{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as et\n",
    "import datetime\n",
    "from skimage.io import imread, imsave\n",
    "from imageio import volread as imread\n",
    "\n",
    "import tifffile\n",
    "import pystackreg\n",
    "from pystackreg import StackReg\n",
    "from skimage.filters import threshold_otsu\n",
    "\n",
    "from pystackreg.util import to_uint16   # make sure version 0.2.5 (not anything below)\n",
    "from ims_to_tiff import convert_to_tif   \n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 10\n",
    "NUM_FOVS = 217"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to TIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles = glob.glob('Cycle_*') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_files = []\n",
    "for i in cycles:\n",
    "    try:\n",
    "        sourceFile = i\n",
    "        destFile = 'tif/'+i[:-4]+'.tif'\n",
    "        convert_to_tif(sourceFile, destFile)\n",
    "    except:\n",
    "        print(f'{i} has B-tree error or import error')\n",
    "        error_files.append(i)\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['000',\n",
       " '001',\n",
       " '002',\n",
       " '003',\n",
       " '004',\n",
       " '005',\n",
       " '006',\n",
       " '007',\n",
       " '008',\n",
       " '009',\n",
       " '010',\n",
       " '011',\n",
       " '012',\n",
       " '013',\n",
       " '014',\n",
       " '015',\n",
       " '016',\n",
       " '017',\n",
       " '018',\n",
       " '019',\n",
       " '020',\n",
       " '021',\n",
       " '022',\n",
       " '023',\n",
       " '024',\n",
       " '025',\n",
       " '026',\n",
       " '027',\n",
       " '028',\n",
       " '029',\n",
       " '030',\n",
       " '031',\n",
       " '032',\n",
       " '033',\n",
       " '034',\n",
       " '035',\n",
       " '036',\n",
       " '037',\n",
       " '038',\n",
       " '039',\n",
       " '040',\n",
       " '041',\n",
       " '042',\n",
       " '043',\n",
       " '044',\n",
       " '045',\n",
       " '046',\n",
       " '047',\n",
       " '048',\n",
       " '049',\n",
       " '050',\n",
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       " '052',\n",
       " '053',\n",
       " '054',\n",
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       " '056',\n",
       " '057',\n",
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       " '066',\n",
       " '067',\n",
       " '068',\n",
       " '069',\n",
       " '070',\n",
       " '071',\n",
       " '072',\n",
       " '073',\n",
       " '074',\n",
       " '075',\n",
       " '076',\n",
       " '077',\n",
       " '078',\n",
       " '079',\n",
       " '080',\n",
       " '081',\n",
       " '082',\n",
       " '083',\n",
       " '084',\n",
       " '085',\n",
       " '086',\n",
       " '087',\n",
       " '088',\n",
       " '089',\n",
       " '090',\n",
       " '091',\n",
       " '092',\n",
       " '093',\n",
       " '094',\n",
       " '095',\n",
       " '096',\n",
       " '097',\n",
       " '098',\n",
       " '099',\n",
       " '100',\n",
       " '101',\n",
       " '102',\n",
       " '103',\n",
       " '104',\n",
       " '105',\n",
       " '106',\n",
       " '107',\n",
       " '108',\n",
       " '109',\n",
       " '110',\n",
       " '111',\n",
       " '112',\n",
       " '113',\n",
       " '114',\n",
       " '115',\n",
       " '116',\n",
       " '117',\n",
       " '118',\n",
       " '119',\n",
       " '120',\n",
       " '121',\n",
       " '122',\n",
       " '123',\n",
       " '124',\n",
       " '125',\n",
       " '126',\n",
       " '127',\n",
       " '128',\n",
       " '129',\n",
       " '130',\n",
       " '131',\n",
       " '132',\n",
       " '133',\n",
       " '134',\n",
       " '135',\n",
       " '136',\n",
       " '137',\n",
       " '138',\n",
       " '139',\n",
       " '140',\n",
       " '141',\n",
       " '142',\n",
       " '143',\n",
       " '144',\n",
       " '145',\n",
       " '146',\n",
       " '147',\n",
       " '148',\n",
       " '149',\n",
       " '150',\n",
       " '151',\n",
       " '152',\n",
       " '153',\n",
       " '154',\n",
       " '155',\n",
       " '156',\n",
       " '157',\n",
       " '158',\n",
       " '159',\n",
       " '160',\n",
       " '161',\n",
       " '162',\n",
       " '163',\n",
       " '164',\n",
       " '165',\n",
       " '166',\n",
       " '167',\n",
       " '168',\n",
       " '169',\n",
       " '170',\n",
       " '171',\n",
       " '172',\n",
       " '173',\n",
       " '174',\n",
       " '175',\n",
       " '176',\n",
       " '177',\n",
       " '178',\n",
       " '179',\n",
       " '180',\n",
       " '181',\n",
       " '182',\n",
       " '183',\n",
       " '184',\n",
       " '185',\n",
       " '186',\n",
       " '187',\n",
       " '188',\n",
       " '189',\n",
       " '190',\n",
       " '191',\n",
       " '192',\n",
       " '193',\n",
       " '194',\n",
       " '195',\n",
       " '196',\n",
       " '197',\n",
       " '198',\n",
       " '199',\n",
       " '200',\n",
       " '201',\n",
       " '202',\n",
       " '203',\n",
       " '204',\n",
       " '205',\n",
       " '206',\n",
       " '207',\n",
       " '208',\n",
       " '209',\n",
       " '210',\n",
       " '211',\n",
       " '212',\n",
       " '213',\n",
       " '214',\n",
       " '215',\n",
       " '216',\n",
       " '217',\n",
       " '218',\n",
       " '219',\n",
       " '220',\n",
       " '221',\n",
       " '222',\n",
       " '223',\n",
       " '224']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lst = make list of 000 to 225 strings \n",
    "lst = []\n",
    "for i in range(0,225):\n",
    "    lst.append(str(i).zfill(3))\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "057\n"
     ]
    }
   ],
   "source": [
    "cycles = glob.glob('tif/Cycle_9/*')  \n",
    "nums = []\n",
    "\n",
    "for i in cycles:\n",
    "    nums.append(i[-7:-4])\n",
    "#print(len(nums))\n",
    "for i in lst:\n",
    "    if i not in nums:\n",
    "        print(i)\n",
    "        \n",
    "# WRITE DOWN THE CYCLE NUMBERS AS WELL IN YOUR NOTES!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    os.makedirs(f'tif/Cycle_{c}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    if c == 0:\n",
    "        command = 'mv tif/Cycle_F* tif/Cycle_0'\n",
    "    else:\n",
    "        command = f'mv tif/Cycle_{c}_F* tif/Cycle_{c}'    \n",
    "    ! {command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Registration (Testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 2, 2048, 2048)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test image sizes\n",
    "im1 = imread('tif/Cycle_8/Cycle_8_F053.tif')\n",
    "im1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Binary Registered Images... reg_bin_Cyc_8/Cycle_8_F003_bin_reg.tif\n"
     ]
    }
   ],
   "source": [
    "ref = imread('tif/Cycle_0/Cycle_F000.tif')\n",
    "mov = imread('tif/Cycle_9/Cycle_9_F000.tif')\n",
    " \n",
    "ref_max = ref.max(0)   # max proj of z for each channel in reference\n",
    "ref_binary = ref_max[0] > threshold_otsu(ref_max[0])  # binarizing for channels - T/F\n",
    "\n",
    "mov_max = mov.max(0)  # max projection by each cycle in for loop (ref above)\n",
    "mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # binary of moved image\n",
    "    \n",
    "sr = StackReg(StackReg.RIGID_BODY)\n",
    "tmat = sr.register(ref_binary, mov_binary)   # creating transformation matrix \n",
    "out_binary = sr.transform(mov_binary) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        , 5.51255444],\n",
       "       [0.        , 1.        , 1.33795096],\n",
       "       [0.        , 0.        , 1.        ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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WlhvXOpwLZJqux4RaABb9oHKj33YdyIXMr4moYRgTocv/AjuS98gtIKeC3omzBkpcv707MwBXAJdNccy2F+izcx4jqQ61FUllZ8nIx9RosypKlUjLhYjYJCtL4TCPsbSaoESPahhGzyh0n57g4W+t3zBF0lxTSwbLnpGjzASQK4F3UmzF0yC5MlhBGUsVSUvAz0EuBm4j7loG/rwvA+e5mWheh5up3o1TOFyCs2ZZARZJ+mPgxMzGH4C8ehalaQABXuQsEFhkaIm0WEHZaBhG59CtwFGMW7qm3yO7GPbtwlB5lD4m3W8IyBRi5KnOf4yk+aG7urUg0UqkwHEhJVJwe0+Zx1haTZBnmdflazIMI5/aMfgifNpa6zdMddQfBDggsy3rtpV1BzuR6baBS3CKmib9OQcg5wC3Eq8QW8K5Lvxb0J3A2Qxd7lYBF4F+Avgm/ZXi/SBJFWeRleVk0MeAfaZYpqYYgP4KN1BM6s+3tc5ek2EY+QjoS4AjyJ94SFiV+sxzd832G58A9mY2cZRWnEVS7BkzsLppqypk7EtZQVIfecvzVkyyi1RoKp0jaJk3I0s8wzDap5Iiqcy3raCTaKv/yJsNM2ZHJRlMcEG272NcOM6zSFrN9Ny3BPRwYCth4T3GImlH6ncyCDgK5EGqX8ePQF+RyiObJzgLlhkMHFonaSsP+t8hi7Bk21UglzE1q7WJEWA96KMFh1yFC7relWsyDKMYAT0K+AnjCzok8lZawZSWwbLbs9+T398GniiwVl3LcHKkhiVrngxmiqTC9KumPiE6+eRXuJZrFSXzJT+9Pkr3pbG0oNOKl9znoMPXZBhGPlFu3XXIUwKE8i8+pRBTJM0frctgG4EtVXOYjJHyxTTa7bgYPgDng9wNrMcJ6rdSO5ZRbjnIbB/MQFadAno3cFzyg3CHosC9ICfRnVXrBDgEdBthhWVyTacCz06zYIZhtIneDRyb2pCnIEr2VVUkLfptC8C1IBf5fd7tTe8EngIe8cqminHmTJEElRU1XVAkRRWxriKpwox03+SYeY6lNSmF19bRazIMo5hWX/UFyqSahk1jmCJp/ujdZB6p8hVYYo9sPhv4jv8xwAnxyQo3dSxKBDgU9H7GBxd5x38G5Ldp13orca3bwfLApE0K41XBqGV8l3qGPMuErEx9OfAuzCrJMHpC1Lsvzxop77j074QkNp+fZNBvAq9nGHNp4P++D/KaKuW3GEnA/A+Qq4QympkSKdk/JR1kk+EKKmXaxDHzSETgp65emmEYMyCnc455jRgriAHIi3P2ZeMjFbj+tMmyUmIHTvDeyfiKYQqywR97Q2r7kj9nkfoKAMUFZE3LbsK40Jf+vYn2lEhrfbVcC3qPf2a/6Pe1ERh6AByNmynPCn996DAU+Dyl904T17Y+ui4axixZ6//AWZOunlK+F+Ge6bp9WiVLGHe8KvBavy1xt07eG6eCXgHsVSHdUFYryiIJKg2UZzLLkVO+6KI0VOZKzaJFK5ZIo6Dm853jWFqTUnptPbc2M4xZEuEt3FrGU3vbhy6oYIYt5vrNImn+qCWDAbwYt2JVmvRMrALbQF7OzJa417OBQ4FfAveD3JM5IFlZrQ2+C3pmYHveDLTfV1lmTWJmFFgX6R3ACQyVY2tYnu3mVpBXVcwzkqCckuNq2CmLJEDfBlxNcayw5Hm4GuQd0yqZYfQY32frXcCTwBm4uEKPe1evKVA6/iqySAq5+GbPTc7Jfs+ah6fftZ8BeQ+lcZPMtS1Bg1/H017+N2VqKHDaKEOlG9uSImmWt2Leze8nIqZ+JfjVMIwJmJliPCbzVBmmrXAyRVI3qa1IgnFlUlrYFZDjcIGvZ0XiopbEnKgRnLQ2bwW9hvwVeyCskK36hNyFs/w5NbxbtwEH45RHoYHJbtwsdwsTar1VJAlwmLc6CwXdTUju8SJIG5ZfRnusZahs3THjshhD7gf9BfBKXN+axK9LXL0eBzmAcKDqCdEkHtHzDGPqJaSf+xdA9gB9wZcxbZEY4+6W7I9RJKV/RvSh5toWoPC+zerFFJNvtF/blKghSpZZLAeTLMin0YFPd3WrzRGpcDXmE6uz+SLWgnnW9Sapz2m+ZmZ93cYMeCJHeN0OPAhsm25xxkhcEBJ3tWkhwO3kPxRFD+Y/Uqx8SjgcdDPoUaAnOaXNiOJmNeg64CUUu30kebVt7ZgdACWDoiUga9k27wyAh4mfVVgAPajdIhkNczPoJ4G7Z10QI0G3+efoVTjlTNJ3pV291vt+8Aqacykd4PrQ53HP/B6MClhZhc5pbpucB3JZTlefJ1DWFdoUNGQhGcnKs0iCsQoIKunmQVFTpn1sMd9aNzZyVqpS2hWm8Bu9TRXuQZdmw6pYJgCtui0azTMztykjSJRCvKarV638C579GIvpVojsY8wiaf6YyCIphSrL1i3ycmBP4JYGEk4PBrq0qtehoPdSHEcn9DQcBXJfeRa6FTiS4VRyNvbS94HTqTzD3bQsNBL4PFSGJfcpezJdZV9dVoPuorhDze5bAraDvIhuXONKJYlJsxl4BU55sBtY060xQt9QcJMShxC2AMwZZzdSZ+L7sLznPb19EWQB2BundEosYv35uWO32EF3ut8OsQukRHlmFklpAjFg2ph9jZ2FzmXeLI9KqKxEKrtBM57CjzUO6zXd1TOvOGLeVcYMyevvAtsbr7MJXkattx+dUj7G3CICcoYX3u8FfjRJYiwPGPRroN9iNCB2F6gZe6lUibQKdDVwIOPSf1ppc1pshrQqpy4P5kJxshRYAGnBDaVx9vCfXwduZmSAONI353WCvyrYZ8yeAW6wrwwtXoRla74O22t0m8TC5gCqLQygoFdR20InnU5pDJ3kbxXwcYaukIlFLKDJipxZS6bYflcynyGXoFX1r3llKpKguBImfClm287ECqW+EXODqtywFgciRU1Byg6YE/LcB2PpwCWueMYUtJmN1v/MmFileIMVpWNfIo4PCCj2/BtTIbFA2k39Fc+S+DM3+sHba4ANoCfjVqfZu4Fyto3iAnw33WkL8FKcZdaeJcemRwYVOoBWZDABftP/2MlQaTTogKWHH5Tpl1x71DNw1irZcod8ipO/XSDrA+cY88NSSlkUUtAugj5GO6scGvksAucS56aWVax4F7PaLICuwfVZIWVxNr8l3AIPIQv1j+DaTkj53BQKnBTOvwxr1iUBqKpSZmDThXdBW4PO0nQnyVgbFipS7aILdZaHKRBWEFnlbPLZ5QbcB6o+hE3WWZW88wY30+hErJ0adZVHaRT0m8AR/ncyuyrAO0AXQDbR3mprk7IW2IFzfwqRfk4ybhHLK6rlufAp6Lf9dxndXpms3AywBPJLnAtJwyvtyXU+z9P953bgtmbzaBwBDgR9kOH9CE3dl/V7e9PMs2FMh1B9LgDrmdkKlCuaEyg2mQn1hQIczmT1tZhzft7znvTtC4Hz9mRZITliMRTSXcTIUqH9Arw5kHcEKzNGUksErQISOhJrJtcPs4QY65yqaYdklbITGr+/ZWaJc0xhe6zA3M/6rXCinqs2ng2jlNqv1wnrq6o1UmGeOiWFdMk1W4yk+WNuZLAkrtA1uBV5VhOOf7Hbu0LNG16BxI9BT/Tb8gYJ2W3Jz4ino7A/yiqpBGQ7Luh2Nu9A/gC8EuT28nLUJj2I6oByRe8GjmN4b2PuIYy221NBmogXZrSDgB6Pc8fN9i3ZZ/Rg4JHpFMtw6AuMr5IG+c9ksu8JkJfUzFRAD8YtGlH11Jw+QRNF0oBhuUPvuOzvSpnXk8FWrmtbw4wI7vMhWlViKq5PFZVIldJui5bcH6fGBBXbsRBdRgkd7JY6z0yfnwYrfBrXYX1NRwkJ6dNGQb8OnELxYG5ebfC3eNen4yuel1hNbyTuAbqZYveI1DYBOCEnWWE8nSXgk7R7jxdTf/OM4Cyzjio5Jo/dwH0gp4Hc2WTBjMZR4IcM4yKlBef087EIeg/FqyDOkqZWKZszZI+8HeTLSArsT/26UuCbNc/NiVMklzK6wlxMGSpSVwbrtCJpAx3S28xxIScpmox9aZ7YpFsrQshvfc6pa1mWMI16NZohqormtP/pTP89ZWZl5xHMtiPWtMb02QDot+bkGT4Op8RIv6NjG+1qZjqQ0pP8l5ASJq20CewTwQ1kYyrhVPJlmZCc8wDDWeoy+UdAHogsR1dJBqVlA0wF/Qr5gWvL2uUxIJfhrFx2lBxrzJYByIGMxsLJewaeKtg3bda6j+Xiftlvn1dF1yRswilns8rvUJ/n3ctqB/EXXHDvowL5pcnu88p4TfroLFfW8A6p0taOyck3gk4rktLM/NmMGREV7J95+aHyqM4GFtOj7P2UZZK6sXrtGHPReVQj25artu8uUet5muAhrK1Ejjinrb7B+pwO8yrcwPmxGeWfCO5FQnuItanvnwX9mv8+zYHUgs9vB+MdIowqxLJ/96cGFrHP+yWEB1R5LPq89mV8oBwq09uYf2uhqniLO90D9Kv+dn3S71ubcw4gf8rIykuxA0nuBnkMZz1mMXXmH8U9H115ifly6lcYLk2voGd5q8jLce26K9cTgVzJ6CRDntVYQwKorquZVnJOSDOTWIom5c6LhZeN/5cnaKcs5XgQ5P7UsUm/tib1V0BvFElQvd6aGrTUbS/ZtjuLgdTyoKNuxhUsdCr3SxVP6OMgtEyRXXjiJHTE8sqIZ16qcwJ9e/eoeNO78NjllXHScofG0X1VLvaKxIpmPXExwYSR2fAm0LWMD7rzhOZk9w/9wElBzwNe479fzfQewkXQnQwHblnFURFLVFN6DUBuY3ygkZfPJ3GKrlW44NYhMvdWn2R+3QdrkrQTngc24gbdv+nbSp77iuCCgWcbeJkF2AC4AHe/TYnUDRR4mGFctgLLPXlpePvUWMC1XwXODOwDuAj0Svr14h0QVoZnr3E3yM4alj9Z8hQv6fxyLKE4inwlUXKqgLzb/9jJcl8hSRp5CrMsS7gVQjfh+u9EI/QTf/hXcRbHNxaXp9uKpMS3LUVM288qb6bJhIZLK4dEay4r935UbctNsGzCbhgtENtWV+oz3wht3LyAYNyot2/S3xN+N2+YNH1jOiSDlDwEFxQ7NRve2HoveStiZQR1xed5XObcZCB1QYNliuEdhK2EstrUrELsKKq5XiwB9zIay6WId7tj9C6v7MrGgMneowEurkiPFCC6DTg2tSFRkiX37nTQ2wNdrgL3M65ciEDurVdWY44IKRAXcM9QHXepJhDcKoJJ2YrcLt885T6wbQSnCC5CcUqVAivDKBS4nlGFemOCUoqrvULpLJDTvRLp3QytILN/obwVOBinCAW40SvID3b79Ayc8PUK2FAghHVbkZQQqUzKG3RPbLzR5QduCpZI2dPapMtVkSYomExjet6USJ2kU9UWeskF2nZfnuXKlTPPldlw2QQnAPWmrg0A9AqCAxVVN8M5Mhv+POh14ePjMgN5kPGZWL8vt58JWfMkAve0GqSQH9+opX6gcLY9rby6FOdedUyFxBW3cl4PRha6DRcwu8jqS4CXk99eNgHPlmXk/5aotcqTMR8UPldZK8BZoMQHgPZ9YG+USYs4BXdWGZ61BjynofyOZfxdFCJvkuBJyq1NkzZ1M3C7z+fbhNta6J2mwKNeGXWfr+tEWZTkvcDQKrWAHnT3nmkMsguo/M4vGUhNU46pyyQ6qJj9fenDqqLZH6EOIKK9V9X1zfP41ShARj6KDpkpuvyv7KDcn71mLqwBNeKeN+nXljdLVqBgNOaYpD69qfwY24DDGFUa7QGcl3N8LAsg6xl1XSgqXxGJddzVTEVCliQOUVF5Qi4Qi6C/onpMp4G/V79iPJCzH4TIicAVoOfiLChCz2PegOwi5mPgXJcB6GHAS4hz0/N1wVOM1YVcBfLBkvNTgpo0NZA1ZoJs9F8Sq6OdwAu4Z27W73ZA01aYMX0OPXj9Ck4hHHPo1lZLEo0+Rfy7Z5Hh+/QhqlVY2voqz2opgv4okhJ05CO7eaZCqqTzz6MrT22dcsr415AcUjufrty7WGKuJ0JgnoP3lzEl8mT7uSD2+eyZ8iD6UuamoiIINLC6VaYx72PzbZtvArOqy0oOAQ4D3YRu3y0AACAASURBVIwzo88OziWwrSqLwBO4GeeQQsqXaeQRy1OIJtvfRvsKkWQmPOb6s8/HErBXjTyXgCdA3gqc6S95u/88ww947/HHbqM89ktoRr3rPMJonWT7pewMv+Asj0IDj6sySoRQ/BMFXgnMyUDWqMmt3sJjo39U3uT/Zv1uF4YLEiSE2nSIg1sp0XQQXOy+a4gb+19FfcvYGNL5phXyaQTYh/zVGtcyXsYvufR0F2Grqzy8klN/l+G7roYg17OweGFaH5P4l0qRJU2lfmQCzeDck3oBF16ihq3xYujF7auoQFOJt0jJfX90/qatYMoeli7WbQ8e5Kp68Hm/3EbfpenZhB4pDlccefW3J8uCs34dOIpRATirFHkM50b19ARF8RYx+vcMBeoF4Ocgl+IUIw8ybsUTevCmIR0v4WLpHICLX7R3TllC/BJn3VWXmwJWkMnU8m73XQ4F/QXhuCGS+UxY6EZflssS5cqw7AUK6IEgofhQiZVXcvzJoLeOHiKvB+6oUVZjvkjq/06Gfc+8sAZXvpCCNK8PX8RZucwyttMkDHALALwiZ39GsaanNdBvJbHifkm+tWiZYvoFXD3tHj9Gb8MF4z85tS+JsZeXT0ihJMChuP7p/8spZyT9VCTpsNMecxPKOb5sIF6Frr1A68rxrV1nkUaujE5LMJ6mBlU5Fdv122PkkPcgd7jCO1z0IVNWkrSll2ntMkyJ1A/SD+tCRgY7ivFBVeZ4XQ/yFeA1E5bjsyBvYTjweR6n1LoZNzAqWcp4FsjjoL8D/C3Fyy1vx7kCApw9Yf9YoPRY/v40sG6STHpGUed6fMn+hDsCFirzpHAwJid5ttqyzlvLqMXKalx/l1U+JChOUV8mH2bb5ao5sKaqi+AUOo9VOP5IGqkz2Q90O0NFUqxQJrh+N3HRTqOgP8Mpf+r2H9nJm4f89wd9ujXpn2ubZ8RyLdJVYiKPisiHLfqZ1OnK11VcYCZ2l8ma3DXsh9PVfq8VYu+p3bR+MAW/tpCFcFlf1ZugjVXpwXX3zNPQaBuvjFABPRzXeCIFXT0t1VdMovC5Edji/+7wn4s4K5v9yF/ZJuue9xjOzaBqP5p1P4i4FrnCu5ttIn+FodNdWURAHqhYpjbI6RhWpDixjbiOMjTQ74M7oNE+Xqmrm10/ufy3E/TbwOU4q8bVDPucPdxv3UG+JWbW5U1x7TR0TpdYR/6KmCGX3HsYKurrkliZvp96gtMC8LuM9Al6rH8vHo5TFtZVPKfHBAOfHjgleDq+YFroixhH9FKRNOkYqrbgnJfhBIWpa5QTPciTwp9j+xp3gypJZ0UKJBURKF85MO+haEnZYPSTomaW19/UVUJ0vVnOUvkyyb0rczmupUkM5RPj4mx0Dx9YVgBZID/WQwgvIKuCfoL6AvNi6i/tqpRY2WRXeAv9LeDia3wYZ9FUoaHqbaBbUgO970eeeAPIPwGvC+x7PciPU8UIKSRaQE4vOSDdBywBd9PtwWfimlI0sMogx3TYcsPoDj/0CqOTAvvOAL0E9PM4196v4Z7DLztFExA/ySg4a6R1dNOlzaNVzLMFp4Tb3kzeci2uL4ntp5P35AkgX/PlSTQ0/0ClCZnywrE8IYGA7E+4PUT2ab1zbUtfdyXZtimXqGzDzZoNanuuB0UUvAPHChRSUs/kHTmLG9VVqlSQCTxGDao8ivbYdq/7CnULUa7hTWZodI9EdtoFPAjymyy7Wug2wvF1sucnJA/Nbtzy85c1XlqXzWWgH6FcQaS+DO+MTHgPXHyLl/nfPtYQp/jB3VEgbyR/cLbb/cmtwFm44KkAO/y2WbA5sC0tUGbdJY6mWx1fFgX2IzzplnddP2u1RIYBgJ6QfGG8La7y284ZHsJmXCydxO0tj/SgT3BK1DUMVyTsKIUyWKjfP6johIos4d5hVzC8nyF2+mPX4VbLvMtv93WsW3GxAxPyBuihcoeOTX4nizwk7t6h8yP1Ir2xSAoaW1SdKc0cP5GbW4nGt4oMHVuOKBeTvB2hWRYClzEDC5aVpiNZaddrzD9j/UYlv7bq+cnyv+4yM3c+GX7EerVGWSE1SMer1kjjZ1LlWKeg4TbcwGUAcoTfX2TBFmoMftCjV9COdctnQP4H8l3IEnzZYp9l/X7m2MTyCeA9wNkp5VDZdd0IssX/3R6Xf1sIDOsx74DEimE1U7OWagUFuZ/hanUlyDHlxxjGRAxA00vYx75AT/SfsSYjieXSy7y1SoeVSFAitoRc23bQaAw9SVZsvDSzI22l+0GQk/1xd2eOGzCqRMqTxbLb0vGPwL2Pd/o/v1KbCEO3v/uottpboJidJni9ycu/bqJTGgCU1tMkSq0C4b+ShrbK/haJznoljlBW4jUbsyPvRTZhv1nj/dUrSt1T66Zb8jetPCE/z5Va531A3uCF0oeAHzKiRFCvUCpPhHAj2UQ7rhXKUJlUlr5/LguVSQPgCFysibK0vLsbV1Mcj2Mx8zdLBOS3KFe8nVCyv0tsAp4lf6Y/CUx+/9RKZKxEBDgQuJLRYPgxVHSFkpP930Plx3aerICSuPLtLDyrGsmKjZ9z78jlv3Wp75/CxVeDcQX8UznpRsiKcmAqj7NwK0O+HuSNjLvhDkDWM+rOW4FOK5K2ZDdkpdRJpNO2lElVyxSpTNL0F83sCJxYqEya5oijDAl+DR7Wp8FIrCWBYUyNWOujGrFz8iYDusxML6FO5plOVJf/NcOYZavRebYA/MD/CCk8HqbY5H6WKM7tIDQTmy2fn7FeDr6dRnCxlP6ecje+VHp6LvApuhNP6MsgSeym7QzrejewBLIB5N7ZFK0N5CogsVpL4qbsZKh4TGb1Z63kM/qN4vrRM3Aj9qYthH37FQHuxS1O0OG4SMsILkZf2cBZcQqfB1ssS1l95dxv/QmjsnTZrGDyt8Doe+WHgLduXX5fp1kCnmD4LqxIpxVJG/yngguqlaWBh60VfVKkcqRyIWIemBrJzhPzpONqjRzXlOCsfu8u3phLWuws+qhEqoos/2s40SLznzIzoUnzjt0n4c3G/LMBYE3O2GYJOJ98KTNyQKQH1y5eFHIJ4ZV9si9fH3ybjzHeWNcArya+nxRgL9CL6c6g7QXgdj/Dfb5XHAnIRj9gu4VmZ/NnjYJcCfL7LFuayRuBs0FOCczqG8asyL7TY1HgF6m2vJv+KEaXCLtspUm2DUDOmW7xotjIaNnzDEXSf7txSrG0IinSunW5HVRsA90Otr1lVIHU+FgnMtBULZJGUbEsuUWqqmySkWTnm9h7NfcXUo9cI7ueXq8xZ0xL47xC23Prlx2jTApRs94nUXJXfS0as0dT7k7LdSc4Af5LjFZoWZsIVf5DEeelWYsbRKQVNGsIKzkGwI9wwnfIDSSQr76dUTdUYTSeTvYaSsreCRksIRlg3MDwOn/sP6u63XSBJeAfQFaxrEzsTF0Z/Sf7wszrTPIarbAcnLuXDHDK/5/hVmQLWZoqyACndOoKeUKSV57JsUDNFfdEQC8ErsW9MyOsbDttkTQtWhNsa76RxspTp4Bdk9aLZs37aJZUdj19u15jfmmxrS1b4vS0PUdfVs+uv7L1fcZKt8dNov8klZ8EFC2rzKxSJmBuHNWWEoH3ZuAq3MAh4Yup72nz/SVgK07ozjNzDlgqLcdLSlwnns5JI89SPvlbBH5Fd9zbEtKWC32xYMhDGQ7I5iVmlTE9mlpyfVIuxvVXVayP0srtnHADsrWZ4s0lSyBPAB8gvFiA4rQgQvcWCAi9axaAY4Evgn4zdbkVTYbkOm9t+urUxoKJgu4rkpr2F52AkJVZTLiQaIE5Ml5SNHNy3yoRI6j1hazryUq4ZmOZKn1I27TW5PrcliNctub58uuWLWslHN1mAxkm3d1YPESjO8Q0gDJz/Zg0bnYKHj0O9C2gz/jfCnrO8Ds/wAWvTSGXAs/k5Jl99w5SRU4GIcnsb16Zi8yO9oy4NsMwpsdaYJV7l+mNqX5EZyiDfQ7X3+QFgM8Sc8xRzLcQ0hByPfDbuHu3g+EkRxLnbE6RR3EKnLS7dcQYUN8EvMKdo4pb/XTv4nNGSJRGd7AcsHvLzfmHd1uRtCH1Pfvybupp1/LkqgjLecfOSpk0x8+QAVZBK5Cy/qQPyqSVpg/NXu/I7z7diJxrmUSZZPSEyNXRcttQwSpnuhX0WP8jcUNKk5J09RWgn83svxLkfZky5M3mJ5ZEj+FWpVsCHmHo3lYk7AdmkGU13YmTZBgrAL0NdDPu2T41vYPlwfn0C+WVHkkA+ArnhX6LgDwY2N9HngP5LMhbgHOBNwwVJHPNSxlOViRujDGTK6sYtUJ6O24xiKoan0grrW4rkvII3OCpDVzSiqyCil7eVadQSUeQ2jTvz4NhGOVUUUjPgib6mRXVV8n4TynY33maMJ0LDcTNJKk7ZNu0V5LIuzK7tjOuQMlrNwr8Bm6VtbSriQAHAEcQ7x4mwCm4FdgSdzjFxYRIhPa889IuBOtBnsS5yh0PcjyjyyeHnoP09rZXCjIMozK6DTgaOMlvSI+Sk74hWcFxmi6pybjvM8AHU9urKqHTVjhdc+ealBtBNoOEVi7rMwPgQlqLY9dPRVKG0ExwJcVShFVSrjVUBTPtqmOKSJ1VLn0bwxhGr5gXvzYY6SwmUcr3WomSR9HAtM9k2myt5rvSTNf6yK/5gctVgAxnguV8kHcyHm8mbcKfZk/Q8wPHr6WaJbr4c9YzdHHw2+XFEeeni/oMsAT6V6B3M75CUFHbHYCcXS2/zrAGN8heS/fiPxkrl3uBQ3DWHKHYSMk7LVnBcRaWhApyle9TBbg0tS9UnnQ/1AUrnDZpOsbZutR3wVnMrvF/TXEbYde2qvW4qr0hRO8VSWX3uq7ypnxjhbQyg7TKaazkjiHAPIy7DaMqmv5SNDMfo9hugxzrmth32ogr10rrs7I3a8Lrz5ufaLLf06bqyDriFY38zH/JtoPvAdcwXBEr5tlYAL2KxgLg6qdSaS0CT0YMtNIP2uHeve2sKpkCD+BCM/y8WnnnHu96qF8B/Tjoj4DLqRafwzBmhB7N6AqMWdIuRougTzIbRelSqjifS7lpbSo4Z6UrkZrCK48U0C0MY2fdBvpl0G+B3uSPrRjkeoQB6Bpc8OwQsXGy0u7YLbXXSS6zOyQPfoBoGTfRBtZOoIRUGQuKGy5PjTLI8r9+keMO3MdLNfrIvA+6Czqnou5Ixr4YVanSNOau38t7fxr9IfTyfajgeD8rrLcApwT2h9rLEvBKnORaZVY5r+39s3A6IlSLg1Km2NqFm6XejSv7OSD305qrwcz4PugrcPdUgN242FUfAblstkUzjFwSF9mq8tfzNc5pmlT+8nkg6bt2MGIxI0dOuVx9YzXobpyb/Yl+W9oC7ASGq+olcbSuA7mIehZQS4xazMJoW6sjT8UGaq9Iry2SRpQlTcwIN2kJoMVFiC5iSWHy3Pr6SNGtmHVfbxhF6PK/KifMiJKOJLu5r/3NNLH+y5h7svJVrOvWqwLnFlkE7A+aCNiKizNUFOg6L9TAbpDnyZ2hFfHJLAIvRFxHQZH5LeBXIGeBvAbkIfqlREpWsnt56rcwrJeLQR8F9p1F4QyjHN2X8ZhBZRMgLynZP20SYwQBUn2vvAA8OosC9Qf9Ka7PPj61MW2KM2DoEplsD7lix9K0dibKQqUevVUkRT3bNTuA1vqNgABVmFdkoxhLtofapKBbkOYcYxg9YC4e4ZyBX3DzXBS4e+T2WxG+bRP1edZhGhMgR9KO69ZT3h0uzSbc7HBZm013TIlLXVGcEwF5NfCG1LayGav0n5+hlqtB9sfNZv8QF5S7Tyz5GfiQUi5RKB0Aev10i2UYUShwC2GFdOj9KrgVF9cwtysuyi0pl7c9mUgZvtLRbcCh/kesH5ePpaVXUC9m0qQTDUWTMQ3TS0XStN0oGs8mZ1BmlJCdecxuG/9pGPNBHxpmmSWBURnN/ogNhNSH9mR0g2y73A2yEzcDPumqQAErJ04YT1eupDi2Sai8sSum3QZyM8PgtqHlmIvKm6bJQK/zguCWqV6ivG/aAHo19j4w5osBcBDlqy52iZW2IlsbDEAPxVmeVQkElH4HbGJ+Jg4SpX4M6XfXOgr77F4qktpk7F5Kzvaq6UQcUEuhFHLt6yMV/Nq6/G4w+knj/ccsyfYzc13YjlCn0+pwR5eV5TfMtjhGLIoL6LmWyWfA82KxvSJne2w/s9ulLZdEHr/IstWB4M9LrJ9i5KpQ/Kc+IcAvGVWw5d2TBeDCivGnDKNtFGRfxttsqA0n7mPH5Ow3+oPgJhwSJVJsrMesVepmZhOROiuHHwB8KrVtHaOKpbUMF0y4iWEg8S/DhmPys+ldsO1pWyOF8i56R1YqVp6ff1V63tlF3Zq5iz5rGD3HnrVm6NGgqzAQe85+o1vIDpzCoA3LmyOBhwvy9o1pRFHxAs61A9yA8Uyci1mdld8E+JFPc5/Ic26tkU+XWAI9G6egy1syPfv7MeBo4JmWy2YYMSiuX1md2ZYmPSn/OHB/4BijXywC5xBnclNkjX87020r21lWCI3ERloLemHq97eAJ1KTKreAPgWcwej7+7XA/vnZ9dMiqaUBTG6yBfFBcido6pgg2MAsTI0H1Pp/o4tYN7DCmLSjmkFHV7V99sGLwMjQVCeVbhRH4YJUR8QkSWKDiIBckPo+AG72B9VRdCmwFXg/1mDTFCnVskLwKmA96IcZKp7W4GbD1wbOrxNfxDDqcDGlLyMBZD/6Ono2shRY4uRarGVcvXkf9drLqSX782LyHVRQzvT317JsIaqKW2Fzo9+XLm/JpEvvHgWtKMDEHl7HFa2RY5s/vRO0JqMFAtEWxaw1WdGYFmXP9USKaKOTzLKa6+Qtmc8RYjtT64S7i/iYEhXicxS2s1SDkgeqpbvMDZnfDayWJtfipOdnAzvT7XbRf8bGpZgH/JLhY49d0TVcxzB4eZl7W8JloF6Zp9eC3gH6jdSgJvn7fkT+htEA8nlcm93BMK7NruF3WcRZ0q1mboNsG80in2C0b8uTTfLklAHwFWq9d+SOsgMyfwOQG4A1+WF4RlhgVAuUrDpXkd4pktog2l2u7OXZoDlBHy0Tss/mNMYSMenaeMZonVSstcatGQ2jJnWsi9KfwQPyOtSiTtaCJHWLK4gPnPBOluMWBQVz8e2wrgKojaCz6i2cPhjYl+60F/xnRwadCugW7xqYVehcDuzN+Oz2QYwG2i56jgMCnirwJuAwhrPhaU70+f/An9q7gBzG3LDkn+s3OhdYAeRikHN9sz8H2EY9t1ijm+wGziQcF68sno3iYgaeTz0L2N0gG3LyzstyA/DosA+fxgC2l4qkpu7XSH3VdUWLmZmpmm7+z17T2HNgGiHDMFYAE70fapokVbIKNi199wnIOrqJYgXOutT3P2UoiabbzsAP6posa1Mk7fUakPWBfYlLw066ZUmjwMty9r0d9G8ZxptK2EFcoG0Ktg9w9yk0QE8URyf68l2Tc5xhTEryXN+Ei4UmwJeAG4GnUsclSoFVDGPRGP1EcVatyaqgFRU0sh/1+6sl4LTyPEZY1bA4FZFY/xRJOvKRe0gl+XXeJJlIZdK8FbuIKl4PrWQY6dtm4x2jNUoeWMUN0s3rZwXRpU4crEGuZDIdU9HKXInViyroKYTbeRfa/m5csNKsMkmAX4GspTvWSNv8l7xBzwDYBPp36ZNwM+5VqFuvXqGkb8UN5M0yyWiLRYaK8KQfuwf0OxkrvZtAv+73m3Kzn2xl9N1WpizfhVOuLzFUkE+y8MSHcXHiaiixlqlj1JLkF0H/FEklVLnvc+0/1lfLpAhlztjxTeQVc5xhtE2gs08USHlY8+wvk9Tr8jts2lhjXJlECquatXpJS6GKE7q3gqyjOzzB8opxy+1/H7oxuBzg3MpeQvmzK7iAxI+mth2EW8VuGq4USfuARmJdGUYMuhX0lYEdJwOneqXSlZhys28cCnpExXMuxllPnpp6J8ySojJk96XjQFWgn4qkJsxb5qEBlBFQJo3IcV24BlLVEKkwysooU7tMGyAZ0yT9MFvbW3FUrfI2PKkNI4qQEmEJ2MxwcCXAcbgZ26yCJRtX6FjcEsYdQwTkVf5zLZPNRE8RfYTRQXBe5yPAatADQB/DrbL2CMj7CVteNfHektRn0j6yrMl8GkYTJG3tCMJta4Gh6+pFtBOPzZgZ+i3GFxHIDj5TfyIgXwR5gPJA2dOgSBBscEzRT0USONPq8U3DLyU3sTPjtj74tUHcDQ8dM+3rTLUrHd3cnTZjdIaxNlUw62vtrz9EKdenQdfeI8ZsCMWEHIDeysgLU79IeNY+0M71aropoc7DAKIKSzglUOI+EdJEZ985i8B6nAsHuODq2fPLNNqS8z1E1m3yM67cmgz0rwdd7VyPAFMoGc1woLegTJQJefhnYlm5anQeVZwCMdv3xchEu3F9ZNIWhNG4gLEk1qI7U3nX6V/T1JEpS665i6/pyYi5iRJ/6FzQ9SnoKjc6O3CeUSWllUmmUDLaIFqZYA2un9St13TQYybrk6xpGdGELJLOZdge1+OWzg4Rkl/eBtpF96WOWCE1ge7bVsIUzvxzsR/o3ejdil6HG2wlbkYfB/ai23KxMXP02xEHpS3l0spVo/tkrZHy4hR5d+wxZdHVvj+6DfRj9Yqgj1Q8oQ0rpJLzVpYiqQmXt3lmpfg1+PqZZ/e9rjYhY87oe59ljFG7KgOxtbLpWjMxWiP0Lk7PuDyOG2xlZZS8d7jFG5kfsoMpbwkkTzOZDFa3Q0pGLq/ynwuZz02gH8GtMDenMqIxxyRuuAeRawG+THbbVXQjNpoRZgCatgIKWSSF+sOsO/Y60HNxEyonAL/JcpB2IM5qMmstWtYWi8g7Ppn4SCtAKy4QsaIUSfY+6Q8ykyBJBdgozWiQutZ21gR7wgQVWdYVVeqqptCgLLZTj8i4tnEeI3FDCus3NMv7FMMYJMZsCFma3c14cPFZULLCHIkyyTCqoKBfAvbIbC9yF/LPhm4oOM7oBr/M2R5Zr6qgL+DaT/IuTLQtXpmkn5y0kBUoUoCe4WM7rfOfgus7k2NeoFSx1Os5n5E6r6O9s85gLlmullnVUdkoLeMaac3ImArWZ61c8tyxCzqhWTeXmPB+SfG3tFwWoyEyjUruTe0bAPvjhPQY5ZAAz2La8VmRnn1PIyAXzKA8VRmA+gGRvIsV5XJoNMCxOduLXpxLwMEF+435Z4l898Sk7tOfmf2lr6ukbVziFEqtruyWp0Dyyi1JlFwZ5Dp/zEmgLwKegS235mfTW4uksbqxB7sXSPqLBLbPA1r40zDKqdlo5uo5MOpRp+6zAkxe8LYKfVOb/Vbs+9mslLqLHMl45e0d2AZhkzQBDsFWQpoGl+BmnSPcJgTgXkaVgQOQ/YDnMmmE0ipyEXqhTuELGACXgZoSyWgbAVaBrKGya5AxRwyAFzHu2pZHQTiB0nwAvQL3Xgzs1yNwSq08t7oi8vrYJ925ywqsUBzCZNudIDeC3F6cVacVSVvIl0mM7lC5vhqu4FbcKkx7ZEwZ6/f6wUT1GOO3Ftk3tdWexiYDJPM7hJkkzSVbAFmf2uAH7HIk8BDjyswYzNdxugxAfgQ873+X3fMTAtuWcPFBLiNOOy2jnyIgrwR5g9++yGi8kSxZa4BsmbMuks8VpGUYk+IH3rIR67N6gOyL63+quJVUrffk+HcAfx84X0GWyNfSxCi4sn8DYH2Foi4xXIGugE4rkqaB6QPap8o9jlQMRxOw2m7uPWCNxzCMaVGzI809bVb9l+R8GvPJDuBiXDyjVcDrgfsJWxLVqEt7jbbMEuhWYD9G3TbSwV1T6J/jRg5Zq4tdIDcRdkdMD8gysZXk1/zvO4Dvg2wAWYWLL7IL174WcdZKoaC3aULuJgMs+LExGdl2l22DSbDlLViH1XWWcO+vfXF9R7a+iz5Df2lFTs4x+nrCVklFZPvSKu3uKhqVq3qhSApOaJrw2RlmNfFYlGdImVsLe6kYNag7uWH0g6nU54z6JoXyCzQlUnd4DuSzwNu8Zcn3Co6NWanGmDoyAEkCC4fig6QHPufjBiIwFkSWx4HfYWjdlJfGEvCAUxrxs8xxt7HseiGvA3k9yGkgH8ApqaIvKvV9DfBZTKFkRBMVuyb1DpX7WiuKMQOiYxcl/WVVeSqthFwPfJS4/ilPSV+hDPr2+GNjENXujnRFRIM3I68BxATCCqRlsmzLxMyOJ0jOzyppjCcTnsnKMMmT0mpANaN/6MhHIaY87xkV+rLM5H49ZORjJPPGpQMJfo1CVa2FzxkioqxlKEwnyxTnBSu9F/ToqplY1zZNdBNwhf/xArAudBBOgfRFkAsZdX0Q0IsYKptS20d+Ho2b+Q/F6EhIBlZe+aSvBm5KbcuSluPS3xe9lZPFrzGq8A7QKxntz9bi3J4SRcAiiK0M2D8EWAO6HRcvCfeb7bg+8XlgT5Czge9AUJWStlZK0oTxvkuB+0GOZ2jJOwAOB/0pLh5dKJ1QWtn9efkNqo9L82SwzlskqaQmOgpMR6pafhlTJDO4qGIBtFyvdQcooRgdOQlMYplkbc+oQ16bG2uqNtLqDxF92aysOCemosLfmH/0606IVgX9MPB1xpdcX+Xcp/Qwwub9xtwgVzFcBvpdhOMLJZ3P+aC7QT/jf68DFORzFHdSxwA/p1iJBE5BtTg8Tn4Isgk3sI81Gxdc4NrrI/IzjDTX+GfhbOBs4NV++0kgZ4G82pRIvUWBHb4ffD2wCdcX+RhxcoHfl2eBW/W9lrVGWgLuwymvqi4WEDkDqVcDe1VMO5Rcly2SNoro5px9QYVcjUs1S5IpEaib3OoqUPSM+fPnHVd5OjyiXAVYOzIqE9vQrG31k4odzaRWSSGLpInTLcgv9RGFWSTNHxtFdPMuXGwkcALv88BXnYKRkAAAIABJREFU/YA/EYCfBt2H4QxtBSQdX2KV/76K5lf4MsbQvYGjcbFfVmV2JnUiuEHPAOQK4LcZ1rtmZO8lYMFbB02wkpoqzrIoW6Zs+XbgLEgeBDmsfn7GCicZ5AvOYmQ1Q6WkrQjYfxZw9a24ut/lt2XqfkSdErKMzLMkUuAhb6UZiC2oFwLX4qyhBoy6iBcJaGWWS8+C/AzYEM53rBx9tUiKYRJBOC9+ltEwgRmlXHkzXQlZBU9bQ41U+WKzsDAfRutY41q5VDXfnCCbxmlTSWVMl/RM6gAXNPRinJDtBV69m9F3dl7FhwKRKugt/vMm0O85KyigWJFgTIw8ixtklHUCyQz3pYy5ubkd/vsCy+5AtRmAnMnQ3SPvD2c1AN6ixDDqkljGJYPtXaltRv9ZZPjO2pXalmXjBHkU9LFynd9/Hm5lzDSKa5ehVS7T79lQH7k36GYmFsS6/RreAKRNklIVMXZfGpZYQwpGowHKNKhE7E9pgIXxZ2nSSqsSt8bah1Ebazwrm2xfWNIesn1d3axCCZY1RVMIGSN4BRDfxSmWfi18zFjDymvEJ/rPk/3nIujjVFvK2KiOAN8g7gH3cYx4DGfF5ANjyx6gz/l9T4CkY2rVKc+BoDf49CSzL/3TD5YksWBrkyQ2WHKfdhYcaxhGL5FbfYgd7xY3Zj2Uh4IcSP7LbMnv+h7j/d6JwJ6gxwB/y7irWjZOU3bfawvyjaTbiqQsedqdFqXcGpbaRiyxI6NUvS/XR965kyqR6ozWptBAxpRlhmH0hxiLAK2mh4/ONyKhtOFBNPby7D+v8Z+h1WiKYtpkG9JC4HOtO05DbplGc0S4PADLFke6HuSjwHtw7oeJO4biBjnfB06lXryi9aCf9d+z/hTpNrOU2Za9hiTuyCJuFLSK8Ix+LJ8DPQ63at1qkFMnSMswjG6yOzX+PBv0xcA1uH4wCfafRRlaapYRsoK6y50rW1z++mni37cCvJX4Pj6HfimSEpoUUIusXoz5wtd7WpmUDQE2SdNoSh/ZtH4rm56NzwxjBZIZgMcqlUr7isa1U8aKIW8540jFaBRXAL+FuZnMmnSYgfNAPgVckpLBBNgD9JcgNYNe66+AV5BvzZZQsoy2/ginODoZ59VwL8g7GcY/qcJW0CNx7e8Ilq3xLC6mYaxgvuf7uaxSKWGX37d6wr4i6UsHwK24fiiv/wv1m/dOkLen28G2N4puzou23Tax8XyMydCKYxcZ/TrSvCX4NbYYmS8lxSjIoEnd5EQDRMMw+kdJHxXsEiftLGrGPKoiQFmw7fljo4huTpvaZxvXpLMmabP8UHopH3JrHC2QLEF9F84CLE3I9Tb9qSCDjCIJnNvXmgrP/gBYGp8ULIsvkZv+NtBD/HmrcLPx3ppKrgMuJK4j89epSeDd7L7PgHyA8Kp3hjEL1mBul7MgHaw7+dgI7HLucE1SqNIJKJKakMFWRLDtVrCo2/NJNvh27L6YJCucVBRLtOo5UcenA5TWTM8w2iLdNK1dtkwq2GzR7saUSE2lYXSTbJyaorZQtZ3kmeMnf4qbgX2KvtrXz5ZkCep1jN73lLIIyK2noIyyGhfj6jHGlVM5Zagz3z12io9Xoi/BtZW0m6QfCekFoRMDeFc9VYYrCWZ5B+hHcNdrGLNiT5zLKaBfdJtMBpsy6WDd+C70ZpDbG8xDgKL0spX+HMtK/UkxRZLRP5rU1tQkJFeMKKQCih/Gf5YTUmjaW8KYE7LNU3O2Gy0wpjHKOaYh5jB0nDEr8hRATaaXMAD2Y+I4D0YBm3D3Ny23hPqWsr4m+VsA1lMedDtmhJKVp9SVVRKlVcIS6A7cwDpPQAuEQwiyxNC6I/syS9Ie4FZYquoqZxgNoptBk2f3HN++vRK0ww5J3aepVf8SRdCPQU8K7M+r49NxCx80YKFmiqRJsQexdWrJn0Uj1aqWRU2OekPp1LAkWi5XjewMYxrEtmNro1Mga01QpmCqwHIdRqbXYNbGvNNwWwPKlRlG48iVDC1vQhZJMYQUPpsptiQrs0bK69dWAZ+gOKB3gbJTr6A4vtdxXimVrNaW1w4V9KqCtAyjRXQbcGxqQ2DEr2oyWCfZw3/+k6/D4yg2KhCWFfciID9triimSGoIexBbokUhsc06C6ZdlmGTBbIGacyQMZcGo3eMjSMj6llyfxjGZFg30yIDnAXR04wq8dLkKZbylD0CemvOOQnJakZpy59s/iEF1SLoJQxHOD7WUzCdbBnBWWDlWQso8KWCMmfTPxXr64ypo9uAQyh2rczxjDA6wBavQDrT/84q5EN1emoqJlKDi1OYIqkJ7CFsncbfw7F1Nu261chso82WDGO6BJULIWG/ohWeMT+M1VdJBY5N2NvAymiaFxXsSyTdPQqOMfJZAnkC+B3iBiDZBz7U9y8Bb6C4L0hii2Qtf9KB10NKqgVgb4ZuZXnKr7zylcVwOtannfM+G8GskYxpIt46JYkFFolZznWIraCJpVmoztJ9kbfKlA0gDazQFsIUSVPClL4TICMfKx7zazY6gSk7e4mmv0S82Mb6bevIjUnIaz/HBrYlQvZNfvb2G/53TJBnYwy5Ajc4LYqrUWWSrizQ6wCnIMyzJCrK65TUdyFfAZaniCqI4VTptWX9nTFNFLiecqV5uhEvAifTriJpNe55byC484pFgENAj6DY0izTl4kAN9Pain2mSGqZrJxtCqXJmPY7uW5+Y+dVjSVgGF1mTtp7gRGUUQMd+1IBi2djtMnmzO8B8FKvQDrVbzvV//7WlMvWI0SA9/ofeQHOY/uHl1LaJ8i+JWmELJ0eAbkznQjwcIVyNUWRFZRhNM0AOAI4MLM9bb1H6ntacXoszSka1uCU9av8Y6mg33H9rt7QUB4rDW+Fqd8k39KsqK9pse8zRVJDFK7SVXGfEUBGvzbxbrb3u2G0wIw6t1ilkSmUJqTqzTMFktEE6XYXaE9jrpOHgH7Wn5dIuokAfirFAZWNQuQKr1C6BHjWb8xa/GTrK2D1I2soXtVsCbifoWtbboFSfwPgIEYHxYs45VfeiCf08mhqFcAIZZlhNEKiSK3g0tYYa1OPz7Wgd4BuYdgPnAZsAF4F+gOTwSqjwAHAUVS/eQc1XpoRTJHUNgUjG3uQKiLjP6cxRpmJDNBQ4zD5xVgJWF86JWrcaKsbY2LKGtGRjMfk+Rb5gY5XA5tAGww4uhKRL4Ds45VKmxgqlRJCWvsaHcJygNgiC6hMjKPRBIBvR+SdDuD9SPVy1lKWGUbTVLVsmASvRdCvMHz+3gQcBpyYOm6BYfD8VwKPAfu0VKa+knULzCrrQwrxRdB7KHaFmxBTJE1Iwaqf4QfXpsMnI0czUlVh0urxDWhvTAFkdJK6fdsEfWLhqQVmStYNG0YHyLNE2un+5AWQR1PHCfAy0ANxA5eQIiNRNjzFbGbv+0Lq3sq1wPsJxyLKKnl2ppRDkSwrq57BxS4qUs4MgCtwAbdhdJBVmpE7X86uVj5g9DqXgMdrpGEYbRKSiZZwrp97UF3hsORc13hdatvAp5Nn8bkGWA/60YJjjFEEeAj3vsoqrNPHhHiUatqetQyVVhHtwRRJE1D4HrRRSnukH56My1vs6ZXyCmRbiLarLzQlk9FH6jwzuceX+bYZ1ZnyfbM5lxVImfVK+vebQE4H9gReyBxzu99eJGwPgP1ozoXJQH5I+KFN18MqkJrBzuVakLcAZwLvZNwCKpWPbgL9qt++CriPche5hCOBnxeUA8o7pyVgX0xRaUyPJN5RUdsMuZkOgINBvwR6eYX8BqBbfX5VByZLOHc3G9DEkXbRztsP4/dTcO+5nIUDgtwKePdD/WT54dbFVSTY5us8CBaEb3LSyqQ6cTuqHKujP4nMMl3NMcWUsS9RRSpOyzA6ROWu0bQNvaBI77dxmgUxpkv2gc9rCEsMV6CB4pnsW3HxONLkTEAZDRESjNKWQMn+IyfM5yaf1K2g7wM+w3CAlc5HWQ6uLlcD73VtRxU3qBrg2tQ6nHXbIrAnyAacdUaegnEArAd5vOTVs6q61ZVhTMQSQ4VpHV6LUw6pb7uryH8OBLgNOMb/riOHmTVSNZZwVpn/wKj2JqQ8SupDcS6ERXWZHJqck1h7+r5bFTYW9GVmkTQpgZtry7PPgMgXtlQ4tiz92GTyLPPrpheNCTFGV8i01RhrlIm6WeujqyONdZ1jWHUYYwTizYw0pkVyl3TXjYHzYdyd49Hmi71iSQ9isw998oBvAHl4wnx2+z8FuYZhuwiZjPvBlr4JuIGhIvI84D24larAxXR5k993C+UBwJ8A1gNPE3SbBmx0ZcyOopdu0cs2eZa88kCvTO1bzaiC6g7QkyrkaeORRpArcXVR5iKTjtO2mmKFIBm9xSrGzYyyEzOZw41IYq1ForGHq3HyLHUauc0xs6bZ/clDKiAaWRaLsWSsNHLMckNdpOb+MLrIWBXGat6NFcdEzSHUV+wiaqbWiGQA8mLQJ3D3dg3LLhmSuNAsNZcXh+Gsi7IWGMkgKqnzfUAfA/GKR/kew9WtEiVlQmz5nsDFhMpZ/U8OAx6ITMswmiIJap33XED82PMtoI+DvBT4CHA0cBbovcChmXSy7hektuexs2CfkU+sF85zOKXTasLK8ZTScBJMkVQFE2o7QfYZk+zONjLJo4pvW8WyFSVnOkpjJtRxM00TUCgVxkGaAHs+6lGlisvucalSMM/v31gZJPW/BNxLvkCcIMCBOEulMosQBV6Cta0mWQKe9C5kbwc+y1CJlOxvkl2E6zkUW+vM1O/Ekm1X5ndF5POg6VFUku8i6P3Yim3G9FkkTjEeM0hYAF4M6i0AWfSfiQI+nU4oraJZfQE5P7KsxgiCM05Accq4vJhzp4PcmbPPv0ujlEglx5jxZSStyBomwDSLjH4d6dvKzABroHV92yTzvWrZIo4133xjZjTR9lr1azNqI8OPGKvqIjT9paw+C8yqjR4RmqFO2tybiRuUb2fc3Sn0t4CbsbeBfivI55wc0posIqAPkz9Yzcpa61oowyJwKUMXtySvVSAfpXnFmWE0SfbZiVEsQXF8nhiS5+K+GucagO/eBOeiC6MLTmz3fe89BQn8tLkwPKZImiE21p8Sbd3oKg9h1Q67DBn5KM3OMKZGU41PM58Npm997wQUKewz3/NutI59MXqJZv5CpFeTeRPOHD9rMXISsDUyv6ciyuKXZrd+oEXafrYXgXOY7SgmCeR9AWNWTfqB8W2GMRWeqHBsnpzVBtv9Z2KlaNZIEyNXeqXRBSwLY7KH35nnznYc6EsjEo9sE6ZIIn/CKr0vWuIwwXi2FI5qZkvjTaPk2qwpGp0nLeTEWK6U0IJh4sokcBPH7q0pkYwYNgwtV+SHIL8OstEPNF4HcgrIzyqkVxawIRnA7B1xrDHfHEO8Sw2gBzecf+JK+T2G8ZaS/BuIPWIYtdhGvrwUq9yMabt5z17e5F+qrzcaIrm3N0TKtwp6PbBHwTEV62fFK5LKzPOr0lTsCGNCpjBirJr8LOrc5BhjnpjFMxD0HrUOeHLy7mElvzZjpSN3pH48D/wQuN3vuxn4EXFBWf1KNnojw1geeQrol4Gsw2bEO458nLAbI4zX/26Qh3DtpCkU+GZgW+q7KZOMqXMa4WfhSZBVOEuVNtpljuJKHvQKpLtbyNNwxLzLBsAROOU3xFkMRya7YokeS0QcuFwPMfFrKmVuGPnkxmnS0a8myxjzxLS6v2A+1vc2R54pbw66/M8wQI5kvM0sMgyWvYBrLxGDf/0p6BLwKvIDMD8EvJrJl6A35oPs4CkUQiD5SyyGmoqJJbhAxEel8s6zhFK/yyzgjGmQVW4mz8C5wEHAr4EMCL+zs3G90rG/8rw98qyQfg5yOi6OGJjiftYo6CLxyvRIWXnFKpIk/SXnT5PPkrSqyMU2hukXs6zP0kGZDdiMKROzEIhQvJhHU4ylt8L82uZRgbyCbr9RghyFU+zkNdJtoN9x+/WKvESAw0E3A4f4bUVS7TqQzdiy0z2i0E2mzQ5wAPoEbuBdJoctAg8C3wH2xDpCox3WgJ4F8nZc7LmsxcmtuEDw94LujesHk7a7E+R58i380mSVSCEE5Ddx1qWbJ7usTrLgb/ua4e+Zo846LLHejZoAjJCbV6wiqYi0AF75PVRwgox9MYzpMW+DSqOf5LYzmaFyY4X1uZrzfebMVWGMRsgOVhQ3iNnBuLC6C7dSz9n+MzRDPfDJHA6c7q2MLgI+z7gwPgAewa3qt4ahJjsk/CrIs5hVSF/ZznCWIt0GkuDqDzaYl+CUQztx7S5vQjphAOwNeiLOfdP6QaMNdgK3eOVF1nrI97/6r33ze45hO1zCLSG/J0PFaKzLU6jdL+Hil/0A18evtKDzq73lz3eAm0A/zfzfg6SvTP7SM74lbaCXiqRo5VqFwUXefRwJHFpys2Nd34yOICMf80mmXc6jlYLRH6RsZjYwwJPM7kbKkf4y1w9o84SqwJ55Yyoo8DDIG4EzAvuPBbkU5KaCNLIWHv751bcyLozvB5pnXRRwc9JBYLvReURwykkYd11L2sc5LWR8K2FFaqiN7eP+zM2tJ6zN/F5Hs/G36jAA2Q58lnD7WgX8EcM+di3Ocsi7uekO/yxVcFcPcjLIz+pcQMfxbUJ/iru/rwFeAVwC+ktcHzBreXQArGfUGq2MPDeCVJK9pGyCoLQyq4y4TTBZ8cRYBzbOBO3Ommy3iZVdp4qSH7MrdUxokwBpxWwjz8usX9hTpqwNzLx9eFZYtfSftIA1wLmYfRfkNoarsSV/D+OCaVeNU5O4B2XJLnNd5uaUxF4yusGa8kMS5BY/CD49s+MU4EyQrQ2WK2EjxQONUHv0HbV+BlMmdRj9ilcKJn9fBr3c71w3o0Itge7CKfHzYiAtpLY9B+z2Mpji2qNGymBFAsUdBft6jN7s2gKH+Q0L/m8V8BLQD+OsvmbJEsgTuL61ipXU7fm7Vt4rNeYJyfoF6Pjm4LExeRudonSQHrCuCMoVs677nAG80S1ilAUzq9eSWYvsMWOPhDajFJuL523KjN2vudEuZqhpxbml8YIYrZMN2gr13BySl+hCTnMOxQJJn5c6n6Ow+Ehd4iuge2W6sjzlkneTlDsyysutIE13IIpb+Si7OmAMSed3EagFH+4OA+Aw0M1eWXBmZv9rgbd7pdLHgL2ppAhtpHwJWTelHGRPhgsavOC+6wMVmnO2310CHl1x4hcAug63IhqMu2AnN2QT8PeB/TNABHhHyUEv+M8NsKVg8qc1RZKIHC4iXxeRO0XkxyLyP/ntLxaRr4jI3f7zRX67iMhfisg9InKbiJxWL2OWg2SH9kUmMY6GBz5GPwl1pLmda1fcZ+ZxYGlEU6XqZlHN0YJH4PhGy9uFZ7FBgkqknN/z9PjHdJtd6VrnkZnJYDDZkucDkPXA00li/vP+TFsYAO9lLoRyo4DEOmM1bmCdfOYhLAdQ1zOBZxla8Sjo90vyyypn2ojRMgB+wXhg4jzS+xM5bBF4jHEXKWP+EOAQ0KuAk/22bL+TtvS5FPQ64B+ZntXZUqrfjbSQG5HBkuf0SEbjJGUPzJJOcwl4hpVlaTcAXY1TuuRZG6UNUk5jboQa+QLIAakN23ETLTv8741eGX8LhW2gTYuk3cCfqOoJOEO7fy4iJwL/Bviqqh4PfNX/BngzcLz/+z3gv9fKNetbX9PPM3t40EK1RmOYJ0G+79R1/yk7ppJCaR5HQnM6sDTy0eyPiIa9Eut23h61tgm2i9IDZ0zAijN0yP/P3pt9z3JU956fXb8zaUACNICQkA6SMLOmcwQYENhmtBFIWIOvzXQNXvexu9fttbr79n/QL3390E93GWywwQYhCQT4YoOxNQASOkcDIAnQDBoRaJbO+Kvoh4isisqKyIzMyqzKzNqftX6/qsohMjIzIjLim3vvmFmuJkl1WE4fLNQWjYGnqBczZAw8CfJZ99uJAPKB+e3M9YTLdkFdMFeh4tMy2O5uw14nAN0A5j/AfBH4HnBMZD8D5lrgHMID0je59J7AxhzpG76F3HFgDpRsr6weA+bfgXNJE0kEeAeYj7GcKe+PAM7AugzXGfTkqdOREmx8vD+j+4Glm2IDOxlEZuWact2c+2An2AR+xzQu1vuxseT+0AlId7vtQtbFHq0JScaYR40xt7jvzwF3ASdj59/4gtvsC8DF7vtFwBeN5UbgpSJyUu3j1875lFINqupBulJ41oC67j+lb/dTVtUULysRspCruD+s38C719Twa1tWk2OaOtgCBXIdXdomNKGQL5MSl+C5FUplVtoHM2CewVqj1L1/V7nO7HmuoxuKcZNZdhTkY07getMCeVKSMXux1/y1bsF52PhFfwbmXWC+G9nvXmyMkdjoJBMBj8fGHFmFKOgGVhIrRwcj3wNoUewJZ2JFgBQXfoMtl5tgfku54LmNqWVamcVeiNuIx9xKsYpYFFfG5T3ArXSrr9EWAryUcmst/8+5R3ZRaBNAbgTZA3KzW5gogi4lRpKI7MRquTcBrzDGPAq2owOc6DY7Gfi1t9tDblk+rf8iIntEZM8TT0QO2EYhDlQ+fQD0m2gxySv5i6r7bZAYa0TL6ECo6tdWYfu6L7BaqRI1Elt7raErbVJVtPFaGq31wWZWeH8bWBeJRYKLZm9Bs8Ct+U7tCORBpmJV7AVOviP/x4G0lOZ5k/vMBrdZ4NmMc5l1gRTgldgSV2b1ke13OSsflMnuwMIPYV3ycJZ0T0V2fkM7eVKWROzZm7VBzuqMLxAWPJ3Fpvm++zNgfgDmJm9/nx2zy4wBczOY07Czg8XyUbQ85Tkbspo7iG1HDcj7nKj6a1ZeH5fKDibXINghzi/fBB5kubGzqrDp/VWgdU9GETkauBL434wxz0pUwg97j80tMOZ/AP8DYPfuwsmmJztX7o+mKM410b7xksnfx4CPcOV74u1Ua/+GqTIem8lvFzKvtIe7v7HbXGbcRGC/QvF1iQSL7ZqV5caswFbJmt2zVdBqH0xK+mCX2lTl8kpZniXWqc2EpkuxsUgSLVPkvgXyopQj2MFzWf9iCxOLDTkZOzA9SHnMoCxNZ2E22X9VLmK3RyyTMiuUDZCXTxfPXJaiEdh2t/FBrOBQdaZDZbkUlHVzIUjWjmXl+wB2mvidzArb52ItOg3wDZCLvXV7wbyInS0w4xzCbV/Ks1W8T//FeZ43APe77/uAI0DeixVLb/eOv2YCvSnSCkLLs5cZJa5ifaNViyQR2YrtwHzJGHOVW/x4Zi7tPn/jlj8EvNrb/RTgkSbyUevNeWJ8pSr9YO0zL5HYDS8rDFXMMVZNjQKVuksXjbCU+oRekqTuV1oOWi4g+aZY21FHneuulXmtWHkfbAT8KTPTZDeNXMnU5aSocdgEOb1kG6UZtjN9Uw/z7U4uTtBk5r2jic/CR25Zfv88/uB6B9YKIAv23SRlg+ecEDpT/Ar2Nbc7axPAfMktbDrvShoj4Fims/SVdUby3gxbwFzhlt2OjRVmmJ8mPvueCYwfmW07zWuxQpNPXRGpKN8+3wF5nOksiEc64fRGplPCr5MV0qIMcNbQNmdtE+BzwF3GmP/XW3UN8Gn3/dPAN7zln3Izh7wdeCYzv65FpMPSaD9GOySdJPke5zesWjhM8W4hl6HG+9GLmK5WKL8qKK2W2k2Nmf06b14Q+UtJtwuFYo3VpTqnHDdGUYbG0vpg+ym2lvB7mZtgfpd4Aqmk1P8xdral+1l9m7UGFF7ionv1K6wIFXtzENnXXMW8dc893gD8u2CudQP4rwBH0d3Z0jawgtEpWEsQA1zizuOKwj2VthiDPMt0ZrYyi5LQ269MUD+dqUVRmRVlvrxvoXzUXvcZH8qzYF2B9wW2r+ECNSgExMXBCq0L/o2sG+DQLLfEtPGKCBCRdwHXAz9lWu3+b6yP/leBU7GPjcuMMU+6Ts//h/UufhH4S2PMnsJj7BbDnnoPrcb60yViwtzxtCPfPqZCX1Fyrl5U7GfKzEel/btaFPqe/0FSp2z6VPVtC+2zggGYZP9ivnbrTJV2ztFnIcmYOvOkri9L6YOJGN4F5jjg65GNfF+e7PtvQV5FdXed7cxan+zAduQPYd3ovuiOkQ3QBGuF8nqQX3j77M+l4c+8oyyGYKdKv595cSfWH/eXXwTmSsID5lBHawzcD7ILeNatMthyEXK5EeDLwJkg7wdeKDyb5eLcAs0TzD7zsnozZnpdBGQbWm6XjDFYl66jq+7IfF8mtiy/3k+DyLL88qqE2mn63WdYBlEJJWSocC/I2YSFuR4Q64O1JiQtg0xIykg6k8DAP5Ui145A/Zt3EZdwOlpPG6biAEv8G0a1fW0C04/KA7uqx1oCZvLP0ZBrp7IAJvg1nVwZ7wvaiSmgYns1EeV6igpJ3UNEDBvYAW7MdW2uI5TtW/145jrgAm/BdcCTwMfcIc4DcxTIQSb9ANkP/JzpgPsOMG/00rjBumuwq3p+lDhVhhYzZWEDTOiNfVF6AvwI5F1g7sHGncmW5/fNBJl9wC9BzkvPZ+tkItwDzIqhJveZcTXIXzArjCqtYy4FqlqHtS0kLSoi+Wlk1uYj7YOVcinW9TTvdhp65p1JePbRnrAWQhIkdKol+LWU0nRNSXpS/hxUGqKukOT29T4SE5h+TPaLJRBqXKocawlEm4S2rfuUYkzhz8HRd+GjdVRIUlaMBIJtJ72hHQPPgZxIuVWSi39kslmCfCuXLA7P08AZIC8Sf6MzcmkcZHbWHCdayNeB/8R6u2s0SFI5MMCjIKfMbxN0w46lNwL+DuR/B/M4syJMfl+/fGwCXwf5z3TDMkmAk8Hcx7ScFwlJzwPfA/kz1DJpmbiAyZWGzylCEoF1+TQILF+E0EvjF4EsFpJSSvQFiv/9cZCddL+ebsc+n4W5vMb6YK0G226dvVSvUDVGXwtHDwndAAAgAElEQVQP2EpEpOwYQx8Yrg1FNzKwrlP3vSzvWlBXh8z/HOpzfqjn1ShVL1IPL6o2NT0jC0rrLIKizwzBDoJT3GtPdAIQzMcUyeLJnICdYttghaHN+b9JGvk3xy64rbkUjIpIzXExE0s1YHYQnP2NgA/m9hsBL6ccvz27FvgbrHWaH7+mrHxtYGPXfJ7kWf+WTtE5HI29zl0fnA4NV8ZEQF6NtQjLt3cpDy/xPmPP57zQk3/xvUifPLbfbhWRFibfzp1EN+vpNqx4lKlBtwB/D/yb/Wmyly4F5aHfQlJG1RFVhUo3Y2FS1FBIwV8+wToNjpKOpBeHue0quj4KIL41Wsq9HMJ9X9TVSqnHwB/uM03mwM912XS1nsYeif74U72OeoLBTnveVN01YK5N3PZjhIPgCnB8+XF6/0zuGPJN7AgjNvA12CDG/8CsldkYeArkuISDHAZ5GOQPQH4EfJL5UU2RhXU2yLucbliiCVbQqpgXc0sbmVGS2IONs1Y29itj2W1P/ni+OHb3kvPSV5wrairmarqnunwTzJ1gbmUyM6C5DMzbnaXVAfu563XxJPKh8PpNzKR5UcqsNFIaDO2gdJOCe1dWnOZ2Xbd7nFr2lWbxzPUHW+S0XCVhUp95Hbyegy2764xgLX42Ke5dOheepNlrzsztlycrSEUzKR2DdaHL8uQ/uzpYNwaBYC2Lnoysyz5vxr4Rz6ZWz3jSDmonbiP7mQ7CNrGuN6czO5D7IbNBtouOm+ds4Pbo2SyHMfAgVpgIEWvv3xhYprTPNjCvxE4AkLd0hNly5pe9Ljz88nVgw1s2sFnFWqPKiw6ACymf8a9tPLHa3IydzTRzD4bpMzLfhh4ZT7Jr2tjCNN4nSLUwWTCJWrF5lEKa0PeSDM0WKXR9v+F9z3+PGVrEGLVEapEO1dNCw4/8ig7lW0nAgDxD+A19YFt+SFxwEiBmlZIVIj/9mHhgwHwPO9AL5SdnjWSuKkhLSWcT5Cmm1zri1mH+Evg80YGKiPt7H8i73d97sbMfPY4NcQF2gHYvVpRK8Q7Is2oRySezriuzlMvOa/uS8qVMGYG5C3tvtrG4RVKezCptiTN8aderApk10k7SPYxWbbojwClgbgQzBs5xedpK+TOvoHCs+rSGQ4F1hqRaDqiFR3M41d8z3ogyd9mrvDGQ+Z+Vxz4du++Vz6Fj+V8H2h5f58dZS0HLUCXM5F9/CGa3TDwywO5WsqM0jQGeiqzL128B3lGS1u9K0vLLSkFdkNcklr0xcC7aFjWIuH/mY8BXsQMWv8/g4lMJwCeIB1+/KbAs4AI26b+E+iWhQmCARwtOYNkIyE/AjJieQ8/a+UGTWXPcAZzecNoHsaPyDewMXw8Cu53lSNNEXNuURDJrpJCK0tUxUZbnU93vhkyJBmeRlIw2zIrHXHFIeaPQUEPRqfZGZj6UDtJm0xUq9loWBkKXnnkak2a4FPUqvdg4PLHAMUIWSUXHfaRC2mqN1A4/Iy78jcBcTvEMfoEA6kGK3NdCViMjkD8oy/wSMcD9IMcDzyVsPwbuIexapTSLAK8Acy9WRKry/Epxf3gLyLnWAo+H3fLb3O9QeT7o/VUlXw+03avOmZHlXe00bwCvofF7vb5CUgraye03KWJQI75t3rYJh+wLqecxlPPtAyb6oz4plthFxV/v/4ro4/NJxaPhk/pm+znSbOIvxcbsyIuP4v0+jI1Tsi2UADYeUypdnFlnCPxPZqe0D7kXfpX4PUxFsIG6n2W+zOR/j4EHgF8teMymcdZ48ln32xfYDmDL6AH3W0D+HI1rswx8i44U4S72rNvvPp3bmgDyTpAHgTvdusOzn4Ln5un+uAB4N8jbq52G0jJd7OMIdnbTcW5Z/nuREB9heEJS4sknbSY6mB4EiWJSk3W/UrnpcOEp0syacANX0mlKREpy40+4wf5qLQeKssZsJ90iIrMCeT9Jg1+5EitAhMSHbNkWkB0Ui0CXAs+X54sPpuVLqcgZlHYozGUsLuQZbKDuT1McWwhsOfxoA8dsi6ucaHAJ8IJbdi7wRyB/6NaNsFN2d3HwWsSI2bhYW+m2VZWAyWLi+IJoRl6wDOw/+Xqeu3dHetZGN2HLYYUZ++QWkL3AzwqslvL5C4mqY+JuxMowMMBjVFN9isqzx/CEpFQqjHyaGCSpILU6qgQ7X5gh3MDAORSKDz0853xsvN4YSrSZ0dgNTlQMVVhUytDyMVBuwVqTQHr7dF968pIVnFjapyWkcRXwlwUbjIGHQB5Iz5eSwAjM6Vjri7KHblNWSQDX2GOXPpjuauBYbZFZD3wb5GgnPtwDchN2trv8dj3C3A/mn7FTjhsw14PJ2pDYrHWrxADXYkWk/AC7LFabX9b3EbaAqyAgzeyzyZzVUmUMcCwaNbkJEsWXVTFXPuq6Rnr0WkjaRb3BYJ16VvQsKrUq8VyetCPdbZZpldRpa6SBKp9lbUSvRKUaSP5HFdOykH9bQ41az4qRUgdXqVooPsqK2IUbBL4WzEeng8Io2YCnxk2fuHNkbiHZcU4HSYmBZKyYNHPsQ9hO9CFsb/gC6g3o2mKb++uypUYZWQD2Iyiv/E1ZJbm05OWR/ABc78pCH6zP/Dp1mOIYUV1mw/WvDHASs8H2dwFZG/LFjvbBYjFxUngROGitkHixofzEuNgdo6hD69fBDZCt9KMudImLmZ1dMY9//Tcpdr9eIjMuku9iYdfIXgtJVajdaZXCnwuJUhJY1hV6aa0RIfm6tmCVVGTJ3XlCvktrNALsXJlfMEPif2n63nlp1kp64GWpDapespVf4oIMrEFzMmwy1zMfvwN90HVa31HzjXnG2SAfdGmOgEdBHiJ9AOQsN/xONO8GLgA5G+Thop2XhLPGMID5Ppj/sJYanXsepWJAYjP5RbbnKhYPCLsJPAVynFc0s/L47yDvXjB9pRoCvIJpQd7CrAXMBtMbdYndzkAnBt4Tfh8rOlftAxvgbSDb28rYLPJd4DLKG42sPii1kG8ytXqMbuQ+Nyh2v16VFd6tTFwjJ8/mikJ+vw3ZdgF7mfrKQ7DiLNxBlWm6wXqZ+ja/LG8d6EmXWWtAJ7JZjVX0wHL3O3jN+nIh82JSTymNMRQ4PxNe3CuW2sa4ch9p7pSGaeo659NorZiUZFjLzUDIbuIY24HOepq3Brbdih2cbXP7xWbuGgG/BJO5SG1irRqKZvoq45ZcWV/1oEqwU32/2f0+PF0+GVgLyDa6G9enKiH3tnNJb4S2YsvGgcj6J21aAvAmbFyeNqZTV4ox2EDVKXjjOfNVkM8wjRG1Sn7kPv0yW1RODwLbQd4E/Ly1XM3zIsh3wPimIrE+ry/gKdUwVnwxBttWxyxH9zEbDyyU1F7gjdPfC71wqULumScC5hLga0zKbxn9t0jKBw5LIXeDknaViOValZvd8deuqZdv0B19U+38al+LDpeDQVNU2SPrulLe61igrFKoTtXXtSosRtn1m1zjyIYxi+zWKHFvWRODx+GSc5lAmHawN5ne2KPc5z+C2QDzPaZxljJ2YDuyW4CxE5GyY7jeq1lEFEiZTn6JmDuYikhgr98Gsz11A+bvl5qtZqhQoY0fhyaEZ61k/g642/0oe6t/Fzael7IafNew1PJwKZjP04np6aNZPuj9jbGipgF5nxME7llO/mYwOTFiH8Fg3rJTn7W1ydz1BTuZA1hBKbvWmQXs7ogw5JYZA7w2l7QBnmYl5j5ytbNQeptbsL9wc8QUOrR3m927xezZU75d/gbWiaNk5r7Mb9T3yhi12IicWK/Ot4pAJOXnFktrbr9EyxelfSo3dR2r30X5L83bsjNvCn92zhKzt1Qp0xGLu4q7tEssQ7vB7Kn02kZZArtFzB5fgA+ZlAk20PXlTKyIzCPAdVi3sjFWKDnsPp2rgICdCnsH8ByYs3LpZuaih7BCVcJzu7OMgC1gnsS+uQ5Z2Psn567Z0t5aN4S5CPhH5gWfwHkUntu92ADeMLVi2wbcDPJWrOjQAWFQcYyAk8Hcw7zVRr6sZ21I/v53oH5P+mDZZ9b2vAvksFu+DxsP7Ga6Uw4/DOZe4CVYgeN271puQWMjNUFWbs/FviQ5iBUUb2dqdevjysZMmaraDoJ90eJbYzZprZqV3wuA68GYcB+s365tKUip51uQ4HYhE9xVt2wNERTKQstkdnGfTr8tN5D8Oh0kD4SOF/DkrK3iHFJcO5XFSW3UykSkBXzbGnWLU9+2/hK78QbMLhBnjWS+7pb/fm6/jdw+AN/DdmJD1ghZWdmC7ez+FngN8FztM1gdYzAx16wQI+w1ylsddBz5Hpg/B65gdvTh13k3e17RgMicjh38Zmlk4sTZ7pp8Hfgk8HxjWVcWYQz8muJ4R9G3927/3wInE3dhXCYur/J7wIPMtV2Tc+mCiAR21r+thGf3UxGpGbLy+hPm+zF5EUmAV4CJxeTLylAWL+4y5stSJk79Kxg/3tvtIOfQjIiZ7f+jwq0G4NqWSNBrpYpbXJE7TMkmvSHlBEosDTqLzHw0Q/7thPezN9dFKaYDNzJUnpIskFbtH5Tq26YsRopvW46ZFwcFLw/KRPPYrgtVmy6UXaU+AUsCTnXlYoyNbQlprzHfgx18hoQkf8C2ARzn3Jw64AKzTEwTgamXxQsg32BiQRat686VcY4RtjwcYFp+/H2doGQ+6tyhlG6T70P7Vkl5F9njWL6I5NxqjYDZgx1YZ+5KRwCZEOC7x47pjoDkc4jOuPAOmk3KZ1Ysihfm14Wxs8QNKTW/tKK5eUdu+Rvd8itpzlSoRGwcvpAUsiLye7omsC60fZVj9JGqgYF6fM6l45OCczPZv9ADsK8i2zqwyM1IGFS3QVI1C8Wb6dIAXLzPLuez78Sub9E1rvlcW2rzn53D3mUdUGmcTeABpoWmSq+zqCPsv/V1nW7Oo5/tSuyalNXjTJiLrc9cyATrbrOkWaMKGYEcBzzLrFiUnecYO7tX6Jwyy63tzPflfQS4GMxvsO48fSwTfWIbVsQrm2HtYorvWwlL74Pd7gSkMXAOU9fbTeBRZt1QlTBdmnWvS5yZ+x3qK28AZzC1aHIvDMwDYE6eXTYhe2ZexNKszYYvJPlU8WvruViyFEqujwn8rZTEBn8hU3EVk5ZKcvnqgH99FZLG+H05oRXlU+teATUvzpw7XEEjr9d/zfE7w68h/gKvKeoIVV0hZH0DaQ+CkwhbgQFmr3s7/RMw/wKmxEVhKYyxM6n9FeGZuB51bjiLzMYn2AHVCWD+htIZk5SaHOGq9XfBfBHMvxVvLt/C3puqMVyytuMrtG99d4Q75B5gJ3amP5i2K8bmwbwU+DtWN2171xm55us7wDErzkuXGAGvZhKQvfRvE9tebjCxQuJVFL9kcfuZp4jPJNcgaxFse2lxF3o2WPWpWwxioktZciu9TgUWJjL5F9k19TpJ8KvSILFbEb3ei1oWLal+B/MX8W3TshWmsivgGjGxqkzBK2dzIlLJPrmvCxML9Kisjt0iZs+Y+QLi36msQxwSeDL3NN9NrSidojgq7rf0xc0rR7Rv4bv7BK6NnAncG9jtTuAN2IGIf03+HjgN5EJWF0/KnYsx2BmBBFs+fgHyZuBootO9m9/HBmrPD6T8suQv62FQ8l6w3VmH3Y8VXDK+AvIq4ELsvc2LRruZnWkx3waUPFsks67b5y3PAg43FGjY5M8p3/ZkA/xnnXWdMmU7VsQ4jK2nF9DrsXHjCJhXYetNvg2Llf/9IHdj3dz82HBFdcYAvwY5g8bcGWN9sD6+u6lOf7Wy5dDw9UlJrgu3JPISrxn0zXyrFF3TMuukNh5oTd3juXRKTkbL1iydsHwcEiHxNcVKQm/C+lA0cMsGXL+OrJfcZ/Y9LyrFhKb8sR6jP/GC8lyOtcJJeYBlLmC/w7oN+myA2Qac6n7ne/kfB94B5nOLZngB3PmJAO/Dzt63C+R/wZ5bREQC4IfYgVSKObJgB7W/ZSlv5teCzNptD/a6n5JbfzmYd4L5JvBPWFHQ57aKwl4WFynb5yngy+57Vrbv9KxfMmreb2OwFiNlbAAvx7pPdsFltCOYn3hlI5tQoW5/wHfN3cJwrL+Kyn/InXkHmDe670XPN3/fEXAaS4mJ1WuLJNktxqRYJC2LPquupnpdj1nvBAfDkQuzsutVR2moY9GilkmNU+X6z1zzwI6V0pLKhhyVqDRY9w6i5crStBVkZYu3vlC1rS8bwBfspxZJw0ZEjDkHuBX7pvQgtgPrd/jvBl4bSaCgbzBniZMvh3krBgM8BXJ89fNYOYKdxedR97vsfAE2QQzWbSvnBjbTrQ9ZU4C9X8924Hr5AyOhPK7Hx8B8hepCQZ/75x3DgK3rW4mXr02sNdjVwJ8RHtAaMAeZBlDfjrViGmHLReYWe7/dXK4FMwJeiW1Tfga82eVlGzPWd/INbEymVDZc2s8zDQbvn1PoHA3wBMhJVH8+DhDzANbtKhN+ctetVEDcir1/+116dwBvBO4AXsSWgz/reT12rm3m58wKkH57n/9e9CyMWSSNgUecxWoDVnowYIskyf2lrmuFnjckjVvk+G+KIm+NVnbJQidbVlAWVgeURYla7KSUr8D9zbcP+TZj5uV4hXu5kFGGlpnKFJaL2DaB/ctecJelM0jUwkgpQH5qBwjyVpAPMn0Lfci1n3/s1h+DHeilVLIUQvs/Q3Mz1SwTZ001GWildPw3mE4BXUSs37IFO9PdE9iA1KvCn/GqTEQaYafX3lewTb5PYFy6BrVKagBzH2nXcsNuZ84mXicFZLdrJy5wi3aBfBzkFLfuIZCzgB9jZ6h6G1ZEMsDrXTpZXrwRrfmwV7bL+u4CvIypIKVUxtzDNHZPxGrGXM38vcgCSAP8LTbAuXH97ewFxOuBc4FL7IY9tn+xAs+D2JctqQJFbH3Zfq9kKX233lskUSFGUutn2uc3HokDLvDOMXKySUUqMKDvBQtYJfXmHDuMmftSQJE1WI3GYLJLPrGit+rFq+bTX7HVRx9JsuQqqIN1nwu9ve7LeBY6moxNohZJ3UNEpkVpg2ml8AWBLdPfk76BXwBjd/UAttN9BFYsyQaL+cIr2AHgFuBtIHsD2/QMcwnwNffjEPY6ZHFgtoPsxA5GYvuXKeH5a341yKXEg353jYvdoLQKXwT5DDr9+YIYgy2TvjhUZL3j1kcb76x92MDemy1MLYsM0/v1VjA3uPUxK42QtcZX3X0vcpcU4FXOoiYvguTTzdLOPp9m7eMkmT8AvsesaUroum2C5EXF+8HszBLClq1t3u+8xY2Xbp9jn5lLsK6f/nMTZq+ZHw8pW+ezSL2rwTAtkvZSScmrekGrbL80q6clELLSgPRzTLoOPe/oVbrXPT/XzpF6PYu2S72BEkkqZnG3yJt2LSeVSRKRvHVVLI8auacdJbUdX+SZ1udOnlKDzKIkb1WS/d5wA65nSLO4+aizRBDsm+jYMQF+D2QD5BYGUVflamfFJcAnQN7nll/klsViToF1Jzqe8KxAMT5Cf0QkgGuc8PW8+x2yQvL/xlhLFm2TmiH0Mi2Pv+wq4rFdsvZh0/udWZE59zhOBnMT1awNszxeCnyh4Pj+9rHzKCo3+yvma0iMwGwF/p349fXroUxFbgOYPdjA0z4pVoMDqMdyJVPRNIQBeQfICdjnZVYOi87db+8eXt5lGkbxj1kIBBqFWFsRSy4kQscO22t8FT+weO5cmzj5sga646SUJaVhmrzgRe1GvhEwue3K8NJIKuZakBajwvULblrwzAjuKD1uvtx5xk63l+ekdB83jbF8BMyx2LexWwgGqhVhEidFAP4Z64b1O6ZxmLZi4z88yLQn2ycxpAjvPOSr2OuQXZPc+jmyhikWADhUwfs2EjDAVfaamCsC6wNv5vkw5a5zSjFlpgd5i8NsYHsuiz1YHidsBRV7Xuf6a+YcKzTXtkaLPezHwAmRdevAmNlrGhlH5tdPLCYzobAm5myQ2+vvv1LciwLzEeCa+eWAvTZjZi20yhgDT1ItPtiC9Nu1zTerTsEU/rRpJibRirDSFaoobQX7J9+cvrnnJJSjPPp2vhlqNVcLli8z+Vfv2N5H4UFqHaJvdadhKt+blDepqUn1+cJXPO+ql0km/5pDXdu6R+U+GNjB4FbgLDAnAv8IHJUlmNC92I2Nv3Q7U3cYZZ5jnfCWdzeJ0Mv2bAvwUjD3Eo/z5NylZCsqJDWAMVgrnLxQGRN2xthpyF9H7aC/c/2+iKtTEYXluygAsol897bpY9VpiqQ+ef5+5ZfnBagiQcr3MhrChc/EoqL1LwVzP3AM5dfyRaz780uYBC1vilgfbK2FpPIDJO43hMIco44ZVlUhiR42CBXEpDYGVevKSoSkRZvIlOPXFJJ6V28apm55aMICrPfXvrTRmt0m9ZK11d6pkNQ9aglJGRvYDnTVFDIXChWQSimdvc2r59LnQBeXgfky8zPAGezMRfkp6pV6jMAcjXVPDREb5LLY87KykJQv9/eCvInSwPTB/kRMSDLA6SAPFKc5WDKXw3sIu6PFXtpVEZKyZZL7NPR3hs66XOqsL7NZDQ2zoucBrOXSW0DuaCcLKiT5pHSgy/bRLm2cOoOPPl7PxPNUIalB6gguKxaSksp2HfF18m99WeXjq5dtVp6U51rgGhdarrd0XVRI6h4LCUnKUpgJcB4Tkt4A8vOlZqtxoqLZ6SD3ryBDA8ZchI17lI99EWsNxswGWd7uti2zUBLgFWAeImxZlxcZCC+XM4D7So4FcBHTIO4hccPYc2ED+DDIPyekOXAq9cFigl+ZcBT6PAByNOtjZZhZLl2ADQz/EHCDt/71wEtBbmovCyokhfALJqz9oKxRqogsM196Rsl56mC/YWoILnn3sljfI3rIRYUk/+DRg8x8pKWZku7AWdXja63qdV1L3oZRIal7qJDUcUZYF7cnsS4PR3rrxsB+N8B/mVu/KNuo7b60MLeAOcd9N9hz34cN8L5vRXkaKiMwmUVgzrJtjjHwAHAGyDHAc2DuAjkE5izvcbGVeYshwbr1PJlbHhMX/PVgrTR2VHjpswHmMJPZEWfSyltYZbPKrXMLKGBCblnJnetpOtHl+Xv8AjY+XiwG3NDJLHlHzFrlbsFeoxYtdYc5a9ui+Gq6dlGbRWa/hi7vIC65OwkVkZZLpcvqiTRmdlHr/YDkMpATu4o2UxHJYVZzCdauXlc513W6LorSdcZYFxABeU9u3SMgH8QOmBcRkXxLk+9gBYFltgOCfUP/Zu/3CPgPkCNREakNjCfO+OLPGHu9syDM+7D34gy327Pu5c8ZYF5v0zHuj6uBI+aPw1M18ncQWw7OrWg5PHbbf9xbtp/p4NyA7HLb1HHLHSAz17duqIFs36L9s3q8e41FJLBlMSQYHQ4sWxLrbZGktE/KHVrEd7rZ5OrnIZCRtRtwLgsT/xl9piXeh7nNarjSzaWRWgY6YvnRK+pYqNVEoj/WjBU1umqR1D20D9YjtjDrBnI2cAf1XEO2YduBQ9ggsDu9dQKyZMskczdWrPBjqNwP8gZK4+IoC/JmMD9133di41E9D7wWzNPAN7FiZVlsnBeB74J80u3vb55vZTz3JrZjB9BefCx5nVv+C+qVQz8A8juwVjC3B9YplkvA/B3W4jGL31PlaZ3vxPvlI7OkfDfI9azW6nHNUdc2ZTWU3aGaQ4OWkq2Mmfvi5UGHPe2RWPMNhH3aMvLmyoEEklwzi6j7QG0ivaGzBCGptjCoNIoKSd1D+2A9JnXGO8FaGW16298AZht2UPcGZqenFuBqkEtYisJvwIpFG/MrJL9MaR5foMys0zKhZacT+WKxlPLikgH5GvAXTMvaFjB5MTArZxeCfBt4M/CzXJ5CVhtbsULHgcDymOCowf3LETCXAp/Dzp7YhJDk3BJ5I8hd6AydHUCFJGX1FAzgq+yawjJGHEUiUpYJHfm0SEKhSC43EvwatTYLbZuyMsmYo6A8KQES47FVRQWkbqFCUvfQPtgacArwFTBHAlkMIt+KaYP5lzWbzAZXbonCWbYAvg5yGToAXRU/AfOWwPKSuLSSiZeA+QFwLrPl7CFsudwCklkjpdzj+8HsDBzv60DAEkqpyPlgfoxtH1Lrf74OZ/c4mwAgb0mprAwVkpTeUniTY40Q7Y375vJToA7oyGdJBIItVm4cYjcr5rZYIZ2uWNANkgJXx7qoG1v3UCGpe2gfbA24F8yp7ns2OIz1u7Llm8DzICfSrGuZEwyiwxaT+/5rkDPRgeiKKL1PsVgEAtwMnB9Pu6rFv8kslEJWamNnCfUJ1BVyEZxll/kIcE3JtqGysYltY3aD/BR1YesYKiQpvSRJtMnToohTKT9qkbRSajVtoRtWJCQl3uAZ67XYW7i0pJQQkXu9cBHQm9IZVEjqHtoHGzAb9s88yXS2t7xgFGOMFXFez7wbUV2yoNoPlWyXy5us95RCq+MCMNcVrA+8/JsgFFu1/AfIe0mLVTTClpv7XHoFTxENR9EQXqyyaFMRWvEIcIqrsxqHqnMMcta2XavOgNI+JveXuk9LWZn5oV3ozlL71uTfaMaszWamT6uYdqRMa3FagDr3oyiJBtJTlKHTWh9sW/kmSstsYkWgo6jeFgo22HFTIhLYQenPE49d9xmtNIb5EtOZzUJ9nrJ7E4h5NYl7dG/FzDzGbP8rmGEwVwWOq1THYNWF47Gz3sXuf+5PLnDFQkWkXtFrIQl08DVkOnlvKwpInTwHpZiGRcKyOEtaSBpk0YGDDj4UpRKNGbVvY9oj/eeG0lQWYubWSuB7vr3Mfo+ACxrOzAg4EevuUuQylfszN6DiwCp4NfaeBQSDGfLCQoxs/Qbwh6SPXsfYYN3bAsfIl5dzS/KgpJNd0x3M3//8dd8Efo0V/JTesYRweO1T4C1SOZ0Q2q6siAU6qK3cs6r5aapgKv0mpaflZI8AACAASURBVNxoWWmOVPeL2H6KolTCmAbcQm4DcxA7LT3MDDAFdNaePuDfs6pWI2UIsC9hmzzvajgfSjGCFfw2mRd7YiJSHTZoPvbVIEbEHeJYbMyplOv6SjSWWU/pt0WSZ1e96EsxNRjoHjquU2qjlXbt0SKgKEsgs/yoW+GctYg5E3hTOH1jgNtqpq/UpnIfbBO4x33f0Wxe2AQ+SHzUkrdyGAO/Af4A7UwuEwM8znSWtapuhinbHUnzgZhdDC4VMxrCgPlX5kWkkIXaBtZiTAOd95J+C0nQSIyRVIMBHZh0n655pnQpL4qihNEgm4qyOOZqqrsRneiEoq3F+5rXN+hGp6QhwMXAi4F1/r0Yu20eBfm4a0/3t5Cf7zANmFwWP3MTeDnITZH1ynJp0kK4DRcUA/y2gXSVqSi3s9ouSj/pv5AECz0kZmZTiv0pKyNVGOqagATdy09fWLjKGb32iqIoS8F/+H6YdPczt4/5Oml9rcy17RWVc6gsgHwP5M8DK37pfT8T5IMgFwG3tpiZ25iWlSJLFwP8BmQr5e5wiiULkE4DfbAjQP6EabDtKqRsf7hCuoL1XjlE+bjuedS9rQkMcC1T0bcoPtIYuBv7IkHpJcOpMu7BUiXUiJn7UrKhjk6XS9boUOHSd+kedSkvPcBEvle+jF656ROicZIao4e3X1H6iV/ZNsD8EOTdFLuIuOmdTRZoNSWuWeYCcRLWdUZZDi8A3wpYbV4APAP8xP3+tftsI47VEVhBKOT6COHYO+9pIR9DRYCTwXyZSZB0462r3C3ZB/wL5aYKh7DtxJG55VlfKGQ19CvgdRXyYoCzCvLiBY6X96OubQ1hzoysyBcmAd6PurX1mGFYJHk0LiIFd1I6SYuDcI3b2x5tGf6V3YO6Vmx1ZkJuJWElSOkseTn0sivKAvhvm0cg76B8MDYG80GmAXljD4HQ22uNlbR8QtNx/wi4w/u9SXvB0Pd67o95y4YQb2kh2PeAMQbMdcDv51ewUMyQGfHxwPx3eRPIH+T2C80C6M8G+BEnVFd4cMuXmI3ZFOO+9DSVOOY+ptZoZX+APLiKXCpNMSwhyVSzdqzTQKqWtGRijX7oYdMSte65jk4bY5E6F4rrly8uVW6V3tYBojdVURojuQ/2HWoF5J2Jk6R1d3Ucpv1Z9E4B9oA5PbI+dv/vbCk/Q+UB7LUOxSirG0g/EwnE/f2h932HE5keAG4HOQ54mrBwlfstPwN+ENiuiMPApcBzkfUHsFZU2yqkqcR5DVZdiLXpghWaqgZiVzrJsIQkKpTHJuIqKcshpgIsQUCqY7mmbWI6pTHK6lLxJrRtNVRWVLXMrAa97orSEM5NTTLLkSJGwEsJxy3xv4fcILKB7Uluu+EEaFAysvhZV4I5i/gAP2Sx9oS261Uw92IDI4fqUZN97JsDyw5jZ197EuRDlMc+Os3lp2rcKwNyJch7c8fN3KleB9LGTHDKPFl5ci8R6hp1KN1hUI/gZT489KXYsDHRHwlogWiOGrHPJsRiJWXLU+Jy5HarRS7Wl7YdLZNwP/W6K0rDvAhyFGmVa4yNr+MLTiFLBN+NKb/6Effl6yCfIm5toPSLfPysqq+7n8WObDTWTTGCFWNPJWqJNGEM5hlnOVT3upZZr23Btgf7sfc8Ew8Pu+WngTzCYqLDbdYSypzPpJ2S/cAjRTsplbkU+CemFqcBZOeyMqO0zSAskuoOCmrtF+rQ1Dy+0k3mrGSUVqgc7L4ORdZsgU2LkmiKoDWvKhuNkRIba/JFr7uiLIwAnEPl+iSXlGwQs0zynwkXg/m3asdVOkwmIkF8hOKLjPkYOu9DRaQUDJhriZsT+Nd1AziGdq/rTc7d7QPA5d7yT4Oc4kSkRY/vLJDkFpC99o870EDPDSNXMivmHsK6D7rfIsDDq8iZ0gZiajm/dgPZLYY9OWuF1I6MCX6tcPCkRUoPqRqsdwbRcpBKpevc9nVNyUcTGYhZSCnNYWY+ZlDxrr8YY/SOdQwRmTTjcgzWGqQqe8Bk03Nvpbjihsw5DROrhbmZxZR+IdjZw7IZ4GLmu0UmyuvaB/Pd/xJdtAqHf/l1Y5Bl+LBsMA3UnKEWZv0ic0E+Gzs5woeAXwJHg/x0pTlTFiDWBxuWRdICUXMrP3gC1ir9leSUjIVEJNazA1MnrFGd69xq/Uo2Y2ngOCELKaVxCi+1XndFaQyBeiISwG7gXSCXud9j5mcJy1sm5euvG+CaqxlIr3ZNMS5mT0bRczIfV2sM3Afyvsj2Q2WLuxTfB3MdmBu9dTsi+2y4S3eA9PiUAmYPsxZM25i6xe2gPDZaCpuB46uI1C+yUA53WHFfvgvyEIgGwR8kw4mRtMCDI983qTRgjZleK2vHuhWBWD1pJQZQ4A1k/vgLHyvfAORjKSn9IBYbK1unKEqnkBuxAXEF+D0wvyjaOPA7q+8XEp6qXukPPwF25ZYV+Z1njEAuBtbF4mEDzCbwI6wYm4ktWTB6gJtB3hrYdxP4BOHYSBl+fygT825xy5yFkPk+cBTWrfU6kN8BnwKer3dKysDIymTo5YAyGPrt2ubMqoH6A76mYh7JzIfSUxaxSJLJv/WggldaeN8q11lmv6pOoBSiLoSDQ13busdMHyxjO9OpnavOgpS5RBS9pfAfAKEQA1pKek3pkCTf1/d+r9u9N/cDO7HCkC8KZbOfbQWuAPkMs+LOCMyxwOPMmxPEXqhl38VeZ/NjrICVHc+ls273QFHWhUG7tgEzD5ZKOkBTsY4K4nIoPWIREWmNqHKZgttWvc6JMc20/imAuhAqyqq4BczfA1kA7CruLplLRIoFSnESyjpgsO5ZgOxcXTaWzgaYrcCJ7nd+JLfBVCC6zFkO+YyB64mLSHn8Z+geMGPg3NzxADbB/BZ4SfkpKIoyDAZjkVR0FsljiNzbrspXZl2D/A2IOtVhHWOvVK14+ctSq9mJmjbNr1uT26Aoa4NaJHWPSR9sK5hD2Pb4ENOAuVuAa0AuqpowcAyY31AsRKlF0uCobJH0BpCfY8tc2RTzQ+F4ME8wO2bJW2nhrTsMsnV2G/MR4CpmLZliaWS/han1kQmsy7gK5HKq3Y+t6OxpitJhhm+RVEDymHXRDojRt2HrxNoH8E3p8EU2XcTqLxgQskqkb0VRFKU57mLa/m7BNvDZAPXCGi8OBDi2Xlb0MdBzHitZ77+g2g1yn/uxLiISWPdRLyZSKVucFVHGyAZCnolbU9YpywJzx+IqZX2wMXAWaaPLEVOh+B8StlcUpXP03iJpLvcFsZIqDV5rWiXp27CekyAGBm/xGt33WnGk8tZ6iaKr/7It6ZAFVlAhNJSOovQDtUjqHpM+2CGmbjL52RY8y4bK/aOi50Skr6d9sB4zAo5y7lGZwJC3etkHcgTwP4GLqR6HawCYk4H7KY5vlFA3JgOo0PauTssdYN4KcgKYu7H3JaWOlXloOAukSR7GTMQnrcOK0j2Gb5HkWyREprDsr2SmdIW5WrSG8VcC4VXLCexTdNkk9z35kIkbFhkwaTuhKIqSiCF5/l9zBXB0YrojrFVS5jKX79dJbtkm1mpiW2L6SvcYA89h72G+b5UF1H4LcAPwJ6yliASJXc58nTHAD5mpqxPBZn/gGI9h6+BnQfaBeZDpfcnXxfxxNoGnCbcL290uv8xZKvpud9oJU5TeMAyLpFTTBiqM+WtYJK3brF2DpOC+r7Ubm09NF86Zt0wlwbOLwiGVHyiexlw6kRhLRfsrirJ81CKpe4gUdCFDVg5jkG8Al5HsihRMPxYbT0vIYJi77wdBtmEtWQ4HdlgzKtU7R6x+mN3Azd6CF0E+APyAaewpv98XEYDyy4PHuxPMG5iZ6W0m317+tT4rK2XE1P1zB1PXzTWN5RXrg/VfSKqs9FQTk1RIWjNS4/qs831uQkhy6RTvMN2m8vEidT0oIiXuqyjLRN0uZ1EhqXtUFpIMNvBvBashFZLWl5l7f4YXD0nBXAR8jflg2UUvx2L1Ywvz4lwuePnMvYgJSXMHzK2+F8wpWMumfPsQaC+0Pisr4QhgH5ibgd1u2Q+AI0EeAD4FPL+ivK2QYbq27aq3W2vSmTZ6/ScXY8f/m1m45lS9BJXjSon32dD1ThKRvHX9ldiVvlM0NtdyqfSCvAtMVnC3gMnPFhXb//iCdfn01UplcIh4fyoizSDfYhrYHsL9JO9hIUX1LVR3UoOXFz2UrsaOMt2xzakUz8KYpZd9TWknFKVpfuyE03O9ZW8F3mwFXPNFdBzo0W8haW/F7WsECFbWkNh91/JgafI6hISiJtKPWZaljsR1tK6sCC2eyqDJOugp7fwO4jGS8mk+WSMv27AxW7axeGwlHfAqy2QTuBRrGZHyQDi73ezMxEvKuBgYgzmetFhqfhpj4E1ov1tZHgK8HMzr3G9fIdni/kbARzWOl0+/hSRqtDEduvllfaNVpaXQjrgxMFIvSdJ2c6Zf9Y6Vx3e7r1ox9JYrKyMUKFUbdqWDyPHAs6SXVUO5VUJGFXHnIMlBv7PG3VwD5k4wN4C5scYx/bRutIOL7E9R2kauAj4DvBDZwAXRFgF+tuCxLsOKVmPKA1BmFoL7wewAOeA1CTHLqbwLwAjkT1BLQ2V5CPAMxc+wmUGFAgOIkVQnXkuq323VS1PFn7co6aoD2JJQL4rSDlUCZi9aGBeo53VEJH9/RVkWSWV1TWN4aYyk7iFi5/A0FwNXMlso/cC5fpneBNmCFWxKgpaae4DTc2nObOA+x8BjIDspH3gKcBKYh21e2MS+Us0ErqtBPg7sK0kHJsFYjXHpZFZJ7rz1GaIshfPB/Nh9P4gt41tA3o2d4a4JBMylwOexMy/GOn35YNni6kdM6I0NysW1E6kudorSAIUx/2A2/Mmate/DjJHk6MS9bEhESlmfum1/JUKlb+RfJrVRJ6ukKTCpAFJHRKq+i6Isjvq1KT1ErsH2Josa/2z5FuBfmc6GU8QZpD9UTkzYBuxLiWxwPcKKRxtMB7nvB3MF5a5qG0xFpCwtn00wv2Uy3bmitMatXiypd4KcDzICftTgMQzI17AWUGAF25AQfNB+yGnefp8k3crf1XURVERSlocAp0TWaZ+rkEFYJEG1+9yGRVJymkULJPi1PL0CcxAdECutkmg90cZxSi3xZLphpfZh7ouitE9ly7n8jDgDRy2SukdmkcQGmCOBR5haK+QtkgQ7yNyW0F8aASeDuZtiV7Mab4fNmPmpx/30nHWRFLnfuVga3AxmJ7PnmN906KV2K1OLLsPaTo3dGTIBdFERZivTOhvibWBOAr6Mve8bWNH090B2AD/HloUR8BrrQjpxafX6ZjMWScZuP/g6o3QSs5+w8B8Zp2sfzJLqUd5dXCPkt0tFm3aCMt/LKsk0lFZfyT+H1uCUu0Ws4jV9IwLmz63eay1ISsfRIqp0hk2QfcA7wdxuf8/0Lg8C20F+D3gwIb0s2G7IZt5/5mTb3Ud4CvMQDwMnF6Tr3IJK2QbmNYH91wHn0gdg/hbkbc766jGQjzGZPltZAYsISBve/tcALwXeRzgG0x6QTeBdwJHYOr4fW783vXTGwL2Uxx5zItJa1SOlGwjwKuDfgQ+QXAa1qFr6b5GU0UK8ltRLIxXSTEo3QeU0c1/C6XgfgyHltgztnNeFUj0qtbnyd0y0Wmw0ppOiVKTNmHxDQC2SusdMHwwmYo75KPAN7MByO/BukBtIF3scwTqRfzs8dgLVvQkJjoCjwTwIHJtLJ0dh/RqBGRG2vvHzNwaeAzkxsm3fcC90zIPAqW5ZZoU08v5uBnnrarKoLMADYDKXtE3gRZDrgAsL9tlg9kVfgZA1V58PYNuHfcAR6/dMUzqCAC8D8zhpMwsa4CnsRBNrxKBjJOUpjNdSyWcs7ViVaCoGRtnMKG6bobXLqWOt/sqj60msOM8tK4uXUbAuaZe2AjwpiqIMHScSyTddvJYLbKwW+dHs+lJGwBuwA0yT+8vYb3/LW0gTkcAKO88C762Ql1g6h7AD4Vj+wL4UTJ2evetkgy0DnJRbl8WZyp6d51QXxpXVYu4H8ypvQSa6fqhkx01sXfKtkCLIebkFHwAOgnxARSRlxWxnvi2PtWFj7IuI1NlHB07/XdsyVuDbVjeWSsBLpzWG5OEWjC+lfm29p3ZdSL3fXhkp3EXLj7IiKlsjtZMNRWmGrDzf6j6rutuMgb/Dukjl0/XECjkRuLN69uQ4MNkgYJHOWFlAbgbUBxuBeYZ4fCmfbLatp4ETGIY11hDZAex3z59DzN7XrNBuAK8EHmvgeD/NCUZZXKWEeqQorTEC8yjpRhzPrJ81UhHDs0jKP7HLLBhS06m2etHklSJ8pdgEltcIwK4snznBP/I2oJH7WFbhtEIqfULLq9IH6sZrOQp4G2GLn98Au0AeoP6sVN8H2e2+50WOTSZBwYOMsLPJ7cMOvKPm7wxHRRJsvJxMRCp6G+qHmXiKIY4yuss2768Id0/Mv3gvMfLiYFbfNsHcQzPWF3krwM3cp6Ksgk3gk6S3VccyJDOchRnmpVjwwV02e85CsVQC1hFtxikeQh9mQhVFYSgduB4Rm9igbNvwAm95U/cxVOG0jCg9Q4usMnTMzVjXtskC7/OlIHcSn00qhU3gdmcdcRY2QHjGBkhRz9gwiemS0ieR4xlGpd2BFQIy6xE/OHksMOmxWDFQaQ8vQLb5PnZUt825kW0nfP3HTkAaR9LM38+nA8sUZSgIcD1pg/ERiFrQzTBMIWkBJuUo8nCs6842l4iZ/VmFdWzP1/Gc+0DZS8lQ2Z6zQlo2Q+jUK2tHI88eRek45l7glNxCz82mUOSpQmYdcWfF+CwGzC8i6/LpbDCYXraB4kCD+Wf5GHgZ1pJFXduaxwlI5kYgs67zLH6MAX4M8rb5Xc29xF/ShQbTMcFJUYZAVg/ys43mNzsN+NUS8tMz1Og0RmSAa5oKxluURmVzjkY27TQylBMZEAvdkpRg8YqyJqjnpbL2ZFMwn8qsG43v1rYJ5gmaDXJaNej2CJvHQ8y7teXd8DbBPEm5q1HXMSAPMw2onT/XDL+hGgH/iooQbSDAK5xYdK63fMP7AzgPuJrpSE+A45nOuJfKItZ/itJ1DMgLWKvL2GQ9pwGPLDFPPUKFJA8z96VlQqKUjhgaQy9lewRd0wIdS9WKFCWNopf9pRspSt8xYK5l/o2wL9RsYAfCq7RwGWPdIFItjZ4HXt9edpbGCDgGe+3LXM4NdnrsD6Lxb5rGiXjmWve7aBS3AeZCZsW8rYRji2Zp5/9G2NnVFpnlUFG6jAGeKOiDbQEeRutABBWS8iSMfBsfHNcNCJ5I6oyGXafK5dHxVnvMCK6xAhVZXhZ/TFGUKSoiKWvFme4zFLTa+5sECF5VrIr/RlggyQ/CN4BXg/lrm2f/r3eMQZ7DChFFwbbdvZNj0dm42sCAOR7YWbCNH78qNxObeYKkQOkz3Fcph4rST7J6cwBrhXcIZAz8ETpuKWAg3tsN0ZOCUvQMT6V3saibOGmlEYQFOsI19utVOVWUBfCbORWRFMURK/9eoOGlMQK5I/IMjAUHfFdgU1MxNlNXuAS4EjvYGjF1M9yPDUCezXinIlJryFZX/vwg9L54FGNMvL7krZSyz/tAtqHubcpaIALmnVgR6QBwO1YpURfdKGJ6+WrEItJs5JzkSyEzH8vHVBiPZyOTmN9nMzlaDib8M3gOvTqxflHZqki8CdMq1liZ/FOUgVPyhn/dMcbolegYjfbBRmBeAvyG8hhIY+A5kJc1dvTKGH+wDQUqMPN9MG+73olJI+z1vwDMccCXQI4E3gjctdKcrQ0GrJCXEitsDDwP8nKms7UFE3Rk5XgfcCTI+cBeqvX5FKXP+CK4uuZOiPXB1CKpDh0w5wnNZB4k39nJJdCBU0lnwdnulIZYUodC76+yVoSsLrUSKOvE0aSVecHGHlohIsBxYO5jPt9lHStvXa/6YDB9M/9DkE3gPcAemg2ArsQZAX9B8fX2C9QGcCzVLCoE2AXyc3c8FZGUdULFo0pojCSPXj3MHbXzPNQHQ4uxphRLnctbJTbXzC3Ue6msE/kYK4qyRsgW0h8UXejD/A7kr4AXAuuK4tB4s7rxFP0UYbLB1q3uc5UB0NeJMcg/kvasyMrZuCA2l79s0/7JW0GyuEghAWp7vawrijI8VEjqI1L4M50udMSqEntw6sBrqbR1qTUujKIoyhoyBh7ExtcJzR7VVa4Aea/7nn+TnX+DErI4/G1geZ/Qt/crQY4DniXN0qhIaPK/P+vE3JuZj4nkjxbvdJ9b6acIqihKY6iQlKPL/ZUZAmJSb/LeFH3oZCr1OsmBezqU2QcVRVGUACM3QD5Ivxr626yrm1xKsctdaGr1c9BppZVqbAJPAn8FvFiwXZW+sQGOIR7w5JXerIOngrkBzLXAV4CjKhxHUZbNduwLim2rzsgw0WDbPmbmo+TgHdEvIgGok+nKeSitkcVgaDQWQ5W6kkhRYO2i42j5VbpO4cQAShIabLt7NN4Hc5i8pUQ+KKQB7gc5s42jL4CAuQg7q1nIajp/tcbAPhcIWV3DhskIzAjksFeUt2ODZS+KgLkEuCKXnu96VuHhUxT43dwLnO6lmVmijUCuAD6FzuymdJOfg3kG2ApyHs3VvzUj1gdTISlP6gC5awKMCX4tpXczhiiVaG0iqIJCVrdSxsqiCf1oyr1TUVpEJ2JrDhWSukdbQhLkZkUL3fkzvDguXWIEJmWKdbDxbh4HTkOtkoaIuHKcv+9XgPw5zbgFZrPone9+HwBze72kgn2wDTAbWAuoI5kXRA3wGMjJ9Y6pKG1i9gBvZjoTm3PdlLeuLk99RYWkVBIFmc4JMDWFpM4JYkqjtDqQLSloVStnqZCUn6I2/lNRVo5a0TWLCkndo00hCQNmP7CDYGwhEWx8lg5a8pjLgS9S7kqRWVa9HhWSBoq5F3g1diB7GNv4b214DJEXd84FtoG5MZQhog+gaB/sbiCz/svv74Qy2ZjbTVFWh4C5h6kVXcYhbP27GriEfrhOd4RYH0xjJOVJMf9sPxfV8WIFVclfJ89FaZ1G2s6GC09hnvIrc/Ez9FmgdImy8qjlVVFKEOAC9/0wdgBwEOsKlm3TQREJAbmecCUPxEiSD6Ii0hAR4CTgZKwVRCZ8uhhE5iqmVhKLki9rPwH2Ahdhy1ZivLHoat+FNOSiKWCuQGPQKJ3B/JR5EQmm9e+jhAu87xZ6ROPZGiRqkRSiJO5QUSyXlVNxYN05yyqlUUz2z3/41xAcyw+SvDhOxDouqYlSyzqlQwSt6DIk+DW4uZbpKWqR1D1atUgC2+k/DOZtwGuA3wAPgtzb6lEbwXwMGyvJxw9WuAk8DfIKdOazISLACWAeJdzQ/wrktbQnIm6AGQP7mZ9ZLdaSpvbBIlZNOp5QVo4TcM2DhAPHZ2V3jBXyc2XW/BTrCgfqApdDLZKqEJkRrRcThDUtEij9xuQ+89+bIF9B3N+yy2B/JXFlSBSKSLnlZdWyL5NWKUoruEG27AH5Csh/gPxqpTlKRq6huNN4ADgW7YUPGcP0/ubLQduuYJsgnyBcvkJT4GaWS3nRKVY+A+KSuZr2z0tRijDY2QRjsw+6bbL66Iuk5jrgTd5259j15gnsjIZKELVIKqPkjXInSYiXJHNflMFhCgahyxB6io4fIOqfn2iR5H0oysqYWAGWUbGwrnvZVouk7rGUPljPMdkgfYPZN+FvAX62ypwpyyBozWOAR0F20p5FkgCnuvhG+UF11RhJoXMIpXMIRN3blBVTOGYoMvv22+ls2+z7TSBvJ+zauSYM0iJp1zIOkn+j1IeubOwhkV/dh3NRFmJtbrHGSlK6QmohLDJBUlMkpQfsgtmYEso8AlwKvACyD3gG5GfAz1ebLWUJjECOA571lmWdshNpt4NmgAexFkaLulVcjHW/zJ5Lfjr+82qLi5V01EI5V5TVUGRNdx5wBdovC9BrIQmWeE974dfm4Vlo+Fnv0yko7dGnctCnvCpKJXzX05iwVLKJoqwS8w+rzkG3EUC+AfKHwJEuuPZb0ADb68AYeBL4bG75yFnuxILFZ+5l25h3NavCUdiydoiwO1v2NwZ+F09GvsXUUkNy++ZFqj8FvoC6uCmroUzVqDuW3wrmUjDPUOw2t4b0XkgC7VxHKaosOjofPgExsagNDfUvGsrCclCrJKWPpEzxpoVa6SoXuRgSL1l1RjrOre7zlpXmQlkBcpV1G/P/ZnCzQxnj/n7gPq8FvgIcXfPAL4DsYn6kFwr+fQzxAXJRMPhAx9GcVZCWorTJuGCd348qCDhfuP8xzL8E8EXTrSwm/vaQQQhJoP3sKHm3vL5ZVq0Jqyy/RePUhcawFcpZ2aZaZBXFEgu5oR5xykrYAhwP5u9Is0LYYNYdbl1c4zZzn0o3OALrkgXTAeAI2NHgMYoGt4DZm4vrcq773A3mIjCfo76Fz5eZtSby3ROyvw1nIVVgJZc8I5sAL8cGk1eUVXAx0+DxRR2jqgMPZ41nvspE/AUwN3oi8PVgrkpIe0C0LiSJyIaI3Coi33K/XyMiN4nI3SLyFREbmk1Etrvf97j1O0sTrxkkae063SoedRa/DLZSJkvu+zLKf1LRa8p6bi0qtLKWBNzcigRgRWm1/+UzBs4ibbC7CWa/63TvBfMFmompshXrCqQoKbiyar4Dxrl+mYOuXN6Edc9aQh7MGcBp4byxgR2lXU59AfIQyMXEHxibwEGataLYh1okKavj29jylxLTpcxKKS8YCHCZtRbMxKOJ8AtWl/hjt3xNxt3LsEj6X4G7vN//D/DfjTGvBZ5i6j38WeApY8yZwH9325VTscdcEoZCUZZG2cQCjZXLFN+2mO+8t7otqrzoUpSu02o51YeVUo12+19gC/wIOBM7IC3B3Me0HJ+F7ZR/nsUHsp8H/s19ncbAVAAAIABJREFUV0FJKUKAV7jB3jsC688Bc4lb31ab6/LAl7DWTyVm4QtNsP0td7yQldAGyHbi8Zo8JlZJZa5u2llTVogcxk5w4FslFVG0PlaWffHIV1Iy8XdtLFVaFpJE5BTgw8DfuN8C/BHwNbfJF7BGaAAXMX0H8DXgvW77cqrerIZucNHYe03Kj1ITE/oRKDRLaYvKFK1FkJmP2OpK6WgfRek6SyujKxJ/le6ztP5XVV7DtIL4FhcJA9kJbn+zw3Mp+DiYt7sB94+1/CsFZPGHIGxFl5VLXLP645bysR843x0rVNuaCkex6USgC+aPX6mWu1noJLP2iL2UbHtGOkUpQa7EWiUlvNwAqotJCda35moGFEAoTtun+NfA/8HUQ/g44GljTOaJ+xBwsvt+MvBrALf+Gbd9eyzQ00gZe2tHRikkpDxGlKM2ypJJTbihg0cNo2p0ODSOvNJZSsTThVG/NiWNbve/oF4lEeAEJxjty63L3JXeyKwVh1ooKRkCvArYWWGfXe2ISeYIrHXPkuJtyG25oN9HlO8zQzYLnUSqrgBbKJ6RTlGWhAjwbmy5PMTUki4mLtWxTCra9kJK46MNgdaEJBG5EPiNMWavvziwqUlY56f7X0Rkj4jseeKJoi0j5LdrotEuSGONrNuURJIEnGUE8aqS9iL5aEr1kcKflZNTlLaJxTZtvZzqQ2etaav/5dKe9sHqZO5i4MU6O2YZAH5Tso0Xn8UYMP/ETHBUZY3JrJGqxPAZYS2HlkVbD4iCYNqVycSkA9hBunObk50NHkNRFkT2grwTuABktxNQt9vvQFgTiPWfUuqlv00f4oRlL1kWcC1v0yLpncBHReQB4J+wJtV/DbxURLLLewrwiPv+EPBqALf+WKz2PYMx5n8YY3YbY3afcEL1TAUHn3U73XmLkoLBv/brldqYmY9+Eho9N9BZWurgXFFS6Yjo2fU2Yxl6+ZrSSv8Lcn0wsDdvDDycljH5HvDnuWVnkF4hxsAnqBZ8+P1grqiwvTJszvS+lwXizf42wTxNs0Gp+x5PSEA+APJJkLc4C5CHVp0pRfEYAzdZQYmfecv3gvyQqVvpooOJUJiBTTBP0Wyb0RSZ6+4PXXb/kdozQ7YmJBlj/psx5hRjzE7gPwHfN8Z8HPh3bBgsgE8D33Dfr3G/ceu/b8xC4eWiLDz4TA3epb1jJUSdctFCWVpJ/2VR1UcVI6UvxMpqFt+l7eN3/PnT8ez1mqX2v7Jg2+9JzNwLIN+cdbHBD8CdcrwbmHcZKHouHAV8KDF9ZX0pK4MvJGxT4VjyCNPZpfr6RuyHwBXAg+533dnlFKUtNr0/DxMKtJ+KRL7neaZk/aoYO9fvc7Bt2jnAkfWSWkUYqP8T+K8icg/WB/9zbvnngOPc8v8K/F9VEu3ifQI0+KmitMHC0bsVZUmEBglrXk5Dz0J9Pi6F5vtfTtCRB4s3m2HRmx1z0Sl6LmyAeeWCx1XWmxNotu0ewnMgi/GkMZGUvrCBtRI6QNiSqA4hq6YN4DTSA34viw13mgeZqkBngPlbalklSUtGP0th924xe/e4H1Ua5PwpV23MTY2y1nYAVqU31K1yjc+hU7EctzSHz+L03TxcWTuSA90vSiwo6oqZO/+E56MxpounstbsFjF7l93+CnAymPuIuwyE6tYYaw11PDroXXPm+mBZGfY/yS2jhT7QBpgjgaeIz9zm0dk+mKL0BQFeBuZ3NfYtC8Ydaleecs+cjmE+CfwtUyHJAHeDvJnoi5pYH2wNJqYLsAoz0v7qdUoHaKWYVhhkdrr/0unMKcos+ihYXcwopQWWfeOyN72h2XDy4mT+7fAxqIi07oywtngHSWuMsyDS+2k+1skmyHPYslkSCFtOQxtJRVkUAfMM9jnQlDVSLv0JBhttsGsxkgTkutnfjEA+RK2A/L0WkibTkfSocdVBhFK1uLZdvMvS71H1UpR+sKQHQe2JJNqmjdlTlaWzt3yT5hlj47FsZ34w4BMLfvo0Pe/5KgsxBnkSO1tRLJ5PNhMZwMXOEugI2hEhszhhF0XWb4IcBJ5A20lFWZQxVizZSnqQ7br1bgNkK917eWGwz9AN7Dm/gBXWy2ZDjdD/x6mOcpUe0jVLIK1GirIkliUiTf51kw5nTekygo07scn8YKBon8wq6VhmrZn6MEWz0jgiwCXuxz73mb2NPxs77+HZ2FkG28Q9D+Q6wmV4Ayua7gusUxSle4yZCsRdZQPk5e77bmwb82K9pHotJO1ayeuwBfCmcddJ3ZRQ/N2oON5WgySzX6N56HKDqCjKhD5N/NPXiYoUy642Ex9hrUC2ue+Ze4ABvs3UvS2lI5WzTDLeH1e6bY5oMO9KL5jMHvgh7MDv993v+0BuBbmD5c1C9oJrC2X+T1GUJVNngO7EXnlbD+rtJjZ+k4Dcs1gfTN/FRCgsQ6GgWg0cp+vlTmmIQPkpvPfL8G0rCyKnKEqnmaumXa63+Tany3lVimmwP+Rj7gd+hRWQjgT2A+cDe4AzvWP7HMIKT1WOcyE2/z+2AwBljcjK7Y9cUbrN/a4RJ2TQbBAW1LrotqMoMQQ4kfCEW36A/RgHIusNsANkN3AX/TPRWbC9UyEpQGmfaJFOU8EMJzr51BpR1PleRSHIB4gLLV8hoUvVkawpDZLyLFdm6f216v0JrDlNikgCnALmauCVwElMTday45zD7BTFh926bcDHgSuYxk5KEZWy8ncumCtA/jM2ZoSyPmQDqWVZH/WNe8DsnF8sXwc+CTy/5PworWHABl4OTWbQdwzwOPX7HOdgA/XnhBcZATcyVVSGeO0KUCEpR90+UUykLD2IzK/SfvWa0NUb3aF8FdVHFR2Gh97LOEnXpk8XsE95VZaDAXMtcCrxt7obs9vzBHCyK07ZFOqXA4+A+TbwksRjbwH+FMwY5C9QUUFZb5wVkjFErY7MR4EvglyCxuoYCHIEw47Hlc3a+ChTd+myvsgm8C8gDwJ3F2y/ppaMYkx/a/9uEdN0mCQz96WcxgK9S3F6irIuVKk7Wl+U3mBmPpIZnIhUA2PMwM+wfzTaBxPgDDC3E49X5FsmGeyg4M0ulk1+OwFzMfAVZsUnPx3/01+tJU1ZZwTMScDD7nfRG+5NEDVJUHqG8Z8jIdc2v8yfYeOmDY7MYvdg2uaxPljfPPlmaTjSo8n+1enlBy5v3b5If6U9RWmB0DTOZna1ovSCGg+FmYD3RX89QyecUGYwYL5DedDrrLyPsC4En2fetj6bCet2Zmeikdn1M5/Z3yaYpwNpKsq6YIBrE7bJgtc/g9YXpV9kz4KYFdGB6XYrE5G2UjnmX3K6gPk+mBvdsgXqb7+FpA4w8+Zq0Q699qoVZUrZSFPri9JTyh4VXZg1MT/WbiPtttJXesoZFAukOcGHkQuQHRoMjIF7gU9TPpmEf6wRcGwkTUUZOgK8HNiZsJ1gB6DHoPVFSWO7+8vYghU2ljxrZjZDIpe4BQeZikeAnOdmNFtutmDH9Kv5BvB99/g6gqkr3qL80llkvQ14I5iHgevrp997DbnRmEJN9Wi9t16VJzTRIEnKmjOxDEzdWOuLkkgnJif0Hgp5z5pgcV5i+c5fn7yY1ERWQvdAq3F/aezexRLJWxGVbZ/f7BowI+xAoeztrhZCZd0RZ2E0Zl7xD9XFMfAQtm4lusgoa8oGmFuAZ4G3u2W3YMvQJsjbo3u2hnwbjICc7/J3oyvmK1JHzI+Bt7gfYxuzD+OWPwZy0QKJj7CTWZzofm+4v1eB2Q9SUwPptZC0l34891uaHVdRFEVJpKwNXqqYkROT/MVz2y2JlGfUotcoGIOwDw9xJcikD7bVfVlkIFlWAPPlRJjGcClJVwTMWcDtbtk+bKd6+/y2Wh6VtSYvIJUhWGukNZupSqmOeQo7+YFvvXZWttJayYiwXFFy7Jr8W+zPSfNfx8JuwXwbgNd7C4RpfL9z7ffJNdpSI4/GCVNbmBeDR9T2Ueu1a9suOh5nQWa/Jnu+dfaEFGUJVCz/dVV0Rcmz1OdJim9bIoHQYa2xyHEkS0AZBLsAcwOY67HTH2fUiesQ642GzOQyK4j3JKSbCbZ3OleFs4A/Ad4JPBfZPkWgUpR1I2v8/QHNCOQDqGubUs7PseXHn/wgs4pxZi3GgPkH5idIaJtNFpqp0+CehXV2HmFfxhji5j1bmLyANAbM16kXczP/siSLN7iT2iJYry2SfBY2va9x9+seK2aprShKDfQNslKVmm4yjZM3V62Rj5g7WpWk5p5FLV2f4CSxagXSb95pPwzAtSBPAh+rkU7MoiFk0m2AE0HurZD+YWyH+WeuuN0M5hjs4OEgUwulA9gYMeqqoyhpDHFGK6V5zve+hzoqBttOXwZcvqxMLY75BbAfa111PZgNrBvaX7gN9pckMHZpHMYKaEUzyBngEPBW4GjgedLFBAGzBfvMy4SpBhiMkJRRW1ASq9RVuB/pG0YSDcWRrJS2ogwQdQVVWiNWsHJixtLd3GqSEou+UvJF1yeXWJ1rpHV74LzDxSPKzO+rMAI5FsyTTK2TQmm4N9q1phz3xarzXfK7gTcC9znLqteBPFgjbSWdrdh7sc19HijeXFku8hiYV4ZWuE8/eN7vlpMnpefkLYxinYHMMulqkEtZyEqoVdysoWYPcBrT88viPG0Bcx1wH8hnsbOHhs5XgOOwFkEpVliZW9sJYP4G5DPAC4l5Httnm9mRW/4C0xniarw8GZyQlFG5k1uhd1t5Jp28qpgoLCnt0RWDBEVRlkRKkCToTWPQaDz6JQa3DxphqVXSMPA6wqaqmDTGBmHN0ghZIW0CW2uIVEXcig32NPJiTyjt8i0wb8FashwJnKfVvzOMgNcBjzMfQyx0k/ZTL16Lsj4IcAKTmTYLt8va/QvprogEVkTK3Kz9euGLQecC52GDeX+GuJi0I7I8Rna8S7F173IqXSs5H8zNs7+5q8Lxc/T/kVmgCFTqm7oCXPbGtLKIFNq5zLdNn6itUeGFu6Io64h7cHRZ2zDRH45cxovOpbKFkJdY3WvU1euqNIQrVJXFJFxgbIO1UsnenB4AngAuBrmlsVxaNnOfOiBuFfMAcAp2AHZ8ttAVGXGWZnoPVkcm6OatFmKUue0oigEeo9zixrd02wBzhXMPO9Rm5mogYI6bfo+y4dZf6vpKEeuhOQuhyYpc+oHOmjmvxouV23L7LKgE9TrYdpAGIo6G7slM7NOmesGhgKraw26NlGKhbhfdoJIArCjrSNGzLrAu+t6iTqO3ULTtCsuV/lFpZpFIEruAT7kfBngvyEUgP1k4d8oKMQ8Ar8KOPLZ4fzBps+oGkVWaJXgL8sG2R1i3UBX+lEXw3+T7f5fSPREpYzvpeRthxaS/Z151MSC3MZ35NP8H8fbQ74dVmeAiX18XrL/DE5IyzMxHGjL7NdgfauMB10DHSykmGEy2AdFRaYfGY5Up603Vej6UdqHN86jzjAV9eTJ0cs9WcxXVZ+C5HeRK+9ZURiA3AT9BB6x9ZQPMNqx7S9nb7w8xnPa3r2xYdxwOUtpXlrOXly2lx4yAY7Hii6HfYzAhPDlE0bmMgI8E9hsBr6b8uvgCbva3AewEcxj4Z+Al1JsxdUH679pWRJPRQLWzOwxC9zbn19Zll5a1oGLge0UppWaZ6mxbUOVESk6iiXo2F/OojE5eVGUhQgVpDJxD9fudj/fQ5VgZSjmbzIuAsb6YAfNbkJPorjXC0NkEeT82cH4Ze1vPjTIExiDPgtkaWFfQCelkH0yARykPT5PPeEhxGYP5cW5dfr+S+MoYMGPgW8AzIJdgRaYDBfs0yHAtkhZBct87V4qVWqQG203YVGkXvf7KyjEDavrrWg/VPJTW3zWiLMCgWhH9/+zdebA0V3nn+e9T976bJLQgIaENrYCRASG9ryQwCGELDLYBSdaC7QbbYI8jJmKmpycmYtoTM/PHxERHTEd0RM90dIxjOmzsBrwgCQnJuI3BYIRwg9ArIZCQxKJ9l0C79K63nvnjZN7Kysq1qrIqM+v3ibi3lsyqzKo6mXnyyXOes9qM0J2tynxxsvOjwRVEWir/CqMhwnUeJPNyBfASkxWFdGscBw5GXe+zgk/LtAF8gvIISsZn8s8zGVA6k9m3MwPeBbwf/FbwvwAOm/K9aup3i6RZaKfZG775r+LM+u1FeqlsMIVey9i3zfxdFOwvtStdQVndFpVDZbU5+E0Zzyd3xukd0RB4DjgWtUpaNAPOIHzv6TPE9EBBWd17RArYdeAbwBfIDsSkk0C3sRJhwDcprkDltbi8kLHjod9K9mh203zuOOfcW4Gzwd8Y5RtsWO9bJLWxDIpIvpU90Zdm2dhNp83yGeaemqDgDbUtr6jED68cKiss3lGdOcXrXkY7kGVw8L8nu5lBOtl23dxnIg52I8XRh3i7f3kB6zMNB3uI8gTZ6b8BcDy4j/44h/kEkZIGYR05E/wGGm8y1PtAkqy2Wq2Rxl4kIn3Vh2BSK9UYLU56Lm6toJHWVtMAeF10sjQkO5Fs0Y5YO47FM+C1wKkV5z8NHUylPmd8+Pm8VofHL2JlZnAFozxE0yYQrxNEOgDsrfi+Rki+/dEoGXeDeh1I0v5NpFvqBP60fUttlnm3k2Ze/ymaJVVephIlrbaDwCA6WVDXpNW0DXg6up931R7ydyr7G1qntuVbaRMDf4HKXdbsQbSfl/qiMmMGnAj8CvBq9Adh298gDNTQYvYFwj4la/82zXaRty88QPhuLgE7i8ljatEgUhvA8zQa7eltIKnrJwkyu7Yd33RuJdICqWBS1nlN63OLTttNb4orZp34PmR59mU8967UFWdZPd+NWiNB/s4jPdxjsgvIB5lfbq3oTMfvAP8CcGj0vLpmjRsSvvOtZJ8cx38HCdv9EoYal36xx8H+GexC4OLoubPBzgX704IXtqTs2fl5E+q+UcGkt4AdCvZl4AHyt8+s91sDjqDRfGbm3t1TW9tlzu6iGRa2KtIiPnGnvnlWgstGiJSgdmskfYEyi5Ky5kUH9vmuyXQWNBJb1ki007zBrN+Ze9EvIstgZs47ge+kJgxQIt5VNYiGot5PceufZJApmZnfgcfBTiFcTZ8Dfxh4CngbIV9IFECyLxJGX3oFXeWjoA6WfO4gsEWBYpmzNUZByiinz2aXrIwyad8ntFjaSjOtF6taA/8IcA2Tgekq20h635ee9nOw4xnbF04VtrkA7LtTvC65Ojl1sN62SGpHTV+6aJ5Fpyypv+ou9WnTlrkoKUhl6TuWvu0uMnm4NjrJc2vGcwoira44H1JWEClvlLYkA7uMuZUhvw94PaOTzsRZj38U/M8YtVBaZeuE3ywr50vMgafBTl/86knPbTBqgXgw/Bnk7iv8rCigspvlRjIc7HbKA7B5+768IFJsH/XqX8nlJP/+NxprgdlwLu8lUIVXYOqzvEaLTxx5To2LrWGyq7OJOyIzyGpuU6NsVckZ26hp++LXXUbydprBC7S99tecWo3Iisjaf+xnM9eIwWjUoVkYIVnvG8g/gRoAV4GfCnbBjMvrsq2EYNtDJfMZ2EXAw82vkkihuNXSU8BwiVWMIWF7qJt7rUq9qG7dKS9Y5cDba75XDd3u2mbW3ZWXxtQt0hPb1pw2ttIudlb4cGV47oNJCiTJIhV2t2xJ4u5GD+GW8dl8uu5t6trWP6qDSZbcfVL6+Q1gPcqP8q0G1uNO4BczJiT3JM7Kd9fyzwE7gV8on9eMcBLf8EhQsuIGwFbw5xnlQ0pvt0YIQm9d4rZrjEamzJgGZHfjzQoSpfePw/BnhzKeYPtS8KyudOllp97PBsB2RoH7+CJQ8rsskFcH61+LJJEamgoibSrr27ZiFZfCs44qpyQr9n3J8pTm7Epsv8valLt0Fq9ccSI9NyCMwlSWHwnCDuEpsJNopsuFAZ8C/pnyM514+nXAlfS3ld0O8D1kt1jQ6IrSJsOorJbZQri45UsIJuVtT7G6FbT0+q8xue8aAD8gBJnK9pup9/PbgZcJybdfIHx3Q7CHCfvt34im122MUdQiycyOAP4X4FLgddHTTwM3AP+Xuz9fb3HzpathkqXOFXrLfbDY9VhovpMlmTmIxOpeMZTFq5z8fUnbbp2WfNPK3N6mbJE062yr2CJJdTDpFANOBL+fyROgrK33TLD7mludwjpYVjDlEbAz6G0gye8G3pI1Ibot677sYHcQWi9BaNWQNWqjyKzWwDcI5SsZlC4aBGXBNYTc7WlzhtT9uMtuUYuk1LS8zzSxb8vLvZR0MGf58eAY14F9ijD4QOYyp0u2fTXwHPA+dz/a3Y8Gfjl67pqS14q02ubIX1lDJy5az6vjuR+v5ufOyv8o0oiqhczrzT4PiwgiFam9u6y4jtquJ6gOJr3VZBCpfOGp2wFwCv0NIt0HnFHzRZb6G4A/Bn5HtK++O5qvJUOxS49sAB+nViLthdUf1sG3EPKv5a1IOoj0YHS/LPCazHP0WM48A7CjgRcpH5QguR5xC6fkbTxa3jpwBWHwgZrbc1mLpB+5+5vrTlsUXQ2TCTWuljcdva6du2QOeUTaaK4nvTlfUB+/N1mutm6/02xP6dQClV+T0yKpzvtUW1Duw9FiV7NFUvvrYGtMnnwve0jmpiUr2uoWNGLAGdGV+gotkpZeB0tOj/M1bad3rWz8RMKJbHzSODFDdJvVIik9/wFGJ55RfhquBbtyXmsrQsg9dArwY8b3JXmt5obAy2Cvo/H9sZ9ACPLUyGlgpwJHE/aPVwOvAoek3zi63ROm2fmEUely9mN+JfBp4LCMefLyMxVx4EmwE3MmT9ki6SEz+5/N7LjNdTM7zsz+NfBIyWsXwnP++myVPmtdbfo+Vu6sp0yDP06bfnfph9Zvv1MW+ioNMAunz7srX+pApm15TPvrYLvDCfvY33eXvVYN2RFu/Ovg3wT/Bvjn0fDxMQf/IaErSro1y6INwI4gBIiKKstGCBwdBL5CP5NI30w4GZ/md0h/b3FLBhh1OboE/GdTrptIDqu7Lb5CsxWIOGJyc3SbF5RN/m0A9wE/B74PfCEE0O2ijNfGQdqdUZD9Ngo/j10LfDJ6sIcQ2N1PdiC86HtJtDjkhPpfYVmLpKOAPwYuAeKKzJPAjcC/dfdnay5vrnaZ+e6C6a0/EaiprBxIvVYEjfenrdM6auJOf1TONVNHTh/jHn59six1cwEtoEWST9wpt9myqOA1mRfWChMW5T9V5cJXroIg1Yq2SGp/HewAk61PDtC/UbAM/AfAWxkFJyBUvq8F+zjdbZm0jcmNdsqWVn4z8O6MCUtokQSJOmFRTpI3gx0F3NL8+izcgJBrpo49hNGdkqqMQPUzsNOBl6ZcV5HYADgZ/F7C/ilW1CIpa4SzOfOTgB+xeVFhTE7lx54gJLNOSo58GNU17T2EZNf3UL1Fb7zMd4NHOZBsAP6d7HUprZwNgZfAjmXie8yrgxUGktquLJAE/Tqx7GsgKetzTft52hZIStxkr0Pug/5ofBejYJI0oa2BpJrb01gXtdqR7bKVmXmWzOUqkNQNu8x895D8E3R6FEy6CzweSj6j60+XP6ffQ8i3EX+uIdiTwCfITbya+17nkT1SWvr72QBb0LjRHgfFkkl7HXgU7FLgrrA+vc2PVHdHfCZwJ9nBpKxAUtJ1YFfR2+9SFiszqXRe2VtEHWydsE9Mj0pZsGA7HXigwpvHLf2m2XbSI7hF75G77Rc9/zjYaUy0zsyrg5Xuxs3sg4QRQ04cLYIb3P3LZa9t3E5C/8Es0cet0YWx1XKvRHf8w5WV8Y5/PCA7SN2Hz9UafdnIpdMW1hqpLsu5X9TVo+r7zjtIrG15QqvrYEWcMITzz6JKaVdbKUQj2vibyC+fTveGjo+2X3cyu3P5APg02O9Q7zMluzUmT/pIPX9knZWdjRn4Rwjt+OLnAC4nDKXdx+5sMEogntXCKJZVnreQ3eIii6funw2ZedNEprET+A6jaEXWRQsHnmu46jAg5Bp7uGCerFZSG8BTFZcxyzZT97VF9bea9brCQJKZ/d/Am4DPAI9GT58E/Esz+zV3/x/qLW6BEgf8rtdNC7szRB+ui5+xiYYqVc9tFvJdJVamsEFA1364hk11ftrFDUBar1X7E5iuNdJUE+ewAG8m1rRKOl0HM8IJ5dHgf9HhVgrDnKu6ceGO/vycDrVKMuB48HhUoPTV7Hieq4CP1XjfAdgx4PcTEsCmA9fx47PAdgE3sbAdhH0pLN+dkENkO/0PeAwJLSHygkLp3FEHCV1S3wv+CpPJgNMyWoRwBv1OtC8LZeeC76O02Ysdznh3sXlLbytZ05OGwNriWl1mivfHj5DdujDrxNSpvf2WfcRfd/c3pZ80s88Tcqm3txID/Tq5LCvAHfucnvsg0mAQcKFfVepMahW6ss3CErfTBpM6uDlIG1UshG0ta0tfrymjSEtf73bpXh3MM2530s0f1ggj7ZRxJrs6tJmD31Q+T+0WZUNCUtk/JIwmtIXx/CZxt4mfAD9lsVHm+KJeshz2OYiUdCnwBcaHV8pq2fEw2LPAfwZ7DvyvyA8yJl+XNFQdTObo04R9yl8Syu+21PS4i2xWOV2kdIFfa8GFBSd8Z2UnVqlW6/ZBagXkykZt22tm52c8fx6wt/piZCZ9vqRboWld3Y+fDLRmTRu/swBZy1r2DmZR6uSsmtMiV+WrlcVozf5kmuOANoaua38dLKtVQvrxyXSz+5ARWmUcYKz10dh0oueOpRvbmwHHAKdGj4u6uCZalNU5UbMvgP23wPujJw4SWgE52GWEgFMXy0NXfYnw+xmTB7T48QDsA8D5hN/qGkYjvaX/0ttA8m+tG5uBdMQQ7Ctgvw9cnHh+T7ixM1lMgTOKm94kjw9t2bc58AzwO1SvP76NMMpcDWUtkn4f+BMzew2jZtUnE9Ly/X69RS3JioTGO/cRqxQFLcAyAAAgAElEQVTqGX+75PFu6S2BkhXOzv1YMzAwr3EOnOoOOE2rJLfV+oplMZZdpupsD7b5b/lqr7ck/T5dqIMVHdcc6OrQ4ENCTox0kub4Nj55Xm/B1ec6tlC9q4YDb6deN7Ah8JeE0YMMOCdqNXBr9D59vjjaQrZR8JU78CqhTKRyuVjcFTDvdVnPPbd61Vxp2CvANWF/slmwLgRupkKm5zlxsFfBnwCOL56Pp8BOWtB6VWA3EHLe5Ylan/Ik2I/qv3/hT+DutwMXmNnrCYkeDXjU3Z+svyiZSoU8E13bYTdahyjqSpb5xIIte/lLUqkMZzS/nCaYtKJfsTRBSX7mQl/jdDpVB8s7e4xPVLcRWjp0UOEJNWCnLG5dZmbgz1DcEin9+Ezq571xQrcTCAmtY6vSnaxlMsvwAWArsAvs3qwXgR0RujeOnS1m5VaJHx8RBQ3b0ipD+iHan2z6dnS7qHLmwNOEHGDPM7q4YBm3Fy1onaryxPa/j7CO6S6CjwO/SQgope1gswVYlrKubZjZEYSv5SJCDPAiM1vgeAtz0IMabGnXii6p83vMsztHJ7+sHrCxm4lJmT+Lfitpi6KyWNTvrQFVFtWm1kjJ9SjrIpi33nGKnVXV+TqYAUfQ2SBSzHZFd/YRgirRCYydSqiEd8WQUQAh3S2pKRsogNQCdnnqiQ+DnQD205wXOKHt4xYmu7Y5k+VnLZpXQSRp2pLKmO1hfN+ZNU/NrmGNS+SHs4uBd07OYpeD3UHmObffBjsLKmGFgSQz+13gduB9hJ7ihwK/DNwWTeuETjU5TsuoiC/iuN95+rJaKfcnyYsy1X1//c7ShKz9SVvLWtvWyyYfZn6NOUGkVdbpOlii1YIdQfXhxNvq+1FF/P1g7wU7LzrePEq3Tpzzav3pkXvivyHwCN1KJi7Zro/KcPz3NUIOlWnLr6f+Hm3f4Udk7tIB2aRTaPdGcAvYXan9wAC4jcz9gD9IaIVVoKx34f8K7HT355NPmtlRYXX4TNV1lwa1udDOgzpci8iqi64EF3UT026yd9pfB0sen5NJCePn14CzCE3ju9zH8SChxvzPiee62IUnq+sC5HdXilswdfV3kxFnPGn6BvnlAULQ8ZhovvSIb+nycJCQcH4r9btBinSIXQf+G8DVjKIoDjwL9vDy1quSuGVoevCEdItRI+SCqtBtuyyQlHfYH9KROqtB9wMRPTtzqFuX7OBHlLS8H32OP67KiayKrM2p1eU/7yQ1PT1hbNbV7dvW/jrYPmB7dP8AoUK6g7CGa2DHEJJtH0kYQr7LAYl00KhrQSQIo3Ol897klSQHngD7GOqa1hdVf8cB8AbgQcqToEQJ9e2EGdZLpEPsn8B/mfFeQweWtjr1le0HHPymam9VFkj6N8DtZvYVQuNWCLuWDwD/Z7VFNOc2is9PPfmg67Ku+HVRzUpkq/J9yGym+B29y1ewReYtsT10MrdY3fWLt/1dhXP1WfvrYGeB3x898UnCyFw/AXsb+J2EjnhPAi8AdwNnEwJOshxDRnlvoDi4a6EbH48UzCf9tAV4MCfJfNZz6RYOIn22B+z2Za9Ew86sNpt50VAUbDah/iCJEUOAf3D352Zbw9nZLnN2V5250VWROioOB1+UO0P6zzf/VaP8SLIyGm7d1wbJqsmuXbB7t/fsE1bT6jqYmbOF8cBQ/DgeLj5+rIsCreKXANcTuiElR/DZR2iBshbyQHEX6qrUViWjKc3kAHjNodVVBxPpj6p1sNLdRFRZ+Zu5rdkyaOfWOpV7Oum3a4VF96ysG0RSOZGVovK+MlpfB0u3Loofb6QeK4gUbAf2RveXmGPJvhS1+P0Q8E/Rkw72K8ArwPcJAaWiHDqyGOvgB5nchm4FO7+ZRfoPgHObeW8RabEBcBLhosLWarNPxczunPa1c2UZt+k/VWA2pQdZWAob3XRhEKRVV1RO2rRptWldRESa1Jo6mNTit4Urve7g11Lhcm5DhtGIPd9Mjd5zC6EVUjSPLJmBf5vsCs47Eq0G5lWO4txIb2byhCG9Dqlpfg3q4ibSdc5kgv0ChbseM/vNvEnA6+usVyNui27TwaQ0RSiA7ONQa/OQt3KlVk+V4EwjZahuVKi1BVlEZDqtr4NJLX4f4Upv7JLQ0mQph674GJsOFimpdjtE3Qt9P/mt1tYJARwH+yLwCeDlGZfrhDKxpWxGJgvuFYTy08XRDEUkcOAxqu0DKI9hfx74S7JP67ZnPCdSTeeGHVo9nvsgwUaT2/DztWU9RGQGal4YUx2sDwx4LWEo5fRVXh20JC3qUuh5Q3Vn8I+CfYYQzJmlJZkRzgzL3iNrj7QB/ijYg8B7ZlgHEemMskDSD4B/5+53pSeY2fubWSVpVHLnv+wggCpPreUTdwpmnHc5mvEksusDG4qIeqVHVAfrAwd+znhAwEe3/gxYnJNCZJgzWlqRAfhHwGbtjuiEHFnpMHWVpKbrwIngx4X5zQiJ3IdopMY+i5P1a//VK2bV9kNlPeD+FWGg0CyX1VwnWbas/s0ieaqWj7aUI5982JZVk+a0Iu+bzJeN3awy1cH6Klm415jfSdjW1P2K3ROkxYrSdiSnzSNPkgPP1Jg366AbBUzdwb9CaFd52BzWTVrJ7wD/xij3m/RHlZEYCwNJ7n6zuz+cM233dKslS5G3cTe40Wt/0mHT5Cia4mV55nkCqQBDP+X9rvq9+2WVg0mqg/XMpYTWGclmswPgqBnfN9l65KvAa8LdzZN4JUDujgFwKqEb5H6yE14XHODmVgers+MtWugvgV8C/hfAIbOtk7TIlqgoOnAGsCt6Ph5I4GfLWzWZr7J9QeVR28zsw0WPpeWs2aHa845zOqlbIT7fMjbVexVUtFQO+6NqEnjpsMQO4Lb