{
 "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles = glob.glob('ims/Cycle_*') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n"
     ]
    }
   ],
   "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": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ims/Cycle_2_F160.ims',\n",
       " 'ims/Cycle_3_F098.ims',\n",
       " 'ims/Cycle_5_F049.ims',\n",
       " 'ims/Cycle_6_F073.ims']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 225 - len(error_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    os.makedirs(f'tif/Cycle_{c}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CYCLE_NUMS: 10 \n",
      " NUM_FOVS: 221\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": 12,
   "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": 13,
   "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": 14,
   "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"
     ]
    }
   ],
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 221"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 tmat_Cyc_8/Cycle_8_F005_tmat_001_004.npy -15.030241619004979 5.897869380189945\n",
      "8 tmat_Cyc_8/Cycle_8_F012_tmat_002_002.npy -15.488249373318695 5.563057136561383\n",
      "8 tmat_Cyc_8/Cycle_8_F068_tmat_003_011.npy -16.48167834764058 9.269199971987518\n",
      "8 tmat_Cyc_8/Cycle_8_F144_tmat_008_000.npy -15.704018612018217 5.0940124303184575\n",
      "8 tmat_Cyc_8/Cycle_8_F146_tmat_009_001.npy -16.366181013512005 11.121144506322935\n",
      "8 tmat_Cyc_8/Cycle_8_F147_tmat_009_002.npy -15.074722740775883 3.3379215234721187\n"
     ]
    }
   ],
   "source": [
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):     \n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    for sFOV in range(0,NUM_FOVS): \n",
    "        tmat_name = next(tmats)\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        if (moveX > 15) | (moveY > 15)|(moveX < -15) | (moveY < -15): # 50 pixels is max\n",
    "            print(c+1, tmat_name, moveX, moveY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 1 field 000 \n",
      "Got threshold\n",
      "cycle 1 field 001 \n",
      "Got threshold\n",
      "cycle 1 field 002 \n",
      "Got threshold\n",
      "cycle 1 field 003 \n",
      "Got threshold\n",
      "cycle 1 field 004 \n",
      "Got threshold\n",
      "cycle 1 field 005 \n",
      "Got threshold\n",
      "cycle 1 field 007 \n",
      "Got threshold\n",
      "cycle 1 field 008 \n",
      "Got threshold\n",
      "cycle 1 field 011 \n",
      "Got threshold\n",
      "cycle 1 field 012 \n",
      "Got threshold\n",
      "cycle 1 field 013 \n",
      "Got threshold\n",
      "cycle 1 field 014 \n",
      "Got threshold\n",
      "cycle 1 field 015 \n",
      "Got threshold\n",
      "cycle 1 field 016 \n",
      "Got threshold\n",
      "cycle 1 field 017 \n",
      "Got threshold\n",
      "cycle 1 field 019 \n",
      "Got threshold\n",
      "cycle 1 field 020 \n",
      "Got threshold\n",
      "cycle 1 field 021 \n",
      "Got threshold\n",
      "cycle 1 field 022 \n",
      "Got threshold\n",
      "cycle 1 field 023 \n",
      "Got threshold\n",
      "cycle 1 field 024 \n",
      "Got threshold\n",
      "cycle 1 field 025 \n",
      "Got threshold\n",
      "cycle 1 field 026 \n",
      "Got threshold\n",
      "cycle 1 field 027 \n",
      "Got threshold\n",
      "cycle 1 field 028 \n",
      "Got threshold\n",
      "cycle 1 field 029 \n",
      "Got threshold\n",
      "cycle 1 field 030 \n",
      "Got threshold\n",
      "cycle 1 field 031 \n",
      "Got threshold\n",
      "cycle 1 field 032 \n",
      "Got threshold\n",
      "cycle 1 field 033 \n",
      "Got threshold\n",
      "cycle 1 field 034 \n",
      "Got threshold\n",
      "cycle 1 field 035 \n",
      "Got threshold\n",
      "cycle 1 field 036 \n",
      "Got threshold\n",
      "cycle 1 field 037 \n",
      "Got threshold\n",
      "cycle 1 field 038 \n",
      "Got threshold\n",
      "cycle 1 field 039 \n",
      "Got threshold\n",
      "cycle 1 field 040 \n",
      "Got threshold\n",
      "cycle 1 field 041 \n",
      "Got threshold\n",
      "cycle 1 field 042 \n",
      "Got threshold\n",
      "cycle 1 field 044 \n",
      "Got threshold\n",
      "cycle 1 field 045 \n",
      "Got threshold\n",
      "cycle 1 field 046 \n",
      "Got threshold\n",
      "cycle 1 field 047 \n",
      "Got threshold\n",
      "cycle 1 field 048 \n",
      "Got threshold\n",
      "cycle 1 field 049 \n",
      "Got threshold\n",
      "cycle 1 field 050 \n",
      "Got threshold\n",
      "cycle 1 field 051 \n",
      "Got threshold\n",
      "cycle 1 field 052 \n",
      "Got threshold\n",
      "cycle 1 field 053 \n",
      "Got threshold\n",
      "cycle 1 field 054 \n",
      "Got threshold\n",
      "cycle 1 field 055 \n",
      "Got threshold\n",
      "cycle 1 field 056 \n",
      "Got threshold\n",
      "cycle 1 field 057 \n",
      "Got threshold\n",
      "cycle 1 field 058 \n",
      "Got threshold\n",
      "cycle 1 field 059 \n",
      "Got threshold\n",
      "cycle 1 field 060 \n",
      "Got threshold\n",
      "cycle 1 field 061 \n",
      "Got threshold\n",
      "cycle 1 field 062 \n",
      "Got threshold\n",
      "cycle 1 field 063 \n",
      "Got threshold\n",
      "cycle 1 field 064 \n",
      "Got threshold\n",
      "cycle 1 field 065 \n",
      "Got threshold\n",
      "cycle 1 field 066 \n",
      "Got threshold\n",
      "cycle 1 field 067 \n",
      "Got threshold\n",
      "cycle 1 field 068 \n",
      "Got threshold\n",
      "cycle 1 field 069 \n",
      "Got threshold\n",
      "cycle 1 field 070 \n",
      "Got threshold\n",
      "cycle 1 field 071 \n",
      "Got threshold\n",
      "cycle 1 field 072 \n",
      "Got threshold\n",
      "cycle 1 field 073 \n",
      "Got threshold\n",
      "cycle 1 field 074 \n",
      "Got threshold\n",
      "cycle 1 field 075 \n",
      "Got threshold\n",
      "cycle 1 field 076 \n",
      "Got threshold\n",
      "cycle 1 field 077 \n",
      "Got threshold\n",
      "cycle 1 field 078 \n",
      "Got threshold\n",
      "cycle 1 field 080 \n",
      "Got threshold\n",
      "cycle 1 field 081 \n",
      "Got threshold\n",
      "cycle 1 field 082 \n",
      "Got threshold\n",
      "cycle 1 field 083 \n",
      "Got threshold\n",
      "cycle 1 field 084 \n",
      "Got threshold\n",
      "cycle 1 field 085 \n",
      "Got threshold\n",
      "cycle 1 field 086 \n",
      "Got threshold\n",
      "cycle 1 field 087 \n",
      "Got threshold\n",
      "cycle 1 field 088 \n",
      "Got threshold\n",
      "cycle 1 field 089 \n",
      "Got threshold\n",
      "cycle 1 field 090 \n",
      "Got threshold\n",
      "cycle 1 field 091 \n",
      "Got threshold\n",
      "cycle 1 field 092 \n",
      "Got threshold\n",
      "cycle 1 field 093 \n",
      "Got threshold\n",
      "cycle 1 field 094 \n",
      "Got threshold\n",
      "cycle 1 field 095 \n",
      "Got threshold\n",
      "cycle 1 field 096 \n",
      "Got threshold\n",
      "cycle 1 field 097 \n",
      "Got threshold\n",
      "cycle 1 field 098 \n",
      "Got threshold\n",
      "cycle 1 field 100 \n",
      "Got threshold\n",
      "cycle 1 field 101 \n",
      "Got threshold\n",
      "cycle 1 field 102 \n",
      "Got threshold\n",
      "cycle 1 field 103 \n",
      "Got threshold\n",
      "cycle 1 field 105 \n",
      "Got threshold\n",
      "cycle 1 field 106 \n",
      "Got threshold\n",
      "cycle 1 field 107 \n",
      "Got threshold\n",
      "cycle 1 field 108 \n",
      "Got threshold\n",
      "cycle 1 field 109 \n",
      "Got threshold\n",
      "cycle 1 field 110 \n",
      "Got threshold\n",
      "cycle 1 field 111 \n",
      "Got threshold\n",
      "cycle 1 field 112 \n",
      "Got threshold\n",
      "cycle 1 field 113 \n",
      "Got threshold\n",
      "cycle 1 field 114 \n",
      "Got threshold\n",
      "cycle 1 field 115 \n",
      "Got threshold\n",
      "cycle 1 field 116 \n",
      "Got threshold\n",
      "cycle 1 field 117 \n",
      "Got threshold\n",
      "cycle 1 field 118 \n",
      "Got threshold\n",
      "cycle 1 field 119 \n",
      "Got threshold\n",
      "cycle 1 field 120 \n",
      "Got threshold\n",
      "cycle 1 field 121 \n",
      "Got threshold\n",
      "cycle 1 field 122 \n",
      "Got threshold\n",
      "cycle 1 field 123 \n",
      "Got threshold\n",
      "cycle 1 field 124 \n",
      "Got threshold\n",
      "cycle 1 field 125 \n",
      "Got threshold\n",
      "cycle 1 field 126 \n",
      "Got threshold\n",
      "cycle 1 field 127 \n",
      "Got threshold\n",
      "cycle 1 field 128 \n",
      "Got threshold\n",
      "cycle 1 field 129 \n",
      "Got threshold\n",
      "cycle 1 field 130 \n",
      "Got threshold\n",
      "cycle 1 field 131 \n",
      "Got threshold\n",
      "cycle 1 field 132 \n",
      "Got threshold\n",
      "cycle 1 field 133 \n",
      "Got threshold\n",
      "cycle 1 field 134 \n",
      "Got threshold\n",
      "cycle 1 field 135 \n",
      "Got threshold\n",
      "cycle 1 field 136 \n",
      "Got threshold\n",
      "cycle 1 field 137 \n",
      "Got threshold\n",
      "cycle 1 field 138 \n",
      "Got threshold\n",
      "cycle 1 field 139 \n",
      "Got threshold\n",
      "cycle 1 field 140 \n",
      "Got threshold\n",
      "cycle 1 field 141 \n",
      "Got threshold\n",
      "cycle 1 field 142 \n",
      "Got threshold\n",
      "cycle 1 field 143 \n",
      "Got threshold\n",
      "cycle 1 field 144 \n",
      "Got threshold\n",
      "cycle 1 field 145 \n",
      "Got threshold\n",
      "cycle 1 field 146 \n",
      "Got threshold\n",
      "cycle 1 field 147 \n",
      "Got threshold\n",
      "cycle 1 field 148 \n",
      "Got threshold\n",
      "cycle 1 field 149 \n",
      "Got threshold\n",
      "cycle 1 field 151 \n",
      "Got threshold\n",
      "cycle 1 field 152 \n",
      "Got threshold\n",
      "cycle 1 field 153 \n",
      "Got threshold\n",
      "cycle 1 field 154 \n",
      "Got threshold\n",
      "cycle 1 field 155 \n",
      "Got threshold\n",
      "cycle 1 field 156 \n",
      "Got threshold\n",
      "cycle 1 field 157 \n",
      "Got threshold\n",
      "cycle 1 field 158 \n",
      "Got threshold\n",
      "cycle 1 field 159 \n",
      "Got threshold\n",
      "cycle 1 field 160 \n",
      "Got threshold\n",
      "cycle 1 field 162 \n",
      "Got threshold\n",
      "cycle 1 field 163 \n",
      "Got threshold\n",
      "cycle 1 field 164 \n",
      "Got threshold\n",
      "cycle 1 field 165 \n",
      "Got threshold\n",
      "cycle 1 field 166 \n",
      "Got threshold\n",
      "cycle 1 field 167 \n",
      "Got threshold\n",
      "cycle 1 field 168 \n",
      "Got threshold\n",
      "cycle 1 field 169 \n",
      "Got threshold\n",
      "cycle 1 field 170 \n",
      "Got threshold\n",
      "cycle 1 field 171 \n",
      "Got threshold\n",
      "cycle 1 field 172 \n",
      "Got threshold\n",
      "cycle 1 field 173 \n",
      "Got threshold\n",
      "cycle 1 field 174 \n",
      "Got threshold\n",
      "cycle 1 field 175 \n",
      "Got threshold\n",
      "cycle 1 field 176 \n",
      "Got threshold\n",
      "cycle 1 field 178 \n",
      "Got threshold\n",
      "cycle 1 field 179 \n",
      "Got threshold\n",
      "cycle 1 field 180 \n",
      "Got threshold\n",
      "cycle 1 field 181 \n",
      "Got threshold\n",
      "cycle 1 field 182 \n",
      "Got threshold\n",
      "cycle 1 field 183 \n",
      "Got threshold\n",
      "cycle 1 field 184 \n",
      "Got threshold\n",
      "cycle 1 field 185 \n",
      "Got threshold\n",
      "cycle 1 field 186 \n",
      "Got threshold\n",
      "cycle 1 field 187 \n",
      "Got threshold\n",
      "cycle 1 field 188 \n",
      "Got threshold\n",
      "cycle 1 field 189 \n",
      "Got threshold\n",
      "cycle 1 field 190 \n",
      "Got threshold\n",
      "cycle 1 field 191 \n",
      "Got threshold\n",
      "cycle 1 field 192 \n",
      "Got threshold\n",
      "cycle 1 field 193 \n",
      "Got threshold\n",
      "cycle 1 field 194 \n",
      "Got threshold\n",
      "cycle 1 field 195 \n",
      "Got threshold\n",
      "cycle 1 field 196 \n",
      "Got threshold\n",
      "cycle 1 field 197 \n",
      "Got threshold\n",
      "cycle 1 field 198 \n",
      "Got threshold\n",
      "cycle 1 field 199 \n",
      "Got threshold\n",
      "cycle 1 field 200 \n",
      "Got threshold\n",
      "cycle 1 field 201 \n",
      "Got threshold\n",
      "cycle 1 field 202 \n",
      "Got threshold\n",
      "cycle 1 field 203 \n",
      "Got threshold\n",
      "cycle 1 field 204 \n",
      "Got threshold\n",
      "cycle 1 field 205 \n",
      "Got threshold\n",
      "cycle 1 field 206 \n",
      "Got threshold\n",
      "cycle 1 field 207 \n",
      "Got threshold\n",
      "cycle 1 field 208 \n",
      "Got threshold\n",
      "cycle 1 field 209 \n",
      "Got threshold\n",
      "cycle 1 field 210 \n",
      "Got threshold\n",
      "cycle 1 field 211 \n",
      "Got threshold\n",
      "cycle 1 field 212 \n",
      "Got threshold\n",
      "cycle 1 field 213 \n",
      "Got threshold\n",
      "cycle 1 field 214 \n",
      "Got threshold\n",
      "cycle 1 field 215 \n",
      "Got threshold\n",
      "cycle 1 field 216 \n",
      "Got threshold\n",
      "cycle 1 field 217 \n",
      "Got threshold\n",
      "cycle 1 field 218 \n",
      "Got threshold\n",
      "cycle 1 field 219 \n",
      "Got threshold\n",
      "cycle 1 field 220 \n",
      "Got threshold\n",
      "cycle 1 field 221 \n",
      "Got threshold\n",
      "cycle 1 field 222 \n",
      "Got threshold\n",
      "cycle 1 field 223 \n",
      "Got threshold\n",
      "cycle 1 field 224 \n",
      "Got threshold\n",
      "cycle 2 field 000 \n",
      "Got threshold\n",
      "cycle 2 field 001 \n",
      "Got threshold\n",
      "cycle 2 field 002 \n",
      "Got threshold\n",
      "cycle 2 field 003 \n",
      "Got threshold\n",
      "cycle 2 field 004 \n",
      "Got threshold\n",
      "cycle 2 field 005 \n",
      "Got threshold\n",
      "cycle 2 field 007 \n",
      "Got threshold\n",
      "cycle 2 field 008 \n",
      "Got threshold\n",
      "cycle 2 field 011 \n",
      "Got threshold\n",
      "cycle 2 field 012 \n",
      "Got threshold\n",
      "cycle 2 field 013 \n",
      "Got threshold\n",
      "cycle 2 field 014 \n",
      "Got threshold\n",
      "cycle 2 field 015 \n",
      "Got threshold\n",
      "cycle 2 field 016 \n",
      "Got threshold\n",
      "cycle 2 field 017 \n",
      "Got threshold\n",
      "cycle 2 field 019 \n",
      "Got threshold\n",
      "cycle 2 field 020 \n",
      "Got threshold\n",
      "cycle 2 field 021 \n",
      "Got threshold\n",
      "cycle 2 field 022 \n",
      "Got threshold\n",
      "cycle 2 field 023 \n",
      "Got threshold\n",
      "cycle 2 field 024 \n",
      "Got threshold\n",
      "cycle 2 field 025 \n",
      "Got threshold\n",
      "cycle 2 field 026 \n",
      "Got threshold\n",
      "cycle 2 field 027 \n",
      "Got threshold\n",
      "cycle 2 field 028 \n",
      "Got threshold\n",
      "cycle 2 field 029 \n",
      "Got threshold\n",
      "cycle 2 field 030 \n",
      "Got threshold\n",
      "cycle 2 field 031 \n",
      "Got threshold\n",
      "cycle 2 field 032 \n",
      "Got threshold\n",
      "cycle 2 field 033 \n",
      "Got threshold\n",
      "cycle 2 field 034 \n",
      "Got threshold\n",
      "cycle 2 field 035 \n",
      "Got threshold\n",
      "cycle 2 field 036 \n",
      "Got threshold\n",
      "cycle 2 field 037 \n",
      "Got threshold\n",
      "cycle 2 field 038 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "cycle 2 field 039 \n",
      "Got threshold\n",
      "cycle 2 field 040 \n",
      "Got threshold\n",
      "cycle 2 field 041 \n",
      "Got threshold\n",
      "cycle 2 field 042 \n",
      "Got threshold\n",
      "cycle 2 field 044 \n",
      "Got threshold\n",
      "cycle 2 field 045 \n",
      "Got threshold\n",
      "cycle 2 field 046 \n",
      "Got threshold\n",
      "cycle 2 field 047 \n",
      "Got threshold\n",
      "cycle 2 field 048 \n",
      "Got threshold\n",
      "cycle 2 field 049 \n",
      "Got threshold\n",
      "cycle 2 field 050 \n",
      "Got threshold\n",
      "cycle 2 field 051 \n",
      "Got threshold\n",
      "cycle 2 field 052 \n",
      "Got threshold\n",
      "cycle 2 field 053 \n",
      "Got threshold\n",
      "cycle 2 field 054 \n",
      "Got threshold\n",
      "cycle 2 field 055 \n",
      "Got threshold\n",
      "cycle 2 field 056 \n",
      "Got threshold\n",
      "cycle 2 field 057 \n",
      "Got threshold\n",
      "cycle 2 field 058 \n",
      "Got threshold\n",
      "cycle 2 field 059 \n",
      "Got threshold\n",
      "cycle 2 field 060 \n",
      "Got threshold\n",
      "cycle 2 field 061 \n",
      "Got threshold\n",
      "cycle 2 field 062 \n",
      "Got threshold\n",
      "cycle 2 field 063 \n",
      "Got threshold\n",
      "cycle 2 field 064 \n",
      "Got threshold\n",
      "cycle 2 field 065 \n",
      "Got threshold\n",
      "cycle 2 field 066 \n",
      "Got threshold\n",
      "cycle 2 field 067 \n",
      "Got threshold\n",
      "cycle 2 field 068 \n",
      "Got threshold\n",
      "cycle 2 field 069 \n",
      "Got threshold\n",
      "cycle 2 field 070 \n",
      "Got threshold\n",
      "cycle 2 field 071 \n",
      "Got threshold\n",
      "cycle 2 field 072 \n",
      "Got threshold\n",
      "cycle 2 field 073 \n",
      "Got threshold\n",
      "cycle 2 field 074 \n",
      "Got threshold\n",
      "cycle 2 field 075 \n",
      "Got threshold\n",
      "cycle 2 field 076 \n",
      "Got threshold\n",
      "cycle 2 field 077 \n",
      "Got threshold\n",
      "cycle 2 field 078 \n",
      "Got threshold\n",
      "cycle 2 field 080 \n",
      "Got threshold\n",
      "cycle 2 field 081 \n",
      "Got threshold\n",
      "cycle 2 field 082 \n",
      "Got threshold\n",
      "cycle 2 field 083 \n",
      "Got threshold\n",
      "cycle 2 field 084 \n",
      "Got threshold\n",
      "cycle 2 field 085 \n",
      "Got threshold\n",
      "cycle 2 field 086 \n",
      "Got threshold\n",
      "cycle 2 field 087 \n",
      "Got threshold\n",
      "cycle 2 field 088 \n",
      "Got threshold\n",
      "cycle 2 field 089 \n",
      "Got threshold\n",
      "cycle 2 field 090 \n",
      "Got threshold\n",
      "cycle 2 field 091 \n",
      "Got threshold\n",
      "cycle 2 field 092 \n",
      "Got threshold\n",
      "cycle 2 field 093 \n",
      "Got threshold\n",
      "cycle 2 field 094 \n",
      "Got threshold\n",
      "cycle 2 field 095 \n",
      "Got threshold\n",
      "cycle 2 field 096 \n",
      "Got threshold\n",
      "cycle 2 field 097 \n",
      "Got threshold\n",
      "cycle 2 field 098 \n",
      "Got threshold\n",
      "cycle 2 field 100 \n",
      "Got threshold\n",
      "cycle 2 field 101 \n",
      "Got threshold\n",
      "cycle 2 field 102 \n",
      "Got threshold\n",
      "cycle 2 field 103 \n",
      "Got threshold\n",
      "cycle 2 field 105 \n",
      "Got threshold\n",
      "cycle 2 field 106 \n",
      "Got threshold\n",
      "cycle 2 field 107 \n",
      "Got threshold\n",
      "cycle 2 field 108 \n",
      "Got threshold\n",
      "cycle 2 field 109 \n",
      "Got threshold\n",
      "cycle 2 field 110 \n",
      "Got threshold\n",
      "cycle 2 field 111 \n",
      "Got threshold\n",
      "cycle 2 field 112 \n",
      "Got threshold\n",
      "cycle 2 field 113 \n",
      "Got threshold\n",
      "cycle 2 field 114 \n",
      "Got threshold\n",
      "cycle 2 field 115 \n",
      "Got threshold\n",
      "cycle 2 field 116 \n",
      "Got threshold\n",
      "cycle 2 field 117 \n",
      "Got threshold\n",
      "cycle 2 field 118 \n",
      "Got threshold\n",
      "cycle 2 field 119 \n",
      "Got threshold\n",
      "cycle 2 field 120 \n",
      "Got threshold\n",
      "cycle 2 field 121 \n",
      "Got threshold\n",
      "cycle 2 field 122 \n",
      "Got threshold\n",
      "cycle 2 field 123 \n",
      "Got threshold\n",
      "cycle 2 field 124 \n",
      "Got threshold\n",
      "cycle 2 field 125 \n",
      "Got threshold\n",
      "cycle 2 field 126 \n",
      "Got threshold\n",
      "cycle 2 field 127 \n",
      "Got threshold\n",
      "cycle 2 field 128 \n",
      "Got threshold\n",
      "cycle 2 field 129 \n",
      "Got threshold\n",
      "cycle 2 field 130 \n",
      "Got threshold\n",
      "cycle 2 field 131 \n",
      "Got threshold\n",
      "cycle 2 field 132 \n",
      "Got threshold\n",
      "cycle 2 field 133 \n",
      "Got threshold\n",
      "cycle 2 field 134 \n",
      "Got threshold\n",
      "cycle 2 field 135 \n",
      "Got threshold\n",
      "cycle 2 field 136 \n",
      "Got threshold\n",
      "cycle 2 field 137 \n",
      "Got threshold\n",
      "cycle 2 field 138 \n",
      "Got threshold\n",
      "cycle 2 field 139 \n",
      "Got threshold\n",
      "cycle 2 field 140 \n",
      "Got threshold\n",
      "cycle 2 field 141 \n",
      "Got threshold\n",
      "cycle 2 field 142 \n",
      "Got threshold\n",
      "cycle 2 field 143 \n",
      "Got threshold\n",
      "cycle 2 field 144 \n",
      "Got threshold\n",
      "cycle 2 field 145 \n",
      "Got threshold\n",
      "cycle 2 field 146 \n",
      "Got threshold\n",
      "cycle 2 field 147 \n",
