{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as et\n",
    "import datetime\n",
    "from skimage.io import imread, imsave\n",
    "from imageio import volread as imread\n",
    "\n",
    "import tifffile\n",
    "import pystackreg\n",
    "from pystackreg import StackReg\n",
    "from skimage.filters import threshold_otsu\n",
    "\n",
    "from pystackreg.util import to_uint16   # make sure version 0.2.5 (not anything below)\n",
    "from ims_to_tiff import convert_to_tif  \n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to TIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles = glob.glob('ims/Cycle_*') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 'ims/Cycle_4_F148.ims',\n",
       " 'ims/Cycle_4_F149.ims',\n",
       " 'ims/Cycle_4_F150.ims',\n",
       " 'ims/Cycle_4_F151.ims',\n",
       " 'ims/Cycle_4_F152.ims',\n",
       " 'ims/Cycle_4_F153.ims',\n",
       " 'ims/Cycle_4_F154.ims',\n",
       " 'ims/Cycle_4_F155.ims',\n",
       " 'ims/Cycle_4_F156.ims',\n",
       " 'ims/Cycle_4_F157.ims',\n",
       " 'ims/Cycle_4_F158.ims',\n",
       " 'ims/Cycle_4_F159.ims',\n",
       " 'ims/Cycle_4_F160.ims',\n",
       " 'ims/Cycle_4_F161.ims',\n",
       " 'ims/Cycle_4_F162.ims',\n",
       " 'ims/Cycle_4_F163.ims',\n",
       " 'ims/Cycle_4_F164.ims',\n",
       " 'ims/Cycle_4_F165.ims',\n",
       " 'ims/Cycle_4_F166.ims',\n",
       " 'ims/Cycle_4_F167.ims',\n",
       " 'ims/Cycle_4_F168.ims',\n",
       " 'ims/Cycle_4_F169.ims',\n",
       " 'ims/Cycle_4_F170.ims',\n",
       " 'ims/Cycle_4_F171.ims',\n",
       " 'ims/Cycle_4_F172.ims',\n",
       " 'ims/Cycle_4_F173.ims',\n",
       " 'ims/Cycle_4_F174.ims',\n",
       " 'ims/Cycle_4_F175.ims',\n",
       " 'ims/Cycle_4_F176.ims',\n",
       " 'ims/Cycle_4_F177.ims',\n",
       " 'ims/Cycle_4_F178.ims',\n",
       " 'ims/Cycle_4_F179.ims',\n",
       " 'ims/Cycle_4_F180.ims',\n",
       " 'ims/Cycle_4_F181.ims',\n",
       " 'ims/Cycle_4_F182.ims',\n",
       " 'ims/Cycle_4_F183.ims',\n",
       " 'ims/Cycle_4_F184.ims',\n",
       " 'ims/Cycle_4_F185.ims',\n",
       " 'ims/Cycle_4_F186.ims',\n",
       " 'ims/Cycle_4_F187.ims',\n",
       " 'ims/Cycle_4_F188.ims',\n",
       " 'ims/Cycle_4_F189.ims',\n",
       " 'ims/Cycle_4_F190.ims',\n",
       " 'ims/Cycle_4_F191.ims',\n",
       " 'ims/Cycle_4_F192.ims',\n",
       " 'ims/Cycle_4_F193.ims',\n",
       " 'ims/Cycle_4_F194.ims',\n",
       " 'ims/Cycle_4_F195.ims',\n",
       " 'ims/Cycle_4_F196.ims',\n",
       " 'ims/Cycle_4_F197.ims',\n",
       " 'ims/Cycle_4_F198.ims',\n",
       " 'ims/Cycle_4_F199.ims',\n",
       " 'ims/Cycle_4_F200.ims',\n",
       " 'ims/Cycle_4_F201.ims',\n",
       " 'ims/Cycle_4_F202.ims',\n",
       " 'ims/Cycle_4_F203.ims',\n",
       " 'ims/Cycle_4_F204.ims',\n",
       " 'ims/Cycle_4_F205.ims',\n",
       " 'ims/Cycle_4_F206.ims',\n",
       " 'ims/Cycle_4_F207.ims',\n",
       " 'ims/Cycle_4_F208.ims',\n",
       " 'ims/Cycle_4_F209.ims',\n",
       " 'ims/Cycle_4_F210.ims',\n",
       " 'ims/Cycle_4_F211.ims',\n",
       " 'ims/Cycle_4_F212.ims',\n",
       " 'ims/Cycle_4_F213.ims',\n",
       " 'ims/Cycle_4_F214.ims',\n",
       " 'ims/Cycle_4_F215.ims',\n",
       " 'ims/Cycle_4_F216.ims',\n",
       " 'ims/Cycle_4_F217.ims',\n",
       " 'ims/Cycle_4_F218.ims',\n",
       " 'ims/Cycle_4_F219.ims',\n",
       " 'ims/Cycle_4_F220.ims',\n",
       " 'ims/Cycle_4_F221.ims',\n",
       " 'ims/Cycle_4_F222.ims',\n",
       " 'ims/Cycle_4_F223.ims',\n",
       " 'ims/Cycle_4_F224.ims',\n",
       " 'ims/Cycle_5_F000.ims',\n",
       " 'ims/Cycle_5_F001.ims',\n",
       " 'ims/Cycle_5_F002.ims',\n",
       " 'ims/Cycle_5_F003.ims',\n",
       " 'ims/Cycle_5_F004.ims',\n",
       " 'ims/Cycle_5_F005.ims',\n",
       " 'ims/Cycle_5_F006.ims',\n",
       " 'ims/Cycle_5_F007.ims',\n",
       " 'ims/Cycle_5_F008.ims',\n",
       " 'ims/Cycle_5_F009.ims',\n",
       " 'ims/Cycle_5_F010.ims',\n",
       " 'ims/Cycle_5_F011.ims',\n",
       " 'ims/Cycle_5_F012.ims',\n",
       " 'ims/Cycle_5_F013.ims',\n",
       " 'ims/Cycle_5_F014.ims',\n",
       " 'ims/Cycle_5_F015.ims',\n",
       " 'ims/Cycle_5_F016.ims',\n",
       " 'ims/Cycle_5_F017.ims',\n",
       " 'ims/Cycle_5_F018.ims',\n",
       " 'ims/Cycle_5_F019.ims',\n",
       " 'ims/Cycle_5_F020.ims',\n",
       " 'ims/Cycle_5_F021.ims',\n",
       " 'ims/Cycle_5_F022.ims',\n",
       " 'ims/Cycle_5_F023.ims',\n",
       " 'ims/Cycle_5_F024.ims',\n",
       " 'ims/Cycle_5_F025.ims',\n",
       " 'ims/Cycle_5_F026.ims',\n",
       " 'ims/Cycle_5_F027.ims',\n",
       " 'ims/Cycle_5_F028.ims',\n",
       " 'ims/Cycle_5_F029.ims',\n",
       " 'ims/Cycle_5_F030.ims',\n",
       " 'ims/Cycle_5_F031.ims',\n",
       " 'ims/Cycle_5_F032.ims',\n",
       " 'ims/Cycle_5_F033.ims',\n",
       " 'ims/Cycle_5_F034.ims',\n",
       " 'ims/Cycle_5_F035.ims',\n",
       " 'ims/Cycle_5_F036.ims',\n",
       " 'ims/Cycle_5_F037.ims',\n",
       " 'ims/Cycle_5_F038.ims',\n",
       " 'ims/Cycle_5_F039.ims',\n",
       " 'ims/Cycle_5_F040.ims',\n",
       " 'ims/Cycle_5_F041.ims',\n",
       " 'ims/Cycle_5_F042.ims',\n",
       " 'ims/Cycle_5_F043.ims',\n",
       " 'ims/Cycle_5_F044.ims',\n",
       " 'ims/Cycle_5_F045.ims',\n",
       " 'ims/Cycle_5_F046.ims',\n",
       " 'ims/Cycle_5_F047.ims',\n",
       " 'ims/Cycle_5_F048.ims',\n",
       " 'ims/Cycle_5_F049.ims',\n",
       " 'ims/Cycle_5_F050.ims',\n",
       " 'ims/Cycle_5_F051.ims',\n",
       " 'ims/Cycle_5_F052.ims',\n",
       " 'ims/Cycle_5_F053.ims',\n",
       " 'ims/Cycle_5_F054.ims',\n",
       " 'ims/Cycle_5_F055.ims',\n",
       " 'ims/Cycle_5_F056.ims',\n",
       " 'ims/Cycle_5_F057.ims',\n",
       " 'ims/Cycle_5_F058.ims',\n",
       " 'ims/Cycle_5_F059.ims',\n",
       " 'ims/Cycle_5_F060.ims',\n",
       " 'ims/Cycle_5_F061.ims',\n",
       " 'ims/Cycle_5_F062.ims',\n",
       " 'ims/Cycle_5_F063.ims',\n",
       " 'ims/Cycle_5_F064.ims',\n",
       " 'ims/Cycle_5_F065.ims',\n",
       " 'ims/Cycle_5_F066.ims',\n",
       " 'ims/Cycle_5_F067.ims',\n",
       " 'ims/Cycle_5_F068.ims',\n",
       " 'ims/Cycle_5_F069.ims',\n",
       " 'ims/Cycle_5_F070.ims',\n",
       " 'ims/Cycle_5_F071.ims',\n",
       " 'ims/Cycle_5_F072.ims',\n",
       " 'ims/Cycle_5_F073.ims',\n",
       " 'ims/Cycle_5_F074.ims',\n",
       " 'ims/Cycle_5_F075.ims',\n",
       " 'ims/Cycle_5_F076.ims',\n",
       " 'ims/Cycle_5_F077.ims',\n",
       " 'ims/Cycle_5_F078.ims',\n",
       " 'ims/Cycle_5_F079.ims',\n",
       " 'ims/Cycle_5_F080.ims',\n",
       " 'ims/Cycle_5_F081.ims',\n",
       " 'ims/Cycle_5_F082.ims',\n",
       " 'ims/Cycle_5_F083.ims',\n",
       " 'ims/Cycle_5_F084.ims',\n",
       " 'ims/Cycle_5_F085.ims',\n",
       " 'ims/Cycle_5_F086.ims',\n",
       " 'ims/Cycle_5_F087.ims',\n",
       " 'ims/Cycle_5_F088.ims',\n",
       " 'ims/Cycle_5_F089.ims',\n",
       " 'ims/Cycle_5_F090.ims',\n",
       " 'ims/Cycle_5_F091.ims',\n",
       " 'ims/Cycle_5_F092.ims',\n",
       " 'ims/Cycle_5_F093.ims',\n",
       " 'ims/Cycle_5_F094.ims',\n",
       " 'ims/Cycle_5_F095.ims',\n",
       " 'ims/Cycle_5_F096.ims',\n",
       " 'ims/Cycle_5_F097.ims',\n",
       " 'ims/Cycle_5_F098.ims',\n",
       " 'ims/Cycle_5_F099.ims',\n",
       " ...]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cycles"
   ]
  },
  {
   "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:-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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ims/Cycle_1_F178.ims',\n",
       " 'ims/Cycle_4_F026.ims',\n",
       " 'ims/Cycle_6_F139.ims',\n",
       " 'ims/Cycle_6_F192.ims',\n",
       " 'ims/Cycle_7_F150.ims',\n",
       " 'ims/Cycle_8_F060.ims',\n",
       " 'ims/Cycle_9_F053.ims',\n",
       " 'ims/Cycle_9_F219.ims',\n",
       " 'ims/Cycle_F075.ims']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 225 - len(error_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove all erroneous files \n",
    "for i in error_files:\n",
    "    command = f'rm tif/*{i[-7:-4]}*'\n",
    "    ! {command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    os.makedirs(f'tif/Cycle_{c}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 4, 2048, 2048)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test image sizes\n",
    "im1 = imread('tif/Cycle_0/Cycle_F000.tif')\n",
    "im1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = imread('tif/Cycle_0/Cycle_F121.tif')\n",
    "mov = imread('tif/Cycle_1/Cycle_1_F121.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": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.99999998e-01, -5.78643668e-05, -6.12333926e-01],\n",
       "       [ 5.78643668e-05,  9.99999998e-01, -1.31286747e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\n"
     ]
    }
   ],
   "source": [
    "reg = np.zeros(mov.shape, dtype=np.uint16) # initialize with the right dtype\n",
    "for Z in range(mov.shape[0]): # Z \n",
    "    print(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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 100)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_max = reg.max(0)\n",
    "reg_v = reg_max[0, ...]\n",
    "reg_v = reg_v[100:200, 100:200]\n",
    "reg_v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[134 118 112 ... 116 112 119]\n",
      " [129 130 130 ... 115 116 112]\n",
      " [127 133 129 ... 109 115 109]\n",
      " ...\n",
      " [139 137 142 ... 116 108 108]\n",
      " [152 139 159 ... 107 112 108]\n",
      " [144 143 152 ... 112 108 115]]  \n",
      " \n",
      " [[128 124 114 ... 110 108 110]\n",
      " [127 119 125 ... 107 108 108]\n",
      " [132 122 113 ... 112 112 111]\n",
      " ...\n",
      " [148 155 156 ... 110 108 107]\n",
      " [163 158 154 ... 112 106 113]\n",
      " [148 157 163 ... 106 112 114]]\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_9/Cycle_9_F000.tif')\n",
    "orig = orig.max(0)\n",
    "orig = orig[0, ...]\n",
    "orig_v = orig[100:200, 100:200]\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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CYCLE_NUMS: 10 \n",
      " NUM_FOVS: 216\n"
     ]
    }
   ],
   "source": [
    "# check again\n",
    "print('CYCLE_NUMS:', CYCLE_NUMS,'\\n', 'NUM_FOVS:',NUM_FOVS)\n",
    "# CYCLE_NUMS = 10\n",
    "# NUM_FOVS = 214"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define jaccard\n",
    "def jaccard(img1, img2):\n",
    "    assert img1.dtype == 'bool', 'input must be boolean'\n",
    "    assert img2.dtype == 'bool', 'input must be boolean'\n",
    "    AND = np.sum(img1&img2)\n",
    "    OR = np.sum(img1|img2)\n",
    "    J = AND/OR\n",
    "    return J\n",
    "reg_J = pd.DataFrame()\n",
    "base_J = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\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",
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\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",
    "        base_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, mov_binary)\n",
    "        reg_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, out.astype('bool'))\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'})\n",
    "base_J.to_csv('base_J.csv')\n",
    "reg_J.to_csv('reg_J.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at Max and Min Shifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 10\n",
    "NUM_FOVS = 216"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 tmat_Cyc_5/Cycle_5_F034_tmat_001_005.npy -169.00286466795455 -649.6314317378203\n",
      "5 tmat_Cyc_5/Cycle_5_F163_tmat_012_001.npy 353.12565394036966 35.02016426077728\n",
      "8 tmat_Cyc_8/Cycle_8_F039_tmat_002_009.npy 9.66500868574849 20.25105975783572\n",
      "8 tmat_Cyc_8/Cycle_8_F040_tmat_003_009.npy -13.380937202910786 19.43926983668871\n",
      "8 tmat_Cyc_8/Cycle_8_F206_tmat_011_013.npy -35.662020617679104 -53.87008717782612\n",
      "9 tmat_Cyc_9/Cycle_9_F206_tmat_011_013.npy -46.51707516257602 -44.66362570396666\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 > 19) | (moveY > 19)|(moveX < -19) | (moveY < -19): # 50 pixels is max\n",
    "            print(c+1, tmat_name, moveX, moveY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for c in range(CYCLE_NUMS-1):\n",
    "#     refs = iter(sorted(glob.glob('tif/Cycle_0/*'))) # list of cycle 0 .tif \n",
    "#     movs = iter(sorted(glob.glob(f'tif/Cycle_{c+1}/*'))) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "#     for FOV in range(0, NUM_FOVS): # \n",
    "#         #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "#         ref_name = next(refs) \n",
    "#         ref = imread(ref_name)\n",
    "#         ref = ref.astype(np.uint16)\n",
    "#         mov_name = next(movs)\n",
    "#         mov = imread(mov_name)\n",
    "#         mov = mov.astype(np.uint16)\n",
    "#         FOV_num = mov_name.split('_F')[1][0:3]\n",
    "#         print(f'cycle {c+1} field {FOV_num} ')\n",
    "\n",
    "#         ref_max = ref.max(0)\n",
    "#         ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "#         mov_max = mov.max(0)\n",
    "#         mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "#         print(\"Got threshold\")\n",
    "#         sr = StackReg(StackReg.RIGID_BODY)  \n",
    "#         tmat = sr.register(ref_binary, mov_binary) \n",
    "#         out = sr.transform(mov_binary) \n",
    "#         out = pystackreg.util.to_uint16(out) \n",
    "\n",
    "#         base_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, mov_binary)\n",
    "#         reg_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, out.astype('bool'))\n",
    "\n",
    "# base_J.to_csv('base_J.csv')\n",
    "# reg_J.to_csv('reg_J.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.609613</td>\n",
       "      <td>0.564445</td>\n",
       "      <td>0.534134</td>\n",
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       "      <td>0.564220</td>\n",
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       "      <td>0.624069</td>\n",
       "      <td>0.605826</td>\n",
       "      <td>0.576953</td>\n",
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       "      <td>0.614113</td>\n",
       "      <td>0.592440</td>\n",
       "      <td>0.346746</td>\n",
       "      <td>0.385184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>213</td>\n",
       "      <td>0.763931</td>\n",
       "      <td>0.692444</td>\n",
       "      <td>0.653242</td>\n",
       "      <td>0.635069</td>\n",
       "      <td>0.620420</td>\n",
       "      <td>0.602219</td>\n",
       "      <td>0.573930</td>\n",
       "      <td>0.310150</td>\n",
       "      <td>0.367964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>214</td>\n",
       "      <td>0.776464</td>\n",
       "      <td>0.699298</td>\n",
       "      <td>0.658347</td>\n",
       "      <td>0.646370</td>\n",
       "      <td>0.622944</td>\n",
       "      <td>0.611471</td>\n",
       "      <td>0.583164</td>\n",
       "      <td>0.348755</td>\n",
       "      <td>0.370556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>215</td>\n",
       "      <td>0.779950</td>\n",
       "      <td>0.709672</td>\n",
       "      <td>0.670465</td>\n",
       "      <td>0.651956</td>\n",
       "      <td>0.637751</td>\n",
       "      <td>0.618317</td>\n",
       "      <td>0.596479</td>\n",
       "      <td>0.370825</td>\n",
       "      <td>0.390774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1         2         3         4         5         6         7  \\\n",
       "0    0.727214  0.599278  0.562585  0.536534  0.523343  0.515182  0.506021   \n",
       "1    0.724609  0.608337  0.570673  0.546480  0.538071  0.525316  0.511452   \n",
       "2    0.726580  0.609613  0.564445  0.534134  0.523691  0.510580  0.500444   \n",
       "3    0.738595  0.627715  0.590469  0.564220  0.562250  0.545792  0.531971   \n",
       "4    0.739166  0.630936  0.595126  0.567113  0.556932  0.540122  0.529329   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "211  0.771028  0.700752  0.653970  0.639096  0.624069  0.605826  0.576953   \n",
       "212  0.775164  0.702361  0.660083  0.643032  0.633088  0.614113  0.592440   \n",
       "213  0.763931  0.692444  0.653242  0.635069  0.620420  0.602219  0.573930   \n",
       "214  0.776464  0.699298  0.658347  0.646370  0.622944  0.611471  0.583164   \n",
       "215  0.779950  0.709672  0.670465  0.651956  0.637751  0.618317  0.596479   \n",
       "\n",
       "            8         9  \n",
       "0    0.353252  0.378581  \n",
       "1    0.323257  0.371884  \n",
       "2    0.301084  0.346253  \n",
       "3    0.337534  0.387639  \n",
       "4    0.317623  0.365094  \n",
       "..        ...       ...  \n",
       "211  0.354787  0.381956  \n",
       "212  0.346746  0.385184  \n",
       "213  0.310150  0.367964  \n",
       "214  0.348755  0.370556  \n",
       "215  0.370825  0.390774  \n",
       "\n",
       "[216 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_J = pd.read_csv('base_J.csv')\n",
    "base_J = base_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "base_J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.661131</td>\n",
       "      <td>0.753357</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.887727</td>\n",
       "      <td>0.871288</td>\n",
       "      <td>0.871269</td>\n",
       "      <td>0.863033</td>\n",
       "      <td>0.869344</td>\n",
       "      <td>0.862571</td>\n",
       "      <td>0.861681</td>\n",
       "      <td>0.666411</td>\n",
       "      <td>0.740598</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0.882589</td>\n",
       "      <td>0.881625</td>\n",
       "      <td>0.877818</td>\n",
       "      <td>0.865818</td>\n",
       "      <td>0.865724</td>\n",
       "      <td>0.668971</td>\n",
       "      <td>0.737419</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.886836</td>\n",
       "      <td>0.877408</td>\n",
       "      <td>0.877635</td>\n",
       "      <td>0.873249</td>\n",
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       "      <td>211</td>\n",
       "      <td>0.883751</td>\n",
       "      <td>0.857671</td>\n",
       "      <td>0.861534</td>\n",
       "      <td>0.859740</td>\n",
       "      <td>0.853829</td>\n",
       "      <td>0.843453</td>\n",
       "      <td>0.834093</td>\n",
       "      <td>0.655214</td>\n",
       "      <td>0.691984</td>\n",
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       "    <tr>\n",
       "      <td>212</td>\n",
       "      <td>0.887919</td>\n",
       "      <td>0.866081</td>\n",
       "      <td>0.869589</td>\n",
       "      <td>0.869102</td>\n",
       "      <td>0.866856</td>\n",
       "      <td>0.859986</td>\n",
       "      <td>0.858055</td>\n",
       "      <td>0.661868</td>\n",
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       "      <td>0.867397</td>\n",
       "      <td>0.866639</td>\n",
       "      <td>0.864264</td>\n",
       "      <td>0.856595</td>\n",
       "      <td>0.847866</td>\n",
       "      <td>0.645022</td>\n",
       "      <td>0.709103</td>\n",
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       "      <td>0.873739</td>\n",
       "      <td>0.870596</td>\n",
       "      <td>0.864498</td>\n",
       "      <td>0.850387</td>\n",
       "      <td>0.860238</td>\n",
       "      <td>0.845169</td>\n",
       "      <td>0.849377</td>\n",
       "      <td>0.650742</td>\n",
       "      <td>0.713281</td>\n",
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       "      <td>215</td>\n",
       "      <td>0.889153</td>\n",
       "      <td>0.871893</td>\n",
       "      <td>0.870383</td>\n",
       "      <td>0.866013</td>\n",
       "      <td>0.865268</td>\n",
       "      <td>0.859982</td>\n",
       "      <td>0.855604</td>\n",
       "      <td>0.653679</td>\n",
       "      <td>0.700257</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1         2         3         4         5         6         7  \\\n",
       "0    0.878906  0.870263  0.862243  0.869302  0.862748  0.859984  0.855823   \n",
       "1    0.881276  0.868336  0.866450  0.868531  0.864940  0.861375  0.854056   \n",
       "2    0.887727  0.871288  0.871269  0.863033  0.869344  0.862571  0.861681   \n",
       "3    0.884023  0.883017  0.882589  0.881625  0.877818  0.865818  0.865724   \n",
       "4    0.886836  0.877408  0.877635  0.873249  0.868408  0.861011  0.862110   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "211  0.883751  0.857671  0.861534  0.859740  0.853829  0.843453  0.834093   \n",
       "212  0.887919  0.866081  0.869589  0.869102  0.866856  0.859986  0.858055   \n",
       "213  0.887029  0.860962  0.867397  0.866639  0.864264  0.856595  0.847866   \n",
       "214  0.873739  0.870596  0.864498  0.850387  0.860238  0.845169  0.849377   \n",
       "215  0.889153  0.871893  0.870383  0.866013  0.865268  0.859982  0.855604   \n",
       "\n",
       "            8         9  \n",
       "0    0.734630  0.775523  \n",
       "1    0.661131  0.753357  \n",
       "2    0.666411  0.740598  \n",
       "3    0.668971  0.737419  \n",
       "4    0.652314  0.689079  \n",
       "..        ...       ...  \n",
       "211  0.655214  0.691984  \n",
       "212  0.661868  0.716064  \n",
       "213  0.645022  0.709103  \n",
       "214  0.650742  0.713281  \n",
       "215  0.653679  0.700257  \n",
       "\n",
       "[216 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_J = pd.read_csv(\"reg_J.csv\")\n",
    "reg_J = reg_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "reg_J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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s3yJNQ0WHFQeokaim7JRERwsiUf3fsUTtpHqpUtQSbiT6nRqJ6hFXzY7Tf5uknXXrq9Onq+0YsZnZqKjSbIiynIjNrFbcIzYzG7LxRoXGMUtyIjazWhnFoYnR++gwM2ujESq9FJE0X9LPJK2QdGq/Yi4qg3mKpD369eBmZqlFqPTSjqSpwGeBo8lKPBwvab9+xFzUI/4YcKOk70l6l6Qd+hGEmVkqCWtNHAKsiIi7I+IZ4CLg2H7EXJSI7wZ2J0vIBwF3SrpK0ol5/YlJSVogaYmkJVetXZEwXDOz9joZmmjOVfmyoKmp3YD7mn5ela9LruhgXUREA1gMLJY0naybfjzwCWDSHnJELAIWAXxj5+NHcOjczEZVJ7MmmnPVJCYbu+hLPitKxBsFEhFjwBXAFZISnTJnZpZOwky5Cmg+RrY7sDpd839QlIj/qtWGiFiXOBYzs56VmQ1R0s3AsyXtDdwPvAk4IVXjzYrqEf+8Hw9qZtYvqYr+RMR6Se8GrgamAudGxB1JGp/AJ3SYWa2kvDhzRHwL+FbCJiflRGxmtZKqGuEg9T0RpypDWCWpylemKqeZ6pzOqUlaqadU5SJTlYVNdUrsw2MzkrQzW+uL7zQg612P2MxsuNwjNjMbspRjxIPiRGxmteIesZnZkLlHbGY2ZOPuEZuZDdcIXimpfSKWtBnZaX2rI+JaSScALwOWA4vy2hNmZpUxilNmi3rE5+X3mSnpRGAWcClwBFmtzhP7G56ZWWdGsdxjUSJ+YUTsL2kaWdGLXSNiXNIFwNJWO+U1PRcAvHP2wbxm5r7JAjYza2cUD9YVnaAzJR+emA3MBLbO128OTG+1U0Qsioh5ETHPSdjMBqkhlV6qoqhH/HngLrKzXxcCF0u6GziU7LIhZmaVMj7sALpQVAbzbEn/ld9eLel84EjgPyPipkEEaGbWidrNmoAsATfdfhy4pK8RmZn1oI6zJszMRkodZ02YmY2UWg5NVEWKJ/fF263pvRFg6Zo5SdpJdVBByfoAad7BUxLFk+IrZqpYUtXtTfWtuWpTtKpUaKdqz00ZI5OIzczKGK/OZ0JpTsRmVivuEZuZDZkTsZnZkI3gJeuciM2sXmrZI5b0LOANwB7AeuAXwIUR8USfYzMz69gonuLctuiPpFOAzwEzgIOBLcgS8o8kHd736MzMOtRQ+aUqiqqvvQOYHxH/l6zGxH4RsRCYD5zdaidJCyQtkbTk6rUr0kVrZlag0cFSFUWJGP4wfLE5WTlMIuJeXAbTzCpoFBNx0RjxOcDNkn4MHAacCSBpB+DXfY7NzKxjtas1ERGfkXQt8HzgUxFxV77+EbLEbGZWKVUa+y2rTBnMO4A7BhCLmVnPRnHWhOcRm1mtNEZwcMKJ2MxqpUoH4crqeyJO9TVh80bvn3KPPb5FgkhIdtHB6ZHmk3vtlKlJ2pmWKJ6xCpVEHE8Uy/REvawpiZ7jVMlmx+m/TdLOmlRlQhMYvf6we8RmVjPuEZuZDdl6DaZPLOkA/nDm8XrgXd1eVLnMCR1mZiMjOlh6dBZwekQcAHwk/7kr7hGbWa0McGgigK3y21sDq9vcty0nYjOrlQFOX3sfcLWkT5CNLrys24aciM2sVjpJw5IWAAuaVi2KiEVN268Fdp5k14XAEcD7I+Jrkv4S+DxZcbSOORGbWa10MjSRJ91Fbba3TKySzgfem/94MVltnq74YJ2Z1co4UXrp0WrgFfntV5FdNKMrRYXht5b0T5LukvRovizP123TZr/f1yNe7HrEZjZAAyyD+Q7gk5KWAh9n4yGOjhT1iL8KPAYcHhFzImIO8Mp83cWtdmquR3yU6xGb2QBFB/96epyI70fEQRHxooh4SUTc0m1bRYl4bkScGREPNj34gxFxJrBntw9qZtYvo1gYvigR3yPpQ5J22rBC0k6SPgzc19/QzMw61yBKL1VRlIj/CpgDXC/p15J+DVwHbAcc1+fYzMw6NsAz65IpukLHY8CH82Ujkk4CzutTXGZmXVlfqRRbTi/T105PFoWZWSKDOliXUtsesaTbW20CdmqxbSNVqt17//qZCSKBKYlewKenppnGPTXR+ylVfeQqSTVRPtWBnapN3K9SHeFUqnQQrqyiM+t2Al5DNl2tmYAf9iUiM7MeVKmnW1ZRIr4SmBURt03cIOm6vkRkZtaD2vWII+LtbbadkD4cM7PejI/gEJuL/phZrVRpfnBZTsRmVit1HCM2MxsptRsjNjMbNaM4NNGXaY3NZTCvcRlMMxugUTyho+tELOnbrbY1l8F8tctgmtkAjUeUXqqi6My6A1ttAg5IH46ZWW9GcWiiaIz4ZuB6ssQ7UcsrdJiZDUsdD9YtB/42Iv7oWkySXI/YzCqnSmO/ZRUl4o/Sehz5PWlDMTPrXe2GJiLikjabt00ci5lZz6JCB+HK6mUe8emUKAw/pUJPyniidlLN+dt6PM1oVqpnONXzk0qKEqoNYFqF3oOpxKSHbTqXqvdYpfKe43XrEaeoR2w2THVMwtZe7YYmcD1iMxsxdRyacD1iMxsptesRux6xmY2aOk5fMzMbKVU6dbksJ2Izq5XaDU2YmY0aJ2IzsyEbxVkTbedhS9pK0v+T9CVJJ0zY9m9t9vt9PeLFrkdsZgPUIEovVVF0Qsx5ZHOGvwa8SdLXJG2ebzu01U7N9YiPcj1iMxugUSwMXzQ08ayI+PP89tclLQS+K+mYPsdlZtaV8Ri9QphFiXhzSVMist8sIs6QtAq4AZjV9+jMzDpUuzFi4BvAq5pXRMQXgb8DnulXUGZm3RrFMeKiM+s+1GL9VZI+3p+QzMy6V6Wx37J6qV53erIozMwSaUSUXnoh6ThJd0hqSJo3Ydv+kn6Ub/+ppBnt2up7Gcw9p60tc7dCvxrfsuc2picaO0rVztNT0lRxPSPuTtLOR9k7STsp6ghDtWpZp6r/+5LDHkjSzg9v2CVJO6lU6fDYAHvEy4A3Av/RvFLSNOAC4K8jYqmkOcBYu4ZcBtPMamVQsyYiYjmA/rjjcRRwe0Qsze/3aFFbLoNpZrXSyZCDpAXAgqZViyJiUY8hPAcISVcDOwAXRcRZ7XZwGUwzq5VOhibypNsy8Uq6Fth5kk0LI+LyFrtNA14OHAysBb4j6ZaI+E6rx3GtCTOrlV4PwjWLiCO72G0VcH1ErAGQ9C3gQKBlIq7SNf/MzHpWgVOcrwb2lzQzP3D3CuDOdjs4EZtZrYzHeOmlF5LekJ9p/FLgm/mYMBHxGPAp4GbgNuDWiPhmu7Y8NGFmtTKoU5wj4jLgshbbLiCbwlZKX3rEzWUwL/3Nyn48hJnZpEbxFOeiesQ7S/p3SZ+VNEfSR/OzRL4qqeWM8uYymG/ccm7yoM3MWomI0ktVFPWIv0A2yHwf8N/AOuC1wPeAz/U1MjOzLgzqFOeUCs+si4h/AZD0rog4M1//L5JazjE2MxuWUSz6U5SIm3vM50/YNjVxLGZmPatjYfjLJc2KiKcj4h83rJS0L/Cz/oZmZta5Ko39llV0ivNHWqxfIantvDgzs2Go0thvWa5HbGa1MoqzJvpej3hlgjrCqaSqI7w+Ub3dVPGcO63US1Ho/vVJmqlUbdqqufGGyerHdE4VOyBVpVN0qzQ/uCzXIzazWqlST7cs1yM2s1qp3awJ1yM2s1EzigfrXPTHzGqljkMTZmYjpY5n1pmZjZRNokcsaceIeLgfwZiZ9WoUx4iLJjtvN2GZA6wEtgW2a7PfAmBJviwoMam68D4lJ2e7nRGIxe34Nfey8aL8CZqUpAZwz4TVu5NdHC8iYp/O0n7Lx1kSEfPcTv/aqVIsbmcw7VQplpTt1FHRCTEfIivuc0xE7B0RewOr8ttJkrCZ2aaubSKOiE8AJwMfkfQpSbNhBA9JmplVWOEp4hGxKiKOI7tCxzXAzD7Escjt9L2dKsXidgbTTpViSdlO7bQdI/6jO0tbAM+KiGWSToqI8/oXmpnZpqGjRLzRjtK9EbFn4njMzDY5RVdxvr3F8lNKlsEsImm+pJ9JWiHp1C7bOFfSw5KW9RDHHpL+W9JySXdIem+X7cyQdJOkpXk7PdVtljRV0k8kXdlDGyvzq2/fJmlJD+1sI+kSSXflz9NLu2jjuXkcG5YnJb2vi3benz+/yyRdKGlGp23k7bw3b+OOTuOY7H0naTtJ10j6Rf7/tl20cVweT0NSqVkGLdr55/y1ul3SZZK26bKdj+Vt3CZpsaRdu2mnadsHJYWk7cv8bpuEgnl/DwEHAHtNWOYCqxPMK5wK/BLYB9gMWArs10U7hwEHAst6iGUX4MD89mzg513GIrKKdQDTgRuBQ3uI6wPAV4Are2hjJbB9gtfri8DJ+e3NgG0SvP4PAnt1uN9uwK+ALfKfvwq8rYvHfwGwjOy4xzTgWuDZvbzvgLOAU/PbpwJndtHG84HnAtcB83qI5ShgWn77zKJY2rSzVdPtU4DPddNOvn4P4GqyabE9vyfrshQdrNtQBvOeCcvK/E3Sq0OAFRFxd0Q8A1wEHNtpIxFxA/DrXgKJiAci4tb89lPAcrI/+E7biYh4Ov9xer50Nf4jaXfgtcA53eyfkqStyP64Pg8QEc9ExOM9NnsE8MuImDhXvYxpwBaSppEl0tVdtPF84McRsTYi1gPXA28ou3OL992xZB9Y5P+/vtM2ImJ5RHR0TcgW7SzOfy+AH5OdA9BNO082/bglJd7Pbf4mzyabFuvZV02Kpq+9PSK+32JbijKYuwH3Nf28ii6SX2qS5gIvJuvNdrP/VEm3AQ8D10REV+0AnyZ70/ZaYDWAxZJukbSgyzb2AR4BzsuHSs6R1OvlV94EXNjpThFxP/AJ4F7gAeCJiFjcxeMvAw6TNEfSTODPyHpsvdgpIh7I43wA2LHH9lL5G+Db3e4s6QxJ9wFvBia9lmWJNo4B7o+Ipd3GUVfDvsLJZNccGuonpaRZwNeA903oCZQWEeMRcQBZD+QQSS/oIo7XAQ9HxC3dxDDBn0TEgcDRwP+SdFgXbUwj+6r57xHxYuA3ZF+9uyJpM+AY4OIu9t2WrOe5N7ArsKWkt3TaTkQsJ/vKfg1wFdnQWKILRlWHpIVkv9eXu20jIhZGxB55G+/uIoaZwEK6TOJ1N+xEvIqNeyC7091XzCQkTSdLwl+OiEt7bS//6n4dML+L3f8EOEbSSrIhm1dJuqDLOFbn/z8MXEY2JNSpVWRnVW7o3V9Clpi7dTRwa0Q81MW+RwK/iohHImIMuBR4WTdBRMTnI+LAiDiM7Kv0L7ppp8lDknYByP8faoEsSScCrwPeHBEpOjlfAf68i/2eRfbBuTR/T+8O3CopzUX8RtywE/HNwLMl7Z33kN4EXDGMQCSJbPxzeUR8qod2dthwdFrZvOsjgbs6bSci/j4ido+IuWTPy3cjouNen6QtlZ0RST6UcBTZV/JO43kQuE/Sc/NVRwB3dtpOk+PpYlgidy9wqKSZ+et2BNmYfsck7Zj/vyfwxh5i2uAK4MT89onA5T221zVJ84EPk5UoWNtDO89u+vEYuns//zQidoyIufl7ehXZwfEHu42rVoZ9tJBsXO7nZLMnFnbZxoVkY4VjZC/w27to4+VkwyK3A7fly5910c7+wE/ydpYBH0nwHB1Ol7MmyMZ2l+bLHd0+x3lbB5BV1Lsd+DqwbZftzAQeBbbuIZbTyRLCMuBLwOZdtvM9sg+UpcARvb7vyCoUfoesZ/0d2lQpbNPGG/LbvyObuXR1l7GsIDsGs+H9XGa2w2TtfC1/nm8HvgHs1k07E7avxLMmfr90fUKHmZmlMeyhCTOzTZ4TsZnZkDkRm5kNmROxmdmQORGbmQ2ZE7GZ2ZA5EZuZDZkTsZnZkP1/lPXimMLnht8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the table for the X and Y shift heatmap   ------ THIS TAKES A VERY SHORT AMOUNT OF TIME\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    fig, axes = plt.subplots()\n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    dfX = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\n",
    "    dfY = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\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",
    "        \n",
    "        col = str(int(tmat_name.split('_')[6]))\n",
    "        row = str(int(tmat_name.split('_')[7][0:3]))\n",
    "\n",
    "        dfX.loc[row, col] = moveX\n",
    "        dfY.loc[row, col] = moveY\n",
    "        \n",
    "    sns.heatmap(dfX, vmin=-20, vmax=20)\n",
    "    pl.suptitle(f\"Cycle {c+1} X-shift\")\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": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the table for the X and Y shift heatmap   ------ THIS TAKES A VERY SHORT AMOUNT OF TIME\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    fig, axes = plt.subplots()\n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    dfX = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\n",
    "    dfY = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\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",
    "        \n",
    "        col = str(int(tmat_name.split('_')[6]))\n",
    "        row = str(int(tmat_name.split('_')[7][0:3]))\n",
    "\n",
    "        dfX.loc[row, col] = moveX\n",
    "        dfY.loc[row, col] = moveY\n",
    "#     fig, axes = plt.subplots()\n",
    "#     sns.heatmap(dfX, vmin=-20, vmax=20)\n",
    "#     pl.suptitle(f\"Cycle {c+1} X-shift\")\n",
    " \n",
    "    sns.heatmap(dfY, vmin=-20, vmax=20)\n",
    "    pl.suptitle(f\"Cycle {c+1} Y-shift\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfX_shift = pd.DataFrame()\n",
    "dfY_shift = pd.DataFrame()\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    tmats = iter(sorted(glob.glob(f'tmat_Cyc_{c+1}/*')))\n",
    "    for sFOV in range(0,NUM_FOVS):\n",
    "        tmat_name = next(tmats)\n",
    "        FOV_num = 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",
    "\n",
    "        dfX_shift.loc[FOV_num, str(c+1)] = moveX\n",
    "        dfY_shift.loc[FOV_num, str(c+1)] = moveY\n",
    "dfX_shift.to_csv('X_shift.csv')\n",
    "dfY_shift.to_csv('Y_shift.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_J = pd.read_csv(\"reg_J.csv\")\n",
    "reg_J = reg_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "reg_J\n",
    "base_J = pd.read_csv(\"base_J.csv\")\n",
    "base_J = base_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "base_J\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfY_shift = pd.read_csv(\"Y_shift.csv\")\n",
    "dfY_shift = dfY_shift.drop([\"Unnamed: 0\"], axis=1)\n",
    "dfY_shift\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfX_shift = pd.read_csv(\"X_shift.csv\")\n",
    "dfX_shift = dfX_shift.drop([\"Unnamed: 0\"], axis=1)\n",
    "dfX_shift\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2160 with 27 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(CYCLE_NUMS-1, 3, figsize=(15,30))\n",
    "\n",
    "for cycle in range(CYCLE_NUMS-1):\n",
    "    axs[cycle][0].hist(reg_J[f'{cycle+1}'], bins = 50, alpha=0.5, label='reg_J', range=[0,1])\n",
    "    axs[cycle][0].hist(base_J[f'{cycle+1}'], bins = 50, alpha=0.5, label='base_J', range=[0,1])\n",
    "    \n",
    "    axs[cycle][1].hist(dfY_shift[f'{cycle+1}'], bins = 50, alpha=0.5, label='dfX_shift', range=[-20,20])\n",
    "    axs[cycle][2].hist(dfX_shift[f'{cycle+1}'], bins = 50, alpha=0.5, label='dfX_shift', range=[-20,20])\n",
    "    \n",
    "    axs[cycle][0].title.set_text(f'Change in Jaccard Index - Cycle {cycle+1}')\n",
    "    axs[cycle][1].title.set_text(f'Y Shift - Cycle {cycle+1}')\n",
    "    axs[cycle][2].title.set_text(f'X Shift - Cycle {cycle+1}')\n",
    "\n",
    "#     for ax in axs.flat:\n",
    "#         ax.set(xlabel='', ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a2d93af4cd04ed09b61699ab5f5c88f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# We are trying to look for registered binaries that have intensity percentage less than 0.1 percent (< 0.001)\n",
    "\n",
    "hist = pd.DataFrame()\n",
    "err_lst = []\n",
    "err_lst2 = []\n",
    "\n",
    "for c in tqdm(range(CYCLE_NUMS-1)):    \n",
    "    binas = iter(glob.glob(f'reg_bin_Cyc_{c+1}/*')) \n",
    "    for FOV in range(0, NUM_FOVS): # \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        bina_name = next(binas)\n",
    "        bina = imread(bina_name)\n",
    "        bina = bina.astype(np.uint16)\n",
    "        FOV_num = bina_name[-15:-12]\n",
    "        \n",
    "        percentage = (np.sum(bina))/(2048*2048)    \n",
    "        \n",
    "        if percentage < 0.001:\n",
    "            err_lst.append(bina_name)\n",
    "        if percentage < 0.01:\n",
    "            err_lst2.append(bina_name)\n",
    "            \n",
    "        hist.loc[FOV_num, f'{c+1}'] = percentage    # this is the table of percent of signal in images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_bin_Cyc_1/Cycle_1_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F076_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F034_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F163_bin_reg.tif',\n",
       " 'reg_bin_Cyc_6/Cycle_6_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_7/Cycle_7_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F127_bin_reg.tif']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_bin_Cyc_1/Cycle_1_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_1/Cycle_1_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_1/Cycle_1_F206_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F000_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F076_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F206_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F000_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F206_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F000_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F000_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F034_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F163_bin_reg.tif',\n",
       " 'reg_bin_Cyc_6/Cycle_6_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_6/Cycle_6_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_7/Cycle_7_F000_bin_reg.tif',\n",
