{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Automated analysis routines to measure AP metrics\n",
    "# copyright © 2022, Bum-Rak Choi, Taeyun Kim, Allison Navarrete-Welton\n",
    "# This program is free software: you can redistribute it and/or modify\n",
    "#    it under the terms of the GNU General Public License as published by\n",
    "#    the Free Software Foundation, either version 3 of the License, or\n",
    "#    (at your option) any later version.\n",
    "#\n",
    "#    This program is distributed in the hope that it will be useful,\n",
    "#    but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "#    GNU General Public License for more details.\n",
    "#\n",
    "#    You should have received a copy of the GNU General Public License\n",
    "#    along with this program.  If not, see <https://www.gnu.org/licenses/>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.polynomial.polynomial import Polynomial\n",
    "import math\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.patheffects as pe\n",
    "from netCDF4 import Dataset \n",
    "import cv2 as cv\n",
    "import sklearn\n",
    "from skimage import filters\n",
    "\n",
    "import scipy\n",
    "from scipy.sparse import csr_matrix\n",
    "from scipy.sparse.linalg import spsolve\n",
    "from scipy.io import netcdf\n",
    "from scipy.ndimage import gaussian_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set plotting aesthetics  \n",
    "pal = sns.color_palette('colorblind')\n",
    "sns.set_palette(pal)\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load sample data\n",
    "filename = 'sample_data/2021-07-30-060.nc'\n",
    "fid = Dataset(filename,'r')\n",
    "temp = fid.variables['Data']\n",
    "data60 = temp[:]*1\n",
    "fid.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot fluorescence signal at single pixel over time\n",
    "test_trace = data60[:, 550]\n",
    "plt.figure(figsize = (12,3))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(test_trace)\n",
    "plt.title(\"Fluorescence Trace from Single Pixel\")\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Signal Intensity\")\n",
    "sns.despine()\n",
    "\n",
    "#Plot tissue image at single timepoint\n",
    "test_img = data60[50,:-2].reshape(100,100)\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(test_img)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.title(\"Tissue Image at Single Timepoint\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Thresholding & Spheroid Labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AutoThr():\n",
    "    '''\n",
    "    A class to automatically detect and label spheroids from a cardiac microtissue spheroid image.\n",
    "    Once made, the thresholded spheroid mask can be applied to subsequent time points.\n",
    "    \n",
    "    Attributes\n",
    "    ----------\n",
    "    raw_img: 2D array of floats\n",
    "        raw image data\n",
    "    thr_method: str\n",
    "        Determines the image thresholding method applied. \n",
    "        Must be 'otsu', 'histogram', entropy', or 'cross_entropy'\n",
    "    plot_mask: boolean\n",
    "        if True, plot thresholded image with different colors for each labeled spheroid\n",
    "    img: 2D array of floats (same dimensions as raw_img)\n",
    "        image data rescaled to lie between (0,255)\n",
    "    thr: int\n",
    "        threshold value\n",
    "    mask: 2D array of floats (same dimensions as raw_img)\n",
    "        image mask with all spheroid regions set to 1 & background set to 0\n",
    "    n_spheroids: int\n",
    "        number of spheroids found\n",
    "    spheroid_mask: 2D array of floats (same dimensions as raw_img)\n",
    "        image mask with each spheroid labeled by a different index & background set to 0\n",
    "    spheroid_pixel_groups: list of lists of floats\n",
    "        pixel values grouped by spheroid\n",
    "     \n",
    "    Methods\n",
    "    --------\n",
    "    rescale_image(self, input_img = self.raw_img)\n",
    "        Rescales image pixel values between 0 and 255\n",
    "    smooth(self, arr, width)\n",
    "        Smooths signal using boxcar averaging method (analogous to low pass filter)\n",
    "    filter_histogram(self, h)\n",
    "        Sets threshold value above histogram mode according to distribution shape.\n",
    "        Reverts to Otsu filtering if histogram values are not amenable.\n",
    "    calc_entropy_from_hist(self, h)\n",
    "        Calculates entropy from a histogram distribution.\n",
    "    calc_cross_entropy_from_hist(self, h, thr)\n",
    "        Calculates cross entropy from a histogram distribution.\n",
    "    set_mask(self, use_slide = False, thr_up  = None, thr_down = None)\n",
    "        Make mask to threshold image based on preset thresholds. Can threshold\n",
    "        below one upper threshold or between an upper and lower threshold.\n",
    "    remove_small_pixels(self)\n",
    "        Remove small pixel groups from mask using an erosion & dilation operation.\n",
    "    get_spheroid_inds(self, plot_mask)\n",
    "        Counts number of spheroids in mask and returns spheroid indices grouped by spheroid.\n",
    "    get_spheroid_pixels(self)\n",
    "        Gets all pixels from original image that correspond to each spheroid.\n",
    "        Results stored in self.spheroid_pixel_groups \n",
    "    thr_pipeline(self):\n",
    "        Thresholds image, finds spheroids, and labels pixels according to spheroid index\n",
    "    apply_mask(self, new_img)\n",
    "        Apply existing spheroid labeling mask to another image (ie, an image at a subsequent time point)\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, raw_img, thr_method = 'cross_entropy', plot_mask = True):\n",
    "        self.raw_img = raw_img\n",
    "        self.thr_method = thr_method\n",
    "        self.plot_mask = plot_mask\n",
    "        \n",
    "        img_shape = np.shape(raw_img)\n",
    "        self.pixel_inds = np.arange(img_shape[0]*img_shape[1])\n",
    "        \n",
    "    def rescale_image(self, img):\n",
    "        '''\n",
    "        Rescales image pixel values between 0 and 255\n",
    "        \n",
    "        Parameters \n",
    "        -----------\n",
    "        input_img: 2D float array\n",
    "            raw image data. Default is the self.raw_img used for thresholding but \n",
    "            can be applied to images from subsequent timepoints\n",
    "        '''\n",
    "        img_min = img.min()\n",
    "        self.img = 255*np.array((img-img_min)/(img.max()-img_min))\n",
    "        return \n",
    "\n",
    "    def smooth(self, arr, width):\n",
    "        '''\n",
    "        Smooths signal using boxcar averaging method (analogous to low pass filter)\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        arr: 1D array of floats\n",
    "            signal data\n",
    "        width: int\n",
    "            window size over which to smooth\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        smooth_arr: 1D array of floats\n",
    "            smoothed signal\n",
    "        '''\n",
    "        #ensure width is an even integer\n",
    "        if width%2 == 0:\n",
    "            width = width + 1 \n",
    "        \n",
    "        #take average along sequential windows\n",
    "        half_width = int(width / 2)\n",
    "        new_arr = [np.sum(arr[int(i - half_width):int(i + half_width + 1)]) \n",
    "                   for i in np.arange(half_width, len(arr)-width)]\n",
    "        new_arr = np.array(new_arr) / (width - 1)\n",
    "        \n",
    "        #concatenate with original data at array edges\n",
    "        smooth_arr = np.concatenate([arr[:half_width], new_arr, arr[int(len(arr)-width):]])\n",
    "        return smooth_arr\n",
    "\n",
    "    def calc_entropy_from_hist(self, h):\n",
    "        '''\n",
    "        Calculates entropy from a histogram distribution.\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        h: 1D array of ints\n",
    "            partial histogram distribution from image pixel values\n",
    "            \n",
    "        Returns\n",
    "        -------\n",
    "        entropy: float\n",
    "            entropy value\n",
    "        '''\n",
    "        positive_inds = np.where(h >= 1)\n",
    "        h_sum = np.sum(h)\n",
    "        res = np.zeros(len(h))\n",
    "        if np.any(positive_inds):\n",
    "            p = h[positive_inds]/h_sum\n",
    "            res[positive_inds] = -p*np.log(p)\n",
    "        entropy = res.sum()\n",
    "        return entropy\n",
    "\n",
    "    def calc_cross_entropy_from_hist(self, h, thr):\n",
    "        '''\n",
    "        Calculates cross entropy from a histogram distribution.\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        h: 1D array of ints\n",
    "            partial histogram distribution from image pixel values\n",
    "        thr: int\n",
    "            index to divide the histogram \n",
    "            \n",
    "        Returns\n",
    "        -------\n",
    "        cross_entropy: float\n",
    "            cross entropy value\n",
    "        '''\n",
    "        h1 = np.array(h[0:thr])\n",
    "        h2 = np.array(h[thr:])\n",
    "        h1_total = np.sum(h1)\n",
    "        h2_total = np.sum(h2)\n",
    "        q1 = h1_total/(h1_total + h2_total)\n",
    "        q2 = 1 - q1\n",
    "        positive_ind1 = np.where(h1 >= 1)\n",
    "        positive_ind2 = np.where(h2 >= 1)\n",
    "\n",
    "        res1 = np.zeros(len(h1))\n",
    "        if np.any(positive_ind1): \n",
    "            p = h1[positive_ind1]/h1_total\n",
    "            res1[positive_ind1] = -p*np.log(p) + p*np.log(q1)\n",
    "\n",
    "        res2 = np.zeros(len(h2)) \n",
    "        if np.any(positive_ind2):\n",
    "            p = h2[positive_ind2]/h2_total\n",
    "            res2[positive_ind2] = -p*np.log(p) + p*np.log(q2)\n",
    "            \n",
    "        cross_entropy = np.sum(res1) + np.sum(res2)\n",
    "\n",
    "        return cross_entropy\n",
    "    \n",
    "    def filter_histogram(self, h):\n",
    "        '''\n",
    "        Sets threshold value above histogram mode according to distribution shape.\n",
    "        Reverts to Otsu filtering if histogram values are not amenable.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        h: 1D array of ints, size 255\n",
    "            histogram distribution of pixel values\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        thr: int\n",
    "            threshold value\n",
    "        '''\n",
    "        #smooth & filter histogram \n",
    "        h_smooth = self.smooth(h, 17)\n",
    "        h_filtered = scipy.ndimage.gaussian_filter1d(h_smooth, sigma = 11, order = 1)\n",
    "\n",
    "        #Find local max above which to filter\n",
    "        ind_max = np.argmax(h_smooth)\n",
    "        h_max = h_smooth[ind_max] \n",
    "\n",
    "        #if local max index is >240, use Otsu thresholding instead\n",
    "        if ind_max > 240:\n",
    "            thr = filters.threshold_otsu(hist = h)\n",
    "            print(\"Hist thresholding error- max in h_smooth is too large-- using Otsu\")        \n",
    "\n",
    "        #Set histogram threshold to 20% of maximum or 2, whichever is higher\n",
    "        if h_max*0.05 > 2:\n",
    "            h_thr = h_max*0.2\n",
    "        else:\n",
    "            h_thr = 2\n",
    "\n",
    "        #set initial threshold to first index above ind_max where histogram values decrease\n",
    "        #below h_thr\n",
    "        iB = np.where(h_smooth[ind_max:] < h_thr)\n",
    "        if np.any(iB):\n",
    "            thr = iB[0][0] + ind_max\n",
    "            #adjust threshold to next non-zero value of filtered histogram \n",
    "            i_min = np.where(h_filtered[thr:] >= 0)\n",
    "            if np.any(i_min):\n",
    "                thr += i_min[0][0]\n",
    "            else:\n",
    "                thr = filters.threshold_otsu(hist = h)\n",
    "                print(\"Hist thresholding error- threshold set over derivative-- using Otsu\")      \n",
    "        else:\n",
    "            thr = filters.threshold_otsu(hist = h)\n",
    "            print(\"Hist thresholding error- no values less than threshold- using Otsu\")  \n",
    "        return thr\n",
    "        \n",
    "    def auto_threshold(self):\n",
    "        '''\n",
    "        Finds threshold value according to method specified by self.thr_method.\n",
    "        Result stored in self.thr\n",
    "        '''\n",
    "        h = np.histogram(self.img, range = (0,255), bins = 255)[0]\n",
    "        \n",
    "        if self.thr_method == 'otsu':\n",
    "            thr = filters.threshold_otsu(hist = h, nbins = 255)\n",
    "        elif self.thr_method == 'hist':\n",
    "            thr = self.filter_histogram(h)\n",
    "        else:\n",
    "            i_start, i_end = 10, 220\n",
    "            h_elements = np.zeros(len(h))\n",
    "            for i in range(i_start, i_end):\n",
    "                if self.thr_method == 'entropy':\n",
    "                    h_elements[i] = self.calc_entropy_from_hist(h[:i]) + self.calc_entropy_from_hist(h[i+1:])\n",
    "                elif self.thr_method == 'cross_entropy':\n",
    "                    h_elements[i] = self.calc_cross_entropy_from_hist(h, i)\n",
    "                else:\n",
    "                    print(\"Error: No such thresholding method exists\")\n",
    "            thr = np.argmax(h_elements)\n",
    "        self.thr = thr\n",
    "        return\n",
    "\n",
    "    def set_mask(self, use_slide = False,\n",
    "                thr_up  = None, thr_down = None):\n",
    "        '''\n",
    "        Makes mask to threshold image based on preset thresholds. Can threshold\n",
    "        below one upper threshold or between an upper and lower threshold.\n",
    "        Results stored in self.mask.\n",
    "        \n",
    "        Parameters \n",
    "        -----------\n",
    "        use_slide: boolean\n",
    "            if True, remove pixels between a lower threshold and upper threshold\n",
    "            if False, remove all pixels below threshold\n",
    "        thr_up: int\n",
    "            upper threshold when use_slide = True\n",
    "        thr_down: int\n",
    "            lower threshold when use_slide = True \n",
    "        '''\n",
    "        if use_slide:\n",
    "            below_thr = np.asarray((self.img >= thr_up)+(self.img < thr_down)).nonzero()\n",
    "        else:\n",
    "            below_thr = np.asarray(self.img <= self.thr).nonzero()\n",
    "            \n",
    "        mask = np.ones(self.img.shape)\n",
    "        if np.any(below_thr):\n",
    "            n = len(below_thr[0])\n",
    "            if n > 0:\n",
    "                mask[below_thr] = 0     \n",
    "        self.mask = mask.astype(int)\n",
    "        return \n",
    "    \n",
    "    def remove_small_pixels(self):\n",
    "        '''\n",
    "        Removes small pixel groups from mask using an erosion & dilation operation.\n",
    "        Results stored in self.mask\n",
    "        '''\n",
    "        #Ensure datatype compatibility with OpenCV\n",
    "        mask = self.mask.astype('float64')\n",
    "\n",
    "        #Erosion & dilation\n",
    "        self.mask = cv.morphologyEx(mask, cv.MORPH_OPEN, np.ones([3,3])).reshape(mask.shape)\n",
    "        return\n",
    "\n",
    "    def get_spheroid_inds(self):\n",
    "        '''\n",
    "        Counts number of spheroids in mask and returns spheroid indices grouped by spheroid.\n",
    "        Number of spheroids stored in self.n_spheroids\n",
    "        Mask with spheroids labeled by separate indices is stored in self.spheroid_mask\n",
    "        '''\n",
    "        s = np.array([[1,1,1],[1,1,1], [1,1,1]])  #include diagonally-touching points in spheroids \n",
    "        spheroid_mask, n_spheroids = scipy.ndimage.label(self.mask, structure = s)\n",
    "        \n",
    "        self.n_spheroids = n_spheroids\n",
    "        self.spheroid_mask = spheroid_mask\n",
    "    \n",
    "        if self.plot_mask:\n",
    "            plt.imshow(spheroid_mask)\n",
    "            plt.colorbar()\n",
    "            plt.title(\"Spheroids: n = \" + str(n_spheroids))\n",
    "        return \n",
    "\n",
    "    def get_spheroid_pixels(self, input_img, store = True):\n",
    "        '''\n",
    "        Gets all indices and pixels from original image that correspond to each spheroid.\n",
    "        Results stored in self.spheroid_pixel_groups\n",
    "        \n",
    "        Parameters \n",
    "        -----------\n",
    "        input_img: \n",
    "            2D image data rescaled to lie between (0,255)\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        spheroid_pixel_groups\n",
    "        '''\n",
    "        flat_mask = self.spheroid_mask.reshape(-1,)\n",
    "        region_labels = np.unique(flat_mask)\n",
    "        spheroid_pixel_groups = [input_img[self.spheroid_mask == n] for n in region_labels if n > 0 ]\n",
    "        spheroid_ind_groups = [self.pixel_inds[np.nonzero([flat_mask == n])[1]] for n in region_labels if n > 0]\n",
    "        \n",
    "        if store:\n",
    "            self.spheroid_pixel_groups = spheroid_pixel_groups\n",
    "            self.spheroid_ind_groups = spheroid_ind_groups\n",
    "        return spheroid_pixel_groups, spheroid_ind_groups\n",
    "    \n",
    "    def thr_pipeline(self):\n",
    "        '''\n",
    "        Thresholds image, finds spheroids, and labels pixels according to spheroid index.\n",
    "        Pixel groups stored in self.spheroid_pixel_groups\n",
    "        Labeled spheroid mask stored in self.spheroid_mask \n",
    "        '''\n",
    "        img = self.rescale_image(self.raw_img)\n",
    "        self.auto_threshold()\n",
    "        self.set_mask()\n",
    "        self.remove_small_pixels()\n",
    "        spheroid_inds = self.get_spheroid_inds()\n",
    "        pixel_groups = self.get_spheroid_pixels(self.img)\n",
    "        return \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dIjEJD42i5GfmqyLkquj2qQlfd0eFx+4rua9weWYAOropbps0qzf+GNtZ6b4N4TMlBHScuKWx5fDee+/B2toaI0eOxLRp02BpKbtiGjdunN6DIzWz8NAtLDx0C2UrBwMA+sQozhXZcP6R0tcaS6EVlwWHYG/F3hxPiaW5wmNNqt1XqGMjxhuNFBNDZw8ntKqnWFqW2g1EyDR+e+bPnw9vb2/5z+fPn0eXLl2wfv16vQYmVELuCajeTZH4NAfejRxw9mGWwrarlNRlMDaqWkfaer9jI7zdwgXjd8tG2qm7cn2+qB+sLJQ3xI9N9Ff7PkJalVXISsqMr0xobkEJ0vIK0ayuRKvtH7/Mh8TSXG23JltUJoeLFy8iOTkZGzduxNixYwEApaWl2LZtG/bvN7zx7qag+tIWHb89ge2jfJRu+zKf34IuhsBcJIKX6+sv7ciOjbDmpGy1WQfrql8dVzsrlfuxtVT+NRPyhYYQLTlyB2E9m/EdBqt6rj2NxNQceStfkyZLjkBiaY7cZQP1HJmaew4ODg5IT09HUVER0tLSkJaWhqysLEREROg9KFIzT7LzFR6LvfGch0iMg0hUtbWwanA7+b/tWOyy4rMv/dzDLBQouSclROmV7qMZi5qsVZZXxM3fS+Un3MvLC15eXhg5ciTq1avHSTCGQMgXe8pOMlRykR0iiGBuJsJ73g3xf4mpml+gzT55/jA9yHwF/x9OYWwXD34DIYKkMjmEhYVhzZo1GD58uMJzp06d0mtQQibk4YdcXVGYCmV/6e2jfLGF5Ut9vhoOWeVdi79fe8ZTBKbn7zvp8PNwhIO1uMrj+cWlsBErDnrgk8rkUDERzZQTgTLN62qe+UoMW0gnN2y7nIKxXRornLjNzEQwq5Y2xvi6K93P4Lb1Efuv6m49Ec/t0Ip3N5T7T22UjPgyJOl5hej7cwL6t3LFgU+7VXlu8p5r2PBBR54iU07jPIcLFy4gPj4eJ06cQN++fREbK5zlj9kwpF19pY8vH9RG6eMuEv2PEiC18zBT+4UHny/qp/DYlg99ULZyMHpXmrSmqsFYtnIwNgZ3UvrcH6FdtLrRyNeqrAJuBCs1oI1hd2/nF8tGW914prjS9MaLj1FYIqyWv8bksGLFCnh6emLTpk3Yvn27wjIYhm68v6fSx7WtjEaEp+myo1pv61ipeT/n7RYwq/Znb99Atnz81DebshJbZYZ2cuabsXz3VP0aCw/d5jQOTTQmBysrK9StWxcWFhZwdXVFUZFxjRgIal0Pxyd2V3jc0L63ebUc02+qLCvNTVg2sA1KVlS90ne1s0LZysEY1qGh3mIwknOeRhbVM6+ODP04afrtn1Wqz/4o6xV28DyYRGNysLOzw9ixYzFgwABs3boVDRvq70vCF+9GDgofXFVXdUK92gvf/y/fIRAd8f1R4uuex3veNTuHGHtRpMq/XfcfTiNk6z+8xQJokRy+//57LFu2DEOHDkXnzp2xcuVKLuLilJONGEXfvKP0uWk9q3YnCPXzmS41rhadKeHrM8XXhc7mEOUTMzUR6FdPrV/OPsTtNGmVx7T5ez+t1Irgi8bkkJGRgWPHjuHHH3/EX3/9hV9++YWLuHj3QUc3uDlaY1J3zyqP929l2DfFCP8mdvfE9IBmvA+LZvvdo7Wsj13TX1uoF2bqTNidiC7fy+rV1OT35rO1pHGa57Rp0+Dv72+U3UmqLBvYGo0crfF4fqDCc0IbrfRzwgO0b+BA6/NA9kX64s+bfIeh0dryk+gPp2RLcXD1t0t8moPDt9NQWFqG9g3sMXbHFVb3P6G7JybtVV6jxRT8desFXkiLMKp8aPPddFm545yCEsw9kASnanMbVMl89boXYNb+JMx+uzlcJKqXZ9EXjclBIpFgxowZXMRiEIR2Ep64R/ZlHN6hAc+R8O95biG+OZas9fb/N0ZWgjY2tAu2/PNEX2GpxHW7oeO37BQn0kX08A46JQzvhg4ql5QoE3jTIWjdOQCQJ4d31p+TP/fV0defS03nkPB9r+8frjpxF6k5BdjyYc264mpDY7dSy5YtERcXh3v37uH+/fu4f/8+F3EJllA/n3tplqvGtO1ZxwaOlRbM+493IwDAoLb1sX0Uf7XKhfqZYsOEat2ymhz4VHURMUM7TgUl6leRHbnpYpWft11OwX8vPEZRadXXVf8ZADZeeKz30UwaWw5JSUlISnpd51YkEmHTpk16DUrIDOzzaVI0XYnvC+2C4jIGvt/FcxKPJkId+abOl0GtsODgLZ1e8/vHnXEnPQ+RfyrWyz4+sTuijtzG0TvpAIAG9qq7TwzteKkKtyLJ7a62RldpGYOxO68gpJObxn2H7pR1CX6gxbY1pTE5bN68Gbm5uUhJSYGHhwckEu3WHTdk5gbyKZzxx3W+QzAo7Ruqr3/OF0O6Ip7X10vn5DCkvazLU1lyCGheF4eb+8MsXLbygkgkwsxezfCtknojtZ0noQ8Mw2D0tsv4tFtj+WP9f0nArtF+Kl/zSsMquMoWyywrYzBm++WaB1oDGpPDoUOH8NNPP6G0tBRBQUEQiUSYNGkSF7FxbnNIJ0z/33VM7uFZ5fHVQ9qhqbMthvx2QVBjrb8/KdwuvmYCXoNq78d+8qUM+MT32ko1FTPCG+cfvcS77epj6G8XAAAXpves1T5XvNMWbzZ1VruNhZlWVY05VVBShm2XU7D32utWwOHb6dh19anK1+ToWL6WYYCXBcWcr7Cs8Wj/9ttv2LVrF5ycnDBp0iQcOXKEi7h48aGPO9K+DFIozhLWsxl6lH9whZMahK2VK3eLpMWceQCz8Fitx4YPbd8QwXpsjuvK0D5Tn3Vrgl9HvoF3270eBOHr7lSrfX7+VnN0bVKnymOtXKv2UojNDSeZjt+diAdZivVVKlhE6LZGnZiHxKix5WBmZgZLS0uIRCKIRCLY2NhwEZfgVHwsBdRwEDQuW1gVo2F+PPWAs/dkg4H0XiJh6pvw/0H56szHJvojrdIEzIvTeyIxNVfeJ64Mw8haGtdTFRegq6z6PBB3R+M595Tp8PUQifj5rGhMDn5+fvj888/x/PlzLFiwAB06aDfRxdjwPWGpuhQlVd9M3caLj/kOoUaE1FWpTPUr+sp6NXep8rOPuxN83J3UJgdA1tJQ1dqoWAyxgb0Vbr54PbtYaMPIK9M0Mqk2diemKty85oLG5DBz5kzEx8ejTZs2aNasGd5++20u4hIsoXxA96upEyAEwjhKwiasy42qEj/vBe9V+pkXoek6a1bv5pBYmmNKj6ZYfiwZWfnFWB1/j1rtHFPZkVVaWoqioiJMmTIF/v7+GDNmDLp3744xY8ZwGZ9gCKlb6dKTl/LJb0Ixwb9JlZ+FcJwMhdAOVV1bMa8ju6wszDGzV3NYWphhfqAXIvu0ACCs41Rxn+vtn87wGodZeCwaLv5LL/tW2XLYs2cPYmJikJ6ejqCgIDAMA3Nzc/j68jdZiE8VVztCOOltucT9bF5lfv+4M4ZtlI1WWTm4Lbo0rgOGYfDj6fuCaWEJmcB6KuUSw9/iO4QqKkZ1Can7bU75sNxzj17yHIlsZQB9UJkcRo4ciZEjR2L37t0YMWKEXt7ckAhh2OGCgzex5MgdvsOQqxi/DgC2lhb4uLOsUP2v5x4JIokaCiEdq9G+7mjoYA0AaOEiQXL5+kAAYGVRuxEz7RvY48rTHJ2/SUJNosZO4z2HHj16YN26dSgsfJ2dpkyZoteghIzP77GQEoM69GXWjtAGOQDA0gGt5f8+NbkH/n0uG1F0akoPeDhpP1ro6ue9FCaT/jW+G64+zYGFec2SjIByqKASur7Qqqxaet2tZAKfCg1cJZZIy1NfP4Kro5Sep58mNZeE1AVXuXVQz94K9cqXs+juqX6CWnUdlNyzcJFYoU9LV51jEtL9PlNCq7JqSf4B5TUKbh2b6I/ePyUoPH4t/C2k5qqecCYCd1/kxy/5L4pSU8JrNwhTRQtLSEnUFGhMDhWrsrZp00b+R2raVHWx9eLiYkRGRiIlJQVFRUWYOHEiGjRogAkTJsDT0xMAEBwcjIEDB2LXrl3YsWMHLCwsMHHiRPTu3RsFBQWIiIhARkYGJBIJli9fDmdn3a5a9EGAPQB6V30Me4XKV5TKiEQizlpYxvBnoSti9ajlwA/WV2Xdt28fnJycsGLFCmRlZWHYsGGYPHkyxo4di9DQUPl2aWlp2Lx5M/bs2YPCwkKEhISgR48e2L59O7y8vDB16lTExcUhOjoa8+bNq+WvyR5T+4A+mR+IyynZ6NDQHp5Lj2r1GhFMq4VVU/KuSg7eq0TJss+GQkgjBSuYQitGq1VZdREUFIT+/fvLfzY3N8f169dx//59HD16FE2aNEFkZCQSExPRqVMnWFpawtLSEo0bN8bNmzdx6dIlfPLJJwCAgIAAREdH6/gr6YepNm0bOVqjkaO1yuetLcwUZody2cr68+YLrbYb2l54xZCuPpUVtfnx1H0sf6etXt8r7H/areBrIzbXaxw1IYSRgqZIZXJ4//33VY6m2LFjh8odVizpLZVKERYWhunTp6OoqAjvvfce2rdvj59++glr165F69atYW9vX+V1UqkUUqlU/rhEIkFurvr1V7jCR9M2v7gUKdkFaOGiv2XSM77sj4xXxfD6+m8AQEgnN51Wf0xd2E9pMRKujtOVlGy1z79Y3A9Xn+bgLRVdZHyqGCb6+/Vnek8OsVrMqP/rs26ws9J4vcgb07os45/KT8K3335b452mpqZi8uTJCAkJweDBg5GTkwMHB9nohcDAQERFRcHPzw95ea/HUOfl5cHe3h52dnbyx/Py8uSv4xsf9xxCtlzCHzeeo+DrQVqvOKqrOraWqGP7ui62o412dW7VbS+CiLMWlqY/S01HyHChIoFWnkugL9qUQujk5qj3OGqCRgryQ+WAYzc3N5X/qZOeno7Q0FBERETIJ8+NGzcOiYmJAICEhAS0a9cO3t7euHTpEgoLC5Gbm4u7d+/Cy8sLPj4+OHFCtqZLfHy84GZkc/nxPHQrDQBQyjB4ITWcIZsikbD6hwnwTItZtHUllhq34YMQRwqawueb9TZkTEwMcnJyEB0dLb9fMGfOHCxbtgxisRguLi6IioqCnZ0dRo8ejZCQEDAMgxkzZsDKygrBwcGYPXs2goODIRaLsWrVKrZDrBG+p/Bz9bb9W7nipzMPlD7X1NkW9zNfadwHlzekzQRYHUxbXN6/Ki413LOZEG9ImwLWk8O8efOUji5Sdp+iYomOymxsbLBmzRq2w6q1ig/onTT9dwFUqPxd0HfLoeDrQSgpK1ModFTZrdm9tTqdyYaycjM6pnItAWKchDYYZNs/T5BXpL7UpzHQOI/9+fPnCA8Px7hx47Br1y5cvXqVi7gEp+L6dBNPi94NKS/HqC+WFmZqEwMAWJibQazF0gdc3p/5OzmduzcjvBBa23DUNm5rOfNF4zd9/vz5+M9//oOioiL4+flh6dKlXMRF8PpLcS01h9c4akIY13jEmFC3Erc0JofCwkL4+/tDJBKhWbNmsLJSPTPWmPG5SFq3NcpLNOpqSLv6rOxHEy6XzzBkdIy0w+VkQU1MqQKjxnsOlpaWOHnyJMrKynDlyhVYWgpzRIO+8ZEa2PwypC3uj7oSS5y6n4GAtfotUCISCeOLLHS61BE2ZXwPBqksv9hwZ5rrSmPLISoqCnv37kVWVhY2bNiARYsWcRCW8Bjy2ko2YjM4WGs/9mDVu21RX83aSZqIwN3aSuroc/IgG0opO2hFSC0HU6IxOVhZWWHEiBGIi4tDly5d4OgozIky5LVmdW3l/57U3RN5Xw3S6kZyhRkBzZG6sF+N35+LRPrfC4+R9Fz97PkTk7rrP5BaKBNAAq2w8YOOfIegkpAW3hPCRQ9XNJ4xZs6cKV/CwtHREREREXoPSoiEWJhFFXWf38rr1PRtqb8lJfT9HRq78wrarTiu3zfRs1IBnWgGtK7HdwgqyYeyCuBwCSAEzmhMDvn5+QgKCgIADB48GPn5pnNDhm81/TJoc3XT3bMO/hrvX7M30IBWZdVO5W4lfSyh8V38XSQ+1W6km6udcAeaGM5lmXHRmBzEYjFOnz4NqVSKhIQEmJnVro4s0QeIYmIAACAASURBVJ6yBe20sTnER+Vzvu6O6NWsLn4Y1qGmYWnEZT0HQ/Yyv1j+76B1Z1nf/+f7/kXHb08gQ0PVPkMhlElwpkLjXcolS5Zg+fLlWLJkCVq0aIEvv/ySi7hILfRoqro4krXYHMf03Bf/JDsf11JzUVJaVuN6waagpFLLoUCPo2AM/aQqlOUz1p19iPXnHvEbhAoMw7De9a0xOTRp0kQwNRWIZkfKu4oCvVxw+HY6L6OsrqXK7lEdv5uBvl78rIg6rmtj1BdwVwlQtVtJn38nTfUQ+vH0N9LW6+Uz+DV+dyLPEajGMOx/hjQmh5iYGPz666+wtn5d8OXUKXYmZRHVatItU7ZysPzfQ9o1xOHb6TDj8UY6n1/mde+9weO7a6dyy0Gf6wf+dTtN7fMf+qpfaVkoqKtSNX0cGY3J4cCBAzh58iRsbGz08PZEX0K7eODf57lY3L8VbzHQl1m9yslBn9XOPtz6j972zRWaWKme7LvG7mdIY4ewm5tblVYDESY7q6rlHa3F5vhxeAc46Vi8R+j2JD5Fv58T1G4zt29LjqKpHWuL11+/zHx2bhr3/yUBexKfsrIvIaERS+rx0nIoLi7G4MGD4eXlJe/7E0qNBWOm60X3mSlv6ieQWtDHB/a9TZc0v6+BXGKuGdYB7244DwCQFrKzBPTh2+k4fDsdZSsbyR9r4SLhpNqcvhnK35UP+jg2GpPDp59+yv67GrjQHVcgMgPWjxTOrNJW9ez4DkEBX19mQxmdU6daqy6noBgO1uy09BIeZMr/rSkx2FiYq31eCEQi7krPVtdo8V9aVdLjkz6OjcZupbZt2+L06dP43//+h5cvX6J+fW5W9hSyjRcf47fzj/X6Hrr+qXVZHsPYGcoVZvUv9JUU9pZm//rvZK23Hd6hIWvvqy98rvTLdmLQx2AJKz0keI1nlMjISHh4eODBgwdwcXHB3LlzWQ+CGCfeSqry8q660+fhif33udbbGkKpVa5vSGe+KoJZeCxibzyr0evLVg7Gd0PaKX1On8vWsEljcnj58iVGjBgBCwsL+Pj40AgUjuhynHt41tFjJDXH1yfFUD6ibMep63fT0twM/856i90g9ES20i9371dRYGvVibus77uJs63CYw3srbBrtC/r71UbWvVF3L0rO0DPnj2j5TM4sv689jMxW9e312Mk/Bmw7iw8og7r/LqGDsKe/FaherfSWz+dgePcA0q3/Xj7ZVjN3g8AuJMmhVl4LI7eqTp/QdeT59st6qJ1PcP47BSVluGbY9p3ldXG0N/Oo/dPshFx8fcyNWytm7q2Ve8pBXrJWhE/Du+AEW80UvYSAPqdB6OKxhvS8+bNQ2RkJO7evYuwsDAsXLiQi7hM3pqT97XeVqidArW90jt0K618P7IdaVoeYGE/LzRysEZol8a1e2OOKDs+uYUlSretqF3OMIz8hLXtcgr6tHSVP67t4XZztEZKdoHO8ZqKfTe075Kr4CKxxBuNHHD0TjpauVatIyI2F+GPsV1gKzZXqDEy5+2W+LRrEwxr30B1PKFdENDMGU7zDlZ5/NhEf9iIzWu8BpsmGpODp6cnFi5ciLZt2+LIkSPw8vLSSyCkqqQXUq23FWovyjfHkjGobe0HMDjNO4iGDla4Oftttdst7MffhL+acLbVvaqiecR+hRuaOQXFCicOdXzdHZGSXWC0LU4+vFjcH09e5qPxkiPo6Fa15s0Ef08EVVsSfbx/E/yc8BD17KzQu4X6exDvlH+HBrauhz9vvpA/3qu5fu9daOwjCg8Px9WrVwEA9+/fx5w5c/QaENHeF31a8B2CWifvs9Mkzy0swe00wx+nX513IweljxfrcCVYVFKGR1m6LaPv4+aEoxP88fXANjq9jqjn7mSD+MndsX6k5tFIq4e0w98T/NGugeoEvfVDH5yf1lP+8/ZRvjg1pQcrsWpDY3J4/vw5goODAcjmPLx48ULDKwhX3mgou0Jpr+YDZkxOs5RshO7tn7Sr8c0wwBurjsN71Qmd9i8SAb1buMDSgu4fsu3NpnVhaynrkGlRV9aF1La+4hwkKwtzvKWhxRDcyQ1+Hk7yn+2tLdDdU/WKy2zTqrDw/fv30bRpUzx69AhlZaZTYJsPma+KqiyroE6glwvOhfWEn4dplG5NeJjFdwicOP1Au9/zVVEpbhlhi8pYDGpb36C/nxqTQ2RkJKZPn46MjAzUq1cPixcv5iIuk+Wy4FCVGtDqmIlE6NzYSfOGRoKGUVe162rN1lAyoIq3BqH6umaVGfL3U2NyeOONN/DHH38AAJ4+fYpGjVQPtyLsuJfxSqvt+FyOmw/pRlLRrLZupWk/WIHo36N5gaztK3VhPzAMU2XFXr5oTA6bNm2CtbU1cnJysHfvXvTs2RNffPEFF7GZnBIdh6QZwMRWVq04rnpCUsV4cVOwUs1xINxjc+Xj+vaa5+g4WFsgp0D5kGc2aezcjouLw9ChQxEfH4+4uDgkJSXpPShTVapjt4khLHvAldjQrnyHYDAM+VNDXYtAyvxAZC8ZoPf30dhyEIlESEtLg4uLC0QiEbKzs/UeFNEO5YbXaOSN9no2rct3CDVWxgDmJv65l1hpNY6o1jR+o7p27YpRo0Zh1KhRWLZsGfr168dFXEbr3MMspOexs8qjqd1zILW3cnBbBDQ33ORALQfuaExBM2bMwIwZM5CdnY3w8HBYWqqf1VlcXIzIyEikpKSgqKgIEydORJ8+fQAAsbGx2LJlC3bu3AkAWL9+PeLi4iASiTBhwgQEBgaioKAAERERyMjIgEQiwfLly+HszN3YXn3z/+EUmte1xZ0v+tR6X5QciK5sxcKv3aCOAO7TmgyNyeHChQtYvHgxSktLERQUhEaNGuG9995Tuf2+ffvg5OSEFStWICsrC8OGDUOfPn2QlJSE3bt3yzN/Tk4ONm/ejL/++gv5+fkYOnQoAgMDsX37dnh5eWHq1KmIi4tDdHQ05s2bx95vzJNbL6TILigGANzVcjSSJsbYrXTx8UtYW5ghMZW92gaG6MnLfEQduY3Qzo1ha2nYJ3Q2CaWQ05B29fFHDdZgMiQau5VWr16NLVu2wMXFBRMmTMD27dvVbh8UFIRp06bJfzY3N0dWVhZWrlyJyMhI+eM2NjZo1KgR8vPzkZ+fL19U7dKlS+jZUzZlPCAgAAkJ6usFG4o23xxDtzWn5D9fePRSYRtti8wvGdBatr0Rthy6fH8S3qtOYNS2y3yHwqvGS45g3dlH8P/hFN7QcQa0Om8bSC0BVYTSchBIGHqlMTmYmZnByckJIpEIVlZWkEgkareXSCSws7ODVCpFWFgYpk2bhrlz5yIyMlLhtQ0bNsSgQYMwbNgwjBkzBgAglUphb28v31dubm5NfzdByyksrvFrI/u0RNnKwSxGoz/7/32O2zQuXxCCO7nBy1V45WR1EX36AQpL2Km3XRumcOtDY7dS48aNsWrVKrx8+RK//PKLVpPgUlNTMXnyZISEhMDT0xMPHz7EokWLUFhYiOTkZCxduhTdunXDixcvcPToUQDAuHHj4OPjAzs7O+TlyZYEyMvLg4OD8sXJDJ22rYTqGmgxDlpI3t1wHgAMJpkZs/HdmvAdQq1F7P8XOYUlWNyf3xV4J/XwxMUnLzH1zaa8xqFPGpPDwoULsWfPHvj6+sLGxgZRUVFqt09PT0doaCgWLFgAf39/ALK5EgDw5MkTzJw5E3PnzsXFixdhbW0NS0tLiEQi2NvbIycnBz4+Pjhx4gS8vb0RHx8PX19hVUdi07HkdBy5k4ZezeqiX6t6Gref1rMpvhvSnoPIiDHq2sRwl3KoLPOV/mbKl2nZb9XD0xkpC4x75KbG5DBhwgRs2LBB6x3GxMQgJycH0dHRiI6OBgCsW7cO1tbWVbbz8/PDmTNnMHLkSJiZmcHHxwc9evSAr68vZs+ejeDgYIjFYqxatUrHX8lw9ImR3U/56miyVlfWJtCSNTnfDWmHGX/c4DsMUk7b2tvUrQTA3t4eR48ehaenp7xEaNOmqptS8+bNUzm6yN3dHbt27ZL/HBYWhrCwsCrb2NjYYM2aNVoFLwSxN56hjAGGqKnkpI0zDzKR+FT9CB1T+ECammk9m1FyEJD8Yv7vZwiFxuSQmZmJjRs3yn8WiUTYtGmTPmMyKEN+uwCg9n3qb/54WuM2MwOa1eo9iGmzNKdZ5ER7GpPD5s2bkZWVhcePH8Pd3d2oJqTpqvrY5qjDt2u8ry3/PNFpe2O8ofv4ZT6+OZYMG7E5xvi646GOFc2Iboxl6PPRO+n4/uQ9TOtJF0v6pDE5HDhwAKtXr0bz5s1x584dTJkyBUOGDOEiNsFbeOhWjV+78cJjFiMxTKE7r+DonXQAsuNR0yW5OzQ0jUp4RObmCylm/HGDt+TwWbcmams4GAuNyWHjxo3Yu3cvJBIJpFIpPvroI0oOpFZ+v5aKc49eorTSyJDa1Gp404AXkiPCos1tvZgR3nqPQwg0dkKKRCL55DU7OztYWRnWOHs20f1g7TR1Vl/J7j//vYhvjiVzFI3w+bgZZhlJYty0mgT39ddfw8/PDxcvXkTjxo25iEuQaLSQdlq5SnA/s+r6Uf88eQkf96rj7HML2SlYYmXgy3UP69AA/6TQUvhCsO7sQ75DEAyN36ply5bBw8MDZ86cgYeHh8ZJcISM66p4ARH481mFxy49YeeEuLgfv7NlifE4fjeD7xAEQ2NyuHz5Mry8vDBgwAB4eXnhypUrXMRlVJb/bVpdKFwOmbwysxfsrbkpfkIM3382XsCs/f/yHYZB0PitqliFlWEYJCcnw83NDZ07d9Z7YEJU026lL/40rdKqxjJkkit0vLjz+/VnAIBv3mnLcyTCpzE5fPvtt/J/FxUVYfr06XoNyFCZhcfiy6BWWHDwFtYO7wA3R2sM/e0CXCXqiyNpo0kdGxYi5FdWfjHMwmORGRXE6n6Fsr5/bYiNsTAHy2zEZsgvLuM7DJOiU/u/tLQUjx/T+HxVFhyUzXuY8vs1fHX0DgAgrRZDNAHgx2EdcHJyj1rHJhRJz41zCfbacLat/QWEsavpKsZsSVvcn9f354PGlsObb74p/3dJSYm87oIp0vYqlWGAc0qK+dTEsA4N0NDBWvOGBoLtLhRjGEHmwkLr0tg1dbbFDR4vLOqa4N9IY3I4deqUpk1MhjGciIjwvNuuPvq2dMGR8tni+nBnztt62zcXvh/aHn1/5r4q5NstXDA/sCUA4PLMAFiYGfawaV2oTA4zZ85UeZVnzMtoq9Oqnh3+vPmC0/c09DH81VHvuiKRSIT/eDfUa3Jo7qK+gqPQ2YgVvwfZ+cVwtBHXar97Ep/ivU2XcHN2b6VV8ho5WKFXc1lp1TcamdZkRZXJ4YMPPuAyDoPw1cA2+C7+HqfvaWz90QUsl3g0lsYcjVhSr7BE8Wb0C2mhPDlIC0tgZ/X6dJZfXAqxmQgW5mYoLWNQWFIKW8vXz0sLS2AjNsemi7IFMP95kq00OQilZjUfVCaHixcvYtKkSQCAFy9eoF49zZXKjJ0lx1fxhnq+UBd275/Y7RpgjKSvz9MIRqTpk7I+/6N30tHS1Q6XU7Lh+108dozyxciOsjLGki/+RP9WrjjwaTeM2vYPdl55WmVlY4e5B2BtYYaC8qSj6lNUZiSfr5pQebY7e/b1jNbw8HBOgiFVmfDnUmvGcoj6tHTV275vzu6tt31zpUNDxVry8fdks5kvPZEN/jh0u2qX76FbaQCAnVeeAgCeZhdUeb6gUmuEYRiF5wHj+XzVhMrkUPmKzFiuzgg3GnN4FczGPBJj17yuYd9vUKXirFRxetI03NU96rDafSl7Xtua0sZIZXKo3AdK/aFEF8qu8vTh5uzeaFxH/QqwBDA30kl2IsgWdHyQJVvkUdlpStuyn0nPpUofN+HcoPqew40bN/DBBx/Il82o+LdIJMKOHTu4jJEQpVoa+Aicytg6fQd3csP2yyks7U3YEh5mwW/1SfnPyo6h5Is/tdrXsvJJq9X1buFSk9CMgsrksG/fPi7jIERnxtSiNWPp6j60i4fJJAdlZWWf5xbiWa7ivYOaSFkQiAb2plu/RmVycHNz4zIOosTUN5vyHUKNOVhbIKeAnXoNRDuOSlantbcynRVrRSKg4eK/WNufMa1MUBPGNcPKyKwe0o7vEGpscX+qscCVAa1fDzOv3kduSmsC8b3+krGh5CBghtxtYriRGx7PSmVZ29SrOpGL67k5fGLz68JlTRKhoiNA9MKEB3nU2AflE7iU6djIQX7ye/XVwCrPVT4nujvZVJnsZUp+TmCvxOcPw9qzti9DRcmB6EWgHid1GSt1CXV6QDN8Wd5VV31oasXN7MotTXdHawxpV5/1GE2FmQG32tlCyUFHVz/vxXcIBqFtA3u97PfnEd562a+QbPvQB280qjpXZIyfB+b29ULZysEKJy5lp7FH8wPx+9gueozSuBnr3BBdUHLQUVNnmnTFp35eptci6d+q6u9sJpKt1utsK5b/DNBKBmyi3EDJQWd2JjQ0UAiCKp0Yy1YORhMjTs6qzu0HPu1W5WeRSIT8rwdhgr8nANPpAoke3oHvEEwKJQciaO0acLMUhxBUXPmLRNqN9vq4swecbMT40Met/HWmkSS40L8VrULN+mVwcXExIiMjkZKSgqKiIkycOBF9+vQBAMTGxmLLli3YuXMnAODEiRNYu3YtAKBt27ZYuHAhCgsLERERgYyMDEgkEixfvhzOzs5sh0kMhMTSnO8QOFPRcNB2vH4LFwkyo4KQ9ap2dcqJovomPDO6Austh3379sHJyQnbtm3DunXrEBUVBQBISkrC7t275VdHUqkUK1asQExMDHbt2gU3NzdkZWVh+/bt8PLywrZt2zB06FBER0ezHWKtNTLxmZNcUrae/vZRPhjeoQEP0XCDGgDKSaxM50JBCFhPDkFBQZg2bZr8Z3Nzc2RlZWHlypWIjIyUP3758mV4eXlh+fLlCAkJgYuLC5ydnXHp0iX07NkTABAQEICEBO7rxmrioGSZAqIfyrrh3+/oht0fdeY8Fn2rnAcpQSgylXsrQsH6WU4ika2UKZVKERYWhmnTpmHu3LmIjIyEldXrplpWVhbOnTuH//3vf7C1tcWHH36Ijh07QiqVwt7eXr6v3NxctkPk3cxezfDtCW7LjRoqUxqAw5SnQl1PgU42Yozt4oHPujZhPygBGdbeeFuLQqSXG9KpqakYM2YMhgwZAk9PTzx8+BCLFi3CzJkzkZycjKVLl8LJyQkdOnSAq6srJBIJ/Pz8kJSUBDs7O+Tl5QEA8vLy4OAgvBuS5rW8glk52HDXTNIFG/cLTOliseJzpevvLBKJsH5kR3RtUkcPUQmHraUFlg5orff3+aRrY72/hyFgPTmkp6cjNDQUERERGDFiBLy9vREXF4fNmzfj22+/RYsWLTB37ly0b98et2/fRmZmJkpKSnD16lW0aNECPj4+OHHiBAAgPj4evr6+bIdYa7+P1W+XRtfGTjgX1lOv78GF2nYDfNatCSLeas5SNMK3ZlgHTOzuiXfb0RWyKlw0JE2osaoW691KMTExyMnJQXR0tPxm8rp162BtXfUmrrOzMz7//HN88sknAGT3Kry8vODh4YHZs2cjODgYYrEYq1atYjvEWmuh5yIzCUaQGADdJxINaF0PB26+rgMcYwKzoSurb2+FteVj+SWWdF9LGS4m+plSV6Y6rH8C582bh3nz5il9zt3dHbt27ZL/PGjQIAwaNKjKNjY2NlizZg3bYREesDXu/s9PuiKvyLRqQ2wf5QOPqCN8hyE4XJy3lY2QM0V0eUL0xoKlNQiCWpvehCQ3Rxu+QxAkLs7btAyJDCUHgTkz9U2+Q2CNvZUF0vNqNkHrwnTj6Fqrjcg+LfkOQXC4OG9XL5hkqig5CIifuyO6GfmIE235ujvxHQLvlnAwMsfQ1ClfbFCf6kos9f4ehoDWVqqhxM974dNuug95OzGpe5Wf369U4IUuWICL03vi2ER/vsMgAjXRX/9zOb4aSEkZoORQY+0bOuDnEW/o9JoODe3Rs1ndKo995Och/7e1kZZ07O6pvjXkWUfWv964jg183J3Qq7kLF2ERA2TBQflOKwtapgOgbiXe9W/liujhHTBp7zW4O5nWTcjMqCAcS05HoJcrDt58gUFtqXIZ4degNqY3+EEV47xUFShlN9NEIhGcbPTfj8qHilEfLVXMC3GyEWNYh4aws7LAiDcawUZMV2yEX/ZUr0WOkoOAGOsIusg+LXHg065wrLRg4Y/DqHALIUJGyYFDqk7+jcv73Ns31E/dZb74e8rqcDhai9G/VT30bvH6XkKflnRfgRAhozYUhxgV45G6ezojYeqb8PMwruGbv458A+FvNUe98sIpFTNP3/NuiFb17PgMjRCiAbUcOKRuFE7XJnVgbmRVzW3E5ujk5ij/uWtj2ailL2hyFyGCR8mhlh7N64urn/fSuN3Xg9pg9RDTWKpbldm9W+DfWW+hY6WEQYiuni3sh70f+/EdhtGj5FBL7k426NBQc82Jli4SiCuN0W5tgt0qZmYitK5nXPdVCPfq2VuhW2P9rCQQUG0ekimj5KBHG97vKB83XX2B0ovTeyL9y/48REWI4WvAYh33yhdq4zmYgW0o6Ia0HvVs6oySsjLEJb1Am2pXzLaWFrClJVwI4V3FPKOPO3uwtsy8MaDkoAcLAr3wea/msLe2QLO6tnj/DTfYW9OhJoRP6mq35y4dAGuahFkFdSvpQaCXqzwZiEQiSgyECED/VqqXxpBYWRjdaMHaorMWy0pXvENNU0IEqJmzLcpWDgYAmIXH8hyN8FHLgWWUGAjhxtstaj7Lvk09O2opaEDJgRBikI5M0K3uR+X1CW7M6o34arVVSFWUHAghJqF6beg29WUjCGf3bsFHOIJH9xwIISbJyUYsvwdBFFHLgRBi9MxEgIeJFdOqLUoOhBCDNfXNphq3ORfWEyUrBtM8Bh1RcmDJno/8EOhFNQoI4ZJF+Ygji0ojj5rVtcXgSiVnOxhZnRSuUHJgybAODXHoM91GTxBCaqdi5PjSAa3ljyV/0QebgjsBABysLajFUEN0Q5oQYnQcrC0wxtcdn9FCejVGyYEQYnREIhE2lrceSM1QtxIhxGCJQLOc9YWSAyHEYFlayJJD5UJahB3UrUQIMViRb7dEcSmDCf5NYGVhBj93J75DMhqsp9vi4mJEREQgJCQEI0aMwNGjR+XPxcbG4v3336+yfVlZGT755BNs374dAFBQUICpU6ciJCQEn376KTIzM9kOkRBiJCRWFvjmnbawFptjYndPdG5MyYEtrCeHffv2wcnJCdu2bcO6desQFRUFAEhKSsLu3bsV1jdZvXo1srOz5T9v374dXl5e2LZtG4YOHYro6Gi2QySEEKIB68khKCgI06ZNk/9sbm6OrKwsrFy5EpGRkVW2PXjwIEQiEQICAuSPXbp0CT179gQABAQEICEhge0QCSGEaMB6cpBIJLCzs4NUKkVYWBimTZuGuXPnIjIyEhKJRL7d7du3sX///iqJBACkUins7e3l+8rNzWU7REIIIRro5YZ0amoqJk+ejJCQEHh6euLhw4dYtGgRCgsLkZycjKVLl0IsFuP58+f46KOPkJKSArFYDDc3N9jZ2SEvLw8AkJeXBwcHB32ESAghRA3Wk0N6ejpCQ0OxYMEC+PvLlpOIi4sDADx58gQzZ87E3Llzq7zmhx9+gIuLCwICApCcnIwTJ07A29sb8fHx8PX1ZTtEQgghGrDerRQTE4OcnBxER0dj9OjRGD16NAoKCrR+fXBwMO7cuYPg4GDs3LkTU6ZMYTtEQgghGoiY6sOHDNTw4cOxd+9evsMghBCjQNMKCSGEKDCalkPXrl3h5ubGdxiEEGJQ6tSpg/Xr1ys8bjTJgRBCCHuoW4kQQogCSg6EEEIUUHIghBCigJIDIYQQBZQcCCGEKKDkQAghRIHJVoIrKyvDokWLcOvWLVhaWmLJkiVo0qQJ32Fxrri4GJGRkUhJSUFRUREmTpyIFi1aYM6cORCJRGjZsiUWLlwIMzMz7Nq1Czt27ICFhQUmTpyI3r17o6CgABEREcjIyIBEIsHy5cvh7OzM96+lNxkZGRg+fDg2bNgACwsLOk4q/Pzzz/j7779RXFyM4OBgdOnShY5VNcXFxZgzZw5SUlJgZmaGqKgoYX2mGBN16NAhZvbs2QzDMMzly5eZCRMm8BwRP3bv3s0sWbKEYRiGyczMZHr16sWMHz+eOXv2LMMwDDN//nzmr7/+Yl68eMG88847TGFhIZOTkyP/94YNG5g1a9YwDMMw+/fvZ6Kionj7XfStqKiImTRpEtOvXz8mOTmZjpMKZ8+eZcaPH8+UlpYyUqmUWbNmDR0rJQ4fPsyEhYUxDMMwp06dYqZMmSKo42Sy3UqViwp17NgR169f5zkifigrznTjxg106dIFgKzg0pkzZ5CYmIhOnTrB0tIS9vb2aNy4MW7evGlSxZmWL1+ODz74APXq1QMAOk4qnDp1Cl5eXpg8eTImTJiAt956i46VEk2bNkVpaSnKysoglUphYWEhqONksslBKpXCzs5O/rO5uTlKSkp4jIgf1YszTZ8+HQzDQCQSyZ/Pzc2tUoSp4nGpVGoyxZn27t0LZ2dn+ZcRAB0nFbKysnD9+nV8//33WLx4McLDw+lYKWFra4uUlBQMGDAA8+fPx+jRowV1nEz2nkPlokKA7B6EhYVpHo7KxZkGDx6MFStWyJ+rKLhU/Xjl5eXB3t7eZIoz7dmzByKRCAkJCUhKSsLs2bORmZkpf56O02tOTk5o1qwZLC0t0axZM1hZWeHZs2fy5+lYyWzcuBFvvvkmPv/8c6SmpuKjjz5CcXGx/Hm+j5PJthx8fHwQHx8PALhy5Qq8vLx4jogfFcWZIiIiMGLECABA27ZtmZnJfwAAA9RJREFUce7cOQBAfHw8/Pz84O3tjUuXLqGwsBC5ubm4e/cuvLy84OPjgxMnTsi3NdbiTFu3bsWWLVuwefNmtGnTBsuXL0dAQAAdJyV8fX1x8uRJMAyD58+fIz8/H/7+/nSsqnFwcJBf+Ts6OqKkpERQ3z2TXXivYrTS7du3wTAMli1bhubNm/MdFueWLFmCAwcOoFmzZvLH5s6diyVLlqC4uBjNmjXDkiVLYG5ujl27dmHnzp1gGAbjx49H//79kZ+fj9mzZyMtLQ1isRirVq2Cq6srj7+R/o0ePRqLFi2CmZkZ5s+fT8dJiW+++Qbnzp0DwzCYMWMG3N3d6VhVk5eXh8jISKSlpaG4uBhjxoxB+/btBXOcTDY5EEIIUc1ku5UIIYSoRsmBEEKIAkoOhBBCFFByIIQQooCSAyGEEAWmOeuLECW+/vpr3LhxA2lpaSgoKICHhwfq1KmD9u3bo1u3bvD29mblff744w/Y2toiMDCwRq///vvvMWjQILRo0YKVeAhRhoayElLN3r17ce/ePYSHh7O+71evXmHq1KlYv359jfeRk5OD8PBw/PLLLyxGRkhV1HIgRIM5c+Zg4MCBSE9Px7Fjx1BQUIC0tDSMGTMGR48exZ07dzBr1iz07dsXBw4cwMaNG2FmZgZfX1+FBBMbG4sePXoAkCUhTfubM2cOHj16hMLCQowbNw4DBw6Eg4MDrKyscPPmTbRu3ZqPQ0JMACUHQnSQl5eHDRs2IC4uDhs3bsSuXbtw7tw5bNq0CX5+fvjhhx+wZ88e2NjYICIiAqdPn5YnAwA4f/48hg8frtX+unXrhnPnzmHPnj0AgNOnT8tf16pVK5w/f56SA9EbSg6E6KBNmzYAAHt7ezRv3hwikQiOjo4oLCzEo0ePkJmZic8++wyA7MT/+PHjKq/PyspC3bp1tdqfnZ0d5s+fj/nz50MqleLdd9+Vv87V1RXPnz/X969LTBglB0J0ULGcsjLu7u5o2LAhNmzYALFYjL1798pP/hWcnZ2rLK2sbn8vXrzAjRs3sHbtWhQWFqJXr14YMmQILCwskJ2dXSXJEMI2Sg6EsMTZ2Rkff/wxRo8ejdLSUri5uWHAgAFVtunatSuuXr2Kzp07a9yfq6sr0tLSMHToUNja2iI0NFS+rHxiYiJmzJihl9+DEIBGKxHCqby8PEyaNAn//e9/a7yPly9fYs6cOYiJiWExMkKqoklwhHBIIpFg6NChOHToUI33sXHjRmo1EL2jlgMhhBAF1HIghBCigJIDIYQQBZQcCCGEKKDkQAghRAElB0IIIQr+H4+nia4u/yTLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Auto threshold the sample frame\n",
