{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 expand_labels import expand_labels\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 10\n",
    "NUM_FOVS = 211"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cycles = glob.glob('tif/Cycle_9/*')  \n",
    "# nums = []\n",
    "\n",
    "# for i in cycles:\n",
    "#     nums.append(i.split('_F')[1][0:3])\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": [
    "# remove all erroneous files \n",
    "for i in error_files:\n",
    "    command = f'rm *{i.split('_F')[1][0:3]}*'\n",
    "    ! {command}"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 2, 2048, 2048)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test image sizes\n",
    "im1 = imread('tif/Cycle_9/Cycle_9_F053.tif')\n",
    "im1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = imread('tif/Cycle_0/Cycle_F053.tif')\n",
    "mov = imread('tif/Cycle_9/Cycle_9_F053.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": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.99999994e-01,  1.13348894e-04, -8.83918751e+00],\n",
       "       [-1.13348894e-04,  9.99999994e-01, -4.30257115e+01],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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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\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": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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(f\"Z-plane: {Z} registering\")\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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 900)"
      ]
     },
     "execution_count": 28,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[105 107 101 ... 140 136 121]\n",
      " [107 112 107 ... 120 113 115]\n",
      " [106 110 109 ... 123 108 114]\n",
      " ...\n",
      " [104 108 105 ... 108 108 112]\n",
      " [105 108 110 ... 107 118 108]\n",
      " [112 103 108 ... 115 107 112]]  \n",
      " \n",
      " [[113 110 107 ... 120 112 118]\n",
      " [107 107 114 ... 113 111 118]\n",
      " [105 111 116 ... 107 119 145]\n",
      " ...\n",
      " [104 117 113 ... 111 115 112]\n",
      " [109 108 112 ... 118 113 105]\n",
      " [108 108 110 ... 115 110 117]]\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_F053.tif')\n",
    "orig = orig.max(0)\n",
    "orig = orig[0, ...]\n",
    "orig_v = orig[100:1000, 100:1000]\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[100:1000, 100:1000]\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='100:1000', ylabel='100:1000')\n",
    "\n",
    "print(orig_v,\" \\n\" , \"\\n\",reg_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Registration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check again\n",
    "CYCLE_NUMS = 10\n",
    "NUM_FOVS = 211"
   ]
  },
  {
   "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": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CHECK IF REFERENCE FOV IS BEING MATCHED TO SAME MOVED FOV, ACROSS ALL CYCLES AND FOVS IN EVERY ITERATION\n",
    "\n",
    "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",
    "        mov_name = next(movs)\n",
    "\n",
    "        ref_num = ref_name.split('_F')[1][0:3]\n",
    "        mov_num = mov_name.split('_F')[1][0:3]\n",
    "        if ref_num != mov_num:\n",
    "            print(\"False\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 1 field 000 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F000_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "cycle 1 field 001 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F001_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "cycle 1 field 002 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F002_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "cycle 1 field 003 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F003_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "cycle 1 field 004 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F004_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "cycle 1 field 005 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F005_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "cycle 1 field 006 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F006_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "cycle 1 field 007 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F007_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "cycle 1 field 008 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F008_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "cycle 1 field 009 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F009_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "cycle 1 field 010 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F010_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "cycle 1 field 011 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F011_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "cycle 1 field 012 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F012_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F012_reg.tif\n",
      "cycle 1 field 013 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F013_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "cycle 1 field 015 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F015_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F015_reg.tif\n",
      "cycle 1 field 016 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F016_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F016_reg.tif\n",
      "cycle 1 field 017 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F017_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F017_reg.tif\n",
      "cycle 1 field 018 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F018_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F018_reg.tif\n",
      "cycle 1 field 019 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F019_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F019_reg.tif\n",
      "cycle 1 field 022 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F022_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n"
     ]
    }
   ],
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 tmat_Cyc_8/Cycle_8_F169_tmat_013_004.npy -13.603085383402345 -39.58153737823659\n",
      "8 tmat_Cyc_8/Cycle_8_F034_tmat_001_005.npy -14.013227484784805 -38.14859807205096\n",
      "8 tmat_Cyc_8/Cycle_8_F045_tmat_004_005.npy -13.483971384554593 -38.5496782479446\n",
      "8 tmat_Cyc_8/Cycle_8_F115_tmat_008_005.npy -14.175900235638892 -38.53165923215613\n",
      "8 tmat_Cyc_8/Cycle_8_F184_tmat_011_005.npy -14.174756656027625 -39.52013593986521\n",
      "8 tmat_Cyc_8/Cycle_8_F000_tmat_000_000.npy -13.008288471975902 -38.22659056699172\n",
      "8 tmat_Cyc_8/Cycle_8_F010_tmat_002_000.npy -13.058575588405915 -38.1459952256414\n",
      "8 tmat_Cyc_8/Cycle_8_F125_tmat_005_000.npy -13.408615051809988 -38.268076225804634\n",
      "8 tmat_Cyc_8/Cycle_8_F135_tmat_007_000.npy -13.76368634485425 -39.30123230079778\n",
      "8 tmat_Cyc_8/Cycle_8_F145_tmat_009_000.npy -13.109517357265075 -38.681516860538295\n",
      "8 tmat_Cyc_8/Cycle_8_F155_tmat_011_000.npy -14.216270821834428 -39.457103821447504\n",
      "8 tmat_Cyc_8/Cycle_8_F165_tmat_013_000.npy -14.177474918327334 -39.6671398493163\n",
      "8 tmat_Cyc_8/Cycle_8_F001_tmat_000_001.npy -13.199907614809149 -38.40515489175618\n",
      "8 tmat_Cyc_8/Cycle_8_F011_tmat_002_001.npy -13.360287296904403 -36.94894630581007\n",
      "8 tmat_Cyc_8/Cycle_8_F133_tmat_006_001.npy -13.858512652430477 -39.48850182133458\n",
      "8 tmat_Cyc_8/Cycle_8_F143_tmat_008_001.npy -13.405562137635002 -39.01730402861915\n",
      "8 tmat_Cyc_8/Cycle_8_F153_tmat_010_001.npy -14.20807773651211 -39.54867506493554\n",
      "8 tmat_Cyc_8/Cycle_8_F166_tmat_013_001.npy -13.516701045941772 -39.8200840094363\n",
      "8 tmat_Cyc_8/Cycle_8_F002_tmat_000_002.npy -13.17088813059479 -38.246412559415376\n",
      "8 tmat_Cyc_8/Cycle_8_F012_tmat_002_002.npy -13.553944288260936 -38.239715302062336\n",
      "8 tmat_Cyc_8/Cycle_8_F022_tmat_004_002.npy -13.357540739531942 -38.77911426337323\n",
      "8 tmat_Cyc_8/Cycle_8_F132_tmat_006_002.npy -13.55005712104662 -38.67982569480114\n",
      "8 tmat_Cyc_8/Cycle_8_F142_tmat_008_002.npy -13.526192873136665 -39.39912934915719\n",
      "8 tmat_Cyc_8/Cycle_8_F152_tmat_010_002.npy -13.552626301322675 -39.92382116104807\n",
      "8 tmat_Cyc_8/Cycle_8_F162_tmat_012_002.npy -13.773802849426943 -39.15455205281478\n",
      "8 tmat_Cyc_8/Cycle_8_F172_tmat_014_002.npy -12.44770703747622 -39.38480732918822\n",
      "8 tmat_Cyc_8/Cycle_8_F006_tmat_001_003.npy -13.577587513613025 -37.94631439348632\n",
      "8 tmat_Cyc_8/Cycle_8_F016_tmat_003_003.npy -13.855006467221074 -38.38821943771643\n",
      "8 tmat_Cyc_8/Cycle_8_F128_tmat_005_003.npy -13.584690417398974 -39.012118340965344\n",
      "8 tmat_Cyc_8/Cycle_8_F138_tmat_007_003.npy -13.832172429625414 -39.21896693832957\n",
      "8 tmat_Cyc_8/Cycle_8_F148_tmat_009_003.npy -13.583629398833637 -39.61258881491267\n",
      "8 tmat_Cyc_8/Cycle_8_F158_tmat_011_003.npy -13.681368044161559 -40.038794323454454\n",
      "8 tmat_Cyc_8/Cycle_8_F168_tmat_013_003.npy -13.713029355850608 -40.236672045227465\n",
      "8 tmat_Cyc_8/Cycle_8_F004_tmat_000_004.npy -13.32339940817451 -37.91728718045363\n",
      "8 tmat_Cyc_8/Cycle_8_F015_tmat_003_004.npy -13.711110140392066 -38.426723487566505\n",
      "8 tmat_Cyc_8/Cycle_8_F130_tmat_006_004.npy -13.764655601344202 -39.112919419502646\n",
      "8 tmat_Cyc_8/Cycle_8_F140_tmat_008_004.npy -13.683841635003319 -39.286617040145074\n",
      "8 tmat_Cyc_8/Cycle_8_F150_tmat_010_004.npy -13.483378979582197 -39.57374771818445\n",
      "8 tmat_Cyc_8/Cycle_8_F160_tmat_012_004.npy -13.712807769808023 -39.005185226120716\n",
      "8 tmat_Cyc_8/Cycle_8_F025_tmat_000_005.npy -13.946795953610263 -38.02526063957566\n",
      "8 tmat_Cyc_8/Cycle_8_F044_tmat_003_005.npy -13.490175053029702 -38.54804536015092\n",
      "8 tmat_Cyc_8/Cycle_8_F114_tmat_007_005.npy -14.438161196999658 -38.73372574641314\n",
      "8 tmat_Cyc_8/Cycle_8_F175_tmat_010_005.npy -13.9847487525102 -39.26432531576381\n",
      "8 tmat_Cyc_8/Cycle_8_F194_tmat_013_005.npy -14.2331335843071 -39.90971544890397\n",
      "8 tmat_Cyc_8/Cycle_8_F009_tmat_001_000.npy -13.422985979358714 -38.23747996007057\n",
      "8 tmat_Cyc_8/Cycle_8_F019_tmat_003_000.npy -13.549624521941382 -38.33839520754259\n",
      "8 tmat_Cyc_8/Cycle_8_F134_tmat_006_000.npy -13.571700471179724 -38.720923945457\n",
      "8 tmat_Cyc_8/Cycle_8_F144_tmat_008_000.npy -13.533923988035099 -38.64270444248518\n",
      "8 tmat_Cyc_8/Cycle_8_F154_tmat_010_000.npy -13.968562165751564 -39.5535559834575\n",
      "8 tmat_Cyc_8/Cycle_8_F164_tmat_012_000.npy -13.92039045446461 -39.48806439416512\n",
      "8 tmat_Cyc_8/Cycle_8_F174_tmat_014_000.npy -12.849438597959534 -39.93874956575246\n",
      "8 tmat_Cyc_8/Cycle_8_F008_tmat_001_001.npy -13.528125015923933 -38.117130589537396\n",
      "8 tmat_Cyc_8/Cycle_8_F018_tmat_003_001.npy -13.614593839294457 -38.11037409770006\n",
      "8 tmat_Cyc_8/Cycle_8_F136_tmat_007_001.npy -13.662693231998674 -38.77143701695218\n",
      "8 tmat_Cyc_8/Cycle_8_F146_tmat_009_001.npy -13.152470700045228 -39.361015045102135\n",
      "8 tmat_Cyc_8/Cycle_8_F163_tmat_012_001.npy -13.846966098214693 -39.908872865919875\n",
      "8 tmat_Cyc_8/Cycle_8_F173_tmat_014_001.npy -12.439518765482035 -39.95532167064164\n",
      "8 tmat_Cyc_8/Cycle_8_F007_tmat_001_002.npy -13.408890791773159 -38.077515102904385\n",
      "8 tmat_Cyc_8/Cycle_8_F017_tmat_003_002.npy -14.166684716204713 -38.355276288411346\n",
      "8 tmat_Cyc_8/Cycle_8_F127_tmat_005_002.npy -13.361736316309134 -38.633259597509095\n",
      "8 tmat_Cyc_8/Cycle_8_F137_tmat_007_002.npy -13.61274497775753 -38.86034619438567\n",
      "8 tmat_Cyc_8/Cycle_8_F147_tmat_009_002.npy -13.637168402467637 -38.82426341821986\n",
      "8 tmat_Cyc_8/Cycle_8_F157_tmat_011_002.npy -14.034350317117628 -39.45435706619935\n",
      "8 tmat_Cyc_8/Cycle_8_F167_tmat_013_002.npy -14.012361177434514 -39.96623069752991\n",
      "8 tmat_Cyc_8/Cycle_8_F003_tmat_000_003.npy -13.203630204272827 -38.15068663093484\n",
      "8 tmat_Cyc_8/Cycle_8_F013_tmat_002_003.npy -13.758848584878592 -38.5917373798211\n",
      "8 tmat_Cyc_8/Cycle_8_F023_tmat_004_003.npy -13.589483523141212 -38.704830489982555\n",
      "8 tmat_Cyc_8/Cycle_8_F131_tmat_006_003.npy -13.830834260665938 -39.070709864318815\n",
      "8 tmat_Cyc_8/Cycle_8_F141_tmat_008_003.npy -13.586144848841514 -39.311304804848646\n",
      "8 tmat_Cyc_8/Cycle_8_F151_tmat_010_003.npy -13.702937485229995 -39.32879528741637\n",
      "8 tmat_Cyc_8/Cycle_8_F161_tmat_012_003.npy -13.670363522409843 -39.37263891856344\n",
      "8 tmat_Cyc_8/Cycle_8_F171_tmat_014_003.npy -12.536095997404232 -39.927138254575766\n",
      "8 tmat_Cyc_8/Cycle_8_F005_tmat_001_004.npy -13.50361998638607 -37.941451485229095\n",
      "8 tmat_Cyc_8/Cycle_8_F024_tmat_004_004.npy -13.754045589119073 -38.12697551882411\n",
      "8 tmat_Cyc_8/Cycle_8_F139_tmat_007_004.npy -13.725975884666354 -38.90843154295237\n",
      "8 tmat_Cyc_8/Cycle_8_F149_tmat_009_004.npy -13.662409717014839 -39.40126843709788\n",
      "8 tmat_Cyc_8/Cycle_8_F159_tmat_011_004.npy -13.791475742733041 -39.29547320341237\n",
      "8 tmat_Cyc_8/Cycle_8_F170_tmat_014_004.npy -12.506181601396406 -39.71090384374486\n",
      "8 tmat_Cyc_8/Cycle_8_F035_tmat_002_005.npy -13.801138321159783 -37.96172601352566\n",
      "8 tmat_Cyc_8/Cycle_8_F105_tmat_006_005.npy -13.697835428165488 -37.99073162884497\n",
      "8 tmat_Cyc_8/Cycle_8_F124_tmat_009_005.npy -14.549500478590858 -38.37302035346681\n",
      "8 tmat_Cyc_8/Cycle_8_F185_tmat_012_005.npy -13.300436466209135 -39.43865453534738\n",
      "8 tmat_Cyc_8/Cycle_8_F080_tmat_006_010.npy -14.346351164544558 -37.997057198689276\n",
      "8 tmat_Cyc_8/Cycle_8_F090_tmat_008_010.npy -14.861929780385935 -38.75779896273002\n",
      "8 tmat_Cyc_8/Cycle_8_F200_tmat_010_010.npy -14.619098554914444 -38.98150538510026\n",
      "8 tmat_Cyc_8/Cycle_8_F210_tmat_012_010.npy -13.734800366221862 -39.484863477535555\n",
      "8 tmat_Cyc_8/Cycle_8_F220_tmat_014_010.npy -14.884974029566933 -40.003302797690935\n",
      "8 tmat_Cyc_8/Cycle_8_F058_tmat_001_011.npy -14.87063605904234 -38.064020982050124\n",
      "8 tmat_Cyc_8/Cycle_8_F071_tmat_004_011.npy -14.444123347844993 -40.236138930817674\n",
      "8 tmat_Cyc_8/Cycle_8_F088_tmat_007_011.npy -14.595118642411535 -38.54901825222271\n",
      "8 tmat_Cyc_8/Cycle_8_F098_tmat_009_011.npy -14.934192370484652 -38.63406408388039\n",
      "8 tmat_Cyc_8/Cycle_8_F195_tmat_014_005.npy -14.370279058009487 -39.55829585663332\n",
      "8 tmat_Cyc_8/Cycle_8_F026_tmat_000_006.npy -13.949786415858512 -37.6611938037978\n",
      "8 tmat_Cyc_8/Cycle_8_F033_tmat_001_006.npy -13.98672706130742 -38.20571965571878\n",
      "8 tmat_Cyc_8/Cycle_8_F036_tmat_002_006.npy -13.893746048974549 -37.720739159602545\n",
      "8 tmat_Cyc_8/Cycle_8_F043_tmat_003_006.npy -13.554810900817984 -38.2527482772922\n",
      "8 tmat_Cyc_8/Cycle_8_F046_tmat_004_006.npy -13.308431926694084 -37.93999931556084\n",
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      "9 tmat_Cyc_9/Cycle_9_F221_tmat_014_011.npy -8.731435466071957 -45.385578063627804\n",
      "9 tmat_Cyc_9/Cycle_9_F052_tmat_000_012.npy -8.696627947831871 -42.009949577784255\n",
      "9 tmat_Cyc_9/Cycle_9_F072_tmat_004_012.npy -8.733229868844091 -43.64343980987701\n",
      "9 tmat_Cyc_9/Cycle_9_F222_tmat_014_012.npy -8.819558837109867 -45.2311308554855\n",
      "9 tmat_Cyc_9/Cycle_9_F053_tmat_000_013.npy -8.839187514081573 -43.025711548725326\n",
      "9 tmat_Cyc_9/Cycle_9_F063_tmat_002_013.npy -8.724748209597124 -43.05841736899504\n",
      "9 tmat_Cyc_9/Cycle_9_F083_tmat_006_013.npy -8.66134537797052 -44.2277062199355\n",
      "9 tmat_Cyc_9/Cycle_9_F216_tmat_013_013.npy -8.965327958692455 -45.359123192955394\n",
      "9 tmat_Cyc_9/Cycle_9_F054_tmat_000_014.npy -8.750539611429758 -43.109790893679474\n",
      "9 tmat_Cyc_9/Cycle_9_F065_tmat_003_014.npy -8.24798845278201 -43.307331114854605\n",
      "9 tmat_Cyc_9/Cycle_9_F204_tmat_010_014.npy -9.340652873436511 -44.67655468135581\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 > 15) | (moveY > 15)|(moveX < -15) | (moveY < -15): # 50 pixels is max\n",
    "            print(c+1, tmat_name, moveX, moveY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6556217872748187 -0.07104054375543001 3.5738681511628556 -1.6091925793780408\n",
      "tmat_Cyc_1 \n",
      " X_max:2 X_min:-1 Y_max:4 Y_min:-2\n",
      "2.1055806261847314 -0.09260808000135512 3.8841588741960322 -1.1678599205441222\n",
      "tmat_Cyc_2 \n",
      " X_max:3 X_min:-1 Y_max:4 Y_min:-2\n",
      "1.9212794958110777 0.15428063993282587 3.594357286251352 -1.3382483779409045\n",
      "tmat_Cyc_3 \n",
      " X_max:2 X_min:1 Y_max:4 Y_min:-2\n",
      "1.7311555070662052 -0.2442079396881809 3.2770836057696897 -1.9364291813841077\n",
      "tmat_Cyc_4 \n",
      " X_max:2 X_min:-1 Y_max:4 Y_min:-2\n",
      "1.9326422924593345 -0.5614762136438003 3.251695243838981 -2.205458036576407\n",
      "tmat_Cyc_5 \n",
      " X_max:2 X_min:-1 Y_max:4 Y_min:-3\n",
      "2.0711436851520317 -0.4996894195381856 4.256350690834665 -1.9416228430158864\n",
      "tmat_Cyc_6 \n",
      " X_max:3 X_min:-1 Y_max:5 Y_min:-2\n",
      "2.3795465304813126 -0.5469230247349515 4.409624416366228 -2.489751394032975\n",
      "tmat_Cyc_7 \n",
      " X_max:3 X_min:-1 Y_max:5 Y_min:-3\n",
      "-12.439518765482035 -15.166826883065141 -46.60897243418512 -53.03447605568677\n",
      "tmat_Cyc_8 \n",
      " X_max:-13 X_min:-16 Y_max:-47 Y_min:-54\n",
      "-6.407133327127326 -9.340652873436511 -40.71511128790897 -47.509614721221624\n",
      "tmat_Cyc_9 \n",
      " X_max:-7 X_min:-10 Y_max:-41 Y_min:-48\n",
      "\n",
      " X_max_total:3 X_min_total:-16 Y_max_total:5 Y_min_total:-54\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",
    "X_SHIFT_df = pd.DataFrame\n",
    "Y_SHIFT_df = pd.DataFrame\n",
    "\n",
    "for c in 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_SHIFT_df.loc[fov, c] = moveX\n",
    "#         Y_SHIFT_df.loc[fov, c] = 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",
    "\n",
    "    print(X_max,X_min,Y_max,Y_min)\n",
    "\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',f'X_max:{X_max}',f'X_min:{X_min}',f'Y_max:{Y_max}',f'Y_min:{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('\\n', f'X_max_total:{X_max_total}', f'X_min_total:{X_min_total}', \n",
    "      f'Y_max_total:{Y_max_total}', f'Y_min_total:{Y_min_total}')\n",
    "\n",
    "# X_SHIFT_df.to_csv('X_SHIFT_df.csv',sep=',')\n",
    "# Y_SHIFT_df.to_csv('Y_SHIFT_df.csv',sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging and Cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_total = len(mov[0][0][0])    # 2048\n",
    "X_total = len(mov[0][0][1])    # 2048"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################\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": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_DIR = 'reg'\n",
    "REF_DIR = 'tif' # cycle0\n",
    "MERGE_DIR = 'merged' # output directory to save\n",
    "Z = 10\n",
    "final_ch = 38\n",
    "\n",
    "pad = 5 # 5 pixel padding\n",
    "\n",
    "#!mkdir merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "image max: 4289\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F000_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F000_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F000_reg.tif\n",
      "12748\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F000_reg.tif\n",
      "39118\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F000.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65459\n",
      "FOV 001\n",
      "image max: 10373\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F001_reg.tif\n",
      "12813\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F001_reg.tif\n",
      "65116\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F001.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65116\n",
      "FOV 002\n",
      "image max: 5582\n",
      "Appending...  reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F002_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F002_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F002_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F002_reg.tif\n",
      "14026\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F002_reg.tif\n",
      "39543\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F002.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 003\n",
      "image max: 4170\n",
      "Appending...  reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F003_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F003_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "14417\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F003.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 004\n",
      "image max: 3889\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F004_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F004_reg.tif\n",
      "65523\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F004_reg.tif\n",
      "48390\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F004.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65523\n",
      "FOV 005\n",
      "image max: 3864\n",
      "Appending...  reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F005_reg.tif\n",
      "13932\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F005_reg.tif\n",
      "65493\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F005.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65493\n",
      "FOV 006\n",
      "image max: 4757\n",
      "Appending...  reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F006_reg.tif\n",
      "12663\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F006_reg.tif\n",
      "47590\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F006.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65410\n",
      "FOV 007\n",
      "image max: 4184\n",
      "Appending...  reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F007_reg.tif\n",
      "13103\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F007_reg.tif\n",
      "55018\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F007.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 55018\n",
      "FOV 008\n",
      "image max: 5806\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F008_reg.tif\n",
      "10493\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F008.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 009\n",
      "image max: 3711\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F009_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F009_reg.tif\n",
      "12170\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F009.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65533\n",
      "FOV 010\n",
      "image max: 3889\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F010_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F010_reg.tif\n",
      "11496\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F010_reg.tif\n",
      "39388\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F010.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 011\n",
      "image max: 6094\n",
      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F011_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F011_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F011_reg.tif\n",
      "12118\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F011_reg.tif\n",
      "65534\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F011.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 012\n",
      "image max: 4388\n",
      "Appending...  reg_Cyc_1/Cycle_1_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F012_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F012_reg.tif\n",
      "12136\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F012_reg.tif\n",
      "51387\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F012.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 51387\n",
      "FOV 013\n",
      "image max: 3875\n",
      "Appending...  reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F013_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F013_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F013_reg.tif\n",
      "12597\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F013_reg.tif\n",
      "39628\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F013.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 39628\n",
      "FOV 015\n",
      "image max: 3753\n",
      "Appending...  reg_Cyc_1/Cycle_1_F015_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F015_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F015_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F015_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F015_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F015_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F015_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F015_reg.tif\n",
      "11798\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F015_reg.tif\n",
      "45733\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F015.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 45733\n",
      "FOV 016\n",
      "image max: 5204\n",
      "Appending...  reg_Cyc_1/Cycle_1_F016_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F016_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F016_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F016_reg.tif\n",
      "13191\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F016_reg.tif\n",
      "65528\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F016.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 017\n",
      "image max: 4568\n",
      "Appending...  reg_Cyc_1/Cycle_1_F017_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F017_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F017_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F017_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F017_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F017_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F017_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F017_reg.tif\n",
      "11715\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F017_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F017.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 018\n",
      "image max: 5167\n",
      "Appending...  reg_Cyc_1/Cycle_1_F018_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F018_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F018_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F018_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F018_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F018_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F018_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F018_reg.tif\n",
      "10817\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F018_reg.tif\n",
      "53847\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F018.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 53847\n",
      "FOV 019\n",
      "image max: 5874\n",
      "Appending...  reg_Cyc_1/Cycle_1_F019_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F019_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F019_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F019_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F019_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F019_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F019_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F019_reg.tif\n",
      "10288\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F019_reg.tif\n",
      "51673\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F019.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 022\n",
      "image max: 4734\n",
      "Appending...  reg_Cyc_1/Cycle_1_F022_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F022_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F022_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F022_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F022_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F022_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F022_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F022_reg.tif\n",
      "11416\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F022_reg.tif\n",
      "65534\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F022.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65531\n",
      "FOV 023\n",
      "image max: 5216\n",
      "Appending...  reg_Cyc_1/Cycle_1_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F023_reg.tif\n",
      "11222\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F023_reg.tif\n",
      "65528\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F023.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65528\n",
      "FOV 024\n",
      "image max: 4568\n",
      "Appending...  reg_Cyc_1/Cycle_1_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F024_reg.tif\n",
      "11887\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F024_reg.tif\n",
      "65458\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F024.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65458\n",
      "FOV 025\n",
      "image max: 3711\n",
      "Appending...  reg_Cyc_1/Cycle_1_F025_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F025_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F025_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F025_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F025_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F025_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F025_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F025_reg.tif\n",
      "11020\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F025_reg.tif\n",
      "45128\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F025.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65498\n",
      "FOV 026\n",
      "image max: 3260\n",
      "Appending...  reg_Cyc_1/Cycle_1_F026_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F026_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F026_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F026_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F026_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F026_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F026_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F026_reg.tif\n",
      "10361\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F026_reg.tif\n",
      "44748\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F026.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 44748\n",
      "FOV 027\n",
      "image max: 4673\n",
      "Appending...  reg_Cyc_1/Cycle_1_F027_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F027_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F027_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F027_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F027_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F027_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F027_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F027_reg.tif\n",
      "10758\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F027_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F027.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 028\n",
      "image max: 3530\n",
      "Appending...  reg_Cyc_1/Cycle_1_F028_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F028_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F028_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F028_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F028_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F028_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F028_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F028_reg.tif\n",
      "9814\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F028_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F028.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 029\n",
      "image max: 3968\n",
      "Appending...  reg_Cyc_1/Cycle_1_F029_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F029_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F029_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F029_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F029_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F029_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F029_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F029_reg.tif\n",
      "10269\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F029_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F029.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 030\n",
      "image max: 3393\n",
      "Appending...  reg_Cyc_1/Cycle_1_F030_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F030_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F030_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F030_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F030_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F030_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F030_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F030_reg.tif\n",
      "10247\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F030_reg.tif\n",
      "61531\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F030.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 031\n",
      "image max: 3929\n",
      "Appending...  reg_Cyc_1/Cycle_1_F031_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F031_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F031_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F031_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F031_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F031_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F031_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F031_reg.tif\n",
      "11132\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F031_reg.tif\n",
      "65338\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F031.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 032\n",
      "image max: 3933\n",
      "Appending...  reg_Cyc_1/Cycle_1_F032_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F032_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F032_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F032_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F032_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F032_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F032_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F032_reg.tif\n",
      "10176\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F032_reg.tif\n",
      "65391\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F032.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65391\n",
      "FOV 033\n",
      "image max: 3846\n",
      "Appending...  reg_Cyc_1/Cycle_1_F033_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F033_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F033_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F033_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F033_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F033_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F033_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F033_reg.tif\n",
      "11503\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F033_reg.tif\n",
      "60399\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F033.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60399\n",
      "FOV 034\n",
      "image max: 3242\n",
      "Appending...  reg_Cyc_1/Cycle_1_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F034_reg.tif\n",
      "11072\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F034.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 035\n",
      "image max: 3731\n",
      "Appending...  reg_Cyc_1/Cycle_1_F035_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F035_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F035_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F035_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F035_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F035_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F035_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F035_reg.tif\n",
      "11603\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F035_reg.tif\n",
      "54516\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F035.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 036\n",
      "image max: 3962\n",
      "Appending...  reg_Cyc_1/Cycle_1_F036_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F036_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F036_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F036_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F036_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F036_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F036_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F036_reg.tif\n",
      "11541\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F036_reg.tif\n",
      "61057\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F036.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 037\n",
      "image max: 3728\n",
      "Appending...  reg_Cyc_1/Cycle_1_F037_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F037_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F037_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F037_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F037_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F037_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F037_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F037_reg.tif\n",
      "10011\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F037_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F037.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 038\n",
      "image max: 4190\n",
      "Appending...  reg_Cyc_1/Cycle_1_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F038_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F038.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 039\n",
      "image max: 3198\n",
      "Appending...  reg_Cyc_1/Cycle_1_F039_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F039_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F039_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F039_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F039_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F039_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F039_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F039_reg.tif\n",
      "9178\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F039_reg.tif\n",
      "40091\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F039.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 62108\n",
      "FOV 040\n",
      "image max: 3191\n",
      "Appending...  reg_Cyc_1/Cycle_1_F040_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F040_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F040_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F040_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F040_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F040_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F040_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F040_reg.tif\n",
      "8928\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F040_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F040.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 041\n",
      "image max: 3586\n",
      "Appending...  reg_Cyc_1/Cycle_1_F041_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F041_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F041_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F041_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F041_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F041_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F041_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F041_reg.tif\n",
      "10807\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F041_reg.tif\n",
      "65526\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F041.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65526\n",
      "FOV 042\n",
      "image max: 3597\n",
      "Appending...  reg_Cyc_1/Cycle_1_F042_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F042_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F042_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F042_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F042_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F042_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F042_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F042_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F042_reg.tif\n",
      "64216\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F042.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 043\n",
      "image max: 4053\n",
      "Appending...  reg_Cyc_1/Cycle_1_F043_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F043_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F043_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F043_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F043_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F043_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F043_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F043_reg.tif\n",
      "12835\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F043_reg.tif\n",
      "38668\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F043.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 42522\n",
      "FOV 044\n",
      "image max: 4571\n",
      "Appending...  reg_Cyc_1/Cycle_1_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F044_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F044_reg.tif\n",
      "11527\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F044.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 045\n",
      "image max: 11028\n",
      "Appending...  reg_Cyc_1/Cycle_1_F045_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F045_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F045_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F045_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F045_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F045_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F045_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F045_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F045_reg.tif\n",
      "57432\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F045.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 046\n",
      "image max: 4507\n",
      "Appending...  reg_Cyc_1/Cycle_1_F046_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F046_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F046_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F046_reg.tif\n",
      "65530\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F046_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F046_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F046_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F046_reg.tif\n",
      "12729\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F046_reg.tif\n",
      "52204\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F046.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 52204\n",
      "FOV 047\n",
      "image max: 4145\n",
      "Appending...  reg_Cyc_1/Cycle_1_F047_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F047_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F047_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F047_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F047_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F047_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F047_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F047_reg.tif\n",
      "9852\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F047_reg.tif\n",
      "64262\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F047.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 64262\n",
      "FOV 048\n",
      "image max: 4172\n",
      "Appending...  reg_Cyc_1/Cycle_1_F048_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F048_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F048_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F048_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F048_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F048_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F048_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F048_reg.tif\n",
      "9858\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F048_reg.tif\n",
      "65436\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F048.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 049\n",
      "image max: 4111\n",
      "Appending...  reg_Cyc_1/Cycle_1_F049_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F049_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F049_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F049_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F049_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F049_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F049_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F049_reg.tif\n",
      "9882\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F049_reg.tif\n",
      "32887\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F049.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 34470\n",
      "FOV 050\n",
      "image max: 2705\n",
      "Appending...  reg_Cyc_1/Cycle_1_F050_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F050_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F050_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F050_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F050_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F050_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F050_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F050_reg.tif\n",
      "8760\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F050_reg.tif\n",
      "52070\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F050.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 051\n",
      "image max: 3090\n",
      "Appending...  reg_Cyc_1/Cycle_1_F051_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F051_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F051_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F051_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F051_reg.tif\n",
      "65531\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F051_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F051_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F051_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F051_reg.tif\n",
      "46729\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F051.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 052\n",
      "image max: 2842\n",
      "Appending...  reg_Cyc_1/Cycle_1_F052_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F052_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F052_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F052_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F052_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F052_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F052_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F052_reg.tif\n",
      "8341\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F052_reg.tif\n",
      "50907\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F052.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 053\n",
      "image max: 7048\n",
      "Appending...  reg_Cyc_1/Cycle_1_F053_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F053_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F053_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F053_reg.tif\n",
      "65530\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F053_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F053_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F053_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F053_reg.tif\n",
      "8730\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F053_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F053.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 054\n",
      "image max: 2773\n",
      "Appending...  reg_Cyc_1/Cycle_1_F054_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F054_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F054_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F054_reg.tif\n",
      "65530\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F054_reg.tif\n",
      "65529\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F054_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F054_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F054_reg.tif\n",
      "7907\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F054_reg.tif\n",
      "60813\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F054.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 055\n",
      "image max: 2249\n",
      "Appending...  reg_Cyc_1/Cycle_1_F055_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F055_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F055_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F055_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F055_reg.tif\n",
      "65529\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F055_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F055_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F055_reg.tif\n",
      "7712\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F055_reg.tif\n",
      "62414\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F055.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 62414\n",
      "FOV 056\n",
      "image max: 2439\n",
      "Appending...  reg_Cyc_1/Cycle_1_F056_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F056_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F056_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F056_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F056_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F056_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F056_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F056_reg.tif\n",
      "6696\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F056_reg.tif\n",
      "33272\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F056.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 33272\n",
      "FOV 057\n",
      "image max: 2314\n",
      "Appending...  reg_Cyc_1/Cycle_1_F057_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F057_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F057_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F057_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F057_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F057_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F057_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F057_reg.tif\n",
      "8802\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F057_reg.tif\n",
      "45273\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F057.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65523\n",
      "FOV 058\n",
      "image max: 3059\n",
      "Appending...  reg_Cyc_1/Cycle_1_F058_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F058_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F058_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F058_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F058_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F058_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F058_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F058_reg.tif\n",
      "9017\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F058_reg.tif\n",
      "47898\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F058.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65516\n",
      "FOV 059\n",
      "image max: 2967\n",
      "Appending...  reg_Cyc_1/Cycle_1_F059_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F059_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F059_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F059_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F059_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F059_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F059_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F059_reg.tif\n",
      "9701\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F059_reg.tif\n",
      "61842\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F059.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 060\n",
      "image max: 3247\n",
      "Appending...  reg_Cyc_1/Cycle_1_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F060_reg.tif\n",
      "8444\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F060_reg.tif\n",
      "44590\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F060.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 062\n",
      "image max: 2962\n",
      "Appending...  reg_Cyc_1/Cycle_1_F062_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F062_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F062_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F062_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F062_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F062_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F062_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F062_reg.tif\n",
      "7861\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F062_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F062.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 063\n",
      "image max: 2950\n",
      "Appending...  reg_Cyc_1/Cycle_1_F063_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F063_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F063_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F063_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F063_reg.tif\n",
      "65531\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F063_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F063_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F063_reg.tif\n",
      "7899\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F063_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F063.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 065\n",
      "image max: 32284\n",
      "Appending...  reg_Cyc_1/Cycle_1_F065_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F065_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F065_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F065_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F065_reg.tif\n",
      "65529\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F065_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F065_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F065_reg.tif\n",
      "8029\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F065_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F065.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 066\n",
      "image max: 37664\n",
      "Appending...  reg_Cyc_1/Cycle_1_F066_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F066_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F066_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F066_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F066_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F066_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F066_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F066_reg.tif\n",
      "9598\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F066_reg.tif\n",
      "36423\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F066.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 067\n",
      "image max: 3161\n",
      "Appending...  reg_Cyc_1/Cycle_1_F067_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F067_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F067_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F067_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F067_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F067_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F067_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F067_reg.tif\n",
      "8982\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F067_reg.tif\n",
      "60039\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F067.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60039\n",
      "FOV 068\n",
      "image max: 22266\n",
      "Appending...  reg_Cyc_1/Cycle_1_F068_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F068_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F068_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F068_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F068_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F068_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F068_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F068_reg.tif\n",
      "8215\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F068_reg.tif\n",
      "49528\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F068.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 49528\n",
      "FOV 069\n",
      "image max: 3062\n",
      "Appending...  reg_Cyc_1/Cycle_1_F069_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F069_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F069_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F069_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F069_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F069_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F069_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F069_reg.tif\n",
      "9006\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F069_reg.tif\n",
      "57548\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F069.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 57548\n",
      "FOV 070\n",
      "image max: 3353\n",
      "Appending...  reg_Cyc_1/Cycle_1_F070_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F070_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F070_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F070_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F070_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F070_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F070_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F070_reg.tif\n",
      "8349\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F070_reg.tif\n",
      "65531\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F070.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65531\n",
      "FOV 071\n",
      "image max: 3190\n",
      "Appending...  reg_Cyc_1/Cycle_1_F071_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F071_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F071_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F071_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F071_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F071_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F071_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F071_reg.tif\n",
      "8048\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F071_reg.tif\n",
      "53428\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F071.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 072\n",
      "image max: 2869\n",
      "Appending...  reg_Cyc_1/Cycle_1_F072_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F072_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F072_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F072_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F072_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F072_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F072_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F072_reg.tif\n",