8uVaG6mAVbaO1rXDsbwm17jgRcTw6zk+oNMxynrGR4N4N/mJ0gvce8MuiXDszvL8s0BB4ELiY4gBgupLtwAEwZ27lf+wE8gAhsFW2PmlrhDJ/GfgrwOvms26yXP5jRr/5OqOyGpeZo9U6qU9uuzd/WuVAEqN4Y+y8aVZmFbQ5eDLvYFLXej9JNUttBWDjd9OttyvL2BDbvG1KNRO/X8GVWv3WHbfKzZEmqQ6WZ1vi/u3g16S2/XkNjT6rOG/M5aPHOPAhik/Ss+yI3uJWQouA2CB1P1qGfw61TOoQ+yzh96pSCYoTyj0d3Z9jPiKz6O/dwDsTE/bWfaNw40/ruNxp6+BbgGNzpicDnBtRy6RtOfNKd7ySP8m8JGRoZr8AXEIYtHQIPA7c6O73zG8Np2Nmrdkfla1IK+rDOa00DGqvYOHnzUjoPcUiZFmm7KY2TTmqsh41J2WLK1rKvdJ5lRLBa7/TLzvBd/tK/pSqg+UtnBAgOgDcBP7e6PkDjFpBxG4Eu2TB61ckGpWLt4OfHQUN6rz2BPC/B95KaNE0YLSj85z71OyuJEvnTmi9FgeU0ltbul5zOvBAQysTr0PUms6vIIwnmdX6KVnOsnKzvgR2zOi9pENOAH8s9VzJCDfa7/SDe3YdrLBFkpn9a+BvCMXjFuDW6P5fm9kfz3slu6ZOgtdWRLwyisBcgkhFfbdb8cGllimCLY0cJwretPbyVA57oVIQKZ4+57xdskQr2rdNdbAcRug6cW/UneuXEtPWmTxAfDjRzaINrZPikbXuAvsc1Q9oBhwDfh0hiATFtfj0+55dfRVlyaKWQFwC7CmYL5pmO4H0Cf48bTAW+LFrCd0lLeMvLfncADgCBZG6xoDXgt9U/6V+HYttDaluvAtV2CLJzH4M/KK7H0g9vxX4obu/seH1K7TMq2G5C05dAcqy9OBs1lWNaV5e59tvMEeTNKTiibhN3GmQZ96tR2Wxk6bd7yRuGldyYU5mkHc1rM9UB8tbMPgJwKPR43QrHBjfCJPP3QB2Bd08kTXgNPAfMkqwnf68BXVQtQzooEPA3w/cwPjIfk4oA2eB3cOoldsC+aXA1UwGZyuUM5XFjlmLcq3l7fGLnn8Y7I0sZJ/rAF8H+zXmNxKmAPl1sLJrM0PgBOCh1PPHs/BdVrtstjLN2nhKAjUVYk3NKmp2WsW0r9GBo5PyistSfs5EZTmrpbf0XN0fXPsd6TbVwXKYJXYHVbbxeJ5LwA92dLfgwD9QnnMkfaCOO0RK97wK9iXC73gebJ7KDcC+w+gsbtF7gwHYneB7gMMYbVB5XSxJ3L+P0CVujrmcpGEbwEUF0/Mq5HNM/l5JvA4KIi1MWSDpXwFfM7OfAI9Ez70BOBP475pcsdarcyLb5svUdVsjTXnm3saPLiUyCnjm77iMH3eWKJKCC92kfY+sHtXBshiwljoMlF3liKcPgZ8BpwEvNbWCDYi7lpyW8XyZAdh7y2eTloqDRLdn/NzLalk3JASEfh/4HJvJ3zNZ6v6HUBCpi84omZ5TL7djWVxFLJk3TFeaF6JKsu0BcD5wIuGneRS41d03ml+9YktN9Dht/o2OJ4GdJpC00K5PMn8VkxovXFuSgsti+HT1AjWh74dV7NoGqoOV2ayTVAkkJZ+/HuxKwpX2Dpn4vOnPmm79MYjy59xF/ZHhRMocCv4+Qhe37YxydqW3t2RS8JXck3fcADgd/Cf1X7rQ33sHxTnFZGp5dbDSQFKbtSGQlLpbTYdztNQtLgoiSWOm3AZViemoaQOHi/y929z6tONWNZDUZm0IJEFUL9nHKMlqUYuk+GT2FeAoOtcyYnMkr7z+BPFAAxvAlmj/t4T8ObIiDNgB/gHgM8Dh0fPpQFI8muJ5YHeg8thhtc8DdeTuhalGbZNqVmYb8eqfNW/wBpG5qdm6T2VyNS30TFeFTGThzICPE4InZRv8kNAy5zA6F0TaHMnrioJ5DhKS274zcQKnk3ZpihPyON0AvD/x/DA1z7vA1hRE6oV4sIKiEbsT/PaFrJVUtZWQt2pOo9upRdKsaraK6HwLnZLPO/Gxuvo5pTum6tsmneOFD/N1uAVo31Q6ZuS9Vi2SWqcVdbAEd2AvoxHNkvYB6+FktvM7hCj/R1b13d4AvB74Ht0cmU66a51RmXsbcGdimlrF9UtRqoH0hP1gWftkWawdUXL8mwiDL5wEXDh7HUyBpHmocILTmwDLFENvizSuVhRXOq1uFzcbu5ElmTXVmgJJ7dOaOljSLvBbJ5+2NxMCTD9Y9Aot2Dqjrm0iy7KGymDP5YYPsp6/Aew3c6bJQvidwFsJ22U8mud6uG9bKc2hp0BS01YpwFL2Wbv++aS7ZmnyIN0wTa4ktUpaulkPGwoktU+r6mCxZKuI9PMKsIiIzM7ADweeJnSTSk2bsAFWNk68NCNqDej7Cb9V+qhtwNVgnwJezZgeUY6kpq1SFbcoD8gqfQ/SPpbxJ/0yZQuj9p3xro6xIdrzKikLWhfpubzuXAdREElEZE7sMPIHNsj48xfIHyRA5pq3aMwwaj2WDvjB6Pe7AvzTwKH110E/6TzFP8iqtIro42cSkfaLcoTI7LIuTjW+oJyh2tMD/YiIiEjLGCEwscFkJCHrXHiNMKKf8rZN8PjfrYSRNs9hfl1DB8AJhIEliiI+A+BK8NeD/Rz4BIWtk5LUtU1ERLqnKNljFnVvmzBr3qLKy5miX1v6KXVtax/VwUREVpABR4I/W/NlOoqPDMDXGOUmikfC20LIKfUJ4KX5LCqzDpa+chd3/R4A10Zd3V5JvodyJImISF8okDSzRaW7q1TNKAkmKZDUPqqDiYisrrohBAWSxnlWS+34O7oV7Pw5LGQd/EDVFWKUiPsGsCvZbBmlQJKIiPRH3UASqsQk+cSdiBU+nGpBdQN+WctWIKl9VAcTEVldrQ8kxV3w4vv7CDmASkYoa9wa+Ea0PlnJyp3QQmnLjN+ZASeAP5IzLS0OJEXLt0S+JCXbFhGRXulKZCGdd7I18po7l8xSywxZ0Vv1XYmIiEgwADsaeJHJCk660jMEnlnCOp4AfB38JvCvh8CXfzcxfVmZojcIeYiKojDRum0G67KSZReJR2i7meyBiPIqWPHz6+DXEfI1FVAgSUREuqcDUaS8wFEbAkqFX19qBadZ35kCZ8v+ckRERCTfEHgW+D3yR8pIPvciCw/c+HXg5wO7gLib2FlRQMmBa6N1mmd9cmviL4+B3UR5XScKwvnPanRPiz6L/xP4HeAnkn01M50fKb0uQ+BtlP5m6tomIiLd5GM3hWzzX/Nqd7lrYNml71m1y1lOV7Mq6zD5oKZEXit1bWuf3tXBtgN7o/tbCCPd5Im7TCy7i4SIyDINom5aMN41CsbrD6cBDy5ofeIk1gcZb1ETr5uNP2fbCN3MZhGNtObfAtbBtgA7gaz3jufN69pGxnpSoZtb9Pk87hoXLyvr/ZOSv1eyVdmzYK+LHqprW/u14Sq1iEjXdD3CMK/9flbexrm+6fxnF2kHA98LfjfwRYqDSBAKuoJIIrLqhmBXAS8zHqiJ78eVggcXtz78iNB9LG5tlFyf9HrdGgV0Zq1IboA/ALwbuAD83Oj5rADVBuH4sTW1fqkA11groo2wroUthBz8PsLxa53Jz5/3GbO6uzmwh+KWVSiQ1EpVAkoKOonIyqvYWmZprZHSzYkbTJS0iC50VY5LtV5QousBQukIA44ilNczwT/CZBLZHdFtdPXYj0zMMyC0ZhIRWUXXgn0yup8MsEcBmkUm2fb7gJOoFuFwQvetV2dc6Br4VuDY1Nt74jiRPkZ8n+K6YfoYtAb+g4znYwZ+AvAGZutCGP9WA7DXFyxvNJu0VZVzkFYmcBURWYJ0XaXoAkyjWnY1IGtRtRY9bYKkWenAJosQNeEHQqAo0T1g8+/L0e1/jU4Mnote6uDfA/8L4NAlrLuIyLI5cF0UMEoMWW/nLjCIZMAxVA+kxBXEbYQLBbPUN+IR2A7JnuwO/s3Uc7uYbC2UbpGU/NsALiQ/+bUDNzGfPFRxC62tlLbOVY6kFvH43wwbna7gisjKKTsSLLo10pRJkmZZzc3jR857px9Odei34nXMXYf6i0ndUY6kNupVHSyZ5wMm62IHCEGmg0xW1Deieb8YdfHYoJo4aKUuciLSF+uM8vMk7zdtAH4c8BCT++h03iHPeO66mvvvlNI6VXQMKayDpRNgZ0wrCszNHNLZC2wHOwu4J/3eypHUbskruFWvZs95sSIindSmEMOydqh5y/XJh42t4jyDSCKLNASuIFyJzSrH8YlJ1tXguCa9k2oFOH6PGwhDUkP9oZ1FRNroYM79phnwBPn9/LOei58fAm9lugqIAccRAlBF3YTWJxc78T7TMuDoGV4fe0cUqPpJjUWrRVKL+Az18BpXtAu6V4qIdF+8k1vwTm2Ww+mszb8rLbvoalcVZS2S5p0TSS2SWq13dTASZTjdIinrSnZyWsSKLs/GI/U8AJzK6MRjndCa6XeBl6ZfdxGRVeb/DfAnjDeTSSfZzhkVzc4A7p9h2VUagOTVwfKONUzOM3OLpKx5hsDDYKcXvXd2HUyBpDaZJZAE1bodlL+FiIhMofWBpDlotFn15kIyztUVSGqd3tXBAL8cuJbQ3SxrWOaS9AOF28eDwCmMusmlA963gL2z9iqLiAjgewk5j2B8/1olkNREHSxrHbKmpV+TtS4bwBrYNnK7Q8+lDpbVMMXAh+ra1gnLrin3rlYoIrIgS9t/LyqIVLIOM3/+RLLJ5EfaOev7ilRk1xPK4JWEbhlFlf067gNOiO7nJUPdCX49+clURUQk2wDsOEKQpe7++RRmr8BcSmjZUyd/QFkr8WRXuTWwv6cwh5M9XnG5eRLLc9gMyu18a/5LFEhqk0QUcKryXFBwPf0g/SciIgs3l5HlSlqjLsSsK5Dxeh2eZOGG0TZ5I6HVUFEi7KrNvE8ArzKS0BrwYaZO9ioisrKGwAuEkcby6iPJ/fm+cGN7wZ5h5sqG/S0hqlK1LlQn1UA8KMOvU3x8OLHisqtw8K+B30FhDj8Fklqsbr28dP6iWrlq7CIiM6uz31568KeizWBXkyusY5C0jBnwXspzBkTl1rKGSXaqDckcl/014DrUKklEZAqbXdT2MQq6DKPbD0a3DnwgmncHsGcOC052SStKMp6u5xTVe/YAP43qYNsK5qtimvrVBcAvUngMVCCpbVKXp+tcrS4ctKdGAVJdXkRkelbxb+wFc1zuPM39gkaZrICS+rbJktj3wK6IHpSMjOhvJrtl3Znxm+XcJt/Xwc/Jfh8RESlnBnYx2K7o/jnR7bei2wHYt+e80Kh7vxlwefRcUYvWPPsS988Bu4rqx4MB2DHAXubT82iN0osaCiS1Vc1KROEJRJ0CpCiSiMh0pjn5m9cJ45QXIPLeaqpg1zxPftX1WtrgIKGFULxB7I2eP0A4SdgHPBmdnDzAfOpbao0kIjKbW4C7ovt3R7fJbmENdiG2vwc7CbgYeLHii+LjQdRSygzsfuDOGguORwHdTsnVy7wVr7GsSFljW1mmxNWqKiMGiojIkmUN25o1zwKWXWVV0i+v9mQ+xX2kd+I6mAEXgt8MnEe4+uuEoFKevKBQ0YYyzVVsEREZWVDQKNNB4DGwx8A/AHyXcJzYWvCaQdSaKXnMKOoil8XAv1XzNTNSIKmDcuv1iiqJiCzfMvfFGcEkmDK2pWOKyLhvR5vF7RXnHwLnArsp3p6S3d0+SP0TCBERaR37HiHA83bg+0wGlOKBtuJjwCxBrx8CZ5bOVU3Fq4Lq2tYFZU3SSqYvPMeFiIgsT8ZOvOgwYemZpugbp15oshLiAM9G6i+Pgx1N+Wg+Pnofe3DWlRQRkVaIjhl2dxQsSuZ8jEeOm+XEewCcBL4b/DTmWxHbR3GLWxRI6pbSjK3FLxURkRWRc4zIPYTMcJBoKoDURPJwkYW7KXGikFcpj7s0GIvvhiEiIs2KA0r3JnIgbZ8xiGTAMeDXAW9n1NJpHlf24iTlV1F4TGo0kGRmR5rZtWZ2r5ndY2bvMrPXmtlXzewn0e1R0bxmZv/BzH5qZj8ws3ObXLdVVKVSrkq7iEjPNBiR8dwHs0mu7m3ze9uVojpYS0TBI3sP2OXR4z2EXEj7GQ09vWvGkwoREWm3eXZbNvB14G3MPVmRnQ78FLgBbvtx/nxNt0j6f4Avu/svAGcD9wB/DHzN3d8IfC16DPBrwBujvz8C/qThdVsdOVekk/c3n1MlRkSkn9LNkKbsxjZhzv3adBiaG9XB2uQW4B/BdoJ9ELgI7L2EYaotyqWh/qEiIlLV08wvmhMdfwzgYUZBrz3ZswOYezNHLTM7nJBW6nRPLMTMfgS8z92fMLPjgW+4+5vN7P+L7v91er6CZeiQW0eVb0s1eBERqWjeVQhLP3BgF/hu19GpBtXBWmwQ/SW/PXVnExGRmuZeB8upabln18GabJF0OvAM8Odm9j0z+1MzOxQ4Lq6YRLfHRvOfCDySeP2j0XNjzOyPzGy3me1ucN37aYaE3SIiIvOWm/Iv2WpKfdumoTpYWw0JV3qrJOsWERGpy1P3039D4JFEviabrmt1k4GkdcKgp3/i7ucArzBqQp0la/Un4mzu/p/cfZe775rPaq6YGRJ2i4iIJDVy+NAxaR5UBxMREemxieBP+qjtZJ/3D4CTZ19+k4GkR4FH3f2W6PG1hErNU1FzaqLbpxPzJz/SScDjDa6fiIiItIUubMyT6mAiIiJ9dynVclUmWyRtENogby18RanGAknu/iTwiJm9OXrqYuBu4Ebg96Lnfg+4Ibp/I/C70cgh7wReKOqbLyIiIss3dexHLWMbozqYiIhI/9mXCHWovTVe5IRLRzPmWGos2TaAmb0D+FNCvOt+4JOE4NXVwBsIOcGvdPdnzcyA/wh8CHgV+KS7F/bBV6JHERGRJfOxm8rqjBSal+hR8qkOJiIi0nPRwCR+AfBV4DWMurTFtzBRSbNvAL/KaHS2Anl1sEYDSU1TJUZERGTJPPNuqTqJHRVIah/VwURERFriYvCjgc8TLgcdAuxn1H0tPmJHtSn7f4H/CdhHaeVtGaO2iYiISN/Z+F31VBMRERFZoJvAro1GYHtv9Ny/SEzfl5r/3wCfAQ6LHq/XX6RaJImIiMjsyq5opZ/IiDblBaDUIql9VAcTERFpoXVCl7W4exvglwGfBo5MzXsTcBFwI9glwHYm8i2pa5uIiIg0q8JReXOWnNBQ5jj0CiS1jupgC7CNyavIIiIidQ2AIUyEfg4AW8I0BmBXA39A6Ba3P8yirm1SW3KUQBERkVIlI7F57oPxp3XskZW2BdzAbwZ/Z/TctqWukYiIdFlWEAlGXdqiOptfCf534J8vf8spesPJKkiXs1R+LhERkXLJg0ZyFJHkc1nzJibvamK9RFrM7wHOIFwp/na0mdwKdj4hoDSMpomIiJQZAKcTjhvJwFHeFbtfCq9xh10FlTC1SJJadKVYRESmUdo5TQcXWXUGHAOcEj1OXu59R6jU+83gVzNKkCoiIlJkAP73TDYhyquXrRVMG39bkRwlUSPV90VEpAqfuFMysw4wsoqMMGTzBpPbQHwC8A7go+CfXuSKiYhIV/mzwJnMfWhdBZIkW17fNhERkWnUPY7EAaWdDayLSBsNgUcI3dfiyn68HcT3t0T3rwS+hHIOiIhIsSsJo7h5xl98DLHE4+Txp4ByJEl1GYmSHNVhRERERGZmwHGEFkkVLvX6h0Bj54mISKEvV5yv5km9WiTJpLJKSarbgeowIiIiIjNy4ElG+SmKuiFEdTF/AV0WFhGRhVMgSSZVjUYqgiQiIlXoeCEyf2vA4YQuCyIiIgukQJJM8hot23zsRkRERERmYJcCrzKZywImc1igOpiIiDSgJCCgQJJMUtIjERERkeX4R+AqYE/q+XQCbifkU3qOkIRbREQkaQAcwWg00KwLFDO8tUimuq2SREREsuj6hEgNr4L9E2GknY3UtKw618s5z4uIyGobgr1Idu69GStnCiRJIVX+RSINwsIAACAASURBVERERBbICcGkfyAk0j5QMO8cTgZERKTH1oCjgRejx2qRJI2yzLsiIiJT0bFkBWyL/mJbUJerWWyAvQe4nOxubhBOBI5DG5iIiGTbAHsW+FT0WC2Sss25699qqxFMUv1FRERy2diN9JR/H/wb4B79fRP4PHDYklesy24B+zrw29Hj/cC+6P5eYA1sC8WtlkREZOXZF4Cn5vd+vQok5QWNFFCaQSqYZKn7alEtIiIi/gBwGrAr8eQu8EvA/4zQtF7qO0jo5vYlMAMuArsgqn99KHpORESkzBrwJsJFiDm0vlmf46otlQJFDYpHCEk8FBERqSU6lqQOKdIDHo8elr48GSf3vAr42KLXqmeG4cZ2M7qKp+CciIhUNQQbgG+vOH9JZa0XgSTPfRCx0SQFQaZUVPPXlyoiIlVYFHSQflgDHxC6W+XlQop+b3e1npmLjZz7IiIiRRz8+erzlulV17bCvm0yu6whA1UpFBERWU1HEYJIW3Omq64gIiLSDnEr1n0Ud22rGDvpTyCp7AMrmCQiIiIyP1sIOXxUxxIREWm3DeBXye4WnRwJtKJedG2r/IHVt01ERGS5lCSpP54g5O5JV0pV3xIREWmff5jfW3U+kKRcCyIiIh2i43Zv+O+R37ZdwSQREZFuqXHc7nwgSURERESW4KvAAUYtktKtzTxx+9wC10tERESqiy/+1LjY1+kcSTuXvQIiIiIiK2gnwD8DOwpmSuZcOIL8kd1ERESkWQPCsfgA2Ym242BSxYEyOh1IgnqtptXCWkRERGQ+7L9Ed/YRRm/L4sAa2BZC5VVEREQWbwi8SLiokxcwSgaVSlondTuQtHMycJbHKJlBREREGqdDcX/4/w5mYBeDvTN6Mg4q7YseD8I8IiIislyZh+O8gFHJsbt3OZJUVxERERFZgIeAI4HvhodmwLuBg4RLld9e1oqJiIhIpiuBTwOHURw8GRa/TfcDSVWTQinCJCIisnwG5tXzOd7W6MrITJ4n1K8OJp77zpLWRURERErZF8A3gL8kP8/hAWAr3FZQCet217ZYWd82BZFEREQ6R4fvlsuKBm4k/kRERKRdHPgK8FvR4wOMuqTH3dLfVN4tvfstktJU6xQREWm95IBeedMANUlqs35cjhQREVkp9grwJXADrgJ+BjwAHAt2C7BW/h79CySJiIhIuyW6pRde/9HFoXY7Fv1GIiIiXTSMDuFXEy4MrQEPR9MqtCrufCBJ9RcREZEOKstxqAN869nWZa+BiIiIzGxIaXLttM4HkkRERKSj0v3bFDwSERERab1O925X2gSR/qk6kpOI9EjRgBnSSqqDifTMtlQdTC0ORaRApwNJItIvnrh1FFQSERERaVTUP8W/Cjh4/HdLNF0BJRHJoECSiCxdUdBIASURERGROdsCGPi3Q+CId6Wm/2IUUPoscOjiV09E2s3cu3uKZmbdXXkRAaoHidTrRWR1ubt2AS2jOphIh0WDHfitwC7CCE1rjFfKDDgAbAHTHlhkZeXVwZRsW0SWIvcMJCfprk8+JSIiIiJ1DMCHhIrVxui5TOthHn8O7FhCYElEBAWSRKQNsqJK6atiIiIiIjKbOIgExUlOkvWw50vmFZGV0+ldws5lr4CITMWTd6p0jlAHChGRVlEdTKSb/H7q1asMeA2wr5n1EZFu6nQgSUQ6TMEhEZFuUy1SpHtOY9TSu2qL7z2oH4tIU7ZFtx0bIVFVABERkRXnqT+RSobLXgERmYtkYCkruKTWSKttG2GUv/hW5srvjepfN3erDtb52LIS8IrMLie/dfMLrEHb+erKKi4qD/ORtynq2CpVqJyI1BS3ONhgM9H1Zh1se/TcohNaZ23INn7f3g8cXNwqSUtsB98L3A18Bzgv3LdLwzT2LnPlOm4L+AHC9rc/uj0Ybp1ujJTY+RZJHfiORVol3fLAU9MWoe52q+18dRUFOrp01aaNyr4/fcdSxm4mDBkuIsWiVhz+dfBvgu8G9/AX72z9H4DPA4c1uyp2BZsnrLni9RqC7QOeanadpJ38NkI5OB34GHAK8JGo7P4V2v9PaxBaIW1ug3Err/j7jPYNba+DmXvbVzHfLjO/bdkrIdIRdfMqNqrmztE2/8kqqVJGVCymM/HdFoyS2Ibv2N3bsBqSoDqYSHX+AHAq46160v1CDgIDsOsJJ+0bDa5P3PphnewWSYnnutAyQubPHwBOYNSKLl1POADWsZw+beF3A29JPkH4TpO3hPtmhABTg/uDMnl1sG63SNrZ/kidyLJN06pgEdtV1XqJ6i+rKTfQ4SXzSSlPP0h/iann9B1LHj9u2Wsg0mJbwLdHQZsTo+fWEn9pa4QLbWfnTJ+X+OT0N6PHWV3poucURFpN7sDJFOdDWge/DrVKqsm3A2+guHKVyFXm0NrvuNuBJHSSKVIk82S87G+B8nI6TkzThr5ScgMdnvGcTK9K3zaRAnYXPci2KY3aGv2tYIJev5cw2hlU207ilgdnEnKmNCXat9vfRYGiKzNW5U0KIq0qv49QRoqiBHE97BxUR69qQNgP7gEOKZgveU62AbxAs/uDGXT+8K9Ej1KmoMdGrxV2XSl7YdNfVLLZ5gIWJx1VJdChwlOLT9yp8AJ9x5LDTwJTAl7JEh3n/WuE4MhTYJexGgl6Dfx4QquDNotGXbS/ZXI/3/kzRKnNgKOoXm7jYMcaSsRexRA87p5Wp15VFHRass7vJlS/lTxZ50kLH52sDdraqiAVTMqdR1ZK7UCH1Ffzu9VmKHnsbcDuZa+FtI4lkkjHJ5iD6Lmrwf4AeHlpa9c8B26iO2dZWccEBQZWzwD8WUKAMdmVKu+CkhGCHPsWsG5dNyB0bz3A5H4h2QUj/T2vRS0Dt7D40Rwr6HzXNpEs6rERmfaDLnL4tqK+bbKaVmYDXQJ9tzJPCiJJ2iARRIJRPqD4mH4V+D8uZc0W68zUY0vdTz5OdmU5QOjGoiTGsmgbwAfIjg7EwaTk3wAoy5OXldunKwHWeStLlp1O3zAEHouea2EQCToeSNJoIVKqIPdP78+nZviAC4/hpA9OCiKtrjlvmEtOA9Yqq/zZZf5uA+2rZdKQ4p2NEwbLuZ6On4VMKa9lB4Tv5mmwbbQ2J4p02PboNhmk3Jaa5yuEQE9WvTxdmRoCP89ZlgEng98SAsvJP74QzbNKedMM/HEm9415+4M4UPeepldsNqu4C5dVUTISkWTTeYE0pSy/+zSbZ1F5zXu/lQ0qTfGBtT+QQlllKr4CbYQTki2MTmCk3ww4psI8a8CH2czR00tPUu8qRnTiaC0/cZTu8tujYM6+RGDnM8BhhJaEceuidJktqEzZ4WS3MHLwm4B3ZEz6jWjZ187yaTpmA/g445GXsgrWG8AebW6V5sHcu1uVtl3mec2Ssj6VKsSro1Kxtp6XCR+7qUSjpElTqqbDqntIsuSLay4za/m95lN8Jy35Yty9JWsiMTObLE4nA9eB7wLuIuTO2AAeAPtDQt6VvidaXmUGnAD+ANl5QNJP9XWrHgCvAR4APzI1reAz2ynA4+TnJxoQgm/xLYQWJcpRIyX8PuAkJrtMDoEbwH4zmq9mgtnMbXgQluX3ADsyXhd3k4vLct/Px2Lr4HEXtay8U/Fze4Ad7do/5tXBut0iKSOIVBT0727ITGQ6Vcv8WG+ygh2XugnJXBQUnDrHzdJAR1kTqGqr1CtVv98W1V+kQ/wb4PEV6LcAZwPnEvLifAn4FuEEW/pLO4/gcPBzqZTbxBxsJ/AE2UGkqJWf3xq15PheopvQ5+a2xtJHUXCXN5DdlWwAfDQx+9GEbpVlJ9TDgk3dGbVGjR/nrVu0HP9Z3pv1yEGw86L7WcHfuDvrziiI1IF9abcDSSlVTgR08rsCqv7AK14Q8gYIyKNuQjItT95JJxPMnHGOy8ybuIp50yJF23zVoLJImjtwCqOubQPGEy1fCP48+J+TnYBV+qOvO9Ot0V9Zbpch8Ajwa4yXdUvdteiEcQDcQX7Q6bho+zo7enzWaJJfFgWVKn4EWTFxF7OiBNdrjLqgPUso42UVhUHBPA7+1cQyy94L4GjwYwvm64vboqDx72ZMe3/YH9hPoscd2Ki73bUt1azaN/9lzVz4UPqkzgG1z80pS76HzM+tLkLSgMJ9M2wWGkvMV7UrXGFFptLaMVm577OObszq2tY+m3WwaFhjv4/8k5X4ynPU2sI0IlU/DQgJdu8EDmU8SW+WG8Eup3w0o7b4FmG484eAywi5vwq6avqjhCG/M9TptuL3Aacz6vqS7BaT7HbU5zqtTG2adB/ujEYPzOjaZm8B7i1aaEF1I1mOY0PgZbDj6H9XzTU2W3VNPN/SfWE/u7bVkboC3d3wmayiGj10KpnYG1RpQjnvlZDVUVZOfLL4VbmANcsip5+546p8eR1pUi0tUedYsB7+/DrUKqmPhoQgyx8Ar1JcLobAR+jEmYhvi1r9XAD+VvCPRifaf0V+Of4640GkeDvZAHuIaiNWDQgn8sdR6Ti6UscymV5GOZmogxmQ7IaVCHrYqcB9Be8/IOQGS3aRyzpvSI4ItwYcQf+DSBCCRVkDDbQ0iFSkqKFb91Tt26YKcu8VXQDrmqLPUVScvcqXUGFbKG1NkrpSoU1MZpIot6XlqChpKTX3AatUcLO+nFX57LIcnrp/LipzPWbXRj/5n5GdEys+cfwMo6HGY06lvEILMQBfY9TqKBU08g+DpU/+ov2rX0Q4Wcy6SlI1eDYET59Yl+y7V+lQJlNI74uL6lE/jKb/EvAScGc0oULLGTsCvM+jMgrQp65tdfsHq/lnf3nhwzFm2dPbVDZ84g6lXXEyX5NStVl1aSBp7E3z10lWWIX980R5nDUIOk3OCB0XWktd29pnrGvbyeA/YnJEIMgOXDqYWiT133ng3yUEYuKWB4mWOPYq+A8ZlY8h8BTYZQtezwKePvGOReXavghcSTixjkZT23xN1kEoyp9kbyecnNdZfvI9013b4ueuB4vXRyQyUSbj/fIQWCs5J8jrilV1mZtPFMw8BB4DO4vy1oyycHl1MAWSpJ9yCsPE0yWFYNllpDQglFGOPfdB8ety16HuLkLblqRNE0hKvHZy5mrLzHv5VOshS6VAUvtM5KnM29iyTiZ+DrYKiVVX3TohL9a7gQ3w1xNaIcVJq2Ey4DFgs8XOUvfHA/ATCV14kv03koFRB3sYeCOb+b+q1pnsekYBqJzlcyL4TxnvBpduvUXq8cNgifURgcRF4YOEMreN0I1sG83V268gDK5wWHIlCuwD+0fgt4BXmlghmZYCSRMv1slu7xUEkzbrARUKwTLLSZXWQOmKVqUWRDVaDimQJDPzzLtAoqzMu9AULLOQym8rKZDUPpUDSbmvn+faSKvFrc82wC8EOxz8i+TnF0q2ul5WOTHw4wk5n9IjryVaUNmTwKmMAknfYzS6Wur9xhygUtL5whZJWdMeAzsdBZIkk18FPA7cDLwZbBtwF820ADLgcvBr4oVXeM0rYA8QRpFT17jWWIlAUuKm4hvohGElZBSKwnKSUSiWVU6qBoUmRlooo0CSLFphH9Pml1mpGJd0GZXlUSCpfdKBJC6NThhyhjuffANtZytpKyGH44vkJ5xOdrt5KWq9toS8SRN1sPSB5CChHB/C5voV1plS08a6xeW9pG73oKfATkGBJMkW5+jaILS0c5rtBpns7rk3epzXwi72VbBfbXCdpLb+j9qWk+umaH5ZEclRAarwxN+SWYV1mKooVw28ujYVmZMlF6RKu4BU4KkluwGR9jPw28nfYDz1twE8T59qoVLVfsIJ5Vbyd8zJcrTEcjLWGioroPM02BbGg1yXEYI46TKffv0Q/O2UfjY7r3j6xDqdwNKPt9JiycDRQZrPpRW1KjKLgkMfYzw3WNa2cVEIPHnc22hbw+soU+tPiyRQ9zaprk5ZWWY5KQn42Oa/8dfM9bNpu5J5ShamRRSUqvnSklrUKlECtUhqn4k6GNFV5/2MrjjnJQTefI/oznbCCYfTnhG7pDlZ9YqsnbIDz4Ed0/wqZYpaRm22prDE81ujLmQPJOYfAKeB38XkyW/OHqxK172yhN9j7/1ZsE+iZNvSPvFob+eD31IwX7pMfxfsAmAHsKfB9ZNc/e/aBvW7t+Xt1KdeI+mMrgSSIHddc3PL1PxsWW+Rfr/ETaX31DYkreOFDyfp+NAqCiS1z0QdzIDfAX8EuIFQ6Y9PpguvhgDfIpxktGzELmnAAHgN+NPkd22LOfA42GkstauWQ0gYHrc0AmwvcA/jgU8DjgJ/kvEE3fG0zTeLDAk5YY6hNIC6ebo2TLxXViBpiEZElHZbB98FfJPs7SRdpjeA24Eno+ODgqQLt1KBpJyHGW8w02TpshrBlqUnBK2bW2begaTEOlTNM7Psr0wk1xwuOKh8L54CSe2T1SKJNeBkYAf4GcBfE0bsOUh2YuX4V42DBPGIXTqO9NcAOB78fioFkuwB4BdYfs6frPKbdUK7FfwDQDqZeFYgKXp9MsdSEXfgZeAQRt0CPXE7BO4FO5owktz+6E+kZUpziWUdAO4D3gKmVqsLtxqBJKjfjUHBpNXUpRZJUH41N2PeeXdvK1uVrHVa+vcmkmOaJPKJG1kwBZLaJ7MOFttCOEmO8mP4JcBnGQ0Dvfkm0W36naKTZLOMadJtA+Bk8B8Ar0lNy9rKzwS7r/nVmqs18IPkd/NMfs6adUy/EvgzQo6p5BDuyff9W+BosKcJOZtE2iTePvIkg6OxYXjd0i/ur6iVDyRVnDyxN1d57akawZbO7bRqBpIqf76SFn9j+3y14pCWm+rQp2DS0iiQ1D6FgaSJmQmjul1NeSuN+PE+sEMzpkk/XAr+BUbJprNOHg1snU52ZfF3A0cBf0Mo89ujCXsT998Cdu8Ub34h+NHA9cD7gG+AvQq+g/Ad7mezZR9rwItgJzGe5FhkGQw4DvxRJhPNlx3lN4Cfg53I8lsorpjVCSTFpkmwuvnGmXelT1YgkJTzcExmsu6K75uetFn/U24Z6QAFkrpFgaT2qRVIGgCHgT9DdnemjFx/7AfbnjGvdEPcSgYmu1etAUeAP0BopZbupgXhpPGNUde2rnot+M+j+1FwlO1stiKy5OeexgXALeBvB/t+9DYZOZPYANtKyEejE3BZNgMfTvG6DeAA2OFoUIYFy6uD9XfgVUv8RSrvp+t045FWc2b7DTt55pLRqi65KWRsGlO9b+6kWYK4IoswbWFUIRaZjoO/huoHHgO2aZPrpPg3vgP8W+C3ZsyzATwL/CHwKtk/9Ns7HkQCeDYEi+I/Phjdf3fiIuUshXx3uLEfMl7JS4paJbkTWjD198xPuqKszHvG3wYhAL0DBZFapNO7k51VZ4z72td586jgdjKQIBMBpImAUqJlQd5v3OnfvqlWQSURqM1JyZ1/wqyBPZG56PTGLdIOm3WwsoTJkdzNrupFCpndVir/XlMxwqhlHiVbPwd4S/TYgRcYy4tk14J9CnglemI/myeJ9uMG13MZDOyfo/t3zOk94y5/Q+AQsB2E7zBd2bIwj58VtQRpsgyIzCIut1kBUWmdTgeSQCelXeGJ26aDCWUDAWRNz22t0+UdV9m6T9UsKfXaKs2bcgJKIl3U5V2CyLy5A1czmTR5YkbgMcIJbNaxI331+SCh+8/W+a/zytkKHnUR9K+GPFUOzQQTDPxFwu+3TmhBkBje2w8H/7PE/A5cA/bL0csvBjsvaq3Txy5YG6nbeXFCy669hG0mq142AE4P8/q1c16+yDzkDLqAEcrvY6qDtU2ncyTtMvPdicelhatuEuJKbyplSnP0NLW8rB1S+u6iV67vykbDUzJ7aYs6IzcmdC5nWk8oR1L77DLz3fFGdCvY+RVeVGO707Y2H/4AcGr04CCjBMw3gl3SwPLSzcFh/GB/AOxl4FhGwaI4d0+ciL2DybVbwcDPAb7DWAAvnrZpCAy0jcmSDIAjwZ8gP6Cdzh32GPA+ujeCY0+sRI6keYbEtG+dj6rdYOe+0Kw3zcp91ct+bctV56vrbhhbRESAsCN/RyKAUNSKqOIBwk6ZcZ0k5MXZQgjYJJ7brPl/OOpyNq/lDYCTye5aReK5QdRqKVkW4oDSBgoizcLBzqZai3TAr6G8NaHIvA0JOdLyWs/Fkt1F3gs81PyqST3dDiTtpF6XmbrBAQUT5qekT9vCAgp5/dqqdtOSYmXfnZIkSYvU2dQNXb0VyRUN0e6PAvdQ3G3qtwmBg7x6wenA402s5AqJchWxn5CcNm8eGLUSm7UbYfxbFp0Uxq2hTkEJc5swAG4mnKgXiX+jy4D/wqglmMgCldaphsBPo3mfButjd9eO63YgKVbz5LRioF5mlNvFLOf3mkd8odJ7KJCxfIkyoJ9DlqlyKjEdGESyJQMEJwJnUBgksE8TAk/pHEnRya89TD/z4yyYb2P0PeYdaBMJLP1zzBZQMPA1slsUZY3A9DyT3a9kNvGJd7KlR/yXVQbWwC9ErcBkaTaDSQcJx4190R+EBPX/IprnlaxXy7L1I5AUq3JiWnIy0Iskyy1hMF3m61mWWeP9FMBojjYfab2snGmpySrHIiUyAgT+LPktktaAQ4A9jNcBkjlbdFI7FxN1sKKRRgCuZLbvfgj2ELCd7JbeyWUNwI9AAcMmDMCOIJyU5w1hnAzoFW2vIgtgBlwIfBx4KyEXkoENgNuWuWZSpl+BpBkpiDRnVSM1y4joKIrUDvodZJlSwaTM3q2KKIlUZ4TRo/L27RvRtB2Mb3Br6jraiKwWRpa6TbpgDsu8jBAYTAct4mVqn9qsIfAi4yMk5nHgOVQXk6Wz3WDXhhaplhxJq6ybpixV/wJJJTvD0kYwOrgtxxwPYvoJl0w/gHRJUXlVWRYpZqn7A+A4clua+E/BdWLQPCeMcjQgO0qe6NI21jrlPzJbdzMD+0HBcmVxdpVMN+AJ4GOoFaAsX3yR4QAqjx3Sv0AS+cer0liFDnTLNY9gko3dNL88yVW17qjNTpYuWVh14iMyE3sT8Mzk8+6E/Em9rHm21BOpx0W5kp4EzmO27mYO3K/WZW1gd4K9NvHEfkYj6sU5zC4C+x6qD4vIVHqb5k77xHbIy++XN6/0ROKHT/+uRYO6iCyVCqbI9JyQ++i/gj8I3AKcv9Q1Wm0DsDeDv1hx3gvnu2wOJ3SbigOH2r8uVhQ42gzqnRdufA/YDuBWNkdbFBGZRi8DSV50pqoI0+LUiCI1Ub+oE8SSBuT8ABP5Z0REpLsOElq+/PfAreCPA0dSXsNMHh/U3W3+hsBLFFeG9gI/A7sUeHS+y7aXolHc9gLbUtM3gPsJeXwKRviTObo93FjyHEnJzkVkBr0IJGWdi04EkxRRWJqygM7ck5wncgAULVsxDBERkdnY6cDxhNF1omHH/SDlXdiSB+FZhp2XQrYD/NeBawgBnXVCkGkNeEfUOuUuGmmZYgZ+KfA54NDEhDWwX2U8iLSN8Qrb/vmvz0pTyyMRmbNeBJImxNGDmsEjdblpzjK/17g4JINKZYNYqBzMib5IEZH+OoOQW+cJQnBiL6E7W1lgKF0/2wgXAG0raqEyb3vAvgH8MiE4833gbcCdhLOAONF2Q+zL4O8KSbg362CnEpKBJ30feCFRDs4lBCZBQSURkRbqfCBprDVLIlqgbk0tkR4hpGieJpadytOT160qvXqePZuIiIhE7P6MJ79boQ6WPrCuA3+Jurg1ZQ+bXZsAuDu6XUTXpr1g9xBGdIufW4+WbcBJ4NcDp7IZgPT4YvBno6DTbxC66YmISGuYe3fDLbbLnN1kZvOd5lNplIkGLbN/WcmylQxcRKTd3F274JYxM8+LGPlvAZ+lepe1Idh+4AjUImnF+P3AG5jsCmmEwKIDN4Jdjq4Qi4gsQV4drNODsO6E7LN7VTfbx3L+Fr3s5HNpnvoTERGRTDsh/1j554RWJ3nH/vTfGnAICiKtkgFwEvB68s9GLJr2UVQvExFpmU4Hkrgtf5JiSZIpK4CVFzjywociIiKSNgB7HSGvTdULNMqBs3qGhJHidlB+gdHBX6AHCTlERPqj24Ek5ndyr8CTZFLrJBERkUz+M8IQ7mlHUyvfkZ2JKmKrZgCczGTAMcsacDgarl5EpEW6HUjauewVkM6rGiRSMElERGTc0Ux2R3PgRWA71bqzn0YY9U3H2dUyBB4hjMyWl/pg0akQRESkskYDSWb2P5rZD83sLjP7azPbbmanmdktZvYTM/u8mW2N5t0WPf5pNP3UqsuZte6h49MK048vIiI9tJA6mEcjbKWe4/ka6/kgamkiIiLSMY0FkszsROBfArvc/a2Ehqm/Bfxb4N+7+xuB54A/iF7yB8Bz7n4m8O+j+Sqb+UKWAgqrqU7B0dVSERHpgIXWwdLBpHVCEmWRMgNGI/VpwBMRkU5pumvbOrDDzNYJ43E8AfwKcG00/T8Dl0b3L4keE02/2MzKwzueebfeMUhBpNVl+vlFRKSXmq+DxTbAfx7dvx/8WkZDtxcFBk5HB+FVNgR7kZBnS93YREQ6pbFAkrs/Bvw74GFC5eUFwjhrz7t73Ij5UeDE6P6JhN7SRNNfIPS+r7Cw8bsOOhCJiIjISlpoHQxCe6fXhpZJ/nrgXPJHSHVCkOnJaO3U+kSuAF5GrZFERDqkya5tRxGucJ0GnAAcCvxaxqzxISMr9DNxODGzPzKz3Wa2+5lnCuaseCBSvElA5UBERPpjIXWw9Jzx3OuEwNLYC1N/A+D1YBuVPo70nF0HfArIKg9x4PHnGdOmtTXxJyIiU2mya9v7gQfc/Rl3PwBcB/wScGTUzBpCL/rHo/uPEgYCJZp+BPBs+k3d/T+5+y533/W61+UsucbVDFcEQWzspmy2maXTAOjim4iIzFnzdTAIuW32EYZwj/PcpBUcPP242p9L+sjBriEEIbPKywbYEYQucLPYEYzSsgAAIABJREFUEtW7vg7+TfDvJIrsesHrRERkQpOBpIeBd5rZIVE/+4uBu4F/IjRiBfg94Ibo/o3RY6LpX3efGAukWN2zcgWRJKXp7vl5xVMBJRERmaPF1ME+AbwVeC9wOdktSmLpKygbwI+rfyDpP4vzVu5LPDkEniEEkQ7M9v7+Y0LZuwDYSSi7UbJ4v5bZA1UiIivE6sZqar252f8BfIwwsOv3gD8k9MP/G+C10XMfd/d9ZrYd+CxwDuEq2G+5+/2F77/LnN3jz9X+NEq2LDHPfrhZPmYsKFXKpsqiiMgkd7UfrqvxOpiZY4xacpwM/kNgW3rG6Naj+z4+zZ4Djo3WUgRgF/it4a49RAht7i56QTmPg5drTJTBzbvay4iITMirgzUaSGravAJJiRtZdUUFaIZC4rkPJt9XZVFEZJwCSe1jZhNHTHdCq5FkN6GiQJIDj4OdhgJJMrLOqDycRwh5zlA+/H7g1OhBRjAzeV87GhGRcf0MJJn5ZsUEQvPU2m8ydiNSO8dDrbesEKhSWRQRGadAUvtkBpLeAxwJfB7Yznh/8VR9bfP2MbDTUSBJsq1R3GWygolTnay62BB4CexYZu5CJyLSJ3l1sO6nlstozVErmJSs2IjAFIWoorL3VFkUEZEOs2+BvxY4JPFksp941gl9k9k6pftmCSIZcHTquayAZjzv8zRT/xMR6aF+Hb4TLTrqnI/r3F0mpIcqnpUqJiIisgLs2VSumb1kt/AwYB/YcagiJs3ZxmhEwWRLuKx62VrN907Pv7Xm60VEOqxfgaSUKjEA1V2kq9ID4GjkNxERaQuzKKD0QeCq6Mn9hC5E+whDvn+QuYzGJZIpTgZftVXT/upv7SeB3xKN+Bb/3RJNVEBJRFZA93MkZan7kRRNkibVyN1VZ8SQsvdUsRaRvlCOpPbJrYOlxaNkDYFdhBZKO4BbmUv+G1kBWwk5tLZFtzUDj4U5kpKjtp0BFI5VmHiLWwhjHG5JPHkgenw12KeAV+qtp4hIG/U32XaRymfvs6+L9FvJQGuVXlylOFYJJNXZYlW0RaQPFEhqn8qBpKQ4qGQogCSV+b2E1kLPAs+BXUYILlVtQXQJ+HWEfhgFFbpKdbD7gNMJ5Xcw/vrNsj0ErgX7OGpttyhbCN99jVZlIlLNagaSkhoYiUtWQ4WB1iq9QaUWRHUCSXlvaJl3pQEVfgIRmQMFktpnqkCSSFVRcnZ3QjAm3gMMor9rwH6bagHJAXg8X04gyU4BHsmYnngPhuD7CUGLZBL5WGrQlDqtzKWmbeD7GHWN/TvgCOA90U9QJ9AoIoUUSBKpqVILoppvVhp4KHnD0iBSxvuoHjN/auwoslgKJLWP6mDSGIsCSMnk2KnpUCNQkwxK7f3/27v3YOmu8r7z36fPeS96hYUuIFkIdJcAcdMrCQmMYRxDQDguwLbAMM4YO6RSlUolmUxSiSdTldSkyn9M1VSSyaVcNWPjYDuJAXGJ4tg4WGBuse4SQggwuiMkIYQECPTezuln/lhrn969e9/37u7d3b9PVZ/TvXvfeq/u3ms/vdazJsvjwEGw84FvE7rNlfCfBj5L/TGvTfWAeXCAzwNvTE3cjU8kZfNlsMsXvGMia6qoDlb3q1BksxX0p8/8+FQpXXdpXbmoU3XvtAEp0+TKKe8HSxERESlRNnpIDAoxBn8K7ALg2YJ500mvPQae3gC+A+aEVkY3UT9X18Ea+50+4X8ceHfNdW+qA4Tk+2kHCd0DE0mrNMD3EVoaZYN+2eGjXhbeH1wI9sO+dlZE0hRIEslR2vKnQXRgb/FY8bHUwxl1og1NoxiKYJRqmm8qd/4ax1lFISIiUsMIfJsQLNiX83xyQt0CzgD+A2FUwHSw5iTgCPiNhCsdB56IXeFuARszrW6g52KKT+bZ1lMOXF4y/6bbit0NP810yyKALxLKLck39Z2QF8sfAs6bLF9qP+H98WfgV4Mpsb9I79S1TSSH7/2pYFP/ppcvWHHrIBI5I49UUbPqQp2+PFr0bVM5iLSnrm3DozqYzEtuXSf9i0w6WPMA2GFC4Cm2XPF7gZcz6e4EITBxD9irOuzXQ4RARl4r9byR4R4EexmVXeY2joGfQ8hJtcNss4a81kYjJqPiwewvdNkfeZPnjwP7wT5JaB2mshBpTDmSRBroEkjqNbdShgJJ/Wn95dFh2DyVhUg7CiQNj+pg0jsDLgb/KuV9JrLn1t8Dzg7L+EuBFzKdEDtZJgYt2ibB9nQAK7s/eU2PLwJ7oN22ZiTd9FLdvFaZHyO8przjVjRAUjaYWCeQlEx/AuxFHXdaZEMpkCRSU+0gUqKqmXPOfIsKJPU1YkjJS1lZjcq56BfHutQySaQTBZKGR3UwmQf/AXBK3hMUB2zGTM7PYybdogoCEnY98F6KuzplR/yKeXxyA0klyS871cEOEAJfu3FzXwQOAQ+Hbl6rzu8gdP2DfgJJ2WnZ94sD3wU7l9mcTCJSSoEkkZp6CSSVLV/QHS5ZLK+eVHu0tuymOlRi5tmyaggW/tVXUu4iUk6BpOFRHUx6l+TNSYZ1z6r7w10yb8FAKZBTPzoIfjQudgvY1anVfw54U872shW1ZJsxkXfjOtg+9rpj+TeAI8Br4nM7cb1bYd18D+wFDdc/FKNYzkXqBJKq5HU1fArsrJrLD80BJq/peNmMIv3TqG0idTWtGhdGfcrnL1qsqOV0U61bPTWcV1d3myFdb1a5i4hI73YJAZvsCFxQ3hKlcDSMzLIp/pNgT6Qe3wG8PK5mJ/NjUzavTtkJ8AQhJ0+bk+QN4JcCPwLOZ/o4ZJNLnxH2sa+W5wuVTXY+D3nvkTMXsN058bsIwcND4FcoebgMQ95XtYg05alby8X7YrS7yC/d/ZLXp5+ka1rBA5VX7H0FOkVERGZc3HD+Oicjz9x2gfvZyzvktwEXpubPBm2qRgiDvVYidmm74I4Dfi1wLnAZoXVS2XYdGBOGuM9rvTV01zFJkJ69GdOV2b5+wapTjgPiPrlxEXAN+KsAj4ElkSVT1zaRLF/QRbIV/IiWlzCw3uoqJpQr3I8aG1zFH8QWVs4pq/LLYR8pwkT6pK5tw6M6mPRuBFxISLadDY6UJVluGWgwixfpu3HbVdsoW9cbmQxb32RkMANOA/8exV26yDzXZy6mJSq8BC3LddSBHQZWIACTm2IjW/4dE8eLNKGubSID5IUPch5nlJ47upxY6l4apAJe6upUzWAlDlRpq7TEwF+DiIisoDFwH6Gl0A71WpCMC+Yrq8uMgYdTgYwW/TNs70+U7EPT4eUN+AH1X2/2F8gx+DNgZ7IWo7n1Vk/KrMfOAx7vYb3zNAJPJ44vOxa6gpcBUNc2kRxNzmFtu5LtbadDl7jilTbTNpl3h00OQpf9blXuAz9Qua3S8t6fqWlqkiDzduWyd0BEFsoMeCchMHKE4gCJh6TYlndCLjvfGiEZdvpxVSLv2JUMgP+WM3/bfDVj8Ni6ZKY7V7Jdp3gfHewZVu9kPAI7AzhKeT/6AgbY/ZQHz5Iy+ybw3Yp5s9JBvW3gpHh/f4N1NDQVRILy93BSB/s4K9dlT1bLlZcVP6dAkkiBbBft9HRynmsaI7AOAaTcbmxdIlpdglmrVnlJtDxW6cNc54ezNsWSlzKg73hjrZ3oYx6RDvQWE9lM9hmwK8HeDrwnNf0XgTtC8MhG7HVVqtXFJzkhj8DeDFxBCC7k5ejJLhOX40NgP9/lleX4RSajsjVpkeOEVjbvYfUSL4+Bp4GD1KsoJTmodsHuC/P7xWDfLl/MRmB/lRCQrGLANriB35TKUXQz+Ofi+ejPgUM11tXG/UwHLsu6/kE4hpcz+B8pZXU5TIKoORRIEsnK+ULOy/tXc9HS7XS+SOoSPOrJppy/soe6TtJpK3xQrOo9Ma+A0lSrNF29i4jIMh0F7gW+BHZDDBwZ8MkQYNqTGgHM8k7SWbuEoMTj4HkX4dmWQNkk3e9u9WqKGXB7an/rVqqSgNibgDtZ3fP2u4FnKd7/2IrIfi4emqeAXwvBHRz8fOBGwvsgXX/5MdiTcf0P19gPA84JASTGwOHUc68mBGwc/JXgHyZcXPdVATbwFxESrUN5rqysRYyAJ5vJgBeUz6JAkkieJifyTISh7o9iTTYzs2zPAaROXfNWXNMWRXXjLPMIIrWdV2SlqW+byGYaE4I3DU54VlTBStaxBXYAOAb2BaZzGuWdq9P1vC2wk+l3lDQHeySus27Xqx/HXbsSeIzVa42UYtcDbyEc3xNMjkEsC7sW7ATwGeBJ8LPAvwhcnVrJm5ipj9vpwFnAx6hXB3PwzwKviY/TV8hbTHISnQz8DPh/ivf74MCn43aMmdeSO5pd0kLurTTPyyVS1z5Kv38VSBIpktddrKhfG9OPq7rmdzGvsYua9I6bmm+Vo0k2+VdUf0zba3Feo3lap4Y9Nfu29R5MarHCVS5+ERFZP5Z0xThKaH20A/xlfC45aSWJvZOuVTX1ct7NyWlj7wP72cyuOKFLVjpQ9BzYO+PruIu9Ll+rzG4DewPwPzHdjdGAz7KXl8jPIiQV/+9MWiBBbuJpPx7ry9tUF5oBLwTOpzrfkBECSO8A/70a89f1z5gETcu6WmZ354Geti+SZYTcYiWU812kjrxgUtF8Pj1b7jmg5dV3XxftVa1mNzU4UKd1kqfKGCivoFjoX1/7eNbNSzSPAlITJxkiB65a9k6IyEo5GoMQrwffBXuOcPF/c8681wH/mfBdU5FI2c6Ld9qOjhbrD34TM99r6ZZUe6f4VwCnEbp+fTk1cxK8WJduTWPg5vi6xxTWcezN4DcSWuHkySaq9pCM2q6juo5zSmqeOvWhEfDz9Nca7F9SflWerXuOCa3R9rMWwUQZoKRFaEkdTIEkkb5lvuyrgkjZc0PZavuQu62mAY+4zFpIR/wKeKzYeZNmRl36L1ZJBZPmFVeqY13eAjJMim2KSGOHgOfi/b8AO0hp4Mc+Br6P0BLmI6knThCCBfFKya6k+/DxMXnzTPAhTp9JGP61uA/ZL8MV7spWqOQ1uYE5+JeAb9VcX3LMLqf6ZOLAHTTrsuiExNwfBfsbhGBfF+dWz5Ldvp2NTpQyH/G6sIq6ton0qLQnUkl3uGy357xbX/vX5Xmg3x0akrxujPG2FxOq2X0RaH5yn/f8FdaxSGV1qW4sIrUdDP/8JPAbmIy25cD1hFYbJYEKM7Dr2UvobRa6WtlVk8fcRfuWSNvAvtSFWd7V1xj8B0z/xD8mdMtbx8BRA+bA68GOAD8k5LdKj3K3N2PO7UJCrqSyK95t4PmElj1VXcuy6/8F4I/p1sVtBHYK+SMI5nHgezTLqyVS10n1fzhXIEmkJ7lBI4tdofq6Su+wnplRufJOlIuKaA1Z1etMtQSq5NPzziun0dIuujfh/SALpyCSiDRyR7zweY6QZyfh4D8DfJTqC/3sF8+dwD2px227kRlwZsjZUznfT6DEyUXuIByj84FLwbfJr5/m1G/9CeBrJeeWLeB9NH+PxGX9jXQL9o0JLZr2Ua/O7YTgmMgceHoUyQoKJIl0VCdoW/p8nQBNxyCOz9wpm0kKdThG2bRKVT84LdpQ9kM218znQW9KESlzKHRL83S3oHQwwOI8b6P5hf5ui2XyOPjnip+b+qXpCeZ/ZXaA0EJrP/2OPjdvxwjdDx8HHi6Zr6Cu7BcyCSylbwCfI+TJyhs1LZ2mwJitwO0SEhIfaP/SGMX9KOrWkP3Bdwv4fXQVL/2x+Da7ldCKrya9BUX6VNK3rfKaaNn92hZkaAGUpqxjF7S8xTsdj2UdSLVGkh7NvI1X9QtCRBbnZkJ3pKJh2JPvkS3wj9PfCFtNXZx5nA1OGOGK7GzmnkDb7wL/AvifAx+mvyHsF+Ejobsh6W5tdSuVsezzzjX+o9iiLRkJrmidea33t4AXEAJdbSXbrRvEGgNvRPUw6UeSD8mBw1QOOpCmZNsiHdRu6bPsL/u6F2Vz3teic/IqaXV9myw00BfrMKmkiCyYgkgi0ogBp4G/tGSebDPgwwz2HDx3I0IOJifk1Em66I/APgj8Lwx/5K/kNdxO9dVruptbHW/KLJddT9ocKq5+P/n7mq2XpQNL57PxubOkB+kgEjRuYqQWSSIt1QoiNZlnCBYYRKqaPkSd9zXvF7ShBJlWqSBkbcx0ZdP7UESqGPgPKW49kverlbO8Fkl5FvldlwSRIARhtuJtBP4ehh9EgslrOIv6rZGKAkFZeYGpsh/X+iq75Co8b8S2vFZr6dc755ZrshkajUadQ4EkkS5W5KKndoxiTq8n92JxRY5dYq/VzrxW3uV4ePvFO25WpB96M4lIHbFlCicIXTDyugFlxVxJSwmYjMDOIIw2lhfwSgcGvslc8hb5Q+R/xybbvYNh91Ex8IsJZX4y7VJAZAND6fxDeUGbhhymy65O96AkOFaUOLxq2e+zWnmuZFhGhPdPerTCFqsQkTbW8MJnro1iivqb50werIHvqGf+N15w3suIpDRq1SkiAiHwcS7FQ7UnshWa/Sznu2YMPA28v2D7qRxJ9nb6Hc7dCPmZXhgfF2zfnyl4bigc+BTDC3Yl77kd4HHwhyeT+WPCKHxJK7hsYMkIuZXaMsJIb0MuNxmuk8DHhO/RbEC+wcWgAkkiMleV5zidBHuVDibN9dAuuyueiIhsph2KWx7ltTRxwshaDZLI9moL+DzwIyb7mO6ulLi/5+2OwB9kttVK9qLxfQw/385F5I+aVtSNMblZ5r5lphtwpGC92e1lu5kl768t4HnA2YQcTn8O/mbwZwnH9aOEC/b0ezZpJXesxmvKo7yW0sUR4EFC4Lrpey9FgSSRBWk82lff2+9tphYqXvtKxCRW9IRdeX5YRp842XhqjSQirT1GCI4U/Yqevb8F9jaWlwtoF+xp4APxcXo/kvvn9bxNA04lBN2SYwW5FS7Ly9GziorOJ8eZDpQlrb6Oxv+voLolWDbQlpRbcjyT0e9eDfzUZH/cwX8p/k/v3wFCueRdidetFK9E5bmG/SwvyLtpkkT7DpzDbCu/hu8pBZJE2li1ix+b/Cv6EW9u56NVO1Zz0uUY56UDaLuudEBp04tmRdN1rTQdbxHppOjkV/bFch7w6Bz2pSH7OJgBV6cmvhrs1WCPz2GDBwiBpJLUAgDcNodt9+06Ji3R8ipDRTmgAH4W7Kp47A14d5yejPr3+vg/L5h0FOxRsGvC8nu3dBmm35NbFHfBc/Db4kX8/wf+GaYDogXLFL62VQ0kxe5+fjC+vBuBm+JzB5a0T5vAMgHNHrqKDq23qcjasZk7yzegXVkZbVsR1xlNNi27jbL5p/apKPdCQWXDCa3kNu2ivuj1lqXWkH7MHPtNe/OJSHd1TsZO6DJ0EOx8QgumnbnuVT1xpC27l8mJZpuwv313LXPC695OPc4+b4Qg2wp0k7KPgW8TWgLVTTA9isGj1Gh9BvBf4513AacAHyG0iPll8N8mBDPiyHZ2ZXx8T2bd94Z1712U1604vCb88/eB7cbl6yybVz5nNtjuEGyB7wL3Aeenpu8wGT3sk2C/sIydW3NG9QhtLd5LCiSJtGH1LsIH9f1eVVGYx86WbG9Qx2ZOurRA6qVOl9f0yGafbmvVyrDu613lH/pWwsAvWERkwJJgTPoiPi0Zze0w2NcJAYGh5f/ZKbg/D9cBv8sk+HIQ8Bgg2QaeYDW+k0fAGcD36i9iyYk8W/7J6/1vhPfL21PPPQ52Jvh3CXmtkm2PM+toW25JUMtiYKxphSOpIG6BrdhVvN9PCFxmW36lAn28JXyuTZWwfs0hiAQKJIl0UnTBP/N5HMoXYno/mv6K0mGT2byGq6qsBXXRvLkvuEaAzfImNlhPoQWV+9DUyg+1YcdkkZQTSUR6NQI7Hfzp+HiHcFXzV8B+BDwQpw8tiLRg9jHwJ8B2wA0s6UI0IpzzVuX4jGOeqaIAYkatQEROVzb7C2A/2HPT266tbou5ZL2PABdk9qcsX5ATEiUfarBPA+APAS+KD8qiDyeXPCftjCpa83Wo+yqQJNJR5edvqBeni9iveEItyfE43OOTlaoc1G4xVPTasgG9sghV7b5tDaWWy4svVlmVYoMGXapSZbHqQc9BUhBJRPoyBp5JBQxeDdzNpHXDqgRIFiAJHk2d05oERwYmtzVa8niX8Nr2UZ1AO88O9VsbPRC3VVVZKKsLnpp6D18D/pPAJzP7c4IQ+BsRgkxXxtZ2K8IfIASRqqIOqTL0Z0LLsFZlKNPGhK6+c6BAkkhby+gqti7W4NiUFX+jl2c592u2GprHdfkaFE2xOgdsQ1tsiYistK/G/wogzVq3Y5K0RnuM6eTMDnwf7IUL2Ic/Ar+gerYZ2TrfaanHt4FlA1OHCa10TjAJCDzAaml6nAx4Cv34tAIUSBLpky4+Z63TMUkFeirPb11ed41lezu/9pVFfOhUIVkuHX8Rmad1C5ZIKX+GkO8pzYAXzHnDMeeWX0t+a6QudaO89/DdzNbTVuW9boS8VlWjmeQdw5czjAT5+wldwlaZAVcSRsbLRn461uVH3RYX2XCWuclmaNuMuSe955tp+h5etfd6i+M0lJeYly9dRERkY41Z3knxlNitbotmQSTPue0CD1OeE2mXEFDZTd1WyQGmA0LpSk1ZvtBXzW+XKu1nL0Lifxr/L21neuDAFeR3b+v4wtQiSUSkT4sKIvW5wrzudWtiFRtbFaV+qPpRb8h6G4lQRERkSfwZQjezPq6gLa6nSb6qA0xOpscZfIsZr/PaMq2tbAz85Zx2qMwB4Bj43cBR4DXAziQf195uWqyDDfzYp9kH48AEH2Z6lLyO1CJJRKRPi7harhrGs4G2AYked2G+VmIngybHdGWOv4iISJ/yrl6dEJD5JuUtfLrYAj5QsP0qea2+LSaUbtIa/A7gC+A3xTrAnxCCUUP7dWlESHj+OLOBsqKWXA78gBDQ+fFc9y6X3xaDRhcCr4gT84IuHubzmxe3b5058BXyK44dKpNqkSQiskr6jh40qHyUnX+GVoeB1WmN1KVINbqciIhslLxWLkmg5u3Mp5VIkhj7nzAbSGrS5DaZ9xghx1OdoFdcxu8FXkboKhZ/TXLCYxtK65j9hPLZAW8yUpgBJ8Cex14eqoU4CfwI0+VXNzrySvCPgv068KOW28++1n3xf9+j1TnwxyhHkojIkK3SRX2TfV2l1i9tW+vYzJ35y93PvFwK6VudddTcdo3V98JX6YMhIiLDlj2nxOCSPTyn7TlhJLF/yGyQo+mJ04DDYNnXcIDJKHT7Uvc91VImyc+0PXkOB36fMLrbou1nutXOZ8BvnXQHa8IuJQTXFhVEMvBbaF/x2QauA/+zhsvF4+XElmWeun2e0P3sJyrW0bDVnQOc33A/a1CLJBGRFvJ+gLK9P4vdbpt1ZOW+lrxtFSTqWVbLpNJj0eBALSOINKNJv7ZEi/2t2syQW5mJiIgY8QeK5IQ1ygnMzGO7X415f7JdntL7UrYfu2CXAI9OT3aALwNPA68HuyXM669PzbSPQn5d2K69p/Il9CNpJZXOJwT1R1pLWisdDOuxq4DH+t3FSl9h0oWtiyuBPwR+hVpBML8JuCo+yM5/Ffg2cA7YNXHa/tS8yfx/CvwV4CRCELWs9dc2/bdwSq1aRERaWPiF9gKbBZUGkLLTMgGlRR2XrkGkZQdK+hx9r+5xb7KpJvNWDVYjIiLSp0X8eDdlBDxAs9Yg2ZPzq4EHM/NsxduLCa2OAL+i4b4Z8K6QDNzOoFkC7zbOBv82IUCRfn11Ezm/Buw0IMkzNGL++5ww4Azwl9K80po3/zb4PwErCyIlgbckl1ci2zdsK27jcKpVV9Jq6gBweZy2G6d9CfgJsCSQlyRwh70ukP6XFFfEOn5+FEgSEWmjsEnSgrfZYhXTd2qo04RlwcGkwi5hNRTu26olSEovX2PfawUHa66rbFWWN13RJBERWWVjwoX/8wmtZ04iP4l2Cbs3Z+Iuk5Y8ybmybkDGUv+3gVNDiynbRwjyHIj73XOLFP9CvFMnkpA+/4/Bdggtsh6Ynr5Q6dHvsvWobHlmK7V5ldyyLpXGJPCWt/486eOaF1RMAlBXA8fBvwlcEvbNPhi2xz8A/wohQJm3T2nJa2rYJF2BJBGRtlp86fa92dYryNNj1uf0YZlrYKmPAMWymib1GFypegmNAjod39OKGYmIyFoagx0F/xUmeYnqnCuPEwJGVUmxq9aVrQBmAwDpJNyAfR78abBfIHQjO1pjX8uMgBcBP0n9k322xdIQow9N6zuZelLp4g7+uea7tKcsqLgdbxdM9skBzgM+Bby8fL+ATnVgJdsWEemi4a9RvWyv5SZLm4H3cfVfkgi6z+CCJ3+6rNRYfNmlLbObYpMF694ypg6rIksiIrIujoPdAHyA6mHqj8T/PwV2iPYjqxX9IldRh/GfAt4Ru0l9CHhey+2n1/kk7YNBXQNZfXDg24ScU13qgUk3tWMUd3c08NOZJLruuz6U7P8WIXg0Bu4E+zD5782eK+VDjAmKiEgNdVsmLTRWkv51zGaf6mN/5tYia0XVejnzDuakyj1JeGquGJKIiKwhB/so+EOEHDYQgiRGaPmTeBvYF2nedCO3j/jsPlRWAFKtWfw9wAVgVzfcl7Qx2LGSc3vV/ixjZLkyR5kuryrHCK9xTOiedwlY2fIG/DDOn9eSLG9+qF95Ss/3cHhs7wHOAPsx+D2EYOaB3KU7UyBJRGTVpCIpRXWN3HP5orNgF0SOOnd16xCdGFIMqWl9IW/ZQUmXe/IrmSJJIiKypuxO9k7I/gZC97WbUufoJJAzrxxATc63DlweWid1GuHuCULXtib7F9lh4PYO2+6RGWGUtFtLZnImuaYgJLs+JS57ArizYiNjykdya1IOlnM/u/wrgPuAhwh5t65hOlDWc51MgSQRkVWUU2lom0QtCn/UAAAcbUlEQVR67tf6S8gjNXgFwcDW6yqzgGCO5T1wxZJERGSNpYa7t5tynq8aDn4UkmNX/hJ4lJDcO0+TYNI23YJaI+Bi4GlC17C0vATO6f8G3N1h2/NwV0VQ7RXAISAJNm3TX6WmxwSiU6krLgJuBC4Guznu7glmyyvtOCFYdoLw/qjZSks5kkREVlVV3+5l5gDK0zWvUUelOaJW0YDKN9tiGxjMvomIiMzdLtWBo6wkiFSWt9GAy1KPj+XMU/aDXbr1itHt6t+BMwu2UyRud68Vz5DsVDz/NeCOzPy71C/rEXAqIVBTkVuyUI1j5rDXSszvA55i+j11VZwxeb1H4z4lQcW3xud+jTD6W01qkSQisurmPLpW3uo7xYM6/gqzNl3B2vRtG+QLCZxQUcy2TrIWBaZWTCIisrb2gZ8APl89q11EuLhPTq7Xgf8BxQmeE01PpPsJwYRtQpDBCK2gnmOSmPo4+I1MWrdkR43LOgIc6tiVbpn66JL4/JxpVcdjl1AODwJ/HfhSzjyZY+6Px7hRZt0G8NV450pC18I3EAJUx4AvE7pgJmW6D7xmc3IFkkRENpAXPpi26HN/Xp2kSy6h9PKDDcLMeb/adC8raqFetVBlnq4G21cwSURE1pF/HbiQ2RYxyYlvDNwfEibzBPCt1DzXAx9tusH4PwaDpmzH/fgM+ClgR8CvBu4Bfgz2t4F/C76P0P3pHOqdoGPgwr7ecF/XyRjsEfAk6Jc9bgU/rNqlhCASwDb4uwjlvjU779TqdoBHgJfEJOBJ67Wk9dRd8X+2G2a6ddWJ+nUwBZJERDbMsi/Qq4IUVY/bbmeTdQ3MdFq+biQwPdqf8iuJiMga8nOAc+ODdGAg+0vMIeClBSt5F/AxprupVVV6jgEHwZLWTSNgF/wvCF2fdsJ0T/bj5cAY/A6mA155wYykBc0RQismA3sV8EDFPm0IS45r0uIrLV3uY8LxTQcOd8DuSpVLmW3gJfF+XhfI3cz/wh1O7XMJ5UgSEdlUdftpW+bWQauWLjXWmXebmWHD1T0ErQ9V1fupqKDS5VM0EomIiMg6+Dz1m3IUtUD54/hc3bqZAYdjt6dvx2mnxkDB4dS2RqltjlL7uZW6pe3Gzb8udm0/lOri/lVmWz9tMDPgOuDZvCfjbQvsD5itSxXlcior+1eVPFel5iA55rXCW8Nk1ibzgojI5vKZOyWy+W4yK+rUXcpa7E+d9TZ6coNk+9KXzJqblLxJedvUv/aSDV4FfpurJAdGdTARkQYM/CJCTpqiEdgSDjwOdh6zrUcMOBv8G8DJqWlZu4RWKYeA88EeY5K42YAXgT/EJCCVbRHlOfez+/g9sDMJQac+8gltgteC35J6nD62u2Bj4HlMJ9m2OLpfA33mpXLPr4OpRZKIyKZZ4OVfYQuhZDf6Hsmtx9ZTayUn+WLR4ekcsmkzKkkelZ+IiKwLB/47s0GkvBa6I0IuorwuSEZoKXQF+SN6JV2aLgK7NrYQenR23qmEym3P18cIgSgFkeq7g+mBSdLlvh27EmbL9fSS9WVHg0veMwuoQylHkoiINJfzC1XlOWse/dpqbViARkmHOg6sN9leH+u4sof1iIiILNsFPazDgR8A30893gXbAQ6m5ttm0o2tzlD1daXrEsdQHaypXcIxOxV4ksloaclxvDJ0ObQkYLgLfK/B+keEgGXMgTVPapEkIrJJegreNO4VnTcqRR87ogpMMw1aak0V8TKP8+1L3LaIiEhfHi+Ynteq5CFgf868Bv4jQt6c5FefLaaDSMTni3LrALmdk7N1hKL8hUmrqbeVb0MKjIDTmD7WSXlsh/vuwCcJI+VtA7cy+z5Jyj97extzDyKBAkkiIhult3hAnRUtIHGy4kgtWUlMUZlvREREemf/B5MAUFk3cCMEkfK6jI2BrzNJfD0Gvkvl0PAz69/O2XY2QFH0OFnN/Q22KRO74I+Rf/yTiu0J4HXAUcJ75rUUD1SSXceCKJAkIiL9KBuFSwalbj1jr87YsGLSZ5JHERGRlTcCvgAcyXkur+v/C3OmG/CTTIZ4TxzNmbfMGHiYEKxqmlPyeLwdYdItS+obge8jdAssOn5JvqTTwR+O0y4gHPeqPJSvXFzVW4EkERHJVXoi6ukXjy4nO8Uq5qjBKG9ZKhcREZGMMXAf8AGKux0lJ9AtsP3kJ9M+SujuZJlpTY3AziDkW6rLgcuB14IdKtg/KTcmBISgvIVR8vjc2M3tQaYDfwWVLbt3HjtdsC1vnOhiODT0rMhyZUcKlRXgU/9KFbUqyW2hYi3eAw32JbOpggdSV+1WRnn99+ss0nO5FA09K8ujOpjIkhwEjsav5HhfX5CrZe/yO/mfdBsz4DngUMM62H6wlkEdfy1wCyEYNSK0kjlOCFYdi9vaitNfDvb1OJ9GamtuK9z8WfJbI6XfB1WjnmTfA2PgcbCLmASqelJUB1OLJBFpLK9FZV7+NxmuspbMVa2c8y4fW1Vird720rMriNRdo65qbYYGVrmIiPQv5sDxO2Igwgndi2Ji3r3bU6qDDd3e8O/HmLTqiRf/dlVJ9/AR2AWEch+z17LJfobW5167M27vrcB7CW+eN8cn3wFcBlwZ9/m+OF1BpHZ2wY8x27KoqOyafJAdeHHDZTraXtymRGQdqHKy4jJDwPc1QnsfkvVUDj2vQMVi6UMvIrJcBjwf/DbgnIp5z2B6NCd9hw+Tgf00cDpwPfAW4IuUX52PCV2cTiIEnvbHINCI9uUcR12z/0EIdFwD9pdMRmbbYhI40ght7RmhrLMqK70l60vbBn6bhYzWtrcL6tomInXlfuDSzXIL6Lp/gKq+PasKzTPnvq6FXPfbXG+mzuZ62m/TxbEGdW0bHtXBRBZoBG7Uu5BPWisdI3SRmuuOSSdJkCbpPlY3CPA+8JvBTgbuobwOlXRRg9Cdyijv+tRkP6SxmTpYtitb8j9puQT1P8TngT3Sx15OK6qDKZAkIrX5zJ0ClntXhqaoHGsEkZrM32j2omFwpTfzPO3Pa7Q2BZKGR3UwkcXyJDjU4OJSI2iuqTiqV7p724yYx8gfJIz45cCtYNvAFUwHmGRhKgNJu4Rg3huBTwGHqF0PXnQdTDmSRKSWvbwqdS4dlCRpNaT7ZVf1084uV0PR26A0j1bZCBbSXYOE2Tr0IiIDMALfzyQBcpMv6OfPba9kkfZnHjuhdVrJ6G9+cwxavIjJuf8w+KtiLq0vxGlb/e+u5DDgLEKZ5VWCTxACg9fEfFR3Ae/JmW9AFEgSkbka8PefJBoEhmaWy1m2SRxR748FqyjrvNFnRURkicZMWiI1HNXED8xzx6SWA0wnV27jU7G4k5G+jDBiX9YW+D7wMXB5nLZv+vm9wNFrYqDpeuB5HfZN6vsO4fin3wvJ5/hSsK2Q/ByAH4F9ihCteZbZz3x6MBQHvj3PHc+nQJKIzI/rvDQEXUfTSy+3t54mBVsxpJ8asC1OWW/GslH8RERkgCpOnnbeYnZDivmXwT8P/qmGXcuTUfoc/E2E3JTH4+OPAn/CdGLumJB9L/9RVUujGGDyd4D/DqELlfQnCSCO4n0vKP+3xBZI34qP0yPieeyu9qtUV+De2M9uN6EcSSJSS9uvCvXPX662aZCqli9aT+08Wjkr0FulX+lu91MTM6zwwfT8td4LypG0MVQHE1mQEXjShanup24MbIHtZzK8vCycP0gYZS8ZAW0EfA/sBRULGnA2eNLKJDuylwMnwNItzkbgW8CPmQSYLLOsF0wfg30C+GWUaLurmHvKvwb2LPhB4AhwdWoeZ/IZrTPiXvIdcIJJC7N0+R0BOyNuZw6UI0lEGumjlYiu/Janqvzqprqqmlhnnrob0VVpP7ItyGrL+8DWDPTNM4gkIrIRUkGBve/x9NDrZbK5BbfiD3kKIi2FE3+AfQkhqJN0ZwI4A/yp6hX45yrm2Y4tk5KWR2NCa6RkZLY65+RUS3F/DYoM9MDvjGV/Efhh4DJCcvO0+Bmd/bWvaKXx83xdalryvXAU7BBzCyKV0dtFRIDinkfqdrQGuhRi3hujJJjUqo2Cgkm9KT1+ZU9WRokmd7M5lBQ/EhHp4KR4iv107LaUvri/s8F5MT3jhX3vpNRihODRbYQL/aIr7TOoLtiLM+vNbmcEXJdqrTaKLZ2OU1yhzzuBJ0GNi8EVeOzEDwLnxgdJAHHEdDfDpFx2ge8z3T2xcMXhn/3XGFAy4HDsEndSL7veigJJIjKXi3e1UFiewq5MmUpFZYulqhkKnm9V7Iog9a9OcqyWI/XNLKbPuohIK34L4Tv69TlPvhL4MiHZ9tSvNtmVxP+xlYI90usuSl1JS6LLqTwvOsAnmL4aT7q03cz06F55dbkkGPEMIRgxJnSbSxJ7F0l3c8uu6zvUC2zINAMuIrQKqso1lQ7ePZ8w+l5dSUAJsHub7mT/lCNJROpdwzf4tNnMHVmUqnw4eyq6LFUGkjIL763Dqzdde13SSOP8VND8mOetewEFphxJw6M6mEhHSdDgYcLFe7bFCHHaEULS3u14/yD5XWLiRaxyUy6JgV9ECPwVtRLJls0OWHpUtREwBt9htrlHXpmPgcfBLmAqIOHOZKS/9LJV742Lwe6vmEdy+X2EYFJugsqCaWNge/j1XuVIEpFcUxefRTfqN1pQEKm7zt0J66ygZJ5G21arpGFpcQwblVU2D4c+5yIi7SStV/JagCTn6DHwHbCfjtNfBvww3j9B6MZ0HDCwKxVE6iQeO/eW1REHPkX9IBLMlv04BoHSQ8QntwY/5JgB7ytYpoyCSO1dFP9b5n9aXi6zBezavCiQJCL1gg4UXzuqm0t/BhFL6RLJqpmcWeagY34qERFZsLw8ONnv5f3AV2I+lEvB/iYhwPQB4OfAXgb2auCB+e/u2jJCEuyq7uBVLiL/B5ckEJR3+yIhoGTA6RX7mFXSysi+TLjSP1qwPp3/52/Nf3RT1zaRDVf3K6D2d+CaflkuSlkKhFrLt2mRktpQ7S5tUyvI2deWCbT1a2p7bU/nq3DM1bVteFQHE+ku93s7PW0cbnYyoQXS9mQaMEnkO0bDtneR7WZIu3NjpzrYViz6I8y2VCoKMo7DNDtE4Qh9/kvA9YSubgfST6TWuxO2uQr1gaHaxDqYWiSJSCWrG1FfgS/DlbAOl2eZlkkt8jmLiIhsnmzXl/1MggQ7TIJIxPsnUBCpKwceYxLAeWXL9byL2STZZbed+P8AcGp8vI/ibm05XaPYR2EQCcA+Eevx15Ts90Wpur60cx2T8qxzi8nRV5kCSSLSTF7fX518+uGQ/Mbf9rf+zsUwxxw7RW8TvX36oWMoIrIGunavktbsjcC7aT3ysP0R+fmNiio6I0LXth2qgwoFAQn7EtPDy2clI/ndA/ZLqekxObedB/ZYahvSin2MSRfFOrkknTBq276C51eAuraJbLg6XwGr0OxyLeSN8NBiHY17pqW7tnXsGperxShi0kKLEfPaVpYXTV3bhkd1MJHuZs656cex9ZGdQkioLfOXdBPcprSVT5nCelRet7RvgX0feB3wEvB7mIy0llZyBmxUR0+3broSuJ3wWpsMQS+lCrurpo/9mNDacEVqNuraJiK51FttQJZ9sOd1WVina6SaJfWm0WHUMRcRWZrCr2AHngQ7iIJIi5R0S2sZRCqV1y3tfOA2Qv6iTzAJIpW1Ykluu3G5Ji1a0vW8u+J/BZF6Zb/AbBe3vSeZlP0a1L/UIklkw5V9BdjMHVmIklE46izaJll28m8uLZLSGgxfKy00aJW0Kq2RQC2Shkh1MJGORuA/ATzJJBiQzi94MRqOfcX4DrPNNKrOXknrlLPAvwk8j+nWK+l1ZOuHeYOdyPIYoWXZfRQnTAc4D+yRxe1WV2qRJCK5ik5ACiIt0SKPeddubW0WqNN3XNqpeTxXKYgkIrKWHOw0pr+LYxJeA3h4KXu1ufK6lDX15Zxped0Xs61VHPyVwLXAcxXbcODHwHGwFc6vs5YceITphOlZ6ZxUK06BJJENVpTLUUGkxSgayKHL+hazUKBgxOpR7E5EZIlSSZHdwT8yPY0x2IjwRa0uR/07MLnrxDJIbrfEKtGBgmXLjMD3A5e23K8fAn8G3A38z3HaDnCE6W52J+KOXwN2gPl0wZPurgOejfePxRtg58cg0pp8ttW1TWSD5H5gciYqkDRftbsdtVlv18BQ0XqL5tV7ZHg89+50Ua1Yualr2/CoDibSjAPcClyVmrhL+Fk/3aVN33bzYcDdseVPnh1Cd6Rbwa5uvvpal9RFiZifADsntZ8OXAE8D/w04JPxubcB94Opy+PwvTYEJ3k58HywmwlB493l7lYbRXUwBZJENkThh6UokKSKTO/mOXiZz9xprlU3NRmmsvfBCpabAknDozqYSH17l1u7TLdAyun2ZHcAr2dtWi0Mhd8DvKJsBiY5I18EPEW9Fj9b4CPgR+Qnvs52Xczb7lMhR1J2vUDIoZQsN4rT1RJp+JLR8LaZJEdfUcqRJLKhSrtLFT1R45Ktj65YG6+kX9uiu6kBzfo96bJ+NancREQWYwt8X6alSt6VVzpn4Ai4hRA8SNvPdLc4VAerLTlul1J9wGKOKn8M+DjTQb8iu4SR9fZTnQcy7/kRcGbBepNR5BJjFERaFTup/yscRCqjQJLIJumYjKdoUVVkqk21GCo7/qnnFnpcM6OAlCbDVjBi+NJlp8TmIiKLlwQYmvrfmA0k3Q1++ySfT7ouscKdS+bPgLPiMUoSINdZZgz+qprzA/wis0O+F7U+yt7GwHdrbmdVpANwbXJOyUpQIElkjc0ELzqsp86POJKvj25nVebayWQFR1nrK4H5yluR8hIRWTd+W90ZM7ejmacd/CJKu2W5A+e22Ml15+CfazB/cs4cEY5nne6FI7C7mQ3+pfahOGFh9Cyzw8WvML8plcT8D5a9NzIvypEkssZaJ1+2Sa6/FotKRtdyqLuRLl+I65QXq0OPTRko5UgaHtXBREpYbNFyO7Pdo8py5jjwPbAzMy2Nkvt534Sp55Soe1aXS90mx3NmO0nOpXTZpfIwTW3nYqBpAu398X+bVm9z5Ekrq6S5SswLpvfm6lrXZNvPAt9Y9n7IjBcQUtTJcKhMhknlMjwqk+E5z91fuOydkGmqgw2WvsOGR2UyPCqTYVK5DE9hHWzVG9F9w92vqp5NFsnMblO5DIvKZJhULsOjMhGpTXWwAdJ32PCoTIZHZTJMKpfVohxJIiIiIiIiIiJSiwJJIiIiIiIiIiJSy6oHkv7fZe+A5FK5DI/KZJhULsOjMhGpR5+VYVK5DI/KZHhUJsOkclkhK51sW0REREREREREFmfVWySJiIiIiIiIiMiCrGwgycyuNbNvmNl9ZvYby96fTWFmLzGzz5rZ18zsq2b29+P0083s02b2zfj/tDjdzOzfxHK628yuWO4rWF9mtmVmd5rZH8XHF5jZzbFMPmxm++P0A/HxffH585e53+vMzE41s+vN7OvxM/N6fVaWy8z+QfzuusfM/rOZHdRnRaQZ1cGWQ3Ww4VIdbHhUBxse1cHWy0oGksxsC/j3wNuBy4D3mdlly92rjbED/EN3fznwOuDvxGP/G8CN7n4JcGN8DKGMLom3vwX81uJ3eWP8feBrqcf/F/CvYpk8A3wgTv8A8Iy7Xwz8qzifzMf/A3zK3V8GvIZQPvqsLImZnQP8PeAqd38lsAW8F31WRGpTHWypVAcbLtXBhkd1sAFRHWz9rGQgCbgauM/dH3D348AfAu9c8j5tBHd/3N3viPefJXwpn0M4/h+Ks30IeFe8/07g9zy4CTjVzM5e8G6vPTN7MfDXgN+Ojw34WeD6OEu2TJKyuh54c5xfemRmpwBvAn4HwN2Pu/v30Wdl2baBk8xsGzgEPI4+KyJNqA62JKqDDZPqYMOjOthgqQ62RlY1kHQO8K3U40fjNFmg2MTwMHAzcJa7Pw6hogOcGWdTWS3Gvwb+MTCOj88Avu/uO/Fx+rjvlUl8/gdxfunXhcB3gd+Nzd1/28xORp+VpXH3bwP/N/AIofLyA+B29FkRaULfVQOgOtigqA42PKqDDYzqYOtnVQNJedFIDT+3QGb2POBjwP/q7j8smzVnmsqqR2b288CT7n57enLOrF7jOenPNnAF8Fvufhj4MZMm1HlULnMWcyG8E7gAeBFwMqE5e5Y+KyLF9LlYMtXBhkN1sMFSHWxgVAdbP6saSHoUeEnq8YuBx5a0LxvHzPYRKjD/0d0/Hid/J2kCGv8/GaerrObvDcA7zOwhQheDnyX8OnZqbDoK08d9r0zi888Hnl7kDm+IR4FH3f3m+Ph6QqVGn5XleQvwoLt/191PAB8Hfgp9VkSa0HfVEqkONjiqgw2T6mDDozrYmlnVQNKtwCUxy/t+QqKuG5a8Txsh9k39HeBr7v4vU0/dALw/3n8/8F9S0381jobwOuAHSZNS6Ye7/+/u/mJ3P5/wWfiMu/8K8FngujhbtkySsrouzq8If8/c/QngW2b20jjpzcC96LOyTI8ArzOzQ/G7LCkTfVZE6lMdbElUBxse1cGGSXWwQVIdbM3YqpaHmf0cIeK/BXzQ3X9zybu0Eczsp4EvAF9h0hf8nxL66H8EOJfwRfFud386flH8O+Ba4Dng1939toXv+IYws58B/pG7/7yZXUj4dex04E7gr7v7MTM7CPw+IbfC08B73f2BZe3zOjOzywnJN/cDDwC/Tgjg67OyJGb2fwK/TBj96E7gbxL64euzIlKT6mDLoTrYsKkONiyqgw2P6mDrZWUDSSIiIiIiIiIislir2rVNREREREREREQWTIEkERERERERERGpRYEkERERERERERGpRYEkERERERERERGpRYEkERERERERERGpRYEkEVkoM7vWzL5hZveZ2W/kPP8mM7vDzHbM7LrMc+83s2/G2/tT0680s6/Edf6bOIwrZna6mX06zv9pMztt/q9QREREZHhUBxORviiQJCILY2ZbwL8H3g5cBrzPzC7LzPYI8GvAf8osezrwz4FrgKuBf56qlPwW8LeAS+Lt2jj9N4Ab3f0S4Mb4WERERGSjqA4mIn1SIElEFulq4D53f8DdjwN/CLwzPYO7P+TudwPjzLJvAz7t7k+7+zPAp4Frzexs4BR3/wt3d+D3gHfFZd4JfCje/1BquoiIiMgmUR1MRHqjQJKILNI5wLdSjx8FzjGzf2Fm72izbLw9mjMd4Cx3fxwg/j+zw76LiIiIrCrVwUSkN9vL3gER2SiWM83d/Z+1XbZkuoiIiIgEqoOJSG/UIklEFulR4CWpxy8GHuu47KPxft46vxObXRP/P9lin0VERERWnepgItIbBZJEZJFuBS4xswvMbD/wXuCGmsv+KfBWMzstJnh8K/Cnsbn0s2b2ujhSyK8C/yUucwOQjCzy/tR0ERERkU2iOpiI9MZCXjQRkcUws58D/jWwBXzQ3X/TzP4FcJu732BmrwU+AZwGHAWecPdXxGX/BvBP46p+091/N06/CvgPwEnAnwB/193dzM4APgKcSxiJ5N3u/vSCXqqIiIjIYKgOJiJ9USBJRERERERERERqUdc2ERERERERERGpRYEkERERERERERGpRYEkERERERERERGpRYEkERERERERERGpRYEkERERERERERGpRYEkERERERERERGpRYEkERERERERERGpRYEkERERERERERGp5f8HSbgWHv1Is1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x4320 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize = (20,60))\n",
