcJeOu6tzDi8xGoNGpRVF2KUn1dG9PGSRsxqM2gljj1escctOVedEsanFAQIAzugaAGLpwGgi+VC6f75t5zU9E4kBHk2cLMOtNvqJevv4bmEUNfQ5cOt/mt/oaOr76OyPP5PzmcJ9ycxDQWudhUkq03YeBwi8fmP/jUhuszWmaUVVPprCW1Wx9AuKKiCBuqy2un0xR7+XzoiiJPSIhumBdyFphZeBpEM0byRnw7vFOWy6KChfJibCrIaCiUUyY9PtHmo9pmZWAgRRwKZVp0a5YPtamEjJVpi1NwtlzAVeehs1g1kPsEkLgQKOLwwnolLSIFmVPrGrW91Ttf15sqOv18jf9i7aelGqqDBuDG7Uh6fGZmAhR0npbm3BYBQ3//zVFWDhxIgAxQXg6oVMDMmYDRyJUfPJj7O/1/3gIbTruANsJWJQlGlJ5wBmIohx3llG5OYozYjKL+dfNpq3PHIUH1MorWJMIurYCh539gSfm6ztBjt/8DhdZBhyksQEAUTVcpEkMmE8EsSK/94IPAnXc2J004xkLrv3Mn8P33wPbt3sdI63rTTdwfd/kP6b37qoD9wHuLdUtQjkOhpMiVHilrH4pMZA5B0TW5CV8pOotSq0P5vjoqNmSc7uoE6Y8DpUn37VzK56ym1WxgWnc9C87fDPx8zNH2rR2AJwYCg5dxvw1vOxyrJqwKTOfBVlqUBib/Mhkf7/m4dsxhijAsuWEJxnUbB1eWiLlXz8XcjXNRYayoLT+512QsvXlpi5qzL4MN2nJ90VKwTEvTQDCCsKWtWHC8F40Ggi+V0drAlAAAIABJREFUi2YpAzqRpqRxcAdk3BvRCt2UvuVlcZ2ov4bmNkkDMfrGz/zWF1E2btFX4JipGneGxdUBG+prkAAIErULANj3kTIU7OWMZOQwfnKtEjJyjW8C2XrMiNvvFSRwcOlj/ONmvDaWiy5saUKUkhT5RZ7gpON0uYr9udhEa7XgpSIO9OFzKPFzdHdfUURoa5nCKZcnlb8rPB59VI6ERJSvj0A3fyJI6Rmxz6gD6Z7y/NRXlzzzd+kr2T3TQ6HxmOuz3GphealIHohMZLS/vLxbmgOhEZAHOZeU5eKY0YGYDVKH47hJXwfsaytX4bGoJKctMT3/FEt0X58siG/nBEgGck8VVxcj6Y0kmKzOwCQZyRMEWH2rUC56sFMscKSIiyLMq3SMJL8KsLpMQy6RI2d6DmLUMYEcckDaasro9IAM8BJtpKUaqi9WAzABMTYbIHHxGyotBY4dAxITgZQUx2al8jNmcOANybRpgFwOxMQA8fHAtm3Azz9zwN4VV3AAD4F+emdsJSC7P89iZJHZ7oTGWXZCDrvUDmksB8xIxEBIw46AARmvp0ZKOz+M6taO86LIHIbII4uhzr8DoigztGcdNKDm5G9g7DUNdpnDIBmWMwmtjyyBSiyGSiQGMY0SdalGLEb79qLaHIFNOokmbLyyEnjuOd/yFJJfz9ChAFGiduzoDES7sorcE5EAonl9s+QcsuoBmvmpuQKEvp7/6P3+ZIEzXV0vZSjuPo95C4P0x024QQFcquesptVqYFp3PQv+OR7o+C5gEVAyS8XA0UeAa7/g8rtSTtYz086gTXibwAwi2EqL0cCa02sw7oexKNQVY1DyIHwx+gukRqSy8btjv7i5w824a8Vd2JS9CXGaGHx12zcYljasxczX14EGbbm+aipYriVpIAgQtqTVCo71otJA8KVyUS1nwCZTH43DO9e/A/LaIq+sR35/BF/s/6JOv55oHM6ZjVhYUjdapqERTQ0xNNPnxb3/txZhoYmN0hdRERHIkuCFhrDPQ2Uo3M+BgDKNHaf+UjOA5HC5Htde72zVv/ypUnx6cysoRGJkmgy4b64O2RtVGLuoDK91bY0yqxnnzCaky5Uw2u3Yb6zCZaown2hZVxyswtQHPYOP4almvLsEGBLqP0hI0Vtkw6R57TVU4dfKEjaHseGxUIskKLdZkCZTNhpooTx7FVYL2slVTm15oqjyZV8R6FZoMaGNTAllA3L20Z6mVUz2k5aKjNpb9ZUsH4+QvpZ+/1tXxtrrrHBYTCl6ztWQxc+vvpyenjY51SVdvlADyFG5qzSRuDk02qf7YnHpOSfgkdortphRaKX9qardk7Q3ZhefBYF/JHSNN+Y9FZ2MRIHeXNfx+dgUloepxGJmbQhlUkQCyyv4VMGp2uhBbwO/JiQKcRIZSq0W9FeF1oKR9dUboolAukyF7YZKJErlGKGJbPQ+FvaXWZaJGatm4IcjPzgN47ZOwLwRQNtIz6PLLAOeWgWscKRiZYXHdB6DecPnIS3SJRTIm4LO0/Wmik4/T8O/aLsRGqrFIsBW83raN2Ufusd3b7bzbqkG4IoK4OBBIDbWmTJSqwVGjfKsbqJ7rKoCqL5CASQnN/3SWOycEwn9J5STJodzBkVhEwsCOYS4RmO7jtBYKUYFgWtSO5StTQwgVDv8QRo1IaLopHd6IMQQtRbaHnfDIiuBvOxyRB34GFIDh8paDIBOEPVH/l+qqLMo7XYfTJFbIDVH46ribzBvytUMzKV1pijBzZsBk4kDaQM150DMtTFtFBZyVKL+yooVQGTNO46P4hPmXjxjMoDOGt6kzGph9Ky8+AoQemqXHI3GhMXWXiamgjNmA8LFUkYBG0gJ0h8HUpvu27oUz1lNr9XG9+B6FiQHOOEzle9BIwM6xgC78rhfZl81G7OunNX4AQRbaHEayK3MxYojKzCl7xRIBVTcntgvLDYLluxcgtGdRiOxkXaf5qqsoC23ua5McFyN0UAQIGyM9oJ1gxpohAaCLxVn5ZGhfZe+itEc9VGGBtQQ3IhlOu9V66NxcB3Mlwe+xEMrH/JK40BRSs+4oRZtJVVgRkzDLFwNNTQP7Pcf9O/9cED0yns6uzZGQM+WYh3G3uoMyj06X4+zMeXYMj8KxYfrGhrkoTaYKusCed3vqcD+z8LcjvmjP4y4NiwSOTozA+im3MwBkl3HV6D9zVX46/F4yFQ2aM84vN3dNdTuOh3WPhfLAJc/C7Uo1dvQt5USGrEEX5UXYog6AperuTHsN1Rhv1HHIsNcRVfAhTxo4p0jCIhSK0mqwC2h0XWiKYVtHDbqsLSM+xIkMPDRqCRsrC7HD4K8cnRtsDocB43VDDh1FaJvfSUuDWEutJLCcqdNBnxQlguj3caAqP9Et66leXWXq4bqFlhMWFlVAinE2GNwnvu9EQno5iFXJXnBkzc8ybjwOKZPoRA1Jsl0F292HgT8srwAO1x03UGhxt3h8dDZbHjVBUDztrlnxqS4rTM9OhmtpXK3UX7ugDq+n5EhUVilKwNPAzo1qjVS5UrsM1ThU22+x+E8HJWIg4ZqBiweFUQCehv/+bougQhWQZQhzbOfKhQakQSKBgDKnsa97sw6TPtrGvbm760tIpcAq+8GrnDjKL0xC7h6OWAS3GKx0Z3w3Y3vY0jqkPOlHr/7qS863Vtj6VHpOP7ocY8RqN7qB6/XrwFXD2y+9OKRi/H4gMebtfoupAGYIuIoNxtF+rVpQ44ynKro93ffBX6owf579eIi9vbvd69KAosI/PMm+fkA/eElKorrt2nE7hS5TQ5RITVGOa3NzJxCGioGrRiV52RQiSUMJIpJtoIiD+sTHoSk57JQCMChyDxyuCEnsJoVQKXNyt7ZJNQPnT/onE8gplwkYnVIKO8sCZWtstlYC3x0uUJZAEvyz4iP+T/kbnGEdtOxQ9TaCHmCiTl/JbWz4tbBcvQzh+HllUswqdftGDU8kUVxNjehaOEBHw2AzlQJjTwU2yZvgzgA7BVEMfvRRxzwnZXFRafWqLheFVBexVtvrVuE3heuZyKiACX5V6fFnzW5zIV5COm9nCxtfB5BimCk8z2daRaX5iDbbGDvHjpz0TVfJUh/7Kummr5cIM5ZPRN6YtG1i5r1OavpNdn4HrydBTOigBN108ixjoNnwcbr/2JqoaWyXwRqDYK23EBpMthOc9JAECBsTqsRHMslpYHgS8V5ud8vy8XxGiM1/3F4SW2ImsnWR+PgTh9ntGe80ji4UgTy7RAwQh/d/kpjaFAjwlJw/7jVMHugFySDxq+TEmqBumvfLkBoomd6zieikvHI1yUo2K9E35sM+HlmhL/TaTblz6xX4dU1Wnw8Vw6rkVuX7hPLoS+RILaLCYn9DHg8qjXmHMhH/h4FSk/IWZQjSe8pWuxe4ph7dEcTekwsh65IilZ99CAPe7JBZShVuD4kCh9q80A5KYdpInFTTeSaO4MQ5YJbVAOu+aIoonTNt5pZnpmr1BG4KzzOaY9Rn79VlWCTIC8ftXttSBR6K0OwTJsPilYkIU9yvhxRZPKRcN7GQRFu1E5jZUZ0MotSXV5ewMDf8ymuz0BPkZqexmSBnVFfKhpwf5/PedbXV57FhFZuIgYIeL4/ohWjcA2UWG1WUE7VmWtmolDHAcj39AA+dWM4vecnYPk+rme1KgYD+09H5/ajsTixQ6CG0yTteIpOp84oWpKiIkn6JvbFztwarkTBSDxFpzfJYC+yRv0xVAunrpFpEKH0/E4b22UsFl67sFloa9HRlXj1n6dQVOIIq/UXaO8U2ROvDV2EriFDavPx/fYb8MYbzWKKKCkBsl2IGFq35qhEAy1VNkvtu5DaFkOENLmSo3kz632K3h72ehHWPO2IxuLHSM5Q3y2XIFKnwtatwF9/AWdrAsWN3bTI3uWMrHFRYSJIpcAffzhmSqDrokXAn38C6enA+++DlSGhs9wwAaPY6tVcnkUSiwUYUZNesH17rp7Q54PoM3/5hYvWvPdeQCbwr6L8tT8erYTeYMdDvaMY+wA5jbSUV50rrWVTRQlT/kqKEvzpJ9+AwqVLOTrcUAcDOiqtFnygzWNnL3I8iqnJHUiObMQgwAudHencR45o5CAWCHk8Kgl0jnmvNLe2OblIDB6k9NbHxUp/7G3ezfl6Q89Z8Zp4zBk2BxN7ToSkCfPFN2fdNXZswjMIUesXVRd5bJJo9YlO35PEqmNBVPokzekM0lgdBev7r4FLnaY5aMv1f88EazR/DQQBwua/RsERXqQaCL5UHAsrzH/F//pqXBrzOvZH6GN2XXU5o1gcogn3if7Rl/aPGKtZDrx4iYwBGf7kC/OlfdcynmgcPLXlicaBPLsrrNY6FIHUTkP0y/ffWBrU3+9Zi+tSh+CAoQqf1AA5dhtQdEiOvJ0qnPjNQe+Y0NuIHpO0+OuxeNZ9ZDszNPEWhCVb0GFUJTa8FIOiYzKI06uh3aWBUmWHQuo/6NmQdQpsHTteWmDDgjfsqMytsbC56YAAU14X/vYf0sqCgc+UIqw1Rznpj1BKI5tJBKnK2fhDtqCig3KYdWK06mtAjt3IogKlIjFSawAcAtpayRTMyMTnuXPXN0W0eoti8GfMDSlLhi+is+LFV7qskmNy/DszBv0eL0PKkMAllqIoRgJMXy/O8mk6epsNOS6RIFzkiX/PUp86a+JCZ81GFpUSK3Effeuaz7G+4ZCx8LCpmuXm6qcM9egYQSDhpJ8nsaYm9wKW3ly31cm/AB/v4X4fMfQ1dOlwG/u7L7S6Tawyp+ZdQakyQxmqze7DpIQGIQKlyPijNWid8kSqZWpEKiODBiE/F9EfQ7WfTSMtIgWZU7ncoRdSeJYCm82Kw8dXYPP2hajWl7Ah+QK0y0zxaJs5Bwn5EyFipNnNU4haNDPTMbbwcCA11RG56GnUFBFnqqED79xBjOPHuZL0e0mUDpo0E8tpWLiCOB+580uuxQg+h/Jt3+Uid4eSMRHEdjZCqrRj53uRaH9TFVKHVaPwoByWajES+hhwbpMKEWlmdkYimR2XxsBEcrLhxVOUPl1fVV6GF+fakLVRhci2ZoyfpcfTHRKa54K0wFG5y9fUlFHCBw4Aj/sZhEzgrBAodKdmd05L9A50zWkY6CXi37PEDEGU+mKRCLeERDtRplOfLZX+uCH6IorYnypLECGWsNQHBrsNlPu5uYq/56xltyxj4GBQGqYBf84gsWog9wkgcSFQ5ENUfXM5gzRMM8FajdXApU7THLTlNnYHBes3Rw0EAcLmuCrBMTENZGVl4a233sLKlSvZ3xUKBdLT03HHHXfg4YcfhjpACST+/PNPfPjhh9i+fTuKiooQGxuL/v3744EHHsDIkSObbDWCLxWHas+aDXWilIhuz5/cYnQAnluSXZt/hXKLTYxovFHDldpPGHHFz4CoqTZuBDp0ADPyNAfZUF2OFS60kPy4SC/C3Gv+jjcQNKgLhy7Fyy8De/cCumg9zp1ruNdxlcUGq95BDRou8ID2d24XorwyQw+dToSqY0rWPWE5oQHKCeRuPkR/ajWJcG6zCpc9VYrwGkMilSWQdt1/uXCIbhMq0OHWKpxdq8KOt2uS1YiAq+YUIyLNxCISV4x1zieZOOscYjqaIJbaWc47XhbGt8MTLhSeF0LX3vrkc/Tx5XwBCE+vVmPXe44on9DWFly72JnG1Fu/nq4TuMdHVPrSBgFgFHknFPL6jxDki/ClncaU0RVJkL1BhdAkC5IGOMBWf9sUroUv60Dtq8USPB+T4pTTkijtni887dT9S7GpzGC/tlrLchtdrYlghsbhy4dj9enVrOzGScCA1sAbW4BDRUCXWGD65cDWc8DgZVxzbZIGYvSNn7G/NyeA0JNBSJjfjleILwYhYb2GGoRoHbbrKxhYXR9Iy4+L9v1nNRHFRF33VFQyomsiWPzdSxeyvC+GanK0KNCBRf3yQnTr8RoH+JRf5XydyhGQmzM9BzHqJghh81FpBD7NK8lGXqWZgUoUyR45ZikOVExjLfgCtHc4ugyt8s+PAdgGO8igTroOl0igFDkDkkJgTkjrSXM5qzOi6JwYcokIl3WXggDCvJrcTAT3Db3GjimTRfj9NxHLcXfTTcDGrTY8+mY1+j1WhrjuRkwIj0dvFXdIIYBjja4mdBcAUVc/GJEIm82OGcWZjY7Gei4mxe97Rm+z4q3SHPbeoefpg5GtWJ7goARGA57yNQWmdfetUOQrUe7m5KAWnPa1vzVr3APgRC//cpFzfmI+H/IcP2nXfR2Lp3LxUjmejk6u4/hzIemPGzsn1/rEYkH0+ETTe0NoNCgXOwmlBfiwNBcVlH9UQPk/P64tOio5Z0t67xZbzYzVw1/H20DPg9rz95w1vO1wrJqwqimGckm06csZhBTRKhQgZ7FOsQDlJKQowjxBNofmega5JBbxAk3SH/YLV0fDlsJ+0RjVBm25jdFesG5z1UAQIGyuK3OJj4tAwfHjx6O8vNytJjp06IDff/8dbdty+RAaImRAmzJlCgMHPQmBhEuWLGkQDaO3MQVfKg4NCXOE8b9SPjHKK+arEO3QO6U5TsUDYbClDzL6uBIK5ffKkKtwrSYKleUijBrluPrww8Dtt/s66qYrVx8lYWP1Egga1MJtw/DBB/z87TDa7SCIj8Bif0QUYYY22znCiAxzsuYbhFBneopuOhRudkRNUoHzCXIS3RhFIRz/uW5ul2sWFeLvaXE+LwkBhKZSCcJbWdGzk5iBiO7EohfhwBdhKDslR+tB1ci4UXfBKcKIGivTZf9RHkZv8ajf3+YMktJ8b/k8DxKFHTazCD+Nb8VUcMVzJUjoVX+eJ58V7aGgMB8QXyRFroTM6ywa2zNX31Qlwi/3cPMlIYrc9jfp/G68wmZBoSDHlq8AobAjylfomjuyvoFMU8qQtjiNRfWkRwF/3wXc9SOwWUApOCgZ+HwUMOJz4BTZ9e0i3Np/E1J7x2JOXFq9+T39VkIjKhToipD0RmtYbc5gMd2OoQpAI+OMvt4MQmQcsrmMoyGgFN1bQgcBX6jiFpRkI8fsfL80lBa7EaoMSFVvhupKI9Cz9n3o6HLfFCBEDjy1CljhYO5kBcZ0HoN5w+chLTItIGNsaCOUC/exF0zI3uRwCCnqcwOM0WtZk74A7ZGlw9Fjf+ANwMSiQIwP9B8vlOuu0sZF18X3MuJyWTh6dhUzis3V622g8+jwBUUIT+Vy/C1qlc7+T1HceWYTu29KT8jQq5MU02KSuOee3YY3Ss6xPHoUqfdoZFItHaM7inf+DPZxWR4OGp2fjwvi2+HJADjT3B/ZCp0VzucKX9eY9EaOJpQfuCVGn/s6z/Ndrjnka6LPanc5B+vTxZ13Av37A5TLUyiUu5hySROQThSkRPdK4i8leiDW4cXY1NooWToHHTFVs/u+o1yF9WfXNzrPcGPy39F4dHYblCJxvQw0xLJBrCr0DLo+JBojQjgQ0N03slBn7pgj5GIx2rjJB/nf2BREemBlCMQ6eGvjrPas3+csRqs87QzahDdZwldvw27x172dQYhm3pMQ/XxzPoO0+MVpphMIRp56X5igLde7joIlWp4GggBhy1uzi37E+/btw8CBA1FdXY2QkBA8++yzuOqqq6DX6/H1119jKSVKANCxY0fs2LGDlWmIzJo1C6+++iqr2qtXL8yYMQPt2rXDqVOnMG/ePOzZw3GIUbnZs2c3pIt661zqLxUyQBAlCnk/Um4V8uh2FX+ALDJSfVFe4NSEqzHxoEHHjB49lBrEuclt5W7BFpeewxkTB1pVF0sgkduhCLOBWPx+npRAiWEgqpYgRCJBrJTM8CL8+2/At4tfDVKet2cLBTxYgtpTIhOZl3pjRUiDWlIkxcmTQJcuQFgYsGQJR8M1Zw6gUABffAEs/cSC3MQlWHDvaPRpn4gJE7yP4KTJO7+JOcyM6hxngDAslCfp8t5HcyhRoRXBLnGOoDyfAGEgdRByUwlyv4mCVG5HfKoNIxYWQhi8RpEyxYfl2L88HGUnHes25OVilmdRVyCBrlACmcYOqcKOkEQLjFoxZBobbBYRDn0dxqIT1bFW7P0ovHboV75YjLhuzmCIcF6s3SIJojuYYCyX4PcHOcra694vgCaOy3FJNHnZLvSc5AwghQhbF0ayiEuSTmMq0WWcw63WHUBI5SiSsPKcM13sjR/lQxnpgFxIH4HOndSQKEhve4AiSwnsjenkrOP9n4Xh+C8htUDgnqXhOPWns1F61Ne5cLVHkX3+5O8hjE439apqlJ+V4thPoRCJ7eh1fzkqO2mdhkR0WR6wZm9D9/n6tt3vYsuORax830TgeAlQ4QbPDVMAGVHArprIobATL+DGe+7FgD5iliO0OcgeQxXeztqODVvn4eTpv5yGdFsnYN4ILu+gJ/HFIETGA6Iyk3kxelIf9J5fWlYbasW65Wmu/1degJ167n4iIzPdc57eYWQ07abUMLrvnYYqRsPYSxnSYiKc1p1Z59ZQPaUP8NZ2bjVob50o5f4+dQDw/k7AJEjD2xhDdaD25hF9NQ4UGnFjUjgG3WhGcaVjgBZlFvIHdwZE/gHtCRuOIMSUitYuRm06KxJtNUW+SHx2dLCDnoNd7qxExzGVqJqXglN7ZZBIgIy3M0GU2eoY7jlMIBqBaSSvFJ2tcxZ9KjqZ0Rd6olSket9XFDnl1e2kUOOBSM5xxF29uXFt2b3zUlFg6GG7KDQseogcKW4OiQZF3Aal+WmgOeVrorPHU08Bu3b5p6dvvgHivPiMCRldHo1KAjlaUQQ5fSE94+HbxL9R1C19R1gcLleHsQvCdwr9RtcuVP47ch6gdx89jyg3N0Xkxrr5/rTa7VhYcs6JZp/yLFOdfV5yYLtzDKM+wj1Qy9M1irjkovorWa7HIerw8+LgNHv9bDz/7/Nsnfw5Z82+ajZmXTmrsdvkkq/v6Qyy+m7gCjf468Ys4Orlze8Mcskv5HlQQDDy1LuSL3VbrncNBUu0RA0EAcKWuGoX+ZgJDFy7di2kUinWr1+Pyy+/3GnG8+fPZ2AeyUsvvYT//ve/fmvk5MmT6NSpEywWC/r27cv6Uakc3s8ETg4ZMgQ7d+5k4zh69CgDDwMpl/pLZX21Fj9WFHtU6RBNBG4N9U6bRXkHDxqrkW81Yb3O2aj8YGQi824lGaQOrzXgkEHzyejWWFByDmR8co38IMonon4iodx4+lIJi646+bvD8E2RMrJUI6rOyJnJiqLWEuWU60vKAEL6+LbZwAxS9PdjxwCNBkhOdky5uBhYvBgoKwOmTweEAbHk5fvee0BBAReROGiQ77tvU3U5M1ix8dtt+PrHMbBYqiCVhiB7yh7IApCP7K+/gNde8z6m+fM5I0RDxB1ASEZClUhca8TTWW0s9w4vKiUgd5+yrCFDOC91yvMkQIjAAnyeIwgDMUmR3AZZghklJ2WwGbn14EHOrndVoOOoKuhLxVh5v/uoYHmIHQm9Dchaf/7zpgybW4SItmasnBWJ6LHcfU8S19WIZJkSlmIp/pjCAYq8DHy2FDEdjQzI/GFM3QjC+nQ6+ptcFpVZlecADzvdXsmM2b7IprlRyNvJUb4RO95t3+ay55TFIIJMbWe0dHwkZOVxBUx/R0MVY2XAZlkmd3PEdXUG+k7+oXECXMf8wD03SXgANEwiRZxEjs//NDKKvad/KcM/Cx18vr0f1GL3Bw6qVb6+Jt6Kke86nDcIED38bSgOf+OeC5god1vdX4TwZDPLe6k7I0fbBAnCYxpOQ+xNr2Rg+/Tr4SivqJvrMSy0Nfr3fhjbd7+HispzdZqS6tohftM+3P5DXrOhGV1SlotjRs7B4lzuNqzbPAdFJY4QNLkE8McgFBvdCZ/f8A6uSh3K3mcEzBENoVAogkMiEtVGHJFhlGgKKQqJ3vW7DXX3NwEbh1wiqHyh1SWGAcphS0LUsDOj2/hNpehtT9D4V+u0qLBacKU6nOVRbai40jRRLshyYzkooohEKgYsNX4DQpom4e9ikRjhinBQLkhexnYZi4XXLmzosPyup9UC/W4ywkAImwepSHsdFRkvs6v+GIAJaA87PYM5jPH5swx2K4pD9Bj0bAnLPzwqNwW/fiVD9+6cY8XmQ2aI7stCxyglHo1MrGX7IGpaouQTCjmckRHeXXQe74z238IztdGFrnXrAwj9veZu3/u9GDUVKGKR7r2AS24uMHcux6FKh9CxYwPexaXWYHPM1/TRR8D//uf/ShBzir+5DfleKPKQnFiOmqrZ87WphXdGoZyelP/ukV8nsy59oT/2Nf8dRfkVWMzoolAzoI0oWIkZhaLghc6rfVWhDPT7q6rGCyRAk6fc2UT3zIsv7BeuXfNgboCGVKcZOmdlvJ2BU2Wn6lxLi0jDrMGzMGfDHJzWOlPCU+H0qHQcf/R4kzA6NdV8m2u7DQbLLUrMGfU2JvacBEkA7AjNVT/BcTk0EIw8rX83XOq23OC9cnFqIAgQXpzr2mJnRRGBlP+P5MEHH2T0nq5is9nQtWtXHDlyBJGRkSgoKIBM5h8i8Mgjj+A9Ql8AbNmyBZdddlmdfrZu3VoLTj766KN4++23A6rXS/2l4o2CZlRYDGIkMpjtdnRVaJBjMbJInv1GHaNb6akMYZ7frrmlGrpIfLQhGQWfLuCi7zxFBtG18iwZEGlmRioengoJETnlXfN3LP/8wwGKJFdd5Vz7+++B6GjvLdIH2HQBPRUZhf/3/c21FfdN2Yfu8d29N+RSwmIBioqAhARg2DC/qzeogpAKjG+APOTJyHe6hgqS7KsVNQxdtBZhHgKKX5xtxx19Neg8sm5U4p132fHMHRr0v8XAqMJ8FYVIDPojVdtQWlW3HtmTXRjy3DZtgQ26SmeP/+YYQUiGfr3dxqJ3Sm0WJ8OOPNmImLuLcOQZRwSVcA5x3YwoPNBwA7uva9LYckQvKU01oOwWmUDNAAAgAElEQVSgCioFEK+UuY1u9tQPgRtEI0XGmoZIYn8Di6rr/YCWRVTysu3NSGRvdA+eEnXprvcjoC9x8Ore9N05HDoIHH8piT033cnV84uQv0uJyAwTNr7CPVzS5Cpm2NIyo5335xnl86LWz5qNsHi5dwik1Bd75/4VKW1o9UQuIlLNOPJSK1RnyaFWizDwiVJIpICmlQWaWM8AhSe9E6ZhNXIgKi/MnkaAV8FOfPfLuDpVO2Xcih4Jr2Dj821hk1RA2+k/qE78uk652O3/YPzHKU0GEBZZTMi1mHDYWM0or8nhZVRoDAPkeDlnNmJhiYAPVTBKm82Kw8dXYPP2hajWcyD4PT2AT2+tq617fgKW7+N+V6li0KfvVLROvxVVRL0qyCvakP3dVHV6K0MxIcIZxG9sX99WFGJLNUcvThFZL8emgqhR/ZWLgabJZAKIZpCcmeqL7KfnZ/4V3WFV12UwkFSnQnPqKVSmzYc9pG7kXFhICiaOWwV9kRRvd0xj7AMfleXVAZB5MM/dfid6Tron3J0vCSQgg727qL358e0Y9R/lbab8za5C50PhuYq/TmNxPXPx1yjSdVJ4Ap4rOgMCJgIpvMMb5SG7PiSqaYzmr7wCUAI6Xj7/HGjdPCKkA6nLQLbV0vM10fuQPnV//NF3rdDnM/np5udz28Ofz/FcsxHzPbyzfB+B95KUd53yM39XUYQffrsH2TmbWSVf6I99yX9HYOdybT73rqh5R1DUM4nYbMbV73yI9M3bUNQ2FX88NRW6GB8+5rxPy6mEMIowEIwL8+LbsnPg8vICjA6NRZrcJQ+p0Qjs2AHExBClk0+j3Zi1EYOXDa5TdkL3CXjn+ncQpghDhbECj/z+CL7Y/0WdchsnbcSgNn54y/o0qgAXIvqczZuBTp2APn0C3LifzVmtwLp1QN++nKPHd98BkZHA3XcDoaEMLJ/08yTWqE9g+dEOmBh7DfDWW34OpAmLU4JVojAi/mO53HNHej1Antfx8fXTtvz8M7CIYxLBt98CsbFNN3h64NLD9p13gIUL63I4U89Uhg5gNLdA0834MbNg5Kl7ZV3qtlw/tlCwaAvSQBAgbEGLdSkMVUj7SQDdgAED3E77tddeY9SjJH///TdGjBjhs3roEJ2cnIycnBxGU0pAoyeh68eOHUPr1q2RlZUV0I/wS/2l4g0g9HlBG1iQGE0p4uboilCc+CWEgZFPLzLihakyn4w5RQcVkKdw/HMS2CGBFeoQMSKkCkhEQATjVfSWvcx58OnpwMiR3FnRVTIygHnzuChETx/g7mjc9hz4lEWP8LJ45GI8PuBxn7TGO6Nu2AC88IJPVQJeSGiMlIrESJXRRypHH8YL3dPlJRJIQ6wIUYoQEmdFVaEzEPHeWyLc1EeF5EF1AcLHpgEzblejw2Cjh7WndawbvfTJOxKM6KXANXcbcORUXYBw1XcyPPaaAUd3SSC3GXFj8Q+wiGX4M+pmpBpOQW2txr6Q3rBBjArnYIfzloMwVCxFvFTOQKESF5pfAgSJ+pcibtvIFE55nIQLTWskSzQhdlKhR4AwUBuD8iERLZJQyFOajD8UreRO0uVqFvHki5GWgH9TBQf8UmRSx452nBM79hq1zxlfuHubgdhWM64pW4n7ct/FzynPYHv0dW6N6KRrAqA1YgmiCOmqaUNvt9bJtcbPgyL5aLor7vA9StGsF0F7ivtQ7tZJBI2Ky8CVaTKAAD2hJEoVAaGVokhsPn9onCkPA8vXoVTZButDLvfpo1ZtrcLl5RtQIovB3tB+iB5bzCidS1dxNLIEzNBzVShd/68CHW+rAuWzzNulZNGR3SZUOH1D05b+85F4J/CU2rjuvQL88bADUDIPfwAFYkcYhUwaitDdb0Gdf0edLVWd8A2MHe6HTuHYh1FZd6JPt0X4e5rvIBU5OnxbUYSdhkokSuWYFJHAou1chZ7rlJeIyguFwIEr1OFYVHqOAYYra6Le+TJEqZu1WglNkg3JV+iZXg4d+wGr1j7DivhiEOpxxStIzrilttsYqazm3RaoOzow7dC99UJsCrsfKTrDlWKRnhkakdivM5zrGUVIYefPqFsaTRM5A81/14K/N1lRVSBBhFwCi0WEaGMuLitagV9CB+O0KsNJBW0MpzG66CscTSjARzd8VEc96txxaK19HemvlcNqrcTBra8i59RvdcrdccvXSEzgjKqPRCWB8ve5CtFzUq4zd2dIun+ei2njFswj+l8CAd9wA0jcE5HAnM48nUtfiUtz64xGY6F3z+zis/5sCb/LUoTgH1WlMNntuCYkkuV8a3Jx9VKjw+nTTzd5t03aAT1DhR5uX34JtHLkzGW0HwYDx43Pe+sJB1RZyRlq9+3juDnJ8N6vHyvRpI4Aqnhkpr0N0LduRN0o/abQmevye+qDcCKyz5vNgFLJMaHceCPw5JO+gYX03vpHRwl9PYjdDkVVFSxyOay0LjUi1+nQ47c/YReJse/GkTCrvbNPVFTm4JMvyfvSd/pjljbi4SOICE3CcZMem6vL0V6hRke5Gr9Uciw4BAL2/eFnRGWdw+HhQ5HVu2ftONtu3YGR8xfX/nvfTddh08TxDV+yenjp/y88HisqixhFd1MIOWCQi4zIbkfhDdejvEILtViM0P88gbBRozkAhtiYBOskHMfkXybj4z0f1/5EgOCSG5ZgXLe6zllfHvgSD618iAGGvEzudR+W3uzyfiFA7r77uCKffgqkpDR+6qTjn34CDhwABg4EiMmK/k0f37fcwgE2WVkcCCikc/7tNw7o4eXllwFC0HNyOKCJPuCbQrKzuedCqICVo7DQc9R3mzbAXXdh+K6pWG3kbGA+geWlkVi1vwfgyWOZ1oIolG66yT0PMT0/Dx/mAOVwR4qIBqtk5UpgwQJH9RUrOACUhO4Bsu9R3hN6ppPNkMDEHj0AojZyZ0ih1EJE5yQUcpJxBeaobW803qdOAefOcet/5gy3LygAYtIk7oH51VcceCsUYVg26eqxx8CSI5PQe4f0LlzjBiuuYRUbHHmqicecYXMwsefEiy7y9FK35TZsJwVrNXcNBAHC5r5Cl9j4rrzySmzYsAEajQZarZbRe7oTivqjPIUkRDFKVKO+SmZmZi1dqKcoRb4tuv7hhx+yf1K9tLQ0X7vxWu5SfamQkfOXyhIc37MDpa0TYdJoINMbcP9dHOULydIvPoJZpURIcQlCC4tgtYhQlRgLfVQE7IJDGTloHvlGA8O6KojaayCLl4IMwuk36FiESeEBOXK3q9CqrwEbXo5m+cfiuhtx5Dv31HbeFk1p1cMikjKQp/iYnIEiErsFM4rm4QrdBuSrkjGvzQvIVySBaJ9iJY0z4nSq2IZwUxH2RA6DUeKcN1CY53D1amDmy1YGglBUUGRbRz7HD2ZPhz7219qpjeo4CivGrmD/prPmqtd2wSRW4unlXWqdQF1tKJ70EmouhVkkh0miQpQpHyXyVuxj3Wex2yG2W2EjnkTBAZwMkTfkfYS9EUOxP+JKWO02ZtjlQRlq/4SpCoUDhkAkq4TdHIq4beswfqIIr9+vcQIBCZiziiSY+7oM4war0H+cDnlZIrSrPoZXM6exoRbOmYNbH70Co5+pwo4NzuPvMMiMb58NQ48bnUEiqrdiuRT92slxzZ0mHMl2pkkiI/Xe9TKMnlmJHesk+ObgdW7VYhLLMavtIhwSpTI6RRJibgltfJpIn5YhQiJFjEQOivw4JQBdqTKBsQTKehOZqRg26QmUxIch50grGEScpzFFcwbK4ZFANQLtIiVSBpRQxBppi4BLfoyZJn0dAKw+AJTGSPPX27gcVyw/4iHOAEVUtiQE2lvVFpSY9Zh59jmUKdPwV/JUp4lNO/YgkvXHndwBPmnzLLbJUnFr0TcYqv2HtSUTiTCj11ondSbqT0Ev1mCHiMudU5+QUZw3/PSv2IQISxn+jrwBoRIZA3mrbBZk5luhy+fem6SvEFTj9oQNqA6Jw8GQnsh2iWxsI1My8E1kt+Hhk9PRTrcPz3T7nd3TvCis1Zh74Ab2zxnd/4JFzD3X6N59+eAoqK0cdeTYzr9BbK3CJ0c4UE0hErGInWkZS5GncI48STDmYvEJzqizIWIYOlYfRKypsLbPfxJH4g35VFh0YkSHixChEbN8OnzksDdduV4nB5BiN3lu+XKGqLXQ9rgbFlkJ5GWXI+rAx5AaOEPTZeXrMSVnEZ5p9zaK5PG4qfgHDNUvwcvXHcX+pApEGaR48ddOmBe+FdmbfL9xjxirsehEAXK3KRGeasZt/dSgyHna3wtKshngzQMT9K4zaLl7sfysjFHiKsM9GwHNOiDmhp9wTd6vKJTH45dHpyP8wRi/oydiEi/DZddyZyASug9bNRCcCMsvwG3PvgSLQo5v578CoxdDh0pbjhGL30fr/QdZ31+8uxAVCQ4AlozGdEYoTW4Nu4sxf4AqDHeGc4myCixGzCw8Db3VCqtIhMUJ6WwvUc62ZKkCpEWKjFbXOAVQHTL2P3nuKDqvXgd1WRkOjxiGy1LScUNowyI/mpSmSZkAXH89t0B//MFZ6RsgJ04Ab74JHDhsc4qApnvnkwPXsBbpWUmyPmIY3m3NcYfTGei9Y3ez59HcEcfxWzcukoZEatag965HMXDJRBjlYmQLQupzTq3Eoa1zYDI5vGO6drwDw4dwDk3koEF0yf7Kw1GJeK/UQZHM1yfAkc8H5tom5Qyk/IYHXehu+XIELr5VWkMvbLdDajTBolTg6Zg2DJT+tCZ6SGS1QqGrhiHU+8tPbLFAYjI7gA06D1HEB9k2Bd8+rrm0ncZO+iGEhgzy/r5seaCBDJZkwCQh7zRKJk3iihANHgyQwdubkGHzZgdrBXxJWldfm/w4CbkmFEqQDoIZoylyhIzjPCjiznDLG3TdoV5//82BgWTcvv9+55GQcZgiT6hPT9QZZIBetgzFD05AUuc/YBI771lybEkQMFu0CgWIRrhTLHCkCLj7JyBPwL6cXwVYXba93CZCzpbLEROaABCo6QGA8bY0INpYigwdMgRISuKM5G4YdPh26JnwwAOeW6U0CWQDJ0myZWO64VV0lJ7Emeg++C55OrRyR7JCYkAhPGXoUKBbNyAqCijOMSLm6fsgLsiB8a3FeEVmhj5CABzY7Rix6D103bQNxvAwrJgxFfnt0zHo0/8xcJCXnK6d8fNLM2v/nbZtJ4Ys/ZT9+98p9+Fs314Iz8tH+cJxWJTBDdgf+uP/nGiNh04n4fCIqxB38jRyO3fA5rvHIaygCGaFHF1WrUHf73+u7f/HV57DqOdne1Tcez/UjY7jC9M374SH/sOchXZ//Tl+F9d8W9jtuHbh2+izfTdiJTL23qJzK0VSk5henYMuL84GyJt0yBBU3z4GzxWfZd/MGaXluPXnP/FLaiKODb0CCcdOoCypFXsHE7jZZu8B2EUitF+/Cam797Jv8j+fmoqzvXvgsi+/g0mtQr9vuO9Gd0LngsQrh3KRcwTQ0H1GYI1Qbr8da65tj3Hf3IZCcwUGlYfhiyveROroe7lSR48CDz3kqLF6Nc4Un8Rdv92LTdmbEGeR4auDnTFMGwnMns3l3dDpODRaKJT0npJkegtlragA7riDe37Ss43CYOnv9P6kj2qiV/ZFli3jnj0USUngIS/V1VyOEKHd6OOPOWCMnmlUXij0fKOPcrq3CSiifxPIRHMhnVJk2/vvAwTukc2LHBs8gYDkzPGn4/5wN42zCgPSLtsKu4joW4G/7wLu+hHYLCChGJQMfD4KGPE5cKqMpRXGma2Xoc3U/wLXuXzTkiexMOUPvVeIdoAeIjQH8nDeudMxFAIJicnL9b1Fz1t6rvNRqZMnc/qieROwR+2RYwat79VX150a0WHTPhI+rwlk8wQck9NL164cgEt6cye016ZOBZYvBwgE5oXeD+Qo8uuvwL33AqtWcXOlg1RDhTfuuHtXUS4bAkUvsPgdeXrLMgYOXoxyqdpyL8a1DM7JoYEgQBjcDc1KA7GxsSguLkaPHj2wd+9ej2MrKytDFH1dgFJj3I5viQbAR1m5ciVurDlQvvnmm5g2jQMJ3Aldn17jTUT1rueNMD72VV+xS/WlQgafZ04fxP0THB/iZPwsPuzwDI3KMLEzo/asjFHCCcXQKgJknzYmReC9mGl4+hfH+uUqklAujcCnCVNwRpUOimRpX30EW8KvhFXkDDZrrJUItVQwMC/aVAitLKq2DBnLiTivnf54LYjkupYvPD4Huq+lGKv9Gn2rd0Ik5QCFtRHDsSTpP7gn/wOMLvsNv7Z6EFHmAgwq/oldp3GcUXeB1G7GiZBeGF7IRa3Qb2+1fwdh5hKk6A5j0hnn3JpPdf8bydXHUCWNQLGSM7bTxyEZ/GkuvFF+UqfvoO4qh32fFlppOEqHxqFS6QCvopRRyBy5HbtzEhEx/npEGXNBgVcmVRhS8rdj4vBzDOzrpV2D/qX1f2B4uw1OaXrg3YxF6F22Gned5T6WX+zyPSqlkVBbKnDf6eeQWn0IpzVdkabjjMCusiX6RvyaOAWvHuA+AgtnLsY1d0Sg0wOncWzg0NriHdf9hnsmD8EzoySYMGQ1NksG4fPDDv68fj3EkP32MzZdcT9EZx0GTGogvR0Qd8swHP1sNUpLRCyqr0fVbta2tldnXL9yATYn1hhgBQPs3U0E5YZ/8cpbZfjoGxlUturadfijx1S8MnA7Dn+zGdpS75Gk9/ZahtzSBPYhdAV2Ic5ShDCrFu2rj6J35XbW69guv7OPqTT9CfxfwTJ0r9pTO5r/JdyL8fmf1P77q/h7MK7gM/bvNZHXYlfoAOQqWiNXUZME026HxlaFa8aE4q0n1SAb2fTZJlhMZZiYtwTppmx8n/IcClSpUFkqsTR2JlqVHmS57gg723r9K5i5chDe2Mfxze6I7IUX1FxkktqqY8aGEI0IWll0HdA4yZCFZOMZBgx31u3H2ogR7H6lj88bjQeQXrUXG2NuRYk8DmHVZxAqsqNAmcIiWK4pWA6rSIYoUx4rx8vsTl9Cajditz0EBgLT7XaMLVyOk/GjQcD+08cmMeP2i2mvI8VwGpPylkAuEqFIlYZPUl9BvjweU3ffjbn6GRzQJxLhVEhPZsiSia1YsG+409b8LOUFZKk7okweX6sDYQH6njW4UG5S9AqBhNN7rMG0Ew+jTfXR2io2O/BGm5nYFnoFTHZrHZ0R0P3dkdEMyCXQjReiM53d+SuMy56H9Ko9OG5ti0fxDjJsx9DHsh1X2DYgWXQOqhrcijfw68UqzG/zX9xd9gs2xo7C/Znc2vEys+uvePXgTa63o9d/Uw4c0h/R/NGceJotqrg7tH/tXvbW0FlrMu4XLcU46/e4y/wpwkJs2B51HXqU/YMfI2/GithxMIiVtcaFRJsOpXYxbATy2G0w1ACcGpENT+YuRp8yDqSl8dC4pnT4HEuOTagdxtLEx3Br+QIs7JMBS96TWHDCe5S1RWTHTz1yMeREDGJ1CvwQfzcWHq/xZBdMcMdJI37fasGArhKM7MmBN2VVNjy17wz0jx3G4IJ/kK1Ixam5N+P9Mcl4Me8stsyLYlGRNy3LZ5GUu6aJEWsuRKy5ADcXf49fYsbgWP+B6DCqCptf485CCb2NiNhwAkqbHlWSUMzJ/E/tKHaEXY7X2k9E/uDOzMrjq0GIolz7p/6G0i3doUqz4YauOxAhV8Acpkbr1bvQ85OfoImz4qvFr6MsOYkZGxm4UWP0kZhMEFusCCkpxbhpztFHR4ddiY2T7sLkCQ4L9IHrr0FZUiI6rV6L2My6NJT8hH5/ZjqufucDKKp0KE5LxQ9znoeVp5cSGJwI6Dtt0iN17wHcvPwbfDn9YVRGhCNZFQJNVRVMKiU6rtsIZUUV9t58Ha4w2kEMtD3atEXpddc6bdPi1BSoqqqg7dkDrWLi0MYGqPYfAGbNck4gTLUIJCHqKIo0IACBDHsA1oVrMS39JPaGOkAxf/NB9rQnYFHbhzFk2RrmVW4xGlFgNTGgn8A84dvG/tJLsHz3HaQHDzr/TmPp+TB++r0a4wuWscg6XghEm5m2EHflf4wO1YdZPd45wRWuu7PLSvSr3IInsrj3+4mBhXiiz0mUyCxoXZCKxStj0T5ECmtaGOYveQPlNisUej3is86huFUCNAoT/lrzJHLzd0GjisTIqxcjJb4fM2oLncGEC5F46Ahu/S8HItIeWvvgvU6AGh/5LqyTsnMPBq1eh4jtO2GVyfDZB1xEz9APl+HkwAG45g0HbcMnn7wHQ7izwwbph55ttL+fuXIkNKVlzGiuSEnD9m4dsfqxB0GA3/jHHAmX/5gxjRnYbTWG6uGL3kP7DRy1oS9CIMK9Ea3QTaEGA3bIoEwv6tJSzpBJxngh1RsBe8QPuWkTF7FCoNknnwD0jUPG4vXrOcMlRVAQqMZHJhCgIIxs8WSgJCM6hYd99hlHwUbtCEE5MqqT4d6d0BjoHiADvFAIRBg92n0dosEjg6w7IbrTG27gonhI2rfnEoHzQjnjybh+niVTqceMdqfwQ6xzfvXbOgHzRgBta4Jb3A0rswx4ahWwwoXUZkxhLOZltkWawUOEHNF7EHBKgAEZuolGsCaFBgNpCDAlUIIM3Pv31+2aIiCnTOGiIgnUIDBUmFKDAJc334ShsALP7bsdYtiglcWyM1mpXoWkw3/jSSN3PxrFaihUYkYFvjvianyR+hxkNiOGFXyFLTE3Ia3qAGJNOVgdNw6Din/G6BxnqkI6n5AT5ltzP0RCohbtcAaj5i9AhFmOUKUYmzLaoNWRY25X9cfZz2PUc6+4vfbh/z7G/ePvxYgr9iFL7bIHAaRUSvDUNjnmDTAhK7QuHXCKToG/N/XwyKLh7zZ77/vP8fAYx/nDtT5F5SVJHd/EBAZSFDwxaHh02SNA6aDgO4ruATdOOBSFzLM9+Dvu+sq3lilQbrWw9xA58nn66smVG7EitghTchMhtbuZjRB17tkTFpENSxJzMbooFommBqQpoGcmPacIxOnZs64TQCCVQG1Rol7i26WoNRJ6Tnpz3iCwjZx7zqPMTjmD59O4M5Y/YPnszDTMKu7EAVV0vqHnPr2TKJlpQ4RANor0e+SRhtSuv47Qjkj5UehPSxehd/gFmsvw5cOx+vRq1rtPkadth2PVhFUXaLRN2+2lasttWq0GW7/QGggChBd6BYL912rAYDBAVeMZesMNN+A3oZeOGz2FhIRAp9Ox/IEUUeirUF7Dh2q81L777juMGTPGY9Xvv/+eAZAkVI8iCn0VemnUJ3l5ebX5FrOzsxmN6aUi3y+ehy6f/ghztRiqaCuKaqJ2WtL8qweqoVrvoD5h539GG+gQlRcKCooUoWrkKd8Q4Y39nuqeiKnCxLs5oEsoX3/RF+2LQ9iHu6HSEW3IxhLuIYlfQwbYBHXIA1mpAKYjG28OdRiAXvtTiWkHFFD06obN++t+dA7szf127JwVJYXO+u7UQYRIjQgnz1lR6HKN7GEJEWJUGezYf9jZPHp5by6mMbvEiuyzzm3GJwDtEsU4dNqK8rL615fWYVdCH3TL3cuAIbFY5P2DMkC6bZ0EtIkXM+JJ/luKd9gnW5PMfRC3U+979gL7IzrjWc0L6K7fixmFr0NmN0NSwwn5ZvKzLErutqIvmcGZhI8uEzbEG6IbMzVyQOABNOpDeG/R/UbGEV5c78+t5j742XAtZts4T3S7SAJpmHfaKk/jdb0/CRwUGuJ9qVckj2NgT5r+FPjx0hTIhYF2n7tHx68VV+J6++/cHCCGJExdW47qmmviLOXUQsMePT4tkTuQ1JeK1fYIPKt/EQNtGzHBvhwGsQbqEOe5ultnWlkeoKS/k8uGt2drfc9QAl+r7Vbc/397YFBYoTRKsPTLXk6RzML50HbvnL2O2d1p7pXTnsPmEzLojVZ0sp5AqTwW2259Dvf9jztTnG/5dMBZLB3E0RX5YxBqt2cSRu7si3Fl/wOk7g1/IrEdlGOSMHGK3qegcLsV7P+1QntPL2IOBhKF/5FhDdXXl+PH4M4ly2qrGzXq2gjhhrYprEeOBhSJ6+utVGG3oN3ArShWOBx3fMkHSX3G6UTI+SgU0k5dGThIhmPXyO+MmjyRpGGOWtnGHBOITrhCC+QW2WHWcdG9QqEy/D3kqhceIBP+roqyMvpjZYQN8lBHNGuBwoS/40oxLiseUgpPqHkdv/TGbISVa/Hos7NhlUnZu46eaQUpSXhpfAZuPiXHyG/+rrMkRMeXsmsvWh84hMq4GIQWOoMvfAWzUgkZb5QFGAsFRcI0RA6NGMaigurowWBE/EkB8KRQICe9bUDe15Tfs9Rqqc15S/sqec06iK91BqobMh+nOhR1QoCjUMhwLpTUVEdUYaM7bIIGCMg6fty5YYqIa8p8UX5MIyCOAJUhWHQyHUPKzw+lqK/To2cE4QGEzcpsBsgqtaiE47tBwqgjfG3NuRw57/B5wOl5JMyxW3uf2+3ce14EyEJskChsUMdYwbI61CM7Iyrxf/2586dQbsmJxisbwyCXqlAlteLlnvn4JaVuHtKvtndGH23D2GfcDYsYHpIZdT8npFeiK6bfae5+i6tTNTkI0H3sRlxBwlCxhDmBFFkc34N+9y+oQAw6dH6iWRCo6fNsCFijP7x07lx/XrnGDLKp6tJziZ5PvFD0XyBoTwM4XnI0zBiwDadUdd+PaXolZh2Px5zUbJx2w1KRXq3C8e39Ifp3bd0o8wCOsdFNUcJk3gmGb8zVkaTRnTSuATo1kZM33Y90z6e4OUdawX230nmA3UnuKE8bNwy/ap/VnkXa4jTmrOqroyHRNJ+ZdgZtwtv41VdLKBwECFvCKgXH6K8GggChvxoLlm8yDRQVFSGOQvMpUmbsWHz99df19hUfH4/CwkJ07doVB4gn3keZP38+ZsyYwUr/8ccfGOmJUqDmOh81uOCtBF8AACAASURBVGDBAjzxxBM+9kL2Ap+PxLikAMKSEmhvG4vDewLzIeLzggS4oLibFdY9AgMbYTp+AIRGm40Z90h8BQ5cp+ANIPyxdy4WDD3JqmVEASdKuRaeWpuOcXtbQ1SuhQWOr2qCK6Xh/uVIiI7iIsqKigKsYA/NkTMm4WdDWx/AuvSS2lI3HpXi+980UEhFqOzUHQcOORpQRljRuy2X2+tolhWlxc73JuVpC1WJcDrPxvK4C4W/Rr8dPm2DtiZVSttUICGqhvLPYMMhF7tD9y4ihChE2HvKiury+p8FkeZiRNuLcVKSzoAFd2Az9U/f+5XlYBFQruZ1udqO2BjKhSJGdp7vxve2aUBCJDePMq3DJkh9pbm3LdRZGbLB/pzTFq+ppuHdc1PYdU9zoGvMcCBy0NXxDfoCELZrC5zKdB5CRjoXLEB6cSfkWE8GLdfgBdeyCksV4vVnkWtLgAkKtq8pUL3U5DtoTvYYgx7IL6g7P28OA8LxuLu3falPtL+hhiJk25Jqm5Oolb4hvQG+hRsKEFYgDmuqBuAO+ze1IxKFhLD1CLR40/PxmCrceZeDFumz5b2RXux5P9BHPNv/AN6vuhv3SZfCJqlBSMRiiLzlLQn0BGvao4/5sffuQE5EXYNQolaJu7cnY3n/bOS6ud66TImvlvbioifI2EcP4AAJ0X6LZfU8r+xgOSbFMtRfzsN4ZHo9pOUV0Gkc0UUUQSZrIA2np2lTTjgyspLQbIi+lKIpKJKCovqIwtRstzM6SneGal/yQfJ9b/wmBIPKQhgFV7nNgkIXoy7lZCUq4hKLGcW5YhjKKEuy2zS6Pq2iMtLKntciiR0SuR02iwgEDjqBvz60tGDWdEx/xpEOINBArQ9DaHSRmNNnodDpYBOLkZOcxM759GxqjFC0EIG3dFfxIAHFMJFhPeCPPIqkISo/ivwQiitA2JgJNXVderEQzZzQCE99Eh94I9cikEMnw+6nCfmY2TYThXLue8cXR4B4kwxzMttiYn4CZxBupsK20mEbksoOINOWCiskYK86lZI5kjSV+HqukKltoD+qaBt7d8zsnInvWzs+VELMErx8JBU35sfARhHfAlmZUokXeuSiSuaIJrz9XCzmHG7r97QI8IuTyphjUaWNHADoq0uEZLmC/T8gQrn/Tp92boqoKsmj0oPQs4ZYLlh+Z0GZM2YDe1cFWogGP8zbIc4V5KT3NE81GegBNVV7BEoRONWMn68bw7UY3KsuS9eE/Hi8cyIDYQeOowJGPHJVNb7oVNdWs3F3LwyqCEAOwaZaA2rXDQuZvXv3es/gtOtPClJt0NmNznCNFYr+pXMaOXYSCEgOV+QMkEf0TS7CO3jRmZGnD+aLUNoPGbEBBPj86s/8Zq+fjef/fZ5V8cfRcPZVszHryln+dNUiygYBwhaxTMFB+qmBIEDop8KCxZtOAwSStSGOcQATJkzAck/UMjVDoLJUp127djhJCX99lFdeeYXlLSRZvXo1hnnKK0HUfGvW4OoajnOq99xzz/nYSxAg9KQoa14hfh35LpLPrgN9epEnaEuUyPhSVGlVMOlknDWYDEUuHz8EhKRl2FFWKEK5I9iQ5ZgSRjLR/H0x/gv1xPJUXXkSa9q7R+ZoSBVKCwwyDoaknCeU64T1ZRJDY5SzHGJCoZUgg9c1x+MwfUO6T8tCDnlke6UoMn+E6lHAMBkZeEYWilajRwAxxZEqK8qBTJdv3l49ueipyMs2sfnxElktQu57oVBKxYxGpqDSipxiGyQyO7okykE/k+w/aUVVhfNHef+eYnb9bKENOS6Bvz06i6BROspba1Bd3ubP9795t3M+MD5iMa/citOnvAGEJUg0nYM2JAKl0iiopWZUmKNgdk5ryOxfcWFctN+eIzYGRJFQ8HGbOG6CpM+te53HEqYRoULn/j7rkiFCeGjjjRRnymXIPa5z3gJuPqyYF2QNiOIOnOnZScxsS4RBHKhhSyIQOjGJY8qi32kmFKBNqSqIRYlfC9pHp11YCQkcpLRlBiPHYiMUsp8QK1NN2ifEG84i1MJ92IdoakDO9u2x57hzXjnelkpaIya3QsEtSPuTpKSUc15luQ1rIiaF0WzdunKAJq1XOUX1uADT/DjJm56AGV89yilnKVF4mewynLalMJ0x+mNh3qZ6blRimyPbKy89unPRcPSMc93zNHayq8kVwPFjdQFaXw15fF+kTzKmEf1wVbVLFLBaDYnMez5Mf55BVNb1WSzcn3T9y57nap0s6N/T/m2H2/c4wFdP/YntFlRCw+h2a4XeES658vwdb0PL70ssx8N37qtT/YbD8Xh6bQaLjqyUWzDvqhP4q7MjFyRf4YNPO6FLYSQgEkMplTADoucMiI5uhFGvtI2ElLP+zkVJoBQ51ITYoAi1wVAuhqVaDEWYjf3mTignXMS5XJREOaJwxHRTCCkV/R2Ih/KUF4oiJ3hw0FOzroZqKucLTRPf3uQDcixdrQY6dEChXIpyiwUWg4iBd5QyNpCMDLFdjA2OBnKd/4ePTsbkuYLcPFIpxD4+lwK0RI1rxm5HXOZpdm4tiotlbfkLEJJxkCKNLyjw40pFSBMhuoAmBHUap3g3tYlS85DAC4yKNFOQ89OEPEzqyNFi+uIIsOxoB0zMbxVwlTVZgzWGeLNNApNdClVaK+zLatqoR2/OkeycWHPk5U++u5LL8OJNR1CqtKB7ThjmbM5AhlrGHB/sOg4gtJglsFtFkIYCeRFiPNntJHZHViHaLMUb+9Jxeal7UIScMujdRKe9TJOzEw5FBjf+hO1l9QjwP+zipUjgYANyVdI7OkuQK5Z6JpCkzOryQdLADUW6KrKY2PmBQEOnU5271DLN9L72OH0KrSWPRKFQroILdPZzN87JHY7i41aOSM0wiwRLjrfHuMJ47qNEQEX8ZQcTHrpajwq54xtycm4rLD3eoYE74DxUs9th37eX2VnoXWuTSZGfkgyjQsEcuWjfubsnc2oYF4QjFN6/lTYryw1OEiuVIUIQtkxn4nyrCXESzhmZv4coz73W5d5xx8jA90n9kQiBSv4a0Y7PjGmD2AbmAW+s5omyP+PtDJwqq0vfnRaRhlmDZ2HOhjk4rXUx3JD/TlQ6jj963K/gicaO93zUDwKE50PLwT7OtwaCAOH51niwP48auNgiCIMUo+6XevdugAIxb8l5D0OKvmOe7i0BIqQoNInSDl0+5022e0w/DF2/ChX5GtjtIohVYthNdQ3YsV2NDNEoOaSo16DqK0BIgCodROm/2+/fjrzQuvk0XDUfSylsngASFwJFAuYTTzdjUqUCv358uU9Pq65duFzh9QGEBNK4Rua1d0l746kzwv4ra9I1UaQYOYjv01ShZz9BsvOayruXh6JXqYTzOPXgYZdTasNZFxCJB/N0Bjv2eaAR9aYMMkYczbUwAKt9osMv2GoHtu2p35SutFajncGByhBgdyqmOwpc7PSX9RKz9knoG664wg6FDAjXOH/qZGYCJeXcXRUVJkJqGpB1zo6KSkBndL7bLutJlKbeZuf9+rlqJbIOCVBwquIGICQAqEN7DoBzBVWpCr8W3nt0X8J1H/KAHZV2vcZ/s5Mu6Xs+J9uKdroDUMgFrEY9ezJD09FjXJofsqP6EkBFZQ+62C9DQ7ggB3fiL8Du2gYxMVEUJUVJpuiOQGbnngukZ/TswT7KikuA7Gznmh07+Iwd1rsktKtcbTtEz3v4pO/Pd/4ZqLGUQ1FdhiJ7jKNPhRISpf+evGoV0L6DA+OgcVLgCdnTKD0PkQ/Q+lK0BxkSCLsmILRdOuf4MKrLQfwkyCd11ckYvP5rl1oqU093drilDFUSNSxWgW/+BYwgnDviOH7r5jAIhRglmLmmPUYei6+zrn90KMDcYcdRpXA4kNyyOwbP/ZoKkVwGsYrLA8RHO9M7ifRK+uMNs/Rvd/cJnwPStVMCZl2dZvx5BkS2M8FmFbGIEQLJhEK54dQFhagI4+jhxGQ05fMV+tNJgMpuiSrH1B4noK2JTvGVpom6jzYA3/6mQbKmEzM4Uo7m8qy6uZobMtSQVhYWGdhU1myxTgdLTc46BqyxB1Tjou8aMs/G1Pn95pHosX4zWmXnsGaI1lQuc877SF7+hVYTo3alZxo5IdD/YyQuxvDGDKSxdelAVlDAtUK5C2vyJDa22fNanzx76IBInkLNzAAv1MPwHnuxOlLLfvLFEWB4aSRW7e9xXlXZqM5cX/z1nL+pn6JizsGLziZqDUCMt/6KkGKc6hKdPAlBWHT65t89rk5KRRoj1mUU49Z9lP+Oq1NhDwUS7QjN5taIPwTQE0ocasW37fJr8wzzeaQJoAxNMsNuE0FhkLJ0GXTm40WTZoRCJkKcWM4wunPZHEMHpckklglfqPtZW/fcw+X89EVospRHkoTYmBITfanltgw5ufCgBuUWVNW8VE8Ioqsa3LhLRT5iih0oiPpYSEdDHqNE49GSpKICoI8wXs4nwEkf25S79fXXnTVGjjiUI3D8ePb7mogyjOt6GIVSMwaVh+GLI52QKsxxqtM5PAUTEnAmUoS7LsvBpvAKxFlk+OpgZwzT1pNQtYnXi74qBvTejXKpGQqzBN9v6wKVSII2Mu5syih0TXokHeK8QnPapXn80ImWyBhoSPe2O1COnwrRAWe7AOd05qVvB3dCubFjXD1WawoWtUtD7Km6IJontZWktsG382ezvMyvxqUFlCLfn6XamLURg5cNrlNlQvcJeOf6dxCmCEOFsQKP/P4Ivtj/RZ1yGydtxKD/Z+86wKMqs/Y7JZNeCAkJCSSEohSBoCAqTYoLdmkiKwhoXFEUEX9hFRULWHBBoujiIoLAoqAguGLDAtIRlSIEKSGhhJDey9T/OfdOudOSSZlkZnLO8/hI5n71/b57v3vPe0rCgLp06fFlmSD0+CXiAdYDASYI6wEaV3EPAr6Wg7A2lFryoXLgAPDPfwLPn5iISHW24ElDnjJNRRSSpT0pFPPSrBOeh8RqQeG0bH+ntRSIPkoUf1mJynwFMpOTcF32AWgVSui1cigiA6ApUgrXTRKRpIZfsDir3D9rTq5uCi9FilL6N6lmTXpOR4rTogAN7vnHfmiU1qiRdXqsROfWNlT0HuwWDaTlil6ElyXRdLLLyIvGerf6aWX49sMb0apKJVwwEXNkVGjyoDPVSM79AbKiQmjHjBeU7Y6kW1cg7aT1lV697D2SartnTNdT4y9iVhf70KlLdwTiyZJuoubfSYhf2mu/HtdBpxaVA62iDejW3qLEl5JWIa306JVUd1LCdh70AUGWh6dOAtoqx2xcj3Lxw56GHRYig653sh2xWBfyTAi3aRC97qRCXnYmr0wicCIaKUrMleoAnD1qTRDK6d6wASMuUm5Oi+IOgpC6I89LyoVmywEQJib9CRF1RNjZienDODxcdGetq9XvHXcAhw7BkJ1tR5hRiiSj44ldtzQ2IilJf05eiY6EvA61OmtPSFui3aR8S9ScRaRfqZVXhSMST0qgCn2Sco9C0JmEblSTNfGGDaK75ZIlQP/+oqbrmWeAv/0N+OEHVJbrhb1F5Bs9M8gh5Xia6C1d27OdnlsU6sok8ZVnRPdOhQKtVOWQJyVC0boVKiqBv0RnDLOYiFda94wMIIpC7crFx4CZpCIF8sCBwL//bVVXHxOLI99ZiDNTCGNMmgT9tKmIeisahVWWcFGRGiVy9gyAulIG8g7OzqrBN0CtgUHCkukVCoc5lRyvdv1/JWUp3XcmwpOUmVLvieRLYVjwXTcMiwuERg0B04J8oFoS7SgrrBLPj0zD4fgSRFXK8epnndE/M0w0vHBZu1m3OdTmFVK31iylSw0h+KL6Ltx040FcW3IccqNimJS6JoMfKt366mohfCYJ5UisyFWgIld8/tNZDoMMmgqZ8I5A5wed9QoVENZeY0eqUVhUEmUAxYy2jMX0HvDewHSsv150V69LmKaRmutwR9FwZKZfgwlH/wN1qfUDflHCfPyt4Cskl/1mB9eqto9i2mXr/U+FTF6CH3zyER6Z+KBdvX2T78P5Pr0wYbaYm7W+EnfiJC62j7fOC1oHgpAU1bYht+o7lvrWW5MyCTkxYioE/4pKDNyxC33+MLq6A4KikcLNeoWYSMKmVGDXBgxZDxKL8uqr9iXp/KFD0phD3lxg2TKR5Pzf/8SzSSqTJgEPPQTs2gUYI8eYL//978DDD4sWI7ffXtvI6nU9078KSTfsp0eH6/maDEDG/huQUB1Qrz6bvBJZJZnCM9RCDjobGx31QtSQEvtImc7qEElI5xw9AWvKNWxrhGprjHJa1wnPJ76KTzPuFbrSyZTCe4Ptu/P7nd5Ee00ezgVfg7l/TbMa1uzkn3FN0W48mCGG3qtNKLS/6T38le4bcEP+NtxesAbhYYKTvsCR7Wt9B86Pexp3XlmBzgfWC0cvvY6ePgP8Ej0OW+JngCJGzD8hjpscBun1TDPiNoS+/H9QfL4B+OADx0Ohl6Q8m1yu48cDjz0GjBhhCashrf3zzzAUFeHM7aNqm575+p5p9yPjuj5I3vq1XU7Xza/NR/bVXfDKP19FaOZ5oQ4ZG2lhgP8TMyHbvt3+ZY8Kbd0KhIWJFkrSCFBEiNKzg16iyZLvyy9dHqdQ8KabgL1761anptJkuFBfD/mff7afn6mvG28ULf2IZbcVqkcydKj1FXpm3nuvaBRy333CtSxVNTZH52J6FpHlchFTugFJaCPRBz2RswkJYm5imR4fxGVhTG404tQ16zOQlCR+I9SSKkg6SGOgErspUa5k0g8JuSxpb/7wg52R8Jd7e6JrWRCO3DEKvb/61tzGf554WDAMq4yOwoPvrkC8DSlX0D4eny59E60zMjHh6bqFv0zv3xdZ3bti4Cp7Iow8/UgeGzvJaj7lka3w8Yp3hd/ufXoeojLEnOAkmdcm49C4uwUScNw/51vVe//ztWadxtuxrkV3aryNbGkp5csUrPxjpfkHIgSX374cE3tOtOtu/bH1eHTbowJhaJKUPilYcdcKdwyt2dpsybrcZgOdO3Y7AkwQuh1i7qAuCERHRyMvLw+9e/fGYUehJoyNFRYWItJoVTZ+/Hhs3LjR5W6++uor3HnnnUL5t99+G7NmzXJal67Pnj1buL5t2zaY8hG63FkNBVv6ofLOO8AXX1gACqrOxsOnZ6KN2mjVLMHuXGAnJFXahzRYnPA8nj6/oE7LkTu6N07e/zfkdOuM6fdORXWJXFDikULQJOU5ClTkWIihoGgtLo3rD1VlFRIP/o7yK0pBQRgWXAqDvwY6PyWUAWIYGbLoV5fJ4ResF9o1vSg+PGoyis5ZFEfUH5Wl3Bh6jb2SOaKjWiCLCs/aK5v0Q2Pw/YtPouc9T+H9Qeew4yrrD72x3YBFtwAdazDwSy8EntkObLYJuzjoTALm/NIW8SVimAuSLhd+Fgz9S/LUONtlpPn3v0L74r7zb4l/5+fjVO9xIH7FVvocXYOcUQ/gUpZ4JSYGiJv/D+A//3G4drM7ncHGNk7MikNCUWioRIVWdIWUhk4N1soREdbGmP/BhpKIisaEHhOweHk6qvIKhfCjpOduF6WAnDYiJd275x6guFggTElBIKwKfWhRmGH68DQ+C8yDNiUL370beMFGMTB/PtCnD/DuuxTLWKhy6IQO6irHhEKothgJ1RkICwFkAnuqQHG1HmfPGaAKAK5KkEPliutane4GAO+/b8lTsm0b8K9/iS2Q2TPlOqAFJe+CWjxuCjZ9h5NTXrH0LpNBRR90MhnoA09oUiZDcm+ZOVSlywQhWUPTGIiQIpxJezJ5stgXKQVpc1If9EFuq+gzfTCTBuqWW6zR+eYbcQ9SqLJTpyzXWrcGPvvMQjKTIpWUiSQrVwLErJqElGM7d4que9S3Sauk1eKPaOv+kn/7CDLKz0PjJfZKKo88Yh4DTe+iA5Kwz38eE9aL9B9EwgmheEk7RgpS+rgnRs40TmrbQTL7ghWbcOXFZQKBmji2L4LfN96/dd03jsofPAjMnSteef55XEm8Hln97hL+lIbypHCpRF5R/hsSmoK/RBtHYWPLywyIkuXDL0gFXHutEE4RTz0lkoZ0ZktJTAoX+dVXwK+/0kEtXs/Ph6CkcMU9ltafmGNSvFAdoxzJPoLkD4wxYyXzPTL9CHrF9MJ/53yOpA/Ej31HUqb3R7De8kA0KBVCHqK6yJVHb8Tto0aBlJpHZr6Icon3s1xhQJtOWiQE+uPwUfEe69NbJjyrtCUVUFH0a5kYBvYM5ehMqMbenrl41KQQ+uEHy761HZS/P7QGHZa3Pocxv+QirlwO0H3Rvr1Qkght0quT/os8QEjvRMrNS2XhCNYVm1sjxSmFsg7RFKLErzWWHBnmcPov9PgCL50YD7VOZCkb6lH4eZu/Y1vr0Zh14XWoi2T4uHoyznS5GqGKMoTIKlAR1gbxVefRu+w37Im4GcVK68MyWFeKEQXfIFBfgdG5llyY6YGd8XKHN1ElDzQ/H2QGPQL0ldDK/LDuxN1W83u90wL8GXkt6LjacPw24VpD8kEqSpMQ9MNRBPjLsTz9QcSqLbGJl7SfhwPhA4U+YtSX8c4pC9n38E2b0O2haoR30CA0ToMZ443PTwBfzfs/nL9W3Ocd9x3EqH+9Y55DZXgYVn30vvB327S/MPp5B8SNixs65tQZXG4bU2eCkPZCgl+AcOe44sGyZ+r9GLD6v1ajutizB47ePhK3vWFDIBlL0Xva9Z98hr6fbzXXI48B8qK5GBON8MuiEcH/xtyBv7pdZS5T2rsnXr5XJAvISzDemEuwVkicKXLp3HWWLHfsWPGdghTZ9MzfvFnshkg1MkyhQ4Gej3Uhu6gdYh1sSbfaJrBpk3je3G2934VqdKZSrGp6Rtu2S0p/qmerwKZ6lFaCni+2in+6dvPNAL1TmYQIv6VLxTqmcMGO6q1fD5BXDVlGjR5tPSv6CCHDERJ6t/vjD/tZ9+snnitSoTOOctfTu8A6e+WwtOiCxAy8kCQ+tOtiCLAgPQnzzifWtgrieWWbe85Ua84c4NZbgX37gOcaRu7XOhAiFOg9id6hyZXOZIllqugIx1obtS9A7z5lZaIXoonLcNTM0fDBiK3KwJWARPQs3mUOk07PkGd6fY/EijTkq9qiQ84WjL+yDnMMr8MQK8cbl+eaMkbYvTrMu3anuauE8jTMOv0YSpStsbDbOmgUIpnbofw4Zp5+3GpIL3ffiGJVNBLLT+DJ0zOsrr3U/TOUqKLQqfQwZpx9yurapvgnsSf6HnO0HelFUz36bclhkQwq0EfgnxUv4kxwb4FlTOpgwMqMYUJEjG7dARmR6HS/k0jfaU0Nv/UW0LevSOrYEouvvw7ccINQUn/2LKofelDA6dTSxTj5/TcobhuDqtBQjHor1WoO38yZhXP9+wq/2ZIlpm9jZ9cWRXeA39YvxW8oqZienabf6DlDzzCy+jMZhu7ZI7yDWgk9P+g5Qi8sEyZYXzO91//jH9Yx9uuxR81VyGOPnhX0LmorZMhAz3KSV14BpHOib1GTcsTRc9LZ2fH22xaDQPLENHoLgqze6FlpEts2yUCE6pJ8/jnw3nviM5ju4eRkVBj0yNZQSFgDIhV+iHSUk490avQtRkLfMkYSUvh76FAhBzOFqCVTXPLYE3Pw6VFp0CFIpgCF9jTJt/83E1cPHoohYyfigqbKbFy4/p23UBTfFkEFhZC9Oh4Lu1rItcmBd6LNhNeh9/PDo+MfEJraO/hG7B0kRkLKuqYb/Kqq8eY063vz2G1/w66HHoBMrzfXc3XJTft36kMzEFRkeeel+qZrvb761opAJGOrP+65Q+ii094DGLlY3Nvkbfj56/OF8ZMoq6rxt7ffQ06nJBy613JuPRjRFj0DKCxM88hP537CxE0TkFOehwHtB2DdmHXoENHB6WAyijIwafMk7LmwB22Co/DJ2A0YluT4G6B5ZtTwXlu6LrfhCHILnogAE4SeuCoteEyDBw/Grl27EBwcjKKiIiid5MTYt28fbqKPZJBR6It4+eWXXUYtPT1dyFtI8sgjj2D58uVO69L1/xhJDKqXJFEeutyhk4It/VCx/ZYnL6uLVcWI0BagwC9KsOIkJZ1a5g+NXIUAXSUGF/+ILhVpOBPYFT9E3orBrxdh+l0WRZct1GtiH8YD2RZrpS/n/h+qH+yGf8V0wkl1BT4syEL7I3+iKjQEuR07mD8u2p44idufeg2lF5UIT9Qg66ae+Or5OULz7Q8fw52vWsJ3rHpoElQKOTTXXovo8xdw+2uLzcP477J/obhtrPB3VHoG7v2/5825gn55bBqCCwpBFmWnBt6Ee/pYXlxzHuiHnfOmi/Vu+BRDC7dbTe2zv0QLruohezEpeyX+aFeE1JvP4nQbizJapQB+fAAYKKb1tJLd54HhawC1JAVhcmwylo5cinf/3gXz0sQwJCQGyNGn4Efzd9ePPxiwae5BlCgj8e8fuljzHKTYGjVKeKEnfQzp/0NSF0J1803A+vVQv7dCzMf21Axg3DjLmGgzUN6GkBAYgoOR9F4nZJYZ2cQabrS6hk5NikhE+vCvgCeesG7V9MH19dcAfSRLRUqy0Afff42KRzJKiBbzEAli+9FlapP+Tx+AED0XNdXWBEFMBx1yLhBtYEBXVSYiqq29vqzGQibCJkttIixJQy+VQYMAehba5lUlK3pniixbEokUjFVVdQ79Vl2lx+8xN8MgIUC6dZIh45xlvqTHI4834QNy2DDs7TLeavjnAzvjvmyLdWINS+/8Eu0l+vgnUonclKUiJQmJSCIM3SiGl1/BlQ0/Q10NtO3ZGn5bPq+5N6PHHH2cG778H7JfXo7sDDGfTdR7r6D9342hXWiOxcUAeTk68ZStsSPSsJE5uptzTpGzx86ej6NDhU2s1RoGJ3hEHvipfvNq5LVM3Z+KWd+JBkRd+zZSvwAAIABJREFUIoHTxrQyqaNSMbP/THw2byPil73ntNeV4VPxYP6HwnW9TAEFuUnWImcDr0KnSpGsXvbvlfhgQgeEGhUyGeoqrMw4hbgd+xH+Wybyrr8KEx+4D9EUVomUUMTWOdrTJoUf3YBEKjgi+4U4qzr7PUGKRFIa0TOBniOx4nlm9YwhS3EizUeOBBITzaFHTVOlpmkvkO6D+IHl/zbg5VdkmOfAYJvyT6r0VShSiV5aJNdcWoWJV1ab/57b6V1Mz1oqGA29lfAiDoXegAcvv4+RBV/h/zr/GxcCrBUWJReVqC5SQB6khypaCz+VAQG1GMBLl2lw0Q+C592pwG74IP5JFPhJnvs26xmiLcHKk6LycVP0RGyMEZVVUqmO2Ivc622MFQAEZU1Euz9fQpvKKqQHR6Oo+2xUxH1qVz9wxw9QFtyIiCAdHsz+N/5WsA1vt38W+8MHO9xdgVE63P6BveGVtLDJKv2p7DPouP9XgWAj6/jiJ2fi/pgEbCnNw87yInTZtRe3LBUJw7pKQGkp6MQye/sQee9gv+7/+3hc7t4Vl7tehbePnBXDpZECcsAA5D3ztKBopBBdX7zyPPyqqoQwn/ccPIycv9Kw//57cXdsInK+/QY3vP2ukKtnyar3kRUgKt4U1dV45O8PCf+O9/NHELnrrFiBeWEKVOh1gvJx6sNPCKeYkAuI7h2yaKK9rVZDK5dj1v9NF8KKlSUmoFVklEAQhpF3sJT8J+Us3Rf0jKYwgVJ59lnR65qEvLOffFL8N5UlwxQ6w0nBLRWpEtcV4EkJvmWLeNORspjOCRoTEXDGvOrCewm9M5DQTUrfOq54fJASfaLRY4BYGluSUKrsphCgROLRWpO3HrnRk2RnW9owzUf6LiJVcNN1W1LAGQZkDUGEAr2LkscMkSImIcU3PctIBg8GXnrJtXNmwQKzkZdQ1zQW248YMqBITAQoj4LREKBL/wM4G2idk46uJVUGYF5mIhYmZuKcg+udVbE4ddUyyGh9yDCG8KBk00QstKJcsBQL2xj5gp790vdp6oDI5MclynB6F6LQLSREhg4ZIj7TSZHvKA88vQvTHpo61RrpFStEQpZI10WLADKyMomJjKW/pe/DhMlqy/NbKE7nChl+0RzoIKjNc4vW0kQ2Gz0+adi0zJQDmeCorgKUfoBy1Yc4peskRHCgKRYsENNZkLx59SpcCZSeDwYhrGBxphKaMgUi4orQWXsaOZFdcGfpLxhzcalwVz/b52fIbGNXO9mDiw8Pp6xnwtXMoO5IvcryjjD2wlIMyBeNEJZ1TkV6SC/h3wq9Bm8dNT4TjO2+1ONzwajG0TUyvJFKh8Lf8fPp9ij2ay26IBofJ0QSUm5tjdwfBf5iXks6/smW4LV27+OGi5+JTy0yKiBDAwKSWFjTM8LUiQv337vnT2HUROvzTur5JOz7A4fgX1aOv4YOEjylpOJfVga9XAENxYWnUPB6Ha4JCMEg/xAk/7ADJSo/5A8bigQV5XGt5V3KEQlI9z7do7b7k77jTEbmFL6D3nOcCT2j6d2bhNqj84ve28noT/rdRX8TYUbXyPOZvh2lIsWTjDDpm5SIfno+UkhZaeQS6b1kMoiktsiSkO4LEltSzvkMxJew116zlDAZyJp+IS9OgwG6YUOFMy1Lq0ZJMWDQQcj33D7AEnJW+AamZ1K7djDo9dhTnIeT0GFocAQo1yRJlU6HH1+Yiw6Hfsd3s5+w0p84GqaJXBv/zPOITheNK0qjo7B2uYXkPPL6APwcaTEi7tThFtw5UnwveXDqdOi1Wrw/+zHh77ykRKgpXwBxsf94CiGmnCUAvn3mSaTf0E+49vCkFPhV2j+r6RqRiINWrjEP96OP3kcVWcQB6LdxM/ptMBrrADh26y3YlWI586/6ZQ+u3rELl7tejd9H32EmAakuGR75n8jFUUUvtL4e8AuyNmymiBV/fRGKC3sCcdvcUizpX/+wwTVtibpcyyrNwua0zZjedzqUkhyMztrQ6rVYfmg5xnQbg7jQ5h9/XebqStmWrst1BSMu430IMEHofWvm0yN+7rnn8DpZqgHYv38/+tMHtgN544038Cx9ZAP47rvv8DfTx7YL6FCS3Xbt2iErKwtdu3ZFmknh7qBut27dcPLkScTHx+PChQuNmlyXDxURcMqbRQZzx48bcP+zlQ5XMPoaNdoPrMDvyy2J77tPKEX3e0uRM/wEZly0kHJTu32OqyvScEUVi7ZPhiBpRAUKz/ghsLUOgZHiRxspw3K0aryeJ4Y1cSSkKOrz5dc4MXwICtu3E4pMiYjFLxVFuJybg9bnL0Dj74+9HUSFhymxNH1sKzQa6JVKuw8guibX6YTfbT+OnI3j+yejseJb0QuHZG6nZej7tfji/fnYOEy/tAS3Ff8IncyAySPvR8F1zyOnXHxxntIbWH2PfctTtgBrjKkq/NQx6Jy5EMfWTIVCrhC4FfXFHLx4YgK+i52K72KnuKybqfX2o69SIiVq8OrJq8hD/JJ4syeJqc3GCJ2qUqhwafYlRD01z+IxJlWSUWf0kWdSmKSkWKwwa5sc1SFlCX0wkpWqVMh6e98+HDymg9bGW5TChpo8qQQfiVWrxBuClEDSD0N6Fr7xhqVVstImhQ998JJynpQmVM/04UwKGPo4NXr8CAoYUlyRhbDx2Sl8iFIYzUaSY21vRmmF+IET4CdDr09fgnLhy1Abc7OoIoyeXsb+XrlqOUZc+cTc+6cJ/8Q7x25tpNF4SDO0L+g/VzzZPGTIjTmMxycVofv+lbgx/ytzs8V+0fiq7cO4/7yopKiWB+JE2I14cO1QMQxoE8ns72Zj43Hn0QcotGiFxoGnsl8wIgIiUF6ugV+xMW+RZMzD/4rGE790wr3tPsGGjPGCsYtMbhCUWlujxiNacwXFygjcmm8dAmtqj024dWOR4KHuH6GDKtggnFW2QuGWcrUawRI7wIf2FekliaMgR2FypDLaVeCMugIdKs+iY+VpHAq7ASVKy3uAs63SWRUErUGPc+oq5P+lgsEYOjRQKUNsHCCP0KJETxmrRIlT+iOIYtyRQkmnNudfcsdWLOj+GCraWfJKyTRhaJWWiqBso7JP0mlF7AYUdpsFg58lTJPfuakIOroMoYH2StL+Txciuls1AlrZZ8eUkoC28zJdu6JV4w3jexF5C8yLSjATeovzLwghPsfNfRFtzqSDPOzoaU/hsy+1CkdIXj7CFUohFNglm1w9Qn+UF6hdWyT+dUYgrogETB90E275ZT8CM88LRPi8le+YFcT3hrXBjUGiEs4khrw8nMlMx4GEOPymE5V5RPT9X2vRs9WRvJl3HtlaS/zc5K3bcN8nX4h5y8iT2xiKbkd5EbaW5qGrfxBSItraK6Hp2+T773G2XVtkxsWizcuvood/EGSkVDZ5tNMAiDwhssQkUg8R8mK39eZydkYQSUMGjFRn+vSmyRNYWAiMGWMPIxE8338vEptE+EiFrMGIbLv+epH0q015b6q7Y4dIhpJ88onF+MAdN51x/+HPP0VWiQjXuhjI0LsUGVDQg0kq9DvtH4poQ+999BCj96zwcOz2v+I8X9N75xCmU6JEocWMLqexLtaewDfna6Lxkos2EdW2/dcFq8xMkdyj8JImoTlR2D6TENkrjapDMbtp3xMRTO+ftmtfU/+EjZTIrKksjYPOsrVrxXdgEvJCJSKTsCWPTem+ItKdjApNUpd3dWMOb+KoaQsS50m3IMFDW7m5UnL2y/8WEy+Ixqffxk7D97EWsq2V+gpeOHEfzoQk4z8d34RWbh1ZhgxwpHBQG8TdmPh4Z9CHqfMwbLgMcT1bg279AQNED83E8CLEvvyoeE9SVBEXQu3/VlmKX7d8juHLxLCm5D3Ybuhw7Kqw9q6qaRuQfua8thoa4zeY+bvaptL0VnG42r8WAz/jd5dQlaKypEq8G6n9EyfEZwBFp5AKfSuRUQqFKaXnGhlrkPHCVVeJhLqz5waRkvTcN0XysM2dSPcSGeaZjKxsgaD7pS7PpLrc+476omeVA2OxMr0OU7ZexrMvjBPeW3VlCmjzRQObB7p/gVteuIiAa4MQLFfgn1EJOFldgWK9VjDc2VPLWksNdJwN3+xZajAg8bfDghEQhfM0edcZDHos//h6VFdb9lWAfwQemXJAIPEjLmZh2PsrcLxDO+wbNggFCeK7AY23U3EZRr79HlpnnkfasCGgaAOm5wrlqqYICTGnz+KbuU8hr0MCVJWVyE8Uv5EVajWCC4tQYgwxbho/Edtjn30ZEVmXUd46El+8+ry5DIWaVwYahOdL9h/+CGylR0SSBjnHVNj/r9ZQl1m/wyWnFKPzraKhd+aOQPz6rhjZIlSuRIxSJdhU0OPRzbatDdlZLa4u63Jb3JK3iAkzQdgiltl7Jnnw4EEzKejMu0+v1+Oaa64RiL2IiAjk5OTAz+iW7+pMH3vsMfzbmIuIvBFvMIbOkNYngvJGCjlGoTEeewzvkfVpIwofKtZg0gvUuM8uIfeEP2KTqxB/QxUq8uRQheqhdGLt/1xUAkbOqEDBMdESkRSxHYZXoPXVajzePQrdrjHgX/kX7FaNlGGUl+LZHEkicRfWlur9VV2B5YUW7zZq56K22kIQutBOXYrQC+aWSW3FL1iZDP2eKETizSKRGpgRgr3PxkDMWijKlLdXY9pWMeRVSh9ghYVbNJdJ+RJYaYymdK9qFTY8a7ESpmhKFJnIJHQLSI0N6zL2+pZNL0zHnO1zsCltk1UTDQmdOq77OCwasQhJrYwhBE0KCVeVWfWdjKTeV0P/gcjfLQnUKMxs/89eFT9CST76yCrEobDmFD6HrFFNRF8jjMNdTVz67k+UTJ8DhaYSAU/8AwlzjV4GRGaSgsGGzHh8chH6/vwqOlScwLHwgYh9fS7G39fwnI/umh+3W3cEKE0KRas6sLMKQ3M3wl9fiR7zx+GNFa2FZ1qQrhRV8iC8/a5S0OE0lZAiKik1EZnF9ueD7Rjq6qkcUBmHxz5/Ad8PHY7sfa3xYMFK9NQew7HQ67Eh5gHhnBLEYADlHq2WB+BMUFeM/TzLTrfenPlGmmotnPVjstN44txZbJ0sej/cvDAPu5+PFizcSfxkcmiMoWql7RBBSFKl0+N0jgb6cgX0ZUJWRiESWUiIASV6nfAeECSXI0RiCU0hQM+qHRsrUZvUCnmQuSIBcgXaKf0FktMkVZE7UNT7AWj98qEqvBGRx1ZCWeU8nKA2IBN53R+CNmofFOrWUO1fg7Cym+1SQY7blCUQyrS3Z1+xD8lu2ktEmr5TYMlf9Hqbji4RzfTus6b4CihX5Lx7p6K1JNSYYeBAGFq3hnzrVsHg5YKEICSFX8Sly3juw/U4GeyPtffeDV1cW8GIanJ4DK4NtIRbLtZpBY+etkoV4sgzthahuVIY65qE3gFtCcu3o43hhxvr/CcDHSLRyBPHGYlifIerbU7Nfv3QITG3rEkoTKX0pbDZB+gdA6gxXxORARTykMiIjz/G+uLdzZuvidgx2p9kndHcYnrw13ZvUjQP8swy5ZlshHETZ0Ld0n80DOLE6Xam1IpSoXIUlZ4+I7pT+E6ZGI2fHMMWW+xVGzwiaVq42hqzzcpC0XNd4PVqbJZ4MuL9XZECnQav5lrCPlKd19okIVCuwOclubWSR1SeDDmIoDJJK4Wf1TkjHceSmE7Cs/+8pgpv518UwlY+GdkOlKtWEPruIG85WizyAK+jnsiVOftimXlf5+PrS6X48F0xEkJ5egAonP2xkOvwWoeFwm9jNmYJOUNtpeC0n+DtdtVdZWaDbGkZeZUaj9z3IGQO6lK5XQ9OxrHbLWlMHOGbm5+G/35ur9gYP3Ib4jtYwn/TDXxGYzEiUlYqhPQvZCzuaOymvsjw2iQhsTqMXHbF6r08L02FnGP+uOrOMoH8Iy8/dSHQFlkojIhB3ulAXH6TjMplVu99pjbFd1PRc9lW2ihVgvG6rST6BQjvuiQULZaixrJ4BgKsy/WMdeBRNC4CTBA2Lp7cWiMgYAozSuFFf/nlFzNJZ2r6rbfewhzjx+r8+fPxkkm5biywY8cODDV63kyZMgWrbUObUNz8U6fQo0cPaLVa9O3bV+gnUJJMurKyEjSOQ4cOCWFOT5w4gS5kzdiIwoeKPZgU3qouQgqvR/48h/3/igS9mF51dxl6PSBa2juzlr85OAJ3h4oWtLb9kYfHgugkIc/NR0WW3D6mMVGbtso1ukbeHNEU28ZNQvrPsmyFYH1GL6QkZFlPFvZkBEkRqij12LRpwIg1I/DjOTHn3e5pQH+KSrQPOJ4L9IgGZt8I7L8IDDIa6Y7oOALbJ1tCmNJHMUUMIWc4yvlOUYScGTy6abrmZndm7BRC/B3OPmz+rb6hU4d0GOLu4dba/qfbtqPDlFeFnJOkUDj6yN145E0xx2lLFAr/NH68aLFMSggyVPchh6iWuKQuz5nSRVLKJ9KTklNrU1vEutNTGXoV2u48jagxBmR8GA2/IL1wv1OOsluW5KD0khKqML0Qrmn/4kgEt9Gh72OFVl5fUUo/zGnd3qwUcBlYHyz4fVkBvikzxncFMD8yCft+VAj6P9L7vV92UVASkhRlKJHi1x43XUv5T8Uoefv3i2nbyCmb0sGavERMEfhIz0zO1xQ5jP6jvUlR+Igo1PQvwPldlny81AcpeCjaMzlWkFJ2/ssGlFcbhHw5Uhn6Wi5iS0LRryoSr3xZjKJ0yzuCzv8yKttsRfDFFMgMtRtFGGRaqLv+B+0iRkFbHIc5b2jxm8biVSh9R3H0bvNQRFtc0wi5ayiXKOUi8luz1hI2kIAmzx/ynPv2W+Tl5+Lt67qjOiQYOqOHAoX6JIMuIvR2V5YIhlZdVIEYHBReK8HX0C29rTQfP5QXWjXTkon3WvEkhTpFF6CbhZgRuhma+gFd6yA9vwDna/L8NXLXCMnQks4ecriV8lLEw5LKgpzJyFGXHCGJBKTvLjrL6EwziSlNHTmQ0rcdBQUxCTksU12yH5RGkiUHWXqnEo1g3DU7sV3ityl6JjmkUoRM8m4i73PyQiehCAfPR4uGL2T48VKuJIEygEnhMVhXbO0564g0ceZF6Gx2b8Z0hMrFcLDuRcgzWjfZpUgzaNC6bd1qwPr1MiFSqinyarVej6tGliP2qct44OMPkPzTQegq5ZDJDJja4wuoFeK70HWPFSFpuGj0RASZXivDn+vDcG67xauTojfRtYv7AgW9zNGPw0D5mj89frtQj0jHyKvVpoi4wm9kTKTTAFf+CBDyJQdG6nD6qxBk7gxC76nFaNOzGod+/xh7flsglJeG/g8/+RYefXISqjqUmcf1+wcRiFIq8fr9YZjyqE7Ym2R0PuDZfPz8XLRgGHX42wAcv6jBf0MysevlKOQet/bOdbaK7QXSToZ0B0SfyUBNahhmasd0LUtbLYTRNQkR2wFG5lRaj4jvDn6Wd1AHqeWbfaPpDXr0/7A/ytWlCFaF4kDKAchbyD3Iutxm3348ADcgwAShG0DlJhuGwB9//IEBAwaASLqQkBBQ2FEi/OjvTz/91JwT8KqrrhIIvFAKESQRVwhCKk4hSilUKUmfPn0wd+5cITfh2bNn8eabb4LGYSr3mhtcqPhQsd8njgjCN9p0FAi7lTaE3XWBocIHxn8Ks5BWbZOLTUIQVup1eC7nnNBZW6U/nmndzqyQsu3PpMSiso7GQkolskS39UqkD5hrA0KxscQSE79hd0HttR19BGUWZSIpNUlQbHaOBL6fBEz6AtgrcZIZ0B5YOxq4ZS1wtpBs3GTImJWBhHDrUJP0UdqQaEa1z8C1Ejq9DqsPr8ZzPz1Xp9CpMcExWDhsIaYmi6FTPUGy1FX4/t1/I/G7Ayjoloi2zz+Jm6J9Lya/J2DNY2AEakPAHZ7KgdmjEX56AZSVHdDmH1cQEKuB5pJKyMfad6AeARH2oR+l46SQTRRKiMWCAJFKh6vLBeXOdQEhQl45qVDIVSKAKMTV4KAIIfRlY4npPYCUVnSMkM7DGbn09JWz0Nvk8+oZEIIHI2JhS1JRsYyfgnDm62AkDKoUjJtInDnN2Dqf0fk/94p9BATT2Aiz1/LPI0+rwfiwaNwUFN5YkIjtkBaamFXKm0QeZpJ0AJmaKizNt3goUvHm3NekhHslLxOk/CRxFLq0ccHh1hgBEQFvydfUkhW8nrJXyQmZ0nsSEUgBjiiipUkoBSWlD6YIlEQsEhlnEqpHv9E3Gzn+UphQSpVHBnj0aG4KoXHR+PbtAwLj1OjeVYbWNkaztt/Ui2M6CeGrpb83BkFIHovkudjShQydbrvNMQqleq2ZyHVUwhCog+5yJSYVrkUrXSE2R4zFlSjrUKwUseC7J9qgNKt2AydpHxv+tN6UUd2qMeeJzfjtfTF0fNFVc1EZa8npJ61L0Rs0foUwKOxD/8t1wVBqI0BGTCah926DTowwEF84ATj5qtV0KTx5oANSTlpILmQUtY4YEaFQIkohvofakoD+MjmIPCQp1+twWVttbk5KAlYb9ILHLIXTFdur3chcmorVk/b3kewjSP4g2TykI9OPoFdME4aEaUYwWJfbjOBz125DgAlCt0HLDTcEgf/973+YNGkSSijvgQMhcnDbtm3oTGZyNuIqQUihSh9++GF8RGH9nMhDDz0kEJJyN7i18KFiD7ozUo5K2l4jUm5GZDy+LM3HzzbW4VTeFQvxr8vysb3MYllOeWzohdFRfxTeYVFMR4e5CykXAuVEqKsHZEPuEUfzW/DLArzw8wtCs33jgFP5QInl3dTcXZi/aHn3m9EidcHQBZg3eF5DhuP2ukQS1iV06qq7VwnkoKfJqeoKHK4uE8LO3RgY5nbvCU+bP4+HEfA0BBrDU9mvpBci/lqEe9/pLHh5kxVyXlwp/EL0QgBoslSme74mIc928nBn8RwE3i/IwmlJeNCa3i2k+fukM6CzmsipVcXZguecrYiRCSrxTsElq0s9/INxvFrMRyMVUxSEmt6XmhPBDHUVUiUhTGksL0YngsLFNZdQ+LvDVWUC+U64sjACjIAFgZas4PWGfVBfApe8GCmvPMk994hpy6WeiO6a+2OPiencc3OBvDzRGXni/QacixW/t8lznIg8Euk5lq/TCIY+JolVqoSctvWRuVEJoPq+LrTGRCxTKvoZMwDyXm3XDnhBVAVYCRkPk5kMvW/UJMXn/YBWRndWGeCnAIKsAynUG9aZF97EgOIdVvUnXPON8De9J18adBUq/Rs/9L+yNBF9D6bjojF3sfhOLhJ5JGRoZopEQSQfvStQKPlAmRxqyo1pjBARJleCQoGaRNyzFtdfqkc5Ay1iQKVBjwAZUY01h0OvDVQKYeyJkro/VYj2ZJLUUamY2X+mJw610cfEutxGh5Qb9AAEmCD0gEXgIThGIDMzE6mpqQIRSA9glUolEILjx4/H448/jiAnIW9cJQhNvX799dcCCfjrr78iLy8PUVFR6NevHygH4q1uNL/jQ8V+3etCEN4aEom/hUTiz6pyO+9CatkVgpDKnagux4nqCoGsMZGD9Puaoiv4o6rUPMgxYdEYFBQuvAi+YpNnoaMqEE9ExjslCGksjU0e2s6PPAa6vNsFZwvtcw/V9ozpHNkZpx4/5dFkVUNDp9aGAV9nBBiBlotAfT2V5dVtEH7mJQRdmgQZFCCrapNIreHJoti/lpxKppw6LXcVPG/m+ypK7CID1PRuYXvOJ/gF4KnWlI+GwsiReg44Wl2Oj4uyhd8o7KbJI/K/xVdwqNLyzjE/ugNetgnLRnVM+8S2L4qoQJEVmlschWGnuZCVPAsjwAh4HgItWcHreathP6LGJnDJAfz++8UwqE0tRHJQWgHyOgwIAHaWF2FLaZ55GOQBVg2DQKiIWdcaJr7yXkUO8EePiqk/KKwr2a3v3u2YCBTeN2AQIh5RTuC65E3Wa4H8k/5QJVYLEQ1oDYijdZUglBJkVQYdLkryEdO4otXZWHZqmnlRt0aNx/rYB4W/44PKcPCm9jDIrfPwKWRArCRkbttQYM09QLdoIC0XeGALcNny6oTsMkBnmyZaq0LvHy+hlb+YYqbxxIBcnUYIoyvmzGx8Q6h33wV69HAeYaLx5lK/lkZvGI0tJ7eYK4/uOhqbJzj2Aq1fD55bi3W5nrs2PLL6I8AEYf2x45qMQIMQ4EPFHr6aCMJVRdk4WiWG4SIxJT8npdvsK/akmKsEobNFJCXXB0VZguU/vfCRdyFZnFHIiOeNIUuldam/Y1Vl+Mio+LO91pgE4czIdkhSWSzfqK/d53dj0KpB9d6Tu6ftxoCEAfWu786KjRk61Z3j5LYZAUbAuxGoq6fywJ5vQbd/GpRBBvSYWIKAcEsIUSlB6EoeHV9RZHn3DrAevdZgwMK8TBQZvRruDG2NYcGtnE7x9bzzQpQBk7wV0wmUQ8YVoXeZ36vKcFZTiWFBrUB5KB0ZJJnebWh/vWf0OqRQptPCYzzCyCdLU4238q09AF5tk1RvTxBXsOMyjAAjUH8EWrKCt/6oNV1NdxC4lEWF0ouaZONGIDoaOHgQmDu36eZGhKHt9zGdcXSG0FnSGELRfygKkDeIKZw4kbjffANcdx2weDHw++91Gb0BF7VqVEly3NWltqZchgq9AXIZoFSIZKQ05QgZRVvQFN9viJAU/2X9vkO/52o1Qg7uYLlCyNmXXPor/lawDZf82+OzNpOglvsjSK5AnNIflQHpONtpDvKiN1kNeWw3YNEtQEfnr19ILwSe2Q5sTrOerd+pcQj/dRGuiW+a0LM33ggsXCh60dqKKYcg3W8USpiE8mPPmSPmDiXyl3KLPvqoeG3rVjFPqacKeTdHLYpCYZUlGldkYCRyn8ltEXkIWZfrqTuTx9UQBJggbAh6XJcRaAACfKjYg2erXLs+MAwTw8VkCyU6LbaW5qNEr8WI4FagsJ4mcVeK0p1UAAAgAElEQVSoLbIIy9NpkODnb/64oDAUznL/UP4hykNkK/SxQyG3KPRWQyU5IARTImLtmkn5MgUr/1hp/j3MPwx3X3031h5da1d2cq/J2PrXVpRUW0L4pvRJwYq7VjR0eG6p78uhU90CGDfKCDAC9UKgrp7KCfE3YcwdHzvsK11TJeSkC1coES2xKr7GP1hQlJDBCVnPkzwQEYs+ARIT6XqNniu5AwEyCvqjqgwRciWuCag9PCWd8xmaKgwJCm8Uwo4Mo8hAijzwyONQquik9xEKf1XfMGzuwIuIztfzLyDXSJRKvSjd0R+3yQgwAvVHoKUreOuPXNPVdBeBS+E/jx8Hrr8eCDSGkCSCypbYoJCVa9cCp0+L4SwbX8QwjEI4dplC6Ks8ogpztxagVUcNwjtocPp/ITj6cRhmzdVj4ig/vFUgGqFkH/bH7ldbC/++aW4B4q6v+Tub8veqjGQhvX/R2f5YZJz5N9PcKLdjdrYYrlNVh0ilhOnZs0C/fiKxRnjqdICCchg7sRUi78Dhw+uHqtaghxYGYfxE2pHjHHkNkvdgfYXGXFIG+PuJXp4k9M5KefL8hEm4ZvRk2z+RhWcdhDft4BcApQ2BWxS+E2c6z0JZ6GFzMyoF8OMDwMAE+5ntPg8MXwOodZZripxkBO5civCiIejUSVyPxpJ588Swrg89ZN0iEXyffSb+Jg3xS38vWCDmCCWhNScP0AsXgJtvBuLjG2tkTduOrXezqfeWkoeQdblNu9+4t6ZBgAnCpsGZe2EE7BDgQ8V+U5Bi7d+FWUIseFKqzo5shzAXwlLZEoSRCj+8EJ3oll1Xm8ei7Vgo3MY/oxIEgnO+g3Bhrg5SSpaa6sz+bjY2Ht8o/FmtrUZRdQG0ej1UChVaBbQSCMBKrX2ugSBlEEL9QwWLL7VODaVcjgj/SPgbc2RN6DEBi0cudnVobi1XU+jUpIgkzBs0Dwt3LcS5onN24/CG0KluBY8bZwQYAZcRqI+nMilKHvz7DoSFxtn1U6rXgfLSScOLUt5cqTdhpV4Hykzi35iaC5dnzAUZAfcgkK/V4LvyAmFvUzh4ep9jYQQYAc9DoKUreD1vRaxH1BwELoUAvfNOcRwU3vCaayxjWrMGWLXKfahVVwNpaUBiIhAcYckLZ+pR9F6TWeWMczaaq+8pQ8/JFkNYKqcukyHvpD+U/nr88pIl3GRybACSu8vxyCOAVqdHr3f6w+BXCpkmFNOVB/DrQZFdev55keghobCPy5aJJCDxZs88Axw6BJD3HxGCb7wBkKcYeWuSBx7NrTHljE1+ZGdtt+1bheQHi/HNYzEOi7RpJccLSzQ4Ky/H0imiu1plNRAoSZtNupg2CnumtFcv0duN8h8S6fXqq8DAgZZu7roLKDWG/3RGEIrvxBbSkTB9/HEiO3XIjl2N9I7PQaMS4+FO6Q2svsd+GlO2AGuOiL/7qWPQMX0hYrOnQq9TYP58IDLS2mOWylGY3ZQUsc7Qoc5XhojA9HTL9eXLgauvFv9++mlr786XXwYGD7aUpb1A+5kISieZkRpzSzR5W1Lv5i6RwOkCcQgtJQ8h63KbfMtxh02AABOETQAyd8EIOEKADxXH+4JCauVoNWjvJ4accEVsvfPGhkVjYFC4K1XrVaYmj0VKZP12/kVzu2+06WhW/roaZvTRVnECUSqV/oFhuM/oTUm/E3GWlJqIzOLak3nXdZJJEYlIfzKjrtXcUt5Z6FTyglx22zKQpyQRoTO+noF1R9fZjcGTQ6e6BTBulBFgBOqFQH09lW/q9xSuv/Yxuz7JezBHpxFCVJPV9fiwaNzkxnOpXpPmSowAI8AIMAItFoGWruD19IX3VAL30iVg0iQLepMni16G9RED9Pj92v7QKkqhLg1FyKcHIIMcPXsC53Rk5ComlDOFoTT1kampgsZgCeten76ldci4OFKhRFnwURzql2y+1PfXIwgp7+VS86ZcgabCSUlAeAPVEdUGPYr1WighQ7hcAYVM7pQgvXX5FXwz3ZoI/FtqDsLaaYUhHVzaCud3Gd1FAfzv/UAk97b3CGw/oMJqvt3bK/Hv11TIzQUSEoCqKtHrzRXvyqIioKCA8ica0GW4tdEyeT5SlAGTUMhZktWrgY+NwTlaDV2NLwxi3sKUPsCKu+yXIuVLYOUf4u9Xn1yFttlThX//85/AyJHi71OnApmZlrq0X8lDlMQ2n+Ozz4rhdmfOFEN8Erl75gzQvbu9J+jOncAHH4ik5k03ubRNvKaQ1BDd0aDJ0LxCI+4VygtJ+SBJgv2CEREQ4XSenmSI3pDFYF1uQ9Djup6KABOEnroyPC6fR4APlcZbYvI8XFF0GRV6Hdr5+ePxVvFu9ciob0jT2gjCSeExuC4wVADGtqxt7qO8ijzEL4kXPAClIk3mXaYGio0Wi1LLrnB/IMRoCOgomTd5IF6afQlRQY2dzLvua+4odOry25djYs+Jdo2tP7Yej2571GtCp9YdDa7BCDAC7kCgIZ7KEWGJmHLfdiGc5OTwGByqKkVatfjBTN6Ds1sbNRDuGDi3yQgwAowAI8AIOEGAFbzevTW8hcC1DadIqBPJQh5Ub71V8xqUBR+xIuRC1x2BIq+XQMyQ9xaF0SQKi4gxWyHyjDzTAmRyI40Ih2Es67ILShPeQ3HXOeYqiafeRlLWLJeaICLs/HlL0datgfbtXarqsBAZTefrNLU2MOiFfMQkW1wUj64Jw/lfgjDiXzm4NiYQuTqNOex3+RUFKgsU+HBge/j7OQ4XOm1+JX74QSRmFTIZDmwJQExU/UKLmgZP5GniIGviURpe9IkngDFj7Kda19D/rQpGoPfR7Rg/HnhMYrtHnp7vvAPs2yfm/Lv22lphbdEF6mKIHh0EZD0NxC0Gcq2X2CGGnmSI3pBFZl1uQ9Djup6KABOEnroyPC6fR4APlcZd4jK9DkU6LdoqVcLLrDvFlrzrGxiK+8Mdh+6QjqNUp8WLxjCjlDOIxiwVUi5fayQIN5fkYldFsXCZcg69Et1ByFkllYe2PoRP/vzELoxooBIgErBEDVQYvyusLbuAUBVQVA1UiUaFZglUBuLvPf+OD+/60J0Qutz2T+d+wsRNE5BTnocB7Qdg3Zh16BDRwWn9jKIMTNo8CXsu7EGb4Ch8MnYDhiU5yBTu8gi4ICPACPg6Ag31VL737k8RF3sdlsR0ggYG7Cwvhg4GDA4Kd9kT3tcx5vkxAowAI8AINB0CrOBtOqzr25MvEbi2YRp/+snibVVTCMeL8ak408VCwAXuSIX/4ZkCpMkWRz6XIb6orUIVsVH1lLze96Eq5n/m2gFX7kLs0Q2IkCuEcNmyGnLwnToFUP5Ck1BoS/K4q58YcMYmZ9+4TWJ0oYIzfvhpbrTw7y53lKP3NFFf4Ejeju1c5+6rqgxYtLYa2XkGzJigQo+OrkV0qq2ja4dokaslxYQpP7dorUyedwsX2teuT+h/Wp+MWRlICK838LVNo0Vcd8UQnYBoGyp6D3aLBtJyRS/Cy8awsnTd0w3RG7KYrMttCHpc11MRYILQU1eGx+XzCPCh4r1LbEsQDg1uhbtCxSTprgqFRZlzRRLUnmLrR8QiOSBEaEJrMODH8kIU6rQYFBSOeD9JMoA6hhj1dsuurNIsbE7bjOl9p0Mprz2XkVavxfJDyzGm2xjEOcgN5uoacTlGgBFoGQg01FP5mq73YsSQhaiPIqZlIMyzZAQYAUaAEWhKBFjB25Ro170vXyNwiZP75BORIJs2DVBKPtekHoavvAIMGiTitWsXMGbDaORFG2MTklHsmdEI/mozYmMh/FdXKdJpkOeC152jdincadbQBBj8Cs2X5epItN2RKYQ9DZYrEKtUOSQJNRrg5ElAJ7H9dZUgrDLoUKHXI2lCEZLGWsi+Hc9HIS9NhV5Ti3HVneU1QkG5rO8OicLGEjFfH8nzUYlorfSrK4RuK793LzD3OT2IvqXwon36AEuWOO+uvqH/FwxdgHmD57ltHi2l4fTCdMzZPgeb0jZZTXlsN2DRLUDHVs6RSC8EntkObE6zLjOu+zgsGrEISa2SvB5G1uV6/RLyBBwgwAQhbwtGoJkQ4EOlmYBvhG5fzMlAqd7iejendXu0tSHwauuGPgxnXzlrVeyZ1u0R52I7/OFfG8J8nRFgBBgB1xCoj6fyxM33Y/+FvQgObIVRw1NxbeIQPBfFFsuuIc6lbBHQG/To/2F/lKtLEawKxYGUA5A7CKnGyDECjAAj4CoCrOB1FammL8ffcQCde/4vREErIeQU6kg8pcnFoV/tQ4o+/TRw662AQgGMHg1QfjsSIiMpBxzlyDuUpsW2/dbpNyjPHRFSZ9T28Q9VIQaoy8TIQ+qQo8i56Ua7zdBm736oynpa/U7RipL8LPn86CKRpMXFYq674GCgTRv7HITkWUliCnY06l4Njl/SoM/Dxeg0qmYS0NkufTm6A8IUtRvQNv0ut+6R8HnxRWDPHoDCr5LX4NVXOx5VQ0L/d47sjFOPnxJC/7M0HIGdGTsx67tZOJx92NyYSgH8+AAw0MFnz+7zwPA1gFpClifHJmPpyKUY0mFIwwfkIS2wLtdDFoKH0agIMEHYqHByY4yA6wjwoeI6Vp5W8mR1BT4uzhZCmPQPDMN94W3qNcTPSnKx1xhGtLMqEDMi4+vUDn/41wkuLswIMAKMgFME6uOpPGvPEqjjBiIyNBb3h8WgZ0AwI8wI1AuBI9lHkPyBJZ7akelH0CumV73a4kqMACPACEgRYAWvZ+6Hlv4dZ3vumVaJzr9ronth8mQgS4yqKcjPP1uvIxGElFuulcSTKeOCAUPuqwJlJySRQ4blLwbg1pEy7CgvwqwngPy/xNCW0T3UmL9YK/xemqXA5299bs4/2CUSOF0g9hd+8i2EnrcktAttp0XpRSWiFH7okeCHS5fEcmqDGNp0zw45KB/h2LHi7xV6HfxvLkSffxQhxl+FJL8AHKwsARGX5zVVDd6c3hS9gtaL1i0oCPC3Do5khUNDQ//vnrYbAxIGNBhbbkBEQKfXYfXh1Xjup+eQUy56qU7pDay+xx6hKVuANUfE32OCY7Bw2EJMTZ4KhbxxQtV6ypqwLtdTVoLH0ZgIMEHYmGhyW4xAHRDgQ6UOYHlgUSIHKUxoaAMs9sg67kh1OTQGA/oEhEBZT0s3/vD3wA3CQ2IEGIEWgQAphMhGmXLFsjAC9UUgdX+qYKFtktRRqZjZX8zDxMIIMAKMQEMRYAVvQxF0X/2W+h0nPfekhJz0/KOQpWo1EBHhOv79hmiRI+S6A6KVfji0U/Suo5yP/zm6HnqN6FmmUAHhRtKiWK9FZWUJdPpK4RrlVaN8aiRKeRACAkOdDuC6znchOPdl+AUZkHhzBahJIu1+qyzF7spiZKgbTgJeHxiGS9pqKCDDTUFh+LQ4B7eEtMKtwZE+6SnX0ND/KX1SsOKuFa5vGi7pEgJEEk7bOk0om9IHWHGXfbWUL4GVf4i/r7p7lUAO+qKwLtcXV5XnxAQh7wFGoJkQ4EOlmYD30W7pw3/x3sWY++Nc8wxdseyiwm+OeBNP3/i0z1l2+ehS87QYAUaAEWAEfAyB0RtGY8tJSx6m0V1HY/OEzT42S54OI8AINDcCrOBt7hVw3L8vErhEyG08vtEp4IVVhajQiGE/pYRcsF8wIgKcM4ITekzA4pGLnbZLXmqbNxugUslwxx1iOE935nyMCI3D1L/vtBrPgMBw7Km05BNs6K5bEtPJJ4lAZ7jUJ/T/pM2TsOfCHrQJjsInYzdgWNKwhsLO9W0QGLFmBH4896Pw6+5pQP92wJJ9wPFcoEc0MPtGYP9FYNAqseKIjiOwffJ2n8SRdbk+uawtflJMELb4LcAANBcCfKg0F/K+06/th1epuhQl1SXmCbpi2UWFw/zDEKqyWEbW9uHlOwjyTBgBRoARYAQYgeZFgPIwRS2KAilLTRIZGIncZ3I5D2HzLg33zgj4HAKs4PXsJfUVAtedhFxSRCLSn8yo00K6M+ejQu6HlEm7ERgYiTK9DtlaNZQyOTr41RBD08HoKWVJv4BQISrFS3mZKNFphVIL2yQhyMfCM7qyePUJ/b/80HKM6TYGcaFxrnTBZeqAQGZRJpJSk2CAAZ0jge8nAZO+APZesDQyoD2wdjRwy1rgbCEggwwZszKQEO57OdpZl1uHzcNFvQYBJgi9Zql4oL6GAB8qvraiTTsfVz68XLHscjTq+nx4Ne3suTdGgBFgBBgBRsA3EKgpDxPnIfSNNeZZMAKegAAreD1hFWoeg68QuO4k5FQKFS7NvoSooKg6Lag7cj526TgKA/vPQVhoO5QZ9LiiVZvHFKtUIaQOxJ435RKsE/Bc2CcQWPDLArzw8wvCXPrGAafygZJq+6mF+QMUMvi3y+K1BUMXYN7geT6BgXQSrMv1uSXlCQFggpC3ASPQTAjwodJMwPtIt84+vEzTc9WyyxEc9f3w8hFoeRqMACPACDACjECTIeBKHqYmGwx3xAgwAj6LACt47ZeWPLj7f9gf5epSBKtCcSDlQLN5bvsagesOQm5c93FYNGIRklol1fs+bYycj9Gtu2HITfPQLq6/MI5qgx4XNNZsiUwmQ3ulP1QU49RGnotKgEomx29VpUgOCEGkwq/e8+GKjIC7ESDD9C7vdsHZwrN2XSVFJGHeoHlYuGshzhWds7veObIzTj1+yudC5LIu1927jttvDgSYIGwO1LlPRgAAHyq8DRqKgLMPL2