      "Got threshold\n",
      "cycle 2 field 148 \n",
      "Got threshold\n",
      "cycle 2 field 149 \n",
      "Got threshold\n",
      "cycle 2 field 151 \n",
      "Got threshold\n",
      "cycle 2 field 152 \n",
      "Got threshold\n",
      "cycle 2 field 153 \n",
      "Got threshold\n",
      "cycle 2 field 154 \n",
      "Got threshold\n",
      "cycle 2 field 155 \n",
      "Got threshold\n",
      "cycle 2 field 156 \n",
      "Got threshold\n",
      "cycle 2 field 157 \n",
      "Got threshold\n",
      "cycle 2 field 158 \n",
      "Got threshold\n",
      "cycle 2 field 159 \n",
      "Got threshold\n",
      "cycle 2 field 160 \n",
      "Got threshold\n",
      "cycle 2 field 162 \n",
      "Got threshold\n",
      "cycle 2 field 163 \n",
      "Got threshold\n",
      "cycle 2 field 164 \n",
      "Got threshold\n",
      "cycle 2 field 165 \n",
      "Got threshold\n",
      "cycle 2 field 166 \n",
      "Got threshold\n",
      "cycle 2 field 167 \n",
      "Got threshold\n",
      "cycle 2 field 168 \n",
      "Got threshold\n",
      "cycle 2 field 169 \n",
      "Got threshold\n",
      "cycle 2 field 170 \n",
      "Got threshold\n",
      "cycle 2 field 171 \n",
      "Got threshold\n",
      "cycle 2 field 172 \n",
      "Got threshold\n",
      "cycle 2 field 173 \n",
      "Got threshold\n",
      "cycle 2 field 174 \n",
      "Got threshold\n",
      "cycle 2 field 175 \n",
      "Got threshold\n",
      "cycle 2 field 176 \n",
      "Got threshold\n",
      "cycle 2 field 178 \n",
      "Got threshold\n",
      "cycle 2 field 179 \n",
      "Got threshold\n",
      "cycle 2 field 180 \n",
      "Got threshold\n",
      "cycle 2 field 181 \n",
      "Got threshold\n",
      "cycle 2 field 182 \n",
      "Got threshold\n",
      "cycle 2 field 183 \n",
      "Got threshold\n",
      "cycle 2 field 184 \n",
      "Got threshold\n",
      "cycle 2 field 185 \n",
      "Got threshold\n",
      "cycle 2 field 186 \n",
      "Got threshold\n",
      "cycle 2 field 187 \n",
      "Got threshold\n",
      "cycle 2 field 188 \n",
      "Got threshold\n",
      "cycle 2 field 189 \n",
      "Got threshold\n",
      "cycle 2 field 190 \n",
      "Got threshold\n",
      "cycle 2 field 191 \n",
      "Got threshold\n",
      "cycle 2 field 192 \n",
      "Got threshold\n",
      "cycle 2 field 193 \n",
      "Got threshold\n",
      "cycle 2 field 194 \n",
      "Got threshold\n",
      "cycle 2 field 195 \n",
      "Got threshold\n",
      "cycle 2 field 196 \n",
      "Got threshold\n",
      "cycle 2 field 197 \n",
      "Got threshold\n",
      "cycle 2 field 198 \n",
      "Got threshold\n",
      "cycle 2 field 199 \n",
      "Got threshold\n",
      "cycle 2 field 200 \n",
      "Got threshold\n",
      "cycle 2 field 201 \n",
      "Got threshold\n",
      "cycle 2 field 202 \n",
      "Got threshold\n",
      "cycle 2 field 203 \n",
      "Got threshold\n",
      "cycle 2 field 204 \n",
      "Got threshold\n",
      "cycle 2 field 205 \n",
      "Got threshold\n",
      "cycle 2 field 206 \n",
      "Got threshold\n",
      "cycle 2 field 207 \n",
      "Got threshold\n",
      "cycle 2 field 208 \n",
      "Got threshold\n",
      "cycle 2 field 209 \n",
      "Got threshold\n",
      "cycle 2 field 210 \n",
      "Got threshold\n",
      "cycle 2 field 211 \n",
      "Got threshold\n",
      "cycle 2 field 212 \n",
      "Got threshold\n",
      "cycle 2 field 213 \n",
      "Got threshold\n",
      "cycle 2 field 214 \n",
      "Got threshold\n",
      "cycle 2 field 215 \n",
      "Got threshold\n",
      "cycle 2 field 216 \n",
      "Got threshold\n",
      "cycle 2 field 217 \n",
      "Got threshold\n",
      "cycle 2 field 218 \n",
      "Got threshold\n",
      "cycle 2 field 219 \n",
      "Got threshold\n",
      "cycle 2 field 220 \n",
      "Got threshold\n",
      "cycle 2 field 221 \n",
      "Got threshold\n",
      "cycle 2 field 222 \n",
      "Got threshold\n",
      "cycle 2 field 223 \n",
      "Got threshold\n",
      "cycle 2 field 224 \n",
      "Got threshold\n",
      "cycle 3 field 000 \n",
      "Got threshold\n",
      "cycle 3 field 001 \n",
      "Got threshold\n",
      "cycle 3 field 002 \n",
      "Got threshold\n",
      "cycle 3 field 003 \n",
      "Got threshold\n",
      "cycle 3 field 004 \n",
      "Got threshold\n",
      "cycle 3 field 005 \n",
      "Got threshold\n",
      "cycle 3 field 007 \n",
      "Got threshold\n",
      "cycle 3 field 008 \n",
      "Got threshold\n",
      "cycle 3 field 011 \n",
      "Got threshold\n",
      "cycle 3 field 012 \n",
      "Got threshold\n",
      "cycle 3 field 013 \n",
      "Got threshold\n",
      "cycle 3 field 014 \n",
      "Got threshold\n",
      "cycle 3 field 015 \n",
      "Got threshold\n",
      "cycle 3 field 016 \n",
      "Got threshold\n",
      "cycle 3 field 017 \n",
      "Got threshold\n",
      "cycle 3 field 019 \n",
      "Got threshold\n",
      "cycle 3 field 020 \n",
      "Got threshold\n",
      "cycle 3 field 021 \n",
      "Got threshold\n",
      "cycle 3 field 022 \n",
      "Got threshold\n",
      "cycle 3 field 023 \n",
      "Got threshold\n",
      "cycle 3 field 024 \n",
      "Got threshold\n",
      "cycle 3 field 025 \n",
      "Got threshold\n",
      "cycle 3 field 026 \n",
      "Got threshold\n",
      "cycle 3 field 027 \n",
      "Got threshold\n",
      "cycle 3 field 028 \n",
      "Got threshold\n",
      "cycle 3 field 029 \n",
      "Got threshold\n",
      "cycle 3 field 030 \n",
      "Got threshold\n",
      "cycle 3 field 031 \n",
      "Got threshold\n",
      "cycle 3 field 032 \n",
      "Got threshold\n",
      "cycle 3 field 033 \n",
      "Got threshold\n",
      "cycle 3 field 034 \n",
      "Got threshold\n",
      "cycle 3 field 035 \n",
      "Got threshold\n",
      "cycle 3 field 036 \n",
      "Got threshold\n",
      "cycle 3 field 037 \n",
      "Got threshold\n",
      "cycle 3 field 038 \n",
      "Got threshold\n",
      "cycle 3 field 039 \n",
      "Got threshold\n",
      "cycle 3 field 040 \n",
      "Got threshold\n",
      "cycle 3 field 041 \n",
      "Got threshold\n",
      "cycle 3 field 042 \n",
      "Got threshold\n",
      "cycle 3 field 044 \n",
      "Got threshold\n",
      "cycle 3 field 045 \n",
      "Got threshold\n",
      "cycle 3 field 046 \n",
      "Got threshold\n",
      "cycle 3 field 047 \n",
      "Got threshold\n",
      "cycle 3 field 048 \n",
      "Got threshold\n",
      "cycle 3 field 049 \n",
      "Got threshold\n",
      "cycle 3 field 050 \n",
      "Got threshold\n",
      "cycle 3 field 051 \n",
      "Got threshold\n",
      "cycle 3 field 052 \n",
      "Got threshold\n",
      "cycle 3 field 053 \n",
      "Got threshold\n",
      "cycle 3 field 054 \n",
      "Got threshold\n",
      "cycle 3 field 055 \n",
      "Got threshold\n",
      "cycle 3 field 056 \n",
      "Got threshold\n",
      "cycle 3 field 057 \n",
      "Got threshold\n",
      "cycle 3 field 058 \n",
      "Got threshold\n",
      "cycle 3 field 059 \n",
      "Got threshold\n",
      "cycle 3 field 060 \n",
      "Got threshold\n",
      "cycle 3 field 061 \n",
      "Got threshold\n",
      "cycle 3 field 062 \n",
      "Got threshold\n",
      "cycle 3 field 063 \n",
      "Got threshold\n",
      "cycle 3 field 064 \n",
      "Got threshold\n",
      "cycle 3 field 065 \n",
      "Got threshold\n",
      "cycle 3 field 066 \n",
      "Got threshold\n",
      "cycle 3 field 067 \n",
      "Got threshold\n",
      "cycle 3 field 068 \n",
      "Got threshold\n",
      "cycle 3 field 069 \n",
      "Got threshold\n",
      "cycle 3 field 070 \n",
      "Got threshold\n",
      "cycle 3 field 071 \n",
      "Got threshold\n",
      "cycle 3 field 072 \n",
      "Got threshold\n",
      "cycle 3 field 073 \n",
      "Got threshold\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 3 field 074 \n",
      "Got threshold\n",
      "cycle 3 field 075 \n",
      "Got threshold\n",
      "cycle 3 field 076 \n",
      "Got threshold\n",
      "cycle 3 field 077 \n",
      "Got threshold\n",
      "cycle 3 field 078 \n",
      "Got threshold\n",
      "cycle 3 field 080 \n",
      "Got threshold\n",
      "cycle 3 field 081 \n",
      "Got threshold\n",
      "cycle 3 field 082 \n",
      "Got threshold\n",
      "cycle 3 field 083 \n",
      "Got threshold\n",
      "cycle 3 field 084 \n",
      "Got threshold\n",
      "cycle 3 field 085 \n",
      "Got threshold\n",
      "cycle 3 field 086 \n",
      "Got threshold\n",
      "cycle 3 field 087 \n",
      "Got threshold\n",
      "cycle 3 field 088 \n",
      "Got threshold\n",
      "cycle 3 field 089 \n",
      "Got threshold\n",
      "cycle 3 field 090 \n",
      "Got threshold\n",
      "cycle 3 field 091 \n",
      "Got threshold\n",
      "cycle 3 field 092 \n",
      "Got threshold\n",
      "cycle 3 field 093 \n",
      "Got threshold\n",
      "cycle 3 field 094 \n",
      "Got threshold\n",
      "cycle 3 field 095 \n",
      "Got threshold\n",
      "cycle 3 field 096 \n",
      "Got threshold\n",
      "cycle 3 field 097 \n",
      "Got threshold\n",
      "cycle 3 field 098 \n",
      "Got threshold\n",
      "cycle 3 field 100 \n",
      "Got threshold\n",
      "cycle 3 field 101 \n",
      "Got threshold\n",
      "cycle 3 field 102 \n",
      "Got threshold\n",
      "cycle 3 field 103 \n",
      "Got threshold\n",
      "cycle 3 field 105 \n",
      "Got threshold\n",
      "cycle 3 field 106 \n",
      "Got threshold\n",
      "cycle 3 field 107 \n",
      "Got threshold\n",
      "cycle 3 field 108 \n",
      "Got threshold\n",
      "cycle 3 field 109 \n",
      "Got threshold\n",
      "cycle 3 field 110 \n",
      "Got threshold\n",
      "cycle 3 field 111 \n",
      "Got threshold\n",
      "cycle 3 field 112 \n",
      "Got threshold\n",
      "cycle 3 field 113 \n",
      "Got threshold\n",
      "cycle 3 field 114 \n",
      "Got threshold\n",
      "cycle 3 field 115 \n",
      "Got threshold\n",
      "cycle 3 field 116 \n",
      "Got threshold\n",
      "cycle 3 field 117 \n",
      "Got threshold\n",
      "cycle 3 field 118 \n",
      "Got threshold\n",
      "cycle 3 field 119 \n",
      "Got threshold\n",
      "cycle 3 field 120 \n",
      "Got threshold\n",
      "cycle 3 field 121 \n",
      "Got threshold\n",
      "cycle 3 field 122 \n",
      "Got threshold\n",
      "cycle 3 field 123 \n",
      "Got threshold\n",
      "cycle 3 field 124 \n",
      "Got threshold\n",
      "cycle 3 field 125 \n",
      "Got threshold\n",
      "cycle 3 field 126 \n",
      "Got threshold\n",
      "cycle 3 field 127 \n",
      "Got threshold\n",
      "cycle 3 field 128 \n",
      "Got threshold\n",
      "cycle 3 field 129 \n",
      "Got threshold\n",
      "cycle 3 field 130 \n",
      "Got threshold\n",
      "cycle 3 field 131 \n",
      "Got threshold\n",
      "cycle 3 field 132 \n",
      "Got threshold\n",
      "cycle 3 field 133 \n",
      "Got threshold\n",
      "cycle 3 field 134 \n",
      "Got threshold\n",
      "cycle 3 field 135 \n",
      "Got threshold\n",
      "cycle 3 field 136 \n",
      "Got threshold\n",
      "cycle 3 field 137 \n",
      "Got threshold\n",
      "cycle 3 field 138 \n",
      "Got threshold\n",
      "cycle 3 field 139 \n",
      "Got threshold\n",
      "cycle 3 field 140 \n",
      "Got threshold\n",
      "cycle 3 field 141 \n",
      "Got threshold\n",
      "cycle 3 field 142 \n",
      "Got threshold\n",
      "cycle 3 field 143 \n",
      "Got threshold\n",
      "cycle 3 field 144 \n",
      "Got threshold\n",
      "cycle 3 field 145 \n",
      "Got threshold\n",
      "cycle 3 field 146 \n",
      "Got threshold\n",
      "cycle 3 field 147 \n",
      "Got threshold\n",
      "cycle 3 field 148 \n",
      "Got threshold\n",
      "cycle 3 field 149 \n",
      "Got threshold\n",
      "cycle 3 field 151 \n",
      "Got threshold\n",
      "cycle 3 field 152 \n",
      "Got threshold\n",
      "cycle 3 field 153 \n",
      "Got threshold\n",
      "cycle 3 field 154 \n",
      "Got threshold\n",
      "cycle 3 field 155 \n",
      "Got threshold\n",
      "cycle 3 field 156 \n",
      "Got threshold\n",
      "cycle 3 field 157 \n",
      "Got threshold\n",
      "cycle 3 field 158 \n",
      "Got threshold\n",
      "cycle 3 field 159 \n",
      "Got threshold\n",
      "cycle 3 field 160 \n",
      "Got threshold\n",
      "cycle 3 field 162 \n",
      "Got threshold\n",
      "cycle 3 field 163 \n",
      "Got threshold\n",
      "cycle 3 field 164 \n",
      "Got threshold\n",
      "cycle 3 field 165 \n",
      "Got threshold\n",
      "cycle 3 field 166 \n",
      "Got threshold\n",
      "cycle 3 field 167 \n",
      "Got threshold\n",
      "cycle 3 field 168 \n",
      "Got threshold\n",
      "cycle 3 field 169 \n",
      "Got threshold\n",
      "cycle 3 field 170 \n",
      "Got threshold\n",
      "cycle 3 field 171 \n",
      "Got threshold\n",
      "cycle 3 field 172 \n",
      "Got threshold\n",
      "cycle 3 field 173 \n",
      "Got threshold\n",
      "cycle 3 field 174 \n",
      "Got threshold\n",
      "cycle 3 field 175 \n",
      "Got threshold\n",
      "cycle 3 field 176 \n",
      "Got threshold\n",
      "cycle 3 field 178 \n",
      "Got threshold\n",
      "cycle 3 field 179 \n",
      "Got threshold\n",
      "cycle 3 field 180 \n",
      "Got threshold\n",
      "cycle 3 field 181 \n",
      "Got threshold\n",
      "cycle 3 field 182 \n",
      "Got threshold\n",
      "cycle 3 field 183 \n",
      "Got threshold\n",
      "cycle 3 field 184 \n",
      "Got threshold\n",
      "cycle 3 field 185 \n",
      "Got threshold\n",
      "cycle 3 field 186 \n",
      "Got threshold\n",
      "cycle 3 field 187 \n",
      "Got threshold\n",
      "cycle 3 field 188 \n",
      "Got threshold\n",
      "cycle 3 field 189 \n",
      "Got threshold\n",
      "cycle 3 field 190 \n",
      "Got threshold\n",
      "cycle 3 field 191 \n",
      "Got threshold\n",
      "cycle 3 field 192 \n",
      "Got threshold\n",
      "cycle 3 field 193 \n",
      "Got threshold\n",
      "cycle 3 field 194 \n",
      "Got threshold\n",
      "cycle 3 field 195 \n",
      "Got threshold\n",
      "cycle 3 field 196 \n",
      "Got threshold\n",
      "cycle 3 field 197 \n",
      "Got threshold\n",
      "cycle 3 field 198 \n",
      "Got threshold\n",
      "cycle 3 field 199 \n",
      "Got threshold\n",
      "cycle 3 field 200 \n",
      "Got threshold\n",
      "cycle 3 field 201 \n",
      "Got threshold\n",
      "cycle 3 field 202 \n",
      "Got threshold\n",
      "cycle 3 field 203 \n",
      "Got threshold\n",
      "cycle 3 field 204 \n",
      "Got threshold\n",
      "cycle 3 field 205 \n",
      "Got threshold\n",
      "cycle 3 field 206 \n",
      "Got threshold\n",
      "cycle 3 field 207 \n",
      "Got threshold\n",
      "cycle 3 field 208 \n",
      "Got threshold\n",
      "cycle 3 field 209 \n",
      "Got threshold\n",
      "cycle 3 field 210 \n",
      "Got threshold\n",
      "cycle 3 field 211 \n",
      "Got threshold\n",
      "cycle 3 field 212 \n",
      "Got threshold\n",
      "cycle 3 field 213 \n",
      "Got threshold\n",
      "cycle 3 field 214 \n",
      "Got threshold\n",
      "cycle 3 field 215 \n",
      "Got threshold\n",
      "cycle 3 field 216 \n",
      "Got threshold\n",
      "cycle 3 field 217 \n",
      "Got threshold\n",
      "cycle 3 field 218 \n",
      "Got threshold\n",
      "cycle 3 field 219 \n",
      "Got threshold\n",
      "cycle 3 field 220 \n",
      "Got threshold\n",
      "cycle 3 field 221 \n",
      "Got threshold\n",
      "cycle 3 field 222 \n",
      "Got threshold\n",
      "cycle 3 field 223 \n",
      "Got threshold\n",
      "cycle 3 field 224 \n",
      "Got threshold\n",
      "cycle 4 field 000 \n",
      "Got threshold\n",
      "cycle 4 field 001 \n",
      "Got threshold\n",
      "cycle 4 field 002 \n",
      "Got threshold\n",
      "cycle 4 field 003 \n",
      "Got threshold\n",
      "cycle 4 field 004 \n",
      "Got threshold\n",
      "cycle 4 field 005 \n",
      "Got threshold\n",
      "cycle 4 field 007 \n",
      "Got threshold\n",
      "cycle 4 field 008 \n",
      "Got threshold\n",
      "cycle 4 field 011 \n",
      "Got threshold\n",
      "cycle 4 field 012 \n",
      "Got threshold\n",
      "cycle 4 field 013 \n",
      "Got threshold\n",
      "cycle 4 field 014 \n",
      "Got threshold\n",
      "cycle 4 field 015 \n",
      "Got threshold\n",
      "cycle 4 field 016 \n",
      "Got threshold\n",
      "cycle 4 field 017 \n",
      "Got threshold\n",
      "cycle 4 field 019 \n",
      "Got threshold\n",
      "cycle 4 field 020 \n",
      "Got threshold\n",
      "cycle 4 field 021 \n",
      "Got threshold\n",
      "cycle 4 field 022 \n",
      "Got threshold\n",
      "cycle 4 field 023 \n",
      "Got threshold\n",
      "cycle 4 field 024 \n",
      "Got threshold\n",
      "cycle 4 field 025 \n",
      "Got threshold\n",
      "cycle 4 field 026 \n",
      "Got threshold\n",
      "cycle 4 field 027 \n",
      "Got threshold\n",
      "cycle 4 field 028 \n",
      "Got threshold\n",
      "cycle 4 field 029 \n",
      "Got threshold\n",
      "cycle 4 field 030 \n",
      "Got threshold\n",
      "cycle 4 field 031 \n",
      "Got threshold\n",
      "cycle 4 field 032 \n",
      "Got threshold\n",
      "cycle 4 field 033 \n",
      "Got threshold\n",
      "cycle 4 field 034 \n",
      "Got threshold\n",
      "cycle 4 field 035 \n",
      "Got threshold\n",
      "cycle 4 field 036 \n",
      "Got threshold\n",
      "cycle 4 field 037 \n",
      "Got threshold\n",
      "cycle 4 field 038 \n",
      "Got threshold\n",
      "cycle 4 field 039 \n",
      "Got threshold\n",
      "cycle 4 field 040 \n",
      "Got threshold\n",
      "cycle 4 field 041 \n",
      "Got threshold\n",
      "cycle 4 field 042 \n",
      "Got threshold\n",
      "cycle 4 field 044 \n",
      "Got threshold\n",
      "cycle 4 field 045 \n",
      "Got threshold\n",
      "cycle 4 field 046 \n",
      "Got threshold\n",
      "cycle 4 field 047 \n",
      "Got threshold\n",
      "cycle 4 field 048 \n",
      "Got threshold\n",
      "cycle 4 field 049 \n",
      "Got threshold\n",
      "cycle 4 field 050 \n",
      "Got threshold\n",
      "cycle 4 field 051 \n",
      "Got threshold\n",
      "cycle 4 field 052 \n",
      "Got threshold\n",
      "cycle 4 field 053 \n",
      "Got threshold\n",
      "cycle 4 field 054 \n",
      "Got threshold\n",
      "cycle 4 field 055 \n",
      "Got threshold\n",
      "cycle 4 field 056 \n",
      "Got threshold\n",
      "cycle 4 field 057 \n",
      "Got threshold\n",
      "cycle 4 field 058 \n",
      "Got threshold\n",
      "cycle 4 field 059 \n",
      "Got threshold\n",
      "cycle 4 field 060 \n",
      "Got threshold\n",
      "cycle 4 field 061 \n",
      "Got threshold\n",
      "cycle 4 field 062 \n",
      "Got threshold\n",
      "cycle 4 field 063 \n",
      "Got threshold\n",
      "cycle 4 field 064 \n",
      "Got threshold\n",
      "cycle 4 field 065 \n",
      "Got threshold\n",
      "cycle 4 field 066 \n",
      "Got threshold\n",
      "cycle 4 field 067 \n",
      "Got threshold\n",
      "cycle 4 field 068 \n",
      "Got threshold\n",
      "cycle 4 field 069 \n",
      "Got threshold\n",
      "cycle 4 field 070 \n",
      "Got threshold\n",
      "cycle 4 field 071 \n",
      "Got threshold\n",
      "cycle 4 field 072 \n",
      "Got threshold\n",
      "cycle 4 field 073 \n",
      "Got threshold\n",
      "cycle 4 field 074 \n",
      "Got threshold\n",
      "cycle 4 field 075 \n",
      "Got threshold\n",
      "cycle 4 field 076 \n",
      "Got threshold\n",
      "cycle 4 field 077 \n",
      "Got threshold\n",
      "cycle 4 field 078 \n",
      "Got threshold\n",
      "cycle 4 field 080 \n",
      "Got threshold\n",
      "cycle 4 field 081 \n",
      "Got threshold\n",
      "cycle 4 field 082 \n",
      "Got threshold\n",
      "cycle 4 field 083 \n",
      "Got threshold\n",
      "cycle 4 field 084 \n",
      "Got threshold\n",
      "cycle 4 field 085 \n",
      "Got threshold\n",
      "cycle 4 field 086 \n",
      "Got threshold\n",
      "cycle 4 field 087 \n",
      "Got threshold\n",
      "cycle 4 field 088 \n",
      "Got threshold\n",
      "cycle 4 field 089 \n",
      "Got threshold\n",
      "cycle 4 field 090 \n",
      "Got threshold\n",
      "cycle 4 field 091 \n",
      "Got threshold\n",
      "cycle 4 field 092 \n",
      "Got threshold\n",
      "cycle 4 field 093 \n",
      "Got threshold\n",
      "cycle 4 field 094 \n",
      "Got threshold\n",
      "cycle 4 field 095 \n",
      "Got threshold\n",
      "cycle 4 field 096 \n",
      "Got threshold\n",
      "cycle 4 field 097 \n",
      "Got threshold\n",
      "cycle 4 field 098 \n",
      "Got threshold\n",
      "cycle 4 field 100 \n",
      "Got threshold\n",
      "cycle 4 field 101 \n",
      "Got threshold\n",
      "cycle 4 field 102 \n",
      "Got threshold\n",
      "cycle 4 field 103 \n",
      "Got threshold\n",
      "cycle 4 field 105 \n",
      "Got threshold\n",
      "cycle 4 field 106 \n",
      "Got threshold\n",
      "cycle 4 field 107 \n",
      "Got threshold\n",
      "cycle 4 field 108 \n",
      "Got threshold\n",
      "cycle 4 field 109 \n",
      "Got threshold\n",
