{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", "**This is a fixed-text formatted version of a Jupyter notebook.**\n", "\n", "- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.9?urlpath=lab/tree/intro_maps.ipynb)\n", "- You can contribute with your own notebooks in this\n", "[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n", "- **Source files:**\n", "[intro_maps.ipynb](../_static/notebooks/intro_maps.ipynb) |\n", "[intro_maps.py](../_static/notebooks/intro_maps.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gammapy Maps\n", "\n", "![Gammapy Maps Illustration](images/gammapy_maps.png)\n", "\n", "## Introduction\n", "The [gammapy.maps](..\/maps/index.rst) submodule contains classes for representing **sky images** with an **arbitrary number of non-spatial dimensions** such as energy, time, event class or any possible user-defined dimension (illustrated in the image above). The main `Map` data structure features a **uniform API** for [WCS](https://fits.gsfc.nasa.gov/fits_wcs.html) as well as [HEALPix](https://en.wikipedia.org/wiki/HEALPix) based images. The API also generalizes simple image based operations such as smoothing, interpolation and reprojection to the arbitrary extra dimensions and makes working with (2 + N)-dimensional hypercubes **as easy as working with a simple 2D image**. Further information is also provided on the [gammpy.maps](..\/maps/index.rst) docs page.\n", "\n", "\n", "In the following introduction we will **learn all the basics** of working with WCS based maps. HEALPix based maps will be covered in a future tutorial. We will cover the following topics in order:\n", "\n", "1. [Creating WCS Maps](#1.-Creating-WCS-Maps)\n", "1. [Accessing and Modifying Data](#2.-Accessing-and-Modifying-Data)\n", "1. [Reading and Writing](#3.-Reading-and-Writing)\n", "1. [Visualizing and Plotting](#4.-Visualizing-and-Plotting)\n", "1. [Reprojecting, Interpolating and Miscellaneous](#5.-Reprojecting,-Interpolating-and-Miscellaneous)\n", "\n", "\n", "Make sure you have worked through he [First Steps with Gammapy](first_steps.ipynb) and [Astropy Introduction](astropy_introduction.ipynb) notebooks, because a solid knowledge about working with `SkyCoords` and `Quantity` objects as well as [Numpy](http://www.numpy.org/) is required for this tutorial.\n", "\n", "**Note:** This notebook is rather lengthy, but getting to know the `Map` data structure in detail is **essential for working with Gammapy** and will allow you to fulfill **complex analysis tasks with very few and simple code** in future!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 0. Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from astropy import units as u\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "from astropy.coordinates import SkyCoord\n", "from gammapy.maps import Map, MapAxis, WcsGeom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Creating WCS Maps\n", "\n", "### 1.1 Using Factory Methods\n", "\n", "Maps are most easily created using the `Map.create()` factory method:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "m_allsky = Map.create()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling `Map.create()` without any further arguments creates by default an allsky WCS map using a CAR projection, ICRS coordinates and a pixel size of 1 deg. This can be easily checked by printing the `.geom` attribute of the map:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat\n", "\tshape : (3600, 1800)\n", "\tndim : 2\n", "\tcoordsys : CEL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 360.0 x 180.0 deg\n", "\n" ] } ], "source": [ "print(m_allsky.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `.geom` attribute is a `MapGeom` object, that defines the basic geometry of the map, such as size of the pixels, width and height of the image, coordinate system etc., but we will learn more about this object later.\n", "\n", "Besides the `.geom` attribute the map has also a `.data` attribute, which is just a plain `numpy.ndarray` and stores the data associated with this map:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_allsky.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default maps are filled with zeros.\n", "\n", "Here is a second example that creates a WCS map centered on the Galactic center and now uses Galactic coordinates:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tcoordsys : GAL\n", "\tprojection : TAN\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 x 5.0 deg\n", "\n" ] } ], "source": [ "skydir = SkyCoord(0, 0, frame=\"galactic\", unit=\"deg\")\n", "m_gc = Map.create(\n", " binsz=0.02, width=(10, 5), skydir=skydir, coordsys=\"GAL\", proj=\"TAN\"\n", ")\n", "print(m_gc.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition we have defined a TAN projection, a pixel size of `0.02` deg and a width of the map of `10 deg x 5 deg`. The `width` argument also takes scalar value instead of a tuple, which is interpreted as both the width and height of the map, so that a quadratic map is created." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Creating from a Map Geometry\n", "\n", "As we have seen in the first examples, the `Map` object couples the data (stored as a `numpy.ndarray`) with a `MapGeom` object. The `MapGeom` object can be seen as a generalization of an `astropy.wcs.WCS` object, providing the information on how the data maps to physical coordinate systems. In some cases e.g. when creating many maps with the same WCS geometry it can be advantegeous to first create the map geometry independent of the map object itsself: " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "wcs_geom = WcsGeom.create(\n", " binsz=0.02, width=(10, 5), skydir=(0, 0), coordsys=\"GAL\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then create the map objects from the `wcs_geom` geometry specification:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "maps = {}\n", "\n", "for name in [\"counts\", \"background\"]:\n", " maps[name] = Map.from_geom(wcs_geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `MapGeom` object also has a few helpful methods. E.g. we can check whether a given position on the sky is contained in the map geometry:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ True, False])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# define the position of the Galactic center and anti-center\n", "positions = SkyCoord([0, 180], [0, 0], frame=\"galactic\", unit=\"deg\")\n", "wcs_geom.contains(positions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or get the image center of the map:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wcs_geom.center_skydir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or we can also retrieve the solid angle per pixel of the map:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$[[1.21731 \\times 10^{-7},~1.21731 \\times 10^{-7},~1.21731 \\times 10^{-7},~\\dots,~1.21731 \\times 10^{-7},~1.21731 \\times 10^{-7},~1.21731 \\times 10^{-7}],~\n", " [1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7},~\\dots,~1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7}],~\n", " [1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7},~\\dots,~1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7}],~\n", " \\dots,~\n", " [1.217365 \\times 10^{-7},~1.217365 \\times 10^{-7},~1.217365 \\times 10^{-7},~\\dots,~1.217365 \\times 10^{-7},~1.217365 \\times 10^{-7},~1.217365 \\times 10^{-7}],~\n", " [1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7},~\\dots,~1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7},~1.2173468 \\times 10^{-7}],~\n", " [1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7},~\\dots,~1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7},~1.2173284 \\times 10^{-7}]] \\; \\mathrm{sr}$$" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wcs_geom.solid_angle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Adding Non-Spatial Axes\n", "\n", "In many analysis scenarios we would like to add extra dimension to the maps to study e.g. energy or time dependency of the data. Those non-spatial dimensions are handled with the `MapAxis` object. Let us first define an energy axis, with 4 bins:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MapAxis\n", "\n", "\tname : energy \n", "\tunit : 'TeV' \n", "\tnbins : 4 \n", "\tnode type : edges \n", "\tedges min : 1.0e+00 TeV\n", "\tedges max : 1.0e+02 TeV\n", "\tinterp : log \n", "\n" ] } ], "source": [ "energy_axis = MapAxis.from_bounds(\n", " 1, 100, nbin=4, unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "print(energy_axis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where `interp='log'` specifies that a logarithmic spacing is used between the bins, equivalent to `np.logspace(0, 2, 4)`. This `MapAxis` object we can now pass to `Map.create()` using the `axes=` argument:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy\n", "\tshape : (500, 250, 4)\n", "\tndim : 3\n", "\tcoordsys : GAL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 x 5.0 deg\n", "\n" ] } ], "source": [ "m_cube = Map.create(\n", " binsz=0.02, width=(10, 5), coordsys=\"GAL\", axes=[energy_axis]\n", ")\n", "print(m_cube.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we see that besides `lon` and `lat` the map has an additional axes named `energy` with 4 bins. The total dimension of the map is now `ndim=3`.\n", "\n", "We can also add further axes by passing a list of `MapAxis` objects. To demonstrate this we create a time axis with\n", "linearly spaced bins and pass both axes to `Map.create()`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy, time\n", "\tshape : (500, 250, 4, 24)\n", "\tndim : 4\n", "\tcoordsys : GAL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 x 5.0 deg\n", "\n" ] } ], "source": [ "time_axis = MapAxis.from_bounds(\n", " 0, 24, nbin=24, unit=\"hour\", name=\"time\", interp=\"lin\"\n", ")\n", "\n", "m_4d = Map.create(\n", " binsz=0.02, width=(10, 5), coordsys=\"GAL\", axes=[energy_axis, time_axis]\n", ")\n", "print(m_4d.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `MapAxis` object internally stores the coordinates or \"position values\" associated with every map axis bin or \"node\". We distinguish between two node types: `edges` and `center`. The node type `edges`(which is also the default) specifies that the data associated with this axis is integrated between the edges of the bin (e.g. counts data). The node type `center` specifies that the data is given at the center of the bin (e.g. exposure or differential fluxes).\n", "\n", "The edges of the bins can be checked with `.edges` attribute:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 3.16227766, 10. , 31.6227766 ,\n", " 100. ])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_axis.edges" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The numbers are given in the units we specified above, which can be checked again with:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$$\\mathrm{TeV}$$" ], "text/plain": [ "Unit(\"TeV\")" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_axis.unit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The centers of the axis bins can be checked with the `.center` attribute:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.77827941, 5.62341325, 17.7827941 , 56.23413252])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "energy_axis.center" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Accessing and Modifying Data\n", "\n", "### 2.1 Accessing Map Data Values\n", "\n", "All map objects have a set of accessor methods, which can be used to access or update the contents of the map irrespective of its underlying representation. Those accessor methods accept as their first argument a coordinate `tuple` containing scalars, `list`, or `numpy.ndarray` with one tuple element for each dimension. Some methods additionally accept a `dict` or `MapCoord` argument, of which both allow to assign coordinates by axis name.\n", "\n", "Let us first begin with the `.get_by_idx()` method, that accepts a tuple of indices. The order of the indices corresponds to the axis order of the map: " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.], dtype=float32)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_gc.get_by_idx((50, 30))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Important:** Gammapy uses a reversed index order in the map API with the longitude axes first. To achieve the same by directly indexing into the numpy array we have to call: " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.], dtype=float32)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_gc.data[([30], [50])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check the order of the axes you can always print the `.geom` attribute:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tcoordsys : GAL\n", "\tprojection : TAN\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 x 5.0 deg\n", "\n" ] } ], "source": [ "print(m_gc.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access values directly by sky coordinates we can use the `.get_by_coord()` method. This time we pass in a `dict`, specifying the axes names corresponding to the given coordinates:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., nan], dtype=float32)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_gc.get_by_coord({\"lon\": [0, 180], \"lat\": [0, 0]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The units of the coordinates are assumed to be in degrees in the coordinate system used by the map. If the coordinates do not correspond to the exact pixel center, the value of the nearest pixel center will be returned. For positions outside the map geometry `np.nan` is returned.\n", "\n", "The coordinate or idx arrays follow normal [Numpy broadcasting rules](https://jakevdp.github.io/PythonDataScienceHandbook/02.05-computation-on-arrays-broadcasting.html). So the following works as expected:\n", "\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lons = np.linspace(-4, 4, 10)\n", "m_gc.get_by_coord({\"lon\": lons, \"lat\": 0})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or as an even more advanced example, we can provide `lats` as column vector and broadcasting to a 2D result array will be applied:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[nan, nan, nan, nan, nan, nan, nan, nan],\n", " [nan, nan, nan, nan, nan, nan, nan, nan],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [nan, nan, nan, nan, nan, nan, nan, nan],\n", " [nan, nan, nan, nan, nan, nan, nan, nan]], dtype=float32)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lons = np.linspace(-4, 4, 8)\n", "lats = np.linspace(-4, 4, 8).reshape(-1, 1)\n", "m_gc.get_by_coord({\"lon\": lons, \"lat\": lats})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Modifying Map Data Values\n", "\n", "To modify and set map data values the `Map` object features as well a `.set_by_idx()` method: \n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "m_cube.set_by_idx(idx=(10, 20, 3), vals=42)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([42.], dtype=float32)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_cube.get_by_idx((10, 20, 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course there is also a `.set_by_coord()` method, which allows to set map data values in physical coordinates. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "m_cube.set_by_coord({\"lon\": 0, \"lat\": 0, \"energy\": 2 * u.TeV}, vals=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again the `lon` and `lat` values are assumed to be given in degrees in the coordinate system used by the map. For the energy axis, the unit is the one specified on the axis (use `m_cube.geom.axes[0].unit` to check if needed...)\n", "\n", "All `.xxx_by_coord()` methods accept `SkyCoord` objects as well. In this case we have to use the `skycoord` keyword instead of `lon` and `lat`:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "skycoords = SkyCoord([1.2, 3.4], [-0.5, 1.1], frame=\"galactic\", unit=\"deg\")\n", "m_cube.set_by_coord({\"skycoord\": skycoords, \"energy\": 2 * u.TeV}, vals=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Indexing and Slicing Sub-Maps\n", "\n", "When you have worked with Numpy arrays in the past you are probably familiar with the concept of indexing and slicing\n", "into data arrays. To support slicing of non-spatial axes of `Map` objects, the `Map` object has a `.slice_by_idx()` method, which allows to extract sub-maps from a larger map.\n", "\n", "The following example demonstrates how to get the map at the energy bin number 3: " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : lon, lat\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tunit : '' \n", "\tdtype : float32 \n", "\n" ] } ], "source": [ "m_sub = m_cube.slice_by_idx({\"energy\": 3})\n", "print(m_sub)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the returned object is again a `Map` with updated axes information. In this case, because we extracted only a single image, the energy axes is dropped from the map.\n", "\n", "To extract a sub-cube with a sliced energy axes we can use a normal `slice()` object:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : lon, lat, energy\n", "\tshape : (500, 250, 2)\n", "\tndim : 3\n", "\tunit : '' \n", "\tdtype : float32 \n", "\n" ] } ], "source": [ "m_sub = m_cube.slice_by_idx({\"energy\": slice(1, 3)})\n", "print(m_sub)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the returned object is also a `Map` object, but this time with updated energy axis specification.\n", "\n", "Slicing of multiple dimensions is supported by adding further entries to the dict passed to `.slice_by_idx()`" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : lon, lat, energy, time\n", "\tshape : (500, 250, 2, 6)\n", "\tndim : 4\n", "\tunit : '' \n", "\tdtype : float32 \n", "\n" ] } ], "source": [ "m_sub = m_4d.slice_by_idx({\"energy\": slice(1, 3), \"time\": slice(4, 10)})\n", "print(m_sub)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience there is also a `.get_image_by_coord()` method which allows to access image planes at given non-spatial physical coordinates. This method also supports `Quantity` objects:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat\n", "\tshape : (500, 250)\n", "\tndim : 2\n", "\tcoordsys : GAL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 x 5.0 deg\n", "\n" ] } ], "source": [ "image = m_4d.get_image_by_coord({\"energy\": 4 * u.TeV, \"time\": 5 * u.h})\n", "print(image.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Reading and Writing\n", "\n", "Gammapy `Map` objects are serialized using the Flexible Image Transport Format (FITS). Depending on the pixelisation scheme (HEALPix or WCS) and presence of non-spatial dimensions the actual convention to write the FITS file is different.\n", "By default Gammpy uses a generic convention named `gadf`, which will support WCS and HEALPix formats as well as an arbitrary number of non-spatial axes. The convention is documented in detail on the [Gamma Astro Data Formats](https://gamma-astro-data-formats.readthedocs.io/en/latest/skymaps/index.html) page.\n", "\n", "Other conventions required by specific software (e.g. the Fermi Science Tools) are supported as well. At the moment those are the following\n", "\n", "* `fgst-ccube`: Fermi counts cube format.\n", "* `fgst-ltcube`: Fermi livetime cube format.\n", "* `fgst-bexpcube`: Fermi exposure cube format\n", "* `fgst-template`: Fermi Galactic diffuse and source template format. \n", "* `fgst-srcmap` and `fgst-srcmap-sparse`: Fermi source map and sparse source map format.\n", "\n", "The conventions listed above only support an additional energy axis. \n", "\n", "### 3.1 Reading Maps\n", "\n", "Reading FITS files is mainly exposed via the `Map.read()` method.Let us take a look at a first example: " ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : lon, lat\n", "\tshape : (320, 180)\n", "\tndim : 2\n", "\tunit : '' \n", "\tdtype : >f8 \n", "\n" ] } ], "source": [ "filename = \"$GAMMAPY_DATA/fermi_2fhl/fermi_2fhl_gc.fits.gz\"\n", "m_2fhl_gc = Map.read(filename)\n", "print(m_2fhl_gc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default `Map.read()` will try to find the first valid data hdu in the filename and read the data from there. If mutliple HDUs are present in the FITS file, the desired one can be chosen with the additional `hdu=` argument:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : lon, lat\n", "\tshape : (320, 180)\n", "\tndim : 2\n", "\tunit : '' \n", "\tdtype : >f8 \n", "\n" ] } ], "source": [ "m_2fhl_gc = Map.read(filename, hdu=\"background\")\n", "print(m_2fhl_gc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In rare cases e.g. when the FITS file is not valid or meta data is missing from the header it can be necessary to modify the header of a certain HDU before creating the `Map` object. In this case we can use `astropy.io.fits` directly to read the FITS file:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: /Users/deil/work/gammapy-tutorials/datasets/fermi_survey/all.fits.gz\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 COUNTS 1 PrimaryHDU 84 (2001, 101) int32 \n", " 1 BACKGROUND 1 ImageHDU 85 (2001, 101) float64 \n", " 2 EXPOSURE 1 ImageHDU 85 (2001, 101) float32 \n" ] } ], "source": [ "filename = os.environ[\"GAMMAPY_DATA\"] + \"/fermi_survey/all.fits.gz\"\n", "hdulist = fits.open(filename)\n", "hdulist.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then modify the header keyword and use `Map.from_hdulist()` to create the `Map` object after:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : lon, lat\n", "\tshape : (2001, 101)\n", "\tndim : 2\n", "\tunit : 'cm2 s' \n", "\tdtype : >f4 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[\"exposure\"].header[\"BUNIT\"] = \"cm2 s\"\n", "Map.from_hdulist(hdulist=hdulist, hdu=\"exposure\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Writing Maps\n", "\n", "Writing FITS files is mainoy exposure via the `Map.write()` method. Here is a first example:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "m_cube.write(\"example_cube.fits\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default Gammapy does not overwrite files. In this example we set `overwrite=True` in case the cell gets executed multiple times. Now we can read back the cube from disk using `Map.read()`:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsNDMap\n", "\n", "\tgeom : WcsGeom \n", " \taxes : lon, lat, energy\n", "\tshape : (500, 250, 4)\n", "\tndim : 3\n", "\tunit : '' \n", "\tdtype : >f4 \n", "\n" ] } ], "source": [ "m_cube = Map.read(\"example_cube.fits\")\n", "print(m_cube)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also choose a different FITS convention to write the example cube in a format compatible to the Fermi Galactic diffuse background model:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "m_cube.write(\"example_cube_fgst.fits\", conv=\"fgst-template\", overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand a little bit better the generic `gadf` convention we use `Map.to_hdulist()` to generate a list of FITS HDUs first: " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: (No file associated with this HDUList)\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 29 (500, 250, 4, 24) float32 \n", " 1 PRIMARY_BANDS 1 BinTableHDU 33 96R x 7C ['I', 'E', 'E', 'E', 'E', 'E', 'E'] \n" ] } ], "source": [ "hdulist = m_4d.to_hdulist(conv=\"gadf\")\n", "hdulist.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see the `HDUList` object contains to HDUs. The first one named `PRIMARY` contains the data array with shape corresponding to our data and the WCS information stored in the header:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SIMPLE = T / conforms to FITS standard \n", "BITPIX = -32 / array data type \n", "NAXIS = 4 / number of array dimensions \n", "NAXIS1 = 500 \n", "NAXIS2 = 250 \n", "NAXIS3 = 4 \n", "NAXIS4 = 24 \n", "EXTEND = T \n", "WCSAXES = 2 / Number of coordinate axes \n", "CRPIX1 = 250.5 / Pixel coordinate of reference point \n", "CRPIX2 = 125.5 / Pixel coordinate of reference point \n", "CDELT1 = -0.02 / [deg] Coordinate increment at reference point \n", "CDELT2 = 0.02 / [deg] Coordinate increment at reference point \n", "CUNIT1 = 'deg' / Units of coordinate increment and value \n", "CUNIT2 = 'deg' / Units of coordinate increment and value \n", "CTYPE1 = 'GLON-CAR' / galactic longitude, plate caree projection \n", "CTYPE2 = 'GLAT-CAR' / galactic latitude, plate caree projection \n", "CRVAL1 = 0.0 / [deg] Coordinate value at reference point \n", "CRVAL2 = 0.0 / [deg] Coordinate value at reference point \n", "LONPOLE = 0.0 / [deg] Native longitude of celestial pole \n", "LATPOLE = 90.0 / [deg] Native latitude of celestial pole \n", "AXCOLS1 = 'E_MIN,E_MAX' \n", "INTERP1 = 'log ' \n", "AXCOLS2 = 'TIME_MIN,TIME_MAX' \n", "INTERP2 = 'lin ' \n", "WCSSHAPE= '(500,250,4,24)' \n", "BANDSHDU= 'PRIMARY_BANDS' \n", "META = '{} ' \n", "BUNIT = '' " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[\"PRIMARY\"].header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second HDU is a `BinTableHDU` named `PRIMARY_BANDS` contains the information on the non-spatial axes such as name, order, unit, min, max and center values of the axis bins. We use an `astropy.table.Table` to show the information:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=96\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
CHANNELENERGYE_MINE_MAXTIMETIME_MINTIME_MAX
TeVTeVTeVhhh
int16float32float32float32float32float32float32
01.77827941.03.16227770.50.01.0
15.6234133.162277710.00.50.01.0
217.78279510.031.6227760.50.01.0
356.2341331.622776100.00.50.01.0
41.77827941.03.16227771.51.02.0
55.6234133.162277710.01.51.02.0
617.78279510.031.6227761.51.02.0
756.2341331.622776100.01.51.02.0
81.77827941.03.16227772.52.03.0
.....................