       " 'reg_bin_Cyc_7/Cycle_7_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_7/Cycle_7_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F000_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F058_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F127_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F000_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F013_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F022_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F127_bin_reg.tif']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_lst2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LNxw8tMexm+u4D/L5VYe5ujOquaBetohYtMB7PoWSmedn5pGZuRY4HfjccuUtSeov3wcuSYXqy9/YmTkDzPRjLElSZzwCl6RCWeCSVCgLXJIKZYFLUqEscEkqlAUuSYWywCWpUBa4JBXKApekQo3eVZhGUPNFkrq5QJLaq3MBKmlv5xG4JBXKApekQlngklQoC1ySCmWBS1KhLHBJKpQFLkmFssAlqVAWuCQVygKXpEJZ4JJUqJ4LPCKOioibIuLuiLgrIt7Rz2CSpOXVuZjVLuDczLwtIn4K2BwRN2TmV/uUTZK0jJ6PwDPzgcy8rbr9KHA3sKZfwSRJy4vMrD9IxFrgZuD4zPznlnkbgY0A4+Pj66enp3vaxsLCAmNjYz2tO7dtvqf1FrNuzeo97m/fMc+DO/s2fN+MH8iK5WrdR837v12u1nVbNY+13HY6GatZ6+PYzbqDVOd532/N+/fo1atGJlezUdpfrepkm5qa2pyZE63Taxd4RIwBnwcuzMxPL7fsxMREzs7O9rSdmZkZJicne1q39ZrTdbRer/qSTddy8dzoXVb93HW7VixX6z5q3v/tcrW7Hvhy12avc23x1sdxVK5LXud532/N+/fyDQePTK5mo7S/WtXJFhGLFnitd6FExL7AXwOb2pW3JKm/6rwLJYAPA3dn5nv7F0mS1Ik6R+AvB84CXhkRt1dfJ/cplySpjZ5PkmbmPwLRxyySpC74SUxJKpQFLkmFssAlqVAWuCQVygKXpEJZ4JJUKAtckgplgUtSoSxwSSrU6F1Gbwlz2+Z5yzJXomvVzysQLjfuuesGspmiLXfFwDpjdTNv2LrJMipXOlT5PAKXpEJZ4JJUKAtckgplgUtSoSxwSSqUBS5JhbLAJalQFrgkFcoCl6RCWeCSVKhaBR4RGyLi6xFxb0Sc169QkqT2ei7wiFgFfBB4DXAccEZEHNevYJKk5dU5An8pcG9m3p+ZTwDTwCn9iSVJaqdOga8Bvt10f2s1TZI0BJGZva0Y8evAqzPzt6v7ZwEvzcy3tyy3EdhY3T0W+HqPWQ8HHupx3UEyV3fM1R1zdWdUc0G9bC/IzCNaJ9a5HvhW4Kim+0cC32ldKDMvAy6rsR0AImI2MyfqjtNv5uqOubpjru6Mai4YTLY6p1C+BLwwIo6OiP2A04Hr+hNLktROz0fgmbkrIt4G/D2wCvhIZt7Vt2SSpGXV+i/VMvPvgL/rU5Z2ap+GGRBzdcdc3TFXd0Y1FwwgW88vYkqSVpYfpZekQq1Ygbf7GH5E7B8Rn6zmfyEi1jbNO7+a/vWIeHWnYw4yV0ScFBGbI2Ku+v7KpnVmqjFvr76eO8RcayNiZ9O2L21aZ32V996I+B8REUPMdWZTptsj4scRcUI1bxj761ci4raI2BURp7XMOzsi7qm+zm6aPoz9tWiuiDghIm6JiLsi4o6IeGPTvMsj4ptN++uEYeWq5v2oadvXNU0/unrM76meA/sNK1dETLU8v34QEadW84axv94VEV+tHqsbI+IFTfP69/zKzKF/0XjR8z7gGGA/4CvAcS3LvBW4tLp9OvDJ6vZx1fL7A0dX46zqZMwB53ox8Pzq9vHAtqZ1ZoCJFdpfa4E7lxj3i8DLgAA+A7xmWLlallkH3D/k/bUW+HngY8BpTdOfDdxffT+sun3YEPfXUrl+Fnhhdfv5wAPAodX9y5uXHeb+quYtLDHuVcDp1e1Lgd8bZq6Wx3QHcNAQ99dU0/Z+j6f/Pfb1+bVSR+CdfAz/FOCK6vbVwInVb6RTgOnM/GFmfhO4txqvHx/t7zlXZn45M3e/D/4u4ICI2L/L7fc911IDRsTzgEMy85ZsPHs+Bpy6QrnOAK7sctu1cmXmlsy8A/hxy7qvBm7IzB2Z+X3gBmDDsPbXUrky8xuZeU91+zvAduAZH+zoUZ39tajqMX4ljcccGs+Boe2vFqcBn8nMx7vcfp1cNzVt71Yan5OBPj+/VqrAO/kY/lPLZOYuYB54zjLr9uOj/XVyNXsD8OXM/GHTtI9Wf679cQ9/etfNdXREfDkiPh8Rr2hafmubMQeda7c38swCH/T+6nbdYe2vtiLipTSO/O5rmnxh9ef6X/Zw4FA31wERMRsRt+4+TUHjMX6kesx7GbMfuXY7nWc+v4a5v86hcUS93Lo9Pb9WqsAX+wfZ+naYpZbpdvqwcjVmRrwI+DPgd5rmn5mZ64BXVF9nDTHXA8BPZ+aLgXcBn4iIQzocc5C5GjMjfhF4PDPvbJo/jP3V7brD2l/LD9A4Uvsr4Lcyc/dR5/nAvwJeQuNP8z8acq6fzsYnDH8TeF9E/EwfxuxHrt37ax2Nz6vsNrT9FRFvAiaAv2izbk8/60oVeCcfw39qmYjYB1hN4zzWUut29NH+AeYiIo4ErgHenJlPHR1l5rbq+6PAJ2j8CTaUXNWppoer7W+mcdT2s9XyRzatP/T9VXnG0dGQ9le36w5rfy2p+sX7t8B7MvPW3dMz84Fs+CHwUYa7v3af0iEz76fx+sWLaVzz49DqMe96zH7kqvwGcE1mPtmUdyj7KyJeBVwAvL7pr/H+Pr96PZFf54vGB4jup/Ei5O4XAV7Usszvs+eLX1dVt1/Eni9i3k/jRYW2Yw4416HV8m9YZMzDq9v70jgn+LtDzHUEsKq6fQywDXh2df9LwC/x9IsmJw8rV3X/WTSeuMcMe381LXs5z3wR85s0XmA6rLo9tP21TK79gBuBdy6y7POq7wG8D7hoiLkOA/avbh8O3EP1gh7wKfZ8EfOtw8rVNP1WYGrY+4vGL7H7qF54HtTzq+PQ/f4CTga+Uf2QF1TT/pTGbyuAA6onwL00Xp1t/kd+QbXe12l6pXaxMYeVC3gP8Bhwe9PXc4GDgc3AHTRe3Hw/VaEOKdcbqu1+BbgN+LdNY04Ad1ZjfoDqg11DfBwngVtbxhvW/noJjV8ejwEPA3c1rfvvqrz30jhVMcz9tWgu4E3Aky3PrxOqeZ8D5qpsHwfGhpjrl6ttf6X6fk7TmMdUj/m91XNg/yE/jmtpHLA8q2XMYeyvfwAebHqsrhvE88tPYkpSofwkpiQVygKXpEJZ4JJUKAtckgplgUtSoSxwSSqUBS5JhbLAJalQ/x9wHpvvZLt0VAAAAABJRU5ErkJggg==\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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AJalQFnBJKpQFXJIKtfKDeIxAnbFNStX5GDdM1ltfUnnsgUtSoSzgklQoC7gkFcoCLkmFsoBLUqEs4JJUKAu4JBXKAi5JhbKAS1KhGhfwiNgzIr4UEdcPI5AkqT/D6IG/A7hnCO1IkmpoVMAj4mDgJOAjw4kjSepXZObgG0dcC/wP4JnAuzLzdYussx5YDzA+Pr5udnZ2oH0tLCwwNjY20LbzW7cPtF0/xveFB59YtuYH1iTX5Nr9hxumTZPncTmZqx5z1dck28zMzKbMfMoXyA88GmFEvA7YlpmbImJ6qfUy82LgYoCpqamcnl5y1a7m5uYYdNuzl3HkvQ2TO7hgfvUN6tgk1+Yzpocbpk2T53E5masec9W3HNmanEJ5JXByRGwGZoFjI+LyoaSSJPU0cAHPzPdk5sGZOQGcBnwhM98ytGSSpK78HLgkFWooJ28zcw6YG0ZbkqT+2AOXpEJZwCWpUBZwSSqUBVySCmUBl6RCWcAlqVAWcEkq1OobxEOrzkTHWDKbzz9p4Lbmt27fZWyaJm1Juzt74JJUKAu4JBXKAi5JhbKAS1KhLOCSVCgLuCQVygIuSYWygEtSoSzgklSogQt4RBwSETdGxD0RcXdEvGOYwSRJ3TW5lH4HsCEzb4+IZwKbIuKGzPzKkLJJkrpo8q30387M26v7jwL3AGuHFUyS1F1kZvNGIiaAm4GjMvMHHcvWA+sBxsfH183Ozg60j4WFBcbGxvpef37r9oH2U9f4vvDgEyPZVS1Nck2u3X+X6c5jWXd5u20Pb98lV7d1R6nu62tUzFXPas0FzbLNzMxsysypzvmNC3hEjAE3Aedl5se7rTs1NZUbN24caD9zc3NMT0/3vX7nCHrLZcPkDi6YX32DOjbJ1TlCYK/RCOuMVvjnV3xil1yrZTTCuq+vUTFXPas1FzTLFhGLFvBGn0KJiKcBfwNc0at4S5KGq8mnUAL4S+CezPzT4UWSJPWjSQ/8lcCZwLERcUd1e+2QckmSehj45G1m/h8ghphFklSDV2JKUqEs4JJUKAu4JBXKAi5JhbKAS1KhLOCSVCgLuCQVavUN4rGE+a3bObttzI1e43FocMM+lu3tbZist+9uz/NyjqNSZ3wXaaXYA5ekQlnAJalQFnBJKpQFXJIKZQGXpEJZwCWpUBZwSSqUBVySCtX0OzFPiIivRsR9EXHusEJJknpr8p2YewIfBk4EjgBOj4gjhhVMktRdkx74y4H7MvOBzHwSmAVOGU4sSVIvTQr4WuCbbdNbqnmSpBGIzBxsw4g3Aq/JzF+rps8EXp6Zb+9Ybz2wvpo8HPjqgFnXAA8NuO1yMlc95qrHXPWs1lzQLNuhmXlQ58wmoxFuAQ5pmz4Y+FbnSpl5MXBxg/0AEBEbM3OqaTvDZq56zFWPuepZrblgebI1OYXyReBFEfHCiNgbOA345HBiSZJ6GbgHnpk7IuI3gb8H9gQuycy7h5ZMktRVoy90yMxPA58eUpZeGp+GWSbmqsdc9ZirntWaC5Yh28BvYkqSVpaX0ktSoVasgPe6DD8inh4RV1fLb4uIibZl76nmfzUiXtNvm8uZKyKOj4hNETFf/Ty2bZu5qs07qttzR5hrIiKeaNv3RW3brKvy3hcR/ysiYoS5zmjLdEdE/DQijq6WjeJ4/VJE3B4ROyLiDR3LzoqIe6vbWW3zR3G8Fs0VEUdHxC0RcXdEfDki3ty27NKI+Hrb8Tp6VLmqZT9p2/cn2+a/sHrO761eA3uPKldEzHS8vn4YEadWy0ZxvN4ZEV+pnqvPR8ShbcuG9/rKzJHfaL3peT9wGLA3cCdwRMc6bwMuqu6fBlxd3T+iWv/pwAurdvbsp81lzvVS4PnV/aOArW3bzAFTK3S8JoC7lmj3H4FXAAF8BjhxVLk61pkEHhjx8ZoAXgJ8FHhD2/xnAw9UPw+s7h84wuO1VK4XAy+q7j8f+DZwQDV9afu6ozxe1bKFJdq9Bjitun8R8NZR5up4Th8G9hvh8Zpp299b+effx6G+vlaqB97PZfinAJdV968Fjqv+Ip0CzGbmjzLz68B9VXvDuLR/4FyZ+aXM3Pk5+LuBfSLi6TX3P/RcSzUYEc8DnpWZt2Tr1fNR4NQVynU6cFXNfTfKlZmbM/PLwE87tn0NcENmPpyZ3wduAE4Y1fFaKldmfi0z763ufwvYBjzlwo4BNTlei6qe42NpPefQeg2M7Hh1eAPwmcx8vOb+m+S6sW1/t9K6TgaG/PpaqQLez2X4P1snM3cA24HndNl2GJf2N8nV7vXAlzLzR23z/qr6d+33BvjXu2muF0bElyLipog4pm39LT3aXO5cO72Zpxbw5T5edbcd1fHqKSJeTqvnd3/b7POqf9f/bICOQ9Nc+0TExoi4dedpClrP8SPVcz5Im8PItdNpPPX1NcrjdQ6tHnW3bQd6fa1UAV/sF7Lz4zBLrVN3/qhytRZGHAn8MfDrbcvPyMxJ4JjqduYIc30beEFmvhR4J3BlRDyrzzaXM1drYcS/Ax7PzLvalo/ieNXddlTHq3sDrZ7aXwO/mpk7e53vAf4N8DJa/5q/e8S5XpCtKwx/GfhgRPzcENocRq6dx2uS1vUqO43seEXEW4Ap4E96bDvQY12pAt7PZfg/Wyci9gL2p3Uea6lt+7q0fxlzEREHA9cBv5KZP+sdZebW6uejwJW0/gUbSa7qVNP3qv1votVre3G1/sFt24/8eFWe0jsa0fGqu+2ojteSqj+8nwLel5m37pyfmd/Olh8Bf8Voj9fOUzpk5gO03r94Ka0xPw6onvPabQ4jV+VNwHWZ+eO2vCM5XhHxauC9wMlt/40P9/U16In8JjdaFxA9QOtNyJ1vAhzZsc5vsOubX9dU949k1zcxH6D1pkLPNpc51wHV+q9fpM011f2n0Ton+F9HmOsgYM/q/mHAVuDZ1fQXgV/gn980ee2oclXTe9B64R426uPVtu6lPPVNzK/TeoPpwOr+yI5Xl1x7A58HfnuRdZ9X/Qzgg8D5I8x1IPD06v4a4F6qN/SAj7Hrm5hvG1Wutvm3AjOjPl60/ojdT/XG83K9vvoOPewb8Frga9WDfG817w9o/bUC2Kd6AdxH693Z9l/y91bbfZW2d2oXa3NUuYD3AY8Bd7Tdngs8A9gEfJnWm5sXUhXUEeV6fbXfO4Hbgf/Y1uYUcFfV5oeoLuwa4fM4Ddza0d6ojtfLaP3xeAz4HnB327b/ucp7H61TFaM8XovmAt4C/Ljj9XV0tewLwHyV7XJgbIS5frHa953Vz3Pa2jyses7vq14DTx/x8zhBq8OyR0ebozhe/wA82PZcfXI5Xl9eiSlJhfJKTEkqlAVckgplAZekQlnAJalQFnBJKpQFXJIKZQGXpEJZwCWpUP8fngozWDsXrN8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as pl\n",
    "for c in range(CYCLE_NUMS-1):\n",
    "    hist.hist(column=f'{c+1}', bins = 80, range=[0, 0.2])\n",
    "    pl.suptitle(f\"Cycle {c+1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging and Cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 210"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " tmat_Cyc_1 \n",
      " -0.4576078365412286 -2.8066403456601847 5.374034641497474 0.1942583968912004\n",
      "X_max:-1 X_min:-3 Y_max:6 Y_min:1\n",
      "\n",
      " tmat_Cyc_2 \n",
      " -0.9120740801914735 -4.013060154883874 7.510329526574992 1.5266698677673958\n",
      "X_max:-1 X_min:-5 Y_max:8 Y_min:2\n",
      "\n",
      " tmat_Cyc_3 \n",
      " -1.2097726458781608 -4.390940632895877 8.661823880819156 2.160239117473793\n",
      "X_max:-2 X_min:-5 Y_max:9 Y_min:3\n",
      "\n",
      " tmat_Cyc_4 \n",
      " -1.2362686772701181 -4.708326519022648 9.125361490422051 2.230576250165427\n",
      "X_max:-2 X_min:-5 Y_max:10 Y_min:3\n",
      "\n",
      " tmat_Cyc_5 \n",
      " -1.3393331823572225 -4.920370913567012 9.701726122607624 2.5718328030671955\n",
      "X_max:-2 X_min:-5 Y_max:10 Y_min:3\n",
      "\n",
      " tmat_Cyc_6 \n",
      " -1.4182303138184125 -4.911448183627158 10.403287067723227 2.5336291352944045\n",
      "X_max:-2 X_min:-5 Y_max:11 Y_min:3\n",
      "\n",
      " tmat_Cyc_7 \n",
      " -1.7501688234245876 -5.10383387099057 10.116650420723772 3.3064062918537047\n",
      "X_max:-2 X_min:-6 Y_max:11 Y_min:4\n",
      "\n",
      " tmat_Cyc_8 \n",
      " -9.632139812632317 -16.816355741928533 18.786484699690163 10.31039383213556\n",
      "X_max:-10 X_min:-17 Y_max:19 Y_min:11\n",
      "\n",
      " tmat_Cyc_9 \n",
      " -3.995700967296557 -10.51994237849965 12.396801532778 4.543430464208768\n",
      "X_max:-4 X_min:-11 Y_max:13 Y_min:5\n",
      "\n",
      " X_max_total:-1 X_min_total:-17 Y_max_total:19 Y_min_total:1\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('\\n', f'tmat_Cyc_{c+1} \\n', 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'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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2048 2048\n"
     ]
    }
   ],
   "source": [
    "im1 = imread('tif/Cycle_0/Cycle_F000.tif')\n",
    "Y_total = len(im1[0][0][0])    # 2048\n",
    "X_total = len(im1[0][0][1])    # 2048\n",
    "print(Y_total, X_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘merged’: File exists\r\n"
     ]
    }
   ],
   "source": [
    "IN_DIR = 'reg'\n",
    "REF_DIR = 'tif' # cycle0\n",
    "MERGE_DIR = 'merged' # output directory to save\n",
    "Z = 13\n",
    "final_ch = 38\n",
    "\n",
    "pad = 10 # 5 pixel padding\n",
    "\n",
    "!mkdir merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "image max: 4114\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F000_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F000_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F000.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 46440\n",
      "FOV 001\n",
      "image max: 4934\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F001_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F001_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F001.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 33144\n",
      "FOV 002\n",
      "image max: 1041\n",
      "Appending...  reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F002_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F002_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F002_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F002_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F002_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F002.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 003\n",
      "image max: 2115\n",
      "Appending...  reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F003_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F003_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F003.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 64430\n",
      "FOV 004\n",
      "image max: 1518\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F004_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F004_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F004.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 005\n",
      "image max: 2115\n",
      "Appending...  reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F005_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F005_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F005_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F005.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 006\n",
      "image max: 2596\n",
      "Appending...  reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F006_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F006.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 007\n",
      "image max: 1558\n",
      "Appending...  reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F007_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F007_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F007_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F007.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 008\n",
      "image max: 2859\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F008_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F008_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F008.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 009\n",
      "image max: 1261\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F009_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F009_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F009_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F009.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 010\n",
      "image max: 1008\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F010_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F010.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
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      "image max: 65535\n",
      "FOV 011\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
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      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F011_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F011.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 49248\n",
      "FOV 012\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F012_reg.tif\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
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      "Shape = (13, 24, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F012_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F012.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
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      "image max: 65535\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
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      "Shape = (13, 32, 2048, 2048)\n",
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      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F013_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F013.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
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      "image max: 65535\n",
      "FOV 014\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F014_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65533\n",
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      "65535\n",
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      "Appending...  reg_Cyc_9/Cycle_9_F014_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F014.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
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      "image max: 65535\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "Appending...  reg_Cyc_9/Cycle_9_F015_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F015.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
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      "image max: 65535\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F016_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F016.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
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      "image max: 65535\n",
      "FOV 017\n",
      "image max: 759\n",
      "Appending...  reg_Cyc_1/Cycle_1_F017_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "Appending...  reg_Cyc_7/Cycle_7_F017_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F017_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F017.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 018\n",
      "image max: 1724\n",
      "Appending...  reg_Cyc_1/Cycle_1_F018_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
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      "Shape = (13, 24, 2048, 2048)\n",
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      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F018_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F018.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 38370\n",
      "FOV 019\n",
      "image max: 4893\n",
      "Appending...  reg_Cyc_1/Cycle_1_F019_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 12, 2048, 2048)\n",
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      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F019_reg.tif\n",
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      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F019_reg.tif\n",
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      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F019_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F019_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F019_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F019_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F019.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 50400\n",
      "FOV 020\n",
      "image max: 2593\n",
      "Appending...  reg_Cyc_1/Cycle_1_F020_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F020_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F020_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F020_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F020_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F020_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F020_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F020_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F020_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F020.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 021\n",
      "image max: 1708\n",
      "Appending...  reg_Cyc_1/Cycle_1_F021_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F021_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F021_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F021_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F021_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F021_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F021_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F021_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F021_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F021.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 022\n",
      "image max: 1200\n",
      "Appending...  reg_Cyc_1/Cycle_1_F022_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F022_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F022_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F022_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F022_reg.tif\n",
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      "Shape = (13, 28, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F022_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F022_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F022.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 34192\n",
      "FOV 023\n",
      "image max: 6140\n",
      "Appending...  reg_Cyc_1/Cycle_1_F023_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F023_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F023_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F023_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F023_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F023_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F023.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 024\n",
      "image max: 4011\n",
      "Appending...  reg_Cyc_1/Cycle_1_F024_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F024_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F024_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F024_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F024_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F024_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F024_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F024_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F024_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F024.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65289\n",
      "FOV 025\n",
      "image max: 803\n",
      "Appending...  reg_Cyc_1/Cycle_1_F025_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F025_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F025_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F025_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F025_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F025_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F025_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F025_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F025_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F025.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 027\n",
      "image max: 5980\n",
      "Appending...  reg_Cyc_1/Cycle_1_F027_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F027_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F027_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F027_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F027_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F027_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F027_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F027_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F027_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F027.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 028\n",
      "image max: 12783\n",
      "Appending...  reg_Cyc_1/Cycle_1_F028_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F028_reg.tif\n",
      "65528\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F028_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F028_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F028_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F028_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F028_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F028_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F028_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F028.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35180\n",
      "FOV 029\n",
      "image max: 2048\n",
      "Appending...  reg_Cyc_1/Cycle_1_F029_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F029_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F029_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F029_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F029_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F029_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F029_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F029_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F029_reg.tif\n",
      "65528\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F029.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 030\n",
      "image max: 938\n",
      "Appending...  reg_Cyc_1/Cycle_1_F030_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F030_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F030_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F030_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F030_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F030_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F030_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F030_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F030_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F030.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 42160\n",
      "FOV 031\n",
      "image max: 747\n",
      "Appending...  reg_Cyc_1/Cycle_1_F031_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F031_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F031_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F031_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F031_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F031_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F031_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F031_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F031_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F031.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 30388\n",
      "FOV 032\n",
      "image max: 1458\n",
      "Appending...  reg_Cyc_1/Cycle_1_F032_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F032_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F032_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F032_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F032_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F032_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F032_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F032_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F032_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F032.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 033\n",
      "image max: 3124\n",
      "Appending...  reg_Cyc_1/Cycle_1_F033_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F033_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F033_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F033_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F033_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F033_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F033_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F033_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F033_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F033.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 035\n",
      "image max: 802\n",
      "Appending...  reg_Cyc_1/Cycle_1_F035_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F035_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F035_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F035_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F035_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F035_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F035_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F035_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F035_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F035.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 036\n",
      "image max: 1493\n",
      "Appending...  reg_Cyc_1/Cycle_1_F036_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F036_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F036_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F036_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F036_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F036_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F036_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F036_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F036_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F036.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 037\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F037_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F037_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F037_reg.tif\n",
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      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F037_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F037_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F037_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F037_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F037_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F037_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F037.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 52345\n",
      "FOV 038\n",
      "image max: 1181\n",
      "Appending...  reg_Cyc_1/Cycle_1_F038_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F038_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F038_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F038_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F038_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F038_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F038_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F038_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F038_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F038.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35223\n",
      "FOV 041\n",
      "image max: 2877\n",
      "Appending...  reg_Cyc_1/Cycle_1_F041_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F041_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F041_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F041_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F041_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F041_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F041_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F041_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F041_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F041.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 54688\n",
      "FOV 042\n",
      "image max: 4274\n",
      "Appending...  reg_Cyc_1/Cycle_1_F042_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F042_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F042_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F042_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F042_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F042_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F042_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F042_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F042_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F042.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 043\n",
      "image max: 3128\n",
      "Appending...  reg_Cyc_1/Cycle_1_F043_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F043_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F043_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F043_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F043_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F043_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F043_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F043_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F043_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F043.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 044\n",
      "image max: 1172\n",
      "Appending...  reg_Cyc_1/Cycle_1_F044_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F044_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F044_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F044_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F044_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F044_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F044_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F044_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F044_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F044.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65144\n",
      "FOV 045\n",
      "image max: 2406\n",
      "Appending...  reg_Cyc_1/Cycle_1_F045_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F045_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F045_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F045_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F045_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F045_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F045_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F045_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F045_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F045.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 34390\n",
      "FOV 046\n",
      "image max: 881\n",
      "Appending...  reg_Cyc_1/Cycle_1_F046_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F046_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F046_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F046_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F046_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F046_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F046_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F046_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F046.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 047\n",
      "image max: 1667\n",
      "Appending...  reg_Cyc_1/Cycle_1_F047_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F047_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F047_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F047_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F047_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F047_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F047_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F047_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F047_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F047.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35927\n",
      "FOV 048\n",
      "image max: 1503\n",
      "Appending...  reg_Cyc_1/Cycle_1_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F048_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F048_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F048.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65534\n",
      "FOV 049\n",
      "image max: 1611\n",
      "Appending...  reg_Cyc_1/Cycle_1_F049_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F049_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F049_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F049_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F049_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F049_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F049_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F049_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F049.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 050\n",
      "image max: 22087\n",
      "Appending...  reg_Cyc_1/Cycle_1_F050_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F050_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F050_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F050_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F050_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F050_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F050_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F050_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F050_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F050.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 45169\n",
      "FOV 051\n",
      "image max: 1492\n",
      "Appending...  reg_Cyc_1/Cycle_1_F051_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F051_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F051_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F051_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F051_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F051_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F051_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F051_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F051_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F051.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 052\n",
      "image max: 1786\n",