    "auto_thr = AutoThr(raw_img = test_img, thr_method = 'entropy')\n",
    "auto_thr.thr_pipeline()\n",
    "\n",
    "#Separate signal traces from individual spheroids\n",
    "spheroid_group_inds = auto_thr.spheroid_ind_groups\n",
    "spheroids_data = [[data60[:,ind] for ind in spheroid_inds] for spheroid_inds in spheroid_group_inds]\n",
    "\n",
    "#Plot spheroid masking result\n",
    "plt.imshow(auto_thr.spheroid_mask)\n",
    "\n",
    "#Plot fluorescence signal trace from sample point in an identified spheroid\n",
    "plt.figure()\n",
    "plt.plot(data60[:,spheroid_group_inds[0][0]])\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Fluorescence Intensity\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identify Spheroids with True Action Potentials Using FFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FFT_GoodSpheroids():\n",
    "    '''\n",
    "    Class to identify spheroids with action potentials from a set of \n",
    "    previously thresholded spheroid regions. Action potentials are \n",
    "    identified using FFT. Bad spheroids are discarded. \n",
    "    Of the good spheroids, only the pixels with APs are returned.\n",
    "    \n",
    "    \n",
    "    Attributes \n",
    "    -----------\n",
    "    spheroids: list of list of 1D arrays of floats   \n",
    "        signal fluorescence data from multiple pixels, grouped by spheroid\n",
    "    f_scan_int: int\n",
    "        rescaling parameter #TODO: why is this needed?\n",
    "    fft_win: list of floats of length 2\n",
    "        sets spectral window to consider\n",
    "    bad_thresh: float\n",
    "        minimum percent of pixels per spheroid that must lack APs for spheroid to be \n",
    "        discarded\n",
    "    hamming: boolean\n",
    "        determines whether or not to apply a Hamming window before calculating FFT\n",
    "    \n",
    "    \n",
    "    Methods\n",
    "    --------\n",
    "    calc_FFT_dom_all(self)\n",
    "        Caculates the FFT of a fluorescence signal trace & returns dominant frequency\n",
    "    check_spheroid(self, data)\n",
    "        Determines if a signal trace has action potentials\n",
    "    identify_good_spheroids(self):\n",
    "        Identifies spheroids with enough action potentials and returns data from \n",
    "        all pixels with action potentials from these high-quality spheroids\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, spheroids  = None, f_scan_int  = 1, fft_win = [0.2, 10], bad_thresh = 0.2,\n",
    "                hamming = False):\n",
    "        self.spheroids = spheroids\n",
    "        self.f_scan_int = f_scan_int\n",
    "        self.fft_win = fft_win\n",
    "        self.bad_thresh = bad_thresh\n",
    "        self.hamming = hamming\n",
    "        \n",
    "    def calc_FFT_dom_all(self, data):\n",
    "        '''\n",
    "        Caculates the FFT of a fluorescence signal trace & returns dominant frequency.\n",
    "        Hamming window may be used to improve frequency selection by \n",
    "        reducing spectral leakage due to non-infinite signal.\n",
    "        \n",
    "        Attributes\n",
    "        -----------\n",
    "        data: 1D array of floats\n",
    "            signal fluorescence data from one pixel\n",
    "            \n",
    "        Returns \n",
    "        --------\n",
    "        ind_max: int\n",
    "            index in Fourier-space within FFT window of the dominant frequency\n",
    "        bandwidth: float\n",
    "            bandwidth of FFT spectrum #TODO: check this equation, looks like a variance estimate?\n",
    "        \n",
    "        '''\n",
    "        n_frame = np.size(data)\n",
    "        a_fft = np.zeros(n_frame)\n",
    "        \n",
    "        #rescale x axis to lie in (0, 1000/f_scan_int) range \n",
    "        #TODO: why this way...\n",
    "        f_res = 1000/(n_frame*self.f_scan_int)\n",
    "        x_fft = np.arange(n_frame)*f_res \n",
    "        \n",
    "        #select x values within the FFT window\n",
    "        hz_max_ind = int(self.fft_win[1]/f_res)\n",
    "        hz_min_ind = int(self.fft_win[0]/f_res)\n",
    "        x = x_fft[hz_min_ind:hz_max_ind]\n",
    "        \n",
    "        #find maximum frequency within FFT window\n",
    "        if self.hamming: #NOTE: IDL used this but it doesn't help for my method\n",
    "            hamming_window = np.hamming(n_frame)   \n",
    "            a_fft = np.fft.fft(data*hamming_window) \n",
    "        else:\n",
    "            a_fft = np.fft.fft(data)\n",
    "        power_spectrum = np.abs(a_fft[hz_min_ind:hz_max_ind])**2\n",
    "        ind = np.argmax(power_spectrum)\n",
    "        f_max = power_spectrum[ind]\n",
    "\n",
    "        #set indicator action potentials exist\n",
    "        if ind <= 1:\n",
    "            ind_max = -1\n",
    "            sum_fft = -1\n",
    "            bandwidth = -1\n",
    "        else:\n",
    "            ind_max = x[ind]\n",
    "            sum_fft = np.sum(power_spectrum)\n",
    "            \n",
    "            #original IDL implementation, not sure why written this way\n",
    "            bandwidth =  np.sqrt(np.sum(power_spectrum*x**2)/sum_fft - (np.sum(power_spectrum*x)/sum_fft)**2)\n",
    "            #this is the variance\n",
    "            #bandwidth = np.sqrt(np.sum((power_spectrum*x/sum_fft)**2) - (np.sum(power_spectrum*x)/sum_fft)**2)\n",
    "\n",
    "        return ind_max, bandwidth\n",
    "    \n",
    "        \n",
    "    def check_spheroid(self, data):\n",
    "        '''\n",
    "        Determines if a signal trace has action potentials\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        data: 1D array of floats\n",
    "            fluorescence signal trace\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        no_APs: boolean\n",
    "            True if signal has no action potentials \n",
    "        '''\n",
    "        ind_max, bandwidth = self.calc_FFT_dom_all(data)\n",
    "        #TODO: find where in IDL they use this to filter out bad spheres\n",
    "        #TODO: if we use my method, can get rid of bandwidth calculations (never used)\n",
    "        if ind_max == -1:\n",
    "            no_APs = 1\n",
    "        else:\n",
    "            no_APs = 0\n",
    "        return no_APs\n",
    "        \n",
    "    \n",
    "    def rescale_trace(self, trace):\n",
    "        '''\n",
    "        Rescales trace values between 0 and 100\n",
    "        \n",
    "        Parameters \n",
    "        -----------\n",
    "        trace: 1D float array\n",
    "            raw fluorescence signal trace\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        rescaled_trace: 1D float array\n",
    "            fluorescences signal rescaled to lie between 0 and 100\n",
    "        '''\n",
    "        tr_min = trace.min()\n",
    "        rescaled_trace = 100*np.array((trace - tr_min)/(trace.max() - tr_min)) \n",
    "        return rescaled_trace\n",
    "    \n",
    "    def average_traces(self, spheroid_traces):\n",
    "        '''\n",
    "        Averages traces per spheroid\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        spheroid_traces: list of list of 1D float arrays \n",
    "            holds one sub-list per spheroid where each sub-list contains\n",
    "            all fluorescence signal traces that belong to that spheroid\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        averaged_spheroids: list of 1D float arrays\n",
    "            holds one averaged signal trace per spheroid            \n",
    "        '''\n",
    "        #TODO: might be possible to do this faster if in array format\n",
    "        averaged_spheroids = []\n",
    "        for spheroid_tr in spheroid_traces:\n",
    "            scaled_traces = np.mean([self.rescale_trace(trace) for trace in spheroid_tr], axis = 0)\n",
    "            averaged_spheroids.append(-1*scaled_traces) #invert signal \n",
    "        return averaged_spheroids\n",
    "    \n",
    "    \n",
    "    def identify_good_spheroids(self):\n",
    "        '''\n",
    "        Identifies spheroids with enough action potentials and returns one averaged\n",
    "        trace per spheroid\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        averaged_traces: list of 1D float arrays\n",
    "            one averaged trace per spheroid\n",
    "        '''\n",
    "        good_spheroids = []\n",
    "        for spheroid in self.spheroids:\n",
    "            no_AP_ct = 0\n",
    "            pixels_with_APs = []\n",
    "            for data in spheroid:\n",
    "                no_APs = self.check_spheroid(data)\n",
    "                no_AP_ct += no_APs\n",
    "                if not no_APs:\n",
    "                    pixels_with_APs.append(data)\n",
    "            #Determine if spheroid is high quality\n",
    "            if no_APs < np.size(spheroid)*self.bad_thresh:  \n",
    "                good_spheroids.append(pixels_with_APs) \n",
    "                \n",
    "        #Scale, invert, and average signals per spheroid       \n",
    "        averaged_traces = self.average_traces(good_spheroids)\n",
    "    \n",
    "        return averaged_traces, good_spheroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "spher_filter = FFT_GoodSpheroids()\n",
    "spher_filter.__init__(spheroids = spheroids_data, f_scan_int = 1)\n",
    "all_traces, good_spheroids = spher_filter.identify_good_spheroids()\n",
    "all_traces = [trace for trace in all_traces if np.sum(trace.shape) > 0]\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Background Drift & Calculate dF/F "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BaselineAdjALS:\n",
    "    '''\n",
    "    A class to remove baseline drift from a cardiac microtissue fluorescence signal \n",
    "    using asymmetric least squares smoothing inspired by Eleirs & Boelens, 2005. See \n",
    "    Baek et al, Analyst 2015, 140, 251-252.\n",
    "    \n",
    "    \n",
    "    Attributes\n",
    "    -----------\n",
    "    f_lambda: float\n",
    "        weight parameter that balances the first (fitness to data) and second (smoothness)\n",
    "        terms in the regularized least squares function \n",
    "    fp: float\n",
    "        asymmetry parameter (recommended to set between 0.001-0.01)\n",
    "    i_iter: int\n",
    "        number of iterations to minimize cost function\n",
    "    n_max: int\n",
    "        maximum length of signal on which to search for baseline\n",
    "    plot: Boolean\n",
    "        determines whether or not to plot sequential fitting of baseline\n",
    "\n",
    "    Methods\n",
    "    --------\n",
    "    smooth(self, arr, width)\n",
    "        Smooths signal using boxcar averaging method (analogous to low pass filter)\n",
    "    make_second_diff_matrix(self, n)\n",
    "        makes 2nd difference matrix\n",
    "    baseline_adjust(self, inputs)\n",
    "        Calculates baseline and subtracts it from fluorescence signal\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, f_lambda = 1, fp = 0.01, i_iter = 10, n_max = 124, plot = True,\n",
    "                fig_name = 'figures/test.png'):\n",
    "        self.f_lambda = f_lambda\n",
    "        self.fp = fp\n",
    "        self.i_iter = i_iter\n",
    "        self.n_max = n_max\n",
    "        self.plot = plot\n",
    "        self.fig_name = fig_name\n",
    "        \n",
    "    def smooth(self, arr, width):\n",
    "        '''\n",
    "        Smooths signal using boxcar averaging method (analogous to low pass filter)\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        arr: 1D array of floats\n",
    "            signal data\n",
    "        width: int\n",
    "            window size over which to smooth\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        smooth_arr: 1D array of floats\n",
    "            smoothed signal\n",
    "        '''\n",
    "        #ensure width is an even integer\n",
    "        if width%2 == 0:\n",
    "            width = width + 1 \n",
    "        \n",
    "        #take average along sequential windows\n",
    "        half_width = int(width / 2)\n",
    "        new_arr = [np.sum(arr[int(i - half_width):int(i + half_width + 1)]) \n",
    "                   for i in np.arange(half_width, len(arr)-width)]\n",
    "        new_arr = np.array(new_arr) / width\n",
    "        \n",
    "        #concatenate with original data at array edges\n",
    "        smooth_arr = np.concatenate([arr[:half_width], new_arr, arr[int(len(arr)-width):]])\n",
    "        return smooth_arr\n",
    "        \n",
    "    def make_second_diff_matrix(self, n):\n",
    "        '''\n",
    "        Makes second difference matrix\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        n: int\n",
    "            matrix dimension\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        d: 2D array\n",
    "            second difference matrix\n",
    "        '''\n",
    "        dn = np.ones(n)\n",
    "        dn[0] = 0\n",
    "        dn1 = np.ones(n-1)*-2\n",
    "        dn1[0] = -1\n",
    "        dn2 = np.ones(n-2)\n",
    "        d_combine = np.concatenate([dn, dn1, dn2])\n",
    "        d_index_x = np.concatenate([np.arange(n), np.arange(n-1), np.arange(n-2)])\n",
    "        d_index_y = np.concatenate([np.arange(n), np.arange(n-1)+1, np.arange(n-2)+2])\n",
    "        d = csr_matrix((d_combine, (d_index_x, d_index_y))).toarray()\n",
    "        \n",
    "        return d \n",
    "        \n",
    "        \n",
    "    def make_asl_matrix(self, lamda, w):\n",
    "        '''\n",
    "        Makes matrix terms that are part of the solution to the \n",
    "        minimization of the weighted least squares equation. \n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        lamda: float\n",
    "            factor that balances smoothness and data fit\n",
    "        w: 1D array of floats\n",
    "            weight vector\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        lamda_d_gram: \n",
    "            Gram matrix of 2nd difference matrix multiplied by lamda\n",
    "        A: 2D array\n",
    "            inverse of lamda_d_gram + weight matrix, component of the \n",
    "            solution to the least squares equation minimization\n",
    "        weights: 2D array\n",
    "            diagonal matrix of weights\n",
    "        '''\n",
    "        n = np.size(w)  \n",
    "        i = np.arange(n)\n",
    "        weights = csr_matrix((w, (i,i)), shape = (n,n)).toarray()\n",
    "        d = self.make_second_diff_matrix(n)\n",
    "        d_gram = np.matmul(d, d.transpose()) #Gram matrix of 2nd difference matrix\n",
    "        lamda_d_gram = d_gram*lamda        \n",
    "        A = np.linalg.inv(weights + lamda_d_gram)\n",
    "        return A, weights\n",
    "        \n",
    "    def baseline_ASL_iter(self, lamda, p, y_res, i_iter):\n",
    "        '''\n",
    "        Solves minimization problem of weighted least squares equation with\n",
    "        repeated updates to weight parameters that remove signal peaks, \n",
    "        producing a fit to the baseline of the signal.\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        lamda: float\n",
    "            parameter that balances smoothness & data fit\n",
    "        p: float\n",
    "            asymmetry parameter (recommended to set between 0.001 and 0.1)\n",
    "        y_res: 1D array\n",
    "            signal data\n",
    "        i_iter: int\n",
    "            number of times to adjust the weight vector\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        baseline:  1D array\n",
    "            signal baseline\n",
    "        '''\n",
    "        #constrain signal length to to n_max via resampling\n",
    "        n_res = np.size(y_res)\n",
    "        y = y_res\n",
    "        if n_res > self.n_max:\n",
    "            n = self.n_max\n",
    "            y = y[::int(n_res/n)][:n]\n",
    "            i_comp_flag = 1\n",
    "        else:\n",
    "            y = y_res\n",
    "            n = n_res\n",
    "            i_comp_flag = 0\n",
    "            \n",
    "\n",
    "        y = self.smooth(y, 10)  \n",
    "        hold_y = y.copy()\n",
    "        w = np.ones(n) \n",
    "        A, W_mat = self.make_asl_matrix(lamda, w) #A = W + lamda_d_gram\n",
    "        baseline = np.matmul(np.matmul(A, W_mat), y)\n",
    "        \n",
    "        if self.plot:\n",
    "            fig0, ax0 = plt.subplots(1,1,figsize = (9,4))\n",
    "            sns.despine()\n",
    "        save_xs, save_ys = [], []\n",
    "        save_baselines = []\n",
    "        \n",
    "        if self.plot:\n",
    "            ax0.plot(self.data, label = 'Original Signal', alpha = 1, linewidth = 0.5)\n",
    "            ax0.plot(scipy.ndimage.interpolation.zoom(y, len(self.data)/len(y)), label = \"Sampled Signal\",\n",
    "                     linewidth = 2)\n",
    "            ax0.plot(scipy.ndimage.interpolation.zoom(baseline, len(self.data)/len(baseline)), label = \"Iter 1\",\n",
    "                     linewidth = 2)\n",
    "            save_baselines.append(scipy.ndimage.interpolation.zoom(baseline, len(self.data)/len(baseline)))\n",
    "            \n",
    "            \n",
    "        #iterate to update weights and remove peaks from signal for fitting\n",
    "        for i in range(i_iter):\n",
    "            #AsLS method, requires optimizing lamda & p\n",
    "            #weights are set near-zero in peak regions, found by comparing baseline & raw signal\n",
    "            w = p*(y > baseline) + (1-p)*(y <= baseline) \n",
    "            \n",
    "            #update A with new weights before re-solving\n",
    "            A, W_mat = self.make_asl_matrix(lamda, w)\n",
    "            baseline = np.matmul(np.matmul(A, W_mat), y)\n",
    "\n",
    "            if self.plot:\n",
    "                #Plot baselines\n",
    "                interpoled_baseline = scipy.ndimage.interpolation.zoom(baseline, len(self.data)/len(baseline))\n",
    "                ax0.plot(interpoled_baseline, label = \"Iter \" + str(i+2), linewidth =2)\n",
    "                save_baselines.append(interpoled_baseline)\n",
    "                \n",
    "                #Plot function adjusted by weighted baseline\n",
    "                idxs = np.where(y <= baseline)[0].tolist()\n",
    "                scatter_ys = hold_y[idxs]\n",
    "                scatter_xs = np.arange(len(y))[idxs]\n",
    "                save_xs.append(scatter_xs)\n",
    "                save_ys.append(scatter_ys)\n",
    "                \n",
    "        if self.plot:\n",
    "            plt.xlabel(\"Time (ms)\")\n",
    "            plt.ylabel(\"Fluorescence Intensity\")\n",
    "            sns.despine()\n",
    "            plt.tight_layout()\n",
    "            ax0.legend(loc = 'right')\n",
    "            ax0.set_title(\"Iterative Baseline Fits\")\n",
    "            ax0.set_xlim(0,11000)\n",
    "\n",
    "        #interpolate to resize baseline-smoothed signal to original signal length\n",
    "        if i_comp_flag == 1:\n",
    "            baseline = scipy.ndimage.interpolation.zoom(baseline, n_res/len(baseline))\n",
    "        return baseline, [save_xs, save_ys, y, self.data, save_baselines]\n",
    "    \n",
    "    def baseline_adjust(self, inputs):\n",
    "        '''\n",
    "        Calculates baseline and subtracts it from fluorescence signal\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        inputs: 1D array\n",
    "            fluorescence signal\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        baseline_subtract: 1D array\n",
    "            fluorescence signal with baseline subtracted\n",
    "        '''\n",
    "        self.data = inputs\n",
    "        baseline, to_plot = self.baseline_ASL_iter(self.f_lambda, self.fp, inputs, self.i_iter)        \n",
    "        baseline_subtract = -inputs/baseline\n",
    "        return baseline_subtract, to_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9c82aEDZZHo2x/VokpSOpQT+QeOhjkQQKwmOmxBLAUaNGMWTIEADMZjMODg5oNBoMBgMBAQFIkkS7du04cuRIscVglmVUeSSAACbtbQBWabphkFW00G8n8cLqYotFeHD/e6oqc18IxF8Za9umubxadEUJQhEzaSIBiDJ75dgemayj9k9pWJ75CRQqkk/OJ+n4Z6URoiAID6hYEsBNmzbRs2fPHF+hoaE4OjoSGxvLlClTmDRpEhqNBrVabdvPxcWF1NTiq+9mMst5tgACmFMjADiib8CspFcAiPrnDXSxojTMo0alVGCvUuCvsiaAqRYnzJpI0sN2l3JkgvBkMaWGA1ClUs08X9+rb0lY/YUgKUg6MpPkU1+JlkBBeEwUSwI4cOBAtm3bluOrUaNGXL16lVGjRjFx4kRatWqFWq1Gq9Xa9tNqtbi5uRVHSMC9tQBGm71Yp32OLdoOOEkGjqzqy94rYcUWk/BgLPpkPBQa0i32fJ/aF4DU89+XclSC8OSQZRlzRgvgyld6UtdHzaDGFXO8Z/bu67xztjYeXZbxs31jWp4Jwn7lFPzXz2T8kS1Ep4uC7YLwqCqxLuAbN27wzjvvsGDBAtq3t9ZyU6vV2NnZERYWhizLHDx4kBYtWhRbDCZz/gmgOdX6QXfH7AVIfJL0GjeM/lRVhCGfmFpsMQkPxpQx6SPS7M0GbRckpQNpN7dhSLhaypEJwpPBok9ENqUj2buhcHDj0nsdWT+ieY733EpM53x0Au0vJfGpU3uClZ5YgDvpqXx35TANf5vHweiQ0rkAQRAKVGIJ4IIFCzAYDMyePZsRI0bw5ptvAjBz5kwmT57MgAEDCAwMpHHjxsUWg8mSdxewbDZiTosGScGcAe1J+7wH6bIjExImopdVVI9fj+bG1mKLS7h/plRrq2yk2Yd4iwe6gEGATMrpr0s3MEF4QmR2/6rU/gW8ywKVL3JRE055Bxd+KqfhQvK3bDftomP5CsTr03j+72UciQktkZgFQbh3qpI60ZIlS/Lc3qRJEzZu3FgiMeTXBWxOuwPIKJ39eLmltU5gJXdHLidX44vkEUzzWMG1318h8NVmOLoHlEisQsEyy75EmKyrFrxwpCW7fFejubyacm1noHTxLc3wBOGxl9n9q3KtnOfrs7rV4ZNTfyOpk5BNdsReq0+bt8dz+sox2nCRZRFfMKv6x6y+HUbf3Ss43WcS/i7uJXkJgiAUoGwVgjbLqBS5L9mU0f2rzHane2tqF/xcHfhZ04MDuqaUU2q4sHm4mGn6iMjsAn6l09MAhJj82aNrgWzWk3JheWmGJghPBFPGxDjlXS2Aq4c25dJ7Hehc3wm8M8ZHhweC0Yl6C44wOm4qzjX7otAnMu3aNDq6uxGn1zLm4AYxQUQQHiFlKwG0yCjzuGKzNuNOV501wFmSJJ6qWg4ZBVMSxhFndscr9Sgh/4pSB4+CzATQzau6bdsazfMAaC7+jCxb8txPEIR7Y9JYE8C7u4CHNqtEXR9XPr+wC0mSkRMqQJqH7XUD9ujaLse14ViU5nRmh3+JpxL+uX2Nn2+cLNFrEAQhf2UqAbR2AeffAqhSV8qx/cdBTdj9elviLR5MSRhvPcaZ2Xz164biD1YoUGYCqHKtgquDdSTDIX0joi0+mFJC0IXvL8XoBOHxZ9ZYKyOoXCvleu1ozC22R1zGRWXPAL+2uV6fvO0qY0JHkNTgU3zQ8WHqLgA+PrkdjVFfvIELgnBPylQCmN8sYHNGCRilOmeJAw8nOxxU1m/Rv/qmLE/tiUqy0DRkMlFxMcUfsJCvzEkgKvcqJHzaDQALStZrOgCQenFFaYUmCE+EzCLQSpeKuV6bc34vAOMD27Fx2FPsGtsmx+u/BkXx5+UYmu8MxLfvn3Q1htPYdIc7Og1zTooJdYLwKChbCWA+S8FljnXJa7abl4ud7fH85OFcNFSjiiqakJ3jii9QoUAWgwZLehyS0gGlsy9KhcSEZ61dwZu1HQGJtBtbMesSSjdQQXiM2SaB3HVjfEuTwPaIS9gplLwd+AwAnWqWz/c4/f92pe+dz5mqspaDWXT5EHdu7S+eoAVBuGdlKgE0y3mXgTHZWgBzJ4B1fVxJn9MDAAN2vJ30Nj/aNWOuNp2X/pjNJ0f3k24yFm/gQg6mVGv3r9K1MpJk/RV+ubm1m+q22QengC7IZj2aK+tKLUZBeNyZMrqAlXd1Af9w9RgWWebFqo3wdXIFQKGQWNA7MM/j7LoWS4jJn7qd11PXkIhGsmfBjmlorm0q3gsQBKFAZSoBBMirDLRtrIs6d1cHgINKiWV+L8Z28iC06m3mOLdjm30dNsYnMvvyNpr+voBTcRHFGLWQnSklo/vXraptWxP/rPISq1M7AKC58JOYdViCrsSkYjKLyTdPAoshFdmQgqR0ROFQzrbdbLGw4vpxAN6s+1SOfSY+W4Nqns682jrvUlkNvjnL1dvWFsMVdo0I3/kK2uA/i+kKBEEoTJlKAPPKBbIvd5RXC2CmY7G3+CXmLyQ7PXK6mpeSQpiRvg9/k4brKXF02rGE/+7cLK7QhWwyJ4DYuVXJsT1ocgcApgbVQOHohSEuCEP0qZIOr8wK/GI/N+K1/HwivLRDER6SKdtnoiRl3TYfuBPMnfRUarqW52mfqrn2C/6oM98PzL+Yv0XriZyuJlHhxHZldWL+eon0W2INb0EoDWUqAQSQ7moCtOjikc16FA4eKOxc8twnUZ/G4H2r0MtG5CQfuNmMbbcH0Cktil3an+njZEJj0tN/70pupsaXwFWUbaaUUABUbjlbGhwzJuwYsCPapz8AKUFifeCSFPjFfkZvOFvaYQgPKb9ekY0h5wAYVK1xjsQwL5XcHfPYKkGC9UZ7vUcXMBuI+WuYrbC7IAglp0wlgHm1AOZX7DS7icd+J1ybRGvvAN6v05PPetQjRVbzdsIkkBV8cWcJ7Z0dSNCnMWjvLxhFsehildUFnLMFsEb5rAR+yPFGAGivbsCsSyq54AQA0fX+mMusAZj9c9FoMfPrrfMADK7epMD9m/q7sbh/w7xfTPZGrXLkjEHiWkB/LPpEYv8eLX5nBKGElakEEEC6axRgZgmY/Na7PBkXzqrgU9grlKx6diif9wjkg061ADhjqMP7Cf9DicxXtxfiaJQ4kxDJ/KD9xXoNZZ2tBmC2MYCZvF3sAQg1VSTOrS2yKR3N5dUlGZ4AKKdsIyZV1Ht7XOXVArgv6gYJ+jQCPXxpUK5Cgfufmtie3vX9uPReB/a+cVedQFlJ6h3rrOFV7t1QOPugjzyI9ur6or0IQRAKVOYSwLvltQxcdh+d/AuAtwOfoYZb7lIHf6Y/w1fJg3GTDCzUbQNg1tldhKaKEiTFxZiaWQQ692DzT7vXtT3+JMS6TFzq+WViZZBi5v7xjlzbgu6klEIkQlGwzQDO9rm4I+IKAH0DGtzzcer6uNIhrxIxiX4ArA25jGObWQAkHJqGbBYVFQShpJSpBFAmdxdDXsvAZToTH8meqOu42jnwYaPOOV57v1NNfF0dAFicOpA/056msyWY50yRGCxmpp3eWQxXUHbk1x1kMaVjSYsBhR1Kl9ytEGPbVMH4RU8A9qS3RKmuhDHxKumhu4o13rIuVW/Kte3dPy7x7aGQUohGeFjmPJaB2xFxGYBulermuU9Bhjb1t42/Tp/TAwwuyOlqJKWZilst2JWrgzk1DI1oBRSEElOmEkDIPQmkoBbAry4eAGBM7dZ4ODjleO3zHvWY+0I91gxrBkh8lPgmwcaKfJS2C5VsYe3N0yw4ehazRYxreRCynPtnBdmXgAtAUijz3Dez1qMJFZ9FdAQg+czC4glUyCFz5RyA81EpjP/tQilGIzyou1sAb6TEcT0ljnL2TrT2zrvMS0FWD2uGeV4vNo9sgYMq4+82yRcA2T0W9xZTAEg+OV+MBRSEElKmEsC8PleyxrrkTABjdRo23DyLQpIYX69dnsd7uUVlXmpq3S9NdmJc/GTKW/QMN1hnyr139G+Moi7aA7t7vCZknwCS/z+heJ0WfIOh5nF+rOjD/1x6cTLiPIbY88UWqwDzewUyvHkl/nk957JgvZYf46t/g0spKuFB3L0KyM6M7t+u/nVQ5XPjdS/6N7S22s/rGQjJPtbPZNcEdFV7oXT2w5hwGf3tww8XvCAI96RMJYCQuxC0SZvZApizC3jDzbOYZAvP+9ehqqtngcd8v1NNAK6ZqjAzcQyv6k+jks3gFsuN1Lgii70sya8NIGsCSJU8X7+cFE2LP75CKh+B5JCObGfkb1U1+qsHs+C/pcUUrQDQzN+dHwY2pnkljxzbt1+O4d0/LpVSVML9ks1GzGkxIClQOlvH6mWO/+vmX6dIzuHlYg9me9B4Ikkyo/8+gDpwOACpF1cWyTkEQShYmUoA80oqzKmZd7o5lztaFXwSgBE1WhR63FaVPWwtgRvTOnNc25S+xisgwTM/L+WLfTfQ6k2cjUx+uAsoQ2RZzqcLOBTIewJITHoq3Xf9QJg2CXuDO/LNJsjXW6FOKIdFUjA12Zkl58XYzOJgp5Rsg/2d7ZSMbZN3gi48+szaKEBG6eyLpLTDaDFzMNo6lrOrf+0iOcfIFhmftyneAPwVeRHX+qMA0F7/FdlsKJLzCIKQvzKVAELOcWUWgwaLIRlJ6YDCMauV70pSDCfjInCzc6R3QP1Cj9mvYQW+698QVwcVIPFx4hu0TEkFQO+eyLJ/djB33w1e3yy6IO+VTD5jAFPzrgEoyzKj/9tAuDaJtt5ViBgxmeH1AsHgRGpUI942WRP9d07t5miMKDpb1D7MKI0EYK9SsHRAI7aOblmKEQkPKmsVEGuvyOn4CDQmPbXcylPR2b2gXW0sOg3xOxZw64vnCP2sPdEbP8AYn7VCjCRJ1p6TVC9rN7BLEhrnSth51Uc2atDdPlTk1yUIQk5lLgHMLuuDrlKOqvabQ61j+PpXbYiTyu6ejqVSSHSpZW0BSZOdmBY9gXr6RPSSim4V/mDe7gtiQsh9ymudgfxqAP526wI7I6/gYe/Epk4j8XJ2YlCTrHGdB0OaMVp/BjPwyn/rSDeJchPFrXtdHwY2ypqp/fz3R5BlGX3MWdHC8wjLGv9n/fv5N2OJy2f9atzT/rrwIIKnNSF6/WS0F/8h7eq/xG+fS/BH9Uk5sdn2vs971GPva8+C1gNJkmmzYivOVbsBkBYiWuoFobiVqQTw7kkgWUWgc47/+zP8InB/9a5cHFT8OqolSf/XjaUDGqHHgRsx1haQPU6VmOi+jtOiC/ie5TcRMCsBzOoCNlnMvHfCuqj8/zXvTgVnNwB6BvpS39cVgFOGOnzgqqOmOZ6rKXHMv7CvGKMXAOyUCmb3qGd7/s+1OJKOz+H22lZEbeosar49ou6eAXzgjnUCz7N+1QvdV3/7CrfmdsIYE4xD5Ub4v7WBgHd3oG7aG4sulYhvB5N8NKvUS4ea5RlZuykA1/W3cKrS1XqcyINFek2CIORWYglgWloab775JkOHDmXMmDEkJFgLJZ89e5aBAwcyZMgQFi9eXOxxZJ9ZmrUMXFYCGKlN5mRcBE5KO7pUvP/xLm6OdoxtU4U/X2mFIbUCSpOC60ovmrifoon9Nfr8dPzhL6IMkJFzrTUqm/TW8UmSMses7fUhZwnRJFDbzZuxtXPOQA2a0iHjkcRF92HMTN8PwPyg/USnpxbjFQgANbycczy/fNg6EUd/5xjJp74sjZCEQpiytQCaLRYORYcC0N634BZAiz6NiEUvYk6Nw6VhN6p9cgz31oNQN+pG5Xe24t1/FsgWbv84mvRbZ2z7zX4mo8qCOpE0jwaAhD72LBaTrjguTxCEDCWWAG7cuJH69euzdu1aXnjhBb777jsApk+fzoIFC1i3bh3nzp3j4sWLxRbD3YWg8yoBsy3cOluxq3/te+7+zcsLgb4gKzAlWo/9q0M95pT7lr8vRbDjcvQDH7csyTVjO3P8n2tlJIUKsI79++L8XgDea9QRpSL/X+kB+yvylLOSjsYQNCYDn5/bUyxxC1myJ/HlFYlUkKJsz5OO/R/GxGulEZZQAHO2MYBnE26TYtRRTe1JZbVHgfvFbPoQ/e1L2FeoQ+Xxm1HYO9pekyQJ7z7T8IGWf+cAACAASURBVGj/KrJRR+SSl7AYrUsFVnR2p6VXAJLCwrIrN7DzCgSLEUPM2eK7SEEQSi4BHDVqFG+++SYAt2/fpnz58mg0GgwGAwEBAUiSRLt27Thy5EixxpG9USlrDGBWApjZ/durcuGTPwoTNf052pWzdoHtUNUiwC6KcW6beGH5cSb8fkHMCi5AXl3A2YtAZzpwJ5iLSdH4O7szrHqzAo9pRolb47d4V2etM7b8+jFrzUChRFRUWksiXTBUR11vBLJZT9yecaLw7yMmqwWwEgejM8f/Fdz9qwsPImH3YlAo8X9zHQoHlzzf5zd8EfYV6mKIukrcts9t23tXCQTgu/OncajQGgD9naMPfS2CIOSvWBLATZs20bNnzxxf58+fR6lU8vLLL7N69Wrat2+PRqNBrVbb9nNxcSE1tfi65XKNAbxrsLPBbGJ/lHW8S48HWO7obr6uDoxuXBdnsztahT17VdV43fU3Au1u8s1/IewLFjUC85PXLOC8ZgD/dN3apT66Vkvslao8j2We19P2uM6WStRT6mlvDCXNZGTJFVF0trjd/Mi6jKKfMh6AKLMX3+jGoHD0QhexH+21jaUZnnCXrJ6RihyNtf7NtfWpWuA+0esmgWyhXKc3carSNN/3KewdqTBqGQDx2z7HGG89fvdK1hvleMUd7H2aA2CIPfdQ1yEIQsGKJQEcOHAg27Zty/HVqFEjAH755RfWrFnD+PHjUavVaLVZLTBarRY3N7fiCMkmRwugNudg56Oxt0g3G6nv4Yufc9HEMbpVAJ+0so5x+VZ6FpVkYU6571BhQilJxGr0RXKeJ40sy7lWAjFm1gDMSACTDen8GmotrTOyVv4lR7J3Q8YanNhHD17VnwZg8eWD6M2517EVik5VT2dSZndnWEYN4TtmL2b/F49nu88ASPj3fSwGTSlGKGSSZTlbcXx/jsdaW90LWv4t7dpBtBd3o3B2x7vfjELP4VL3WdxaD0E2GYj9/VMAmnhWRDbaY5B0XHO0nkus3CMIxSvvJpNisGzZMnx9fenbty/Ozs4olUrUajV2dnaEhYVRuXJlDh48yLhx44othrs7mrKKQFsTwH1RNwDoVKEWRWlojWZ8eGo7153duJTgT337EMa6bmXC7yom/H4Ry/xeRXq+J0WuFsCUnC2AG0POoTOb6FShJtVdvQo81nO1vdl1LRaAT8I6cqDiZuqZ47isg623LjC4epOivwDBRu2gokcVEynx1gQQQF1/JClBP2CIPknSibl4Pv1pKUcpWNLjwGxA4eBBnMlCqCYRZ5UdgR6++e4Tt20OAJ5d30alLvjvMJN3/1mknNhE0n8r8OoxBQe/2qDxhHJ3+DvVzDAkDAlXkM0GJKV9kVyb8OCMRiMRERHodGJizqPM0dGRSpUqYWd3j+XrijkemxdffJH333+fX3/9FbPZzGefWe/+Z86cyeTJkzGbzbRr147GjRsXaxyZrUrW5Y6ircsduViXO9qbkQB2rFCzSM/p7+JOB78a7LsTzNH67xN45W3GuW1ms7YTMRZPYlL1+Lg6FOk5H3cFjgHMSAC33AoCYEj1/LucMu0c2wbFZGupmEizD/bV+jAo4gIznTqw/NoxkQCWgMwhF1EZCaDrRzsJGfsFht87kXL6a1wbjMbOvfBSI0LxMWkyKyP4czzOWri5hVflfNf/1YUHoTm3HcneCc8u4+/5PA5+tfBoN4qkf5cTv2MBFUcv46cXOjLm8Dp2Rt1kpEcNTEk3MCZexb58w4e/MOGhRERE4OrqStWqVXNVZxAeDbIsEx8fT0REBNWqVbunfUpsEkj58uVZvnw5q1atYu3atTRvbh3n0aRJEzZu3Mivv/7KxIkTSyqcnMsdKVRojXqOxtxCIUm0v8eCp/fjxWrWxDZI5cCOtDY4SEbGuP4BwEurTxX5+R53MnnMAs6WACbq09h7+zpKSUGfe6zXmL2ltffRVvQxXMFRNrEn6jrBKWI8ZnHLTC4M9tYbrjSjmQ9OqFHXG4Zs1pPw73ulGZ5AzsoIxzPG/7UqoPs34Z9FAJRr/yoqN+/7OpdXjykAJB/6BVNKLP2qBSLLEsfjwkjztCZ9hrig+74GoejpdDq8vLxE8vcIkyQJLy+v+2qlLWOFoLOale4udvpfdAgm2UJzr0p4ODgV+bn7ZCwp98/tq4QFvA5AP+cDqDCxLziepHRRFPdu2T9sZLPBWrhbUqBSV2Jb+GVMsoX2ftUp75j3jMOCnDXUJt2xLt2N1wFYef1EkcVdFt3LPN7MIRfT+3a0bVtxPJxyT89GsnMhLfgP0m/tLqYIhXuRvTLCsUISQHN6KslH1wJQrtNb930uhwp1UDfpiWzUkbh3Ce72TpDmhlm2cNjRWoPVECsSwEeFSP4efff7MypTCSBkjSszZ7RGZK4C8l9GuYMHaf1Lv3mCyGUjCP64EaGfdyB+10IshpxZeEVnd9p4V0FnNtHy6QboXGrhpUyhnaN1ptvTi0Tl++zu7gI2aSJAtqBU+yMp7fgto/u3X5X76x4a0zoAbxfrmKKjzi/R33AZgPUhZ0Q5kodQ2MeOLFtskwua1a6X4zX7GafwaPURAPH7JyKbxMSo0pLZTa9wqcCJOGsCmN8EkJSj65D1WpxrP4NDxQermuDV7V0AEvZ8i2wy0CRjicf/zNZJeKIFUMgUHh7O+PHjGTFiBEOGDGHGjBloNLknj12+fLnARSX+/fdfNmzYcN/n79SpE3p9zs+mW7duMXbsWMaMGcPIkSOZN28eFoul0BjuR0REBIMGDSqSY92tTCWA2f+9m2xdHZUAOBpj7V58qpByBzmOJ8vEbp1FyMxWJB9ejT4iiLQrB4heM4GQmS0xxNzM8f6+Vaxdlb+HX8SvwWAA2jtaK+JfjhGzILOTkXMkFdm7f3UmI7sirwLcc/dvph8GNuaPV1oBMOV8DRoqLXhbtNxMTeBUfESRxC7kZk6LAYsJhVN5VPbOuV4/4zYUu3K1MCZeJenU/FKIUICsz8VQOx+SDToqOrtRySXvAtCJB34AwKPD2Ac+n3Pd9jj418ecEkPq2W0Mr2O9odufkgaAIeHKAx9beHLodDreeustXn31VVatWsX69etp3Lgx7777bq731qtXr8DJpM8++yyDBw8ukri+/PJLhg8fzvLly1m5ciWhoaHs2bOn0BgeFSU2CeRRkZlUmLJVuzdZzByPy6x3VSWfPXOL/W0Gcb/PAkmBV7dJuLUejCHmJrFbpqGPuEDo5x2oNu0wdp7WJLNvQAM+OLmd7eGX+fbZ5+DY/9HGQdzh5kWW7yrZkzkD2DWAQzGhpJuNNPasiL+L+30fu3WVcoC1MPSimC5097vOLw5N2HDzLC3KVy6S+IWczKmZLe7WIRerhjZlxNqs5cA6/3CaP3rMpH7iUJKOf4669kDsyt3/UozCw8n8XDxnsq7i0bJ83q1/+shL6EJOonD2wK3liw98PkmS8Gj/KtFrJ5K4/wcqd/0B2aTiVloqYUpPAlLDsBi1KOzuf5iH8OTYv38/LVu2zDFJtF+/fqxbt47w8HC+/fZbkpKSSEpKYsyYMfz111989dVXbNq0iTVr1uDu7o6dnR09evQA4ObNmwwZMoR3330XPz8/wsPDadiwITNnzuTOnTvMmDEDvV5PUlIS//vf/+jSpUuecVWsWJHffvsNFxcXGjVqxNdff41KpeLYsWOsX7++wBgOHDiATqcjLCyM1157jf79+3P8+HFby6FOp2Pu3Ln3PKP3QZSpBDB7D1/2ItDnE6JIMxmp6VoeHyfXezpWyqmttuSv0rhNuLXoD4BTtRaoG3UjbH430m8cIfybflSbeghJZU8td28CPXy5lBTNETyprXKmNhF4KZKItxS8zFJZlDMBDAXAzq2KrfWv6wOs1Xy3DdouLDVO4xeHJmwMPsHcli+gkMpUw3iJyD67FGBYs0o5EkCA2Rd8+SPwZTSXfiFu3zv49ftLjDsqYZmfi0EG6/NmXv55vi/52HoA3FoOQGH/cGOm3Z8aTszG99Fe+JvqzyeC1gPc4zjm2oyApN0YE6/h4FP4TH+h5Gy9EMXZyJQiO14Tfzf6NqiQ7+vh4eEEBOS+GalUqRK3b1tbrdu0acOoUaM4duwYAAkJCfz4449s3boVe3t7Xn755Vz7h4aGsnz5cpycnOjSpQuxsbHcvHmT0aNH07p1a06fPs2iRYvyTQAnTpzI2rVr+fLLL7l27Rrt27fnk08+sb1eUAwajYbly5cTGhrKG2+8Qf/+/bl+/Trz5s3D19eXpUuXsnPnTnr1Kr4ycYUmgBcuXKBBg/vrZnuUZf5DyV4E+khMKABt7rH1z6xNJGqldSKHz6C5tuQvk9LJjYCJ27g5vTm6kJPEbJmO7yDrskd9AhpwKSmaHbdv0NCvJbqIAzS0D2a/rnlRXN4T4+7ReNlrAP4TbF0/9jn/Og98/FdaBfDT8TBSZReuJgdS0TmFCB0cjQnjKd+qD3xcIW+25cVcs1pYF/ZtwDtbL9ieHwtL4kLnKVS/uQ1d2B60VzegrjukxGMtyzJ/Tuc11gL9TfJIAGVZJuVoRgLY+uF/PirX8rg270fKsQ1UvvEraCuAexyH7aswEDAmXBUJ4COmb4MKBSZsRc3X15fz53MXBg8NDaViRes4/rtLn4SFhVGjRg2cnKw3KE2b5v4dCggIsK1G5u3tjV6vx9vbmyVLlrB582YkScJkyn+hgKNHjzJq1ChGjRqFVqtl7ty5fPfdd3Ts2LHQGOrWtY6brVChAgaDwXads2fPxtnZmejoaJo1K3h504dVaFPH8uXLGTRoEKtXryYlpegy/tKQfZB/VhHoihzJqHZ/r92/MZs+xJwSg3PtZ/DqNinP9yjVnvi/sQYkBfE75qGPvARAt4wl5nZEXMbex/rDbWgX/GAX9AS7eyWQzDGA8Q5+nEu4jZPSjqfvY7zm3UY0z/rHtkrTneeM1p/B7yGiJE9xuLsLGGB8u2qET8t5Z915xTWk5jMBSPh3CmZdUskFWcZZDKnIhhRQOnIuKQawrtBxN92tMxiir6N098WlXociObfHs2MASD68GjTWYR2HjC5YAGPi1SI5h/D46ty5M4cPH86RBG7atAlPT08qV7beVN7dWxAQEMDNmzfR6XRYLJY8E8i8ehgWLlxInz59mDdvHq1bty5wcuC8efM4dOgQYF3Ktlq1atjbZxUuLyiGvM49depUPvvsM+bMmYOPj0+xT0wsNAH86quv+OGHH5AkiXfeeYd3333X1sT6OJLIvdxRZgtgYetdAuijrpK4/wdQqqgwaimSIv9voXOtpyjX8XWwmLmz+m1kWaaNdwAe9k5cT4kj0t26AHpbtfX8b/0qlj7KLmcXsDUB3J9ufd7erwaOqgcfG9G+Rnnb4xCTPzXM1kLcf9wU5WCKw91dwJn83Z1I+7xH1gaVgdoH0+nh8RqdFV0Z9ecsriXHlmSoZVZm698ddU0SDemUd3DB3zn3GNuUY9YZlG4tBiDlUyD6frkEdkLl7ocxJpgGaRHYW5xIsMBlhTdGMRGkzHNxcWHp0qV89913DBkyhIEDB3Lu3Dm+/PLLfPfx9PTktddeY+jQobz66qvo9XpUqsJHvXXr1o3Zs2czdOhQDh8+TGJiYr7v/frrr/nxxx/p378/Q4YM4eLFi4wdmzUp6n5j6NOnD4MGDWLIkCFotVpiYmIKjfdh3NMYwLi4OG7fvk1iYiI1atRg586dbN26lc8//7xYgysu2Zc7ijaaCdUkolY50MDDr9B9Y3+fBbIFj3ZjcPAPLPT93i9+SvKx9Wgv7UF7cTfqBl3pWrE2m0LPsd/kTi/gKbcwiIKlR27x3YuNiuAKH3/Z73tki8mWQOxLTADgOf+HH/9nnteTDt8d5r+QBHZFtcS1egLX9HAj6Q417+F3Qbh3WV3AlXK95miXkUQ4JUPABYwqE9dkJ1A6cVMDm36bx9KnBzKqgPWehYeXWQT6qmNV0EFjr4q5WilkWSbl+EYA3NsUXfe8pFDi1nowCbsWMl59ig+MbTA4hHNEVYkmogVQwNqatnTp0jxfmzNnju1x69atad26NSaTiZiYGLZs2QLAsGHDqFChAi1bZn2ObNy4MdfjSpUq0bNnz1zn2Lt3b65tNWrUYMWKFbm2308MDg4OtmN/+OGHfPjhh7mOlz3OolRoC+DAgQOZMWMGdevWZePGjUydOpXp06cXe2ZaHDKTiuzFTk9kLHfUyrsyygJa8wD0t6+QcnQdKO3w7jP1ns6pUntRvod1hYPYrTORZdnWDbwrIQ6FvTtmbRTeivzvMsoiWb5rxrZsRulSkf8yyvV0KILl+iRJsrUy/qdrRjOD9Xf611NrHvrYQk5ZQy5yJ4AA68fUhSpBSCoTssaDV316861jOv0NlzDKFsYc3MDya49vz8PjIPMm65LSuu5vU8/c4//04UEY40JRuvviVPOpIj2/W5uXAGgU8w9O6dZJcYdUAZgSryNbzEV6LuHJp1KpSE9Pp1+/fgwaNIh69erRokWLMhdDQQptAZw2bRqNGmW1Sh0/fpxWrVqxfPnyYg2suEjS3TOArXe9jfMY63K3hH++AVnG45nR2HnlvzzS3cp1/h/xf80j/foh0i7vo1tV6y/AvqgbmLwaoog6SC27MGL15Zi79wbvdyratYgfV7YJOxndv9HqWtzSJOJh73RPrbX3YnKHmvx78zgyClKTK4MPbAsL4j2LCUlRpibJFxtZtuTbBQygMxmZcXkLktKMnOwNEfXYEK2ldofXWZDWn/rpsXzq1J43D/9KbTdvnvET6wUXh8wWwIuoATnPCSCpZ63rabs27lng8JcH4VS9FXbe1XGNvUmlWyFEecBJlT+6NBOm1FtinWjhvk2aNIlJk/Iep1+WYshPvn/BJ0+eZP369bz33nts2LCBDRs2sHbtWmbNmlWS8RWpzPGU2ZeBO5eRADYqJAE0pyWTdOgXADy7vn1f51U6ueL5/AQA4nfMx8/Zjaae/ujMJk65WGdYv17XWvj01/O37+vYTyo5WydwZgJ40qEqYC3WXVhrbXZmbRIJe5Zwe/kYon4ZR8qprbYWhZ6Bvvw0uAkAZ+LboJAtHJfLEXlpXRFdiWBJiwWLEYWjJwq73EWg513Yx9XkWOq6+0BkHUAiRWfiv3Ad5Tt+zUjDOV4znscsWxj13zpSjfe+1qVw7zJ7Ri4YrH9beU0A0WQmgE2LvjSFJEm4Z7QCvpB+mAYefqRLKs4pfTEmiG5gQShq+f4XdXNzIy4uDoPBQGxsLLGxsSQmJjJlypSSjK/ISUiYtVkzgM8nRAGFtwAm/bfCuuxRYCccK9W/7/OW6/wWkp0jmvM7METfoHtGN/B+rAuoN3WxxqQzWe772E+iHF3AGQngcTwBePY+WoCSj23kxns1ufPLWyT9+xOJe74l4pt+hMxqiyE2xHq86tbjWiwOeOrAJCnZceoXZFn8LIqCbfxfHt2/t9OSmXPeOv7l27b9sczra3vtj4vRvHXMH5daLzIp7V8aKnWEahKZdmpnyQRexpg1kSRKjkQYzTir7Kjt5p3jdVNyNOk3jyPZOeBSP++6aA8rsxu4s/4onTP+zo+oKouJIIJQDPJNAGvXrs24ceNYv34948aNY9y4cfzvf/+jffv2JRlfkbKNAcwYj5TuVJHg1HjsFErqufvkv58sk7h3CQCeXcY/0LlVai/b3W3C7m95PiMB3JdmbYny1N8AQC8SQCDnSiCZCeAxvXWyQDvfavntlkPcX/OJ/G4wZk08zrWfwW/EYrwHzEZVzh9dyAlCZz+DIeYm1b1csMy3tmgkpFhLAf2bLpEetqeIr6pssnX/5jEB5MsLB9CZTfSr0jDPcZ2/nIrAs8OX2Nur+SxpCxKw5MphriY/fmOQH3UmTSSXFdbZ8Y3KVczVyp56bjvIMi6BnVE4FM/KHA7+gdj71cZDTqW73loC6KiqklgSThCKQb4J4NtvW7s5+/fvT7t27XJ8Pc6sYwAzZrvhioxMPXcf7JX5j/dKDz6G4c41VB4VcG2Se3bQvSrX1Zo8Jv33Ey1dPVGrHLim1XBHcsGQcAkJi0gAs8msA2hKDSNecuKqzoCT0o7mXnlPJMgu6eAvxGyYApKE70tfUuWjA3h2+R/evT6ixuwgnGs/gykxkvCve2PRp9n2M2ut/wAPqgJIPbekeC6sjMmrBiBAvE7L91ePAvBRo8627c0r5Sw9si9SRbm206lniWOwHIpJtvDhyb+KOeqyx6yJ4pLS2uqXV4+I5swfAKibFN/KBJIk4drcWli/dugJFMBZpR/J8aILWBCKWr4J4DfffAPAwYMHc3097jJbJC6arC1KhY3/S84Y++fedhhSAYliYZyqNMWp5lNY0lNIP72VZ/ysLVnHnBsgG7X4K2OJSE5/4OM/SbKXgTGl3OKU0lp1vq1PlQKTdQBdeBBRK14DwG/YN3h1m5ijnIXSpRyVJ23HvkJd9JEXiV4/GcjoCk53RTIrCVN6cCXkP4zJoUV6XWVRfhNAfr5xAq3JQNeKtWlWPiupP/7OMzne9/z3R/kx6TnsyzfindTdOEoyv4ddIChj+Ibw8GSLCXNaNJfzSQAtRj2aC/8APNRN8L3IXFlJf/ZPmpfzwSgpOZKUWOxFcYVH2/fff8+oUaN45ZVXGDNmDBcuXCh8p/t07NgxJk6ceE/vDQ4OZsSIEbm2HzhwgJEjRzJ69Ghefvll/vjDeuO0ZcsW9uwpml6lLVu2MH/+/Ic+TqEj6U+cOMG///7LgQMH6NKlC3/++edDn7S0ZH6A2Ga7pVuXX2nsmf+SNhaj3rbupfvTudcSvF8ez4wGIOm/lXTJWMv2iJN1SbPadmFYxGcckLESiASyxYwpNZzjKmvyUFj3r8WoJ3LZcGSTAY/2r+LZdVye71M6uVLprQ2gtCNx31LSb55gyYuNAAmLphwAh+wqkxr0fZFeV1mUVw1AWZb56dpxAF6v2zbH+yVJ4n9PV2V+r6w6m1O2X6N81+/xRs8gnbVg+tyg3HW5hAdj1t4BZK7bWUvANLrrMzH9xmFkQxoOlRpi51l4C/zDcKzWgjuK8pgSI3kxo2juYcphTrtTrOcVHl03btxg7969rFixgp9++onJkyfz0UcflXZYeZoxYwaLFi1ixYoVfPfddyxcuJD4+Hj69+9P586dCz9ACSq0OWvevHnMnz+fmTNnsm7dOiZMmFCsixMXN8mswWJIRlI6cD7ZWlS4oBZAzbntWLSJOAQ0xrFyw4c+v1urgdxZ8zZpV/bTaaB19YlDFg9kYGZrC3sPgMUio1DkXiamrJEAszYKLEZO21nH5rXzLXgCSPyO+ejDz2PnXR2/oV8V+F7HgEZ4PT+B+L/mcWf129SddpgbH3ai5tLb4B7HQVUAwy6swKPNNBSqh1vwvizLqwbgsdgwLifH4OOopmfl3AXVF/VryMnwnMvAOc2LJPSFSbx6fglrHRqyIeQsnzbrRjVXr+K9gDLArI3ChESwZO1+r39XmaXM1j+XBl2LPRZJktjj0IZh6dt4JvoKKNw4oqyMMf4yKpeSW39WyNudrX1ID91RpMd0qtodv76/5/u6p6cnt2/fZvPmzTz77LPUq1ePzZs3A9bSdIsXLwZAp9Mxd+5c7OzsmDhxIhUqVCAiIoIXXniB69evc+nSJTp06MCkSZMYMWIE1apVIyQkBFmW+eqrnP8vduzYwcqVK1EoFDRv3pzJkycTExPD5MmTkWUZb2/vXHECeHl58csvv/D8889Ts2ZNduzYgb29PYsWLaJ8+fIMGTKEmTNncuHCBcqXL09kZCRLlixh8eLF2NvbExkZSUxMDHPmzKF+/fqsXr2aXbt2YTKZcHV1ZdGiRUX0Xb+HFkAHBwe8vLxQqVR4e3vbFi1+HMmAIt16FympKxGUWPgM4OTDqwHwKILWPwClszuuzfsB4HdhJz6OaqLMcFNRjlqqMABMohkwa8JOyi30KLms8EBCopV35Xz3MSbeJm6bdXWaiq/8gMJRXeh5yveehtLNh/Tgo2iD/qa6lwtktAAeVFbBqEtAe3XTQ19PWZZXF/AvN04C8HLNFtjls5yYt4s93erk/JCttb05VcpV4gXDNSyybBtDKDwckyaSMIU7ehQEuHjgZu+Y43XtRWsCqK5f/AkgwG57a6uw59V92MkWLiq9iYkp+i4/4fHg6enJkiVLOH36NIMHD6Zbt27s27cPgOvXrzNv3jx++eUXOnXqxM6d1ioB4eHhzJ49m2XLlrFw4UI++OADNm3aZEscAZo1a8aqVavo3r07y5Yts21PSkpi0aJFrFy5knXr1hEdHc2hQ4dYsWIFPXv2ZNWqVXTpkvdM+CVLlpCens6kSZNo164dy5YtyzF8Yc+ePSQlJbF582Y+++wzoqKyhrJUrFiR5cuXM2LECDZs2IDFYiEpKYmVK1eydu1aTCYTQUFBRfZ9LbQFUK1WM3r0aIYOHcqaNWuoUOHxvgNTplm7fyOdq6JNM1DByQ3vfBIFi16L5rz1Tset1aAii8Gj3ShSjqwl+eDPdHxhJhtCz3FYVZl68ZcA60xge1XRFll93GTOAjal3uKi0hsjChp4+OJq55jvPrG/TkXWa3Ft1geXwE73dB6lkyte3ScTs+E9Yn//FJeGz4PRCVnviNYBLiq9id4/n6cCR+S5eLdQMFmWbQlgZhew2WJha5j1n/lL1Zvmu28VT2dWDGlKhZm7bNuM2PFB/OsMU83ld/u6/HT1CDOaPo/DQ4zNFawtgNcU1pbU+uVytv6ZNPHoQk8hqexxrvNsicQzauAg5LULMEXfoE+dVDYr3Pk36ga5R1wJJa2glrricuvWLdRqtW352aCgIMaOHUvr1q3x9fVl9uzZODs7Ex0dTbNmzQCoXLkyrq6u2NvbU758eTw8rKvLZP8cb9OmDWBNBLMv9RYWFkZCKB04UwAAIABJREFUQoJtXV+tVkt4eDjXr1+nT58+tn3WrctZLzY5OZnbt28zZcoUpkyZQnR0NOPHj6d+/azScTdv3qRJE2vtWU9PT6pXz+rVqlevHgB+fn6cPn0ahUKBnZ0dkyZNwtnZmTt37mAymYrgO2pVaJaxcOFCPvvsM/r27UvLli0feuBhcHAwzZs3R6/XA3D27FkGDhzIkCFDbM24xUWWQaGzZttX7K3/jO4e65Kd5vwOZKMOpxptinTci0tgJ1Tl/DHGhtAHa4vqYVVljIlXUWAmWWcssnM9rmRkJEnClHKL8xlLU7X0zn/1Ff2dayQdXAlKFT6D593XuTw7vYlS7UX6jcOkXTvId/0bQpr1w+KAogYVjVfQ3znxwNdSlmWtu10OhZ21dMjR2FtEp6dSTe1ZaP1NX1eHXNvWRgZwNbkBdc2xxBl0DP1D1AV8WCbNba4rrQng3avspF3aC7KMU62nUTjkLuRdHCp7uWKu2x2AvhpryZ8Dycklcm7h0XP16lVmzJhhyxuqVauGq6srSqWSqVOn8tlnnzFnzhx8fHxsrW33csOeOZHk9OnT1KyZVYaqUqVKVKhQgZ9++olVq1YxfPhwGjduTPXq1Tlz5gxAni1xBoOBCRMm2Fr1vL29KV++PPb29rb31KpVi7NnzwLWhDE0NNT22t0xX7lyhd27d/P1118zbdo0LBZLkU6GKvS2OT4+nn379tmaVQHGjct7YH1hNBoNc+fOzfHNmD59OosWLaJy5cqMHTuWixcv5siWi1pmF/AVyQOQC/wHlHLSuoCza8astKIiKZS4tRxIwq6vaRp2GnD6f/bOOz6qOt3/73PO9Jn03iCUhBA6RBAJCFZ0dV0bulfZ5e7dRddVLFd3dW1Xt+oW9/70olvcdcXC4iIqVlRUFJEivSSEFEJ6mUkyM5l6zvn9cTIJMYFQ0jPv12teL5iZc77Pycyc85ynfB626jIItvp5/dp4Gtx+0qPDNWcCEGg5yh5JuyDNPokD2Pj2b7UxffO+jzE567TWEU02Yi74MQ1v/RLHxyu59bZX2dI0mZdqa3hbmMQdbMG59zlMKbPP5nBGJB0agB3p33VHtRPn1aOnnHFU9cnmpfws+c88RAJfVn8M9G1n6nBHdlUdFwHsfFMcqv/rr/QvgEES8GVfim7ny+TWlUBkFpt9+n5bP8zg4pJLLqG4uJjrr78ei8WCqqr89Kc/JSIigquuuoolS5YQGRlJfHw8dXWnrhG6bt06XnjhBcxmM08++SSHDx8GtMjcsmXLWLp0KbIsk5aWxmWXXcadd97J3Xffzbvvvkt6etegUEJCAg899BC33347Op0OWZZZuHAh+fn57Y7jwoUL2bRpEzfeeCPx8fGYTCb0+u6/26NHj8ZsNnPNNddgMBhISEg4rePriR4dwDvvvJO5c+eedepXVVUefvhh7rnnHm677TZAcwj9fj+jRmkX9vz8fLZs2dJnDqCKiujRUsAHZRPgOWEDiBLw4dr9NgCRs3rXAQStGcS+4U+wez3jz7uFIy4HB6QELO7DzHzK0S5MPFJpH9vXUs5uSXMe5pzAAQw0ltP05SoQROKvuP+M1otZuJyG9b+mZcdags21nJeYyUu1UGKwIfsFWovXo8oBBCl8ETodgu0agNrJUlXVTg7gqaD8/koa3D4SH+1IBbeoNpxchlGtps6so6TmAGOT++7GcTjRXfwg6K7mcNuN1uTjUsCqquI+oP3d+6MBJIQvqLDwIyM7dAb0lQdIyZjLYWM0VfYyUmMz+82OMIOHH//4x/z4xz/u8vwDDzzAAw880OX5NWvWAFofw/Hp3c2bN7f/+5577mHcuHHt/58zZw5z5swB4KqrrmpP9x7PX/5ycmWICy+8sNtu3zvu0HSAi4uLycvL49FHH8XhcHDFFVcQExPDb3/72/b3LliwgAULtHKLF1988aTrnQ09poCtVit33303N954Y/ujJ1577TWuuOKKTo9bbrmF888/n5ycnPb3uVwubLaO+jur1YrT6TzDQzk1QhHAAz5NcPlEEjDuAx+heJ0YR03DkDSu2/ecDeZx56KLTSdoP8Z3JS0Kslk3Cr0zLHgK2kVKEKC2uYpjUhQWScek6KRu39v43u9BDhJ57o0YkrpOkzgV9HEZ2nxTOYBj0/PckpeD6jehSgofKRNR/M14q7ecxRGNTORvjIEraK6jzOUg0WTj3MQTR3S/SbzViPy7zlG+3xbkMsmrpQX//slvwzpxZ0Grs4oyMRoRQZvJ3Ia/9giBhqNItjhMo09cr9nb1Dh9eAQzlomLQFW5uUk7L35UEv4NhhnapKSk8Pbbb7NkyRJ++MMfcu+993bKivYnPTqAWVlZvPPOO5SUlFBaWkppaWmPO73++ut5++23Oz1KS0tZu3YtS5cupb6+nh/84AfYbDbcbnf7dm63m8jIyLM7oh4QPFW0YKDc58Mo6brMuwzh3LEWgMi8a/vGDlEk8pzrAbig9hAAX0npZOkr+mS9oYigquxq02qcGZeGrptuUdnTQtPn/wAg/ltnFv0LEbPoVgCaP39BcyZaNUmMdYJ24fOUhqdPnC7fTAF/WKmlWC5KzUYUTq/RSRCELk7g4YY8AP7t1tGy58/dbRbmG3SXdC/ytCILImMjojHrOqLc7gMfAWDNvRDhBN3afYHYVhoQ0TZ1ZFGTdiPxaVVRv9kQZnizatWqTtG//sJisfDss8+yZs0a1q5dy9VXX93vNoTo8Qx86NAhVq9ezaOPPsojjzzCo48+ekYLffjhh6xatYpVq1aRkJDA3//+d2w2G3q9nvLyclRV5YsvviAvL++M9n8qqCqInmoKJG3c1+To5G6dClVRcLalf0OSLX1BqLM4sfATBFVlhy4Vjz0cAQQt9WQINLBb0OqS5iRmdvu+5i9eRPG6sOQsPGudRuuki5CikvDXFuEt3cHfvqWF4L/WaVHi1tLe1b4aCXwzBfxhVcgBPL06zRCCIFD8QEd6xelMRy9DkRTHl1/+jkBzyVlaPPJQAq0UyloEYnJM52ktrQWfApxyV31vERoHaJv2LQDSGirQKTKfhhtBwoTpNXqsAVy1ahVOp5PKykoyMjKwWnt3CPhjjz3GvffeiyzL5OfnM23atF7dfyeUAIK3ngKDtsaJ6v+8pTuQnfXo4zMxpvVdXZF53Bz0caMINJZzterjddHEdlcLEnKfrTlUUFUw+ytO2gCiqir2jSsBiL3oJ2e9piDpiJpzI/YN/0vzlpc5/8qHAHCYFFxOEzb7IWR3LZK1+1R0mK50pIDT8MtBPqspBmifgnMmjImz8P++M5kVb+wHVcTfnIIQW816IYPpG5aTfN0GhNOMLo5kZHdHA0iX+r/CzwCwTDi/X22anqY5gIaETIzpk/FV7Gd+UymfxI6nzGknMyK2X+0JE2Y40uNZ8oMPPmDp0qXcd999vPDCC6xcufKsF924cSNGoybvMH369PZQ6KnO4DtTdP46BFQKjJqY8Inq/5x7tVSfbdrlfar9pg0+1yKM17i0tvGvhCTGGsIjj1TA5K1sl4CZEz+6y3taD32Cv+oQuuhUImZ0LdY9E6Lm3gRA89bVjLFEkWqOBF2Qt+W2NHDlpl5ZZ6QQGgMnRaSzpf4o7qCfSdFJpFmjzmq/S2cd14HXopVxfGjMxlu5Cee+589q3yONoKu6XQLmeA1Af81h5OZapMhEDCkTBso8bG1p4OvqteahT6qPDJgtYcIMJ3p0AP/xj3+wZs0aoqOjue222/joo4/6w64+wRDSAGwbeH6iCKBrT5sDOPXyPrcpYqbmuEw8pukCbdFl8LMZvSf0OJRp9FTSLJpIlCC9G4fBvvFZQOvgFXS9051rGpOHITkbubmW1oJPWZCi1Yh8IGoCnXt2husATxVVVZHbU8BpfHRc/d/ZEmXWc+SBCxgbZwF3NGpQR4kQyRExhqatv0IJes56jZGC7KrsNgLYWqjd7FgmLBhQEfSI6Vrd50x7JagqGyvCE0HChOkNenQARVHEYDAgCAKCIGA2D119Op2vhiACh1VNzHRqTNcIYLC5Fm/pdgS9EevERX1ukyUrH9ESjaG+hDGtDvZISRi94TtcVYVirwOAPKu5ywVIdtlx7XoLBJHo83/Ya+sKgtBem+nc+Qb5SWMA2K/T6kbdxz7rtbWGO4q3EVX2IhqiEA0RfF6r1ectTDmzTu1vMjbOyv9eNRkQwKk5MG9Z5iK7q8JRwNOgxVnJMSkKPSpZxzXFtRZo33VrP6d/v4l53BykiHhsPh9jW+18UlMc7vgeYVRUVLBkiXZeLiwsZPv2MxfmP3r0KFdcEdYNhVNwAPPy8vjv//5vamtreeSRR5gy5ewK7QcSva+GMjEab9u8y5huVO1d+zTBa0vOon5RvRd0eiLaCp3/w1lOUJD42n6Uryua+nztwYyKSmFQU30/J6Zrp3bz1tWoQT/WyRejj03r8vrZEDHzOwA4d77J3DitXMBhUmlVjGTpK5Bbe0+IczgTagCRItLxyUG2NRwDYN4JGnq6w3N0F8f+3zUU/iSewtsTOPb0dXiO7mp/ff7YWPLSo8CpOeirAlpzScuelaiq0ktHMrw56NBKTrJM+va5zJ3q/3IG1gEURKk9G3NZQwHVPh+HW+oH1KYwA8eGDRs4cuTMgiRvvPEGd999Nw6Ho5etGpr02ARyzz33sGnTJiZOnMjYsWO54IL+7QbrTfTeGva0dQD3lP6NmNb36d8QthnfpnnLyyyyH+VXSdM4plN4fV81s9Kj+82GwcghRQIBZieO7fJa0xf/BCB63vd6fV1T5sw2jcYKxjdXYtMZceHji+aJXGLYja/2ayxjLuv1dYcbx2sA7myswCcHyY1OIs50ao1k9o9XUvPSClA6mqKcO9bi3PUmKUufIWbRLUSa9Gy9cz7ST+2osojTpFDCOMY2HcFTtgHLmMV9cmzDiYNOBxBNrjWi/blAfSlBewWSNbZPG+FOxiMXd5QKREy/gubNL3Jp42H+b/Q8NlYVMeE4vcIw/ccVH/6N9yoKenWfl6Xn8PbFPWdyamtrWbduHXq9nkmTJuH1ennqqaeQJImMjAwef/xx1q9fz9q1a1EUhRUrVjB37tz27aOionjppZe4+OL+EzUfzJwwAijLMn6/n9tvv525c+fyve99j/POO4/vfa/3L7j9hcFXwyFRiyZ1NwJOlYO49n8AgG1q/13gbVMXg6Qnsa6YaH8rhwzRBAK+flt/MOINBjiMDUFVmZ3eOersqyrAW7IN0RTRHq3rTQRBaN+ve9dbzE7QooAfC9oFyVf7da+vORzpiACmsblW0w+dlzjmlLa1f/wsNS/+BBSZ2ItXMP73pYz/fSkxF/4E5CDVL9yKY9PfAe3zSrSawaV1hj7WqkWsWvY+19uHNCw56NHONZOiOxyqUPrXMmEBgjgwHdX/2l3Z/m/rlEtBlMhssRMV8LAx3AgyIklKSuLqq69m2bJlTJkyhYcffphnnnmGl156iaSkJNatWwdAZGQkr776aifnD2DRokVYLP0zz3oocMII4Nq1a3nuuedoaGhg8eLFqKqKJEnMmjWrP+3rVfS+Ggokre5vajcdwK1HtqC0NmNIzj7jiRJngmSOxJp7Ae59H3BBYzHrkifzn7oKYHq/2TDYOOQ4SkCQGC/biYnK6PRaKPoXOXtJn6XpI2Z+B8dHz+Dc+QZzr/8jG6uPUGRJhwD46nb2yZrDjZAItM6Wxhe1ZQDMS+rZAWw9/AU1L68AIGXZc8QsuqX9tZTvPYMxZQI1L62g+oVbMY2ajjlzJhdnJ/BySRxENbBNnwqqhOfoBuTWeiRL92LvYTQKAlp97eS4jk77wZD+LazvGBIgmSOxZJ1Ha+HnzG8s5VNbHIqqnLaYeJiz51Qidf2B3W6nrq6Ou+66CwCv18u8efMYNWoUY8ac2o3mSOeEv54lS5awceNGHnnkET7++GM2btzIhx9+2Gle3VDD4KtpF4GeFtM1Auhuq//rj+7fbxIx49sAXGUvQhUE7N6RLQi9u06bjjJDdHXSdFMVmeYtLwEQ1Qfp3xDWCQsQLdH4qw6RL2gF55Y0rdGg7ui2Plt3OBFKAYu2dL6s0yKA+UmZJ91G8bVS+eelIAeJXfzfnZy/ELEX30HMhbeBHKDyuZtQg34UFX6Rr93t+60uWpMvBCWI+/BrvXtQwwxVVTmsaCn5KckdUi/tEcABrv+TlY5mj4i2meyLGwux+1rZa68eKLPCDCCCIKAoCjExMSQnJ7Ny5UpWrVrFrbfe2j7HVxygqPVQo8e/0rx58/jrX//KM8880/4YiqiqitPfTK1ow6ozMC4yrst7XKGxR5Mv6W/z2h3AqfYKDHKQA+6qfrdhMLHXoTUMzDR1ft5zZAtBewX6+NFYsvP7bH1Bp8c25VIAJtZozujulkbsipUIuQ7ZXdtnaw8XQhqAxWI0jb5WUi2RZNpOLuBb/9YvCTSUYcyYStL1vznh+5Ju/D2GlAn4qwto3PC/3Dl/DMtmjEP02RBEhR8Va7I9roJXeu+AhiGNzipqRSsmNci4WC3SHmgsJ9BQhmiJwpQxdUDt8wU76j9D58g59nJ0ihzWAxyhTJ48mZdffplt27bx4IMPsnz5cm688UZeeeUVsrPPXmJqJNFjE8idd97J3LlzSUnpXjR5qKB4GjgsaHOGp8SkdEkdyO4mvKU7QNJhnTC/3+3Tx6ZjGjUdb/luZjVXsOcEItUjhb1OOwCzIiI6Pd/y9RuAFg3o69ok25TFtGz9F+qhjeRkLqCguY6P1FyWsB2/vQBzeCLISQk6NSd+u1f7/3mJmSfVk/PXl9L43u9BEEhZ9ueTajuKBjPJN/0v5b9fTMObjzMz//voImPIMmVQqB5ihyEJj2qCmm0EW8rRRXadJBMG9lVrxfzZgrv9nOguaNP/y8rv1/m/3XHU4WFiknYOMCSORYqOxdRkZ1ZzBR9XF3H35IGNUIbpH9LT01mzZg0ACxcuZOHChe2v5ed3DgRcc801Pe5v8+bNvWrfUKXHK6jVauXuu+/mxhtvbH8MRYKuCg61C0B3da7cBZ+CqmAZNxfRZOtn6zRsbZ3HixqLKcdIVevInHvZ6HVTHghgUgNM+cZoKufXWpFvX85oDhGKBLsPfcK8tu/Ml1ImAAF773bBDTdUVW1PAe90a6LM5yZ0neZyPA1v/RLkAFFzb8Yy/twe17BNuRTbtG+heF00vvcHAJ5bvBCAoK2Zz71a9Kr16IYzPYxhz/7GowDk6DsibZ6iLwCtAWSg2XC4s9yLJescAM5vKOHzmlICSnhsZpgwZ0qPDmBWVhbvvPMOJSUllJaWUlpa2h929Tqyq6q9/q87CRh3KP076aJ+tet4QrWHFzRqqY1PKkdmHeC2hnIAJst1mCI6Ije+Y/sI1JcgRSRgyTqvz+3Qx6RizJiK6m/lYp+my3hIr5UOBOyH+nz9oYzic6AGPQiGSL62a+nyvPiME77fX3tEa+4RJRK+88gpr5PwnUcBsH/8fwSdDZyXmIlBMCAYPbwbbBvfV/bBWRzJ8OZAmwbgRJOx/bnWoi8BsGTNGxCbQlw/NYW0yM41IBEztTTwxY1HcAW87GjTlgwTJszp02MK+NChQxw61HGxEwSBF198sU+N6guCxzmA3TaAHBx4B9A8bg6iNYYkt4OMVgcfl+/ipqzZA2bPQLGtXnMAp8q16CI6Zr62R/9mXtVvqSnblMX4ju0lt/oQYKE1ygYNUFq6i7i+HxQzZAmNgAtaM9jnqEYUBGbEnViwu+Hd34EiEz3/P0+rA9889hxsUy/Dtfc97B8+TeI1jzFan0qRv4zPddrNg7fiM1RV6dRMFEbjoKsFgNwIbdSi3NqMr3I/SHpMmQOr+HDL3Ex0YueSAdv060H3E5K9LdpUkOojzD0NYfEwYcJ00OMZcdWqVZ0eQ9H5A/A4j3FE1ArQp8Qmd3ot0HgMf3UhoikC85hzBsI8AARJh22y1ngw317KJ7VHR+TIo2312l39NLkW6XgHcGdb/V8faP+dCNtUTUjYVPQ5UQYT1QE/NYKVgGNkRmdPlZAEzGHzWIKqwsSoRGx6Y7fvlV12mr9cBUDc5T897bXivvUzAByf/hk16Oc7mVoDSJNZplaOQfE1EXAUnclhDGtUVeWQNwBAbrR2TvQUbwVVxZw5C9FgOtnmfY5BJ+CTO09z0VkS0MVqXcuLGo6EG0HChDkLThgBvOGGG05YsL169eo+M6ivKGiqJSBEMEqvI0Lf+cTmPvgxAJachSctPO8PbNMup2XrahY1FvNK+kxKXXbGRnTtWB6uqKrK9rYU8PRgDboILW3ory/FW74b0WTDmnthv9ljyZqHYLTiq9jPhXk38brfyw4pjSvUwyi+FkRjZL/ZMpQIOrX6v716rXbyZOlfx6bnUf0erFMuxZiac9prWSYswJg2CV/lAVp2vM6PplzE7w6/B9YmdjZkc5l5K76arRhiJ/S8sxFEndeFXRGIUH2MitE+n9YjWvrX3A8lFj1hlCSaPcGuz4/OJli3i/MbS3iprgxPMIB5gM/bYcIMRU7oAP7xj3/sTzv6nL3OFiCCCZauF+zBUP8XQpMeEZjVVIFJDrCx+siIcgCLnY00+lqJU1pJEmREYwwAzrbuX9vUy/s1MiHoDFgnLsK1+20Wuxt4HdgsjOEKDhNoOoIxaWa/2TKUkNsigPuUCCBwQgdQVWQcH/8fALEXrzijtQRBIObCn1Dz4m3YP3qGsXNuYLQthqMuBxvVHC5jK77qbUTkDt0pRn3B/tAMYLkRnU1Lz3tC9X/jB94BNOgE/HLXec6WnPm4d+xiWksVFk8LW+rKuCA1awAsDBNmaHPCFHBaWtoJH0ORA22pjgmRndO/qqq2RwBtg8AB1EUmYsyYhF5VOKfp2IhLcYTq/6bJNQRMKe1R6P7s/v0m1olasd+kei2NuE+nfYcCjsP9bstQITQGbrdfO8WcyAF07dtAoOEo+sRx2Kac+dze6HlLEU0ReIo24686xKIUrY7wa732Wfnr95zxvocrB5s0BzBbbkSypaAqMp7irwAwj597sk37Bb0oUuPsOhLTlDwd0SYg0lYqM8LOkSORiooKlixZAkBhYSHbt28/o/088cQT3HDDDVx77bXtsjIjmRFTFX0waABgQkznETG+yoMEm2vQRadgSJ04EKZ1IWKm5uQsaCzhk6qiEVUHGHIAp8s1+I1as06wpY7Wos0g6dulcvoT68QLAIgq3wmqSpUpCgUINIXryk5E0FWJCz2FXh96Uep29jZA82ZtrF/0gh+cla6jaLIROecGAJo2r+KCFC0iVGXSkhz+xgOoatdo0kjmgEObpJGl2NFZU/BVHEDxOtHHZ6LvplGuv2nyBvjJ6/u6PK+Pm4QUpd0Ynt9QHHYARxgbNmzgyJHT/8y/+uorysvL+de//sWrr77KX//6V5qbR6bUWogeu4CHA7LPySEhGoCc2M4dhu3dv7kXnlSktj+JmH4lDW/+gvMbS/iVx8nBplomxST3vOEwICQBM1WuJWDSohDOXetBVbDmXoJk7v+aO2PGVCRrLLK9gjwUdigSZWI0cvUhYvrdmqGB7KrgoJSACkyJScYodT3VyO4mrbFHEIg+b+lZrxl13lKaPvsbzVteYmFbM0nA7KaqOY5UGgm2lKGPGnvW6wwX9jdqUdpsnYwgGQZV/R/A+DgrOYldNVkNcRMRo0SoUJjnKOPBulKcAW+X2u4wfUP5H7+Fa8+7vbpP27TLGXXPOz2+r7a2lnXr1qHX65k0aRJer5ennnoKSZLIyMjg8ccfZ/369axduxZFUVixYgVz52rXkRkzZjBxYkeQR5ZldLoR4QKdkB5vuWtra7n33nv5r//6L9asWcOePUMvlVLReAS7aCFCDZBmje702mCq/wthypxFUGcgxedkTJvUwUjALwfZ1ag1D0wN1uI3ag0EofRv5ACkfwEEUcQycSEA3/ZreoB7pSQaq8NagN2hqipBVyV7JW1SyonSvy3b1qAGfFgnXoA+7sRNIqeKJTsffXwmQXsF0eW7mBiViCApvE8uAP6G/We9xnBBVVUONmsiy7lmrTvbc2QLMDjq/wASI4wsmdY1EikaIjDEZSKYwCoHmOEo5/OaoalPG+b0SEpK4uqrr2bZsmVMmTKFhx9+mGeeeYaXXnqJpKQk1q1ru1ZERvLqq6+2O38ARqORqKgoAoEA999/PzfccANWq3WgDmVQ0KP7+/DDD/Of//mfrFy5kry8PO6///4hlzvfU6vVao0XPJ2ifGowQGvBpwD92lnaE4IocsSUTo6rhAWNJWysPsLtuX0393awsMdehV+RGa+TicSPw5SG7HHiPvAhCAIRM68aMNusOYtw7nidcxzlkBzHXimJxb6dqKo6aCLHgwXF14QacHPQql28Z53AAWz6Qkv/RuV/v1fWFUSRqHlLaXjzFzR98U8umPodDjXXsUnK5Ad8jr9hH9Zx3+6VtYY6la3NNAcDxCgekmyJAHgGWQTwZOjjcpGiSgh6FRY2FrOxuojLMwZHCc9w51Qidf2B3W6nrq6Ou+66CwCv18u8efMYNWoUY8aM6Xab5uZmVqxYwezZs7nlllv609xBSY8RQJ/Px9y5cxEEgbFjx2I0dq/l1ROqqjJ//nyWLl3K0qVL+cMftNFNu3fv5vrrr+fGG2/kmWeeOaN998SetlTHOBGOv1R7SraheF0YUnLQx6Z3v/EA8ZVxOqAVOX9WU4ysDP/6pfb6P7S6jIApFfe+91GDfszjz0MXNXCzd625Wh1gSuU+UFX26lIxqS4UT8OA2TRYCXUAF+i0z2taTNfRi/6Go3iOfIlgtPZqZDfqvJsBTTPyggRNCLrAoEX9A+EIYDsHmrTpLFlKI7qIFIItdfhrjyAYrZjSpwywdR08/mH3jVaG+MnH1QGW8O6xg/1pVpiJm6mRAAAgAElEQVQBRBAEFEUhJiaG5ORkVq5cyapVq7j11luZM2cOAGI39cRer5dly5Zx7bXX8pOf/KS/zR6U9BgBNBgMfP755yiKwu7duzEYDGe0UHl5OZMmTeK5557r9Pyjjz7K008/TUZGBsuXL+fAgQNMmjTpjNY4Efta7ACM0Zs7PT8Y078h3pYWsIzXmdVcSaC1iV32ypNqqQ0HtrWNdZoWrALAb0qh5etngf4Vf+4OQ+pEpKgkaK5lTKudg9Z4/IgEmoqQLAkDattgI+isxIdEMVZEQWByNw6gc8daACKmX9Grs7eNydkYR03DV76H2XZtzm2jUcDnl9CHHcB2DrZLwNjRWXPb07/msbMRuqnXHGwYEqYhWAQEo5E0Xwti5QEKm+uYEJU40KaF6WMmT57Mk08+ybhx43jwwQdZvnw5qqpitVp58sknqa6u7na71atXc+zYMV577TVee+01AH7961+TkTG8r6sno8df+i9+8QueeOIJHA4Hf//73/mf//mfM1rowIED1NbWsnTpUkwmEw888ACJiYn4/X5GjdLu1PPz89myZUuvO4D7Wz2AyCjTN+r/BpH8yzc5TCZYJXRumbmOo3xSfWT4O4BtEcDJrdqUjaA+HtceLd0wUPV/IQRBwJqziJatq7nC6+BpaxyHxThchbuYlTr4U2b9SdBVSZEYi4xATmRCtyK9Ldv/DUBk3rW9vn5k3rXUl+9B3b2eyfGT2N9Uwx4xidnNxaiyH0E6s5vY4cT+NgmYLKURyZZK68HBo/93KhgSpiEIArpYI4FqH5fUH+at8gPcNyXsAA5H0tPT20vPFi5cyMKFC9tfy8/vXB51zTXXdLuPZcuWsWzZsr4ycUjSYwrYaDRy3XXX8c477zB79myioqJ63Olrr73GFVdc0ekRHx/P8uXLWbVqFbfccgv33XcfLpcLm63j7t9qteJ0Os/uiL6BNxjgSEBAVBVSLant9VqKz01r8RYQRCwTzu/VNXuD/fddgJygRU7mN5aycZg3gjh8rRxuqccoSkwIVKPoozFX7ULxtGBMn4IhadxAm9iuB7jAqd1h7tUlUV15YCBNGhR8U6ZIdh1rn7s9tRv5l4C9Es+RLQh6E7apl/W6PZHnXAeAc9ebnN+WBv5ImgBKMKzd2EYoApgtN6Kzpgy6DuAQdy/ovmtbHz0eQWdBsLoBuKTuMOuPhiO8YcKcDj06gPfcc0+7UxYVFcV9993X406vv/563n777U6PKVOmcOGFWqNFXl4etbW1WK1W3G53+3Zut5vIyN6V+TjQVIuMwBilCYwdqajWws9BDmIak4f0jc7gwYBOFGlNmwZodYCba4rxy13HIg0Xtrelf6dHxmBAQbWkEVH0PjAw4s/dYZmwAIAx9Ue0OkApCV94xiwAx/fBBJ2V7Q7gtNhu0r9fvw6AbeplvZr+DWFMnYgxNRfF7eBSTyMAWyWtxtdvD3duq6rKwaY6QNMAFE0JeEs1YV3LuHMH0rQu7Kpsxu3ret4TRAlD/BREm4BgjWGUtwl7yXbqPL0bQAgTZjjTowPo8XhYvFhT6L/yyivxeDxntNAzzzzDP/+pdf0VFBSQmppKREQEer2e8vJyVFXliy++IC8v74z2fyL22LV6shy5AZ8xub0JxH1oI9Ah8jvY0IsCTYlzEPSQ4Hcz2lHB1rYU6XBka71WrzXToqXnguZUrEc+AAY+/RvCkDIBKSIBo9tOhqeJvVISqVQOtFkDzjd1yoOuDgdwajeCwn2Z/g0RcY627wll2wAo0kchIxBoDDcLNMsuXEEf8aqHGNVL0N6IGvBpda622IE2rxOfFjdSam/t9rVQGtgyVpP5ubi+kLePhR38MGFOlR4dQL1ez+bNm3G5XGzZsqXb7ppTYfny5Wzfvp2bb76Z3/zmN/zmN78B4LHHHuPee+/luuuuIzc3l2nTpp3R/k/E3rYO4BylEZ++o1jffUCr/7NOGjzyL8ejl0Sazdma4CmwwF4yrPUAN9eWAZCn10b2KR4dOncd+vjRGEf17nfiTBEEAUu2Vm9yTksVR8RYzEpNeMIEIBzXXx9sOUaBGEoBd44ABptraT38uTbVZfoVfWZPyLmU975LvGTDL4ocEuPxhx1A6oMOALLlehB1eCs0p2mw1v+daA6SIUE7L+jaxKIvqT/Mm0e7Tg4JEyZM9/TYBPLLX/6SJ554gl/+8peMHz+exx9//IwWioqK4i9/+UuX56dPn96nuoJ7GrTI0iS9DIIOQQDZZcdbvgtBZxi0Jz29JOAwZiFFCsgNcH5jCSuri3hkxiUDbVqvIysKX7VFAM8RtI5tta4BAW0s3mDS2bNkz8f59Tou9Nh5XRApFCOZ7KpEFzG8G3ROxvEXaFVVqWi102Q2E2swk2bpXDPs2vMuqCrW3AuRLD3XE58pxoyp6ONGEWgs57+sOp5oge26NGaEU8DUBbTfWJZsR7KmdHQAD7L6vxDKCUZhhhxARahFiEhgtLOessLPaVrwH0Qbzd1uEyZMmA56DOeNHj2alStX8t577/H000+3d+wOBVRVZW9brctkS8eYIHfBp6CqmMefh2i0DJB1J0cviXjEaPRJKagCTHbWUlRxEHeg63D0oc7+phqcAR+ZthgSPBVaU0GFFu0cLPV/IUIRwClNWs3iXimJQNPwjcyeKiEfXfW3cEjR1PWnxqZ2cd6dbV3dEX0Y/dPsEdojjAsdZYDmAAaajqDK/j5de7ATigBmyY1IlmQ8RQPXAawEFdyVLTTuqqb2i3JqPiujbssxmg7W43No5UaycgIHMH4yCCJBRyFRszSZqAtqD7GuPBwFDBPmVOjRAXzuuefIy8sjPz+//TFUKHc7aAoGiFVaSbMltNcqheRfQuK+gxGdKBCQFYyJU5EitIvouQ3FfFFXNrCG9QFf1mpjnM5LHEPQWYHqBaGlDtkciyV73gBb1xnT6BkIRisxzdXE+dyaAzjCG0GO7wIOuio41N4A0rn+Tw0GcO/fAGizP/sa2zTNAcwo0xoctuvSUcOdwNQHtXGG2UojghhNsKkKyRqLITm7X9ZX/DKNX1dz5J972P3YZxQ8s52yNQepeKeIyveLOfbWYYpX7WX/k1+yOToK8csKvPXuLvsR9Rb00VmgBLHkzAbgW7WHWF38db8cR5j+o6KigiVLlgBQWFjI9u3bz2g/Tz31FNdffz1Llixh7969vWnikKTHFPB7773H559/jtk89ELquxq1BpCJcgP6iDRAq1VyHxzcDSCgpYADioo+fjJS5PsoLSoLGkt4v6KAS9MmDLR5vcrmNqf2vKRMgkXHkJu1mrrW8ZciiNIAWtYVQdJhGT8X94GPmNlcyb74sAMIHRHAoLPiuPq/zg5ga9EXKF4nhtSJGBK6H9XUm1gnLkIwWFCP7SU+aQENNiPFYgyJ9kNa9GgEIisKDQEtAjhetqO4tN+aefxchDOs7z5Vgp4AdV+UU/dVJXJroP15Y4IFS7INfYQBQScie2V8dg+tVU4M7gDyrloO7KolamI86YvHY0rsmN9qSJhGwFGIGCEixY0itbEc16FPqVlwE8mW3lWUCDM42LBhA/Hx8Zxzzjmntd3BgwfZvXs3a9asobKykttuu4233nqrj6wcGvToAKalpWEymXp626Bkj13r0MxV6pFseaiA2lSFv7oAwWjFPHb2wBp4EvSiyEPvFXDHDyZpjSAVCuc5ynju6F7+OPvbg6ou7mzZ3BYBnJcwCtldhdykRZRaJ/S+RlxvYMnKx33gI+a0VPJhYjY19mLiBtqoAeT4BJ3cqQO4cwOIc3db+nfat/rFLtFgwpp7Ia7d61lQW8XrtjHskFLJG8GNIKUuO0FkUnUQiR/ZoY1dNI+f22drqopKw44qqjYUE3Rrjp8lPZL4vBSicxPQR3Q/XlRVVDZsKiOuvAWxyE7zoQaaCxtJmj+K1IvGIupEDAnTcB9eQ6BhPzH536fhzV9wRfV+Xivbwx258/vsmEYyRS/spqWwsVf3GTkhjqxl03t8X21tLevWrUOv1zNp0iS8Xi9PPfUUkiSRkZHB448/zvr161m7di2KorBixQrmztW+27m5uTz//PMIgkBVVRXx8fG9egxDkR4dwEAgwJVXXkl2dna70xGa4zvY2W0PRQDr0dnSwAVK8WcAWCcsQOhmQsFgQRQF7sgfgyE+HdEgIFiNWN0+Yo/t5VBzLbnRyQNtYq9Q4W6i3N1ElMFEjl7hmE9G9aioBhvezAUDbV63WCZoF5ZzXdo81Z3NjfTu7JqhR+h2pKX5GGViNBKQG915dnNoqoutnxxA0GoNXbvXc6mzlNcZwzZdGj8YwQ7g/jYB6AmSVgfpr9NEzS191ADic3goe+0grlIt7WzLjCb1krHYMqN7vIkVRAE12UogM4q8q3Oo+rCEhh1V1H52FOcRO2O/O7m9EcRfv4e4BX+m4c1fcHF9EXcVbA47gMOQpKQkrr76auLj45kyZQqLFy/mlVdeIS4ujj/96U+sW7cOnU5HZGQkzz77bJftdTodTz31FC+++CIPP/zwABzB4KJHB/BHP/pRf9jRJ4Q0AHPleiRbGqpTRS76FABr7uCUfzmeaLMefew4EEQkW5CgW5ODebv84LBxAEPyL+cmjEZxVaI0aSmpYNaFoBuckWfzuHNB0pHRXI016GOXrHKzEkQQB/8M1b7g+CbNA001KIKVHJMB03E3WP66EvzVBYiWKCxZ/VfXGXI257gLMMgLtEYQ+5Z+W3+wcSA0A1htQZVVAnXlIEqYx5xeOu1UcOyr5ejrBcjeILoIAxlXZBMzJfG0shcGSeRgrYu5mbGMvmYicbNSKF19gNZKJ4dW7mDs9VkA+Or3oE8ch3H8eXDkSxIKP2NHw03DfnzmQHAqkbr+wG63U1dXx1133QWA1+tl3rx5jBo1ijFjTlxicvfdd/OjH/2IG264gby8vCHV2Nrb9Fj0kZuby+bNm3njjTdoamoiKSmpp00GBQ5fK0ddDkxqkDFKkxYBVFWUIi0CaBnEDSAhBEDUmdHHTkRsK2eZ31jKOxXDR8pic11b+jdxDEHnMeRmzZsITPzWoE1zi0YL5sxZiKrCtJZq9ooJBFvKBtqsASX0We13tgAwNTKm0+uuPe8CYJt0Sb9G3vWxaZhGTUcKeJjlqKJajKCsuWbEdgLvdbQJ4wdrUVpVUBVMGdN6dSKLqqhUfVRCySv7kb1BonLjyV0xh9ipSaf9m06OMPKj1/a0/982OpqJK2YTOSEOuTXAkZfLCKqLUf0tBJuLiZ2/DICrq/fz54LB4eh/c1Ti8RyqdXLnG+ERdqeDIAgoikJMTAzJycmsXLmSVatWceuttzJnzhyAbvWKt2zZwmOPPQZoI251Ot2gvcb0Fz06gD//+c/JyMigrKyM+Ph4Hnzwwf6w66wJpX8nKI1IqEi2NAzNR1Edx5CssZgyBoe48MkInTaMiTMQrQKCyUqmx0Fl2U4avV274oYin1UXAzA/eQy+ugIUlwqiSCDr4gG27OSYs7Ru+FlNFeyTEvHbR25nqXpcFeB+r1bjNTU2rdN7XPu0sX790f37Taxt84bPq9fSndvFpBHbCbzXrv0Nsr1lKG7tc+tN/T8lqFC6ej/VH5eCAOmXZzHu5qnobYYz2p/F0LUJTGfWM37pVOJnp6IGFVrqb8fvm42vZhuRc24Ao5W85gq27dtAs//MJlf1JjlPfMLhehe+oNzltcP1bp7+opQPCusGwLKhyeTJk3n55ZfZtm0bDz74IMuXL+fGG2/klVdeITv7xJ3ss2fPRlEUbrzxRm666SZuuukmMjJGdoS4RwewqamJ6667Dp1Ox8yZM096NzOYaE//BusQjTGIegtRlW16VxMX9XnHW29iSJyBIAgYkrWi+vyGEt6tKBhgq86eWo+T/U01mCU95yaMxlPwBQDGjGxUUySD+ebM2lYHeE5zBQ2ilbK6of95nA2hj+pgUCvon56U1f6aKgdpLdwEgHVS/zv2tsmaePp5TdrNxtdS6oicCdwa9FPU0oCIwBhPGUrbPWRv6f8pfpniF/fg2FeHaJQY//1pJM0fdVZRFr3U/baCJDLqOzkk5o8CVcLV9BCOvWVI5khi8r8PwHfKd/DPoh1nvHZvUdTgJueJT5j11CbEe9cTkBW++9LXfFlm5+oXNDmTy/66FX8wPFHoRKSnp7cPjFi4cCHvvvsu5557Lvn5+bz66qusXr2a559/nri4OK655hruvffeLvuQJInHHnuM1atXs2bNmnZZmZHMKXlBxcXaibOmpuaMR8H1N7sbtQ7giUo9kk2To4iq2AwMjfq/4zEmzgBAjNBOEAsbi3mrfOinDT5ti/7NSxqDQdLhKdaOyZq7AFWFQez/YW6rY8t11qBTZLa3TTIZiYTuCWW/iwIxGoBpSR134p6yrzX5l6Qs9N+IDPYH5vFzEYxWsn3VJPhcbNeljsiZwPsdNaioJEg2DGoQpW3Ebm9EAGVfUOsOLbKjs+qZsHwWURPOvsvSajhxXa0gCKRfPp7YKQKgp273DFxHm4m98CcAXFlzkOd2f4BfDp61HaeCcpxgdbmjlZd3ViDeu779uYO1LgCMP3uHN/eUkv/M5o6NRZkfvj7wzmqYkUWP3txDDz3Ez3/+cw4ePMiKFSu4//77+8Ous2aPvUMDUGdLQ1UUIiu1mpDBLAB9PN6AljLQOt0EVLEaJD2zmir4smQnriE+FSQ02/iClPEoXheBWq1A3TbzKoBBXZ+hi4jHkJKDUZHJddbytbNpoE0aUAQBSusP4xSMxKk+Uo7TYGs99AmgRd4HAlFvxJqzEID5jqMckeKoqR95Edu9Di39my4ZUH1AUEEXnYI+7uyK4JWAzJEX9+AqbUIfaSR7+SwsqRG9YLHWCHcyBEFg1LV5GE0fgGrgyIu7UQ2ZWCYuwqIEyCv5kldKdvWKLT3x7b9vY2+VVgO7r9rJ0ld2kSbVkSR2lkz5ccRaDqT9Bw9EvQA6D9Mz3sMy4RPWOV/BGwx0s+cwYfqGHh3AzMxMHn30UXbs2MHy5ctPmmMfLPjkIAebahGACXIDki0NX8V+9J5GhKjUflO8P1t+92kx9lY/oiECfUwWghjEPP4cJFTm1hXw9rGhHcUIOYCLUsbj2vcBKCqiVcCUOq1TXdlgJTQWbkZzJbu9A2zMABL6pHbXap9nruTr5Ly72xxA6wA5gAC2KZcCcGmL5gRtcYy8mqu9bTfFmajH1f/NO6sbLVVWKF19AFdJE/oIAxOWz8R8nFBzfyAZI4kauxG9YRtya5Cif+wiMn8FAEuPfc0f9nxIUOlaf9cdm4obafF2dsLeL+j6XfmgsI53DtZS3OCmsM7F6/uqqXf7OdLoZn91C1f+fRvjdcf4MPkOPki+q90JzNRVcWfkvzgsxnI40YOYvZVKo8Kze9by2s5/IvpdZ/nXCBPm1OnRAbz33nvZs0frwiotLR0SEcB9jmqCqsJ4g4CFIDpbavv4Nyl74aCOLH2Tdfu0qJihLQ1sGj0egIvqi3itdM8JtxvslLscHHE2EKk3MTMujZbtrwEgRktIluRBnwKG4xzAlir2EkkwMDwac04XVVURENhrrwBgkrHjk1ODfloPa7WdoSjcQGBtqwOc3liCoKp85RVGXCdwyAEcp3pQ3Fo5ydnU/6mqytE3Cmg6WI9k0pH1gxkY43p/tvr01J4nephS8rBF/xpjTCt+h5eaXSnoUqaS5msh6/Am/lF0aqPD7nnrAEUN2u/433uq8AZkLv/b1i7vu+yvW7ny79vI+u1GJj75Cdf9cwfbjzVx3T93MPUPmtLEtyybMQpBIsRWrrN+AihcE/8mt1kv5/KIm3ndkMs4dwOv73yR6c46Moyx6I29EzkNE+ZU6NEBrK2t5bvf/S6gaQLW1Q3+O+ft9eUATJe0FKlkS8d94EMAdNkDF4U4XSx6iZVfajIpoTpAKVoHgsi5jnI2Hd1Di39ohp5C0b8FyWMR5WC7SLAxNa19/Ntg99NDenazmitwYaCgcvcAWzRwCALsbbEDMNnWcRHzlGxH9bdiSJ2IbgC1Kw3J2VSJCVh8TnJcdewQk0dUJ7Cqqu0p4GzF0SsdwDUby2jcUY2gFxm/bBrm5N6Tkjmeb0/q+XtjTpuPIPiIGfMSxlgznioXctKvURH5Yfk2Htv5Hs5Az+dKFZWArP1tVryxH3vbyDpVVdvr+eytp3bjkG/UZs0qQHRUKSnjP+apuFw+041hshzBX50ifwxmUzbt93ye9xRbsx8gKA/+zEeY4cMpdXSUlmpOSHl5OYoy+DuVtjccA2CqqoXdRWM87oJPAdBNGBr1fwCX5STia+sMMyTOBCDYUoBlwnwMqsycusOsH6Jp4PcrCwG4KDUL98GPUbwuBDPoEzUBz6HQbK5PHIffnEB0wEumx8HWqgMDbdKAEPqs9ru1C+yUqI7i/8GQ/oW2etJs7bc/z17GASkBR/3Qb6Q6VcrdDpr9XhJMVtK8laheQNJhHj3jjPbn2F9H1UclIMDY707GNjq6dw0+DkkUCMonv+6YMrTvV6BuA+O+NxnJoqe1zoSc9HNGexzMPLqDx3d92ONabr/MeU9/wUPvaTWiWb9tyxzd9zYA4r3riX/kgy7bmQUvV5o/Z2Xck/ws6kXixCZyDMX8Sz+J5dY7qI68nnt10/ibP5enPeNY7ovHI42mzBBNEyogECdZETi1VHWYML3BKekA3nXXXeTn53PXXXcNiRRwuwPo1zozgw11qH4P7rgcpOiUk23ar6iqihKQkb1BlEDXH75RJ3Y4gAma+rq/YS8RM7QmiYvqi3ileGf/GdxLBBWZDW0O4GXpE3HuWAuAFC2ii0gHtDtxYZAngQVBICJHSwNPb65ke/2xAbZo4PArAcqCKnpVJjeuQ1vLXTA4HECA2GmLAbio+ShBQeKrypHjsIf0/6bGpBLpKAHAmJqNoDt9fb7Waidla7S/Xdql44memNB7hnaDWS/hCZzcAdTZUtHHTEANuBHk/Yz/3lQEnUhAWkjQtoQ7Sr/g//Zu5Mu2yUPH4wnI2B7QhMpD0b9ff1xEjdN30nUFFC40bWdNws/Zm3ozT8X9ifOtDq6MT+TP45vZY/0/4oRbucY/lhkeEYPPS2vQg4xCpGRhtCGaKUYb5+hlzrc2M22MH0nf/VzkkU5FRUW7bEthYSHbt59aSr87PB4PV111FZs2beot84YsPc6umjZtGm+++SYAVVVVpKam9rlRZ4Mz4OVQUx16USLLraV4vKX7AHBknM9AWh90+2kpstNS7MBT7cTb0Iri63D8JJMOY7wF26hIIrPjsOpFIoy6ttei0UWNJdhcgmmcNnl2vr2Mh4/tp9LdTJo1akCO6UzYUneUJr+H7MgExlmjObxT+35JUSI6W5sDqA7+FDCAfuxcfLvWMbO5kjddzoE2Z0BQgRp/IyoC4xQ75jYnXgn48BS1aW8OYP1fCF32QlRBJKepCnPQz+bGZq4aaKP6iXYHMDYFc7NWV2weO+u09xNw+Sl+cS9KQCF2RjJJC/p+jFZjq5/HPizk91eefOK2adQFBByFeI59Qsyc8xizJJeSV/cTjFxOkr2Omyq/Ztnnr7L1yjuJMWq1ir/86DB6UaQ1ILP8tT2U2ltPvobg5SLzXi4yH2CW2YPROBGntJz92HDIMm7Fp+V8FQBtX2bRQKxkIkpSsPrrMDp2IbhLSLjgR0RPX0ydamN0/NA5fw80GzZsID4+nnPOObPxhY8//viQ6gPoS3p0AF988UVMJhMtLS28/vrrzJ8/nwceeKA/bDsjvm6oQEVlSnQiBkcTgmTEfUjz9B0Z8/s9qqQqKs0FDTR8XUVzQSMonXObgiQg6ETUoILsDdJa0UJrRQt1X1bwY7OOi5IsBN1+dFYDxsQZBJtLUHyVmMacA6XbObexlBeP7OCBaUNH2/C9NhHryzMm4i74DNnViBQZjWByoYvsuKAMhZ9oXO75uNbCjOYqfuUXCSgyerHr9ILhTk1Aq//LkRvQ2bQou6f4K9SAF2P6FHQRZ68Jd7YYI+NpSZqGtWYX5zQd4ytd36UtBxuhEXBTYlLQtWiSRZYJ55/WPlRFpey1g/ibvFjSIxl9dU6/XEif+aIUt1/u0QE0ZyzEuedZvOWf4J15PzFTkkhv8lLx7hECMT/l1uqHeCvpKDd8sooURx5/uX46j7xf2L7937aWt/1LJUr0k6FzkaF3k6lrId9ST6YlhqAuCSfZ2OWx7JZbuzh7ekEiSmdGL8jYqCEnN4+48ROQq/ZR9ZelKK1NGFNzSb/zDYzJmlj66N7/k/Uph9/6E81le3t1n1GZU8n+9l09vq+2tpZ169ah1+uZNGkSXq+Xp556CkmSyMjI4PHHH2f9+vWsXbsWRVFYsWIFc+fObd/++eefZ8aMGUNmoEVf06MD+M4777Bq1Sp++MMf8s477/D973+/P+w6Y0Lp37xI7eQu6JPxHduDYDDTnJTXb3aoiop9Ty01n5bhrWvrDhUFIsbHEjk+FtvoKEwJVnRWTedKVVXk1gCt1S5cpU049tfirWtlbFkL+578kpQLxmBKmoO7aC3emq1EnnMd3tLtXF5XwLNF27h/6gVD5q7m3bZZxpel5+D88E8A6JPjEQQ3UigCyNBwAC2ZM/CJRjI9Dqx+L/sdNcyI63+x44FEVaHa3wBoDqBk1eLsg6X+L0RAVnhfmcy17GKu4yj/L24U/oAHg9480Kb1OaEI4JSIOAS31sRgzb30tPZRu+koLYcbkcw6xt00BVHfPzc6N81M5y9fdS+0rqoqP/jXbh69ZALpaQtAEHFXbmbWo//m07u/RVb+KHx2L/VfVUDUQzxZ/BQ/MFhRW5ooerqBHIODUTpXu7OXoXMxWu8l1piIT5+Mm2ia1GgaArHsVILgDwBaY4iIgFUyE9QJHBO8fC44iBudyr1TFzFOZyDSZEIyRdpuTmYAACAASURBVNH47u+oe+1+UFUiZl5F6vJVSOZwt++ZkJSUxNVXX018fDxTpkxh8eLFvPLKK8TFxfGnP/2JdevWodPpiIyM5Nlnn+207ZYtWzh69CiPP/44O3cOvdKpvqBHB1AQBOrr64mPj0cQBJqbm89oIVmW+c1vfsP+/fvx+/3ccccdLFq0iN27d/OrX/0KSZLIz8/n9ttvP6P9hwg5gDPM2qGpHs3Bskw4H0Uy9kta0VXWRPlbhXiqNU0nfZSRxPMyiJuRjD6i+xoPQRDQWQ1EtjmIKReNwX20mU2vHSDJ7qXy/SMYY6diZAy+6q9IuvxB6tb8jIWNJTzqqGJTbQnnJ4/r+4M7S4667OxzVGPTGclPyKT863UASDESip+OGkBtJPCgR9Dp2S1lM0fZx/TmSrbVHB5xDiBAjU9TB5gotiIatG7QgRaA/ia5SRH8j3UW17KKBY5SnhAuYHvZV8zLGhz29RUtfi+HW+oxiBJjGgupVkAwSehj0k95H66yJio/1GoHxyyZhCHa1FfmduGG6andOoDljlZu/fde3i+sp9EdwGqUeDhuHuaGz5mv+5KcJywEfnMJcXPNbN5ZRJbexyQpn7VNlZj1TcSqhZAsYNQloOjTcIuptKg6qoJeDslu8AWBDoF3k2DAr5qJz4hjZdVhthgqCAgqJknPVWnTeDbvWrKiOuoh5dZmKp65DueO1wFI+M7/EH/Vw0NqDGl3nEqkrj+w2+3U1dVx112aPV6vl3nz5jFq1CjGjBnT5f3//ve/qaysZOnSpZSUlHDgwAESEhKYOHFif5s+aOjRAZwzZw4333wzf/jDH/j1r3/NJZdcckYLvfnmmwSDQVavXk1tbS3vvfceAI8++ihPP/00GRkZLF++nAMHDjBp0slD/SdjW5sEzDSdJgEjO7TOxJAQbF/6f0FPgIq3i2jcqd1t66OMpF44ltgZyYi60/vRC4KALTOarVPjuScznmNvFeKze/DxJ4KBv5FijcCScz4UfMZF9UWsPLR5SDiAa9tSB5el5yCXbCXYXIM+fjSq4ABAZ+twngZ7E0iInfpc5gT2MbO5kq1VBdwyaXg7FN9EVmWqA9rnN9ms3eAofg+e4q9AELDmnF6qsa8QRYG3nKP4pcnGqNYmkrxONlXsG/YO4C67NhZzSkwKgcOfAqCLOfWas6DbT8mr+0FRSVowiqic/k3nh7J1qqoiCAJ3vrGfK3OTGBVj5v3CegDeO1TNOH0Lr9uyucrm5vaYcv4j+gN2rFyHKMD4OC2rIAkmJhhyCOrTaBFs2GWZWr8Dnz8AdAQ3JARidCIRFj1CbDw1qcn81yebsMSX4GkMgBESTTZun5jPLTlziTd1Fr/2Ht3NsWeuI1BXjGiOJPVH/yRy1nf66S82vBEEAUVRiImJITk5mZUrVxIREcHHH3+MxWKhurq625G1f/jDH9r/ff/993P55ZePaOcPTsEB/P/snXdgVeX9/1/n3Jk7sm72HhCSkEAGRBAQcCCuWrSoqFWrFqna4ej62daqtbW2Vr5Va7VurQMHVXHiQNl7j+y9x9373nN+f5wQQEB2SJDXPyQ355z7hCTnvJ/PeH/uuOMO7rjjDux2O3fffTda7ZF3jQEsW7aMvLw85s6diyzL/P73v8flchEIBMjIUOq+Jk+ezMqVK49aADa5rDS7bURp9YwM92KTZYKdnYAyEF4+geNaHTV9NLy5g6DDj6AWSTorg6SpWYjaY0uTbGyxE3X+KMw/P4PmD6roWdOGx3krtS+tI3bcD/Hs+oqLu3ZxS+NWGpx9ZJljj9N3dGLYbV49O3ssjiX/AsA8/gd4O/4JogYxQnm4yMMmCQwbNYUAlNjbeKjfbPe7RKPLil8OEy+5SYxUfn6e6hXIoQD6zFJUxpiTvMI9hAQ1hvxpuDYtYqK1kRU9Q2dtJ4oNPYpBd5klDe9KpeFKk3B47XCyLNO4cBdBhx9jRhSpMwZ/k5nTby6t+uUi/H+9iMeW1fPc8iqmRFr5RXQHhTorozQ29KLSseujEL0KUvAgimZUmkzQJOAWjPSEQ3QG+giGnMCepi1RVCOJIbolB5HpEfgSstkhSWzqbWV970p8XSGEGPCGYWpSDj8aeQazs8agV+87qk6WZWxfPUvHK7cjB/3oMsaSfvtbaBNHDNr/16lOUVERDz/8MLm5udxzzz0DmsJoNPLwww/T3t5+spc4bDikAFy7di333Xcf4XCYmTNnkpKSwuzZs7/1nDfffJMXX3xxn9diYmLQ6XQ89dRTrF27lt/+9rc88sgjmEx7zEONRiPNzUdvpbGsU/ErPDMhC8m9GdkLks+DOjYNbUoBckPjca+Tk8MSrZ/U0rlUiTwa0yPJumI0+rjj44i/aKciYEWtisxZBQj+RfRsycNZb8JvzUPU51NhqyTW5+Txncv4e8X3jsv7ngganH2s6WnGoNYwMymHljXK9A9T8TS8Hf9EbUxBEJSd23DpAgbYrB4FgkChq5N6pwNPKIDhKOw1hitb+w2GlQYQRVgMtfTvbv5wXh4mccaAAPyru2AgsnSqsq5XEYDlcWn4mhTvQ13qyMM6t29jB7bt3Yg6FdlXjUZQDX76MivWAMiM1Di49cG/83RiF6O1VtTCvoX8TUEjVcF4siIimRQpYVXl0B6CVn83Pq+dvSN8RiIw+30spY0PI/zU6vYaPdQNdLfsc+3xcelMihvJTfnlFMYkHnCdIUcX7c/PxdnvahA99WaSrv0novbUrzE90aSlpbFgwQIApk2bxrRp0wa+Nnny5H2Oveyyyw55vYceeui4rm+4ckgBOH/+fF555RV++tOfMm/ePObMmXNIATh79uz9jrnjjjuYNk0Zw1ZRUUFDQwMmkwm3e8/4LLfbTWTkocf+HIzdAnBKYg7hhg8JO5Qdoano/IEb/PG8zQddAepe3Yqr3gaiQMo52SRNzTyhN8nY0kwCLT/D432IgC0BYuejsT3GBZ2VPFsVxx9KZhCpHbz6nCNhd/r3orRCpB1fEHb3oUsrQhWl/MxV5j3pX5nhIwDvOr8EzWfp0NVEgaODjb2tTErcvwblVGWbTRGABVI3KqMyZ3uoNYDsjXH0eQBMtDZglUR22bsoiD7wQ/1UYHcEsFytJmzvARG0KfmHPC9g89H0ntIlm35JHrqYwRUysizj6W6kr3odbyV/Rbpmz7MiJAts9sWy1p9Ah5TCSHMmk6O0TFLbafF38L7HCbQOHC8g0ulX86XTQILLz29UKQiCBslVw5Payfz03HSqXG18XN/IT8/KIEKlJl5voig2mXJL2n4p3m+u07nubdpfvJWwsxsxIpKkHz5B9KRrT+R/z2lOc8wcUgCKokh0dDSCIKDT6TAaj27Qd3l5OV999RXnn38+u3btIjk5GZPJhEajoampifT0dJYtW3ZMTSDL+wXg5MRsQtvaCNuVHaK59BJgd1rx+OBudlD73y0E7X7UZi251xSfEDf8M7P2TVHpks9ApW4n0vJLhKLF9KxpIxh9Bz92LedNn4t/7ljK70rOO+7rOFZkWeaV2vWAkv61f/hnAKImXkPYpTyg1MZ901LDpQZwW4cDKa0UupoodbSyrqf5OyYAFV+5/HAPKlMKks+Ft34NCCKGvCkneXX78vjyeu6dcT7qmFRirK3kubpZ2l59ygrAvRtAsnrq6ABEo4Da/O2NSrIk0/DWDiR/mKjCOCxlg2egH/Q66d21kp4dS/H2KiIuXQN9YR1LPMms8CVhI5XzYy3MipfRCVYavVW0+n20+vvXL4uIhljeapHY7tHRHVIDAktvm8SUJ5YzKyNIfrOGHOMFbIlYQO6kP6OOHM+CTW1cUXL4brH+tp10vPIz3Ns/A8BYeA4pNz+HxnLi/RFPc5pj5ZACMCMjg0ceeQSbzcbTTz991EbQV1xxBffeey9XXHEFsixz3333AXDfffdx9913Ew6HmTx5MmPHjj2q6/f5PWyzdaBTqRkXl057XxOyR0ZQ6zCOPnfguOMRVbJu66L+je3IIQljZhS5VxejiTwxDu6Tsy37pKjU5gxUxmTC7nZSpooY00bS+PZW9OpJvNZo5ffCWm4tmESs7vgPZT8W1ve2sMXajkVn4MK4dBo2vgdA5IQ5eBqUVLDKvKcrcTj5NL2ztYPZo8ZTzLuU2Vv5uue7NRFk++4IYH8K2FO9HMIh9NnjURmGlsFtnyeoNFgVzcC29HkmWhv5unkLcwsmH/rkYcjeDSCButVAvwA0fvt83e6VLThrraiNGqX0ZBDC8a72Wjo3L8ZaswFZCgGg1puIzasgZsQ48h6vY3Z8DH9IUSEKPdR5t1K711xeo95ATk4OKVmZZKWnUdnjpXFdM5kuPy+ua2HHr6bhDoSJ0qs5//qz8Xyynu61XlzeWVT97lJSr/k5syuuOKy1+tt20rPoIewr/wtSGNEYQ8LlDxIz/ZZh3+V7mu8OhxSA9957L2+//Tbl5eVERETwwAMPHNUbabVa/vKXv+z3eklJyUBu/1jYHf2riEtHi0SwU7GlMBSejahTopbHQ1N0rWimeVEVyBA3PoX074064g7fI+HhL2soT4ti9lhFeAuCgC6pAk/tu/g7VhM3/jq8W5+he0cWqWTyeG05b3z4NT+ZNfOEreloeL56DQDX5pbj3/gucsCLIW8K2rhMnNuUh9Tu+jEYXilggKa4yRQDY+1tPNpZd7KXM2jY/F6a3Fa0cphsyYrKmIJ9g2LtMxTTvwCSJGPsF4BnWhv4U0/LoU8apuzTALLpdUARgCrjwSN6vh4PLR/XAJB5WQEa04mrZ5UlCVv9Jjo2fIyrXXlPBIGozGLiRk8hKnMsQncIW1Un7xRoqfXuYpdnz7SOGHMUS+06UjKy+Mkl5ft0f45OMlOYaKbTqYQF8xMU772e+2eiEgXkWRMJ+zfQt8WGV38XzU/9lIhFfyH27J9gKrkETcxe9yNZJtBZjXvHFzhWvoqnaqnyBVFFzPRbiL/8T0PC7Pw0pzkSDikA582bx3PPPTcYazkmvui/eUxJzCHkbidsV+r/zCWXDBxzLKJClmRaP64ZaPZIOT9Xqfc7wSplbEok0jeUqy55giIA21djLryOhJlzcHxdRiD2lxh0U6lYA9uCmyicNXhmrd+GJxTgtbqNANyYV4H1CaU+NGrSdQCEXIoAVJn2TUsNF/03Ms5IOCoFQa/D7PNDZzVWv2dg3NSpzO4JEyMkG2pk1KaUIV3/l2sxEJJkjIXK5JwyWyudHjfNLhvpplNvMsjuBpDxkRa8DUoJhiIADxwBVLp+dyKHlFFv0YUnZs6vLEn0Vq2mfc17+GxKo5tKZyC+aBoJY85Gq4kiVOek9t3N7LDV0ervHjjXL6lZ5TJQPLqAWy8p5+pvuQdPy7UwKsHET6fsKclQif314IJA1hWlhLybcVRDIP5hhObbaH9hHjAPlTkOlTkepDAhWxuSzzVwDUEbQfSk67Fc9Cu08d+dco/TnFocUgDu9tfJysoa2F0dyGTxZLO4VSlWnpE6iqC1HsnZX/9XcvE+xx1NXZkclmh4cwd9mztBFMi6vGDQamIevCCflY1WrtzrNX3yGQD421cBoEspwDByHFQ9wOZiA9l95bCxl8rO9eRcU4wu9siLt59d3URKpI4LCo69NurF6rXYAz4mxGcy0mOltno5ot5E1ISrAAg7d0cA92oCGT4ZYF6+upRuVwBtYjL+xgZK7W2s62nhvNS8k720E87uCROFYeUhjmDA17AeVGoMeUMvrToizkibw0dWbDyaxEzobKTM3sqyrnrmmEpP9vKOOwMNIM5OCIcQDCKC6uARwN517bjqbKiNGtIvOv6/v7IsYa3ZQOuqhfj6u8e1kXEklZxHXOEURKeAfXMX6+vWU+luxCMp0Tu1qCI3NxdNQgYpKSm0rW7i94cYDQeQGWsgM/bgGzFBJZJzdTGVT63H25GMXPAaJvVTeKu+JOzsIezsGThWFZmAIW8K5pKLMY+7DFXE0TcsnuY0Q4FDCsC+vj5eeOGFgc8FQeCll146kWs6YppdNnbauzCpdUxIyMT2ybMggSo6Go0lfeC4o6krk0IS9a9vU6wQtCpyry0mcqTleC7/W+l2BXjws2oemLmna0+bWI6g0hPo2cqi9ZupyMvno8gLmcYyyh0L+GE2/K55NKltsPOxNaRfkkdsaZIyycUb5IuaHmYVKw8ASZLpdPlJjtzTOdzl9LOpzY4naNpvPUdKWJJ4dLsyi/mOorOwfvUMAJETrkbUK9cPuZUoksq4b8pluFhzaFUigbCELmM0/sYGyuxKI8h3QQBu7vc9LAh3IxoS8NasAilMxIiJAz/focQnld2sb7GRFWvAWDANW+eLTLA2sqyzjjk5p5YA7PN7qHJ0o1epSWzfjhVQGSEgmhHV+28Kg04/LR9WA5B+cd7AmMrjhaN5J83L3sDTb9avjYwjpeISLKMmQk+IriVNbG7ZSa23daBhLyYymtFjisjPz0en21Nnfai5wEeCSq9mxA1j2fXkOvw20I+6j7wn3iHsaCfsUaxjNDEpiIboYXNPOtVoaWnhzjvvZMGCBVRWVuJwOBg/fvwRX2fevHnYbDY0Gg06nY5nnnnmBKx2+HBIAfjyyy9jtVppbm4mLS2N2NihZzS8uK0KgOnJI9CIKtzblBSUPnP/B/CR/P1KwTC1r2xV5l/q1Yy8sQRj+uAWtRcmKnUrgZCEtr/WUFRHoM84G2/9h7z07vPk/fRPPOEYy9nGGMKNGxmRMIvrMpZzX2cJk13xNLy5g+UfVzNj7jhaQ2F+tWgHu7pc3D0tlwufWc3n1T1033c+fZ4AL65r4c+fVzM2JZK8+GN/gP+vaRu1zl6yTbFcmpxH3XJl8xAz7ccAyFKIsFvpIt27BhCGTwpYoxIJhmUMeVNwLP2AUnsrT3xHGkF2p4Dzwz2oo4Z2+nc3Uv8+0Fz2A2xLXuTMvkbu6zj16jZXdSnO9+WWNPxb3gZANAk41QeuVWt+v4qwL0RknoWYscevK9pv76Z52QKs/S4AGmMMKRUXYymYDO0BGj/cwebOXQNpXkEQGJGVS3HJGJKTkwdFdGmj9Iy8sZTKf6/DXtlL08JKMn9QgCb28MflnWZw+PTTT4mLizsqAdjU1MQHH3xwWsj3c0gB+NFHHzF//nxyc3Oprq7m9ttv59JLLx2MtR02nw6kf/OQQ0G81ZsAMBRO2ue4I4n/hQNhal/aPNAJN/LGUgwpgz/AuyhZec/pT67grFwL956XR+5fPuf+7BLO5kMuiFhJKCyz0xpmU9rljKl8hnFbPuH1ogu4M3kd37fm8RtnHqnOINvmr+LfAT8tUoh7PtrFPR/tGnifL2p6uPLl9QOfb25z8MmuLhZsauXr2yZh9Qa5+pUNfDx3AgCeQAiD9tt/fcKSxB83fgLAXUVT8WxYSNjViz6jBH1WuXKMpxPkMKIhAUG1p9h8GGWA0aoEgpKEYcR0JJVAst9JY/PWk72sE05ICrPNurcFzFg864emAfRuVKJARH9drLHgXBAF8t3dtHfV0ef3DLnu+WNhVbciAM+0pChj+VAEoFe9f12fbUc31q1diFoVGd8fdVwekOGgn/Z1H9Cx4WPkcAhRrSV5/EUklsyArjCNH+xgXec2uoPKvF21Sk1hQQFjS0uOyQ/2aIlIMDLihhKqn9lA74Z21CYNaRccnmH2d4nwyh7kTv9xvaaQqEM18dBNNJ2dnSxcuBCNRsPo0aPx+Xw8+uijqFQq0tPTuf/++3n//fd5++23kSSJn/3sZ0ycOBGAnp4eHA4H8+bNw+FwMHfuXKZPH5r3qcHikALwhRde4J133sFoNOJyubj++uuHlAD0hYJ81KIImZlp+bh3fons9yLoQZ9Rts+xsnx4UaWwL0TNi5twNdhRm7Xk3VRKROLgp7OkUIBAbxszDM1k23ag2eTlqa1+HjL4MHcF6FXPotgg0vbaXSxM0RKSdHjyv8fUcIBH3b20qPxYjZuZTzNTpSzKJAvzVEZmiWpeku00q6oJoqItHMe1L68C9k35fLhL6aT+46dVPLBYibJ+WdNDeVoURX9bwvs3VVCYaEYtCgd8YLxUu44dtk6yTbHcOLKC1j8pQ7tjzv7JwPFhlxJBUn+jAeRAk0BkWSbo8OPtdOPrdBOw+wi6AoRcAaSghCzJIMuodGpUEWo0Ji26eAP6OCPGNDNq44npZtSpRXxBCU1sHmojSA5I7thFm8dOyhCzQTmeVDt68IVDpGpUROFHVFvwNb0HKg2GEWee7OUdkNevLSMjWkl/ilo9mrh4gl1dnGFtYnlnPZdkHL/U4slmdwRwWsCFHPShiUtDUHfi/UYEMOwL0fSusolOnZF7XAyf7U3bafziRfwOpYbOkj+RtDN/gMqjp/nTKta1baMz0AdAhFbPmJKxFBUXodefXBN7U0YUOdcUU/PSFjq/bkJj0pI4JfOkruk0e0hMTGTWrFnExcVRXFzMzJkzefXVV7FYLMyfP5+FCxeiVquJjIzkySef3OfcYDDIjTfeyHXXXYfdbmfOnDmMGTMGi2XwSrqGGocUgIIgDJg/m0ymfeowhgKL26pwhfyUxqaSY7bQtkaxlFFFi6jN+4fvD7WzDXmDVD+3CU+LA02Ujryby47bWLdDEXD24WytxNleg6u9WjFBlWUe+MbGSECFVh2DTp2OVh1NnGggJOiRBT0SaiQEzkFAEESl6cUoISRISpem7CY+HOavIQkplIIU3oggvYQg1RKSwSvr6AlH0xRKRCuEKNbW0rUlmrsiJ/CFbxwXPeXHJyu/A6X/+JqpORb+dEE+k7L3LQ2w+b38fv3HANxXNpNw3Wp89etQmSxETfrhwHGh3SbQ30j/yoAQBleTHVe9DVe9FVeTnbA3dNT/v/pEI+acGGKKEjBlRSOIxycNYNSq8QTDqPTRqKLNSA4HZfZW1nY3c2nmqSsAB+r/NEq8NuzwgixjyJ2AOEQjaT3uAEvrm5h/aREA+uwigl1fMNHayLJTSACGJYnV/bV2o3pq8QKa5HRkOveLALYtrlNm/aZHEj/x2FKeIa+LpmVv0LtzOQARcelkTf8hRkMm7StqWdW4ifZALwB6jY7S8jKKxxSj0RzfesNjIWpUHFk/KKBhwQ5aPqxBbdQOqhH2UOdwInWDQV9fH11dXfziF0pgwefzMWnSJDIyMg7YqBoXF8dVV12FWq3GYrFQUFBAfX39aQH4bWRkZPDQQw8xbtw41q1bR0bG0HI4f6dRSbVdllWMHAriWP8O0C8A9xMV355YDLmDVD23EW+bE22Mnryby46qg/ZwkWUJd0c9tobN2Oo34/1G3ZggqjDEZIAul509JkxaIxEqARk/bSEXzrAXj89HQN53sPnhICJgUOkx6yZgVp+NUdRhFCSMci+WUCV5gXWEAmtB9mAWPdyqeYdbI98hLIusD4zieefFLPZV8FVdL3ZfkFBYQq0SBzy3/rBlEe1eBxPjM5mTU0LrYz8AIOacW/eZjRnqjwCqTMqDJxwI46jqJXp5M0Kbk8qAtM+6VRFqIpJMRCQY0cZEoDFr0Zi1iFpVv6ATCPtDhL1BAnY/vm4Pvi437hYHvv7IYffKFtRmLXFlycRPTEMbdWxRB6NWhcuvCFNdSjbBps2U2NtY19PMpZlFx3TtoczuDuACQRnRFexSariGavoXQBQE/rm0fkAAGgvPwbn6Cyb2NfL7U6gOcJutA1fIT5YpBrFuGQCa2FgCDvBq9ghAT7uTrpXNIEDGrPxj2hT1Va+lcckrhLxOBJWa1DMuJaHwPJxbuvh0x4fUeJXNnk6tpaSslDFjx6DVDs2Z2ZbSZELuIC0fVNPw9k7UBg1R+UND+HzXEQQBSZKIiYkhKSmJf/3rXwNuJQaDgfb29n38IHezYsUK/vvf//L000/jdruprq4mJyfnJHwHQ4dDCsA///nPvPHGG6xYsYLc3FzuuuuuwVjXYeENBXm3f7j5ZZljcG39GMltRdCLiBHCPl2luznY7S3kDlD17Ea87S50lgjyflx2zMLgYHh6mundtZLeytUE3daB10WNjqiUMURHF+P1m+i2Oml299Hl6sMhWcH3LReVBWRZIihDTMCKMeykTYzDro4krAY0QXSySIQkopFlJEHGFfbiCnsHduQD6yCZKPUcLOZ5mEUt/pAPv387Wv8HpAsbqdDtpEK3k6pgOv9yXM73ng3z87NG8ujXygP0qik63uhbjUZU8fTk2YS66nBu+B+CWkvs2bfu815hZwuSZMLbV0zty1uwV/UihyR2S0RdvAFzdjSmrBhMWVFoo/VHVZ8khSQ8LQ7su3ro29JJwOqj46tGOpY2EVOcQMq5OUcd6dWpRfwhRajqM0txrN5MnrubF9oqofyCo7rmcGDvDmAAf2stMLQbQHIs+/6MDfnnI6t/R2LARW/zZryhIBHqoRONOlpWdjUAMDEuHe9nSjROjFSDA3z9EUBZlpXUrwwJZ6ZhSD66GueQ30Pjklfoq1TqDM2p+WRNvw66tax65yu22msII6ESRMaOHUvZuPIhl0k6EImTMwi6AnR+1Ujtq1vJu6kM0ykc0R8uFBUV8fDDD5Obm8s999zD3LlzkWUZo9HIww8/THt7+wHPmzp1KsuWLeOKK65AFEXuvPPOIdnUOpgcUgBu3LiRvLw88vKUjtpNmzYdVffNieDtxi3YAz7Gx6WTH51A89eKYbUqFgStGVG7b93ewVxggq4A1c9uxNvhQhdnUMTfcR7tFvJ76N25nO4dS/HuNXlAGxlHXPokVOosOrvdVPZ00Na6nYC8b6pTRsQV1tMaUFPjFWkLqukNqrGHVXgkEXkvaTs+4OB5+99IE4xUPNGC5t6lYO5GlV6JJISJU8dgrcokOmjgs+vH8PkXO4lzOtHq/Xg0ATyEsIacWEN7oopqIY44888R1EaEkBtL8GPyhIXMt8znZ8EF3Azk7QAAIABJREFU/N/aKxE4E1nrY6F1BQAFFFMYnUTbMzeCLBN15rWooxUD2oDDj31nN10rC/FZX4NuNaAICWN6JG1xEUQVxlFU9O0jqw4XUS1iyorGlBVNyvm5uBvtdK1sxrqtG+vmTqxbu4gbl0LKeTlHPPlgb0GqjS9ANqhQucP4a1YRksKoxZNvxn0i2N0BPMrfjBySCXbWI2h0ROROOMkrOzhhad+bgNZSgNosELbKjO+tZ3V3I9OSR5yk1R0/dtf/nScHkXxONAm5yLIDAF9/BLBvYwfuRjtqk5bkc48uEuJo2UX9p88QcPUhqrWkT74SS/IEKpdsZVXnFrz9Pn4js0YwYcrEk9LccSyknp9LyBWgd307NS9uYtQt5SelHvy7Tlpa2sDEsGnTpjFt2rSBr02evK/f6GWXXXbQ69xzzz0nZH3DlUMKwNdeew1Qdos1NTWkpqYOGQH4bKUy2/KmvDMI2Ttxbl4Eogp17IFnXR6osSDoClD1zAZ8nW708Qbybi47rnN9Pb0tdG35gt5dK5GCys1QpTeSkD0dVLk0d/awpKYVW2jZPudFRZhJTk4mMT2ZpKQkkh5agfSN+GVRkpnmu6by83e38/iyeqS/X8LP/7eNkXFF7Hj1TQrdm+hb/E9CD/+eZpsXp2DnvA+fpStghWwrMeoRqBOiueO2S2jsc3PlX5bwfVHNdLWIT+XHrXfhMXhwaIJ45RAdgV46+qOFamECScYLiRMl0sJb+D/961wReJ+fRM7EI4vI9jg2t0QS6KzBtvwlZJUJXfEdtC2uw17Zg6d1t7hMAcIYUsFSnkd0YTzaKD27trUTexyK0Q+EIAgDYjBg89H2eT2969voWdOKdVsX6ReNHPBNPFI00SPQGWVCbsi3NrLV2kGpJfXQJw4zurxO2jwOTGod6c46JJcirCJGnImoPbmF/N9GenQEFsOeCJ+oMaBJTCRsbWdin1IHeCoIwKX94whLrIoQNI46i7BbidD5NHGEvEFaPlI8/9IuGIE64siinlIoSOuqhXRs+ASQMSZmk3P2zTirA7z7v3cHsgpJlgQmTZtCUtLx2cgNNoIgkDkrn5AniH1nD9XPbyJ/3ji00UP3d/w0pzlcDikA//GPfwx8HAgEBgouTzZrupv4urMOk1rHldkl2D57HMIhDPlnImvWojIcuGh370kgQaefqmc24utyo08wkndzKRrzsYs/WZZxNO+gfd0HOFv2WK1Ep5VhiJlAQ4eNL2ubcYT3iD6dWktaUirpuZmkZ6Tvt1Pu+9MFGLVqNL9aROCvF6H99QdsvmsqgiDsY3D914sKUIkCgbj5NP5lGr2f/IPY835KZmwMYKD2yl/xwKbFPLLtK2rCNZQt+htX55Ryc94EFv7ubKINWkRfiGde38IMVSqOWitIMkExhFNnxxPpxqYN4pKDtPi7UWKZqURp7yXRYOJF2UOtvYWR9l5qNDY+f3gxiZb/IGvSaFjYuefnoBGJHBmL1DsflfQ+GdesQBOzx7Q7LMmIg+H/Fa0n6/ICEqdk0Px+Fc6avoGpL1mzCw87Grj7J6CJGYloVNZdam9leWf9KSkA1/ePGCuJTULsteJ3Kd/zUE7/AhQkmrlt0r4F4vqcsfh2tTPe1swf2qqh5LyTtLrjQ4OzjwaXlWhtBNH1a3Gj1GXaNikzmj3qBNoW1xFyBTFlRRFbemTizGfvovajJ/F0NYIgkDL+e1iSprH+07VssVUjIaPX6Jg0ZTKj8o+PpczJRFCJ5FxVRNVzG3E32ql+biOjbhl33I2yT3OaweaQAnBvwuEwzc1Dw+D2gU2LAfhJ/kTMag01Xygt34bRZ+CuW4vKtL8A3Dv5E3T4lchftwd9opG8m45d/MmyhK1uI21rP8DTX4MjavQkZF1IrzeaTT1NdLWvHjg+QqMnOyOL3MKRpKamolIdPFUYqVduNr+clotaJTIq3jhwY/35lBz+3zmKX5W+3+NMkz8VQ+HZeHZ8Qfd7D5I05+8AGNRa/jLuIq4fMZ4/bV7MG/WbeLFmHS/WrGNUVDyXpI/mwvQCfnxjKVqVmpA3iKvehrO/E9fb4SItLOMXg7jMVtwmLz1iAHvIhT2kzMq06OKJSM1lWkCFqisGyW9AFkCVYKTNoKY2QsXtV45B0Ag0PP4mEBhoAtlNWNozs3MwiEgwMvLGEvo2dND8QRWOql52PraG7KtGY86OOeT5u1eqjsodEIDFjnb+0V7D7YVDbyTasbKhRxnfVxqp1ERJw0QAHgh9ahmS/lMMviDO6uX4Qj9GP4zrAJd0KLWY0+Iy8Hz1KACGEWdgXetGUBswerR0r20BUSD9e0cm0Ky1G6hf/CzhgBdtZBy50+fSU+njjU3v4Ap7ARidV8CEKWeedEuX44moVTHi+rFUPrUeX6eb6hc2kXdzKSrdET1CT3OaIcUhf3v3zq+HQiGuu+66E7qgw+F/jdv4sGUnRrWWO4um4tzwLsHuOjQJuWiSkqCOg866FASl/qzqPxvw93iISDIx8qbSI6772puBweZrFw3Mt9QbkjAmn09Nj521dc2EUVIxGlHNiIwcRo0tJDkl+YDdSt/GXy8uBGDHr/Y8aEfEGQ94bOKVf6P+j+PoW/xPYqbfgi5pj6lpfnQCr0y9hntLZvBs1Wpeql1Ppb2bSvsS/r5tCTqVmrExyZTFpZFjspBeGI1pjBlBNuHocmFrs9HaCTZriNiwnhiVRIJBwCcG8Uh+avzd1AARiTrS1REEAm38pjWe7QELUXo114eLMAX7IBxA1MciavYtzg/LMqoj+685ZgRBwFKejHlEDPWvb8fVYKPqPxtIvziPhDPTD3Gu8q+oMaCOzSCoayTCH6K7ZgWcc8MJX/tgszsCOFavQQ7K4A0haA1E5FSc5JUdOVpLIRozhH1Q3lPLiq4Gzk4ZvgbAX/ULwEtkH7LfjS6lEPqteoSIZAoqbUrjx8TDb/yQwiFaVrxF58ZPAYjOKSWt8CpWf7WO7U4l3RwXGcvU86YP23TvoVBHaBj5oxIqn1qPp8VB7ctbGHH9WETNqVnje5pTn0MKwGXLlh3qkEElIIWZt+JNAP5cfiHxehMNHz0CgGXGL5C8DQAHqQGUCfR5qX95CwGrj4gkE3k3lx61QbAsy9jqNtK6ciHePiUiEhlViBA1gR09bbQ2bBs4Ns2STP6YQnJG5h4Xz6vD2bVHZJURPfkGbEufp+uNX5L+8//td8zIqHgeGn8xD5RfwLLOej5s3slHLTvZae9iTU8za75tpJkatIkqvp8xhgWfqZG7DJyVHUNXewc/tXRj0Am4JD9VAT9g4JcZEokqK+sdAe5+YxmPX6BEj9Sm/b3HJFlGdZJSR9ooPXk3l9L2WR0dSxppfr8Kf6+XtItGHtQmY+8GI03MKHSmeiQ/pHZW0eSykmE6dBRxOLGhXwCOUQcI99f/GUZOQlAPTVuPvflmL5jGUojKLBDuhgnWRj5rqx62AlCWZZa01wBQ3lsPgHH0uYTdysY0EJhBtN2P2qQl5bzDa/zwO/uo/ehJ3B21CKKKtAlXIHmyeHvxh9hDLkRBpKJsPKUVZUe8oR1uDIyMe2o9zlor9a9vJ+fqIoTB3q2e5jTHgYMKwDvvvPOgIuORRx45YQs6FLtsXXT73JybksdP8s/Eve1TvDUrEI0xRJ/1I3o+nwscOAJocAVofm4TIVcAQ6qZkT8qPeo6DkfzTlpWvI27v9g6OqYcn76YDX2N9Lk2A6AW1RTmjmLMGaVERZ0c+4D4HzyIfc0CnBvexbXlY0xjZh7wOI2oYnryCKYnj+BvFZdg83tZ39vClr42mtw2mt02vKEgEjIx2ggyTTFUxGcwPWkE0boIyqjh1x/sZMltk9Hd9Q7pWx8kO9zKy0l3k5NeiEoM4Ax7cYYhSg8zAi2s+aCOGLECnXH/uaODVQN4MASVSOr5I9AnGGl8eyddK5oJ2H1kX1WEqN7/Zr+zc0/HtDa2AJXxU6TesFIH2NVwSgnAbp+LZrcNo1pLttRHt1ORVMMx/Qugic1HNIlIQpgiZwdPNWyEcRee7GUdFQ2uPprcNmK0EZjqv8SL4nUYdrcjSUZcbUp9Y9qFI1DpD52+dDTvoPajfxPyudCYYsiZNI8dG5rZ0LcYGZlYUzTnXjiD+Pj9x8udqujjDIy8sYSqpzdg29FN48JdZF5WcNyM5U9zmsHioHeAq666ajDXcdgE5TAT4jN5fdq1iEDngl8DEHfxbxF1RkL9O91vCkB3s53ytZ2EghKmnGhG/HDsYd0Av4m7q4GWFW/jaNoOiERFTcCuyWG5vRG3QzGlNmj1jCkew+iS4pNeB6OJTib++/fS9cavaH/hFnIe3IYq4tBpn2hdBOekjOScw4yE/HL6CKbmKo7qO8pW4/+0FW3SKNakXcTDDU4EJO6Kr+XMaC2dYRVdQQddQdCoryWtJ4KuN54nrXgUMSPKUGn1hCV5UGsAD4alNBltlJ7al7dg295N3atbybm6eD8R+OaWdt7o/1hjKdynEWRBRx1zckoHeeUnjvX9NkalllRkdxOSS/FAHMoG0Hvz4c5O7jt/1MDnojoCTWwuorEKXBL62tX0+txY9AcurRjKfNmupH/PtaTg/Xw1iCoM+VNx7XwOr+s6pFAEthgdsSXfnqaVZZnOTYtpXrYAZImojGJiUi7l469WDszuHVs4hglTJqJWf/fq4AzJZmVu8LMb6F3fjkqvVjIEw7zhZajS0tLCnXfeyYIFC6isrMThcByVG8k777zDa6+9Rjgc5pxzzuG22247AasdPhw0br1u3ToqKiqoqKggKytr4OOKipNb4zM6OomvL7yNGJ0B65dP4W/ajDo2jdhzbwcg7FaG06v2SgE7avqoemYjmqCEaZSFkTeUHLH481rbqfnwX+x4/X5czdVERk7GG30ZS9wya6yVuCUfMcYopk+dznU33kD5hPEnXfztxnL+Heizygn2NtH15m9P2PtUZMTgbdiA/7NHlO7Am57ls9um8qcL8pERGXf+VVxWncGC9j5KxB3EqzUE5TD1ARfLezx8/PUuvn7xJaoWPYOxbxeiHD5haz0SzDkx5P24DFWEGvvOHmpf3oIUPPjatJYCBB2EtFosQS819esGcbUnnt3p3zJLGoGeGmQ/SGodEVnlJ3llh8eFBftHnBU/QOXjCdZGPm+vHuRVHR8+aVVcB74fdEI4RET2eFSGKNztLvzei0CQqS20fKtQkUJB6j97jualr4MskVoyC698Bu+s+5TuoA2TzsCll1zK5OlTvpPibzemzChyrx2DoBLoWt5MxxcNJ3tJ3wk+/fRTampqjvi8pqYmXnvtNV5++WXeeustgsEgwWDwBKxw+HDQv95Vq1Zx663K1Ia7776bl156adAW9W3oVWpUokigs4bON34JQNKcfwyMF9stANX9EcCeta00/q8SJJn2JCNnXzX6iIp2/c4+2la/S8/O5agEHQbzNFqIZI27DQmlxibFkkTpGeVkZmUOyR2goFKTctOz1P1xHNbPn8Bcegmm4vOP+/tIfg9t/7kepDCxM36OIW8SAOeMiEO4IJ9IvRoQ+NI7ho971nOj+T+4VWNZIt6NXnBgD3uxh6GqSUW6ppXWxkeR8rKJH30WxoSTO5DdkGIm78dlVD+7EUdVL/ULtpMzp3gg7VOetifFr4ktQBAE1AYZAmBs2ogj4CNyCPvjHQm7I4BlljT8KxTB4Y4vQBgmnbMH+gtV6gDfI9QOE60NvNVaxRXZJYO+tmMhJIVZ3FYFQGlvPWGU+j9ZlundmguoMI1w4vkWn9OAy0rNB4/j7qxHVGtJHXszq3fV0+LbCEBe5gjOOm/asJjkMRhE5lnIvnI0da9to+2zOlQR6kM2jA13Fi1aRGNj43G9ZmZmJhdffPEhj+vs7GThwoVoNBpGjx6Nz+fj0UcfRaVSkZ6ezv3338/777/P22+/jSRJ/OxnP2PixImAMgquqKiIX//613R3dzNv3rwhNYP6ZHBQAbi3t5x8sBEaJ4mQvZOmf1yE7HcTOX42kRWzAZCCbuSgC0GlB7WZlo9r6PxK+UVNnJrJYo3EOYdZrBv0Omlf9wFdW75AlPWojVNplHS0eroBJwICI9KyKZlQTmLi/hGFoYY+Yyzx3/8j3W//jtZ/X0PO/RvRWI7fjUqWZdpfuhV/yza0SXkk/ODBga+dkRnDGZkxfFHdgyiAJMMjjqtxy3qqghl86DWgFSK4PkFkotmOO2yjPuChHgvxVSESK98g1uwhoWgKllETUJ+k1Jwh2czIm0qV2p9t3TS/V0n6pYqNxkV7RZVU+hhUxmTUxhZCNiixtbKiq4GZafknZd3Hm90RwPK4VIKdSvOTI3X4df/ujTa2AMEgIGm0pPkcbK9ZhTxp9pDc0B2MlV2N2AM+8iLjUa37cEAA9m3sIOBIRBD7iDojErHvwOc722uo/eAJgh47+sgU1HGXsGjzBvxyEJ1Ky7RpUxmRnzeo39NwIKY4kUxfiMZ3dtH8fhWqCDWW0gO7UJzm2EhMTGTWrFnExcVRXFzMzJkzefXVV7FYLMyfP5+FCxeiVquJjIzkySef3Odcq9XKunXreO211/D7/cyZM4e33npr2E2nOZ4cVADufeM7HjfBp59+mqVLlwLgcDjo6elh+fLlbNq0iQcffBCVSsXkyZO5/fbbv/U6ks9B/f0TCPY0oEsfQ/JNzwx8LexRjIYFXRb1r27DtqMbRIHM748ibnwq8ufVB50FPHCNgI/OTZ/Svv5jBElHWH8WNWGZXq8yRml3Y8fYCWXD7hcn7uLf4qlainvrJ7Q8cQWZv/nyuE1tsC15GvuyFxG0EaTd/haibn+RVpIayZJbzyQvzkTSfZ8y3zFn4GsBWeA/nTL/6Yzk+fPG4KypRit00R3y0I0RgyOGjFWVmJf9D8uIYuJHn4U5dfBNZg3JZnKvG0P1c5voXt2Kxqwj+RzFWFiW5YH1aGILCHa1EkKpA1zSXnNKCMAen5um/gaQPHM81X3K30Vf+tkneWXHhsZSiCAIaGMiCHUFyGjZQpWjm1FRCSd7aYfNx/3p39mRMQTadiJGRKJLq6Dm/5QSBIPpOYSohxCs+//NdG/7msYlLyNLYaJTJlPnjqOmUTkvIz6V6Reei8l0egTawYgbn0rIG6L1oxoa3tqJSq8muuDUbIw5nEjdYNDX10dXV9fAcAqfz8ekSZPIyMggOzt7v+Ojo6OpqKjAZDJhMpnIzc2loaGBMWPGDPbShwwHFYDbt2/nqquuGhgBt/tjQRB4/fXXj/iN5s6dy9y5SofuLbfcwt133w3Avffey2OPPUZ6ejpz585l+/btjB49+qDXCXRUE4xvQp9TQcYv3kMVsUeEhd2dhII5uFt/RzjQjUqvJufqIiJHWgaOOZhekEJBurctoW3tIuSAHo9+ErVBH26fHQCDRk9xYTFF48YMmdq+I0UQRVLnvkzdvWV4a1fR+tQ1pN22AOEYZ9U6N31A+0tKMW3y9U+iTy8+4HGxBi2Tsy2EwtLAaw/MHMW/VzbSavcNvPajxQ2AhvZfX03nlkq21u7EFnSySwKtUERGg0x31eOYYmNJHHsusaPOQKUZvJSUOTuG7KtGU/ffrbR9Voc+0Ygky3Q4/SRHKr8bWkshXsPnSCoN2V4r8xs3w/ihceM8FtZ0NwFKA0iocweyXwaVgDNxaIyHPBwOlM/QxOYDAoLeA8BEayMfNO8YXgKwf+rQ+XYlQmssmkH7l82EXAHU2l1o9Z8jRCQiCt6Bc6RwiOalr9O15QtAxJR2Jcu7WnGHm1ELKs48YyJFZWOGVST0ZJF0ViZhb5COJY3UvbqNkT8qwZxz6nT/DxUEQUCSJGJiYkhKSuJf//oXZrOZzz//HIPBQHt7+wHtiMrKynj11Vfx+/2Ew2Fqa2vJyMg4Cd/B0OGgAvC99947IW/46aefEhkZyZQpU3C5XAQCgYEfwuTJk1m5cuW3CkBRZyT++3/EctGvBur+AGRJpmt1H46+RwEtEckmcq8pRmfZYzB8oBu/FA7RV7mK1tXvEvLqsWorqBNcBH1Kp1uMPpKS0hJGjS381kkdwwV1ZDwZd31Ew4OTca57h/bnbyH5R08dtQh07/iClieuAClM3CX3ED35+kOvQSVyw7h0XljXzD3n5pEeHcENr2/a7zi9KYIx54ynaFo5DRur2Lh1Ex2eXmpCoBJHk+5S4VjyDvoVbxI/+iwSxpyDzhx7VN/HkRIzOoG0C0bS8mE1DW/uoGRaOjZvcEAADtQBWixIXR0I9Wux+b1E607MfOPBYlW3UlIxIT4T15b3ARCjjXCMm4jBxB0I7ROtBaUTWB2Vg+RXissrrM3c07CVO4umnaRVHhmtbjub+towqDWkNKzDC2izLqVlZQsIYDD9E1FrQtJEI6BstoIeB7UfPYmztRK1JhG7aRJr+j0EE8wWzr34fGJiTwuYIyFlRi4hb4ie1a3UvLSZvJvLMKYNr0zRUKeoqIiHH36Y3Nxc7rnnHubOnYssyxiNRh5++GHa29sPeN6oUaO4/PLLmTNnDrIsc+uttxIdHT3Iqx9aHFQApqYe/fzSN998kxdffHGf1/785z8zZswYnnrqqYH5wi6Xa5+0gtFoPOSoOW1yPvGz7t3nNVeTnZZFVbiblYevKaWOkfNuPGCzx+5ZwFIoSM/O5bSv/xC/L44uVQnNsh3Jrwi/5MgESseXkTUq55Tb/erTikj/+Xs0/f18bF8/S9hrJ3Xuy0ecDnasW0jrv+cgB/1En3UT8Zc/cNjnPnvlWJ64XIkUHszvb2C6hkokZ1w+2eWjaNvRxMb1G2h0ttEQkmgU8shEi3/jcjo2fEJsXgXJ4y7EYNnfXPp4kzA5HU+bk75NHcR+1cS/Ohw8Nkexe9FalIktGrMKfxeMtbfydWcd38s4+OZmOLBbAJ4Rn4ln2T8BUMUn7TNje6jzj6/qmD0mhTMy9xU3WksBIXstYmwykX3tuGtW0Oe/iVid4SBXGjosbFQsqC5OzMb31XxkRHprs0HyEVuig/Z6AhH5/Obd7UzLteDpbqJ60WMEnL3oos5km0+L1dGIgMD4ojLKp1Sc8qbOJwJBEMj43ijC3hDWLZ1UP7+JUbeUE5Ew/CyFhhJpaWksWLAAgGnTpjFt2rSBr+09rQzgsssuO+h1brjhBm644YYTscRhyQnp4Z89ezazZ8/e7/WamhoiIyPJzFQ6Ok0mE263e+Drbrf7sOvqZEnGUd1L96pW7Lt6AFDp/UToHySh/NwDij9ZlgkH/XRuX077usV4whm0UkRX2AlhGwICufEZlEwYR1LGqV3Ea8w/i4y7P6F5/iU4175FQ2cNqbe+ji551CHPlUMBut7+Hb0f/g2AmHNuI+nafx6RUBYEgYj+n9GsoiRa/3Ae+X/9Eqc/9K3npI7OJHV0Jr21HaxfvY4aayMNQT9NwigytWpCVZvpq1xFdE4pyeMuwpR0eNMOjgZBEMi8LB9ftxtanUyssQ9ElpSUIqBSNhRl9la+bK8Z1gIwLEkDKeAz4tNx1a4HQJU6/BoDfKH9bXw0lkKoW0REehbuvnamdlfzUcsursktOwkrPDLebtgCwDWyHznoQ0j/Bd4OH5ooHZbiHnrbwa1OoarbxazYTna++R5IKvxRF7PO1YGEn2i9mXNnziAx9dQc5TZYCKJA9hWFhP0hHJW9VD+7kVHzytHFDO/o/2lOPQbVxGnFihWcddZZA5+bTCY0Gg1NTU2kp6ezbNmyQzaBBO1+al7YhLPBhuRXbuKiRiRhUgYaHsO9cy0qwzX7nee3dzOy4wu2v9yJXcigOZSHW/IBTrSihoL0kYyZVEZkzMmZ2HEyMOafRdb/W0rzY5fha9pE3e/HEjvjF1hm3ok6cv/aJzkcwrnhXbre/C2BzmoQVSTM/guWC+4+piipUafGqFNjf/ACsh/8jEarUqMUqT94i74lN4kZuRdT3tDJ2uWrqbU1Ux8M0CSOJlMjEq7bhK1uI5HphaRUfA/zCRIpokZF7rVj2PZ/qxnlCdG1opnESRmoIiyoDInI4Q5kBAqdnTzashPOuPSErGMw2GHrxBn0k2GMJs7Zjd3ZB2oQkgsOWlt7IpAlGV+3G1eDDXeLE3+fh4DNjxQII4clRI0KtVGDzhKBIdmMKTMKU1b0PuO6guH9C0K0sQUAqGKVB/X0nlpeaNw25AVgh8fB0s56dCo1xW3bcaqSCcgXAKA7J5vHP3+bOcB7TVoq5FXk1FYi6EZSq8mgy6Wky4qzC5h43pTvvC3G8UJQieReXUz18xtxNdgVEXhLORrzafuc0wwdBlUA1tfXM2nSpH1eu++++7j77rsJh8NMnjyZsWPHfus1gq4A9speAHRxBkIjY/hHl50bcqKIXVtHFKDqHy0mhYLY6jezZumXmEN6NESxOhgHKIXekVojY0YVkV9RjE7/3fzD1GeMIef+DXS88nPsy16g94O/0vvxIxgLzyEidwLqyETkgAd/2w5cWz8hZFVsP7TJo0i58RkMeZMP8Q5HRu1vz0H9q0W8df24wzrekpXIzKzv0VPTzpqVq6l3tFIXlNCqS8jRSEjN63E07yAqs4jUiZefED9BbbSeuItH0v3WTlo/qsGcHYMhxYzGUkjY04kmMQuhsx6aNtHtcxGvH57dlAP1fwlZeHZ+AYDKJCAYTny0XJZlPC0O+jZ3Yt3WRdDuP+ixYW+IoMOPt92FbVu3sk69mujCeOInpPLmdeVER+wvdDT9aXtZ6AJjLBnuPiqrlhOYejVa1dA1PF7YtA0ZmRnJefg/vp9g9B3IkopVOoFYNfhsDUhmNYX6OC7U1uI3TGV7wEsoZMOgjuDs6dPJzNu/a/I0x4aoVZF73Viq/rMBb7uL6uc3kffjMtQH+N07zWlOBoN6V7v33nv3e62kpGQgt384dMgyC5MjeLapjzFmA4u/VhwmpBVqAAAgAElEQVT7s9KiOKe3mUhUVDXZiax/mdZGB3bZTFvITEgOA05ERLLj0hg9toi0UVmnXH3f0aCKiCT1x88Te86tdL/7AK7NH+De+gnurZ/sd6w2cSSx5/2MmOm3nBDjX1EU6Lh3BglHuFOOG5HMBbmX0rWrlRWrVtLm6WJXAMy6M8kU+pAbt2Fv3EbMiHGkTpxFRMzxFS3Ro+N58o0t/AANda9to/CnFWhjC/A1f4kuMYVQZz3ltha+bK8ZdgbDu9nTAJKB+8vHARDNAhhSEE6Qob4UkrBu7aRreTOe1j3zljVmLabsGEwZUejjDWhjIlDpVQgqkbA/RMgVxNftxtPqxFHTi6/LQ++Gdno3tBMVpeWPfj+L7j13n/fa3QkcstcSVfID7MtfZnzHDr4Y4hY+b9QpJs3X6rX4PGORossIakR+73Jh++8GXozrxS7MJNeQQpOmkFa/FYARiVmcddHZREScTk2eKNQRGkbeWErlU+vxtruoeXEzeTeWImqHT9PUbr7ZOHWaoceRejYP3W3tQbAh8V77GlxCCl9UhclW2/hN9P8wbU9GY7iEOv1t9NWI9ISc/V2/ykMjQjRT/f/bu+/wqKr88ePvOz0zk95DCiQQIHQIglQLKAI2VlxcF3Wti7KWtbGuDUF3Xct31XV/lgWxrYqKXaxIlSIdQg0JCek9mV7v74+BKAsoQkghn9fz5HkyM3fOnHtPcvPJKZ/jjuC+68YRHtExe2BOtbDMoaTf8TH+hgocu5biLt5CwFGHxmBGH5eBOXs0poxBKKd4cvivDf4OURSFxN6pXNzzNxRt3Mv3G9fS4LOxnTDizWfRxbeH+vz11O/bQFzOaFKHX4re0jJD/jqNwj+DPn6fEoW70kHpV/uITA/1KGkjQ+czvL6IrzvgDhOHrKk6GADGpeHY9R0AGqsGNSwRWjgAVIMqdZsrKPumAG99aNWq1qwndlAS0f0TsaRFHPOPkc6sxxgdhiUtgtjBoUDfXe2k5odSataXEd3oZTYK+a9uIf3inhiiQoufDq0E9jfuI6zHEBpXvc7ZtftYWLi53QaA+U01rKgsxKzTM6SggLrImwC43+WiARhirKZnWAYe82jyfODx1qNTdJx95miyB+W0beU7Cb3VQPZ1g9j1wnocRY3s++82uk/vf9iUhPbOZDJRW1tLbOzPbyMo2o6qqtTW1v6qNHUdLgDsYfLw124afNTjwYgbBXtgEnVBD3VBwOsBPCgomDURFHrCWVijYY879EM71yqrsX6JLiqJyOHTiBw+ra2rckI0Gg3dcnuS3j+LHcs388PezVR7bFSTTFZkH6Jsq6nJW07dnnWkDJ1M4sDxaE6yN1On0eABuk3NYee/11P1/QHMiQcX1BjsqIqGgU1lzNm/pcPtMAFQ53Gyq7EKg0ZLb1c9JbYaFIMWxQiEJaPYWu58bAX1HPhkD64KOwCmBDOJozKIGZj4q7Zx/ClTvJnUiT1IPrcbe78poH51CY27asgrrCdtUg9ic1NCiaDj+uJv3IcuNhz0Jvo1lXP/njV4RlyGsR0OAy/Y+wMA3upYKsti0euNrPZVsgQr08L3cVtsKVXGCRR5Q4uRVKJo7NpPgr9WZogykX3tIHa9uIGm3bXsf38nXS/Lad5Ksr1LTU2lpKSE6urqtq6K+Bkmk4nU1OPPgNH+7mi/wK1qyPNpAf/BrxCdoiVaq8OCk/cauvFJHdQdudCvAyWrECdLa9DRb1wu2UP7sH7JaraW7WKfvQ6jti+9osMw1H5HyffvUb19GamjLic6a/AJB2a6gzdyc5cIksZmUPHdfsqXajHrDPhtewnrlou7YB2pFTvZXFfGoNgTT7PUFlZUFACh/H++XcsA0FhVFEUDYUko2E/6M/wOHyWL91K7IbQwwRBlImVcJjGDklrsD6XWqCNrQnf+SYApdT4adlRTtGgXtsIGMi7thSFhEM59H+Gr34G1zzjsmz9lSMUOvijZxcUZfVukDi0lEAzy3LbVANxXlYxen4TqL+cxDDwWt46xMQnkBXrh8jagUzRsciTxUqWW4C2dd+eDtmRKsNDjmgHs+c8m6jZVoLMYSJ3YvUP8M6jX64+6u4bo2DpOH/RBRq0esxJNL52DwbpCRmhWEeP+nvHe2xnqupkDde/yavXRgz849k4g4vRljAxj5KXn8NtJl9HFkoAn6GNLQxMF5rNRI/vgaapm3+fPs3vRE7hqS0/oMzQahfvO7QFA8jndMCVa8NR5cLn/iOqzY+4e2iljeH0xi0t2tti5tZZlFfsAGJOUiePgAhCNVUETFoeqNZx0+fV5VeT932pqN5SjaBVSxmXS58/DiR2S3OK9JHqtBodOQ+bv+9H18j5o9BrqNlWw64UNaKyhFb+eqo2EDwqt2B5fvYd3Co9MVN7WPjuwE7vq5Nz6NCYrSaD62eL7ihdSt9I/tjcbfVpcQQ8JWg0Tsqp47JZJjOzaOonSxdFZ0iLJurIfilahamUxlcuL27pKohPrcAFgREwUf7j5d7xUWkqc8180OlZyZ9V4Hqy/jh3erjzbdHlbV1G0UzFdE7joqt9w3uCzsGrDqPU08YPdRF3sRBRjDLbSXeS99TAHVr1LwHfsVabH8ti3oQVJGp2GrpflgEbB3TQBv7cX+pTQbjdn1hex+OCWXR3JoQBwbHwGzoM9gNpwBZ0lBTjxf6yC3gBFH+yi4I1t+B0+rJlR5Nw2nORzu53wcO/xUhSF2EFJ9Lp5KMaYMFxlNoo/NRPwp+Ct2ox1yMWg0TGsvpgV+37A5nP/cqGngKqq/L/v97OjwkalzcPeajsFtQ4u+fhDMj1WHqgODecqvELPnGHUGDMo9DagRUN/bSFDPHeSPPhSYswGVsxs2VX74teLyI4N3R8UKP0in5r1ZW1dJdFJdbgA8JDLfnMviwesYXzFs9xwzpkUxV/BhVVPscvXldIHx9MnMfyw4y/olUDwyQs7RHe7OHU0Gg09zuzDFb//HYO65KBBIb+hkq1qL+gygWAwSMWGxWx/434aCrec8OdYUiNIHJ0OKDiabkdj1KAYzPRw1JBfkket2/GLZbQX9R4nW+rKMWi0DHTWEHTb0MV2QTEoaK0p/MqFZ81cFXZ2Pr+OmnWlKFqF1Mk9yL5+MKb41t15IyzJSq+bczGnRuBt8NFU/3/4nEmovlqs/c5Dh8rI8h28VdA6vYCDn15GMKgSCKp8vaea2z/K45ZF2+j75FKSZ3/Fje9upfvTnxARZuPxssGEKX4CscsoypzIFh+4gl4SdBrGd9lNvyEJpFy+FEO8DPu2JzEDk0ibHMpNWvTBLhp2yNw60fo6bAA4dUAKMZFxBNAy+/yerJg5io+vPYNXfjuQ5AgT2+4+i6UzRrDlzrEAfHb9sDausWhPDFYTIy45m8svmEJSWCyugId1FdVURE1AF5ON11bL3k+eIf+z5/Ha6k7oM1LO7YY+3EsgkEHNNj2WXqGfxTPqiviidHdLns4ptaKyEBWVYfHpBHaEhn+Nqd0B0FpPrAewflslu/7fetxVTkzxZnrdMpTEkemt9g/aI1/vOeyxzmIg+/pBRPSIQQ1G0FT/OA1btxExLLQQ6oKqXczbs6ZV6ra5rImk2V/x+/9u5PyX1lDS4Drs9WUFtRhiSnmibAjpigN713I2mVMo89kwKDoUv49LrriQrEv+SdTQezAln9Eq9Ra/TsKINJLO7gpBlYK3ttO078TuM0KcqA4bAALkJFqZ/9sfU2pMzknk6qFpzY/HZMXSNyn8aG8VAoDYzCQuvfpyxuYMx6DoOWCrYl1TFP60S1B0Rur3bWDbG3+lcsu3qGrwF8urtP04dKzRa0kZF+rNatrfG33GhUAoHcyhvVs7gh/n/2Vh3/YFAIbkUHoVnSWZX9MBqAZVSr/cR8F/txP0BogZmESvmWdgTm7d39OUiCNTJWiNOrKuGoAltQZUMwe+jiRoGYuiMzKksYTi0p1sOsE5or9WjcPLO5tDQ4MfbK847DVF72a2O56+UU72JLvJ87nwqwGSdCam/XYqMw9koY849Xthi5OXMj6TuKEpqP4g+Qu2EPQdY/K6EKdAhw4AB6dGcc1PAr6jURQF+2MXtFKNREek0Wroe/YQrpj6WzIjU/GrATaWHaDANApj6kiCPg/Fy95k1/v/wN1Q+bNlNbkPT4gXM2AQxrBPAS11xb1R0YTmAR7Ygf0E5hm2haXl+QCcFR6Dq2AdaPVoI0PJg7XW0Gpm5TjW1wc8fva9toWKpftBgdRJPeh6eQ7aNkiKW9Z09Pl8Gp2GtAsiQm0W1FL4bj66HrejAc6r2sPLu09NL2Cd0wvAK+t+flFAmraCV+Oq6B2vsFFrpz7gxKox0L/2B6bccA3hsTE8e0n7Wq0sjk1RFNIv7knyud1IGpPRoXIDio6vU/y0mQ0dLtuNaAPW+EgmXHkRE4aei0UbRrWrgZXlHpwpl6KERWEv28P2Nx+kfOMXqMGj9wYG/mdCnMYYQUTqCjSaKtzVAdS4a0jwOshoLOfzDrAauNzZxOa6MsK0evpX7wU1iLnnaIKe0JwlnSX5uLLP+x0+9vxnE427a9GG6ehx7SASR7XekO+vYUrKxRz+PGHW9yGo0tQ4Hr/5IiZV7eS1/PVUu08+5c3/invwSyb9Zy33LT76AqEsXQlPRL/OvEwddpOGA74mtGjorgQYvO42emTHo2hCgfTMUZKuoyNRtBpSxmWSMj6zw+QFFKeHThEACnG8FEUh64xeXHHlFfRNzEZFZXtlMTvV/mhTz0YN+ChZuZCd7z56RMqYh87LZvX++iPKNKX0xRzxHAAe4+UEtSmcVbuP9/ZvbZVzOhlfHpyreFZyFp68rwGw9ptAwBEanjyeOYDeRje7X9qAs6QJQ7SJXjcPJaJ726cj+WbP0Sfea83xGGKyCbP8h8RRoV1cfFG30lMdR2pDGf/eueqkP7ukwcXzqwr596r9PLsilGNx8a6qw6YQWBQXl5u/4d34B3mlWzXRMePZ7XcSIEiyIZKzzplA1+2z0QZdRI2YftJ1EkJ0LhIACnEUxvAwxl42nkvPupAYQwRNPgery5uoj78QxZKIo7KQvLcepmzdxwQDoYTk/1xewHULj1w5bEwcgsG4HnPSflB1+KL+zJjaQhaX7MTRzoeBvziYsuaClOzm+X/WfhPw20MBoM6S8rNzAN01Tna/sAF3lQNTooWef8zFFNe6q3yP5byXjj2ca+wSSpcSnrSOjCm9ARV/xB94qLwrz+9Y9avb7ar/biRtTiiAVlWVPdUO/vTBdmZ+sI3bP8o77Fir4uT+yPmsSr6RO1JUHJG3sMUfiyvoJVZroUmnZcSVF5HcsIaAow5j+gBM6bLKVwjx60gAKMTPSOmTztTp0xiaMQAtGvbWlbHVl4Xa5TyCwQClaz5kxztzcFYXkx4ddtQyjIlDALBGvorOrCNoHEhOYDBhzgY+Ks476nvaA38wwNdlodWy5ykBAo2V6KK7YEjqQdBdCxodGnM8cPQddpzlNna/uAFvgxtzagQ9bxiCIeLE9nluaT3jf35LSFPKSADcpSuJG5pC6jgLqD6yGcOswmxe2LLyuD5nU2kjAG9sLKW00U1Zo5u0Od8wad7aox7fQ1fMhwn3cmF8F/IsT7M+0BNbwEOENow+gSj+z1+KdXQfkswR1C99EYDos2463tMWQohmEgAK8Qt0Jj1nTB7F5ZOmkBwWhyvg4YeKWiqiJqAJT8dVc4Ad78zhmewSdATZUNJw2PsN8QNA0eBv/IHUyaH5Wb6IG5lYXcWC/B/a4pSOy7rqAzR4XXQPjyO6IBSwWPtNIOAMbdWmtSSjKJqj5gG0FzWw56WN+O1ewrOiyb5+EDrLye233JK0vzDXynSwB9Bd9j2qGiThnOFYtPMg6GCsI5kenzgpK6w65vu3lTfh8QcY8n/L+fuSvc3Pp875mrImNx7/4XNIIxQ7N1o/YV7XAsrCH2BdoDd1fhdmjYG+SiRdquK4zrAHMizM6DUCT9kunLuWoRgtRI648iSuhBCis5IAUIjjFNM1kUuunsrY3sMxKDoO2KpZ70rCmzSBYDCApXAJC5K+40Dh3sPep9FbMMT1BzVAWGwB5ng7aCxcaRvNktJ8iu1HzhtsDz4u3g7ABam9sG36BABr/wsIOEIBoM6S3HzsTxdzNO6pZc+8TQTcfqL6xNP9moFoje1rIZY3EIpaA8HDo9d9NaEE3bqIrmitqQTddXirt6IoCvFjR2OsnoFXPUAXr5nSl7dS+tU+gr4Au6vsuH0Bahwe9lTbGfDUMtLmfAPAfZ8fe+cXq+JgdswiPulewJlJw9gSyKQx4MaiMZKjWOhbFkcwmMplKaupsnh5Y8yV6DRa6pe+BEDk8CvQhkWcikskhDjNSQAoxK+g0Wroe84QrvjNtOaUMZurq9lvORc1vAc9DE0kbnyBku/fJ+j/MSWMqctoADxlK8n4TX8I2onR9GNCUzyv5a9vq9M5JlVVWXQwV+GU2CRc+1aj6I1Y+52P3x5a/HJoAYj6k1mAdVsr2ffaFlRfkNghyWRe0ReNrv3dZt76fWjPX/09nwKwNL8GgB5/X8LXe6pZuq+W6shQmzkLPwMgasTv0WrriSi/iS8tu9GoChXf7eebR5Zx+xPLWZpfQ8JDX9Hr8e+AUC6/YwlT3DyW+AMLe9STGDWMrf4o7AE3EVoT/bQW+pRHE1mWjm9YBhOti2nU+nh2+KX0iIwn6HHSuPJVAKLPluFfIcSJaX93ZiE6AGviwZQxuedg0ZqodjewwRnJTu1ZgIby9Z+R9/Zs7JWFAJhSDw4plq7EnNGNMF0oqLi7KoeP8jYTPI4k061pa305+2y1JJis9CnZAqqKJWccGpOVgP1QD2BK8/EKUL2ulMK3t6MGVBJGppExpXe7zWs2JDWq+ft9NQ7OeWF18+Mnl+bz2voDvFrRD4D9W95n9le7+bDATdTIq1Dwkq58yPVpqykyOIn1q8zVGbG9upWpGh0/N7sw11TDu5nFvN7DRbg5kX2+ID41QLzOTH/M9CqJxlKURmRaN5Sru3JR4wd41QC39xnDH3qEdvRoWPUaAUcdpm5DCeuWe0qujxDi9Ne+xmWE6EAURSFrWG9Sc7qy+osV5FXtxYaNnYYRxAcqSarbw86Fc0kaPIHo3qGtCD3lq1EDXmKHpFC2Yj1WUy437Mvg8+KdTM7o08Zn9KP3D6aouSSjL441rwAQPvgSgCNSwKgqRO+qpXhrKK1KynmZJJ3VtV3m+DuaHn8PbW/3mwWh+Zhf7wn1BmZE9OS26DCinNt5dclS9vtT+HTyFXTlRfrtW0Hh4H5My1jOxLI+XG/vQqai4R6tgds0ejaqQVarAXarQcxGN9cmOYk3KpT6vFSogM+GFg1p+jBibEH0tYkoqhFjkoXUcVnsS3Bz/lcvUe91cVF6H/6ROxkANRik7ounAYi94K7Wv1hCiNOGBIBCnCRjeBhnTT2P7LyefLNyKTa/nWIlHJ95PCmetVRsWMyGH1bRxzoYnX0jnsqNRJ4xlZpPRuFOeJkhrliWfL6DyTPaRwCoqmpzAPibpEwced+AohA+KLSV3aEUMNqDSaC9Kw8QdzD4S7som4Qzf353nrYSDPgJuB0EfG6Cfh+9DfUYlQAGJYiCSml+FUOMCkEUgkDArfCFfQznha3llogv+GfT5Ty8WuWPYUMZ5t3K7/aWML9/Gp+nbOP7UhfjndFcoPPT2+DmnAgjZ4aZqNGolPqacAQ9HBoRjtGGkeBTsNZY0HljQaMQlRNH3NAUInrEsqh4G9d88RZOv48L0/rw9lnT0WpCPan2zZ/grdyLPi6DiNwpbXcxhRAdngSAQrSQlD4ZBBvG4Nm0lTCljHJPHTVKb7LDDSTbvqfBmUOYAo7Cz4kd+QimpBSCNXNwxz3JOcUxbF6/h4G52W19GqyrKWZXYxUJJiuDq/dS4fcS1mMkushEgOZFIFpzCsUf7sK/vhxVgW5T+xA7KKlN6uz3OPE0VOKx1eJtqsVjq8HbVIvP2YjfZcPvdhDwug57z4Ljqmo8DUxmjBXGWEN5/Mjpi5O+/AG41tGESR+HKTmAT6fQQDilfiMV3jp8vqbmUiyKgYSggYh6C1ZNAmHxFsIGWwnPiiY8KxpdmB6n38udP3zMMztWAPD7rCG8PHIqBm3oNq2qKjWfPwFAzHm3o2jl9i2EOHGtdgex2WzccccduFwu9Ho9TzzxBPHx8WzevJlHH30UrVbLqFGjmDlzZmtVSYgWd83Ibmg+2M4wazo3JNpwBWvJc/qx6M8kiyrwQdGmfAzd9hJ+xlQ8H86m0biCaM9YnB8V4UpLISzR2qbnsGBvaCj091lDcG54C/hx+BfAby9FVXWULTXRuLsMtAqVI7qQ2wrBnxoM4KotxVFdhKu2FFdtGa66UnzHs5JaUdAZLWgNYWh0BtDqWHPAhgctQVVBo6hoUdGgNn9v1GhJN3iJ0+vQaaPQa2PR6xNQNZHY0VPn91Lua6TKV4/bU3PYx0VqgiQpJZQqAX43fRY6Y8xR50SqqspHRdu554dPybfVoFU0/G3IRP7cd+xhw+iOHUtw7V2FxhxF1JjrTvpaCiE6t1YLABctWkR2djb33HMPCxcuZN68ecyaNYuHHnqI5557jrS0NG688Uby8vLo06d9DIUJcSJW3DKS0c+vYq3dwjUJ4YwOr8YRdLCVcJKMo0kJ7GfXe38jtvtgVEVLcuXzfJ6azTn2ZHbO30i/mcPQh7dNwmSX38c7hZsBuDojB9t/PgYgYsilQChY8dtqsTc8iK/KjcaoRZnUHa9Je0rq47XVYS/Px15ZiKOyAGdVEUH/katrFa0OU1QSxog4DBGxGMNjMYTHYbBGoQsLR2eyojWGoSihAEz1B8Ed5OanV5Bi0JNk0JNs0JNk0JF88PtEvQ6jxk+D3069z0aD3xb63t6AM1hxRB38ioZSnZ+mYAG/jSxhmyuHiy//C+HWqCOOBfAE/Cwq2sZzO1awtroYgD5RicwfPY3cuMOH0VVVpfr9+wGInXg32rDwk7quQgjRagFgdnY2BQWhPS/tdjs6nQ673Y7X6yU9PR2AUaNGsXr1agkARYc2slsMk3sn8unOShZUBXmnOpa/pMWQoCunwu+kggRSTd0IFO5G138qxn3fEei2h217w+jXFEX+a1vJvn5Qm+TOe2//Vhq9bnLjUknbv55SjwNT5hkYErMA8DXW0VT9V/y+PujMenpcO5DdqGjqXb9Q8vEJ+DzYSnfTVLydxqI83PXlRxxjjEzAktCVsNguzV/GiHgUjSYU2LkCqO4guANgD6DWBMHjIeBygif0vM/rwxl080bPaBwBN46AC0egDkfATaPXRbnbjTPgJniMje6CKoS7KohpzKPOaGX8+SP5NqDl4fximvxa/uPuyvjUVHQH8hgY04XEMCu+YJAqt428hkqWle/j67I9NBwclo43Wbh/wHhu7Dm8ecj3p+xbF+PatwZteByx429tkWsthOjcTslfmHfffZdXX331sOcefPBBVq1axcSJE2lsbOTNN9/Ebrdjtf443GWxWDhw4MCpqJIQrerj685gTVE9I55biUuFB4sV0o1duCfFhYYaSnwOSkkl3ZpNXE5Xzq/Zz9UpPh4tHkFKCWx8aQP9rh+MKaz1ds9QVZVn8pYDcGPPM2la/DcAIs/8HQDeRjd75m3H7+uDRldPz5suwJRgIXCggV/YWONnP9NVV0pTUSjgs5XtRj24tzKARm8iPKU7lsRMLEmZWOK7oguGoToD4PSjOgKwJ0DAWQPOAKongDvoxR5w4Qi4cAYPBXfunwR6bryq72dq9SOfouOAS0NKQhyLC538+8ozeW93E5MHdqVpyxfEvD0XjTmSxF5PcVVEPOcPtPHgxi94fd8GvirdzVelu3+2/IExKdzY80yuzBqMVX/0Xl81GKDqvfsAiJ14LxpT204REEKcHk5JADh16lSmTp162HMzZ87k+uuvZ9q0aezatYs//elPvPXWWzgcjuZjHA4HERGS1V6cHoZnRJObGsn6ktB+sMUelZmFJi6L9HFNopb9PoUin5MiUkkx9eR5/wE+T/qG8dXjiCyDDS9v5MwZuWj0p2Z49X8tryxgU10p8SYL05IyKd66GBQNEWdcjqvSzt5XNuNr9KPRFhPX+zNMCZcBEFTVX9xa7af8LjuNB3Y09/L5HIfP3zMnZBCV2p/I+L6EGZPAGQSbHzXfj2dTLTU+R3OAZz8Y1B167Ai4CfLLORU1Gg0WiwWr1YrFYmn++t/HOp0Op9eP2aBDt6KA7KxM7gt1hqIm/5b8LfPx7fya6kUPknzN/yMxLJwXR07l0SET+aBoG8srCshvqqbKbceg0RFtDKNnZAK5salMSO1FVkTcL9a1funLeIq3oI9NJ+bcm4/7OgshxM9ptTGmiIgIwsND81ZiY2NxOBxYrVb0ej3FxcWkpaWxcuVKWQQiTiurbx3dvNvEIe81duOzJjdPJXxLt8gcCn16ynwOyohhoC6VYPJ+7LUBLBXZ5C/YQtb0/qG5doqCze0nqKpE/kzPoC8QRK/V4PUHMfyKXTie3LYUgBm9RuDd/Amq34s55xzcdSb2vbGBgMuPKc6DSbkTU+wlNLp8RJh0BIIqmp/J+acGA9grCg4GfNtxVO6HnwytmizJxKYMJSKiB0ZNHNiCOCrsVJfYafBvosFvOzgHz44r6PnF8zAajT8b2FmtVkwm03HnKTQbQrfJ20ZnHva8oiikT/8n++7vT/3Sl4g++yZMGQMBiDNZuKHncG7oOfy4PuNYAvY6qt/7KwCJVzyFxmg+qfKEEOKQVgsAb7vtNu6//37++9//4vf7mTNnDgCzZ8/mrrvuIhAIMGrUKAYMGNBaVRLilNNqFGLMeuqcPoZnRLOmKNTb5VJN3Fw5iUlNq7hRWYMlegIFxmhq/E5q0G0F824AABXoSURBVGCIMtAltgKvvZTNc/azeWAWI/okcdH8dcwY0ZXnp/Q77HP8gSAefxCLUUfy7K+oeWQCiQ9/Se0jE9AcR+/cmqoiPi/ZiUVnYEavETQ+c3EoREu6iT3zNkFQJbJ3HO7499HstqO1ptD9b9+ye9Y5uHzBI3oAPU01NBZvp6koj6YDO5pTsOi1kUSZ+xAZm4PJlIbbo9DgclBSY6O+PJ8G/2Ya/Db8auCo9dTpdERGRhIeHt4czP30y2KxoNe33rC5sUsOMeNmUvfVM5TNu5ZuD65F0bXc51e+fRcBRx3mnHMIz/1Ni5UrhBCtFgAmJiby8ssvH/H8wIEDWbhwYWtVQ4hWVz37fDz+IMsLallWUMvfvs1vfu0z10iWBQeytOhqhhkslA2cSbUuk0a/g0K/HwxgSa1jdK0Nw9oAvXQOPlxbw2/6xFLnhkk5iYTptQz553L6J0fwn8sHUOf0Uef00uj2o7vnU/z/CO0iMe7F1SyZMQIAty+A6SdDyw9sXAzArTmjiWqqpHrXOnwx91O7KxZQiR6ZRreJPfj7Y39hmhXK/bE0uv28su4Ad3+6gxcuzqZeV0LTgR00Fefhrq/EqIvFbEghJmwUgcgEXKqRRp+TUr+N+qpKmvwFh+0j/FNhpjCiY6KJiooiJiaGqKgooqOjCQ8Pb3c7jMRPmYNt40e4izZR8+nfiL/kwRYp17bpExpWvIKiN5E8/fl2d95CiI5NMokKcYopioJJr+W8ngmocFgACGDXWPjGeCaTPcv4ePteNidAVLqFS9VktD4vjqCHAjzghD+nG4nX+tF/9zxW9yYWr7GwwZVBRWV/MqJ68d7W0KrZuAe/bC5f95Mh6En/WUtuWhRzvt6D/x+TeXp5AVndnCwpz0er6hkZ3p+Vr75EVPzLqLoE/BqF77MiuHPpbirGZpCiC+34cdfXtfTTR7F7xQ7+k1RL7ra1lO9OxqeNwa0MxxmuoSTgoMFnx+mpA+qOem0irBFExYSCvOjo6OYvk8nUso1wCmnDwkm5/hWK/n421R/PwdJnHOYeI06qTH9DBWWv3ABAwtS/YUzp1RJVFUKIZoqqqkf/F7ydmjJlCosWLWrraghxQoJBFU8gyI5KGz3iLEyet46VhXUM825hXuMDNBriGRvxIv7wBpSMPLSqwix7IsMMSdSoHuxB92HlGRUd0VoNkdTi9NXg9Jai8e1Br7hxqwZsQTP5vlTWePqy0dsTpxrW/N5/XdqPmR9tInHgVqo8NmJLezJXzWKIM3RLKAzYuTuopZQA6Xo7A4x2/hK/Ea2+Gy5NIh7FhFNVsAV9NPmd+FQ/R6PVaIgKjyQ6Joao2OjmYC8qKgqd7vT5H7TirTup++JpdJFJdJu9AX10ygmVo/q97P/b2bjyv8fc6ywy7v0WRXP8czmFEOJ4SAAoRBtyePyUNLpx+/yEPTMcf+Vu7oi4l6+NIyG2GCWpEDWogeI+jHDGMjvajz7KTYPOT3XAhTt4ZFJkALNGj1WjYMSHERcGmtCrdpoCOqr8Zryqigk3e8IisGsjyXWbyfCHo2ohoPXjx4FbpyGIDi8qzqD/Z/PiARh1BqKtkURFRxOTEEt0XEzzsK2mEwQwqt9H0RPn4dy1FFO3XDLu/RZt2K/LaqAGg5T95w80rnoNXUwqmQ+vb96CTwghWpIEgEK0E3Vf/4uKN/5ESewQVp73Bq9vOED3fuUsqd2KGlSgJAezLY4ZGj2XaXRotG6C4Q00hPsJ6DV4AEcwgC3g+tlA7WQYFS1GRaEmEE6lT0+BW8N2Byy76zzio2R3Cn9TNYWPDMNXXUhYj5Gk37n4uHftUINBKl67mfrvXkQxmOl63zLCuuWe4hoLITorCQCFaCcCrib23t6FoNtOtzlbMKX1Q0VF99Q/UWLLUFW4r/84HnvbSyoabtTqOU/Roj24OKBB9ZOnq8NlaCLCrCHSAFadgl6rBUWLqqgEUQgCARWCqCgoKICCgqoGMahetEEvdsVIQPXi8zeiBsqo9jSRq/uOLtpK/lx3G5+4Rh9W9+CTF7b+BWunvNX72f/YGPx1BzB26UPabR8176RyLAGXjbKXpmPb+BGK3kT6nz/DknNOK9VYCNEZSQAoRDtS/tot1H/7b6LGXk/KtaFV83nlTXxc9QP3H1ypm6RNoHx3OngsJKBwkUbLNLOJSM8vJ0A+REWlXOdksc7O57YIilF5yPYvprq/4h3TBOaEH5lwWIefS7vreTdf5S/ndm9ezDJnQk/+Oi67Bc7+9OGtKqD4/ybjLduJxmQl/tJHiD7nj2gMYYcdpwaD2DZ+ROV/b8dXW4zGHEXqzPew9jm3jWouhOgsJAAUoh3xlO9m3196o2j1dH+iAH1Ml+bXvizdzR9WvE2ly4ZO0eCrS2Dlb6+grlFHWaOLz9YU8/LwTFwVNlyVDgJOH36XH0WjgF7D6hobFdYmtlvK+MFcS3FTNFRmcdvoTF5dtoVvaq/FhJfJ0f9mvy6VO8dm8dSyfZydFcsdY7P4w9ubeGRCL4Kqys0junLZq+v5YHsF624bTW5aVBtetfYp4GqifN71NP3wLgBaayzhgy7CmNYfRavHW5mPfcuneCtDgbQpYzBdbn4LY5IE00KIU08CQCHamZJ/XU7TD+8Sc/4dJP3u6cNeq/c4+cv6z5m3dy3Bg7+6Y5Mymdp1AGOSsugdlYBG+XHBRSAYZGNtCe/t38rLu9fQ6HOj12gZpB1MvKcb5TY3P9w+hifuu45JpfNZbsjl5sgHcfxtImF6Lef+v+95fHIOmbFmwvRaTAd3FjmUk+65lYX8aVS3VroyHZNt86dUf/Aw7v0bjvq6LiaNuIl3E33ODBTt6bMqWgjRvkkAKEQ74yraROGDg1EMZno8XYQu/Mj9Yvc0VvPo5m94r2gL7sCP6VeMWh2p5kjMOgOegJ/99jq8wR931Tg7KYtnz7yUnKik5ucCjgb23NkV1dVIwl1L+NCZyfXDMk7tSXYyqqriKd6CY+cSfDX7Uf1edNGpmHuOxtx9RIvuHiKEEMdDAkAh2qHipydh3/I5sZNmkXj53455XJPXzaKibXxVuptVlYWUOBuPOKarNZoJqb2YnpXL8IQjA7uqDx6m5sPZmHPOoeu937boeQghhGifJAAUoh1y7lvL/keGo+hNdP/HXvQxqcf1PrvPQ4mjEU/Qj16jId0SjVVvPObxAUc9e+/sStDVRMZ9y7H0HH3MY4UQQpw+Tv/srEJ0QOasYUQMnYrqc1O96Pj3lrXqjfSKSmBATAo5UUk/G/wBVH84m6CrCUvOuRL8CSFEJyIBoBDtVMLUx0Crp2HlAtzFW1q8fE/ZLuq+fR4UDYnTnmzx8oUQQrRfEgAK0U4ZErsTc+7NoKqUvXIj6k8Wc5wsVVWp+O8dEPATNfZ6TBkDW6xsIYQQ7Z8EgEK0Y/FTHkEX3QV3wTrqvnm+xcpt/P4NHNu+QGOOJOE3c1usXCGEEB2DBIBCtGPasAiSr/o3AFXv3YenfPdJl+mrK6XijVsBSLryGXQR8SddphBCiI5FAkAh2rnwwRcRcebvUD0OSv41laDHecJlqX4fpS9eSdDZgHXAJCJHXtWCNRVCCNFRSAAoRAeQfPULGJKy8ZRso/yVG1GDx7/v709VvnM3zl3L0EUlk3zty807egghhOhcJAAUogPQhoWTesu7KAYzjavfpPKde/i1KTxrPnucuq+eAa2e1JnvoY9KPkW1FUII0d5JAChEB2FK70/arYtAq6fui6eofPP241oZrKoq1R8/StXCWaAopFw3H3OPEa1QYyGEEO1VqwWADQ0N3HDDDVxxxRXMmDGD2tpaADZv3szUqVOZNm0a//rXv1qrOkJ0SNZ+55M6478oOgN1Xz9L8ZMX4Ks9cMzj/U3VlD7/W6rfvx8UheRrXiRq5O9bscZCCCHao1YLAF988UWGDBnCW2+9xfTp03n66acBeOihh3jqqad466232LJlC3l5ea1VJSE6pIihl5F+91doLTE48r4mf1YvKt68Hde+dQS9LoI+D+7iLVS9dz/5s3rS9MO7KEYLabd+SPRZN7R19YUQQrQDutb6oPz8fO644w4ABg8ezCOPPILdbsfr9ZKeng7AqFGjWL16NX369GmtagnRIVl6jSXrsTzKX78F2/pF1H31TGh+39GO7TMutIgkMauVaymEEKK9OiUB4Lvvvsurr7562HNJSUksWbKEnJwclixZgtvtxm63Y7Vam4+xWCwcOHDs4SwhxI90UUmk/el9XEWbaFj6Mo68b/DW7AdU9NGpmHudRfTY6wnrMUJW+wohhDjMKQkAp06dytSpUw97zm638+ijj3LNNdcwevRokpKSsFqtOByO5mMcDgcRERGnokpCnLbCMgYRdnUoWfShlcES8AkhhPg5rTYHcP369Vx88cUsWLCA1NRUBg8ejNVqRa/XU1xcjKqqrFy5ktzc3NaqkhCnHUVRJPgTQgjxi1ptDmC3bt249957AUhISOCxxx4DYPbs2dx1110EAgFGjRrFgAEDWqtKQgghhBCdkqL+2myybWzKlCksWrSorashhBBCCNFhSSJoIYQQQohORgJAIYQQQohORgJAIYQQQohORgJAIYQQQohORgJAIYQQQohORgJAIYQQQohOpsOlgRk2bBhdunRp62oIIYQQQrR70dHRzJs374jnO1wAKIQQQgghTo4MAQshhBBCdDISAAohhBBCdDISAAohhBBCdDISAAohhBBCdDISAAohhBBCdDK6tq7A8QoGgzz88MPs3r0bg8HA3LlzycjIaOtqdUo+n4/77ruP0tJSvF4vM2bMoHv37syaNQtFUejRowcPPfQQGo2GhQsX8vbbb6PT6ZgxYwZnn302brebu+++m9raWiwWC48//jgxMTFtfVqnrdraWqZMmcL8+fPR6XTSTu3Yiy++yJIlS/D5fFxxxRWcccYZ0l7tkM/nY9asWZSWlqLRaJgzZ478brVDW7Zs4cknn+T111+nqKjopNtn8+bNPProo2i1WkaNGsXMmTPb+hRPjtpBfPnll+q9996rqqqqbtq0Sf3jH//YxjXqvN577z117ty5qqqqal1dnTp27Fj1pptuUtesWaOqqqo+8MAD6ldffaVWVVWpkydPVj0ej9rU1NT8/fz589Vnn31WVVVV/fTTT9U5c+a02bmc7rxer3rzzTer5513npqfny/t1I6tWbNGvemmm9RAIKDa7Xb12WeflfZqp77++mv11ltvVVVVVVeuXKnOnDlT2qqdeemll9TJkyerU6dOVVVVbZH2ueiii9SioiI1GAyq119/vbp9+/a2ObkW0mGGgDds2MDo0aMBGDhwINu3b2/jGnVeEyZM4Lbbbmt+rNVqycvL44wzzgBgzJgxfP/992zdupVBgwZhMBgIDw8nPT2dXbt2HdaWY8aMYfXq1W1yHp3B448/zrRp00hISACQdmrHVq5cSXZ2Nrfccgt//OMfOeuss6S92qlu3boRCAQIBoPY7XZ0Op20VTuTnp7Oc8891/z4ZNvHbrfj9XpJT09HURRGjRrV4dutwwSAdrsdq9Xa/Fir1eL3+9uwRp2XxWLBarVit9u59dZbuf3221FVFUVRml+32WzY7XbCw8MPe5/dbj/s+UPHipa3aNEiYmJimm9kgLRTO1ZfX8/27dt55plnmD17NnfddZe0VztlNpspLS3lggsu4IEHHmD69OnSVu3M+eefj0734yy3k22f/41BTod26zBzAK1WKw6Ho/lxMBg8rHFF6yovL+eWW27hd7/7HRdeeCFPPPFE82sOh4OIiIgj2szhcBAeHn7Y84eOFS3v/fffR1EUVq9ezc6dO7n33nupq6trfl3aqX2JiooiMzMTg8FAZmYmRqORioqK5telvdqPBQsWMGrUKO68807Ky8u5+uqr8fl8za9LW7U/Gs2P/V0n0j5HO7ajt1uH6QEcPHgwy5cvB2Dz5s1kZ2e3cY06r5qaGq699lruvvtuLrvsMgBycnJYu3YtAMuXLyc3N5f+/fuzYcMGPB4PNpuNffv2kZ2dzeDBg1m2bFnzsUOGDGmzczmdvfnmm7zxxhu8/vrr9O7dm8cff5wxY8ZIO7VTQ4YMYcWKFaiqSmVlJS6XizPPPFPaqx2KiIho7iGKjIzE7/fLPbCdO9n2sVqt6PV6iouLUVWVlStXkpub25andNI6zF7Ah1YB79mzB1VVeeyxx8jKymrranVKc+fOZfHixWRmZjY/99e//pW5c+fi8/nIzMxk7ty5aLVaFi5cyDvvvIOqqtx0002cf/75uFwu7r33Xqqrq9Hr9Tz11FPEx8e34Rmd/qZPn87DDz+MRqPhgQcekHZqp/7xj3+wdu1aVFXljjvuIDU1VdqrHXI4HNx3331UV1fj8/m46qqr6Nu3r7RVO1NSUsKf//xnFi5cSGFh4Um3z+bNm3nssccIBAKMGjWKO+64o61P8aR0mABQCCGEEEK0jA4zBCyEEEIIIVqGBIBCCCGEEJ2MBIBCCCGEEJ2MBIBCCCGEEJ2MBIBCCCGEEJ2MZFIWQnQqf//738nLy6O6uhq3201aWhrR0dH07duX4cOH079//xb5nI8++giz2cz48eNP6P3PPPMMkyZNonv37i1SHyGE+ClJAyOE6JQWLVpEQUEBd911V4uX7XQ6+dOf/sS8efNOuIympibuuusuXnrppRasmRBChEgPoBBCALNmzWLixInU1NTw3Xff4Xa7qa6u5qqrruLbb79l79693HPPPYwbN47FixezYMECNBoNQ4YMOSKI/OSTTxg5ciQQCjR/qbxZs2ZRXFyMx+PhuuuuY+LEiURERGA0Gtm1axe9evVqi0sihDiNSQAohBD/w+FwMH/+fD777DMWLFjAwoULWbt2La+99hq5ubk899xzvP/++4SFhXH33XezatWq5oAPYN26dUyZMuW4yhs+fDhr167l/fffB2DVqlXN7+vZsyfr1q2TAFAI0eIkABRCiP/Ru3dvAMLDw8nKykJRFCIjI/F4PBQXF1NXV8eNN94IhIK7AwcOHPb++vp6YmNjj6s8q9XKAw88wAMPPIDdbueiiy5qfl98fDyVlZWn+nSFEJ2QBIBCCPE/FEU55mupqakkJyczf/589Ho9ixYtag7wDomJicFmsx1XeVVVVeTl5fH888/j8XgYO3YsF198MTqdjsbGxsMCSSGEaCkSAAohxK8QExPDNddcw/Tp0wkEAnTp0oULLrjgsGOGDRvGli1bGDp06C+WFx8fT3V1NZdccglms5lrr70WnS50a966dWuH33BeCNE+ySpgIYRoYQ6Hg5tvvplXX331hMtoaGhg1qxZvPDCCy1YMyGECJFE0EII0cIsFguXXHIJX3755QmXsWDBAun9E0KcMtIDKIQQQgjRyUgPoBBCCCFEJyMBoBBCCCFEJyMBoBBCCCFEJyMBoBBCCCFEJyMBoBBCCCFEJyMBoBBCCCFEJ/P/AeQhf3LNbGaFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cOKBs53Xw4EGGDRvGiBEjmDdvnltjKG99R0SAD9HqZOdtjWQn/ogYBq5KcgVrsnOn9+Wv51oRoMoteKyKnIsbsOWmeio8QbijhfhrqVPdjxpq5Y1woi3Eed/3cXoGXJqCpI8m78pfnPrtzaoKUxCEm+CxBHDRokV07tyZr776iv/+97+8/fbbAEyePJnZs2ezYsUKDh06xLFjx9wWQ3Q1P8Z2r1fmfcMaKtnhTnNz5f+b5/P6WrFDSFUqL2H30ajRmBMBuGCJ5JDUAewW/vp+rAejE4Q716HxPQgz+JSZADpuP3zhBZBUPBvwA3lJ+6oiTEEQboLHEsDRo0czYsQIAGw2Gz4+PhiNRvLz86lduzaSJNGtWzd27tzpthh0aom20dXKvC8YpXq0yNifLLs/nX2Psf+vlTy0aLfb4hHKV1mrMSlX2b0lyRbMa1ceBZWWqKRV5Cb86YHoBOHOFlXNl50vdSMmQNl1J9EWXOoxB/Ib49fyJdSSndTfnkW2WTwdpiAIN8EtCeDq1avp379/sX8XL17E19eXlJQUXn31VcaNG4fRaMRgMDifp9fryc7OdkdIQPmtRQCspoLt4Cy1mJP1CADTqs9n+4lTbotHqFhFLRmD7MqQ/RVbCOes0ZwIfwaAzD0zPBGaUIb+X+zi71+JStAdw26GvDSsaDBKQWU+pOXGu4m1RpCfeoRvP3sIu9Xs4SAFQbhRbkkAhw0bxtq1a4v9a9myJadOnWL06NGMHTuWjh07YjAYMJlMzueZTCYCAwPdERJQsLtEmcft2AraiCTZglli7MsOcwtC1ZnMqP4Jc/88X8azBHeqbK+BIFKAwqGppab+mGUdubEbsKSfcXN0QkmyLLPuZDKrDl7m0OXMqg5HcAGb8RIARlUYk+9vAsCIEnsGp+ZpeDXtJUx2X9pafiPx2wexW3I8HqsgCNfPY0PAZ8+e5ZVXXmH27Nncc889ABgMBrRaLXFxcciyzLZt22jfvr1b4yhrJbAtJxnsVlS+IbSpHY4kqXg1/SUy7AZ6+u1n78aZbo1JKE2WZaQK1gHbjErF1pEArjqRy0853QDIOvJ/7g9QKKbokH3bD7dWXSCCy1gLfsfCI+rwWo8GqFUSX/69TanH7cuPYXjKe1yxhpB3eTupm14Q28UJwm3AYwng7Nmzyc/P57333mPkyJE8//zzAEydOpUJEyYwdOhQmjZtSqtWrdwWQ3krSx3VP40hip0v/w0fjYpEWwhvpD8HwMSgL1m2eaNzL2HBMyoaArYWVCe6NWvhPLbMeD8AxmNLRBXCw+xF/uDLMhy8JKqAtzubUWmzpA2IRqWSuPr2/WjUZf/JOGmpw1Opk8jHF9PJ5WT8NdWToQqCcAM0nrrQ/PnzyzzeunVrvv76a0+FUWZNyTHUoTbUBOD4qz05dCWLhxbBKlNvHtFvovre55mfsZJnuzYiJiLAY/F6q8rqB7aCrahG3dOR53YrlYojlgYk6ppRI+8YptNfE9BstHuDFJzuXfBXsdttP9yKfdaAKopGcAVrdvHXxUBfbUUP57S1Nq+kvsy8kFlk7JoGqAjq/KbYX10QblFe1Qi6vFEJx1CH2qDMb7kr2J+BzWoA8G7Gk1y01CBGF4vh2HtMXHcCi83ukXi9XUV/NqzZyg4FGkN0seP7DMMAyNz/EbIsvk+esuV86cbp2y6IZuq3M1vBwjiNIaqCR9khJB4a7IHGO9gQ7s8zxhdBUpGx613St70hhoMF4RbldQlgWW9GHRVAjT6y2PFX/laXHNmPMWljschqngpYS+rZjfi8/rOY6O5mFf3NsFtM2PPSQa1D5R+G9f3+zvsWXGnHFWswlqvHyb2wzgORCuXp/skOss3Wqg5DuEHWgiq7owLoEOirYd/Y7uwe0xVqH0OqcR7JJwdJY0EKSGNLlMTODh+DSkPmvtlkH/6sKsIXBKESXpUAAmUuLCisABZ/oftwkNIU+oilAfOyhgIwo/o8AiQTeVZRXXK38oaOCqt/tZAkVbH9gY+l5PM/ozL0mLF3lvuDFCo0bfMZUTG/TTkWWpWssn8+tBXNawTwv7jfkQLSkK1a5LhmyKc7IadFIqlkRp86S9zdHwOQvmOy2KVHEG5BXpUAlldUKnyhKz3UYZvZnzd6N2R+9sMczG9IlCaVKUFfMGrFAVQTfuJ4ovv6FnqziraCc8z/0wQU/mFa9lhb58erTPeSYTeQd3kH5kvb3RekUMzcwc1LHZux+Sx74jOqIBrhZlmdc6OLvy4Obx3FpiunWXBqJzqVGmJbQHYoWHzhSkPktChs2HnmYgpyrT7Y8zJI3y62ixOEW41XJYBQ9hCw1VTwQqcvnQBKksTQlpHYUDM+7WVy7D48pN9KE5MyvDjsy71ujdeblTcH0GosnQD+vU1NDo1X2guZZD++Mj4AQMZe0cLHU7rXC2FGvxhWPt6u2PHfz4rqz+1Gtlux5SQCUqmpMTa7ndf2/ATA220fIGfyI0WmYUiQWB/ZrOdc9lWWRo0ElZbsowsxX3bfLk+CIFw/r0oAy5uMXFgBrFnm/REBPgBctEbxdsZTALwd9Dm11YmcSBatYdyhojmAjiFgdYmhqRaRhU3Elxj7kmvXkXthHfmpR90So1Bci8hAXu3ZgOElmgW/uV7spnO7seUkgWxH7R+OpNYVu2/5+QMcy0iiriGYl5v+DV+tutg0DGQVXGkAwKyzh8lo+goAqZv/JbaLE4RbiHclgJSuKtktOdjzMpQFBX6hZT4vMtAX20zlHe7qnN78nNMFg8rMhyEfocGKzS5WublaeQt2oMgcwIBa5T4/zV6N1Tm9AMjcN9vl8QnFjWwXXeH9gxftZvuFNKxiPuBtobAFTPFkXpZl5hxTGn3/p3UffNSFncQeb1vkDXROEHJWCDlWC5+o2pAoR2JJPUrmgTnuD14QhGviVQkglE4qClcAR1XYr0q5TwZdLhNNo4mzhtFad4YxgSvRvrbWjRELJVmdcwBLJ4D/6FTb+fHC7IFYZRXZJ1dhyYr1WHzeqG6wf7HbiZPvY1Kfhs7bPxxLot/CXWTniVXBtwNbwbSYkqMiO5IvciDtEqE+ev5et/iuIB8Malb8JKnK7+fyuAN8Kv8LgIy/3sGaFeemqAVBuB5elQCWNaxoLdgFpOQ73ZJ2p8RxV/vjSA33kNPgGPcHPsavmvo8H/gdD/n/QYoxzx0hey2lWlt2Qm5zVgBLV50+H1a4k0yCLYKfc7siyVay9ovKgyeFB/jw9gNNih3LEi1hbhslm0A7fH5Kafj9TOPO+GqKN4au7qcMFYfpC4aMc6shmwLJk/PZ4hOOvuHDyNZcMnZPd3P0giBcC+9KAJFLVfmKVgDLsyXxHL3XzycuN5VQHz2++GHR2vmXvh9f6Vowo/onPDb9XTEU7GJlFWRlWXYuAlEbyh8CdliQ/RAA2Uf/J1pR3AJsuSksXfg8uSknqjoUoQLO18UiCWCu1cL3scp82tENO5R6jlol8dNTHbn01r2FB9OU5ydIFwnqMgUkFdnHF2PJvOC+4AVBuCZelQBC6TmA5fUAdLiSk8WI35eSY7UwqkF7YodPYtt94zGkK8NbU3x7sEcTxcchH9DyP3MZuHC3O8P3GuUt2LHnZSBbTEhaAyqfapWe55SlDlkhPZCtOWQd/NTVYQrX6fzap+iWvZCUtYPFgoBbmNVUfHckgJ8TjmO05tEhtBYNAsueL92vaQQatYqnHVMxskORbRokPyNTD2ZjaPIo2K1kHZjr9s9BEISKeVUCWFZOUVEPQICxu34g2WykV2QD/q/rcHw1WtrUDCJj7LM0kGNAknjBbwAWCeYEf8Cmk8pcs/Unk932eXiLMvdtLjL8W9GczZDqFghMAV8jz55QFoNkHfoUu8XkjlCFa+RzdRcAtszzGE+uqOJohPLYsktXAFeePwjAiHptynxOUc7XWlkFmeEA/Hf/nwS2fhEA48kVyLZ8F0YsCML18qoEEEoPKzp7AJaRAO5Mvsjqi4fwVWtY2O0R1KrCL5ckSRwf/SSavECy1Vre0/amvvYy/672JbP+OEffL3a59fO405U3mF50F5CyZOWbCWxwmrSoHUi1jiPV38eeSBt/5DfDbk4j++j/3BSxUJ4GoXoA/KVcAlU5zuMZe6YjW8Xc2VtRYQVQSQDNVgvrE04CMLROy0qf36FWNd59sGAOaEaE8v/AFDRhrdCGNsduvkpu3EbXBy4IwjXzqgSwrKSirHe6Du8c/A2Asc3uobaheqn71SoV/ap3BWCNbyMSCORR/QZ++nO964L2YmU27XbsTxpYOgHMt1kZsHEh2T5X8Ffr6F2jsTL8ZEjn5Wp9yEJH5v6PxNCjB0UE+DBrQFPurlOdCHUaAAnWMHJ862LNOEv6zslVHKFQkizLhbvtFLwubkk6T67NQpvgmkTrgyo9xz+71OGlrnV5qHkNyA1AzvdB0uaz5swp9PUHAZBzQbxOCkJV8q4EUJZLrSwt+U7X4URGEr9eOoWfWsvYZt3LPee3j/RGzgxFVsGrqsGoJJmXdfMBGaNoeXHDymsEbTU6KoClVwBP3LeObUkXqOlfjf2DxrLuvqfhbHtksz85OpnXAgdjzY7HeHqVO0MXioif1IeBzWowonVNItXKIpwEWzjPJT4PkorMfR9ivryjiqMUirLnpSPbzEi6QFS6AABn9e+B6CYVPbWYAF8Nnz3cEpAgS5kzuOr8YXLCewOQe3F9uXN9BUFwP69KAKF4VUm22wrnAJZYBTz3+DYARjZoR4ivvuKTpioTnvfoq3PWHk5bn9Pc57uLAWJByE0pa45feT0AD169xMfH/0QlSXzTaxQNq4WhkiSw+kBcC2Sbmo1SBOs1DcjcOwtZFg2JPUGjVl5iHmpeg0j1VQASrSFsN9Ynvd6/AJnU3/6J3WquwiiFogpXABe+Jv6SoKzavp4EEJR2QPc1CoOsMAC+jzvCc7+rUPmFYs26iDXjrIuiFgThenlVAljyvaYtNxlkGyq/MCSNj/O42WphxfkDALwY063S8/717IP45YWAys4b9iEAjKu2gm3nk9kTl+Gy+L2JXM4sQOfQVIkegG/sW4ddlnkxphsdwpSEXKWSlGbEFl9IqgfANN+/kXP1JLkXfnFj9EJJ0UF+/KOF0jfuii0EgLu33I09oAGW9FNit5ZbSMnOCGezUjmTlUqQzo/OYbUremqZ1j/bGXIDkS06JF0eP1+Mxbfm3wAwX/rTdYELgnBdvCoBhOIrS8tbAbwu4SRZFjNtQ2rSrHqNSs/ZsXZ1ljzQD4D9+mrEWsNpqE3gIf8tZJrFfLMbIctlrwK2Gh2LdgoTwINXL/HrpVPoNTomtepT7PGj2tdi4z+7QHokcp4fV9QBfKONIWPvLHeGL5QhwJYIQGJBApiPjp+r/RuAzL2zseWkVFlsQqGSoyKbLp8BoHdUQzQq9Q2eVQJjsPKhIQ3faGVajUgABaHqeFUCWHK6iXNBQYn5fyvO7wfg79fQ7sBhQK2mhPkawDeXyXkjAPhX4Bru/3wH9y3YeRNRe6+SI8CyLGMzlq4Azjr6B6DsTlByuL5+qJ5eDUMBCZLrAPC5bwdyL2/HfFl8Xzypnq9SDXckgAAbMhrjV+cBZIuRjN3/rarQhCIK32QpCeCfSecB6Fmj/g2f895GocUSQL+CCmBugkgABaGqeCwBzMnJ4fnnn+fRRx/l6aefJi1NWRF48OBBhg0bxogRI5g3b55bY5Dl4vPKbCZHBbAwAcy2mPk54QQSEo/UvfYEUKfW8Hi9dgBs861BgjWMuzRJdPU5zMYzYgeK61XWALA9NxXZlofKpzoqrZLopefl8G3sESQkXm5ayXB9Vhhyvg9xqkC2aWqLYUcPc8wtu2IrbCK87mQyPXfdi12WyDq8QOwQcQsoHBmpiSzLbE1UEsC/1ah3w+fsXi8EjNWVN+H+WeQE1kflUx1bdhyWzIsuiFoQhOvlsQTw66+/plmzZixfvpx+/frx6afKrgyTJ09m9uzZrFixgkOHDnHs2DG3xlG0qFRYASwcAt54+Qx5Nitdwu+ipr7ynSaKerxBWwBCamRzvNpgAP6u33BzAXuxUiu2HVvABRQm7KsuHCTPZqVXVAPuMgRXekbSIxRTIgIAACAASURBVAH4yqc1Oed+wiImoXuM4/dtSKd2xY6fstThh5zuYLdwdccU7GJLxSpVdBeQi8Y0LuVkEuzjT9OgiBs+Z9c6wWDXQE4gkiSz+cp5fGsqLbTMl7a6JG5BEK6PxlMXGj16NDabDYDLly8TGhqK0WgkPz+f2rWVicXdunVj586dNGvWzC0xlFxYUFgBLBxO/KWg3UHf6JjrPn+r4CgaBYZxOiuFY5F96HXmC/r47SFElQnAN4cv83DL8vccFgqV1R7CuQK4yPfry7N7ARjVoPTepEWNbl+LxXvjlbmA4bH8ob6Lq5Iv1Y5/SfDdb7swcqEsdosJe146qHXkqoOA9GL3f5g1gsGBO8k5tZJvfR9laM/7qyZQodg+wI7qX7eIuqikG68X9GhQUPU1BoM+ix9jj9Mrqis559eSl7iXgKZP3HTcgvtYLBYSEhIwm8Vq/VuZr68v0dHRaLXaa3q8WxLA1atXs2TJkmLHpk2bRsuWLXniiSc4ffo0ixYtwmg0YjAYnI/R6/XEx8e7IyTAMQRceLtwtVtUwf2ys93Bg9fZ7gCU4eXhdVvx7qGNHMhPY4u5Db399jHA/0/gcYZ9uQ/7LJEAXquScwCd8/8KEsAL2VfZlRKHQePD4LuaV3iugc0jiAj0Ycbms2Csjj0gjXXahjxyaAnVO09GuuHJ7cK1KEwqopFRcW+jUH47XTg14pItnIOG4bTO/IoG595CvqeP+J5UkaJzAP88o8zR6x5x48O/xRiDIeIiy04dZf6DyhaN+ckHXHNuwW0SEhIICAigTp06FW7BKVQdWZa5evUqCQkJ1K1b95qe45Yh4GHDhrF27dpi/1q2VLYP+vLLL1m2bBkvvfQSBoMBk6lwb1aTyURgYKA7QnIqvgrYkVAoSdnBtMtczskiyj+QVsE3lqgNq9sKgOM5F/gmpwcAQ/z/oMmMzTccszcqqz+sc8i+YAj4p7jjgJKs+2t0FZ7voeaR/LdvDGqV5Nyaao2mGbq8K+TGi++Nu1mL7CzxVMdazH+49HZio4/fR5KtOtWMB8g69KmnQxQA2ZqHPTcVJDVqv3D+dMH8P4dwg44L4wch29TYtbkk+it/pPJTDyPbRdP8W5nZbCYkJEQkf7cwSZIICQm5riqtx+YALliwgO+//x4Af39/1Go1BoMBrVZLXFwcsiyzbds22rdv77YYiuYUsiw7K4COipKj2/2D0TE3/IPeLKgG9QNCyLDk8vJTI8m062mmu4CUocxt/N/uuBv/BLxMye+A1ZmwK02gf4w7CsDA2tc+ZWDxiNaQHYJsV3FMG0aCFIDx2GJXhCtUwDl/01CTmIgA6oXoSX37fv7bt3CqRbas5830fwKQvv1NLJnnqyRWb+ac/6ePJC3fzNnsVPzUWlrf4BviohLevJe7qushR5lb/c6ek2gC6yJbc7Gknbzp8wvuJZK/W9/1fo88lgA+/PDD/PTTT4wcOZLx48czbdo0AKZOncqECRMYOnQoTZs2pVWrVm6Nw/EFsudlIltMSFo9kk6pOv6eqCwI6BPV8KbO75g/uD0tlrU5ykTnIf5/APDRVvFH7VpUuG9zQE3S8nLYmnQBjaS6rvma7aKrgayGbKUVyUZNPXLO/YjNnF7JM4Wb4RwCLtK+J9hfx+u9GhR73CZzBxKq90O25pD623NixxYPK9obdU+qMh2nXWj0TfT/K+TYFUadq+yrvvj4UXThrQHIE8PAQiXi4+N56aWXGDlyJCNGjGDKlCkYjcZSjztx4kSFHUW2bt3KqlXXvx1or169yMvLK3YsNjaWZ599lqeffppRo0Yxc+ZM7HZ7pTFcj4SEBIYPH+6Sc5XksUUgoaGhLFy4sNTx1q1b8/XXX3skhqILC2ymwonOkiRhsdvYmXwRuPn5Ln1rxTD3xDbWJZyAnJ48ZtjAQP8/+W/mKI4mZt/Uub1JyXczRZtA/5JwEptsp3dkQ4J8/K75nE3CA9g/tjtt/5cE1VL4Tt2C0ZZDmE6tIrDVcy6NXyhkzS6cA1iSj0ZFnrUw0Zub/xzvaHZCwh9kH/4/Alv902Nxerui8//2pCqjFe1Da1X0lOv2RExzFl89B/oMdCHNyDn7HZb0Uy69hnBnMZvNvPDCC7z77rvOItF3333H+PHjWbBgQbHHxsTEEBNTflGge/fuLovrgw8+4PHHH6d79+7IssyLL77Ipk2buPfeeyuM4VbhsQTwViBTuLCg5HZHe1PjybFaaFwtjBr+NzcP8Z4a9dFrdBxKu8zMfk+S+NdMaqjTaKBJ4KzVtS+md6qScwBLNoHecPRHAPrVuv5fMqtdBmMwsgzHdcFkm3Xoji0RCaAbFR0CLnWfXaZphIHjScq7+TWn8jH6PcUnIbNI2/Zv/Orcj7ZaHU+G67WK9kZ1VAA7uDgBXNDvbhYtWoukM5Oob4QvYEkTCaBQvj/++IMOHToUGyEcPHgwK1asID4+nk8++YSMjAwyMjJ4+umnWbduHR9++CGrV69m2bJlVKtWDa1WS9++fQE4f/48I0aMYPz48dSoUYP4+HhatGjB1KlTSUxMZMqUKeTl5ZGRkcG//vUv+vTpU2ZcUVFRfPfdd+j1elq2bMlHH32ERqNh165drFy5ssIYtmzZgtlsJi4ujmeeeYYhQ4awe/duZ+XQbDYzY8aMa17ReyO8KgGEwnllJTc8d7Q76B5x493uHXzUGnpHNeTHuGMYQrMJrnMPxH9HB5/jIgG8RjJysTmARZtASxp/Nl9RtqfqFXn9w/X1Qvx5o0dTpp07iqzP4jd1A4Yk72Pjri306XSPiz4DoajCPZxL//zb7DK7Xv4bAZPWQrUU8DGxPr822zW96GrZTNrWCUQMWOPpkL2S442xyj+SPeeUBLDjDez/WxGNWq3MAwxIY4vZj/tBVABvM98fvcLBS1kuO1/rmoE81Dyy3Pvj4+Od7eKKio6O5vJl5We2c+fOjB49ml27dgGQlpbGF198wffff49Op+OJJ0q3Grp48SILFy7Ez8+PPn36kJKSwvnz53nyySfp1KkT+/fvZ+7cueUmgGPHjmX58uV88MEHnD59mnvuuYe33nrLeX9FMRiNRhYuXMjFixd57rnnGDJkCGfOnGHmzJlERETw2WefsX79egYMGHBtX8QbUGkCePToUZo3r7jFxu2iaFWpcKhDqUhsSTwHQHcXrHYDZSHJj3HH2Hj5NCMa9OJq/Hd09DnOCtP9HL2SRfNI9652vhMUb9lTuAL4dFYKl3OyCPPV0/wa9mouKdhfx6j20Uw7FAL6LJZLHRjCcYwnvgSRALqFtUhvubLE56ZB/X1IPrnOY6OtrVipjqXduR/Jjd2I311lvwgLruN4Y5yoCyPZfIwQH3/qVtpg/QaYgiAgjY3pOUoCmHEO2WZBUruv2iG4zkPNIytM2FwtIiKCw4cPlzp+8eJFoqKUIk7J1idxcXHUr18fPz9lilCbNqV39qpdu7azFV1YWBh5eXmEhYUxf/581qxZgyRJWK3lr1D/66+/GD16NKNHj8ZkMjFjxgw+/fRTevbsWWkMTZooreYiIyPJz893fp7vvfce/v7+JCUl0bZt22v7At2gSheBLFy4kOHDh/PVV1+RleW6jL+qOOaVORcUGGpitdvYnnQRUIZvXaF3pDK5/Y8rZ9HWUJoUt9AqSebkX8W73cqUt2+zxhDN5oLN6XtGNrzhlWkNwwyQrfxhO6arjgxEX/0B2Wa54ZiFstktOdjNV0GlReUfVur+YxO7cN+vC5B8cgmSqiEn10HOCUTW2HjCtx9HVWFc/fM1ZLutCqL3Lo4K4AGrD6DM/3PH6s+W1ZS5oIeyEtEE3AV2C9YssQ2gULbevXuzY8eOYkng6tWrCQ4OplYtZVSh5M9p7dq1OX/+PGazGbvdXmYCWdbP9pw5cxg0aBAzZ86kU6dOZW5K4DBz5ky2b98OKH2M69ati05X2JKsohjKuvakSZOYNm0a06dPJzw8vMJru0KlFcAPP/yQzMxM1q5dyyuvvEJwcDDDhw+nU6dObg3MHYruBFK43VFNjqQnYrTmUS8g5Lq3fytPvYAQauuDiDNlcFIVjL+s4S5NInopl++OJrrkGne6or8fRZtAb7qirNbuFdmgrKdds5wpw/BffBiL1sKftkZ0V5/GfHkbfrV63tR5heKKNoGWSuwmIcsy4/Z9x6WcTNpUr8XW/s9j+PevkFIbueZJ8oKSedkwkLWpS6h2/EsCmj9ZFZ+C13CsAj6Qq1Q9XD3/z2HXP/vit/hPzhtTMAXF4JMdS37aSbTVG7nlesLtTa/X89lnnzFt2jQyMjKw2Ww0btyYDz74oNznBAcH88wzz/Doo48SFBREXl4eGo2mwooewAMPPMB7773HggULiIyMJD29/A4RH330Ee+++y6zZ89Gp9MRHR3NlClTnFvaXm8MgwYNYvjw4QQGBhIaGkpycvI1fHVu3DXNAUxNTeXy5cukp6dTv3591q9fz/fff89///tftwbnarJc1hzAmuxOUVa7dXLhXBdJkugZ2YAlZ/fyR1Isg0OaIqcdpok2ln3517/LiLcp+b7HUQGUDFH8cc41CaCvVoM6pzr2wGSW05HunCbn3I8iAXQx53SLgNLDv9/EHmbDpVME6fz4+f6n8Nfo+P7JDjy0aA9cbozsYyLODz7y7cybOyajbzQMlc5Q6jzCzZNl2bkIZH+20q3A1SuAHXzUGjAHgH8WR3zr0x6wir6PQgVq167NZ599VuZ906dPd37cqVMnOnXqhNVqJTk5mW+//RaAxx57jMjISDp0KNw2tGgHEsfH0dHR9O/fv9Q1Nm8uvWFA/fr1WbRoUanj1xODj4+P89wTJ05k4sSJpc7nrk4plQ4BDxs2jClTptCkSRO+/vprJk2axOTJk92embpL4SrgwjmAu1NdnwCCMkQJsPnKWfQ1lLH/GK0Y5rhWUpFlII7v1yl1GOn5udTWB1EvIOSmr/FALaXisEerzCMxnfvJ7WV3b2PNVhYTlJz/Z5ftTN7/KwDvtnuQCL8AAPo2CVceIKuUJFCGL3Wtic01kbl3pucC9zJ281VkWx6SrhqH0pVRirYhpdv2uEyOMg96r6T0BbRkitdGwXU0Gg25ubkMHjyY4cOHExMT49aNJm7VGCpSaQXwzTffdG7jBrB79246duxYZk+/W53j77rdala2O1JpUPuHs6egAtgh1NUJoDKfcGvieaRmytcwRnvRpde4U5VMwhyrSPdalEniXSPqumRu0od9urHum21k+llJzAqiRnYc+SkH8QkvPWFYuDFFh4CL+j72GCczk6mtD+IfjQqnlGjUKuyzBqCf+DO55gDIDMcalMw8347M2PcBhpjH0Va/8WbtQtkcb7KS9PVIz88l1EdP1E22xKqQIwE0K3UIa+ZF911L8Erjxo1j3LhxXh9DecqtAO7du5eVK1fy2muvsWrVKlatWsXy5ct5++23PRmfSymtRSTnMIdaH4nRms/xjGS0KrVLtjsqKlofRKPAMIzWPI5qlNWqdbXKta+a8l16rTtR8VXAyh+n3QVft7vD67jkGvUDQlBZfUFjZZn1bwDknPvJJecWFIVDwMUTwI+ObQFgfPMeaMvYaWJM94IV+cl1kGX4QdeUJLua1N9fEVVaN3DM/zvlexcArYKj3Lr91/BGylSYvUYjNiQxBCwIHlZuAhgYGEhqair5+fmkpKSQkpJCeno6r776qifjczlJKrrdUU32piYgI9MqOBJfjetbEPQoqAL+lacUW9sYkgDoNm+by691Jym5b7NjEcjujKsAdHFRAihJEo82VvYS3iApf5DiDouec65U1hDwqcxktidfRK/RMbphhzKfVy9Yr3xg8YPsUCxIrPDviDluI6bTntk9yJs4VgCfVCsrtVsGu7fNx6jW9ZHzfTHarJxRBWPNuii2/hMEDyp3CLhRo0Y0atSI4cOHEx4e7smY3MZRNCi23ZGbhn8dukbU5fNTf7EzM40RWj06y1WqSdmcSnHL5e4YRRfsOJpAX/WpwTljOnqNjhY30P+vPHeH1+Grc/s4rwvAaPclMPcklsyLYvcJFyncB7hwQcGXZ/cCMKxuKwxanzKf93Sn2jyz+pBy42pNCEzlc6kFL7CVtC0T8K/zACof16zaFwq/T8dkA2CjVXDZPRtdRQZlGFhn5qBfQ5qY/sJmulJur0hBEFyr3Argyy+/DMCQIUPo1q1bsX+3M4nic5J2u2m7I4du4Upzyh3JsaiDlAUH9QqGgYVr42gCfUivVOg6htZyyeb0Dl0j6gBg98tmi1lpvJlzXgwDu0rJput22c7Ss/sAGN2g7OpfST0i6yHn+ZOnlvg1oCe2nCQy9rzvnoC9lKM11nGL8mehlZsrgLIM5CrzAA/6KK+TVrEQRBA8ptwE8OOPPwZg27Ztpf7drhzDilbnEHBUkRYwd7nlmncZqhPlH0haXg6xgU0BqKdR/iCKeUzlk5Gd848cLWAOaJQ5ZK4a/nVoGhRBkM4PSZfHj/lKQmI6+6NLr+Gt7NbcggVXWtT+ykjC7pR4LuVkUlsfRLeIuhU+P29GPwCSsvMhQ3n+ZFM7ALIOzHX+Lgs3z2a8jAktF/IsaFVqmlRz78hPiF7nXAhyELESWKjY559/zujRo3nqqad4+umnOXr0qMuvsWvXLsaOHXtNjz137hwjR44sdXzLli2MGjWKJ598kieeeIIff1T+lnz77bds2rTJJXF+++23zJo166bPU+kq4D179pCbm4ssy7zzzju88sorbt2bzp1kGVSqwopSui6MSzmXMWh8aFQt1C3XlCSJu8PrsObiYfZra1ET+Hs9E98cBYtNRqdx3yTr213Jno37ZGW4z9UJoEpS0TnsLtZfOsk2dV3ssoQxYTt2qxmVxtel1/I2RfttOppA/xinNEkdWLt5pYsMtGrlOQOb1+DEn6kQcZEMfT5X/PoSmbyOrEPzCe76jhs/A+9hM17ilDoEGeVNkU7t3q3iO99VHfL0aCU152yQjY4gkQAKZTh79iybN29mxYoVSJLEiRMneP31153J1a1kypQp/PDDDwQGBmI0Ghk0aBBdu3ZlyJAhVR1aKZX+hs+cOZNZs2YxdepUVqxYwZgxY27bBBCU3nKORSAnZaWhbPPqNVBJlbZEvGHdIuqy5uJh9tj8GQB0DU4FYPrms7x1n+h8X5Zi+zZnx5OPikMWZdi3c7jrq7VdI+qw/tJJzH5mzlhr0VgbR27ifvTRd7v8Wt7Eml18+BfgxzjlnfvA2s2u6RzbXuxKUnYeWHyRTdWQ9Jk8HtuGTX7ryD7yBUGd3kCl8XN98F7GarrCCbUy7NvKxR0RyvNKt/rMSTiI5J/NUXU4kWI7uFte4veDyL34i0vP6VfnQWo89EO59wcHB3P58mXWrFlD9+7diYmJYc0aZbHe7t27mTdvHgBms5kZM2ag1WoZO3YskZGRJCQk0K9fP86cOcPx48fp0aMH48aNY+TIkdStW5cLFy4gyzIffvhhsWv+8ssvLF68GJVKRbt27ZgwYQLJyclMmDABWZYJCyu9rSVASEgIX375Jffffz8NGjTgl19+QafTMXfuXEJDQxkxYgRTp07l6NGjhIaGcunSJebPn8+8efPQ6XRcunSJ5ORkpk+fTrNmzfjqq6/YsGEDVquVgIAA5s6d66Kv+jU0gvbx8SEkJASNRkNYWJhz0+LbkWMrOMew0YmCbV+bu3BBQVnuLpgHuMuYBygbnwPkWMTephUp2rT7jCqEPBkaBoYS7OPv8ms5q4r+WRzMU5LyT35Y7fLreBursfgK4DOZKZzITCZI50f3GvWu6Rx31wlmcIuC+WhZyovuRT8fdOFtsJuvYjotVm3fLNmWjz031bkC2FMJoFatUnYEAY6ow7FkiFYwQmnBwcHMnz+f/fv388gjj/DAAw/w+++/A3DmzBlmzpzJl19+Sa9evVi/fj0A8fHxzi3d5syZw7///W9Wr17tTBwB2rZty9KlS3nwwQdZsGCB83hGRgZz585l8eLFrFixgqSkJLZv386iRYvo378/S5cupU+fPmXGOn/+fHJzcxk3bhzdunVjwYIFxaZ7bdq0iYyMDNasWcO0adO4cuWK876oqCgWLlzIyJEjWbVqFXa7nYyMDBYvXszy5cuxWq0cOXLEZV/XSiuABoOBJ598kkcffZRly5YRGeneicHuJmF39gE8lmMGoEV1935OrYIj0Wt0nMvJJlXyIyzzAn4aMQewIkW/NLbsBI4V/GFq46adCTqG1kIjqbD4GtljacgjbCTa4vo5Jt7Glu1YAax839YlnADggegmZfb+q1R2CESeBUMaydGPEpR8AOOJZQQ0LT0XR7h2NpOy88dJrfJa2NLNr4kOT3WsxayDSgJ4WBOBNeugR64r3LiKKnXuEhsbi8FgcG4/e+TIEZ599lk6depEREQE7733Hv7+/iQlJdG2rbKQr1atWgQEBKDT6QgNDSUoKAig2LSTzp07A0oiWHSrt7i4ONLS0nj22WcBMJlMxMfHc+bMGQYNGuR8zooVK4rFmZmZyeXLl3n11Vd59dVXSUpK4qWXXqJZs8LRjvPnz9O6dWtASWzr1St8IxwTEwNAjRo12L9/PyqVCq1Wy7hx4/D39ycxMbHSvYyvR6UVwDlz5jBt2jQeeughOnTo4JKJh1VFlkGVlwp2Kyq/UI5lKNvZubsCqFGpndvM7fePQbaZCZKvMvOPc2697u3OsRWc1XiJ4wUJYNsQ97SI0Gt9aF69BpIE+1RKslLTdsYt1/ImhSuAla/p5ivKPs73RV3/1IeBzSIY0PAu5FwDktrOA9v9sat8MMf/7lwoJNwYqykRO3BSUv5IeqoC2CQ8AHIdFcAIbKYr2K25Hrm2cPs4deoUU6ZMIS9PGUWrW7cuAQEBqNVqJk2axLRp05g+fTrh4eHOwsq1NDF3LCTZv38/DRoU7i0fHR1NZGQk//vf/1i6dCmPP/44rVq1ol69ehw4cACgzEpcfn4+Y8aMcVb1wsLCCA0NRafTOR/TsGFDDh5U3uhkZmZy8eJF530lYz558iQbN27ko48+4s0338Rut7u0cFRpBfDq1av8/vvvzrIqwIsvvuiyADxJBjRmpfqn8o/iWIbSlNndFUBQes1tvnKWg771uc+0nwntVIzf7fbL3rYcP+KOJtDHfJV3da3d2JusfWgtDqZd5pLOD6usIlql/DES88tunM0xBBxQE6vdxtZEZYivV+T1b+X2/ZMdAVDN2A1+RlJ8MskK7UNQ8s8YT64gqMPt3aS+KtlMl0mQAslFTZR/ICG+es9dPM8fvUbHJWsgVyU/amZdRBcc47nrC7e8++67j3PnzjFs2DD8/f2RZZnXXnuNgIAABg0axPDhwwkMDCQ0NJTk5ORrPu93333H4sWL8fPz4/333+f06dOAUpkbPXo0I0eOxGazUbNmTR588EFeeeUVxo4dy7p164iOLj0aFRYWxqRJk3jxxRfRaDTYbDZ69OhBt27dnIljjx492Lp1KyNGjCA0NBRfX1+02rI3objrrrvw8/NjyJAh6HQ6wsLCruvzq0ylCeArr7xCly5dXDb0e+7cOYYPH86OHTvw8fHh4MGDvPfee6jVarp16+b25FJjVpK+S/o6mDLzifTzzIudo83MYZWy2viBKCPjC1ofCGWTJKUJtNWWz0m18nVr46YKICgJ4Bend2HzzeGCNYqG2gQsaSfFvsA3wbEIRGOIZt/VBLIsZhoGhlLLEHTD55zWrSv/OX0RDGlkRg4hKPlnTKdWiQTwJthMiZxVBwMQExTh0WtP6tOIrfZ4/ky6wBF1OE0zRQIolPb888/z/PPPlzo+ceJEJk6cWOr4118ruwX5+PgUG97dvn278+Nx48ZRv3595+1OnTrRqZOyL/mgQYOcw71Fff755xXG2bt3b3r37l3q+EsvvQQoOVD79u2ZPHky6enp9O/fn+rVqzN9+nTnY7t370737t0B+PLLLyu83s2oNAHU6/XX3BenMkajkRkzZhQrh06ePJm5c+dSq1Ytnn32WY4dO1ZsvNyVZFlGZVZKs6c1SkLr7uFfh44FQ8CHLVqsSFS3JQC1OJ1ipFGYwSMx3E4cZW6rMYELqiByJS219EGEujFZb1/QDNy/monTibVoqE0gP/WoSABvQtF9gH8/dxyAnpENKnpKpV5s34w3jmmRtPncv1HiULg/pB4Wu7fcBKvpMmdUIQA0C/LMa6KDSpJoH1qLP5MucFgdwcOiFYxwB4uMjGTWrFksWbIEm83GhAkTiuVEnlTpHMCGDRvy888/c/78eS5cuMCFCzf2yynLMm+++Sbjxo3Dz08ZUjMajeTn51O7dm0kSaJbt27s3Lnzhs5/TTEAmoIE8JRK6SnnieFfgFBfPfUCQsiR4YwqBJVR+Tqmmm7fVdXuJKP0AbRmJ3BMrTSkdWf1D5Q3A75qDbmSicNWpWKbmyIWgtwou9WMPTcFVBrUfuFsvqzM/7uR4d+iDD46hjdQ3iRa9NnstbUHIOf82psL2ItVZQXw7d9O0z5EefN1RB2BJeuiR68veKelS5cWq/55ir+/P/Pnz+frr7/mm2++YfDgwR6PwaHSCuCJEyc4ceKE87YkSZWWJFevXs2SJUuKHYuKiqJv3740adLEecxoNGIwFFa/9Ho98fHx1xz8jVDnKgngSZsOMHusAgjKdnPns69ySB1Bq0xlLpTNLlYCl0eSlOa07l4A4qBVqWkdXJO/UmIJbtISLq8k/+qJyp8olMnRBFqtj8Iiy+xIvghAj8ibf9F9sFZjVsceBEM6a1La0DVkKznnf6Jam9tzfnJVs5kSOaNSEsBmHk4ANSqJltWV3+3D6gjRCkYQPKTSBHDp0qVkZ2dz6dIlatWqhV5f+RDcsGHDGDZsWLFj9957L9988w3ffPMNKSkpPPXUUyxYsACTyeR8jMlk2twXQQAAIABJREFUIjAw8AY+jWsjy6AqSACPm5UefJ6qAAJ0CqvNqgsHOaSpgSVjDyDz84lk/lYvxGMx3C4cC52s2fHOFjDuXADi0C40mr9SYtFHR8JluHL5JO5pPHPncwz/agKiOZR2mVybhcbVwgjzvfkpD/fWLFhF7J/B7+bWIKkwX9qG3ZKDSuv6PpF3Okv2Zc6plX6lTT2cAFrtMhuPGQnW6rhqgbjMK3h2EFoQvFOlQ8C//vorI0eO5NVXX2Xx4sV8+umnN3Sh3377jaVLl7J06VLCwsL43//+h8FgQKvVEhcXhyzLbNu2jfbt29/Q+a+V2pxIHmrO5eagkiSPDnd0CC2YB6iJRM7Poroqm/d/P+ux699uJEnCkl20Auj+VMwxD/BgjtKKIsByCVm2u/26dyJbwZaLGoNSVQXo7KI9t6P8qyGb/ZHUdrL97MTSAOwW8q785ZLze5vY3GxyJS2Rvnqqu6HRemVUkkS7ggr/flOu6JEqCB5QaQK4aNEivv76a4KCgnjhhRfYuHGjSwOYOnUqEyZMYOjQoTRt2pRWrVq59PxFyXY7qtzLnFUFY5NlGgaG4qcpe/m1O7QJqYlGUnFGVR0TWmb3FPvMlsexa8vFrGSyJF9CtTqi/N1XHXboUJAA7r16hRRbED6Shay0OLdf907k6M2nNkSzM7kgAXThPs5PNm2pfKDP4LxGWahjvrTVZef3FrLNwql85fctxoMjIsViANoV7Jh0SA7Ebk6rkjgEwZtUmgCqVCp0Oh2SJCFJknMBx83YvHkzPj4+ALRu3do5GdJVq43LI1mzUdlyOK1Tmpw29/CLnZ9GS8vgSOxIHFWH0zsi26PXv91IwOGCSlyb6uHX1NjzZjWuFkaA1oeEnExOSXUA2HV0n9uveycqOgTsqAB2cVEFEKDvXY5h4ExOq5Rk0Jzwp8vO7y1sOUmcdc7/8/zg60vd6hLsr3WOkBxTh2MVK4GFIhISEhg+fDigNIXes2fPDZ8rNjaW/v37uyq021qlCWD79u0ZP348SUlJvPXWW7Ro0cITcbmFJk/Z7uiMj1LlaeHBBSAOzmFgdQRkixe58siy8p/D+UrS1ya0jkeuq5JUzl0QzumVxQrHzhz2yLXvNLaCCmCqLoxYYzoBWh+Xzi/7W4RSMcI/iy8uKL/LeckHxJD9dbKZrnBGrcxD9vQKYIDH2tZk4+kU2ocqUzyOaMLJzxQLQYSybdiwgbNnb2zq1Pfff8/YsWNJT093cVS3p0oXgYwbN46tW7cSExNDvXr16NWrlyficgtNwQKQU+owsHm+AghKP8AFp3ZyUFMDOesC4N45j7czjTWN45LSLLttwfCQJ7QKjmJb0gXO+daAXGUhiHD9rAVzAPfmKz2uOobWRq2q9D3nNQv/f/bOPDyq+uzf91lmJskkk31f2NcQdkEEbbBq0WqtKIi1KK018raW4kKrRUWxdWsV+uqL1u2nsilKqeJSF+qGoICyBghbIGRfJskkk5lk5pzz++PMTNgTQjIzIXNfVy7DzFme8UzOeb7P8nnCoxhoSWSfrQqrSUQIT0ZzVOCuL8QQ4395h+6K217WGgEMwKK4xt7C61uKefXGkSRJUKmEsb/qAKMH+d2UEO3g6k9f5qPizr0nXpkxmPcv/02b21VUVLBmzRoMBgPZ2dk4nU4WLVqEJElkZmaycOFC1q5dy+rVq1FVlTlz5jBhwgTf/tHR0SxbtozLL7+8U+3vrpzWAVQUBUVRuPvuu1m0aBEXXnghqqpyyy23dKkydVfijQAWaHonsz87gL2MS9SjjzukZBRbvt/P313QAKOzzKcB2NUSMMfijQA2RcdDLWTJ5X479/mENwW8uUmf33lhUuelf71cktKXfbYqiKinwjCQJEcFLVXbQw7gWeBqLPNpAPq7AxhAEvUovyAIjDJH8LGtie+tZYz2uyUhgp3k5GSuu+46EhISyMnJYcqUKaxYsYL4+HgWL17MmjVrkGUZi8XC888/f9L+kydPDoDVwctpHcDVq1fzwgsvUF1dzZQpU9A0DUmSGDNmjD/t61Tk5nLqBBPlqkiEbKBvVJzfbRhkSSJKNlLmjqK6sYxbx4ZERk5Hg72IKtFMFAp9/HitvHIze1t0qaAMuQqHSyHcIPnNhu6O5m5GbaoEQWJznV7QP94zDaczuTilLy/v+w4i6vlXcTyzLdBSvRPzgKmdfq7zlSN1JTgEA0mSQFwAOoCzU6L4ySC9039MXDIf2wrZ2hCqjw5W2hOp8wdWq5XKykrmzp0LgNPpZOLEiWRlZdGnj/8yRt2Z0zqA06dPZ/r06bzzzjvccMMN/rSpyzA4yijwzOLNjklBFDovHdVeJFFkTEImX5QfZJtLpr8lVK90KjRN47C9FIAcE369VtkxyUiCyD57Iw5k0qUqv537fMFt16N/QmQa26z6dfRK7HQmrXWA9Ryo0RdTrtr9nX6e85l8Ww0QzuCIwKgSxEUYye2n35fHJveHw4Vsa+n6hq8Q3RNBEFBVldjYWFJSUliyZAlRUVGsW7eOiIgIysrKEDux1OR8ps0awIkTJ/LSSy/R3Nzse+3OO7un2r7cXE6Bp9g5EPV/XsYlZvFF+UF2SMkMcB0Fum9UtavQgIMOKyCRY/ZvVCJMNjAkOolddeXki0mMpRTV1QSGKL/a0Z1xN+gOYHFEPxqdzaRHRJMc3vn//3pFxpFljqHIXudb3LnqD3b6ec5nCuyNQDhDomIDcn5RANWj+zcuczR89ym7tCjc7hZkOTAzUkMEL8OGDeOpp56iX79+zJ8/n7y8PDRNw2w289RTT1FWVhZoE7sNbTqAf/jDH5gwYQKpqYFzmDoLQ3M5+0S9piwQHcBevJGQHVIyo1qKA2ZHsLPf1QxEMComye/nHhGfxq66cg5FDmCsrRSX7ShEDPW7Hd0Vrwj0bmMGOLt2jvOFSb0pKtzGEaO+UHDXHkDTNL/IBp0P7G3WSx2GxqYF5PyiIPgcwNSoRNK0JkqFCPaU7SInM1QJGAIyMjJYtWoVALm5ueTm5vremzRp0nHbTp3advnHN99806n2dVfajJOazWbuuusuZsyY4fvprhic5RRIepQgoBFAjxTMTikZc8gBPCWaBvsU/es5qhO149rLSE8jyF6j7ri4G4743YbujFcEepenu7Qrm3i8tYX2MDd2LRy1pT4kJHwW7HPrYvjDEgJTNyUKAv/e1dpoNVzWs02by3YHxJ4QIXoKbTqAAwYM4IMPPuDQoUMUFhZSWNg9tes0dzOSq4Z9nhRwICOAGeZokmSRejGM6qZS7M3ugNkSrNhcDkoIw6S5GZo00O/nH+FpBNktxACgNBz1uw3dGcXTAbzLrQu+j+rCMX7eBRXhDRS69L9rV10oDdweFKWFA4Kemh+WHBjdFVGA74vrff8eFaF/Z7ZUhSbwhAjRlbSZAt6zZw979uzx/VsQhG4pA+O2l1IiRGEXjCSFRZLUBfVI7UUQBEZbYviP1couu5X3dldw0yj/yZx0B/bZ9IjAYKWaMEvnd4+2hVcKZo9LRkHg4++/55ZQNqrduO2laMB2p7646coIoHfEostkp6AujWEU4q7bD6njuuyc5wuHqw/RJBhJ0BwkmGMCYoMgCDx0eesib3RMPDQ4+MFWFxB7QoToKbTpAC5dutQfdnQ5SmOpL/0bCP2/ExmXmMV/rFaqjSJSqFbpJApq9UkA2dQjGiP9fv6EMDMZEdEUN9VTJEZTUxmKKJ0NSmMZJUIUdW6FxDAz6RHRXXaucNnAyPg0tlQXs0XM5HrAFZok0S52Veod0wNFZ4AtaWVMYh84upudTgW3qiCLIfmlECG6gtM6gDfeeONpi6jffPPNLjOoq3A3Fh/TARy49K+XcWnZULCNQkMUdY7mtnfoYRTY9PTPcJMWMBtGxKdR3FTPbimRdDkkBXM2uO2tIt6j4jO6vCFjXEIWW6qL2WvQaw7dtlD6sD3stuqlDYMC3Gy78NN9PPwTPQWdFD+ATGUjR6VodtdVMDwuMM0pIUKc75zWAXzmmWf8aUeXozSWsk8MvASMl3Epespjj5TAQ2u+IG9CSLjyWPY11gAwPCI8YDaMjEvng6N72CMmMEoKacu1F03TUOxl5Bt0eaMxXVj/52VcYi+W7N1AiSEMFHDbDnf5Oc8HdtdXAzA4PDAagKfCEN2HHKWCo1I0W6qLQw5giBBdxGmbQNLT00/70x1xH5cCDnwEMD7MTJbgxCkY6B8fii4di93VzOEWJ7KmkB2dGDA7vJHifVI8yZIVTXEFzJbuhOqsAdVFvkF/cHelBIwXbydwfZiGBrhtoa7t9rDH3ggQMA3AUyFFppGj6QvALVXds+kwROdSXFzM9OnTASgoKGDz5s0dOs6TTz7JjTfeyPXXX++TlenJ9Bi5bEdDMYViDAKBGXh+KkYZ9fSmFFYTYEuCix21ZWhAf9WK2RK4BYfPAZSTkQSVZluoE7g9KHZdiHW3pDvv/nAAB1gSiDWGo8gKZUIk7oajaGqou/5MaJpGgUcDMDsIsiJeBEFkZJj+aNpcEarlDHE8n3zyCQcOHDjr/b799luKiop46623WLlyJS+99BL19fVt73ge02YTyPlCQUMtbiGdLFM4EUGiLj/GEsW7VW6UcEegTQkqttboEiLZShVy5KUBs2OAJRGjKFGsmmnEwEOrP+Op3+QFzJ7ugruxjBohnCpMRBlM9Ins+jnOgiBwQWIWn5QU8IXQn1+o23A3FGOI7t3l5+6uFNlradIE4tUmEqMDm9mRROE48e6Rllioh502K82KG5PUYx5VQU/RMz+lcfuHnXrMyBFXkXX3B21uV1FRwZo1azAYDGRnZ+N0Olm0aBGSJJGZmcnChQtZu3Ytq1evRlVV5syZw4QJEwAYNWoUQ4YM8R1LURRkuWd/r9qMAFZUVHDvvfdy2223sWrVKrZv3+4Puzqd/Ca9y21AZEKALWlljEdrrsoQYEOCjFYHsBI5quvrx06HQZQYHK03MuyX4qmtCkUj2oNiL2utt41J8dtEjrGeWsPvRf2/oTTwmdldVwHAALUGyRzYOjtF1Vhf2CreHRfTi76KFZemsau2/Ax7huhJJCcnc9111zFr1ixycnJ48MEHee6551i2bBnJycmsWbMGAIvFwsqVK33OH4DJZCI6OhqXy8V9993HjTfeiNlsDtRHCQradH8ffPBBfvWrX7FkyRLGjh3Lfffd1+1y55qmssclggn6R3f+QPqOMjZ1EOLeQg5hwOF2ES6HPEFodQCHKlVIkYGNTAyLTWVHbRkFYjxJQmVAbQlWNO34Tm3FXhaQmdujE3THb4+UAO6QA9gWezwOYD+lFtkc+LKYOkdrja0hug/DlP9ySIpjS/VRxiQEbiEY4njaE6nzB1arlcrKSubOnQuA0+lk4sSJZGVl0afPqZsq6+vrmTNnDuPGjeOOO+7wp7lBSZsRwObmZiZMmIAgCPTt2xeTyeQPuzoVtamKfaJe5DwggDVlJxId158BqhVVENhmLQm0OUFBi+JmV105gqYxWKlGjgzsjd/bMLRfiieBioDaEswcG+Rz28sC0nDl7TYuMkR6GkFCDQRnYrcnsqZHAAPvALrV1oWEbOlNjqIvuL6vCY3LDNGKIAioqkpsbCwpKSksWbKEpUuXMnv2bMaPHw+AKJ7s2jidTmbNmsX111/P7373O3+bHZS06QAajUa+/vprVFVl27ZtGI3BUT93NrgbS3wPpAFRSQG2phU5MtN3k9tUEXpYAeTXVeBSFXqrdYTLEQERgT4Wb8NQQcgBPC0nBACPl1yK818EMNMcQ5wpAqckUipEhSKAbZDvWXQOlBUEKfD3deVYB9AjBQOwpTrUfBWilWHDhrF8+XI2bdrE/PnzycvLY8aMGaxYsYKBA08/NvTNN9/k6NGjvP3228ycOZOZM2dy9GjP/m61mQJ+9NFHefLJJ6mtreXVV1/l4Ycf7tCJNE3jkksuoXfv3gCMHDmSe+65h23btvHXv/4VSZKYNGkSd955Z4eOfyaq645QJkZhQqWXuesL0tuLIBkYZXDxDrCpvAByJgfapIBzbP2fOzzwnYneqTH7xHjSpW8DbE3wItAaAnQ1lrNfGgX4d+qOIAiMjE3nv+X72SUl0S/kAJ4WTdPYY9Plp4YEiQagoh0bAezDUKUKUVPZVVseKpHp4WRkZPhKz3Jzc8nNzfW9N2nSpOO2nTp16imPMWvWLGbNmtVVJnZL2nQATSYTN9xwAxMnTmTZsmVER3dspFNRURHZ2dm88MILx72+YMECnn32WTIzM8nLyyM/P5/s7OwOneN07KrRpwL0FhTkU4SGA8locwQ0weaa0kCbEhRs9aR7hipVuCMDLwCbaY7BYjBhdYHRYEfTVAQhuL5DgebEWS2FTfU0iUbSwszEmSL8asvION0BzJcSuSokBn1aSprqaXC7iVUdJAVJY1yLovp+l8JiiDRF0V+1sk9IYLu1lAuTegXQuhAhzj/afJLdfffdNDQ0ABAdHc28efM6dKL8/HwqKiqYOXMmt99+O4cOHaKxsZGWlhaysrIQBIFJkyaxcePGDh3/TOyq09OsvYNwBTksNhmj5uago4m65pAczDar7ggPVatwhwU+AigIgq+R4bAUjdIUagQ5EV2+o/X3PR5tOX+mf72MT9KbvHZKySiNJWhKi99t6A54O4D7q9agqP8DcLrU4/4tRx9TBxhKA4cI0em06QA6HA6mTJkCwDXXXIPD0baT8vbbb3P11Vcf95OQkEBeXh5Lly7ljjvuYN68eTQ2NhIZ2VrjZTabfc5mZ5LfqB+zlykSPylStJuIGD3VAbClpmff5BRV9TXDZCtVKGHB8WDKjmmdCBKaMXtqvH9XqrOGAiEGgJw4/zdceTuBd0rJaJqKuzHUQHAqvB3AA5QaJHPgF1oLrhhIUuTxDYZydB+GeRzALaFGkBAhOp02U8AGg4FvvvmGESNGsHPnzlN215zItGnTmDZt2nGvORwOJEkCYOzYsVRUVGA2m7Hb7b5t7HY7FovlbD9Dm+xpdgNGMsKCI9VxLLKlD8OVdWyTU9lcdZTL0k5fxHq+U2CrpMntIkNSidWcVJqSA20S0NrJWiDFozQUQeq4AFsUXBybAlYaSykQ9b+zQMzc7hMZh0kwUidCuRBJav1hDNF9/W5HsHNsBFAOAgfwqsHJVDY2H/eawdKbHOUbINQIEiJEV9CmN/eXv/yF5cuXM23aNFasWMHChQs7dKLnnnuO119/HYC9e/eSlpZGVFQUBoOBoqIiNE1j/fr1jB07tkPHPx2aprHXrXe4pUVkHFesHgwYYvox3NPttrmH3+R+8DSADBf0+aRKeHBEAL2pzP1iPO6GUATwVHj/qtz2cvZ5NAD92QDis0MQGB6r147ukpJCncCnwRsB7K9YkSID7wCGGUScbuW41+TovgxRqpHR2FNXSaOr+TR7hwgRoiO0GQHs1asXS5YsOecT5eXlMW/ePL788kskSeLxxx8H4JFHHuHee+9FURQmTZrEiBEjzvlcx1Jsr8eGTKzqwByeGXQpYDm6j88B7Omr3B+qPQ0gqp72UYOgBhD0aRYA+6U4WkLzgE/iWBkYe0MJh8UYJDSGRAdGcmlgZAqbrYfJlxJxhxpBTkLTNPKPrQGMCPxCK0wW+fxADdcPb238ki29MaEwWHSySw1na00JF6eEorkhQnQWbTqAL7zwAi+//DJhYa1SAevXrz/rE0VHR/Piiy+e9PrIkSO7dLLIDk9d3UC1Bpcp+FLAYlg88e5mIrVmSprqKW2qJy2iY53W3R3fBBDnYQDcpsA/mADiw8ykGA2Ut8ChuiISA21QkKHROsN1j7UYRRAZYICwADVdDYtJgyLIl5JwhRzAk2hUHdS1OIjWWkjUmoKiCSTMILFkw2Gem5rje80QrU9zyFEq2CX05vua4pAD2EMpLi7m7rvvZtWqVRQUFGCz2bjgggvO+jiLFi1iw4YNCILAAw88wPDhw7vA2u5Dmyngjz76iK+//pr169f7froTOyv3AzBYdKAhBVkCWE9ZCZG9WtPAVT0zwqRqKlutXgewEE00opmCR7Mx26KnNfMb6gNsSXDi/bvaZasBYKjZv/Ivx3Jd/wG6LaEU8CmpdtcC0F+pQQCkiMDX2sriyXdm2dILEMh26iL5Pb1EJoTOJ598woEDB856v927d7Nt2zZWrVrFM888wwMPPNAF1nUv2owApqenHxf9627s8HSPDTHqNxh/DaY/G5rDepFjr2SDnMXm6qNc22tYoE3yOwdsNTS4mkkPM5NQ78Adlkkw5etz4jJYV11OvsMdaFOCjmNTwPl2OxDNMEvgnPd+lngMmkSVaKbUVkHg1SSDiyqXxwFUaxCMFkTDuTvraouTxp3/oXHb+ziPbMVdX4amKsgxqYRl5GAe9hMsY69DNJlPub/ZKJ30miAZkaIyGGbXR9aFpGCCg/2vbcNWUNOpx7QMimfArJFtbldRUcGaNWswGAxkZ2fjdDpZtGgRkiSRmZnJwoULWbt2LatXr0ZVVebMmcOECRMAGDp0KK+88gqCIFBaWkpCQvBlBP1Nmw6gy+XimmuuYeDAgT7n6emnn+5ywzqLfFs1ANkR4dScJFkbHLgishhhWwf03FXuDx5HfURkFFSAOyw5qKK1OYl9Yd8W9mFGbWlANEYF2qSgwuur72nW/8Zy4gI3w1kURFxNZjDb2OlUGONuRpC73wzzrqLaXQfoDSCy+dyif2qLk9r/Pk/1h0+i1J88KlGxVdJctJ36DcsoXxZD3I9/R8LV9yGGHT/i0RJm4KHLT1ZAMFj6MLBhPSZRZL+tmrpmBzGm8HOyOUT3JTk5meuuu46EhARycnKYMmUKK1asID4+nsWLF7NmzRpkWcZisfD888+ftL8syyxatIg33niDBx98MACfILho0wG8/fbb/WFHl+BSFQqamgAYaonnawgqp8KLOyLLJ3i6pfqoR1g3GC3tOrwO4PAw/XMrQdIA4sXbCVwgJeBuKMIY37nTarozxy6r9iomECAnqX/A7AHQnFEIZhu7PdfLEDsgoPYEE1XeFLBqRYro+HQNx8FNlLw8i5bSPQCYMnKInvALIgZOwpDQGwQRd20JTfu/wfbdWzgOfkv12r9S9/WrpP7qJaJG/rTNc8jRvTGWfMWwcCPf2538UFPMpWmhaxlI2hOp8wdWq5XKykrmzp0LgNPpZOLEiWRlZdGnT5/T7nfXXXdx++23c+ONNzJ27FiysrL8ZXLQ0aYDOHToUF566SWqqqrIzc1l0KBB/rCrUyior8SlQZZSR7Slz0lD64MFNbIPKVojSYKLyhY4YKtmQHTPajXwNoAMl3WpByUsOZgywAyNSUbQNA6LMdjrDoccwGPQNA0BgRpnIxVCOBFaC/2TAqxn6dAjTPmeOsCQA6ijaVqrA6hYkSI6pmlZ+/mLlL3xW1AVjKmDSJ7xNJEjrjpp4WqITSO87wXE/2QuTfs3UL7iLpyHNnF00dXEX/VHkqY9hiCenP71InsaQYbLLXyPvkAOOYA9G0EQUFWV2NhYUlJSWLJkCVFRUaxbt46IiAjKyspOqVe8ceNGPvnkExYsWIDJZEKW5R4XaDmRNptA/vznP5OZmcnhw4dJSEhg/vz5/rCrU9hhLQNgsFqNbE5D04KqrMyHENUbARiu6unqnpYG1jTNpwGYrVqB4OkA9hIhG0kXFRRBZE/lwUCbE3QIAuwoLwBggGZD7oS6snPCqafod0uJuEKNID6qnI041GaiRIEUrfGsO4A1TaPirT9R9todoCrEXTGXvgu3EjXyp20+TCMGXESfBzeSNO1xECVqPnyKkhduRnPr4/pOtT43WHoDMFLVa86+rQrpcPZ0hg0bxvLly9m0aRPz588nLy+PGTNmsGLFCgYOPP3Cc9y4caiqyowZM7j55pu5+eabyczM9KPlwUebEcC6ujpuuOEG3nvvPUaPHo0WrGG0U7CzVncAB3nHHTkIOiFoADEyEw2RnObDfBaWypbqo/yi3+hAm+U3Chut1LU4SAqLJNGxjybAHZYSdNeqn8FAcYvGrtpSLgq0MUGE95aww+MYD5aCQLC3OQIDGkfFaKprD9H584W6J94JIIMM2ll3AGuaRuWq+6j58CmQDKTN+icxl/zqrM4viCIJV99HeL/xHF18Lbbv3kJ1NpA559+n3F72THEZ1VwIxLKhsrBHlsj0dDIyMnxycbm5ueTm5vremzRp0nHbTp069ZTHkCSJRx55pMts7I60PdcNOHhQv7GXl5e3axRcsOCLACrVSJGpQdoCAiZjGK7w9B47EcRb/zc6PgOlqRTQm0CCjb7huj5jfmNICuZEBGBXrR7FHWIKhnuEQJakTwDa7rkPhDhmBrDkBDirCGD1+497nD+ZzDvfPmvn71jMQybT689fIkUl0Lj9Q0penoWgqSdtJ0f3BiC9YQ9JYZFUOe3s9zT2hQgR4txo8079wAMP8Oc//5ndu3czZ84c7rvvPn/Y1Sn4IoCeFDAEZwrYJIs0h2WS43EAt9aU4FKVNvY6f/BqH45JyEBp1K+Z25QSdNcqTNWlTfY4XAG2JLjwLqx22/TasmzzqaU+/E0vYywA2xoaAmxJ8NA6A1jvBG5vBNC2ZQ1V78wHQSBj9gqiRl97zraE9xpF1j0fIYZFYtu4gpzti07aRopIQZDC0Jw1TEzUO8vXVxSe87lDhAjRDgewd+/eLFiwgC1btpCXl3fGHHswUeO0U9JUT7jmIosmxPD4oE1fm2QRhymTGK2ZPiYDDsVFfm15oM3yG5s8dT0XJGSi2D0RwCB0AHNS9O/+HsUYYEuCC++f1e4mB6B33AeavX+aTJJJX/TtbAmwMUGE1wHs69L/K7VDBsZZvIuSF2cCkDTtCSzjpnWaPeF9xpIxZw2IEkN2v4Rt8+rj3hcEwRcFvDBKX1hsqDxQ/q+YAAAgAElEQVTcaecPEaIn06YDeO+997J9+3YACgsLu00EcIcn+jdQqcFgTkUQRDSCUwbGJIvYTXor+iiDLjTcU9LAblVhi2dc3wVRUWhKM6IxGlUOcBPBKRiZkYNRc1MqRFDvCEWVjsWm2LGpGrGqgxRL4KWXjZLIqh90z3SXFoXqdgTYosCjaZovKzLAcRhoOwWstjgpWTIDrdlO9ISbib9qXqfbFZl9Gck3PgVA6cuzaC7de9z73jrAC4z6vfGbUAQwRIhOoU0HsKKigptuugnQNQErKyu73KjOYOdxHcCtmnLBFlUC3QFsMOrpjeGa3u22pYc4gLtqy2lyu+gbFU+cR6BWivSk69vhrmuqiuPgJqrXPk7pK7+heMlNlL02G+unz9Hs0SfrLCTJRC/FBsD2sl2deuzujIZGRYvevT1QrcEQFXgHMD06jD9fMgYJlUNiLPXW/YE2KeCUOxqoaW7CJBhIdhQBAlL4meWmKt++j+aSfIwpA0n91T+7rPki7id3UZR1FaqzkZIXZ6K5W8ssvJ3A2Wo14ZKBfbYqqpyNXWJHiBA9iTa7gEGP/PXp04eioiJU9eRC3WBkh68DuBopUl9BBmkGGJMsst+ZxHBgmPMQENdjIoDfedK/4xKzcDfq6V8pMr3NdL3mdlH75cvU/OdpXGeQZQnrPYaEa+YTNebn5/zwcqsaKS4H+2XYUbmfS/pOOKfjnS9oGpS7dAdwgLfjPsBIgoCoSQwUW9ijhrGtdDeTk3r24Hdv9C9FikJAQ4xIQhBP/whozF+H9ZN/gCSTPnv5ace4dQaCILBl3CP0s+/CWbiF6vcfJ/HnDwGtWoDYDjM+cTBflB/km4rD/LwHjswMEaIzaZcO4Ny5c5k0aRJz587tNingnbW6MzFEaW0A0QhO+YAwWWLht/qKd1D9DiRB9ETGzv/ipU3VugM4PiELxa4/oLwR29NdKkfh9xx6eCzlb/wWV+VB5LhMYi/9H1JufZ70O5aRfPM/iL5oJmJENM7D31P87FSOPJ5LS+Whc7J1QIKZdI9o7S5ryTkd63yjwtUaAQwGB1AUBTQNhofp1+uH6sOBNSgI2OVxADM83dHyGRpA1BYn5a/PBiDx2ocI7zO2y+1792ATCbNeBqDqvUdxHNmq2+lxAN31hUxM1n/fUBlKA/ckiouLmT59OgAFBQVs3ry5w8dyOBxce+21fPXVV51lXrelzQjgiBEjePfddwEoLS0lLS3w6Z22UFSVXZ4mikFK9XEPpM52/9z1FTTt/wZn0TYUWxWa0oIUGY8pPZuIgRdjTOrb5jFMskijZkaMSCK8qZKhljh21lezraaUi5J7d7LFwcWmYyOAh74E9BTw6eJ/devfoOy1PDRXM4bEPiRPf5KosVNPOU1AbXFS99UrVL27kKaCrzj4wHDS71iGZczPO2RrvNlI//BoUCG/ISQF40UDyr0p4CCJAHoZYYnhrSY72+s7d3h9d8Qri9VL0P+6pIjT1//VfPAkLRUHMKUNJeGnf/KLfd8X12PPuoy4y3+P9dNnKX/9t/R+4BsMPgfwoM8BDHUC91w++eQTEhISuOCCCzq0/8KFC4MyEBQI2nQA33jjDcLCwrDZbPzrX//i4osv5v777/eHbR1mv60ap+ImTVSw0OJ7IHVWClhTVRq2rKb2839i3/PfMx44vN944i6fg2X8DITTaCjKov5lNMT0p7mpkjHmcHbWw+bqovPaAbS1ONlTV4lBlBgZl0bjDj1qK0emAyc769UfPEnlKj0CHTv5DpJvegbRdPpmEdEYRtxlv8Ny4QzKX/8ttk2rKH52KskzniZ+yl0dsnnnESAT9jhdIUFaD6qm+moAB8kqohwWYItaGZ2QCeV72d4UBOLUAca7KO6jeTUATx0BbKk4QPUHjwOQcuvzCLL/ut7dqkbq9X/BtvkdHAe/pe7r/0f0xF8A4KovZEJCBrIgsqW6GFuLE4sxeL5rPYV97y2m/vCOTj1mdO/hDPzZ3Da3q6ioYM2aNRgMBrKzs3E6nSxatAhJksjMzGThwoWsXbuW1atXo6oqc+bMYcKE1lKdV155hVGjRgWtIoi/aTMF/MEHH/Dzn/+cr776ig8++IA9ezq3sL4r8AoLDxPtwPE3unN9Xjds/5BD83Mo/r/p2HevQ5AMmIf+mPir7yfllv8j9Vcvkjj1UaLGXIcYFoXj4HeUvHAzhxaMxlH4/SmP6XUiDDH9ARhl0FO/57vcwebqo2hojIxLI0w24PZIwEjm1JN86uoP/6Y7f4JAyi1LSJ31whmdv2ORI+NJ/+2bJN3wGGgaFSvvpuY/z3TI5tqWBKK0ZmpVgfJQJzAAhxutuDSFZLWRuMjAS8Acy8iUwQiaRoFbpkVxB9qcgOFWFXbXe0SgVT16fToNwIq3/ojmaiZ64i2YB1/iNxsBWhQVKdxC8oynAahc9Sc0pwMpKhNUN+GOMsYlZqFoKl+Wh0Yy9jSSk5O57rrrmDVrFjk5OTz44IM899xzLFu2jOTkZNasWQOAxWJh5cqVxzl/Gzdu5MiRI75Ucoh2RAAFQaCqqoqEhAQEQaC+PvhTXz4H0DNbV4pIAk49a7K9uG1VlC/7Pbbv3gLAEJ9F/FV/JHrCL5DMsafcR21uon7jcqreXUhz0XYKH72QpOv/SvxV804ZOfI6gBeoVQB8XXF+jz36tlKf0To+sRcASmNrBFCrbf3c9d+9ReVbfwRBIO22V4m5eNZZn0sQBBKuuR/JkkTZq7+hYuU9iCYzsZPvOKvjlLmTGKTsY4uczs7aMlIjQkPG9ngcCz39G1wlInFxA+it1lEoxZJfV8Go+PRAmxQQDthqaFbc9IqMJb5J74g+lQRM04GNNHy/BsEYQdL0J/xtJs1uvcnQcuEMar98iaY9n1P17qMYYvqjNBzFVXuAS1P7s6HyMOtK93NNVrbfbezptCdS5w+sViuVlZXMnavb43Q6mThxIllZWfTp0+ek7d955x1KSkqYOXMmhw4dIj8/n8TERIYMGeJv04OGNiOA48eP55e//CW//OUveeyxx7jiiiv8Ydc54XUAhzbrnbTeG52maR2aL9t04FsOLRiN7bu3EIwRJM/4O/2f2k/cZb87rfMHIJoiiM29nf5P7iPuij+A4qZy1Z8o+395x8kcePE6gL3sB0gOj6LC0cA+W9VZ29td+LpCb8qY5Knr8UUAI1udCEfh95S+NAuA5F8s6pDzdyyxP7qNlFufB6Dsjd9h3/3fs9r/l5dcyEBFryfzFtX3dHwOoFqDfBajxbqahZ/uQ4xIYqimp6d/qDgQYIsCxw5PU9yw2BTC3d6F8fERQE3TqHxLr/eL/8ldGGL8X8vpdOkOoCAIpPxiEQgC1nX/hyjpi3hX3QF+nDYAgP+WhaR9eiKCIKCqKrGxsaSkpLBkyRKWLl3K7NmzGT9+PMApR9Y+/fTTvPnmmyxdupSLL76YefPm9WjnD9rhAN511118/vnn9OvXj3vvvZff/e53HTqRoij85S9/YcaMGUydOpXPP/8cgG3btjFt2jRmzJjBc88916FjH4uqqfxQo3doZjsOcKLW1dkG02q/fIXDj12C21pMeP+L6PdYPvFX3nNWdTGiMZyUmxeT8ft/IRjCqPvyZUpevAXthHFvsscBdNft52KPU/RV+bl1rgYrblXxpbgvTu6DprSgNlWCICJFJOmi3U4bxc/dgOZyEvOj3xB3+ZxOOXfcpbOJv+qPoCocfe4GWiran0pyS5EMEnVR4Z1VhzvFnu7O3rpjI4DB0wAC+sNiuEH/O/u+fF+ArQkc3vq/4bFprQ7gCc5647b3adr3NVJkPPE//aPfbQT40we7fb+HZY0gesIvQXHRtFt/3VW3nwsTexEhG8ivq6C8yRYQO0MEjmHDhrF8+XI2bdrE/PnzycvLY8aMGaxYsaLbTCoLFtpMAW/evJlHHnkERVGYMmUKaWlpTJt29qOA3n33XdxuN2+++SYVFRV89NFHACxYsIBnn32WzMxM8vLyyM/PJzu742H9A7YaGlzNpIVHklhvRwxP9GldnU3dp6ZpVL//uD7/Eoi74g8k3/g3BNnQYdssY69Dvu+/FP3tJ9i+exMxLOo4cVVvBNBlK+TipD68c3gHX1cc4vZBF3b4nMHK1poS7O4WBlgSSImw4LLp6WApIsV3vcI+uh9X9WHCeo8h9Zb/69RUeNK0x2gu3U3jtvcpfv4m+jzwTbuv7dAwIyiw01raafZ0Z/baWiOAx0Zvg4XhZjM0wtYefL28GoDDYlMI80Swj62N1jSNqjULAEj42QNI4f4vbfjj5P489fnxUdrE6x/FtuktHAU/YBok4649gFGSuTi5Lx+XFLCu7AA39xvtd1tD+JeMjAxWrVoFQG5uLrm5ub73Jk2adNy2U6dObfN4Tzzh//KGYKTNCODixYtZtmwZCQkJzJ49m5UrV3boROvXryclJYW8vDweeOABLr30UhobG2lpaSErKwtBEJg0aRIbN27s0PG9eCdojLLEACd3urXHhdA0jYo37/UNP0+5ZQkpNy8+J+fPS0T/CWTe9T6CMZy6L1/C+vFi33uiMVKvoVJauChK7277+jyNAHplHCYl6zI5SqP+gPI6EOF738ewdSWCMZz0O5Z1eieiIEqk572BHJeJs3AzVf9+uN37Znu+W3sb61C6iTB6V9GiuNlvq0JAo59iDaoUsJcRsXr6cJe9scdeL28EMCc29ZgUcOu1atzxEc4jW5GjU4idPDsgNo5Ojz7pNWNCL2Iv+z0ArhIFV53uIP44VU8DryvtuVHdECHOlTYdQFEUiYmJQRAETCYTZnPbavBvv/02V1999XE/VVVVHDlyhH/+85/cfvvt3H///TQ2NhIZGenbz2w209Bwbp2V3vq/keG6w3BsnUt7A4CVb/8Z63+eAclA+v+8SdyP/+ecbDoR8+BLSM9bCkDFW/NozF+n26dpvijgQLWWWGM4RfY6DjdYO/X8wcBXnvo/b6pbsetpezkyDcXRQPxneuQ1efpTmNIGd4kNkjmW9NnLQRCpfv9xmvZ90+Y+Cz/dR1h4KslqIw5V5VBDz9aX22+rxq2ppGtOInAHVRPIfZf2R9M0UmJ7k6baaFK187qm9nQ0upo51FCDQZToHxGBUbWDZEQ06QsZTdOofu+vAMRNuQcxQNIq8eZTL7ATr/kzYkQMaqNGc0khmruZy9L1VN/HJQWoWs906kOEOFfadACzsrJ4+umnqaur48UXX2yXEPS0adN4//33j/tJSEggNzcXQRAYN24chw8fJjIyErvd7tvPbrdjsZxb6sHrAA436jeF4xxAjTbTiFVrH6PmgydAlMi88x2ix3dNy7jlguuJv/p+UBVKX5xJtGrDpWjIsboDqNYf9EXHvM7S+YKqqb6B7hen6J/x2DFwVf9+BLmxAiVjDLE//m2X2mIedDEJV98HmuZpzml7+ooansEgRY+i7Kor71L7gh1vZGmAqi9STtVZGijCDRKKqiFbejFU0R2/rTU9b4KLVwB6aEwyorM1+ue9Fzbt/RLHgQ1I5jjiLg1M9A9gcr8EHrhswEmvS5FxPjFqV4mblvqDDI9NJdMcQ7mjge+ri/1t6jnjUkJOa4jA06YDuGDBAtLS0hgzZgzh4eE8+uijHTrRmDFj+PJLfdLD3r17SU1NJTIyEoPBQFFREZqmsX79esaO7fjIIUVtbQDJEfRh4Sd2up3J/av9/EVf2jc9bylRo3/WYVvaQ9L1jxI+cBLuujIebHwBp0tprQOsPcAlKednI8juugpqmptIi7DQJzIOAKVRv26aU8L66T/QEHBd87fTimd3Jgk/exBj8gCaS3dT/eHf2txeiMxkoBrqBAbI9zjAg92eFH4QNYEYJRGXqiFH9yG7BzuAWz2L4tHxGbgb9et17Bi46rWPAXqdsxgWefIB/IQoCoinWaDHXT4HIcyE5tCo3/AGgiBwdeZQAN4/uvuU+wQbqqrx+uajZD36KRc9uz7Q5oQI0bYDOHv2bG666SYWLFjAzJkzMRo7Vos1ffp0NE1j+vTpPPjggzzyyCMAPPLII9x7773ccMMNDB06lBEjRnTo+KA7Fg2uZnpFxpLg0le68rGFzmdIAjfmr6PsDT3alHrrC0RPuKnDdrQXQZRIv/11BJOZKc3radjyTqsDWHeAH6Xov68r3XdeKZevK9XlGyan9vdFIbwRwIZvPwXFjW3kTNT0UX6xRzSGkTrrBQCq33v0jF3BT/50CMboXgzyFNLvrA1FAAEGKVWIYXFBNQXEKAu0uFVkS+/WCGAPnOH8vc8BTEdp0ht2vLXRzuJd2PM/RTCZibv89wGzsS1EUwSRIy8CoO6zV9EUt08DcG03cQD7Pb6O4noHpfV2Giu2n6QCESKEv2nTAYyKimLdunUcPHiQwsJCCgs7NoPRaDTy+OOPs2rVKt5++21fp+/IkSNZtWoVq1ev5q67Ojaey4tXVmRCUm/fjU70iECDNwV88n7NZQUUP3cDqArxV/2R2Ml552TH2WBM6kvyjL8DYHv7HqQwPYLiqtvPqPg0Ekxmiux151Xt0icleuH25WmDfK8pjaUoDSrOw7sQwy1YL/7TOU9tORvMQy8l+qKZaK5mKladfvapJUwGcwYDPA5gfg93AL2fX5eACZ70L4BBFGlRVERTDMM80j1bq4+eV4up9uCNeo6KT0ex69fL2wBi/fRZAGIm3XpGTVN/sfDT0zd1RI37OYIJ3HVV1H39/8hN6UekbGK7tZSixlo/Wtl+7np3l+/3I7UO/v7FQZ6IXcKHyfdw8OM/cOe/dtLgdHPVS99ibWq7/CREiM6kTQfQarXy2muv8fDDD/PQQw+xYMECf9jVIb6t0qVEJiT2QrHrDqB8wsDzE50KxV7L0UVXozbVETX65yRNe9wvth5LbG4etQnD0erLqPvqTUDAXV+IoCq+YudPS86Pbrdmxe0b4XRZWmu9j6uxBFepXhcTf+U8lPA4v9uWNO1xBGMEDVtW07Tv1Cmarw9Z2VxlpL/QiKip+tzpU4h69wSa3C0cbKhBFgR6q3VB1QACYJRFXIo+USYjKok4tYk6VzNHgtRZ6Aqcbhf5dRWIgsDw2FTcTZWAHgFUGq3Ub9Cb0eIuuzOQZrYLU2I2cqoEQNWahzEoLi733B/XFuUH0rSTePm7I1Q2NPOPrwvZW9na2GhsqeJ68xcAaHtf5tWNBUQ/sob/WDdzxTvvBMja4Ke4uNg3wq2goIDNmzd36DizZ89mxowZzJw5k9/85jedaWK3pE0HcOnSpfzv//4v8+bN4x//+AdvvPGGP+zqEBtPEQGUjo0AnrC9pmmUvHgrLRUHCMsaSfodS/1Sc3Yigijyt+jfoiFg/ew5BDEZNAW37TCXp7V2u50PfFNRiENxkROb6hujpmkaLSVFaE0aUlQCcVf8QY/W+tk2Q1w68VfeC0D5ynvQTiEZ8vaOUr49Wk9kZBq91ToUTWVvfaWfLQ0O9tRVoqHR12jAiIrcSfV/muJGsdeiuprP6TjbS23kV+gPX0NMb18d4A89qA5wZ20ZiqYyODoJs8GE2tQaAaz96hW0FgfmYVdgSgv+iQiG+GykGAExQsJdV4r1s+eY2isHgLcKtwXYulZK6h3kvb2Db4v0hcbQp77goz3682iCaadvuz1yAjGZmxAHfIuQdIRt7q04euhi8mz45JNPOHCgY1N9ioqKWLlyJUuXLuXll1/uZMu6H20KQX/00UcsXryYfv36sX//fu68806uvfZaf9h2VlQ5G9lvqyZCNjA8LpVSX63LCRHAY9yKmo/+TuO2tYgRMWTM+VdAC6Dvu3U69as3ELP9DVzFTuQ0vQ7winS97uWL8gM0K25MUpuXLKj5zFP/53VsARRHLa6jTYBXhDYKjcaAzEBOuGoedV+8iPPQJmyb3yZ6/I3Hve9WNWRRQLZkMaDGyiEpjp215YzsgTNmvePFBho8HfcdTAFrmoZj/zfUb1iOffdntFQeAo+0hxyXScSAi7CMu5GokVeflRbnsu+LMckilw9MRLb0YWjxZr429GKbtYSpvXM6ZGt344dj0r8Abk9mRAxPoPaz1uaP7oAUkYwUHo+cWk3LQah+/3GunngLEbKBbyoPc7jBSu+ozs8cfHeklnfzy3nsKt1J3llmo3+CmXCDHo1UVA1JFHj44wIevHwgmY9+xq8i16J8cB995Ts55M7gp69sAmCEqYD/yP14xTSarXIqoCJrKndUbqLedRHhnaA129UoG6vRKs5tcXYiQrIJaUJCm9tVVFSwZs0aDAYD2dnZOJ1OFi1ahCRJZGZmsnDhQtauXcvq1atRVZU5c+YwYcIEAKqrq7HZbMyePRubzUZeXh6TJ0/u1M/R3WjTm3jttdf417/+hdlsprGxkVtvvTUoHcCNlXr6d1xCFrKmoDqtIEiIYfG+bY6t/bEXfE3l2/cDkH776xgTTx4e7U+iTDIFE+4hruBfuCqqECwSrroDpPW5kmExKeyqK2dD5WEmp/YPqJ3nyieeSKY3dQNQv/5lNCcIJpnY3DsAAhIBBBDDIkm87hHKXruDqn8/guWCGxBEyff+jJHp7CqzIadnMahyDx8b+vfYTmCvvMgQQXfeOzIFxHFwE+XL5+A4+N1xr4vhFtSWJtzWo9i+ewvbd29hSOxL4nWPEH3Rze1aHLx+00gsJv2BKkf3Jlv5AGiViuoJeOv/RsdnAPgyI81Fh3DVFGFM7k9kzpSA2Xc2CIKAIX4oiuNrwvqMwFm4Hccn/+BnWcN489BW3izcyn3Df9zh47+5tYQZo45fyH17pJYym5MdpTa2ldQTaZIZ8fSXvHrjSMZlxqBoGiOe/pKyBVew8NN9fFdUS6xo44GY1wD4n6g1PFz3G9KMZeyPNPBiYgaVkn7vi9KaubF5J3mHvyO83InGBpTmR5BMER3+DOc7ycnJXHfddSQkJJCTk8OUKVNYsWIF8fHxLF68mDVr1iDLMhaLheeff/64fV0uF7/+9a+55ZZbqK+v56abbmL48OHEx8ef5mznP206gIIg+MSfIyMjMZlMXW5UR/BOzNDTv546l/DE4x7eGnoNoNtWScmSGZ6mj3ldLvfSHoySgMMUR/yV86haswBXiUpLrR4tuyJ9ELvqyvm4eG+3dgCPNtax1VpChGzgYo/GoeZ2UfPRPwAIHzzAJ0LrvVaBIObiWVS//xgtpXuwbXqb6Atn+N7Lig3nmS8PIg/OZJCi1wnu6qGNIDs8o9UGexpiziYFrLldVLx9P9aPnwFNQ4qMJyY3D8von2PKGoFoMKGpCi1lBTRs/5C6L1+ipXwfpS/OxPbtClJvewVDzJnPFxtuwCu3ZojuQ7ba86Rgtlp1Z9cbAfSmgBu3fgxATO4dASl76SjGuKE0l6wnctxknIXbsX6ymF/e9RFvHtrKioM/8KecSzucOfjF8h9OcgAnP7+BV28cyYd7K/lwb2upx6/fOj7lnPrIJ4wx7uHCipcZFdUaxcuJyOfSmDf5PDwDQTBSSQS9lDpKK0fwkrSE4eUFKFZdn0LOCEOQAnTTOwvaE6nzB1arlcrKSubOnQuA0+lk4sSJZGVl0afPyQGdhIQEZsyYgSzLxMfHM2TIEAoLC3u0A9guIegnnniCzz77jCeeeIKsrCx/2HXW/LesVVpEOabQ+SQ0jdJXbsNdV0r4wEkkXf9Xf5p5Rmau2Er8lLsRzTFoTRpNu78G4MoMfRLGe0eDq9D5bHmvSO+Im5I+2JfqqPv6VdzWUgQThA9qlX3RNO24dL0/EWQjCdfok0iq3330OLkGgyjoaWBLrx6tBahpGtu9DmCL7mS0VwNQabRy5G+XY/3P0yBKxP/0Twx45gjJ0x4jvN84RIO+yBRECVP6UBKuupd+j+WTetsriOZYGnd8ROGCsTgKvz/jeWRRRPFE/WVLbzLVeqJwUe5ooKzJ1tGP3m1wqYovSjsyLg1N01DsFWguDXv+5yDJxEy6JcBWnoyqnr5L25jgmRNvsBM15jq0Fgc5m1aSYDKTX1fBd1VF53x+TdN8NjS7Ve7/YE+b+0ww7eSNxIXcEvkRt0e9R7EQxUNhufwsejrvR/TFLhi50H2Uf9rXsrx5I2HVCTj3i7itAo6wXPYmr6I65iMEITgDLMGEIAioqkpsbCwpKSksWbKEpUuXMnv2bMaPHw/oE8xOZMOGDT5n0W63s3//fvr27etX24ONNh3Axx57jMzMTDZs2EBmZmaHhaC7kmqnnW3WUkySzEVJvVF8hc7HO4CaBo71r9C47X297m/2ik6Z79uZiGGRxF+p1+Q07cpHc7u4JKUvcaYICuqr2FNXEWALO867nk69a3sNA0BtcVD174UAGFIl5KjW1ffpJHv8RcykWzEk9KK5dDe2za3deVcOTmLqsFTkqEyy1HpMqBQ31VPX7AicsQGg2F5PbYuDeFMESU59/nZ7HEC3rYrDT0ymae+XyDGp9L7/S5KnP4FoOvOISUGSib3k1/R7LJ+IgRfjrivl8GMX05j/2Wn3kUUBtycEKFt6IwJD3PrisCdEAXday2hRFfpHJRBtDEdtrgO1hWarDJpK1KifIVuS2j6Qn1HOINNjiNPFn101u0m64a8giNi+eoU/JOkp7uf3bmj3eYrrHOyr0gcGPP2Frkxw5792cu2rm5H/+D6z39kBQFHdqf+2zYKD+6Nf47PkO1mW+DBhQgulQiT3hf+YH0fdygrTcNyIXNmyn3cbVrLMvoYfuwtxyf1ZVjefEYa+NCW9BnEP0FuKQ2wW6WEKRR1i2LBhLF++nE2bNjF//nzy8vKYMWMGK1asYODAgafd70c/+hG9evVi+vTp3Hbbbdx9993ExflfbSKYaDMFvHXrVgYOHOj7H7tt2zYuuOCCLjfsbPi8TO8ImpjUm3DZQIM3AniCAxheX4jtvXkApN6yBEN8pn8NPQPxZiORJtkMF7sAACAASURBVD1dHX/FPKrXLkRzuLB+8QLxl/2eazKH8vqBLfz7yC6GxJwishnk1DY38UX5QSRB5KcZejF17boluOtKkeOTEGOsyMc0EWgEpgbQiyAbSbj6z5S9dgfVax/DMm46giAwMj2aMZnRyJYYJDQGaDZ2CTHsqitnUnJg60j9yXZPA8jwuDSEKs+Cq40mEMVex5EnL6W5eBfG1EH0+uM6DHFn1zxjiEml158+o/T/5VG//nWOLv4ZWXe9j3nopSdtK0t6tBZANEQgRaQwVKlkk5zONmsJV2UGf+frufCdRxZrfJKetVGayvXoVo2CAMRcclsArTs1C6cMotmtYpBOHZswxusOYIt1D8bUwcRcPIu6r17l+vwPecjSn1WF2/j7uGtIbEdD39s7SjlS62DxtcPY5OnYXbLhsO/9F789cpo9NS4L28zDMS+TKutZACth/EGYzsbIWBA10CDeZmK58DL91VqOuJNBBndDNFGFyZgTbsUlRSMCxtgwpJEp7LHITJC7Tzren2RkZLBq1SoAcnNzyc3N9b03adKk47adOnXqaY8zf/78LrGvu9Lmt23lypWsXLmSFStWsHDhQl555RV/2HVWeNO/l6bqunKtYqfHSMAobnK+vAetpQnLhTf5ZdLH2RAXYeTuS/oBIJrMhA3QV7TV7y5EbW7i5x65g38X7TztMYKZtUd3o2gquSn9iDVFoDhsVL+vay5GDB2CIAjHRZDaM7e5q4medCtydArNR3dg90SajJJIi1tDjtQXD4Nceoptpycd2lPw1v8Nj4oBzY1LjjnjFBDN7aL4uRs8zt9get/3xVk7f14E2Ujaba8S86PfoLU4KFr8M5xFO07aThIEDte2Rm/0RpCeIwXzrScdemFiLwAUewVqo4bQ7EKOTScy5yeBNO+UPPbZfhZ9dfrRl1JEImJ4IlpLA0rDURJ//jCCIQzlh38zR1ZpURVe3Putb/uvDtYct/+x6WVREPjfrwv56mANdc625VeMuPhN5Lt8lDyXfyY8Sapcw9aW/kxuvp9x5t+y0RIDooZWn4h2YBzVR8fztvUaEq9azict/0ND1V3Y7G+gRtwIUjRSUhiLwmDYPRMYdFlfrh4RXDqaIc5/2nQAn3nmGZ555hkWLVrE6tWrT5lbDySapvk6Sy9N0xskWmsAWyMS1WsfI6ZqO1JsBqm3/J//DT1LwgdfiBAhoNiqsX7yDy5PG4hZNrKlujhoVe/PxPKDPwBwfe/hAFg/XozSWEP4gIkIFt3ROzGFGOhyaNFgItYzHqvmP08DIIkCqqYhyCakiBSGKJ6UYg9zALd5Pm92uD4astlw5lRi+fI/YN+9DsmSRNY9HyHHnNvUEEEUSZ31TywX3oTWbOfo4mtw1x9fHtHkUvjDv1snMegzgfXrta0HOIC+CKDPASxHqdFT4jGTZh3XIBcsuFWNd3eduanKGK/XAbZU78AQn0nitQ8BMOuHdzApLhbv/ooGl5N/7yoj93k9Jfzkfw/ozVt/fB+HS+GrgzWonnxr7vMb+HRf9RnPeZFpBx8k3839MW8w0FBMjWJhTtNtTIucxtEkO8huNHs02qFRvDbpFzx+2WgEBLIy7qD0q35c3zAel3oFIGMyFzPwNyMYNmcCf79rIoIkIokCMeHBVY4U4vznrLw5RVE4evRoV9nSIXbUlnG4sZbk8CguSNCjMidGAB2HNlP1rl5rFjvrlaAYedQWpoRhGNL0y1P94ZMYnQ2+ZpBgEj1tD6VN9fy3bD9GUWJ6nxG4G2t8DlXSDY+hnkKz8Uxzm/1J3OTZCMYI7Ds/xnn0+OirbMlqnTHbg6RFoDUCmC3rDTItxsTTbmvb/A61/30ewWAic+57GBN7d4oNgiiSdturhPe7EFdNEUefuwFNcfveV05oJjBYetNXrSVMgMJGK7XNTZ1iRzBS47Sz31ZNuKTrogK4ag+j1On/T2Iu+XUgzTstGvBDSf0ZtzEm6c1izZVbAYi/8l5MGcOQrUU8Wr0Xa3MTees+YuprWwDYcNjK/R/uYfUOPVr/7PpCcp/fwD3vtT1DOF2qZFHcIpYmPkJfQykHXWnMcz7Ihdp9fJhihnA7WeYY7htwLe9Nvp2mh2Zw0+BkbpUMfGmxcOVBGw0Ha0FtQrKvIWPCQbLn30JUvwRkSSTFEjyzs0P0PNp0ACdNmuT7mTx5MldeeaU/7Go33s7SazKHIgr6xzk2Aqi6mil9aRaoCoXZvyJscPcQfjTGD0WKEpGTElGb6qn+4Alu7jcGgDcObOlW80xXHtyKqmn8NHMosaYIaj54CtVhwzzsCsyDL0Gx6zdm+aQUcKAsbkWKjPM9LGv+8wzQOlFGjspkiKJHDnbVltNyjPNxPmN3NXPAVoMsiPQX9EkbzcZT16W2VB+h9NXbAUie8TQR/cZ3qi2iMYzMP/wbOSYNx771VL3b2qR2ogMoR/dFRmOIrKf7tp3HUVtvN+zYhAwMnkiffecXoEFTfBbGpODsfjzxmp0Kk8cBbKnUswqCbCD1Vy+CIDClYB05tjLeKv4OJH227qTnvgFg4xE9c3JfG129kYKd/8/eecdJUd///zlle7st1wscRz+6dBDB3g0ao/6ixqghxpjE+OVr8jXfxBgTW6KxJCYajV81scdeUVApIghIOY5ylet1b297m5nfH3PcgRwElXKQez4e87jbqZ+d3dl5zbteZX+TN7L+i+W5P+B860piqpF7ur/NWbFbWFGkoHlbkESRa0vmULbwv/ntnLmcZDXR9K9tbLlrJY3vVmKJpRHoRO5+BGvoBwz//kKyL7juqIe2DDLIbv6tAFy5cmXv9Omnn3L99dcfiXEdNK/t0jNLzy8q7Z23ZxZwx2u/IdFUjjFnJDum/NdRKy1yMOzZCN3QE+xsLNCfEP3vP8SpVjs+k43yQOsxE8OkaRpPVepP4peXnECqqwn/B3oD+qxv/g41GUJLRRBkC4LR2bcdA0MAAnhPvxEEke7V/yTd3dr7DZKdQ3CQZJhRIqkqlB/DGdpfhi1dLWhojMnIRuq51pLGfV3AmqbR9PjVqNEA9snn4z7l8Px2yK5s8q/7BwgCHa//lsj25QBML8rg0kl9cVWycygApZouBI5nq+0X3b8A0W36ddg5ZPZRGdPB4LMZ/+06puwpACRaP++dZx0+C88ZP0VQFR7c8R4OLQI5+48l7I8Jhgru99zH2rxr+GXGE4w11hJRzdQ4z+ZU/z084h6OWriTjkSYWZlDWH/+T3lw/JkEVzax9Y+fsvPRDXRtakVTNWz5GqbIHZiaLsPu2krJrR9jL/3qRaoHGeRwsN8s4Jtuumm/Tyr33nvvYRvQl2F7oI3P/Y04DCZO6UkAAT3YGSDV3kTHW3eDIJB37ROojeYBIyr+HYaMESAa0NQWHFMvJLTuZQKv3c5lpefy0LaVPFW5jhN8BUd7mP+WFa3VlAVayLY4OLtgNG1//x5aMoZj6oVYiqeS6tJFr2TL3ev7djTrAH4RY3YJjsnnEdrwGl3LHwfjRQBIDj3kYJycoDop87m/8T+iJdzu+n8TPLkokdVA/wKw+5N/EC1fhmT3knf1Y4fV8mEbswDfuf9Dxxt30PTYdyn53RasJisjMvuyQWXXUADGxutAzuLzzuPXArg7AWS3AIzXbSbt7wQJ2gtPOppDOyCrbpjDqLs/POA6csZwBKMDJdJEOtLSWz0g65t3ENq6DF/9Rn654wN+NvZstO5MCO+/0K+BFNNN5Vxtf4P5lj5BuSY5jv8Lns2r//tz3tixhoD0PoKaJsNo4Q/jzuaceCFdLzZRVtvnRpYdRnwn5CJ2vUTXu7cgahr2yeeT//2nkSzO/g7fi5KMI0gy4jHe6nOQY4v9ftsuvfTS/S0aMOy2LH2reBLmnnp+ajqOmuxGQ6bl6Z+CquA58yasI2ajNXy1BtJHA0EyYHCPJNW5lYz5lxHa8BqBFU/wnZmX8xDwTPUG7px6Nlb53z8xH03+vE13vywaNROlfjPdq54EyUD2t+4GIB3pv4TIQLIAArhP/oH+GXz4CMJp3wBAdujlNUrVLl4nk42dTTDiQHs5PtjdA3iiOw+lVnffJ7/gAk6HO2l99iYAsi+7D9m5/xjBQ0XmN35N6PM3SDRsof3V28i+5O69HiFkewGIMmPilWCfetxaABVVZW2vANS/o4EVTwAguUVipqOfbZqOpUgG4iQDCVLBOEpCQU0qWFIqPxANNH1QjWiQkG0GDA4jBqcJk9uCZJYRBBFT5iTijStItm1ALj4bAMMtS7gg67+5pf4azm7fzs46H4/lG6D6BEjtHWs3ylDLtfY3OM2yFoeox4LGsbLdfQnnLryFYucQspuqmPbOXygPtJKXtHCTeSanBvNIPh+iXtMTDwWDSMbYTLyTc7BmKzQ9erleMUAQ8H3jVjIv+BWCKOpdbcJdJLrbSQTb9/qbDPoRUxKCyUjpd25DHGC1aQcCDQ0N3HTTTbzwwgvs2LGDYDD4lcrRvfzyyzz77LMoisIpp5zCD3/4w8Mw2mOH/QrAdevW9bp729rayMoaWAVD06rC01W6ALxy+NTe+bt7XSodRlINZRizh5N1YV9c0ADSFP2yvKqTeSX6E6vRW0qqcyuIEdwnXUvXh4/gW/oQ04rn8VlHPf+s2sD3Rs08JMdNplXuWlbJr07ffyHNYDyF03zwP0714QCv7CpDFkS+N3IGrQ9cAJqG57QfY8zuydjuif/bRwAepV7A+8NWehqGrBJSbVXkNC8HxiI79ZvrmGQ9kHncCoovsruI8kRvHkrZbgG49+9D24u3oIQ6sI5ZgGvOFUdkXIJsIPfqv1F7+yw6370X58xL0ejrqyqIErJjCCO7a5EFge3d7URSCWyG46v7wkZ/E8FUnGEOL/k2F1o6SffqfwAgewQSB0jYORwoiTThmgDhXd1Em0PEmsOkgon9rn+1ZKB5aU2/ywwuE5ZsO6QuRYmJPP6vpSz68RnEFBVNg1dbHQSdN3F/8E5urFlJgyWDd4cYoHYipI3MMpWxyPEq88x9iXQ7UkVMnX05RVNuYIzFR1Wwgzve/SeNO1o4P+bjzvhYchO6gEwSQpAEHMPceCbmkDEuE8kkEyp7n6rfLCIVCyPkTcQ+5yqilgx2vnYfiWAH6XAIo+jCKHswSm6QMlDEIaQZQdykEZSi2GUTmjawqmwMRJYsWYLP5/vSArCuro5nn32Wp59+GqPRyIMPPkgqlcJg+M8V3PsVgHvG+y1evJinnnrqiA3qYHiuZiNN0SCjXJnMyRraO1+NtqFGNVIN3SAI5F7zd8Se5toDPW3CZZZ5cXNTrwDcHQeY7CzHd8GvCKx6itD6l/n5xPO5qKOeh8pXcO3IGQftWksrKvIXCqx2RZO4rUZst7yNomrU+CMsnj8cn83IjrYwb21r5bpZQ3ls7S7uXFpJ+p5zWVHTyUubm3lo4fgDHu+eLctQNJVLiifh2LaMhh3LkRw+Ms//3951+hJA9rZKaGgDKlhaEEXcC75P2/M3U1LxHHBdrwVwVHgbWKewyd+Mqqm9yUjHIylV6U2eOMFbQCiyrwBMNJYT+PgxECVyr3z4iH6O1pIZeE79Ef73H6Tlyeth+hN7LZddxZi6qxhttVEWCbO5q5lZe/x+HA983KJ7Oubl6IkeoY1voYQ6EMwCglUkIR/+7gfxjihdW9ro3tFBpD4IX0juEA0iRrcFY4YZo8uEaJKRjCKCQeKXb2/DiMAt80tIR1IkgwlSoQSJzhip7gSp7gQwDPg5s4Ett39MWTLNz0QDO9HYYZ7Ng8rV3Bh5nDu3vU1qrMj60Wlu9i/nEvEzfQCylSe6FvA/37uLbCWXRFec8mXNVFR8hqlT4fp0DtD3UCqaJBwj7NiGiBi8adKxNsLBcjrfaSHSUE46lUQadg4m2YNJ9kC7m5ToJC1mkxJkIjaFoBIhlI4STEVIa/uWuhEFkWmoSAy88jx78uabb7Jr1/6KZH81hgwZwrnnnvtv12ttbeWVV17BYDBQWlpKPB7nj3/8I5IkUVhYyG9+8xveeOMN/vWvf6GqKj/+8Y+ZNWsWoLeCGzduHD/72c9ob2/nuuuu+48Wf3AAAbhnlulAyzhNqwp3b14GwOJxC/a6waSC9SR3pUEDz+k/xjbqxN5lAyWzdH9IorBXK6DdVe9TnVsxuPPwnPYTOt+6i4mfPEFe8QK2BlpZ0rSTM/JHHdT+jT97iwm5Ttb+5ESMsshPXi3joZU1qH84rzf77sl1DTy5Trdk/es7U1lZ48dukrlzqX5T+eV723v/31MAqqpGWUuICXl6rEtDJMBjO9cgIPCL0bNp/b0eAJ258DYkW0bvdsr+XMADzAIIkHHid2l/+ZfkNC0n2V6DwTcUwejAm+wk3+KgMRaiMtjJSNeRtbAcSbZ2tZBQ0gx3+Mgwmgn0JoH0vefWF34Omor75Osx5Y0+4mPMvOi3dK95jljVpxTlvAP0jcHgHEocmGCCsohuzTz+BKCe/HBSjl5Yvtf96xUQrZkgHJ44s1Q4Sef6JvybWok1h/sWCGArdGIf5saW78CS68DksSCI/V/hf39TL7f08Nl7x1NoqkbCHyPWGiZS14J/zWukU8MhncV4UWL8nsLJdRndjoWY1A7ubg2xK+igwziF9WqcTMmBQ87grLhC+QMNQINedkpIUiRFQIqSMocxutMYbQk0QqTiXXR1xggHHJhkDwbJA5IbRSxBcI4hrUkE1ATBHoEXTYSBMPvDZDDhtDtwuZw43Rm4Mlzk5ub+xwuSf0d2djYLFy7E5/Mxfvx4zjzzTJ555hm8Xi/3338/r7zyCrIs43Q6+ctf/rLXtl1dXaxbt45nn32WRCLBZZddxksvvYTTeeD4zOOZ/f4S7CmqBpIlBuCh8pWUB1oZandzecmUvZb533sULQ6i3an3ivwCA+297IlREkmpat/r3QVPO/VMZ985P6Pro0eIbf+Y34w9g2tj8OsN73J63sje97W0op0JuU7qAzGmFGSgaRrPb2yiK6aXvtjcHMT887do/fXpPLRSd7OIi9/odzwXPam72HeXTwDY5e/rrPDT18q47/xSfvHOdq6eXsTMB1cQvescAH678QOSqsK3iifh+/gROjtqMRdNwj1/0V7HSB/IBTzAPirZ4cM57WK6P/kHXR/9jeyL70B2FJLqLGeCw0ljLMSGzobjWgCu73FzT/EVoMY6QE2jmTwg6W7UyPaPCW98A9Fs7y3Qe6SRLA6yLvotzU8sYsLGP6Amf4Bo1F14uxNBSgX95ny89QRWVJWVrfp1fVLOMNKBFsKb3wZRRnaDaM0+pNeVpmmEawK0r2kksLUNTelpvWeSyBibibs0C0eJG8n89UWnIAqYfVbMPivu0ixov5yUfztXtd5JLD2aUYLISEFklCAyVBAximY0UU+UK1RTFKaiet0LrRVFrUF2REhJERQ5jCTGkAQVWbRhlN0YJDei7CKt5ZIUzChmA1GTRkiJEUxHCCWjpLUAEOh3rKIgYLfYcTmcODNcOD0uXC4XTqcTp9OJyXTshh0cjKXuSOD3+2lra+PGG28EIB6PM2fOHIqKiigu3rctZ0ZGBtOnT8dut2O32ykpKaG2tpYJEyYc6aEPGPZ7VW7dupVLL70UTdOorKzs/V8QBJ577rkjOca9iCkp/nfDOwA8OHMhxj2ypsJl7xNe9x4AGadcuE+D+YFSXHh/tEeStIb6YmNk1zAEgx0l3IgSaUWyZZN53i9ofW4xc1Y+Tv7ES1nbUc/rdVu5YMg4AE575FOev+IELnl6PZdOykMSBf65Yd+b3Ii7ln2lMT7zed++HlhRwykjMrlrWSXnl+b03lge+XwLj+1cgyyI/Cozn84nr9Ld8d99FOELWW59FsC9u4DovYAHmAIEMuYvovuTf9C98kmyLrod2TFEF4BmiXfQO0xcOmzy0R7mYWN9hy4Ap3oLSId7smgtuqjQNI22F28BwHv2zciuo9ezOmPe1fjffwgatuBfcj++c38O6C5ggLHpZiCXdR0Dq7D912VzVzOBZIyhdjdD7B463v4DqAqWUbPAsA7Bcmg+E03V6N7eQfOHtUQbgvpMAVxjfPim5uEc6UX8mn1tP6sLMK0oo99lqpJGzjmJmL+Fcxyf82HcgiIlaBUTJKUEkkejhF0oiTgqRhAkRMGALDkwSA5k0YEm5ZAW7CQFM3FkwhqElThhJUYoFSWVjAGxfo8PYNQU7CYT7uwCXB43TnefyLPb7QOua9bxgiAIqKqK2+0mJyeHhx9+GIfDwdKlS7FarTQ3N/d77qdMmcIzzzxDIpFAURSqqqooKio6Cu9g4LBfAfj6668f0gM9+uijrFixAoBgMEhHRwerVq1i48aN/O53v0OSJObOncsNN9xwwP1sD7QRV0Zx9YjpnFM4tnd+OtBC06NXAiDniliKT9hn24HoVtwTRdV4fWtfLTlBlDBmTSLRuJJE2wasxWfhOe1HBJb/nURTOQ9Hm7nA4OaW9W9zRv4oQnHdevjtf+oFUp/b2H+Zi0yxC3c6RJQ80siAxhCpBYOQpiqdj9ZTHtIsJJhorEBEY3NyOBHNss++zv/7WgAeWV1LLKUiLn4dij9HsGqMk0cjPP8zNFXBtuCHWIbtG7TbFwO4b2uwgWYBBLCOnEudmENRoInI1g96E0HGifqNYoP/+LIofZH1nbpgOsFXgBLpyaq36OI9uv1jYpWfINk8eE6/8WgNEdCvnez/dx9195xGx1t3415wHZItA4NTF4ClkQpkIZ+yQAvBZByn8fjoyLC8pQqAeTklaJpGd4/71zZ2GpHadYiWrK91XWmaRqCsjaalNcRbIwDINgO+6flkTs/HmPH1zqOqpMmXI+RIUZ58+SXy5+SSjnWTigb1KdJNKhZEievHRjyXUxxGzsyowSA5EEUHquglLVpIC6UoZgNJRBIaxNU0USVOTE0QScZRSQD7T0aRRRmHxYpVUDCEdiE1rcUcbcKS9JM14WQKvvXbr9zPepCvzrhx47jnnnsoKSnhF7/4BYsWLULTNGw2G/fccw/Nzc39bjdq1CguuugiLrvsMjRN4/rrrycjo/8HjP8U9isA8/MP7Rd70aJFLFqku/++//3vs3jxYgBuvfVWHnroIQoLC1m0aBFbt26ltLR0v/tR0bhwyHgemrmwb14yTv2DC0l3tyD7MpGzu3rbwH2RIykq1KRCsjtOOpIiHU2RjqVA7Ymp1DREo4xskZEsMkaXGYF9E1VMWSfsJQAF2UjOdx5m153zKVnzT0466Ud83N1G9p8eY6gyBtD7aX4RuxDhu/a3ONu6ipEG3YoTUi2sT4xmiNxCsUG/aHakivhz8Js4xTA/cb5ApqS7OOKakQ9jU3gzNpeGdBajDLvIlAJUpfL5OD6lN26QrFoEawgtZWTyuo9Qw5/RJno4b/OJhPo5R/u1AA6wuNPdCIJA97hLYPMDBFY8gX2aHoIwTtM7gmzoaOi1lB9vJJQ0m/z692SKtwBlp/5Ap1myEdCLMAN4zrgRyeI4auPcjb30VLbZJzMm/DmdS+4na+Gvey2AUqiKyYXf5rOOetZ21HFq3v6z348lPmiqAPT4v3j1ZySaypGcWRiys6EWBGv2V7ash2sDNLxdoSd1AAaniex5RWROy0c0frnEhVQ0SKyzkZi/kVhnI3F/M4lQJ8mwn5fz9GtfEGTaP3NiEJ1okhNFdKMIeaRlI0mHREITSWoaMTVJTE0QVRIk0ykg2TMdGJOmYlGT2AwidqsRm82I3QRmLYoxUIPa8Dmxz1aDoofPCLIR58zL8J13C6ac4+P7cqxQUFDACy+8AMD8+fOZP39+77K5c+fute6FF1643/1cddVVXHXVVYdjiMckR7zq5JIlS3A6nZx44omEw2GSyWSvGXbu3LmsXr36gAJwkiefF0/+Tu9rTVVoevxqYlWfYvAWYZlUQKrzs31iyuDwZQFrqka8PUKkIUi0IUi8LUq8M9qTrXbwrDJYqddUqp/dgq3Qha3QhdHX0/aodX3verbRJ+GacyXdq57ivrrVnJA3g5Cjmkh3Lnwhg8wuRLjC/i5X29/AI+kSLKRaCKgOCuW23uKnfsWBhsAoQx0Peu/r3X5nqoCoZmGCoZKzrJ9ylvXTfcbdqTh5KzabLUYPr/i8oAlMrJb4UVgPFfil40dERD0T+79e38q955fy4IpqfjgzBzURAMmIaN47M1F3AQ9M6ku+wfgtDxLa8CrOWacCkBWtJ8cyhpZYiIpgx3EZB1jW1UJKVRjlysRpNNPVY73FkoO1eQOR8qWIZgeeUw9sxT+S3CVdzJN8jv+9P+I9/SeIVg+C0YGWDDHLk8NnHfV80lp7XAjAeDrFRz0ZwGfkjyLwws8AcM2+HDWhP6AIluyD0UZ7keiM0vB2JYFyve+1bDeSd2ox3hPyDsrNm05EibTWEGmpJtxSRaS1lnQsCIgY5QwkyUtacpMUC0jYTMQRiakqCTVFRI0TU+KoisbBCDsBsAgqJlHD4c7D5nBhtduwOuxYHVasVismJUri0/8j8uk/SXXU7rOPVM+k71DEUjIT58xLcc2+HNm+/6LSgwxyrHFYBOCLL77Ik08+ude8O+64gwkTJvDII49w3326wAiHw9jtfZX6bTYb9fUHjsmR9uwWoaRp+vu1BD99FsFko/Anr9H+4f/T17P2H+tyKOLKNE0j1hImVOknWOEnXNeNmlD2XVEUMLpMGOxGZKsByWpAkIRe65CSUFDiadLRFMlADMIphgkiXZvb6Nqs9zMWjTlIwi9JVlbiDsR7XSzZl/6e8MY3MVas4holn8eLCqm2rQNpCigGMsQQV9nf5Dv2t3H2FDpdmxjDQ8GLWZsoJY1MvtTGZONOWhU365OjkVG51PY+VzneIqRa+WtoIe/EZgECOVIn51hWcbplDU4xQkWqkBbFw2zTFsYYdzHR9Tl/sC0E3NihuwAAIABJREFUQeDG6Cdc7V+DgMpLltNZZdTd8dPuX876hm5kUeD3H1Vx1Wj9Z1a25SF8oXTKQEwC2U3Ykot59MnEty0lXqsXhFVCdczIOpPX6spY0153XArA3fFyJ3j1Dii73feaJYfcVXp7P/cpP0SyuY/OAPthvXEctrGnEClfSud7fyTrwt9gcBaT7NjMDKteRP2TttqjO8hDxMq2GqLpFBM9eWTLBnaueRbQs9eDm+4AeiyAqQPtpQ81rdK6YhfNy2rR0iqiQSR73hCyTyxCMu3/1qGmU4SbK+iu20qwbivR9nok0Yws55KSs4hLMwjbZIJKiu50mEQ6BWkF6N7vPk2SEavJgs1sxWKx8FZlN+0pgaakxqnGJZxrWYqFbgxiCuf4a/GceBeivG/ISi+Fv0X75u0k6rcQq15LsnUn6WA7oCGabBh8QzHljcE6fDaS/fCXzRlkkKPBYRGAF198MRdffPE+8ysrK3E6nQwZorcnstvtRCKR3uWRSOSgU7LTgRYaH72CyNYPEEw2im56G/OQSb2FoPsTgF/HraipGqHqLvwbW+je0Uk6vPeTqMFlwlboxFbgxJJjx+S1YnKbEaSDDwR+YmUt97xaxlhB5N6ZxUR2BYi3R1GZTap9NlvuXoU5y4pzuBfnCA/Zl/+Vpke+xaLq13jf/j3qPKAVlfHtjkZudjyLXYwD8Gm8lD+HvsknifHsaVdrVLJojPW5ypNIPBU5mxuuvZOTH1iOTUiTK0VxiCmcokKFMpWa8AnIgookaEhorEudgsvSSJMnxISUTFFU5Zv1EoowD1wZnOPLY3iyia5UIc7mOC7ZjHFVEwtkM1UP16MlnkP0a3z+v0sZnufE5NIr/nu7o6RMBtJmA7JlYJVG+M37O5k99RsUbFtK+PMPEDyQDtUxo7SI1+rKWNu+iyuG7xuDeqyzOwN4dwtCpScJRE0KOGuWIhhMeM/86VEb3/7IXHgbkfKl+N+7H++Z/4XsGkqyYzMnyPqD0Zr2OhRVRTrGg/bfbdgO6Na/4NoXUaPdmIunYi4Yh3+1HmohWrIQ9q+zeglVd1H32nbibfo58kzKoeCs4Ric/WevpqJBAtUb6apaT6hxB5JmRTUMISYNp9s6ho50lJAS7XGn+vfaVhYlnGY7TpsTl8uJw+Xkrxtbeb8+QkCRCCoSzb8+E29Pn2C9akFfgt9yFvI/t91JOlSHnFGCZD44wSYIAuaiCZiL/nOzQAf5z+aIuoA/+eQT5s2b1/vabrdjMBioq6ujsLCQlStX/tskEC0Vo+1fv8S/5AHUeAjJmUXhj17GOnIOaiqKlgwhSCZEk2vfbflyViVN04g1h/FvbMG/sYVUqE/0GZwmnMM9OIZ7cJa49/vD+GX47tyhXPPqFnZoCk+eO4KhJplkIE79C7cTaTKTVmbq7uW2KG2f1CNIXsSh/0D2r+JP1X5ukkdTZw+xNS9CJCqxITaJPwW/yfrkmH2OJaKRK0f4+TQXZdW1/HCKm1BXB50drRg+WM7aoQE09YtWTQHdhiqAoP/VNA2NdF8stQQMHUFClUGxQtrFCCmMYGiClBcUR+/2ahzAhZKCfCBWHyTWYwAuBAKbOwgARrcZe5FLP9cjPBhdRz9gv23I6RRZnMR3bcRkMwFtTPPoDx1retpwHW982mMpm+7TQzZ2x28q5Z8goOGcfgmyc2B1DAIoM48la/R8ots/ouujR5F7EkEy440MtbupDXexNdDCBM/Rb5H2dXivUbdGn1kwmq6/6d1X3AuuA0AJ97jrrQd+j0oiTcPblXSs1ZOZTD4rRd8YhbNkX1GVjoXxV3yGv3Id4aYqZEMxETkPvzGXlmQ3yVQaUn1qUxYkPLYMvG4P3iwf3rxM3F4PVqt1n5jZqk2rqU6ke1/XB2Jsbg5y0+tb91pvQYmXs8ZkI9n0aZDDx/Ea23w88WWNXEdUANbU1DBnzpy95t12220sXrwYRVGYO3cuEydOPOA+Eo3ldLz+LgD2SeeR+52HMXh6LBI91j+91lX/X9SD+fomumL4N7Xi/7yFeFufhdLoseCdlIN7QhbmLNthvRjOfXwNH10/B2OGGd/UbMTli7GNuhzrmPsIVnTqrueGIJqSA/aLyAGe6Sku3ybFKTdcQEfSxDmqwDmmECZDkHG+FNFoBznmCEYtgCyoCE0GTrRlIFcFyJYzKLSM1etgWWyIghlJMCOJJiTRhCj2b4nTNBVVTaNqKRQ1TkoNkVZCpJQQKaWbeKqDeHo7yXQXgpZA0gJElATlcScLLMt5PnQOT4a+yRMLxzPGZiIRiLNpezt5KZV0W5RkVxx/Vxz/Jv3zNWfb8IzPxj0xG7PP2u+YDif/d+kkhvlsOKdfQuDjv6GFrGAKM8moISCwyd9ELJ3Cchz19AwkYmwNtGIUpV4LYDragqZqaGVLAPCcMjD7aqqaXpYmuv0j/EseIPuKmwFId9cyK2sOteEuPmmrPaYFYF24i/JAKw6DiSnxbuorVyNanLhm6uW7lIhurRWs2QhC/wWKw7UBal4sJ+mPIUgCOfOHknPSEERDX1yxpip0122lo3wlwZptaMaRBMRCmg2ZdKcjkA72rusw2sjxZpGbn0dOcT5en/egS6MM99n4oKKj9/WUPy7vd72lP5h9UPsb5OthNpvp7OzE6/UOisABiqZpdHZ2YjYfvIHkiArAW2+9dZ95kyZN6s3uORhEoxXX7MvJmL9ory4fAEpEFwjyfuL/DiSO09EUXWVt+D9vIVzbV9xTshrwTMjGMzkHW6HziH35k0rfYC1FJwMQr3+fzDMc2Idm8LpN4ufVLZQKIvPVTi6kEiweNJOETw7jk4PgDCLJGkY5A4OcgVFyYXKW9v6vFzy17W8I/aJqGipa719JEDAIEpJkRMKIARtm+g+UVrU08VQ70UQ9kWQDjkQ93SmJbIuRjGQrJ72isu1nJ1MyIZtdJpgwNodcu5FYW4RwTYBgpZ9QVRfx1ghNrdU0fVCNNd9B5ox8PBNzvnQm4lelMMNCSlHJmHslgY//Rqo9iujVsMRaKM3IpizQwuedjczOHnpExnMkWN1eC8BUXyEmSe4RFS0oXRrEuolmT8RSMv3oDnI/SIKAfcKZmArGkWgoI1FXC0AqWMvsMd/m2erPWd22i+tGH7ti4tVdZQCcljeS8PLHAXDNvgLRZENNdKOlYyDbuHtFK8Xeva95Na3S9H41rSt2gQaWXDvF3yrFktMXn52MBGjf8iHtZatR1DwCYhYN0mTCyRjQCYBRlCnw5lFUPITCUUO/VoeFP184nr+uPrTtxgb56hQUFNDQ0EB7e/vRHsogB8BsNlNQUHDQ6x/xLOCvizFvDPnff7rfZUp0d0mR/buh9hRwakqhe0cn/s9b6N7R0VvFXjCIZIzJxDs5B+cIz5eK4ztU7CkzDd5SJNdwUt2NdG58jrd22amqquEmbxdjzUlGmkUssgejbMcouzFKGT2iz4UsHiAQGlA0laCaJKDG6VCjNGhhqtQudigBqtQAHcQIakm6tQRRdJeMKAicljeSa0bO4PzCUgj7qbv7DNKdzWyxnMDCnz8HKZGbX9pCocnISIuZ0VYzhSYjVmMuVmMuPnSxkFJCFMQqOMlTQU14K39+pIythpHsSLr04tKSiDXXgTXXQdbsQtS0SqjKj39TK4HydqKNIXa9vJ2GtyvxnpBL1pxCTO4Dv+evi1kWCSbSWEbOwZA5jFR7NWpIIh2qZ3pmEWWBFtZ21B1fArBNvxnPytLjd9W4H01JonTq39TOCVcetbEdiDeu1r9ngiDgPWsxTX+7iuCaNxGzNdLdNczuaQN3rCeCvLxrMwDfzBtO9/M/AsC94PsApHusf5olh7+tqeeuc/pCQuLtUaqf2UKsJQwC5MwfSu4pxb3ZvZHWGlo3fkCwpp6QPIJ6pZSudBjQuwNZZTMlBcUMGzuC3KI8JOnQPIQJgtBTXPyQ7G6Qr4nBYOi3u8YgxzbHnAA8EEpUz5yVrPuWgAH9x0RTNUI1XXRubCVQ1oYS74kzEcAx3IN3cg4ZpZkHzHI7HKjpFMlIF5NMHeRIUaazi8olFSSCUdLRNGp6EaLDQWSTzBRRZHJ2KQgmFCCupYloCoqmoSoqqpJATbSg0ISCQreSJEaSiJYiqqWJaikipAlrKWKkUdBICxpJNBKoJAX9b0rQcMkShVYXXqeLooxMZuQWc2LOMHxm3YqgRALsuv9c4o2fYyqayD/zb+KiHsvBfY1te71HuyRy8ZBMFk8qJLpzM6X2TAxyNl77FLz2KQz1KkyNV9MV3czqwHqilQbUCXMQ93ClirKIa5QP1ygfakqha0sb7Z82EKkP0raqnrbVDXin5JI7fwgm7+FxD5tkkXhYRRAEMuZcSfurv0bxq6RDu5iRdRZ/r1h73MUBru4RSLv75irRFrSohhpRwOqme/T5R29wB8AoiyQVvUC6a+ZltL30C5LNFRgtEmlpF+NcWbiMZqpDndSFuyiyD5wM5oOlJRpkZWstRlFiXuNmuuIhLCPmYC7Ue3X3xv9ZckgpKlJPD96usjZqXypHTSiYvBaGXlyKfYhLL/Zcs5Hmz94jHLDTKnioV3JRFd29a5ZMDC8oZsS4UeQOyT9sXpH0Peci/feb/S577vITuHNZxWE57iCD/KdwfAnAnqD0lNHHgyuq+fGJw4C+ZI7OD6op39C2V30+a54Dz+QcPBOyD0kiR3+o6RTJcBeRQAdBfyvhgJ9QqJtoOEI8rpJKQ1qVSCPz0wKRFAZSqsJ7FdGePez+mOI901fH2jP5kPhizcB+SQJRoCMO1NMiNvKmZb3eT9FiQtv6JnLIiL1wPiOufYSXCof3bpq+51yeXt/Ad5/fyN8unsjiN7YSd8mMmZ2L9PJazrE8j9U2gULTCE53O5jrtOO0jMBpGUGhJ0X3th1UbnoA68hisiefhsG6t0tJNEh4p+TinZJLpDFI24o6/Jtb6VzXROeGZrxTcsk/bdgh/1zNskQ8rSfIuOZcoQvAbo1UZzUzRukJEmvajx/3VVpVegXtbouZEmkh3d7Tt3rqFWgHKrlxFDFKAom0Pk5BNuI5/Se0PX8zSqeM5EqjRZuYl13CG/VbWdZcyVUj9u1WM9B5ta4MDY3T8kYQW/5XoM/6B30WQCzZxNMqoqZR/1YFbSv1z9Q9PoshF41BNIr4K9fTsvYDAtEsdim5+NMhdmftFrrzGDt+LMVjhx8yS9+BOJCwtBklrIYjE/IxyCDHK8ecAEz4dxLv2IaYMYr3drThsxmZNVTPUNttAYzJXu7/sJpFo7L1ZI7NrSQ6olwpGUh1JzC6zXgm5eCZlIMl68vFwPWHpiokw13EAm34WxsJdLbT3R0gFIkSTWjEVQMJZNL7pKCIPdNulJ5p91IBgyjrk2xAFjWItyBpYUQtBGqI8kQOcs50FozMQRQlJFQCH/6VdOsOZLOdzLMXo2YUYDMZEISerN2eSVX1G6OqqiiKQiqVIpVKkU6ne/9PJBLEYrHeKZVKEYlEiEQitALYJkCxXkZhw5vvI0nLcLvdeDwePB4P83weum49GZfDxrxhHio79KSa35w5hoc/sVB382kYbn6Textb8coSC31uLsvMYI7Tgds2DrdtHPGmdpoqX0IcYid76mkY+7HS2PKdFF86jtxThtHyUS2dG1voXNdE1+ZWck4aQvbcokMWI2g2iL2iwpg1DFPROBJ1ZUQr1jP27GxcRjO7wl3UhwMU2o/9VkNbulqIpJOUOLxk93T4SLZXoAR0/5w28xqE1MAMDDfJEoFYX+E790nfo+O136B0h1GjMunuWk7OHc4b9Vv58BgVgP+q1d2/V0oC8dr1SHYvzmnfBOCsv33K7YVVeIE21YMPgbxldbSFUyAKFJw9nMyZ+XRVraN57Sra45nUpLzEVP3h0yKZKC0Zw5jp43G6vnpM36FmwXAvp470He1hDDLIMc0xJwCFdJh17/ySK+quY1eX3n/12hlFPHrxRNLhZlLJcQS3DuW+KGz9Y1/XCslm4NlglP/9wQzsQzO+ktsinYgS9zcR8zcR6Wiks62FzkCIYEIjKtiJYELdS+TtbRWRELFKZn0SzVglE1ajGYvZjNlmxWS3YHZYWfTGNqoSKlXxFFtuPpkSX59ITYebuOHe/8VImrdis6lJ55O66dxetw6AUjqa+ge/QbT8PcSn11Bw/fPYS07/0u+333OQTuMvX0nNPxYTSYukfCORJl9KOJGmu7ubcDhMR0cHHR0de23ndDrJzs4mLzub1lbwWg3cMKd4r3F3phUea+ngsZYOCowGqr49E2pDmMkk33UGij9O+8tLUPI1cmaeidG2r7gyZ1oZevFYcuYPpeHdCrrLO2h6v5r2tY0UnT+KjLFfv0CzbgFU+97bzG/RXldGorYGSRSZk1XM2w3b+Li1isvtx349wFWtNUCf+xcgtO5N0MBYUEzCW4zQ2l+jv6PPnhZAAMmWQca8a/AveYB0u0I6WMuCXP3a+LC58pgrdbEr7OfD5ipMksyULW8RRy/9IhotzH94Fcur/cxqXc8Vdnhzk4d/yGa84RQGp4niy0pRqKf8uZdpjWVRmbSS1nQ3r8fiYtL4iYyYPAZZHli3iR/OGYrVOLDGNMggxyLH5FUkt62gvutK8hApFATEz+rZFogRq/0ummpB6NLryIlmmS0WkSe7I1QoKg1qiruL/32MTyoW6hV6MX8Tkc4mOjs76Y6rxAQHMcFBBAMqRsC7V8aGTTTjlG29k8Ngw+V04nS5MLusiHYD2GQEqwQWCUHc92bz6oTCnmKnMOKuZah/OA9V1RBFgQfXx3g01NcHufnW0/cSUQCSxUHRT9+i8S+XEdrwKnX3nknmwtvwnfs/CNJX/8g1JU3XW3fS8dpvcChpckpPpeCHD+3V+SGRSNDV1YXf76ezs5OOjg7a2toIBoMEg0EqKvS4HZPJxMS8PLZsifPHM4bw0/dquXFeCfcvrwagIZlCHuuCMS60ljjp7Z1IQTPZzrmowRT+Vz9GGyqTNe1kJOO+7kdzppXhV0wkVOWn/q0KYs1hqp7eTMa4TArPG4Xxa7iFTbLIdS9tZtFMPSEiY+41tL/4K5RAjGR7DSflDOPthm2saKnm8pJjXwAua9bbi52UUwLoFu/wZr0PsH3yycQHcKS+UeqLAdyN5/Sf4H//QZQujUTTJsaNvZJMs43GaPcx18bvH5Ub0ND4jjub+LLfg2TAfcr12P/nbaIp3ZtQJLURi1zMBan5iIJAs9PI3Et8NKx5noaQk+qkmbSmJ3UUZuQyefoJFAwvGhBCeOG4HF4pa+GGucX8aaX+IPLQwvFHeVSDDHJ8cMwJwHR6CEb/DayUrRj2+IGKVkcAC6JUz0upDJaoBjaGVJTdhonE3v2PNE0jFQkQ72ruFXrxzibC/haCcZWIYCUuuokKViKaFQ3r3t5awCXZ8Bkz8Bky8NncZPp8mL12BKcBwSaDTQKj+LV/SH/7wU4eXb2L5T+cw+I3yvdalu3oX8iIRjMFP/oXHa/dTvtrt9H+8q8IrX+V3O8+gqV46pceQ2THClqf+SnxWr0nseeMn5L9rbsRvlDrzmQykZOTQ05OXyKOqqr4/X5aW1tpaWmhqamJYDBITU0NNTU1yMCvCyTGGWy8aY5THTcxLtelnzcBhDwLxrwCNH+S1NZ2xE4DPttU1LYUbS+9izQ6g8xJ8xDEfV28jhIPY26YTvvqBhqXVBEoaydY4afg7BH4puV9pc/G/IX+pwZXHpLHjNIZJ7D8UU6cpxffXd5S/aX3PdBQVJWPW6oAODlXj+8Mb3obNRxEMIJ1rF7Y/ehLhf4xyiKXPL2eiyf21fgzZhZjGXkCsR3rCG34AN9pAgtyR/BCzUaWNVccMwJQ0zSerPwMgNN3rAVN5R3TSYz7XV/fcDswOvZNYqnJiMBLQifzstpZuyyD6oSG0pPNO8STz9Q5M8gpyj0K72T/uMwGHv/WRK6aVsgDF5QSTfbTcnOQQQb5ShxzAhDNiKZ6MAjQpqn4hRgTLR8gG8uRDdvQxE7ubnwOFf2pX0QjR4pSbAhSbAhR88HfifmbifubSCSTRLASE5zEJTcRHEQ0x965EZp+c/PITrwGFz6TC5/LR2ZWJiaPFVwGBKeMYDq0Acn/Pb+E33+k33h/9a5e4T+tfjlLiyCKZC68FcvwWTQ/8T3iuzZQ8+tpOKZcgPesxViGz0Y4QGFWTVWIbP0A/5IHCG9+BwDZU0Detf+HvfSUgx6HKIr4fD58Ph+lpaUABINBGhoaeicvMZqrt/OTXAgrIlNL9b7Q+fn5vcVjBY8R44n5aMEUiY1NyH4DmdbpKLVxWipfxz5rHI7CEf2cB4GsOYVklGZS99oOurd3UPfKdrq3dTDkwtEY9iOi94dJ3vecmYYOIdq5g+7VzzD5G7djk43sDLbTHA2Sax04sVNflo3+JgLJGMV2D0Mdeqytf+mfAZB8IgZ7PgPX/qeXLOqPjPnfJbZjHfGKbaiJCAtyh/cIwMpjph7g8tZqqkKdWGIaeWWvA/CE8bze5SMRuFs2YUhNBrGT9c46zvTmsDMhk9T0WqdDPflMO3EmWQX9V0442pwzNoth3r6i+7YjXJ1hkEGOZ465qykgdJHhu5wnI3O5O/htvutYwnTnv1Axk8JOtzqUn3k2kStFyZOj5MhRZEEjiZEoNrbuTBATnIQ1D1FpD9dQz11MRMBjcPZZ9Tw+PNk+jG4LgqvHfduP2/ZQc35pTq8A3M2kez/u/f+WU0bwWlnLQe3LPv50Su7YSvurt+H/4E+ENrxGaMNrGHxDsI09FXPxVAyeAkSjFTUeJtleTbx2A+Et76KE9MKfgtGK7+yb8Z69GNH09RNnnE4nY8eOZezYsWiaRmtrK9XV1WzethN7PML27dvZvn07NpuNESNGMGrUKHw+PehbcBowzxuC2pUgub4ROWwm0zid5JoAzWVv4ps3F0M/8YHGDDMlV06ga3Mrda/qQrD8gTUMuWgMGWMO3uoj91MX0lw8geimHaTa61BqNzA7ayjvN+1keUs1lwyb9NVP1FFmWbPusj85TxfWiZYKIlveA1FA9opI1my05JdrsXgkkfYzMMf482mx3oAWTRNY+SSnTr8EgKVNFaRUBUM/1uSBwmd1AW58rQypSPcGXFhbg0OLss4wlnKDbqU9X5C4WTJiFDRwrCaVlY057aEsobdmy3dmMefkeWTmD+z2aRdNOHa7swwyyEDnmBOADlnDL5/KuS6Z8zL0p94A5wCgIpAUjZzsjJES3SSFfHZhJKhqpHssgmg9E6rem1J24jNlkOn0kunz4cnxIbvN4DQgGI9ec/hZQ/aNVdwd03PJpDxOHeHj9jNHHfT+RLOd7Et/j/esxXQueYDuT54m1bGLwPLHoadzQH8YMotxz19ExknXIjsOT9adIAi9buNZs2bR1dVFZWUlO3bsIBgMsnHjRjZu3IjP52PcuHGMHDkSg8GA6DZhPnUY6eYQqfXNGMkgMzGZ0JtlKMMFvFNmIQjiPsfyTMzBPiSD2hfLCVV3UfXUZjJn5FNwzoi92l4diG9Pyd/rtdEzEsktorSrBFY9xUkTF/J+005WtB7rAlCP/1vQ4/7tWvYXAGSvCUFWkGw5kDxwyY6jicvS/0+cZM/HkGchWRml850/MHzBdYx2ZbG9u41P2mp74x2PFssqOjh5xN7X27r6ANe8sJFaf4yQGka0VyClVa5q19ukPWX5BibgvyUjF4gymrEFNTfALnEIbQk9ucNhsHPCzFmMHT9iwH5mgwwyyJHhmBOAmmChzTQLVRBJawIpBJIaxDWNpJbuc0f1Cj1dNJlFI17ZhdeWgS/Dgy8zC3eOFynDCHZ5wP0YigewMt56+khGZzm+0n5lVzbZF99B1kW/JVbzGbHK1cTrNqEEW1GTUUSTDYOnEFP+OKxjFmDKH3tEz40gCHg8HqZPn860adNobW1lx44dVFRU0NHRwUcffcQnn3zC6NGjGTduHG63GznXgXSOnWR5C1TEcJiK0eoUOnYtxXHSBMzefa0cxgwzI66ZTNuqOhrfq6J9TSORhiDDvj3+oDqJ/HNDI0//vym9rw0ZI5E9ugAMfvos807RuzEsbTp2i9UmlTQrW/U4xgU5JaiJKIEVTwAgedIgGhHNHjQGZgYwgMdq7He+IAiYi8eRqltLqr2G8MY3OadwDNu723irvvyoCsDbluzgtiU7Wf2juczoeRD81lPreGlzc99K2Y1oaFywq44ctZMKqYidxhk8LpsZKUcgu5FWs4faJKAEMQoyhcMmcPrpMw66H+8ggwxyfHPMCcAEArWKxp718nYjAA7JilWyomImohjpSBtxZ/p4uLyN7bEEkXsOTTmUI0H8rnMw//ytfeYPcX/9DheCKGItmYG1ZMbX3tfhYk/L4Ny5c6msrKSsrIyWlhY2b97M5s2bKSoq4oQTTiA3NxdTaS5qSZr4mmoMfitucSyJD9vozKvEM3PWPvGOgiiQfeIQ7MPcVP9zC9HGENseWkvxt0pxjf5y1k6DewSiVUC0W1HCfkqbt+IymtkZbKcm1Emxo//+yAOZFa01RNMpxmXkkGN10vXRY6jRAOahkxCsW5Gs2b0W1oH1+HRwGH2jkTPXkWpU6Xz3Xs69+u/cW/Yxb9Vv455p5/37HRwGkmmV25bsBKAtnCCaTPP8xqa9xZ+UBE8TkqpyTbOejd3qOY8XjRoGTxVdGU4qEiJK0o+EQLbQyWPqVH4+cuSg+BtkkEF6OeYEoNVoYZw7G3O0EklLYaSbhNFF/qxFnPfMZmrjKbrSCvNLvHxU1QmkWX1eEVfkOnAcYwHEcj9WwCn5Liz/gRXwJUli1KhRjBo1ivb2dsrKyti5cyd1dXXU1dWRnZ3NlClTKC4uxnrSSFItAZJrmjHJHkxtHvyvfIz1xFFYsvaNKbLlOxlzw3RqXywkrp43AAAgAElEQVSne3sHlU9uImf+UPJOG3bAeM+UomLoiQc0ZOguUskDahhCq57mlPHf4OVdW3ivcccxk1iwJ2/V6zFm5xTqcZr+pX8CwDHjAsI7t+ruX47dfq0G9ygkr0i6TSa6YzlTIh1kGC1s726jMtjBcOeRKzScTKvcuayiV/wBXPDEZ/2v7KtHEFWubvicwnQnyYzvMTVjOtGcEGUpiXhCz+z1Wr0s3mmh6vbvU/5RFTlfMtlpkEEGOb455h4HbS478y5dSJF3Fd7I7TgiDzJ2zjjyRmZzy4WldKUV3v/+TJZeN2uv7W6cN4xrZhQdpVF/NfpzA7/63WOvU8GhJjMzkwULFnDllVcybdo0TCYTra2tvPPOOzz77LNUVFQgZ7uwnDeaRHYEVUvjkkbCiiD+T9ag9aNYZKuBkismkH9GCQjQ8lEtVU9t6usV3Q/t4WTv/6LFh2jKQHImQRAJbXqLczy6QHqvccehPwmHGU3TeLNXAI4hVrGKRN0mJGcWlmHjAJBtumtdY+AmgRwIo2cUgiRgGloIQOD9BzmzYDQAr9eVHZJjbG87OPf4+obAXuJvfww3V2L07kLQNL7fuI1E7h0kxs6l3KeyKdFNXE1ik510Zk6ha9RMHrliJiZZ4henjmREpv3rvp1BBhnkOOKYE4AAgiiRc/7LeE/+E5ln/QNryTcAentDnjIiE0EQ+OMFpUdzmIeEPftdrrvxRAoyBmbP1aOBxWJh+vTpfOc732Hu3LnY7Xa6urpYsmQJzz//PDW1NVhnjkCc5yEhdmGQHDjbCuh6bSXJgH+f/QmiQM78oYy4ZjKSRaZ7Ryfb/7KOhD/W7/E/qurrdiIIAoaMEQgGAcuI6aCkOLFRb9G1rKmSpLJ/ITkQ2RlspyrUicdkZWbmEPzvPwSAe/4i1EQnAJK1r2accAw6gQ1uPYlK8gkgSgTXvsDFGbrV74WaTYfkGGPv+ajfBw7QY/0+quygsTtGtT/a7zoAIgrjDFXc637g/7d332FSlefDx7+nTC/bKLuUhWWXpRcpYllBYkUUkJ8YG2iMUYxYMIjEiEoEI4klSswbTVDsioANg2JEpSNSpUvdZSnby8zstHPO+8fA4grYQFjc+3Ndc83MmVOeM8+1w83Tbjq2nEdM0fhbwR7Mjo+ws01zvoxXU2kE8dlcfFKZwXx/Z8Zefjq/65PJwI71e5avEOLkOSUDQABFs+HvejPedlfWTlK4uH0TopMH1u5zR14WTw3pTPMk58kq5jGrfmQAL19zGld2a0aPFqd+Xtmfg81mo1u3blx33XWce+65eL1eSktLmTNnDjNmzKAwUIbr0o6E0ysxrRh+2hD/ZC+Vq9ce8Xz+7FQ63NYbZxM34aIgG59ZTvX28sP2m75mT533ekpiqRR3bjcAlC9n0Cm5KYF4hMVFO4/vTf/MDnb/DmjRHrNiL1UrZoGqkdL/FoxQYvkh7WAL4CnQB/zV3qrDtunJOaComJEC/L2GghGn94a5eHUHy0sK2FpVcoQz/TiKcngX+WOfbaM6HGfC3C38Zd7X9HxyPsNfW1VnnzS1ksvdn/FM6l9Z3uxG3m06lgz/NmbrufwnkEX7ZoNZqtWwN1aBTdE5o+NpXHPjCFp1yuXpoV1w2rQjLlckhBAH/aJ+IRRFqfOjpygKt+dlndKtZoqicG2PFrwx/NRPKfZz0zSNTp06ce2113LOOefgcrkoKipi9uzZvPfee9RkN4az/EQow64l496RQtns+cRDwcPO5Uhz0/7W3iS1S8MIxdgydRXFy3bX2ee99fvrvD84DlBv7EN1+Qlv/4JfexLdbu8X1M3gUt/N2vkVAANbdKT8s+fAiOPreTm21BYYwQMBoPvQ4sH1vQt4d8Xhrbiq7kT3Z4Fl4j9rKADV86cyLKMNAG9uX33M11UA80AEqI55H3XM+4ydvYGk+xMLq3+8pYSibwwlaKYV8WTqk3zR7EYeS53Cxe5lJKsBtsab8p42nI+s3kQ1je3RUhQUFDWNsbua0rP/Wei6zpODO+M9xcY6CyFOjl9UACgEgK7rdO3aleHDh3PmmWficDgoLCxk+vTpLFi3CqN/K2qSE92Y/ng24f9+TfWWw5dr0Zw62SO60bRvJpgW+e9sZvd/v8Y6SkYWW0ouAPGq7fh7DwNgwJ7EWLJZO9eeEi1lALsCZSwp3oVbtzEwI5vyz54FIPX8xNI2h1oAD0wCOTnF/FGMoxTSlnqgG9hrx517DmaokuvLEguwv7595U+qs9P/Pp9AJI5lWZgWFFSEiX0rH/G3ZWglTEh+jk/Sb2eQeyEGNuaHu/Fg+U0MLZpCjf9RLlAy2RQpJW4ZZKBwWadGbEzvxEMDu/7oMgohhASA4hfLZrPRo0cPrrvuOrp164aqqmzatInX33idje4w4S4WUbMSp94Yx3o7ZR8vwohF65xDURVaDGhLqys6gKqwf0E+O95Yx5FWl7OndQAgWraRpLNHAOBd8x7NXV7ygxUsLyn4uW/5uHjrwPi3S1t2Ir7ibYzK/ThadMHd7hwA4sFEy+c3ZwHX8wbAowZgB8cBxso3k3rx3QA0/eINmjpcbKwsYmnxrh91nT/N2ciXuyvp/ff5bC9NjOvL/ssnPLNo5xH3b6KWMSH5Oeal38Z13o/QMXgvlIcy5Ev+6XyMXmn9GdvKSUGsgpAZIUV10TN/Nt2LX6BFv2FYFmgnIDOREOKX54QFgNXV1dx0001ce+213HDDDRQXJ1KMrV69mmHDhnHVVVfxj3/840QVRzQgTqeTvLw8rr76atq0aUM8Hmf58uXMWPAJO3NNgs79qIqOP9iawHtrqCksPOwcjXo2o+1vuqM6NMq/KuIFl5uMby3HY0vOBUUjXrENZ5ue2BpnES/bzW0HcgfP3HnkMYf1zZs7El2fv27djdL//hWAtAF/qB1rW9sF7PlmF3D9DkJiR2kCtKceDAC34DvtMmxNsokX7+B+JTFp51+blhz1nIZpYVkWV7y4nDvfWceW4gDTlieC/M3FQdr/9dPafe9+b32dYx1E+L1vBv9Lv7028Hs/dDYD9j/J6LLRRPJD/NGzB00roiwewK3ayXSn0m3VXfj3fUiTYY+iKEpiBvaxfDFCiAbrhA0WmTVrFrm5uYwdO5bp06czdepUxo0bx4MPPsiUKVNo2bIlN998M+vXr6dTp1N/9q6of5KTkxkwYAB79uxh0aJFFBUV8ennn5Oenk7vFipNCj14tGYYy8KUN15G0tm9UL+RE9afk0r7kb34etpqcisjPKs62Ly1lHY5iUWeFd2BLTmHWPlm4hVbSDprOCXv/pkL9qzhPl8WM3au4dFeA+t1sLShYh8rSwvx25z0LdvBvsL16CnNSTrjagAsy8QIHWgBdB+cBPLTrmXGDCKlNUTKaogFosSDUeKBKGbMxIybtV3tml1DtWtoDg2bz4EtyYE9yYkj1YXm/P6fsJevOY2pX+ziyu6HrwF5sAUwWrYJRdVIu/Au9r1yO/03zkVp3Z+3dq7hidMHkeY8PP+1bexsAJKcOpXhOG+sKqQ4eKgF2TjCUAG3UsP5zuXcnfQ6LfUiAObWnM7jldewNd6Si/wmj2XU8NlXS7EAXVHJMN0sa+HlgsK3qYpU4+t5Od5O5wEw8sxW+GXMnxDiJzhhvxy5ubls355IKxUIBNB1nUAgQDQaJTMzsT5fXl4eS5YskQBQ/KyaNWvGFVdcwebNm1m8eDH79u3j/X376NA2ly41SaSozfGVtqDy3WV4+rXDnnooi4cr3Uv7W3ux+rkVZJSFKXlxLS1+1wNPZhIAtrSOxMo3EyvdQPLZiQDQte4jWve9g52BchYX7eTsplkn69a/1783LwPgqjbdqfroSQBSL7wLRU90epvhMjBjqI5kVP3Q7PrvC2mNcJzg7iqCBVUECyqp2RcgWhE+5gGEtiQHrqZeXOkePJlJeFslY/PW7aDvluHn4y1HntFrS02s+xcr3YhlGiT3/Q3F7z2MsWslo1v14QnDzb+3LGVc1/OOWoaDAf03g79vO8+5nKs8H3O2cw2OA62LG6OtmFj5G5ZGuvDywBwcX6+nNF5CQdRAQSFT8bAoGOb9TlFea9OI3TNeRbE5aXr1E7Xn7ZLh/2FflBBCfMvPEgC+9dZbvPjii3W2PfDAAyxatIhLLrmEyspKXn31VQKBAF7vocVJPR4PBQWnxjgpcWpTFIX27duTlZXF8uXL+eqrr9j49Ra2Oxx0T6+kU7QNPrUV0U+LCGUVkNyje+2x9iQnPW/vw/97cB5nxmHzf1bS5qrOJHdsjD2tI6GtbxMt3YC3w7W4cs6iZuti/mgEuQUbz3/9Rb0NAGviMV7a+iUANzudhDZ9huryk9L/5tp9jEBi6RvNe6g1zTpCFGeZFsHdVVRtKaVqSynB3VWHB3uqgiPViSPNjc1vx+axo3vtqHYNRVMOpO6zMKMGZtTEiMSJVUWIVkaIVoaJlNYQq4wQq4xQtaW09rSORm58Wcn426Xhz0n9zsw5misNzdscI1BIvHIbtpRcGg8az75Xbue6jXN5qsMgnlw3n1Ht8/DaHZSHotg1lc6PfXboHN8R/aaplTyQPJVL3YsAMC2FDWYHXq3sy1vB89j6h3P5/O1PiG5aRIUZASBddaNWOPhj8jaad23K2+dex76JiUwyaZeMxd649dEvKIQQP9DPEgAOGzaMYcOG1dk2atQobrrpJq666io2bdrE7bffzuuvv04weGgJjmAwiN8v/6MVJ47D4SAvL4+OHTuyYMECdu/ezbJdW9iaXERvPZNWjjbY86GscAH+83qguxNdgZpTZ7QRYRx2hsR0tr2ylpYDc3E3SkwEiZUlln1J6X8zNVsXc9bmTyDnIt7asYa/9xmMz1b/1qZ8a+caKqI19GrUAue8/2ABKf1HorkO/U3Gg4mctJonEQBalsWOshA+h45lWlTvKKd87X7K1xVjhGK1xymagivDh6elH0/LJNzNfThSXaj6Tx+GbJkWkbIaavYHqNlTTWBXJcH8SiIlISIlIUqW70HRFGwt/Vyp6sSqI9iOkA7N3qgrNYFCosVrsaXkktL/Zvb+93FsxdsY0SifF5q2JufZV9jyuxGkPfARTw3pzK7yQ8vKlH7jPr9ROoa6P+P+5BdIUoMETSdPV13J26F+ZDdvzeZwBW92Vljw7jtUW0GwIFV10rzKw5taFVObbGRg64680u9agh8+Tjh/NbZGrWg08N6f/H0JIcQ3nbAuYL/fj8/nAyAtLY1gMIjX68Vms5Gfn0/Lli1ZuHAho0aNOlFFEqJWamoqgwYNYvv27SxatIjSigo+pILWSXs529kdP20I/3crVhcnvnaJcWMGMMmI0r5NKu13VlEwewtpPVtiWQrR0o0A+Ptcxf43x2IUrufG3PN4Pq7w5o413JTbB4ArXlzOjOuPnt7v6+LACUnhZVkWf18/H4C7khthffUuMcVO6oV31tnPOBAA6p50ZqzZQxOvg3GvrOKlLi1Y+/YW4tWHukHtqS6SctPw56bha5OMdpzHqimqgrORG2cjNymdmiTuwzAJ7ammams5lZtLCOZXEt1ZyT2anTWPLMSfk0pq96akdGpSO37Q3rgLNTvnEClZiyf3ChTdzr2x/+OvPM41W+bxRtp1FDm/JvnB9wEbd77z3Wni/EqAP6c8x2UHWv3mh7vzQPnvKDDS8ajwr2yD9c4y9gYDAHhVO5k1LvYYXkYkr6TAHuKPXc/jzz0uIrZ3C8XvPARAxg3PoTrcx/U7FEI0XCdsFvCdd97JO++8w7XXXsuoUaN4+OGHAZgwYQJjxozhiiuuoGPHjnTr1u1EFUmIOhRFITs7m6uvvprevXujaRo7K4t5s2Qey6vWoGspODY4KZ27gHg0XHvcltZ+Wl/ZEUVTKF0RIlB1P7GKQsxYCNXmIOVXtwJww57ELOCn18+vXV9u1lf76pTh2+vOtZucmEm6qrCydtuEuYdyC89cu4e9VWGOxPzGJATLsvjHwh1H3XfO7k2sKdtDhsvPWStnArA043JsKYe6ev+5aCfxA13AhiOdf7y+hui7m5lhc+HeVEq8Oooj1UV6/9Z0vLMPncecSebgdiR3aHTcg7+jUTQVT8skMvq3pv3IXnT70zm0+L8OfG7GMYDqrWXsmrGRNZMWsGP6egI7K7ClJdbRixYfmqU9x3EOWmYP0o1ybt26EkWPQ5Md33N1i4GuRcxNv5PL3IsImk7uqxjFb0ru5/nhA/ln6xj/zi5n0a71VByY2ds+5qNZUUsec5UwIm0hIR/M/NUNTOw5AMWy2DP1t1ixCMl9b8Tb5cKf7XsTQjQ8JywAbNq0Kf/+97959dVXefPNNzn77LMB6N69O9OnT2fmzJmMHj36RBVHiKOy2WycfvrpXHPNNYllYwyDldU7eKNoLjvC+/EHswi+t4bVwxK5cB/+eAtpp2XQ9jfd0Zw6sfBZVJX/hZrCRCtgav+RKLqdlC2f09WIsb5iP3N2bzritds+Oo/hr608bHvPJ+fXvp4wdwsAG/dXM2XhDrYUBygOJMaPDZ22HIAPNxUxcuZaTNNi3tclOO79gDveWUfBgYwYoeih3MSWZfHo2k8AeKBROsGV72DqLj5tOaJOGUa9/RVrt2wnHBrI3oVn87hiJ6W4hrBlsbWJi/a/702nMWfS/MJsXOneejHbWffYadqrGWOMKA9k2Mm8vD3erGSsuEnZqn1sfnYFu2Y3JRwaTHj/ttrjLEUlOPAxTBSG71tCdqAEUvaCu+KI12mt7+GFRg/zdNoTNNYq2Kl3Y/Vp7zPt7odZ1MfOvkUfYyh7KY1X41JstIt76VjWmtkei4EtP+FD524ubtGeNUPGMKRVZwBK3v8LNVsXoydn0PSqx0/I9yWEaDhkIWghjsLv9zNgwAAGDRqUGLYQj/C/suW8W/I5IdNLu8IU3sqqIFmNEDdMfNmptBvZE81RjRHrwLbXSggXh9CT0/H3uQosi4vXJVKs3b98Lg9+lGjJe2XFbqrDcUzTYntpiFdX1l2H8OByIu+s28vDHyeCv0AkTpfHPiMYNbh39kaaPjSXHaUh3lmXaFEsr4mxdFc5+6ojnP/sEuLfaA00TIvGD36EZVkEI3EGz5jDoqKdWHEd/e0XANicfSXTtxtAIoXZ9AU7uFO14dwwhFD1KMygk/2Wyft+jUvjNaxom4ynpb9eBH1H83F+BY1Pb067m3vS+Z6zSD+3FbrXTqQ0Tqh6JKU7JrPtlRVUbipBA3rNCPCGcwA6Jg9smI+GCS02gvbN2b4W13g+5L9N76avcw0Vppc/ld9Cz0tnkLO/ktdfe4OVRdsOtPjZyI376VyRzZc+D/3TP+Zp/SsyPH6m9x/B7At+S4Y7Md4yuHkBxW8/CECzm15A80gecCHE8aVYp0p+qgOGDh3KrFmzTnYxRANjmiYbNmxg2bJlhMOJbtR27kx6+zsSjhTw6J4dPHH39SiqSvFnj1L4STJGvC2aSydnRDd0ZSfbHziNGuxc0e/35Csa1s4uEEwFEtkcft2tGa+tOhT8vXT1aYx4fRV392vDE59v/94yPnhhLhPmbmH5Xefw6spC/j7/u4/JaeThfyNPp/Ubk1EcNXTbDq/kP04Ndi5O+zelago9FJWrVJ2+ioZ2ILjTbet5Kubn5VgaxoFz3X9eDuP7NScWrCBWU40RrcGI1CSeozVYRhzLMsGyap8VTUfV7XUeutOD7vKiO32JZ4fnwGzgY6OOeR+AjWP7067JoTGVlmFSsbGE3e+9Q7Q6u3Z7qWXxqRlntRlkXNkdNDb28GSL83k+pxtWjZcLHefx6cZi3uk0g/aVMwDY7h/CxspBZPvsFETLiVmJVla/6qBZjZOC4hQKuoeZGFxGXDFxaTbu7HQO93U9D4/t0OSUeHUJ2x84jXjZbtIGjqPplX855vsXQohvkwBQiB8hEonw5ZdfsnbtWkzTRFc0unlz6OzOpMr8Cu8ZHbFZheydOZRQ+FEi1e1QdJXMQe1487nL6R/9glebXsijHbpghT2wrScnM5dDm9wSdtjWk27z8cSnr9AlvpXn3Fez3XsdV2k22imJ4MvEoFDfQrukl0Ev5x/V15OhR2imB2mkhWlqi6Ba353v9qdR0N0+7N5U7L5UHL7UxOsD7+2+NGyeJBTlu4PEgwEggPnYZYd9Xrb4AcqWPI+aej/Bsq5ESw7N8jUtC80ohNhOPkppzA6PSrLTzpnlG2mrlmI5Mqn257IXi32xQ13EaaqLZpEk1GYtmWrbyEsVidZfm6rxu9w+3Nft/NoWv4OseJRdf72A0Ob5uHLOpPUfP0fRbT/pmxNCiO8iAaAQP0FFRQWLFy9mx47ExACHYqO7L5c2djcBxyYiu+5Fs6rQMpdSsjyR8WFJvJz+xSOoweLCXiOp9GhYe9pCed0MFT4lyDDPJ+TohXjVEHYlhmGpVJo+KiwvlaYHhxKjsVpBK30ve41GvBvqS43loIttG9m23eyIN2NLrCV2JU6SGsCnhPCpIbxKCLcaIWC62Kn5eCfDj6kovLXrdXJ3xYj6BlPjGYJDUcBWhmUvRHVuBr0GU3F953eiOTzYvcnoLj+aw4VmP/hwouo2QE205ikKCgqmEccyohixKFY8ihGPYoSDxGqqiYcDxGsCGJHgd14TQFE17N4U7L60A49UHLWvE+9tf5xbu/+RAsDQro/Z//ZA1Ca9yFr5RzoqKmcpKn0Ujc6Kin6g9dNSw2ArQXUHMJPclGguCqKVRA+09umotNCT8KQ2ZVlKlGdLV7ArlAgKXZqNETm9GNulP619qYeVwbIs9k79LRULXkBPbkbWg19gS23+vfcvhBA/hQSAQhyDtn96i0tTK8lxJiZhuFQHp/lyyVBClFe8ROMzB6BwPjvf2QQxk7BRgr98MlPdzZjSvT2WocK2XigxB5e6FtHbsYGLXUtJ06p+1nJXY+cK75VsU1P5U3kZQ0qaYLqbYdnLwVYKeuDwg6w4GtXsjnv4KNSZ/LiXwriHYsPJron/h6rbDz/mGJlGnHioikigjGh1GdE6z6VEq8uI11R/73kqDDv7DDf74i6uPKMDmsON5nCj291oDhdYcYo+HI5pmIwpvx0DJz41ik+N0VwL0dYWoJtbJ+rMphwve2LVhA4s3Azg0ZzstcPL7n1sDVdifKM1NNOTzE25Z3BL+zNpdISUcpAI/opm/InS2X9Bsbtofd8CXFk9j/0LFEKIo5AAUIhjUBWOkXz/HNq7wgxKqaSlIzFBwKna6ehuRbpVzubYDqblpzA41ppcJbEcihX6hGubOdjapBol5OWxys8Y7F5ce97lkQ68F8qjyvQStWxoikGSGiBJDZCsBghbdsoNP3uNNLratzLAtYSwZWddLJttsea0tRXQXCsmgp1K00O16abadBOyPLhVnXiSg+6mk2wzik0LHNYLHbcs9sV1NkZT+CLcjNWRJiQp++njWM8rwYsJW3UXVD5Sq9qJYsajRKvLiFSX1gaF0epSItWHgkTLjH//ib5BU5zYbE2J6hlUK37KTIuiaAUGhwI7j+4k6HPxr9g2NpiVdY7vmpJBv/RshrbuQl7TLNTv6aIufntCYr0/VaPl7TPx9Rj8o8orhBA/lgSAQhyjQ+PLLBYNz2H3pnUUlRQDoKHS1t2CDCXI/qpVbK9OpmukBUosHdMyeD25kBcbbaW/sZbxwUVMrR7MF5EOLI925GBU1q2ZnzV7fnyLoEMxyLFV0t5eQXt7BX18ARqZZWjKt/7kLQUlYrI0ksLH8Sw2RVPYHvNhfM8iAW0befi6JMj8284iLyvtO/c9mSzLJB6q5rRJ75Cu1dBED5OmWdybl4kVNojWKAQiCqXVETTVotrSqYiHqTKCh2WvS1UtGldsxb/7fZp1PpPmt7wIikpBsIKqWBiHqtPSk4zzB47bs0yD/a/dTdnHT4Oi0vzW10nqc+Xx/xKEEOJbTlgmECF++RQq7GlcceUwnp+3Gt+2/1IcS2ZTKJ9NQJqjMx28NmzRLcRqPkeJNefaaGOuLOjCPGcOt+q/Yll1Wx4f1Inl7yVSyc29+QwKK8O8u35f7RIv3+ZQDFroAVrpAbJs1bSyVdPGVkWWrRr9m8GelSgjsSTMWCpbY3785cvIKpvBu46+jPcNBRKzg42SumPvrjmtOa+tKmTkma1w6CoTLmqHz6Gj3TP7mIM/0zSJxWLE43FisRjRaBQzbmLGDcxY4mHFDYyD2wwz8blhYBkmRtzEMgwMw8QyTEzTTOzzzWfT4NHsZEzLS9yKETFj/G9jBSEzcsRcxge+KVJVk1T2kmSsoGlTF83OuR+jqjv5j0+laumrmKEymo98jUxvyo++byNQRuG/byCw+n0U3U6z370owZ8Q4oSRAFCIY/Tm8J78+uUVAHy4uZgBHZqyqETh0qxczll/N/n6QHYr3SiNV1EaB4U0mnqySCOEK7YV3VjJhcCFhpNYxj7sG7/i76kmOW0yaFu8AF8gSrI3QvPkPfRKd5GdrLFy2x4aaTWk28L4lOgRy2VZiWCPWCpKLBUrlsI6XWGuq5yefU6n4H8zuKX0dcoUP495flN73G1nt+alLwuYeHF7Bk79glV396VLup9/XdGVqGGS6j401i/VfXhLl2EYhEIhgsFg4rkqSKgqQCgYJBgMEaoJURMNJwI+I45hGYed40Ry606CcZ2QaaMippCjLKGnMhsPe9E0jaTuo/B3+ze6PzNxQDpkjvmIgr8PIrB2Djsm9KbZTS/gzs37wdcMfPURe6b+lnh5IaonhZZ3vI2nfb+f6Q6FEOJw0gUsxDF6Y1Uh17x6KHvHI5e052+fbuPc7DS6FEziet9/MdAp1vuyV7+AfYarTquTV3WQqhq4jX044nuwE/uRC8OoKJYXK+qHmA8lngRxP8RTMDSdZbYSPvftY4F3Pz0yWzN3XhrhMT3YfF9XtGiA+/x386u0RDgAABClSURBVM6Ux1m4o5S+zyzm5WtO49oeLQBYuKOUvKw0LMsiGo0SCoXqBHfVVQHCgSChYIhgKEQoHCISP3JA+l1sio5N0dEVDV3R0BQVVVVRFBVVVVBVFfXAtjoP7cB+2qHXqqYlXusHtmkaqq4y5sOvqYorVBgKNaaKx+Wkb7tmPD6oC0n3z6kty69yGlG0axmLrkvF2eIcbElZRyxztHgHBU8NIVKQSCGXdNZ1NLrsPhzNOhxxf8uyCG1eQOkHjxJYm7ieK+dMmt/yCvYmbX70dyaEEMdCAkAhjtGrK3cz/LVVR/xMweRy9+f0c64iSQ3QzKsSdLQlO+s6dhaUsaemiti3WsB0RcNjaXjjFvZoBIdp4DBNHIaOYulg2cC0g+EG0wWmE+VAyFioxAg3Nlmpl/Chls8GZyWGYpHja8TDPS9mWOtunP/PBfyn/D5qvl6Eq9cw7L/+f2hmjJqaGkKhUO1zMBgkFEgEeqGaEHHjh7XUqSi4VAduzYlLc+DWXbgdLjxON273gYfHhc3pwOZxoDltqA4NdOXAQ0XRjv/aiLfOTARqzy7ZVbvtjFYpLN1VXme/6kkDsOsqNu37F6A2YxFK3p9Eyey/gJGYaOJs3RNPx/OwZ7RDtbsxAqVECtcTXP8x0f1bAVBdfhoNup+0i0ajaNIRI4Q48SQAFOIYbdhXzcaiaoa9tOKo+7x+XQ+ufmUln956Jv2yGwGJFqGdfxtIVTCVYKuLKcZBSaSCsHn0FjQFDdNSiFsQtkxCGATUOAElTrkWI6wYqCiogFPVyfKmkutNI8PuqR1fFyreTSRUTUz3YKg/fJFhXdFwq07cmhO35ki81p24nW48bjdurwe334sryY3i1lFcGrgSgV19SRG3Zk8lpz0x/zv3+SkzmqPFOyj9YDIVi1/B+o61C7WkpqT0+x2pF9yB7m/8o68jhBDHiwSAQhwn38w28W3mY5fx6dYSumb4SfMcGkMX3b+Vbfd3w4qGaPbbF/B3u4bqgjKK9xRRXFZCdShAtRGiKh4kZIaPe5k1VFyaA5d64HHgtVtz4laduBwuPB43Hr8Hm9eJ4tbBpaG4NXBrYFfrTXD3Q5imhT529lE/n3F9L4Z2yfjp54/WENzwCeEdK4gWb8eKhVHdydibtMHdri+urN7S4ieEqBfkl0iIE6R/TqPDttmb5pAx4hn2/Oc37H35Nlw5fUjq3IGkzunkAFbMhKoYVjBOvCpKTWWQmuoQ4XAN4UhiIkXcMjEsAwMT07JQFQWVRLYNVVFRUbGrOnbFhk3VsZlxbDYdlz8Nh8uO4tLBoaI4NXBo4Dzw2q2h6Meeh7c+UdWjB6u5jT3HnJRPtbvwdb8UX/dLj/FMQgjx85IAUIjj5IPfns7AqV/86OOS8q4nuGEelYtfpuDvg2n9wBJ0b2JpFcWmQpoDJc2BHQ92Ukj6xrGWYUHMBMMC0zrwDKiAoiTWMlEgXlVE/tMDCOzfgK/X5aTfNv2Uark7Edo19p7sIgghxAkjAaAQx8mADk0P2+Z1aNzUp9V3HqcoChnX/5Pw7rVE8tdQ8NQQWt3zMard+b3XVDQFNO0794kHSsl/5iIi+9bjzOpN85teaNDB39392qCg8Pjn22q3/V+XDB4f1LHOEjdCCPFL9svq3xHiJEtxHZpUcd95bSmZcDFPDOr0vcepTi+Zoz9AT2lOzZaF7J4yFDNac8zliZUVsusv5xIpXI+jWUcyx8xBdTbslq7HLuvE3y7rSFPfoXR2L1zVncwUN16H/J9YCNEwSAAoxHG076ELuefcbH6V04iuGX7sP2IMnS21OZl/mIPma0Rg7RzynxiIESz//gOPIpy/lp2T8ojsXoe9WQcy75lb27Us4LHLOgIwomcLCfyEEA2OzAIW4jg7+Cf1U7tZI4Ub2DX5POKV+7A1bkOL22fganXaj7p+xef/Yd8rd2DFwriyz6Dl3bMl+DuCi59bytRfd6N5kutkF0UIIU6oExYAVlRUcM899xAIBEhOTmbixImkpaWxevVqJk2ahKZp5OXlMWrUqO88jwSAoiGIFu9k95ShhHetAlUj7aLRNLrsPjTPd+ecrdm+nH2v303NloUAJPe9kfTrpqA63Cei2EIIIU4RJywAnDx5MklJSYwcOZLFixfzwQcfMGnSJAYPHsyUKVNo2bIlN998M3fddRedOh19zJQEgKKhMKM17H9zLOWfPAOWheLwkHzWdXi7X4ozszu6vylWPEK0aDuhzZ9TuexNar5eBIDma0z6NU+SdNa1J/kuhBBC1EcnbODL1q1bGT16NAA9evTgz3/+M4FAgGg0SmZmIsl6Xl4eS5Ys+c4AUIiGQrW7yBg+heSzR1A04z6C6/9H+afPUv7ps0c/xuUnpf9IGl36RzRP8gksrRBCiFPJzxIAvvXWW7z44ot1tqWnpzNv3jw6duzIvHnzCIfDBAIBvN5DMxI9Hg8FBQU/R5GEOGW52vSm1diPCRd8RdWXMwmu/x/RfZsxguUomo4trRXOzO54uw3E13MImst/sosshBCinvtZAsBhw4YxbNiwOtsCgQCTJk3ihhtu4JxzziE9PR2v10sweChvZjAYxO+Xf7yEOBJnyy44W3aByx862UURQghxijthy8B8+eWXDB48mGnTptGiRQt69OiB1+vFZrORn5+PZVksXLiQXr16nagiCSGEEEI0SCdsDGBWVhb33nsvAE2aNOGRRx4BYMKECYwZMwbDMMjLy6Nbt24nqkhCCCGEEA2SrAMohBBCCNHASCYQIYQQQogGRgJAIYQQQogGRgJAIYQQQogGRgJAIYQQQogGRgJAIYQQQogGRgJAIYQQQogG5pRbBqZPnz40b978ZBdDCCGEEKLeS0lJYerUqYdtP+UCQCGEEEIIcWykC1gIIYQQooGRAFAIIYQQooGRAFAIIYQQooGRAFAIIYQQooGRAFAIIYQQooGRAFAIIYQQooHRT3YBfijTNHnooYfYvHkzdrudiRMn0qpVq5NdrAYpFotx3333UVhYSDQa5dZbbyUnJ4dx48ahKApt27blwQcfRFVVpk+fzhtvvIGu69x6663079+fcDjMPffcQ2lpKR6Ph8mTJ5Oamnqyb+sXq7S0lKFDh/L888+j67rUUz327LPPMm/ePGKxGFdffTWnn3661Fc9FIvFGDduHIWFhaiqysMPPyx/W/XQmjVreOyxx3j55ZfZtWvXMdfP6tWrmTRpEpqmkZeXx6hRo072LR4b6xTx0UcfWffee69lWZa1atUqa+TIkSe5RA3XjBkzrIkTJ1qWZVllZWVWv379rFtuucVaunSpZVmWNX78eGvu3LlWUVGRdemll1qRSMSqqqqqff38889bTz/9tGVZljV79mzr4YcfPmn38ksXjUat3//+99aFF15obd26VeqpHlu6dKl1yy23WIZhWIFAwHr66aelvuqpjz/+2Lrjjjssy7KshQsXWqNGjZK6qmeee+4569JLL7WGDRtmWZZ1XOpn0KBB1q5duyzTNK2bbrrJWrdu3cm5uePklOkCXrFiBeeccw4A3bt3Z926dSe5RA3XxRdfzJ133ln7XtM01q9fz+mnnw5A3759Wbx4MWvXruW0007Dbrfj8/nIzMxk06ZNdeqyb9++LFmy5KTcR0MwefJkrrrqKpo0aQIg9VSPLVy4kNzcXG677TZGjhzJueeeK/VVT2VlZWEYBqZpEggE0HVd6qqeyczMZMqUKbXvj7V+AoEA0WiUzMxMFEUhLy/vlK+3UyYADAQCeL3e2veaphGPx09iiRouj8eD1+slEAhwxx13cNddd2FZFoqi1H5eXV1NIBDA5/PVOS4QCNTZfnBfcfzNmjWL1NTU2h8yQOqpHisvL2fdunU89dRTTJgwgTFjxkh91VNut5vCwkIGDBjA+PHjGT58uNRVPXPRRReh64dGuR1r/Xw7Bvkl1NspMwbQ6/USDAZr35umWadyxYm1d+9ebrvtNq655houu+wy/va3v9V+FgwG8fv9h9VZMBjE5/PV2X5wX3H8zZw5E0VRWLJkCRs3buTee++lrKys9nOpp/olOTmZNm3aYLfbadOmDQ6Hg3379tV+LvVVf0ybNo28vDz+8Ic/sHfvXq6//npisVjt51JX9Y+qHmrv+in1c6R9T/V6O2VaAHv06MH8+fMBWL16Nbm5uSe5RA1XSUkJN954I/fccw9XXHEFAB07dmTZsmUAzJ8/n169etG1a1dWrFhBJBKhurqabdu2kZubS48ePfj8889r9+3Zs+dJu5dfsldffZVXXnmFl19+mQ4dOjB58mT69u0r9VRP9ezZkwULFmBZFvv376empoYzzzxT6qse8vv9tS1ESUlJxONx+Q2s5461frxeLzabjfz8fCzLYuHChfTq1etk3tIxUyzLsk52IX6Ig7OAt2zZgmVZPPLII2RnZ5/sYjVIEydOZM6cObRp06Z225/+9CcmTpxILBajTZs2TJw4EU3TmD59Om+++SaWZXHLLbdw0UUXUVNTw7333ktxcTE2m43HH3+cxo0bn8Q7+uUbPnw4Dz30EKqqMn78eKmneuqvf/0ry5Ytw7IsRo8eTYsWLaS+6qFgMMh9991HcXExsViMESNG0LlzZ6mremb37t3cfffdTJ8+nR07dhxz/axevZpHHnkEwzDIy8tj9OjRJ/sWj8kpEwAKIYQQQojj45TpAhZCCCGEEMeHBIBCCCGEEA2MBIBCCCGEEA2MBIBCCCGEEA2MBIBCCCGEEA2MrKQshGhQHn30UdavX09xcTHhcJiWLVuSkpJC586dOeOMM+jatetxuc67776L2+3mggsu+EnHP/XUUwwcOJCcnJzjUh4hhPgmWQZGCNEgzZo1i+3btzNmzJjjfu5QKMTtt9/O1KlTf/I5qqqqGDNmDM8999xxLJkQQiRIC6AQQgDjxo3jkksuoaSkhE8//ZRwOExxcTEjRozgk08+4euvv2bs2LGcf/75zJkzh2nTpqGqKj179jwsiHz//fc5++yzgUSg+X3nGzduHPn5+UQiEX77299yySWX4Pf7cTgcbNq0ifbt25+Mr0QI8QsmAaAQQnxLMBjk+eef54MPPmDatGlMnz6dZcuW8dJLL9GrVy+mTJnCzJkzcblc3HPPPSxatKg24AP44osvGDp06A863xlnnMGyZcuYOXMmAIsWLao9rl27dnzxxRcSAAohjjsJAIUQ4ls6dOgAgM/nIzs7G0VRSEpKIhKJkJ+fT1lZGTfffDOQCO4KCgrqHF9eXk5aWtoPOp/X62X8+PGMHz+eQCDAoEGDao9r3Lgx+/fv/7lvVwjRAEkAKIQQ36IoylE/a9GiBRkZGTz//PPYbDZmzZpVG+AdlJqaSnV19Q86X1FREevXr+eZZ54hEonQr18/Bg8ejK7rVFZW1gkkhRDieJEAUAghfoTU1FRuuOEGhg8fjmEYNG/enAEDBtTZp0+fPqxZs4bevXt/7/kaN25McXExQ4YMwe12c+ONN6LriZ/mtWvXnvIJ54UQ9ZPMAhZCiOMsGAzy+9//nhdffPEnn6OiooJx48bxr3/96ziWTAghEmQhaCGEOM48Hg9Dhgzho48++snnmDZtmrT+CSF+NtICKIQQQgjRwEgLoBBCCCFEAyMBoBBCCCFEAyMBoBBCCCFEAyMBoBBCCCFEAyMBoBBCCCFEA/P/AS4HBYxMW57SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Adjust baseline to remove background drift from all traces in sample image\n",
    "base = BaselineAdjALS()\n",
    "base.__init__(f_lambda = 1000, fp = 0.001, i_iter = 5, n_max = 300, plot = True)\n",
    "adjusted_traces = [base.baseline_adjust(trace)[0] for trace in all_traces]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot effect of baseline adjustment in sample trace \n",
    "adj_trace, to_plot  = base.baseline_adjust(all_traces[0])\n",
    "plt.figure(figsize = (12,3))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(all_traces[0])\n",
    "plt.title(\"Original Signal\")\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Fluorescence Intensity\")\n",
    "sns.despine()\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(adj_trace)\n",
    "plt.title(\"Signal After Baseline Subtraction\")\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Scaled Fluorescence Intensity (dF/F)\")\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Noise While Preserving Sharp AP Upstroke with Bilateral Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ClsBilateralFilter:\n",
    "    '''\n",
    "    Class that implements bilateral filtering on cardiac microtissue \n",
    "    spheroid fluorescence data after baseline adjustment.\n",
    "    \n",
    "    Bilateral filtering is intended to preserve sharp edges of the\n",
    "    action potential upstrokes. The filter replaces each pixel value by a \n",
    "    normalized weighted average of surrounding pixel values \n",
    "    with largest weights assigned to nearby pixels with similar intensities.\n",
    "    See Paris et al, 2008, Computer Graphics and Vision,\n",
    "    doi:10.1561/0600000020, 6-10.\n",
    "    \n",
    "    Attributes\n",
    "    -----------\n",
    "    window_size: int\n",
    "        number of neighbor pixels to include in weighted average\n",
    "    sigma_s: float\n",
    "        parameter determine the width of the Gaussian used \n",
    "        for spatial weighting\n",
    "    sigma_i: float\n",
    "        parameter determine the width of the Gaussian used \n",
    "        for intensity weighting\n",
    "    sigma_i_weight: float\n",
    "        if sigma_i is not pre-set, this is used to set the \n",
    "        width of the Gaussian used for intensity weighting\n",
    "        as a multiple of sigma_s\n",
    "    n_iters: int\n",
    "        number of times to apply the filter \n",
    "    dist_filter: 1D float array\n",
    "        spatial weights\n",
    "    data: 1D float array\n",
    "        input fluorescence signal \n",
    "    \n",
    "    Methods\n",
    "    --------\n",
    "    make_distance_filter(self):\n",
    "        Creates distance-based (Gaussian) filter\n",
    "    filter_data(self):\n",
    "        Executes bilateral filtering\n",
    "    filter_pipeline(self, data):\n",
    "        Applies bilateral filter to input data\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, window_size = 13, sigma_s = 0.3,\n",
    "                 sigma_i = 0, sigma_i_weight = 1000, n_iters = 1):\n",
    "        self.window_size = window_size  \n",
    "        \n",
    "        if np.any(sigma_s):\n",
    "            self.sigma_s = sigma_s\n",
    "            self.spatial_sd = -0.5/(self.sigma_s**2)\n",
    "        else: #set automatically\n",
    "            self.sigma_s = 0.5 - self.window_size/2\n",
    "            self.spatial_sd = np.log(0.01)/self.sigma_s**2\n",
    "            \n",
    "        if np.any(sigma_i):\n",
    "            self.sigma_i = sigma_i\n",
    "        else: #set automatically\n",
    "            self.sigma_i = self.sigma_s*sigma_i_weight\n",
    "        self.i_sd = -0.5/(self.sigma_i**2)\n",
    "            \n",
    "        self.sigma_i_weight = sigma_i_weight\n",
    "        self.n_iters = n_iters\n",
    "    \n",
    "    def make_distance_filter(self):\n",
    "        '''\n",
    "        Creates distance-based (Gaussian) filter\n",
    "        '''\n",
    "        n_size = self.window_size\n",
    "        window_inds = np.arange(n_size) - (n_size - 1)/2\n",
    "        filter_arr = np.exp(window_inds**2 * self.spatial_sd)\n",
    "        self.dist_filter = filter_arr / np.sum(filter_arr)\n",
    "        return\n",
    "    \n",
    "    def filter_data(self):\n",
    "        '''\n",
    "        Executes bilateral filtering \n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        output: 1D array of floats\n",
    "            bilateral filtered trace\n",
    "        '''\n",
    "        win_size = self.window_size\n",
    "        win_half = int(win_size/2)\n",
    "        n = np.size(self.data)\n",
    "        \n",
    "        #allocate space to track averages & normalization factors\n",
    "        temp = np.zeros(n)\n",
    "        norm_factor = np.zeros(n)\n",
    "        #combine distance filter with intensity-weighting\n",
    "        for dist_idx in range(win_size - 1):\n",
    "            s_input = np.roll(self.data, dist_idx - win_half)\n",
    "            diff = self.data - s_input\n",
    "            i_filter = np.exp(diff**2 * self.i_sd)\n",
    "            temp += self.dist_filter[dist_idx] * i_filter * s_input\n",
    "            norm_factor += i_filter * self.dist_filter[dist_idx] \n",
    "            \n",
    "        output = temp/norm_factor #normalize\n",
    "        return output\n",
    "    \n",
    "    def filter_pipeline(self, data):\n",
    "        '''\n",
    "        Applies bilateral filter to input data n_iter times\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        data: 1D array of floats\n",
    "            fluorescence signal data\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        filtered_data: 1D array of floats\n",
    "            bilaterally filtered data with initial and trailing pixels removed\n",
    "        '''\n",
    "        self.data = data\n",
    "        orig_max, orig_min = np.max(data), np.min(data)\n",
    "        for i in range(self.n_iters):\n",
    "            self.make_distance_filter()\n",
    "            filtered_data = self.filter_data()[:-20]\n",
    "            self.data = filtered_data\n",
    "        return filtered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalization function\n",
    "def normalize(trace):\n",
    "    return (trace - np.min(trace))/(np.max(trace)-np.min(trace))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalize signal intensity and apply bilateral filter\n",
    "bilat = ClsBilateralFilter()\n",
    "bilat.__init__(window_size = 30, sigma_s = 5, sigma_i = 0, n_iters = 5)\n",
    "normalized_traces = [normalize(tr) for tr in adjusted_traces]\n",
    "filtered = [bilat.filter_pipeline(tr) for tr in normalized_traces]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot result of bilateral filtering\n",
    "plt.plot(normalized_traces[0], label = 'Unfiltered Signal', alpha = 0.5)\n",
    "plt.plot(filtered[0], label = 'Bilateral Filtered', linewidth = 2)\n",
    "plt.legend()\n",
    "plt.ylabel(\"Fluorescence Intensity\")\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.title(\"Results of Bilateral Filtering\")\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Automated Measurements of AP Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ClsAnalysis():\n",
    "    '''\n",
    "    A class used to analyze fluorescence recordings from one spheroid.\n",
    "    Part of the image processing toolkit for cardiac microtissue experiments.\n",
    "    The data fed to this class should already have been processed to remove baseline drift\n",
    "    and filter noise.\n",
    "    \n",
    "    Attributes\n",
    "    ----------\n",
    "    max_num_peaks: int\n",
    "        maximum number of action potentials (per cell)\n",
    "    s_num_peaks: int\n",
    "        number of action potentials detected (per cell)\n",
    "    s_rep_type: int\n",
    "        sets which algorithm is used to determine repolarization #TODO: describe these options more clearly\n",
    "        0: percent ind\n",
    "        1: moving average \n",
    "        2: moving average sub baseline  \n",
    "    f_APD_crit: float\n",
    "        percent repolarization to be calculated as start to search for APDmxr\n",
    "    f_peak_crit: float\n",
    "        percent of the fluorescence signal derivative that is set as the minimum threshold \n",
    "        for a detected action potential peak to be confirmed as a peak\n",
    "    s_interpolate: int\n",
    "        interpolation order, ie, the number of subdivisions of the x-axis (time) \n",
    "        for quadratic interpolation of the fluorescence signal derivative when \n",
    "        finding action potential peaks \n",
    "        \n",
    "    i_ref_win_size: int\n",
    "        sets size of window around a reference signal time\n",
    "        used to search for action potential peaks\n",
    "    i_ref_pos_win_size: int\n",
    "        sets search window after a starting index\n",
    "        used when finding action potential peaks\n",
    "    i_ref_pre_win_size: int\n",
    "        sets search window before a given index \n",
    "        used when finding action potential peaks, takeoff, repolarization, and decay time constant\n",
    "    i_ref_rep_win_size: int\n",
    "        sets search window around a given index\n",
    "        used when finding action potential repolarization and decay \n",
    "    i_smooth_win_size_takeoff: int\n",
    "        sets window size for smoothing function when finding action potential takeoff\n",
    "    i_smooth_win_size_rep: int\n",
    "        sets window size for smoothing function when finding action potential repolarization\n",
    "    i_shift: int\n",
    "        sets time shift from reference when analyzing action potential notches  #TODO: what's the notch for?\n",
    "    f_scan_int: int\n",
    "        scaling factor for time axis when fitting exponential function to find decay time constant\n",
    "    i_ref_max_APD: int\n",
    "        maximum action potential duration in ms\n",
    "    repol_percs: list of floats\n",
    "        repolarization thresholds to calculate \n",
    "    criteria: float\n",
    "        Z-score threshold for finding AP upstrokes\n",
    "    AP_peak_inds: 1D array of ints\n",
    "        indices of action potential peaks \n",
    "    AP_peak_vals: 1D array of floats\n",
    "        signal values at action potential peaks \n",
    "    AP_peak_deriv1_inds: 1D array of ints\n",
    "        indices of action potential upstrokes (signal first derivative maximi)\n",
    "    AP_peak_deriv1_vals: 1D array of floats\n",
    "        signal first derivative values at action potential upstrokes (signal first derivative maximi)\n",
    "    AP_repol_inds: 1D array of ints\n",
    "        indices of final action potential repolarizations\n",
    "    AP_repol_multi_inds: 2D array of ints\n",
    "        indices of action potential repolarizations to different thresholds \n",
    "        specified by self.repol_percs\n",
    "    AP_decays: 1D array of floats\n",
    "        holds decay time constants of action potentials\n",
    "    AP_repol_flags\n",
    "        tracks whether or not a repolarization time was successfully found for each peak\n",
    "        0 = unsuccessful\n",
    "        1 = successful\n",
    "    simulation: Boolean\n",
    "        True if running algorithm on simulation rather than experimental data\n",
    "    \n",
    "    \n",
    "    Methods\n",
    "    ----------\n",
    "    smooth(self, arr, width)\n",
    "        Smooths signal using boxcar averaging method (analogous to low pass filter)\n",
    "    get_first(self, mask, start, end)\n",
    "        Find index of first 1 in a 1D array of 0s and 1s\n",
    "    get_peak_window_Zscore(self, dfdt, arr_size, criteria, default_num)\n",
    "        Finds windows containing action potential peaks using min & max thresholding criteria\n",
    "        based on Z-scores of the fluorescence signal derivative\n",
    "    find_peaks_ind(self)\n",
    "        Get action potential peak upstroke indices & values using the first derivative\n",
    "    check_threshold(self)\n",
    "        Confirm that the action potential peak first derivative magnitudes are appropriately large\n",
    "    find_max_peak(self)\n",
    "        Find the action potential peaks using maximums of the fluorescence signal\n",
    "    find_takeoff_local_max(self)\n",
    "        Finds the start of each action potential (\"action potential takeoff\") and \n",
    "        the baseline immediately before takeoff.\n",
    "    find_repol_mov_avg_sub(self)\n",
    "        Finds time of repolarization for each action potential using moving average baseline subtraction\n",
    "    find_repol_mov_avg_sub_baseline(self):\n",
    "        Finds time of repolarization for each action potential using moving average baseline subtraction\n",
    "    find_repol_deriv1(self)\n",
    "        Find action potential repolarization time using the signal first derivative.\n",
    "    find_repol_percent_ind(self)\n",
    "        Find repolarization to the threshold percentage set by f_APD_crit using \n",
    "        signal amplitude\n",
    "    find_multi_repol_perc_ind(self, repol_percs)\n",
    "        Find times of repolarization to all threshold percentages in repol_percs list\n",
    "    calc_asym_y(self, p, y, y_fit)\n",
    "        Adjusts signal downwards for signal values that are greater than the \n",
    "        corresponding values from a fitted polynomial. Used in EAD detection routine.\n",
    "    baseline_poly(self, p, y, n_max, iters, degree)\n",
    "        Fits a polynomial to fluorescence signal baseline by iterating multiple times\n",
    "        over y-data modified to reduce the signal values of largest magnitude.\n",
    "        Used in EAD detection routine.\n",
    "    fit_expo(self, x, y, a)\n",
    "        Fits an exponential model. Used in decay time constant detection routine.    \n",
    "    '''\n",
    "    \n",
    "    \n",
    "    def __init__(self, max_num_peaks = 200, s_num_peaks = 0, \n",
    "                 s_rep_type = 1, peak_type = 0,\n",
    "                 takeoff_type = 0, rise_type = 0,\n",
    "                 f_APD_crit = 0.95,\n",
    "                 f_peak_crit = 0.2, s_interpolate = 10, \n",
    "                 i_ref_win_size = 40, i_ref_pos_win_size = 40,\n",
    "                 i_ref_pre_win_size = 40, i_ref_rep_win_size = 40,\n",
    "                 i_smooth_win_size_takeoff = 100, i_smooth_win_size_rep = 41,\n",
    "                 i_shift = 0, f_scan_int = 1, i_ref_max_APD = 5000,\n",
    "                 repol_percs = [0.15, 0.25, 0.3, 0.5, 0.75, 0.9, 0.95, 1], criteria = 5,\n",
    "                 simulation = True):\n",
    "        self.max_num_peaks = max_num_peaks\n",
    "        self.s_num_peaks = s_num_peaks\n",
    "        self.s_repol_type = s_rep_type\n",
    "        self.peak_type = peak_type\n",
    "        self.takeoff_type = takeoff_type\n",
    "        self.rise_type = rise_type\n",
    "        self.f_APD_crit = f_APD_crit\n",
    "        self.f_peak_crit = f_peak_crit\n",
    "        self.s_interpolate = s_interpolate\n",
    "        self.i_ref_win_size = i_ref_win_size\n",
    "        self.i_ref_pos_win_size = i_ref_pos_win_size\n",
    "        self.i_ref_pre_win_size = i_ref_pre_win_size\n",
    "        self.i_ref_rep_win_size = i_ref_rep_win_size\n",
    "        self.i_smooth_win_size_takeoff = i_smooth_win_size_takeoff\n",
    "        self.i_smooth_win_size_rep = i_smooth_win_size_rep\n",
    "        self.i_shift = i_shift\n",
    "        self.f_scan_int = f_scan_int\n",
    "        self.i_ref_max_APD = i_ref_max_APD\n",
    "        self.repol_percs = repol_percs\n",
    "        self.criteria = criteria\n",
    "        self.simulation = simulation\n",
    "        \n",
    "    def smooth(self, arr, width):\n",
    "        '''\n",
    "        Smooths signal using boxcar averaging method (analogous to low pass filter)\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        arr: 1D array of floats\n",
    "            signal data\n",
    "        width: int\n",
    "            window size over which to smooth\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        smooth_arr: 1D array of floats\n",
    "            smoothed signal\n",
    "        '''\n",
    "        #ensure width is an even integer\n",
    "        if width%2 == 0:\n",
    "            width = width + 1 \n",
    "        \n",
    "        #take average along sequential windows\n",
    "        half_width = int(width / 2)\n",
    "        new_arr = [np.sum(arr[int(i - half_width):int(i + half_width + 1)]) \n",
    "                   for i in np.arange(half_width, len(arr)-width)]\n",
    "        new_arr = np.array(new_arr) / (width - 1)\n",
    "        \n",
    "        #concatenate with original data at array edges\n",
    "        smooth_arr = np.concatenate([arr[:half_width], new_arr, arr[int(len(arr)-width):]])\n",
    "        return smooth_arr\n",
    "  \n",
    "        \n",
    "    #Find index of first 1 in an array of 0s and 1s \n",
    "    def get_first(self, mask, start, end):\n",
    "        '''\n",
    "        Find index of first 1 in a 1D array of 0s and 1s\n",
    "        \n",
    "        Parameters \n",
    "        ----------\n",
    "        mask: 1D numpy array\n",
    "            array of 0s and 1s\n",
    "        start: int\n",
    "            start index of search window\n",
    "        end: int\n",
    "            end index of search window\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        int\n",
    "            index of first 1 in array (-1 if array is all zeros)\n",
    "        '''\n",
    "        non_zeros = mask[start:end].nonzero()\n",
    "        if np.any(non_zeros):\n",
    "            return non_zeros[0][0] + start\n",
    "        else:\n",
    "            return -1\n",
    "        \n",
    "\n",
    "    def get_peak_window_Zscore(self, dfdt, arr_size, criteria, default_num):\n",
    "        '''\n",
    "        Finds windows containing action potential peaks using min & max thresholding criteria\n",
    "        based on Z-scores of the fluorescence signal derivative.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        dfdt: 1D array\n",
    "            derivative of the fluorescence signal\n",
    "        arr_size: int\n",
    "            size of fluorescence signal \n",
    "        criteria: float\n",
    "            minimum Z score above which to search for action potential peak\n",
    "        default_num: int\n",
    "            maximum number of peaks\n",
    "            \n",
    "        Returns \n",
    "        -------\n",
    "        temp_arr: 2D array of size (2, default_num + 1)\n",
    "            column 0 stores the total number of peaks\n",
    "            all other columns correspond to action potential peaks\n",
    "            row 0 stores the start index of the window containing the action potential peak\n",
    "            row 1 stores the end index of the window containing the action potential peak\n",
    "        '''\n",
    "        peak_windows = np.zeros((2, default_num))\n",
    "        num_peaks = 0\n",
    "        #remove first and last small regions (intensity may not be stable)\n",
    "        start_ind, end_ind = 10, arr_size - 10 \n",
    "\n",
    "        #get Z-scores\n",
    "        std = np.std(dfdt[start_ind:end_ind])\n",