    "axs = axs.ravel()\n",
    "\n",
    "im_reg = np.zeros((2048, 2048,3)) # empty color image\n",
    "im_reg[...,0] = ref_binary\n",
    "im_reg[...,1] = out_binary\n",
    "    \n",
    "im_orig = np.zeros((2048, 2048,3))\n",
    "im_orig[...,0] = ref_binary\n",
    "im_orig[...,1] = mov_binary\n",
    "    \n",
    "axs[0].imshow(im_orig[0:1000, 0:1000])   #before reg\n",
    "    \n",
    "axs[1].imshow(im_reg[0:1000, 0:1000])   #after reg\n",
    "\n",
    "\n",
    "axs[0].title.set_text('Before Registration')\n",
    "axs[1].title.set_text('After Registration')\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlabel='0:1000', ylabel='0:1000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n"
     ]
    }
   ],
   "source": [
    "reg = np.zeros(mov.shape, dtype=np.uint16) # initialize with the right dtype\n",
    "for Z in range(mov.shape[0]): # Z \n",
    "    print(\"going...\")\n",
    "    for ch in range(mov.shape[1]): # channels\n",
    "        reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "        reg[Z,ch,...] = to_uint16(reg[Z,ch,...])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 900)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_max = reg.max(0)\n",
    "reg_v = reg_max[0, ...]\n",
    "reg_v = reg_v[100:1000, 100:1000]\n",
    "reg_v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[110 107 101 ... 108 115 109]\n",
      " [108 114 108 ... 107 108 115]\n",
      " [108 107 104 ... 105 111 109]\n",
      " ...\n",
      " [154 141 152 ... 110 106 110]\n",
      " [139 150 162 ... 113 109 106]\n",
      " [143 165 154 ... 107 109 115]]  \n",
      " \n",
      " [[108 106 110 ... 111 108 109]\n",
      " [104 106 109 ... 108 106 106]\n",
      " [103 105 108 ... 112 105 108]\n",
      " ...\n",
      " [157 151 173 ... 106 106 105]\n",
      " [148 179 164 ... 113 109 107]\n",
      " [158 172 186 ... 110 108 108]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x4320 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QC by eye, every single Cycle -- pick a random FOV\n",
    "\n",
    "orig = imread('tif/Cycle_1/Cycle_1_F000.tif')\n",
    "orig = orig.max(0)\n",
    "orig = orig[0, ...]\n",
    "orig_v = orig[100:1500, 100:1500]\n",
    "\n",
    "# reg = imread(\"reg_Cyc_1/Cycle_1_F053_reg.tif\")\n",
    "# reg = reg.max(0)\n",
    "# reg = reg[0, ...]\n",
    "# reg_v = reg[100:1500, 100:1500]\n",
    "#plt.imshow(reg_v, cmap=plt.cm.gray)\n",
    "\n",
    "f, ax = plt.subplots(1,2, figsize = (20,60))\n",
    "ax[0].imshow(orig_v)\n",
    "ax[1].imshow(reg_v)\n",
    "ax[0].title.set_text('Before Registration (DNA Channel)')\n",
    "ax[1].title.set_text('After Registration (DNA Channel)')\n",
    "\n",
    "for ax in ax.flat:\n",
    "    ax.set(xlabel='100:1500', ylabel='100:1500')\n",
    "\n",
    "print(orig_v,\" \\n\" , \"\\n\",reg_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Registration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 10\n",
    "NUM_FOVS = 217"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(CYCLE_NUMS):\n",
    "    if i == 0:\n",
    "        continue\n",
    "    os.makedirs(f'tmat_Cyc_{i}')\n",
    "    os.makedirs(f'reg_bin_Cyc_{i}')\n",
    "    os.makedirs(f'reg_Cyc_{i}')           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for Cycle 9\n",
    "c = 9\n",
    "refs = iter(glob.glob('tif/Cycle_0/*')) # list of cycle 0 .tif \n",
    "movs = iter(glob.glob(f'tif/Cycle_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "for FOV in range(0, NUM_FOVS): # \n",
    "    #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "    ref_name = next(refs) \n",
    "    ref = imread(ref_name)\n",
    "    ref = ref.astype(np.uint16)\n",
    "    mov_name = next(movs)\n",
    "    mov = imread(mov_name)\n",
    "    mov = mov.astype(np.uint16)\n",
    "    FOV_num = mov_name[-7:-4]\n",
    "    print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "    ref_max = ref.max(0)\n",
    "    ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "    mov_max = mov.max(0)\n",
    "    mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "    print(\"Got threshold\")\n",
    "    sr = StackReg(StackReg.RIGID_BODY)  \n",
    "    tmat = sr.register(ref_binary, mov_binary) \n",
    "    out = sr.transform(mov_binary) \n",
    "    out = pystackreg.util.to_uint16(out)   \n",
    "\n",
    "    # save binary\n",
    "    fname_to_save = f'reg_bin_Cyc_{c}' + f'/Cycle_{c}_F{FOV_num}_bin_reg.tif'\n",
    "    print('Saving Binary Registered Images...', fname_to_save)\n",
    "    tifffile.imwrite(fname_to_save, out, imagej=True, photometric = 'minisblack',metadata={'axes':'YX'})\n",
    "\n",
    "    # save tmat\n",
    "    print(\"saving tmat\")\n",
    "    np.save(f'tmat_Cyc_{c}' + f'/Cycle_{c}_F{FOV_num}_tmat.npy', tmat)\n",
    "\n",
    "    # THE REGISTRATION STEP\n",
    "    reg = np.zeros(mov.shape, dtype=np.uint16)               # initialize with the right dtype\n",
    "    for Z in range(mov.shape[0]):\n",
    "        print(\"registration going\")\n",
    "        for ch in range(mov.shape[1]): \n",
    "            reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "            reg[Z,ch,...] = to_uint16(reg[Z,ch,...])\n",
    "\n",
    "    fname_to_save = f'reg_Cyc_{c}' + f'/Cycle_{c}_F{FOV_num}_reg.tif'\n",
    "    print('Saving Registered Images...', fname_to_save)\n",
    "    tifffile.imwrite(fname_to_save, reg, imagej=True,\n",
    "                     photometric = 'minisblack',metadata={'axes':'ZCYX'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS-1):    \n",
    "    refs = iter(sorted(glob.glob('tif/Cycle_0/*'))) # list of cycle 0 .tif \n",
    "    movs = iter(sorted(glob.glob(f'tif/Cycle_{c+1}/*'))) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "    for FOV in range(0, NUM_FOVS): \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        ref_name = next(refs) \n",
    "        ref = imread(ref_name)\n",
    "        ref = ref.astype(np.uint16)\n",
    "        mov_name = next(movs)\n",
    "        mov = imread(mov_name)\n",
    "        mov = mov.astype(np.uint16)\n",
    "        FOV_num = mov_name.split('_F')[1][0:3]\n",
    "        print(f'cycle {c+1} field {FOV_num} ')\n",
    "\n",
    "        ref_max = ref.max(0)\n",
    "        ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "        mov_max = mov.max(0)\n",
    "        mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "        print(\"Got threshold\")\n",
    "        sr = StackReg(StackReg.RIGID_BODY)  \n",
    "        tmat = sr.register(ref_binary, mov_binary) \n",
    "        out = sr.transform(mov_binary) \n",
    "        out = pystackreg.util.to_uint16(out) \n",
    "\n",
    "        # save binary\n",
    "        fname_to_save = f'reg_bin_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_bin_reg.tif'\n",
    "        print('Saving Binary Registered Images...', fname_to_save)\n",
    "        tifffile.imwrite(fname_to_save, out, imagej=True, photometric = 'minisblack',metadata={'axes':'YX'})\n",
    "\n",
    "        # save tmat\n",
    "        print(\"saving tmat\")\n",
    "        np.save(f'tmat_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_tmat.npy', tmat)\n",
    "        \n",
    "        # THE REGISTRATION STEP\n",
    "        reg = np.zeros(mov.shape, dtype=np.uint16)               # initialize with the right dtype\n",
    "        for Z in range(mov.shape[0]):\n",
    "            print(f\"Z-plane: {Z} registering\")\n",
    "            for ch in range(mov.shape[1]): \n",
    "                reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "                reg[Z,ch,...] = to_uint16(reg[Z,ch,...])\n",
    "\n",
    "        fname_to_save = f'reg_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_reg.tif'\n",
    "        print('Saving Registered Images...', fname_to_save)\n",
    "        tifffile.imwrite(fname_to_save, reg, imagej=True,\n",
    "                         photometric = 'minisblack',metadata={'axes':'ZCYX'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at Max and Min Shifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 217"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.825897503683109 0.9283841186154405 2.322898338284176 -2.698294405225397\n",
      "tmat_Cyc_1 \n",
      " 3 1 3 -3\n",
      "4.307972601050096 1.695331957334929 2.557358290047432 -1.941289914914023\n",
      "tmat_Cyc_2 \n",
      " 5 2 3 -2\n",
      "4.576048344142919 1.9854675001972737 2.9922757688725596 -1.2105047719963977\n",
      "tmat_Cyc_3 \n",
      " 5 2 3 -2\n",
      "5.037272932140695 2.265329772301584 3.036799228552809 -1.6115791792959726\n",
      "tmat_Cyc_4 \n",
      " 6 3 4 -2\n",
      "5.227335609246893 2.6471875861350327 3.247288684273826 -1.7749673334094496\n",
      "tmat_Cyc_5 \n",
      " 6 3 4 -2\n",
      "6.1318181453254965 2.6696954394196837 3.02591939698209 -1.2926273405932989\n",
      "tmat_Cyc_6 \n",
      " 7 3 4 -2\n",
      "5.789261668054451 2.47424034507344 2.9402476206073516 -1.8735797509239092\n",
      "tmat_Cyc_7 \n",
      " 6 3 3 -2\n",
      "12.913221019693125 -1.093932927573177 8.318829060681992 3.069074700335136\n",
      "tmat_Cyc_8 \n",
      " 13 -2 9 4\n",
      "8.29717106145416 5.101653388593767 1.9104978305670102 -4.582513286812839\n",
      "tmat_Cyc_9 \n",
      " 9 6 2 -5\n",
      "13 -2 9 -5\n"
     ]
    }
   ],
   "source": [
    "## print('X shift and Y shift max min values')\n",
    "x_max_list=[]\n",
    "x_min_list=[]\n",
    "y_max_list=[]\n",
    "y_min_list=[]\n",
    "# X_indices =[]\n",
    "# Y_indices =[]\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):     \n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    X_SHIFT = []\n",
    "    Y_SHIFT = []\n",
    "    X_RIG = []\n",
    "    Y_RIG = []\n",
    "    for sFOV in range(0,NUM_FOVS): \n",
    "        tmat_name = next(tmats)\n",
    "        #fov = tmat_name.split('_F')[1][0:3]\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        if moveX > 0:\n",
    "            moveX = moveX + 2048*np.tan(np.arcsin(tmat_loaded[0,1]))\n",
    "            X_RIG = []\n",
    "        if moveY < 0:\n",
    "            moveY = moveY - 2048*np.tan(np.arcsin(tmat_loaded[0,1]))\n",
    "        X_SHIFT.append(moveX)\n",
    "        Y_SHIFT.append(moveY)\n",
    "\n",
    "    X_max = max(X_SHIFT)\n",
    "    X_min = min(X_SHIFT)\n",
    "    Y_max = max(Y_SHIFT)\n",
    "    Y_min = min(Y_SHIFT)\n",
    "#     X_i, Y_i = X_SHIFT.index(X_max), Y_SHIFT.index(Y_max)\n",
    "#     X_indices.append(X_i)\n",
    "#     Y_indices.append(Y_i)\n",
    "    print(X_max,X_min,Y_max,Y_min)\n",
    "    #print(X_SHIFT, Y_SHIFT)\n",
    "    def round_shift(val):\n",
    "        import math\n",
    "        if val <0: # if negative,\n",
    "            val = math.floor(val)\n",
    "        else:\n",
    "            val = math.ceil(val)\n",
    "        return val\n",
    "\n",
    "    X_max = round_shift(X_max)\n",
    "    X_min = round_shift(X_min)\n",
    "    Y_max = round_shift(Y_max)\n",
    "    Y_min = round_shift(Y_min)\n",
    "    \n",
    "    x_max_list.append(X_max)\n",
    "    x_min_list.append(X_min)\n",
    "    y_max_list.append(Y_max)\n",
    "    y_min_list.append(Y_min)\n",
    "    print(f'tmat_Cyc_{c+1} \\n',X_max,X_min,Y_max,Y_min)\n",
    "\n",
    "X_max_total = max(x_max_list)\n",
    "X_min_total = min(x_min_list)\n",
    "Y_max_total = max(y_max_list)\n",
    "Y_min_total = min(y_min_list)\n",
    "\n",
    "print(X_max_total, X_min_total, Y_max_total, Y_min_total)\n",
    "#print(X_indices, Y_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 tmat_Cyc_8/Cycle_8_F002_tmat.npy 15.160436900069044 7.9828994185995725\n",
      "9 tmat_Cyc_9/Cycle_9_F002_tmat.npy 24.354831699891747 -78.75207638199242\n"
     ]
    }
   ],
   "source": [
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):     \n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    for sFOV in range(0,NUM_FOVS): \n",
    "        tmat_name = next(tmats)\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        if (moveX > 10) | (moveY > 10)|(moveX < -10) | (moveY < -10): # 50 pixels is max\n",
    "            print(c+1, tmat_name, moveX, moveY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS-1):    \n",
    "    refs = iter(glob.glob('tif/Cycle_0/*')) # list of cycle 0 .tif \n",
    "    movs = iter(glob.glob(f'tif/Cycle_{c+1}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "    for FOV in range(0, NUM_FOVS): # \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        ref_name = next(refs) \n",
    "        ref = imread(ref_name)\n",
    "        ref = ref.astype(np.uint16)\n",
    "        mov_name = next(movs)\n",
    "        mov = imread(mov_name)\n",
    "        mov = mov.astype(np.uint16)\n",
    "        FOV_num = mov_name[-7:-4]\n",
    "        print(f'cycle {c+1} field {FOV_num} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 1 field 000 \n",
      "cycle 1 field 001 \n",
      "cycle 1 field 002 \n",
      "cycle 1 field 003 \n",
      "cycle 1 field 004 \n",
      "cycle 1 field 005 \n",
      "cycle 1 field 006 \n",
      "cycle 1 field 008 \n",
      "cycle 1 field 009 \n",
      "cycle 1 field 010 \n",
      "cycle 1 field 011 \n",
      "cycle 1 field 012 \n",
      "cycle 1 field 013 \n",
      "cycle 1 field 014 \n",
      "cycle 1 field 015 \n",
      "cycle 1 field 016 \n",
      "cycle 1 field 017 \n",
      "cycle 1 field 018 \n",
      "cycle 1 field 019 \n",
      "cycle 1 field 020 \n",
      "cycle 1 field 021 \n",
      "cycle 1 field 022 \n",
      "cycle 1 field 023 \n",
      "cycle 1 field 024 \n",
      "cycle 1 field 025 \n",
      "cycle 1 field 026 \n",
      "cycle 1 field 027 \n",
      "cycle 1 field 028 \n",
      "cycle 1 field 029 \n",
      "cycle 1 field 030 \n",
      "cycle 1 field 031 \n",
      "cycle 1 field 032 \n",
      "cycle 1 field 033 \n",
      "cycle 1 field 034 \n",
      "cycle 1 field 035 \n",
      "cycle 1 field 036 \n",
      "cycle 1 field 037 \n",
      "cycle 1 field 038 \n",
      "cycle 1 field 039 \n",
      "cycle 1 field 040 \n",
      "cycle 1 field 041 \n",
      "cycle 1 field 042 \n",
      "cycle 1 field 043 \n",
      "cycle 1 field 044 \n",
      "cycle 1 field 045 \n",
      "cycle 1 field 046 \n",
      "cycle 1 field 047 \n",
      "cycle 1 field 048 \n",
      "cycle 1 field 049 \n",
      "cycle 1 field 050 \n",
      "cycle 1 field 051 \n",
      "cycle 1 field 052 \n",
      "cycle 1 field 053 \n",
      "cycle 1 field 054 \n",
      "cycle 1 field 055 \n",
      "cycle 1 field 056 \n",
      "cycle 1 field 058 \n",
      "cycle 1 field 059 \n",
      "cycle 1 field 060 \n",
      "cycle 1 field 061 \n",
      "cycle 1 field 062 \n",
      "cycle 1 field 063 \n",
      "cycle 1 field 064 \n",
      "cycle 1 field 065 \n",
      "cycle 1 field 066 \n",
      "cycle 1 field 067 \n",
      "cycle 1 field 069 \n",
      "cycle 1 field 070 \n",
      "cycle 1 field 071 \n",
      "cycle 1 field 072 \n",
      "cycle 1 field 073 \n",
      "cycle 1 field 074 \n",
      "cycle 1 field 075 \n",
      "cycle 1 field 076 \n",
      "cycle 1 field 077 \n",
      "cycle 1 field 078 \n",
      "cycle 1 field 079 \n",
      "cycle 1 field 080 \n",
      "cycle 1 field 081 \n",
      "cycle 1 field 082 \n",
      "cycle 1 field 083 \n",
      "cycle 1 field 084 \n",
      "cycle 1 field 085 \n",
      "cycle 1 field 086 \n",
      "cycle 1 field 087 \n",
      "cycle 1 field 088 \n",
      "cycle 1 field 089 \n",
      "cycle 1 field 090 \n",
      "cycle 1 field 091 \n",
      "cycle 1 field 092 \n",
      "cycle 1 field 093 \n",
      "cycle 1 field 094 \n",
      "cycle 1 field 095 \n",
      "cycle 1 field 096 \n",
      "cycle 1 field 097 \n",
      "cycle 1 field 098 \n",
      "cycle 1 field 099 \n",
      "cycle 1 field 100 \n",
      "cycle 1 field 101 \n",
      "cycle 1 field 102 \n",
      "cycle 1 field 103 \n",
      "cycle 1 field 104 \n",
      "cycle 1 field 105 \n",
      "cycle 1 field 106 \n"
     ]
    }
   ],
   "source": [
    "# finding the intensity sums of each image and finding which z stack is the max\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for c in range(CYCLE_NUMS):\n",
    "    if c == 0:\n",
    "        refs = iter(glob.glob('tif/Cycle_0/*'))\n",
    "        for FOV in range(0, NUM_FOVS): \n",
    "            ref_name = next(refs) \n",
    "            ref = imread(ref_name)\n",
    "            ref = ref.astype(np.uint16)\n",
    "            FOV_num = ref_name[-7:-4]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "            Z_ch0 = []\n",
    "            Z_ch0_dic = {}\n",
    "\n",
    "            for Z in range(ref.shape[0]): # Z \n",
    "                for ch in range(0, 1):\n",
    "                    arsum = np.sum(ref[Z, ch, ...])\n",
    "                    Z_ch0.append(arsum)\n",
    "                    Z_ch0_dic[arsum] = Z\n",
    "\n",
    "            df.loc[FOV_num, 'max_int_C0'] = max(Z_ch0)                 \n",
    "            df.loc[FOV_num, 'z_idx_C0'] = Z_ch0_dic[max(Z_ch0)] \n",
    "    else:\n",
    "        ims = iter(glob.glob(f'reg_Cyc_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006  \n",
    "        for FOV in range(0, NUM_FOVS): # \n",
    "            #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "            im_name = next(ims)\n",
    "            im = imread(im_name)\n",
    "            im = im.astype(np.uint16)\n",
    "            FOV_num = im_name[-11:-8]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "            Z_ch0 = []\n",
    "            Z_ch0_dic = {}\n",
    "\n",
    "            for Z in range(im.shape[0]): # Z \n",
    "                for ch in range(0, 1):\n",
    "                    arsum = np.sum(im[Z, ch, ...])\n",
    "                    Z_ch0.append(arsum)\n",
    "                    Z_ch0_dic[arsum] = Z\n",
    "\n",
    "            df.loc[FOV_num, f'max_int_C{c}'] = max(Z_ch0)                  \n",
    "            df.loc[FOV_num, f'z_idx_C{c}'] = Z_ch0_dic[max(Z_ch0)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>1.119118e+09</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>478641658.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>755377685.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>750686401.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>627118451.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>662533702.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>630114342.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>646428270.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>632197388.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>688360413.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.369988e+08</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <td>003</td>\n",
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       "      <td>842045867.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.167974e+09</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>503179767.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>789136091.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>646637960.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>781059094.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>653479368.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>800582051.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>812305228.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>799187343.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>751397763.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.349049e+09</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>484668016.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>759938674.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>688648557.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>818926880.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>755855202.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>766680391.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>765032117.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>771153369.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>792433192.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.519895e+08</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>494989119.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>639881109.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>770054210.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>772420044.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>632935678.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>646471974.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>776564515.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>786315171.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>817224456.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.058622e+09</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>466946909.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>604967437.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>614119466.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>737417140.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>734456810.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>741981284.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>739099277.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>744091019.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>768008965.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.629803e+08</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>480680474.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>755803785.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>752209511.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>754634762.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>750037327.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>760141288.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>756299903.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>763816373.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>798381963.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.758403e+08</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>475444284.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>618834814.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>745182689.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>747648124.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>743471019.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>751999435.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>748575190.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>755740253.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>634750564.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.916793e+08</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>218 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      max_int_C0  z_idx_C0   max_int_C1  z_idx_C1   max_int_C2  z_idx_C2  \\\n",
       "000  497603534.0       5.0  783796134.0       5.0  774130294.0       5.0   \n",
       "001  474355286.0       5.0  755755326.0       5.0  865768568.0       7.0   \n",
       "002  478641658.0       5.0  755377685.0       5.0  750686401.0       4.0   \n",
       "003  480227678.0       4.0  760329403.0       4.0  634435809.0       4.0   \n",
       "004  503179767.0       5.0  789136091.0       4.0  646637960.0       4.0   \n",
       "..           ...       ...          ...       ...          ...       ...   \n",
       "220  484668016.0       3.0  759938674.0       4.0  688648557.0       3.0   \n",
       "221  494989119.0       4.0  639881109.0       4.0  770054210.0       4.0   \n",
       "222  466946909.0       6.0  604967437.0       6.0  614119466.0       6.0   \n",
       "223  480680474.0       4.0  755803785.0       4.0  752209511.0       4.0   \n",
       "224  475444284.0       4.0  618834814.0       4.0  745182689.0       4.0   \n",
       "\n",
       "      max_int_C3  z_idx_C3   max_int_C4  z_idx_C4   max_int_C5  z_idx_C5  \\\n",
       "000  775605331.0       5.0  776153011.0       5.0  795282102.0       5.0   \n",
       "001  751784855.0       5.0  617024045.0       4.0  763849176.0       5.0   \n",
       "002  627118451.0       5.0  662533702.0       5.0  630114342.0       4.0   \n",
       "003  756242824.0       4.0  755192702.0       4.0  768656992.0       4.0   \n",
       "004  781059094.0       4.0  653479368.0       4.0  800582051.0       4.0   \n",
       "..           ...       ...          ...       ...          ...       ...   \n",
       "220  818926880.0       3.0  755855202.0       3.0  766680391.0       3.0   \n",
       "221  772420044.0       4.0  632935678.0       4.0  646471974.0       4.0   \n",
       "222  737417140.0       6.0  734456810.0       5.0  741981284.0       6.0   \n",
       "223  754634762.0       4.0  750037327.0       4.0  760141288.0       4.0   \n",
       "224  747648124.0       4.0  743471019.0       4.0  751999435.0       4.0   \n",
       "\n",
       "      max_int_C6  z_idx_C6   max_int_C7  z_idx_C7   max_int_C8  z_idx_C8  \\\n",
       "000  816243194.0       5.0  796394123.0       5.0  854276562.0       5.0   \n",
       "001  785165977.0       5.0  765708534.0       5.0  825421937.0       4.0   \n",
       "002  646428270.0       5.0  632197388.0       4.0  688360413.0       4.0   \n",
       "003  784329516.0       4.0  769035748.0       4.0  842045867.0       4.0   \n",
       "004  812305228.0       4.0  799187343.0       4.0  751397763.0       4.0   \n",
       "..           ...       ...          ...       ...          ...       ...   \n",
       "220  765032117.0       3.0  771153369.0       3.0  792433192.0       3.0   \n",
       "221  776564515.0       4.0  786315171.0       4.0  817224456.0       4.0   \n",
       "222  739099277.0       6.0  744091019.0       6.0  768008965.0       6.0   \n",
       "223  756299903.0       4.0  763816373.0       4.0  798381963.0       4.0   \n",
       "224  748575190.0       4.0  755740253.0       4.0  634750564.0       3.0   \n",
       "\n",
       "       max_int_C9  z_idx_C9  \n",
       "000  1.204771e+09       5.0  \n",
       "001  1.119118e+09       4.0  \n",
       "002  9.369988e+08       4.0  \n",
       "003  1.167974e+09       4.0  \n",
       "004  1.349049e+09       4.0  \n",
       "..            ...       ...  \n",
       "220  9.519895e+08       3.0  \n",
       "221  1.058622e+09       4.0  \n",
       "222  8.629803e+08       5.0  \n",
       "223  9.758403e+08       4.0  \n",
       "224  8.916793e+08       4.0  \n",
       "\n",
       "[218 rows x 20 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      max_int_C0  z_idx_C0   max_int_C1  z_idx_C1   max_int_C2  z_idx_C2  \\\n",
      "002  478641658.0       5.0  755377685.0       5.0  750686401.0       4.0   \n",
      "\n",
      "      max_int_C3  z_idx_C3   max_int_C4  z_idx_C4   max_int_C5  z_idx_C5  \\\n",
      "002  627118451.0       5.0  662533702.0       5.0  630114342.0       4.0   \n",
      "\n",
      "      max_int_C6  z_idx_C6   max_int_C7  z_idx_C7   max_int_C8  z_idx_C8  \\\n",
      "002  646428270.0       5.0  632197388.0       4.0  688360413.0       4.0   \n",
      "\n",
      "      max_int_C9  z_idx_C9  \n",
      "002  936998820.0       4.0  \n"
     ]
    }
   ],
   "source": [
    "print(df.loc[['002']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"max_intensity_indices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67c507eb82924cfdaa110ce50e565194",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# We are trying to look for registered binaries that have intensity percentage less than 0.1 percent (< 0.001)\n",
    "\n",
    "hist = pd.DataFrame()\n",
    "\n",
    "err_lst = []\n",
    "for c in tqdm(range(CYCLE_NUMS-1)):    \n",
    "    binas = iter(glob.glob(f'reg_bin_Cyc_{c+1}/*')) \n",
    "    for FOV in range(0, NUM_FOVS): # \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        bina_name = next(binas)\n",
    "        bina = imread(bina_name)\n",
    "        bina = bina.astype(np.uint16)\n",
    "        FOV_num = bina_name[-15:-12]\n",
    "        \n",
    "        percentage = (np.sum(bina))/(2048*2048)    \n",
    "        \n",
    "        if percentage < 0.001:\n",
    "            err_lst.append(bina_name)\n",
    "            \n",
    "        hist.loc[FOV_num, f'{c+1}'] = percentage    # this is the table of percent of signal in images \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>0.062582</td>\n",
       "      <td>0.062360</td>\n",
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       "      <td>0.061614</td>\n",
       "      <td>0.061659</td>\n",
       "      <td>0.063269</td>\n",
       "      <td>0.061531</td>\n",
       "      <td>0.053897</td>\n",
       "      <td>0.052782</td>\n",
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       "      <td>001</td>\n",
       "      <td>0.046272</td>\n",
       "      <td>0.043406</td>\n",
       "      <td>0.045377</td>\n",
       "      <td>0.045292</td>\n",
       "      <td>0.045328</td>\n",
       "      <td>0.046404</td>\n",
       "      <td>0.045244</td>\n",
       "      <td>0.039007</td>\n",
       "      <td>0.038232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000822</td>\n",
       "      <td>0.034982</td>\n",
       "      <td>0.040595</td>\n",
       "      <td>0.036681</td>\n",
       "      <td>0.031898</td>\n",
       "      <td>0.034297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>0.047084</td>\n",
       "      <td>0.046613</td>\n",
       "      <td>0.046205</td>\n",
       "      <td>0.046672</td>\n",
       "      <td>0.045991</td>\n",
       "      <td>0.049131</td>\n",
       "      <td>0.046358</td>\n",
       "      <td>0.043029</td>\n",
       "      <td>0.042183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>0.066303</td>\n",
       "      <td>0.065870</td>\n",
       "      <td>0.065564</td>\n",
       "      <td>0.065358</td>\n",
       "      <td>0.065113</td>\n",
       "      <td>0.068811</td>\n",
       "      <td>0.065474</td>\n",
       "      <td>0.055280</td>\n",
       "      <td>0.057168</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>0.074491</td>\n",
       "      <td>0.070480</td>\n",
       "      <td>0.070524</td>\n",
       "      <td>0.071783</td>\n",
       "      <td>0.070785</td>\n",
       "      <td>0.070633</td>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.055489</td>\n",
       "      <td>0.058527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>0.057778</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.055814</td>\n",
       "      <td>0.057730</td>\n",
       "      <td>0.056369</td>\n",
       "      <td>0.056431</td>\n",
       "      <td>0.055513</td>\n",
       "      <td>0.045425</td>\n",
       "      <td>0.046355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>0.066977</td>\n",
       "      <td>0.066302</td>\n",
       "      <td>0.066057</td>\n",
       "      <td>0.065404</td>\n",
       "      <td>0.065349</td>\n",
       "      <td>0.065322</td>\n",
       "      <td>0.064743</td>\n",
       "      <td>0.046914</td>\n",
       "      <td>0.053117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>0.045626</td>\n",
       "      <td>0.045360</td>\n",
       "      <td>0.045151</td>\n",
       "      <td>0.044732</td>\n",
       "      <td>0.044919</td>\n",
       "      <td>0.044663</td>\n",
       "      <td>0.044737</td>\n",
       "      <td>0.037579</td>\n",
       "      <td>0.038178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>0.053400</td>\n",
       "      <td>0.052982</td>\n",