      "9159\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F072_reg.tif\n",
      "58766\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F072.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 073\n",
      "image max: 4016\n",
      "Appending...  reg_Cyc_1/Cycle_1_F073_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F073_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F073_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F073_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F073_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F073_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F073_reg.tif\n",
      "65530\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F073_reg.tif\n",
      "7614\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F073_reg.tif\n",
      "65531\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F073.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65531\n",
      "FOV 074\n",
      "image max: 2854\n",
      "Appending...  reg_Cyc_1/Cycle_1_F074_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F074_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F074_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F074_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F074_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F074_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F074_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F074_reg.tif\n",
      "8930\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F074_reg.tif\n",
      "49341\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F074.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 49341\n",
      "FOV 075\n",
      "image max: 3454\n",
      "Appending...  reg_Cyc_1/Cycle_1_F075_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F075_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F075_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F075_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F075_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F075_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F075_reg.tif\n",
      "65529\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F075_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F075_reg.tif\n",
      "40531\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F075.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 076\n",
      "image max: 3670\n",
      "Appending...  reg_Cyc_1/Cycle_1_F076_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F076_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F076_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F076_reg.tif\n",
      "65530\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F076_reg.tif\n",
      "65531\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F076_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F076_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F076_reg.tif\n",
      "9108\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F076_reg.tif\n",
      "58553\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F076.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 58553\n",
      "FOV 077\n",
      "image max: 3701\n",
      "Appending...  reg_Cyc_1/Cycle_1_F077_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F077_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F077_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F077_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F077_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F077_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F077_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F077_reg.tif\n",
      "10060\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F077_reg.tif\n",
      "32632\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F077.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 36585\n",
      "FOV 078\n",
      "image max: 3052\n",
      "Appending...  reg_Cyc_1/Cycle_1_F078_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F078_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F078_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F078_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F078_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F078_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F078_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F078_reg.tif\n",
      "7557\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F078_reg.tif\n",
      "33007\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F078.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 33007\n",
      "FOV 079\n",
      "image max: 2993\n",
      "Appending...  reg_Cyc_1/Cycle_1_F079_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F079_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F079_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F079_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F079_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F079_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F079_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F079_reg.tif\n",
      "7432\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F079_reg.tif\n",
      "38909\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F079.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65526\n",
      "FOV 080\n",
      "image max: 3063\n",
      "Appending...  reg_Cyc_1/Cycle_1_F080_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F080_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F080_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F080_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F080_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F080_reg.tif\n",
      "65529\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F080_reg.tif\n",
      "65530\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F080_reg.tif\n",
      "65523\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F080_reg.tif\n",
      "36196\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F080.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 082\n",
      "image max: 3924\n",
      "Appending...  reg_Cyc_1/Cycle_1_F082_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F082_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F082_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F082_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F082_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F082_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F082_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F082_reg.tif\n",
      "10128\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F082_reg.tif\n",
      "65425\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F082.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65425\n",
      "FOV 083\n",
      "image max: 3840\n",
      "Appending...  reg_Cyc_1/Cycle_1_F083_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F083_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F083_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F083_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F083_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F083_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F083_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F083_reg.tif\n",
      "10406\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F083_reg.tif\n",
      "44735\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F083.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 084\n",
      "image max: 4070\n",
      "Appending...  reg_Cyc_1/Cycle_1_F084_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F084_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F084_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F084_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F084_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F084_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F084_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F084_reg.tif\n",
      "11635\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F084_reg.tif\n",
      "51283\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F084.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 085\n",
      "image max: 5017\n",
      "Appending...  reg_Cyc_1/Cycle_1_F085_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F085_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F085_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F085_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F085_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F085_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F085_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F085_reg.tif\n",
      "11922\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F085_reg.tif\n",
      "52391\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F085.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 087\n",
      "image max: 4785\n",
      "Appending...  reg_Cyc_1/Cycle_1_F087_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F087_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F087_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F087_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F087_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F087_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F087_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F087_reg.tif\n",
      "10088\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F087_reg.tif\n",
      "65506\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F087.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65506\n",
      "FOV 088\n",
      "image max: 41280\n",
      "Appending...  reg_Cyc_1/Cycle_1_F088_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F088_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F088_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F088_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F088_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F088_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F088_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F088_reg.tif\n",
      "9856\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F088_reg.tif\n",
      "60733\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F088.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 089\n",
      "image max: 3926\n",
      "Appending...  reg_Cyc_1/Cycle_1_F089_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F089_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F089_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F089_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F089_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F089_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F089_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F089_reg.tif\n",
      "9092\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F089_reg.tif\n",
      "65521\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F089.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65521\n",
      "FOV 090\n",
      "image max: 3906\n",
      "Appending...  reg_Cyc_1/Cycle_1_F090_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F090_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F090_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F090_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F090_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F090_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F090_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F090_reg.tif\n",
      "8745\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F090_reg.tif\n",
      "46125\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F090.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60142\n",
      "FOV 091\n",
      "image max: 44014\n",
      "Appending...  reg_Cyc_1/Cycle_1_F091_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F091_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F091_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F091_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F091_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F091_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F091_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F091_reg.tif\n",
      "9294\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F091_reg.tif\n",
      "65190\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F091.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65190\n",
      "FOV 092\n",
      "image max: 4299\n",
      "Appending...  reg_Cyc_1/Cycle_1_F092_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F092_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F092_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F092_reg.tif\n",
      "65530\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F092_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F092_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F092_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F092_reg.tif\n",
      "9389\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F092_reg.tif\n",
      "65148\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F092.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65148\n",
      "FOV 093\n",
      "image max: 4449\n",
      "Appending...  reg_Cyc_1/Cycle_1_F093_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F093_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F093_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F093_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F093_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F093_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F093_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F093_reg.tif\n",
      "11702\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F093_reg.tif\n",
      "41801\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F093.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65514\n",
      "FOV 094\n",
      "image max: 5090\n",
      "Appending...  reg_Cyc_1/Cycle_1_F094_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F094_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F094_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F094_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F094_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F094_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F094_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F094_reg.tif\n",
      "12223\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F094_reg.tif\n",
      "43195\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F094.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 095\n",
      "image max: 10949\n",
      "Appending...  reg_Cyc_1/Cycle_1_F095_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F095_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F095_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F095_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F095_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F095_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F095_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F095_reg.tif\n",
      "11207\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F095_reg.tif\n",
      "45892\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F095.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65411\n",
      "FOV 096\n",
      "image max: 4399\n",
      "Appending...  reg_Cyc_1/Cycle_1_F096_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F096_reg.tif\n",
      "65531\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F096_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F096_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F096_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F096_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F096_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F096_reg.tif\n",
      "10773\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F096_reg.tif\n",
      "60914\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F096.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60914\n",
      "FOV 097\n",
      "image max: 3618\n",
      "Appending...  reg_Cyc_1/Cycle_1_F097_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F097_reg.tif\n",
      "65530\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F097_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F097_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F097_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F097_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F097_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F097_reg.tif\n",
      "8532\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F097_reg.tif\n",
      "44065\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F097.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 45728\n",
      "FOV 098\n",
      "image max: 11380\n",
      "Appending...  reg_Cyc_1/Cycle_1_F098_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F098_reg.tif\n",
      "65532\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F098_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F098_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F098_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F098_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F098_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F098_reg.tif\n",
      "9053\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F098_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F098.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 099\n",
      "image max: 3198\n",
      "Appending...  reg_Cyc_1/Cycle_1_F099_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F099_reg.tif\n",
      "65531\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F099_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F099_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F099_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F099_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F099_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F099_reg.tif\n",
      "8940\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F099_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F099.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 100\n",
      "image max: 4990\n",
      "Appending...  reg_Cyc_1/Cycle_1_F100_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F100_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F100_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F100_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F100_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F100_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F100_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F100_reg.tif\n",
      "12994\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F100_reg.tif\n",
      "51236\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F100.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 51236\n",
      "FOV 101\n",
      "image max: 6568\n",
      "Appending...  reg_Cyc_1/Cycle_1_F101_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F101_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F101_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F101_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F101_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F101_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F101_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F101_reg.tif\n",
      "12195\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F101_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F101.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 102\n",
      "image max: 5081\n",
      "Appending...  reg_Cyc_1/Cycle_1_F102_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F102_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F102_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F102_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F102_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F102_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F102_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F102_reg.tif\n",
      "12871\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F102_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F102.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 103\n",
      "image max: 4688\n",
      "Appending...  reg_Cyc_1/Cycle_1_F103_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F103_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F103_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F103_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F103_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F103_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F103_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F103_reg.tif\n",
      "9220\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F103_reg.tif\n",
      "60611\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F103.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60611\n",
      "FOV 105\n",
      "image max: 4567\n",
      "Appending...  reg_Cyc_1/Cycle_1_F105_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F105_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F105_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F105_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F105_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F105_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F105_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F105_reg.tif\n",
      "11049\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F105_reg.tif\n",
      "63189\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F105.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 63189\n",
      "FOV 106\n",
      "image max: 4388\n",
      "Appending...  reg_Cyc_1/Cycle_1_F106_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F106_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F106_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F106_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F106_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F106_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F106_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F106_reg.tif\n",
      "10341\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F106_reg.tif\n",
      "65533\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F106.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65533\n",
      "FOV 107\n",
      "image max: 5706\n",
      "Appending...  reg_Cyc_1/Cycle_1_F107_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F107_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F107_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F107_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F107_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F107_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F107_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F107_reg.tif\n",
      "12419\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F107_reg.tif\n",
      "64064\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F107.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 64064\n",
      "FOV 108\n",
      "image max: 5461\n",
      "Appending...  reg_Cyc_1/Cycle_1_F108_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F108_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F108_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F108_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F108_reg.tif\n",
      "65531\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F108_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F108_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F108_reg.tif\n",
      "14338\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F108_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F108.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 109\n",
      "image max: 5677\n",
      "Appending...  reg_Cyc_1/Cycle_1_F109_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F109_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F109_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F109_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F109_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F109_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F109_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F109_reg.tif\n",
      "13074\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F109_reg.tif\n",
      "64963\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F109.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 64963\n",
      "FOV 110\n",
      "image max: 5477\n",
      "Appending...  reg_Cyc_1/Cycle_1_F110_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F110_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F110_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F110_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F110_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F110_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F110_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F110_reg.tif\n",
      "13526\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F110_reg.tif\n",
      "65265\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F110.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65265\n",
      "FOV 111\n",
      "image max: 6600\n",
      "Appending...  reg_Cyc_1/Cycle_1_F111_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F111_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F111_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F111_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F111_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F111_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F111_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F111_reg.tif\n",
      "15520\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F111_reg.tif\n",
      "55845\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F111.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 112\n",
      "image max: 6411\n",
      "Appending...  reg_Cyc_1/Cycle_1_F112_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F112_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F112_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F112_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F112_reg.tif\n",
      "65531\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F112_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F112_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F112_reg.tif\n",
      "14761\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F112_reg.tif\n",
      "65456\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F112.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65456\n",
      "FOV 113\n",
      "image max: 5958\n",
      "Appending...  reg_Cyc_1/Cycle_1_F113_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F113_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F113_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F113_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F113_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F113_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F113_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F113_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F113_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F113.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 114\n",
      "image max: 4740\n",
      "Appending...  reg_Cyc_1/Cycle_1_F114_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F114_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F114_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F114_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F114_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F114_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F114_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F114_reg.tif\n",
      "12972\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F114_reg.tif\n",
      "65518\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F114.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 115\n",
      "image max: 4629\n",
      "Appending...  reg_Cyc_1/Cycle_1_F115_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F115_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F115_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F115_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F115_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F115_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F115_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F115_reg.tif\n",
      "14323\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F115_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F115.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 116\n",
      "image max: 4864\n",
      "Appending...  reg_Cyc_1/Cycle_1_F116_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F116_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F116_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F116_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F116_reg.tif\n",
      "65531\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F116_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F116_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F116_reg.tif\n",
      "13045\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F116_reg.tif\n",
      "63430\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F116.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 63430\n",
      "FOV 117\n",
      "image max: 6532\n",
      "Appending...  reg_Cyc_1/Cycle_1_F117_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F117_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F117_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F117_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F117_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F117_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F117_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F117_reg.tif\n",
      "14988\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F117_reg.tif\n",
      "63979\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F117.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 118\n",
      "image max: 6406\n",
      "Appending...  reg_Cyc_1/Cycle_1_F118_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F118_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F118_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F118_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F118_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F118_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F118_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F118_reg.tif\n",
      "15488\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F118_reg.tif\n",
      "64829\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F118.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 64829\n",
      "FOV 119\n",
      "image max: 5436\n",
      "Appending...  reg_Cyc_1/Cycle_1_F119_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F119_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F119_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F119_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F119_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F119_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F119_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F119_reg.tif\n",
      "65533\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F119_reg.tif\n",
      "65505\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F119.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65533\n",
      "FOV 120\n",
      "image max: 5529\n",
      "Appending...  reg_Cyc_1/Cycle_1_F120_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F120_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F120_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F120_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F120_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F120_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F120_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F120_reg.tif\n",
      "14819\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F120_reg.tif\n",
      "65425\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F120.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 121\n",
      "image max: 5844\n",
      "Appending...  reg_Cyc_1/Cycle_1_F121_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F121_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F121_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F121_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F121_reg.tif\n",
      "65531\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F121_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F121_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F121_reg.tif\n",
      "13723\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F121_reg.tif\n",
      "50341\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F121.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 50341\n",
      "FOV 122\n",
      "image max: 5623\n",
      "Appending...  reg_Cyc_1/Cycle_1_F122_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F122_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F122_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F122_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F122_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F122_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F122_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F122_reg.tif\n",
      "11998\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F122_reg.tif\n",
      "42001\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F122.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 42001\n",
      "FOV 123\n",
      "image max: 4660\n",
      "Appending...  reg_Cyc_1/Cycle_1_F123_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F123_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F123_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F123_reg.tif\n",
      "65530\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F123_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F123_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F123_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F123_reg.tif\n",
      "10685\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F123_reg.tif\n",
      "56735\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F123.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65437\n",
      "FOV 124\n",
      "image max: 8805\n",
      "Appending...  reg_Cyc_1/Cycle_1_F124_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F124_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F124_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F124_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F124_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F124_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F124_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F124_reg.tif\n",
      "11469\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F124_reg.tif\n",
      "65534\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F124.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 125\n",
      "image max: 3719\n",
      "Appending...  reg_Cyc_1/Cycle_1_F125_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F125_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F125_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F125_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F125_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F125_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F125_reg.tif\n",
      "65530\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F125_reg.tif\n",
      "10454\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F125_reg.tif\n",
      "64153\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F125.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 64153\n",
      "FOV 127\n",
      "image max: 4160\n",
      "Appending...  reg_Cyc_1/Cycle_1_F127_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F127_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F127_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F127_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F127_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F127_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F127_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F127_reg.tif\n",
      "10660\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F127_reg.tif\n",
      "44027\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F127.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 128\n",
      "image max: 4547\n",
      "Appending...  reg_Cyc_1/Cycle_1_F128_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F128_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F128_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F128_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F128_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F128_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F128_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F128_reg.tif\n",
      "11207\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F128_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F128.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 130\n",
      "image max: 5243\n",
      "Appending...  reg_Cyc_1/Cycle_1_F130_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F130_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F130_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F130_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F130_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F130_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F130_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F130_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F130_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F130.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 131\n",
      "image max: 5211\n",
      "Appending...  reg_Cyc_1/Cycle_1_F131_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F131_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F131_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F131_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F131_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F131_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F131_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F131_reg.tif\n",
      "10305\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F131_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F131.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 132\n",
      "image max: 4325\n",
      "Appending...  reg_Cyc_1/Cycle_1_F132_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F132_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F132_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F132_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F132_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F132_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F132_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F132_reg.tif\n",
      "11664\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F132_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F132.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 133\n",
      "image max: 4718\n",
      "Appending...  reg_Cyc_1/Cycle_1_F133_reg.tif\n",
      "65530\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F133_reg.tif\n",
      "65530\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F133_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F133_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F133_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F133_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F133_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F133_reg.tif\n",
      "10173\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F133_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F133.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 134\n",
      "image max: 4573\n",
      "Appending...  reg_Cyc_1/Cycle_1_F134_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F134_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F134_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F134_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F134_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F134_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F134_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F134_reg.tif\n",
      "11073\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F134_reg.tif\n",
      "51599\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F134.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 135\n",
      "image max: 6752\n",
      "Appending...  reg_Cyc_1/Cycle_1_F135_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F135_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F135_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F135_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F135_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F135_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F135_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F135_reg.tif\n",
      "10685\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F135_reg.tif\n",
      "65527\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F135.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65527\n",
      "FOV 136\n",
      "image max: 4057\n",
      "Appending...  reg_Cyc_1/Cycle_1_F136_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F136_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F136_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F136_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F136_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F136_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F136_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F136_reg.tif\n",
      "9689\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F136_reg.tif\n",
      "55420\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F136.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 55420\n",
      "FOV 137\n",
      "image max: 5105\n",
      "Appending...  reg_Cyc_1/Cycle_1_F137_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F137_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F137_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F137_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F137_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F137_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F137_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F137_reg.tif\n",
      "65535\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F137_reg.tif\n",
      "65514\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F137.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 138\n",
      "image max: 5181\n",
      "Appending...  reg_Cyc_1/Cycle_1_F138_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F138_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F138_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F138_reg.tif\n",
      "65530\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F138_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F138_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F138_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F138_reg.tif\n",
      "14326\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F138_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F138.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 139\n",
      "image max: 5466\n",
      "Appending...  reg_Cyc_1/Cycle_1_F139_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F139_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F139_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F139_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F139_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F139_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F139_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F139_reg.tif\n",
      "13162\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F139_reg.tif\n",
      "65517\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F139.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 140\n",
      "image max: 5245\n",
      "Appending...  reg_Cyc_1/Cycle_1_F140_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F140_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F140_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F140_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F140_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F140_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F140_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F140_reg.tif\n",
      "13842\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F140_reg.tif\n",
      "53383\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F140.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 53383\n",
      "FOV 141\n",
      "image max: 5024\n",
      "Appending...  reg_Cyc_1/Cycle_1_F141_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F141_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F141_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F141_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F141_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F141_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F141_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F141_reg.tif\n",
      "11910\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F141_reg.tif\n",
      "58349\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F141.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 58349\n",
      "FOV 142\n",
      "image max: 4780\n",
      "Appending...  reg_Cyc_1/Cycle_1_F142_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F142_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F142_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F142_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F142_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F142_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F142_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F142_reg.tif\n",
      "10403\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F142_reg.tif\n",
      "65311\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F142.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65311\n",
      "FOV 143\n",
      "image max: 4028\n",
      "Appending...  reg_Cyc_1/Cycle_1_F143_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F143_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F143_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F143_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F143_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F143_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F143_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F143_reg.tif\n",
      "10134\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F143_reg.tif\n",
      "64960\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F143.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 144\n",
      "image max: 3840\n",
      "Appending...  reg_Cyc_1/Cycle_1_F144_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F144_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F144_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F144_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F144_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F144_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F144_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F144_reg.tif\n",
      "9631\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F144_reg.tif\n",
      "54199\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F144.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65474\n",
      "FOV 145\n",
      "image max: 3704\n",
      "Appending...  reg_Cyc_1/Cycle_1_F145_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F145_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F145_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F145_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F145_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F145_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F145_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F145_reg.tif\n",
      "8275\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F145_reg.tif\n",
      "50169\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F145.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 50169\n",
      "FOV 146\n",
      "image max: 3831\n",
      "Appending...  reg_Cyc_1/Cycle_1_F146_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F146_reg.tif\n",
      "65532\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F146_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F146_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F146_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F146_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F146_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F146_reg.tif\n",
      "9935\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F146_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F146.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 147\n",
      "image max: 4323\n",
      "Appending...  reg_Cyc_1/Cycle_1_F147_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F147_reg.tif\n",
      "65532\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F147_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F147_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F147_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F147_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F147_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F147_reg.tif\n",
      "10288\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F147_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F147.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 148\n",
      "image max: 5240\n",
      "Appending...  reg_Cyc_1/Cycle_1_F148_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F148_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F148_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F148_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F148_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F148_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F148_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F148_reg.tif\n",
      "11405\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F148_reg.tif\n",
      "48406\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F148.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 48406\n",
      "FOV 149\n",
      "image max: 5243\n",
      "Appending...  reg_Cyc_1/Cycle_1_F149_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F149_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F149_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F149_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F149_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F149_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F149_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F149_reg.tif\n",
      "14013\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F149_reg.tif\n",
      "56923\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F149.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 56923\n",
      "FOV 150\n",
      "image max: 3643\n",
      "Appending...  reg_Cyc_1/Cycle_1_F150_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F150_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F150_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F150_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F150_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F150_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F150_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F150_reg.tif\n",
      "7831\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F150_reg.tif\n",
      "30291\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F150.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 34709\n",
      "FOV 151\n",
      "image max: 10609\n",
      "Appending...  reg_Cyc_1/Cycle_1_F151_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F151_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F151_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F151_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F151_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F151_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F151_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F151_reg.tif\n",
      "8779\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F151_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F151.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 152\n",
      "image max: 4114\n",
      "Appending...  reg_Cyc_1/Cycle_1_F152_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F152_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F152_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F152_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F152_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F152_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F152_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F152_reg.tif\n",
      "9427\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F152_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F152.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 153\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F153_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F153_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F153_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F153_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F153_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F153_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F153_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F153_reg.tif\n",
      "13086\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F153_reg.tif\n",
      "65348\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F153.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 154\n",
      "image max: 5159\n",
      "Appending...  reg_Cyc_1/Cycle_1_F154_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F154_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F154_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F154_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F154_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F154_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F154_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F154_reg.tif\n",
      "14368\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F154_reg.tif\n",
      "65306\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F154.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65306\n",
      "FOV 155\n",
      "image max: 5802\n",
      "Appending...  reg_Cyc_1/Cycle_1_F155_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F155_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F155_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F155_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F155_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F155_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F155_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F155_reg.tif\n",
      "14097\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F155_reg.tif\n",
      "61366\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F155.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 157\n",
      "image max: 4046\n",
      "Appending...  reg_Cyc_1/Cycle_1_F157_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F157_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F157_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F157_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F157_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F157_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F157_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F157_reg.tif\n",
      "9151\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F157_reg.tif\n",
      "65528\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F157.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65528\n",
      "FOV 158\n",
      "image max: 3710\n",
      "Appending...  reg_Cyc_1/Cycle_1_F158_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F158_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F158_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F158_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F158_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F158_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F158_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F158_reg.tif\n",
      "9245\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F158_reg.tif\n",
      "53241\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F158.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65530\n",
      "FOV 159\n",
      "image max: 3326\n",
      "Appending...  reg_Cyc_1/Cycle_1_F159_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F159_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F159_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F159_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F159_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F159_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F159_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F159_reg.tif\n",
      "8023\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F159_reg.tif\n",
      "46502\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F159.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 44007\n",
      "FOV 160\n",
      "image max: 3003\n",
      "Appending...  reg_Cyc_1/Cycle_1_F160_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F160_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F160_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F160_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F160_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F160_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F160_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F160_reg.tif\n",
      "7561\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F160_reg.tif\n",
      "37327\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F160.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 161\n",
      "image max: 4548\n",
      "Appending...  reg_Cyc_1/Cycle_1_F161_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F161_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F161_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F161_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F161_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F161_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F161_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F161_reg.tif\n",
      "8319\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F161_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F161.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 162\n",
      "image max: 3498\n",
      "Appending...  reg_Cyc_1/Cycle_1_F162_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F162_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F162_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F162_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F162_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F162_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F162_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F162_reg.tif\n",
      "8607\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F162_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F162.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 163\n",
      "image max: 24682\n",