      "cycle 4 field 110 \n",
      "Got threshold\n",
      "cycle 4 field 111 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "cycle 4 field 112 \n",
      "Got threshold\n",
      "cycle 4 field 113 \n",
      "Got threshold\n",
      "cycle 4 field 114 \n",
      "Got threshold\n",
      "cycle 4 field 115 \n",
      "Got threshold\n",
      "cycle 4 field 116 \n",
      "Got threshold\n",
      "cycle 4 field 117 \n",
      "Got threshold\n",
      "cycle 4 field 118 \n",
      "Got threshold\n",
      "cycle 4 field 119 \n",
      "Got threshold\n",
      "cycle 4 field 120 \n",
      "Got threshold\n",
      "cycle 4 field 121 \n",
      "Got threshold\n",
      "cycle 4 field 122 \n",
      "Got threshold\n",
      "cycle 4 field 123 \n",
      "Got threshold\n",
      "cycle 4 field 124 \n",
      "Got threshold\n",
      "cycle 4 field 125 \n",
      "Got threshold\n",
      "cycle 4 field 126 \n",
      "Got threshold\n",
      "cycle 4 field 127 \n",
      "Got threshold\n",
      "cycle 4 field 128 \n",
      "Got threshold\n",
      "cycle 4 field 129 \n",
      "Got threshold\n",
      "cycle 4 field 130 \n",
      "Got threshold\n",
      "cycle 4 field 131 \n",
      "Got threshold\n",
      "cycle 4 field 132 \n",
      "Got threshold\n",
      "cycle 4 field 133 \n",
      "Got threshold\n",
      "cycle 4 field 134 \n",
      "Got threshold\n",
      "cycle 4 field 135 \n",
      "Got threshold\n",
      "cycle 4 field 136 \n",
      "Got threshold\n",
      "cycle 4 field 137 \n",
      "Got threshold\n",
      "cycle 4 field 138 \n",
      "Got threshold\n",
      "cycle 4 field 139 \n",
      "Got threshold\n",
      "cycle 4 field 140 \n",
      "Got threshold\n",
      "cycle 4 field 141 \n",
      "Got threshold\n",
      "cycle 4 field 142 \n",
      "Got threshold\n",
      "cycle 4 field 143 \n",
      "Got threshold\n",
      "cycle 4 field 144 \n",
      "Got threshold\n",
      "cycle 4 field 145 \n",
      "Got threshold\n",
      "cycle 4 field 146 \n",
      "Got threshold\n",
      "cycle 4 field 147 \n",
      "Got threshold\n",
      "cycle 4 field 148 \n",
      "Got threshold\n",
      "cycle 4 field 149 \n",
      "Got threshold\n",
      "cycle 4 field 151 \n",
      "Got threshold\n",
      "cycle 4 field 152 \n",
      "Got threshold\n",
      "cycle 4 field 153 \n",
      "Got threshold\n",
      "cycle 4 field 154 \n",
      "Got threshold\n",
      "cycle 4 field 155 \n",
      "Got threshold\n",
      "cycle 4 field 156 \n",
      "Got threshold\n",
      "cycle 4 field 157 \n",
      "Got threshold\n",
      "cycle 4 field 158 \n",
      "Got threshold\n",
      "cycle 4 field 159 \n",
      "Got threshold\n",
      "cycle 4 field 160 \n",
      "Got threshold\n",
      "cycle 4 field 162 \n",
      "Got threshold\n",
      "cycle 4 field 163 \n",
      "Got threshold\n",
      "cycle 4 field 164 \n",
      "Got threshold\n",
      "cycle 4 field 165 \n",
      "Got threshold\n",
      "cycle 4 field 166 \n",
      "Got threshold\n",
      "cycle 4 field 167 \n",
      "Got threshold\n",
      "cycle 4 field 168 \n",
      "Got threshold\n",
      "cycle 4 field 169 \n",
      "Got threshold\n",
      "cycle 4 field 170 \n",
      "Got threshold\n",
      "cycle 4 field 171 \n",
      "Got threshold\n",
      "cycle 4 field 172 \n",
      "Got threshold\n",
      "cycle 4 field 173 \n",
      "Got threshold\n",
      "cycle 4 field 174 \n",
      "Got threshold\n",
      "cycle 4 field 175 \n",
      "Got threshold\n",
      "cycle 4 field 176 \n",
      "Got threshold\n",
      "cycle 4 field 178 \n",
      "Got threshold\n",
      "cycle 4 field 179 \n",
      "Got threshold\n",
      "cycle 4 field 180 \n",
      "Got threshold\n",
      "cycle 4 field 181 \n",
      "Got threshold\n",
      "cycle 4 field 182 \n",
      "Got threshold\n",
      "cycle 4 field 183 \n",
      "Got threshold\n",
      "cycle 4 field 184 \n",
      "Got threshold\n",
      "cycle 4 field 185 \n",
      "Got threshold\n",
      "cycle 4 field 186 \n",
      "Got threshold\n",
      "cycle 4 field 187 \n",
      "Got threshold\n",
      "cycle 4 field 188 \n",
      "Got threshold\n",
      "cycle 4 field 189 \n",
      "Got threshold\n",
      "cycle 4 field 190 \n",
      "Got threshold\n",
      "cycle 4 field 191 \n",
      "Got threshold\n",
      "cycle 4 field 192 \n",
      "Got threshold\n",
      "cycle 4 field 193 \n",
      "Got threshold\n",
      "cycle 4 field 194 \n",
      "Got threshold\n",
      "cycle 4 field 195 \n",
      "Got threshold\n",
      "cycle 4 field 196 \n",
      "Got threshold\n",
      "cycle 4 field 197 \n",
      "Got threshold\n",
      "cycle 4 field 198 \n",
      "Got threshold\n",
      "cycle 4 field 199 \n",
      "Got threshold\n",
      "cycle 4 field 200 \n",
      "Got threshold\n",
      "cycle 4 field 201 \n",
      "Got threshold\n",
      "cycle 4 field 202 \n",
      "Got threshold\n",
      "cycle 4 field 203 \n",
      "Got threshold\n",
      "cycle 4 field 204 \n",
      "Got threshold\n",
      "cycle 4 field 205 \n",
      "Got threshold\n",
      "cycle 4 field 206 \n",
      "Got threshold\n",
      "cycle 4 field 207 \n",
      "Got threshold\n",
      "cycle 4 field 208 \n",
      "Got threshold\n",
      "cycle 4 field 209 \n",
      "Got threshold\n",
      "cycle 4 field 210 \n",
      "Got threshold\n",
      "cycle 4 field 211 \n",
      "Got threshold\n",
      "cycle 4 field 212 \n",
      "Got threshold\n",
      "cycle 4 field 213 \n",
      "Got threshold\n",
      "cycle 4 field 214 \n",
      "Got threshold\n",
      "cycle 4 field 215 \n",
      "Got threshold\n",
      "cycle 4 field 216 \n",
      "Got threshold\n",
      "cycle 4 field 217 \n",
      "Got threshold\n",
      "cycle 4 field 218 \n",
      "Got threshold\n",
      "cycle 4 field 219 \n",
      "Got threshold\n",
      "cycle 4 field 220 \n",
      "Got threshold\n",
      "cycle 4 field 221 \n",
      "Got threshold\n",
      "cycle 4 field 222 \n",
      "Got threshold\n",
      "cycle 4 field 223 \n",
      "Got threshold\n",
      "cycle 4 field 224 \n",
      "Got threshold\n",
      "cycle 5 field 000 \n",
      "Got threshold\n",
      "cycle 5 field 001 \n",
      "Got threshold\n",
      "cycle 5 field 002 \n",
      "Got threshold\n",
      "cycle 5 field 003 \n",
      "Got threshold\n",
      "cycle 5 field 004 \n",
      "Got threshold\n",
      "cycle 5 field 005 \n",
      "Got threshold\n",
      "cycle 5 field 007 \n",
      "Got threshold\n",
      "cycle 5 field 008 \n",
      "Got threshold\n",
      "cycle 5 field 011 \n",
      "Got threshold\n",
      "cycle 5 field 012 \n",
      "Got threshold\n",
      "cycle 5 field 013 \n",
      "Got threshold\n",
      "cycle 5 field 014 \n",
      "Got threshold\n",
      "cycle 5 field 015 \n",
      "Got threshold\n",
      "cycle 5 field 016 \n",
      "Got threshold\n",
      "cycle 5 field 017 \n",
      "Got threshold\n",
      "cycle 5 field 019 \n",
      "Got threshold\n",
      "cycle 5 field 020 \n",
      "Got threshold\n",
      "cycle 5 field 021 \n",
      "Got threshold\n",
      "cycle 5 field 022 \n",
      "Got threshold\n",
      "cycle 5 field 023 \n",
      "Got threshold\n",
      "cycle 5 field 024 \n",
      "Got threshold\n",
      "cycle 5 field 025 \n",
      "Got threshold\n",
      "cycle 5 field 026 \n",
      "Got threshold\n",
      "cycle 5 field 027 \n",
      "Got threshold\n",
      "cycle 5 field 028 \n",
      "Got threshold\n",
      "cycle 5 field 029 \n",
      "Got threshold\n",
      "cycle 5 field 030 \n",
      "Got threshold\n",
      "cycle 5 field 031 \n",
      "Got threshold\n",
      "cycle 5 field 032 \n",
      "Got threshold\n",
      "cycle 5 field 033 \n",
      "Got threshold\n",
      "cycle 5 field 034 \n",
      "Got threshold\n",
      "cycle 5 field 035 \n",
      "Got threshold\n",
      "cycle 5 field 036 \n",
      "Got threshold\n",
      "cycle 5 field 037 \n",
      "Got threshold\n",
      "cycle 5 field 038 \n",
      "Got threshold\n",
      "cycle 5 field 039 \n",
      "Got threshold\n",
      "cycle 5 field 040 \n",
      "Got threshold\n",
      "cycle 5 field 041 \n",
      "Got threshold\n",
      "cycle 5 field 042 \n",
      "Got threshold\n",
      "cycle 5 field 044 \n",
      "Got threshold\n",
      "cycle 5 field 045 \n",
      "Got threshold\n",
      "cycle 5 field 046 \n",
      "Got threshold\n",
      "cycle 5 field 047 \n",
      "Got threshold\n",
      "cycle 5 field 048 \n",
      "Got threshold\n",
      "cycle 5 field 049 \n",
      "Got threshold\n",
      "cycle 5 field 050 \n",
      "Got threshold\n",
      "cycle 5 field 051 \n",
      "Got threshold\n",
      "cycle 5 field 052 \n",
      "Got threshold\n",
      "cycle 5 field 053 \n",
      "Got threshold\n",
      "cycle 5 field 054 \n",
      "Got threshold\n",
      "cycle 5 field 055 \n",
      "Got threshold\n",
      "cycle 5 field 056 \n",
      "Got threshold\n",
      "cycle 5 field 057 \n",
      "Got threshold\n",
      "cycle 5 field 058 \n",
      "Got threshold\n",
      "cycle 5 field 059 \n",
      "Got threshold\n",
      "cycle 5 field 060 \n",
      "Got threshold\n",
      "cycle 5 field 061 \n",
      "Got threshold\n",
      "cycle 5 field 062 \n",
      "Got threshold\n",
      "cycle 5 field 063 \n",
      "Got threshold\n",
      "cycle 5 field 064 \n",
      "Got threshold\n",
      "cycle 5 field 065 \n",
      "Got threshold\n",
      "cycle 5 field 066 \n",
      "Got threshold\n",
      "cycle 5 field 067 \n",
      "Got threshold\n",
      "cycle 5 field 068 \n",
      "Got threshold\n",
      "cycle 5 field 069 \n",
      "Got threshold\n",
      "cycle 5 field 070 \n",
      "Got threshold\n",
      "cycle 5 field 071 \n",
      "Got threshold\n",
      "cycle 5 field 072 \n",
      "Got threshold\n",
      "cycle 5 field 073 \n",
      "Got threshold\n",
      "cycle 5 field 074 \n",
      "Got threshold\n",
      "cycle 5 field 075 \n",
      "Got threshold\n",
      "cycle 5 field 076 \n",
      "Got threshold\n",
      "cycle 5 field 077 \n",
      "Got threshold\n",
      "cycle 5 field 078 \n",
      "Got threshold\n",
      "cycle 5 field 080 \n",
      "Got threshold\n",
      "cycle 5 field 081 \n",
      "Got threshold\n",
      "cycle 5 field 082 \n",
      "Got threshold\n",
      "cycle 5 field 083 \n",
      "Got threshold\n",
      "cycle 5 field 084 \n",
      "Got threshold\n",
      "cycle 5 field 085 \n",
      "Got threshold\n",
      "cycle 5 field 086 \n",
      "Got threshold\n",
      "cycle 5 field 087 \n",
      "Got threshold\n",
      "cycle 5 field 088 \n",
      "Got threshold\n",
      "cycle 5 field 089 \n",
      "Got threshold\n",
      "cycle 5 field 090 \n",
      "Got threshold\n",
      "cycle 5 field 091 \n",
      "Got threshold\n",
      "cycle 5 field 092 \n",
      "Got threshold\n",
      "cycle 5 field 093 \n",
      "Got threshold\n",
      "cycle 5 field 094 \n",
      "Got threshold\n",
      "cycle 5 field 095 \n",
      "Got threshold\n",
      "cycle 5 field 096 \n",
      "Got threshold\n",
      "cycle 5 field 097 \n",
      "Got threshold\n",
      "cycle 5 field 098 \n",
      "Got threshold\n",
      "cycle 5 field 100 \n",
      "Got threshold\n",
      "cycle 5 field 101 \n",
      "Got threshold\n",
      "cycle 5 field 102 \n",
      "Got threshold\n",
      "cycle 5 field 103 \n",
      "Got threshold\n",
      "cycle 5 field 105 \n",
      "Got threshold\n",
      "cycle 5 field 106 \n",
      "Got threshold\n",
      "cycle 5 field 107 \n",
      "Got threshold\n",
      "cycle 5 field 108 \n",
      "Got threshold\n",
      "cycle 5 field 109 \n",
      "Got threshold\n",
      "cycle 5 field 110 \n",
      "Got threshold\n",
      "cycle 5 field 111 \n",
      "Got threshold\n",
      "cycle 5 field 112 \n",
      "Got threshold\n",
      "cycle 5 field 113 \n",
      "Got threshold\n",
      "cycle 5 field 114 \n",
      "Got threshold\n",
      "cycle 5 field 115 \n",
      "Got threshold\n",
      "cycle 5 field 116 \n",
      "Got threshold\n",
      "cycle 5 field 117 \n",
      "Got threshold\n",
      "cycle 5 field 118 \n",
      "Got threshold\n",
      "cycle 5 field 119 \n",
      "Got threshold\n",
      "cycle 5 field 120 \n",
      "Got threshold\n",
      "cycle 5 field 121 \n",
      "Got threshold\n",
      "cycle 5 field 122 \n",
      "Got threshold\n",
      "cycle 5 field 123 \n",
      "Got threshold\n",
      "cycle 5 field 124 \n",
      "Got threshold\n",
      "cycle 5 field 125 \n",
      "Got threshold\n",
      "cycle 5 field 126 \n",
      "Got threshold\n",
      "cycle 5 field 127 \n",
      "Got threshold\n",
      "cycle 5 field 128 \n",
      "Got threshold\n",
      "cycle 5 field 129 \n",
      "Got threshold\n",
      "cycle 5 field 130 \n",
      "Got threshold\n",
      "cycle 5 field 131 \n",
      "Got threshold\n",
      "cycle 5 field 132 \n",
      "Got threshold\n",
      "cycle 5 field 133 \n",
      "Got threshold\n",
      "cycle 5 field 134 \n",
      "Got threshold\n",
      "cycle 5 field 135 \n",
      "Got threshold\n",
      "cycle 5 field 136 \n",
      "Got threshold\n",
      "cycle 5 field 137 \n",
      "Got threshold\n",
      "cycle 5 field 138 \n",
      "Got threshold\n",
      "cycle 5 field 139 \n",
      "Got threshold\n",
      "cycle 5 field 140 \n",
      "Got threshold\n",
      "cycle 5 field 141 \n",
      "Got threshold\n",
      "cycle 5 field 142 \n",
      "Got threshold\n",
      "cycle 5 field 143 \n",
      "Got threshold\n",
      "cycle 5 field 144 \n",
      "Got threshold\n",
      "cycle 5 field 145 \n",
      "Got threshold\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 5 field 146 \n",
      "Got threshold\n",
      "cycle 5 field 147 \n",
      "Got threshold\n",
      "cycle 5 field 148 \n",
      "Got threshold\n",
      "cycle 5 field 149 \n",
      "Got threshold\n",
      "cycle 5 field 151 \n",
      "Got threshold\n",
      "cycle 5 field 152 \n",
      "Got threshold\n",
      "cycle 5 field 153 \n",
      "Got threshold\n",
      "cycle 5 field 154 \n",
      "Got threshold\n",
      "cycle 5 field 155 \n",
      "Got threshold\n",
      "cycle 5 field 156 \n",
      "Got threshold\n",
      "cycle 5 field 157 \n",
      "Got threshold\n",
      "cycle 5 field 158 \n",
      "Got threshold\n",
      "cycle 5 field 159 \n",
      "Got threshold\n",
      "cycle 5 field 160 \n",
      "Got threshold\n",
      "cycle 5 field 162 \n",
      "Got threshold\n",
      "cycle 5 field 163 \n",
      "Got threshold\n",
      "cycle 5 field 164 \n",
      "Got threshold\n",
      "cycle 5 field 165 \n",
      "Got threshold\n",
      "cycle 5 field 166 \n",
      "Got threshold\n",
      "cycle 5 field 167 \n",
      "Got threshold\n",
      "cycle 5 field 168 \n",
      "Got threshold\n",
      "cycle 5 field 169 \n",
      "Got threshold\n",
      "cycle 5 field 170 \n",
      "Got threshold\n",
      "cycle 5 field 171 \n",
      "Got threshold\n",
      "cycle 5 field 172 \n",
      "Got threshold\n",
      "cycle 5 field 173 \n",
      "Got threshold\n",
      "cycle 5 field 174 \n",
      "Got threshold\n",
      "cycle 5 field 175 \n",
      "Got threshold\n",
      "cycle 5 field 176 \n",
      "Got threshold\n",
      "cycle 5 field 178 \n",
      "Got threshold\n",
      "cycle 5 field 179 \n",
      "Got threshold\n",
      "cycle 5 field 180 \n",
      "Got threshold\n",
      "cycle 5 field 181 \n",
      "Got threshold\n",
      "cycle 5 field 182 \n",
      "Got threshold\n",
      "cycle 5 field 183 \n",
      "Got threshold\n",
      "cycle 5 field 184 \n",
      "Got threshold\n",
      "cycle 5 field 185 \n",
      "Got threshold\n",
      "cycle 5 field 186 \n",
      "Got threshold\n",
      "cycle 5 field 187 \n",
      "Got threshold\n",
      "cycle 5 field 188 \n",
      "Got threshold\n",
      "cycle 5 field 189 \n",
      "Got threshold\n",
      "cycle 5 field 190 \n",
      "Got threshold\n",
      "cycle 5 field 191 \n",
      "Got threshold\n",
      "cycle 5 field 192 \n",
      "Got threshold\n",
      "cycle 5 field 193 \n",
      "Got threshold\n",
      "cycle 5 field 194 \n",
      "Got threshold\n",
      "cycle 5 field 195 \n",
      "Got threshold\n",
      "cycle 5 field 196 \n",
      "Got threshold\n",
      "cycle 5 field 197 \n",
      "Got threshold\n",
      "cycle 5 field 198 \n",
      "Got threshold\n",
      "cycle 5 field 199 \n",
      "Got threshold\n",
      "cycle 5 field 200 \n",
      "Got threshold\n",
      "cycle 5 field 201 \n",
      "Got threshold\n",
      "cycle 5 field 202 \n",
      "Got threshold\n",
      "cycle 5 field 203 \n",
      "Got threshold\n",
      "cycle 5 field 204 \n",
      "Got threshold\n",
      "cycle 5 field 205 \n",
      "Got threshold\n",
      "cycle 5 field 206 \n",
      "Got threshold\n",
      "cycle 5 field 207 \n",
      "Got threshold\n",
      "cycle 5 field 208 \n",
      "Got threshold\n",
      "cycle 5 field 209 \n",
      "Got threshold\n",
      "cycle 5 field 210 \n",
      "Got threshold\n",
      "cycle 5 field 211 \n",
      "Got threshold\n",
      "cycle 5 field 212 \n",
      "Got threshold\n",
      "cycle 5 field 213 \n",
      "Got threshold\n",
      "cycle 5 field 214 \n",
      "Got threshold\n",
      "cycle 5 field 215 \n",
      "Got threshold\n",
      "cycle 5 field 216 \n",
      "Got threshold\n",
      "cycle 5 field 217 \n",
      "Got threshold\n",
      "cycle 5 field 218 \n",
      "Got threshold\n",
      "cycle 5 field 219 \n",
      "Got threshold\n",
      "cycle 5 field 220 \n",
      "Got threshold\n",
      "cycle 5 field 221 \n",
      "Got threshold\n",
      "cycle 5 field 222 \n",
      "Got threshold\n",
      "cycle 5 field 223 \n",
      "Got threshold\n",
      "cycle 5 field 224 \n",
      "Got threshold\n",
      "cycle 6 field 000 \n",
      "Got threshold\n",
      "cycle 6 field 001 \n",
      "Got threshold\n",
      "cycle 6 field 002 \n",
      "Got threshold\n",
      "cycle 6 field 003 \n",
      "Got threshold\n",
      "cycle 6 field 004 \n",
      "Got threshold\n",
      "cycle 6 field 005 \n",
      "Got threshold\n",
      "cycle 6 field 007 \n",
      "Got threshold\n",
      "cycle 6 field 008 \n",
      "Got threshold\n",
      "cycle 6 field 011 \n",
      "Got threshold\n",
      "cycle 6 field 012 \n",
      "Got threshold\n",
      "cycle 6 field 013 \n",
      "Got threshold\n",
      "cycle 6 field 014 \n",
      "Got threshold\n",
      "cycle 6 field 015 \n",
      "Got threshold\n",
      "cycle 6 field 016 \n",
      "Got threshold\n",
      "cycle 6 field 017 \n",
      "Got threshold\n",
      "cycle 6 field 019 \n",
      "Got threshold\n",
      "cycle 6 field 020 \n",
      "Got threshold\n",
      "cycle 6 field 021 \n",
      "Got threshold\n",
      "cycle 6 field 022 \n",
      "Got threshold\n",
      "cycle 6 field 023 \n",
      "Got threshold\n",
      "cycle 6 field 024 \n",
      "Got threshold\n",
      "cycle 6 field 025 \n",
      "Got threshold\n",
      "cycle 6 field 026 \n",
      "Got threshold\n",
      "cycle 6 field 027 \n",
      "Got threshold\n",
      "cycle 6 field 028 \n",
      "Got threshold\n",
      "cycle 6 field 029 \n",
      "Got threshold\n",
      "cycle 6 field 030 \n",
      "Got threshold\n",
      "cycle 6 field 031 \n",
      "Got threshold\n",
      "cycle 6 field 032 \n",
      "Got threshold\n",
      "cycle 6 field 033 \n",
      "Got threshold\n",
      "cycle 6 field 034 \n",
      "Got threshold\n",
      "cycle 6 field 035 \n",
      "Got threshold\n",
      "cycle 6 field 036 \n",
      "Got threshold\n",
      "cycle 6 field 037 \n",
      "Got threshold\n",
      "cycle 6 field 038 \n",
      "Got threshold\n",
      "cycle 6 field 039 \n",
      "Got threshold\n",
      "cycle 6 field 040 \n",
      "Got threshold\n",
      "cycle 6 field 041 \n",
      "Got threshold\n",
      "cycle 6 field 042 \n",
      "Got threshold\n",
      "cycle 6 field 044 \n",
      "Got threshold\n",
      "cycle 6 field 045 \n",
      "Got threshold\n",
      "cycle 6 field 046 \n",
      "Got threshold\n",
      "cycle 6 field 047 \n",
      "Got threshold\n",
      "cycle 6 field 048 \n",
      "Got threshold\n",
      "cycle 6 field 049 \n",
      "Got threshold\n",
      "cycle 6 field 050 \n",
      "Got threshold\n",
      "cycle 6 field 051 \n",
      "Got threshold\n",
      "cycle 6 field 052 \n",
      "Got threshold\n",
      "cycle 6 field 053 \n",
      "Got threshold\n",
      "cycle 6 field 054 \n",
      "Got threshold\n",
      "cycle 6 field 055 \n",
      "Got threshold\n",
      "cycle 6 field 056 \n",
      "Got threshold\n",
      "cycle 6 field 057 \n",
      "Got threshold\n",
      "cycle 6 field 058 \n",
      "Got threshold\n",
      "cycle 6 field 059 \n",
      "Got threshold\n",
      "cycle 6 field 060 \n",
      "Got threshold\n",
      "cycle 6 field 061 \n",
      "Got threshold\n",
      "cycle 6 field 062 \n",
      "Got threshold\n",
      "cycle 6 field 063 \n",
      "Got threshold\n",
      "cycle 6 field 064 \n",
      "Got threshold\n",
      "cycle 6 field 065 \n",
      "Got threshold\n",
      "cycle 6 field 066 \n",
      "Got threshold\n",
      "cycle 6 field 067 \n",
      "Got threshold\n",
      "cycle 6 field 068 \n",
      "Got threshold\n",