       "      <td>0.052596</td>\n",
       "      <td>0.052108</td>\n",
       "      <td>0.052348</td>\n",
       "      <td>0.052107</td>\n",
       "      <td>0.052089</td>\n",
       "      <td>0.040501</td>\n",
       "      <td>0.042863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "000  0.062582  0.062360  0.061846  0.061614  0.061659  0.063269  0.061531   \n",
       "001  0.046272  0.043406  0.045377  0.045292  0.045328  0.046404  0.045244   \n",
       "002  0.000802  0.000823  0.000847  0.000822  0.034982  0.040595  0.036681   \n",
       "003  0.047084  0.046613  0.046205  0.046672  0.045991  0.049131  0.046358   \n",
       "004  0.066303  0.065870  0.065564  0.065358  0.065113  0.068811  0.065474   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "219  0.074491  0.070480  0.070524  0.071783  0.070785  0.070633  0.070312   \n",
       "220  0.057778  0.052209  0.055814  0.057730  0.056369  0.056431  0.055513   \n",
       "221  0.066977  0.066302  0.066057  0.065404  0.065349  0.065322  0.064743   \n",
       "222  0.045626  0.045360  0.045151  0.044732  0.044919  0.044663  0.044737   \n",
       "223  0.053400  0.052982  0.052596  0.052108  0.052348  0.052107  0.052089   \n",
       "\n",
       "            7         8  \n",
       "000  0.053897  0.052782  \n",
       "001  0.039007  0.038232  \n",
       "002  0.031898  0.034297  \n",
       "003  0.043029  0.042183  \n",
       "004  0.055280  0.057168  \n",
       "..        ...       ...  \n",
       "219  0.055489  0.058527  \n",
       "220  0.045425  0.046355  \n",
       "221  0.046914  0.053117  \n",
       "222  0.037579  0.038178  \n",
       "223  0.040501  0.042863  \n",
       "\n",
       "[217 rows x 9 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as pl\n",
    "for c in range(CYCLE_NUMS-1):\n",
    "    hist.hist(column=f'{c+1}', bins = 30, range=[0, 0.3])\n",
    "    pl.suptitle(f\"Cycle {c+1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_bin_Cyc_1/Cycle_1_F002_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F002_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F002_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F002_bin_reg.tif']"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## print('X shift and Y shift max min values')\n",
    "x_max_list=[]\n",
    "x_min_list=[]\n",
    "y_max_list=[]\n",
    "y_min_list=[]\n",
    "X_indices =[]\n",
    "Y_indices =[]\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):     \n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_1/Cycle_1_F000_tmat.npy'))\n",
    "    X_SHIFT = []\n",
    "    Y_SHIFT = []\n",
    "    for sFOV in range(0,NUM_FOVS): \n",
    "        tmat_name = next(tmats)\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        X_SHIFT.append(moveX)\n",
    "        Y_SHIFT.append(moveY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging and Cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_total = len(mov[0][0][0])    # 2048\n",
    "X_total = len(mov[0][0][1])    # 2048\n",
    "\n",
    "###############################\n",
    "X_abs_shift = [abs(X_max_total), abs(X_min_total)]\n",
    "Y_abs_shift = [abs(Y_max_total), abs(Y_min_total)] \n",
    "\n",
    "def crop(X_x, X_n, Y_x, Y_n, img, pad):\n",
    "    Y_total = img.shape[3]    # 2048\n",
    "    X_total = img.shape[-1]     # 2048\n",
    "    print('image shape', Y_total, X_total)\n",
    "    \n",
    "    if X_x * X_n > 0:   # same signs --> both negative or both positive\n",
    "        Xlength = X_total - (max(X_abs_shift))\n",
    "        print(Xlength)\n",
    "        if X_x > 0: # moving left\n",
    "            img = img[...,:, 0+pad:Xlength-pad]\n",
    "        if X_x < 0:  # moving right \n",
    "            img = img[...,:, abs(X_n)+pad:X_total-pad]\n",
    "\n",
    "    if X_x * X_n < 0:   # diff signs --> one is positive and other is negative\n",
    "        Xlength = X_total  - (abs(X_x) + abs(X_n))\n",
    "        print(Xlength)\n",
    "        img = img[...,:, abs(X_n)+pad: X_total - abs(X_x)-pad] \n",
    "        \n",
    "    if X_x == 0 and X_n < 0:\n",
    "        Xlength = X_total  - abs(X_n)\n",
    "        print(Xlength)\n",
    "        img = img[...,:, abs(X_n)+pad:X_total-pad]\n",
    "    if X_x == 0 and X_n == 0:\n",
    "        Xlength = X_total\n",
    "        print(Xlength)\n",
    "        img = img[...,:, 0+pad:X_total-pad]\n",
    "    if X_x > 0 and X_n == 0:\n",
    "        Xlength = X_total - X_x\n",
    "        print(Xlength)\n",
    "        img = img[...,:, 0+pad:Xlength-pad]\n",
    "### Y ## #\n",
    "    if Y_x * Y_n > 0:   \n",
    "        Ylength = Y_total - (max(Y_abs_shift))\n",
    "        print(Ylength)\n",
    "        if Y_x > 0: # up\n",
    "            img = img[..., 0+pad:Ylength-pad, :]\n",
    "        if Y_x < 0: # down\n",
    "            img = img[...,abs(Y_n)+pad:Y_total-pad, :]\n",
    "            \n",
    "    if Y_x * Y_n < 0:  # Y_x > 0 , Y_n < 0\n",
    "        Ylength = Y_total  - (abs(Y_x) + abs(Y_n)) # \n",
    "        print(Ylength)\n",
    "        img = img[...,abs(Y_n)+pad:Y_total-abs(Y_x)-pad, :] \n",
    "        \n",
    "    if Y_x == 0 and Y_n < 0: # down\n",
    "        Ylength = Y_total  - abs(Y_n)\n",
    "        print(Ylength)\n",
    "        img = img[..., abs(Y_n)+pad:Y_total-pad, :]\n",
    "        \n",
    "    if Y_x == 0 and Y_n == 0:\n",
    "        Ylength = Y_total\n",
    "        print(Ylength)\n",
    "        img = img[..., 0+pad:Y_total-pad,:]\n",
    "        \n",
    "    if Y_x > 0 and Y_n == 0:\n",
    "        Ylength = Y_total - Y_x ## \n",
    "        print(Ylength)\n",
    "        img = img[..., 0+pad:Ylength-pad, :] ## up  \n",
    "    Xlength = Xlength - (pad*2)\n",
    "    Ylength = Ylength - (pad*2)\n",
    "    return Xlength, Ylength, img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_DIR = 'reg'\n",
    "REF_DIR = 'tif' # cycle0\n",
    "MERGE_DIR = 'merged' # output directory to save\n",
    "Z = 13\n",
    "final_ch = 38\n",
    "pad = 10\n",
    "\n",
    "#!mkdir merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F000_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F000_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F000_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F000_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F000.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F001_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F001_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F001_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F001_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F001_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F001_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F001.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F003_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F003_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F003_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F003_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F003_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F003.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F004_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F004_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F004_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F004_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F004.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F005_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F005_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F005_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F005_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F005_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F005_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F005_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F005_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F005.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F006_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F006_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F006_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F006_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F006_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F006.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F008_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F008_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F008_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F008_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F008.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F009_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F009_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F009_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F009_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F009.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F010_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F010_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F010_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F010.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F011_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F011_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F011_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F011_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F011_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F011_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F011_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F011_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F011.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F012_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F012_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F012_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F012_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F012_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F012_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F012_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F012_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F012_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F012.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F013_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F013_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F013_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F013_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F013_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F013_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F013_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F013_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F013.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F014_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F014_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F014_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F014_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F014_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F014_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F014_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F014_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F014_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F014.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F015_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F015_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F015_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F015_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F015_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F015_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F015_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F015_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F015_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F015.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F016_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F016_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F016_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F016_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F016_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F016_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F016_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F016_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F016_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F016.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F017_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F017_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F017_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F017_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F017_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F017_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F017_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F017_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F017_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F017.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F018_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F018_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F018_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F018_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F018_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F018_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F018_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F018_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F018_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F018.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F019_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F019_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F019_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F019_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F019_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F019_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F019_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F019_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F019_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F019.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F020_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F020_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F020_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F020_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F020_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F020_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F020_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F020_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F020_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F020.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F021_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F021_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F021_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F021_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F021_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F021_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F021_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F021_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F021_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F021.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F022_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F022_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F022_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F022_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F022_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F022_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F022_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F022_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F022_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F022.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F023_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F023_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F023_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F023_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F023_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F023_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F023_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F023_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F023_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F023.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F024_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F024_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F024_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F024_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F024_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F024_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F024_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F024_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F024_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F024.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F025_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F025_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F025_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F025_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F025_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F025_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F025_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F025_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F025_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F025.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F026_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F026_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F026_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F026_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F026_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F026_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F026_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F026_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F026_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F026.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F027_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F027_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F027_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F027_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F027_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F027_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F027_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F027_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F027_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F027.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65531\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F028_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F028_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F028_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F028_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F028_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F028_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F028_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F028_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F028_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F028.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F029_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F029_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F029_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F029_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F029_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F029_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F029_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F029_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F029_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F029.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F030_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F030_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F030_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F030_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F030_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F030_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F030_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F030_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F030_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F030.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F031_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F031_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F031_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F031_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F031_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F031_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F031_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F031_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F031_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F031.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F032_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F032_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F032_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F032_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F032_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F032_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F032_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F032_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F032_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F032.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F033_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F033_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F033_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F033_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F033_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F033_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F033_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F033_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F033_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F033.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F034_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F034_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F034_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F034_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F034_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F034_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F034_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F034_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F034_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F034.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F035_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F035_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F035_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F035_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F035_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F035_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F035_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F035_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F035_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F035.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F036_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F036_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F036_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F036_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F036_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F036_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F036_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F036_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F036_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F036.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F037_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F037_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F037_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F037_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F037_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F037_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F037_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F037_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F037_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F037.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F038_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F038_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F038_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F038_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F038_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F038_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F038_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F038_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F038_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F038.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "54369\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F039_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F039_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F039_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F039_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F039_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F039_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F039_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F039_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F039_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F039.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F040_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F040_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F040_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F040_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F040_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F040_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F040_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F040_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F040_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F040.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "54427\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F041_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F041_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F041_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F041_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F041_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F041_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F041_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F041_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F041_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F041.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F042_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F042_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F042_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F042_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F042_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F042_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F042_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F042_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F042_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F042.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F043_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F043_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F043_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F043_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F043_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F043_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F043_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F043_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F043_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F043.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F044_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F044_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F044_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F044_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F044_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F044_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F044_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F044_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F044_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F044.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F045_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F045_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F045_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F045_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F045_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F045_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F045_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F045_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F045_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F045.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F046_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F046_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F046_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F046_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F046_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F046_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F046_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F046_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F046_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F046.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F047_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F047_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F047_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F047_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F047_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F047_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F047_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F047_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F047_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F047.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F048_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F048_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F048_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F048_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F048_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F048_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F048_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F048_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F048_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F048.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F049_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F049_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F049_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F049_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F049_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F049_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F049_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F049_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F049_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F049.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F050_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F050_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F050_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F050_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F050_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F050_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F050_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F050_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F050_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F050.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "60529\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F051_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F051_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F051_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F051_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F051_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F051_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F051_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F051_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F051_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F051.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F052_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F052_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F052_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F052_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F052_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F052_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F052_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F052_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F052_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F052.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F053_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F053_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F053_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F053_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F053_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F053_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F053_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F053_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F053_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F053.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F054_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F054_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F054_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F054_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F054_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F054_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F054_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F054_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F054_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F054.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F055_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F055_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F055_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F055_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F055_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F055_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F055_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F055_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F055_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F055.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F056_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F056_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F056_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F056_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F056_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F056_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F056_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F056_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F056_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F056.