      "Appending...  reg_Cyc_1/Cycle_1_F163_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F163_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F163_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F163_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F163_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F163_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F163_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F163_reg.tif\n",
      "11690\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F163_reg.tif\n",
      "37331\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F163.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65529\n",
      "FOV 164\n",
      "image max: 4590\n",
      "Appending...  reg_Cyc_1/Cycle_1_F164_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F164_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F164_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F164_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F164_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F164_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F164_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F164_reg.tif\n",
      "12671\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F164_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F164.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 165\n",
      "image max: 4748\n",
      "Appending...  reg_Cyc_1/Cycle_1_F165_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F165_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F165_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F165_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F165_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F165_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F165_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F165_reg.tif\n",
      "10944\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F165_reg.tif\n",
      "56516\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F165.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 56516\n",
      "FOV 166\n",
      "image max: 4564\n",
      "Appending...  reg_Cyc_1/Cycle_1_F166_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F166_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F166_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F166_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F166_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F166_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F166_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F166_reg.tif\n",
      "11605\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F166_reg.tif\n",
      "65534\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F166.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65534\n",
      "FOV 167\n",
      "image max: 4563\n",
      "Appending...  reg_Cyc_1/Cycle_1_F167_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F167_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F167_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F167_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F167_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F167_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F167_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F167_reg.tif\n",
      "8920\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F167_reg.tif\n",
      "36549\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F167.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65513\n",
      "FOV 168\n",
      "image max: 9395\n",
      "Appending...  reg_Cyc_1/Cycle_1_F168_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F168_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F168_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F168_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F168_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F168_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F168_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F168_reg.tif\n",
      "8394\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F168_reg.tif\n",
      "44001\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F168.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 169\n",
      "image max: 5058\n",
      "Appending...  reg_Cyc_1/Cycle_1_F169_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F169_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F169_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F169_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F169_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F169_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F169_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F169_reg.tif\n",
      "7585\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F169_reg.tif\n",
      "65519\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F169.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 170\n",
      "image max: 6527\n",
      "Appending...  reg_Cyc_1/Cycle_1_F170_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F170_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F170_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F170_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F170_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F170_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F170_reg.tif\n",
      "65530\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F170_reg.tif\n",
      "7956\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F170_reg.tif\n",
      "47103\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F170.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 47103\n",
      "FOV 171\n",
      "image max: 4202\n",
      "Appending...  reg_Cyc_1/Cycle_1_F171_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F171_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F171_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F171_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F171_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F171_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F171_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F171_reg.tif\n",
      "8028\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F171_reg.tif\n",
      "32747\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F171.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 36056\n",
      "FOV 172\n",
      "image max: 3400\n",
      "Appending...  reg_Cyc_1/Cycle_1_F172_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F172_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F172_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F172_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F172_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F172_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F172_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F172_reg.tif\n",
      "8609\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F172_reg.tif\n",
      "32100\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F172.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 34993\n",
      "FOV 173\n",
      "image max: 5752\n",
      "Appending...  reg_Cyc_1/Cycle_1_F173_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F173_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F173_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F173_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F173_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F173_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F173_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F173_reg.tif\n",
      "10509\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F173_reg.tif\n",
      "58883\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F173.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 174\n",
      "image max: 4327\n",
      "Appending...  reg_Cyc_1/Cycle_1_F174_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F174_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F174_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F174_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F174_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F174_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F174_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F174_reg.tif\n",
      "11357\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F174_reg.tif\n",
      "46949\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F174.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65501\n",
      "FOV 175\n",
      "image max: 7368\n",
      "Appending...  reg_Cyc_1/Cycle_1_F175_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F175_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F175_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F175_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F175_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F175_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F175_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F175_reg.tif\n",
      "15305\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F175_reg.tif\n",
      "52851\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F175.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60973\n",
      "FOV 176\n",
      "image max: 4295\n",
      "Appending...  reg_Cyc_1/Cycle_1_F176_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F176_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F176_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F176_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F176_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F176_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F176_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F176_reg.tif\n",
      "11834\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F176_reg.tif\n",
      "44422\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F176.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 44422\n",
      "FOV 177\n",
      "image max: 4146\n",
      "Appending...  reg_Cyc_1/Cycle_1_F177_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F177_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F177_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F177_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F177_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F177_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F177_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F177_reg.tif\n",
      "10742\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F177_reg.tif\n",
      "52304\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F177.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 52304\n",
      "FOV 178\n",
      "image max: 4376\n",
      "Appending...  reg_Cyc_1/Cycle_1_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F178_reg.tif\n",
      "10660\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F178_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F178.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 179\n",
      "image max: 3765\n",
      "Appending...  reg_Cyc_1/Cycle_1_F179_reg.tif\n",
      "65530\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F179_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F179_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F179_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F179_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F179_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F179_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F179_reg.tif\n",
      "9760\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F179_reg.tif\n",
      "36468\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F179.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 180\n",
      "image max: 9316\n",
      "Appending...  reg_Cyc_1/Cycle_1_F180_reg.tif\n",
      "65530\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F180_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F180_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F180_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F180_reg.tif\n",
      "65528\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F180_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F180_reg.tif\n",
      "65528\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F180_reg.tif\n",
      "10895\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F180_reg.tif\n",
      "34482\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F180.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 37308\n",
      "FOV 181\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F181_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F181_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F181_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F181_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F181_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F181_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F181_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F181_reg.tif\n",
      "11081\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F181_reg.tif\n",
      "44778\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F181.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 182\n",
      "image max: 14733\n",
      "Appending...  reg_Cyc_1/Cycle_1_F182_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F182_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F182_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F182_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F182_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F182_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F182_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F182_reg.tif\n",
      "10200\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F182_reg.tif\n",
      "54299\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F182.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 183\n",
      "image max: 4519\n",
      "Appending...  reg_Cyc_1/Cycle_1_F183_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F183_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F183_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F183_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F183_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F183_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F183_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F183_reg.tif\n",
      "12957\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F183_reg.tif\n",
      "62513\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F183.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 62513\n",
      "FOV 184\n",
      "image max: 4849\n",
      "Appending...  reg_Cyc_1/Cycle_1_F184_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F184_reg.tif\n",
      "65532\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F184_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F184_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F184_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F184_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F184_reg.tif\n",
      "65530\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F184_reg.tif\n",
      "12417\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F184_reg.tif\n",
      "64907\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F184.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 64907\n",
      "FOV 185\n",
      "image max: 4905\n",
      "Appending...  reg_Cyc_1/Cycle_1_F185_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F185_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F185_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F185_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F185_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F185_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F185_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F185_reg.tif\n",
      "11879\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F185_reg.tif\n",
      "47984\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F185.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 186\n",
      "image max: 4467\n",
      "Appending...  reg_Cyc_1/Cycle_1_F186_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F186_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F186_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F186_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F186_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F186_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F186_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F186_reg.tif\n",
      "10293\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F186_reg.tif\n",
      "42147\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F186.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 42147\n",
      "FOV 187\n",
      "image max: 4511\n",
      "Appending...  reg_Cyc_1/Cycle_1_F187_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F187_reg.tif\n",
      "65532\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F187_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F187_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F187_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F187_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F187_reg.tif\n",
      "65528\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F187_reg.tif\n",
      "10329\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F187_reg.tif\n",
      "48563\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F187.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 188\n",
      "image max: 4093\n",
      "Appending...  reg_Cyc_1/Cycle_1_F188_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F188_reg.tif\n",
      "65530\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F188_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F188_reg.tif\n",
      "65527\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F188_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F188_reg.tif\n",
      "65529\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F188_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F188_reg.tif\n",
      "11326\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F188_reg.tif\n",
      "56567\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F188.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 56567\n",
      "FOV 189\n",
      "image max: 3704\n",
      "Appending...  reg_Cyc_1/Cycle_1_F189_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F189_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F189_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F189_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F189_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F189_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F189_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F189_reg.tif\n",
      "9432\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F189_reg.tif\n",
      "44707\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F189.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 190\n",
      "image max: 3878\n",
      "Appending...  reg_Cyc_1/Cycle_1_F190_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F190_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F190_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F190_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F190_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F190_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F190_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F190_reg.tif\n",
      "10606\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F190_reg.tif\n",
      "36413\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F190.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 191\n",
      "image max: 4132\n",
      "Appending...  reg_Cyc_1/Cycle_1_F191_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F191_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F191_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F191_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F191_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F191_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F191_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F191_reg.tif\n",
      "10942\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F191_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F191.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 193\n",
      "image max: 3624\n",
      "Appending...  reg_Cyc_1/Cycle_1_F193_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F193_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F193_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F193_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F193_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F193_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F193_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F193_reg.tif\n",
      "11635\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F193_reg.tif\n",
      "49670\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F193.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 49670\n",
      "FOV 194\n",
      "image max: 4119\n",
      "Appending...  reg_Cyc_1/Cycle_1_F194_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F194_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F194_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F194_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F194_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F194_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F194_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F194_reg.tif\n",
      "10221\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F194_reg.tif\n",
      "56363\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F194.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65438\n",
      "FOV 195\n",
      "image max: 4130\n",
      "Appending...  reg_Cyc_1/Cycle_1_F195_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F195_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F195_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F195_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F195_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F195_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F195_reg.tif\n",
      "65531\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F195_reg.tif\n",
      "9540\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F195_reg.tif\n",
      "47421\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F195.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 47421\n",
      "FOV 196\n",
      "image max: 8007\n",
      "Appending...  reg_Cyc_1/Cycle_1_F196_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F196_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F196_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F196_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F196_reg.tif\n",
      "65534\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F196_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F196_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F196_reg.tif\n",
      "10914\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F196_reg.tif\n",
      "65465\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F196.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65465\n",
      "FOV 197\n",
      "image max: 7167\n",
      "Appending...  reg_Cyc_1/Cycle_1_F197_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F197_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F197_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F197_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F197_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F197_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F197_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F197_reg.tif\n",
      "11585\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F197_reg.tif\n",
      "65513\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F197.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 198\n",
      "image max: 3812\n",
      "Appending...  reg_Cyc_1/Cycle_1_F198_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F198_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F198_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F198_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F198_reg.tif\n",
      "65528\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F198_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F198_reg.tif\n",
      "65528\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F198_reg.tif\n",
      "10886\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F198_reg.tif\n",
      "65417\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F198.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 43199\n",
      "FOV 199\n",
      "image max: 18545\n",
      "Appending...  reg_Cyc_1/Cycle_1_F199_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F199_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F199_reg.tif\n",
      "65533\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F199_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F199_reg.tif\n",
      "65532\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F199_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F199_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F199_reg.tif\n",
      "9927\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F199_reg.tif\n",
      "65238\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F199.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65518\n",
      "FOV 200\n",
      "image max: 10351\n",
      "Appending...  reg_Cyc_1/Cycle_1_F200_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F200_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F200_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F200_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F200_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F200_reg.tif\n",
      "65530\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F200_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F200_reg.tif\n",
      "9294\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F200_reg.tif\n",
      "38292\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F200.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 44452\n",
      "FOV 202\n",
      "image max: 2975\n",
      "Appending...  reg_Cyc_1/Cycle_1_F202_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F202_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F202_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F202_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F202_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F202_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F202_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F202_reg.tif\n",
      "7793\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F202_reg.tif\n",
      "65534\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F202.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65502\n",
      "FOV 203\n",
      "image max: 11725\n",
      "Appending...  reg_Cyc_1/Cycle_1_F203_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F203_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F203_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F203_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F203_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F203_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F203_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F203_reg.tif\n",
      "6536\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F203_reg.tif\n",
      "46117\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F203.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 46117\n",
      "FOV 204\n",
      "image max: 2826\n",
      "Appending...  reg_Cyc_1/Cycle_1_F204_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F204_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F204_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F204_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F204_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F204_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F204_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F204_reg.tif\n",
      "6525\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F204_reg.tif\n",
      "45806\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F204.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 45806\n",
      "FOV 205\n",
      "image max: 4094\n",
      "Appending...  reg_Cyc_1/Cycle_1_F205_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F205_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F205_reg.tif\n",
      "65530\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F205_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F205_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F205_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F205_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F205_reg.tif\n",
      "6979\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F205_reg.tif\n",
      "56602\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F205.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 56602\n",
      "FOV 206\n",
      "image max: 10580\n",
      "Appending...  reg_Cyc_1/Cycle_1_F206_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F206_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F206_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F206_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F206_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F206_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F206_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F206_reg.tif\n",
      "6456\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F206_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F206.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 207\n",
      "image max: 17709\n",
      "Appending...  reg_Cyc_1/Cycle_1_F207_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F207_reg.tif\n",
      "65531\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F207_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F207_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F207_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F207_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F207_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F207_reg.tif\n",
      "6610\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F207_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F207.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 208\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F208_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F208_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F208_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F208_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F208_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F208_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F208_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F208_reg.tif\n",
      "8395\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F208_reg.tif\n",
      "65531\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F208.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 209\n",
      "image max: 38763\n",
      "Appending...  reg_Cyc_1/Cycle_1_F209_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F209_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F209_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F209_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F209_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F209_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F209_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F209_reg.tif\n",
      "10273\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F209_reg.tif\n",
      "49578\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F209.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 49578\n",
      "FOV 210\n",
      "image max: 6570\n",
      "Appending...  reg_Cyc_1/Cycle_1_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F210_reg.tif\n",
      "8949\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F210_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F210.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 211\n",
      "image max: 25906\n",
      "Appending...  reg_Cyc_1/Cycle_1_F211_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F211_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F211_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F211_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F211_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F211_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F211_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F211_reg.tif\n",
      "8309\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F211_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F211.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 212\n",
      "image max: 3109\n",
      "Appending...  reg_Cyc_1/Cycle_1_F212_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F212_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F212_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F212_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F212_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F212_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F212_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F212_reg.tif\n",
      "6625\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F212_reg.tif\n",
      "43662\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F212.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65525\n",
      "FOV 213\n",
      "image max: 4456\n",
      "Appending...  reg_Cyc_1/Cycle_1_F213_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F213_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F213_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F213_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F213_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F213_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F213_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F213_reg.tif\n",
      "6776\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F213_reg.tif\n",
      "65452\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F213.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 214\n",
      "image max: 3288\n",
      "Appending...  reg_Cyc_1/Cycle_1_F214_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F214_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F214_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F214_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F214_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F214_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F214_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F214_reg.tif\n",
      "6069\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F214_reg.tif\n",
      "55342\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F214.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 215\n",
      "image max: 2940\n",
      "Appending...  reg_Cyc_1/Cycle_1_F215_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F215_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F215_reg.tif\n",
      "65532\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F215_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F215_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F215_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F215_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F215_reg.tif\n",
      "7255\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F215_reg.tif\n",
      "45765\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F215.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65318\n",
      "FOV 216\n",
      "image max: 8474\n",
      "Appending...  reg_Cyc_1/Cycle_1_F216_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F216_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F216_reg.tif\n",
      "65530\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F216_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F216_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F216_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F216_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F216_reg.tif\n",
      "7003\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F216_reg.tif\n",
      "62274\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F216.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65215\n",
      "FOV 217\n",
      "image max: 3729\n",
      "Appending...  reg_Cyc_1/Cycle_1_F217_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F217_reg.tif\n",
      "65531\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F217_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F217_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F217_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F217_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F217_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F217_reg.tif\n",
      "7063\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F217_reg.tif\n",
      "48950\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F217.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 52710\n",
      "FOV 218\n",
      "image max: 13257\n",
      "Appending...  reg_Cyc_1/Cycle_1_F218_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F218_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F218_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F218_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F218_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F218_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F218_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F218_reg.tif\n",
      "7791\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F218_reg.tif\n",
      "65530\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F218.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65530\n",
      "FOV 219\n",
      "image max: 10062\n",
      "Appending...  reg_Cyc_1/Cycle_1_F219_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F219_reg.tif\n",
      "65530\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F219_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F219_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F219_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F219_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F219_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F219_reg.tif\n",
      "8506\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F219_reg.tif\n",
      "52664\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F219.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65534\n",
      "FOV 220\n",
      "image max: 12574\n",
      "Appending...  reg_Cyc_1/Cycle_1_F220_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F220_reg.tif\n",
      "65531\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F220_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F220_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F220_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F220_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F220_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F220_reg.tif\n",
      "8096\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F220_reg.tif\n",
      "34619\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F220.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 37412\n",
      "FOV 221\n",
      "image max: 3091\n",
      "Appending...  reg_Cyc_1/Cycle_1_F221_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F221_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F221_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F221_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F221_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F221_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F221_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F221_reg.tif\n",
      "7485\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F221_reg.tif\n",
      "32319\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F221.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 222\n",
      "image max: 23038\n",
      "Appending...  reg_Cyc_1/Cycle_1_F222_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F222_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F222_reg.tif\n",
      "65531\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F222_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F222_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F222_reg.tif\n",
      "65534\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F222_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F222_reg.tif\n",
      "7355\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F222_reg.tif\n",
      "29873\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F222.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 32859\n",
      "FOV 224\n",
      "image max: 2922\n",
      "Appending...  reg_Cyc_1/Cycle_1_F224_reg.tif\n",
      "65531\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F224_reg.tif\n",
      "65534\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F224_reg.tif\n",
      "65530\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F224_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F224_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F224_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F224_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F224_reg.tif\n",
      "6954\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F224_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F224.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 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.split('_F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\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(\"image max:\",img.max())\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",
    "        print(im_to_add.max())\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",
    "    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",
    "    ### 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(\"image max:\", img.max())\n",
    "\n",
    "    tifffile.imwrite(\n",
    "        f'./{MERGE_DIR}'+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\n"
   ]
  },
  {
   "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": "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "image max: 4289\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "65530\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F000_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F000_reg.tif\n",
      "65532\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F000_reg.tif\n",
      "12748\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F000_reg.tif\n",
      "39118\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65459\n",
      "FOV 001\n",
      "image max: 10373\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F001_reg.tif\n",
      "12813\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F001_reg.tif\n",
      "65116\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65116\n",
      "FOV 002\n",
      "image max: 5582\n",
      "Appending...  reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F002_reg.tif\n",
      "65531\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F002_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F002_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F002_reg.tif\n",
      "14026\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F002_reg.tif\n",
      "39543\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 003\n",
      "image max: 4170\n",
      "Appending...  reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F003_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F003_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "14417\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 004\n",
      "image max: 3889\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F004_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F004_reg.tif\n",
      "65523\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F004_reg.tif\n",
      "48390\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65523\n",
      "FOV 005\n",
      "image max: 3864\n",
      "Appending...  reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F005_reg.tif\n",
      "13932\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F005_reg.tif\n",
      "65493\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65493\n",
      "FOV 006\n",
      "image max: 4757\n",
      "Appending...  reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F006_reg.tif\n",
      "12663\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F006_reg.tif\n",
      "47590\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65410\n",
      "FOV 007\n",
      "image max: 4184\n",
      "Appending...  reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F007_reg.tif\n",
      "13103\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F007_reg.tif\n",
      "55018\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 55018\n",
      "FOV 008\n",
      "image max: 5806\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F008_reg.tif\n",
      "10493\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 009\n",
      "image max: 3711\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "65534\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F009_reg.tif\n",
      "65532\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F009_reg.tif\n",
      "12170\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65533\n",
      "FOV 010\n",
      "image max: 3889\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F010_reg.tif\n",
      "65533\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "65533\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "65531\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F010_reg.tif\n",
      "11496\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F010_reg.tif\n",
      "39388\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 011\n",
      "image max: 6094\n",
      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F011_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F011_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F011_reg.tif\n",
      "12118\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F011_reg.tif\n",
      "65534\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "FOV 012\n",
      "image max: 4388\n",
      "Appending...  reg_Cyc_1/Cycle_1_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F012_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F012_reg.tif\n",
      "12136\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F012_reg.tif\n",
      "51387\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 51387\n",
      "FOV 013\n",
      "image max: 3875\n",
      "Appending...  reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F013_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F013_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F013_reg.tif\n",
      "12597\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F013_reg.tif\n",
      "39628\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 39628\n",
      "FOV 015\n",
      "image max: 3753\n",
      "Appending...  reg_Cyc_1/Cycle_1_F015_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F015_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F015_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F015_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F015_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F015_reg.tif\n",
      "65532\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F015_reg.tif\n",
      "65534\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F015_reg.tif\n",
      "11798\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F015_reg.tif\n",
      "45733\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 45733\n",
      "FOV 016\n",
      "image max: 5204\n",
      "Appending...  reg_Cyc_1/Cycle_1_F016_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F016_reg.tif\n",
      "65534\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F016_reg.tif\n",
      "65533\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F016_reg.tif\n",
      "65535\n"
     ]
    }
   ],
   "source": [
    "df_before = pd.DataFrame()\n",
    "df_after = pd.DataFrame()\n",
    "\n",
    "refs = iter(glob.glob('tif/Cycle_0/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    ref_name = next(refs)\n",
    "    FOV_num = ref_name.split('_F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\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(\"image max:\",img.max())\n",
    "    im_max = img.max()\n",
    "    if im_max > 65000:    #first, is the max pixel value greater than 65000?\n",
    "        loc_ref = np.where(img > 65000) # all locations where pixel value is greater than 65000\n",
    "        df_before.loc[FOV_num, 0] = len(loc_ref[2])\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",
    "        print(im_to_add.max())\n",
    "        imtoadd_max = im_to_add.max()\n",
    "        if imtoadd_max > 65000:\n",
    "            loc = np.where(img > 65000)\n",
    "            df_before.loc[FOV_num, cycle] = len(loc[2])\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",
    "    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",
    "#     ### FINAL CHECK before saving ### \n",
    "#     assert img.shape[0] == Z, \"Check ZCYX\"\n",
    "#     assert img.shape[1] == final_ch, \"check final merge size\"\n",
    "    \n",
    "    print(\"image max:\", img.max())\n",
    "    fin_max = img.max()\n",
    "    if fin_max > 65000:\n",
    "        fin_loc = np.where(img > 65000)\n",
    "        df_after.loc[FOV_num, 'merged'] = len(fin_loc[2])\n",
    "\n",
    "#     tifffile.imwrite(\n",
    "#         f'./{MERGE_DIR}'+fname,\n",
    "#         img,\n",
    "#         imagej=True,\n",
    "#         photometric='minisblack',\n",
    "#         metadata={'axes': 'ZCYX'},\n",
    "#     )\n",
    "\n",
    "    del img # clear memory\n",
    "done = open(\"done.txt\", \"a\")\n",
    "done.write(\"done\")\n",
    "done.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "image max: 4289\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F000_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F000_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F000_reg.tif\n",
      "12748\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F000_reg.tif\n",
      "39118\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65459\n",
      "(array([8]), array([14]), array([313]), array([551]))\n",
      "FOV 001\n",
      "image max: 10373\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F001_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F001_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F001_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F001_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F001_reg.tif\n",
      "12813\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F001_reg.tif\n",
      "65116\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65116\n",
      "(array([4]), array([35]), array([319]), array([108]))\n",
      "FOV 002\n",
      "image max: 5582\n",
      "Appending...  reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F002_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F002_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F002_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F002_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F002_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F002_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F002_reg.tif\n",
      "14026\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F002_reg.tif\n",
      "39543\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 8, 8, 9]), array([28, 28, 28, ..., 28, 28, 28]), array([324, 324, 326, ...,  68, 192, 192]), array([ 231, 1643, 1736, ...,  615,  108,  108]))\n",
      "excessive edge brightness detected\n",
      "FOV 003\n",
      "image max: 4170\n",
      "Appending...  reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F003_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F003_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F003_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F003_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "14417\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F003_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35]), array([1590, 1591, 1591, 1591, 1592, 1592, 1592, 1592, 1593, 1593, 1593,\n",
      "       1593, 1593, 1593, 1594, 1594, 1594, 1594, 1594, 1594, 1594, 1594,\n",
      "       1595, 1595, 1595, 1595, 1595, 1595, 1595, 1595, 1596, 1596, 1596,\n",
      "       1596, 1596, 1596, 1596, 1596, 1596, 1596, 1596, 1597, 1597, 1597,\n",
      "       1597, 1597, 1597, 1597, 1597, 1597, 1597, 1598, 1598, 1598, 1598,\n",
      "       1598, 1598, 1598, 1598, 1598, 1598, 1599, 1599, 1599, 1599, 1600,\n",
      "       1600, 1600, 1600, 1600, 1601, 1601, 1601, 1601, 1602, 1602, 1602,\n",
      "       1570, 1571, 1572, 1574, 1574, 1576, 1576, 1587, 1588, 1588, 1588,\n",
      "       1589, 1589, 1590, 1590, 1590, 1590, 1590, 1591, 1591, 1591, 1592,\n",
      "       1592, 1592, 1592, 1592, 1592, 1592, 1593, 1593, 1593, 1593, 1593,\n",
      "       1593, 1593, 1593, 1593, 1594, 1594, 1594, 1594, 1594, 1594, 1594,\n",
      "       1594, 1594, 1594, 1594, 1595, 1595, 1595, 1595, 1595, 1595, 1595,\n",
      "       1595, 1595, 1595, 1595, 1595, 1596, 1596, 1596, 1596, 1596, 1596,\n",
      "       1596, 1596, 1596, 1596, 1596, 1596, 1596, 1597, 1597, 1597, 1597,\n",
      "       1597, 1597, 1597, 1597, 1597, 1597, 1597, 1597, 1597, 1597, 1598,\n",
      "       1598, 1598, 1598, 1598, 1598, 1598, 1598, 1598, 1598, 1598, 1598,\n",
      "       1598, 1598, 1599, 1599, 1599, 1599, 1599, 1599, 1599, 1599, 1599,\n",
      "       1599, 1599, 1599, 1599, 1599, 1600, 1600, 1600, 1600, 1600, 1601,\n",
      "       1601, 1601, 1601, 1601, 1601, 1601, 1601, 1601, 1601, 1601, 1601,\n",
      "       1602, 1602, 1602, 1602, 1602, 1602, 1603, 1603, 1603, 1603, 1603,\n",
      "       1603, 1572, 1572, 1573, 1573, 1574, 1575, 1590, 1592, 1592, 1592,\n",
      "       1592, 1592, 1592, 1592, 1592, 1592, 1592, 1593, 1593, 1593, 1593,\n",
      "       1593, 1594, 1594, 1594, 1594, 1594, 1594, 1594, 1594, 1594, 1595,\n",
      "       1595, 1595, 1595, 1595, 1595, 1595, 1596, 1596, 1596, 1596, 1596,\n",
      "       1596, 1596, 1596, 1596, 1596, 1597, 1597, 1597, 1597, 1597, 1597,\n",
      "       1597, 1597, 1597, 1597, 1597, 1597, 1598, 1598, 1598, 1598, 1598,\n",
      "       1598, 1598, 1598, 1598, 1598, 1598, 1599, 1599, 1599, 1599, 1599,\n",
      "       1599, 1599, 1599, 1599, 1600, 1600, 1600, 1600, 1600, 1600, 1600,\n",
      "       1600, 1601, 1601, 1601, 1601, 1601, 1601, 1602, 1602, 1602, 1602,\n",
      "       1602, 1602, 1602, 1602, 1602, 1603, 1603, 1603, 1603, 1604, 1604,\n",
      "       1606, 1590, 1591, 1592, 1592, 1593, 1593, 1593, 1593, 1593, 1593,\n",
      "       1593, 1594, 1594, 1594, 1595, 1595, 1595, 1595, 1595, 1595, 1595,\n",
      "       1595, 1595, 1596, 1596, 1596, 1596, 1596, 1597, 1597, 1597, 1597,\n",
      "       1597, 1597, 1597, 1597, 1598, 1598, 1598, 1598, 1598, 1598, 1598,\n",
      "       1599, 1599, 1599, 1599, 1600, 1600, 1600, 1600, 1600, 1600, 1600,\n",
      "       1601, 1601, 1602, 1602, 1602, 1602, 1602, 1602, 1602, 1602, 1603,\n",
      "       1592, 1593, 1593, 1594, 1594, 1594, 1595, 1595, 1595, 1596, 1596,\n",
      "       1596, 1596, 1597, 1597, 1597, 1597, 1598, 1598, 1598, 1599, 1599,\n",
      "       1600, 1600, 1600, 1601]), array([1477, 1474, 1476, 1478, 1472, 1473, 1475, 1477, 1472, 1474, 1476,\n",
      "       1478, 1479, 1484, 1472, 1474, 1476, 1477, 1480, 1481, 1482, 1483,\n",
      "       1473, 1475, 1477, 1478, 1479, 1484, 1485, 1486, 1473, 1475, 1476,\n",
      "       1477, 1478, 1480, 1481, 1482, 1483, 1484, 1486, 1471, 1473, 1474,\n",
      "       1475, 1476, 1477, 1478, 1479, 1484, 1486, 1472, 1474, 1476, 1477,\n",
      "       1478, 1479, 1480, 1481, 1482, 1484, 1473, 1475, 1479, 1481, 1473,\n",
      "       1477, 1478, 1479, 1481, 1473, 1474, 1475, 1476, 1477, 1478, 1479,\n",
      "       1481, 1483, 1482, 1480, 1481, 1480, 1481, 1476, 1475, 1477, 1478,\n",
      "       1474, 1476, 1475, 1477, 1478, 1479, 1481, 1473, 1475, 1485, 1472,\n",
      "       1474, 1476, 1478, 1479, 1480, 1481, 1471, 1473, 1475, 1477, 1478,\n",
      "       1482, 1483, 1484, 1485, 1470, 1472, 1474, 1475, 1476, 1477, 1478,\n",
      "       1479, 1480, 1481, 1486, 1470, 1472, 1474, 1475, 1476, 1477, 1478,\n",
      "       1479, 1482, 1483, 1484, 1486, 1470, 1472, 1474, 1475, 1476, 1477,\n",
      "       1478, 1479, 1480, 1481, 1482, 1486, 1488, 1470, 1472, 1474, 1475,\n",
      "       1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1485, 1487, 1470,\n",
      "       1472, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483,\n",
      "       1485, 1487, 1469, 1471, 1473, 1474, 1475, 1476, 1477, 1478, 1479,\n",
      "       1480, 1481, 1482, 1484, 1486, 1470, 1472, 1482, 1483, 1485, 1468,\n",
      "       1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1484,\n",
      "       1469, 1470, 1471, 1482, 1483, 1484, 1472, 1473, 1476, 1477, 1480,\n",
      "       1481, 1480, 1482, 1480, 1482, 1482, 1483, 1479, 1475, 1477, 1478,\n",
      "       1479, 1480, 1481, 1482, 1483, 1484, 1487, 1475, 1477, 1485, 1486,\n",
      "       1488, 1475, 1477, 1479, 1480, 1481, 1482, 1483, 1484, 1486, 1475,\n",
      "       1477, 1479, 1484, 1485, 1486, 1488, 1476, 1478, 1479, 1480, 1481,\n",
      "       1482, 1483, 1484, 1485, 1487, 1472, 1474, 1476, 1477, 1478, 1479,\n",
      "       1480, 1481, 1482, 1483, 1485, 1487, 1473, 1475, 1477, 1478, 1479,\n",
      "       1480, 1481, 1482, 1483, 1485, 1487, 1472, 1474, 1476, 1477, 1478,\n",
      "       1479, 1480, 1484, 1486, 1473, 1475, 1480, 1481, 1482, 1483, 1484,\n",
      "       1485, 1476, 1477, 1478, 1479, 1480, 1484, 1471, 1472, 1473, 1474,\n",
      "       1475, 1480, 1481, 1482, 1484, 1476, 1477, 1478, 1479, 1475, 1480,\n",
      "       1480, 1476, 1484, 1477, 1485, 1478, 1479, 1480, 1481, 1482, 1483,\n",
      "       1484, 1478, 1484, 1486, 1475, 1477, 1479, 1480, 1481, 1482, 1483,\n",
      "       1484, 1486, 1477, 1479, 1482, 1484, 1486, 1475, 1477, 1479, 1480,\n",
      "       1481, 1483, 1485, 1487, 1476, 1478, 1479, 1480, 1481, 1483, 1485,\n",
      "       1474, 1476, 1484, 1485, 1475, 1478, 1479, 1480, 1481, 1482, 1484,\n",
      "       1476, 1483, 1472, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1477,\n",
      "       1479, 1478, 1481, 1479, 1480, 1483, 1478, 1482, 1484, 1478, 1480,\n",
      "       1482, 1484, 1478, 1480, 1482, 1484, 1480, 1481, 1483, 1478, 1483,\n",
      "       1480, 1481, 1482, 1477]))\n",
      "FOV 004\n",
      "image max: 3889\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F004_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F004_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F004_reg.tif\n",
      "65523\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F004_reg.tif\n",
      "48390\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65523\n",
      "(array([1]), array([32]), array([1771]), array([960]))\n",
      "FOV 005\n",
      "image max: 3864\n",
      "Appending...  reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F005_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F005_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F005_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F005_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F005_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F005_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F005_reg.tif\n",
      "13932\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F005_reg.tif\n",
      "65493\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65493\n",