      "cycle 6 field 069 \n",
      "Got threshold\n",
      "cycle 6 field 070 \n",
      "Got threshold\n",
      "cycle 6 field 071 \n",
      "Got threshold\n",
      "cycle 6 field 072 \n",
      "Got threshold\n",
      "cycle 6 field 073 \n",
      "Got threshold\n",
      "cycle 6 field 074 \n",
      "Got threshold\n",
      "cycle 6 field 075 \n",
      "Got threshold\n",
      "cycle 6 field 076 \n",
      "Got threshold\n",
      "cycle 6 field 077 \n",
      "Got threshold\n",
      "cycle 6 field 078 \n",
      "Got threshold\n",
      "cycle 6 field 080 \n",
      "Got threshold\n",
      "cycle 6 field 081 \n",
      "Got threshold\n",
      "cycle 6 field 082 \n",
      "Got threshold\n",
      "cycle 6 field 083 \n",
      "Got threshold\n",
      "cycle 6 field 084 \n",
      "Got threshold\n",
      "cycle 6 field 085 \n",
      "Got threshold\n",
      "cycle 6 field 086 \n",
      "Got threshold\n",
      "cycle 6 field 087 \n",
      "Got threshold\n",
      "cycle 6 field 088 \n",
      "Got threshold\n",
      "cycle 6 field 089 \n",
      "Got threshold\n",
      "cycle 6 field 090 \n",
      "Got threshold\n",
      "cycle 6 field 091 \n",
      "Got threshold\n",
      "cycle 6 field 092 \n",
      "Got threshold\n",
      "cycle 6 field 093 \n",
      "Got threshold\n",
      "cycle 6 field 094 \n",
      "Got threshold\n",
      "cycle 6 field 095 \n",
      "Got threshold\n",
      "cycle 6 field 096 \n",
      "Got threshold\n",
      "cycle 6 field 097 \n",
      "Got threshold\n",
      "cycle 6 field 098 \n",
      "Got threshold\n",
      "cycle 6 field 100 \n",
      "Got threshold\n",
      "cycle 6 field 101 \n",
      "Got threshold\n",
      "cycle 6 field 102 \n",
      "Got threshold\n",
      "cycle 6 field 103 \n",
      "Got threshold\n",
      "cycle 6 field 105 \n",
      "Got threshold\n",
      "cycle 6 field 106 \n",
      "Got threshold\n",
      "cycle 6 field 107 \n",
      "Got threshold\n",
      "cycle 6 field 108 \n",
      "Got threshold\n",
      "cycle 6 field 109 \n",
      "Got threshold\n",
      "cycle 6 field 110 \n",
      "Got threshold\n",
      "cycle 6 field 111 \n",
      "Got threshold\n",
      "cycle 6 field 112 \n",
      "Got threshold\n",
      "cycle 6 field 113 \n",
      "Got threshold\n",
      "cycle 6 field 114 \n",
      "Got threshold\n",
      "cycle 6 field 115 \n",
      "Got threshold\n",
      "cycle 6 field 116 \n",
      "Got threshold\n",
      "cycle 6 field 117 \n",
      "Got threshold\n",
      "cycle 6 field 118 \n",
      "Got threshold\n",
      "cycle 6 field 119 \n",
      "Got threshold\n",
      "cycle 6 field 120 \n",
      "Got threshold\n",
      "cycle 6 field 121 \n",
      "Got threshold\n",
      "cycle 6 field 122 \n",
      "Got threshold\n",
      "cycle 6 field 123 \n",
      "Got threshold\n",
      "cycle 6 field 124 \n",
      "Got threshold\n",
      "cycle 6 field 125 \n",
      "Got threshold\n",
      "cycle 6 field 126 \n",
      "Got threshold\n",
      "cycle 6 field 127 \n",
      "Got threshold\n",
      "cycle 6 field 128 \n",
      "Got threshold\n",
      "cycle 6 field 129 \n",
      "Got threshold\n",
      "cycle 6 field 130 \n",
      "Got threshold\n",
      "cycle 6 field 131 \n",
      "Got threshold\n",
      "cycle 6 field 132 \n",
      "Got threshold\n",
      "cycle 6 field 133 \n",
      "Got threshold\n",
      "cycle 6 field 134 \n",
      "Got threshold\n",
      "cycle 6 field 135 \n",
      "Got threshold\n",
      "cycle 6 field 136 \n",
      "Got threshold\n",
      "cycle 6 field 137 \n",
      "Got threshold\n",
      "cycle 6 field 138 \n",
      "Got threshold\n",
      "cycle 6 field 139 \n",
      "Got threshold\n",
      "cycle 6 field 140 \n",
      "Got threshold\n",
      "cycle 6 field 141 \n",
      "Got threshold\n",
      "cycle 6 field 142 \n",
      "Got threshold\n",
      "cycle 6 field 143 \n",
      "Got threshold\n",
      "cycle 6 field 144 \n",
      "Got threshold\n",
      "cycle 6 field 145 \n",
      "Got threshold\n",
      "cycle 6 field 146 \n",
      "Got threshold\n",
      "cycle 6 field 147 \n",
      "Got threshold\n",
      "cycle 6 field 148 \n",
      "Got threshold\n",
      "cycle 6 field 149 \n",
      "Got threshold\n",
      "cycle 6 field 151 \n",
      "Got threshold\n",
      "cycle 6 field 152 \n",
      "Got threshold\n",
      "cycle 6 field 153 \n",
      "Got threshold\n",
      "cycle 6 field 154 \n",
      "Got threshold\n",
      "cycle 6 field 155 \n",
      "Got threshold\n",
      "cycle 6 field 156 \n",
      "Got threshold\n",
      "cycle 6 field 157 \n",
      "Got threshold\n",
      "cycle 6 field 158 \n",
      "Got threshold\n",
      "cycle 6 field 159 \n",
      "Got threshold\n",
      "cycle 6 field 160 \n",
      "Got threshold\n",
      "cycle 6 field 162 \n",
      "Got threshold\n",
      "cycle 6 field 163 \n",
      "Got threshold\n",
      "cycle 6 field 164 \n",
      "Got threshold\n",
      "cycle 6 field 165 \n",
      "Got threshold\n",
      "cycle 6 field 166 \n",
      "Got threshold\n",
      "cycle 6 field 167 \n",
      "Got threshold\n",
      "cycle 6 field 168 \n",
      "Got threshold\n",
      "cycle 6 field 169 \n",
      "Got threshold\n",
      "cycle 6 field 170 \n",
      "Got threshold\n",
      "cycle 6 field 171 \n",
      "Got threshold\n",
      "cycle 6 field 172 \n",
      "Got threshold\n",
      "cycle 6 field 173 \n",
      "Got threshold\n",
      "cycle 6 field 174 \n",
      "Got threshold\n",
      "cycle 6 field 175 \n",
      "Got threshold\n",
      "cycle 6 field 176 \n",
      "Got threshold\n",
      "cycle 6 field 178 \n",
      "Got threshold\n",
      "cycle 6 field 179 \n",
      "Got threshold\n",
      "cycle 6 field 180 \n",
      "Got threshold\n",
      "cycle 6 field 181 \n",
      "Got threshold\n",
      "cycle 6 field 182 \n",
      "Got threshold\n",
      "cycle 6 field 183 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "cycle 6 field 184 \n",
      "Got threshold\n",
      "cycle 6 field 185 \n",
      "Got threshold\n",
      "cycle 6 field 186 \n",
      "Got threshold\n",
      "cycle 6 field 187 \n",
      "Got threshold\n",
      "cycle 6 field 188 \n",
      "Got threshold\n",
      "cycle 6 field 189 \n",
      "Got threshold\n",
      "cycle 6 field 190 \n",
      "Got threshold\n",
      "cycle 6 field 191 \n",
      "Got threshold\n",
      "cycle 6 field 192 \n",
      "Got threshold\n",
      "cycle 6 field 193 \n",
      "Got threshold\n",
      "cycle 6 field 194 \n",
      "Got threshold\n",
      "cycle 6 field 195 \n",
      "Got threshold\n",
      "cycle 6 field 196 \n",
      "Got threshold\n",
      "cycle 6 field 197 \n",
      "Got threshold\n",
      "cycle 6 field 198 \n",
      "Got threshold\n",
      "cycle 6 field 199 \n",
      "Got threshold\n",
      "cycle 6 field 200 \n",
      "Got threshold\n",
      "cycle 6 field 201 \n",
      "Got threshold\n",
      "cycle 6 field 202 \n",
      "Got threshold\n",
      "cycle 6 field 203 \n",
      "Got threshold\n",
      "cycle 6 field 204 \n",
      "Got threshold\n",
      "cycle 6 field 205 \n",
      "Got threshold\n",
      "cycle 6 field 206 \n",
      "Got threshold\n",
      "cycle 6 field 207 \n",
      "Got threshold\n",
      "cycle 6 field 208 \n",
      "Got threshold\n",
      "cycle 6 field 209 \n",
      "Got threshold\n",
      "cycle 6 field 210 \n",
      "Got threshold\n",
      "cycle 6 field 211 \n",
      "Got threshold\n",
      "cycle 6 field 212 \n",
      "Got threshold\n",
      "cycle 6 field 213 \n",
      "Got threshold\n",
      "cycle 6 field 214 \n",
      "Got threshold\n",
      "cycle 6 field 215 \n",
      "Got threshold\n",
      "cycle 6 field 216 \n",
      "Got threshold\n",
      "cycle 6 field 217 \n",
      "Got threshold\n",
      "cycle 6 field 218 \n",
      "Got threshold\n",
      "cycle 6 field 219 \n",
      "Got threshold\n",
      "cycle 6 field 220 \n",
      "Got threshold\n",
      "cycle 6 field 221 \n",
      "Got threshold\n",
      "cycle 6 field 222 \n",
      "Got threshold\n",
      "cycle 6 field 223 \n",
      "Got threshold\n",
      "cycle 6 field 224 \n",
      "Got threshold\n",
      "cycle 7 field 000 \n",
      "Got threshold\n",
      "cycle 7 field 001 \n",
      "Got threshold\n",
      "cycle 7 field 002 \n",
      "Got threshold\n",
      "cycle 7 field 003 \n",
      "Got threshold\n",
      "cycle 7 field 004 \n",
      "Got threshold\n",
      "cycle 7 field 005 \n",
      "Got threshold\n",
      "cycle 7 field 007 \n",
      "Got threshold\n",
      "cycle 7 field 008 \n",
      "Got threshold\n",
      "cycle 7 field 011 \n",
      "Got threshold\n",
      "cycle 7 field 012 \n",
      "Got threshold\n",
      "cycle 7 field 013 \n",
      "Got threshold\n",
      "cycle 7 field 014 \n",
      "Got threshold\n",
      "cycle 7 field 015 \n",
      "Got threshold\n",
      "cycle 7 field 016 \n",
      "Got threshold\n",
      "cycle 7 field 017 \n",
      "Got threshold\n",
      "cycle 7 field 019 \n",
      "Got threshold\n",
      "cycle 7 field 020 \n",
      "Got threshold\n",
      "cycle 7 field 021 \n",
      "Got threshold\n",
      "cycle 7 field 022 \n",
      "Got threshold\n",
      "cycle 7 field 023 \n",
      "Got threshold\n",
      "cycle 7 field 024 \n",
      "Got threshold\n",
      "cycle 7 field 025 \n",
      "Got threshold\n",
      "cycle 7 field 026 \n",
      "Got threshold\n",
      "cycle 7 field 027 \n",
      "Got threshold\n",
      "cycle 7 field 028 \n",
      "Got threshold\n",
      "cycle 7 field 029 \n",
      "Got threshold\n",
      "cycle 7 field 030 \n",
      "Got threshold\n",
      "cycle 7 field 031 \n",
      "Got threshold\n",
      "cycle 7 field 032 \n",
      "Got threshold\n",
      "cycle 7 field 033 \n",
      "Got threshold\n",
      "cycle 7 field 034 \n",
      "Got threshold\n",
      "cycle 7 field 035 \n",
      "Got threshold\n",
      "cycle 7 field 036 \n",
      "Got threshold\n",
      "cycle 7 field 037 \n",
      "Got threshold\n",
      "cycle 7 field 038 \n",
      "Got threshold\n",
      "cycle 7 field 039 \n",
      "Got threshold\n",
      "cycle 7 field 040 \n",
      "Got threshold\n",
      "cycle 7 field 041 \n",
      "Got threshold\n",
      "cycle 7 field 042 \n",
      "Got threshold\n",
      "cycle 7 field 044 \n",
      "Got threshold\n",
      "cycle 7 field 045 \n",
      "Got threshold\n",
      "cycle 7 field 046 \n",
      "Got threshold\n",
      "cycle 7 field 047 \n",
      "Got threshold\n",
      "cycle 7 field 048 \n",
      "Got threshold\n",
      "cycle 7 field 049 \n",
      "Got threshold\n",
      "cycle 7 field 050 \n",
      "Got threshold\n",
      "cycle 7 field 051 \n",
      "Got threshold\n",
      "cycle 7 field 052 \n",
      "Got threshold\n",
      "cycle 7 field 053 \n",
      "Got threshold\n",
      "cycle 7 field 054 \n",
      "Got threshold\n",
      "cycle 7 field 055 \n",
      "Got threshold\n",
      "cycle 7 field 056 \n",
      "Got threshold\n",
      "cycle 7 field 057 \n",
      "Got threshold\n",
      "cycle 7 field 058 \n",
      "Got threshold\n",
      "cycle 7 field 059 \n",
      "Got threshold\n",
      "cycle 7 field 060 \n",
      "Got threshold\n",
      "cycle 7 field 061 \n",
      "Got threshold\n",
      "cycle 7 field 062 \n",
      "Got threshold\n",
      "cycle 7 field 063 \n",
      "Got threshold\n",
      "cycle 7 field 064 \n",
      "Got threshold\n",
      "cycle 7 field 065 \n",
      "Got threshold\n",
      "cycle 7 field 066 \n",
      "Got threshold\n",
      "cycle 7 field 067 \n",
      "Got threshold\n",
      "cycle 7 field 068 \n",
      "Got threshold\n",
      "cycle 7 field 069 \n",
      "Got threshold\n",
      "cycle 7 field 070 \n",
      "Got threshold\n",
      "cycle 7 field 071 \n",
      "Got threshold\n",
      "cycle 7 field 072 \n",
      "Got threshold\n",
      "cycle 7 field 073 \n",
      "Got threshold\n",
      "cycle 7 field 074 \n",
      "Got threshold\n",
      "cycle 7 field 075 \n",
      "Got threshold\n",
      "cycle 7 field 076 \n",
      "Got threshold\n",
      "cycle 7 field 077 \n",
      "Got threshold\n",
      "cycle 7 field 078 \n",
      "Got threshold\n",
      "cycle 7 field 080 \n",
      "Got threshold\n",
      "cycle 7 field 081 \n",
      "Got threshold\n",
      "cycle 7 field 082 \n",
      "Got threshold\n",
      "cycle 7 field 083 \n",
      "Got threshold\n",
      "cycle 7 field 084 \n",
      "Got threshold\n",
      "cycle 7 field 085 \n",
      "Got threshold\n",
      "cycle 7 field 086 \n",
      "Got threshold\n",
      "cycle 7 field 087 \n",
      "Got threshold\n",
      "cycle 7 field 088 \n",
      "Got threshold\n",
      "cycle 7 field 089 \n",
      "Got threshold\n",
      "cycle 7 field 090 \n",
      "Got threshold\n",
      "cycle 7 field 091 \n",
      "Got threshold\n",
      "cycle 7 field 092 \n",
      "Got threshold\n",
      "cycle 7 field 093 \n",
      "Got threshold\n",
      "cycle 7 field 094 \n",
      "Got threshold\n",
      "cycle 7 field 095 \n",
      "Got threshold\n",
      "cycle 7 field 096 \n",
      "Got threshold\n",
      "cycle 7 field 097 \n",
      "Got threshold\n",
      "cycle 7 field 098 \n",
      "Got threshold\n",
      "cycle 7 field 100 \n",
      "Got threshold\n",
      "cycle 7 field 101 \n",
      "Got threshold\n",
      "cycle 7 field 102 \n",
      "Got threshold\n",
      "cycle 7 field 103 \n",
      "Got threshold\n",
      "cycle 7 field 105 \n",
      "Got threshold\n",
      "cycle 7 field 106 \n",
      "Got threshold\n",
      "cycle 7 field 107 \n",
      "Got threshold\n",
      "cycle 7 field 108 \n",
      "Got threshold\n",
      "cycle 7 field 109 \n",
      "Got threshold\n",
      "cycle 7 field 110 \n",
      "Got threshold\n",
      "cycle 7 field 111 \n",
      "Got threshold\n",
      "cycle 7 field 112 \n",
      "Got threshold\n",
      "cycle 7 field 113 \n",
      "Got threshold\n",
      "cycle 7 field 114 \n",
      "Got threshold\n",
      "cycle 7 field 115 \n",
      "Got threshold\n",
      "cycle 7 field 116 \n",
      "Got threshold\n",
      "cycle 7 field 117 \n",
      "Got threshold\n",
      "cycle 7 field 118 \n",
      "Got threshold\n",
      "cycle 7 field 119 \n",
      "Got threshold\n",
      "cycle 7 field 120 \n",
      "Got threshold\n",
      "cycle 7 field 121 \n",
      "Got threshold\n",
      "cycle 7 field 122 \n",
      "Got threshold\n",
      "cycle 7 field 123 \n",
      "Got threshold\n",
      "cycle 7 field 124 \n",
      "Got threshold\n",
      "cycle 7 field 125 \n",
      "Got threshold\n",
      "cycle 7 field 126 \n",
      "Got threshold\n",
      "cycle 7 field 127 \n",
      "Got threshold\n",
      "cycle 7 field 128 \n",
      "Got threshold\n",
      "cycle 7 field 129 \n",
      "Got threshold\n",
      "cycle 7 field 130 \n",
      "Got threshold\n",
      "cycle 7 field 131 \n",
      "Got threshold\n",
      "cycle 7 field 132 \n",
      "Got threshold\n",
      "cycle 7 field 133 \n",
      "Got threshold\n",
      "cycle 7 field 134 \n",
      "Got threshold\n",
      "cycle 7 field 135 \n",
      "Got threshold\n",
      "cycle 7 field 136 \n",
      "Got threshold\n",
      "cycle 7 field 137 \n",
      "Got threshold\n",
      "cycle 7 field 138 \n",
      "Got threshold\n",
      "cycle 7 field 139 \n",
      "Got threshold\n",
      "cycle 7 field 140 \n",
      "Got threshold\n",
      "cycle 7 field 141 \n",
      "Got threshold\n",
      "cycle 7 field 142 \n",
      "Got threshold\n",
      "cycle 7 field 143 \n",
      "Got threshold\n",
      "cycle 7 field 144 \n",
      "Got threshold\n",
      "cycle 7 field 145 \n",
      "Got threshold\n",
      "cycle 7 field 146 \n",
      "Got threshold\n",
      "cycle 7 field 147 \n",
      "Got threshold\n",
      "cycle 7 field 148 \n",
      "Got threshold\n",
      "cycle 7 field 149 \n",
      "Got threshold\n",
      "cycle 7 field 151 \n",
      "Got threshold\n",
      "cycle 7 field 152 \n",
      "Got threshold\n",
      "cycle 7 field 153 \n",
      "Got threshold\n",
      "cycle 7 field 154 \n",
      "Got threshold\n",
      "cycle 7 field 155 \n",
      "Got threshold\n",
      "cycle 7 field 156 \n",
      "Got threshold\n",
      "cycle 7 field 157 \n",
      "Got threshold\n",
      "cycle 7 field 158 \n",
      "Got threshold\n",
      "cycle 7 field 159 \n",
      "Got threshold\n",
      "cycle 7 field 160 \n",
      "Got threshold\n",
      "cycle 7 field 162 \n",
      "Got threshold\n",
      "cycle 7 field 163 \n",
      "Got threshold\n",
      "cycle 7 field 164 \n",
      "Got threshold\n",
      "cycle 7 field 165 \n",
      "Got threshold\n",
      "cycle 7 field 166 \n",
      "Got threshold\n",
      "cycle 7 field 167 \n",
      "Got threshold\n",
      "cycle 7 field 168 \n",
      "Got threshold\n",
      "cycle 7 field 169 \n",
      "Got threshold\n",
      "cycle 7 field 170 \n",
      "Got threshold\n",
      "cycle 7 field 171 \n",
      "Got threshold\n",
      "cycle 7 field 172 \n",
      "Got threshold\n",
      "cycle 7 field 173 \n",
      "Got threshold\n",
      "cycle 7 field 174 \n",
      "Got threshold\n",
      "cycle 7 field 175 \n",
      "Got threshold\n",
      "cycle 7 field 176 \n",
      "Got threshold\n",
      "cycle 7 field 178 \n",
      "Got threshold\n",
      "cycle 7 field 179 \n",
      "Got threshold\n",
      "cycle 7 field 180 \n",
      "Got threshold\n",
      "cycle 7 field 181 \n",
      "Got threshold\n",
      "cycle 7 field 182 \n",
      "Got threshold\n",
      "cycle 7 field 183 \n",
      "Got threshold\n",
      "cycle 7 field 184 \n",
      "Got threshold\n",
      "cycle 7 field 185 \n",
      "Got threshold\n",
      "cycle 7 field 186 \n",
      "Got threshold\n",
      "cycle 7 field 187 \n",
      "Got threshold\n",
      "cycle 7 field 188 \n",
      "Got threshold\n",
      "cycle 7 field 189 \n",
      "Got threshold\n",
      "cycle 7 field 190 \n",
      "Got threshold\n",
      "cycle 7 field 191 \n",
      "Got threshold\n",
      "cycle 7 field 192 \n",
      "Got threshold\n",
      "cycle 7 field 193 \n",
      "Got threshold\n",
      "cycle 7 field 194 \n",
      "Got threshold\n",
      "cycle 7 field 195 \n",
      "Got threshold\n",
      "cycle 7 field 196 \n",
      "Got threshold\n",
      "cycle 7 field 197 \n",
      "Got threshold\n",
      "cycle 7 field 198 \n",
      "Got threshold\n",
      "cycle 7 field 199 \n",
      "Got threshold\n",
      "cycle 7 field 200 \n",
      "Got threshold\n",
      "cycle 7 field 201 \n",
      "Got threshold\n",
      "cycle 7 field 202 \n",
      "Got threshold\n",
      "cycle 7 field 203 \n",
      "Got threshold\n",
      "cycle 7 field 204 \n",
      "Got threshold\n",
      "cycle 7 field 205 \n",
      "Got threshold\n",
      "cycle 7 field 206 \n",
      "Got threshold\n",
      "cycle 7 field 207 \n",
      "Got threshold\n",
      "cycle 7 field 208 \n",
      "Got threshold\n",
      "cycle 7 field 209 \n",
      "Got threshold\n",
      "cycle 7 field 210 \n",
      "Got threshold\n",
      "cycle 7 field 211 \n",
      "Got threshold\n",
      "cycle 7 field 212 \n",
      "Got threshold\n",
      "cycle 7 field 213 \n",
      "Got threshold\n",
      "cycle 7 field 214 \n",
      "Got threshold\n",
      "cycle 7 field 215 \n",
      "Got threshold\n",
      "cycle 7 field 216 \n",
      "Got threshold\n",
      "cycle 7 field 217 \n",
      "Got threshold\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 7 field 218 \n",
      "Got threshold\n",
      "cycle 7 field 219 \n",
      "Got threshold\n",
      "cycle 7 field 220 \n",
      "Got threshold\n",
      "cycle 7 field 221 \n",
      "Got threshold\n",
      "cycle 7 field 222 \n",
      "Got threshold\n",
      "cycle 7 field 223 \n",
      "Got threshold\n",
      "cycle 7 field 224 \n",
      "Got threshold\n",
      "cycle 8 field 000 \n",
      "Got threshold\n",
      "cycle 8 field 001 \n",
      "Got threshold\n",
      "cycle 8 field 002 \n",
      "Got threshold\n",
      "cycle 8 field 003 \n",
      "Got threshold\n",
      "cycle 8 field 004 \n",
      "Got threshold\n",
      "cycle 8 field 005 \n",
      "Got threshold\n",
      "cycle 8 field 007 \n",
      "Got threshold\n",
      "cycle 8 field 008 \n",
      "Got threshold\n",
      "cycle 8 field 011 \n",
      "Got threshold\n",
      "cycle 8 field 012 \n",
      "Got threshold\n",
      "cycle 8 field 013 \n",
      "Got threshold\n",
      "cycle 8 field 014 \n",
      "Got threshold\n",
      "cycle 8 field 015 \n",
      "Got threshold\n",
      "cycle 8 field 016 \n",
      "Got threshold\n",
      "cycle 8 field 017 \n",
      "Got threshold\n",
      "cycle 8 field 019 \n",
      "Got threshold\n",
      "cycle 8 field 020 \n",
      "Got threshold\n",
      "cycle 8 field 021 \n",
      "Got threshold\n",
      "cycle 8 field 022 \n",
      "Got threshold\n",
      "cycle 8 field 023 \n",
      "Got threshold\n",
      "cycle 8 field 024 \n",
      "Got threshold\n",
      "cycle 8 field 025 \n",
      "Got threshold\n",
      "cycle 8 field 026 \n",
      "Got threshold\n",
      "cycle 8 field 027 \n",
      "Got threshold\n",
      "cycle 8 field 028 \n",
      "Got threshold\n",
      "cycle 8 field 029 \n",