    "        z_scores = dfdt/std\n",
    "        \n",
    "        #Calculate upper and lower z-score thresholds\n",
    "        crit_below = -criteria\n",
    "        gt_crit = z_scores > criteria\n",
    "        lt_crit = z_scores < crit_below\n",
    "        \n",
    "        #adjust thresholds \n",
    "        i = 1\n",
    "        while np.sum(gt_crit) < 15 and i < 3:\n",
    "            gt_crit = z_scores > criteria*(50-10*i)\n",
    "            i += 1\n",
    "            \n",
    "        #print(np.sum(lt_crit))\n",
    "        if np.sum(lt_crit) < np.sum(gt_crit) - 1:\n",
    "            #print(\"readjusting lower cirt\")\n",
    "            lt_crit = z_scores < crit_below*0.8\n",
    "            if np.sum(lt_crit) < np.sum(gt_crit) - 1:\n",
    "                lt_crit = z_scores < crit_below*0.5\n",
    "                if np.sum(lt_crit) < np.sum(gt_crit) - 1:\n",
    "                    lt_crit = z_scores < crit_below*0.3\n",
    "\n",
    "        while start_ind < arr_size and num_peaks < default_num:\n",
    "            #Find next index where signal is greater than upper criteria \n",
    "            #This will be the start of the action potential peak window\n",
    "            first = self.get_first(gt_crit, start_ind, end_ind)\n",
    "            if first == -1:\n",
    "                return peak_windows, num_peaks\n",
    "            if first > start_ind:\n",
    "                start_ind = first\n",
    "                num_peaks += 1\n",
    "                peak_windows[0, num_peaks - 1] = start_ind\n",
    "                \n",
    "                #Find next index where signal is less than the lower criteria \n",
    "                #This will be the end of the action potential peak window\n",
    "                first = self.get_first(lt_crit, start_ind, end_ind)\n",
    "            if first == -1:\n",
    "                peak_windows[1, num_peaks - 1] = arr_size - 1\n",
    "                return peak_windows, num_peaks\n",
    "            if first == start_ind:\n",
    "                start_ind = start_ind + 1\n",
    "            else:\n",
    "                start_ind = first\n",
    "                peak_windows[1,num_peaks - 1] = start_ind\n",
    "        \n",
    "        return peak_windows, num_peaks\n",
    "                \n",
    "    def find_peaks_ind(self):\n",
    "        '''\n",
    "        Get action potential peak indices & values using the first derivative\n",
    "        Stores indices in self.AP_peak_deriv1_inds\n",
    "        Stores corresponding values of fluorescence signal derivative in self.AP_peak_deriv1_vals\n",
    "        '''\n",
    "        peak_windows, num_peaks = self.get_peak_window_Zscore(self.deriv1, arr_size = self.i_num_frames,\n",
    "                                              criteria = self.criteria, default_num = self.max_num_peaks)\n",
    "        peak_windows = peak_windows.astype(int)\n",
    "        self.s_num_peaks = int(num_peaks)\n",
    "        \n",
    "        if num_peaks > 0:\n",
    "            smoothed_deriv = np.diff(self.smooth(self.data, 100))\n",
    "            \n",
    "            self.AP_peak_deriv1_inds = np.zeros(num_peaks)\n",
    "            self.AP_peak_deriv1_vals = np.zeros(num_peaks)\n",
    "            \n",
    "            for i in range(num_peaks):\n",
    "                ind_max = np.argmax(smoothed_deriv[peak_windows[0,i]:peak_windows[1,i]])\n",
    "                self.AP_peak_deriv1_inds[i] = peak_windows[0,i] + ind_max\n",
    "            self.AP_peak_deriv1_inds = self.AP_peak_deriv1_inds.astype(int)\n",
    "            self.AP_peak_deriv1_vals = self.deriv1[self.AP_peak_deriv1_inds]\n",
    "        return\n",
    "    \n",
    "    def check_threshold(self):\n",
    "        '''\n",
    "        Confirm that action potential first derivative peaks are appropriately large\n",
    "        '''\n",
    "        n_peaks = self.s_num_peaks\n",
    "        v_deriv_max = np.max(self.deriv1)\n",
    "        peak_crit = self.f_peak_crit * v_deriv_max\n",
    "        bad_ind = np.where(self.AP_peak_deriv1_vals[0:n_peaks-1] < peak_crit)\n",
    "        if not np.any(bad_ind):\n",
    "            return\n",
    "        self.AP_peak_deriv1_inds[bad_ind] = -1\n",
    "        return\n",
    "    \n",
    "    def find_max_peak(self):\n",
    "        '''\n",
    "        Find the action potential peak maximums using the fluorescence signal (NOT the derivative)\n",
    "        Store indices in self.AP_peak_inds and corresponding signal values in self.AP_peak_vals\n",
    "        '''\n",
    "        n_peaks = self.s_num_peaks\n",
    "        \n",
    "        if self.s_num_peaks > 0:\n",
    "            self.AP_peak_inds = np.zeros(n_peaks)\n",
    "            self.AP_peak_vals = np.zeros(n_peaks)\n",
    "            \n",
    "            for i in range(self.s_num_peaks):\n",
    "                #Set search window starting from the indices found using the \n",
    "                #first derivative criteria\n",
    "                ref_ind = self.AP_peak_deriv1_inds[i]\n",
    "                start_ind = ref_ind\n",
    "                end_ind = ref_ind + 2*self.i_ref_win_size\n",
    "                if start_ind < 0:\n",
    "                    start_ind = 0\n",
    "                if end_ind >= self.i_num_frames:\n",
    "                    end_ind = self.i_num_frames - 1\n",
    "                    \n",
    "                ind_max = np.argmax(self.data[start_ind:end_ind])\n",
    "                \n",
    "                self.AP_peak_inds[i] = ind_max + start_ind \n",
    "\n",
    "                \n",
    "            self.AP_peak_inds = self.AP_peak_inds.astype(int)\n",
    "            self.AP_peak_vals = self.data[self.AP_peak_inds]\n",
    "\n",
    "        \n",
    "        return \n",
    "    \n",
    "    def find_peak_deriv2(self):\n",
    "        n_peaks = self.s_num_peaks\n",
    "       \n",
    "        if self.s_num_peaks > 0:\n",
    "            self.AP_peak_inds = np.zeros(n_peaks)\n",
    "            self.AP_peak_vals = np.zeros(n_peaks)\n",
    "           \n",
    "            deriv2 = np.diff(np.diff(self.smooth(self.data, width = 40)))\n",
    "       \n",
    "            #Do not include the final peak\n",
    "            for i in range(self.s_num_peaks):\n",
    "                #Set search window starting from the indices found using the\n",
    "                #first derivative criteria\n",
    "                ref_ind = self.AP_peak_deriv1_inds[i]\n",
    "                start_ind = ref_ind - self.i_ref_win_size\n",
    "                end_ind = ref_ind + 2*self.i_ref_win_size\n",
    "                if start_ind < 0:\n",
    "                    start_ind = 0\n",
    "                if end_ind >= self.i_num_frames:\n",
    "                    end_ind = self.i_num_frames - 1\n",
    "                   \n",
    "                ind_min = np.argmin(deriv2[start_ind:end_ind])\n",
    "                self.AP_peak_inds[i] = ind_min + start_ind\n",
    "               \n",
    "            self.AP_peak_inds = self.AP_peak_inds.astype(int)\n",
    "            self.AP_peak_vals = self.data[self.AP_peak_inds]\n",
    "        return\n",
    "        \n",
    "    def find_takeoff_deriv2(self):\n",
    "        '''\n",
    "        Finds the start of each action potential (\"action potential takeoff\")\n",
    "        using the 2nd derivative maximum. Baseline indices and values are\n",
    "        stored in self.AP_takeoff_baseline_inds and self.AP_takeoff_baseline_vals\n",
    "        '''    \n",
    "        if self.s_num_peaks > 0:\n",
    "       \n",
    "            self.AP_takeoff_baseline_inds = np.zeros(self.s_num_peaks)\n",
    "            self.AP_takeoff_baseline_vals = np.zeros(self.s_num_peaks)\n",
    "           \n",
    "            deriv2 = self.deriv2\n",
    "           \n",
    "            for i in range(self.s_num_peaks):\n",
    "                #set search window before action potential peak chosen by 1st derivative\n",
    "                ref = self.AP_peak_deriv1_inds[i]\n",
    "                start_ind = ref - self.i_smooth_win_size_takeoff\n",
    "                end_ind = ref\n",
    "                if start_ind < 0:\n",
    "                    start_ind = 0\n",
    "                   \n",
    "                #find takeoff value: maximum of 2nd derivative\n",
    "                ind = np.argmax(deriv2[start_ind:end_ind])\n",
    "                if ind == 0:\n",
    "                    ind = ind + 1\n",
    "                takeoff_ind = int(ind + start_ind)\n",
    "               \n",
    "                self.AP_takeoff_baseline_inds[i] = takeoff_ind\n",
    "                self.AP_takeoff_baseline_vals[i] = self.data[takeoff_ind]\n",
    "       \n",
    "        return\n",
    "    \n",
    "    def find_takeoff_local_max(self):\n",
    "        '''\n",
    "        Finds the start of each action potential (\"action potential takeoff\") and \n",
    "        the baseline immediately before takeoff. Baseline indices and values are \n",
    "        stored in self.AP_takeoff_baseline_inds and self.AP_takeoff_baseline_vals\n",
    "        '''    \n",
    "        self.i_smooth_win_size_takeoff = 5\n",
    "        if self.s_num_peaks > 0:\n",
    "            local_sub_raw = self.smooth(self.data, self.i_smooth_win_size_takeoff) - self.data\n",
    "            \n",
    "            self.AP_takeoff_baseline_inds = np.zeros(self.s_num_peaks)\n",
    "            self.AP_takeoff_baseline_vals = np.zeros(self.s_num_peaks)\n",
    "            \n",
    "            for i in range(self.s_num_peaks):\n",
    "                #set search window before action potential peak chosen by 1st derivative\n",
    "                ref = self.AP_peak_deriv1_inds[i]\n",
    "                start_ind = ref - self.i_smooth_win_size_takeoff\n",
    "                end_ind = ref \n",
    "                if start_ind < 0:\n",
    "                    start_ind = 0\n",
    "                    \n",
    "                #find takeoff value: greatest negative deviation from the smoothed signal\n",
    "                ind = np.argmax(local_sub_raw[start_ind:end_ind])\n",
    "                if ind == 0:\n",
    "                    ind = ind + 1\n",
    "                takeoff_ind = int(ind + start_ind)\n",
    "                self.AP_takeoff_baseline_inds[i] = takeoff_ind\n",
    "                self.AP_takeoff_baseline_vals[i] = self.data[takeoff_ind]\n",
    "\n",
    "        \n",
    "        return\n",
    "\n",
    "    \n",
    "    def find_repol_mov_avg_sub(self):\n",
    "        '''\n",
    "        Finds time of repolarization for each action potential using \n",
    "        moving average baseline subtraction. Indices stored in \n",
    "        self.AP_repol_inds.\n",
    "        '''\n",
    "        n_peaks = self.s_num_peaks\n",
    "        local_sub_raw = self.smooth(self.data, self.i_smooth_win_size_rep) - self.data\n",
    "\n",
    "        #Calculate rough recovery thresholds for each peak\n",
    "        crit = (self.AP_peak_vals - self.AP_takeoff_baseline_vals)*(1 - 0.75)#self.f_APD_crit) \n",
    "        crit += self.AP_takeoff_baseline_vals\n",
    "        \n",
    "        #Allocate space, excluding final peak\n",
    "        self.AP_repol_flags = np.zeros(self.s_num_peaks - 1)\n",
    "        self.AP_repol_inds = np.zeros(self.s_num_peaks - 1)\n",
    "        self.AP_repol_vals = np.zeros(self.s_num_peaks - 1)\n",
    "        \n",
    "        #Remove last peak\n",
    "        for i in range(self.s_num_peaks - 1):\n",
    "            #Set search window between action potential peak and next action potential takeoff\n",
    "            start_ind = self.AP_peak_inds[i] + self.i_ref_pos_win_size \n",
    "            if i == self.s_num_peaks - 1:\n",
    "                end_ind = self.i_num_frames - 1\n",
    "            else:\n",
    "                end_ind = self.AP_takeoff_baseline_inds[i + 1] - self.i_smooth_win_size_rep\n",
    "            if end_ind < 0:\n",
    "                end_ind = self.i_num_frames - 1\n",
    "            if end_ind > self.AP_peak_deriv1_inds[i] + self.i_ref_max_APD:\n",
    "                end_ind = self.AP_peak_deriv1_inds[i] + self.i_ref_max_APD\n",
    "            if end_ind < start_ind:\n",
    "                start_ind = end_ind - self.i_ref_pre_win_size\n",
    "            if start_ind < 0:\n",
    "                start_ind = 0\n",
    "            start_ind, end_ind = int(start_ind), int(end_ind)\n",
    "            \n",
    "            #Find first time signal falls below rough recovery threshold\n",
    "            data_arr = self.data[start_ind:end_ind]\n",
    "            flags = data_arr < crit[i]\n",
    "            first = self.get_first(flags, 0, end_ind - start_ind)\n",
    "            if first == -1:\n",
    "                adj_start_ind = start_ind\n",
    "            else:\n",
    "                adj_start_ind = start_ind + first\n",
    "            \n",
    "            #Set repolarization index at the maximum of negative signal deviation \n",
    "            #from smoothed signal after rough recovery threshold\n",
    "            ind = np.argmax(local_sub_raw[adj_start_ind:end_ind]) #end_ind])\n",
    "            self.AP_repol_inds[i] = ind + adj_start_ind\n",
    "            \n",
    "            #Flag to confirm repolarization index is under 90% repolarization threshold\n",
    "            f_base = self.AP_takeoff_baseline_vals[i]\n",
    "            crit_rep = 0.9*(self.AP_peak_vals[i] - f_base) + f_base\n",
    "            if self.data[ind + adj_start_ind] < crit_rep:\n",
    "                self.AP_repol_flags[i] = 1\n",
    "            \n",
    "        self.AP_repol_inds = self.AP_repol_inds.astype(int)\n",
    "        self.AP_repol_vals = self.data[self.AP_repol_inds]\n",
    "        \n",
    "        return\n",
    "    \n",
    "    def find_repol_deriv2(self):\n",
    "        '''\n",
    "        Find repolarization using the local maximum of the 2nd derivative.\n",
    "        Results are stored in self.AP_repol_inds.\n",
    "        '''\n",
    "        #allocate space to store results\n",
    "        self.AP_repol_inds = np.zeros(self.s_num_peaks - 1)\n",
    "        self.AP_repol_vals = np.zeros(self.s_num_peaks - 1)\n",
    "       \n",
    "        deriv2 = self.deriv2 \n",
    "       \n",
    "       \n",
    "        #Remove last action potential\n",
    "        for i in range(self.s_num_peaks - 1):\n",
    "            #set search window based on peak & action potential takeoff\n",
    "            start_ind = self.AP_peak_inds[i] + self.i_ref_pre_win_size\n",
    "            if i < self.s_num_peaks - 1:\n",
    "                end_ind = self.AP_takeoff_baseline_inds[i+1] - self.i_ref_pre_win_size\n",
    "            else:\n",
    "                end_ind = self.i_num_frames - 1\n",
    "           \n",
    "            if end_ind < start_ind:\n",
    "                self.AP_repol_inds[i] = 0\n",
    "            else:\n",
    "                start_ind, end_ind = int(start_ind), int(end_ind)\n",
    "                repol_ind = np.argmax(deriv2[start_ind:end_ind])\n",
    "   \n",
    "                if repol_ind == start_ind:\n",
    "                    repol_ind = end_ind\n",
    "                self.AP_repol_inds[i] = repol_ind + start_ind + 2 #2 to correct for derivative-taking shift\n",
    "        self.AP_repol_inds = self.AP_repol_inds.astype(int)\n",
    "        self.AP_repol_vals[0:self.s_num_peaks - 1] = self.data[self.AP_repol_inds[0:self.s_num_peaks-1]]\n",
    "       \n",
    "        return\n",
    "    \n",
    "    def find_repol_percent_ind(self):\n",
    "        '''\n",
    "        Find repolarization to the threshold percentage set by f_APD_crit using \n",
    "        signal amplitude. Results are stored in self.AP_repol_inds. \n",
    "        '''\n",
    "        crit = (self.AP_peak_vals - self.AP_takeoff_baseline_vals)*(1 - self.f_APD_crit) \n",
    "        crit += self.AP_takeoff_baseline_vals\n",
    "        \n",
    "        #allocate space to store results\n",
    "        self.AP_repol_flags = np.zeros(self.s_num_peaks - 1)\n",
    "        self.AP_repol_inds = np.zeros(self.s_num_peaks - 1)\n",
    "        self.AP_repol_vals = np.zeros(self.s_num_peaks - 1)\n",
    "        \n",
    "        #Remove last action potential\n",
    "        for i in range(self.s_num_peaks - 1):\n",
    "            #set search window based on peak & action potential takeoff\n",
    "            start_ind = self.AP_peak_inds[i] + self.i_ref_pre_win_size\n",
    "            if i < self.s_num_peaks - 1:\n",
    "                end_ind = self.AP_takeoff_baseline_inds[i+1] - self.i_ref_pre_win_size\n",
    "            else:\n",
    "                end_ind = self.i_num_frames - 1\n",
    "            \n",
    "            if end_ind < start_ind:\n",
    "                self.AP_repol_inds[i] = 0\n",
    "            else:\n",
    "                start_ind, end_ind = int(start_ind), int(end_ind)\n",
    "                #find first time signal repolarizes to less than the criteria threshold\n",
    "                data_arr = self.data[start_ind:end_ind]\n",
    "                mask = data_arr < crit[i]\n",
    "                first = self.get_first(mask, 0, len(mask))\n",
    "                if first != -1:\n",
    "                    self.AP_repol_inds[i] = first + start_ind\n",
    "                    self.AP_repol_flags[i] = 1\n",
    "                else:\n",
    "                    self.AP_repol_inds[i] = end_ind\n",
    "        self.AP_repol_inds = self.AP_repol_inds.astype(int)\n",
    "        self.AP_repol_vals[0:self.s_num_peaks - 1] = self.data[self.AP_repol_inds[0:self.s_num_peaks-1]]\n",
    "    \n",
    "        return \n",
    "        \n",
    "        \n",
    "    def find_multi_repol_perc_ind(self):\n",
    "        '''\n",
    "        Find times of repolarization to all threshold percentages in repol_percs list.\n",
    "        Results are stored in self.AP_repol_multi_inds and returned as temp_rec. \n",
    "        \n",
    "        Returns \n",
    "        --------\n",
    "        temp_rec: 2D numpy array (len(self.repol_percs) x self.s_num_peaks - 1)\n",
    "            holds times of repolarizations to each repolarization threshold in self.repol_percs\n",
    "            columns represent different action potentials\n",
    "            rows represent different repolarization thresholds, in order of self.repol_percs\n",
    "        '''\n",
    "        n_peaks = self.s_num_peaks\n",
    "        \n",
    "        self.AP_repol_multi_inds = np.zeros((len(self.repol_percs), self.s_num_peaks - 1))\n",
    "        \n",
    "        #TODO: should be a faster way to do this with arrays\n",
    "        for repol_idx, repol_perc in enumerate(self.repol_percs):\n",
    "            crit = (self.AP_peak_vals - self.AP_takeoff_baseline_vals)*(1-repol_perc) + self.AP_takeoff_baseline_vals\n",
    "            #ignore last action potential\n",
    "            for i in range(self.s_num_peaks - 1):\n",
    "                #set search window \n",
    "                start_ind = self.AP_peak_inds[i] + self.i_ref_pre_win_size\n",
    "                if i < self.s_num_peaks - 1:\n",
    "                    end_ind = self.AP_takeoff_baseline_inds[i+1] - self.i_ref_pre_win_size\n",
    "                else:\n",
    "                    end_ind = self.i_num_frames - 1\n",
    "                    \n",
    "                #find first time amplitude repolarizes under threshold criteria\n",
    "                if end_ind > start_ind:\n",
    "                    start_ind, end_ind = int(start_ind), int(end_ind)\n",
    "                    \n",
    "                    data_arr = self.data[start_ind:end_ind]\n",
    "                    first = self.get_first(data_arr < crit[i], 0, end_ind - start_ind)\n",
    "                    if first == -1:\n",
    "                        #if fails to repolarize in window, return minimum signal index\n",
    "                        min_ind = np.argmin(data_arr)\n",
    "                        self.AP_repol_multi_inds[repol_idx, i] = min_ind + start_ind\n",
    "                    else:\n",
    "                        self.AP_repol_multi_inds[repol_idx, i] = start_ind + first\n",
    "                else:\n",
    "                    self.AP_repol_multi_inds[repol_idx, i] = 0\n",
    "                        \n",
    "        temp_rec = self.AP_repol_multi_inds\n",
    "        return temp_rec\n",
    "\n",
    "        \n",
    "    def calc_asym_y(self, p, y, y_fit):\n",
    "        '''\n",
    "        Adjusts signal downwards for signal values that are greater than the \n",
    "        corresponding values from a fitted polynomial.\n",
    "        Used in EAD detection routine.\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        p: float\n",
    "            percent of the difference between y and y_fit \n",
    "            by which to adjust y values greater than y_fit\n",
    "        y: 1D array of floats\n",
    "            ordinate variable data\n",
    "        y_fit: 1D array of floats\n",
    "            ordinate variable data calculated from a polynomial fitted to y\n",
    "        \n",
    "        Returns\n",
    "        --------\n",
    "        y_res: 1D array of floats\n",
    "            y-data decreased by (1-p) percent of the difference between y & y_fit\n",
    "            for all values where y was greater than y_fit        \n",
    "        '''\n",
    "        diff_y = y - y_fit\n",
    "        crit = diff_y > 0\n",
    "        y_res = y - (1-p)*crit*diff_y\n",
    "        return y_res\n",
    "    \n",
    "    def baseline_poly(self, p, y, n_max, iters, degree):\n",
    "        '''\n",
    "        Fits a polynomial to fluorescence signal baseline by iterating multiple times\n",
    "        over y-data modified to reduce the signal values of largest magnitude.\n",
    "        \n",
    "        Parameters\n",
    "        ----------\n",
    "        p: float\n",
    "            used in calc_asym_y as the percent of the difference between y and y_fit\n",
    "            by which to adjust y values greater than y_fit\n",
    "        y: 1D array of floats\n",
    "            data to fit\n",
    "        n_max: int\n",
    "            maximum length of signal to fit \n",
    "        iters: int\n",
    "            number of iterations that... #TODO figure that out\n",
    "        degree: int\n",
    "            polynomial degree\n",
    "            \n",
    "        Returns \n",
    "        --------\n",
    "        y_fit: 1D array of floats\n",
    "            fitted signal baseline\n",
    "        '''\n",
    "        #constrain signal length\n",
    "        n = np.size(y)\n",
    "        if n > n_max:\n",
    "            y_reduced = np.resize(y, n_max)\n",
    "            y_reduced = y[::int(n/n_max)][:n_max]\n",
    "            i_comp_flag = 1\n",
    "        else:\n",
    "            y_reduced= y \n",
    "            i_comp_flag = 0\n",
    "        n_reduced = np.size(y_reduced)\n",
    "        \n",
    "        #fit polynomial over multiple iterations,\n",
    "        #reducing greatest y values with each iteration\n",
    "        x = np.arange(n_reduced)\n",
    "        poly_fit = Polynomial.fit(x, y_reduced, degree)\n",
    "        y_fit = poly_fit(x)\n",
    "        for i in range(iters):\n",
    "            y_res = self.calc_asym_y(p, y_reduced, y_fit)\n",
    "            poly_fit = Polynomial.fit(x, y_res, degree)\n",
    "            y_fit = poly_fit(x)\n",
    "            \n",
    "        #resize to original signal length\n",
    "        if i_comp_flag == 1:\n",
    "            y_fit = scipy.ndimage.interpolation.zoom(y_fit, n/len(y_fit)) \n",
    "        return y_fit\n",
    "        \n",
    "\n",
    "        \n",
    "    def fit_expo(self, x, y, a):\n",
    "        '''\n",
    "        Fits an exponential model (constant raised to the xth power).\n",
    "        Used in decay time constant detection routine.\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        x: 1D array of floats\n",
    "            abscissa variable data\n",
    "        y: 1D array of floats\n",
    "            ordinate variable data\n",
    "        a: list of floats, length 3\n",
    "            initial guesses for model coefficients\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        popt: list of floats, length 3\n",
    "            fitted model coefficients\n",
    "        '''\n",
    "        def f_expo(a0, a1, a2, x):\n",
    "            return a0*a1**x + a2\n",
    "        \n",
    "        popt, pcov = scipy.optimize.curve_fit(f_expo, x, y, p0 = a)\n",
    "        return popt\n",
    "\n",
    "\n",
    "    \n",
    "    def set_data(self, filtered_data):\n",
    "        '''\n",
    "        Loads data and calculates relevant parameters to enable analysis\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        filtered_data: 1D array of floats\n",
    "            signal fluorescence data filtered as described above\n",
    "        '''\n",
    "        self.data = filtered_data\n",
    "        self.has_data = 1\n",
    "        self.i_num_frames= np.size(filtered_data)\n",
    "        self.deriv1 = np.diff(filtered_data)\n",
    "        self.pderiv2 = np.diff(self.deriv1)\n",
    "        self.deriv2 = np.diff(np.diff(self.smooth(self.data, width = 40)))\n",
    "        return\n",
    "    \n",
    "    \n",
    "    def fit_expo(self, x, y, a):\n",
    "        '''\n",
    "        Fits an exponential model (constant raised to the xth power).\n",
    "        Used in decay time constant detection routine.\n",
    "        \n",
    "        Parameters\n",
    "        -----------\n",
    "        x: 1D array of floats\n",
    "            abscissa variable data\n",
    "        y: 1D array of floats\n",
    "            ordinate variable data\n",
    "        a: list of floats, length 3\n",
    "            initial guesses for model coefficients\n",
    "            \n",
    "        Returns\n",
    "        --------\n",
    "        popt: list of floats, length 3\n",
    "            fitted model coefficients\n",
    "        '''\n",
    "        def f_expo(a0, a1, a2, x):\n",
    "            return a0*np.exp(a1*x) + a2\n",
    "        \n",
    "        y = self.rescale_image(y)\n",
    "        \n",
    "        popt, pcov = scipy.optimize.curve_fit(f_expo, x, y,\n",
    "                                             p0 = [-1, 0.005, 250],\n",
    "                                             bounds = [[-100, 0, 0],\n",
    "                                                       [0,0.005, 260]])\n",
    "        #bounds = [[-np.inf, -np.inf, -np.inf],[0, np.inf, np.inf]])\n",
    "        print(\"coefficients: \", popt)\n",
    "        plt.figure()\n",
    "        plt.title(\"data being fit\")\n",
    "        plt.plot(x,y)\n",
    "        plt.plot(x, f_expo(popt[0], popt[1], popt[2], x), color = 'red')\n",
    "        plt.ylim(0,250)\n",
    "        plt.show()\n",
    "        \n",
    "        return popt\n",
    "    \n",
    "    def find_risetime_deriv2(self):\n",
    "        #allocate space\n",
    "        self.AP_rise_times = np.zeros(self.s_num_peaks - 1)\n",
    "       \n",
    "        #calculate second derivative\n",
    "        deriv2 = self.deriv2\n",
    "       \n",
    "        #Remove last peak\n",
    "        for i in range(self.s_num_peaks - 1):\n",
    "            #Set search window before takeoff and after peak\n",
    "            start_ind = int(self.AP_takeoff_baseline_inds[i] - 100)\n",
    "            end_ind = self.AP_peak_inds[i] + 100\n",
    "            if end_ind > self.i_num_frames:\n",
    "                end_ind = self.i_num_frames - 1\n",
    "            if start_ind < 0:\n",
    "                start_ind = 0\n",
    "               \n",
    "            #Find difference between 2nd derivative max and min around AP takeoff\n",
    "            deriv_arr = deriv2[start_ind:end_ind]\n",
    "            min_ind, max_ind = np.argmin(deriv_arr) + start_ind, np.argmax(deriv_arr) + start_ind\n",
    "            if max_ind < min_ind:\n",
    "                self.AP_rise_times[i] = min_ind - max_ind\n",
    "            else:\n",
    "                print(\"ERROR WITH RISE TIME\")     \n",
    "        return \n",
    "    \n",
    "    def find_risetime_subtr(self):\n",
    "        self.AP_rise_times = self.AP_peak_inds - self.AP_takeoff_baseline_inds\n",
    "        return\n",
    "    \n",
    "    \n",
    "    def find_risetime(self):\n",
    "        if self.rise_type == 0:\n",
    "            self.find_risetime_subtr()\n",
    "        else:\n",
    "            self.find_risetime_deriv2()\n",
    "        return\n",
    "    \n",
    "    def find_takeoff(self):\n",
    "        '''Find AP takeoff according to method specified by self.peak_type'''\n",
    "        if self.takeoff_type == 0:\n",
    "            self.find_takeoff_local_max()\n",
    "        else:\n",
    "            self.find_takeoff_deriv2()\n",
    "        return\n",
    "    \n",
    "    def find_peak(self):\n",
    "        '''Find AP peak according to method specified by self.peak_type'''\n",
    "        if self.peak_type == 0:\n",
    "            self.find_max_peak()\n",
    "        else:\n",
    "            self.find_peak_deriv2()\n",
    "            \n",
    "    def find_repolarization(self):\n",
    "        '''\n",
    "        Find action potential repolarization times using \n",
    "        self.s_repol_type to determine method.\n",
    "        '''\n",
    "        if self.s_repol_type == 0:\n",
    "            self.find_repol_percent_ind()\n",
    "        elif self.s_repol_type == 1:\n",
    "            self.find_repol_mov_avg_sub()\n",
    "        else:\n",
    "            self.find_repol_deriv2()\n",
    "        return \n",
    "    \n",
    "    def analyze(self):\n",
    "        '''\n",
    "        Obtain metrics\n",
    "        '''\n",
    "        self.find_peaks_ind()\n",
    "        self.find_peak()\n",
    "        self.find_takeoff()\n",
    "        self.find_repolarization()\n",
    "        self.find_multi_repol_perc_ind()\n",
    "        self.find_risetime()\n",
    "        \n",
    "        if self.sNaN:\n",
    "            self.check_threshold()\n",
    "        return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "analyze_MA = ClsAnalysis(s_rep_type = 1, takeoff_type = 0, peak_type = 0, \n",
    "                          criteria = 1.3, i_ref_max_APD = 1000, \n",
    "                         i_ref_win_size = 500, i_ref_pos_win_size  = 50, f_APD_crit = 0.95,\n",
    "                          repol_percs = [0.3, 0.5, 0.8],\n",
    "                         i_smooth_win_size_takeoff = 100)\n",
    "\n",
    "analysis_dict_MA = {}\n",
    "cell_idx = -1\n",
    "for f in filtered[1:]:\n",
    "    cell_idx += 1\n",
    "    analyze_MA.set_data(f)\n",
    "    analyze_MA.sNaN = False\n",
    "    analyze_MA.analyze()\n",
    "    \n",
    "    #get metrics from the second last AP in each spheroid's trace\n",
    "    AP_start = analyze_MA.AP_takeoff_baseline_inds[-2]\n",
    "    APD_subs = [row[-1] - AP_start for row in analyze_MA.AP_repol_multi_inds]\n",
    "    \n",
    "    APD30, APD50, APD80 = APD_subs[0], APD_subs[1], APD_subs[2]\n",
    "    APD_mxr = analyze_MA.AP_repol_inds[-1] - AP_start\n",
    "    rise_time = analyze_MA.AP_rise_times[-1]\n",
    "\n",
    "    analysis_dict_MA[cell_idx] = [APD30, APD50, APD80, APD_mxr, rise_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APD30</th>\n",
       "      <th>APD50</th>\n",
       "      <th>APD80</th>\n",
       "      <th>APD_mxr</th>\n",
       "      <th>rise_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>569.0</td>\n",
       "      <td>723.0</td>\n",
       "      <td>772.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>399.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>267.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>249.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>556.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>551.0</td>\n",
       "      <td>683.0</td>\n",
       "      <td>728.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   APD30  APD50  APD80  APD_mxr  rise_time\n",
       "0  569.0  723.0  772.0    791.0       50.0\n",
       "1  399.0  662.0  779.0    832.0       50.0\n",
       "2  494.0  706.0  767.0    793.0       51.0\n",
       "3  267.0  400.0  579.0    552.0       54.0\n",
       "4  249.0  369.0  556.0    549.0       53.0\n",
       "5  551.0  683.0  728.0    746.0       53.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display metrics obtained for each spheroid\n",
    "results_df = pd.DataFrame(analysis_dict_MA).T\n",
    "results_df.columns = ['APD30', 'APD50', 'APD80', 'APD_mxr', 'rise_time']\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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