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F058_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F058_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F058_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F058_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F058_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F058_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F058_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F058_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F058_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F058.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65496\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F059_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F059_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F059_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F059_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F059_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F059_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F059_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F059_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F059_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F059.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65531\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F060_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F060_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F060_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F060_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F060_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F060_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F060_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F060_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F060_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F060.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F061_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F061_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F061_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F061_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F061_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F061_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F061_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F061_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F061_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F061.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65528\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F062_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F062_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F062_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F062_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F062_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F062_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F062_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F062_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F062_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F062.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F063_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F063_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F063_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F063_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F063_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F063_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F063_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F063_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F063_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F063.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F064_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F064_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F064_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F064_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F064_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F064_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F064_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F064_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F064_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F064.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F065_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F065_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F065_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F065_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F065_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F065_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F065_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F065_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F065_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F065.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F066_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F066_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F066_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F066_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F066_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F066_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F066_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F066_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F066_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F066.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F067_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F067_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F067_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F067_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F067_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F067_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F067_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F067_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F067_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F067.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F069_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F069_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F069_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F069_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F069_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F069_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F069_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F069_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F069_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F069.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F070_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F070_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F070_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F070_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F070_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F070_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F070_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F070_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F070_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F070.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F071_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F071_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F071_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F071_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F071_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F071_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F071_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F071_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F071_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F071.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F072_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F072_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F072_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F072_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F072_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F072_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F072_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F072_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F072_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F072.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F073_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F073_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F073_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F073_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F073_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F073_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F073_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F073_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F073_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F073.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F074_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F074_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F074_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F074_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F074_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F074_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F074_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F074_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F074_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F074.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F075_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F075_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F075_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F075_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F075_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F075_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F075_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F075_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F075_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F075.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F076_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F076_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F076_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F076_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F076_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F076_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F076_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F076_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F076_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F076.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F077_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F077_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F077_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F077_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F077_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F077_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F077_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F077_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F077_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F077.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "62375\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F078_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F078_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F078_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F078_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F078_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F078_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F078_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F078_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F078_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F078.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F079_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F079_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F079_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F079_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F079_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F079_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F079_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F079_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F079_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F079.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "52194\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F080_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F080_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F080_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F080_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F080_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F080_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F080_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F080_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F080_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F080.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F081_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F081_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F081_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F081_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F081_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F081_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F081_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F081_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F081_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F081.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65501\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F082_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F082_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F082_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F082_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F082_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F082_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F082_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F082_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F082_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F082.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F083_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F083_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F083_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F083_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F083_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F083_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F083_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F083_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F083_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F083.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F084_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F084_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F084_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F084_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F084_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F084_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F084_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F084_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F084_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F084.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F085_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F085_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F085_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F085_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F085_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F085_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F085_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F085_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F085_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F085.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65513\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F086_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F086_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F086_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F086_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F086_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F086_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F086_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F086_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F086_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F086.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "62970\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F087_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F087_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F087_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F087_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F087_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F087_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F087_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F087_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F087_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F087.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F088_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F088_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F088_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F088_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F088_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F088_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F088_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F088_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F088_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F088.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F089_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F089_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F089_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F089_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F089_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F089_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F089_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F089_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F089_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F089.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F090_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F090_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F090_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F090_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F090_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F090_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F090_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F090_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F090_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F090.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F091_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F091_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F091_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F091_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F091_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F091_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F091_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F091_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F091_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F091.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65519\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F092_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F092_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F092_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F092_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F092_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F092_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F092_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F092_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F092_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F092.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "60789\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F093_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F093_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F093_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F093_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F093_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F093_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F093_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F093_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F093_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F093.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F094_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F094_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F094_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F094_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F094_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F094_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F094_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F094_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F094_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F094.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65414\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F095_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F095_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F095_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F095_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F095_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F095_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F095_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F095_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F095_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F095.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F096_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F096_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F096_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F096_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F096_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F096_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F096_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F096_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F096_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F096.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F097_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F097_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F097_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F097_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F097_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F097_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F097_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F097_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F097_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F097.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "51096\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F098_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F098_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F098_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F098_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F098_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F098_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F098_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F098_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F098_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F098.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F099_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F099_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F099_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F099_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F099_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F099_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F099_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F099_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F099_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F099.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "64714\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F100_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F100_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F100_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F100_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F100_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F100_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F100_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F100_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F100_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F100.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F101_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F101_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F101_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F101_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F101_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F101_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F101_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F101_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F101_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F101.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F102_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F102_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F102_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F102_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F102_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F102_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F102_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F102_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F102_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F102.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F103_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F103_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F103_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F103_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F103_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F103_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F103_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F103_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F103_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F103.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F104_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F104_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F104_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F104_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F104_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F104_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F104_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F104_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F104_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F104.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "64727\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F105_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F105_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F105_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F105_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F105_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F105_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F105_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F105_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F105_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F105.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F106_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F106_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F106_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F106_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F106_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F106_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F106_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F106_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F106_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F106.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F107_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F107_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F107_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F107_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F107_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F107_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F107_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F107_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F107_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F107.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F109_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F109_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F109_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F109_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F109_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F109_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F109_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F109_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F109_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F109.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F110_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F110_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F110_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F110_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F110_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F110_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F110_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F110_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F110_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F110.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "57052\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F111_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F111_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F111_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F111_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F111_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F111_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F111_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F111_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F111_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F111.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "60663\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F113_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F113_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F113_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F113_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F113_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F113_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F113_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F113_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F113_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F113.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F114_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F114_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F114_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F114_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F114_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F114_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F114_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F114_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F114_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F114.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F115_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F115_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F115_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F115_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F115_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F115_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F115_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F115_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F115_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F115.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F116_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F116_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F116_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F116_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F116_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F116_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F116_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F116_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F116_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F116.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F117_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F117_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F117_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F117_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F117_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F117_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F117_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F117_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F117_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F117.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F118_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F118_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F118_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F118_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F118_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F118_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F118_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F118_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F118_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F118.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F119_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F119_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F119_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F119_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F119_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F119_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F119_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F119_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F119_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F119.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F120_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F120_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F120_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F120_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F120_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F120_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F120_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F120_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F120_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F120.