      "Appending...  reg_Cyc_1/Cycle_1_F052_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F052_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F052_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F052_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F052_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F052_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F052_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F052_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F052_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F052.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65532\n",
      "FOV 054\n",
      "image max: 3128\n",
      "Appending...  reg_Cyc_1/Cycle_1_F054_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F054_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F054_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F054_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F054_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F054_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F054_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F054_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F054_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F054.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 49060\n",
      "FOV 055\n",
      "image max: 851\n",
      "Appending...  reg_Cyc_1/Cycle_1_F055_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F055_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F055_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F055_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F055_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F055_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F055_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F055_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F055_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F055.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 056\n",
      "image max: 1344\n",
      "Appending...  reg_Cyc_1/Cycle_1_F056_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F056_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F056_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F056_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F056_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F056_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F056_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F056_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F056_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F056.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 057\n",
      "image max: 2427\n",
      "Appending...  reg_Cyc_1/Cycle_1_F057_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F057_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F057_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F057_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F057_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F057_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F057_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F057_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F057_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F057.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 39805\n",
      "FOV 058\n",
      "image max: 6549\n",
      "Appending...  reg_Cyc_1/Cycle_1_F058_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F058_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F058_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F058_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F058_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F058_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F058_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F058_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F058_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F058.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 33558\n",
      "FOV 059\n",
      "image max: 904\n",
      "Appending...  reg_Cyc_1/Cycle_1_F059_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F059_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F059_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F059_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F059_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F059_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F059_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F059_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F059_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F059.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 31967\n",
      "FOV 061\n",
      "image max: 1358\n",
      "Appending...  reg_Cyc_1/Cycle_1_F061_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F061_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F061_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F061_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F061_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F061_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F061_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F061_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F061_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F061.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 37722\n",
      "FOV 062\n",
      "image max: 987\n",
      "Appending...  reg_Cyc_1/Cycle_1_F062_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F062_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F062_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F062_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F062_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F062_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F062_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F062_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F062_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F062.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 063\n",
      "image max: 1123\n",
      "Appending...  reg_Cyc_1/Cycle_1_F063_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F063_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F063_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F063_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F063_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F063_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F063_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F063_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F063_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F063.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65534\n",
      "FOV 064\n",
      "image max: 22770\n",
      "Appending...  reg_Cyc_1/Cycle_1_F064_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F064_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F064_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F064_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F064_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F064_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F064_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F064_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F064_reg.tif\n",
      "65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F064.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 34510\n",
      "FOV 065\n",
      "image max: 2290\n",
      "Appending...  reg_Cyc_1/Cycle_1_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F065_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F065_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F065.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35314\n",
      "FOV 066\n",
      "image max: 1423\n",
      "Appending...  reg_Cyc_1/Cycle_1_F066_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F066_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F066_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F066_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F066_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F066_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F066_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F066_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F066_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F066.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 39190\n",
      "FOV 067\n",
      "image max: 1289\n",
      "Appending...  reg_Cyc_1/Cycle_1_F067_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F067_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F067_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F067_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F067_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F067_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F067_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F067_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F067_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F067.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65523\n",
      "FOV 068\n",
      "image max: 1153\n",
      "Appending...  reg_Cyc_1/Cycle_1_F068_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F068_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F068_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F068_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F068_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F068_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F068_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F068_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F068_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F068.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 38651\n",
      "FOV 069\n",
      "image max: 1853\n",
      "Appending...  reg_Cyc_1/Cycle_1_F069_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F069_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F069.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 070\n",
      "image max: 1196\n",
      "Appending...  reg_Cyc_1/Cycle_1_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F070_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F070_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F070.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 071\n",
      "image max: 879\n",
      "Appending...  reg_Cyc_1/Cycle_1_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F071_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F071.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 072\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F072_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F072_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F072_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F072_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F072_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F072_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F072_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F072_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F072_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F072.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 073\n",
      "image max: 1377\n",
      "Appending...  reg_Cyc_1/Cycle_1_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F073_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F073_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F073.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 59611\n",
      "FOV 074\n",
      "image max: 871\n",
      "Appending...  reg_Cyc_1/Cycle_1_F074_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F074_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F074_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F074_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F074_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F074_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F074_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F074_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F074_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F074.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 077\n",
      "image max: 6256\n",
      "Appending...  reg_Cyc_1/Cycle_1_F077_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F077_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F077.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 078\n",
      "image max: 2129\n",
      "Appending...  reg_Cyc_1/Cycle_1_F078_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F078_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F078_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F078_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F078_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F078_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F078_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F078_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F078_reg.tif\n",
      "65528\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F078.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 079\n",
      "image max: 1550\n",
      "Appending...  reg_Cyc_1/Cycle_1_F079_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F079_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F079_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F079_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F079_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F079_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F079_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F079_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F079_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F079.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 080\n",
      "image max: 2060\n",
      "Appending...  reg_Cyc_1/Cycle_1_F080_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F080_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F080_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F080_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F080_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F080_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F080_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F080_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F080_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F080.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 081\n",
      "image max: 3525\n",
      "Appending...  reg_Cyc_1/Cycle_1_F081_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F081_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F081_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F081_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F081_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F081_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F081_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F081_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F081_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F081.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 082\n",
      "image max: 5450\n",
      "Appending...  reg_Cyc_1/Cycle_1_F082_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F082_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F082_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F082_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F082.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 083\n",
      "image max: 6846\n",
      "Appending...  reg_Cyc_1/Cycle_1_F083_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F083_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F083_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F083_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F083_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F083_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F083_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F083_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F083_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F083.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65527\n",
      "FOV 084\n",
      "image max: 4803\n",
      "Appending...  reg_Cyc_1/Cycle_1_F084_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F084_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F084_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F084_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F084_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F084_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F084_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F084_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F084_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F084.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 085\n",
      "image max: 1689\n",
      "Appending...  reg_Cyc_1/Cycle_1_F085_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F085_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F085_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F085_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F085_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F085_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F085_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F085_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F085_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F085.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 49396\n",
      "FOV 086\n",
      "image max: 1161\n",
      "Appending...  reg_Cyc_1/Cycle_1_F086_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F086_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F086_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F086_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F086_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F086_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F086_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F086_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F086_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F086.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 087\n",
      "image max: 9960\n",
      "Appending...  reg_Cyc_1/Cycle_1_F087_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F087_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F087_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F087_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F087_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F087_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F087_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F087_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F087_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F087.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65534\n",
      "FOV 088\n",
      "image max: 2318\n",
      "Appending...  reg_Cyc_1/Cycle_1_F088_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F088_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F088_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F088_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F088_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F088_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F088_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F088_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F088_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F088.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 45419\n",
      "FOV 089\n",
      "image max: 2006\n",
      "Appending...  reg_Cyc_1/Cycle_1_F089_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F089_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F089_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F089_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F089_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F089_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F089.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65529\n",
      "FOV 090\n",
      "image max: 1500\n",
      "Appending...  reg_Cyc_1/Cycle_1_F090_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F090_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F090_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F090_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F090_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F090_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F090_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F090_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F090_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F090.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65501\n",
      "FOV 091\n",
      "image max: 2118\n",
      "Appending...  reg_Cyc_1/Cycle_1_F091_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F091_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F091.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 56043\n",
      "FOV 092\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
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      "Shape = (13, 24, 2048, 2048)\n",
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      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F092_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F092.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 50149\n",
      "FOV 093\n",
      "image max: 2191\n",
      "Appending...  reg_Cyc_1/Cycle_1_F093_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F093_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F093_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F093_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F093_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F093_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F093_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F093.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 53063\n",
      "FOV 094\n",
      "image max: 1493\n",
      "Appending...  reg_Cyc_1/Cycle_1_F094_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F094_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F094_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F094_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F094_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F094_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F094_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F094_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F094_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F094.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 59957\n",
      "FOV 095\n",
      "image max: 1461\n",
      "Appending...  reg_Cyc_1/Cycle_1_F095_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F095_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F095_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F095_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F095_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F095_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F095_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F095_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F095.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 096\n",
      "image max: 1169\n",
      "Appending...  reg_Cyc_1/Cycle_1_F096_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F096_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F096_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F096_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F096_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F096_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F096_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F096_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F096_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F096.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 097\n",
      "image max: 1873\n",
      "Appending...  reg_Cyc_1/Cycle_1_F097_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F097_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F097_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F097_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F097_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F097_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F097_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F097_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F097_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F097.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 098\n",
      "image max: 11639\n",
      "Appending...  reg_Cyc_1/Cycle_1_F098_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F098_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F098_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F098_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F098_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F098_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F098_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F098_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F098_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F098.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 42755\n",
      "FOV 099\n",
      "image max: 1596\n",
      "Appending...  reg_Cyc_1/Cycle_1_F099_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F099_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F099_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F099_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F099_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F099_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F099_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F099_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F099_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F099.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65512\n",
      "FOV 100\n",
      "image max: 2277\n",
      "Appending...  reg_Cyc_1/Cycle_1_F100_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F100_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F100_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F100_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F100_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F100_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F100_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F100_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F100_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F100.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 101\n",
      "image max: 2551\n",
      "Appending...  reg_Cyc_1/Cycle_1_F101_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F101_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F101_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F101_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F101_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F101_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F101_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F101_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F101_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F101.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 102\n",
      "image max: 1541\n",
      "Appending...  reg_Cyc_1/Cycle_1_F102_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F102_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F102_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F102_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F102_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F102_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F102_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F102_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F102_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F102.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 42636\n",
      "FOV 103\n",
      "image max: 1220\n",
      "Appending...  reg_Cyc_1/Cycle_1_F103_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F103_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F103_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F103_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F103_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F103_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F103_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F103_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F103_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F103.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35952\n",
      "FOV 104\n",
      "image max: 1240\n",
      "Appending...  reg_Cyc_1/Cycle_1_F104_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F104_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F104_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F104_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F104_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F104_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F104_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F104_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F104_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F104.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35918\n",
      "FOV 105\n",
      "image max: 1750\n",
      "Appending...  reg_Cyc_1/Cycle_1_F105_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F105_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F105_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F105_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F105_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F105_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F105_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F105_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F105_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F105.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 63395\n",
      "FOV 106\n",
      "image max: 1247\n",
      "Appending...  reg_Cyc_1/Cycle_1_F106_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F106_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F106_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F106_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F106_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F106_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F106_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F106_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F106_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F106.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65470\n",
      "FOV 107\n",
      "image max: 8554\n",
      "Appending...  reg_Cyc_1/Cycle_1_F107_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F107_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F107_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F107_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F107_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F107_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F107_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F107_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F107_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F107.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65522\n",
      "FOV 108\n",
      "image max: 9930\n",
      "Appending...  reg_Cyc_1/Cycle_1_F108_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F108_reg.tif\n",
      "65527\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F108_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F108_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F108_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F108_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F108_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F108_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F108_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F108.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 47031\n",
      "FOV 109\n",
      "image max: 2497\n",
      "Appending...  reg_Cyc_1/Cycle_1_F109_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F109_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F109_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F109_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F109_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F109_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F109_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F109_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F109_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F109.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 110\n",
      "image max: 6319\n",
      "Appending...  reg_Cyc_1/Cycle_1_F110_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F110_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F110_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F110_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F110_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F110_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F110_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F110_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F110_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F110.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35665\n",
      "FOV 111\n",
      "image max: 4409\n",
      "Appending...  reg_Cyc_1/Cycle_1_F111_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F111_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F111_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F111_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F111_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F111_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F111_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F111_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F111_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F111.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 112\n",
      "image max: 8073\n",
      "Appending...  reg_Cyc_1/Cycle_1_F112_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F112_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F112_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F112_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F112_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F112_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F112_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F112_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F112_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F112.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 43201\n",
      "FOV 113\n",
      "image max: 1789\n",
      "Appending...  reg_Cyc_1/Cycle_1_F113_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F113_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F113_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F113_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F113_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F113_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F113_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F113_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F113_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F113.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 40523\n",