      "(array([4]), array([35]), array([1200]), array([402]))\n",
      "FOV 006\n",
      "image max: 4757\n",
      "Appending...  reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F006_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F006_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F006_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F006_reg.tif\n",
      "12663\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F006_reg.tif\n",
      "47590\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65410\n",
      "(array([0]), array([16]), array([1207]), array([1406]))\n",
      "FOV 007\n",
      "image max: 4184\n",
      "Appending...  reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F007_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F007_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F007_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F007_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F007_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F007_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F007_reg.tif\n",
      "13103\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F007_reg.tif\n",
      "55018\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 55018\n",
      "FOV 008\n",
      "image max: 5806\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F008_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F008_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F008_reg.tif\n",
      "10493\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F008_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 9, 9, 9]), array([34, 34, 34, ..., 34, 34, 34]), array([  3,   4,   5, ..., 493, 493, 493]), array([495, 495, 495, ...,   6,   7, 111]))\n",
      "excessive edge brightness detected\n",
      "FOV 009\n",
      "image max: 3711\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F009_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F009_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F009_reg.tif\n",
      "12170\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F009_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65533\n",
      "(array([5]), array([35]), array([1669]), array([1535]))\n",
      "FOV 010\n",
      "image max: 3889\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F010_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F010_reg.tif\n",
      "11496\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F010_reg.tif\n",
      "39388\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 7, 7, 7]), array([6, 6, 6, ..., 6, 6, 6]), array([ 966, 1094, 1094, ..., 1606, 1606, 1728]), array([1901,  493,  743, ..., 1814, 1895, 1895]))\n",
      "FOV 011\n",
      "image max: 6094\n",
      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F011_reg.tif\n",
      "65533\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F011_reg.tif\n",
      "65535\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F011_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F011_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F011_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F011_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F011_reg.tif\n",
      "12118\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F011_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9]), array([13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 28, 28, 28, 28, 28, 28,\n",
      "       28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
      "       28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
      "       28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
      "       28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
      "       28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
      "       28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28,\n",
      "       28, 28, 28]), array([ 64,  64, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
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      "        64,  64,  65, 454, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 577,  64,  64,  64,  64,  64,  64,  64,  64,  64,\n",
      "        64,  65, 454, 454, 454, 454, 454, 454, 454, 454, 454, 454, 454,\n",
      "       454, 454, 454, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
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      "        64,  64,  64,  64,  64, 454, 454, 454, 454, 454, 454, 454, 454,\n",
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      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
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      "        64,  64,  64,  64, 454, 454, 454, 454, 454, 454, 454, 454, 454,\n",
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      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
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      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
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      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576,  44,  45,  47,  51,  64,  64,  64,  64,  65,  65,\n",
      "       532, 533, 535, 535, 576, 576, 576, 576, 576, 576, 576, 576, 576,\n",
      "       576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 576, 577,\n",
      "        62,  64,  68,  68,  68,  68,  68,  68,  68,  68,  68,  68,  68,\n",
      "        68,  68,  68,  68,  68,  68,  68,  68,  68,  68,  68,  68,  70,\n",
      "        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
      "        91,  92,  93,  94,  95, 101, 102, 103, 104, 105, 106, 107, 108,\n",
      "       109, 110, 111, 112, 190, 192,  62,  64,  68,  68,  68,  68,  68,\n",
      "        68,  68,  68,  68,  68,  68,  68,  68,  68,  68,  68,  68,  68,\n",
      "        68,  68,  81,  82,  83,  86,  87,  88,  89,  90,  91,  92,  93,\n",
      "        94,  95,  96,  97,  98,  99, 100, 101, 102, 103, 104, 105, 106,\n",
      "       107, 108, 109, 112, 113, 190, 192]), array([1004, 1510,  620,  646,  647,  653,  654,  655,  656,  749,  761,\n",
      "        762,  763,  764,  765,  884,  885,  886,  887,  889,  890,  891,\n",
      "       1128, 1004, 1035, 1036, 1377, 1510, 1512,  620,  620,  646,  655,\n",
      "        656,  657,  665,  666,  683,  684,  747,  748,  749,  750,  760,\n",
      "        761,  765,  766,  867,  884,  885,  891,  892,  953,  954,  955,\n",
      "       1128, 1004, 1035, 1036, 1037, 1038, 1039, 1375, 1376, 1377, 1412,\n",
      "       1512,  642,  643,  644,  645,  646,  647,  652,  663,  665,  666,\n",
      "        694,  695,  696,  697,  620,  645,  646,  656,  657,  662,  663,\n",
      "        664,  665,  666,  682,  684,  685,  747,  750,  759,  760,  766,\n",
      "        866,  867,  868,  878,  879,  881,  882,  892,  893,  898,  899,\n",
      "        900,  951,  952,  953,  954,  955,  956,  962,  963, 1126, 1510,\n",
      "       1004, 1034, 1035, 1038, 1039, 1375, 1376, 1377, 1378, 1391, 1412,\n",
      "       1413, 1508, 1509,  620,  640,  641,  642,  643,  644,  646,  647,\n",
      "        648,  649,  650,  651,  652,  653,  654,  655,  656,  661,  662,\n",
      "        663,  664,  665,  666,  667,  668,  670,  694,  696,  697,  708,\n",
      "        620,  644,  660,  661,  662,  663,  664,  665,  666,  667,  668,\n",
      "        681,  682,  683,  685,  686,  746,  750,  758,  759,  769,  770,\n",
      "        865,  868,  869,  878,  879,  880,  893,  894,  895,  896,  897,\n",
      "        898,  899,  900,  944,  945,  946,  947,  948,  949,  950,  951,\n",
      "        952,  953,  954,  955,  956,  957,  958,  961,  962,  963, 1069,\n",
      "       1071, 1126, 1128, 1004, 1034, 1035, 1036, 1037, 1038, 1039, 1041,\n",
      "       1374, 1375, 1377, 1378, 1379, 1508, 1509,  620,  639,  640,  641,\n",
      "        642,  643,  644,  645,  646,  647,  650,  654,  663,  664,  665,\n",
      "        666,  692,  693,  694,  696,  697,  698,  620,  641,  642,  663,\n",
      "        664,  665,  666,  667,  668,  679,  680,  681,  682,  683,  684,\n",
      "        685,  686,  744,  745,  746,  747,  749,  750,  751,  752,  754,\n",
      "        755,  756,  757,  771,  772,  773,  774,  863,  864,  865,  866,\n",
      "        867,  870,  878,  879,  880,  895,  896,  897,  898,  900,  938,\n",
      "        943,  944,  945,  946,  947,  948,  949,  950,  951,  952,  953,\n",
      "        954,  955,  956,  957,  958,  959,  960,  961,  962,  963,  964,\n",
      "        980,  981,  985, 1005, 1031, 1037, 1084, 1086, 1126, 1128, 1004,\n",
      "       1510, 1512,  620,  620,  640,  641,  642,  645,  646,  648,  650,\n",
      "        658,  659,  660,  661,  662,  663,  664,  665,  666,  667,  668,\n",
      "        672,  682,  684,  685,  686,  744,  753,  754,  755,  756,  757,\n",
      "        758,  759,  760,  761,  762,  763,  764,  766,  767,  768,  769,\n",
      "        770,  771,  772,  773,  774,  775,  863,  864,  867,  868,  869,\n",
      "        870,  871,  877,  879,  880,  881,  882,  883,  884,  885,  886,\n",
      "        887,  888,  889,  890,  891,  892,  893,  894,  895,  896,  897,\n",
      "        898,  900,  938,  943,  944,  945,  946,  947,  948,  949,  950,\n",
      "        951,  952,  953,  954,  955,  956,  957,  958,  959,  960,  961,\n",
      "        962,  963,  964,  965,  966,  967,  997, 1003, 1007, 1011, 1126,\n",
      "       1004, 1510, 1512,  620,  620,  639,  641,  643,  644,  645,  646,\n",
      "        647,  648,  649,  650,  651,  652,  653,  654,  655,  656,  657,\n",
      "        658,  659,  660,  661,  662,  665,  667,  752,  757,  758,  759,\n",
      "        760,  761,  762,  763,  765,  766,  767,  768,  769,  770,  774,\n",
      "        874,  879,  880,  882,  883,  884,  885,  886,  887,  888,  889,\n",
      "        890,  891,  892,  893,  894,  897,  898,  943,  944,  945,  946,\n",
      "        947,  949,  950,  951,  952,  953,  954,  955,  956,  957,  959,\n",
      "        960,  962,  964,  965,  966,  967, 1005, 1126, 1510, 1512, 1385,\n",
      "        620,  620,  742, 1004, 1382,  744, 1384, 1126, 1128,  618, 1001,\n",
      "        492,  651,  652,  655,  658,  659,  759,  887,  889,  946,  947,\n",
      "        951,  953,  956,  960,  963,  964,  968,  970,  971,  998, 1000,\n",
      "       1132, 1639,  108,  110,  111,  112,  113,  114,  115,  116,  136,\n",
      "        137,  138,  139,  140,  141,  142,  143,  144,  145,  153,  155,\n",
      "        159,  162,  614,  108,  108,  108,  108,  108,  108,  108,  108,\n",
      "        108,  108,  108,  108,  108,  108,  108,  108,  108,  108,  108,\n",
      "        108,  108,  108,  108,  108,  108,  108,  108,  108,  108,  108,\n",
      "        108,  108,  107, 1132, 1639,  108,  113,  114,  115,  116,  117,\n",
      "        118,  119,  120,  133,  135,  136,  137,  138,  139,  140,  141,\n",
      "        142,  153,  614,  108,  108,  108,  108,  108,  108,  108,  108,\n",
      "        108,  108,  108,  108,  108,  108,  108,  108,  108,  108,  108,\n",
      "        108,  108,  108,  108,  108,  108,  108,  108,  108,  108,  108,\n",
      "        107]))\n",
      "FOV 012\n",
      "image max: 4388\n",
      "Appending...  reg_Cyc_1/Cycle_1_F012_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F012_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F012_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F012_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F012_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F012_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F012_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F012_reg.tif\n",
      "12136\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F012_reg.tif\n",
      "51387\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 51387\n",
      "FOV 013\n",
      "image max: 3875\n",
      "Appending...  reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F013_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F013_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F013_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F013_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F013_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F013_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F013_reg.tif\n",
      "12597\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F013_reg.tif\n",
      "39628\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 39628\n",
      "FOV 015\n",
      "image max: 3753\n",
      "Appending...  reg_Cyc_1/Cycle_1_F015_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F015_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F015_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F015_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F015_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F015_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F015_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F015_reg.tif\n",
      "11798\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F015_reg.tif\n",
      "45733\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 45733\n",
      "FOV 016\n",
      "image max: 5204\n",
      "Appending...  reg_Cyc_1/Cycle_1_F016_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F016_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F016_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F016_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F016_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F016_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F016_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F016_reg.tif\n",
      "13191\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F016_reg.tif\n",
      "65528\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 7, 7, 7]), array([9, 9, 9, ..., 9, 9, 9]), array([198, 199, 200, ..., 301, 302, 320]), array([106, 106, 106, ..., 106, 106, 488]))\n",
      "FOV 017\n",
      "image max: 4568\n",
      "Appending...  reg_Cyc_1/Cycle_1_F017_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F017_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F017_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F017_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F017_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F017_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F017_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F017_reg.tif\n",
      "11715\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F017_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([8, 8, 8, ..., 9, 9, 9]), array([35, 35, 35, ..., 35, 35, 35]), array([105, 105, 105, ..., 877, 877, 877]), array([1786, 1819, 1820, ..., 1772, 1773, 1774]))\n",
      "excessive edge brightness detected\n",
      "FOV 018\n",
      "image max: 5167\n",
      "Appending...  reg_Cyc_1/Cycle_1_F018_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F018_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F018_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F018_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F018_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F018_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F018_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F018_reg.tif\n",
      "10817\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F018_reg.tif\n",
      "53847\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 53847\n",
      "FOV 019\n",
      "image max: 5874\n",
      "Appending...  reg_Cyc_1/Cycle_1_F019_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F019_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F019_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F019_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F019_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F019_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F019_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F019_reg.tif\n",
      "10288\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F019_reg.tif\n",
      "51673\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]), array([18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,\n",
      "       18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,\n",
      "       18, 18, 18, 18, 18, 18, 18, 18]), array([1349, 1445, 1445, 1474, 1350, 1350, 1350, 1350, 1350, 1350, 1350,\n",
      "       1352, 1350, 1350, 1350, 1350, 1350, 1352, 1356, 1474, 1350, 1350,\n",
      "       1350, 1350, 1350, 1350, 1350, 1350, 1352, 1354, 1357, 1350, 1350,\n",
      "       1350, 1350, 1350, 1350, 1350, 1350, 1350, 1350, 1350]), array([1515, 1514, 1897, 1896, 1390, 1391, 1392, 1398, 1399, 1400, 1895,\n",
      "       1389, 1390, 1398, 1399, 1400, 1895, 1389, 1389, 1896, 1390, 1396,\n",
      "       1397, 1398, 1399, 1400, 1401, 1895, 1389, 1389, 1389, 1390, 1391,\n",
      "       1392, 1393, 1394, 1395, 1396, 1397, 1398, 1400, 1895]))\n",
      "FOV 022\n",
      "image max: 4734\n",
      "Appending...  reg_Cyc_1/Cycle_1_F022_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F022_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F022_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F022_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F022_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F022_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F022_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F022_reg.tif\n",
      "11416\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F022_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65531\n",
      "(array([4]), array([35]), array([3]), array([69]))\n",
      "excessive edge brightness detected\n",
      "FOV 023\n",
      "image max: 5216\n",
      "Appending...  reg_Cyc_1/Cycle_1_F023_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F023_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F023_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F023_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F023_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F023_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F023_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F023_reg.tif\n",
      "11222\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F023_reg.tif\n",
      "65528\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65528\n",
      "(array([5]), array([35]), array([1801]), array([62]))\n",
      "FOV 024\n",
      "image max: 4568\n",
      "Appending...  reg_Cyc_1/Cycle_1_F024_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F024_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F024_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F024_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F024_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F024_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F024_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F024_reg.tif\n",
      "11887\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F024_reg.tif\n",
      "65458\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65458\n",
      "(array([3]), array([36]), array([717]), array([1766]))\n",
      "FOV 025\n",
      "image max: 3711\n",
      "Appending...  reg_Cyc_1/Cycle_1_F025_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F025_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F025_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F025_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F025_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F025_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F025_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F025_reg.tif\n",
      "11020\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F025_reg.tif\n",
      "45128\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65498\n",
      "(array([0]), array([12]), array([994]), array([1098]))\n",
      "FOV 026\n",
      "image max: 3260\n",
      "Appending...  reg_Cyc_1/Cycle_1_F026_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F026_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F026_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F026_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F026_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F026_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F026_reg.tif\n",
      "10361\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F026_reg.tif\n",
      "44748\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 44748\n",
      "FOV 027\n",
      "image max: 4673\n",
      "Appending...  reg_Cyc_1/Cycle_1_F027_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F027_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F027_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F027_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F027_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F027_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F027_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F027_reg.tif\n",
      "10758\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F027_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35]), array([1365, 1369, 1369, 1369, 1369, 1370, 1371, 1371, 1371, 1371, 1371,\n",
      "       1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1372, 1372, 1372,\n",
      "       1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372,\n",
      "       1372, 1372, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373,\n",
      "       1373, 1373, 1373, 1373, 1373, 1373, 1373, 1374, 1374, 1374, 1374,\n",
      "       1374, 1374, 1374, 1374, 1374, 1374, 1374, 1375, 1375, 1375, 1375,\n",
      "       1375, 1375, 1375, 1375, 1376, 1376, 1376, 1376, 1376, 1376, 1376,\n",
      "       1377, 1378, 1378, 1378, 1378, 1368, 1368, 1368, 1369, 1369, 1369,\n",
      "       1369, 1370, 1370, 1370, 1370, 1370, 1370, 1370, 1370, 1370, 1370,\n",
      "       1370, 1370, 1370, 1370, 1370, 1370, 1370, 1370, 1370, 1370, 1370,\n",
      "       1370, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371,\n",
      "       1371, 1371, 1371, 1371, 1371, 1371, 1371, 1372, 1372, 1372, 1372,\n",
      "       1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372,\n",
      "       1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372,\n",
      "       1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372,\n",
      "       1372, 1372, 1372, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373,\n",
      "       1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373,\n",
      "       1373, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374,\n",
      "       1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374,\n",
      "       1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374, 1374,\n",
      "       1374, 1374, 1375, 1375, 1375, 1375, 1375, 1375, 1375, 1375, 1375,\n",
      "       1375, 1375, 1375, 1375, 1375, 1375, 1375, 1375, 1375, 1375, 1375,\n",
      "       1375, 1375, 1375, 1375, 1376, 1376, 1376, 1376, 1376, 1376, 1376,\n",
      "       1376, 1376, 1376, 1376, 1376, 1376, 1376, 1376, 1376, 1376, 1376,\n",
      "       1376, 1376, 1376, 1376, 1376, 1376, 1377, 1377, 1377, 1377, 1377,\n",
      "       1377, 1377, 1377, 1377, 1377, 1377, 1377, 1377, 1377, 1377, 1377,\n",
      "       1377, 1377, 1377, 1377, 1378, 1378, 1378, 1378, 1378, 1378, 1378,\n",
      "       1378, 1378, 1378, 1378, 1378, 1378, 1378, 1378, 1378, 1378, 1378,\n",
      "       1378, 1379, 1379, 1379, 1379, 1379, 1379, 1379, 1379, 1379, 1379,\n",
      "       1379, 1379, 1379, 1380, 1380, 1380, 1380, 1380, 1380, 1380, 1380,\n",
      "       1380, 1380, 1380, 1380, 1381, 1381, 1381, 1381, 1381, 1381, 1381,\n",
      "       1381, 1381, 1382, 1382, 1382, 1382, 1382, 1382, 1382, 1382, 1382,\n",
      "       1383, 1383, 1383, 1384, 1384, 1384, 1384, 1384, 1384, 1385, 1385,\n",
      "       1385, 1386, 1386, 1387, 1387, 1387, 1388, 1388, 1389, 1389, 1389,\n",
      "       1391, 1392, 1393, 1393, 1394, 1395, 1395, 1369, 1369, 1370, 1370,\n",
      "       1370, 1370, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371, 1371,\n",
      "       1371, 1371, 1371, 1372, 1372, 1372, 1372, 1372, 1372, 1372, 1372,\n",
      "       1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373, 1373,\n",
      "       1373, 1374, 1374, 1374, 1374, 1374, 1375, 1375, 1375, 1375, 1375,\n",
      "       1375, 1376, 1376, 1377, 1377]), array([983, 936, 955, 956, 979, 933, 930, 931, 932, 937, 953, 954, 955,\n",
      "       956, 957, 958, 959, 960, 978, 923, 933, 934, 935, 936, 941, 942,\n",
      "       944, 950, 951, 952, 955, 956, 960, 961, 974, 921, 929, 931, 932,\n",
      "       933, 937, 946, 947, 948, 949, 953, 954, 957, 958, 959, 962, 921,\n",
      "       926, 928, 930, 934, 946, 950, 951, 952, 955, 956, 931, 932, 947,\n",
      "       948, 950, 953, 954, 959, 929, 934, 949, 951, 952, 953, 955, 950,\n",
      "       934, 951, 952, 953, 956, 960, 981, 932, 935, 952, 975, 924, 926,\n",
      "       927, 928, 929, 930, 931, 936, 953, 954, 955, 956, 957, 958, 959,\n",
      "       960, 964, 967, 977, 978, 980, 982, 923, 932, 933, 934, 935, 950,\n",
      "       951, 952, 960, 961, 962, 963, 968, 970, 971, 975, 976, 919, 920,\n",
      "       921, 924, 925, 926, 927, 928, 929, 930, 931, 936, 937, 938, 939,\n",
      "       940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 953, 954, 955,\n",
      "       957, 958, 959, 964, 965, 966, 971, 972, 973, 974, 977, 978, 921,\n",
      "       922, 923, 932, 933, 934, 935, 950, 951, 952, 955, 956, 959, 960,\n",
      "       961, 962, 965, 970, 974, 978, 921, 923, 924, 925, 926, 927, 928,\n",
      "       929, 930, 931, 932, 933, 935, 936, 937, 938, 939, 940, 941, 942,\n",
      "       943, 944, 945, 946, 947, 948, 949, 952, 953, 954, 957, 958, 963,\n",
      "       973, 921, 923, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934,\n",
      "       935, 936, 940, 942, 949, 950, 951, 952, 953, 955, 956, 959, 921,\n",
      "       923, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 937, 938,\n",
      "       941, 943, 944, 946, 947, 948, 949, 950, 951, 954, 921, 923, 925,\n",
      "       926, 927, 928, 929, 932, 935, 936, 939, 940, 942, 945, 947, 948,\n",
      "       949, 952, 953, 955, 921, 923, 925, 926, 927, 928, 930, 931, 933,\n",
      "       934, 937, 938, 941, 944, 946, 948, 949, 950, 951, 921, 923, 925,\n",
      "       928, 929, 932, 935, 939, 940, 945, 947, 949, 952, 921, 923, 926,\n",
      "       927, 930, 931, 933, 934, 946, 948, 950, 951, 919, 921, 924, 925,\n",
      "       928, 929, 932, 947, 949, 920, 922, 923, 926, 927, 930, 931, 947,\n",
      "       949, 920, 924, 947, 917, 919, 921, 922, 923, 946, 919, 920, 947,\n",
      "       916, 918, 918, 919, 947, 915, 917, 914, 915, 916, 915, 915, 913,\n",
      "       914, 913, 911, 912, 950, 959, 953, 961, 962, 965, 946, 950, 954,\n",
      "       955, 956, 957, 958, 959, 960, 965, 967, 973, 941, 944, 945, 951,\n",
      "       952, 953, 957, 958, 935, 943, 944, 946, 947, 948, 949, 950, 954,\n",
      "       955, 959, 961, 945, 951, 952, 953, 956, 946, 947, 948, 949, 950,\n",
      "       953, 947, 951, 948, 953]))\n",
      "FOV 028\n",
      "image max: 3530\n",
      "Appending...  reg_Cyc_1/Cycle_1_F028_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F028_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F028_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F028_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F028_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F028_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F028_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F028_reg.tif\n",
      "9814\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F028_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35]), array([ 99, 100, 100, 100, 100, 100, 100, 100, 101, 101, 101, 101, 101,\n",
      "       102, 102, 102, 102, 102, 103, 103, 104, 104, 104, 105, 105, 106,\n",
      "       106, 106, 107, 107, 108, 109, 110, 110, 111, 112, 112, 113, 114,\n",
      "       115, 116, 116, 117, 117, 118,  97,  97,  98,  98,  98,  99,  99,\n",
      "        99,  99, 100, 100, 100, 100, 100, 100, 100, 101, 101, 101, 101,\n",
      "       101, 101, 101, 101, 101, 101, 102, 102, 102, 102, 102, 102, 102,\n",
      "       102, 102, 102, 103, 103, 103, 103, 103, 103, 103, 103, 103, 104,\n",
      "       104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 105, 105, 105,\n",
      "       105, 105, 106, 106, 106, 106, 106, 106, 106, 106, 107, 107, 107,\n",
      "       107, 108, 108, 108, 108, 108, 109, 109, 109, 109, 110, 110, 110,\n",
      "       111, 111, 111, 112, 112, 112, 112, 113, 113, 114, 114, 114, 115,\n",
      "       115, 116, 116, 102, 102, 102, 103, 103, 105]), array([579, 576, 578, 580, 581, 582, 583, 584, 579, 583, 584, 585, 587,\n",
      "       578, 580, 582, 587, 589, 578, 581, 576, 578, 580, 576, 579, 575,\n",
      "       577, 578, 577, 578, 578, 577, 575, 577, 575, 574, 577, 576, 576,\n",
      "       576, 574, 576, 574, 575, 574, 592, 596, 593, 594, 596, 585, 588,\n",
      "       592, 595, 580, 581, 582, 583, 584, 587, 594, 576, 577, 578, 579,\n",
      "       585, 586, 588, 589, 592, 594, 577, 580, 581, 582, 583, 584, 587,\n",
      "       590, 591, 594, 577, 579, 586, 588, 589, 590, 591, 592, 594, 577,\n",
      "       579, 581, 582, 583, 584, 585, 586, 587, 588, 589, 577, 579, 581,\n",
      "       582, 584, 577, 579, 581, 583, 585, 586, 587, 588, 577, 579, 582,\n",
      "       584, 577, 579, 581, 583, 588, 577, 579, 580, 582, 577, 579, 581,\n",
      "       575, 577, 580, 575, 577, 578, 580, 576, 578, 576, 578, 580, 576,\n",
      "       579, 576, 577, 577, 578, 579, 577, 578, 577]))\n",
      "FOV 029\n",
      "image max: 3968\n",
      "Appending...  reg_Cyc_1/Cycle_1_F029_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F029_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F029_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F029_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F029_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F029_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F029_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F029_reg.tif\n",
      "10269\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F029_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35]), array([1894, 1894, 1895, 1895, 1895, 1896, 1896, 1896, 1896, 1897, 1897,\n",
      "       1897, 1897, 1897, 1898, 1898, 1898, 1898, 1898, 1898, 1898, 1898,\n",
      "       1898, 1898, 1898, 1898, 1898, 1899, 1899, 1899, 1899, 1899, 1899,\n",
      "       1899, 1899, 1899, 1900, 1900, 1900, 1900, 1900, 1900, 1900, 1900,\n",
      "       1900, 1900, 1900, 1901, 1901, 1901, 1901, 1901, 1901, 1901, 1901,\n",
      "       1901, 1901, 1901, 1902, 1902, 1902, 1902, 1902, 1902, 1902, 1902,\n",
      "       1902, 1902, 1902, 1902, 1903, 1903, 1903, 1903, 1903, 1903, 1903,\n",
      "       1903, 1903, 1903, 1904, 1904, 1904, 1904, 1904, 1904, 1904, 1904,\n",
      "       1904, 1904, 1905, 1905, 1905, 1905, 1905, 1905, 1905, 1905, 1905,\n",
      "       1906, 1906, 1906, 1906, 1906, 1906, 1906, 1906, 1906, 1906, 1906,\n",
      "       1907, 1907, 1907, 1907, 1907, 1907, 1907, 1907, 1908, 1908, 1908,\n",
      "       1908, 1908, 1908, 1908, 1909, 1909, 1909, 1909, 1910, 1910, 1910,\n",
      "       1910, 1910, 1910, 1911, 1911, 1911, 1911, 1912, 1912, 1912, 1913,\n",
      "       1913, 1913, 1913, 1914, 1914, 1915, 1915, 1915, 1915, 1915, 1897,\n",
      "       1898, 1898, 1898, 1899, 1899, 1900, 1900, 1900, 1900, 1900, 1900,\n",
      "       1900, 1900, 1900, 1900, 1900, 1901, 1901, 1901, 1901, 1902, 1902,\n",
      "       1902, 1902, 1902, 1902, 1902, 1902, 1902, 1903, 1903, 1903, 1903,\n",
      "       1903, 1903, 1904, 1904, 1904, 1904, 1904, 1904, 1904, 1904, 1904,\n",
      "       1905, 1905, 1905, 1905, 1905, 1905, 1906, 1906, 1906, 1906, 1906,\n",
      "       1907, 1907, 1907, 1907, 1907, 1908, 1908, 1908, 1908, 1908, 1908,\n",
      "       1908, 1909, 1909, 1909, 1910, 1910, 1910, 1912, 1912, 1912, 1912]), array([584, 591, 586, 587, 588, 574, 584, 585, 589, 571, 575, 583, 586,\n",
      "       588, 572, 573, 574, 577, 578, 579, 580, 581, 582, 584, 585, 587,\n",
      "       589, 565, 569, 570, 571, 575, 576, 583, 585, 587, 568, 572, 573,\n",
      "       574, 577, 578, 579, 580, 581, 582, 586, 568, 570, 571, 572, 574,\n",
      "       575, 576, 582, 583, 584, 586, 568, 570, 572, 573, 574, 575, 576,\n",
      "       577, 578, 580, 581, 585, 568, 570, 571, 572, 573, 574, 575, 578,\n",
      "       579, 583, 566, 568, 570, 572, 573, 574, 576, 577, 580, 581, 566,\n",
      "       568, 570, 571, 572, 573, 574, 575, 578, 566, 568, 569, 570, 571,\n",
      "       572, 573, 574, 576, 577, 581, 565, 567, 569, 570, 571, 572, 573,\n",
      "       575, 565, 567, 569, 570, 571, 572, 574, 567, 569, 571, 573, 565,\n",
      "       567, 569, 570, 572, 574, 565, 567, 569, 571, 565, 567, 571, 565,\n",
      "       568, 569, 570, 565, 567, 564, 565, 566, 568, 569, 573, 572, 574,\n",
      "       575, 567, 573, 567, 569, 570, 571, 572, 573, 574, 575, 576, 578,\n",
      "       579, 566, 568, 572, 573, 568, 570, 571, 572, 573, 574, 575, 577,\n",
      "       579, 567, 569, 571, 572, 574, 576, 564, 566, 568, 569, 570, 571,\n",
      "       572, 573, 575, 565, 567, 569, 570, 571, 573, 566, 568, 569, 571,\n",
      "       573, 566, 568, 570, 572, 574, 564, 566, 568, 569, 571, 572, 573,\n",
      "       566, 569, 571, 566, 568, 571, 566, 567, 568, 569]))\n",
      "FOV 030\n",
      "image max: 3393\n",
      "Appending...  reg_Cyc_1/Cycle_1_F030_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F030_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F030_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F030_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F030_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F030_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F030_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F030_reg.tif\n",
      "10247\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F030_reg.tif\n",
      "61531\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]), array([14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
      "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14]), array([ 582,  583,  704,  710,  710,  832,  832,  582,  703,  704,  710,\n",
      "        710,  831,  832,  832, 1216,  582,  703,  704,  704,  710,  710,\n",
      "        832,  832,  582,  703,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  704,  710,  710,  826,  832, 1216, 1698,\n",
      "       1703, 1728, 1728,  703,  710,  832, 1094, 1216, 1216, 1216, 1216,\n",
      "       1216, 1606, 1606, 1728, 1728, 1728, 1728,  710,  832, 1094, 1216,\n",
      "       1216, 1216, 1216, 1216, 1216, 1216, 1216, 1606, 1606, 1728, 1728,\n",
      "        664,  703,  710,  832, 1094, 1216, 1216, 1216, 1216, 1216, 1216,\n",
      "       1216, 1216, 1216, 1216, 1606, 1606, 1728, 1728, 1728,  664,  703,\n",
      "        704,  710,  832, 1094, 1216, 1216, 1216, 1216, 1606, 1606, 1728,\n",
      "       1728]), array([1896, 1770, 1896,  232,  488,  232,  488, 1896, 1770, 1896,  232,\n",
      "        488,  362,  232,  488, 1516, 1896, 1770, 1772, 1896,  232,  488,\n",
      "        232,  488, 1896, 1770, 1952, 1953, 1954, 1955, 1956, 1957, 1958,\n",
      "       1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
      "       1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980,\n",
      "       1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n",
      "       1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n",
      "       2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n",
      "       2014, 2015, 2016, 2017, 2018,  232,  488,  488,  232, 1896, 1982,\n",
      "       1000, 1000, 1896, 1770,  232,  232, 1896, 1388, 1485, 1490, 1492,\n",
      "       1896, 1000, 1896,  876, 1000, 1516, 1896,  232,  232, 1896, 1485,\n",
      "       1486, 1488, 1489, 1490, 1492, 1494, 1896, 1000, 1896, 1000, 1896,\n",
      "       1770, 1770,  232,  232, 1896, 1483, 1486, 1488, 1489, 1490, 1491,\n",
      "       1493, 1499, 1862, 1896, 1000, 1896,  876, 1000, 1896, 1770, 1770,\n",
      "       1772,  232,  232, 1896, 1489, 1496, 1497, 1896, 1000, 1896, 1000,\n",
      "       1896]))\n",
      "FOV 031\n",
      "image max: 3929\n",
      "Appending...  reg_Cyc_1/Cycle_1_F031_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F031_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F031_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F031_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F031_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F031_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F031_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F031_reg.tif\n",
      "11132\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F031_reg.tif\n",
      "65338\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([8, 8, 8, ..., 9, 9, 9]), array([15, 15, 15, ..., 15, 15, 15]), array([835, 835, 835, ..., 859, 961, 961]), array([236, 237, 238, ..., 490, 234, 490]))\n",
      "FOV 032\n",
      "image max: 3933\n",
      "Appending...  reg_Cyc_1/Cycle_1_F032_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F032_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F032_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F032_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F032_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F032_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F032_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F032_reg.tif\n",
      "10176\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F032_reg.tif\n",
      "65391\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65391\n",
      "(array([5]), array([36]), array([1075]), array([1633]))\n",
      "FOV 033\n",
      "image max: 3846\n",
      "Appending...  reg_Cyc_1/Cycle_1_F033_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F033_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F033_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F033_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F033_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F033_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F033_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F033_reg.tif\n",
      "11503\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F033_reg.tif\n",
      "60399\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60399\n",
      "FOV 034\n",
      "image max: 3242\n",
      "Appending...  reg_Cyc_1/Cycle_1_F034_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F034_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F034_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F034_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F034_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F034_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F034_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F034_reg.tif\n",
      "11072\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F034_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7,\n",
      "       7, 7, 7]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35]), array([1317, 1319, 1319, 1319, 1319, 1320, 1320, 1320, 1321, 1321, 1321,\n",
      "       1322, 1322, 1322, 1327, 1317, 1317, 1318, 1318, 1318, 1318, 1318,\n",
      "       1319, 1319, 1320, 1331, 1331, 1331, 1332, 1332, 1333, 1333, 1333,\n",
      "       1334, 1334, 1334, 1335, 1336, 1336, 1337, 1337, 1338, 1338, 1339,\n",
      "       1342, 1317, 1318, 1318, 1319, 1319, 1320, 1320, 1321, 1321, 1321,\n",
      "       1322, 1322, 1322, 1323, 1323, 1324, 1324, 1325, 1325, 1326, 1326,\n",
      "       1327, 1327, 1327, 1327, 1328, 1328, 1328, 1329, 1329, 1329, 1329,\n",
      "       1329, 1330, 1330, 1330, 1330, 1331, 1331, 1331, 1331, 1332, 1332,\n",
      "       1332, 1332, 1333, 1333, 1333, 1334, 1334, 1334, 1335, 1335, 1335,\n",
      "       1335, 1336, 1336, 1337, 1338, 1338, 1342, 1323, 1324, 1325, 1331,\n",
      "       1332, 1333, 1338]), array([677, 671, 672, 673, 676, 670, 671, 673, 671, 673, 674, 669, 671,\n",
      "       673, 672, 681, 682, 673, 677, 679, 680, 682, 683, 684, 684, 676,\n",
      "       678, 683, 679, 683, 678, 680, 683, 679, 682, 683, 683, 681, 683,\n",
      "       682, 683, 682, 683, 684, 681, 684, 683, 685, 683, 685, 683, 685,\n",
      "       681, 683, 685, 683, 685, 687, 683, 685, 683, 685, 683, 685, 682,\n",
      "       685, 680, 682, 683, 685, 681, 683, 685, 679, 680, 681, 683, 685,\n",
      "       680, 682, 683, 685, 680, 682, 683, 685, 679, 681, 683, 685, 681,\n",
      "       683, 685, 681, 683, 685, 680, 682, 683, 685, 682, 685, 685, 682,\n",
      "       684, 682, 686, 686, 686, 686, 686, 686, 685]))\n",
      "FOV 035\n",
      "image max: 3731\n",
      "Appending...  reg_Cyc_1/Cycle_1_F035_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F035_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F035_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F035_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F035_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F035_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F035_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F035_reg.tif\n",
      "11603\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F035_reg.tif\n",
      "54516\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7]), array([30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30]), array([64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64]), array([1383,  877, 1383,  877, 1383,  877, 1383,  749, 1255,  749, 1255,\n",
      "        749, 1255,  749, 1255]))\n",
      "FOV 036\n",
      "image max: 3962\n",
      "Appending...  reg_Cyc_1/Cycle_1_F036_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F036_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F036_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F036_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F036_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F036_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F036_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F036_reg.tif\n",
      "11541\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F036_reg.tif\n",
      "61057\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7]), array([31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
      "       31, 31, 31, 31, 31, 31, 31]), array([1863, 1879, 1883, 1887, 1888, 1891, 1895, 1896, 1900, 1902, 1903,\n",
      "       1904, 1906, 1908, 1909, 1913, 1915, 1916, 1917, 1918, 1922, 1923,\n",
      "       1924, 1926, 1927, 1929, 1933, 1935, 1936, 1937, 1938, 1939, 1942,\n",
      "       1944, 1945, 1946, 1948, 1949, 1951, 1952, 1953, 1954, 1955, 1957,\n",
      "       1959, 1960, 1961, 1962, 1963, 1964, 1965, 1967, 1968, 1969, 1970,\n",
      "       1971, 1973, 1974, 1975, 1976, 1977, 1978, 1872, 1874, 1876, 1887,\n",
      "       1893, 1897, 1898, 1899, 1900, 1903, 1904, 1907, 1912, 1913, 1915,\n",
      "       1917, 1918, 1919, 1922, 1928, 1929, 1930, 1931, 1932, 1935, 1936,\n",
      "       1939, 1941, 1942, 1943, 1944, 1945, 1948, 1950, 1951, 1952, 1953,\n",
      "       1954, 1955, 1957, 1959, 1961, 1962, 1966, 1967, 1968, 1969, 1970,\n",
      "       1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1860, 1881, 1886,\n",
      "       1891, 1895, 1898, 1899, 1900, 1901, 1904, 1907, 1914, 1917, 1918,\n",
      "       1924, 1926, 1928, 1929, 1930, 1932, 1933, 1934, 1935, 1936, 1938,\n",
      "       1939, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1950, 1951,\n",
      "       1953, 1954, 1955, 1957, 1958, 1961, 1962, 1964, 1965, 1967, 1969,\n",
      "       1971, 1972, 1973, 1974, 1975, 1977, 1978, 1860, 1886, 1887, 1892,\n",
      "       1900, 1904, 1905, 1906, 1907, 1911, 1912, 1914, 1917, 1919, 1920,\n",
      "       1924, 1925, 1927, 1929, 1930, 1933, 1936, 1941, 1944, 1945, 1946,\n",
      "       1947, 1948, 1950, 1951, 1953, 1954, 1955, 1958, 1959, 1960, 1961,\n",
      "       1964, 1966, 1967, 1968, 1970, 1971, 1972, 1973, 1974, 1975, 1976,\n",
      "       1977, 1884, 1888, 1891, 1892, 1896, 1899, 1900, 1902, 1907, 1909,\n",
      "       1911, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1923, 1924, 1925,\n",
      "       1926, 1927, 1929, 1930, 1931, 1933, 1934, 1936, 1940, 1943, 1944,\n",
      "       1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955,\n",
      "       1956, 1957, 1958, 1959, 1960, 1961, 1962, 1964, 1965, 1967, 1969,\n",
      "       1970, 1971, 1973, 1975, 1976, 1977, 1978, 1879, 1893, 1894, 1895,\n",
      "       1900, 1903, 1906, 1907, 1908, 1909, 1912, 1913, 1914, 1918, 1922,\n",
      "       1924, 1927, 1929, 1930, 1932, 1935, 1936, 1937, 1939, 1940, 1941,\n",
      "       1943, 1944, 1945, 1946, 1947, 1948, 1951, 1952, 1953, 1954, 1955,\n",
      "       1956, 1958, 1959, 1961, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
      "       1972, 1973, 1975, 1976, 1977, 1978, 1860, 1885, 1893, 1896, 1898,\n",
      "       1902, 1905, 1908, 1911, 1912, 1913, 1914, 1917, 1919, 1920, 1922,\n",
      "       1923, 1924, 1925, 1926, 1928, 1930, 1931, 1932, 1933, 1935, 1939,\n",
      "       1941, 1944, 1945, 1946, 1947, 1950, 1951, 1952, 1953, 1954, 1956,\n",
      "       1957, 1958, 1959, 1961, 1963, 1964, 1965, 1966, 1967, 1968, 1969,\n",
      "       1970, 1971, 1973, 1974, 1975, 1976, 1977, 1978, 1873, 1900, 1904,\n",
      "       1909, 1910, 1911, 1912, 1914, 1915, 1917, 1918, 1920, 1921, 1922,\n",
      "       1924, 1925, 1926, 1929, 1930, 1933, 1934, 1935, 1936, 1938, 1939,\n",
      "       1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941,\n",
      "       1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941,\n",
      "       1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941,\n",
      "       1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941,\n",
      "       1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941, 1941,\n",
      "       1941, 1941, 1941, 1941, 1941, 1941, 1941, 1942, 1942, 1942, 1942,\n",
      "       1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942,\n",
      "       1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942,\n",
      "       1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942,\n",
      "       1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942, 1942,\n",
      "       1942, 1942, 1944, 1945, 1946, 1947, 1948, 1950, 1951, 1952, 1953,\n",
      "       1955, 1961, 1965, 1966, 1968, 1971, 1973, 1974, 1975, 1976, 1977,\n",
      "       1978]), array([1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515, 1515,\n",
      "       1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961,\n",
      "       1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972,\n",
      "       1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983,\n",
      "       1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1994, 1996,\n",
      "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
      "       2009, 2010, 2012, 2013, 2014, 2015, 2016, 1898, 1899, 1900, 1901,\n",
      "       1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912,\n",
      "       1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923,\n",
      "       1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934,\n",
      "       1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945,\n",
      "       1946, 1947, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387]))\n",
      "FOV 037\n",
      "image max: 3728\n",
      "Appending...  reg_Cyc_1/Cycle_1_F037_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F037_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F037_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F037_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F037_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F037_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F037_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F037_reg.tif\n",
      "10011\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F037_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 7, 7, 7]), array([13, 13, 13, ..., 14, 14, 14]), array([450, 450, 450, ..., 319, 320, 321]), array([1644, 1751, 1753, ...,  488,  488,  488]))\n",
      "FOV 038\n",
      "image max: 4190\n",
      "Appending...  reg_Cyc_1/Cycle_1_F038_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F038_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F038_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F038_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F038_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F038_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F038_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F038_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F038_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6,\n",
      "       6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32,\n",
      "       32, 32, 32, 32, 32, 32, 32, 32]), array([1933, 1936, 1936, 1937, 1938, 1938, 1939, 1939, 1940, 1940, 1940,\n",
      "       1940, 1940, 1940, 1941, 1941, 1941, 1941, 1942, 1942, 1942, 1943,\n",
      "       1943, 1943, 1943, 1943, 1944, 1944, 1944, 1944, 1944, 1944, 1945,\n",
      "       1945, 1945, 1945, 1945, 1946, 1946, 1946, 1946, 1946, 1946, 1946,\n",
      "       1946, 1947, 1947, 1947, 1947, 1947, 1948, 1948, 1949, 1949, 1949,\n",
      "       1949, 1950, 1951, 1951, 1951, 1952, 1953, 1953, 1953, 1954, 1954,\n",
      "       1931, 1933, 1934, 1934, 1934, 1935, 1935, 1936, 1936, 1936, 1937,\n",
      "       1937, 1937, 1937, 1938, 1938, 1938, 1938, 1939, 1939, 1939, 1939,\n",
      "       1939, 1939, 1939, 1939, 1939, 1940, 1940, 1940, 1940, 1940, 1941,\n",
      "       1941, 1941, 1941, 1941, 1941, 1942, 1942, 1942, 1942, 1942, 1942,\n",
      "       1942, 1942, 1942, 1942, 1942, 1943, 1943, 1943, 1943, 1943, 1943,\n",
      "       1943, 1943, 1944, 1944, 1944, 1944, 1944, 1944, 1944, 1944, 1944,\n",
      "       1945, 1945, 1945, 1945, 1945, 1945, 1945, 1945, 1945, 1945, 1945,\n",
      "       1945, 1946, 1946, 1946, 1946, 1946, 1946, 1946, 1946, 1946, 1946,\n",
      "       1946, 1946, 1946, 1946, 1946, 1946, 1946, 1946, 1947, 1947, 1947,\n",
      "       1947, 1947, 1947, 1947, 1947, 1947, 1947, 1947, 1947, 1947, 1947,\n",
      "       1947, 1947, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948,\n",
      "       1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948, 1948,\n",
      "       1948, 1948, 1949, 1949, 1949, 1949, 1949, 1949, 1949, 1949, 1949,\n",
      "       1949, 1949, 1949, 1949, 1949, 1949, 1950, 1950, 1950, 1950, 1950,\n",
      "       1950, 1950, 1950, 1950, 1950, 1950, 1950, 1951, 1951, 1951, 1951,\n",
      "       1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1951, 1952, 1952,\n",
      "       1952, 1952, 1952, 1952, 1952, 1952, 1952, 1952, 1953, 1953, 1953,\n",
      "       1953, 1953, 1953, 1953, 1953, 1953, 1954, 1954, 1954, 1954, 1954,\n",
      "       1954, 1955, 1955, 1955, 1955, 1955, 1955, 1955, 1955, 1956, 1956,\n",
      "       1930, 1932, 1932, 1933, 1933, 1934, 1934, 1935, 1935, 1936, 1936,\n",
      "       1936, 1936, 1937, 1937, 1937, 1938, 1938, 1938, 1938, 1939, 1939,\n",
      "       1939, 1939, 1939, 1940, 1940, 1940, 1940, 1940, 1940, 1941, 1941,\n",
      "       1941, 1941, 1941, 1942, 1942, 1942, 1942, 1942, 1942, 1943, 1943,\n",
      "       1943, 1943, 1943, 1943, 1944, 1944, 1944, 1944, 1944, 1944, 1944,\n",
      "       1944, 1945, 1945, 1945, 1945, 1945, 1945, 1945, 1945, 1946, 1946,\n",
      "       1946, 1946, 1946, 1947, 1947, 1947, 1947, 1947, 1947, 1947, 1947,\n",
      "       1948, 1948, 1948, 1948, 1948, 1949, 1949, 1949, 1949, 1949, 1949,\n",
      "       1950, 1950, 1950, 1950, 1950, 1950, 1950, 1950, 1950, 1951, 1951,\n",
      "       1951, 1951, 1951, 1952, 1952, 1952, 1952, 1953, 1953, 1953, 1953,\n",
      "       1953, 1953, 1953, 1953, 1953, 1935, 1936, 1938, 1939, 1939, 1941,\n",
      "       1943,  625,  625,  625,  626,  626,  626,  626,  626,  626,  626,\n",
      "        626,  626,  627,  627,  627,  628,  629,  630,  632,  632,  632,\n",
      "        632,  632,  632,  632,  632,  632,  632,  632,  632,  632,  633,\n",
      "        633,  633,  633,  633,  633,  633,  633,  633,  635,  636,  643,\n",
      "        644,  645,  646,  647,  648,  650,  651,  652,  653,  654,  655,\n",
      "        656,  657,  658,  752,  753,  754,  754,  754,  754,  755,  755,\n",
      "        755,  755,  755,  755,  755,  755,  755,  755,  755,  755,  756,\n",
      "        756,  756,  756,  756,  756,  756,  756,  756,  756,  757,  758,\n",
      "        758,  758,  758,  758,  758,  758,  758,  758,  758,  758,  758,\n",
      "        758,  758,  758,  758,  759,  759,  759,  770,  771,  772,  773,\n",
      "        774,  775,  776,  777,  778,  779,  780,  781,  782,  783,  784,\n",
      "        785,  786,  787,  788,  789,  790,  791,  792,  793,  794,  795,\n",
      "        797,  805,  806,  807,  808,  809,  810,  811,  812,  813,  818,\n",
      "        819,  819,  820,  821,  826,  827,  828,  828,  829,  830,  831,\n",
      "        834,  835,  836,  837,  838,  839,  840,  841,  842,  856,  858,\n",
      "        859,  860,  861,  862,  863,  864,  865,  866,  867,  868,  869,\n",
      "        870,  871,  872,  873,  874,  874,  874,  877,  879,  879,  879,\n",
      "        879,  879,  879,  879,  879,  879,  880,  880,  880,  880,  880,\n",
      "        880,  880,  880,  880,  880,  880,  880,  881,  881,  881,  881,\n",
      "        881,  881,  881,  881,  881,  881,  881,  882,  882,  625,  625,\n",
      "        626,  626,  626,  626,  626,  626,  626,  626,  626,  627,  628,\n",
      "        629,  630,  632,  632,  632,  632,  632,  632,  632,  632,  632,\n",
      "        632,  632,  632,  632,  632,  633,  633,  633,  633,  633,  633,\n",
      "        633,  633,  635,  636,  637,  643,  644,  645,  646,  647,  648,\n",
      "        650,  651,  652,  653,  654,  655,  656,  657,  658,  752,  754,\n",
      "        754,  754,  754,  755,  755,  755,  755,  755,  755,  755,  755,\n",
      "        755,  755,  755,  755,  756,  756,  756,  756,  756,  756,  756,\n",
      "        756,  756,  756,  758,  758,  758,  758,  758,  758,  758,  758,\n",
      "        758,  758,  758,  758,  758,  758,  758,  758,  758,  759,  759,\n",
      "        759,  769,  770,  771,  772,  773,  774,  775,  776,  777,  778,\n",
      "        779,  780,  781,  782,  783,  784,  785,  786,  787,  788,  789,\n",
      "        790,  805,  806,  807,  808,  809,  810,  811,  812,  817,  818,\n",
      "        819,  820,  821,  826,  827,  828,  829,  830,  831,  832,  833,\n",
      "        834,  835,  836,  837,  838,  839,  840,  841,  842,  865,  874,\n",
      "        877,  879,  879,  879,  879,  879,  879,  879,  879,  879,  880,\n",
      "        880,  880,  880,  880,  880,  880,  880,  880,  880,  880,  880,\n",
      "        881,  881,  881,  881,  881,  881,  881,  881,  881,  881,  881,\n",
      "        881,  882,  882,  882]), array([ 886,  889,  891,  889,  888,  890,  890,  891,  887,  889,  891,\n",
      "        892,  894,  895,  889,  891,  893,  895,  891,  894,  896,  891,\n",
      "        893,  894,  896,  898,  890,  892,  894,  895,  898,  900,  891,\n",
      "        893,  895,  896,  897,  892,  894,  896,  898,  899,  900,  901,\n",
      "        902,  894,  896,  897,  898,  901,  895,  898,  893,  895,  897,\n",
      "        931,  931,  931,  933,  934,  931,  932,  934,  935,  936,  937,\n",
      "        886,  887,  887,  888,  890,  887,  890,  887,  889,  891,  887,\n",
      "        889,  891,  893,  887,  889,  891,  892,  886,  888,  890,  891,\n",
      "        892,  893,  894,  895,  897,  888,  890,  892,  895,  896,  888,\n",
      "        890,  892,  893,  894,  898,  885,  887,  889,  890,  891,  892,\n",
      "        893,  894,  895,  896,  899,  889,  891,  893,  894,  895,  897,\n",
      "        898,  900,  889,  891,  893,  894,  895,  896,  899,  901,  904,\n",
      "        888,  890,  892,  894,  895,  896,  897,  898,  900,  902,  903,\n",
      "        907,  889,  891,  893,  894,  895,  896,  897,  898,  899,  900,\n",
      "        902,  903,  905,  906,  907,  922,  929,  933,  891,  893,  894,\n",
      "        895,  896,  897,  898,  899,  901,  904,  912,  915,  916,  919,\n",
      "        928,  933,  890,  892,  894,  895,  896,  897,  898,  900,  902,\n",
      "        905,  917,  918,  919,  921,  922,  923,  924,  929,  930,  931,\n",
      "        932,  934,  892,  894,  896,  897,  899,  901,  921,  925,  926,\n",
      "        927,  928,  932,  933,  934,  935,  893,  895,  897,  899,  922,\n",
      "        924,  925,  929,  930,  931,  932,  937,  894,  896,  898,  923,\n",
      "        926,  927,  932,  933,  934,  935,  936,  937,  938,  895,  897,\n",
      "        928,  929,  930,  931,  932,  933,  937,  939,  895,  896,  933,\n",
      "        934,  935,  936,  937,  939,  941,  928,  929,  930,  931,  932,\n",
      "        940,  894,  933,  934,  936,  937,  938,  939,  940,  932,  941,\n",
      "        889,  887,  889,  887,  890,  887,  889,  887,  890,  886,  888,\n",
      "        889,  891,  888,  890,  892,  888,  890,  891,  893,  888,  890,\n",
      "        892,  893,  895,  887,  889,  891,  892,  894,  896,  889,  891,\n",
      "        893,  895,  897,  888,  890,  892,  894,  896,  898,  890,  892,\n",
      "        894,  895,  897,  899,  889,  891,  893,  894,  895,  896,  898,\n",
      "        900,  891,  893,  895,  896,  897,  898,  900,  902,  892,  894,\n",
      "        896,  898,  900,  889,  891,  893,  895,  896,  897,  898,  900,\n",
      "        893,  895,  897,  899,  933,  894,  896,  898,  928,  929,  933,\n",
      "        895,  897,  928,  930,  931,  932,  933,  935,  936,  896,  928,\n",
      "        931,  933,  934,  895,  928,  929,  937,  930,  931,  932,  933,\n",
      "        934,  935,  936,  937,  938,  891,  889,  893,  891,  892,  893,\n",
      "        892,  676,  677,  678,  692,  693,  694,  695,  696,  697,  698,\n",
      "        699,  700,  632,  637,  756,  630,  630,  630, 1782, 1790, 1791,\n",
      "       1792, 1793, 1794, 1795, 1796, 1797, 1958, 1959, 1960, 1961, 1974,\n",
      "       1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,  756,  756,  629,\n",
      "        629,  629, 1781, 1781, 1781,  755,  755,  755,  755,  755,  755,\n",
      "        755,  755,  755,   76,  113,  113,  859,  860,  861,  875,  876,\n",
      "        877,  878,  879,  880,  881,  882,  883, 1042, 1043, 1044,  113,\n",
      "       1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066,  113, 1129,\n",
      "       1130, 1131, 1783, 1784, 1785, 1786, 1946, 1947, 1948, 1949, 1950,\n",
      "       1951, 1952, 1953, 1954, 1967, 1968, 1969, 1137, 1137, 1137, 1137,\n",
      "       1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137,\n",
      "       1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137,\n",
      "       1137,  629,  629,  629,  629,  629,  629,  629,  629,  629,  115,\n",
      "        115, 1139, 1139, 1139,  628,  628,  114,  628,  114,  114,  114,\n",
      "       1138, 1138, 1138, 1138, 1138, 1138, 1138, 1138, 1138,  112,  112,\n",
      "        112,  112,  112,  112,  112,  112,  112,  112,  112,  112,  112,\n",
      "        112,  112,  112,  112,   47,   50,  112,  630,  663,  664,  665,\n",
      "        666,  667,  668,  669,  670,  671,  684,  685,  686,  847,  848,\n",
      "        849,  850,  851,  852,  853,  854, 1136,  868,  869, 1030, 1031,\n",
      "       1032, 1033, 1034, 1035, 1036, 1037, 1038, 1051, 1052,  677,  678,\n",
      "        692,  693,  694,  695,  696,  697,  698,  699,  700,  756,  630,\n",
      "        630,  630, 1782, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797,\n",