      "Got threshold\n",
      "cycle 8 field 030 \n",
      "Got threshold\n",
      "cycle 8 field 031 \n",
      "Got threshold\n",
      "cycle 8 field 032 \n",
      "Got threshold\n",
      "cycle 8 field 033 \n",
      "Got threshold\n",
      "cycle 8 field 034 \n",
      "Got threshold\n",
      "cycle 8 field 035 \n",
      "Got threshold\n",
      "cycle 8 field 036 \n",
      "Got threshold\n",
      "cycle 8 field 037 \n",
      "Got threshold\n",
      "cycle 8 field 038 \n",
      "Got threshold\n",
      "cycle 8 field 039 \n",
      "Got threshold\n",
      "cycle 8 field 040 \n",
      "Got threshold\n",
      "cycle 8 field 041 \n",
      "Got threshold\n",
      "cycle 8 field 042 \n",
      "Got threshold\n",
      "cycle 8 field 044 \n",
      "Got threshold\n",
      "cycle 8 field 045 \n",
      "Got threshold\n",
      "cycle 8 field 046 \n",
      "Got threshold\n",
      "cycle 8 field 047 \n",
      "Got threshold\n",
      "cycle 8 field 048 \n",
      "Got threshold\n",
      "cycle 8 field 049 \n",
      "Got threshold\n",
      "cycle 8 field 050 \n",
      "Got threshold\n",
      "cycle 8 field 051 \n",
      "Got threshold\n",
      "cycle 8 field 052 \n",
      "Got threshold\n",
      "cycle 8 field 053 \n",
      "Got threshold\n",
      "cycle 8 field 054 \n",
      "Got threshold\n",
      "cycle 8 field 055 \n",
      "Got threshold\n",
      "cycle 8 field 056 \n",
      "Got threshold\n",
      "cycle 8 field 057 \n",
      "Got threshold\n",
      "cycle 8 field 058 \n",
      "Got threshold\n",
      "cycle 8 field 059 \n",
      "Got threshold\n",
      "cycle 8 field 060 \n",
      "Got threshold\n",
      "cycle 8 field 061 \n",
      "Got threshold\n",
      "cycle 8 field 062 \n",
      "Got threshold\n",
      "cycle 8 field 063 \n",
      "Got threshold\n",
      "cycle 8 field 064 \n",
      "Got threshold\n",
      "cycle 8 field 065 \n",
      "Got threshold\n",
      "cycle 8 field 066 \n",
      "Got threshold\n",
      "cycle 8 field 067 \n",
      "Got threshold\n",
      "cycle 8 field 068 \n",
      "Got threshold\n",
      "cycle 8 field 069 \n",
      "Got threshold\n",
      "cycle 8 field 070 \n",
      "Got threshold\n",
      "cycle 8 field 071 \n",
      "Got threshold\n",
      "cycle 8 field 072 \n",
      "Got threshold\n",
      "cycle 8 field 073 \n",
      "Got threshold\n",
      "cycle 8 field 074 \n",
      "Got threshold\n",
      "cycle 8 field 075 \n",
      "Got threshold\n",
      "cycle 8 field 076 \n",
      "Got threshold\n",
      "cycle 8 field 077 \n",
      "Got threshold\n",
      "cycle 8 field 078 \n",
      "Got threshold\n",
      "cycle 8 field 080 \n",
      "Got threshold\n",
      "cycle 8 field 081 \n",
      "Got threshold\n",
      "cycle 8 field 082 \n",
      "Got threshold\n",
      "cycle 8 field 083 \n",
      "Got threshold\n",
      "cycle 8 field 084 \n",
      "Got threshold\n",
      "cycle 8 field 085 \n",
      "Got threshold\n",
      "cycle 8 field 086 \n",
      "Got threshold\n",
      "cycle 8 field 087 \n",
      "Got threshold\n",
      "cycle 8 field 088 \n",
      "Got threshold\n",
      "cycle 8 field 089 \n",
      "Got threshold\n",
      "cycle 8 field 090 \n",
      "Got threshold\n",
      "cycle 8 field 091 \n",
      "Got threshold\n",
      "cycle 8 field 092 \n",
      "Got threshold\n",
      "cycle 8 field 093 \n",
      "Got threshold\n",
      "cycle 8 field 094 \n",
      "Got threshold\n",
      "cycle 8 field 095 \n",
      "Got threshold\n",
      "cycle 8 field 096 \n",
      "Got threshold\n",
      "cycle 8 field 097 \n",
      "Got threshold\n",
      "cycle 8 field 098 \n",
      "Got threshold\n",
      "cycle 8 field 100 \n",
      "Got threshold\n",
      "cycle 8 field 101 \n",
      "Got threshold\n",
      "cycle 8 field 102 \n",
      "Got threshold\n",
      "cycle 8 field 103 \n",
      "Got threshold\n",
      "cycle 8 field 105 \n",
      "Got threshold\n",
      "cycle 8 field 106 \n",
      "Got threshold\n",
      "cycle 8 field 107 \n",
      "Got threshold\n",
      "cycle 8 field 108 \n",
      "Got threshold\n",
      "cycle 8 field 109 \n",
      "Got threshold\n",
      "cycle 8 field 110 \n",
      "Got threshold\n",
      "cycle 8 field 111 \n",
      "Got threshold\n",
      "cycle 8 field 112 \n",
      "Got threshold\n",
      "cycle 8 field 113 \n",
      "Got threshold\n",
      "cycle 8 field 114 \n",
      "Got threshold\n",
      "cycle 8 field 115 \n",
      "Got threshold\n",
      "cycle 8 field 116 \n",
      "Got threshold\n",
      "cycle 8 field 117 \n",
      "Got threshold\n",
      "cycle 8 field 118 \n",
      "Got threshold\n",
      "cycle 8 field 119 \n",
      "Got threshold\n",
      "cycle 8 field 120 \n",
      "Got threshold\n",
      "cycle 8 field 121 \n",
      "Got threshold\n",
      "cycle 8 field 122 \n",
      "Got threshold\n",
      "cycle 8 field 123 \n",
      "Got threshold\n",
      "cycle 8 field 124 \n",
      "Got threshold\n",
      "cycle 8 field 125 \n",
      "Got threshold\n",
      "cycle 8 field 126 \n",
      "Got threshold\n",
      "cycle 8 field 127 \n",
      "Got threshold\n",
      "cycle 8 field 128 \n",
      "Got threshold\n",
      "cycle 8 field 129 \n",
      "Got threshold\n",
      "cycle 8 field 130 \n",
      "Got threshold\n",
      "cycle 8 field 131 \n",
      "Got threshold\n",
      "cycle 8 field 132 \n",
      "Got threshold\n",
      "cycle 8 field 133 \n",
      "Got threshold\n",
      "cycle 8 field 134 \n",
      "Got threshold\n",
      "cycle 8 field 135 \n",
      "Got threshold\n",
      "cycle 8 field 136 \n",
      "Got threshold\n",
      "cycle 8 field 137 \n",
      "Got threshold\n",
      "cycle 8 field 138 \n",
      "Got threshold\n",
      "cycle 8 field 139 \n",
      "Got threshold\n",
      "cycle 8 field 140 \n",
      "Got threshold\n",
      "cycle 8 field 141 \n",
      "Got threshold\n",
      "cycle 8 field 142 \n",
      "Got threshold\n",
      "cycle 8 field 143 \n",
      "Got threshold\n",
      "cycle 8 field 144 \n",
      "Got threshold\n",
      "cycle 8 field 145 \n",
      "Got threshold\n",
      "cycle 8 field 146 \n",
      "Got threshold\n",
      "cycle 8 field 147 \n",
      "Got threshold\n",
      "cycle 8 field 148 \n",
      "Got threshold\n",
      "cycle 8 field 149 \n",
      "Got threshold\n",
      "cycle 8 field 151 \n",
      "Got threshold\n",
      "cycle 8 field 152 \n",
      "Got threshold\n",
      "cycle 8 field 153 \n",
      "Got threshold\n",
      "cycle 8 field 154 \n",
      "Got threshold\n",
      "cycle 8 field 155 \n",
      "Got threshold\n",
      "cycle 8 field 156 \n",
      "Got threshold\n",
      "cycle 8 field 157 \n",
      "Got threshold\n",
      "cycle 8 field 158 \n",
      "Got threshold\n",
      "cycle 8 field 159 \n",
      "Got threshold\n",
      "cycle 8 field 160 \n",
      "Got threshold\n",
      "cycle 8 field 162 \n",
      "Got threshold\n",
      "cycle 8 field 163 \n",
      "Got threshold\n",
      "cycle 8 field 164 \n",
      "Got threshold\n",
      "cycle 8 field 165 \n",
      "Got threshold\n",
      "cycle 8 field 166 \n",
      "Got threshold\n",
      "cycle 8 field 167 \n",
      "Got threshold\n",
      "cycle 8 field 168 \n",
      "Got threshold\n",
      "cycle 8 field 169 \n",
      "Got threshold\n",
      "cycle 8 field 170 \n",
      "Got threshold\n",
      "cycle 8 field 171 \n",
      "Got threshold\n",
      "cycle 8 field 172 \n",
      "Got threshold\n",
      "cycle 8 field 173 \n",
      "Got threshold\n",
      "cycle 8 field 174 \n",
      "Got threshold\n",
      "cycle 8 field 175 \n",
      "Got threshold\n",
      "cycle 8 field 176 \n",
      "Got threshold\n",
      "cycle 8 field 178 \n",
      "Got threshold\n",
      "cycle 8 field 179 \n",
      "Got threshold\n",
      "cycle 8 field 180 \n",
      "Got threshold\n",
      "cycle 8 field 181 \n",
      "Got threshold\n",
      "cycle 8 field 182 \n",
      "Got threshold\n",
      "cycle 8 field 183 \n",
      "Got threshold\n",
      "cycle 8 field 184 \n",
      "Got threshold\n",
      "cycle 8 field 185 \n",
      "Got threshold\n",
      "cycle 8 field 186 \n",
      "Got threshold\n",
      "cycle 8 field 187 \n",
      "Got threshold\n",
      "cycle 8 field 188 \n",
      "Got threshold\n",
      "cycle 8 field 189 \n",
      "Got threshold\n",
      "cycle 8 field 190 \n",
      "Got threshold\n",
      "cycle 8 field 191 \n",
      "Got threshold\n",
      "cycle 8 field 192 \n",
      "Got threshold\n",
      "cycle 8 field 193 \n",
      "Got threshold\n",
      "cycle 8 field 194 \n",
      "Got threshold\n",
      "cycle 8 field 195 \n",
      "Got threshold\n",
      "cycle 8 field 196 \n",
      "Got threshold\n",
      "cycle 8 field 197 \n",
      "Got threshold\n",
      "cycle 8 field 198 \n",
      "Got threshold\n",
      "cycle 8 field 199 \n",
      "Got threshold\n",
      "cycle 8 field 200 \n",
      "Got threshold\n",
      "cycle 8 field 201 \n",
      "Got threshold\n",
      "cycle 8 field 202 \n",
      "Got threshold\n",
      "cycle 8 field 203 \n",
      "Got threshold\n",
      "cycle 8 field 204 \n",
      "Got threshold\n",
      "cycle 8 field 205 \n",
      "Got threshold\n",
      "cycle 8 field 206 \n",
      "Got threshold\n",
      "cycle 8 field 207 \n",
      "Got threshold\n",
      "cycle 8 field 208 \n",
      "Got threshold\n",
      "cycle 8 field 209 \n",
      "Got threshold\n",
      "cycle 8 field 210 \n",
      "Got threshold\n",
      "cycle 8 field 211 \n",
      "Got threshold\n",
      "cycle 8 field 212 \n",
      "Got threshold\n",
      "cycle 8 field 213 \n",
      "Got threshold\n",
      "cycle 8 field 214 \n",
      "Got threshold\n",
      "cycle 8 field 215 \n",
      "Got threshold\n",
      "cycle 8 field 216 \n",
      "Got threshold\n",
      "cycle 8 field 217 \n",
      "Got threshold\n",
      "cycle 8 field 218 \n",
      "Got threshold\n",
      "cycle 8 field 219 \n",
      "Got threshold\n",
      "cycle 8 field 220 \n",
      "Got threshold\n",
      "cycle 8 field 221 \n",
      "Got threshold\n",
      "cycle 8 field 222 \n",
      "Got threshold\n",
      "cycle 8 field 223 \n",
      "Got threshold\n",
      "cycle 8 field 224 \n",
      "Got threshold\n",
      "cycle 9 field 000 \n",
      "Got threshold\n",
      "cycle 9 field 001 \n",
      "Got threshold\n",
      "cycle 9 field 002 \n",
      "Got threshold\n",
      "cycle 9 field 003 \n",
      "Got threshold\n",
      "cycle 9 field 004 \n",
      "Got threshold\n",
      "cycle 9 field 005 \n",
      "Got threshold\n",
      "cycle 9 field 007 \n",
      "Got threshold\n",
      "cycle 9 field 008 \n",
      "Got threshold\n",
      "cycle 9 field 011 \n",
      "Got threshold\n",
      "cycle 9 field 012 \n",
      "Got threshold\n",
      "cycle 9 field 013 \n",
      "Got threshold\n",
      "cycle 9 field 014 \n",
      "Got threshold\n",
      "cycle 9 field 015 \n",
      "Got threshold\n",
      "cycle 9 field 016 \n",
      "Got threshold\n",
      "cycle 9 field 017 \n",
      "Got threshold\n",
      "cycle 9 field 019 \n",
      "Got threshold\n",
      "cycle 9 field 020 \n",
      "Got threshold\n",
      "cycle 9 field 021 \n",
      "Got threshold\n",
      "cycle 9 field 022 \n",
      "Got threshold\n",
      "cycle 9 field 023 \n",
      "Got threshold\n",
      "cycle 9 field 024 \n",
      "Got threshold\n",
      "cycle 9 field 025 \n",
      "Got threshold\n",
      "cycle 9 field 026 \n",
      "Got threshold\n",
      "cycle 9 field 027 \n",
      "Got threshold\n",
      "cycle 9 field 028 \n",
      "Got threshold\n",
      "cycle 9 field 029 \n",
      "Got threshold\n",
      "cycle 9 field 030 \n",
      "Got threshold\n",
      "cycle 9 field 031 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "cycle 9 field 032 \n",
      "Got threshold\n",
      "cycle 9 field 033 \n",
      "Got threshold\n",
      "cycle 9 field 034 \n",
      "Got threshold\n",
      "cycle 9 field 035 \n",
      "Got threshold\n",
      "cycle 9 field 036 \n",
      "Got threshold\n",
      "cycle 9 field 037 \n",
      "Got threshold\n",
      "cycle 9 field 038 \n",
      "Got threshold\n",
      "cycle 9 field 039 \n",
      "Got threshold\n",
      "cycle 9 field 040 \n",
      "Got threshold\n",
      "cycle 9 field 041 \n",
      "Got threshold\n",
      "cycle 9 field 042 \n",
      "Got threshold\n",
      "cycle 9 field 044 \n",
      "Got threshold\n",
      "cycle 9 field 045 \n",
      "Got threshold\n",
      "cycle 9 field 046 \n",
      "Got threshold\n",
      "cycle 9 field 047 \n",
      "Got threshold\n",
      "cycle 9 field 048 \n",
      "Got threshold\n",
      "cycle 9 field 049 \n",
      "Got threshold\n",
      "cycle 9 field 050 \n",
      "Got threshold\n",
      "cycle 9 field 051 \n",
      "Got threshold\n",
      "cycle 9 field 052 \n",
      "Got threshold\n",
      "cycle 9 field 053 \n",
      "Got threshold\n",
      "cycle 9 field 054 \n",
      "Got threshold\n",
      "cycle 9 field 055 \n",
      "Got threshold\n",
      "cycle 9 field 056 \n",
      "Got threshold\n",
      "cycle 9 field 057 \n",
      "Got threshold\n",
      "cycle 9 field 058 \n",
      "Got threshold\n",
      "cycle 9 field 059 \n",
      "Got threshold\n",
      "cycle 9 field 060 \n",
      "Got threshold\n",
      "cycle 9 field 061 \n",
      "Got threshold\n",
      "cycle 9 field 062 \n",
      "Got threshold\n",
      "cycle 9 field 063 \n",
      "Got threshold\n",
      "cycle 9 field 064 \n",
      "Got threshold\n",
      "cycle 9 field 065 \n",
      "Got threshold\n",
      "cycle 9 field 066 \n",
      "Got threshold\n",
      "cycle 9 field 067 \n",
      "Got threshold\n",
      "cycle 9 field 068 \n",
      "Got threshold\n",
      "cycle 9 field 069 \n",
      "Got threshold\n",
      "cycle 9 field 070 \n",
      "Got threshold\n",
      "cycle 9 field 071 \n",
      "Got threshold\n",
      "cycle 9 field 072 \n",
      "Got threshold\n",
      "cycle 9 field 073 \n",
      "Got threshold\n",
      "cycle 9 field 074 \n",
      "Got threshold\n",
      "cycle 9 field 075 \n",
      "Got threshold\n",
      "cycle 9 field 076 \n",
      "Got threshold\n",
      "cycle 9 field 077 \n",
      "Got threshold\n",
      "cycle 9 field 078 \n",
      "Got threshold\n",
      "cycle 9 field 080 \n",
      "Got threshold\n",
      "cycle 9 field 081 \n",
      "Got threshold\n",
      "cycle 9 field 082 \n",
      "Got threshold\n",
      "cycle 9 field 083 \n",
      "Got threshold\n",
      "cycle 9 field 084 \n",
      "Got threshold\n",
      "cycle 9 field 085 \n",
      "Got threshold\n",
      "cycle 9 field 086 \n",
      "Got threshold\n",
      "cycle 9 field 087 \n",
      "Got threshold\n",
      "cycle 9 field 088 \n",
      "Got threshold\n",
      "cycle 9 field 089 \n",
      "Got threshold\n",
      "cycle 9 field 090 \n",
      "Got threshold\n",
      "cycle 9 field 091 \n",
      "Got threshold\n",
      "cycle 9 field 092 \n",
      "Got threshold\n",
      "cycle 9 field 093 \n",
      "Got threshold\n",
      "cycle 9 field 094 \n",
      "Got threshold\n",
      "cycle 9 field 095 \n",
      "Got threshold\n",
      "cycle 9 field 096 \n",
      "Got threshold\n",
      "cycle 9 field 097 \n",
      "Got threshold\n",
      "cycle 9 field 098 \n",
      "Got threshold\n",
      "cycle 9 field 100 \n",
      "Got threshold\n",
      "cycle 9 field 101 \n",
      "Got threshold\n",
      "cycle 9 field 102 \n",
      "Got threshold\n",
      "cycle 9 field 103 \n",
      "Got threshold\n",
      "cycle 9 field 105 \n",
      "Got threshold\n",
      "cycle 9 field 106 \n",
      "Got threshold\n",
      "cycle 9 field 107 \n",
      "Got threshold\n",
      "cycle 9 field 108 \n",
      "Got threshold\n",
      "cycle 9 field 109 \n",
      "Got threshold\n",
      "cycle 9 field 110 \n",
      "Got threshold\n",
      "cycle 9 field 111 \n",
      "Got threshold\n",
      "cycle 9 field 112 \n",
      "Got threshold\n",
      "cycle 9 field 113 \n",
      "Got threshold\n",
      "cycle 9 field 114 \n",
      "Got threshold\n",
      "cycle 9 field 115 \n",
      "Got threshold\n",
      "cycle 9 field 116 \n",
      "Got threshold\n",
      "cycle 9 field 117 \n",
      "Got threshold\n",
      "cycle 9 field 118 \n",
      "Got threshold\n",
      "cycle 9 field 119 \n",
      "Got threshold\n",
      "cycle 9 field 120 \n",
      "Got threshold\n",
      "cycle 9 field 121 \n",
      "Got threshold\n",
      "cycle 9 field 122 \n",
      "Got threshold\n",
      "cycle 9 field 123 \n",
      "Got threshold\n",
      "cycle 9 field 124 \n",
      "Got threshold\n",
      "cycle 9 field 125 \n",
      "Got threshold\n",
      "cycle 9 field 126 \n",
      "Got threshold\n",
      "cycle 9 field 127 \n",
      "Got threshold\n",
      "cycle 9 field 128 \n",
      "Got threshold\n",
      "cycle 9 field 129 \n",
      "Got threshold\n",
      "cycle 9 field 130 \n",
      "Got threshold\n",
      "cycle 9 field 131 \n",
      "Got threshold\n",
      "cycle 9 field 132 \n",
      "Got threshold\n",
      "cycle 9 field 133 \n",
      "Got threshold\n",
      "cycle 9 field 134 \n",
      "Got threshold\n",
      "cycle 9 field 135 \n",
      "Got threshold\n",
      "cycle 9 field 136 \n",
      "Got threshold\n",
      "cycle 9 field 137 \n",
      "Got threshold\n",
      "cycle 9 field 138 \n",
      "Got threshold\n",
      "cycle 9 field 139 \n",
      "Got threshold\n",
      "cycle 9 field 140 \n",
      "Got threshold\n",
      "cycle 9 field 141 \n",
      "Got threshold\n",
      "cycle 9 field 142 \n",
      "Got threshold\n",
      "cycle 9 field 143 \n",
      "Got threshold\n",
      "cycle 9 field 144 \n",
      "Got threshold\n",
      "cycle 9 field 145 \n",
      "Got threshold\n",
      "cycle 9 field 146 \n",
      "Got threshold\n",
      "cycle 9 field 147 \n",
      "Got threshold\n",
      "cycle 9 field 148 \n",
      "Got threshold\n",
      "cycle 9 field 149 \n",
      "Got threshold\n",
      "cycle 9 field 151 \n",
      "Got threshold\n",
      "cycle 9 field 152 \n",
      "Got threshold\n",
      "cycle 9 field 153 \n",
      "Got threshold\n",
      "cycle 9 field 154 \n",
      "Got threshold\n",
      "cycle 9 field 155 \n",
      "Got threshold\n",
      "cycle 9 field 156 \n",
      "Got threshold\n",
      "cycle 9 field 157 \n",
      "Got threshold\n",
      "cycle 9 field 158 \n",
      "Got threshold\n",
      "cycle 9 field 159 \n",
      "Got threshold\n",
      "cycle 9 field 160 \n",
      "Got threshold\n",
      "cycle 9 field 162 \n",
      "Got threshold\n",
      "cycle 9 field 163 \n",
      "Got threshold\n",
      "cycle 9 field 164 \n",
      "Got threshold\n",
      "cycle 9 field 165 \n",
      "Got threshold\n",
      "cycle 9 field 166 \n",
      "Got threshold\n",
      "cycle 9 field 167 \n",
      "Got threshold\n",
      "cycle 9 field 168 \n",
      "Got threshold\n",
      "cycle 9 field 169 \n",
      "Got threshold\n",
      "cycle 9 field 170 \n",
      "Got threshold\n",
      "cycle 9 field 171 \n",
      "Got threshold\n",
      "cycle 9 field 172 \n",
      "Got threshold\n",
      "cycle 9 field 173 \n",
      "Got threshold\n",
      "cycle 9 field 174 \n",
      "Got threshold\n",
      "cycle 9 field 175 \n",
      "Got threshold\n",
      "cycle 9 field 176 \n",
      "Got threshold\n",
      "cycle 9 field 178 \n",
      "Got threshold\n",
      "cycle 9 field 179 \n",
      "Got threshold\n",
      "cycle 9 field 180 \n",
      "Got threshold\n",
      "cycle 9 field 181 \n",
      "Got threshold\n",
      "cycle 9 field 182 \n",
      "Got threshold\n",
      "cycle 9 field 183 \n",
      "Got threshold\n",
      "cycle 9 field 184 \n",
      "Got threshold\n",
      "cycle 9 field 185 \n",
      "Got threshold\n",
      "cycle 9 field 186 \n",
      "Got threshold\n",
      "cycle 9 field 187 \n",
      "Got threshold\n",
      "cycle 9 field 188 \n",
      "Got threshold\n",
      "cycle 9 field 189 \n",
      "Got threshold\n",
      "cycle 9 field 190 \n",
      "Got threshold\n",
      "cycle 9 field 191 \n",
      "Got threshold\n",
      "cycle 9 field 192 \n",
      "Got threshold\n",
      "cycle 9 field 193 \n",
      "Got threshold\n",
      "cycle 9 field 194 \n",
      "Got threshold\n",
      "cycle 9 field 195 \n",
      "Got threshold\n",
      "cycle 9 field 196 \n",
      "Got threshold\n",
      "cycle 9 field 197 \n",
      "Got threshold\n",
      "cycle 9 field 198 \n",
      "Got threshold\n",
      "cycle 9 field 199 \n",
      "Got threshold\n",
      "cycle 9 field 200 \n",
      "Got threshold\n",
      "cycle 9 field 201 \n",
      "Got threshold\n",
      "cycle 9 field 202 \n",
      "Got threshold\n",
      "cycle 9 field 203 \n",
      "Got threshold\n",
      "cycle 9 field 204 \n",
      "Got threshold\n",
      "cycle 9 field 205 \n",
      "Got threshold\n",
      "cycle 9 field 206 \n",
      "Got threshold\n",
      "cycle 9 field 207 \n",
      "Got threshold\n",
      "cycle 9 field 208 \n",
      "Got threshold\n",
      "cycle 9 field 209 \n",
      "Got threshold\n",
      "cycle 9 field 210 \n",
      "Got threshold\n",
      "cycle 9 field 211 \n",
      "Got threshold\n",
      "cycle 9 field 212 \n",
      "Got threshold\n",
      "cycle 9 field 213 \n",