      "FOV 114\n",
      "image max: 1075\n",
      "Appending...  reg_Cyc_1/Cycle_1_F114_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F114_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F114_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F114_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F114_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F114_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F114_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F114_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F114_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F114.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 115\n",
      "image max: 4481\n",
      "Appending...  reg_Cyc_1/Cycle_1_F115_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F115_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F115_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F115_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F115_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F115_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F115_reg.tif\n",
      "65527\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F115_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F115_reg.tif\n",
      "65529\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F115.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 51108\n",
      "FOV 116\n",
      "image max: 1726\n",
      "Appending...  reg_Cyc_1/Cycle_1_F116_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F116_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F116_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F116_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F116_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F116_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F116_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F116_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F116_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F116.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 117\n",
      "image max: 4270\n",
      "Appending...  reg_Cyc_1/Cycle_1_F117_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F117_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F117_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F117_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F117_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F117_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F117_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F117_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F117_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F117.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 118\n",
      "image max: 7622\n",
      "Appending...  reg_Cyc_1/Cycle_1_F118_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F118_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F118_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F118_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F118_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F118_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F118_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F118_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F118_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F118.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 47932\n",
      "FOV 119\n",
      "image max: 1383\n",
      "Appending...  reg_Cyc_1/Cycle_1_F119_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F119_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F119_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F119_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F119_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F119_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F119_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F119_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F119_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F119.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 53282\n",
      "FOV 120\n",
      "image max: 1315\n",
      "Appending...  reg_Cyc_1/Cycle_1_F120_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F120_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F120_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F120_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F120_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F120_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F120_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F120_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F120_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F120.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 51151\n",
      "FOV 121\n",
      "image max: 3504\n",
      "Appending...  reg_Cyc_1/Cycle_1_F121_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F121_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F121_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F121_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F121_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F121_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F121_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F121_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F121_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F121.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 122\n",
      "image max: 2001\n",
      "Appending...  reg_Cyc_1/Cycle_1_F122_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F122_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F122_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F122_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F122_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F122_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F122_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F122_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F122_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F122.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 64797\n",
      "FOV 123\n",
      "image max: 2020\n",
      "Appending...  reg_Cyc_1/Cycle_1_F123_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F123_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F123_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F123_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F123_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F123_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F123_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F123_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F123_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F123.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 124\n",
      "image max: 1826\n",
      "Appending...  reg_Cyc_1/Cycle_1_F124_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F124_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F124_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F124_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F124_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F124_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F124_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F124_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F124_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F124.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 54336\n",
      "FOV 125\n",
      "image max: 1015\n",
      "Appending...  reg_Cyc_1/Cycle_1_F125_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F125_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F125_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F125_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F125_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F125_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F125_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F125_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F125_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F125.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 46421\n",
      "FOV 126\n",
      "image max: 1021\n",
      "Appending...  reg_Cyc_1/Cycle_1_F126_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F126_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F126_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F126_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F126_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F126_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F126_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F126_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F126_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F126.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65531\n",
      "FOV 127\n",
      "image max: 3612\n",
      "Appending...  reg_Cyc_1/Cycle_1_F127_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F127_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F127_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F127.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 128\n",
      "image max: 3012\n",
      "Appending...  reg_Cyc_1/Cycle_1_F128_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "Shape = (13, 28, 2048, 2048)\n",
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      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F128_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F128.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 129\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F129_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "65535\n",
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      "Shape = (13, 28, 2048, 2048)\n",
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      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F129_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F129.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 130\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F130_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
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      "Shape = (13, 24, 2048, 2048)\n",
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      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F130_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F130.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35769\n",
      "FOV 131\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "Appending...  reg_Cyc_9/Cycle_9_F131_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F131.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
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      "image max: 65384\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
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      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F132_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F132.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 133\n",
      "image max: 1158\n",
      "Appending...  reg_Cyc_1/Cycle_1_F133_reg.tif\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F133_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F133_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F133_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F133_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F133_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F133.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 42505\n",
      "FOV 134\n",
      "image max: 2028\n",
      "Appending...  reg_Cyc_1/Cycle_1_F134_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F134_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F134_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F134_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F134_reg.tif\n",
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      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F134_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F134_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F134_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F134.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 135\n",
      "image max: 1150\n",
      "Appending...  reg_Cyc_1/Cycle_1_F135_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F135_reg.tif\n",
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      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F135_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F135_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F135_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F135_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F135_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F135_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F135.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 41501\n",
      "FOV 136\n",
      "image max: 1777\n",
      "Appending...  reg_Cyc_1/Cycle_1_F136_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F136_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F136_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F136_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F136_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F136_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F136_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F136_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F136.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 31902\n",
      "FOV 137\n",
      "image max: 1284\n",
      "Appending...  reg_Cyc_1/Cycle_1_F137_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F137_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F137_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F137_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F137_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F137_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F137_reg.tif\n",
      "65527\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F137_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F137_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F137.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 41202\n",
      "FOV 138\n",
      "image max: 1198\n",
      "Appending...  reg_Cyc_1/Cycle_1_F138_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F138_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F138_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F138_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F138_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F138_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F138_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F138_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F138_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F138.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 38264\n",
      "FOV 140\n",
      "image max: 33895\n",
      "Appending...  reg_Cyc_1/Cycle_1_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F140_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F140_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F140.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 39410\n",
      "FOV 141\n",
      "image max: 1531\n",
      "Appending...  reg_Cyc_1/Cycle_1_F141_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F141_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F141_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F141_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F141_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F141_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F141_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F141_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F141_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F141.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 142\n",
      "image max: 1441\n",
      "Appending...  reg_Cyc_1/Cycle_1_F142_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F142_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F142_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F142_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F142_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F142_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F142_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F142_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F142_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F142.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 41924\n",
      "FOV 143\n",
      "image max: 2661\n",
      "Appending...  reg_Cyc_1/Cycle_1_F143_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F143_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F143_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F143_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F143_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F143_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F143_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F143_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F143_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F143.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 56509\n",
      "FOV 144\n",
      "image max: 604\n",
      "Appending...  reg_Cyc_1/Cycle_1_F144_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F144_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F144_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F144_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F144_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F144_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F144_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F144_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F144_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F144.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 40752\n",
      "FOV 145\n",
      "image max: 56904\n",
      "Appending...  reg_Cyc_1/Cycle_1_F145_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F145_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F145_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F145_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F145_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F145_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F145_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F145_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F145_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F145.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 146\n",
      "image max: 2741\n",
      "Appending...  reg_Cyc_1/Cycle_1_F146_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F146_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F146_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F146_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F146_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F146_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F146_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F146_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F146_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F146.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 64719\n",
      "FOV 147\n",
      "image max: 1003\n",
      "Appending...  reg_Cyc_1/Cycle_1_F147_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F147_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F147_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F147_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F147_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F147_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F147_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F147_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F147_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F147.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 148\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F148_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F148_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F148.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 149\n",
      "image max: 35693\n",
      "Appending...  reg_Cyc_1/Cycle_1_F149_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F149_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F149_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F149_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F149_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F149_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F149_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F149_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F149_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F149.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 151\n",
      "image max: 2697\n",
      "Appending...  reg_Cyc_1/Cycle_1_F151_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F151_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F151_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F151_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F151_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F151_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F151_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F151_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F151_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F151.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 152\n",
      "image max: 1897\n",
      "Appending...  reg_Cyc_1/Cycle_1_F152_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F152_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F152_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F152_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F152_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F152_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F152_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F152_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F152_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F152.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 153\n",
      "image max: 12344\n",
      "Appending...  reg_Cyc_1/Cycle_1_F153_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F153_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F153_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F153_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F153_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F153_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F153_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F153_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F153_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F153.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 40963\n",
      "FOV 154\n",
      "image max: 8067\n",
      "Appending...  reg_Cyc_1/Cycle_1_F154_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F154_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F154_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F154_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F154_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F154_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F154_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F154_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F154_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F154.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 155\n",
      "image max: 2618\n",
      "Appending...  reg_Cyc_1/Cycle_1_F155_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F155_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F155_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F155_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F155_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F155_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F155_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F155_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F155_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F155.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65534\n",
      "FOV 156\n",
      "image max: 1175\n",
      "Appending...  reg_Cyc_1/Cycle_1_F156_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F156_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F156_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F156_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F156_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F156_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F156_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F156_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F156_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F156.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 55066\n",
      "FOV 157\n",
      "image max: 2090\n",
      "Appending...  reg_Cyc_1/Cycle_1_F157_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F157_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F157_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F157_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F157_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F157_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F157_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F157_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F157_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F157.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 51381\n",
      "FOV 158\n",
      "image max: 1168\n",
      "Appending...  reg_Cyc_1/Cycle_1_F158_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F158_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F158_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F158_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F158_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F158_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F158_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F158_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F158_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F158.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65264\n",
      "FOV 159\n",
      "image max: 36737\n",
      "Appending...  reg_Cyc_1/Cycle_1_F159_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F159_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F159_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F159_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F159_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F159_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F159_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F159_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F159_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F159.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 36737\n",
      "FOV 160\n",
      "image max: 11720\n",
      "Appending...  reg_Cyc_1/Cycle_1_F160_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F160_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F160_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F160_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F160_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F160_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F160_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F160_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F160.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 37646\n",
      "FOV 161\n",
      "image max: 944\n",
      "Appending...  reg_Cyc_1/Cycle_1_F161_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F161_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F161_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F161_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F161_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F161_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F161_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F161_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F161_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F161.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65087\n",
      "FOV 162\n",
      "image max: 1801\n",
      "Appending...  reg_Cyc_1/Cycle_1_F162_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F162_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F162_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F162_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F162_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F162_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F162_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F162_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F162_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F162.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 164\n",
      "image max: 8374\n",
      "Appending...  reg_Cyc_1/Cycle_1_F164_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F164_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F164_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F164_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F164_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F164_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F164_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F164_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F164_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F164.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 165\n",
      "image max: 1126\n",
      "Appending...  reg_Cyc_1/Cycle_1_F165_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F165_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F165_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F165_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F165_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F165_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F165_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F165_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F165_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F165.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 166\n",
      "image max: 2031\n",
      "Appending...  reg_Cyc_1/Cycle_1_F166_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F166_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F166_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F166_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F166_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F166_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F166_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F166_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F166_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F166.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 30854\n",
      "FOV 167\n",
      "image max: 2238\n",
      "Appending...  reg_Cyc_1/Cycle_1_F167_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F167_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F167_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F167_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F167_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F167_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F167_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F167_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F167_reg.tif\n",
      "65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F167.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 29843\n",
      "FOV 168\n",
      "image max: 3933\n",
      "Appending...  reg_Cyc_1/Cycle_1_F168_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F168_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F168_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F168_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F168_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F168_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F168_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F168_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F168_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F168.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 34186\n",
      "FOV 169\n",
      "image max: 980\n",
      "Appending...  reg_Cyc_1/Cycle_1_F169_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F169_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F169_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F169_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F169_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F169_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F169_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F169_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F169_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F169.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 56893\n",
      "FOV 170\n",
      "image max: 3343\n",
      "Appending...  reg_Cyc_1/Cycle_1_F170_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F170_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F170_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F170_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F170_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F170_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F170_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F170_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F170_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F170.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 171\n",
      "image max: 4329\n",
      "Appending...  reg_Cyc_1/Cycle_1_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F171_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F171_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F171.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65501\n",
      "FOV 172\n",
      "image max: 2411\n",
      "Appending...  reg_Cyc_1/Cycle_1_F172_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F172_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F172_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F172_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F172_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F172_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F172_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F172_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F172_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F172.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 173\n",
      "image max: 1162\n",
      "Appending...  reg_Cyc_1/Cycle_1_F173_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F173_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F173_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F173_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F173_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F173_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F173_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F173_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F173_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F173.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 174\n",