q3LpZd0nE0xodXQ+fF9RkBRoARYAQYAV9BoLnyMPkKfjwPRoARaDgCrOB1jKGtB/eR6UfQXJ7bvkrgNgYhlxybjKUjl2JIhyENvxkANCTn48vDXsWl+GEopWSHEsnRalCiF8OCkgTLFWirVFmVYS/BRlk+bqSJEdh9fjcGrRpk1+vkXpOx7LZlQroaSnMz4+sZWHd0nV253dN2Y0DCgCYetXu7Y12ue/Hl1psHASYImwd37pURYIKQ90CjIUAfXg9tfQhni+ytuqiTpAhg3iBg4S7gXJHjbjtFdMLKu1c22odXo02OG2IEGAFGgBFgBLwUAVfCgZumFh0EZD0NxC0GcitqnzCHA68dIy7BCHgSAs3prcYKXsc7QerBTSVSR6ViZv+ZTb5tfJ3AbQght3DYQkxNngpFHcJ1urqADcn5WKXXY23xFZyoLhe60xkMOK+tFv6vksvRTukPuWQgL0QnsqegqwvD5TwKgZQvU7Dyj5XmMREhuPz25ZjYc6LdONcfW49Htz0qEIYmSemTghV3rfCoOTV0MEwQNhRBru+JCDBB6ImrwmNqEQjwodIilrnJJkkk4c0f32zX3+RewLLbAIoHT3HiZ3wNrDtqP6ydU3dicOLgJhsvd8QIMAKMACPACPg6Ap6Yh8nXMef5MQKeikBzequxgtfxrhi9YTS2nNxivji662hsnrC5ybeQMwJ3Us9JOJ57HFXaCgQog9A9ujv+e+y/duPzFg+dhhBy7liUxsj5mKtV47W88xgUFI4kv0DsqChCmFyBk+oKxChVmBkZL4QTZWEEvBWBn879hImbJiCnPA8D2g/AujHr0CGig9PpZBRlYNLmSdhzYQ/aBEfhk7EbMCxpmLdO3+G4WZfrU8vJkzEiwAQhbwVGoJkQ4EOlmYD30W5tP/xpmkMTgZ+m2k946GpgR6b1775o2eWjS83TYgQYAUaAEfAiBDw1D5MXQchDZQR8AoHm9FZjBa/9FiKPzqhFUSisKjRfjAyMRO4zuU2eh9AZgUuEYPIHyebxUQjUP3P+9FoPncYg5BrrYeBrOR8bCxduhxFwhEBWaRY2p23G9L7ToZQrawVJq9di+aHlGNNtDOJC42ot720FWJfrbSvG43UFASYIXUGJyzACbkCADxU3gNqCm5R++Jtg2D0N6N8OWLIPOJ4L9IgGZt8I7L8IDFplActXLbta8HbgqTMCjAAjwAh4GAKemIfJwyDi4TACPo1Ac3ursYLXenvZenSarjZHHkJnBK4zUtkbPXQ8jZDz1ZyPPv0Q5ckxAh6CAOtyPWQheBiNigAThI0KJzfGCLiOAB8qrmPFJV1D4ODFg+i/sr9QuHMk8P0kYNIXwN4LlvoD2gNrRwO3rAXOGg1mD6YcRL/4fq51wqUYAUaAEWAEGAFGoF4IeGoepnpNhisxAoyAywh4kreay4P28YJS8q1LJHC6QJxwc+UhdETg1kQqe5uHjicRcr6e89HHb12eHiPQ7AiwLrfZl4AH4AYEmCB0A6jcJCPgCgJ8qLiCEpepCwKe9OFVl3FzWUaAEWAEGAFGoCUh4Gl5mFoS9jxXRqA5EPAkbzXp/Im47P9hf5SrSxGsCsWBlANNHl7TXesx+7vZ2Hh8o9PmKbRohaZCuL7mHuABYyrCYL9gRAREOK03occELB652F3DNrfrS6SypxFyznI+Tu41GctuW4Yw/zCUVJdgxtczsO7oOru19pacj27fpNwBI9BCEWBdbgtdeB+fNhOEPr7APD3PRYAPFc9dG28cmad9eHkjhjxmRoARYAQYAUagKRDwpDxMTTFf7oMRaOkIeJq3mmk9bInL5giv6Y69Qd9FSamJyCyWhFFx0lF0EJD1NBC3GMgV+cIaJSkiEelPZtRWrMHXPZVUrs/EPI2Qc5bzcWLPiXbTW39svdfmfKzPWnEdRsAVBHzZuMSV+bMu1xWUuIy3IcAEobetGI/XZxDgQ8VnltIjJuJpH14eAQoPghFgBBgBRoAR8DAEPC0Pk4fBw8NhBLwSAW/1VnOW484rF0Ey6LyKPMQviYdap7aaikIGxIZYfmobKnoPdosG0nJFL8LLpZbr2WWAzmCNhkqhwqXZlxAVFOVWmDyVVK7PpD2NkHOW89HZ3Lwx52N91onrMAKuIuCrxiWuzp91ua4ixeW8CQEmCL1ptXisPoUAHyo+tZzNPhlP+/BqdkB4AIwAI8AIMAKMgAciwOHAPXBReEiMQAMQ8GZvtZpy3DUAEo+oml6Yjjnb52BT2iar8YztBiy6BejYyvkw0wuBZ7YDm9Osy4zrPg6LRixCUqukBs/RW0nl+kzcEwk5Rzkfa5qbt+V8rM86cR1GwFUEfNW4xNX5sy7XVaS4nDchwAShN60Wj9WnEOBDxaeWs9kn44kfXs0OCg+AEWAEGAFGgBHwIAQ4HLgHLQYPhRFoJAS81VvNl3Lc1bSUOzN2YtZ3s3A4+7C5mEoB/PgAMDDBvubu88DwNYBaZ7mWHJuMpSOXYkiHIY2ya7yZVK4vAEzI1Rc5rscIeB4Cvmxc4grarMt1BSUu420IMEHobSvG4/UZBPhQ8Zml9JiJ8IeXxywFD4QRYAQYAUaAEbBDgMOB86ZgBHwTAU/3VnOEui/luKttV+n0Oqw+vBrP/fQccspzhOJTegOr77GvOWULsOaI+HtMcAwWDluIqclToZArauvG5eveSiq7PEEuyAgwAj6LQEsxLqlpAVmX67Pbu0VPjAnCFr38PPnmRIAPleZEn/tmBBgBRoARYAQYAUagaRHgcOBNizf3xgg0NQKe6K3mDANfynHn6joTSTht6zSheEofYMVd9jVTvgRW/iH+vuruVQI56A7xRlLZHThwm4wAI+BdCLQk4xJnK8O6XO/aszxa1xBggtA1nLgUI9DoCPCh0uiQcoOMACPACDACjAAjwAh4LAIcDtxjl4YHxgg0GgKe4q3WknLcubp4I9aMwI/nfhSK754G9G8HLNkHHM8FekQDs28E9l8EBq0SWxzRcQS2T97uavP1KudNpHK9JsiVGIFGQoA81/p/2B/l6lIEq0JxIOUA5DJ5I7XOzbiKQEs0LrHFhnW5ru4WLudNCDBB6E2rxWP1KQT4UPGp5eTJMAKMACPACDACjAAjUCsCHA68Voi4ACPgEwg0p7daS8xxV9umySzKRFJqEgwwoHMk8P0kYNIXwN4LlpoD2gNrRwO3rAXOFgIyyJAxKwMJ4Q6SFdbWYR2uewqpXIchc1FGoMkRsPVcOzL9CHrF9Grycfh6h2xcUvsKsy63doy4hPchwASh960Zj9hHEOBDxUcWkqfBCDACjAAjwAgwAowAI8AIMAKMgASB5vRW4xx39ltxwS8L8MLPLwgX+sYBp/KBkmr7cmH+QJdI4LfL4rUFQxdg3uB5TbK3m5NUbpIJcieMQAMQkHquUTOpo1Ixs//MBrTIVW0RYOMS1/YE63Jdw4lLeRcCTBB613rxaH0IAT5UfGgxeSqMACPACDACjAAjwAgwAowAI8AIAPAEbzXOcWfZiqT07vJuF5wtPGu3P5MikjBv0Dws3LUQ54rO2V3vHNkZpx4/BZlM5va93Zykstsn5wUdcAhLz16k0RtGY8vJLeZBju46GpsnbPbsQXvZ6Ni4xLUFY12uazhxKe9CgAlC71ovHq0PIcCHig8tJk+FEWAEGAFGgBFgBBgBRoARYAQYAfI68yBvNc5xB+w+vxuDVg2y25uTe03GstuWIcw/DCXVJZjx9QysO7rOrtzuabsxIGGAW/e2J5DKbp2gFzTOISw9d5GIvI1aFIXCqkLzICMDI5H7TC7nIWzkZWPjktoBZV1u7RhxCe9DgAlC71szHrGPIMCHio8sJE+DEWAEGAFGgBFgBBgBRoARYAQYAQCe6K3W0nPcpXyZgpV/rDTvTyIEl9++HBN7TrTbs+uPrcej2x4VCEOTpPRJwYq7Vrh1f3sSqezWiXpw4xzC0nMXx5a8NY2U8xC6b83YuMQ5tqzLdd++45abDwEmCJsPe+65hSPAh0oL3wA8fUaAEWAEGAFGgBFgBBgBRoAR8CkEPNlbraXmuPvp3E+YuGkCcsrzMKD9AKwbsw4dIjo43XcZRRmYtHkS9lzYgzbBUfhk7AYMSxrmtn3qiaSy2ybrwQ1zCEvPXRwpeUs5Qk8XiGPlPITuXbOWblziDF3W5bp333HrzYMAE4TNg6JEuFUAACAASURBVDv3ygiADxXeBIwAI8AIMAKMACPACDACjAAjwAj4DgKe7K3WknPcZZVmYXPaZkzvOx1KubLWDafVa7H80HKM6TYGcaFxtZZvSAFPJpUbMi9vqsshLJt3tWZ/Nxsbj290OggKLVqhqRCur7kHeMCYijDYLxgRARFO603oMQGLRy5u3sn5QO8t1biECUIf2Lw8BZcRYILQZai4ICPQuAgwQdi4eHJrjAAjwAgwAowAI8AIMAKMACPACDQnAp7qrcY57ppzV9TctyeTyp6LWuOOjENYNi6edWmNPGiTUhORWXyh1mrRQUDW00DcYiBX5AtrlKSIRKQ/mVFbMb5eCwIt2bjEETSsy+VbxhcRYILQF1eV5+QVCPCh4hXLxINkBBgBRoARYAQYAUaAEWAEGAFGwGUEPNFbjXPcubx8TV7QU0nlJgeiGTvkEJbNB35eRR7il8RDrVNbDUIhA2JDLD+1DRW9B7tFA2m5ohfh5VLL9ewyQGewnodKocKl2ZcQFRTVfBP08p7ZuMR+AVmX6+WbmofvEAEmCHljMALNhAAfKs0EPHfLCDACjAAjwAgwAowAI8AIMAKMQAtBgHPcef5CeyKp7PmouT5CDmHpOlbNUTK9MB1zts/BprRNVt2P7QYsugXo2Mr5qNILgWe2A5vTrMuM6z4Oi0YsQlKrpOaYks/0ycYlTBD6zGbmidSIABOEvEEYgWZCgAnCZgKeu2UEGAFGgBFgBBgBRoARYAQYAUaghSDAOe5ayELzNB0iwCEsvWdj7MzYiVnfzcLh7MPmQasUwI8PAAMT7Oex+zwwfA2g1lmuJccmY+nIpRjSYYj3TNxDR8rGJY4XhnW5HrpheVgNQoAJwgbBx5UZgfojwIdK/bHjmowAI8AIMAKMACPACPgCAnqDHv0/7I9ydSmCVaE4kHIAcpncF6bGc2AEGAEPQYBz3HnIQvAwmgUBDmHZLLDXu1OdXofVh1fjuZ+eQ055jtDOlN7A6nvsm5yyBVhzRPw9JjgGC4ctxNTkqVDIFfXunytaEGDjEse7gXW5fJf4IgJMEPriqvKcvAIBPlS8Ypl4kIwAI8AIMAKMACPACLgNgSPZR5D8QbK5/SPTj6BXTC+39ccNMwKMQMtDgHPctbw15xlbI8AhLL1vRxBJOG3rNGHgKX2AFXfZzyHlS2DlH+Lvq+5eJZCDLI2HABuXOMaSdbmNt8e4Jc9BgAlCz1kLHkkLQ4APlRa24DxdRoARYAQYAUaAEWAEbBBI3Z8qhNMySeqoVMzsP5NxYgQYAUagURHgHHeNCic35qUIcAhL71m4EWtG4MdzPwoD3j0N6N8OWLIPOJ4L9IgGZt8I7L8IDFolzmlExxHYPnm790zQC0bKxiWOF4l1uV6weXmIdUaACcI6Q8YVGIHGQYAPlcbBkVthBBgBRoARYAQYAUbAWxEYvWE0tpzcYh7+6K6jsXnCZm+dDo+bEWAEnCDA4YR5azACnoEAh7D0jHWoaRSZRZlISk2CAQZ0jgS+nwRM+gLYe8FSa0B7YO1o4Ja1wNlCQAYZMmZlICHcQbJCz5+yx46QjUvsl4Z1uR67XXlgDUCACcIGgMdVGYGGIMCHSkPQ47qMACPACDACjAAjwAh4NwJEGEQtikJhVaF5IpGBkch9JpfzEHr30vLoGQE7BDicMG8KRsCzEOAQlp61HtLRLPhlAV74+QXhp75xwKl8oKTafrxh/kCXSOC3y+K1BUMXYN7geZ47MR6ZTyDAulyfWEaehA0CTBDylmAEmgkBPlSaCXjulhFgBBgBRoARYAQYAQ9AwJYwMA2J8xB6wOLwEDwKAV/wvuNwwh61pXgwjAA4hKVnbgKDwYAu73bB2cKzdR5g58jOOPX4KchksjrX5QqMgKsIsC7XVaS4nDchwAShN60Wj9WnEOBDxaeWkyfDCDACjAAjwAgwAoxAnRCQEgZkAX+6QKzOeQjrBCMXbgEI+IL3HYcTbgEblafoNQhwCEvPXard53dj0KpBTgcofV9yVGj3tN0YkDDAcyfII/N6BFiX6/VLyBNwgAAThLwtGIFmQoAPlWYCnrtlBBgBRoARYAQYAUagCRCY/d1sbDy+0WlPFFq0QlMhXF9zD/CAMRVhsF8wIgIinNab0GMCFo9c3AQz4C4YAc9AwNu97zicsGfsIx4FI2BCgENYetZekL4vSd+NaJSUW9BP4Qe1Tm33vuSv8Bd+p1yFJgnyC0KrgFbCn/y+5Fnr7CujYV2ur6wkz0OKABOEvB88CoHz589j27Zt2LFjBw4fPgx68Op0OkRFReG6667Dfffdh/Hjx0OpVDZo3NT+0KFDXWpj/vz5eOmll1wqW5dCfKjUBS0uywgwAowAI8AIMAKMgPcgQCGyklITkVl8odZBRwcBWU8DcYuBXJEvrFGSIhKR/mRGbcX4OiPgMwh4u/cdhxP2ma3IE/EBBGoKYZkUkYR5g+Zh4a6FOFd0zm62HMKy8TeAo/cluQzQWzg/c6e1vS/Z1uP3pcZfL24Rgp66ffv2AhQXLlxAu3btGBZGwOsRYILQ65fQdybw4osvYsGCBaAXhJqkb9++2LRpExISEuo9eSYI6w0dV2QEGAFGgBFgBBgBRoARqAWBvIo8xC+JN1u8m4orZEBsiKVy21DRe7BbNJCWK3oRXi61XM8uA3Q2r8YqhQqXZl9CVFAUrwMj4PMI+IL3HYcT9vltyhP0IgSchbCc3Gsylt22DGH+YSipLsGMr2dg3dF1djPjEJaNu9jO3pcoi2DbEMCUTrCm9yVSIV4ug8SPUBwjvy817lpxayICTBDyTvBFBJgg9MVV9dI5paSkYOXKlQgODsbo0aMxfPhwdOnSBQEBAUhLS8M777yDX3/9VZgd/f77778jJESiYanDvKUE4UcffYR+/fo5rd2mTRvQf40tfKg0NqLcHiPACDACjAAjwAgwAp6DQHphOuZsn4NNaZusBjW2G7DoFqCjGAHLoaQXAs9sBzanWV8e130cFo1YhKRWSZ4z0f9n717gbCr3x49/GbcMGbeEyIiKEv2TTiS5dCglt44UxWk6KRU/RRfHUedQHaVDV4U4SLoYlVQoUqIUkkQKg5Bb7pLr//V95uwxZmbP3mvPWntd9ud5vc6r32/2Ws/l/azH2vv5rudZ1AQBBwX8sPqO7YQdvADIGgGbBdLeS5OxS8dm5aoBwVFtR0nXel1zlTR5+WS5a8ZdJmAYSmkXp8nodqNtrlViZ8f3pcTuf7+1nrlcv/UY9Y1GgABhNEocExeBBx98UMqXLy933XWXlC5dOleZutXozTffLG++mfkul3/+858yaNCgmOqWPUA4d+5cueqqq2LKpyAncVMpiB7nIoAAAggggAAC/hCYlzFP+s7sK9/++m1WhYsliXxyq8gVeWyIMX+DSMsJIoePnWxfgzMbyIjWI6RZjWb+aDS19IyArsC7bMxlcuDwPkkuVlq+SvtKChcq7Jn6RaqI11ffsZ1wpB7kcwS8JTBn3RzpOrWLbDuwQ5pUayKTOk6SGik1wlYyY3eGdEvvJl9s/ELOSK4gr3d6Q1qktvBWowJSG74vBaQjA94M5nID3sEJ2jwChAna8X5t9s6dO6VKlSpy+PBhueiii2TZsmUxNYUAYUxsnIQAAggggAACCCAQg8Cx48dk/Lfj5ZE5j8i2A9tMDrfVFxnfPndmt70jMuF/X3ErJVeSoS2GSo8GPSSpcFIMJXNKogvkXIG3rNcyuajSRZ5h8fvqO7YT9sylREUQiFpg877Nkr4yXXo17CVFCheJeN7R40dl1DejpGOdjlKldJWIx3NA7AJ8X4rdjjPjI0CAMD7OlBJfAQKE8fWmNBsEdDvQb775xmxFun///phyJEAYExsnIYAAAggggAACCBRAQIOEPd/taXJIu1hkdLvcmaW9JzJ2aebfx90wzgQHSQjEKpB9BZ7mMbLNSLnvsvtizc7W84Ky+o7t8Wy9LMgMAQQQMA9V8X2JC8GLAgQIvdgr1KmgAgQICyrI+XEX0JWDy5cvN9uQ7t17ci94KxUhQGhFi2MRQAABBBBAAAEE7BBoNaGVfLLuE5PV/J4il50l8sxCkRXbRS6oKNLvcpEvfxFpOi6ztFY1W8ns7rPtKJo8ElSgwxsd5J1V72S1vsP5HSS9S7onNIK2+o7t8TxxWVEJBBAIgADflwLQiQFtAgHCgHZsgjeLAGGCXwB+a/62bdukatWqcvToUdGVhIsWLYqpCdkDhJdffrls2LBBNG9dlVijRg3zTkJ9F+K5554bU/7RnMRNJRoljkEAAQQQQAABBIIhsH73ekkdmSon5ITUKicyq5tIt2kiCzaebF+TaiITO4hcPVFkzS6RQlJIMvpmSPUyebysMBgstMJBAX3/YIVhFWTXoV1ZpZQ7rZxs77/dM+8hDNrqO7bHc/CCJmsEEEgIAb4vJUQ3+7aRzOX6tuuoeD4CBAi5PHwl0L9/f3n66adNnZ966il54IEHYqp/9gBhuAwKFy4sgwYNksGDB0uhQoUsl6M3jfzSli1bpFGjRuaQjRs3yllnnWW5DE5AAAEEEEAAAQQQ8IfAkM+GyKC5g0xlG1YRWb1TZO8fuet+enGR2uVEFm/J/GxI8yEy8MqB/mgktfSUQM73D4Yq57X3EGq98lt917iayGVjRA4cFkkuJvJVWmZgveUEkcPHTpI3OLOBjGg9QprVaOZ6P7A9nutdQAUQQMCnAnxf8mnHJUi1CRAmSEcnWDMJECZYh/u5uV999ZVcccUVZvWgBtN+/PFHKVmyZExN0gDhzTffLB07djR51qxZU4oUKWJWEk6fPl0mTpwoR44cMXk//PDD8vjjj1sux0pQkQChZV5OQAABBBBAAAEEfCOg71qr/VxtWbNrTa46p6akysCmA2Xo50Nl3e51uT6vVa6WrL5ndUwPrPkGiIo6IpD9/YMadP7pt8xivPQewuwND7f67v/+JNLg5ZNHLuslMnyhyIRlmX+rlFxJhrYYat7XmVQ4yRFLq5myPZ5VMY5HAAEERPi+xFXgdQEChF7vIeoXiwABwljUOCfuAlu3bpWGDRuK/kOsgbePP/5YWrRoEXM9Dhw4IMWKFZOiRYvmmYduXfrnP/9Z9uzZY8pbsmSJNGjQwFJ5BAgtcXEwAggggAACCCAQWIH5G+ZL03FNc7Wv+0Xd5flrn5fTi58ue//YK70/6C2TvpuU67j5PedLk+pNAutDw2IT6Dezn7y54s2wJ+vWogePHDSfT2gvcuv/XkWYXDRZUkqkhD2vywVdZHjr4bFVyoazcq6+u/AMkb4zT2Y8so3Id1tFxi7N/Nu4G8aZ4KBXEtvjeaUnqAcCCPhNgO9LfuuxxKsvAcLE6/NEaDEBwkToZZvbqCv4wgXWrBQ1btw46dEj8g+5ffv2SfPmzWXx4sUm+yeeeEIeeughK0XFdOxrr70m3bp1M+empaXJ6NGjLeXDFqOWuDg4AAL6npvLxlwmBw7vk+RipeWrtK88836bAPDSBAQQQAABHwukvZcmY5eOzWqBBgRHtR0lXet1zdWqycsny10z7jIBw1BKuzhNRrez9l3Ux1xUPQoBXWWROvJsWb8n20ssw5xXsaTI5vtFqgwX2Z4ZL8w3paacLWv7ZEQ6zLHPc66+e2qByLs/niyu/Xki9zcWaTou82+taraS2d1nO1YfqxmzPZ5VMY5HAAEEMgX4vsSV4HUBAoRe7yHqF4sAAcJY1BL8nHgGCA8dOiTXXnutzJ0716j369dPhg+Pz9Os2s4KFSqYVYS1a9eW1atX29rz3FRs5SQzDwjkfM+NF99v4wEmqoAAAgggkIACc9bNka5Tu8i2AzukSbUmMqnjJKmRUiOsRMbuDOmW3k2+2PiFnJFcQV7v9Ia0SI1994wEJA98k3cc3CFVn6kqh48dPqWtSYVEzix18k+VS2euHqxTUWTl9sxVhFv2nfz81/0ix06cylUsqZhs6rdJKpSsEHfHnKvvPrpF5PwXRI4eP1mVIoVFVvUWaT1JZM0ukUJSSDL6Zkj1MtXjXt+cBbI9nutdQAUQQMDHAnxf8nHnJUjVmctNkI5OsGYSIEywDreruatWrSpwVpUrV5YyZcqEzUcDdPqOQH0noKZYVvEVtJKNGjWSr7/+2rzrULcltTNxU7FTk7y8IJD9PTdaH6++38YLVtQBAQQQQCDxBDbv2yzpK9OlV8NeUqRwkYgAR48flVHfjJKOdTpKldJVIh7PAYknsHbXWhkwe4BMXTn1lMZ3qiMy7GqRmmXDm6zdJdJ/tkj6ylOP6Vy3swxrNUxSy6a6Appz9Z0GNQ9kvhr+lJRcVOT8CiKLt2T+eUjzITLwyoGu1Dl7oWyP53oXUAEEEPC5AN+XfN6BAa8+c7kB7+AEbR4BwgTteK83+/jx43LLLbfIlClTTFW7dOkikydPlsKFC8e16pdeeql88803BAjjqk5hfhXo8EYHeWfV/15uIyIdzu8g6V3S/doc6o0AAggggAACCPhCYF7GPOk7s698++u3WfUtliTyya0iV+SxqG7+BpGWE0QOHzvZvAZnNpARrUdIsxrNXGtzfqvvtFK1y4n89Fve1atVrpasvme1eX+8m4nt8dzUp2wEEEAAAQScFSBA6KwvubsjQIDQHXdKjSBwxx13yJgxY8xR1113naSnp9vy3kMr8LqCsXz58rJ3716pVauW/PTTT1ZOj3gsN5WIRBzgIwF9/2CFYRVk16FdWbUud1o52d5/O+8h9FE/UlUEEEAAAQQQ8KfAsePHZPy34+WROY/ItgPbTCNuqy8yvn3u9tz2jsiEZZl/r5RcSYa2GCo9GvSQpMJJcW98v5n95M0Vb5pydbvU7Qe3h62DbpWqW6SGSxVLVhTdHlVTlwu6yPDW8Xk1Rfb6sD1e3C8hCkQAAQQQQCBuAszlxo2aguIoQIAwjtgUFZ2AvmfwP//5jzm4ZcuWMmPGDClevHh0J9t41KRJk6R79+4mx9tvvz0rYGlXEdxU7JIkHy8I5Hz/YKhOvIfQC71DHRBAAAEEEEAgUQQ0SNjz3Z6muWkXi4xul7vlae+JjF2a+fdxN4wzwUE3kq4YTB15tqzfszFi8RVLimy+X6TKcJHtByMeLqkpZ8vaPhmRD3TgCLbHcwCVLBFAAAEEEPCAAHO5HugEqmC7AAFC20nJsCACjz76qDz22GMmi8aNG8usWbMkOTnZUpYZGRmSmpr5zoxmzZrJp59+esr5u3btkmXLlslVV10VNt9FixZJ69atZffu3WabGv3/GzZsaKkekQ7mphJJiM/9JJD9/YPZt3/iPYR+6kXqigACCCCAAAJ+F2g1oZV8su4T04z5PUUuO0vkmYUiK7aLXFBRpN/lIl/+ItJ0XGZLW9VsJbO7z3al2TsO7pCqz1Q1Kwezp6RCImeWOvmXyqVFdPVgnYoi+k5CXUW4Zd/Jz3/dL3LsxKlN0JWEm/ptkgolK7jSNgpFAAEEEEAAgeAJMJcbvD6lRSIECLkKPCPw3HPPyX333WfqU7VqVXnjjTekTJky+dbvvPPOy7X1aKQAYejziy66SNq3by+XXHKJVK5cWZKSkmTDhg0yffp0mThxohw5csSU3b9/fxk2bJjtTtxUbCclQwcFsm//lFcxurXowSOZj3Nn3/4puWiypJRICVszt7Z/cpCKrBFAAAEEEEAAAVcE1u9eL6kjU+WEnJBa5URmdRPpNk1kQbYFek2qiUzsIHL1RJE1u0QKSSHJ6Jsh1cvk8bLCOLRi7a61MmD2AJm6cuoppXWqIzLsapGaZcNXYu0ukf6zRdJXnnpM57qdZVirYZJaNvOhURICCCCAAAIIIGCHAHO5diiSh9cECBB6rUcSuD66om/evHmWBNatWyc1atQ45ZxoA4SRCtKA4aBBg+Qf//iHIy+756YSqQf43CsCQd3+ySu+1AMBBBBAAAEEELBDYMhnQ2TQ3EEmq4ZVRFbvFNn7R+6cTy8uojs+LN6S+dmQ5kNk4JUD7ahCzHnMy5gnfWf2lW9//TYrj2JJIp/cKnJFHrHL+RtEWk7Q9xaeLLLBmQ1kROsR0qxGs5jrwYkIIIAAAggggEA4AeZyuTaCKECAMIi96tM2xStAePjwYXnvvfdk4cKFZuvQTZs2yY4dO+TQoUNmxaKuStS6pKWl5Qo+2knLTcVOTfJyUoDtn5zUJW8EEEAAAQQQQKDgAvpAV+3nasuaXWtyZZaakioDmw6UoZ8PlXW71+X6vFa5WrL6ntWOPBRppWXHjh8TfYfiI3MekW0HtplTb6svMr597lxue0dkwrLMv1dKriRDWww171JMKpxkpUiORQABBBBAAAEEohZgLjdqKg70kQABQh91FlUNlgA3lWD1Z9Bbw/ZPQe9h2ocAAggggAACfhaYv2G+NB3XNFcTul/UXZ6/9nk5vfjpsvePvdL7g94y6btJuY6b33O+NKnexBMEGiTs+W5PU5e0i0VGt8tdrbT3RMYuzfz7uBvGmeAgCQEEEEAAAQQQcFKAuVwndcnbLQEChG7JU27CC3BTSfhLwJcAbP/ky26j0ggggAACCCAQcIG099Jk7NKxWa3UgOCotqOka72uuVo+eflkuWvGXSZgGEppF6fJ6HajPaHUakIr+WTdJ6Yu83uKXHaWyDMLRVZsF7mgoki/y0W+/EWk6bjM6raq2Upmd5/tibpTCQQQQAABBBAIrgBzucHt20RuGQHCRO592u6qADcVV/kpvAACbP9UADxORQABBBBAAAEEHBCYs26OdJ3aRbYd2CFNqjWRSR0nSY2UU9/Vnr3YjN0Z0i29m3yx8Qs5I7mCvN7pDWmR2sKBmlnLcv3u9ZI6MlVOyAmpVU5kVjeRbtNEFmw8mU+TaiITO4hcPVFkzS6RQlJIMvpmSPUyebys0FrxHI0AAggggAACCIQVYC6XiyOIAgQIg9irtMkXAtxUfNFNVDIfAbZ/4vJAAAEEEEAAAQS8I7B532ZJX5kuvRr2kiKFi0Ss2NHjR2XUN6OkY52OUqV0lYjHx+OAIZ8NkUFzB5miGlYRWb1TZO8fuUs+vbhI7XIii7dkfjak+RAZeOXAeFSRMhBAAAEEEEAgQQWYy03Qjg94swkQBryDaZ53BbipeLdvqFl0Amz/FJ0TRyGAAAIIIIAAAghEFjhx4oTUfq62rNm1JtfBqSmpMrDpQBn6+VBZt3tdrs9rlaslq+9ZLYUKFYpcEEcggAACCCCAAAIxCDCXGwMap3hegACh57uICgZVgJtKUHs2MdrF9k+J0c+0EgEEEEAAAQQQiJfA/A3zpem4prmK635Rd3n+2udF36uo703s/UFvmfTdpFzHze85X5pUbxKv6lIOAggggAACCCSYAHO5CdbhCdJcAoQJ0tE003sC3FS81yfUKHoBtn+K3oojEUAAAQQQQAABBCILpL2XJmOXjs06UAOCo9qOkq71uuY6efLyyXLXjLtMwDCU0i5Ok9HtRkcuiCMQQAABBBBAAIEYBJjLjQGNUzwvQIDQ811EBYMqwE0lqD0b/Hax/VPw+5gWIoAAAggggAAC8RaYs26OdJ3aRbYd2CFNqjWRSR0nSY2UGmGrkbE7Q7qld5MvNn4hZyRXkNc7vSEtUlvEu9qUhwACCCCAAAIJIsBcboJ0dII1kwBhgnU4zfWOADcV7/QFNbEmwPZP1rw4GgEEEEAAAQQQQCA6gc37Nkv6ynTp1bCXFClcJOJJR48flVHfjJKOdTpKldJVIh7PAQgggAACCCCAQKwCzOXGKsd5XhYgQOjl3qFugRbgphLo7g1049j+KdDdS+MQQAABBBBAAAEEEEAAAQQQQAABBHIIMJfLJRFEAQKEQexV2uQLAW4qvugmKpmHANs/cVkggAACCCCAAAIIIIAAAggggAACCCSSAHO5idTbidNWAoSJ09e01GMC3FQ81iFUx5IA2z9Z4uJgBBBAAAEEEEAAAQQQQAABBBBAAAEfCzCX6+POo+phBQgQcnEg4JIANxWX4CkWAQQQQAABBBBAAAEEEEAAAQQQQAABBBCwIMBcrgUsDvWNAAFC33QVFQ2aADeVoPUo7UEAAQQQQAABBBBAAAEEEEAAAQQQQACBIAowlxvEXqVNBAi5BhBwSYCbikvwFIsAAggggAACCCCAAAIIIIAAAggggAACCFgQYC7XAhaH+kaAAKFvuoqKBk2Am0rQepT2IIAAAggggAACCCCAAAIIIIAAAggggEAQBZjLDWKv0iYChFwDCLgkwE3FJXiKRQABBBBAAAEEEEAAAQQQQAABBBBAAAEELAgwl2sBi0N9I0CA0DddRUWDJsBNJWg9SnsQQAABBBBAAAEEEEAAAQQQQAABBBBAIIgCzOUGsVdpEwFCrgEEXBLgpuISPMUigAACCCCAAAIIIIAAAggggAACCCCAAAIWBJjLtYDFob4RIEDom66iokET4KYStB6lPQgggAACCCCAAAIIIIAAAggggAACCCAQRAHmcoPYq7SJACHXAAIuCXBTcQmeYhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQsCzOVawOJQ3wgQIPRNV1HRoAlwUwlaj9IeBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiiAHO5QexV2kSAkGsAAZcEuKm4BE+xCCCAAAIIIIAAAggggAACCCCAAAIIIICABQHmci1gcahvBAgQ+qarqGjQBLipBK1HaQ8CCCCAAAIIIIAAAggggAACCCCAAAIIBFGAudwg9iptIkDINYCASwLcVFyCp1gEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMCCAHO5FrA41DcCBAh901VUNGgC3FSC1qO0BwEEEEAAAQQQQAABBBBAAAEEEEAAAQSCKMBcbhB7lTYRIOQaQMAlAW4qLsFTLAIIIIAAAggggAACCCCAAAIIIIAAAgggYEGAuVwLWBzqGwEChL7p7K/2iQAAIABJREFUKioaNAFuKkHrUdqDAAIIIIAAAggggAACCCCAAAIIIIAAAkEUYC43iL1KmwgQcg0g4JIANxWX4CkWAQQQQAABBBBAAAEEEEAAAQQQQAABBBCwIMBcrgUsDvWNAAFC33QVFQ2aADeVoPUo7UEAAQQQQAABBBBAAAEEEEAAAQQQQACBIAowlxvEXqVNBAi5BhBwSYCbikvwFIsAAggggAACCCCAAAIIIIAAAggggAACCFgQYC7XAhaH+kaAAKFvuoqKBk2Am0rQepT2IIAAAggggAACCCCAAAIIIIAAAggggEAQBZjLDWKv0iYChFwDCLgkwE3FJXiKRQABBBBAAAEEEEAAAQQQQAABBBBAAAEELAgwl2sBi0N9I0CA0DddRUWDJsBNJWg9SnsQQAABBBBAAAEEEEAAAQQQQAABBBBAIIgCzOUGsVdpEwFCrgEEXBLgpuISPMUigAACCCCAAAIIIIAAAggggAACCCCAAAIWBJjLtYDFob4RIEDom66iokET4KYStB6lPQgggAACCCCAAAIIIIAAAggggAACCCAQRAHmcoPYq7SJACHXAAIuCXBTcQmeYhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQsCzOVawOJQ3wgQIPRNV1HRoAlwUwlaj9IeBBBAAAEEEEAAAQQQQAABBBBAAAE7BY6fOC6XjblMDhzeJ8nFSstXaV9J4UKF7SyCvBCISoC53KiYOMhnAgQIfdZhVDc4AtxUgtOXtAQBBBBAAAEEEEAAAQQQQAABBBBAwH6BZb8ukwYvN8jKeFmvZXJRpYvsL4gcEYggwFwul0gQBQgQBrFXaZMvBLip+KKbqCQCCCCAAAIIIIAAAggggAACCCCAgEsCI78cKX1n9s0qfWSbkXLfZfe5VBuKTWQB5nITufeD23YChMHtW1rmcQFuKh7vIKqHAAIIIIAAAggggAACCCCAAAIIIOCqQIc3Osg7q97JqkOH8ztIepd0V+tE4YkpwFxuYvZ70FtNgDDoPUz7PCvATcWzXUPFEEAAAQQQQAABBBBAAAEEEEAAAQRcFtD3D1YYVkF2HdqVVZNyp5WT7f238x5Cl/smEYtnLjcRez34bSZAGPw+poUeFeCm4tGOoVoIIIAAAggggAACCCCAAAIIIIAAAq4L5Hz/YKhCvIfQ9a5JyAowl5uQ3R74RhMgDHwX00CvCnBT8WrPUC8EEEAAAQQQQAABBBBAAAEEEEAAAbcFsr9/sHY5kZ9+y6wR7yF0u2cSs3zmchOz34PeagKEQe9h2udZAW4qnu0aKoYAAggggAACCCCAAAIIIIAAAggg4LBAv5n95M0Vb4YtRbcWPXjkoPl8QnuRW//3KsLkosmSUiIl7HldLugiw1sPd7j2ZJ9oAszlJlqPJ0Z7CRAmRj/TSg8KcFPxYKdQJQQQQAABBBBAAAEEEEAAAQQQQAABxwVOnDghqSPPlvV7NkYsq2JJkc33i1QZLrI9M16Yb0pNOVvW9smIdBifI2BJgLlcS1wc7BMBAoQ+6SiqGTwBbirB61NahAACCCCAAAIIIIAAAggggAACCCAQWWDHwR1S9ZmqcvjY4VMOTiokcmapk3+qXDpz9WCdiiIrt2euItyy7+Tnv+4XOXbi1PKKJRWTTf02SYWSFSJXhCMQiFKAudwooTjMVwIECH3VXcGv7FVXXSXz5s2LqqH6pJEdaeHChfLiiy/K559/Lr/++quULVtW6tevLz169JCbbrrJjiLyzIObimO0ZIwAAggggAACCCCAAAIIIIAAAggg4HGBtbvWyoDZA2Tqyqmn1LRTHZFhV4vULBu+AWt3ifSfLZK+8tRjOtftLMNaDZPUsqkebz3V85sAc7l+6zHqG40AAcJolDgmbgLxDhD+85//lMcee0yOHz+eZxuvv/56efPNN6VEiRK2G3BTsZ2UDBFAAAEEEEAAAQQQQAABBBBAAAEEfCYwL2Oe9J3ZV7799dusmhdLEvnkVpErquduzPwNIi0niBw+dvKzBmc2kBGtR0izGs181nqq6xcB5nL90lPU04oAAUIrWhzruEAoQNiwYUMZN25cvuVdeOGFBarPmDFj5I477jB5nHPOOfLII49IvXr1ZPPmzTJy5EiZO3eu+eyWW26RSZMmFaisvE7mpmI7KRkigAACCCCAAAIIIIAAAggggAACCPhQ4NjxYzL+2/HyyJxHZNuBbaYFt9UXGd8+d2Nue0dkwrLMv1dKriRDWwyVHg16SFLhJB+2nCr7RYC5XL/0FPW0IkCA0IoWxzouEAoQNmvWTD799FPHytu9e7ekpqaK/rd69eqyePFiqVDh5L7kx44dkw4dOsj06dNNHXTb0yuvvNLW+nBTsZWTzBBAAAEEEEAAAQQQQAABBBBAAAEEfC6gQcKe7/Y0rUi7WGR0u9wNSntPZOzSzL+Pu2GcCQ6SEHBagLlcp4XJ3w0BAoRuqFNmWIF4BQifeuopGTBggKnH66+/nue7BvUf/Ro1aogGC6+77rqsYKFd3cdNxS5J8kEAAQQQQAABBBBAAAEEEEAAAQQQCIJAqwmt5JN1n5imzO8pctlZIs8sFFmxXeSCiiL9Lhf58heRpv/beKxVzVYyu/vsIDSdNnhcgLlcj3cQ1YtJgABhTGyc5JRAvAKETZo0kQULFsjpp58u27dvl2LFiuXZpDZt2sjMmTOlePHismPHDilVqpRtTeemYhslGSGAAAIIIIAAAggggAACCCCAAAII+Fxg/e71kjoyVU7ICalVTmRWN5Fu00QWbDzZsCbVRCZ2ELl6osiaXSKFpJBk9M2Q6mXyeFmhzz2ovrcEmMv1Vn9QG3sECBDa40guNgnEI0B4+PBhSU5OlqNHj0rr1q3lo48+Clv7J554wrybUNOcOXOkefPmNrVUhJuKbZRkhAACCCCAAAIIIIAAAggggAACCCDgc4Ehnw2RQXMHmVY0rCKyeqfI3j9yN+r04iK1y4ks3pL52ZDmQ2TglQN93nqq73UB5nK93kPULxYBAoSxqHGOYwKhAGGlSpWkWrVqsmrVKjly5Ih5P+All1winTp1kq5du0rRokVjrsOKFSvkwgsvNOf36dNHRowYETavadOmSceOHc3nL7zwgtx9990xl5vzRG4qtlGSEQIIIIAAAggggAACCCCAAAIIIICAjwVOnDghtZ+rLWt2rcnVitSUVBnYdKAM/XyorNu9LtfntcrVktX3rJZChQr5WICqe12AuVyv9xD1i0WAAGEsapzjmEAoQJhfAXXr1pW3335b6tSpE1M9dMXgNddcY87VdxE+8MADYfP55ptv5NJLLzWfP/TQQ6IrCqNNetPIL23cuFEaN25sDlm0aJFUrlw52qw5DgEEEEAAAQQQQAABBBBAAAEEEEAAgcAILNq0SDq90SlXezrW6ShDWgyR0sVLy74/9snAOQNl2sppuY5L75Iul1bNnMMjIeCEwJYtW6RRo0Ym63Xr1kmNGjWcKIY8EYirAAHCuHJTWCSBFi1aSOHCheXaa6+V+vXrS/ny5WXfvn2yZMkSefnll2XlypUmC11hqEG16tWt7y/+1ltvyV/+8heTz0svvSS9evUKWy0tTwOSmu655x557rnnIjUh63OeWoqaigMRQAABBBBAAAEEEEAAAQQQQAABBBBAAAFfCOi8dGhRiS8qTCURCCNAgJBLw1MCu3fvlpSUlDzrpFuN3nHHHfLf//7XfN6hQwdJT0+3XP+JEyfKrbfeas4bO3as/PWvfw2bx9q1a+Wcc84xn99+++0yZsyYqMsjQBg1FQcigAACCCCAAAIIIIAAAggggAACCCCAAAK+ECBA6ItuopJRCBAgjAKJQ04VOHr0aIHeARjKbdy4cdKjRw9LvFp2vXr1zLsJNek2nlWrVrWUR7xWEEbaYvTQoUOmHboasmLFilKkSBFL7fDzwdmX5LO9qp97krp7WYBx5uXeoW5BEGCMBaEXaYOdAowJOzXJC4HcAowxrgoECi7AOCq4ITkgEEkgyONM56W3b99uCHR+ukSJEpE4+BwBzwsQIPR8F3mvgm4GCFXj6aeflv79+xuY1157TW6++WZLSPF6B6GlSiXYwbzUN8E6nOa6IsA4c4WdQhNIgDGWQJ1NU6MSYExExcRBCMQswBiLmY4TEcgSYBxxMSDgvADjzHljSkDATgEChHZqJlBeoRV8BWly5cqVpUyZMpaz+OCDD6Rt27bmvGHDhmUFC6PN6PvvvzdPeWjq06ePjBgxIuyp06ZNk44dO5rPX3jhBbn77rujLYbj8hHgywKXBwLOCzDOnDemhMQWYIwldv/T+twCjAmuCgScFWCMOetL7okhwDhKjH6mle4KMM7c9ad0BKwKECC0KsbxrgvMmDFDrrvuOlOPWAKEhw8flpIlS8qxY8ekdevWoisKw6UnnnhCHnnkEfPxnDlzpHnz5q63PwgV4MtCEHqRNnhdgHHm9R6ifn4XYIz5vQepv90CjAm7RckPgVMFGGNcEQgUXIBxVHBDckAgkgDjLJIQnyPgLQEChN7qD2oThcBTTz0lAwYMMEdOmjRJbrnllijOOvWQxo0by8KFC+X00083e0cXK1YszzzatGkjM2fOlOLFi5vjSpcubbksTsgtwJcFrgoEnBdgnDlvTAmJLcAYS+z+p/V8v+MaQCDeAtx34i1OeUEUYBwFsVdpk9cEGGde6xHqg0D+AgQIuUJ8JaDvP9TtQUNbnG7YsEGqVatmuQ268vDBBx80573++uty00035cpDb2g1atQwKw2vvfZa0ZWLJHsE+LJgjyO5IJCfAOOM6wMBZwUYY876krv/BBgT/uszauwvAcaYv/qL2npTgHHkzX6hVsESYJwFqz9pTfAFCBAGv49908K5c+fKxRdfLCkpKXnW+ciRI3LHHXfIf//7X/P59ddfL++9916uYzMyMiQ1NdX8vVmzZvLpp5/mOua3336TmjVryp49e+Tss8+WxYsXS/ny5bOO06Bghw4dZPr06eZvbC9q72XElwV7PckNgbwEGGdcFwg4K8AYc9aX3P0nwJjwX59RY38JMMb81V/U1psCjCNv9gu1CpYA4yxY/Ulrgi9AgDD4feybFvbo0UOmTp0q7dq1k6uuukrOO+88swXo/v37TQDv5ZdflpUrV5r2nHHGGfLll19mBQKzNzKaAKEer/n16tXLnHrOOefIwIEDzerEzZs3y4gRI0QDlpq6du0qkydP9o2jHyrKlwU/9BJ19LsA48zvPUj9vS7AGPN6D1G/eAswJuItTnmJJsAYS7Qep71OCDCOnFAlTwROFWCccUUg4C8BAoT+6q9A11YDhKHVgfk1VIN4U6ZMkbp16+Z5WLQBQj158ODB8q9//UtOnDiRZ166tagGLUuUKBFo+3g3ji8L8RanvEQUYJwlYq/T5ngKMMbiqU1ZfhBgTPihl6ijnwUYY37uPeruFQHGkVd6gnoEWYBxFuTepW1BFCBAGMRe9WmbdHXgzJkzZeHChfLDDz/I9u3bRbcCLV68uFSqVEkaNmwonTt3Nlt/JiUlhW2llQChZrJgwQJ54YUX5PPPP5etW7eaLU7r168vPXv2NKsHSQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAkAQIEAapN2kLAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAhEECBByiSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQQAIECBOos2kqAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgQIuQYQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSCABAoQJ1Nk0FQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEChFwDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCSQAAHCBOpsmooAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAUKuAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSSIAAYQJ1Nk1FAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAgAAh1wACHhdYsmSJfPTRR/L555/L999/L9u2bZOiRYtKlSpVpHHjxnL77bdL06ZNo26F5vXKK6/IokWLZPv27VKxYkVp1KiR/O1vf5M2bdrkm88ff/whS5cula+//tqcr//76aef5MSJE+a80H+jqczhw4dl4sSJ8tZbb8myZcvkt99+M+2qWrWqNGnSxNTnT3/6UzRZ5XvM8ePHZdWqVVn11bp/9913ouVrmjt3rlx11VURy9mwYYPMmDFDPv30U/n222/ll19+kWPHjkmFChXkkksukZtuukluvPFGKVKkSMS8OMB7AkEdZ4cOHZJXX31Vpk6daq77PXv2mDHfoEEDufXWW6VLly62d8bChQvlxRdfNP9m/frrr1K2bFmpX7++9OjRw4yT/NLRo0dl+fLlZryG/p354YcfzFjTtG7dOqlRo4btdSZD5wUYY/YZF2SM7d27Vz744AP55JNPZPHixbJ27Vo5ePCglClTRi644AK57rrrJC0tTVJSUuyrMDnlEmA82HdRFGQ8fPPNNzJv3jxzv9F7jX4v1u+jxYoVM9+z9Xuo3ruaN29uX4XJKW4CjDP7qAsyzsLVYsuWLVKnTh3z3VRTs2bNzO8skrcEGEf29UdBxtGjjz4qjz32WFSViXZ+I6rMOCguAowz+5gLMs5y1uLjjz+WSZMmyfz580XvWTrXV6lSJbnoooukZcuW0r17dylVqpR9lScnBFwUIEDoIj5FIxBJQH8offbZZ5EOMzemMWPGmAmNcEmDd7169TLBwXBJg3KjRo2SQoUK5XlIz549Zfz48fmWEbGyIrJx40Zp27atCQTkl/7v//5Phg8fHrY+0ZT13//+10zuhEvRfIH+xz/+IUOGDIkYAG3YsKEJxFSvXj2aqnGMRwSCOs5+/PFHueGGG0T/Gy7pQwFvv/22JCcn29Ib//znP82PVw3M55Wuv/56efPNN6VEiRJ5fq7n6g/gcIkAoS3dFPdMGGPeGGMffvihdOjQQfRhn/yS/vB9/fXXCYo4NFIYD94YD9q9V1xxhXzxxRcRe1ofAJswYULYe1fEDDgg7gKMM++Ms3Cd37lzZ/O7KZQIEMZ9mEQskHHknXFEgDDi5erbAxhn3hlnoYto165donOf7777br7XlS6e0AevSQgEQYAAYRB6kTYEVqBWrVqyZs0a8xSzTk7oSkENPulqGn0yRoNnmzZtMu3v2rWrTJ48OazFwIED5fHHHzefX3zxxTJgwAA555xzTP7Dhg0zKwM16XEaDMsraaBNA26aSpcuLf/v//0/E3zQVUKaollBqCuE9LxQcFCfvunXr5+cd955sm/fPvN0jrbrwIEDJk+tW//+/WPuYw1o6s1dk65QvPDCCyW0Skn/Fk2AUFdTjB071gRRdHJVnxaqXbu2mShauXKlPPvss+bpc036d30CjCeJYu6yuJ8YxHGmqyB0ZasG4zXpvx+33Xab+bdk8+bNZhzr6l1NGrR77733CuyuDynccccdJh/9t+WRRx6RevXqmfJGjhxpxpqmW265xTyJl1fK/uNXx5d+4da26L9TmggQFribXMmAMeaNMabjTh8oKly4sFx99dVm1wBd3aurBXVV/GuvvSZvvPGGuUZKlixpAif86LV/yDAevDEetGf1+5x+p9YdOXQl05lnninly5c39x3d3UIfmtP7jiZdcT9lyhT7LwhydESAceadcZZXB0+fPl3atWsnZ5xxhtkdRxMBQkeGQoEyZRx5Zxxl/40U6SHr1NRU2x7+LNAFxMlRCTDOvDPOtMN0Vbt+P9SdVjTpwgbdBUn7Sb8zrl+/3sz96UPWei/jt1JUlzkH+UCAAKEPOokqJq6AbvWl2wB26tRJkpKSckHs2LHDbMe5evVq85muNsxru9Gff/7ZTHxoYExXuelxp512WlZ+ur2Y/ijTrZZ02bxuyakT/DmTThz+/vvvcumll5r8dJJRt+fU7Zk0RRMg1CdF9YlRTZdffrnZhjBn2/RmrJ8dOXLEbE+oPxxj3bpTtyr88ssvzTaqevPWoEP2L9jRBAgffPBBM2F01113mcBozqRfFG6++WazMkqTrqIaNGhQ4l64Pmt5EMfZPffcIy+88ILpicGDB+e5Kk//rteqJh2XHTt2jLnndu/eLfpjVP+rDzHoGNbtd0NJx4gG1/VLtCb9N+PKK6/MVd7MmTMlIyPD/BujDw/ouM/+YAIBwpi7yNUTGWPeGGN6D9d7ngbvw610f+655+S+++4z10uLFi3MVqQkewUYD94YD9qr+r04v++X+p1XJ4n0oTxNulW3PvhC8r4A48w74yzn1bJ//36pW7eueYhNV+bqb11NBAi9N64YR94ZR9nnL6KZc/He1USNwgkwzrwzzrSP9J6kr0LS74f6cGW4V7LoONQ5jljnKRkRCHhNgACh13qE+iBgUeD99983K4A06aSertTJmXr37m3eCaZJJznyerefBtE0KKdJgws6SRhNshog1NWC//nPf0zWumopVPecZWmwYtq0aebP+pScrvyzK1kNEEZT7s6dO83qLH23oQY29MlzUnAE/DTOQu/G1GDd2WefbVbf5fWAgR5Xs2ZN0fdr6oMDoVWwsfTaU089ZVYla9KtCfN616CuUNL3B2q5+kMoFCyMVB4BwkhCwficMZZ/Pzo5xnKWrAF6fWBIHwLSB3T0ARlSfAUYD94ZDxpYD93Tnn/+edHv1KRgCDDO3Bln+ntVf2fquz3nzJmT9SoJAoT+HFeMo/iMIwKE/hwfdtWacRafcaa7mYUWXOiY0weqSQgkigABwkTpadoZWAF9CjO0qk2Xv+uXh+xJn2ypVq2a2Yr0/PPPN1tihkv6uW4ZetZZZ5mgQbh3EWY/32qAMPvKpu+//14uuOCCPKuj24o+/fTT5jOdqNTtEu1KTgQItW6hSVXdilT7hRQcAT+NM10BrCt8Nen2uKNHjw7bEaHtc/WAgqzO05XMCxYskNNPP91szRbufai6paGuEixevLjoCuhotuIlQBiccZRfSxhj+fezk2MsZ8nZ77/64IA+QECKrwDjwTvjIfuknH4vvf/+++N7MVCaYwKMs/iPM93ZRR9I1RUXuiJXXzER+r1JgNCxS93RjBlH8RlHBAgdvYw9nznjLD7jTB8I0wfDdI5iy5YtUc1VeP7ioYIIRClAgDBKKA5DwKsCv/32W9bT/Xm9S2zt2rVZ24Xeeeed5n0q4ZJ+/sorr5iP9TzdMjBSshog1Pf19enTx2QbzQpC/dGoK6E08GBXcipAqCsHdbWjBmz37t1rV3XJxwMCfhpnGqjTYIKm/N4pGvo89G5SfS9haJsnK+S6alaD4rpVW+vWreWjjz4Ke/oTTzxhtjfUpE+N69PjkRIBwkhCwficMRa+H50eYzlLDq3u0L/rdsH63mBSfAUYD94ZD/re3NA7vjVYqA/jkYIhwDiL7zgLvepCd1nRVzGEtrknQOjv8cQ4is84IkDo73FS0NozzpwfZ/p7q0yZMnLo0CG58cYbs14fpPcuXWyh9yp9V3W4B6EL2secj4DbAgQI3e4BykeggAK6DWfo3WH61P+wYcNOyXHGjBlmOz9NurVn3759w5aon+sWoJr0vGuvvTZi7awGCHV1kb7gVwNoGsTQd5Hl3P5w6dKlZhtUvUl37do1a2ImYmWiPMCJAKFuw1a1alUTJNGVhPqELCk4An4aZzrxEnpZdqTtgrOv6NV3bT755JOWO23FihVZWwBr8H/EiBFh88juqO9IvPvuuyOWR4AwIlEgDmCMhe9Gp8dYzpLr169vVnboCg/dPtvOB3QCcbHGoRGMB/fGw/Hjx81KeB13ug3iO++8YyqjK5105wveNROHARCnIhhn8R1n+h3z4YcfNg+u6ljS98JrIkAYpwveoWIYR/EZR9nnL1q1aiVLliyRffv2SUpKinmnp+7Sog97ly1b1qGeJls3BRhnzo8z3TWlUaNGpqBnnnnGzEPqPeutt96SAwcOmL/rfUsfcP773/8ujRs3dvOSoGwEbBcgQGg7KRkiED8BncTQbVpCwai8tgLTFYN33XWXqZTe3Dp37hy2gm+//bZ5WkaTnqdfMiMlqwFCzU+/4OgT2b///rtcfPHFJmh57rnnmm05v/jiCxk+fLj5wqtBjg8//NA8qWNnciJAmH1LNn1X1AMPPGBnlcnLRQG/jTP9Aqs/Do8cORLxfZihVa/KG2swXlcMXnPNNaaHIl37ul2wBtA1PfTQQ6IrCiMlAoSRhPz/OWMs/z50eoxlLz37Q0V5bVvu/6vN+y1gPLgzHvQduevXr8+zcH2fr46NcNvie/+qooY5BRhn8R1nujONvk9ef/vpVvN//vOfsypAgNC/45NxFL9xlH3+IlypGiwcP3683HDDDf69qKh5LgHGWXzGme6mpPMOmnSHJQ0S6itR8kr6nnads8xv8QWXMgJ+EyBA6Lceo74IZBPQm1IoENWhQwdJT0/P5aMT9gMGDDB/12CbPl0WLunnoVWD0b5nJZYAoZb/ww8/mJvuq6++KvqexOypUqVKoquZ/va3v5mtC+1OdgcIv/rqK7niiivM6kF9f6O+x7FkyZJ2V5v8XBLw4zjTgF1oq0/dGk2DfznT66+/LjfffHPWn3Wl8fTp0y0r64MHf/nLX8x5L730kvTq1StsHvoOVH3KVVOk1Y2hTAgQWu4S353AGMu/y5weY6HSdfsi3U5UgyS6sl8fOtKHeEjxFWA8uDMe8goQ6mrBf/zjH2ZrfFbSxnccOF0a4yy+4+zqq6+Wjz/+WLp06SJTpkw5pXAChE5f7c7lzziK3zjS+Qud62nfvr1Z5VSlShXzMKjOO7z22msuUitOAAAgAElEQVQya9YsUxn9/qa/50IPbzrX++QcLwHGWXzGWfbd1IoXLy5//PGH2YlNx54+4LJnzx6ZOnWqechZd0PTe5c+PMZYi9dIoBynBQgQOi1M/gg4JKBbc+r2EhqUOuOMM8x2YBpYy5n+9a9/mckNTZ988om0aNEibI30nWAtW7Y0n+t5unQ+UoolQKhfZh977DEZPXq06NaceSVdZTR48GBH3vViZ4Bw69at0rBhQ/nll1/MlwT98ZufcSRPPveWgF/Hma4q1i189d+HokWLmn8D9P2ClStXNi/cnjBhgnn3i16zupWvJh37ev1aTRMnTsx6d+HYsWPlr3/9a9gssr8T9fbbb5cxY8ZELI4AYUQiXx/AGIvcfU6PMa3BsWPHzI/g0IMFev/VeyUpvgKMh8jeTo2H1atXm/uhPqmvW+vqjhb60Is+Pa5BjRdffFFKlSoVuYIc4XkBxlnkLrJznOl3zttuu80E2VetWmW+i2ZPBAgj94cXj2AcRe4VO8fR7t27zXai4dLLL7+c9ZCmBg9//vlnOe200yJXkiM8LcA4i9w9do2zIUOGmPfjhtL1119vtpnX1YLZ0/z586VZs2bm+6IGDnUeNnQfi1xbjkDAuwIECL3bN9QMgbAC+l6Upk2byq5du0SfbtGtWvQmlVfy2gpC3f5QVyl+9tln5gm3+++/X3r27Ck1a9Y0LwTW1XgauNAbr95o9UkefXI7e9L3VoRLqampEVcd2hUg1G1QdQ/yxYsXm+rodon6RBEpGAJ+HmfaAzohc8cdd2QFAHP2io4/fV/gvffeaz7SJ1J1+99QCk2W5tWbulI29CPV6dVNBAiDMZ7yagVjLDMg4fYY0/J1S/FXXnnFVEW3Fn333XdzvR84uFeiN1rGePDOeAhdEfo9u3Xr1mY1rW7Jrd9NS5cu7Y0LhlrEJMA4i+840wB7nTp1TKBd3+mpu0fkTAQIY7qUXT2JcRTfcRRtZ+vvvtDDl5MmTTKvdCH5V4BxFt9xpjuo6WuDQkkfaNH3T+eV9LVM+nomTRogrFevnn8vNGqOwP8ECBByKSDgM4F169aZ7Sw3b95sJu90cl63Fw2XvPYOQt0SVbdJ0KR75OsTpTmTrnrSd1PMnTvXPLGzdOlSMzETSvk9oaPn6KrG/JIdAUINZmqgU8vT1K9fv6x2+eySorp5CPh9nIWatGzZMhk6dKhZFaQBbU06pjSwrX/XLXxDX2g1UK9b/oZSfu9jGjduXNYe/U6/H40AYTCHKGNMxCtj7OGHH5Ynn3zSXGj6/UK3qeKp8/iOO8aDd8ZDzp7PvjW2jhV9Lw3JnwKMs/iPM929Qld36G4r+hBozpUYeiURIPTXeGIcxX8cRXuFZH/XuwYLQw9+RXs+x3lHgHEW/3GWfRWuLjrQnY/CJQ3E6xjTFGkHJe9cVdQEgfwFCBByhSDgIwENCurKQb1Z6Y8pDbDpD6/80vvvvy+6PF6TrsbL70W62ffd1v20Q+8jzC9/K1uM6rsGK1SoIPqeo3PPPdfsmR8u6dZOOlGpSeusdQsltwOEGsDs2LFj1vva0tLSzHappGAI+H2c5dULun2gbi2qgW3ddib0jkx9P2Ho6VJdbRx6p6nmEW3wQlf0hoKMutpXVyWGS7pCUceOphdeeEHuvvvuiBcNAcKIRL47gDGW2WVeGGP//ve/s1a+6/sHdavxMmXK+O6a8nOFGQ/eGQ/hriP9zvrTTz9J7dq1RVfXk/wnwDiL/zhT86pVq5qCBwwYEPadtqH3ZOtKw9BrMXRy9rLLLvPfhRbwGjOO4j+OrFxSBw8ezNpJSedxdD6H5D8Bxpk74+yDDz7Ier2Rzrnqjmfhku7g1qZNG/Mxu4j5b4xR47wFCBByZSDgEwHdmkW3Ef3hhx9MjZ9//nnp3bt3xNpnf+eXbiGmKwrDpexbjOl5+uMsUrISIPz111+z3juR10vqs5elgYzQCga9+X744YeRqhL15wVZQah7jWtAZcqUKaY8bYcGWfJ6IjbqCnGgZwSCMM6sYGqATt+xpEm/BOuXYatJt0jUgKMGIXUrttA7zPLKR79AP/LII+YjDUToSsZIiQBhJCF/fc4Y884Y03eqhb5H6MSs/hugD/GQ4ifAePDOeMiv1/WdvgsWLJBixYrJH3/8Eb8LhJJsEWCcuTPOMjIyovotmVcn6w4z+iAsyTsCjCN3xpGVK0Bf5RJ6Vy4BQity3jmWcebeOFu/fr15eFNT48aNzbuow6XswcScD1l752qiJghYEyBAaM2LoxFwRWDPnj3SokULWbJkiSlftwJ78MEHo6qLrtrT94Xpk0jnn3++6FZJ4ZJOEOpe2/q058aNG6N62a6VAKF+4alYsaIpvlOnTln7dudVH90OUV9mr+m6667LWq0XVaMjHFSQAGH2vf21Xunp6VK0aFE7qkUeLgsEZZxFy6iBPf23Yfv27WbM65di3bY4lqRfohcuXGjGrOank6h5JQ326xN3+u5UPS6adzkRIIylR7x5DmPMO2NMt3zTCVj9jqDvAP7888/N6mJS/AQYD94ZD5F6PbTat1y5crJz585Ih/O5hwQYZ+6NMwKEHhoIBawK48i9cWSl6/R9uY0aNTKnsMORFTlvHMs4c3+cnX322bJhwwapVKmS6OKGcEnfqXvfffeZj3WxQGglvDeuJGqBQGwCBAhjc+MsBOImoFtF6Pv4Qk+wDBw4UIYMGWKp/OyrhHQS/09/+lOu87/88ku5/PLLzd/1eN3+L5pkJUCoq+/Kli0re/fuNRORGpAoUqRInsVk3xr13nvvlWeffTaa6kR1TKwBQn3PYGir05YtW5ptOzTQQfK/QJDGWbS9oU+76ZZPmv71r3/J3//+92hPzXXcsGHDsh5aeP311+Wmm27Kdcwvv/xinsrTlYZWnmolQBhzt3jqRMaYd8aYPtjyl7/8xYxFfUhAg4OhJ2Y9ddEEuDKMB++Mh0iXWfYJV93J49NPP410Cp97RIBx5o9xxjsIPTJgwlSDceSPcaTdp0FBfR+aJn0QrFu3bt6+uKhdlgDjzBvjLPt8n86/6kPQeSXdBSn0fVAXVujvKRICfhcgQOj3HqT+gRbQFT76/sBZs2aZdkZ6v1c4DH1fygUXXCD67jx9SbxuIxbavlPP+f333+XKK68UfbG1Bux0G1N9z0o0yUqAUPO7+eabRQMImjRQN3jw4FzF7Nq1y7x/MLSdqq440iCpXSmWAGH2c/SLgvZJcnKyXVUiHxcFgjjOlFOffqtevXqestOnTzereI8cOWLG+nfffSclSpSIuRf0vaK6CkmffNQn7xYvXizly5fPyk8DER06dMhaCRzt9qKaAQHCmLvFMycyxrwzxvTepd8rtE/OOOMM833gvPPO88y1kggVYTx4YzwsWrTIfOfVd2+GS5s2bZKrr746a/eNMWPGyO23354Il6nv28g488Y4i+ZCIkAYjZI7xzCOvDGOli9fbuZuatWqFfZCePnll6VXr17m8zPPPFN+/vln5ircGTaWS2WceWOcheZP9HeRvu7okksukXnz5uUaR5MmTZLu3bubfm7btq3owgYSAkEQIEAYhF6kDYEV0Al8fdJfk24xOmLEiHy3/dRt/c4999w8PR5++GGzNammiy++2Kz2Oeecc2TNmjXy73//W5YuXWo+0+Mef/zxPPPQZfY53y+mef7444/m+HHjxp1yngb5cn6R1S1M9WarT0lp0olK3eZMgwt6I9aVjNpODW5o0pV6H3/8cYH6OOc7LN555x159913TZ7qoFuvhpLWV+udPWXfQkC3YnzjjTekTJky+dZJv1iw9WiBui1uJwdxnCmebvepq4JvvPFG84CA/vug2z299dZb5hrWpCt6P/nkE/NvQkFT9h+m+m+LrnauV6+e2d5Yx/TcuXNNEboFh27FkVfav39/rq2HdUI2tIJaVz1mf0dagwYNRP9H8rYAY8wbY0zvr3pP1fuv3p/03njRRRfle/HoE7EpKSnevsB8VjvGgzfGg17/PXv2NE+H63dRvZeEtsHXwKDes/R7rT74oqlVq1Zmi2zeOe2PAcc488Y4i+ZqIUAYjZI7xzCOvDGO9H6lqwN11dI111xjfl/pg5j68LfOrWjAYvbs2eYi0ddF6PxRu3bt3LloKNWyAOPMG+Ms1HHZd1mqW7eu2XHpwgsvNN8HdWyNGjXK7MKicy26wCLahRWWLwxOQCDOAgQI4wxOcQhYEQj9YIr2HF25owGAvJJu76nvz3v11VfDZqdPRb/yyithJz90Gb1+MY026cSKrv7JmTTgp0ECfSdhfkmDom+//bYJYhQkWXHUYGXOgGL2VZLR1mPdunVs2RYtlsvHWbk+tKp+GWf6knp9WX24pF949QelHcHBUBm6Ili3K9X3muWVdGvRqVOnhl2taPV9NVqeru4leVuAMVbwALwdYyz7Svhor5hw9/Foz+e43AKMB2+Mh1CAMJprVL/L6tb7JUuWjOZwjvGAAOPMG+MsmkuBAGE0Su4cwzjyxjiK9n6lQUPdYvSGG25w54Kh1JgEGGfeGGfZO08XTegiinBzGroLiy46CL2iKaaO5yQEPCZAgNBjHUJ1EMguYOeXhVC+H3zwgQkC6jtVNECnq3EuvfRSufPOO80TafkluwKEWsbOnTvNF9gPP/xQVqxYIbt37zZbPemWGFof3YpUn3yzapBX/a3kQYAw8cagletDdfILEHppnE2ZMsVshavbqG3ZskV0dZ6ujtAVQ507dzZbYzixynXBggVmIlXfa7Z161az+qh+/fpmpUakF3gTIAzm+GOM2duvsY4xAoT29kOsuTEeYpXL+7xYx4Nur6/3Kd3yWvPQVYPbtm0z2+/qU+H6RHiTJk3MvTLSSlt7W0RudggwzuxQPJlHrOMsmloQIIxGyZ1jGEf2usc6jvTepNsYLly40Oz6pL+vdC5FgxflypUzv7PatGljHszW+xfJXwKMM3v7K9ZxlrMWOt5eeukl811R51L0dSy6W5vOUd57770RdxSzt1XkhoDzAgQInTemBAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQ8I0CA0DNdQUUQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcF6AAKHzxpSAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgGcECBB6piuoiApEu/92s2bNRN+HV9Ck7+gaN26cfPfdd7Jr1y7z/rumTZtK79695U9/+lNBs+d8BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBzAgQIPdcliV2heAUIDx06JDfeeKN52XNeqXDhwvLoo4/KoEGDErtDaD0CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggEToAAYeC61N8NCgUI77rrLrn77rvDNiY5OVlSU1Njbuwtt9wikydPNuc3b95c+vTpI1WqVJHly5fL448/LmvWrDGfjR49WtLS0mIuhxMRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAa8JECD0Wo8keH1CAcLBgwebFXxOpHnz5slVV11lsr7++utl2rRpkpSUlFXUjh075JJLLpENGzZI2bJlZe3atZKSkuJEVcgTAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIi7AAHCuJNTYH4C8QgQtm3bVj744AMTFMzIyJCzzjorV5X03YRdu3Y1f3/66afl/vvvp+MQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUAIECAMRDcGpxFOBwj3798vFSpUkD/++EPatGkjH374YZ54hw8flooVK8revXulcePG8sUXXwQHmZYggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAQgsQIEzo7vde450OEM6ZM0datmxpGv7EE0/IQw89FBahdevWMmvWLClSpIgcPHhQihYt6j0waoQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIWBQgQGgRjMOdFQgFCOvWrStHjx417wHUAN2ZZ55pVvL16NFDmjdvHnMlXnjhBbnnnnvM+fruwfbt24fNq0+fPvLss8+az1esWCFaJxICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4HcBAoR+78GA1T8UIMyvWRrUGz9+vJQpU8Zy63XF4L///W9z3tdffy0NGzYMm4e+e7B///7m848++kh0RaGV9Msvv+R7+KFDh2TVqlVSqVIls52pBkJJCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAt4S0MUs27dvN5WqV6+elChRwlsVpDYIxCBAgDAGNE5xTiA5OVnatWtntgE9//zzpVSpUuYf3nnz5smoUaNk586dpvBmzZrJ7NmzLW/72bt3b3nxxRdNHitXrjRlhEsvvfSS3H333ebjt99+Wzp16mSp4dEEOy1lyMEIIIAAAggggAACCCCAAAIIIIAAAggggAACrgosWrRILr30UlfrQOEI2CFAgNAORfKwTWD37t2SkpKSZ35bt26Va665RpYuXWo+HzlypNx3332Wyr799tvl1VdfNeesWbNGatasGfZ8PU6P1zRx4kTp1q2bpbIIEFri4mAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMDzAgQIPd9FVDBKAQKEUUJxmDcE1q5dK3Xq1JHDhw9LrVq15KeffrJUsXiuIIy0xejGjRvNexU16U2lcuXKltrCwQgggAACCCCAAAIIIIAAAggggAACCCCAAALOC2zZskUaNWpkClq3bp3UqFHD+UIpAQGHBQgQOgxM9vYLXHfddTJjxgyT8aZNm6RKlSpRFxLPdxBGqpQGEKtVq2YO02DhWWedFekUPkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIswBzuXEGp7i4CBAgjAszhdgpMGDAAHnqqadMllaXcz///PNy7733mnOnTZsm7du3D1u1Pn36yLPPPms+X7FihdStW9fOZgg3FVs5yQwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHBEgLlcR1jJ1GUBAoQudwDFWxfo37+/PP300+ZEqwHCOXPmSMuWLc25TzzxhOiKwnCpdevWMmvWLClSpIgcOHBAihUrZr2y+ZzBTcVWTjJDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQcEWAu1xFWMnVZgAChyx1A8dYF2rZtKx988IE5Uf9hrlq1atSZ7Nu3TypUqGDeYdimTRv58MMP8zxXP69YsaLs3btXLr/8clmwYEHUZUR7IDeVaKU4DgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcE+AuVz37CnZOQEChM7ZkrMDAmvXrpXzzz9fjhw5IjVr1pQ1a9ZYLuXaa681gUFdGagvlM3r3X9TpkyRrl27mryHDRsmumrR7sRNxW5R8kMAAQQQQAABBBBAAAEEEEAAAQQQQAABBOwXYC7XflNydF+AAKH7fUAN/icwffp0ueaaa0zgLq+0detW8/nSpUvNx8OHD5d+/fqdcuj48eOlZ8+e5m+DBw+WRx99NFdW2bcZbdeunaSnp0tSUlLWcTt27JBLLrlENmzYICkpKaJBybJly9reT9xUbCclQwQQQAABBBBAAAEEEEAAAQQQQAABBBBAwHYB5nJtJyVDDwgQIPRAJ1CFTIEaNWqYlYGdOnUy23rq/3/aaaeJBuw+/fRTGTVqlOzcudMce8UVV8jHH38sxYsXP4UvmgChnqCrA3WVoKbmzZtL3759pUqVKrJ8+XIZOnRo1spELfPOO+90pIu4qTjCSqYIIIAAAggggAACCCCAAAIIIIAAAggggICtAszl2spJZh4RIEDokY6gGpkBwvXr10ek0ADimDFjzOq+nCnaAOHvv/8unTt3znqXYc58ChcuLIMGDcpzBWLECkZ5ADeVKKE4DAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcFGAuVwX8SnaMQEChI7RkrFVgXnz5on+b+HChWZbT105uHfvXilVqpRUq1ZNGjduLLfddptZXRguRRsgDJ0/efJk0XOWLVsmu3fvlkqVKknTpk3lnnvuybccq23L63huKnYokgcCCCCAAAIIIIAAAggggAACCCCAAAIIIOCsAHO5zvqSuzsCBAjdcadUBISbChcBAggggAACCCCAAAIIIIAAAggggAACCCDgfQHmcr3fR9TQugABQutmnIGALQLcVGxhJBMEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBRAeZyHeUlc5cECBC6BE+xCHBT4RpAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQS8L8Bcrvf7iBpaFyBAaN2MMxCwRYCbii2MZIIAAggggAACCCCAAAIIIIAAAggggAACCDgqwFyuo7xk7pIAAUKX4CkWAW4qXAMIIIAAAggggAACCCCAAAIIIIAAAggggID3BZjL9X4fUUPrAgQIrZtxBgK2CHBTsYWRTBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAUcFmMt1lJfMXRIgQOgSPMUiwE2FawABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPC+AHO53u8jamhdgAChdTPOQMAWAW4qtjCSCQIIIIAAAggggAACCCCAAAIIIIAAAggg4KgAc7mO8pK5SwIECF2Cp1gEuKlwDSCAAAIIIIAAAggggAACCCCAAAIIIIAAAt4XYC7X+31EDa0LECC0bsYZCNgiwE3FFkYyQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEHBVgLtdRXjJ3SYAAoUvwFIsANxWuAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwPsCzOV6v4+ooXUBAoTWzTgDAVsEuKnYwkgmCCCAAAIIIIAAAggggAACCCCAAAIIIICAowLM5TrKS+YuCRAgdAmeYhHgpsI1gAACCCCAAAIIIIAAAggggAACCCCAAAIIeF+AuVzv9xE1tC5AgNC6GWcgYIsANxVbGMkEAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwVIC5XEd5ydwlAQKELsFTLALcVLgGEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB7wswl+v9PqKG1gUIEFo34wwEbBHgpmILI5kggAACCCCAAAIIIIAAAggggAACCCCAAAKOCjCX6ygvmbskQIDQJXiKRYCbCtcAAggggAACCCCAAAIIIIAAAggggAACCCDgfQHmcr3fR9TQugABQutmnIGALQLcVGxhJBMEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBRAeZyHeUlc5cECBC6BE+xCHBT4RpAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQS8L8Bcrvf7iBpaFyBAaN2MMxCwRYCbii2MZIIAAggggAACCCCAAAIIIIAAAggggAACCDgqwFyuo7xk7pIAAUKX4CkWAW4qXAMIIIAAAggggAACCCCAAAIIIIAAAggggID3BZjL9X4fUUPrAgQIrZtxBgK2CHBTsYWRTBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAUcFmMt1lJfMXRIgQOgSPMUiwE2FawABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPC+AHO53u8jamhdgAChdTPOQMAWAW4qtjCSCQIIIIAAAggggAACCCCAAAIIIIAAAggg4KgAc7mO8pK5SwIECF2Cp1gEuKlwDSCAAAIIIIAAAggggAACCCCAAAIIIIAAAt4XYC7X+31EDa0LECC0bsYZCNgiwE3FFkYyQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEHBVgLtdRXjJ3SYAAoUvwFIsANxWuAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwPsCzOV6v4+ooXUBAoTWzTgDAVsEuKnYwkgmCCCAAAIIIIAAAggggAACCCCAAAIIIICAowLM5TrKS+YuCRAgdAmeYhHgpsI1gAACCCCAAAIIIIAAAggggAACCCCAAAIIeF+AuVzv9xE1tC5AgNC6GWcgYIsANxVbGMkEAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwVIC5XEd5ydwlAQKELsFTLALcVLgGEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB7wswl+v9PqKG1gUIEFo34wwEbBHgpmILI5kggAACCCCAAAIIIIAAAggggAACCCCAAAKOCjCX6ygvmbskQIDQJXiKRYCbCtcAAggggAACCCCAAAIIIIAAAggggAACCCDgfQHmcr3fR9TQugABQutmnIGALQLcVGxhJBMEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBRAeZyHeUlc5cECBC6BE+xCHBT4RpAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQS8L8Bcrvf7iBpaFyBAaN2MMxCwRYCbii2MZIIAAggggAACCCCAAAIIIIAAAggggAACCDgqwFyuo7xk7pIAAUKX4CkWAW4qXAMIIIAAAggggAACCCCAAAIIIIAAAggggID3BZjL9X4fUUPrAgQIrZtxBgK2CHBTsYWRTBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAUcFmMt1lJfMXRIgQOgSPMUiwE2FawABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPC+AHO53u8jamhdgAChdTPOQMAWAW4qtjCSCQIIIIAAAggggAACCCCAAAIIIIAAAggg4KgAc7mO8pK5SwIECF2Cp1gEuKlwDSCAAAIIIIAAAggggAACCCCAAAIIIIAAAt4XYC7X+31EDa0LECC0bsYZCNgiwE3FFkYyQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEHBVgLtdRXjJ3SYAAoUvwFIsANxWuAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwPsCzOV6v4+ooXUBAoTWzTgDAVsEuKnYwkgmCCCAAAIIIIAAAggggAACCCCAAAIIIICAo3jAZrEAACAASURBVALM5TrKS+YuCRAgdAmeYhHgpsI1gAACCCCAAAIIIIAAAggggAACCCCAAAIIeF+AuVzv9xE1tC5AgNC6GWcgYIsANxVbGMkEAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwVIC5XEd5ydwlAQKELsFTLALcVLgGEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB7wswl+v9PqKG1gUIEFo34wwHBZYsWSIfffSRfP755/L999/Ltm3bpGjRolKlShVp3Lix3H777dK0adMC1+DRRx+Vxx57LKp85s6dK1dddVVUx1o5iJuKFS2ORQABBBBAAAEEEEAAAQQQQAABBBBAAAEE3BFgLtcdd0p1VoAAobO+5G5BoFmzZvLZZ59FPKN79+4yZswYKVasWMRjwx1AgDBmOk5EAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSSoAAYUJ1d8I0lgBhwnS19xtaq1YtWbNmjVkteOONN5qVgtWrV5djx47JwoULZfjw4bJp0ybTkK5du8rkyZNjblT2AOHy5cvzzSc1NVWSk5NjLivcidxUbCclQwQQQAABBBBAAAEEEEAAAQQQQAABBBBAwHYB5nJtJyVDDwgQIPRAJ1CFTIHrrrtObr31VunUqZMkJSXlYtmxY4c0adJEVq9ebT7T1YaxbjeaPUB44sQJV7qAm4or7BSKAAIIIIAAAggggAACCCCAAAIIIIAAAghYEmAu1xIXB/tEgAChTzqKamYKvP/++3L99deb//u+++6TkSNHxkRDgDAmNk5CAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQSToAAYcJ1eUI0mABhQnRzcBq5f/9+KV26tGlQ27ZtTcAwlkSAMBY1zkEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBJPgABh4vV5IrSYAGEi9HKA2vjbb79J+fLlTYt0JeF7770XU+sIEMbExkkIIIAAAggggAACCCCAAAIIIIAAAggggEDCCRAgTLguT4gGEyBMiG4OTiOnTZsmHTt2NA3q37+/DBs2LKbGZQ8QtmrVSpYsWSL79u2TlJQUqVu3rrRp00buvPNOKVu2bEz5R3MSN5VolDgGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwV4C5XHf9Kd0ZAQKEzriSqwMCx48fl8svv1wWLVpkcv/666+lYcOGMZWUPUAYLgMNFo4fP15uuOGGmMrQm0Z+acuWLdKoUSNzyMaNG+Wss86KqRxOQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHBOgAChc7bk7J4AAUL37CnZosDw4cPlgQceMGd16NBB0tPTLeZw8nANEOr57du3N0G6KlWqyJEjR+THH3+U1157TWbNmmUOTkpKkunTp8s111xjuaxChQpFfQ4BwqipOBABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIirAAHCuHJTWJwECBDGCZpiCiYwb9480a1Ajx49KmeccYZ89913UqlSpZgz3b17t9lONFx6+eWXpVevXuZjDR7+/PPPctppp1kqjwChJS4ORgABBBBAAAEEEEAAAQQQQAABBBBAAAEEPClAgNCT3UKlCihAgLCAgJzuvMCKFSukadOmsmvXLilevLjMnDlTmjVr5njBd9xxh4wZM8aUM2nSJLnlllsslckWo5a4OBgBBBBAAAEEEEAAAQQQQAABBBBAAAEEEPCkAAFCT3YLlSqgAAHCAgJyurMC69atkyuuuEI2b95stvt86623zPai8UjffPONXHrppaYoDRa+8sorthbLTcVWTjJDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQcEWAu1xFWMnVZgAChyx1A8eEFNCioKwfXrl0rul3n+PHj5dZbb40b2cGDByU5OdmUd+2118qMGTNsLZubiq2cZIYAAggggAACCCCAAAIIIIAAAggggAACCDgiwFyuI6xk6rIAAUKXO4Di8xbYsWOH2Ub0hx9+MAc8//zz0rt377hyHThwQEqVKmXKJEAYV3oKQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEPCNAgNAzXUFFbBQgQGgjJlnZI7Bnzx5p0aKFLFmyxGT45JNPyoMPPmhP5hZy+frrr6VRo0bmjLS0NBk9erSFsyMfyk0lshFHIIAAAggggAACCCCAAAIIIIAAAggggAACbgswl+t2D1C+EwIECJ1QJc+YBXRbzz//+c/yxRdfmDwGDhwoQ4YMiTm/gpyoQcGxY8eaLCZOnCjdunUrSHa5zuWmYisnmSGAAAIIIIAAAggggAACCCCAAAIIIIAAAo4IMJfrCCuZuixAgNDlDqD4kwKHDx+W66+/XmbNmmX+2KdPHxkxYoRlIn1XYc+ePc15gwcPlkcfffSUPJYvXy6nnXaa1KpVK2zeL7/8svTq1ct8fuaZZ8rPP/+c9T5CyxUKcwI3FbskyQcBBBBAAAEEEEAAAQQQQAABBBBAAAEEEHBOgLlc52zJ2T0BAoTu2VNyDoFOnTpJenq6+atuMarBwUKFCoV1KlasmJx77rm5Po8UINTPdXVg8+bN5ZprrpF69epJ+fLl5ejRo7Jq1SqZNGmSzJ492+SblJRk6tSuXTvb+4ubiu2kZIgAAggggAACFgSOnzgul425TA4c3ifJxUrLV2lfSeFChS3kwKEIIIAAAggggAACCCCAQGIIMJebGP2caK0kQJhoPe7h9uYXDMyr2meffbZkZGTEFCAMrTDMj0ODhrrF6A033OCIGjcVR1jJFAEEEEAAAQSiFFj26zJp8HKDrKOX9VomF1W6KMqzOQwBBBBAAAEEEEAAAQQQSBwB5nITp68TqaUECBOptz3e1ngFCLdt2ybvv/++LFy4UJYuXSpbt26VnTt3yokTJ6RcuXJSv359adOmjfTo0UNOP/10x9S4qThGS8YIIIAAAgggEIXAyC9HSt+ZfbOOHNlmpNx32X1RnMkhCEQnwCrV6Jw4CgEEEEAAAQQQQMD7Aszler+PqKF1AQKE1s04AwFbBLip2MJIJggggAACCCAQo0CHNzrIO6veyTq7w/kdJL1L5nbvJATsEGCVqh2K5IEAAggggAACCCDgBQHmcr3QC9TBbgEChHaLkh8CUQpwU4kSisMQQAABBBBAwHYBXdlVYVgF2XVoV1be5U4rJ9v7b+c9hLZr559hkFfZsUo1zhcTxSGAAAIIIIAAAgg4JsBcrmO0ZOyiAAFCF/EpOrEFuKkkdv/TegQQQAABBNwUyLmyK1QX3kMY/14J8io7VqnG/3qiRAQQQAABBBBAAAFnBJjLdcaVXN0VIEDorj+lJ7AAN5UE7nyajgACCCCAgMsC2Vd21S4n8tNvmRXiPYTx75igrrJjlWr8ryVKRAABBBBAAAEEEHBOgLlc52zJ2T0BAoTu2VNyggtwU0nwCyCAzQ/yFmkB7C6ahAACARfoN7OfvLnizbCt1K1FDx45aD6f0F7k1v+9ijC5aLKklEgJe16XC7rI8NbDA64X3+YFdZUdq1Tjex1RGgIIIIAAAggggICzAszlOutL7u4IECB0x51SERBuKlwEQRMI8hZpQesr2oMAAsEWOHHihKSOPFvW79kYsaEVS4psvl+kynCR7ZnxwnxTasrZsrZPRqTD+DxKgSCvsmOVapQXAYchgAACCCCAAAII+EKAuVxfdBOVtChAgNAiGIcjYJcANxW7JMnHKwLx3CLt6PGjUubJMnL8xBEpXKio7HlojxQpXMQrFNQDAQQQcFVgx8EdUvWZqnL42OFT6pFUSOTMUif/VLl05urBOhVFVm7PXEW4Zd/Jz3/dL3LsxKlNKZZUTDb12yQVSlZwtY1BKdzPq+xYpRqUq5B2IIAAAggggAACCEQjwFxuNEoc4zcBAoT/n70zAY+qvP7/d/aZ7AkJgSQsCWHfFY3siiCodUFRflYUtbFq9a8UK63iUitYi0WltRZFC0XrggWtFRUQBEEWxQIisiaEJSGQQPZl9v9z7p3lzpbMJDPJLOd9nnnu5N53/bx35k7e73vOibQZ4/5GDQF+qETNVPJAbAQ60kUauc2b+e+ZDvYf3vIhZgyawXPBBJgAE2ACNgLFVcWYt2EeVh9c7cLk5oHAoilAXqpvVMVVwGMbgDUHXfPQ9+yiyYuQm5rLnINEIFKt7NhKNUg3AFfDBJgAE2ACTIAJMAEmEDEEeC03YqaKOxoAARYIA4DFWZlAMAnwQyWYNLmuzibQ0S7SbvrgJnx06CPHsG8acBNWz3RdBO9sJtw+E2ACkU0gWuKqbinZgjnr5mBv+V7HhKgVwMY7gTE9gII3gQYDEK8GdhUC208BV64EDGbn/I3oNgKvTH0FE3tPjOxJ7YTeR6uVHVupdsLNxE0yASbABJgAE2ACTIAJdCoBXsvtVPzceIgIsEAYIrBcLRNojQA/VFojxNcjiUBHu0jr/ufuKG8oh1wGWKxAt/huOPObM5GEjPvKBJhAmBOIpriqZosZK/auwBObnsC5hnMC+dnDgV9fBox43TkR++4HFu8AVu4Tz8Xp0nH1ZY9h/KD/Q2FaNuLlijCftfDqXrRb2bGVanjdb9wbJsAEmAATYAJMgAkwgdAS4LXc0PLl2juHAAuEncOdW2UC4IcK3wTRREDqIk2rAJptlidLpi3BwwUPB3WoFH9Q9ZzKo07jU0aOQxhU0lwZE4htAq/sfAW/XvdrBwSyoHvkskciGgqJhHf/525hDIUjgSFdgTnrnENaMg344Szw1h7x3PBxz6FH3xuE9xfrEjE5PhUXaRMQx0KhX/eBv1Z2FBdSbxLjPVKcSI0SoPiP9hTusSBbslId19MT1baTbKXq1w3EmZgAE2ACTIAJMAEmwATCigCv5YbVdHBngkSABcIggeRqmECgBPihEigxzt+ZBAJxkSbtZ7wqHinaFJ9dnzl4JhZPXRzQ0NzjD9oLcxzCgDByZibABFohMOmfk/BVyVeOXJNyJ2HjnRsjgpuv7+yKxkoYzHphDNvuBl7cDvznsHNIN/YHHh0DjF8unpPLVVBrxWCFcpkMA/pciyvGPIFBmnhcqk3EQE0cFDJZRDDprE76Y2W3r9zTknNYJhBJsSB9WamuuNGT/OyPnVaqmfGZWDhpIe4acRcULDx31m3K7TIBJsAEmAATYAJMgAn4QYDXcv2AxFkijgALhBE3ZdzhaCHAD5VomcnoH0cgLtICpZGb0gvFj5QEVMw9/qC9MMchDAgjZ2YCTKAFAhR/MPH5RDSaGh254lRxqHu8DnKZPKzZ0Xd27yW9cLLmlM9+5qcBX9wODPgbYLI4synlwKEHganvAEVVnsVTErNw18+3OC6Qy1GyKLxEl4QcpRoyFgt9Mm/Jym53GfBriSXnK1OBi7Mi08rO3Up12fWeSAo/cVqpLr9huSAOcmICTIAJMAEmwASYABNgAuFOgNdyw32GuH9tIcACYVuocRkmEAQC/FAJAkSuokMI+Osi7UIT0GRydkmnBNJ0zr/9dZF28esXY+/ZvT7HRgv3vlJLC/cju43E7l/u7hBm3AgTYAKRTaCj46q2lZbZakWF2YhSox5lJgPOmPQ4VleOl/55GcwWo0u1ZOdntZ0ZlQUcrAAaXLMIV+NVwIB04HsvYV0VchUKZ22DTpfm0eVuSjUu0SXiYm0ikhXKtg4paOXoWVHwZgEaDHWIVydiV+GuThd3fVnZnawBvpLslZmUC+QkRaaV3eSVk7HxuGhpS1aqBTnASzuAAxXA4Axg7mhg52mnlerkvMnYcMeGoM07V8QEmAATYAJMgAkwASbABEJFgNdyQ0WW6+1MAiwQdiZ9bjumCfBDJaanv8MGT/H6kl9IhsVqhFymQs3vatoUp681F2m9UwDtAsAo0e5UcqD5SaCkGnhsA7DmoOuwZwyagUWTFyE3NddxwWKxQLtQDaPFFsQwiKTUCgX0T0oUzCDWzVUxASYQXQSk8Qf7pgFHL4jj68w4hHVmkyACii89zpgMKDcZQCKhe6qpPYmtOxfh2HGJWVoLUyQdo69so/J/hhGXPoqExJwWJ1sGGfppdLhEm4ih2nioO8ni0l3k3Xf/PgzLHBYWN6rUyu4XI4D3fgQaJY+nOBXwf4OBf9j2ykSKld2J6hPIXZILK6wgK9X1s4BZHwHbJcasY3sAb08HprwtWqnS/VIypwQ9k70EKwyL2eJOMAEmwASYABNgAkyACTABkQCv5fKdEI0EWCCMxlnlMUUEAX6oRMQ0RXwn3//xfdy2+jbIZYDFCnww4wPcOvjWNo/Ll4u0v18L/OITz2r/cT1w/1rAINH7RnQbISyyT+w90aPA4crDGPC3AR7nFTKgW4J4ut4A1IghtCBd1E7WAAlq8bw3a0U6f+jBQ+if3r/N4+eCTIAJRAeB1uKqnm86j2ZTszDYlTcCd34sjlun1CHNi/WcnUpb4qq6EzVZrTjrJgSWGQ2oswS2wWHL9udx6OjH0BvqYGmlrHSM3mY4RZOCeHW8YIE4of+NuGz071BiEPm0lDQyOYYLLkgT0Uel7VAXpEt2LsGcdXMc3VsybQkeLni4tS4H9bqv+4ws8/W2WJDLbwDu/o9ns9LzGoUG6XHpjkzBuM+CMVD38dXqa1FnqBOqJivVI+eBWtvzWtpekkZ8ftutVJPUSUjUJIbd+ILBiOtgAkyACTABJsAEmAATiB4CvJYbPXPJI3ESYIGQ7wYm0EkE+KHSSeBjrNlJKybhqxNfOUY9qdckbLxLdP3V1uTNRRq5DSP3YZSkop30fGZ8JhZOWijEGlLIFT6bv+XDW7Dm4Bq4uxIll6UkAtYagEabWzzpoja5xUtUA9V6oNltHZ1cj84YOAMf3PJBW4fN5ZgAE4gSAoHEVc2IA8oeBbIWAxXOcIQ+SQQSV5X6UWsxo9RmDUgiIFkGnjUbQdfak6j8ivcmoqbOi59Qt4rbOsYKkwG7m+vxXVMdqsxefJW6tZOqUGGULkGwLMxQ2nZztGeQrZS9/t3r8d+j/3VskLm+7/X4z8+9KHEh6oM/9xlZ2ZGl4IKtns/PpyYA7+73HgsykPssRMMT7tHcJb1wooVYl/a2aZMSbSR6YK24Wam1FA7ja62PfJ0JMAEmwASYABNgAkwg9gjwWm7szXksjJgFwliYZR5jWBLgh0pYTkvUdSrh+QQ0GBsc44pXxaP+ifqAx+nNCqLR2Iiq5iqhLruFIr2XinbS86naVMSp4lzadreCCGTBsa2L2gEPngswgSASIN3HbAYMBsBkAoxGzxedD/Q6ldHrgS++ACwW52v8eEBniwUqkwH2l/C5lYt/U3K/1pl5v/tOZKNQiH0cOhRISxP/tp+zv2/tSOXd81TpKzF4RTdYrK6ujKWWysSke6L4fTYwQ4zXR1aEZ0TjKCF5s1RWyBQo/025i7UX5TVYLYI7UHILSvEC6UiuQhvd3CnT/UGhAy0mme1lf0/n7efoKP5tNslgNQE6ixKpVhVSLCokWVWAoRq/Le4Nk9XgcvcGY4xKmRofXFqK/Kx0ZGTQ3FhxStYsCIX79PXQ0w3YSuqt1gpC4QhtAuJa2DDSWj2+rtMGk/iF8Wg2O60cyfqTnn8txapta3veyvmK3yvNS664pa653S053a9Lyz4w6gG8du1rwexyQHX5Mz6qsIsO+Px24JJs4LtSYNq/AIpZ3FJSK9Q4+evTHp+jgDoYgsz7m8XfU4M1cZDLZFDYv0BD0BZXyQSYABNgAkyACTABJhB+BHgtN/zmhHvUfgIsELafIdfABNpEgB8qbcLGhQIgQPEHVc+pPEoYnzJ6xCE8dw5ISADiXPU7oSyJdql/SkaNXrIy7qMfgYp2SapE7Jxe6xA0zjdV4qp12TBagr+orZCpMb/vUcQrnW7a7MNop7FOALPimbWyzoyjp82gPlgtMqiUwEV5KgcTqyD2yEC+/UhYoiNZYAhr8NJztnxifqqL5g4g/YEMNuznqC5HHvt1oW1pHrEM9cdZzq1OWx8oT73egqOHZLCSAAUraL1/2CA5ZHKrIDyRQAOZKPbQNToniFP0tyBSWYUj5aG8gjjlNa/VWYYELpuAZC8ntiHW5ajb0YZTEJMrxH5J8+hNFhwoNYm8iJ1ZhqE9VZBbZYKIZzYBJiO9ZOLRBJjpPZ032K6bbNeEfJSHBBzJdRJ0gh9e03FT0Wf1WLHrPdarJ6BW2VTAdt2pHVe4+JQFZoNduQS6dwfidc4xUHwz8cvJs09+GCfBAisOj+sFg6bco4KbBwKLpgB5qb7HW1zlPa4qldCZs3DFhSMoLgEMJissagusSgt08YBKbhP2SPwTxD6b6Gemv+mzKoObZunRCYrXppbJQK471XLxSC853fxuqUlbjKI+81CZsdrlSnvGmHFuBvKKF0HX7IwdS5UnJ0MQC1O7WGFM1aM6uQnVic3QdTFB18UsvFRxnrOjlMkwRBOPUbpEDFDHBU1wcY8/aAfQ0XEIiy4UYfjSoWgwtqKIAQj0+Zlo7Yl7ux1A11wzuueZkZxphVVmFb/3hSiA4pGeF9L3zmv0LHHmozwUz1LMS2XEsmZ7XZJr9nKVNSewYccfcbD4C4/7j1x+v/Ez4Lahnp8lsoy871PRZbh76ps3DeMK5iE5qUfHfen40RJ9v54w6UEugOm+zVPrsDAjF1rh4cWJCTABJsAEmAATYAJMIBYI8FpuLMxy7I2RBcLYm3MecZgQ4IdKmExEFHfDHn/QfYjvTv8AF2tvxZtvAuvXA1VVQHMzMHgwkJ4uikQkYtitkJrkFdg4sqsg3khTMCxRaCVyzPYKqI1O0S4Ui9q68ulIProAyqbeYTfj1iQjakudQi6JVknxYdfNFjtkTTShtkzpyEPiW5ItZmSkjMSqNqP2vKvrWxpDJBmI0IJ+bZ3rBzUhHlBE2Pp1vdECc7Oz07RxQeXbK3HAt5hZVYkzE/sCci/qBAC1Ath4JzCup2fV204CV650javqksuiRtaOw9Cb0mCodH4mtFpA47lfo8W+KwXxzyYG2o4q4YYMTPCtTt6CI/1+hcb4nxztBTpGxbkR6HHgFfSWecaO9TUIk9WCOotZeJEFpVJnhS5NFAuFl/19mkX4mwTG0ZnxuFSXiGylul3xCt3jD9r7GMo4hI2NwIULwPnzztfJyko8dq4brAi+tSqscnTffBwK2/OT+Kb0NiK5lxHJvY3C+6SeRig1AX9EAi5wumwXNmx5AnV1Jx0uRMn6cdNs35+jSf90Wk+St4HExJ6YMvF55GQVBNx+RxSot5gFK2B7Usvl6KvW4TJdEibEJSNNEeAHvCM6zW0wASbABJgAE2ACTIAJBJUAr+UGFSdXFiYEWCAMk4ngbsQeAX6oxN6cd/SI3eMP2ttPrZ6E4Xs34swZ4OxZZ69IHMzJ8eylQVWJ7aOzALlnjKn2WKIILVlUGLOjzEUgtPeAFrWP5c9BfeJe54JcgAv3qtphSDm8CJqq8R2N3+/2rMlG1J6OcIEwwYTaM04xhPSL5AgTCKExo6YywgVCimlX7yoexccDyggTCOv0VljsFoQQLZuDKRDSh9OkO46afk+iKfNjr5/V2cOBFTd6Xpr9MbByn/ePt3QjQpPSBEOV8zNBbl7Vko+ItAay/rNbA6ptoiAdvVkF+v3F4pbxeK8/4ETuMy5n/RqjIQG6La9A/dNdyO6uQNeubemBFXoh3qIJ9WazYJHmK8mVEMTCLl2Aft2UGNZdjR5dFYJ4SM8oOtK1x76ci1UHVvmsp7KhEnqL3uO6Vq5Fl/guPst5ur0G6uqcgp+7ACj9mzbauCcS7XeOzoZe4xkLsr3PT6U+C922HG15Qkh4yzKJgiEJh7niUZtmCfrmB4vFjD37V2Dn93+B0SgG7PTnHlOp4nHZxf8PI4feBXkI3M225Y71Vua0SY9miftcEgh72tRXsuwdqo3H5XHJ6K3StkvcDlZ/uR4mwASYABNgAkyACTCB4BPgtdzgM+UaO58AC4SdPwfcgxglwA+VGJ34IA774tcvxt6zTvHMpWrBxZgzDlTfNODoBVsOYW1W7tU9Hwk7CfUjMer73S7V+bLqo0yBWqLYK/blqo46ZrBa0Wg1o8FixJm8p1Gfu9hhNOPPgiOtPycc/w2Sjz0NGYJoehTE+XNUlWJEzakIFwjjTagtj3CBUGtCTYWrgtPhFoTkIlVJLysUKqv4XjhabUfxmv28wu28TGnBWb1Z+KzYZcKMBDnUZO5rczPo0GasMsEVocNbp80lrXBfCufJWazzO8Mlr62clXzKUnIrK5SzewGlPNJ2HGXFPI46BHfG4rlztRbB/Sa5r6W6k3QyYQzk9pVccApucu2uYMkNLr23XRPc4gp/21zk2tzy+vro6lO3orr/PBiTfnDJUjgSWHa9Z6nCT4C39rie97YRodFigbHBqcyKAqEMZAFIVoFSITAQq0ClElCpADqq1eLR/rf0KH1P98lf0RcXUBTwGJMUXbG4Wznq6sT7hWJjVlQ4X95EsZa+JkkwIzZ1FhMaLHZnly2VkCFOLkeiXIEEuQIkxEBmxXdjeqFBdSro38hd5L1wv75EsAIk8Y9e5Ea4rcmgqsCO0TmwhshatfuWow4LwkD6qE60ILmXCSm5BtHqkKwNc0zCd0t704HDq7Fh8++Eavz5HE25/AUM7n9ze5sNaXlyL1pvtaDabIKefFDTbx+JQChtvKdKK1gUUnxNjlEY0mnhypkAE2ACTIAJMAEm0OEEeC23w5Fzgx1AgAXCDoDMTTABbwT4ocL3RXsIWCwWqJ5TCvGF/EkrbwTu9G4o41GcFmAnbnaKi9IMZNV3qH8hmuOOuZTzS7SzldA25mPAkTfRpW6iGJOOtEq5KAg2woxGmQkmmRhrDnIrSvteh8bELY72/FlwpMwpzZdj6PlP/MEj5OksV5INMOPMKZnYvgxQqoC8HLkYI49i89ni9xEP8WUFuWMT+JCgJImn58hj4yfU4YjpxQCcCQAAIABJREFUJzKVxt6jhXZ7/WIsQLF+Zz328q5lxdh9Vlv7QIPJjON1RocQREMZ3EVri6soxlYTxCBbbEQhJpYtFqIQU5HEHMd1UYiw55XGQRTyCnXY8kjLkaBlj70oiccorOXay7nlEeM0iuWMMKPosFzkRUwVVvTLkwvzobSJdXbRjo7CyybmKdXiPJCHORL0KI6iQi0KeSTO0Hn70fW6WIdYn21+A/Me6XJ/0yL2D/oGl3MU100TYTGyDugbhDhfYpIJbvxIHBL/cnWwSd9Xwn0rGbU0D10XqqJ7gsRFu5hom3v622y0YFfR+1j9/W9htoqW0tvuBgpygJd2AAcqgMEZwNzRwM7TwPjlYmNymQpX9/gzhqT8HDKLUqjbfj/vOWpGrckMlVkOtVmBsX00uDRHC51KLgh63kQ9X6KfPa89PqffX2q2jNtObsP45Z5W1P6MUWSxDWN7jvVolriSW83KSlfR0C4g2s/X1nrvMT2/BBekZhOabaJLS2Mji0q6D7TaKuwZ09NDdJO6vT5bD5hst5B0g4xSBmTarJvL6ynmnmuLMosao3eUerVqb407iZ8kHtEGFzqS1aTBYoFBVxx0a9X8pOsx2PgHNJ3Ix7liBfT1ohjt+BxIPhPOz4Ygr7rkk35u6P7L7GFBTp4V2XkW9MyzIKePBUnJVK+MthUJgheVER8/MuFZRPMinLO9v/O9a7G95Cu/P0cTcifh3z//vDW8YXGdvmNPmvTY2VSHw4ZGKFtw95usUGJ8XDJG65IQ18mWkRarBQVvFqDBUId4dSJ2Fe6CXPgRwYkJMAEmwASYABNgAkwgEAK8lhsILc4bKQRYIIyUmeJ+Rh0BfqhE3ZR2yICqq4EffwS+3HMIz10YKLER9N18RhxQ9iiQtRioEL1+tZhoke/DiYeQl9xfEO9oUfrFH+bii1OiO7daQxWazK4V+SvaUfk4VRxStakwwYrR/W7AuNGPo9RoAC2uuicSQl99czAsVqcJh7+L2iq5Cs3zmyGPMHGktfnh60yACbSfwNx1ri4qTRYTzjaIPpfz04D1s4BZHwHbJUZqY3sAb08HprwNFFWJfciMz4RSYnbl7qKy/T1tfw2FnxTirT1vuVQUyBj/b/D/4b0Z77W5I3q96KKThENfYmL5eQtqzRSv0CQRh303WXp5D1hVF/wKx+j3BhkroDR2wbjtla2O1WwTAKWCIAmDpBBTLEBtihnaVAt0qeJRm2pGvXob9lU+gdomV7eggTw/R3QbgVemvoKJvZ2xIKlZ4lpUJL6OHROPp0/brHJbHU3LGcila34+0KeP89Wjh7i5xz2dqD6B3CW5wvPc33uM5MWSOSXomewl6Gc7+x7K4udNRnzdWINdTbUOq0Jv7alkciGu5sS4ZGTQbpJOSPvK92HE6yMcLe+7fx+GZQ7rhJ5wk0yACTABJsAEmAATiGwCvJYb2fPHvfdOgAVCvjOYQCcR4IdKJ4GPoGZp0e/ECVEQtL9KS8UBkNuy7WO6eiyOSi0oKF/3RIAWRwdmAAcrRCvCM3VOCN4sKOhqxWMVSI9LFzLSjvncJb1wosa3Ozd/RTt3/CmJWbjr507rQPfr+358B19986zjtL8LjvYCr13zGh645IEImnXuKhNgAqEm0Np32qgs4Mh5oNYzhB2SNABZpH3vGVJO6HZuSi8UP1IS6iEEVP+m45tw2+qZONfgFL4CGeM9I+7BWze4CowBdcCPzGaz6M6zosKKvWf0+K5Uj5/OGlBXKUfTeQWaLiiEI7mepSiGZ69KCtkGmQlfSTeriC6vSQhUJFigSDEBKUbIkk028c8sxPMjQdAuBiq1vi37d37/V+zc/RcXIv48PxUyBV7/2eu4a8RdUPhpjUbuX48fdxUNi4tFq8/2Jo0GyM11FQ1JQHz5+wV46qunhOoDuccWXLEA8yfMb2+3OqV8k8WMXU11glhYZfaM1Szt1CBNvCAUklW0rAPdFizZuQRz1s1xdGXJtCV4uODhTuHFjTIBJsAEmAATYAJMIJIJ8FpuJM8e990XARYI+d5gAp1EgB8qnQQ+jJulxbyDB0Ux8MAB8VVf773DBlUlto/OAuSei1E3DwQWTQHyUn0PtrgKeGwDsOagZx6yvCt7tMwhEFY2ViL7pWwYzAavFQYq2kkrUchVKJy1DTpdmkfd5J7r9Xcno7zaGTcrkAVHqrB/l/449NChMJ517hoTYAIdTaC17zRpf3KSgN9PBH6/BTjtw1WmNL9aoUbp3FLH92dHj81Xe6W1pYIFEY3dPeWm5GL++PlYuHUhjlcf97ien5aPIw8d6VBBgzphsFrwQ3MDvmuuw1F9Eywk1NXKcbJoHz777iaPfgZrg8wdqZvQretINCcZ0JCohzLVJAiA7YnPRy4muytUWPj2eJTXOAXkQJ6fJY+UoFdKr3bdUrTxqLxctDK0WxqStSGda28iq8Hvx/RFvdo11iXVG673WHvHLC1vtlrxo74BWxqrcdzQ3GLV3ZUaTIxPxkXaBJCFYajT9A+m4+NDTj/z0wdMx5qZa0LdLNfPBJgAE2ACTIAJMIGoI8BruVE3pTwgCh9hpW3UnJgAE+hwAvxQ6XDkYdfguXOiCGi3DqTFOoqv5m9q0hajqM88VGas9iiiVgAb7wTGefHYte0kcOVKwEDxstzSjEEzsGjyIuSm5rpcKa4qxrwN87D6oGdbgYp29or75k3DuIJ5SE7qIZxSy+TIV+vQX6NDP3UcMhUqLN6+GI99+ZhXJK0tOFKhFye/iN+M/Y2/SDkfE2ACMULgnv/cg/d+fA/NJt8L+XcMA169BoLVIFkTPvgZ8M4PvgFplVrcPuR2vHnDm2FH0VccwjuG3YFXr3kVSZok1Opr8eBnD+KdH97x6L+vOIQdNdBqswm7m+vwXVMdzpkMOHT0E2zY8gTMZlczz/ZskFEoNLjq8hfQP/9n7RoWuZHMUqqRrdQgSyUek+UKfHPqG49YkIE8P++96F68cd0b7eqbr8INDZ4uSsn60OB9X5DXaqqTt2HvSM9Yl9mVd2BC/atI1iYhI6cWm7QP4pt6z3vsg6nbUNB9rM/4nG2NwRkSYK1UetLYjC0NNdijrxe8MPhKFFNzbFwyxuqSkEgBaUOQKP5g+qJ0VDXb/CIDSNOlCZ4iOA5hCIBzlUyACTABJsAEmEBUE+C13Kie3pgdHAuEMTv1PPDOJsAPlc6egY5tn9yn0S59uxhIwiAJhG1JOh0waBAweDAwdChQmbAFv9v8CPad3edS3ezhwIobPVuY/TGw0jUrhmcOB7mcksY18ta3LSVb8MgXnm3Z8+amAPPHAwu3AservY8uPW0ALh/7JHpkXYYclQb9BVEwDrkqLRRuLrcifVG7LfPLZZgAEwgtgdZcjFLrz5DV4OWe/XjmK+APX/vuXzi6GKXeuschJEFw6bVLcdvQ2zwG8+7+d/HA2gcEwdCeCkcWYtn1y0I7MX7UTnN3yqQXhML/Nddj7Zanse+nfwmx/+wp8A0yMgwfdDuuGP+MHz1wZiHLr+4kBKpIENSIgqBSDY2PuLfeYkEG0mCCOgG1v6vtMEtO+t1CcQzdYxtSPElv6VD/QpR3d7qiVZiS0O/IUmSeu02IP2l3kU5ljQPeRfPkB2BWOu+x7mWF6H+k5XtMqQTopVK5Hn2dt+fzVoaukejoXpe3uu3n1q0D6DfYhAlAZiaQk9PyDJKwvbWxBjubatFo8bIry1acfvtcrBXjFGapNIHcFq3mdY8/aC/AcQhbRccZmAATYAJMgAkwASbgQYDXcvmmiEYCLBBG46zymCKCAD9UImKa2txJcg36009OQZDe673Es/KnAVqEGjLE+aK4P7SoJU1mixkr9q5A4X8LHacLRwLLrvdsofAT4K09zvNvXvem33GN6swmvHt0A+7/4BqPiv21tnn65o8xPf9KIQZPfCuxlKJlUdufeeY8TIAJdAwBby5G5TLAIjH08XeDhXu5cHUxKo1DOLbHWLxz0zvondLbJ/CS6hLMWjNLsHrrGp+O927+AJNyJ3XMBPnZCrl0/EnfgG9qK/DMh9fjXOWPjpL+zl/X9CGYcd27UKt1LbaapFAi22EVSGKgGukKFeQBxJGjObh51U2obq7xc4Se2TrbkpN6VF3tFA1JPCTvBxQvuTJxEw4NmQm9shJJNWMx8OA70DWL9xhtiCorc44nLg7oMawEBwfOQm3yN9CY0jHgxw+QWh1e95jL7yyz6PXB7umhZ09g6VLg0ks9f5O5zxy5y/3WFqewwtSyWWZfdRwuj0/GQHVcUMRgafxBip969ILYO45D2OaPIRdkAkyACTABJsAEYpgAr+XG8ORH8dBZIIziyeWhhTcBfqiE9/wE0jvyHkULX7RwtH+/eCwpAdriwJmEv759RetAuyiYnu5fb745+Q3GLR/nyLztbqAgB3hpB3CgAhicAcwdDew8DYxf7qxz+z3bMbrHaK+NGK0WFBuacdjQhMOGRpQZ9fjP57/E8ZNfueS/ohew6S7PKq5YAWw+4Xr+un7X4ZPbPvFrUNG4qO3XwDkTE2ACISXQkttkatjfDRbSTvpy0RzSgQRQeVldGdYcXIP7R90PpR8B9UwWE5buXoqbBt6ErMSsAFrq+KxPb/kDntvstAD0d/7GXPJrXHrRrxwdlslkgnvrbJVoDUhWgfSeXEEGI922+ja8/+P7Xqvyx232L0b+Am9eH34ubI1GUST89mAZPj6yBlml9+N4kRK1tWTbacGuYQUwyepg1Sci4f1d6JImBwlsFpkJZVlLkVFxEzSG8L7HLlwATp50Th1ZEvbvD6SlAVOnAldfDfQQPab7TGQB+5OhUXA/etTQ2GJeclU7IS4Zl+oSBRfsvtLcdXOx6sAqn9fJtWijUWxr5Y3AnbZQhPGqeKRoU3yWmzl4JhZPXRyM257rYAJMgAkwASbABJhA1BDgtdyomUoeiIQAC4R8OzCBTiLAD5VOAt/OZhsbgVOngG3bgN27RStBWhTr0gVISgq88sREVzFwwABA48W7FMWQKXizAA2GOsSrE7GrcJdH7Jixb43F9tPbhU7kpwHrZwGzPgK2n3L2a2wP4O3pwJS3gSJbOJpxPcZh6z1bhUy0eHXGZHAIgkWGJpjclM6PPivEqdNbXKxt/BUjydpmWp9rsPb2tX7DiuZFbb8hcEYmwARCQoDcJv/ik1/geHVRm7/TclP64K3r32rVRXNIBsCVCs+tvn/ti6KqIgcNf59JGcm98fe7dyFHrRXEwEylCuQ6NFSJrLnmrp/jcq9RW/7EgqTn58tXLcHDlz0cqu4FtV766VBZCXyxZx/u3DHCUXfaB/swMG0YunUDSFg0mcQXvW/LxqqgdrqFyo4eBShWoz2RFWS/fq4FaGPXNdcAEycCdL2lRBuutjTW4PvmOpA1rK8UJ1dgtC4J4+OSkewWp9Afd8n2ejPigLJHgazFQEXL2qRQJFzdJXfUfHM7TIAJMAEmwASYABPwRoDXcvm+iEYCLBBG46zymCKCAD9UwneaKO5OebkoBLq/7LF3SBg0SLxEZWUBXbu2PiaKV0NxA+0WgrSD3h8PZe4xZNxjx1gsFiifU8Jqi8M0Kgs4ch6o9eLWNEkDkJup78+I/ZVBji9+fRYnzUacNOlBbkRbSqdKd2Dt+l+h2VAvZAtEjEzVJuPft64JO1d1rc8c52ACTCBaCZCL5qe/ehrPb3s+4O+0+ePm49krnoUiSBZm0co4lONyj1UbyDOJ+tWRbjtj0W221MUl8V48eQkeuOhhIZafeyL3nVLR0C4cejv6OudvXmqHfu/Z23M/utdDYid5i6AjlSMBkDw+eEtaLXDFFaJVIYmGLf3Oo99c3zTV4pvGGtS3EKeQ3NmO0CRgYnwyeqq0QrPe3CXTeYUM6Jbg7FlmPHCwUozUKQMwMB04KxE7y+sBs5tGGa7ukkP5XcJ1MwEmwASYABNgAkygNQK8ltsaIb4eiQRYIIzEWeM+RwUBfqh0/jTW1HgXAUtLxd3sLSWyGqyyWeBRPnIxRWKfNKlUAFkE2l2FDhoEpPj25tRie+4LbO6xY/727d/w0OcPtRnq4IInMHLoHUh3253urUKNXI69uxZj3f9eEy4HIkbOGzsPf5r8pzb3kwsyASbABEJBYMHXC/DUV08F/J224IoFmD9hfii6xHX6ScBddAvkmURNFI4sxLLrl/nZWvuyxaLb7OkfTMfHh2x+LQFMHzAda2auaR/ITixNVoRffQV89hlw8GDrHaGNYSQUkhtS8jbhK5FL9/8112NzQzXKW4lTmKvWYmJcCoZq4lFSfRzzNszD6oOrXaq+eSCwaAqQlwqsOgDM/Lfz8oe3ADMGAcVVwGMbgDVu4wh3d8mtU+ccTIAJMAEmwASYABMIDQFeyw0NV661cwmwQNi5/Ln1GCbAD5WOmXzaEU6CH1kCUuwYOp4+Lb6vq2t7H2gHOdVjT/HxwKWXOsVAEgXJ9ZRS2fY2pCXdF9iu7ncDXr7pXdRazKixmPB/S4fgfH2Z18YSE7JQcPFD2PX9q6jzkUejy8RNt38lxF5yTzLI0FOlQX9NHPqrdeip1GDAq/1c3LnZy7QWQyk/LR9HHjoCivPEiQkwASYQDgS8uai09yuN4ox1AQ6fBy40efaWv9M6fwY3HNuAq/51lc+OjMkZ43C/7S3T+tvXY0r+lA4bSCy5zSb36OmL0kFx8JyfqTRUPFbh4Sa9wyYgiA1RvOnPPwfWrweqq1uumH72FBSILkhHj/b9+5C+j44YmrClsRoH9S37Ak1TqATXo5fpkrDr5FbMWTcHe8v3OjqiVgAb7wQWbwc+Puzs3/QBYkzqK1cCBrPz/IhuI/DK1FfYXXIQ7xGuigkwASbABJgAE4guAryWG13zyaMRCbBAyHcCE+gkAvxQCR54Ct1Crj+lIqDdNSi5Cg12TBta5KHYgcePA/n5wLBhwIQJojuptupetCBErqXsgl8dvTeL4l+12YhHXh+ERn2NA5pWk4L7Zu+CzBYr6Z8fTENVtTP+kj3jwL434vJxT0OjToTeUIevtj2LQ0f/4wE/ITkPV8/4L7KVauEaLTrZBcG+ah0oBo49ubtzs5/3J4YS5e1Id27Bu8u4JibABKKVgK/vNIrZJY3V5f634zvx7m0Y23NstOIJ+3HNXTcXL+982fO5pkrAt/d+i4EZA3Gw4iAuWXYJGowSv4q2Eo9e9ij+PPXPYT/OSOygu3t0+xjc3aRH4tikfSavE7t2iVaFO3cC5Cq1pZScDFx1FTBtGpCX5zvnWZMBXzfW4LumOpCFoa9Enh3S5Cpcn5CCb35ahSc2PYFzDeeE7LOHA2uPkjtSZ2n6Lru6L7Byn3guMz4TCyctxF0j7mJ3yZF+M3L/mQATYAJMgAkwgZAS4LXckOLlyjuJAAuEnQSem2UC/FAJ/B5oanJa/7nHBmxuDry+1kqQCNijh/NFLkTpb4o3qBZ1tFaT2Sb8kdBHgh8Jf8J7QQC0HS0m4bzFh5JZcf4g/vXv6z3aun3Gf5HRZYBw/qPPCnHq9BZYbDFkVKo4XDlhAQbkX+dR7tDRT7Bx61MwGsXVIrkMSM8ejxuuXoY7U7qhvzpOcDXqy8ovFmMotTrRnIEJMIGIJeD+nUbxu8jypsmLq2mdEtCb4fiupUF3pIvKiIUcwo4nv5CMWn2tSwtX5V2FdXes82h1yttT8GXxly7nkzXJqP5dK+ZfIex/NFctdY9OsY+PXhBH6+4mPZoYXLggWhSSWEi/VVtL/fuLLkivvBJIkMQNlJZrsJixo6kWWxtrhN+OLSWdXIGUkxvw27X3CdnuGQ78wyYESsvdPRxYbju//IblgjjIiQkwASbABJgAE2ACTKBlAryWy3dINBJggTAaZ5XHFBEEYvmhUmEyoskq8WkkmTHadV1RDpSdlqHslAxn6EivkzJU2RaWgjnB5AK0WxbQLduKrB62V44V3XtYkZTsuyUS/mjBhqz+SNyj0VhgFUTAWocAKF63wqbatbHje/avwJbtC4XS0gW2iWOexMihs4Xzp0p34LMv/x+ammvQPfMiTLtyMZITc4RrWrkcyXIlEuUKJCuUSJIr0FhXhj+u/SV+KNuFFG0q3rn5A1zrp4u1WIyh1Map42JMgAlEAIGNxRtx9b+ugtGL2U9rbpNVcjk+v309rsy7MgJGGp1dvP/T+7Hsf68Loq1CpsB7N7+HWwbf4nOwHx74ELetvg1mq1nYIHPvRfdh6c+WRiecEI+KrDdXHVjlsxVyLdpo24y08kbgTlsownhVPFK0voMyzxw8E4unLg5x70NbPe35+uknUSikmIW0ya2lRBvPxo8XXZCOHOndIwX99tzbXI8tjTU4ZfS9M271p7NxqnS70NyzlwPPbPZs+Q+XA0/bzk/Om4wNd2wILRCunQkwASbABJgAE2ACUUAgltdyo2D6eAg+CLBAyLcGE+gkArH8UPlHdTl2VzSgvkyFulIl6sqUqC9TorZUiYZyJSwtb45u04xpUy1IzDIJr4Rso+N9XFczJN4z/a671GRAk8UpcpLwluElfp8/FW7Z/jyOFn/mM2uzvhYmk7iyJF1gUynjoNMkQU7+osm3qcUMo0kPnSoOCrkM0wfdilemLoba5obUvQGTxYSlu5fipoE3ISsxy5+uOvLEUgylgMBwZibABCKOALtNjrgp8+jwumPr8LuNv8PWu7ciQe3DDEtSqt5Qj/HLx+OFK1/A1PypkQ+gE0ZArtFzl/TCiZrWzeTIpWXZo0DWYle3vb66nZvSC8WPlHTCqELTJImDW7aI8Qp/+KH1NjIzRatCckFK790TsT9ubMbmxhr82NzgshGttq4U/3j3CgBW5KcBeSnA+mKxBukms6l5wLEqoKgKoFjTJXNK0DO5Z+ud66QcFM+y4M0CNBjqEK9OxK7CXVERx7KTcHKzTIAJMAEmwASYQBsJxPJabhuRcbEIIMACYQRMEncxOgnE8kNl7G1NOHmyfVZ13u4KhdpqEwBFIVAQA21HVVxw26O4MGQ5aE9tFQhpkWfFuxNRU3+m1Rs91hfYWgXEGZgAE2ACbSDAbpPbAI2LxDyBysZKZL+UDYPZ4MKCXPR2k2i03RPFzU0DM4CDFaIV4Zk6Z5HyesDs9hNNrVCjdG4p0uPSo47z6dPAF1+IL4qf3VKivV8XXywKhWRd6M29/bClI/HjOafqSL8rSRz0lqSbzNyvk0godS0/sttI7P7l7rDhL41nSZa/A9MH44cHfmCRMGxmiDvCBJgAE2ACTCA2CMTyWm5szHBsjpIFwticdx51GBCI5YfKlb9swJEDsrbNggyIyzA7BMDEbBMSuptAR10XM2gxpSNSpdmIakkcmNYEQlp0IdeeSXLRxWeSQnxvba7BDUsHwMgLbB0xbdwGE2ACTMCDALtN5puCCbSNQHFVMeZtmIfVB1e7VHDzQGDRFCAv1Xe9xVXAYxuANQdd88wYNAOLJi9Cbmpu2zoVIaXMZmD3btGq8JtvAFMr3jMoPiHFKSQXpH37ii5ILRYLtAvVMEo2rPkafqCbzNQKBfRPhsClRxvnRxrP0l7Fvvv3YVjmsDbWyMWYABNgAkyACTABJhA4gVheyw2cFpeIFAIsEEbKTHE/o45ALD9UZj5bhx3rlS3OqSpedAmalC2Kfw6LwG4mKDWdfztcMJtQYTYKHVFChhyVGiO0iTYRUIz1J8T8swmC8XIF5D7US15g6/z55B4wASYQ2wTYbXJszz+Pvn0EtpRswZx1c7C3fK+jIrUC2HgnMM6L18ptJ4ErVwIGSTjqEd1G4JWpr2Bi74nt60wElq6pAb78UoxXWGxzB9rSMPLyRKGw10WHccnKAR5Zg2HFSZVuvX8/Lu06yKer+o5EPf2D6fj4kC2Ipa3hJdOW4OGChzuyG9wWE2ACTIAJMAEmEOMEYnktN8anPqqHzwJhVE8vDy6cCcTyQ+X994HXXwcUCiArC+jRA+jZUzzaX8nJ4u7ocE3NFgvMsCJOJndxydSe/vICW3vocVkmwASYABNgAkygswiYLWas2LsCT2x6AucazgndmD0cWHGjZ49mfwys3Ceez4zPxMJJC3HXiLugaEtQ6M4acAjaJe+gR4+KQiEJhg0NLTeiUFqxa1wm6lHhkbE9VpxUWUJcOgrv2CHUmyBXIE2hQppCiS5uxxSFEiofsa6DhYjiD3b5UxdU66tdqrxxwI34aOZHwWqG62ECTIAJMAEmwASYQKsEYnktt1U4nCFiCbBAGLFTxx2PdAKx/FCpqhIXPbp3F0VCTk4CvMDGdwMTYAJMgAkwASYQqQRIJLz7P3cL3S8cCSy73nMkhZ8Ab+0Rzy+/YbkgDnJyJaDXA9u2iS5Iv//eOx2DqhI7RmfDKneNA2nPHagVp7QVhVyFwlnboNOltTo1SQqlQzxMlSvRRalCGh0VKpCAqGjnjj9p/EFpZ5I1ybjw2wsch7DVGeIMTIAJMAEmwASYQLAIxPJabrAYcj3hR4AFwvCbE+5RjBDgh0qMTLQfw5y7bi5WHVjlkrPB2IDqZnGntD8LbCnaFMSr4l3qmDl4JhZPXexHDzgLE2ACTIAJMAEmwATaT2DyysnYeHyjUNG2u4GCHOClHcCBCmBwBjB3NLDzNDB+udjW5LzJ2HDHhvY3HMU1lJcD69aJYuHZs64DbdIWo6jPPFRmuMaBtOfyx4rTHV3fvGkYVzAPyUk92k1VBhmSFaIFYhdBSBQtEe3HFLnSpwt+e+Pe4g/ar0VSHEKyhCx4swANhjrEqxOxq3AXi5vtvsO4AibABJgAE2ACHUuA13I7lje31jEEWCDsGM7cChPwIMAPFb4piIDVakXukl44UXPKJxB/Fti8Fc5N6YXiR0oYNBNgAkyACTABJsAEQk7gRPUJ5C7JhRVW5KcB62cBsz4Ctkt+4oztAbw9HZjyNlBUBZCAVDKnBD3S4OIhAAAgAElEQVSTvQQrDHmPI6sBckG6Z4/ogvTrrwGjGApbSNXJW3Asfw7qE51xIOm8P5vM7HXkZgzFzAm/R9ful+KC2Yh6iyRIZIhQUXzuHTv+iIPH1gpCoRyAHDLIZeKRog1UNVeh0djo6EHfNODoBfFPsiJMUCd47V24bZRzt4SMJHEzRNPP1TIBJsAEmAATiDgCvJYbcVPGHfaDAAuEfkDiLEwgFAT4oRIKqpFXZ2VjJbJfyobB7N09lL8LbN5GrlaoUTq3FOlx6ZEHhnvMBJgAE2ACTIAJRBSBBV8vwFNfPSX0eVQWcOQ8UKv3HEKSBiCR5/sz4rUFVyzA/AnzI2qsnd3Zujpg0yZg7VoxbiElK8wo77YCh/vfC8iswjl/NpmRDLfsumUecSD1FguqLCacNxtxwew8knhYZTahMQgCIm2UW/HuRNTU224GP8CuvBG48+PWMypkCjxS8EjYeNNwt4RcMm0JHi54uPWBcA4mwASYABNgAkwgbAjwWm7YTAV3JIgEWCAMIkyuigkEQoAfKoHQiu68xVXFmLdhHlYf9HQPFcgCm5TSjEEzsGjyIuSm5kY3PB4dE2ACTIAJMAEm0OkESOjp+9e+KKoq8uhLbkou5o+fj4VbF+J49XGP6/lp+Tjy0BHI2hmrrtMhdFIHvvwSWLgQUKmASu032HPRODLN9NuKk7r9+c3bManfaKjV/g+iyWIWhEPx5SYiWowggbG11NR0AcveHgtYTWg9N5ARB5Q9CmQtBiqcRoU+m+mVnIOSOb69dLTWv2Ben/7BdHx8yKlsTh8wHWtmrglmE1wXE2ACTIAJMAEmEGICvJYbYsBcfacQYIGwU7Bzo/4QOHnyJP7yl79g7dq1oPcajQb5+fm49dZb8atf/QpxcXH+VNNqni+++AJvvPEGvv32W1RUVCAjIwOXXnopfvnLX2LatGmtlm9rBn6otJVc9JbbUrIFj3zxCPad3ed1kLkpwPzxwMKtwHExPKFHGp45HLQjeWLvidELikfGBJgAE2ACTIAJhBWBbSe3Yfzy8R59umPYHXj1mleRpElCrb4WD372IN754R2PfNvu3oaxPceG1ZgirTMmE1Dwt8n4X7UYAzKQTWapFyZj+A8boNEAiYlAUhKQkCAe7X97O9rP6XSAVN8lwbjJasF5m3hot0K0C4kkKhqtoiR4tmI/3ltzs2ADKU0KGdBN4j20eyJA1oMDM4CDFaIV4Zk6Z4nyesDsWgXCxZsGxR9MX5QuuEu1pzRdGioeq+A4hJH2QeP+MgEmwASYQEwT4LXcmJ7+qB08C4RRO7WRPTASBW+//XbU1NR4HUj//v3x2WefIS8vr80DpX9c77//fkEc9JVIJFy6dGlIdjTzQ6XNUxfVBUkkvPyfl3uMcdZQ4EAF0GwCtEpgUAbwr/2eKLbctQUTek2IakY8OCbABJgAE2ACTCC8CBR+Uoi39rzl6BQJgkuvXYrbht7m0dF397+LB9Y+IAiG9lQ4shDLrl8WXoOKsN5YLBZoFmhgspoC7rnMosT4r/WQC1EAA08KhSgk+hIT3cXGhAQrFIkWGHRGPPnlb/DZT6tQ11wDk8XV5f7NA4FFU4C8VN99Kq4CHtsArDnomicluTdmXLscQ7rkI1elFV9qHRLkisAH2M4S7vEH7dVxHMJ2gm1HcRJtC94sQIOhDvHqROwq3MVibTt4clEmwASYQKwQ4LXcWJnp2BonC4SxNd8RMdp9+/ZhzJgxaGxsREJCAh5//HFcccUVaGpqwvvvv49ly8TFgwEDBuC7774T8rQlzZ8/H88//7xQdOTIkZg3bx769OmDoqIiLFq0CHv27BGuUb4FCxa0pYkWy8T0Q6W6GtB7CUoTdMohrJCCvzQ3OxvQasVVkXamws1z8dahd11quaIX8PI0YMTrztP77gce+RzYfMK1wcIBt2PZ5Yv960VDA9Ao8c9EfqWSk/0rGy65qP80DntSKoHUFlaRwqXf0n7QZ6HWuUgKuRzo0iUce+q7T0YjQJ9racrIiKwxmM3AhQuufU5PdzWJCPcRWa1AZaVrL+nzQJ+LSEoVFa69pe+lQPzehcNY6V6ie8qe6PlAz4lISvSZps+2PcXHA0Hy3tBhGOi7Vfp7g8yc2vi7scP67N5QfT3Q1OQ8SyZeZNYVZmlT6Tbc9uUvca7pAsZ2uxTvTHoVvZN6ir10/72hUqFEXotZmx7CN+XfoqsuDe9NfgOTsseF2agk3aE5oLmwJ1LE0tLCo7/Et74er5WswoN7Fra5T8N/egF5p+9qc/m2FLTCii8vvxj16jKfxdUKYOOdwDjb7STNuO0kcOVKwCD5urVfT43vhgenfuRRbxeFEj1VWvRUatFTrUGGQhWSzaDShpf8sAxztovxOSn+5lHbz40lw3+Hh/NvF3/30e+/SEruz+oI+72xp3I/Lvr3FAfxPVd+iBE9L4VgRhtJyf33Bj3j6FkXSYk2hRskmwPotwb95oik5L42EG6/N4ixdO2CGLuvXYT72gC5rZZ+79B3pvv/m9GwNuB+39P/cZG2NhBJn9029DWm13LbwIuLRAYBFggjY55iqpckBm7evBlKpRJff/01Ro8e7TL+F198URDzKD377LN4+umnA+Zz7NgxDBw4ECaTCaNGjRLa0Ul+SJM4OXHiROzevVvox6FDhwTxMJgpph8qNGdbtwYTZ8fXdeoUcP68s10SEnJy2t2PTSlVuG3ITzindC7Kbrsb2FUKPLreWf1LU4FLsoDxy53nuppUeO/HQZhU7adAVl4O0MueaBE+N8JiFpIYcvq0cwz0z2Tfvu2ehw6tgP5hOy6JyURCyKBBHdqFdjdG/1AePeqshvyMDR/e7mo7tAJamPjpJ9cmhw2LrAU7Egj3ubkoHjgw8ha79u93Fdfy8yNP1Dl0yHUhpndvICWlQ2/pdjd27JirIJKd7bkQ0+5GQlzBiRNAldOlHzIzge7dQ9xokKsvLXVdECNRqqcXpSTIzbalujK1HmsyKnB/WRaUVonYce4cUCYRgEjgzMuDSWbB0qwy3FSRgSxDmC/K0yL8yZNOLPR/Q//+bcEUsjL9L92JI3GSzWuSlnKbtLhnf3+80fcnnEqVCP+SPL0u6PDRyoKQ9c9bxVVaA6YV7oBR6eobVAZxf47Fdnr2cGDFjZ41zP4YWOndMz9UFhm2bhmJNKOqxTEpIINWLoNWpoBWLodWFrgd5dw+x7Cq6zmf7VQpTWhUiC5VyU0quUelFG8AUvQyMYAkDdotzTzXFYuL8jt0TvxubO9e16z0eYggYWpeXhFe7CnGp5TLgK71MpRuGgl5YvhtwGhxTg4fdt1EQs+HcNm84O/NVFzsulkyKwvo2tXf0uGRj54P0o2GJFzR76ZISmfOAGfPOntMon+vXpE0AvH3Ev1usicSzOn/iEhONAcrVkTyCKKu7zG9lht1s8kDshNggZDvhbAiQBaBFP+P0n333Se493RP5D5nyJAhOHjwIFJTU3H27Fmo6J+qANKDDz6I1157TSixY8cOXHbZZR6ld+7c6RAnH3roIfz1r38NoIXWs8b0Q4UFwhZvkG8TalFw8f+EhYL8NGD9LGDUMuCCxIAgTQfsvheY8jZQRGufVuDb7y/CJfUB/FPLAmHrH9SOyMECYUdQbr0NFghbZ9RROVgg7CjSLbfDAmF4zEMECYQ+gfkQCMMDsJ+9iACB8MH8w1ja44xDVLOP7I7yTLx6tC+SqptQe+oYHryiEe8MdBUJSSC5tyQLiw/0g9kEmMxwHt3eU6xDMpCmfG4h//yE6ZrtdFITlowvwsa+rhbwF3cHvj8j5i0cCSy73rP6wk+At0SnLy5p0tF0/L/veqOnQQOl2gqFxgoFHdVWr0KctDDpdBqZ3CEW6mRyKKUBFt3aIivI3NE7cULTuneUjDig7FEgazFQIXHi4Qtcrl6D4h2um2XbBDkUhSJcIBw26jvsT5B4IQGwb/NADENmKGiFrk4WCEPHNpCaWSAMhFbo8rJAGDq2XLODQEyv5fJ9ELUEWCCM2qmNzIFJ3X6SQFdQ4H0X6wsvvCC4HqW0fv16TJnidA/S2sgp9mCPHj1QWloquCklodFXouuHDx9GTk4OTp48GVT3MzH9UGGBsMXbdEGvEjyVWyLkGZUFHK4E6lxDsgjXEtVAvy7OxZMFxbmYfzKAXX4sELb2ddEx11kg7BjOrbXCAmFrhDruOguEHce6pZZYIAyPeWCBMDzmIQIEwsL+h/BWd6dniCSTAkuP9MNt52yCB7nAKyoSeL7b34AHJjWhVuOU+ArLumPZEf+tIqkkeXyTCoaCsGj2cs4uOtqERbtloHRyv8+uxp8nHsPhrhJXrrYM5E2jIAd4aYcYk3twBjB3NLDztKs3jf7nEvCbLfm4uFS02LZYrSC7PTOsDuFUEArtgqHtqNRYIVf5ljtVMrIwFEVDEgzVMrnD4K9SZUD26B0wyF3LK2RAN0kkjO6JovXgwAzgYIVoRXimzkmgvB4wu3VBbZGhdMdopBvV4fE5kPYiggVCC6zQTvgaRrc5e+XHHDxSGWHWRiwQhsdngwXC8JgHFgjDYx6ivBcxvZYb5XMby8NjgTCWZz8Mxz5hwgRs3boV8fHxqK6uFtx7ektk9UdxCimRi1FyNepvKi4udrgL9WWlaK+Lrr/xxhvCn1QuN4juF2P6ocICoc/blXYh9y3YhSKddxdRLd3n+Y06HPn2Usi8+SjyVpAFQn+/NkKbjwXC0PL1t3YWCP0lFfp8LBCGnrE/LbBA6A+l0OdhgTD0jP1pIQIEQqmb+rE1SXjn4ED0bpbEIpMIhDTkkjQ5Zt1gwjfJtQjYTb0/zFrIQ8KiICS6WSfqzVbcc9mPWNfD6cbf7k1j1kfAdtEjpJDG9gDenu7qTeO/ywuQXes7/hoJkyQOkWhI4qFUjyMjQadwaBEtDm0CotztX1JyoCuKhQpBOCyN0+PxPkVYneFqBXnzQGDRFCAvVXSXWvAm0GAA4tXArkLRtWVxFfDYBmCN257VGecysKg4D7nSOWwn96AWj2CBcE98HS665HsPHJMqErDxwKigYgp5ZSwQhhyxXw2wQOgXppBnYoEw5Ii5AYpwc1owOqF06tQpwaCEExOIdAIsEEb6DEZZ/zMyMlBZWYnhw4djr/s/HZKxVlVVIc3mW/+WW27BqlWr/Caxdu1a/OxnPxPyv/zyy5gzZ47PsnR97ty5wnUqd8011/jdTmsZY/qhoteLW44jORmN4vZoe1IoAIod18607fR2jH93ss9a+qYBRy/4bmTbzzdibI6froio/zQOe6JA35owjwHkPvRoGAOtkJE4ZU+0QqXVtvNO6uDi9Hmmz7U0RVAcGqHbFL+v2U2Yp3lowa1YB1NuvbloGAONskniT5n+pu8l+n6KpOT+nCNX6D42PYXtsKJhDPTdSt+x9kRzEKBb+k6fnxD93ujQcUXDszqcx2Dvm1yOMsN5rDn6H9w/4l4o3VUtL783TGollu5dhpv63oCsxKwOvS28NUaeXvouG4qi6mLHZfKmceQ8UOvFg2eSBqDfxnZXpPlFzyCv5Ld+j8NktUBvtaDJakGzxQIDPUe9OE5VxlmR0N2EhG4m8Si8zMLfKp1V2JzXXaVGfcUevLP9ORyu/NHRB7UC2Hin6PljxOvOru27XxzTlSsBg+RrakTXYXhl0ouY2HO83+PolIwR/Kyet3k+Xvz2ZQ9sWoUWDXMrIZdF0G+OaHhWR8MYwv33hj/9C/ffG+7/53j7nzmcn9Vt/aKOxLWBto41QsrF9FpuhMwRdzNwAiwQBs6MS4SIQHNzM3S2BeVrr70Wn376aYstJSQkoKGhQYgfSBaF/iaKa/jAAw8I2T/88EPMmDHDZ9F///vfIAGSEpUji0J/Ez00WkpnzpxxxFvkXSf+Uo3OfHPXzcWqA6LIXdVchUajMygJLTioFWrozeKqCLkmIpdElDQKDQxmA6T7n+NUcUjVpgrXZw6eicVTF0cnNB4VE2ACTIAJMAEmwASYQFQR2HZyG8Yv9y2MJaiBqX2A/50Bjld7Dj07MQdf33gSpaUy0L9ip07RTn/xWO0lv3sNZF0oCIYWC5ptoiGdaylpUyyCYJiYLQqH8d30KLe8jz3HXkRDk2gJOXs4MLIbMGeds6Yl00Rhc+U+8VxmfCYWTlqIu0bcBYVcEVXz2tGDkf5v5a3tisYK4X8ob6lrXFeoFCqv1/h/q46eSW6PCTABJhB+BFggDL854R61nwALhO1nyDUEiUBFRQW6du0q1DZz5ky8//77LdacmZmJc+fOYciQIdhP7sj8TC+++CLmzZsn5P78888xbdo0nyXput1q8M9//jMeffRRP1shoxMKce9fYoHQP07RmIt2Sucu6YUTNRKfST4GmhEHlD0KZC0GKpwaok8suSm9UPyIGMuQExNgAkyACTABJsAEmAATCDcCLW2U89XXgmygewLw8WHPHOm6dGiUGo+Ncg0NoljoLhzS340+f1eTYGh1iIVkaUhWh/6khqy3UTXkfiFr4Uigsgn4+JCz5PQBQJoOeGuPeG75DcsFcZBT+wjQ/1apf0pGjV4S4LGVKlvz0GIvnqJNRNVva9vXQS7NBJgAE2ACEU2ABcKInj7uvA8CLBDyrRE2BEgk69mzp9CfO+64AytXrmyxb5SXyvTp0wfHKE6On+m5554T4hZS2rhxIyZNmuSz5KZNm3DllVcK16nck08+6WcrLBD6DSrGM1Y2ViL7pWyPXawKGdAtwQmne6JoPTgwAzhYIVoRnpH831teD5jdNjiT5WHp3FKkx6XHOGUePhNgAkyACTABJsAEmEC4EQhko5x738m9aBedd0tCyuvvRjnyWldV5V08pBCg0ogCVC8JhIJ1oc3SkNySukYzFHtacfG10HfZLLz/+i7ghvfJU4hzFCQOfjQTmLhCPNcrcSIeHLMamd2Arl1kiFM4YxzqhHiHctiPFPcwkM2o4TbvoexPRUMFuv5Z3HTsnijmozRRXEhKUg8tlIX2+dqvudfxwKgH8Nq1r4VyCFw3E2ACTIAJhDEBFgjDeHK4a20mwAJhm9FxwWATiDYLQnYxGuw7JHrrK64qxrwN87D64GqXQd48EFg0BcgTPYZ6TcVVwGMbgDUHXS/PGDQDiyYvQm5qbvSC45ExASbABJgAE2ACTIAJRCwBXxvlSMhxF2hykoDfTwR+vwU47cOIS1ouGBvlKMTy2bMACYVSd6VkdVheLoYvJnHQ7o7UEctQewLl4wcBMivy04AlU4Fr3/OcprU/Bx7+HCiqotCHMnTbehDK5h6g8JG6LmbhFZdhRly6STzSi86lmxEfLxNFQ5lCEA5JNJQKiOJ78ZpUXNTJFUJepUwGi9WCgjcL0GCoQ7w6EbsKd4Vt/D0SkylUo8Eqxor0dTzXUIHrX+sPq9UEf2w9A/XQ0is5ByVzWvf8ErEfSu44E2ACTIAJtEiABUK+QaKRAAuE0TirETqmaItB2No08EOlNUKxd31LyRbMWTcHe8v3OgavVgAb7wTGica1LmnbSeDKlYCB/lu2pRHdRuCVqa9gYu+JsQeQR8wEmAATYAJMgAkwASYQUQR8bZSTDuKOYcCr1wBkNVirBx78DHjnB9/D7IiNckYjUFbmzWWpFd8lLMCpPNFjzagsYP9ZQG/7vS51Z6lRAEO6irEIKSUdfQZJx8VQGK0ldYJVEA51JBZ2NQtHUUAUxURtqkWwhPOVSCCsuXAYy1Zd68jy3M83ol/mEGhJWJQIjg4BUjgnCowamczFipEEPBNIuJOKd873RqtFcNdKwp7RKsZ6pLz283SOrtF58b2YxyjUKealNvxJZyv24701N5Pq6pKdPbT4Q4/zMAEmwASYQEsEeC2X749oJMACYTTOagSPKSMjA5WVlRg+fDj27nWKJO5DqqqqQlpamnD6lltuwapVq/we9aefforrrrtOyP/yyy9jzpw5PsvS9blz5wrX165d64hH6HdjLWTkh0owKEZfHWaLGSv2rsATm57AuYZzwgBnDwdW3Og51tkfAyv3iecz4zOxcNJCIXaJQq6IPjA8IibABJgAE2ACTIAJMIGoJUAb5W5adRMuNF1wGeMzZDV4ueewn/kK+MPXrufTdGlYc+uaDt8oJ42jSD0qry+H2SrZwSfpptSdpceoLArIDZmw63q68puRcuSFNs253QqRrA0FETFDFBNFi0Txtf/ocmzZvtBR/8QxT2Lk0NmttmexWnHabICc3HHaettFoYK2JUWy1VqDm6Gm9iTWb/4dSs9851Jxezy09M+7GreNewoj0/siW6lBD5UGGQpVSNy9RpJ1Z3BnjmtjAkyACYQ3AV7LDe/54d61jQALhG3jxqVCRGDChAnYunUr4uPjUV1dDaVS6bWlHTt2YMyYMcI1iif47LPP+t2j4uJiIW4hpfvuuw9Lly71WZauv/HGG8J1KpebGzx3jfxQ8XvKYjIjiYR3/+duYeyFI4Fl13tiKPwEeGuPeH75DcsFcZATE2ACTIAJMAEmwASYABOIRAIbijZgxoc3oVZf7+i+PxvlKHOSJgH/vmUNpvSZ0qFDDySOYqDuLNXNPZG19TBIkBPdZfpnQecvgAujZqIx7VNH9kzN1RjXbxnibZaJvqwQacxFRklARQA5Ko1gWRhu6WTpDqzf/FvU19vMNAEE6qElo8tATBwzHzlZBR7D08jkyFZpkKPUCAzsoqGinWLpvvJ9GPH6CEd7++7fh2GZw8INL/eHCTABJhBzBHgtN+amPCYGzAJhTExz5AzyiSeewB//+Eehwzt37kRBgeePcLr2wgsv4PHHHxfyrVu3DldddZXfg6R/aHJyclBWVoYBAwbg4EG34G2SmgYOHIhDhw4hOzsbp06dCuruQH6o+D1lMZlx8srJ2Hh8ozD2bXcDBTnASzuAAxXA4Axg7mhg52lg/HIRz+S8ydhwx4aYZMWDZgJMgAkwASbABJgAE4gOAmV1ZUJs7n/t/5cwIH82yt0+9HYsmrIIWYlZHQ6B4ih2fbGrEItQmoLhzpLs855POYfGynSUl1tRfg6orhFj8VlgFeI0Ckf62xajTzwvnjPbjva/pX2kHGVXZMGqqnN0W2ZMQtZXpZAJtoFwxEJ0iYNosz680LMGijgr5DZNsKdKC3U7RbFAJ08ukwltqmVyzyOc5+VWC+57cwSqGs46mvBHeI7TpWPMpXMxqN9NkAfgoUUliIZqh5UhiYfdlGoEIhou2blECD1hT0umLcHDBQ8HiojzB0CArTYDgMVZmUAME+C13Bie/CgeOguEUTy5kTi0b7/91iEK+rLus1gsGDJkiCDspaSk4Ny5c1CpVAEN91e/+hX+/ve/C2XIGvGyyy7zKE8C5ejRo4XzlP9vf/tbQG20lpkfKq0Rit3rJ6pPIHdJrrDQkJ8GrJ8FzPoI2H7KyWRsD+Dt6cCUt4GiKtG9T8mcEvRM9hKsMHZR8siZABNgAkyACTABJsAEIoxAJG2Uo/9NNQtUMFlFGz9pao87S6pHJZfD8JSrq9LmZuDcOfF19qzzSO/pVVEBmEzeJ5z+t7ALiXXxe7H/0os9MvbZtRvquqEwS4RHqRhpL1BvssCil0OutUChtSAlFYhLtEKptUKpswhHtc4KjQ7Q6gAdHbVW6OKAOJ0McVogPg5IsP2doJMhIU6GOLUMGrld8PMh/tlEQX8FN/rfqveS3i5j9Ud4vmfCs7hkRCFOG/VosHh3GevvR4tiPna3WxkqNYKAmKXUgM57S9M/mI6PD33suDR9wHSsmbnG3+YiIl+4CXJstRkRtw13kgl0OgFey+30KeAOhIAAC4QhgMpVto+A3c0ouRf9+uuvHSKdvdYXX3wR8+aJwdufeeYZ/P73v3dpcPPmzbjiiiuEc7Nnz8aKFSs8OnTkyBEMHjwYJpMJo0aNEtrR0X8tttTU1ATqx+7duwU3pz/99BP69u3bvoG5leaHSlBxRlVlC75egKe+ekoY06gs4Mh5oFbvOcQkDdA3Dfje5jFnwRULMH/C/KhiwYNhAkyACTABJsAEmAATiB0CkbZR7nDlYQz42wCfExSoO0v3ig49eAj90/v7fQNYrcCFC67CobuYWFsLnM5egmN9RQs1+n/iqC30Y/7RJcgp9WWp5rRMPFBkAWlmgi5qBXr3kCEpQYxISJZ9dLTHJ/S78wAUClFMlL7i4jzPueehv93z2f/+3abf4KWdi1264Y+Hll7JvYQNmOSBqNpiwmmjAadNekEwpGOt2YcS6+eAiVN3paulYZZKDQqykr4oHVXNVY6aKL5mxWMVkIehG1c/h+uRbc+ZPbjojYsc5/fctwcjujndqra13raWY6vNtpLjckwgtgjwWm5szXesjJYFwliZ6Qga5549ezB27FiQSJeQkAByO0qCH/39/vvvO2IC9uvXTxDwEhMTXUbnj0BIBchFKbkqpTRy5Ej89re/FWITFhUV4U9/+hOoH/Z8zz//fNAJ8kMl6EijokL6B7TvX/uiqKrIYzy5KbmYP34+Fm5diOPVxz2u56fl48hDR4LqCjcqoPIgmAATYAJMgAkwASbABCKCQCRulNtcshmz1sxCaV2pV8b+uLN0L5idmI13b3oXE3pPaNO8zV03F6sOrPJalkREEp+aTI3C9ZU3AnfajNWUlngojCk+ox12PTcTfY4txr59rlUPHAhoNG3qakgLkdXkN2PTYVLZFFDAbw8t1LGSR0rQK6WX1z7WmE0oNelxigRDm2hY3U7RUCaTwVx1FH/54GqPNqMtDiG5En5x+4uOcc4bOw9/mvynkN4PLVUeC1abnQaXG2YCUUSA13KjaDJ5KA4CLBDyzRCWBP773/9i1qxZqKXtjV4SiYNr165Ffn6+x1V/BUJyB3PvvffiH//4h08Gv/jFLwRBUm4PrhBEWvxQCSLMKKpq28ltGL98vMeI7hh2B1SZoIgAACAASURBVF695lUkaZJQq6/Fg589iHd+eMcj37a7t2Fsz7FRRISHwgSYABNgAkyACTABJhALBCJ9oxxZID2+8XE0mZpcpssfd5b2AjqlDn+88o945LJH2jzlxDF3SS+cqJHEJ/BRW0YcUPYokLUYqBD1whZTkrkXxu4uwY4drtmGDYMjHmFrdXTk9erkrdg70lVkDcRDy4DGe3Gr9g107QqPV1oa4O4htN5iFsRCEg3t4uEFszGgIe/ZvwJbti8UykitO2dNXIB7LnkQOSo1KK6hLoC4iAF1oIMyD/v7MOw/t9/R2rDMYSARtDMSuTuNBavNzmAbi22Gm/vcWJyDUI6Z13JDSZfr7iwCLBB2Fnlut1UCJ06cwJIlSwQhkL6A1Wq1IAjecssteOihhxBHPkO8JH8FQnvRzz77TBABv/vuO1RWViI9PR2XXHIJKAbi1Vd77txrteN+ZuCHip+gYixb4SeFeGvPW45RkyC49NqluG3obR4k3t3/Lh5Y+4AgGNpT4chCLLt+WYxR4+EyASbABJgAE2ACTIAJRDqBaNgoZzAZ8MtPf4l/7vunYzr8cWdJmWcPn403fvYG1Ep1u6aysrES2S9lw2A2uNSjkAHdEpyn6L3eBJitAF3TKIHyeud1ek/XpEmtUOPUnFLUnElHaSmEF7kwHTkSaGryfDU2tnyOYiqGMv0w5Ge4kL7WZxPymlxo/jcXTWMfB9SSwdtKyM3xGL+1zqu7VKUSSE8XhcOMDCAz0/N9QgLQZDVLLA0NeGPzfOw79qnPPjXra2Gyicy5KcCJGsBiBZQKHbTaJEc5uaRXKpkMtwy+Fa9NeyWUOINWNwko2gVaGC1O8VQlV6H5yeZOcaPqHn/QPtBos9oM2gRyRS0S4HiW0X2D8FpudM9vrI6OBcJYnXked6cT4IdKp09BWHZg0/FNuG31TJxrqMTYHmPx/9m7DzCpqvOP47/tS+9dylLEhkJEjaCioMFgBVFExBIwfyxRYldiScQYUQzYohHFCIoVC1GDxo5gVFRsKEoXBEE6Alv/z3t3Z3d2d2ZnZvfeqd/j4wPs3HvOuZ9zD3c47z3nzBw2U12adgla1xVbVjhLGr2/+n21btBSs057SgPzBsbltVEpBBBAAAEEEEAAAQSCCSTLi3K2j2KXqaXf37s3l147Wzr7eWm+34S+/h2lGUOl42ZIS8u2mls5fqU6Nenkyg3yuxd/p1lfztLuwsoRuNP2lSYdJ3VtJi1aJ/V+sKK4ReOkA9tIyzZLV70uzV5cuSq5mbkadcAoTTtlmit1tExsuVMLElpwMVAwMdygo//5/kHHRb2GaEuLVwMumZr19WjVf/tepeU3Vkn2NhWdOUQ7mr9f7dr6fDJPTbbVboUWW3bVP3DYunWJrl3XWev3hJ7d2SzXloINn7pBo3Y6e+TbOrp+U3XJzlWXrFy1yciKy+0nqu4/6LvKWO1D6L//oP+szanHT9WlhwXbkzP8tuHI1BJgP8vkbm/GcpO7fVP16ggQpmrLc90xF+ChEvMmiNsKrN2+VrMXz9a4vuOUmW7b1NecCosL9cDHD2jYvsPUvlH7UIfzOQIIIIAAAggggAACcSeQLC/KxXofxVBLjGZnSG+cIy1cK42fW3EbTD1e+lU7adBjUn5R4Nsjr2lnLbtsRdzdO/4V8g86vv79mzpn7m+VX1wxm7J+RmONafOAcr8bqY0bpU2bpM2bpbZtpc/rT9U3na+Q0ioA2q0dq55L3FmhJT9roxYc3kEl6TXP7mzXSDouT7rNL17ZJEdqWDa5NNDszvT0LJ0+6h21q9+qnCM3PV2ds0qDhfZ/56z4WJrUf/9B/4CcV/sQ1rQnp2HZnpy/FFTfk7NBVgM1zW0a9H4fsf8ITR48Oa77A5WLvgD7WUbfPJolMpYbTW3KipYAAcJoSVMOAlUEeKhwSyCAAAIIIIAAAggggAACFQKJ/qKcBec6/r2j1mxfE3Gz7tV4L60av6rOM76CLTHqX6FzD5K27pFe+Kbip0P3kRrlSI/VsA2cLTG65vI1alm/ZcTXF6sTnv7qaV0w53fatmdnyBVaLLj4+ervddIzg7V6xzI1zWqp3zV5Si22DtT69dKGDaX/FxbW/mp25S7T0m5Xa2Or5ypl4j+70z449UnpxW8rDjl1H2nybwLP7mzb+Tjtd8gf1b15N9VPSw9auTSlqXVmVmnA0MNZhqECcht+2VC+BO5jp0rnvFBaZbu/WvkFOKteSG0CcqEC5v5lRLonZyIEzGt/p3JmbQTYz7I2aol1DmO5idVe1DY8AQKE4TlxFAKuC/BQcZ2UDBFAAAEEEEAAAQQQQACBmAkE20cx3ArNO3+e+neq3XKW/mUs27xMNkvrucWVg1C+Y8b0lmZ/U3kJy+b1pFN7So98Fri2w/cbrknHTlJes7xwLydujnMz8GxBRJt1aIFC238x0P/2uR1XU9rS5B193328djSqAPfN7uzXUWp2u7RtT0UODbJKZ3YWFFf8LGtnL2UumKysHf2U06pQrbsWqW23QjVsX6hGHQrVqH2hshsVKy0teE3cnmVoAblmtzfR1j3bQ7Z/pAG5prmNtPmabSHz9T8g3D05bdamBSv3bSUt3lAatPzR7xKC7cmZaAHziPA4OGIB9rOMmCzhTmAsN+GajAqHIUCAMAwkDkHACwEeKl6okicCCCCAAAIIIIAAAgggEBuBMS+O0SOfPRKwcNs6YFSvUXr8i8dlWwQESmP6jNG0k93b4++dFe9o/Nzx+mxd5ajf9FOk818srUF6mlRcFszy/7mvfr3b9taUwVM0oMuA2KAmYKk2w9CWLw0WQLSfb98ulahI69o+qmVdr1dB9k/Oldrszj/+uvL+kFUJsvLbqOuyW7Xj/fO0c3uGSuy/7GK1aFesnEbF2l1SrMKyCGV2wxI1bF+ghu2K1LiD/VoaPGzQtlCZOYFx22Rmly1Jmqu87Mj2Mtywc4Na39m6WsYZaVLbhhU/rk1Azs7ecNWGiGewBguYV521GUgj2J6ciRwwT8AulTBVZj/LhGmqWleUsdxa03FiHAsQIIzjxqFqyS3AQyW525erQwABBBBAAAEEEEAAgdQSuOzVy3TvR3eXB9x8V9+1aVe9c/47smVEf9j2g46afpSWb1leCccCdX845DJN+e0UV9GKios0ef5kXfPGNU6+3ZtLIw+Qbnm3ejE3HiU9/oW0dHPpZ7cPul1X9LtCGekZrtaJzKTduysCiI9//aimLD/fYRnbRzqgdcX+kP579Pncen4zXe3WnaevvpIKCio0u3SRmpZtmVdYUhoo9P2/p9gJI1YcnCbVa2FBw8LSoGH7QjUsm3VYv2WR/FcqjWSWoc3Yaz+5vQqK/SpWVmpdAnKWRVZ6ltZesTbiAKHvogMFzH2zNo/oVP2unLeq+p6cBMxTu/eGWj6X/SyT//5gLDf52zgVr5AAYSq2OtccFwI8VOKiGagEAggggAACCCCAAAIIIOCKQM97e2rJz0sq5XXJIZfoniH3VMv/4lcu1v0f3V/p5z1b9NQ3l/htDFjLWlUdxN6ev13b9pQuzZiVXnmZyqpF+H/eOKexGmU3Kj+kNnvA1fISkvq0qu1jQbU9RaXridqymzsLpF/K4mv+e/T5UDJKcpRd2FJ79lReyrT1hhHad9XkgHYWHNxjAcPiiqChb5Zh1RPSM1W6TGnZbEPfrEObiZjTqDTIaLMMO9tehlnVZxnWtMRtpAE5X93cmrFnAfNHP3tU1795vX7aWTFr89FTq7Od+0LFnpxtGrTRrQNv1Xm9zyNgntS9M/jFsZ9lijZ8lctmLJf7IBkFCBAmY6tyTQkhwEMlIZqJSiKAAAIIIIAAAggggAACYQnYTL1r37hKhcUlTmDtlVGv6IhORwQ91/YsHPL4EFkALzM9TX8bdIczY68uKZJB7EjLyWvaWcsuWxHpaRzvJxBJ+0S6R1/LjM76v90r9MMP0po1ki11WlMKOcswwMm2p6Ez29A367Bsv8OW7YvVtUFpwLA0cJijD1fN02X/uUyL1i+qlJMtoxoqIOc74aA2B2nq8VNdX+LWgoTnv1gxa/PBk6TDpkk786UG2dL/xkq/nyM9/GlpTaafMt0JDpJSV4D9LFO37f2vnLFc7oNkFCBAmIytyjUlhAAPlYRoJiqJAAIIIIAAAggggAACCIQt8MmPn+i2ebdpxtAZys3MDXne7sLdGv38aF13xHX6VbtfhTw+1AHhDGLb9nTrd0pFfitO2h5xbRpIaWmlJazbUflz+1l2RrbWXL6m1ks8hqp7KnweTvuYQ2326PNvn6Iiaf16afXqyv9b8HDDhsDSkcwyrJZDmlS/VVGlWYedO6Vpv47S6s3P6t63rteO/O3OabaM6kMnV6/D2JcqAnKNsxvrrsF3eTZj79jHjtUby99wKjHvfCk3U+r7UEWdFv6+dBbnkdNLf3Zs12P1+ujXU+EWjctrtH1bm/ytiYpLCpSelqWt126V7esa7cR+ltEWj7/yGMuNvzahRnUXIEBYd0NyQKBWAjxUasXGSQgggAACCCCAAAIIIIAAAjUIhBrE3r5H6v1g9QwWjZMaZktXvS7NXlz5c7eWeKThpN+9+DvN+nKWLDjsn+qyR58Fo0cdMErTTpkWknjXrtIZhv7BQwscrlol/fJL5dNrM8uwagXSs6R1/X6l/JxvnY8sIHfYXtJdC6SvNkj7t5IuP1z64IeKgNx+rfbTVxd9FfJawj3AZm4WSbJlVZdtWaED7unu7Mloe3K+drY08DFpxZaK3PKaSm+cIx03o3RPzjSl6YnfL1KLRnupSCVOPoVlv64t2KPP9+xUg/QMNUxP10E5DTWwQTO1yMhUmi/iHm5FOS6gwNNfPa0Rz44o/+yZ05+R/Z0Uq8R+lrGSj325jOXGvg2ogfsCBAjdNyVHBMIS4KESFhMHIYAAAggggAACCCCAAAII1ELABrHHvDRGSzcvLT/b9oAbd7B094elP+rRXPpuU+nvLztM+sfHUr5FUspSt2bd9PDJD7u+xGMtLicpTgm1xGht9+gznLouAWszS7dsCTzrcO3a0iVLazPLsDB3ldYdua9F2coDcqNmSwt+kNLTpOISqd9e0sxhFQE5u57bjv1ce7XroJzGxUrLLg3KlQfnyn5fUP6z0uCfBe2K/IJ3vt/br770v0/u04KPpjh/7NteWvKztK10C8hKqXFOaf9Y+GPpj/sd8kcd+quLqh23qahQm4rKNo2U1DQjSy0zMpWTnq52mdnqkJmj9pnZap+Z4/zZfk6KTGDYU8P0/DfPl580bJ9hem7Ec5Fl4vLR7GfpMmiCZMdYboI0FNWMSIAAYURcHIyAewI8VNyzJCcEEEAAAQQQQAABBBBAAIFSgcvnXi6bcWNpT9Ee2bKW/ikzXSosLv3JY6dK57xQ+nv/n/uOb1W/lbO0qKUR+4/Q5MGTYa6DQLAlRv2zjGSPPv/zvFwC1pYsXbcu8KzD9RuLtbuk4v89xRZGrAjIbdn7Ou3ocrdT1UgCcg2Xj1fT7251zsvIKVFOk2InWFj6f1Hpr02KZfsi2q+59rOyP2fWKylfLtffyAK0jz55rLZuWxWwFX0By0AfNm3cWeee+Xq1WYHrCvO1o7giqt4kI1OtMrIC5m8zEVtkZjrBQgsaOsHDrGw1S2e2YU3dqv3k9vpxR1mk1pbgbdhOa69YW4ee6N6pVfezDLV8LvtZumcfi5wYy42FOmV6LUCA0Gth8kcgiAAPFW4NBBBAAAEEEEAAAQQQQAABNwVCzVDzL6tVfWntFVL7ydKGKktLBqpTXWeouXmdiZxXsCVgfdcUzh59Va8/lkvA2pKltkSpb8nSlatK9M2qIi1fXaLNO4v03VHtVZy9OeImS89vrnZvr3KW94w02fZ0OU3Kgoi+oGKTYv1Sb77+t/XUSLMrP/6MU55U+7YHVzp/ZcFu2UxGX6opQBis4Nz09PKgoQUPO2Rlq21mtrLTmG1o+w9m3VIacPUP4BbcUBCTfQirtmHV/SwP6SB1u1vamS81yJaWXip9uIb9LGvd6eLsRMZy46xBqI4rAgQIXWEkEwQiF+ChErkZZyCAAAIIIIAAAggggAACCAQXCDZDLSNNatuw4rx2jUpnD+7bSlq8oXQW4Y/bKz5ft0Mqqoh5OB94OUMtFdvUloC9+JWL9dWGynvthbNHn8+rd9vemjJ4SlwuAWsxs5e/eE8nPX9UrZu35YevK3dLv1qfX/XETftdpF/2+lf5j9MKGqveTyfqlw5PVCujQ9oI/aRXVFBS0TH6dh6l846ZrIaNrD+kKSMtTTuLCrW9uEjbnP8LtbukRBku1NgCoy0zs9ShbHlSCxraEqVNU2y2YdX9B320sd6H0OqxcstK5U3Nq7Sf5dCnpEXrK26A3m2k2SMq72e5YvwKdWrSyYW7hCyiLcBYbrTFKS8aAgQIo6FMGQgEEOChwm2BAAIIIIAAAggggAACCCDgtkCwGWqn7StNOk7q2ix4ics2S1e9Ls1eXPmYWM5Qc9snnvKzfcwmz5+sa964xqlW9+bSa2dLZz8vzV9dUdP+HaUZQyvv0Xf7oNt1Rb8rlJHuRjjKG5UTnzhRL3/3crXMLdicX5Rf8fOSbCnN789lnzTccII6LZrtLBdaOo+wdD5hpf/Tqv6s7M9l56zsfqV+bvWMc3Zx+h4VZm2S0oqVVpytzILmKsrcpuKM6lNo04saKKOwkXN8SXq+VJKurILmSivJccrvsGWEjtg5Wdu3Sxs2SM2bSy1aSD32LlHvXxepoEm+djXO1/YGe7Qxc49+KiyotPRqbcXrp2eU72loy5PaMqVtMrOUlaSzDavuP+hzi4d9CCe+O1E3vHWDU6Wqy+f6z3asup/lxGMmasJRE2p7C3BeDAUYy40hPkV7JkCA0DNaMkagZgEeKtwhCCCAAAIIIIAAAggggAACXgnYDLXxc8frs3WflReRnSG9cY50RIDJK/NWSYMek/IrtlNTPM9Q88ot2vnWFGTwr0siBhmGPD5Ec5e+quKy2agNsxtqwhETdN2b11VjvumYiZr8/t+0I3+H85kFWI7tPEQPD3pZW7ao/P+tWyt+bz/3/3NBQeVsbS/EDw/vrF05ftFWlxq4/p7OOnTBCq1dK/30U0WmzZpJnTtXLqRePalp8xLlNC1SRuMiFTcpUEGjfO1unK/0JoXKbVqk3Ga2l2KRgmxfWGOt09LS1DrDZhuW7mno2+OwcXpGtT0TXbp817I5+MGD9dn6ir+jqmZcXFK2YWqAEtNrCIr2adtHH//+Y9fqWTUjW865xz09tHTz0ojL6N68u5ZcsiTu2ybiC0uBExjLTYFGTsFLJECYgo3OJceHAA+V+GgHaoEAAggggAACCCCAAAIIJKtAYVGh2t/VXht+2VB+ieceJD0aYBu2c1+QHltUIdG6QWut+eMaZWZkJitPzK+rpiBD83pSzxbStz9Lm3ZVr2oiBBneXP6mznhmuH7etVn99uqnx097XC9+86ITuLbUo7n03abSa5t6/FSd3PNkjXpulOb/MF8t6jXT06c/q4F5A8NqJ1vS1PZD9A8YrtywUaM/66DCksqzE/2X3C0okn4KsAdn6/pSVtnkzEBL7toMxMMXrNG6pS21qewarKKtWkkdOoRVZUklKiwp0Z6SEuWXFGtPSbFK6heXBw0toJjbtNgJIOY0KVZus8p/DjV5tIEFCCXtKC7S3jn11Te3kQ7MaaCc9NK9DS34dti0w7Qzf7saZDfS/8b+TzUF3cK9qnCPKy4uVu6t2Soo9nsrIdyTQxyXnZGhPX8qdCm36tnMWzVPR04/stoHGWkZKiqpuJ6qf/adMO/8eerfqb9n9SNjbwQYy/XGlVxjK0CAMLb+lJ7CAjxUUrjxuXQEEEAAAQQQQAABBBBAIAoCgQaxx/aRHjq5euFjX5Ie/rTyzxnE9raRggUZWtWXNvgFrar+OZ6DDJfPvVy2b5wvWbBkd8FuNchu4Pxo8+7N+qWg9OJsH0zb/9JSg6wGaprb1Pn9zvydys3KlQVXfGnE/iM0efDkiBsk1JK7c76Vxs8tzbZywFI6ce/AS+62+mm4ui6bpHq787R0qZxlRn2pXTupTZuIq1npBJv5aMHCfCd4aIHDEuUXF6tYfhuDpkk5jYqVUxZAtECif0AxtyygmNXAAovF8g8mtiubabhr0xL9/rGKPSIXjVukA9scWLfKR3D2txu/1T737VPtDDcCuJbpNxd/o54te0ZQo/APHfvSWD386cPlJzTOaazr+l8XcHbsbYNu023zbtO2PdvKjx/bZ6weOvmh8AvkyLgQYCw3LpqBSrgsQIDQZVCyQyBcAR4q4UpxHAIIIIAAAggggAACCCCAQG0Eqg5iWx7zzpcO20u6a4H01QZp/1bS5YdLH/wgHTm9cikMYtdGPfxzqraPBUZsGdhdASY+1cuU9hTZrK+K/OOtfWxGZN7Uzlq5NfSSnhb0XHuF1H5y5WBoML28pp217LIV4eOWHekLWO4p2qOtu7eqoLjyOqS5mdLuMm//gKV5V22HrPQsNc5uosz0HB3bdoTObz9Z8+dLK1ZIGzfKmUnYurWUk1P6+z17Iq5uDSeUqKB8pmFFALGghiU4g2XW+ZhfnJmJazP+ocXbbi4/7LDeE3RUnzFqnpuhvBwL0KaV/q80ZabJ+dX+nFnp1wA/Lzs2s+zc0jzkl5cvzzS9t+JtjX5+tNZsX1Opur49U2sTwO3QqIOeGPaEjupSEfysa0vYvW3zAm3GpwVq31j2psa8MFIbdv6sQ/fqp7tPfkSvfPuS/vLG1dWKunLg33RUjxP0p39foM/XfKBm9ZvrhhOn66BOR6qoRCpSiYrK8i/9tfTPTTIydXzD5nWtOue7KMBYrouYZBU3AgQI46YpqEiqCfBQSbUW53oRQAABBBBAAAEEEEAAgegK+C/xaCV3by69drZ09vPSfL8YTv+O0oyh0nEzpKWbS+sY6RKP0b2y5CjtjWVv6LeP/0YFxdX3WctrmqcJR07Qre/dquVblle74Kz0dL066jUN6joobjA2/rJRHe7qoPyi4Et6WmXbNSqdPbhvK2nxhtJZhD/6zcILtKRndka21ly+Ri3rtwz7emMZsPQteWqBQvt/8+bSX4P9vrCWq2FasKp0edKyoGFx6e/tv1Dpp4N/q/wW75YflrF+gOq//4oz27BRfTm/pmWUKD3Tfl/6a1pmifN7m9yZbr8v+6zan6v+vNL5pefaFoLf7r5R6/fMUWHJThWWVMyw81UqkgCunZOT1UjZmQ10QJeTdMGvb1VhiVRQVKKi4hIVFkuFxRZ8kwoKywJ+RaU/K/Z9bgE6O67s5/Z7O764qEQLl/1Fq35+2VaGdZKFCouKdyszvb7z5z0Fm1Ws6lHhdOUqO6N0dmxh8S/KSMtVmoGV5dOu3snap+HNcmK9JVJJcZrs/mnbLF3/Gt0qVDNG9fNYL0sb1YsNUBhjubFuAcr3QoAAoReq5IlAGAI8VMJA4hAEEEAAAQQQQAABBBBAAIE6CVz9+tW6Y/4dTh5920tLfpa2BZjZ1DindInFhT+WFnd1/6t1+7G316lsTq5ZINgSo6MPHK17h9wrW7bQliW8+JWLNfPzmdUyi8clYEMt6dm1WXCTZZsDL+k5fL/hmnTsJOU1y4voloq3gGWwylswaMeOmgOI/oFFO77mVDrb0JYnXV9YEDBYaMGtNYNaSxl+G1wW1lfDF9crMzNdDeuFKqPun1sQ86ej9lFB7g8hM4t0xmnmrk5q+97ikPmGe0AkdbU8/ZerDVVG1u5OavNu9bq26VSsj2c1DHV6VD9ftG6Rej/Y2ykzPU3at+X++vzCz6O6d2VUL7hKYYzlxlKfsr0SIEDolSz5IhBCgIcKtwgCCCCAAAIIIIAAAggggICXAjaDqsc9PbR089JqxYSaoda9eXctuWSJ0tLSvKxiSucdaB+zB054QCN7jazm8sQXT+jCly9MmH3M3lnxjsbPHa/P1n1Wfi22fOob50hHdKre7PNWSYMek/JtHcey1Lttb00ZPEUDugyo9X0STwHLWl+E34k22XTbtopgYk2zE7durVxiYdm+hhYy3NrgMy077JBqVar/+gfK3dVL9XPdqG3NeRRlbdSPA3pI6e7POFVRttq9+50yCsKfcVpTbZ26Hp0npVWf7WuBMksWuPXFbv2Xq7WPfX+N+i8RXF5eSbravb28Wl1bdSzWJ0/GV4Bw6gdTnX7tn6K9d6X3d2bwEhjLjaU+ZXslQIDQK1nyRSCEAA8VbhEEEEAAAQQQQAABBBBAAAEvBZJxhpqXXtHO25aAHfncCP20c6P6d+yvmcNmqkvTLkGrsWLLCp09+2y9v/p9tW7QUrNOe0oD8wZGu9phl1dUXKRHP3tU1795vX7a+ZNz3rkHSY+eWj2Lc1+QHltU+vM2Ddro1oG36rze5ynD1rmsQyrfg7Bwj7buqbwHYaQBS9uDsElOE+Vk5mjE/iM0efDkOtTM+1Nt2dLly6XvvpOWLpW+/15q21Zq1Ej6989TNFd/dCrhP9st+4M71XLNJWreNM0JdvmCXr5FS30BMOcz+68sKFb6Z0uBfxbsagvrLdfWvf+kXW1eqHSIbw/C2sw4zfxhqOotnqgmJcH7UqT6dq3rj+6uwux1IU+NdLZj9p726vZe6TLCvmCi/dp2rxK9/kROyPKiecDQp4bqhW8qt9XU46fq0sMujWY1YlYWY7kxo6dgDwUIEHqIS9YI1CTAQ4X7AwEEEEAAAQQQQAABBBBAwEuBZJ6h5qVbNPNeu32tZi+erXF9xynTNnQLkQqLC/XAxw9o2L7D1L5R+1CHx8XnFiQ8/8XznbqM7SM9dHL1ao19SXr409KfTz9luhMcrGsKZw/CcAKWgeqR17Szll22oq5V9Ox8X2A0WAE/7/pZuwt3Ox/7b8S9/QAAIABJREFUz3bLSa+nJtnNlZ5eGhy05L+k6cA2I3RRt8my4KPv/6IiqaCg9M/2e//P7PcFBSUqKJTyC0uU7/uznWM/KyjdI3C13tWCepdrc1ZZlFhSpAHc9E0HKeejO5Sx/ijl5EhtW6aVBtyc/0pT6Z9tRl/ln9ksQJvll5Ge5ly7/dl+zciQMtKkwqyf9faBHVXiwWzHjJJsXbR7jRqmt3TqYOXar61aSX/4g2e3SMQZ2/6DLSe11ObdZRvVluVwas9T9fyZz0ecXyKewFhuIrYadQ4lQIAwlBCfI+CRAA8Vj2DJFgEEEEAAAQQQQAABBBBAwBFI9hlqNHNiCBz72LF6Y/kbTmXnnS8dtpd01wLpqw3S/q2kyw+XPvhBOnJ66fUc2/VYvT769TpfXLA9CC1A5JsJF07A0v94X6WyM7K15vI1alnfnSUs63yxfhmEExj1HR7pbDcvA6M247TXP3pp8caK/fjCDeD2aLqvnj/uC6UpQ7YMa3Z2aYDNCfaVBdyq/t4JCJZNUA1nJeVkW6420nvOf/9B/3MbZjfU1mu3psQ+hIzlRnrXcHwiCBAgTIRWoo5JKcBDJSmblYtCAAEEEEAAAQQQQAABBOJKIBVmqMUVOJWpJLByy0rlTc1zlqPs3lx67Wzp7Oel+asrDuvfUZoxVDpuhrR0c+mMrxXjV6hTkwCbFUboGyyo48smnIBl1SKH7zdck46dpLxmeRHWJjqHW2C07Z1tVVTit6GjSmfCtfXb0q5do9LZg/u2khZvkM55Qfpxe0Ud1+2QinyR1LIfZ6RlaN2V6zwJjBYXFyvn1hzZLFlfCieAa8fa8q+7J+xWukUBPU7xsr+mx5dZLftA+w/6DkqVfQgZy432XUd50RAgQBgNZcpAIIAADxVuCwQQQAABBBBAAAEEEEAAAQQQSGaBie9O1A1v3eBcYt/20pKfpW17ql9x45zSvfAW/lj62cRjJmrCURNco7GgzrCnh2nTrk3leYYbsPSd0KJeCz13xnMa0GWAa/XyIiObQbjX3zto7fYyTL9C6rK3n2WzV6P2Wn35Gi+qrfs/ul8Xv3JxpbwjCeDeP+R+XXjIhZ7UrWqmvv01L3z5QhUUFzgfhzPb0WaeWj3d2F/T7QsNtSytLS36S8Ev5cX6711pe3PaTMJAKRH26wzXkrHccKU4LpEECBAmUmtR16QS4KGSVM3JxSCAAAIIIIAAAggggAACCCCAgJ+ABap63NNDSzcvreaS1zRPE46coFvfu1XLtyyv9nn35t215JIlzl5xbqXXl76uE2edqPyifCfLSAKWFtj598h/67hux7lVHc/ysRmEbe5sI9szLlCKdG8//zzS09K1/sr1rs0g9A9KrduxrtKsx0gDuDa7sW3Dtk51oxGUstmxXaZ2KecJd7bjyvErXZkd6+YNFMmytL5y/feurKku1i6XHXaZJg+e7GaVY5IXY7kxYadQjwUIEHoMTPYIBBPgocK9gQACCCCAAAIIIIAAAggggAACySowb9U8HTn9yGqXN/rA0bp3yL1qnNNY2/Zsc2aNzfx8ZrXj5p0/T/079XeNx4IgXad21YqtK6rlmddUmnCkdOt70vIt1Yvs0rSLll26zNWApWsXViUju86Of++gNQFmEPoODWe2W6D6dWzcQav++IMrVQ8UlPLf7zGSAG7VfSK93CvRd/H+s2PtZ+HOdnR7dqwbjWFB5faT26uouECBw8qVS4l078rOTfbSivF+6wq7UekY5MFYbgzQKdJzAQKEnhNTAAKBBXiocGcggAACCCCAAAIIIIAAAggggECyCox9aawe/vTh8suzgOADJzygkb1GVrvkJ754QrZcowUMfWlsn7F66OSHXOMJFrAc1Uvau0Xp/ofdmknf/iw98UX1Yt0OWLp2YVUysmBPh7s6lM+U9H3sH0QLZ7Zb1aCb5WMzKddcvsaVGYTB6hnIZa/G0s0DpJvfkX6ouEWCErpZz0CFWHCzy5QuWrVtlfNxJLMdOzfprOWXLY+7YPPHaz/WYdMOqzbz1I29K71uD6/6UtV8GcuNljTlRFOAAGE0tSkLAT8BHircDggggAACCCCAAAIIIIAAAgggkKwCby5/UyOfG6Gfdm5U/479NXPYTNlMvGBpxZYVOnv22Xp/9ftq3aClZp32lAbmDXSNp2rAMj1Nuus30tNfS/P9Jjf17ygN30+6Yq4qzaZyO2Dp2oUFyGjZ5mW6+vWr9dzi5wIWE+5sN/+Th+83XJOOnaS8ZnmuVT1UPa2g0QdK9w6RbJ9K27/y4lekmZ8Hr4IX9axaWtVgcySzHS2veAw223KvD3z8gHYV7qqGW5e9K22J0fMOOk/TTpnm2n0Tq4wYy42VPOV6KUCA0Etd8kagBgEeKtweCCCAAAIIIIAAAggggAACCCCQzAJrt6/V7MWzNa7vOGWmZ4a81MLiQidIMWzfYWrfqH3I4yM5wAKWpzx5knbk/+Kc1qu1tHJradCparJgVKfG0pcbSj9pmF1fL545x9WAZSR1r+2x76x4R+Pnjtdn6z4rzyKS2W52Uu+2vTVl8BQN6DKgttUIeZ7Vc9jTw7Rp16ZKx95kswaPrn76TW9Jf3m38s+b12uu2WfM9rSevhLHvDhGj3z2SMDrCrW/pp00pveYuAqYlc6I7KQftv0QdInRuuxdGY0lX0PeZC4cwFiuC4hkEXcCBAjjrkmoUKoI8FBJlZbmOhFAAAEEEEAAAQQQQAABBBBAINYCpXve5Wnl1pXVqhIqqJNIexBWvbii4iINf3q4Xvj2BeejSGa7ndrzVD17xrPKSM/wvPleX/q6hj8zTNv27CgvK9y9EhvnNNSzp8/Wcd2O87yeVsDUD6Y6gdeqKdz9NaceP1WXHnZpVOoaTiG+PQgLigtqPDzc9qiaCUuMhtMKHINAbAQIEMbGnVIREAFCbgIEEEAAAQQQQAABBBBAAAEEEEAgOgLB9iAMN6gTj8tChiNXc2BUmnCkdOt70vIt1XOLdmDUZpza0qiPf/G4U5lw9koc1WuUJh03yfUZpzXZxtv+muHcB6GOseVeL3r5Is1dOjfooeG0R9WTf9v9t7pvyH2uLk0b6lq8+pyxXK9kyTeWAgQIY6lP2SktwEMlpZufi0cAAQQQQAABBBBAAAEEEEAAgSgKJGNQJxy+YIHRUb2kvVtISzdL3ZpJ3/4sPfFF9RyjHRg99rFj9cbyN5yKhLNX4rFdj9Xro18Ph8K1Y+Jtf03XLkySLfc6ds5Yfb/p+2rZhtMevpO6N++uaSdNi8qSr25ef015MZYbLWnKiaYAAcJoalMWAn4CPFS4HRBAAAEEEEAAAQQQQAABBBBAAIHoCCRzUKcmwTEvjdEjn1bsl5eeJt31G+npr6X5qyvO7N9RGr6fdMVcVdqHbkyfMZp28rSoNNLKLSudZWBLVKJw90pMU5pWjF+hTk06RaWOvkLiaX9Nty/clqU97anT9OKSF8uzDrc97ITbBt2mq/pdFZWlad2+9pryYyw3mtqUFS0BAoTRkqYcBKoI8FDhlkAAAQQQQAABBBBAAAEEEEAAAQSiJ5DMQZ1girZf3uWvjVdxSekRvVpLK7dK2/ZUP6NxjtSpsfTlhtLPLJj4999M1aW/js5+eRPfnagb3rrBKTuSvRInHjNRE46aEL0bKclLsmVpe9zTQ0s3Ly2/0kja45ZjbtGfjvpT0ikxlpt0TcoFSSJAyG2AQIwEeKjECJ5iEUAAAQQQQAABBBBAAAEEEEAAgRQRGPPiGD3yWcUMQv/LzmuapwlHTtCt792q5VuWBxQZ03uMpp3i/QzCQEEpX4VC1dOWs1xyyRKlpaWlSKt6e5nBlqX1ldq8ntSzRemytJt2Va/LXo320qo/rkq69mAs19v7jtxjI0CAMDbulIqAeKhwEyCAAAIIIIAAAggggAACCCCAAAIIeClgMwjHzx1frYjRB47WvUPuVeOcxtq2Z5sufuVizfx8ZrXjph4/VZce5v0MwmBBqXDrGe29Er1ss1jnXXW/Tv/6tKovbfil4idV/+z7JBnbg7HcWN+ZlO+FAAFCL1TJE4EwBHiohIHEIQgggAACCCCAAAIIIIAAAggggAACtRaoGuyxgOADJzygkb1GVsvziS+e0IUvX+gEDH1pbJ+xeujkh2pdfrgnJko9w72eRD7O9uscPPM3KiwuKr+MjDQpO0PaVVj9yuplSnuKVL6MrR0Rrfsmms6M5UZTm7KiJUCAMFrSlINAFQEeKtwSCCCAAAIIIIAAAggggAACCCCAAAJeCliwZ+RzI/TTzo3q37G/Zg6bqS5NuwQtcsWWFTp79tl6f/X7at2gpWad9pQG5g30sopO3olST88h4qAAW+617Z1ttXHXT5WCfr6qtazfUiqRNu7aGLC2WenpenXUaxrUdVAcXI17VWAs1z1LcoofAQKE8dMW1CTFBHiopFiDc7kIIIAAAggggAACCCCAAAIIIIBADATWbl+r2Ytna1zfccpMzwxZg8LiQj3w8QMatu8wtW/UPuTxbh2QKPV063rjNZ+a9iA8c/8z9eBJDzpVv2DOBXr6q6cDXgZLjMZr61IvBCoLECDkjkAgRgIECGMET7EIIIAAAggggAACCCCAAAIIIIAAAgggEFAg0B6E9TLr6eGTH662NK0tS2vH7yrcVSkvlhjl5kIgMQQIECZGO1HLJBQgQJiEjcolIYAAAggggAACCCCAAAIIIIAAAgggkMACttzraU8PdfaiLC6R+rbrq2fOeCbo0rS2LO3wp4dr4Y8LnatulttYz57xfFSWpo0mM2O50dSmrGgJECCMljTlIFBFgIcKtwQCCCCAAAIIIIAAAggggAACCCCAAAIIxJuALfc6/dPpapjTUBcfcnHIpWltWdr7PrxPO/J36Pw+50d1adpo2TGWGy1pyommAAHCaGpTFgJ+AjxUuB0QQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4F2AsN/7biBpGLkCAMHIzzkDAFQEeKq4wkgkCCCCAAAIIIIAAAggggAACCCCAAAIIIOCpAGO5nvKSeYwECBDGCJ5iAwusWrVKL7/8st5++2199tlnsr94i4qK1LJlSx188ME688wzdfrppyszM7NOhJb/McccE1YeN910k26++eawjo3kIB4qkWhxLAIIIIAAAggggAACCCCAAAIIIIAAAgggEBsBxnJj406p3goQIPTWl9wjELjxxhs1ceJElZSU1HhW37599dxzz6lTp04R5F75UAKEtabjRAQQQAABBBBAAAEEEEAAAQQQQAABBBBAIKUECBCmVHOnzMUSIEyZpo7/Cx07dqwefvhhNWjQQEOHDtWgQYPUo0cP5ebmavHixbr77rv10UcfORdiP//kk0/UsGHDWl2Yf4DwkUce0SGHHBI0n9atW8v+dzvxUHFblPwQQAABBBBAAAEEEEAAAQQQQAABBBBAAAH3BRjLdd+UHGMvQIAw9m1ADcoErrnmGrVo0UIXXnihGjVqVM3Flho966yz9PTTTzuf/eUvf9ENN9xQKz//AOFbb72lo48+ulb51OUkHip10eNcBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgOgKM5UbHmVKiK0CAMLrelFZHgZ9//lnt27dXfn6+DjzwQC1atKhWORIgrBUbJyGAAAIIIIAAAggggAACCCCAAAIIIIAAAiknQIAw5Zo8JS6YAGFKNHNyXaQtB/rxxx87S5Hu2LGjVhdHgLBWbJyEAAIIIIAAAggggAACCCCAAAIIIIAAAgiknAABwpRr8pS4YAKEKdHMyXWRNnPwiy++cJYh3bZtW60ujgBhrdg4CQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSDkBAoQp1+QpccEECFOimZPnIn/66Sd16NBBhYWFspmEH374Ya0uzj9AePjhh2vVqlWyvG1WYpcuXZw9CW0vxL333rtW+YdzEg+VcJQ4BgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiK0AY7mx9ad0bwQIEHrjSq4eCVx11VW68847ndzvuOMOXXnllbUqyT9AGCyD9PR03XDDDbrpppuUlpYWcTn20Kgp/fjjjzr00EOdQ1avXq299tor4jI4AQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBbAQKE3vqSe2wECBDGxp1SayHwv//9T0cccYQze9CCad9++63q169fi5wkCxCeddZZGjZsmJNn165dlZmZ6cwknDNnjmbMmKGCggIn7+uuu05//etfIy4nkqAiAcKIeTkBAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIigABwqgwU0iUBQgQRhmc4monsH79evXt21f2F7EF3v773/9q4MCBtctM0s6dO5Wdna2srKyAedjSpb/5zW+0detWp7xPPvlEvXv3jqg8AoQRcXEwAggggAACCCCAAAIIIIAAAggggAACCCAQlwIECOOyWahUHQUIENYRMBVPtxl8wQJrkXhMnz5d5513XshTtm/frmOOOUYLFy50jr3tttt07bXXhjyvrgc8/vjjOvvss51sxo4dq4ceeiiiLFliNCIuDkYAAQQQQAABBBBAAAEEEEAAAQQQQAABBOJSgABhXDYLlaqjAAHCOgKm4unRDBDu3r1bQ4YM0VtvveVQX3755Zo8eXJU2O06W7Zs6cwi7NGjh5YsWeJquTxUXOUkMwQQQAABBBBAAAEEEEAAAQQQQAABBBBAwBMBxnI9YSXTGAsQIIxxAyRq8d98802dq96uXTs1adIkaD4WoLM9Am1PQEu1mcVX10oeeuih+uijj5y9Dm1ZUjcTDxU3NckLAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwRoCxXG9cyTW2AgQIY+tP6UEEiouLNWrUKD355JPOESNGjNATTzyh9PT0qJodcsgh+vjjjwkQRlWdwhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfgRIEAYP21BTdwTIEDoniU5uShwwQUXaNq0aU6OJ554ombPnu3KvoeRVNFmMLZo0ULbtm1T9+7d9d1330VyeshjeaiEJOIABBBAAAEEEEAAAQQQQAABBBBAAAEEEEAg5gKM5ca8CaiABwIECD1AJcu6Cdg+g3//+9+dTAYNGqSXX35ZOTk5dcu0FmfPnDlTo0ePds4cM2ZMecCyFlkFPIWHiluS5IMAAggggAACCCCAAAIIIIAAAggggAACCHgnwFiud7bkHDsBAoSxs6fkAAI333yz/vznPzuf9OvXT6+99poaNGgQkdWKFSuUl5fnnDNgwAC9/fbblc7fvHmzFi1apKOPPjpovh9++KEGDx6sLVu2KC0tTfbnvn37RlSPUAfzUAklxOcIIIAAAggggAACCCCAAAIIIIAAAggggEDsBRjLjX0bUAP3BQgQum9KjrUUuOeee3TppZc6Z3fo0EFPPfWUmjRpUmNuPXv2rLb0aKgAoe/zAw88UKeeeqoOPvhgtWvXThkZGVq1apXmzJmjGTNmqKCgwCn7qquu0qRJk2p5VcFP46HiOikZIoAAAggggAACCCCAAAIIIIAAAggggAACrgswlus6KRnGgQABwjhoBKpQKmAz+t55552IOJYvX64uXbpUOifcAGGogixgeMMNN+jGG290ZhG6nXiouC1KfggggAACCCCAAAIIIIAAAggggAACCCCAgPsCjOW6b0qOsRcgQBj7NqAGZQLR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width=\"900\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/erigler/anaconda3/envs/test_GIMP_py36/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
+ " warnings.warn(message, mplDeprecation, stacklevel=1)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x1a311d2dd8>]"
+ ]
+ },
+ "execution_count": 170,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# plot differences between different adjusted (and quasi-definitive)\n",
+ "# and static traditional HDZ baseline-adjusted data\n",
+ "#\n",
+ "plt.figure(figsize=(9,6))\n",
+ "for interval in range(len(utc_xyzf_weekly_007_causal)):\n",
+ "\n",
+ " plt.subplot(3,1,1)\n",
+ "# plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ "# xyzf_def_weekly_007_causal[interval][0] - \n",
+ "# xyzf_trad_weekly_007_causal[interval][0], 'black')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ " xyzf_adj_weekly_007_causal[interval][0] -\n",
+ " xyzf_trad_weekly_007_causal[interval][0], 'turquoise', alpha=.75)\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " xyzf_adj_weekly_007_acausal[interval][0] -\n",
+ " xyzf_trad_weekly_007_acausal[interval][0], 'blue', alpha=.75)\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " xyzf_adj_weekly_inf_acausal[interval][0] -\n",
+ " xyzf_trad_weekly_inf_acausal[interval][0], 'red', alpha=.75)\n",
+ " \n",
+ " plt.subplot(3,1,2)\n",
+ "# plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ "# xyzf_def_weekly_007_causal[interval][1] -\n",
+ "# xyzf_trad_weekly_007_causal[interval][1], 'black')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ " xyzf_adj_weekly_007_causal[interval][1] -\n",
+ " xyzf_trad_weekly_007_causal[interval][1], 'turquoise', alpha=.75)\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " xyzf_adj_weekly_007_acausal[interval][1] -\n",
+ " xyzf_trad_weekly_007_acausal[interval][1], 'blue', alpha=.75)\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " xyzf_adj_weekly_inf_acausal[interval][1] -\n",
+ " xyzf_trad_weekly_inf_acausal[interval][1], 'red', alpha=.75)\n",
+ "\n",
+ " plt.subplot(3,1,3)\n",
+ "# plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ "# xyzf_def_weekly_007_causal[interval][2] -\n",
+ "# xyzf_trad_weekly_007_causal[interval][2], 'black')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ " xyzf_adj_weekly_007_causal[interval][2] -\n",
+ " xyzf_trad_weekly_007_causal[interval][2], 'turquoise', alpha=.75)\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " xyzf_adj_weekly_007_acausal[interval][2] -\n",
+ " xyzf_trad_weekly_007_acausal[interval][2], 'blue', alpha=.75)\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " xyzf_adj_weekly_inf_acausal[interval][2] -\n",
+ " xyzf_trad_weekly_inf_acausal[interval][2], 'red', alpha=.75)\n",
+ "\n",
+ "\n",
+ "# plot differences between absolutes and static baseline-adjusted data\n",
+ "idx = (utc_bas_all_inf_acausal > start_UTC) & (utc_bas_all_inf_acausal < end_UTC)\n",
+ "plt.subplot(3,1,1)\n",
+ "plt.ylim([-5,5])\n",
+ "plt.plot([utc.datetime for utc in utc_bas_all_inf_acausal[idx]],\n",
+ " abs_xyz_all_inf_acausal[0][idx] - \n",
+ " np.interp(utc_bas_all_inf_acausal[idx].astype(float), \n",
+ " utc_xyzf_all_inf_acausal[0].astype(float), \n",
+ " xyzf_trad_all_inf_acausal[0][0]),\n",
+ " '*', c='green', mfc='yellow', ms=8)\n",
+ "plt.subplot(3,1,2)\n",
+ "plt.ylim([-5,5])\n",
+ "plt.plot([utc.datetime for utc in utc_bas_all_inf_acausal[idx]],\n",
+ " abs_xyz_all_inf_acausal[1][idx] - \n",
+ " np.interp(utc_bas_all_inf_acausal[idx].astype(float), \n",
+ " utc_xyzf_all_inf_acausal[0].astype(float), \n",
+ " xyzf_trad_all_inf_acausal[0][1]),\n",
+ " '*', c='green', mfc='yellow', ms=8)\n",
+ "plt.subplot(3,1,3)\n",
+ "plt.ylim([-5,5])\n",
+ "plt.plot([utc.datetime for utc in utc_bas_all_inf_acausal[idx]],\n",
+ " abs_xyz_all_inf_acausal[2][idx] - \n",
+ " np.interp(utc_bas_all_inf_acausal[idx].astype(float), \n",
+ " utc_xyzf_all_inf_acausal[0].astype(float), \n",
+ " xyzf_trad_all_inf_acausal[0][2]),\n",
+ " '*', c='green', mfc='yellow', ms=8)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 171,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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Cfi8qbhswlaonIQAAlYC5KAVEychEJsAORgbLQ0XTIBcKVhAmU6uBMi/XIWLweZUgDzLaeAYdiEEXPLP53u5hQg6kwgUoEDIwkAgJQGfNxWXTTilMNEtAoUUIAcLGVYmlSMBcjBHwWKoqQqQK6ny07nnAuSf5wuA6SciQJ4lwkwnCAQKuOSfz/dyWUbFFaBAWNzYMrOMC/i8qbhswhkPJ8NDIFcC5GCuwsVgCyhADhYwqEwpFgFyJRZWGkXASoD8s2LiJAQaEiDPGuLjYgQaEnDJP5/v5TaEzcWZFqBAmOnwMLgiC/i8qbhswkVeC8wNgaQFyMGkxekPgXIBcpAVgYCdALli58RZCMQhQP7FoUqbCPA7IWsAgawIuOxzPt/LzUrcGEf0AhQIozelRQSsBHzeVFw2YStUTkIAASsBctCKiZMQiE2AHIyNloYLJkCuFCygTCdXAuRfrsLFYHMqQJ7lNHAMuxACLvnn873cQgSdSQQKUCBkYSCQkoDPm4rLJpxSmOgWgUIKkIOFDCuTypEAOZijYDHUVAXIlVT56dxzAfLP8wXA9BMRIM8SYaYTBAIFXPLP53u5LKPiClAgLG5smVnGBXzeVFw24YyHk+EhkCsBcjBX4WKwBRQgBwsYVKYUiwC5EgsrjSJgJUD+WTFxEgINCZBnDfFxMQINCbjkn8/3chvC5uJMC1AgzHR4GFyRBXzeVFw24SKvBeaGQNIC5GDS4vSHQLkAOciKQMBOgFyxc+IsBOIQIP/iUKVNBPidkDWAQFYEXPY5n+/lZiVujCN6AQqE0ZvSIgJWAj5vKi6bsBUqJyGAgJUAOWjFxEkIxCZADsZGS8MFEyBXChZQppMrAfIvV+FisDkVIM9yGjiGXQgBl/zz+V5uIYLOJAIFKBCyMBBIScDnTcVlE04pTHSLQCEFyMFChpVJ5UiAHMxRsBhqqgLkSqr8dO65APnn+QJg+okIkGeJMKfbydtvS/vuK02YIP3wh9IVV0idO6c7JnpvFnDJP5/v5bJsiitAgbC4sWVmGRfweVNx2YQzHk6Gh0CuBMjBXIWLwRZQgBwsYFCZUiwC5EosrDSKgJUA+WfFxEkINCRAnjXEl4+LjzpKuuqq1rE+9JC0ww75GHvBR+mSfz7fyy34cvB6ehQIvQ4/k09TwOdNxWUTTjNW9I1A0QTIwaJFlPnkTYAczFvEGG9aAuRKWvL0i4DbkxW4IYBAOAH2uXBeuTy7Q4fyYQ8fLr3ySi6nUrRBu+Sfz/dyixZ/5tMqQIGQ1YBASgI+byoum3BKYaJbBAopQA4WMqxMKkcC5GCOgsVQUxUgV1Llp3PPBcg/zxcA009EgDxLhDndTioLhN/4hjRxYrpjovdmAZf88/leLsumuAIUCIsbW2aWcQGfNxWXTTjj4WR4CORKgBzMVbgYbAEFyMECBpUpxSJArsTCSqMIWAmQf1ZMnIRAQwLkWUN8+biYJwgzGyeX/PP5Xm5mA8nAGhagQNgwIQ0g4Cbg86bisgm7KXMVAggECZCDrAsE0hUgB9P1p/f8CJAr+YkVIy2eAPlXvJgyo+wJkGfZi0nkI6JAGDlpVA265J/P93Kjcqed7AlQIMxeTBiRJwI+byoum7Any4JpIpCIADmYCDOdIFBVgBxkcSBgJ0Cu2DlxFgJxCJB/cajSJgLlAuSZByuiskC49trSq696MPHsT9El/3y+l5v9iDJCVwEKhK5yXIdAgwI+byoum3CD3FyOAAJtBMhBlgMC6QqQg+n603t+BMiV/MSKkRZPgPwrXkyZUfYEyLPsxSTyEVEgjJw0qgZd8s/ne7lRudNO9gQoEGYvJozIEwGfNxWXTdiTZcE0EUhEgBxMhJlOEKgqQA6yOBCwEyBX7Jw4C4E4BMi/OFRpE4FyAfLMgxVRWSBcay3ptdc8mHj2p+iSfz7fy81+RBmhqwAFQlc5rkOgQQGfNxWXTbhBbi5HAIE2AuQgywGBdAXIwXT96T0/AuRKfmLFSIsnQP4VL6bMKHsC5Fn2YhL5iCgQRk4aVYMu+efzvdyo3GknewIUCLMXE0bkiYDPm4rLJuzJsmCaCCQiQA4mwkwnCFQVIAdZHAjYCZArdk6chUAcAuRfHKq0iUC5AHnmwYqoLBAOGya9/roHE8/+FF3yz+d7udmPKCN0FaBA6CrHdQg0KODzpuKyCTfIzeUIINBGgBxkOSCQrgA5mK4/vedHgFzJT6wYafEEyL/ixZQZZU+APMteTCIfEQXCyEmjatAl/3y+lxuVO+1kT4ACYfZiwog8EfB5U3HZhD1ZFkwTgUQEyMFEmOkEgaoC5CCLAwE7AXLFzomzEIhDgPyLQ5U2ESgXIM88WBGVBcI115QmTfJg4tmfokv++XwvN/sRZYSuAhQIXeW4DoEGBXzeVFw24Qa5uRwBBNoIkIMsBwTSFSAH0/Wn9/wIkCv5iRUjLZ4A+Ve8mDKj7AmQZ9mLSeQjokAYOWlUDbrkn8/3cqNyp53sCVAgzF5MGFFCAs8995zuv/9+jRs3ThMmTND06dPVpUsXrbrqqtpqq610+OGHa8SIEbGNxudNxWUTji0QNIyAhwLkoIdBZ8qZEiAHMxUOBpNhAXIlw8FhaIUXIP8KH2ImmAEB8iwDQYh7CJUFwjXWkN54I+5ead9CwCX/fL6Xa0HKKTkVoECY08Ax7MYEtt12W40dO7ZuI4cccoiuueYade3ate65YU/weVNx2YTD+nI+AghUFyAHWR0IpCtADqbrT+/5ESBX8hMrRlo8AfKveDFlRtkTIM+yF5PIR0SBMHLSqBp0yT+f7+VG5U472ROgQJi9mDCiBATWWGMNvfXWW81PC373u99tflJw0KBBWrx4sZ588klddNFFev/995tHcuCBB+qmm26KfFQ+byoum3DkAaBBBDwWIAc9Dj5Tz4QAOZiJMDCIHAiQKzkIEkMsrAD5V9jQMrEMCZBnGQpGXEOpLBCuvrr05ptx9Ua7IQRc8s/ne7khaDk1ZwIUCHMWMIYbjcBuu+2mQw89VPvuu686derUrtGZM2dq66231qSvPhxsnjaM+nWjPm8qLptwNJGnFQQQMALkIOsAgXQFyMF0/ek9PwLkSn5ixUiLJ0D+FS+mzCh7AuRZ9mIS+YgoEEZOGlWDLvnn873cqNxpJ3sCFAizFxNGlBGBf/7zn9p9992bR3P88cfr4osvjnRkPm8qLptwpPg0hoDnAuSg5wuA6acuQA6mHgIGkBMBciUngWKYhRQg/woZViaVMQHyLGMBiWM4lQXCoUOlt96KoyfaDCngkn8+38sNycvpORKgQJijYDHUZAXmzJmj5ZZbrrnT73znOzIFwygPnzcVl004SnvaQsB3AXLQ9xXA/NMWIAfTjgD950WAXMlLpBhnEQXIvyJGlTllTYA8y1pEYhgPBcIYUKNp0iX/fL6XG406rWRRgAJhFqPCmDIhMGvWLK200krNYzFPEt59992RjsvnTcVlE44Un8YQ8FyAHPR8ATD91AXIwdRDwAByIkCu5CRQDLOQAuRfIcPKpDImQJ5lLCBxDKeyQLjaatLbb8fRE22GFHDJP5/v5Ybk5fQcCVAgzFGwGGqyAnfccYf22Wef5k5POeUUnX/++ZEOwOdNxWUTjhSfxhDwXIAc9HwBMP3UBcjB1EPAAHIiQK7kJFAMs5AC5F8hw8qkMiZAnmUsIHEMhwJhHKqRtOmSfz7fy40EnUYyKUCBMJNhYVBpCyxZskRbbrmlxo8f3zyUZ555RptsskmoYZlNo9Yxbdo0bbbZZs2nTJo0SQMGDAjVfp5Pnj9/vsaOHds8hZEjR6pbt255ng5jRyB3AuRg7kLGgAsmQA4WLKBMJzYBciU2WhpGoK4A+VeXiBMQaFiAPGuYMPMNdO/Ro2yMSwYP1oJXX838uH0YoEv+mXu9w4YNa+aZOnWqV/dyfVgTvs6RAqGvkWfeNQUuuuginXzyyc3n7L333rr99ttDi3Wo/CuhGi1cc801WnnllUP3wQUIIIAAAggggAACCCCAAAIIIIAAAgggkD2BPffaq2xQX/Tpowf/9KfsDZQRWQnMnDlTRxxxRPO5FAityDgpBwIUCHMQJIaYrMBjjz2mnXbaSYsWLVKfPn300ksvqW/fvqEHQYEwNBkXIIAAAggggAACCCCAAAIIIIAAAgggkDuBXpMmqefkyZq+4Yaa16dP8/jbFQj79tWDV1+du7kx4JIABUJWQhEFKBAWMarMyVlg4sSJGjFihD755BM1NTXpgQce0LbbbuvUHq8Yrc7m8hi/UxC4CAEEAgXIQRYGAukKkIPp+tN7fgTIlfzEipEWT4D8K15MmVH2BMiz7MXEdUSdRo9W1+9//+vL57/+upYOHCheMeoqGv91LvnHK0bjjws9JC9AgTB5c3rMqMDkyZO1zTbb6IMPPlCnTp00evTo5teLxnX4/GFblw8BxxUH2kXARwFy0MeoM+csCZCDWYoGY8myALmS5egwtqILkH9FjzDzy4IAeZaFKEQ0hsrPDHXvLs2dK1X+9yFDpMmTI+qUZhoRcMk/n+/lNmLNtdkWoECY7fgwuoQETFHQPDn49ttvy7wa9Prrr9ehhx4aa+8+byoum3CswaBxBDwTIAc9CzjTzZwAOZi5kDCgjAqQKxkNDMPyQoD88yLMTDJlAfIs5QBE2X1lIdC0vXRp+wLh4MHSlClR9kxbjgIu+efzvVxHZi7LgQAFwhwEiSHGK2DeH21eI/rKK680d3TZZZfpmGOOibdTST5vKi6bcOwBoQMEPBIgBz0KNlPNpAA5mMmwMKgMCpArGQwKQ/JGgPzzJtRMNEUB8ixF/Ki7pkAYtWjs7bnkn8/3cmMPCB2kJkCBMDV6Os6CwOzZs7XDDjvoueeeax7Oueeeq9NOOy2Rofm8qbhswokEhU4Q8ESAHPQk0EwzswLkYGZDw8AyJkCuZCwgDMcrAfLPq3Az2ZQEyLOU4OPo1rZAOGiQ9M47cYyANkMKuOSfz/dyQ/Jyeo4EKBDmKFgMNVqBuXPnatSoUfrPf/7T3PDpp5+uc845J9pOarTm86bisgknFhg6QsADAXLQgyAzxUwLkIOZDg+Dy5AAuZKhYDAU7wTIP+9CzoRTECh0nt15pzRxonTssVLPninoJtxlUIFwxgypd+/ygVAgTDgw1btzyT+f7+VmJnAMJHIBCoSRk9JgHgQWLlyo3XffXWPGjGke7gknnKA//vGPiQ7d503FZRNONDh0hkDBBcjBggeY6WVegBzMfIgYYEYEyJWMBIJheClA/nkZdiadsEBh86yyWGa+u/e970m//W37b/IlbB5bd0EFwn79pGnTyrscOFB6993YhkHD9gIu+efzvVx7Wc7MmwAFwrxFjPFGIrDvvvvq9ttvb27LvGLUFAc7BG3mX/XWtWtXDRs2LJK+WxrxeVNx2YQjxacxBDwXIAc9XwBMP3UBcjD1EDCAnAiQKzkJFMMspAD5V8iwMqmMCRQyz2rcW9MOO0gPPZSxKEQ0nFrzbtsFBcKIwBtvxiX/fL6X27g4LWRVgAJhViPDuGIVqFUMDOp48ODBmjJlSqRj8nlTcdmEI8WnMQQ8FyAHPV8ATD91AXIw9RAwgJwIkCs5CRTDLKQA+VfIsDKpjAkULs+++EJadtnaykuXZiwKEQ3HtkA4YIA0dWpEndJMIwIu+efzvdxGrLk22wIUCLMdH0YXkwAFwphgLZt12YQtm+Y0BBCwECAHLZA4BYEYBcjBGHFpulAC5EqhwslkciZA/uUsYAw3lwKFy7Nvf1u6777asTDFsc6dpVVWyWXMqg7atkBovkk4fXqx5p7T2bjkHwXCnAabYdcUoEDIAkEgJQGfNxWXTTilMNEtAoUUIAcLGVYmlSMBcjBHwWKoqQqQK6ny07nnAuSf5wuA6SciULg8sy2SmfPOOUf6xS8ScY69k8WLS0VP2+ODDyTzfUKOVAVc8s/ne7mpBovOYxWgQBgrL40jUF3A503FZRNmLSGAQHQC5KF5BEIAACAASURBVGB0lrSEgIsAOeiixjU+CpArPkadOWdFgPzLSiQYR5EFCpdntgXClqB+8onUq1f+Q3z33dKee4abR1FftRpOIdWzXfLP53u5qQaLzmMVoEAYKy+NI0CBMEjAZRNmLSGAQHQC5GB0lrSEgIsAOeiixjU+CpArPkadOWdFgPzLSiQYR5EFCpVnn34qrbBCuHD961+SeS1p3o8ttpCefjrcLEwxdcmScNdwdqQCLvlHgTDSENBYRgQoEGYkEAzDPwGfNxWXTdi/FcKMEYhPgByMz5aWEbARIAdtlDgHAYlcYRUgkJ4A+ZeePT37I1CoPPv978O/MnT//aVbbsl/wIcNk954I/w8Zs0KX1QN3wtXVBFwyT+f7+WykIorQIGwuLFlZhkX8HlTcdmEMx5OhodArgTIwVyFi8EWUIAcLGBQmVIsAuRKLKw0ioCVAPlnxcRJCDQkUKg8C/t6USO33nrSSy81ZJiJi13mbgb+6KPStttmYgo+DsIl/3y+l+vjGvFlzhQIfYk088ycgM+bissmnLkAMiAEcixADuY4eAy9EALkYCHCyCQSECBXEkCmCwSqCJB/LA0E4hcoVJ65FMl8LxA++KC0447xLzR6CBRwyT+f7+WyjIorQIGwuLFlZhkX8HlTcdmEMx5OhodArgTIwVyFi8EWUIAcLGBQmVIsAuRKLKw0ioCVAPlnxcRJCDQkUKg8cykQrrOONGFCQ4apXGy+N3jEEdLnn0vnnScdcIDbMK67TvrhD92u5aqGBVzyz+d7uQ2D00BmBSgQZjY0DKzoAj5vKi6bcNHXA/NDIEkBcjBJbfpCoL0AOciqQMBOgFyxc+IsBOIQIP/iUKVNBMoFCpNn8+ZJPXqED+/aa0uvvhr+urSvGDJEeued0iiWWUb64gv3ET33nLThhu7Xc6WzgEv++Xwv1xmaCzMvQIEw8yFigEUV8HlTcdmEi7oOmBcCaQiQg2mo0ycCrQLkIKsBATsBcsXOibMQiEOA/ItDlTYRKBcoTJ6NHi3tv3/48A4YIE2dGv66NK944YVoC3o77ST9+99pzqjU9/Tp0ptvSuuvLy27bPrjSWAELvnn873cBEJCFykJUCBMCZ5uEfB5U3HZhFkxCCAQnQA5GJ0lLSHgIkAOuqhxjY8C5IqPUWfOWREg/7ISCcZRZIHC5Jl5Vegrr4QP1c47S2PGhL8uzStcXqVab7xLl9Y7I96fP/+8tP320uzZ5f2MGydts028fafYukv++XwvN8VQ0XXMAhQIYwameQSqCfi8qbhswqwkBBCIToAcjM6SlhBwESAHXdS4xkcBcsXHqDPnrAiQf1mJBOPItMBLL0l/+IPUr590+uml102GOAqTZ65Fs8svl44+OoRYBk51nWutoaddIBw6VJo8OXiEaY8txpC75J/P93JjDAVNpyxAgTDlANC9vwI+byoum7C/K4WZIxC9ADkYvSktIhBGgBwMo8W5PguQKz5Hn7mnLUD+pR0B+s+8gPnuXNtXMW60kfTss6GGXZg8cy2a/fa30i9+Ecos9ZNd55rlAmGtOb31lmQKiAU8XPLP53u5BVwCTOkrAQqELAUEUhLweVNx2YRTChPdIlBIAXKwkGFlUjkSIAdzFCyGmqoAuZIqP517LkD+eb4AmH59gVNPlS64oPy8kE9bFSbPXItmK60kzZxZ3XrJEmnhQqmpSXLto34k7c8wr+Ds1cv+fNszQ64b22atz6tla14xal41WsDDJf98vpdbwCXAlL4SoEDIUkAgJQGfNxWXTTilMNEtAoUUIAcLGVYmlSMBcjBHwWKoqQqQK6ny07nnAuSf5wuA6dcXCCqqhCz0FCLPWgp49cWCz6hmNmuWZL5t+OGHpeumT5d693btpfHrzDi32EIaP77xtipbCLluIh3AvHlSjx61m6wcnynczp1beqVuFgq3jiAu+efzvVxHZi7LgQAFwhwEiSEWU8DnTcVlEy7mKmBWCKQjQA6m406vCLQIkIOsBQTsBMgVOyfOQiAOAfIvDlXaLJQABcJSOC+5RDrhBPfQViuOHXusZL5R2HL06SN99JF7P41euf/+0ujRjbYSfH2aBcLPP5eWX772vE46STrzTGm55aQrrpCOOab1/KlTpQED4nGJuVWXfc7ne7kxh4PmUxSgQJgiPl37LeDzpuKyCfu9Wpg9AtEKkIPRetIaAmEFyMGwYpzvqwC54mvkmXcWBMi/LESBMWRagAJhKTydOknmiTLXo1pxLAJf1yG1u+6FF6QNN4ysuXYNpVkgNE9qmle91juOO65UDM5SXOqNuc7PXfY5n+/lNsjN5RkWoECY4eAwtGIL+LypuGzCxV4NzA6BZAXIwWS96Q2BSgFykDWBgJ0AuWLnxFkIxCFA/sWhSpuFEoigUFKIPGv0FZPvvisNHNh+aQS1+/jj0tZbJ7+Mzj9fOu20+PqNqkD4zjvSrbdKa64p7bWX3XhPOUW68EK7c804I1j3dp3Ff5ZL/vl8Lzf+iNBDWgIUCNOSp1/vBXzeVFw2Ye8XDAAIRChADkaISVMIOAiQgw5oXOKlALniZdiZdEYEyL+MBIJhZFcgqFBinqQLUTArRJ4FzfeNN0pFKtsjqEBWzbF//9KrPrfc0rb1xs877zzpf/6n8XaqtRBFgfCTT6QVV2ztwTztZ576q3eEWK+iQCif7+XWW0r8PL8CFAjzGztGnnMBnzeVQvwSnPP1x/D9FiAH/Y4/s09fgBxMPwaMIB8C5Eo+4sQoiylA/hUzrswqQoGgwsqXX0qdO1t3Uog8q/ZEWZjCkxEzBa5evVrtal2/7bbSo49aOzd84rnnSj//ecPNVG0gigLhfvtJt91W3oVNu2Hi9P77kinQVh42/cSn59yyS/75fC/XGZoLMy9AgTDzIWKARRXweVNx2YSLug6YFwJpCJCDaajTJwKtAuQgqwEBOwFyxc6JsxCIQ4D8i0OVNnMp8Nln0p57lgpSP/qRdPnlUpcuwU8KnnOOdPrp1tMsRJ5FVSA0am0LTfUKV7WKUg88IO22W+k1m7fcInXsaB2TwBPPOks680z7NiZNkoYNsz8/igKby6s/H3tM2m47+3H27St99FH786MYv/0oIjvTJf98vpcbGTwNZU6AAmHmQsKAfBHweVNx2YR9WRfME4EkBMjBJJTpA4HqAuQgqwMBOwFyxc6JsxCIQ4D8i0OVNhMTMAULU8iYMUP6xjekTp3cu95xR+nhh1uv32EH6YILpI03Dm5z+nSpd2+r/gqRZ1EWCLt3l0zRqls3af31axtWK0q99po0fHjrteZVp6Zg18hhnlgcO9a+BfMtwMGDg88/5phSkbntEUWBzaVAWK8IazvjKMZv21eE57nkn8/3ciOkp6mMCVAgzFhAGI4/Aj5vKi6bsD8rg5kiEL8AORi/MT0gUEuAHGR9IGAnQK7YOXEWAnEIkH9xqNJmIgJz5kh9+kjz5pW6MwWiiRNLT/25HGGLKMceK116qVVPhcizaoWpddaRXnml1eGww6TrrrNy0aBB0rvv1j63WlHKplC2eLF0xx3SiSdK8+dLu+4qXX211KNHcJ9h1sDQodKbb1Z/anHDDaXnny/vx6bAtmCB9LOfSffeK5mC5WWXScsu29qOzbzb9mq+ExnmKcdq0bjrLmmPPezimrGzXPLP53u5GQsfw4lQgAJhhJg0hUAYAZ83FZdNOIwt5yKAQG0BcpAVgkC6AuRguv70nh8BciU/sWKkxRMg/4oXUy9mNHq0tP/+7ad6663Sd78bnuDzz6Xllw9/nU3BR6aGOU9jxoxpbn/UqFHqbp6gy9tRrTA1e3brNwWNoXmac+edwz2JV8ti7lwpyMumUGYKWvfcU976DTdIhx5a/t9eekl67jnphz+0i4p56tEU7kaMCH4FrWnlxz8uFSPbHjbr5aabpIMOar3K9GOeRvzHP6Snn5YuvLD9GKu1O3Om9VOudSf+zDPSJpvUPS2LJ7jkn8/3crMYQ8YUjQAFwmgcaQWB0AI+byoum3BoYC5AAIGqAuQgiwOBdAXIwXT96T0/AuRKfmLFSIsnQP4VL6aFn9HUqWp+8izoMN9Ze+SR8ATmm4K/+lX462wKPkUvEAapvf++NGBAeM+gK8yTeKZ4V3nUKxC++KK0wQbBY2gbt4svLj1hGOb49FOpZ8/SFTvtJD30UPurTzml9Iratsd770n9+wf3ZF6PasZyxRXtf/6Tn0hXXVV9hNXWoZm/cYjiME9DVvOMov0Y23DZ53y+lxtjKGg6ZQEKhCkHgO79FfB5U3HZhP1dKcwcgegFyMHoTWkRgTAC5GAYLc71WYBc8Tn6zD1tAfIv7QjQf2gB8zRVUBHFNGReyfjoo6GbVJhXS7Zt3ecC4aqrSqYQWO1wNQ1qz7wqtGPH8p8EtW+emFtppdJ5++xTer1o0NE2bi7jPO006dxzSy2bp+qefbZ9L2ef3b7obAqAxx/f/lzzWlHzPUbXI8xrWF37ePllad11Xa9O9TqXfc7ne7mpBovOYxWgQBgrL40jUF3A503FZRNmLSGAQHQC5GB0lrSEgIsAOeiixjU+CpArPkadOWdFgPzLSiQYh7XAvvtKt98efHrSBULLp6oKkWeVhTTznbyLLqoetvvvL33zL4pj0SKpU6fylqoV9syTdkceWf3bgKaVRguEbds44gjp2mvbz9IUKIPWqfn+oSm03XKLNGRI6TrzSlHzalHXo2U+5v998EHphRdKr0vt3du1xfbXme9MDh8eXXsJtuSSfz7fy00wNHSVsAAFwoTB6Q6BFgGfNxWXTZiVgwAC0QmQg9FZ0hICLgLkoIsa1/goQK74GHXmnBUB8i8rkWAc1gK1CoTDhkmvv27d1NcnujxF1nKxxVOEhcizSqOHH5a23766tSnUHXVU+FgEXbFwodSlS/lPasXs5pulAw+s3neUBcIrr5SOPrp9X3/6U6lQWe3Ye+/WAmIj68+03zIfU7T9wx+iMa9sxTyZawrwOTxc8s/ne7k5DDFDthSgQGgJxWkIRC3g86bisglH7U97CPgsQA76HH3mngUBcjALUWAMeRAgV/IQJcZYVAHyr6iRzfm8vvxSMt8FNN+eM09XHXBA64S22Ub6z3+qT9CiYNfu4kYKNBb9FSLPTIHOPMnXcpjYmO8DVjuuuUb60Y+iWYjmFZxdu5a3FUXMTOwqX11qO+KWuJvvCg4c2P4q863MoP/e9syWNhqZi2lvyRI1vya30XZqzX30aGm//Wx1MnWeS/75fC83U8FjMJEKUCCMlJPGELAX8HlTcdmE7WU5EwEE6gmQg/WE+DkC8QqQg/H60npxBMiV4sSSmeRPgPzLX8y8GPEFF0innto61f/+V9p449L/Xq8Ict990i67hGOq12at1nwpEJpXfJpCVMvx4ovS+utXl3n8cWnEiHBxqHb2vHntv9EXRczMK2I32qj2GN9+Wxo6tP059Z5CND+vN8aoCoSmgPrAA9Iee0TjHdTK5Mmtr0SNr5dYWnbZ53y+lxtLEGg0EwIUCDMRBgbho4DPm4rLJuzjGmHOCMQlQA7GJUu7CNgJkIN2TpyFALnCGkAgPQHyLz17eq4hUFlY2XpryRSczLHqqtK0abX5LIp2ZQ3UK+TU6s2ir1znmZnfJZdIJ55YrvDyy6Vv6VU75s+XunePZpnPndu+rShiNm6cNHJk/bUU1FcUBcLFi0tPMDYyFzP6zz+XBg+WZs2KxjuoldmzpeWXj6/9GFt2yT+f7+XGGAqaTlmAAmHKAaB7fwV83lRcNmF/VwozRyB6AXIwelNaRCCMADkYRotzfRYgV3yOPnNPW4D8SzsC9B8oUFkwWWGF1uKHTTHFomhX1q9Nm9VC9cUXUo8eNQOZ6zzr2VP67LP285s4UfrGN6rP2xS/OneOZoGbAtiyy5a31UjMWtbHI49IO+xQfYzmSdaTTw4u4EVRIBw/Xtp008YLhNOnS336RGNdrRUKhPH60joCCQhQIEwAmS4QCBKgQDimmWXUqFHqHtVfj7HUEEDASiDX/xC1miEnIZBtAXIw2/FhdNkRIFeyEwtG4p8A+edfzHMx48riz3LLtRapbApDSRYIjzuu9IRdjSO3eWZeKWpeLRp03HOPtNtutZeTTaxsFuSnn0qmUNn2MN8kNN+qdDla1od5MvWJJ6q3cP310ve/H1zAa1u0rPaEYb35mwLldts1XiA03+n8299cJOyvsSiE2zeW7Jku+efzvdxko0NvSQpQIExSm74QaCPg86bisgmzeBBAIDoBcjA6S1pCwEWAHHRR4xofBcgVH6POnLMiQP5lJRKMo0ygsrCyzDLSnDmlU+oVXcw577wjDRpUH/X116WHH5aOPrr+ubXOqFOQzGWemeKgeTKtX7/gmRu7YcNqu9nEykZ+5kxppZVazzTe5tWcroftt//MN/3uuit4zfXuXfKptiZtvkF49tnSL39pt6Zd5xrVdWGL7lH1G0E7Lvnn873cCMhpIqMCFAgzGhiGVXwBnzcVl024+CuCGSKQnAA5mJw1PSEQJEAOsi4QsBMgV+ycOAuBOATIvzhUabNhgcrCUrdu0q9/LZ17rmSeJqt3mOLNk09Kq69e/cwbb5QOOaT6zy+/XDrmmHo9lX5etAKhKcaapzZrHZMnS0OG1D4nqgKh+ebkKqu09rVggWTWhOthWyA86ijpiiuqF/BqtWN+9rvfSaefXnuU//iHtN9+rjMJd92UKcExmzFDuvfe0tOS1Q4KhOGsORuBDApQIMxgUBiSHwIUCHnFqB8rnVlmUYAbPlmMCmPySYAc9CnazLURAXKlET2uRaAxAfKvMT+ujkkgqsLSOedUL9DU62Pu3LrfFvx69kUqEL73njRwYP3Avv22tNpqtc8LMm5qkg44QLrhhvp9tJwxdao0YEDr+c89J228sf31lWfaFgj/8pdSEbnaWqnWjvkmpXkl56uv1v5Oo/sM3K6s9lSj+cak+cZjtacyzetdbQrzbqOK/SqXfc7ne7mxB4QOUhOgQJgaPR37LuDzpuKyCfu+Xpg/AlEKkINRatIWAuEFyMHwZlzhpwC54mfcmXU2BMi/bMSBUVQI1CvehQEzT8OZV5RWHvX6sHlFZEubRSoQmqfITGGs3mHzGtdq3+Yzbdfzb9t/5dOKpmBo8wrZanOwLRCa7/p973vBY73wQumkk0o9mOKaKQi2HOa7gub7gqaIWusp1nrGUf+82po2T2SabzpWi4kpyG64YdSjSaw9l33O53u5iQWGjhIXoECYODkdIlAS8HlTcdmEWTcIIBCdADkYnSUtIeAiQA66qHGNjwLkio9RZ85ZESD/shIJxlEmEKZ4VI9uwgRpnXXan1WrD/N6TfNU1ZFHSn/+c70epE8+kXr1qnpervLM1n7x4vrfAYyqQPjGG9Iaa7T6midDf/Wr+nGpdoZtgdA8Tdm/f6lIePPN5a1NmiStuWbpv/32t6VvCZrDzPmll6R115XefLP1HPfRRndltQKh+d6kGXdQvMxrSQcPjm4MKbTkkn8+38tNIUR0mZAABcKEoOkGgUoBnzcVl02YFYQAAtEJkIPRWdISAi4C5KCLGtf4KECu+Bh15pwVAfIvK5FgHGUCtkUqGzbzbbVdd21/Zq0+Ro6UHnusVCQ0r1dsOfbZR7r99vZt3XKLtP/+VUeTqzyztbf5Jl2tAuHvfy/94hc2EZReeUUaPrz1XNsxVmvdtkA4a5a0wgqlJyorv8/XtkC6aFHpe4Mvvigdeqi0556lnp99VtpkE7s5JnFWtQJhvW8pJjG2GPtwyT+f7+XGGAqaTlmAAmHKAaB7fwV83lRcNmF/VwozRyB6AXIwelNaRCCMADkYRotzfRYgV3yOPnNPW4D8SzsC9B8o0GgBqG2j5kmul18u78Y8MdWpU3V888SYeb2kOcx35K68UhoyRDr+eKlLl/bXbb+99PDDVdvLVZ7Z2PfrJ33wQf3FW6tAGOb1m5dcIh13XOlJTVOsO/HE+n3XOsMUxMwrUk1Max2m8GfWiYntjjuWn2lTIJ09u+aTpY1NIuTVq6wiTZtWKmD+9a/Bc7n+eumHP2z9mVnvF18csqPsne6Sfz7fy81eBBlRVAIUCKOSpB0EQgr4vKm4bMIheTkdAQRqCJCDLA8E0hUgB9P1p/f8CJAr+YkVIy2eAPlXvJgWYkY2RaowE20p5phv15lvw5nXgbY85RXUzimnSOefH9yDeX3kN7/Z/mc1Cka5yjMb+6Ymaf78+hGoVSC0KdC17cEUdTfaSHrhhfr91jvDxGqttSTzmtBaR0tMTd/mdaKmqGmO/faTRo+u14tkCoxBBeX6V0Z/xhFHlF6Xe8YZ0tlnl7ffMk8T05/8pPSU7Oabl4rkffpEP5aEW3TJP5/v5SYcHrpLUIACYYLYdIVAWwGfNxWXTZjVgwAC0QmQg9FZ0hICLgLkoIsa1/goQK74GHXmnBUB8i8rkWAcXwuYp8RWXDFaEFMAmTxZGjrUrl3z1JR5eqraUavwFXBNbvIsjL3NE3S1nEyxdtAgu3hEfZYp+HXsWL/VtnM0T0z+4Q/S8stLJ50k9ehhd71NP/Vbaj3jf/9X+tnPwlxROvfb35b+9S/p448l8zShKV6a49hjpUsvDd9ejq5wyT+f7+XmKLQMNaQABcKQYJyOQFQCPm8qLptwVO60gwACEjnIKkAgXQFyMF1/es+PALmSn1gx0uIJkH/Fi2nuZ2SeYLr6avtpHHigdPPNtc+v9u21alfddptkvjdY7ShqgfCuu6S99rKzb7RAaApu/fvb9RX1WeYpUvNa2HqHzRzrtWHzRGa9Ntr+/J57Sk+3jhsX5qpSQfOLL0rX/Oc/knltq3nF6pln2hU7w/WWqbNd9jmf7+VmKngMJlIBCoSRctIYAvYCPm8qLpuwvSxnIoBAPQFysJ4QP0cgXgFyMF5fWi+OALlSnFgyk/wJkH/5i1nhRxy2oGJeObnBBrVZwhYIn35a2myz6m0WtUAY9H26ago2xbNaTh9+KJlvGWb1MMXSPfZofHSdO0uLFzfeTksLEydKf/qT27cBbWIW3Ugz05LLPufzvdzMBI6BRC5AgTByUhpEwE7A503FZRO2U+UsBBCwESAHbZQ4B4H4BMjB+GxpuVgC5Eqx4sls8iVA/uUrXl6MNmyBcN48qXv32jRhC4Tvvy+tumr1NpMoEC5cWCoCmSftzGsgV189/vAfcoh04412/dgUm2o5TZ8u9e1r15fNWbfeKu2/v82Z1c8x3+B7881S4TKq19ya7zWaWEZ1mMLqU0/ZP+nZtl+bmEU1zgy147LP+XwvN0OhYygRC1AgjBiU5hCwFfB5U3HZhG1dOQ8BBOoLkIP1jTgDgTgFyME4dWm7SALkSpGiyVzyJkD+5S1iBR3vffeVvpHmcpiixy67SA88UP3qsAVC8322Tp2qt5dEgfB73yt/dar5dlxURatqMzvqKOmqq+yiYFNs6tNHmjGjvL2W62bOlHr3tuvL5iybQnG9dmzmVK+Nyp+HLXjXa998P9G8ZnTPPeudWf5z80SseTLWw8Nln/P5Xq6HS8SbKVMg9CbUTDRrAj5vKi6bcNbix3gQyLMAOZjn6DH2IgiQg0WIInNIQoBcSUKZPhAIFiD/WBmZEGikiGKKOtddJx1+ePWpmFc81ir4VV5Zr1CURIGwso+ttip9Oy7OY5VVpI8+suuhnpFp5aWXpG9+s7U98zrYlv991ixppZXs+rI5K2wROKhNmznZjKXtOdXW9nnnSaedFrY1yYzR5elLU4w113l4uOxzPt/L9XCJeDNlCoTehJqJZk3A503FZRPOWvwYDwJ5FiAH8xw9xl4EAXKwCFFkDkkIkCtJKNMHAsEC5B8rI3WBJ56Qtt7afRimYHLnndLee1dv48ILpZNPtu+jXqEojQKhGX29cdnPMPjMMIVa27G89VbpybXNNy9/Tap5stA8YRjFsfvu0t13S2HGH9Sv7ZzCjLnamFwLmua6zz6TevYMM4rSuXHML/woEr/CZZ/z+V5u4gGiw8QEKBAmRk1HCJQL+LypuGzCrB8EEIhOgByMzpKWEHARIAdd1LjGRwFyxceoM+esCJB/WYmEx+NopKiz6abS+PHS/fdLu+4aHWK9QkrcBcLXXpOGD28/n7bjevxx6aGHpO23l0aOjGbuYWJRz6jeiD7/XFp++Xpn2f/cjMeYGTvXo9E5BfUbR4HQvE61R4/gWT7zjGTyIuiIY36u1gle57LP+XwvN8HQ0FXCAhQIEwanOwRaBHzeVFw2YVYOAghEJ0AORmdJSwi4CJCDLmpc46MAueJj1JlzVgTIv6xEwuNxhClKVTL97/9KP/2pFEexqVZIgsZsvg1XZS6h82zDDSXzOs7Ko6XAY141OmJE6Ykw0+cjj0jbbtv4Igoa/447lgqR1cbSSK/77ivdfnsjLbReayzMk4pbbOHeXhwFtCDTFVaQzCtWXda+GaP5RmaXLsHzNOuwY8f2P9thh+A4umvl5srQ+SfJ53u5uQksAw0tQIEwNBkXIBCNgM+bissmHI06rSCAgBEgB1kHCKQrQA6m60/v+REgV/ITK0ZaPAHyr3gxzd2MXIokLZO85BLpuONqF0xcQOoVioKeWJwyRRo8OLC30HlW66kz08Naa0mTJrX2ZZ6ce+UVl5mWXxPU79Ch0ttvt2+7npHNaBYuLH0/8mc/M/94tbki+Jy+faUPPyz9rJH1FMWcKkdYrej64INuYzVjNP8TVAQ06/Jb3yqtw3ffLR/Jyy9L667rbpzjK0PnHwXCHEebodcSoEDI+kAgJQEKhGOa5UeNGqXu3bunFAW6RcBPAZdfhP2UYtYIxCNADsbjSqvFEyBXihdTZpQfAfIvP7EqxEjnz5cmTJAGDSp9f65aocN2sr/6lXTWWY0Xhir7q1coeuopacsty6+67z5pl10CNfsj9gAAIABJREFURx46z+oVCEO+4tSWM7C4ZozPPrt9E/WMrDuVtNNOjT/d1jIe86pZUyhzOaKcU0v/QbHaZx/pttuk/fYr/b9hjpYxdu0qffll+ZXnny+dcoo0ZIj0zjvlPzNF3tVWC9NTYc4NnX8UCAsTeyZSLkCBkBWBQEoCFAgpEKa09OgWAZ4gZA0gkLKAyz9GUx4y3SOQigC5kgo7nSLQLED+sRASE/jss9K38l58UerWrfRazC++KBWHXA/zHTbThjkaeXKssv96haKgV5qed5506qmBM7HKM9PnlVdKDz9cvWjUMq4kC4T33CPtvnv7edUzChPTnXeWzBN1rof5luHs2aWrzZOUpnhr1luY49FHo3lNa2WfphA+Y0b5fzWvgzX9HXCAdMstYUZZKqqbY5llpLlzy6897DDp2mtLRfMzzyz/2ccfSyuuGK6vgpxtlX8Vc/X5Xm5Bws40AgQoELIsEEhJwOdNxWUTTilMdItAIQXIwUKGlUnlSIAczFGwGGqqAuRKqvx07rkA+ef5Akhy+nvvLd15Z7Q9brqpNH58qc0kC4TmO2+dOpXPxRSlnngicH5WeTZ6tLT//rV90igQVvumXZQFwlGjpH//231ttLxqtqUF8/TcM89IG20krb66XbtRzqdtj0Hr8sYbpYMOkv7f/5NuvdVufC1ntYyzV6/WomjLzx5/XNp661KemXxre5ind5uawvVVkLOt8q9irj7fyy1I2JlGgAAFQpYFAikJ+LypuGzCKYWJbhEopAA5WMiwMqkcCZCDOQoWQ01VgFxJlZ/OPRcg/zxfAElN3zy9tPLK0fdmiiym2GKOK66Qjjkmmj5sikWVhZ811pDeeCOw/6p5Zlyuvlq65hpp8uT6YzdPYJqn7cxrPysPmzHX66Hak4mV/918M/Cii+q1Zv9z82rWBx6wP7/yTFPErFYgtikcz5olrbCCe/+1rgzq/6abpAMPlIJeE1pvFC1xXmklyYy77fHqq9Laa5de11r5ZO4nn0imqOjh4bLP+Xwv18Ml4s2UKRB6E2ommjUBnzcVl004a/FjPAjkWYAczHP0GHsRBMjBIkSROSQhQK4koUwfCAQLkH+sjEQETjxRuvji6Ls68shSga3lMK9cnDOnVHixKficdppkXg1aedgU2yoLP3vsId11V+AcA/PMFLWGD5cmTYrGxWbM9XqqnNMJJ0h//GP711kuWtT+Ccp6bdf6eZjvBq6zjjRxYnlrteZuUyA0r4xddtlGZlD92qD+X35ZWnfd+k+9mlentn1V6nHHSeZpSXMEFQjffLP0xKT5NuGqq0ozZ5bOXXNN6fXX6/cXj0Dqrbrscz7fy009YAwgNgEKhLHR0jACtQV83lRcNmHWEwIIRCdADkZnSUsIuAiQgy5qXOOjALniY9SZc1YEyL+sRKLg4+jcWVq8OPpJjh0rjRgR3G6977tVe13ngAHS1Kn1x1pZ+Nlxx6rf0QvMs/vvl0xhLKojigJh796tRSUzLvOdyO22K8Xu5JMl84Ta9ddLq6wS1ahL7XznO9K999q1aZ6aa/stvZbv7lW72qZAGOfrN4P6/+gjyXybsN7Y/vUv6c9/Lr0ydK+9pH/8o7Uwa57INU+gtj3Mq1UHDSr9F3OtKcybYvlVV1XPEzv1XJ/lss/5fC8318Fm8DUFKBCyQBBIScDnTcVlE04pTHSLQCEFyMFChpVJ5UiAHMxRsBhqqgLkSqr8dO65APnn+QJIavrf+pY0Zox7b2efHfxazXHjpG22CW436DuBbc9sKaj95z/lbbz1ljR0aP2xVhZ3rr1WMsWqgCMwzy69VDr++Pr92J4RRYGw8qm0xx6TzGtN4z5231365z/r9zJ4sDRlSqmAe911pTgdfHDtQlu9IpzpNeonItvOJKj/BQtKhbtllpHMU6/VDjNXM+egI6jdDz6Q+vWr7+jZGS77nM/3cj1bHl5NlwKhV+FmslkS8HlTcdmEsxQ7xoJA3gXIwbxHkPHnXYAczHsEGX9SAuRKUtL0g0B7AfKPVZGIgE2RptZATNHOvI5z4cLys1qecKt2ba1+2xbUXntNev75UqFw4EA7kk02kZ59tvXc//s/6Qc/CLw2NwVC84262bNb5/D449LWW9t5NHKWeT3rPffUb8GlCGqz9mp9w7D+qGqfEdR/S3/9+0umqFftCPvq1JYnExsdc8Gud9nnfL6XW7DwM502AhQIWQ4IpCTg86bisgmnFCa6RaCQAuRgIcPKpHIkQA7mKFgMNVUBciVVfjr3XID883wBJDV9myJNrbGYgspPf9r+O4Yt31yrdq1tgdDFYYstpKefbr3ymmukww8PbCmRAuErr5SKqI0c5pt35nt8LYd5unKrrRpp0e5a8/rMKt9v/LqB996TTEEt7GGz9lwKj7bjCOq/pb+OHaVqfe+2W+2iaVC7cX5L0Xa+GTzPZZ/z+V5uBkPIkCISoEAYESTNIBBWwOdNxWUTDuvL+QggUF2AHGR1IJCuADmYrj+950eAXMlPrBhp8QTIv+LFNJMzsinSVBu4KRyZJ8zM03k33FB+lvkm3tprV5/yr38t/eY3wT9vtCi02mql1122HJdfLh19dGBfiRQITc+mYLnZZu5LYNllpS++aL3+qaekzTd3b8/2ymOOka64ovbZrq8B3W8/6bbbarfd6Fqo1XqtAuHpp0u/+137q3/0o9J/N98ZrHbUatfW3ZPzXPY5n+/lerIsvJwmBUIvw86ksyDg86bisglnIWaMAYGiCJCDRYkk88irADmY18gx7qQFyJWkxekPgVYB8o/VkIiAa4Hw73+X/t//Kw3RPMn25JPlw631DUJz5gsvSBtuGDzFRotClXMyT8HdcUdgX1/n2eLF2mXBAjU9/LB0883lxbgoAmGeAGz7itCwbVbOafx4adNNw7YS/nzzTcFBg6pf993vSrfeGr5dc8W990rf+U7taxtdC7VaP+EE6ZJLys9o6e9Pf5J+/OPyn914o3TQQfXnSoGwvtFXZ7jscz7fy7WG5cTcCVAgzF3IGHBRBHzeVII24Rfmz9HvZr6rL5Ys1pEr9NOey9X4i6iiLALmgUBKAi6/CKc0VLpFoJAC5GAhw8qkYhAgV2JApUkELAXIP0soTmtMwLZAeMEF0imntPbVtnCzzz7tC3CffCKZ7+bVOqr13WhRKESBpiXPNjn/fPV/4onGLOtd3TIvUyicP1/q27feFaWff/ml1LVr+bn//a+08cZ21zdylnk6sEuX8hbME4WzZkk9epSezGxqcuvh/vulXXetfW2ja6FW6wsWSN26tZ5x2mnSueeW/vf995dGjy6/ukahuezEEOvPDa44V7nscz7fyy1O5JlJpQAFQtYEAikJ+LypBG3CR06bpDcWzmuORhd10O0D19GyHTulFB26RaDYAi6/CBdbhNkhkKwAOZisN73lV4BcyW/sGHn+Bci//McwFzOwLRCaQo0p+pnXXJrvzbW9LqiNxYsl8x23WkdSBcLllpM++yxwJCbPxv797/rWYYfFHy5j+M9/SgceKM2ZI5nXd152Wf1+gwppY8ZIO+9c/9pGzzDfmOxUcV8oqtebPvCAtMsutUcYZ4HQ9LxwofTMM9KAAdLgwa1jCVqbBx8s/fWv9UUpENY3+uoMl33O53u51rCcmDsBCoS5CxkDjkpg+vTpGj9+fPP/PPPMM83/8/HHHzc3//3vf1/XX399VF0FtuPzphK0CW//zotlTj9bcYB2X26lWGNA4wj4KuDyi7CvVswbgTgEyME4VGmziALkShGjypzyIkD+5SVSOR+nTYHQfM+vbfGkcsquBZG4CoSmIGgKcC3HSSdJF14YGCiTZ89deKG2PuOM+AM5Y0bpdZ3zSn+Y3XxMnCh94xu1+z7/fMk83db2ME9zmv+exHHAAdItt5R6Mq+FffbZ8gKx6xj+/W9p1KjaV8ddIKzWu/l+5uuvl//0ueeqvxa37Zmu+eDqmOPrXPY5n+/l5jjUDL2OAAVCloi3Ah1q/CJKgTDeZWFTIDxxxf68ZjTeMNC6xwIuvwh7zMXUEYhcgByMnJQGCypArhQ0sEwrFwLkXy7ClO9Bmqf8OneuPwfzFFmtQqJrQSSuAuEqq0gffdQ6rz33lO68s/08ly7VgtGj1dTyLcX6Eo2d8be/tf+G3YgR0tix1dt96SXpm99s/3NTsDOvwUziME/ZXXVV6enRo46q/+pY2zE9+GD9pyDTKhBeeql0/PHlM7F9ratrPti6Feg8l32OAmGBFgBT+VqAAiGLwVuBtgXCgQMHavjw4RpjXpPAE4SxrwmbAuHOy6ygX6xc42PUsY+SDhAoroDLL8LF1WBmCCQvQA4mb06P+RQgV/IZN0ZdDAHyrxhxzPQsgr6zFjTgekUa14JI0HWmYGm+udfIYTse2/k3Mpa2195zj7T77u1ba/E1/68p/L32mvS970nDhlUvzI4fL226aVQjS6edhx6Sdtqpdt/11l5cIw/67qP5bzYFddv1F9fYc9Suyz5HgTBHAWao1gIUCK2pOLFoAmeeeaY23XTT5v/p27evpkyZotVWW615mjxBGG+0bQqE5i3zDw4O+Eu1eIdG6wh4IeDyi7AXMEwSgYQEyMGEoOkm9wLkSu5DyARyLED+5Th4eRm6zetFzVzqFWm23bb9U3D1rjHtmtdLmtdMtj1uuqn0jb5GDtsCje38GxlL22vPO6/9q0Lb+l5yiXTCCaUrlllGMgXFHXYI7j2q7wBGNTeXdh55pPr8WtqzWUcufdtcc8EF0qmnls4034o034y0Oe67T/r2t1vP3Htv6fbbba707hyXfY4CoXfLxIsJUyD0IsxM0kaAAqGNUjTn2BQITU+PUCCMBpxWEKgQcPlFGEQEwgh8+KG0YEHtz8WEaa9o55KDRYso84lLgFyJS5Z2EagvQP7VN+KMBgVsC2T1ijTm23p9+rQOZpNNpGeeqT+4RYukLl3KzzOvPe3Ysf61tc6oVSA07ZtXZnbtavc0WGMjsbu6xbdy3CuuKM2aFdyG+UXfzCHPx6OPSttvX3sG9dZeFudv1tjJJ0t//rO00UaSKXoPGJDFkaY+Jpd9jgJh6mFjADEIUCCMAZUm8ylAgTC5uFEgTM6anhAIEnD5RRhJBGwFTjxRuvji0tknnSRdeKHtlf6cRw76E2tm2pgAudKYH1cj0IgA+deIXs6unTxZGjq0fNAtBaA335TWXLP1Z+ZJJPNEUtvjuuukww+Xdt219K0928JRVAVCM5abb5bOPlvq27dUGFljDbsgnHOO9Ktflc41T9Add5zddbXOqlYgfO89aY89pOefl7baSnriicb7iqKFagXCWm3nsXBWOZ/HHpO22662YBHmGcUaKWgbLvscBcKCLgbPp0WB0PMFwPRbBSgQJrcaKBAmZ01PCAQJuPwijCQCNgI/+IF0ww3lZ5p7If3721ztzznkoD+xZqaNCZArjflxNQKNCJB/jejl7NqggtaOO0oPPhj8DTrz383PzfF//ycddlj5hG2+lWaecrL5npppOW9FmmoFwuHDS9/3y9rx6adSz57VvzcYNN68xSRoDuPGSSNHVo9GUDE8a7FjPA0JuOxzFAgbIufijApQIMxoYBhW8gIUCJMzr9yEu3Xrph3efandAHjFaHIxoSe/BFx+EfZLiNnWE/j889LbkVZaqfVMc58g6I1M5r6RKRxytAqQg6wGBOwEyBU7J85CIA4B8i8O1Yy2We1JPvPLXa2fvf9+8KsL775b2n332pOdOVPq3dsOJG/FqCAz8zpT24KonUrprGnTpH79wlzR/txzzy299qPydau1Ws1bTILm8vjj0ogR1Wdp/rETxqSxKHB1CgIu+xwFwhQCRZexC1AgjJ2YDvIiEHWB0GwatY5p06Zps802az5l0qRJGuDRO8Hnz5+vsWPHNs995MiR6trUpN1mvNGO694+w/KyfBgnArkSqMxBU6TnQMBWYJddumrs2E5fnz537rzm//+LL3bQllsGr6WWc2z7KPp55GDRI8z8ohIgV6KSpB0EwguQf+HN8npF9x49Aoc+b+5cVfvZwvPPV4fp09Ul4F3yS9ZbTwuefromR8c77lDTQQdZkZlx5OnocvDB6myePkvgmPfBB+q+6qoN9zR/3Dh1q1Usq+ghbzEJAur45JNqankSNuCEIsyx4YVR8AZc9jlzr3fYsNK9yqlTp3p1L7fgy8Hr6VEg9Dr8TL6tQNQFwg6279OXdM0112jllVf2NiBLzOv+NxjSbv4nvjDFWxMmjgACCGRVYK+99iwb2hFHvKzddntbF120scaNGxA47FtvvUddu5r/a8+BAAIIIIAAAgggkCWBPffaK3A4d915p6r9rN74zbW1jnWvvVar33NPvWaaf16vLatGEjxp9bvu0rrmFRoJHP+68UZ95+CDG+7pueOO00aXXmrdTt5iEjSxlV5+Wdu0fH8y4IQizNE6oJxoLTBz5kwdccQRzedTILRm48SMC1AgzHiAGF5yAhQIk7Ou7IkCYXr29IwAAgiEEZgyZTmdeOIO7S658867VFk4bHvSiBHv6aSTng3TFecigAACCCCAAAIIxC2wdKn23HvvwF4+Hj5cK736qtMI/vW3v2nRMstUvTZM4TFvhZrV/vlPrX/NNU5uYS8yNiu++qpG/PznYS9t6Py8xSRosmvcfrvW+ctfAh3u/ctf9OXyyzdkxMXFFKBAWMy4+j4rCoS+rwDm/7VA1AVCXjFafXFVPsbfualJe0T0itGnFszR2Pmfa40uTdqz+wrqFOJJTtIBAV8EXF6l4YsN86wtcMcdHXXQQU3tTjKvEO3Ro3vNiw87bJEuu+xLiCWRgywDBOwEyBU7J85CIA4B8i8O1Qy2uWCBuq+wQuQDW3jeeVp83HFV26326tLKCxZedpkWH3ZY5OOLs8FOf/6zup5wQpxdfN12y2swbT2jGlQRXr/Z+bzz1OU3v2lHMm/2bL49GNVCyXg7LvscrxjNeFAZnpMABUInNi4qokDUBcJ6Rj5/2LbyQ8CduzVp1LsvtyN7ZPA36zGW/fyNhXN15LTWbxkev0J/7b28v69uDYXHyV4JuHyM2ysgJltV4IADpFtuaf/jt9+Whg6tD/f889IGG9Q/r+hnkINFjzDzi0qAXIlKknYQCC9A/oU3y+UVH38sxfW5ky++kD78UDLfyKv85rntH/KOGSPtvHO+aK+7Tjr88GTGvHRpqR9bzyhGteeeUp1XyEbRTextnH22dMYZ5d2Y/z2gaBj7WOggFQGXfc7ne7mpBIlOExGgQJgIM53kQYACYXJRqtyEO3Vr0rciKBCe8OGbemnBF2UTCVtkTE6BnhBIT8DlF+H0RkvPWRKI4t5Dy32MLM0r6bGQg0mL019eBciVvEaOcRdBgPwrQhQt5nDzzdL3vmdxYoOnPPWUtPnmrY3Y/lL54IPSjjs22HnCl//1r9KhhybTaRoFwvnzpab2bxRJZsIR9nLWWdKZZ5Y3+MQT0pZbRthJqalJC+bq3UULtGm35dSzU+fI26dBNwGXfY4CoZs1V2VbgAJhtuPD6BIUoECYHHblJtyxqUm7TG38CcLdp07QnCWLyyZCgTC5uNJTfgRcfhHOz+wYaZwCtvdyao3hnnuk3XaLc5TZb5sczH6MGGE2BMiVbMSBUfgpQP55Evd//1saNSqZybb9KzHbXypjKtjEOuE//EH62c9i7aK58b59S09omsPWs5FR7bGH9MtfSptu2kgr2bk2oQLh43Nn68wZU7RE0sqdOuvafmtpeYqEmVgHLvscBcJMhI5BRCxAgTBiUJrLrwAFwuRiV7kJd2hq0q4UCJMLAD15L+Dyi7D3aBEATJggmXsw5o9St9giggZTaCKqew/msyx//GMKE8hIl+RgRgLBMDIvQK5kPkQMsMAC5F+Bg9t2as88I222WTKTXbKktZBl+0vlf/8rbbxxMuOLqpdhw6Q3Wj99ElWz7dqZMaP19bC2no0MpmivAbn4YunEE8tF/vMfaautGlFqd+0u776kBW3sDunZV4f1WiXSPmjMTcBln6NA6GbNVdkWoECY7fgwugQFKBAmh125Caupq749dUK7AYR9+o8nCJOLIT3lW8DlF+F8zzj90Zvi4CabSAsWlMaSx7clmXFHee+haPcYwqwycjCMFuf6LECu+Bx95p62APmXdgQS6v/YY6XLL0+ms3POkU4/vdSX7S+Vn3wi9eqVzPii6uX3v5d+8YtQrT38xz9q5KRJ6nzFFbWvM69bNf+QqDxsPUONquLkov3yPnly+4+oP/64tPXWjSi1u3b7d14s+2+DuzTp+lXXjrQPGnMTcNnnKBC6WXNVtgUoEGY7PowuQQEKhMlhUyBMzpqeEAgScPlFGMnGBLbfXnr00fI28vhv7CjvPeRx/o2tgtarycGoJGmn6ALkStEjzPyyLED+ZTk6EY7NFJwefjjCBus0FfabeQG/MC5durT5dY0dm+uMHZIbu21PV14pHX207dnN5911553aZdll1bTTTrWvM68iMa9drTyCHNZeW3rttVDjqHrybbdJ++wTTVtZaeX996UBA8pHQ4EwK9FJZBwu+xwFwkRCQycJC1AgTBic7rIj8Pjjj+vNN9/8ekAzZ87UKaec0vy/b7311jriiCPKBvuDH/wg0sH7vKlUbsJLm7rqOzxBGOn6ojEEagm4/CKMaGMCQf9mX7xY6mjubOToiPIeDAXCMc2RHzVqlLp3756jVcBQEUhOgP0qOWt6QqBSgPzzZE2Y+xw33JDcZM0vgB98IPXv377Piy6STjqp/L9X/MI47csF+uWMKXr7y/narkdP/XzlQeraIWO/UI8cKY0bF8rUFAhHbbyxug8cWPu6Y46RLrus/TlBv6QPGSJNmRJqHFVPzuM/XOrNPKhAaOK2zTb1rgz1c54gDMWV6Mku+5zP93ITDQ6dJSpAgTBRbjrLkoAp+N0Q4hdh81dqUR4+byqVm/CSpq7ajQJhlMuLthCoKeDyizCkjQkE/Zt90iRpzTUbazfJq+fOlZZZJroep0+XeveOrr08tUQO5ilajDVNAXIlTX369l2A/PNkBdx4o3TIIclN1txXue466fDDy/s0r9swTzKedZZ05pnSpptKTz/d7lWkl816X7d9PvPra8/qPUQjevRMbvw2PW20kfT88zZnfn1Oc4Fw883VvV+/2tddfbV05JHtz3nxRWmDDUL1GerkiO+Hheo7rpNTKhAO6tykG/rzitG4whqmXZd9zud7uWFsOTdfAhQI8xUvRhuhAAXCCDFDNlW5CS9u6irz/cDKg28QhoTldAQsBVx+EbZsmtOqCAQVCB95RNpuu2ySPfecdP310jrrlO5BmPGbz9OYz9REdbzxhrTGGlG1lq92yMF8xYvRpidArqRnT88IkH+erIE//lH66U+Tm6wpNB14oPT3v5f3ee210mGH1R1H5dNYy3fspLsGrvv1dQuXLtFz8+eoT6cuGto1pTc0fPe70j/+UXcubU9oLhButZW69+lT+7p335WqPWXYtjB5332lX+KnTg01jqonF7FAGPQk69ix0ogR0Zh91UrlmqVAGClvQ4257HMUCBsi5+KMClAgzGhgGFbxBXzeVCgQFn99M8NsC7j8IpztGWV/dEEFwmp/AJzWbMz9A/M/q64qrbZa6yjOP18yb+B2eb1onwFLNP294Nc+XXNN+z8eT2vuSfdLDiYtTn95FSBX8ho5xl0EAfKvCFG0mMN660kT2v+xrsWVbqeYAtepp7YvEC5ZYvXLZmWxpXuHjrp30HrNY1m0dKl+PG1S8+tHzW+f5vWjOy2zgts4G7nql7+UfvvbUC00Fwi32UbdV165/XXmVfTz5pVev3rhhfbtRvX6WPMakSK+Dn/atNI/fNoeCRQIB3Zu0l94gtB+Hcd4pss+5/O93BhDQdMpC1AgTDkAdO+vgM+bSuUmvKhrF+3x3sR2i4EnCP3ND2Yer4DLL8Lxjqj4rQcV1556Stp882zMfcwYaa+9Svcego4HHpC+9a1wY112vbna4Oa39Pi6pZs2QUcR/xjZRokctFHiHATM/02apzHm/0DxvU6WAwKJC5B/iZOn06HLX4A1OtLf/176+c/LW7H8pbCyQGgaablv8O85n+h3H79b1u49A9fVsh07NTricNf/+c/BrwGt0UpzgXC77dS9V6/ys84+WzLfHVy4UOrbN9w4Dj5Y+tvfql9jXhly993Sr38dfE4RvzvYdqZBBcLHHpPMNyQjPCrXLAXCCHEbbMpln/P5Xm6D3FyeYQEKhBkODkMrtoDPm0rlJvxl1y7akwJhsRc8s8uUgMsvwpmaQIYHM2OGZP4tvcoq5YPMeoFw3XWlie3/TqMh6c0eflU9hi7Uo0O+WbUdy3tBDY0jixeTg1mMCmPKogC5ksWoMCZfBMg/TyIdZYHwww/b/xJsy2j5S2GtAuGZM6Zo7NzZZT1269BRV/VbU4O7dLMdSejzPln8pZo6dFSPlkLkZ59JPQO+i2i+vVjlNarNBcJRo9R9yBDJfKi75fjyS6lz59Bjar7gySelrbaqfq35C8BRo4Kf3FxhBWnWLLd+83KVWa+V33ykQJiX6EUyTpd9zud7uZGg00gmBSgQZjIsDMoHAZ83lZZNeHEHaf3tt9Oy3bvrwPdfbRd2niD0IROYYxoCLr8IpzHOvPVp/hj6F78ojfrHP5auuqp1BkH3Xsy/2bfYIhuzjPLeUMuMRr7xojp2EQXCgBCTg9lY94wi+wLkSvZjxAiLK0D+FTe2ZTOL8pdA80RWZcHFljGCAuEZM6ZoXEWBsKX7sPcWbIf9vx+/p3vmfCxTiPxN78HarPvypUsrv7N4++3S3ntL3/62ZL4PWHF8XSB8//3Sq0TNaz3Na0o328x2KO3PM69t7VTj6cmW15kErYGNN5b++1/3vvNwZVCB8NGMxiK8AAAgAElEQVRHpW23jXT0PEEYKWekjbnscz7fy40Un8YyJUCBMFPhYDA+Cfi8qZhN+O6HHtRtq/fVjB5Nzb9Mz1+6pF34w/4Sv/vUCZqzZHFZO2Hb8GkNMld/BVx+EfZXy27m5qnByj/uNU8TtnxGJOjf3Y8/Lm29tV37cZ1l/ij52mulo46KvoeRk15Sx65LKRAG0JKD0a83WiymALlSzLgyq3wIkH/5iFPDowxbIDz2WOmyy4K7/fhjaaWV3IYUQYHw1I/e1jPzPw/s/8ZV11b/Lk1uY6ty1eSF83XYtNfD3X8wH/YO+I7g1wXCqL/1Vyu+ixaVCohB55hvC9x/f6RemWvM/EOoa9fyYZmnJs3TkxEeFAgjxIy4KZd9zud7uRHz01yGBCgQZigYDMUvAZ83FbMJn/Xi03qqX/VfvFzey06B0K8cYrbuAi6/CLv35seV5g+BzR8Etz2OPlq6/PLSfwn6d/f++0u33JKez/z5UqP3IHpuNkezxy/bbhIr7TRb610zpfm/13rFqPnD5rD3pdITi65ncjA6S1oqtgC5Uuz4MrtsC5B/2Y5PZKPbfnvJPDXVcgwYIPXoIU2aFNyFKeRV++Wt1s/qDTiCAmHQ60dbut2xRy/9svfgeqMI9fOffvimXljwRdk1df9A+ayzpDPPbNdPKgXCFvOgeB55pHT11aE8qp08Y9GXuvvzmdqg27LauPtykbQZWSOXXCL99KeS+UeJiUu17zE20KFZl3Ne76Z5k5vUa8s5Wm2lTvpr/+ENtMilUQm47HM+38uNyp12sidAgTB7MWFEngj4vKmYTfjb06v8g+Or+O/Qo5d+FfIX+D2mTtDnPEHoSQYxzUYEXH4RbqQ/H65de23p9fI/IG6edq1/d7f9eRpGu+7a+B8Gbzflxeahm5sh27w4Qe9fv7I6Ni1R/x/OVKduS5t/9uwea+rzl3oETtF8hsU8wejbQQ76FnHm6ypArrjKcR0CjQuQf40b5qKFyuLQDjtIDz9cfeg5LRCu0LGzbh+4TqQhqfU9xKodXXmlZP6KsOLIXIFw3Dhpm22cveYvWaKlWtr8hqf923xO5sDle+vIFVZ1bjeWC997r/QR+cHRFpBbxrredW9r4o9X09JFHdTUf6H2HDNZt3xjrVimQqPhBFz2OZ/v5YbT5ew8CVAgzFO0GGuhBHzeVCgQFmopM5kcCrj8IpzDaSY65Fp/SG0GUu/niQ72q86ieHKvpUB478B1dd7HU/VYwHdfvnizSc/stHbVKVr+wXgaRLH1SQ7GRkvDBRMgVwoWUKaTKwHyL1fhCjfYKk+x1W2kSxdp4cLMPkG4wzsvqvTnacFH3af76gKUn+BUIDTfaVy1fYEs1QKheWJ03rzyyTXwC/oZ06do3LzZicUhZNgSP71Lz0Va9Fnnr/td/+QZevGC3omPgw7bC7jscz7fy2UNFVeAAmFxY8vMMi7g86ZCgTDji5PhFV7A5RfhwqM0OMFqxbaLL5Z+8hOpqconT15+WVp33QY7d7y80QLh5uNeUfeBXzb3bl7b1LVDR933xax2o1n4cSc9sXH1STZw/8Fx5ulfRg6mHwNGkA8BciUfcWKUxRQg/4oZV82dKy2zTO3J/fzn0u9/3/6cgw+W/vrXzBUIbSOViQLhggVSt27thhxbgXCjjaTnnw8mavkl/LPPpJ49W88xr0Z59VVb1rLzFi5dom+9+3LNa6OOg9NAE7yo8t9cPYct0KevR/s9zASnU6iuXPY5n+/lFir4TKZMgAIhCwKBlAR83lRsCoQu3wjgFaMpLWa6zZ2Ayy/CuZtkwgOuVWy79FLpuOOqD+jTT8v/TZ7U0BspEK55znvqf/DHVkNdPL+Dxq29ftVzKRCOUvdGPwZpFQlOQiB/AuxX+YsZIy6OAPlXnFg2z8T8wmW+t3biifUnZt7/fvjh7c8zhcPf/S64QHjssZL5pdf1F0yLXwhnLvpS333/lfrjr3JG1IWpoCcIHx60vjrUMwj4eWwFwgcekHbZJVikrfmECdJvfiOttZZ0xhlS165OzmfNeEePzP205rUPDlpfneoZOfWezYsqp7r8mgs0exIFwixEy2Wf8/lebhZixhjiEaBAGI8rrSJQV8DnTYUCYd3lwQkIxCrg8otwrAMqQOPdBy7Q/Pfc/qFn/rB36lRpxgxp6FBp4sTAPyyOVGn2bKlXL/cmR77+kjo21XqJU3nbjw75ZtXOLO4HuQ80o1eSgxkNDMPKnAC5krmQMCCPBMi/ggV7zBjpW9+qO6kpPzpcgz+aoQ53393+3JZXXwQVdw46SLrxxlgLhEdOm6Q3Fla8CrPGjBbO6KwF0ztrmbXmq2Pn0jezozyCCoT/HrS+OtcrflX8fE6/fnroyis1alRMfzR2wQXSqaeWT/3mm6UDDoiSo7mtIJPKTs5YebC2X6aBf4hEPup4G6RAGK9vI6277HM+38ttxJprsy1AgTDb8WF0BRbweVOxKRBu1X15/bbPaqFWAE8QhuLiZI8FXH4R9pjLaur17gNYNfLVSVtsIT35ZJgrwp/7wgvShhuGv85cse3kF0P/cfj897vo2T3W1Jcfd2nXqfmj5g8+kMwfpcdwn8JtkjFfRQ7GDEzzuRZ47ItP9bfPpmvFjp31k2VW1osPPdI8n9hunOZai8EjEJ8Ae1V8tqm0bPnL6gXnnqFT/ues4CF+/LG04orBRcCbbpIOPDDyAuGcJYu1+9QJock+eWJZTThyiBbP6aTlN/lCG9z0lv459BtarlPrt+BCN1pxQVAx7F8D11WPjp1qN10Ri3v/8hd9ufzy8e1z5rWmvXtLn39eGpfpf9EiqWPHRgnKrl+8dKl2evclqzbP6j1E5p6PD08StisQrrFAs99w+8NSK1xOshZw2ed8vpdrDcuJuROgQJi7kDHgogj4vKnYFAhNnMP+hR8FwqJkB/OIW8DlF+G4x5T39i3vuVhPM+6n6saNk0aOtB5O2YnbTXnR6cJ6rxrt1EmaNq10/6LoBzlY9AgzP1eBzxYv0j7vTdTirxrYsusy2nz8xOb/jQKhqyrXIeAmwF7l5pbZqyx/Wf2s5/Ka1WdlDXnj7fZTafkFNaithQulLl2CC4SPPiq9/bZ02GFVeb5YvEivLZir/l2atErn0ustly5dqh0sC06VDY/faS3NfbP1W3/DL3lHI/ZboKv7DYssREEFwhtWXUuDurT/xmBZp+bVIYMGNf+nhVdcoftWXTX+fc483Wk+jG5+4b7uOmnffSNzaGnI/IHPr2e+Y91ur46ddcfAdazPz+uJFAizGzmXfc7ne7nZjSQja1SAAmGjglyPgKOAz5sKBULHRcNlCEQk4PKLcERdF7YZy3su1vOPu0Bo7hEccoj1cMpOdC0QmkZqvWrU/PzMM6Vf/9ptXHm6ihzMU7QYa5ICN3z6oa6f/VFZlye+MCX+G6dJTpK+EMiJAHtVTgJlO0zLX1YXde6sX19+vs758c/at9zyC2rQL5K1ioe1fvZVL9tPeaH5/9dFHXR+36HaoNuyunzW+/rH5zNtZ/j1eUsWdNDYtcq/f91rizna4O9vhf4j5JZGZy9epA8WLdSQLk3q/tUTgkEFwn6du+qm/sOtx1ykPLN5vWglzJ0D1lHPCJ/qtIZP8MTK1Ftu9QX67E2eIEwwBFW7csk/n+/lZiFmjCEeAQqE8bjSKgJ1BXzeVCgQ1l0enIBArAIuvwjHOqACNG55z8V6pnEXCNdZR3rlFevhfH1i331nafhFU8Nf+NUV9QqEJ58smc+kFP0gB4seYebnKhB0M5gCoasm1yHQmAB7VWN+mbva8pfVxZ066YRbr9Vl+/6g3RSWLlmiDqYd84tq29dTmr/uMn/lZY7zzpP+53/Kr61TIDxu9P9pwqat775vKbK5FJxMx9P/2VOvHDukbAw9N52jDUe7FQjfXDhPP5o26ev2bh/wDa3QqUvV7+3d+//Zu+74KKot/KVn03uvQBJq6EUBsWF/2HtDrNiwYHk2sPvsYkORZhexYEFFRboIItIhCSWk955sspvN+93dTHZ2dsqd2dnNJjvzj5i95dzv9vPdc07q8B4SUWoc9Kd5pqS/3k/MQpZ/kBRMffp3jSB03+5TMv88WZfrvj2pSeYoAhpB6CiCWn4NAYUIePKmohGECgeNlk1DQCUElByEVaq63xZDqXOhbr8zCcLly4GZ9jofKtmmHdkFLwfClWgEoQVmbQ5SDTctkQcisKC2BN9wrEU0gtADB4LWZLdAQNur3KIb1BOC8rD6/ZUX47cLzsEbl99oVzex8nsxbgDG60ItJOGxY0BkJBARYZs2KwsoKLD87aefABJwmnwCMpy9bwv0wbYk0fepwxXFHiTVCJ03iRcMuWFMSHlc4otYOa5JzxUkCO+ITMIlYXQ+8/vTPGPj1FHrg7/PzkFHhR+yny1C0tW1vGN5cWI2Bvjr1BvnbliSHUE4oB2NhzULQnfoKiXzz5N1ue7QZ5oMzkFAIwidg6tWqoaAJAKevKloBKHk8NASaAg4FQElB2GnCtQPCqfUuVC31JkEIZ+sgent0BeKX1SVuBYN9vJGS5epp91SBOH99wMvv0wNU59N6Oo52NnVhV36ZoT5+GIQhRKmxNCOgx2tGBYQ3BMHqM+CrQnepxB4vaYYq5prbGTWCMI+1YWasP0IAVfvVf0IOvdrCtfiT0TCtx6fi01nnorPp5xjl4pxA7oieShiff3E20kCSwcFAeHh1nRjxwL//GOXb3redhj9bcvTeXmjjXWGpAXVUOeDzaOH8yZXQhDqTSacXbTHrrzrw+OxnOMSm0mU4uuPjyjdjPaXedZm6sQ5RXsFz/xjVuUhbGSbHY63RiTiivA42u7tk+k0gtB9u03J/PNkXa779qQmmaMIaAShowhq+TUEFCLgyZuKRhAqHDRaNg0BlRBQchBWqep+W0xfIQj//RcYbfXg1NMfRGGSPy8ZJctjBPtILkE4OzIJ54RE2bz+3ndHOqp+5LwyZ9V4773Aq6/222HS0zBXz8GHKo5gm77JXP+dkUm4WORV+6H2VtxTcRj6LhMIwft2YhbS/QL7f6doLXQLBF6tKcb3GkHoFn2hCaEh4Oq9SkNcZQSOHwf27gVOPNFiuce18hOojljz+RqN+H7kSXYpGIKQ/KDEEg87dwJjxtiXe3SnoHWhXFQO/TcFZZ9F22XzjTRiys59WJE8BLG+/tTF/tJcixdqhN3rmwxA3eZQ+McYETrcSoDR4tNf5tlnDZV4v77MjCvho9dnjrTDmO8uMSwgCG8lZFH3hzsnrOs04KHKo8jvaMPM8HhcFx5vdslrRxBmtqPxiGZB6A59qWT+ebIu1x36TJPBOQhoBKFzcNVK1RCQRMCTNxWNIJQcHloCDQGnIqDkIOxUgfp44Vu3AiecoG4jnGFBSPRE6en8cpILu6HBG5tHjhBsiFyCcEliNjL9dTbul469EY9jryUI1jFnDvD66+pi6Y6luXIO7m9vwR3l3W6+usEQU1rdVZ6Pve2tPbBN0YXh6bhMd4RRk6kfIvBSTRFWN9u6IdMsCF3f0UTJWW40YIBfIALYccZcL4pWYy8i4Mq9qheb2T+r/usv4PTTgeZmICEB2LwZGDiQqq2EBPQymbB2AA+Rd+zfnjJoCTCbSg8fBgYNspODTTxSCSmSSMhbRciwVoz7MR8Xhsbg7qhk6mrE4uqR8/rOSwahcUcw4NWF7GeKe1xp0uLTX+YZGycxF69c4LP9dXgvMZu6P9w54bnH96CVZfX6fFwmJunCNILQjTtNyfzzZF2uG3elJpqDCGgEoYMAatk1BJQi4MmbikYQKh01Wj4NAXUQUHIQVqfm/lfKjh3AuHHqt6uqCogRNuZTVOGttwLvv8+flZB/Qq99mRxyCcJvUoYhwsfXhiBsO+6Pv04aIij/3XcDb7yhqHl9KpMr5+DHDRVYXF9ug4+Y0opPEUar5OpTnaAJ65YIvFB9HL+01NnIphGEru2qtS11eLr6eE+l36YMQ7iPr2uF0GpzCwRcuVe5RYP7kxAXXwx8/bW1RSefDKxbR9VChqz7I2OUXXo2kXd/VArOC7W31BOthMQszLR/dKSEICSWe+2l/vCLMcI32OLOvvWYP7adLHzOzHmxCImX1cqyfhQjCGs3hGD3dbbEK3Ne/iVtBPwpAnf3l3nG4NTZ6o2NQ/kfHPLdJTL9ArEkKYdqbLp7IqEzNNeCMCSjHU1HNQtCd+hPJfPPk3W57tBnmgzOQUAjCJ2Dq1aqhoAkAp68qfRFgpDEY3q9thjNpk7MjEjARF2YZB9rCTQE3BUBJQdhd21Lb8rV2Qn4CuhMIyY3oX5zqEPiqW1FOGwYsH+/rUhefiZM3bcX3v5d5h/EYgTKJQi/Tx2OEG8fG4LQUO+DzaP448KQ+u+8E3jzTYdg6xOZXTkH368rxdID9SheFgO/iE6k3lSF37KGw09AaaURhH1iCPVbIZ+rPo5fNYKwV/uXuwb4wQtr0nN7VSat8t5BwJV7Ve+0sB/X6oDve1qCkKDHPCD6p63J7F7yRF04rgmPg7dQ/QK+7uUShIYGH2w/Mxsd5RZXoeN/PYjgrHYcnJuK8pVRoh078tPDeGNGNKYEhcOHAicxgrDg6SQUL461qY85L9MSX/1lnjE4FTyVhOIltpgwAPHdJbL8dXi/n1gQagRh31tTlcw/T9bl9r0e1iSmRUAjCGmR0tJpCKiMgCdvKn2RIJxbcRg79M3mURDo5Q1iGROouVxSeVZoxbkKASUHYVfJ1pfqeeAB4OWXhSWednQXb/wN2jY6ShB2dACvvAIcPQrMng3ccAOwa5dt7dyLupiFH9+lfnRACHa2W9ZG7vdD6nAEcwjCjlofbBkjTBCeeirw+++0CPXddK6cg7cX52Px2Ax0VPqZAUu4pBYrlvtgalA4L4AaQdh3x1V/kPzpqkKsba23acrtuwvhb+rCGWecAZ1O1x+a6dZt0NYAt+4elwrnyr3KpQ3zhMooiC8hGOQShCsbq/B2XalNcYKeBw4eBIbYW/jJIQiFzqqTNu1HwTNJqP5ZONY1IyQ5054eHIFHYwR877Naw7cmHlsQh2OvJvJCyD4v03hg6A/zrNCgx8zSQ2Y85D42vCsyCReJxMbuS9OVmiBM70DTMfo4mH0Jg74mq5L558m63L7Wv5q89AhoBCE9VlpKDQFVEfDkTaUvEoTcw94D0Sk4J0SmSxVVR5BWmIaAcgSUHISV19Z/c0rpXoiCQOySLIWMowShFIEZc1Y9hi8s5BWDG48w5aZKDHqszC4to/hY0ViJd+tsf1+dOhw6DkFIwnKsHzBStOnEMrO/v79w5Rw8cWER/pydaoP59Dua8eNrIfCzcIb4/ntgxgzLv0/8ex/8Y4w26WkUXFLjWftdQ4AGAW4MTJInrN2Ay/PLcOGpp2sEIQ2IDqRp6jRiRvE+wbXegaK1rH0QAVfuVX0QHvcWWeqQKiK9EEH44ovz8dNlF9jkfCo2A09UHbMrbVlSDtL9Au1raW8HAu3/LocgzJ+fhJJl/BZqtJ3CkHg0LpS5eoBt03PQms/Ttu7KyQNBBn6a81N/mGd3ludjX3f8arG7z5Q9e+AbanEHy3wPRafirBBxq0/afu3tdBpB2Ns9IL9+JfPPk3W58hHWcvQVBDSCsK/0lCZnv0PAkzcVtQnC9S31+LG5Ftv1TU5TaHAPeySwOQlwrn0aAn0RASUH4b7YTmfLLKZ7IS5GR31yBNW/hmHvzfaxVmhkc5QglNINSbkM7WzzQvWacOgy2xGW28YrMqP4MHV14bTju3vS+MILv3a7pFtWX47lDRU9v9GQptXVQHQ/foPhyjk47oFq7HjZfr/68kvgkkuAxsYuhId72fTviKVHUL81BFHTmhB5YrOsWD00Y1tLoyEghICQK7dRVY14btg4jSB08tA5btDj+m4rEHZVNEpuJ4umFd8LCLhyr+qF5vXvKqUOgSKtZ8i6jEMFWHrmJeaUlYnxuHzLz+hhvrrzk9ODxUm97TdRF4oX4gbw18IjmxyCkOYcKdW5zBl4cWI2BviLW6aTfYk8cCtZFoOCp5Klijb/zpCENGtnf5hnzN5tbPbGpuH88QcZ4MgZM/oUq95mblQKzpUby5KqF1yfiO8M83taLny8bc/ZwWkdaC7ULAhd30P2NSqZf56sy3WHPtNkcA4CGkHoHFy1UjUEJBHw5E1FTYKw2NCOa0sPCuJNcyiX7CzAJoYWSX9PVDLO1whCGui0NG6IgJKDsBs2o1dFOnwYGDRIXASifDC2eGPTMNuLctjoFnjrTKjfIh6jsLcJQhqA2WsscS+0tL4cfl5euDkiEXG+lovvj001eLm2uKc4GsXO/PnAvHk0EvTNNK6cg6MvbMW/3wbxAkXGWPLATpQe8REEcvTKfPxzcVbfBFqTus8hcF/FYezsdunOFX51XLZGEDq5R2s7Dbi4mBOslhVnzMnVa8W7GQKu3KvcrOl9XxwagjAsjLwSsmurHLJODCjBe7gbEYQLE7KQE8B/RmLaRkifwrficPRlfpeifBhk3F+GjLsq8WtaLnwl+qI/zDOGGKtYFYEDc6Tdtg595xhMem/EnNGAuWmJ/UavwkcQ/i8uExODwmyGiUYQus8Sq2T+ebIu1316TpNEbQQ0glBtRLXyNAQoEfDkTUVNgnBe1TFsaG0QRN1ZBOG9USmY0U9eulEOWS1ZP0JAyUG4HzVflabk5gJ79ogXRQhCQsCsz7R1qTnwsRKk3FgtGZ/QEYKwoQGIkAjBImVBKAXULRGJuDI8TioZNrTWY16V1ZUpDUFICnWk/ZJC9XICV81BgqGYu1byu5QOMSi7DS2HtLhvvTxkPKb6V2uK8X1zDW97NYLQ+cPgz9ZGPFJ11K4itc7Tzm+BVoOaCLhqr1JTZq2sbgSkNneSLCEBKC+3g8yTCMIF8QMxIjBEdNgQ0of27MouiJyzX40fiNES5feHecYQY0pwWlFThUujHHMZ6y7zXsgLAhcXjSB0lx4DlMw/T9bluk/PaZKojYBGEKqNqFaehgAlAp68qahJEM4uy8fBjlZB1NVSaHAPexpBSDnQtWRuiYCSg7BbNqQXhZLSu2Q/W4Skq2vNEnIvhScV7IK3r/3fuc1RSpCRfDt3AmPHigPkKEG4Ni0XXlJAAPhX34x7Kw73CEOrPFDafjWHxerVQH09cPnlgI+wkZ3sKl0xB00mIDm1C+Wltm6N2MLSEIQkvTv0hWyQtQxujcA/bU2oNRkxRReOQBaL/VpNMb7TCMJe67vfWurwbPVxu/rVOk/3WsO0ihUh4Iq9SpFgWiZpBCjOZ0KFqEUQvp+YhSx/Hus8N7IgvDosDjdFilsGOkIQXhMWhxslyu8P88wRgnD24nq8M0viVaP0iO/1FI2dRpyz7wDy56WgeY8OsefVI+PuCnj52N/5glI70HJcczHa650GjSB0hz7QZHAPBDSC0D36QZPCAxHQCMI8yV6nUUb0FkE4VReOp+IyJNugJdAQcEcE+sNFtLdxldK7MLFHiJwmI1AwPxn6Yn/kvFiEgDijWXwpokwJKbN/PzBsGB06gSntmLRJ2EWzWClfpQxFlI8fVUVVxg5cVnKgJ61Uu5mEStpPJRBlouRkoLTUmlhNeVwxBz/9FLj6avHGagQh5WDQkqmKwOcNlXivvsxc5gC/QCxKzIZ396LqiQShocuElY3VaDAZzfGt47vdM6sKOmVhh9pbcVt5vl3qn1JH2BC5lMVpyfo4Aq7Yq/o4RO4rvtRBlUh+/fXA8uV2bVCLIDwrOBIPxaTZYySTICTnaL+FGfj95XDV8GY/khPSOej1APHIcXnLLqwfaOsNhEYQpg4pnUZ/mGfmOI08XlNocIJXF7pMwo/ZiOvrgo42DPDTIcaX7u5BVa/KiT6oK8OzT3vh2GsJPSWP/OQwIic32935NIJQZfAdKE7J/PNkXa4DUGtZ3RwBjSB08w7SxOu/CHjypqKuBWEeDna0CQ4UqQM57QjjWhAm+Pjjs5QhtNm1dBoCboWAkoOwWzXADYQR07voBugxce0hSSl3z8pE7VrbmBTsTEoIKRp9ELsOJVaEv6XlwkdmRXMrDmNHd1yxdZm5QJewIoCRb/duYIRt+EZJTNVKQKwGIyNtS1u3Dpg2TZ0aXDEHabrot3UmnH6yt2SjlIxFyUK1BB6LAPdM9XRsBqYEWRS/YgRhlm8A6rtMuCY8DjP6URzol2qKsLrZYnEe7eOLL5KHyl5j1RpMe/QtuLuiwK64VSnDEObjq1Y1Wjl9BAFX7FV9BAr3FPOnn4CHHwaCgoB33wVGjbLKSXMIWLMGOOMMu7apRRCSgnnv4i++CDz0UE+9jy56HVumnwxjozc25VoOfsGD2zDuxzyz9dWuawagbpN43G65HSRFEB48CJx7LnDkCKDLaEfbsQC5VWDc6kMIGarnx4BVWn+YZ2Rf76jyxZbxlK8UOWgKnTOLDe24ttT6mHFpYg4y/ANl94UrMswpL8CCRNsA9cydkPs4UiMIXdEjdHUomX+erMulQ1VL1RcR0AjCvthrmsz9AgFP3lT6A0GY5OuPT5I1grBfTEYPbISSg7AHwiTY5LY2iy6G+2U9VQyfIBPiL6wzKzSkvvKvI3HwPp6X1d0Z5ZIyUvHm+OTxizJi8j/7pES1+V3JwwtiIbOmuQ4v1xajek0Y9t6SKVnnqacCv/9uSVZZCcybBzQ3A489BuTkSGZ3KIGQXk1unwgJ4Yo5SKMbpAVJrXbT1qel698IcAnCK8JicWtkkrnRYgQhgwp5XrAieShqOg0I9/FFQi9a3KnRU1w8Ho9Jw6nBnBcKalREUcbWtkb8t9I+BmFvykQhtpbESQi4Yq9ykuj9v9j2diA+3mLiRr5x44Dt263tpjkEkJdPJ59sg9Vj772KzWeeqhp+vGdGcqhYvhz6bdvwyKL5Q2YAACAASURBVNTR2HniBHN9GwaPgElvfbSUcX8ZUm+sxsah6r8WkyIIL70UWLnScRhIPVLn5v4wz8g+JnWvEUNT6Jx5S1ke8jmPwaXwdLzXlJVwd3kB3uQQhL6RRkzZuc/OgpB4cWkrkk86K5NMyyWGgJL558m6XG009V8ENIKw//at1jI3R8CTN5X+QBCm+PrjI40gdPNZpoknhICSg7CGphWBTZuAqVPtEZFrjXfokWSUfRojCG1xMUDcXNJ+Tz1lIdHkfnLkvjwsFrd1K/Ll1kPSEwUCcRW1YRCdqyZGYTB9OvDbb5YadTqgqUndmIDctvRFgrCxEQhXz/uWDSQaQahktGt5hBBwlCBkl+sHLzwak4ZpwX0zflFXVxdOPb7bBqqZ4fG4PsLqosxVI+lYhx43lAlbv7urUtZV+HhiPc4+L3Z2dWFTawPInkvikTKuhj0Ra9lt/vZb4MILbbOR4MPEBUJUFFVxj325DOd98CEm/bK2J72a1oOk0DOCI7GxtQGPxKT1WIozlR3t0GMWa83hc0EfdXIjatcJe9ugaWhQdhta83Q2Scf+kIfQ4RYvRHxrGw2/SlO3JxGEf04egvYSZXH1bio5ZPYM8J/QaBtYuecF8uNg/yBE+fji7qjkXnXJze1/oViV047swvoBtveegOQO/JGnxwlBjo1tmjGopRFHQMk+58m6XG089V8ENIKw//at1jI3R8CTNxVagnBZUg6KDO0YFRiCEG9+c5zZZb3jYjTVNwAfJg9281GmiachwI+AkoOwhqUVgQcfBF56yR4ROUQbyV28NAYFTwozgDfdBCxaBJC4guQVc24ucMEFwj2hVJkRNa0RucvtLUb4anJUQcxc9GniEPr4AEYjQPRd5N/sb+1a4JRTnDcq+bD08wM6OtSp0xlzUGn/07RIIwhpUNLSCCFA4gcRa79MPx18vbzMDwXY35VhsbhFhgUht54gL2/8mKa+hYkrenRVUzVery3hreqXtBHw95J2AayGnHxEJbdcR9d/NeTUynAtAs7Yq9gteKaqEL+31pv/dHpwBB6NSXdtA/tybZ99Blx1lW0Lnn0WePRR6lbN+WIxdk8ci7DaOvgajaiNi6XOqyThC3GZmKizEiLrW+oxv7rQXFTTXh12nJetpFjRPOSs6HNuJX6bG2eXLum6akx9thKfpwy1+02tMxW5G3yfOlxQl0EqdvY8Ux1UngKFyDHaupk71DkhUXggOrUnGx9ByPw4RReGp+OkPZLQyuBoOiEMRn5agF1XWVyPhgxtw7jVeT1Vueu+ysb9lohEXBluP38cxctd8iuZf56sy3WXftPkUB8BjSBUH1OtRA0BKgQ8eVOhJQh94QUjukDi/S1KyuY9WPcWQZjuF4BlSRpBSDXYtURuh4CSg7DbNaIXBeJTGgSmtmPSRmuMDGJlTNaI0zmWIWyxq38Nw96bxS+2JPbJgAHWXCTEzG238TdeqTIj9ZZKDHykTBJRNS6x5x3fg5Yuk52rHaHKCTHFZxn3wAMACWHjrK8vWhAq7X8aDDWCkAYlLQ0fArv0zbin4nDPT4T0OvP4HpukbILw9ZpirGqukQ2mGuuT7EpVyHBj6SEcMegFS1qZPBTRvn4q1CReBNeShy91X8XY6eD14wqceV5s6DTigqK9mLBusxnBbSdPxrepw81ug7WPAoHPPweuvJIioXAShiB0qBCZmdnrCNt95N7Z6aj+SX1LcHJerBtSiw9m8VtVnrh9HzaPs4+bp9aZihBfD0Wn4qwQYatOZ84zmd2jOLkjBCEJ0ZB8nXXfZ48RMYKQCOtO+5IUBoFp7Zi0wXpXJPK7Y3zfDa31mFdlIe6ZT4rkVjxw3CCjkvnnybpcN+gyTQQnIaARhE4CVitWQ0AKAU/eVGgJQjaGs8ITcG1EvB2svUUQZvgFYmmSk4NgSQ0i7XcNAYUIKDkIK6yqX2bjUxpMO7rL7CKL+c4MjsTDMWl2ljJsQJr26LDjP/JfS/ORNSQUTWCgPLh1A/QIztEj+9li+Ed1SmZW4xIux4KQCETaWlEBJHC87ZFH8598IimyogT5+UC2QLeoRZSpPQfbjCYE+TnP0uikvN0YFRqEl+IHuMyiSVHnaZncDgGucu/8kGg7AtCTCUIp5edVYXG4OTLR6f36Tm0pvmyqEq1nbVouvNTSmju9Rc6vgFhdlho7zA8YpUgtYkV7cfF+s1A3RiTgmnD7O43zJZZfg9p7FVsCQkofuPZKnLPiW/OfV192AYZ9/BnS/WQeZuQ3q3/kcDJBSFw41nYaVceKvY4w65+x2RubhjvHCvyRR4CkS2px5xh+gm7IgkLsv8tiubpX34LNbQ0YEhCkmtvqKXv2wDfUJEpkOXOeqd6BAgVKkWNicgyaV4KUG6p7kqxOHQ5dt/coqT1SjbuJUoyaTZ34uKECbSaT2cLuypIDog8gM+4pR8Y9FXbV9WYb+NrOh/lNEQm4uo/sW3L7U8n882Rdrlx8tfR9BwGNIOw7faVJ2s8Q8ORN5VBTPW6rtX2VJNW9QhZ7riAI+dwuEXluj0xCuLcvcgKCpMTXftcQcCsElByE3aoBvSwMn36U6170ydh0nBQUIUoQdlT5Yst4+1fLUs3bswcYPtw2lRyd7ZS9e+AbYrKrhiiiP22sFKxejQvsrWV5yOtok2VBWFICpKTYinXDDcCSJVJIKfs9JwfIs3r/sSnEXQnCBX/VYc6kSGUNpsiVfnc5Mu+zKDXUGAcUVWpJ+gkCUso90swrwmJxa7eLUaUWhL+l5cJHzkLoJvjS4OPsOWfq6sJpItbuDFRspa2bwNdrYnDvBjdHJOCqbuUp+W1jWwPKjQaz28woHz+7s8DypByk9QEizJnnxcLyEqQn2m7uRRWlaNm9G83ffwf/yZORexnHhWav9bgbVuwEgtDU7oUNObnmxvpFG8yuEAPi1SUJ30nIMhNw5GPWv6IPYnD4GRlBt2V0x3/+A5z3fC1uHS5swdduMqHQoMctZfk9JdO4wqcRI+vpYiRfW9PvCcLTC3fh9wy6+OJc3DIfLEP67db7x7XhcZgVkYgaowGXlFgeVgh9zt4fheo1dnVhOmvfjPfxQ0WnQfR+w4wFbpkvxw3AWF0ozXBySRqhc0lvYe3sRivZ5zxZl+vs/tDK7z0ENIKw97DXavZwBDx5U3mvqgift9bKGgG9SRBKKU7ujkzGhWExstqjJdYQ6E0ElByEe1Ned6ubhiBkXLFIKX8dVUDMng289po860GhWIlEwS7mElWNi+FvLXV4tvq4LILwq6+ASy6xHQW33gosXOickSHGMbgrQTjig6OS7modRYsZN2qMA0dl0fI7B4G6TgOOdOgxwD8QkT7quLWUWgNJS8YFhuCl+IHmRr1RW4xvm+S7GP1fXCYmsGJbOQch9UulwefC0GiMCAjBKcHqu98jLfpf9XH83FIn2bgvk4cixgXuTiUFcYMEKxqr8G5dqY0kzNr4eUMl3qu3uu1empiDG8oO2UndF9ZSZ50XiXL92z/X45LJtsGEdy98C7m33dmD1b7F72HYrFvcoMfdUIQvvgCuuMIhwe5esQR7Jowxl9HZ6o2NQ22t+MLGtGDM1wUO1cGXmRn7zPqX90QySj903l16T6EBI9KF97R1zdZYiERectZbn6mM7OK2NyhLjwm/Hur3BOFJ/+7FxtGc14uUIydokB4TfrNdI8kY+a6pBq/VFouW0lvr6ILaYnzDc1YRu9cNeLgUabfxW+q/Fj8QowJDKBFzXrJtbY14qJI/Ln1vYe281lpKVrLPebIu19n9oZXfewhoBGHvYa/V7OEIePKmsrCqCF/IJAiFXHq6woKws6tLVGlOhnJ/PTB5+DTtt81XchDut2DIbNjq1cC559pn4pJuv6blwtfLC9MLd5tjqbK/CG9f1JssL7IdJQhJGSedBGzYQNeQqFMbkbtE+OInpKyeqAvFC3GsYIh01dmlauw04vzifdTt/v57YOdO4IknbIt67DHg6acVCiGSbelSYNYs4QTuShBGntiE+j+d+/pYIwjVH2/uVGKRoR1zygtQZzIi1NsHbyUMUsXCiYYAIzgwcXiUEoR99Sz2VWMV3uIQTULjQiqOlZLx1GbqxDlFe6mynhsShbnRqVRp+3sivnFN4mv6e3mLeg5g49IX7g7OOi8+WnkUpf/+g6Vncl7/8A0ctTbe/jYoiZ/1a65xqFVsglDoPMo+37ZX+sIn0ITAsC67s60cQewIwkeTUfqJ8wjCgoIuDBrEigPAEfatmhKsbLK6uCRH9A2D1CEISVV3lOXjrYQsQYicNc/k9ImjaVNmVqNkufI+5N6jliTmYE97M16rLREVrbfWUaGzjdi9LvOBMqTcWIWKryIBHyDh4lp4d/PWmX6BWNLL4Ws+a6jE+6zHLVzgewtrR8emVH4l88+TdblSeGq/910ENIKw7/adJnkfR8CTN5V3qo7jy1bpl8rsLh7gF4jFPIcmjSDs4xNBE79XEFByEO4VQd2sUqKj8hYI88a92DKXqN9b6vBM9XGblhD3o+MCQ3Fu0V5qokwtKITci94dlYwLQ2PsFJukueN1obg/KhWxKliOEAuli4r3y2o3iT9YXm6LAPGsdfnlaqFiLUfKQ2FRkb27UyVSODoHiTss4qo1wdff3C9SciuRkZtHIwjVQNF9y3iqqhB/tNb3CCh07pLbAlqCkJRLCBZilaXEgpDk74vKKzkEoTPaWG004FIeF25tRf4oXxkJXXoH4i+s61lj+iLGcscsX3riNnRXewv2trdgTGAI7ii3t6qK9fHDipShGkEoAXilsQOXlxxAxqECKoLQdPQovDMy1OjG/lPGggXAnDkOt0cOQbhp9DAY63x76iSxib39bR/A0QrEJQh3XjYQDducZz3V1gbodMLScc/wnXovbBxscbUq9P3vf8BDD9G1eG754R4reb4cjp4J6aRwXiribcnHW5iApalZyLuJVF419iRytifhG8aNA+LipGq0/K6EIIw5sx4N20NgqLXOo0l/7kdgosFcphptoZOeP5XUeY0tHzuurlqPSB2R3ZG8SuafJ+tyHcFay+veCGgEoXv3jyZdP0bAkzeV72rL8VqTfYBmse7uTYKQ62OeT87ePtD146miNc0JCCg5CDtBjD5XJLFY41qyMY0QIgj51o+1abnw8vIyXy7VsCCUA6TQBfzn1BEI6GY/l9eXY0NrA56NyzQTUGp+jMvmqp/CsW+2VeE37udD+PusHOqqPvwQuPZa6uTUCaWItgkTgL/+oi5OMKEjc7DF1Ik7ywtwzKBHkJc3XojLRK7OeYo17hj/PS0X3lJAOQ6RVoKLEeBTDKlxtpFSOLGbSR5P/Ktv5nXbRQOHGvLS1KNmmhWNlXi3zuqOUqrs71KGIdTHqlyUSi/1O1+MJ2OTNzaNsLoaTLmpEoMes8jYFzGWwoDmd0JcE7eiUh/Z30+liOforlgSrymd6DJbQpLPkb1KCKs9+hb8b+vveOq2+zHgEJ37yo7SEvgnJknB379+JxaCxD3EVVcB06ZZ2mY0An7quH8mxdEQhJM27UftxlDk/dfWejjpumpkPyVu3SXUIVyC0Jln4bFjge3bhR/4ERm5Z2N9mR+2njBUdDzV1gKffgrcafWIK5iePM7bOMzWfSs7sZJ5dqSjDc9XH0dNpxHXRsSbH/n1xtfRZTKHDngqzp7Ef+AB4KWX6KQa8kYh4s+3PlLi5qr7MxiFb8bDL7ITgx4vQUCCxROLo3sS8VJy8slAYyMQHw9s3QqIvUcgj0VKjB24tvQgb8PExrJ3QCdM7T52+dzlAZ7UeY2NNTftw9GpODNEOM4n3SjonVRK5p8n63J7p5e0Wl2BgEYQugJlrQ4NAR4EPHlTuahoL+pMnbLGxSC/QCzqJQtCQ5cJZxzfIyrvJF0oWk0mzIpIwEg38B8vC1wtscchoOQg7HEg8TRYjBNhKxeGBwThTZYrIaLwvrfisLnEhQlZyAkIMv/b1QThiKVHEH1KE29XupL0YS6V1b+HounfYESf2oiw0a3Ye2s6qn+hi7H1+utAXR2Qk2MJwaMGX/Xzz8DZZ0uPdDW8nTkyBz+rr8T8d9pR+UMEQke0YdzlLfjmtExpwVVIkTa7Al++ouuTsd5UaH6/LsIdCMJIb1+cHByuCkFY32lEVWcH0v0Ce8gOd+zAV2qK8EMzfVxuoZjcStvGtgJgyjj8fCKK3rM1o3AXBSaRsdzYgZ36ZmT56zDIX8QsSCkonHxEAX6mxD2AyfJ+YhZuKcunqplxSUqV2MFEu/TNZvKdWD+OELin/NXWiIdZsacI2anX67FmzRpz7WeccQZ0YmZYlDJeUbQPr0w9G8mFRZQ5gJLrrkHy8o+o0/f5hDfcACxbZm0GeZlEXiipcdhhgUNDEIphOWRBIeJn2JI6HyRm46ayPNEucCVB2NwMBAeLQ8clCI+8lIDjb8eLtsFgsJTpy3qv8dZb/ITh6K/y8c9F6roYnVtxGDv0zT0yuipGLCHINrU1oszYjtOCInFJtwU6HzFWVgYkJtLPRqFHjJ1tXtg8ZhhMbRZyLfq0BoxYfMz871fjB2K0wJr2dq3VdSzjxpwrzSmnAOvWWf86cyZAwg0IfS9WF+GnFuue3bhbh84Wb0RMbAF5V6GE7B6/5iCCs9sdJjvpkeZP6QhBSEp0lKx1VH6l+ZXcyTxZl6sUZy2f+yOgEYTu30eahP0UAU/eVKQOH3xdTpQA7ydm2/3kChejchQDwV7e+CZ1GPy6X9720+GrNauPI6DkINzHm6yK+EJ6mciTGjHyw6M4OzjK7O7x6vA4KoX045VH8Uy8a4gdAoCY+x5XXuqE9oB9t6ejajUdQcju0FdfBe6917EuJu6ngiy8reTX2wThad8cxdqLHBs3w98/ir23KCtj3E+HsF2GtackoFoCt0DAUYKQxBfd2taIEQHBSPQL6GmT3DPfuMAQ/M1SesoBh1nHDrS34qHKI2gydYI8MHsjYRCCvO1f7csp21lp5eKjthKOcftMyjU2e+PPE4ais8l9LRxKDe24pSwPLV0mc5eIKYfV6rMyQzuuErAW4dZBrGDnVRXaVU2w3TTcYkE04KFSpM22WCMyHgXUkpWvHEIM3ldx2BwNmdgFvh4/CCMCg+2ScsfivVHJmO4b7DBBeKxDj19b6pDqF4BJujDMXfM1PjhHgY9wNTZfZwKtZtl8B0455liUsjhKEJJqmLPlsQVxOPaqhQ0a810ewnLbBKUg5HiJoQOzyg6Z0yghVbiFx55Xh6ofIu3qZIYN7SM/UgCNy1OTyUIQkv/u3w/Exlos0PjqiftPHSq+s5eNEVbJvYw7Xwf6BeIDF8SwE7J635AzAqZ22zgMBHs5nLbQPaX0syg7C1YmbYZfIJbytJv9OJPBme+uwyef0FJTaNBjZqllzJKv8O04HH3JMuZjz63H0DcLsX6A/NiVY77NQ9iotl4l2Ij1+OkS1u9iFoQEA1feJSmXOKpkSuafJ+tyqUDVEvVJBDSCsE92myZ0f0DAkzcVJcoQpQThsqQc8+tx8pGYTUsaykEOd+eGRGNqUDjVUNKbTDi7SNyCkF3Q/Jh0TAuWr+SmEkZLpCGgAgJKDsIqVNvnixC65I7+Oh+bzx8AnUwF9NqWOpwWIqwwUBswtycI70zjVe7Q4MBc5t9+G3jlFYt7oMWLgUxKDuyss4BffqGpCWhvB/wd9LzqyByMGNaGhv2OWc1M3HAAf500hK7BPKk8SU+rGKQ+ltERgvBoh75H0Uua/WB0Ks4OiQKNwklNmBjl1JzyAuxub+kp+q7IJFwUFqtmVaqVpeRMrKYSjk0Qiinp3cWC8Lnq42ayif2piQdfxz5YcQTb9fzW97QDgYstsSgKH9uK3IBgM4HtzO/K4gMo7+zoqUIobAPfWFwdl+0QQUgsea8o2Y/27k1jii4Mxh9/wPOz7pbf5Px8YFA3VsTPOPH5HhUFLFoEED+S/emTw6o40G6GICTdsz5TPrFBqp52ZBdqfguze3Q0Ye0BBA2wjju2mMm+/pgcFN7jtlcNgpDEd+N6oXj+eeDhhy01i0E67fAueLHeRdDII3QOWr4cIFZo7C9iUjPq/hR2BS/3TEge5JxfvM+u5529FpIKhfasXddlom5DmI1MahGEx9+PxZHnbF0Ms+80fO2mPdPIIQg/b6jEe/VWl+DccTJofgkK5ifLnpHDFx1FzPTGXiXY2kydOKdor6jsDM7L6suxvME+XJArxp9scCkyyJ1/pEhP1uVSQKol6aMIaARhH+04Tey+j4AnbypKlCHZ/jq8p8CCkJz1f0u3XHg+qCvDJ42VPYPno6TBSGG9chcaVTQHJnbeh6JTcVYf9cHuLjOLYP5I5VHzC+cbwhPM8dq0Tz0ElByE1au975YkNAyn7N6Djax4TbQt/LO1EScG216mafMqSefuBOH+OWmoXKWMMCVKiKNHgQEDrMhceaUlPozUR9xPhYZKpbL+Xl0NREfTp2dSEhmJ1zCiPCLf/PlbMGpUlWy3bX5hnTDyWPhwJWJcyvIpuk4q2IUNg5QpA0k9GkEov//dPQetMo2vHUJ5W02dOFdC4aQmLly3deyy3VVxpeRMrGZbGBej7ZW++HPCMMHumHZ0l1nBTqw14n388Je+CTE+fiBn6cDu+LVy+5LEUyVxaeXEVOTDy9muOpX0ERcLvnXYWaQrcQFIXA/qu0w4QRfGaxWyNDEHGf6WB5TMx22nH7ywKi7LIYJwYV0pvuDEbvwjY5TcoWJOX3/lFYj49DOLf3FirtXZHa5i1CiABBLrT5+L7j0MQUhcOG4ckqsIQd0APdqO2I4lpiCxcye7MhpCjqQnscAff5xfzKhTGlH7h+2Zmn0OFIM088EypN9u1RFIyZOcTAgCYbi4dQWmtKOtyGpZz80p9172RNUxbGxtsBOAvTeQtb2gow0D/XSI9lUvbqXQesjFLGtIF/L2e6liQeguBOEXDZVY2E0QOkKqcztOl9mOiX8c7FWCsKnTiBk8pDNX1qdjM/B4lcW9K/d7JW4AxuhkXKgUrTjqZ5I7/4gEnqzLVb8HtBLdBQGNIHSXntDk8DgEPHlTUXLRzvHXYaECgpAMLCGF0bSgcMyPtQ+ozR2MchVcfTlIsztMRGLpeeaxPWjaq4N/tBGBKYZePTC7AyZqy6DkIKy2DGqXZzQCf/8NJCSIB5d3pF4h5QJRgChR2P7aXIfLzvJF/WbXXKbcnSA8cF8qKr5WFuCeXNRvvx14913bHmaILBJfcN8+4IILgIEDbdPI1cM99ZSwgkpsfG3aBEydapvi669X4ayz6OM6Eb0oMZig+Zj+3nXtANRttI6xyKlNGPnREUGXXgMeLsWRF2xfanPr80SC8MgRIC8POPFEIMx1vD5NV6uSxhkEYY3R0BOfSBUhJQq5JiwO6f6BeLb6uF1KJWu0M2Um1pWlxg5cR+m6ki2Lmm2pNhpwacl+VP4Yjv13CJ+JyUMY3zCLW0/up0Sen5tr8WpNMTrRhZsjEnFFuG3MQyHs+capkIs5NfqPkG2nSrhdo6nHlQQhm5QjMdK3tglbP4q5jCPueRdEpjlEEHKteeOLSvD51HNpIONPQzafa64BPvmEf7NXXrL75CRtVEi6y20EQxDWbQnBrqs4hyO5hfGkV5sg1OuBQH4uEknXVKP04xg7KWhcjJJMbFmlCEKSXuwcJMcqjZQl914mpEth5vNxgx53lxegwdSJMG8fLEgY1ONRydFupSUIGYxuvhn44AO6WoXGS9GiWBx+VtiCkM9VM+2ZRk5fsQnCsi+icOihVLqGSaTy8jdhWt4eRXdJVQQAsLiuDB+zHtIrKdeV8eyVyCeUR+78I+V4si5XTey1stwLAY0gdK/+0KTxIAQ8eVNRQhDG+vhhRcpQuxFyW1keDnUIxzggGUg8nCgfX6znvLQb7K/DuzykI7eSZlMn/iPjBbxGEDo2kS88vg/fpltfsQcPbkPj/kB4y9XiOyZGv86t5CDszoCQR+QkyPzGjRbXj59/Dlx4ofoSq00QEiXe4q1N2HF+FroM3oBXF3KXHcXu61lmcCo2w90JwoNzU1G+kpL94uBCFDXEpRNjncf8TP6emmr70ruszEIkk2/NGuDMM+WBPGkS8Oef8vKQ1Hzj58UX1+POOydAp5N2GUpIcD8Zj8CZ/m4+EIjdl2Sho8Ub/kEm3PFTJWaMDcQpIfausG99thWhl1Xi5SzxxzP9gSA83NGG9+rKQOzTb4tMRKa/cB/88Qdw6qmWPieWA7t32xK1S5cCt90GXHwxsGyZ4y5o5Y8ux3PQKtP4ahLKW2xox7UKCDDHW2NfghISS205jF1dMJkjwQFnHqd3Xc+VQ822VBk7cFnJAaqYW0J7iFx5CDl6xvHdYNONP6QORzCFm24pxbjafaYGyW0yABuy7C22nWFBKJfQfCthEIYFWOIROsPFKJcgHL59J9689Abl3XTJJcDKlfb5+8OmxLRK7mYvguYpR3fij8zRgikYgrB6jb2LUOWdZM3JWB6LldWp98LGwXTWi2IuK0feX4Vdr9i7kqYlCCfv2ovOVi8EJhqpYiLKJQiNpi74CFwkxO5lZI329/JGuI8vSFy9f/RN+KjBau3IxpZZi5+pKsTvrfU9P03WheGZOEqf+xIdL5cgJHe07GyAPLIiX0sL8OKLwJNP2ldE1kQSr7Wr0wt+4d0WwgCKPojB4WdsXXey96Pf0nLtsKWxNv+7rQnjg+wfaQr1LTv+Ig2JLGcOKX1sylcHOWsQ69FoHz/EUlqPKtHPseseFRCM15zsLlsOnnLSKtGLeLIuVw62Wtq+hYBGEPat/tKk7UcIePKmovQAQg69DZ1GfN9cY34NR+II3lGeL0kQCg0bjSB0zwmV9aSw//7LL7eQP9rnGAJKDsKO1ejc3KtWWSzD2J8zdEWWez1R7tq6/5yhYQAAIABJREFUvFV6qfujpR5PVRearWVrN4QifEwLIia1oH5rMP69Qv2YRO5OEO67Ix1VPyqL3/rccxblA/eVckeHPVkzYQLw119AQQGQlaVsbCoZX3x6oauv3o9FizKpCMLZs4GFC+nlZfqbvKw+csTL3OaJEy0WlMRSO9DH266wvFIjfgmowIPjowVdhplngYXj6LMfUaJfXXoQZUZLfKR0vwAQl3tC7qy5fTdnDvD665bmk4cJJ51khYI8Vli7tu9Bw3c243uVz9cyIYKQxFl9msearzfQkUtiqS3j0vpyfNgds4ecYRtNVuWn3LrUbEuFsQNXlBygUoirRRDyuTJ7OW4AxlK4JnM1Qaj0zsLu05q1odgzy/7hD4MnLTlKM06UxP0k48nQZcIZPKS1ozEI55TlY8pjT2LGJytRnJmO5ffciqduu5+mKfLSmEziQeaY0mpqgGeesQQTfuQRICVFXj2uSL11K3DCCQ7XtOiBu/DpHTdCzKXrnC8WY/fEsSj/OhIH70tzuE5uAUysTbGCd8/MRO06OrN8cvY47zzgxx/tS7z+5k4sX8QKJNidhJYgJMmZOSlF/px/PvDtt8Kt4jvvFXbokebHb/4odC97q7YEXzVVw9/LC+eGROHbppruJyb8dTvbzXapod18duL7+DATOisSS1C+d3GDXys0W+V1Gb2QObe8x+2rFEH4a1oufDmg863d7DAwepMJZxft4d37hOS+pSwP+d0P06XGiNzJpPQuya2HrOXnF+1DW5flCc6LcQMwXmRv/bihAovry81pTe1eOPZGPBp3BiHmrAYkX1dD7SJ2cWI2Bog8tJOLhyvTK9GLeLIu15V9o9XlWgQ0gtC1eGu1aQj0IODJmwrfgY3EPmjaHYTA5A6zS0m+j7gtIC/RiUsm8s0IicahjlbFBKGQ21Ju3UKBwIWG83+jU3GGFoNQ9mwnh3Fi2UOsM8S+XbuAXLqHprzFfNNUDRL35uqwOI+NbajkICy7Q12YISfH4vqP/TmDwDDfPb27AJOVICQWruN/zlPkFobtqoYr+/pM5fHhUm6uROSJzdhzg1UhmTm3DOl38r84JnWrqXCW6vrby/JxoKPVLpmjl+1Zs4AlS2yLvesu4M03pSQS+N2nC9lPFyPvEXsXQkrGF5/C6IIL8vHppym8BOGxY0BhoYXUI8qUSBnhGcf+kIfQ4W3mGGFf8ljfE4LM29s+titpF1FI3RUtvhArab/CXnBKtkKDHjNLD9mU/VnyECT4+vPWJ+aCSo57Kqc0RqVCaV7bC1UlRBDu0Tfj7orDKknoWDGuXOO4ksr1RCHVUjXbsq+9BXeWF1ARhNOO7IKX/bsC0BLJTLv48HgpbgDGOUAQjgsMMVskEmvgLP8gKQipf1eDIMx7LJnX9SFDRixMyEJOgDoyCxF9Yg2+PCy2J06gt9EIP4MB7d3ae0cJwld++gr3n3MJNd6KExKrOx97csiuvNNOs77gIGZNBw/SEYuKBVOQUSWPKY8vfBWbzjqViiAsXhKDgqckLmAKmuIT3Imp+/aK5pRz9iNnj+++AwhBx/7IA9IvvrCvZvJkgLh3J9/HHwPXXiveCDInyaO9HedliyYkXgOI1wqhj68LVzVWY0aovQtUUgbfvazM0I6rZFrgO5sg/KyhEu93x+Bj2t56xB/756SjeY/9GiZ2ViSeXsRIVlK+l28X/OMNCIgzoHGnxdKZ+dgPVvji0PKt3eRxDjkTE4vM56uPY01LnSyCkClTjtUr7bQ5cfs+bB4nHAeYthw24cfkEToz7G9vwR3lBT1FFy+NQcGT1nUgfU45Mu+toKqaL64tVUY3SKREL+LJulw36DJNBCchoBGETgJWK1ZDQAoBT95UuAe2/XelofJ7q+bTP86A7GeKEX16o40i4tnYDDzKCYpMSD4pF6NCfUFLEBKrxQsogjYz9dAQhAcOAN98A4wcCZzrQCgOqXHWV34nF4jTL+3A2q/4lbPcdihVTucuO9JDmgx6shj5T7jhy2EXdJqSg7ALxFJchasU9KQen6BOTN1vr+xQorAlrm0eqOz2ucNpvRyFCRe4cT8fQnCWHscWxJst8kJzW5E1v0QwftQUXRieVsn1EE0n5rW34tbyfLukjrSZFEaUDWRdVeubuPEAAlM6wEfWPvww8Pzz8mriG6fnn1+Azz5LtiMIScxE0h5CDI4YASQlAb/8Ql8fUeTfF5MsqIwiJQnNm7drS/D8/f4oWWbvqouRQOkaTN8C56Yk7kVvKrN9VbA8KYf3dT+xQg0IsJfnyy8B4u3OVeuPcxHhdy/4fepwhEi4fezoMgm6yxwbGIId+mZni05VvpI1mqpgikQLaktAHifRfEP8g3gfULDzqtEWU1eX2YKduN8nLt02DR8hKR5xwcd2+8ZkWJE8BLEC5DpfoXwEoZSVA1MODWEnl7AUa7hYfYZ6H+Q9moKgQXpRJarQ3sYouJ+LzcQJQXQWVFKdJDYfxfISY5Pse3bg/e9u7El26pF/8GPCYIdiENYmJiGqvExKbMd/J5sl30LNLrm5GQjluBPcsQMYM8bx+tUsQSWC8NFFr2PL9JMxf/ZcTPvpN14J565Ygh0TxuDoa/EofKPb97qabeHE9uMrWs7Zj5w92C6/mfLI41Fyp+Z+7JjR+/cDwyT4FzInaeR54gl+F5lM/XxduKWlUXCe893Lvm6swpt1pbJ6w9kEIdvFJiOYGF5iZ0XiaYHw9TRf6uwKFL0bb5NUCUFICkj1DcCL8QNwZckBc3k0lo9VRgNIXMe53Xe2Iy8m4Pg7tvLQtEMqjRpnaznu4rlpxWLlSsn+YdJgpPrxHJalMrrB70r0Ip6sy3WDLtNEcBICGkHoJGC1YjUEpBDw5E2FfRgRU0qEDGvFuB+tSuTZkYl4t872kukKgrC+04gLVSQIiUXI0KFAa7cBzUcfAddcIzVi+vfvP6wz4j+n+FI3UskButLQgXgSoI71ratpxLQodZQy1MLzJCTWPEKu7RwpVyivkoOwM+RQq0xXKehJPSHDWzHuB1ty6/6oFJwXGi27OSSWyL0C1jU0CgqhCsf9cgghOXoqeU4LisA90SmSJABVYTIS8V1gHWkzqZpYkh6yNQqTIZF9UilXUwYD4Eu/bPESSTNmFODzz+0JwrQ0oKhIufhiiiim1MQralD+hXXcpt5aieML4/BuXSk+PlyHLeOFNWlK1mDlrVE/55GONtzIIQiXJeUgncf9F+ljEkOH79u+HRg/3v4XWm936rdMWYlCccu+ShmKKB/xwJdfNVbhLZlKTGVSKs+V6ReIJUk5ygtwMCcNqcVUcUdkEt6WwFMNgvC1mmJ811xjrpbPfRtfk8kaMfC//GSPHJmIF4fzOLG9/xeXiQk66fMYDZavxw/EyMAQB3vNkl2oPm5cQZ/QTkzdw28pJUUQzo9Jx7RgZe61uY1sM3XiHA62QU3NaA0Vx6Pmj1DsvmGgTXEfzv8vLn3wcYcIQmr/dI72FglsFiRhhVlZCcRzlPrr1gHTpjlau7r5VSIIF8x/CN/MvBJhdfVYNfpkXhnnf7kc68ePRP78JNFHQY40UCoOoZyzHzl7cN16E9n27gWGD7eXkjzIHTzY8nfiVTaQ38NnT8ao0xpQ+3u4ZHOrq4FokWM/XxfeWpSPhSn8fu357mXfNFZjQV2JpCzsBM4mCJfUl+OjblfZLQUBOPZqAqpW869d5BxLdB5CHyF1R42ia96gJ0rsLFzZBOFPqSMQ6G1r3i62V5wfEo1V3fufFEH4XVMNXqstthFUzpila6EllSNna/Lwhjxqv4bH6lRof2ZjROrmexA59cBu+Oik4wp8nDQYyRpBKKe7tbQaAm6HgEYQul2XaAJ5CgIaQWjp6eLl0SiYJ2zFNXnXHviFm5Dk64/zQ6PtCELyd8blqNyxQ2tBWNtpwMXF+6mLl7IgJLGLFiywLc6RAyG1YG6cMDS2E83VFK6ButugBK/LllTiyxvjbFCIP7MB5T9LXwSdBd2m1gY83m0VS9yerErlud06oXKNIFQGKrnwn/DXPgTEG20KkGs9wWRmXLvxSdNW5I+/pg4x/5T9XBGSrqqletVM0jPuJaVauSZtBPz4/MVJZVThd2cQhCqIZS4iINSECdv3wifQciEWUgQsXw5cdx19rUI6v9bWNjsLQkf1g41GI0J9xNnLyTv3Yt+tGWjYHoKIE5ow/L1j2DhiBBbVleHTxkrR8banqAPDU+gsvukRcl3Kox16zCqzZZOXJOYg099eeyjWF0QfzTz2YUu/YgVw6aWua4+jNQlZHTFuVwmB+GNzLX5tqcNA/0DcHJEIXbdlIQ1h46h8JL+pwwuln0XB1OaNpKtqBC2i+erK8tfh/URxl3FqyChUhhyM5kQl441aYaVwbkAw3khwPEYtW6Z1A3OBTnuXw3ztUSMOobMJQiK3HMJSqN/4HgjWbwtG9c/hKP8mEsY62zWWjU3h23E49loC/CKN6KjiJ9mZ9FL3Bjljkw/bV664CddcuRIH5qSbi4o+vQEjPjhmU+yB+1JR/rU949HW2orFWzagLDgAlw8didwwOl/XJL7XyzVFuDjlbAzpsFjqOPMz1dfDO1ziPF9SYh9zcPNm4MQTnSma/LJlHAA2nz4Nk39bz1vH3jG5uOvrD82/Bba2YdyGP/H0bffZpH34y6X4a/xoOBIDWqqBAYkdOOFP/jGgL/PD1hOGShXR8zu5/5EumzLFNguxDiSPb7kft3tlQCsqk9Q99Oyzu/Dzz7Zr6lmrj+KnszN5y+W7l33bVC26F/AVNDUoHKMDQniJRTXWxDvK8rG/oxUt+QHYPr2beRVAiu3elS8J8Ypx1ll0XT9oXomN60uSK+qURvgEmTDw0VKsnZjTcyZhSqTdd6UIQm45xkZvbMqVtrina5ltKqlxxaQm7mf3tLdgcECQ2fPFXn0LHq06KhjbmK/vufFqSbifjUPsY7gkXlmDyKlN2H97BgKSOzB+zSH4BlviG7I/2jjCSnBxdh4lehFP1uU6uz+08nsPAY0g7D3stZo9HAFP3lTYB60tk4ago1xYyTj+t4MIHtSONN8AnBsaZUcQOjKMsv11eI9CYVRtNODSEnqC8JHoNEwPEb5ADxliCXnB/mgPhI60153zyr2wff018NtvwL33AoMo9WTkgso31noTe+6lQ003U2L9reQg3NfGjzP61TesE+N/OmQXJ/XXtFz4yh3EAA61t+I2HlebQljTvlgd/VU+wsfax/gj5V4SGoM7otSPMyN3fPBd3DePGQZDrQyTPLmVUqbnxtlS6j6JbayQmipsEegMgrC1s9NOWcJtPukDMk/YQ5coEZg9T6zdK3e24OJRtvFgKOF1i2R8MQiXJGYj019nJ5+CqY3PPgOuuMItmkolhFCsZcbtKnetIjF8b4pMNJdNq4SjEkQk0f6701D5neVsxWfJLVY+cdv5TiK/5YajctHkl4PR3KgUvMyxVmDXMTEwFC/EW+PL/tpch88bKxHv6497o5JtXH0au7pALDyJQtfQ1WV2/zUzPN68NtgQhBn0MW/VIAj5XIy+EJeJiRIWhFyFphj2aijDbyg9hGMGqzV+075A7DhX2BJ10PwSpMysRnu5L/6cJB1LisGSWI1eEibs0plmjDFp+LBdmzEK3rC1ABn23lHEntnYU/Sm0cNgqLMlMvWBgdhadhxPNljdHH6QmI2BPOskV0YmxvL8oXMwrZWfwJLTLqm0xm3b4Mtnzs3OePQoMMA6d8w/ETPwceOkiqf7nZj9k8vJ6NFW0yjyguSddyymQbffDgRT7JsyNp3rfv8WRQMz8P2IKQhpsnXn/NILT2D1FRf1yB5ZWY2vJ5xu05Ynv/wQ68bnUj9AowPCPpXQutHwTxB2XkS3Nv/vf8CDDwJbtwInnGBbB7lXM5aC7F9WrwbOPtv6F2IwesopSlthzSd1x/jpJ+Ccc2zrufSFBqx4iJ/E5ruXrWqqxusij0XktkKNNZHZN2juI//5jyVepNAny4JwfgkK5vPfXSJObELxxiAEc9yh0+67cghC4op5+/QctB6WMEWV2znd6cXGFZ9Fqb+XF16LH4j36sqwu71FsFa+vueG0DE2eWMTiWlA8fHN51fiBmAMRRxhiuJdnkSJXsSTdbku7yCtQpchoBGELoNaq0hDwBYBT95Uzjm+G23dJyCpA2b2s0VIuroW6X4BOCfEdQRhjdGAS7pJQfISb2c7fQwdKYKQ79734YfA4cMWaxTu3dUT5k7WKW0oWGevmKVpOwlKf9ll0imF7ttSlzzpkpWnkBMnQHkt9jmVHITVrF/tslzpYpTvUqT00l1qaMfVPK5ghPCp+ikc+2ZnSMI3+d+98Ivg94n4dcpQREq4DJSsQIUEfGN/60mDoT/e+/EruH2slCCk1fFxCcL33gNuu80xkA2mLknSWmz9Ib+JtXvMDQ3YsaT3rK8dQwfmWDLXl9paEAopvmn7kS0TsW4gbtD6ykdeo1/FsxYtSszGIH8dphfuhpFDMIi5MlO73UQpt36ALYk1ZlUewka2UVeldJ2mrkAkIa2ikhRxWVkGHrzJD4Z6X+Q8X4Sok2zPn+MCQ/BSvMUVJPcBW4qvPz5KtlieG7pMOOP4HjupmPzuRhA+H5eJSSIEIYmZSIjQRfXlVF2iRn+zMWqv8MWfE+lIv90zM1G7TtpdKrPXkDvOsiRxaxyqRgMo6GjDzWV5aK/0RVeHl/lB0x8ZoxCBOuQjC7GwxsL8FufjjWNPmosm630XbC2eCgZn4+afV9hUTeJ3fZgsLSuD3byh9+Dk1nW04juWjsQYJARcQwNQUwOkpwM+Vu8k7QcOIIBjZlaycR2Sp6jgYpT4UiT+EuvrLb7HCTM1fTowYYKFhCTfxIkWdkvqk7HpnFbwN0y+vuY+5n43//A5CoZb+4qPIPzn5x9w/+AUpxOEU/bsgW+ovdXR7uszUbteeq6Qtv37ryXOIIGTwMr+8vKAbB4jcT5X8OXlQGkpQPhiEkdYySd1dyRuxllDz1zFhDl1+Ot1/gfE/ZEgvOEGYMkSYXRJ2FAd5dV/0JPFoh6nGoxGhHG8ZjCP4FoOBcI/zgD/KP57kRyCsGZdKPbM5DwyUDKABPIIjSux+yJNuB2+/ZA8Lnqu5niPJIY6H2weTefJiO8u/G3KMIRLeC5RESpVi1KiF/FkXa6q4GuFuRUCGkHoVt2hCeNJCHjypkI24bf+2oQtSZH4Nkf81ebAx0qQelO1UwhCMZdTcpQ53HGrhCBkyggLA44fB9hecogVCnHDQe60Y8f2z1ky5vJm7FyhPF6M1EWNoOauBCGJY9Nl9Orx76+GUktqlCg5CEuV2Zu/u4IgZDxTpd9djsz7Kmya60ifyV1rdl42EA3bxOeK0EttIrQjsqrZx7wE4ZTB0Bf3LkE4edde+IXbKhFInJXtp4srRP/6y15hRavjYxOEVVVAnK0nZGrYw0a3IPKkJmTMqcAfGbmScU1XNFaZ4w0y352RSbi424rl84ZKXBkhLgjNukstvIsT8hGEhAwrWKvDtm3AeecBY8ZYhKLtR24T+hI+DKnAbcNbCYMwLCCY10qQWUuuKN6Pik6DU3uwqxNYP9CWIBz69jHEndtAXW9vrn006zxpY9uxAGw7zXatOSl/F7xZhl2jA0PwajdBOLssDwc7bElSpp3EvSNxC8v3kTSMTEJuxYSAFdpf5CgHmzqNmMGJ7S3lQeHjhgospiQHiexr06TXQKnBw+63bWdkozVPWptN8JF6/MjUy2BJvKQspyDdpOQlvxcb2nHaq5XIfyzFfLYkcSOPvpcAHw7Bz5SVjGIk/tCCHedl2xGEJM0px/61q5ZmLjHYPTHsHpzS4iKCkJiYJSUB115rlZkEnuuOP96461+EjRpt057iV19Gyr33S0NLAtGSOIchIQAn1pk5M3nVQ173MF9KCvDPP/Yb+rFjFuJS7JOx6TD9w0cQzvz1KxRmWeNKRlVW4asJ021qzlvzE27NTuQds2eeabl/qvGd+Pc+/D56CM4usn20QDtXiAzkfky8MRAjTcK9sj/yt5UrgYULbf8utQ/LgNqmYKlySWJu2QPHd6BgG7/XJL57GTveH18fdFT7Yss4y6MF76BOTNm5D94BwrHiaOatVF/LsSCcOxd46SXhEsmUoo3jnfVUMfKfEA5JU9VhQIyfdaMkbtOn5+/Fhhyry8ysp4uRfK0l7i77EyMIuVbrx9+PxZHnkqRgUvy70LiiOUOwK9WX+qFqdTh0me2IOa3J5u5HXMa3dJnM4QSYOMQkb/nKSBycm0YlO9m7yNmBxCrvbPaBd6AJhwu7kBFHHy6GqiIXJVKiF/FkXa6LukWrphcQ0AjCXgBdq1JDgCDgyZsK2YTnzduLl14aLzkYcl46jsRL68xvWm+NSMTC+jLJPLQJ3JEgJLLPmwfMn29pBVEWk6DrhCQkLxGJa80ZM2hb2HfSRQ3qQN1h5fGslFzUGHTYeXfqm1Fk0IPEcHCFldXYVXnYe0smOir9kHhVNbKfLaFWLDnSu0oOwo7U5+y8riAImToiT2rEyA+P2jTJkUu33EsfqZjE/iBxN7ZO5o/bIqTAZbsFdHafSJXP1+4t44cKxmqSKk+t34Wwo1FicdchWsUTmyDki1FL2za27DRjkijpn6ouxA59M8YEhmBeTHpP3MJ2kwmBPt6iVdOsu7SyuzpdkaEd13Es5rLeHIpFr1gVTMQVGTEKuf56ZdL1JXx26ZtxT8Vhc0Ob9ujQqfdG+LgWvJYwEISQErM2/ayhEu+reDbjQ9tkBDYMsiUIhywoRPyMerPnvvYyP/gEd5pjVgt9NHNCWU9L55Ja5zv1Xth93QDexx/MQzmmFnYMQikrYCHJViYP7fGSQSzN/pwgbRnHlKXGAxQ+l7ZSBKEUhty2vp0wCEMDKNw5CoDEVgyT/tk42D42E19W4qKaa+0q1A+TNu03W/iReOqfdFt+So8m8RSVhg7EdxNiTMpWBEKHdsGMXt3kIdeCkGRwhCD07uzEywNvxGjYk4yOtpM6/1NPAY8/bk5et20bIokVH+urvP8+xL38inhx3NiFxDoxKso2D7Fc5AakJX4m2f4tSY5ffwVOt3Xz2VMQISCJ30USvI3yc5QgfGPWb7h/xclmJb8zv482tGKwLgijx3bh9OO7e6qiOVsxiRkD0a++srf8+/RTC3k4daptK6T2YdpzGhcbqXJJem7ZF96hx9dv8bum5LuXPV1ViLWt9YLdwsUu+fpqZD0pHL9WjT2QrMPl30Tg4L0SJDeA++4DXpGYWl6+XVTxb6UIwpLWDiTprHoEEot10OUNKF9pO0/59i8xgpDrXr1oUSwOP+veBKGhwRubR1pdhRJiNO8xC7lKYsNySXpmgMmZi0IPYWjmhTPXGaVlK9GLeLIuVynOWj73R0AjCN2/jzQJ+ykCnrypFBa2ISPD+grXDx3ogMVqpAVBiEUV2hBk/v/0OeXIvLfCowjCk08G/vjDMvDJ6zv24ZpYGBLPOc74iItT8ig2kz9+Om+V5C5MXIiQ++6bbwJXX00nGTm4E9UziYNDXrIlXFiPylXCcRulSqU5kEpZEP7cXIv/1RT1VPVD6nC7eAZScsj9PWpaI+o2WF3rjPkuDzv+w+MjR27BEumVHIRVFkFRcX+3NeGN7thMd0elYHx3rANnE4TMo3Gr0F3QpXfgokea8PEtMYrawmSSq/RkV1axKgIH5thf0oUUuGpYVDjUWFZmvnbLuZyqJQe7HL8YAyb/LRxvVko+9jrEjj8oJSubIFSqrCJ1yCUISR6y/noJVColC826K9X23vqdjyCU6l+5svYlfLa0NuDRqmPYe1s6qn+O6Gnq1tZGc1w4vvnKrCefNlRQu31kiBC56x6xtN+QZUsQDn6tEPEX1OPAvWmo/DYSPiGdGPZ2IaKmNfF2lRrKUbljgHadl1K4ctf0l+IGYJwuVLRfxDA+OSgc61oth8mOGh9sGUvnVoy7znDx+G90Ks4I4RAnPKBxYx+RJM/GZuDEIGG3xXLHTIZfIJYmCccLlOpLdn1thf74a5rFdaua36gVBYiYYIkbxYzPbW2NeKjS8ghpRkg07o0Wtprhk6WkvQNXB27BEsxCCJpxP17BR7hOVOxz8QN+xHm8aZQShDN2bcCrl9+MQYfy1IQMbbpA6NqscSGpCl++3BzDoWbdWkSfcppNlqpH/ovYZ58TL4ZYJZaxHqhedBFAWCr2Ry5oTZy1Z8EC4O67bdPxxUVobATI5W/nTqrmsBOJEYSX/LUGNfFWTwB8FoRTsQGbwGHVZEshL8O0o7t6yDM5+y5x20nOJZs3A8SNN/sj1oXEaPOMMywWhiQdMei8+WZx2UbkdmHvHlvXujStodnfuWeoRz5swrPXhvIWz76XnTZ9OgJ1OqxursFrIjEI+bBT4wGHWPvN7ucH5lKRem+9BdxxhziaPkGdMLVJk9OE5Mp/XHgtPNLYjsxQqwcS4hUhK8De4puLT9EHMTj8jH1sQ6Z/2esxaYlQeqFWJicD5H0B7fdRfQW2tzWB7F+3RiYiqDuuopz97+DcVDtilGnPjKK9aDLRu1oVklsjCD3b2IN2PGvp+h4CGkHY9/pMk7ifIODJBCH3wMz3YpV5zUq6mxxCyNHxlshEvFvX/y0ISZuZgxx5DVlcbDvoaS4lcqcJeVz7zDOWXEwQeJoyJk0CiGs95pu/qQqzJoQh1U/YTeBz1cfxa0udTfH585JRslw50UKDiRRBOHnHPuQ9nozWwwFIuqoWc+4C7oziD4hOgw1NGq5MoSNb0fivhRx35tcXCUISf+jykgOo7nZlF+Pjhy+Sh8Dby4vXBSCjSFCCI9HVsN38CpXx4j/VeGC08nFLypVz6eOTg4x9El/j3ysHInREK0YsOiboYqg3FeRc2bntJmv8hrFDYahh+dJT0nkO5Bl7GANhAAAgAElEQVT7fR5CRwjHNJNSZLHHHFkXyfpI8/UmQSgmnxRB2NEBsDw60TTVbdJwXYzKsRCibQTNvkRblrPTkVg0z1Yet3Pj+f7GZtw8RdyC8MP6CixtoIsLx7hSlLvumTq8sCHb1oJr8MvHETSwHf9cmNUDD3GnNfGPg7xwzSYudENj4CM1sJ0AtlR7pdaWE7btQ0Cc0Uay1+IH4t5uq0/2D8uScpDuF0i9txz6bwrKPoumbrWYAlrMMwe7ArkEIZ9LYBqBv08djpBuBStNenYadp9tmTQEHeXKvVyI1T3yk8OInNxsJgjJg41TWdZVJN+K5KGI9aXfFxdXlmNi/OkYjn3masnDy2C0ym1+T3qlBOGqay7D+Z98qbheoYz5Q3OQtd82fixVJV9/jSo/X8T+x9YNS9X8eYid1+22RaggqVdoJO5gJM8jR0IakgMl+yMXLmLVyHzE5aicl5kcGZ1JEJI7aJH13aRNzSTeH4n7p+TLfqEISVdY3B9LrX3s8pk91Wi0P3sw5y/itnLTJiAmBhhGYRi9YwcwTiTayejR/Lwtzf4ePbQdtQes9+EHFjfhxVniBOHeqBCsS4vtiSGtJwF4Bb5eIwgzbB/rCMlHiDHCrYt9vqGdVNarUgThwXo9csKt1pnrW+pxcoj1sRMjA3v/MtT7YPMo/scxTP++XVuClU3WuK0HH0xB+Qr6/fLKK4FvfjRB3yjukYNPvmvD4zArItH8k9QZgo2xkEWkkCt5klfuGVgjCDWCUMnar+VxfwQ0gtD9+0iTsJ8ioBGElo4dh+3YDk6kcQDhqEcjLC+JySHE38sLN0YQgtAaL8nRoeGuLkaJ1xrivYZ8UndSRzEg+evq7D3lkMDhARShwPjkI/1FiJs4X3tlCp9SiMiQ/2QSSpbGKm5Oc2enpLWfj38XTAb7V6LMJSD5mmqUfmIle8b9fAjbz1T++pymMa7oXz45+iJBWNihxxmvVoGQycHZeuS8UIyV09NQty+wJ1YYu61EgUDc8ir5aPXHzx8ox8ODE5RU0ZNHzqXPoYrcKP4gacd3TeRltPX1wxvxAzE+099pSlga7MQU3yS/lCKLuKNM6w7fsXYtcJqtoYKgCGoThFN0YXg6ToYpuIBkUvPg7W/0uP0CfndZNHj3ZppCgx4zSy1KZql+VSonjQJRadlq5/u2qRqvFpdh41CrWypSx/ATDNizxY9XOUXcVEb7+kEqThJb1gF+gViclCNL2UXyF74Vh6MvWxRlzJfzYhEadwbZkVti83hWeAKujYhXGz7J8qTWeakxmPlAGdLvqLSpJ90vAIUGe7eRjCtpqTpJYXKtB0ke4tkjbXYlfAL5Y139npZrjn1IrHTPCInEIH97K466TgMuKra11n4mNgOTBSwIlca5vCcqGeeHKnvEw8ZPqn8kB4BIgpDhrRj3Q76ZIOS6s2OyyXncM+3Av1g/1DbO3t8Yi3HYIVvMo/6ZmJX3jV0+KnmkNhBOqQ/gRbyEByVlvP3rD/HOReIWkUKFVHz+KeKvuMrm53adDgGEpBML/it2WCeuVIh7UTkfszkQi0NCIjrwiRGEF2/7FbVx1ruVXAvCH36wxOPl+0izgwTeM5IHdlLebph1mm9uXXYZsGKFfa3sPXX1auCeeyxE4bvvAiedpBxEsaFKCEdCkmZkWMt//nng4Yel64vJ1aNmj/WMdPe7TXjjNmGCcPWva/DWSFZF3VVwyZsT/toHv8hOu0czJLnY/pfqG4CFiVk9VmnSLbBPYbYgpCQICwqAgdYQmLzV+YZ1orNJ+rImRRD+UdGEk+Os2K5qqsYFYfZrf/CQNsSc0YD0OytQ8U0UDj2YyisXM9ZIe4kb7q1ThqCrg47kYxf455/AdQ/pkb+B7qzM7T9mraXZz5l6hQhCsTJo+5SpQyMINYJQyfqh5XF/BDSC0P37SJOwnyKgEYRdZvc3N2AZbw/PwmIsxSzzb1JKW6VDRE2CkMTHKV4Ui+aDOtwz0xf/vcBySC0tBfbvB8gLxPXrgYsvlpaWuKNgrAZdQSBt3Gh/sSIXL27YDD7JhQhCkpZPgbC8vhzLGip6iqr7MxiGWl+UfhKN+i38lyZpxIAvGqpwWZjlEkwuc+RSQl6PRrMe+Y26pAW7vrK/xDOXAG5bIqc2oXaDcpmk5CYvxb29hQlLqfyO/N7XCELSR+NO7MQ/W20vkat3tOP8SQEwGOzR0Ou7EBAg320QKYlWr7XtSDvGZ1Iw6SKdxXV1+Gb8IHzWWIktbZxX5450eHdeKqWeCvXQFEHiO61srML+jlZM0YVjekgkdGnt0Bc5hidN3XxpSNwoL4m7/9/nZaF5r7CFL/E4RjyPkY8orQhJTfOpSRBeHx6PmRGOkdaMzMRN1/Tp4i1oMBgR5utL00yH0+S1t5pfcsf6+OHa8HgEEp/YCr9jHXrcUHaIWtGlpJq+RBASN6EP3uONkmW2D3WCI0xorvPmJfRuiUjEleFxuKUsD/kdwpa3bOyy/XV4PSIbubfVonSrzuwOdMCDZfAWMZAiOK7PtLdYyH6uCE27g1D2ue1rfqkzY2+sg1LKPSnlXNAgPSb8psBqSmLgVv0Shn23KntMIIQz2aXZDsyuD4/DzG4rCEac2k4DLuYQhE/HZmCKAEEohZ9QM++ITMIl3WdDuXOYqVNo/MktTyw9wZKMy++bavAq6+EMk0fOmD151z9YN2qsTXWz8TbehYSvPx4B6xCBa/b8gHsfexbDduzCltOnYeEj9+HXLBGTK6Yc2oNUd/oo1KAW0pY50w79i/U5oxTBX/HWAsTfyXH5yZQk9jJS7DIms53m6pjNgexhDm4UYgThaYd3wMR6KSeXIMzPB7KsBto2mBOxhZp+5pnAL7+Id5GjBKGiASCQSagdR45YjTsPHQI+/BAglpPXXUd3T+CWe8oD9Vj7or1VGxGL3MuWbl6PL7NsH8KQ37guIwPT25F6YxXyn7B3uSm1/00MDMUL8QMUwyeHICQ6kET75tjUrZaL0Rn7DmDVUIsb6GZTJ2aVHsKXqfxx2kkaoQdGjHBsgnDzmGFmXYWS7+hR4PHPG/Hxf+keApyUv8vmPCREEBLrx+YDgQjOaod/jO1lwxUEoVCsXQeXMyUQq5JHiV7Ek3W5qoCuFeKWCGgEoVt2iyaUJyDgyZsKOTDzuRVl9/seDEcu9pj/JHXYVTpeBvkFYpFAfBK5yojjC2Nx5AWrH40DBwASt4xxWxISApDg6rSfEGlF8pMLC7FUIe7rAukepJmrra0FiCuVCRNs3SfecguwaJG9ZOwHro89BpDLEfkvO+6D0KVqyBuFWHd7op0VobNeZJOD6rrMkebwH+zHuC8tbcfcmRbCIWNGIwq/tz+gGzu74OPN76bSmQddQpD4agSh4JQgiomnnwYO13Vgyw/yXXs1t3YhWCefICRuimgtDyvbDYj1p3f9JdTYcmMHduubke0fhAz/QNR3GnFhscU9mJqfHCWjmvXSljX8/aOKldW0dbDTPVJxVBYRe/i5RBS9b43nw1cnWTPIWst+nCAlW0lJG5KSdObHDUq5Nu8AE36srsdZFPG/pORh/75tG/D668Bnn/Hnmv5YHdY8rTx+rJgsZI0kM5i4EW41deKi4n1o716UzwuJwv3R/C+/adp3tKMNs8ryXEIQrlkDPPkkcOutFsWiO36L6spwSxS/Jo9ALnQmImuKnPPSUP8gRLyVhedYIb8Gv3IcCRfbuh1nY8QXf5D8TiwKmvbqUP6FLakwedde+IXzx9gh+XpjHZTCSIog9PLtwrSC3aoPneo1Ydh7i7oEIZ+QXMyFCMJQbx+0mkyYoAu1cQXLxY+MCUIME8Vo7NnCgbkvCo3BXQpdxTN1Ghu9sSnX1rJW7Y5gCMKZpQd5rUL5xizB8I+WeiT7BWCSznK2Nbti3bUPf4y3JfCIk0Klzyk+vnkWrlm0pKfJ8955CfNuu9+8LoudaRJEQg3w5SOeYxrAT56w00+891/89ZoyglC03x591BprgVyyiBtQ8tKHLN4JPI9uyML4f/auAzyqYgv/6Z30kEBCS+ihSu/VRlOkKYJYsYCKIggiiKIoWBBRsaAiPAUEBQWUJtKRHiAQQmjpvdfdZPd9Z5dNdve2uXd3Q5Cd73ufj+zMmTNn5k45/yn0MGrVSv5yoLYy4pDHdmoP14oK3tCqBoDw9Vfm4Z5ft5rwYh4eVi5ASHcZimojdNcRWgI0tO7dxcUiBhAKtbTVm0wqBYX8Cda3YDXyzaxU6VIoCBWp88G4HYvOxJIzUA5AyJLqgRVfl/Ig7H3mPA52iIZxyHM5cjOXvTFAqJQObRtk8L36Uh6ebMu9Jz/0sAabfjbdmXscugD3hjVWr3wAYXmyC06NiTKJuBL5Rioins7SDYOP30qNFkPMwlcbj1nuGHsdj8XhrtwYvrb6RpV+g6zt7AAhq6Ts9f7rErADhP/1GbaPr85K4E4GCF9x+Bgf41XJuTHkIWS57EoS46lgTYDQ/GI1aBBAIeaUFjGAkEBBMnRt317/vmQBCc0Tuq9fD1AIF6EHDP3dwEPHjkBMTM1I0tP1XnoUQoaAT6HSeUs88l5uAbKeo/LRR8AfD+kJ8eUTUiorakf5eeii2nVEOU5sNUVNxWRJbdNLKhHs4aQDCc2LLS+6OZVqBPEk8LJln4bxKbkIWzI/ctruLM7FmvxMbOkRiaIU5eDb8tgsOO4NRlmZXjHvw+gMKgfcyaushJ+TMqtSKZlIKZSl2vP9bolSQEl/ctv0T4hB7HNNkLNbH17aloVCRk2qMtrYGDpjUabT90sKNQrdzFoWLlRj/nwXCKUxYqETMKgQWbt9RJW2LHSE6rRakoRLs8VDMVlC37gtgYHDks5X/8nP0VnnIf51vmkO4pWhzdHSTVnO1quqMjxy9Dr+7a+3OJdTgu7LR/af0opsytH455/AqFE11N99F5g7V05vtVP3k5xkvBLE9Uag3q0JELZ388LysCjOoMTueZoKB+xvaZp/kAg0fCwLGpUjJ8RoxNRMRM4Rzld9K/ZBqf2cRTlni7tw9q56OP+07QFC8vjt4eGDNm76KA50/xmTYhpilPJTJlbqQ6a2dvXEF2E1rkvm8jOXl5BsnvILxURfZSFlDX0eG9oSpZdlWOMp+GT7xJ7Thfcdl3wBWTfzLBuTMV+zZZoq3G+0R071C8ME3xAdWO+YqsKeXtzUDQrYEmxCwJPYd0SekCN4wvsJEVyDR/EcvkQxuBe1eXgHi/BmdVN6G0oZmSoaa9++wP79+g2PQD9Dgr1evYDDh7kkMzKA+srWlq4PsgaixxRD+XH60/AoKcXY7/7HqW0AAZ3Uauxu3tXkd0sBQgJ4hBz1xTwIxX4zMEgAQ8raQNz4lD3iga3eR+2+u4rzT3K96iztjxUgtMb5YJCrYS+sLHJE3KuNEP31dc6aseQMHJEQi63NGZI7GukRxJY4M0C4KBmX5/HfUYh+r5Pnsb5dc0xKrclBzHKuCvFmDYBw3Tpg/HiADNKauXFDbVNo3OeeM+XA/P6yo1E7OMMBg43APUq1kbKaGz7VYBzFN+5fCrLwuUCanrIkF/zbV9jbkmGLqq5i6Tcjpy9r1lWiF7mTdbnWlL2dVt2SgB0grFvzYefmDpLAHX2oMN4GbQ0QRrq441sreRBacgnlW/bkbUjv0bvvFv8oVq7Ugx9SRUl0HClgjd7F9D6WUwxh/K59FIobnyl8WPN02H7tFcRMjBS11hRadu0WpWL7q4GI8OCGNrTlRTerUoUQF65nnC37NIhOyUVYzjwrrWvwnCs46YnTDwnENVJAvH1XDWKOsdnPZ2WJp6Ix7j5VVYEwmVbyrOxLKQxY6RjqWTP0pNy+WevTmGn984UTZKHh07EERWfEcwF13R2HXf2j4OvsLMvzifrXaoB9zbihDo15Y1GM8Y2F2q1YAUyfzjJSbp32a64g5lGJZC/KSOtaDbgag32R/GO39p41N/MajpiF2K3v5IIMHsV5kJMLfgkXVmyQl83HOcm6fGgjfAKrww1eUZUhikdhwyKiDj8noOGaSGzfLu6hTJ6DfGe4teXFwrNYnX/+AcY9o0bWZX6DDDGAcFZgBJbkJJmQF8qNR5U6u3vj41DuOhUDv8zzLxk6I6+60LG5HIBQt16vC4P/lihHlcpaaj8/fm8LlMRxFYjG/dkCIGQJMfr55wCFTzY3euuy4xK8W5bLEsk3YS10OQmzK9UYawYQmhOaH9QYA730QLyx/BLeboDk70xD4UbOS0HEU9m8vCiZbwoBP+imQtba93s+Jrtsv4Tj97XEU6mXcEXNlennoVHV4Oqh0gLMy+Iq/bdFROsMK5wSVdjdjwsQXkArtEGN8lzWxJlVJuBpQVBj9PKsh3sS9dFeqGwKb4MAJ33O0r1N2L38PFGiA/3KwDX4MAcEbQYQ6g55LXDqFHCXaYhWXlkpeQQZCJH1iCt7ZIyP3puHQn8/LHxuJocVYxCQPAQXP/EibkQ1xeKPF0Frhu7J9SBUCgIqvQdJrUlbnZ1C9xtL+zN/d7YfVoaYrab7vEarNQF/+GQgZw8ynBMEvhbHeqDl4ppc3wbaSvZEQ9t7Yi5iZ0c2wyoW+TGqhNBcCiA8HgvXYOlQm1JrzPC7NQBCAw2hiEF8AKH5/eXrsOYId3YzMQgRWg8+HUpw15YEXg9CsftD6s8BiJ+jPCKHQWbkNXz0KKuE61Y9JXqRO1qXW7emz86NFSVgBwitKEw7KbsE5Ejgjj5UGG+D72M25uB9m4UYrcsAoZy1pPQCLgUA0u/kZdOokRxuxOt2/u0yTo2OArTyQz+KUXYNViM2uRLNzRW+Dlos/DMf9TwcMKO/sMfHibwSdPEXzk9oPQnUUMpSqxHCE56SZT4t5UfJRdjSPlnav5N1A3+X5iPzDz9cmN6YpQlznT1/azFooPS6o3AwEYzvpAqNBq5SieuYOTStyBeCTSEpXbPN4W3hayNvR0v4Mm5rUALLUYYYt49akIKEhQ0F2al3Vwk6b0pgZvf9kKaYn3UdKqOPUoq33r0B8tiWWygsdWs2nQsv6dfTrmFxqDJPIBZeKfTd6ob8odSsvWdJgSnm/G6PiIaHo2l+UkOd59LiEWeUH+/7sJa6ML4JqjLueSEmCActGk7ORr2Opaj/YD7eCGoEryJX9AoTBqTJk5Q8ks2LteXFMn9CdZYvB156SZyCGEDI19LX0QmbwtvyhrPq6u6DpaFcT43+V2LgwD+FqCp11HlXmRcKqxs6Jhep/+Na0tcFgFCl1eB4WREqtVq8lX1DVMgxjzZF3kHxHEU2AQj/8kXss004vH31FbB5sz6U/Zw5wAMPAJSb2rzI5Yk8SD8NjUJWpRrjJABC6osvvJrQHizEiwGUFJqA7cU5WJqjV6I/4RuKEGcXHZA97mbIP6k935Lvz9CWIm6cHNlClzfrGg9ASDeXt4Ibo5Wrp2Aowgn1grGuMAuaQ5XYN5GbIzAHAQgEz4akYAAGUCrM2RVplSoTCoaww3IAQgL9XKCCCqaGep9hGl7EZ1iH8RiPDbp+qO43eApPYZUCziWakBs/xdWWss60fs+iFN9d9i72jLwPfzfrbFLv8Z0bcb0F1yNbiNjtABBGRgJXrnBH8McfwPDhthG8UNhMS8/q4EGFyN5bs693eTUHxz80DYlNubiFPLsMo5WzBzWbkwq/7sU49UALQf3J1ohoaAH8kJ+OvKpKXS5hMtxgKYE9i5F7VCR80E0iPqGVKEyTjrDCpxLq1w94+GFT77pms1Nx9YOaVC7mvPb8NxZu9a0PEPa9cA4H2yoLMZ2sqtCFgNbtWzzPTzIKnDZN/FxdGtIMTV3cTTzuxdZDs7mpuPoeV06G85EMHemNnfRNsGhOdZa1YF7n9GmAok7djkWJXuSO1uXejpNs55lJAnaAkElM9kp2CVhfAnf0ocIIEJLUvVGETueuwdmHMmhYt9gBQr08haaD3siUr/B2KYPjz2FPi5pLvHfbUhTHsoWf25aaj2ENuACipY9DPtlRvjlSjtXTOqOJZ+16LRr4UXIRro11QI/0nL99cO4JrgLZGv2zzCdZP/bsKd0bea2eeaQZHGTsZ9JUTWuQZfE3+WnYVpyLsT7B+K4gXS6J6vqWWAwr7lRmQ2sAhCEj8nFiWAuoMky9oXy7F6PDj1fh6EZqEbZCoX1OlhVjbtbNOMkCuT3YqNmuVqeNlzGinwsWBnMV/dbqlQDrQGdhDzMl/aSoK3TK5bZuntUAn7HnDivNGQENMdKHCxIRODMr86oJmb4evng7pAkuq0rRQkZ40rDxOWj5gakl/qf1I/Fi+hVAA+T/642YR9g8OFn2IdaxW1qPZfvSaGq8qVj7E8pNGF3ohxXtucYfHX9JgF/XEl7ylcWOOBjNAxB6VCF0TB5S19Q9gJA8BsTy/ZgPlEUBLBeMY5mrg53aojKPq8Q1X6OEl+zaxaWohCdaGxRBwQDAifG5ukFLNHJxN/EglAsQEn2h8++GuhxTUi+JioplblhkLVan47oEfDYiGB/kJKFYwx92ksLMVerU+vxlZkA4PsxNRkxkBHKrTEEIamFJHkLzHs1DVxr/rvv2KQRp007MYiHQb+3qSkx8zPSMGYNfsAlj0A5ncRZ6D3aq2wg3cAM2OO/eegvo3BkYOZKZ99qoOPfbT3FkSH9EXLmOHwc/oOvy83mvYuNTk2R1fzsAhN98AyxZAlAecuNy4gSbY6csgdysvKEwE4v/LMSZCTVg67Hr5eja2LLQwg6OWhODWO92pSiM8dC9G/Kq1EhWq/BiBr/RGuU+Vec7wz1cJRm5gjNmJy1Q5SAIEC4Mboz9JQXYU5qva+rm4IAt4dFwE4ona9SBs3cVqkoErHnMGGG564hFOOrVT4MjB/TRXxq/nI4by4TD0fY4cgHuYTW5+6iNJXs38U5vsC6/JuD0GPGINgSMdeLZ7vpfi6nmgW+cFy4AbQQCYLg1VOGuP+IxsXEA7vX2x5Np8To5lKe44Ghv+eFADWf1pbkNkfYT986k5Lsxb0PjscTQ0Ro8KKWhRC9yR+tylQra3q7OS8AOENb5KbIzWBsSSExMxPLly7Ft2zbQ/3dzc0NUVBTGjRuH559/Hp6ebCCDHF7v6EOFRSN1U5iv4kOsqP8iOqy5Aq8W+twk1irNXNyxqo6GGJUzRqUX8OPHgS5dgMmTgTVr5PRYN+t6R5cqtoZbeDEVC1pzLe5YZCtHGivzUrG+UJ9EXChsmrX75ONPyUVYzjiV1u19+jwOd45W2lyyHeVTMWw/JOcPPwTobzNnAk4337v0X/qbWOm29yI8m6pEc/BIMqOgAqtnFXk3fZSTjHIyFQXwemAE7vEOUNBj7TaxBkAY/nhNmLnS667I/ssXXq3KETigSNZgOrnpQyGS989Qo9wfligcZDEgozIpIUb6BOKVQOH8LDLICVYlD/DTo7mKknPngGiZn+22ohydMttQNjRsg2BnFxwoLdB7bWY7I/b5xig45o2wh3N4w2QZ2kY4u+FHHu/GsckXkG0WltQwr5cqStHKnf1uxweEkGfS0zeVNvnHvXBmLJsnR23s8VLzTTxcvw40Y7DF2FWch3ezE6VIVv/e3NUDX4e14A3h6/dXGDY/G8Kh5d64Aj328Yc/VBc44lAHLkDo5F2F0IfyeHPx3GoPwhNlRXjNDJwWEyDLvqIEjDP0SV6Yx4a0REWqPqxhr1PnocpywYl7WvKyZb5Gp0wBVq/mVlXCEwFImZUqQU84814CnJyRW1XjGaIEIPy7UXvdeUge/05GbxCpM9Wa+bJb4wL+h4mIQBI+wqt4H3Oqh9p25TUE31vI/I3R/CR+EYK09QHwalGu2x+fjgrE6oIMnGvSENkwDcHKTJixohRAOGnv71gzyCj5qgRdAv1SkrUoCG+D1jfDoJbBHcHIQgn03kqGvIOG9BM2yUM4ZAgwYoS0WzWjnKxV7e5Lx6B2Yw9JatwvhX19KFmf79PaAOFrr+nv0ebFkhCjH3+sz3tvvt+cPKnHbm1RDAYd6gInlCe6wqNpBf5pE22yVyjpl0/d8VTKJcwMjMCz6WYIqFEH2bvr4fxTlkeEENqfJ/vWx48FpjlCnvdvgLH1pPcNzwgVypLZ1iLLXUcMIBz3aBV++Z/+cdb4xXTcWC4CEB68APdw6wGExVVVGJ50Hvn/euHMePG7ndB67/A/MiTVG47xjZMclv0k0lkbz2HcrHCkb+Aaf7CsTaJDfCpN4cDSx6VLQIsWLDXrXh0lepE7Wpdb96bQzpGVJGAHCK0kSDuZ21cCBApOnDgRBQUFvINo2bIltm/fjmYsWhQZYrijDxUZAKHuUgUtgu7JR/RX4iGaZIhfV9UaAGH+CU/4dSm1yEpNLt98DzEpGnwipxBSzzwDTJoErF0rReG//Xv0t9d4H2Msjxs5kulx4CLi5zWEKtMFzWal8XrKWbtPPv6UXITljFNpXd/OJSg8LZ5DTiltavfJJ8DLLwNFRUA9s2huY8boQ6pVmkao4e3O8GCrba88KWWmgVnii7yz9pcWoKmrO3p4iIeus0Sm1mxrbYBQKW8P+gTixYAasM1Y7iyKfKX9Ujv/PkXIO+gjiwStRy8HR2xtpCwMEmtnBBTd7ePPqd50Vhr+fsdfF7qTtUit5SO9W6MipUYJFTE1E5Fz0gTJ832LfH0oAQgbT89A01e53rujvAOxpThHx5OmwgH7W7ZnGn5t7PFijFCOYx8ZS6z7vovwaGwaRlCM/uO+oZjsV58XIBT7foSUmep8JxzqyEWgnetVov7oPKT8wFVq3mqA8KX0BJyt4PeINMiO1uLpimLdP83l0rYtEBtrKmUlYJxubaqB/c3Fc6eaz6f5GiWvHrovmhclPNG3mlGpwoSbITyZPhqjSkJrqPWnN1B/lN4jRqysa9ga9Z31e4vUPhT7QmNkbZPQ4Ep1ePN3c0CrC36c6PYAACAASURBVI7jJPShQIPuzUf0SvY3TuFZD5waWaOJDX8iC1HzU6GpBC5GhSET4jm+cx54AoGbv2PknFtNDCCk2g999z9Me3spE/1ERKAxEpGeDjwaugtrMAneKMYMfIJv8XQ1jVoBCKm39u2Bs2eZeK+tSlLyFuKDzqiXA8Or17lfcjZ+6zPEpHpf7MdB9OUlIQb00W/Z2UCw2fbr5QXQGSPzqV/d/9y5QKtWeuNV41JRIStto+ypoYgDn+WmQAMtpgc0RHcr3Jv5ZMCyZ1rrninU16sB4fjIyEBLtwdJ5HM2CNS3SwkKT7K901juOmIA4dPPavDtV3oPwkbT0pG4Qhgg7L7/Ijwamd5TLJHjT/mZ+Do/DXEzI5C+UdzAUug7CX8qE0nf6A2i+MZJe16o8JB07QxzWBznjhP38hv0sCx2oiPnnspC07wOhQa2srpUCRuK2ijRi9zRulxFUrY3uh0kYAcIb4dZsvNoMwnExMSgV69eKC0thbe3N+bMmYOBAweCDol169bhG3oRgy6qrXD8+HFdHWuVO/pQkflqMFiLSl2qk74NwrWPQuESUIU2y2/A965S0elq4uKO7y3wICxPdcHxu1ui7/nzdR4gpJCJ5omjyWvw0UeBceOAX36x1squK3QoDJN0vjkpbkurqgRzW0m1Nf89v6oSIfW1UOfwh+kz1Gd5UMnt27y+kouwpX2ytJe5NbCQ5NSxxLLZQOxWAYSLsm5UhwQSG3xtA5eKJoKnkUFZe/zeFiiJY8uHYkym+cJkNHxMD9hYUszlZ6xEzj3gjbOT2EJJKuGBFMXZf8lTSNfmeuT7Rg3gnZx1J6WY51Ps9DoRC9cgfgRfLkAYW1aKaMboEGL58YznmFUZZctQaSxrbsAAYN8+lpr6OsHD8tH2c3bw4jn/MIyrF8IBX0qvueLYQOFEm0J3PFWuE69nuUtAJUJG5SHl+7oHEEqtb5Lrk36hWJWvB55Z1g7rOjSf2QPtolFVxBYSTugeQhEn+ELOS93L+VYZfasUav1hKwOE1BcLP61dPfFFmN4TWmqeWObFMEZSjlL+NL5SpaqCo6tpKNeVmIrnsFJX3atlGbru0IeQYylnHm6G/COmKD+NvfSqKxIH+SENwrm6iH5Guhb1Q5XfkaUAKzn5Bw+gD/rhALKyuGCTsSzMAcIv8SyexVcs4rrt64jJe0ZAOLYW5+CyUa5dw4DfCW6CPp6+1evc91oONg8cbCIPSwBCAu3czeyCyNBu1CjlAOE//wC9enHBwNp4G1l7odRVgHBOYAQW5yRxhit1h6MQ8N4tK1B6mc0YjGXOxABCYw9VJ58q0XOs2z8X4dnEegCh4SyROgNmvKbBx0sc4eCsD+tqXEIbapGWrP8b3zirqmqi1witPQMf555qgpzdvoqXKNG5vjwE1z8OU0xDqiFFpWjMjSAv1axO/K5EL3JH63LrxKzZmbCFBOwAoS2kaqd520iAwMB//vkHzs7O2L9/P3qaJZ5aunQpZs2apRvPwoULMX/+fKuN7U4+VCq2bIHbA/ocCiyFBSBUZTnjcLc21bH+fbsVo9MGniznRh36ODqho5s3MqtUGFcvGIO8ajwjpJQGRObkqCiUXnG/5QAheTxRWET6r4sR9vTFFzUJvkNCoHt8mxe6vFOqDUr+bi9cCWxLKMb9kZYZBtCD6vlrV/F126bQlOstIcXKipwU3OXhY1OvLyUXYSm+xX6nPD8/F2TCy9EJFNrG14k/cb0dIBSX8u9FOfjEzOrXvMWeRu3hWBuCtGRBCLQ17LsmHi+Uw0Ujrch0dNegX9w5i7mi9fm4n6lJ74WKEryQXpMnZl+LdtCqpL9lJcz4dilGwQn2PSegfyHar9bnSJRS7ijhx7xN5yWpOD2bq3zufzUG/zRl91LiO2Mpt8rV98NQVe6InF1cRYhbAxXIm82Rx8aCFSDs6OaF93yj0O/uKpw4KA6aUF9/9W6OB5PNXLkEBCmlSDI0o3M6Ph5oYoMUWixzrGR7YAFeDH1P82+Ah+oFm4AvsdMaIWsr1/vUmF9BgDDbGYe7tOUMzTVYDco5mvzd7QkQPuMXpvNQoMKydrrujoNXlLxQ+xWZzjjSjSs7sXXSvz9ASnrzwrduuvx1Cd6tylmWXXWdnY3a4a2sGzhcxh5S07gDKVmFPJCH1p8kinowGfYLqbu+VF/GfBkbHzlDjRexHE1wHV9hKs6PmMu5aF9CC7SCPv+ha301ev2rDwNJkcGvvNsAxRfd0WJRMjybcb13TwxvzgmpT9/PxVci4PlrPq5C3IglP08LP3/pc1VoYpd+sADbxz+o+7lJfAKeWvIZfntsAk721SdwlgMQ3kAjNMEN5OYCASKOOuYAYQOkYB/6Iwribz1Zi7OOVhYDCA13Pr61/F5wU/T0rFfjQXgjG7/1Z/MgpCgbFFyJ77vv0wc4cEAvrI4d9SFBqdDZRhE63Nyg8wgNU4BFGN60cXHA4sX6NbFwITfqRx2dKhO2lACEp8dHouBf9jugmBzknNu677ax+B0utqIEnRu7cnJ8v/gisHw5lxNLAUJKCUpzz1K67YmDZ2TN+WhpOE1WgHDpUi1mznSAX3glClKE8/kKAaEb/6nA2IFugkPssuMSvFuWM90RxOREORqP9pSfu5BF9oY6yclAw4ZyWtSdukr0IneyLrfuzJydE2tLwA4QWluidnq3jQTII7DbTXPYqVOnYuVKvRWncdFoNIiOjsbFixfh7++PjIwMuBgjMBaM9k4+VOgQPrV0KXovWMAkQRaA8PpnIbj+kelLRO7FeGPDNgh01msepZQGVIcUB2TR1vfcrfUgpEg47doB48cDGzaYipR+o78tWsQvarpA33MPsHMn01TcFpU8mpSj7DqbdaPUgN7ZXIh5oywLz0hrSY6SqV9CDBydgQ9DmumAQlsUJRdhpXwYcnsY2gvlC8vLE1cOKe3fvN3t7EEolbdpS3hb1BMAX60lP1vSMd93K4sc4eiq5Q3dWP/BXGT8VqNNDL4vH22/ZPdyEhrH7+Ft4cMjw/eyE7GrJK+6GYXqgaNWdug+Kfl5NK5A2Q1hZYGhvWdkOVyCKtHyveRqpYiUckeqb5bfj+YXoac/d1+iMOCZf/rCgQF9iq8oxdT0y8jZ64O4VxuhwaRsNJ2RwbRPtvo4EaGja+bBwLNh7OsLMrHyJujCN54Gzq7otjsSL0+RzqEj9w4hZ58n7/1blfuXYYo4opMji8dU4YhbFYgNmjSEP5kFRzctU94boT7Kklzwb1+uYotAFVWGsEc+n0cBDaw2vhOpOyStw+HegbIAQtYQmsaTd+X9MCSt5OZ8FPvWjXP1GtcTWjdtvriOkPv50zTw9fNmUCO8IyOnpTkNlu+M1l3Um6mCw9wc3lZnqCQ1Tyx9USfklbt3bw2Y8j5mYzaWSG6pi/AG3oT+gm5Y/4fuamMSaaLP2XNwrmeaGPnkyOYoOmuaQ5XaE7+xaIM2uCjad0mxFl7eygFCIj44/jh6/nMQi56ZUd3XpikPY8Vbs2UBhKOxCb9htA6M8hVxkOmDAziAfrq0E4bihnI8i5VYhhoeJIV+G1aQyvlIQ+Jby0tDmqGLhw+uq8rxeNol+F7JwebBlnsQbt8O3HefXpDkXfzcc0BpKbBkCTB8eI2AlZw1LKDS7TKFcgFCVY4TDt8lM6GziDDMQTMpuRnORjJs3VOaj0R1BYZ4+aGRi/5Nva8kH4NCfKApNTWu+vZb4KmnuNRZ5jIlBQg3Sp+dlFTzb8pvSV6ELKXrrjh4Na8BCEuvuOHY4FYsTXnrsAKEGzcCDz0ETJ5ThjXvm0Y+eXiiBj+t1RsTCgGEhy9VoHcr4Tt/6+U3UH9kPtP9WGyw7hEVKE+SflsoFhiAtDTpkKmW0LdlWyV6kTtZl2vLubDTvrUSsAOEt1b+9t5voQTeeOMNvPfeezoOjh49iu7du/Ny8/777+tCj1LZuXMnhg4dahWu7+RDRXcI79iBUQ/qrU+lihhAWJ7mgoS3GiB7BzckmxyFFvEw1icYzwfoPSOklAZUhx7izvWq0OfsrQUIP/gAIEdXpQ8xJe2k5uxW/k6Kc/LstEaZ+E4R1s7TK8N/KczCF3mpcHdwxMbwNjpvOENJVldgXtY1OMMBy0Kj4G30W+NpGUj8XDwfjDGvDR/PQuiYXDRrX4UN4bax9lNyEVYqzz0leVhkpgz8u1F7DpDw0+YqTHxQXhg0JTxRpGjKj6K0uIaq0OuoXvlWG4pmcz5X5qVifWEWSFJVRj/Wc3QCKT1ZABqlY7d1O6F9N+61CKT/UgMGRr6Rioins3TeEhm/6v8ud7/nG8sTvqGY5Cf8rfLxx6pAZpWdk2cVqsyUL3xt+cZbG+uxUquFiyO/YnlrYQ6G+QRKDvX3omy8uaXAJFSro5sGmgo2r8zAIQVo+ko6vNvUeC7tatQepZoqjGLw9jvcvY0osEQDaDQtA81mcvMOig1OzlqgUIQJNU6pkjKzZgUlZz7L95X1Vz3cWFGf49kUveoqzj/ZTHIIfH1UljjiaO/WqMznWubTXqxKFwd6+8Seg7OXKbiyoWFrBN/MQSfJlMIKUndI8mS9rCpH+nkX3fd+ZmyUSU+Ux6vELIVhl+2XTNY8C2ty1qSBnpBSV2zdtFySBDLScPYxlTULj3LrsI6JZc1K9c3XF+VcM4/I8eWXwLPP1tzDzfMNivUzAr9jK0ZUn2HmfQbdl49oM+MXimBSFGOaB8wAEDL1bQ1LKYFBEZglx4MwENnIRaDuXiaVyYPGthqTMQWrTXpnGrPUZNfh35UChBRiNONPX/z0E9C4gxp5EWfw/RPdTEaqJMQoGfT5MURCV3LWsIBKdXiqTFiTCxAmrw5EwgIjtMzCgRIo1ONAXHVoSQoXTilYHASeWoY75MbCLHyepzewoDfvTw1bwd/JBb8VZWN0vSAOV0LhgVnnsqwMIKCQvM88jDA22leff55NCGSw3emXBJCRnZOHFmkb/HFpViO2xjy1WAFCAsaJ50038jGmielHcSleixbNhUOMknwOX1Chd1vhe0zw/fk6j3jWHNeKB2yFhrQOgrjLwwqUbU9CiV7kTtbl2n5G7D3cKgnYAcJbJXl7v7dcAv369cOBAwfg5eWF/Px8XZhRvnLkyBFdnkIqFGKUQo1ao9zJh4rhEPZMT8fAv/+Gk6srHNauFRSrASDkS0Ad+0JjZG3jf6XIVQ54OjhiW6N2Oj6klDtURw8QVqLP2ViLLbssWVP16+tDuSh9iClpZwm/t1Pbh+YXYeNCHxhCDJKy0tFZq/OIMDykKjQa3Jt0Dhlb/FAU44kmL6djX3S0Lswjec85CyjTxeTg4KJBp40JODmyhU3EpeQirJQRvm9pe0Q0J7fjxLeK8dNC64TVUcorSzvydmrxToquam0AMnw85VSq4eLggIuqUnyemwpnBwfMCGiIdu51X35iMhbad8tuuOLsY81Qdt0NPh1LdCE1XXyrQB6GB8l92koAIYFMJEuhcl/iOZRT/DejUnjGA6cesM13KiarWwUQEk9CIiKeWL4JyqX5Zoj1EpV02nRZMuewsSzNPXT45FzvrhJ03iQPwbuyOAxJX7F7a+Xk1I7XtPH41q8HJkxg2elM6/S/FiN6x1DnO+FQR8s8H/jW9PVP6+P6J6Yhfw2cuQSooc4Vz+nb8LFsNF+o368NhXJ2jWQAsuVLCdBotbpcYM+mXxZt3tndGx+HCoeBJI8cUo6al3bfX0XgwCJm1ljBNGOCQkpdoVD1xm37XjyrU87aqpiEn5boRO4bwJyc8fkiNR4K00+eU4a9US5gRe8c4lejcsD+Fu053ZmPZV/zdtCqTQ0q+l06q1MiS/V9FU3RTHtV2aNBShAA7ok7ih2tejDU1FdxRQXUcAWBBMbgAB8BGlsVHOFsYh4FyTEzM1MHKxb61sOomP3o41EPB3nC8oqFyw2J98eGu2tAknH35WD9n6Ya/NXvH8OU17vyjlwIR2YFfigX4e+/yxMqK215VG9NbbkA4bVP6uPGp/znndIRhI7LQfqGGsMtsZzCQmuJ0rA8598AE5IvYH0E13CVvM4deey7LJ3LH38EHntM6cgta8cKEBrGWFRViS7vZCB+oT7G5uC1Sdg9MaKaifRMLcLq17wvfv0VIDv5346WYXRP8Zzrfr2KkH/YNhGFLJOSaWsKE+0vHknemt1ZlZYSvcidrMu1qvDtxOqUBOwAYZ2aDjsztSmB4OBgZGdno0OHDjhz5oxg13l5eQi4mRRh7Nix2GAew1Eh03fyocJ7CIsoZQ0AoXGuJYPYxZQfSpQDrHlJqH/q29GjCk1fTceVRbc26LpSY2Cl7RQu+9uu2fBZRfjjAx+d4mafUX6t9j9eQcwkvXKPQI2j/Vuh3Cgs4Fdp6XgmNBRlVRp4OrN5xZgLh4CQwtOmFuLWEqCSi7DSvvlAH+Nwvga6078qxIpnLQvnqpRHOe36xp2Fk7teAcoChsihfafXFTPMqCp3gDrPGa5BapMcdIUxHkheFYw2yxMtFp/UfL6ReY03b1bmVl9cmCacUI7ApsKT1v2Wzc+3aDdPfBba3GIZsBCwFCA8WFqAvl4iseRYmDCrI+e8ZwVN5NAkduTmvHn4Yeg8O2qzKDUIIiv2tl/cgLYKKDrvAdeQSriHqatZv7ywAVK+5+YClDM2PhBSbK48m5ej9LJ0tADzebzXyx+zg5R7FgiNicDBwYlnmYYcfCwEv4wTTtDVt29Nji9zgm0+v46QYTUhPclmgYzlcvbUQ9edl+DZpCZnHetaN/SxejUweTL/EHbsAO69V3x4SjxvmQR2s1LBKU+cHs22z7VdeR3B97KHPjXng0V2azERE3HzI961Cw5DKb+bFlrIu/cZAMLc/d44O5kLHBuvYaGwsR1+TkDsw411YJtYWYEXME27wmYAoZz5pLqGN55KBbjysE4AAa1L3R6Lm944RmFG6e+DsRu7wY3w0xRXcQ3S3styebZW/cOD+6HXnv2i5MYd/gtZDUIhFJpX7N0a2zcaWUmm7mL70A/9oE8geB5tcXj7cUy9nwtQtG4NXLig9wgiYxbjIgf4oRQWBBxQCN6vv5aWnBza0tRubY0Jb5Rg/Xs1dz+XQDV6n9TnGi1PddGdXz7tS6GtdMDhrvJyxVoyMqG7zXdhLdHU1Z3XSJrWmVDKDEuBZKGxEIhG4TtvRZECCD2aVug8Fg/dVTNvFGnoy5xUeDs7YW5QI/TwMH3TkpwuXQICAwHyRKcSn6NCyyDpkPe3QgbmfdL9Uez7ZPUsrgtjMedBiV7kTtbl1sU5tPNkHQnYAULryNFO5TaTQHl5OTxumikOGzYMW7duFR2Bt7c3SkpK0KNHD5BHIUuhQ0OspKWlVedAjI+PR7hxAHaWDm7jOiT//fv1DyLy5HR3d4eHp2kuDePhGeebML/Uij3g+12OMVEks4hse4jeE+T+zHjJ6izKA0kiVqpQWloGT09xCzS+rkpKytChgxsSEuQpM4TY9mpdhpKL8vmwkhisToYsL6/+4ImOn2Yjfk6NJSB1RDKncm/SZd7QH6nFRfglLx8vRZi2k8OkoQ85bVjq8n2DLO2U1OH7lr4NaALKwWQo9ODw8pJeN1FvpSDhLWEw3hZAjPGYm81ORaPnsqr/ZNgvlMjF3oYrgZGZ8ai8hYKRms+PCtOxp7yQl8Pry+rj+jJ+y2/ycDv9EJtSm3X45mfh5uAouDpYZx+X4kHorCGepGRItM+qStHDTzoUqRQfxr+T57ZPh1IEDCiS9KZnObvdwyvQ42CcHBZ0dVloGxO11R7Px3hMjAN69pQG1IQG3f9KDM48HImCY3pPZQodGjRY780md9x8fXQ/cBEeETXglpA3la6tgxYtFicj/nXp85VPGbotuLnVwzEvL8zAX+VsgJSUvL77ToUnnhBWGhqPyZxW7zPn4eKnD0At1Y/5PBQWlkEgoIouBFzz5tLntFxgnfUjo5QCR3vKC7vOF2KWtT8p2bXDWZxFBxNy9F7phUM4hD6s3eiX800PwoKTnrxnhUGmZChzoBXXw5BotP3qGlpOPYVNGCPa91LMxLTSt+HWtSscY2Nl8WmNynnwgz/yUQ43RCEBKQjHlCmVWLFCDW9v7vrKzCxDSIj+7zsxFEOx2yQPIf3dH7m6MKXmheQq5VFpjTEZaPwzbCgGbNvFTHLKrk34Yag4AjI0/jgqXV3wtm9DzC8w9YamjsTerXxrmEK6voF34QI13sNczN/qheeG8xvs0PlESv+GDWvm5ZlnKrFsWY1xCPNgAWzZ4ogrVxzxww9Ogu/O2jwT5fCupO6LK1T4dlaNbH27FaPThisoPOuBMxMiObn8lPShpI3YHk3rie/dNsUrCD+UZPOeKXw6iCVLVJg2zTgRgnxOd+92xMiRts2bJ8QVyUjI6Msgv3oOjlgXbBoenCIHUXFitMYqUmlQ38+6BoTyJW3aIjJSo/tOjUvv3lWYOrUKL7zggqIi/kgrOTllkl7glvJmq/ZK9CKk623RQq83TEpKuqN0ubaaBzvdWy8BO0B46+fAzsEtkEBWVhZCKFYOgPHjx2PdunWiXNSvXx+ZmZmIjo7GuXPnmDiWkwfq22+/RdDtGrSbSRrSlUY98IBgJWOAkB7AQXcXVisAxR7wlKeo3bfXpTs3qvHyGX39ZR1rvEFyD3jrciU1fjEdTV/JqK4tpTyQ1fEtquzuXonycn14Xf++Rcg7YFkIi7v+iMfJEZaH22vWLF83x1euMCS5MJOdb9diFBy3XqjFzZu34IEHRnFmiP5O5ckVA5Czm/u4Jm8IVaYzjnRXbhVq6OMWLQ+rdGv8LRkIeqsqUeyqX3fd0/OxJzEMJ8ZJr5vob65BU+6IC9O54Qmbv5OMkOH5ONTJsjB35oOOnJeCiKeyObIgvnum51tFRnYiegmsahOOopvr4lbIxLD/C/W9v4E/ToXwK9LEwAwCVijnS0WmMwjDO9xF+Z5g4M1YwROdXYQhyWYm/jYUIN9+SN0RT1IypHqng3ywsM8gm3BI+3+nX66I0mY5u8MezkHLxeKGXnydsNA2bvfzz1vh4WGZEo30UenpXvD2VsHHh19xKzRnciah5ZJETk4fKSt7OfS77o6DV1RFdRMK6X2wrT6EMF8JGZWHzC3s8az8ehah489XdaRGJ6SjUXFNDks5fArV5Tvr+OqyhCX+6KN/8OqrAwTZEgMIDd8i/VfOehw8+AamTxeOqKJSOWLcuBGSomIFCPP/9cLl+Q2hrXJA1IIUBPQVTw4sZywGJlu8m4QGE3MleTavkLnNFxdeEPYKp/p8wFOTgAx0zj2IXyHP7YXCbDaal4WKdBckf8sNU0z3kPDHs5F/zAsxD5sqpA286yJbTOb/zXh8y/ASmm4eCEe1GiPGjpUtG0sbbMYoPIjNJmQMd12hu3ZFhRPGjx+OxriOT/ESHoD+/m1czOdjC0bq6tUmQPjDS1Mx5dOvmEUkla/xRmRTTNnzG4LKVHg4PhWfdeCuSb53q4EBlm/m5Y/+xbJXu/PybJiXggJXnDoVgvDwYjRvbvm9d8OGFvjpp9acPgMDy7Bq1U5m+dX1it8fbIEtH9aMs17nEnT+NQGnRkeh8NStA4V6HLlgEgHAWI7Pn72BL9oLh4DnW1O0Tl5+eQCuX6+5H2/atAVOFqaVv3AhAHPn9rXaNDs7a1BZyWZIR+dY7kFvnH2U69E96OoZdMguRP/UPIt5q9QCYx7k6hgsJmwBgTfeOIp33zUNE23YC2JigrFggT71knn5L+gs5IiNItE99dRTuiZ2gFCO5Ox167IE7ABhXZ4dO282kwBt4o0a6cMLTZo0CT9SkHORQnWpTWRkJBIS2HLS2AFCedPHChAS1SYz0tHkpQwUx7njxL0tRTtquTQRoaPzBBNymzc2f2jxJQy3pkJMnpRsWNtBi37xZ7G/uak1tNweSTYsD1IpunTJjI/3x6xZ/aSqcn73jCpHaYJyLwm+Cy+f0uK3zVt0wY7EFK+OnlUWWYj+Fy7bpDTVVDgga4cvnH2qeD18WNdMu++uInAQv8eKITwdKy3WhdX9wAV4RJgq3TtkFWJASu7NYFeslOz1pCTwvxZhyPK8NdbCky6mILBC3Cq+yMUJq9oKeywJrT05nu98MnLyqkJVSY2mJXR8Dlp9UANeDUzKQYcc9rxkUvMg9bvQntfrRCxmJUvfkX5uEYb1bbpJdaP4914nz8M1kB90oxCZ+yKlzznvdqXo8od4Hjk+BvOPeuHMBGklvaHtvHlH0KVLpuKxUu6fxYu74fjxMHh6qjF79nF06FDj5UyES0ud8cgjw6r7oBy3jadl6u5RRbHuODlM/B4lxlyfc+fg7KOxyrlvnme6qtQRB9oIA4Q+7UtQdFaekrXz7/Go174MAeUqTI5LVSx3voasACHLGbVx4+8YM2akIH8U8pU8KB3dNLweZUruqf37J2HGjFOiMmEBmlkAQgK1/6XQ7In6/d41WI2eRy+Y3NUJxP01qsYrm0Vu5syT4VuHNXpQmLWIeemtWLEH06YN1pESAp6G4w9shTSQasxPQyQjFeKpCshLWshLnWg1DruB62nioKZuP4AHdm3+Wde92NuLVV5y61FY1kkwzTtvuOu+/XYPnDpVv5rkO+8cQrt2egMtqbW3DuMxHhuq25JXYT78MRvv433Mkcumovor57yMZxcvY25LAOGGHncjON30DFj6/nzkBgfh6GD9G6hzZgH6peaZGK/S30NLyjHhcrquDt/+w/LNCAGE7dtn4e23DzOPRU7F5GTv6u/IvN1/4d1jGNOaw82xaUmN1zOFE73r98tWOS/lyNu8bsjIPMGw/A9cycDmyJpv0LytEEBYWemAP/9sisJCV9x333UEBFhugHP1aj288spAS4Zq0nbRooOYN4/Nu7vLjks4cQ//3ciaa5T8DR/kMUK22qBlEho142WVQwAAIABJREFUKgGPPRaLN9/sjdhYfb7SuXOPols3vYE83Tl//z0SP/zANca1plxksn1LqtsBwlsidnunNpaAHSC0sYDt5OumBGrDg9AeYlR47vnc+N06doRjPH9Yzy44jpPoYkJQLhBFlu9h46QtvcxDtfBdhHvHnINzPQ32RbYHNPxhFsxHv2yZCn/84YQ9eyw0p7PRJ+XRrBzd/74EjRoWgYRy54VvOD/+qMKYMVU4dswBAwZYD+gz7+utZaV462Xh0LaG+kLhWy8VFCDCxVVRaFfWabRVqB0loTRYeTavd19GvEn+xvAnM+HRVIWAvkXwaKySlber3fdXEThQD4SQEi9+TjjUBc5o9WEiXAOUhVSTGlf/qzE6ry/jwhJGUYqu/XeuBOLUZXglL6nWRBPq6IJ0jRpv+jZATzc2r2Ox8NN854XBe9B4UFJKu5ARecj8o8YzquMvCcjd64PU/wXCs1kFWi9PNAnF+Lx3CIZ7yve2VirooA4OvLnfKDfaiceFFUuG/raW5mNckHD+NaV8GbejCAKNp2egXgd9KGhDyfrLF7HPSivRWTwRhfhM/j4ICQvZ8xJ/+60KjzyizItwzx5HjBhRA6o3b65BTEyNFx7xOH68q+7+YSiRb6Yg4skar2ip9Sg2H3dtjYdPdJlVFJ49Dl+AewO1Ls9h2voAZGz2rw5nyseDd3Qpis9Ln+HmbQ0AlrX3cZbQ9JR36mgv6TCZLGHjyViu4eRsXq95sfBoQvP5+utqzJ8vHuSZJZQ9Xy5J8z75woV23hyPeh1rvlfzUHdK1mnDKVlo/hY7EHyfuy9+mh6ImF/4o2nQvEyf7oJVq5wFAcJh2IptGC5rG+uBI/gXph4bsggAoNChM/ERU7Oy0lJdvSn192J9UY3xAFNjCyu1wCVchmnECMNdt6ICeO89Z5w+7YgHHqjC449XVUeMee01F3z+uT7yhFAZhc3ohmNYglkogP5MdIZaMi+jhUOqbv7FvFfx/CLxOdj54DDc/ds2XRsCCP0zs/FrN8pdqS8PnPwbBYEBJixN9wnBfR5+uKIux/S8mnzLawOb4dO33bF0qUt1fYq0ETS0UBfG8tRI6cgcK7cV4Nlh3MgIM2ao8e67tgn6npEBNG3KH67YVu8ea82xHDqPfVeEX6aZegQrPbfk9MtSl4yCk1cFQZXjjIaPZVcbQj7iGYCfSoW9rs334fBwDeLjTe8cLP2z1LlxwwGtW1tPD8Byrhr4cg1RQ5VZ810Z/j5hQiW++05ZiF2hMbOcqyzyskadlJQy+PvrgcDz5x0QFKRFgwZcynw8387frhK9iD3EqDVWnJ1GXZOAHSCsazNi56dWJFAbOQilBnInJ7blTQR89CjQsyev2D7DNLyIz0x+I0vzf/txw5OIyb3P+XNw9taITs3swAj09KiHB5L1eTmEFBKkfCTQIHsnf7g5807IWnrKFGD1aqmVcWt+D38yC1Fv6pUoFNbraO/WqMwXf4jzcWopQDhhArBmDXQ5cI4dA7rzR72xipAOXKhA3zbS3krJqgqEu3LrHbusQkhjDZq4Wu/xYj4wjUZr9TxJ1IeSZNxKhd7x5wTEPGLkUeOgBbR6YD3yjVRceZfn5SHQ2aR1GUjqobeWFipKlIhi9Pi8IfY2lvZAUiqvO7mdVqvFrMyrOFEuHmrOWjJSMo8Db8QIdn9iRHMUnzMFLPjWj9Qa7fLXJZx/ponOuyZsQo7OU0gsnclLAQ3xgI/e0rc2yuy4JCxpzfWkDLonHxe2eiHYWa9UOVlWhP8VZiLIyQVjfILwZ0ku9pcWILeq0iqAEutYCSgk0NWrRQVzvwGDCtH+u2usXXDqSc2xeYObaWtk9zduHPDLL6bNzGmZrx3zNUlrjfUuw8cg0Tt0Vxuoc7jKNKEBuflXoiLP9I5hAAgvv9UAKT8ES8rCq00pSi4oBwiVfP9iTIntDYZ2rOuC5pAlhVHPY7E40o0/ZDF9j9k72A0Htm8H7rtPXOwsPHVclwC/HiWihCrSnXGkhynfZAjh11Xf7lm/MIz3DYFBpuoCRxzqIOxNKtRZk1fT0GQ6u4futohoRAQ7IVdAP07zolYD7q5VqAL/HXkktuB3yAsZ9zI+wad4WXLNG1cYjU2Yjs9wBZGYiQ8xClvwAx6XpLEYr2OOdrGuXlYWEBVSAE+U4kPMxET8JNne0grGaSMMtFj2vxdfBD4zfQoys/ItnsST+I65vtKKK+a/hpZnYzF083ZBEi+tX4VPxz+p+50AQioOGg2cVWqo3Vx5P/w3ghphiJfeaIhynKm0Grg7OKK83AGefFugkxaoYjNg/W5XKZ4YyiVy+jTQsaNSSYi3S0sDL+hArVjWgm24sj7Vtitu8KZEsH5P8ilSGPW0n2vydhJg6Oiqz58nVszP+r17gQHC0bClyIn+XlkJuLBfKyT7Yj1XxQgVFAD16kl2JasCy7kqiyBIblqo1Wx7gDFt1u+Pj2fWtnLHUhv1lehF7mRdbm3Mib2PWyMBO0B4a+Ru77UOSCA4OBjkGt6hQwecOSOccyMvLw8BAXpLvrFjx2LDhprwJZYM404+VHgPYa0Wn7w7DzPefI9XrOYPyvoP5iLjN1MLS6n5oPA8TV6uySEoVZ9+F1XmyHiA0aXp+eeBL79k6bX26/ApdITGbu7dYszto8kXsTZcHnBraG9+sSQlTGgoBBU1YlJy8q5CVbG4t+aR+Ar0bCENEFLYPL6cYd32xMEzkl3hrGRW381KxKzACDjLeD0QyLIsNwW/F+tzkm2PiIaHo6kslFyElfBPbWSwLtnF8j9K8Gs78RCGrApYyc5uVjBXpg/x8sMbQcL5OVjp2uvxS6BMU4X7k85bVTwucIAapooPH0cn/B4hP1+lGAhQVeaAA63bV/PedVccvJpzLaul1qjO+0cDnSeVI4NyZEFQYwzwYgcCLBWuWqtBy1kZuPYh1wvw69w0PO0fhp3FuVicU+MNSmGGry+rj6JYD9QflY+4V/Vh3muz0J59bHArpi4pvJR3S+UhspK+DcKVRexehARIkNW23DJ+PGB+LZULEMZMboq8/co1XrReL86IYL6T9egBaIan49i8mvCRNO6uO+NkgbhercpQEsfvhSImR8OePiMgHMFOLujh4WMVQxwpgFCV44TDd7HtOayKTAo1mrXdOt/+ggXAW2+Jr0DW81wqzGjWn76Ifc7Uk7fj+gT4ddcDhJvD28LXybkaICw46YnTDzWX+3mg6WtpaPwCO0BIoLHYGLV/bAVmzQIuXpTNi1iD9zEbc/A+M80IJCIRNfcQCtuZjHC8jg8kaczAx/hEO6O63mOPAZRpowFSEBMyFEGZF4G2bbEqtrtVQbU9GIR78RcqYXqo5eUBfgxL2N0dIA9DpSUAOaC3ZDakjQ+U9vHknxuQ1LQxdrYStm4ceO009jbtpOvCABBK9bckpBm6enC9Wr//HnjiCanW4r8LAYTkQcT6vcvlgPY3R4FUcLczyGAuh+ivriH22aZyxXNL6jd6IQPNXhM3wKR73P6WNXdcYnTfPqCf/GwgzGO86y7glHjka2ZatLYefxz44QfmJpyKpJsgI2ZrFlt8Z9v+qcSwAfIZZf3+vv4amDq1RgqUiu+bb6wpldqlpUQvcifrcmt3duy91aYE7ABhbUrb3ledkkC/fv1w4MABeHl5IT8/H84Cp/2RI0fQq5c+Ge/8+fOxcOFCq4zjTj5UhA7hwTdi8PKcdzDi500cGXfGSZxGZ4tlL6W0MO7AXNlrSed04SLPuMmTLaFiu7bNZqei0XOmeYuElNijk2LxawS/xfqXuam49HYDLGNPwVE9KL5L6fr1emCVEp2TpbPc8uW2Mjwx1B1urlwrus//KsEL98rLXyS3f0vr03r9MKQZ7uJRDAjRJq+dmZlXUVnkCAcXLZzctTD3lFByEVY6Fms+fL7eXoZ7hjjgsdRLguxIgS9yx2G+ZywKboLenmyew3L7steHzjL+nsRzVhUFrf80dYVu3RiAwt/C28LPSf7jWQoEoH1MU+YIB1cNHAXIS61ROecUCWpXo/ayjAisIdxCdRV8XblGGMQ7ydtcTje+CMG1JbYNKyo1Lr/eRcg/xB860Lyt3Dng61tqno3beHsD164BQTIdQR9+GFi3zrR3uQBh6s8BiJ8jnFtTSq5dd8fh+BA24JVoDR0KfLaxHK18ud733f6+iGOD2IyMPFuUoTReOUBoGNfTfqF4xFc6NK6YHMgwZ1DiWVFRZW71xYVp0uFtiQgrQCg1N3J+798f+Ocf8Ras53m/S2dRcMIL7uEqXShx45Lztw/OPdGM01GHnxPg31MPEBruLIZ95OTI5ig6K99b1Nm/En1O6yOCSJXSK27wWtQK5BHDV+bhHbyD+VJkFP3+F+7BffhLP3YMwADsq6bD9bjTQgsBdIWh98uIQnOtaX7VspuRXT2cVEB2tm4jOh88ENGF1stBZzyOt98GkpOB114DohhTtrKuPSkRCOWOlGrH8vuImP0o9q2HvU2EXe8IFDT8zgoQ7mnUHo48Apg5E/iILaqsIPsEEP692hNrTdNC2tyTT2g+WQEKlvm41XXuXZOCHZPZDYVuNb9i9x5VrhPOjItCaYLpuX3wINC7t205T0wEGlvBLpPWlkoFuEnbBgsOyBbAubX2NmOmLyZWonUj+W8c1u+vpAQYMwb46y+gfXvKS2idObLtShKmrkQvcifrcm/VPNn7tb0E7ACh7WVs76GOSmDu3LlYvFgfYuXo0aPoLhDL8P3338ecOfrk5jt27MDdd99tlRHdyYeK0CFMioDOh/7FRxONTJJuSjsbgQhGTc4cpZNA1sQUTpNAE6mi1GLZnG5YGJCaCly+DLSQTgchxZZNfndw0qL/FVPlFp9y09Fdgw+TU+G4Nhwv80RDOlZahMNf+/D+JsY4WTBLWfQJXaD37wceXpWNlNVczeroGaXY9LEnrxXsj7vLMXGguw58rM1CninXloYiZ7c0yESPtWYu7ljVgD9ROh/f9B0Zz13LD5IQN8tU+avkIqxURtZ8+Hz7Vxmm3O2OIWaK2JWhzXGhohTNXN3x9MFkHB/KrqyWGpf5g/mDkKbo5qHc20aqvzv990qtFkMlFO1yZFTP0QlbbnoKlms0yKtSI8jZBS7miSUZiUoBhCxkpIAjueCUtUMlsoyB6vB92xSmal/z9hyAUGrMrH3aql7AgEKEP5EFOAD+fYqt4jmhZMxylU+PPAL8/LOpVOQChEXnPXByuOnlJHRcDtI31IQfs6bcKWwdhRB3deVSdXDRQKtmAz88I8tRekV+iG9bhI1Or1Th4RRxr7JzTzVhOvdJKrcCIAwJASg3mFhhPc89o8r1imQnLdp9cw2Bg/S5g6kcbN8WlYVcxWWHnxLg34sfIFTyLRn6M8+JSGDL1LR4JKj1HsIXX4lAxq/iEUlqI48dAWhOqOR42A3BLuxBTY665ZiO6Vhh2SfJoAXOadwJgYnCEXbkMDAdy7EC06ubMHTPIc+69qT4siVAOOr0Pyj097MaQJi+0R9xMxuBDEimTydDZYA8KQ3lzTeBRYukRiz+OwGEAyI90bp1jYcmvcfoXWbLcicAhGM2pGLTePY0CraUNwttsbvngXbRqCriPpiPHAEoKoCtizW+f8O+YwktJXuXlGws4UeI9vWMKjSpL1/BIWd8VJeMSwhwrW1dipRM5f6uRC9yJ+ty5crXXv/2kYAdILx95srOqZUlcOzYsWpQcOrUqVi5ciWnB41Gg+joaFy8eBF+fn7IzMyEi5WCod/Jh4oYQOhSocLOlt14Z5svb4WSZeHWQIWeh8UVOXTpiX+jIdJ+kmnOz8MQ5c349FPA2rH0lYxdrE3g4AK0W3W9usqxIS05loKUs2DHd+7wSfdCU56oKXHlpfjzS0/MqIlexMTmlStAM65BuUlbscfk0OXp2P2Sabgyatz32SLs/5LCh3HZ2HdWhX7tXK2iCGYa5M1Ke4vzMaG3OzJipBWb9Fir7+SCdeFtmLsgQDD+dVNA0PzCr+QizMyAWUVrPny+31mGKUM9sLYgA6vy9aFw+nv64q3gGq8MAnDKk11wenwUQh/K1a1hS0KwmT+YF4c0RQ87QKh0OUi202i1GGxFgPCbsBaIcpXvZSTEaF0DCId5B2BmoHLvL8kJEanA9203nJKF5O+DqwHCshuuIM8cPo8hlr6bzUnF1cW2V7CRF5xXlAXx63gGowTUkKuYnTgR+MksbZjxfk9eieZnq/meRuFs9zUzzavabe9FHBvI5snHMo/GdRo2BMgjwFKFkmdUGUoT5H/btgAINxRm4su8NFFRyFkPtwIgfOUVaW8kpee5scyF5NB+7RX49SiGpsIRB9ro8w0a9ls5sjOfhMChBWj3Tc3dlgwq7ks8h3KtBpWFjjjYXjq3YTAykQnLvEylvhN64wzAXuzFIE5Vw/unPtKRDss8sZMHjUX4Hul0GZeGTkPL3Z9Lsc30u/n7TY4S2tCB0rVnzqAtAcJl1y5gi4PKKgDh9eUhuP6x6VzPnQu8+27NiOj/z5vHNAWClQggfHyIJ86dAzZu1OcdfPBBy2iytL4TAMIpm9Ox+kHu25RFPnLr0DqwFCwWAggrix1xMJp/n/z3X6Abv9pG7hBE61v6/Q8cCPz9t74LS2gp2bukBGEJP0K0M/M0CPHnN7Yij8/DxzXQqri/22J8UuOvC78r0YvcybrcujBndh5sIwE7QGgbudqp3iYSMIQZpfCi+/fvR8+ePU04X7p0KWZRrgkACxYswFtSyTlkjPtOPlTEAEISoVBoFmsBhNRHj4MX4B6u5p2xqlJHXHixEZOlt0ugGuoc8SRRbdsC588DVVXWj1svtuQuXYLuwUchIFhLv8sx1Tmv+BQyEc9m4tRn/ihNdeEN95GqqsDfG9zw6KPiPVLs+mee0dehfADffSfNodhj8pXvCvHJk1yvrjf3ZOPtQUG8j4GkzCqEBztZ9FCQ5ppbQ47i765tl+DTtpwTIlSsXz45zZ6tRc9HStGshRbRbl4oLy/Hzp07dWTIK9rDQ76SlXXs1nz4/Li7DJMG63mt0urVPOZhl8wBnKtLQpH4hbhiz/Ao5lvzdg9C1pm2Xj1LQLin/cJ0XoLxqjKM8AnEEC8Fid1EhmIJbwayUspuFg9CCovo7eiEYd6BcLLmRyZjGsX2ZJITnaOZv1sm/zN5pejoLz+0oIxh6KqyyFwuTbl5CA30//c/gDwDqdB5cfgwQFt0Z54o65MmgRMa7nJFWTUovn07MGyYKed8YzWeK8rt1/WvePEczHKFYVSfQpC/9JJlSjoi5xygRmUuQ5JOM15tARB+kZuKX4rE46BLfffGbMq5J1gwFSZNKcQgAc5y7xcs/bMAhPVH55p48lEIs2FZMTrycmTHx49x/wQQ0r+zd9TDxVcaQVMq7WUxEWuxFpNYhqqozjF0RXccwxy8h/fwBoeG4f1jDXDryrLfEfnSCEk+9838A/0/GilZz7jCNHxm4ilo+M0aACFfvlUKi0vpBy5cYGdTiQwf3bsFaweOkuzkm9xU/FSYKQkQTl38CSZ8tRqDE05AI5DiRGjNGxT4sbFANFtKU1G+DQCh5OCsXOFOAAhf+zUPHz5k2R2IVewFBdB5mlpieCN0D8raUQ+xU/lzKZ44AVCeQFsXS6+59JlR/kAqltCyBYBmCT98cg8MBK4nauHjxWMdLTFRthifrdeGNejbAUJrSNFO478gATtA+F+YRfsYFEvg9OnT6N27N+hQ8Pb2BoUdHThwoO7f69atw9eEYoDCQrbAiRMn4OPDlruGhSE7QMgFJ55Ji8dlVVmtAISdt8SjXgd90o3iC+5I/DoEroGVaPBIDo4NZg9PGLUwGQkLwiWnXJeXSmPZxV2yE7MKlEelqAgYKeN9b2xpzfc4rdelBMlH3ZCa4IxWPGIqqqqCi9rJJAQOH98kj+vXAVIAtWnDdlkXe0xOW5OHzydzH2F/na7APR3deB8DarUWzs4OFj0U5M4J1Zej+KM8Zt3/icORnuwehEJycvSsQs8jFzEh3B9PegTclgDhT3+X4+GB4p6X5gBO4WlPnHqwuehUiQGEk5LjkFSp9yzycnDU5d90VRieUsl6uRPbWALCrQprgWZW9Bg0l78lvBloSSm7WcCqWxVW1FgeYntyyPB8ZG3zRbPZaQh7JBeHOrBpMl2D1Qgelg91vjOaTM/An/0jMW2KE35eyxZ2Usn30nRmGhpPy2Ru6gwHVEI6TDkZGx0b3BIVaTyxNCV6i4sDWrbkno0G5c2R0kLsKcnDzukhOLDe1MBDCozpuD4Bft31oRyNS0m8G4hnnw5lunNRap2KDcHLCxgwVINtm7nzRjlr7rmH7dyXmhS/nkXIPyLvbs73fU3xrY+DpQVo6+aFqf5h2FKUg9yqSkzwDUaAkzQI+XNBJr7ON/UgpLlK3xCAxC9D4NO+VBZYLueeICUj1t9ZckkREENGb3KLb7diBA0t1IX4N/dWFaNlMFoTWosTJnBzcPLR6xt3FmXX3NC8sSN+at0c4xYX4Je50mHeDbSUgEqC48rPB/z8OD8TiHYNTdAENzi/dcUxXEZz5MNysOHUh3+j86sDJadw35u70H8Re1qN82iLdjgPPlkZA4SnT+u91OQWijRinq+QorNQlJYPPgBef52Nopy5PDqwD36d8giO9+8lCvrFR7fGtE2rMbl+I110C6kchI5VVbh//W/wKC7BL8/wx/IUAwitaXBqBwjZ1o2SWn3HVODgJgsS3sno1BrhM/ucOwdnH43uHkDGlRTCmyIHZW3z0/2Pr9QWQOjrq0VhoXzAy8Az2fgvWKD/F4XP/fFHGcI1qmoLAE3oLk35mnftks/njh3A4MHiRun3P1uK7Su5xne2GJ/8EdR+CztAWPsyt/dYNyVgBwjr5rzYuapFCfzxxx949NFHUVhYyNsrgYPbtm1DFGsWdUbe7QDhrQUI26+5goC+xagqd8Dhrm154+qzTGX7H6/g7ORIyap04aL/OfLoOB9+WO8dIORFR3l6KKm23JKTAxw/Dtx7r4yWjloMuKrPRXioSxuos7mKsQqNBi5w5IyF8hOqSh103ixS1nBKLqCiAOGPefj8Ma7iZP95Ffq25Q8jao3HlAzJVlelfr1bl6Ekjs1rj3JBpa1nzwUlJXsKpbejZwT279qt46muehD69ylC3kFTxe/6feUY108eQEjy3tfUNHye+byJAYSny4rxUU4SKrRavODfAP29+B/JStaCvQ2/BCwB4WwNEKapK/BIapxFUycFvNzuAGFJmRZeHg7w6VCKqAUpuLygIYrPsXkB0pka0K+4Wr4EhFKOE0+25ormpe/Fs3DykAb8DMTXNmiFmIpiLM1JluxPne+EuNcikLOLHYwgoqNHA/XqcXPzHj0KBHcqw5Np8bq+L74agYxNpvnTDOunKNYdJ4fx569lWWPdvuiAJUskh8hbgXIMbtmqxbtvcxV6FOaLwn1JnVWsPft0KEFRjBdrddneorsatYezBLPGYa8NjGT96YvY52rCX7MySF3JzUXJSlusHoXBl/I+qagwzYEmt992P1zFuSkS8eQZia5fD6xZA2zdKt3AraEKFSl6oJ4iesj1vJIDKolyI4KOE4iWgEhE4iqHhAYOmIdFvN6F0qM3rfHHa/sxYklfyWYbfyzFmMfYv6vVmIwpWC0KEBrWtmTnPBUoP2aoWbRGA0D4xRfACy+wUR2KndiJe5gqD40/jkpX/TtICPQbeF2fp5Humk3XReLXjQ4oOOktSD+3UoWHkrkuj+PrBaOPhy8aubjhlYwrWNWQf+/+7TeADDDuZsduRcdqBwiZloKiSveMV2PnBmkDE0XEzRpZ400bNT8F4U9k48riMCR9FVLdg6NHFTRl/J7WJ0/yRzewxpiMaQwYAOzbp5wqgeoGHcyhQ0CfPspoKdFfSPXEd71Y/IUarz/nouieRMbh5E0qdm15ck4FVi3mgte2GJ/U+OvC73aAsC7Mgp2HuiABO0BYF2bBzsMtl8CNGzfw6aef6oBAAu5cXV11gODYsWMxbdo0eNpAM2UHCLkA4bNp8bhUSx6EwcPz0HZFIlLWBOLym9IegEKLtOO6BJyZECW5hoUu7p98Arz8sr4530WOLGZJOSpXmUH0SJFDVmRyPAipXf+rMfBzdsIfLVtzHgT1Opcg/4QnHHhAwE6bLuPUaL2nltildOoXBVj5nDxlqRhNku09LxZj52fcB/mxyyp0jap7AGHEM5lI/qbm8SW2gDybl6MkXjpfoYGGlNLVs0UZ4k6qcWrPXl2TugoQdvgpATGPmH5bmw6WY3RveQAhjZFyoJ0aHYXKAmdeUQsBhK711ahIr53HveQmcgdVsAQgtHbOQT6xW8If0fuvA4Sd52Xi1KIQtFlxHRemyQNIDBbsJKcxPkF4IaChbgpGjNRi6x/KrcfFPh8WsMzQ/p3gJujj6YtkdQUmMQLFGpUD9rdoL+sLppw+BLLxldnpV/FveZHup7iZEUjfaAoQtlyShNCxuTh+d0uUXubfL528qtD+h6to2w5Idi/l7WdrUAedkklJuXgRaC2QwvCbb4CnnrIeQOjiX4nIN1MRPzccmnJpT1M5801jf8ovFBN9xcNUf5abgl+Lsk1EJfWd01ktpIy7FR6ELIpBSz2X3MJUijxq+dYg8RsRASRL4/RKlnB1GwdooIF0GFLJTj76SH/hJy01z0WNAMJziEY0YiVJWVLh6MeH0WOGaUoNPnpjxwLdN87ETHyk+3k5puNFfCbYdWtcQBxaCwKEtB+cOqUcYKZ5pvk2Ls8+C3z5JUDer32lMc+bTbXYhmG4H39KinHQxePIORYAZ79KnBzFD9gZAMK8w17Vd1YxQDkjXYsJ5frQucbFPCqA2F3+iSfYUjNIDhDA97tKMWWIDS1wBJjgGx8ZzJrn1GUZQ10WPvBWAAAgAElEQVSts2SJFrNn2+beYjzmjz8GZszQ/4VPrhSVQcgD0Fx2dD5KnV3GbWoLICSjon/+YZvpGzdgkgaFDKX/NPrc6eygv93MtMFG1LB7sNuSMdPlm7OFK8sxf6q7IoCQBSzu3kuDfw/bcxAaJskOEDIvV3vF/7gE7ADhf3yC7cOruxKwA4RcgPC5tHjEKQAIw5AKf+QhFQ0wCWuQhWCswwS6JgsuACfvKvQ9fx4XZ0Qg4zdT5ZqcVRP5ZgquvKNXYIoVw2WNFGOG3HtkqU05A8gSVOhSL3XJE0tKThbo06YBZFkrp9DjYDdZzDty5df0tTRcXRKmI/fozHL87yO98pFCj3b6JaH6USH2sP15fzkm9GUHvAy889Fs1Aigh8CgEZXYu5UL/py5pkaHJvwWeAbZBjZTI/ea9UCgUaOALVuEJU79dnoxB2c+Y/MKtDZASJwdjytGStweHZO2BAiTKirQyF1+eB3XEDXafH4DZ8aaAoS/HarAA73E6W0szMLneam8E3DorjacnKEeTSp0YVypXPu4Pm4srzFRp1ylR3qzh3eV853Z6wpLwBIA7o+IaF1uPluWrEo1VuWnQaXVYopffUQ4u2FZbgp+L85h6lZK+cICYNTlEKMGIQTdnY/snfI8br9Lz0C+s1qXQ2+4d4DOGIXK2bNAB3FHYCbZm1dq+UESwsbnMrX1dHDE7xHROi/5gqpKPJDMrsiXmnNzBrp0ASh0F18Zm3QBWVX6ZDpxs8KRvoF7lpAnQMLb0ncTAvKe8+Aqq4k2rTEpgxMhwREQwJczkepTam8KCaiUNl+frZfdQPDwfOyPkl4kLN+XeR/G31tOpT7HKa3RYGcXUFSFe5POcdiSmvPnnwc6dQKefpo7IronEIi6ahXT0rRKJRaAkDqy5rxZwnhtgajtEYMYKIiJaTw4c+EKAISX0AItcNkSsUi2zfngWwTOelKyXteu+j0oFGk60C8Dobzgn4GQWJ5E+o1SClhib5uSAoSb2XNSGL3duwEl+fiCkIUyeKAYwiGKnVBZDQ4LgX4GgPBQ57ZQ5+rfIWIA4YvTtTj3qmUAoeTkyahQlwDC7GyA8qf9VwqBUvffL380L76oxvLl0u/S4cOBXr2A116rCSfJtz+TzqOqmO1eLBcgrK0Qo2QM8NVXbLKk7ZaiKG3YoAcK+eaAjF0ov3NQkD7VCUuh/Yv2MWsXvjl7/a0qLF7gpOi8ldIdEf+TJ3PDrL73HjBnjrVHd3vQswOEt8c82bm0vQTsAKHtZWzvwS4BXgnYAUIuQPh82mVcVJWK5m7YiIfwMpYhBfpX4mhswlo8Cg+Um8j5A8zC6/hAdPXRJfhIr9aoSJWfH8hAuP3qqzj7mHS4JGPdQEwMQJ6BQ4boQ4gZCt8FUeySt3w5MH26sLKG2q5cCTz3HL8YCFxLTOT+RnIhZVhQfQ1yMk2ty5rOSsPVD/QAoUajxf0/pSIxtxLB9xbg80aRiHbXo51iCqQ1u8vx6GD5AOGBA0C/fqb8kgKSrBdHj9Hit01cQPNiihqtGogDhG1HleDC7+xhlKS2NAJmu7yVgVNvcz0OKMxVebIr7ptViL+WGk2+CFFbAISbTufB6cZ+Xa+2BAjD+5Yg5aB82Xo0rUCrDxNx+iHT3IHb/q3A/d3EAcJiTRUWZF3HqfJitHL1RJizK/aW5uvGenVpKBI/N50Xyq/RcnGNC0L2Hh8UxXgi9KE8eDRW6b4Fe6ldCQxPPIcSrcak09mBEfggJ0mSkVs1X1VaLYYk6sMzSxUp4EAKwPgqtDlauNW+xb/5uKSAAq9W7KGUDbQTSssR6cF/Pkj1JyV3vt/JU9m/l17jsyCoMRZmm+b/+rFBK0S48O85coBsqTmXyzuFbg1/PBuXXg9H2jrlGlXKKfe8pzBA6NejGAX/yncjpLsF3TH4CoVJ7d7dykCToxb/FBZgcJiPZMh4Q147OTI37CuJ6nJMT09AoaZKZ4iwvH4UPshJ1EW/MC4soa2nTtXfaSZO5HJC7a9dA5pJXy/lDAOd5mfgNM/dhIhwAEJKFke52IkJck9x1oMftvgOZQ3iZmXil5zxWIFNJX3o5CJibMhMkxEgFMpByNyPUcXiTn3gffogp6l2yVI4vDZTkiR5cpmvTTFZEAhICveevbh3cfrN0rC5ZFBpnrqR9hHaT5QAhAYBCI1pPcZhAtZXy4mvXn6APx48pY/GYbzHF8Eb3uBHEkgWfGe8HA9CycmTUaEuAYTl5YCbfJtCGaOt3apqNUBpQuSUzz7bgylTesHHRzwNxbJlwEsvcSlbuj9TvtiCY+xnPqUyIYMmW5fr14GmTdl6kXsmOHlomKIPUJjjELbgP2yM3qzFN2cNGmiRkuKANo/n4eIP8nLPsgCEBCovXWrKZloaN4yzrIHcxpXtAOFtPHl21q0qATtAaFVx2onZJcAuATtAyAUIX0i7jAsSACFJeBNGYww26YQt9VgVmxF6oO1v1Y7pUihEh/K5HR/SSnLiWS6rcgFCAv9IwST0GKA+k5L4lXRkpenjw/8Qu/f/7F13fBXFFv7SSUgIEEKAhN5BilIEFVCfIqCIomIHVKwIYhcVCwL6sIOIWBALoiA+RLChAkoXld4RCIGEJKT3dt/vZLPJ7t7td/fWmb8gd8qZb+qeb845J3bjh1Y9cPujJfj8DbGitkGfQuRu1yZ81D5QPvm5BGMvN04QknJBGh+HD+fy5lsOPPKws1LiyJlytG+qThB2vrwIh34RK9uHjCtCxZFIbNxo3DUM4V5cVYkomWA+bR5NxbHXmmPkM7lYNUufm1U7CMKvN+UiNJ3z1WIXQXjkCNBRzO9prhM+Q+OL89Dm4TT8PaqTqMyPf5XiivO0tQcOhwPlcCAUQTheXlIbr6v0TCg2n99dVKdW/DFPEU66wfLDjK+dPYnVBWKrrt9a9cTPhdl4RYUkXNi8E9qG64vtaQdsSoTR1LiWeFkgtxpZNOjALoTUc/ZhdF1MEzxY427TDtnN1KmliIruVoSCfcaITLLOahwi/3J+8KVV+GOtjAvJIAfaP52KozNbGO7GwC17EdGsorrctCat8FKm+NXMr616Ilihow+mHcbeUnn3nFJB0r5uhANPtASqjJ8pSp1qPTkNZRlhSF1iniD89Vfg++5H8VdJXdxHvj3a+578LR2z/2NcI0bWPuQu7nfuHYoouUoQUlxeOatJOnu7vHoSBwlnlUSub5telWtorvDnwIsZx7GuqK4sWZUWSR4zUMVSa3SlxkjBS3HUpEmPgs9QB2oyUzxrJYW16J5KmsIWgvVEri9qzDe01r0ZucyUIXldiSWlt013EoSn0AItkKpXNNV8mz/ah4F3yZjG3H67s+mITE1kBdynj/gHrW8uGpO0oGZohjOigkSK6fkOUuuQXPzLHj0463KyYiKLR7l1pDVfpX1KRzxm4hm8h/tQhrr75l50QzfsFzUx/+mHsfSecajID8YGEqYm9cAu7IL84zI9BCFPflsyETQq8SaCUBgnzh19t7sNue9WrTZXrPi2+rssKkr9LkvegeQeAGvNd632jf5OZ73wqDBaXm/+oqI6j0taZYzuNaFkYVmobWFptF4tOfnfo7sVo3C/eLzJG9GKFUCr+9JF8SD11Kl1f6C6KfzMXRJD8vR0ID5eTwv+l4cRhP43pqxH5hBgBKE53FgphoDLCDCC0Jkg5JVtSoHghaCrubLh8/F5lAbrgr/3YNN557g0luf/vh9bBysE2hHUrOdSaZQgpIfd5JpKjSAkFxvkPkOa1C6PVyXvAbnpG3l9JVYtd74wm+0LL8OKnyoxaqj2RVxuYCioOCmE+MTLIhebhPKkZ1chvmGwLEZqGPQYXYhdy+ubeimvVu+lr6bj18ea4vynM7DtZX238KgOJSiUiSO1r7SwOg5W/8gGaBhS515Vz8dhj4X/Iu7SfJyfloPne/ZHZKT1pIoeOZQW30W7dqM4OQJ/XSUmCH/YUYJhvYyRy0QWXiqw7Np6SRcUH+OUPvValWLA7weqrQR/LczGDAk5QHkYQejSFmmqsBxByI+DHAm3qEXnajefSkSOKSFMFFIiCMll89DkXeBtIuUIwuCoSnR84RSaj8mWbdkXCUJy31t8XJvQF3aY3DWGB8nHkZPbU15bnY/E1g5c1jkS8WHaLrmk4A4+shPBNdsnxa+8O/WQKIva+t9fWgQijM5WVuD22AR8nJumOmsKDtRD2ZkwZP7SAKc/kzmYTcw5V4tQnMMnmipbEP5YkIXhMcbdsJNilGKCCc9rXtbffgMonpDZM6LZjWeR9pUzKUpn78X/7sT69upW3xSPqfs8saWoFo5q+49cWb0Wo2SYR7Gole5oZDVFLvdIcW5FIozU7oy1bZCpFjUuTDWXG7PjZoX8MuKAln0Fx/FXu6+dPdu6lohQDQvXQeovXAjQPYpYcbmk04LQSoLwwKoj6DLpcs4UVZjIby0FsdNI5J1DahmkRBAOwGZsxYBqErBl0EmcRJ358C1YjCW4xWWCUI40o/jsu3dzsQ2lZCZ1j8qQAawUAtE8kliI3oUPsRDOLlifwH/xXzwlQm3u80/gmztukY3Zdh7+wl9wNq1SIgiX1e9VbT1Hjzfl3KlqjZfZ372JINTzfWm2n54oZ4bo1UsQzpsHkJtqaSLXkZ99Jv4r6QHoYbAdyV1jVlysz0XxzJnA008b62log0pNzwP8fmKsZn25W07IQMpHYp0APd669FKgzZQ0nHi7LvSFnhq1CELCkiw/pV6ZMjLkdUZ62vT1PIwg9PURZPJbhQAjCK1CktXDEDCIACMInQnCSWmHsadU3cUoD7MVBCG5y8z8KbbanaDZNGDzPmwZqO28Xs8FWo0glLvwT5wIvPOOurJH6UKtdnnkXYxecqkD69aKFSMh9StRoSOOAX20zJ8vjypv9WcWc1IE0CV26NC6vsu5HhJe5sc+WYLPZtcRS616leHEDs7vi5qyzKgiLCisClVlnHJbruw5T53B7pcTED8sB5k/6YvNFdmuBEVH6+HT42fx1mdlaJ4I3DQ6GAsFymhh3DUjMtMa+PqZCJzXwLgCWGv8nOUgqygdijag2v1S3j9R+PtasQniF7+V4OZLjBGEJGdaRRmeyziOwzVu4IpPhCMozIF6Lbg4Xowg1BpN9/7++tmTWCWxIFRT0HsLiStHEN7fqAXGNBB/+EvXhjAOphLSvkgQ0nmh51W2sM/kupqPOyjFgpT/UpdIvMsnci0cI2O1rTVzha7eaB49lHYEu0o513DXxsRhcmNJ0CuVCveWFuLBtCNaTVYrrde39Q7XxeTSbWr2UfxTKrYgjAgKwo+teuLL3HS8c+IMNvaqs47hO9i/PzB5MnDLLZyrR2GiPlJspM2bneEgzwYUS8zIWSWsRY0gpDWoh5zTcuMrldpTBCHJkZcHxOpzOIDoaM4b6EsvyU9D3QShyoXU7LhpLgwDGdrjCI7M/6XabMxxXh+QxwJy2Z+QADz/PDB9uoHKFLLy81TXRCWN/G236WRf3eOnNeWPY0ha9R4X8FOYdAYNk7POlCMIm+IMMtAUFBOd5h3BsG3xIVyH5diB3vgRw6tb1/MdpDVq0rk3YwbwzDOAkvtBapMMXyl+mVJajREYgR+qfy5EFOKrYxM6fxfKEYRvT38Kn513h9NjNr4tObzo+3Vhdho+za2zskx4tQu+mhdRPYfJtSvF222pbgitBZXod+KvlThhRhAagtJwZqX9cvwdDiz62PmbiCcIFy6MxIMPKje3aBEwbpzz73JWi2PGcPH47EhWrGs9ctFdRfqWlR6D/PsvF2+Q7hT0PkPOklir/rDGFajIrntoq5Tfrr62ujcdJ98Xe2rYuhWgO1a7J1NxbDYX2kVv0iII6Xd6VNOsGYcdpU6dgAMHzN/L9MrmrfkYQeitI8PkcjcCjCB0N+KsPYZADQKMIHQmCCenHcHu0kLVGIT8BKIPrASkIQ3KlyYtC0KqK2F0Fs58Y54cuWD7XmzqK3ZZKJ3kEyYAH3ygPfXVCMJPPgHGj3euQ0vZo/R6UQ9B2KmTA4cPO3+86L0g9/jwGLLWx+D052JLiV9+Af7zH208jOSgi66c8Qgva8ZZB84bUoGUvWEIj6rC7j1Ap7bKRF6jpApknQzVpReSyqmGbYN2Zcg9Gm6gXgcSrs3GXXPy8Pr5LWrjZbZ9PBWtJ6bXNt0urB4+atG5+v9GlXfd3j2Ovfe3MQK3rrxCOcg6qqpIv9UoKW/TV8di30SxXEtXVeCGK7U/4pQEVLLwIuXv13kZmJd92qmot5BPukD3k0xvnE3BdwU1X601ffIFgvCr3HS8lyN2D0fWgyGSRem0RkMcuPioevzC0TFNMMnLXIzS2UbGKEoponkZSlP1B+Ahy670VcoPJ557zpn4yM6ui0tldO8juXmiaFZ8WwyMaoAyRxXWFuYgMjgYgyJjFclKo3uMNL8eEssdy5ks077KT8f7knlLbdOaW5LL/SaVt359oEDAKebnA+S5kGIEvfEGdw6RZU/Pns694N3ImRkvqq35TWdl4y7S2Xv5iV34pY1MoxIxvIUgpJhtRKQq3SNq77z63tZUkyVXXOFsGcDXQxiRJYmcGzHR3c6LCcIOOIzDEHgXWLYMuP76WgjNxP0S4v/ii8DUqTV3SpqsNbEXVdcjMTB33OFVBOHen0+he4tsgMzshIle1AkDoCt07OefubkkTHmIQQzEjwn4by1yAUgKfCXrN73fDmo4S6fl118D113HlVCasqtWASNHKtfaEsl4Ew+jMbLwEqZhLS6VzfwUXsbLEJsmvfXSVDw8bZZi5UoEIT2E+aEwCztKCpF4Ohbje+l8AWDyUCDsyQ0keQ6WJkYQmgRVZzEisNq3d85M7kHlHtLyBGG9epFOD2/4Wuj8pYeySs5fiEwjK7quXTlPQ4mJwGnnzxudPVDPZsW61iOIHEG4cqX62tZTL+UJjy9Heaa6BwraX4h8tSNFd3WO10371pVXAh2eO4WjLyUqNksxEck1KJ/oQQR5BFfaE8eNc2DRIu5C8f333IMi0p9Q2Bry0hSoiRGEgTryrN9SBBhByOYEQ8BDCDCC0Jkg5F/u63MxWgUH5N2Q8UM6CiuwEqNURziE/M7rsIhTquTCnXuwsZe6m9IPP3T28y5XH/mCJx0Dn84/H6BYPZTI6m449whXlLQIQqULoh6CkF6vDRgg36aeZUMuMCemHXFSLFa7bDLuCU6zSTWClQoTiUiKsUaNxLEX5crdMLUAS2dFGybbqB01bBO6lSFtbzhufKYQS2dpx3KMH5mN0pRw5P3jnFeq4CRlLlnKNQ/Tr5DnQbXjA0+Ia1C9SjhKjBGEeSsb4+/J4ifUu3c7cM45OjWlMjNGjiBsEhKKZUnd8VthtlP8MaqCEYSaS885w4kTwKOPcgwCmXLQM1gDSY0gvPv0QRwpLxHV5k1j9HzGcfxeE6NsdtN26BcZ49RzuT1Hi7DwRoKQLB1uvVV5YOOvykbGqka6Rr7lvel45OlKTGml/OiHrKjImkqY6P/kkk3prNNqnMf9zYT26F0vWiu75u9KjxCkBb2FIKS9/7OcMyKLdJKVble/tu6FhTmp+Cw3HbvvboOza+qU2Pv3A100wi8rxV/Set2uBXLzm886xV0kl6XkunR3SQFu+igT+x5Uf/Sitd6kMthlQUgEN91JpEl6JuslU8ntHCmepRad0rNeWp/TQzaVCxXdpchyhda/MJHV0055b7VaQ2ro98W4BbdgibiMBDC9eEkb/vxzyZ5GjOssZQKotnxaGme+KNfwwIEAMcHCZFZAA0j9ueIU+o1q4SyTzgsfKY9JQS3ab2UIwitHODBtmvh7gR400sNGYdLZrGoPKXYWkQKUaN2QBTn/PaE0ZV2Jhy0U5hnMwAxME8n31rNP4eEZLyvKrEQQCrEgq0t6/GJnovZoP577YQWm3Ct+ZMcIQjuR5+qWm5tkaUv7jTTxBCGFfqDvfvr+FybSDdC1mrzo6E12bjdWrGs9/ZB7CHzwIGf55mqKblaBwjPqj0/5Ld7VtuTKy40P7x2j86yTOPSMsjnx449zsRlpnlx8MUAPXPg4w3L1vv468MgjdvTCt+tkBKFvjx+T3joEGEFoHZasJoaAIQQYQehMEE5JO4KdpYVonpyCLwZfpYpnOEpFweOVMuuxIjQ0cJLMF+3ZjQ3nOLveEmZzUjgoNEiPeineB33M0ocvudJsU6Pj+ucf4LzzxAVHjABWrwYmTeJcjUqTmhKO/23JEs49GJ86Tk9B4tiz1aSInNvOpiOzcWalPoUvXyfJTjKSu1PydHTZZa4grlxWiyBUnCMyfNPFszKwdmq85QRh5z4VOLA9FM98mYdZNzfQBKIlBSd/T+x2RFqofudi9P3hED5I7IgpaUfxfWv1+SjXqNUfeJUOB0KD64ANCq+Co8b1qlan+z2ZiWsfK0L/oFhc1kT8qrqw2IGoetYShP3qxWB2QjuQzJcJYhXycnoT+aSFnVf8LjVbpgBa9GzdwKsANRejUjeOj8clYUS0czwyT2JBcS/Joa5STEQzBOG1MU0w2cssCClGitp+Hn9lNjJW6zsv1hXkYHCUusWeHG70qpziNlH6+MhZ3NnR2FzwFEEo50LZE3OWlutNKftwppJztyxMtPcdKyvBnakHUVkShBNvJ6BgXyQSx2di1/h2usRVO5fNKi2b35KJZ6+MxcRbuZdGRIZRvLTevTmR/irOx+JfSoFD9fHmY/LxdQdu2YuIZjWB63T0xC6CUO6RF92Z+L7woim5UJSKTt4qiOxTwpY/64nQIfeMfCoslMR3kquATPME1nTr1wPXXAPk5HCWZmTER9YqdifZOHiSi7bZubVrF9BDeIXSWxGPjVx+YsooToAw6a3XBTB3rstGryENOWaITJWiojhXJDrbprh9FL9PmApQH/VRJP6jzAWSyHqppxAr7pn0yI9cmdKcI+5WOFZKe41eI1AtqKdhOqbjeVG2Cc//g49erNl4ZCrQQxDSd9FT4tCGWqIY/p3H/mBKBbq0FBMhn/1Sgtv+Y9x1v2EhJAXogQkRPMJkxRxxVS47ysvNzb17ge4yDoiEBOHRo9yeunYtMGQIQFZl5EbaaNK55I1WW21x9scfhouZLkAuV+kRDKWrrgK++850VaKCCUlVSD+l/ujcrsfNJIjc+NAeR4/iur6ejAOP1cV0lfZYaEUt/U2uXqtIVWuQ955aGEHoPWPBJPEsAowg9Cz+rPUARoARhM4E4SNpdXFwtKwIR2M5vkGNXxmVeWQ3QTjk3534vUsPVfKDFDutW+ub7PQhS5dCsojgX4BRSTmXPfSCm2IQ0OXwhhuc69dDENKFt919Gcha1wCx/QvQ8fnTCImqqrWakl4uL9y5Gxt6Gieg9PXetVxmCcLm/YqRtl2sROw06yQOTm2pV48i+4ErJ8/UV8ox68kw3PJUEZb813zsSylSEYlluOPnU3ive1tTIFr9Uf5VTgZuaiSIuxZWBZSrf3zxgtMrUQolRiTLec+lY8eMhOqfhkzPxLppYne1Rjv7+Jmj2F4ido9F8eEoThwlOesfRhAaRJkCgdCGJUwG/QCpEYRU7e9FOdhQlIeuEVG4JjrOsBtIgz2yPLu/EISkd6ZHLBSrSy7FDsxH7mZnC0q5vHr2IDpHk5PFpSWcBdak5OOXP6qwfF8Bjs5QdsvE18IThG8ktMe5FlgQrsjPxNtZkvmvMIPcbUU45OhOZG+Mxu472yG2XwG6v3cCG3qeI7vvkchWuPW1gyBscVsmtiyMwbaVESBPB1dfrewai9ysvSxj4BPasAIX7dire20bIQiryoHfO2rHmCSDM7JKELrObdy4LiaQUDjpuwslwXkuiiwq160T56KxELpI27YNIAU0Wak0FHr2VfLRSZrq998XVZqVxclLLvQWL3bmwXQDLJORrBzIXa00yRKElEmwiZhViFMMps6ct3Yu6a1I7cL9zTfAtdeKuzFoELBhgyvwaJZ1VMdz1cymmEFuzi3HaIzG/8RlZDZv+tPgwXVdfOst4KGHzMuip6Qde42w3emYhmkQsOrVUbXpKZByUiII6WELuXxs3hyg2Lpz5+rpoXKeKVM4EknJgpcfInqrRa5GhWn9egcGD3ZhopgUvVs3gCzRhUnPPcBkcx4tJjc3yS037/1AKJyQILRKaFf2ATUZhC7erZJVdT05uIfR9OCYtlQ93p/1yNWidRVSk9W/UXnX6HrqM5pHbnyoj/XqAd3fOYF9k5SVSPw3s1ybcvWSg5dWynyjUdH9Jj8jCP1mKFlHXESAEYQuAsiKMwTMIsAIQhmC8MxR/FOjvJ/y7EyM+nyZWXhry2l9vLnaACkX05Y3wsGpSXCUB6Ht42lOwaSt+OCh193SV4MPPMC9pFOLT0j9k14Q6SHxfffV9VyNFOkw7VStkpXiNV74diqWJ6nHXHQVU7PlzRKEo5/Nx/9mipXYd7+Ti/cnkjWLcWnU9ESvLyrDI+PC0bUfZ0loZYrqWIKiw+ZeAVsxR4V9afdEKo69KnAVGOIAKvWBKZQltbwUC45nIiIYeKBNAmJDXMPsx4Is/PesmM34qHkntAvnCGJGEBqYkRQAhVyIEkMkXChyi4Z/zaCzei2CUGc1XpvNXwhCAvjQIeCeaSVYv9R576nXshQlJ2vM+zRGQ88eJOcqTqkcnc3r22qTNDxB+HrTdjhPxh2s0UlEDxsulbFElqtn5+3tkP1H3dlDLllvGhyBuU9Y93iEb3fwoV0IDndWZhPxJbfvzWvWAd0iONfWruyLdijtyR3thrkN0CpM+7xTsx4y4mbUCEFYkhKGLRd105w6FPuHJ8BIWUyKPjl3o3xFZLF7550AxXp7eHYJnrnTuf+8BeFNNwFffeUsgp51Vm2OSe4s5JJKBV9+Cdx8s2a3dWeQs66kwnoIQqX+azV++DDQod1MwAoAACAASURBVIMgl95LoNrFj3wJfvaZuOmlS4Ebb9QSx7XfdQ22ehMU5mDy5Crk5OTirrt24+OnmmIneiECZdUFb8BSLHPIvFAEQA8QKY4hxbskt4h2Jzv2GqHM5+Jv/I0+tX8qRBSiUajaLSWCkC9EinrpoxejONEYEb4UooJCVUgTuWVdsYL7qzxBqByz1KgsRvK/+y4wcWJdiXPP5Tzn+GOSWoDT2wDyPEyPIaXJFwjCjz/m3A/LxbL1xfFLaleFU8fUCUILtlNFaOT2LnrMQ3+/7fN0LL5d3pPQa69x0RyUkly9drpK9cWx52VmBKEvjx6T3UoEGEFoJZqsLoaAAQQYQehMED525ij+qiEIo/IL8OZNE9Bp7wEDqDpn/RdtEY0CPI5X8SnGuVSXXGFewVSRFwxHVRDCGlaiNC0UR15KRFCIAx1fOlX9Qt/VJPeSd/NmLuZHSgrQUsY9PX+ZpQ90cv9EiYJZ0weiMD7NW2dT8G3B2VoRGwWH4puWHAkojU30VWJXNA01HuPO1f7rKW+WIIxuXYbCZHGfnvqwEC/fVV+RIIyOdeDNuVW4e6zz152anujFhUV47o4o3DsvD+8/qO1iVE+/rchj5YdPSYkDkZESMjDIATiME4RW9E1YR0lVFYaf3C2q9rdWPWst0KSKcLIsJAtDliQI0AuDBQvq/sibce3bJ+8ziXLyX7s6wGQEoTNI3uhilJdyZX4mnlhQhIOPi58lhzYsR0WOOOBs/JU5yFgtNFfiatG7B5GnPl7XToSEko6d1nLxyXBsHdRVdcbxZ/jLTdtiQKQ1e/L6why8kHlCc6Y7KoHk95qC3I3GD8tFwnXZGBbdCFPjrX/eLUeGDa3fCFObtMKktMPYUyp2G/hJi861BJx0X3wpvg0uihK7f1bqrNq5TC7CyBrAUApyYODWffiyd3tdBCHVTdZt//7r3IpRglDJDbW0Zr2WoeQiUejm0wgOpVVVqBfirNAkw7Tff9d2MarY1gsvcEGMlJLKQqXHZ/RwzYpE3jDpYZzs/IHCXYIuxMQIv/kmsrtdCLLGVEpkzfb2286/kvF7rYUVvX4QmROq9Ezt4tekCUAPaoSJ/PKRiZ2dSe+mqiGDUHG6ffsIrJqxByPxHbajL451GeFkBWZnl9Tqls4Vfg5RGb08r7rsDnyFGzEGy1CCCIzFp1iGMapFtAhCK7AiC16a6+Rl9/bbnWsUerEhcoCsFoWJXAXbPRXl+kkPHS64gLN6pAew5C6SYqj5ayKL0Y0bAYrVysfNk5uXvkAQWrS1eM1Q69kf7Owz6XBIlyNMfHsXLj6OTbc5x1TWI49cv9xt9ek1g2zgnBs6dCgoBqhWCmRdrhY27HffRYARhL47dkxyH0cgkA8VpVc6cu7/tFyNGpkG9VGAInAv4q1KehRMVrkovO46gDwVUWrQgHNFSpc/iskh94pPeHkk3QTlI12HkBykumZmnsAvhTm1kFwQ2QAzm9a5qqSYX2TZeVV0HBq6aMFlFe5y9ZD3qw8/rPuF3GtRzAatJHeBfnZRAV4aF40h9xTg9w+cAz6o6YLUfvvnYAV6dwpF39WH8NdVFkRW1+qczt/lYh7pLCrKlpfHxUxwTmS54nmCkORampeB+dmnq0V8Oq4VLo+ui5HGx9ui35qEhGFJYleE6vly1AMW+ZSiABoffQSccw4XuEPk101PJV6SR0nrL/SVJxWV/M8JA56qdEX6MIGyWrWPegOCYY0qUJFTZw3b9vFUtJ6YriqaNxOEZyrKcP3Bg9g8sBsq84WPJpzXfb9fDuDPy7o49VWPsoMvRC6SSPmr9nqdJ7W0yBr+DH+laVucbxFBmFZRhptPSXyn6Zx4RNo9LUMQhsRUotHAAmT+rI+YkzYnd1e5MroxHotriZ8KsvCKxLL68xZdkBjGWX9uLsrD0xnHqv89KCoW0+OdlVVK3SP35+QGnU+07VHcIkoU742UpUZSl9eS0ez6bHzaogta1sinVZ4Us4ky3mb13N/4umn/+a0wGy9lSnzcShqnWI1/dOmpJVL177y1n67MMpnktmGKabVnjzwhMn06QLEHFRNp7Otr3JF5NkKmkpEjuRhZViTeK7UhglDYcGUlgmQIVMpCbkTpniIlSui39HTBvmLk7Fe7+D32GPDqq2JYiJ0kv5B2JiObqoocwm+2vn2HYvjwSOzezT04pAeIRtewXV1+6SXOXS+f+EeU9H8jQ6kunwNdsR85aIhUSHx1yhR0B0HIDzNdschYVZqE08CbCEKSs7SUsxokS0q5PdquueIt9bqLIKQ9r6v6WyndkNBnjKsucXU35qaMevYHi7ZT2R7JeYni27vtw0wsvts5vIYeeeT6Rce8Du7LTch7TzPMgtB7xoJJ4lkEGEHoWfxZ6wGMACMInS0I7SYIJ+IdvAuBPxMT82/Axn3YcmGd6yg9CiarFNvkroeMdsirHxnw8K6oiCiUc0ul5/JIELjiPswEhLYVIXJq8mSAyC7iIR5/3JkMlWtc7gI98PVUbHqkOS6fVIBf3rGOIMzIq0STmJBqzLUU17YBpVDx1KnArFnmW6V4Rla4kdI7b81Lql6y3FGFUocD9YOCrY1tt2SJM0Hm6c6aAZE2ILnAKUr+4IRt6OxvQVUlRp/ci/Ka+D48kWFGXG8s03vJEeye0BZVRSGo36UY531zpDr2q1q6NiYOkxsneWN3qmWiPY2G98ScBBx/s5minEOOybv+1Dk1dPefP9e2XdYZRUeUXVHyZ/jSxG6IDxVbO+puTJIxv7ICV6foj28nLJ4YGo7FSc6avAZ9CnHe8iOmzw25u8rV0XF4OC4Ji3LS8EnuGVEv6HFEM4G3AFqTZIHdxCBGpJgkt5MU647OZ1LiC89ccoNJ7iD1Jr4fRghCqlvunB98ZCeCdXqtVnLFKpXbyLmuwrXpgkOuT+SylFyXyv1GhGwPtRDSxPr01CA3yUpcQdNsZVg9Pt6TaYJw1iycv2Iq6F4iTbTXKHGhon1Ij9aYr1yNIKRAmf37i8V48klg9mxd42w6k0WbqlRxCkSCXLFSuGE1K03TcpssSFcTuvfv2MFdtYjI4IfQyFCabF62mDsJQtpfpcRNnz7A9u11onkbQWgl1r5Yl7sIQqUz0AxmRGbRAy1/Snr2B4u2U0XYNm0CLryQc8JCRwb/VueZ56swa7qztwA98sj1Sy1moT+NqdG+MILQKGIsv78iwAhCfx1Z1i+vR4ARhM4E4RNn/sWfJfmisbt/xusY86EkdocLo+tqTEJSTgkVQBfu3IOw2EpViawiCNUaMetek+r0F4LQ7LSQw67pqGycWdEIC34oxn0jnN1MmLUgzC6oQsP6wViZfxavHj+N43MTkLokDpUFMoEozHbIhXKkYNEyIFCqXs8Hlh7R9Hz06KnH6/LIAURWhRH6YrR5TX8o4A0FT5Em0nbHxWmLqdPVKFkuL8vLQHxIGO5s2AyRwd6xRrQ7qJ2D9tyS02EoORWOmB5FCKnnHBtOWsuchA7oUc9aC3htSfXn2FNSiElnjkDJgiokuhID30/GrFGN8ePMWNFjBHqYQA8UrEz8uVaaHorN/ZXj5vKEk5XndIXDgct1xiGU9nlkdBwaf5eEO+5wRkN6/zCClxxByJPOL2Qcx/qiXFF1SxO7It4N7sTXrAGGDtXfE74fQheoekrLbb99Vh1CzDnFeorj51Y9MDRZ7J5arqARgtDVs06uTxTbipSMcr/98AMwbJhKd3/6SSNDTVkFwUmxSfyhkTQQmxCBUqwD+Ras8zKgesfS6Y1gyGBHtbtVaaK6yXKpnsy7AVsIQgqeSn5uhen55wEy6bQzuTrBamQzozi1s1tm6rbqfmq0bSsJQqUrFj/M77wDTJokP9/5vzKC0OgI2ptfOi/Hjt2L0aOPQK+LQyPSWbUGLNpWjIhue1492Hiq3/PmcY8dhIlc7X/yiTYsruiGtGv3rxxmzrlA1uX61+iz3ggRYAQhmw8MAQ8hEMiHitIhLEcQ0vBY6WbUcoLw7z0Ia8wIQg8tI0ualbtAd7igFIc3RmDD/lIM6uZM4JglCEvKHIgIC8L24nw8ns4FRdo3pRXSV9S5ubSkUyYrkdNj6a1KzweWnro89RGmRzaX8sgBlJwsH0DUpYZsLjx6NPC//zk3QlYl+3W4Vfz0U/lAOTaL7U3Vyz3KkJOvXlAwShxV1W4dX2zS2lqLVhsA4ft1dFZznHy/qVML77xbhYn3B1dbGj71FGdAQ3pyCntmdRJirEba2EEQUl/0jrFcvy/5pFc1LtJkNUF4XUwTPNg4ERuLcvFsxnFRc8uTuqFxiDUWlWpj++uvwGWX6R99KwnC1g+loe3DYstJJUm+TeqOUTqsQj1NEL7/PkDu1k1ZEJLPXvJFr5UUDulx4wDa3oWJlJj0d7k0C1MxFa9U//QJxmI86jSefBPEoUnXghzpIlf/FUMd1S4wpYnqJiuKMJnprUkQkrnrnXeKWW36P7kOpyQHPG8OKRRE6aGNFvZGfrfoMmVGcWpETHfktep+alRWKwlCJScN/DAL4/Pycl5yCfDbb3VSM4LQ6Ajam18YlmHChApcdRUXlNfdBOEjjwCvv67PFa9F24q9wBqsXc/+4Kl+yx0V770H3HuvdieXrqjEjdfWPa6MqOdASbG+cB/atftXDjPnXCDrcv1r9FlvhAgwgpDNB4aAhxAI5EPFSAxCGh6vJgj/2YOwRhxBGFxRgbDycpTSs2TBbdNKywSl6Sq93JIIxfoexmP86QM4UV5aW/WlUQ0xLb61h1aG+5tt06scJ3aJNUXLVlXi+itDsONYGc5tF+4kFP+hcMdr+Vj0eEzt79M+L8D0WzmXpGMWpmPZXWIlOV+uzFGFq0/uqXZluaFXd1Tk6vRzZjM85AauXTtzjej5wNJTs6c+wvTI5lIeOYCsCv7okmAGC1sx0H47yPqw1EMejY1NwKiYOJBrx5ahEV5PDlLPhf0qywzFpr5iy71Fi5TJAn3I6c8llGXrpZ1R/K+8m1FvJAjbLGmPRVOdXVtbTRCeExGFuc064svcdCzISRWBK4xBqB914znXrgUuvVR/OStdjFKretzEU77/RDXEr0V1sZqVJPY0QUhWRBMncuQXkWDCpOlaTO/errB/y8XDpqxPPOEcgi8MZSiD+PFVEk7iFDg3ynwTWVliw/RgVKIS+u5L14xy4NtvnUdKzwOv6lJKVv/h4cCcOQBpafv25fw68vGEqcFrrhE3KoeXkqtu/UtBNee7uB8PON61pDYzilNLGrawEr1T28ImuXksY+1q9qEqTaMffwSGD6+TkncpTH954AFg/nxxD8hCW/gAIzUVaCEJnbh+PTB4sNU9Z/UZRcDudaa2Bsg1Lz3Y0lonBw8CnToZ7Zn359fqt/BMcndv6H1JqOTI0+vtp7wcGDvegS+/CELTBAc2bghChw7u7oFvtGdm/QWyLtc3RpVJaQYBRhCaQY2VYQhYgEAgHypKh/BjZ47ir5ICJ3StJAibXpmNyqJgZK1tYHgU2zySijaT08UuRnfsQVjDSrQ8cgwz734ILY8lY92IyzDzrZdRER6GZidPYcnW/cDIkbbeqqWXWwr4fuKEvi5KldVjGsTj/kaSL0h9VflkrneWlmLSjWJFFa9IO5xagU4tnJVRvL6nqsqBSe/l49e1wNUjgvDf8dG1ivxjhaXo0acKhQc5F6V3fJaBhbfF12K0oSgX87NP4wuZeFOeAnLH4Qr06qBP+SaVUc8Hlp5++S13JAfQZ58Bt92mBxbvyWPFQPvtIOsbJjWCsEt4JM6tF43xDZshPMg57oi+FjyTa1ZmMtYUZtc2vuPmdsjZzD2goJg1GRnui10jxFjtEYY3EoRn18Vg93jnlxoka8qiJjjyQqLhAVYiwugBk1wMwo+bd0abcOXYjYYFUChAyumLybukzmTWgpAs6+Re3OslCPWIV54dgo3nnuOUdeNGzu2nNLm6Daq5EZ02DZgxQ9yiZnt693aViv77X846mBKRvzS2pHx+7TWxLA2RjWw0Fv3xMqzBr+DMSYVNnHsuF1eOUiccxEF00TMcuHtUOj78tu7OxRdyiSDUBBFAdDRAgbookQklmXZJE73gszGQ18tPZGPqfxvqwkkrkxnFqVad7v6dnDW0lrx7pHiobdrY69DAaoKQPLTTGiMnDhTzm/7NT6MvvgBuvVWMLJH2Qs/vcgQhueGl+KEseRYBu9eZ2vb+9NPAzJnyBGG3blxMTzKUbt7csxjZ1bqeo0/P1m+XfMKQtRMmAB98YKwltZi+xmry39xm1l8g63L9dyawnjGCkM0BhoCHEAjkQ0XpEL711H6crihzGhErCcJLjnNaBiOvvHmBWt6bjvZTU7FlUBeUnOQIJT4G4ZOPTsOw5d/Vyv70h28jv2EDzL1eEEho2TLg+uttmXFDhkAUa+Xdd4H779duKruyHKNTnIPGuMPqUVs69+SorHQgNLTO5Ub9RlUoyOIU86ezK5HY2Dn2md4Phe+ysvDRhgK0TwrGc72aITbEmXxT+zChWIjp37rP/ejmQ6UY0NF4TLw/svMwuLEy6d7m0VQcf137y7L3l0fwz41++rxRbqApEn3//u6Z6Fa1oudLWqutCy5Ate83swEvter38t/vPn0QR8pLnKQcFR2HKXGcBY2vJnr4MK3GXWXRv+E4/FwS2hZH46XpQRgxwn29EhKEfw7rhMIDzrFkSRpvJAhzttbHDpl9kJfVzP1FjghrFByKb1p2x9biPDyVfkw0OF8mdkWCG2IQ/vGHMesVswSh3Ct84fibmZnlOSE4/XkcQqKq0OK2syhNC8PWwV2dqtJyDWimbSojtxXzj5soXlmTJnU1X3cd8PXXGi3p3dt1XICEfSbygWIjClMscpAD8d3mcvyMX3B5dTZhE/TvbduAqL/+QI+J+k2d9jS/DD1S18iOhxJ+te2S6QVZCkqTjr7rGs+yMtviD39602rcuGiEZeGNzShOdWHg5kyvvMK5s05IAL76ChgwAFAaZqtEs5ogVJNLbo+TTldGEFo1stbXY/c6k7Pk5nuRkgIkJsqfKV9+Cdx4o/X99aYab7qvDF8tkNnvBUJatfWb7Te9KSEZbHxXYlY0vyhnZv0Fsi7XLwaddUIWAUYQsonBEPAQAoF8qCgdwsOTd1fHW5ImKwnCE+3b4t1nH8XsOyYaHvl6SaUYsOEAdt/VBmd/ja0uX6tcbNNbVF9Km5ZIOn7SuQ3y+0RfpPSknJTk9LwzIgKIqXNTaVgwAH/+iWrlK1XXrx8Xc4IeMWslJUuWQCIICaNT6VV4Zk4pGjcMwiuTIxAezhGGecVViI1ytuKx8kNh7NxsfDZZngSM6V2I/B31tYbRst83HyzFgE7GCcK4i/OQtV6ZINTrGm/oiV34qVVPy/rj9opIQ0Oap9JS7sktrW0+ySlfN2/mtFS+lPQqkbX6RF+5vJWFVl4/+12OkKEurm55DqKCnR8k+Fr3hZaEtzRoirsbaT8OsLqPwrNt74OtkLFKfo+1iyC8MWUf0ivLTXWrsiQIf3Rx3gd5WTN/boA997SVrTuybSmKjznv4XIEYRiC8HPrniiuqsSIk3tE9a1p1ROhVq11FRToKnTRRfphMksQUgty3Rl8eCeCTYRapDvAn1d0QtEhjnhOuDYLbR5Jw9ZB3USdob4RCSrXtqv3iJ9+AoYNE2MnrJO8Xb78Mmf1Qa5HSQGsmEhD3LKlvoGYN48733i3mhql5PreALnIhdjC7Qr8iJ9xRXVtstiYmI9xyEQW4mol7NwZOHCA+6/qmOTnAw1k7jSuDhovCZmChdiw19N3BS0qC5MZxamFzdtalV3DwAvtToKQ2ty3D/jPf7ipS9NA+EiAfmcEoa3TyaXK3bHOrroKWM2FORQlNatqOkN4q3CXOujFhR9alIs5d3B6HaVk1dbvxTAEtGhm1l8g63IDerL4eecZQejnA8y6570IBPKhonQIX5G8C2UyNzArCUKaEbmNGiIuO0MxjknvZUew44Y6K6YLsBHLcR0OJ3TCG6tfxN+LuiH5nYTqyaVEEJ5s26ra3ahq6t2b85tE5CD5hqGbuwuJYrWcPs15MpV7+CxXtRxBODgqFi/Gt3FBEv8pKrUu5Htm5YfCVxuLcdNF8tYt7kZyy6FSnG/CglBNbzdw215ENK2QtdrtOCMFh5/lLKb6r9uP1/u1wEVR6h9p7sbEUHtSIIQTRQ4kij5PfqJ8KZlQ0rIvbmcEvj6binkF6aIfViZ1R4yMlbEvTQ+S1eFwYE9pIcKCgtElIsoj4s/LOoWv8zOr2977QGtkfC/vbs8ugjC5vATjTh801Xd6J7W+XS+nskKSL/37WOx7wPmcJivsHTc5W2GruRilhpbkpuP9mjiEUxonYlSMwPzMVC/0FaI3EsRp6E1WE4TU7uAjOxFs0LN2zp/1RfdEquecj/7FnrvErmHJUIzehdlBEFKbffoAf//NoUduPB99VC+SknxyAcy0quLNTjTyvfoqF4dQmNxFED6IuZiHB0Vt63IxSvGBzztPLDQFbzt1SgsV/b/rOUu7dgX279dfZ3o6EO/sVlV/Bc45zShOXWnP3WW1hiE4mHPlSa5yjSZ3E4Ra8jGCUAshz/3ujnVGltxy7mT5PZHc7j73nBgDPq6t55Cxv+U5+zLwUHf1fdPK7377e8RaMIqAmfUXyLpco/iy/L6DACMIfWesmKR+hkAgHypKhzBZD5XD4TTSVhOE1MBF+AMb4fxsvdu7x9F0RG4tmVEfBSiA2LqvzaTTODGXs4hwiSAU9pSeeBO75+YkRxBGB4fgu5bOcXTcLJrXNGeXYo/v4NJNxbjxQu8gCDceKMUFnY1bECopWAZu2YtL20bh6SatUF/mtbxUaf1Tqx4+F3etdqLKxRQSWgjKgURf63LBqbxm9ksEUfLTZ1beAP7izi8qwg2pB1AaylmRJIWG49MWXWpjmJqFlJXjEKh0OPBNfibezT6N43Oa4vgb8laMdhGEwnFYnX8Wr2WlGBoaqRvRHov+RdzF+bV1ZG+qj523iInA7guOIf6KPNnHGFoEIVWcU1kBsp2Xc4VtSHgDmemNxMCB+gvYQRCGNSnHhdudXa2rSXVmRUPsnyIOahZcrwpVJWKPA2ShRFu/XfcI2pIp1l+jRhxZaDppsSRqFe/ZA3TvrphD7miUIwiH4Qf8hGEgV5AUc0mUTJ49U/Am3sYUUVVSgrA5TuMRvIFEnMLN5x/jPADwQQ+FJTdtMjZZtQZDD+bU7549gb17tWoD+EBT2jkN5TCjODXUgIczqw0DPbj85htg5UqA4rRpJSJYKP4nn6wiCMeM4ZxTuJoYQegqgvaVd8c6o/cNSTJe7Pk9sajI2fM/laG3Ef6c5uzPwEPdlAnCjz7iYjCy5L8ImFl/gazL9d+ZwHrGCEI2BxgCHkIgkA8VpUN4WPIulMoojN+4aQLO3bLd0pG69f5l+GK+czzAi3bvRmhMFQ5OTULqkjisx2AMxh+itm++9mt8+b/rEIQqDN+9GUUx0ZCSmLosCKU98oCynLkY1Z5Wdin2+JaXbS7GmAvsJQiDI6pQVersKlXa+1nbM/DEeU0QokdxJSgsl73vTwcR3bkEvLtaaZ6ojiXov0ZsYePTrm3pibnUDw91mjTElORAIutB0pB7a+IDWvGyr1oFjBxpnbQ2KTStE9C+mugcnL/lD6xLikNMZCQeb9IKvevp8Attn0h+WTOdcRV5wdg8sBsqC8Uu/ZLuTkeHZ1Kr+23n3mOGIDz1WRwOT+M0eeRq+rz/HRFtIbnbo/DP9R1FY3bB33sQ3rjSNEHoiQlA7s6MOE9whSA85xxlnmXQ/l0IiXR+oKaEyZmVDbF/spgglMurZK1G3jmzsz2BuEybJsk3UU0a99cjR4COgukqF4NwOL7H5tjhSEsD6tWTyEleNm691TBgczAJD2GOrKh0rMk9AlRsxGpNOcUCIH+QaolwHT8e+OQT9XzkUzI31zA+egqYUZzqqddb8ihdd99/H7j7bk5KCiFPJJ1WojdhwgcPVhGERE7OnKnVuvbvjCDUxshTOdy1zqTznUKU9O1b12vyIP1gjdG1VfPOU5jqbfetfRl4WGJBGBzsQMOGQbjjDmD2bIAsiVnyXwTMrL9A1uX670xgPWMEIZsDDAEPIRDIh4rSIXz36YM4Ul7iNCKdd+7Be6Nus3Sknhv7Ml769CmnOnnlU/HJcGwd1BVyH3dUKAgOPIlXMGzEbzjRoS3GzXlfVFdGs6aITxO7j9PsgJcQhO3C6uGjFp01xQ2UDHYThMu3FOP6gfYRhK0mnkGz0dnY9p8umkPW57tDuHEAkRU6YxHV1CiHEa2lG2Li8UBj7umpNE+9lqUY8EdNMKCaeuxU0mt23tUMSlomNX9m1KYH1r1mV0lhTDGmli7l/OPt2gV06SJPcmpWppKBrKbJejoAk5mP0QCEyeUujzq5B3lVlSg6GoHUrxqjYB+310a1L0XbJ1IRWp8j8O3cew6WFuG+tMOyfRkQGYMtxXWWgcJM+XsiUZYZikYX5jvFyXNUApvO74byTC6AXlSHEvRbc7B6n838pQH2TKiLUdjv5wOo36lUtn07+61n8CZMAOh1vp4UFOrAkCO7qrN+0qIzWoVJWST1Wn7+GbiCC3HnlILCq9B8TBYiW5ch6c4MBGmEh0tfHYt9E7VdsfPb+7FjQDuB91EiwRI4T/WeTydOAG20+6IqKG8qqZJJeEQ2RDay0ViUe+m41Rj08gj5I8HgoyW+Yrqnz4bYHFHoTq/nc6MwCiv1jYEdZzU9wqHz1okRrRGJ2rz2WmDFCnUZGzcGzp7V1w+Dufz9rJKbWuTwgoaGTzQMeggCsvK78UZBuWqbbHGi70ejaft2Fy2EaxpkBKFR5N2X313rjLabjz8GDh0CXngBoHDg0kThwcvLdYeZdR9INrX07Zix6AAAIABJREFUxeFs3NpJHKf601+Kcft/7Ps2t6krrFqTCJhZf4GsyzUJMyvmAwgwgtAHBomJ6J8IBPKhYpQgpBlgh5tR6Uda40vy0PPjY7UT7sDjLZG6LE52AlJZJfLQ9Iy1Q/mgIYycBeHtsQm4s2Ez093wt4JS5cHfu6pwbg/rnhJ+s7UY1w2w7yOEJ72lLuvUxmnskUN4MikR3SLq12arcjgwPfME1hflIgxB+DSxC5qFhmPxYuA2Gf6e2l3V8hzUD+Y0rUokolAOTyurXZq7SgrMdeuAIUOUyTUPrHvNfhIxKNRyUQHyszV6tGZRpwxjxwKffipf7sABoHNgPkYw8zFqHHxW4s2zKVhZoK04t3vvkZ61F0Q2qI71m1VZjhtPGYgxJhjS7E3RODqjBYLCHOj4Ygoa9C6u/ZXIxbwdUdXkYlTbMsWJYHe/tWZgv34AKb+lKSq2CkW54nM2OKoSg/ftqc5qhiA8ehTo4Bye0ant1pPOoO2jaaqip/2vIQ48rN+CkCoj921khNaypTIfpIWXLb/v3AlQTGylRKZ/5CeUYg4qJboI0KMSlSS04GyELGRBcr/+7jtlc1KTBCGJI73ri45cI/XaeVarPTD6/HPg9tvVh95Gd+X+flbJQZ+fD0RLDPopJCWFplRL27Zx5Mv8+Vwuue/EDz9w1Fom6l3Phw/r27u06mMEoRZCnvvd39eZ55DVbnlXShl6tQwXZfxkTTHGXmbft7m2VCyHOxEws/4CWZfrzrFhbbkXAUYQuhdv1hpDoBaBQD5UvIUgbN0mGcnH6yylhhzd6fRqXImYDGtUjvJs7uW+ZclO5YOCkHIE4Yqk7m6NQWQZfjZVRK+Ie/SpwoFdwfhyqQM33uD8ItiVpldsK8a159v3EWKGIEy4LgudZqTgk3ad0Dacs9JYmX8Wb0riaJFyWUmvVV7lQKjgR7l8t6XsR0oFp7y+p2Fz3Bzb1BUoPVtWTdFIik8l15xS/z6e6sX+/RwBePCgdVaNvI8uJWyeeAIg16wBmMx8jAYgTC53uaCqEiNPcqSSWrKbKJOetYMiYzG9KUcQXpdiLP6dVl+M/G53v7Vkee014PHHnXM1TqxE1imxGV9obAUu2snFYluS2LX6gYrRpJcPGrB5H+o1L1es/sS8pjj2qrb1sweudUYhAf74Axg8WL4cEYcU5PCGG4BfflGvmxjQSOW7DLlp42MLxiETmZDEfLKJIByKn7AGQ2tl90qCkExbycRVmkjYfftUYzxWWxguX269hX+NLP5+Vkn3hJMn5eO0kavBRYvUlwAReaGhQNsaA27Zh6QOh+K9Wal2suiSs/QyutgZQWgUMffl9/d15j4kjbd0srAMraLF94nfkwsxqGXdI1njtbISvoSAmfUXyLpcXxpbJqsxBBhBaAwvlpshYBkCgXyoeAtB+NSCOViRNwrBYQ40HZkj61JKiSA8mdgKLU8lWzYfqivygCZJjiD0tMLQWlC9v7Zv/yzGNf29iyDkURu4dS829e9e/V+aK0KrCYrZ9HuXnoqKDul0llPMFlRWYl1hDpqEhuH8yAbeP1hqEqppnik+UF6ecmkPrH0nYW66CSD/WFamnBwgNlZdcekNfbeyzzrrMvMxqrNqlk2CgFKsXT7bhZENMKNpnUtOOwCUynBRZAO8VNOmlnx2yMPX6enznjihq6927mHzjhVIPRwq+iGmVxH6fHsYrcMisKiFtstsOdz0EoRUdtC+3QiJqokhK6ls1x1tkbVW+8zyie1NKRaeUPhhw4CfftKeiipMxoIFwH33cVU0QQYyIHkQtHIlMGIEl4F8PAqTkYGTkTIJJzEE67EDvbHXwd1pqpOReu0cTDI9o5jE0qTmojwjg7tXEBtlpB/aoyjK4e9nVVYWcOWVXDho4lmVHCXocYd8/DjQunXdcBglCH/9VT4spVVTjxGEBie/G7P7+zpzI5SGm0qvKEP/R7NwYg7nPanF2Ez8/H4kugu86BiulBXwKQTMrL9A1uX61OAyYQ0hwAhCQ3CxzAwB6xAI5EPFWwjCnMaNcO3fa1UH1Q7XpooNWvUFaGCaShWT50ZE441m7Q3UwLK6isDK7cUY1c8egjCkfiUG7eWsZ4y4GBX2iZ+W/X48iO3Dxe4g//pLPi5K6yFFOL5OHNjC7liOro6Dy+VdUdCR37kWXKxGt6fPPgPIDagdqbQUCA9nBKEMtmY+Ru0YokCoU46A6xgeicNlxWgSEorZTdvXWkrbhYeci9GZHiYIX4pvg4uiYu3qsq56168HLr7YOeu1jxfif6+KX++PWLgHvYdH4r64JLQMi9BVvzTThx/CkHu/9s+eQtIdmaIHZEXHwrHtkq662vfAtU6XXKJMq1bJW7gLhb/nHuCDD7TrnjYNeOYZ4KmnACIe77oLmDy5upwQ+3ikIx2SIIzUBlmdU3rgAWDevLr2XDlfpVKTRSQ/6YzUa+dgkgtXORMxJYLwmmuA//1PezwsyMHOKg7Ee++tm55KsJIX3sREcwQhDSl5cqfyROQJk1VTjxGEFiwIm6pg68wmYHVUm1FRhjGn9qNgXz04KoMQfU4x3mnWAefUYxaEOuDziyxm1l8g63L9YtBZJ2QRYAQhmxgMAQ8hEMiHitIhPOH0QRwtL5EdEaNEXUFMNDYMvQTDln+nOsKXHN+h+rvRdl2aThQRnHzTuClVOhy4LHmXqLVHGidhZIx83EU3iRVwzaz6uxgj+ygThJ1nJ+PgE61M4XLR7t0IjeEsIMwShFKFhx5BZqacxNOJde57qYxUD9fvlwPY9h9zViB6ZHB7HiOKRqlwFNyG2FZ3JwqoQ23bkQYNAn7/natZDRurNF929MHGOs18jNoojl9XLUcQ/tSqB9IqyhAXElYbJ9VOEFwlCBNDw3Gqxh2zGTn/27Qtnkyvi7FMdfzaqieCXdm3zAgiKUMelvv3d65o3v5MbHulCT75hPstKqocX3zxPYYOHYpIFTeWWiLRNtu3r1Yu8e9tHk1Fm0nptX80cpb6xPYmF3OWeisUnuLIjhunD7j69QGyJOQTkXFr11bHZrvzTu6PTXEGZ6AR65oGiz+frJ6napZ5Sr20ezDVXlERpuTClU/k+15qZalvdAznYmcVB9n99wPvvacOX1oakJBgjiCsquLKkUVjnOAz7IsvgJtvNjxssgUYQWgNjnbUwtaZHajqqzOjohxjToldvc9J6IAejCDUB6Af5DKz/gJZl+sHQ866oIAAIwjZ1GAIeAiBQD5U3EEQ8sSfFsFH+cJKy/D+VTehzeF/8cP1V+PV2S+gWcppnL92Ax56/hX3zZDPPwduvdVt7R0qLcK9aYdF7S1o1hGdIsSWX24TKEAbWv13Ca7qw8X5k0sUQ7A8NwTl2SEoywjDjhs6aCLV5c0TaHZtjijftv90RtFR5XaUKv3k6wqMvS7UkAerdQU5GFK/oajKgRv3YctF3ar/1uqBM2j3RBo87d5OE0gjGVxVYL7zDvDll1xALjmfe0Zk0ZuXFIyklXIllZVxlgzt2gHNmnExBSMiOCuSRo24moODlV0ou/lhhCtdtbKsmY9RK9sPpLq8wZW2VIYBkTF4uWm76mH4p6QAj5w5qjokvSPqY0epgHTROYArk7ojJoR7ePRVbjrey+FMU5YndUPjEIvjKOuUSZht1y6gVy9xwcYX5+HlbwowIbZF9ZZ49mwZEhJ+RkREpcsE4dGjQAftI9SpJ3wsX/pBL0F4wQXAxo0mQHF3kZkzgWefFbdKA9OjR93fyMo9Kcm8ZC+8gFVVIzByer/qOpohFanQYTWvRuRRXMRly8zJNH06QBbu1Hc96c03gSlT9OQ0n0d6h/j+e2D4cK4+OqfpTD1wANVMa0Px/cp8o9ol2VnFYTRpEkDXNLV0+jTQvDlH6NHeZcTFqJR/Jj6Y3oySEwarEiMIrULS+nrYOrMeU701yhGEcxOYBaFe/Pwhn5n1F8i6XH8Yc9YHeQQYQchmBkPAQwgE8qGidAjfdfog/lWwIBz98ReY9OJsp9Gq6NoFofsPOP3dCEGoRSK6dYrY/UJZ0Jn1hTl4IfOEqHuftOiMVmHGSSS3YuRnja36qwQj+6oThHyX83dH4q+RnTQRECozKXO9oGBsva0Nsv+I0SwrzRAa7kB5aZAhglBuGksV5CTTD60ECkjDknlZAVcJQmF3yBcbuWazO1khs549S8093dCh+mJb2Y2Fm+s38zHqZhH9pjlvJAjPj4zBKzUEIQG9oSgX0zKOK2JOEdkqTYzIb60oTmyQiZLuKUIxuyiEmjD1/20/otqV1T4gsXKt0HsEMwp3MwQhEQpz5rgHR5dakZsfUrfXZLEW5jqh3BM7sRs90RyncRqJ+sQeMIALECdNJNPBg0B3QUxBfTUayzVkCEDB4ey22CMXrnRW8knP2WqsJ6ZyW7n+TAngJYXIU+7cuerCpKcD8fFAfj5AoaddIQjt6DYjCO1A1Zo62TqzBkcztTALQjOo+VcZM+svkHW5/jX6rDdCBBhByOYDQ8BDCATyoWKGICRXR2vbnus0WsWvvIJIinUiSXoJwmcXvIEZ9z7ioVkg0+zJk6690jbQkzUF2Zh1NllUwtuViQa65zNZV/5ZglH9lQnCFrdlotOMU9X9KTwcgT8v13bL+fSZYxjfMAEp5WVILi/BZfUb4fL5adj/UGtTuJCeSq+Ouft7x7DnXonGF4Caiz1TQnlbIb0A6ZXbHcpBV2XWu1/JsQBCHNzRV724uymfmY9RN4nmd808k34Mm4rzRP1yt/WydP/rXy8G/03gLAj5lFVZjutSxG6u+N/MEoTu7qeZyUOGanu4ULmIbFuK/r8dQHxoGJYlcRbnVq8VM9seTxCWZYRiUz/9hJTXb21Sf4b8AGZmiv0c0t/NACeZED/jclyBn9ECp3AKLlgkUr08uKNH2xuPj/f9aGZymylj5MJlpn6DZaxefwab95rseghC4VSh5cIIQq8ZPq8XhK0zzw1RZkU5bnByMdoePepFe04o1rJbETCz/gJZl+vWwWGNuRUBRhC6FW7WGEOgDoFAPlRMEYQA5Cz9Sp59FvVmzBBNrS/uuwMfPPVQ9d+8yjpQ7wJwk0bJG6wq9ELiz/lWbivBqPNVrDaDHGhx21k89EYxWpZG4ZYk7RiRclPo2bRjmNncmbjTg60RfRUpUuWU0h9mp2JxXl0cpw+ad0KHcOXYi3rk8qo8FihPRf1xxz7giszffANce62+IcjOBho3Vs7rjr7qk9Rtucx8jLpNOD9rqNxRhaHJu2t79U1SNzRys3tN6Xnbr14MZksIQhJQ7lymv09s1ALzsk8bHhlfIAgPHwYumJwDVAWh7ROpqN+hFHEhofg6iSPirF4rZrY9niDceVs7ZG/Qb4nv9VsbuRaVc7NZUsK5ihYmiv1If3cxBcGBRKQgBeI4xYarlYJL/yfXm3nixwCG6xUWyM3lTMECOFm9/nwVSvL2+uOPYulXrQKuuor72/r1wODBdb+Tc4Sf18hYbzsceO01zps8n95/H7j7bvuRYRaE9mNstgW2zswi53o5RhC6jqGv12Bm/QWyLtfXx5vJr4wAIwjZ7GAIeAiBQD5UrCQIixd+hMg7xa74rjiwBWX1OMKFEYTKE5wRhB5a/JJmP/uxFGOHSxRxMqKR7ut0eSkSw/XllVZxsLQIXeqZiy9pBUFYVFWJBdmpOF5egmHRjTE8WoUw8o6h0S+FRe7XRA3arVlOS+OC5ZhJFGuQgu3o1bRr4WN3X8300eYyZj5GbRaJVW8jAtLzdlj9RniySSunFkee3IOCKmdnossSuzm9cNcjri8QhNQPKT6twyKwqAVnLW/1WqGwqVdcAaxbpwdBLk/fnw4iunOJ7viDfM1ev7UtXw5cf70zEHKCf/stcM01+kFTyBmJIqzANdWWhKbThRcCGzY4FydyMDbWdLWigtu2Af24mImBnKxef76K5XffOYeHVlvf1ffmYHmC8OxZ4KabuD1o2DBg8WL38NCMIPTe2cfWmefG5mxFOa6XWBC+ndAePZkFoecGxc0tm1l/gazLdfPwsObciAAjCN0INmuKISBEIJAPFbMEYft9B/HhiBtrYZz1xgxMve8RBEXVkR5/X9Afj37xfm0eryII778fmD9feyEcOwacOAFcfjlAiviWLQGywHnxReCGG7TL68zBCEKdQNmcbe7iMky+LVyzFb0EYetJZ3B8ToJsfW0mn8GJudxv4fHlKMvQF1PICoJQs4O+moEPNmO1/HZrlkm5OmiQOakrK4HgYGNlaZ8uLpYvY3dfjUnqltxmPkbdIhhrxBYE3sk6heX5mbV1f9GiC5qHOT/2mJWZjDWF2U4yENE3P/s0luZlGJLPVwnCBxq1wA0N4qv7atda0fu+gQd8yNGdWN++lyH8vX5rM0IQkttRCrDmYroHC/A+7nWtlr17gW6cC1qn5IrL0R07gE6dALKWZMnW9edr8NK1JzS0TuqJE4F33tHohdwm48FNobAQiJZ4TSQP8K3NRR/wtSH0anntOue8utNeIlx2ZTlGS9y7z0nogB716nuJhEwMuxEws/4CWZdr93iw+j2HACMIPYc9aznAEQjkQ0XpEB6evBsljirVmdFp114M+O0P7D+3J/4ccgF+bdUTU75ehHFvL0BBgxjMm/YYMlo0Q5fwKFQ4qvBBona8NrdMxTlzuJfIAwdqN0evn8mtkVzKybHsdTQjCLWHwh05vvylFDdfrs8q8FR5KZJkLAi7LziGtKWNEdWhFG0eTsPvnXvKip5bWYGrj+0FgoBxh0/hruEjdHVRL0EY1qQcZ9Lgdvd9ujphVyajWma9ctitRPr6a/MPDszIRsF55s4FpkxxRuCxxziLxEceAfr00YuQT+cz8zHq0x0OcOHJKnBB9mmcKi/DqJg4DKnfUBaRfaWFmJh2xOk3Ivo+yUnDotwzhpD0VYLwscZJuDKGc6dt11oxunVTfN299xlz021mqzQ0wK5mvuUWYMkS51qUBDcKmqvyKZUnUyh6QCeXtmzRd9eWK5uczD3KY6kWAbvWny9CXFrKWf0lJQHd9YQi9TKCkDC/807g44859K++GiDDYJY8jwBbZ54bA4fDgXGnD+JkRWm1EA2DycV5N4R4y3nnOWgCpmUz6y+QdbkBMzECsKOMIAzAQWdd9g4EAvlQUTqElWLvqI3YL6164rLkXU5Zbo9tijsbNsfZ3r0Qt9P5d7fPAorbsmcP0Leva02/9x5wr4svr2skYASha0NhVekla8pwy1B9FoRKBCEfH4lkGhubgDsaKijOaoTm1+A114zS1Q2avjVee1XzD9y6F5v669Ga6GrW+zORC7Lzz7dHTjs1y1Q37UV//21OdrOy0URSs8yg39LTnZ+4m5PSq0uZ+Rj16g4x4SxBQI0gfPNsClYWnDXUjq8ShI/HJWFEtHcRhIaAB3fl00UiGK3Yqvzk57BJE/navJ0gpAcnaspbs4pdrXqtwt6H6mFnlQuD5YUEIU1xCiFdXs69ERNaRbrQU1bURQTYOnMRQBeL7ykpxFtZKaiAA+TBoH9kYMeedRFOnytuZv0Fsi7X5waYCawbAUYQ6oaKZWQIWItAIB8qVhKEa1r1xOUyBOFV0Y3xaFxLHHjkIXR5c461g2emNlK2nDrFPTt1Jc2cCTz9tCs11JZlBKElMLpcyeIfy3DbcHWCsPXkNBx/uxkKqyoRHRLi1Oamwjx8X3AWbcLq4faGCQgPUnf/aJQg7NvPge1/ysRSkUiyMDtNk5x0GTBvqkBLCUk+qWTGS1cXzLjx1FUxgJdfNr+P3HMPsGCB3pac82lh9sYbwMMPm6/fR0qa+Rj1ka4xMV1AYHdJISafkbcgXJqXjvnZqYZqZwShMlxaW5EhoGUym31H4Wq7ussPGABs3SqfXUn4114DHn9cdxO2ZdQC1+zgatVrW4e8t2J2VrkwNl5IELrQG1bURgTYOrMRXFY1Q0ADATPrL5B1uWxC+S8CjCD037FlPfNyBAL5ULGSIPypVQ9ckbxbdrRJMbbm+CEM6NUXMXn5np0RvNLBrNKCl54RhJ4dRxta/2VXKS7vJXYxOmDzPmwZyMXXiepUjPOWH8Ef5/So/r8V+gajBKGebg8+vBPr2vdEkKtzXE9j3pJHq696fbPK9WfxYoDcv9mRtORWa7OsDAjTF7tSthqttgPE55WZj1E7pgKr07sQ2FFSgIfPHHUSiu4zSr+p9cBXCMJhybtQKiBnliZ2Q3wot8/YtVYWLgTuusvY+De+NA9Zv+mzLPB6rkltL1YTXmsPNwapudxa4JqVUatec9L6dCm71p9Pg6JXeCsu7HrbYvl8GgG2znx6+JjwPo6AmfUXyLpcHx9uJr4KAowgZNODIeAhBAL5ULGSIFRyMUrDSoqxtYU5WLBjMwb9+CtOdGiHeiUlmH7fo+4fdasIwqlTgVmzXJaf/O1fKmN56SvKRJcB8KIK/i0rxnkXVSL3z+hqqeKvzEH3eSdkFcT0Ryv0DXYQhGVVVQjTsFz0IthdFyUtDWjeXL0eWvfvv2/eLbBdykpPKk+12m7RgrO29vNk5mPUzyFh3QOwt7QQDyrEIDxYWoT70g4bwslXzvTfCrPx37MnUeZw4OYG8binUYvaftq1VrKygDjOi6ktya7t2zJhlfbi554DXnxRuRmtPdwyARUqql8fKChQb8WsjF4/aHaD61y/XevP/T3xQItWXNg9IDZr0v0IsHXmfsxZiwwBHgEz6y+Qdbls5vgvAowg9N+xZT3zcgQC+VCxkiAk5ZdS7EL6bUtxHqamH6ubDQ4H1rY91/2zwyqCkFw7zZ7tsvzbivPwpBAXAIMiYzG9aRuX62YVGEPgSFkx7jx6BKlfNkZQmAPNb8xCcLhDVMnQ+o0wtUmr6r9ZoW+wmiDs82Iatj+nHvfQGCo+kFvNPRsvPq37vDwgNtZch+xQVq5cCYxSiD1JgWlGj1aW1Qp5tBS348YBixaZw8uHSpn5GPWh7jFRTSJQ6XDIxlWm+8zxshLckXrQUM2+QhBSp/IrK1AOBxqHiC2U7Vor+flAA33GgIYwF27/pgq6q5DSXqy1z2vt4VbJf//9wPz58rW5IuOuXcDAgUBhoXPdWvVa1Tcfqseu9edDEJgX1YoLu/nWWUkfQoCtMx8aLCaq3yFgZv0Fsi7X7yYA61AtAowgZJOBIeAhBAL5UFE6hK9K3o1CR5WhESHl19tZKViRf9apHP22s6QAUyTuuoas/hkvTHzCUDsuZ7aKIPzkE2DsWJfFmZ99GkvzMkT13NOwOW6Obepy3awCYwgcLivCPaliqxAia/8ozq2taGVSd8SEhFb/v+eif7H7jna1v3V8KQWHnjUW29IKgrDDiynI3xWFhKtz8OftbTTjHhpDxQdy61GS0rqvqnKOQ/jgg8BvvwH79ql31Gpl5dGjQIcOym0WFwORkcq/WyHP9dcDy5crt3HvvcB77/nABHBNRDMfo661yEr7CgJK8YGVyEO1fvkSQajUD7vWCm1ntB3++y/XclQUUFRkbJZcey1w660AbWvSZMV2aUwag7m9mSCk2L0VFfIvoqibWuBquU8123eDEPtDdrvWnz9go9kHRhBqQsQycAiwdcZmAkPAcwiYWX+BrMv13Eixlu1GgBGEdiPM6mcIKCAQyIeK0iF8f+phHCgzpp0h5decrBT8T4EgPFRahHtlXHKtbdPb0rn5ymvTMf7N+Wh2KlW+XqsIQnpR/e67Lsv+ac4ZfJybJqrnpfg2uCjKpKWTyxIFbgVybuM+adEZG4vykFpRhqtj4tAhvI60IeVx7l9RSPkoHs1uyELcJfnV7nSNJH4N3jBhBMozzcWT2/aXA/3OCzLSrH/l1SIIU1OBZjVWldK8pIWuVw8IDlbHREsJahRRss779FPlUkRmqslkhTxkEfLAA8oyMILQ6Kiy/H6GwP2ph3CgrLi2VzPi2+DCmrNZyWOCEgRGzwZvhNKM4kZvP1avBsaPB0pKgLffNh6TkK5jdC3zSR7ALEmmdfbpBV8tX8uWQHKyfQQhDTo9uJMmK844K/rvRXXYuf68qJv2iOKTG4M9ULBa1RFg64zNEIaA5xAws/4CWZfruZFiLduNACMI7UaY1c8QUEAgkA8VpUN4/OkDOFFeqnvORAUFY3WrHpibdQrf5Gc6lSPF2MnyUow9fcD5N4sJwkuO76huQ5F4tIogpEYsUGBsLsrD0xkC16sAvkrsiqah4brxZxmtQeBAaRHul5DYi1t0QYuwCNkGlKxLjEjDr8EXK7vhn+s6GilamzfllAOJLQKUIKQ1aITco2BXQ4cCJ04Aq1YB55/P4aimaJ0+HZg2zdTYiAqlpABnzgBduwIUu0ktUb+0LC9clYjiCyapWLzecw+wYIGrrXh9eTMfo17fKSagJQiQ2+ln0o8hvbIcAyJj8GJ8nYX21Sf3IL+qUlc7T8a1xLDoxrryenMmd64Vo9zXBx8AEyb4EUG4ZAlw003q02HMGGDZMnunzOLFwC23mCcI33kHmDRJXkY6544dA9rVeWKozWjB/dpeYNxfuzvXn/t7Z3OLjCC0GWD/qZ6tM/8ZS9YT30PAzPoLZF2u740wk1gvAowg1IsUy8cQsBiBQD5UrIpBOL9ZR3SJiMI7WaewXIEgzKwoxw2nnN34qVkQLnzkAXTcsx+Dfl4rGvWi+lG4cu8mWRJQN0FIinreqohq//57YMQIY7Prww+BF1/klOjDhxsrW5N7VmYy1hRmi8r6g6WBKTA8XGhvaSEeTDsikuLLxK5IUCBrpQRhbHAIVrQ8x1Av+DX4Zq82OPhUEtK+ijNUnjJXVmpzZIYr9ZUCBQVATIyztORrjki4V1/VF9xKTRv90kvAs8+6hsjHHwN33qm/Dl45+sQTXB+kyQrlaXY20FiFtLj9dnUrR/298eqcZj5GvbpDTDhLESitqkIjg1ihAAAgAElEQVSxowoNgkMQLNgnXjt7EqsLslTbCgEwMLIBnm7SCpHB9D/fTu5cKwsXGrMipHCpZJjt9TwAMZn0+ILSP/8AvXubFzojA3jsMXv3aa1HdVpn0fHjQNu28hOfypLJqJw7ba16fXspmZLenevPlIDeXMjrNwZvBi+wZGPrLLDGm/XWuxAws/4CWZfrXaPHpLESAUYQWokmq4shYACBQD5UrCIIeUJrXtYpfK1AEOZXVuDqlL1OI6NGEBLZt3jQlWhx8pRTOfpNrqwqQThqFLBiRV1dpIAgopAIBiITfvzRNNGHiRMBeiltMN2csh9plWWiUowgNAiiRdl3lRTgIUmczGWJ3dAkVN71Z1ZlOa5LqSO91chEJRH5NfhW7zbVWSoKg7Ghew9DPQpoPdq33wLXXOOMl1FQBg0CNmyQx/2FF4Dnnzc0Jk6ZjZrDCOXfsgUYOLCuSlKmGg3QJSd9ZiYQH6/cLwoEVljoWr99oLSZj1Ef6BYT0WYEMirKMP70QRSpxGv2t7Pc3Wtl0yZg7Vpg505tQ7l58ziPyV7NA5DlehvurK9N9MKH9tpSgdeON94AHn5Y/ww2er7or7nOU4ZZN6jUllxZGiwaNLnYwFTG6BlupE8+mtfd689HYZIX26s3Br9C2uc7w9aZzw8h64API2Bm/QWyLteHh5qJroEAIwjZFGEIeAiBQD5U3EkQljmqcEXybqdRXnu2EujTR3b0lUhAymyKINy1C+ihTL7kVlYgVoEM0jU9TSg0rHBTqUs2lkkTgR0lBXhYQhAuT+qGxiHKsQFpXpN1bFxIGCK0XF3KSCAlCCnLujbG4hiamHaaWPhMBrJy+/xzZ3GNgqJkqUc1kxu3r75yDRKjClyp/KQlv/RSgPpL8ZqM1icnvZJiVpjXKI6uoeSR0mY+Rj0iKGvU6xBILS/F5uI8tA+PxBTJ2UHCMoLQuiFLSwOaN1eu79xzgb//9nKC8OKLgfXrxZ2gwIkUD1aY6OHL1VfrB08YT7ZpUyA9XX9ZrZyuWhBS/XLnFblPJTeqSr8HwNmjBb30d3ZWGUVMkJ8RhC6AF1hF2ToLrPFmvfUuBMysv0DW5XrX6DFprESAEYRWosnqYggYQCCQDxWrCcJ3s05jWX6GE/q8kkyRDFNQdltOEG7eDAwYIDs7luSm44OcVKxp3wch9KLbTDpyBGjf3lBJRhAagsvWzH8V5+Ox9H9FbaxI6o7YkFDb2mUEoYvQyu0dt90GfPaZsYrJhejMmcplXFFWarnylGvVlfaM9FyLaDx7Vt0NqZG2vDSvmY9RL+0KE8uDCATCWe7JtVJczBnaqSWl0K3u2k5VhauoAMKUHxuJytL5ReeYkUQeMA4fBm64QZ1JNVIn5VUjCOnMfPpp7Rq1rA8ZeaONIQBPrj9dAnpzJjbHvHl0vEo2ts68ajiYMAGGgJn1F8i63ACbHgHVXUYQBtRws856EwKBfKhYTRDOzz6NpXnGCUJHcDCCZDQ4rhCEN7+7EPfMniOeahs3Ahdc4DT9CqsqcdXJPdV/jywoxPfnXGhuilIsGYopYyAFglLRABwezVpQVYmRNfOAF+SXVj0RokWiuCA1vwbXtIzD3jgulp6cBWHvZUew44YOsi15hfLTBQxcKmqV0ufll9UVnXv3At26GROVt/ozVorL7a5B1TO3yQ0zWaX4aTLzMeqnULBuuYDAQ2lHsKtU7JKXWRC6AKhM0cmTgblz5ets0ADIzXU2VmvYEKA3Gh5PRoIqUoxCim1tNpHraHIhbUVSIwjJB6zQ/bVSe4wgtGIkGEHoCopW3RVdkYGV9QkE2J3QJ4aJCemnCJhZf4Gsy/XTacC6Rc43HA53aYMY3gwBhoAQgUA+VKwmCB9OO4IdEgUZYa1lQVi8/U9E9usvmpizZ7+AH8Zcg37rN2H2uAdEv2UkxGPM1jWqMQj7/r4Jr44Vl0NWFtCokdMCkLqWVIuLqLl6DG7ljCDURNStGT7PPYOFOWkIQRCmNE7ElTFxtrbPr0GyWZ1bE4dQjiA8UlqMCamHZMlDg1PO1v64vXKrlD4//QQMG6Ys/ogRwOrVxrqnh3yTq/Hmm4EvvjDWltncemRs3Ro4ftxsC15fzszHqNd3ignodgQeO3MUf5UUiNplBKG1w0BnHW3FZCwnTRQCmkJBS7c0ehNGb8M8nvTstbyQ7doBR4+aF3nGDGDaNPPlhSXVCEJyZaoWx5avR67vERFASQmXY8IE4KOP6lolS8gO8g+irOmUb9bCzioXxs2qu6ILIrCivoEAW2e+MU5MSv9EwMz6C2Rdrn/OAtYrQoARhGweMAQ8hEAgHypWE4RXJO9CmQxboUkQVlUiUuLG8ZJj/3CaHocDa9ueK5odb09/Cs0fehgh+w/gugFDRL+R1SGlfus2Yvb4iaLf9qaeRFaDGAyIjEFYUHDtb4wg9NDi89JmsyvLEYwgW12L8l0XrsFLL78cj+am4IMWnZyQUdPRMYJQApcZQHbvBnr2VJ+RRuqlvCZiUlYLUFoKhIe7Z3XoVVob6bt7JLesFTMfo5Y1ziryGwQYQeieoVTaWrduBfr3B958E3jkkTpZ+L+7RzqVVvTutVQFuaonl/VmEz2GixM8biLX+uRi34gMfNv83v/vv84u9Mkdv55zTq5dir347rt1PSQWd906YPx4IDHRbM/9uhw7q1wYXkYQugBeYBVl6yywxpv11rsQMLP+AlmX612jx6SxEgFGEFqJJquLIWAAgUA+VKwmCIcl70KpCYKwwuHAzdvW4J3R49AgOwcTflyG061b1o6i1KJv9J+/4qGu52FI/YZOCg+eIKxXWIRvzx2C8LLy6noym8ZjzJafQO5Mu0dEYW5CBwTVfDAygtDAgmFZLUVAugZLw8PQaVQBMlY3rG3n/N/3Y8ugrtX/7z7vOPY92Kb2twu278XGPt0tlclnKlOK6WSGzCJFZ6hGrEmqNzkZ+O47oEcPYPBgZagefRR44w1zUJqR31xL+hXG7pTJbF9MljPzMWqyKa4YYblnD0A+Eck6kyW/QIARhO4bRjldf2oq0KwZUF4OkMfov/4CbrwRuOUW98ml2pIRco4sAJ95xjXBP/gAoNi6RBR++inQt6/+/V7YsnDvF/p4Xb4cGD1an4xyfTdSXl8rfp/L7WeVPyHKCEJ/Gk1b+8LWma3wssoZAqoImFl/gazLZdPJfxFgBKH/ji3rmZcjEMiHitUEoZy7TBp+LQtCyqNUln679Nsf8MQTLyCitBSfTrobHz86EdPj22BQVKwiQUjlrl20BA/MeB3lYaF4dfaLWDvyitrZ+HrTdjgvkov55imCMK+yAqNS9jqtEK9yS0aunjp1Aii+Imnc9LwW9/I1703iya3BIUd34uSCpig8XA/Nx5xFowsKRWuoPDcEJcnhqN+pBMERjtrfvKlfbpFFK66RESH0WPxt384pWfn01VfAmDHyrRhRBktrcCcZR/GjtmzRRqqgAKhfXzufXTmKi4GrrwbIpR25ek1KsqwlMx+jLjVOjMWSJRwh/eGHwLhxLlXHCnsHAowgdN84+KSu38iZ8OuvwKWXWg+oERn41q04j+TapRiJQitH63vrdzW6/azyJwR9ctPwpwHwnb6wdeY7Y8Uk9T8EzKy/QNbl+t8MYD3iEWAEIZsLDAEPIRDIh4qvEIQ0Nern5iGsvAI5TRpXz5SX4tvgoqhYnOl5DhJ215FsvAUhP53CS0rgCApGeYTYZd+dsc1we8OE6mw7Swow5UxdvBd3xSDcUJSLaRnOsb28hiAkBY40vowVyiIPrXVvbFZuDarFpWQxK2tGsahImbAyO0etVJ6aqctKhazeyf7330CfPtq5H3oIeOst7Xx25ZDiqde1nQ55zHyM6qhWPoucK1u9cbxMN/p/9q4DyopiaX+khSVnlpwzgihi/BUEAUEFMwYEFRPmhBHDEyOYCJIEQREVUTAAKgZQTKgIKqgoILBkdsksLLD7n9rhsjdM6OnJM9XnvPNwb3d11Vdd0/f2N1XNA91AgAlCN1BW5pgyRalEGd9kH/uuaW1mT9i+HahYWEXANh3N6ECTtm8P0B5htanNm5sLlChhVXKkxru6V4UNWSYIw+ZRx+zhOHMMWhbMCBgiIBN/UT7LNQSUOwQWASYIA+s6VjzoCER5UzFDELYpWRp/HNin6m69DEEaEPv8q7078L9ta47KmFWn9dF73vQyCNUmfbJaA5xSugLmjhmBswfdfrRLMkGotT4HVKiB/hUzCj62NYPw2WeBwYOBhQuB//u/wul37QLKKRmLsfbgltX4PmdXwt+Glq+JU9/7EChWDOjXz7jsoZMBqPaDmup3GZVidFKnkMkWIQj7V6iBAUfWapc1S5EXh8GgSrVwcflq/kKFSn9Sth3Ve2tQWA7VViW7dgUoy0KtyZ4Umz08jc09fLhy6VVsPJUgpWw3mdaqFbAsNatYRpTwGBG7qU9e3MpbsgTYuxegDESns4rXrQPq1Us05+WXASp3Z0OT+TEqPe0zzwAPPJA4vFIl4NdfC8uNUvy8+aaSJdmli/RUPNBdBBbl7MJ9W1YfnbRtyTJ4OaOJu0o4PJursaJjCz2Kzj0XmDNH6bRjB1ChgsPGWxUv8pyNzSG7hxnpSHf7bdhg1Kvwc3pe3XefeH+tntddp2RLxzenbLSurW8l+CX+fAuQnmJMEAbSbV4ozXHmBeo8JyOgICATf1E+y+V1E14EmCAMr2/ZMp8jEOVNxQxBOK9eW9AB2EM6GW9GJUYLNv68w8jJz0OlosWP3gFIfzdLED5VrSFOLl0eL29ajYtOOgO116wrWGmiBGHf8tVwQ6VaBWNsJQhJYO/ewAcfpK58OtWK+5F6ceZybDus3JEYa/O6X4Lif68o/IONmTKmQ1HtBzWRIXS/mh2NMFqxAqDspLTEDE87xAdBhloMfrtvJx6Oi7OP6rZB2aLFCszZfCgXfdf/WfDvlmmlMTqj8C5NX9hL65VKo339NVCyJPD220CfPvarpnXYOmoUcPPNcvOZOcDVmmH37pQXAYSVoRKeH38MdOokPMSWjqJ2xw5040muvn2VcplONjtLyaroKfNjVNrcgQOBiRNTh192GTBtmnI/YTzhSuQhZXJTidX0dOfJWGnDeODh/Hw8vm0Nvtm3s+D7zdDqDdCqpIdleR1wiaux4oD+pkXSywlE6NPLEI89BrRrZ1pEwYADB4BSpcTHOkWeDRkC0P2GIq10aYAudqS7Uq02YnDpWRZr9ELN889blRq58ZGLPzs9zAShnWiGWhbHWajdy8b5HAGZ+IvyWa7P3cnqWUCACUIL4PFQRsAKAlHeVLQ24W5rfsNB5CfAGssCvGL9n9hwKFf1MzWS77T08niiekNDF5klCJ+t3hAd08vjpaxMfL7hP3T++FNsrVkDP3aOy9rTmfWsMpXwYFUlKyWZIKyQlY1Zxyfd/7JqlXJfitXX1GMHP+vWoXfWKuyqVFhGqkbmBrx9Ws9ErUeOBG65xRA/Wzt8+SUwYoQ6yUkT2XF4lZwBRlkzlDUZsaYVg6tzc7D+UC7alSyDcsWKBweVDz9UCPJYq14d2LzZfv21SKOlS4G2beXmEyXK9KQTiUNkjmijWKL+VMatfn1b79YTVSH+pQXdMaSr2l2Nq1c7lylKCoWJINRbY4Tt+PHADTckuoHW82+/AWecAcycmXjQLuxk7ugGAvn5+diedwjpRYoi/chLHW7M69YcMgc3bunmyDyUBR+/fxFRSMSZ2fbGG8BVV4mPsuM7ltpsO3cq32PpRR69duONyos2bdqI62zUk75DT50KNG0K0Islduy3RnOG7PPIxZ+d/mOC0E40Qy2L4yzU7mXjfI6ATPxF+SzX5+5k9SwgwAShBfB4KCNgBYEobyoyBOHl6//ERhME4eAqdXF2WeXeQL1mliB8rnojnJBeDi9kZeKjPVlG4lM+PzG9HJ6p3qjg78kEIf1t7jNjUGrsOGUcHZaMGaP82+qhxllnAfPmHdVn0l2D8MZt1xf893Hf/ojnr0g6HKYPnDosUkONDsNq1UosJ5jcT/aQLF5OMo7nnANQacaINZkvwr6G6NJLgenTE1V0Yv1qxaGVErhWY1vGMU5gY1YPUbtJV4r9smUTZ5g8Gejf3+ys4v3DQBDSCxB0ME+lprUa4WvkC7oHkjKuuTECHiAQuv1KD0PKfqfM3vh2zDEKWW+2NWsG/POP+Cgn94WffgI6dtTXxcn5xVHgnkkIRCr+7PY+E4R2IxpaeRxnoXUtGxYABGTiL8pnuQFwKasoiQAThJLA8TBGwCoCUd5UtDbheXu246mstUehvbBcVdxSuXbBf5slCK+qUANXH7k/Tc9XZgnC4dUb4fj0crhg3bKCN/ZlWou0dOzNy0OH9LKYuTuRZJxZpzUqqmVuGR3gSigSK4t6w1MvoO/411MlmD2soYOo9euBU04xX7qT7k8cNszYiqRyqcYD4npoldsya6epSf3ZWeaLsD8tOaLVxRcDM2YkquiEX50gjRyIbV1f2XW/k9UFIWJ3o0bAypXAli1AjRqJM15+uXJnnhMtKwuoWlVdsk3rypUYnDQJuPZafYRECEKSYJPdTriLZYYbAVdixS8Qaj0X9+9XymebaSLP2Hh5Tse4kT5Oz28GO+57FIFIxZ/dfmeC0G5EQyuP4yy0rmXDAoCATPxF+Sw3AC5lFSURYIJQEjgexghYRSDKm4rWJrw/Lw9PbFuD73J2oUmJUgUlQjOKK3fEXZb5JzYdFi8x2ql0BTxarYGhm8wShM9Xb4Tj0suZvrvQUJEjHdwkCB8dMxxfn90VH7c5FWX27E1VUfSwJjc38eCKMlY2bQKKmyhRSYf9IneK1akD0P08Mu2rr5R76pKbqJ0yc/p0jMwXYZ+aoqh1wQVKGcT45oRfw0AQWsl2tHMRGB0W01z00sA99yh3UlGGcXyj7EHKInSi3XknQFlzas2mdeVKDIpgTFmGIs9qm+x2wl0sM9wIuBIrfoDw33+VUphqje4kfOopc1qKxL/Te2a8fCN9+Bljzr8u9Y5M/DmBJxOETqAaSpkcZ6F0KxsVEARk4i/KZ7kBcSurKYEAE4QSoPEQRsAOBKK8qehtwnSXziHkoxiKoGjcD6u+mcux+fDBBOhj9xOqkXydS1fEI9XqG7rKLEH4Yo3GOLZUWfcJwkceAZ54wtAesx0oi/CrBsdaOwg/77zUMp01awIbNoir06+fck+MSPvzT6BFC5GeiX1GjQJuvTV1XAQPpWS+CJsH3MURdOfg1q2JEzrhVycIQrpfVK8EpJ0wxpcttlOujCyjw+KYTMpMphKjxyY9p66/Hhh3pByzzPx6Y4zu7LNhPkdikMjfNOWlGuFGpMODDxp3dyKejGflHowAHIkVv+FKZdbp7kG9phWDVFmhTx9g/nzg118BupP2669TS5Ua2ex0jOs9V+++Gxg+3EhD/twDBCIRf07hygShU8iGTi7HWehcygYFCAGZ+IvyWW6AXMuqmkSACUKTgHF3RsAuBKK8qchswmYJwjNLV8QQBwjCl2s0RttSZfG/rWvw1b4ddi2Ho3I0MwiXLk09ILdhdl2CkA6bFy0CiOyjH7lELpx+OkBvssf/6NU69FmyBGjXTkxLygZ6XaXMqdro114DBgwQkxvfa+BAYOLE1HFOH4qZ19TxETIx6LhSViZw6xBGbR6rRNUHHyiHu240v2QPkq033wy88oqY1VQmMzl2idR68kmx8WZ60QsIrVppj7DpeeFIDIqSrmbwiPW1yW6ZqXlMtBFwJFb8Bil9p5kyRV8rrRhMjvtixYDDh81b6HSM6z2fVqzQzp40bwmPsBGBSMSfjXgliHLru6lT+rNc1xDgOHMNap6IEUhBQCb+onyWy0sovAgwQRhe37JlPkcgypuKzCZ8aeZybDGRQdildEU8LEkQXl0hA6/t3KS6gkbUaIJjSpXB2O0b8M6upIwlG9acJkG4fDnQurUNMySK0CUItWZr2xYgwpIavblOh1FaTfTAyczB9oQJAJF9Ztv99wPPPps6SlRHs/OJ9KdDPMpsXL0auOEGoGVLkVGW+8jEoOVJnRTg1iGME/NQicehQ4GPPwZOPRW46y6ggXF5ZCk4vVzryQr//rtyX+mePcon27Zp3/unZuz06QDdPWl3M3oWydwFpqKjIzFopLsVrPy0dqzYwWMDh4AjseI3FERiVy0GZbKGrX5fk8VOz8a//waaNZOVzOMcRCAS8ecUfk58Z3RKV5brKQIcZ57Cz5NHHAGZ+IvyWW7El0uozWeCMNTuZeP8jECUNxWZTdgsQdi1TEU8VFWuxGisdOmz29bik73bE5bRqIwmaF3SA4KQ3q5u3tz2Jd3l35/xRZMO5uUuWwbQfTllywJdumiPVzvQ+uILpaQdEQQ5OebnvugigMiB2bOVt+TPOUefpIzNQATMueemzrdzJ1C+vHk97Bhxxx3Ayy8XStqxA6CSkw43mRh0WCVr4t06hEmeh3xFPrO76d2BZ2Uuv5E82dnAqlVAmzZAqVKJmclGdlLGMZUmtrsZHdTHvyBhYW7bY5B8W7SoBY0Mhvpt7ThnKUv2GQK2x4rP7CtQx+i5Q33UYpBKudeubd0i+l6mdkezdcmFEvRs3L1b+T7JzXcIRCL+nELdre+mTunPcl1DgOPMNah5IkYgBQGZ+IvyWS4vofAiwARheH3LlhkgsGfPHixevBiLFi0q+N9PP/2E//77r2BU/fr1j/7bKSCjvKnIbMKXZC7HVhMZhGeVqYQHq9YzdJ/aHYQxgvC5beswd292gozRGU3QykGC8P06rVCpWIlUvekQvXHj1L/37g1QiULJdveb4/D8FTdIjhYYlnyglZsLlCwpMNCgy8knA99/X9hJ5PD6/feBCy9MFfzLL8Bxx1nXSUZC8uEBkZ/vvisjydQYmRg0NYHbnd06hEmep2lTgMh7u5tdcZKsl0ic2G2LGXkih+Tx8uy2Z/t2oHJlY41tmNf2GKQMHJm7WY2tVXrYYLPoVNyPEYhHwPZY8SO8Is8+tRj86COA7oG22tyIbxfudrUKA49PRSAS8eeU4936buqU/izXNQQ4zlyDmidiBFIQkIm/KJ/l8hIKLwJMEIbXt2yZAQKdO3fG/PnzVXsxQejs8pHZhC/OXIZthw8lKBYj8tRIvjNKV8Bj1YzL9OkRhMOy1mHOnkSCcGxGUzQvWRrjtm/A2w6UGNUkCNeuJeY61TFUXqqECqEo6MKc9FIoefAQilKZQycaZQmmpyuHy02aAFdeCbz5pv0z/fGHcQnWadOAK65Infuaa9TvJrRfy1SJHh0eyMSgG3DozrFgAfDEE0CVKsCLLwK1ahV2dwvH5HmIqP7uO/uhcSobzI1DYCtoEJ4//CAugUrzEqEXnwFM5OrChcr6MEOYGZVLjtfKBhxtj8Evv9TP5hZHVb2nDTZbVYHHRxMB22PFjzCKEIT/+x9w1lnASSfp730y9rkR30wQynjG8zGRiD+nUHbru6lT+rNc1xDgOHMNap6IEUhBQCb+mCDkhRRGBJggDKNX2SYhBDp16oQFdOAMoFKlSujQoQO+//57UGYhE4RCEEp3ktmEzRKE3ctUwv0WMwiHZ63D7CSCcFxGUzQrWRrjt2/AWw4QhO/VaYXKahmEWmWk6FBH5GBJx1v5vXqhCJXrdLqVLg3s2+fcLEYHXHTX3623ah9+J9/lM2sWQBmaTjaPDg9kYtBJGFRlU/lYWpdUevK004Dq1YG9e5WuvXopd/bFmhs4/vpraqYplbelDA4nmpW47toV+PzzVK2MYsQJO8zIlLG5alUli5ruM6QXHegex0WLgOLFgalTgUsvFdPghReAu+8W60tr02I5T9tjsG9f4J13xPSX6UV3RZYpIzOSxzAClhBIiJXjj0c63Xtcs6Ylmb4bTC9S0f2mRo2eO/QSxQknKD3NPjPpRQK1UqJu7A1aL4jR/ctjxxpZzp97hIDte5VHdngyrRvfTT0xjCe1GwGOM7sRZXmMgDgCMvHHBKE4vtwzOAgwQRgcX7GmNiMwfvx4lC1bFh07dkQTymwC0KBBA6xZs4YJQpuxThYnswlflLkMWSYyCHuUqYT7LBKEL2Rl4qM9WQnqT6jZDE3S0jFh+0ZM27XFdqQ0CUIqf9uwYep8egQhHaLRQXZUmtEBV8eOwE8/qaOhhaORTKvYenR4IBODVk01Pb57d+Czz7SHkW927VIIoE8+Se1nA4mTIFTNVwMGAK+9Zto0oQGPPQY8/nhh14svFi8/u3IlQHdKXX994firrgKmTBGa2rNOZg+7Y4pSRg2VHJ45E7jggkL16TBd5BlIfYhQFG2UbSir65E5bI9Bi/oYmu5S+WNDPbhD5BCIxUq9L77AsWPHogi9zPPww0pGeViamfiNPe/IdjPjYi+00B3QTz9diBzts5SZ6HSjF3vmzEmchYhOehkrviKA03qwfFMI2L5XmZo94J09+o4fcNQiqT7HWSTdzkb7BAGZ+GOC0CfOYzVsRYAJQlvhZGFBR4AJQnc8KLMJmyUIzy5TGYOr1jU0SK/E6ItZmfgwiSB8tWYzNPaCIFy+XL2EJpEkdLCjli306qvAwIGGGISmQ4zMoywiOsymrKJnngHuvVfJ9jEqb+XFD3kv5gQgE4OurhMqHdmokf6U8+YBRITdeKN6Pyo1aaH8LnbsUAindu0Ucl7tbjoql3v55c5AQ+v5vfeAjRuVOai0quhhMJGDRIifey5ApbTp/lI6mG3WzBld7ZIqap/afIRXv35K1mB8o7/T/z78EFi2TLmHtHnzxD5m5yV81bJwRHCgew4HDcLBOnXwSceOyEtLQ7du3ZBOGUSijeyZOFHJGDz+eIVIpkxbp5vTL0w4rT/LDyQCBfvVp5+i12WXoXh8lh09GzMygE8/BYYPB+rUUf6fnpVBa2afQbFYNLXkdRIAACAASURBVDOOsqTvvFNB5p9/gJ9/VjKu6xnf120LnMkvvZDQ3buBsmVtEc9CnEHA998XnTHbHqkefce3R3mW4iYCHGduos1zMQKJCMjEHxOEvIrCiAAThGH0KtskjQAThNLQmRooswlfmLkM2SYyCG+oWBN9K1Q31EuPIHwpKxMfJBGEk2o2Q8O0dLy6fSPedCCDcEbtVqhSXOVOweTylzHL6JCIDsd69Ei1lQ5/Hn1UKdMYhRY7MNPLFNTCgTMI/bVCiPigkolGrXZtYP169V5UztYM6RIvhcqXErlm1Ci2evY06mXf56KHwX/+qdy/R5lx27YBFSq4QyBZtVTrZQcRuRTDV18NTJ6c2Jv+Xq2agkOs0ZqJZayYuXswXrIMWZadnUBeHE5Lw8fTp5snCOneSzrYd7vJ2Oy2jjxf6BAo+M44dy56E7kf3956S3lBikjC2D3K9JLA668HDwPRZ3vMslgstmmjvPgg0kaPLng5wbOmdrcuP1M8c4foxDK/2URlh74fE4Shd7FdBnKc2YUky2EEzCMgE39MEJrHmUf4HwEmCP3vI9bQRQSYIHQHbJlNWI/Iu23Tv/j9wJG7yY6YMDqjCVqVNL4vSU/uTRtX4K/cnARQJtdqjvolSrlPEJIWWj806TCsf/9U59Gb2ZQ1c8UV7jjW61lk3qiP6bxqlXrGGmX7VKxon2VZWcBTTwF0n9cddwCtWqXKduHATCYG7QNBQNL48QDdS2SlUfnRcuXkJIge1o4YoX2vpdzM+qPovsPzzjOWHNSsDCr3TVmhMo3ihjKmKbMuvhFxkFw+tG1bYOlSgLJMS5aUmU3JSjTbbr4ZeOWVhFHzxo3Daf36mcsgpKzBxYvNzm69v4zN1mdlCRFHgPareXPn4rxkgnDSJICyzZNLjQZxnYruObG1QM86yggsX158dUyY4H1VCXruHnusonNmJkAv+XDzNQK+/77oZ/SYIPSzd3ylG8eZr9zBykQMAZn4Y4IwYoskIuYyQRgRR7OZYggwQSiGk9VeMpuwHpF31+aV+HX/ngS17CAI1eacUqs56pUohYnbN2KqAxmE79ZuhapqGYRkXfIPzfr1AbqbcOhQYMiQVLdQBhUdIp92mlWXBWM8lXukUqKXXWafvpSpc/LJ1uURUdu7t5gcu+/OU5lVJgbFlLepl1ommFnRVshd0cPaWKaeWd2s9Kf1/fbb+hKoDJ8s8WVFN6tjRXFXm4fKfk6fDowbl/gpkc3xdzHGf0ok/Usv6WtN2dlqd1zKkBAq9n332GNoP3iwOEGoloUjgztlWi9aZG4kka10ty03RsBFBAoIwjlzcB6VDo9vRLYvWQJQjMc3mdh00R7VqZKfDaVLA/QdTq/Ry0Z0n6Boozto6S5aboyACQR8/33RhC2ud2WC0HXIgzohx1lQPcd6hwEBmfhjgjAMnmcbkhFggpDXBCMQh4CdBCFtGnpt48aN6EgHdABWrFiBOnR3SkTa/v378fXXXxdYe/rpp6OUwN1JPbesSEFnTnXlPq37t6/DbwcTM/1erFQXzUsY3+mkJ1ftswmVG6B28TS8vmcb3t6XbbvHXq/SEFWLqZQYBZBOB0ZxLWfduoJydUW/+w4lu3ZN0SWHCJK0NKSXMc6ktN0QGwTmNWmCov/+a4MkeRG5L7+Mw9ddJy+ARubnm/JBDmUZypbGFNRUJgYFRdvSLa1rVxQjctZCI98Ve+stFPvhBxyYNQt53bqJScvNRbpg1mgOZa/UqCEm185eVBaTngkadzfl7N0rfl+hnXpZlJX8jDMr7tB116E4ZcnY1A68+y7yOnVCOpUoTWoFz1eTJKyafX9ecQUyRo4U2gdJhVLVqqEI+Vew5Wzdqqr/gbfeQkmJlynymjXDgV9/DeT6EoSMu/kMAdqvvvniC5x38cUJmuU+/zyK/vEHir/2WuLfR4/GYXrJJEAt+dlw8KGHUOLJJ221IPf113E4mWS1dQYWFkYE/P590c+Yq+35OUbEv58NYt0cQ4DjzDFoWTAjYIiATPzRWW+zZspZ5Lp16yJ1lmsIKHcILAJMEAbWday4EwjYSRAWMZEJ8eqrr6Jq1apOmBQamS8d2yDFljuW/FfwtxmNayCzXCIZ2HfFBmTsyzW0X0+u2mcDlmeiYu4hfJ9RET9m2Fh68oimA5etQ9mDh1X1Ttu1Cz3690eR/HwsGjwYG085paBf2cxMdLnllpQxH02fjry0NPTu08cQB7s6rOnSBfUpk8eG9sGsWWg1eTKazpplgzQ5EVvbtMF3lKFpoRXbvx/niNynd2SOuVOmIJfujItoK71xI8666Sbbrf/qpZewq0HqcyR5oip//IHTHn5YaP6P33oLhx0mc/UU0Yptip0gNjefVSL4fPD++wVZyXbhrCZn4ZNPIqt16xR1SmVlof3IkSi7fj3+694d/1x4IWp+/z06PveciOoFfWLrQG3eL0aPRhcqeSrRvnn6aWS3bCkxkocwAnIIFD14EOcmEYS/X3NNQXw0pHuYk1qQnoEU692vvTbBgl8HDUL7pHLEcsgVjlpy001Y0727VTE8nhFgBAQRUNt7g/RsEjSTuzECjAAjEDkEtm3bhoFU7p0Jwsj5PswGM0EYZu+ybaYRYILQNGSuDfCCIFQjHq9enokKDhKEl/29ATVyjInNeOBL7NqFniplo/QOh/Uct+i++9Dx2WelfPv1M8/g9PvvlxqrecCXn4/e559vi0yzQra0a4fvH39caFj1X35Bmc2bseHkk3GgUqWjY8ySHt8PGYItdMdYBFvJ7Gz0uOYaxywXOZip+d13wiTMBzNneppJ1fytt9DinXdS8BKx0zGQLQg2GysWpjIcOv+FF7CzUaOCflp6zZ08GbmC2aZacr5+9llsb948RZ+2Y8eiYVxpUzrcP3bMGEO94zvE1sE5l1yCYnTf4pF2OC0Ns6dNSy3ZaEJ6UNeYCRO5q48QKJqbi3MvuSRBo78vuQRpu3ej4dy5gX4G1vzhB3R85pkEGzZ27IiaZksAG/jLjheefLQkWBVGwPcIMEHoexexgowAI8AISCHABKEUbDzI5wgwQehzB0VdvUOHDqFECfVyi2awee211zBgwADDIXYShFxiVBtumTT+C7f+g5yke2ViJUYf2L4OSx0oMTp+9xbMytmRYMjkKg1RvVgJTN2zDdMcKDF6T/kMnFmqvOFaTeiQl6daajBWxsZs2b79P/+M/EqVkN64cYoe+RkZyH3jDRSbORPFVd5uz9m2Dek2ZMPuX7AA+SeccHR+szaYA1C/t0g5oLTLLkOxDz44KujAzJnIO/KmvozuB4cOxSG6H43uVHSgycSgA2qkiJTByoxe+//8E/l0d6dOKzprFkpefrmQWJG1ISTISqesLKTXrXtUQt4xx+DAjz9akejZWKf9L2pYzpYtQFz5Vi29DnfpgtyPPtIXu2cPimzciPxGjZBerlxK31W9eqHSG2+klBi1A4vY+kxe0wemTUNenz4pZatF8aF+vlj7ZhS22nfvXpS47TYU+/RTHHzgARweNMjTlwOsmhOk8bRfLZw3D+deemmK2oeuvRbFJ05M+XuQ1meJW25B8UmTEmzIHT8eaVp3p0o673CnTsidM0dyNA+LKgJ+/b4YBH9widEgeMkfOnKc+cMPrEU0EZCJPy4xGs21EnarmSAMu4cDbl+QCUIj6KN8sa3MRcBnr/0d+/OVe7di7av67Qr+edfmlfh1/56Ez17JaIqWJRPv7FPzSec1S1P+HJM7Ons9ZuzelvD59NotUa14Gibv2IQpOzcbudn05xNqNkOTNOO7E1MEq5W0zc9HXn4+ipolmeh+yP/7P+3DTyJq//4baNEi1b6DB4ERI4C77zZt+9EBlL04eHDieLrTM44EkRcuMTKJmFaVoIb/oUNAsWLyh8jTpwNJJdUktFcdIhODds2tKYcynEze6SalU8yfVDp2yBBFxA03AGPHKv+m9Xv77caiv/8eOOkk435u9Ni1C7jvPqBNGyDIxIWJ0tyOwpoc83p66T0f6DC+Vy9DVYnMSE8uVWsViy+/BDp3VuYmHemFjs8/B7p0Aai0KMlXm4Oe3Q0bAiplqxMMEXkuGloeoA5nngl89VWhwpTdGV+ukfDYv7/g3t+C5z432xCg/eqvwYPRftQocZl+X5+kn953sw0bgFq1xO2N9aT7qGkPuOCC1LFUMtmjagzmDeERfkHAl98X/QKOkR4av82MhvHn0UOA4yx6PmeL/YOATPxF+SzXP55jTexGgAlCuxFlebYj8Ndff1mWWbNmTVQQuNPLzgxCI6WjvKnIbMK6BOGmlfj1QCJBOCajKVpYJAhHZa/He0kE4YzarVCleAlM2bEJkx0gCN+o1QJ1SpQ0Wj6pn2v8CF2yfw+OTU/NWtGdYMEC4PTTgR49AJW7fQoOmyku1e6gos/++AM45hjzNlDGIN3Vp5U5Z/XA3LxGyojYQeP27cC77wKUgZZ8j4+abnSY3KmTPEFIh4eH1e+jlDUlNk4mBq3OaTh+5UqgSRPDbpY7kD+JUEveE37/HVi1Cujd23iKzZuB6tWN+3EPcwh4FePxWv73nxLj8Y2Itvnz1W159VXlWXjkTtiEToL2pBCEP/0EdOxoDrvk3nl5xs8eNf0oBmrUAMqU0Z/f7wSMNfSE99iCjgcOAFdcAbz3nrIWKJu8aVO7NYisPNqvTGfU+n190gsdeveJkv6Cz4+UhbF3r3r8LlsGtGoV2XXEhssh4Mvvi3KmuD+KCUL3MQ/ojBxnAXUcqx0KBGTiL8pnuaFwOhuhigAThLwwGIE4BJggdGc5yGzCPdb+hgNJBz5HMwgdIghHZq/H+0kE4ft1WqFSMecIwtdrtUBdGwnCT/Zko0e5KgmOnd+zK3457SSctW4z2o6ZkOr0rCygcmWA7jV8443Uz8kPS5YA7durf0Z/NXuwRXNNmaK/AM3KtGs5k710AFyqVKHERx8FHnus8L/VdItlQlrR26FDTpkYtAtOTTl0vxplPjjdrBy8xnRzyC9Om+57+VZixS7j1Hwrkt16xhmpJKKgPQkEIWUeE/lMLyRYaSJrVO/wsl074LfftDUQkW9Ffz+NJRwIj+QWw4DKzJ53XuGnV18NJJWM9JM5QdNFiiDcs8eY5PYKCKPsQdLLyj6lNZbuNIwr3e6V+TxvsBDw5ffFoEDIBGFQPOW5nhxnnruAFYgwAjLxxwRhhBdMiE1ngjDEzmXTzCPABKF5zGRGyGzCamVEYwThwn07MWTrfwmqjM1oiuYWMwhHZK/HzCSCcFad1qhQrDhe37EZr+3cJGO+7pgptZqjXok4Ikp0Bo0fofdvWYWfdm/HqAsHoOXSP/B19zPx6NjnCwi84dUb4fjSKvcdxg49tQ631QizmJ5GY7Xs6dcPeP11fWsFD9tFIRPuRzapve0fs3XNGqBBA2Fxpjo6dAgvE4Om9Jbp7JZ/rRy8Jq9zGTt5jDYCbq0BPR9oxZxIVl/8WBES4IgeCQRhfOlbK2tF5Nmhd3hp5AsR+Vb099NYLSwoY5BKOdLzn/aB+BYlfBz2lRRBSKU0qaSmH9sPPwAnn6yvmZV9Smvs7NlAz55+RIR18jECvvy+6GO8ElRjgjAonvJcT44zz13ACkQYAZn4Y4IwwgsmxKYzQRhi57Jp5hFggtA8ZjIjZDbhlbk5uG7jCuQfmfDeKnXQs6ySGbfh4AFcsSGxFK09BGEmZu7OSjDxo7ptULZoMbyxYzMmOUAQTq7VHPVtJAjV7lGMGfRenVaoXDwt1YV6JB/dEUf3LFGzcricPCvdi0X3Y+k1owNrrbGU2fHhhzJLVRlDpfrU7gqi8p/096pVAcq6dKI5dMgsE4O2mUclbBcvVu5ma9ZMEUtlHenes6A0h/wSFPMd01M2xu1SiMi5hx7SlmakX/y6MLGmEwjC5LvuZG0TWaN0ZyWVHYw1Kp1Md+tRu/deYPhw7dlF5Mvq7rdxen4nskftLtIo4WPWXzk5wF13AZR5SS8HPfEEULy4phQpgpCk+dUHlGE6ebI+ak4QhNu2AVUSK0qYdR33jx4Cnn5fDDrcTBAG3YOu6c9x5hrUPBEjkIKATPwxQcgLKYwIMEEYRq+yTdIIMEEoDZ2pgTKbME2wOGc3vsnZieZppdG9TCUUOfLDyymC8OXsTMxKIghn122D0g4ShJNqNkfDNBsyCOnA8vvv8cHubXgpe72qfz6s0xrlFv2UeHcW3TtFpbmoqf2wve024OWXtT+XzSB8+GHlkFCv6d0DpjUuJlfNlrPOAubNM167GzYAtWql9luxQrlnyog0SB65ezdQTvBeSIcOOGVj0Bgsgx4zZyoZN9RordG9f0QMHnsssHSpZfGuCXDIL67p79eJzMaS3XbQyw/0EoRWM9KP7v4qXVoZ/d13wKmnCmmYQBAazSEi8cILgRkzjHtS1tvxxysvOFBZabo/tmZNZRz9jV5+0GpRigEZn8ReIDH2QvR63HQTMHZsod3TpgGXXaaJQ87evUgvW9Y8TrR3x9az+dHOjKBnTHq6sWxZgpBKoRMBy8SEMcbcQwgBz74vCmnn804chz53kH/U4zjzjy9Yk+ghIBN/TBBGb51EwWImCKPgZbZRFYF///0XCxcuTPjsnnvuQVZWFqpUqYLhSW/O9+jRAxkZGbahGeVNRWYT1gPeKYLwpaxMfLAnMTNsbt1jUKpoUUzduRkTdySWGE1DEeQezXGUWyqTajZDwzSBw6Nk8XQf3uOPF/71yF0zD25Zje9zdqkqM6duG6QXLQZ8/TXwwANA377ALbcUHiyp/bDt1g349FNFnp0ZhMl3+qlprHUPFOn02WfqgBPpSCShmq6UKfTkk8aOKlECOHgwtd/PPwMdOhiPT+5BB390iEf3Gho1hw7h7Y5BIzOOfk5ZInRwHmuxuyfNHMBTmTTKPlRrdE+Y00Rj//7G2R/CgHDHBATMrAO7oSOyrF49falG+tEzgQg3avRMoow8gWYbQTh3LlCtWqEOAnNj3z5g40agdu3Ee1a1nvExmX4kX0Tslelj5Hc1ma+9BgwYIDNb+MeYPDTP2bUL6RUqyOFiRPrLSZUfdc01AK0NoyZLENapA6xbB9CdqPTdLr459H3CyBT+PNgIePZ9MdiwKdqbfNaFwWS2QQ4BjjM53HgUI2AHAjLxF+WzXDswZxn+RIAJQn/6hbVyAYHJkyfjairzI9i++uordOrUSbC3cbcobyoym7Aeok4RhC9mZeLDJILws3rHoEQRdYLwygrVMXXnFmPn6/R4tWYzNJYhCCnrb/BghRy58krgxhsLfpjO3LUNI7arZxB+Xq8tiukdfB53HPDrr6naxg6Z5s8HKKsv1igzjO5k0vpRrIeMUWk/GpudrV4eS+8gbdgw4J571H+kX3edQhBWr27JZ6YHmzn4c+hAz+4YFMYgeb1RuTMqe2bmAJ4wUbvfjUrUUslEG1/kULXr778LS6MKG84dhRCg5w09d9xoRDjs2GFuJnq2vvmm9piBA4EJE0w/A20jCO1+XujF5ZIlABHyUWhmnk8xPN5+G7j00iigY95GNTyJpNZ4duds2oR02UxAv73QIbqWzHxPiPfAiScCVPaWXnKkMsHxze7ng3nP84gAIuDZ98UAYpWiMhOEYfCiKzZwnLkCM0/CCKgiIBN/UT7L5WUUXgSYIAyvb9kyAwSYIPRuichswnrarj94AFdK3kH42Nb/sGDfzqPiW6SVxpiaTQv+W40gjJFqb+7cjFeTMgifrNag4G+rDx65o08C4gk1m6GJDEGoMdeCvTvw2LY1qp9+Vd/gcPeZZ5TMwuQWf8hEh6CDBinZhyNHAsWKKb31MuQoS3HUqESp77wDXHKJPmJaJe9IHzrs36WSKUk6xWdFxs/w8cdKJprogZ2EP1WHkL7NmwNUotSoOXSgZ3cMGplx9PNkrImc3bxZ3AeVKilEMTUif4kUp0ZZnrm5yr+d9Ofll+sTRMJAcEdNBEyU5rSEokwJSMo6fuop/WkpZqmMcPnywurl7NhRmCFlZf3a/bzQ02XIEOB//xO2MRAdCb/nnwfeegugrHa6v1b2mUJkF5Fe3BIRUHu5g3pQ2dFXXklFi15+Ei3JrYU13bO5dStw2mmF31G88Mvq1UCjRmIzyxKEVMa7Tx+Ayh3XqKH8PzV6IXLSJLG5uRcjEIeAZ98Xw+AFJgjD4EVXbOA4cwVmnoQRUEVAJv6YIOTFFEYEmCAMo1fZpkAgEOVNRWYT1nOqFYIw8+AB3LhxBfbm5yGtSBG8UKMxWpcsUzDdC1mZ+Cgpg/DLem0L7j6ctnMzJiQRhM9Wb4jD+cCDW1dLr0G7CcLhWeswe88RQiVJK0OCcPp09QwIkUPojh2Bn35Sx2HBAqXE1uTJyueUGbB2LUDlJ/Wa1t09pM+//yr3ASa38eMByhRU+5FOZbioHJeVA3kZT5O+dLBOB9BGTQRrIxkqn9sdg4YqaPmHBhLRYHT/ZGyCJk2Af/4pnG7TJqX8a926hX9zwp90wEykD61rJ+QbAhixDk5iTCWSqSyxTKP1ZpTJRDFL63TlSuEZDg4ejBLPPqus5bQ04XEpHe1+Xuj5IT5bUl5jf42kFxaISIo1yhY9+WRxUifemvj7fP1lpbfaUAZ2ixapOsReFkn+xI5nQdGiQF4eQPcOU/zbIVMGxREjgNtvNx55/vnA++/L6fnNNwoRSo0qSrzwgnKHMu2zsftRjTXgHozAUQRc/74YJuyZIAyTNx21hePMUXhZOCOgi4BM/EX5LJeXU3gRYIIwvL5ly3yOQJQ3FZlNWM+dVghCkrv5UC5+P7AXzdLSUa9EqaNTPZ+1Dh8nkWsxUk2NIHyueiMcys+3RBCOy2iKZiVL27Z6O69ZqinLkCCkO6boXqrkJnIITYfHEyeqz03lr449FnjxRaW85G23Gd/9FZNk9t5Dym4cPVr9oI3erKcDM7cPCwk/0YNCEazVUL755sJsDCrbSHjHNbtjUHfBamWMyKzytm2N7xi02590lxwdLHNzDwG7fRivuUzmYPx4I91IfiyT2gxiFCdEKhK5KNtknxda8+nZShmSOwuz72VV9tU4I9+aUbZVK4BeLOCWiADdO/vGG+qoJK/fQ4eU7HA721dfATZeF2BKNdH1Rd9L6PuJaP94Jahs+oMPmlKLOzMCegi4+n0xbK5ggjBsHnXMHo4zx6BlwYyAIQIy8Rfls1xDQLlDYBFggjCwrmPFg45AlDcVmU1Yz99WCUIt2WrZdzFS7a2dWzB+R2L5sGHVG+GgRYJwbEZTNPcLQUgHVGXLpsIjcghNZa7oTkK1pkJYCcfzffcBzz1X2L1Dh8JMRbUf4lRii7J+1D6jjAL6u8whnLDCKh0JPyqlRiSeURPBOlnGn38CdDid3Fq2VA6sixSB3TGoa8YffwDHHGNkqdjnsfuV9Hrb7c9Vq4CGDcX04172IGDVh1TCmEoLJ7f47BpZTa3qpjUvZVbRHYdamddG+saX2TXqK/q5ka0yzyfRud3uR2RnxYr2zhomfOxCRm9NxeNF//7tt5SXWyyrQd8hqHy6F80onuJ1ki0xStn4Dz/shXU8Z0gRcPX7YtgwZIIwbB51zB6OM8egZcGMgCECMvEX5bNcQ0C5Q2ARYIIwsK5jxYOOQJQ3FZlNWM/fagShHZl4ZgnC4dUbITdMBKFW5pfIoSdlBlarpu62xYuB9u3lQpjmpkP0adOA1q2VA0QqH0ZN7Yc4Zerdeqv6ZzE7zBzayWmdOIrmnTABuP56Y2mUQWE2G0nPng8/BM49112CkDJJKaPUjkaZH5QBotfs9qfIerfDNpZRiIAVH154ITBjhnInKRHKf/0FULlHunPUjswhK7o56eOLLwaoLLSdzcjW2EsWds7phSy63/aTT5S9xc4WxGfHgQPKCyz79ikvsdhNmooShP37A6+/bqc3FFl33w0MH26/XBGJRvEUL4PWzi+/APQSlJk2Zgxw441mRnBfRkAXAbt/s0UKbiYII+VuK8ZynFlBj8cyAtYQkIm/KJ/lWkObR/sZASYI/ewd1i3UCER5U5HZhPUWg1ME4bCsdZijUWL07Z1bMC4pg/D56o2wPz8PD239T3rtvpLRFC39kkFIViT/uKUsCyotZ9S07gukcVRilA7u7W5qP8Rj941t3w5Urpw4o5ME4fr16uVZSQOal+5gvOYaYwQoq6hZM+N+8T2MDiHz890lCI300bKOysPSQXV869kTmD1bHw+6Q4zus7KrBfGQ3y7bvZJDGT4PPCA3+xdfAGeeqYwlkmPRIuUOuXr15OQlj5Jdz/bMri2FiNDmze2dhZ71dPemViPStVcve+d0W5rWXbt26BHEZwdle1PWd6zZZQPJobLiRNBptdhcVGXguOPs8ECqjDvuUPRwq739NvDll0CPHsCkScb7VzLup5wCfP+9uLZE8Fq5x1R8Ju4ZEQTs/s0WEdgUM5kgjJS7rRjLcWYFPR7LCFhDQCb+onyWaw1tHu1nBJgg9LN3WLdQIxDlTUVmE9ZbDGoE4fiaTdE0zdpdfs9tW4e5e7MTpo6VGH1n5xaMTSIIX6jRGPvyDuNhCwTh6IwmaFWyjG1r39IdhKQFHQ7HCMFvvwXosEq0aR2kUwk9s2/Fi8ypNh+VOu3TRxlNpOW4cUCtWsBFFxX+cLf7wD9WBlNLLh2CTp0K9OsnYhVgNkvHyJ68POTs34/P6G49AN26dUN6erqYLjK9jPRRk/n000qZ1N69Ez+NZYcZ6fH554X3Blatqtx1KdvsOiCXnT+q4378EcjOVvxo5h4ys88ps/g+8ghAZfz81Dp2BAgvu9uKFcako9nnk906WpUn83wSnTNoz46DB1PJJbvuYD3tNIBiU6/F8Bo8GBg2TBRlc/2o9PDIkebGyPamjP3kPUxEFr0c8dRThT3pu0vx4kCXLsDXXyt/pxce1q5N7GX/SAAAIABJREFUlRa0NSeCB/fxFAG7f7N5aozbkzNB6DbigZ2P4yywrmPFQ4CATPxF+Sw3BC5nEzQQYIKQlwYj4BECUd5UZDZhPTc5RRA+u20tPtm7PWHqGEE4fdcWjNmeeAfhizUaY2/YCEIr8UEHi927p0qgjJ4TTrAiWX2s2g/xq64CpkzRn0trnGx5s5tuUjLf6C1+OnBNbnSARwTYgw+KYbBwIXDqqWJ9qZfRgXf//sgZM8YdglCrTK2RNXSIe/bZqdlJRKqa9Qtlj61ebTSj9ud84CqPnV0jjdZ0/DwyZXnN6Cm7ps3MYbZvTg5QqpTZUWL96SURyug64wz1/rEyzmLS/NVLNtuYnil0N6lRiz076D5fymYnIrdOHaNR3n3+3nvKyzPx7aSTzGWwaWkvEsNuEISx/dkNlEVsVtOD7lm+997UTzZvBugFBYr3IUPUqwvwfuWGZyM1h92/2SIFHhOEkXK3FWM5zqygx2MZAWsIyMRflM9yraHNo/2MABOEfvYO6xZqBKK8qchswnqLQY0gnFCzGZqkWcuKMksQvlSjMXbnHcYQCxmEI2s0QZtSzmcQ1ihWAm/XaeV8jFEGUJUqifOsW+fMIanaD/Hx44HrrtO3U20cjaGxRFLR3VRmGmXdNG0K9O0LvPNO6kg6wDNLWpk59BM4lMzZvBmfHSlb5lgGIRE1ZjK/4pGicrB0zySVZYtvdG8jZYGaaUTEEiGr1+bMUTKwHn9c3V9m5uO+9iOgtqaplF7Jkt74i+KRXnSg+9latLDfXrMSzTwfzMqO9dcr++rG/LJ6640TeFaqDl+2TLkD16gdPgzQftegQWFPKjl56aVGI735XA2PChWAHTus6ZObqx6ryVJj6+iuu5wrAyqzh8haL7u+6Jny55/GszL5YIwR97CMgN2/2SwrFCQBHKNB8panunKceQo/Tx5xBGTiL8pnuRFfLqE2nwnCULuXjfMzAlHeVGQ2YT1fZh48gH4b/kroYgdB+PS2tfhMM4NwK8Zs35Aw54gajbHTIkE4okYTHGMTQZifn48z1/6mCt0JpcrhuRqN3AkRKrFFpbaoEeEzd64z86r9EBe5l0ttXKwMGd0ZdO215vSNldyjuwP/+Sd1LB2CUklPKhsm2swcwAscSua+8QbmlitXMLtjBOHAgcDEiaIWpvajMqFduyb+/fbbgZdeMieTDvPbtNEfo3cfpRnszWnGvUURaN8eWLKksDfF5Kuv+uN+H4F4EzVTup8ba1St/GRMYTfmlwZHZ6Cs78heIqjpZQV6Jmm1r75SXhShzK/45le89MpiW8F/6VLg2GONJWRlKXcFd+4MzJ9v3F+mB2VIvvuuzEhzY+geYtlsUXrxgO5NNmpMPhghxJ/bgIDdv9lsUCk4IjhGg+MrjzXlOPPYATx9pBGQib8on+VGerGE3HgmCEPuYDbPvwhEeVOR2YT1POkUQTh06xp8sS/xzflYidF3d23FKykEYRPszDtkKYOQSMZjSpW1ZeHm5ueh+9rftc8u67ezZR5DIZQ98Npryl16V1/tXCk8tR/iK1cq2Xp6TW3c3XcDw4crWWxXXGFoYkIHPbKJDgwpo6RXL4Cy1kSbmQNlgUPvg888gzlHMp8cIwgF9NA0n8olEj5nnpnY5f77jbMB1YQa6cIEoehK9Kbfd98pZDGV1qtWDaB7TOvXV7Js6K7K+GYmVuywhu5opTKcXja3bHaKQPIKO6Pngppev/+e+MLB7NnABx8AEyak9ta6/9Qtf5nF1S7/EiZXXqlkHlJZVcq2FWmZmUDt2sDppwPffCMyQq6PG/iLrq2TT04t4VqzJrAh8QU0VUOT52jXLvFFCjl0eBQjkICA3b/ZIgUvE4SRcrcVYznOrKDHYxkBawjIxF+Uz3Ktoc2j/YwAE4R+9g7rFmoEorypyGzCeoth06FcXLY+sRzT5FrNUb+EtTuZHt/6H+bv25kwdYwgnLFrK0YnEYSjMpog+/AhPGKhxCiVKW1nE0H4Y84u3L9F++61mC2hCTS1H+Jr1wJ16+qbqDaOylI++SQwcyZwwQXmINIjm0gSfU5ZcHfeKS6XxsyaBQwbpmQlvPwykJGhPl7gYPLAu+/ikyPlP31JEI4dq9yvlEwQUglQuoPJbNu4Ucn00cocYYLQLKLu96ds3N9+A045BaAD9Fijlw4mT1b+i4jDDh3c1Y3IQSIJvWqyMSGjr10EkujcP/wAUCYWlXouXVp0lHg/gWflUWFERuuVk6VnyyWXiM3tBkElpkliL6v+ff554J57ZGZWxnzxhfLMN+MXmdncwF/UBtqXaD+Pb+eeW1h1Qc8+eu7R8y/WnLrfWQZjHhMaBOz+zRYaYEQMYYJQBCXuA3r/Lcedu+EZbUaAEUhBQCb+onyWy0sovAgwQRhe37JlPkcgypuKzCas504qpdl/w99Yd+hAQbdaxdMwtVYLFBE9oNEQvuLAPtywqbBEZIdSZTGsRuOC3moE4eiMJiiCIhgUN8bsMnyxRmMcaxNB2G3NbziIfE0VQkcQ0qHaxx8n2hvLSNBzBJENmzYl9ogdulMW4b33mnOjHtlEpB6RVdu2KZlQso2yM954Q310WhpA5QAF2saOHVFx3jykO0FwWIk/yjalw+Kzzkq0gghSKwfQVBLwxhsTZXbqBFApQGoPP6wQw7FGhNQxxwggyV0ijYDRHWvXXANQuWI7G2VlV6+ukGdWYs2MTl9+CXTpkjpi+XKgZUszkoz7Emlyxx2F/dasAerVMx4n2uOzz4Du3cV6jxgB3Hqrfl96XiWXRNYa4QZBJWZZYi8rBCHttUYv44joRHfBnniiSE/5Pm7gLxqT9H0g/oUHsuq998ReTCI76LlCxCCVcu/ZUx4THskIaCBg92+2SAHNBGGk3G3FWI4zK+jxWEbAGgIy8Rfls1xraPNoPyPABKGfvcO6hRqBKG8qMpuw0WJYmZtTcCcg0WE3VqqJpmnWsw2IeHwxez0+2pOFqsVK4IlqDdCipCJ38o5NmLIz8V6hIVXroXPpipr3/hnZQJ9fWr4a0osUxamlK6BJWrrIEM0+ndcs1R0fOoJw9erUcqI7dxpn99DdSHRHUnx75hngvvuAiy8GZsxIxfGqq5SMtOOPT/1MjyCkQ+/bblPKrRYrZsm/BZmIas1k+dLcYcOQZoV007JC9IBUbTzZRiX7rr8+8dNRo4Cbb5bHTY3goCzRPn0UmXSv2BNPAFRGcMAA4Pzz5efikdFC4NNPlTtW1drXXyuH+HbGmRskh5otanFNJEV8JpMdnnf6YLVoUSWbW6TF7pXV62smk1R0XhHd7Oizb59S5lKL4BTRt0wZgOQEof37L9BYednLsSay/114ofL9IrmvCN6OKc6CGYFEBJz4zRYZjJ3exyIDZPgN5TgLv4/ZQv8iIBN/UT7L9a8nWTOrCDBBaBVBHs8ISCIQ5U1FZhOWhNmWYQfy8lCsSBEUj/uh90r2Bry7e2uC/Nsq1cb55avCiJgTUaoEimBireaoW6KkSPeUPqRzj3Xa9w/SgNARhGRUt27AvHkKHkTuTZ9ujF+NGsCWLYn9KHOQ7iEcMgQYOjRVxp49AB2I6v34V/vs9deBfv0UeSIHiHraax0i0iEvZbOYaU4cSFqxj/ShrCvKkopvVHr0hhvMWJbYl+6wSy5VSNmWxYvLy+SRjAAhQGuWSCe1RhmGR0r62hL7sfm8QF4trv/3P+VZaUc7dEh5NqrFZOy5a8c8VC7077/1JWVlAZUri88m+sxz4nkrrmViT8K0XDn90SL6itoem2ngQODVV2W1Vh9Hz3YRkjK2v9s7e6I0ETzoZSF6aYjIZcpkpxea6L5Vygzmxgj4BIGg/WbzCWyKGkwQ+sodflaG48zP3mHdwo6ATPxF+Sw37OshyvYxQRhl77PtniIQ5U1FZhP21Fkqk1O24vRdiQTh7ZVro085ewhCmvLU9PIYWr2hadP35B3G7Zv+xaqD+3XHhpIg3L8fmDJFOYwnIi7+UF4LDSpbt25d4qcPPaQQg5RZSBmGyY0yzaiUp1mCkEpZ0kEgNTqYbdUqdW5Rj2sd2rZpAyxbJipF6SdyAGxOojUClPShOxrprsb4ZsfBbnwWIf27c2ezlnF/RkAdAbXnwcknK4f+8U2EPDDC2ImYNZqTPn/2WeD++1N7ErFnNSuasnkpc3fXLnVNqNzvkiXaRKyI/rE+HTsqd1bqNbMYi/rVrFwzdpntK6IzZVTTvqLXROTExlOZWsrgGz/erLb6/WkNklwqR2vUnPaBCB633AKMHGmkKX/OCHiKQBh+s3kGIBOEnkEftIk5zoLmMdY3TAjIxF+Uz3LD5Hu2JREBJgh5RTACHiEQ5U1FZhP2yE2a047dvgHvJBGEd1Sujd4aBGHd4iXxeu0WprIL04oUwaf12po2Xe1+xGQhGcXS8FYdm++MMq2pTwaMGQMMGpSoTKVKQHY2sH49UKdOqqKxknNmCcK9e1Mz2EQOEo2gIsKSshCqVjXqqf65E4elVuwifejwODlbcPJkoH9/ORt5FCPgNAKih4FmY4OIt8OHE7V3ImZF8KFnWNmyqT1PPRVYuFBEgtInWQ6RdSecYDy+bVullOtddwGU/S3bRHxgFmMRmaTvN98Ap50mq7m940R0ju2HejOLyImNpxdjqCSt2ft9jSwnf/3yC9Chg1FPZ16Kic0qkpVJfUXvGjS2hnswAo4hEIbfbI6BYyRY9DuBkRz+PPQIcJyF3sVsoI8RkIm/KJ/l+tiVrJpFBJggtAggD2cEZBGI8qYiswnL4uzUODWC8M7KtXFeuaroumYpko5yC9SgjD0z5UepzOhn9c0ThCJztCtZBi9lNHEKnmDJ3bpVvaQXHTZS6VG1Q2i9ewb1PqMMx5JJZWPNHKzqIXv55cC0aXLYz5oFnHuuPZk5pAGVdr30UjldiBi87jpg8eLUOx7pb+3by8nlUYyA0wjQ/ZWPPJI4ixrJZCbmKcN5wQIl25hK5FKzI5PWChZa+psh1Ciz8ocfrGihjKWXIqhUqJlSoJs3AxkZxnObsYekifqVStGSzk083oO//VacqDTCQtR2wumOO5S7ZJs2NfaBmR56e2+yHCN7zMyb3JfuczzlFGMJdmTdGs/CPRgBSwiE4TebJQCsDGaC0Ap6kRrLcRYpd7OxPkNAJv6ifJbrM/exOjYiwAShjWCyKEbADAJR3lRkNmEz2LrRV40gvKtyHZxbrgrOWvMbDiE/RQ2zBGFxFME8hwjCmyvVwkXlq7kBVTDm0PoRv327+sFz7HBx+XKgdetCG6ks5gsvKP9NZQUpqya+xTIP4/9m5mDVSTQpo2PSJOszxJfwlJEWw1btTjc1/GTm4DGMgBMI0Jp9+GHgqaeUO1Ap4zX5zkuaVzTm6Q7OPn2AihWBv/4C6A5TujuPyieLynDCTjsIQrv1N0P4EH5TpxojY0YmSaOsQCLdRBq9kPHhhyI9nelD9yuayTgnLOh/992nlH5u1AiYMaOw9GiDBmKlPcmaUaOAa68F0tPlbWvYEFi9unD8rbcCI0Yo//3oowDdi6nX6L6/8uXl59cbKbq2za4vZ7RlqYyALgJh+M3mmYuZIPQM+qBNzHEWNI+xvmFCQCb+onyWGybfsy2JCDBByCuCEfAIgShvKjKbsEdu0pxWjSC8u3IdnFOuCs5cs1SFHjSfQVgUwBf125k2XSSDcF69tigueohlWoMADkjGgsqK0r2EWqXC4g/2fvtNufeQDu4HDkw8uC9XTpFBbdgw4J57UsHxkx+IECUyQrY9/7y6jWbkxWNLd1/16qWUV/z0U+N7sMzMw30ZAa8QEI15vxIIfiQIKSOPyFi1ktDJfnYKf8oIE7n3NqbPjz8CdBei223DBqB2bXOz0lqkva5d3HcSuoeXyltTE8WU+taqpeyvsndWXnYZQIRg167Avn1KNuiiRUDduooutOdS+VLKOKe/qzW6W/jXX81hINKbqgSIEp9+jW8RO7lPZBAIw282z5zFBKFn0AdtYo6zoHmM9Q0TAjLxF+Wz3DD5nm1JRIAJQl4RjIBHCER5U5HZhD1yk+a047ZvwNtJdxDeU7kOepWrollG1GwGIU1OY8w2PYKQ7jV8qlpDHJ9ezqzYcPdP/hFPB6g1awJah31mDvZ27FAOQoksVGtUZu3ff/2BL+nRuLGcLpRVQhmUVpsZbK3OxeMZAS8QECVT/BoLfiQIY34UKb8qgj+VSH77bfOrQ0R2TOr//R/w9dfm57A6woyOsbkoM/Lpp4GPP06cXe8+Xj09aW3L6EEyb7oJeOUVYOVK4I8/gJNO0r6PUu+lFSfiy4xNTsxvdW3weEYgCYEw/GbzzKlMEHoGfdAm5jgLmsdY3zAhIBN/UT7LDZPv2ZZEBJgg5BXBCHiEQJQ3FZlN2CM3aU47fvsGvJVEEA6uUhdnl63sKUG4+/AhnJe5TFXv2XXboHTRYn6D0h/60N1eVB6OMg7mzAHOPlvRizLXihdP1dHOgz3KPKTsFz80OnCl0nEyzczBqJ58O7GVsYPHMAJOIyAaK36NBT8ThOQ7I9xE8KfS0UQ+mW0isuNlGulqdn6R/mZ11JNJZT6pvKgZmeecA3z0kbkx8To0b66U3BVtdqxXq3OpjffC96J2cD9G4AgCYfjN5pkzmSD0DPqgTcxxFjSPsb5hQkAm/qJ8lhsm37MtiQgwQcgrghHwCIEobyoym7BHbtKcVo0gvK9KXfSwmSB8v06rAh0qFSshBMFjW//Dgn07VfvKZCMKTRr2Tk7/wB8yBBg61B8oMkHoDz+wFuFGQJRM8SuBoKX/ihUAZUSLNFEMRGQl96GXOqjcJ73sQCUxiei74ILC56xIGVB6LhvdY6emG2VRUza1aKMs9ZIlRXvb089O7OkuwFWrzJF9dI/hM8+YGxNvOZUYnTZNHIsbbgDGj0/t70R8mcHWifnFUeGejIAQAmH4zSZkqBOdnP794ITOLNMTBDjOPIGdJ2UEChCQib8on+XysgkvAkwQhte3bJnPEYjypiKzCfvNnRO2b8S0XVsS1JpSqznqlShlawYh3UNIbVClWriwfDVDGC5Ytwzb8w6l9OtVtjLuqXLkfh5DKdwhAQGnf+D/9JM391CpuZkJQl78jIDzCIiSCH4lEKpWBbKyUnEaNw64/nox/EQxEJMm1ovKYy5ZAjz8sHF/uttO9C65eGlm7yGku/hE7k001lisB9lVpoxYX6d6jRih3CEouwYGDQJGjxbXbudO9bt17Y6vvXuBsmXF9bJ7fvGZuScjIIxAGH6zCRtrd0enfz/YrS/L8wwBjjPPoOeJGQEmCHkNMAJHEGCCkJcCI+ARAkwQflaAfLdu3ZAucwjnkd9i06oRhHPrHoNSRYvaShDGm/lpvWOQViRGGaYCkJN3GD3XqZdE4+xBCwvG6R/4v/4KHHecBQUNhg4YAEyeLCafMkEoI0SmyR72Js/Fh6Yy6POYICEgGit+jQUiterVS0WcyiX/+ae2J8geurOO7mQVxcALvz7+OPDII9ZmFrWP7si76y5rc5kZXaUKkJ1tZoRcX8JPKwNzxgzgwgvl18DNNwOjRpnTS80fBw+qlxA3J7mwt1ZcfPklcOaZqVL9Gt+y9vO4UCLAxIUFtzr9+8GCajzUXwhwnPnLH6xNtBCQib8on+VGa3VEy1omCKPlb7bWRwhEeVOR2YR95LoCVdRKjMYIvM5rlqqqSySd1mci9k2t1QK1S6iXIhu7fQPeSboTMSaTyUERdHX6OP0D32mCkA7ki2oTywmWL1sGtFLK2ppqdNCpN8f06cAllxiLpKwSyi7hxgiEGQFR8sjPBMI99wBEbiU3LZ3prrrevYHffwd69QJmz/afhym7jp5jdpT8FPUxoTBrloKNG82MXlb0obKubdqoS/j2W+CUU7wnCB96yN7y3j/8AJx8snpM/N//AQsXFn723HPAvfdaQZjHMgKuIBCG32yuAKU2idO/HzwzjCe2GwGOM7sRZXmMgDgCMvEX5bNccWS5Z9AQYIIwaB5jfUODQJQ3FZlN2G+O94IgfL1WC9RVIQjXHtyP/hv+1oSICUKLq6dTJ2DBgkIhdMh36qkWhcYNX7QIOPFE++QlS6ID+1deASjrwqhR9g9lAZlt332njwnpoHZQUrlyYjaL3RkdZu3g/oyAGwiIkDSlStGlGG5oIzdHLBNQ7XmjJvH22/1N/jdrBvytvY+aBokyLCmjTLS5QQYfOADQunKjUTnXc85RnymWqS4SB2oS5s0DunY1Z4XWXHbifsIJwM8/p+pFc2zcCBx7LLBli0KOEkFesaI5G7g3I+ABAmH4zeYBbMqUTBB6Bn3QJuY4C5rHWN8wISATf1E+yw2T79mWRASYIOQVwQh4hECUNxWZTdgjN2lOO277BrydlLE3r15bFC9SBJdkLsfWwwdTxlrNIIzdcZgsWC97kPoyQWhx9Rw+DFx3HfDOO8D48cAVV1gUmDT8iy/MH3aa0YAOJ0Xvxfr3X6BxYzPStQ9B6JP77weeeko5JFE7KNm/H6AsjhUrgLFjgVq1zM/NIxiBoCEgQozk5gIlSvjbMjNlG0Vs9tLayy8H3nzTPg3oZYe0NHF5RCBlZIj3N9tz4kRg4ECzo+T7//ij9osvu3crd/VJrInDnTqhGJXsNDvWDYLQKB7ouwTteVRWXzSrX94DPJIRsAWBMPxmswUIGSFMEMqgFskxHGeRdDsb7RMEZOIvyme5PnEbq+EAAkwQOgAqi2QERBCI8qYiswmLYOpmHzVS7vN6bVGsSBH0zVyOzQ4QhJNrNUf9Eqlv/4/OXo8Zu7dpms8EoZsrQ2Ku994DLrpIYqDgkFiGhMiB6ldfAVQKje4IM9PUZN94IzBmTKEUPigxgyj3DTMCerEYBGIw5hs1OwYNAkaPVnps26a8XLFkCfDff3IepftT6R5Vp9tPPwEdOtg7S4UKwK5d4jLpLkK1sq3iEtR7ir4gYnWe+PErVwKUfa+WRUnZp1ovjejokN28OdJ//hnpRC6abfTyS9OmqaMoS9eurEre48x6hfsHAIEw/GbzDGZ+JngGfdAm5jgLmsdY3zAhIBN/UT7LDZPv2ZZEBJgg5BXBCHiEQJQ3FZlN2CM3aU6rRhB+Ua8tijpIEE6q2RwN01IJwhHZ6zGTCUK/LRFxfZzO7IgRhH37KlmQRu3005XyZ6KHsJmZQN26qVKTiQ4+KDFCnj+PCgJqsdCgAfDii0CfPsFBQS8ry667VSkTz41MSjtLTcY8SHcLnn++OX/u3QuULm1uzOLFSvnWOnWUjGzKUItvdBcg3S+r1eh5T/dC2tkoW472nHffTZVq5qWVuNGrevZEzRkzkJ5sn6jeWuuV8GvfXlRKYT8iXsnHRDASfmpZgU6sK/Oa8ghGQBqBMPxmkzbe6kD+3msVwciM5ziLjKvZUB8iIBN/UT7L9aELWSWbEGCC0CYgWQwjYBaBKG8qMpuwWXyd7j9m+wZMTyox+mW9tijiIEE4sWYzNEpLOvgDNDMWYxhwBqHTq8GifMoYuecei0J0hscOKKdOBfr1E5uHsnb69xfrK1q6jQ9KxPDkXuFHICkW8ho1QlHKuApa04t9kYxlNXuPOQY480wli/nhh4FKlQCjO06t4ha7E8+qnOTxRJIR4Uv38Ym22rUBeulCtFGGImUqxtrZZwNz5iSONvLFjBn2Z7HTvtO6NbB8eaolkgThmq5dUf3DD+0nCElDGSLv3HMLfXvLLcCoUdq2ivqT+zECPkMgDL/ZPIOUv/d6Bn3QJuY4C5rHWN8wISATf1E+yw2T79mWpJ+M+fkyv4gYRkaAEbCKQJQ3FZlN2Credo9/JXsD3t29NUFsjIhzqsTohJrN0ESFIOy8ZqmueUwQ2u19m+X973/Ao4/aLDROXGybN1PKlA6cd+wQ04kJQjGcuBcjEEMgmSBs0ABFV68OHj5asW8l62/BAoCymJObEcllBT0nfwqRbCrb/O234hqa0YdKOY8blyg7ebwRdpMmAddcI66fUc+tW4GqVZVsRrqDNrlJEoTrzjgDVefOdYYgJDLXzL2A//wDNGtmhIQc8WgslXswAq4hEIbfbK6BJbJvmXm+e6Y4T+w2AhxnbiPO8zEChQjIxF+Uz3J57YQXAc4gDK9v2TKfIxDlTUVmE/abO/UIwk/2ZOPZrHUpKhNRZ0Tm6dk5vmZTNE1LLT1mJJMJQr+tniR91qwBqLygE40Ofq++WpFMWSyU8SDaPvsM6NpVuStKq9FBh9ahqsghNR+UiHqD+4UJgbBkFWg9G6hs8rXXynksKwuoXDl17BdfKM8juxuVdb3jDrulJsobPBgYNkx8jngM6B7H6dOB448HTjwxVYbIWjIiCIlgvOGGRNmXXw5cdpm5PSMmIfZc378/tdwp9Yl93qQJYCJzdv0pp6Dy5587QxDSGqBS2aTTeecZ++qjj8T68R5njCX38DUCYfjN5hnAIs9nz5Tjif2EAMeZn7zBukQNAZn4i/JZbtTWR5TsZYIwSt5mW32FQJQ3FZlN2FfOAzA6ez1mJN37FyPi/svdj6s3/p2islWCcFxGUzQryQSh39aCLfrQfX909xS1jh2BRYvUxZ51FjBvntiUGzcCGRmFfYcOBYYMERsb6zVgAPDaa9pjFi5UsmPUGhOE5rDm3tFBICyHhkbEk4xHte7gowyv4sVlJOqP+eMPpRSmk43moNKpoo3KjFJpzrVrE8c1apRKqBmtpb/+Alq21J9506bEvYJ6U9lVenHFTFYdjaP9KUbkar1AEtsb9PYPFY03nnACKi5Y4AxBGD/fyy8Dt92mj9mHHwK9ext7lAlCY4y4h68RCMNvNs8ANno+e6YYT+w3BDjO/OYR1idKCMjEX5TPcqO0NqJmKxOEUfM42+sbBKK8qchswr5x3BFF9AjCNQf3Y8AG+wnCsRlN0ZwJQr9Rxa0uAAAgAElEQVQtBXv0ycsDvvoKSEtTCDetg3c6JKe7uYwaEYwnnJDYq0cP4NNPjUamfr5lC1CtmvL3iy8G6L4qahddVPjv5FETJgADByb+NdkmulssO9u8PjyCEQg6AmE5NHSCIDx0SPsZpzffcccBixebXxnr1gF16pgfZ3aEWayefhp44IHUWeh+QiIQY81oLc2eDZxzjr62RGIlyzFTBnTFCqBhQ8VvyXKM9HvzTeDKK4XQ3NKuHcp9/708QUilvKmkt0gzIvZmzgQuuEBfUrducnuuiH7chxFwCYEw/GZzCarUaYyef54pxhP7DQGOM795hPWJEgIy8Rfls9worY2o2coEYdQ8zvb6BoEobyoym7BvHHdEES8IwlcymqIlE4R+WwrO6KN3r5/IQbPa4WbPnsDcueb1JeKyUyeA7pWqXl1svNp9Tsl6UwaGmZKnYjNzL0bA/wiE5dBQ5Flk1ht6xAy9QEH3G8a39u2B+vWB4cOV8pBmG72c4YQdyXqYnUMvkzweI6O1RPgsWaKNCr1IQi+UEA5z5igZg2efXYiJXqbcN98o2Zf0sodWM9KP5lYrnaoib1vr1ijz00/yBKFoWVCa+5dfACKd1Rq9NFOjhthKMyIaxaRwL0bAMwTC8JvNM/CMnn+eKcYT+w0BjjO/eYT1iRICMvEX5bPcKK2NqNnKBGHUPM72+gaBKG8qMpuwbxx3RJGR2evxvkaJUacyCEdnNEGrkmVSoLhg3TJszzukCRHfQei31SOgjxMEIR3+9uolMHlSl5o1gQ0bxA/Rb70VGDEidZ4pUwAqWUqNDqV//FFcpnmteQQj4F8EwnJoaJb0EvGIHqFCZR9HjkyUQi8uVK2q/E1GH7cInO7dAbrXVbQ1bqx9P58RQUglRYkUpJKsRhnn8ffUaummhivd0/fBB8bWJI+lbEYi6mKNiLgOHYzlAMhu1gzpS5bIE4R0t2NsrYjMWKsWcNddyh2V8ThWqSKe/e7W+hKxh/swAhIIhOE3m4TZ9gwJy15vDxosRQcBjjNeHoyAdwjIxF+Uz3K98xTP7DQCTBA6jTDLZwQ0EIjypiKzCfttIXlBEI7KaIIGJUohrUgRlChS9CgkF2cux7bDSVkVRz49Nb08hlZv6Df4WB8jBNQOFagU2+WXix2Cqx1K5uYCJUsazaz+uVoJOi1JegeidK/W5s1KGVUn7hOTs45HMQLuIhCWQ0MZQs4Iab3nR9u2wO+/J0rYvh2oWFH5m4w+bhE49EIElaSkly2sNiOCkOQTEXfppUC/fvqzffyx8YsjargmlzrVmoVINroPN9beey+xNCcRmZTlKNB2NmiAtOXL5QlC2TVCZUnj7+81s87cWl8C+HEXRkAGgTD8ZpOx25YxYdnrbQGDheghwHHG64MR8A4BmfiL8lmud57imZ1GgAlCpxFm+YyABgJR3lRkNmG/LSQ9gjAn7zB6rvsjRWXK5Ou8ZqktphBZ2PpINqGezOHVG+H49HK2zMlCXERA7VAhdjeX0eHkyScD332XqiwdVFL5OJlmF0EoMzePYQTChsCddwIvvXTUqv3z56PUGWcEz0qygWyxq/XvD0yerC1N7dm3ezdQtqwyxujZqCbZTQKHynhS+WW6K/Gkk6yh9vbbCgEoY3P8zNOnK3fL6rW//wZatCjsQXfZ0v16Io0y1+m+2pwc5cWQL79MfDnkt9+Adu1EJGFVz56oOWOGNYKQ1kv58kLzJXSKrZN//gGaNRMf7+b6EteKezICwgiE4TebsLF2d2SC0G5EQyuP4yy0rmXDAoCATPxF+Sw3AC5lFSURYIJQEjgexghYRSDKm4rMJmwVb7vH6xGENJcaaWcnQUhzxEqH6hGEI2o0wTGlUsuS2o0Hy7MZAb1DBbXPTjlFIQUpk4YyQk49VV0h2cNkJghtdjCLizQCW7bg8IAB2P/LL1jbpQsavPoq0kuXDh4k77wD9O1rj97DhgFUQpTuGdRqas+v/fsLM6PXrlXuIzTTvCJwZJ/F8bZRedBrrjFjbWpfymikMtJGjQi+TZuA2rX1faQmh7INaeyxx6Zmji9bBrRpYzR7weeLBg/GMY89Zo0gJEEy2MfWidmxXq0vIUS5EyNgjEAYfrMZW+lMj/lFOqETFhwV/gpuwqD8V5yZjKUGGgGOs0C7j5UPOAIy8Rfls9yAu5vV10GACUJeHoyARwhEeVOR2YQ9cpPmtCOy12Omxh2ENMgvBOFF5ari5sq1/QYf62OEwHPPAffdV9hr3Djg+uuV/1Y7oDx4EPjrLyAjQ/+Opd69gQ8/NJo99XMmCM1jxiMYAR0EwrAPIjmzzIrHRYiUq64C3ngjcRbKyot/Ju7ZAyxdqtz3R6UhjZrIvEYyZD4fOxa46SaZkfaOESUI7Z21UBrtW3RnokD7+e670fqJJ4JDEFJZ2Y4dBSzjLoyAfxEIxV7lEbzHFVmML3EmKmInMlEbJ+EHZObX8UgbntbPCHCc+dk7rFvYEZCJvyif5YZ9PUTZPiYIo+x9tt1TBKK8qchswp46S2XyoBCEVYuVwLt1WvkNPtbHCIGsLKV83Pz5QPfuwLRpQIUKyigrJYvoMHzqVBxatAjzTjgBZ1NJP5HGBKEIStyHERBGIAz7YIGx558PzJolbLdmRxGibutWoHr1RBFa43buLLybUE87kXmtW5cqwUrJZzv1ob2mcmU7JZqTZaJk57xx43Bav37WCcLsbKBKFXN6ymQQUjlZ2bLe5rTj3oyAYwiEZq9yDCFtwfR1vTYy0RJ/YjGOQzaqwKstxwPzeUoTCHCcmQCLuzICNiMgE39RPsu1GX4W5yMEmCD0kTNYlWghEOVNRWYT9tvqCApByBmEfls5NuhjhSA8Mn0sBnv36SOmEB101q0LULaJXps6FbjiCjGZ3IsRiDACYdgHC9xHz4ZPPgHOOceaN0VPTWOZd1WrArNn62doEQlEZJBeE53XmnXqo82Wq3RCBy/tJ3tWrQIaN1a37OWXgTvuAJ2or+nSBUtuvRXdunWzThDSbFdeCbz5pjii69cDtWqJlyctWRKg8rfcGIGAIxCavcoDP9jwdd0DrXlKLxDgOPMCdZ6TEVAQkIm/KJ/l8roJLwJMEIbXt2yZzxGI8qYiswn7zZ1mCcKqxYrj3TqtVUuPytomcgfhPZXroFc5k2/KyyrE49xBwIYTB9ME4eOPA99+q5Tt02vJ5f7cQYRnYQQCh0AY9sEE0B98EHj6aWM/EDFDLxIkNyeIKpEsPSfmNUZB6fHLL0CHDqK9nennpf1k0erVQKNG6raRbn//jf3Z2fh08+YCcs42gpD2qmLFxDG95RZg5EhxgrBcOWDXLnH53JMR8CkCodurXMTZhq/rLmrLU3mJAMeZl+jz3FFHQCb+onyWG/X1Emb7mSAMs3fZNl8jEOVNRWYT9pszzRKEU2o1R70SpXDLpn+w7MA+W8yZULMZ1h88gMe2rdGU92jV+uhUpqIt87EQnyCQfKj87rvARReZUs40QSgine79attWpCf3YQQij0AY9sEEJ27cqGRYGTXqV7Nmai+niCqjLD2n5jXCIfY5ZWVTmc1SpYCTThIdZV8/r+3XyiAkvxGJJ/lmtxBARmsjWQjpY6ZkqNfYCoHAnRgBfQRCt1e56HAmCF0EO+BTcZwF3IGsfqARkIm/KJ/lBtrZrLwuAkwQ8gJhBDxCIMqbiswm7JGbNKc1Igh3HT6Eazb+jazDh3Bn5do4r1zVAln3bV6FRft322JOUQDK8Zl2e7Z6Q3RML2/LfCzERwj89BMwZw5wyinAWWeZViwWg51uvx0V1mgTzKYE5+Qoh9zcGAFGwBCBMOyDKUbu3g2U19lvypQB9uwB3noLuPzywuGUmSzxHDMEmTqMGAHcfrt2Vz+SOGaJKyEgVDrdey/w3HOyo+0Z9++/QNOmqbJoL6E9xU8EIa1beiHn/ffFbPfj2hLTnHsxAkcRCOVe5ZJ/mSB0CegQTMNxFgInsgmBRUAm/qJ8lhtYR7PihggwQWgIEXdgBJxBIMqbiswm7IwX5KWOyM7EzN1ZCQJiJT/1pD64ZTW+z3Gv7NSojCZoXbKMvKE8MpQIxGKw5dSpaDZjhj028mGoPTiylEggEIZ9UNVRWVkA3Q+o1lq3Bv74Q/lk8WLgo4+ASy8FWrRwzud0D1x6urZ8Pz63nCIIKcubfLBokXKnbJ06zuEuKlmLIKxYEdi+vUCKY7EigzPdtfnxx2LW+XFtiWnOvRiBowg4Fn8RwJgJwgg42SYTOc5sApLFMAISCMjEX5TPciUg5iEBQYAJwoA4itUMHwJR3lRkNmG/rQBZgnDIltVY6CJBOKlmczRM46wuv60fr/WJxWCJ3btx9sCBKHLggDWVPvgAOO88azJ4NCMQIQTCsA9quovuX6NMweR2+unAggXue1mPCPIjiSNDXBmhOncu0KOHUS/3P//vP6Bhw9R5a9QANm0q+LtjsSKDM2Xtf/edGE5+XFtimnMvRuAoAo7FXwQwZoIwAk62yUSOM5uAZDGMgAQCMvEX5bNcCYh5SEAQYIIwII5iNcOHQJQ3FZlN2G8rQJYgfGzrf1iwb6dr5rxTuyWqF09zbT6eKBgIxMdg9+rVUWryZGD8eHPK33Yb0LIlcOKJQPv25sZyb0Yg4giEYR/UdeGvvwLHHZfYxSuSKiwE4V9/yWdb+pWsIr2aN1fuYYxvlOG4dm3BXxyLFSptShmMTjW/Yu6UvSw3lAg4Fn+hRCvRKCYII+Bkm0zkOLMJSBbDCEggIBN/UT7LlYCYhwQEASYIA+IoVjN8CER5U5HZhP22AmQJQrdLjM6p2wbpRYv5DT7Wx2MEVGPQbDbFyJHALbd4bAlPzwgEE4Ew7IOGyHfvDtD9grGWlweYfc4YTiLQISwEIRFOsvj5mayicrPHH5/oSLqj8s03C/7mWKxMmgRce63AAtLp0r8/0LcvcPbZqZ38jLk1q3l0hBBwLP4igCEThBFwsk0mcpzZBCSLYQQkEJCJvyif5UpAzEMCggAThAFxFKsZPgSivKnIbMJ+WwGyBOENG1dgRW6Oa+aI3IvomjI8kW8QsIUgXLgQOPVU39jEijACQUIgDPugId5EkMycqdwld9llQOnShkMc6RAGgrBSJSA7O5wEITk92UeU1U7km5ME4erVQKNG1pbc7bcDL72k7hcmCK1hy6N9gUAk9iqHkGaC0CFgQyiW4yyETmWTAoOATPxF+Sw3MI5lRU0jwAShach4ACNgDwJR3lRkNmF7ULdPysvZmZi1OytBoAgZNzxrHWbvybZPEQNJIjq5pgxP5BsEbCEIV660frjqG0RYEUbAXQTCsA+6i5iF2YJGEI4aBdx6a6rBYc0gJEuTfUSlPxs3LsDAsVjZtg2oVs3CwjoyVMsvTBBax5YleI6AY/HnuWXOK8AEofMYh2UGjrOweJLtCCICMvEX5bPcIPqYdRZDgAlCMZy4FyNgOwJR3lRkNmHbHWBRoCxB+NqOTXh952aLs4sPZ4JQHKso9bSFIPSqXGCUHMW2hhaBMOyDgXFOxYrATpW7f9u1A5Ys8Z8Zb70FUInN5BZmgnDECOCOOwCy8ZprgIkTj1rvWKzk5gIlSyaiPGcO0LOn+JqoXx/47z/OIBRHjHsGDAHH4i9gOMioywShDGrRHMNxFk2/s9X+QEAm/qJ8lusPr7EWTiDABKETqLJMRkAAgShvKjKbsACkrnaRJQin7tyMiTs2uaYrE4SuQR2oiWwhCDk7IlA+Z2X9hUAY9kF/IaqjzapVR7PRjvYi0vCDD4DTT/efGb//DrRtm6oXPXOvvPLo3XymFA/C8/qff4C9ewEibuNO1h2NlYEDC8nILl2Azz8H9u0D3n5b/H5CziA0tRS5c7AQcDT+ggWFaW2ZIDQNWWQHcJxF1vVsuA8QkIm/KJ/l+sBlrIJDCDBB6BCwLJYRMEIgypuKzCZshKfbn8sShG/t3ILxOza6om6t4ml4s3ZLV+biSYKFABOEwfIXaxs+BMKwDwbKK1OnAv36Aa1aAa++CjRrBlSp4k8TDhwASpVSJwi3bAFq1DCvdxAIQg2rHI0VyoR/5x1g/34lazM+o3DXLqBCBWOsmSA0xoh7BBYBR+MvsKiIKc4EoRhO3MvBUtoMLiPACBgiILPPRfks1xBQ7hBYBJggDKzrWPGgIxDlTUVmE/abv1/KysQHe8zfQfjOzi0Y6xJBWLd4Sbxeu4XfoGN9fICAMEFIB5/z5wOdO6sfVvvAFlaBEQgiAmHYB4OIe2B0Tj5ZnjwZ6N9fUX/rVuWOQiK2RBsThKJIJfaj8qENG2qPnTkT6NOHS4zKocujAoAA71XyTmKCUB67qI3kOIuax9lePyEgE39RPsv1k+9YF3sRYILQXjxZGiMgjECUNxWZTVgYWJc6jsxej/d3b0uYTaSc57u7tuKV7Rtc0hIQ0ck1ZXgi3yCgGoNjxgCDBhXquHo10KCB8t+zZwPnnFP42dChwEMP+cYeVoQRCBoCYdgHg4Z5oPS94AKAyKdYo/vySpQo/O8ZM4CLL0416fnngbvvTv07E4Ry7qfMwvR07bErVwKNGgF33gm89FJhv1mzgN695ebkUYyAjxDgvUreGUwQymMXtZEcZ1HzONvrJwRk4i/KZ7l+8h3rYi8CTBDaiydLYwSEEYjypiKzCQsD61LHTYdycdn6P4/OdnJ6eTxVXect8yM939u1FaOYIHTJSzyNFgKaMfjnn8A33wAXXQRUrpw4fMUKgEjEM88Ezj2XwWUEGAELCIRhH7RgPg81QoBKX1LW4KZNyl14yWVF1TK7iaj65RegUqVE6cWLAwcPGs3o2889j5UHHgCeeUYdnxjxum6dsm8S/pdeqtxrqFYm1rcos2KMgDoCnsdfgB3DBGGAneey6hxnLgPO0zECcQjIxF+Uz3J58YQXASYIw+tbtsznCER5U5HZhP3oTioXOm3XFlQpVgIPV62HRmk6b5kfMWB17n5cs/Fv18zhDELXoA7URGGJwUCBzsoyAhZ/jDKAjMBRBBYtAk48MRGQrCzlxQ4ipwYOLPxs7lygR4/AgueL/UrtpJ8QDXBmZmAXBCvuKgK+iD9XLbZvMiYI7cMy7JI4zsLuYbbPzwjIxF+Uz3L97EvWzRoCTBBaw49HMwLSCER5U5HZhKWB9tnArYcO4pL1y13TiglC16AO1ERRjsFAOYqVDS0CHIOhda07hlGGIZWApsw1au3bA4sXK/8+dAh47jlg4UIl2/vGG9XvyHNHU8uz+CJWMjOBunUTbaEMwZwcy/axAEbAzwj4Iv78DJCObkwQBtRxHqjNceYB6DwlI3AEAZn4i/JZLi+c8CLABGF4fcuW+RyBKG8qMpuwz90prN6uw4fQO3OZcH+rHZkgtIpgOMdHOQbD6VG2KmgIcAwGzWM+1HfJEuDBB5W7CYcNA5o186GS1lXyTaxs2wZUq6YYRGVfJ0wINPFq3TMsIQoI+Cb+Agg2E4QBdJpHKnOceQQ8T8sIgN71ysFnn31WgEW3bt2Qrnf39BHEonyWy4smvAgwQRhe37JlPkcgypuKzCbsc3eaUq/zmqWm+lvpzAShFfTCOzbqMRhez7JlQUGAYzAonmI9vUaAY8VrD/D8UUaA40/e+0wQymMXtZEcZ1HzONvrJwRk4i/KZ7l+8h3rYi8CTBDaiydLYwSEEYjypiKzCQsDG4COTBAGwEkhVzHqMRhy97J5AUCAYzAATmIVfYEAx4ov3MBKRBQBjj95xzNBKI9d1EZynEXN42yvnxCQib8on+X6yXesi70IMEFoL54sjREQRiDKm4rMJiwMbAA6MkEYACeFXMWox2DI3cvmBQABjsEAOIlV9AUCHCu+cAMrEVEEOP7kHc8EoTx2URvJcRY1j7O9fkJAJv6ifJbrJ9+xLvYiwAShvXiytAAhsHbtWsyePRvz58/HkiVLQA/5w4cPo2rVqjj++OPRt29fXHzxxShevLgjVkV5U5HZhB1xgkdCmSD0CHie9igCUY9BXgqMgNcIcAx67QGePygIcKwExVOsZxgR4PiT9yoThPLYRW0kx1nUPM72+gkBmfiL8lmun3zHutiLABOE9uLJ0gKCwCOPPIKhQ4ciPz9fV+MOHTrgvffeQ7169Wy3LMqbiswmbLsDPBTIBKGH4PPUBQhEPQZ5GTACXiPAMei1B3j+oCDAsRIUT7GeYUSA40/eq0wQymMXtZEcZ1HzONvrJwRk4i/KZ7l+8h3rYi8CTBDaiydLCwgCAwcOxMSJE1GmTBmcf/756NKlC5o2bYpSpUr9f3tnAi1XUe3vYshwA+EyJRCCJIAEEENASYQAYRJFRBAEgSACAgr64L2HDKIyI6MoD5UxAk+RQQbBCCiCEUTQIOQhyAxhSAgBIxACScjgf+3Svv/OTQ+nq8/ps6v2V2uxdKXPqdr72/W71V27Bvfkk0+6iy66yD300EPeG/n3Rx55xK244oq5emd5UAkZhHOFX3JleScIBy67nHtn8aKaXk0aNqpkb2leIwHrGtQYE2yyRQAN2oo33oYTQCvh7HgTAu0SQH/hBEkQhrOz9iY6sxZx/NVEIER/ludyNcUOW/IlQIIwX57UFgmBE044wa222mruyCOPdAMHDlzKajlqdPz48e7nP/+5/+z00093J510Uq7eWR5UQgbhXOGXXFneCUJJAh712rPu8fnvLeHZsD793NVrbVSytzSvkYB1DWqMCTbZIoAGbcUbb8MJoJVwdrwJgXYJoL9wgiQIw9lZexOdWYs4/moiEKI/y3O5mmKHLfkSIEGYL09qS4jArFmz3FprreXef/99t+mmm7pHH300V+8sDyohg3Cu8EuurIgE4TEzn3dT5s1ZwrMN+3a5S4eMKNlbmtdIwLoGNcYEm2wRQIO24o234QTQSjg73oRAuwTQXzhBEoTh7Ky9ic6sRRx/NREI0Z/luVxNscOWfAmQIMyXJ7UlRmD06NHuL3/5iz+KdM6cJZMv7bpqeVAJGYTb5a3p/SIShPe/97Y76Y0Xl3DzgsHruY90Lb1DVhMLbCmHgHUNlkOdViHw/wmgQXoDBLIRQCvZOPEUBIoggP7CqZIgDGdn7U10Zi3i+KuJQIj+LM/laoodtuRLgARhvjypLTECsnPwscce88eQzp49O1fvLA8qIYNwrvBLrqyIBKG41Lve362zqVum1q/Tkv2n+fIJWNdg+RHAAusE0KD1HoD/WQmglaykeA4C+RNAf+FMSRCGs7P2JjqzFnH81UQgRH+W53I1xQ5b8iVAgjBfntSWEIHXX3/dDR061C1cuNDJTsLJkyfn6p3lQSVkEM4VfsmVFZUgnLd4sbvyrdfcCssu677QvYZbjuRgyZHW27x1DeqNDJZZIYAGrUQaP9slgFbaJcj7EAgngP7C2ZEgDGdn7U10Zi3i+KuJQIj+LM/laoodtuRLgARhvjypLSECxx13nPvud7/rPTr//PPdscce25J3Mmg0KjNmzHBjxozxjzzzzDNu7bXXbqn+mB+eN2+eu++++7wL48aNc/3794/ZnZZt3/X1Z1p+p94Lm/Tpcuev8oHc6qMiGwSsa9BGlPFSMwE0qDk62KaJAFrRFA1ssUYA/YVHfMCArqVefu+9ueEV8mayBNBZsqHFsQgIhOhP5npHjBjhvXvllVdMzeVGEFJMDCRAgjAQHK+lTeDPf/6z22abbfzuQUncPf30027AgAEtOd3K0Y4TJkxwq6++ekv183C8BC7cbHhuxg+dM8/t89xrudVHRRCAAAQgAAEIQAACEIAABCAQTuCzn91jqZdvvfW28Ap5EwIQgAAEVBD4+9//7g477DBvCwlCFSHBiBwIkCDMASJVpEVg5syZbosttnCyKkSSfHfffbfbcccdW3aSBGHLyMy8kGeCcO135rq9n59phh2OQgACEIAABCAAAQhAAAIQ0Eygd4JwwIAF7tpr79BsMrZBAAIQgEAGAiQIM0DikegIkCCMLmS2DJYdfH369Gnb6auuusodfPDBTet555133A477OAefvhh/+zZZ5/tvvGNbzR9r9YDHDFaH1vINv6gICh9Kc8jRoct19ddslp+OxKVIsOsnAlY12DOOKkOAi0TQIMtI+MFowTQitHA47YKAugvPAwXX7ycO/bYvj0V3HTTfLfrrovDK+TNZAmgs2RDi2MREAjRH0eMRhBYTGyZAAnClpHxQicJdDJBKAPDrrvu6iZNmuRdPOaYY9wFF1xQmLuWL7YNuQi4sECUUPEOLz2aa6uTho3KtT4qS5+AdQ2mH2E81E4ADWqPEPZpIYBWtEQCOywSQH/hUZ8717njjnPugQec23135046ybnllguvjzfTJYDO0o0tnuknEKI/y3O5+iOKhaEESBCGkuO9jhF46qmn2m5ryJAhrru7u249kojca6+93MSJE/0zcp70FVdc0Xa7jSqwPKiEDMKFBqPDlX9v1jQ3cc6s3FolQZgbSjMVWdegmUDjqFoCaFBtaDBMGQG0oiwgmGOKAPozFW6cLYkAOisJPM1CwDkXoj/Lc7l0mnQJkCBMN7Z4lpHA4sWL3QEHHOCuv/56/8a+++7rrr32WrfssstmrCHsMcuDSsggHEZZ51uzFy105856xT0+/123VddK7jfvvtmWoSQI28Jn8mXrGjQZdJxWRQANqgoHxigmgFYUBwfTkieA/pIPMQ4qIIDOFAQBE8wSCNGf5blcsx3FgOMkCA0EGRcbEzj88MPdhAkT/EO77babu+WWW3K597AZd8uDSsgg3IxnzJ+3e+QoCcKYo1+O7WiwHO60CoEKATRIX4BANgJoJRsnnoJAEQTQXxFUqRMCSxJAZ/QICJRHIER/ludyy4sULRdNgARh0YSpXzUBuWfw+9//vrdxp512crfffrvr169fR2y2PKiEDMIdCUpJjZAgLAm84WbRoOHg47oKAmhQRWL2pH8AACAASURBVBgwIgICaCWCIGFisgTQX7KhxTFFBNCZomBgijkCIfqzPJdrroMYcpgEoaFg4+qSBE499VR32mmn+X8cO3asu+uuu9wKK6zQMUyWB5WQQbhjgSmhIRKEJUA33iQaNN4BcL90Amiw9BBgQCQE0EokgcLMJAmgvyTDilPKCKAzZQHBHFMEQvRneS7XVOcw5iwJQmMBx91/EfjBD37gjj76aP//hw4d6m644QbX3d3dEM+GG26Y69GjlgeVkEE45b5LgjDl6Or0DQ3qjAtW2SGABu3EGk/bI4BW2uPH2xBohwD6a4ce70IgGwF0lo0TT0GgCAIh+rM8l1tEDKhTBwEShDrigBUdJrD99tu7e++9t6VWp06d6oYPH97SO40etjyohAzCuYFXWBEJQoVBSdwkNJh4gHFPPQE0qD5EGKiEAFpREgjMMEkA/ZkMO053mAA66zBwmoNAFYEQ/Vmey6XzpEuABGG6scWzBgRIEJbbPUIG4XItLrZ1EoTF8qX2pQmgQXoFBMolgAbL5U/r8RBAK/HECkvTI4D+0ospHukjgM70xQSL7BAI0R8JQjv9w5KnJAgtRRtfVRGwPKiEDMKqgpezMSQIcwZKdU0JoMGmiHgAAoUSQIOF4qXyhAiglYSCiSvREUB/0YUMgyMkgM4iDBomJ0MgRH+W53KTCTyOLEWABCGdAgIlEbA8qIQMwiWFqSPNkiDsCGYaqSKABukOECiXABoslz+tx0MArcQTKyxNjwD6Sy+meKSPADrTFxMsskMgRH+W53Lt9Ax7npIgtBdzPFZCwPKgEjIIKwlbIWaQICwEK5U2IIAG6R4QKJcAGiyXP63HQwCtxBMrLE2PAPpLL6Z4pI8AOtMXEyyyQyBEf5bncu30DHuekiC0F3M8VkLA8qASMggrCVshZpAgLAQrlZIgpA9AQC0BxkG1ocEwZQTQirKAYI4pAujPVLhxtiQC6Kwk8DQLAedciP4sz+XSadIlQIIw3djimXIClgeVkEFYeTjbMo8EYVv4eDmAABoMgMYrEMiRABrMESZVJU0ArSQdXpxTTgD9KQ8Q5iVBAJ0lEUaciJRAiP4sz+VGGmbMzkCABGEGSDwCgSIIWB5UQgbhImKgpc5aCcJN+63gNuk3wN373tvu1YXvNzR10rBRWlzBjkgIoMFIAoWZyRJAg8mGFsdyJoBWcgZKdRBogQD6awEWj0IgkAA6CwTHaxDIgUCI/izP5eaAnCqUEiBBqDQwmJU+AcuDSsggnHKP6J0g/FjXQHfO4PW8ywe/+pR7acH8uu4f2D3YfWnlISnjwbcCCKDBAqBSJQRaIIAGW4DFo6YJoBXT4cf5kgmgv5IDQPMmCKAzE2HGSaUEQvRneS5XaRgxKwcCJAhzgEgVEAghYHlQCRmEQxjH8k7vBOGWXQPd2RkThCes9gG3y4qrxuIqdiohgAaVBAIzzBJAg2ZDj+MtEkArLQLjcQjkSAD95QiTqiBQhwA6o2tAoDwCIfqzPJdbXqRouWgCJAiLJkz9EKhDwPKgEjIIp9yR2kkQnjFouNtmQHfKePCtAAJosACoVAmBFgigwRZg8ahpAmjFdPhxvmQC6K/kANC8CQLozESYcVIpgRD9WZ7LVRpGzMqBAAnCHCBSBQRCCFgeVEIG4RDGsbzTO0G4VddK7qzB63rzD5r+lHt5Yf0jRn+zzkjXd5llY3EVO5UQQINKAoEZZgmgQbOhx/EWCaCVFoHxOARyJID+coRJVRCoQwCd0TUgUB6BEP1ZnsstL1K0XDQBEoRFE6Z+CNQhYHlQCRmEU+5IvROEY7tWct/JmCCcNGxUymjwrSACaLAgsFQLgYwE0GBGUDxmngBaMd8FAFAiAfRXInyaNkMAnZkJNY4qJBCiP8tzuQpDiEk5ESBBmBNIqoFAqwQsDyohg3CrfGN6ngRhTNFKw1Y0mEYc8SJeAmgw3thheWcJoJXO8qY1CFQTQH/0BwgUTwCdFc+YFiBQj0CI/izP5dKT0iVAgjDd2OKZcgKWB5WQQVh5ONsyr1GC8IvTn3KvNDhilB2EbaE3+zIaNBt6HFdCAA0qCQRmqCeAVtSHCAMTJoD+Eg4urqkhgM7UhAJDDBII0Z/luVyDXcSMyyQIzYQaR7URsDyohAzC2uKXpz0kCPOkSV1ZCKDBLJR4BgLFEUCDxbGl5rQIoJW04ok3cRFAf3HFC2vjJIDO4owbVqdBIER/ludy04g6XtQiQIKQfgGBkghYHlRCBuGSwtSRZnsnCLfuWsmd+e87CNlB2JEQmGsEDZoLOQ4rI4AGlQUEc9QSQCtqQ4NhBgigPwNBxsXSCaCz0kOAAYYJhOjP8lyu4a6SvOskCJMPMQ5qJWB5UAkZhLXGMQ+7SBDmQZE6WiGABluhxbMQyJ8AGsyfKTWmSQCtpBlXvIqDAPqLI05YGTcBdBZ3/LA+bgIh+rM8lxt3tLG+EQEShPQPCJREwPKgEjIIlxSmjjRLgrAjmGmkigAapDtAoFwCaLBc/rQeDwG0Ek+ssDQ9AugvvZjikT4C6ExfTLDIDoEQ/Vmey7XTM+x5SoLQXszxWAkBy4NKyCCsJGyFmEGCsBCsVNqAABqke0CgXAJosFz+tB4PAbQST6ywND0C6C+9mOKRPgLoTF9MsMgOgRD9WZ7LtdMz7HlKgtBezPFYCQHLg0rIIKwkbIWY0TtBuE3XSu6Mf99BeOD0J920he/XbXfSsFGF2ESlaRNAg2nHF+/0E0CD+mOEhToIoBUdccAKmwTQn82443VnCaCzzvKmNQhUEwjRn+W5XHpPugRIEKYbWzxTTsDyoBIyCCsPZ1vm9U4QbtvV7U4fPNzXSYKwLbS8XIcAGqRrQKBcAmiwXP60Hg8BtBJPrLA0PQLoL72Y4pE+AuhMX0ywyA6BEP1Znsu10zPseUqC0F7M8VgJAcuDSsggrCRshZhBgrAQrFTagAAapHtAoFwCaLBc/rQeDwG0Ek+ssDQ9AugvvZjikT4C6ExfTLDIDoEQ/Vmey7XTM+x5SoLQXszxWAkBy4NKyCCsJGyFmLFUgnBAtzt9EDsIC4FNpZ4AGqQjQKBcAmiwXP60Hg8BtBJPrLA0PQLoL72Y4pE+AuhMX0ywyA6BEP1Znsu10zPseUqC0F7M8VgJAcuDSsggrCRshZjRKEH4helPuuncQVgId8uVokHL0cd3DQTQoIYoYEMMBNBKDFHCxlQJoL9UI4tfmgigM03RwBZrBEL0Z3ku11r/sOQvCUJL0cZXVQQsDyohg7Cq4OVsTO8E4bgB3e60f+8gbJQgHNG3y102ZETO1lCdBQJo0EKU8VEzATSoOTrYpokAWtEUDWyxRgD9WYs4/pZBAJ2VQZ02IfAvAiH6szyXS79JlwAJwnRji2fKCVgeVEIGYeXhbMu8VhOEo/qt4Eb1X9Ed3L2GW2aZZdpqm5dtEkCDNuOO13oIoEE9scAS3QTQiu74YF3aBNBf2vHFOx0E0JmOOGCFTQIh+rM8l2uzl9jwmgShjTjjpUIClgeVkEFYYQhzM6lRgvCA6U+6V3sdMTpp2Kjc2qYimwTQoM2447UeAmhQTyywRDcBtKI7PliXNgH0l3Z88U4HAXSmIw5YYZNAiP4sz+Xa7CU2vCZBaCPOeKmQgOVBJWQQVhjC3EwiQZgbSirKSAANZgTFYxAoiAAaLAgs1SZHAK0kF1IciogA+osoWJgaLQF0Fm3oMDwBAiH6szyXm0DIcaEOARKEdA0IlETA8qASMgiXFKaONNs7QbjdgG53aoM7CNlB2JGwJN0IGkw6vDgXAQE0GEGQMFEFAbSiIgwYYZQA+jMaeNzuKAF01lHcNAaBJQiE6M/yXC7dJ10CJAjTjS2eKSdgeVAJGYSVh7Mt8xolCDlitC20vFyHABqka0CgXAJosFz+tB4PAbQST6ywND0C6C+9mOKRPgLoTF9MsMgOgRD9WZ7LtdMz7HlKgtBezPFYCQHLg0rIIKwkbIWYQYKwEKxU2oAAGqR7QKBcAmiwXP60Hg8BtBJPrLA0PQLoL72Y4pE+AuhMX0ywyA6BEP1Znsu10zPseUqC0F7M8VgJAcuDSsggrCRshZjRO0G4/YBud8q/jxhlB2EhyM1XigbNdwEAlEwADZYcAJqPhgBaiSZUGJogAfSXYFBxSR0BdKYuJBhkiECI/izP5RrqGuZcJUFoLuQ4rIWA5UElZBDWErci7CBBWARV6mxEAA3SPyBQLgE0WC5/Wo+HAFqJJ1ZYmh4B9JdeTPFIHwF0pi8mWGSHQIj+LM/l2ukZ9jwlQWgv5nishIDlQSVkEFYStkLMIEFYCFYqbUAADdI9IFAuATRYLn9aj4cAWoknVliaHgH0l15M8UgfAXSmLyZYZIdAiP4sz+Xa6Rn2PCVBaC/meKyEgOVBJWQQVhK2QswgQVgIViolQUgfgIBaAoyDakODYcoIoBVlAcEcUwTQn6lw42xJBNBZSeBpFgLOuRD9WZ7LpdOkS4AEYbqxxTPlBCwPKiGDsPJwtmVe7wThDgNWdicPGubrHD/9STdj4ftL1D9p2Ki22uNlCKBB+gAEyiWABsvlT+vxEEAr8cQKS9MjgP7Siyke6SOAzvTFBIvsEAjRn+W5XDs9w56nJAjtxRyPlRCwPKiEDMJKwlaIGSQIC8FKpQ0IoEG6BwTKJYAGy+VP6/EQQCvxxApL0yOA/tKLKR7pI4DO9MUEi+wQCNGf5blcOz3DnqckCO3FHI+VELA8qIQMwkrCVogZvROEG/cd4C4esoFvix2EhSA3XykaNN8FAFAyATRYcgBoPhoCaCWaUGFoggTQX4JBxSV1BNCZupBgkCECIfqzPJdrqGuYc5UEobmQ47AWApYHlZBBWEvcirCjd4JQ2qgcI0qCsAji1IkG6QMQKJcAGiyXP63HQwCtxBMrLE2PAPpLL6Z4pI8AOtMXEyyyQyBEf5bncu30DHuekiC0F3M8VkLA8qASMggrCVshZjRKEO4/7Un32iLuICwEvOFK0aDh4OO6CgJoUEUYMCICAmglgiBhYrIE0F+yocUxRQTQmaJgYIo5AiH6szyXa66DGHKYBKGhYOOqLgKWB5WQQVhX9PK1hgRhvjyprTkBNNicEU9AoEgCaLBIutSdEgG0klI08SU2Augvtohhb4wE0FmMUcPmVAiE6M/yXG4qccePpQmQIKRXQKAkApYHlZBBuKQwdaRZEoQdwUwjVQTQIN0BAuUSQIPl8qf1eAiglXhihaXpEUB/6cUUj/QRQGf6YoJFdgiE6M/yXK6dnmHPUxKE9mKOx0oIWB5UQgZhJWErxIxGCcL9pj3hZi5asES7lfsJCzGGSk0QQIMmwoyTigmgQcXBwTRVBNCKqnBgjDEC6M9YwHG3FALorBTsNAoBTyBEf5bncuk26RIgQZhubPFMOQHLg0rIIKw8nG2ZR4KwLXy8HEAADQZA4xUI5EgADeYIk6qSJoBWkg4vzikngP6UBwjzkiCAzpIII05ESiBEf5bnciMNM2ZnIECCMAMkHoFAEQQsDyohg3ARMdBSJwlCLZGwYwcatBNrPNVJAA3qjAtW6SOAVvTFBIvsEEB/dmKNp+URQGflsadlCIToz/JcLj0mXQIkCNONLZ4pJ2B5UAkZhJWHsy3zSBC2hY+XAwigwQBovAKBHAmgwRxhUlXSBNBK0uHFOeUE0J/yAGFeEgTQWRJhxIlICYToz/JcbqRhxuwMBEgQZoDEIxAogoDlQSVkEC4iBlrqJEGoJRJ27ECDdmKNpzoJoEGdccEqfQTQir6YYJEdAujPTqzxtDwC6Kw89rQMgRD9WZ7LpcekS4AEYbqxxTPlBCwPKiGDsPJwtmVe7wTh0OX7umuGbuzr3G/aE27mogVL1D9p2Ki22uNlCKBB+gAEyiWABsvlT+vxEEAr8cQKS9MjgP7Siyke6SOAzvTFBIvsEAjRn+W5XDs9w56nJAjtxRyPlRCwPKiEDMJKwlaIGb0ThNt0reTOGLwuCcJCaFOpEECD9AMIlEsADZbLn9bjIYBW4okVlqZHAP2lF1M80kcAnemLCRbZIRCiP8tzuXZ6hj1PSRDaizkeKyFgeVAJGYSVhK0QMw599Wn3woJ5PXWfN3g9N7prIAnCQmhTKQlC+gAEyifAOFh+DLAgDgJoJY44YWWaBNBfmnHFK10E0JmueGCNLQIh+rM8l2urd9jylgShrXjjrSIClgeVkEFYUehyN+XReXPcSW+86N5ZvMht3bWSO33QcLfsMsuQIMydNBVWCKBB+gIEyiWABsvlT+vxEEAr8cQKS9MjgP7Siyke6SOAzvTFBIvsEAjRn+W5XDs9w56nJAjtxRyPlRCwPKiEDMJKwlaYGXMWL3Ly3xrL9XHL/Ds5KI1xB2FhyE1XjAZNhx/nFRBAgwqCgAlREEArUYQJIxMlgP4SDSxuqSKAzlSFA2OMEQjRn+W5XGPdw5S7JAhNhRtnNRGwPKiEDMKaYtdJWybPne1OeH1qT5N7DVzdHbXq0E6aQFsJEkCDCQYVl6IigAajChfGlkgArZQIn6bNE0B/5rsAADpAAJ11ADJNQKAOgRD9WZ7LpSOlS4AEYbqxxTPlBCwPKiGDsPJwFmbewn/+010w6xX323ffdOv37XKnDRru1ly+b2HtUbENAmjQRpzxUi8BNKg3NlimiwBa0RUPrLFFAP3ZijfelkMAnZXDnVYhIARC9Gd5Lpdeky4BEoTpxhbPlBOwPKiEDMLKw4l5EIiKABqMKlwYmyABNJhgUHGpEAJopRCsVAqBTATQXyZMPASBtgigs7bw8TIE2iIQoj/Lc7ltweZl1QRIEKoOD8alTMDyoBIyCKfcF/ANAp0mgAY7TZz2ILAkATRIj4BANgJoJRsnnoJAEQTQXxFUqRMCfCekD0BAC4GQcc7yXK6WuGFH/gRIEObPlBohkImA5UElZBDOBJWHIACBTATQYCZMPASBwgigwcLQUnFiBNBKYgHFnagIoL+owoWxkRJAZ5EGDrOTIBCiP8tzuUkEHSdqEiBBSMeAQEkELA8qIYNwSWGiWQgkSQANJhlWnIqIABqMKFiYWioBtFIqfho3TgD9Ge8AuN8RAuisI5hpBAI1CYToz/JcLt0oXQIkCNONLZ4pJ2B5UAkZhJWHE/MgEBUBNBhVuDA2QQJoMMGg4lIhBNBKIVipFAKZCKC/TJh4CAJtEUBnbeHjZQi0RSBEf5bnctuCzcuqCZAgVB0ejEuZgOVBJWQQTrkv4BsEOk0ADXaaOO1BYEkCaJAeAYFsBNBKNk48BYEiCKC/IqhSJwT4TkgfgIAWAiHjnOW5XC1xw478CZAgzJ8pNUIgEwHLg0rIIJwJKg9BAAKZCKDBTJh4CAKFEUCDhaGl4sQIoJXEAoo7URFAf1GFC2MjJYDOIg0cZidBIER/ludykwg6TtQkQIKQjgGBkghYHlRCBuGSwkSzEEiSABpMMqw4FREBNBhRsDC1VAJopVT8NG6cAPoz3gFwvyME0FlHMNMIBGoSCNGf5blculG6BEgQphtbPFNOwPKgEjIIKw8n5kEgKgJoMKpwYWyCBNBggkHFpUIIoJVCsFIpBDIRQH+ZMPEQBNoigM7awsfLEGiLQIj+LM/ltgWbl1UTIEGoOjwYlzIBy4NKyCCccl/ANwh0mgAa7DRx2oPAkgTQID0CAtkIoJVsnHgKAkUQQH9FUKVOCPCdkD4AAS0EQsY5y3O5WuKGHfkTIEGYP1NqhEAmApYHlZBBOBNUHoIABDIRQIOZMPEQBAojgAYLQ0vFiRFAK4kFFHeiIoD+ogoXxkZKAJ1FGjjMToJAiP4sz+UmEXScqEmABCEdAwIlEbA8qIQMwiWFiWYhkCQBNJhkWHEqIgJoMKJgYWqpBNBKqfhp3DgB9Ge8A+B+Rwigs45gphEI1CQQoj/Lc7l0o3QJkCBMN7Z4ppyA5UElZBBWHk7Mg0BUBNBgVOHC2AQJoMEEg4pLhRBAK4VgpVIIZCKA/jJh4iEItEUAnbWFj5ch0BaBEP1ZnsttCzYvqyZAglB1eDAuZQKWB5WQQTjlvoBvEOg0ATTYaeK0B4ElCaBBegQEshFAK9k48RQEiiCA/oqgSp0Q4DshfQACWgiEjHOW53K1xA078idAgjB/ptQIgUwELA8qIYNwJqg8BAEIZCKABjNh4iEIFEYADRaGlooTI4BWEgso7kRFAP1FFS6MjZQAOos0cJidBIEQ/Vmey00i6DhRkwAJQjoGBEoiYHlQCRmESwoTzUIgSQJoMMmw4lREBNBgRMHC1FIJoJVS8dO4cQLoz3gHwP2OEEBnHcFMIxCoSSBEf5bnculG6RIgQZhubPFMOQHLg0rIIKw8nJgHgagIoMGowoWxCRJAgwkGFZcKIYBWCsFKpRDIRAD9ZcLEQxBoiwA6awsfL0OgLQIh+rM8l9sWbF5WTYAEoerwYFzKBF588UW37rrrehcnT57shgwZkrK7S/gmg/B9993n/23cuHGuq6vLjO84CgENBNCghihgg2UCaNBy9PG9FQJopRVaPAuBfAmgv3x5UhsEahFAZ/QLCJRHIER/M2bMcGPGjPFGT5061Q0fPrw8B2gZAjkRIEGYE0iqgUCrBB566KGeQaXVd3keAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEOk9ANnuMHj268w3TIgRyJkCCMGegVAeBrARIEGYlxXMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABHQQIEGoIw5Y0T4BEoTtM6QGCAQRmDdvnnvsscf8u4MGDXLLL798UD0xvlS9Jd/a8aoxxgub0yOABtOLKR7FRQANxhUvrC2PAFopjz0tQwD90QcgUDwBdFY8Y1qAQD0CIfpbuHChe+ONN3yVI0eOdP379wcwBKInQIIw+hDiAATiI8ClvvHFDIvTIoAG04on3sRHAA3GFzMsLocAWimHO61CQAigP/oBBIongM6KZ0wLEKhHAP3RNyDwLwIkCOkJEIBAxwkwCHccOQ1CYAkCaJAOAYFyCaDBcvnTejwE0Eo8scLS9Aigv/Riikf6CKAzfTHBIjsE0J+dWONpYwIkCOkhEIBAxwkwCHccOQ1CgAQhfQACiggwDioKBqaoJoBWVIcH4xIngP4SDzDuqSCAzlSEASOMEkB/RgOP20sRIEFIp4AABDpOgEG448hpEAIkCOkDEFBEgHFQUTAwRTUBtKI6PBiXOAH0l3iAcU8FAXSmIgwYYZQA+jMaeNwmQUgfgAAEyifAIFx+DLDANgE0aDv+eF8+ATRYfgywIA4CaCWOOGFlmgTQX5pxxStdBNCZrnhgjS0C6M9WvPG2PgF2ENI7IACBjhNgEO44chqEwBIE0CAdAgLlEkCD5fKn9XgIoJV4YoWl6RFAf+nFFI/0EUBn+mKCRXYIoD87scbTxgRIENJDIACBjhNgEO44chqEAAlC+gAEFBFgHFQUDExRTQCtqA4PxiVOAP0lHmDcU0EAnakIA0YYJYD+jAYet5ciQIKQTgEBCHScAINwx5HTIARIENIHIKCIAOOgomBgimoCaEV1eDAucQLoL/EA454KAuhMRRgwwigB9Gc08LhNgpA+AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAHLBNhBaDn6+A4BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIGCOAAlCcyHHYQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAcsESBBajj6+QwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEImCNAgtBcyHEYAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDAMgEShJajj+8QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALmCJAgNBdyHIYABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACELBMgASh5ejjOwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgDkCJAjNhRyHIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAELBMgQWg5+vgOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBgjgAJQnMhx+HYCDzyyCPu17/+tfvDH/7gHn/8cff666+7Pn36uLXWWsuNHTvWHXrooW7bbbfN7JbUdfnll7vJkye7N954ww0aNMiNGTPGffnLX3a77LJLw3rmz5/vpkyZ4h566CH/vvz37LPPun/+85/+vcr/ZjHm/fffdz/96U/djTfe6B599FH3j3/8w/s1dOhQt/XWW3t7ttxyyyxVNXxm8eLF7qmnnuqxV2z/61//6qR9KZMmTXLbb79903Zefvlld/vtt7vf//737v/+7//ctGnT3KJFi9zqq6/uPvrRj7r99tvP7bPPPm755ZdvWhcPxEUgVQ3OmzfPXXnlle7mm2/2mnj77bf934PNNtvMffGLX3T77rtv7oF68MEH3cUXX+z/nr322mtulVVWcaNGjXIHH3yw11CjsnDhQvfYY495LVf+Bj3xxBNeh1KmTp3qhg8fnrvNVFg+ATSYXwza0eDs2bPdHXfc4e655x738MMPuxdeeMG99957rru7222yySZut912c4cddphbeeWV8zOYmjITQCeZUTV9sB2d/OUvf3H33nuvH6dkjJLv2vIdt2/fvv67u3y3lTFvhx12aGoHD8RFAA3mF692NFjPihkzZriNN97Yf9+Vst122/nfdZS4CKCz/OLVjs5OPfVUd9ppp2UyJut8S6bKeKhUAugvP/zt6K+3FXfffbe75ppr3P333+9krJM5yTXWWMNtuummbqeddnIHHnigW3HFFfMznpqSJECCMMmw4lQqBOSHy3333dfUHfmDP2HCBD/5UK9I8u6II47wvBg3gQAAHoBJREFUycF6RZJyl156qVtmmWVqPnLIIYe4q6++umEbTY11zr3yyivu05/+tJ/sb1T++7//211wwQV17cnS1v/+7//6iZh6JcsX1pNPPtmdeeaZTROgW2yxhU+2rLPOOllM45kICKSqwaefftrtscceTv63XpEFAzfddJNbYYUVconU6aef7n9IStK+VvnMZz7jfv7zn7v+/fvX/FzelR+j9QoJwlzCpK4SNKhDg3feeafbc889nSwUalTkx+h1111H8qPDSkInOnQiYd9mm23cH//4x6Y9QBaV/eQnP6k75jWtgAdUEUCDejRYr2Psvffe/ndapZAgVCWhTMagMz06I0GYqcsm9RD606O/Ssd68803nczR3nbbbQ37mmzykEXgFAg0IkCCkP4BAcUEPvjBD7rnn3/erziWiQTZKSjJJ9kxIytOJHk2ffp078H+++/vrr322rrefOtb33JnnXWW/3zzzTd3xx9/vFt//fV9/eedd57fGShFnpNkWK0iiTZJuEkZOHCg+8hHPuITDLITSEqWHYSyC0jeqyQHZVXLMccc4zbccEP3zjvv+FUv4te7777r6xTbjjvuuOAoSUJTBk0pskPxwx/+sKvsRJJ/y5IglB0RP/7xj32iRCZIZRXOBhts4Cd1nnzySXfRRRf5leJS5N9lZRUrdIJDpurFFDUouxlk16sk6qXI35aDDjrI/5159dVXvcZlZ68USdr98pe/bDsmsoDh8MMP9/XI351vfvObbuTIkb69//mf//E6lHLAAQf41W+1SvUPUdGefMkVX+RvmBQShG2HSWUFaFCHBkWXshhp2WWXdTvvvLM/cUB2/8puQdlR/7Of/czdcMMNvg8NGDDAJ0j4Ido5SaETHTqRiMt3RPmeLqd8yG6lNddc06222mp+vJITM2QhnoxXUmSn/vXXX9+5jkJLhRFAg3o0WCvIEydOdLvvvrsbPHiwP41HCgnCwuRQWMXoTI/Oqn+XNVv0ve666+a24LSwzkXFTQmgPz36k2DJbnj5zimnukiRDRhyIpPESb6HvvTSS36OUhZ8yxjI77KmXdz8AyQIzXcBAGgmIMd1yVF/n/vc59xyyy23lKl///vf/XGczzzzjP9MdhvWOm70ueee85MUkhiTXW7yXFdXV099ckSY/EiSY5FkO7ocySmT+L2LTP7NnTvXjR492tcnE4VyPKccpSQlS4JQVm7KCk4pW221lT9qsLdvMsjJZwsWLPBHEMoPudCjO+U4wj/96U/+GFUZFCWxUP2FNkuC8IQTTvCTO0ceeaRPjPYuMgCPHz/e736SIjulTjrpJM1dC9syEkhRg//xH//hfvSjH3kCp5xySs1defLv0o+liGb32muvjMSWfuytt95y8sNQ/lcWOIi+5WjeShH9SOJdvrhKkb8n48aNW6qi3/zmN+7FF1/0f39kYYH8TahetECCMDhEql9Egzo0KOO/jJeS3K+3S/4HP/iBO/roo31/2nHHHf1RpJTOEEAnOnQi0Zbv2o2+s8r3aJnQkYV+UuSIb1kwQ4mbABrUo8HePWnOnDnuQx/6kF8YJ7t25be1FBKE8WkOnenRWfV8SpY5oPh6Gxb3JoD+9OhPYiNjmVzZJN85ZSFnvethRJ8y3xI6n4oS7BAgQWgn1niaKIFf/epXfpePFJmYk904vcvXvvY1f++XFJmQqHW3nyTRJCknRRIIMtGXpbSaIJTdgt///vd91bIzqWJ777YkIfGLX/zC/7OsSpOdf3mVVhOEWdqdNWuW34EldxtK8kJWiVNsEIhJg5V7MyVZN2zYML/7rtbiA3luvfXWc3L3piwqqOyQDYno+eef73csS5GjB2vdNSg7kOT+QGlXfnxUkoXN2iNB2IyQjc/RYOM4F6nB3i1LAl8WG8kCIlncI4trKDoIoBM9OpGEe2Us/OEPf+jkezolfQJosBwNyu9j+V0r937+7ne/67m6ggRhmppDZ53RGQnCNPXTrlforzP6k1PXKhtDRIuyuJsCgXYJkCBslyDvQ6BkArIqsrKrTbaVy6BcXWTFyAc+8AF/FOlGG23kj8SsV+RzOTJ07bXX9omBencRVr/faoKwevfS448/7jbZZJOa5sixot/97nf9ZzLZKEci5lWKSBCKbZWJUTmKVOJCsUEgJg3K7mDZ/StFjs694oor6gapcrSuPNDO7jzZ5fzAAw+4lVZayR+xVu+uVDmyUHYJ9uvXz8nu6CzH9JIgtKGxZl6iwcaEitRg75arx25ZWCALDCg6CKATPTqpnkCT77pf//rXdXQSrCiUABrsvAblJBlZACs7J2S3rlxpUfl9S4Kw0O5eWuXorDM6I0FYWhdX3TD664z+ZJGZLDaT+ZIZM2ZkmjdR3XEwTgUBEoQqwoAREAgn8I9//KNnhX6t+8JeeOGFnuNCv/KVr/i7T+oV+fzyyy/3H8t7cixgs9JqglDu6/vP//xPX22WHYTyI052O0lyIa9SVIJQdg7KbkdJ2M6ePTsvc6lHOYGYNCiJOkkWSGl032jl88q9pXIvYeVIplbCITtqJWEuR6598pOfdL/+9a/rvn722Wf74wulyApvWendrJAgbEbIxudosH6ci9Zg75YrOzXk3+U4YblzmKKDADrRoxO5b7dyb7gkC2WBHyV9AmiwsxqsXK0hp7rI1Q+Vo/NJEKatNXTWGZ2RIExbR6Heob/i9Se/7bq7u928efPcPvvs03PNkYx5silExji5/7reouzQ2PJe+gRIEKYfYzxMnIAcw1m5H0xW7p933nlLeHz77bf7I/ukyNGe//Vf/1WXiHwuR4BKkfd23XXXpvRaTRDKDiK5OFcSaJKokPvGeh9xOGXKFH8Mqgx++++/f88kSlNjMj5QRIJQjlIbOnSoT4TITkJZsUqxQSAmDcokSeWC6mZHCVfv9pV7OM8555yWA/q3v/2t53hgWRhw4YUX1q2jmqPckfjVr361aXskCJsiMvEAGqwf5qI12LvlUaNG+V0asltDjt7Oc3GPic5coJPopDydLF682O+gFz3KUYe33nqrN0Z2M8lpGtwLU2DHV1Q1GuysBuV764knnugXyorO5B56KSQIFYmiAFPQWWd0Vj2f8vGPf9w98sgj7p133nErr7yyv/NTToaRxeerrLJKAVGmSq0E0F/x+pMTWsaMGeMb+t73vufnS2Wsu/HGG927777r/13GO1ls/e1vf9uNHTtWa3fBLmUESBAqCwjmQKAVAjLhIMemVJJRtY7zkh2DRx55pK9WBo299967bhM33XSTX4UiRd6TL3XNSqsJQqlPvjjI6um5c+e6zTff3CctR4wY4Y/l/OMf/+guuOAC/wVTEhl33nmnXwGTZykiQVh9rJrc93TsscfmaTJ1KSUQmwblS6P8UFuwYEHTuzIrO2IFfWiiXnYMfupTn/LRa6YLOUpYkutSvvGNbzjZUdiskCBsRij9z9Fg4xgXrcHq1qsXJNU68jz93qjXQ3RSjk7kbt2XXnqpZuNyD7Bopt5R+3p7E5aFEECDndWgnIQj99fLb005vv4Tn/hEjwEkCEN6cBzvoLPO6ax6PqVeq5IsvPrqq90ee+wRRwfCyrYIoL/O6E9OdpI5ECly2pMkCeV6llpF7oSXudVGm0TaCjovJ0WABGFS4cQZawTkj30lEbXnnnu6W265ZSkEMil//PHH+3+XZJus5qpX5PPKrsGsd6KEJAil/SeeeMIPZldeeaWTexKryxprrOFkx9KXv/xlfzxh3iXvBOGf//xnt8022/jdg3J/o9zjOGDAgLzNpj6FBGLUoCTsKkd9yhFnkvzrXa677jo3fvz4nn+WXcgTJ05sOQKyKOHzn/+8f++SSy5xRxxxRN065H5UWXEqpdnuxkolJAhbDklyL6DBxiEtWoOV1uVIITlOVJIhciqALFiSBUAUHQTQSTk6qZUglN2CJ598sj9unx22OvTRCSvQYGc1uPPOO7u7777b7bvvvu76669fonEShJ3o8eW0gc46pzOZT5G5p89+9rN+N9Naa63lF6DKPMjPfvYzd9ddd3lj5Duh/IasLBgtp2fQaicIoL/O6K/61Ld+/fq5+fPn+xPjRJOyMObtt992N998s19wLae2yZgnC9LQYCdUEHcbJAjjjh/WGyYgR3PKcQ6SlBo8eLA/0ksSa73LGWec4ScipNxzzz1uxx13rEtN7v3aaaed/OfynmxJb1ZCEoTy5fG0005zV1xxhZOjOWsV2Ul0yimnFHIvS54JwpkzZ7otttjCTZs2zQ++8mO0EeNmPPk8HgKxalB2HMvxvvK3o0+fPv7vg9wvOGTIEH/J9U9+8hN/T4v0ZznmV4r8XZC+3Wr56U9/2nN34Y9//GP3pS99qW4V1felHnrooW7ChAlNmyNB2BRR0g+gwebhLVqDYsGiRYv8D9PKwgMZu2WcpegggE6ax6EonTzzzDN+HJVV9XLkrpySIYtlZKW3JC4uvvhit+KKKzY3kCeiJoAGm4cvTw3K99iDDjrIJ+Cfeuop//22upAgbB6PGJ9AZ82jlqfO3nrrLX+caL1y2WWX9SwMleThc88957q6upobyRNREkB/zcOWl/7OPPNMf69upXzmM5/xR9fLbsHqcv/997vtttvOfweVxKHMF1fGv+bW8oRFAiQILUYdn6MnIHeYbLvttu7NN990smpEjk6RP/61irYdhHLEoexSvO+++/yKsq9//evukEMOceutt56/aFd240lyQgY0GcBkhYyssq4uco9EvbLuuus23XWYV4JQjkGVs70ffvhhb44ciSgrdSjpE4hZgxIdmTw5/PDDexKAvSMm2pT7Ao866ij/kawOlaOBK6Uy6Vkr0rKLtvKDsejdSyQI09daPQ/R4L8SD2VrUNqX48gvv/xyb4ocLXrbbbctdbew3Z5arufoRI9OKj1Bvrt/8pOf9Lts5Shv+b47cODAcjsKrRdGAA12VoOSfN944419El7u+5QTKXoXEoSFdffSKkZnndVZ1kDLb83Kgs9rrrnGXzFDSY8A+uus/uSkN7neqFJkIYzcaV2ryPVRco2UFEkQjhw5Mr0OiEe5ESBBmBtKKoJAZwhMnTrVH2f56quv+gk4mYCX40XrFW13EMqRqHL8gBQ5k15WePYusrNJ7oqYNGmSXwkzZcoUP4lSKY1Wvsg7squxUckjQSjJTEl0SntSjjnmmB6/OtMTaKUsArFrsMLt0Ucfdd/5znf8rh9JdksRvUnSW/5djvetfImUJL4cB1wpje5Vuuqqq3rOxS/6/jMShGWpoNx20aBzWjR44oknunPOOcd3CPluIkdKsUK8XH1UWkcnenTSu0dUH6ktGpI7ZCjpEUCDndegnIghuzTkdBdZdNp7R4X0MhKEaWkNnXVeZ1l7UPX98pIsrCwmy/o+z+kngP46r7/q3bmyOUJOYapXJEEv2pPS7DQn/b0NC4smQIKwaMLUD4EcCUhSUHYOyiAgP24kwSY/hBqVX/3qV062nUuR3XiNLqitPs9azqmu3EfYqP5WjhiVuwZXX311J3cVjRgxwp9RX6/IMUwy2ShFbBbbKqXsBKEkMPfaa6+eO9kOO+wwf1wqJX0CsWuwVoTkeEA5WlSS3nIETOX+TLmfsLLSU3YiV+47lTqyJidkt28lySg7gWVXYr0iOxRFV1J+9KMfua9+9atNOxQJwqaIknsADf4rpBo0eO655/bsmpf7B+WY8u7u7uT6XIwOoRM9OqnXf+R78LPPPus22GADJ7vyKWkRQIOd16AwHzp0qG/4+OOPr3sPbuXubdlpWLmGQyZZP/axj6XVCQ14g846r7NWutV7773Xc7KTzCvJ/BIlHQLorxz93XHHHT3XMMncsJzMVq/ISXO77LKL/5jTztLRXlGekCAsiiz1QiBnAnJUihwj+sQTT/iaf/jDH7qvfe1rTVupvtdLjgGTHYX1SvUxYfKe/FhqVlpJEL722ms990DUujS+ui1JVlR2IcigdueddzYzJfPn7ewglDO8JWlSufBe/JBESq0VqpkN4sEoCKSgwVZAS4JO7kqSIl885Qtoq0WOQJSEoyQh5Ui1yh1lteqRL63f/OY3/UeSaJCdjM0KCcJmhNL6HA3q0aDcnVb5DiKTrPI3QhYAUcongE706KRRb5C7gB944AHXt29fN3/+/PI7DhbkRgANlqPBF198MdNv11qBlhNtZOEtJR4C6KwcnbXSQ+Rqmco9uyQIWyGn/1n0V57+XnrpJb9QVMrYsWP9/db1SnUysfeCb/29DAs7TYAEYaeJ0x4EAgi8/fbbbscdd3SPPPKIf1uO8zrhhBMy1SS79uROMFnhs9FGGzk51qhekUk+OcNaVl++8sormS6xbSVBKF8kBg0a5Jv/3Oc+13Medi175MhDuVxeym677dazWy+T000eaidBWH2Wvth1yy23uD59+uRhFnUoJpCKBrMilsSe/N144403/N8D+SIqRxqHFPni+uCDD3o9S30yGVqryEIAWeUm96rKc1nuZCJBGBKRON9Bg3o0KMe3yWSqfL+Q+4P/8Ic/+N3HlPIJoBM9OmnWGyq7gFdddVU3a9asZo/zeSQE0GB5GiRBGIlIcjATnZWns1bCJ3ftjhkzxr/CiUutkNP9LPorX3/Dhg1zL7/8sltjjTWcbMKoV+Qu3qOPPtp/LJsaKjvodfcwrCuLAAnCssjTLgQyEpCjGeQ+vsrKkG9961vuzDPPzPj2vx6r3gkkE/VbbrnlUu//6U9/cltttVXP83LEX5bSSoJQdt+tssoqbvbs2X4yUZIOyy+/fM1mqo9GPeqoo9xFF12UxZxMz4QmCOWewcpRpzvttJM/JkOSGZS0CaSkwayRkhVmcjyTlDPOOMN9+9vfzvrqUs+dd955PQsarrvuOrfffvst9cy0adP8SjjZadjKClMShMFhiepFNKhHg7Io5vOf/7zXqiwikORgZRVrVJ0qQWPRiR6dNOte1ZOmcjrI73//+2av8HkEBNBgHBrkDsIIxNTARHQWh84khJIUlHvPpMjisi984Qtxdz6sd+hPh/6q5yVlnlgWZNcqciJT5TumbACR324UCNQjQIKQvgEBxQRkF4/cH3jXXXd5K5vd4VXPFbnbZJNNNnFyd55c2i5HgVWO75R35s6d68aNG+fkImlJ2MkxpnInSpbSSoJQ6hs/fryTJIEUSdSdcsopSzXz5ptv+vsHK8epyq4iSZLmVUIShNXvyAAsMVlhhRXyMol6lBJIUYOCWlacrbPOOjWpT5w40e/wXbBggf878Ne//tX1798/OEJy56jsMpLVhrLa7eGHH3arrbZaT32SaNhzzz17dglnPV5UKiBBGByWaF5Eg3o0KOOefCeRmAwePNh/l9hwww2j6UspG4pOdOhk8uTJ/nu03MlZr0yfPt3tvPPOPSd6TJgwwR166KEpd08TvqFBHRrM0tlIEGahpPMZdKZDZ4899pifS/rgBz9Yt6Ncdtll7ogjjvCfr7nmmu65555j7kSnrDJbhf506K8ylyO/weRapo9+9KPu3nvvXUpf11xzjTvwwAN9fD/96U872YBBgUAjAiQI6R8QUExAJulltb4UOWL0wgsvbHjspxzdN2LEiJoenXjiif5oUimbb76539Gz/vrru+eff96de+65bsqUKf4zee6ss86qWYdsX+99h5jU+fTTT/vnr7rqqiXekyRf7y+OcoSpDGKy+kiKTDbKUWWSQJABTnYyip+SwJAiO/XuvvvutqLU+06JW2+91d12222+TuEgR69WitgrdleX6q35ctziDTfc4Lq7uxvaJAM2R4+2FTYVL6eoQQErx33KjuF99tnHLx6Qvx1yNNONN97o+7cU2e17zz33+L8X7ZbqH4nyd0d2Qo8cOdIffSx6nzRpkm9Cjr2Q4y9qlTlz5ix1LLFMrFZ2V8uux+o70DbbbDMn/1HiJoAGdWhQxmYZj2XslrFNxtVNN920YeeSVaorr7xy3B0wEuvRiQ6diC4OOeQQv5Jbvt/KGFQ5Wl8SgzLWyXdlWTAj5eMf/7g/Wpt7rCMRWgMz0aAODWbpSSQIs1DS+Qw606EzGetkd6DsTvrUpz7lf9PJ4k9ZjC5zPZKY+O1vf+s7kVxRIfNZu+++u85OhVWZCaA/HfqrBKz6xKcPfehD/vSnD3/4w/47pmju0ksv9Se+yLyPbATJugEkc4fgweQIkCBMLqQ4lBKByg+YrD7J7hyZ5K9V5HhPuT/vyiuvrFudrGC+/PLL605UyPZ0+SKYtcgkiOzw6V0k4SeJALmTsFGRpOhNN93kExXtlFY41rqkvnqXZFY7pk6dyrFrWWEpfq6VviNuxKJBuTBeLo6vV+RLpvy4yyM5WGlDdgvLcaVyb1mtIkeL3nzzzXV3K7Z6t4y0Jzt/KXETQIPtJ+jz0GD1LvqsPared4Cs7/NcdgLoRIdOKgnCLJGT78dynP+AAQOyPM4zygmgQR0azNJNSBBmoaTzGXSmQ2dZxzpJGsoRo3vssYfODoVVLRFAfzr0Vx002dwhmz3qza/IiS+yOaJylVRLAedhcwRIEJoLOQ7HRCDPQbji9x133OGTgHL/iSToZMfN6NGj3Ve+8hW/AqxRyStBKG3MmjXLf2G888473d/+9jf31ltv+WOZ5AgKsUeOIpWVZq0yqGV/K3WQIIxJIcXb2krfEWsaJQg1afD666/3x+TKcWgzZsxwsjtPdjnIjqC9997bH0dRxA7YBx54wE+Iyr1lM2fO9LuLRo0a5XdcNLs0mwRh8f1dYwtoMN+ohGqQBGG+cci7NnSSL9FQnciR/TK+yVHZUofsGnz99df9sbyygltWb2+99dZ+jG22Azdfj6itaAJoMF/CoRrMYgUJwiyUdD6DzvKNS6jOZFyT4woffPBBfwqV/KaTuR1JUqy66qr+t90uu+ziF4rL2EdJgwD6yzeOofrrbYXo8JJLLvHfP2VeR66GkVPlZC71qKOOanryWb5eUVvMBEgQxhw9bIcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAiwRIELYIjMchAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgEDMBEoQxRw/bIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEINAiARKELQLjcQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQjETIAEYczRw3YIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEItEiABGGLwHgcAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAjETIEEYc/SwHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAItEiBB2CIwHocABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAzARIEMYcPWyHAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQIsESBC2CIzHIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIBAzARKEMUcP2yEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQIgEShC0C43EIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIxEyABGHM0cN2CEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCLRIgARhi8B4HAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIxEyBBGHP0sB0CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACLRIgQdgiMB6HAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQMwESBDGHD1shwAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgECLBEgQtgiMxyEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAQMwEShDFHD9shAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0CIBEoQtAuNxCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCMRMgARhzNHDdghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQi0SIAEYYvAeBwCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACMRMgQRhz9LAdAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAi0SIEHYIjAehwAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEDMBEgQxhw9bIcABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAiwT+H7rHUlIYFc8kAAAAAElFTkSuQmCC\" width=\"900\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(9,3))\n",
+ "\n",
+ "for interval in range(len(utc_xyzf_weekly_007_causal)):\n",
+ "# plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ "# np.linalg.norm(xyzf_def_weekly_007_causal[interval][:3], axis=0) - \n",
+ "# xyzf_def_weekly_007_causal[interval][3], 'black')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ " np.linalg.norm(xyzf_adj_weekly_007_causal[interval][:3], axis=0) - \n",
+ " xyzf_adj_weekly_007_causal[interval][3], 'turquoise')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " np.linalg.norm(xyzf_adj_weekly_007_acausal[interval][:3], axis=0) - \n",
+ " xyzf_adj_weekly_007_acausal[interval][3], 'blue')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_inf_acausal[interval]], \n",
+ " np.linalg.norm(xyzf_adj_weekly_inf_acausal[interval][:3], axis=0) - \n",
+ " xyzf_adj_weekly_inf_acausal[interval][3], 'red')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 370,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ "output_type": "display_data"
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\" width=\"900\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x1b3fbb17b8>]"
+ ]
+ },
+ "execution_count": 370,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(9,3))\n",
+ "for interval in range(len(utc_xyzf_weekly_007_acausal)):\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_causal[interval]], \n",
+ " vector_dist(xyzf_adj_weekly_007_causal[interval][:3], \n",
+ " xyzf_def_weekly_007_causal[interval][:3],\n",
+ " 'euclidean'), c='turquoise')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_007_acausal[interval]], \n",
+ " vector_dist(xyzf_adj_weekly_007_acausal[interval][:3], \n",
+ " xyzf_def_weekly_007_acausal[interval][:3],\n",
+ " 'euclidean'), c='blue')\n",
+ " plt.plot([utc.datetime for utc in utc_xyzf_weekly_inf_acausal[interval]], \n",
+ " vector_dist(xyzf_adj_weekly_inf_acausal[interval][:3], \n",
+ " xyzf_def_weekly_inf_acausal[interval][:3],\n",
+ " 'euclidean'), c='red')\n",
+ "\n",
+ "plt.plot([utc.datetime for utc in utc_xyzf_all_inf_acausal[0]],\n",
+ " vector_dist(xyzf_adj_all_inf_acausal[0][:3],\n",
+ " xyzf_def_all_inf_acausal[0][:3],\n",
+ " 'euclidean'), c='magenta')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xyz_30_list[0][0].shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "#\n",
+ "# configuration parameters for BOU\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'BOU'\n",
+ "validate = True # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "#\n",
+ "# configuration parameters for BSL\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'BOU'\n",
+ "validate = True # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Barrow (BRW) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.953 -0.258 0. 0. ]\n",
+ " [ 0.258 0.953 0. 0. ]\n",
+ " [ 0. 0. 1. -138.482]\n",
+ " [ 0. 0. 0. 1. ]]\n",
+ "[[ 9.564e-01 -2.690e-01 0.000e+00 -3.468e+01]\n",
+ " [ 2.690e-01 9.564e-01 0.000e+00 -1.031e+02]\n",
+ " [ 0.000e+00 0.000e+00 1.010e+00 -6.839e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.611e-01 -2.860e-01 1.670e-03 -1.769e+02]\n",
+ " [ 2.658e-01 9.611e-01 -1.831e-03 3.168e+01]\n",
+ " [ 1.549e-03 2.803e-02 9.611e-01 2.059e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.463e-01 -2.909e-01 2.291e-02 -1.248e+03]\n",
+ " [ 2.656e-01 9.592e-01 -2.012e-03 4.351e+01]\n",
+ " [-2.062e-02 3.236e-02 1.050e+00 -2.752e+03]\n",
+ " [-0.000e+00 -0.000e+00 0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for BRW\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'BRW'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Stennis (BSL) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1.027e+00 1.268e-02 0.000e+00 0.000e+00]\n",
+ " [-1.268e-02 1.027e+00 0.000e+00 0.000e+00]\n",
+ " [ 0.000e+00 0.000e+00 1.000e+00 9.470e+01]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.006e+00 2.007e-02 0.000e+00 4.933e+02]\n",
+ " [-2.007e-02 1.006e+00 0.000e+00 1.682e+02]\n",
+ " [ 0.000e+00 0.000e+00 8.865e-01 4.722e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.683e-01 1.253e-02 4.848e-02 -6.055e+02]\n",
+ " [-6.530e-03 9.683e-01 -3.909e-02 1.440e+03]\n",
+ " [-4.063e-03 -3.342e-02 9.683e-01 1.474e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.938e-01 1.807e-02 4.521e-02 -1.063e+03]\n",
+ " [ 1.660e-02 1.032e+00 -5.847e-02 1.702e+03]\n",
+ " [ 1.286e-02 -9.395e-03 8.882e-01 4.348e+03]\n",
+ " [ 0.000e+00 -0.000e+00 0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for BSL\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'BSL'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [College (CMO) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.96 -0.326 0. 0. ]\n",
+ " [ 0.326 0.96 0. 0. ]\n",
+ " [ 0. 0. 1. -57.574]\n",
+ " [ 0. 0. 0. 1. ]]\n",
+ "[[ 9.596e-01 -3.294e-01 0.000e+00 4.678e+00]\n",
+ " [ 3.294e-01 9.596e-01 0.000e+00 -4.416e+01]\n",
+ " [ 0.000e+00 0.000e+00 1.025e+00 -1.426e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.970e-01 -3.495e-01 -3.531e-02 1.486e+03]\n",
+ " [ 2.957e-01 9.970e-01 3.063e-02 -1.307e+03]\n",
+ " [ 9.227e-03 1.401e-02 9.970e-01 -4.730e+00]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.512e-01 -3.564e-01 -8.754e-03 5.854e+02]\n",
+ " [ 2.907e-01 9.725e-01 3.049e-02 -1.245e+03]\n",
+ " [-3.643e-02 1.433e-02 1.048e+00 -2.274e+03]\n",
+ " [-0.000e+00 0.000e+00 -0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for CMO\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'CMO'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Deadhorse (DED) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.919 -0.355 0. 0. ]\n",
+ " [ 0.355 0.919 0. 0. ]\n",
+ " [ 0. 0. 1. 22.798]\n",
+ " [ 0. 0. 0. 1. ]]\n",
+ "[[ 0.915 -0.365 0. 31.443]\n",
+ " [ 0.365 0.915 0. -96.716]\n",
+ " [ 0. 0. 1.005 -242.835]\n",
+ " [ 0. 0. 0. 1. ]]\n",
+ "[[ 9.385e-01 -3.521e-01 7.930e-03 -6.235e+02]\n",
+ " [ 3.762e-01 9.385e-01 8.013e-03 -6.399e+02]\n",
+ " [-8.826e-03 6.592e-04 9.385e-01 3.586e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.218e-01 -3.630e-01 3.892e-03 -2.486e+02]\n",
+ " [ 3.587e-01 9.029e-01 8.678e-03 -5.393e+02]\n",
+ " [ 1.709e-03 -4.195e-04 1.005e+00 -2.993e+02]\n",
+ " [ 0.000e+00 0.000e+00 -0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for DED\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'DED'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Fredericksburgh (FRD) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 9.785e-01 1.822e-01 0.000e+00 0.000e+00]\n",
+ " [-1.822e-01 9.785e-01 0.000e+00 0.000e+00]\n",
+ " [ 0.000e+00 0.000e+00 1.000e+00 7.087e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.044e+00 1.641e-01 0.000e+00 -1.408e+03]\n",
+ " [-1.641e-01 1.044e+00 0.000e+00 -3.916e+02]\n",
+ " [ 0.000e+00 0.000e+00 1.117e+00 -4.638e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.006e+00 1.617e-01 3.380e-03 -7.428e+02]\n",
+ " [-2.269e-01 1.006e+00 -1.492e-01 7.756e+03]\n",
+ " [ 7.804e-03 -8.029e-02 1.006e+00 2.759e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.642e-01 1.658e-01 2.112e-03 2.130e+02]\n",
+ " [-2.168e-01 9.504e-01 -1.926e-01 9.512e+03]\n",
+ " [ 1.226e-02 -1.969e-02 1.103e+00 -4.244e+03]\n",
+ " [-0.000e+00 -0.000e+00 -0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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eXrCdr4d3pUJyOxLqPEPD9kep5etB1eeyktSzY26n/5gvSIxLZclna3FLWMIjU78mqJ0vFVPhyHMDufvlaQT9M4uKL00j1a0KW7uOBrd6dLzDQnz0ChpMXoPrL/NZOz+MG7ue5ZYnxgEwa8xqrOlmTOlJ9N1ilMHR/qeolFK7tNZ+1zyhwsxLkqeiyytxuBh1nAoVqrLsqe+JqdmWxm29aHTTTho060mNui0LHD+XzB2WAynJCaSnpVC5qvdVlWH3+t+J+b8J1Jz4Lh37PmrvvmX5HKrX8cGs2+Ub46zRAVgtYDJDtdiRWKqYGTIra+WPOX+Ebf9+w51PTGbey+tJS7Zg5TLNOm8lamsgfd+eQ8OGN6LcPIkIjeGv6fvwarCMAU+PZPWMsbjXrMI9Y37iwNY/SIo9j0+bnpwf/ASnR/bh7tGz7fNZ7j+axDNHadL+LvZtuYl7X+uYlbhoDdpqBJnNvJcDSUu24uquqNtsPh7VfBgwxNipa6uVmc+tBpMrWNMZPW9QoepzwXtbuXTOaCVxrZhAvyc0GSYT+1bNJ+b4Czi+AKXpHzjGqPcu75Li4U2F5Gjqdd9AdOBaOu81sbM9dN4L4aPbcW7/cKxmVwfTyWDUzIEkxl8g4LvxDH5pNudO7OLSoCH2xKL+sencuvQnlj7fB7NPPR4av5Qlnz5G40Vx7O7wEh33fEmj3z9mwwtPctNR7Af7o2MH09RB8qStVnasmk6Vl41lUdCOz5KWhNnNkx+GdKFzUM5Wmexlbxb+Pt7xObsVdIAJ7D4Fq6tnjh1xdgG9vrQvz/4bX7Z3T57+Jh4vfMLRDh5EVJ+CtuZujTFZ0um78aWc0zK7giXntAL6zHC8rWpN//VjcsZThGEBDjaCM40dl2FtnxmoPPYRJks6PYeepUWHQXz/Si96bsx6M83W22tTd9d5fLLdFqR/mkzcc29TLRHW9ZiCdq0I1kRahb9Onaic88izDGTVWWHLWdCyzmv5FeRqxwNY13sq2pR7W1PWdPpteMnBGIWIp4jL/WrH3+Y3jqSKRkLrmXiWrkF5t9oVVEcHn+1K8pa2XPRqS40L+6j7QHVqfzDF3v9cNagTa3wOb/YIkfWMHxzxSDpPN1uivLf1CPv4F73a5VmGYe/XpVKdVrn75SHzONqk01HcqhxmwCNTJHkqDY6SpwUPdqTDAeMAGNLSxIN/hPDLq4Nov+IkQY81wf1sAjcFXiCgz1TABZSVnptexM12G31gr6kOD24mSzq1mv6M3r6H3l8tZNHH59AWDdZ0PL2n0HXJWQCaHwgGBVHHYljy6U5u2v8lvbet5dSxwzS8oRknQjYSHxtF2x4P8PcAX5qczvugpbVGKYXVaiXy+H42zXiT/q/PpGrV2mxZMJFz27fQfpPRxBzUzsQd01ZQs7YPYS19SXOBLX387cnRU593ZvrILty5rWQOEuu7f4LFtSLm9ET6bHF87T/VrQoHWj1Fm9D5uKfFE9LCTJ8Zv3Nh4P0AbO7yLqme3lTxqkDbW89xeG8gLP+XJufhuI8HbSb6s3ZmHpeAtMbr8qtcqPR5ngeE52f2NQ5Stv6hO1ZSqVot/voin2v8tnLmVw9AsdRni9Ax1I82via6Q0XbrUTZE4vGUW/R5EjOs+nsrR+ZO7u8rBpQkUqJGfTYmvs+Jd/wMIICl1Jx5DsAbG6l6BFqlC/eA6o4qPr8DsCOyljU9as419WC+ud1sIXcCVhRDsz5rjsF1F1eB/rsLTm1Y3Je2ypsnSVUrM9Ov7cdDps534DeU40D85Ws6fTfUHBydbXJxrUmKWDsb7Z1Ho/FxcOYltZ4JEfRce9U3NPiHQ6fff9UEnFddX06mEdhYsnveJZ9fc5ruKJQ1gz6bRib43iWeYWgWdfT9H/icf6c+j+63PMibhWrEXPLYIeJX6uD4ZI8lbTmVTz0gkXfUfHF9zhT20StC1ZcC7jF5Vo39oL6J3hA5WRbIuDhjXtyND0KOOtOnPYN2xclUu+MPylup/BMsWAa0I8b52Xd0BddDbxj899pFraM57zaE9rqKVAm+04FbaF1yPxc04yp1pzgNiOxmt3sw5osqbQNnk2NuMNF2pkYZzU9qRe5iZaHFxY63uzW9f4Kbcr9XtjMDbcoZcu0t21TEjyGk16hWlYcWuOWGku74JlUTow0DjSd3gBlzjHdzrs+YaffO4WOP6Zac/a1HZ1rOu33zaBG3OEcw5bUgak4XUvytK93HS7ydrEtz4ISmoKmlepWha2dx2PNPNjaYvaO3kvzI7/lOKDaD8yuHoAxLWVNpvv2iYS3NdMu6JJ92Lzma18H8qir/A70+bU2FLSNZLe183iSK9Y2bqRSCtB4JGXN11ECYs5IpuvOibinxee7DnbdNob4ykbZtTIZJy6F2B7zq7PWIfNJq3yahidi8hwX8kkG8tk2sreutA9x/AxVfvuBs492pPk3ywlp6Urr8Nz3JCWNeBTzd3+z+eYPwOxC5nqDNYMe2yfgnhZPQsX67G0zKue+CI1bSta+KN86wkLrA/M55r2fbuG60PuIVLcqbO42yZhWYWQ/adQadAY9thll8A0PQ1uthLdqTUDvT0F55GpRzi+uMXMHlFrydN3eMO6WBhVffA+A+ucLTpwAWoV8DVZL1sK3rbytDxgbS7t908Cakat/+71fFWr8nV1mENDXn1TPWqAUqZ61COjrb6wseQhaGAPKxPk6T9M+LInmx1NzJE5gJE4Aob7DAAhp9b+rLmOdC3uNS2GZ/QC01eHOrGbsIZQ1I8ewypphP9C32zcNU0ZqjnmZMlLs9QXGjiygrz+R9XuDMhFZvzcBff0J7DU1zzLkpfu28cbNktnLZkk3uhexbJna7z+KqyU5axzbeC4ZyfadVeXEM9gv29lPVhQRNSPpHDQZK7nXmc5BHxH6bM6nhWrGHsq6+qe1/fOViROA0o5X6MzuSjv+OZa8uhenfW09AOgcNNl2zLW9EBOj7I62oc5BH+EbHsaedlY23lMdl3PRuZcnWctTT/+oSMuz+7YJDufbfdsEII91A2Nap94ZSpUls6CCh7FMMtcDBa7pCYS0z2BfO6PMR195jH87xpPhYgaUffvQJjPuafFol5wns/b5Klv5lMKqrHQOmozJxZotFnKsf1ZlzpU4BfQx9i9knsyY3XLtX7pvG4/Ovj5mq9PwjtUACL7BWPEsLhVQppzrdeZ8z1bHKE/mJXOdbhzvbeXc2M5Y/jnqnKxlfen1IUbZyapznW35xX+Q9TRsxJuP2D9HV7HVmSnbclTKPt6t/2xC/zAd3/AwjjQ1lkmlRT9ybMStHBs1mMhxz9Bt+wRqn99p7AcBSMcj6Tw1LuZ86u9UY0/7/umit3E56qJ3e/v+6dLk5+3Dhr871rYfyLasbAlAuimSwa9/Sujw/nSb9ieOtB/7f8byzExQMveryoR7WjznJ7xC5cQzxr4o+zpI1r4oI9vRPq75qaz16opt42Il42tBx7NMoSO7UfvcjhzzRGtIT3K4fNFWtAKVWb+2MgAsfP1hZo5aZ6ynJk9jfXermGM9LegYVVqu2+SpKILbGzd959iBkrXCnetknCnmOrjZ/mce3Opc2Asq98482Sv/pzgcydwRWlwrglJYXCvmmWgVZqeZKa8DTvTom7IGMptR1nQanAoA0sHsYlxytDn37QT7Z6vZFZf0RGqdXYqyJmI1uxHeyOhXM/YQysM1a15KoXQG3jOexzc8jIRp4+j8qMXYkdljslL7/A66bTfm4XtgssMNXCV/nqtsYb1M9p2PvXHAbGJfX2MnagUwmexlU9Z04yyxABmunqCtuKVewi3lImgL1joN2dOjPmBcjnVLT6BCciTdH6hBxcRIXDISONy2BpUTz2DK3Awzy2AyUXvBJ/Qb+n8A7G1lZm9fLyNusxmX9ESaHl2Km6cLSjt+IqagRLH7tgmY05Ny9DenJ9F92wS2djbi2dHcccvGxldfyLc+zn8wOqtuFn5t/3x0lJEMVhtg/K+ceMb+6Dxa574rLFtiUDHJSEQfX3iQEVO2UO2pZ40drtlYPvYdsdlE0ON1aNilr/E92/LUZC3Pgw2yZtMyJBj3OeMwV3ADRVbCb4LAbh6c+/BZ47syYXIx4clWlFmByYXYKtDjvlHccGNHfG7yplJ1N7yjdmFO3k2lmhU451ObO75Yxu2z/+HwC3dy14gJvPZdGE071KdNn/o0OjKFVMtGKnknAuDRth3Ng/exp1+2exmVCZPZRNv+DTGZFSaTG2lTn8VsdslxBq6VBa+WK2nVuy6mjJxvl/ENDyvwoLO+jcI9LR5l+9Uuo04VKBPHfBU14ozxaicZ61zPreNo0taL+pEb6Bw0mRZdvah8+TSxFcF9ivEAQ42LYfj2rMMj47vj26MONS4al2XajZ3EgYaRkNnKlW1ZV06MZMCQcZx643Ywu2IyKyypAZjMyr782t49mqN14N+BVej76Gucamzh2Pih7G+SWR9Zy91kVmiTmSXdjX6tugwA4O4Vu/ENC6VhGz/ufOUr7hz7MbcMfZWjEx5H9e0MJhNY00C5klL1FO2DZ3Fw1G32OP1+WoTVQSsdgNXkQvt+We+tuuOB4ZxuonFxy6BG/UrcOqINNepVxLO6Jz12GnXywJv+1G/UhOr/LuVULbgxOJim+3dxQ9AmzGYzkV19qFQhkkZtzLTbNwWzCsTNZDy12O3+oYCxL1ImhVKXcfW0gkmRVK0qLUNDaBOSdc/ivfM3gYsryqzs+7rMRNfSpilgO57ZV66s5ZP9ZG3/c315/PmppLRvDdi2QVtSZsq+78w2vldMMC41j+G36xPqR26gbgtPe7+2fwUXeOKXK6lWFHiSWxKu28t2bXyb6t+1GwDRNTTeFxUuv89h/SfPUve0iaSBbUg5c5yqzdvRZ+iHRPboj/nrz1nzoyvKaqH1LY05EHgaNAyd0pbfXr+NXs+8T8APVVDJCficXM3JRrdiMbvRYMAKmr37LwBr+07DZLHgnrCR1Mq90MpMxcsvc/NuS77Nulc+hRLQa6px4+qVdDr91xvXoz1+mUXyY6OKfDkqoPc0lLZQzSeOSxFVQZt5YGJTpr93N9Uva8Z+HcSRdp0IvakSD/ye9Z6OJT+8ytFD23j9w8289WF//DreSwefTpwd9gynb2uKafdRbjoGoUO6Ytm9hZtCTQQOmIkpKYHGEas53fZh0tMsjJrRzz5N/5F5vINVa3wPv0DfNfuZNWpdziZjbSWVN7h9QwoRzTNo9NonxEaE023IOyx84Avc0+LoOes1QjZGkhSXyumIN+m55AQAN+zZSvLlS5zt5fjR2b2doP0VT67vGFybLn+eZ88zftz+5ESq1/JxHHMmSwaYzGirleB9f7NrYXVMpjQUAVjVLWiLK8M/6QnA/s2Ladq6JxWr1Sb58gU8Knnx+zv/w233Pgb/s4ewlr55ziagzzTAhNJWY8dotdB/w4vs6VeHDuvOEdD7C1BuKG1BKzMmSxo1Yl6h4/hP2D7hLZL61aTXvKxHr481hBtOgdeWTRzd9ScNbmjL6cN7qfqSkahGvD+Y9ev/4oOvdvPV621Ij3Xlre9C+fXelrjWhQdmhRN5ZAv1buxuj3vb4Hmkxh6jqnsAeI0i8eIlTG4VMUWewOfESg41ux3XWk0Y/nEPh031K2fvJ+7fxdxwfDOxz3+MJd2N20e2BeBS1CEqV2vA4badAPiri+LuHcb+rtH2LUTc3B3Ium9w5ez9eFZ1x0wgKWldSU9W9mllxluYJ4K2LfuMGzvdg1f95gUOi9VqlEspIo8FUe8G44rDqu/eptHHf7Cqo0JVcmXs3Nzb6bdvbqJCRVf87vQhaMUJUhLTs9abTQsImzSRioP7gHbhrlEzWDTuXs5HjcpKWMB+6S61oiumb6eydsuvtGQMqSsWY0reTNWn/UmKS+X2kW1JSU5h7cM30/OH1USGbOX8gZ30HTmJ3+5tgXbXPLIw51vLP3nBj1bd7+Pux8bZu61a8AatuzxEgxs728uQcjmShgf/4FK3UaScjqbn1nF51vPujQsICfyVoeOXOewftOFXVvwyiXc+38qWnyeTkhDFba8UvUUic11o3auefT+RfV1I8IQuu8OM+3L4t1egAAAgAElEQVRm7ENbNVlnzhY8LHN4at4iFj3RDFffNgz+v6VFjiE/qZdO4l6tUY5t4psJz1I1JIQHF2/Jc7xFr96G69mzDF6wHwCtNaG+rdh5k4nHftjOyu/e4t6R01l7y03Ui7SwrteXmCqYuKGjleN73UhPSaPJwZfpuTSAyMhD+LbOah3/e/Z+0lYspl7kZi6OnETcP5u54N0Gc3oKPidXE9H6fjKS0ui38WXqblrNmsdv41LfZox450/+uN2XFsdtZXOrYlyeNLlkHa9slyf/uPkyj2y0EtBnGspqof6ZjZxq1A9l1fTf8KLc81Qa/Pz8dND2raA1ScnxHDmwjrbdHsqxM3PIdgaPi1ue0/70aV/u2mx8Pl3PlT5/b2b+kJs52NLKlx+Gs3fdTNr1eY4VP75B08l/4x34F9F97waMxOXKRKD/hhfZMkjTdJuiw4o1RPYYmOcK1nzAMZLWfIrbGVdu/zucpQ+2omWI7WZDZdtpKuxPC8V/9QZVxk5hTwcXrPEW4j3h+d+zzlCO7g/g4J5V3DHsk6zyF/Y+FZvMDX3e1NvwWn6KVp9NpUW7gfZ6PrRvHR6VatCwabtc42Y+1ZeLsjJ6lnEG+e0bGzGZU2nQ/AzHg6sCVahY/Sfa/7yV473M3DHvgH20FXNfRKO5a8R0e7eIQ0EEvPYklR98iAeeNO4xyysp8Q0NYf7YzgwYPY1GLbob9WFyXgPulXFubguDv/gNTw8PNvyeRKptZ5b8mj/xF+Lx7Z/Mxjnv0HV1DOu6PkvVhp54um6kRrMJOQ4Qmf7+/l2aTP6NczWgzrwfCN6xnIeHZ92HZ7Vk8OsDbUj3rcewyXknukCO9aagZCRg4TvUfm8pNf9ZQJ3GHfKtg8sXjnIk+E/a93ulwDqKGncX5w9s4uFPthJ5aD1JaWnc2GZgvtMH+PmjB9AuZoa88VuBwxaXiLC/aNTyriJvb/nxH7EGtAUzG7GY+wJmmoe+QLflAVSqkvVUbezF07i7ueNRqRBP81qtxbYNFCVJdZbg7X/gXb81dRo0A2Dm8wFkbwBWJnh+Zvn52dezJ/dRtUYDPCvXtHeLPL6fuNsf4ejoztz1wg+FnlZYS19SXKFDcNbyS4g+SlTYWpr2HsGSD24m8UIMQ6flfJ3Isukv0cx/lf175nFQaStamezHwSgvqGU7n4uqpagVpbkxeB9/zhlOZe9mDHr0fUmeSlpJvufpwulgogc8jBVonbkTKCDpyNxprO09iUpJificWMmRmx9HX0yjTscl3P2C8d4Wq8XCwdbGC94Cek8zdlpK2a4iWhk96xbb/IxH9K0Z6SycPJiLZ15Dk4YybcKk+mO1aAY8HkqLXmP40/9/9HpwPDVq1DVuJM0nMbxm6cng6lHowRPjUvn+nUB0RlaSaHK18uSk3gW+Qys86BdadHgIZXbctJ4f/+Et6L/VOCDsHuhFxzUX2DkgjSdnOH65n7P8+/uH1B//MwBnhnsx4I0NOdaznx5oiftlxUOrsnZmyQnRLP7oHu59YxGVqtfPd/qbls6k5tvTOesF/TcV3wGtNA+SmfM6UQtu31B2D8ql4dyxzSjlSu0mXQoe2AnKQ/J0pVlj1uHqZqbjoMbs/udkrtbz68mpI1vwqFQDrzotCx44m9XfvEfDT7MeBNrf+lnc0+KoF7mZY0+8SlRYGL2eqIk5+jCu4xdz9pZEun60jZgLx2jYtKd9vNJ8VUHRjyqiQF4NbuLUFw/TsNWdWR0LOHvceZ8nqXFxjJ5+D+GtjfuL+n6+ANISwe1++3Amc9Z1ZK+YYOo8eGeOpmX7fGzXm00urjw2/u9sc8r+3iIj0Ro85vuiF/JqFSFxAqhY1R2zyZ0MZcE4AVGYlGuhXj7a0u+xq42S0fOCCWtjtIR5VK6H7/5V+Lp5FjBW6Rvw0P/x/ZG/cHWvxuOvrMrV/9EfNuZ6o7BHZW+GTM790x2OWNKNZj9dfI0fpS59+gj++XEew96VX5Kqc0MPZ4fwn5M9Uep4a2MnRuJ8DW/sflXjWStUyfG9bbYnFts91Azfhrdn9Rw4At8qDcBkwrNqvauaX3GQ5KmEdLgj/1cMXOnJj4Lsic+MuxR+non4ArhVzDXs1jur0W1FLC7x8+jzmHGpos9jLa415DKrUesaue4/KHHZWt8u1/GCMpg4ZRr29vY8+7lUvLqXqGay2hKv8pw8tR34Mm0HFu0liUKI0lPHu2aO7ztu1nTZbux01OUr3qVXrVFphZUvSZ7KimwtU/6f5f4ZjOyGjFuAv/sAhgyZVtJRlQnZ78NxRpI4ZFTuJ/euF9pa/luehBBlW7Oug4jgY/v3Kr59Yft6ABo0aOKkqPJX4ne6KqUaKqXWKaXClFIhSqmxtu4P2b5blVJ+V4zztlLqiFLqoFLqtmzdB9m6HVFKvZWtexOl1Hal1GGl1EKlVAnetON8bjWa8PJHR/HOfllQFLvEyfdw9k0/TK4VnB2K01gyMlueJHsSQpSMitkeVgDQqfFY5jyNx/wXqFTZ+b+H6khptDxlAK9qrXcrpSoDu5RSa4ADwP3AnOwDK6VaAY8CrYF6wL9Kqcxnfv2BgcBpYKdSapnWOhT4BPhSa/2rUmo28DQwqxTKJv7D/O77xNkhOJ22SMuTEKJ0uXtWpE2f15wdRr5KvOVJa31Wa73b9jkBCAPqa63DtNYHHYwyGPhVa52qtT4OHAG62P6OaK2Paa3TgF+Bwcr4Vcz+wCLb+N8D95ZsqYS4PtzQvSeBNylO35X/U3lCCHEtDmW797vmzTc7L5BCKtV7npRSPkAHIO87XKE+sC3b99O2bgCnruh+M1ATiNXa/rsS2YcXQlyDG1v2o8GkL3D36e3sUIQQ/2UeZsBC8B0uPNzzKWdHU6BSe7ufUqoSsBh4SWvt+GenbYM66KavorujGEYopYKUUkHR0dEFhSyEACo0H4Qqw08bCiH+C4xDublmZ6e+eLiwSiVCpZQrRuL0s9Z6SQGDnwYaZvveAIjMp/sFoJpSma/PtnfPRWs9V2vtp7X28/a+tke4hRBCCFE8VOaPx5eTh1NK42k7BXwDhGmtvyjEKMuAR5VS7kqpJkAzYAewE2hme7LODeOm8mXaeEX6OuBB2/jDAMc/TS2EEEKIsqucJE+lcc9TD2AoEKyU2mvr9g7gDkwHvIEVSqm9WuvbtNYhSqnfgFCMJ/VGa238nLJSagywCjAD87XWIbbpvQn8qpT6ENiDkawJIcqoRcNSCc7wpPR+KU4IUZYlVDKSJqtH+XjTUIknT1rrTTi+LwnA4U9Na60nAZMcdP8b+NtB92MYT+MJIcqB8SPWwKUTzg5DCFFGuN5am698zvJaz87ODqVQ5A3jQojSV7Op8SeEEMDDD//IPYGTqNBhiLNDKRRJnoQQQgjhXFXqUuGeGc6OotDK/vOAQgghrgux8kYMUU5Iy5MQQogy4eOn4ZI2s8HZgQhRAEmehBBClAm/NLmL1Igtzg5DiAJJ8iSEEKJM8LzjU+TKnSgP5J4nIYQQQogikORJCCGEEKIIJHkSQgghhCgCpW0/xne9UUolAAedHcd/iBfGjzSLayP1WPykTouH1GPxkvosfi201pVLY0bX8w3jB7XWfs4O4r9CKRUk9XntpB6Ln9Rp8ZB6LF5Sn8VPKRVUWvOSy3ZCCCGEEEUgyZMQQgghRBFcz8nTXGcH8B8j9Vk8pB6Ln9Rp8ZB6LF5Sn8Wv1Or0ur1hXAghhBDialzPLU9CCCGEEEVWbpInpVRDpdQ6pVSYUipEKTXW1r2GUmqNUuqw7X91W/eWSqmtSqlUpdRrV0zrZds0DiilflFKVchjnsNs0z2slBqWrfs/Sql9tmnMVkqZS7LsJaEs1We2/suUUgdKorwlpSzVo1IqUCl1UCm11/ZXqyTLXlLKWJ26KaXmKqUOKaXClVIPlGTZi1NZqUelVOVs6+RepdQFpdTUki5/cSsr9Wnr/phSKlgptV8ZxyOvkix7SSljdfqIrT5DlFJTCgxea10u/oC6QEfb58rAIaAVMAV4y9b9LeAT2+daQGdgEvBatunUB44DHrbvvwH/czC/GsAx2//qts/Vbf2q2P4rYDHwqLPrpzzXp63//cAC4ICz66a81iMQCPg5u07+Y3X6PvCh7bMJ8HJ2/ZTHerxiuF1Ab2fXT3mtT4xXDEVlrou2+b/n7Pop53VaE4gAvG3DfQ/ckl/s5ablSWt9Vmu92/Y5AQjDqLDBGAXF9v9e2zBRWuudQLqDybkAHkopF8ATiHQwzG3AGq31Ra31JWANMMg27fhs03EDyt2NY2WpPpVSlYBXgA+LqXilpizV439FGavTp4DJtvlYtdbl5qWGZaweAVBKNcM4AG68xuKVujJUn8r2V1EppYAqeYxf5pWhOr0BOKS1jrYN9y+QbytzuUmeslNK+QAdgO1Aba31WTAWBMaGmSet9RngM4ws8ywQp7Ve7WDQ+sCpbN9P27plxrAKI/tPABZdZVHKhDJQnxOBz4Gkqy5EGVAG6hHgW9ulkfG2HWu55sw6VUpVs32fqJTarZT6XSlV+xqK4zRlZN0EeAxYqG2n9+WVM+tTa50OjAKCMRKEVsA311CcMsHJ6+gRoKVSyseWfN0LNMxvnuUuebK1UiwGXsrWAlSU8atjZLVNgHoY2fsQR4M66Gbf4LXWt2E0OboD/YsaR1nh7PpUSrUHbtRaLy3qvMsSZ9ej7f8TWuubgF62v6FFjaMsKQN16gI0ADZrrTsCWzF20OVKGajH7B4FfilqDGWJs+tTKeWKkTx1sI2/H3i7qHGUJc6uU1sr1ChgIUar6AkgI795lqvkybbSLAZ+1lovsXU+r5Sqa+tfF6M1KD8DgONa62hbBr8E6K6UujnbDY33YGSk2TPPBlzRDKi1TgGWYSy0cqeM1Gc3oJNS6gSwCWiulAosnhKWjjJSj5lnX5nN3wuALsVTwtJXRuo0BqM1NDOx/x3oWAzFKzVlpB4zY2kHuGitdxVL4ZygjNRnewCt9VFbC95vQPdiKmKpKyN1itb6L631zVrrbhi/e3s4vxmWm+TJdgniGyBMa/1Ftl7LgMw75ocBfxYwqQigq1LK0zbNW2zT3K61bm/7WwasAm5VSlW3ZbW3AquUUpWyLVQX4A4gvLjKWVrKSn1qrWdpretprX2AnhjXnfsWVzlLWlmpR6WUi7I9cWPbGd0FlKsnFzOVlTq1HZj+AvrapncLEFoMRSwVZaUes03nMcpxq1MZqs8zQCullLdtegMx7hUqd8pQnaJsTyfbuj8PfJ3vHHUZuOO+MH8YB1aN0US51/Z3B8Zd8msxssS1QA3b8HUwssx4INb2OfMpufcxEp4DwI+Aex7zfArjWugRYLitW21gpy2OEGA6xtmU0+uoPNbnFf19KH9P25WJegQqYjzFlLlefgWYnV0/5blObd0bAxtssawFGjm7fspjPdr6HQNaOrte/gv1CYzESJj2YyT4NZ1dP/+BOv0F4+QolEI8QS9vGBdCCCGEKIJyc9lOCCGEEKIskORJCCGEEKIIJHkSQgghhCgCF2cH4CxeXl7ax8fH2WEIIYQQohjs2rXrgtbau+Ahr911mzz5+PgQFBTk7DCEEEIIUQyUUidLa15y2U4IIYQQoggkeRJC5ElrzdI5r3M+6oSzQxFCiDJDkichRJ7Cd62h5ZfL2TbmCWeHIoQQZYYkT0KIPFkunAagUnSckyMRQoiyQ5InIUSeVHoCAFpbnRyJEEKUHZI8CSHyZLXlTEp+xUmUguS9e4n/+29nhyFEga7bVxUIIQp2/nIa9QH5DUxRGk48+hgAVe64w8mRCJE/aXkSQuRJKWV8kNxJCCHsJHkSQuRDOTsAIYQoc5ySPCmlJiql9iul9iqlViul6tm6V1VK/aWU2qeUClFKDc82zjCl1GHb37Bs3TsppYKVUkeUUtOU/VRZCCGEEKL4Oavl6VOtdVutdXtgOTDB1n00EKq1bgf0BT5XSrkppWoA7wI3A12Ad5VS1W3jzAJGAM1sf4NKrxhC/MepHP+KjdYabbEU81SFEKJ0OCV50lrHZ/takaw7KjRQ2dZ6VAm4CGQAtwFrtNYXtdaXgDXAIKVUXaCK1nqrNu5o/QG4t7TKIcR/Xgk15O7s3Jrw1m1KZNpCCFHSnPa0nVJqEvAkEAf0s3WeASwDIoHKwCNaa6tSqj5wKtvop4H6tr/TDroLIYpFyZxfVb4sd6ALIcqvEmt5Ukr9q5Q64OBvMIDWepzWuiHwMzDGNtptwF6gHtAemKGUqoLjqwY6n+55xTRCKRWklAqKjo6+htIJcX3I3JjkPU9CCJGlxFqetNYDCjnoAmAFxj1Nw4GPbZfgjiiljgMtMVqU+mYbpwEQaOve4IrukfnENBeYC+Dn5yeHAyEKIs9fCCFELs562q5Ztq/3AOG2zxHALbZhagMtgGPAKuBWpVR1243itwKrtNZngQSlVFfbfVJPAn+WUjGE+M9T8jYTIYTIxVn3PH2slGoBWIGTwEhb94nAd0qpYIxLcm9qrS+A8XoDYKdtuA+01hdtn0cB3wEewErbnxBCCCFEiXBK8qS1fiCP7pEYrUqO+s0H5jvoHgTIYztClITMVxXIRW4hhLCTNnkhRN7knichhMhFkichRD4kefpPuXgcNnwK8kPPQlwTSZ6EEHmTHwb+T/nm67t54c/viLtwzNmhCFGuSfIkRDmX+FkbLq14t0SmrWUX8Z9Sf4WF51dYiTx50tmhCJHD0cC/2DKgK+nJSc4OpVBkzyhEOTc8GaYE/1Qi05aLdv8tVRIh1a0Ke5ZDYlyqs8PJX8Q2sGQ4OwpRSk6+P4Hqp+M4sneds0MpFEmeypCYHb9xYeuCggfUmsi5DxK9ZmrJB3Wd2rzrV8bPu4+EpFhnh1Kg93+2MOQ7t6saNzEulaWf78rzQGp/w/hVxlYWFFTG682hpg9y6axmsf+aUplf9KkE5r20nujTCYUeJypsI7OWPMSBRW+WYGSlR2tNSkaKs8MoM7Jvk//+8iG/DOmKLmf3BkjyVIZ8vnIcH69+L0e3hONBRHzSHGviRXu3fRt+5cnEcD7cNbNwE06Jg7TEYozUyazWEr/htcYT7/P45+H8NuaWEp1Ppr8XTuL78SuKdICP272AMa+2Bq5+Q17zzRYiD8exceEhxwOUwtN2V3NwLWj89ORYvp7Yh/hLEWxaFk7kkTiCVhwvrpDzVJhE7djhlcTHniz2pC779C6eCGL2BB+ij2yy9589JpA9fv5E1+4ESpEQ4Yn/yABmjwnMd7pnYk4UOZY/nu/PH492BuDvuftIS7Gwau6BQo+/Zvtf9J1diZU7AgjbEYn/yAAO7Tpf5DhKS0HLcs5wP4636UDY/n+LdV4Rh3ezefGMHP2jTyUw58V1zB0bmGObSE5LIi0jNc9pXSksdD0/3dmKowdDWfDBOpZOHFGk2PKz7qcwIg/Hse7ncOq//zPtg+Kod9YYJzqycNvpte43rpUkTyXg/MlgVn873DjIF8HQX1x4+hczAPs3fMfq74by4+8vs2SvCyvXzLMPtyskgOlzLIz8zlyo6S7/rBVrprQuUiwlLiUeMtKuatRlHzRk87xHcnSzJF0i/Sp28gXpviXv6++J0UfISCmeDTf+d08uR1Vg3U/hBQ9s8/tnnzB6Rf7rWERoDDNHBRARfjFH99ljAvEfGcCZQ0YSenR3tP1AuvnzPsx9uSnpiTHoEs6dzhwOZMHEv0hLyWB1toNrgTthqxWSLwGwbOpm0lIy+Gd2sL334o8fIT3iHX58+whHNseAhgMbjIPwrDEBecaTvb7WLnqfoMBcr5bL18aFh4g8HMevE3fkjD0tCdKNlofUu1/hTNdB9mE3/pYzcV0wtgMLXvFj8oe3kVrI1opTh8/y7RuBRB6OZcnkeZwfNJQ+v3nw56z/sw9jyXC8rmR2j4mN5GzU0Rz9dvwzn/get7P6x/eJOr6fXX8aJ2zzX+jJkjkvAvDPN18w96WcB+oWAWc5U/Vj/EcGcDna2M7jopLxHxmA/8gAEhMvEX50S4557Q341v65wlmjxbdFWDoBX+8HrVk7P4TTkeFERee8Xyt49c+EtfQlKvJIju7FmZz+MqwrK2a+BEBCchwWqwWAmAvG79IH/hpM5OE4ti7NiiEtI5U/PxxKWnoKfbYZ+5HwP/wdbpNaa86fzuME5gor52wm8lAs6xaEkHj3E9QY5w9AakIc1tRU/p0fQkaaJj3Vygr/nSx5eTBWq5UTbTtxtE17AJZNepKDu1ex7qdwI4lxsN/Z/9EbdDqqWTdtC5fOWEnd68uZw3vyjCvhUjI/jAvMdTK2LyDcnvxm7ndOBhtlP7k/hoC+/gT2yrqKomPjgIKTo3/mBJOWYuGf2cFordGl/ASpKu0ZlhV+fn46KCioRKY947Wb6Lomg9Rp79Ojz8OFHi+spS8AvuFhfPWkLz32wanGLrQ8lEHI4515cMIPAPz+f4/SZtE++7CFmW6KK3QINoY9tHMJsXHn6NLpTqjeuKjFA63RiRdQlbzzHMSSEofZvYrRcpGRCi7uLH+jA+pcHHf+cIwJI1pTu0Y6oz8u3A4DwH9CezJiE7l1tQthDeD+f7PKPuOtlkRfVrw/w9YtIw0yUqBClQKnGxj4O6fPhDLkiXeNFi2l7MsC4MY9Ozl2YBUtOj9AbNw5UpLjqVOnObMeakVaI1fGfraXhDNhVG7QqtBlAcCSweyxG7Fk5N4GzS4mRs7o63C0QzvPseabUKpemEunA/vs3U/PeZ3W9dqiK7jj4lmZ2jV9mDs2gPRUUKYUOmx/lYqfvkjbvs/h/3xgnmHVPTEa3xNwasrrHPnpM/rt10RVhz5bjbrNSE9h/ru3c/tzX1KvQRsuxp/Cu3qTHNP4/p5WnKilePfrEIfzCGvpS0CfGXm2bJk8TmBNakzjtl7UarCM6o070az9Hfb+Kye25+jpz1EOx9c0iHiRxqcrcaTp/Zyv1QmUCbBS+3wQNx5dSvv929m3ZxX16jfHu1ZW7P7PrQAq4ObhQs9/RmJR0CbkAItm9OLO4X9wKtzKmm9CGfhsa5p3qm0fb/aYQIfJiTJZeX7mANb2bUntKEXVJd+w/PMErGbX3GU2wyj//jnWPYBLFaH7LsfbefSpBH6btNNhP5MlnRpp73H33NV4ulfGf+QaIPcJl8lFMWpGvxz7n4jQGJZP30cV/SOdArcR7FeZm4IS7P0zhz0/7nVCNjUETNSoV5HH3u0K2JZvr6ngoJzKBM32jaZBDNy4bzdnjyaxfPo+qlX5mw5/rgAgdHhfzh170PH6oTWj59xCYnI8FT2qsLFrK7xiNRGPdCXp5DFqhEXRZ3sYy/33cjL4Io3b1uSu59s5rKNMGz97nYa33I1Ph94O+2eWt2bgCmL63snpWmYut6jHOV51uCyxplO35ix8lx7kROuq+IQYCcHh59pw/uQY0lIsuFUwcybxebo9OIozv/5Et61xnPpgKDU9ahC1Zyu3T/geS2qqcTnLZGLu6HVok4P1xpLO07P7Me/5jfm2FPcPHA1Aww1r+XFccB7roGaUv9Ha7v/cKlCO19MBT2YQG3GEzg89T8bFS3w9fg8WS+55ml1MWFJTwOSKyUVhzdB5LtP+68fYv/qGh/HjuM3Ex6RSxcudoR/2sPfzH5nXyY9mzJwBu7TWfnlWQjFy1s+zlEnakoGaWJP4Z7ZRpYFvHgPZDnRXrADJsVF4VKsFgO+2DCqmwuoF7+LX9W7c3T0KnPeKn1/jBtvnlfc249ZwY9GYbK1XOlsrVsy5w/bPK3v74vbC4zRt1hOf9v3swyqTCWtGBnNnPk8foEI6fPNaC56aEoZl6DgqA788NpmalRpz66tZTfuFsWDuc8T+u4mB78/Gu1ZjIo7tom2X+1n083vc0LQTblXqYL7/SX66twZd2/ThzJ9L6T1hFk2XpQDuWFKTeWyDFTDDZM3p/YE0aNvX4UYVHXmYdZNvpYHf7fT/LZXMVdb3NGz4x5/eg4wdwi1/GONOerEFw1/6jU3+g7l81JP/LQ5m8dwhVKhUm1sfmMjHr3WhXaO63PvmWlLizlOhWh2iPplAp+PAE+/yxkut8XRTZG/XmvF0Fwbt1qz88DBnFn1HnXOKPn9tpW+whuA0/mk8nMYzt3Nu/P30e2KSfbyLEaGY3SvgWrkGJzt2Y8/d1ek6/GPWvz2Su2b/wZYpg7Ckz3NYbkuGlYiwzWz6aDj9Jy/jzJ6FnAzbwP1vrGXVN3tRuBJX43/Ay/ZxGjz3KTvrQfNIbIlJ1uPo2lqB3Z39YaEmbE1nKkd7keA1FnC3zV9jzkjBkv4lvieMcSKiE+m3PyuxmzOyHbpWE+p4NaDXknNsCB/CydqK+9ZlELdkPoGLJ5MRcYzn5gbT5ZCmy6GcSeHan8Zx6ZfFDF5inL0qnYF2sHMGsCb7gIKTwTGcDO6BsqbTbC6kXjyGW9VGVP4rFdXB4aig4WJDb043HH9F3Zo4X7sL52t1xjfxMm6PvcQFIG7xTFZNsxrDKmNbTUuxENDXH7Rm1/j76bL4Eofn9GFjry9Bu/Lv/BA8q0WQGHuWFp3uybNVR1tNzBoTQN9zRhwXTh/DaqrruMwZmiN71+fqXj3RSJL++Hw3977Wkd2L3+Ly5u088FtQnokTWtNt+wTc0+I52a4LdbZvoceW/2Nz14mgzEZZtQal8a7wNdAvx+h/Td8J2pV46/3ANkzmdHu/5EQjEQjoMwM2K/sNcRfPJtkPbP2BHtsnsLXnRKyWrPmZM5Ixm6bRIMYY59TO5axdVAut4VL8AIzfiIeMmChahXxNaOunAFPWcrRm0DpkPmGb4+HpdzjaqjLetpOPxEqe+G6LIrDXVA5kO8Ce3B+D/8gATGYYOaMflthYXKpX50xYEPGRJ2jZ/wG8vl5O8tfLsYYcYN9f39Lunqf46ene3PbtelcAACAASURBVBQUYz/pBNi//CfqAw2iLBB1irOOcy0UkJJs3CqRZk3jUiXY02kGHFSAkWWkpVjwNk/nyBJN/61GnabP/w3PE6n4AHq85lA7o6Vo/60N0crx/V9WkwsJ8efoHDSZvW1Gke5RDftC0Rq31Fia3J0BgUanqOAgrKaKjqeVARmpKRxu1wH6zMhjGE3GsLFUAvSDo5j35g7HCSS2Vk2zcT+m1ULWeke2z9pC+31Z8wroM4OAbMsv/kKqfb0aNimfKyil3A4kyRMQsuobXGs2JvbUVir/Wo9VMYN5afohUlOTcXf3YO13Y0k+sYm73tvFjLHNqVkhjcemnGTr2h+4wbcXq78fhd/3J4l7+CJWn8HUu2BM97718NP/tePpT7NaVzaunM+FHTO5d8JOln7w/+ydd3wURf/H33OXXigJPQFCTYMk9F4EQcUCIoo8iopYQFDRn4+PFQsqYi9gQVFUFLFTFJEqRVoCCZCEXkMIkAAhhJTL3fz+2M3thVwahOQi83697nV7s7Mz35mdm/nstG1Nnmcjho37iZZTfrf7Cdlp3Ja2e3XxlLqBRXMm07Hvf+i31hhKCjkB5179jt8i5uIV3Ihxb6xg3q3tya/jh61FS/rNibf77bnIxC/nWxGBFwAxc/2ADH71HUeLdteSk7aXHiOeKJI3pzNOYDKZqV030O7W6Ks1dDgFiYt/JvD7v6iXBefjBhE5ZR4wj5/v788twJ2/nSJx50L67YQdv31pf4fOmr++o/CZ/c/3bqT5p/v47NaGDLr9ZVJ3/kPPW57i27HhWNx88HG30H6ZFywtvgKj/qTpsHMCaQeM3pfhf5mYZ7uNa5d5A5Klv7xNu/e1PIhv1Z/bVgAc4xtG0Hl2Ekffe5L2+hC7tNkYs0Ry4b/w2i3a77R/ltMrXquUjh3aZT+fs2YbANm/L2etLOD8jgUMfj2Z3x4ZwRlfaDLyTqKBDgtPk7fwQboD8bcOpW26J4F1PmBbh0eRNscnMisDxjQn8cGhdDhhZuEro+i9Kpdj/aZrjQD65HCTh72BL3xqa5uqByELnD41IguI+vU8cJi/e0usZrSeRD3qBsdS7V4z09Psx0JC31X5wC523KqVn45JVjomaeeP74il17eaqN9/yBi6SNyxGtOIBzn3/tM0eeUXmiBY+N4jhFMsm0tFCjeWvDOKZjPjiY+w0SbPRJfYqWzu8hRIUaRx7RI3jdS2Fs77FTjt+cBWwK6lf1B4Juv2h6C7814SbAV0/HV30V4yAdIK89/MAulL6KfgWfAWeW6PgHR32BtLEpCxg6b3NQV9qktG2hG6xM4mrsPj2Myehqiw5tJx6zukJd5MYHEr+HnaSqwWXxZ+sJkeC1awQi8LJWeYDc/8s/afa5f+TOv8s5hsVmxmM8JWgBRmTAUWIhclYH1dW9VmNFxaGZMmX3sZi0QrYwcTN5bcayglAV4/AOCZfxZp1R+SrBYwuSFNZnyPptjjYo4hJpAO5XmBFleSzQYmky70tHQ1zEhg06e76Aq0SsqiQO9Mc9MbZZvJebNmK5D8078TAcdz8P/iQ7LufRiA2Md24af7mT9uAGFrTrDo74V0Wa8pvHyHofmgt+YCkOuuPZBK4TwuKdyQJm1WzHFTrlbf2Uouj4WYHP4UueeMxSorfFKJyin5+n271uKffRR3aw4W6hSZE+pWkMOx7G0E6b9Pxv1DdEIyCVETigppXcSkH9ByIzrhgxL9FJJ9+gQ9Nk4mOfQOTgVEFin7WLURByRF/p8gtO7HQhslBGQaHQKlcaRXf7r4BhHb7RmktbDOlGCzEnxwWrnCqCyu2DlPuanxfPdSU5CS3CffYuXUR0heNhuAa5aaWftsOL8N7cT+5C00ef0vWn1/nnPncxj4lxsxC3w4mrCEOhOmsvWBa+j8lTYGX/uHAHzfWVMknp4LzRzet50HXghnV9yf+D35JmFzszmWeoDwuR7EzD7Fxq9eLtPe9qvdqfP+j3z95VvFzvnlwjVbJP0WHGPrP0uITrTRZd1Zmq3bV8xvxAqvYm5h7/2N531PU+e5WSyfejXrJrfi28+1HpS9A/uxfkQvsjJPMfPxVsSv/J7G+lC9OLidenq9smvZLHt4x3OOAnC4Hnid18Sfm6cRb87mdfbj5p9qNvb+8Tg5tzxI3We/4szp43RcB93+Po/pUOnzojYv+4LT191exK3HP0axzvrOsCtnlSHA6v+lPU1mrDBEa+pBQ4Q5o/sfKfbjTR+MMU4IrfLL8/cm8JUFNP0Nss5l0WOn5Lo4yaHkjXavhf0Tbiat4gg84zBsKXXhZrVhvWcoISc0Zy9PTWpGJH6unbdXjFJ7Et9hzIcrpNeGyWCzOFRQEqwWzV3HbM3DNzuVyMRZ+J1LxWzNo8E5o9I9n3HCfuzlMG3E5FW8DHln5tiP16w2JsWmPvggAH6PTsWiN3JWm9aL0XPjZO1x1NFGm1WrYC9IY0z8+9iWaD1WMUkmvPPBP/to8bxD4J+disUs6bVxMmbL+SLhmy3n6bVxMvvnvGikzaL1kjjNr42TcbPpee9oq0Pen0lPpdfaA4jCcQtp3COvvNPknjtijys77Yhut0M8+pd/diqnTx0ukq8r+k1nRf8ZWAv8QAhyzqIJjJLQ7Q44XXSYz5qjCYGA08kEpa6mc9wbBKWutvs7flCf82IraWsAw/1I4ka6xE7FMyejaDqkxCvnJN6NDUFRJyuJoNTVdNnyJj756wg4lcyplgGlx+XoLkwIm4XgIysAq9aQA8nBWhnKdYcjjbSGueEPqwCt0XcsQ9KhDAUc18ppylLjf+/3rrHNhzVFK/P7M3aT51GLuJhJ/D18WDETM+po311ip2p2FSnDBXSJfY06B7WKspZ0B6mXMS4o23oZK+R0RFPjeG+SQzaY9TJaUCyuXhsnc2LNIgAK3H0AGx55p8F6GqSVAncfAo5n2sMy7dyv1Tt2MwqFqSQgcw97XtXmyRXzg+GnkF3XDsYz/yxeuacMf7pfM5rYMq6XIG0IaQV5nlb7fsVkzUZQtNe2S+xU7eHvgnQOuU/rsfXPPlrcLgTYUqlKrtyep1PudJjrx/H8YHzyatE3UUKisdw78GcIRJL0f6NppbvN+2kmPYG0ejGs+MiNyMBomu8t2uB6FECWbxBbOkyi49Z38c9OZfZrI3lsHSzd+TgDLZBRpy0rpuwnpnYbAjL3sGbPSkaUYzF4YBYMnbuuVD87pzxGjH58qKGkcQUXGDX56ijggayziRlxK4jxbEPzo3v47Yne9FnjAX+8ZPcrU1IonEORv3oGXnpxqn9A63lolg6FcsGSfc5+Xc7h9QDYBOgagn2tvWi1V5scu3jsIHsaIveUni9+E98s5pZVW1L7vHZdZLIhpGovXAyYyfINYn+rSQRkvkuB2RBnq+Y9T8dS4trd2kbbvVp4nVYbIqPBfq0it3gEEBdzO+2SvmD1yG72YVj3gw4VV2FYzT1pdEKLW0oLbpY8mh/+i30tBiPwwCyhwARuNvD09QegUXo8SVgBs0PFoj2JX4jW66DHZrOAcANhKtIb0Xv9s/bjhumaMImP8qTBKU0pBZ41JmD7G9oIy7bicwW93zYm+55eO89+nF5fEJyh2equ16Pm2AS7jV55p8j1qgfSArhpvz3q2itzPYMIyNzDcX/tnu4KglBdNwmZjU/2WUIOLmZ3zAiEvqi0Xaw2mVyaChWb0fPhmX+WUIeFXykNBcHHz+rzogy/jvnVKD2eJGm74IlZy/vdq77HH3AvyKPABLXPHiDHO5B8jzrke9Qi/UCi/am/7eeagDdb8/DOPUXIwcXsDLses00bLmw9Y2GxvC0JISxIm1vRoRDAhI2Y7R8X8Rv66mwAohINoR265wf78d6FX1IfrYFf1+1lPf3C3nAFWt61+83elUjT7KOYrXl6I6bHbbNgM3tgzTQmQnfYYsTXfb3Wa/N7A61no9fGyazrOQWkuUhcjmJiwOpH7Mc5zf4merUmAmunavWKlwVaHNXir3XO4YFE09EOwqBo70a2m5XaTvI0Uq8vG7v7c6D5dWTWboVvdh9gXhF/+e5aoP7ZR7FRoPUG6/fAZLPgn52Kv6593MxuSPL1sqTXZw5lzM1i/CdNBYZwzBx1n/04OM2CZ36+JkaE0ZNnklY888+Ss08Tv73XP8vOno0I25DG4WbuNDusD7euN2w/d2gffoDAhtmSS/PDf3Gw2WBs+vBavc3GQ6Kjn0PNBmM1F90SxeesVofle9TCZMvHIy8T75yTZNZuidmah9XkgZAWgo6uISW4HwgzVznc0+Ypy4vdA00ciaJ1AIKjL42398qazfl4Zp0k5OBidkXdjCnPTJNTxYK6rFyxE8bbeXnLH0NCLuraFX3eBZM72CwMWPNYsfP/dH2BXO/6eOWcpOeml4qd/7vXG1jdfDAXnKffuicvyoaLIc+jFjsi7qVd0hdFGlFnlGVjRcIqya/FbDSol0pGnbYkRE0kJuHDMruAy7o/F4rf8pDnUYtNnZ7C4lGLRmkbidj1zSXZWBIr+n6AkFaCjq7haFAfpDAXaWAc2RZ5P575mTRJXUdqk17kedQu0ni6As5szAiIwGzNL1JhX+Xkf3ax4ZeUB2X5rUjeVxZZvkFs7vRksaGTLnHTiOv4BDaT3mgLgbBZaJK2/pLv84q+H2gi0VagNfDS5jSda3u8irslm5CDizkYch0Wd98igrwy46ooK/u8e9FlaFWf95xPprZa6L9mUjH3svLhSJAbTY9qoqisMmZxF7hbSm6Ta8J/ujK4mLJ1oJmJ65cmVdmE8XKJJyFEA6AX0ATIAXYAsVLKiq3FdyEuRjyVNs4/4O+Jl3z+YqhIQ58UOpq0Rt1KbNyh7DQWktDuATICowhM30Z04sxLirciQqwkyiNIy5u2ssTVhZS3sq0u0ayo2dgFRiG6wLhcDWlVNtCuKAbyPGqxt9VwTtaL0ualSYlnbjqdt75z0fWTomqI2LXTNVbbCSGuAp4CAoCtwAnACxgGtBJC/AS8LaW8IkpUROLnJEXcq1VkDk+BkYnl3AumHBMGoWKCaHvkfVjN3myPvL/Ehv7Cxj2tcXfSGnd3/iRVykRjZ2Fl1I9mRf8ZTsMqb7y7W40gs3Zrdre6hfbJX1IRLhREVnffYpOoLzasXJ8GZYZVknACY1i+Mm0EONgA+3yoqiQh0kx0YsldhQcGh9NCn0u2o42Jdnsq59lqXyNolVa2v+pkS4SgY1LJD6L5btqQPsDuNm603VP0P78uXNArWbt+Q1cr3TcZWwp4WLKKPYVDyUNwl0pU4mdsCoPQ7JLD3dsIWuv3xCrAfJEDGJWdhg2dbHSPu7SpvJ75ZzneoKN9fhVCkOddn3U9p5bY++RI4XD7wQbgLiRBx2vy/vyKkiirlA0B7pdSdpFSPiClfE5K+YSU8iYgGk1QDbrsVl4GrE2NPVqWDjT2AjrSuHhBT3v8FkCb9yBNeoPgMO/BPUJb/Vba5EFwMmHQYVLqrtZGZekoiEqicCJprk8DEMLe0K/oN5347kXX65RUrzlz77Vhsjb/5AIbI1KeocBUykoWkxvf9dOK045onzLjPVJPEx4r+hs7Hp9s2Nm+YVpKv6KbUxZO3szzuGDfJlnCpFNhNPLbxmgzmUKTpjqdcFl4f8rD6m6e9uNdEe4lLxiTkkNeL2jHJU2MdbAxrY1zAVbIwZgG9uM9bbQyuKjL5amUD8UE2I/jBxjplfU095RGRqqTuhkzR/x79rMfWxrUBWBrd3+7W26rJsXiSveH7OLzz0mKNPIjp4kWRnKEQ94/cYP9eMv93ezHyT1K39frcD3jeG9LLf+2RRoib9f9xtrzLVcZeZ6qXxc/vLXdLWFUjP3YQ59E/3cv4398vr6xHHzDLS3txzZPLW07uxr337uhYZhvgzZFbO69/lm6xb5Gw/StdIt9rcjwxeaeWnxHA7go4m41LkzpZmzIGZCr/c9juxvbrCQ9aOy1cy66mf3YmXDa+9AA+/E+/bZvb26U1023tXVqzyF967gjDmVs7b297ce/99HczzistD8cbhSg+tFX248Xdi3+/9g5wLinWU7K3eEOhVsyOP9vOSb1vMP0nw9HG/+Dwz21fJR+Eq9cLZwFg4wyNr9H6f/bXUHG8bYB2vzDLa0hpbEW++HeRrn58hojrD2R2j3LdahKDoQY8f7Z0fC7tUPxejzuRmNi48c3F7drZ7Bze39xsl3DP+FGXCcdJpftbaR9n/Iz3DZ3vLiHrLVXGdtnpNep+ulHZYmn9VLKw85OSCkLpJS/SSl/vgx2XXbcPX3sx/1v0cbYd94UStNjxk0oLMRXPfAKKc/8B/O3HyKEO5ghamBThM0CwkzByBvxmv8VDb+fhuP+GhoC31ztEc0z/6w2cRe0CX9gn5RqNZUuiCqCsGk2bO2jLQmRJQgeZ+5FbLQZNubVPoObTV/JguPrUYyVLKEpmps131Is3CLxCmiaXooQM7uRZtLuT3KEVlE49k450mvDZC0vLxB7LYP/tPup0zhMT9tRjErRuD97H9V6eTXxW7q4um+Wsa9OaJKFnhsnY84/UTQ/pMQz9zRtjmpLEXttdG5ju77GSrv01sUXqP8x3GhwreFGg9pQX5tdK6/s99klhFX8KTzbZggAv4ZGI+mXrjUK+V4me8Nj6mL0kHcadDt7GsPaYfUR+ZpgdFxlaR5hCJ5CzgSY8NLn7O8c29PubnNY0Wd11+w5UMeoZJvHGA25b8Pm9mORVXoleuxaY3OoRmma34ZnjWv8mrezHwsPw4ba+nqH+o2N/d98AwwxKKyabbV9jZbP2jnKflwvwBBdHue1/8eZRoaA8DAbIsXsZdRNhexo7rzB9bNowvJEWPnVk2MjKkL72I8DjxvitPVB7f7trWWUn+YdjVbSJIv/d/NqGfnlVcsQKaY6mt/gHKPRb9N5sFPb8n20+BxHKX2bdjKua3cNABYHgWAadKNh9wCj1fd1Ug15NjDKSsao4mrA0kkLv9f6Z/HyK1oXeJ8/rq0Q1Zl5nWFkaBPjXrvratLNZiNQXysS6GVsxNrRU3swdBQQBxs7iP6bjXJx2Kode5lMCL34ZzXtaj/ftccI+7HU/7ezrzbs2t7RKFd9gk7bj7OdbMxra3Gt/bh/gHGvj2nPQZz3c/7fCsvW2plzDtsZNg5yKM9tjYQ20xeP5EcZQs3Np+yNjAvZcYvxmjFvPy1Pd7SAemeqvnevrJr1uTLO11jcHTaujBpwB5Z5H3D9a/M45/A0Mmx5sn0H70F3PU/bTlcz4eMBTPhoIH1ua4tn/wWk2ybRe8C9tAjtSvM2nfDwM+GbnUqTQ7PwzU7Fw5JF8hBj5+kWUfUISl1Nu8Q3qRd8knoZ29nUVoCt9Eo/o27RnYG7xF7QiwL2ht6+saY+VKSJggsFj5Uusa+ROLEHAI3j1iJ/eoOcOa/QLNKfoNTVhJz7nKCQDOplbKfhuP8C+koWx7089KUstmk3kldLq3yb6ZMje26cjNldFInXVJBL+4TJ/NJXs0uSVyQN7p5mbnuuK9lttc3QUht8WGLvFMBZL4dVUoX7xQgTnnWMyr1uQ61BS6sj8PQzgzjGoLERFJCKlFmEt9WebP2zjyJMbtqeRw7id1lno1Exu7mz6CZfYidqvQ6e+WexmQpXtVkBgZS55DU4Td17tEo99gafoiu5dBsjehoN1wmT8Vc0//w27w418fiUBXY3j/AwI4+yNNEUesxoHTb0LN67leUN1hKmJe5vBLMGO//7hz73vPGjvVZRL+8rON5Y+8/kenraJ/r71DPEgnfd+ty0Mpn7X19NgS68Lf5GF0HIsFHkuaGVdx0TYNZN9A00Gtw8H6O8m/K0p++Gnn7s1KMLb2+8c9Dby6ic3c6W/jqT095GufDTOzf9zhr50K671qivCxcIDy2f9wQJe+9YvdAo1kQKlnYQePrWsV8nChsjhyFvr149SGymb9nhZeSDZ46WYJ8gQ5haaxmCyexbfFPdtk8ak52THjfWhJ4I08RTdkRQsWsK2dMYTr1o9M5Z/Ywy0anjdfbjtPZGembrq/NvvGeK3S2yy1AjDP2/ts6hhyGnRWOevcvMqyNNuHsbPY672mr5m+zQwxfVywjr+Tu1e/3xEJOxl4cw6oSGLaJ4brSZh8eZ8TBr+VT/DMwd7saxutD7rkftfuuHGOLWJ7d4g+of3cp+3O/ez7jnMTO77zVWw/rla9vOeOafxc1dvw96WZYXrFYN9jTesBAVZAg80zmtDMhsoww362r0xHmbNLsCzhlCdve1xg7ovUd8AEC2J7h7aPlw2t/dniWeAUa80ZHG/yBH7+HOd4OfR1n4rbvghr7Gliqn+//XftzwdPH2pkFLo4yYfYzymKdXfx79uvHt+Dx+1XvOUgLhn/vOcdZdu2mJfY2HCc9wo2470kurX9d3MpPUU8vL/AgjvZnNyy/8gwZ9aD9289AM8zBd3EvRL5Urdp8nIQTHuzUl9zVth+qo6EF4uHnSccsOfr7ah6C40rcEABh7x0e88HkiZpPxJ7n/rQH0/rAH1/8+m7yw7+i9/lnajBzF6ZdvZc/YGIaMj2LYwpfo988yRj5/Ox7P+nPHrwlE7LU5EURGz0fO//4DwJ+jNbWt7RVTuLtvYcUt8Hh9BCd89D1QCiyGX+G434bAZM3HPzuVERO/IHxnMnV8A4lodyMdO9/CjQ93ZchvzzLk2x8Y9vRt3PbTY3ToN45/9Idyk9kEIpsC9wV4+rhh8vCgT//7uX7qDBKbg/nNRzkYLPHMP4uPLqiE1QIIPCxZ7OySz39nbOHXe91w9/S+QKxI6gf7c/8j37PnnWEInA9n2czuhCcn0W19PMHRAYjWR7nt2e5k+q/F59x2Ym57yO43svdN/NhbsOfWcO57ZwATPrmTtl0ac/8HQxkz/WpqBxp/em9/DwKb+BGZaIjf3olHODp1EJse1hq7/74Ry+iJc/Gd9wLzuwkCQ+uSK9dQN/UN2vULolXHpkx4835uuel1mm1dx32vraRexnaCUldTN3oV/r6J+OXuILC+sadLxxuMV4+0jRzCzGmJCLOZnLdv4+AjLWjTXuvh2djZRuDNmqDJetjYFTq0ZTDZeiWXXksbIln7n8Z46QJhY6jRkCwbXouoGW/y1jvF31O1rVct2kUNYmV7wdoIQYMmrRj7qJkzEV60HjiA1LqQN7gdCY92YEkHQZ8bH7RfKxwEoOwfytIYQbNBA1n4H38WdRHUqtuImO1J3D3feGVLy0NGg1pfGk/GZoen0br6u9F8PQMImf46m9+5Fi9Pfxqu/I6mG5bQb+Ao/ugt2fX6cBKbaA3IQUOH2TnQEEIbF3+Fjqm5Hyd03dCwcWuSx2QSNbgBJndNaFl93dnUz8rGtoIWXQcQddfN9L7rTtzqOIg9fR8ki7vRKNXv2ZfMq8+x93ZPzuvF+HA92Bau5ZNPiNGzaI42ei7cHMRg/ph8/rw9l8get9rdrr55CrMGm4gb15jhw58l6b5Mbr3d6BH59lZYHi14brSZL2+Arl9+Qa/bZ9vPp3kZ9ymkudGItRr+AOvDBG/dI5l01wzSHm1L55jBzB9WwMxrTXj71ObrASbev8lEyNAbWdBNEHCTMXRJy2b8cs8c5jz0Ax5+xljNxGeWsHEC3PP2JrubX0AQzz4E8+/P4WbvHG572o0Hbp1CmyNaeTjnZtSpzQP8+fmhRSwbv55Mfy2fv7nKxItP/kmvT1/F2y+Qr66Dr4fbqFu3kf06z8GB/N1OsPD+HN6/28a3/U10aGf0qPjUD2HjA9sZ+mQqb42RnKgNDXsP4r3xBTzyoJkGzWuT5r8a78YvE5i5Eb9z2v4Yb9+s5V+rbkZPTaP6Ro+WR+dAUgIh99qzfK4/oLTq0p/l0YIVUYIT+u7eue7QfcR1PPm4ZMKEqbw71MTsgSYCGwQTf/9Z5KgMWvXpwKxBJur3CaKurvFq+dflwYlm/m+smQZBRo+0+3mtjvcsgAcjb2B06zQiB0/kvIc2zBkaHMXiToLH7zPTPK34Q1XrNoYAbFQ3j1mDTHw6zkJ8C63u8A1pzyujlxHaRCufaREWbvFrj3eP5nx6rYkGvQxR79/W6B0b0Hco307MJfTq9tzcJIPT95yi/02P8dT/ScZMMtOwaUgxWy7kRG2tp65WE4de5sIecpOwP1RVJaWuthNCnAf2OjsFSClllJNz5Y9ciCeAN4H6Usp0ob2o6n20uVbngXuklFt0v3dj9IS9IqX8SnfvBMwGvIE/gEdlOZYQXs532xVikzaOZB2hea3mZfotfHdSkZU1QiCllUbdv2bEmG+wSRsmYbL7XdXrVeo0D6Lz9SHE/n6Q3GwLY6b1Zv2GL9j+xltEPfYIte9/X/M74E1869em1VV+HF6dz7kz2Qx6vAEhwe2c2uMMS9Yx0o9soHGEkwHxC8jOPsGp0/vZsciHPHmCZh3cyDrQmLSUgwx7uDueXlrl+uX/1uLlY6bzNU2I/SvNngYo+X1hEsnI57pSP9i/2DlHksPCWRUtGD8vqVR/Z9KPcKy31uNQ2NPo+H6xnWO6cvP/vio9wbbC13s47z4+lLyQX9a+xGP3O5Q5KXl+XDu865t55pVtRd4t5jQ9391CywEv49koEnl4E6JZV/s15z98mo5Xj+bsiUTOnTmEh7s3gU17squ9Nky1pzGs72sl65w70982xItjOuNbw6hFReM+EPcXPn/czgna0v659ezZ+Sut29yIcHMvFkYRu/PPcyrxJwJiRmu9bZbz4F2n2DWxbUxkROQS/o8H7WdM48stn+Hr7k2j2HNEL9Y23Ul97iqavLIS71/eJSTCaLCcsXPPX3wy4zFGjHudwGHaisbY/3bi2nY9Ee4+bD94jkbPaJtMpgZAk1OQPvkGAluGs3/nPG64ZwlkZ4CHLz+8fCftf0xkZ1sP+r7998R4EgAAIABJREFUIfvjvqPLyI/t93hP8gbe/3wsfQIyaRk5kje3L+HDUR/y+7SJhG610W19PG7umqJNSVzKh/PHMyTkaur7B7B0zxzGjVnH9C97s9DNn99HLuRwvyEcqws5Q/vQcvYadoeYGbpoC5w7DnWaMvz9cKQNfn0sGTL2Qe1gbQdnnfWLJ3P06HpGjPkDtv0AjaO1d1d6+hfJ88+uMRFxWNIrWRKenMSdL7YjxDOXV57Zx/mk3/BuNQjhafSUWTP2UJCdgWez7qTvXYpn7WD864eTffgffJv2YPaoMLrFmzj35oN0uVGbTL35r7n4PfJysXJxxwuRND8Br32caN9iIfWlUJrINA78Zy25NxV/F2jTDWvwq6PNCbNk7Oe7r/oypPn11L/lU7ufrM3fYPJviG/YYKM8xm9Evt4UHtrIuS3fc37zBzR8Np2fro1kTxPB07ONesF6OoVzfz5F7VFzICsNLDkQ0EJ7obPJjbW39Cdwlybua0+sxW8Fx3jooZ1s6N4Jkw3a/LWAtL43sS1EcNsv6zj5fnvqPxLPifci2O0m6PNkGt+/2xIfaz5+gZ3ZsGYPJ2IKeO9RY+h+xy9PkH/+BB3v/BrOnwKv2lCQy/FfnqDBTa/x6pSu3PKrG7kfP0JUu0Fw9ijmFr1JjtDq8EkPmHhvpo2vX+rD1JHGCuiYL9oRnZvPV+N3kfNyXWxDP+fw7Vrvcm5db7xOa0NorbZuYsqk7pysDTMe+57cmX0Q3R5n8c7fmSGy+XrwdILD+7N20TTeSPyGiT71GDxxJbas46T/9DAN/vM5yTHauw1tn09j//oHkR51uemRRGxbvsbUfoT2ntM9SyF6JJYpjbDacjnS6QUKnih5xfYnd9i42ussx9zcuHvifva312THX/dGMPiLJFb08yGnVQZrbH4seDqxylbb2d9G7OwDJALNS/qUdm1ZH6ApsAQ4BNTT3YYAi9HEWXdgo+4eAOzXv+vqx3X1c5uAHvo1i4HryhN/p06dpCvx4mtdZFJomPz0saXyi//7Va6d86n87qUN8ov/rpbSZivi961xbWVSaJhc/MG9ZYY78+ZwmRQaJndv/fVymX7ZOHcmVy75fIec/uAyOf3B5fbPJw+vLNf1u3YvkifTd5XtMT9Hvnt7uLznmQi7U1JomP2z4qupF5mCssnJOi4t+eeLxFkRCq/Zueobp+dHPxchk0LD5KxP73R6fvak3vYwtq39qNj5vZuXSPlCLbl9SvcSbXjp3nD58S3hF2X34aOxMu9onEz4qHOR83MnXSeTQsPkN7eGaeU/60SFwneMY/umJXa3Q/vWy6TQMPl/EyPk2u/HysdebiPzMlOdXj/v+REyKTRM/npDlPMIrAXS9kItad29TP9tlVJKmZ95VJ45/E9x//tWSllg0fydS9fcbDYpszOklFI+/UorOe/9HnLp/Jflsu5hctorXYpeb7MVqwsqwpwhWl3wycTecseqaXLxnGHaiY0zpUyNv+hwT+9bJRe90ciefimlXLb8R6flOWXZmzJp9vAibhmvhkn5Qi2ZcSRZPjm1q0wKDZMfjNe+N0VX7P8gpZTjn4iQj0yKKPH8X9Mayn8+6luhMH/7ZpxMCg2TT73YWktn3jkppZRXvxMpe0+PlNKSK1/6oLlM+Lh30QttNiktudpx1gntXlutMnvObVLmZlXIhiPfjZa/vNlY2vTyUsiExyPk+JdD5fGv7pGbptaXmYe3Fzmf8d0DMnPxlCJuM0dHy6TQMDn3ldvlbVMi5MTHI6S02eSCNxvJuNfqa55OH5bSZpMFaUny3E8TtLIrpZSZR6XtxbrSmrKlmI03vxopZ98YLnfsXC9lXraU+TklpseWc0aeXDBZygJLkfo2KTRMvvViCzn+Ca3uWr9ompTnT0l5Nk1KKeXQ1yLlVe9FygPrvpZj3msjd/44UWYvfErKF2pJtC2ULlqXVORTlsDZetkihp/QVuwddBBPnwKjHPzsAhoDo4BPHdw/1d0aAzsd3Iv4K+3jauKpIuQX5MmFC16UVpu1TL9Wm1UezNhTBVZdHlbOSS4inD57bJX84sk1lR/R4Y32ClHKouJpyWUUT45siAmTr4y5OBFyLOlvp+etNqtcd2CZtJXQ6M57ZkSpom33Jk08JZYinlKObpK7DjmPvyy7S6JQPP1wW4cKhessjv2JG4u4//X9LfJkymatMShBOEkp5dzJWt78MqT9RdtQIbJOSJmfI22WfPncV73kqYy9lRr81yM18TT7voGVGq4zjqTukh/eFi7HvFq2+LEk/S4tb7SV0pIrCwoscnn8LLln8Q8yKTRMbnhydIXjPrhxlty/bkbJHgrypbQWVCzQ86flnlcCpeXg+qLuB9ZImZpghHs5sdmcxrHrj+fk8e0LpbTkaQK9HGz+7n7Z57MwuXfdFzJlSl255bV62okTO6U8k3LRJqZ+OVQufqORtJ0/VaHrbn05Uj7wZIRsN7udnDSjpTw7tZk89nJduWRaQ5l/ZGsRv7ZNn0u5b5V2D9d/pIk0naoUT2W9nqXsiT8XgRDiJuColDJBFB3mCAKOOPxO0d1Kc09x4v6vxt3swQ03vlAuvyZhornDSp+aRk5WPu36BRHZpwmJa1I5n5nHdeMuabTYOQ4rWC7Ekp9T4rnK5L5JbnTILX3Cc0n4N2zl1N0kTPQMGej0HFB0wYETrIXT6Up5i29Qky5l2ledNApuWuT3oJE/GT9qNS7xOqvUV35V1SpoP20isACm3LW20oM/1qMB6fuOc7pb+Vc3XSzBDVrQvUM6t/cre5NZt/AhEK7N+TMDA6LvhWg4+1EAYX2crIMvg+Zd7y3dQwn7spWKdx1aP5te3D3E2EbhosKtCEI4jaPtdcakflr2L1dQnYe/w+rYL6D7XRDSgaBMvXmtH3pJJjYe/RONz6WBd90KXfddcAhuh9aA/nocHkvCf84tNDqZDI2KzlMUXcYaP7qPvyR7L4VSxZOUciKAEMITuAUIcbxGSlniG22FEMuARk5OPQs8Azhbq+pswoi8CPeSbHoAeACgWbNmJXlTuBCOQqnfqEv7Y1eEeX1MjFyjr1rML/3lxJXF34dS8K3gpv1/txP02yHxqd2wbM/OKEM8tainzX1pGlB8+byr88pYdwKPWfnA31k1VDb5jbQldlta2Cj+atiax+MP/MYMn4E8fNecsj1fKmZ3Oj91aTu51hpQiuhXXBqeftBLfw1OkxjtUxmY3bS5eBXEbcwiTv8wgbpJczg78A1q1Q6CCRsqx6bLRHlfDDwfyATigLwy/AIgpbzambsQoj3QAijsdQoGtgghuqL1HDk+JgYDqbp7/wvcV+nuwU78l2TTTGAmaBPGy5MOxZXJiG6BzG6UxrDfzQRENC37gkogwFbxzeKiOmbwU08fnjdf3Du+LT6lL/P11Dds9Os+plR/rsjn45dx5tSeEifxl8VNI5/ntvxhvHdd+TdRdWXcvOvw6P1x1W2GQuGUum16QNIcajUt/yKm6qS8NW6wlLL0ZS7lREq5HbCv8RVCHAQ6S2213QJgohDie6AbkCmlPCaEWAK8JoQo7AscDDwtpTwlhMgSQnQHNgJ3AR+iUFwi7e5bzQubZ7G09vPcFHNj2RdUBsM/g0btK3RJz/8eomfumYuOMvza/uxYm8BPvcQF743X8WsAL2Y6O+PyePs1wNvPyZ4F5SQwsDXLJ+2oRIsUCkWJxNwBzXtCQMuy/boA5RVP/wgh2uvC53LyB9qKu71oWxWMAdBF0hSgcHvnl6WUp/Tj8RhbFSzWPwrFJePTZSxDO96tdUVXBVHFl2mXibsXuF/csBRAx84PsO+pg0yLuvuiw1AoFIpLRogaI5yg/OKpN3CPEOIA2rBdpezzhBZIiMOxBCaU4O8LoNgbeKWUsUDN6OdT1DyqSjhVF0Jw61VTq9sKJ6iXqSoUCtelvC3DdWV7USgUCoVCofj3U6p4EkL4SSnPSSkPleWn8k1TKBQKhUKhcD3KerfdfCHE20KIvkII+379QoiWQoix+kTuSplIrlAoFAqFQlETKGufp4FCiCHAg0AvfbVbAdrO378Dd0sp0y6/mQqFQqFQKBSuQZlznqSUf6CtglMoFAqFQqG44ilr2E6hUCgUCoVC4YASTwqFwnVR7wFQKBQuSKniSQjxhxAipGpMUSgUCoVCoXB9yup5mg38JYR4VghxmV8ZrVAoFDpqj0yFQuHClLXa7gchxO/AZCBWCPENYHM4/85ltk+hUCgUCoXCpSjPDuMWIBvwBPxxEE8KhUKhUCgUVxpl7TB+LfAOsADoKKU8XyVWKRQKBajhO4VC4ZKU1fP0LHCrlDKxKoxRKBQKhUKhcHXKmvPUp6oMUSgUCjtqiwKFQuHCqH2eFAqFQqFQKCpAeSaMKxQKRaUyv7vAYobnq9sQhUKhuAiUeFIoFFXOt1eZgbLFk1DDdwqFwgVRw3YKhcL1UKvsFAqFC6PEk0KhUCgUCkUFUOJJoVBUC162svfbVaN2CoXCFalW8SSEeEIIIYUQ9fTfdwghtumff4QQ0Q5+rxVC7BJC7BVCPOXg3kIIsVEIsUcIMU8I4VEdaVEoFOVn88EjrDl8tLrNUCgUioui2sSTEKIpMAg47OB8AOgnpYwCpgAzdb9mYAZwHRABjBJCROjXTAPelVK2AU4DY6smBQqF4mLxkhIvqfqVFApFzaQ6e57eBZ7EoWdeSvmPlPK0/nMDEKwfdwX2Sin3Synzge+BoUIIAQwAftL9fQUMqwrjFQqFQqFQXJlUi3gSQtwEHJVSJpTibSywWD8OAo44nEvR3QKBM1LKggvcFQqFQqFQKC4Ll22fJyHEMqCRk1PPAs8Ag0u59io08dS70MmJN1mKe0nhPgA8ANCsWbOSvCkUCoVCoVCUyGUTT1LKq525CyHaAy2ABG3UjWBgixCiq5QyTQgRBXwOXCelzNAvSwGaOgQTDKQC6UAdIYSb3vtU6F6STTPR51F17txZTbhQKBQKhUJRYap82E5KuV1K2UBKGSKlDEETRh114dQM+AUYLaXc7XDZZqCNvrLOA7gdWCCllMBKYITu725gfpUlRqFQXFbUXpkKhcIVcbV9niajzWP6SAgRL4SIBdB7lSYCS4Bk4AcpZaJ+zf+Ax4UQe/VrZ1W92QqFojKRQskmhULhulT7u+303qfC4/uA+0rw9wfwhxP3/Wir8RQKhUKhUCguO9UunhQKxRXII/EgXK3jW6FQKMqHEk8KhaLqCWhR6mk1aKdQKFwZ9einUCgUCoVCUQGUeFIoFC6H2kdEoVC4Mko8KRQKhUKhUFQAJZ4UCoVCoVAoKoASTwqFQqFQKBQVQIknhULhckiTtt7OqmoohULhgqitChQKhctxJKoBh0/s43gXP0ZWtzEKhUJxAeq5TqFQuBzSbOLbAWYsnqqKUigUroeqmRQKhctRz88TAF9P1TmuUChcDyWeFAqFy9GpTkMAevoEVLMlCoVCURwlnhQKhcvRwSeIvw4f5SaPRtVtikKhUBRD9YkrFArXo1lPGlutEDG0ui1RKBSKYijxpFAoXI8GYfBiZnVboVAoFE5Rw3YKhUKhUCgUFUCJJ4VCoVAoFIoKoMSTQqFQKBQKRQUQUsrqtqFaEEJkAbuq245/EfWA9Oo24l+AysfKR+Vp5aDysXJR+Vn5hEop/asioit5wvguKWXn6jbi34IQIlbl56Wj8rHyUXlaOah8rFxUflY+QojYqopLDdspFAqFQqFQVAAlnhQKhUKhUCgqwJUsnmZWtwH/MlR+Vg4qHysflaeVg8rHykXlZ+VTZXl6xU4YVygUCoVCobgYruSeJ4VCoVAoFIoKU2PEkxCiqRBipRAiWQiRKIR4VHcPEEIsFULs0b/r6u5hQoj1Qog8IcQTF4T1mB7GDiHEXCGEVwlx3q2Hu0cIcbeD+59CiAQ9jE+EEObLmfbLgSvlp8P5BUKIHZcjvZcLV8pHIcQqIcQuIUS8/mlwOdN+uXCxPPUQQswUQuwWQuwUQtxyOdNembhKPgoh/B3KZLwQIl0I8d7lTn9l4yr5qbuPEkJsF0JsE1p7VO9ypv1y4WJ5OlLPz0QhxBtlGi+lrBEfoDHQUT/2B3YDEcAbwFO6+1PANP24AdAFeBV4wiGcIOAA4K3//gG4x0l8AcB+/buuflxXP1dL/xbAz8Dt1Z0/NTk/9fPDge+AHdWdNzU1H4FVQOfqzpN/WZ6+BLyiH5uAetWdPzUxHy/wFwf0re78qan5ibbF0InCsqjH/2J1508Nz9NA4DBQX/f3FTCwNNtrTM+TlPKYlHKLfpwFJKNl2FC0hKJ/D9P9nJBSbgYsToJzA7yFEG6AD5DqxM81wFIp5Skp5WlgKXCtHvZZh3A8gBo3ccyV8lMI4Qc8DrxSScmrMlwpH/8tuFie3gtM1eOxSSlrzKaGLpaPAAgh2qA1gGsuMXlVjgvlp9A/vkIIAdQq4XqXx4XytCWwW0p5Uve3DCi1l7nGiCdHhBAhQAdgI9BQSnkMtBuB9scsESnlUeAtNJV5DMiUUv7lxGsQcMThd4ruVmjDEjT1nwX8dJFJcQlcID+nAG8D5y86ES6AC+QjwJf60MjzesVao6nOPBVC1NF/TxFCbBFC/CiEaHgJyak2XKRsAowC5kn98b6mUp35KaW0AOOB7WgCIQKYdQnJcQmquYzuBcKEECG6+BoGNC0tzhonnvReip+BSQ49QBW5vi6aqm0BNEFT73c68+rEzf6Hl1Jeg9bl6AkMqKgdrkJ156cQIgZoLaX8taJxuxLVnY/69x1SyvZAH/0zuqJ2uBIukKduQDCwTkrZEViPVkHXKFwgHx25HZhbURtcierOTyGEO5p46qBfvw14uqJ2uBLVnad6L9R4YB5ar+hBoKC0OGuUeNILzc/At1LKX3Tn40KIxvr5xmi9QaVxNXBASnlSV/C/AD2FEN0cJjTehKZIHZVnMBd0A0opc4EFaDetxuEi+dkD6CSEOAisBdoKIVZVTgqrBhfJx8Knr8Lu7++ArpWTwqrHRfI0A603tFDY/wh0rITkVRkuko+FtkQDblLKuEpJXDXgIvkZAyCl3Kf34P0A9KykJFY5LpKnSCkXSim7SSl7oL33dk9pEdYY8aQPQcwCkqWU7zicWgAUzpi/G5hfRlCHge5CCB89zIF6mBullDH6ZwGwBBgshKirq9rBwBIhhJ/DTXUDhgA7KyudVYWr5KeU8mMpZRMpZQjQG23cuX9lpfNy4yr5KIRwE/qKG70yugGoUSsXC3GVPNUbpoVAfz28gUBSJSSxSnCVfHQIZxQ1uNfJhfLzKBAhhKivhzcIba5QjcOF8hShr07W3R8CPi81RukCM+7L80FrWCVaF2W8/hmCNkt+OZpKXA4E6P4boanMs8AZ/bhwldxLaIJnB/AN4FlCnPeijYXuBcbobg2BzbodicCHaE9T1Z5HNTE/LzgfQs1bbecS+Qj4oq1iKiyX7wPm6s6fmpynuntzYLVuy3KgWXXnT03MR/3cfiCsuvPl35CfwDg0wbQNTeAHVnf+/AvydC7aw1ES5VhBr3YYVygUCoVCoagANWbYTqFQKBQKhcIVUOJJoVAoFAqFogIo8aRQKBQKhUJRAdyq24Dqol69ejIkJKS6zVAoFAqFQlEJxMXFpUsp65ft89K5YsVTSEgIsbGx1W2GQqFQKBSVgsViISUlhdzc3Oo25bLi5eVFcHAw7u7uRdyFEIeqyoYrVjwpFAqFQvFvIiUlBX9/f0JCQhA1/+1MTpFSkpGRQUpKCi1atKg2O6pszpMQwiyE2CqEWKT/FkKIV4UQu4UQyUKIRy7w30UIYRVCjHBwu1sIsUf/3O3g3kkIsV0IsVcI8YH4t5YahUKhuBKQEsvSGcj8Gv26yyonNzeXwMDAf61wAhBCEBgYWO29a1U5YfxRiu6Ceg/aNulhUspw4PvCE0IIMzANh91phRABwAtAN7TXTryg7wQK8DHwANBG//yr3jKvUCgUVxI5f85i78PTOf3SPdVtSo3j3yycCnGFNFaJeBJCBAPXU3S78/HAy1JKG4CU0vHdNQ+jvevG0e0aYKmU8pTUXuK3FLhWf1VKLSnleqnt+Pk12huRFQqFQlEDyT94GICc3all+FQoqoeq6nl6D3gSsDm4tQJGCiFihRCLhRBtAIQQQcDNwCcXhBEEHHH4naK7BenHF7orFAqFogZyMu8cACes+dVsiULhnMsunoQQNwAnZPE3aXsCuVLKzsBnwBe6+3vA/6SU1guDchK8LMXdmS0P6GIt9uTJk+VOg0KhUCiqjq2njgFw0PrvXjX2b2fixIk0b968VD9jx47l999/Z+vWrTz11FNVZNmlUxWr7XoBNwkhhgBeQC0hxBy0HqKfdT+/Al/qx52B7/UxzXrAECFEge6/v0O4wcAq3T34Anenfb1SypnATIDOnTurl/opFAqFK+ICc1oUl8aBAwdYtWoV+fn5ZGVl4e/v79RffHw8L730EsHBwXTo0KGKrbx4LnvPk5TyaSllsJQyBLgdWCGlvBP4DRige+sH7Nb9t5BShuj+fwIeklL+hjZ5fLAQoq4+UXwwsERKeQzIEkJ011fZ3QXMv9zpUigUCsVlRj3i1lheeOEFnnvuOSIiIkhMTLS77969m969e9O+fXveffdd0tLSCA4O5s4772TVqlXVZ3AFqc59nl4HvhVCPAacA+4rzbOU8pQQYgqwWXd6WUp5Sj8eD8wGvIHF+kehUCgUNRHV83TJvLQwkaTUs5UaZkSTWrxwY2SZ/hITE9mxYwdfffUVa9euJTExke7du1NQUMCdd97J9OnT6dq1Kw899BBhYWEAbNu2jaioqEq193JSpeJJSrkKbagNKeUZtBV4pfm/54LfX2DMjXJ0jwXaVZKZCoVCoVAoLpJnn32WKVOmIIQgPDycHTt2APDLL78QHh5O165dAYiMjMTb25v8/Hyys7MJCAggOzubhx56CA8PD/r3788dd9xRnUkpEbXDuEKhUChcClG4DkgN21005ekhuhxs3LiRJUuWEB8fz4QJE8jNzbX3KG3bto1OnTrZ/cbFxdG/f3+SkpIIDw8HNIE1YsQIbrzxRkaOHOmy4qkqN8lUKBQKhaJM7JpJKPVU03jmmWdYtGgRBw8e5ODBgyQkJNh7ngIDA+3HcXFxzJ07l5iYGBISEoiOjga0V8w0bdoUALPZXD2JKAdKPCkUCoXCpbBPeVLaqUaxdOlS8vLyGDhwoN2tYcOGZGdnc+rUKUaPHk18fDwxMTG88cYb1KlTh/Dw8CLiKTg4mJQUbetGm83mNB5XQA3bKRQKhcKlEE6371O4OoMGDWLQoEHF3DMzM+3HmzZtKnZ+1apVPProowAMHz6ciRMn8vvvv3PjjTdePmMvESWeFAqFQuGaKA31ryY7O5s+ffowaNAg+2aavr6+fPnll2VcWf0o8aRQKBQK10QN2/2r8fX1ZcuWLdVtxkWh5jwpFAqFwqWQap8nhYujxJNCoVAoXIrCOU9KQilcFSWeFAqFQuGSqFE7hauixFMNRGaf4eT4AViPJle3KQqFQlHpqFE7haujxJMLcjgpg4/Gr+DwzlNOz8dOf5r0lcfYPXA4uzenMWPcCnbHHS8z3JNHsvhs0t+cTMmqbJMVCoWi0lF7ZCpcFSWeLoKyBEtZ4qek6/MteUgpWTJzB1LCkk+2O73+h8w4+/Hy2Una9xeJTv068uen28nPtfJnCeGWx8bqwtXsUSgUlxPV9aRwba5Y8ZS7I5HksHCSw8LZ26M124a2YGdMWwoOJBD33xtIf20cALazp4td60ywWA7sJC9+DQBLPtuOlJI/P4q3nz+T8De77ulX4vVHtv3JZxPX8tH4leTnWgHIz7UyY9wKZjy4nJzFszg3fxY5a37nvC2PFf2ms6L/DGyaV2xWNL/jVhSx1VZQwIwHlzNj3ArOpucCcDY91x6utNnY/fi1ZG9bhcxKJ3vu5BJtdKQiYqYsMVnWeaRk6axtlWZPef2eST/E30M6kX2mcnr1sjPz+PXtOLIz88oMr9xISVK7MI5Nn1B5YSoUCoWiVISUV2a/aDsvb/ljSEiFrlnRb7rzwXgpGfD3xEs6HyAncYq3wORe/LzNwoDVk4o4pdWLISniXhAmLUwpQVqJTPyCI+GxtD3oTa2UAg6GmNnf/P0S4+3k9hy1l58B4HhrK4lBH5cw4UAy4RNjy/2PJ6zAZgWTGcbPGODEP+TEb8A7pjufP/43eeetePqYueu1LphMZtw8PO3+HM/f906/ImFcKAbLY0+fziuIuON5zhxI4GzSJkJunljkyrJs3705jaWzkqh3bg5RsesBCN/pfH7ZsUMJ1G8cxo9Tt3Lq2HkQcNuzXagf7F/M76IZCRzankHz9oHcMCHaaZyD7o+kbaeGJaS5uF+vxF/Ifu0D7cT0R9j8/oeM+HQ+Pk3aALBr8zGWzUri6nvDCWktSHx1PDFTf8Dk4WEP70x2Bp7CHW+fWqXGWxLJ2/4k7UgSV13/+EVdr6g6/rm2NW5e7nT9zbXnS/46ZQxh325gV2s3hi0qu6dcoZGcnGx/we6/HWdpFULESSk7V0X8V7x42hAq6L6rfHlQmmBpmJHAyr7vI03F9x0VtgKuWv1omdfnedRiXbeXweRmnLcV0GvjZDzzzxYLd0Wf9zS/hdgsDFjzWDF/Wb5BbO70JAhzkXi7xE3DPzu1QmksSyDm+trwytY6NEvze//YXD77wouSuucnfDKAbX/O5MTGdHYd6wLy4uwBcOvciOO70kjoWIJfoF7YV1gOHObO91faxRXSwoC/NdG6pW9tOq7ORDzUH/nRKgDqzv+cn9/JLTHMCZ8MYP0744nfews2W/FOXrObibFvdSU/L4evn0koU4wW4ij+2p99jsDNWu/o0QAIuqDzbkX/90GawWYhMvExGp6Cww233ZZQAAAgAElEQVQg8D+3cG7OLzT/ZBV/fJBATMKHdFgyi9hr+tLys48IjrrKadxnMo/j41MbD3cvNs+4n5gx77C3Y1cA8r+dSnSnYaXaXl4qIiZPHsnit7e3MOyJjsVEa0F2FmZvX4Tp4jrZ5w8IJ7mNJ099Gl+2ZxdC2mycXD6HBoPusrvZbDZ2RUQCJT8MuAqF4mlnazduVuKp3LiaeJo4cSILFy7k0KFDJfoZO3Ysw4cPp0mTJsybN4/XX3+9XGFXt3i6Yoft8ps35Zy3oOObi/j53U8YfdtDhCVrQ1XZnrDzKaMRyNdf7Hyo4X6Q2osK7aJT2miYkQBAzw3Pg9WiNfCaJ7Ba6LnheWxAaq0E+/U4ud4z/yzSrN8Sm0X7FiaSm52z27K/kUMiTCaEzULdEysQNgsIM9/2L35L/bOPYhcpdrEsigkngEbp8aXaGJH4uTZG6JhGWwGROz4DsAsngOiEDzAV5BXxayrIJSb+ffbd9QTR8SWc3/oeyWHhuE96l6C534D14u0BKIhNIzCrFL/bZxL1ySYyc59jxrgV9qFQhDsr+s9gRb/pdFytvZupUDgBnB56H11ipzrkaVFmPLicOjNXYbM6F1dWi5WZj65n9v8SnAy/Luf8yYMsfGYYCZHhHFm3kBnjlhexz2aFBN9XNAFJUeFUOKwLuhA3e5AYpaWl2Qnwfe9nGqZL/nxvAwgT2yPv41D3vtTPhKzbHmJbZJh9WHvuqCh+/XQSq8Zfw7Fu/dnXvgML7uqB34dr7cIJwOOOp+3XZCXHMufp69nw9zfkF+Tx9dNDyEpP5XDcMvYdLrqj8NY1u5nx4HIS1u1hZXft+mVfJgGSpZ9p9zkzLxOLzYLVZuW1aTdx9uQR+/V/fqLP5ftYa2TXxP5MTv551sT9zJ5OXZlz8x3MGLecVb1ux1ZQYLcxvyCP5LBwVrz3ELn7t/Hr/JfZuTHVPqSbcmgbbVNh6N957PnpS7b8+RlJ6w6VOd9xxrgVzJ2ysczh2TKHqy+BhcM7k/HwVFa++ZDdbfuedZUeT2lk5mVy1slDn+LK4MCBA6xatYr8/HyyskqezhAfH090dDTt20cwdeprVWjhpVFl4kkIYRZCbBVCLNJ/CyHEq0KI3UKIZCHEI7r7HUKIbfrnHyFEtEMY1wohdgkh9gohnnJwbyGE2CiE2COEmCeE8ChuQVFq+deiy9Ykotu25Lnr+hH78sMIIQjfmUznhGSu/8/LJLYyMfeqSLYOjwEg/LHnQWiCpV3fhiAtYDLTNCGO+Vc3xv+v+WB/wrVpesVkImbbRoK3bGPUokQwmxE2C7WC0sEE0uxGaFKS3a6AZhCUupoG6e/jm7mGehnbOR/ellXjbuVwa8GQlYbfBz7qz8mgz+j+8cPUdf+AAasfYdgdb3IiQDvfIn4LqyMFq25rg8WUhW92Kv7pX+KTnYqHJYulY7rbwzr9vdYAL+vsDmY3kBZCutYCoXVxbP3iR0ATVzZTyWLGkcAzuxG2giJ+ha2AgMw95TpvR8/z4COGSCykLLHnyKUKQ2f4Zx/FK+ek3iNmXOeVk06XWK0iiE74AGwFxcKNiX+/FEH3GYf6XEfrX3bhYYVzY58kYkf57SsrLfY5c25+IARWDz+7UARwdxB8MVsthL27hIYrD9vd2mw6U2KeAKTcPJpOv+6n9oOvsa9dDF1+PUBK74Fk3/Ew+YPvsAuY5LBw/vl6PwBrZ+8jKVqzS7tNAnBjxrgVzHkklr0RUeyOaMfNX+7haJ/Bxly+DH0uX4Y2l2/bZ3U4GNWJenc8p7k3uBskJEbcw6527e027mun/a8bf7KSA0NGEva/uSz/fJs2x+7TrSx7wxjuLXjuDbwnvcPK2cmAZOmsRLIyjrE5bgGLNnzNvriVJIeFs+gdTaCcOnqO9fN3cWB3LN++eic2aePPv79g8jvGi07/+lxbGPLXTOc9K1JKzpxJIzksnJ/f/ZAZ41bw5WtTkVKSb80n25JtF2Db16Tw6SMrmfnoKk6mZNFmZw4Ahzes4ev/dOLrm9tzeOsqADLqtGX6g0tZ1+0mjh7dxf+3d9/xUVXp48c/z5QUQgIkhBqqtNCbiBog0kG/iiu7Cy6o2BHFsu6CbS3gWn6rrAri6uoK6qIuoougIspGRFSkBEjoQqgiEFpIn5nz++PeTHqDVHjer9e8Mjn33HPPPZmZPHPOueeePPkrb/7nEYwxLLyuB1/cFlvsvMBj6cc4ln7M//vR/Sm8fm8cH/51TaFg8VCP/hzsfkn+18X+Lcx/+g/4ct6H5MxB/JqVi1ey61ACtz/UDS9ezlVFzDHMO5fxvvu78Oyc68+5XheKxx9/nEcffZTOnTuTmJg7V3XHjh3ExMTQrVs3Zs2axeHDh2nUKIKJY65j8Xvz8BkftWFErCrvbXcvsBXImVhxE9AC6GSM8YlIIzt9DzDIGHNCREYBrwOXiIgTmAMMAw4AP4nIYmPMFuA5YJYx5n0ReQ24BZh7LpUNCHAzdmkiY7G6uw/cd5roiPoc3vYXTu3dRuwfPqTPb9oS5A7A7XIxfbY1N6dRi0TcP66k+8wb2b8lmzT7jVuvjjWXacprQzmVnk294Pxzm9aNvxi3uw7XPzyMRa+tIubqucQvmkvr2R8QMP0fXHTxQLjvKQCOz34VV1h93E43jz/xEQAtXv6Unw8coW/rpmyf25Cje3cSHRTMHR9Zwdau7RtIv/Z6fp35Z5I+nU3M96dp8Mh/WOp8h3pBIYztOYStSz7hthZtCQrMU7dbcp/mdPQ7xA0mm5Y967AvPhXEScKINnRdtgeAow9OIvJv1o0dvYFBuDJTCU/+kiORw/E5A9gxdQQdXl4GgC8oBFfGGVrt+5KfLxqBzxnArjZO2u2xPjyT7rmGwa9M9dehw+5FHMszNWfr8OaQYQVXF/WP4OcfkjHiZPOEi+n27k/s6hSAa+hQWs/+jGZr1sCDPyC+bDrENGbHql8xzty3QJNj8WwxPmvIMk9wtaX3Nhovh9PBsOemIfSY+zWHpt+I9615tDgCpmFjJCUTk2e+mifEyYHwQ0Snwt72+63V/oR8wUxOkFjUMYsL/ioqb4+NL7O56534nAH+4VCHN5Pum18rVFZZHG/oI/xY+b6LFRpyddrfeXLqW2CYtryKKn9F7Jx8Q7rF5pUAUr1PsGJQMfMVfTD/ka1gQhj8zTS+GDQbYufkKVHYvvoY21eDwzuR7dFdaAW0AuZs/zpfWZlp1oUhOfWaf1Nrbng7KV/9Dm9pDw5D2p5ebIvu7E//5rLn8LlD+Oa9bdaq3AaWTP8vOV+L+iV6APsLyhP/BmBz51sRHGzqchvhQ8ZwGrgM2PbYIqxBvV9ZTjzG6eaLN+KJO7aduO//wYNjXyV4rHUhTVjiRhb89DaeJe3JzjQc3XeG1R/vYthNXfD4PHhNbvDzyr//ieObVgRc9B0B331Iv+2GD523cfDUfpy//Io7+EUwsPnTNDYsuJP7NxiWXbyeLsBph9c/hMvQA0wZewMJxxJoGdaSsIAwvkj6ghGtRuA1XhZu+Q+/7zKO9UfWcyTtCN6lTTm08xSL3/yJ8Q/EFPka8RkfDrFet4t/XsxVba/Ca7x89v2HXH3Z9Xz2RjxZGV6Wv5nIHZ/7gA1Q4NoMr8+L0+Ek25uN13gJcgUVeawcqacy+fKfCQy/tSsh9QJLzFuUkoap8/l8OhzOH5gbe9lROdurGZt0g1GlD6slJiaSkJDAvHnzWLVqFYmJifTv3x+Px8OECROYPXs2/fr146677qJTp04c+2U3CTt20LdNB/Ye2oLTFUjTBm05/ssZ6jUOxh3gJNuXXWrbVqUqmfMkIlHAPOBp4AFjzFUisga43hizq4T9GgAJxpjmInIp8IQxZoS97SE727PAUaCJMcZTMF9x+vbta9auXXvuJ1cLebKyOLJzC8269CzXfttWfErdRs2J6trbn/b1n35Hs083c/LhSdT/qxUwddq6xf8B32zlcn66fgT17ribg8eTCHxvMRfP+5ijo64FoGNiAp9Mv5qmfYZwYMlCuq49wYkwQ4PT1ps78qvPODp0NAA/PDCU/i9+xb5IaHD37WS//Brt3vqQLX++iSbbM4jetpVvZ02jYYuLaD38t6y6fRQdp71Iy16X+eu7bsk7ONwB9Brxe3/ayf27+WXYlbRc/S1vPxwPXg9RB7/lYPMBGHEy7rE2bJ5wJfXvuoOevy96UvQbkxaBnCF2ygjWLk0iIzWbQdd52L34LYbOmMerty/D6c2i17Ud2fDxdrzOAFo0+hftPtrEithXCh1z8MqpNPthNYf6X0ZKPcPR+gG03ZvNikEvg89L1MFvOdByEPiEwSun0mbdGtYP70/H/3zGkcEjAVgROxvxZtP84LccbD0E4/ESvetujjQIpkdiJt9c/jxeVx3/OYgvjY6e2fR5+nV+GTySPV3rErE3lbAU6zMiY9rtBD73Om1+XM3n18fgcgfQYPT/0fDF/xC88G1EnPzw5G3EzHiX09eMBeDg3dfQfPZ/Adh8cUO6/XSMfRc5afmzt9j5ddbnem7v4tnO5Stt/l5eJeU9NSiMsJUpJZaVXL8Dm7reickJRgGMof6JbXTZNj/fnMXk+h2KDVwL9rqWNKev1JUkCwSJZZkfWJY8JeYDHN5sYr/NvcDlm8uew+sOwZmdyqDV08pVH4CvB81CcIMvm2Z77qfTASv9SD1odArOBMF3nYUR6w3zhjho4XkRpOgLb3Zd8RFN96fQ8Yt4Yj//gZ8v7s+WlkLWzRO59OH5rI4Wmh8Xfu70crF1677+bvY3EpKvGE5qwpdcucaw//WXOfbqbronvEXU6v/x8MyLiWzQjuj9hiWNfmb6ze9y4tQRPn39AQa1f4cDCSeJ6lKPq+/uzTs/zue63mP59eSvvD3vKR774xu483wRu+/+LtRp0oEZf1rI46ufpN1nw0k9nkVYRBATn76M5PRkRITwoHC2bt1Ku4s6cPJwKiE/PEHQmW048gbqGak4DLiDQ/Dhw/jAm+3D5XbgKDAvMCfQ8vkM3mwfDpcgzbqz45JJdAzviEMcGGMQETK9mQQ4AhARTmSc4KZxNzHxponEjohlwRvvsyVhG7NffZmPFi3k0yWf8s78dwCYM2cOu3fv5i8TJtDr6quJX/45B/bs57k33iA5w8Nbc9/F4MGVdYg6meCK7siu4zsQA5mHfHz72hFk4F6mXH8zcB5OGBeRhcAzQCjwoB08JQMvAtdiBT9TjTE7C+z3IFbP1K0iMhYYaYy51d42EbgEeAL4wRjTzk5vAXxujOlaUp0u5OCpIvm8Xn5duZSmV1ydL31Tn2iyGjrou6zopQW+nTYZ+XkXMQuX+9OObYvn6JjxhDx0PeGDJ3D0s/doc8ejrLqqOy6P0H3BV6z+7SBk3I0MuTX3Q9iXlY43PQ13vYgKOSeTlcm27rmBZVkm1/qyMxGHE3EW3Zm7oVs0QdlWWSun/AZnlz5ccuO9bH77OXre8TifTLuaTkutnrtOWxLB50FcAXgzUnG4g0j48kNc9z9F8Kv/j5R7HsTlFZp8vYTDQ64qVMfkLWs5NP8Zuj37EUc3rcHpcBLetQ+b/vEUUcN/x1dP3kK3H46zYtDfcToNrU9+zL6Gv8frEybPtiaKG68Hcbr44o3HaPXCQvY2E0au2FL4xIqxtZM1kbPBwnmcGHsjAB3i1/HVtN9x+aOvs3+AdaWkf0I7gAgOJ/h8IJ4sK+hrPgDjdDPQ+SJdX1nMts5d/W206k8xbEqbCb6cnirA52XwynsJmv8KGTfcU/iiCvEwOO4+mi16m0O/uQmAZks+5tBV11p5nS789wSxg7aM+fMIuuFGe7s7zxw3D4O/uY+tHSB6B3xz2fN4A+qQ954izQ59S6edH/h/PxZhaJgsVl53buBaMLDIUVxQ1zLpCw60HJYvAMsbtAVknqTH5lfzzWss7YKWko5XMOBMCWnOT30fKjLAEF82V6y8r9Tg6FwvTinO2e6XV0pIczZ2u5OswAb+ugVlJNMt4R9FzhWN73I7xxt2J/zYRnomFj3MHzfg7/ichYM6hzeb1tvvI/4iF2O+9/DsWAdpQcKEFV5WRzu46WtriPPrQbORIs7LYEhLu4fGf3yadmfCaNuiJ05jHcc4vEQ0DyUtO43QgFAy7OGzQw2CaHYig5TQFoBgMDSIqkNGVjppmWlEhkWSun0rbi+cDm1h92oa6p7ZjwDJoZDpBqcP6jZoRsieQ6QGOskOjCR+3SZ+M340jSKsz+P0bA+dO3Xh/fc/4tlZTxHlCuL6e+7GlR3Knx6bypChg2lZrw7PvfgK770xB09gSwBumTyRN+e+k+9cQ1Nypw5sPpnFT++cAF82/+r3IJ0OGN57akuVBU+VPmwnIlcBR4wx60QkNs+mQCDDGNNXRH4DvAUMyLPfFViDRjn9rUVfP198elF1uR24HaBly5blPBNVFIfTWShwAui+tuR/tAOeKzyq2rBTTxrmCQLq3mHNWYn5ZK096dnN0K8Kl+sICMYREFzeqhdLAgJJiW1JaNw+mn2+qEz7ONwld7+3+2AenpPJAAyck1tmrykzALj2hc/YutQKOMThAIc1hOUMCgGg26jxMGo8AElPHuTUsoU0aH4RDYoI7CI69yXiWWs4N7J77oTu7ndYa3hd+vvJnPnhaa4Zc5SoUROAkYXK8AeBZ3mfjNRruxH45UYadeyJ75kHMV4vzqA6jHhpSYGcDhwuB10HNSfhm4P4fIYpcwf7g68myQu55MftgBVshcy8B1e9CMThYMALq9k1bRXejOPUS/qArB6TyTyTTegD42jW5wq2g3++nD8QcwX4A01Z+C/SNq8mtPVF9rk6AA9R+1dyoPkAcLrJjm1Kr3798K76ihWPbcfhgManVnK4bgzGDvpMvTDgND6nG3EIIRwmnQi8PjdZAWE47xhJlseH880vCe4UBd8dxOd043L7aLXtv+xtORyPy/p7b+kZSottKYRmwNE//pbAb5dhzZ905gZtDmi373N+6TgaXxZ24ES+CxdcnnTaTe5EvVGzOfDR27S8ayaZvWOsq3mdLrD/GYrDcNkPj3GyvZtT3XtQd9VaHG4nPo/JLddnDffW+3Q+mx+dwtA3l7Gj72UEp/1Kep3Gua8TYwhOP0Lv+L8D9tBwn3vwecR+HRkcHquH7WS4r9Sh5c6J/yw2uCpJj40vs7H7lEI9kj03zi7DK9cSmnoQr8N+T9t1c3izCgVOBQOi45E9WRE7p1DvG4CviMA1J731EWh9xBpenb4wdz5Yh0O5z4t7J4qB/1tjWPFqGO6J4f7ACUB8To7vTwPAnZL7RTbU04iUPCN+gnDygDVPrl7KMbI4RkZoSzLyHleEM6HW/82IPEEMJ602cVIPnwngr397knfe/ICBl8eCCEeOHmHolQNwZDsJD2rExl3buSGrLhsTNvDhhx8wcfz/sXP9Nrp17EhwFqSUMpKZYteBkz/7L4aZtO5lMIb3GFryzhWoKuY8XQ5cLSKjgSAgTETexZq39JGd52PgXzk7iEh34J/AKGNMsp18AGuOVI4o4BBwDKgvIi5jjCdPeiHGmNex5lDRt2/fmj8jrTaryJtTuUqd/1/hLp69BHN0D46mHSqkvNDO/UrPVEatx94BY+846/1bjJoAoyaULfNZ9kz3feZDeNoHDgfNrr2l2Hw3P9qS4CirjQf8rnBbH43I/0295di78v0+6bmc71Z5A/jcZRYGr5yK65XpeO5ZRIfdi/L10IV17U9YV2t2UMNpv2Nc5H62fP8xDeN2EdVuFX1e/oacoM3ZsDlT5jbPKRWAZbcMAyDcHYJj0sUM+nEaXRYVnH9mLzthDEcaPk6Ha++B9GRafTyT4Dvns63zVFod+JpfW1ofxWENI3D7TgNC/ebt6T7/KTZMXoHDAVGuBA5kd8VnXERvSSBu6kqc5gzG68DnDgIxBDgzyDJ1oHlrwm+1/satpj4HgGdgJHgcgFjxik8wCOGjW9PolocIam/1tm6cvAIRHy1Dd7M/rQM+XHRcswpHWATNPlgDQJt3X2bVnBT8a2bYPV8+cRKYdZpfBkfRdMUOXAFusjwe/3ZXSBDdX7yZgDZd2D34ausCGm+e4Na+GGR7OxcddxUIrgR/cBX+7zkcum0y2V3rE3nbdDJufZi0oa3I2LyXiF935Da/fz8IP7WTtZfVp+9q62KHrFAHASm5wQlA4NThLNswusgeorSQpvl+P9HdUWJAZNw+JNsaCvM4zj2ou3jtM8UOU+dsZ8JzhfYTn5c66UfypQWlHyMjqGH+iMwYgjOOlSsPQErdFv7P+2++XUFWVhYDY3Lfg40iG5GWnsaJk8f57W/Gcf2ksQwePYCL2rYjLKwe0e1jWfjR/4jpZL3/Q1J/IbVOk3zHwBhC0g7nr1eebWc7L/JclCt4EhEH0ANoBqQDicaYEpdfNsY8BDxk7x+LNWw3QUSexfpkeQsYBOyw87QEFgETjTF53gX8BLQXkTbAQWAc1pwpIyL/A8YC7wM3Av8tz3kpVZC43EgFBU4XrDKsrZQTOBV0vKWD8H0+vAHOIreXhbtXS4Iv60PzYTeS/MQxsvfuLDZv5KQnAYgZOp7k8HuIuLf0ifP9b3uAX7feQ987/kToJaNKzixCo0nWBR/Uj6TOXe8CEDSiDRnL9tBr+mMk/fsfDJnxL9bfMQY2naJjr4EATJmbs+5X/vW/Js++As/h3Zz68GUi7plV6heWvrOXcPS1TdSpF0iXAc1I/PYQaacyaXnnB/ny5R6veEF9hxHzfTSbutxGk7FX+ss7vTOTji8sJ7phFMbnI27qNwSGuOk9shXrv9hLdpaX0MutZWCit20lmtwh3g67c3tkA3PORRzggO6xUSSsPISPACJvHErDXlfQeN12f1CW/Z+GuDpfyvEvF3Dk/r/icIAjK5VW+75kf7ff4sl2EL1tK8dvzv2n3uMnqyfGm57Kjl7WSE+bO2cxYPgI9g39M3uTfPhw4xAPkYfX0+7njzn65/FEPr8AHND7mffJvP1RtnaYal0daveu4bMCooZvvUbyRCvYb/buLFzX328FIjm9hCL+oK7R3ydz5L65eIN9uDxgsh246mXjaeyBHcF0+HElOy4ZaLeJ5PtSE5p6CF/rOoQmHeQM+YNBADE+HDlL3wDpoS7qpKeTUcTAjcuT7n/u9qSRISXnyQwS6qYeIj0oAq8riEEDBjNoQO4XBhAQ2LV5vz9t2Sf/KxSQ/bD6f9z/u1kAnDh+lEeefo7NiZt4ac4L3Dvlj1Z9wtwERHUkGMjYmzOH0G5HX9EXzlSmMgVPInIRMA0YCuzEmqMUBHQQkTTgH8A8Y0zhv1zxngXeE5H7gTPArXb6X4AI4FV7fNdjjOlrTwa/G1iGNZv0LWNMTj/kNOB9EZkJbADeLEc9lFLFqabb2yf1bE/4vu14nWd//HYLlvmfR4z7Y5n2kaBgGv7xn2XKW6//KOp9t/Oc2qj1//sIM+UHHB2uoMfg3wHQ+9WFeHatJ6Bpq1L3dzVpS8TUv5f5eKPu7O5/Pmh8x/JXuIDuiW8Q/dEDecrLLVMcDv8cOoDew0s/nxyp4XWBEwxeOZWs15+hx8CODPh9Ttl5gju77d3drBkfEaMmEjFqItEAXg9kXA4huXMh+z83nx0Dhuc7ljPYGhqngQNxOOj61XKOvbcN395DOPHixYXTk0Fg1mkahDUjeksCGB843Vzx9X/ZetcKOxjIDRbCT+0ksvcAkgFfgCGi90hCX95L3GI37gAHvYdFsX75QbIzDC0emUjdkVOpM20fgVfdg6NhSzIWPEzg2L8gAcH+QKvuzYMJTMoipEk9+l7ZmrVLk0g/lUZI6zRaLN7Atu69MPbqQw4xGK8X43BhxIEjyIEvw/r3HORwE9SxA6f3WesHBmSlkOWu629LCTSYzJzXtBWoBWSfISsgFEQI6tieM0k7cGUKPocDZ0QIzuzc5fjy/mkK9VsXeK+kpqVyzW9HMqz/JbRs1sz6G9avzwvPvWQNgdcLJPVUJsZnCIhqW6As6D64hTXcb5w0vaoJbN9W8IiVpqw9TzOxLv2/wxSYYW4vMXA9MBHrirpiGWPigDj7+UngyiLy3EpuIFVw22fAZ0Wk7wYqblxEKWWppvVWomgAQFNf+S/lrlLnGFxKQDDSIf9q7o6GUQQ0jDqncms7jzu3x9FZzEUYpXK68gVOAM7IFkVmLXhRSHpKFl0HNvf3qJ1c3RR2QpfYEdYwZZ6rQcUhBAQ58/SuCR2WfYA4XUQ9dhsBHaylCgOG38HkPHFb71H2XDt7aDh40t/824KufyY3o/0aa/HnOfn+Mbb3r7xvrd7dceXnbPvlOI1a5b/NkvFmI87OmKwMsg/swd0kCsThz5eVdJg6vhTcrdvj3Sc4G7XEc+oEnuQUIpsFIi43mLr2eVtcwXUgM51gp4vAJi3JOJIG6R5EBKfbgSfLi8kJxkSoExZA2uksa/0m+yPFnXWakOBQvlr6LaEp+0gPgOAsa1vDPEswBIUUHkJt1CqM5LQg+l3eIc9w/zB4oeq+7JX1VfmSMeaHojYYY44AZf/qo5RSpahrT5iva6pyKTpVY+SJ2Z3uqp/zWKiHblwHyBwPQYXv/1hS71roH6rufo+ORq2R5PRC6WLP35KAIALaFr51S0Dr3KFzV2trpS93cCjuxt7cK1ULxCRBjVvgydqJq6kV5NdrVIfSFFzT6vjP+wk9c5L0ekEEd+0KCQkApAUJFXf5T+Up66p2r1ZqLZRSNVL1DNpBWD3r4zMgtIb3PKlKIXl6PB1n2/NUkUSKDJzOWyL5l/gouNkdgLttF2tY8WwV06ltittQw1yw97ZTSpXOVNOcp3a/u5bGfU7S7vax1XJ8VXXOBNuLsF6TO6fliEnzP48ICa/Q4zUcWI/giKwKLVOVX5B9ZWVwgSsb62QXlRnoIf4AABTcSURBVLvmKWtI31ZEFhe30RhTeKEfpVTtV01znuSiQYS/sBwad6mW46uyaTS86DlE5XG6rlA3HVIvHkXQf61b3LQ5mHs/uvDIyHM+Rl6Rr64k0pNRoWWq8gsMrUNW+mkC69YFwFW/Dp6TabjDSh8CrAnKGjwdBV6ozIoodaELa5VG3Z5tqrsaNUeTEm8SoKpZ9PrvILCEe6uVkeSs/ZlneK6Dw42XTKRBNo6I5sXseZZcAdWydpzKzxnZguCwFP9wqKtZK0SScDapHQtYlzV4SjHGfFOpNVHqAtf8k43grh3fupSiTsUMp4l9iXve4KnVQ9PYfcdTtHpwcoUcQ9VABeaRicOJq/lFJexQs5Q1eEqqzEoopYDg+tVdA6WqXM6sOlee3qDAQeOJ/mkwhDYueielqlmZgidjzG9ynovIZUDrvPsaY+ZXeM2UUkqd91x2z5O74P0hNXBSNVi5rrYTkXeAv2HdrPdi+1EldzBWSil1/on0Wj87NKrguU2q2t199920alXyyvK33HILS5cuZcOGDUyfPr2KanbuyruARl+gc8FVxpVSSqmzEdoqiFMJGbjr1avuqqgKtGfPHuLi4sjKyiIlJYXQ0KIvLoiPj+fJJ58kKiqKXr16VXEtz15513lKAJqUmksppZQqg6avfcxFz96Aq3W36q6KqkCPP/44jz76KJ07dyYxMdGfvmPHDmJiYujWrRuzZs3i8OHDREVFMWHCBOLi4qqvwuVU3p6nhsAWEVkD+Bfi0HWelFJKnQ1p2JqAMQ9VdzXOO8+teY5txyv2Rrmdwjsxrd+0UvMlJiaSkJDAvHnzWLVqFYmJifTv3x+Px8OECROYPXs2/fr146677qJTp04AbNq0ie7du5dScs1R3uDpicqohFJKKaXOD4888ggzZsxARIiOjibBvm/dokWLiI6Opl+/fgB06dKF4OBgsrKySE1NJTw8nE8++YSlS5dy5MgRpkyZwvDhw0s6VLUpU/AkImIsxa71lJOn4qqmlFJKqbNRlh6iyvDjjz+ybNky4uPjmTJlChkZGf4epU2bNtGnTx9/3nXr1hEbG8uWLVuIjrZuWjxmzBjGjBnDiRMnePDBB2ts8FTWOU//E5F7RCTf0p8iEiAig0VkHnBjxVdPKaWUUrXFww8/zJIlS0hKSiIpKYmNGzf6e54iIiL8z9etW8eCBQvo2bMnGzdupEePHvnKmTlzJlOmTKny+pdVWYftRgI3AwtEpA1wEggCnMCXwCxjTHzlVFEppZRSNd3y5cvJzMxkyJAh/rTGjRuTmprK8ePHmThxIqNHj6Znz5507NiR+vXrEx0dzdtvv03//v0BMMYwffp0Ro0aRe/evavrVEpV1kUyM4BXgVdFxI01cTzdGHOyrAcSESewFjhojLlKRASYCfwW8AJzjTEv2+kvAaOBNOAmY8x6u4wbgUftImcaY+bZ6X2At4Fg4DPgXh1CVEopparOsGHDGDZsWKH0U6dO+Z+vWbOm0Pa4uDjuvfdeAF555RW++uorTp06xa5du7jzzjsrr8LnoLwTxjHGZAO/nMWx7gW2Ajk3s7kJaAF0Msb4RKSRnT4KaG8/LgHmApeISDjwONZaUwZYJyKLjTEn7Dy3Az9gBU8jgc/Poo5KKaWUqgKpqakMGDCAYcOG+RfTnDp1KlOnTq3mmpWu3MHT2RCRKOBK4GngATt5MnC9McYHYIw5YqdfA8y3e45+EJH6ItIUiAWWG2OO22UuB0aKSBwQZoz53k6fD4xBgyellFKqxgoJCWH9+vXVXY2zUt5FMs/W34E/A748aRcBvxeRtSLyuYi0t9ObA/vz5Dtgp5WUfqCIdKWUUkqpClem4ElERpSw7bel7HsVcMQYs67ApkAgwxjTF3gDeCtnlyKKMWeRXlRdbreDtbVHjx4tqdpKKSjmnaSUUhe2svY8fSYi/xORonp0Slsa9nLgahFJAt4HBovIu1g9RB/ZeT4GcpYWPYA1FypHFHColPSoItILMca8bozpa4zpGxkZWUq1lVJGoyellCqkrMHTJuDfWHOQCvY0FdXz42eMecgYE2WMaQ2MA1YYYyYAnwCD7WyDgB3288XADWLpD5wyxvwCLAOGi0gDEWkADAeW2dtSRKS/faXeDcB/y3heSqkSmJLf3kopdUEq64RxY4x5Q0S+Ad4TkdHAFGNMGmffsf+sXdb9wBngVjv9M6xlCnZhLVUwya7AcRGZAfxk53sqZ/I41uTzt7GWKvgcnSyuVIVwaOyklFKFlOtqO2PMDhG5FGt9pg0ickM5948D4uznJ7GuwCuYxwBFLitqjHmL3LlRedPXAl3LUxelVOnsi2GVUkrlUdbgyf/90xjjAaaLyBfAAkAnDymllFLqglHW4OnJggnGmDh7Ze87KrZKSqmaQqSqVjNRSqnao0yfjMaYT4pJP2GMebZiq6SUqin0LkdKqbN19913+1cOL84tt9zC0qVL2bBhA9OnT6+imp07/VqplCqBBk9KqfLbs2cPcXFxZGVlkZKSUmy++Ph4evToQa9evXj22drTF6PBk1KqWLpUgVLqbDz++OM8+uijdO7cmcTERH/6jh07iImJoVu3bsyaNYvDhw8TFRXFhAkTiIuLq74Kl1OV3NtOKVU7aeikVO10+K9/JXPrtgotMzC6E00efrjUfImJiSQkJDBv3jxWrVpFYmIi/fv3x+PxMGHCBGbPnk2/fv2466676NSpEwCbNm2ie/fupZRcc2jwpJQqgT1sp1GUUqqMHnnkEWbMmIGIEB0dTUJCAgCLFi0iOjqafv36AdClSxeCg4PJysoiNTWV8PBwtm7dyksvvcSxY8cYMmQIkydPrs5TKZYGT0qp0unUJ6VqlbL0EFWGH3/8kWXLlhEfH8+UKVPIyMjw9yht2rSJPn36+POuW7eO2NhYtmzZQnR0NADR0dG89tpr+Hw+brvttmo5h7LQOU9KqWLpnCelVHk8/PDDLFmyhKSkJJKSkti4caO/5ykiIsL/fN26dSxYsICePXuyceNGevTo4S9j8eLFxMTEMGTIkGo5h7LQ4EkpVSwNnVRVanrrcMJjWlZ3NdRZWr58OZmZmfmCnsaNG5Oamsrx48eZOHEi8fHx9OzZk+eff5769esTHR1dKHi6+uqrWb16Ne+99151nEaZ6LCdUqoEOudJVZ36D75U3VVQ52DYsGEMGzasUPqpU6f8z9esWVNoe1xcHPfee6//+aJFi8jMzGT06NGVV9lzpMGTUqp0OudJKVXBUlNTGTBgAMOGDfMvphkbG0tsbGz1VqwMNHhSSimlVJULCQlh/fr11V2Ns6JznpRSpdNhO6WU8tPgSSmllFKqHDR4UkoVq3NEYwDCjX5UKKVUjir7RBQRp4hsEJEl9u9vi8geEYm3Hz3t9Hoi8qmIbBSRRBGZlKeMG0Vkp/24MU96HxHZLCK7RORlEdFBBqUqQGRgGACNxFnNNVFKqZqjKr9O3gtsLZD2J2NMT/sRb6dNAbYYY3oAscALIhIgIuHA48AlQD/gcRFpYO8zF7gdaG8/RlbuqSh1odHvI0oplaNKgicRiQKuBP5ZhuwGCLV7j+oCxwEPMAJYbow5bow5ASwHRopIUyDMGPO9McYA84ExlXEeSl2wNHZSSim/qup5+jvwZ8BXIP1pEdkkIrNEJNBOmw1EA4eAzcC9xhgf0BzYn2ffA3Zac/t5wXSl1DmSwAAAXHV02E4pVTYff/wxIsK2bdsAa+HLq666qpprVbEqPXgSkauAI8aYdQU2PQR0Ai4GwoFpdvoIIB5oBvQEZotIGEV/9zUlpBdVl9tFZK2IrD169Gi5z0WpC01Qjz40ufgkTW8cUN1VUUpVgtRTmXz8wjpST2VWWJkLFiwgJiaG999/v8LKrGmqoufpcuBqEUkC3gcGi8i7xphfjCUT+BfWPCaAScAie9suYA9WkHUAaJGn3Cis3qkD9vOC6YUYY143xvQ1xvSNjIysuDNU6jwljTrS4JlPcV7zfHVXRSlVCdYu3cOhXadYu3RPhZR35swZvvvuO9588818wdPp06e59tpr6dy5M3feeSc+nw+v18tNN91E165d6datG7NmzaqQOlSFSl9h3BjzEFYvEyISCzxojJkgIk2NMb/Yc5vGAAn2LvuAIcC3ItIY6AjsBnYBf80zSXw48JAx5riIpIhIf+BH4Abglco+L6UuGFF9q7sGSqkK9trdcXg9uTNpElYeImHlIZwuB3fOjj3rcj/55BNGjhxJhw4dCA8P968gvmbNGrZs2UKrVq0YOXIkixYtok2bNhw8eJCEBOvf/8mTJ8/pnKpSdS7e8p6IbMaa19QQmGmnzwAus7d9DUwzxhwzxhy3t/1kP56y0wAmY01G3wX8DHxedaehlFJK1S4Tn76U9hc3xuW2wgCX20GHfo2Z+PSl51TuggULGDduHADjxo1jwYIFAPTr14+2bdvidDoZP348q1atom3btuzevZt77rmHL774grCwsHM7qSpUpfe2M8bEAXH288HF5DmE1atU1La3gLeKSF8LdK2oeiqllFLns5B6gQQEOfF4fDjdDjweHwFBTkLqBZa+czGSk5NZsWIFCQkJiAherxcRYfTo0RRcflFEaNCgARs3bmTZsmXMmTOHDz/8kLfeKvQvvkbSZYOVUkqpC1B6ShZdBzZn7LQ+dB3YnLTTWedU3sKFC7nhhhvYu3cvSUlJ7N+/nzZt2rBq1SrWrFnDnj178Pl8fPDBB8TExHDs2DF8Ph/XXXcdM2bMqFU3Ca7SniellFJK1Qyj7uzufz5ofMdzLm/BggVMnz49X9p1113H3LlzufTSS5k+fTqbN29m4MCBXHvttWzevJlJkybh81lzr5555plzrkNV0eBJKaWUUucsLi6uUNrUqVOZOnVqkfl79OhRq3qb8tJhO6WUUkqpctCeJ6VUlWsRm4w49eNHKVU76aeXUqrK1X05CURvmKeUqp00eFJKVb2AOtVdA6XOS8aYQssCnG+MKfIObFVK5zwppZRS54GgoCCSk5NrRHBRWYwxJCcnExQUVK310J4npZRS6jwQFRXFgQMHON9vfB8UFERUVFTpGSuRnM8RaklEJAXYXt31OI80BI5VdyXOA9qOFU/btGJoO1Ysbc+K19EYE1oVB7qQe562G2P0jqcVRETWanueO23HiqdtWjG0HSuWtmfFE5G1VXUsnfOklFJKKVUOGjwppZRSSpXDhRw8vV7dFTjPaHtWDG3HiqdtWjG0HSuWtmfFq7I2vWAnjCullFJKnY0LuedJKaWUUqrcak3wJCItROR/IrJVRBJF5F47PVxElovITvtnAzu9k4h8LyKZIvJggbLut8tIEJEFIlLkalsicqNd7k4RuTFP+hcistEu4zURcVbmuVeGmtSeebYvFpGEyjjfylKT2lFE4kRku4jE249GlXnulaWGtWmAiLwuIjtEZJuIXFeZ516Rako7ikhontdkvIgcE5G/V/b5V7Sa0p52+ngR2Swim8T6f9SwMs+9stSwNv293Z6JIvJ8qZU3xtSKB9AU6G0/DwV2AJ2B54Hpdvp04Dn7eSPgYuBp4ME85TQH9gDB9u8fAjcVcbxwYLf9s4H9vIG9Lcz+KcBHwLjqbp/a3J729t8A/wYSqrttams7AnFA3+puk/OsTZ8EZtrPHUDD6m6f2tiOBfKtAwZWd/vU1vbEWmLoSM5r0T7+E9XdPrW8TSOAfUCknW8eMKSkuteanidjzC/GmPX28xRgK1aDXYN1otg/x9h5jhhjfgKyiyjOBQSLiAuoAxwqIs8IYLkx5rgx5gSwHBhpl306TzkBQK2bOFaT2lNE6gIPADMr6PSqTE1qx/NFDWvTm4Fn7OP4jDG1ZlHDGtaOAIhIe6x/gN+e4+lVuRrUnmI/QkREgLBi9q/xalCbtgV2GGNylmb/Ciixl7nWBE95iUhroBfwI9DYGPMLWH8IrDdmsYwxB4G/YUWZvwCnjDFfFpG1ObA/z+8H7LScOizDiv5TgIVneSo1Qg1ozxnAC0DaWZ9EDVAD2hHgX/bQyGP2B2utVp1tKiL17d9niMh6EfmPiDQ+h9OpNjXktQkwHvjA2F/va6vqbE9jTDYwGdiMFSB0Bt48h9OpEar5NboL6CQire3gawzQoqRj1rrgye6l+Ai4L08PUHn2b4AV1bYBmmFF7xOKylpEmv8Nb4wZgdXlGAgMLm89aorqbk8R6Qm0M8Z8XN5j1yTV3Y72zz8YY7oBA+zHxPLWoyapAW3qAqKA74wxvYHvsT6ga5Ua0I55jQMWlLcONUl1t6eIuLGCp172/puAh8pbj5qkutvU7oWaDHyA1SuaBHhKOmatCp7sF81HwHvGmEV28q8i0tTe3hSrN6gkQ4E9xpijdgS/CLhMRC7JM6HxaqyING/kGUWBbkBjTAawGOuPVuvUkPa8FOgjIknAKqCDiMRVzBlWjRrSjjnfvnK6v/8N9KuYM6x6NaRNk7F6Q3MC+/8AvSvg9KpMDWnHnLr0AFzGmHUVcnLVoIa0Z08AY8zPdg/eh8BlFXSKVa6GtCnGmE+NMZcYYy7Fuu/tzpIOWGuCJ3sI4k1gqzHmxTybFgM5M+ZvBP5bSlH7gP4iUscuc4hd5o/GmJ72YzGwDBguIg3sqHY4sExE6ub5o7qA0cC2ijrPqlJT2tMYM9cY08wY0xqIwRp3jq2o86xsNaUdRcQl9hU39ofRVUCtunIxR01pU/sf06dArF3eEGBLBZxilagp7ZinnPHU4l6nGtSeB4HOIhJplzcMa65QrVOD2hSxr0620+8C/lniEU0NmHFflgfWP1aD1UUZbz9GY82S/xorSvwaCLfzN8GKMk8DJ+3nOVfJPYkV8CQA7wCBxRzzZqyx0F3AJDutMfCTXY9E4BWsb1PV3ka1sT0LbG9N7bvarka0IxCCdRVTzuvyJcBZ3e1Tm9vUTm8FrLTr8jXQsrrbpza2o71tN9CputvlfGhP4E6sgGkTVoAfUd3tcx606QKsL0dbKMMV9LrCuFJKKaVUOdSaYTullFJKqZpAgyellFJKqXLQ4EkppZRSqhw0eFJKKaWUKgcNnpRSSimlykGDJ6WUUkqpctDgSSmllFKqHDR4UkoppZQqh/8PweMJ66MkcXcAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for FRD\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'FRD'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Fresno (FRN) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.965 -0.22 0. 0. ]\n",
+ " [ 0.22 0.965 0. 0. ]\n",
+ " [ 0. 0. 1. 67.484]\n",
+ " [ 0. 0. 0. 1. ]]\n",
+ "[[ 9.408e-01 -1.886e-01 0.000e+00 5.645e+02]\n",
+ " [ 1.886e-01 9.408e-01 0.000e+00 7.482e+02]\n",
+ " [ 0.000e+00 0.000e+00 1.006e+00 -1.822e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.869e-01 -2.097e-01 3.919e-02 -2.164e+03]\n",
+ " [ 2.192e-01 9.869e-01 -2.772e-01 1.167e+04]\n",
+ " [ 2.441e-02 1.699e-02 9.869e-01 4.362e+01]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.863e-01 -2.097e-01 3.932e-02 -2.155e+03]\n",
+ " [ 2.187e-01 9.819e-01 -2.749e-01 1.158e+04]\n",
+ " [ 2.281e-02 1.457e-02 9.959e-01 -2.955e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for FRN\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'FRN'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Guam (GUA) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1.016e+00 -1.991e-02 0.000e+00 0.000e+00]\n",
+ " [ 1.991e-02 1.016e+00 0.000e+00 0.000e+00]\n",
+ " [ 0.000e+00 0.000e+00 1.000e+00 2.546e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.014e+00 -1.241e-02 0.000e+00 6.956e+01]\n",
+ " [ 1.241e-02 1.014e+00 0.000e+00 2.643e+02]\n",
+ " [ 0.000e+00 0.000e+00 1.002e+00 2.396e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.014e+00 -1.094e-02 6.216e-03 1.006e+01]\n",
+ " [ 1.323e-02 1.014e+00 -1.077e-04 2.364e+02]\n",
+ " [ 6.604e-03 1.718e-03 1.014e+00 -8.888e+01]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.986e-01 -1.997e-02 -7.584e-03 6.747e+02]\n",
+ " [ 2.301e-02 1.042e+00 8.549e-03 -1.704e+02]\n",
+ " [ 5.469e-03 1.060e-03 1.007e+00 1.046e+01]\n",
+ " [ 0.000e+00 -0.000e+00 0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for GUA\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'GUA'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Honolulu (HON) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.984 -0.172 0. 0. ]\n",
+ " [ 0.172 0.984 0. 0. ]\n",
+ " [ 0. 0. 1. 126.704]\n",
+ " [ 0. 0. 0. 1. ]]\n",
+ "[[ 9.788e-01 -1.868e-01 0.000e+00 1.451e+02]\n",
+ " [ 1.868e-01 9.788e-01 0.000e+00 -4.136e+02]\n",
+ " [ 0.000e+00 0.000e+00 9.885e-01 3.686e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.774e-01 -1.942e-01 -1.865e-02 5.747e+02]\n",
+ " [ 1.812e-01 9.774e-01 -1.553e-03 -2.277e+02]\n",
+ " [-1.599e-02 -1.161e-02 9.774e-01 1.039e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.650e-01 -1.889e-01 -3.199e-02 1.194e+03]\n",
+ " [ 1.759e-01 9.978e-01 -1.360e-02 1.705e+02]\n",
+ " [-1.586e-02 -1.173e-02 9.779e-01 1.024e+03]\n",
+ " [ 0.000e+00 -0.000e+00 0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for HON\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'HON'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Newport (NEW) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 9.721e-01 -2.663e-01 0.000e+00 0.000e+00]\n",
+ " [ 2.663e-01 9.721e-01 0.000e+00 0.000e+00]\n",
+ " [ 0.000e+00 0.000e+00 1.000e+00 7.207e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.700e-01 -2.408e-01 0.000e+00 3.933e+01]\n",
+ " [ 2.408e-01 9.700e-01 0.000e+00 4.574e+02]\n",
+ " [ 0.000e+00 0.000e+00 1.123e+00 -5.502e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.014e+00 -2.590e-01 -3.328e-02 9.257e+02]\n",
+ " [ 2.330e-01 1.014e+00 8.769e-02 -3.834e+03]\n",
+ " [-2.713e-02 -2.756e-02 1.014e+00 4.784e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.718e-01 -2.582e-01 -4.197e-02 2.130e+03]\n",
+ " [ 2.345e-01 9.673e-01 8.515e-02 -3.738e+03]\n",
+ " [-5.235e-04 -2.378e-02 1.121e+00 -5.417e+03]\n",
+ " [ 0.000e+00 -0.000e+00 -0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for NEW\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'NEW'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [San Juan (SJG) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 9.793e-01 2.221e-01 0.000e+00 0.000e+00]\n",
+ " [-2.221e-01 9.793e-01 0.000e+00 0.000e+00]\n",
+ " [ 0.000e+00 0.000e+00 1.000e+00 2.738e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.546e-01 -1.800e-01 0.000e+00 6.194e+02]\n",
+ " [ 1.800e-01 9.546e-01 0.000e+00 -1.083e+04]\n",
+ " [ 0.000e+00 0.000e+00 1.085e+00 -1.845e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.069e+00 2.643e-01 -1.131e-01 3.983e+02]\n",
+ " [-3.623e-01 1.069e+00 -7.689e-01 2.285e+04]\n",
+ " [ 1.750e-02 1.077e-01 1.069e+00 -1.892e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.394e-01 2.398e-01 -1.682e-01 5.247e+03]\n",
+ " [-3.408e-01 1.275e+00 -7.971e-01 2.300e+04]\n",
+ " [ 2.411e-02 1.038e-01 1.077e+00 -2.282e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for SJG\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'SJG'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Shumagin (SHU) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 9.720e-01 -1.919e-01 0.000e+00 0.000e+00]\n",
+ " [ 1.919e-01 9.720e-01 0.000e+00 0.000e+00]\n",
+ " [ 0.000e+00 0.000e+00 1.000e+00 -3.212e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.018e+00 -1.868e-01 0.000e+00 -9.111e+02]\n",
+ " [ 1.868e-01 1.018e+00 0.000e+00 1.102e+02]\n",
+ " [ 0.000e+00 0.000e+00 1.127e+00 -6.488e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.015e+00 -1.951e-01 1.547e-02 -1.617e+03]\n",
+ " [ 1.461e-01 1.015e+00 9.451e-02 -3.671e+03]\n",
+ " [ 3.074e-02 3.505e-02 1.015e+00 -1.674e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.025e+00 -1.969e-01 1.167e-02 -1.624e+03]\n",
+ " [ 1.552e-01 9.891e-01 1.208e-01 -5.137e+03]\n",
+ " [ 1.607e-02 1.415e-02 1.099e+00 -5.467e+03]\n",
+ " [ 0.000e+00 -0.000e+00 -0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for SHU\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'SHU'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Sitka (SIT) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Magnetometer altered, discarding measurements prior to 2018-07-11 20:40:00\n",
+ "[[ 0.95 -0.325 0. 0. ]\n",
+ " [ 0.325 0.95 0. 0. ]\n",
+ " [ 0. 0. 1. 72.418]\n",
+ " [ 0. 0. 0. 1. ]]\n",
+ "[[ 9.387e-01 -3.454e-01 0.000e+00 1.788e+02]\n",
+ " [ 3.454e-01 9.387e-01 0.000e+00 -3.249e+02]\n",
+ " [ 0.000e+00 0.000e+00 9.845e-01 8.970e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.480e-01 -3.502e-01 -1.832e-02 1.005e+03]\n",
+ " [ 3.401e-01 9.480e-01 2.784e-02 -1.718e+03]\n",
+ " [ 2.619e-02 -1.562e-02 9.480e-01 2.418e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.351e-01 -3.416e-01 -5.063e-03 5.054e+02]\n",
+ " [ 3.414e-01 9.468e-01 2.617e-02 -1.649e+03]\n",
+ " [ 1.103e-02 -2.313e-03 9.783e-01 1.048e+03]\n",
+ " [-0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for SIT\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'SIT'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## [Tucson (TUC) Observatory](#Adjusted-Data-Algorithm---Contents:)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 9.770e-01 -1.619e-01 0.000e+00 0.000e+00]\n",
+ " [ 1.619e-01 9.770e-01 0.000e+00 0.000e+00]\n",
+ " [ 0.000e+00 0.000e+00 1.000e+00 3.476e+02]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 1.006e+00 -1.654e-01 0.000e+00 -7.028e+02]\n",
+ " [ 1.654e-01 1.006e+00 0.000e+00 -8.159e+01]\n",
+ " [ 0.000e+00 0.000e+00 9.449e-01 2.559e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.646e-01 -2.046e-01 1.408e-01 -5.351e+03]\n",
+ " [ 1.704e-01 9.646e-01 1.626e-01 -6.730e+03]\n",
+ " [ 2.146e-02 -2.987e-03 9.646e-01 1.240e+03]\n",
+ " [ 0.000e+00 0.000e+00 0.000e+00 1.000e+00]]\n",
+ "[[ 9.841e-01 -2.074e-01 1.418e-01 -5.866e+03]\n",
+ " [ 1.705e-01 9.642e-01 1.629e-01 -6.746e+03]\n",
+ " [ 2.056e-02 5.086e-03 9.400e-01 2.250e+03]\n",
+ " [-0.000e+00 0.000e+00 -0.000e+00 1.000e+00]]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x432 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#\n",
+ "# configuration parameters for TUC\n",
+ "#\n",
+ "start_date = '2018-03-01T00:00:00Z'\n",
+ "end_date = '2018-09-01T00:00:00Z'\n",
+ "obs_code = 'TUC'\n",
+ "validate = False # validate against QD data\n",
+ "\n",
+ "#\n",
+ "# Run do_it_all()\n",
+ "#\n",
+ "M0, M1, M2, M3, pc = do_it_all(obs_code, start_date, end_date, validate=validate)\n",
+ "\n",
+ "#\n",
+ "# save type 0 matrix to state file\n",
+ "#\n",
+ "import geomagio\n",
+ "from geomagio.algorithm import AdjustedAlgorithm\n",
+ "adjAlg = AdjustedAlgorithm(\n",
+ " matrix = M0,\n",
+ " pier_correction = pc.mean(),\n",
+ " statefile = 'adj' + obs_code + '_state_' + '.json'\n",
+ ")\n",
+ "adjAlg.save_state()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}