8617.78279510.031.62277621.521.022.0
8756.2341331.622776100.021.521.022.0
881.77827941.03.162277722.522.023.0
895.6234133.162277710.022.522.023.0
9017.78279510.031.62277622.522.023.0
9156.2341331.622776100.022.522.023.0
921.77827941.03.162277723.523.024.0
935.6234133.162277710.023.523.024.0
9417.78279510.031.62277623.523.024.0
9556.2341331.622776100.023.523.024.0
" ], "text/plain": [ "\n", "CHANNEL ENERGY E_MIN E_MAX TIME TIME_MIN TIME_MAX\n", " TeV TeV TeV h h h \n", " int16 float32 float32 float32 float32 float32 float32 \n", "------- --------- --------- --------- ------- -------- --------\n", " 0 1.7782794 1.0 3.1622777 0.5 0.0 1.0\n", " 1 5.623413 3.1622777 10.0 0.5 0.0 1.0\n", " 2 17.782795 10.0 31.622776 0.5 0.0 1.0\n", " 3 56.23413 31.622776 100.0 0.5 0.0 1.0\n", " 4 1.7782794 1.0 3.1622777 1.5 1.0 2.0\n", " 5 5.623413 3.1622777 10.0 1.5 1.0 2.0\n", " 6 17.782795 10.0 31.622776 1.5 1.0 2.0\n", " 7 56.23413 31.622776 100.0 1.5 1.0 2.0\n", " 8 1.7782794 1.0 3.1622777 2.5 2.0 3.0\n", " ... ... ... ... ... ... ...\n", " 86 17.782795 10.0 31.622776 21.5 21.0 22.0\n", " 87 56.23413 31.622776 100.0 21.5 21.0 22.0\n", " 88 1.7782794 1.0 3.1622777 22.5 22.0 23.0\n", " 89 5.623413 3.1622777 10.0 22.5 22.0 23.0\n", " 90 17.782795 10.0 31.622776 22.5 22.0 23.0\n", " 91 56.23413 31.622776 100.0 22.5 22.0 23.0\n", " 92 1.7782794 1.0 3.1622777 23.5 23.0 24.0\n", " 93 5.623413 3.1622777 10.0 23.5 23.0 24.0\n", " 94 17.782795 10.0 31.622776 23.5 23.0 24.0\n", " 95 56.23413 31.622776 100.0 23.5 23.0 24.0" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Table.read(hdulist[\"PRIMARY_BANDS\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Visualizing and Plotting\n", "\n", "### 4.1 Plotting \n", "\n", "For debugging and inspecting the map data it is useful to plot ot visualize the images planes contained in the map. " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "filename = \"$GAMMAPY_DATA/fermi_2fhl/fermi_2fhl_gc.fits.gz\"\n", "m_2fhl_gc = Map.read(filename, hdu=\"counts\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After reading the map we can now plot it on the screen by calling the `.plot()` method:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m_2fhl_gc.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can easily improve the plot by calling `Map.smooth()` first and providing additional arguments to `.plot()`. Most of them are passed further to [plt.imshow()](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html):" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "smoothed = m_2fhl_gc.smooth(width=0.2 * u.deg, kernel=\"gauss\")\n", "smoothed.plot(stretch=\"sqrt\", add_cbar=True, vmax=4, cmap=\"inferno\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the [plt.rc_context()](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.rc_context.html) context manager to further tweak the plot by adapting the figure and font size:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAowAAAFdCAYAAACJuTzBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXusr3te1/f+rsve++yz50iQgokNWqg4gBap04uJBeJMBA1ttNoIBgstjQbFBtuxGiMR0TqRKEk1xXbCGK62XmpDSpBqGkFsrHZGO5IxU4oXRuR6QJmZs8/e6/b0j7U+5/d63r/39/v77X3OOmudxfedrDzPen7P871f35/Lty3LoomJiYmJiYmJiYkeDm46ARMTExMTExMTE7cbc8E4MTExMTExMTExxFwwTkxMTExMTExMDDEXjBMTExMTExMTE0PMBePExMTExMTExMQQc8E4MTExMTExMTExxFwwTkxMTExMTExMDDEXjBMTExMTExMTdwSttV/WWnvSWvuOzu+ttfYnW2s/c/X3Da21tivcuWCceMPQWvv2m07DxERhtseJ24LZFifeZPz3kv7vwe+/U9JvkvQ5kv4tSV8s6XftCnQuGCfeSPzHN52AiQlgtseJ24LZFifeFLTWvkTSv5L0fwxe+3JJf3pZlh9dluVfSPrTkr5iV9hHb0gKbwF+W2ufJ0lPdFlSwvXjkh5f3Z9os0q+h2sVRP12hN8fXv3x91NJr17dV9jCey9cXY+v4qz3Kk0fxbMzC9vTc3F1z2u9+wDxPsA39d4J7is/j3A91hqnuiwvXV3rvvL4BOmt9Bxs4jx421U9XFh6PQ9CHuv6UNJLV/efIOkTr+7fdnW9L+np1f3Hrq4/q01ZnmlT/pXHF5HH06vrK/im6oP1wPIlUj2kfB3gWmVedfNImzzW9aE2ZfD0Kn1CGj+qTT2c2NXjrPiOECfbOduGdJnni/BNleMD3Fc5HiLeM23K7RzvvXh1X3XHPNb7j3VZf5L0Mu57dcKr3xOou4Pj1j7vAHGzT7MeHtrvp9quBx9HUr+tcEZhnyAc1m3l56E27be+5UDd60Mcm+pa8dSVeXii9fjg4bMPsE17W+u1aea30sA2XeXHOklj2BN8z/Kv99i+65rGwArnBM8qDYyTZepjFMG+U9/6GPbS5vWD34w5SlqPs5Uvjuuse/aZCuMC70qX9cB5qtrBU3zj9c02y/gu8A3bTqWB6ZDW41Hqb2mO5fjJcY11k/r968APL8vyY29MULcTrbWXJH29pHdK+srBq58t6YP4/4NXz8bh35WzpFtrdyMjExMTExMTE284lmXZqad33fiiL/qi5eWXX36ubz/wgQ98SJt1uiS9d1mW99Y/rbX/TtKPLcvyJ1trXyfp31yW5cs8nNbauaTPXpblw1f//zJJPyTpYBksCu8MwyhJv1mXJVnMQNrBnWl7h/xA27sf30nfs99PtKk11t4Du/rukGxFfcvdnsfH3d6ZXfnNLobxRJs8kr2oXWX1onOtd/SVzko3GcYC2Y3ChaXXmaL6jlcyK58g6Rdc3RdLdagNi0WGsNJ4pm2G7CHStm89sO7Tbpj5eh6GkQxv/VblT1aox2x53NzR1/WBttsimZeUh4PwzQNtmMVjvFdg3db1SBuGnQxjlUXF94o2bOK/1IZhTOw7kRhewpmiA233bzI4ZGbIgjoT9Fjr/uR1T4aWdZzCTowfGUZnfe6F+Oq5dNk3ivmtPnKqdV+u+NjmU7901sz7g5fvA2236R6jzLp1huxZGEayu97HegxjYlZHDKOPw4md9/J7oDXb+jY8ly7ryNPzUa3nkHqfbOGFfcP32Y4fhG84TyXpkJcfJQg+H9Q1MYy7xhGfQ+s7TyP72Kh9vlXx8ssv6/3v/3vP9W1rh0+WZXlH/q39KknvkvS5ewT1ca0IcL0k6eOjxaJ0xxaMp7ps4GlAK5A6Z2NOC0Y2TKfyewtGnzy94/pAdaFt+p4DJyeKRNsn0akPbHVfcVZ6KXavxQAHW6aT+R+JbvhdEp+mAZqDMsOpia++b9qIWdKAz285SfkiaYdYvSs+8bLg935f6fGFXG/iYR0nMaFPvi4u9DbECcdFogyHZcH2wPzXCFJlT5xru833Fnmu/vBEmzpmGRRSu/KFuy+iUlmkcA60LgMu+io8zwf7RlIVSRPh4/A7F0EcE/iti3N77YybzX1FLOy/3hbTIpzl19tM+SLoQtth9xZqozQyHC5u0rMk/nSVi7qmfuR99Z76i1BpPQdwPOG4V+LgwqFyP/EyZzhMb1JL6uHc/k9qDRynWU5818fptElkf+FCmxsokiiFJ/asl6dnUUn5eYwvkPRLJX3kyuD5kaTD1tpnLcvyb9u7H9KlwUutXD/n6tkQd2rBODExMTExMTFxe7Hompa775X0P+P/d+tyAflV4d1vk/Rftda+5ypB/7WkP7srgju1YHxF6102d6OJGRjtXKX1LszZpSfavbv0+LgjdSVhppciHrJCLhogeiLTtJsl41a70GKhDrXenTvjxN0nd9/ekJjXxMYwzJ5itCt6V7jSuuxZj4np9DKgqKPgTIW3B9Ytsavbj9iuxF4mRXq2AcZHlsTrJDGMLJueSC6pFiT2gxgxJvXsVNsM47nWovaU78RmjeqRLGlSOWH6UjgeX4VZV7Kx+yjkJxUOHydcWsC+Q9YnxUMx/8ioIKlMJDF3j2FMolnmx/sqjQsTe85+m8Yrhp3Ep+wTIxY/9bekosB7xpHGuBEzSFCtgfXocwnbPr9Nkp6RiJb10LQZ21Pd8xtvf96HRix/CpvfU93gEX4vpHmX4bFPSHl89LLbxVLeDrzxqVmWhdoCaq19XNKTZVl+urX2H0j6a8uyVDX8j5I+TdIPXv3/zVfPhrhTC8aJiYmJiYmJiduN61++Lsvydbj/AW3W7LrSVfxvrv72xp1aML6irJfnrKEzRb6LdfAbsnxJR893RETPQGCkbE39y8QcUP/kib1H/ZS0K067w2NtdNXS7jvtHhOD4wzDSC+P7Bh3nK60nfQiXQeUOmj1TdqBpl1zSgdZBW8bqXwYN8NMafS0etoS29Wru5HRy/2ra6pbD9uZtl26ZnyX+fG6Y3tg2CNdvqRb7IxIYhhHCvcFtqXEApLNSsYzqV1J2/2SBi6JCUqMiafTf6P+WtKFZj2MdH13SVP4fmLaesxhXb2tupGIM3as/6Rvl9jNBGfhU//39lDPGfc9u/e4mR5eR/WYpFWJZU75dziL/8Te9d9TepJhCfOaxquebjZ1+VNZ1nx8X9tI7WYXevrcPdwehvHaRNLXjju1YHyidWOn37g0SKaOxPeSYjCvLpImegvPkXivJ+LyQYmLtt5gXFd2xFFl1zdlOFT3nqaeuGFkVMABhnlz8ZKLj3zypRoAJ9mkkK/wexILpfJJAyK/T+I5vscw63dOuL4Q6S3+0kIiiUSZb6Y7bZxoAVlxjBaFSSy8zwCdytc3DT55Mm91dUV5V0HwSSMtGB9q24ck+6+U+3Lqd6lORmMKJ8BkIJDEe0m9JC1W/P8kbkxjShLrpsUf23sS76ey4MIxtYGUnmQUl/p0T1XExz1uTtICmPnepYKQ+v9IfO+bN68T36hUen1s8T6d2gEXihVHMsjbtbHp+XStuN2fa30nrS29P2q/eRqSUVZ5h+DCchd5M+qDvQW7tN/m983DXDBOTExMTExMTEx0MRnGW4Ha7SR2qJCYK+6yekxk2hUmZf9nhbOAFTbZj7Sj9zz2dvTcmSamqHZ9yXXNU20zevyeaUgGDUlxOomkewr1qZ48PdyR9uohsYB1T1HQSJyRWLwDZVcRScRGhib5mkvqBmfh9yQedaMEhs37ptwGPN2eH29r9HxLxpJhukj6cXjW87PGvHp+yPIlMWxiwI+1EYMx7SO2q8f8MY2pDAtMTzKoGTFgjGvX2JLYshFYprsMOZJRVZIWXGi7TSejF/8mGbik+k5qOSOxb0+Kk/IwYs19LErqBolhHKWRLCCv6V2G4/XUY3XT+DmSVDAPaWy5p20fm0eI82P4psL5qLbLiu7NRlI1VxfwOa1XvonV9TJNxqI3h7lgvHFUx0uTYhr0k+iwN1h4J++JV/ZFEqlwUcrf0yLAF1tJX9F1K31yZbmUv7BF607uYfbK0kWdx1ovGF2EzsVWzyLP40li9wNlP5iprFJ7kP3m90yDL9p4rBYXzKf2nrQWj7r+1CnCThaTSfTlYvPRgtHFXUzvPWX90oJPHh4m4zq0K5EsRnv9Ji0quEhJoni2IZ8oevv5JAbriY3r/bSYTW2Wbc3rhu+ljV5v87LvOJMm5vR7UqlIeoK+eE5tMYnvU32PxO6pvvn7Wfg9lR/7Kst/V90lJFFyIgpSf+stwlN6/b2eOkESU/e+GYVfSHNfhS+tvS2Ul+d7CLvK1h18Jx/FvmHstfG0UN61sO952iB+VhOvF3dqwTgxMTExMTExcXsxRdK3Au41ft9dpItuZfdpt5Z22uo867FX/k3ajZ0pG4ekOHaJVNN92nGODAA8bXUtQ5lkaORisApv1wkNu0Q7nsbExu4qe2eo6tk+Zcl8kWFMyt3JYpdlz1MZkhVlUtxnme7TRjydldZdbdoPX71Aepfwe09MPWIqU9xkWxPDyOe7RJSpf48YnCQydaOLNL74N4l9O9C6jpM6jH/D9OxirhSeJZaJhl4jwxSya2TnU91S1SOJtneJTBM7OurLqVyYnl3Hvo76eU+0nRhn4qxzX/+P5o1kJMb0sh8kAyDGc4B7jzuVb7Icl7YlB8dIR0mmvJy93TE/PcMepqW+SfOuw9VQRoz+7cHtS9E+uFMLxomJiYmJiYmJ2425YLxx1I5kZMDS210mxmoE7mL5re/2XC9kH9aM/rTOtGGnqIs20lPq6eqN4mR8THvaxY4YRrJLdJngu9fEMB7b98/KDFwo+zNLDM8+zzzstMutMugZU1QeKl9MD98rvUe6DkpuN8jSjc5CZ5wsi+RSJbFdTJufSSutz4Cu8ItZPdA26+isD/NS4SQd3FF/TDp4iX1zvai6OpvhV4/b9RGdyTjXun5Sej2+NAYlowy27WRwl3zBXnR+Z3zeFpNPxQNlhjHldVe/SxhNnWwXnqbe9+wbSWe415dTOpJeZDqdJLF4PWMWn3QTI3eidZv2OHcZO11oW4f0TNtjN9PDMkmGhnU91Kb/91ywJX3FNC8naSCZUW+LDLsnvUjjy+1ank2R9K1ADZCJDmdGvYP1Bp/UIPneSLmZYSQRZhpEme6Rtabnp35LA3gaYBP2FQGlQYeDG8EFRBpkd6UpwQfMnlgoTVyFXXXsC23/joN6mhzSIucc73GQlS4H1Vev7pPCOOuWE+FDPHNDEy7yGq5etxQvMd+juuV7aQA/07bo+1D7bT543xPfFUbOlz0c/519sdLCaw/J0IiGZ733e+lkX+aGMDmOZ1p9IZjE7j0xNg1HfMGYxs9nGTvS4mTfsk3vMA/sB2kcYZmNfMQmH7q72hrDH43ddEDPzdRITJqM9rwefJHO+156kkqAx63w3hNtLIo/rs3YUuN5jVXCe69oXabJsMfTmYyPvHy8nac+5pt9D+t2Ls2uJ1Wtte+Q9E5JL0r6CUnfsCzLN4f3vkLS+7Suzi9eluX7RuHfqQXjxMTExMTExMTtxbUyjO+R9JXLsjxtrb1d0ve11v7BsiwfCO/+nWVZfu2zBH6nFoxJvFLPR4wI2QKGkYw2dol7khgrGSfsYnV2iZKSwYg/8+PWRkzcrmc9owNpLTpg2fLexeqMp67n2uxi0y6V6WCZJhH8SDTWE0kl8L3RbrdXjp6eU61PG5HWO/qeKyNnjx/qcgsprRlGioqKwST7OGJwWOaprApNmSkpHGhbHC5tGwN5PSQmOLWrnli5niXxcmoXI6O4xEz7MWmex3vasLmsj6qTxL7xeY/t9vQSzOO+7ZwMY8JofOyNhUkKMhInEqk+e2Js/z7Vo4+FdP3Cq7Tug7vCHjGMCs92sWqpXJKUKTHBvO/1h2dVCeD4VuMR+1MxiDzar4xePqZLNrLeSwyjp4vsOsuH2MfoxdtfYiFvH8t4PSlaluVD/Pfq79MlpQXjM+N5pIITExMTExMTExO3DK21b2qtPZb0YUk/Lul7Oq9+bmvt5dbaD7XWvra1tpNAvFMMo7NY3Bkl/Y2kfEtGg8xBYiCd/Us7aIbNUz4YXtJlSTt1hulM5QOtd27122M8811cYt/ccCft7EY6PHynfk+HzUuZcUrK1iOdIBp8ePzSekc/KlOvO29DZyE9ycm2x+077XQeKw1dyC6nPJA5qXKlsdDI+MMZXGmte5TOcyYqPVR6Zxug+w3XYTzShn1L+rCp7/TaH/vtiFVLjH2vTJ2dT3qsiV3jfdOmDKqfn2v7lCjXLxvlu/dsX/1A/51llnQCE7vpZ0k7y5DYRD5LjuF77PBojFLndx8LyQTTvRclGUkalXT9RlImKTNgu8aElAcHxzrOCyPpD8tvXx3SxDy70WXVXzGIlOokKQkZxhR3TwpV8Y1YrKOQj5607/YavUivI0Wf1Fp7P/5/77Is7+ULy7L87tba75X0ayR9gTZEMPG3JP0KST8i6bMl/UVdFuN7RpHfqQXjS8qiTKe+k3GIL9p8cTJSQudA443dleNp2Vq/j5TVd02aFFF62pgWGi+wLJLhAAeLkeFAGgT5TrJ+Zh5dRORGJL5ASyKeNMFJ67zWQqUWSwwniUyIUXp6VryjBWOabNzq1ScUPwFHuiyzlGYuytKkyHvpcpBPA30SgTFuLva5kJTW9X0evulZ+yYRpsfhixc/ki71S4bVE4F7f+qJG+s9twKX1vlm/uvey76epUWdW/v7mCC738doo5D6Tk/Fo35LRmajBQjHDmI04aSx2Te1o76RFpmH2tQV/Z+OFn898WiB9ZWOjE3jVZqLdo09qf3uWjzz/X1VAjwOj9c3kclLh+d71+LUn/UW1ImcSH2Zi9001t4uvC4dxpeXZXnHzhiW5VzS326tfZmkr5L0Z+z3f4J/f7C19vWSfr9+Pi0YJyYmJiYmJiZuN940zvNIlzqMu5DOYIiB3Rm8pDUTRHFsVQ89wXMX67sS932XlHNHSsVpF7+LYbzAey7O6SGJTAo88zOJNR9oW3RIMUHy98bvRwrWFEveV97RJ8YlGRAl1wxpd5mYTiJ9s69Ij4xJYsiSeJTf9wywKuwRe5zOR+WZ34kpZng0uiCzWFcyjAUyI0klgvGkuuVJMPV/YmOSYUoh9THvQw/sm9RGXATsSHXXE8kzj65ScaCNmgBZpHqPPukSC91r0ykd6b4wUh9xiQfHgvp9V5wOiglHotneZNMbKz09uxhGhseZL/kjPcY9f/M8pPFPeOZt2g0NmaZCUpFJv6WxZ1c4qd+SadynjxGJ5WTcqe56BliOXRKj3qktXiduJJrq6nbheqykW2ufLOnXSfpuXbrLeZekL5X028O7v0HS31+W5SevrKm/VtJf3hXH7WVtJyYmJiYmJibuHC6e82+IRZfi5x+V9C8l/SlJX7Msy3e11j61tfbx1tqnXr37Tkn/sLX2ii6NYv6qpD+xK4I7xTC+qMsdibM6rn/HHYy03uUX9tX38Pukw5iU6yvuZu/WlTuu0e497YDpNiLt1MkwOMNI3TjXtSr4bjcpLx9qs7NPDqNpIOC6lvUsMUXC74XEsibmkLtQZwHIkvR2UakemJ7EXI9274wvsQyJYSycaa3JnNKe2rS3FzIiiRlJToWd4Up5K1at0phOjHCXT4mV8Hw5w+jMamITe+2mkPQI07v+/74cQb1Hw6R0nvhIT7jH6rJO9tFz6zG0u9paGu8Sa8k+5s6xR2Vbz56VYaSOcqF8iVTcia11I7EknfBxNrUr7ztuCMd+VmGmOvWxg3EnSU+v/NIcMWrbSXrmbS21IZcysSxS3L0xdSRBeKhsJOpl6kaDu6RytwNvPMO4LMtPS/r8zm8fkfQI/79b0rufNY47tWBMjSthJH7iAJIG456Sr3/P/9OCZpehxkhUkkQhUjZgSQYCLB8u4KRL332n+MbzmCj/ixC3D9Q+aR5qu35o0dgbwJN1c/pmdHyXqxtUOGnAHNUJ65oDHQf/fUTkbvAxUn/gtzyer5CUUFx0yrhdPJ+GMZ9wmR4XCXv4NGga+a9MC5XeBMdy9lMomE5OvpzY/Bmfs88nn6lpwOS3yeDu1J41rftLSluaxLmQ9mPWetPPaMGYJuQkkmZ8ySjrvraNnJ4iTI5Rni5/lsbmtAD2cUtaLxJZHt4neu3KF/FJDMr73kIuiWHThjAtwFKcafGdNqicpzxOxyjuNOfs+sbLZ7ThIdIGgO2zVjgVHz00VJmU9Xald5/N383irXs04O0qx4mJiYmJiYmJiVuHO8Uwlhi0R63X1XcgZ+HZPmzCPgrEPZGK8MzFI2mnzbRxx8nfnHUgA/NAm7OHU77oV48soPu2JCNVaUiK+9J6Z+/uT4jEgCX06i6xEbuU2RV+T8xh4UzbYjfWE9kaqjoktiHlYYTeXnT0fU9UnNLQYxv9WWKpUpineEZ2zcVTZEnoT7PHPlUaRufy+n19OzI0IHrsk9RnjNM54fzNmdUF4SQXJbvYQuYnjTMJiR1KTFFi0nv9pQx8XtRGtaX68mNtj2HO6jqcIfO4+X8SdfZUadytFselVKYFr+9ULyOJhjQWx1M1yplefks/wjScGhlvJlFyT+3Fxy1n3pMKwkgKkuav3nya0sMx4QVL27G2T7ByBjzhQNJHB/G/+XhrMox3asE4QhL/sbGniaQ3IRVcLJcGct5zQEwijl1NaJQHDiAFLhiZHlqwpsVL/X6o7UVS0oOjGDVNeoyHiw8fyJ9q3fFHixfhvTRQ7Vp8jPR2qF/JeNKCkX4wa0HeG3grD0m3qKd6UNekysCJ2MulaTOJpzymcumJc/ybXaIe1ncSFXMCSosX92XoaaC/x0K1Gz+2sL5J7TPlleXs+sbsX/QrWddThMU+9tSeedpcFNpr5ym9u9Qf9tnU+vvulJ1p5BhGS+O6r9/o6SGpfezCqL942lJ/KVCnverpSNt1wfruLcJHutsMJ6kOeJqkrEta5XgY4iNcFYdXTweRCI1kKV/gOOxhVDrqvVE9Jf+efDf1u3vabErofcDVUKj+kBx311j5U7oteOuKpH/eLBgnJiYmJiYmJm4ec8F4q5HEudzdJDHWiJlJBizJX15P5FfgzmzkL4vpTTtbT1v9T8MIPx+I7AdPQSFL4iKXtGtOu1Cml+xcXcniVRpPtfYPuG88iWFMqgVpF5uYyEXbDERqD25A8OLVPZkvimcrXYlhHLURdtKKm3Es2mZjmR9nf4hdrGN619t+Pa80pFNdpFzWiTElM5rEiWQe9+kHHqd/lxjGJKo7tPeY30pjMghzuBTD2xVZc/aBZHhCVsgZsiQNYHrONO4vBfekUGD/9nrqMU7PIkWR1oYl7CcFjoVJTSUZYCTjwNS/j+xdF+2yDfTGIO8bjJ/MdYXNtp/qMUklenPNCElUnPo085ikTEnq1WPsR3Mj0zViwKX1XOVgPPsaH94M5oJxYmJiYmJiYmKiiymSvhU419qPYI+lSzuhQo+dSLoqvlO8r229MXeBw50z0yR7bxfbOGK7yDSQ6fFd4am2XbNwh8dzWBM7l9yj9PR3nP3o6cGkExMSO9LrbqNySUwwd72sB2de9jHaoP6RlJm2xJI6GzNqv3RlUnWT2jzjoB5hYjdT+IlN7PnqS3GmOhudRsN8FyubmK0L/M62SoOSlJ5Cj73wfk8fj9SZHIWdQJ3LnnujipvngI+YNmmsB8f43PflE63beWLIWAaebrbVpGNLacEuQ4TEhqd23PNxKOUxgWWadP2k7f7dC4dSKG87iY31vIzmDUo5kjssjsNJz9XB+JIOuL/r3/R0RD3fdJHV0zlP/cnrIaXR235JwKp8niqfSc/vvXyTy66bx1ww3jieaD1Q9ajxNFikjp0GtJ44UspH7VG06YvHeuYLRvdZt8+CMU3sdJ7NiZZWnWnC4MTuhgcpnjSYujhs5Ew1iTp6fvsKPTUBn0hpjJLSW0ii4HpeafCBlYMpwUnTJ+yRFV+lTXbPiYn527VgTOXnA71PEknNwsX3PgAnMaz3nZ6fxdTOffHBdHqf9vJP+U7iO9ZdT72k+nJPJJswGjPSgpBIaiE9Qw4fex5pu52z/X0MYdPJcRIJ+uLERbxpkh+J/Bl2obcBG32TNvtp7JbWi5Lks9Y3Gj3jjtHCiXPEaFHGb1IfSwtmggtGvpc2f+l+tOFOBpREz0gqjTeszzRejZx9s145B9S7Po/yG26C3joi6bcm7tSCcWJiYmJiYmLi9mKKpG8FXlHfKz53Hc4wJo/93HFyd5l2Lbw6I0fDEcZZYSfR65ndj3biaefa81N3jnteGQ4ZpcQckMXr7aDrt56IvX4f7ZATs5oaqzNOxbw8wrXuKTKuskhs6y5xozMl0nqXy98e43emk9cDu3fWd9cpMmfasMfcqadTcXx37sxVEuekEyWIkUJ+UmWo3+iGpqeuwbQXEmuZGLCUxx5L6r+zL7NNn4f79KyQXAN5vG4w0pN4jKQbPBWDPhHdP6K3810i2fomtfldokwfj3rMKr9NYxyNTFwa05MM8NvUriptIwmCY6RWU/BxbV+1CBoLSdvGXdVGOPam8TOlQ+H3xOKld1M/Yd2e4T3+7v0/9VV+z3hZT4n9HDGnybjG37kduH0p2geTqZ2YmJiYmJiYeNNw8Zx/Y7TWvqO19uOttY+21n6otfZfDN79fa21n2it/Vxr7c+31u733i3cKYbxo8o6TK5o7LujC23vVly3wwvqCL8nw4mCpyfpX+zDvlX4dd2lkyWtd30Mh45565t0Pi9BBWwP/0J5V5f0GROr29vZJwMMxy7drpe0cXdTvcHZz3rGdIwU5Vlf/N6ZsXTeuJ9kUHGkcik8xDfFGJFhTE66PZ1MA59VWJUOOiKvNKaySP2kxwJWOO7Q/ExrHdtiYxmetw3vi0l3zlmoZMTk4SSjA4ZZoJNu6qrWb55eShiSC6Gerpn3BVfmd7arad1HZfc0PmBgP9qZAAAgAElEQVT5jnT5WFb7smqFxPT09BwTK5mmxl26ks+S3qSvmO5HUiavx3p/F1OZnqdv3IF6L71ejqMwva3tej+VZXJLJHtvJA1LEiWmi21xxGglqVhiRHt2ADeHaxVJv0fSVy7L8rS19nZJ39da+wfLsnyAL7XWvlDSH5T06yT9mKT/VdIfvXrWxZ1aMJZYqzfYSNs+pvib8K1P/t5J0uCVGrgvTqhwXt+4KJQdKS0Ee5Z29TuV6zkRJhGGf0NDGaYpYSR+apa2keJ0z7ou5XE0QKcFD/0jcqNQk3xqKxRtc3KodCZfkUm0w7QxDxV2HXt1bO+ltucLxtT+PO7UrtLgzzJnuXncBP1LJtFX2rx4mZ5rPQAlUfLIEMH7iadnNHH7pJY2SemYvzPcjwwwmC5f3D2wPLgxWtpgeR+q56e4un9Zadsy+1DrI0K9jaRNDse1nvHSLoMRvltX7//J4It9umes5uH4mJkMmkaLsJRGabsdpcVQTww6WrQdhGcuZt1n/EyLfWLfhSPHcy//9HuFNzIsTeNR2hzzm+RJhHFzjErH3o424TeP61kwLsvyIf579ffpkj5gr365pPfV+621PybpO7VjwThF0hMTExMTExMTbwqKYXyeP31Sa+39+PudHnpr7Ztaa48lfVjSj0v6npCIz5b0Qfz/QUmf0lr7haOU3ymGsXaOaZef2IZdBhhufOBIO+SRYu8uA5eeSMB3cxTvpdMCyJZQbLaPMQB9B7JcXMGfONBG3Ps2pKfefaK+IY5fEyOVzvJl2VN1IBltuH9EL1//NrGbB9r4BKtnbqjRYysZD8OuMruHb9OuOolLeyzSiFlIbIunNRnXOPu2aOxbkO2K6fEzoB9oXSfOvo/SKfVVMxL74eI0F2Ml5qqese0nJm6ExIIealP30ibfibVNLF/6nafMJCOT+o1x39fa6KuuyRVU6idkkZyVTGyg58fH1xNt1x1dGvXYIxdjeztPKiCp3SQJQVK3SOMHkdpdYsJTP0lj8y4RO+cS1mNifZM0wOvBDS29LiiFYv4O7D1p24dnujIcjvFJwsB3OR/SlVzyfbtPX31z8dwM48vLsrxj9MKyLL+7tfZ7Jf0aSV+g7UPepEsbuZ/D/3X/Nkk/0wv7Ti0YP1HbnUZad4Cew+y0aBuJmpIoJFmUjsLkVeqLONgx6uqiw4fa9hfHDvdE2xa7Z9oe/Hmf0uv3ugqjFoqfgPTUQudVbeuvpbCZnpTHnoU7B/VdelOFKquaMLkoo55mmqSehmdpwbhrYmKcXEz4AnHRtr5svetI9chFeM/SeZQ2XxRycujpNSZxGY/Q8zQeaL2QrLCTmFD4PVmcO9gXR46uHT7Jc3JNk1nSrUzTwqG9O8pDWlSkdnUP7478R3JTIG3qhJsgj4/oLV5GizZiNBZS7aPSeGzx1PNKJ1U4eio7vllN5ZfSy7Jn/04b7QTqJhfSvJGe9TagSS2B16SfmnTsU3/qxVPf10aDqgzVd7wv+HPvO57GRCikudzTlpDGntu3YLxeLMtyLulvt9a+TNJXSfoz9srHdaniX6j7j2mAKZKemJiYmJiYmHjT8Nwi6WfFkS51GB0fkvQ5+P9zJP3ksixddrECuzP4ZK2Vv1+5utI4IbEfiWGUMhvWE7fV+24gwN1YEntwB5eQxKyJfaM1a+GpNmzYY2WfgBRDOJI4jSDzWYYlxTC+hG8ea8NY0V8gWYKKY5TH+1ofu1fvJdGZ8MzF6AfKBhiFZMNGn4G9bkt2pa6JPXIxPxmu0lKW1rviVD/JWpNsoouaksGXMz3+DdlAGkYdhOcFhsP24+/RHybz98DeIc6VGR72u0N7xnaRxFi7GL0kRk0iVd6zjSRRXN33rLMTSzISu/Oe6XF1jGNti/1k/4+MAXdNWyP2x8dWH69SfNKmjI61Gc8Sm9grn5HUgYy6/+4+dL0e9wk7MeRJJJvGFr73rEsFjnGMI7WrfQ1CaEx5z54xTEpEUjyp/3I8TqoOSV2FeaFa0pl9Q2PT24HrsZJurX2yLq2ev1uXgr13SfpSSb89vP5tkr6ltfadutRz/MOSvmVXHJNhnJiYmJiYmJh403AtDOOiS/Hzj0r6l5L+lKSvWZblu1prn9pa+3hr7VMlaVmW75X0DZL+pqQfufr7I7siuHMM44k2Qni6IKGPt5E7DDIRo101d/wjXRPeJ/2fnk8rpseVtpPRC1110C9c3SeXPrt0WlK+e2zW1skT96SDevHJNuubdIuog8jfS3eGZ3WTGU1GKGSPnWWhIQcZSxoAFJNBlycjlxVMUzJOGrmPIXgWOnfpzmQc2rPa6SeGkRjpT5H9oF5Y0i9iO04Mmu/oGU+V6bE2DOZTbTOu9xE30033Mc2+IYNGnckLe+++MuvraazvK+6Rwj6ZrcQOVbqdFRz5YU1thfE8DM/ogikxjDRYcr3SpFvYY7gSEzliFqmzxvFq9L2PR97HEsNIPU1KXkZpYxsosN2ks+3JMCb3Zp72+nYkzeqxu2nOGukoJ6Oj1L8T8+YsaMVZbYTjDeOggaWXFeefUfk9xLNzbY+f7srM0+tx1nVfFvXNwfUwjMuy/LSkz+/89hFtDoOqZ98o6RufJY47tWB8m9YdgE52R6LV1LGJNFH0BtYktkjf9CZff4/vsnPto+zvA4T7aUtK2fy9N6Bx0cI08JuzM6kdbJ4lsXBSPE+Tb01697U9gJzj9yNtNgac+D3u5ET6Quvy8cGNVuapXJifniVuvcdJyMPjgigZJ9U3D5CvJB7mYDwSP7n4OE1CBa/3+iYtbrytUWxUdeSTWt2/gN9dvLzrCEdusLgYcvFxEkES/CYpz/f6RjKcSAtCjg/eHnqT276qLWmxlMaJE20fSce66/mKrfQmZ8lpI+HprLTVRrD6r0/8Uh43GDfFriwflkGy6k4bZVeP8LJIR876OOL173Wb1J/S5tkXQK465Jtr6XKxNVJRSmNLErn35g2Os3Wf/Pc+1faClB5C0nzLfknjt6Qy4QtGppH5SCpntwdv/ILxzcCdWjBOTExMTExMTNxuzAXjjeNQ650OGaqe6Fda79a4Q07siO8ypfUu1Sl/7sjpKoE7ZY/bWR9nCRKTweZHw5LEEiQxTMoP08T/K+7kQ++jlYYL6fBik45Xr55zxz4Sdyfmyd1lFChiK1Gyi1Gk9bF6zgw8xT3FzxRNJ+bV08j7xAT11Bt47ywAmb/EiEjZHyLjlPruo0YMY08MmPoGr86W8VliGI+0dp8krdnjghsS1H3Kf3L5kU74ofFNEn0nhjaNGalf9kT2ZBhlvzNtieHtSURcLeFYa1coKZ5q35XXZBz3ROsxI7Fpqd14fyEj90jbR3amtu8MozPOzD/VKGhE4WL5NBam9FKMukuk2gvHxaNpDkjqBD33MmfhGx6DWr5REsN4ok3d9uaauib2zsd9huP1lKQOroKT+oa73HLRd2JEXWXC4+6pVNwcrvVowGvFNHqZmJiYmJiYmJgY4k4xjK9o7VYnOTHtGW24bozrmrlyMBVp046y4Lso12Hkbpj6K9xR+u6KO6/k1qB3akNiGH3XN3J1Ub/X935KBJ+5647a2RYDyfSknXbayRxomz1m3VL/ZcGzFE4C9RWr3J7it8QU7dIBTQxuYpL4bXJW69+cK++qT/F+0uUZGVgkNpF1khilxAoxHubBHSwnZoW/U1pAowzhvv6vekr1ncqCRk5kMXmqS9JzK6Q2RDcs1IdzlknalEU6m1nadvvUK1O2L2dJpVx3rPtqLzwfPdUnw0uSgSQl8bbPvD7Q+lQovietGatdRjGj9JBl9e94PdN2ep1h7Okc+zUxdaksE3ucxsIj5Xp0Pc6H2lg1PNR2uVKKMpIQsEwTw8g5Lblo6umIJsbZy8fPrvd+n9pV6g+99Nwe3L4U7YM7tWD8GV0OgLQMrutokmcj5eKNoolCUrhnRxoZsKQJh4MBn40Uxv14L08b85/EhAzPn3m8qYG42ORc2yJpPynExZFpwbjLyo/gAolW4YXWeVdXaTmw91hWnCgqvWlCcWVqTy8HY6YlLSL9O16T6sA5wqZINeVhtJDzwdTzcBKe7RKhJ2v1iovPOCn2RN/ptJLRBi2d2nKu7Tbi7bPh3UJKT8EXBtLawCot0qliwMnMx550VCEnblqKsky9/9MKOomKdxkQ9DbcI0OaNLEnPK9oyw2v3FCunnHT0fN1KeW+nBaMSZ2D4yfHe5b1SJWJm65UVik/LPN0gk0yjGTaTsM3abFKcPFYv6cxLC2+mZfR+JjaH4+XTeocDDvdM57bhbeuSPpOLRgnJiYmJiYmJm435oLxxvFTWu/ek+EJd77cwdXunq4eegrVvPI+GVP0RGh85grwnl7fNfXOOh6duJF2fbuaLHfII9Er73vMUhLduBqAM4yVt8oXd8j0+UXxcaWDbGPloQxvDrW966TYvMfOOcPAMu2JbpP/tMRUJHFvYm7YtsmSVnkkJf3EMDINaVfO+NxFjjPuSRUi5TEhsV3Poqye0uQK+NI2s+hMpBvNUByeDOGSm5WkQpD8nzJMln/yRcf4RlIL1gNZmXSSzkhtITGnZM16ZzMrfFNI7fmpNuVCRvQE9x7mLqT+4mFJa6M2MoAjQ62UnzTX+JiYfiezWNeRVIFxplOxUr5PtO1jM7kG69XT6CxuzgsjKYinO+U7zRuJtRwZSzLslI7pVueNw51aME5MTExMTExM3F5MkfStwMvKOmJeNcmFAZWkpW3XAWmXvIshYxrU+T+xb76D6xmAeHoc++hPjhxL99iGFE9y1JrKn4xTOgM25YvOhd3dBk8VONVYSZr58nqmHmbSZ+qxDVTiTzv+xJo5o8x6SMYJ3FXT4S7L0g0r6B6FzF/qCyOjI8ZDFi8xpok5GOlhET12yPUIec+0uZ6V54HnEUtrBpGM5GH4nenmaTRe5vu2G3cn5H2QJ7QkKUdPZzAxvamsyTruM231DDBGDBfDZf5oFFQncqXTli7wLKWdY0LSjU2SDobhunOu711pSFKAkZ7cLskK2VrWlxtV+jjscw3bTeFEmzJl3dOQKx0K4HXmxmg+NvUkEkkqxrr3/uLfV3pS+aexJY2fLgW4vZgLxhtHLVJGC8aekrR3AirKp4kgIQ0qNJ7pif+S2HKXCIMGD4XFrgyTGA3+bnDji5veotjL2sWfvoCgUnZvcef1+FQba1iKYEd1wnA4OdSEnE6ZSOElUbIvglK5eNy9xab7IOQ36TgsppMT5GgCrHcZtxtdjQwZUvvrbSo8nl2n3vTS5os2fpNEoWlyldYLQemy3tOieGTc0XBPi2guqJPIv8D808DF200y3Olt/tJES6OBkbqBNG4PvU3iPsZoFb+0zmvljRu9NEZR3YKbwKQW4u2cG0+OvxxH91nYexvYR1XEw05iWu+rDJtxp7FwdLoO46txkmFyXklji5dT3Xsb2dWmjjSu07TJ4TiZRP4joyFXBfO4990YvXl46zKMo74/MTExMTExMTExcbcYxtpRp3NsE4vCa4msqIA98vKfRMXcEfkunc8qHf5NUtJNuyymJ/lh7ImhPb293eMo7dzNJYaRu/BkDJB+H5UpQfFUYn8TQ9ZTUahnp3h/xJCxrHoMTWKXvVz8d6nPMLIevCwpnt+l6D1izfxc8sSieNyP8VtiCVK7YrtJrqtc/FdwhrHZs57qh6dnBKaXRjFudMUwd/EDSXqRVFd6Z/76vbOXiZlxf3mM20Xy9buzgImN2SVmlf3O8KV1vis9ZBjPBt/4md5pzEjhsB3zvOl6LzF/SezbY+IZlrTN7kp5nEjjEU/SIevYmzsqPG/XXnfeVll+yd0a5y5+k0T+Pk70pAWp76VyTOoWSbzMuaRXN2ncu3144xnG1tp9Sd8k6V2SPlHSD0v6Q8uy/LXw7ldIep82tqCS9MXLsnzfKI4bXzC21r5P0r+vTT3/i2VZfnlrrUn6HyT9Jkl/ZVmW37MrrEfKegucKB9oe+I61vYxYu6LL/kmk33TG7xSOtLiI4nV0mLhsdaLCSkfb8Z4EtLgxUVOGrA4ydCKNOngeVy9uFO+E5J+XxJ3E0lE0UvPCGnBlzYMfN4TI6bB1ifKFB6f+SI85cfbWhIL9yZFhuN9yifu0UI6iY1qcez1mRbX3qbdetkXwCNxIbF0vqE4OJVp0gdNahaFpLvJemB+Cmda653Vs12qF75oOdL6OMyKr9J7LOmFTlgENwgc4ygKTZvRXjqltR5hWoik8XWXp4IkemU60gaX6XVn6T2/sGlsTmRAWryk9LIt9cIppA3WLozE9z3dbJZ5UlsYLcbSIpzPExlScP+8SeRf6BEWqT3cLlybSPpI0j+X9PmSPiLpN0r6S621X7ksyz8L7/+dZVl+7bNEcFsW31+9LMujq79ffvXsXVfXXyzpqLX2zhtK28TExMTExMTEG4SL5/zrY1mWV5Zl+bplWf7ZsiwXy7J8t6R/KulXv1GpvnGGcYDaDPM6xIvqMzT0ccbjkyqiUqwmo5EMCLgTHIkEuAMjA+G7tV4TSF71E8NYoKipJxocKYwL3wi/j8TYPIYvsWaj3UgSa3iZ7rMHS8wfkboZRc49EU4S7+9KbwpntNtN8Z1oIz5MIvTErFZamDayXYkxSQxjT8zl91533qaJkTicokK2RZYLVQ/qW6YjWeomJLEbxbSeB6o/JJEeyyWx66mNJIZxF4vCb+ub1B5Smz7Tdvk5w1jY17CEqhD1exrXkkHSid2nOkkGNyOmKMXj/Sn19RSOi+fp+SC1bcbDdp7Yu5EIONVnr49xvE5eJkYTeorHf6/fTsLzpLa0S+w7kpTt8o+4q2/sK8bexc7fDJ6bYfyk1tr78f97l2V5b3qxtfYpkj5D0oc6YX1ua+1lST8r6dslvWdZlmFR3RaG8T2ttZdba/9na+0Lrp79dV32iR+9+v9v3EjKJiYmJiYmJibeEJRI+rkYxpeXZXkH/nqLxWNJ3ynpW5dl+XB45W9J+hWSPlnSb5H0pZJ+/66U3waG8Q9I+ke63BB8iaT/rbX2q5Zl+ceSvvxZAnJjDO6CqD/1EPf1XlLcTzvbxMwQiVHirjmFSVao/q9vuCNlHL7j6rE7iT1KW4ikP5bCPdD2iTKjs1o9zBHj1GO70i62l99dzGG9l1jHpAPVU7L29Kb2kPLAdFBfNtXNLt0tpstZu+SuJTGMPdq+GKe0o+yxiomN3YX0Pds2DXukbd9zbtDgblikvn/ECvNUeczYxfz5NzTIYb376TLezk/tm6btM7TPldtaYhiTDl25WWFfpf4k2UtnwLz8Rvp/Sc8t6awl34N8N/WXXv/f1dbS96n9jhhGotjnxLSxbu5pbAzzLPqIaQ5w5i8ZzDFtaXxM9ck2kFjUxJr3jIJGY2rSR3R3QaNxuudizPvtLibzZvDcDONOtNYOdMkYnkj66vTOsiz/BP/+YGvt63W5YHzPKOwbXzAuy/J38e+3tta+VJfKmn/2WcM6Vh78z7RukG700rRp5DWwunguifeSAUtS7FX4nUidj+950+otRJo96y1o2KF80OH3aYC50PZRW/6/f3sU8uDv1nXfBWPhqHOfFlYjMYwvGNMGIolzR450U9wcoJNIhQuZ0YKR3x1p05apbpGOcEuLHC6wRiKrFB/b6iiNPfF8ahepfdb1vtbOt/1IT+aHC/JXQtgpvQUet5h8orL/06fnaPGSLLD5faX7vrYXmefKm9U06aZNkx8d6fHUN4fabucPtC6rNB6lDUtqx8mxv4dFuNrHLhFoCm80jqS2mt471/ZGpPcNN/ujBVpP9YBXTwfT7eXn4+yorNLGifWVxsKUx7RIZZ0ldYREplBlLKlrsN2mo0oPwjejRfHN4vr8MF4ZC79P0qdI+o3Lspzu+ISJ2sX93BqRNLFXwiWptfbtrbVXWmuv7H57YmJiYmJi4ucrar1w9fftN52ea8Cfk/SZkv7DZVle7b3UWvsNVzqOaq29XdLXSvquXYHfKMPYWvsESf+epO/X5Sbgt0n6PElfs8/3y7L8Dkm/4yqspXY+vgre9T+NXhJYSElcMWIYkyiBz5NYw3fF/jtdAxWzkhTYyXrxaKaRsjXj4Y41sUKn4RnD6J3owTg87JGbih7Srnxf1wy72C6WRTq5Iol7C67kzytxoSz6Zho8j64m4S5rXtK2mJDtijuyai88HYLMk7NHziq6iGnEnDDtzpIkUdKI4X6kTfsvZpXxuF9CaZt9rN8TY+JjAtsI657uuTy9p9o+3o6gSJpIJ9wUzsP9fW2PRxTpVx3x+MyeayFnGN1Xp/fR1DeSEV6lqdLTM1rw60j1h0hqCz31CY8nqbD0mDaGMTL+4Ph5FL5hX/b25youLlZPcXsad80r/owYqZckqY63gcSAe33zG6qJcZzxb5hHZxpHeaj7ZVle7Lz+JuONZxhba79E0u/S5VD+E5dko3T17Ad0qfr3WcuyfETSOyV9S2vtkaSflPQdkv7ErjhuWiR9LOmPS3q7Lse+D0v6Tcuy/L83mqqJiYmJiYmJiWvBG79gXJblRzSWzj7Cu++W9O5njeNGF4zLsvy0pH/njQrvSOudGXVJRvpp1HHYRx9PWrM6xQb4qQT1/iju3s6VO0rPT3I+3jt/tsKmAnaq9LRDTiwgmQHuIn0HTB2mnm5nYmGpfO875J5eZ92faw/fS+p31ZT3tIPu6QR63Ek5PDl3dp2glG/f5buukjM8L+jSzZS01vnjNxVOlSv19hg39eg8fjKMu84G3sU2Jhckif3o6U35b4zDHVizzfrZzkxXvevf8Hc/p5q/n2q7f7txDE+Xqt/ZpmX3HKOKYWV9s19+/Or+566urg+bxo9R23d98Lo6s1jpYrpZlz3jmTQ+prpI9cB072LNRkZkPV3nxPJ53K5D530hsdkX2mZ1H3TywPgSSzqSGPF5Kj8Pv4demY8YRubxJHxTDCOlBtT1JVObmNWUn115vDm8dc+SvmmG8Q1FNSa3VJSyKISK/+f2Xs8isncKgMdDkWiyZKQYzxu2d1YfTEjbc2AeNcGUh7Sg6SmEs/zq5IeRyOSB/eZihJPwjJMIBxMOFqOJQNo+GeRM2+XCxWwP+4hudg3QvbhTHEm0nRaMyXgmLcaOtT15s/w42NaCpWlbPHqkbZEqFxfn2tQfVRRGC9yCqwsk9Yf0TbpPcaVFXX1zrM3CixuwKr9z5YVO2mQm9QcadyRRXDrmkgvB+j6JdpP6wrG2N5Fn2jZ2edW+9/5KA8C0MXoQvqF6DttaKj/C6+nE4vRvextPF0WzP6Q+msJJ6UmLj1158MXLaCPNRZWrN7iFtsefRPVuyOXGKmlT1ksj40nzk9cTx2uOPcLvacPoG7X7Whuwnds3PfWR0Xh/O/HWSKXjTi0YJyYmJiYmJiZuLybDeKuQGMYkQuOOJ+04e8q9dU3GFgXfGTENHs+uHdzIP1gpLCzaMBV1ddbGd5ApD876uJjxCe7Tjr63yx/5kkwe+V2UX2Gn0w0KzC/DJkNZ12SwNGK0+DvTm8RgZANdMZviY5Y5y8/TRrFm/eY+7BKL4uoVB9oWn7rIM7FCdV/f0B3Log0jUHEn1YAz+72+Te2TZTFiXg4QVhnspHoim013Nay7UV9OrCIlFazjU3v2VNvtz++rnkbn0DMdF/h+JEo+0Jrx4zsMT1qfenNqz9hf2D4phr6P3wvOTPfYtyQqTiwqx5HeOM24CskoK7mkGUkVkhjWx8qKi+86G/ZA2xKupJLTOw0opZd9MeVxxKymcnSxbxpb0nzo4wTT1lNJ8fI51FgJj+Mnw9iVbykbHN4c5oJxYmJiYmJiYmJiiLlgvHHU7mLkxmKX/gp31Q/wW9JhHLESFfaptnfSUmY3+RuZFVcMfqDM4BQzUGzLE631A0e6Rz19z0JyuJuYP7Jn3Pm6Dl7y0p92rkTamfZ0nJLhBHf5ftJJMliqMD2dybijp+PodUuF8N7u3PNONiy1m9SmT7RpB9TVre/J8pGZph5iXan3J12WHcstMSVeFk+1fWqLD5npf2ezqL9LJ93MQ2JR6p4n2Iz0U5OjZq9Pz89TfENdXa871kP9TyRm3xlw1/060VqyUBix5mQOqTdW4Ak01PHkOCRdtgtnrMlWJwlCYvSTviF1wPk8tf3UZnuSjCR1KCSpAZ+PxkqXkjhDToasdMGZdsaVxrgCw04uspwNr/h6YUl5fCTDmFyDJX34HkZlXTjXuu5cJ53zMg2xEvPvfeh2MYxvTdypBeMrypMeG/M9bSa+1IApsuOkSpFN/e4igYQ0oEl5UGJ6kwUin1Fcqau0cmCQ1gr3uybfnvjYOxkHWw6gvvD0fPkA7RN2hc30uoWch19XH1QqLA+HA6Pn2/OVrChH4hwibRCo4J7KL01C6f802fcmMN9U3FcWE9Y3T5XL0uvpUJt+sGufvMsH4WiSYZulv9EkjmV9pcVWpSP1VYqSeepKT7TlSIsbTlre/riBYnqoDuCTnBsn+KTIzSHLxcWxrprihjLS+rSrunLseGTfcPykv8dk9d7bbFU4SaxOJEOPkapI2kyxDAh/5qpBaUPofdkXwjRKqqsv7F2M7aBVPcP1duULR98wpsUzw3mEa/KF+BjX0XhO8OjJNH6mxRzHoKQ6kIwC2Q+8DVUc5THg5jF1GCcmJiYmJiYmJnbhYi4Ybxyvar0r6e2k3WdYcl2RlPQJ7vhGp8QwTO5skzEMd6uJfRqxMWQYuSOkeMTjfqBtH33u+24fMYyLRys9CcnQICmR90RN+2LEWiR/eeda5ysxjElsRHg6T8JvycCKdUxmy7+Vsg9CPueu2921nGp3W3LFdTcOqfDofsfrnP2Bqhmn9t6x1iogbihGdvg4fJOMAZIKSI/9qHRSDFZpSP4TyThRJSWNHyPDCLJQyWiF8fTavP/O87LJVFLaUO9zXBy5BgULLWEAACAASURBVEtt5Vjbkgq6P2Fe3SjI/eEmaYEznm6MkoxMRicNsaxZNy6G7U2ErGM3pkxthPH4/QieL443qY303B8RzqYnNSrOjcUwfoLWJydV+X4UafBTlHyecuO6Xj9IEqwR+5uMAhkO69YZ5Z8K4d4IlmUuGG8DeuKAe9ro3bygbX0dip/Z6DmZ+aBDHR33S0f44D9agCV9JQ68wu/eKXq6WyO9jfMQzi4Rem/Q9nxRnMiwkkiV8aRFVlpE9sTBSRzhC1MKBJiH5CMyhe3ibE8b9UZdBOdiLg8nxXegbfEfHbHz3TTxp7xU2+aRctxMpQ1Rr33WPReEvmim7msvvx6P39f/PQfkjmT1yUm46mnRttV5Wgy4akuNIyyzdNxd2nyk4wAZj6uhODy/J5I+hvtC5esxfkthpk3i84BW634soS9WXayZLLCTI3sp91suilP7ZB9MC9MUT91zwVi40PaijHEnn7++Me2lkWNHr258zKXo+lybjQznqbS4TM6zX8I3ruKVNtQ+91bcaQObxii2hbQIZbp9ke8qBj6nvZ72fG2YC8aJiYmJiYmJiYkuFknnc8F44+gpTPtReu6F/kKZReEzF7uRSeSRXoUk9k2iOu4uE1VPRiBZaHNH7yJp37n670faFqc7K+miG9+9S9vsjL+XdsVkGNI3KZ5kQODhO5Pku8965mIsGgil/LshUsWbGIHRMzIizlDWM2/HbpUsXdb3yCiI+UiMMY1Jqm+8pA3LwPhodV/XYgtonUtxLn+va2KU2I9GPvZYPn7MH+Nmv6U4kSciVRyVxzKWYzhUSWFaWDfpJB1X8aBYmOWf2NQCmbaE1DeY3lfxXs9itJ65KLnHxlCSUWGxH3j5sV7Z51mfnvdkyEEjxWOtGavKQ5IqpHGPjNMu0Tevde/zRjoq0yU0XvccM9I4zfD4nnt12OcIVM8H1QmSagsNVJKonsaiXg+pv0hr9ZHE0FJlytPTG+OTNNDnKd7fPobxrSuSvn1lOTExMTExMTExcatwpxhGac3QUEcxGTyMfLfRD5u0rWPicUrrHXDP1Y6fBEEXBWnHmXZP6SzkE631lCqcxNZw1ztqAAfKu7Skb9NjGSu9zvomBiUxJx6f/+7K1s6UpG9Yh+mZ66pJl0wEXbt42hKDxrQlV0V8j9/6KT7OzBToc83Tw7pne/Dd+5HWuksvXN33ToKR1mws79mHXH+txzCOzstNDAOZCuqVVTw9AzU3UHFDF/dhyvSSwRrtsMmQpdMuUtp4Kg77Gseueo/17WEl/3NsA6m/kO0mM+r1wHCeaNuNyzG+oW5mxVPty3VT05i6azzyuIke8590HNOYOkJiRC+0ZqTrmvSwkz5dGpt5TazlQef3+i2NBWxLnl/miX4zmYfFrglsI6dau2mqeJzNTpIRSgPvhXcvtK3/SxsEabsN3UpWbHlrMox3asHoVZBo+zNtOhAXbz7BJQtJv3rnS46wD7Ue9H1hRWMADsppwZhEs8JvI0Vkim74zBdyrlTsv1PEQ7FEMrhJi5y0mFJ4Ly0wevCJKF2ZthQ3xVSMM6kW0AdhMirqKYI/63sjsZMvhD2/PQe2brDjYkC3BD3X2M8o40zpSSKrno/CZLnq/S6Jgiud9Z7HzcmEi83CqbJT8bRw4u+HeO7PKvy34b7nU9E3mVwI09E4HWnLfqchQlrMsy8d4T3fwHLyrcVz2gAQNHJiWadjPEcbuTSO7HK2zHGYbZtjvI97rMe0EUlEQfIKkNRYeqJZlr+PPUndxce8kch61M5lz7yP+WZAuvRXSKO4+p3Hb6Z+yXz5oo5GL8lfJuuwNq33tS5rXut7fltpSwvpW4VrspJurd2X9E2S3iXpEyX9sKQ/tCzLX+u8//sk/QFdFvn/IumrlmV5mt4t3LqynJiYmJiYmJi4s7i4eL6/MY4k/XNJny/pF0j6Wkl/qbX2S/3F1toXSvqDkt4p6ZdK+jRJf3SfCO4MkgKttPZtl5RnKUYob/C+s02Mkysi8xnf9x2uNDZ84HMXufozps/z6GEno4ykZN4TBdZ7/k1P1JlEj2QYE3NF9sO7R8q3tM6PMxPcDSexE9OT0kbGKCm9c9c8OlUjlUt6xjyyfboxgbPizjak9JB9S6DLDzKVT+1ZhVXXpMzu7HCPdUxK6onxZBoZT2Kpnd1g3yATWWHT8If9MolzGaYz/6cIM/mxPMR7ZKHSKVKuEuAiveS+KzFpyW0Jf3f1CPpUTBIYosK5r8y2Opvlvgy9PTzRtloNJTA0kmK/HImXyQKmtkgWKhlDspzdsCe5DnJVBu//VCdKY2oSq/NdIhkYES4t6KHK93F4xrLkmOpjGJleZ+8rvH3Gx3uSXsQz9i1PB9OamOtbK/RddC0M47Isr0j6Ojz67tbaP5X0qyX9M3v9yyW9b1mWD0lSa+2PSfpOXS4iu5gM48TExMTExMTEm4LluhjGFVprnyLpMyR9KPz82ZI+iP8/KOlTWmu/cBTmnWIYnXFIJ3okVqIp73apq0el3ELakTrcaCApDvsuy58l3a6kw5jYFjJFSb/P801mJeXHmbheGqm8fF/brlDSjpQ7yqTPlOrOmaukg+rMIlnfHsPo8SzaZmhPtXFh0tMXS0ywu/R4YO+53hTbUGqfPfbOy4JtJpXZq1ozAtL6fGkaibFtOAuYjLKSvieZaZZ/cqtBpoFsV8VD1z/OMFK/Lznbb9rWi3yq7X7gfSzpkHn4zFfS80vleKBt9og6YKPzuXtIbDbjp+JSxcNTYhLDyDDSRFL5qd/ua1y3HIc5bqXxiGOHM4scl8+UmbYRC5WkUNQrZx903TrXy/O+7hKcii9JhdLvyaiKxlCJue6duV5X1xNkX019mWM3mWky5HXP8SSN594ekp7lrjy4oU96fqvw+vwwflJr7f34/73LsrzXX2qtHeuSMfzWZVk+HMJ5JOnn8H/dv03Sz/Qiv1MLxiOtB8QkFt6FNJFyoUJxQzXoWjRQYZyDFL9NFnsuMuWgko5zSqcOcDKmWDIpJY+srV1U5OI4dkh+k0QLNfnyNJEkcuFicmQkwYVRwQeIJIZNjXwkFqKonGLAZJw0sgLuGW0kv5q0pE3+IAscLNOkt0uVocAye2LPGHfaaHBhtEs8mhaEyfgo9duKi1dX0nfR7WOtN3rS2ndgaguLtvsGFe576g0jDwJcBPFoxnrH2xK/PdKmPdDvZjI6SGB9+8Tu6iG+iOUYxQUjyzRtPFPcVTcN1wPc+xh239KhcO/x0PKXRmksy5HKRFKPYP/nYsz7Ja3MWY9s06PN4ajdeJlW3vzEJ2nTLnpjB+NL8aRNOO+TOpang+lJfYxtPonsXW1jhKR6keasEfFxs3hdRi8vL8vyjtELrbUDSd+uy2bw1Z3XPq7NgT7C/cfCu69hiqQnJiYmJiYmJt7iaK01Se+T9CmSfsuyLL1TSD8k6XPw/+dI+sllWbrsonRHGUZ3f0KQHeF1tN7n7ojw1fZT5XOq+T7FehW2sxvOtnh+kjjiXNu7vR6Dk8QjSUSRdqlJkb7HAJA5dBaAxgCn+G10HvGpsi+vJOJIDEVitvheOlu8QCaud+Z0MtpI4vvEvlEMnQx3hHcLjMfroMcwFCiKJJPkZfVU22oUzm6OxGlMQ2I8E6s+Yl54n5g4sg0VTvKz6FID76/JxRLvkyEXmamU3n2Z6Z4BS6rTlLYk0kuuv/gtmXm2h5TGFE8am5w14hnuSfWFrCMZLLY1H8Po7srHXk/HqF1R5K/w3i7JCq+p/4/UiVLYbJ9H2vYB+0DbJ4xRVYTGQkyv37MP9vqvt7U0J3nZOnOdpENJvUlaj4UeTvqGInTe91zk3Qpc30kvf07SZ0p617Isrw7e+zZJ39Ja+05JPy7pD0v6ll2B36kFY4k8Eq09WkBw4vfFW/0+EhPWsyRu8AHEJ4I04fqg7PlJfsJo4doTayQ9Q19guT+3NJC5ZWBaqPkg6JMHG14NhtQ34uRCZ8s+qNMC/om2Bz3X62E+pM3EQ72bJD5N7YdtgQuIB+EZrWZ9McpNDBcG1Bsb6fclsK2d2HPiVJdH43naRk7Be3WbFjxJRM7JM21e0hGbBd+weH2nyZeqGdxgcaKkxTTf47OGe6pzcNHqm6gzbRaptKhnHnyDUGnycJI6DOH9m2XT2yR6W04+adm/Uzvn0Ysjn527RPEXykffnYXfk350gfUgbbcHPmN6km5oKnOODd7+XA3IFzdpYZTUS7wPuSV9OjzA9UJdXO4btLomf5kJzFfSC026vGnTwXHARdsc47n4oz/S5BQ8LR6Zrlslll50LY67W2u/RNLv0uWQ8xOXZKN09ewHJP0jSZ+1LMtHlmX53tbaN0j6m9r4Yfwju+K4UwvGiYmJiYmJiYnbi+tx3L0sy49ovGd7ZO9/o6RvfJY47tSC0Q8l75WcW326ZVuFUfdkesg0OjPwWJlZGZ0Ew/u040wiNml7h0wF7GQk4uLpuqbdZGIBkkh6JHbjezxtoXCurDJQYNpqR5kUpyv8es939DSCYLk4Q3OgDUtChpFtyJk/ipIZJ9kYMjv1jqslnGpt5ZuYAY8jsXienzQkJRGQs4rpfWm7zfXCPNSaEah30mkWLJ90Covwe6VhJGJKbYSi9lRfyXKVhkhkxckYu8FNYsAvtGaPPW6KXFPfSelO6WU47C8+rrFsz5X9Ho6YqSTSPlZWwXGcatMueiJeZ3qb1oxo5YeeFdL4mcbpxDomkX86rrInCh1Zjyejt4rL05BYwl3iWn/mTGUyAEzzHOuxrkntaTQPsd891ZpNZxqkNauY5kOWaeo73v54zGdi7Kn+cGtwfSLpa8WdWjBOTExMTExMTNxaXNPRgG8G7tSC8YkuMzRiGpOeVk8vIrniSErFyV8jGQQPm2EmdijpYfjviZUYKfv22Mmkq5d0GNm8k3sEj9v1Cf18Wu5iuZtluTjj1GMYa8e569zZkXueI6QtncawaHtny3QkPSR+P2Ia3J9bKt8Un6ff/991sk2CM0Vs04w7nVs7Sif7AVkOso7eHhh3cnVExorxJt94XqaHyvqrqf2xHd7He+f2DSUV1ZZo3MWypa7uSF+RfZhpc31F9iHqYTZ7xpNckmFZ0uemGxXe94zv/LfEwPppN5UXsqNMQw/JiJGnE/V0MmX3PV3nQjL0SGOiM2GjMSMZ6/UY7Mpb6RvzHHVKctIyhPU5Yq5ppEgJi0vkFm3re1KHPhmRHkh6iHsp63jy21QnlCJxXq2y4tzJ87J3zalvOuaC8eZRIuGkaJzEsKPJgUrx0nYj7Ik6CkmMkiwM/V1pvfjj5DFanKRnFFOlhQrvuUhMk0chDbac2JO4lgvTZMxDkV8NAslPWw8Uhdb35+F3it98sV+bDely4PQ24osk4bm0rrMkNi40rY9cq6tPatJ6svFwuDjptaU0KYzEXAfhvWTVyW965dLbCDFsLhCo2M8F48gIIk0o7C891QxpPTHRl18h9R2WBUWlaWOWfPm5RTfTLOW+nFRbeg6hR4srLkqTb1GWlYstH2ij/ESrZE7SHg/bROXX1Wa8v1xou759g9/wbn3r9e0i0eQrMY3DaaPWU/fohXNk73FsKqTFlC+CfO5xEW/yQdpbMDJeLyv6vj3FM7bfVAa+QZC2877rGfsYDV3SWMhwktcQqkxVGY4IghvFNR0N+GZg1wZuYmJiYmJiYmLi5zn2Zhhba58p6bdK+kXLsvye1trbJd1bluUfXlvqnhFJRFzXtDsn0+E72zNtdlFJtJCU1XtIzEGhp7Rd4E48uRPhrm3XbtjTk0TOHl/aDad737kyvWTvRmzrA4RDhpFK1yNxO3f0FJvVbrnieaztXSxF6MwXd/F+bF7asfdAsRCNpOrKNpuMQwo9sffI3c4uZpq7c2e7pW2XHmR1aNBUz87DN0wX80eWIDGVyc8bkRjcKmu2OWfSaTSQyo/HeDIvian39iVtyuIYeaj6TqxoxVnfer6oYnCstVsZaX83Pyfq93XGJW1EiI9w/0DZzdcuZqviYNv3ejrSttGQi7adVUssqR+bmYy0Rv2FYD0kV1MMs67PwlD69z3jJDe4S3OAjwlJSpLag5eFu3eqd934RVqrazAdHAvqvVTmribgR3J6HtifkiunB9q0VVeT+qhuC5bXczTgjWIvhrG19p9I+n5Jv1jS77h6/EjPaJI9MTExMTExMfHzFiWSfp6/G8a+DOPXS/r1y7L8P62133b17INaHy1z46jdnTN/abfFeyq974Lr93iYvgJ3Q5ikx+G6UK63ODKu4c406W4SHk5iGFxvMel7jlhLxpXKPzGMruhe7y14t8Lkuan1HnefSeftxfDM003dGT7n7nx0ckCPbfVw+H1Pb7F2yK4kznedEUrGD26UkdhfgmwC9Qmd5SP7e6Ft5pVxUuePSvXStvuOZDhQSENl07bu4b7heZjOEh4pM6sHV4VwcbHNDHr80mXbfmy/8cQiOp4nvPz8tKT7nfcI1g2vqf8zXtePfKg1w0gWtpcHtqXeeezOdpGBLFBvkWXLE6GcfeMYlnR9abhDViwxkalfO7PMsO/hvcTOMZwRDjv3HKd3saSjMZf5domctD59S3i3cGS/O/OcXPF4eskgJqf1yUl8cutEfXga3/jpLz+l24LlWhx3vxnYd8H4ybpcIEqbtrBoW3/7RvFI6w7QU/Z3/4yH2h7cuOBJEyEbbjIaSEgGBMlX5EXnnnlwEUby8eiLu8oHRWM+CPIZFyLpCDem0cPpTQ4cvB4MvqHIquCnAFR4HLTSEWW+MHVLPGnb6j1tMEYTCuuEz9Lg1bOorDT6gvFI25OUK9Qni/J9jYZ6Yu4K261Ce+G6da20LvuRKImLjpFfSCKJFrl4Sz7cCm6E4BPcIdJ5eBX48dF6wZg2/EfWOZ6eSPeuElD5f1XrMvdyO9CmnbNeebJKap9ebhdaG0RIl+08HQXJ8cTbn1tJe/+nFa+Po4RvdNO4mdoiF8f1LvOTRK8pTubhtbq9urLPp2893Po9bSqSOJx59f7PhSV/23VqzsjqO80bHM+T+gTDq/dOleesNNYllZXRXMJ+WYv9+1rX52jBSOMXptENcljmtwa3gC18Huxr9PIBbUTRhS+R9Pfe2ORMTExMTExMTNxRlB/GOyyS/i8l/fXW2ldKerG19r9L+gxJv/7aUvYceEmXO6KeiLOuI0V6+vIauTpJvhVTmM4guJiAzEramdIIgkjuHNKzJDapsO9pLZ7y9HOHR9bB05NYM4qXknuelF5nQ5PbosRK1I6SfvlSmAW6YeB1V3d0dQRXRUiqAEkNwPPlopd0OkxSbC/c1zbrR9bH08/7Xp6TmJAMAduvlzENYbjLr35HESP9c/qZyz2jq11tvsC+XEgGLBRjkcEtHF8Feu/emkFM4/eBFca9e9Lx1UBycHU91LoNVH5ZLvWMTC+ZWfd9mfoI667Gsle1djOTJBXPwzAmtYTEMjrbTyRVmxe0USmhCsNJeEbmasTiP9RYBYljzKh801xCMSulFiOGKzFyjC+5CEvSC9kzHyuTwSfjSt8mMTYlHkxvGkuSGgDnoQv8XuFUfu8rGwC6OJx1mRY0x9o2prpx3ILF3/NgrwXjsiwfvrKK/mJJ3y3pn0v67mVZPn6diZuYmJiYmJiYuFO4ywtGSVqW5bGkv3SNaXndoHscwl12pBMlvPq4Y+KuMTlTTsxNwZmypHQsu3f9k5HeAMNJ3zOPzr5xt0Y2K+md0MUGnWsXXF/sRGtdKWcw0znLvuv1/HDHTyOPxBgnxpQ76dRdU33ytxErl9gRpiMxo8mgoeeA2b9PjCKRFIx77XTUvhJzwHZBpoOM/FO8W888zTztg7p1ZNr95BDqvjLPI30vfpN0kMn4FWhsce+qAA4OtnUUpewh4/Bg89u9q4hOryJ5cLZmhdKY5XqjXkeJvSuMDDVcnzhJEF64ui9Gk2WVxliOLdRDTe2m4ulNl85Avk1rptN1LtnvGHdiqSvdZEl36VwSaY7wdPvvSbfbx4kjjZlKf1daS3mSwWJyep30CPk9yzGNlWn8q7AptTlSzqMb5h1ofbpWhc0x3nXSWd80ouO46HXK02omXh+6C8bW2g9oD6OWZVk+7w1N0etALYB2+Y2T/c7G7scKMmxprXCbFip+msNT5UEgWZQlZV9+M1occsJLg3ELzzmRVth+NFgSWdWEwvy76OWp1oOFH7O469gs5nGkBO2GRD5gPtX2ZEZRHSegVG6jhaU/S0rzSWSaxKyjRSJF/jXAsmOea1MXbnzg6U2bivQe25wb1PQWW3VsGdObwuZkXvmhqDSdOlLX+8JCTtuTHUW87OvebqhakTYa54Lo7CoSkgIHB9skwTkyzQnqzAvD4vTypZoKFxqpzrgJ9EXmPW1PyG74wPKQ1j4XK10UDSYjqGQBzGdcqKWFPdssRdGVrjplpmcoU2HSwKfSmAx7OGZwvB9tCNPij+9WOO6pwRembNNVpve1bfXu7WPBu54HqhikeAppPD/Sutyk/Y6E9bLqbU7TItJ9afI9zqu7Ti/qwReUt26xuLx1/TCOGMZvxv2nS/rPJX2rpB+R9KmSvlzSn7++pE1MTExMTExM3DHcNZH0sizfWvettf9L0hcuy/IhPPsLulww/pFrTeEzoJTBfQfXM2hIbN/IQEVaK9wmn38eDsXhia1JYSdGiM8vtM2QJb9miaUjkqiObGD6LvnT487fd/Oyd9OuusAzYBOzyvRSnJ38s/F/N+xJYhYv88SQ+bv+TRJJy97tsYmjuKVsEMKwe8w4nyVm2tPteaOBBcXC9MOYRHfuT491RNFXsSM8fSe5sap2Q1E7XaEkcWRiGMmkUVnez+qlIczZVWGdnW3YwnawcaVWz05ONmJnulmruSGJC6nkT3hbYptNUhIpq9rQX2HFxzD3dVvSO9Gjrt5m6XolSXKSVIKsGE+w4djkvkV7kgFKbfaZmpu2fXE2rZmzZMAyUmNJDGP6hqdR9cLz8ZN9LElY3CCvrt6fkmj6qdYGY8n40MHyO9B2fjj2cDz2vuqSE79PbDbHoFNtxkrGc6uWZ2Ul/RbEvjqMnynpH9uzfyrp7W9scl4fXrT/U+dLAwiPAePA2hPrOZKIgh2BixO3bmSHpWilN7nw6kj6MqMFMMUaFGNRNJh0p1L8ni86F6Z4j7pSLhbiAN3Tk/FB3R3GuvUjrYW5IBkt/tLim0j6U0RapI8mGbbJNPkyDz0ryKQzNEobF88cBJIjbA/nzO59sE/iOy4OuchLeq6FZAVNkSa/T2VNbwDVLkrUSV3cC4Rzjmt9/9oiHQtGiqRPrhL++El2rJ78IxJpUeegZapvQuvqfVDa5JELqCrLp/gmiYp7+khet3RE7uLASm89S5u7KjOmoe7ZlnaJFlm+aQwspA1ZOrzB5wpfKKfjQB9IW/ONX5M6kfcx73dUD5DWenmJ8GC601GcydsHF+PpEIME1m3yl8kweKyhrv5/BfcObhBSmdc3FF0nzwC3bsEovWUdd++rIvD9kr6ltfbLWmsvtNY+Q9L7JP3A9SVtYmJiYmJiYuKO4Y77YfwKSd8k6UPa6Ln/VUn/2fUk6/nwCVor0ifGg7tYFr9T485wpR1TYlGSmKXQU+L1XfmpvefNJLFvPTYpMVtk57wB+PtJhJkskEdK0DyJhGJNF8ufKp9kQtGNsygHymwGWYmqU4riPN0uwklGB44ew7jL55obW7jhjoNK7z1xuN8nUTHTlsTzPTGWf/tEm3o61aXBirTpdwyrZ9BU4SUPAokRGVnC90D/fCV9qOvDA+nBFb1ydiZdnKzDJJv1mtFUh2EsMfSFto+upG/QxDxfKDOMri7jYkuqDPgzflusz4L3G+6dtWOdEFQJ4LtMN9PDse4gPKv0yd7zfkZmmv1/ZI3N9CRRqLTNWB9qbITHOF3FhXkYfe/pocTDRcnOrrtHiXvaZnUX5T5BdYxqD9WfaCRa4Zwrjx27mMakrkXJiNfDU+Wxo75he+GY4engeJT8Gt86H4zXKJJurX21Ltdrv1LS/7Qsy1d03vsKXZJ+r+LxFy/L8n2j8PdaMC7L8rOSvqS1diDpX5P008vyFuVUJyYmJiYmJiZuCtfHFv6YpD8u6Qu10cDp4e8sy/JrnyXwvRaMrbVPs0cvtna5pl+W5Z88S4TXiUe63KnUbiPpRUjbuxbq4CSF+wTuCtMpHskFAcNMOnI9FsufUw8p6dNxJ5d2wym+nh6bsyJkRPjM2YS0EyS4iyXbNfLjlM6spQ4e85DOGU2sXE8ZfVRPZAZTWdEgycsvGZaw7qhLlc7DJguX2M/EWibduH3bH58noxQy+p5Wpmfks7O+ccMUnj9dWBAf2W6ypDSsqvC2jKXs1JaDk3V6D0I6zy7WDKPD2e6C6+C54r7r2Ca2ld8klo7xjPRuGRbLkq6nntg3NCwh283x09OT9E9dUpP0Jl0f9gnSSIObgrPU9V5hZbyEMNOYQUMbaXvsPbXfe/1p11LA03Ogsdsx+p+kiyLHU20Y9DSe87QVllFPt7aepfS6JIknPtH1VT2jjnK1r6dSZKmZHp+DezqtPf/Lnt67jmVZ/qoktdbeIelff6PD31ck/cNa+7KVNvWa6upGcE9r5Vo2vCQ24SR9br+5QYg30nNtdwAeBZUWY2mAWflrw7NdCwxfBKWKcXGid3watfQWSy6uPED4nLh3iTB4fF/9X+XGCXO0COL3HGCTYU/6diTWoKiYltds3C6+u691uVTaRn7Geh0uLRiTn0CKL5NlIUVsB/bNou1246LM0QKv4AYdFX+ahPi/1y3r4J7WFszS5fbYF2Dc+PQMsKrc6MsvHol2sbkm0SE3P6/FfZWAo4NLS2lpo79+oG3RIcsnbVA9T9J2eVe6mXZfbLn6CZ9X2uq7tOBJE3fqL8l/LNOTFjxJvcHDqt/r+JG/bgAAIABJREFUnRpbnij3o7TY5EYr9d+SvfWOKvV5gUYX97RZUPbUluo6Ujvi4iWRCqlumA6GV9/QyTn9QfqYk1RfksjeiQb3j8oFLueAAj1pML2jzTePdeTcmFQzUntiu/Dxd1EeJ24Mt8dK+nNbay9L+llJ3y7pPcuyjDSq9hZJr/pAa+0X6dKdzjR6mZiYmJiYmJjYF8+/YPyk1tr78f97l2V573OE87ck/Qpd+tX+bEl/UZfr9feMPtqXYVxhWZafaK19jaQfkvQXnieM68CTq7/aAboCurTefSb/iD3GjuIMaX2s2YhVdPFxb7dNHHfuKe5p9kxas58F7h6TQQ7ZOU9XUqQnnDXs/e7pqGvaaY+2NxSNMV1U4udpGRX2KN+9OqtdO91YJOMFfpOO60u7/ELacdM9BVkxF2fyBJu0Y2f7JZOb0pHaJ/vDrmMIfRAhY5+Ma1KZ86hIXkdMBU8Tqmc0lCHb52k8PduM2Wdn23mi6JV9/zWm7UI6BrMoXYqp60jAulJ0zfhexbfPoh6gkBdpbVyT3q1kuGsbZ/TIlCe3LYlBYxthfSZXSIWe78lkmJb8aqa4Ka6lGxpPL419dqlK8LfEosruvW6Sf9QRA5nKj++zz7tPXxp3sc6Su6UC2w19vPbYxgrHDUrcdQ3bW72X/IQW0tjMd9M4TOyaD28Fn1dY9HpOenl5WZZ3vO4krFUJf7C19vWSfr+uY8F4hV+ujRRpYmJiYmJiYmJiiFsjkiZc5TBiX6MXP1f6oS5pzK9/rqRdE35Wa6VtKpFzB0cXE9JaWb2nC5EU/h3UEUk6iFQ65o7Ud64XWjsVLiQ9t6TvwfSkdOxS0B7piPD7ekYdkV3dgKyis3weR/rdy5/uMBZtn4TCHXJPL9LTJm3rESbGqRdOYoirPpPj4jNt2sB9bTNt7KQVt7tEGZ3yUemm/wS2i8Q6FjiCMK+9s3Prt+SmynX1qBdGNrHSkZyGN637QWI33Wgo6o0Zq+jMNN8tXCizXBw7ilksg5qjo8196Tyen0lH5Sn8JPcdZ/dozJOYmWTAwrpLbYlh1nMaCpJpHLFirJORnrX3abblSsOIVUt5vNBa/62e0SjExySef064biIlLK6jJ2WG0XU8OUZ6mKzPZPDF392dUHL2faRNfyLLTzg7d6xNvskGFjhHJJ1XXkeO1TnHEp4vnvZ1rmyIxLTJnicGksaStwbX5GSmtVbd6FDSYWvtgaQz101srf0GSX9/WZafbK29XdLXSvrLu8Lfl2H8Zvv/FUkfXJbl/9vz+zcFH9VlY3PDiGR1KG0aMBXBORkl4wVOlCPRBAdiTsjuYZ/xMI6R5Vr6Jp0Iw4GegxLTWgNUssxNi7peM0/i5V3vpYVimpiYNxehP1A+Li9Zrhf8NIYUd5pkXLxE4wUumjnguojXj0P0eNlG0mL1PDyTcv31rB555QSWkNQKPD63vKThWTo5hRNmidC4YDzANw4XL6UJne9K6wU5jbhe678QJR9dvXDvYvvUlnOtF6GvLXCvvm2hII+OLi2y617aWFrXfUmnRgu0mgGkvBjghF3hsK1xUcb70QaYZZtUFFhfLhLstauRIU0aH7nJfkXbZAD7GhcvXAS5IcmBsr9MLz/Pg7exdGymH52YvFNw7KorT8CqZ5ycR4ZnbNus21G/TvnifEm/kD5vpM3AgdYL+5GBC+c+jqUVH8kZN0TqbWDTppqkgXtyuFFcr9HLH9b6uOYvk/RHW2t/XtI/kvRZy7J8RNI7dXkYyyNJPynpOyT9iV2B77tgfLwsy9bqs7X2W5dl+St7hjExMTExMTExMXENWJbl6yR9XefnR3jv3ZLe/azh77tgfJ8yXfleSbdmwVi7RPc7x91ZyjB3hWR1yDC6YvB9bYunaCxB0XM6SzWdu8mdadqJj0Qlh9pmuFwU76JHij/JRnDH6aL4Z9kXjRgu5iGJwJMBQDot4ERrA4sD+507cYrlRu4lpG22gSATxDaWWOERK1HwE1KSUri3ZTdYSmyt77SfKu/8E8imuuiw0in125CXL8VzFc6LWjMq3qZPte0iiyKr3hmxbrTBNnKM314zljqQ7l91fBqpFBN4alfpklV05lDaEAcMp+7rvYODTVhHR9LhyXbaRv5Ik2qAlMvCmT9XtXHjBIZPxonjSBL5O5PeM3Rh+/O4X5D0gpXV+cWmrB5r0x4+ru1nhQv1XRNVenlSUX3jrDjz0/MdV2G6CkuFk8rBJVOPJL10dZ8Yd0rNkuu1QpKK8Mp6YlnUdyxHzqFuiESG8T6eCe/x5BUPh/k/tPeYhl0nOe0CjeN6PplvDLdPh3EvDBeMcNh90Fr7N7Ru/5+mW1gPExMTExMTExO3Fndxwai1w+5/bL/9hPrU543ggdZ6Rj0Hyr4bTs6SyQZSP4iK+cnlR9LBSawlXU6kc3LJsjjb1WMfklsd6o0k3Zm6506Au27XX+kpGjsrRj2YXRgZ1PC+pxM1YvRYJ2S2vO56jrB7p3PUNTEZKQ8jVxBk5rjrZhrctQXd6iTHvufaMJc8H3lXnXjfkHKZFtgPksECv3E9YTcCc/aYp0OQMWadVBkmhXp3pF7fVHyvnTwAtjCxhsU0npxc/kmXLKG/e35xadAirecDOgiva9J5Zzt3hqZneMJvncF5oLUko/edtGYNnQFzxs3d5SQ3PbvA9BbL/LZ70qMrodk9DDxVbicn0uMrY6GPXdXDx3Spuy5lPe5eW0xzQ3rmusPEobbLgDqiB+q7ApLWzGoxi2+7utL9SE//zpuQM9Oua8qDBliHiz1j2IdIC8s0sYVMRyp3tkvp8jz3queq43snm/J7RWOWkWWf2neyR7gVuD2Ou58ZwwVjOexurX3/siyf/+Yk6fnxQGufiX6V1oMFxQlcKNaVyvc+kTIcWiUmf2QjAxeGk6w6D7U96KRFJMUWXDhWp+LClUYKboRBhXEiTQ5J6b2wj+W0L8a8nnyxSsvA5DswKWMzzbRYdqtlLsqSj0NfmBZYfi66TYvM3nFWVIlI4vLUljkxVVz0j+YnNLhosa48Ss8XGEnFg3CxvKeNBgfJX17Vw4HWC8VK98eQH2m7DbjXAbZp9k9PO+vz/GIjNn7wYHN1I5UnTy7/6ptjiJgrPa9N7lg4ntoq/fRss/A8O9u2ar6He5b9SMXjUJuTbbj5jSfchHDSBJ9OFSKeZxJO7e7hVeQPH17+SdL9q4QfI+GnZ9Kjq/J/eCWTvv+vNvl9BekeifSl7U1oUlPZBRo+cdxPxhhJNM1xyQkLbgL3LWe2cxIjtDR2q/iDA+msNjJIz4v4pjDauDPM1N84nld9P3okHdsgf++JdPDx9XdMm4cpbZMu1Qer7SeL8hvH8/thvFHs1UfeCovFiYmJiYmJiYlbjbvIMLbWvndZli+6unc/jK9hWZbPu6a0PTOOlUWmyS2AtPb3xvtChcUdUzKgIfvoLkbSrq4H7uA8jYybbCLdSyQDFbpo8bT1DF0oBnRmgeL7dHbrLpBVXJ3Rq23xSKXJRSL1u7TNnLoLjeQWItUJ2Tf6OHTWVlrXN3e2yT2F7P5U27tdZ73J7jlSGyHIODszyF0+89/z/Vbh0J+p7J7M7IgRYRuh70XWVzJW4ZnCwneVBzcy8zqR1uwODaheiw+nvhRbeO/ehuVa8BvFoz7mU9S8EklfFdApWMViKk/wHn0HOut+pnxyFfuBi/zuac3QVV5eE43j+WvtCo2q3tvH0CidlOPl76eP3L9K24tXDfDhw02Zv3B1vXfPDGDqLG/k6+BKJl0n71CVocdMkZmtNLpxx0jVpcJJLobq2YX2F9uzXUrrcuY4TLI6zSejZcihLl1ISWsXUAfVvq/+v2/pdfEzy6/nh9HHApZLiaGP723q+bU0oo+dPd483+XjMZVFfdNzo3ajuGsLRknfhnv3w3gr4aKIpGPH58lXYoFiQFpZUb+vBoOacLkQ4aLKF1j1vSMtEvkuFzFutf2KpY3h1b2L1ZkGigPhU3i18KrryBn1yCJPWg8wJ/Y7B51dAyzD5oLHxp8tv1698KT14L7gXspqCfe1XmB5uSdntj1xzci6lGH2HNjSgXOl1ydFHplWuK+NKJOWmUyj68Z63EnPKOl2ub5T0tdkHpIonuEcKy8Y99m8cMH4VBuxcxrHa3I9ONgsqJ5FX7FQounzs7UD6tcsTq9uOIlSHF6SutQvaWFcYt1797Z9Q56frfPqC+W0YDw8kZ5ipeKLsZ5Vt+frPhZ/BwfSIZ5Ll2oAW2L+o7U+Y/nLrDycYbFfBXRw0ddfl9bzQWXrgfImcaRK4n4567ektuOLLn7Dds6xqufMmmmo/Di4AUtzzSHq2ccoinjpBYH18Jpeb22glPWMR2Mu08D2V23k/tGmj428AXjY7rvxWVUN3hRck+Pu60a3LJdl4RnRH16W5e/6O621f/daUjUxMTExMTExMXFrsO/i+29o4yqK+F5Jn/jGJef1wcWnFDOT5XMxAndrFIfxVAG31pTWorW6+hFR9zvxUCE/sTEHTuNsLjpRNjJxC1hXwHYxAkXAxXicKotkeIKAizWTeJRMZDIMOdE24+QGHcmiPIHif1cT4DcUrXpZ0bijxyy4dSMZ5X0ZRra/lMYeW+MGI37ahxsY0e8h2Q8Xz9NIhIZehWQ8xHCSMQXbDY9Ec/izlG/PA9say3KUHr+X1hbjp9oYofB6YAk8Azt3erI2gBmhyIQyLqC6AEWzb7uyEH74cNvg5vFj6fCKQXsMQ5nXfPk9lF64YhYpzk0MY+Xx6ck2S+p5rmfL401eR0ZQhWPk6xe8tMkXLcsTq+lg2VIMTav2el7PzqC/QFWT19pIYFEp8UgMY0ybtvv3gdYMl4+VFI9y7HCGkWPzrmP3Cjyi8ULbUqhdhkAEpUhkrKXLMjvxCfFi3f99DlkZaoId9jZARv7wYMOau2GOI3mzaHa9NbiLOoyS1Fqr9t9aa03rsv907e85ZWJiYmJiYmJi4i4uGHW5IExeT6TLxf5/+4an6HXA2RnqMCRFeyqE1047sVDp3OO0e0zGFGT5qAhO+Fm0x0frXXezY0uo30JldK8gZ/5cB+1Z2KOkkzli/uiagUxQZYV6ez3GwhnI5E9v1xmhNGhypXbG7SBTV+lNOngsA2dEGU6BDERitntud0Z6eTSCSnpLqb7pvzAZhfBKZXhpbWxCxrLnxkrKbf9Mm7KidIDuZVwnlb8npJN0pG0Ggv7ypA1jUqzhwcH6zOd65zVjFfhkTIYII9bMddyowyddMoU1NrlLHkk6frKZb4r1oUuaCufoaMO+vcaknW10Bw8OpFev8lOGORcX26zP0dEmTD1RpHl8bDnUhumsdL300oYF5dhLQyLXJaWRDnGAMZN6kdLVeEpm8uo5yyWxx3R1JF2yWmyXyT+qnw7D/iJtGyIJv6dxLfVbSmsIl1Qw28kN0oW2y/L8YrvfrubQUL5JZ9fT4c9WYzfqu1BzH1X7WE9HjMfq7uJirVNZ+ehJY24cd5VhlFSnu3y/pM/D80XSTy/L8up1JeyNADtcKfa/7d62w92Li81E8KQGToRDUR1FyWkBkY7NSuLN18TlR5vBk+liB3rNqqwmsydjv141WHCRc4C0JWVgLqp4/FSBYtiRKJ/i/teU3kNaL7Quy3rGck/