      "image max: 2538\n",
      "Appending...  reg_Cyc_1/Cycle_1_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F174_reg.tif\n",
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      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F174_reg.tif\n",
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      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F174.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 175\n",
      "image max: 1124\n",
      "Appending...  reg_Cyc_1/Cycle_1_F175_reg.tif\n",
      "65528\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F175_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F175_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F175_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F175_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F175_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F175_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F175_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F175_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F175.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 176\n",
      "image max: 2040\n",
      "Appending...  reg_Cyc_1/Cycle_1_F176_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F176_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F176_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F176_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F176_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F176_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F176_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F176_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F176_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F176.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 43855\n",
      "FOV 177\n",
      "image max: 36841\n",
      "Appending...  reg_Cyc_1/Cycle_1_F177_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F177_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F177_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F177_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F177_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F177_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F177_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F177_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F177_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F177.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 179\n",
      "image max: 2319\n",
      "Appending...  reg_Cyc_1/Cycle_1_F179_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F179_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F179_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F179_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F179_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F179_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F179_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F179_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F179_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F179.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65534\n",
      "FOV 180\n",
      "image max: 1411\n",
      "Appending...  reg_Cyc_1/Cycle_1_F180_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F180_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F180_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F180_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F180_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F180_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F180_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F180_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F180_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F180.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 35918\n",
      "FOV 181\n",
      "image max: 1268\n",
      "Appending...  reg_Cyc_1/Cycle_1_F181_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F181_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F181_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F181_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F181_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F181_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F181_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F181_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F181_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F181.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 39979\n",
      "FOV 182\n",
      "image max: 2106\n",
      "Appending...  reg_Cyc_1/Cycle_1_F182_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F182_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F182_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F182_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F182_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F182_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F182_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F182_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F182_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F182.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65522\n",
      "FOV 183\n",
      "image max: 4022\n",
      "Appending...  reg_Cyc_1/Cycle_1_F183_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F183_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F183_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F183_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F183_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F183_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F183_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F183_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F183_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F183.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 38506\n",
      "FOV 184\n",
      "image max: 916\n",
      "Appending...  reg_Cyc_1/Cycle_1_F184_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F184_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F184_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F184_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F184_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F184_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F184_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F184_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F184_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F184.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65013\n",
      "FOV 185\n",
      "image max: 4629\n",
      "Appending...  reg_Cyc_1/Cycle_1_F185_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F185_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F185_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F185_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F185_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F185_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F185_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F185_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F185_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F185.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 45721\n",
      "FOV 186\n",
      "image max: 1420\n",
      "Appending...  reg_Cyc_1/Cycle_1_F186_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F186_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F186_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F186_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F186_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F186_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F186_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F186_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F186_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F186.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 39122\n",
      "FOV 187\n",
      "image max: 1442\n",
      "Appending...  reg_Cyc_1/Cycle_1_F187_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F187_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F187_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F187_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F187_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F187_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F187_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F187_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F187_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F187.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 188\n",
      "image max: 11524\n",
      "Appending...  reg_Cyc_1/Cycle_1_F188_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F188_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F188_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F188_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F188_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F188_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F188_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F188_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F188_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F188.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 189\n",
      "image max: 5418\n",
      "Appending...  reg_Cyc_1/Cycle_1_F189_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F189_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F189_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F189_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F189_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F189_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F189_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F189_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F189_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F189.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 190\n",
      "image max: 1887\n",
      "Appending...  reg_Cyc_1/Cycle_1_F190_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F190_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F190_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F190_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F190_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F190_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F190_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F190_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F190_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F190.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 36271\n",
      "FOV 191\n",
      "image max: 13921\n",
      "Appending...  reg_Cyc_1/Cycle_1_F191_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F191_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F191_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F191_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F191_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F191_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F191_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F191_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F191_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F191.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 193\n",
      "image max: 7437\n",
      "Appending...  reg_Cyc_1/Cycle_1_F193_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F193_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F193_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F193_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F193_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F193_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F193_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F193_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F193_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F193.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 44865\n",
      "FOV 194\n",
      "image max: 4516\n",
      "Appending...  reg_Cyc_1/Cycle_1_F194_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F194_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F194_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F194_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F194_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F194_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F194_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F194_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F194_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F194.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 45965\n",
      "FOV 195\n",
      "image max: 1191\n",
      "Appending...  reg_Cyc_1/Cycle_1_F195_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F195_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F195_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F195_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F195_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F195_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F195_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F195_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F195_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F195.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 196\n",
      "image max: 1356\n",
      "Appending...  reg_Cyc_1/Cycle_1_F196_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F196_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F196_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F196_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F196_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F196_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F196_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F196_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F196_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F196.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 57970\n",
      "FOV 197\n",
      "image max: 1645\n",
      "Appending...  reg_Cyc_1/Cycle_1_F197_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F197_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F197_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F197_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F197_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F197_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F197_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F197_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F197_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F197.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 42219\n",
      "FOV 198\n",
      "image max: 13599\n",
      "Appending...  reg_Cyc_1/Cycle_1_F198_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F198_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F198_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F198_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F198_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F198_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F198_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F198_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F198_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F198.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65476\n",
      "FOV 199\n",
      "image max: 8010\n",
      "Appending...  reg_Cyc_1/Cycle_1_F199_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F199_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F199_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F199_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F199_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F199_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F199_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F199_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F199_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F199.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 200\n",
      "image max: 2433\n",
      "Appending...  reg_Cyc_1/Cycle_1_F200_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F200_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F200_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F200_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F200_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F200_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F200_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F200_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F200_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F200.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 50905\n",
      "FOV 201\n",
      "image max: 2971\n",
      "Appending...  reg_Cyc_1/Cycle_1_F201_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F201_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F201_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F201_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F201_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F201_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F201_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F201_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F201_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F201.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 46476\n",
      "FOV 202\n",
      "image max: 2186\n",
      "Appending...  reg_Cyc_1/Cycle_1_F202_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F202_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F202_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F202_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F202_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F202_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F202_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F202_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F202_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F202.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65505\n",
      "FOV 203\n",
      "image max: 1385\n",
      "Appending...  reg_Cyc_1/Cycle_1_F203_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F203_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F203_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F203_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F203_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F203_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F203_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F203_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F203_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F203.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65433\n",
      "FOV 204\n",
      "image max: 1583\n",
      "Appending...  reg_Cyc_1/Cycle_1_F204_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F204_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F204_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F204_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F204_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F204_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F204_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F204_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F204_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F204.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 205\n",
      "image max: 3575\n",
      "Appending...  reg_Cyc_1/Cycle_1_F205_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F205_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F205_reg.tif\n",
      "65527\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F205_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F205_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F205_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F205_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F205_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F205_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F205.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 207\n",
      "image max: 18977\n",
      "Appending...  reg_Cyc_1/Cycle_1_F207_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F207_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F207_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F207_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F207_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F207_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F207_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F207_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F207_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F207.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 208\n",
      "image max: 6396\n",
      "Appending...  reg_Cyc_1/Cycle_1_F208_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F208_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F208_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F208_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F208_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F208_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F208_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F208_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F208_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F208.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 38979\n",
      "FOV 209\n",
      "image max: 998\n",
      "Appending...  reg_Cyc_1/Cycle_1_F209_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F209_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F209_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F209_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F209_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F209_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F209_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F209_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F209_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F209.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 210\n",
      "image max: 1183\n",
      "Appending...  reg_Cyc_1/Cycle_1_F210_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F210_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F210_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F210_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F210_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F210_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F210_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F210_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F210_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F210.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65514\n",
      "FOV 211\n",
      "image max: 21425\n",
      "Appending...  reg_Cyc_1/Cycle_1_F211_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F211_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F211_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F211_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F211_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F211_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F211_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F211_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F211_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F211.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 54813\n",
      "FOV 212\n",
      "image max: 1001\n",
      "Appending...  reg_Cyc_1/Cycle_1_F212_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F212_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F212_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F212_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F212_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F212_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F212_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F212.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 213\n",
      "image max: 1849\n",
      "Appending...  reg_Cyc_1/Cycle_1_F213_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F213_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F213.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 46823\n",
      "FOV 214\n",
      "image max: 4215\n",
      "Appending...  reg_Cyc_1/Cycle_1_F214_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F214_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F214_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F214_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F214_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F214_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F214_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F214_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F214.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 45408\n",
      "FOV 215\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F215_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F215_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F215_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F215_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F215_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F215_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F215_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F215.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 216\n",
      "image max: 1599\n",
      "Appending...  reg_Cyc_1/Cycle_1_F216_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
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      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F216_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "Shape = (13, 28, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F216_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F216_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F216.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 217\n",
      "image max: 973\n",
      "Appending...  reg_Cyc_1/Cycle_1_F217_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F217_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F217_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F217_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F217_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F217_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F217_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F217_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F217.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 43560\n",
      "FOV 218\n",
      "image max: 992\n",
      "Appending...  reg_Cyc_1/Cycle_1_F218_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F218_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F218_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F218_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F218_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F218_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F218_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F218_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F218_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F218.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 63567\n",
      "FOV 220\n",
      "image max: 6454\n",
      "Appending...  reg_Cyc_1/Cycle_1_F220_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F220_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F220_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F220_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F220_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F220_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F220_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F220_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F220_reg.tif\n",
      "65528\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F220.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 221\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F221_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F221_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F221_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F221_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F221_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F221_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F221_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F221_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F221_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F221.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 65535\n",
      "FOV 222\n",
      "image max: 1147\n",
      "Appending...  reg_Cyc_1/Cycle_1_F222_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F222_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F222_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F222_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F222_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F222_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F222_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F222_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F222_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F222.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 42334\n",
      "FOV 223\n",
      "image max: 991\n",
      "Appending...  reg_Cyc_1/Cycle_1_F223_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F223_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F223_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F223_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F223_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F223_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F223_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F223_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F223_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F223.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\n",
      "image max: 41871\n",
      "FOV 224\n",
      "image max: 1736\n",
      "Appending...  reg_Cyc_1/Cycle_1_F224_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F224_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F224_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F224_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F224_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F224_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F224_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F224_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F224_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F224.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2031\n",
      "2029\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": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "0\n",
      "1\n",
      "2\n",
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      "12\n",
      "FOV 001\n",
      "0\n",
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      "11\n",
      "12\n",
      "FOV 002\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 003\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 004\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 005\n",
      "0\n",
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      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 007\n",
      "0\n",
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      "11\n",
      "12\n",
      "FOV 008\n",
      "0\n",
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      "11\n",
      "12\n",
      "FOV 011\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 012\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 013\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 014\n",
      "0\n",
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      "2\n",
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      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 015\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
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      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 016\n",
      "0\n",
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      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 017\n",
      "0\n",
      "1\n",
      "2\n",
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      "4\n",
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      "6\n",
      "7\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 019\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
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      "6\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 020\n",
      "0\n",
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      "2\n",
      "3\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 021\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 022\n",
      "0\n",
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      "2\n",
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      "4\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 023\n",
      "0\n",
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      "3\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 024\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 025\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 026\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 027\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 028\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 029\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 030\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 031\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 032\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 033\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 034\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 035\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 036\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 037\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 038\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 039\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 040\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 041\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 042\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 044\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 045\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 046\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 047\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 048\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 049\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 050\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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      "5\n",
      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 051\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 052\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 053\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 054\n",