      "Got threshold\n",
      "cycle 9 field 214 \n",
      "Got threshold\n",
      "cycle 9 field 215 \n",
      "Got threshold\n",
      "cycle 9 field 216 \n",
      "Got threshold\n",
      "cycle 9 field 217 \n",
      "Got threshold\n",
      "cycle 9 field 218 \n",
      "Got threshold\n",
      "cycle 9 field 219 \n",
      "Got threshold\n",
      "cycle 9 field 220 \n",
      "Got threshold\n",
      "cycle 9 field 221 \n",
      "Got threshold\n",
      "cycle 9 field 222 \n",
      "Got threshold\n",
      "cycle 9 field 223 \n",
      "Got threshold\n",
      "cycle 9 field 224 \n",
      "Got threshold\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",
    "# base_J.to_csv('base_J.csv')\n",
    "# reg_J.to_csv('reg_J.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.745817</td>\n",
       "      <td>0.685230</td>\n",
       "      <td>0.629229</td>\n",
       "      <td>0.611321</td>\n",
       "      <td>0.609511</td>\n",
       "      <td>0.615352</td>\n",
       "      <td>0.602370</td>\n",
       "      <td>0.409441</td>\n",
       "      <td>0.444415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.815280</td>\n",
       "      <td>0.744768</td>\n",
       "      <td>0.679836</td>\n",
       "      <td>0.661148</td>\n",
       "      <td>0.658609</td>\n",
       "      <td>0.664596</td>\n",
       "      <td>0.667042</td>\n",
       "      <td>0.471169</td>\n",
       "      <td>0.475886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.848202</td>\n",
       "      <td>0.794707</td>\n",
       "      <td>0.731219</td>\n",
       "      <td>0.689994</td>\n",
       "      <td>0.688210</td>\n",
       "      <td>0.687102</td>\n",
       "      <td>0.665078</td>\n",
       "      <td>0.485590</td>\n",
       "      <td>0.438166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.820908</td>\n",
       "      <td>0.776545</td>\n",
       "      <td>0.747112</td>\n",
       "      <td>0.725911</td>\n",
       "      <td>0.721313</td>\n",
       "      <td>0.707849</td>\n",
       "      <td>0.686661</td>\n",
       "      <td>0.356905</td>\n",
       "      <td>0.384804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.862036</td>\n",
       "      <td>0.814620</td>\n",
       "      <td>0.765621</td>\n",
       "      <td>0.752663</td>\n",
       "      <td>0.750929</td>\n",
       "      <td>0.752334</td>\n",
       "      <td>0.741749</td>\n",
       "      <td>0.508741</td>\n",
       "      <td>0.533078</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216</td>\n",
       "      <td>0.810520</td>\n",
       "      <td>0.713738</td>\n",
       "      <td>0.688933</td>\n",
       "      <td>0.668599</td>\n",
       "      <td>0.681789</td>\n",
       "      <td>0.694496</td>\n",
       "      <td>0.676153</td>\n",
       "      <td>0.434554</td>\n",
       "      <td>0.503064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>0.750771</td>\n",
       "      <td>0.710453</td>\n",
       "      <td>0.690698</td>\n",
       "      <td>0.624167</td>\n",
       "      <td>0.689017</td>\n",
       "      <td>0.701531</td>\n",
       "      <td>0.679131</td>\n",
       "      <td>0.426700</td>\n",
       "      <td>0.491771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>0.761610</td>\n",
       "      <td>0.681662</td>\n",
       "      <td>0.733223</td>\n",
       "      <td>0.657203</td>\n",
       "      <td>0.664038</td>\n",
       "      <td>0.728787</td>\n",
       "      <td>0.713151</td>\n",
       "      <td>0.433429</td>\n",
       "      <td>0.515525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>0.825662</td>\n",
       "      <td>0.768444</td>\n",
       "      <td>0.704778</td>\n",
       "      <td>0.728979</td>\n",
       "      <td>0.695263</td>\n",
       "      <td>0.733504</td>\n",
       "      <td>0.683549</td>\n",
       "      <td>0.450629</td>\n",
       "      <td>0.511743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>0.867595</td>\n",
       "      <td>0.783650</td>\n",
       "      <td>0.766003</td>\n",
       "      <td>0.742142</td>\n",
       "      <td>0.716129</td>\n",
       "      <td>0.751865</td>\n",
       "      <td>0.734503</td>\n",
       "      <td>0.466927</td>\n",
       "      <td>0.537342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1         2         3         4         5         6         7  \\\n",
       "0    0.745817  0.685230  0.629229  0.611321  0.609511  0.615352  0.602370   \n",
       "1    0.815280  0.744768  0.679836  0.661148  0.658609  0.664596  0.667042   \n",
       "2    0.848202  0.794707  0.731219  0.689994  0.688210  0.687102  0.665078   \n",
       "3    0.820908  0.776545  0.747112  0.725911  0.721313  0.707849  0.686661   \n",
       "4    0.862036  0.814620  0.765621  0.752663  0.750929  0.752334  0.741749   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "216  0.810520  0.713738  0.688933  0.668599  0.681789  0.694496  0.676153   \n",
       "217  0.750771  0.710453  0.690698  0.624167  0.689017  0.701531  0.679131   \n",
       "218  0.761610  0.681662  0.733223  0.657203  0.664038  0.728787  0.713151   \n",
       "219  0.825662  0.768444  0.704778  0.728979  0.695263  0.733504  0.683549   \n",
       "220  0.867595  0.783650  0.766003  0.742142  0.716129  0.751865  0.734503   \n",
       "\n",
       "            8         9  \n",
       "0    0.409441  0.444415  \n",
       "1    0.471169  0.475886  \n",
       "2    0.485590  0.438166  \n",
       "3    0.356905  0.384804  \n",
       "4    0.508741  0.533078  \n",
       "..        ...       ...  \n",
       "216  0.434554  0.503064  \n",
       "217  0.426700  0.491771  \n",
       "218  0.433429  0.515525  \n",
       "219  0.450629  0.511743  \n",
       "220  0.466927  0.537342  \n",
       "\n",
       "[221 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.868873</td>\n",
       "      <td>0.854811</td>\n",
       "      <td>0.837175</td>\n",
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       "      <td>0.831140</td>\n",
       "      <td>0.826090</td>\n",
       "      <td>0.823439</td>\n",
       "      <td>0.632101</td>\n",
       "      <td>0.676461</td>\n",
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       "      <td>0.852689</td>\n",
       "      <td>0.830417</td>\n",
       "      <td>0.837775</td>\n",
       "      <td>0.834175</td>\n",
       "      <td>0.831933</td>\n",
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       "      <td>0.644280</td>\n",
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       "      <td>0.846401</td>\n",
       "      <td>0.842787</td>\n",
       "      <td>0.596469</td>\n",
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       "      <td>0.845894</td>\n",
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       "      <td>0.837286</td>\n",
       "      <td>0.839910</td>\n",
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       "      <td>0.633153</td>\n",
       "      <td>0.663210</td>\n",
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       "      <td>0.861516</td>\n",
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       "      <td>0.639433</td>\n",
       "      <td>0.683824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1         2         3         4         5         6         7  \\\n",
       "0    0.773819  0.759900  0.758220  0.742892  0.741481  0.743687  0.739028   \n",
       "1    0.854576  0.838683  0.835455  0.827766  0.830000  0.823954  0.829800   \n",
       "2    0.871888  0.868873  0.854811  0.837175  0.816400  0.803593  0.785682   \n",
       "3    0.865873  0.860067  0.846622  0.833321  0.834075  0.830708  0.828976   \n",
       "4    0.865045  0.847346  0.840340  0.829437  0.826572  0.824603  0.823227   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "216  0.851914  0.855563  0.831002  0.837576  0.831140  0.826090  0.823439   \n",
       "217  0.854561  0.852689  0.830417  0.837775  0.834175  0.831933  0.829328   \n",
       "218  0.875031  0.858743  0.859182  0.852525  0.850643  0.846401  0.842787   \n",
       "219  0.870469  0.859291  0.845894  0.847388  0.837286  0.839910  0.834159   \n",
       "220  0.885256  0.872529  0.875289  0.861205  0.861516  0.853740  0.857972   \n",
       "\n",
       "            8         9  \n",
       "0    0.614580  0.643520  \n",
       "1    0.695501  0.690509  \n",
       "2    0.610870  0.551538  \n",
       "3    0.655769  0.603169  \n",
       "4    0.645079  0.590585  \n",
       "..        ...       ...  \n",
       "216  0.632101  0.676461  \n",
       "217  0.644280  0.681774  \n",
       "218  0.596469  0.642180  \n",
       "219  0.633153  0.663210  \n",
       "220  0.639433  0.683824  \n",
       "\n",
       "[221 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qGk8kaSdV7ehU15VNUfc5EsVSobn/SaWqI5yqRncKtR0jNjMbFlWaDdEtJ2IzqxX3iM3MBmyiUaULN3XHidjMamUYhyaG76vDzKyNRqjrpRNJR0n6iaS7JJ3er5g7lcE8VdKe/XpwM7PUItT10o6kEeCTwNFkJR7eIOnAfsTcqUf8IeAGSd+V9DZJO/cjCDOzVBLWmjgEuCsi7omIjcAlwHH9iLlTIr4H2IMsIS8E7pD0LUkn5fUnpiRpsaTlkpZ/bf09CcM1M2uvl6GJ5lyVL4ubmtoduL/p5zX5uuQ6HayLiGgAVwNXSxol66a/AfgIMGUPOSKWAksBli04fgiHzs1sWPUya6I5V01hqrGLvuSzTol4s0AiYgy4ArhC0ux+BGRmVkbCTLkGaD5GtgewNl3zv9UpEb+u1YaIWJ84FjOz0rqZDdGlm4ADJO0L/AJ4PXBCqsabdapH/NN+PKiZWb+kKvoTEeOS3g5cBYwA50fE7Ukan8QndJhZraQs0BQR3wC+kbDJKTkRm1mtpKq4N536nogbiV6UFOU0U5Xq22ZkvPOdurChMZKknbFIc4Jkqp5EqtM1n07QUqoP+MxEr85EhUqEwnAmrU7SlXOdPu4Rm1mtDOOXixOxmdXKMBbxdyI2s1pxj9jMbMDcIzYzG7BUB0SnkxOxmdXKEF4pqX0iljSL7LS+tRFxraQTgJcCq4Clee0JM7PKSDVldjp16hFfkN9njqSTgLnAl4EjyGp1ntTf8MzMejOM5R47JeLnR8QLJM0kK3qxW0RMSLoIWNFqp7ym52KAd81byDGz90sWsJlZO8N4sK7TqUsz8uGJecAcYLt8/VbAaKudImJpRCyKiEVOwmY2nRpS10tVdOoRnwfcSVZ5aAlwqaR7gEPJLhtiZlYpE4MOoIBOZTDPkfSF/PZaSRcCRwL/ERE3TkeAZma9qN2sCcgScNPtXwOX9TUiM7MS6jhrwsxsqNRx1oSZ2VCp5dBEWakKcCjJpJQ0sfxmIs3Ltu3oxiTtPD62VZJ2UtURTiXFq1y1ur2pXuNhLGwzXYZx+pp7xGZWKxND+B3lRGxmteIesZnZgDkRm5kN2BBess6J2MzqpZY9Ykn7A68F9gTGgZ8BF0fE432OzcysZ8N4inPb2TSSTgU+BWwNvAiYTZaQfyDp8L5HZ2bWo4a6X6qi07TGtwJHRcT/JasxcWBELAGOAs5ptZOkxZKWS1p+5fq700VrZtZBo4elKrqZX75p+GIrsnKYRMR9dF0Gc//yUZqZdWkYE3GnMeJzgZsk/RA4DDgLQNLOwK/6HJuZWc9qV2siIj4h6VrgucDHIuLOfP3DZInZzKxSqjT2261uymDeDtw+DbGYmZU2jLMmPI/YzGqlMYSDE07EZlYrVToI162+J+IqlSGcqTSxzJqR5o+f9eNpXv6Rin30RhO9zhMJ3vNUn7+xSFPAcrbSfHY2xkiSdlK9PjMq1AutTiTdc4/YzGqlWt2S7jgRm1mtjCf6i6wTSQfx2zOPx4G3Fb2octUuymBmVkr0sJR0NnBGRBwEvD//uRD3iM2sVqZxaCKAbfPb2wFr29y3LSdiM6uVaZy+dhpwlaSPkI0uvLRoQ07EZlYrvaRhSYuBxU2rlkbE0qbt1wK7TrHrEuAI4F0R8SVJfwGcR1YcrWdOxGZWK70MTeRJd2mb7S0Tq6QLgXfmP15KVpunEB+sM7NamSC6XkpaC7w8v/1KsotmFNKpMPx2kv5R0p2SHs2XVfm67dvs11SP+J6isZmZ9Wway2C+FfiopBXAh9l8iKMnnXrEXwQeAw6PiPkRMR94Rb7u0lY7bV6PeL+isZmZ9Sx6+FfqcSK+FxELI+L3IuLFEXFz0bY6JeJ9IuKsiHig6cEfiIizgL2KPqiZWb8MY2H4Ton4XknvlbRg0wpJCyS9D7i/v6GZmfWuQXS9VEWnRPw6YD5wvaRfSfoVsAzYETi+z7GZmfVsGs+sS6bTFToeA96XL5uRdDJwQZ/iMjMrZLxSKbY7ZaavnZEsCjOzRKbrYF1KbXvEkm5rtQlY0GJbX6Som5qqpqwaadqZNSPN4YL1E2lq044kqlo1Z9ZYknae3DirdBuzZ44niAQ2jm2VpJ26mlGh68RV6SBctzqdWbcA+COy6WrNBPxXXyIyMyuhSj3dbnVKxFcCcyPi1skbJC3rS0RmZiXUrkccEW9ps+2E9OGYmZUzEfXrEZuZDZUqzQ/ulhOxmdVKHceIzcyGSu3GiM3Mhs0wDk30pR6xy2Ca2aAM4wkdhROxpG+22uYymGY2KBMRXS9V0enMuoNbbQIOSh+OmVk5wzg00WmM+CbgerLEO1nLK3SYmQ1KHQ/WrQL+OiJ+51pMklyP2Mwqp0pjv93qlIg/SOtx5HekDcXMrLzaDU1ExGVtNu+QOBYzs9KiQgfhulVmHvEZTGNh+JhymLo3MxU0ErxHKWKBdOUrU5UgTFUG87ENaUpGTiR4nTeMbcVoglHDFGVYoU/zRSsgxe9VKhN16xFXqR5xClX6sNj0SJGEbbjUbmgC1yM2syFTx6EJ1yM2s6FSux6x6xGb2bCp4/Q1M7OhUqVTl7vlRGxmtVK7oQkzs2HjRGxmNmDDOGui7fxySdtK+n+SPivphEnb/rXNfq5HbGYD0SC6Xqqi04k+F5DNGf4S8HpJX5K06bSpQ1vt5HrEZjYow1gYvtPQxP4R8Wf57a9IWgJ8W9KxfY7LzKyQiRi+syk7JeKtJM2IyJ5ZRJwpaQ3wHWBu36MzM+tR7caIga8Br2xeERGfAf4W2NivoMzMihrGMeJOZ9a9t8X6b0n6cH9CMjMrrkpjv90qU5XvjGRRmJkl0ojoeilD0vGSbpfUkLRo0rYXSPpBvv3HkrZu11bfy2DOSPTtlKKEZap6u6nqEc8dHUvSzobxNNPB1zfStDNLE0namZngdU5V+jTVe55KqvrIqaSqiZ3CNPaIVwJ/Cvx780pJM4GLgL+MiBWS5gNtf9ldBtPMamW6Zk1ExCoA6Xe+hV4F3BYRK/L7PdqpLZfBNLNa6WXIQdJiYHHTqqURsbRkCM8CQtJVwM7AJRFxdrsdXAbTzGqll6GJPOm2TLySrgV2nWLTkoj4aovdZgIvA14ErAOuk3RzRFzX6nFca8LMaqXsQbhmEXFkgd3WANdHxCMAkr4BHAy0TMR1vZahmW2hKnCK81XACyTNyQ/cvRy4o90OTsRmVisTMdH1Uoak1+ZnGr8E+Ho+JkxEPAZ8DLgJuBW4JSK+3q4tD02YWa1M1ynOEXE5cHmLbReRTWHrSl96xM1lML/mMphmNo2G8RTnTvWId5X0b5I+KWm+pA/mZ4l8UdIzWu3XXAbzT1wG08ymUUR0vVRFpx7xp8kGme8H/hNYD7wa+C7wqb5GZmZWwHSd4pxSxzPrIuKfASS9LSLOytf/s6SWc4zNzAZlGIv+dErEzT3mCydtG0kci5lZaXUsDP9VSXMj4qmI+PtNKyU9E/hJf0MzM+tdlcZ+u9XpFOf3t1h/l6S28+LMzAahSmO/3XI9YjOrlWGcNdH/esSJ6pTOm1X+ykwbxtMMaz89kaaddeOjSdqZqTRjYkr0wZyING96I0EN4FT1sFPV/001epnqedVRleYHd8v1iM2sVqrU0+2W6xGbWa3UbtaE6xGb2bAZxoN1LvpjZrVSx6EJM7OhUscz68zMhsoW0SOWtEtEPNSPYMzMyhrGMeJOk513nLTMB1YDOwA7ttlvMbA8XxZ3Mam64326nJztdoYgFrfj99zL5ovyF2hKkhrAvZNW70F2cbyIiCTFhiUtj4hFbqd/7VQpFrczPe1UKZaU7dRRp1Oc30tW3OfYiNg3IvYF1uS3XfHdzCyBtok4Ij4CnAK8X9LHJM2DITwkaWZWYR2L/kTEmog4nuwKHdcAc/oQx1K30/d2qhSL25medqoUS8p2aqftGPHv3FmaDewfESslnRwRF/QvNDOzLUNPiXizHaX7ImKvxPGYmW1xOl3F+bYWy4/psgxmJ5KOkvQTSXdJOr1gG+dLekjSyhJx7CnpPyWtknS7pHcWbGdrSTdKWpG3U6pus6QRST+SdGWJNlbnV9++VdLyEu1sL+kySXfmr9NLCrTx7DyOTcsTkk4r0M678td3paSLJW3daxt5O+/M27i91zim+txJ2lHSNZJ+lv+/Q4E2js/jaUjqapZBi3b+KX+vbpN0uaTtC7bzobyNWyVdLWm3Iu00bXuPpJC0UzfPbYvQYd7fg8BBwN6Tln2AtQnmFY4AdwP7AbOAFcCBBdo5DDgYWFkilmcAB+e35wE/LRiLyCrWAYwCNwCHlojr3cDngStLtLEa2CnB+/UZ4JT89ixg+wTv/wPA3j3utzvwc2B2/vMXgTcXePznASvJjnvMBK4FDijzuQPOBk7Pb58OnFWgjecCzwaWAYtKxPIqYGZ++6xOsbRpZ9um26cCnyrSTr5+T+AqsmmxpT+TdVk6HazbVAbz3knL6vxDUtYhwF0RcU9EbAQuAY7rtZGI+A7wqzKBRMQvI+KW/PaTwCqyX/he24mIeCr/cTRfCo3/SNoDeDVwbpH9U5K0Ldkv13kAEbExIn5dstkjgLsjYvJc9W7MBGZLmkmWSNcWaOO5wA8jYl1EjAPXA6/tducWn7vjyL6wyP9/Ta9tRMSqiOjpmpAt2rk6f14APyQ7B6BIO080/bgNXXye2/xOnkM2Ldazr5p0mr72loj4XottKcpg7g7c3/TzGgokv9Qk7QO8kKw3W2T/EUm3Ag8B10REoXaAj5N9aMsWWA3gakk3S1pcsI39gIeBC/KhknMlbVMyrtcDF/e6U0T8AvgIcB/wS+DxiLi6wOOvBA6TNF/SHOCPyXpsZSyIiF/mcf4S2KVke6n8FfDNojtLOlPS/cAbgSmvZdlFG8cCv4iIFUXjqKsy16xLYapr4Qz0m1LSXOBLwGmTegJdi4iJiDiIrAdyiKTnFYjjGOChiLi5SAyT/H5EHAwcDfwvSYcVaGMm2Z+a/xYRLwR+Q/andyGSZgHHApcW2HcHsp7nvsBuwDaSTuy1nYhYRfYn+zXAt8iGxsbb7jSEJC0he16fK9pGRCyJiD3zNt5eIIY5wBIKJvG6G3QiXsPmPZA9KPYnZhKSRsmS8Oci4stl28v/dF8GHFVg998HjpW0mmzI5pWSLioYx9r8/4eAy8mGhHq1huysyk29+8vIEnNRRwO3RMSDBfY9Evh5RDwcEWPAl4GXFgkiIs6LiIMj4jCyP6V/VqSdJg9KegZA/v9AC2RJOgk4BnhjRKTo5Hwe+LMC++1P9sW5Iv9M7wHcImnXBDENvUEn4puAAyTtm/eQXg9cMYhAJIls/HNVRHysRDs7bzo6rWze9ZHAnb22ExH/JyL2iIh9yF6Xb0dEz70+SdsoOyOSfCjhVWR/kvcazwPA/ZKena86Arij13aavIECwxK5+4BDJc3J37cjyMb0eyZpl/z/vYA/LRHTJlcAJ+W3TwK+WrK9wiQdBbyPrETBuhLtHND047EU+zz/OCJ2iYh98s/0GrKD4w8UjatWBn20kGxc7qdksyeWFGzjYrKxwjGyN/gtBdp4GdmwyG3ArfnyxwXaeQHwo7ydlcD7E7xGh1Nw1gTZ2O6KfLm96Guct3UQWUW924CvADsUbGcO8CiwXYlYziBLCCuBzwJbFWznu2RfKCuAI8p+7sgqFF5H1rO+jjZVCtu08dr89gaymUtXFYzlLrJjMJs+z93MdpiqnS/lr/NtwNeA3Yu0M2n7ajxr4r+Xwid0mJlZGoMemjAz2+I5EZuZDZgTsZnZgDkRm5kNmBOxmdmAORGbmQ2YE7GZ2YA5EZuZDdj/ByBzwnj2WfirAAAAAElFTkSuQmCC\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",
    "        \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": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sZpUy0kOzIZrlRGxmleIe8RgmKc2IeIq6xgOJYum1WrCbNqZ5G1ONrWlS7xwFUaL3vNeulVvFOsKpVHaM2MysX/TSbIhmORGbWaW4R2xm1mUjtfKX2JpoTsRmVin9ODTRf18dZmbjqIWaXopIOkLSXZLulnRap2IuKoN5qqTdO/XgZmapRajpZTySBoAvAEeSlXg4XtI+nYi5qEf8aeBGST+T9D5Jz+tEEGZmqSSsNXEQcHdE3BMRm4BvAkd3IuaiRHwPMIcsIR8I3CHpx5JOyutPjEnSAklLJC1ZvPb+hOGamY2vlaGJ+lyVLwvqmtoNeLDu55X5uuSKDtZFRNSAq4CrJE0m66YfD5wDjNlDjohFwCKAZfPe0odD52bWr1qZNVGfq8Yw1thFR/JZUSJ+TiARsRm4ArhC0vROBGRmVkbCTLkSqD9GNgdYla75PyhKxG9vtCEink0ci5lZaSnKIeRuBvaWtCfwEPAO4IRUjdcrqkf86048qJlZp6Qq+hMRWyS9H7gSGAAuiIjbkzQ+ik/oMLNKSXlx5oj4IfDDhE2OyYnYzColeqxSXjP6JhEPJCitmOrUx1RjUIOT0nx3V7F8JcDg4EjpNlKVCE1FA731GlfRFtcjNjPrLveIzcy6LOUY8URxIjazSnGP2Mysy9wjNjPrshH3iM3MuqsPr5Q0fiKWNIXstL5VEXGNpBOA1wArgEV57Qkzs55Rq2CP+ML8PkOSTgKGge8Ah5HV6jyps+GZmbWmH2dqFyXifSNiP0mDZEUvdo2IEUkXA0sb7ZTX9FwA8Lez9+XYmXOTBWxmNp4qHqyblA9PzACGgFnAk8BUYHKjnVyP2My6pabqDU18GbiTrPLQQuDbku4BDia7bIiZWU8pf2L8xCsqg/lZSf8vv71K0kXA4cCXIuKmiQjQzKwVlZs1AVkCrru9Bljc0YjMzEqo4qwJM7O+0o8HpZyIzaxSKjk0UdbwzI1J2klx+ZNJA701seWJJ2YkaWfmlDSvcSpSmj7Jli0DpdsYnJLm0M26dVOTtNPKFYbHM3NGmve812pQp9Bbv+XNcY/YzCplxD1iM7Puco/YzKzLnIjNzLqsDy9Z50RsZtVSyR6xpBcCxwC7A1uA3wCXRMRTHY7NzKxl/XiK87hzaSSdCpwHTANeCUwnS8g3SDq049GZmbWopuaXXlE0qfEU4IiI+D9kNSb2iYiFwBHAZxvtJGmBpCWSllzy5Mp00ZqZFai1sPSKZsaIB8l6+1OBmQAR8YCkpspg3rPvG6s3Y9zMelYvJdhmFSXi84GbJf0COAQ4C0DS88jqEpuZ9ZR+7PkVlcH8vKRrgJcB50bEnfn6x8gSs5lZT+mlsd9mNVMG83bg9gmIxcystH6cNeF5xGZWKbU+HJxwIjazSqniwbrSnklUPrCXjCQahJoxfVOSdtY8PT1JO6mk6o+keJUHJqX5tZw53FulRlOJfhxQLdB//WH3iM2sYtwjNjPrsi2JLkxQRNL+/OHM4y3A+9q9qHKaywWYmfWIaGEp6WzgjIjYH/hk/nNb3CM2s0qZwKGJALbLb88CVo1z33E5EZtZpUzg9LUPAVdKOodsdOE17TbkRGxmldJKGpa0AFhQt2pRXitn6/ZrgF3G2HUhcBjw4Yi4VNJfAl8mK47WMidiM6uUVoYm6guUNdjeMLFKugj4YP7jt8lq87TFB+vMrFJGiKaXklYBr8tvv57sohltKSoMP0vS30u6U9IT+bIiX7f9OPv9vh7x4rX3txubmVnLJrAe8SnAP0paCnyG5w5xtKRoaOJbwE+AQyNiNYCkXYCTyLribxhrp/ru/m17vqUfT3Qxsz4VE3SwLiJ+DhyYoq2ioYl5EXHW1iScP/jqiDgL2CNFAGZmKfXjFTqKEvH9kj4maeetKyTtLOnjwIOdDc3MrHU1oumlVxQl4rcDOwLXS3pS0pPAdcBs4LgOx2Zm1rIJPLMumaIrdPwO+Hi+PIekdwMXdiguM7O2bOmpFNucMtPXzkgWhZlZItHCv14xbo9Y0rJGm4CdG2zriBQ1gDdsTnP+yoypm5O089Qz05K0k6qi7KzhDYla6h3r1k9J0s7wDmnqEa99Is17Pm0ozWdww/qGF2NvyfThNLW1U+ilg3DNKspMOwN/Bvxu1HoB/9mRiMzMSuilnm6zihLx94HhiLh19AZJ13UkIjOzEirXI46I94yz7YT04ZiZlTMS1esRm5n1lV6aH9wsJ2Izq5QqjhGbmfWVyo0Rm5n1m34cmuhIPWKXwTSzbunHEzraTsSSftRoW0Qsioj5ETH/2Jlz230IM7OWjUQ0vfSKojPrDmi0Cdg/fThmZuX049BE0RjxzcD1jH0WbcMrdJiZdUsVD9atAN4bEX90LSZJrkdsZj2nl8Z+m1WUiD9F43HkD6QNxcysvMoNTUTE4nE275A4FjOz0qKHDsI1q8w84jNoojD82g1pyhBuP1S+ROOUwRHWrC9fhnBgUm+NQqUqX1kbSVNQM1WJxs2bB0q3MXN4I5s3lW9n1QOzSrcBMDyUppzmY48NJ2knVTwDk3vnd2Kkaj3iXqpHnEKKJGz9JUUStv5SuaEJXI/YzPpMFYcmXI/YzPpK5XrErkdsZv2mitPXzMz6Si+dutwsJ2Izq5TKDU2YmfUbJ2Izsy7rx1kT45bBlLSdpP8r6WuSThi17V/H2e/39YgvX39vqljNzArViKaXXlFUj/hCsjnDlwLvkHSppKn5toMb7VRfj/jooT0ThWpmVqwfC8MXDU28MCLelt/+rqSFwE8kHdXhuMzM2jISvXO6dbOKEvFUSZMismcWEWdKWgn8FEhzsruZWUKVGyMGvge8vn5FRHwV+F/Apk4FZWbWrn4cIy46s+5jDdb/WNJnOhOSmVn7emnst1llruJ8RrIozMwSqUU0vZQh6ThJt0uqSZo/att+km7It98madzSj31TBnOkVuY7IzNtcEuCSNIZTFTXOFX93w3rJydp59En0hw+mDWjfJ3l4e3S1NudvGEkSTupzNru2W6H8Bxrn+ydErMT2CNeDvwF8G/1KyUNAhcDfxURSyXtCIz7S+oymGZWKRM1ayIiVgBIf3RBhTcCyyJiaX6/J4rachlMM6uUVoYcJC0AFtStWhQRi0qG8GIgJF0JPA/4ZkScPd4OLoNpZpXSytBEnnQbJl5J1wC7jLFpYURc3mC3QeC1wCuB9cC1kn4ZEdc2ehzXmjCzSil7EK5eRBzexm4rgesj4nEAST8EDgAaJuLyR8DMzHpID5zifCWwn6Sh/MDd64A7xtvBidjMKmUkRppeypB0TH6m8auBH+RjwkTE74BzgZuBW4FbIuIH47XloQkzq5SJOsU5Ii4DLmuw7WKyKWxN6UiP2GUwzaxb+vEU56J6xLtI+qKkL0jaUdKn8rNEviXpBY32cxlMM+uWiGh66RVFPeKvkA0yPwj8O/As8CbgZ8B5HY3MzKwNE3WKc0qFZ9ZFxD8DSHpfRJyVr/9nSQ3nGJuZdUs/Fv0pSsT1PeaLRm0bSByLmVlpVSwMf7mk4YhYFxGf2LpS0ouAuzobmplZ63pp7LdZRac4f7LB+rsljTsvzsysG3pp7LdZrkdsZpXSj7MmOl6PeJed1rYa05hS1MrdGGmGtdesT1N7ddedn07SzrqnpxbfqQmDg2nG1rYfLl9HGCDF70mq12bm9mme06SBNL/8q1dtl6QdqXeSUSq9ND+4Wa5HbGaV0ks93Wa5HrGZVUrlZk24HrGZ9Zt+PFjnoj9mVilVHJowM+srVTyzzsysr2wTPWJJz4+IRzsRjJlZWf04Rlw02Xn2qGVH4D5gB2D2OPstAJbky4ImJlUX3qfJydlupw9icTt+z708d1H+Ao1JUg24f9TqOWQXx4uI2Ku1tN/wcZZExHy307l2eikWtzMx7fRSLCnbqaKiU5w/Rlbc56iI2DMi9gRW5reTJGEzs23duIk4Is4BTgY+KelcSTOhDw9Jmpn1sMKiPxGxMiKOI7tCx9XAUAfiWOR2Ot5OL8XidiamnV6KJWU7lTPuGPEf3VmaDrwwIpZLendEXNi50MzMtg0tJeLn7Cg9EBF7JI7HzGybU3QV52UNlttosgxmEUlHSLpL0t2STmuzjQskPSppeYk4dpf075JWSLpd0gfbbGeapJskLc3bKVW3WdKApF9J+n6JNu7Lr759q6QlJdrZXtJiSXfmr9Or22jjJXkcW5enJX2ojXY+nL++yyVdIqmt2qSSPpi3cXurcYz1uZM0W9LVkn6T/79DG20cl8dTk9TULIMG7fxD/l4tk3SZpO3bbOfTeRu3SrpK0q7ttFO37aOSQtJOzTy3bULBvL9HgP2BuaOWecCqBPMKB4DfAnsBU4ClwD5ttHMIcACwvEQsLwAOyG/PBH7dZiwiq1gHMBm4ETi4RFwfAb4BfL9EG/cBOyV4v74KnJzfngJsn+D9Xw3MbXG/3YB7gen5z98C3tXG478cWE523GMQuAbYu8znDjgbOC2/fRpwVhttvAx4CXAdML9ELG8EBvPbZxXFMk4729XdPhU4r5128vW7A1eSTYst/ZmsylJ0sG5rGcz7Ry335R+Ssg4C7o6IeyJiE/BN4OhWG4mInwJPlgkkIh6OiFvy22uBFWS/8K22ExGxLv9xcr60Nf4jaQ7wJuD8dvZPSdJ2ZL9cXwaIiE0RsaZks4cBv42I0XPVmzEITJc0SJZIV7XRxsuAX0TE+ojYAlwPHNPszg0+d0eTfWGR///WVtuIiBUR0dI1IRu0c1X+vAB+QXYOQDvt1F/BYAZNfJ7H+Z38LNm0WM++qlM0fe09EfHzBttSlMHcDXiw7ueVtJH8UpM0D3gFWW+2nf0HJN0KPApcHRFttQN8juxDW7bAagBXSfqlpAVttrEX8BhwYT5Ucr6kGSXjegdwSas7RcRDwDnAA8DDwFMRcVUbj78cOETSjpKGgD8n67GVsXNEPJzH+TDw/JLtpfLfgR+1u7OkMyU9CLwTGPNalk20cRTwUEQsbTeOqipzzboUNMa6rn5TShoGLgU+NKon0LSIGImI/cl6IAdJenkbcbwZeDQiftlODKP8SUQcABwJ/E9Jh7TRxiDZn5pfjIhXAM+Q/endFklTgKOAb7ex7w5kPc89gV2BGZJObLWdiFhB9if71cCPyYbGtoy7Ux+StJDseX293TYiYmFE7J638f42YhgCFtJmEq+6bifilTy3BzKH9v7ETELSZLIk/PWI+E7Z9vI/3a8Djmhj9z8BjpJ0H9mQzeslXdxmHKvy/x8FLiMbEmrVSrKzKrf27heTJeZ2HQncEhGPtLHv4cC9EfFYRGwGvgO8pp0gIuLLEXFARBxC9qf0b9ppp84jkl4AkP/f1QJZkk4C3gy8MyJSdHK+Abytjf1eSPbFuTT/TM8BbpG0S4KY+l63E/HNwN6S9sx7SO8AruhGIJJENv65IiLOLdHO87YenVY27/pw4M5W24mIv4mIORExj+x1+UlEtNzrkzRD2RmR5EMJbyT7k7zVeFYDD0p6Sb7qMOCOVtupczxtDEvkHgAOljSUv2+HkY3pt0zS8/P/9wD+okRMW10BnJTfPgm4vGR7bZN0BPBxshIF60u0s3fdj0fR3uf5toh4fkTMyz/TK8kOjq9uN65K6fbRQrJxuV+TzZ5Y2GYbl5CNFW4me4Pf00YbryUbFlkG3Jovf95GO/sBv8rbWQ58MsFrdChtzpogG9tdmi+3t/sa523tT1ZRbxnwXWCHNtsZAp4AZpWI5QyyhLAc+Bowtc12fkb2hbIUOKzs546sQuG1ZD3raxmnSuE4bRyT395INnPpyjZjuZvsGMzWz3Mzsx3GaufS/HVeBnwP2K2ddkZtvw/Pmvj90vYJHWZmlka3hybMzLZ5TsRmZl3mRGxm1mVOxGZmXeZEbGbWZU7EZmZd5kRsZtZlTsRmZl32/wGBzIm9Ed4fBgAAAABJRU5ErkJggg==\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": 9,
   "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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eca8c72d1ee4468815105c8ae187874",
       "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": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_bin_Cyc_1/Cycle_1_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_1/Cycle_1_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_1/Cycle_1_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_1/Cycle_1_F155_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_2/Cycle_2_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_3/Cycle_3_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_4/Cycle_4_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_5/Cycle_5_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_6/Cycle_6_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_6/Cycle_6_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_6/Cycle_6_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_7/Cycle_7_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_7/Cycle_7_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_7/Cycle_7_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_8/Cycle_8_F144_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F003_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F012_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F071_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F142_bin_reg.tif',\n",
       " 'reg_bin_Cyc_9/Cycle_9_F144_bin_reg.tif']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_lst2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fX6G6Eynzj0euWHCAa4bb/yWnbVe/acz6RDGvXjKzbcerjHa+j2U4rnwcVz5l4pLUMNG3YujmPCpn87trd0TELyNiJFteCxwqaXYL+jQzsya1ItEvB26ut0PSyyQpW16c9fdEC/o0M7MmlRpLkPRi4BzgPVXb3gsQESuAC4H3SToAPAssi1ZcFDAzs6aVSvQR8QzwkpptK6qWrwOuq61nZmad4ykQbIzqi7e1F3bNbHryFAhmZolzojczS5wTvZlZ4pzozcwS50RvZpY4J3ozs8Q50ZuZJc6J3swscU70ZmaJ852x4ygzLfFkyRvz8I79XDoNX6eZNc9n9GZmiXOiNzNLnBO9mVninOjNzBLnRG9mljgnejOzxDnRm5klrnSil7RN0rCkjZLW19kvSX8raYukByX9ftk+zcysea26Yao/Ih5vsO88YF72eA3wpezZzMw6oBNDN+cDN0bFvcAxkk7oQL9mZgYoIso1ID0G7AMC+HJErKzZfxtwdUT8IFu/E/hoRKyvKTcADAB0d3cvGhwcLBTPyMgIXV1dherWGt6xvyXtAHQfCbufbVlzLTNeXAvmzupsMFVa+T62kuPKx3HlUyau/v7+DRHRW29fK4Zu/jAidko6Hlgn6acRcXfVftWp84LfLtkviJUAvb290dfXVyiYoaEhitat1co5YK5YcIBrhqfe1ELjxbXtor7OBlOlle9jKzmufBxXPu2Kq/TQTUTszJ73AGuAxTVFtgMnVa2fCOws26+ZmTWnVKKXNFPSUaPLwLnApppitwLvyr598wfA/ojYVaZfMzNrXtmxhG5gjaTRtm6KiO9Iei9ARKwA1gJLgS3AM8BflOzTzMxyKJXoI2Ir8Ko621dULQfw/jL9mJlZcb4z1swscU70ZmaJc6I3M0ucE72ZWeKc6M3MEjf1btW0KaOn5s7gbVe/aVq0bWZj+YzezCxxTvRmZolzojczS5wTvZlZ4pzozcwS50RvZpY4J3ozs8Q50ZuZJc6J3swscU70ZmaJO6inQKi9Dd/G52kLzKYnn9GbmSWucKKXdJKkuyQ9JGmzpP9Up0yfpP2SNmaPT5YL18zM8iozdHMAuCIi7pd0FLBB0rqI+ElNuX+JiDeX6MfMzEoofEYfEbsi4v5s+SngIWBuqwIzM7PWaMkYvaQe4Czgh3V2v1bSA5LukHRmK/ozM7PmKSLKNSB1Af8MfC4i/rFm39HA8xExImkpcG1EzGvQzgAwANDd3b1ocHCwUDwjIyN0dXU1VXZ4x/5CfRTRfSTsfrZj3TWtTFwL5s4q3G/tsa9tK8/72EmOKx/HlU+ZuPr7+zdERG+9faUSvaRDgduA70bE55sovw3ojYjHxyvX29sb69evLxTT0NAQfX19TZXt5Ncrr1hwgGuGp963WcvEVebrlRN9VTPP+9hJjisfx5VPmbgkNUz0Zb51I+CrwEONkrykl2XlkLQ46++Jon2amVl+ZU4x/xC4GBiWtDHb9p+BkwEiYgVwIfA+SQeAZ4FlUXasyMzMcimc6CPiB4AmKHMdcF3RPszMrLypN2hs00beaxytnDKhuu9UpmLwFBPWLp4CwcwscU70ZmaJc6I3M0ucE72ZWeKc6M3MEudEb2aWOCd6M7PEOdGbmSXOid7MLHFO9GZmiUtuCoThHfu5tMGt+b6lfHKVmRZ6vLoTtZvnfc87DcF45VsZ13RR+5pXL5k57v4Uj8FU5DN6M7PEOdGbmSXOid7MLHFO9GZmiXOiNzNLnBO9mVniSiV6SUskPSxpi6Qr6+w/XNIt2f4fSuop05+ZmeVXONFLmgFcD5wHnAEsl3RGTbHLgH0R8XvAF4D/VrQ/MzMrpswZ/WJgS0RsjYhfA4PA+TVlzgduyJb/J3C2pHH/obiZmbVWmUQ/F/hF1fr2bFvdMhFxANgPvKREn2ZmlpMiolhF6W3AGyPi8mz9YmBxRPxVVZnNWZnt2fqjWZkn6rQ3AAxkq6cDDxcKDGYDjxes206OKx/HlY/jyifFuE6JiJfW21FmrpvtwElV6ycCOxuU2S7pEGAWsLdeYxGxElhZIh4AJK2PiN6y7bSa48rHceXjuPI52OIqM3RzHzBP0qmSDgOWAbfWlLkVuCRbvhD4fhT9E8LMzAopfEYfEQckfQD4LjADWBURmyV9BlgfEbcCXwW+LmkLlTP5Za0I2szMmldqmuKIWAusrdn2yarlXwFvK9NHAaWHf9rEceXjuPJxXPkcVHEVvhhrZmbTg6dAMDNL3JRO9GWmWJD0sWz7w5Le2Gyb7YxL0jmSNkgazp5fX1VnKGtzY/Y4voNx9Uh6tqrvFVV1FmXxbpH0t0VueCsR10VVMW2U9Lykhdm+Thyv10m6X9IBSRfW7LtE0iPZ45Kq7Z04XnXjkrRQ0j2SNkt6UNI7qvatlvRY1fFa2Km4sn2/rer71qrtp2bv+SPZZ+CwTsUlqb/m8/UrSRdk+0ofryZj+5Ckn2Tv152STqna17rPWERMyQeVC7yPAqcBhwEPAGfUlPlLYEW2vAy4JVs+Iyt/OHBq1s6MZtpsc1xnAXOy5fnAjqo6Q0DvJB2vHmBTg3Z/BLwWEHAHcF6n4qopswDY2uHj1QO8ErgRuLBq+3HA1uz52Gz52A4er0ZxvQKYly3PAXYBx2Trq6vLdvJ4ZftGGrT7TWBZtrwCeF8n46p5T/cCL27F8coRW39Vn+/jdz+TLf2MTeUz+jJTLJwPDEbEcxHxGLAla6+ZNtsWV0T8OCJG7zXYDBwh6fCc/bc8rkYNSjoBODoi7onKJ+xG4IJJims5cHPOvkvFFRHbIuJB4Pmaum8E1kXE3ojYB6wDlnTqeDWKKyJ+FhGPZMs7gT1A3RtoCihzvOrK3uPXU3nPofIZ6NjxqnEhcEdEPJOz/7Kx3VXV571U7keCFn/GpnKiLzPFQqO6zbTZzriqvRX4cUQ8V7Xta9mfiZ8o8Cd/2bhOlfRjSf8s6Y+qym+foM12xzXqHbww0bf7eOWt26njNSFJi6mcRT5atflz2RDBFwqcYJSN6whJ6yXdOzo8QuU9fjJ7z4u02Yq4Ri3jhZ+vMserSGyXUTlDH69uoc/YVE709X5wa78i1KhM3u2diquyUzqTykye76naf1FELAD+KHtc3MG4dgEnR8RZwIeAmyQd3WSb7YyrslN6DfBMRGyq2t+J45W3bqeO1/gNVM76vg78RUSMnsV+DPg3wKupDAd8tMNxnRyVOz7/HPiipJe3oM1WxDV6vBZQuSdoVNnjlSs2Se8EeoG/maBuodc7lRN9nikW0NgpFhrVbabNdsaFpBOBNcC7IuL/n21FxI7s+SngJip/9nUkrmyI64ms/w1UzgJfkZU/sap+x49X5gVnWx06Xnnrdup4NZT9gr4d+HhE3Du6PSJ2RcVzwNfo7PEaHUoiIrZSub5yFpU5XY7J3vPcbbYirszbgTUR8ZuqeMser6Zjk/QG4CrgLVV/4bf2M1bmYkM7H1Ru5tpK5WLq6IWMM2vKvJ+xF/G+mS2fydiLsVupXBiZsM02x3VMVv6tddqcnS0fSmXM8r0djOulwIxs+TRgB3Bctn4f8Af87sLP0k7Fla2/iMqH+7ROH6+qsqt54cXYx6hcJDs2W+7Y8RonrsOAO4EP1il7QvYs4IvA1R2M61jg8Gx5NvAI2UVJ4FuMvRj7l52Kq2r7vUB/K49Xjs/+WVROrObVbG/pZyxX4J1+AEuBn2UH4qps22eo/OYDOCL7oGyhciW6OhlcldV7mKqr0vXa7FRcwMeBp4GNVY/jgZnABuBBKhdpryVLvB2K661Zvw8A9wN/UtVmL7Apa/M6spvsOvg+9gH31rTXqeP1aiq/ZJ4GngA2V9V9dxbvFipDJJ08XnXjAt4J/Kbm87Uw2/d9YDiL7RtAVwfj+ndZ3w9kz5dVtXla9p5vyT4Dh3f4feyhcmLzopo2Sx+vJmP7J2B31ft1azs+Y74z1swscVN5jN7MzFrAid7MLHFO9GZmiXOiNzNLnBO9mVninOjNzBLnRG9mljgnejOzxP0/sJK8CUkf3z0AAAAASUVORK5CYII=\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 = 215"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " tmat_Cyc_1 \n",
      " -0.06911219424080173 -2.1257164435357936 3.4055502186970443 -1.8883525876651013\n",
      "X_max:-1 X_min:-3 Y_max:4 Y_min:-2\n",
      "\n",
      " tmat_Cyc_2 \n",
      " -0.49333815679085546 -3.4995412522382594 4.530662702168797 -1.7702159903757133\n",
      "X_max:-1 X_min:-4 Y_max:5 Y_min:-2\n",
      "\n",
      " tmat_Cyc_3 \n",
      " -0.4760069907295019 -4.197026513704145 4.602583572787694 0.11795869804177528\n",
      "X_max:-1 X_min:-5 Y_max:5 Y_min:1\n",
      "\n",
      " tmat_Cyc_4 \n",
      " -0.47899234504984634 -4.4309285761603405 4.9110267032973525 0.09909872036348588\n",
      "X_max:-1 X_min:-5 Y_max:5 Y_min:1\n",
      "\n",
      " tmat_Cyc_5 \n",
      " -0.47671860429522894 -4.546590732263212 4.618288422063529 -1.0825984263064734\n",
      "X_max:-1 X_min:-5 Y_max:5 Y_min:-2\n",
      "\n",
      " tmat_Cyc_6 \n",
      " -0.373146060988347 -4.637534303990492 3.8657078496735267 -1.7369283739076238\n",
      "X_max:-1 X_min:-5 Y_max:4 Y_min:-2\n",
      "\n",
      " tmat_Cyc_7 \n",
      " -0.5524643944531817 -4.859923243347794 4.115634081531198 -2.054494726204876\n",
      "X_max:-1 X_min:-5 Y_max:5 Y_min:-3\n",
      "\n",
      " tmat_Cyc_8 \n",
      " -4.995376642732367 -14.104358094097392 14.217221334080591 -11.830522960406709\n",
      "X_max:-5 X_min:-15 Y_max:15 Y_min:-12\n",
      "\n",
      " tmat_Cyc_9 \n",
      " -0.8983578287323688 -8.598227641443032 8.810588361243617 -6.932314074255885\n",
      "X_max:-1 X_min:-9 Y_max:9 Y_min:-7\n",
      "\n",
      " X_max_total:-1 X_min_total:-15 Y_max_total:15 Y_min_total:-12\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": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_DIR = 'reg'\n",
    "REF_DIR = 'tif' # cycle0\n",
    "MERGE_DIR = 'merged' # output directory to save\n",
    "Z = 13\n",
    "final_ch = 38\n",
    "\n",
    "pad = 10 # 5 pixel padding\n",
    "\n",
    "!mkdir merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "image max: 2289\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "65528\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",
      "65531\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F000.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 001\n",
      "image max: 11862\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "65534\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",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "65530\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",
      "65528\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 002\n",
      "image max: 8111\n",
      "Appending...  reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F002_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F002_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F002_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F002_reg.tif\n",
      "65532\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",
      "65535\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F002.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 59014\n",
      "FOV 003\n",
      "image max: 5405\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "8453\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "8751\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F003_reg.tif\n",
      "39208\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F003.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 39208\n",
      "FOV 004\n",
      "image max: 4074\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "65530\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",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "65528\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",
      "2033\n",
      "2021\n",
      "image max: 37338\n",
      "FOV 006\n",
      "image max: 1544\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "65535\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",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 007\n",
      "image max: 1543\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",
      "65535\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",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F007_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65518\n",
      "FOV 008\n",
      "image max: 1739\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "65528\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "65527\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",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "65529\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",
      "2033\n",
      "2021\n",
      "image max: 65534\n",
      "FOV 009\n",
      "image max: 4009\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "65529\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",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "65530\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F009.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65494\n",
      "FOV 010\n",
      "image max: 3989\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F010_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F010_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F010.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65511\n",
      "FOV 011\n",
      "image max: 1358\n",
      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F011_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F011_reg.tif\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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      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F011_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F011_reg.tif\n",
      "65534\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "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",
      "2033\n",
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      "image max: 53034\n",
      "FOV 013\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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      "Appending...  reg_Cyc_9/Cycle_9_F013_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F013.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
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      "image max: 65526\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "Appending...  reg_Cyc_9/Cycle_9_F014_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F014.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
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      "image max: 65524\n",
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      "Shape = (13, 32, 2048, 2048)\n",
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      "Appending...  reg_Cyc_9/Cycle_9_F015_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F015.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
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      "image max: 65535\n",
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      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
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      "65535\n",
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      "Shape = (13, 32, 2048, 2048)\n",
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      "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",
      "2033\n",
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      "image max: 65535\n",
      "FOV 017\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F017.tif\n",
      "dtype of  uint16\n",
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      "2033\n",
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      "image max: 61511\n",
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      "Appending...  reg_Cyc_9/Cycle_9_F018_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F018.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
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      "image max: 65535\n",
      "FOV 019\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F019_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F019_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F019.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65515\n",
      "FOV 020\n",
      "image max: 7218\n",
      "Appending...  reg_Cyc_1/Cycle_1_F020_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",
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      "65535\n",
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      "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_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",
      "2033\n",
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      "image max: 65116\n",
      "FOV 021\n",
      "image max: 3923\n",
      "Appending...  reg_Cyc_1/Cycle_1_F021_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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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F021_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F021_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F021_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F021_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\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",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F021.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 022\n",
      "image max: 10397\n",
      "Appending...  reg_Cyc_1/Cycle_1_F022_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F022_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F022_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F022_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F022_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F022_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F022_reg.tif\n",
      "65528\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F022.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 023\n",
      "image max: 2295\n",
      "Appending...  reg_Cyc_1/Cycle_1_F023_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F023_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F023_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F023_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F023_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F023_reg.tif\n",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 024\n",
      "image max: 1363\n",
      "Appending...  reg_Cyc_1/Cycle_1_F024_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F024_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65534\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_F024_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F024_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F024.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 025\n",
      "image max: 1934\n",
      "Appending...  reg_Cyc_1/Cycle_1_F025_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F025_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F025_reg.tif\n",
      "65531\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",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F025_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65496\n",
      "FOV 026\n",
      "image max: 1783\n",
      "Appending...  reg_Cyc_1/Cycle_1_F026_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F026_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F026_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F026_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F026_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F026_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F026_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F026_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F026_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F026.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 51192\n",
      "FOV 027\n",
      "image max: 1604\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",
      "65534\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",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F027_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F027_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F027_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F027.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 39867\n",
      "FOV 028\n",
      "image max: 1659\n",
      "Appending...  reg_Cyc_1/Cycle_1_F028_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F028_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F028_reg.tif\n",
      "65527\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F028_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F028_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F028_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F028_reg.tif\n",
      "65527\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F028.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65413\n",
      "FOV 029\n",
      "image max: 1162\n",
      "Appending...  reg_Cyc_1/Cycle_1_F029_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F029_reg.tif\n",
      "65532\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",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F029_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F029_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F029_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F029.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 030\n",
      "image max: 2118\n",
      "Appending...  reg_Cyc_1/Cycle_1_F030_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F030_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F030_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F030_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F030_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F030_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F030.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 031\n",
      "image max: 3461\n",
      "Appending...  reg_Cyc_1/Cycle_1_F031_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F031_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F031_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F031_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F031_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F031_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F031_reg.tif\n",
      "65534\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F031.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 032\n",
      "image max: 2582\n",
      "Appending...  reg_Cyc_1/Cycle_1_F032_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F032_reg.tif\n",
      "65535\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",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F032_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F032_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F032_reg.tif\n",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 033\n",
      "image max: 853\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F033_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F033_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F033_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F033_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F033_reg.tif\n",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 47167\n",
      "FOV 034\n",
      "image max: 1045\n",
      "Appending...  reg_Cyc_1/Cycle_1_F034_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F034_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F034_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F034_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F034_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F034_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F034_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F034_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F034_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F034.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 37162\n",
      "FOV 035\n",
      "image max: 1425\n",
      "Appending...  reg_Cyc_1/Cycle_1_F035_reg.tif\n",
      "65534\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",
      "65527\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F035_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F035_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F035_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F035_reg.tif\n",