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F121_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F121_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F121_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F121_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F121_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F121_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F121_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F121_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F121_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F121.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F122_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F122_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F122_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F122_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F122_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F122_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F122_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F122_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F122_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F122.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F123_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F123_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F123_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F123_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F123_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F123_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F123_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F123_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F123_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F123.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F124_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F124_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F124_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F124_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F124_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F124_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F124_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F124_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F124_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F124.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F125_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F125_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F125_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F125_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F125_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F125_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F125_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F125_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F125_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F125.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65532\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F126_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F126_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F126_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F126_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F126_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F126_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F126_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F126_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F126_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F126.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F127_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F127_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F127_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F127_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F127_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F127_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F127_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F127_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F127_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F127.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F128_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F128_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F128_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F128_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F128_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F128_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F128_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F128_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F128_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F128.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F130_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F130_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F130_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F130_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F130_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F130_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F130_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F130_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F130_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F130.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F131_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F131_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F131_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F131_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F131_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F131_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F131_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F131_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F131_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F131.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F132_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F132_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F132_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F132_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F132_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F132_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F132_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F132_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F132_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F132.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F133_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F133_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F133_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F133_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F133_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F133_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F133_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F133_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F133_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F133.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F134_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F134_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F134_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F134_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F134_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F134_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F134_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F134_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F134_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F134.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F135_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F135_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F135_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F135_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F135_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F135_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F135_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F135_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F135_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F135.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F136_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F136_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F136_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F136_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F136_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F136_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F136_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F136_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F136_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F136.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F137_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F137_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F137_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F137_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F137_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F137_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F137_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F137_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F137_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F137.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F138_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F138_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F138_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F138_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F138_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F138_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F138_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F138_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F138_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F138.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F139_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F139_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F139_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F139_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F139_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F139_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F139_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F139_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F139_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F139.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F140_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F140_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F140_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F140_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F140_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F140_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F140_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F140_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F140_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F140.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F141_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F141_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F141_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F141_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F141_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F141_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F141_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F141_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F141_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F141.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F142_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F142_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F142_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F142_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F142_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F142_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F142_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F142_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F142_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F142.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F143_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F143_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F143_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F143_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F143_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F143_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F143_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F143_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F143_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F143.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65526\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F144_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F144_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F144_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F144_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F144_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F144_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F144_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F144_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F144_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F144.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F145_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F145_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F145_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F145_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F145_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F145_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F145_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F145_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F145_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F145.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F146_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F146_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F146_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F146_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F146_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F146_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F146_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F146_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F146_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F146.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F147_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F147_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F147_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F147_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F147_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F147_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F147_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F147_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F147_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F147.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F148_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F148_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F148_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F148_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F148_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F148_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F148_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F148_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F148_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F148.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F149_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F149_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F149_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F149_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F149_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F149_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F149_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F149_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F149_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F149.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F150_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F150_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F150_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F150_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F150_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F150_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F150_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F150_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F150_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F150.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65527\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F151_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F151_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F151_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F151_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F151_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F151_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F151_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F151_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F151_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F151.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F152_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F152_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F152_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F152_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F152_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F152_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F152_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F152_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F152_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F152.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F153_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F153_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F153_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F153_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F153_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F153_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F153_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F153_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F153_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F153.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F154_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F154_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F154_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F154_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F154_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F154_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F154_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F154_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F154_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F154.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F155_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F155_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F155_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F155_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F155_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F155_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F155_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F155_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F155_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F155.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65528\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F156_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F156_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F156_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F156_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F156_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F156_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F156_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F156_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F156_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F156.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F157_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F157_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F157_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F157_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F157_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F157_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F157_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F157_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F157_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F157.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F158_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F158_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F158_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F158_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F158_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F158_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F158_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F158_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F158_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F158.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F159_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F159_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F159_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F159_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F159_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F159_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F159_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F159_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F159_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F159.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F160_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F160_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F160_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F160_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F160_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F160_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F160_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F160_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F160_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F160.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F161_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F161_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F161_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F161_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F161_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F161_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F161_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F161_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F161_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F161.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F162_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F162_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F162_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F162_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F162_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F162_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F162_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F162_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F162_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F162.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F163_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F163_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F163_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F163_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F163_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F163_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F163_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F163_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F163_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F163.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65532\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F164_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F164_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F164_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F164_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F164_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F164_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F164_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F164_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F164_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F164.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F165_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F165_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F165_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F165_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F165_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F165_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F165_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F165_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F165_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F165.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F166_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F166_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F166_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F166_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F166_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F166_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F166_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F166_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F166_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F166.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F167_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F167_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F167_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F167_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F167_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F167_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F167_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F167_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F167_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F167.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F168_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F168_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F168_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F168_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F168_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F168_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F168_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F168_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F168_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F168.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F169_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F169_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F169_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F169_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F169_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F169_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F169_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F169_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F169_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F169.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F170_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F170_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F170_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F170_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F170_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F170_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F170_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F170_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F170_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F170.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F171_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F171_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F171_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F171_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F171_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F171_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F171_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F171_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F171_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F171.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F172_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F172_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F172_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F172_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F172_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F172_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F172_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F172_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F172_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F172.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F173_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F173_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F173_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F173_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F173_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F173_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F173_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F173_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F173_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F173.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F174_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F174_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F174_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F174_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F174_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F174_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F174_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F174_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F174_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F174.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F175_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F175_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F175_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F175_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F175_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F175_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F175_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F175_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F175_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F175.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F176_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F176_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F176_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F176_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F176_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F176_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F176_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F176_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F176_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F176.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F177_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F177_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F177_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F177_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F177_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F177_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F177_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F177_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F177_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F177.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F178_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F178_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F178_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F178_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F178_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F178_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F178_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F178_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F178_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F178.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F179_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F179_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F179_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F179_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F179_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F179_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F179_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F179_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F179_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F179.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F180_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F180_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F180_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F180_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F180_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F180_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F180_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F180_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F180_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F180.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F181_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F181_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F181_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F181_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F181_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F181_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F181_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F181_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F181_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F181.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F182_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F182_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F182_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F182_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F182_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F182_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F182_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F182_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F182_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F182.