      "0\n",
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      "2\n",
      "3\n",
      "4\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 055\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 056\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 057\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 058\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 059\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 060\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 061\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 062\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 063\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 064\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 065\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 066\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 067\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 068\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 069\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 070\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 071\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 072\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 073\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 074\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 075\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 076\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 077\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 078\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 080\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 081\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 082\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 083\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 084\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 085\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 086\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 087\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 088\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 089\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 090\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 091\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 092\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 093\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 094\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 095\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 096\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 097\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 098\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 100\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 101\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 102\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 103\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 105\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 106\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 107\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 108\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 109\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 110\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 111\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 112\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 113\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 114\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 115\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 116\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 117\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 118\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 119\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 120\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 122\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 123\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 124\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 125\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 126\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 127\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 128\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 129\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 130\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 131\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 132\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 133\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 134\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 135\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 136\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 137\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 138\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 139\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 140\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 141\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 142\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 143\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 144\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 145\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 146\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 147\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 148\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 149\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 151\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 152\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 153\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 154\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 155\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 156\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 157\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 158\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 159\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 162\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 163\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 164\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 165\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 166\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 167\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 168\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 170\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 171\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 172\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 173\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 174\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 175\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 176\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 178\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 179\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 180\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 181\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 182\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 183\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 184\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 185\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 186\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 187\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 188\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 189\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 190\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 191\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 192\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 193\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 194\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 195\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 196\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 197\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 198\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 199\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 200\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 201\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 202\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 203\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 204\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 205\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 206\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 207\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 208\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 209\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 210\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 211\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 212\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 213\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 214\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 215\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 216\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 217\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 218\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 219\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 220\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 221\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 222\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 223\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 224\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n"
     ]
    }
   ],
   "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",
    "int_counts.to_csv('pixel_intensity_counts_table.csv')    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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>493858.0</td>\n",
       "      <td>13396.0</td>\n",
       "      <td>17891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>480241.0</td>\n",
       "      <td>10310.0</td>\n",
       "      <td>14353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>002</td>\n",
       "      <td>2416700.0</td>\n",
       "      <td>40575.0</td>\n",
       "      <td>55541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>003</td>\n",
       "      <td>944596.0</td>\n",
       "      <td>12743.0</td>\n",
       "      <td>24385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>274.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>11087.0</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>220</td>\n",
       "      <td>220</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>221</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>222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>223</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>224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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          493858.0      13396.0      17891.0\n",
       "001     001          480241.0      10310.0      14353.0\n",
       "002     002         2416700.0      40575.0      55541.0\n",
       "003     003          944596.0      12743.0      24385.0\n",
       "004     004             274.0        483.0      11087.0\n",
       "..      ...               ...          ...          ...\n",
       "220     220               0.0          0.0          0.0\n",
       "221     221               0.0          0.0          0.0\n",
       "222     222               0.0          0.0          0.0\n",
       "223     223               0.0          0.0          0.0\n",
       "224     224               0.0          0.0          1.0\n",
       "\n",
       "[211 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3063"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sixfivek_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3150"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_to_five_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3014"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(z_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('000', 0, 32),\n",
       " ('000', 0, 33),\n",
       " ('000', 0, 37),\n",
       " ('000', 1, 32),\n",
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       " ('000', 1, 37),\n",
       " ('000', 2, 32),\n",
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       " ('000', 2, 37),\n",
       " ('000', 3, 32),\n",
       " ('000', 3, 33),\n",
       " ('000', 3, 37),\n",
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       " ('000', 12, 32),\n",
       " ('000', 12, 33),\n",
       " ('001', 0, 12),\n",
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       " ('001', 1, 12),\n",
       " ('001', 1, 32),\n",
       " ('001', 1, 33),\n",
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       " ('003', 6, 29),\n",
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       " ('003', 6, 34),\n",
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       " ('003', 8, 32),\n",
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       " ('003', 11, 33),\n",
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       " ('003', 12, 33),\n",
       " ('004', 0, 32),\n",
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       " ('004', 5, 32),\n",
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       " ('004', 12, 33),\n",
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       " ('007', 0, 4),\n",
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       " ('007', 5, 4),\n",
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       " ('007', 5, 33),\n",
       " ('007', 6, 4),\n",
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       " ('031', 0, 32),\n",
       " ('031', 0, 33),\n",
       " ('031', 1, 9),\n",
       " ('031', 1, 32),\n",
       " ('031', 1, 33),\n",
       " ('031', 2, 9),\n",
       " ('031', 2, 32),\n",
       " ('031', 2, 33),\n",
       " ('031', 3, 9),\n",
       " ('031', 3, 32),\n",
       " ('031', 3, 33),\n",
       " ('031', 4, 9),\n",
       " ('031', 4, 32),\n",
       " ('031', 4, 33),\n",
       " ('031', 5, 9),\n",
       " ('031', 5, 32),\n",
       " ('031', 5, 33),\n",
       " ('031', 6, 9),\n",
       " ('031', 6, 32),\n",
       " ('031', 6, 33),\n",
       " ('031', 7, 9),\n",
       " ('031', 7, 32),\n",
       " ('031', 7, 33),\n",
       " ('031', 8, 32),\n",
       " ('031', 9, 32),\n",
       " ('031', 10, 32),\n",
       " ('031', 10, 33),\n",
       " ('031', 11, 32),\n",
       " ('031', 11, 33),\n",
       " ('031', 12, 32),\n",
       " ('031', 12, 33),\n",
       " ('032', 0, 26),\n",
       " ('032', 0, 32),\n",
       " ('032', 0, 33),\n",
       " ('032', 1, 26),\n",
       " ('032', 1, 32),\n",
       " ('032', 1, 33),\n",
       " ('032', 2, 26),\n",
       " ('032', 2, 32),\n",
       " ('032', 2, 33),\n",
       " ('032', 3, 26),\n",
       " ('032', 3, 32),\n",
       " ('032', 3, 33),\n",
       " ('032', 4, 26),\n",
       " ('032', 4, 32),\n",
       " ('032', 4, 33),\n",
       " ('032', 5, 26),\n",
       " ('032', 5, 32),\n",
       " ('032', 5, 33),\n",
       " ('032', 6, 26),\n",
       " ('032', 6, 32),\n",
       " ('032', 6, 33),\n",
       " ('032', 7, 26),\n",
       " ('032', 7, 32),\n",
       " ('032', 7, 33),\n",
       " ('032', 8, 10),\n",
       " ('032', 8, 32),\n",
       " ('032', 8, 33),\n",
       " ('032', 9, 10),\n",
       " ('032', 9, 32),\n",
       " ('032', 9, 33),\n",
       " ('032', 10, 10),\n",
       " ('032', 10, 32),\n",
       " ('032', 10, 33),\n",
       " ('032', 11, 10),\n",
       " ('032', 11, 32),\n",
       " ('032', 11, 33),\n",
       " ('032', 12, 10),\n",
       " ('032', 12, 32),\n",
       " ('032', 12, 33),\n",
       " ('033', 0, 25),\n",
       " ('033', 0, 32),\n",
       " ('033', 0, 33),\n",
       " ('033', 1, 25),\n",
       " ('033', 1, 32),\n",
       " ('033', 1, 33),\n",
       " ('033', 2, 25),\n",
       " ('033', 2, 32),\n",
       " ('033', 2, 33),\n",
       " ('033', 3, 25),\n",
       " ('033', 3, 32),\n",
       " ('033', 3, 33),\n",
       " ('033', 4, 25),\n",
       " ('033', 4, 32),\n",
       " ('033', 4, 33),\n",
       " ('033', 5, 25),\n",
       " ('033', 5, 32),\n",
       " ('033', 5, 33),\n",
       " ('033', 6, 25),\n",
       " ('033', 6, 32),\n",
       " ('033', 6, 33),\n",
       " ('033', 7, 25),\n",
       " ('033', 7, 32),\n",
       " ('033', 7, 33),\n",
       " ('033', 8, 32),\n",
       " ('033', 8, 33),\n",
       " ('033', 9, 32),\n",
       " ('033', 9, 33),\n",
       " ('033', 10, 32),\n",
       " ('033', 10, 33),\n",
       " ('033', 11, 32),\n",
       " ('033', 11, 33),\n",
       " ('033', 12, 32),\n",
       " ('033', 12, 33),\n",
       " ('034', 0, 32),\n",
       " ('034', 0, 33),\n",
       " ('034', 1, 32),\n",
       " ('034', 1, 33),\n",
       " ('034', 2, 32),\n",
       " ('034', 2, 33),\n",
       " ('034', 3, 32),\n",
       " ('034', 3, 33),\n",
       " ('034', 4, 32),\n",
       " ('034', 4, 33),\n",
       " ('034', 5, 32),\n",
       " ('034', 5, 33),\n",
       " ('034', 6, 32),\n",
       " ('034', 6, 33),\n",
       " ('034', 7, 32),\n",
       " ('034', 7, 33),\n",
       " ('034', 8, 32),\n",
       " ('034', 8, 33),\n",
       " ('034', 9, 32),\n",
       " ('034', 9, 33),\n",
       " ('034', 10, 32),\n",
       " ('034', 10, 33),\n",
       " ('034', 11, 32),\n",
       " ('034', 11, 33),\n",
       " ...]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13396 FOV: 000\n",
      "10310 FOV: 001\n",
      "40575 FOV: 002\n",
      "12743 FOV: 003\n",
      "483 FOV: 004\n",
      "261 FOV: 005\n",
      "13229 FOV: 007\n",
      "491 FOV: 008\n",
      "276 FOV: 011\n",
      "6758 FOV: 012\n",
      "22700 FOV: 013\n",
      "906 FOV: 014\n",
      "9488 FOV: 015\n",
      "259 FOV: 016\n",
      "9009 FOV: 017\n",
      "8890 FOV: 019\n",
      "20979 FOV: 020\n",
      "7228 FOV: 021\n",
      "6169 FOV: 022\n",
      "236 FOV: 023\n",
      "252 FOV: 024\n",
      "1532 FOV: 025\n",
      "20910 FOV: 026\n",
      "16435 FOV: 027\n",
      "293 FOV: 028\n",
      "122 FOV: 029\n",
      "19996 FOV: 030\n",
      "20796 FOV: 031\n",
      "17966 FOV: 032\n",
      "6296 FOV: 033\n",
      "297 FOV: 034\n",
      "6564 FOV: 035\n",
      "9635 FOV: 036\n",
      "10787 FOV: 037\n",
      "12896 FOV: 038\n",
      "530 FOV: 039\n",
      "266 FOV: 040\n",
      "14310 FOV: 041\n",
      "249 FOV: 042\n",
      "213 FOV: 044\n",
      "17357 FOV: 045\n",
      "1296 FOV: 046\n",
      "282 FOV: 047\n",
      "11319 FOV: 049\n",
      "288 FOV: 050\n",
      "8799 FOV: 051\n",
      "22111 FOV: 052\n",
      "241 FOV: 053\n",
      "272 FOV: 054\n",
      "10538 FOV: 056\n",
      "8455 FOV: 057\n",
      "7541 FOV: 058\n",
      "15578 FOV: 059\n",
      "1128 FOV: 060\n",
      "12456 FOV: 063\n",
      "7995 FOV: 064\n",
      "16546 FOV: 065\n",
      "252 FOV: 066\n",
      "17669 FOV: 067\n",
      "253 FOV: 068\n",
      "17737 FOV: 069\n",
      "103 FOV: 070\n",
      "7564 FOV: 072\n",
      "154 FOV: 073\n",
      "229 FOV: 074\n",
      "8178 FOV: 075\n",
      "814 FOV: 077\n",
      "6173 FOV: 078\n",
      "15252 FOV: 082\n",
      "208 FOV: 084\n",
      "11 FOV: 085\n",
      "22280 FOV: 088\n",
      "21023 FOV: 089\n",
      "6802 FOV: 092\n",
      "3115 FOV: 094\n",
      "186 FOV: 095\n",
      "222 FOV: 096\n",
      "264 FOV: 097\n",
      "278 FOV: 098\n",
      "6034 FOV: 100\n",
      "276 FOV: 101\n",
      "1 FOV: 102\n",
      "1 FOV: 103\n",
      "492 FOV: 105\n",
      "14 FOV: 106\n",
      "7563 FOV: 108\n",
      "5384 FOV: 110\n",
      "1 FOV: 112\n",
      "31 FOV: 113\n",
      "16 FOV: 114\n",
      "20 FOV: 115\n",
      "514 FOV: 116\n",
      "239 FOV: 118\n",
      "11061 FOV: 120\n",
      "17628 FOV: 123\n",
      "28627 FOV: 125\n",
      "3 FOV: 126\n",
      "302 FOV: 127\n",
      "269 FOV: 128\n",
      "261 FOV: 132\n",
      "247 FOV: 133\n",
      "229 FOV: 134\n",
      "264 FOV: 135\n",
      "18655 FOV: 137\n",
      "263 FOV: 141\n",
      "262 FOV: 142\n",
      "13344 FOV: 143\n",
      "288 FOV: 144\n",
      "259 FOV: 145\n",
      "9822 FOV: 146\n",
      "84 FOV: 147\n",
      "20992 FOV: 148\n",
      "8 FOV: 149\n",
      "236 FOV: 151\n",
      "6382 FOV: 152\n",
      "1158 FOV: 153\n",
      "6600 FOV: 154\n",
      "12743 FOV: 155\n",
      "9 FOV: 156\n",
      "9077 FOV: 157\n",
      "98 FOV: 158\n",
      "8316 FOV: 159\n",
      "5700 FOV: 162\n",
      "10379 FOV: 163\n",
      "255 FOV: 166\n",
      "274 FOV: 167\n",
      "12906 FOV: 168\n",
      "9 FOV: 170\n",
      "208 FOV: 171\n",
      "5733 FOV: 172\n",
      "27 FOV: 179\n",
      "274 FOV: 185\n",
      "400 FOV: 186\n",
      "1246 FOV: 187\n",
      "335 FOV: 188\n",
      "294 FOV: 189\n",
      "2 FOV: 190\n",
      "1 FOV: 192\n",
      "216 FOV: 193\n",
      "491 FOV: 194\n",
      "12067 FOV: 196\n",
      "4671 FOV: 197\n",
      "246 FOV: 198\n",
      "4586 FOV: 199\n",
      "1 FOV: 200\n",
      "10 FOV: 204\n",
      "13573 FOV: 206\n",
      "9 FOV: 208\n",
      "8328 FOV: 213\n",
      "346 FOV: 214\n",
      "6349 FOV: 217\n"
     ]
    }
   ],
   "source": [
    "for idx, val in enumerate(int_counts['one to five']):\n",
    "    if val != 0:\n",
    "        print(int(int_counts.iloc[idx, 2]), \"FOV:\", int_counts.iloc[idx, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000 493858.0\n",
      "001 480241.0\n",
      "002 2416700.0\n",
      "003 944596.0\n",
      "007 480474.0\n",
      "012 481041.0\n",
      "013 1457160.0\n",
      "015 585413.0\n",
      "016 304764.0\n",
      "017 1017722.0\n",
      "019 301044.0\n",
      "020 980228.0\n",
      "021 443780.0\n",
      "022 492682.0\n",
      "026 996027.0\n",
      "027 779911.0\n",
      "030 922826.0\n",
      "031 1232731.0\n",
      "032 1083472.0\n",
      "033 483832.0\n",
      "035 311925.0\n",
      "036 499382.0\n",
      "037 498260.0\n",
      "038 912944.0\n",
      "041 488841.0\n",
      "045 795532.0\n",
      "049 475110.0\n",
      "051 436913.0\n",
      "052 1371664.0\n",
      "056 995475.0\n",
      "057 503126.0\n",
      "058 441043.0\n",
      "059 946451.0\n",
      "063 484210.0\n",
      "064 595159.0\n",
      "065 782397.0\n",
      "067 795241.0\n",
      "069 774615.0\n",
      "072 494978.0\n",
      "075 484242.0\n",
      "077 524754.0\n",
      "078 315622.0\n",
      "082 796600.0\n",
      "087 315520.0\n",
      "088 972739.0\n",
      "089 1406480.0\n",
      "092 483494.0\n",
      "094 309390.0\n",
      "100 502231.0\n",
      "108 417115.0\n",
      "110 454808.0\n",
      "120 497535.0\n",
      "123 922866.0\n",
      "125 1459560.0\n",
      "137 958329.0\n",
      "143 954651.0\n",
      "146 901530.0\n",
      "148 1848175.0\n",
      "152 311785.0\n",
      "154 311847.0\n",
      "155 1000128.0\n",
      "157 496514.0\n",
      "159 481375.0\n",
      "162 495074.0\n",
      "163 596200.0\n",
      "168 608370.0\n",
      "172 313516.0\n",
      "187 655927.0\n",
      "196 493471.0\n",
      "197 312634.0\n",
      "199 298643.0\n",
      "203 1048146.0\n",
      "206 911753.0\n",
      "213 497038.0\n",
      "215 643200.0\n",
      "217 308059.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['000',\n",
       " '001',\n",
       " '002',\n",
       " '003',\n",
       " '007',\n",
       " '012',\n",
       " '013',\n",
       " '015',\n",
       " '016',\n",
       " '017',\n",
       " '019',\n",
       " '020',\n",
       " '021',\n",
       " '022',\n",
       " '026',\n",
       " '027',\n",
       " '030',\n",
       " '031',\n",
       " '032',\n",
       " '033',\n",
       " '035',\n",
       " '036',\n",
       " '037',\n",
       " '038',\n",
       " '041',\n",
       " '045',\n",
       " '049',\n",
       " '051',\n",
       " '052',\n",
       " '056',\n",
       " '057',\n",
       " '058',\n",
       " '059',\n",
       " '063',\n",
       " '064',\n",
       " '065',\n",
       " '067',\n",
       " '069',\n",
       " '072',\n",
       " '075',\n",
       " '077',\n",
       " '078',\n",
       " '082',\n",
       " '087',\n",
       " '088',\n",
       " '089',\n",
       " '092',\n",
       " '094',\n",
       " '100',\n",
       " '108',\n",
       " '110',\n",
       " '120',\n",
       " '123',\n",
       " '125',\n",
       " '137',\n",
       " '143',\n",
       " '146',\n",
       " '148',\n",
       " '152',\n",
       " '154',\n",
       " '155',\n",
       " '157',\n",
       " '159',\n",
       " '162',\n",
       " '163',\n",
       " '168',\n",
       " '172',\n",
       " '187',\n",
       " '196',\n",
       " '197',\n",
       " '199',\n",
       " '203',\n",
       " '206',\n",
       " '213',\n",
       " '215',\n",
       " '217']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOOKING FOR WHICH FOVS HAVE THE BLACK BOXES\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": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(black_box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_to_five_set)               # num FOVs that have pixel intensity 1-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3150"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('000', 0, 32),\n",
       " ('000', 0, 33),\n",
       " ('000', 0, 37),\n",
       " ('000', 1, 32),\n",
       " ('000', 1, 33),\n",
       " ('000', 1, 37),\n",
       " ('000', 2, 32),\n",
       " ('000', 2, 33),\n",
       " ('000', 2, 37),\n",
       " ('000', 3, 32),\n",
       " ('000', 3, 33),\n",
       " ('000', 3, 37),\n",
       " ('000', 4, 32),\n",
       " ('000', 4, 33),\n",
       " ('000', 4, 37),\n",
       " ('000', 5, 32),\n",
       " ('000', 5, 33),\n",
       " ('000', 5, 37),\n",
       " ('000', 6, 32),\n",
       " ('000', 6, 33),\n",
       " ('000', 6, 37),\n",
       " ('000', 7, 32),\n",
       " ('000', 7, 33),\n",
       " ('000', 7, 37),\n",
       " ('000', 8, 32),\n",
       " ('000', 8, 33),\n",
       " ('000', 9, 32),\n",
       " ('000', 9, 33),\n",
       " ('000', 10, 32),\n",
       " ('000', 10, 33),\n",
       " ('000', 11, 32),\n",
       " ('000', 11, 33),\n",
       " ('000', 12, 32),\n",
       " ('000', 12, 33),\n",
       " ('001', 0, 12),\n",
       " ('001', 0, 32),\n",
       " ('001', 0, 33),\n",
       " ('001', 1, 12),\n",
       " ('001', 1, 32),\n",
       " ('001', 1, 33),\n",
       " ('001', 2, 12),\n",
       " ('001', 2, 32),\n",
       " ('001', 2, 33),\n",
       " ('001', 3, 12),\n",
       " ('001', 3, 32),\n",
       " ('001', 3, 33),\n",
       " ('001', 4, 12),\n",
       " ('001', 4, 32),\n",
       " ('001', 4, 33),\n",
       " ('001', 5, 12),\n",
       " ('001', 5, 32),\n",
       " ('001', 5, 33),\n",
       " ('001', 6, 12),\n",
       " ('001', 6, 32),\n",
       " ('001', 6, 33),\n",
       " ('001', 7, 12),\n",
       " ('001', 7, 32),\n",
       " ('001', 7, 33),\n",
       " ('001', 8, 32),\n",
       " ('001', 8, 33),\n",
       " ('001', 9, 32),\n",
       " ('001', 9, 33),\n",
       " ('001', 10, 32),\n",
       " ('001', 10, 33),\n",
       " ('001', 11, 32),\n",
       " ('001', 11, 33),\n",
       " ('001', 12, 32),\n",
       " ('001', 12, 33),\n",
       " ('002', 0, 10),\n",
       " ('002', 0, 22),\n",
       " ('002', 0, 32),\n",
       " ('002', 0, 33),\n",
       " ('002', 1, 10),\n",
       " ('002', 1, 22),\n",
       " ('002', 1, 32),\n",
       " ('002', 1, 33),\n",
       " ('002', 2, 10),\n",
       " ('002', 2, 22),\n",
       " ('002', 2, 32),\n",
       " ('002', 2, 33),\n",
       " ('002', 3, 10),\n",
       " ('002', 3, 22),\n",
       " ('002', 3, 32),\n",
       " ('002', 3, 33),\n",
       " ('002', 4, 10),\n",
       " ('002', 4, 22),\n",
       " ('002', 4, 32),\n",
       " ('002', 4, 33),\n",
       " ('002', 5, 10),\n",
       " ('002', 5, 22),\n",
       " ('002', 5, 32),\n",
       " ('002', 5, 33),\n",
       " ('002', 6, 10),\n",
       " ('002', 6, 22),\n",
       " ('002', 6, 32),\n",
       " ('002', 6, 33),\n",
       " ('002', 7, 10),\n",
       " ('002', 7, 22),\n",
       " ('002', 7, 32),\n",
       " ('002', 7, 33),\n",
       " ('002', 7, 35),\n",
       " ('002', 8, 19),\n",
       " ('002', 8, 28),\n",
       " ('002', 8, 32),\n",
       " ('002', 8, 33),\n",
       " ('002', 8, 35),\n",
       " ('002', 9, 19),\n",
       " ('002', 9, 28),\n",
       " ('002', 9, 32),\n",
       " ('002', 9, 33),\n",
       " ('002', 9, 35),\n",
       " ('002', 10, 19),\n",
       " ('002', 10, 28),\n",
       " ('002', 10, 32),\n",
       " ('002', 10, 33),\n",
       " ('002', 11, 19),\n",
       " ('002', 11, 28),\n",
       " ('002', 11, 32),\n",
       " ('002', 11, 33),\n",
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       " ('027', 6, 33),\n",
       " ('027', 7, 30),\n",
       " ('027', 7, 32),\n",
       " ('027', 7, 33),\n",
       " ('027', 8, 23),\n",
       " ('027', 8, 32),\n",
       " ('027', 8, 33),\n",
       " ('027', 9, 23),\n",
       " ('027', 9, 32),\n",
       " ('027', 9, 33),\n",
       " ('027', 10, 23),\n",
       " ('027', 10, 32),\n",
       " ('027', 10, 33),\n",
       " ('027', 11, 23),\n",
       " ('027', 11, 32),\n",
       " ('027', 11, 33),\n",
       " ('027', 12, 23),\n",
       " ('027', 12, 32),\n",
       " ('027', 12, 33),\n",
       " ('028', 0, 32),\n",
       " ('028', 0, 33),\n",
       " ('028', 1, 32),\n",
       " ('028', 1, 33),\n",
       " ('028', 2, 32),\n",
       " ('028', 2, 33),\n",
       " ('028', 3, 32),\n",
       " ('028', 3, 33),\n",
       " ('028', 4, 32),\n",
       " ('028', 4, 33),\n",
       " ('028', 5, 32),\n",
       " ('028', 5, 33),\n",
       " ('028', 6, 32),\n",
       " ('028', 6, 33),\n",
       " ('028', 7, 32),\n",
       " ('028', 7, 33),\n",
       " ('028', 8, 32),\n",
       " ('028', 8, 33),\n",
       " ('028', 9, 32),\n",
       " ('028', 9, 33),\n",
       " ('028', 10, 32),\n",
       " ('028', 10, 33),\n",
       " ('028', 11, 32),\n",
       " ('028', 11, 33),\n",
       " ('028', 12, 32),\n",
       " ('028', 12, 33),\n",
       " ('029', 0, 32),\n",
       " ('029', 0, 33),\n",
       " ('029', 1, 32),\n",
       " ('029', 1, 33),\n",
       " ('029', 2, 32),\n",
       " ('029', 2, 33),\n",
       " ('029', 3, 32),\n",
       " ('029', 3, 33),\n",
       " ('029', 4, 32),\n",
       " ('029', 4, 33),\n",
       " ('029', 5, 32),\n",
       " ('029', 5, 33),\n",
       " ('029', 6, 32),\n",
       " ('029', 6, 33),\n",
       " ('029', 7, 32),\n",
       " ('029', 7, 33),\n",
       " ('029', 8, 32),\n",
       " ('029', 8, 33),\n",
       " ('029', 9, 32),\n",
       " ('029', 9, 33),\n",
       " ('029', 10, 32),\n",
       " ('029', 10, 33),\n",
       " ('029', 11, 32),\n",
       " ('029', 11, 33),\n",
       " ('029', 12, 32),\n",
       " ('029', 12, 33),\n",
       " ('030', 0, 19),\n",
       " ('030', 0, 25),\n",
       " ('030', 1, 19),\n",
       " ('030', 1, 25),\n",
       " ('030', 2, 19),\n",
       " ('030', 2, 25),\n",
       " ('030', 3, 19),\n",
       " ('030', 3, 25),\n",
       " ('030', 4, 19),\n",
       " ('030', 4, 25),\n",
       " ('030', 5, 19),\n",
       " ('030', 5, 25),\n",
       " ('030', 6, 19),\n",
       " ('030', 6, 25),\n",
       " ('030', 7, 19),\n",
       " ('030', 7, 25),\n",
       " ('030', 9, 35),\n",
       " ('031', 0, 9),\n",
       " ('031', 0, 32),\n",
       " ('031', 0, 33),\n",
       " ('031', 1, 9),\n",
       " ('031', 1, 32),\n",
       " ('031', 1, 33),\n",
       " ('031', 2, 9),\n",
       " ('031', 2, 32),\n",
       " ('031', 2, 33),\n",
       " ('031', 3, 9),\n",
       " ('031', 3, 32),\n",
       " ('031', 3, 33),\n",
       " ('031', 4, 9),\n",
       " ('031', 4, 32),\n",
       " ('031', 4, 33),\n",
       " ('031', 5, 9),\n",
       " ('031', 5, 32),\n",
       " ('031', 5, 33),\n",
       " ('031', 6, 9),\n",
       " ('031', 6, 32),\n",
       " ('031', 6, 33),\n",
       " ('031', 7, 9),\n",
       " ('031', 7, 32),\n",
       " ('031', 7, 33),\n",
       " ('031', 8, 32),\n",
       " ('031', 8, 33),\n",
       " ('031', 9, 32),\n",
       " ('031', 9, 33),\n",
       " ('031', 10, 32),\n",
       " ('031', 10, 33),\n",
       " ('031', 11, 32),\n",
       " ('031', 11, 33),\n",
       " ('031', 12, 32),\n",
       " ('031', 12, 33),\n",
       " ('032', 0, 26),\n",
       " ('032', 0, 32),\n",
       " ('032', 0, 33),\n",
       " ('032', 1, 26),\n",
       " ('032', 1, 32),\n",
       " ('032', 1, 33),\n",
       " ('032', 2, 26),\n",
       " ('032', 2, 32),\n",
       " ('032', 2, 33),\n",
       " ('032', 3, 26),\n",
       " ('032', 3, 32),\n",
       " ('032', 3, 33),\n",
       " ('032', 4, 26),\n",
       " ('032', 4, 32),\n",
       " ('032', 4, 33),\n",
       " ('032', 5, 26),\n",
       " ('032', 5, 32),\n",
       " ('032', 5, 33),\n",
       " ('032', 6, 26),\n",
       " ('032', 6, 32),\n",
       " ('032', 6, 33),\n",
       " ('032', 7, 26),\n",
       " ('032', 7, 32),\n",
       " ('032', 7, 33),\n",
       " ('032', 8, 10),\n",
       " ('032', 8, 32),\n",
       " ('032', 8, 33),\n",
       " ('032', 9, 10),\n",
       " ('032', 9, 32),\n",
       " ('032', 9, 33),\n",
       " ('032', 10, 10),\n",
       " ('032', 10, 32),\n",
       " ('032', 10, 33),\n",
       " ('032', 11, 10),\n",
       " ('032', 11, 32),\n",
       " ('032', 11, 33),\n",
       " ('032', 12, 10),\n",
       " ('032', 12, 32),\n",
       " ('032', 12, 33),\n",
       " ('033', 0, 25),\n",
       " ('033', 0, 32),\n",
       " ('033', 0, 33),\n",
       " ('033', 1, 25),\n",
       " ('033', 1, 32),\n",
       " ('033', 1, 33),\n",
       " ('033', 2, 25),\n",
       " ('033', 2, 32),\n",
       " ('033', 2, 33),\n",
       " ('033', 3, 25),\n",
       " ('033', 3, 32),\n",
       " ('033', 3, 33),\n",
       " ('033', 4, 25),\n",
       " ('033', 4, 32),\n",
       " ('033', 4, 33),\n",
       " ('033', 5, 25),\n",
       " ('033', 5, 32),\n",
       " ('033', 5, 33),\n",
       " ('033', 6, 25),\n",
       " ('033', 6, 32),\n",
       " ('033', 6, 33),\n",
       " ('033', 7, 18),\n",
       " ('033', 7, 25),\n",
       " ('033', 7, 32),\n",
       " ('033', 7, 33),\n",
       " ('033', 8, 32),\n",
       " ('033', 8, 33),\n",
       " ('033', 9, 32),\n",
       " ('033', 9, 33),\n",
       " ('033', 10, 32),\n",
       " ('033', 10, 33),\n",
       " ('033', 11, 32),\n",
       " ('033', 11, 33),\n",
       " ('033', 12, 32),\n",
       " ('033', 12, 33),\n",
       " ('034', 0, 32),\n",
       " ('034', 0, 33),\n",
       " ('034', 1, 32),\n",
       " ('034', 1, 33),\n",
       " ('034', 2, 32),\n",
       " ('034', 2, 33),\n",
       " ('034', 3, 32),\n",
       " ('034', 3, 33),\n",
       " ('034', 4, 32),\n",
       " ('034', 4, 33),\n",
       " ('034', 5, 32),\n",
       " ('034', 5, 33),\n",
       " ('034', 6, 32),\n",
       " ('034', 6, 33),\n",
       " ('034', 7, 32),\n",
       " ('034', 7, 33),\n",
       " ...]"