      "       1798, 1799, 1959, 1960, 1961, 1974, 1975, 1976, 1977, 1978, 1979,\n",
      "       1980, 1981,  756,  756,  756,  629,  629,  629, 1781, 1781, 1781,\n",
      "        755,  755,  755,  755,  755,  755,  755,  755,  755,  113,  113,\n",
      "        859,  860,  861,  875,  876,  877,  878,  879,  880,  881,  882,\n",
      "        883, 1042, 1043, 1044,  113, 1058, 1059, 1060, 1061, 1062, 1063,\n",
      "       1064, 1065, 1066,  113, 1127, 1128, 1129, 1130, 1131, 1783, 1784,\n",
      "       1785, 1786, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1967, 1968,\n",
      "       1969, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137,\n",
      "       1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137, 1137,\n",
      "       1137,  629,  629,  629,  629,  629,  629,  629,  629,  115,  115,\n",
      "       1139, 1139, 1139,  628,  628,  628,  114,  114,  114,  114,  114,\n",
      "       1138, 1138, 1138, 1138, 1138, 1138, 1138, 1138, 1138,  112,  112,\n",
      "        630,  664,  665,  666,  667,  668,  669,  670,  671,  672,  684,\n",
      "        685,  686,  847,  848,  849,  850,  851,  852,  853,  854, 1136,\n",
      "        867,  868,  869, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037,\n",
      "       1038, 1051, 1052, 1053]))\n",
      "FOV 039\n",
      "image max: 3198\n",
      "Appending...  reg_Cyc_1/Cycle_1_F039_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F039_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F039_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F039_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F039_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F039_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F039_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F039_reg.tif\n",
      "9178\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F039_reg.tif\n",
      "40091\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 62108\n",
      "FOV 040\n",
      "image max: 3191\n",
      "Appending...  reg_Cyc_1/Cycle_1_F040_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F040_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F040_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F040_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F040_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F040_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F040_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F040_reg.tif\n",
      "8928\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F040_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35]), array([1047, 1053, 1054, 1047, 1048, 1050, 1052, 1053, 1053, 1054, 1054,\n",
      "       1054, 1054, 1055, 1056, 1056, 1056, 1056, 1057, 1058, 1058, 1059,\n",
      "       1059, 1062, 1018, 1023, 1052, 1054, 1054, 1055, 1056, 1056, 1056,\n",
      "       1056, 1057, 1057, 1057, 1058, 1058, 1059]), array([759, 760, 759, 759, 758, 760, 761, 761, 764, 761, 763, 764, 765,\n",
      "       761, 761, 762, 763, 776, 758, 758, 759, 758, 774, 756, 786, 786,\n",
      "       761, 762, 763, 762, 763, 766, 767, 769, 760, 767, 769, 760, 770,\n",
      "       770]))\n",
      "FOV 041\n",
      "image max: 3586\n",
      "Appending...  reg_Cyc_1/Cycle_1_F041_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F041_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F041_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F041_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F041_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F041_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F041_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F041_reg.tif\n",
      "10807\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F041_reg.tif\n",
      "65526\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65526\n",
      "(array([5]), array([35]), array([762]), array([930]))\n",
      "FOV 042\n",
      "image max: 3597\n",
      "Appending...  reg_Cyc_1/Cycle_1_F042_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F042_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F042_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F042_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F042_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F042_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F042_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F042_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F042_reg.tif\n",
      "64216\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 9, 9, 9]), array([21, 21, 21, ..., 33, 33, 33]), array([1732, 1732, 1732, ..., 1514, 1515, 1515]), array([681, 709, 710, ..., 100, 113, 114]))\n",
      "excessive edge brightness detected\n",
      "FOV 043\n",
      "image max: 4053\n",
      "Appending...  reg_Cyc_1/Cycle_1_F043_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F043_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F043_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F043_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F043_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F043_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F043_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F043_reg.tif\n",
      "12835\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F043_reg.tif\n",
      "38668\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 42522\n",
      "FOV 044\n",
      "image max: 4571\n",
      "Appending...  reg_Cyc_1/Cycle_1_F044_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F044_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F044_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F044_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F044_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F044_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F044_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F044_reg.tif\n",
      "11527\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F044_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 9, 9, 9]), array([18, 18, 18, ..., 34, 34, 34]), array([ 839,  839,  961, ..., 1133, 1133, 1134]), array([1773, 1895, 1773, ...,  623,  624,  626]))\n",
      "FOV 045\n",
      "image max: 11028\n",
      "Appending...  reg_Cyc_1/Cycle_1_F045_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F045_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F045_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F045_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F045_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F045_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F045_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F045_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F045_reg.tif\n",
      "57432\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 9, 9, 9]), array([8, 8, 8, ..., 8, 8, 8]), array([1350, 1350, 1350, ...,  192,  192,  193]), array([621, 622, 623, ..., 102, 103, 105]))\n",
      "excessive edge brightness detected\n",
      "FOV 046\n",
      "image max: 4507\n",
      "Appending...  reg_Cyc_1/Cycle_1_F046_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F046_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F046_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F046_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F046_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F046_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F046_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F046_reg.tif\n",
      "12729\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F046_reg.tif\n",
      "52204\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 52204\n",
      "FOV 047\n",
      "image max: 4145\n",
      "Appending...  reg_Cyc_1/Cycle_1_F047_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F047_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F047_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F047_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F047_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F047_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F047_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F047_reg.tif\n",
      "9852\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F047_reg.tif\n",
      "64262\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 64262\n",
      "FOV 048\n",
      "image max: 4172\n",
      "Appending...  reg_Cyc_1/Cycle_1_F048_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F048_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F048_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F048_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F048_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F048_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F048_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F048_reg.tif\n",
      "9858\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F048_reg.tif\n",
      "65436\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]), array([13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13]), array([ 581,  582,  582,  582,  582,  582,  582,  582,  582,  582,  582,\n",
      "        582,  582,  582,  582,  582,  582,  582,  582,  582,  582,  582,\n",
      "        582,  582,  582,  582,  582,  582,  582,  582,  582,  582,  583,\n",
      "        584,  585,  586,  587,  588,  589,  590,  591,  592,  593,  594,\n",
      "        595,  596,  597,  598,  599,  600,  601,  602,  603,  604,  605,\n",
      "        606,  607,  608,  609,  610,  611,  612,  613,  614,  615,  616,\n",
      "        617,  618,  619,  620,  621,  622,  623,  624,  625,  626,  627,\n",
      "        628,  629,  630,  631,  632,  633,  634,  635,  636,  637,  638,\n",
      "        639,  640,  641,  642,  643,  644,  645,  646,  647,  648,  649,\n",
      "        650,  651,  652,  653,  654,  655,  656,  657,  658,  659,  660,\n",
      "        661,  662,  663,  664,  665,  666,  667,  668,  669,  670,  671,\n",
      "        672,  673,  674,  675,  676,  677,  678,  679,  680,  681,  682,\n",
      "        683,  684,  685,  686,  687,  688,  689,  690,  691,  692,  693,\n",
      "        694,  695,  696,  697,  698,  699,  700,  701,  702,  703,  704,\n",
      "        704,  704,  704,  704,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  704,  704,  704,  704,  704,  704,  704,\n",
      "        704,  704,  704,  704,  705, 1093, 1094, 1094, 1094, 1094, 1094,\n",
      "       1094, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103,\n",
      "       1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114,\n",
      "       1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125,\n",
      "       1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136,\n",
      "       1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147,\n",
      "       1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158,\n",
      "       1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169,\n",
      "       1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180,\n",
      "       1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191,\n",
      "       1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202,\n",
      "       1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213,\n",
      "       1214, 1215, 1216, 1216, 1216, 1216, 1216, 1216, 1216, 1216, 1216,\n",
      "       1216, 1216, 1216, 1216, 1217,  581,  582,  582,  582,  582,  582,\n",
      "        582,  582,  582,  582,  582,  582,  582,  582,  582,  582,  582,\n",
      "        582,  582,  582,  582,  582,  582,  582,  582,  583,  584,  585,\n",
      "        586,  587,  588,  589,  590,  591,  592,  593,  594,  595,  596,\n",
      "        597,  598,  599,  600,  601,  602,  603,  604,  605,  606,  607,\n",
      "        608,  609,  610,  611,  612,  613,  614,  615,  616,  617,  618,\n",
      "        619,  620,  621,  622,  623,  624,  625,  626,  627,  627,  628,\n",
      "        628,  629,  635,  636,  637,  638,  639,  640,  641,  642,  643,\n",
      "        644,  645,  646,  647,  648,  649,  650,  651,  652,  653,  654,\n",
      "        655,  656,  657,  658,  659,  660,  661,  662,  663,  664,  665,\n",
      "        666,  667,  668,  669,  670,  671,  672,  673,  674,  675,  676,\n",
      "        677,  678,  679,  680,  681,  682,  683,  684,  685,  686,  687,\n",
      "        688,  689,  690,  691,  692,  693,  694,  695,  696,  697,  698,\n",
      "        699,  700,  701,  702,  703,  704,  705, 1093, 1094, 1095, 1096,\n",
      "       1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107,\n",
      "       1108, 1109, 1110, 1111, 1112, 1113, 1114, 1114, 1115, 1115, 1116,\n",
      "       1117, 1118, 1119, 1119, 1120, 1120, 1121, 1122, 1122, 1123, 1123,\n",
      "       1124, 1124, 1125, 1125, 1126, 1126, 1127, 1127, 1128, 1128, 1129,\n",
      "       1129, 1130, 1130, 1131, 1131, 1132, 1132, 1133, 1133, 1134, 1134,\n",
      "       1135, 1135, 1136, 1136, 1137, 1137, 1138, 1138, 1139, 1139, 1140,\n",
      "       1140, 1141, 1141, 1142, 1142, 1143, 1143, 1144, 1144, 1145, 1145,\n",
      "       1146, 1146, 1147, 1147, 1148, 1148, 1149, 1149, 1150, 1150, 1151,\n",
      "       1151, 1152, 1152, 1153, 1153, 1154, 1154, 1155, 1155, 1156, 1156,\n",
      "       1157, 1157, 1158, 1158, 1159, 1159, 1160, 1160, 1161, 1161, 1162,\n",
      "       1162, 1163, 1163, 1164, 1164, 1165, 1165, 1166, 1166, 1167, 1167,\n",
      "       1168, 1168, 1169, 1169, 1170, 1170, 1171, 1171, 1172, 1172, 1173,\n",
      "       1173, 1174, 1174, 1175, 1175, 1176, 1176, 1177, 1177, 1178, 1178,\n",
      "       1179, 1179, 1180, 1180, 1181, 1181, 1182, 1182, 1183, 1183, 1184,\n",
      "       1184, 1185, 1185, 1186, 1186, 1187, 1187, 1188, 1188, 1189, 1189,\n",
      "       1190, 1190, 1191, 1191, 1192, 1192, 1193, 1193, 1194, 1194, 1195,\n",
      "       1195, 1196, 1196, 1197, 1197, 1198, 1198, 1199, 1199, 1200, 1200,\n",
      "       1201, 1201, 1202, 1202, 1203, 1203, 1204, 1204, 1205, 1205, 1206,\n",
      "       1206, 1207, 1207, 1208, 1208, 1209, 1209, 1210, 1210, 1211, 1211,\n",
      "       1212, 1212, 1213, 1213, 1214, 1214, 1215, 1215, 1216, 1216, 1216,\n",
      "       1217, 1217]), array([ 873,  699,  700,  702,  703,  704,  705,  706,  707,  708,  709,\n",
      "        710,  711,  712,  713,  714,  715,  716,  717,  718,  719,  720,\n",
      "        721,  751,  752,  753,  754,  755,  757,  760,  849,  871,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  763,\n",
      "        765,  766,  767,  768,  769,  770,  771,  772,  774,  775,  776,\n",
      "        780,  781,  782,  783,  784,  785,  786,  787,  788,  789,  790,\n",
      "        791,  793,  794,  871,  873, 1897, 1878, 1879, 1880, 1882, 1883,\n",
      "       1884, 1895, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1421, 1422, 1426, 1427, 1433, 1437, 1438, 1440, 1457,\n",
      "       1808, 1811, 1815, 1895, 1897,  873,  701,  702,  706,  707,  710,\n",
      "        714,  716,  717,  718,  719,  720,  721,  722,  723,  745,  748,\n",
      "        749,  750,  751,  752,  753,  754,  755,  871,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  363,  363,\n",
      "        363,  363,  363,  363,  363,  363,  363,  363,  363,  871,  363,\n",
      "        873,  363,  235,  235,  235,  235,  235,  235,  235,  235,  235,\n",
      "        235,  235,  235,  235,  235,  235,  235,  235,  235,  235,  235,\n",
      "        235,  235,  235,  235,  235,  235,  235,  235,  235,  235,  235,\n",
      "        235,  235,  235,  235,  235,  235,  235,  235,  235,  235,  235,\n",
      "        235,  235,  235,  235,  235,  235,  235,  235,  235,  235,  235,\n",
      "        235,  235,  235,  235,  235,  235,  235,  235,  235,  235,  235,\n",
      "        235,  235,  235,  235,  235,  743,  745, 1897, 1895, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387, 1387,\n",
      "       1387, 1387, 1387, 1387, 1387, 1387, 1387, 1895, 1387, 1897, 1387,\n",
      "       1387, 1387,  233, 1387,  231, 1387, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107,\n",
      "       1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,  107, 1387,\n",
      "        107, 1387,  107, 1387,  107, 1387,  107, 1387,  231, 1424, 1767,\n",
      "        233, 1769]))\n",
      "FOV 049\n",
      "image max: 4111\n",
      "Appending...  reg_Cyc_1/Cycle_1_F049_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F049_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F049_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F049_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F049_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F049_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F049_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F049_reg.tif\n",
      "9882\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F049_reg.tif\n",
      "32887\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 34470\n",
      "FOV 050\n",
      "image max: 2705\n",
      "Appending...  reg_Cyc_1/Cycle_1_F050_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F050_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F050_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F050_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F050_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F050_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F050_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F050_reg.tif\n",
      "8760\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F050_reg.tif\n",
      "52070\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 7, 7]), array([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
      "       19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
      "       19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
      "       19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
      "       19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
      "       19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
      "       19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19]), array([1097, 1219, 1097, 1101, 1219, 1097, 1099, 1100, 1101, 1102, 1103,\n",
      "       1104, 1105, 1106, 1110, 1219, 1097, 1099, 1100, 1102, 1103, 1104,\n",
      "       1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115,\n",
      "       1219, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106,\n",
      "       1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117,\n",
      "       1118, 1119, 1219, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105,\n",
      "       1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116,\n",
      "       1117, 1219, 1097, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106,\n",
      "       1108, 1109, 1110, 1111, 1112, 1113, 1114, 1118, 1219, 1097, 1099,\n",
      "       1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1110, 1199,\n",
      "       1204, 1205, 1219, 1219]), array([1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517,\n",
      "       1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 1517, 2014,\n",
      "       1504, 1389, 1389, 1895]))\n",
      "FOV 051\n",
      "image max: 3090\n",
      "Appending...  reg_Cyc_1/Cycle_1_F051_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F051_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F051_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F051_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F051_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F051_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F051_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F051_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F051_reg.tif\n",
      "46729\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 7, 7, 7]), array([24, 24, 24, ..., 32, 32, 32]), array([1480, 1480, 1480, ...,  239,  239,  239]), array([749, 873, 876, ..., 928, 929, 930]))\n",
      "FOV 052\n",
      "image max: 2842\n",
      "Appending...  reg_Cyc_1/Cycle_1_F052_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F052_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F052_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F052_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F052_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F052_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F052_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F052_reg.tif\n",
      "8341\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F052_reg.tif\n",
      "50907\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
      "       3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
      "       4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,\n",
      "       6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
      "       7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9]), array([17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
      "       17, 17, 17, 17]), array([1478, 1478, 1600, 1600, 1602, 1602, 1734, 1856, 1862, 1862, 1478,\n",
      "       1478, 1600, 1600, 1602, 1602, 1734, 1734, 1734, 1734, 1734, 1798,\n",
      "       1856, 1858, 1862, 1862, 1919, 1921, 1921, 1923, 1478, 1478, 1600,\n",
      "       1600, 1602, 1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734,\n",
      "       1734, 1858, 1862, 1862, 1478, 1478, 1581, 1582, 1583, 1584, 1586,\n",
      "       1600, 1600, 1602, 1734, 1734, 1734, 1734, 1734, 1734, 1856, 1858,\n",
      "       1862, 1862, 1478, 1478, 1581, 1582, 1583, 1584, 1585, 1586, 1587,\n",
      "       1588, 1589, 1591, 1600, 1600, 1602, 1602, 1734, 1734, 1734, 1734,\n",
      "       1734, 1734, 1734, 1856, 1858, 1862, 1862, 1478, 1478, 1580, 1581,\n",
      "       1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1591, 1592, 1600,\n",
      "       1600, 1602, 1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734,\n",
      "       1734, 1734, 1734, 1856, 1858, 1862, 1862, 1478, 1478, 1581, 1582,\n",
      "       1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1600, 1600, 1602,\n",
      "       1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734, 1734,\n",
      "       1734, 1734, 1734, 1734, 1734, 1734, 1856, 1862, 1862, 1478, 1478,\n",
      "       1580, 1600, 1600, 1602, 1734, 1734, 1734, 1734, 1734, 1856, 1858,\n",
      "       1862, 1862,  326,  448,  454,  576,  966,  966,  966,  966,  966,\n",
      "        966, 1088, 1088, 1088, 1088, 1088, 1088, 1090, 1090, 1090, 1090,\n",
      "       1090,  326,  378,  450,  454,  499,  501,  503,  576,  966,  966,\n",
      "        966,  966,  966,  966,  999, 1000, 1001, 1001, 1002, 1006, 1088,\n",
      "       1088, 1088, 1088, 1090, 1090]), array([ 493,  615,  493,  615,  492,  616, 1901, 1901,  109,  359,  493,\n",
      "        615,  493,  615,  492,  616, 1901, 1995, 1996, 1997, 1998, 1773,\n",
      "       1773, 1772,  109,  359,  359,  315,  360,  233,  493,  615,  493,\n",
      "        615,  616, 1773, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,\n",
      "       2002, 1772,  109,  231,  493,  615,  615,  615,  615,  615,  615,\n",
      "        493,  615,  616, 1773, 1992, 1993, 2002, 2003, 2004, 1773, 1772,\n",
      "        109,  231,  493,  615,  615,  615,  615,  615,  615,  615,  615,\n",
      "        615,  615,  615,  493,  615,  492,  616, 1773, 1990, 1991, 1992,\n",
      "       2003, 2004, 2005, 1773, 1772,  109,  231,  493,  615,  615,  615,\n",
      "        615,  615,  615,  615,  615,  615,  615,  615,  615,  615,  493,\n",
      "        615,  616, 1773, 1989, 1990, 1991, 1992, 2000, 2001, 2002, 2003,\n",
      "       2004, 2005, 2006, 1773, 1772,  109,  231,  493,  615,  615,  615,\n",
      "        615,  615,  615,  615,  615,  615,  615,  615,  493,  615,  616,\n",
      "       1773, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
      "       1999, 2000, 2001, 2002, 2003, 2004, 1773,  109,  231,  493,  615,\n",
      "        615,  493,  615,  616, 1773, 1994, 1995, 1998, 1999, 1773, 1772,\n",
      "        109,  231, 1901, 1901,  359,  359,  493,  615,  877, 1127, 1261,\n",
      "       1383,  493,  615,  877, 1127, 1261, 1383,  492,  616,  876, 1260,\n",
      "       1384, 1901, 1773, 1772,  359,  359,  283,  233,  231,  493,  615,\n",
      "        877, 1127, 1261, 1383, 1383, 1261, 1275, 1384, 1260,  749,  493,\n",
      "        615,  749, 1127,  492,  748]))\n",
      "FOV 053\n",
      "image max: 7048\n",
      "Appending...  reg_Cyc_1/Cycle_1_F053_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F053_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F053_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F053_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F053_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F053_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F053_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F053_reg.tif\n",
      "8730\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F053_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([4]), array([35]), array([654]), array([280]))\n",
      "FOV 054\n",
      "image max: 2773\n",
      "Appending...  reg_Cyc_1/Cycle_1_F054_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F054_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F054_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F054_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F054_reg.tif\n",
      "65529\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F054_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F054_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F054_reg.tif\n",
      "7907\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F054_reg.tif\n",
      "60813\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]), array([13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
      "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13]), array([  64,   70,  192,  453,  454,  454,  576,  576,  577,  965,  966,\n",
      "        966, 1088, 1088, 1089, 1349, 1350, 1350, 1472, 1472, 1473,    2,\n",
      "          3,    3,    5,    6,   64,   70,  127,  128,  192,  453,  454,\n",
      "        454,  499,  500,  502,  503,  503,  505,  506,  576,  576,  577,\n",
      "        965,  966,  966,  999, 1000, 1003, 1005, 1006, 1088, 1088, 1089,\n",
      "       1349, 1350, 1350, 1370, 1371, 1374, 1376, 1377, 1472, 1472, 1473]), array([1901,  359,  359, 1387, 1389, 1895, 1389, 1895, 1387,  363,  365,\n",
      "        871,  365,  871,  363,  875,  877, 1383,  877, 1383,  875, 1899,\n",
      "       1851, 1898, 1771, 1773, 1773,  359,  359,  314,  231, 1387, 1389,\n",
      "       1895, 1895, 1818, 1387, 1307, 1386, 1259, 1261, 1261, 1767, 1259,\n",
      "        363,  365,  871,  871,  762,  362,  235,  237,  237,  743,  235,\n",
      "        875,  877, 1383, 1383, 1370,  874,  747,  749,  749, 1255,  747]))\n",
      "excessive edge brightness detected\n",
      "FOV 055\n",
      "image max: 2249\n",
      "Appending...  reg_Cyc_1/Cycle_1_F055_reg.tif\n",
      "65533\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F055_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F055_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F055_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F055_reg.tif\n",
      "65529\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F055_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F055_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F055_reg.tif\n",
      "7712\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F055_reg.tif\n",
      "62414\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 62414\n",
      "FOV 056\n",
      "image max: 2439\n",
      "Appending...  reg_Cyc_1/Cycle_1_F056_reg.tif\n",
      "65532\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F056_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F056_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F056_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F056_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F056_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F056_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F056_reg.tif\n",
      "6696\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F056_reg.tif\n",
      "33272\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 33272\n",
      "FOV 057\n",
      "image max: 2314\n",
      "Appending...  reg_Cyc_1/Cycle_1_F057_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F057_reg.tif\n",
      "65535\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F057_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F057_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F057_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F057_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F057_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F057_reg.tif\n",
      "8802\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F057_reg.tif\n",
      "45273\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65523\n",
      "(array([0]), array([26]), array([1447]), array([622]))\n",
      "FOV 058\n",
      "image max: 3059\n",
      "Appending...  reg_Cyc_1/Cycle_1_F058_reg.tif\n",
      "65534\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F058_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F058_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F058_reg.tif\n",
      "65535\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F058_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F058_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F058_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F058_reg.tif\n",
      "9017\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F058_reg.tif\n",
      "47898\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65516\n",
      "(array([6]), array([28]), array([768]), array([109]))\n",
      "FOV 059\n",
      "image max: 2967\n",
      "Appending...  reg_Cyc_1/Cycle_1_F059_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F059_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F059_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F059_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F059_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F059_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F059_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F059_reg.tif\n",
      "9701\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F059_reg.tif\n",
      "61842\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,\n",
      "       9, 9]), array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5, 5]), array([ 581,  581,  583,  583,  705,  705,  705, 1093, 1093, 1095, 1095,\n",
      "       1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217,\n",
      "       1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217,\n",
      "       1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217,\n",
      "       1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217,\n",
      "       1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217,\n",
      "       1217, 1217, 1217, 1217,  581,  581,  583,  583,  628,  631,  632,\n",
      "        633,  633,  635,  705,  705, 1093, 1093, 1095, 1095, 1115, 1118,\n",
      "       1120, 1122, 1176, 1177, 1178, 1179, 1180, 1182, 1193, 1194, 1196,\n",
      "       1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207,\n",
      "       1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1217,\n",
      "       1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217, 1217,\n",
      "       1217, 1217]), array([ 876, 1384,  877, 1383,  877,  928, 1383, 1388, 1896, 1389, 1895,\n",
      "       1389, 1693, 1694, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704,\n",
      "       1705, 1706, 1707, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748,\n",
      "       1760, 1761, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771,\n",
      "       1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782,\n",
      "       1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1792, 1793, 1795,\n",
      "       1796, 1797, 1798, 1895,  876, 1384,  877, 1383, 1383,  875,  874,\n",
      "        748,  795,  749,  749, 1255, 1388, 1896, 1389, 1895, 1895, 1387,\n",
      "       1371, 1261, 1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767,\n",
      "       1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767,\n",
      "       1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767, 1767, 1261, 1740,\n",
      "       1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1761,\n",
      "       1764, 1765]))\n",
      "FOV 060\n",
      "image max: 3247\n",
      "Appending...  reg_Cyc_1/Cycle_1_F060_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F060_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F060_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F060_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F060_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F060_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F060_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F060_reg.tif\n",
      "8444\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F060_reg.tif\n",
      "44590\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9,\n",
      "       9, 9, 9, 9, 9]), array([20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
      "       20, 20, 20, 20, 20, 20, 20, 20, 20, 20]), array([1607, 1659, 1665, 1666, 1668, 1669, 1670, 1671, 1672, 1673, 1674,\n",
      "       1677, 1681, 1682, 1729, 1729, 1735, 1857, 1857, 1607, 1675, 1677,\n",
      "       1729, 1729, 1735, 1857, 1857]), array([1005, 1127, 1127, 1127, 1127, 1127, 1127, 1127, 1127, 1127, 1127,\n",
      "       1127, 1127, 1127, 1005, 1127,  237,  237,  615, 1127, 1127, 1127,\n",
      "       1005, 1127,  237,  237,  615]))\n",
      "FOV 062\n",
      "image max: 2962\n",
      "Appending...  reg_Cyc_1/Cycle_1_F062_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F062_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F062_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F062_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F062_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F062_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F062_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F062_reg.tif\n",
      "7861\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F062_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5,\n",
      "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35]), array([140, 140, 141, 142, 142, 142, 142, 143, 143, 144, 144, 144, 144,\n",
      "       144, 144, 145, 145, 147, 149, 140, 141, 141, 141, 142, 142, 143,\n",
      "       143, 144, 144, 145, 145, 146, 146, 147]), array([1700, 1701, 1701, 1697, 1698, 1701, 1703, 1701, 1703, 1698, 1699,\n",
      "       1700, 1701, 1702, 1703, 1697, 1700, 1700, 1694, 1701, 1698, 1699,\n",
      "       1702, 1700, 1703, 1700, 1701, 1700, 1701, 1700, 1702, 1699, 1700,\n",
      "       1696]))\n",
      "FOV 063\n",
      "image max: 2950\n",
      "Appending...  reg_Cyc_1/Cycle_1_F063_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F063_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F063_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F063_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F063_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F063_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F063_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F063_reg.tif\n",
      "7899\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F063_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
      "       5]), array([35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35,\n",
      "       35, 35, 35, 35, 35, 35]), array([1937, 1938, 1938, 1939, 1939, 1939, 1939, 1940, 1940, 1940, 1940,\n",
      "       1941, 1941, 1938, 1938, 1939, 1939, 1940, 1940, 1941, 1942, 1944,\n",
      "       1945]), array([1691, 1693, 1694, 1689, 1690, 1691, 1692, 1687, 1688, 1689, 1693,\n",
      "       1690, 1691, 1692, 1693, 1690, 1693, 1692, 1693, 1690, 1691, 1692,\n",
      "       1689]))\n",
      "FOV 065\n",
      "image max: 32284\n",
      "Appending...  reg_Cyc_1/Cycle_1_F065_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F065_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F065_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F065_reg.tif\n",
      "65532\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F065_reg.tif\n",
      "65529\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F065_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F065_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F065_reg.tif\n",
      "8029\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F065_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([4]), array([35]), array([1632]), array([1295]))\n",
      "FOV 066\n",
      "image max: 37664\n",
      "Appending...  reg_Cyc_1/Cycle_1_F066_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F066_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F066_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F066_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F066_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F066_reg.tif\n",
      "65530\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F066_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F066_reg.tif\n",
      "9598\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F066_reg.tif\n",
      "36423\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 65535\n",
      "(array([0, 0, 0, ..., 7, 7, 7]), array([5, 5, 5, ..., 5, 5, 5]), array([ 325,  325,  327, ...,  965, 1089, 1089]), array([1390, 1391, 1511, ..., 1766, 1261, 1767]))\n",
      "FOV 067\n",
      "image max: 3161\n",
      "Appending...  reg_Cyc_1/Cycle_1_F067_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F067_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F067_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F067_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F067_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F067_reg.tif\n",
      "65531\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F067_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F067_reg.tif\n",
      "8982\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F067_reg.tif\n",
      "60039\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 60039\n",
      "FOV 068\n",
      "image max: 22266\n",
      "Appending...  reg_Cyc_1/Cycle_1_F068_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F068_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F068_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F068_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F068_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F068_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F068_reg.tif\n",
      "65533\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F068_reg.tif\n",
      "8215\n",
      "Shape = (10, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F068_reg.tif\n",
      "49528\n",
      "Shape = (10, 38, 2048, 2048)\n",
      "image shape 2048 2048\n",
      "2029\n",
      "1989\n",
      "image max: 49528\n",
      "FOV 069\n",
      "image max: 3062\n",
      "Appending...  reg_Cyc_1/Cycle_1_F069_reg.tif\n",
      "65535\n",
      "Shape = (10, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F069_reg.tif\n",
      "65535\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F069_reg.tif\n",
      "65534\n",
      "excessive edge brightness detected\n",
      "Shape = (10, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F069_reg.tif\n",
      "65532\n"
     ]
    }
   ],
   "source": [
    "before_lst = []\n",
    "before = open(\"before_crop.txt\", \"a\")\n",
    "after_lst = []\n",
    "after = open(\"after_crop.txt\", \"a\")\n",
    "high_int = open(\"high_int.txt\", \"a\")\n",
    "\n",
    "refs = iter(glob.glob('tif/Cycle_0/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    ref_name = next(refs)\n",
    "    FOV_num = ref_name.split('_F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\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(\"image max:\",img.max())\n",
    "    im_max = img.max()\n",
    "    if im_max > 65000:\n",
    "        loc_ref = np.where(img == im_max)\n",
    "        if (loc_ref[2] < 50).any() | (loc_ref[2] > (Y_total -50)).any() | (loc_ref[3] < 50).any() | (loc_ref[3] > (X_total -50)).any():\n",
    "            before_lst.append(ref_name) \n",
    "            before.write(ref_name)\n",
    "            before.write(\"\\t\")\n",
    "            before.write(str(loc_ref))\n",
    "            before.write(\"\\n\")\n",
    "            print(\"excessive edge brightness detected\")\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",
    "        print(im_to_add.max())\n",
    "        imtoadd_max = im_to_add.max()\n",
    "        if imtoadd_max > 65000:\n",
    "            loc = np.where(img == imtoadd_max)\n",
    "            if (25 > loc[2]).any() | (loc[2] > (Y_total-25)).any() | (25 > loc[3]).any() | (loc[3] > (X_total-25)).any():\n",
    "                before_lst.append(fname) \n",
    "                before.write(fname)\n",
    "                before.write(\"\\t\")\n",
    "                before.write(str(loc))\n",
    "                before.write(\"\\n\")   \n",
    "                print(\"excessive edge brightness detected\")\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",
    "    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",
    "#     ### FINAL CHECK before saving ### \n",
    "#     assert img.shape[0] == Z, \"Check ZCYX\"\n",
    "#     assert img.shape[1] == final_ch, \"check final merge size\"\n",
    "    \n",
    "    print(\"image max:\", img.max())\n",
    "    fin_max = img.max()\n",
    "    if fin_max > 65000:\n",
    "        fin_loc = np.where(img == fin_max)\n",
    "        print(fin_loc) \n",
    "        high_int.write(fname)\n",
    "        high_int.write(\"\\t\")\n",
    "        high_int.write(str(fin_loc))\n",
    "        high_int.write(\"\\n\")\n",
    "        if (fin_loc[2] < 25).any() | (fin_loc[2] > (Y_total -25)).any() | (fin_loc[3] < 25).any() | (fin_loc[3] > (X_total -25)).any():\n",
    "            after_lst.append(fname) \n",
    "            after.write(fname)\n",
    "            after.write(\"\\t\")\n",
    "            after.write(str(fin_loc))\n",
    "            after.write(\"\\n\")\n",
    "            print(\"excessive edge brightness detected\")\n",
    "\n",
    "#     tifffile.imwrite(\n",
    "#         f'./{MERGE_DIR}'+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\n",
    "before.close()\n",
    "after.close()\n",
    "high_int.close()"
   ]
  },
  {
   "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": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = imread('reg_Cyc_1/Cycle_1_F000_reg.tif')\n",
    "img[...,60:1988, 60:1988] = 0\n",
    "#img[1, 1, 1000, 1000]\n",
    "img[1, 1, 59, 1989]"
   ]
  },
  {
   "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 007 \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 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 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 057 \n",
      "cycle 1 field 058 \n",
      "cycle 1 field 059 \n",
      "cycle 1 field 060 \n",
      "cycle 1 field 062 \n",
      "cycle 1 field 063 \n",
      "cycle 1 field 065 \n",
      "cycle 1 field 066 \n",
      "cycle 1 field 067 \n",
      "cycle 1 field 068 \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 082 \n",
      "cycle 1 field 083 \n",
      "cycle 1 field 084 \n",
      "cycle 1 field 085 \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 105 \n",
      "cycle 1 field 106 \n",
      "cycle 1 field 107 \n",
      "cycle 1 field 108 \n",
      "cycle 1 field 109 \n",
      "cycle 1 field 110 \n",
      "cycle 1 field 111 \n",
      "cycle 1 field 112 \n",
      "cycle 1 field 113 \n",
      "cycle 1 field 114 \n",
      "cycle 1 field 115 \n",
      "cycle 1 field 116 \n",
      "cycle 1 field 117 \n",
      "cycle 1 field 118 \n",
      "cycle 1 field 119 \n",
      "cycle 1 field 120 \n",
      "cycle 1 field 121 \n",
      "cycle 1 field 122 \n",
      "cycle 1 field 123 \n",
      "cycle 1 field 124 \n",
      "cycle 1 field 125 \n",
      "cycle 1 field 127 \n",
      "cycle 1 field 128 \n",
      "cycle 1 field 130 \n",
      "cycle 1 field 131 \n",
      "cycle 1 field 132 \n",
      "cycle 1 field 133 \n",
      "cycle 1 field 134 \n",
      "cycle 1 field 135 \n",
      "cycle 1 field 136 \n",
      "cycle 1 field 137 \n",
      "cycle 1 field 138 \n",
      "cycle 1 field 139 \n",
      "cycle 1 field 140 \n",
      "cycle 1 field 141 \n",
      "cycle 1 field 142 \n",
      "cycle 1 field 143 \n",
      "cycle 1 field 144 \n",
      "cycle 1 field 145 \n",
      "cycle 1 field 146 \n",
      "cycle 1 field 147 \n",
      "cycle 1 field 148 \n",
      "cycle 1 field 149 \n",
      "cycle 1 field 150 \n",
      "cycle 1 field 151 \n",
      "cycle 1 field 152 \n",
      "cycle 1 field 153 \n",
      "cycle 1 field 154 \n",
      "cycle 1 field 155 \n",
      "cycle 1 field 157 \n",
      "cycle 1 field 158 \n",
      "cycle 1 field 159 \n",
      "cycle 1 field 160 \n",
      "cycle 1 field 161 \n",
      "cycle 1 field 162 \n",
      "cycle 1 field 163 \n",
      "cycle 1 field 164 \n",
      "cycle 1 field 165 \n",
      "cycle 1 field 166 \n",
      "cycle 1 field 167 \n",
      "cycle 1 field 168 \n",
      "cycle 1 field 169 \n",
      "cycle 1 field 170 \n",
      "cycle 1 field 171 \n",
      "cycle 1 field 172 \n",
      "cycle 1 field 173 \n",
      "cycle 1 field 174 \n",
      "cycle 1 field 175 \n",
      "cycle 1 field 176 \n",
      "cycle 1 field 177 \n",
      "cycle 1 field 178 \n",
      "cycle 1 field 179 \n",
      "cycle 1 field 180 \n",
      "cycle 1 field 181 \n",
      "cycle 1 field 182 \n",
      "cycle 1 field 183 \n",
      "cycle 1 field 184 \n",
      "cycle 1 field 185 \n",
      "cycle 1 field 186 \n",
      "cycle 1 field 187 \n",
      "cycle 1 field 188 \n",
      "cycle 1 field 189 \n",
      "cycle 1 field 190 \n",
      "cycle 1 field 191 \n",
      "cycle 1 field 193 \n",
      "cycle 1 field 194 \n",
      "cycle 1 field 195 \n",
      "cycle 1 field 196 \n",
      "cycle 1 field 197 \n",
      "cycle 1 field 198 \n",
      "cycle 1 field 199 \n",
      "cycle 1 field 200 \n",
      "cycle 1 field 202 \n",
      "cycle 1 field 203 \n",
      "cycle 1 field 204 \n",
      "cycle 1 field 205 \n",
      "cycle 1 field 206 \n",
      "cycle 1 field 207 \n",
      "cycle 1 field 208 \n",
      "cycle 1 field 209 \n",
      "cycle 1 field 210 \n",
      "cycle 1 field 211 \n",
      "cycle 1 field 212 \n",
      "cycle 1 field 213 \n",
      "cycle 1 field 214 \n",
      "cycle 1 field 215 \n",
      "cycle 1 field 216 \n",
      "cycle 1 field 217 \n",
      "cycle 1 field 218 \n",
      "cycle 1 field 219 \n",
      "cycle 1 field 220 \n",
      "cycle 1 field 221 \n",
      "cycle 1 field 222 \n",
      "cycle 1 field 224 \n",
      "cycle 2 field 000 \n",
      "cycle 2 field 001 \n",
      "cycle 2 field 002 \n",
      "cycle 2 field 003 \n",
      "cycle 2 field 004 \n",
      "cycle 2 field 005 \n",
      "cycle 2 field 006 \n",
      "cycle 2 field 007 \n",
      "cycle 2 field 008 \n",
      "cycle 2 field 009 \n",
      "cycle 2 field 010 \n",
      "cycle 2 field 011 \n",
      "cycle 2 field 012 \n",
      "cycle 2 field 013 \n",
      "cycle 2 field 015 \n",
      "cycle 2 field 016 \n",
      "cycle 2 field 017 \n",
      "cycle 2 field 018 \n",
      "cycle 2 field 019 \n",
      "cycle 2 field 022 \n",
      "cycle 2 field 023 \n",
      "cycle 2 field 024 \n",
      "cycle 2 field 025 \n",
      "cycle 2 field 026 \n",
      "cycle 2 field 027 \n",
      "cycle 2 field 028 \n",
      "cycle 2 field 029 \n",
      "cycle 2 field 030 \n",
      "cycle 2 field 031 \n",
      "cycle 2 field 032 \n",
      "cycle 2 field 033 \n",
      "cycle 2 field 034 \n",
      "cycle 2 field 035 \n",
      "cycle 2 field 036 \n",
      "cycle 2 field 037 \n",
      "cycle 2 field 038 \n",
      "cycle 2 field 039 \n",
      "cycle 2 field 040 \n",
      "cycle 2 field 041 \n",
      "cycle 2 field 042 \n",
      "cycle 2 field 043 \n",
      "cycle 2 field 044 \n",
      "cycle 2 field 045 \n",
      "cycle 2 field 046 \n",
      "cycle 2 field 047 \n",
      "cycle 2 field 048 \n",
      "cycle 2 field 049 \n",
      "cycle 2 field 050 \n",
      "cycle 2 field 051 \n",
      "cycle 2 field 052 \n",
      "cycle 2 field 053 \n",
      "cycle 2 field 054 \n",
      "cycle 2 field 055 \n",
      "cycle 2 field 056 \n",
      "cycle 2 field 057 \n",
      "cycle 2 field 058 \n",
      "cycle 2 field 059 \n",
      "cycle 2 field 060 \n",
      "cycle 2 field 062 \n",
      "cycle 2 field 063 \n",
      "cycle 2 field 065 \n",
      "cycle 2 field 066 \n",
      "cycle 2 field 067 \n",
      "cycle 2 field 068 \n",
      "cycle 2 field 069 \n",
      "cycle 2 field 070 \n",
      "cycle 2 field 071 \n",
      "cycle 2 field 072 \n",
      "cycle 2 field 073 \n",
      "cycle 2 field 074 \n",
      "cycle 2 field 075 \n",
      "cycle 2 field 076 \n",
      "cycle 2 field 077 \n",
      "cycle 2 field 078 \n",
      "cycle 2 field 079 \n",
      "cycle 2 field 080 \n",
      "cycle 2 field 082 \n",
      "cycle 2 field 083 \n",
      "cycle 2 field 084 \n",
      "cycle 2 field 085 \n",
      "cycle 2 field 087 \n",
      "cycle 2 field 088 \n",
      "cycle 2 field 089 \n",
      "cycle 2 field 090 \n",
      "cycle 2 field 091 \n",
      "cycle 2 field 092 \n",
      "cycle 2 field 093 \n",
      "cycle 2 field 094 \n",
      "cycle 2 field 095 \n",
      "cycle 2 field 096 \n",
      "cycle 2 field 097 \n",
      "cycle 2 field 098 \n",
      "cycle 2 field 099 \n",
      "cycle 2 field 100 \n",
      "cycle 2 field 101 \n",
      "cycle 2 field 102 \n",
      "cycle 2 field 103 \n",
      "cycle 2 field 105 \n",
      "cycle 2 field 106 \n",
      "cycle 2 field 107 \n",
      "cycle 2 field 108 \n",
      "cycle 2 field 109 \n",
      "cycle 2 field 110 \n",
      "cycle 2 field 111 \n",
      "cycle 2 field 112 \n",
      "cycle 2 field 113 \n",
      "cycle 2 field 114 \n",
      "cycle 2 field 115 \n",
      "cycle 2 field 116 \n",
      "cycle 2 field 117 \n",
      "cycle 2 field 118 \n",
      "cycle 2 field 119 \n"
     ]
    }
   ],
   "source": [
    "high_ints_before_mc = pd.DataFrame()\n",
    "high_ints_before_mc_outer = pd.DataFrame()\n",
    "before_reg = pd.DataFrame()\n",
    "\n",
    "for c in range(CYCLE_NUMS-1):   \n",
    "    imgs = iter(sorted(glob.glob(f'reg_Cyc_{c+1}/*')))\n",
    "    movs = iter(sorted(glob.glob(f'tif/Cycle_{c+1}/*')))\n",
    "    for FOV in range(0, NUM_FOVS): \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        img_name = next(imgs) \n",
    "        img = imread(img_name)\n",
    "        img = img.astype(np.uint16)\n",
    "        mov_name = next(movs)\n",
    "        mov = imread(mov_name)\n",
    "        mov = mov.astype(np.uint16)\n",
    "        FOV_num = img_name.split('_F')[1][0:3]\n",
    "        print(f'cycle {c+1} field {FOV_num} ')\n",
    " \n",
    "        count_65 = np.count_nonzero(img > 65000)   # counting >65K in registered images\n",
    "        img[...,60:1988, 60:1988] = 0    # test first in upper cell\n",
    "        mov[...,60:1988, 60:1988] = 0\n",
    "        count_65_outer = np.count_nonzero(img > 65000) # counting >65K in outer region of registered images\n",
    "        before_outer = np.count_nonzero(mov > 65000) # >65K in outer region before registering\n",
    "                \n",
    "        high_ints_before_mc.loc[FOV_num, str(c+1)] = count_65\n",
    "        high_ints_before_mc_outer.loc[FOV_num, str(c+1)] = count_65_outer\n",
    "        before_reg.loc[FOV_num, str(c+1)] = before_outer\n",
    "\n",
    "done = open(\"done.txt\", \"a\")\n",
    "done.write(\"done\")\n",
    "done.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "FOV 001\n",
      "FOV 002\n",
      "FOV 003\n",
      "FOV 004\n",
      "FOV 005\n",
      "FOV 006\n",
      "FOV 007\n",
      "FOV 008\n",
      "FOV 009\n",
      "FOV 010\n",
      "FOV 011\n",
      "FOV 012\n",
      "FOV 013\n",
      "FOV 015\n",
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      "FOV 065\n",
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      "FOV 099\n",
      "FOV 100\n",
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      "FOV 164\n",
      "FOV 165\n",
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      "FOV 167\n",
      "FOV 168\n",
      "FOV 169\n",
      "FOV 170\n",
      "FOV 171\n",
      "FOV 172\n",
      "FOV 173\n",
      "FOV 174\n",
      "FOV 175\n",
      "FOV 176\n",
      "FOV 177\n",
      "FOV 178\n",
      "FOV 179\n",
      "FOV 180\n",
      "FOV 181\n",
      "FOV 182\n",
      "FOV 183\n",
      "FOV 184\n",
      "FOV 185\n",
      "FOV 186\n",
      "FOV 187\n",
      "FOV 188\n",
      "FOV 189\n",
      "FOV 190\n",
      "FOV 191\n",
      "FOV 193\n",
      "FOV 194\n",
      "FOV 195\n",
      "FOV 196\n",
      "FOV 197\n",
      "FOV 198\n",
      "FOV 199\n",
      "FOV 200\n",
      "FOV 202\n",
      "FOV 203\n",
      "FOV 204\n",
      "FOV 205\n",
      "FOV 206\n",
      "FOV 207\n",
      "FOV 208\n",
      "FOV 209\n",
      "FOV 210\n",
      "FOV 211\n",
      "FOV 212\n",
      "FOV 213\n",
      "FOV 214\n",
      "FOV 215\n",
      "FOV 216\n",
      "FOV 217\n",
      "FOV 218\n",
      "FOV 219\n",
      "FOV 220\n",
      "FOV 221\n",
      "FOV 222\n",
      "FOV 224\n"
     ]
    }
   ],
   "source": [
    "high_ints_after_mc = pd.DataFrame()\n",
    "\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    mer = imread(merged_name)\n",
    "    mer = mer.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    count_65 = np.count_nonzero(mer > 65000)\n",
    "    high_ints_after_mc.loc[FOV_num, 'merged'] = count_65\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "execution_count": 382,
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   "source": [
    "high_ints_after_mc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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   "source": [
    "high_ints_before_mc_sum = high_ints_before_mc.sum(axis=1)\n",
    "high_ints_before_mc_sum = pd.DataFrame(high_ints_before_mc_sum)\n",
    "high_ints_before_mc_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "scrolled": true
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     "execution_count": 387,
     "metadata": {},
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   "source": [
    "high_ints_before_mc_outer_sum = high_ints_before_mc_outer.sum(axis=1)\n",
    "high_ints_before_mc_outer_sum = pd.DataFrame(high_ints_before_mc_outer_sum)\n",
    "high_ints_before_mc_outer_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
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     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference = high_ints_before_mc_sum - high_ints_before_mc_outer_sum\n",
    "difference = pd.DataFrame(difference)\n",
    "difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize = (20,5))\n",
    "\n",
    "axs[0].hist(high_ints_before_mc_sum[0], bins = 50, alpha=0.5)\n",
    "axs[1].hist(high_ints_before_mc_outer_sum[0], bins = 50, alpha=0.5)\n",
    "axs[2].hist(difference[0], bins = 50, alpha=0.5)\n",
    "    \n",
    "axs[0].title.set_text('entire image 65k pixel count (BEFORE CROP)')\n",
    "axs[1].title.set_text('edge 65k pixel count (before crop)')\n",
    "axs[2].title.set_text('edge subtracted from entire image 65k pixel count')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'entire image 65k pixel count (AFTER CROP)')"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEICAYAAABbOlNNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAbN0lEQVR4nO3de5wdZZ3n8c9XIiDXJKTBkAQT3AAiAxJbDF4ZIgjIEGYX3WQVIkayrDBcHOQizqLjoIgXlJc3IgGigwFERiKDK1mEcVwkTHMPhEAImDQJpBECDCAQ+e0f9TTWczinu9Pn0rfv+/U6rz711FNVT52qnG89T1V3FBGYmZl1e8NAN8DMzAYXB4OZmWUcDGZmlnEwmJlZxsFgZmYZB4OZmWUcDC0iaRdJ/ylpswat7/OSLm7EugYrSY9K+lAD1vNxSTc0YD1flPTP9a6n1SQdLOkXA92OoUjSbZLePtDtaDUHQ5NUfqlFxOqI2CYi/tyI9UfEVyLi041YV7NJOlnSI5Kel7Rc0m6p/ABJr6bA7H7NafT2I+LyiDi40esdaOnz6+xD1a8A51UsK0mrJN1fZb03S/pTxXH5YMV0pOPZPf1+SZdJermi3t1pnZPTMt3lj0o6s5f9k6STJC1L2+qU9DNJf5Xml7f3lKQlkvaoWMdESZdL+mNax22SDq+oU96XxyR9q3QB9w3gH/vwGQ8rDoZBIP0DGJbHQtKngbnAR4BtgMOBJ0tV1qbA7H4tHIh2DleS3gVsHxG3Vsz6ALAjsGuqU+nEiuPyb+XpVGefUtm/p7LzK5bbp2K9o9PyRwH/IOmgHpr/HeBk4CRgLLAb8AuKc6nb+Wl9E4DHgAWlfR8L/A54GXg7MA64APippKMqtrVPWs8M4H8Ax6XyxcBfSxrfQzuHnWH5ZdQoknaW9HNJXemK96TSvC9KukrSjyU9J+k+Se1p3k+AXYBfpquQ00tXTKNSnZslnSvp/wEvUPwD3V7SAknr0pXLP9UaeioPa5TWfaykNZKelnS8pHdJukfSBknfLS37Vkm/SVdRT6YrqtGl+dMk3Zn262eSrpT0T6X5h0u6K633Fkl712jjG4BzgFMj4v4oPBwRT/XjWOyRjsGsGvMjXV2uSvv09e6wlfRJSb9L79+T5k9K0/uk/dgjTdc85n1o48z0uTwr6WFJh5TWuThd1a6UdFxpmcsqPtusF6Diyvq0dByfScdiS0lbA78CdtZfrsJ3rtKsQ4F/q1I+B7gWuD69b6mI6ADuA95Rbb6kqcAJwOyI+E1EvBQRL6Te33mV9SPiReCqivWdCvwnMDciHo+IFyNiEXAu8E1JqrKeB4B/B/ZK038CbgeGXY+zJw6GGtKXyi+BuymuRmYAp0j6cKnaEcAVwGiKK4vvAkTE0cBq4G/SVdP5NTZzNDAP2Bb4A7AQ2Aj8F2BfipNxU4aL3g1MBf478G3gbOBDFFdLH5P0we7dA74K7Ay8DZgEfDHt9+bAvwCXUVylLQL+tvS5TAMuAf4nsANwEbBY0hZV2jMxvfZKgfWIpC9V9I52lPREmndB+sLLpG3eAPxdRFzRw/7/LdAOTANmAp+qrBARt6Q2L5T0JuAnwBci4oE+HvOqJO0H/Bj4HMX58AHg0TR7EdBJ8XkfBXxF0oze1lnyMeAQYAqwN/DJiHie4ku/3ONaW2XZvwJWVLR1q9SOy9NrVjruLSNpOsWX78oaVWYAnRFxWx/XtzUwu2J9BwE/j4hXK6pfRXHhtluV9ewJvB+4s1S8HKjs+QxrDoba3gW0RcQ/RsTLEbEK+BFQvmL9XURcn+4b/IRNP3kui4j7ImIjxZfwocApEfF8RKyn6PZWvUKu4csR8aeIuAF4HlgUEesj4jGKq6B9ASJiZUQsSVdhXcC3gO7QmA6MAi6MiFci4hqg/I/zOOCiiFgaEX9OQz8vpeUqTUw/D6b4gvprin+8c1P5AxRXeOOBA4F3praUvZ8idOdExHW97P/XIuKpiFhNEYyza9T7IrB92q+1wPdSeV+OeS1zgUvS5/pqRDyWwmYS8D7gjHRs7gIuprgo6KsLI2Jt6mn9khpX2TWMBp6rKPuvFMfsBuA6iuP9kYo6F6ae1AZJd2zC9k4rLbdBUuXQ4JOSXgR+D3yfYmiomh2AdX3dHsU+vo/8cx1XYx3rSvO73SHpaYrP92Lg0tK85yg+xxHDwVDbWyi66a+d5MDngZ1KdR4vvX8B2FJpqKiP1lRs743AutL2LqIYB+6rJ0rvX6wyvQ2ApB0lXaFiuOpZ4J/5yz+SnYHHIv/ripXt/PuKz2VSWq7Si+nn+RGxISIeTft0GEDq3t+fvkgfAU6nuJItOx64JSJu6sP+l9v5hxptIiJeoegR7QV8s7SvfTnmtUwCHq5SvjPwVESUv5z/QNEj6avK82ybWhWreJqiR1o2B7gqIjZGxEvANbx+OOmkiBidXtM2YXvfKC03OiIq1zsutf804ACKc76aP1JcMPRpe8BkivNt99K8J2usY3xpfrdpETEmIt4aEV+o6GVsC2zoQ1uGDQdDbWuARypO8m0j4rA+Lt+XP1tb+eX7EjCutL3tIqIZj8p9NW1774jYDvgExfASFFdTEyrGXydVtPPcis9lqzR2W2kFxY2/vv4J3yi1o9vxwC6SLujD8uV27kLRG3gdSRMo7n1cSjHW3D0MVs8xXwO8tUr5WmCspPKX8y4UN0qh6NltVZr35j5sq1tfPtd7KA2ZSJpI0Tv7hKTHJT1OEcaHSRpXYx0NlXqa3wT+BHymRrUbgYlK9+36sM7VFDeqv5OGCAH+L/Df9PoHOz5Gcbwe7GOT30YxvDhiOBhquw14VtIZkt4kaTNJe6n6ExzVPAHs2teNRcQ6iq79NyVtJ+kNKm4Sf7C3ZfthW4qbchvSl+TnSvN+D/wZOFHSKEkzgf1K838EHC/p3SpsLekjFV983fv0AnAlcLqkbdOX0nEUwxfdN1p3SeuZRPFI5bUVq3mOYnz9A5Jed9OxwuckjUnrOjltO5MC7zKKp1fmUgThl9Pseo75AuBYSTPSsZsgaY+IWAPcAnw13TTeO2338rTcXRRfymMlvRk4pQ/b6vYEsIOk7Xuocz1/GSaEYqjlQYor63ek124U90BqDb01y3kU58aWlTMi4iGKoaZF6TzZPH1+s1TjMdeIWEIRxPNS0QXAdsACSW9Oy8+muPf2uYpecVXpouGdwJL+7OBQ5WCoId03+BuKfziPUHQ7L6YYm+6LrwJfSEMSp/VxmWOAzYH7KYYArqZv3elN9SWKG7TPAP9KMZQAQES8TDEGPZei+/wJii/yl9L8Doov9++mNq4EPtnDtk6kCKG1FKHzU4qb16Q2/J7iqvkWYBnFo4mZiNhAcSPxUElfrpxfci3FEyR3pf1aUKXOSRRDQ/+QvhiOpfhCf389xzzdJD2W4svoGYongd6SZs+mGOpYS3Fj/5z0JQbFvam7KW5U30CVMOthmw9Q3Nhelc6z1w2dRcQdwDOS3p2K5gDfT8N4r72AH9KYp5NOV/57DE/2UPdfKc6h42rMP4niPPsexbn4MMUDBr/sYZ1fT23YIiL+SHHfYUuKf1N/BD4LHB0Rff2cjwBurnFjf9hSH0LTRjhJS4EfRsSlvVYeIJICmBoRtZ5yGbEkHQx8JiKOHOi2DDXp3J8bEcsGui2t5GCw10nDVysorpg/TnE1uWsa7hqUHAxmjbMpT9DYyLE7xbPe21B0348azKFgZo3lHoOZmWV889nMzDKDYihp3LhxMXny5IFuhpnZkHL77bc/GRFtjV7voAiGyZMn09HRMdDNMDMbUiT9oRnr9VCSmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllBsVvPtfjgiW1/3e+Uw/areY8MzOrzj0GMzPLOBjMzCzjYDAzs4yDwczMMg4GMzPLOBjMzCzjYDAzs4yDwczMMr0Gg6RLJK2XtKzKvNMkhaRxaVqSLpS0UtI9kqY1o9FmZtY8fekxXAYcUlkoaRJwELC6VHwoMDW95gE/qL+JZmbWSr0GQ0T8FniqyqwLgNOBKJXNBH4chVuB0ZLGN6SlZmbWEv26xyDpCOCxiLi7YtYEYE1pujOVVVvHPEkdkjq6urr60wwzM2uCTQ4GSVsBZwP/u9rsKmVRpYyImB8R7RHR3tbWtqnNMDOzJunPX1d9KzAFuFsSwETgDkn7UfQQJpXqTgTW1ttIMzNrnU3uMUTEvRGxY0RMjojJFGEwLSIeBxYDx6Snk6YDz0TEusY22czMmqkvj6suAn4P7C6pU9LcHqpfD6wCVgI/Aj7TkFaamVnL9DqUFBGze5k/ufQ+gBPqb5aZmQ0U/+azmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWabXYJB0iaT1kpaVyr4u6QFJ90j6F0mjS/POkrRS0gpJH25Ww83MrDn60mO4DDikomwJsFdE7A08CJwFIGlPYBbw9rTM9yVt1rDWmplZ0/UaDBHxW+CpirIbImJjmrwVmJjezwSuiIiXIuIRYCWwXwPba2ZmTdaIewyfAn6V3k8A1pTmdaay15E0T1KHpI6urq4GNMPMzBqhrmCQdDawEbi8u6hKtai2bETMj4j2iGhva2urpxlmZtZAo/q7oKQ5wOHAjIjo/vLvBCaVqk0E1va/eWZm1mr96jFIOgQ4AzgiIl4ozVoMzJK0haQpwFTgtvqbaWZmrdJrj0HSIuAAYJykTuAciqeQtgCWSAK4NSKOj4j7JF0F3E8xxHRCRPy5WY03M7PG6zUYImJ2leIFPdQ/Fzi3nkaZmdnA8W8+m5lZxsFgZmYZB4OZmWUcDGZmlnEwmJlZxsFgZmYZB4OZmWUcDGZmlnEwmJlZxsFgZmYZB4OZmWUcDGZmlnEwmJlZxsFgZmYZB4OZmWUcDGZmlnEwmJlZxsFgZmYZB4OZmWV6DQZJl0haL2lZqWyspCWSHko/x6RySbpQ0kpJ90ia1szGm5lZ4/Wlx3AZcEhF2ZnAjRExFbgxTQMcCkxNr3nADxrTTDMza5VegyEifgs8VVE8E1iY3i8EjiyV/zgKtwKjJY1vVGPNzKz5+nuPYaeIWAeQfu6YyicAa0r1OlPZ60iaJ6lDUkdXV1c/m2FmZo3W6JvPqlIW1SpGxPyIaI+I9ra2tgY3w8zM+qu/wfBE9xBR+rk+lXcCk0r1JgJr+988MzNrtf4Gw2JgTno/B7i2VH5MejppOvBM95CTmZkNDaN6qyBpEXAAME5SJ3AOcB5wlaS5wGrgo6n69cBhwErgBeDYJrTZzMyaqNdgiIjZNWbNqFI3gBPqbZSZmQ0c/+azmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpapKxgknSrpPknLJC2StKWkKZKWSnpI0pWSNm9UY83MrPn6HQySJgAnAe0RsRewGTAL+BpwQURMBZ4G5jaioWZm1hr1DiWNAt4kaRSwFbAOOBC4Os1fCBxZ5zbMzKyF+h0MEfEY8A1gNUUgPAPcDmyIiI2pWicwod5GmplZ69QzlDQGmAlMAXYGtgYOrVI1aiw/T1KHpI6urq7+NsPMzBqsnqGkDwGPRERXRLwCXAO8BxidhpYAJgJrqy0cEfMjoj0i2tva2upohpmZNVI9wbAamC5pK0kCZgD3AzcBR6U6c4Br62uimZm1Uj33GJZS3GS+A7g3rWs+cAbwWUkrgR2ABQ1op5mZtcio3qvUFhHnAOdUFK8C9qtnvWZmNnD8m89mZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmmbqCQdJoSVdLekDSckn7SxoraYmkh9LPMY1qrJmZNV+9PYbvAP8nIvYA9gGWA2cCN0bEVODGNG1mZkNEv4NB0nbAB4AFABHxckRsAGYCC1O1hcCR9TbSzMxap54ew65AF3CppDslXSxpa2CniFgHkH7uWG1hSfMkdUjq6OrqqqMZZmbWSPUEwyhgGvCDiNgXeJ5NGDaKiPkR0R4R7W1tbXU0w8zMGqmeYOgEOiNiaZq+miIonpA0HiD9XF9fE83MrJX6HQwR8TiwRtLuqWgGcD+wGJiTyuYA19bVQjMza6lRdS7/d8DlkjYHVgHHUoTNVZLmAquBj9a5DTMza6G6giEi7gLaq8yaUc96zcxs4Pg3n83MLONgMDOzjIPBzMwyDgYzM8s4GMzMLONgMDOzjIPBzMwyDgYzM8s4GMzMLONgMDOzjIPBzMwyDgYzM8s4GMzMLONgMDOzjIPBzMwyDgYzM8s4GMzMLONgMDOzjIPBzMwydQeDpM0k3SnpujQ9RdJSSQ9JulLS5vU308zMWqURPYaTgeWl6a8BF0TEVOBpYG4DtmFmZi1SVzBImgh8BLg4TQs4ELg6VVkIHFnPNszMrLXq7TF8GzgdeDVN7wBsiIiNaboTmFBtQUnzJHVI6ujq6qqzGWZm1ij9DgZJhwPrI+L2cnGVqlFt+YiYHxHtEdHe1tbW32aYmVmDjapj2fcCR0g6DNgS2I6iBzFa0qjUa5gIrK2/mWZm1ir97jFExFkRMTEiJgOzgN9ExMeBm4CjUrU5wLV1t9LMzFqmGb/HcAbwWUkrKe45LGjCNszMrEnqGUp6TUTcDNyc3q8C9mvEes3MrPX8m89mZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmmX4Hg6RJkm6StFzSfZJOTuVjJS2R9FD6OaZxzTUzs2arp8ewEfj7iHgbMB04QdKewJnAjRExFbgxTZuZ2RDR72CIiHURcUd6/xywHJgAzAQWpmoLgSPrbaSZmbVOQ+4xSJoM7AssBXaKiHVQhAewY41l5knqkNTR1dXViGaYmVkD1B0MkrYBfg6cEhHP9nW5iJgfEe0R0d7W1lZvM8zMrEHqCgZJb6QIhcsj4ppU/ISk8Wn+eGB9fU00M7NWquepJAELgOUR8a3SrMXAnPR+DnBt/5tnZmatNqqOZd8LHA3cK+muVPZ54DzgKklzgdXAR+tropmZtVK/gyEifgeoxuwZ/V2vmZkNLP/ms5mZZRwMZmaWcTCYmVnGwWBmZhkHg5mZZRwMZmaWcTCYmVnGwWBmZhkHg5mZZRwMZmaWcTCYmVmmnj+iN2RdsOTBquWnHrRbi1tiZjb4jMhgaBQHjJkNRw6GJnBgmNlQ5nsMZmaWGdY9hlpX7sOVeypm1gjDOhgaZTgHjMPEzCp5KMnMzDLuMZQ0u2fgq3MzGwqaFgySDgG+A2wGXBwR5zVrW8PVQAbJpm67UW1t9j73FP4OaLNCU4JB0mbA94CDgE7gPyQtjoj7m7G9kWZTezaN7An5fkvv9Wtx8IxsQ2nEoFn3GPYDVkbEqoh4GbgCmNmkbZmZWQMpIhq/Uuko4JCI+HSaPhp4d0ScWKozD5iXJncHVvRzc+OAJ+to7lA3kvff+z4yjeR9h3z/3xIRbY3eQLPuMahKWZZAETEfmF/3hqSOiGivdz1D1Ujef++7930kasX+N2soqROYVJqeCKxt0rbMzKyBmhUM/wFMlTRF0ubALGBxk7ZlZmYN1JShpIjYKOlE4NcUj6teEhH3NWNbNGA4aogbyfvvfR+ZRvK+Qwv2vyk3n83MbOjyn8QwM7OMg8HMzDJDOhgkHSJphaSVks4c6Pb0l6RJkm6StFzSfZJOTuVjJS2R9FD6OSaVS9KFab/vkTSttK45qf5DkuaUyt8p6d60zIWSqj1SPGAkbSbpTknXpekpkpam/bgyPcSApC3S9Mo0f3JpHWel8hWSPlwqH7TniaTRkq6W9EA6/vuPlOMu6dR0vi+TtEjSlsP5uEu6RNJ6SctKZU0/1rW20aOIGJIvipvaDwO7ApsDdwN7DnS7+rkv44Fp6f22wIPAnsD5wJmp/Ezga+n9YcCvKH5fZDqwNJWPBValn2PS+zFp3m3A/mmZXwGHDvR+V3wGnwV+ClyXpq8CZqX3PwT+V3r/GeCH6f0s4Mr0fs90DmwBTEnnxmaD/TwBFgKfTu83B0aPhOMOTAAeAd5UOt6fHM7HHfgAMA1YVipr+rGutY0e2zrQJ0gdH/L+wK9L02cBZw10uxq0b9dS/J2pFcD4VDYeWJHeXwTMLtVfkebPBi4qlV+UysYDD5TKs3oD/aL4PZcbgQOB69KJ/SQwqvJYUzzptn96PyrVU+Xx7643mM8TYLv05aiK8mF/3CmCYU36ghuVjvuHh/txByaTB0PTj3WtbfT0GspDSd0nVrfOVDakpS7yvsBSYKeIWAeQfu6YqtXa957KO6uUDxbfBk4HXk3TOwAbImJjmi6397V9TPOfSfU39TMZDHYFuoBL0zDaxZK2ZgQc94h4DPgGsBpYR3Ecb2dkHPeyVhzrWtuoaSgHQ69/dmOokbQN8HPglIh4tqeqVcqiH+UDTtLhwPqIuL1cXKVq9DJvyO07xZXvNOAHEbEv8DxFV7+WYbPvaZx7JsXwz87A1sChVaoOx+PeFwO6v0M5GIbVn92Q9EaKULg8Iq5JxU9IGp/mjwfWp/Ja+95T+cQq5YPBe4EjJD1K8Vd4D6ToQYyW1P0LmOX2vraPaf72wFNs+mcyGHQCnRGxNE1fTREUI+G4fwh4JCK6IuIV4BrgPYyM417WimNdaxs1DeVgGDZ/diM9PbAAWB4R3yrNWgx0P3Uwh+LeQ3f5MenJhenAM6mL+GvgYElj0hXZwRTjrOuA5yRNT9s6prSuARURZ0XExIiYTHEMfxMRHwduAo5K1Sr3vfszOSrVj1Q+Kz29MgWYSnEzbtCeJxHxOLBG0u6paAZwPyPguFMMIU2XtFVqW/e+D/vjXqEVx7rWNmob6Jsxdd7IOYziCZ6HgbMHuj117Mf7KLp99wB3pddhFGOoNwIPpZ9jU31R/EdIDwP3Au2ldX0KWJlex5bK24FlaZnvUnHDczC8gAP4y1NJu1L8A18J/AzYIpVvmaZXpvm7lpY/O+3fCkpP3wzm8wR4B9CRjv0vKJ40GRHHHfgS8EBq308oniwatscdWERxP+UViiv8ua041rW20dPLfxLDzMwyQ3koyczMmsDBYGZmGQeDmZllHAxmZpZxMJiZWcbBYGZmGQeDmZll/j8MXdQWdt63aAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(high_ints_after_mc['merged'], bins = 50, alpha=0.5)\n",
    "pyplot.title('entire image 65k pixel count (AFTER CROP)')"
   ]
  },
  {
   "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": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "int_counts = pd.DataFrame()\n",
    "one_to_five_list = []\n",
    "sixfivek_list = []\n",
    "z_list = []\n",
    "\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    overall_count_zero = np.count_nonzero(img == 0)\n",
    "    x = np.count_nonzero((0 < img) & (img < 6))\n",
    "    y = np.count_nonzero(65000 < img)\n",
    "     \n",
    "    for Z in range(img.shape[0]):\n",
    "        print(Z)\n",
    "        for ch in range(img.shape[1]): \n",
    "            count_zeros = np.count_nonzero(img[Z,ch,...] == 0)\n",
    "            if count_zeros > 0:\n",
    "                z_list.append((FOV_num, Z, ch))\n",
    "            count = np.count_nonzero((0 < img[Z,ch,...]) & (img[Z,ch,...] < 6))\n",
    "            if count > 0:\n",
    "                one_to_five_list.append((FOV_num, Z, ch))\n",
    "            count = np.count_nonzero(65000 < img[Z,ch,...])\n",
    "            if count > 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": 918,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_counts.to_csv('pixel_intensity_counts_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "927"
      ]
     },
     "execution_count": 737,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sixfivek_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ('168', 6, 22),\n",
       " ('168', 6, 26),\n",
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       " ('199', 6, 26),\n",
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       " ('207', 5, 15),\n",
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       " ('207', 5, 19),\n",
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       " ('208', 4, 35),\n",
       " ('208', 5, 1),\n",
       " ('208', 5, 5),\n",
       " ('208', 5, 8),\n",
       " ('208', 5, 9),\n",
       " ('208', 5, 13),\n",
       " ('208', 5, 17),\n",
       " ('208', 5, 22),\n",
       " ('208', 6, 8),\n",
       " ('208', 7, 8),\n",
       " ('208', 7, 28),\n",
       " ('208', 8, 8),\n",
       " ('208', 9, 8),\n",
       " ('210', 3, 18),\n",
       " ('210', 3, 22),\n",
       " ('210', 3, 26),\n",
       " ('210', 3, 30),\n",
       " ('210', 4, 14),\n",
       " ('210', 4, 18),\n",
       " ('210', 4, 22),\n",
       " ('210', 4, 26),\n",
       " ('210', 4, 30),\n",
       " ('210', 4, 35),\n",
       " ('210', 5, 18),\n",
       " ('210', 5, 22),\n",
       " ('210', 5, 26),\n",
       " ('210', 5, 30),\n",
       " ('210', 5, 35),\n",
       " ('210', 6, 22),\n",
       " ('210', 6, 26),\n",
       " ('210', 6, 30),\n",
       " ('210', 6, 35),\n",
       " ('211', 5, 35),\n",
       " ('211', 6, 35),\n",
       " ('211', 7, 35),\n",
       " ('212', 2, 10),\n",
       " ('213', 1, 22),\n",
       " ('213', 1, 26),\n",
       " ('213', 1, 30),\n",
       " ('213', 2, 14),\n",
       " ('213', 2, 18),\n",
       " ('213', 2, 22),\n",
       " ('213', 2, 26),\n",
       " ('213', 2, 27),\n",
       " ('213', 2, 30),\n",
       " ('213', 3, 10),\n",
       " ('213', 3, 14),\n",
       " ('213', 3, 18),\n",
       " ('213', 3, 19),\n",
       " ('213', 3, 22),\n",
       " ('213', 3, 23),\n",
       " ('213', 3, 26),\n",
       " ('213', 3, 27),\n",
       " ('213', 3, 29),\n",
       " ('213', 3, 30),\n",
       " ('213', 3, 31),\n",
       " ('213', 4, 10),\n",
       " ('213', 4, 11),\n",
       " ('213', 4, 14),\n",
       " ('213', 4, 15),\n",
       " ('213', 4, 18),\n",
       " ('213', 4, 19),\n",
       " ('213', 4, 22),\n",
       " ('213', 4, 23),\n",
       " ('213', 4, 25),\n",
       " ('213', 4, 26),\n",
       " ('213', 4, 27),\n",
       " ('213', 4, 29),\n",
       " ('213', 4, 30),\n",
       " ('213', 4, 31),\n",
       " ('213', 4, 36),\n",
       " ('213', 5, 6),\n",
       " ('213', 5, 10),\n",
       " ('213', 5, 11),\n",
       " ('213', 5, 14),\n",
       " ('213', 5, 15),\n",
       " ('213', 5, 18),\n",
       " ('213', 5, 19),\n",
       " ('213', 5, 22),\n",
       " ('213', 5, 23),\n",
       " ('213', 5, 25),\n",
       " ('213', 5, 26),\n",
       " ('213', 5, 27),\n",
       " ('213', 5, 29),\n",
       " ('213', 5, 30),\n",
       " ('213', 5, 31),\n",
       " ('213', 6, 10),\n",
       " ('213', 6, 14),\n",
       " ('213', 6, 18),\n",
       " ('213', 6, 22),\n",
       " ('213', 6, 26),\n",
       " ('213', 6, 30),\n",
       " ('214', 8, 28),\n",
       " ('214', 9, 28),\n",
       " ('215', 4, 30),\n",
       " ('216', 3, 30),\n",
       " ('216', 4, 22),\n",
       " ('218', 2, 35),\n",
       " ('218', 3, 35),\n",
       " ('218', 4, 35),\n",
       " ('219', 7, 14),\n",
       " ('221', 8, 7),\n",
       " ('221', 9, 7),\n",
       " ('224', 0, 34),\n",
       " ('224', 1, 34),\n",
       " ('224', 2, 34),\n",
       " ('224', 2, 35),\n",
       " ('224', 3, 34),\n",
       " ('224', 3, 35),\n",
       " ('224', 4, 34),\n",
       " ('224', 4, 35),\n",
       " ('224', 5, 34),\n",
       " ('224', 5, 35),\n",
       " ('224', 6, 34),\n",
       " ('224', 6, 35),\n",
       " ('224', 7, 34)]"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sixfivek_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for idx, val in enumerate(int_counts['one to five']):\n",
    "    if val != 0:\n",
    "        print(int_counts.iloc[idx, 2], int_counts.iloc[idx, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_FOV_set = set()\n",
    "for i in z_list:\n",
    "    z_FOV_set.add(i[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# LOOKING FOR WHICH FOVS HAVE THE BLACK BOXES\n",
    "\n",
    "black_box = []\n",
    "for idx, val in enumerate(int_counts['total Zero count']):\n",
    "    if val > 2000:\n",
    "        black_box.append(int_counts.iloc[idx, 0])\n",
    "        print(int_counts.iloc[idx, 0], val)\n",
    "black_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 576,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(black_box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_to_five_set = set()\n",
    "for i in one_to_five_list:\n",
    "    one_to_five_set.add(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_to_five_set)               # num FOVs that have pixel intensity 1-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "638"
      ]
     },
     "execution_count": 524,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_to_five_list)    # number of single images that contains -- surrounding black box, black circle artifacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " ('002', 5, 28),\n",
       " ('002', 6, 28),\n",
       " ('002', 7, 28),\n",
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       " ('139', 1, 24),\n",
       " ('139', 2, 24),\n",
       " ('139', 3, 24),\n",
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       " ('143', 2, 24),\n",
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       " ('146', 4, 35),\n",
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       " ('146', 6, 35),\n",
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       " ('152', 1, 15),\n",
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       " ('152', 3, 15),\n",
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       " ('173', 5, 7),\n",
       " ('173', 6, 7),\n",
       " ('173', 7, 7),\n",
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       " ('178', 1, 13),\n",
       " ('178', 1, 34),\n",
       " ('178', 2, 13),\n",
       " ('178', 2, 34),\n",
       " ('178', 3, 13),\n",
       " ('178', 3, 34),\n",
       " ('178', 4, 13),\n",
       " ('178', 4, 34),\n",
       " ('178', 5, 13),\n",
       " ('178', 5, 34),\n",
       " ('178', 6, 13),\n",
       " ('178', 6, 34),\n",
       " ('178', 7, 13),\n",
       " ('178', 7, 34),\n",
       " ('179', 5, 30),\n",
       " ('179', 6, 22),\n",
       " ('179', 6, 30),\n",
       " ('181', 4, 9),\n",
       " ('181', 4, 17),\n",
       " ('181', 4, 29),\n",
       " ('181', 5, 13),\n",
       " ('181', 5, 21),\n",
       " ('182', 0, 7),\n",
       " ('182', 1, 7),\n",
       " ('182', 2, 7),\n",
       " ('182', 3, 7),\n",
       " ('182', 4, 7),\n",
       " ('182', 5, 7),\n",
       " ('182', 6, 7),\n",
       " ('182', 7, 7),\n",
       " ('185', 8, 14),\n",
       " ('185', 9, 14),\n",
       " ('187', 0, 4),\n",
       " ('187', 1, 4),\n",
       " ('187', 2, 4),\n",
       " ('187', 3, 4),\n",
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       " ('187', 5, 4),\n",
       " ('187', 6, 4),\n",
       " ('187', 7, 4),\n",
       " ('187', 8, 4),\n",
       " ('187', 9, 4),\n",
       " ('189', 4, 22),\n",
       " ('189', 4, 30),\n",
       " ('189', 5, 22),\n",
       " ('189', 5, 26),\n",
       " ('189', 5, 30),\n",
       " ('190', 0, 17),\n",
       " ('190', 1, 17),\n",
       " ('190', 2, 17),\n",
       " ('190', 3, 17),\n",
       " ('190', 4, 17),\n",
       " ('190', 5, 17),\n",
       " ('190', 6, 17),\n",
       " ('190', 7, 17),\n",
       " ('191', 3, 35),\n",
       " ('191', 4, 35),\n",
       " ('191', 5, 35),\n",
       " ('197', 0, 12),\n",
       " ('197', 1, 12),\n",
       " ('197', 2, 12),\n",
       " ('197', 3, 12),\n",
       " ('197', 4, 12),\n",
       " ('197', 5, 12),\n",
       " ('197', 6, 12),\n",
       " ('197', 7, 12),\n",
       " ('199', 5, 18),\n",
       " ('206', 3, 35),\n",
       " ('207', 3, 35),\n",
       " ('207', 4, 35),\n",
       " ('207', 5, 14),\n",
       " ('208', 0, 8),\n",
       " ('208', 1, 8),\n",
       " ('208', 2, 8),\n",
       " ('208', 3, 8),\n",
       " ('208', 3, 35),\n",
       " ('208', 4, 8),\n",
       " ('208', 4, 18),\n",
       " ('208', 4, 35),\n",
       " ('208', 5, 8),\n",
       " ('208', 6, 8),\n",
       " ('208', 7, 8),\n",
       " ('208', 8, 8),\n",
       " ('208', 9, 8),\n",
       " ('210', 4, 30),\n",
       " ('210', 4, 35),\n",
       " ('210', 5, 22),\n",
       " ('210', 5, 26),\n",
       " ('210', 5, 30),\n",
       " ('210', 5, 35),\n",
       " ('210', 6, 35),\n",
       " ('211', 5, 35),\n",
       " ('211', 6, 35),\n",
       " ('213', 2, 18),\n",
       " ('213', 2, 22),\n",
       " ('213', 2, 26),\n",
       " ('213', 3, 14),\n",
       " ('213', 3, 18),\n",
       " ('213', 3, 22),\n",
       " ('213', 3, 23),\n",
       " ('213', 3, 26),\n",
       " ('213', 3, 27),\n",
       " ('213', 3, 30),\n",
       " ('213', 4, 10),\n",
       " ('213', 4, 14),\n",
       " ('213', 4, 15),\n",
       " ('213', 4, 18),\n",
       " ('213', 4, 19),\n",
       " ('213', 4, 22),\n",
       " ('213', 4, 23),\n",
       " ('213', 4, 26),\n",
       " ('213', 4, 27),\n",
       " ('213', 4, 30),\n",
       " ('213', 4, 31),\n",
       " ('213', 5, 10),\n",
       " ('213', 5, 14),\n",
       " ('213', 5, 15),\n",
       " ('213', 5, 18),\n",
       " ('213', 5, 19),\n",
       " ('213', 5, 22),\n",
       " ('213', 5, 26),\n",
       " ('213', 5, 30),\n",
       " ('213', 5, 31),\n",
       " ('213', 6, 14),\n",
       " ('213', 6, 18),\n",
       " ('213', 6, 22),\n",
       " ('213', 6, 26),\n",
       " ('213', 6, 30),\n",
       " ('214', 8, 28),\n",
       " ('214', 9, 28),\n",
       " ('221', 8, 7),\n",
       " ('221', 9, 7),\n",
       " ('224', 0, 34),\n",
       " ('224', 1, 34),\n",
       " ('224', 2, 34),\n",
       " ('224', 2, 35),\n",
       " ('224', 3, 34),\n",
       " ('224', 3, 35),\n",
       " ('224', 4, 34),\n",
       " ('224', 4, 35),\n",
       " ('224', 5, 34),\n",
       " ('224', 5, 35),\n",
       " ('224', 6, 34),\n",
       " ('224', 6, 35),\n",
       " ('224', 7, 34)]"
      ]
     },
     "execution_count": 733,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_to_five_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Location of Pixels in Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 910,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 0 pixel image \n",
    "for i in z_list:            \n",
    "    if i[0] == '122':\n",
    "        print(f\"Z:{i[1]}\", \" \",f\"ch:{i[2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 912,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),\n",
       " array([], dtype=int64),\n",
       " array([], dtype=int64),\n",
       " array([], dtype=int64))"
      ]
     },
     "execution_count": 912,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = 'merged/F122.tif'\n",
    "img = imread(img)\n",
    "np.where(img == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:8   ch:14\n"
     ]
    }
   ],
   "source": [
    "# >65k pixel image\n",
    "for i in sixfivek_list:           \n",
    "    if i[0] == '002':\n",
    "        print(f\"Z:{i[1]}\", \" \",f\"ch:{i[2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, ..., 9, 9, 9]),\n",
       " array([28, 28, 28, ..., 28, 28, 28]),\n",
       " array([324, 324, 324, ..., 194, 194, 194]),\n",
       " array([106, 108, 109, ..., 614, 615, 616]))"
      ]
     },
     "execution_count": 736,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = 'merged/F002.tif'\n",
    "img = imread(img)\n",
    "np.where(65000 < img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1-5 pixel image\n",
    "for i in one_to_five_list:            \n",
    "    if i[0] == '000':\n",
    "        print(f\"Z:{i[1]}\", \" \",f\"ch:{i[2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = 'merged/F000.tif'\n",
    "img = imread(img)\n",
    "np.where((0 < img) & (img < 6))"
   ]
  },
  {
   "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": 590,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <td>646</td>\n",
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       "    <tr>\n",
       "      <td>647</td>\n",
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       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <td>648</td>\n",
       "      <td>224</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>567 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num    Z  channel\n",
       "0       002  0.0      2.0\n",
       "1       002  0.0     28.0\n",
       "2       002  1.0      2.0\n",
       "3       002  1.0     28.0\n",
       "4       002  2.0      2.0\n",
       "..      ...  ...      ...\n",
       "644     224  5.0     34.0\n",
       "645     224  5.0     35.0\n",
       "646     224  6.0     34.0\n",
       "647     224  6.0     35.0\n",
       "648     224  7.0     34.0\n",
       "\n",
       "[567 rows x 3 columns]"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_black_box = pd.DataFrame()\n",
    "for idx, i in enumerate(z_list):\n",
    "    if i[0] in black_box:\n",
    "        df_black_box.loc[idx, 'FOV_num'] = i[0]\n",
    "        df_black_box.loc[idx, 'Z'] = i[1]\n",
    "        df_black_box.loc[idx, 'channel'] = i[2]\n",
    "        \n",
    "df_black_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.502752e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.502752e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>002</td>\n",
       "      <td>0.527760</td>\n",
       "      <td>0.006799</td>\n",
       "      <td>9.459403e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>003</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>3.573930e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.005505e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.508257e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>0.131216</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>0.031753</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>2.655420e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>0.125411</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>2.882920e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count  one to five   greater 65K\n",
       "000     000          0.000000     0.000000  2.502752e-07\n",
       "001     001          0.000000     0.000000  2.502752e-07\n",
       "002     002          0.527760     0.006799  9.459403e-03\n",
       "003     003          0.000014     0.000030  3.573930e-04\n",
       "004     004          0.000000     0.000000  5.005505e-07\n",
       "..      ...               ...          ...           ...\n",
       "219     219          0.000000     0.000000  7.508257e-07\n",
       "220     220          0.131216     0.000000  0.000000e+00\n",
       "221     221          0.031753     0.000273  2.655420e-04\n",
       "222     222          0.000000     0.000000  0.000000e+00\n",
       "224     224          0.125411     0.002211  2.882920e-03\n",
       "\n",
       "[211 rows x 4 columns]"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total num of pixels is 3995601 (because 2019*1979)\n",
    "# here we get percent of 0 intensity count, 0<x<6 and greater than 65k\n",
    "percents = int_counts.copy()\n",
    "percents['total Zero count'] = percents['total Zero count'].div(3995601)\n",
    "percents['one to five'] = percents['one to five'].div(3995601)\n",
    "percents['greater 65K'] = percents['greater 65K'].div(3995601)\n",
    "percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {},
   "outputs": [],
   "source": [
    "percents.to_csv('percent_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>003</td>\n",
       "      <td>1.351486e-05</td>\n",
       "      <td>2.978275e-05</td>\n",
       "      <td>3.573930e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>027</td>\n",
       "      <td>027</td>\n",
       "      <td>2.277505e-05</td>\n",
       "      <td>4.479927e-05</td>\n",
       "      <td>4.259685e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>028</td>\n",
       "      <td>028</td>\n",
       "      <td>9.009909e-06</td>\n",
       "      <td>1.977174e-05</td>\n",
       "      <td>1.954650e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>029</td>\n",
       "      <td>029</td>\n",
       "      <td>9.760734e-06</td>\n",
       "      <td>1.101211e-05</td>\n",
       "      <td>1.816998e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>034</td>\n",
       "      <td>034</td>\n",
       "      <td>1.001101e-05</td>\n",
       "      <td>2.627890e-05</td>\n",
       "      <td>1.761938e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>041</td>\n",
       "      <td>041</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>5.255780e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>053</td>\n",
       "      <td>053</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.251376e-06</td>\n",
       "      <td>8.008808e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>058</td>\n",
       "      <td>058</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>1.001101e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>063</td>\n",
       "      <td>063</td>\n",
       "      <td>1.251376e-06</td>\n",
       "      <td>5.506055e-06</td>\n",
       "      <td>4.404844e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>065</td>\n",
       "      <td>065</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>1.101211e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>073</td>\n",
       "      <td>073</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.508257e-07</td>\n",
       "      <td>8.008808e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>098</td>\n",
       "      <td>098</td>\n",
       "      <td>1.751927e-06</td>\n",
       "      <td>3.754129e-06</td>\n",
       "      <td>2.828110e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>099</td>\n",
       "      <td>099</td>\n",
       "      <td>1.001101e-06</td>\n",
       "      <td>2.002202e-06</td>\n",
       "      <td>1.526679e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.252477e-06</td>\n",
       "      <td>3.128441e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>8.008808e-06</td>\n",
       "      <td>1.226349e-05</td>\n",
       "      <td>1.391530e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>2.753028e-06</td>\n",
       "      <td>4.254679e-06</td>\n",
       "      <td>4.780257e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>132</td>\n",
       "      <td>2.527780e-04</td>\n",
       "      <td>4.167083e-04</td>\n",
       "      <td>6.416557e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>144</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.005505e-07</td>\n",
       "      <td>2.502752e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>1.426569e-05</td>\n",
       "      <td>2.853138e-05</td>\n",
       "      <td>3.155971e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>4.004404e-06</td>\n",
       "      <td>5.330863e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>2.753028e-06</td>\n",
       "      <td>2.928220e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.002202e-06</td>\n",
       "      <td>1.151266e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.508257e-07</td>\n",
       "      <td>3.979376e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>5.005505e-07</td>\n",
       "      <td>1.251376e-06</td>\n",
       "      <td>3.704074e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>189</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>2.502752e-06</td>\n",
       "      <td>7.232954e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>191</td>\n",
       "      <td>1.351486e-05</td>\n",
       "      <td>2.577835e-05</td>\n",
       "      <td>2.813094e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.502752e-07</td>\n",
       "      <td>2.502752e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>1.001101e-06</td>\n",
       "      <td>2.502752e-06</td>\n",
       "      <td>1.476624e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>1.001101e-06</td>\n",
       "      <td>4.504954e-06</td>\n",
       "      <td>4.655119e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>5.756331e-06</td>\n",
       "      <td>9.510459e-06</td>\n",
       "      <td>1.161277e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>4.004404e-06</td>\n",
       "      <td>9.510459e-06</td>\n",
       "      <td>7.383120e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213</td>\n",
       "      <td>213</td>\n",
       "      <td>2.552807e-05</td>\n",
       "      <td>4.254679e-05</td>\n",
       "      <td>4.570026e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count   one to five   greater 65K\n",
       "003     003      1.351486e-05  2.978275e-05  3.573930e-04\n",
       "027     027      2.277505e-05  4.479927e-05  4.259685e-04\n",
       "028     028      9.009909e-06  1.977174e-05  1.954650e-04\n",
       "029     029      9.760734e-06  1.101211e-05  1.816998e-04\n",
       "034     034      1.001101e-05  2.627890e-05  1.761938e-04\n",
       "041     041      0.000000e+00  2.502752e-07  5.255780e-06\n",
       "053     053      0.000000e+00  1.251376e-06  8.008808e-06\n",
       "058     058      0.000000e+00  2.502752e-07  1.001101e-06\n",
       "063     063      1.251376e-06  5.506055e-06  4.404844e-05\n",
       "065     065      2.502752e-07  2.502752e-07  1.101211e-05\n",
       "073     073      0.000000e+00  7.508257e-07  8.008808e-06\n",
       "098     098      1.751927e-06  3.754129e-06  2.828110e-05\n",
       "099     099      1.001101e-06  2.002202e-06  1.526679e-05\n",
       "102     102      0.000000e+00  2.252477e-06  3.128441e-05\n",
       "108     108      8.008808e-06  1.226349e-05  1.391530e-04\n",
       "115     115      2.753028e-06  4.254679e-06  4.780257e-05\n",
       "132     132      2.527780e-04  4.167083e-04  6.416557e-03\n",
       "144     144      0.000000e+00  5.005505e-07  2.502752e-07\n",
       "146     146      1.426569e-05  2.853138e-05  3.155971e-04\n",
       "161     161      2.502752e-07  4.004404e-06  5.330863e-05\n",
       "162     162      2.502752e-07  2.753028e-06  2.928220e-05\n",
       "164     164      0.000000e+00  2.002202e-06  1.151266e-05\n",
       "179     179      0.000000e+00  7.508257e-07  3.979376e-05\n",
       "181     181      5.005505e-07  1.251376e-06  3.704074e-05\n",
       "189     189      2.502752e-07  2.502752e-06  7.232954e-05\n",
       "191     191      1.351486e-05  2.577835e-05  2.813094e-04\n",
       "199     199      0.000000e+00  2.502752e-07  2.502752e-05\n",
       "206     206      1.001101e-06  2.502752e-06  1.476624e-05\n",
       "207     207      1.001101e-06  4.504954e-06  4.655119e-05\n",
       "210     210      5.756331e-06  9.510459e-06  1.161277e-04\n",
       "211     211      4.004404e-06  9.510459e-06  7.383120e-05\n",
       "213     213      2.552807e-05  4.254679e-05  4.570026e-04"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circles = percents.loc[(percents['one to five'] > 0)&(percents['greater 65K'] > 0)&(percents['total Zero count'] < 0.00050055048)]  \n",
    "black_circles\n",
    "# THESE MUST BE THE BLACK CIRCLE ARTIFACT ONES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_circles.to_csv('black_circle_pixel_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "black_circle_lst = []\n",
    "for i in black_circles['FOV_num']:\n",
    "    black_circle_lst.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['003',\n",
       " '027',\n",
       " '028',\n",
       " '029',\n",
       " '034',\n",
       " '041',\n",
       " '053',\n",
       " '058',\n",
       " '063',\n",
       " '065',\n",
       " '073',\n",
       " '098',\n",
       " '099',\n",
       " '102',\n",
       " '108',\n",
       " '115',\n",
       " '132',\n",
       " '144',\n",
       " '146',\n",
       " '161',\n",
       " '162',\n",
       " '164',\n",
       " '179',\n",
       " '181',\n",
       " '189',\n",
       " '191',\n",
       " '199',\n",
       " '206',\n",
       " '207',\n",
       " '210',\n",
       " '211',\n",
       " '213']"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circle_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>003</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>003</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>003</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>003</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>003</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>616</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>617</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>618</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>619</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>620</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num    Z  channel\n",
       "10      003  4.0     35.0\n",
       "11      003  5.0     35.0\n",
       "12      003  6.0     35.0\n",
       "13      003  7.0     35.0\n",
       "14      003  8.0     35.0\n",
       "..      ...  ...      ...\n",
       "616     213  6.0     14.0\n",
       "617     213  6.0     18.0\n",
       "618     213  6.0     22.0\n",
       "619     213  6.0     26.0\n",
       "620     213  6.0     30.0\n",
       "\n",
       "[124 rows x 3 columns]"
      ]
     },
     "execution_count": 739,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_black_circle = pd.DataFrame()\n",
    "count = 0\n",
    "for idx, i in enumerate(one_to_five_list):\n",
    "    if i[0] in black_circle_lst:\n",
    "        df_black_circle.loc[idx, 'FOV_num'] = i[0]\n",
    "        df_black_circle.loc[idx, 'Z'] = i[1]\n",
    "        df_black_circle.loc[idx, 'channel'] = i[2]\n",
    "        count += 1\n",
    "        \n",
    "df_black_circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_black_circle.to_csv('black_circle_locations.csv')"
   ]
  },
  {
   "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": 963,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = imread('merged/F002.tif')\n",
    "image = image[0, 28, ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 969,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f74d83fe110>"
      ]
     },
     "execution_count": 969,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 0\n",
    "binary_mask = image <= t\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7), sharex=True, sharey=True)\n",
    "plt.title('Zero Intensity Masked Image')\n",
    "plt.imshow(binary_mask, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 971,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('002', 0, 2),\n",
       " ('002', 0, 28),\n",
       " ('002', 1, 2),\n",
       " ('002', 1, 28),\n",
       " ('002', 2, 2),\n",
       " ('002', 2, 28),\n",
       " ('002', 3, 2),\n",
       " ('002', 3, 28),\n",
       " ('002', 4, 2),\n",
       " ('002', 4, 28),\n",
       " ('002', 5, 2),\n",
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       " ('224', 2, 35),\n",
       " ('224', 3, 34),\n",
       " ('224', 3, 35),\n",
       " ('224', 4, 34),\n",
       " ('224', 4, 35),\n",
       " ('224', 5, 34),\n",
       " ('224', 5, 35),\n",
       " ('224', 6, 34),\n",
       " ('224', 6, 35),\n",
       " ('224', 7, 34)]"
      ]
     },
     "execution_count": 971,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2800e779725041648703a0546880ed35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=211), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "FOV 001\n",
      "FOV 002\n",
      "FOV 003\n",
      "FOV 004\n",
      "FOV 005\n"
     ]
    }
   ],
   "source": [
    "# BINARY MASKING FOR 0 INTENSITY PIXELS -- ANOTHER WAY OF FINDING LOCATION OF BLACK BOXES\n",
    "validation = []\n",
    "t = 0\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in tqdm(range(NUM_FOVS)):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "\n",
    "    for Z in range(img.shape[0]):\n",
    "        for ch in range(img.shape[1]): \n",
    "            binary_mask = img[Z,ch,...] <= t\n",
    "            if binary_mask.sum() > 2000:\n",
    "                validation.append((FOV_num, Z, ch))\n",
    "                \n",
    "done = open(\"done.txt\", \"a\")\n",
    "done.write(\"done\")\n",
    "done.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>002</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>002</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>002</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>002</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>002</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>531</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>532</td>\n",
       "      <td>224</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>533</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>534</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>535</td>\n",
       "      <td>224</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  Z  channel\n",
       "0       002  0        2\n",
       "1       002  0       28\n",
       "2       002  1        2\n",
       "3       002  1       28\n",
       "4       002  2        2\n",
       "..      ... ..      ...\n",
       "531     224  3       34\n",
       "532     224  4       34\n",
       "533     224  5       34\n",
       "534     224  6       34\n",
       "535     224  7       34\n",
       "\n",
       "[536 rows x 3 columns]"
      ]
     },
     "execution_count": 994,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation = pd.DataFrame(validation)\n",
    "df_validation = df_validation.rename(columns={0: \"FOV_num\", 1: \"Z\", 2:'channel'})\n",
    "df_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation.to_csv('black_box_locations_correct.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1312,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = pd.read_csv('black_box_locations_correct.csv')\n",
    "df_validation = df_validation.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1320,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_lst = list(df_validation.itertuples(index=False, name=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1326,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55d5da288eec4ddbaf6ff65e6758afb7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for idx, val in tqdm(enumerate(validation_lst)):\n",
    "    FOV = str(val[0]).zfill(3)\n",
    "    img = imread(f'merged/F{FOV}.tif')\n",
    "    Z = val[1]\n",
    "    ch = val[2]\n",
    "    \n",
    "    cnt = np.count_nonzero(img[Z, ch, ...] == 0)\n",
    "    ttl = img.shape[2]*img.shape[3]\n",
    "    df_validation.loc[idx, 'percent_bbox'] = (cnt/ttl)*100\n",
    "df_validation.to_csv('bbox_locs_and_percs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1327,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "      <th>percent_bbox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.640204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4.576158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.640204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4.571352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.640204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>531</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>1.566723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>532</td>\n",
       "      <td>224</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>1.567749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>533</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>1.569101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>534</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>1.570527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>535</td>\n",
       "      <td>224</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>1.554835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num  Z  channel  percent_bbox\n",
       "0          2  0        2      1.640204\n",
       "1          2  0       28      4.576158\n",
       "2          2  1        2      1.640204\n",
       "3          2  1       28      4.571352\n",
       "4          2  2        2      1.640204\n",
       "..       ... ..      ...           ...\n",
       "531      224  3       34      1.566723\n",
       "532      224  4       34      1.567749\n",
       "533      224  5       34      1.569101\n",
       "534      224  6       34      1.570527\n",
       "535      224  7       34      1.554835\n",
       "\n",
       "[536 rows x 4 columns]"
      ]
     },
     "execution_count": 1327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 983,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'002',\n",
       " '008',\n",
       " '010',\n",
       " '011',\n",
       " '016',\n",
       " '017',\n",
       " '019',\n",
       " '026',\n",
       " '030',\n",
       " '031',\n",
       " '035',\n",
       " '036',\n",
       " '037',\n",
       " '038',\n",
       " '040',\n",
       " '042',\n",
       " '044',\n",
       " '045',\n",
       " '048',\n",
       " '050',\n",
       " '051',\n",
       " '052',\n",
       " '054',\n",
       " '059',\n",
       " '060',\n",
       " '062',\n",
       " '066',\n",
       " '071',\n",
       " '072',\n",
       " '075',\n",
       " '079',\n",
       " '080',\n",
       " '083',\n",
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       " '094',\n",
       " '101',\n",
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       " '113',\n",
       " '114',\n",
       " '117',\n",
       " '120',\n",
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       " '127',\n",
       " '128',\n",
       " '130',\n",
       " '133',\n",
       " '134',\n",
       " '135',\n",
       " '137',\n",
       " '138',\n",
       " '139',\n",
       " '143',\n",
       " '151',\n",
       " '152',\n",
       " '155',\n",
       " '160',\n",
       " '169',\n",
       " '173',\n",
       " '178',\n",
       " '182',\n",
       " '185',\n",
       " '187',\n",
       " '190',\n",
       " '197',\n",
       " '208',\n",
       " '214',\n",
       " '220',\n",
       " '221',\n",
       " '224'}"
      ]
     },
     "execution_count": 983,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_FOVs = set()\n",
    "for i in validation:\n",
    "    validation_FOVs.add(i[0])\n",
    "validation_FOVs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 988,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_FOVs = np.array(sorted(list(validation_FOVs)))\n",
    "black_box_arr = np.array(black_box)\n",
    "np.array_equal(validation_FOVs, black_box_arr)"
   ]
  },
  {
   "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": 659,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "yes_box = percents.loc[percents['total Zero count'] > 0.00050055048]['total Zero count'] #greater than 2000/3995601"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "000    0.000000\n",
       "001    0.000000\n",
       "003    0.000014\n",
       "004    0.000000\n",
       "005    0.000000\n",
       "         ...   \n",
       "216    0.000000\n",
       "217    0.000000\n",
       "218    0.000000\n",
       "219    0.000000\n",
       "222    0.000000\n",
       "Name: total Zero count, Length: 140, dtype: float64"
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_box = percents.loc[(percents['total Zero count'] < 0.00050055048)]['total Zero count']  #less than 2000/3995601\n",
    "no_box   # percent zeros in images with BO black boxes present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'percent 0 intensity')"
      ]
     },
     "execution_count": 725,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize = (50,20))\n",
    "axs[0].hist(no_box, bins = 50, alpha=0.8)\n",
    "axs[0].set_ylim([0, 20])\n",
    "axs[0].set_xlim([-0.001, 0.01])\n",
    "axs[0].set_title('no black boxes - percent 0 intensity', size = 50)\n",
    "axs[0].set_ylabel('count', fontsize = 30.0) # Y label\n",
    "axs[0].set_xlabel('percent 0 intensity', fontsize = 30) # X label\n",
    "axs[1].hist(yes_box, bins = 50, alpha=0.8)\n",
    "axs[1].set_ylim([0, 20])\n",
    "axs[1].set_xlim([-0.001, 1])\n",
    "axs[1].set_title('black boxes present - percent 0 intensity', size = 50)\n",
    "axs[1].set_ylabel('count', fontsize = 30.0) # Y label\n",
    "axs[1].set_xlabel('percent 0 intensity', fontsize = 30) # X label\n",
    "\n",
    "\n",
    "# the little bit in the no black boxes --> that is the black circle artifacts that correlate with 65k pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_to_five = percents.loc[percents['one to five'] != 0]['one to five']\n",
    "greater_65K = percents.loc[percents['greater 65K'] != 0]['greater 65K']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_to_five_FOV = percents.loc[percents['one to five'] != 0]['FOV_num']\n",
    "greater_65K_FOV = percents.loc[percents['greater 65K'] != 0]['FOV_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "greater_65K_FOV_list = list(np.asarray(greater_65K_FOV))\n",
    "one_to_five_FOV_list = list(np.asarray(one_to_five_FOV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab9f22ad39ef4bdab847c43a082f1ab3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=146), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000 (array([], dtype=int64),) [551]\n",
      "000 (array([], dtype=int64),) [313]\n",
      "001 (array([], dtype=int64),) [108]\n",
      "001 (array([], dtype=int64),) [319]\n",
      "004 (array([], dtype=int64),) [965 960]\n",
      "004 (array([], dtype=int64),) [1766 1771]\n",
      "005 (array([], dtype=int64),) [402 402]\n",
      "005 (array([], dtype=int64),) [1200 1201]\n",
      "006 (array([], dtype=int64),) [1406]\n",
      "006 (array([], dtype=int64),) [1207]\n",
      "009 (array([], dtype=int64),) [1534 1535 1537 1532 1533 1534 1535 1536 1538 1532 1535 1536 1537]\n",
      "009 (array([], dtype=int64),) [1667 1667 1668 1669 1669 1669 1669 1669 1669 1670 1670 1670 1670]\n",
      "022 (array([], dtype=int64),) [68 69 67 68 69 68 68 69 67 68 70 68 69]\n",
      "022 (array([], dtype=int64),) [0 0 1 1 1 2 3 3 4 4 5 6 6]\n",
      "023 (array([], dtype=int64),) [59 60 60 60 59 60 60 62 60 62 60 62 60 62 60 62 60 62 60 60 62 60 62 60\n",
      " 62 59 60 59 60 60 60 60]\n",
      "023 (array([], dtype=int64),) [1785 1785 1786 1787 1790 1792 1794 1794 1795 1795 1796 1796 1797 1797\n",
      " 1798 1798 1799 1799 1800 1801 1801 1802 1802 1803 1803 1804 1805 1790\n",
      " 1790 1792 1795 1796]\n",
      "024 (array([], dtype=int64),) [1957 1957 1764 1766 1765 1957 1955 1765 1767 1764 1767 1765]\n",
      "024 (array([], dtype=int64),) [462 463 717 717 718 461 461 716 716 717 717 718]\n",
      "025 (array([], dtype=int64),) [1098]\n",
      "025 (array([], dtype=int64),) [994]\n",
      "032 (array([], dtype=int64),) [1631 1633 1632 1633 1631 1632 1633]\n",
      "032 (array([], dtype=int64),) [1075 1075 1076 1076 1076 1076 1076]\n",
      "057 (array([], dtype=int64),) [622]\n",
      "057 (array([], dtype=int64),) [1447]\n",
      "070 (array([], dtype=int64),) [1041]\n",
      "070 (array([], dtype=int64),) [889]\n",
      "079 (array([], dtype=int64),) [788 786 787 781 782 783 784 785 787 780 784 785 786 780 783 785 787 788\n",
      " 788 784 786 788 784 787 784 787 787 788 787 786 787 783 784 785 787 786\n",
      " 788 787 784 785 786 787 782 783 787 780 781 787 781 782 783 784 785 786\n",
      " 787 786 785]\n",
      "079 (array([], dtype=int64),) [403 404 405 406 406 406 406 406 406 407 407 408 408 409 409 409 407 404\n",
      " 405 406 406 406 407 407 408 408 409 403 405 406 406 407 407 407 407 408\n",
      " 408 409 405 405 405 405 406 406 406 407 407 407 408 408 408 408 408 408\n",
      " 408 407 407]\n",
      "082 (array([], dtype=int64),) [1888]\n",
      "082 (array([], dtype=int64),) [16]\n",
      "087 (array([], dtype=int64),) [1245   93]\n",
      "087 (array([], dtype=int64),) [687   5]\n",
      "089 (array([], dtype=int64),) [1039 1040 1041 1039 1040 1039]\n",
      "089 (array([], dtype=int64),) [1171 1171 1171 1172 1172 1173]\n",
      "091 (array([], dtype=int64),) [927]\n",
      "091 (array([], dtype=int64),) [414]\n",
      "092 (array([], dtype=int64),) [780 780 781 780 780]\n",
      "092 (array([], dtype=int64),) [325 326 326 328 329]\n",
      "093 (array([], dtype=int64),) [1794 1795 1795 1794 1962]\n",
      "093 (array([], dtype=int64),) [ 242  243  244  245 1377]\n",
      "095 (array([], dtype=int64),) [1228 1226 1226]\n",
      "095 (array([], dtype=int64),) [617 618 619]\n",
      "106 (array([], dtype=int64),) [1017 1017 1017]\n",
      "106 (array([], dtype=int64),) [936 937 938]\n",
      "110 (array([], dtype=int64),) [740 739 739 741 742]\n",
      "110 (array([], dtype=int64),) [315 314 315 315 315]\n",
      "112 (array([], dtype=int64),) [386 386 657 649 652 653 655 656 652 654 657]\n",
      "112 (array([], dtype=int64),) [937 938 668 674 667 668 668 668 669 669 669]\n",
      "119 (array([], dtype=int64),) [665 668 665 668 667 660 571 571 573 572]\n",
      "119 (array([], dtype=int64),) [1906 1906 1907 1907 1908 1915  460  461  461  462]\n",
      "123 (array([], dtype=int64),) [1624]\n",
      "123 (array([], dtype=int64),) [1226]\n",
      "131 (array([], dtype=int64),) [ 842  843 1416  844  841  842  845  841  843  844  841  842  843 1417\n",
      " 1414 1415 1416 1417 1418 1414 1417 1419 1415 1416 1418  840  842  843\n",
      "  844  840  841  845  841  842  843  845  844  839  841  842  843  844\n",
      "  843  843  843  842]\n",
      "131 (array([], dtype=int64),) [281 281 245 277 278 278 278 279 279 279 280 280 281 244 245 245 245 245\n",
      " 245 246 246 246 247 247 247 276 276 276 276 277 277 277 278 278 278 278\n",
      " 279 280 280 280 280 281 276 277 278 280]\n",
      "135 (array([], dtype=int64),) [1075 1076 1077 1075 1076 1077 1078]\n",
      "135 (array([], dtype=int64),) [707 707 707 708 708 708 708]\n",
      "142 (array([], dtype=int64),) [679]\n",
      "142 (array([], dtype=int64),) [447]\n",
      "147 (array([], dtype=int64),) [146 147 144]\n",
      "147 (array([], dtype=int64),) [1926 1927 1928]\n",
      "153 (array([], dtype=int64),) [387 388 389 835 834 835 836 834 835 836 834 835 836 834 835 836 835 835\n",
      " 836 835 836 837 838 837 838]\n",
      "153 (array([], dtype=int64),) [ 637  638  639 1135 1136 1136 1136 1137 1137 1137 1138 1138 1138 1139\n",
      " 1139 1139 1140 1136 1136 1138 1138 1135 1135 1137 1137]\n",
      "154 (array([], dtype=int64),) [624]\n",
      "154 (array([], dtype=int64),) [1518]\n",
      "157 (array([], dtype=int64),) [1833 1834 1835 1836 1832 1834 1835 1836 1833 1835 1836 1837 1833 1833\n",
      " 1834 1835 1836 1837 1838 1838 1833 1834 1832 1833]\n",
      "157 (array([], dtype=int64),) [746 747 747 747 748 748 748 748 749 749 749 749 750 751 751 751 751 751\n",
      " 751 752 750 750 751 751]\n",
      "158 (array([], dtype=int64),) [1773 1773 1776 1769 1778 1773]\n",
      "158 (array([], dtype=int64),) [25 27 29 30 30 34]\n",
      "163 (array([], dtype=int64),) [599 601 599 601 599 601 601 601 600 600]\n",
      "163 (array([], dtype=int64),) [765 766 764 764 765 765 766 767 764 765]\n",
      "166 (array([], dtype=int64),) [1093 1093 1092 1769 1767 1769]\n",
      "166 (array([], dtype=int64),) [530 529 530 460 461 463]\n",
      "167 (array([], dtype=int64),) [1805 1806  885]\n",
      "167 (array([], dtype=int64),) [1364 1364  688]\n",
      "168 (array([], dtype=int64),) [992 992 994 993 993 992 992 992 993 992 993 992 992 993 992 993 992 993\n",
      " 993 992 994 994 994 992 993 994 992 993 994 993 995 993 993 995 994 993\n",
      " 993 994 992 994 994 993 992 995 994 993 994 992 994 992 994 992 994 992\n",
      " 994 993 993 995 994 995 995 994 993 994 995 995 994 993 994 994 995 969\n",
      " 969]\n",
      "168 (array([], dtype=int64),) [1594 1595 1592 1593 1594 1595 1593 1594 1594 1595 1595 1596 1592 1592\n",
      " 1593 1593 1594 1594 1595 1596 1596 1592 1593 1594 1594 1594 1595 1595\n",
      " 1591 1592 1592 1593 1594 1594 1595 1596 1592 1592 1593 1593 1594 1595\n",
      " 1596 1596 1590 1591 1591 1592 1592 1593 1593 1594 1594 1595 1595 1596\n",
      " 1597 1592 1593 1591 1592 1593 1594 1594 1594 1593 1592 1593 1593 1594\n",
      " 1594 1291 1294]\n",
      "174 (array([], dtype=int64),) [1288]\n",
      "174 (array([], dtype=int64),) [1619]\n",
      "194 (array([], dtype=int64),) [1550 1551 1549 1552 1552 1551]\n",
      "194 (array([], dtype=int64),) [138 138 139 139 140 141]\n",
      "196 (array([], dtype=int64),) [1080 1081]\n",
      "196 (array([], dtype=int64),) [1257 1257]\n",
      "202 (array([], dtype=int64),) [1963 1964 1965 1964 1963 1964 1965 1962 1963 1964 1965 1966]\n",
      "202 (array([], dtype=int64),) [543 543 543 541 542 542 543 544 544 544 544 544]\n",
      "212 (array([], dtype=int64),) [394 395 393 396 393 395]\n",
      "212 (array([], dtype=int64),) [1712 1712 1713 1713 1714 1715]\n",
      "215 (array([], dtype=int64),) [955]\n",
      "215 (array([], dtype=int64),) [173]\n",
      "216 (array([], dtype=int64),) [849 614]\n",
      "216 (array([], dtype=int64),) [1605  384]\n",
      "218 (array([], dtype=int64),) [419 418 418 419 420 421 422 423 424 425 426 427 417 418 417 419 422 417\n",
      " 418 419 420 421 638 639 635 637 639 635 636 637 633 634 636 631 632 634\n",
      " 635 629 630 632 633 628 630 631 634 626 627 629 625 627 623 626 622 623\n",
      " 624 625 422 419 420 421 636 636 632 633 634 635 631 633 630 632 629 630\n",
      " 626 628 624 628 626]\n",
      "218 (array([], dtype=int64),) [1231 1233 1230 1230 1230 1230 1230 1230 1230 1230 1230 1230 1231 1231\n",
      " 1232 1232 1232 1233 1233 1233 1233 1233 1332 1332 1333 1333 1333 1334\n",
      " 1334 1334 1335 1335 1335 1336 1336 1336 1336 1337 1337 1337 1337 1338\n",
      " 1338 1338 1338 1339 1339 1339 1340 1340 1341 1341 1342 1342 1342 1343\n",
      " 1230 1232 1232 1232 1333 1334 1335 1335 1335 1335 1336 1336 1337 1337\n",
      " 1338 1338 1339 1339 1340 1340 1341]\n",
      "219 (array([], dtype=int64),) [1368 1367 1367]\n",
      "219 (array([], dtype=int64),) [353 355 356]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(greater_65K_FOV_list):\n",
    "    if i not in one_to_five_FOV_list:\n",
    "        img = imread(f'merged/F{i}.tif')\n",
    "        x = np.where(65000 < img)[3]\n",
    "        y = np.where(65000 < img)[2]\n",
    "        x_where = np.where(x>1989)\n",
    "        y_where = np.where(y>1949)\n",
    "        print(i, x_where, x)\n",
    "        print(i, y_where, y)\n",
    "\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 958,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'count')"
      ]
     },
     "execution_count": 958,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# USE THIS TO SEE IF THE 65K PIXELS ARE IN THE SAME VICINITY AS 1<X<6 PIXELS FOR THE FOVS\n",
    "fig, axs = plt.subplots(1, 2, figsize = (50,20))\n",
    "axs[0].hist(one_to_five, bins = 50, alpha=0.5)\n",
    "axs[0].set_title('percent 1-5 intensity', size = 50)\n",
    "axs[0].set_xlabel('percent 1-5 intensity', fontsize = 30) # X label\n",
    "axs[0].set_ylabel('count', fontsize = 30.0) # Y label\n",
    "axs[1].hist(greater_65K, bins = 50, alpha=0.5)\n",
    "axs[1].set_title('percent >65k intensity', size = 50)\n",
    "axs[1].set_xlabel('percent >65k intensity', fontsize = 30) # X label\n",
    "axs[1].set_ylabel('count', fontsize = 30.0) # Y label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "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": {
      "text/plain": [
       "<Figure size 288x144 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pyplot.hist(percents['total Zero count'], bins = 50, alpha=0.5, label = 'total Zero count')\n",
    "pyplot.hist(one_to_five, bins = 50, alpha=0.5, label = 'one to five')\n",
    "pyplot.hist(greater_65K, bins = 50, alpha=0.5, label = 'greater than 65K')\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.figure(figsize=(4, 2))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('002', 0, 2),\n",
       " ('002', 0, 28),\n",
       " ('002', 1, 2),\n",
       " ('002', 1, 28),\n",
       " ('002', 2, 2),\n",
       " ('002', 2, 28),\n",
       " ('002', 3, 2),\n",
       " ('002', 3, 28),\n",
       " ('002', 4, 2),\n",
       " ('002', 4, 28),\n",
       " ('002', 5, 2),\n",
       " ('002', 5, 28),\n",
       " ('002', 6, 2),\n",
       " ('002', 6, 28),\n",
       " ('002', 7, 2),\n",
       " ('002', 7, 28),\n",
       " ('002', 8, 28),\n",
       " ('002', 9, 28),\n",
       " ('008', 0, 34),\n",
       " ('008', 1, 34),\n",
       " ('008', 2, 34),\n",
       " ('008', 3, 34),\n",
       " ('008', 4, 34),\n",
       " ('008', 5, 34),\n",
       " ('008', 6, 34),\n",
       " ('008', 7, 34),\n",
       " ('008', 8, 34),\n",
       " ('008', 9, 34),\n",
       " ('010', 0, 6),\n",
       " ('010', 1, 6),\n",
       " ('010', 2, 6),\n",
       " ('010', 3, 6),\n",
       " ('010', 4, 6),\n",
       " ('010', 5, 6),\n",
       " ('010', 6, 6),\n",
       " ('010', 7, 6),\n",
       " ('011', 0, 3),\n",
       " ('011', 0, 13),\n",
       " ('011', 1, 3),\n",
       " ('011', 1, 13),\n",
       " ('011', 2, 3),\n",
       " ('011', 2, 13),\n",
       " ('011', 3, 3),\n",
       " ('011', 3, 13),\n",
       " ('011', 4, 3),\n",
       " ('011', 4, 13),\n",
       " ('011', 5, 3),\n",
       " ('011', 5, 13),\n",
       " ('011', 6, 3),\n",
       " ('011', 6, 13),\n",
       " ('011', 7, 3),\n",
       " ('011', 7, 13),\n",
       " ('011', 8, 28),\n",
       " ('011', 9, 28),\n",
       " ('016', 0, 9),\n",
       " ('016', 1, 9),\n",
       " ('016', 2, 9),\n",
       " ('016', 3, 9),\n",
       " ('016', 4, 9),\n",
       " ('016', 5, 9),\n",
       " ('016', 6, 9),\n",
       " ('016', 7, 9),\n",
       " ('017', 8, 35),\n",
       " ('017', 9, 35),\n",
       " ('019', 0, 18),\n",
       " ('019', 1, 18),\n",
       " ('019', 2, 18),\n",
       " ('019', 3, 18),\n",
       " ('019', 4, 18),\n",
       " ('019', 5, 18),\n",
       " ('019', 6, 18),\n",
       " ('019', 7, 18),\n",
       " ('026', 8, 2),\n",
       " ('026', 9, 2),\n",
       " ('030', 0, 14),\n",
       " ('030', 1, 14),\n",
       " ('030', 2, 14),\n",
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       " ('220', 6, 3),\n",
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       " ('224', 0, 34),\n",
       " ('224', 1, 34),\n",
       " ('224', 2, 34),\n",
       " ('224', 3, 34),\n",
       " ('224', 4, 34),\n",
       " ('224', 5, 34),\n",
       " ('224', 6, 34),\n",
       " ('224', 7, 34)]"
      ]
     },
     "execution_count": 1018,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1132,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = imread('merged/F002.tif')\n",
    "arr = arr[0, 28, ...]\n",
    "#plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1979, 2019)"
      ]
     },
     "execution_count": 1123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "182845"
      ]
     },
     "execution_count": 1126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len((np.where(arr == 0))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0\n",
      "0.0 1\n",
      "0.0 2\n",
      "0.0 3\n",
      "0.0 4\n"
     ]
    }
   ],
   "source": [
    "percent = 0\n",
    "while np.percentile(arr, percent) == 0:\n",
    "    print(np.percentile(arr, percent), percent)\n",
    "    percent += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cfc8141c5dd42859e2bd7d7182f0e8e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=211), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n"
     ]
    }
   ],
   "source": [
    "percentiles = pd.DataFrame()\n",
    "\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in tqdm(range(NUM_FOVS)):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "\n",
    "    for ch in range(img.shape[1]): \n",
    "        arr = img[:,ch,...]\n",
    "        percent = 0\n",
    "        while np.percentile(arr, percent) == 0:\n",
    "            percent += 1\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_cutoff_perc'] = percent # by the +1, you will know if there are ANY 0's\n",
    "        \n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0th_perc'] = np.percentile(arr, 0)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.001st_perc'] = np.percentile(arr, 0.001)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.01st_perc'] = np.percentile(arr, 0.01)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.1st_perc'] = np.percentile(arr, 0.1)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.5th_perc'] = np.percentile(arr, 0.5)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_1st_perc'] = np.percentile(arr, 1)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_5th_perc'] = np.percentile(arr, 5)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_10th_perc'] = np.percentile(arr, 10)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_90th_perc'] = np.percentile(arr, 90)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_95th_perc'] = np.percentile(arr, 95)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_99th_perc'] = np.percentile(arr, 99)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_100th_perc'] = np.percentile(arr, 100)\n",
    "\n",
    "\n",
    "percentiles.to_csv('percentiles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1236,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>ch3_0th_perc</th>\n",
       "      <th>ch4_0th_perc</th>\n",
       "      <th>ch5_0th_perc</th>\n",
       "      <th>ch6_0th_perc</th>\n",
       "      <th>ch7_0th_perc</th>\n",
       "      <th>ch8_0th_perc</th>\n",
       "      <th>ch9_0th_perc</th>\n",
       "      <th>...</th>\n",