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65519\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F183_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F183_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F183_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F183_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F183_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F183_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F183_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F183_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F183_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F183.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65487\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F184_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F184_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F184_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F184_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F184_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F184_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F184_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F184_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F184_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F184.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F185_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F185_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F185_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F185_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F185_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F185_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F185_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F185_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F185_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F185.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F186_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F186_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F186_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F186_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F186_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F186_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F186_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F186_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F186_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F186.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "63429\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F187_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F187_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F187_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F187_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F187_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F187_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F187_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F187_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F187_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F187.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F188_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F188_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F188_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F188_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F188_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F188_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F188_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F188_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F188_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F188.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F189_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F189_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F189_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F189_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F189_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F189_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F189_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F189_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F189_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F189.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65519\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F190_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F190_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F190_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F190_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F190_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F190_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F190_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F190_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F190_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F190.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65469\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F191_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F191_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F191_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F191_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F191_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F191_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F191_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F191_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F191_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F191.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65527\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F192_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F192_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F192_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F192_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F192_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F192_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F192_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F192_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F192_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F192.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F193_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F193_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F193_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F193_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F193_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F193_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F193_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F193_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F193_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F193.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "49761\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F194_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F194_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F194_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F194_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F194_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F194_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F194_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F194_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F194_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F194.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F195_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F195_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F195_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F195_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F195_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F195_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F195_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F195_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F195_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F195.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F196_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F196_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F196_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F196_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F196_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F196_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F196_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F196_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F196_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F196.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F197_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F197_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F197_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F197_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F197_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F197_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F197_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F197_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F197_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F197.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65225\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F199_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F199_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F199_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F199_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F199_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F199_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F199_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F199_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F199_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F199.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F200_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F200_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F200_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F200_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F200_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F200_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F200_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F200_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F200_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F200.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F201_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F201_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F201_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F201_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F201_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F201_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F201_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F201_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F201_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F201.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F202_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F202_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F202_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F202_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F202_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F202_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F202_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F202_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F202_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F202.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F203_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F203_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F203_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F203_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F203_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F203_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F203_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F203_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F203_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F203.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F204_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F204_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F204_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F204_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F204_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F204_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F204_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F204_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F204_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F204.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F205_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F205_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F205_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F205_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F205_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F205_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F205_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F205_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F205_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F205.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F206_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F206_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F206_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F206_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F206_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F206_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F206_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F206_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F206_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F206.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "63265\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F207_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F207_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F207_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F207_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F207_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F207_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F207_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F207_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F207_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F207.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "60996\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F208_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F208_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F208_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F208_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F208_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F208_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F208_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F208_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F208_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F208.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F209_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F209_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F209_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F209_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F209_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F209_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F209_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F209_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F209_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F209.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F210_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F210_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F210_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F210_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F210_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F210_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F210_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F210_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F210_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F210.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65489\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F211_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F211_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F211_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F211_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F211_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F211_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F211_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F211_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F211_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F211.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F212_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F212_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F212_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F212_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F212_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F212_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F212_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F212_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F212_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F212.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F213_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F213_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F213_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F213_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F213_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F213_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F213_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F213_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F213_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F213.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F214_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F214_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F214_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F214_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F214_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F214_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F214_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F214_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F214_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F214.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F215_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F215_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F215_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F215_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F215_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F215_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F215_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F215_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F215_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F215.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F216_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F216_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F216_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F216_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F216_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F216_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F216_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F216_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F216_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F216.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F217_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F217_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F217_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F217_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F217_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F217_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F217_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F217_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F217_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F217.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F218_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F218_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F218_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F218_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F218_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F218_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F218_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F218_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F218_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F218.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F219_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F219_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F219_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F219_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F219_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F219_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F219_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F219_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F219_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F219.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F220_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F220_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F220_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F220_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F220_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F220_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F220_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F220_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F220_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F220.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F221_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F221_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F221_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F221_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F221_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F221_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F221_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F221_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F221_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F221.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65532\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F222_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F222_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F222_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F222_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F222_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F222_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F222_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F222_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F222_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F222.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "56817\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F223_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F223_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F223_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F223_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F223_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F223_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F223_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F223_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F223_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F223.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F224_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F224_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F224_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F224_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F224_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F224_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F224_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F224_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F224_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F224.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2034\n",
      "65535\n"
     ]
    }
   ],
   "source": [
    "refs = iter(glob.glob('tif/Cycle_0/*')) \n",
    "for FOV in range(NUM_FOVS):   \n",
    "    ref_name = next(refs)\n",
    "    FOV_num = ref_name[-7:-4]\n",
    "    #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "    img = imread(f'{REF_DIR}/Cycle_0/Cycle_F{FOV_num}.tif') # reference image that did not move\n",
    "    img = img.astype(np.uint16)\n",
    "    print(img.dtype)\n",
    "    for cycle in range(CYCLE_NUMS-1):    \n",
    "        fname = f'{IN_DIR}_Cyc_{cycle+1}/Cycle_{cycle+1}_F{FOV_num}_{IN_DIR}.tif'\n",
    "        print('Appending... ', fname)\n",
    "        im_to_add = imread(fname).astype(np.uint16)\n",
    "        im_to_add = im_to_add[:,:,...] \n",
    "        img = np.append(img, im_to_add, axis=1) # concatenate along channel index\n",
    "        print(f\"Shape = {img.shape}\") \n",
    "        \n",
    "    fname = f'/F{FOV_num}.tif'\n",
    "    print('saving', './{MERGE_DIR}'+fname)\n",
    "    print('dtype of ', img.dtype)\n",
    "    \n",
    "    ########## CROP ################ --> CHANGE everytime depending on shifts\n",
    "    Xlength, Ylength, img = crop(X_max_total, X_min_total, Y_max_total, Y_min_total, img, pad)\n",
    "    \n",
    "    assert img.shape[3] == Xlength, \"Check X size\"\n",
    "    assert img.shape[2] == Ylength, \"Check Y size\"\n",
    "    \n",
    "    \n",
    "    ### FINAL CHECK before saving ### \n",
    "    assert img.shape[0] == Z, \"Check ZCYX\"\n",
    "    assert img.shape[1] == final_ch, \"check final merge size\"\n",
    "    print(img.max())\n",
    "    tifffile.imwrite(\n",
    "        f'./../../../Expansion2/Cov_8_merged/'+fname,\n",
    "        img,\n",
    "        imagej=True,\n",
    "        photometric='minisblack',\n",
    "        metadata={'axes': 'ZCYX'},\n",
    "    )\n",
    "    \n",
    "    del img # clear memory\n",
    "    del im_to_add"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intensity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double Check\n",
    "CYCLE_NUMS = 10\n",
    "NUM_FOVS = 217"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_counts = pd.DataFrame()\n",
    "z_list = []\n",
    "one_to_five_list = []\n",
    "sixfivek_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 0 field 000 \n",
      "cycle 0 field 001 \n",
      "cycle 0 field 003 \n",
      "cycle 0 field 004 \n",
      "cycle 0 field 005 \n",
      "cycle 0 field 006 \n",
      "cycle 0 field 008 \n",
      "cycle 0 field 009 \n",
      "cycle 0 field 010 \n",
      "cycle 0 field 011 \n",
      "cycle 0 field 012 \n",
      "cycle 0 field 013 \n",
      "cycle 0 field 014 \n",
      "cycle 0 field 015 \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-2f356ed31a23>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mFOV\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNUM_FOVS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mref_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrefs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m             \u001b[0mref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mref_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m             \u001b[0mref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m             \u001b[0mFOV_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mref_name\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/all_your_base/lib/python3.7/site-packages/imageio/core/functions.py\u001b[0m in \u001b[0;36mvolread\u001b[0;34m(uri, format, **kwargs)\u001b[0m\n\u001b[1;32m    452\u001b[0m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"v\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/all_your_base/lib/python3.7/site-packages/imageio/core/format.py\u001b[0m in \u001b[0;36mget_data\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m    337\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_BaseReaderWriter_last_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m                 \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/all_your_base/lib/python3.7/site-packages/imageio/plugins/tifffile.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    248\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tiff support no more than 1 \"volume\" per file'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m                 \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# request as singleton image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m                 \u001b[0mmeta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_meta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/all_your_base/lib/python3.7/site-packages/tifffile/tifffile.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(self, key, series, level, out, maxworkers)\u001b[0m\n\u001b[1;32m   2942\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilehandle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2943\u001b[0m                 result = self.filehandle.read_array(\n\u001b[0;32m-> 2944\u001b[0;31m                     \u001b[0mtypecode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2945\u001b[0m                 )\n\u001b[1;32m   2946\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpages\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/mambaforge/envs/all_your_base/lib/python3.7/site-packages/tifffile/tifffile.py\u001b[0m in \u001b[0;36mread_array\u001b[0;34m(self, dtype, count, out)\u001b[0m\n\u001b[1;32m   8352\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'size mismatch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8354\u001b[0;31m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   8355\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8356\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'failed to read {size} bytes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# If you are running the reg files and not merge files\n",
    "\n",
    "for c in range(CYCLE_NUMS):\n",
    "    if c == 0:\n",
    "        refs = iter(glob.glob('tif/Cycle_0/*'))\n",
    "        for FOV in range(0, NUM_FOVS): \n",
    "            ref_name = next(refs) \n",
    "            ref = imread(ref_name)\n",
    "            ref = ref.astype(np.uint16)\n",
    "            FOV_num = ref_name[-7:-4]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "            overall_count_zero = np.count_nonzero(ref == 0)\n",
    "            x = np.count_nonzero((0 < ref) & (ref < 6))\n",
    "            y = np.count_nonzero(65000 < ref)\n",
    "\n",
    "            for Z in range(ref.shape[0]):\n",
    "                for ch in range(ref.shape[1]): \n",
    "                    count_one_to_five = np.count_nonzero((0 < ref[Z,ch,...]) & (ref[Z,ch,...] < 6))\n",
    "                    if count_one_to_five > 0:\n",
    "                        one_to_five_list.append((FOV_num, Z, ch))\n",
    "                    count_zeros = np.count_nonzero(ref[Z,ch,...] == 0)\n",
    "                    if count_zeros > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                    count_65k = np.count_nonzero(65000 < ref[Z,ch,...])\n",
    "                    if count_65k > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                \n",
    "            int_counts.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "            int_counts.loc[FOV_num, 'total Zero count'] = overall_count_zero\n",
    "            int_counts.loc[FOV_num, 'one to five'] = x\n",
    "            int_counts.loc[FOV_num, 'greater 65K'] = y\n",
    "    else:\n",
    "        ims = iter(glob.glob(f'reg_Cyc_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006  \n",
    "        for FOV in range(0, NUM_FOVS): # \n",
    "            #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "            im_name = next(ims)\n",
    "            im = imread(im_name)\n",
    "            im = im.astype(np.uint16)\n",
    "            FOV_num = im_name[-11:-8]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "            \n",
    "            overall_count_zero = np.count_nonzero(im == 0)\n",
    "            x = np.count_nonzero((0 < im) & (im < 6))\n",
    "            y = np.count_nonzero(65000 < im)\n",
    "\n",
    "            for Z in range(im.shape[0]):\n",
    "                for ch in range(im.shape[1]): \n",
    "                    count_one_to_five = np.count_nonzero((0 < im[Z,ch,...]) & (im[Z,ch,...] < 6))\n",
    "                    if count_one_to_five > 0:\n",
    "                        one_to_five_list.append((FOV_num, Z, ch))\n",
    "                    count_zeros = np.count_nonzero(im[Z,ch,...] == 0)\n",
    "                    if count_zeros > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                    count_65k = np.count_nonzero(65000 < im[Z,ch,...])\n",
    "                    if count_65k > 0:\n",
    "                        sixfivek_list.append((FOV_num, Z, ch))\n",
    "                \n",
    "            int_counts.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "            int_counts.loc[FOV_num, 'total Zero count'] = overall_count_zero\n",
    "            int_counts.loc[FOV_num, 'one to five'] = x\n",
    "            int_counts.loc[FOV_num, 'greater 65K'] = y\n",
    "            \n",
    "done = open(\"done.txt\", \"a\")\n",
    "done.write(\"done\")\n",
    "done.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>total Zero count</th>\n",
       "      <th>one to five</th>\n",
       "      <th>greater 65K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>425776.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>012</td>\n",
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       "      <td>425776.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>013</td>\n",
       "      <td>013</td>\n",
       "      <td>425776.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>014</td>\n",
       "      <td>014</td>\n",
       "      <td>425776.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>015</td>\n",
       "      <td>015</td>\n",
       "      <td>425776.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count  one to five  greater 65K\n",
       "000     000          425776.0          0.0          0.0\n",
       "001     001          425776.0          0.0          0.0\n",
       "003     003          425776.0          0.0          0.0\n",
       "004     004          425776.0          0.0          0.0\n",
       "005     005          425776.0          0.0          0.0\n",
       "006     006          425776.0          0.0          0.0\n",
       "008     008          425776.0          0.0          0.0\n",
       "009     009          425776.0          0.0          0.0\n",
       "010     010          425776.0          0.0          0.0\n",
       "011     011          425776.0          0.0          0.0\n",
       "012     012          425776.0          0.0          0.0\n",
       "013     013          425776.0          0.0          0.0\n",
       "014     014          425776.0          0.0          0.0\n",
       "015     015          425776.0          0.0          0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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