      ]
     },
     "execution_count": 28,
     "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  0,  0, ..., 12, 12, 12]),\n",
       " array([32, 32, 32, ..., 33, 33, 33]),\n",
       " array([1802, 1803, 1804, ..., 1993, 1994, 1995]),\n",
       " array([2028, 2028, 2028, ..., 2028, 2028, 2028]))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = 'merged/F000.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": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
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       "      <td>37.0</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
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       "      <td>3009</td>\n",
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       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <td>3010</td>\n",
       "      <td>217</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <td>3011</td>\n",
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       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <td>3012</td>\n",
       "      <td>217</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3013</td>\n",
       "      <td>217</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1635 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num     Z  channel\n",
       "0        000   0.0     32.0\n",
       "1        000   0.0     33.0\n",
       "2        000   0.0     37.0\n",
       "3        000   1.0     32.0\n",
       "4        000   1.0     33.0\n",
       "...      ...   ...      ...\n",
       "3009     217   8.0     12.0\n",
       "3010     217   9.0     12.0\n",
       "3011     217  10.0     12.0\n",
       "3012     217  11.0     12.0\n",
       "3013     217  12.0     12.0\n",
       "\n",
       "[1635 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "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\n",
    "df_black_box.to_csv('black_box_locations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>FOV_num</th>\n",
       "      <th>total Zero count</th>\n",
       "      <th>one to five</th>\n",
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       "      <td>4.360853e-03</td>\n",
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       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>0.117057</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>3.498481e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>002</td>\n",
       "      <td>0.589060</td>\n",
       "      <td>0.009890</td>\n",
       "      <td>1.353787e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
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       "      <td>0.230241</td>\n",
       "      <td>0.003106</td>\n",
       "      <td>5.943737e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>2.702408e-03</td>\n",
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       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.437456e-07</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          0.120376     0.003265  4.360853e-03\n",
       "001     001          0.117057     0.002513  3.498481e-03\n",
       "002     002          0.589060     0.009890  1.353787e-02\n",
       "003     003          0.230241     0.003106  5.943737e-03\n",
       "004     004          0.000067     0.000118  2.702408e-03\n",
       "..      ...               ...          ...           ...\n",
       "220     220          0.000000     0.000000  0.000000e+00\n",
       "221     221          0.000000     0.000000  0.000000e+00\n",
       "222     222          0.000000     0.000000  0.000000e+00\n",
       "223     223          0.000000     0.000000  0.000000e+00\n",
       "224     224          0.000000     0.000000  2.437456e-07\n",
       "\n",
       "[211 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total num of pixels is 3995601 (because 2019*1979)\n",
    "total = img.shape[2] * img.shape[3]\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(total)\n",
    "percents['one to five'] = percents['one to five'].div(total)\n",
    "percents['greater 65K'] = percents['greater 65K'].div(total)\n",
    "percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "percents.to_csv('percent_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>004</td>\n",
       "      <td>004</td>\n",
       "      <td>6.678630e-05</td>\n",
       "      <td>1.177291e-04</td>\n",
       "      <td>0.002702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>005</td>\n",
       "      <td>005</td>\n",
       "      <td>2.803075e-05</td>\n",
       "      <td>6.361760e-05</td>\n",
       "      <td>0.000646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>008</td>\n",
       "      <td>008</td>\n",
       "      <td>4.801789e-05</td>\n",
       "      <td>1.196791e-04</td>\n",
       "      <td>0.001091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>011</td>\n",
       "      <td>011</td>\n",
       "      <td>3.022446e-05</td>\n",
       "      <td>6.727379e-05</td>\n",
       "      <td>0.000836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>014</td>\n",
       "      <td>014</td>\n",
       "      <td>1.043231e-04</td>\n",
       "      <td>2.208335e-04</td>\n",
       "      <td>0.005722</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>198</td>\n",
       "      <td>198</td>\n",
       "      <td>2.729951e-05</td>\n",
       "      <td>5.996142e-05</td>\n",
       "      <td>0.000289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.437456e-07</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>7.312368e-07</td>\n",
       "      <td>2.437456e-06</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>208</td>\n",
       "      <td>208</td>\n",
       "      <td>7.312368e-07</td>\n",
       "      <td>2.193710e-06</td>\n",
       "      <td>0.000036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>4.631167e-05</td>\n",
       "      <td>8.433598e-05</td>\n",
       "      <td>0.001038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count   one to five  greater 65K\n",
       "004     004      6.678630e-05  1.177291e-04     0.002702\n",
       "005     005      2.803075e-05  6.361760e-05     0.000646\n",
       "008     008      4.801789e-05  1.196791e-04     0.001091\n",
       "011     011      3.022446e-05  6.727379e-05     0.000836\n",
       "014     014      1.043231e-04  2.208335e-04     0.005722\n",
       "..      ...               ...           ...          ...\n",
       "198     198      2.729951e-05  5.996142e-05     0.000289\n",
       "200     200      0.000000e+00  2.437456e-07     0.000002\n",
       "204     204      7.312368e-07  2.437456e-06     0.000031\n",
       "208     208      7.312368e-07  2.193710e-06     0.000036\n",
       "214     214      4.631167e-05  8.433598e-05     0.001038\n",
       "\n",
       "[73 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_circles.to_csv('black_circle_pixel_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['004',\n",
       " '005',\n",
       " '008',\n",
       " '011',\n",
       " '014',\n",
       " '023',\n",
       " '024',\n",
       " '025',\n",
       " '028',\n",
       " '029',\n",
       " '034',\n",
       " '039',\n",
       " '040',\n",
       " '042',\n",
       " '044',\n",
       " '046',\n",
       " '047',\n",
       " '050',\n",
       " '053',\n",
       " '054',\n",
       " '060',\n",
       " '066',\n",
       " '068',\n",
       " '070',\n",
       " '073',\n",
       " '074',\n",
       " '084',\n",
       " '095',\n",
       " '096',\n",
       " '097',\n",
       " '098',\n",
       " '101',\n",
       " '102',\n",
       " '105',\n",
       " '106',\n",
       " '112',\n",
       " '113',\n",
       " '114',\n",
       " '115',\n",
       " '116',\n",
       " '118',\n",
       " '126',\n",
       " '127',\n",
       " '128',\n",
       " '132',\n",
       " '133',\n",
       " '134',\n",
       " '135',\n",
       " '142',\n",
       " '144',\n",
       " '145',\n",
       " '147',\n",
       " '149',\n",
       " '151',\n",
       " '153',\n",
       " '156',\n",
       " '167',\n",
       " '170',\n",
       " '171',\n",
       " '179',\n",
       " '185',\n",
       " '186',\n",
       " '188',\n",
       " '189',\n",
       " '190',\n",
       " '192',\n",
       " '193',\n",
       " '194',\n",
       " '198',\n",
       " '200',\n",
       " '204',\n",
       " '208',\n",
       " '214']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circle_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>168</td>\n",
       "      <td>004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3131</td>\n",
       "      <td>208</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3132</td>\n",
       "      <td>208</td>\n",
       "      <td>12.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3141</td>\n",
       "      <td>214</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3142</td>\n",
       "      <td>214</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3143</td>\n",
       "      <td>214</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1407 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num     Z  channel\n",
       "168      004   0.0     32.0\n",
       "169      004   0.0     33.0\n",
       "170      004   1.0     32.0\n",
       "171      004   1.0     33.0\n",
       "172      004   2.0     32.0\n",
       "...      ...   ...      ...\n",
       "3131     208  11.0     35.0\n",
       "3132     208  12.0     35.0\n",
       "3141     214   8.0     35.0\n",
       "3142     214   9.0     35.0\n",
       "3143     214  10.0     35.0\n",
       "\n",
       "[1407 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = imread('merged/F000.tif')\n",
    "image = image[0, 32, ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f229444af50>"
      ]
     },
     "execution_count": 43,
     "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",
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       " ('220', 6, 3),\n",
       " ('220', 7, 3),\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": 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",
       "        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>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",
       "        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",
       "      <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",
       " '084',\n",
       " '085',\n",
       " '088',\n",
       " '094',\n",
       " '101',\n",
       " '111',\n",
       " '113',\n",
       " '114',\n",
       " '117',\n",
       " '120',\n",
       " '124',\n",
       " '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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = imread('merged/F000.tif')\n",
    "arr = arr[0, 32, ...]\n",
    "#plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2012, 2019)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len((np.where(arr == 0))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 211"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/html": [
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       "\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_0th_perc</th>\n",
       "      <th>ch1_0th_perc</th>\n",
       "      <th>ch2_0th_perc</th>\n",
       "      <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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\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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ezIMPPlgJz5aIVCWaz72K69WrF7NmzSIzs9gpm+NCz7WETVnmc4+ewz3a7vTBRctVveeu+dxFRKoZDctUEZMnT2bTpk2nlU2ZMoX169cnJiARSWpK7lXEnDlzEh2CiISIhmVEREJIyV1EJIQ0LCMi1d62g42Lli9MT1wc8ZQ0yT361qZ4KOn2KBGRMNCwzDmcaz73DRs20LVrV1JSUli+fHkCIhQROZuSeynKMp97ixYtWLhwIWPGjDkv8YiIlIWSeynKMp97eno6GRkZfOc7534q169fT8+ePRkxYgTt27fn9ttv59SpUwCsXbuW7t2707VrV0aOHMlXX31VdPwZM2ZwzTXXsGzZMnbt2kW/fv3o3LkzXbt25aOPPop/w0Uk6Sm5l6K4+dwrOid7oby8PH75y1/y3nvv8dFHH/HCCy9w+PBhfv7zn/PKK6/w5z//mczMTB555JGifWrXrs3GjRsZNWoUN910E5MnT+bdd9/l9ddfp2nTpjHFIyLhlDQfqCZCPOdkL5SdnU2rVpH5KkaPHs3GjRupXbs227dv5+qrrwbgm2++oXv37kX73HjjjQB8+eWX7N+/nxEjRgCRpC8iUhwl91LEc072QiXNzd6/f38WL15c7D6F87NXhUneRCQ5JE1yT8Sti2WZz7288vLy2L17N5dffjlLly5l0qRJdOvWjcmTJ7Nr1y5at27N3//+d/Lz87niiitO27d+/fqkpaWxYsUKhg8fztdff83JkyeLfmVJRKRQTGPuZjbNzN43s21mttjMaptZSzPLNbOdZrbUzGrFK9jzLXo+93bt2nHDDTcUzee+atUqAN566y3S0tJYtmwZt912Gx06dCj1mN27dycnJ4eOHTvSsmVLRowYQePGjVm4cCGjR48mIyODbt268cEHHxS7/3PPPcfjjz9ORkYGPXr04G9/+1vc2y0iya/CPXczaw78GGjv7v8ws98Do4DBwKPuvsTMngImAk/GJdoEGDx4MIMHDz6tbMaMGUXLWVlZ5Ofnl/l4devWZenSpWeV9+nTh7feeuus8j179py23qZNG1599dUyn09EqqdY75ZJAeqYWQpQFzgA9AEKv82zCBge4zlERKScKtxzd/f9ZjYL2Av8A1gLvA0cdfcTQbV8oHnMUSaZ9957j5tvvvm0sgsuuIDc3Fx69eqVmKBEpFqJZVimITAMaAkcBZYBg4qpWuwtHmY2CZgEkW95hkmnTp3YsmVLosMQkWoslmGZfsBudz/k7t8CLwA9gIuDYRqANOCT4nZ293nununumY0bNy6uioiIVFAsyX0v0M3M6lrk5u2+wHbgNeD6oM5YYGUJ+4uISCWpcHJ391wiH5z+GXgvONY84G7gTjPbBVwCzI9DnCIiUg4xfYnJ3X8G/OyM4o+B7FiOW5xDs5+I6/Ea/+iHcT2eiEhVkjTfUE2U9PR0LrroImrUqEFKSgqbN29OdEgiIuek5F4Gr732Go0aNaq04584cYKUFF0KEYkfTfkbJ7169WLq1Kn06NGDjh07kpeXB0BBQQETJkwgKyuLLl26FM0Hv3DhQkaOHMmQIUMYMGAAAA8//DCdOnWic+fO5OTkJKwtIpL81F08BzNjwIABmBm33XYbkyZNKrFuQUEBr7/+Ohs2bGDChAls27aNBx54gD59+rBgwQKOHj1KdnY2/fr1A+CNN95g69atpKam8tJLL7FixQpyc3OpW7cun3322flqooiEkJL7OWzatIlmzZpx8OBB+vfvz3e/+1169uxZbN3Ro0cD0LNnT44dO8bRo0dZu3Ytq1atYtasWQAcP36cvXv3AtC/f39SU1MBeOWVVxg/fnzRDI+F5SIiFaHkfg6F87c3adKEESNGkJeXV2JyL2mu9ueff562bdueti03N7donnaIzNUe6w+BiIgUSprknohbFwsKCjh16hQXXXQRBQUFrF27lnvvvbfE+kuXLqV3795s3LiRBg0a0KBBAwYOHMjs2bOZPXs2ZsY777xDly5dztp3wIABzJgxgzFjxhQNy6j3LiIVlTTJPRE+/fTTop+0O3HiBGPGjOHaa68tsX7Dhg3p0aMHx44dY8GCBQDcc889TJ06lYyMDNyd9PR0Vq9efda+1157LVu2bCEzM5NatWoxePBgHnzwwcppmIiEnpJ7KVq1asW7775b5vrf//73eeihh04rq1OnDr/+9a/Pqjtu3DjGjRt3WllOTo7ukhGRuNCtkCIiIaSeezlNnjyZTZs2nVY2ZcoU1q9fn5iARESKoeReTnPmzEl0CCIi56RhGRGREFJyFxEJISV3EZEQSpox97wXP47r8bKHtIrr8UREqhL13EsxYcIEmjRpQseOHYvKPvvsM/r370+bNm3o378/n3/+eQIjFBEpnpJ7KcaNG8eaNWtOK5s5cyZ9+/Zl586d9O3bl5kzZ1bKud2dU6dOVcqxRST8lNxL0bNnz7Pmd1m5ciVjx44FYOzYsaxYsaLE/e+77z5uvvlm+vTpQ5s2bfjNb35TtO0///M/ycrKIiMjg5/9LPJLhXv27KFdu3bccccddO3alX379rFmzRq6du1K586d6du3byW0UkTCKGnG3KuKTz/9lKZNmwLQtGlTDh48WGr9rVu38uabb1JQUECXLl247rrr2LZtGzt37iQvLw93Z+jQoWzYsIEWLVrw4Ycf8swzzzB37lwOHTrED37wAzZs2EDLli01x7uIlJmSeyUbNmwYderUoU6dOvTu3Zu8vDw2btzI2rVri2aH/Oqrr9i5cyctWrTg8ssvp1u3bgC8+eab9OzZk5YtWwKa411Eyk7JvZwuvfRSDhw4QNOmTTlw4ABNmjQptX5Jc7xPnz6d22677bRte/bs0RzvIhIXSZPcq8qti0OHDmXRokXk5OSwaNEihg0bVmr9lStXMn36dAoKCli/fj0zZ86kTp063HPPPdx0003Uq1eP/fv3U7NmzbP27d69O5MnT2b37t1FwzLqvYtIWSRNck+E0aNHs379eg4fPkxaWhr3338/OTk53HDDDcyfP58WLVqwbNmyUo+RnZ3Nddddx969e7nnnnto1qwZzZo1Y8eOHXTv3h2AevXq8dvf/pYaNWqctm/jxo2ZN28e3/ve9zh16hRNmjTh5ZdfrrT2ikh4KLmXYvHixcWWr1u3rszHuOKKK5g3b95Z5VOmTGHKlClnlW/btu209UGDBjFo0KAyn09EBHQrpIhIKKnnHgfPPPMMjz322GllV199taYHFpGEqdLJPVnuFhk/fjzjx49PdBgV4u6JDkFEKkGVHZapXbs2R44cUfKpRO7OkSNHqF27dqJDEZE4q7I997S0NPLz8zl06FCiQwm12rVrk5aWlugwRCTOqmxyr1mzZtE3M0VEpHyq7LCMiIhUXEzJ3cwuNrPlZvaBme0ws+5mlmpmL5vZzuCxYbyCFRGRsom15/4YsMbdvwt0BnYAOcA6d28DrAvWRUTkPKpwcjez+kBPYD6Au3/j7keBYcCioNoiYHisQYqISPnE0nNvBRwCnjGzd8zsaTO7ELjU3Q8ABI+lT5soIiJxF0tyTwG6Ak+6exeggHIMwZjZJDPbbGabdbujiEh8xZLc84F8d88N1pcTSfafmllTgOCx2J8qcvd57p7p7pmNGzeOIQwRETlThZO7u/8N2GdmbYOivsB2YBUwNigbC6yMKUIRESm3WL/E9CPgv8ysFvAxMJ7IC8bvzWwisBcYGeM5RESknGJK7u6+BcgsZlPfWI4rIiKx0TdURURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhJTcRURCKNYpf0VEqoS5W+YmOoQqRT13EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRDSN1RFpOp47aF/Lveenrg4QkA9dxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJoZiTu5nVMLN3zGx1sN7SzHLNbKeZLTWzWrGHKSIi5RGPnvsUYEfU+i+AR929DfA5MDEO5xARkXKIKbmbWRpwHfB0sG5AH2B5UGURMDyWc4iISPnF2nP/FfAfwKlg/RLgqLufCNbzgeYxnkNERMqpwsndzP4NOOjub0cXF1PVS9h/kpltNrPNhw4dqmgYIiJSjFh67lcDQ81sD7CEyHDMr4CLzaxwtsk04JPidnb3ee6e6e6ZjRs3jiEMERE5U4WTu7tPd/c0d08HRgGvuvtNwGvA9UG1scDKmKMUEZFyqYz73O8G7jSzXUTG4OdXwjlERKQUcfmxDndfD6wPlj8GsuNxXBERqRh9Q1VEJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhOLyDVURkapq7pa5Rct3XHlHAiM5v9RzFxEJIfXcRURKkPfix0XL2UNaJTCS8lPPXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhHSfu4gk1msPnbu89/TzE0uIqOcuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhVOHkbmaXmdlrZrbDzN43sylBeaqZvWxmO4PHhvELV0REyiKWnvsJ4N/dvR3QDZhsZu2BHGCdu7cB1gXrIiJyHlU4ubv7AXf/c7D8JbADaA4MAxYF1RYBw2MNUkREyicuY+5mlg50AXKBS939AEReAIAm8TiHiIiUXcw/1mFm9YDnganufszMyrrfJGASQIsWLWINQ0TC7Dz+cEdB3ltFyxdmZ1XquSpTTD13M6tJJLH/l7u/EBR/amZNg+1NgYPF7evu89w9090zGzduHEsYIiJyhgr33C3SRZ8P7HD3R6I2rQLGAjODx5UxRSgi4VPST+uV09wtc+NynDCKZVjmauBm4D0z2xKU/ZRIUv+9mU0E9gIjYwtRRETKq8LJ3d03AiUNsPet6HFFJKTi1FuXstE3VEVEQkjJXUQkhJTcRURCSMldRCSElNxFREIo5m+oikjJHn35L0XL0/pfkcBIpLpRz11EJISU3EVEQkjJXUQkhDTmLlJFabxeYqGeu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAjpbhmRM1SXu1RKbOd5/L1SqTzquYuIhJB67iLlUJV79VUyNv36UsKo5y4iEkLquUtSqJK90ijR8ZWlTnQbqnrbyk299SpBPXcRkRBSz11EykZ30SQV9dxFREJIPXcpl2QfH64q8Zc0Rl+WsfvyHue8tFPj7FWOeu4iIiGk5C4iEkIalhGpoPIOoSRKeYdo3vj4SNFy996VElJM5m6Zm+gQkoJ67iIiIaSeu4RSsvSqyype7Ynb86IPUKs89dxFREJIPXepUsoyPnw+b/ML2zuAcgtZDz16vH5kOffNe/HjouXsIa3iFFHlUc9dRCSEKqXnbmbXAo8BNYCn3X1mZZxHKk95e9DRzuekWLH0rKvKF5qqgjfm31V8efSdM60uOV/hSBzEveduZjWAOcAgoD0w2szax/s8IiJSssrouWcDu9z9YwAzWwIMA7ZXwrmY/szwouWHxq+ojFMkpUT2qsv7lfhqP65dybrtnVe0/GaLScWWS/EK8t4qWr4wOyuBkZRfZYy5Nwf2Ra3nB2UiInKemLvH94BmI4GB7n5rsH4zkO3uPzqj3iSgsBvRFviwgqdsBByu4L7JIMztU9uSV5jbl0xtu9zdGxe3oTKGZfKBy6LW04BPzqzk7vOAmN8Xmtlmd8+M9ThVVZjbp7YlrzC3Lyxtq4xhmbeANmbW0sxqAaOAVZVwHhERKUHce+7ufsLMfgj8icitkAvc/f14n0dEREpWKfe5u/sfgT9WxrGLEfaP/MPcPrUteYW5faFoW9w/UBURkcTT9AMiIiGU1MndzK41sw/NbJeZ5SQ6nliY2WVm9pqZ7TCz981sSlCeamYvm9nO4LFhomOtKDOrYWbvmNnqYL2lmeUGbVsafACflMzsYjNbbmYfBNewe1iunZlNC/5NbjOzxWZWO5mvnea0DH8AAAL0SURBVJktMLODZrYtqqzYa2URjwc5ZquZdU1c5OWTtMk9hNMcnAD+3d3bAd2AyUF7coB17t4GWBesJ6spwI6o9V8AjwZt+xyYmJCo4uMxYI27fxfoTKSdSX/tzKw58GMg0907ErlJYhTJfe0WAteeUVbStRoEtAn+JgFPnqcYY5a0yZ2oaQ7c/RugcJqDpOTuB9z9z8Hyl0SSQ3MibVoUVFsEDC/+CFWbmaUB1wFPB+sG9AGWB1WSuW31gZ7AfAB3/8bdjxKSa0fkxos6ZpYC1AUOkMTXzt03AJ+dUVzStRoGPOsRbwIXm1nT8xNpbJI5uYd2mgMzSwe6ALnApe5+ACIvAECTxEUWk18B/wGcCtYvAY66+4lgPZmvXyvgEPBMMOz0tJldSAiunbvvB2YBe4kk9S+AtwnPtStU0rVK2jyTzMndiilL+lt/zKwe8Dww1d2PJTqeeDCzfwMOuvvb0cXFVE3W65cCdAWedPcuQAFJOARTnGDseRjQEmgGXEhkqOJMyXrtziVp/50mc3Iv0zQHycTMahJJ7P/l7i8ExZ8Wvg0MHg8mKr4YXA0MNbM9RIbP+hDpyV8cvNWH5L5++UC+u+cG68uJJPswXLt+wG53P+Tu3wIvAD0Iz7UrVNK1Sto8k8zJPVTTHARj0POBHe7+SNSmVcDYYHkssPJ8xxYrd5/u7mnunk7kOr3q7jcBrwHXB9WSsm0A7v43YJ+ZtQ2K+hKZ4jrprx2R4ZhuZlY3+Dda2LZQXLsoJV2rVcAtwV0z3YAvCodvqjx3T9o/YDDwF+Aj4H8lOp4Y23INkbd7W4Etwd9gImPT64CdwWNqomONsZ29gNXBcisgD9gFLAMuSHR8MbTrSmBzcP1WAA3Dcu2A+4EPgG3Ac8AFyXztgMVEPj/4lkjPfGJJ14rIsMycIMe8R+SuoYS3oSx/+oaqiEgIJfOwjIiIlEDJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhP4/43gA6rHqmBIAAAAASUVORK5CYII=\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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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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 1,
   "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",
    "from skimage.segmentation import *\n",
    "from skimage import measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 9,
   "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": 9,
     "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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000', '001', '002', '003', '004', '005', '006', '007', '008', '009', '010', '011', '012', '013', '014', '015', '016', '017', '018', '019', '020', '021', '022', '023', '024', '025', '027', '028', '029', '030', '031', '032', '033', '035', '036', '037', '038', '041', '042', '043', '044', '045', '046', '047', '048', '049', '050', '051', '052', '054', '055', '056', '057', '058', '059', '061', '062', '063', '064', '065', '066', '067', '068', '069', '070', '071', '072', '073', '074', '077', '078', '079', '080', '081', '082', '083', '084', '085', '086', '087', '088', '089', '090', '091', '092', '093', '094', '095', '096', '097', '098', '099', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '220', '221', '222', '223', '224']\n",
      "210\n"
     ]
    }
   ],
   "source": [
    "_allFOVs = sorted(glob.glob('tmat_Cyc_2/*'))\n",
    "allFOVs = [x.split('F')[-1][:3] for x in _allFOVs]\n",
    "print(allFOVs)\n",
    "print(len(allFOVs))\n",
    "NUM_FOVS = len(allFOVs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000', '001', '002', '003', '004', '005', '006', '007', '008', '009']\n",
      "['010', '011', '012', '013', '014', '015', '016', '017', '018', '019']\n",
      "['020', '021', '022', '023', '024', '025', '027', '028', '029', '030']\n",
      "['031', '032', '033', '035', '036', '037', '038', '041', '042', '043']\n",
      "['044', '045', '046', '047', '048', '049', '050', '051', '052', '054']\n",
      "['055', '056', '057', '058', '059', '061', '062', '063', '064', '065']\n",
      "['066', '067', '068', '069', '070', '071', '072', '073', '074', '077']\n",
      "['078', '079', '080', '081', '082', '083', '084', '085', '086', '087']\n",
      "['088', '089', '090', '091', '092', '093', '094', '095', '096', '097']\n",
      "['098', '099', '100', '101', '102', '103', '104', '105', '106', '107']\n",
      "['108', '109', '110', '111', '112', '113', '114', '115', '116', '117']\n",
      "['118', '119', '120', '121', '122', '123', '124', '125', '126', '127']\n",
      "['128', '129', '130', '131', '132', '133', '134', '135', '136', '137']\n",
      "['138', '140', '141', '142', '143', '144', '145', '146', '147', '148']\n",
      "['149', '151', '152', '153', '154', '155', '156', '157', '158', '159']\n",
      "['160', '161', '162', '164', '165', '166', '167', '168', '169', '170']\n",
      "['171', '172', '173', '174', '175', '176', '177', '179', '180', '181']\n",
      "['182', '183', '184', '185', '186', '187', '188', '189', '190', '191']\n",
      "['193', '194', '195', '196', '197', '198', '199', '200', '201', '202']\n",
      "['203', '204', '205', '207', '208', '209', '210', '211', '212', '213']\n",
      "['214', '215', '216', '217', '218', '220', '221', '222', '223', '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": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "      <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>223</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>210 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",
       "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",
       "223  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",
       "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",
       "223  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "224  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[210 rows x 38 columns]"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "mask shape (2009, 2011)\n",
      "F000, no mask needed\n",
      "FOV 001\n",
      "mask shape (2009, 2011)\n",
      "F001, no mask needed\n",
      "FOV 002\n",
      "mask shape (2009, 2011)\n",
      "F002, no mask needed\n",
      "FOV 003\n",
      "mask shape (2009, 2011)\n",
      "F003, no mask needed\n",
      "FOV 004\n",
      "mask shape (2009, 2011)\n",
      "FOV 005\n",
      "mask shape (2009, 2011)\n",
      "FOV 006\n",
      "mask shape (2009, 2011)\n",
      "F006, no mask needed\n",
      "FOV 007\n",
      "mask shape (2009, 2011)\n",
      "FOV 008\n",
      "mask shape (2009, 2011)\n",
      "FOV 009\n",
      "mask shape (2009, 2011)\n",
      "F009, no mask needed\n",
      "FOV 010\n",
      "mask shape (2009, 2011)\n",
      "FOV 011\n",
      "mask shape (2009, 2011)\n",
      "F011, no mask needed\n",
      "FOV 012\n",
      "mask shape (2009, 2011)\n",
      "FOV 013\n",
      "mask shape (2009, 2011)\n",
      "FOV 014\n",
      "mask shape (2009, 2011)\n",
      "FOV 015\n",
      "mask shape (2009, 2011)\n",
      "F015, no mask needed\n",
      "FOV 016\n",
      "mask shape (2009, 2011)\n",
      "F016, no mask needed\n",
      "FOV 017\n",
      "mask shape (2009, 2011)\n",
      "F017, no mask needed\n",
      "FOV 018\n",
      "mask shape (2009, 2011)\n",
      "F018, no mask needed\n",
      "FOV 019\n",
      "mask shape (2009, 2011)\n",
      "F019, no mask needed\n",
      "FOV 020\n",
      "mask shape (2009, 2011)\n",
      "F020, no mask needed\n",
      "FOV 021\n",
      "mask shape (2009, 2011)\n",
      "FOV 022\n",
      "mask shape (2009, 2011)\n",
      "F022, no mask needed\n",
      "FOV 023\n",
      "mask shape (2009, 2011)\n",
      "FOV 024\n",
      "mask shape (2009, 2011)\n",
      "F024, no mask needed\n",
      "FOV 025\n",
      "mask shape (2009, 2011)\n",
      "FOV 027\n",
      "mask shape (2009, 2011)\n",
      "FOV 028\n",
      "mask shape (2009, 2011)\n",
      "F028, no mask needed\n",
      "FOV 029\n",
      "mask shape (2009, 2011)\n",
      "FOV 030\n",
      "mask shape (2009, 2011)\n",
      "F030, no mask needed\n",
      "FOV 031\n",
      "mask shape (2009, 2011)\n",
      "F031, no mask needed\n",
      "FOV 032\n",
      "mask shape (2009, 2011)\n",
      "FOV 033\n",
      "mask shape (2009, 2011)\n",
      "FOV 035\n",
      "mask shape (2009, 2011)\n",
      "F035, no mask needed\n",
      "FOV 036\n",
      "mask shape (2009, 2011)\n",
      "F036, no mask needed\n",
      "FOV 037\n",
      "mask shape (2009, 2011)\n",
      "F037, no mask needed\n",
      "FOV 038\n",
      "mask shape (2009, 2011)\n",
      "FOV 041\n",
      "mask shape (2009, 2011)\n",
      "F041, no mask needed\n",
      "FOV 042\n",
      "mask shape (2009, 2011)\n",
      "FOV 043\n",
      "mask shape (2009, 2011)\n",
      "FOV 044\n",
      "mask shape (2009, 2011)\n",
      "F044, no mask needed\n",
      "FOV 045\n",
      "mask shape (2009, 2011)\n",
      "F045, no mask needed\n",
      "FOV 046\n",
      "mask shape (2009, 2011)\n",
      "FOV 047\n",
      "mask shape (2009, 2011)\n",
      "F047, no mask needed\n",
      "FOV 048\n",
      "mask shape (2009, 2011)\n",
      "F048, no mask needed\n",
      "FOV 049\n",
      "mask shape (2009, 2011)\n",
      "FOV 050\n",
      "mask shape (2009, 2011)\n",
      "F050, no mask needed\n",
      "FOV 051\n",
      "mask shape (2009, 2011)\n",
      "F051, no mask needed\n",
      "FOV 052\n",
      "mask shape (2009, 2011)\n",
      "F052, no mask needed\n",
      "FOV 054\n",
      "mask shape (2009, 2011)\n",
      "F054, no mask needed\n",
      "FOV 055\n",
      "mask shape (2009, 2011)\n",
      "FOV 056\n",
      "mask shape (2009, 2011)\n",
      "F056, no mask needed\n",
      "FOV 057\n",
      "mask shape (2009, 2011)\n",
      "F057, no mask needed\n",
      "FOV 058\n",
      "mask shape (2009, 2011)\n",
      "F058, no mask needed\n",
      "FOV 059\n",
      "mask shape (2009, 2011)\n",
      "F059, no mask needed\n",
      "FOV 061\n",
      "mask shape (2009, 2011)\n",
      "F061, no mask needed\n",
      "FOV 062\n",
      "mask shape (2009, 2011)\n",
      "FOV 063\n",
      "mask shape (2009, 2011)\n",
      "F063, no mask needed\n",
      "FOV 064\n",
      "mask shape (2009, 2011)\n",
      "F064, no mask needed\n",
      "FOV 065\n",
      "mask shape (2009, 2011)\n",
      "F065, no mask needed\n",
      "FOV 066\n",
      "mask shape (2009, 2011)\n",
      "F066, no mask needed\n",
      "FOV 067\n",
      "mask shape (2009, 2011)\n",
      "F067, no mask needed\n",
      "FOV 068\n",
      "mask shape (2009, 2011)\n",
      "F068, no mask needed\n",
      "FOV 069\n",
      "mask shape (2009, 2011)\n",
      "F069, no mask needed\n",
      "FOV 070\n",
      "mask shape (2009, 2011)\n",
      "F070, no mask needed\n",
      "FOV 071\n",
      "mask shape (2009, 2011)\n",
      "F071, no mask needed\n",
      "FOV 072\n",
      "mask shape (2009, 2011)\n",