lW6BqAMXphWTlx/y7OPhQayVzV4pP6eDiOS3G0sIltQcaMSVjkxQ2VRCSD06fuOrbJOb3dlPhVzi+4H6gzaGkx51vPO40PFKkR9WMB3jGtEvaEsO7mI3qJ6NFtvfFSl85hj4729xzoqxJ7vQsbwbSMYA1N7SwCDrEoq6uhzCuqbHh/N5abJwWjA8erJ9xHDkI354m0ezZtoj33r3NIvPoSGpX5XKITTaNkqQrJ9EWzoMHG4voo6P1QrHiTulJBkQFlhUXkcyvO2Wnj83C2RmOgIRawmpBZPGcnUnHtdG4eod9OXnf6Imke5vuem80UafxhqSCq+lIG1H/+dn2gtGLmXVRV98s0SjwSNsHQiTjGS78llDOXkf1W1owVnoamJO0Cb81uIsLxmVZfuTq9pe8CWmZmJiYmJiYmLi7WHQ3F4xEa+0/kvT5kj5J2BAty/KfXkO6ngvOKFDsRpcTtROvncpTiJoIZ6akzASt/J5ZGLt8OB4fZWYgubfgTvn4apd/fLYJr+KurPjRaS5qvqe+4YXnsZAMXMgo8fqa8veBvay1qJ7lk8Q5SdTL4CiGcVEM03Ef37hPQDKrdGNxFr7p+Z90Ju5IG2amsFxsFLjpv3AkSqq46l3psm7pl8/dVzi77Oklw0iWzlkGim6rnb+otSupZCjj+T4/27RLppvf+gktSZ2A3bRnSJNOzUisRBL/ufHLCEmVxNlCMnrJdQ8ZE77nzygaJGpMIFN5YOla3QeRM9N2erb9/b17YNVwf1gM7Mm2m58H/z977x5r676fdT3vvK251l57d3N66GnaxkCBeimEGiFGBEWQSyNaFEnK5UAhhNhiGjGIgEdaKhElEdFQEUxJpbSlpBQhERT+ECkBqfzTcIqxQAUlLW1Oz9ln77Xmmpcxx+sfY3zH+3mf8fzeMdc6e+019+rvSdZ6x3wvv/v1+V5+51MbcMMbR2LxWn97flguNMpY4bsdc4u6qXcrnAeCWtLl9B5Zz8RKFlNX7CRVmYjkaie5yOL7SY3qJNTpMcpiJt7fvsdjbZdUJQo+xjvDe3U9udNJcxqLqfw+np/PVQJ2cYX0sD1U+SYXPLM0s02bH7VjLR8x+MFj/NCe9HKH5iMNw/ANkv7Y9v1fLenHJf0ySe+8vKR1dHR0dHR0dLxGKIbxRf69YtyVYfzNkn7JOI6fHIbhN43j+NuHYfhOSZ94iWl7brgRBt2FcJeZduq14OeujAyjM4eu4yhtWEw6N5W2yvEIe8eeYFe8pHs0S+Nqel55KKbx+HJKbzn45ZnI1Cvh9ZDOZWIbXecl6TqeIMxD7by1a3GGl/qehQfQPeLJIIVkbEH2jU6FC1fX8zClzEZJc/2hHXMbXH4kPbidvpzmxirJRUQqc+otkkmu9HjdkmVmHvzUoArTv3mE685R9sl89y/lfK9W2p0RuwY7UeV6hHjogqTSVHl5pjmb6iwqw3kU6oGgThadaktzvTzqgqW+TobL7zG/ZKsSweA6fY7EWh4Zy0SM6/1xhIzm0dGcnfJw6jfLL+kUzowTQlkUeMKNs2EMr773sAnqfdfpMEwX+5vn7egIzCL06dzIyZHaUKWjJD2UECS3O+xPS7q2Pl67QVPqY9Jcx/ZqW9ZktlNZnoV8VxqHo/36vg3vU8+frO8jnOJTaWebdP/VblCTTlNK8LYmbRzOS9LJdWZ9O54fd10wvj2O4ye3v6+HYTgdx/H7hmG4V2dMu3iMk2MN0Mnq7nY1NVxO1pw0XRwpTeIBKlOnBSMtK6uz8Dgs93PVEhusw6J3dnLCdgFSlXqh+WkqNeGX9fKbWKzWIfDHR6YQjUm18sAFMK/SfOJORhtUjE5W3cJzirekrNjPgf7phfR0uyipMm+J1WvBXvVwino7P99XxKe1Kgc5ioVcuZ6TZhrUd3X3ZFLST8eacRFZwVCcSxESF1s+MT3Q/gJrjfiOtX8qTmujsdRWeYpHTagDyue4Fg3I4wnS+wCj0qPVPG6eaHSLvFXbfijpsVnnthburNuaXMtqeFjtn1YhxEfL4VTfnBS50WMaPB1pYclvDvkr9O99cVjpqjDPz3P/dhEvRbhHR1P7rT7DI95q8cZxpEAjEvbbJHbnNYkwd0ZBZ9JDjO2FS1ssVRkxPobDhSnLsfr9uL6beJR/jtpfDPJ0Ik4VyYqf419Zl5dPy8eP91WrblaTwc7lJQw5URZpsV+L3fMa91E+PKqP94/CNZ3OU9eHj6Y2kqytCaaR5V/3khFUOilm55ez8v1E9wTj63fSi+EfDMPw5eM4/oCkT0r62mEYPiPpMy8vaR0dHR0dHR0drxnugXj5RXDXBeMnJH3+9vfvkvQd2njW+LqXkagXRTGMteuhIQd9P7mYhjuvZNSSfDuStaidXmIYyTqsVplhrO+LleHufGwwBy5KmgGHtpPRKxbm87c+Ud5+e7NTrXRWeBQVkR2VNn/X72Jj6Jqh4O5PdswWRUV4Xmkla3tuTNFbb23+SfN01y70vXelH//05vc7W+3aq8vcN12EdmoMQ91f0k2myO847GyTiJLMSuXhyRPp3Xen3zzLW5ob9pDVZZm7GsZDTUzGQ5RpMv5IBl/0l+nGR67WUfm5DTt+sjG7st7+TZH0ufaZQRodkC15hr7qqgWf99ZUrknJniCr5mcY60IzpXlpUxZVllTiZx1Hn3V1L4hwWf4UzTqLQiMiVuEaDaPUMTg2eJs8PpKOjU1nWTBtD4L4kwY5VWYnJ1O/3PlrRH5mrojAJjpzxT60y2uDVU11yjJLBkYjxtGzwFIVbvFescN0NzSi7nb50XTlqVhuCMIzvznG7Fz6bN871mQwcnY2zRdVzh/5yNRPqozoG/TiYlJ5ebq9Xl1msbvXQ+u88cJ4tK/mM9ic5OPrcbjHeTipVCRGlPeSj1JpzkbO8nBfGMbX3Up6HMe/iN/fJ+mnS9IwDF/6ktL1QjjR/OB0LpYKq9XGqk92j+I/Xut3OlbJG37sKDa50m9aXdPENhMBX0/h8xnBQXI3gV/Pddve3sbzke1hjh/9aB7oqyNeXU6DDifsulfp5SJyhYKjGDX51vLF2OnJvHwqbW+/PaW30l7Pzs6muD/96alcSzfpyZNQLutpUOEEnyahlmis0BJv1dVFMxR9l/joyZNJ5PTo3WnxeLEt8+SncXbUlvZVJt7QpMPnagfSfPKk9WNl8ZDOT5qcq+5H6EqxTKpcHteCEAvV8/OpTt/YLvgens/TJm3aZNX37XpatLyBTdCbtgm6XU9lzb7DPuZtmioe1ClkWSYRc4GTcJI+8Rs/BpDP04TLMuf7fgQh0RJtVziVr+vrqdweWNv1dDPsM2trlc5D+eJ7rfCXnjFttbi7up6XaZqbfUxgOijWpV9OX3yntJ2spz5KveY3sKFJC720adhtsM6mvstNdG2M6E+4+gnJC27evN0cmnOSM3XWQ7K4b42lhbTJXnqfcbPtp808F557pMs/Xo7jg8Pr67i7iWEYHkj6e1r2j9vR0dHR0dHR0VH4ibZg3OJzPtd7GIaPSPoWSb9U0qck/e5xHL9je3b1fy/pV0r67nEcf9uhsE40t5LcidJO5uKTAqlt939XbKU03ylyR5UsuJKY5RQMYol5igV59Ghil9xSV9oYcTwxKj1ZuyXx08OVdLpNx+nJxNSRYXzzrXncMyteiKeWfKnNRGhgY3YM71EWzTqoPP/225v0SdIXfMHm+oVfKP2kj24/fPzmNsDjHQ1wfn4ZxfcuGlut5s8rXazbxAQ/AHsibXauycIzKWBTfFnixVuwOzsGEuk4eTKlm+IiaS6SXmvf6OXR+b4xFU/XSJaTp5qMXpY6tqtJFKO160NBjEWGkUfgFR6eT+2TrHe1O/rIY5lX3timKxwy4MXa0gBgBUbqfFvWSZ1gZ0BF1qcxcibDiISkuM+xKRl/FJIBDMcjWny7dMI9DXj/Zj0li++UdpblkqUx+9Xp2VRGZIp8nG5ZSXM8qmPjqNLDfB2Fb9xCm6eXFKv43hPpafUTSWdgtplWhn20nhuzlKrE52/b50c+MrHhhavLaYxPp96cnOT25Gwtxxu+z75O1QMp18PN9Vyi5AZEHDPcFyTTLM1ZztRmnaF0rwuJwVxSMUrv3ROPNBPG19wP4wKSpPZ58c3aqEx9TNKvk/RHh2H4ckn/+vb5F0s6GYbhF78PcXV0dHR0dHR0vDrcxedi+veK8bkyjJ8ThmF4Q9KvkvQzx3F8IumvD8PwFyR9XNJf1WZBe4zrIuqlylTyWSXt7y5vV/PzP6W5W5JzSeeN3XZ9L819ANa3xzA0OAODVml7/Hhi+d4A41K7Oro1YLpdj4PsRzFhZB0enE87WxoFPDT9SbbLmb/HYsXO9nepSb+PBj6tEzD4vMqHSt1f9EWb31/yJZvrwy96e6KSimFcr6Un70mS3lz/sN5+sqnU2rHzTGAi7XbpCqbYuWKr3n57uldw3SPvzy2d1tPAAvDECFfmvrjY9813tJ6YlQpXmrs38bqV9tvSbJevuUFK3XP3PHsniKznz08C283ze5OPPOow1vXRo339PupXrddTP/rCL9xcP/YlJ9JHf/Is45/35D195Mc2VlA7Y6jrOWv5wPo3x4yLoMtY8TNtZILIeFR9tgzVBmsjznb7N6yzZChXLoIuLvaNhiRphN/Cek49y+R+hvf2xs/1xIZz7Ei6bzuDmuCnkflsMVf+HtN+qHwLSd+bkpX3tnl5bzWzH5yM89A/jz2dJ1N6hqO5cWNd3zSGkXksQ8Kb63lf9Xp88mTed6S5UVuSOHH8Sq7g2J4ZT42l1Odcap9SZsiTIZyPma7XmNq5jyMtFvreMowvEcMw/PuSvkbSz5L0neM4fs3Cu79d0n+sjS3Wn5X0teM4uivjGRYXjMMw/H9qs4ifszha0pdJuh3H8Qdx7/u1OYLw90j6Ndqoqv5Pkv7KXQI81r4CNicrdoZdB9E0GNSkx0Xio0fZCa2LD5JY8vgkFzIb+0MsHutZKTnfYiDjoL8TfQeF8pr8KDI5P58Gr6T0fsjCcEnRmT72uDjm+0tGEBzQqgw+8pFJFP3wi7arti/64mkFd761B17BHvvRI52fX8/CbE2+rsDt+Tu3OqFFeeESoqTLhjW232N9uTWftKmzqr8rDJwu6uNi6wQLXLZ5DrLSdtK3xb6HuYaPRGnTyR9anbJuV6t9C2Omh+nyNjIcTd9ywcgNjVurU1x2u54m31Jf0Bd98bTTqAjfeUen298/+ezHJG3qrYwbjk8UF9K+kHNxePpmZ1E0IlFXAAAgAElEQVSN9vTAxo7U/yTpqGGY4mD7pIeFyk+1q2eXh9UxbmzhtFpNG5Hr0EZo6ZzE0PU7LQZdrO6+OluTPjdQS6pFqVypPlHPbzU3iKj3q11d1OJM+Ui5nXrSavqj+tgD80SQxPmuDpPAMWq93t/0Mr3sa8mIhQtLXzw+fryvenC7lp5t43v33Y0xoTRdk1cFz7PPAycnUC9ZT9dj62P8fXqWVRSSv9uU7zSP3Qu8XCvpH5b0+7U5ie9h66VhGH6ZNh5vftH2mz8n6fdt7zVxiGH89c+T0hfAY0mftXuflfTmOI5rSb/xJcff0dHR0dHR0fEB4eU57h7H8XskaRiGnyPpSxZe/Y2SvmXrW1vDMPxnkr5dn8uCcRzH//25Uvv8eCLpLbv3lqT3XiSwo+0/iv+kfZZpb4esfaOBx2C7Hj2awqp6JqNE1qHi2YmpVtLqaHqv0naFHf8eLQ+WiaKb5FajdSpEgWJfT9vTi7zb5QkYS7s0Mpor21FSpzedrsPvyQiX2Pytt6TP25KJM7nwW2XssuWCr6+ks+3vk5O9XTvL6ibkhbv4IzQCF/fwRB7iNrAbDNvdZVCsSQOpK7yXTv7YqSBACX8nBTvZZ9UpnkpMcdqxD2C2qn0eg83iaUAJrM9TtN+654wn407upZYMrTzOnZ/C8wfSo+3G+nx7Xa+ld7f70m0kD64vd305qUycnk0nIlFlIhkDJAMWTx9/J4abv1sqBMyr13fq8yPYuXQCSIuVGWycoKucJFnhOFHxJHaIRkqr1eQztGWoIGVVD4aZ+h3VOvx0mVZ8zFfyu8vfK15L3QBjNNnVKks3qGE6rq/nouhKT/XBG4wZDKekGzUuPXy0r1rBeM7O5hITafM3/Z4WKj3nwbUV00HQvZy7EWsZ4fgK5OSkLWKX5m2f0ouhHPiu17vyqvTeNlwrvVK8+gR9uaQ/j7+/X9LHhmH4/HEcf7z10R2G5JeKH9TGoOVn4N7PlvQDd/l4GIZvG4bh6TAMT19K6jo6Ojo6OjpeC9R6Yfvv215JIkZpXI8v9E/SR4dh+Nv491tfMBUu3a3fby599EqNXsZxfDoMw/dI+qZhGH6LpK+Q9FWSft4dv/+4NgYyGoZhlDRzEn0aduqrBmu2c3ANnSruYFz5fr2edjBUgk7xFci67RTqz6dd5441MP0oV0JPDGPSm/FdZn1PNwnPwk49KQsv6TW2lI5nzJ7l4Xa9z6jsGQXFozRO2wlerfbYj8TgJv0zGg0d2vwx3UlftEB9LzKMiaGhAUbVD1noXRtYSA/Btub6WlJ2tUOjjXIhQuX5lpPd9DwxQMmx9MzxspVV0o1LOsiS9PbWbc6b77wzKUNebx39PHlPuryaBX51wHUI88P6Pg75Sn0wsY6JYWRbPKTTRqOBpfIl2K6kubPzxEKTOSod2svLuX4bT26p61VJVMBMuW7c+fnEXJ2dSedVp9Btq/Reh7ZPdzk0NKLBmLRh5Gj44+XLU0l25RTmgjPNXeQc2ZXnubsh4C7cKn/Ud+WNUivv83SJxDI4RZ1cIo/SpnzJ7CcmLulmD49qnH2wS+Tjiw0Hc3O9np1ItbmX57cC2yr1U+ubHROpqR7omDwdZNHqRwxP2pQbnfxLc7dk4zi+oXuAz4Fg/NQ4jj/nfUiCS3fr96J095UuGLf4Okl/QtKPSfpxbSx17sQwJrQUxtnAvbJONHV8UvotMW2F5wq7FBtx8kuDSXXmi4vJR1yBYoAnTyYF5BogqMxOC8xkyJDykE5eKBxqyLPFS+jEKe7jo+k4rcLpyeEJcm9Uur6WLp/NA794No2YFxezMuKnlQ5Juj2a++CT5mJzKtmzHqvMa4DiIpNK/IVnF9KzMCG71S3bCOO5DWkfLbzKo1tRthbxSxaGHNRTm26Ft+S3L/W7urphhE/sNHjgQoJ19mg7iVX5/JSTH9PDKsASSV9f7TT2P7NV3H96MfUrHqPGBVPVZxm/UaTKU4mSL87CzWrfuMkXjF6uHMNSPUj77ZfpuMUYVUhW+Derqdx4Wk/50ywDQBpi8d0K51KaHe8pSavLadPBeubG28uNi6m0gE8iypvVtDC4wgag+v+zNY4prYXnejJO2hmwoMwfbN97oP16YJpYVrvFEP3QHu23Cf7Nuk2LHOZ9sH7PjQbLjAvGh2bk+PjxXOVCkobzs/2jZ9brncXTycn13jen8JSRNqMMin+nuSadKkSVlD3DQGwY55udzQv0ilEL3GeX87H0VWMcpfXt4fdeMn5AG2nun9n+/bMl/eiSOFq644Jxe6rLehzHG9w7lXR0yAz7EMZx/LQ2zrk7Ojo6Ojo6Ol5rrN8PD9YBwzCcaLOuO5Z0PAzDuaTVOI6+ZP6Tkr51GIZvl/Qjkj4h6VsPhX9XhvGvSPqdkv4P3PsXJP0Xkn7hHcN4pWgpj0vS0XqusCvNRS9JNEZRcRJF1W72BjuvU+yQKU5wJXOKCXlKRV2fXezvwA/5bjskXk5MEHeFS6ztXcRq7lctMWA3q/lu+fZi88dxZfzsbMMWMaLLq51zvat3r3e7y9pRDoF5ZR5vGuzQTnQO5i6VL8VtLtIutqDilDa7XYpC61lqVwXuuskSk+n19kDGM/mnazGIO0OxwCIvGXd4+IUlUfzJycQGJNdVSfWCvi/X60l8RVbhC7Y+Fx89emf3npf5pdWDK/GfnEhnxi6xHT16NLFYiRWiBCDdI3PifvDccMLjvg31TTZ2UYKgyWhjxqTXPe3f84PFd2yvpqs3ibXQjq+nPKQxg2OP15P3hTQeuWj28hIsKpJfQR0LBjd172i/nyQXLh5fnWy1KyKolxyfzH0yVtg36KOVf2fSbtfzMfOQwU79TeO4nVHN+RTmXt9tDezbgDg+0BDTz2a/tc+Xxgrmxct31s5X+/2fLLSL9usbCJwkbfrpjfXv1xifkPQN+PvXS/p9wzD8CUl/V9I/N47j/zuO4/8yDMMflPS/afLD+A17oRnuumD8WZL+lt37Pm1ozI6Ojo6Ojo6OjgN4mW4Yx3H8Rknf2Hg88yQ8juMfkvSHnif8uy4YP6vN0X3/BPc+JuleWSev1TZoKByfaHZChj8n80S9B6/gpLMm7e+IqCe0Xk9KwGQdji6mOCvsek73CUW0PXmST51Iu1Cm0XeFV9dzx7V1JQPkO0CGTf0yZxhT2fA5HbmyzCuv77wzsUcf1ac2YV5eTS5TjrZaSNcTw/juu9kgwssl7VKpj0h9RjKerotKVpEGEWuUszMmN9DBSwy2hNMj4KLoKA0w0CO6NVcSZCWoY+jtmM5zaXSQBjTqeCYDmiUXTc8uJ51B10uTNifTJMMhMi7SpvxYd5W3OsHl05+WfviHN79LWZ/sEFkZ18WT5kr6FXadnPTQWGR3lfL0Ys4qVzw3YBalzTvP8N4D1LOUHR8nCUGl05F033Z1rIlpW2tihm5xb6fXZ1fhHV6JBbXkPQMq16dlHgvjej5OuK4uf7M/Lc3HlAaWN5bkWun4ZDrsgK6F2J52bbHSIpTbCsaU2wSdrqSzhcQlg03qbhbYfpPhyXo9jUePMU67e65NJzJDwusrjVvLNI6LdOV0hXGzgvH4pazvzry66yxKf5ILJ85zbD/pG469qYxeGe6HDuML4a4Lxj8r6TuGYfh6ST8k6adpszL9M4tffcC41qazurL1bAF1pJ3pGy3uvPNxMbUOk2PqxHEhormYx59z4KP/tHr+5Ml0VNVTnCpS7Z8VmDotJzgfbNmRrqvDaRpsz66lR7YI4KkjFR+VimuQenA279CtBaQ0V8IvcBCoReTjx+/p0aP3dukocJHporpxvZ9eijVZ/pwUbqysWJYUD7uomN/QmOIpxGVXNsFVfuvqlqvSZFmcxOGCeLra7PVqPxyKP3dWl9cNlQpLF59x0cYNURIrzUTJ1eYrPElH1VefzA0QpLY1687HnqbF+bvb6zvvTAtFnhjjYwHzNVscon9Xe3gT/liTaJL5HW0C5eKPz8qq+FZT36lwuBAu+NjiGzRu7pgvF7MeaxqPVprGkZq/eLRqBXd+Ph8rOZ5Jm0VRGZGUocwZw4GxROt0jkqjj6lsAyyDlppQhVNBp2EHU8BsMeSbXY5haby/vp7yc9UY76tcapF6cr2/wWLakzoQF9cp3xy7d8evXU9W6EktZLKsXutB8T6o5KTaUv0hGea5WtLaxqPVar/8jo72+wvLgPEkv4+tzZL7r+T8fl/wshjGl42lDSHxn0j6v7QRQ7+njS7j/63N8X0dHR0dHR0dHR0HMI4bo5cX+feqcSeGcRzHS0m/bXuw9Ue18QV0D5I/x4U2O7ydawN4wk8MVxKvXoG94E6xds4le0je/KV9f3kM4+h6n1lsibXr+dOLiVl8UswUwq/P19f79P/KWKbd7rLYNU1sTxXZStMu4pE2O2JpvsOjuFySLtYTU3EKZo+sjqsBHF1n1QC6T6FBhLQRDZafxhITnp5MdffsYn62c6XRy/zJE/g43MZ9Zsxe7U4rPLYVulNxporl8s47c0OlSoOzJDxhpaXU7ixKErlLmvk4dLHQ0Xo/nNuw8680SXPRNl0MPUEbKnbqQbFqmtpQJYds1rU9q3B2LNX2wdl6n3Vcr+ciUQ/rUtJFqXBsr+dn+77dyMg9COes88SJuvIkDZa/u/kgkij5+nqe7lO8W9fdebtgS4qlfgbfgpUfnkK0U414Dhaj0nAsqYj2R5b/QrHCld1T7TOMdMVD1ympfSdDmF1ebGxlnym4SxfW7fp6f5I70fwUMCmzm66qtGT0dlqMG75PDO4JxuGkupGkAcn469nlZtyV5ox91cNxkHZdXu67nDk5kR5uB40HkJWTLawxlwaAfprameUhqU+4+Dm1T56glsbK1ri3JMZOrvReNe5beu6K5oJxGIafMo7jP9z+/lJ7/OYwbMjvcRx/6KWl7jnxnjYLtF1dYJGTxNMUP9eB81svf7qUVP6COIhWh3zUqPAa4GqhyFX1SpPu0myxGqxDaYF5iYVipW1vwah90S77JyfXOp3qPU2DTaX3VpMFoTSJbo4hmt0NWuspvPp+V06IcDiaT5aV56Uj4OifkpM8F4/S3D/c1fXcYW9dfeC8hn5qRT3YhOF6UdTbq/Q8fjwtPGnt/gwLxtKtqwH66cW+ZTZFhxxsOdDT92AhlZvrrPnvhDRh7xaJKBfWYSkvv6dpUixx2APt68HN4ltOzg50jLxz/N4Iiwuw6rfVts+up43PTkx6NLWlt96a2lHLEbE0XzwfsuCmI2ZXk6Dax5EmPTq2c9ctfvIEbQmLZp5WmfQe9zYImusrVvnWuHYu6YHprNHaN+EI35fuHR1Hz/Sosdn0sp6JOhEfVYOS+NkXcjw+zsOSsuPzll7o9cIYNvumwta8zfuC8VLSkelZU4TLjbk76+bzJ+vJw3K1c85T19pPbxpTGfft9uPjk2nMaG1ml8DFEMvKw6E/XOr0UkyddM2X4uSCcabTeo8WaC/T6OVlY4lh/Duajon5+9rkc7B3Rk1jRUdHR0dHR0dHRwuvo9HLOI5v4vdddR1fKW6UD44/hrj2+AjinqLYtZNe765k8bhrrHrmroWK5btd1Pa9gd/s/tOO5qPhRGI/r6/3xds3+F2fXGlaubdYHTKUlYT6zc1+pZfK7PXC1Wp6l2Xl5XNdkWp/hy9Jp5f7zEpLJMS6S8ddkSWoneoN2Lnod3J73YnVbBef2CMXJT1+PDGZn3k0N1qQ5hbuZCw9PScnmbWoe1dmJCFtd2/FQCAf7nvxeZEYnEKxQ0/WE8P4rqY2VK/ySLViPE41iSvToDPgOdkaip+lOUN2ralNX+F54RjXPSZtLa0hlnNvABcXkyiWIme2h+S9oO7V9eJizjZWGllWLvpOHgQuLzflLm3KfE+8/2R6t8JJRni3msZIdkuW1an1RxqOXV1PZV1pONTUijG6grHO1eV+v08eLpx9c6aJdbcTD59IY7CMb6kMSG2G8djqge9eXMyNXaRNG2a5+rpgJYiQIdpfh/dLved2NVe5kDbsYvVBnsyzU51COqmK42WeyuTsbMP+SfM2lHyYJsOS2/U+I52kIYfGqNv1FH7Lh2ldk5/GUs+5ltyVaMcL4k7TyjAM/23j/h9+f5PT0dHR0dHR0fF6YtTzGbp86IxeJH2NpK8P9z8u6T9431LzOaJ27jOWS5ud2M7ti6bdBnWdLvG7rrVJOkNYu+v1vl5k0qfhdpO6SyswdvVqMW6u0O0E3Yg8kClzfYHWN8wL2UTHjSY9mdrFniAcpqEYijPcq3huNNebKux8k5UhEZ7dIs5ZPNt3z7cMDs97Xa/300awsXv13MDQKLEJz3B6ROHycmKS0kkviREhi0LDBrqx2DOSWM/LUpoz6TeamIUHZXQUFMKPjqY8nEJfjnn13Tnrq9LwnjYslzRnGKsNnUt6Y/v7FPeqfl23toVV+O3GM95vk0uTE831K2W/aQywY2hP5u2q3qN7E7pzkjY+IMk2StkFybFQLmf7DBn7P5luSgjcWO1G0nW5SrnYfb6n40m2ZcZm4Xdi3XZs92qKu75nfR5VHhtGJE9R4YntWmIBeY544cHZVF7UAU19OIU5Mywr1jKkgewymTsvS7LZlNDMpEyVhgpbuR9UH6eUiu3dxwLqkjILlHgk1zaFyheNnNbrOUMpzY350piZsF7ttysad/EEsOSWiz5MXRpzvZ5LXpJEzprNq8X4euowahiG31zv4XfhS6WtR+V7ghLN7RZl26v7DasOdoXnLjoYNBez+iLzSpOfqyVLO2k+GKQJvyaz4xqw1tloICmLUmTnUfvE7IvMY4TJCYPflNijrikNtJJMA+NK+wsehrVb7Fjcvhi41v7Ef6J9a2spG2BwUexlcb2axL5nUMDeDXLS3iL05nryLXjyZF/skgwF3PectFlIUKTqIkOqWnDhxAmnxvjdxLXe/0Zr7RyAH9fCUdMEuV7P+0R96xuN97Tx5C9t1BJ2xiWawiywjr0eKF5OCtJ1n3lwMbS3Efbj3UZMc9G4g2KsMlg6PppbhUobK+lTbArqfi0Y3313mkgvoHKyW5wgPTvr0rN9Twk8MIDH/XFc4zgkbcqkDPYqr8laXcplPuKZL7BWq8nAjYtVLhhdxeMIakDMC42TkmV1OsaSBwR4Hzw+2hdRtsbhAjdtN2HRsZvMr+cT+27RXOnSfKHoOFZeVPsiPi0YPfl+dOPM+TiubEoriyiJ/KWpvh+WQeHZ/JkvGD/77uS5o8qPhkSpTa+v81GavmA8P58WrDODHKiC1Jhb+6JLZYKA4+d9Uxl87XQYt/j49nqG39Kmvf6opN/4MhLV0dHR0dHR0fG6YXxdGcZxHP81SRqG4feP4/iJDyZJL44SyyYDDO4EucOu787wXJqLXtf4hju92nH6CQsS2CY0DLJHaUfEOOr5SXi+JHqWMmvI9knRYToRgWxq/aavrwKzXXFxB5x2ePye5V/xFW4QZ7Gbz/B95ZEMInfYdINU97grdyMJputkDdYX933XvxYYU7B3BI0bdmksRhosB/Pqm0+qG7D9UMxS+SHzmo59E55Xusr92hHyQxGvq2M80VQnF8rGD0fhXoH9MhmrJIMxsmsUA1Z51PVa+2wXTx1h+0n9iAYNyS0JmZNiPXiKz94xdSFf0lzh30+7IMO3MypYz11f+Thyq33W8VxTe0jtmZiJ8Yr1KYMDzcXhFT5Fprt84feJGWrdIpyVgiHSCoZRFJXCeMH7/y1YM6p10NgnGb254aO3q3rnGPXo7ppovERWlziy+5QM8JoIp3X4zf7AucrjpkpQnQQzBGM+unB7CKOrAl1FJVdldOVE1QIy8ZJ0td73betqXw6qFrA+i1kso0u6dZNyG79vhN590Ed8EdzVlvKvDcPwZbwxDMM/PQzDL3kJaero6Ojo6OjoeO1Qfhhf5N+rxl2NXr5Z0r9i997b3v+y/ddfDTwz1AU7pAvoK2e6ruHOlvdq51u7tXSm7Kg5o5kUzj09LSaSu0zfcTIPMxcZ+DY9J9NUoJ7RM/yWpt01caLMQDqT698kp55Uu6kwKw1ksyrdt5qzd25YQaaY6XIlcnfJVGlLZ+MW1prXgzMuLP+ZwU3YptFdhrupOdacOZQ2eSLj5OGwTSVDIDKxZEdcVyoZhD3FbzImBZYLFc9dN5NhJ4agtZulfq/3HTIrh7CLp2Gg4Ywfz9Wle67SyWqdV+tM77Em4xAPn1cJzIpy+3TmlGD/TgP9CmHe2H1/b8mA7QjfVH2OmvfRuqY269Idac7Wk4nzfN4GYzW6Wbm+nsZp12WvdEpzhtEZammuL5sOOUj1QMlA0mFMhhp8RtdpZNg9nsGuUjZuOpV0U3PWlp5braRH24y9B1186o26W51rRH6GjNFhfH1P9nznlmf7fp3MxvwcofMmhvFKc5ZamkujqJfL04u6s+j3B3ddMH7BOI4/Yvd+RNIXvs/peV/BQTVZaBJuZHKu+eKoGinFafX8FI18z/pOcwMCH/BoVUcpdjJu4KDBidhBkfTSQvlId28AjDspaLsitw/uLrpJ6eFkz8VEUm7mJMM8+gDNOLgornLjwoZp9+8faL8NpUmG4MkLO8MeToSYyGjR/1RznGua2NySXJpvaAqsk7SoTSIyllVaMHofqHgKabFPtQhXW/DFqIvY6Jsx1QPVS7gwZz0WfHPCTcPR0b6lrZRPHWlZl0tzBmB3PN+qoc4Csa8vkH2hIm36AFU2XNQszS3Spc0pMOf2HtVd0gLsRvNxqL5Jmz6m0xeZ6X3fYHk4KwWR7tF88ehleSFpVX4ya+Go+fi51EcLvmEs0NjqAr+Zbmlu8EUDQF+QU/ycFqstFQ1XSaFIOqkekfDgYr7aza4tQo0iHTGawpFQT9v3TrCw5JGJVPG4CMYqFWWV6Qn+OLren1eulDcdTI73I44Z9wIfYh3Gu4qkf2gYhl9k936hpP/n/U1OR0dHR0dHR8fri/Xti/171bgrwfSNkr5nGIZvkfQPJP00Sb9p++/e4FZzloSMBXdjS7vcMzzjzj/5vNoxHaDfdz7bNF2LMaIomaLgJILjrtBFimTiKHao3WNLn9ZFgtzxk1WgkYOLQpJ7GYV7/HvQxHSQpUunINTu80JzpeZKj+9wKM65tfuy+0nUlBTyyeSQFTsN95LqANnJqttd2CvtGseIXTzF75VvosIho8S6Sayal29yxUGQqSB7mVwi8Xtvi2QYk9EWWRCqGxTYF/3EmAeas41ep2QdkwsS4d6uP6z3T51wsWbduwHDWKwkz32u/k+XSq7+QJF5YlFTHqgGIO2LcU+1X1aPtPHzKE0s6QUNarQvoUhsIv0JthiGpPbgaLn9WpKmuCFVUplwl15SPs1miTFN0hCmjW2aajEu6eGYMGh/fEh5pMFS9YcrzaUBqdyrvt0dmTRXf6JRWsVDxnnXrqBGldJboMHYzo2aqQb4qVdPnuTx3Mc1GsIwP2Rgl1SdKDWr8nmouWTrVaMcd38YcSeGcRzHPy/pl2rjj/ff2F5/2fZ+R0dHR0dHR0fHIYyHjVs+7EYvGsfx+yR930tMy+eMUsjmTl3ht4MsFXfIZFmW9Dh2bnxQoTT9547KFXJbjBBdOLgz5ZZbjZTGtNv1HS7BsKlgzG9cJ8hdvNSzKp8H2jf+4E6Feot1ssxTzXVdKkzXI6QbpVH7bFfS00q6emT5yOpSL8ydY/P7ZJBAYwDu7HeuLbb32EZ4vjf1e5JhAFmAu+SbYSYGZxV+k2Egu57cMJGNSYxn2p2mbwpn2mcGyKTRNQtdRRXY3j09M9229dR36eqo3IiQYeSZ0zzDXNqcQlLK/uyrbN91rec0jCLTOIZvrvBesUp1JRu79b+sx4+m9O5OzAB9zbQt6ShT/8tdt9TVx4RW2+PY7O9QgrBrK/YS2en6xvsg9djIjhaSCx1pLpGqb6mDmCSCzh4fckAvzY3i6pmP55cI+yh8k/qtx5EOLHA2lq6BWvXp+ucPNLW7Co+GLtK8T1R+lpzs75hKzavcpUdpnnOyzl2r8eSp+4BR90O8/CK484JxGIavkPQLJH1UaIPjOP7el5CuF8JTzS0DCzPfYNofdKT5gFlX+ppzQ4RB00BKUYVbC/IkDA6IVIKuuCkSrOdXmospCmnxxwGGaZCyXz7mv6XwnSaCVFa+GKCh0bmmSezh9prEbj6oJBGQixtONVcIT3Xrk9AZ3qPlLxdtblzD8qGoKG1EmEYX3TzTfn0+1XTUHhertLzkwqrykpT508TPxaGXRcs6NrWrNPFwQiE8Hi70qMjOPCbRtqtwnCpb/tMqPp0oU2AcO9WL68lHXImSLy+lC7PqlKS1WXFK06TIY/OSCgeNuFiPNbGlzV2yKE2LrQprdoX1Nw0a2O/SYsvF1Eeayjwdd8cxIYl9k6UwkdpxMmRbab6Jl7KlPS20k8Ej637p5CmqL42aj8+eRo5baSzlyVO++Ru0v8j041/T+Or1wEVies50JRWCNI4I6aWIl2Lwen8N1axS1yg/l1eaxrsltYWV5mVxF2KEYmqKyzn23Ct8iI1e7rRgHIbht0r6ryX9ZUlfKekvaSOi7iLpjo6Ojo6Ojo474sOqw3hXhvF3Svrl4zh+7zAMnxnH8d8ehuErJX31S0zbc+Ozmu+GuSupHWVLNO27arpReKL9kzQInrBAJXVpLn5yQ5C6V7sjnk7CHXBSlk9wca/7OnQXDxSzpFMb3AVHhb2krM7daNXDG5qYRZafx906EYbMoBvPnCmzZYXEfpBZIcNIpWxnGKV9sZAbLJzaNyx/iq5cNPMEcbvSd6W70sM0kFFOfuB8J8/3kjucJT91xNC4z3bnBhjnuFfpphi1ZUTjxmh0d0W1B7KK9ZsivyR2I3t3jDOkpc15taUekXxbHvHUjPX0nvfVxA4n8XulXZozW4dcwrBduCuoM/EfQk0AACAASURBVESyE0lrX9LAuCk2TuJauo0ppHaTWB2GnXwLSvt9da15PSbDKTc8oXHhkf2uq5dVMphJTKuU+yBF5Mk9F31S1vdkGl0twd1Dpb5c4LdUySCTL2VRPOcfguJ5F/E+DG5zVusp7Ju1dLyewpey2yKOE2xTZLuTSNrVfJwlTu3zru5gOpbxPH4Yv3f7ez0Mw9E4jn9pGIZvf1kJexG8o7mIh524RKK0Sk5W0JzYkwUnxUIF6vz5N3Sg2tIZ9HDobJoDECcZnyhptc1JugYTdyAutS2ek0PZQhJrMl0cnDn4JZ+LFY8fNVbfe+PkIjQtypj2JXDQ4eKNIukkzqVYtO6lwbraGtUAKIaueN7DPVoyJmfLrm4gzduvf3Ol/UXSobBTfR8CB3VOjq4PxnZT4OBOx+hc/Ho9rDTX/9vpD+PbJTUKn0QrnPqowrvStOmjjzi2/WE9jzupcCT9Z09PtX/WSfWFVO8MnwuAW793LZ1vE5+srQ8dp5bgVsut54f07irNTC/HIo55FCn7OM02xPGTC+C00Ku+mnQZCW6kPR9rPK/0UgeZGx46x3dd6Afa76scrxk+4fMYN2C0iE4+V1s6gwxPyuLymQeB7cdPlTcOXFC7qg3HTLZ9ql55f2I4XERSp9fnjZb+6avCa3uWNPCPh2H4KeM4/kNJPyjpq4Zh+JTufqhCR0dHR0dHR8dPeLzuC8Y/KOmflfQPJX2TpO/WZjPz9S8nWS+GdzWn8rljTVZzyeIvUd9JZELl24KzVNL+KRzeTpKPLWcYk1hY9jv5rHMDARdrVj54ZTwt5eSWP7VKe125a/Y6oVGRGwpUmC4+PdP+UVxk8fhuEqEncW2L9XHr0cTOUcR2rv12lToXLbmZblcsZ17qO6andexb2sEl0fVReM4yIJYY8sQwHgo7iTqpbsD4UnoJv99izb2+bzTPjzN5K+23S7KgZNCSL86ltLJNUlzG+Dxsxnek3G/9dB36uaMRTTHbSVRHg5rE+F1qzljVM7K+lcY0znq+CPY7Mv9U1UmnVbkHBmcDk0QpjQluAOTjUWJhXZ2D7S/NP5ea1HPIXi6dRPIkpPdU+6oXzuLXfVolu/ieIulDvjZnajxQw6g0Mp4lgxtKwEoaw/fTOEFm2dnCBwgzGeQkFvVVYhz1eltJj+P4rfj9l4Zh+EmSzsZxTD6GOzo6Ojo6Ojo6Al47o5dhGFobDWm7kdrqMt6bxXvtJNLukYq75ZOJ+n2uF3FIZ+hG+7uoZNBAhjExOGlnSdasdU6w7y6TOxHPR6pQZ66SHpYjsaQVN3VFKx3JVxoNhBLLx90y85rOBE6nOpBR9h0r9YPSeeOJ1WW5FAtyprmOjutk8TfDc6X3c83rxvXsEjt3rn2dSoKMMlm+tLFdGrsSY9JqF0m3i6yE58FdHzlDfheG0dlN9peUV+qIkdHz84HX2mfNBs0V7tMZ0D6gpjSwzycXLzwDPukoV/qJ1Ebo9zXpWV8pnzLjRgV0W8I6qfho2Me2uHSuOw3GqAfobZ/jBHWyicTsL+lXtvQR3ZiPeWgZ+3i+qTtMF25JB5fx3JVJr3CSQR31+6R9SQbZuSWjNRpQ8d1kLEmdfRqjpXxU+6XPVDd6IxuY5j5pnzE+1TSncyxlmd+bRcoWr6NIeqXluaSMJe+qL/3SwcWKlJVr35D02J6TqmfnYif2SZOOVYV7/p5PuN5OkhiLAw2/4UC0tJofceWiwQ12okKz2gs4v8fBq8qeHZficD92j4YeBc9XyqM3ttYxVmmS4sKSx8s5mMclMaOLTy7DO26NnY4YpJVkEodz4KRSe1owUoRZYF6S2Lglaq5v3OjKN2JpsvPF9ZHmZVVp5cLdN09ML+sxLTj5zDcng6Z65qTIhdUl7heSMQWNk2S/ucmhmK++d2fHlQcv3yvt+xul9SvjpiFH2uR6O6Y/PE78rXJhOuvqizb2Azdcq7TV30mdg+l1lQpX/UneHPx4OariVLgVlsLvgi9w3e+rt89r7bdF3wykvuXqBCwzqrikdC8ZKT7TvHy8HqmGwY2yEw1cHFNFgeH4uMa5j2EVaFFO7NoIVoxLcxu9YlBVKakt3FcDi9dVJP1TP7BUdHR0dHR0dHT8BMBrxzCO4/iPPsiEvB/4iP3Nw9nJ6rjvJxZCsQA3mjOVzhxyd1ng3y3XIM7SOJvo6ZEyK3QX+A7X08vdWOtkFGcTyUCmHR6v3OnxVBNpLqIka3Yoj6nMucu/i4I7RSbJrQNZ4dkpH3Yd7b11+CYdHejsZivuZExE1oaMyGDvnWmZqUxIZc82y/CY5tQ+qbpR6U0GKiOeOzuaVAw8Hc64UJRcIIvsrl4qjclHnN9baX7Eo7c1sihUV/H+TPaH4mfWk9e3+0w8teet0zFcLYH5TuXHPr8K7621L1pkuOwjVQ+pfaS+zrDpWoUsaGLxky/PJGIns+fGKolxZ7+iS6/kju0S7wnPkxrHaNckyRHie6C5SkHlwU8DkuZjR6rHQjIK8meFZAzkRozJKLAFjte7vrqa0thKpzR3i8c+kNyEsS1+SAm9e4e7WklrGIZ/S9K/qv2jAX/DS0hXR0dHR0dHR8drhVGvodELMQzDN0j69yT9aUm/WtIfk/RrJX3Xy0va8+Mna876pF2JlHdzdO8hzZlIsjVkGJPult9ruTxJendkHZcU6fmun8/bei859k0GLkea60t52lM83KW6fl594/o0l8qK/Wmnzzy4rmlibRl/S1fP4x40d/ieTtfxHbu7cHGdIz9xQpqzsWQImVfPN9sA2XHqbjkrcKt95mWtyd1Tqx6dDWqVb/qGSDptS3CjDo8vlWlyW0K4bpvHtzQm3OJ3YpsPnU/rLEcrbXSPxLR4G2Da6Jw4GbolhpHPi3Ft6d0W3H2OlPVpyR6zzSXWkQ7WnRVP4whBJpjl4rrZ58rtjoyc67clQxj2sdtGmpJEIzkNJ7z9sg6SzjVPL1rZM2neP9OpRAyPeorS3FUUDaTS/ESm0XUhjzWve9oH1NXD4RzAOYftzeuEhjJJEnYX/cpXjp8Ajrt/s6RfMo7jJ4dh+E3jOP72YRi+U9InXmLanhsfU/afRhHQpfZF0WlCeaD5STEu5nLqvMLzBSMVjVsWp0nsSySLXa+4lh8vdtjKAw1Pkr9BLpS5iPX0LoGTQzKcYB4OGbosLRB80ebW4+kYOy4GWorYvnBKdefi4/TcJ7NUT5wIZ2Ia5MHT4+Xk90fNxcEFn6R8QXLIWlPaN07ydpusxDnJtBZ3yXLVjTKYr+R/k33QRYj1vYfN75PIOon0uLhZ8iFH0WGBcfPYwiTm5wKK41bKm7dlLl7q/RvlBU3q/0xvKgMuHP3IzjSx+CLcra2lvGhjWS1Z7HPB6GoevPLYV/YR3+ymU8H4O4lm11pe1HID9hTP3dra+1cyTvJ4rjVvS6mfpXve5t0nb/JrnFQcuInxPniLcOrbp/jNekibrbTB4gKX/Y7HvUobYyCqONwHfFgXjHdVi3t7HMdPbn9fD8NwOo7j92kjou7o6Ojo6Ojo6DiAspJ+kX+HMAzDR4Zh+HPDMDwdhuEfDcPwaxvvfeMwDDfDMDzBvy89FP5dGcZ/MAzDl4/j+AOSPinpa4dh+Iykz9zx+w8Eb2vukoK7SLI2Ve7pPOK0Y+IOmmE4Y3KC98j4kGlzapyuQxgfd/6JuUkMo++6yW5QzFrXZ9oXzXB3ntgwxsP4XORMVoI+urhbTcYNLZbVwyxwl08DIoq+HDxtZUlZuvV9YjeIxJhy9+5sAcNKjFTLZUrKI9sSleb9PbItbKtueOWuRTzdDGtJAZ5it3TaR2IOUr4pDUgMBPtgUsJnWmlwU0gM7iGjocS2kDlZ8pnI8Kqe6MOUrngSU3pIVO9iQLavJO6m+xj2f6qSJEMZsnvSvC8mUWZiq1L/luZ95y7qD7cIh/2N459LPNj2k2sgiqTJkvppVfThmpCe3Wi/rXJOaiG5RCuwrJbKLBlledpoaFPfuoSGcVCEXmD/Zt1UuVWZs80lQyHWLeNkf6s6LR+RPIXmvuAl6jB+szZF8DFJXyHpfx6G4fu3azfHd43j+OufJ/C7Lhg/Ienzt79/t6Rv18ad4dc9T2QvG9RBk+ZiLA4QvkCj366k18RJiOII7/w8ronWdRwkfZGZFoG+YEziXB8E/OD5upcs0moAoKNxDlRJVJdEfoSLOk/sXlowukgqWbzymp55PbhFJAc3frskPl1rvyxT3fmE637ckkif1sDJv1yaxD3tlZ60qUjWj4d0qogk6l5aMCYn5613PY38nSaZsfHb/SNKczGie0GgKJN9iW3FRaFMC/NFsZq3edd5q7iX2i/Tyav3O1r7pjKnT8Xkn5JWttX/U5umP0xufqmqkPRgU/mt7JkTJEnVJvX5tPBPi8hq577w9A0N313qY2st91GKtpPOM3XrUvhpEcN6Z76TioKnneXLNrS08Dy0iK93pKwKwnRTb7G+ob52So/r1Sd9bD5nnXABzwWwHy9JwuI+YNTLEUkPw/CGpF8l6WduT+H768Mw/AVJH5f0u96POO60YBzH8S/i99+S9NPfj8g7Ojo6Ojo6On7C4OU57v4ySbfjOP4g7n2/2qqD/+YwDJ+W9COS/sg4jn/0UAQHF4xbfcWb7e+fr/nG5m+M43hvDJBWmjNONO5IuyPu1shQSPvMiO96uKNausejcMg4VfgUc6UdZRIvJQ/2/KbySDE6d17cgSW/kPXdSvu70xMtK7ZzN19hX4S4yQKk00Ck/d15YquclagwyTK7KKQl+io80P4JMBR1UCTCuJ3RI+OUfH4mP2xsv2lMcZF7pcMZqRb7s2RgkU5RSIYwZI+kzOAsMStk4ZcsT8lUJHEhv6cYNTHKzjq0xMvJ6ID9l+LVCusK7y0xVgWyvxx7lox03OiKomhpzjAugZKGZERGKUkaj9aa8kt2M6nDLLF4BPuQj480wmG51D0ygGxLiZEqUNz7AO9536BaR2vs8XblZZoYew8nMcbOMNKnsDS3eC6wv3BsSqpFKR38231F8jfVYYik7lFIaXARujRvfx6mf8M+xLK8sm+SNPBDjI8Ow/C38fcfH8fxj29/P5b0WXv/s5LeDOH8GUl/XNKPSvoXJf3ZYRjeGcfxO5ciX1wwDsPwtZJ+njaUpiT9ZUmf0qatPpL0OyV9y1IYHR0dHR0dHR0dG3wOIulPjeP4cxrPnkh6y+69Jek9f3Ecx7+LP//GMAz/jaR/V9KLLxgl/QZt/C8WrsZx/KckaRiGr5D0R3WPFozvKuuV8NzntNNISvruNiMxLykc33m5Lk7yIeW7J2fZ/H5iyFoGFNTjSrqQyYiEDK0zQPRX6PHxN3ff19pn58j6kL1IDTI9T7p+3NGzvlPfdCMn6mYmPU22q7oyj9J+3ZJhXDLAYH1eaa5/yas0sSWua5qwtDsnq0iXKC39TMLbpKezxXgearNLLEAqi9Qv/R0pn9dMhjG1G+qIFauz0pz9TOfPp3Q7a0Zfcufad0lDdij52COLcoPnzpBRH4w6lcJ7/g1PEDpkdEGG0U86SW2g9X0yNkss/QPcJ/N6iO1K5b/EelLXLrG+rHfqTUr7Ooyp33sfY92yDyTjI44tPhY6m5r0z1O+vf2ybSe90lTex3bfdQbZPtPYQrYw6Q9T79PdhfH9K7XZ3vuCcdTLMnr5QUknwzD8jHEc/9723s+WlAxe9pKlw13+4ILxp47j+P34m6vS75f0pXdIyAeGT2vTSHwwdR9wiYJfUkBOYthDJUv6Ph0P53HUu7xK+6Iov7ck+qNhCb9PfrXS98l3notq/ZulAcvfd6MBT28Sn6ZnNMpI9ZNEgsnnGsNpHY8lZcMJ1gkH+rRgrPQwjRSlpwEvierO8cwV14+UF+kFpucR0uuDOhdWKT2cCJhvn+xa4m5OUt4HuehIXgyOlMVy/pu+UO+6KbvR/mKB4mP3KdiKm+2CRgH1+1zSG9vf7FtumVppkjYbFm5apHl7oLjVnWNz08UNJQ0A3ajIx1TffHPTW2lMfchVOApu8b0EXwC7uLyQ1CRYj27E2CIA3I+lNB8LkiFhsuIlEbGkttD6NhEaLvJn3aT5zP1TetxpwUhVJs4L3h+ZXlqmp7koeUFIm37e53jU8iFb93y8v6tl7weJl2H0Mo7j02EYvkfSNw3D8Fu0sZL+Km2kxDMMw/BVkv6apHck/VxJXy/p9xyK49DC+/HW8qYS9C/j2SNNY11HR0dHR0dHR8cCykr6Rf7dAV8n6aGkH9NGvPy14zj+wDAMv2AYhid476sl/X1txNV/UtJ/OY7j/3go8EOL709K+qWS/lx49st1N6rzA0MpVxaSuLGldJyUl+s5GUaKXJZ2a0lkl5DYjUPP0z33p1dYUiDm88QE0RXCkng+nTrAHS7Fz0tiArIxaSdONiGxWWSAuLNNolD6vJM2u+90qgaVv303vdI8n+6ORNpnWVimBbpo4Q6aO/u0a2aZuqET3bC02nS9f47fwrt1TQr5wm9n3xlmy3BCmvsOTP7rkosch7c7sl2Etxu6N5KmcSP5lUts9lr7bYT1lIy7aLhDt1tVVjTASIw9fZ06y0L2yMPjezeat/MC23YSUR7yw+jjxIn22UCHt+kR77qxk+zvJaaD/YnMFv2Snts993NZ4FhXdUb2zUXxlCAkkXSSoLD8kqFL6qPsTxTRUkXhCu9K2a1QMizjyWZJ9YJSBeaFah0VJvvBknFXwQ0tve9caV/lrOKX5n2UTHvrJLRXgpdnJa1xHD8t6VeG+9+rjVFM/f1rXiT8QwvGPyzpvxuGYZT0F8ZxXA/DcKQNzflHJP2HLxJpR0dHR0dHR8dPRLxEx90vFYsLxnEc//QwDF8s6U9JOhuG4VOSPqrNQv+bDplgf9B4V3O3MK0D1tPu1VkU30k7E8cdHndbS7qFSek46ZU5fFfd2l0vGSekNJHB4Q6WO/FkHJL0VzxuMpHJoMGVwwuH2E9nT97QPpvFcC61zOCyTI/De9RRSsYoZPnSudxuICRlfbzEHifjpMR6M708dcTb+UqZtaXBg/Bu5UH2m8xJYkdo1EFm2tk7P53E3UUxnpaumetfsR4LrkjP7wrOopDZo24qw6k8JifHxX7Q4IZxPq+DdddTTSyfu3Bq6VknZrXAcNPpGYmlToxdGutaroyYr+Tyh3qENIBhGphe6tAJ6aB7JP+eYR8aZ3lCkDOHlNBwLkoMZGGF96jvWrrFD7U/7nEeKzDPz6Mi5/qT7nop6UL72CxlxtDHMuKQbrs01w2tqzOMTM8jzeu0Fc+rxDi+HB3GDwIH9UHHcfyvhmH4HyT9S9osFn9c0t8cx9H9/bxyXGh/UJfmkysn0mpwSRmb4kZS8EnEdshw5Sz8TpZgBYZHUV2apClqS4s/LtqSONdFHWngvAtcOdzFCsnfY6G1sHQR2yNNgyjTzRMpvPxb1qzJEpS/06kObozC9LKeWM414PmRkQzHB/6ljUjL+jMtBAss86VxKk04bDeprSVx5APN26qHT+X5pcU8kdpk65u7TPxe11WuddQbJ810dCDvJVF+pe2BpiPKmNfb8Dv1AxrULFn+MxyG5xO2H1WYNi8Fhpf6ZfV1bhCSX83WySCpr7pqkI+jyejOLadp/MGFNNVQPA+nkk62FTBsr8dH0i0ScLPaT6+rabiKzJJ6AOHqGkeaj3W+IOdmNdUT8825y0Wza+2PP1QN4OZkSUXGF45LbZpIZAjHLvrylTZ9KansCL+9Tac22fFiuJMB0TiO70r6X19yWjo6Ojo6Ojo6Xmu8LB3Gl437aHH+wqhdCHfT0nwHQjaBO6fkp407mCP7psUwOtxQw0UUCb6Dc3+FSQzIsLkzTTs8MnelBZsY2MQMtsSfS2gpf3v5uejVGdMH2lecppJ+hSHNRRjP7NmRprZxg/fSCSMM19uN72xdtEPW50r7aJ3p68wqRS5ss0sMI40gEpvL3X7aqae2z/pgPdD4ye/R2MLFjYfywPS2xFc+7pJN8HQRrjKxxv2Cn3bBsmixFnc9ozgZsCyd+07WLDE8DKdApieVOdPZYnjSe87Ytc41bvl+bIEMI90GJSlJYhoTc51E/i1/hCUmPN4+JLs4rqc64RzBE6Wkeb8j25jUZQpHyuoNlcdz7ec3GUb5OCL7TUOkY3yTmL9kyOXhOZKKEsdEZ7tpqFmgikx9Jy2f0OQSN8/jke7gYPADxGstku7o6Ojo6Ojo6Hh/8FoavXzYQJ1Eaa4wz51X7ciKebrQnJGS9t2/pJ24x0O9HOEZmRnXweM3yY1FMlQgkn5KS4exwq/d6mNNDCP1sIrVeGbpqHBSGngCScGd+fJ5q0wLa3vXkdgLT7u0OSvJdV64o69nLQfATNtd9e0K3J3Toa4zAzyLlwxPvXemfZZZmrNG3hYTM0hXPAXXvUxl4AruzjAWkn4h85h0QPmtD0YtfcXEWhQScz+G9+h4m/pgLb1Sjzu1Adcz9DQm/egL3CeL7Lp8T5XdIyWjgmTM53pohaSb7S7EnJlytyXSPtOWWDzW8VH4xnWC65qYcuogu3sjIrHQqe7obmi1mqervnHWd41vkluy5OJF2h/XzjQf+2XPT5UZMuq3VnqWcKtlfb4lV1pMD/OVjH2Szq8buNU16YCm+ZQSj9QelnSml+bPV4Hyw/hhxGu1YEyiW2nTyLiYTGJLV7Jmp0iLG3/Oq5QXACeaK4rXc+8UHPg4iKbJKi1g1XjucT/WxgKP9zi5cnHjE08LyXCE3yRjiYR1eD5qf2FPJKV5bgZSnSSrdyINTq3Fs3+fJsBB+6ItbgqSKCmVlYuPk6+1lHZ+42lsqQ5UGVEESWvNQjrdiCL/VA+tzUJKW8WXjD/OcM8n0qSG4mn3EzLov499IInvUzwMx+O+1LxOfKObTuxwP5VLdbrkA9IX2V7fFKmmSZwLtAJP0qk2wLGOxiYuLmQ8rTaQ1BaSX1jmJdUP+51/I01lndLGsqSVtBujtDYaydAoHQV5aJzlmHpoPvDNS1ITWGmuGiPtn2BVSGMC79HYpHVMZoVfV98s+ViXxti0sUxzb+FG9wxdJN3R0dHR0dHR0XEI3ejlHsAVYrmjOURzF8j8tdgeac5IpbAP7RTTubNMdxIPtIwt0jceN8WwdH2zdFqFn0CSwvS4mZ6kSF9pSMxUq8wL3FUzPsbpLMul9ut5qV5l7/LvJZF1qyMlkWFyHZLCWRJryu7586Ss7s8rLUuMHxX3yTAuMcnJFUdqx1LuO4lR4nVJpEXGhIr9S644+HdLXCnNGfekfuKsr6cxpZtjE8NZkiok354tca73h2RwwTCT6yUySsx3Og2E4bk49kxZJLp0BrmrpqT24O5jGAfL2plcKffbFtNY4fAEEZ7OU1caXfrYzvSyfNL4c2N/C+FdKbdljlHpBJc0piY1imT0lthEMv9LfhhbLrRSfSY1oNZzv+f5qOuHlNC7d3itFozVMHyAbh2bx0kxOVhdAhvp0mJUmg9Y9U115qQH5EcVLukRpkUbxbUUNXkZjNqfUG406f9RjLikU7lSTmOV5SPN9d8qXS4e4eCUxIXX2reE9wkwLTpctJj0PamDw3CX2o3sXlq8JCvzdAxl2ogkEVFrYZMWJclZbqrP+iZZU7P87ip+TjpQhxa6/k6lx+uOSAtltgf2pyUdZX7f2kBImzxf2d8EJ820oUl1wzHB08J0+BjldUJ1DVoYL3k5INJin2C/9DGM7ZOHJcjuUUzN75OOdxo/+ZvXtABjPXj5p+M8OZ774kx4h/Fw4VS40bw+3Z8rw0l6idRHTOLnJRUPhs2NVbXZQXmMchUOIqnLUKeVYL5dr5J5SJ4ySGb4Iry+l91PfZTvcvxMY+GrQreS7ujo6Ojo6OjoOIhuJX0PULuhJHYjC+h+7s407Wzr6qLBtLNNO69b/K64W6IofivNd0RkRBKblowTPJxLTXmklVpdrzTtPpNfOBqMpKP8uHtMu12WuYuaaG1ZGDQ/mL52sRX3eyEPLSVpGoE423WkZZ+VSYTh8VR4ZGiTFXBiXhPDWEjW2on1SSwvr2RRlsTh7gsxtXNnWznWkcVaEpHxd2I3EpLY18WJ/n2quxZDm34nhpH18LwK+WnsaPlC9LCkuccDjlt+HOZac3Waivva3muxmaksEkuamKuW8YLH6Ywa/SJKc4Yx9ZckdidrWeGc4t410smTYNLYXSDDSIbQWTUybYldO9K+6JbxuHGj3+Ozyi+9QCwZkSVpQRKRM+4WXAKR2i5PCCOS1TfVk5JFfpUZjamYFm+zlEQwvfTpea8YRqnrMHZ0dHR0dHR0dCygi6TvD5LiOXfFLR9SPOhd2uyYyAgkXaClOj+0Q06H0Sedi2S0caL9M6B5nmtKB10qUOfMz9V2n5RLunyJAfP363f63hmHB5rnhzvNQtqBk8GgXlVdk9HLIZ+VS/p0fI/1dILfUma7Ulm5D73UKT1uN05Ieo/+baWZV9kz/77FqKW+scSAt5TVPX7HIQV3R9JrpDSgZWSTyiW1/RT3Kvz2+vA0JgOgAhlw3kvuuXgCSDE8xUKRwUlgOpKObctYIjGMziIea5/5Yzwcj3hCUOXbTwXy9FYa6cOwQKYrMU9E0ldM/nDZhqh/7mMY852kOtJy+3XWVZr3ZUqClsa1FlPsaSALSl3Q5KaL37uu6rnmBpTVNpJudr33hvaZco7hiYGltID9oSVhq2+SG7ZXib5gvAdodcRDdcNOQ5FoElG6+I3xtmhxdtgkbkxhJ/9rSQGeovQlcdhK0yBMMQqVqKXNgtENSypOad7xE9bhvbToSOLGteYWlekYq0ovB+qUH4qCvMwrfH5LETidOlc4PJ4rGSpxklrKY2vRkRS5z8K9NAG1JgJHivtI2cCgJT4tpM0N697TnkTTvphJoiYHy56LqFZ5+HtpAZUWcJwoIG6m/gAAIABJREFUOSmmiTx9k8qfaUgLU8Z3aHFdaB17WPH5RoNtoKVO4Isyn2z9+Ur7/Yli4Zblf1rI+EbOPTWksbLCr7i9/n1s57uF5KvTVW2WNgH0x5jGlNQ+Oca4aJvvt6SXSayeNmhLRkycSxheUknhAq3SmwyxuFhl26H4uq5p40PDMm9rHG9a+U7fLKm+fNAYpa7D2NHR0dHR0dHRsYAukr4faIlffHfnTMZKc6pfmhuwuBuMuiZF7yQabIlp69vk4iOxFmQoeFqLNBdJ0ycaWQBXgGflk6loKfl72g+JEJeYtJaPsnO857tu5rulYL2257faF6+wDVD8kY4bTEeipU7zQFPbWBIDcveexD4sg+dhdZdYtSXDKDI0LXG4M0HOTCWGLDF/LcOLurckSi6wblriYWdwyEC4+I3fSfN8ORNKhtENQZhu3qOYMPUN9tF1eJ7KbK18esVdThAie5nEjYn5ZH2TwRHuLakOMF/FHvE4zDSOUNJD8XxiwL0dsGyONGc9mUaGwzmg2tiV9tsFv2G7SqwZxxRXB5Kla+n0nJX2mWT2b9ZtS+WiwvE2y/JJ7mxY3xQpe1m21EuSf98TPEtuh5KvyaXxZq3clu8rw/hhxmu1YOzo6Ojo6OjouK/oVtL3BK4onvQwpP1d46WkJ/bNqeYGFEs7nSVdvZbLlMQ2tpgXdwP0SHN3MNLGWMd1/qiXc8iQg7vqli5LXZdclDDdKZzEWniaKg9vhOcP8LzCYdhJD8nvJRblStOuOekbkQleqm//nXQGl1hbGiokg5xW3Etsr+t4SftnFXvals65dR3bxLS7ziDZjxaWGJGWfmBqn4lFcZY1MTb+t5cFmaBDjAX7rDNtdNbNeK7DvdRWrpV1Zw+5a/KwW4ZcDk9P+sbrdq25w+hCcjyd4qGkgIYgXu5sfzzhh/VdYyVPuHJmi+NEgQxhi82uMk/uwO6KdAIOz2NO+vJ36WPOmJItTAZUNJhhfNQprLSlfincc0f5lMgx7AKf8Zt00EChxb6n8X5JQvOBo4uk7wce2d9JhJaMTDiAJ6ONQ4ukZLCQRDyt+2nSK6QFI/2VUdm64ufmpZUOaa5szQGkBo4khqGYq5XeuvI9F6FRJMVva4DgAMqFnPs1q7C0/fYCvysPS+J9IolHaHmaDAy4iHLLziTSbyn7F5I4knXChRxP+ThUFw5aMVLMurQwTXlgHpN/NbZTL3Nayq6VVRTSIvLQRsSRFoyejgINJLgIqCvVVLweW5s7P36ztXEstOaSpbGH9w4Z36RF0tKGQ8rll1Q4uChOC6elk0o4HlGEm3z5LW082Yce4ncyTKtv6TGC8OMBea3veJXm49XSuoAqN0lczntelocWQGljmhbcBNuxzwuyb6vcElF2o/1Th1p+EH2zKU1j09Pw/ZHyWLi0gL2Pi5xu9NLR0dHR0dHR0dHEOHaR9L3AI813VS2F5SRic9bM6X1nUc6VGTKPm4xmivtI++yIKyf7joo+r8h61fctFsRZCYpcmIfk/mNJmV3aFw+477Wls0VTuVFcRbGQMwNXms6+vtCkWsCdaWLfkiHCEhtwq2VDg3QiT2Jjne1O76X0urHGA83r3tnRlBcyenw/iW4OMeDr8E0yyqLKhCvV8xSOlN7E7LvPtSVmNTGeqZxpLFWg+Jls95JbmBNl/6jOMHq6U/8/xCD5OJLyfxaeu5g/nUSUDJYYtpcvDUYoRnVW7FTzsclF6Kn9sXz8fsWTyiCdZc16qHer/yYWU5qrMLgR3pGmOn2KuJhvJ5KSWkjq5wwnTdKtuYTpXjJ24wlfiaFl3+B9KRsssZ6SChfbFd/zOWCtLJJe6mMnmrcHj6fC/ie6P+gi6Y6Ojo6Ojo6OjiZG9QXjvQAVzKW8MyUzcFdF2MQ0Jj0hhsfd85JOUWJyiKQATx0Tno+anHCTqat8UB8kuXZxPSyPu0AF6qRMfYgJWnL7QkbvEMNYbOITTQxj0mH0ePmb+UqK/QSZAephJcY5sdmJYWTdpHJx4yQpsxVL9TRqn9VJJwn590t6t/5u/V33yJg4S0rjAzJOrOOlM2DJLDDuZDSUdMCqfTmLVUjOlBNTl3RNkxEETzZJjH2qu2Sg4t9X2tI9Z+yd/fG22NILTYwy0+iSCkoVkm7wgL+TARXDaenR3iUPZKSSuyv25RRPOrmGhk8u1bnRvJ7cmCXFzT7PNLL9HdJFrSvDKf3hpTHhUlM/SONwa4506V3rsImWZMDDSfWZThpLh1ZwHEjxtMrvVeJDul58vRaMR8qNg0rm/lua+zCk77tkWSi85+GkBc2R5ou2s/DNktiI99Oit8AFFg0/lo5EWikPaC2LSl7re6ltMb7UuFz0Le0rpV/iXd5n3O6ryxdELT+Yh8Rud7Hy9W+9btfaXwQlESTf83SmtEnZAOcQkhiQC0bpxQaEpfbJSdGtMG81icaSEYT3A4W/fTI80ST6XrJcvba0eRtpbXbSwikt6grpBJEb5TymRTjDZj9JKghpM+pWpmxrnJApGkziRo5nS+pXhxY5hItkvS1Kebz2tPnihXGzzafNR6WB7SP56jy0cGLdpsVfirOQxnqWcxJZJ1Umin2lfetn5iG1/YKLs937BsPifHfXI2UL7E9sk95mpfmi3zdlDzQvN9/sc67u+NzwWi0YOzo6Ojo6OjruMzrD+JwYhuFrJH2LJpsFSfoV4zj+1e3zXyHpmyX9qKSvHsfxhw6FucRGcJfqoiTS3EusoofjHvIpYis4S5LEI6742/o+7aoTC5AYN2dH6urMKtPBMuB7VH6W5iLpCpusTkuJOrktSeI04Z6HQ4OFlEeyXYlVTHnlDpviTZ7gIG3YMaYjMZAe5yFR70qZFXaWJbW1ui/N2QAyMMk/mpcP40viUSIp3zMe5t/Z7BtNnf+JsgHGUt9g+VY9PdC+UnzyVeii2cSI1G+qfyyxa2SF6lv6n6v+0mLFUztm2MngKbUBxuf5cobbWbfExjgTlOoiGb05C06jq1vdzVCLKgZJBSGpEnibXGIYK73sv8nFDZGMCsluFih2T+Xv7YL3TjSx75wD0skp7DeshzPcl73LU4EuNAfHP0rfElrjmbc7llVSFUuibfbvJLniWeW3+O3SsDM9v2/MlwkaXX3Y8KoZxr85juPPbzz7vZJ+rqSfIen3Sfr4ocDKn+LSgCbtLxiT6JXvUdxDev/UvjnSYZ0rn+DSxN/SqUoi65a+k8fHiWlJPN8aJNNEkOJmupIoI1k0ehiyd6jLmDYFKaw0CaWFYoqf7aHqOC0YfUJc2iwcKl+mMS3AXJR81EiH68vV961wkuNtgov9FnzSdMfU0nxzwnQl9YmkT3tooc36cufunra60pI+LZoLTPfSAo31zfJPVtBLk2drc1dgv+W3S06mD6G1cfLwkppASo8031j5vaSCwcVqXblg4YKRbSVtytICjd94PGlR5F4IfIHLdpMcqdd3ROpL1C0ucC5peWBIeoSsM2/L3Nhf4OobVOp9pg1CGhNa825auKdwWjr/fJdXgpsPgpvnB+H5q0RfML7/KBKPZF5HR0dHR0dHx4cSnWF8cfzzwzB8StKnJX2bpD8wjmNtMP5zSf+npB+T9NV3CexCmZJ2sUbawRTSLp870gJ3cAyvdtNJ3OXhS/tHX0lzxd4Wg8Z01NV3Yc7KLDEzsnelnO9k+UclfIbrVqb1vcdDJCOcJQVtF7ElBvMu4mAXoxyySq5vaB1513aV2lISufBeErWn5+mUH7IKrlDuPkj9G/5ula2XLw1ckniZ4SYGgoynM/Itw6MlBoJhk1lJvjqXLER5JJqU85OY1SVGztkwTw/riScwpR20G0mQkW+NE0mUnNpfIYlU2VaYl1QWhRv8ndRzkuV5OtKP5cL4UnthO04qHs4ye/0ni15nxV1lof6m+o2LvN1bgLTJXzIEScZqrbnG0825b8lAheDYslq453OK1zlVbZbC8baUmHRnVjmnJL+RSTL1qnHf0nNXvMoF41+T9DMl/SNJXy7pu7RpC39AksZx/G5J3/3KUtfR0dHR0dHR8T6jLxgPYBiGXyfpj23//N5xHL8Sj//OMAzfJOk/0nbBeMcwv03Sv1N/X2q+W+UuNe1yuTta2pkt6VTwN3eF1M2q75OOTrrHXXVSICbo566QGDWyLCntiX0j08GySm4aPDz3FecdxM+I1TZc6uV53MkNyKHnienw51LWr5Emxpgn+ySW9Fb7LANPUUjpTmwW6z7pmjLeQ3qaSccpncDA972NOINb1yUm19MuzVlzvp/aZOEQw9hiQVN6ySxKGyObJfcdSVevlc6kO7eks+p5cKODus9rSU8KzsSn88QTs+JlusQwFpxV9DbNOJd0QRnPobKq3+4SaclnKI2dkt7jko4oDZqSLijLgK6gPD3O4LrLsDc0+Q+sZ3SJlpi/1KaT/0nmi22afWyJ0Ut9NUk3WmNPcifENCbDskPh3MVVGUH2mGFX+Q7D8BSvf884jgdtI95vdJH0HTCO47dL+valV/Sc7uW2lf1xSRqGYayG5Jlyantp8k1Iojq3SKv3lowK0iS0UhapuFidz9MCgt8kg4eWKD7lmx0tiTC9E1PJlL6/0mRZC8Jb7StbrzUX3aSJ38UaPkguDeBpA8G4U5icuHwyZHouNbdqrGtyKpwGQW4glhbkLTHg0rtL7S+pMnj4Pgm1+ktaDNDK/Dy8z/aZVDzSomJpUXyh/XbORRmPjmRbch+RqVyOlRcTTGMp19NCO20ICzRUSP2OZX+Be2lRl+o21VNaoCVjiZY4N/XBKv9Dm0l6W3B/eof8Fiafluw7VeaPlY+NS8cA1rh1rn3/na7q4QYuLR+GNAqsMnxje31T0udtf7PNVbss+PySxnvG6ellG1raMPP7pfikvFFgelLdc+xZUtlhWrhgdO8lTEfLA8hReG+nYjSOVRUdL4DULj4QDMPwlcMwfGz7+5+R9J9K+vOvKj0dHR0dHR0dHS8b6xf896rxKnUYf7Gkbx2G4bE2vhb/lDaGLi8ML9iWyO4uLEtLnMsd/10Ucp3y9zSd43fLF+JdXLKs1Va45jv+dyojPvcd/aXmO0Bpzh7RrUtyUZQUxlmmFU5LsToxvel3oSXWdH+YXt+JnfPdLnfx6YSCVKbcNbfEMZ6mVA+8d8g9UhI3Mg1kA51tYDyHdpdLDDnzTbBO7sKonti9JKrzQc2Z4LpSbHeXwZjuOxILeqaJSWJbcd+Yzg6t7ZuWv0D2k9SXve0fEvmxntjm0riWRIvsv0d2r9X+kgi09XfFx9/OLpGJq+upJrEvxdlkGI/tG4qx6XeT8SeGssC2z7D9mMo3JD3avny2fXizkm63EV3hW9bNDX5LeYzycWJJCrDUBnzcTmN3ge0mMZ1p7EhMYys9h4yX6rokYuecdR/QRdIvgHEcf4ek3/Gq4u/o6Ojo6Ojo+KDRF4z3ALUb8p0Qdy2sqKRzle7xuyU9Dr7H610bB/WnlvTp+C71e1wB9EaZGaCOh7s/YdwtNsF3pC3duJZRQsXtbAEZiJaBkCPtgCus+iadE+73fFec8p3Yk+T6gvXl8TBfSbcwMcqpHFus5Br3ZL/P8Zv1saQ/SXaJ6U0Dx/P0p7q3pDdFRollkeqJ7y2F0zrdxccHfsP0JpaPbKKzXYP2mcVDuoWP9LmdrtGSrNQ1sb5LuofMa2KPEtvaYhiF3zydp+BjGMO5DeGneKg3mtx0nWrukF/btJziuTRv+0eaWL56nly43EXHa72eX6XsloyoNsR+7n2e6W3pV/rY0uqrrTCl3P8ZDqUFh/QnHZ6eJPFYKus0/1823n2V6AvGe4BWo2gtuhINfhS+OUTLM34fHNNCikji7jQoe9rSwoFh1jVZazM8NzxpLRiXDDlajZ+nH/gCLPkw84VTWmwtLWhY/klcnsSfHJAYty/WWBZ3HaC5QDsP99LiOakJpIURy4oLbYp9k1h4abJPIiAuKFNZpDafVBGSyPSQIjyfpYXwoUVSiiflO92/DM8YPsuFZVXvlmjxWFM/aG1A65taLJ1rX4zt7c/HM7aH1EYKrpawtGCsb+mxgKo4d1VRYHipry/5OuW9Q/ExjUlcXqCRHo31HuC57DfHZKZ3Sf2E99mudnV2PT2r9CaDGi5MualwMByO4+z/Sxu91FfTOM28ehx1LxEnjlQ3Pv4lkXYSqyf1kkPkzqtCF0l3dHR0dHR0dHQcRF8w3gOcq81MJfYj7VrS7ikxFEmJPPk1cyxR9YeYDIqSU7qdhSJzlTz6+ykf9WyNb5b89qX0MC9J3Fu71Qvt7wApppL2RVZkKrmLTGzYEsOY6r0lCllqA2n3Xfnw8Nn+kksUlm9izZJ7I7JuSyJO9gP3NXdp79OtkYfDtp3qie85K+lMdt1rqRHUt4ltTZKBJYa2xcinNp981l2Gb5jeJHJtqU84aEhDow0/+/YK6eDZzMxX6v/ORDp75nmgSDiJeJM0psXYO7zslyQUqV8mcSSZtALHoyvtM5SsGzKMSRrENplYvQrzKtwjKo1PNW9j0rz/p/GRYS+x+Dw9x1UPpLl0g98m6VliwBPb5/3G85NY8YQWu+nzU2prHs4Ss30f0BnGe4JHypZptIBr6T34JHNp9xK1njpky9KscEj/wuPz+0tIuhtJPLUkxubzJC6Xljskj1tLZZDEsBwAku5hTZ6czJLuW9J5YXtoiUrrmhb7XDgu+YD0fHg4ScUgLUbTAO2i73q2Dr/TYJom3xH3WAbJsv7QROrW44yncB3C5gR3on2HxmwjwntpI8LNy5KYuqVvl1RSPG4vi7Qo9Dbt8Tta41W1+dRu0oTcUplwkWFr0baEpMoizfvV0j0uKpbKYkA6k0PeYy2XeSoLgm02iXsLlUbqPzLuEhtT/YPppV/JipMbtfIcvca96o+1OHT98wqHFthHeJfXgo8F3Ii0Fpl1bam+VLi+cPDxyDe4aYwi0pzD+0tqSd5/l8TcHZ8bXqsFY0dHR0dHR0fHfcaHdQH7Wi0Y39JcXJYMDdJB77S+S8r8Cc6yVDz0I1hIrOQS++bsRmKPfJfFXWhCi/F05opoKc0nEaSH3RLjMY++Az7XxDKRrU2Hyyf2qMUwOqvbYowL/IZhJ3FYUpBPjCaZ2sS8LDGMLaOXlDaeuOF1y3JsGbAo/E6iQ7Z51pnCu/V3ulfxUNyXrBvJcqRj31gnS4xJyz/qIav5Ciexyy1xub9HsM17v6U6AFkoHh+3ZGzRap+eB0pRlsa6lviYbF8ysOL3FQcZT2fNTkN6yb6lskxjnntgcIYs+QNtlTnv39q7LekF2VZvv/SmUNfEcvJYUeaZRlAD3q0r+/UhKZbHQ1aQcXvdluoXcWgM4/dL6WnNfUsLLLar1th+3xZo9y09d8VrtWDs6Ojo6Ojo6Liv6DqM9wRvba++y6XfrUN6Oy1F7iXF/7PwXmIYEwOWGBHfHboeUmIYfffuaXTl8Qo3sVlMj7OAyXcbmZ5kZJP0J480Z4ClDcv0GL9dX+lSc7cTHnZifZMhEnehTCP9mvkO2nV0/L3k77GlHJ7qm3lY0hHlt+kblr/nm+0+uQNiPS61T4aZ2LkT7ber1s6fp2u4r7nE8nkf8zbdMjjwsiCLcaL9tsi4U5smE8L3EqPnv9n22W/J0BRrVO39meY6tEmv9BzPK1/ebhhfcrXVmsiWxoRkiMC6SSwT+yoNrVzXkmzXSvtsXGvyXdKb5N9k6dxAiywezxHn9QjPCzyLulD1SP2+dDa4t+f67VIAuvZhupMOOcN2CU1iPL3MliR2bFepDx5iu9OYmp635kbZ80M6zPcF9zFNd8FrtWB8pOxMVcqLpENiHXY4X4i0FowJS0YZdOzLtPvE7eEcMm5gujwPLAsfLFoL6rRgTJNegcrLSeyZFnePNC36H2kql+SbrCVyWvLdWOBAlKyTk0i61W6YBzfauMuio95Lom2md2mTkyaUmb83SxfvceGUnKUr3GuJL+s+jRcKSezGb05DuGyzh6wtU9/g5Ls0yaQ2ncTdLOe0gWgtrv2e1/Wl3acoNC3uDi3aGF9rIq6wl3yqttpC2hgtla/Ce4c21+zzXASlMP1bNwDyeqTqEK8+DrPPUZWJ8fmC0cuiwqj80No9qQG1xnMu8BwcC7nYTQRBUodJBiosK47PdY/HJwrfVTipfab69vbnHkB8bk1jKtu5bzCkPF68SnSGsaOjo6Ojo6Oj4yD6gvEewNkIKvtzx54MPZbYk8ToUYy1JIJshXlXJMaE4VPEkHbfS8r13Aky3LQjXRLPk91IBhQJDCeJpB/jORmGxKIsiYWTawt+n3bFLbZkSZE7MaYUYyXRTWJGydYu1UNKn6etcMjVS2IyDoFlUeEz/55eln1Ko/dRT6NwL+WnkNpnK5wk5l6SFnheva0ndoh5TfVJBk24l9ihQipf5iGBba7yQF9/S2Oil0+K29v0ofEoMaI3mtoJ0/hk+zsZ6aRwnGVKRhvFlr25vR5rYsvSmOoGMI7W2FJucorFe6a7iUyZr6SakQxLXA0ozYepXTkj52OZz3M0emE4S2xi65SuuscTatbhm2RMynQnFZv3aw5+GfiwLhjvWzl2dHR0dHR0dHTcM7xWDOOVsjsCXxUnHYfEEiwxSunc3bswNL6rTLvHliEH0+bhtHZWKb3+jN8cMno50v7uMcHLwfOQyu9Uc7bRd/QX2m+wd9GLcmX1ZOxzyKlwizFJumEF13laQtLtTHp5CvdSGtIpH636Yt14uZA9YhzUqUrhez2NIWwybWSuZO8x7JZe3lLcrXbObxNr5t87e7mkR1hwFyWeh3RmdYuhSQYlCt/w6mdSU2eNbGNiBltlkOrp0BhWeSBcL4+Op+v3hSYGlmWV2k2LNUvlQimAND/phXqHbCfuVkfh2a3mfXDphCYiGR+l/srxailsZ9BTHHXP73tb4visbbqSY3UPo96V5nqjZP5dWsAxNUnxOEalsS61u2qzSWf0VaDrMN4TFN2fFoxLixxXmuX9uuedryUqdvh7rrDLeNJCrbVY8E5DazcXES6ll6dq1Dcp7iQCSiLO1BGSuDYd17gk8klhenpa4imK/6R85GFrwU2kgWjJKpmLLU6kaXJYsmwl0oImpZFhLoHh0LKS5ZjaXxJxpo1TWuglpf+04En91o2Pkng6hZMWd0lcfKjM0+aNk5q3v9YiMsVNH5pJFLe0ceTzmtjPtW+xe6V5f1gyeEibu5RHtoHkvSC11Vb7TKLXZMixtHhuhZkWFdzUJStotgcfnxJB4GJoWkfzO16Tugvrk/c53qS6YxwpHtnvlkFi2sASVW7MX+pDbDe++GPaGR/vLREWqQ/xXd/kvBvy8arQF4wdHR0dHR0dHR1NdIbxnuBdZZEKd6Ct377DYzh3pfITnIXzMFuMxpKojt9zZ5/cBKXdZUpfa3e59JxoKW7XN1Q4r6srMq80dzFS39DoxcUKyWChwqrrksJ4a9d9lw7tTLDHnRhGspseVusbxsM8JoY3MZlp539k79fvtONPrGarH3k8vLdUDxQTpjPgmUY/09t/30VlwstuSVzeKj9vd8k/XUuNhHWT+m1SS1hicPn7FFc/1WTQxKAlI7FD6iwt1iuJRz0chn9oDEnSljPt94MkVXDmaYm1PLV3pTnDSDH5Untiu6ELnTReeX9qzTWpf6fTWFqqVUsqU+kbYknVZq3986s5rrHvsL68f0tzd0S8erxJAtNSwfK5pqRZ/0T3B33B2NHR0dHR0dHR0URnGO8JnmizQ3Hz/RO7lxiDxNilXW7aIadCTGxMYjK4u6z3nBVIDM6SXg6/W9IzTMYfDJtxL+2uE9PmzJDfpw4jd8+f3f6+0rT7rF31u9pnl9JOms+Tbk1i7LxM0zeeFzJyfCfpkC4xfyvtt1niSNmtRKpb6jh53C3WILWHpTbg+n/JgX3S93I405b6mOu3OgObHP+2ypDvtYy7EsN4qG8kJo7vpDZEVnFp8jjE7B9CGq94oo6fDpPYS9dbToydt7vE/pJNTcZUHmelcUlSlPqYS0YS+576SaHev9GcYXScaN9tFsNmuaT5oMXy+710P7nVcdY7uX3zcYJpb41/zrzyd0vC5fWTDI14uhPTQmlMGit9rnZDF3c0nk4Ne9XoC8Z7gGvNxRYty76lhVMygpD2JzPh+dJgcKI8qC2JIF7UJ2AabA8NnEmUlCbIJE5LAzgt4aqTHhIXVtj0C/cenq/w/MK+aeUx1W2aZFoK+WmST508nTawlMe0sGd8aQHMNpQWED7ItpAWJ4cWyowzlRsH6LRwTyLOJCLzxaN/SyyJ0NbaL4O0GPX+7eG0JvGlBfWheuJ76Zi/tLhLdZPAsrzBvUI6mSOpASSjKx870jiS6i61taVxLVnFPtJ8s+Rxt8ao1O7YBqqM0jF/dY+W/fz+ULtZMqRL44335fTt0riWwjlUt4UV0nTo5JQlEfohooFp4+avUKoBNMpimghf9LJNpnzfxwXjhxWv1YKxo6Ojo6Ojo+M+ozOM9wBeCS3maYlNTLvmFMda+4XnO7y6JncYfC/tHukrLe3GEwuwxEzxG+bbz5JODBjjSQwjRSEpPooWGF7a7S6xXcm9Ucs9SmJgE/PKckyK1dz5J5b5ULk4mK/E0LIseXbrIYbRjVUOscMpDQktsWFSZl868SixSy2VCW+n0pzdSCyfwvNkTJKYspYxT2pLS+2zJYpLDCPdWaX8FpIYNbGWRDFkV0gbxYDOMkrzujskir8LC50kMa4usCSNKXboseYMozPpLRdZrNvkZiW58Foa96SJlUzftlx20VVS3fM6azGISW0htb9D40gyPlxqPy5x87HlLKTHw1ySAqRTn27wztJ4xd8tKdtSf7sP6DqM9wRrzRtcS5yQdCDSgjFNBGmBwc6RBkw2YF/UcbJP6Uni6bQw5e/0LE2k9OWV8n1PrjeeAAAgAElEQVRoYkqD8oPt9Vjz8qelM7/lb1+4p0GykMSeXHwnfZrCoYU9B9YUtsfrfx9ahDm8rSyJVFJ9pM1LS0zYirOurfBb3/BIv7RBc992BPPKPLAOvUxbk0hSA/D087fn5S4TqU9mKaxyaHyCv10P80x503aEe95XuVhiW2X5eRmvNS0O6Wi57nFBuTTZ+4LRwUUxx0RfAPsmPI1TXJTUlQvG8nHIsTLpTyYR5ZLKxI2m+qGD/1THFGf7eHOpaay7aHxfSHNSWiy11Fg8HEeKe0l1oLXI9/bJ32mxn+ZgzmN1j20y+XOscB1pnF3KD9vIfUFfMHZ0dHR0dHR0dDTRGcZ7gtpJeKb8dIIlkdb7DWeMXKl7rX0lfRdHpF23Mwxu2MOwPMx0MgDTm5i2QwYYdc9PlvDfSYyQ1ABau/skFk5Mb9ql3pVpS50isQCu6pDS9rxIZX6sOWNV95bK4jykN6WR91riHNk3/D0qs4dksbR9x9ubi2s9brLvrTJ1jwKt95LUoZDiZt22wk+sjzN6p9ovy5Wmckl+VplvqqawzyfGuX4na1aKoQ+x+EuGPYfEjWRByZh63IdYy0N9iOlN49qSGFaas4gFP5XFLX9TmlxE7kcZkun0tLOdp1NbEmtG3FXFI7HUrXiE+/VeEm0nTyRpnK94LnAvqT+0pEhLviRZ5um4SzKjqd+/SvQFY0dHR0dHR0dHxyL6gvEeYKXNbiK5Fkk7zgJ370m5PukoukFJvedsIFnFc0kPt7+LiUu7Ou5MK0+ehyW9HIbHcJw5SAr51J9iWSV2M+1mqRiemMOWn8uUxudlGJMuVGvHvcS0kZ1LLAnjbTGpLdyFOXEWluxcxTFqrgPkaaDu1yEdpkJL32tJj6ilG+R5YH2y/bQYigo7+fVjm0zGVp5GlmkySOC7Leab7xSW6puMXtVT1cetpvJlXycT5Okha5b0AFv6p56HZODnSPlMkowlNqvVFw/p5aX0kl1PLGDL+KGV3rX23eqkMvNTmVKfcTaLbFca45hHhXt3ZRhb0oClsTlJmZLrHx9v3XCH6TkkSWLcPrYkv6WVj7py7pQ280rV2RN8w/ZHVrPivk8LnS6SvidYa96YW+LGNID4osPFd75oa01W/p6LoWuhSEOBJPa9y+KD8REtEW8hWbMy/1xwHxJ71LdLEy6NazjApsH0kBhGeF5xcNBJ4pOUXgcnRb7TUqz2Z60JO32bJso0qFPMsiQCSgtHLv6Y3rT44fMkAkqL6yXRbEt8v1Q37E+cxFNfpo8+34C1NjG1QLgIz10FpNK4NIkfEmPz6kZBDPtGy5P8oUk8GRowDG9LXASlid8X8dJ883Gu/fbGCTD1AdZ7y89tpcdFvDQ2OwrPUzhs+zxekuP00maA9Z421/zGy7clxuY3Kd9pg7CkOtWqp6VjKlvpSeGnTTzbnNezpzeRE0uL3kTonGljJS9NHiNoMMa4Ww7E695d59OOZbxWC8aOjo6Ojo6OjvuMzjDeA9SOZclQI7F3yU0FWYDEbri7B9nvtOujgnuJrFq7xyR+WhKxtcRYS+zRSfhGjXsJaRdKMI2HlLrreqgjJTFMEnEk9ndJHMZwkpI0xc8tFiUZA3keU7697i7teVIDOLHfSc3C22KF5e+5WJrxMB28JtWAJBZiuEvGUun5rXJZV797KOkNu5fq+1r7JwS5qJGMVsWX+tMS086yZPj13mm4d8gf4FJ8LSyx1TxNqSXCTX2IfcxPjVlpYoXThNIah5P0w8uPxhLsl8kokGNuMVL048gx1d183UW07feSNIvpaUkakgg4jRMJaa5pjYWJYUxYYiIPzTUsi0N+jZcYxpY0sOqu+vkp0ltSA/dTW2HR6OU+MYxdJN3R0dHR0dHR0XEQfcF4D1A72KRcm3YgiTk8xEgl3UR+4+9Rb7G14/TdWmsnnnQyuPtOu90W01lh+26MrISns64pPc5auL7Mkj4O0VKi9nst/Sqvk8QUuTK7h03ma4nhcR28JRcQSUmf4SztkFvO25f0aRNLeshgISHVt+seORue9Fj9d32bdEWXHCczD6ea+lZdy3G8x+fnKzu74Wlf0kv230u6kKlszzTPY9Kn8zi97hKj7OWbTnJyHcaUt0Iqq1afdIYxsUeHGEamR/9/e2cfq1lVnfFnzQwDDMOoMCkWFCigNYEEjRWCBrXBIsRam2paVLRiwVai1KhVTPmSQtQKbbVqVYQqpRjFaiVVqbQVGkpDpSJaLSEBOwU6AiMfMgMMzMzqH+dsZr3rPnufc2fuzH3n3ueX3Lznno999jln733Wftba+4R1rYEn0XNSiApjVKFL2izOLd6XWCZzHHCNeH9aE4THMsLeL0yxH6IWf9pqr9jzZl4QFsMIzKy3WYnM97emjOb3JSvb+RpYzHZkrIdsPpHBOAUUY5EZRuwFyQaCsBcue8mzudBi2rFSxM9yFdgXMJhrgUn50YXG3COFbDznOSCXYOaLdFuIjQUb1RrTb7n8o8svHlNzV5T02Ih01lizxosZwixvNXc5M1zj/W25KGMazBXMgu9jGuwesHTieVovjEgr+D6mE12UuRMTj4mudmYExWcf7xkrT/GZFLdoDPFgL0r2NZqYHisbY+5pzm9+kdY6fIU8qrbslzsAsVPAYEZHfHGzUJCcfvkdMnjyc9qMyVHhwGT5ZPWGdabGhEYwQ7rV7u2OmQMN4/2NaWdDLneGWJgK69DEe5rrRM34ZW38WFg6m1Ley3bWfuZ0smiQyy1zuw8JBPE5MsMzPu/SjkYjvrynPBzDvg7D3PvTZpzJJS2EEEIIIQaRwTglMNk9rqv1fvJ8T9k1mAciMDdNzZ0wpN61goFbriKAK0FMRYkqS+uh15S0Idcsg7ktay6Msl/NZRh/y755P5Y3dnzNRR6fbe5V1warMFWYlaF8jrIdmKnqsMD+VkB+VARq96WcJ1O7ZzG/2X2f086DIGrpZIWGlXtg8lqza2s5JpVK5sJk6/I5auWmFXKSFbf8TJgSvAwzVb7lmCxf7DvrWQHK06Qwdy7SMlPF8nXnMhunoWmF6cTzDLV1Q8ohu4aaEpzTieuHVOjo1WH1suUir3k+mPoe7ylT3Vvt9BCsjaopg1nhjXmI3hCm7Mfpp/K7rzZNTUvly+cv21joT7zG3GbFeRjjF3WGBlNOE7uywrg9nkghhBBCCLEIWHAKY61304oXYT3tGBvDesNDahaL7VhK9otf8ahNQcAGNLC4EtazYj3t2NMrvbXYQxsaoJJ7pDVVl8X3tVSxGHfD1KPa1AyFqIYxFaA2uW5Jm/VyW3Gs8TxM0YtliN2Xsl9Us3IPO19DrWy3VPWoNOT81AbCMOUaYb/WAJeaUlSLSyu/OYaMTc+TFcRcD6JCxupnCZivqdmtmNSstLEJglmw//KwPecrxna2BmKx+lCOj+eLx7BykdPO7d4K8DjgWtmonTuur9UbVj5zOjk9Vs5Z3cjTCcW0NqI9zVds41sDPXKey7ZYt3KZz+1MjdY5yjrWRsW8Mk9QVNDLbx7YE+8Z8wQNDSapedpi3nLaLSU3nju2j0VhXA/+HKeZXSGPjAVlMG7C5AsuFjL24oovz1xgLSwvxdYK1mrUawZqHEHIKnkO7M8vaTYvGnOFsEEkrOGMDQT7UkFMpzUCdgvZr5AbJ2YwsoE7rXnE4n1h+WBGHSsPbFR3zEfN3cMMp5Ybhr2Eah0B9myZ8VfI58v3ipXz2EAzA5Y1BjVXfKxbrTLSGpiTj2nVg5qhHA3F8psNPTbXYcl7vgZmSNfuD+tQZgM2LrN0WJlmzy4PYmCdzBx6wAY05M+m5mP2wNaR5rVBesyAqBk1QL3MjwknyHltdZ7LsbkNy+1MdLnW2r2c7yH3dOykDA2SYgxdd8vwirBOfMxjrhsrMNPtG43wWlvZet41WsfEvEYXeO7sM7Ekj/zfFdhV8plZUAajEEIIIcS0sivHMC4og7EoarkXugm8B8gUgicxkxhoy25Yy90N8C8ixN4Rc48UYt5abk2WnxycnNWluC7O0xZ72tmNFnufMW024IOpS1FhzPmNxzNXCevtZgWxpQyyY2r3OudtjFuIDQ7JveGaO4e5R7eFlgszXjdT15ZjpiqRl0s6UQXICnvteWZ1A9ha354cyC97xlF1Y+pxOV+cm7HUv5obFWF7y6VaC93I94+lk+8Zu2/MzRiPYfOesu+Ns6lV4vnYtecpiLZgsg1jXol8DczbksvaWCWO1Y1WXc15zG127RrY82TPLOaLqXM1d3r5ZfeoFTowVBaZR6MWZtEKvSh1Y3dMegNaA1NyHjK191feFtNhZZUpjKyNZ/moKbTzybTlZywLzmCM7jI26WqsSLFyleVSaeKIT2YI1lyQ+YUc1xkmJ+wtv2yutAJzYzP3c4RVbDZH3Ja0HZg0GBnR/cnmbmNzX8YXBctn3BbnTysv+pLHpeAGAjMo2QjjoUa7ZUTUKgpzQcb73DIWxlIz3uL9G5p7tOSBhWvE9JjbOBv5MYyiNql4TocZjLl8Drndy36xnjB3OHve7BrKdnat0SiruZ7zeeJ1s44RM3xqrsbc+Ygu59q583lqbnWWDrt/Jd7TMdk+5MnAWexmXi75Yp34WptRfludqVo73Op81647E9Ou3d+8LueR5S3fl2WV5Xhszh+77uw2Z52XofwCXTvbMuqGOrWsjcthNzkddq3ZAB5DfF+2OjHziRRGIYQQQggxiAzGKaD0ulq9ZtbzYm7bkl5eZj3Fmkug/DLFKdLqNQN8Dkjm6mypCWWfWn5rAeysl8YG4WT3327g1xpdgjntWi+S9e5rbp+hL1vU0kHaL9/LWm946Nmz3i2bD5HR6lVnl1MOCajNr9a610ypjOU3DkphimlrIA1zbWWFJn/yi9XlrPwxFSs/26zO5XXsXrGyVCOmE/NezpPveayXMR8s2J+5GNl1R69D/GUKbFa9gfao4i0YVhhbA0ZiesvT/8BkWWNu6lZdrXloxj67obZnrDo1pIC12iCmvsVjaorzWOK5cxl5EjPnUWXvtpzfljIY70W8rpaiXHue+TpZGanVDdZ2TAvTlJfZMFvvmBBCCCGEWGQsOIWRBcLXenDMWm7FQsXlWqxUWY695pZVHnteQwM5mFLJ4ldqU9NkFYENKsjqBesJtabQ2S2sK7CgeDaAZQkmn1n+cgjLb+4N53s11EOuxRu1plmpxUqN6fXXFE3Wo6/FHJVfNqgoKoxZXWLniaoPi0UdUnpyPoB6fWMDx5gCGfPa8hbkmK1MK24sHlNTY1k8bMx3jM+spRPzy8pSfDYtFZB5IfL6HJ8at9farawes3NmtZUN0mu1lbVyM8bDULvuVgz4E5hZbnI+WFljaY9VglgbxLwoMR9xvstch2I+4vNspTek/rAY5o2Y/A47UJ+mJo4JYN61nMeYp9l4QWI6rIyw9iamw+K04/r5RjGMUwRz5+SC13IBxwaUub7YS2joZR/XMbdcNjJrhkjLyGGSf/ycWO3FxNyDQ9fYeonHUeZxRHg+pjbJcST/z16k+YUxZBiU3/wcamVkqHGL7kQ2cjXngQVlZ2NzyH1V/mcGIzNKWutqBmOeSSDDzs1ci4XN4dzRgCpu6N0qxwwZA/l6avM9MgOMhRtE8jHxZRbLQ4G5w0HW5ZCEMTMe1F52uWMW2QTe1rWOjcZhPIbVt1onM5+n1jFiITI5b/nZsLozlB9WRlj9Z8+5wO7/UFvIiOE7zJiKZZa12axesjac5Tk+2/jpv1we89zArHy26gYLw2CGXvx0YryeWEdz+R1jeOYBfeW9EK95vpHBKIQQQgghqkhhnBKyesiUv9pxWcl4HFy5GutuqO3X+uxWTWGouaVqxzLXawyKj640dg1MTWDKQLxXLRcbcz8zVSKqb/nzVOXYluKZXYaFrOzEOR5jj50pV8zFG3vPNZUw5z3+z3rnrYrI3LWx972cbI/nbrnYsvuYKVLMvdeaLofl4UnMDDHISmRLCaoNhsjbayrp0HNgg0OYehTPzbaz51BTgGJeaoxtw5hCXVOU43JLsY/pMTd2pFX+WFsY72+s62xAQ2uqp6FBEPEetOp/7bpZXW+9A+L9Yedm11MLkUFjOwv7yK5Z1ubW3LWRrBC2FPshd3mBqe/x2TFVsabSM6UypsPuFQA8RPI1X8hgFEIIIYQQTWQwTgk15Y89oNirzlNF5Ji0Vu8ywhRGBpu2hKlvTJGK52EqClNOmPo2pChF5ZBNhB1n3y/rakHbrbi+GAPXCuJnMYpx/9jrZvGIs1EYQY5h9zLmMfdst5B1NaUiqhJMMcnnY+pahE0kXostYqrGkDocn1lRGMuAJ68cw1ToeG5L2zeHbUMNbEt5YXWoppqxMjukMDK1ht3f+NtSMmtty1iVMZarrPpm1ZbVsZY3gJVppoCxe55jkJmil8v+8rTM4j9Z+S3U6jKr/7ntHopHrNFSP2ttAsL2cu6WB4sNdMuDqcpyjEdsvcdq+WZKZr4vWdlrKfot71Dt/V1g9Ykp6XH7tCGX9JTCKn5txG7r6wW1yrs9BmOspLnRzudrpcUqX2yImRthKPg7jlTOgcL5yypIy+ylyNxYMfibja4dakyWkWNq7hWE7eWXGVusAWcDI2rGSW6gWDoswD3nJ78UmZsfmOx0FNhgqSGDsRCfPasbta/DlLyX+dziedh8eNF9VAZJbUzHA5Nz/jGjLeZz7Is2npu5cWO+W4OYagZPfrZDn8CrGYdjYe3R0MCIaGC0XvzsnuZQiPI7dhAJe46sg8tGwMbOSdlvMzl3XH4cvMwzlzQLf2i5imvucFaXWd4iuYzF8sXmT10GYEW/zEJCWmV76Bpymcp1p3bdtfJdfll5yOfM4kK+7iFhJO7L3ivTwq5qME6rES6EEEIIIaaEaTO85wTWA2EKx5DCuL0uonJudgxzY9VcuGMURqZ4xJ4XCxZmPfYtmHQ1M7JKEF3XzL3ClA6WdlbpavsA3L3Ezs2+FsBgveZaHuK2lusxLrfmOszuJTbNRVYg8vmYsprVOWDm/cvlLz8zpvItA1cO4rlz2EJUekoeYr1jg4qewFaFe+grM2zQRjxffg75PFlNrLmKkdbF9bGutga3xeUhtYapPgzmUo3U0m65RxmsHWF1MKrVMY/M6xPXsXRq6ijAwxZqXoahupyfQ20wCnML19zd+QWbv2YEdNfA6gt7F5X04mCzFWFbvHZWrvK9GVJRax61lvcn71v2b037xN5jsT2K6bY8aUwBX4Gt4TLTgFzSUwIzFIGZLpFWIR1yC4/NQ4S9XFglZo0pqzR539q2/KLMDQKL99qMrQ0RYwlmfqpwKNaHuTWGXAvshVLOD7TjkcrxOb/MqCjklwNrqNi6FuzlugLcFR+X2Uslk/PTisGL5YHFPcU02byHbBJf1lmIz47FBOcX97Kwnbn043NiL8+YD+Yuj/Pdsc5ANBhZm5HLZ80QaRlo2bgu22I5ZudhHYRtecmwtFsdhKFyHvMey3YuT+wZZbdlLg9xO8vP5rRcWJp+8z0d09ln11UzpuJ1F2Mt/sZ7wepyniib1ZdHMVkHc95qoS2RVvllbXd8duz9EsviUBgVOw9Lhz3v1rtvSBhhbdwSTJfBCMhgFEIIIYQQA8hgnBKYi6ysB8b1asq6lit4TD4ybNABO6Z2DS2GgsiBSXUv5yf22Eve4kz8Qy7nmus9w46pBUFnN2HcPjawnLnqWG+45tpuhSXURn9HmPuOKYzs/kYlojWAgPX4n8BMBScqQVEFKXmPbuOYHlMYY9hCVDDLsVkZjApjISp/8R606mVWHfI1stHqtYFNLZV/TJlu1eV47tzI5pG/Ob04Lycr+0N1K+7X8rbE6x5SiiLs/rJBZMwl2sp3XGbegDgwKipGpe0q9aWmDLLrYmWADaiLnqnoLSjK4sqwrjUDBlPvWOhF/tJYDlPJam3Zj3kY8m85vuyX19VUPnYvh8Ko8v5xufYObKnD7D1XCzl5IqyLXyCbb+SSFkIIIYQQg8hgnEJasYNI28bE8mwLMR3WKxw6Zsy+BRYThLCcVZ+4PapdMRZoaVifzxPjxpakbUMqyJggaJbW8rQu3998ntq58/ZaDCPLA7sGNrhmKN6r9WWUeO781aGyrdXjj9fYGvgUp1Zhz5jFp+bnluMHmSLKnndUYOLxhdnUy5YKnZX2DHu2bF3tmLiOxZK2FDmmZjPFeTYxjEMqfmwnhpSi8jum3YrHzAZW5mPZj/eqrG/Vndl4h+L9zSppTflnMYx79b97Y2ubGb02rJ2I67LCmD0ETLHL7X30ysQBZUxhjOR08jGt8h/LDztPbJvZeRituldrh5kXIb7vpslA25EKo5ntA+BSAMcDWAfgA+5+JdnPAHwYwKn9qksBvN/d8we5JliQBmM2IPLLtXVM7f+5oGbczNU5Wy9S5h6JL7NINBKZOy27bse+TGIe2TNpuR/KeVoGRK0xYcZUTDPnZ1nlGJa/liuPucbGuMuYm5AZjIyY33x/ay/FuD3D7m80MvMLvezXcsWxOjDkrhxrwI01MueqQ1hLMxrRLffyUBnZ3vywl3SrnYgv/tkYqHN1f1m9a30goHavWu7Rclz+f6g9Y52/PMAijvbfhJmu0Hg9rcnSxxhqzGBsuaRr96JVHoaOqdWxMYbebN6BLcOTPUuAl5tFwCfRPf79ADwfwDfM7FZ3/1Ha720AfhPAkehs2GsB3Ang063E56JdEkIIIYQQI9iyjX8tzGwvAK8FcLa7r3f3GwBcDeBNZPffBXCxu9/t7vcAuBjAW4bybQMK5C6DmS2MCxFCCCHEnOPuY2Yt26GY2TUAVm/j4Xtgq3gKAJ9198/26b4AwI3uvmc413sBvMzdX53y8DCA4939pv7/XwHwHXffu3XyBeOSnoaCsNgxsw3uvtfwnkLseFQexbSgsigK7n7CDkp6JYCH07qH0YXWDu37MICVZmatOEa5pIUQQgghdm3WA1iV1q0C8MiIfVcBWD806EUGoxBCCCHErs3tAJaZ2XPCuiMB5AEv6NcdOWK/CWQwirnkq/OdASECKo9iWlBZFDsUd9+Arpydb2Z7mdlLALwGwN+Q3S8H8G4zO8DM9gfwHgCfHzrHghn0IoQQQgixWOnnYbwMwK8B+BmAM939SjM7FsC33H1lv58B+Ai2zsP4OYyYh1EGoxBCCCGEaCKXtBiFmb3DzG42s41m9vm07Tgzu83MHjWz75jZQWHbYWb2n2b2EzM7cadnXCwKzGwfM/uamW0wszVm9oZ+vZnZZ8zsXjP75HznU+z6mNkVZrbWzH5uZreb2an9+oPNzM1sffg7Oxy3u5ld1h/3UzN7d0r33H79VWa2+86+LiGGkMEoxvJ/AC5AJ3c/hZmtRhc3cTaAfQDcDOBLYZcPAjgDwNEAzt0pORWLkfiFgzcC+CszOxzAK/rtB6ALCD9unvInFg4fAnCwu68C8BsALjCzF4btT3f3lf3fn4T15wF4DoCDAPwqgPeZ2QkA0A9UOKrfdguAk3f8ZQgxOxbMPIxix+LuXwWemuDzWWHTbwH4kbtf1W8/D8A6M3ueu9+G7kuD5RPVu+3UTItFQfjCwRHuvh7ADWZWvnBwHbqO8dLwK8Q2kz6z5v3foehixlq8GcAp7v4ggAfN7BJ0X9e4Bl25tP63/AkxVUhhFNvL4QBuLf/0I7Xu6NcDwPkAPg7gpn5ZiLnmuQA2u/vtYd2t6Mrgt9F94vfufv21OzlvYgFiZp8ys0cB3AZgLYBvhs1rzOxuM/vr3gMDM3sGgP0R2kpsLaPoO9c/ALAGwAvBR7YKMa9IYRTby0oA96d1T80u7+4/xuR8T0LMNdUvHLj7FnTfTRViznD3083snQCOAfByABsBrAPwIgDfB7AvujCJvwXwSnRlFJj5dY2nvsLh7mcCOHNH512IbUUKo9heZjO7vBA7ApVBsdNx983ufgO6EJ23u/t6d7/Z3Te5+70A3gHgeDNbha6MAjO/rqEyKnYZZDCK7WVixvg+nuxQjJg1Xog5YjZfOBBirlmGrs3LlDnrrI9bXItt+LqGENOCDEYxCjNbZmZ7oA/INrM9zGwZgK8BOMLMXttvPwfAD/qYHCF2OLP8woEQ24yZ/YKZnWRmK81sqZm9EsDrAfyLmR1tZr9sZkvMbF90sdvXuXtxQ18O4Cwze4aZPQ/AaRjxdQ0hpgUZjGIsZwF4DF2Mzcn98lnufj+6EaoXAngQ3fQ5J81XJsWi5XQAewK4D8AX0bkIpd6IucYBvB3dIKoHAVwE4F3u/nUAh6Ab8fwIgP9CF9f4+nDsuegGBK4BcD2Aj7r7NTsv60JsH/rSixBCCCGEaCKFUQghhBBCNJHBKIQQQgghmshgFEIIIYQQTWQwCiGEEEKIJjIYhRBCCCFEExmMQgghhBCiiQxGIYQQQgjRRAajEGKbMLODzcz7L/7MddrrzeyQuU53rjGzA/u8Lp2j9HbYPRVCiO1BBqMQi5j+M2c3mdkGM7uvXz7dzGwn5uE6Mzs1rnP3le5+5yzTebmZ3T23uWvj7v/b53Vzn4cZ1yKEEAsBGYxCLFLM7D0APgbgowCeCWA/AH8A4CUAls9j1oQQQkwZMhiFWISY2dMAnA/gdHf/irs/4h23uPsb3X1jv9+rzOwWM/u5md1lZuc10jzFzP7bzB4xszvN7PfT9teY2ff7tO4wsxPM7EIAxwL4RO/a/US/r5vZYf3ynmZ2sZmtMbOHzewGM9tzttdrZpeb2f19OmeZ2ZJ+21v6NC8yswfN7CdmdmI49pfM7F/76/onM/ukmV3Rb3vKhcyuhbmYowppZkv7864zszsBvIrk+1IzW2tm95jZBXPl/hZCiNmgOBkhFifHANgdwNcH9tsA4M0AfgTgCADXmtn33f3vyb73Afh1AHcCeCmAb5nZd939e2Z2FIDLAbwOwIB5YAcAAAQVSURBVD8D+EUAe7v7NWb2EgBXuPvnKnm4CMDhAF4M4KcAjgawZfylAgD+EsDTABwCYF8A3wawFsCl/fajAXwBwGoAbwNwqZkd4O4O4EoA/wbgFQCOAvBNAFfnE7j7H+drMbODB/J1Grp79gJ09/rv0vYvALgXwGEA9gLwDwDuAvCZcZcthBBzgxRGIRYnqwGsc/dNZYWZ3WhmD5nZY2b2UgBw9+vc/YfuvsXdfwDgiwBexhJ092+4+x29Unk9OqPs2H7z7wG4zN2v7dO6x91vG8pkrwK+FcAf9sdsdvcbiwI6hl6R+x0AH+iV1P8BcDGAN4Xd1rj7JX0s4hfQGbT7mdmBAF4E4Bx3f8LdbwAxFreD3wbwF+5+l7s/AOBDId/7ATgRwLvcfYO73wfgzwGcNIfnF0KIUUhhFGJx8jMAq81sWTEa3f3FANAPHCnu2qMBfBidurgcnSp5FUuwd+OeC+C5/fErAPyw3/xsdMrcbFkNYA8Ad2zDsTGN5QDWhHVrABwQ/v9pWXD3R/sxPyv7Yx9w90fDvnehu565YP8+vZivwkEAdgOwNoxBWpL2F0KInYIURiEWJ/8OYCOA1wzsdyU6Re3Z7v40AJ8GMGMEtZntjs6dehGA/dz96egMxLLvXQAOrZzDG+dfB+DxxrFjWAfgSXQGWOFAAPeMOHYtgH3MbEVY1zIW87Vs6H/j8c9M6cf0DgzLd6F7Rqvd/en93yp3P3xEvoUQYk6RwSjEIsTdHwLwQQCfMrPXmdlKM1tiZs9HFytX2BudwvZ4H4f4hkqSRX28H8CmXm08Pmy/FMApZnZcf54DzOx5/bZ70cUWsnxuAXAZgD8zs/37QSLH9AYqxcz2iH/o4h2/DOBCM9vbzA4C8G4AV7TuUX/+NQBuBnCemS03s2MAvLpxyMS1uPv96AzTk/u8vxWTxu+XAZxhZs8ys2cAODMcuxadW/9iM1vV37dDzYyGBAghxI5EBqMQixR3/1N0htP70A1YuRfdYIr3A7ix3+10AOeb2SMAzkFn4LC0HgFwRr/9QXSG5dVh+38AOAVdDN7DAK7HVsXvYwBe149Q/jhJ/r3oXNvfBfAAgI+g3nYdAOCx9HcogHeiU/vuBHADOuX0skoamTeiGyT0MwAXAPgSOuWPwa7lNAB/1B9/OLbeWwC4BMA/ArgVwPcAfDWl92Z0xviP0d3Xr6CLrxRCiJ2KdYMAhRBCjMHMvgTgNnc/d77zIoQQOwspjEII0cDMXtS7gpeY2Qno4j7ZtEJCCLFg0ShpIYRo80x0ruJ9AdwN4O3ufsv8ZkkIIXYuckkLIYQQQogmckkLIYQQQogmMhiFEEIIIUQTGYxCCCGEEKKJDEYhhBBCCNFEBqMQQgghhGgig1EIIYQQQjT5f456awkfPLrrAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rc_params = {\"figure.figsize\": (12, 5.4), \"font.size\": 12}\n", "with plt.rc_context(rc=rc_params):\n", " smoothed = m_2fhl_gc.smooth(width=0.2 * u.deg, kernel=\"gauss\")\n", " smoothed.plot(stretch=\"sqrt\", add_cbar=True, vmax=4);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 Interactive Plotting \n", "\n", "For maps with non-spatial dimensions the `Map` object features an interactive plotting method, that works in jupyter notebooks only (**Note:** it requires the package `ipywidgets` to be installed). We first read a small example cutout from the Fermi Galactic diffuse model and display the data cube by calling `.plot_interactive()`:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "filename = \"$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits\"\n", "m_iem_gc = Map.read(filename)\n", "\n", "rc_params = {\n", " \"figure.figsize\": (12, 5.4),\n", " \"font.size\": 12,\n", " \"axes.formatter.limits\": (2, -2),\n", "}\n", "m_iem_gc.plot_interactive(add_cbar=True, stretch=\"sqrt\", rc_params=rc_params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can use the interactive slider to select an energy range and the corresponding image is diplayed on the screen. You can also use the radio buttons to select your preferred image stretching. We have passed additional keywords using the `rc_params` argument to improve the figure and font size. Those keywords are directly passed to the [plt.rc_context()](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.rc_context.html) context manager." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Reprojecting, Interpolating and Miscellaneous\n", "\n", "### 5.1 Reprojecting to Different Map Geometries\n", "\n", "The example map `m_iem_gc` is given in Galactic coordinates:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy\n", "\tshape : (88, 48, 30)\n", "\tndim : 3\n", "\tcoordsys : GAL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, -0.1 deg\n", "\twidth : 11.0 x 6.0 deg\n", "\n" ] } ], "source": [ "print(m_iem_gc.geom)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example we will now extract the image at `~10 GeV` and reproject it to ICRS coordinates. For this we first define the target map WCS geometry. As `.reproject()` only applies to the spatial axes, we do not have to specify any additional non-spatial axes:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "skydir = SkyCoord(266.4, -28.9, frame=\"icrs\", unit=\"deg\")\n", "wcs_geom_cel = WcsGeom.create(\n", " skydir=skydir, binsz=0.1, coordsys=\"CEL\", width=(8, 4)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we extract the image at `~10 GeV`, reproject to the target geometry and plot the result:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " ,\n", " )" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m_iem = m_iem_gc.get_image_by_coord({\"energy\": 10 * u.GeV})\n", "m_iem_cel = m_iem.reproject(wcs_geom_cel)\n", "m_iem_cel.plot(add_cbar=True, vmin=0, vmax=2.5e-9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.2 Interpolating Map Values\n", "\n", "While for the reprojection example above we used `.get_image_by_coord()` to extract the closest image to `~10 GeV`, we can use the more general method `.interp_by_coord()` to interpolate in the energy axis as well. For this we first define again the target map geometry:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "m_iem_10GeV = Map.from_geom(wcs_geom_cel)\n", "coords = m_iem_10GeV.geom.get_coord()\n", "\n", "m_iem_10GeV.data = m_iem_gc.interp_by_coord(\n", " {\"skycoord\": coords.skycoord, \"energy\": 10 * u.GeV},\n", " interp=\"linear\",\n", " fill_value=np.nan,\n", ")\n", "m_iem_10GeV.plot(add_cbar=True, vmin=0, vmax=2.5e-9);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5.3 Making Cutouts\n", "\n", "The `WCSNDMap` objects features a `.cutout()` method, which allows you to cut out a smaller part of a larger map. This can be useful, e.g. when working with allsky diffuse maps. Here is an example: " ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "position = SkyCoord(0, 0, frame=\"galactic\", unit=\"deg\")\n", "m_iem_cutout = m_iem_gc.cutout(position=position, width=(4 * u.deg, 2 * u.deg))\n", "\n", "rc_params = {\n", " \"figure.figsize\": (12, 5.4),\n", " \"font.size\": 12,\n", " \"axes.formatter.limits\": (2, -2),\n", "}\n", "m_iem_cutout.plot_interactive(\n", " add_cbar=True, rc_params=rc_params, stretch=\"linear\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned object is again a `Map` object with udpated WCS information and data size. As one can see the cutout is automatically applied to all the non-spatial axes as well. The cutout width is given in the order of `(lon, lat)` and can be specified with units that will be handled correctly. " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }