{
"cells": [
{
"cell_type": "markdown",
"id": "261d10af",
"metadata": {},
"source": [
"# Installation"
]
},
{
"cell_type": "markdown",
"id": "71b45751",
"metadata": {},
"source": [
"To set up the python environment, we will use Anaconda ( www.anaconda.com). Click the link, find `Free download`, and follow the instructions.\n",
"\n",
"\n",
"Once installed, search for the app, which should be called something like **Anaconda-Navigator**. Once *Navigator* starts, go to *Environments*, and *Search Packages*. Please check for the following packages:\n",
"- numpy\n",
"- scipy\n",
"- matplotlib\n",
"- notebook (jupyter notebook)\n",
"- numba\n",
"- uncertainties\n",
"- pybind11\n",
"\n",
"`pybind11-abi` probably exists, but `pybind11` probably not. `uncertainties` is also probably missing. \n",
"\n",
"When in *Environments* of *Navigator*, please change the packages which you see from `installed` to `All` and than search again for `pybind11` and `uncertainties`. If you find them, install them.\n",
"\n",
"If they still don't show up, then you can issue the following in the command line:\n",
"\n",
"`conda install -c conda-forge uncertainties`\n"
]
},
{
"cell_type": "markdown",
"id": "cbe261a4",
"metadata": {},
"source": [
"We will also need a text editor for python codes (in addition to jupyter notebooks). **Spyder** is part of Anaconda, and is very powerful editor. You can find it inside *Anaconda-Navigator*.\n",
"\n",
"But other editors of your choice are equally good (for example *emacs* or *Aquamacs* or *vim* or *vi*)"
]
},
{
"cell_type": "markdown",
"id": "f556e632",
"metadata": {},
"source": [
"Some examples to speed up the code will be given in C++. It is not essential to have it, but you will learn more if you can set up the environment with a C++ compiler (such as gcc), which can be combined with Python. For installation instructions, see the `Optional installation of C++` below. We will also show examples below."
]
},
{
"cell_type": "markdown",
"id": "bec6c2f1",
"metadata": {},
"source": [
"We will test the installation with an excercise of plotting **Mandelbrot set.**"
]
},
{
"cell_type": "markdown",
"id": "22f41c3b",
"metadata": {},
"source": [
"## Mandelbrot set"
]
},
{
"cell_type": "markdown",
"id": "ab9d4c3d",
"metadata": {},
"source": [
"Wikipedia: The Mandelbrot set $M$ is defined by a family of complex quadratic polynomials $f(z) = z^2 + z_0$ where $z_0$ is a complex parameter. For each $z_0$, one considers the behavior of the sequence $(f(0), f(f(0)), f(f(f(0))), · · ·)$ obtained by iterating $f(z)$ starting at $z=0$, which either escapes to infinity or stays\n",
"within a disk of som finite radius. The *Mandelbrot set* is defined as the set of points $z_0$, such that the above sequence does not escape to infinity."
]
},
{
"cell_type": "markdown",
"id": "f6cadfbb",
"metadata": {},
"source": [
"More concretely, the sequence is : $(z_0,z_0^2+z_0,z_0^4+2 z_0^3+z_0^2+z_0,...)$.\n",
"\n",
"For large $z_0$ it behaves as $z_0^{2n}$ and clearly diverges at large $n$. Consequently, large $z_0$ is not part of the set. \n",
"\n",
"For small $z_0$, it is of the order of $z_0+O(z_0^2)$, and is small when $z_0$ is small. Such $z_0$ are part of the set.\n",
"\n",
"To determine that certain $z_0$ is not part of the *Mandelbrot set*, we check if $|f(f(f(....)))|>2$. \n",
"This treshold is sufficient, because the point with the largest magnitude that is still in the set is -2. Indeed, if we set $z_0=-2$, we see that $f(f(0))=(-2)^2-2=2$ and $f(f(f(0)))=2^2-2=2$, and for any number of itterations the sequence remains equal to $2$. Such sequence remains finite, and by definition $z_0=-2$ is part of the set, and $f(f(f(...)))=2$ might lead to finite sequence. \n",
"\n",
"For any other point $z_0\\ne -2$ and $|z_0|=2$, we can show that once $|f(f(f(...)))|$ becomes $2$, it will lead to diverging sequence. For example, for $z_0=1$ we have $f(f(0))=2$ and $f(f(f(0)))=5$, and clearly grows.\n",
"\n",
"We will make density plot, with $Re(z_0)$ on $x$-axis, and $Im(z_0)$ on $y$-axis, and color will denote how long it took for the sequence to have absolute value equal to 2. The mandelbrot set will have one color, and all other colors \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1338f8ce",
"metadata": {},
"outputs": [],
"source": [
"from numpy import * # because arrays are defined in numpy\n",
"\n",
"def Mand(z0, max_steps):\n",
" z = 0j # no need to specify type. \n",
" # To initialize to complex number, just assign 0j==i*0\n",
" for itr in range(max_steps):\n",
" if abs(z)>2:\n",
" return itr\n",
" z = z*z + z0\n",
" return max_steps\n",
"\n",
"def Mandelbrot(ext, Nxy, max_steps):\n",
" \"\"\"\n",
" ext[4] -- array of 4 values [min_x,max_x,min_y,max_y]\n",
" Nxy -- int number of points in x and y direction\n",
" max_steps -- how many steps we will try at most before we conclude the point is in the set\n",
" \"\"\"\n",
" data = zeros((Nxy,Nxy)) # initialize a 2D dynamic array\n",
" for i in range(Nxy):\n",
" for j in range(Nxy):\n",
" x = ext[0] + (ext[1]-ext[0])*i/(Nxy-1.)\n",
" y = ext[2] + (ext[3]-ext[2])*j/(Nxy-1.)\n",
" # creating complex number of the fly\n",
" data[i,j] = Mand(x + y*1j, max_steps) \n",
" return data\n",
"# data now contains integers. \n",
"# MandelbrotSet has value 1000, and points not in the set have value <1000."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "07e8719a",
"metadata": {},
"outputs": [],
"source": [
"data = Mandelbrot([-2,1,-1,1], 500, 1000)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "17549e0a",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pylab import * # plotting library\n",
"%matplotlib inline\n",
"\n",
"ext=[-2,1,-1,1]\n",
"# pylab's function for displaying 2D image\n",
"\n",
"imshow(data.T,origin='lower',extent=ext)\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d5ea20e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function imshow in module matplotlib.pyplot:\n",
"\n",
"imshow(X: 'ArrayLike | PIL.Image.Image', cmap: 'str | Colormap | None' = None, norm: 'str | Normalize | None' = None, *, aspect: \"Literal['equal', 'auto'] | float | None\" = None, interpolation: 'str | None' = None, alpha: 'float | ArrayLike | None' = None, vmin: 'float | None' = None, vmax: 'float | None' = None, origin: \"Literal['upper', 'lower'] | None\" = None, extent: 'tuple[float, float, float, float] | None' = None, interpolation_stage: \"Literal['data', 'rgba'] | None\" = None, filternorm: 'bool' = True, filterrad: 'float' = 4.0, resample: 'bool | None' = None, url: 'str | None' = None, data=None, **kwargs) -> 'AxesImage'\n",
" Display data as an image, i.e., on a 2D regular raster.\n",
" \n",
" The input may either be actual RGB(A) data, or 2D scalar data, which\n",
" will be rendered as a pseudocolor image. For displaying a grayscale\n",
" image, set up the colormapping using the parameters\n",
" ``cmap='gray', vmin=0, vmax=255``.\n",
" \n",
" The number of pixels used to render an image is set by the Axes size\n",
" and the figure *dpi*. This can lead to aliasing artifacts when\n",
" the image is resampled, because the displayed image size will usually\n",
" not match the size of *X* (see\n",
" :doc:`/gallery/images_contours_and_fields/image_antialiasing`).\n",
" The resampling can be controlled via the *interpolation* parameter\n",
" and/or :rc:`image.interpolation`.\n",
" \n",
" Parameters\n",
" ----------\n",
" X : array-like or PIL image\n",
" The image data. Supported array shapes are:\n",
" \n",
" - (M, N): an image with scalar data. The values are mapped to\n",
" colors using normalization and a colormap. See parameters *norm*,\n",
" *cmap*, *vmin*, *vmax*.\n",
" - (M, N, 3): an image with RGB values (0-1 float or 0-255 int).\n",
" - (M, N, 4): an image with RGBA values (0-1 float or 0-255 int),\n",
" i.e. including transparency.\n",
" \n",
" The first two dimensions (M, N) define the rows and columns of\n",
" the image.\n",
" \n",
" Out-of-range RGB(A) values are clipped.\n",
" \n",
" cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`\n",
" The Colormap instance or registered colormap name used to map scalar data\n",
" to colors.\n",
" \n",
" This parameter is ignored if *X* is RGB(A).\n",
" \n",
" norm : str or `~matplotlib.colors.Normalize`, optional\n",
" The normalization method used to scale scalar data to the [0, 1] range\n",
" before mapping to colors using *cmap*. By default, a linear scaling is\n",
" used, mapping the lowest value to 0 and the highest to 1.\n",
" \n",
" If given, this can be one of the following:\n",
" \n",
" - An instance of `.Normalize` or one of its subclasses\n",
" (see :ref:`colormapnorms`).\n",
" - A scale name, i.e. one of \"linear\", \"log\", \"symlog\", \"logit\", etc. For a\n",
" list of available scales, call `matplotlib.scale.get_scale_names()`.\n",
" In that case, a suitable `.Normalize` subclass is dynamically generated\n",
" and instantiated.\n",
" \n",
" This parameter is ignored if *X* is RGB(A).\n",
" \n",
" vmin, vmax : float, optional\n",
" When using scalar data and no explicit *norm*, *vmin* and *vmax* define\n",
" the data range that the colormap covers. By default, the colormap covers\n",
" the complete value range of the supplied data. It is an error to use\n",
" *vmin*/*vmax* when a *norm* instance is given (but using a `str` *norm*\n",
" name together with *vmin*/*vmax* is acceptable).\n",
" \n",
" This parameter is ignored if *X* is RGB(A).\n",
" \n",
" aspect : {'equal', 'auto'} or float or None, default: None\n",
" The aspect ratio of the Axes. This parameter is particularly\n",
" relevant for images since it determines whether data pixels are\n",
" square.\n",
" \n",
" This parameter is a shortcut for explicitly calling\n",
" `.Axes.set_aspect`. See there for further details.\n",
" \n",
" - 'equal': Ensures an aspect ratio of 1. Pixels will be square\n",
" (unless pixel sizes are explicitly made non-square in data\n",
" coordinates using *extent*).\n",
" - 'auto': The Axes is kept fixed and the aspect is adjusted so\n",
" that the data fit in the Axes. In general, this will result in\n",
" non-square pixels.\n",
" \n",
" Normally, None (the default) means to use :rc:`image.aspect`. However, if\n",
" the image uses a transform that does not contain the axes data transform,\n",
" then None means to not modify the axes aspect at all (in that case, directly\n",
" call `.Axes.set_aspect` if desired).\n",
" \n",
" interpolation : str, default: :rc:`image.interpolation`\n",
" The interpolation method used.\n",
" \n",
" Supported values are 'none', 'antialiased', 'nearest', 'bilinear',\n",
" 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite',\n",
" 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell',\n",
" 'sinc', 'lanczos', 'blackman'.\n",
" \n",
" The data *X* is resampled to the pixel size of the image on the\n",
" figure canvas, using the interpolation method to either up- or\n",
" downsample the data.\n",
" \n",
" If *interpolation* is 'none', then for the ps, pdf, and svg\n",
" backends no down- or upsampling occurs, and the image data is\n",
" passed to the backend as a native image. Note that different ps,\n",
" pdf, and svg viewers may display these raw pixels differently. On\n",
" other backends, 'none' is the same as 'nearest'.\n",
" \n",
" If *interpolation* is the default 'antialiased', then 'nearest'\n",
" interpolation is used if the image is upsampled by more than a\n",
" factor of three (i.e. the number of display pixels is at least\n",
" three times the size of the data array). If the upsampling rate is\n",
" smaller than 3, or the image is downsampled, then 'hanning'\n",
" interpolation is used to act as an anti-aliasing filter, unless the\n",
" image happens to be upsampled by exactly a factor of two or one.\n",
" \n",
" See\n",
" :doc:`/gallery/images_contours_and_fields/interpolation_methods`\n",
" for an overview of the supported interpolation methods, and\n",
" :doc:`/gallery/images_contours_and_fields/image_antialiasing` for\n",
" a discussion of image antialiasing.\n",
" \n",
" Some interpolation methods require an additional radius parameter,\n",
" which can be set by *filterrad*. Additionally, the antigrain image\n",
" resize filter is controlled by the parameter *filternorm*.\n",
" \n",
" interpolation_stage : {'data', 'rgba'}, default: 'data'\n",
" If 'data', interpolation\n",
" is carried out on the data provided by the user. If 'rgba', the\n",
" interpolation is carried out after the colormapping has been\n",
" applied (visual interpolation).\n",
" \n",
" alpha : float or array-like, optional\n",
" The alpha blending value, between 0 (transparent) and 1 (opaque).\n",
" If *alpha* is an array, the alpha blending values are applied pixel\n",
" by pixel, and *alpha* must have the same shape as *X*.\n",
" \n",
" origin : {'upper', 'lower'}, default: :rc:`image.origin`\n",
" Place the [0, 0] index of the array in the upper left or lower\n",
" left corner of the Axes. The convention (the default) 'upper' is\n",
" typically used for matrices and images.\n",
" \n",
" Note that the vertical axis points upward for 'lower'\n",
" but downward for 'upper'.\n",
" \n",
" See the :ref:`imshow_extent` tutorial for\n",
" examples and a more detailed description.\n",
" \n",
" extent : floats (left, right, bottom, top), optional\n",
" The bounding box in data coordinates that the image will fill.\n",
" These values may be unitful and match the units of the Axes.\n",
" The image is stretched individually along x and y to fill the box.\n",
" \n",
" The default extent is determined by the following conditions.\n",
" Pixels have unit size in data coordinates. Their centers are on\n",
" integer coordinates, and their center coordinates range from 0 to\n",
" columns-1 horizontally and from 0 to rows-1 vertically.\n",
" \n",
" Note that the direction of the vertical axis and thus the default\n",
" values for top and bottom depend on *origin*:\n",
" \n",
" - For ``origin == 'upper'`` the default is\n",
" ``(-0.5, numcols-0.5, numrows-0.5, -0.5)``.\n",
" - For ``origin == 'lower'`` the default is\n",
" ``(-0.5, numcols-0.5, -0.5, numrows-0.5)``.\n",
" \n",
" See the :ref:`imshow_extent` tutorial for\n",
" examples and a more detailed description.\n",
" \n",
" filternorm : bool, default: True\n",
" A parameter for the antigrain image resize filter (see the\n",
" antigrain documentation). If *filternorm* is set, the filter\n",
" normalizes integer values and corrects the rounding errors. It\n",
" doesn't do anything with the source floating point values, it\n",
" corrects only integers according to the rule of 1.0 which means\n",
" that any sum of pixel weights must be equal to 1.0. So, the\n",
" filter function must produce a graph of the proper shape.\n",
" \n",
" filterrad : float > 0, default: 4.0\n",
" The filter radius for filters that have a radius parameter, i.e.\n",
" when interpolation is one of: 'sinc', 'lanczos' or 'blackman'.\n",
" \n",
" resample : bool, default: :rc:`image.resample`\n",
" When *True*, use a full resampling method. When *False*, only\n",
" resample when the output image is larger than the input image.\n",
" \n",
" url : str, optional\n",
" Set the url of the created `.AxesImage`. See `.Artist.set_url`.\n",
" \n",
" Returns\n",
" -------\n",
" `~matplotlib.image.AxesImage`\n",
" \n",
" Other Parameters\n",
" ----------------\n",
" data : indexable object, optional\n",
" If given, all parameters also accept a string ``s``, which is\n",
" interpreted as ``data[s]`` (unless this raises an exception).\n",
" \n",
" **kwargs : `~matplotlib.artist.Artist` properties\n",
" These parameters are passed on to the constructor of the\n",
" `.AxesImage` artist.\n",
" \n",
" See Also\n",
" --------\n",
" matshow : Plot a matrix or an array as an image.\n",
" \n",
" Notes\n",
" -----\n",
" \n",
" .. note::\n",
" \n",
" This is the :ref:`pyplot wrapper ` for `.axes.Axes.imshow`.\n",
" \n",
" Unless *extent* is used, pixel centers will be located at integer\n",
" coordinates. In other words: the origin will coincide with the center\n",
" of pixel (0, 0).\n",
" \n",
" There are two common representations for RGB images with an alpha\n",
" channel:\n",
" \n",
" - Straight (unassociated) alpha: R, G, and B channels represent the\n",
" color of the pixel, disregarding its opacity.\n",
" - Premultiplied (associated) alpha: R, G, and B channels represent\n",
" the color of the pixel, adjusted for its opacity by multiplication.\n",
" \n",
" `~matplotlib.pyplot.imshow` expects RGB images adopting the straight\n",
" (unassociated) alpha representation.\n",
"\n"
]
}
],
"source": [
"help(imshow)"
]
},
{
"cell_type": "markdown",
"id": "91fc0a63",
"metadata": {},
"source": [
"This resolution is somewhat low, and we would like to increase $N_{xy}$ to 1000. But this would make the code 4-times slower. The code is already time consuming.\n",
"Let's time it. \n",
"\n",
"For that we need to include *time* and use *time.time()* routine."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b3ada506",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"clock time: 12.905179023742676 s\n"
]
}
],
"source": [
"import time # timeing\n",
"t0 = time.time()\n",
"data = Mandelbrot([-2,1,-1,1], 1000, 1000)\n",
"t1 = time.time()\n",
"print ('clock time: ',t1-t0,'s')"
]
},
{
"cell_type": "markdown",
"id": "7e8bada7",
"metadata": {},
"source": [
"We can speed up the code with package **numba**: https://numba.pydata.org.\n",
"\n",
"In the simplest case, we just add two lines of code: \n",
"\n",
"`from numba import njit`\n",
"\n",
"`@njit`\n",
"\n",
"\n",
"Limitations of Numba:\n",
"- Numba only accelerates code that uses scalars or (N-dimensional) arrays. You can’t use built-in types like `list` or `dict` or your own custom classes.\n",
"- You can’t allocate new arrays in accelerated code.\n",
"- You can’t use recursion.\n",
"Most of those limitations are removed if using Cython.\n",
"\n",
"Numba has been getting a lot better, even just over the past few months (e.g., they recently added support for generating random numbers).\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "23b6e039",
"metadata": {},
"outputs": [],
"source": [
"from numpy import * # because arrays are defined in numpy\n",
"from numba import njit # This is the new line with numba\n",
"\n",
"@njit # this is an alias for @jit(nopython=True)\n",
"def Mand(z0, max_steps):\n",
" z = 0j # no need to specify type. \n",
" # To initialize to complex number, just assign 0j==i*0\n",
" for itr in range(max_steps):\n",
" if abs(z)>2:\n",
" return itr\n",
" z = z*z + z0\n",
" return max_steps\n",
"\n",
"@njit\n",
"def Mandelbrot2(ext, Nxy, max_steps):\n",
" \"\"\"\n",
" ext[4] -- array of 4 values [min_x,max_x,min_y,max_y]\n",
" Nxy -- int number of points in x and y direction\n",
" max_steps -- how many steps we will try at most before we conclude the point is in the set\n",
" \"\"\"\n",
" data = zeros((Nxy,Nxy)) # initialize a 2D dynamic array\n",
" for i in range(Nxy):\n",
" for j in range(Nxy):\n",
" x = ext[0] + (ext[1]-ext[0])*i/(Nxy-1.)\n",
" y = ext[2] + (ext[3]-ext[2])*j/(Nxy-1.)\n",
" # creating complex number of the fly\n",
" data[i,j] = Mand(x + y*1j, max_steps) \n",
" return data\n",
"# data now contains integers. \n",
"# MandelbrotSet has value 1000, and points not in the set have value <1000."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "177c553b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"clock time: 1.1185107231140137 s\n"
]
}
],
"source": [
"import time # timeing\n",
"t0 = time.time()\n",
"data = Mandelbrot2(array([-2,1,-1,1]), 1000, 1000)\n",
"t1 = time.time()\n",
"print ('clock time: ',t1-t0,'s')"
]
},
{
"cell_type": "markdown",
"id": "b1a649b8",
"metadata": {},
"source": [
"This is substantial speedup of order of 20, considering a small modification required.\n",
"\n",
"We can slightly improve the code by making the outer loop over i parallel, and by allocating array for data outside the optimized routine.\n",
"\n",
"- We will allocate array data outside `Mandelbrot` function\n",
"- We will change `@njit` to `@njit(parallel=True)` and use `prange` instead of `range` for loop over j. Namely, if we don't change `range` to `prange` both loops will be attempted to be parallelized, which leads to nested parallelization, which either fails or is very innefficient.\n",
"\n",
"\n",
"The example code is:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "48285424",
"metadata": {},
"outputs": [],
"source": [
"from numpy import * # because arrays are defined in numpy\n",
"from numba import njit # This is the new line with numba\n",
"from numba import prange\n",
"\n",
"@njit # this is an alias for @jit(nopython=True)\n",
"def Mand(z0, max_steps):\n",
" z = 0j # no need to specify type. \n",
" # To initialize to complex number, just assign 0j==i*0\n",
" for itr in range(max_steps):\n",
" if abs(z)>2:\n",
" return itr\n",
" z = z*z + z0\n",
" return max_steps\n",
"\n",
"@njit(parallel=True)\n",
"def Mandelbrot3(data, ext, max_steps):\n",
" \"\"\"\n",
" ext[4] -- array of 4 values [min_x,max_x,min_y,max_y]\n",
" Nxy -- int number of points in x and y direction\n",
" max_steps -- how many steps we will try at most before we conclude the point is in the set\n",
" \"\"\"\n",
" Nx,Ny = shape(data) # 2D array should be already allocated we get its size\n",
" for i in range(Nx):\n",
" for j in prange(Ny): # note that we used prange instead of range.\n",
" # this switches off parallelization of this loop, so that\n",
" # only the outside loop over i is parallelized.\n",
" x = ext[0] + (ext[1]-ext[0])*i/(Nx-1.)\n",
" y = ext[2] + (ext[3]-ext[2])*j/(Ny-1.)\n",
" # creating complex number of the fly\n",
" data[i,j] = Mand(x + y*1j, max_steps) \n",
"# data now contains integers. \n",
"# MandelbrotSet has value 1000, and points not in the set have value <1000."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "514a077b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"clock time: 0.22400474548339844 s\n"
]
}
],
"source": [
"import time # timeing\n",
"data = zeros((1000,1000))\n",
"t0 = time.time()\n",
"Mandelbrot3(data, array([-2,1,-1,1]), 1000)\n",
"t1 = time.time()\n",
"print ('clock time: ',t1-t0,'s')"
]
},
{
"cell_type": "markdown",
"id": "afa8299f",
"metadata": {},
"source": [
"With this modification, we get speedup of the order of 50.\n",
"Note that pure C++ code with OpenMP could gives us speedup of the order of 200, i.e., extra factor of 4 on MAC with 8 cores.\n"
]
},
{
"cell_type": "markdown",
"id": "28442589",
"metadata": {},
"source": [
"Finally, let us improve the plot a bit. We will transform the data to plot `-logarithm` of the data (density logplot), so that *Mandelbrot Set* has the smallest value (appears black), and borders are more apparent.\n",
"\n",
"We will use different color scheme, for example `hot` color scheme from `matplotlib.cm`."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "fb9d9862",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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iesSi047VCu0ybXcwHlH2McKpx0rgihqsJQnTbr/pwy7TD7VkDTp9UI/qHuBxxJ1XhRC4CqetESZ6X4dHyVwx1lxpifKxwn0QftyfKjx9zDqyYRVqR0Ywj5mJAWZGRmmIdZ+ks82BxC4xF25iRbWq1Drve5G/oyY7XIa1ID2PjYMfQeZbNxN01HxuN/svBA7Ba7+HI+bBc22y6mTg48A3zS4LEffZdwDOMu2rZWqB+b4XIUjabgViHVKr0E6sm28FIr5Wt14QYcVdh/uhvJrMTTO5sBLlc/4hhY79R+W0F4UGT4ayjkxbhV7FE6GZjJkklFbSl67/U4okSLsQTKxYhrie6rHush7gFIRIVCCE55BZrjqiUbNOXVNN5vsuYAf8+gCcu1l2+wCwtA0eQsiQ2q3covTPAlcBb/m98KH5pyFkqw4hO5rvSDNXK4FTQldmlp/u9EVF1W5UnB44LGEjETJbiDXbWqJ8zz+k8IQoVXgylFVkUtyJaXsmJNLzCEcvM8MtBumNGJsCCYKJOYTAFjF1iUMzQibU+qP7jSJanAcQg8NRSEqbAYTxaC4fU0rjJuAbyFS8G3gyQdfazPsO4B5z+A9vEePOFQsRArYLuNT0Qd1iFc7nbqxFqQOrD6rGaoZUY6Q6pFJCxNQRMm9JyaaVSGUICzN8nOliH/JjZJKMFg88GcoqXiRzfvQIkoBrJkyEHhMRYWYIpYeRc03H/2iKJAjCK9G7lhJNjFiNEByNHnND5lUP1IqQjWYk9GunWbYFaIHX7rOGnKlkJhtBpu4bEdfZ8vvhLuCTwBH92FD/KDJvanLFBoSI7UYE0nuwLjZF0BIUJqYeHoLyXrJDUrJlJRpE7sOGDB5juhhG5oRy8ruf+QFPhrKGXuTGzAQ0u3SxT4Qe4TiIEKFiR7r0QdMgQRBOhIJQC0sZsYRB/Vma80f1Rh3AA/D6QdH5zHsB/noWsFgsO3+PBJZNF08i0WVzgPcCR2j4PggJOxkbvq9WqVYklL4DsWAdQKxGxzJRP6Ri6prA8uF+KC8nO66lbFiJCsVdpoToRPK7n7mHJ0NZQzuZsQoNIDmLZoJGxCMWM6m0RjrcYsPSztjQ1EiQwhVMu4gGvishUpdSmbMc870NsbQsgEc2wycQA0wp0NELS7bC10kPEVJ0IsabnUD13bDwYjn+uGh6MUJ8lCTVIlasOsRKtNOcw8lxDhBPPzTcbWacbEzK2bASFYq7TOcIT4gSwZOhrGCYzCRZVIuQJ0IzDzMlYiwdbrEB2X9sdHokCGypjWSg7iaFkiIQNjIPscK8JN/f1AjRTpsA+i5sBH660Q58EPgi8PHdwC7o2wG1S0y/B5Aos7NMH+uw9dRKEdNS0PqjiCumxhCiYbIX/ZVpK1EhuMvAzhW+jlk8+DxDWcFu0i+c9vXGZi40YqzYiVAv07OomoCF4W4R8maSCAWLqYKMrmEjrAqPGxCS0Yf8nCNwMcIhlCNlgggp+oAfAftegCt2iN3g+ztg351IyP9ihPSoGLwWIXALEa2Tq38KQmuchWG4n8yVSwk9IJnLS6TuskJ4II3g65jFhydDWUEmtELt5H8CMI/0oxcZ0Aph8J0qpjN5GVdYsskSk0UyFqFkyZZbAgNEk3MO8BZxif0H8PaUOzg17ACuBn6DeMXGtUmNGF8dsRFk+l6BJXvxLnE0wbrhIbJLiGD65Doe1HpZCDiIT8oYDk+GMo5M3Hzt+BD6mYgIxR86P9UJS/VAneklQZBeIjSImGQ0gmsUmx26HsreLL/yHSk0OR2MAOud78cCy5sRwXQrcAI2x1AiS1e8xhNx9uFR5GyzSexdop1OHCJzkcLpxj78g/REeM1QxrGd9E5eEbypcyai2CPGpqoNmmZk2GRIViMUFE/H22Y3osXpxGZ0jiIWmE1AO9yaZHPpxIVITsgrQDJkNyMEqIGJEpOywLsmagwjSmG5h1wMjwLdprDrLLI3Jel91pDGYx5ETjTfp1Ufch+GfP/VChy9pHcC8yH0Mw8zIWJsKpFiaYoMiwcNn0+GCIXphdx1Cq1SP4oQiRcRXY7R43Rvh1uYWi6h6eJ54D+qkLj7JmARdp4cwuqGXNJT5jQwQnw/Q1iKgSCGh0xyRg1Vz8bUlG5xdZ9psyENbWUaPuQ+iKy4yb773e9yzDHHUF1dzamnnspjjz0Wd9sPfOADlJSUTHideOKJ49vcfvvtodsMDOSbjiKd/mkVTOfbOXpkDsVOhKaiDTIWpLFOmUAzRYT6SD5qLJ4ZZzTwWS0oWmdsELE8HZJls88QbpTsYdOJvcC3NTDqOWyNsh7EitVpPsfrXCJTlrrLJvNcDo864uoBsvPQl+4iv4Uipgbpp/cyKDJOhu666y6uvvpqPv/5z7N161bOPvtsLr74Yl555ZXQ7b/5zW+yf//+8dfevXtpbGzk3e9+d8x2s2fPjtlu//79VFfnE8NNdzi9F0zPLAwgdeaKlQilqg1yNUEZIkFg63Wl0n68bZU4aHmLTsQaFEUEypps8QDwMNAm9cSy7SIDOYV7gaGHgSWmE13ARqRi/bOIySpeeJuG0yc6QLIcYXhUIgDH3abZmKwjpCfarJDE1OAF1RYZt0X+8z//Mx/84Af50Ic+BMDNN9/MAw88wPe+9z1uuummCdvX19dTX18//v1Xv/oVr7/+On/1V38Vs11JSQktLS2Z7fy0kE6RXgQvmJ5JKOYaY71Y90QK+2RKE6RIxS3mYpDwfgUjxvoQX1QbEp5ejSQv/A2wHH7dBl/D5jnMBU4DKt+IkKA+xGrVg4TSlyF5hcIq1ivUHRbvEXsy/VAQw6PSibJ+KKnEus8yNW3pfdkyzWMcQtxu2cqlNF3sQ/rakON+5BYZtQwNDQ3x9NNPc8EFF8Qsv+CCC3j88ceTauO2227jvPPOY8GCBTHLe3t7WbBgAUcddRSXXHIJW7dujdvG4OAg3d3dMa/M48U0tRPB64RmEiIUJxFyXWLJEiGTWHI4w0RIrUGpEqFEWiFtSzMoRhHBtL6agZXAUujbIgkWnyG3ZKgTGHvUdES9PSPI3N6ECKsThdIn4w4bIHUv0ghiDRzuFstgRt1o6pqerrwhEzmNMgXVD0Vy3I/cIqNk6ODBg4yMjNDc3ByzvLm5mfb2yU1z+/fv5/777x+3KimWLl3K7bffzn333cedd95JdXU1b3jDG9i5c2doOzfddNO4xam+vp7W1tapn1RSSJdw2meYnlmIUHzFVjXvT6ouMSdZYqYwgFg+Jqszlmj/sP3c/Dr63oiQoBUIsZiNiKlPg9rz4B+Bn5jNcoVNwNHAL/cATyGkSBMtdiLnWok957BzT8YdFk1im3iYQIxU76PkKB3/HSXu0yFEKqYuFPi5JisC6pKSkpjvY2NjE5aF4fbbb6ehoYHLLrssZvmqVat473vfy4oVKzj77LP5+c9/zvHHH8+3vvWt0Hauu+46urq6xl979+6d8rkkh3RUp/eC6ZmDYUQfVGwWISVBqTwlqy4oQyRI3WHTIUEgLqR4RMjVzrjbaNJCTVy4CLG2nAJHvxMumytcI1eoN12LgAw9jUiZ+4Y4OyTKL9RPYgvSdAiR287wqCVHw50BgqRur6mSpOkKqw9N8bi5wswWVGdUMzR37lzKysomWIFee+21CdaiIMbGxvjBD37AunXrqKxMPESUlpZy+umnx7UMVVVVUVVVlVrnp4xh0qPv8YLpmYFijBibaqh8JDMkKIqN5FK31VSRqGJ90EUUPFYptj5ZKWIEfBGxvswByuDNwA+n0b3pYDnw/4DZSxAX3gLEilWDsKRapN/uecXLL6QWouoE6xXpjHsZ0U4NEeP3LANKSpEpzw3dn2wKjJj3hil0RusH5rO2NYiDyA99VK47knVk1DJUWVnJqaeeyoYNG2KWb9iwgTVr1iTcd+PGjbz00kt88IMfnPQ4Y2NjPPvssxx55JHT6m96kA7htApoPYobxRYxpoN/qnqJJKxBo1irS6LXAGKVUPFvDzbb8xCZIUK6PNj9sOiqMmx+Hv35n0fEQp25iSRT1ACzTwIuQSxWrQh5qzQvd7bQc01k3VFCNJhgfTosRMkgoRVpsoi1CFO3EHVReNb9fRSWiy89yHg02TXXXMO6des47bTTWL16NbfeeiuvvPIKV111FSAurH379vHjH/84Zr/bbruNM888k5NOOmlCmzfccAOrVq1i8eLFdHd3c8stt/Dss8/yne98J9OnkwSmK5xWy9LMNFXOHBRTxJipCj+l7NGRiSQoaMlRZKM+RRiUiAUtPdq/MAYTJby/pVhCVIEIqfcCu2BtVHQ7ZXF2zSSqMdmnWxDvzouIteocRNsUTJg44ixTC1AYRpxXBeHt6MlmOzPKuBXJsOdyDXULS/oYMe8NKR5EQ+0LyTqkc9CxzKS8zBk/07Vr13Lo0CFuvPFG9u/fz0knncT69evHo8P2798/IedQV1cXd999N9/85jdD24xEInz4wx+mvb2d+vp6TjnlFB599FHOOOOMTJ/OJEiHcNq7x4ofEYpDKD1VEmT2HeuOdbeoJSGVgqeJMF2791RIEGZdour2YEtw1CIWmE74KsJBrkb4UbZxE/DL3Uj/67A101yrkLrF9PwrsJaxoPXIhRtVF+Y6G3DWJcpUnUnEhPLXMDE0PmLeG1JsuNBC7WEmustKxsbGxnLdiWyju7ub+vp6urq6mD17dhpbfgnJUjZVRJA60oU+SXrERzHUGJsOCYIJeYPUVTIZCXKzOOvkOoBczmZiSz64pSL0szvJhk3awfZhIgkaIjEJC4um0v0g1p0XcT63mdcv4G398FCCQ2QC5wIfAy6qR6xBtUjk2xLzWSWXLuEpY6I1p5TEpEj3098quJ0uz7SVSH8T/T1GsOejFqzyUsJrl80ldWJTT+ERi3LkBmjIcT9i0d39J+rrP5H2+Xvm2MCygj9OY19fd6z4Uej6oOmSoIBbLJjoMCjGda0oMNFC42Z1HkEsGSPIhBbmXtPjuESJkO2CSIYEaT/DLELu+Wkbph7Z+Ll1IqHsDVDdP8lx0oyFwOeBVZciYfSLEQFRA/Za6W8zhCU7rnUIZzslgy5pCl5z/S2DpChoQUqnlcglQHqsKBJAFUHOtx65jyqB6lGo6AyxEqnlPhVC1EX6aqBlCzOrfpknQ2lDhKmnNfdh9MUNFRYXKhGaStboIOIQoT7sk7laSSazDLiCaRVJ7zFt1iAuHo18CkMybrjJXGFuO4mi1KLONq7LTbdVDVEpEsq+C96HcJJ/S6Kb00VMYum9po/zEYmLEhGNftPr6RIiJXphpMXNtRQkIJgDa96iMqz1Sbfrw5KlyaxNieCSoKClb8D0ow/JCN6CXAz9TWuB0n4oHybWQnIQG5mWLCIUFhkCG25f/Pqh4j67rOIlpm7VieB1QsWKAUSMWIhEV3050+17QB+kTR/CplwuM8t2YRMU6gSrmTVcq4EWPN3mtKNRYyvN9lOxLLhWoHiRY5DcxOySPNf1FrQwlSIEpBVYBm/bBmWb4RdkPifwacC/A/ObsXmPmom11Chhc61AQUKkAulEVje9tm3I77wc+a1VoB2PFOk1VGKUqOQHxJJTl4QFoeuUfEdNu0rQK5x1w0NQHmEiIUqldEcX4mIrNCvLQURNPzfXHckoPBlKC6aTW0iZt0fxIULhCaWHsSQoHf0OqSumJRk6gO3YiVCtPU3YUPVSJNinz+yr84hOjvPMvhGzvAFridCwLHXTJNIJ6WSN+bwDIVkqcNYyFD3Y4CCdtF03UDAHT9AaES/KrNk5573C5z6NiJozRaNrgaXA/AVITqE12JxCmL6UMfEauVFkqhGCWLeXiyA5iSBWqE7gLOzvX+lsF4w80+VDhOvB3O3c93gIujQ1iGyHcw4R4GQs6RsegvJerHVHc4SlQogOUnjaIZA5ahaFR+SShydDaUGEqVl2vHuseBGhsIiQhgBrxt50oFfqirlwXSYNiD6zA7EUdGLJzYCz/RBCmjqBE7AP5wPEurE0AqqGidFOySbw0UmyApkYlRBEEMLyEpIXaAVi2QhrX48dZlVyJ+ugG64MIV5roWUbXLsROqLwU3Pq6UQD8BXgrxcgmR7nYK0uLoF0+6ekZcTZrg8hC2rdSVSvLYoQW7WUDSEn1oDMsermdK9NvHD8sM+pwD0v7bNqznab5a3Y3I3KAcb6ocR1j+n/JlmrSaFah4rfXVacZ5V1bJ/ifhG8e6wYUUhC6emKouMhhAjp4XQCq0Am0XZkcm1CJsdqhCDNR57IVUzbjSTwU+tMD3AP4mppwYZ3q2VBq6THc90EoURIJ8o5iAXjOWQS34lMnHXmpefitj+EECb33OYT6/oJ6odci1SN2fcUoBm+ej+UdsJtWOPYdNEKfAb462OQMLJWc1yXCLmaJr2e+h3stdqGcH6XUIVd8yjWjVmLXFctxKYWOBDCqTwhKKZOV4pgt5SKG0GI6X+b6ZsStCixovGybkOIdPrsNRsnqwcqVOtQcbvLPBmaNqaaLdq7x4oPhSSUTpceKAwRcSkEERZtpRPOUVgB9b1IiFOPWd+ATKQHEJLUiPDNvdgJdg1CptzJugJLbCYjRG7fdPI/yrT9FDKBVphjLEaef6IIkXAn1gHgSWzS4lazrgVrYdJj1WGJh6srUuHuPOBiuOlhWN4G30MCzqaKWuA44O+Bc08Azjbno0RHNTPaD13egdzSS4g1aGzD1nnbjhCqFdjfVBFFIv42InPpErPd88CzSNFaJWOrzT7ucVxLYZilKFmE6cCU/PYhpEzTNFQiRFatRa51aAQoH2BihFlYssYwFKp1CIrZXebJ0LShAohU4d1jxQUlt/nsFku3HigMkXAiBPHLUyzCEqFRhAj1IRPsAmffGqzWKGK2ebtZp8RCtUJhCFsedGlFEeuTRqrda/brQMKu9yIkYB4yKTdiJ+dRs1038BSMHYQS7c8bzTbtwJ3meOcgrraNCFlqxUZQKeYAl8AVHXDFvfCDUfgnrCcnGZQhQulPApfNQqxB85D5TMXPqtc6CthiPp9ldt5mXitNn9VdFkFcic+Ya/N2hDRCrI4oirX8bEQI0EpzrrtMP7ST2o+g9sh1LyrRnUxI7WKQiRF/eqw+xBrUgdxjTcj9NJtYouhGtI31Q0mQ/ERI3mpSqNYhDQg5NtcdSTs8GZo2puIii5D5OBGP7CFCfuuDMuUKczFJodUwLYmKc92HzCbEOrALmYC2IxPtfGSijSCutL3IhF6LTFzuU3vQwuGGbwcRFmq9F3GJVSMkpd30QYlbA0JSWomtSbYPcdtFpK89wOw2059TEJLRBuyBvijULgMehNful12OPwu6N5n6YJh22hACEpFj/3UZ/NUO4VPrTZM9zuaY7vUggWHnmfcrShGLTB02FcE24Cg49AeYc4r5/lfAbmj/A7QcQqxHu2Dfdpi/y/wu7zDXbZOc/1gnlIyYdRFElb2MiVams+W6UG06rlaaVuBMrF7MFb+HGSBc3RIkJkVhaQ+CEYNaO60fq0erRFybrjBew/D1e/kwsVNoKu6yQrYOqbusIcf9SC88GZoWpuIiU9F0vk6cHsljGBkYDuW6IyFQK9B08wMle6xI4kKrwSKpwclLrQ2qtVmMTNpqCXrerN+FPMkvRohTFbFh4Aq1EmmYtObNCZs43ZxCbUj65zazbTvjFpuhR6Gyz7TVgLh+Fpp+gM2r1yP927ENVrZB2SKEK1fI+fw0Kk2efUC+P2kOP3+TuMIWb5fTPA64D2h6WIwve82lwRxCT0+XRcx7nVnegXCzXwDfG4WGF+TUlyFz/RDifnse+OJW+Afghrvg2V453l8/bxp7Hu4CPt0PJUNYjdcS4F7p/6o689vsQubJPmLF5Up81piO1iFaLN0GhIx0mc65EWvxcgwlGzmmCCbPjJrjPSdtPHc9LH8j8BaEgLr7qV4oJjGokh8XEZJ3lxWqdUjnsFkUE4UonjPJCdpJfaJpZyZWBC4+5Ks+yCVA2SDcSRAhmDx7s7uNEpZSrDtqD8I5qxEiNAd5SnfD6FV3o57A2cizSgdilVBxcrxjRxEyczrCECKIm64C+I0QgsqXYS3A44gl5VzE/FKHWI9Wyr6v7YG/M1/P2wTnGyH42Ba4BeEKv90CDyPt9gM/BDabZpSLJTPP9wS+a12z9pBlAA+adzV6VCMJHkeB53qtxGflQej4LfwGsULVAVdWItezA175LTwgl4Z7exCiswCx4qkLSt2KPaaBRsQq1GE6eInZbgfWMnMmlrjq7+kWuE3GNeaK0l2BtEJ1W93mArQbgXo/NpWC7q8WIY2eqzXrx0ahJGgdSiW6rJCtQ73ID1iIZC4cngxNC6+muP1UxdYe+YV81AdlUhAdDykQoclmdTdLsOp1nkesAs+Z762IWGYvMmktwGpUIoC6l/oRc0UTQqBakL/dfGwixmDZD1c31IQIfKNm/zbZZxNiMTn0MrzLNDX7RURbo5ffJI/8HsKXHkfIzfmb4QdGM1WBGIouQwLPNGBOoVFjU40aTxbavhul9oDz+Z3mvRHrWdu3CR7aBOuWiHbpR8jl+3oXXHsvcK3ZSW/DTsRatBvJ2bMNntsh0qGP1yMnvgy5xu3EJnF0XWGjxKYwSOQWm+yk3XttNuNpuFd9BRtKr8Ra9xkk1lql5GiCqwxknE9WZFyo1iGQP1UDhZdVOxyeDE0ZqRIbTcyYTxOoR2oYRn73fNEHaX96yX5/kiRCMFGzEYYOpAxSEzLxvGSWqT/oNLO8FitgVgHwLoTwvIrM3BrT8KJZ1myOoRFhi7EWhqAFYcQsr0F+5k3IhN4hu+wB/i9i3fk48PFRhE08CU88CqtOkn5pNPgAsBV4e9Tqe/RS7HAOnY9oD7x/G9GTLwOiO4S/xGji5yGE8Fnkd1zOuB9v/UFofFB47YMIGfpwF1TWyXpWIhdLI7lcIXyY1sv93ZRAuxFwup+7r3sfKkEvRX6oWnPsncjvra49TSqpJMk1p5VCuKsMbGbOyVDI1iGd04oj91Dhn0HOkKqLLILPKVTI0Gyz+eAWS0etsDT0IRkiBJMnPNRJ6iBCeIYQ3c5CRIzbh7jF9HRrTZvPIQ+mWpdsBJmAR8zydrNvBDHpNCEz+QATS0e4k6qeVgR4Cg71ipvoDuxcuhdZ9vEIsEv0RHcC928XLjAHS4bWT3L6hYIoVhb0AKIlr0OMaOeB/HYPwR9fgGMrgA8jBHSjWJAewHKcJUBljVmgSSwXYF1QLjnVyDS1BgWJ0F6EmQ2YdsqwAislV0FL4FBIWwfM8VuctgeJ1Ql1mc9LsWLqUCgjL3brUPHkHvJkaMpIxUWmbhWPwsQAdmbNZR+yqQVKhEj88PmpoB+xAh0y7xphVIFMZq54ptW8PwbtvdCi7oolyJi8DRFVNyBkqhMxx6zCZqhWcuZyOddFpxNhHTAfanaIgShowdkLjPTDobtFc/NL0p8pOl+hMh8Qj+RbgSVt8Ddt4j57QqPNFsBPR+F+YvXOzwPf7IdPqTarFvm9tHHVgGnyzDAoqalGDBTqMq0HXkDYmrrF5jHRteb+1p0Ii42a/VSrpO1rfw4h5KrPLA/VDSmSrV3Wh/ynC3U6Lo7cQ4V69XOMVF1kB/E5hQoR+eAWywcrkItIakTIfQqPh0PSLA1Ya00dMo/UYSemXYxPaCO9IlDe3S9656/cB5WXIpPac4iFaLdptwmZb+aZtqOB9wFkdlYxz3FYa8Q5cFgfXPyqRGW5v0Ir8FnE5bOL/PmFso0ocpmfRFyBNcCfj0LTNvj+H23EfBA/At6zHY44BvvbhG0YJnp3I8NKsVF8O7G/+T5sCoZ6JlqIlPwOIPfHQmeZRsN1YYu1DiHPwKon0/6G6oZAxoxkSI5GpSbjVstHaDBJoVq3BJ4MTQmpTE4DyAjrUVjIpVssn6xALiKpW4QSPdUrmpDJZhdChuqJdV+oC6wa0RV1yNsD0iO2IUFJZ94n/OfnwHs2CSf6DrCh37S7HiFJK7DuGNX8PIfM5mXI5LkUIUUdwDlw2UaIvirh53ux/Gljalej6DGCcJIHkEtZ3QsXI1KuoNXsOuCICxASolovsK6vBsQyqEJqN+ePa2YaddroQu6jl7BWxVZi80G5+Y/0NQdxie3F1kwbMifzkvm83PRJtUlu3qG4iJCadqhQp+QDFK72SVCoVz7HeCmFbfcxc58ZCxW5CJvPZl6gqSAyNddYPFmRO6Hp+Kl6Hjfvj1srbAhxhZinezf6+U7gU4jBPgp8A5m3WoE/jsKx+xBdSQ+iTXEF1FsYLyfXHYXZdQgJegae3QEnl8Jro0J81GOC2cQjPkaw7sP5WDJUhqTxWaNfFpl3JaXq7jwOIRtKXN2wevceca1JPYi761iEgzSaNgaRe6cdm5ipllgXrJKfTrPtY0jOoVOQqDOtm+aWMJkUyWqH1ArdkGzDeYYBCj0ztSdDKWOY5JPs9eJF04WEXLjFlADlc+6p3qkToXihUq6ougw7V5jQ9HERraICmVEHZN/jt8G/DgoJuhdxubgYQea9Q8D1wL/8FmbXIxPZS8RahuaZDSvgpigMvAR/85IYGz4LVI/K3PgM+UlT8x2dWCJ0HpJG6Fqg8jTk2j+ARJM1I6FmbdhivVGENbnJGcESoQiWEB1EzHUvIYRKLUMR7L3UhS0a0IhEFirprkaI0i6ERb8fsVLORghVHXLfTOA1k40V8SLOgjhEYScyPIhc9MIMtS/Uq55DHCQ5guND6QsL2bQG5bsVyEVvePX5ZBAvpD7MWlSGDWEudZZpbhkVNC9EJrxzYc39QnQSBatFkaSAq4F/6ILLGhBCpJdd48bfAvxImn4QMRYtR+bNSOKz9EgBem1/D5y1Bf5mi3gn//wZhHy0Arug/QA07oHKediEjUELUCfyg+1FSM0I8Bxs6ITl90NLH5Lb6EXkvjkO4SQjCOFqNm3sxrK1erNNH9Y9p8Vh3WSgOO8MkJgAqMVnsulW3eKFOi0Xdqh94fU450jWRRbBW4UKAdm0BhWCFcjFNIgQxGcp8UiSluRQUjRCbDIenaSeh7Ht8FH5OClGECNDLchs3Io86XfA0M1GjnKcbFBp6lQ9w/QqxHvERwR4CvFmPY/QhD8fAR6Ap3ZYT9ab5iJfIkysE9aJZLTsQSw5DfK5+yW5L84GftAJbEB8p2Wm0ReR550OYu+tjcCFCKmqQNymbWa9utNAyLl+VpF90medTPh5oQuRCzfU3pOhlJCsi2wYL5ouBGRDJJ3LxIjTwTSJULIusiDKAutLkUkrgmhLAOrgx0jdrmTRB9wEnF+HTHam6GsfIrI+7yXJI+QJUPYQRTxkjcANvfDeHXAFwnN+AkJaVyC/l5sBWjNaLgV+jRCiB2GsTUqbgBh7rt0uba0EPv6c2Wcz3NApy8/aIZGICy8G3osQqgasS05zF9VhhdKuOzclJGv16SP5/ET5igMkZwnLLxRWb3OOZDVAEbxxPd+RabdYoVmBXEyTCMHEwqyKwTjLFa4bQl0UI1hldCOwU7wZuipZ1OJUWH8WeBAOb4THOqUu2CEmzw/pkX50IoT0DsRgU4fxYmpOqL3YDNEqeNa6YnPgd38QYvwclsy2Ie43EAH3va/CP74qhqGvOcv/UzsxH7m3lCzXIffhdiSHVZs5rmaaTLZG2jhSCbOPULhh9mDnv8KyDnkylBKScZF5q1B+I5NusULSAsVDGogQhOuCkqlR5m7bh0w6/cjEVw/shN8dhFtJ/QpHgBJTBbX7t2Ip2jIogUu5TKfpIddff4O3I94q9iDsphPhBp/BaodeQsxAc2QzNzt4EFHEo3YZNmenYiEIydK8Qg3Y+nULkfuww/RhsVnvEnaQTOxxcw25SFZIXehh9lCI1qHC6WleIJmn/AjeKpSvyJQ1qJCtQC7SRIQ0OieIASbOWPFmsBEki/Bu8/1Y+Tz0kkSQPU/yVd0VCwAaoO8++AvThg+Pzz/8EHGf/WCHGIVqgMuasWVURs17D/z9q1IzLZn7IGJeFYjc512YkiA7kCFhJbZWmSZabEBMhgPYfFgq9o9BMmQoWVdZoYfZQyFahzwZShrJZJ32VqH8xADyx+wivdagfMsOPR2kiQhBuK8pmA9GEVzmWpSasaHOfwTOg8qF8KNn4bmDEgCWSvmL5cDv9kjiRK8Nym+0IfqhQeArIPfPJmyYfRuMdYlrrCfFtt8F/DMw+wTkpmhByPtRxFqGNIJM8wrVYMlQSi4yRbKuMih8MgSFZh0qjF7mBZKZ9CJ4q1A+IRMusUIVRCdCGomQ1nNyoeHxQYwy0Z2mGYU1c3APMuPNR9wW1UAjLN8Eb2tL7B5xobv+JcVBXWcCOpHb4CmgZjucux3m1yEangHJNH7/FNrdhNFiL0cSfTYgpLvBrCjFEqFqrLUoGFo/JSTrKisGIXWEQrIOTetnnVmYTC/krUL5BS2umq5cT9req8gf3BOhCQgjPVpXLIyxBEmTaz3SjMM1SJa+05AJayeSOGhIFq1IolsVSGT2jXgiVGjQ7OJXIyTmoZ8jIqFOEUOn4iZVdGLKqPQgfrgqwstqKDFSPqLvoSH1ybrIkx03VEhd6MhlXcfU4MlQUkgmpD5Ccdy8hQ4dRPaQHm1QL0KC2im+qTSSPiIE4aQnHhEKWoVcnZFLktRNUWdeK4GTZN+/roCbEa6UqDqCVlgvtl9vJmEA+ARSXk59nPEqvUyGPsSiyDNYDVIUIUcmw/l442XOeylTCKkPIpV6g+l26+cCEQplXvRusqSgmpN48Fah/EDEvKYbG1SMrjAXhjAOT3U6CUGYeyxeGL1aixQuEXK7pCU53CKdDYhuZCkQgaX3we2I4PZh5Im/AonELkb6OpPRh9Q6a9gOX4iKhytVEb1iKYhpUbORR5Ah/DiEdLdgw+whNr9QmAlhbBRK0q0HKgYhNRSKdii/e5c3eJXEk2KEQmG/xYl0RYkVS1RYImSACGm5DBeDhEeUuUQorNDmUGDbYLbqRmRiGgD2QO1VUHsArnwRruyAoYNSq+xWbCCaR/EgiqREeHEHfBHhK5EU29CKHGzB1rt7CSFGbabRVmLzXU3mQxkhyfB6SO0BqxjIUIRC0A5lxU323e9+l2OOOYbq6mpOPfVUHnvssbjbPvLII5SUlEx4vfjiizHb3X333SxbtoyqqiqWLVvGPffck8EzSJRo0VuFcgfV8UzXJaZkqh1PhFJEmCYoXmh91Nl2IM5+brvuu64fxbormpEJbB4yqZVJor3rkbwyHsWLe5C0Q0umsG8NkuTxoQOIKfFVxEXWjOQSWolUlFBtUNIa5mRJTiquMhVSFzryXzuUcTJ01113cfXVV/P5z3+erVu3cvbZZ3PxxRfzyiuvJNxvx44d7N+/f/y1ePHi8XWbN29m7dq1rFu3jm3btrFu3Touv/xynnzyyQQtThXmETQuInirULbh6oIOMfU/mZKpYidBAL2SfjmdRAgmEpowEbWSoAFkbO/HEht9ue1oG5rFWvdTK9IgoouPIP6xH8HTD8JHD0g49t60nqBHvmIjNvVQsqgAvo9krD7XtTLWYWP0G7Di6ZTC6JMlLamMV+oqK3REyPd5smRsbGwskwc488wzWblyJd/73vfGl51wwglcdtll3HTTTRO2f+SRR3jzm9/M66+/TkNDQ2iba9eupbu7m/vvt4GVF110EYcffjh33nnnhO0HBwcZHBwc/97d3U1raytdXV3Mnj17kjPoBe4m/g28A1+QNVvQgUGzoE0VKhIohieuZJDGiDEX/Ux0a7mkRsmLkpt9SM6gRViLTw2xolTdfsi0P4CYeUYR4XQUuAf6NkHtAvjTHokSqwb+ianpRzxmBsqAs4D1NcAliFWxh/ESL6zFusg0p1AlEm2mqCQ88kwPUNJIcq6yFpI3OVUjKUMLXdUyl6nZ8mLR3f0n6us/keT8nTwyahkaGhri6aef5oILLohZfsEFF/D444kN2aeccgpHHnkkf/Znf8bDDz8cs27z5s0T2rzwwgvjtnnTTTdRX18//mptDSZlT4QXiU+Eesl3tlsccC1B+5g6iXEtQTOBCA0DB7NDhMASoSjW+uNahTqBzUho/EPAY8RG8Ljb70EqzG9BUkXvQ+pm9APt4g771R672dfwRGimI1GgVy1SlP49IIxI81idhyixVyH8ZB42pxBMNDsluslGIHmrTyrWHi3zU+iIkM9WroxSzYMHDzIyMkJzc3PM8ubmZtrb20P3OfLII7n11ls59dRTGRwc5N///d/5sz/7Mx555BHe+MY3AtDe3p5Sm9dddx3XXHPN+He1DCWHRDdh/vtBCxvpsgQVU6boZNELY/3pZwhaMyzYrkaODWCtPurq0n36kKfqXUiCoPlmGzckSK1CIGPnL8znMiSGvhqolTJlnzKLd6br3DwKGucgXGYb9p6oBi4GTgY+rpbFvQiLbkKix5oQrVA1k1elV8tlXDNCskkVU4GOg7PS3G62ofra/DyPrNjdSkpKYr6PjY1NWKZYsmQJS5ZYU9rq1avZu3cvX//618fJUKptVlVVUVVVFbouMRLphVR065F+eHfY1JEBkbQiERFSt1YUWwAK870Dm0y3BZmpOpAn8hFiI8bUsrTXvFfAK4Ny7PmboOxZ+FOvGJcqECe1hweIofGLwPdroK9fbrEmoPYkLLl5BzIftyIem9nYOmQVJJdHaIj4fCfpEPtUH6L7SL6URz7jIPIUlH+ZtTN6ZefOnUtZWdkEi81rr702wbKTCKtWreKOO+4Y/97S0jLtNpOD1pIJw8EE6zymBk+CpocMWYPAurKCbav1x7iv6EcsP82IELUPeVTXCedeJHz5ZKzQ2pCemDZbEYVsmUxof4l42c7qlarmt+Id1B6xiCKRhD398KWzoHYZogGqxmrTGpEkQ1WI70yF0qkIRqLEn8uTDrHXiLJkp2B1leWnVSV5GNc9R+W6IxOQUc1QZWUlp556Khs2bIhZvmHDBtasWZN0O1u3buXII48c/7569eoJbf7+979Pqc3kEE8vNIAPp08n3BD56WiChklOEzRMYqJbaHC0QdkkQiqMHkW0P7uQFMF7zfc2YCtCiHYjf6cmRLxRZfbtQjIB7yBWPwTiurgEDquRp/ynEH7013gi5BEf/wTcsAm5F3sQIlSLzVGlZeuTiRQLq6k3Emf5ODKhiymWqDKQuTP/HlQzbnO75pprWLduHaeddhqrV6/m1ltv5ZVXXuGqq64CRM+zb98+fvzjHwNw8803s3DhQk488USGhoa44447uPvuu7n77rvH2/zUpz7FG9/4Rr761a/y9re/nXvvvZcHH3yQTZs2pbn38X6wmaY/yQT0zx1B/CzTISb6tBH8TYaZSHyGxZStKCnF/g3KnRfkv0k6gy4xiO8WU6hgegSZYJqQSLFOZEZagViHXkQmjz3IA+EiZIJShqOe6EUIAVpktl+E3B5N8I49smhbgu54eIDcH98Bel6FD90KSy9H3LMnhWycUP9D4ns/Xkx/Sq6yVMaYLiQiK9/HpcmgyW3zy1WW8au6du1aDh06xI033sj+/fs56aSTWL9+PQsWLABg//79MTmHhoaGuPbaa9m3bx+HHXYYJ554Ir/97W95y1veMr7NmjVr+NnPfsYXvvAFrr/+eo499ljuuusuzjzzzDT2PJFeaLI6ZR7xoaFF03WFgY0yCyYxM8cYG0pi5nQVu+ZddQMllcQSpHwZhMxgkkkS5BKdMLilNrRkRhMyQXQhT+HbEENfm9lmAaIT2m2O0Yy1KmkuoYXYXETrTVsRy588EfJIBn3ADxGJ0Is/h6XHIDdRP0J+WpGbSV1ko8TXC4URJg0WCJvPk3aVpUoIiqU8B8j4n18ZqTOeZygf0d3dTX19/SR5CuLlF+oF/idkuUd8qL87ghUCThdqVRoOLBvIDEkow1iRqskNOdJrmKHzA8sLw1xiLjT8XfdR4tSP/CTtiHVoB/Css1y1QB3m+5lmu93w3EFYvgQJEzsX6IGh78A3EC/ak2Y3D49UsAJJJv0roOw9iAt3CeKqrUHcZaVYTZuG1VcTW44jLLeQbhdmISovZfLJflYS2wQxBzFzFTrKgROZigYqU3mG8uVRNw8Rrx6ZD6dPHi4BSpdbMYwEDUt25T5sWLdmj00lPW0ijGAaNzl7skKO1L2XrJVrikjGEuRuG6aXUP3FAGIRUuayHBH6rAQegn1bxDPWBbzr9/J5k9nlyzvgxLlIsEkTVK4GNkuS6YQSDQ+POHge4T07gaW/hO5BmH0IyTXkWnsiiK6oCbmX3YgxzXoeDEhW61CY9iilwq2poFhcZfkXZl/oVzSDiIQsG4iz3MNC3WARpq8FchFPF2SyK2sodi0TM8a6T3npQlxyVI4lSIpUwmzVAmS0TZn0C2mSw2RIEITXIQsmXlRB9WIkkmcLMsE0Aatg/kZ4d1QmqV8gFp8+hLM+AfzjQVj3HNAJ/9op6zNkB/OYAYgC9yPWxcWDcC1wbrVZMWhepYgiohRYQ/gDlIqmw5IwDjDRcjQClGcq+qsYosrAShzyQzvkyVAohhGxQxBeOB2OTBEgiK8LAomgGrVlG3Yh/6uFyERciq1vpWbwTGGcHA3Jy9UflEzCxFTQnQ1BTKoEyEWQCAWhuqE3m2M8b7bXJ+4tMBaV+mE/QpJQu93qRDJJv+sliSC7E5nEvE7IYzoYQQyVy4FzmxHfmUYtPo+dj+c5n8uIJT9uUtCghUgfCCa40iYjLar/SWUaLpYEjJBvQmpPhkIRL4zRC6ctMkmAFGEuMYUhQprnphZxr+zFRkDpf6wCa7HIhJUoDDEzeA5tG6PYp9qpECCFK5hOhApkfNeilwPAKWbZaiipgY8/A78YDN99F3AHsKlfrEIeHumA5gJ97QAccQB4GOtKfxLREVUgnhvNPwSxLjA3QWgFsVaiIbOdO697V1kSyB8hdTFczQwgwsQJ2LvIskOAIL5LTHHQioh1gnYHph4sCah2ttGBrJbsEKJcwCU/MD0CpFDCmQy0tEYNIpauM68OxPTTAH8aTPxP+g1Sb8zDI53YBtwDfGQnDL0ElRVIqodDiHANbFZ0LRMTlm16JLCNJm50v+t23lU2CSLki6vMk6FQ7A5ZNlMzTmeLACkSWYNgQl4dnfQrsE9nbdinvhZkYh5A3GnzkEGqkolPd4UEl+yMOp/d93QdJ5F62T22e9wyJHS+HrlteoDngDYr44qHdGcL8/AAGQJuBPpekijFliic8zL8DXBsMxKopWTeDZ0fwEadudDthrAkSB+2xseVTJCWYnKV5U9Gak+GQhGciIcR0+RMQbYJECRXTHUAhhOYKHQw60QGKI1oakCe/lTT0o+V91SabSoC79lCIqIRL/ut+55JaORYvGONxvmsKDOvKmRS6ZD2hpCcjP+ABJoF4VV5HplCBPgyckt3IsVdj11tvhyFjA8QW2y4Arkp9QFL9YBKhgaRxKLzEL2iblNKBl1lxVKrDGRubSHX51IMVzLNCEu2WOwuMieCKasESI+dyCXmbDfWPXGxPo3pZ61BNIAMaJrzpgJJugaxT32BfItJFWpMJ/JVHRwWORaEXrMwV5x+1+tZAbwL2AGH1cEbHoebD4o77OeIJ+0Zivtf5pEfqEDyf44CbwG5V9+BPDSpuwtix4cKs0MwtYQ7lmxDHq4WYAM2Jk3AOFVCU0xeigj54PbzZCgUwRutGOuQKQHqJb15gFLBZC6xwLZhE3OQvFRjrT71yKl1IKHertVHB7GgqyzT5MTV9LhEIZ80TMkQId1O30cCy3WZnms1kmtIswADx4/A8W3w0T3w9U4vmPbIDgYQA86vK5ASHecjBKbObKD3r7rH3IhUd7xxtXnqTtaixVr/rBQSR0xNNZpKH1zzQ3w8feQ+55AnQxNwMPC9mKxC+UCAtB/JWIOc7ccmUfC67hzNIxIkO0FXjjuBZ9I95rqbNFPzXsSsXovVHKhpPZc6plSJUNAq5GawHg2sL0MmiEGEnG4EtsO5UUlHlK9GMo/iw5PAXVFYC5L5E0Q8HQw80OzTlc53nG20fp/iAKKPO51w8XVaUUwO5Qi5FlJ7MjQBwczTSiAKFflCgBQDpC5Gj2MVAvu05pqu1crSg7ijOxEtQFhKfR30wvKHpAMakq79HzD96sCSnlqnL2B1CtkmRckSIbDuA9cqNIBc6xHkb1SHdRWo0PQ5hPnUwSNbJMJ5R5KH9PBIF/qAe4GztsrQULkL+CT2PxdF/qPPI3mJmggIo7GWoQPIXN6J/AfOctqoRgI+kqpVNpWzKBbdUFgeueyiGK5imlEsLjIVJOcDAVKk4hZTJGEVCkLdZF3II+APgZuRp7sGwgsvqik8XXmI3OSGwWU95rUX+6RZ52wXdC9lw4UWJG2JoDmblOCA3GKDwOOMC6VpQp62NUT5J/D6Jpkzjq2SU/4pxWN39SgsrEdu1xuA97dhk7fqw1EUEbKpweIsYDb2/xhFNIm7zPZaqLgWWwQ2bKxJG4qpcCvk2lXmyVAMguLpQnOR6Z8jQnZF0JMhVbeYi4HUzQZDyCBUDxyHhC01IrOv+vXDrLHxdESpwCUKQR2NkqEK058DCCHqQEhDWEp/rX2UCauV9itZa5Bu7+ok3OKufUhfDyDXugV7zoYcVSOh818blLmjUB81PAofelteDHAecnP2YQOHKxCd207E0tmAWIlUTB0xy78GvBdJLjobufcnRFdmirTkyxifDkTIpaXLk6EJKDQXmfbxIPlFgBRTsQY5GOtPbXu1UKh/vw55UmtAntA0FBYS5w4ZJXVhcyLrygAy0OoIrPlMdiFESEN34yV40/NJJylKxRqk0L+DWq7U8KhFoJoQggdS/2Ancv3rgCVw2Juh7mGpS5Yoat/DIxsYAW4BPnMfHH4mci8/CPwRWIWtB7MYST83D3mQAasnertZ32Be7k2tFqKMjctdpF7SI1+hKV1yYx0qhiuYRgTF0/n83KrWlnxygwVxkPCyJskiRauQEiFNsKhWFdUMKTQ5o5qwXfGyu42bTC2epSgojg5DP2JO70d4odZEKkXCWhqIjUxxs9gqXFI0XT1RmAsvGbhEaAA5p+cRl18Tcm77kPNZgJCin5rvTcC5QJ98bERyY3p45BI9yK18+DzEQmtE/Q9F4dw2+OYgfAioPRe5p3uwtcuGnO/6EOMGDkB4cde0It8efqeL3LnKPBmKgSuezkerUL5bgRTTcYu5SGH/fqxWJYLMtF3IQNRMbKZmiBVcu64fJUSVzvogUXGXu+9BKOnoQwjZLvO5H3mirEVKVlQ525dhB9NEpChYCmAyxHPfJQN1hbmJ6FRgusss24VMJFHE0tWJ/A7V8OKrsDQCzIH1f4C/I78fMzxmFn4BXNgG5++F/90KH0NSBlUN2pRln70XuBDr4l5oVjyFJfsaUt9ltmnFkqSMiaiLLcRehdTZpyaeDMWF3mT5AFcL1Jd405xjmm4xF6kIp5UgKCFqwdbE0giuMqwgWUmBPuUpdMIP8rB4FiQXLtlwicMo1rS+ERk8F5v+ucnc9DzKnGVR59jBSJZg2Lrb19GQbaeCYDkOt61W83k7MnsYN+DIJnvZf4NUqL+kF77woHQtMo3ueHikGx2IkPrcuyX34m6zXEfaG4GGA1LTjAhSEFojy+YgrjMVUs9DHhzcgIiMI18fiqeCCJ4M5RzBkhtBl1kuoISsi/yzUoUhQvoIZJIuMnX3VCAESPP2jCLWl35EA7AcMXOrSVsJjSZWCzNlK8EaQSxNLWbfZM3ebrSVZsPW4wctTFoaJMwClYgYTWadmircXEFuP908LFrlezdC9hqkb0N74AvIZdeKKOpNizjLPDzyBR3AlwivSglC6PduFoPnd7uQ+78HGVPasNGTIGNQWBqPjKGYqthDrmqVFcvVSwOGkdlKkUsLjOYFOkRhsP50ucWCbSYB1zJSgTyRudFRNUhESK156boKZz91A4XVHVJrkwpc5pn3ZAiRa1HpxhKMOmKJhfYjaAgLKw0SRowSWatSQZhly0WQIJVho+C2AmcCB6Tcxhe2w2ZiiY+Kpj088g33Y//aYdiOEPpW4FAbzClFHrQWmA2qzecW7INBVjPLF0u+IcjV3FssVy/NyFVIfaGRIMgMESL5KDJ3dnXT5WuIvD6tHSA2walGi2loubqD6gPtDyLZFqrM9sHjJeqXEjIN5W8ybfVhXVmamLAOIRWqBdKst2GDabBKfVA7lCxBCmaRDiNAYefjogZxG5QhhLFW3hfugiX9sU/a3iLkka9oJ/ZROIgR4FvApcDhZyAPWPOxEWYDyBhTa74HS3dkFCqjyF325vQiQi6yUXsyNI6XsASkl+ySEXWHFRIJgrTqg2KQZHthZgYNT9eIp1GEgESRMO+FyJNbGWKtGUQm9E7k8i/DurG0jQGsBiBYaiJevSI3mmQUS1iakSgrdbeNYonSAmwW51bTz0SEawDR/Neb/dXdFywmOV247r7gciVuqtEyOqLufvhHs8lLyPzweBq75OGRTbQgdYZr/xJxj9UhnqkOxArUis0qr//trGaQL6R5YzLkhtx5MjQO17LRFXer9KKQhNFB9JI5XdVwchqYeMJgJUQRZIBagg1v34vN6fOq2b4aIUajyNNeJzY/2hBCNHaZz61YXZKb/8ftE4RHblUjZKwO65prwyZeVMF3D/ZpU9t3j6FJDvWcIFawmc5BOOjOc+Faklwt0yKY/WaYXQb//iDcA3w9jV3y8MgE6hDSszOwvAz4HFB7BlJoeB7j+rhxS3IjNiii2tkxayg23ZCeT/ZQLFcujchWSL0maik0EgTpFUqHIYmnnKD1Qy05igokG+wiJiYI7EOsKPOwdcIGkAGuHUtAVBfQhz3dJmLJyRA2skT7ECRoQXdWA7aQbBMSiVXh9HUvQuBcobVrjVLypFan57HWmXgVtqeCoJXLXT4a2EbrMmmiy8XALvm4GxuB7+GRr+gHPowYkn9pvp+E5BlaAfKgtB25x+dgM1JXmu/q6i4lvos7oygm3VD2Q+yL5cpNE8OIj0Q/RzJ8rAiF5xKDjOmDgkhGL6STviJs4FFBtU7orcQKldVFVYFM5nuRx8IFzvIDCNnYiyQNVBID8jMeQIhLHeE6HRU8K0lSC4p+VzdcJ0KKQAiS8mQ3V4nqm3qQEXsEG7mi56UuQsVUCVGiRJJuWL/mWtmETAwLketyL/zPATmlO6bYBQ+PbGIEuBN4bC587KDc2sfPQvRBDQir13QSNch/vhEbIZpKpGnaUYy6IU+GcgA3kiyTIfWFbA3KEhGailUoHiqIJSEtyKWvxobfq49/BNgHf/w5HHseQjh0+xHgdISkqA5pACFIFVitn7YHViAdFqLuosu0vQ8pAQDCkzuQwpB6PG2vBynzrpakOvPS9doXPe9gGH8yiBKuEdJ1bp01tax1mOPsYtwqdStSI9fnFPIoFLQD9MDRnyT2wWYh8n9vQkh/A5b8aBi9S4SStgrlLslg/iO7Ifb+F5iAwck3SRmFbA2C7BEhc6zJZs+gVQhspFdweRlistYSHNXYsHTdvhQhFE1w7FXIZK/aHSUj9dgINS3S2Ie4g8qItaQEw/OD6flV8wPWVddg3g+YtjX8fpBYS5KeezVWsKkaJD0XdVcpUVOT/WSkaLKyInoMJUHtiIVK+/wc7HsU5h8FNMO8NulaZJLDenjkCzqAnw7CFbuxucnqkP/Sccj/rhZLhFx9kEuA3Az2CZHO+aCY6pRBZubi+CiWqzZN6CSfCRdZIVuDILtEiMmPE8yInAxcC5ESC9dd1YmIqXuQJ0DVF2ldsxpknGlDrDG1ZpsdiFag2mlP+9aP1ROp1WYEIQ49CLnSMPtRaat7k4y7JW9FSNYWJPrsZKcNEFP9HrNsLxIBt8isqwmcrxJBCA+5d4laIhLqluQYQNxiu00/uhE9xR7pzkOvwumvyuWJJGjSwyPfEEVcu1fcj/wfjsM+ULlRYprKwk1nkXMU4oN2IkTIptXMkyHAhtXrKx0odGsQ2DpoWez/ZCU44uW7mQyuCdslRDrI1SNWmlLkgWQRcvrbkJjwSsSVpRqk3djCqyNm31rTvnG58SpwitlXyUkfwhj2IWzhGcTs/qzkMDkHuGGX2aceIT6aEBKEjDRgiyYNmP5VmH6fgAjH9Xw1ck6fVKdy7ZQIgSV7CxB91U7o6xd+9wlEPtQJrDan5+FRaPgNcGEUzm3BPhiB/PeTtvjkCgPkqtBp+qHzcXZoStb17vmNdE38qkE6kKb2coEcEKHJSnAMEF/LAonFi8HwdIW6yBZiB7tShNj0A0fBK4PAvcgs34boYgYRi8hDZrkKqwfMfgew5UD6nPVqRTIut9e64PuPwtm98CywHnjkBYTgaISWG/GmbrJS51gbEeJ1yDmORs4NYAuqanLJRMkVXag2yCVC+vs0IvqJk6C2HloaJQF1h9lkEz7JokdhogY4V9NRP2lebfG3n4CwSLKsWI7ysbj4dKBeiezAk6EYpEPqqZnwspWrKBPIBREi8fGmkkgwTFfkkiKN9Cp11pUiP90hZAB8Hv4BWD4KT7wk38eLbh1A3EPVZlslG1qosR0hS8aCMn5LzEeIRJ3s+nXEQBQ1zV8J/O8B024/QmR2ms+D5l3zDA0g1qMnzHZKegYY1/GwyfRF9xtytomGvHSdq81yi8mCkMWzgLcDl8j5nAOsxeZf9PAoRHQC17XBU7ch//U2Jmamh4n/iUQoKSU7Fo5CffiOh+yFX2SFDH33u9/lmGOOobq6mlNPPZXHHnss7ra//OUvOf/882lqamL27NmsXr2aBx54IGab22+/nZKSkgmvgYGpsOJhJIwnHXqhXqwQpFChyRRz8KdKFFIfJpqeDGEDVVAzE4z0imLF2CaBWg3CMf4B+Mh2+HQbbHgQ7tqDWG12IreOkpMoslMT4oH9CTKovoiQEsfas5GJD50HcHhfn1nQ5RznGcR9p9nhahGfVDvWfdaDaI6eR25JPW7QMjQS8grCjYpzUW2u0TLgEjj9jfCDRvlaTM+nHjML7cB3gO8BfXsQdgSx2jr9PBRYl3N0UVyEKEK2zifjZOiuu+7i6quv5vOf/zxbt27l7LPP5uKLL+aVV14J3f7RRx/l/PPPZ/369Tz99NO8+c1v5m1vextbt26N2W727Nns378/5lVdPZUcC2panK6J8SAiBCnkGzFT5TWSQQIX2VRE08lA29TBbR+i54kghMi4mlrMZg8hOXNuBd6NeM5GHkQsLxp51mk+dyFEqF0KO9KGkJqtSMXSTqDaeudcNGOCVPrMft0wdiciZoiY4+yE1+6HV+6Df9xilrkahy7EelULnIYIsvU8p+Iqc38bbScC3IeQs1JgG+zuFI6WiZ/LwyNb+BbwT0DtWYjJs4HYYAP3wWKy9BmAl+dOFTo3Zx4ZJ0P//M//zAc/+EE+9KEPccIJJ3DzzTfT2trK9773vdDtb775Zv72b/+W008/ncWLF/OP//iPLF68mF//+tcx25WUlNDS0hLzmh6mWo9M/ZqFLJSG3BIhiEtENXopHVYhF667KIq4nzRc/SGEhLTBP74sFqGw3dcjLi2qsDqdDsQaE0F0NQPwJeAjbYgO6Dn46g4Y+zm88nsxIP05NkK3Afg0YuB5/bfw3E+AjXALcGgPQjwq5Bj/F/g/SKK4oUGzrg9roapGyN0u7GDthvZPdo0SEaFRYLshg2XAHhjpgr8mNXmFh0c+4hng8DcCFyNJVd0HCf3vqCu6jViiFIpsiZo1+WKxIHvnk1G6OjQ0xNNPP83nPve5mOUXXHABjz+eXNnG0dFRenp6aGxsjFne29vLggULGBkZ4eSTT+YrX/kKp5xySmgbg4ODDA7anAXd3d0hW03FteUSoUJGronQcPwosqm4x5hkHy0fsRdhH5qPR3MLVQJPwqGXxNOUyGD1FPD6y3D4Sqxi+OewLwrzz4ChLngYubp/3wV9XUJe7jD7a6ogNw3Rd5Cn0iazbl6vaDgrgNpH4f0VMBIVg1QU8YJ9D/hUBUKE1NvYhBCiZ80BTkZymLl5kNyyHW74vms1C550D+NC8rIK4AHY0CUWrqnwVg+PfMM2oPtRmL0GuecPIA83DWYDfSBQ6y1IBGrOMlC7KOSH8jD0kY06ZRklQwcPHmRkZITm5uaY5c3NzbS3t8fZKxb/9E//RF9fH5dffvn4sqVLl3L77bezfPlyuru7+eY3v8kb3vAGtm3bxuLFiye0cdNNN3HDDTfEOYIbVp8KioUI6eNNLv9AcVxk03GPhbUX1MaoOLkFIV3PIO4lU2L9erMoEXow/KEaGUF74I9RsSat/IMYibS02U3Ofm6klWsT68PScv2HaB/+rznMt6NiuVeNdj0S6c6xCPE5GbFWRRHCtxlxlz2DLR/g5lwKRtolYn8jyF/mIfjTZiFidxmC14B4Gj08Ch1NSLHh8eCInQjRWYP4sfX/okRpHvH/N1nPQVRsZCg7yIojs6SkJOb72NjYhGVhuPPOO/nyl7/MvffeyxFHHDG+fNWqVaxatWr8+xve8AZWrlzJt771LW655ZYJ7Vx33XVcc80149+7u7tpbW0139QnGUnhjIqJCOVILD2O4XDh9FTdY7pvmAvIJVcN2KzS+5DkiauQn7QMOBe+uxHWRBOHiFcAcyqAHbBvC1xtmtiJaIrSLSQeQAjW8+Z7GVLN41jEZVWmiRhXIuakWmABjNwPZX+JdeXpSa0klhyFwc2iHUUG/oVwWBss3iNf25E5w1uFPIoBQyD3+g+RJ56TsELqC827WpMrzDbxXM9ZiyRTFFsF+4NICG5mzyejrc+dO5eysrIJVqDXXnttgrUoiLvuuosPfvCD/Md//AfnnXdewm1LS0s5/fTT2blzZ+j6qqoqqqqqErSQqmUogidCaexHcAZV83OqM2s8145O4q7epQxRLzchFiLdpgabMLwKWqK2fmoYFgGHovDLLeKqynaiwRHgAUSOtBioG4V/eRmOBkZeFb3SPOCjsxBidBCxHmlJAU3KWEZ8DZHrwwMxSZ0FLICydnjX7bL79fiM0x7FgTrgiU1izV0MrHwUrjgFG6nZB/wYIUJnIQOB626OQbGQklwhO8kXMyqgrqys5NRTT2XDhg0xyzds2MCaNWvi7nfnnXfygQ98gJ/+9Ke89a1vnfQ4Y2NjPPvssxx55JFT7Gkq4ulisAipZSvXRCiOVWgqFqFogs9utIfbruYXqiG24nQZMqvPgc8weR3o8xGLUC4zLvcgXrCNwFuBH70M1wDfAH4Ock7VSKLILuT8jiM2CiYszD6sVMcI8hs9KV8PXyHpjLxVyKNY8BJwAZIR43vAZ0GeivoRa1AXQoBWAGcjjEkxIUv1VKKcp4tcj+3pRHZE1BmnrNdccw3r1q3jtNNOY/Xq1dx666288sorXHXVVYC4sPbt28ePf/xjQIjQ+973Pr75zW+yatWqcavSYYcdRn29ZL664YYbWLVqFYsXL6a7u5tbbrmFZ599lu985zsp9m4YcfgmK57upfCjxvKFCAH0TpxBJ8syHQ9KeNQv5VqDILakBM4ytYhEzecehCicA3TBGw7Bv/TC57DZlV1smkJXM41dSGkMTXX0HhAr2CGz4DjEz7YDcRcqeYxXzNUll3qt9iAnv1c+b0cunYdHMUADMBWdwHWjcMM9UDkP8YPPQ9zMGgHh1iqLQbYtQ0oeckHCMoXMi6gz/iutXbuWQ4cOceONN7J//35OOukk1q9fz4IFCwDYv39/TM6h73//+wwPD/Oxj32Mj33sY+PL3//+93P77bcDEIlE+PCHP0x7ezv19fWccsopPProo5xxxhkp9i4VYqDEKR9IxFSR7aKridALwwF2omHuqWIQa+EI0xoFiVAwuaD7WQXGAwgxeDu8+3n4860ScX8Pkuw53+tujSDkpAwhcU9sgzO3QcmlWDLTj2Svnmc2rMFm5IaJbrMR5DpuAX4D/xuFbS+Jm66Q04x6eASxCLF2KhqQ+3wtcPImZJyoRAaCAeLXLSvPtl7IY6ooGRsbG8t1J7KN7u5u6uvr6eo6wOzZv0bs/YlIjtYaK+QSG2DTEOcawzDWGUtCtAzEVNxjagHSxIcugkRILRvusTQvj+bf0XpiWoi1DXgQvh6Fb2J1lIWERuBTwLU1sMF4Js+fB7wPq35+B1LkNWghCgqonwNugddHJQ3AN8iPu8rDI114DzLab0B4znXAR+cCSxHrag8SgHEO8jCxGIlKrUD0ePpAUV7K5BaNo0g/YZqDTRdbDCgHTgXK6e7+E/X1n6Crq4vZs2en9QgekyKCJ0LpgoncSwcRUouQusc6EKZSh5CZBiZO6pqhGSwx2mfeq7FRIfuA3wNN8LttElTymxS7l0/oBG4EHuoXsfXHgfMbzYo9yICuxLEM6zaEiWUIFgIfhsPb4PP3ybgflpjSw6NQ0QL82xJ4eofwn9p5wGpEH/QMEl6vicAWImONPkTEuMpyVUG+2CLKMg9/pSaF6oQKGfniGjNEaNjxv0yFCLnWIMz+g8hkruU0mhCztYqi1boRQSZ/TT6obrk2Z10zsB1uaIMVbRIltSuF7uUrRhCBdR1SoWM8JFiTNSpZBJuDyN1ZfzYVT1fIx19kuuMeHlnG8wCNcOr1yDjyDBLd3YwU39OaOUsRC1FceY6fYtOHzEaUzfBf6n+RCvPxXGTFoBOKkB/p2UNcY6oRmowIqYtG34Nt6PIaxFz9PPKzVSCkyE2QFkWMZEPI01w1Mtg1Ahvh2e3SzPE1wqtuIT9oZDrRgwisN22Fr28DamCsF0oWIBoija5TBAvZ7gb2wmMHxVo2iIdHcaEFZOhsQMaGJcj/ohb5j1SbVw0yfqg1yM1A7fVCaYTqXY/K2BFm+C81TPyZWHVChTwVRsiPzC8DMNadmAi54d3uZ0I+u224BElJTwU2qaAOVF1IVuZ+s34LYh7R9f0IcULKEZ3XLxaPYi042okQmf5R6OsVTcTSXUiUzDlmIzcztV5jU0Pk0EGxMv0Cmynbw6NYMAIyJmxCBgQlQpqCQ8lQGbEEKOvZphNhgNy56TKBzCbvmOFkKBEGKGydUITcEyET4ulGjWm0l1ssNCyXTSJoG+53EFJTjalNgfi2nsMWAWtBLslOc6y9Zt86s301nNwIKzqldlixYy/wI+CvgKUnYHVTqqWCidougGUwpwO+sB02dnoy5FFcKEOMy0/sgVWtiHUoaP3RMHpXI+RuA+SWiAxTfGQos5jhZGiEcMKg7rFChRZezRXMH3GsP1wonax7LAxqDXLhtqPRHAuQQWwIG2W2D9EG7UBiZ3cgFqJmYAU8d7+EzyfKOF2M+A3wVy/AqQeQ22YRYiWrIdYypALrNtj3qHDKQlfTeXgEUQHcDJz6TqTWTSnWKlpJ7PhTGtgxBjN8ek07IohwKzPwv1aoGywSZ3khIJdEyFiCxobCq533Ica2Pmy19GSrPCdTtFVz5Gi2Zd2vEvH5v4QIpGvgv7aI4eg+YMWr8IWIuH2uZ+ZlUu4ALgPmd8LdwPxnECK5mom5U6LAM0KEPkdxCMs9PFw0ILIgliIPUoPY/0E7oiFqIXb8ChY79nqhDCCzc/IM/7XChNEDFO7zbi4q0Ks5dgDGRsPrjA2ZTTRvj1pmXP97WOZW3TdezSwILxmhhKjS+Q5iGWoEauDAqyIijiK5RJp7JYfQTCNCik5gOTB/HtY65EaWgb04K+BNnfCePfB3We2lh0fm0QQcUYH4kBchY9dpyP9gF/K/eA+x5GdCSH0+oAuhdjN8mk8SM/wqvRyyLEJhRo9lo/CqW9B2QCxAEJ9BuGHzg+Z7BRKK+jyS20afrDRnh4Z0JyJALkadd+2HGzKvnzuBkxFytU0MRboqCvx/eO3LRiSdQHUbfFZ/B625pNe3H7gTvjkI385RPz08MonngflRaHgVfvwqnNqKuNefQ3IKaY3xhC6yfCiFUYjzWO6Qd1w2uwjWGStU0XS6640p6RlA3G4H5TXWCcPd5jU0McxdoQJnJUKqD9K7bY5534E8afU7rzBNUDxoeQiIX89sBKsh0txDZWIGX4sYisrwREjxPUxY8SFkVogSW+5kSDa4BHnm9PAoNqiSdDfiWSeK+IW3Ihyn1WxY5rx7vVAWoPNcZjDDf7GgDzJC4bFpTQEw1X4PE0t+EHcXpO4z0skyKI52v6s5eR5ChtRaNIQVKurgkihM1U0QGPwZ3YrratHQ45YCi2F2KXz3QThvEK7C19ZS9AA3AR0vw8dehsOWIU/D7u83ItW8vV7Io9hQhhSi34HkVuwGsQq1A5ciRY5bsRFlMNEIVAYzfmrNGDKX1cz/YuMoRKvQVCxCA9horymSnjDEyyQ9GlhW4SzTpyktiqqhqW7eICUxYVFN8SrRu/XH9FhaZkPbaYP/GYSv4YlQEHuRqLr3AYftwYpFQa5pp2xTqCEGHh7xsAB4bBaM9MqwU3IBdtzQFB3LiLUKBR/aSvJJPJ3ZrM3FBH+VxhGhsKxCyVagV6tPb7jAebqIZw1SBCPAyrCl3pSkjGBr+7hFDhO54VzLT/B4A8iTXA+SQ2gUccE9hjzFNcDurfBPwIvJnOMMxHNIEdYb9iKDfx3j1/tQv5AhD49iw3kA50PZCuz4oWPUYkRIrYEfEGshyjuYcT8v9Ev5jxlOhtSsMExh2Qcmc41lmACBJSST1RULW9eIDCI1iAm6E9HzaPh9vIyu6hprI7bMhrtedUSViK271izTyuwDQI9Yuh+ieDNMTxediKC6+yWYfQ72NxmS+WFnznrm4ZEZ1GGS0D+JpJU4CZt1utpsoOV7ghGrMfCJDjMH9WykHzOcDKlbLHMXOP1IRIQ0xD0kz086kWxx1TBrkVZFrzXrSxlP5DeeMBFiC6yCdX0NITN1D2LTLgtso9aiasT534mIW56HV16QzNKbgTPJfX7ufMdzyLxw/k4k7h6gU5YXckpSD48wzAP+7hgkskJdw5qzTOv1VRCbWyhvrULFigjyS6UfM5wMDSB3cy+FQYY0sisIE/WVKSuQYjKXWNj2we9u6YwKxD1Whsyu/cSG1gcrzqtbbR8yWPUTP2mjRpFpe21w9F7Y1gvPIlYhj8QYAP4BOO9RKJmPPBlH5WG4UDNxeXiEoQy4EOh7GWrvRfILjSBJWldj3WGuhtFXqi8qeF7LMIUhnI4wkQiZ3ELD3TCcYSKkLqxkLEKKMKuQi1okzL4Jyd3RiQgUe8x7h3NMfYEMQlrSI+jncpfp3V0LnAK8De6qhw+RZ/UU8xjPI+U6iDCePHMD3r3oUVwoBW4D3gBipd6FFGmNIOzfpOSY1CrkM08XLPyvlvcxMWFCaWMJGk42Ic80kKo1SBEWXq/ddV1bmoG6CSE/jyNp8DHL3EgyJTrGQhGTx0jhZqPWjNdRpOBYD+zrgntTPJWZjD7gx8DbHkeSCw0knwbKw6NQ8OfO50NdMOcZxPq8mIlWIUi+jFDO0QvMzXUnCgKeDOW1iyxCbP+y5A5TJKsNirevYjTw3Y0WU3eZVkxfijyVNZqXus50e00CuNO024yNUAObCXAEmcl3mddG+K9RmdhfmsLpzGQ8BvxvFxw/AHR48bRHcaER+Bvg9HciD2RtZsUS86pGxpgqszwsnD5vMYgPr08OM/wKpRJF5hKSyaDO5KlcXg2HdP1Cw0AkeyRoqtYghbufZqMOg5vID6w4sQchSf1mmwOINagOIT37zPshs6wUiRxrxtY5GzXtLpT+jI2Ky+ehKZ7STEYP4iq7pgMYEFG1h0exYA4SMc8ahBn1IGPHSYhwWh/YFAmF0z6MPbPIXOT3DCdDyprjIYyYpAKXFFUHlrlwEiEG639l0xIElrzEK2+RDNQKlIgIBRFBNEOawGYUGZT6zLJ5TrtdCFHqMNs1YhMsusfTa9YIJSvg9G357xTNV/wQuGY7UO+Fhh7FhQ8BJW9GhuYoEjlZgw2rD5bbSOgim+FTalbQnZFWZ/gvF89FZiwx9E6zfXfqTaYtpyyGhse7Ao1Mz0LTcYsp3DpW8ZhH0G02gg1fbUUGnzbElDOAWIAGzDbqPjsEf/oJHPZGxK//HPYprhPx95ciP+M2eHEbXENhSOXzERHgT51wWD3MznVnPDzSgDJElvg94ONPAisRN/tybG6hYNRYQbnIPFLBDCdDQSKULhIUbDuRPcIQoLFRK/Z1RcMudJmuT2eeC43Omi4R0jIZiU55iNhwea1PVmfWN2JJzU5E89OBLbRaARyAe4A1j8LCMrPtkwgx6sNmtK4AFsHSXVC7x7vIpopORG/1kai3DHkUB+YhOcc69UsPNsO0utuDY6zPLVS0mOFkqN/5nGx5i0RQV9dAcq4tN4uzyYxMD0IGKrGCPUWwPZcQaVbUVJEOa5CbEHGyqvN6rhpdFowGK8OW0IgC8+BXW2BjJ3zhBZhTD093yQB2C0KILn5YeOSyPXBRBJiP1NmYb9psh2f3eOHvdNEO0CapVzw8ChllwCeBwy+Fw89DbuoVSH6hKmyh6KBLLJGLzBdoLWjM8F9u2HmfTuV3oytKJdQ9SELUKtOBtZBMVvfGrd2lxQSTJUYaph6v/tdkcImMvrtEKGjd0m36ECYzgk1lr33QGkCjiFvY5BraCtwKvApUdEn0fafZ9TngAXO4pcAPX4YvvgwnanbqBjlOu2nOY+p4EPjIqCFFHh4FijIk1qIH7Jh7MTLuqk5INwyOo4lcZHlVoNUjVfhfblpEyLjVUiFBYQJlt4xEBdY4FWWidSge4hEjt7qybhMNbDvqtJEs3P1VzFxD/CcnjVAbNNseQJ7C3Mr1nQhZ0iRnphbQ3yBlNB4i3G6np/WceW0DPrMHruwDTpZjbIqzr0fy6MAXtvUofFwO/NsCbC4yLUTsFmANswp5rVBRY4aToRGmXq2+F8b6U09EGM8lpYQkgvwJ55htp1IVOZ61x43ycpMTpgolU+rKGsCGus/DPlm51eXd71p/bB+24KomU+wDnkLOuw14Bm5CJuJku7oX+DLQfxA+dUj605Z4F48k0A48k+tOeHhMA03A+QDvQIIsjkPc6cG6YxBuFSpIvZDPM5QMZvgVMqHrKWEK1iCQJ5BE4eoqKq5GrCdqxijFus2CcC1K8ZiCG9nVY9qvIvzJJxHUsuOetlaIV91TBJsdOqxtJVEVCGnSSvI6EKlFax6wHX61GR4G/i2FbioiwNcBtsKnThCjlcf0EMVH43kULhqBFzDPav1IHqE52KgxtwJ90ViBND2Mz380GWY4GUp1aE+TWywI1e8MIH9IN7LLrcfltpmsZcfNv3MI+fNXOG1MRopcK1DYOn2vQYiNmzFan6T0WEqmNMqrFvFrNSAjVQc210e1/Dp3TXJ6idCJEKn3vuDF0+mCdzV6FCKWATcAh12MVGRdgow7+iBWSqzVJ2xMLEirkEeymOFkSKvWJ4NhGOu0UVMweXh7MkRoFDFjuLNMBOtO0uirOmyEgyIRiQlacvoQsjIAHOv0OR4pCiuC6kIJm2aqrkQIkbr23OzSQeuVK9yuRSxEfYjlSo/5HPzILJoO9iB6o1en2Y6HYO/km3h45BXeAnwGWHU1QoIWI+OO6xZzx9V4Y3rRWIs8wpAVrvvd736XY445hurqak499VQee+yxhNtv3LiRU089lerqahYtWsS//Mu/TNjm7rvvZtmyZVRVVbFs2TLuueeeKfRsBDEhHoSx17CsJCT/0FCnrHarqPeZVz8TiYOSnB6z3n1pCH0XQlB2ImaMTsR6swuZwXdjidQA4j6LOq9+LClxoX3T7fWzkrduZ98Bp09aNb7LfHfD4F3rla5TsqXEptq5hLp9J8JI+pw+HzDblAH1ps+bkSSLT8l5jx1Iz8Q7giRV85N4evBgrjvg4ZEkKhAi9B/HwKovI+U2FhObQ0jhic6MR8YtQ3fddRdXX3013/3ud3nDG97A97//fS6++GKef/55jj766Anbv/zyy7zlLW/hyiuv5I477uC//uu/+OhHP0pTUxPvfOc7Adi8eTNr167lK1/5Cu94xzu45557uPzyy9m0aRNnnnlm8p0be93ynigQHQKG5F90WA1yeQageyjcujOKzbgM9inDdS0F6aYbfq5i6gpkti4DdkHfP8BHgR89CXweMeeWYfU5aiEKq9gedJuNEqvrUVKl27vRXIMIGWsh9qnJteR0mW0bAufgkiqwT16uVqnUtHUIsSRpWOsojGyDa+X0OedlGbMOkB54IpQ+ZKYqkIdHerEI+AFw+ieR/EFNWJeYm7xW370LbMajZGxsbCyTBzjzzDNZuXIl3/ve98aXnXDCCVx22WXcdNNNE7b/7Gc/y3333ccLL7wwvuyqq65i27ZtbN68GYC1a9fS3d3N/fffP77NRRddxOGHH86dd945aZ+6u7upr6+nqwNmz0bIQj92slaCoRO95u1RclPprNM/llpJesznBueArqtIoftHEevQbuAOeP0FOME00wq8+D4k8qEW+zTjkhi37YhZVhdYPoQwi92m78cFtnH71IkdOPrMcd2w/A7TuRbT926zbQ/COh4ETkfM0UrgtiNOe71eEcQ15mSO/uc2uN7pypn4gqAeHh7JowIZZr4IXHQG8H5kHK4hNlosWGajlIklN8IiLvQBLx7KS4G5U+j5UWTWLtHM1PqVn+jubqK+/mt0dXUxe3b6igNl1DI0NDTE008/zec+97mY5RdccAGPP/546D6bN2/mggsuiFl24YUXcttttxGNRqmoqGDz5s18+tOfnrDNzTffHNrm4OAgg4OD49+7u02htwNYlw/YaCa1hGhyQNXr6DZaJ0sne5d4aHHRfuQPpa4hffKoNOsrnO2Ne+j8F2zuQRBOMl7FXQmaVnNXAbLuUIHNRNho3lWzVAEcgte+AkdUADdj3VR67mVY3U4EEVprWQt3O71emsUwavoxBPweVm2DJ3YCf43VEHUgREk1RoMIMTPE6NNtUurBhSdCHh4eyaIBuA849RTgXVgSFNRZenjEQUbJ0MGDBxkZGaG5uTlmeXNzM+3t4Xls29vbQ7cfHh7m4MGDHHnkkXG3idfmTTfdxA033DBxRTNSdTKeZaiO1C1D1YgOpsE5TtAy1OTsr2HzNbBhb6xlqANs/h3XMtRArGWo0Tm29jtoGYrCEdebvjcydcuQmpRbEMLkWoYugCdKEcuQZoAeMm22EmsZ0vD+TvhGJ7R6y5CHh8cUEQH+DFi2Fb641bEMaS3DVNKIeMxIZCWarKSkJOb72NjYhGWTbR9cnkqb1113Hddcc8349+7ublpbW21IpVp5NCoqGc1QFTbpoLqtKhCikYxmSPU0FQh5agKuhcN3wR9VM3QUsJqJZMjVDLmuNyVvLpQMqXWnESExwVxDGtYfxZIt18KkZGjU9KUhcMxqs+7Pzfm4mqFl5vyUbPYhEW163c6BT90pxqNd8pXFwF+GnI6Hh4dHGKJI9vl3Aov+AD/4Q0AzpA+ubsZ9bzXyMMgoGZo7dy5lZWUTLDavvfbaBMuOoqWlJXT78vJy5syZk3CbeG1WVVVRVRVS16LkcCg3GQ7LRuGwSmRWL8demmqYbaLJVD2qM3QpYopVUuE+faiGxy19Ada1ZZoed4G1mmU1UPtl+NFzSD6MRqz4WImawiVG7nKN5nLrhA1iqzFXhbRVa171WHGzojKwnSuodsuH1JhjqWVJ+6h5iPSzkjEVn/dA2Qr4Rqm5Zk0w9gcx3KUjc3SraccTq+mjFi+i9sh/7EIyTZ9/i0ST8X7koayR2PxnZeY97MHVY0Yhoz9/ZWUlp556Khs2bIhZvmHDBtasWRO6z+rVqyds//vf/57TTjuNioqKhNvEazM+yoBZwFwoOQKbhSvIEcuhslFWayG/aiyBCKvJVWqaqzPr3VededUj2ZYXI39StdosAt4LLMQSESUxFc7LFQa60L7p9vpZCcxsZ99qp0+NCJGpx9bqcYWHKj7Udeo+1JB9jTJz92lEXGZqKapBWE41NjoNxAK2DHGxLYSSZssPp4MypLZZOtrygPNy3QEPjyQRBdYD734ZnvgyUuF5J9Y67z4d+SelGY+Mc+FrrrmGf/u3f+MHP/gBL7zwAp/+9Kd55ZVXuOqqqwBxYb3vfe8b3/6qq65iz549XHPNNbzwwgv84Ac/4LbbbuPaa68d3+ZTn/oUv//97/nqV7/Kiy++yFe/+lUefPBBrr766hR7l0qK8nIoaYTK0liSkOgKqguucpJtGgJdaUA0OUqW5iEkpYHYysqJTLx6bO1frWlnPtaapJmgwwiVEpd456j76LvqrqqJJYs1xBJG15qlOY9aELbShBDBOmC5PMzFq0SSLBYgvPKoabbjIfCk0qPQsB74BLD+ZkRl/TzWaq/60CAxCsu47wlTUSPjmqG1a9dy6NAhbrzxRvbv389JJ53E+vXrWbBgAQD79+/nlVdeGd/+mGOOYf369Xz605/mO9/5DvPmzeOWW24ZzzEEsGbNGn72s5/xhS98geuvv55jjz2Wu+66K7UcQ4CwjY4Uti8HGqA8knxJDnWlQfxM1K6LTUtSKNFQq00YkqlNploesHV4XO3RZNC+hdUm00zTFdjouRpizwes6NptQ7VDmgRNz1cj4wbk11nL1GqTgRilPgTMOQEWvyCV6z2mh1QeHzw88gXPAx8B/vd+qL4fSj4MXIqMiWDHOH3wiyLWdBcplqP0KCxkPM9QPmI8z1DXqcye/TqpF2uFtFat1xD7AeBFxEKixKWO9NnvMlm1fq9ZNlnVeo1Y24UMNips1HbaERVkBeNV6z81CD9MsasNwN8CnzpF+vPXm6dX58xDfpIvEhv15+FRSGgCvgqsvZrYqvX6EOdqHYO5hipDlrnIyzxD5Yh9vHgeYwoyz1D+Q+PUB5hYgmMyzIKS6tSsROoe0lIWLpTwNGCjuVw3VypQF5Sbct4Nj3dF3a7QOlm24YbkK8FqYKJ2Si1CrmVJ8xmBddkp1Eh3OnKNjBD7uofhN0ws4RYPrUgtoivnMl6Ydl5yZ+aRAC3Aylx3wsNjGugANgBr70HGsMXAhxH3PMRazIOi6ngRwnmPGT7NJwl/lShHhvl2UidE5cBcKO8FBpIjReo20ygszd2j5MVUbE85N4ZLgFTYHHZsJVnqLw/mGkoWwQKvmmDRjWBzNU1u1uwmxIdV6awrNcvqkSizbsatZd9Dfp23mCYex+aX1C6UAkuRZ6AvAicuQJIVNQAdcBbSTjJkyiMcTcg19vAoZPwc2LhHXOifXYH40JrMSh3P9KHRdZV5zVBRY4aToXKEACkhOsjUpstZ8iofZrwA2Nho4j+PmytowPneZD7HIzQukiFAiY5fQXzX3WQIWn2iWIIXxgm1rwq1SLnHrcMWvZ1t3nvgFGTg+iIwpx6e6hIr0fWIFegC2YxlwEXHIBanZuQnBRiSj034OmXTwXlASym0jAo59fAoRIwgxQcawI659yP5iJZgg0/CLEEjxB9nx0ahROcTj0LDDP/VahATBIxbeYgwNQ2RtmGIUcmwc3UTEaxhOGxY/kiqEYqXQt4tMOgSoelAj2WyVE/p6aeU2Cco1QaFQQcZ3U7772qYepCfpR9oh8sa4bImhOBUwulGT9SxTQpRL3wzokFqRMzefYgJQy1tLXDyAli8x5Oh6aAFYB4seNWTIY/CxgjwDeDy+6DzPjj2OGzwxlLsWBu0DulDX7xGyz0ZKlTM8F8tJJ8Qc5FnhghTJ0XBtmdNvnkJcPgwYqkagLGhiSGemfJVu1m4p2IlUuigMWTai0eINNO1HhtkINJXJzbb4nzTVqt5b0RI0ojUrz3sjYi/P4K4xZrM/qq36gd2wYt7hCNN1Ss409EIvA+gwgfVeBQH2oDTkKHif9qQB9GXkPxublHs4ENbQeqGPCbDDCdDsxATRFAr5JIioweakvtMFfzlzucwVb8KuPW9WshR+YAcfzKXW7qQSOCdLDS5I8QnROpe0+30mP2I6eYAMjC1mm13IQroWrP9q8AoHPaXiEBaS6DMwQrQMe03ACtgaSf88zZ4G1Zv5JE8ZgOH1cvn7oRbengUBkYQC+dXQR6kFmFd/a6OcwAbRebWaPQoKsxwMlSF1Q2FweQVAmebZEiRS4KSgWs5GsYSMNNWybBErWWDFLnJGqfqNlOio20lc8kanP0OmH3rkJ9Ii9XWIRagesTM04R9imsL9N111XXC2DZ4imIKMM0uPgiiqWjzliGP4sK/AR97GEouQUjPcwgROgk7prjusUSuspSDcDxSR/rC6V3McDJUjpgbkpmt9VIl4fKaFlwCFjEvY6kqGZDItUyToum6zVSYPRkhcsP9wUa41WBD9auRJzbddg4SbdZvPmvNsyWmDWU7Iwhh2g3shZJSWDYK5wJ3TuGUZjLqgEtg3AV5JlLVwMOjGHAI2AKc/jgSe9+Gdb1fiHXrKwFK6CobIPNzxEyGztmZaXmGYxZSICsfGX0D0j+NcjN1LpQUJZvfaKqYjrjadYOpW8xN+gg22aK2rUknlzrrlezg7F+FkJ8Wp48aOeYesxYZ1Fpluzf0wMLNYiHakcKpzHScDRxfj9x+TaJR9/AoFnQiaTe+f7d8/yowZxEySCxELKJa7Nqt8VgQrjL1fnhMhoL4OTOLfHecaNh/g7OsGslvNNtkPc0g1LKjlqJkEYyIc2ucuaRKQ/s7EGvPGiRZUCu2IKxb06wCEVnj9Mstgqv9VCLVYJatABbC/Hp4e4qnMpNRixFOr2E8R5UfNDyKDb9EypZtQdJ3sBK533cSmwZEH+SiIY3kJbyVKln4cY1yRISS72hgYqr3ACnK5AxfQXIFYl0EtwsWrO1DbNQdiE6oEXHF1Jn3JueY+gJrJAtLTOku04GrD9gK/BrWdolGwLvJksMyjIusAfn9quF8UssH6uGR7xhFdHH/BRKssQjJ1NqAWMaHsIkYIX4x1+FR8tPL4DEZZjgZ0tl1FoVhSpxFeB0bQ4pKZkN5ZeZIUapWouDdVeos00SNESSCzLXsVCPWXTexo1qH6rDh9rq9u16hZT86gD3APnilVwxEJwPX4Sf0yVANfB4oeSNCVM11HsLWt/TwKAaMAA8AtccgpuNqhAgtw45VwcLYcaWmngwVImY4GVKLUDWFQYbAhv2H9df8g0saobwmc9YitRLVkrj9sOSRlVhxs2arrsSGzgetPeryqkAIkuYaWmT2KzPvlc6+Gg67DSFDdcAyOPok+Lvj4NfNQoYaUj/zGYXliFiaxdhr2yjLm3PYLw+PTKAN+MeXgR8j44ZqgzTthwaTTGYd8sggGjLWcqEwgAxB/TapRJXlA6pJXD4kmAk7yRIhqUCtNpMJrMOyHHaa1yFE/1OHjdaoJT5F17pqi7ED1WhgvT7BDSEi6zrzvcUcy/DFvT+WyLJfUkDu/yyiETgHmH0c1lpnrncP8hNsy133PDzSjh5MregzzZfNyPjRhwwSy5EsjVolAGyC2Rj0hi30SAsyZ7iY4WTIRQP5G1UWBrUQTVZPzSVGGGKkWa4Nk5gOQZosDL+C2ASOmghxBHGRNWCtQkEi5JYcKXP2V7KjRCx4PLCac912AGE/A0AnLDwFPrNVajQ+l8r5zhAsBz4GVsiuv0UU5tRAa38OO+fhkSE8CLABRu6RYafkAmxB62pkLDkHeY7WHGip1oXMGnTs90gGefkT5gbVFIaQ2kUil1k8VCN/kLlQcoRxqc22bjV1raXqXosnsA5Wr486y9Qk42qEwA48biQZzroKZ13wAczVGbn71yEaIyVr8+DEKvhbMpW1onDRivDGOpDIPleXZdyUWh3Fw6OYsAc4uxeOBN4M/OvvkXFqCLn/m4i1grt50hRj+SSi9vaOZDHDr1RwOG+gsKxDEGshmkq/y5mQULLEtFPu1EqDyd1saiWqxFqJRohNVqaWHa09pq4t1f/EK1IbhGspCiZ1VDKnQke3xtAosBO674fPAb+mcJyj2UAdoqd6/zHYDN9qndPrWSEV7P8Nsax5eBQLRoBnzOengL8BsVw3IrH3+sBXi028OECsztEXbM0gqibfZIqY4b/WHORZQEmEWocO5axHU4OJJpsyIQqiPPDukKRypyzJmPFRBQlSKWKF0RxCYCMywF7eJdhBpAZLhJIVJqp4esh5D9umD/mZ1a02IgFsdznda8FXYgcZ/NtB/hrLsBYh/T0qgTb4DeLl9PAoNqghuQE4DuT+X4wtUbkXSQyriRdVVB0TnlpID9SFAn3wz4zKc4aToWOwzwGKBgrPOgTpJ0RhcK1I1aJBIoFA281gDXbQ6EImWtfVVRrYD6zeJxExKnXeg+U99JgmCoqNjBO0Hc5mFYiV6GtYg9VMxDnAl+YhoWIrsIUrXe1WDfAe+NTDwB74uxz11cMjU1gGPFGB/A8WIU9K8xDf8C5kiIXYcWkCGcqHshwzfHpPETP8asULT5+DZAEsNGhyjAjZI3PByLVesRi5ZTFcHVEDUgDRzSodD7ov2LIdQehEHXSHjWCj3HTQmo9w33YZ576FWL5XAFfOgsFemdxnYkLGRiTAb18bzF9iviwhNlGm6ry2wSN7pMabW2XFw6MY0AG8FoUj3m8WLMOm/ViC/FnCHrri1ivLFeqZ8VN8Csirny43CJOBNsRZXgiYRe4y6Jgis5rnKFiOoxabZVoHl2RRgVglEt2xrq5lALFARRC33A7zXgH0wxtOg3Wr4T8q4AtHAeeIZeQr2DJnMwVNwK+AJxphfilSiuBMYoXTet0rZP1i4P9DHpw9PIoJEcQTxouIv6wK+z9oQYqmKxlycw655Mhnos4AMjsnz3DaWIYQh97A8nLEdrAn2x1KE2Yhf8RIjo5vrEUl1aagrPGTuS4X12rjRmZMhmqzT1AwrftGsRqhXeaYA8ilmI8NHRtBnvIWY8KmYPnFsPxJ2Ng5s/RDlwCnnoCIpTViRjVcilKs0H0ezH8jzN8Oczqz3FkPjwwjClwN/L+7YdUB4JPEWrHdB7KErjIvok4vGpDr6TVDWYaKqbty3ZEposG8R3LYB2MpKh+AsW4rOFQLj0tidFAZCXwm5LO2oRms3RIfAwgR2oFM6ovM+i7kCa8fsU7tQDQAmiOkU/Z9tlOSCb4XEVgXswtIQ+h7gBdfgKWazFJTE7gpFvT6DwLPw6FHpdL3rux22cMj4xhBPGOrFiBjSITYhwPNO+SW5yjFJmAcJ0u5TL5YnsNjFyZmOBkqJ34ct1aLH6Rwg68bzHskh30AEVuXQ1mnnVTd/6lOwBDuBgvWBHLb0J/GzVsURQaxoxA+qwZAtWzsQjLJNpg2NOqtQ3a/Hzi+Bkb64R4K99dPhEbEIvR1QyzHehHi2Iqt+aYYJTZdQRPMmQvnHBQi9Rtgdxb77uGRaZSB/A/OYty1Po7gg1kUG/HtjmU5R7GRoUxWIp/xZOh4xESwj3D/rrrL4q0vBDQgfQ+6ArONcqMlihh/OokzV7twC7yCFVO7OYbKkAFrJ+Ima0bcYm6OIz1WC0KUlED1IZahFXByg9l3O7TugNuA6ykuC0gdIh6/7BQkRPgQlNQiZCheoksloxWIO60Vzl4Ep/0BNmSx7x4e2UA72FiUfiTwohVYjViem5H/ylLkgUt1da6rbHjU5xtKGzSsPrNH8EiIWRRudJliLjHJE3MGdZtFLCHSgWMyQuRCxYyDWEIEMhi1IjbuPuTJzp3UNcy+xnwfddbPQ8jAYmRAq4Mv9QBNUL0NfohYQAoZZciD7uPAFuAyc560Y6+VS4RcF1kwyeWZQJt8fBfwD1nov4dHtrAMoBOe/orwndp5yPhyAMk0uhvrQjsd+X/UhLXkyVD6kNnr6H+lpNCADU8qVGhawTwhRK7LbCqECGxtIJBZuYlxMTSN5l1zFYHVKbnWKI0QcVPu9yOk6j1ABC5qhosehq9H4ZuIEanQ0Ah8HPhsFWwYNJe506xYgCRYKsVqqIIlVVTHNYpMBLfB61H4DvCNbJyAh0cW0Q68e4dYPeuA69rgo5sQMtSE/F86zOfd2HxpE0Lsc6Ub8mH1qcJfrXHzW6L4oWLQD0FyhV2zgXIoqSHGEV9BalFl7n6qJaplottNJ3iXEKlAWI/lRoAoudJ2jJZoJCr5iN4GPIE4VwsBDUhW6fMQYw4XwvlR5Om2CRmn12CfbDW5IlgC5CZdHEF+tiboaJOAvHeY5nwVe49iwXZsAedOxPL5bwfgBwfg5BXIQ4QWMACZHuaQR3qhYpvaW/CWoYxCidAfk9y20PVDyVa6zwZmiT9dw+7BPkClmvWwiljyo09oqisKEiIVArvERz93YnlxF/Br+I9eyVDdMYWu5QrKbTRSftUKJNllE/LT9yEuwXnYnE9Bi5B+dq9TJSI+r4bjvwfHL4B37zEEy8OjSBDUCEaA92HcZ2ch2sQh5GlAqxYPMbF0ltcNFQzyhsfmBkpwkq1brvqhQr6xp1LpPlOYNTFAQAu9pgrVuiih0s9aAFYJkQu1dLgWKS1KtBF4HP6rF65C+FEYEToLGQ/zCYsQgfRahMDdCWLKn4Ncp5eQc9TacPqKF6zhJl1Uq9oC5OTPApYJz6oL39vDo+CwiNi/QyNwUylUvgMhP2djhdVuqH1o6aBsPzxrVYBiQrJz9NSRDzNiHmAWcimSuWlV0V7IgurpVrpPYz+C7jKwlp1U3WXROJ9dyq+DFljrUD8Ts8k2AHvhn5jchrYB+CWSdydX7rM6bP7IfwGOPgbe+7J8nwc2K3cTIieoRUhRC/b6xCNDKqB23WYqom6H17fBcuDnaT4nD49c4TjgZuAm5H+1EsRPXoP8h/oQ89Fe5E92um6EzTc0joHggiygmKb27JC7jFqGXn/9ddatW0d9fT319fWsW7eOSCQSd/toNMpnP/tZli9fTm1tLfPmzeN973sfbW2x5TPf9KY3UVJSEvP6i7/4i2n01C1AmgwakMfsQoYWds31n6Z64iRcitX/pALV/gT308k7qH3ZjYgDnkJ8/53I4DZqjj84eSbqXcCcCrjyNPg1cCHCERqJlSJlCmXmmBuAR4D1pXD0AmAllB0Ff78EPnocjPQiA/hcRCM0z3RSrWcVk7zcDLw9wCbgLhi7XXIxXWMWe3gUA3qAVWfBvQvg641wxRuRh6VqhPy0IJmp34FMBe7D2wTrUKHKKvIFqc7PUz9KxnDFFVfw6quv8rvf/Q6AD3/4w6xbt45f//rXodv39/fzzDPPcP3117NixQpef/11rr76ai699FK2bNkSs+2VV17JjTfeOP79sMMOm2IvqxmPcEq6CIOb8+DQFI+bD8hGpfvJEMc6pNqfVCPMdF83Akqhk/kQIgLQ2mXzzbInkFGwBXgIPjo6eRX7KHAoCnOWwPwK+GUP/O92qdu1EhEWr2c8n+O0UY2Y8M8BHkSkC08hqrfl55nzOBnRLrQiJO83UHYxQmhqkcF7gdOgm2kaJl5vd30FclH+CH96WZrfi1wyjdIvxGg7Dw8XlSD3+l+ZBbsQE9EabKBBnXltN+9BK7RibFSKWGftwbPYIsmy89CesSO88MIL/O53v+OJJ57gzDPPBOBf//VfWb16NTt27GDJkolKi/r6ejZsiE3h9q1vfYszzjiDV155haOPPnp8eU1NDS0tyZXUHBwcZHBwcPx7d3e3s/Y44GlSvxTFRIgayG6l+5A+lPVPnIQ1wmwqqmXXrRNcppN7DcJYtH4ZyKC3B1gEX3lJJvcHEhymDjP+DSBmdOD4HfCVKMw/A4b+IPKjKBLV1YdoeOrMsg7n8FGEq6jGucmsmwc8CfytWf/+ColuO970oct0mT8i4W5NZsUANlFcM0KSgvmEXK2QEkjXzehee/09jpN2DpsNSzfCl2pgTZec0zV4MuRR+OgAuh+G2WciJKgJsaQ2YEtuaBSryk4TuZmzyk2KiQhlDxlzk23evJn6+vpxIgSwatUq6uvrefzxx5Nup6uri5KSEhoaGmKW/+QnP2Hu3LmceOKJXHvttfT0xDfS33TTTeOuuvr6elpbW0O2mopASwlRM4V9A2ql+1ydQzmUxFFNqwA6VSTapwIZ2JZgXUXKQuoQ68qZMGe1BE4l0hWfDhx+DDbPUSVwOcz/GLAcKuvhzUjpiy/Uw03NQor+G/g98Afgz4k1vHwM+E+kLMh3gbtmwacRQfT73whcCmVVtlLAAqRNogjZ0TCyDsQVuFjOh3lYl1glNtu0m+FbP2ul7uDJV5hrtBBYJKSMC+H8y+VaVIfs4uFRaFgBzH4j1uLTjCVC7v+nAfl/tZIdv3hSKOS5KAyZF09DBq9ae3s7RxxxxITlRxxxBO3tybmjBgYG+NznPscVV1zB7Nmzx5f/5V/+JccccwwtLS1s376d6667jm3btk2wKimuu+46rrnmmvHv3d3dIYQoFRG1i6CFqFD9wypQi5Cbc9DY1ACm6i6bjOZrfiLXKqLmmHMZFxz/3TFQ9jLcGLL7W4B/BUk/pYSqEhk4+xEfWTXc0AVz5iEjbBQ+2yCfj47Ac78X8bUapiJIEsMLgcPfCoc3yMJP/hZKjBaIXcAy+H9b4SvAHUBllVlXi5AhTYnVipAg1Uq5FqHJrlEwHxPE6q5OgrLzzLpFUFYPP+iCixDXmYdHoWIl8PqjcPgo4pPWpyLV0EGs1i6YlmICerG1IjOJYosky975pEyGvvzlL3PDDTck3Oapp54CoKSkZMK6sbGx0OVBRKNR/uIv/oLR0VG++93vxqy78sorxz+fdNJJLF68mNNOO41nnnmGlStXBpuiqqqKqqpgAgiFVvdVxf9Ua3jNNfsXch6iXBKiaijrDic803GXJYK2q4PYfGydsnaEWFRbJdm5CM+pQ4hQJ4YMNJqFKq7URI7HAbtgThlCSJpMA2civLldDDdBm+YBDDmqNfsBJe8h1ky/GI5okeP83V45Du3muKbfNJnjbDF90eSKyRAhRSJC1ABcak6gE1gJC7fB4k7pik895FGo+ATi3v7WJmOXOA75b+tYoSZQ9wEj4X+qUOeEXEM1vZlHykf5+Mc/Pmnk1sKFC/nv//5vDhyYGH7e0dFBc3Nzwv2j0SiXX345L7/8Mg899FCMVSgMK1eupKKigp07d4aSocQoB45F8uc2ML2CprMQp0U7MqsWItRClgNRdZiQWlGJTaKYLMJE1COBZVqE1C0NosUWe4ABeVsEfB5YdRIy8S8DXsTGtGvNszLEwNWJuKmOAy7Akgct91EHNMlD5zxihdrNOBZ3V0S02Cxbafp7AGFTA4jLrwVbO6wUeZrdaxpvIdYt5p5/EMFrHEaIMMftQSxgW+DpHVKewxjEPBnyKEi0AJcj7uvaBdjSPm7NPv1cGViXcxSbeLqBvCVDc+fOZe7cyavHrl69mq6uLv7whz9wxhlnAPDkk0/S1dXFmjVr4u6nRGjnzp08/PDDzJkzeQj7//zP/xCNRjnyyCOTP5FQpOOOrkZKordTuLXMchVlluB21Ak5FTIUFFErmXKzVSth0nWj2CrU/cAQfPFlOLoUYUTLsDN9N2IIXIRwYDeViEalnY59chwybe5jnEgMANcirq7nEL7z/4Djm802NchgrHogzait66oRwvPXZrlWntf0BA1m2SCWrE32FKvERwWiekxNSQDC9TchhHCHtPkwcFeCZj08CgGNwE3zgIvNggrCh3JXazcZshZRVkxECLLJMjMmoD7hhBO46KKLuPLKK3niiSd44oknuPLKK7nkkktiIsmWLl3KPffcA8Dw8DDvete72LJlCz/5yU8YGRmhvb2d9vZ2hoZET/LHP/6RG2+8kS1btrB7927Wr1/Pu9/9bk455RTe8IY3TLPX6Qrh01pmhSyszkUeopCcQ4HVCbNTJzJFxHOzjSLEZbd5HzLL+hDy8CocXQW8HRkltbp9FTAb8Z1pUqFq86rB6oZqsOUu1Bs7yjihOqIePvJGeGyWBHu9BXjTCYhLS8XdxooUk1F71DnWOQj/nuMcR11l6i5TkjQVF5lec1dQrZav7dDXBe2dEvHWZDY5CxvU5uFRSOgHHlJT7ZnY4INkoWOIi6zU8VHJR7HA1eNm52gZw09+8hM++clPcsEFFwBw6aWX8u1vfztmmx07dtDVJbT71Vdf5b777gPg5JNPjtnu4Ycf5k1vehOVlZX853/+J9/85jfp7e2ltbWVt771rXzpS1+irGyqLPI44H+wyZ3SYQ3RH3IWhes2q0ZIXRZrmZW4RcRCMFX9kGv5cZOjDSGn1oXobhaabXchs3ojUuW01XxuQojGUoQQNCADpVpdtI/znW3dMNxa05ZmgV7CeBr/+zaZkhaLzPI2xOpzcqCNCBJLX2r6usa0qeJpN6pFq9BDuFXIvR7xrqlb203zAOwxnxdDbTfUzob/iMATeyT30enA1/CWIo/CwyXAuRVM1OCNYP8DeYtiI0PZexjP6JEaGxu54447Em4zNjY2/nnhwoUx38PQ2trKxo0b09I/C72BUk2+mGzbRyEzWCFGm2W7uGucqDLFVMiQqzVyCVEU6346yhx2t1mvevpGrOtsHlYoPUJsaL6SDiUctU4/9XgaoltvjrsAeQztkbZm1yA6oA6EUZyDWHtqnL4PYUsAVCBuOyVdavmB2IzRbt+CcLcLuhFduISoGjH9HIeQoueRzI/LoHUPrDpK+rRkq81g5eFRCKjApAu7GKkxo/+JIexDhNYi04hU132cUxSqFyIeGigaMlSYiBd1Nh0UupUom4So3Iqa4yFMTB1PXK0DGVhtUJRYU7a6yjrhj7fBsech1pl52BpEXVhiUon8T3sQ4qLExCUWQS2S9kUHUJBLqeb4iHlVmbaVVFVhrTllWDfZiOmf8nhX5Ow+HKaS+EfDhPX6BKF90FxD6sI7ZPq8HOYvNsvvkVPzJTo8CglzgCuqEAtxH6KHGzHf95kNVhAbJa9aPiVGEFKfLB7SOQUXm3g6E3NxfBTTlZsGVOPzKjLp78nQcQrZSpQtQpTELZmsmFqJj1pmOrBCZrAkKIKQnSgcezlisdEkagfMPk8hE38zVqtTatarRSboggpGYYX1tx6pq9GPWHlALD1HYXMs6aCqJGoJMkjvxLrGTne2h4nZpVNBoj7rdddtVM/UZJYvRq7nCHwYkVt8icnLmnh45APmAdTBK7fIKHf8LGxur73IuNCH3NhakwxsUIXO36HV68OQvdDxwkP29ELgfwWDcuTOfhXrKotk8FiFaiVS0hgho86PRCH2iqB1KGzwiSJkR11ie7E8ThOmqXVHExQ2mu3nYF1Djdhy9CpQVleYuqiUeAQJhEacqOjZ1S6VYclOHfLEOWKOdRpWNK0ES5886xDCpkRPE1K6Yb5TJUIKV2gdJER6LiriPh15ch7A6qzeDifugmUPCmf72jS64uGRDZQB7wE+e1ASofYDJ/XChx6Vv+bx73A27Ef+b93mXXP4ug9AWUWxJVtsINv0xJOhCciWIl+tRL0IsSgkUtSAzUWUCUzBOhScsDX0fRdiOVFtThQhGaUI9wU5nW5kMtdcPXVmXYWzzVFY8qIEp55Y0qH9csPSy7AkKIr83Cre7kAGUo360u/qenN1SIpFZpt+hGksQgiIkrd0DcbuMUcCy90EjBXm+P3INTxk+lUml3yh6eKuNHXLwyMTqAFuRW5dxSZgM/BPwPH7kNTwzdhAhueQsaHG2akO6ybLWLx2GIppOs++xayYrt404RKgetIroo4HtULNovBcZ5nMVp2EbggsSQhup9aSMoRgPIOQkFKERDQ56zT/Tidy+euxlh9t9xAym2tBRpd0BC0wSngg1qKD6dNuxELluta0T41OX8qIFUS7qHbWtTh9TicRUui5xRNVu5Y5PfddUuSyDfgs8BLifvBkyCOf0UO4xm0E+P+AK/4AtUuRKvXHIWNCFzKEdyIWXc1F5uYEywqKTS9Un/UjFtPVmya0ev0wU69TNlW4rrNeCocUzUJm4XbS298kb0sN83ahRMglJAsQbc88bBboMqz1R7M2NznLdF8lGaozUgIUj3goQVPXlRv9FjX90DB4iBUi15j3FqxQO2wwVXJVhuiHgiU2kkmsqO0Eo+zCoMLtoKhaz7MfuQXaEMvWAYmM+0C/yKFAOKCHR6GiHfgF8I4fw+wzkAejg4hBP2I2mI/8xweIzWSfFRTTVJ4bl18xXcE0ohqZNTPlBkp03GoKixSpjijNwupkdEMQ6ypTwbQSkSgyObcRG3quL7AWmKA7ynVxLTBt6OCWjAVGEyyqlmfA9EUzQ2tIvpusMZHFyYUu10izZIhPGNwMukHXXpAYxUtpoBm1tyJk6QAQgd39VmalaMJ6CT088gktyLPSM3HWlwGfAr4OPPIHmNOG3PtvRm7qVmzhQk3LkbVw+2LUC2U/X5InQ+NwI8pAZqxskyGFzo4N2FCnLCU9nBIyIaxO8tZ0yYsKpt0s0v3ICLccsbwGRckQTm508i/DZp9tibNtPLhEbTaW4PSYfigRUqtOMkTIDW9PN5Qc6VNtkBQFjzmCzTEUNZ/L4E974O+ZqIJ7F3J33Jn2jnt4TA8XI6NtPDJ0EpJsvhOYMw+JmnR9apqItB9xg69A/ttuuH1GUUxTee3km2QAxXQFp4lyZLZUMpTJEPtkoe6zBgpDaN2A9DnC9C1a1fGr2LtQ4hBFTNUd5nM1cqn6kAzSDdgHDndwSpSHx7UULWFyK0zQsqLupQGzTwtiLlGLkXscdYmFEaFEBKgs8K7buyQmXlh/Irg5h9Tl6BJPjahpRhTSTyI/e1RO5e+BLwC/AX6EZPX9ArABcZ11pNgdD49Mogm5P+8h3KX7fuAjq5F7fD6SdLQC+T/PMw2oIbuPpIza6UOx6YWyG1KvKKYrmGZkOsQ+FbhC6wHEYjVIfrrQVEeUBrfZZKU5XChZaEJ+sjbEoKbWF9XmlAa2DyMeYMPU3e3dd5gYyeZaVsCSCK1ivwsbdbXL9EfFlkE33WQWoDJnfTJPnm6+pVSIkV4jtbbpcTVVwVbT3iLzPQplp0OL0V59/Ck47yAsnQWsgtEHrd40L5L2esx4NCEVbcreCXffDZ8AtmG90B8GPtKMWIM0crIJGzk5glie6xBeMkiWw+uLaRpvIFfnU0xXMQ04CqH6Wl043+q8qG94FtJHVfDlmwvN1RH1TqOdSUpzuKjBRofVIm6pPsQE0WW645IGN0liIouPEo54g5ubOygsQaHupxqhCNal1IeNLHPdZZORoET9iQd3e7X2JMtGVEg+gJyvuv+aED1VDzZhZB9yvRuR3+ExWDoPsSCtgLd0Q90f4ANkJ17Tw2MyvAs4fx7QCktPgQ3b4aEonFsF/zwIHwEp1LwAG2ihDwinIw82HdiErHMQYlSK/GdqgPJSMjPdFpteKHdJKD0ZikHQPNdM/g7ZSjiGyV9r0VwsA5hKv5J0lTmbj2PEeTUGtlNtTrxoLXebyawupdiss4OEk6Ia5Kfqw1qG6hAi1IENq1crVhiUsKUjQ72SqXj9jYdqpO8qOm/BCkajyHPEYuBe5CdfC3wIIUV1ZlmtnHKE5LIneHhkEmowfr0NDm9C7tk5cO4fgVVwzTPITboXIT9rmJhTaAD7POo+YEEWLETZLWaaeTTn7MjFdBXTBDekXq1D+WZ5cRG0Fqm2KF+I0TTdZslGlSmUTKguR5OHuJmjq0hMOqZiecG0q0UcgyTD7ZdaZcoQ95KuD+tTOklQWH/dDNbJQP8Oep00+3YUUaE+SWzSyMVmP0OW/vSw/BzvQjjgLSkc2sMj3SgDPgkcfilyL1chGsPzsIWVdyJS0jOxgQ+jyLDUj5D/OiQ7yyix6TnG3eqZmmqLSS8UbxDMDrKaHzP/UY3YQt3vDbnpypSg2qKFiMpvDvnh6lMr1lxS/+NWp15WQi0+XUjGv88jIhUlRZVx9kuU6DBZVCADalCPpFoiJQ6dyDjWioiza5gI7U8m6xWWYnMaJXOdXU2UWs70mtUizKbZfG/HktAqoEN41FnA96vgHeTyOdBjpkP/jveDqPoHkHtYaxNWI+FlrUgI/UnYFB3qlj8K+FtsIPJOZJyZMLNmypVVLEQIcqkXIqdHzlsEL0k+u8oSwbUWDSAWo1zrizSZ5UGSt1qVpyakBhumXo88zWkOEC2dEfYIUEl6SUeYK0q1NnXmNQ+rrXExHevUVJGKlchNGaA/jXLuNWbdHmz0nmbifQ8cvgQO3wLUQc8muAL4N/IjTMFjZuEtwP9DOA3ziNXugQyXK5Ew+SasFVRRgYwtldigjRqz33TyfyWNYtML5fbRyJOhCXBF1FAYrrJECLrRck2MVGySittsFpR1hk/SYUJgDStXf/4QMpCFWYQyTTyUZGg/NfdIE3aADYq4sxqJ4sAVSk9GiNyUAeoWrEbI3SAiqI4i116j6qqRqJuFQATeNABv2gaPRWFLEof08EgXahFN9PxTEAO66wJTq64RVcdkgw9Gk1ZiLaEDZnvNP6T/44yJp2sz1G4ukDvhtKJYrmQaERRRq6usEK1DQeQLMVK3meqbJrMSJWEdchMFDjibunlygk9p2bLAuCJrrfReR6zIMl8c1skSIjflgFvWoxJLRkec9frbDJn3ncgkchI8tAu+3iWV7fM5i5ZH8eBMYG2F82UB9kHEDZ5wSVDQ9a2JXmsRtzfIPT3f2TejyAcJRLrQQK7Px5OhUATrkhWqqywRwojRMNkVX6ciro5jHQp+V4HyKKIZ6kEGqw4mChuDCRh1eSahg6r+7/PRHJIKIdIMu25oWKmzTIlRBNFf7EB+h8fhfw+KVOPnWL21h0emUY0kVjwxCt/aCudWIOmlGxBdkN6/+h9NVIcQbJkeJVI1BMaWRK6sqbq5VB9aLMi9etCToQlQEfWOwLIGilfZ4PqeG7CxohEyT4xcK1Ev8UlROZTMBrpjF0cDn/uRp7QhJCFaE3JKhxBC1EAsEdKnt4rAe7YQTXFdMNFjppAMIVJXWdA6BDYxo+qlokily374U7/won8ANmak8x4e8RFFUgMBrAfOLUNST1+KzaOl44JLhMLqF7r/kxVY7ZHuXwaJp9mpTsHFNHU3kGurEBTXFU0jgpdFS3VEst+VnECVhA1kjxiplSqR66waygdgOI67TAlPPzJINWArwGuEfgVWA5BtkXIYEh0/0TolSm79sHQTJVe/FNZmaZzPCiVCg8gt1AS0yaX/DPBcnMMWskLPI7/RgNx730CGBoA/boZjm5FosA5kqHddY667zIX+P6qRwIEK57tuW+L1QpMjP9ID5L4HeYmFwP8Els1FynPnQ+6ebCLbxGgy11kDlB+EYcMANLIpitWrzMPmDNEHDs2uVo0VLRcy4lmy1ELjkqXpkKNEZMiFPjG7xMxUsB8Xshvx9ND9icnOWYj7zMMjnagGvgh85Dj4+EtQWYo4AToQd7qW1tB7WUlNopxkLmEKTc2RCYtHMUWRaf3N3MOToVA0MFE3pIQgV5Xs8wHZIkaTCaznWkLk+u0VLvEBK3zUZYVOhBIhWB/NJUdTJUaaZC7MIBdWFFYT0u1F/GFLkIidRll2WBU0DMY/3CXIHfaLKXTVwyMeViC5rVgMlcuxBOZJ5B5dhjwoueVwUolADS4rg8y5f3LvVkoPGsiXc/FkKBTKvCOB5XOY2WTIRTaIkVqJIliBt8IQIkZlwOpADHc12PIPwaKs2fzPuULskknY15hhE5nQAYUVj50KMapKcp8otv4ayDXfCpwGbIaxbfAd4keNLQLeC1xZA3uNtiiRrMrDIxlUIEPCEc2IVncl8h99Hsk2XY24cTVMPqwIcqJs8JVMHF8y5iLLD7dSejAn1x0YR7Fc0TSjHImPjASW6+TsFQ2xyCQxUjOqFqZ1r/1cKO+Fmn45/GKse0yhg1SmrUFlOINfMGfGJH+zkmFnMyeyb2w0/QTJfXpNlRhNJqgeQUjpJsQKpOkD+hivwVZSAT+NSmT9uchDueaoq0eS+R52DNAF7+mXO+rBJLvn4RGGMmyR+YcOwLnbEI1PrXkvRZKEutnUYaKVJ57GUCPIJmCypy9NQpsKislFVk0+nYsnQ3HRELKsmHIOZQpBYhQhPXmM4rnOZkF5NSzslMOoy2ay6u/TxTj5UeIznYKJQeJkBtGSYSg35GhsKHPEyM08PVkofZAQaUSZ22YpwnZAJokO83oK9kWF8HQh9cmeQrjTNuDLwIlzgZOBJrjyOejcDA9N0i0Pj3ioAC4EbgCWVsHrKubXsjnKV47DltoIyxwdbyxR93vYcu8imwQN5NO5eDIUF0cxUTcEYkdNpZzETEY1QmDU2hFBGMt0rl1Y1Fk5lBwBs0xo/vBogv2niLSSn2Shx6g2xMhY3dJ9flqfTHVBYVm93W0rQtarVqgaEWd0ItkpnsXWeKqG+YthvpatPxPe1Alv2g3PHYQTlyDmoSVADwxtlqYvRCxIHdM/U48ZhmWIfG0xwJ/D4XuR+0uJj0aJqc4wTCsUzzUWL1cZeBfZpCgnH3ILuSiGq5oh6EQUnLhn4YXUqcJN8KhutENMz1oUpicyx1DSMFVriup9SiqJJT65/Ls417B8AOjNDCnSCSKRpcgVVKsmyXW3VZr3PsQ/sQNbx2mZaXsUGQt3AB2wvMy0twpxsVVD5Rnw2S6gHX7VJRXun0zvGXsUMaqR2mNzgLJjkEzTZ2IJvVv+RgsVB5OuxrP8wCSpOTLl/skfS8r00EA+ucjAk6EEqGZi8kWFF1JPHepGc607U9UWxdMTmWOMu5n0BeNaHEXME1yQ9OTr38OcX7nJGJ5JS1Ef4YTIFVRr9ukOxD3WiSSxW4FI73oQsrTHbHe+aT+KfSqvQ9TTFdg6cm+R0+Me2NUlT/huomsPj3ioBd6HjNRLL8fmG9Php4xY8pNIUxi2LhFJmjTRoiJVMlBMeqH8EU4r8nW0zxPEu9u9kHr60HTyDVgXWhdTJ0XqjnNJURxrTol7jEL+CxgymElSpDmCwlxn+hfQ2mMdSGrfWiSzXZn5XiPdYwlCiHYhXuiFCDHajdwG87BWJ8x2A9LuPUiSxpWmKc0g7OERRBnwMeBLRyGEegFyT4ZhssCKeOV5EhloknaRpTr2FIuLLL+E04piuLIZxFIk+WJwgq5GbPx7st6j4oRqi+Yi1qKputCUFCm5itdGsd32Sop6Yaw//aYTd+B323b1Q2rZaUGYSh1CcOqR0PoG084WRC39Xmw28JXEuhuiyE+3U7b9U79421YC5yDVxi9j5uSD90gNnwG+dBbikq1DTIpgs6C77jGtrRcP8UTTCQMzMjHRF5NVqJl8dPdlNOD49ddfZ926ddTX11NfX8+6deuIRCIJ9/nABz5ASUlJzGvVqlUx2wwODvKJT3yCuXPnUltby6WXXsqrr76agTNIpBOZm2Cdx9Sg1qIFiH9lqn8YJVct02ijEDELShqhPAN/62psQjoXbnHKFkQXtMK81yHW8BUIUWpGmMxJSIk5zf3kJslUHcZe096IGJxuBh4B/n6W7P5hxNDk4aGoAL4CfKkG+jbB7luh75+Ae5FqwHciVsiNwAtY9+0osYlDkzlQPCTtIgum35gMTpRpQSN/Mk4HkVEydMUVV/Dss8/yu9/9jt/97nc8++yzrFu3btL9LrroIvbv3z/+Wr9+fcz6q6++mnvuuYef/exnbNq0id7eXi655BJGRjLxSLwgwbr8/FELH+kmRXOn0UahQa1ENfFN/FNFKeGEqApLiKqwxXFrzbJWxAU2gGSlqDbbdBFb0kCJlYqtK4AoHF0KR1dA2VnA++GwFZKlOoqJEvLwAM5GFJ6r+uFo4FTgeOBD2+Hb2xAL5s+Bx4A7kJwO3diYjkRRlC7CslIrMuYiK5ZaZPk7Fmfs6r7wwgv87ne/44knnuDMM88E4F//9V9ZvXo1O3bsYMmS+M91VVVVtLS0hK7r6uritttu49///d8577zzALjjjjtobW3lwQcf5MILL0zzmST64XyYfWahpEjF1lN1n7mRbJEptlFomAUl1VAeSa+WSHVE/cSW51BBdTU2maPmHIoiY3mt2W+RWbYP4bsa2QOxk1ED8HEkPL8Oa+zrEx71FcSRvQ5JJOwxs7GRiVxmADEI3Qds3C6F6dddgFgs5yH3YydSR08jzWqxFemDCMtBFAPvIouP/Aund5Exy9DmzZupr68fJ0IAq1ator6+nscffzzhvo888ghHHHEExx9/PFdeeSWvvfba+Lqnn36aaDTKBRdcML5s3rx5nHTSSXHbHRwcpLu7O+aVPJYSnzNqmL1HZuHdZ1ODYyVKN2qY+ISsUTqqBQrWhGtEJpxLkPTTZxOb38Wt+r0AKZNwGqL9mI+422qAJmnmsgV2s2tJvyHMo7CQyKjTBzyAECM2mY33IunNnweeQKyWbcQmFQ3Wgkl0kyXtIoPUyE2xuMgayGdSlzHLUHt7O0ccccSE5UcccQTt7e1x97v44ot597vfzYIFC3j55Ze5/vrrOffcc3n66aepqqqivb2dyspKDj/88Jj9mpub47Z70003ccMNN0zxTOLlG1L4MPvsIWgpOsDUrHJKimaKpWiWXLp0i6uVY6mFKJihWsmNhtC3Iu6xMEOV24a7vgJRTWvU2giwFmrPktWHbYSbnoene2UuW4886HsUP8oQ/djzJFe/bgR4HFjfD40/h1VzkXtyIULsnwGWI0PDqLNTskgp0WIqU2+xRJHlXzi9i5QtQ1/+8pcnCJyDry1btgBQUlIyYf+xsbHQ5Yq1a9fy1re+lZNOOom3ve1t3H///fzv//4vv/3tbxP2K1G71113HV1dXeOvvXv3pnDGiXRDYMPDPbIH11I0h6kPFK6lKH+fWNKDDImr1RqkcIvCKtTaoyJstRqVOi+3nWAiPN1PLU1ViKWoAUlP/X449Tz4fjP8FJnfPIof52C9sskiCnwEcZc9dBBxk1UjYuo6s1EEK6zWzOpJIVnrTarC6WIYmxrI93ky5Vnk4x//OH/xF3+RcJuFCxfy3//93xw4cGDCuo6ODpqbk/cbHnnkkSxYsICdO6XYUUtLC0NDQ7z++usx1qHXXnuNNWvWhLZRVVVFVVVYPvVkMZfw5Itg/aCRabTvMTUomWlArHNd02jHTQTZm47O5SEMiUynjiisXpmbodqFkpkBbPbEaGA//VzqvAfX6yR1wBzjAKI/GhH32VeAWxErgEdx4h3AF4E3T2HffuCTwLnNCKOqR+7JA8j92I7cSFq7bIA4hViDSHY6TSWSTOuGFDqayXfrVsq9mzt3LnPnTh5FtXr1arq6uvjDH/7AGWecAcCTTz5JV1dXXNIShkOHDrF3716OPPJIAE499VQqKirYsGEDl19+OQD79+9n+/btfO1rX0v1dJJEvDpligbzimTo+B6JUY38RrOw9c+m2k418lsqKSo2cXwGCJGSFNeloM8eQULkkieNJIs621US6zIrwyZ1BPGB7TXvEei7T2T1DwAPIyJaTWLt06IWHyoQfdgXlsBdO8Sgkyr01uM0ZI5uMK8DiKi6DmsZAkvAExlVU9ILpTLteqtQtpAxAfUJJ5zARRddxJVXXskTTzzBE088wZVXXskll1wSE0m2dOlS7rnnHgB6e3u59tpr2bx5M7t37+aRRx7hbW97G3PnzuUd73gHAPX19Xzwgx/kM5/5DP/5n//J1q1bee9738vy5cvHo8vSD50g4yG/VfIzBw0IKZrP9J5C1A13FPkcCjp1qLA6UYxwiqhmoqC6inDBqRIihbrEdJ3CnYzUihRBTD6/Ae6HF4EPAP8XSScTQfIS7cYToWJDLfAh4AsnAa2iFZqqBO5FkASgHci92IqQo0XmcwP2ngN7M8Vzm6WkF0qW4BSLiyz/rUKQ4R7+5Cc/4ZOf/OR45Nell17Kt7/97ZhtduzYQVeXuDfKysp47rnn+PGPf0wkEuHII4/kzW9+M3fddRd1dXXj+3zjG9+gvLycyy+/nD/96U/82Z/9GbfffjtlZZmKJylncqF0A946lA9QIlPN9FxnimIOy2+QrNXD/ZNumRSqmWghCrrQFKoV0olFdUZDxFqHRhArkE5MzwDbgRr4QRR+aBYlQgUyx+2huH69mYRq4FuI/p6VQNvUn+SrkQTorMCK+11dm+rZwAYCpKQdmuzoyU67xSCcbqAQrEIAJWNjY2O57kS20d3dTX19PV1dXcyePTvJvdqBxCJumXzjaYs8so9hphd1lqjNYnKhpZEQaQI7F6OEE6LRkG01+Z2204P4wMoQRqPhz5vgo22SOy8Z68BCJGv1jSGH9MhflAGXI9Kec4H5lyOpx3fAXT+HK0ndOtSKVIg57HIkbUMD4hprNO9KjFS87wr83WKvLspnhywMwyySS9ZbjgSIFLpVegnpTk7c3f0n6us/keL8PTkymoG6uKDFWROhgUJhwTMD6Yo6C2uzmFxos9KXiyjMXRYWYabLgyOQJrXTPDBtiH9kH+L72g48BM+18f+3d+ZhUpV3vv/0XvZClU03NI2ERUijARFwAUbjDpi4xGSMRkNwYkjivcZLRpNokmfEzESjk5iZZ9QYM4Z4Ex2duCRxXCaYIOoVF0Y0KEIIym4jgt20kKbb7nP/+J1fn7dOn6quqq693s/z1NNdp87ynlOn6v3Wb+VREp8ItyAi6F7EsGDJfxqR2+l4YNE0GHs2kvruBoR9Fjg7hf2ehOv9Wof42t4CXiY6i0wD+7uBQ0jUtVqHhmUhStTtVQyB0xEKaT60Yihh6pFYlHjY2KH8RLPOxiOm53RRT/Gk5qdREAUJH7PKtIl/mfmNtBuZsLYjE9JT0PMULH4PziV5B+g6YOF4eBKxNDQnub0le7QiZRL+BfcbtQpRMWHk49wKZWHRRg0x9hGLB4HJwNffRGLPXkbutR14nnC1Su5DAox0HbVwpiSI4vW69FPo3ydQKLFCihVDSZHIDRqhkNRwaaGiaLgB1kH7baLwrUVpEkRmQLSJvyYRAc/N5UchbgyAzUArVE+Gz7mLk3WPbAXogLrz4H5kP/OQ3+CW/OHvgIeBk9vgkib4VDkiRNQ9VYNYbhrguzPgBhKrPh5B7pteJN/0QaDnIOLJacMrCaFiaB8SkKbZi3vwxJA/SzKtzVmLIXA6QqHNg1YMJcXkBNax1qH8xnSdpdNKpPsudGtRmgRR0DeLpsonur32iKoFRrjPT4SFTRL/k6zkjABOJ9AKIz4JN9XAijDMRN6teM3ILZmlDrEGVSFZgZtBPqKfB65DCgOp3yyEfBVfJBvpajNi7LsKEb0PIa3uTLaACK06xMSkrrEu98X1iId9HKKg9jHYVVaZaCZZot8HxRA4XVhWISi00eYcDX4bqv1GBJtZlu+olQiGn3EWa/+FWrPIbd8xnKDqaoK7gGtD11hmHXOi0QyyCmTGm+QunwKd7yVXeRhkLitrdPd3LNAM7/9CWqTdgARjP4HEaFuyRyOSMv95YCGiN0Iglpgq5L3XCuTgxfVEgL3i+lzYC84u+HfgFkS7zHL3NQu48gjgeJizCrbsk+UnYRgwdyI3yDj3OPuQeLVWRIy3El1hJZHaQ1Ek6iLTH2uFTIRCPAcrhpIikRR7Xc9Wpc5/KvEsOOnMOPMfI+I+uimcCtfDFERqBQoSPVUxlhOwvB+Y4i4PIRUWu+ALwArgkQSHU4cYGOhCJtNaIOItr5gMc/bBhfukv5kl81QhGWIR4Ho3JPO+jSJGQyDuqdeQVECzZlU58tW6Afn4HgRmQVkXXPSIiKEJwA+nIffNViS4aBLQA9d3IkJLy931IUp4AV422S5EpbUiX/n+DLKkCxwl6iIrhsDpwrMKQSGOOOdMJrH0+QiJWZEsuSXddYniYVqLVBjlc6L3MAVRFQGxFXiVp/0TilqSehARVIFMWBrDcRCZK46GsnFwxxPySVw/xDAqkGDpAyAt7psZKLRX/Q13pU7gHm+4s5D5Uws5WtJHBNG3JwFXAC+CvEkL4Pg2YBW0d0LPe1Ct3Xaq8awwVYhQmYf35jfLshGT4Y6/yHtHI2IB3IBYfLTa9AZ3H22I+3ULcBZyz21C7o0GRAg1M7hNTJ/7t4ckdEskwfXSm4aefZooRKsQWDGUAk0kJnIqkUDdDgrHPVLKqNssk1YiRWOLtJhjPluLhiGIYomeINeCKYK095hOOjoZaRxHFfBHKUR9NDJ/xXKZVQGfAO4ERoTxSlS3uvuegJgi/kPGejRiQLjCfWkzcmccRGJp81m6FgJnIm2/rgGqjwNmwKffQtTnaOBZIAQtEUQxaXsMfzZiBC/OZxZer7rpcNY+9yBtyEf6CLyUs053P+OQm0ZrC/W4z7e6f+uI7p+nQt104w7EDQ2liOpJb4B1vqJzXmGeQ2GOOqck6ioD+RBEElzXkntMK1E7qfc4S4ZCsBbVQ+WH8GGQmScOibrKNGhViy0GdQvvReI6tgIHYPMhKbi3z93VYkQcbXdXrUKu6DhgGRIwzUZ3/5Pwiun1Iy6RkcAecZmNmIwooe1w80Y4thze7ZeCjWuQsjTZuDOKhUZkilyH1z/uReCONTB2NJ57qhxRSyMR99hkROjoe6U1qEDeN60U3Yuo1SZEze5E3ny9xxrxgvHDiDiCwYJnCyK4p7l/70GUdBixII12lzfj9d4Dhp5GEw2cTlcttFzRRGEmjQiFfOVzSKKuMpDov3yc4CyxCSHfmO1k1m1mEmQt6iZ/rIoRt7lrCoIoCLUaaSpzF6IyKpDJx98kE8RVUivrmosrkDT5f0Hms/90n68Dbgc+Wo7Mxl2I/0SzklQMHYe81TthRASZmJuBuXCs2/pj1Co4ZQc8h6fhmhEjkyWYCiRN/mxEuIJn8HsKEa8X9iLv+zTkuh/vrtiAVxlarUIVxk4gWhhhbHesu09Nya9D3vNqd5/6vps973rd17UStZYl60LKVfcg904EuV8bCC4fMYigctVBFHo6fYih6/DlN1YMpYRWo05E4ISQnxRbMzoiS7rJRnB1LMwvUHWh5YOYjiQviKoJHropkvYgk9c+ZMLbhZheWvCEkVqNxsnzIxGDwhZk/jwRqD4PProPvrsOmA5HboFPdSCqpQ/5la9WAs1MqnCfT0esELuR3zrV7hiagcfhNzvgq75TOdp9rHKHnw/vUK7RjhXzkEv3r/Xw9Afy1vq5CTjl9zBqIuIO0/oG2nol5D731zxQK1EPnrVoC55ZsAN5DxvwRI9WRjfvOxVB/UhgfoexXqN73E7EShhyj7fDPWYvCcYLRRJZicJPpx9NoQd+F/LVzyFajXpzgus34ZUvtRQOuXCb+ck3a1EkOUFUTnTj1SBGIrPlW3iT1x5EFB2H58qY5K4DVNTDjR9AS6372gJk0qpDJsKIu/4r7ut1yK98neTMGJSQe5xevMw1rcy3Cv66Q1Lu/Z/e7UhLkL1ISvdPCJ70i50q5PK2IbFWPwIeLkcE5njY9btgb+liYNQ0RITsQpRlBV5vsCAhpJj3Vb+7jwrkHggDbyJf0aPd1+uInwZfhwiqXuReUMHcgFcsVAWQ+mFxl8ecRpNJpy/kwGktOlvYWDGUMkeQuBgKITZX28S1MMmF2yxoDPliLYqk5jKLRS1iRhiJF8z6CjKx7cazEnXj9SubDi0aarUeiRnZi1Te03TqLe42jUgGUbNxLH9ArlobdNIDEU87ZdcnITHW5oQ+DqiohVFnwzUPeaFHr7p//5vizUSrQrTmLuSS3wwc2wqEYdabyHU8CeiCS8rht/1yPbRsVBvwf2oRAVSFCNg+Y+dmPzt1R/ndYrp+NyJ8ut3BVOC5vEYz2CLUj9xjVca+NPOsD8/apB+5Kt/2WhC0HCiLV3CxKc5rJnUJrpevFEND2cJ+B3JMC4m7ysCm2hc6uXSb+VFr0YfkrqBjvSuIEmjSFCvFXlER0oQIlUNIAZqdiIg56O7jkLv+AeTX/3R3vyqWpiATYDeiQhqQeSaCuMBUBOkvfY0d8aOTbAQ4Hkbug0WbYfUH8ACet26BrjMJqj8On38G5rjBt9/d6n0zfMId5hp5KfkSNXmC1r48Gjn3J5Cfd88hMUDH1gCnw5ETEBGilrxTYPFK+DoSx/UU4lrsOQjVVe4OqxEBq94WvSeCetrpc7UcTULekD48ARU29qGYGYpqVdJU+Qr32JuQ3zxaDruGaDFVh9e5Pi6JxgpBYVtVCjeV3o8VQymTrKvMptoXPqbbbCu5fx/9BR07yJ61yD12IoIoVoq9iWboHEJOYTIyWWka0hq8lggzENOLukD0Y3WEsT+3jhDNeMGwYwn+pd/v+78fEWAhxLqxC9gPfR/IvPt3wN+6Lw9YCU6HObPc8/yVHELfiZnAt6vg567FaTkiIo4A/oKXEJVvtLh/GxGD3JcQN9gfgUVt8NrGaOMKu5BbcQEiGEAuUhd8ogk4FuZshzkbpep3dVheowu5IH2Ida8ZETOxmvuC9/6ZFiQIFreKZpepZagC7347gNf/TF2ppp6pxrtvoixNsYKeI3EGYqKdZwuRwk6l91McZ5EzknGVgSegbDB1YaNuz0wXaUwGrZOUzdiiBAVRvBR7c51qvJihBsRi0OW+thX5xR5BRI5ZFVhbNujkGQLmIunQO5H4o7HIBB0Ug2JOqL3u+uvxrEtVsvwkxGB10UR3HK8BU/GK9NUit0NIRMNziEHqeGQ8X+wFZzXchei43yCp5g8g2isMrHb/7krgkg0XM2RKjWQnyamyAfg1cin+C6nKPR4YexIsmiwvXL1RksB+B1wTRkRQj7FTEGExCRE4DcBkmN4A0/cg72cVcq1VvI5A7gO/Vcd0YybSAsN0pflTD8397kdEUDu88I8w5zjEhGUK5gq8VHq9v/T/wCm0ntKwCo2lsDPgorFiaFgk6yrTbfaSv0X2LImh4qMSeT/zBVUIH5KdukVJCKKh0HXM4na7kBm5F+/H9ib3NY0BUhGkf2uNdWuQSdZM0faPRQUYyAT9Mp4aWe9uPwku2gHVE5G3fRaSwjbBHQfuNq/IsUbNgBtfk9UqTkIUUTuU9cJVa2SXHzkOFndD4+ty+LOA2xBv31uIcex37u61ebrWDtSuIgeMZbvcv+Pc01C92OfuI+KeunqlNFt8PfAPwPeBG+rh1Q/keMc2yTme9RyM7xTLED0MWNs+UgdLfgvTD7mDWO8+FiBqr8o9mQnuQLvwqojvQSx+b7kDneGuGzHeKwiwxCRIkOVILUL9xrIRiNBukGFRi1xo7YPnFz9mIHZgvJBaaxOhkK1C2pC6eLBiaFgk6yoDueQaTJ1rN4tleGgcUYjcxxH5yWaV6wQEkb9xq381TXHuxas5pBWBw3ilpluRlK1NeEpAA2xVEOnkqcLIfO5HY0j09VbE/KNtGZ5DJsc+qJ6GfHQj7rpT3LHp8bXCdZ+Mvw2oaHXPZ7S7nxBcUgUHehlw8534umxWdxJc8RyMmOburwM+tQs4xd1vlxzX2SjB3I/jxSGZdOFpxTPdS3dJOTIgjaMKIZatI2DvS1A9E254DbgIjl0HLS+5Gx4r1/ui1VCm17LFPccngAlw4kb3oLMQS1AznmhQ4dGHCKHn3WNrGrxef9z3ynSPaRBzLCqMv7HEkr+aeYjoAGqNPWuH6f+IvP+jffswLURRxwmyikRIfFotVKuQzmHFJR+K62xywlSSE0PgxXnYYOriIIJ8lHaSX4JIUWtRBC+2KN3jdNODY2WZBbnKzGwgrSO0BylPfMBdfxoyuXbhWXy0hYJWO+jA6y6ugqicaGtRrPgTTaM3J8txeL6qdne7fe6yjXiiqQrPAgJiAfk8olL2QMNBdz0N7D7N22ddr3te02FUFYxqkeOOOMkYZw9eTMse4Lfw835JXd8ScCpKh/t3nfuoAP69H77+Jpxbj4i9VkQcVcHI091rcDLi+e+ClrGIz8xNVx+rYucUPHFzklyPsj3utZmEFBhS0aTXF3f/zyMiN4ykr7vicCAET+O7qoxHEOb7majF6BCDRZGmyqvhtBbPzafX3rQKmcIs0EWmPz4SoZCtQsUTNG1ixdCwSaYAo4mtTF1c1JN/cUR+/NaiDtJ//0ViC6KgrDLXijIQRbwdmeknIJOrCqBuvKDmRmSi6kICWkAimjXwNVEhBNHp9GotqMGLAzofcZup66YZETdd7vhMt4maY0YAx0NZByKsZuK1fugHrnSP1eAuOxvPMlaDVwywB/HAroT7dkkdo1dinEY8+hB9eTkw+QP4p9/B6Uch4sd0NaoFTQWPipGZ7rm2ET1/RxD3VjPee6WZVmbGVwUiWvvcfbe5128f8pEZ4W6n9XuChJA/gyxZavCqnZsp/FqDSsXtbkR4drrbTDfWNYVXWS2Dp89IEgMqVKtQiEKvNB0LK4aGTSquMrC1h4qREF5KU74KIsUfcJ1OF1oEKj8Y3NxVJ1xTEHUjk8wO93kvIkC0hYaKJHU16eR9BPLjehMigt5yl48wjqV/Ywkh/9jAyzLqdce0B7GIHMRz+3QgIkCtGP5A3xPxCva1u+ehAbiaAafWKE3rrnCvhQqmDsSK9ARctw/uZvglPw8gnrH/BVz9JizpRqxE4/BEhsbomJYXN2YqKnanHxFJr7nnOQ2J/THFqFKFiIpJ7jkeQtyPWtsnjBuwg1fk0EStMsnGDfkpR+6Rg3iCSN23dXiu263u2MbjvTdm640KAgaZaE0hKGyrUHHUFArCiqG0MI3kxRDY2kPFyhHkRz2iRDBdaB2kLwstRrd7jeg144PUfbYPL2ZDJ0WN7+hGhE6N+1oDMql9HhEcHXgtEzStW7OSEsVvcdjr7nO6u//RSC78enfcEwK2qUK+DlTsNBqvafyKigwzn16Xd7rHeg1YBd/qhftIb+3z7Ujz2oq34YtPIe67kXgZeaZlTc/JfA6eYJqBV+AwnutKxdZBxPI3Dk8gaqS3prMrFaRuCYqHKYj0XHXMGssFnlD3Z5aVjSB66kzGPQaFaxUqTveYYsVQWoiQmqjRQDTrLis+Iu7fQhBE4LUE0Cy0DoY/blcQOQejm2u6qep0IIbRKrw4IC1RHMKzRKjAAG+yVLeSOVFqipXZW2qolGwzwNbfCb0Nz4VyhPt6NV4fLTPdWkWXTrBagMcMGjfXUQsQxut9iFJ5HNp3wK+QNh+Z+GboAK4F1myFO1YiX0PqKjKtMH4h5BdJZoC6/3XFzNZTl5xaoBqJdtOZ+4gXMxT0P0Rfy3hUEX1PViBWwC68qtgd7phMq1CZv+JiMtljULhWIfVkFK9kKN4zyypafCoVC491lxUvEeT93UnhiF0zrigdbT/qoawSKvZ7k49afJrxRI66id7Ca6ypk6FWkVbRooKnC3EldRiH68ATI4lYFTSTzJ9+fTTR8SVKyB13LJeNv2aRCisVBPqaf7+7jbGMg1d2wI/J7F1zAKkntHMrjO1GApyPxnObmenpKib91iG/CAqyxOm2+loEEV7HImIoZGxr1vTx7yPRwGl/R3oYfL1VrKrLttt9tOG18NBAej1WZTXRFiDNJk1mGo0ksW4+UbzuMcWKobQxGXiD1H5NR7DusmJFAw7zObA6FukSRSERRGbqfQjPPaLWk148d0vQ965OxhrTo2VO1rn7GodYGTR9PJUYEzP9Wi1YsTDLBPQRPOFirKPblPvW7Uf08npgDTx+MHt9zdYAnwJ+vhumv4W4j6rxYnhMq1osIVTN0KLT3M84vPfKjE+KJYLM4O5EMAWu6Wo1rXTgBfRrJqMKJC09YFqrKqsZLGSSiRMCUVaFWKSwuN1jihVDaSOCfDvvGGK9IKy7rLgppMDqINJRr8hXi8isIaO/zvsJbpNh/rpX60qVOxyNHxpPdO+xeMSKIzInSnNCNYv1Be1HJ2zNADNRd4xfTKgLTYVUPxIzFYHlByU7Pxto+Z8DIAJFSxTsl7EEVoNOVAiZViN/kLm53J+ppa+Z1sHhoPeaBu+bAldLhPXi1Y1SkabHL6tlsIhpInlLSSTJ9fOB4nePKcV/hlnlSFITQyA33ZHYYozFzBF4376FiAZbm9aiZNBaRB9IHBF48SJBvwF0glSLgSkc1JXWiJT6Gs3g2BZ8/5sTa7z4ITPGJahYX6xC2zqp+8/FbAqqVqE+RIGoVaIRyVp7MPs/h7Yg1af/9+9gYRhJf69DgqPb3JVM8RMvPqicoa04Q4mgRARtKuj7aL6XFUiWm8ZxqQCqAirLCS6i2ETyFp5CtApVInNScbvHFCuG0koL3kSRChFs77JiR03rhRJYHcRwRZEvjkjdJEGuDBMVE4q6NEbGWT8ZgvZvpsCbosiMRzHRNGzTQlTue133+xqeWeY12Pa6eMo2JTnsdPA8MsyFzXglrfcg1qK6GBv5rT2JuLLipcmn0xo0FKYwChpHoDUI5Ds6FVETSWGbXDOWwhx3algxlFbqkZ+ow6nZ0oJ8O9r4oeIlQn5XrE6U4YgiXxyROTmpADEtNDBYJA237sxQlOMV6/OLIg0IDhJFZs2ioH3qOs2I+tku+/oGUn6nK2CzbPBdkHIBRyBCaA9iptJilKZYNK1CpsszFkNliMUSJtmk0rwJg6bGCKmJg5EUnlWoiWLrPTYUmf46KUGmDnN7zUyzOrW40YrVsX52FxIhvC/PZL701W1WO7hIXwgvGFoDohN5aGxIUKxLqqgoMisr63J/Krp5DkHH9mc67WbAVfNAFZxDZjvVx6IbqWc00OF1DlI8shZPDJqY5xZPxKilTN2hQa/nSghVIAKoshYqG/HcX+kUQsmm3ucDpTkHZVQMvf/++yxatIhwOEw4HGbRokV0dHTE3aasrCzw8c///M8D65x66qmDXr/44oszeSpJ0MTwi2ppVWtLcaOB1eFcDyRNqChKNsumHsoa3V/mMfCny8d6qIjyCykVScMRR2o48O9DlwcJIj9m1pm+/UcjVqLG7HiIYnEQ2P860uJELVYaFO6PldJzHUoIhQjOEtPXMxUfFHSsynLJCqscIeKnbCgBpERIXdAUYl2hsRSeJWv4ZFT6XXLJJezYsYMnn3wSgC9/+cssWrSIRx99NOY277zzTtTzJ554gssvv5zPfOYzUcuXLFnC9773vYHnhx12WBpHPhyGU3PIxLrLSgOtVQKFmWkWhJmS30FirkBfcHU6zSOmeyZWmnWiBLV0UNTFp1YUtUyZKfiaQdaP1+S1nYH6SitTGFK6WAfMBq7bCF/cA8xFRFo3cr5zia4J5I+xMhmqhUamhFAFUFaO3E/mA1Kb7iKkLoT0x0EhUXruMSVjYujNN9/kySef5IUXXuDEE08E4Gc/+xlz585l48aNtLW1BW7X0hL9Rvz2t7/ltNNOY9KkSVHLa2trB62bP0xF2nMMJ3bIptuXDpUUVguPRDGbGHeQ2HnVQ1kouiZROjHDQrpJXRTFEkRmwUWIFkOaOaaC6S2kaORW2PmUNGENCjPKFp3u8CIgXz37kA6v44muwqzEsrJlSwhFCR8zzidd01oqWWMmI9M4lmxQOmn0QWTMTbZ69WrC4fCAEAKYM2cO4XCY559/PqF97N69m8cee4zLL7980Gv33nsvTU1NfOxjH+Oaa66hqyt22OGhQ4fYv39/1COzaCD1cNF0+0Izs1pSI4LcN8X0ZaQVrZOJJ1Ir0Yj4rrPhEsLrgZaK+yzWdv7qzSDCYjuSQbYHqePTBayBA0/Bt4CL3dVyxUnANuDT44HjkVT/A4hga8TL+DPrK/kZSgjB8IRQBZ6ra8DNFcETQ+n47GhrmuEIobphbp9t7FyTsW/d9vZ2Ro0aNWj5qFGjaG9vT2gf99xzDw0NDXz605+OWn7ppZcyceJEWlpaeP3117nuuut47bXXWLFiReB+brrpJm644YbkT2JYqHVouESw9YdKiQjFkWnmRyeYEIlbO10TTiZcZ/7DVONlTiWKmVHmR/dn1tTZ7j663WO+AmyAuuPgojVSUGOPu0ouaATKPo4YNEYjlq8K9/keJI5oFqm7xiC1YOkKoEyrIKZL8MQilRYbQSQbN5dLtJ5QJMfjyC1J/+xatmxZzCBnfaxZswaQYGg/juMELg/i5z//OZdeeimhUPSnZ8mSJZx55plMmzaNiy++mAcffJCnnnqKV155JXA/1113HZ2dnQOP7duz8XWTjkBqJYINqC4liinTzI9aiZKZLDTAunZ4AdDx0FigZK1ENTHW98fT1CGB0q2IpWU7Eo+zWJaf2wrPHgEXpDD0dLEG6HkGifmtc8fXgFixokpVxyCoiKKJ6plE0UyvskairT+ZQu/N4R6j0FLpS6ueUCySftevvPLKITO3JkyYwJ/+9Cd27x5caXfPnj2MHj20C+nZZ59l48aNPPDAA0OuO2vWLKqqqti0aROzZs0a9HpNTQ01NbFSGjJFJfAxYFWa9mcDqksLTTVqp3gCq000nqiDxGLrXHdbmVqKejJjKVIr0QES3/9Q1iF1C2m17F2IPyyECI4e4DRgExy7Y+iWaJmgAjgfqD4NMUJPR8ThKe4KdXjZebF2EC8VTq1GiVBZjpfhlS3rSoT0CIJCS6Uv3YBpP0nfaU1NTTQ1DW3xmDt3Lp2dnbz00kuccMIJALz44ot0dnYyb968Ibe/++67mT17NjNmzBhy3TfeeIPe3l7GjBkz9AlkleFWpDaxAdWlRzFmmpmYrrMOEs86i0DZh5kTReXI5J+o20wbfgbtx/xfM7Ba3P3vxysB0A/775Xq09VkXwyNA66sQebxZrz2FNo41yxPEEQiQmgoP8SQRQ8zgYqXdFlyRlM4cTelHTDtJ2PRiUcddRQLFy5kyZIlvPDCC7zwwgssWbKEc845JyqTbOrUqTzyyCNR2+7fv59f//rXfOlLXxq0382bN/O9732PNWvWsGXLFh5//HEuvPBCZs6cyd/8zd9k6nRSJF2B1IoGudmbt3TQTLN03kf5RrIB1uCJogy5z9RtFmvy968b6/jmN2wVEjSt/cmm4nl/DsCIafDdJmkJlm2OBm4+BNseQRTZW3jBS9qMVV1/muRnitB41z9e5WlloCv8UDV/0kkq9108CilourT6jiVCRosu3nvvvUyfPp358+czf/58jjnmGH75y19GrbNx40Y6O6N/9d5///04jsPnPve5Qfusrq7mD3/4AwsWLKCtrY2rrrqK+fPn89RTT1FRkamAguEwjfR+uCNYNV+KNFHcVWHVSpRs4Km6zzIkihIVRIlUS6xC2l3MQlK3WvHq9jS7y6bDlxPcXTr5b+B7wBdAAqW1k3sHgw3Rfb6/avEKYqg4ocpyyQwLbIiaSepJf5BzoQRN24DpIMocx3FyPYhss3//fsLhMJ2dnYwYMSILR/xvUu9mH4sd2IaupcgHFF+mmZ8PSTyWKGjbbnmks07RQeK7zPoJDi7WukLdxvND3hA5gHyMDwA7oO8/4I/Ab4H/S+Zbc1QAC5BvqD4krOlxYPpcpJv7SKRz/SQ8D5bZgkRdZ0EhmRWIsSSWUIrZFT6TpNstpoykcGJvxiPW5sJk//6/Eg5/Le3zt+1NlhWOzMA+NSPHUlpoplkxm7dTtRLptu6v/sra9NUpSsRClKhVyp9pth3Js3gcrgEuRMRQNmgD/gXpidaMNGttBAnw7ncXVhEt6rRopFbSjnWJ42WXDbjFsm0NSqdbTCmkoGkbMB2LQrDpFQETgDdIbyaYBlTrr2hL6RDCa/lSjIHVSrIZZ0Hb10Nlt2zv9A/P1FLr/o0VLO3PKlOx4KfCXd6BiIo65O38CzzhLtqHCJVdZK6LfR2S2T/2KLivF/b/BUa0IW68biR2aDSijvoQ69VB5Fy1FpGej5947rHKWrIbW5Mpa5Duu1CCpiPYEIvY2KuSFdKdZq9oQPUb2AyzUiNEcWeaKalknPlx/TvpyECLJ4j8+I9hpoj1IEpnE3Ja++DpfdFtvy5CBNE33FXTSQtiEZoO8lttPIwYh1iCJiFFFkFceCPxutdvwUujP53gUljx0uwrR5Bd4VBPZi1QmoKX79gK00NhxVDWSGeavUkIEVpWEJUemmkWQiJeixm1Er1H6ve5mZbfnXpVa51P/ILIn2Lfa/ztR47VbyzvQ4KotwNb4dQmeOY9uN1d/MV6YArsXgv/RPpadTQiGfNTgAmfQfLqQQTPFHeFsDuITYhFqBMRSCFELGk7keaAA8QSQ1kVQpm0BpnHKASXk84RVgjFw4qhrFGPKPPXMrBvVf22ZUdporE1xdTkNQi1EnUw/CbIWsAxBVGkgcN9xN7OFDy9iEhSUaSCaRKSB1GNiIppcHgXfHcTIlB6gRa4YK0EOX8a+YQPhxOBfwUewI0NqnCPrW03mtz/NS6oAbncBxGBM9p9TELEkz8mKFZxxcraGC9kgkxbg6Bw3GM2hT5RrBjKKtqvLN3WIfB6mG2muCdESzARirOnmZ90uM3MfaUoilQQdeNtY8YN6bJu5C3pR4SFiqN+PGvLdkT8VCF+qxZgLyI4emHUecAaCO0SMbMb8VYliuqdL7uHmX42TN+D1BKqRVxdqlXM8tcd7t8J7usjA9b3ExQ0XVlNdmKEsmENUgrBPWZT6JPBiqGsorUtMiGGcPfdjU25L1U002wnxe8y1eJ87zF88ZeiKKoiuOaOWlW6EVfSbvev9vyqwsvC2ouUIpuCzFlamLHFWHeP7P86JExnL1IP6GgkDd+MJ9JhtCLa62jgXKSE0djjEJfXJPeFGe4Ge92xabq8BkSPcHeubUMajDFVGA8VP0FB0wPp85kmG9YgpVDcY2OxGceJY8VQ1pmB2MYz9etdP6RWEJUmpZJpBl4Q+XDiiExSEEU6+et6VYjg2I3E1RxCxE0vsBbRqs3G+g3ucw3kAYmaVoHRi4iXNjh3EjAB6vbCf/4eRrXCul0ifUcC/4W4wHoRXXIsEn902HzkMjW4Y9Fjdbvj94sctV71ucs3Ee0aBK88gAqnmEHTkTgXLx1k0xqkxysE91hh1xLKBVYMZZ0m5CbdkqH9m79arCAqTUol0wzSF0fk36eKoo6hizeG8GKBVCAcQCKeNehYTTVbgOfxLD8V7tAb8FpzqKjodffT765viKZR58my6V0wfRP8bA3chufh2uI+3gKefg4JOmo2xlGB+My63L+1eJaePnccR7vrvoWEOq5HfssdjZdVpwSJocpaMt9lPpLhY/gJk/9uJyuEUsGKoZyQaeuQCqJDSNdzS+mhmWb1lE5gdSXprbnl7rfSbY4cTxSZAdWaldWMCIlu5OMYQcRHq/tX44j2IJp1HIMbmmpckWrbCCKiGvGEywaxAAXZxl4B7jsIl+xxjwteBekGvCDpEJ7LSzPf1PgxHjE9deFZuiJ4wi0oaHqg83wmyLY1SKkj/91OLRSGCy//sGIoJ2TaOgReUUawgqiUibh/i10QgWclSGdxU/CKN34Q23VmBlSD1+29F69RaRXiAgvhZZcdRMSOtrNQcYH7txHxynS562oQtsYWdcGBD7yyQH76kPYal7yMGDVALpO20KjGa8Rqnos2qh2PFwvUjYi7fch8q+IpsNJ0poRKLqxB4P3AzOcpswVbVDF17FXLGR8js2IIrCCyCBFkNiuVwGoYfqZZjH2XVULF/mBBpAHVfquKBlSHkLfiANHB1QcQy4sKKHVX4W6j1ptD7r67EfXTBeyDF4cY9XQQY3Szuy+NG4Lo1iDVRIuaOjwtrQIs7G6r2WQVDG5TUqnKMJ3kyhqk5HuckBVCw8X2JssZ2nsp06ggimThWJb8RQOrw0OtWARkoiO5EoKyxtg9z0J4okKFjbql1DpU5z5vR0TNOLw4nmpj3Rq85qghPAFiurjCcHob/G2M0VYB60C88lowsREvvR/juOXG85DvocJorLuuNmDNilUoUz3FEqUuh8dOhCasEBo+VgzljErk51q2jmXrTVg0sLoUBJGeayYmCI0l8kcRu2hTVxU/6k7SeCANstaCzBMQ99lod1vdDqJFlek+iyDzXwvQJ5v9LdHergYkHf9XrXjxS9sRd1s7opK015g/PV6PVc5gt1kbXsB1Rq1Cw2nYmy4yeR+lgwhWCKUHewVzyhHIBz3dMQ5BaJXqzdjGrqWMGVi9M8djyTQa59FOZuKl6uUQQXFEZhq61h7qIToVvxvRpZqg5M/GqiA6Q82saq2Bzr1AI9zUBuyFFe/BXcAG4FFgwlFIrdcwXjD3BrxKjJqHD56AK2ewywxjHTO+adA66RJCIXIrgnCPPZb8dY9FsNWl04cVQzlFrUN/yNLxrCCyKBH3b7EHVmdBEAXFEfkDqmvcZRo7VAdM9j1XNO5IRYm27+hBPrbaxl6FTTNwPLAHzmqH7c/AC7raFLzaRke7CzuBo5A4pYi7L9OSFVQvqML3WpBVCEjPxJyrIGmTfK8nFMEKofRixVDOyaZ1CDxBtDWLx7TkJxG84JUDuR1KRsm0IApBGYMFkRlQrc81w6wC8WP14MXu+LfT2kVqIdqNZ90BiTc63v1fCykegC+OhwVbYewMvErYVXhZZLuRjLAReNYdfSQihNxTHtx2o5zhTSm5DpI2yed6QhojZIVQOrExQzknm7FDigoiW4/CUipxRJlOjQ5B2YjAxYMsKFVIzI0ZEO13OfkDr5Xx7uNoZK5uRIKiQ0j8Tx1wmtt6ow1p9aEB11qfaBwikPYioqua2JYeDaY2x+BPxR9gOCJG3598EEL5XE+oBWsRygzWMpQXZNs6BDbt3uIRQu7BSmSGLFayYCGq/BA+PDhocWCHew2M1ngiFRha9FAtSCHEzVVnrLvdXb+B6KKJqmn3uds0u6+pFagGcc+FEJeZGdQdqwO9f3ks61HK00k+xAcp+VxPyKbPZxJrGcoLcmEd0uNqSorF0oIEjBbzl61ZrToT1A/OMtP4oYqA1cvxUujNZWo9Mis81xrr9uN9dGsRQdRIdJVprXIdMvYZwqtppJ6WoHHFEkJmVptJWaouMk2bz4d7Lp8Dpq0QyjRWDOUNah3KNpp2b3vZWKA0UnXVEpEp6gfXIYoVmGy+HjQH63Kz9k8dEhitPcI080utTHWIRagbCQXb6v5vurzM/UKwKy9odoi1PCUBka1aa4mgAdP54KbzcwTyHV3Mn8ncY8VQ3lAJzCZ3N/x45ANnsYSQ+6GY44gyLYgigy0oQfFDJlUxXjdrDanwCeO5x1TUtAN/YaBD64H/RIKszVT5CuO57huiXXixOtDHzCCD5L631DqXL8KjkujUunyiDS+cwZJJrNTMK44g8z3L4qHm6o05Or4lf9A4omKuR1SPxA51ZGDflW5A9f7oxf4MMz81MV7X7YJafvQj1qDxiKVoKzAD6lrwgqU1K03Fj/9nsO4bYluwzO39yxOeSvIpY0wJkz8WKpM28nNcxYkVQ3lHpjvaD4V++NS2biltIu7fYq1HFHH/dmRg3yGo7IYPe7xFQ4khcx0/1XjuLrOgo24TwjudLjyX2ThEJIEIp3juOnOfQeMKIuF4IbUI5VNMTj5mjqllNt/GVdxYN1neoR3tcz0Gm75pUSIUt9ssQubu9frU3GWxgq3jva4xQRH3f23mqkHP8URYP4PrHfn3HVNEJWLlyVchpFmU+YKWPbFCKNtYMZSXzCD3H9AI8DHy049uyT7qNhuZ64FkiExN1JVQFtDDLFYWlxLrtXLf67FEUQQJoi4nWgT1Ey2I4gmgRMaTEPkohPKx51gE+52bO6wYykuakA9FrrG/Uix+ijX9PpMp96HBYiJeur2+Ho94r/fh9UHrRTLKDuLVKDpEdJ8zxfDmDSKWVWjIxqz5KoTyLYXeWuNzjRVDectU8iPI0FartviJUJxuMw3uzcB+g6xD8dLtY7nCFNP1pQ1bQWKKdiMhf1qraIf7fJ/7dz9eTFGfsW2/b1/+YwUSTzxaIZQYtqp0PlBsP++KiHrkA/JargeCV4uoDmnyarEUa9XqTGWYhaAiRnd77UGWKuoG60WG/RaiU6fgxRF1A+vcddxO9wNxQNosNl5wd8yfzbEmcBWW+TTB52NRRftDM1+wlqG8Zir55T9uQdI98+nLxJJbitFtFiH993gllMWImo5XBdokKMi5D7EEdRmPWuRtaUBE0TgkYLsBETwd7nZalHEPsAlxpXUj4uhQIqcUz0UWIT8s20q+CaEQ8l1qhVC+UEzfYEVIPRJMvSrXAzHQYmmbyUw6sqXwiCBf7u8BnbkdStrQXoHpLC8RItAEpEUV/RaZoFpA/ue9iJjpQKw9XXhtOurc9ardZUcgImc/Yj0CEVftSBuPDndfYUQ3gNQ9ins+QeRTQUXIP9dYBOsWyz8yahn6/ve/z7x586itrSUSiSS0jeM4LFu2jNbWVg477DBOPfVU3njjjah1Dh06xNe+9jWampqoq6vjvPPOY8eOHRk4g3xgAvkXwGzjiCx+1G1WLFaiTARUBwRSK9o5Ph59vv/VSqStNx4x9qNurxo8S1GLu67+3QM8Cyu+A7yOCCStr6mFGv3xQ0qFez6DqMcKoXjY+KB8JaNiqKenhwsvvJArrrgi4W1uueUWbr31Vm677TZefvllWlpaOOuss+jq6hpYZ+nSpTzyyCPcf//9PPfcc3zwwQecc8459PXFq2RWqOS6TUcsVBDZFh4WkwjFE1ydgYDqWK6yII+TGbjsd5GZwc8diPCZgrjCtHmr9jEzG7XWutsdRH5nzYOzrgKmufuN4AVma3xRzPPwfyfVk19u/XwTQvp9mS/jsZiUOY7jZPogv/jFL1i6dCkdHR1x13Mch9bWVpYuXcq3vvUtQKxAo0eP5uabb+YrX/kKnZ2dNDc388tf/pKLLroIgF27djFu3Dgef/xxFixYMGi/hw4d4tAhzwne2dnJRz7yEbZv386IESPSd6IZ5WlgW64HEYO9wNsUZ4ViS2p8CLzLoHYUBUkH6XP/HYIPu2K/rJ3mlYOIMDlkLDdT5A8iH7/dSJ/RXiRGKMzgjDTdTosxjjX2UYtkm7W727ciWuf/ARcBhxv7qQDKDvftXBud5suPthrkJOL6+bJEJTCR4q3RlV327/8r48Z9k46ODsLhNP7ocrLA8uXLnXA4POR6mzdvdgDnlVdeiVp+3nnnOV/4whccx3GcP/zhDw7g7Nu3L2qdY445xvmHf/iHwP1ef/31DmAf9mEf9mEf9mEfRfDYvHlzaoIkBvki4wFob28HYPTo0VHLR48ezdatWwfWqa6u5vDDDx+0jm7v57rrruPv//7vB553dHQwfvx4tm3bll5lWYLs37+fcePGFZiVLT+x1zI92OuYPuy1TB/2WqYH9ew0Njamdb9Ji6Fly5Zxww03xF3n5Zdf5rjjjkt5UGVlZVHPHccZtMxPvHVqamqoqRlsLg2Hw/amTBMjRoyw1zJN2GuZHux1TB/2WqYPey3TQ3l5ekOekxZDV155JRdffHHcdSZMmJDSYFpaJDupvb2dMWPGDCx/9913B6xFLS0t9PT08P7770dZh959913mzZuX0nEtFovFYrGULkmLoaamJpqaMpPqPXHiRFpaWlixYgUzZ84EJCNt1apV3HzzzQDMnj2bqqoqVqxYwWc/+1kA3nnnHV5//XVuueWWjIzLYrFYLBZL8ZLRmKFt27axb98+tm3bRl9fH6+++ioAkydPpr5ealFMnTqVm266iQsuuICysjKWLl3KjTfeyJQpU5gyZQo33ngjtbW1XHLJJYC4ti6//HKuvvpqRo4cSWNjI9dccw3Tp0/nzDPPTGhcNTU1XH/99YGuM0ty2GuZPuy1TA/2OqYPey3Th72W6SFT1zGjqfWXXXYZ99xzz6DlK1eu5NRTT5UBlJWxfPlyLrvsMkBif2644QZ++tOf8v7773PiiSdy++23M23atIHtu7u7+cY3vsF9993HX//6V8444wzuuOMOxo0bl6lTsVgsFovFUqRkpc6QxWKxWCwWS75iG7VaLBaLxWIpaawYslgsFovFUtJYMWSxWCwWi6WksWLIYrFYLBZLSVMSYmjLli1cfvnlTJw4kcMOO4wjjzyS66+/np6enrjbOY7DsmXLaG1t5bDDDuPUU0/ljTfeyNKo85fvf//7zJs3j9raWiKRSELbXHbZZZSVlUU95syZk9mB5jmpXEd7Twbz/vvvs2jRIsLhMOFwmEWLFg3ZGNrek8Idd9zBxIkTCYVCzJ49m2effTbu+qtWrWL27NmEQiEmTZrEnXfemaWR5jfJXMenn3560L1XVlbGhg0bsjji/OSZZ57h3HPPpbW1lbKyMn7zm98MuU067smSEEMbNmygv7+fn/70p7zxxhv8+Mc/5s477+Tb3/523O1uueUWbr31Vm677TZefvllWlpaOOuss+jqitN1ugTo6enhwgsv5Iorrkhqu4ULF/LOO+8MPB5//PEMjbAwSOU62nsymEsuuYRXX32VJ598kieffJJXX32VRYsWDbldqd+TDzzwAEuXLuU73/kOa9eu5eSTT+bss89m27Ztgeu//fbbfOITn+Dkk09m7dq1fPvb3+aqq67ioYceyvLI84tkr6OycePGqPtvypQpWRpx/nLgwAFmzJjBbbfdltD6absn09r2tYC45ZZbnIkTJ8Z8vb+/32lpaXF+8IMfDCzr7u52wuGwc+edd2ZjiHnP8uXLnXA4nNC6ixcvds4///yMjqdQSfQ62nsymPXr1zuA88ILLwwsW716tQM4GzZsiLmdvScd54QTTnC++tWvRi2bOnWqc+211wau/81vftOZOnVq1LKvfOUrzpw5czI2xkIg2eu4cuVKB3Def//9LIyucAGcRx55JO466bonS8IyFERnZ2fcrrdvv/027e3tzJ8/f2BZTU0Np5xyCs8//3w2hlh0PP3004waNYqPfvSjLFmyhHfffTfXQyoo7D0ZzOrVqwmHw5x44okDy+bMmUM4HB7yupTyPdnT08P//M//RN1PAPPnz4953VavXj1o/QULFrBmzRp6e3szNtZ8JpXrqMycOZMxY8ZwxhlnsHLlykwOs2hJ1z1ZkmJo8+bN/Nu//Rtf/epXY67T3t4OMNAgVhk9evTAa5bEOfvss7n33nv54x//yI9+9CNefvllTj/9dA4dOpTroRUM9p4Mpr29nVGjRg1aPmrUqLjXpdTvyffee4++vr6k7qf29vbA9T/88EPee++9jI01n0nlOo4ZM4a77rqLhx56iIcffpi2tjbOOOMMnnnmmWwMuahI1z1Z0GJo2bJlgUFo5mPNmjVR2+zatYuFCxdy4YUX8qUvfWnIY5SVlUU9dxxn0LJiIJVrmQwXXXQRn/zkJ5k2bRrnnnsuTzzxBH/+85957LHH0ngWuSfT1xHsPRl0LYPOf6jrUir35FAkez8FrR+0vNRI5jq2tbWxZMkSZs2axdy5c7njjjv45Cc/yQ9/+MNsDLXoSMc9mdFGrZnmyiuv5OKLL467zoQJEwb+37VrF6eddhpz587lrrvuirtdS0sLIKpzzJgxA8vffffdQSq0GEj2Wg6XMWPGMH78eDZt2pS2feYDmbyO9p4czIQJE/jTn/7E7t27B722Z8+epK5Lsd6TsWhqaqKiomKQ9SLe/dTS0hK4fmVlJSNHjszYWPOZVK5jEHPmzOFXv/pVuodX9KTrnixoMdTU1ERTU1NC6+7cuZPTTjuN2bNns3z5csrL4xvFJk6cSEtLCytWrGDmzJmA+IZXrVrFzTffPOyx5xvJXMt0sHfvXrZv3x41qRcDmbyO9p4MZu7cuXR2dvLSSy9xwgknAPDiiy/S2dnJvHnzEj5esd6Tsaiurmb27NmsWLGCCy64YGD5ihUrOP/88wO3mTt3Lo8++mjUst///vccd9xxVFVVZXS8+Uoq1zGItWvXlsy9l07Sdk8mFW5doOzcudOZPHmyc/rppzs7duxw3nnnnYGHSVtbm/Pwww8PPP/BD37ghMNh5+GHH3bWrVvnfO5zn3PGjBnj7N+/P9unkFds3brVWbt2rXPDDTc49fX1ztq1a521a9c6XV1dA+uY17Krq8u5+uqrneeff955++23nZUrVzpz5851xo4dW9LXMtnr6Dj2nozFwoULnWOOOcZZvXq1s3r1amf69OnOOeecE7WOvScHc//99ztVVVXO3Xff7axfv95ZunSpU1dX52zZssVxHMe59tprnUWLFg2s/9Zbbzm1tbXO17/+dWf9+vXO3Xff7VRVVTkPPvhgrk4hL0j2Ov74xz92HnnkEefPf/6z8/rrrzvXXnutAzgPPfRQrk4hb+jq6hr4LgScW2+91Vm7dq2zdetWx3Eyd0+WhBhavny5AwQ+TABn+fLlA8/7+/ud66+/3mlpaXFqamqcj3/84866deuyPPr8Y/HixYHXcuXKlQPrmNfy4MGDzvz5853m5manqqrK+chHPuIsXrzY2bZtW25OIE9I9jo6jr0nY7F3717n0ksvdRoaGpyGhgbn0ksvHZS2bO/JYG6//XZn/PjxTnV1tTNr1ixn1apVA68tXrzYOeWUU6LWf/rpp52ZM2c61dXVzoQJE5yf/OQnWR5xfpLMdbz55pudI4880gmFQs7hhx/unHTSSc5jjz2Wg1HnH1p2wP9YvHix4ziZuyfLHMeNNLJYLBaLxWIpQQo6m8xisVgsFotluFgxZLFYLBaLpaSxYshisVgsFktJY8WQxWKxWCyWksaKIYvFYrFYLCWNFUMWi8VisVhKGiuGLBaLxWKxlDRWDFksFovFYilprBiyWCwWi8VS0lgxZLFYLBaLpaSxYshisVgsFktJ8/8BzHNFPdeRbRIAAAAASUVORK5CYII=",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.cm as cm\n",
"\n",
"imshow(-log(data.T), extent=[-2,1,-1,1], cmap=cm.hot, origin='lower')\n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "c7553894-b15c-42f5-9a2b-655fd8f7bd6a",
"metadata": {},
"source": [
"## Optional: Using C++ with `pybind11` to speed up the code \n",
"\n",
"To make the code as fast as in compiler languages (C++ or fortran), we can rewrite the slow part directly in C++, and use pybind11 to produce a python module from C++ code.\n",
"\n",
"### Optional installation of C++. These instructions are MAC specific\n",
"\n",
"- Install `Xcode` package from `App Store`\n",
"- Install \"Command Line Tools\" parts of `Xcode`. You can check if \"Command Line Tools\" are already installed by issuing the following in the`terminal`:\n",
"\n",
" `xcode-select --print-path`.\n",
"\n",
" If you get no output, then issue the following command in your `terminal`:\n",
"\n",
" `xcode-select —-install`\n",
"\n",
" Xcode contains `C/C++` compiler (`gcc/g++`). Check if installation was successful by issuing in `terminal`:\n",
"\n",
" `gcc --version`\n",
" \n",
" It is just a link to apples native Clang. It contains `make` utility.\n",
" Xcode also contains many libraries, for example, BLAS and LAPACK libraries, which can be linked by adding linker option: `-framework Accelerate`.\n"
]
},
{
"cell_type": "markdown",
"id": "94deb9c3",
"metadata": {},
"source": [
"### Return to `pybind11` to speed up the code \n",
"\n",
"The function `mand` is written in conventional C++ language. For storage, we use type `py::array_t`, which can convert any numpy arrays to a simple C++ container, that can be accessed conventionally.\n",
"\n",
"We do that in the very top of the function, where we extract proper type from `py::array_t`\n",
"\n",
"`auto dat = data.mutable_unchecked<2>();`\n",
"\n",
"\n",
"The `dat` object behaves as normal 2D array, which can be accessed via `dat(j,i)=...`. \n",
"\n",
"To have access to these Python types, we need to include a few `pybind11` header files:\n",
"\n",
"`#include \"pybind11/pybind11.h\"` \n",
"`#include \"pybind11/numpy.h\"` \n",
"`#include \"pybind11/stl.h\"`\n",
"\n",
"The crucial difference between normal C++ code or Python module is that the `main()` function is replaced by `PYBIND11_MODULE(imanc,m)`, which creates shared library that Python recognizes as module, rather than executable. Here, the first argument has to match the name of the file compiled file (`imanc.cc` and `imanc`). The second argument creates object with name `m`, which can than be filled with functions or classes that are exposed to Python. We just add our function `mand` to it : `m.def(\"mand\", &mand);`. The string `\"mand\"` gives name to this function in Python, which could in general be different, and `&mand` takes the pointer to existing C++ routine and binds it to Python module. The line `m.doc() = \"...\"` adds some documentation to the module as seen from Python.\n",
"\n",
"\n",
"The entire C++ code below is just a long string (because jupyter has very limited support for C++). The last three lines in the cell open new file `imanc.cc`, write the code to this file, and close it, so that all content is safely written to this file.\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "149367bd",
"metadata": {},
"outputs": [],
"source": [
"CPP_code=\"\"\"\n",
"#include \"pybind11/pybind11.h\"\n",
"#include \"pybind11/numpy.h\"\n",
"#include \"pybind11/stl.h\"\n",
"#include \n",
"\n",
"namespace py = pybind11;\n",
"using namespace std;\n",
"\n",
"void mand(py::array_t& data, int Nx, int Ny, int max_steps, const vector& ext)\n",
"{\n",
" auto dat = data.mutable_unchecked<2>();\n",
" #pragma omp parallel for\n",
" for (int i=0; i z0(x,y);\n",
" complex z=0;\n",
" for (int itr=0; itr4.){\n",
" dat(j,i) = itr;\n",
" break;\n",
" }\n",
" z = z*z + z0;\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"PYBIND11_MODULE(imanc,m){\n",
" m.doc() = \"pybind11 wrap for mandelbrot\";\n",
" m.def(\"mand\", &mand);\n",
"}\n",
"\"\"\"\n",
"file = open('imanc.cc', mode='w')\n",
"file.write(CPP_code)\n",
"file.close()"
]
},
{
"cell_type": "markdown",
"id": "e42c41e0",
"metadata": {},
"source": [
"Next we check that the file now exists in the working directory, where our jupyter notebook is located."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "a69a3d20",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 3144\n",
"-rw-r--r--@ 1 haule staff 588532 Jan 2 2024 Interactive MandelbrotSet.html\n",
"-rw-r--r-- 1 haule staff 4788 Sep 3 21:38 Interactive MandelbrotSet.ipynb\n",
"-rw-r--r--@ 1 haule staff 548683 Jan 11 2024 Introduction to Comp Phys 488.html\n",
"-rw-r--r-- 1 haule staff 261741 Jan 22 18:05 Introduction to Comp Phys 488.ipynb\n",
"-rw-r--r-- 1 haule staff 828 Jan 22 18:17 imanc.cc\n",
"-rwxr-xr-x 1 haule staff 192256 Jan 23 2024 \u001b[1m\u001b[31mimanc.so\u001b[m\u001b[m\n",
"-rwxr-xr-x 1 haule staff 1887 Jan 15 2024 \u001b[1m\u001b[31mmanp_dynamic.py\u001b[m\u001b[m\n",
"drwxr-xr-x 6 haule staff 192 Mar 10 2024 \u001b[1m\u001b[34mpybind11\u001b[m\u001b[m\n"
]
}
],
"source": [
"cmd = 'ls -l'\n",
"!{cmd}"
]
},
{
"cell_type": "markdown",
"id": "b168c682",
"metadata": {},
"source": [
"Next we need to compile C++ code to create proper shared libray, which Python recognizes. This string is platform dependent, and in its current form is most appropriate for MAC. It needs some modification on other platforms. Please read the documentation and instructions to modify the compile line.\n",
"\n",
"We will again create a long string, which is a line that is normally written in comand line, and we will than execute it on the system.\n",
"\n",
"As said, the complile line is system dependend, and will most likely need modification. Here is the explanation of the command:\n",
"\n",
"- `g++-12` is tha name of the C++ compiler on the sytstem. Most likely needs to be changed to your existsing C++ compiler.\n",
"\n",
"- `python3 -m pybind11 --includes` is line that can be executed in command line (try it) and should give the location of `pybind11` include files. Each computer will have these files at slightly different location, but this command should work in most systems.\n",
"\n",
"- `-undefined dynamic_lookup` is something MAC specific. Withouth this flag, the compiler will issue errors of missing symbols in the module. In other operating systems should not be needed.\n",
"\n",
"- `-O3` switches on agrresive optimization.\n",
"\n",
"- `-fopenmp` allows the code to be parallelized by openMP. Namely the code above has a line `#pragma omp parallel for`, which parallelizes the first loop over i, provided that we also add this flag during compilation.\n",
"\n",
"- `shared` tells the compiler that we are producing shared library\n",
"\n",
"- `-std=c++11` forces compiler to use C++ 2011 standard. We could use later standard, but not earlier.\n",
"\n",
"- `-fPIC` makes code position independent, which is required for shared library\n",
"\n",
"- `imanc.cc` is the name of our file that is being compiled.\n",
"\n",
"- `-o imanc.so` says that the output file will be called `imanc.so`. The Python module is thus named `imanc`.\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "57ca239f",
"metadata": {},
"outputs": [],
"source": [
"#cmd=\"g++ `python -m pybind11 --includes` -undefined dynamic_lookup -O3 -shared -std=c++11 -fPIC imanc.cc -o imanc.so\"\n",
"cmd=\"g++-14 -fopenmp `python -m pybind11 --includes` -undefined dynamic_lookup -O3 -shared -std=c++11 -fPIC imanc.cc -o imanc.so\"\n",
"\n",
"!{cmd}"
]
},
{
"cell_type": "markdown",
"id": "911b1ab0",
"metadata": {},
"source": [
"Finally, we import this generated module `imanc` and use the exposed function `mand` as `imanc.mand()`, which requires several arguments (see C++ code `mand` to understand the arguments). The arguments are `data, Nx, Ny, max_steps, ext`."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "0ba2fbae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pybind11: walltime: 0.21619701385498047\n"
]
}
],
"source": [
"import imanc # We now import pybind11 module, produced with C++ code\n",
"\n",
"data3 = ones((1000,1000))\n",
"\n",
"t0 = time.time()\n",
"imanc.mand(data3, 1000, 1000, 1000, [-2,1,-1,1])\n",
"t1 = time.time()\n",
"\n",
"print('pybind11: walltime: ', t1-t0)"
]
},
{
"cell_type": "markdown",
"id": "ddf0922c",
"metadata": {},
"source": [
"The walltime should be around 150 - 200 times shorter than pure python code above. This close to the optimal performance of modern computers. \n",
"\n",
"Unfortunately, my current computer has `silicon` architecure, and anaconda seems to not yet properly take advantage of its speed. I can only demonstrate the speed in terminal running different `python` version from `homebrew`.\n",
"\n",
"Finally we replot the data."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "7acda3b9",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.cm as cm\n",
"\n",
"imshow(-log(data3), extent=ext, cmap=cm.hot) \n",
"show()"
]
},
{
"cell_type": "markdown",
"id": "6e1ee084-4d0f-4f22-a8df-0ae71c101ae2",
"metadata": {},
"source": [
"## Popularity of programing languages\n",
"\n",
"https://www.tiobe.com/tiobe-index/\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a77157de-5f46-4cc1-99db-216dafa14fd2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}