      "F072, no mask needed\n",
      "FOV 073\n",
      "mask shape (2009, 2011)\n",
      "F073, no mask needed\n",
      "FOV 074\n",
      "mask shape (2009, 2011)\n",
      "F074, no mask needed\n",
      "FOV 077\n",
      "mask shape (2009, 2011)\n",
      "FOV 078\n",
      "mask shape (2009, 2011)\n",
      "F078, no mask needed\n",
      "FOV 079\n",
      "mask shape (2009, 2011)\n",
      "F079, no mask needed\n",
      "FOV 080\n",
      "mask shape (2009, 2011)\n",
      "FOV 081\n",
      "mask shape (2009, 2011)\n",
      "FOV 082\n",
      "mask shape (2009, 2011)\n",
      "F082, no mask needed\n",
      "FOV 083\n",
      "mask shape (2009, 2011)\n",
      "F083, no mask needed\n",
      "FOV 084\n",
      "mask shape (2009, 2011)\n",
      "F084, no mask needed\n",
      "FOV 085\n",
      "mask shape (2009, 2011)\n",
      "F085, no mask needed\n",
      "FOV 086\n",
      "mask shape (2009, 2011)\n",
      "F086, no mask needed\n",
      "FOV 087\n",
      "mask shape (2009, 2011)\n",
      "F087, no mask needed\n",
      "FOV 088\n",
      "mask shape (2009, 2011)\n",
      "F088, no mask needed\n",
      "FOV 089\n",
      "mask shape (2009, 2011)\n",
      "F089, no mask needed\n",
      "FOV 090\n",
      "mask shape (2009, 2011)\n",
      "F090, no mask needed\n",
      "FOV 091\n",
      "mask shape (2009, 2011)\n",
      "F091, no mask needed\n",
      "FOV 092\n",
      "mask shape (2009, 2011)\n",
      "F092, no mask needed\n",
      "FOV 093\n",
      "mask shape (2009, 2011)\n",
      "F093, no mask needed\n",
      "FOV 094\n",
      "mask shape (2009, 2011)\n",
      "F094, no mask needed\n",
      "FOV 095\n",
      "mask shape (2009, 2011)\n",
      "FOV 096\n",
      "mask shape (2009, 2011)\n",
      "FOV 097\n",
      "mask shape (2009, 2011)\n",
      "F097, no mask needed\n",
      "FOV 098\n",
      "mask shape (2009, 2011)\n",
      "F098, no mask needed\n",
      "FOV 099\n",
      "mask shape (2009, 2011)\n",
      "F099, no mask needed\n",
      "FOV 100\n",
      "mask shape (2009, 2011)\n",
      "FOV 101\n",
      "mask shape (2009, 2011)\n",
      "F101, no mask needed\n",
      "FOV 102\n",
      "mask shape (2009, 2011)\n",
      "F102, no mask needed\n",
      "FOV 103\n",
      "mask shape (2009, 2011)\n",
      "F103, no mask needed\n",
      "FOV 104\n",
      "mask shape (2009, 2011)\n",
      "F104, no mask needed\n",
      "FOV 105\n",
      "mask shape (2009, 2011)\n",
      "F105, no mask needed\n",
      "FOV 106\n",
      "mask shape (2009, 2011)\n",
      "F106, no mask needed\n",
      "FOV 107\n",
      "mask shape (2009, 2011)\n",
      "F107, no mask needed\n",
      "FOV 108\n",
      "mask shape (2009, 2011)\n",
      "F108, no mask needed\n",
      "FOV 109\n",
      "mask shape (2009, 2011)\n",
      "F109, no mask needed\n",
      "FOV 110\n",
      "mask shape (2009, 2011)\n",
      "F110, no mask needed\n",
      "FOV 111\n",
      "mask shape (2009, 2011)\n",
      "FOV 112\n",
      "mask shape (2009, 2011)\n",
      "F112, no mask needed\n",
      "FOV 113\n",
      "mask shape (2009, 2011)\n",
      "F113, no mask needed\n",
      "FOV 114\n",
      "mask shape (2009, 2011)\n",
      "FOV 115\n",
      "mask shape (2009, 2011)\n",
      "F115, no mask needed\n",
      "FOV 116\n",
      "mask shape (2009, 2011)\n",
      "F116, no mask needed\n",
      "FOV 117\n",
      "mask shape (2009, 2011)\n",
      "FOV 118\n",
      "mask shape (2009, 2011)\n",
      "F118, no mask needed\n",
      "FOV 119\n",
      "mask shape (2009, 2011)\n",
      "F119, no mask needed\n",
      "FOV 120\n",
      "mask shape (2009, 2011)\n",
      "F120, no mask needed\n",
      "FOV 121\n",
      "mask shape (2009, 2011)\n",
      "FOV 122\n",
      "mask shape (2009, 2011)\n",
      "F122, no mask needed\n",
      "FOV 123\n",
      "mask shape (2009, 2011)\n",
      "FOV 124\n",
      "mask shape (2009, 2011)\n",
      "F124, no mask needed\n",
      "FOV 125\n",
      "mask shape (2009, 2011)\n",
      "F125, no mask needed\n",
      "FOV 126\n",
      "mask shape (2009, 2011)\n",
      "F126, no mask needed\n",
      "FOV 127\n",
      "mask shape (2009, 2011)\n",
      "F127, no mask needed\n",
      "FOV 128\n",
      "mask shape (2009, 2011)\n",
      "F128, no mask needed\n",
      "FOV 129\n",
      "mask shape (2009, 2011)\n",
      "FOV 130\n",
      "mask shape (2009, 2011)\n",
      "F130, no mask needed\n",
      "FOV 131\n",
      "mask shape (2009, 2011)\n",
      "F131, no mask needed\n",
      "FOV 132\n",
      "mask shape (2009, 2011)\n",
      "F132, no mask needed\n",
      "FOV 133\n",
      "mask shape (2009, 2011)\n",
      "F133, no mask needed\n",
      "FOV 134\n",
      "mask shape (2009, 2011)\n",
      "FOV 135\n",
      "mask shape (2009, 2011)\n",
      "F135, no mask needed\n",
      "FOV 136\n",
      "mask shape (2009, 2011)\n",
      "F136, no mask needed\n",
      "FOV 137\n",
      "mask shape (2009, 2011)\n",
      "F137, no mask needed\n",
      "FOV 138\n",
      "mask shape (2009, 2011)\n",
      "F138, no mask needed\n",
      "FOV 140\n",
      "mask shape (2009, 2011)\n",
      "F140, no mask needed\n",
      "FOV 141\n",
      "mask shape (2009, 2011)\n",
      "F141, no mask needed\n",
      "FOV 142\n",
      "mask shape (2009, 2011)\n",
      "F142, no mask needed\n",
      "FOV 143\n",
      "mask shape (2009, 2011)\n",
      "F143, no mask needed\n",
      "FOV 144\n",
      "mask shape (2009, 2011)\n",
      "F144, no mask needed\n",
      "FOV 145\n",
      "mask shape (2009, 2011)\n",
      "F145, no mask needed\n",
      "FOV 146\n",
      "mask shape (2009, 2011)\n",
      "F146, no mask needed\n",
      "FOV 147\n",
      "mask shape (2009, 2011)\n",
      "FOV 148\n",
      "mask shape (2009, 2011)\n",
      "F148, no mask needed\n",
      "FOV 149\n",
      "mask shape (2009, 2011)\n",
      "F149, no mask needed\n",
      "FOV 151\n",
      "mask shape (2009, 2011)\n",
      "F151, no mask needed\n",
      "FOV 152\n",
      "mask shape (2009, 2011)\n",
      "FOV 153\n",
      "mask shape (2009, 2011)\n",
      "F153, no mask needed\n",
      "FOV 154\n",
      "mask shape (2009, 2011)\n",
      "FOV 155\n",
      "mask shape (2009, 2011)\n",
      "F155, no mask needed\n",
      "FOV 156\n",
      "mask shape (2009, 2011)\n",
      "F156, no mask needed\n",
      "FOV 157\n",
      "mask shape (2009, 2011)\n",
      "F157, no mask needed\n",
      "FOV 158\n",
      "mask shape (2009, 2011)\n",
      "F158, no mask needed\n",
      "FOV 159\n",
      "mask shape (2009, 2011)\n",
      "F159, no mask needed\n",
      "FOV 160\n",
      "mask shape (2009, 2011)\n",
      "F160, no mask needed\n",
      "FOV 161\n",
      "mask shape (2009, 2011)\n",
      "F161, no mask needed\n",
      "FOV 162\n",
      "mask shape (2009, 2011)\n",
      "FOV 164\n",
      "mask shape (2009, 2011)\n",
      "FOV 165\n",
      "mask shape (2009, 2011)\n",
      "F165, no mask needed\n",
      "FOV 166\n",
      "mask shape (2009, 2011)\n",
      "F166, no mask needed\n",
      "FOV 167\n",
      "mask shape (2009, 2011)\n",
      "F167, no mask needed\n",
      "FOV 168\n",
      "mask shape (2009, 2011)\n",
      "F168, no mask needed\n",
      "FOV 169\n",
      "mask shape (2009, 2011)\n",
      "F169, no mask needed\n",
      "FOV 170\n",
      "mask shape (2009, 2011)\n",
      "F170, no mask needed\n",
      "FOV 171\n",
      "mask shape (2009, 2011)\n",
      "F171, no mask needed\n",
      "FOV 172\n",
      "mask shape (2009, 2011)\n",
      "F172, no mask needed\n",
      "FOV 173\n",
      "mask shape (2009, 2011)\n",
      "F173, no mask needed\n",
      "FOV 174\n",
      "mask shape (2009, 2011)\n",
      "FOV 175\n",
      "mask shape (2009, 2011)\n",
      "F175, no mask needed\n",
      "FOV 176\n",
      "mask shape (2009, 2011)\n",
      "F176, no mask needed\n",
      "FOV 177\n",
      "mask shape (2009, 2011)\n",
      "F177, no mask needed\n",
      "FOV 179\n",
      "mask shape (2009, 2011)\n",
      "F179, no mask needed\n",
      "FOV 180\n",
      "mask shape (2009, 2011)\n",
      "F180, no mask needed\n",
      "FOV 181\n",
      "mask shape (2009, 2011)\n",
      "F181, no mask needed\n",
      "FOV 182\n",
      "mask shape (2009, 2011)\n",
      "F182, no mask needed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 183\n",
      "mask shape (2009, 2011)\n",
      "F183, no mask needed\n",
      "FOV 184\n",
      "mask shape (2009, 2011)\n",
      "F184, no mask needed\n",
      "FOV 185\n",
      "mask shape (2009, 2011)\n",
      "F185, no mask needed\n",
      "FOV 186\n",
      "mask shape (2009, 2011)\n",
      "F186, no mask needed\n",
      "FOV 187\n",
      "mask shape (2009, 2011)\n",
      "F187, no mask needed\n",
      "FOV 188\n",
      "mask shape (2009, 2011)\n",
      "F188, no mask needed\n",
      "FOV 189\n",
      "mask shape (2009, 2011)\n",
      "F189, no mask needed\n",
      "FOV 190\n",
      "mask shape (2009, 2011)\n",
      "F190, no mask needed\n",
      "FOV 191\n",
      "mask shape (2009, 2011)\n",
      "F191, no mask needed\n",
      "FOV 193\n",
      "mask shape (2009, 2011)\n",
      "F193, no mask needed\n",
      "FOV 194\n",
      "mask shape (2009, 2011)\n",
      "F194, no mask needed\n",
      "FOV 195\n",
      "mask shape (2009, 2011)\n",
      "FOV 196\n",
      "mask shape (2009, 2011)\n",
      "F196, no mask needed\n",
      "FOV 197\n",
      "mask shape (2009, 2011)\n",
      "F197, no mask needed\n",
      "FOV 198\n",
      "mask shape (2009, 2011)\n",
      "F198, no mask needed\n",
      "FOV 199\n",
      "mask shape (2009, 2011)\n",
      "F199, no mask needed\n",
      "FOV 200\n",
      "mask shape (2009, 2011)\n",
      "F200, no mask needed\n",
      "FOV 201\n",
      "mask shape (2009, 2011)\n",
      "F201, no mask needed\n",
      "FOV 202\n",
      "mask shape (2009, 2011)\n",
      "F202, no mask needed\n",
      "FOV 203\n",
      "mask shape (2009, 2011)\n",
      "F203, no mask needed\n",
      "FOV 204\n",
      "mask shape (2009, 2011)\n",
      "FOV 205\n",
      "mask shape (2009, 2011)\n",
      "F205, no mask needed\n",
      "FOV 207\n",
      "mask shape (2009, 2011)\n",
      "FOV 208\n",
      "mask shape (2009, 2011)\n",
      "F208, no mask needed\n",
      "FOV 209\n",
      "mask shape (2009, 2011)\n",
      "F209, no mask needed\n",
      "FOV 210\n",
      "mask shape (2009, 2011)\n",
      "F210, no mask needed\n",
      "FOV 211\n",
      "mask shape (2009, 2011)\n",
      "F211, no mask needed\n",
      "FOV 212\n",
      "mask shape (2009, 2011)\n",
      "FOV 213\n",
      "mask shape (2009, 2011)\n",
      "F213, no mask needed\n",
      "FOV 214\n",
      "mask shape (2009, 2011)\n",
      "F214, no mask needed\n",
      "FOV 215\n",
      "mask shape (2009, 2011)\n",
      "F215, no mask needed\n",
      "FOV 216\n",
      "mask shape (2009, 2011)\n",
      "FOV 217\n",
      "mask shape (2009, 2011)\n",
      "F217, no mask needed\n",
      "FOV 218\n",
      "mask shape (2009, 2011)\n",
      "F218, no mask needed\n",
      "FOV 220\n",
      "mask shape (2009, 2011)\n",
      "FOV 221\n",
      "mask shape (2009, 2011)\n",
      "F221, no mask needed\n",
      "FOV 222\n",
      "mask shape (2009, 2011)\n",
      "F222, no mask needed\n",
      "FOV 223\n",
      "mask shape (2009, 2011)\n",
      "F223, no mask needed\n",
      "FOV 224\n",
      "mask shape (2009, 2011)\n"
     ]
    }
   ],
   "source": [
    "NUM_FOVS = 210\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",
    "    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_num, ch] = count0[ch]\n",
    "        count20_df.loc[FOV_num, ch] = count20[ch]\n",
    "        count65K_df.loc[FOV_num, ch] = count65K[ch]\n",
    "        if ch in final_inds:\n",
    "            # whether to make mask;\n",
    "            if count20[ch] > 2000: # if there are more than 2000 <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",
    "        #sFOV = str(fov).zfill(3)\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",
    "        \n",
    "    \n",
    "# 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": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask percentage >3% and <5%\n",
      "005\n",
      "3.2053174934574624\n",
      "mask percentage >3% and <5%\n",
      "010\n",
      "3.5624622069904723\n",
      "mask percentage >5% and <7%\n",
      "021\n",
      "5.394075739233123\n",
      "mask percentage >5% and <7%\n",
      "023\n",
      "5.386798689834086\n",
      "mask percentage >5% and <7%\n",
      "029\n",
      "5.898469319687463\n",
      "mask percentage >3% and <5%\n",
      "033\n",
      "3.9816598553649305\n",
      "mask percentage >7%\n",
      "042\n",
      "7.693375830641774\n",
      "mask percentage >5% and <7%\n",
      "046\n",
      "5.138883972892743\n",
      "mask percentage >3% and <5%\n",
      "080\n",
      "4.436945728310123\n",
      "mask percentage >7%\n",
      "111\n",
      "8.348062757868062\n",
      "mask percentage >3% and <5%\n",
      "117\n",
      "4.368135533312426\n",
      "mask percentage >3% and <5%\n",
      "152\n",
      "4.298137248616928\n",
      "mask percentage >3% and <5%\n",
      "164\n",
      "4.221901493007969\n",
      "mask percentage >3% and <5%\n",
      "204\n",
      "3.325067034248418\n",
      "mask percentage >5% and <7%\n",
      "220\n",
      "6.567784601317938\n",
      "mask percentage >3% and <5%\n",
      "224\n",
      "4.0688854406785575\n"
     ]
    }
   ],
   "source": [
    "NUM_MASKS = 48\n",
    "masks = iter(glob.glob('mask/*')) \n",
    "for fov in range(NUM_MASKS):\n",
    "    mask_name = next(masks)\n",
    "    img = imread(mask_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = mask_name.split('/F')[1][0:3]\n",
    "    \n",
    "    mask_perc = 100*np.count_nonzero(img == 1)/(img.shape[0]*img.shape[1])\n",
    "    \n",
    "    if mask_perc > 7:\n",
    "        print(\"mask percentage >7%\")\n",
    "        print(FOV_num)\n",
    "        print(mask_perc)\n",
    "    if mask_perc > 5 and mask_perc < 7:\n",
    "        print(\"mask percentage >5% and <7%\")\n",
    "        print(FOV_num)\n",
    "        print(mask_perc)\n",
    "    if mask_perc > 3 and mask_perc < 5:\n",
    "        print(\"mask percentage >3% and <5%\")\n",
    "        print(FOV_num)\n",
    "        print(mask_perc)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max Projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "import tifffile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 210"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 001\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 002\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 003\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 004\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 005\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 006\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 007\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 008\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 009\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 010\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 011\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 012\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 013\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 014\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 015\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 016\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 017\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 018\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 019\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 020\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 021\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 022\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 023\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 024\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 025\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 027\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 028\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 029\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 030\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 031\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 032\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 033\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 035\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 036\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 037\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 038\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 041\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 042\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 043\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 044\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 045\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 046\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 047\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 048\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 049\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 050\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 051\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 052\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 054\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 055\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 056\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 057\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 058\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 059\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 061\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 062\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 063\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 064\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 065\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 066\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 067\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 068\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 069\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 070\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 071\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 072\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 073\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 074\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 077\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 078\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 079\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 080\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 081\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 082\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 083\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 084\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 085\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 086\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 087\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 088\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 089\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 090\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 091\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 092\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 093\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 094\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 095\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 096\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 097\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 098\n",
      "Prepare CP inputs - max projection only\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 099\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 100\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 101\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 102\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 103\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 104\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 105\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 106\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 107\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 108\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 109\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 110\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 111\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 112\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 113\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 114\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 115\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 116\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 117\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 118\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 119\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 120\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 121\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 122\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 123\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 124\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 125\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 126\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 127\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 128\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 129\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 130\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 131\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 132\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 133\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 134\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 135\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 136\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 137\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 138\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 140\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 141\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 142\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 143\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 144\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 145\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 146\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 147\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 148\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 149\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 151\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 152\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 153\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 154\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 155\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 156\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 157\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 158\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 159\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 160\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 161\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 162\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 164\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 165\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 166\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 167\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 168\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 169\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 170\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 171\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 172\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 173\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 174\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 175\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 176\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 177\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 179\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 180\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 181\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 182\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 183\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 184\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 185\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 186\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 187\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 188\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 189\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 190\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 191\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 193\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 194\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 195\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 196\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 197\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 198\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 199\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 200\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 201\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 202\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 203\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 204\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 205\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 207\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 208\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 209\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 210\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 211\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 212\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 213\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 214\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 215\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 216\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 217\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 218\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 220\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 221\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 222\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 223\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n",
      "FOV 224\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2009, 2011)\n"
     ]
    }
   ],
   "source": [
    "MAX_DIR = 'max'\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",
    "    print(\"Prepare CP inputs - max projection only\")\n",
    "    img_max = img.max(0) # take max projection # ZCYX\n",
    "    im_to_save = img_max[final_inds].copy()\n",
    "    fname = f'F{FOV_num}_max.tif'\n",
    "    \n",
    "    print('Saving MAX...', f'./{MAX_DIR}/fname') \n",
    "    print(im_to_save.shape)\n",
    "    tifffile.imwrite(f'./{MAX_DIR}/'+fname, im_to_save, imagej = True,\n",
    "                    photometric='minisblack', metadata={'axes':'CYX'})"
   ]
  }
 ],
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