       "      <th>ch28_0th_perc</th>\n",
       "      <th>ch29_0th_perc</th>\n",
       "      <th>ch30_0th_perc</th>\n",
       "      <th>ch31_0th_perc</th>\n",
       "      <th>ch32_0th_perc</th>\n",
       "      <th>ch33_0th_perc</th>\n",
       "      <th>ch34_0th_perc</th>\n",
       "      <th>ch35_0th_perc</th>\n",
       "      <th>ch36_0th_perc</th>\n",
       "      <th>ch37_0th_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>38.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>41.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>32.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>36.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>49.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>29.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>42.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>51.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>39.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ch0_0th_perc  ch1_0th_perc  ch2_0th_perc  ch3_0th_perc  ch4_0th_perc  \\\n",
       "000          55.0          35.0          59.0          56.0          45.0   \n",
       "001          43.0          39.0          65.0          49.0          56.0   \n",
       "002          37.0          53.0           0.0          71.0          38.0   \n",
       "003          38.0          53.0          67.0          68.0          64.0   \n",
       "004          41.0          44.0          55.0          55.0          57.0   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "219          36.0          52.0          52.0          63.0          81.0   \n",
       "220          29.0          56.0          48.0           0.0          62.0   \n",
       "221          42.0          51.0          58.0          57.0          57.0   \n",
       "222          51.0          59.0          59.0          69.0          71.0   \n",
       "224          39.0          57.0          67.0          67.0           7.0   \n",
       "\n",
       "     ch5_0th_perc  ch6_0th_perc  ch7_0th_perc  ch8_0th_perc  ch9_0th_perc  \\\n",
       "000          48.0          44.0          38.0          43.0          43.0   \n",
       "001          51.0          48.0          50.0          43.0          49.0   \n",
       "002          46.0          27.0          36.0          54.0          52.0   \n",
       "003          58.0          57.0          38.0          49.0          39.0   \n",
       "004          53.0          56.0          44.0          46.0          28.0   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "219          81.0          72.0          78.0          79.0          76.0   \n",
       "220          76.0          71.0          71.0          78.0          76.0   \n",
       "221          59.0          51.0           0.0          72.0          63.0   \n",
       "222          64.0          72.0          71.0          71.0          66.0   \n",
       "224          75.0          73.0          68.0          61.0          57.0   \n",
       "\n",
       "     ...  ch28_0th_perc  ch29_0th_perc  ch30_0th_perc  ch31_0th_perc  \\\n",
       "000  ...           57.0           70.0           71.0           75.0   \n",
       "001  ...           55.0           51.0           67.0           40.0   \n",
       "002  ...            0.0           43.0           51.0           54.0   \n",
       "003  ...           48.0           38.0           55.0           61.0   \n",
       "004  ...           52.0           49.0           42.0           57.0   \n",
       "..   ...            ...            ...            ...            ...   \n",
       "219  ...           49.0           50.0           61.0           52.0   \n",
       "220  ...           59.0           62.0           64.0           60.0   \n",
       "221  ...           63.0           62.0           63.0           69.0   \n",
       "222  ...           52.0           55.0           61.0           54.0   \n",
       "224  ...           75.0           75.0           75.0           73.0   \n",
       "\n",
       "     ch32_0th_perc  ch33_0th_perc  ch34_0th_perc  ch35_0th_perc  \\\n",
       "000           49.0           44.0           60.0           80.0   \n",
       "001           55.0           50.0           36.0           83.0   \n",
       "002           56.0           52.0           46.0           91.0   \n",
       "003           47.0           37.0           40.0            0.0   \n",
       "004           24.0           46.0           39.0           77.0   \n",
       "..             ...            ...            ...            ...   \n",
       "219           54.0           40.0           46.0           89.0   \n",
       "220           47.0           21.0           58.0           99.0   \n",
       "221           58.0           32.0           69.0           93.0   \n",
       "222           44.0           41.0           48.0           90.0   \n",
       "224           51.0           41.0            0.0            0.0   \n",
       "\n",
       "     ch36_0th_perc  ch37_0th_perc  \n",
       "000           73.0           49.0  \n",
       "001           49.0           46.0  \n",
       "002           70.0           49.0  \n",
       "003           67.0           36.0  \n",
       "004           58.0           32.0  \n",
       "..             ...            ...  \n",
       "219           95.0           34.0  \n",
       "220           90.0           55.0  \n",
       "221           91.0           58.0  \n",
       "222           87.0           40.0  \n",
       "224           79.0           56.0  \n",
       "\n",
       "[211 rows x 38 columns]"
      ]
     },
     "execution_count": 1236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeroth_perc = percentiles.filter(regex=(\".*_0th.*\"))\n",
    "zeroth_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  2.,  0.,  1.,  3., 15., 13.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  0.,  1.]),\n",
       " array([ 60. ,  63.6,  67.2,  70.8,  74.4,  78. ,  81.6,  85.2,  88.8,\n",
       "         92.4,  96. ,  99.6, 103.2, 106.8, 110.4, 114. , 117.6, 121.2,\n",
       "        124.8, 128.4, 132. ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 1241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sf3GX837g9CnnfJDB8eM7uq9Lp51zlaxf6H6f7gL+EThuo597T6WXpEbN5CEUSdLaLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUqP8FKyZPA5Tt1a8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of maz 0th percentile values across all channels\n",
    "\n",
    "plt.hist(zeroth_perc.max().to_frame()[0],bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Channel 35 -- percentiles')"
      ]
     },
     "execution_count": 1225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Channel 35\n",
    "plt.hist(percentiles['ch35_0th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0_perc')\n",
    "plt.hist(percentiles['ch35_0.001st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.001_perc')\n",
    "plt.hist(percentiles['ch35_0.1st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.1_perc')\n",
    "plt.hist(percentiles['ch35_5th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '5_perc')\n",
    "plt.hist(percentiles['ch35_10th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '10_perc')\n",
    "plt.legend(loc='upper left')\n",
    "plt.suptitle(f\"Channel 35 -- percentiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Channel 36 -- percentiles')"
      ]
     },
     "execution_count": 1226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Channel 36\n",
    "plt.hist(percentiles['ch36_0th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0_perc')\n",
    "plt.hist(percentiles['ch36_0.001st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.001_perc')\n",
    "plt.hist(percentiles['ch36_0.1st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.1_perc')\n",
    "plt.hist(percentiles['ch36_5th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '5_perc')\n",
    "plt.hist(percentiles['ch36_10th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '10_perc')\n",
    "plt.legend(loc='upper left')\n",
    "plt.suptitle(f\"Channel 36 -- percentiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a78e1bbff44540e5ab7e22aa4b0533f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=38), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/egunim/.conda/envs/all_your_base/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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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d/PyYQzYnjV5vqc5SceYePZ771txBR03LlCF/yxvPXZdCioiEkLplqokJEyawatWqo9omTpzI8uXL01OQiGQ0hXs18cgjj6S7BBEJEXXLiIiEkMJdRCSEFO4iIiFUYZ+7mc0GfgDscff2Qdt8oOSrkWcA+9y9k5nlAhuBj4Jpb7r79ckoNPpSqGTQFyFEJMziOXOfAwyIbnD3K9y9k7t3InLj7OejJm8pmZasYE+nisZzX7FiBV26dKF27do899xzaahQROR4FYa7u68AYg4lGNw8exgwL8l1VQvxjOeek5PDnDlzGDly5AmpR0QkHon2uX8f2O3um6LaWpjZu2b2X2b2/bIWNLPxZrbGzNYUFxcnWEZqxDOee25uLnl5eXznOxW/lMuXL6dnz54MGTKE8847j+uvv54jR44AsHTpUrp3706XLl0YOnQoX331Ven6p06dyoUXXsizzz7L5s2bufjii+nYsSNdunRhy5Ytyd9xEcl4iYb7CI4+a98F5Lh7Z+BnwB/N7PRYC7r7THfPd/f8rKysBMtIjVjjuVd1TPYShYWF3Hfffbz//vts2bKF559/nk8//ZRf/epXvPrqq7zzzjvk5+dz//33ly5Tt25dVq5cyfDhw7nyyiuZMGEC7733Hq+//jpNmzZNqB4RCacqf4nJzGoDPwT+raTN3Q8CB4PHb5vZFuBcYE3MlVRzyRyTvURBQQEtW0bGrRgxYgQrV66kbt26bNiwgQsuuACAf/3rX3Tv3r10mSuuuAKAL7/8kp07dzJkyBAgEvoiIrEk8g3Vi4EP3b2opMHMsoDP3P2wmbUEWgMfJ1hj2iRzTPYSZY3N3rdvX+bNi/3RRcn47NVhkDcRyQzxXAo5D+gFNDazIuB2d58FDOf4D1J7AlPN7BBwGLje3ZNyX7d0XLoYz3julVVYWMjWrVs555xzmD9/PuPHj6dbt25MmDCBzZs306pVK77++muKioo499xzj1r29NNPJzs7m4ULF3L55Zdz8OBBDh8+XHqXJRGREvFcLTPC3Zu6ex13zw6CHXcf4+6PHzPvAndv5+4d3b2Lu/85VYWfCNHjubdt25Zhw4aVjue+ePFiAN566y2ys7N59tlnue6662jXrl256+zevTuTJ0+mffv2tGjRgiFDhpCVlcWcOXMYMWIEeXl5dOvWjQ8//DDm8k8//TQPPfQQeXl59OjRg3/84x9J328RyXwaOKwCgwYNYtCgo8d6njp1aunjrl27UlRUdOxiZapfvz7z588/rr1379689dZbx7Vv27btqOetW7fmr3/9a9zbE5GaScMPiIiEkM7cU+D9999n1KhRR7WdfPLJrF69ml69eqWnKBGpURTuKdChQwfWrl2b7jJEpAZTt4yISAgp3EVEQkjhLiISQhnT51484+Gkri/rJzcmdX0iItVJxoR7uuTm5nLaaadRq1YtateuzZo1GTlMjojUMAr3OLz22ms0btw4Zes/dOgQtWvrUIhI8qjPPUl69erFpEmT6NGjB+3bt6ewsBCAAwcOMG7cOLp27Urnzp1Lx4OfM2cOQ4cO5dJLL6Vfv34A3HvvvXTo0IGOHTsyefLktO2LiGQ+nS5WwMzo168fZsZ1113H+PHjy5z3wIEDvP7666xYsYJx48axfv167rrrLnr37s3s2bPZt28fBQUFXHzxxQC88cYbrFu3jkaNGvHyyy+zcOFCVq9eTf369fnss6SMtyYiNZTCvQKrVq2iWbNm7Nmzh759+/K9732Pnj17xpx3xIgRAPTs2ZP9+/ezb98+li5dyuLFi5k+fToA33zzDdu3bwegb9++NGrUCIBXX32VsWPHlo7wWNIuIlIVCvcKlIzf3qRJE4YMGUJhYWGZ4V7WWO0LFiygTZs2R01bvXp16TjtEBmrPdEbgYiIlMiYcE/HpYsHDhzgyJEjnHbaaRw4cIClS5dy2223lTn//Pnzueiii1i5ciUNGjSgQYMG9O/fnxkzZjBjxgzMjHfffZfOnTsft2y/fv2YOnUqI0eOLO2W0dm7iFRVxoR7Ouzevbv0lnaHDh1i5MiRDBgwoMz5GzZsSI8ePdi/fz+zZ88G4NZbb2XSpEnk5eXh7uTm5vLiiy8et+yAAQNYu3Yt+fn5nHTSSQwaNIi77747NTsmIqEXz52YZgM/APa4e/ug7Q7gWqA4mO0Wd38pmDYFuJrInZh+6u5/SUHdJ0TLli1577334p7/Rz/6Effcc89RbfXq1eO3v/3tcfOOGTOGMWPGHNU2efJkXSUjIkkRz6WQc4BYp6sPuHun4Kck2M8jcvu9dsEyj5pZrWQVKyIi8anwzN3dV5hZbpzruwx4xt0PAlvNbDNQALxR5QqrmQkTJrBq1aqj2iZOnMjy5cvTU5CISAyJ9LnfaGZXAWuAn7v750Bz4M2oeYqCtuOY2XhgPEBOTk4CZZxYjzzySLpLEBGpUFW/ofoY8F2gE7ALuC9oj3Utn8dagbvPdPd8d8/PysqqYhkiIhJLlcLd3Xe7+2F3PwI8QaTrBSJn6mdHzZoNfJJYiSIiUllVCnczaxr1dAiwPni8GBhuZiebWQugNVCYWIkiIlJZ8VwKOQ/oBTQ2syLgdqCXmXUi0uWyDbgOwN0/MLM/ARuAQ8AEdz+cjEIL//xxMlZTquDSlkldn4hIdVLhmbu7j3D3pu5ex92z3X2Wu49y9w7unufug919V9T8d7n7d929jbu/nNryU2vcuHE0adKE9u3bl7Z99tln9O3bl9atW9O3b18+//zzNFYoIhKbhvwtx5gxY1iyZMlRbdOmTaNPnz5s2rSJPn36MG3atJRs2905cuRIStYtIuGncC9Hz549jxvfZdGiRYwePRqA0aNHs3DhwjKXv+OOOxg1ahS9e/emdevWPPHEE6XTfvOb39C1a1fy8vK4/fbbAdi2bRtt27blhhtuoEuXLuzYsYMlS5bQpUsXOnbsSJ8+fVKwlyISRhpbppJ2795N06aRz5ObNm3Knj17yp1/3bp1vPnmmxw4cIDOnTtzySWXsH79ejZt2kRhYSHuzuDBg1mxYgU5OTl89NFHPPnkkzz66KMUFxdz7bXXsmLFClq0aKEx3kUkbgr3FLvsssuoV68e9erV46KLLqKwsJCVK1eydOnS0tEhv/rqKzZt2kROTg7nnHMO3bp1A+DNN9+kZ8+etGjRAtAY7yISP4V7JZ111lns2rWLpk2bsmvXLpo0aVLu/GWN8T5lyhSuu+66o6Zt27ZNY7yLSFJkTLhXl0sXBw8ezNy5c5k8eTJz587lsssuK3f+RYsWMWXKFA4cOMDy5cuZNm0a9erV49Zbb+XKK6/k1FNPZefOndSpU+e4Zbt3786ECRPYunVrabeMzt5FJB4ZE+7pMGLECJYvX86nn35KdnY2d955J5MnT2bYsGHMmjWLnJwcnn322XLXUVBQwCWXXML27du59dZbadasGc2aNWPjxo10794dgFNPPZXf//731Kp19ACaWVlZzJw5kx/+8IccOXKEJk2a8Morr6Rsf0UkPBTu5Zg3b17M9mXLlsW9jnPPPZeZM2ce1z5x4kQmTpx4XPv69euPej5w4EAGDhwY9/ZERECXQoqIhJLO3JPgySef5MEHHzyq7YILLtDwwCKSNtU63DPlapGxY8cyduzYdJdRZe4xR2UWkQxWbbtl6taty969exU8Kebu7N27l7p166a7FBFJomp75p6dnU1RURHFxcUVzywJqVu3LtnZ2ekuQ0SSqNqGe506dUq/mSkiIpVTbbtlRESk6ioMdzObbWZ7zGx9VNtvzOxDM1tnZi+Y2RlBe66Z/dPM1gY/j6eyeBERiS2eM/c5wIBj2l4B2rt7HvA3YErUtC3u3in4uT45ZYqISGXEcyemFcBnx7QtdfdDwdM3idwIW0REqolk9LmPA6Jvp9fCzN41s/8ys+8nYf0iIlJJCV0tY2b/QeRG2H8ImnYBOe6+18z+DVhoZu3cfX+MZccD4wFycnISKUNERI5R5TN3MxsN/AC40oNvGrn7QXffGzx+G9gCnBtreXef6e757p6flZVV1TJERCSGKoW7mQ0AbgYGu/vXUe1ZZlYreNwSaA18nIxCRUQkfhV2y5jZPKAX0NjMioDbiVwdczLwSjD2y5vBlTE9galmdgg4DFzv7rrxp4jICVZhuLv7iBjNs8qYdwGwINGiREQkMfqGqohICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJobjC3cxmm9keM1sf1dbIzF4xs03B74ZBu5nZQ2a22czWmVmXVBUvIiKxxXvmPgcYcEzbZGCZu7cGlgXPAQYSuXdqa2A88FjiZYqISGXEFe7uvgI49l6olwFzg8dzgcuj2p/yiDeBM8ysaTKKFRGR+CTS536Wu+8CCH43CdqbAzui5isK2o5iZuPNbI2ZrSkuLk6gDBEROVYqPlC1GG1+XIP7THfPd/f8rKysFJQhIlJzJRLuu0u6W4Lfe4L2IuDsqPmygU8S2I6IiFRSIuG+GBgdPB4NLIpqvyq4aqYb8EVJ942IiJwYteOZyczmAb2AxmZWBNwOTAP+ZGZXA9uBocHsLwGDgM3A18DYJNcsIiIViCvc3X1EGZP6xJjXgQmJFCUiIonRN1RFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQmhuMZzj8XM2gDzo5paArcBZwDXAiV3vb7F3V+qcoUiIlJpVQ53d/8I6ARgZrWAncALRO689IC7T09KhSIiUmnJ6pbpA2xx978naX0iIpKAZIX7cGBe1PMbzWydmc02s4ZJ2oaIiMQp4XA3s5OAwcCzQdNjwHeJdNnsAu4rY7nxZrbGzNYUFxfHmkVERKooGWfuA4F33H03gLvvdvfD7n4EeAIoiLWQu89093x3z8/KykpCGSIiUiIZ4T6CqC4ZM2saNW0IsD4J2xARkUqo8tUyAGZWH+gLXBfVfK+ZdQIc2HbMNBEROQESCnd3/xo485i2UQlVJCIiCdM3VEVEQkjhLiISQgp3EZEQUriLiIRQQh+oiogk7LV7/vvxRVPSV0fI6MxdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQmhhMeWMbNtwJfAYeCQu+ebWSNgPpBL5G5Mw9z980S3JSIi8UnWmftF7t7J3fOD55OBZe7eGlgWPBcRkRMkVd0ylwFzg8dzgctTtB0REYkhGeHuwFIze9vMxgdtZ7n7LoDgd5NjFzKz8Wa2xszWFBcXJ6EMEREpkYzx3C9w90/MrAnwipl9GM9C7j4TmAmQn5/vSahDREQCCZ+5u/snwe89wAtAAbDbzJoCBL/3JLodERGJX0Jn7mZ2CvAdd/8yeNwPmAosBkYD04LfixItVERCJPruS5ISiXbLnAW8YGYl6/qjuy8xs7eAP5nZ1cB2YGiC2xERkUpIKNzd/WOgY4z2vUCfRNYtIiJVp2+oioiEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQsm4h6qISGzRd1y6aEr66qiBqnzmbmZnm9lrZrbRzD4ws4lB+x1mttPM1gY/g5JXroiIxCORM/dDwM/d/R0zOw1428xeCaY94O7TEy9PREJD9009oaoc7u6+C9gVPP7SzDYCzZNVmEi0B175W+njm/qem8ZKRDJDUj5QNbNcoDOwOmi60czWmdlsM2tYxjLjzWyNma0pLi5ORhkiIhJIONzN7FRgATDJ3fcDjwHfBToRObO/L9Zy7j7T3fPdPT8rKyvRMkREJEpC4W5mdYgE+x/c/XkAd9/t7ofd/QjwBFCQeJkiIlIZVe5zNzMDZgEb3f3+qPamQX88wBBgfWIlikhGSfEHp4+ufbT08Q2dbkjptjJZIlfLXACMAt43s7VB25pPwacAAAV8SURBVC3ACDPrBDiwDbguoQpFRKTSErlaZiVgMSa9VPVyREQkGTT8gIhICCncRURCSOEuIhJCGjhMapyyvu2qb8FKmOjMXUQkhBTuIiIhpG4ZyWjqShGJTeEuIonTcL7VjrplRERCSGfukhap6E4pb53R00QA1u/579FoT8lNXx2ponCXakuBLFJ1CndJu8qexccT+vH+YShrPn1QK5lO4S5SAQW9ZCKFu1RZqvvNRaTqFO5ywii4RU6clIW7mQ0AHgRqAb9z92mp2paIpIGuba/WUhLuZlYLeAToCxQBb5nZYnffkIrtScWS1YUSzweQYab+d8kUqTpzLwA2u/vHAGb2DHAZkJJwn/Lk5aWP7xm7MBWbyEiVvaqkrLCqKcEtEiapCvfmwI6o50XA+Sna1gmVyJlbWSEZT6iWNTRtvOuKpw6dlVZOPH/0QvE6Znj3S/GMh0sfZ/3kxjRWcmKZuyd/pWZDgf7ufk3wfBRQ4O4/iZpnPDA+eNoG+CiBTTYGPk1g+epM+5a5wrx/2rfq4Rx3z4o1IVVn7kXA2VHPs4FPomdw95nAzGRszMzWuHt+MtZV3WjfMleY90/7Vv2lauCwt4DWZtbCzE4ChgOLU7QtERE5RkrO3N39kJndCPyFyKWQs939g1RsS0REjpey69zd/SXgpVSt/xhJ6d6pprRvmSvM+6d9q+ZS8oGqiIikl27WISISQhkd7mY2wMw+MrPNZjY53fUkwszONrPXzGyjmX1gZhOD9kZm9oqZbQp+N0x3rVVlZrXM7F0zezF43sLMVgf7Nj/48D0jmdkZZvacmX0YHMPuYTl2ZnZT8G9yvZnNM7O6mXzszGy2me0xs/VRbTGPlUU8FGTMOjPrkr7KKydjwz1qiIOBwHnACDM7L71VJeQQ8HN3bwt0AyYE+zMZWOburYFlwfNMNRHYGPX818ADwb59DlydlqqS40Fgibt/D+hIZD8z/tiZWXPgp0C+u7cncoHEcDL72M0BBhzTVtaxGgi0Dn7GA4+doBoTlrHhTtQQB+7+L6BkiIOM5O673P2d4PGXRMKhOZF9mhvMNhe4PPYaqjczywYuAX4XPDegN/BcMEsm79vpQE9gFoC7/8vd9xGSY0fkwot6ZlYbqA/sIoOPnbuvAD47prmsY3UZ8JRHvAmcYWZNT0ylicnkcI81xEHzNNWSVGaWC3QGVgNnufsuiPwBAJqkr7KE/Cfw78CR4PmZwD53PxQ8z+Tj1xIoBp4Mup1+Z2anEIJj5+47genAdiKh/gXwNuE5diXKOlYZmzOZHO4Woy3jL/0xs1OBBcAkd9+f7nqSwcx+AOxx97ejm2PMmqnHrzbQBXjM3TsDB8jALphYgr7ny4AWQDPgFCJdFcfK1GNXkYz9d5rJ4V7hEAeZxszqEAn2P7j780Hz7pK3gcHvPemqLwEXAIPNbBuR7rPeRM7kzwje6kNmH78ioMjdVwfPnyMS9mE4dhcDW9292N2/BZ4HehCeY1eirGOVsTmTyeEeqiEOgj7oWcBGd78/atJiYHTweDSw6ETXlih3n+Lu2e6eS+Q4/dXdrwReA34czJaR+wbg7v8AdphZm6CpD5HhrTP+2BHpjulmZvWDf6Ml+xaKYxelrGO1GLgquGqmG/BFSfdNtefuGfsDDAL+BmwB/iPd9SS4LxcSebu3Dlgb/Awi0je9DNgU/G6U7loT3M9ewIvB45ZAIbAZeBY4Od31JbBfnYA1wfFbCDQMy7ED7gQ+BNYDTwMnZ/KxA+YR+fzgWyJn5leXdayIdMs8EmTM+0SuGkr7PsTzo2+oioiEUCZ3y4iISBkU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iE0P8HWrk8Ad4F8t4AAAAASUVORK5CYII=\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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x3CtKjx49mDFjBpmZhQ7ZHDd6vqWqCft47roVUkQkhNQtU0lMmDCBDRs2nLFs4sSJrF27NjEFiUhSU7hXErNmzUp0CSISIuqWEREJIYW7iEgIKdxFREIoafrcZ2+ZHdfj3XnVnXE9nohIZaKWewlKGs993bp1dOrUiZSUFJYsWZKACkVEzqVwL0ZpxnNv2rQp8+fPZ+TIkeelHhGR0lC4F6M047mnp6eTkZHB975X8lO5du1aunfvzpAhQ2jTpg133HEHp0+fBmDVqlV07dqVTp06MXToUL788suC40+bNo1rr72WF198kV27dnHdddfRoUMHOnXqxIcffhj/ExeRpFdiIpnZPDM7ZGbbopbVM7PVZrYz+Fs3WG5m9oSZ7TKzrWbWqSKLr2iFjede3jHZ8+Xk5PDrX/+a9957jw8//JCXX36ZI0eO8Itf/ILXXnuNt99+m8zMTB599NGCfWrWrMn69esZPnw4t9xyCxMmTODdd9/ljTfeoFGjRjHVIyLhVJoPVOcDTwLPRS2bAqxx9+lmNiWYvxu4HmgZ/LsaeCr4m5TiOSZ7vqysLJo3j4xdMWLECNavX0/NmjXZvn0711xzDQDffPMNXbt2Ldjn5ptvBuCLL75g//79DBkyBIiEvohIYUoMd3dfZ2bpZy0eDPQIphcAa4mE+2DgOY+k4ptmdqmZNXL3A/Eq+HyK55js+Yoam71Pnz4sXLiw0H3yx2evDIO8iUhyKO+tkJflB7a7HzCzhsHyJsC+qO1yg2XnhLuZjQfGQ+RDyZIk4tbF0oznXlY5OTns3r2bK664gsWLFzN+/Hi6dOnChAkT2LVrFy1atOCrr74iNzeXK6+88ox9L7nkEtLS0li6dCk33ngjX3/9NadOnSr4lSURkXzx/kC1sD6LQpub7j7H3TPdPTM1NTXOZcRH9HjurVu3ZtiwYQXjuS9fvhyAt956i7S0NF588UVuv/122rZtW+wxu3btypQpU2jXrh3NmjVjyJAhpKamMn/+fEaMGEFGRgZdunThL3/5S6H7P//88zzxxBNkZGTQrVs3Pvnkk7ift4gkv/K23A/md7eYWSPgULA8F7g8ars04ONYCky0AQMGMGDAgDOWTZs2rWC6c+fO5Obmlvp4tWvXZvHixecs79WrF2+99dY5y/fs2XPGfMuWLfnzn/9c6scTkaqpvC335cDoYHo0sCxq+Y+Cu2a6AJ8na3+7iEgyK7HlbmYLiXx42sDMcoH7gOnA781sHLAXGBps/kdgALAL+AoYUwE1V3rvvfceo0aNOmPZBRdcQHZ2Nj169EhMUSJSpZTmbpkRRazqXci2DkyItahk1759e7Zs2ZLoMkSkCtM3VEVEQkjhLiISQgp3EZEQSprx3A/PfDKux0v9l3+O6/FERCqTpAn3RElPT+fiiy+mWrVqpKSksGnTpkSXJCJSIoV7Kbz++us0aNCgwo5/8uRJUlJ0KUQkftTnHic9evRg0qRJdOvWjXbt2pGTkwNAXl4eY8eOpXPnznTs2LFgPPj58+czdOhQBg4cSN++fQF45JFHaN++PR06dGDKlCkJOxcRSX5qLpbAzOjbty9mxu2338748eOL3DYvL4833niDdevWMXbsWLZt28aDDz5Ir169mDdvHseOHSMrK4vrrrsOgI0bN7J161bq1avHq6++ytKlS8nOzqZ27dp8+umn5+sURSSEFO4l2LBhA40bN+bQoUP06dOH73//+3Tv3r3QbUeMiHzfq3v37hw/fpxjx46xatUqli9fzowZMwA4ceIEe/fuBaBPnz7Uq1cPgNdee40xY8YUjPCYv1xEpDwU7iXIH7+9YcOGDBkyhJycnCLDvaix2l966SVatWp1xrrs7OyCcdohMlZ7rD8EIiKSL2nCPRG3Lubl5XH69Gkuvvhi8vLyWLVqFffee2+R2y9evJiePXuyfv166tSpQ506dejXrx8zZ85k5syZmBnvvPMOHTt2PGffvn37Mm3aNEaOHFnQLaPWu4iUV9KEeyIcPHiw4CftTp48yciRI+nfv3+R29etW5du3bpx/Phx5s2bB8A999zDpEmTyMjIwN1JT09nxYoV5+zbv39/tmzZQmZmJjVq1GDAgAE89NBDFXNiIhJ6Vhl+ui0zM9PPvn98x44dtG7dOkEVlV2PHj2YMWMGmZmZiS6lXJLt+RaJVfQXI3enf/ebDVkDmyeinHIxs83uXmjoqOUuIqEze8vsgulE/ERnZaBwL6MJEyawYcOGM5ZNnDiRtWvXJqYgEZFCKNzLaNasWYkuQUSkRPqGqohICCncRURCSOEuIhJCSdPnnvPKR3E9XjLd7iQiUlZquRdj7NixNGzYkHbt2hUs+/TTT+nTpw8tW7akT58+fPbZZwmsUESkcAr3Ytx6662sXLnyjGXTp0+nd+/e7Ny5k969ezN9+vQKeWx35/Tp0xVybBEJP4V7Mbp3737O+C7Lli1j9OjRAIwePZqlS5cWuf/999/PqFGj6NWrFy1btuQ3v/lNwbpf/epXdO7cmYyMDO677z4A9uzZQ+vWrbnzzjvp1KkT+/btY+XKlXTq1IkOHTrQu3fvCjhLEQmjpOlzrywOHjxIo0aNAGjUqBGHDh0qdvutW7fy5ptvkpeXR8eOHbnhhhvYtm0bO3fuJCcnB3dn0KBBrFu3jqZNm/LBBx/w7LPPMnv2bA4fPsyPf/xj1q1bR7NmzTTGu4iUmsK9gg0ePJhatWpRq1YtevbsSU5ODuvXr2fVqlUFo0N++eWX7Ny5k6ZNm3LFFVfQpUsXAN588026d+9Os2bNAI3xLiKlF1O4m9lk4DbAgfeAMUAjYBFQD3gbGOXu38RYZ6Vx2WWXceDAARo1asSBAwdo2LBhsdsXNcb71KlTuf32289Yt2fPHo3xLiJxUe5wN7MmwE+ANu7+NzP7PTAcGAA85u6LzOxpYBzwVKyFVpZbFwcNGsSCBQuYMmUKCxYsYPDgwcVuv2zZMqZOnUpeXh5r165l+vTp1KpVi3vuuYdbbrmFiy66iP3791O9evVz9u3atSsTJkxg9+7dBd0yar2LSGnE2i2TAtQys2+B2sABoBcwMli/ALifOIR7IowYMYK1a9dy5MgR0tLSeOCBB5gyZQrDhg1j7ty5NG3alBdffLHYY2RlZXHDDTewd+9e7rnnHho3bkzjxo3ZsWMHXbt2BeCiiy7ihRdeoFq1amfsm5qaypw5c/jhD3/I6dOnadiwIatXr66w8xWR8Ch3uLv7fjObAewF/gasAjYDx9z9ZLBZLtCksP3NbDwwHqBp06blLaNCLVy4sNDla9asKfUxrrzySubMmXPO8okTJzJx4sRzlm/btu2M+euvv57rr7++1I8nIgIx3AppZnWBwUAzoDFwIVBYChX6ayDuPsfdM909MzU1tbxliIhIIWLplrkO2O3uhwHM7GWgG3CpmaUErfc04OPYy6zcnn32WR5//PEzll1zzTUaHlhEEiaWcN8LdDGz2kS6ZXoDm4DXgZuI3DEzGlhW3gdIlrtFxowZw5gxYxJdRrlVhp9aFJH4Kne3jLtnA0uI3O74XnCsOcDdwF1mtguoD8wtz/Fr1qzJ0aNHFTwVzN05evQoNWvWTHQpIhJHMd0t4+73AfedtfgjICuW4wKkpaWRm5vL4cOHYz2UlKBmzZqkpaUlugwRiaNK+w3V6tWrF3wzU0REykYDh4mIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJoUo7cJiIVEGvP/zddM+piasjBNRyFxEJIYW7iEgIqVtGRKq8vJy3vpsZ2DxxhcSRWu4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhFBM97mb2aXAM0A7wIGxwAfAYiAd2AMMc/fPYqpSROQ8yXnlo4LprCS+5z3WlvvjwEp3/z7QAdgBTAHWuHtLYE0wLyIi51G5w93MLgG6A3MB3P0bdz8GDAYWBJstAG6MtUgRESmbWFruzYHDwLNm9o6ZPWNmFwKXufsBgOBvwzjUKSIiZRBLuKcAnYCn3L0jkEcZumDMbLyZbTKzTYcPH46hDBEROVss4Z4L5Lp7djC/hEjYHzSzRgDB30OF7ezuc9w9090zU1NTYyhDRETOVu5wd/dPgH1m1ipY1BvYDiwHRgfLRgPLYqpQRETKLNYhf/8F+K2Z1QA+AsYQecH4vZmNA/YCQ2N8DBERKaOYwt3dtwCZhazqHctxRUQkNvqGqohICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhFOuXmEREKoXZW2YnuoRKRS13EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRCKOdzNrJqZvWNmK4L5ZmaWbWY7zWyxmdWIvUwRESmLeLTcJwI7ouZ/CTzm7i2Bz4BxcXgMEREpg5jC3czSgBuAZ4J5A3oBS4JNFgA3xvIYIiJSdrG23P8T+DfgdDBfHzjm7ieD+VygSWE7mtl4M9tkZpsOHz4cYxkiIhKt3OFuZj8ADrn75ujFhWzqhe3v7nPcPdPdM1NTU8tbhoiIFCIlhn2vAQaZ2QCgJnAJkZb8pWaWErTe04CPYy9TRKq01x/+brrn1MTVkUTK3XJ396nunubu6cBw4M/ufgvwOnBTsNloYFnMVYqISJlUxH3udwN3mdkuIn3wcyvgMUREpBixdMsUcPe1wNpg+iMgKx7HFRGR8tE3VEVEQkjhLiISQgp3EZEQUriLiISQwl1EJITicreMiEjcRX9xKQazt8wumL7zqjvjcsxkoJa7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQuUOdzO73MxeN7MdZva+mU0Mltczs9VmtjP4Wzd+5YqISGnE8gPZJ4F/dfe3zexiYLOZrQZuBda4+3QzmwJMAe6OvVQRCaU4/RB2Rch55aOC6ayBzRNYSdmVu+Xu7gfc/e1g+gtgB9AEGAwsCDZbANwYa5EiIlI2celzN7N0oCOQDVzm7gcg8gIANCxin/FmtsnMNh0+fDgeZYiISCDmcDezi4CXgEnufry0+7n7HHfPdPfM1NTUWMsQEZEosfS5Y2bViQT7b9395WDxQTNr5O4HzKwRcCjWIkVECjN7y+xEl1BpxXK3jAFzgR3u/mjUquXA6GB6NLCs/OWJiEh5xNJyvwYYBbxnZluCZf8OTAd+b2bjgL3A0NhKFBGRsip3uLv7esCKWN27vMcVEZHY6RuqIiIhFNMHqiIiYZOX81bB9IVZnRNYSWzUchcRCSGFu4hICKlbRqSSemz1XwumJ/e5MoGVSDJSy11EJITUchdJMmrRS2mo5S4iEkJquYvIGaLfGYDeHSQrhbtISFT67proH+XoOTVxdVQR6pYREQkhtdxF5PyrxD+tFxZquYuIhJBa7iJxVun7vgtx9oeokvwU7iJlkCzBnSx1lod+fal01C0jIhJCarmLnCelaU2XtXtE3SlSFLXcRURCSC13SQoV0YdcWb6JWdGt7zD3v59POa98dMZ81sDmCaqkdJI+3Kc+e2PB9MNjliawEgmTsnahxKubpTT1yPmTzL/KpG4ZEZEQSvqWu1Rt8exyiKV1nCwfhJbn+Spqn41zf1ow3bV5/e920LgxlYJa7iIiIaSWuySE+qsTr1zPS2nGhNG4MZVChYW7mfUHHgeqAc+4+/SKeiypvCoiWM93WFe1F4cue+ecMf9m0/EJqqQIu//nu+lm/1CmXaO/3To0XvVUUhXSLWNm1YBZwPVAG2CEmbWpiMcSEZFzVVTLPQvY5e4fAZjZImAwsL2CHq9QZf3wqCreDxzW56iqtbYrUnRLfmOCaph9bGvcj7ntUGpM+0ff914Z73mvqA9UmwD7ouZzg2UiInIemLvH/6BmQ4F+7n5bMD8KyMEVTIUAAAQISURBVHL3f4naZjyQ35nXCvignA/XADgSQ7mVXZjPT+eWvMJ8fsl0ble4e6FvQSqqWyYXuDxqPg34OHoDd58DnPnJTTmY2SZ3z4z1OJVVmM9P55a8wnx+YTm3iuqWeQtoaWbNzKwGMBxYXkGPJSIiZ6mQlru7nzSzfwb+RORWyHnu/n5FPJaIiJyrwu5zd/c/An+sqONHiblrp5IL8/np3JJXmM8vFOdWIR+oiohIYmlsGRGREErqcDez/mb2gZntMrMpia4nFmZ2uZm9bmY7zOx9M5sYLK9nZqvNbGfwt26iay0vM6tmZu+Y2YpgvpmZZQfntjj48D0pmdmlZrbEzP4SXMOuYbl2ZjY5+D+5zcwWmlnNZL52ZjbPzA6Z2baoZYVeK4t4IsiYrWbWKXGVl03ShnsIhzg4Cfyru7cGugATgvOZAqxx95bAmmA+WU0EdkTN/xJ4LDi3z4BxCakqPh4HVrr794EORM4z6a+dmTUBfgJkuns7IjdIDCe5r918oP9Zy4q6VtcDLYN/44GnzlONMUvacCdqiAN3/wbIH+IgKbn7AXd/O5j+gkg4NCFyTguCzRYANxZ+hMrNzNKAG4BngnkDegFLgk2S+dwuAboDcwHc/Rt3P0ZIrh2RGy9qmVkKUBs4QBJfO3dfB3x61uKirtVg4DmPeBO41MwanZ9KY5PM4R7aIQ7MLB3oCGQDl7n7AYi8AAANE1dZTP4T+DfgdDBfHzjm7ieD+WS+fs2Bw8CzQbfTM2Z2ISG4du6+H5gB7CUS6p8DmwnPtctX1LVK2pxJ5nC3QpYl/a0/ZnYR8BIwyd2PJ7qeeDCzHwCH3H1z9OJCNk3W65cCdAKecveOQB5J2AVTmKDveTDQDGgMXEikq+JsyXrtSpK0/0+TOdxLHOIg2ZhZdSLB/lt3fzlYfDD/bWDw91Ci6ovBNcAgM9tDpPusF5GW/KXBW31I7uuXC+S6e3Ywv4RI2Ifh2l0H7Hb3w+7+LfAy0I3wXLt8RV2rpM2ZZA73UA1xEPRBzwV2uPujUauWA6OD6dHAsvNdW6zcfaq7p7l7OpHr9Gd3vwV4Hbgp2Cwpzw3A3T8B9plZq2BRbyLDWyf9tSPSHdPFzGoH/0fzzy0U1y5KUddqOfCj4K6ZLsDn+d03lZ67J+0/YADwV+BD4OeJrifGc7mWyNu9rcCW4N8AIn3Ta4Cdwd96ia41xvPsAawIppsDOcAu4EXggkTXF8N5XQVsCq7fUqBuWK4d8ADwF2Ab8DxwQTJfO2Ahkc8PviXSMh9X1LUi0i0zK8iY94jcNZTwcyjNP31DVUQkhJK5W0ZERIqgcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhP4/HRsgWlSRqOEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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luokez31v2qCCx1k3tEpEORWi8dxFRKoZdctUERMnTmTDhg1nzJs0aRJr165NTEEiktQU7lXE7NmzE12CiISIumVEREJI4S4iEkIKdxGREEqaPvc5W+fEdX93XnlnXPcnIlKVqOVeitLGc1+3bh1du3YlJSWFpUuXJqBCEZGzKdxLUJbx3Fu0aMGCBQsYNWrUOalHRKQsFO4lKMt47mlpaaSnp/Otb5X+VK5du5aePXsydOhQ2rdvzx133MHp06cBWL16Nd27d6dr164MGzaMzz//vGD/06dP55prruGFF15g9+7dXHvttXTu3JmuXbvywQcfxP/ERSTpKdxLUNR47hUdkz1fdnY2v/zlL3n33Xf54IMPeOmllzhy5Ag///nPefXVV3nrrbfIyMjg0UcfLdimdu3arF+/nhEjRnDLLbcwceJE3nnnHV5//XWaNm0aUz0iEk5J84FqIsRzTPZ8WVlZtGoVGbti5MiRrF+/ntq1a7Njxw6uvvpqAL766iu6d+9esM3NN98MwGeffcbBgwcZOnQoEAl9EZGiKNxLEM8x2fMVNzZ7v379WLRoUZHb5I/PXhUGeROR5JA04Z6IWxfLMp57eWVnZ7N3714uu+wylixZwoQJE+jWrRsTJ05k9+7dtG7dmi+++IKcnBwuv/zyM7a96KKLSE1NZdmyZdx44418+eWXnDp1quBXlkRE8qnPvQTR47m3a9eO4cOHF4znvmLFCgDefPNNUlNTeeGFF7j99tvp0KFDifvs3r07U6ZMoWPHjrRs2ZKhQ4fSuHFjFixYwMiRI0lPT6dbt2789a9/LXL75557jieeeIL09HR69OjB3//+97ift4gkv6RpuSfKoEGDGDRo0Bnzpk+fXvA4MzOTnJycMu+vbt26LFmy5Kz5ffr04c033zxr/r59+86YbtOmDX/5y1/KfDwRqZ7UchcRCSG13CvBu+++y+jRo8+Yd95557Fp0yZ69eqVmKJEpFopNdzNbD7wbeCwu3cM5jUAlgBpwD5guLt/YpFbQR4HBgFfALe6+1uVU3rV1alTJ7Zu3ZroMkSkGitLt8wCYGCheVOANe7eBlgTTANcB7QJ/k0AnopPmSIiUh6lhru7rwM+LjR7CLAweLwQuDFq/rMe8QZwsZnpK5QiIudYRT9QvcTdDwEEf5sE85sDB6LWywnmncXMJpjZZjPbnJubW8EyRESkKPH+QLWo7+YX+bVKd58LzAXIyMgo9auXubOejK2yQhr/8Adx3Z+ISFVS0XD/yMyauvuhoNvlcDA/B7g0ar1U4MNYCky0tLQ0LrzwQmrUqEFKSgqbN29OdEkiIqWqaLivAMYAM4K/y6Pm/8DMFgNXAZ/md98ks9dee41GjRpV2v5PnjxJSoruShWR+Cm1z93MFgEbgbZmlmNm44mEej8z2wX0C6YB/gTsAXYDvwaqzW/Z9erVi8mTJ9OjRw86duxIdnY2AHl5eYwbN47MzEy6dOlSMB78ggULGDZsGDfccAP9+/cH4JFHHqFTp0507tyZKVOmFHssEZHSlNpcdPeRxSzqW8S6DkyMtaiqxMzo378/Zsbtt9/OhAkTil03Ly+P119/nXXr1jFu3Di2b9/OAw88QJ8+fZg/fz7Hjh0jKyuLa6+9FoCNGzeybds2GjRowMsvv8yyZcvYtGkTdevW5eOPC9+gJCJSduoLKMWGDRto1qwZhw8fpl+/flxxxRX07NmzyHVHjoy8Dvbs2ZPjx49z7NgxVq9ezYoVK5g5cyYAJ06cYP/+/QD069ePBg0aAPDqq68yduzYghEe8+eLiFSEwr0U+eO3N2nShKFDh5KdnV1suBc3VvuLL75I27Ztz1i2adOmgnHaITJWe6w/BCIiki9pwj0Rty7m5eVx+vRpLrzwQvLy8li9ejX33ntvsesvWbKE3r17s379eurVq0e9evUYMGAAs2bNYtasWZgZb7/9Nl26dDlr2/79+zN9+nRGjRpV0C2j1ruIVFTShHsifPTRRwU/aXfy5ElGjRrFwIGFR2L4Rv369enRowfHjx9n/vz5AEybNo3JkyeTnp6Ou5OWlsbKlSvP2nbgwIFs3bqVjIwMatWqxaBBg3jwwQcr58REJPSsKvx0W0ZGhhe+f3znzp20a9cuQRWVX69evZg5cyYZGRmJLqVCku35FolV9Bcj96Z985sNWTe0SkQ5FWJmW9y9yNDReO4iIiGkbplymjhxIhs2bDhj3qRJk1i7dm1iChIRKYLCvZxmz56d6BJEREqlbhkRkRBSuIuIhJDCXUQkhJKmzz37j3viur9kut1JRKS81HIvwbhx42jSpAkdO3YsmPfxxx/Tr18/2rRpQ79+/fjkk08SWKGISNEU7iW49dZbWbVq1RnzZsyYQd++fdm1axd9+/ZlxowZxWwdG3fn9OnTlbJvEQk/hXsJevbsedb4LsuXL2fMmDEAjBkzhmXLlhW7/X333cfo0aPp06cPbdq04de//nXBsl/84hdkZmaSnp7Oz372MwD27dtHu3btuPPOO+natSsHDhxg1apVdO3alc6dO9O371mjLIuIFClp+tyrio8++oimTZsC0LRpUw4fPlzi+tu2beONN94gLy+PLl26cP3117N9+3Z27dpFdnY27s7gwYNZt24dLVq04P333+eZZ55hzpw55Obm8v3vf59169bRsmVLjfEuUkZzts4peHznldXmN4POoHCvZEOGDKFOnTrUqVOH3r17k52dzfr161m9enXB6JCff/45u3btokWLFlx22WV069YNgDfeeIOePXvSsmVLQGO8i0jZKdzL6ZJLLuHQoUM0bdqUQ4cO0aRJkxLXL26M96lTp3L77befsWzfvn0a411E4iJpwr2q3Lo4ePBgFi5cyJQpU1i4cCFDhgwpcf3ly5czdepU8vLyWLt2LTNmzKBOnTpMmzaNW265hQsuuICDBw9Ss2bNs7bt3r07EydOZO/evQXdMmq9i0hZJE24J8LIkSNZu3YtR44cITU1lfvvv58pU6YwfPhw5s2bR4sWLXjhhRdK3EdWVhbXX389+/fvZ9q0aTRr1oxmzZqxc+dOunfvDsAFF1zA888/T40aNc7YtnHjxsydO5fvfOc7nD59miZNmvDKK69U2vmKSHgo3EuwaNGiIuevWbOmzPu4/PLLmTt37lnzJ02axKRJk86av3379jOmr7vuOq677royH09EBHQrpIhIKKnlHgfPPPMMjz/++Bnzrr76ag0PLCIJE1O4m9ldwG2AA+8CY4GmwGKgAfAWMNrdv6rI/pPlbpGxY8cyduzYRJdRYVXhpxZFJL4q3C1jZs2BHwEZ7t4RqAGMAB4GHnP3NsAnwPiK7L927docPXpUwVPJ3J2jR49Su3btRJciInEUa7dMClDHzL4G6gKHgD7AqGD5QuA+4Kny7jg1NZWcnBxyc3NjLFFKU7t2bVJTUxNdhojEUYXD3d0PmtlMYD/wD2A1sAU45u4ng9VygOZFbW9mE4AJAC1atDhrec2aNQu+mSkiIuUTS7dMfWAI0BJoBpwPFHXPXpH9Ku4+190z3D2jcePGFS1DRESKEMutkNcCe909192/Bl4CegAXm1n+O4JU4MMYaxQRkXKKJdz3A93MrK5FbmnpC+wAXgNuCtYZAyyPrUQRESmvCoe7u28ClhK53fHdYF9zgXuAu81sN9AQmBeHOkVEpBxiulvG3X8G/KzQ7D1AViz7FRGR2Gj4ARGREFK4i4iEkMaWERGJkv3HPQWPq8rvSFSEWu4iIiGkcBcRCSGFu4hICCncRURCSB+oiki1l5f9ZsHj87MyE1hJ/KjlLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJId0KKSKhMGfrnESXUKWo5S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCKKZwN7OLzWypmf3VzHaaWXcza2Bmr5jZruBv/XgVKyIiZRNry/1xYJW7XwF0BnYCU4A17t4GWBNMi4jIOVThcDezi4CewDwAd//K3Y8BQ4CFwWoLgRtjLVJERMonlpZ7KyAXeMbM3jaz35jZ+cAl7n4IIPjbpKiNzWyCmW02s825ubkxlCEiIoXFEu4pQFfgKXfvAuRRji4Yd5/r7hnuntG4ceMYyhARkcJiCfccIMfdNwXTS4mE/Udm1hQg+Hs4thJFRKS8Khzu7v534ICZtQ1m9QV2ACuAMcG8McDymCoUEZFyi3U89x8CvzWzWsAeYCyRF4zfm9l4YD8wLMZjiIhIOcUU7u6+FcgoYlHfWPYrIiKx0TdURURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiIRQrKNCiojEz2sPffO499TE1RECarmLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEYg53M6thZm+b2cpguqWZbTKzXWa2xMxqxV6miIiURzxa7pOAnVHTDwOPuXsb4BNgfByOISIi5RBTuJtZKnA98Jtg2oA+wNJglYXAjbEcQ0REyi/WlvuvgH8HTgfTDYFj7n4ymM4Bmsd4DBERKacKh7uZfRs47O5bomcXsaoXs/0EM9tsZptzc3MrWoaIiBQhlpb71cBgM9sHLCbSHfMr4GIzyx8nPhX4sKiN3X2uu2e4e0bjxo1jKENERAqrcLi7+1R3T3X3NGAE8Bd3vwV4DbgpWG0MsDzmKkVEpFwq4z73e4C7zWw3kT74eZVwDBERKUFcfmbP3dcCa4PHe4CseOxXREQqRt9QFREJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCaG4fENVRCTuXnvom8e9pyaujiSllruISAgp3EVEQkjhLiISQgp3EZEQ0geqIpJY0R+cStyo5S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCqMJfYjKzS4FngX8CTgNz3f1xM2sALAHSgH3AcHf/JPZSRUTONGfrnHKtc+eVd1ZmOVVKLC33k8C/uXs7oBsw0czaA1OANe7eBlgTTIuIyDlU4XB390Pu/lbw+DNgJ9AcGAIsDFZbCNwYa5EiIlI+celzN7M0oAuwCbjE3Q9B5AUAaBKPY4iISNnFPHCYmV0AvAhMdvfjZlbW7SYAEwBatGgRaxkiofPYK38reHxXv8sTWIkko5ha7mZWk0iw/9bdXwpmf2RmTYPlTYHDRW3r7nPdPcPdMxo3bhxLGSIiUkiFw90iTfR5wE53fzRq0QpgTPB4DLC84uWJiEhFxNItczUwGnjXzLYG834KzAB+b2bjgf3AsNhKFJHQ0Rjula7C4e7u64HiOtj7VnS/IiISO/0Sk4hUfdEt/d5TE1dHElG4i4gUI/uPewoeZ93QKoGVlJ/GlhERCSGFu4hICKlbRkQqj/rKE0YtdxGREFLLXUTODd3bfk6p5S4iEkJquYskMQ0uJsVRuIvEmQJXqgJ1y4iIhJDCXUQkhNQtI1KJqmUXTZLfFZOX/WbB4/OzMhNYSWzUchcRCSG13EXKobiWePT8eO2zLOtXisIt7+hvllaBb5zO2TonIcdNNmq5i4iEkFruIiFRlncA1fIzgGpK4S5J4VyEUmV0ucRLeWtQiIu6ZUREQkgtd6lWEtkKL2+3SWUcV6oPhbuERjy7IqpDIBb3fG3cc/SM9bpThvvWk/ze9jBSt4yISAglfct96jM3Fjx+aOyyBFYi8ZCorotzLQznIFVbpbXczWygmb1vZrvNbEplHUdERM5WKS13M6sBzAb6ATnAm2a2wt13VMbx5NyJ5duUYW6JJ7uN835c/LKoPvjurRqei3LOie2HGye6hEpVWd0yWcBud98DYGaLgSFApYZ77qwnz5hu/MMfxP0YZQmi8n6BJF5fPy9reCbqCy6x3Ktd2ceqqseIt27758a0/Rkftu755gWhskN/zrFt30zU/5dKPVZYVFa3THPgQNR0TjBPRETOAXP3+O/UbBgwwN1vC6ZHA1nu/sOodSYAE4LJtsD7FTxcI+BIDOVWdWE+PxX5WJsAAAPsSURBVJ1b8grz+SXTuV3m7kX2L1VWt0wOcGnUdCrwYfQK7j4XiO09ImBmm909I9b9VFVhPj+dW/IK8/mF5dwqq1vmTaCNmbU0s1rACGBFJR1LREQKqZSWu7ufNLMfAH8GagDz3f29yjiWiIicrdK+xOTufwL+VFn7jxJz104VF+bz07klrzCfXyjOrVI+UBURkcTS2DIiIiGU1OEepiEOzOxSM3vNzHaa2XtmNimY38DMXjGzXcHf+omutaLMrIaZvW1mK4Pplma2KTi3JcGH70nJzC42s6Vm9tfgGnYPy7Uzs7uC/5PbzWyRmdVO5mtnZvPN7LCZbY+aV+S1sogngozZZmZdE1d5+SRtuEcNcXAd0B4YaWbtE1tVTE4C/+bu7YBuwMTgfKYAa9y9DbAmmE5Wk4CdUdMPA48F5/YJMD4hVcXH48Aqd78C6EzkPJP+2plZc+BHQIa7dyRyg8QIkvvaLQAGFppX3LW6DmgT/JsAPHWOaoxZ0oY7UUMcuPtXQP4QB0nJ3Q+5+1vB48+IhENzIue0MFhtIXBj0Xuo2swsFbge+E0wbUAfYGmwSjKf20VAT2AegLt/5e7HCMm1I3LjRR0zSwHqAodI4mvn7uuAjwvNLu5aDQGe9Yg3gIvNrOm5qTQ2yRzuoR3iwMzSgC7AJuASdz8EkRcAoEniKovJr4B/B04H0w2BY+5+MphO5uvXCsgFngm6nX5jZucTgmvn7geBmcB+IqH+KbCF8Fy7fMVdq6TNmWQOdytiXtLf+mNmFwAvApPd/Xii64kHM/s2cNjdt0TPLmLVZL1+KUBX4Cl37wLkkYRdMEUJ+p6HAC2BZsD5RLoqCkvWa1eapP1/mszhXuoQB8nGzGoSCfbfuvtLweyP8t8GBn8PJ6q+GFwNDDazfUS6z/oQaclfHLzVh+S+fjlAjrtvCqaXEgn7MFy7a4G97p7r7l8DLwE9CM+1y1fctUranEnmcA/VEAdBH/Q8YKe7Pxq1aAUwJng8Blh+rmuLlbtPdfdUd08jcp3+4u63AK8BNwWrJeW5Abj734EDZtY2mNWXyPDWSX/tiHTHdDOzusH/0fxzC8W1i1LctVoBfC+4a6Yb8Gl+902V5+5J+w8YBPwN+AD4j0TXE+O5XEPk7d42YGvwbxCRvuk1wK7gb4NE1xrjefYCVgaPWwHZwG7gBeC8RNcXw3ldCWwOrt8yoH5Yrh1wP/BXYDvwHHBeMl87YBGRzw++JtIyH1/ctSLSLTM7yJh3idw1lPBzKMs/fUNVRCSEkrlbRkREiqFwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSE/j9+4DqUZ2YaZgAAAABJRU5ErkJggg==\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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3kpOTQ61atRg4cCAPPPBAFTxbIlKdlDue++kQhvHcq0rPnj2ZPn06OTkxh2xOGj3fcqaJHs99R9bAkunca1umIpxKOdV47roVUkQkhFSWqSYmTJjAunXrTmibOHEiq1evTk1AIpLWlNyriSeeeCLVIYhIiKgsIyISQkruIiIhVG5yN7O5ZrbfzDZHtf3SzN43s01m9gczOz9q2VQz225mH5hZv6oKXEREyhZPzX0eMBN4NqrtVWCqux81s4eAqcAdZtYOGA60B5oCK83sEnc/lmigszbOSnQXJxh/2fik7k9EpDopt+fu7muAT0u1rXD34vFk3wAyg+nBwEJ3/9rddwDbgcp9LbOaKG889zVr1tC5c2cyMjJYvHhxCiIUETlZMmruY4BXgulmwO6oZQVB20nMbJyZbTCzDYWFhUkII/niGc+9efPmzJs3j5EjR56WeERE4pFQcjeznwNHgd8WN8VYLeZXYN19trvnuHtOo0aNEgmjysQznntWVhbZ2dl85zvlP5WrV6+mR48eDBkyhHbt2nHrrbdy/PhxAFasWEG3bt3o3LkzQ4cO5csvvyzZ/3333ceVV17JCy+8wPbt27n66qvp2LEjnTt35sMPP0z+iYtI2qt0cjezUcD3gBv82zEMCoCLolbLBD6ufHipFWs898qOyV4sPz+fX/3qV7z77rt8+OGHvPTSSxw4cIBf/OIXrFy5krfffpucnBweeeSRkm1q167N2rVrGT58ODfccAMTJkzgr3/9K6+//jpNmjRJKB4RCadKfYnJzPoDdwD/4u5fRS1aBvzOzB4h8oFqayA/4ShTJJljshfLzc2lZcvI2BUjRoxg7dq11K5dmy1btnDFFVcA8I9//INu3bqVbPODH/wAgC+++II9e/YwZMgQIJL0RURiKTe5m9kCoCfQ0MwKgLuJ3B1zFvBqkOzecPdb3f09M/s9sIVIuWZCMu6USZVkjslerKyx2fv06cOCBQtiblM8Pnt1GORNRNJDucnd3UfEaJ5zivXvB+5PJKhYUnHrYjzjuVdUfn4+O3bs4OKLL2bRokWMGzeOrl27MmHCBLZv306rVq346quvKCgo4JJLLjlh2/POO4/MzEyWLFnCddddx9dff82xY8dKfmVJRKSYvqF6CtHjubdt25Zhw4aVjOe+bNkyAN58800yMzN54YUXuOWWW2jfvv0p99mtWzemTJlChw4daNGiBUOGDKFRo0bMmzePESNGkJ2dTdeuXXn//fdjbv/cc8/x+OOPk52dTffu3fnkk0+Sft4ikv40cFg5Bg4cyMCBA09ou++++0qmu3TpQkFBQdz7q1u3LosWLTqpvVevXrz55psnte/cufOE+datW/OXv/wl7uOJyJlJPXcRkRBSz70KvPvuu9x4440ntJ111lnk5eXRs2fP1AQlImcUJfcqcOmll7Jx48ZUhyEiZzCVZUREQkjJXUQkhJTcRURCKG1q7oUzZiZ1f41+8uOk7k9EpDpJm+SeKllZWZx77rnUqFGDjIwMNmzYkOqQRETKpeQeh9dee42GDRtW2f6PHj1KRoYuhYgkj2ruSdKzZ08mTZpE9+7d6dChA/n5kcEwi4qKGDNmDF26dKFTp04l48HPmzePoUOHcu2119K3b18AHn74YS699FI6duzIlClTUnYuIpL+1F0sh5nRt29fzIxbbrmFcePGlbluUVERr7/+OmvWrGHMmDFs3ryZ+++/n169ejF37lwOHTpEbm4uV199NQDr169n06ZNNGjQgFdeeYUlS5aQl5dH3bp1+fTTT8s8johIeZTcy7Fu3TqaNm3K/v376dOnD9/97nfp0aNHzHVHjIgMoNmjRw8OHz7MoUOHWLFiBcuWLWP69OkAHDlyhF27dgHQp08fGjRoAMDKlSsZPXp0yQiPxe0iIpWh5F6O4vHbGzduzJAhQ8jPzy8zuZc1VvuLL75ImzZtTliWl5dXMk47RMZqT/SHQEREiqVNck/FrYtFRUUcP36cc889l6KiIlasWMFdd91V5vqLFi3iqquuYu3atdSrV4969erRr18/ZsyYwYwZMzAz3nnnHTp16nTStn379uW+++5j5MiRJWUZ9d5FpLLSJrmnwr59+0p+0u7o0aOMHDmS/v37l7l+/fr16d69O4cPH2bu3LkA3HnnnUyaNIns7GzcnaysLF5++eWTtu3fvz8bN24kJyeHWrVqMXDgQB544IGqOTERCT2rDj/dlpOT46XvH9+6dStt27ZNUUQV17NnT6ZPn05OTk6qQ6mUdHu+RRIV/cXIHVnf/mZD7rUtUxFOpZjZW+4eM+noVkgRkRBSWaaCJkyYwLp1605omzhxIqtXr05NQCIiMZSb3M1sLvA9YL+7dwjaGgCLgCxgJzDM3T+zyO0ejwEDga+Am9397aoJPTWeeOKJVIcgIlKueMoy84DSnyJOAVa5e2tgVTAPMABoHTzGAU8mJ0wREamIcpO7u68BSn9dcjAwP5ieD1wX1f6sR7wBnG9mTZIVrIiIxKeyH6he6O57AYK/jYP2ZsDuqPUKgraTmNk4M9tgZhsKCwsrGYaIiMSS7A9UY33FMua9lu4+G5gNkVshy9tx/h8/SiyyUtLpdicRkYqqbM99X3G5Jfi7P2gvAC6KWi8T+Ljy4aXWmDFjaNy4MR06dChp+/TTT+nTpw+tW7emT58+fPbZZymMUEQktsom92XAqGB6FLA0qv0mi+gKfF5cvklHN998M8uXLz+hbdq0afTu3Ztt27bRu3dvpk2bViXHdneOHz9eJfsWkfArN7mb2QJgPdDGzArMbCwwDehjZtuAPsE8wJ+Aj4DtwNPA+CqJ+jTp0aPHSeO7LF26lFGjIq9ro0aNYsmSJWVuf88993DjjTfSq1cvWrduzdNPP12y7Je//CVdunQhOzubu+++G4CdO3fStm1bxo8fT+fOndm9ezfLly+nc+fOdOzYkd69e1fBWYpIGJVbc3f3EWUsOinTeGQsgwmJBlWd7du3jyZNIjcANWnShP37959y/U2bNvHGG29QVFREp06duOaaa9i8eTPbtm0jPz8fd2fQoEGsWbOG5s2b88EHH/DMM88wa9YsCgsL+dGPfsSaNWto0aKFxngXkbjpG6pVbPDgwdSpU4c6depw1VVXkZ+fz9q1a1mxYkXJ6JBffvkl27Zto3nz5lx88cV07doVgDfeeIMePXrQokULQGO8i0j8lNwr6MILL2Tv3r00adKEvXv30rhx41OuX9YY71OnTuWWW245YdnOnTs1xruIJEXaJPfqcuvioEGDmD9/PlOmTGH+/PkMHjz4lOsvXbqUqVOnUlRUxOrVq5k2bRp16tThzjvv5IYbbuCcc85hz5491KxZ86Rtu3XrxoQJE9ixY0dJWUa9dxGJR9ok91QYMWIEq1ev5sCBA2RmZnLvvfcyZcoUhg0bxpw5c2jevDkvvPDCKfeRm5vLNddcw65du7jzzjtp2rQpTZs2ZevWrXTr1g2Ac845h+eff54aNWqcsG2jRo2YPXs2//qv/8rx48dp3Lgxr776apWdr4iEh5L7KSxYsCBm+6pVq+LexyWXXMLs2bNPap84cSITJ048qX3z5s0nzA8YMIABAwbEfTwREdB47iIioaSeexI888wzPPbYYye0XXHFFRoeWERSplon93S5W2T06NGMHj061WFUWnX4qUURSa5qW5apXbs2Bw8eVOKpYu7OwYMHqV27dqpDEZEkqrY998zMTAoKCtBwwFWvdu3aZGZmpjoMEUmiapvca9asWfLNTBERqZhqW5YREZHKU3IXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBKKLmb2WQze8/MNpvZAjOrbWYtzCzPzLaZ2SIzq5WsYEVEJD6VTu5m1gz4NyDH3TsANYDhwEPAo+7eGvgMGJuMQEVEJH6JlmUygDpmlgHUBfYCvYDFwfL5wHUJHkNERCqo0snd3fcA04FdRJL658BbwCF3PxqsVgA0i7W9mY0zsw1mtkEjP4qIJFciZZn6wGCgBdAUOBuI9WOfMQdkd/fZ7p7j7jmNGjWqbBgiIhJDImWZq4Ed7l7o7t8ALwHdgfODMg1AJvBxgjGKiEgFJZLcdwFdzayuRX4LrzewBXgNuD5YZxSwNLEQRUSkohKpuecR+eD0beDdYF+zgTuA28xsO3ABMCcJcYqISAUk9EtM7n43cHep5o+A3ET2KyIiidE3VEVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhBL6EpOISHUxa+Oskunxl41PYSTVg3ruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIZRQcjez881ssZm9b2ZbzaybmTUws1fNbFvwt36yghURkfgk2nN/DFju7t8FOgJbgSnAKndvDawK5kVE5DSqdHI3s/OAHsAcAHf/h7sfAgYD84PV5gPXJRqkiIhUTCI995ZAIfCMmb1jZr8xs7OBC919L0Dwt3Gsjc1snJltMLMNhYWFCYQhIiKlJZLcM4DOwJPu3gkoogIlGHef7e457p7TqFGjBMIQkdB47cFvH5KQRJJ7AVDg7nnB/GIiyX6fmTUBCP7uTyxEERGpqEond3f/BNhtZm2Cpt7AFmAZMCpoGwUsTShCERGpsER/Zu8nwG/NrBbwETCayAvG781sLLALGJrgMUREpIISSu7uvhHIibGodyL7FRGRxOgbqiIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQol+Q1VEJFTy//hRyXTutS1TGEli1HMXEQkh9dxF5IxXlP9myfTZuV1SGEnyKLmLSOjM2jirZHr8ZeNTGEnqqCwjIhJCSu4iIiGksoyIpK3o8oucSD13EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREEo4uZtZDTN7x8xeDuZbmFmemW0zs0VmVivxMEVEpCKS0XOfCGyNmn8IeNTdWwOfAWOTcAwREamAhJK7mWUC1wC/CeYN6AUsDlaZD1yXyDFERKTiEu25/xr4D+B4MH8BcMjdjwbzBUCzWBua2Tgz22BmGwoLCxMMQ0REolU6uZvZ94D97v5WdHOMVT3W9u4+291z3D2nUaNGlQ1DRERiSGT4gSuAQWY2EKgNnEekJ3++mWUEvfdM4OPEwxQRkYqodM/d3ae6e6a7ZwHDgb+4+w3Aa8D1wWqjgKUJRykiIhVSFfe53wHcZmbbidTg51TBMURE5BSSMiqku68GVgfTHwG5ydiviIhUjr6hKiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJJGVtGROR0mbVxVqpDSAvquYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAjpVkgRSa3XHkx1BKFU6Z67mV1kZq+Z2VYze8/MJgbtDczsVTPbFvytn7xwRUQkHomUZY4C/+7ubYGuwAQzawdMAVa5e2tgVTAvIiKnUaWTu7vvdfe3g+kvgK1AM2AwMD9YbT5wXaJBiohIxSTlA1UzywI6AXnAhe6+FyIvAEDjZBxDRETil3ByN7NzgBeBSe5+uALbjTOzDWa2obCwMNEwREQkSkLJ3cxqEknsv3X3l4LmfWbWJFjeBNgfa1t3n+3uOe6e06hRo0TCEBGRUhK5W8aAOcBWd38katEyYFQwPQpYWvnwRESkMhK5z/0K4EbgXTPbGLT9DJgG/N7MxgK7gKGJhSgiIhVV6eTu7msBK2Nx78ruV0RCKvrLSldNTV0cZwgNPyAiEkIafkAkhkdf/VvJ9OQ+l6QwEkml/D9+VDKde23LFEZScUruInL6xTOejMo4CVFZRkQkhNRzFxGJUpT/Zsn02bldUhhJYpTcRaRy4imbaDjflFFZRkQkhJTcRURCSGUZSQsVvTVRtzLKmU7JXSQQ/YIgFVTVty1G779+vQptOmvjrJLp8ZeNT1ZE1Z7KMiIiIaSeu0g1VVZpSSUniYeSu6S1eBLdmZIMq+w89U3RtKSyjIhICLu5G+sAAAVHSURBVKV9z33qM9/+/vaDo5ekMBKRUyvrA9uySi7VUkXHhAmR6EHEoPoPJJb2yV2kOqv2yfoMtnl/uH/eU2UZEZEQUs9dUi5dP/BM17jLs37O7SfMd2t5QYoikUQouUu1Ek8ZI1mljnj3U9HjVfX6Fd1nmF54Trd0HiFSyV0kCaq6tl7W/lXTl7JUWc3dzPqb2Qdmtt3MplTVcURE5GRV0nM3sxrAE0AfoAB408yWufuWqjienD7x9BRVBqheEi3RrP/oYMx21eKrt6oqy+QC2939IwAzWwgMBpKe3DuuPfDtzOiKb5/IP/zqUNes6Dc0S69XFck6VS8Ap7Nen66iPyx9o/m4mOt0PV3BVMCsQ5u+nTkUtaDFP1dsP1GDiLUlsRen6v7j2VVVlmkG7I6aLwjaRETkNDB3T/5OzYYC/dz9h8H8jUCuu/8kap1xQHHXoQ3wQSUP1xA4UO5a6SvM56dzS19hPr90OreL3T3mt7GqqixTAFwUNZ8JfBy9grvPBmYneiAz2+DuOYnup7oK8/np3NJXmM8vLOdWVWWZN4HWZtbCzGoBw4FlVXQsEREppUp67u5+1Mx+DPwZqAHMdff3quJYIiJysir7EpO7/wn4U1XtP0rCpZ1qLsznp3NLX2E+v1CcW5V8oCoiIqmlUSFFREIorZN7mIY4MLOLzOw1M9tqZu+Z2cSgvYGZvWpm24K/9VMda2WZWQ0ze8fMXg7mW5hZXnBui4IP39OSmZ1vZovN7P3gGnYLy7Uzs8nBv8nNZrbAzGqn87Uzs7lmtt/MNke1xbxWFvF4kGM2mVnn1EVeMWmb3KOGOBgAtANGmFm71EaVkKPAv7t7WyJfEpwQnM8UYJW7twZWBfPpaiKwNWr+IeDR4Nw+A8amJKrkeAxY7u7fBToSOc+0v3Zm1gz4NyDH3TsQuUFiOOl97eYB/Uu1lXWtBgCtg8c44MnTFGPC0ja5EzXEgbv/Ayge4iAtufted387mP6CSHJoRuSc5gerzQeui72H6s3MMoFrgN8E8wb0AhYHq6TzuZ0H9ADmALj7P9z9ECG5dkRuvKhjZhlAXWAvaXzt3H0N8Gmp5rKu1WDgWY94AzjfzJqcnkgTk87JPbRDHJhZFtAJyAMudPe9EHkBABqnLrKE/Br4D+B4MH8BcMjdjwbz6Xz9WgKFwDNB2ek3ZnY2Ibh27r4HmA7sIpLUPwfeIjzXrlhZ1ypt80w6J3eL0Zb2t/6Y2TnAi8Akdz+c6niSwcy+B+x397eim2Osmq7XLwPoDDzp7p2AItKwBBNLUHseDLQAmgJnEylVlJau1648afvvNJ2Te7lDHKQbM6tJJLH/1t1fCpr3Fb8NDP7uT1V8CbgCGGRmO4mUz3oR6cmfH7zVh/S+fgVAgbvnBfOLiST7MFy7q4Ed7l7o7t8ALwHdCc+1K1bWtUrbPJPOyT1UQxwENeg5wFZ3fyRq0TJgVDA9Clh6umNLlLtPdfdMd88icp3+4u43AK8B1werpeW5Abj7J8BuM2sTNPUmMrx12l87IuWYrmZWN/g3Wnxuobh2Ucq6VsuAm4K7ZroCnxeXb6o9d0/bBzAQ+BvwIfDzVMeT4LlcSeTt3iZgY/AYSKQ2vQrYFvxtkOpYEzzPnsDLwXRLIB/YDrwAnJXq+BI4r8uADcH1WwLUD8u1A+4F3gc2A88BZ6XztQMWEPn84BsiPfOxZV0rImWZJ4Ic8y6Ru4ZSfg7xPPQNVRGREErnsoyIiJRByV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJIT+P5x15z6YB1EBAAAAAElFTkSuQmCC\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"
    },
    {
     "data": {
      "image/png": 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zuYuISMpRt0wdMXHiRFatWnVM2aRJk1i+fHlyAhKRlKbkXkc89thjyQ5BREJE3TIiIiGk5C4iEkJK7iIiIVRln7uZzQa+A+x1905B2Xyg5KeRZwEH3L2rmWUBm4CPg3XvuPuNiQj08XWPJ2I3pW7qelNC9yciUpfEcuU+BxgUXeDuV7l7V3fvSuTG2S9Frd5asi5RiT2ZqprPfcWKFXTv3p20tDRefPHFJEQoInK8KpO7u68Ayp1KMLh59ghgXoLjqhNimc89MzOTOXPmMHr06BMSj4hILOLtc/82sMfdN0eVtTKz983sD2b27Yo2NLMJZrbGzNYUFhbGGUbtiGU+96ysLLKzs/nGN6p+K5cvX06fPn0YNmwYHTp04MYbb+To0aMALF26lF69etG9e3eGDx/Ol19+Wbr/qVOncvHFF/PCCy+wZcsWLr30Urp06UL37t3ZunVr4hsuIikv3uQ+imOv2ncDme7eDfgB8GszO7O8Dd19prvnuHtOenp6nGHUjvLmc6/pnOwl8vPzeeCBB/jggw/YunUrL730Evv27eOnP/0pb7zxBu+99x45OTk8+OCDpds0aNCAlStXMnLkSK6++momTpzIH//4R9566y1atGgRVzwiEk41/hGTmaUB3wX+uaTM3Q8Dh4PltWa2FbgAWFPuTuq4RM7JXiI3N5fWrVsDkQnEVq5cSYMGDdi4cSMXXXQRAH//+9/p1atX6TZXXXUVAF988QW7du1i2LBhQCTpi4iUJ55fqF4KfOTuBSUFZpYOfOruxWbWGmgL/DnOGJMmkXOyl6hobvb+/fszb175X12UzM9eFyZ5E5HUEMtQyHlAHtDMzAqAn7j7LGAkx3+R2geYamZHgGLgRndPyH3dkjF0MZb53KsrPz+fbdu2cf755zN//nwmTJhAz549mThxIlu2bKFNmzZ89dVXFBQUcMEFFxyz7ZlnnklGRgYLFy7kyiuv5PDhwxQXF5feZUlEpEQso2VGuXsLd6/v7hlBYsfdr3X3J8vUXeDuHd29i7t3d/ff1lbgJ0L0fO7t27dnxIgRpfO5L168GIB3332XjIwMXnjhBW644QY6duxY6T579erFlClT6NSpE61atWLYsGGkp6czZ84cRo0aRXZ2Nj179uSjjz4qd/tnn32WRx55hOzsbHr37s1f//rXhLdbRFKfJg6rwpAhQxgyZMgxZVOnTi1d7tGjBwUFBWU3q1CjRo2YP3/+ceV9+/bl3XffPa58+/btx7xu27Ytv//972M+noicnDT9gIhICOnKvRZ88MEHjBkz5piyU089ldWrV5OXl5ecoESkQvm/PXbcR+7lrZMUSeIoudeCzp07s27dumSHISInMXXLiIiEkJK7iEgIKbmLiIRQyvS5F854NKH7S//3mxO6PxGRuiRlknuyZGVlccYZZ1CvXj3S0tJYsyYlp8kRkZOMknsM3nzzTZo1a1Zr+z9y5AhpaToVIpI46nNPkLy8PCZPnkzv3r3p1KkT+fn5ABQVFTF+/Hh69OhBt27dSueDnzNnDsOHD+fyyy9nwIABANx///107tyZLl26MGXKlKS1RURSny4Xq2BmDBgwADPjhhtuYMKECRXWLSoq4q233mLFihWMHz+eDRs2cM8999C3b19mz57NgQMHyM3N5dJLLwXg7bffZv369TRt2pTXXnuNhQsXsnr1aho1asSnnyZkvjUROUkpuVdh1apVtGzZkr1799K/f3++9a1v0adPn3Lrjho1CoA+ffpw8OBBDhw4wNKlS1m8eDHTp08H4NChQ+zYsQOA/v3707RpUwDeeOMNxo0bVzrDY0m5iEhNKLlXoWT+9ubNmzNs2DDy8/MrTO4VzdW+YMEC2rVrd8y61atXl87TDpG52uO9EYiISImUSe7JGLpYVFTE0aNHOeOMMygqKmLp0qXceeedFdafP38+l1xyCStXrqRx48Y0btyYgQMHMmPGDGbMmIGZ8f7779OtW7fjth0wYABTp05l9OjRpd0yunoXkZpKmeSeDHv27Cm9pd2RI0cYPXo0gwYNqrB+kyZN6N27NwcPHmT27NkA3HHHHUyePJns7GzcnaysLF555ZXjth00aBDr1q0jJyeHU045hSFDhnDvvffWTsNEJPSsqlu3mdls4DvAXnfvFJTdBVwPFAbVbnf3V4N1twHXEbkT03+4+++qCiInJ8fLjh/ftGkT7du3r1ZjkikvL4/p06eTk5OT7FBqJNXeb5F4Rf8wclvWsfdsSJVZIc1srbuXm3RiGQo5ByjvcvUhd+8aPEoSewcit9/rGGzzuJnVq1nYIiJSU1V2y7j7CjPLinF/VwDPu/thYJuZbQFygbdrHGEdM3HiRFatWnVM2aRJk1i+fHlyAhIRKUc8fe43m9k1wBrgh+7+GXAu8E5UnYKg7DhmNgGYAJCZmRlHGCfWY489luwQRESqVNNfqD4BfBPoCuwGHgjKyxvLV26nvrvPdPccd89JT0+vYRgiIlKeGiV3d9/j7sXufhR4ikjXC0Su1M+LqpoBfBJfiCIiUl01Su5m1iLq5TBgQ7C8GBhpZqeaWSugLZAfX4giIlJdVfa5m9k8IA9oZmYFwE+APDPrSqTLZTtwA4C7f2hmvwE2AkeAie5enIhAy97ANl6pMtRJRKQmqrxyd/dR7t7C3eu7e4a7z3L3Me7e2d2z3X2ou++Oqn+Pu3/T3du5+2u1G37tGj9+PM2bN6dTp06lZZ9++in9+/enbdu29O/fn88++yyJEYqIlE9T/lbi2muvZcmSJceUTZs2jX79+rF582b69evHtGnTauXY7s7Ro0drZd8iEn5K7pXo06fPcfO7LFq0iLFjxwIwduxYFi5cWOH2d911F2PGjKFv3760bduWp556qnTdz3/+c3r06EF2djY/+clPANi+fTvt27fnpptuonv37uzcuZMlS5bQvXt3unTpQr9+/WqhlSISRppbppr27NlDixaR75NbtGjB3r17K62/fv163nnnHYqKiujWrRuXXXYZGzZsYPPmzeTn5+PuDB06lBUrVpCZmcnHH3/M008/zeOPP05hYSHXX389K1asoFWrVprjXURipuRey6644goaNmxIw4YNueSSS8jPz2flypUsXbq0dHbIL7/8ks2bN5OZmcn5559Pz549AXjnnXfo06cPrVq1AjTHu4jETsm9ms455xx2795NixYt2L17N82bN6+0fkVzvN92223ccMMNx6zbvn275ngXkYRImeReV4YuDh06lLlz5zJlyhTmzp3LFVdcUWn9RYsWcdttt1FUVMTy5cuZNm0aDRs25I477uDqq6/m9NNPZ9euXdSvX/+4bXv16sXEiRPZtm1babeMrt5FJBYpk9yTYdSoUSxfvpx9+/aRkZHB3XffzZQpUxgxYgSzZs0iMzOTF154odJ95Obmctlll7Fjxw7uuOMOWrZsScuWLdm0aRO9evUC4PTTT+e5556jXr1jJ9BMT09n5syZfPe73+Xo0aM0b96c119/vdbaKyLhoeReiXnz5pVbvmzZspj3ccEFFzBz5szjyidNmsSkSZOOK9+wYcMxrwcPHszgwYNjPp6ICGgopIhIKOnKPQGefvppHn744WPKLrroIk0PLCJJU6eTe6qMFhk3bhzjxo1Ldhg1VtWtFkUk9dTZbpkGDRqwf/9+JZ5a5u7s37+fBg0aJDsUEUmgOnvlnpGRQUFBAYWFhVVXlrg0aNCAjIyMZIchIglUZ5N7/fr1S3+ZKSIi1VNnu2VERKTmqkzuZjbbzPaa2Yaosp+b2Udmtt7MXjazs4LyLDP7m5mtCx5P1mbwIiJSvliu3OcAg8qUvQ50cvds4E/AbVHrtrp71+BxY2LCFBGR6ojlTkwrgE/LlC119yPBy3eI3AhbRETqiET0uY8Hom+n18rM3jezP5jZtxOwfxERqaa4RsuY2X8RuRH2r4Ki3UCmu+83s38GFppZR3c/WM62E4AJAJmZmfGEISIiZdT4yt3MxgLfAa724JdG7n7Y3fcHy2uBrcAF5W3v7jPdPcfdc9LT02sahoiIlKNGyd3MBgG3AkPd/auo8nQzqxcstwbaAn9ORKAiIhK7KrtlzGwekAc0M7MC4CdERsecCrwezP3yTjAypg8w1cyOAMXAje6uG3+KiJxgVSZ3dx9VTvGsCuouABbEG5SIiMRHv1AVEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREIopuZvZbDPba2YbosqamtnrZrY5eG4SlJuZPWJmW8xsvZl1r63gRUSkfLFeuc8BBpUpmwIsc/e2wLLgNcBgIvdObQtMAJ6IP0wREamOmJK7u68Ayt4L9QpgbrA8F7gyqvwZj3gHOMvMWiQiWBERiU08fe7nuPtugOC5eVB+LrAzql5BUHYMM5tgZmvMbE1hYWEcYYiISFm18YWqlVPmxxW4z3T3HHfPSU9Pr4UwREROXvEk9z0l3S3B896gvAA4L6peBvBJHMcREZFqiie5LwbGBstjgUVR5dcEo2Z6Ap+XdN+IiMiJkRZLJTObB+QBzcysAPgJMA34jZldB+wAhgfVXwWGAFuAr4BxCY5ZRESqEFNyd/dRFazqV05dBybGE5SIiMRHv1AVEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRCKacpfEZET4s37/rF8yW3JiyMEapzczawdMD+qqDVwJ3AWcD1Qctfr29391RpHKCIi1Vbj5O7uHwNdAcysHrALeJnInZcecvfpCYlQRESqLVF97v2Are7+lwTtT0RE4pCo5D4SmBf1+mYzW29ms82sSYKOISIiMYo7uZvZKcBQ4IWg6Angm0S6bHYDD1Sw3QQzW2NmawoLC8urIiIiNZSIK/fBwHvuvgfA3fe4e7G7HwWeAnLL28jdZ7p7jrvnpKenJyAMEREpkYjkPoqoLhkzaxG1bhiwIQHHEBGRaohrnLuZNQL6AzdEFd9vZl0BB7aXWSciIidAXMnd3b8Czi5TNiauiEREJG6afkBEJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJI87mLSCg8vu7x0uWbut6UxEjqBl25i4iEkJK7iEgIKbmLiISQkruISAjpC1URSVnRX6LKsXTlLiISQkruIiIhpOQuIhJCSu4iIiEU9xeqZrYd+AIoBo64e46ZNQXmA1lE7sY0wt0/i/dYIiISm0RduV/i7l3dPSd4PQVY5u5tgWXBaxEROUFqq1vmCmBusDwXuLKWjiMiIuVIRHJ3YKmZrTWzCUHZOe6+GyB4bl52IzObYGZrzGxNYWFhAsIQEZESifgR00Xu/omZNQdeN7OPYtnI3WcCMwFycnI8AXGIiEgg7it3d/8keN4LvAzkAnvMrAVA8Lw33uOIiEjs4kruZnaamZ1RsgwMADYAi4GxQbWxwKJ4jiMiItUTb7fMOcDLZlayr1+7+xIzexf4jZldB+wAhsd5HBERqYa4kru7/xnoUk75fqBfPPsWEZGa06yQInJS2rA3vXT5tKzkxVFblNxFJHR0P1XNLSMiEkpK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkIa5y4iyfXmfcmOIJR05S4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiFU46GQZnYe8AzwT8BRYKa7P2xmdwHXA4VB1dvd/dV4AxURgWOn85WKxTPO/QjwQ3d/L7iP6lozez1Y95C7T48/PBERqYkaJ3d33w3sDpa/MLNNwLmJCkxERGouIX3uZpYFdANWB0U3m9l6M5ttZk0q2GaCma0xszWFhYXlVRERkRqKO7mb2enAAmCyux8EngC+CXQlcmX/QHnbuftMd89x95z09PTyqojIyezN+/7xkGqLK7mbWX0iif1X7v4SgLvvcfdidz8KPAXkxh+miIhUR42Tu5kZMAvY5O4PRpW3iKo2DNhQ8/BERKQm4hktcxEwBvjAzNYFZbcDo8ysK541Et0AAAW0SURBVODAduCGuCIUEZFqi2e0zErAylmlMe0icrzovvNLbkteHCcJ/UJVRCSElNxFREJIyV1EJIR0mz2Rcjz0+p9Kl2/pf0ESIxGpGSV3ETnxqvnDJE0WVn3qlhERCSFduYvUInXvJMi2//3HcqtvJy+OFKIrdxGREFJyFxEJIXXLiNQhoejG0S9R6wQld5ETJBSJW1KGkrtIEkQn+trYj/54iJK7nBRiuWpOVMJNlGRe6Sfs2LrRRtIouUtKO9EJMBWvlNUddHJSchdJsNr4BKAELdVVa8ndzAYBDwP1gF+6+7TaOpaktup2mcRTp2y96saXqH3WFakat1StVpK7mdUDHgP6AwXAu2a22N031sbxRMoKc9KKp2219r6ob73Oqa0r91xgi7v/GcDMngeuAGolud/29JWly/eNW1gbh5AkqCgRhTlxJ0oi36MKv2dIW5CwY0ji1VZyPxfYGfW6ALiwlo51jBPZN1ndY52I2GL5T13dY1f3S8SK2qmkHC5v/3l/6XKv1mcnMZL4FeW/e2zB5a2TE0gCmbsnfqdmw4GB7v794PUYINfd/z2qzgRgQvCyHfBxHIdsBuyLY/u6TG1LXWFun9pWN5zv7unlraitK/cC4Lyo1xnAJ9EV3H0mMDMRBzOzNe6ek4h91TVqW+oKc/vUtrqvtiYOexdoa2atzOwUYCSwuJaOJSIiZdTKlbu7HzGzm4HfERkKOdvdP6yNY4mIyPFqbZy7u78KvFpb+y8jId07dZTalrrC3D61rY6rlS9URUQkuXSzDhGREErp5G5mg8zsYzPbYmZTkh1PPMzsPDN708w2mdmHZjYpKG9qZq+b2ebguUmyY60pM6tnZu+b2SvB61Zmtjpo2/zgy/eUZGZnmdmLZvZRcA57heXcmdktwb/JDWY2z8wapPK5M7PZZrbXzDZElZV7rizikSDHrDez7smLvHpSNrlHTXEwGOgAjDKzDsmNKi5HgB+6e3ugJzAxaM8UYJm7twWWBa9T1SRgU9TrnwEPBW37DLguKVElxsPAEnf/FtCFSDtT/tyZ2bnAfwA57t6JyACJkaT2uZsDDCpTVtG5Ggy0DR4TgCdOUIxxS9nkTtQUB+7+d6BkioOU5O673f29YPkLIsnhXCJtmhtUmwtcWf4e6jYzywAuA34ZvDagL/BiUCWV23Ym0AeYBeDuf3f3A4Tk3BEZeNHQzNKARsBuUvjcufsK4NMyxRWdqyuAZzziHeAsM2txYiKNTyon9/KmODg3SbEklJllAd2A1cA57r4bIn8AgObJiywu/wP8J3A0eH02cMDdjwSvU/n8tQYKgaeDbqdfmtlphODcufsuYDqwg0hS/xxYS3jOXYmKzlXK5plUTu5WTlnKD/0xs9OBBcBkdz+Y7HgSwcy+A+x197XRxeVUTdXzlwZ0B55w925AESnYBVOeoO/5CqAV0BI4jUhXRVmpeu6qkrL/TlM5uVc5xUGqMbP6RBL7r9z9paB4T8nHwOB5b7Lii8NFwFAz206k+6wvkSv5s4KP+pDa568AKHD31cHrF4kk+zCcu0uBbe5e6O5fAy8BvQnPuStR0blK2TyTysk9VFMcBH3Qs4BN7v5g1KrFwNhgeSyw6ETHFi93v83dM9w9i8h5+r27Xw28CXwvqJaSbQNw978CO82sXVDUj8j01il/7oh0x/Q0s0bBv9GStoXi3EWp6FwtBq4JRs30BD4v6b6p89w9ZR/AEOBPwFbgv5IdT5xtuZjIx731wLrgMYRI3/QyYHPw3DTZscbZzjzglWC5NZAPbAFeAE5NdnxxtKsrsCY4fwuBJmE5d8DdwEfABuBZ4NRUPnfAPCLfH3xN5Mr8uorOFZFumceCHPMBkVFDSW9DLA/9QlVEJIRSuVtGREQqoOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJC/wexU4Ozt6+f9AAAAABJRU5ErkJggg==\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"
    },
    {
     "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"
    },
    {
     "data": {
      "image/png": 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iBQUFHDhwgMzMTB588EEmTZoUs462bdsyYMAAjhw5woIFCwCYMWMGU6dOJScnB3cnOzub119//SvbDhs2jI0bN5Kbm0uLFi244YYbeOSRRxrg2RKRpkLjgTcR+fn5zJ49m9zcrwz5mzAaD1wknOo8HriIiDRNakJpZFOmTGHt2rWnzbv77rspKChITkEiEloK8EY2d+7cZJcgIilCTSgiIiGlABcRCSkFuIhISDW5NvBfbPxFlfOPRo0H3qoW44Hf1euuetckItIU6Qo8UNN44GvWrKFPnz6kpaXxyiuvJKFCEZHTKcCJbzzwrKwsFi5cyNixYxulHhGRmijAiW888OzsbHJycjjrrJqfsoKCAgYOHMjIkSO5/PLLufPOOzl16hQAK1eupH///vTp04ebb76Zo0ePVuz/oYce4uqrr+bll19m+/btXHfddfTs2ZM+ffrw0UcfJf7ARSTUFOBUPR54Xcf0LldcXMzPf/5z3n//fT766CNee+01Dhw4wE9+8hNWr17Nu+++S25uLo8//njFNunp6RQWFjJ69GhuvfVWpkyZwh//+EfWrVtHx44d61WPiKSeJvchZjIkckzvcnl5eVxyySVAZFCrwsJC0tPT2bJlC1dddRUAf//73+nfv3/FNrfccgsAn3/+OXv27GHkyJFAJNhFRCpTgBPfeOC1FWts78GDB/PSSy9VuU35+N6NOcCYiIRXkwvwWN3+GnI0wnjGA6+t4uJiduzYwcUXX8ySJUuYPHky/fr1Y8qUKWzfvp0uXbrwt7/9jZKSEi677LLTtj3vvPPIzMxk6dKl3HTTTRw/fpyTJ09W/NqOiAioDRw4fTzwbt26MWrUqIrxwJcvXw7AH/7wBzIzM3n55Ze544476N69e7X77N+/P9OnT6dHjx507tyZkSNHkpGRwcKFCxkzZgw5OTn069ePP/3pT1Vu/+KLL/LUU0+Rk5PDgAED+Otf/5rw4xaRcNN44A2goKCA2bNnV/nDC8mk8cBFwknjgYuIpJga28DN7CLgBeAfgFPAPHd/0sxmArcDpcGqP3L33zZUoU3R+++/z7hx406bd/bZZ1NUVER+fn5yihKRM0Y8H2KeAH7g7u+aWWvgHTNbFSx7wt1nN1x5TdsVV1zBxo0bk12GiJyhagxwd98L7A2mPzezrcCFDV2YiIhUr1Zt4GaWDfQGioJZ3zOzTWa2wMzaxthmspltMLMNpaWlVa0iIiJ1EHeAm1kr4FVgqrsfAZ4B/hHoReQK/edVbefu89w9191zMzIyElCyiIhAnF/kMbPmRML7V+7+GoC774ta/hyQkD5zpXOernL+36LGAy+txXjgGd//Xr1rEhFpiuLphWLAfGCruz8eNb9j0D4OMBLY3DAlNo7s7Gxat25Ns2bNSEtLo3J/dRGRpiaeS9mrgHHA+2ZW3uXiR8AYM+sFOLATuKNBKmxEb731Fu3bt2+w/Z84cYK0tCY3eoGIhFQ8vVAKgaqG5juj+nyXy8/Pp1evXhQXF3PkyBEWLFhAXl4eZWVlfP/73+f999/nxIkTzJw5kxEjRrBw4UL+53/+h2PHjlFWVsbvf/97HnvsMV588UXOOussrr/++ip/AUhEpCa6HAyYGUOGDMHMuOOOO5g8eXLMdcvKyli3bh1r1qxh4sSJbN68mYcffphBgwaxYMECDh8+TF5eHtdddx0A69evZ9OmTbRr14433niDpUuXUlRUxDnnnMOhQ4ca6xBFJMUowANr166lU6dO7N+/n8GDB/P1r3+dgQMHVrnumDFjABg4cCBHjhzh8OHDrFy5kuXLlzN7duR7TceOHWPXrl0ADB48mHbt2gGwevVqJkyYUDGyYPl8EZHaUoAHysf/7tChAyNHjqS4uDhmgMca6/vVV1+la9eupy0rKiqqGOcbImN91/fHIkREoAkGeKxuf6eiRiPMSPBohGVlZZw6dYrWrVtTVlbGypUreeCBB2Kuv2TJEq655hoKCwtp06YNbdq0YejQocyZM4c5c+ZgZrz33nv07t37K9sOGTKEhx56iLFjx1Y0oegqXETqoskFeDLs27ev4ufLTpw4wdixYxk2bFjM9du2bcuAAQMqPsQEmDFjBlOnTiUnJwd3Jzs7u8rhZIcNG8bGjRvJzc2lRYsW3HDDDTzyyCMNc2AiktI0Hngt5efnM3v2bHJzvzI0b5On8cBFwknjgYuIpBg1ocQwZcoU1q5de9q8u+++m4KCguQUJCJSiQI8hrlz5ya7BBGRaqkJRUQkpBTgIiIhpQAXEQmpJtcGXvzfH1c5/2jUeOB/qcV44HnfuKTeNYmINEW6AgcmTpxIhw4d6NGjR8W8Q4cOMXjwYC699FIGDx7Mp59+msQKRUS+SgEO3HbbbaxYseK0ebNmzeLaa69l27ZtXHvttQ025Ku7c+rUqQbZt4ikNgU4kVEFK49HsmzZMsaPHw/A+PHjWbp0acztZ86cybhx4xg0aBCXXnopzz33XMWyn/3sZ/Tt25ecnBx+/OMfA7Bz5066devGXXfdRZ8+fdi9ezcrVqygT58+9OzZk2uvvbYBjlJEUk2TawNvKvbt20fHjh0B6NixI/v37692/U2bNvH2229TVlZG7969ufHGG9m8eTPbtm2juLgYd2f48OGsWbOGrKwsPvzwQ55//nl+8YtfUFpayu23386aNWvo3LmzxggXkbgowBNkxIgRtGzZkpYtW3LNNddQXFxMYWEhK1eurBiV8OjRo2zbto2srCwuvvhi+vXrB8Dbb7/NwIED6dy5M6AxwkUkPgrwGC644AL27t1Lx44d2bt3Lx06dKh2/VhjhN9///3cccfpPxe6c+dOjREuIvXW5AI8Vre/xh6NcPjw4SxatIjp06ezaNEiRowYUe36y5Yt4/7776esrIyCggJmzZpFy5YtmTFjBrfeeiutWrViz549NG/e/Cvb9u/fnylTprBjx46KJhRdhYtITZpcgCfDmDFjKCgo4MCBA2RmZvLggw8yffp0Ro0axfz588nKyuLll1+udh95eXnceOON7Nq1ixkzZtCpUyc6derE1q1b6d+/PwCtWrXil7/8Jc2aNTtt24yMDObNm8c3v/lNTp06RYcOHVi1alWDHa+IpAaNB54AM2fOpFWrVtx7773JLqVaGg9cJJw0HriISIqpsQnFzC4CXgD+ATgFzHP3J82sHbAEyAZ2AqPcPaW/rvj888/z5JNPnjbvqquu0tCzIpIU8bSBnwB+4O7vmllr4B0zWwXcBrzp7rPMbDowHbivLkWEpRfGhAkTmDBhQrLLqJPGbCoTkcZRYxOKu+9193eD6c+BrcCFwAhgUbDaIuCmuhSQnp7OwYMHFTANyN05ePAg6elN67MDEamfWvVCMbNsoDdQBFzg7nshEvJmVmVHaTObDEwGyMrK+sryzMxMSkpKKC0trfaxj/zvFxXTh1p+tSueVC89PZ3MzMxklyEiCRR3gJtZK+BVYKq7H4m3ycPd5wHzINILpfLy5s2bV3wDsTpPrPpzxfS0wZfFV7SISAqLqxeKmTUnEt6/cvfXgtn7zKxjsLwjUP1gISIiklA1BrhFLrXnA1vd/fGoRcuB8cH0eGBZ4ssTEZFY4mlCuQoYB7xvZhuDeT8CZgH/ZWaTgF3AzQ1TooiIVKXGAHf3QiBWg7cGrhYRSRJ9E1NEJKQU4CIiIaUAFxEJKQW4iEhIKcBFREJKAS4iElIKcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISCnARURCSgEuIhJSCnARkZBSgIuIhJQCXEQkpBTgIiIhpQAXEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQqjHAzWyBme03s81R82aa2R4z2xjcbmjYMkVEpLJ4rsAXAsOqmP+Eu/cKbr9NbFkiIlKTGgPc3dcAhxqhFhERqYX6tIF/z8w2BU0sbRNWkYiIxKWuAf4M8I9AL2Av8PNYK5rZZDPbYGYbSktL6/hwIiJSWZ0C3N33uftJdz8FPAfkVbPuPHfPdffcjIyMutYpIiKV1CnAzaxj1N2RwOZY64qISMNIq2kFM3sJyAfam1kJ8GMg38x6AQ7sBO5owBpFRKQKNQa4u4+pYvb8BqhFRERqQd/EFBEJKQW4iEhIKcBFREJKAS4iElIKcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISCnARURCSgEuIhJSCnARkZBSgIuIhJQCXEQkpBTgIiIhpQAXEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQU4CIiIVVjgJvZAjPbb2abo+a1M7NVZrYt+Nu2YcsUEZHK4rkCXwgMqzRvOvCmu18KvBncFxGRRlRjgLv7GuBQpdkjgEXB9CLgpgTXJSIiNahrG/gF7r4XIPjbIdaKZjbZzDaY2YbS0tI6PpyIiFTW4B9iuvs8d89199yMjIyGfjgRkTNGXQN8n5l1BAj+7k9cSSIiEo+6BvhyYHwwPR5YlphyREQkXvF0I3wJWA90NbMSM5sEzAIGm9k2YHBwX0REGlFaTSu4+5gYi65NcC0iIlIL+iamiEhI1XgFLiISZk+s+nPF9LTBlyWxksTTFbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQU4CIiIaUAFxEJKQW4iEhIKcBFREJKAS4iElIKcBGRkNJgViLS5KXygFT1oStwEZGQUoCLiISUAlxEJKQU4CIiIaUAFxEJKfVCEZFG19R6lUTXE60+tVXeZ0Mcp67ARURCSgEuIhJS9WpCMbOdwOfASeCEu+cmoigREalZItrAr3H3AwnYj4iI1IKaUEREQqq+V+AOrDQzB/7D3edVXsHMJgOTAbKysur5cCISJk2tt0m0WD1P4lk/5rG89WjUnW/Voaraqe8V+FXu3ge4HphiZgMrr+Du89w9191zMzIy6vlwIiJSrl4B7u6fBH/3A78B8hJRlIiI1KzOAW5m55pZ6/JpYAiwOVGFiYhI9erTBn4B8BszK9/Pr919RUKqEhGRGtU5wN39Y6BnAmsREZFaUDdCEZGQ0mBWImeoptzFD2rfzS9R29bnsaY1cqLqClxEJKQU4CIiIaUAFxEJKQW4iEhIKcBFREJKvVBE5DTx9E5JZC+Pxuxt0tC9U9Z/fPDLO40wdp+uwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQU4CIiIRWaboT9dn35c5tPrJpcMR3PIDxNfdCe+kjlY0tlsc5brG5utV0nnsetrfp2wUtUF75kDVSVrBqqoytwEZGQUoCLiISUAlxEJKQU4CIiIaUAFxEJqdD0Qll+1vaK6Q5R82v7afD6+fdWTPefNPu0ZfH0DIg5P+3VL3d0zf1fTr/1aJXzG6T3SIzHqu6Yq6wn+lg4fYCe6O0b4hhq28OioXvh1Hf/jdlLqLY9W2JtKzWL7hUX/zpV/9+rD12Bi4iElAJcRCSk6hXgZjbMzD40s+1mNj1RRYmISM3qHOBm1gyYC1wPXA6MMbPLE1WYiIhUrz5X4HnAdnf/2N3/DiwGRiSmLBERqUl9AvxCYHfU/ZJgnoiINAJz97ptaHYzMNTdvxvcHwfkufv3K603GSgffaor8GEda20PHKjjtmGQysenYwuvVD6+MB3bxe6eUXlmffqBlwAXRd3PBD6pvJK7zwNq7jRZAzPb4O659d1PU5XKx6djC69UPr5UOLb6NKH8AbjUzDqbWQtgNLA8MWWJiEhN6nwF7u4nzOx7wO+AZsACd/8gYZWJiEi16vVVenf/LfDbBNVSk3o3wzRxqXx8OrbwSuXjC/2x1flDTBERSS59lV5EJKRCEeCp9JV9M7vIzN4ys61m9oGZ3R3Mb2dmq8xsW/C3bbJrrSsza2Zm75nZ68H9zmZWFBzbkuBD71Ays6+Z2Stm9qfgHPZPlXNnZtOCf5ObzewlM0sP87kzswVmtt/MNkfNq/JcWcRTQcZsMrM+yas8fk0+wFPwK/sngB+4ezegHzAlOJ7pwJvufinwZnA/rO4Gtkbd/ynwRHBsnwKTklJVYjwJrHD3rwM9iRxn6M+dmV0I/CuQ6+49iHRMGE24z91CYFilebHO1fXApcFtMvBMI9VYL00+wD4+6QAAAAJuSURBVEmxr+y7+153fzeY/pxIAFxI5JgWBastAm5KToX1Y2aZwI3Afwb3DRgEvBKsEuZjOw8YCMwHcPe/u/thUuTcEenU0NLM0oBzgL2E+Ny5+xrgUKXZsc7VCOAFj3gb+JqZdWycSusuDAGesl/ZN7NsoDdQBFzg7nshEvKc/rsVYfLvwP8BTgX3zwcOu/uJ4H6Yz98lQCnwfNBE9J9mdi4pcO7cfQ+RXxzYRSS4PwPeIXXOXblY5yqUOROGALcq5oW+64yZtQJeBaa6+5Fk15MIZvZPwH53fyd6dhWrhvX8pQF9gGfcvTdQRgibS6oStAWPADoDnYBziTQrVBbWc1eTUP47DUOAx/WV/TAxs+ZEwvtX7v5aMHtf+Vu24O/+ZNVXD1cBw81sJ5GmrkFErsi/Frwth3CfvxKgxN2LgvuvEAn0VDh31wE73L3U3b8AXgMGkDrnrlyscxXKnAlDgKfUV/aDNuH5wFZ3fzxq0XJgfDA9HljW2LXVl7vf7+6Z7p5N5Dz93t1vBd4Cvh2sFspjA3D3vwK7zaxrMOtaYAspcO6INJ30M7Nzgn+j5ceWEucuSqxztRz4TtAbpR/wWXlTS5Pm7k3+BtwA/Bn4CPi/ya6nnsdyNZG3ZpuAjcHtBiJtxW8C24K/7ZJdaz2PMx94PZi+BCgGtgMvA2cnu756HFcvYENw/pYCbVPl3AEPAn8CNgMvAmeH+dwBLxFpz/+CyBX2pFjnikgTytwgY94n0hsn6cdQ003fxBQRCakwNKGIiEgVFOAiIiGlABcRCSkFuIhISCnARURCSgEuIhJSCnARkZBSgIuIhNT/B0i1XppcEQ5SAAAAAElFTkSuQmCC\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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S03zuyRaG+dyTpaCggBkzZpCXV+WUzQmj51tOZpXnc9+aM7RiOf+Ktic6nJjFNZ+7iIikHw3L1BOTJk1i5cqVR5VNnjyZZcuWpSYgEUlrSu71xOOPP57qEEQkRDQsIyISQkruIiIhpOQuIhJCNY65m9kc4DvAHnfvEpTdDdwIlATVfuTurwXb7gCuB8qAf3H33xxz0DqYtXZWIg5T4ZbutyT0eCIi9UksPfe5wJAqyh929+7BozyxdwJGAZ2DfWaZWYNEBZsKNc3nvnz5cnr27ElGRgYvvvhiCiIUETlWjcnd3ZcDsU4leCXwvLt/5e5bgc1A3b5zXw/EMp97dnY2c+fOZcyYMSckHhGRWMQz5n6rma0zszlm1iwoOxfYEVWnOCg7hplNNLPVZra6pKSkqiopF8t87jk5OeTm5vKNb9T8VC5btox+/foxfPhwOnXqxM0338yRI0cAWLJkCX369KFnz56MGDGCL774ouL406ZN4+KLL+aFF15g8+bNXHrppXTr1o2ePXuyZcuWxDdcRNJeXZP7E8A3ge7ALuDBoLyq6QernN/A3We7e56752VmZtYxjOSqaj73us7JXq6oqIgHH3yQDz74gC1btvDyyy+zd+9efvKTn/Dmm2/y+9//nry8PB566KGKfRo1asSKFSsYNWoU11xzDZMmTeIPf/gDb7/9Nq1atYorHhEJpzp9icndd5cvm9lTQPmMVcXAeVFVs4BP6hxdiiVyTvZy+fn5tG0bmati9OjRrFixgkaNGrFhwwYuuugiAP72t7/Rp0+fin2uvvpqAD7//HN27tzJ8OHDgUjSFxGpSp2Su5m1cvddwepwYH2wvAj4pZk9BLQG2gNFcUeZIomck71cdXOzDxw4kPnz51e5T/n87PVhkjcRSQ+x3Ao5HygAWphZMfBjoMDMuhMZctkG3ATg7h+a2a+ADcBhYJK7J+RTwFTcuhjLfO61VVRUxNatWzn//PMpLCxk4sSJ9O7dm0mTJrF582batWvHl19+SXFxMRdccMFR+5555plkZWWxYMECrrrqKr766ivKysoqfmVJRKRcLHfLjHb3Vu7e0N2z3P1pdx/r7l3dPdfdh0X14nH3e939m+7ewd1fT274yRU9n3vHjh0ZOXJkxXzuixYtAuC9994jKyuLF154gZtuuonOnTsf95h9+vRh6tSpdOnShTZt2jB8+HAyMzOZO3cuo0ePJjc3l969e/PRRx9Vuf9zzz3Ho48+Sm5uLn379uUvf/lLwtstIulPE4fVYOjQoQwdOvSosmnTplUs9+rVi+Li4piP16RJEwoLC48p79+/P++9994x5du2bTtqvX379vz2t7+N+XwicnLS9AMiIiGknnsSfPDBB4wdO/aoslNPPZVVq1ZRUFCQmqBE5KSi5J4EXbt2Ze3atakOQ0ROYhqWEREJISV3EZEQUnIXEQmhtBlzL5n5WEKPl/nPtyb0eCIi9UnaJPdUycnJ4YwzzqBBgwZkZGSwevXqVIckIlIjJfcYvPXWW7Ro0SJpxz98+DAZGboUIpI4GnNPkIKCAqZMmULfvn3p0qULRUWR+dJKS0uZMGECvXr1okePHhXzwc+dO5cRI0ZwxRVXMGjQIAAeeOABunbtSrdu3Zg6dWrK2iIi6U/dxRqYGYMGDcLMuOmmm5g4cWK1dUtLS3n77bdZvnw5EyZMYP369dx7773079+fOXPmcODAAfLz87n00ksBeOedd1i3bh3Nmzfn9ddfZ8GCBaxatYomTZqwf3+sP34lInIsJfcarFy5ktatW7Nnzx4GDhzIt771Lfr161dl3dGjRwPQr18/Dh48yIEDB1iyZAmLFi1ixowZABw6dIjt27cDMHDgQJo3bw7Am2++yfjx4ytmeCwvFxGpCyX3GpTP396yZUuGDx9OUVFRtcm9urnaX3rpJTp06HDUtlWrVlXM0w6Rudrj/SEQEZFyaZPcU3HrYmlpKUeOHOGMM86gtLSUJUuWcNddd1Vbv7CwkEsuuYQVK1bQtGlTmjZtyuDBg5k5cyYzZ87EzHj//ffp0aPHMfsOGjSIadOmMWbMmIphGfXeRaSu0ia5p8Lu3bsrftLu8OHDjBkzhiFDhlRbv1mzZvTt25eDBw8yZ84cAO68806mTJlCbm4u7k5OTg6vvvrqMfsOGTKEtWvXkpeXxymnnMLQoUO57777ktMwEQk9qw8/3ZaXl+eV7x/fuHEjHTt2TFFEtVdQUMCMGTPIy8tLdSh1km7Pt0giVf6S5Nacv/+GQ/4VbU90ODEzszXuXmXS0a2QIiIhpGGZWpo0aRIrV648qmzy5MksW7YsNQGJiFRByb2WHn/88VSHICJSoxqHZcxsjpntMbP1UWU/M7OPzGydmb1iZmcF5Tlm9lczWxs8nkxm8CIiUrVYxtznApVvEXkD6OLuucAfgTuitm1x9+7B4+bEhCkiIrVRY3J39+XA/kplS9z9cLD6LpCVhNhERKSOEjHmPgEojFpvY2bvAweB/3D331W1k5lNBCYCZGdn13iSol//Kf5Io9Tn25tEROIV162QZvbvwGHgv4OiXUC2u/cAvg/80szOrGpfd5/t7nnunpeZmRlPGEkzYcIEWrZsSZcuXSrK9u/fz8CBA2nfvj0DBw7k008/TWGEIiJVq3NyN7NxwHeAazz4JpS7f+Xu+4LlNcAW4IJEBJoK1113HYsXLz6qbPr06QwYMIBNmzYxYMAApk+fnpRzuztHjhxJyrFFJPzqlNzNbAhwOzDM3b+MKs80swbBclugPZDY8ZQTqF+/fsfM77Jw4ULGjRsHwLhx41iwYEG1+999992MHTuW/v370759e5566qmKbT/72c/o1asXubm5/PjHPwZg27ZtdOzYkVtuuYWePXuyY8cOFi9eTM+ePenWrRsDBgxIQitFJIxqHHM3s/lAAdDCzIqBHxO5O+ZU4I1gJsN3gztj+gHTzOwwUAbc7O6hmph89+7dtGrVCoBWrVqxZ8+e49Zft24d7777LqWlpfTo0YPLL7+c9evXs2nTJoqKinB3hg0bxvLly8nOzubjjz/mmWeeYdasWZSUlHDjjTeyfPly2rRpozneRSRmNSZ3dx9dRfHT1dR9CXgp3qDC5Morr6Rx48Y0btyYSy65hKKiIlasWMGSJUsqZof84osv2LRpE9nZ2Zx//vn07t0bgHfffZd+/frRpk0bQHO8i0js9A3VWjrnnHPYtWsXrVq1YteuXbRs2fK49aub4/2OO+7gpptuOmrbtm3bNMe7iCRE2iT3+nLr4rBhw5g3bx5Tp05l3rx5XHnllcetv3DhQu644w5KS0tZtmwZ06dPp3Hjxtx5551cc801nH766ezcuZOGDRses2+fPn2YNGkSW7durRiWUe9dRGKRNsk9FUaPHs2yZcvYu3cvWVlZ3HPPPUydOpWRI0fy9NNPk52dzQsvvHDcY+Tn53P55Zezfft27rzzTlq3bk3r1q3ZuHEjffr0AeD000/nF7/4BQ0aNDhq38zMTGbPns13v/tdjhw5QsuWLXnjjTeS1l4RCQ8l9+OYP39+leVLly6N+RgXXHABs2fPPqZ88uTJTJ48+Zjy9evXH7V+2WWXcdlll8V8PhER0HzuIiKhpJ57AjzzzDM88sgjR5VddNFFmh5YRFKmXif3dLlbZPz48YwfPz7VYdRZffipRRFJrHo7LNOoUSP27dunxJNk7s6+ffto1KhRqkMRkQSqtz33rKwsiouLKSkpSXUoodeoUSOysjRrs0iY1Nvk3rBhw4pvZoqISO3U22EZERGpOyV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQmhmJK7mc0xsz1mtj6qrLmZvWFmm4K/zYJyM7NHzWyzma0zs57JCl5ERKoWa899LjCkUtlUYKm7tweWBusAlwHtg8dE4In4wxQRkdqIKbm7+3Jgf6XiK4F5wfI84Kqo8mc94l3gLDNrlYhgRUQkNvGMuZ/j7rsAgr8tg/JzgR1R9YqDMhEROUGS8YFqVb+uccyk7GY20cxWm9lqTesrIpJY8ST33eXDLcHfPUF5MXBeVL0s4JPKO7v7bHfPc/e8zMzMOMIQEZHK4knui4BxwfI4YGFU+bXBXTO9gc/Kh29EROTEiOnHOsxsPlAAtDCzYuDHwHTgV2Z2PbAdGBFUfw0YCmwGvgTS98dFRUTSVEzJ3d1HV7NpQBV1HZgUT1AiIhIffUNVRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJIRi+oaqiEg6mbV2VsXyLd1vSWEkqaOeu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiIRQnacfMLMOQGFUUVvgLuAs4EagJCj/kbu/VucIRUSk1uqc3N39Y6A7gJk1AHYCrwDjgYfdfUZCIhQRkVpL1LDMAGCLu/85QccTEZE4JCq5jwLmR63fambrzGyOmTVL0DlERCRGcSd3MzsFGAa8EBQ9AXyTyJDNLuDBavabaGarzWx1SUlJVVVERKSOEtFzvwz4vbvvBnD33e5e5u5HgKeA/Kp2cvfZ7p7n7nmZmZkJCENERMolIrmPJmpIxsxaRW0bDqxPwDlERKQW4volJjNrAgwEbooqfsDMugMObKu0TUREToC4kru7fwmcXalsbFwRiYhI3PQNVRGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREIrrN1RFROqLWWtnpTqEeiXu5G5m24DPgTLgsLvnmVlzoBDIAbYBI93903jPJSIisUnUsMwl7t7d3fOC9anAUndvDywN1kVE5ARJ1pj7lcC8YHkecFWSziMiIlVIRHJ3YImZrTGziUHZOe6+CyD427LyTmY20cxWm9nqkpKSBIQhIiLlEvGB6kXu/omZtQTeMLOPYtnJ3WcDswHy8vI8AXGIiCRc0a//VLGcf0XbFEZSO3H33N39k+DvHuAVIB/YbWatAIK/e+I9j4iIxC6u5G5mp5nZGeXLwCBgPbAIGBdUGwcsjOc8IiJSO/EOy5wDvGJm5cf6pbsvNrP3gF+Z2fXAdmBEnOcREZFaiCu5u/ufgG5VlO8DBsRzbBERqTtNPyAiEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGUiB/rEBGpt2atnVWxfEv3W1IYyYmlnruISAgpuYuIhJCGZUQkbUUPucjR1HMXEQkhJXcRkRBSchcRCSEldxGREKpzcjez88zsLTPbaGYfmtnkoPxuM9tpZmuDx9DEhSsiIrGI526Zw8C/uvvvzewMYI2ZvRFse9jdZ8QfnoiI1EWdk7u77wJ2Bcufm9lG4NxEBSYiInWXkDF3M8sBegCrgqJbzWydmc0xs2bV7DPRzFab2eqSkpJEhCEiIoG4k7uZnQ68BExx94PAE8A3ge5EevYPVrWfu8929zx3z8vMzIw3DBERiRLXN1TNrCGRxP7f7v4ygLvvjtr+FPBqXBGKyMnjrfv/vnzJHamLIwTqnNzNzICngY3u/lBUeatgPB5gOLA+vhBFJBSUuE+oeHruFwFjgQ/MbG1Q9iNgtJl1BxzYBtwUV4QiIlJr8dwtswKwKja9VvdwREQkEfQNVRGRENKUvyJyLI2Ppz313EVEQkg9dxFJnuh3AGmktOi9iuXT8nulMJK6U3IXkfhpGKfe0bCMiEgIqecuIqmVpkM39Z2Su0gVHn7jjxXLtw28IIWRSLKUzHws1SEklZK7iMROvey0oeQucjKpnJyT8eGnXgDqBX2gKiISQuq5S9o5WcbDT5Z2SnIouYvUQElW0pGGZUREQkg9d0kL0b3nWOqohx2jVH34qQ9dk07JXVIiUYk4lqSfyHOflC8gSsRpScld6q26JO5E7FsfnfQvKpqvptZCkdxPyn/4KZKoXm8s5fVddc9FLM9RdSDTaOoAAATfSURBVG2O5Tmt9TVQz/ukFIrkLvWLXmz/Ll1fuCT9JS25m9kQ4BGgAfBzd5+erHOJ1FWikm99O07oeutJHqJZvycz4cdMtaQkdzNrADwODASKgffMbJG7b0jG+eqbdOq5xjOEEM++khjVXYN3/rSvYrlP27OrLK+8TcIlWfe55wOb3f1P7v434HngyiSdS0REKknWsMy5wI6o9WLgwiSdiz3F/1ax/PAbD1Qs1+dec217vXVpS217zcn4sFRSr3JvPZZ69blHP2vtrFSHkBbM3RN/ULMRwGB3vyFYHwvku/s/R9WZCEwMVjsAH8dxyhbA3jj2r8/UtvQV5vapbfXD+e5e5QcGyeq5FwPnRa1nAZ9EV3D32cDsRJzMzFa7e14ijlXfqG3pK8ztU9vqv2SNub8HtDezNmZ2CjAKWJSkc4mISCVJ6bm7+2EzuxX4DZFbIee4+4fJOJeIiBwrafe5u/trwGvJOn4lCRneqafUtvQV5vapbfVcUj5QFRGR1NJ87iIiIZTWyd3MhpjZx2a22cympjqeeJjZeWb2lpltNLMPzWxyUN7czN4ws03B32apjrWuzKyBmb1vZq8G623MbFXQtsLgw/e0ZGZnmdmLZvZRcA37hOXamdltwb/J9WY238wapfO1M7M5ZrbHzNZHlVV5rSzi0SDHrDOznqmLvHbSNrlHTXFwGdAJGG1mnVIbVVwOA//q7h2B3sCkoD1TgaXu3h5YGqynq8nAxqj1nwIPB237FLg+JVElxiPAYnf/FtCNSDvT/tqZ2bnAvwB57t6FyA0So0jvazcXGFKprLprdRnQPnhMBJ44QTHGLW2TOyGb4sDdd7n774Plz4kkh3OJtGleUG0ecFVqIoyPmWUBlwM/D9YN6A+8GFRJ57adCfQDngZw97+5+wFCcu2I3HjR2MwygCbALtL42rn7cmB/peLqrtWVwLMe8S5wlpm1OjGRxiedk3tVUxycm6JYEsrMcoAewCrgHHffBZEXAKBl6iKLy38C/wYcCdbPBg64++FgPZ2vX1ugBHgmGHb6uZmdRgiunbvvBGYA24kk9c+ANYTn2pWr7lqlbZ5J5+RuVZSl/a0/ZnY68BIwxd0PpjqeRDCz7wB73H1NdHEVVdP1+mUAPYEn3L0HUEoaDsFUJRh7vhJoA7QGTiMyVFFZul67mqTtv9N0Tu41TnGQbsysIZHE/t/u/nJQvLv8bWDwd0+q4ovDRcAwM9tGZPisP5Ge/FnBW31I7+tXDBS7+6pg/UUiyT4M1+5SYKu7l7j718DLQF/Cc+3KVXet0jbPpHNyD9UUB8EY9NPARnd/KGrTImBcsDwOWHiiY4uXu9/h7lnunkPkOv3W3a8B3gK+F1RLy7YBuPtfgB1m1iEoGgBsIATXjshwTG8zaxL8Gy1vWyiuXZTqrtUi4NrgrpnewGflwzf1nrun7QMYCvwR2AL8e6rjibMtFxN5u7cOWBs8hhIZm14KbAr+Nk91rHG2swB4NVhuCxQBm4EXgFNTHV8c7eoOrA6u3wKgWViuHXAP8BGwHngOODWdrx0wn8jnB18T6ZlfX921IjIs83iQYz4gctdQytsQy0PfUBURCaF0HpYREZFqKLmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiITQ/wJSskIDzwn8sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in tqdm(range(38)):\n",
    "    fig, axes = plt.subplots()\n",
    "    plt.hist(percentiles[f'ch{i}_0th_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.001st_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.001_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.1st_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.1_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_5th_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '5_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_10th_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '10_perc')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.suptitle(f\"Channel {i} -- percentiles\")\n",
    "    \n",
    "#     fig, axes = plt.subplots()\n",
    "#     sns.heatmap(dfY, vmin=-100, vmax=100)\n",
    "#     pl.suptitle(f\"Cycle {c+1} Y-shift\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ch0_cutoff_perc</th>\n",
       "      <th>ch1_cutoff_perc</th>\n",
       "      <th>ch2_cutoff_perc</th>\n",
       "      <th>ch3_cutoff_perc</th>\n",
       "      <th>ch4_cutoff_perc</th>\n",
       "      <th>ch5_cutoff_perc</th>\n",
       "      <th>ch6_cutoff_perc</th>\n",
       "      <th>ch7_cutoff_perc</th>\n",
       "      <th>ch8_cutoff_perc</th>\n",
       "      <th>ch9_cutoff_perc</th>\n",
       "      <th>...</th>\n",
       "      <th>ch28_cutoff_perc</th>\n",
       "      <th>ch29_cutoff_perc</th>\n",
       "      <th>ch30_cutoff_perc</th>\n",
       "      <th>ch31_cutoff_perc</th>\n",
       "      <th>ch32_cutoff_perc</th>\n",
       "      <th>ch33_cutoff_perc</th>\n",
       "      <th>ch34_cutoff_perc</th>\n",
       "      <th>ch35_cutoff_perc</th>\n",
       "      <th>ch36_cutoff_perc</th>\n",
       "      <th>ch37_cutoff_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ch0_cutoff_perc  ch1_cutoff_perc  ch2_cutoff_perc  ch3_cutoff_perc  \\\n",
       "000              0.0              0.0              0.0              0.0   \n",
       "001              0.0              0.0              0.0              0.0   \n",
       "002              0.0              0.0              2.0              0.0   \n",
       "003              0.0              0.0              0.0              0.0   \n",
       "004              0.0              0.0              0.0              0.0   \n",
       "..               ...              ...              ...              ...   \n",
       "219              0.0              0.0              0.0              0.0   \n",
       "220              0.0              0.0              0.0              2.0   \n",
       "221              0.0              0.0              0.0              0.0   \n",
       "222              0.0              0.0              0.0              0.0   \n",
       "224              0.0              0.0              0.0              0.0   \n",
       "\n",
       "     ch4_cutoff_perc  ch5_cutoff_perc  ch6_cutoff_perc  ch7_cutoff_perc  \\\n",
       "000              0.0              0.0              0.0              0.0   \n",
       "001              0.0              0.0              0.0              0.0   \n",
       "002              0.0              0.0              0.0              0.0   \n",
       "003              0.0              0.0              0.0              0.0   \n",
       "004              0.0              0.0              0.0              0.0   \n",
       "..               ...              ...              ...              ...   \n",
       "219              0.0              0.0              0.0              0.0   \n",
       "220              0.0              0.0              0.0              0.0   \n",
       "221              0.0              0.0              0.0              1.0   \n",
       "222              0.0              0.0              0.0              0.0   \n",
       "224              0.0              0.0              0.0              0.0   \n",
       "\n",
       "     ch8_cutoff_perc  ch9_cutoff_perc  ...  ch28_cutoff_perc  \\\n",
       "000              0.0              0.0  ...               0.0   \n",
       "001              0.0              0.0  ...               0.0   \n",
       "002              0.0              0.0  ...               4.0   \n",
       "003              0.0              0.0  ...               0.0   \n",
       "004              0.0              0.0  ...               0.0   \n",
       "..               ...              ...  ...               ...   \n",
       "219              0.0              0.0  ...               0.0   \n",
       "220              0.0              0.0  ...               0.0   \n",
       "221              0.0              0.0  ...               0.0   \n",
       "222              0.0              0.0  ...               0.0   \n",
       "224              0.0              0.0  ...               0.0   \n",
       "\n",
       "     ch29_cutoff_perc  ch30_cutoff_perc  ch31_cutoff_perc  ch32_cutoff_perc  \\\n",
       "000               0.0               0.0               0.0               0.0   \n",
       "001               0.0               0.0               0.0               0.0   \n",
       "002               0.0               0.0               0.0               0.0   \n",
       "003               0.0               0.0               0.0               0.0   \n",
       "004               0.0               0.0               0.0               0.0   \n",
       "..                ...               ...               ...               ...   \n",
       "219               0.0               0.0               0.0               0.0   \n",
       "220               0.0               0.0               0.0               0.0   \n",
       "221               0.0               0.0               0.0               0.0   \n",
       "222               0.0               0.0               0.0               0.0   \n",
       "224               0.0               0.0               0.0               0.0   \n",
       "\n",
       "     ch33_cutoff_perc  ch34_cutoff_perc  ch35_cutoff_perc  ch36_cutoff_perc  \\\n",
       "000               0.0               0.0               0.0               0.0   \n",
       "001               0.0               0.0               0.0               0.0   \n",
       "002               0.0               0.0               0.0               0.0   \n",
       "003               0.0               0.0               1.0               0.0   \n",
       "004               0.0               0.0               0.0               0.0   \n",
       "..                ...               ...               ...               ...   \n",
       "219               0.0               0.0               0.0               0.0   \n",
       "220               0.0               0.0               0.0               0.0   \n",
       "221               0.0               0.0               0.0               0.0   \n",
       "222               0.0               0.0               0.0               0.0   \n",
       "224               0.0               2.0               1.0               0.0   \n",
       "\n",
       "     ch37_cutoff_perc  \n",
       "000               0.0  \n",
       "001               0.0  \n",
       "002               0.0  \n",
       "003               0.0  \n",
       "004               0.0  \n",
       "..                ...  \n",
       "219               0.0  \n",
       "220               0.0  \n",
       "221               0.0  \n",
       "222               0.0  \n",
       "224               0.0  \n",
       "\n",
       "[211 rows x 38 columns]"
      ]
     },
     "execution_count": 1188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoffs = percentiles.filter(regex=(\".*cutoff.*\"))\n",
    "cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('002', 2, 2.0),\n",
       " ('002', 28, 4.0),\n",
       " ('003', 35, 1.0),\n",
       " ('008', 34, 7.0),\n",
       " ('010', 6, 3.0),\n",
       " ('011', 3, 2.0),\n",
       " ('011', 13, 2.0),\n",
       " ('011', 28, 1.0),\n",
       " ('016', 9, 2.0),\n",
       " ('017', 35, 1.0),\n",
       " ('019', 18, 2.0),\n",
       " ('026', 2, 1.0),\n",
       " ('027', 35, 1.0),\n",
       " ('028', 35, 1.0),\n",
       " ('029', 35, 1.0),\n",
       " ('030', 14, 4.0),\n",
       " ('031', 15, 1.0),\n",
       " ('034', 35, 1.0),\n",
       " ('035', 30, 1.0),\n",
       " ('036', 31, 2.0),\n",
       " ('037', 13, 2.0),\n",
       " ('037', 14, 2.0),\n",
       " ('037', 35, 1.0),\n",
       " ('038', 32, 1.0),\n",
       " ('038', 35, 1.0),\n",
       " ('040', 3, 1.0),\n",
       " ('040', 35, 1.0),\n",
       " ('042', 21, 2.0),\n",
       " ('042', 33, 1.0),\n",
       " ('044', 18, 2.0),\n",
       " ('044', 34, 2.0),\n",
       " ('045', 8, 2.0),\n",
       " ('045', 33, 3.0),\n",
       " ('048', 3, 1.0),\n",
       " ('048', 13, 1.0),\n",
       " ('050', 19, 2.0),\n",
       " ('051', 24, 2.0),\n",
       " ('051', 32, 2.0),\n",
       " ('052', 17, 2.0),\n",
       " ('054', 13, 2.0),\n",
       " ('059', 5, 1.0),\n",
       " ('060', 20, 1.0),\n",
       " ('062', 1, 1.0),\n",
       " ('062', 35, 1.0),\n",
       " ('063', 35, 1.0),\n",
       " ('065', 35, 1.0),\n",
       " ('066', 5, 3.0),\n",
       " ('071', 28, 1.0),\n",
       " ('072', 12, 1.0),\n",
       " ('075', 22, 1.0),\n",
       " ('075', 32, 1.0),\n",
       " ('079', 3, 2.0),\n",
       " ('080', 7, 2.0),\n",
       " ('083', 11, 1.0),\n",
       " ('084', 16, 1.0),\n",
       " ('085', 4, 3.0),\n",
       " ('088', 4, 1.0),\n",
       " ('094', 20, 2.0),\n",
       " ('094', 22, 1.0),\n",
       " ('094', 26, 1.0),\n",
       " ('094', 30, 1.0),\n",
       " ('098', 35, 1.0),\n",
       " ('099', 35, 1.0),\n",
       " ('101', 18, 3.0),\n",
       " ('101', 37, 1.0),\n",
       " ('108', 35, 1.0),\n",
       " ('111', 0, 1.0),\n",
       " ('111', 5, 5.0),\n",
       " ('113', 33, 1.0),\n",
       " ('113', 35, 1.0),\n",
       " ('114', 8, 5.0),\n",
       " ('114', 16, 5.0),\n",
       " ('115', 35, 1.0),\n",
       " ('117', 8, 3.0),\n",
       " ('120', 13, 3.0),\n",
       " ('124', 8, 1.0),\n",
       " ('124', 35, 1.0),\n",
       " ('127', 8, 6.0),\n",
       " ('128', 22, 3.0),\n",
       " ('130', 13, 1.0),\n",
       " ('132', 35, 1.0),\n",
       " ('133', 22, 1.0),\n",
       " ('133', 35, 1.0),\n",
       " ('134', 7, 2.0),\n",
       " ('135', 0, 2.0),\n",
       " ('137', 32, 2.0),\n",
       " ('138', 2, 1.0),\n",
       " ('138', 35, 1.0),\n",
       " ('139', 24, 2.0),\n",
       " ('143', 4, 1.0),\n",
       " ('143', 24, 2.0),\n",
       " ('146', 35, 1.0),\n",
       " ('151', 37, 1.0),\n",
       " ('152', 15, 2.0),\n",
       " ('152', 35, 1.0),\n",
       " ('155', 15, 2.0),\n",
       " ('160', 8, 3.0),\n",
       " ('161', 37, 1.0),\n",
       " ('162', 37, 1.0),\n",
       " ('169', 4, 2.0),\n",
       " ('173', 7, 2.0),\n",
       " ('178', 13, 2.0),\n",
       " ('178', 34, 1.0),\n",
       " ('181', 9, 1.0),\n",
       " ('181', 25, 1.0),\n",
       " ('182', 1, 4.0),\n",
       " ('182', 7, 1.0),\n",
       " ('185', 14, 1.0),\n",
       " ('187', 4, 5.0),\n",
       " ('189', 30, 1.0),\n",
       " ('190', 17, 2.0),\n",
       " ('191', 35, 1.0),\n",
       " ('191', 37, 1.0),\n",
       " ('197', 12, 4.0),\n",
       " ('206', 35, 1.0),\n",
       " ('207', 35, 1.0),\n",
       " ('208', 3, 2.0),\n",
       " ('208', 8, 4.0),\n",
       " ('210', 18, 1.0),\n",
       " ('210', 26, 1.0),\n",
       " ('210', 35, 1.0),\n",
       " ('211', 35, 1.0),\n",
       " ('213', 10, 1.0),\n",
       " ('213', 14, 1.0),\n",
       " ('213', 15, 1.0),\n",
       " ('213', 18, 1.0),\n",
       " ('213', 19, 1.0),\n",
       " ('213', 22, 1.0),\n",
       " ('213', 23, 1.0),\n",
       " ('213', 26, 1.0),\n",
       " ('213', 27, 1.0),\n",
       " ('213', 30, 1.0),\n",
       " ('214', 28, 1.0),\n",
       " ('220', 3, 2.0),\n",
       " ('221', 7, 1.0),\n",
       " ('224', 34, 2.0),\n",
       " ('224', 35, 1.0)]"
      ]
     },
     "execution_count": 1264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tup = []\n",
    "for index, row in cutoffs.iterrows():\n",
    "    for col, val in enumerate(row):\n",
    "        if val > 0:\n",
    "            tup.append((index, col, val))\n",
    "tup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>channel</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>002</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>002</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>003</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>008</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>010</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>214</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>220</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>221</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>224</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>224</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  channel  percentage\n",
       "0       002        2         2.0\n",
       "1       002       28         4.0\n",
       "2       003       35         1.0\n",
       "3       008       34         7.0\n",
       "4       010        6         3.0\n",
       "..      ...      ...         ...\n",
       "132     214       28         1.0\n",
       "133     220        3         2.0\n",
       "134     221        7         1.0\n",
       "135     224       34         2.0\n",
       "136     224       35         1.0\n",
       "\n",
       "[137 rows x 3 columns]"
      ]
     },
     "execution_count": 1266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_perc = pd.DataFrame(tup)\n",
    "bb_perc = bb_perc.rename(columns={0: \"FOV_num\", 1: \"channel\", 2:'percentage'})\n",
    "bb_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1294,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb_perc.to_csv('bbox_percentages.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>616</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>617</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>618</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>619</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>620</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  FOV_num    Z  channel\n",
       "0            10        3  4.0     35.0\n",
       "1            11        3  5.0     35.0\n",
       "2            12        3  6.0     35.0\n",
       "3            13        3  7.0     35.0\n",
       "4            14        3  8.0     35.0\n",
       "..          ...      ...  ...      ...\n",
       "119         616      213  6.0     14.0\n",
       "120         617      213  6.0     18.0\n",
       "121         618      213  6.0     22.0\n",
       "122         619      213  6.0     26.0\n",
       "123         620      213  6.0     30.0\n",
       "\n",
       "[124 rows x 4 columns]"
      ]
     },
     "execution_count": 1295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc = pd.read_csv('black_circle_locations.csv')\n",
    "bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1301,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set()\n",
    "for i in bc['FOV_num']:\n",
    "    s.add(str(i).zfill(3))\n",
    "s = pd.DataFrame(sorted(s))\n",
    "s.to_csv('b_circle_FOVs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1332,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = imread('merged/F132.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3, 3, 3, ..., 9, 9, 9]),\n",
       " array([766, 767, 767, ..., 864, 866, 866]),\n",
       " array([648, 640, 649, ..., 696, 689, 691]))"
      ]
     },
     "execution_count": 1333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.where(test[:, 35, ...] == 0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1010"
      ]
     },
     "execution_count": 1334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1276,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = imread('merged/F002.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, ..., 9, 9, 9]),\n",
       " array([324, 325, 325, ..., 192, 192, 192]),\n",
       " array([107, 108, 109, ..., 613, 614, 615]))"
      ]
     },
     "execution_count": 1287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.where(test[:, 28, ...] == 0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1584705"
      ]
     },
     "execution_count": 1288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#import seaborn as sns\n",
    "import math\n",
    "#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",
    "from imageio import volread as imread\n",
    "\n",
    "from skimage.segmentation import expand_labels\n",
    "import tifffile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define inputs\n",
    "##########################################################################################\n",
    "IN_DIR = 'merged'\n",
    "OUT_DIR = 'mask'\n",
    "META_DIR = 'metadata'\n",
    "# define input directory\n",
    "#DATA_DIR = '101222_D10_Coverslip1_Processed'\n",
    "##########################################################################################\n",
    "# os.chdir(f'{DATA_DIR}')\n",
    "# print(os.getcwd())\n",
    "\n",
    "#SOURCE = f'gs://fc-secure-9289bfef-e5cb-493a-83d5-e604cd429e39/Brian/{DATA_DIR}/'\n",
    "\n",
    "# load metadata - load full codebook as well\n",
    "full_codebook = pd.read_csv(f'{META_DIR}/full_codebook.csv',sep=',', index_col=0) # this is \"legal\" codebook\n",
    "Procode_gRNA = pd.read_csv(f'{META_DIR}/PROCODE_gRNA.csv',sep=',')\n",
    "legal_codes = sorted(list(set(Procode_gRNA['ProCode ID'].to_list())))\n",
    "codebook = full_codebook[legal_codes]\n",
    "#AllProcodes = pd.read_csv('AllProcodes.csv', sep='.')\n",
    "#sns.heatmap(codebook, linewidths = 0.3)\n",
    "markers = pd.read_csv(f'{META_DIR}/markers.csv')\n",
    "#markers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 5,\n",
       " 6,\n",
       " 7,\n",
       " 9,\n",
       " 10,\n",
       " 11,\n",
       " 13,\n",
       " 14,\n",
       " 15,\n",
       " 17,\n",
       " 18,\n",
       " 19,\n",
       " 21,\n",
       " 22,\n",
       " 23,\n",
       " 25,\n",
       " 27,\n",
       " 29,\n",
       " 30,\n",
       " 31,\n",
       " 33,\n",
       " 35,\n",
       " 36,\n",
       " 37]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get indices of final channels --> only make mask when happening in these indices of merged file\n",
    "final_inds = []\n",
    "marker_names = ['DNA_0','NWS','VSVG','FLAG','HSV','C','S','Ollas','GFAP','NeuN',\n",
    "               'pRPS6','RANGAP1','NFKB','TOM20','LAMP1','4HNE','TDP43','G3BP1','GM130','Calnexin','Golgin97',\n",
    "               'SYTO','ER','AGP','Catalase']\n",
    "\n",
    "for marker in marker_names:\n",
    "    ind_to_add = markers[markers.marker_name == marker]['Split_Num'].values[0]\n",
    "    final_inds.append(ind_to_add)\n",
    "final_inds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000', '001', '002', '003', '004', '005', '006', '007', '008', '009', '010', '011', '012', '013', '015', '016', '017', '018', '019', '022', '023', '024', '025', '026', '027', '028', '029', '030', '031', '032', '033', '034', '035', '036', '037', '038', '039', '040', '041', '042', '043', '044', '045', '046', '047', '048', '049', '050', '051', '052', '053', '054', '055', '056', '057', '058', '059', '060', '062', '063', '065', '066', '067', '068', '069', '070', '071', '072', '073', '074', '075', '076', '077', '078', '079', '080', '082', '083', '084', '085', '087', '088', '089', '090', '091', '092', '093', '094', '095', '096', '097', '098', '099', '100', '101', '102', '103', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '127', '128', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '193', '194', '195', '196', '197', '198', '199', '200', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '224']\n",
      "211\n"
     ]
    }
   ],
   "source": [
    "# _allFOVs = sorted(glob.glob('reg_bin_Cyc_2/*'))\n",
    "# allFOVs = [x.split('F')[-1][:3] for x in _allFOVs]\n",
    "allFOVs = ['000', '001', '002', '003', '004', '005', '006', '007', '008', '009', '010', '011', '012', '013', '015', '016', '017', '018', '019', '022', '023', '024', '025', '026', '027', '028', '029', '030', '031', '032', '033', '034', '035', '036', '037', '038', '039', '040', '041', '042', '043', '044', '045', '046', '047', '048', '049', '050', '051', '052', '053', '054', '055', '056', '057', '058', '059', '060', '062', '063', '065', '066', '067', '068', '069', '070', '071', '072', '073', '074', '075', '076', '077', '078', '079', '080', '082', '083', '084', '085', '087', '088', '089', '090', '091', '092', '093', '094', '095', '096', '097', '098', '099', '100', '101', '102', '103', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '127', '128', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '193', '194', '195', '196', '197', '198', '199', '200', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '224']\n",
    "print(allFOVs)\n",
    "print(len(allFOVs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000', '001', '002', '003', '004', '005', '006', '007', '008', '009']\n",
      "['010', '011', '012', '013', '015', '016', '017', '018', '019', '022']\n",
      "['023', '024', '025', '026', '027', '028', '029', '030', '031', '032']\n",
      "['033', '034', '035', '036', '037', '038', '039', '040', '041', '042']\n",
      "['043', '044', '045', '046', '047', '048', '049', '050', '051', '052']\n",
      "['053', '054', '055', '056', '057', '058', '059', '060', '062', '063']\n",
      "['065', '066', '067', '068', '069', '070', '071', '072', '073', '074']\n",
      "['075', '076', '077', '078', '079', '080', '082', '083', '084', '085']\n",
      "['087', '088', '089', '090', '091', '092', '093', '094', '095', '096']\n",
      "['097', '098', '099', '100', '101', '102', '103', '105', '106', '107']\n",
      "['108', '109', '110', '111', '112', '113', '114', '115', '116', '117']\n",
      "['118', '119', '120', '121', '122', '123', '124', '125', '127', '128']\n",
      "['130', '131', '132', '133', '134', '135', '136', '137', '138', '139']\n",
      "['140', '141', '142', '143', '144', '145', '146', '147', '148', '149']\n",
      "['150', '151', '152', '153', '154', '155', '157', '158', '159', '160']\n",
      "['161', '162', '163', '164', '165', '166', '167', '168', '169', '170']\n",
      "['171', '172', '173', '174', '175', '176', '177', '178', '179', '180']\n",
      "['181', '182', '183', '184', '185', '186', '187', '188', '189', '190']\n",
      "['191', '193', '194', '195', '196', '197', '198', '199', '200', '202']\n",
      "['203', '204', '205', '206', '207', '208', '209', '210', '211', '212']\n",
      "['213', '214', '215', '216', '217', '218', '219', '220', '221', '222']\n",
      "['224']\n"
     ]
    }
   ],
   "source": [
    "for ii in range(0,len(allFOVs),10): # For each CHUNK (10 FOVs per loop)\n",
    "    print(allFOVs[ii:ii+10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>219</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1    2    3    4    5    6    7    8    9   ...   28   29   30  \\\n",
       "000  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "001  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "002  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "003  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "004  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "219  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "220  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "221  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "222  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "224  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN  NaN  NaN   \n",
       "\n",
       "      31   32   33   34   35   36   37  \n",
       "000  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "001  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "002  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "003  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "004  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  \n",
       "219  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "220  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "221  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "222  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "224  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[211 rows x 38 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# record in dataframe\n",
    "count0_df = pd.DataFrame(index=allFOVs, columns = list(range(38)))\n",
    "count20_df = pd.DataFrame(index=allFOVs, columns = list(range(38)))\n",
    "count65K_df = pd.DataFrame(index=allFOVs, columns = list(range(38)))\n",
    "count65K_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.segmentation import *\n",
    "from skimage import measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "mask shape (1979, 2019)\n",
      "F000, no mask needed\n",
      "FOV 001\n",
      "mask shape (1979, 2019)\n",
      "F001, no mask needed\n",
      "FOV 002\n",
      "mask shape (1979, 2019)\n",
      "F002, no mask needed\n",
      "FOV 003\n",
      "mask shape (1979, 2019)\n",
      "F003, no mask needed\n",
      "FOV 004\n",
      "mask shape (1979, 2019)\n",
      "F004, no mask needed\n",
      "FOV 005\n",
      "mask shape (1979, 2019)\n",
      "F005, no mask needed\n",
      "FOV 006\n",
      "mask shape (1979, 2019)\n",
      "F006, no mask needed\n",
      "FOV 007\n",
      "mask shape (1979, 2019)\n",
      "F007, no mask needed\n",
      "FOV 008\n",
      "mask shape (1979, 2019)\n",
      "F008, no mask needed\n",
      "FOV 009\n",
      "mask shape (1979, 2019)\n",
      "F009, no mask needed\n",
      "FOV 010\n",
      "mask shape (1979, 2019)\n",
      "FOV 011\n",
      "mask shape (1979, 2019)\n",
      "FOV 012\n",
      "mask shape (1979, 2019)\n",
      "F012, no mask needed\n",
      "FOV 013\n",
      "mask shape (1979, 2019)\n",
      "F013, no mask needed\n",
      "FOV 015\n",
      "mask shape (1979, 2019)\n",
      "F015, no mask needed\n",
      "FOV 016\n",
      "mask shape (1979, 2019)\n",
      "FOV 017\n",
      "mask shape (1979, 2019)\n",
      "FOV 018\n",
      "mask shape (1979, 2019)\n",
      "F018, no mask needed\n",
      "FOV 019\n",
      "mask shape (1979, 2019)\n",
      "FOV 022\n",
      "mask shape (1979, 2019)\n",
      "F022, no mask needed\n",
      "FOV 023\n",
      "mask shape (1979, 2019)\n",
      "F023, no mask needed\n",
      "FOV 024\n",
      "mask shape (1979, 2019)\n",
      "F024, no mask needed\n",
      "FOV 025\n",
      "mask shape (1979, 2019)\n",
      "F025, no mask needed\n",
      "FOV 026\n",
      "mask shape (1979, 2019)\n",
      "F026, no mask needed\n",
      "FOV 027\n",
      "mask shape (1979, 2019)\n",
      "F027, no mask needed\n",
      "FOV 028\n",
      "mask shape (1979, 2019)\n",
      "F028, no mask needed\n",
      "FOV 029\n",
      "mask shape (1979, 2019)\n",
      "F029, no mask needed\n",
      "FOV 030\n",
      "mask shape (1979, 2019)\n",
      "FOV 031\n",
      "mask shape (1979, 2019)\n",
      "FOV 032\n",
      "mask shape (1979, 2019)\n",
      "F032, no mask needed\n",
      "FOV 033\n",
      "mask shape (1979, 2019)\n",
      "F033, no mask needed\n",
      "FOV 034\n",
      "mask shape (1979, 2019)\n",
      "F034, no mask needed\n",
      "FOV 035\n",
      "mask shape (1979, 2019)\n",
      "FOV 036\n",
      "mask shape (1979, 2019)\n",
      "FOV 037\n",
      "mask shape (1979, 2019)\n",
      "FOV 038\n",
      "mask shape (1979, 2019)\n",
      "F038, no mask needed\n",
      "FOV 039\n",
      "mask shape (1979, 2019)\n",
      "F039, no mask needed\n",
      "FOV 040\n",
      "mask shape (1979, 2019)\n",
      "F040, no mask needed\n",
      "FOV 041\n",
      "mask shape (1979, 2019)\n",
      "F041, no mask needed\n",
      "FOV 042\n",
      "mask shape (1979, 2019)\n",
      "FOV 043\n",
      "mask shape (1979, 2019)\n",
      "F043, no mask needed\n",
      "FOV 044\n",
      "mask shape (1979, 2019)\n",
      "FOV 045\n",
      "mask shape (1979, 2019)\n",
      "FOV 046\n",
      "mask shape (1979, 2019)\n",
      "F046, no mask needed\n",
      "FOV 047\n",
      "mask shape (1979, 2019)\n",
      "F047, no mask needed\n",
      "FOV 048\n",
      "mask shape (1979, 2019)\n",
      "FOV 049\n",
      "mask shape (1979, 2019)\n",
      "F049, no mask needed\n",
      "FOV 050\n",
      "mask shape (1979, 2019)\n",
      "FOV 051\n",
      "mask shape (1979, 2019)\n",
      "F051, no mask needed\n",
      "FOV 052\n",
      "mask shape (1979, 2019)\n",
      "FOV 053\n",
      "mask shape (1979, 2019)\n",
      "F053, no mask needed\n",
      "FOV 054\n",
      "mask shape (1979, 2019)\n",
      "FOV 055\n",
      "mask shape (1979, 2019)\n",
      "F055, no mask needed\n",
      "FOV 056\n",
      "mask shape (1979, 2019)\n",
      "F056, no mask needed\n",
      "FOV 057\n",
      "mask shape (1979, 2019)\n",
      "F057, no mask needed\n",
      "FOV 058\n",
      "mask shape (1979, 2019)\n",
      "F058, no mask needed\n",
      "FOV 059\n",
      "mask shape (1979, 2019)\n",
      "FOV 060\n",
      "mask shape (1979, 2019)\n",
      "F060, no mask needed\n",
      "FOV 062\n",
      "mask shape (1979, 2019)\n",
      "F062, no mask needed\n",
      "FOV 063\n",
      "mask shape (1979, 2019)\n",
      "F063, no mask needed\n",
      "FOV 065\n",
      "mask shape (1979, 2019)\n",
      "F065, no mask needed\n",
      "FOV 066\n",
      "mask shape (1979, 2019)\n",
      "FOV 067\n",
      "mask shape (1979, 2019)\n",
      "F067, no mask needed\n",
      "FOV 068\n",
      "mask shape (1979, 2019)\n",
      "F068, no mask needed\n",
      "FOV 069\n",
      "mask shape (1979, 2019)\n",
      "F069, no mask needed\n",
      "FOV 070\n",
      "mask shape (1979, 2019)\n",
      "F070, no mask needed\n",
      "FOV 071\n",
      "mask shape (1979, 2019)\n",
      "F071, no mask needed\n",
      "FOV 072\n",
      "mask shape (1979, 2019)\n",
      "F072, no mask needed\n",
      "FOV 073\n",
      "mask shape (1979, 2019)\n",
      "F073, no mask needed\n",
      "FOV 074\n",
      "mask shape (1979, 2019)\n",
      "F074, no mask needed\n",
      "FOV 075\n",
      "mask shape (1979, 2019)\n",
      "FOV 076\n",
      "mask shape (1979, 2019)\n",
      "F076, no mask needed\n",
      "FOV 077\n",
      "mask shape (1979, 2019)\n",
      "F077, no mask needed\n",
      "FOV 078\n",
      "mask shape (1979, 2019)\n",
      "F078, no mask needed\n",
      "FOV 079\n",
      "mask shape (1979, 2019)\n",
      "F079, no mask needed\n",
      "FOV 080\n",
      "mask shape (1979, 2019)\n",
      "FOV 082\n",
      "mask shape (1979, 2019)\n",
      "F082, no mask needed\n",
      "FOV 083\n",
      "mask shape (1979, 2019)\n",
      "FOV 084\n",
      "mask shape (1979, 2019)\n",
      "F084, no mask needed\n",
      "FOV 085\n",
      "mask shape (1979, 2019)\n",
      "F085, no mask needed\n",
      "FOV 087\n",
      "mask shape (1979, 2019)\n",
      "F087, no mask needed\n",
      "FOV 088\n",
      "mask shape (1979, 2019)\n",
      "F088, no mask needed\n",
      "FOV 089\n",
      "mask shape (1979, 2019)\n",
      "F089, no mask needed\n",
      "FOV 090\n",
      "mask shape (1979, 2019)\n",
      "F090, no mask needed\n",
      "FOV 091\n",
      "mask shape (1979, 2019)\n",
      "F091, no mask needed\n",
      "FOV 092\n",
      "mask shape (1979, 2019)\n",
      "F092, no mask needed\n",
      "FOV 093\n",
      "mask shape (1979, 2019)\n",
      "F093, no mask needed\n",
      "FOV 094\n",
      "mask shape (1979, 2019)\n",
      "F094, no mask needed\n",
      "FOV 095\n",
      "mask shape (1979, 2019)\n",
      "F095, no mask needed\n",
      "FOV 096\n",
      "mask shape (1979, 2019)\n",
      "F096, no mask needed\n",
      "FOV 097\n",
      "mask shape (1979, 2019)\n",
      "F097, no mask needed\n",
      "FOV 098\n",
      "mask shape (1979, 2019)\n",
      "F098, no mask needed\n",
      "FOV 099\n",
      "mask shape (1979, 2019)\n",
      "F099, no mask needed\n",
      "FOV 100\n",
      "mask shape (1979, 2019)\n",
      "F100, no mask needed\n",
      "FOV 101\n",
      "mask shape (1979, 2019)\n",
      "FOV 102\n",
      "mask shape (1979, 2019)\n",
      "F102, no mask needed\n",
      "FOV 103\n",
      "mask shape (1979, 2019)\n",
      "F103, no mask needed\n",
      "FOV 105\n",
      "mask shape (1979, 2019)\n",
      "F105, no mask needed\n",
      "FOV 106\n",
      "mask shape (1979, 2019)\n",
      "F106, no mask needed\n",
      "FOV 107\n",
      "mask shape (1979, 2019)\n",
      "F107, no mask needed\n",
      "FOV 108\n",
      "mask shape (1979, 2019)\n",
      "F108, no mask needed\n",
      "FOV 109\n",
      "mask shape (1979, 2019)\n",
      "F109, no mask needed\n",
      "FOV 110\n",
      "mask shape (1979, 2019)\n",
      "F110, no mask needed\n",
      "FOV 111\n",
      "mask shape (1979, 2019)\n",
      "FOV 112\n",
      "mask shape (1979, 2019)\n",
      "F112, no mask needed\n",
      "FOV 113\n",
      "mask shape (1979, 2019)\n",
      "FOV 114\n",
      "mask shape (1979, 2019)\n",
      "F114, no mask needed\n",
      "FOV 115\n",
      "mask shape (1979, 2019)\n",
      "F115, no mask needed\n",
      "FOV 116\n",
      "mask shape (1979, 2019)\n",
      "F116, no mask needed\n",
      "FOV 117\n",
      "mask shape (1979, 2019)\n",
      "F117, no mask needed\n",
      "FOV 118\n",
      "mask shape (1979, 2019)\n",
      "F118, no mask needed\n",
      "FOV 119\n",
      "mask shape (1979, 2019)\n",
      "F119, no mask needed\n",
      "FOV 120\n",
      "mask shape (1979, 2019)\n",
      "FOV 121\n",
      "mask shape (1979, 2019)\n",
      "F121, no mask needed\n",
      "FOV 122\n",
      "mask shape (1979, 2019)\n",
      "F122, no mask needed\n",
      "FOV 123\n",
      "mask shape (1979, 2019)\n",
      "F123, no mask needed\n",
      "FOV 124\n",
      "mask shape (1979, 2019)\n",
      "F124, no mask needed\n",
      "FOV 125\n",
      "mask shape (1979, 2019)\n",
      "F125, no mask needed\n",
      "FOV 127\n",
      "mask shape (1979, 2019)\n",
      "F127, no mask needed\n",
      "FOV 128\n",
      "mask shape (1979, 2019)\n",
      "FOV 130\n",
      "mask shape (1979, 2019)\n",
      "FOV 131\n",
      "mask shape (1979, 2019)\n",
      "F131, no mask needed\n",
      "FOV 132\n",
      "mask shape (1979, 2019)\n",
      "FOV 133\n",
      "mask shape (1979, 2019)\n",
      "FOV 134\n",
      "mask shape (1979, 2019)\n",
      "FOV 135\n",
      "mask shape (1979, 2019)\n",
      "FOV 136\n",
      "mask shape (1979, 2019)\n",
      "F136, no mask needed\n",
      "FOV 137\n",
      "mask shape (1979, 2019)\n",
      "F137, no mask needed\n",
      "FOV 138\n",
      "mask shape (1979, 2019)\n",
      "F138, no mask needed\n",
      "FOV 139\n",
      "mask shape (1979, 2019)\n",
      "F139, no mask needed\n",
      "FOV 140\n",
      "mask shape (1979, 2019)\n",
      "F140, no mask needed\n",
      "FOV 141\n",
      "mask shape (1979, 2019)\n",
      "F141, no mask needed\n",
      "FOV 142\n",
      "mask shape (1979, 2019)\n",
      "F142, no mask needed\n",
      "FOV 143\n",
      "mask shape (1979, 2019)\n",
      "F143, no mask needed\n",
      "FOV 144\n",
      "mask shape (1979, 2019)\n",
      "F144, no mask needed\n",
      "FOV 145\n",
      "mask shape (1979, 2019)\n",
      "F145, no mask needed\n",
      "FOV 146\n",
      "mask shape (1979, 2019)\n",
      "F146, no mask needed\n",
      "FOV 147\n",
      "mask shape (1979, 2019)\n",
      "F147, no mask needed\n",
      "FOV 148\n",
      "mask shape (1979, 2019)\n",
      "F148, no mask needed\n",
      "FOV 149\n",
      "mask shape (1979, 2019)\n",
      "F149, no mask needed\n",
      "FOV 150\n",
      "mask shape (1979, 2019)\n",
      "F150, no mask needed\n",
      "FOV 151\n",
      "mask shape (1979, 2019)\n",
      "FOV 152\n",
      "mask shape (1979, 2019)\n",
      "FOV 153\n",
      "mask shape (1979, 2019)\n",
      "F153, no mask needed\n",
      "FOV 154\n",
      "mask shape (1979, 2019)\n",
      "F154, no mask needed\n",
      "FOV 155\n",
      "mask shape (1979, 2019)\n",
      "FOV 157\n",
      "mask shape (1979, 2019)\n",
      "F157, no mask needed\n",
      "FOV 158\n",
      "mask shape (1979, 2019)\n",
      "F158, no mask needed\n",
      "FOV 159\n",
      "mask shape (1979, 2019)\n",
      "F159, no mask needed\n",
      "FOV 160\n",
      "mask shape (1979, 2019)\n",
      "F160, no mask needed\n",
      "FOV 161\n",
      "mask shape (1979, 2019)\n",
      "F161, no mask needed\n",
      "FOV 162\n",
      "mask shape (1979, 2019)\n",
      "F162, no mask needed\n",
      "FOV 163\n",
      "mask shape (1979, 2019)\n",
      "F163, no mask needed\n",
      "FOV 164\n",
      "mask shape (1979, 2019)\n",
      "F164, no mask needed\n",
      "FOV 165\n",
      "mask shape (1979, 2019)\n",
      "F165, no mask needed\n",
      "FOV 166\n",
      "mask shape (1979, 2019)\n",
      "F166, no mask needed\n",
      "FOV 167\n",
      "mask shape (1979, 2019)\n",
      "F167, no mask needed\n",
      "FOV 168\n",
      "mask shape (1979, 2019)\n",
      "F168, no mask needed\n",
      "FOV 169\n",
      "mask shape (1979, 2019)\n",
      "F169, no mask needed\n",
      "FOV 170\n",
      "mask shape (1979, 2019)\n",
      "F170, no mask needed\n",
      "FOV 171\n",
      "mask shape (1979, 2019)\n",
      "F171, no mask needed\n",
      "FOV 172\n",
      "mask shape (1979, 2019)\n",
      "F172, no mask needed\n",
      "FOV 173\n",
      "mask shape (1979, 2019)\n",
      "FOV 174\n",
      "mask shape (1979, 2019)\n",
      "F174, no mask needed\n",
      "FOV 175\n",
      "mask shape (1979, 2019)\n",
      "F175, no mask needed\n",
      "FOV 176\n",
      "mask shape (1979, 2019)\n",
      "F176, no mask needed\n",
      "FOV 177\n",
      "mask shape (1979, 2019)\n",
      "F177, no mask needed\n",
      "FOV 178\n",
      "mask shape (1979, 2019)\n",
      "FOV 179\n",
      "mask shape (1979, 2019)\n",
      "F179, no mask needed\n",
      "FOV 180\n",
      "mask shape (1979, 2019)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F180, no mask needed\n",
      "FOV 181\n",
      "mask shape (1979, 2019)\n",
      "F181, no mask needed\n",
      "FOV 182\n",
      "mask shape (1979, 2019)\n",
      "FOV 183\n",
      "mask shape (1979, 2019)\n",
      "F183, no mask needed\n",
      "FOV 184\n",
      "mask shape (1979, 2019)\n",
      "F184, no mask needed\n",
      "FOV 185\n",
      "mask shape (1979, 2019)\n",
      "FOV 186\n",
      "mask shape (1979, 2019)\n",
      "F186, no mask needed\n",
      "FOV 187\n",
      "mask shape (1979, 2019)\n",
      "F187, no mask needed\n",
      "FOV 188\n",
      "mask shape (1979, 2019)\n",
      "F188, no mask needed\n",
      "FOV 189\n",
      "mask shape (1979, 2019)\n",
      "F189, no mask needed\n",
      "FOV 190\n",
      "mask shape (1979, 2019)\n",
      "FOV 191\n",
      "mask shape (1979, 2019)\n",
      "F191, no mask needed\n",
      "FOV 193\n",
      "mask shape (1979, 2019)\n",
      "F193, no mask needed\n",
      "FOV 194\n",
      "mask shape (1979, 2019)\n",
      "F194, no mask needed\n",
      "FOV 195\n",
      "mask shape (1979, 2019)\n",
      "F195, no mask needed\n",
      "FOV 196\n",
      "mask shape (1979, 2019)\n",
      "F196, no mask needed\n",
      "FOV 197\n",
      "mask shape (1979, 2019)\n",
      "F197, no mask needed\n",
      "FOV 198\n",
      "mask shape (1979, 2019)\n",
      "F198, no mask needed\n",
      "FOV 199\n",
      "mask shape (1979, 2019)\n",
      "F199, no mask needed\n",
      "FOV 200\n",
      "mask shape (1979, 2019)\n",
      "F200, no mask needed\n",
      "FOV 202\n",
      "mask shape (1979, 2019)\n",
      "F202, no mask needed\n",
      "FOV 203\n",
      "mask shape (1979, 2019)\n",
      "F203, no mask needed\n",
      "FOV 204\n",
      "mask shape (1979, 2019)\n",
      "F204, no mask needed\n",
      "FOV 205\n",
      "mask shape (1979, 2019)\n",
      "F205, no mask needed\n",
      "FOV 206\n",
      "mask shape (1979, 2019)\n",
      "F206, no mask needed\n",
      "FOV 207\n",
      "mask shape (1979, 2019)\n",
      "F207, no mask needed\n",
      "FOV 208\n",
      "mask shape (1979, 2019)\n",
      "F208, no mask needed\n",
      "FOV 209\n",
      "mask shape (1979, 2019)\n",
      "F209, no mask needed\n",
      "FOV 210\n",
      "mask shape (1979, 2019)\n",
      "F210, no mask needed\n",
      "FOV 211\n",
      "mask shape (1979, 2019)\n",
      "F211, no mask needed\n",
      "FOV 212\n",
      "mask shape (1979, 2019)\n",
      "F212, no mask needed\n",
      "FOV 213\n",
      "mask shape (1979, 2019)\n",
      "F213, no mask needed\n",
      "FOV 214\n",
      "mask shape (1979, 2019)\n",
      "F214, no mask needed\n",
      "FOV 215\n",
      "mask shape (1979, 2019)\n",
      "F215, no mask needed\n",
      "FOV 216\n",
      "mask shape (1979, 2019)\n",
      "F216, no mask needed\n",
      "FOV 217\n",
      "mask shape (1979, 2019)\n",
      "F217, no mask needed\n",
      "FOV 218\n",
      "mask shape (1979, 2019)\n",
      "F218, no mask needed\n",
      "FOV 219\n",
      "mask shape (1979, 2019)\n",
      "F219, no mask needed\n",
      "FOV 220\n",
      "mask shape (1979, 2019)\n",
      "F220, no mask needed\n",
      "FOV 221\n",
      "mask shape (1979, 2019)\n",
      "FOV 222\n",
      "mask shape (1979, 2019)\n",
      "F222, no mask needed\n",
      "FOV 224\n",
      "mask shape (1979, 2019)\n",
      "F224, no mask needed\n"
     ]
    }
   ],
   "source": [
    "merged = iter(glob.glob('merged/*')) \n",
    "for fov in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    img = img.transpose(1,0,2,3) \n",
    "    # C Z Y X format. you may use image as it is but should change bottom line to axis=(0,2,3)\n",
    "    count0 = np.count_nonzero(img==0, axis=(1,2,3))\n",
    "    count20 = np.count_nonzero(img<20, axis=(1,2,3))\n",
    "    count65K = np.count_nonzero(img>65000, axis=(1,2,3))\n",
    "\n",
    "    # initialize mask, save_im_flag\n",
    "    save_im_flag = False\n",
    "    mask = np.zeros(img[0,0,...].shape)\n",
    "    print(\"mask shape\", mask.shape)\n",
    "    mask = mask.astype('bool')\n",
    "    # check for each channel;\n",
    "    for ch in range(count0.shape[0]):\n",
    "        # record in dataframe\n",
    "        count0_df.loc[fov, ch] = count0[ch]\n",
    "        count20_df.loc[fov, ch] = count20[ch]\n",
    "        count65K_df.loc[fov, ch] = count65K[ch]\n",
    "        if ch in final_inds:\n",
    "            # whether to make mask;\n",
    "            if count20[ch] > 1000: # if there are more than 1000 <20 values\n",
    "                save_im_flag = True\n",
    "                for z in range(img.shape[1]): # second index is Z dimension, after Transposing\n",
    "                    mask = mask | (img[ch, z] < 20).astype('bool')\n",
    "            exp_mask = expand_labels(mask, distance=5) # expand mask with 5 pixel padding\n",
    "\n",
    "            # check if area is large enough? 200\n",
    "            labels = measure.label(mask)\n",
    "            _df = measure.regionprops_table(labels, mask, properties=['label','area','centroid','bbox'])\n",
    "            df=pd.DataFrame(_df)\n",
    "            df=df.set_index('label')\n",
    "            if df.area.max() < 200: # if area is less than 200, turn off flag and don't do anything?\n",
    "                save_im_flag = False\n",
    "\n",
    "\n",
    "    # export mask if flag is true\n",
    "    if save_im_flag:\n",
    "        fname = f'{OUT_DIR}/F{FOV_num}_mask.tif'\n",
    "        tifffile.imwrite(fname, exp_mask.astype('uint8'), imagej = True, photometric='minisblack', metadata={'axes':'YX'})\n",
    "    else:\n",
    "        print(f'F{FOV_num}, no mask needed') # export later!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save dataframe \n",
    "count0_df.to_csv('count0_df.csv', sep=',')\n",
    "count20_df.to_csv('count20_df.csv', sep=',')\n",
    "count65K_df.to_csv('count65K_df.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>531</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>532</td>\n",
       "      <td>224</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>533</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>534</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>535</td>\n",
       "      <td>224</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  FOV_num  Z  channel\n",
       "0             0        2  0        2\n",
       "1             1        2  0       28\n",
       "2             2        2  1        2\n",
       "3             3        2  1       28\n",
       "4             4        2  2        2\n",
       "..          ...      ... ..      ...\n",
       "531         531      224  3       34\n",
       "532         532      224  4       34\n",
       "533         533      224  5       34\n",
       "534         534      224  6       34\n",
       "535         535      224  7       34\n",
       "\n",
       "[536 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bbox_locations = pd.read_csv('black_box_locations_correct.csv')\n",
    "bbox_locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['002', '008', '010', '011', '016', '017', '019', '026', '030',\n",
       "       '031', '035', '036', '037', '038', '040', '042', '044', '045',\n",
       "       '048', '050', '051', '052', '054', '059', '060', '062', '066',\n",
       "       '071', '072', '075', '079', '080', '083', '084', '085', '088',\n",
       "       '094', '101', '111', '113', '114', '117', '120', '124', '127',\n",
       "       '128', '130', '133', '134', '135', '137', '138', '139', '143',\n",
       "       '151', '152', '155', '160', '169', '173', '178', '182', '185',\n",
       "       '187', '190', '197', '208', '214', '220', '221', '224'],\n",
       "      dtype='<U3')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = set()\n",
    "for i in bbox_locations['FOV_num']:\n",
    "    s.add(str(i).zfill(3))\n",
    "bb_total = np.array(list(sorted(s)))\n",
    "bb_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 010\n",
      "FOV 011\n",
      "FOV 016\n",
      "FOV 017\n",
      "FOV 019\n",
      "FOV 030\n",
      "FOV 031\n",
      "FOV 035\n",
      "FOV 036\n",
      "FOV 037\n",
      "FOV 042\n",
      "FOV 044\n",
      "FOV 045\n",
      "FOV 048\n",
      "FOV 050\n",
      "FOV 052\n",
      "FOV 054\n",
      "FOV 059\n",
      "FOV 066\n",
      "FOV 075\n",
      "FOV 080\n",
      "FOV 083\n",
      "FOV 101\n",
      "FOV 111\n",
      "FOV 113\n",
      "FOV 120\n",
      "FOV 128\n",
      "FOV 130\n",
      "FOV 132\n",
      "FOV 133\n",
      "FOV 134\n",
      "FOV 135\n",
      "FOV 151\n",
      "FOV 152\n",
      "FOV 155\n",
      "FOV 173\n",
      "FOV 178\n",
      "FOV 182\n",
      "FOV 185\n",
      "FOV 190\n",
      "FOV 221\n"
     ]
    },
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28miter\u001b[39m(glob\u001b[38;5;241m.\u001b[39mglob(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmask/*\u001b[39m\u001b[38;5;124m'\u001b[39m)) \n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fov \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(NUM_FOVS):\n\u001b[0;32m----> 4\u001b[0m     mask_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(mask)\n\u001b[1;32m      5\u001b[0m     FOV_num \u001b[38;5;241m=\u001b[39m mask_name\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/F\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m3\u001b[39m]\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFOV\u001b[39m\u001b[38;5;124m\"\u001b[39m, FOV_num)\n",
      "\u001b[0;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "x = set()\n",
    "mask = iter(glob.glob('mask/*')) \n",
    "for fov in range(NUM_FOVS):\n",
    "    mask_name = next(mask)\n",
    "    FOV_num = mask_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    x.add(FOV_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['010' '011' '016' '017' '019' '030' '031' '035' '036' '037' '042' '044'\n",
      " '045' '048' '050' '052' '054' '059' '066' '075' '080' '083' '101' '111'\n",
      " '113' '120' '128' '130' '132' '133' '134' '135' '151' '152' '155' '173'\n",
      " '178' '182' '185' '190' '221']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks = np.array(list(sorted(x)))\n",
    "print(masks)\n",
    "len(masks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132 is masked but not in black box list\n"
     ]
    }
   ],
   "source": [
    "for i in masks:\n",
    "    if i not in bb_total:\n",
    "        print(i, \"is masked but not in black box list\")\n",
    "\n",
    "# It is the really big black circle artifact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>percent masked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>010</th>\n",
       "      <td>010</td>\n",
       "      <td>4.369205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>011</th>\n",
       "      <td>011</td>\n",
       "      <td>2.943737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>016</th>\n",
       "      <td>016</td>\n",
       "      <td>1.826359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>017</th>\n",
       "      <td>017</td>\n",
       "      <td>5.769195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>019</th>\n",
       "      <td>019</td>\n",
       "      <td>2.186004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>030</th>\n",
       "      <td>030</td>\n",
       "      <td>6.457677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>031</th>\n",
       "      <td>031</td>\n",
       "      <td>1.822554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>035</th>\n",
       "      <td>035</td>\n",
       "      <td>1.152918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>036</th>\n",
       "      <td>036</td>\n",
       "      <td>1.728025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>037</th>\n",
       "      <td>037</td>\n",
       "      <td>3.613474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>042</th>\n",
       "      <td>042</td>\n",
       "      <td>11.091548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>044</th>\n",
       "      <td>044</td>\n",
       "      <td>2.091100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>045</th>\n",
       "      <td>045</td>\n",
       "      <td>3.806761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>048</th>\n",
       "      <td>048</td>\n",
       "      <td>4.155895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>050</th>\n",
       "      <td>050</td>\n",
       "      <td>1.802432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>052</th>\n",
       "      <td>052</td>\n",
       "      <td>6.221517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>054</th>\n",
       "      <td>054</td>\n",
       "      <td>7.886949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>059</th>\n",
       "      <td>059</td>\n",
       "      <td>4.183326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>066</th>\n",
       "      <td>066</td>\n",
       "      <td>4.456751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>075</th>\n",
       "      <td>075</td>\n",
       "      <td>5.244192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>080</th>\n",
       "      <td>080</td>\n",
       "      <td>2.223645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>083</th>\n",
       "      <td>083</td>\n",
       "      <td>1.153418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>101</td>\n",
       "      <td>4.438006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>111</td>\n",
       "      <td>12.223017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>113</td>\n",
       "      <td>1.732681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>120</td>\n",
       "      <td>4.331388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>128</td>\n",
       "      <td>7.031383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>130</td>\n",
       "      <td>2.098558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>132</td>\n",
       "      <td>0.376464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>133</td>\n",
       "      <td>3.443137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>134</td>\n",
       "      <td>1.744168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>135</td>\n",
       "      <td>1.910476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>151</td>\n",
       "      <td>1.672915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>2.000750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>1.802933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>173</td>\n",
       "      <td>1.740664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>178</td>\n",
       "      <td>2.217288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>182</td>\n",
       "      <td>1.165382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>185</td>\n",
       "      <td>3.329236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>190</td>\n",
       "      <td>2.283311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>221</td>\n",
       "      <td>1.744719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  percent masked\n",
       "010     010        4.369205\n",
       "011     011        2.943737\n",
       "016     016        1.826359\n",
       "017     017        5.769195\n",
       "019     019        2.186004\n",
       "030     030        6.457677\n",
       "031     031        1.822554\n",
       "035     035        1.152918\n",
       "036     036        1.728025\n",
       "037     037        3.613474\n",
       "042     042       11.091548\n",
       "044     044        2.091100\n",
       "045     045        3.806761\n",
       "048     048        4.155895\n",
       "050     050        1.802432\n",
       "052     052        6.221517\n",
       "054     054        7.886949\n",
       "059     059        4.183326\n",
       "066     066        4.456751\n",
       "075     075        5.244192\n",
       "080     080        2.223645\n",
       "083     083        1.153418\n",
       "101     101        4.438006\n",
       "111     111       12.223017\n",
       "113     113        1.732681\n",
       "120     120        4.331388\n",
       "128     128        7.031383\n",
       "130     130        2.098558\n",
       "132     132        0.376464\n",
       "133     133        3.443137\n",
       "134     134        1.744168\n",
       "135     135        1.910476\n",
       "151     151        1.672915\n",
       "152     152        2.000750\n",
       "155     155        1.802933\n",
       "173     173        1.740664\n",
       "178     178        2.217288\n",
       "182     182        1.165382\n",
       "185     185        3.329236\n",
       "190     190        2.283311\n",
       "221     221        1.744719"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_percentage = pd.DataFrame()\n",
    "mask = iter(glob.glob('mask/*')) \n",
    "for fov in range(len(masks)):\n",
    "    mask_name = next(mask)\n",
    "    m = imread(mask_name)\n",
    "    #print(m.shape)\n",
    "    FOV_num = mask_name.split('/F')[1][0:3]\n",
    "    #print(\"FOV\", FOV_num)\n",
    "    \n",
    "    percentage = (np.count_nonzero(m == 1)/(m.shape[0]*m.shape[1]))*100\n",
    "    #print(percentage )\n",
    "    mask_percentage.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "    mask_percentage.loc[FOV_num, 'percent masked'] = percentage\n",
    "mask_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_percentage.to_csv('mask_percentage.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 042 has greater than 7% of masking\n",
      "FOV 054 has greater than 7% of masking\n",
      "FOV 111 has greater than 7% of masking\n",
      "FOV 128 has greater than 7% of masking\n"
     ]
    }
   ],
   "source": [
    "for idx, row in mask_percentage.iterrows():\n",
    "    if row[1] > 7:\n",
    "        print(\"FOV\", idx, \"has greater than 7% of masking\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 017 5< % <7\n",
      "FOV 030 5< % <7\n",
      "FOV 052 5< % <7\n",
      "FOV 075 5< % <7\n"
     ]
    }
   ],
   "source": [
    "for idx, row in mask_percentage.iterrows():\n",
    "    if row[1] > 5 and row[1] < 7:\n",
    "        print(\"FOV\", idx, \"5< % <7\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 010 3< % <5\n",
      "FOV 037 3< % <5\n",
      "FOV 045 3< % <5\n",
      "FOV 048 3< % <5\n",
      "FOV 059 3< % <5\n",
      "FOV 066 3< % <5\n",
      "FOV 101 3< % <5\n",
      "FOV 120 3< % <5\n",
      "FOV 133 3< % <5\n",
      "FOV 185 3< % <5\n"
     ]
    }
   ],
   "source": [
    "for idx, row in mask_percentage.iterrows():\n",
    "    if row[1] > 3 and row[1] < 5:\n",
    "        print(\"FOV\", idx, \"3< % <5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 011 <3 %\n",
      "FOV 016 <3 %\n",
      "FOV 019 <3 %\n",
      "FOV 031 <3 %\n",
      "FOV 035 <3 %\n",
      "FOV 036 <3 %\n",
      "FOV 044 <3 %\n",
      "FOV 050 <3 %\n",
      "FOV 080 <3 %\n",
      "FOV 083 <3 %\n",
      "FOV 113 <3 %\n",
      "FOV 130 <3 %\n",
      "FOV 132 <3 %\n",
      "FOV 134 <3 %\n",
      "FOV 135 <3 %\n",
      "FOV 151 <3 %\n",
      "FOV 152 <3 %\n",
      "FOV 155 <3 %\n",
      "FOV 173 <3 %\n",
      "FOV 178 <3 %\n",
      "FOV 182 <3 %\n",
      "FOV 190 <3 %\n",
      "FOV 221 <3 %\n"
     ]
    }
   ],
   "source": [
    "for idx, row in mask_percentage.iterrows():\n",
    "    if row[1] < 3:\n",
    "        print(\"FOV\", idx, \"<3 %\")"
   ]
  }
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