      "65532\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F035.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 036\n",
      "image max: 1628\n",
      "Appending...  reg_Cyc_1/Cycle_1_F036_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F036_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F036_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F036_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F036_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F036_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F036_reg.tif\n",
      "65530\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",
      "2033\n",
      "2021\n",
      "image max: 40393\n",
      "FOV 037\n",
      "image max: 1173\n",
      "Appending...  reg_Cyc_1/Cycle_1_F037_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F037_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F037_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F037_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F037_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F037_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 038\n",
      "image max: 3027\n",
      "Appending...  reg_Cyc_1/Cycle_1_F038_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F038_reg.tif\n",
      "65533\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",
      "65528\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",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F038_reg.tif\n",
      "65527\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F038.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 039\n",
      "image max: 2189\n",
      "Appending...  reg_Cyc_1/Cycle_1_F039_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F039_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F039_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F039_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F039_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F039_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F039_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F039_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F039_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F039.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 040\n",
      "image max: 1375\n",
      "Appending...  reg_Cyc_1/Cycle_1_F040_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F040_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F040_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F040_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F040_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F040_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F040_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F040_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F040_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F040.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 49698\n",
      "FOV 041\n",
      "image max: 1330\n",
      "Appending...  reg_Cyc_1/Cycle_1_F041_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F041_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F041_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F041_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F041_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F041.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 35659\n",
      "FOV 042\n",
      "image max: 1409\n",
      "Appending...  reg_Cyc_1/Cycle_1_F042_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F042_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F042_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F042_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F042_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F042_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F042_reg.tif\n",
      "65530\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F042.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65523\n",
      "FOV 043\n",
      "image max: 1061\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F043_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F043_reg.tif\n",
      "65528\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",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F043_reg.tif\n",
      "65530\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F043.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 044\n",
      "image max: 2073\n",
      "Appending...  reg_Cyc_1/Cycle_1_F044_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F044_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F044_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F044_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F044_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F044_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F044_reg.tif\n",
      "65529\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F044.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 50715\n",
      "FOV 045\n",
      "image max: 907\n",
      "Appending...  reg_Cyc_1/Cycle_1_F045_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F045_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F045_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F045_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F045_reg.tif\n",
      "65529\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",
      "65528\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F045.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 046\n",
      "image max: 792\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F046_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F046_reg.tif\n",
      "65535\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",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F046_reg.tif\n",
      "65529\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 047\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F047_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F047_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F047_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F047_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F047_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F047_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F047_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 048\n",
      "image max: 2558\n",
      "Appending...  reg_Cyc_1/Cycle_1_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F048_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F048_reg.tif\n",
      "65535\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 32509\n",
      "FOV 050\n",
      "image max: 926\n",
      "Appending...  reg_Cyc_1/Cycle_1_F050_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F050_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F050_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F050_reg.tif\n",
      "65527\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F050_reg.tif\n",
      "65533\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",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F050.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65131\n",
      "FOV 051\n",
      "image max: 1037\n",
      "Appending...  reg_Cyc_1/Cycle_1_F051_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F051_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F051_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F051_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F051_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F051_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F051_reg.tif\n",
      "65530\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F051.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 35246\n",
      "FOV 052\n",
      "image max: 1565\n",
      "Appending...  reg_Cyc_1/Cycle_1_F052_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F052_reg.tif\n",
      "65532\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",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F052_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F052_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65531\n",
      "FOV 053\n",
      "image max: 942\n",
      "Appending...  reg_Cyc_1/Cycle_1_F053_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F053_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F053_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F053_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F053_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F053_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F053_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F053_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F053.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 054\n",
      "image max: 774\n",
      "Appending...  reg_Cyc_1/Cycle_1_F054_reg.tif\n",
      "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",
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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F054_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F054_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F054_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F054_reg.tif\n",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 055\n",
      "image max: 1702\n",
      "Appending...  reg_Cyc_1/Cycle_1_F055_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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      "65535\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_F055_reg.tif\n",
      "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_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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 056\n",
      "image max: 1351\n",
      "Appending...  reg_Cyc_1/Cycle_1_F056_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F056_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F056_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F056_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F056.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 32202\n",
      "FOV 057\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F057_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F057_reg.tif\n",
      "65535\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",
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      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F057_reg.tif\n",
      "65535\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",
      "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_F057_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F057.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 35384\n",
      "FOV 058\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F058_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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      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F058_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F058_reg.tif\n",
      "65535\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_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",
      "2033\n",
      "2021\n",
      "image max: 39873\n",
      "FOV 059\n",
      "image max: 2112\n",
      "Appending...  reg_Cyc_1/Cycle_1_F059_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F059_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F059_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F059_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F059_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F059_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F059_reg.tif\n",
      "65528\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",
      "2033\n",
      "2021\n",
      "image max: 35531\n",
      "FOV 060\n",
      "image max: 1514\n",
      "Appending...  reg_Cyc_1/Cycle_1_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F060_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F060_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F060.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65529\n",
      "FOV 061\n",
      "image max: 1459\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",
      "65535\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",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F061_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F061_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F061_reg.tif\n",
      "65535\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F061.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 062\n",
      "image max: 2189\n",
      "Appending...  reg_Cyc_1/Cycle_1_F062_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F062_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F062_reg.tif\n",
      "65530\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",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F062_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F062_reg.tif\n",
      "65534\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F062.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 51507\n",
      "FOV 063\n",
      "image max: 766\n",
      "Appending...  reg_Cyc_1/Cycle_1_F063_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F063_reg.tif\n",
      "65535\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",
      "65535\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 41161\n",
      "FOV 064\n",
      "image max: 2986\n",
      "Appending...  reg_Cyc_1/Cycle_1_F064_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F064_reg.tif\n",
      "65535\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",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F064.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 065\n",
      "image max: 1236\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",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F065_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F065_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F065_reg.tif\n",
      "65533\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",
      "2033\n",
      "2021\n",
      "image max: 37113\n",
      "FOV 066\n",
      "image max: 1438\n",
      "Appending...  reg_Cyc_1/Cycle_1_F066_reg.tif\n",
      "65533\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",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F066_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F066_reg.tif\n",
      "65533\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",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 65531\n",
      "FOV 067\n",
      "image max: 1305\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F067_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F067_reg.tif\n",
      "65535\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",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 069\n",
      "image max: 939\n",
      "Appending...  reg_Cyc_1/Cycle_1_F069_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65534\n",
      "FOV 070\n",
      "image max: 2154\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",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F070_reg.tif\n",
      "65530\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",
      "2033\n",
      "2021\n",
      "image max: 55347\n",
      "FOV 071\n",
      "image max: 1443\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",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F071_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F071_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F071_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F071_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F071_reg.tif\n",
      "65534\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 072\n",
      "image max: 912\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",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F072_reg.tif\n",
      "65532\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 45595\n",
      "FOV 074\n",
      "image max: 1759\n",
      "Appending...  reg_Cyc_1/Cycle_1_F074_reg.tif\n",
      "65535\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",
      "65535\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",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F074_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F074_reg.tif\n",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 075\n",
      "image max: 1568\n",
      "Appending...  reg_Cyc_1/Cycle_1_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F075_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F075.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 39663\n",
      "FOV 076\n",
      "image max: 1266\n",
      "Appending...  reg_Cyc_1/Cycle_1_F076_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F076_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F076_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F076_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F076_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F076_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F076_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F076_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F076_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F076.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 64530\n",
      "FOV 077\n",
      "image max: 1958\n",
      "Appending...  reg_Cyc_1/Cycle_1_F077_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F077_reg.tif\n",
      "65528\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F077_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F077_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F077_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F077_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F077_reg.tif\n",
      "65534\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",
      "2033\n",
      "2021\n",
      "image max: 41004\n",
      "FOV 078\n",
      "image max: 1506\n",
      "Appending...  reg_Cyc_1/Cycle_1_F078_reg.tif\n",
      "65535\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",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F078_reg.tif\n",
      "65532\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",
      "65529\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F078.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65351\n",
      "FOV 079\n",
      "image max: 856\n",
      "Appending...  reg_Cyc_1/Cycle_1_F079_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F079_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F079_reg.tif\n",
      "65535\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",
      "65535\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",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F079.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 080\n",
      "image max: 2428\n",
      "Appending...  reg_Cyc_1/Cycle_1_F080_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F080_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F080_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F080_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F080_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F080_reg.tif\n",
      "65531\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F080.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 58163\n",
      "FOV 081\n",
      "image max: 2542\n",
      "Appending...  reg_Cyc_1/Cycle_1_F081_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F081_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F081_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F081_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 082\n",
      "image max: 1536\n",
      "Appending...  reg_Cyc_1/Cycle_1_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F082_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F082_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 083\n",
      "image max: 1166\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",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F083_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 35486\n",
      "FOV 084\n",
      "image max: 10088\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",
      "65531\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F084_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F084_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F084.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 64654\n",
      "FOV 085\n",
      "image max: 1614\n",
      "Appending...  reg_Cyc_1/Cycle_1_F085_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F085_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F085_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F085_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F085_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F085_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F085_reg.tif\n",
      "65529\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F085.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 45466\n",
      "FOV 086\n",
      "image max: 963\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",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F086_reg.tif\n",
      "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",
      "2033\n",
      "2021\n",
      "image max: 53357\n",
      "FOV 087\n",
      "image max: 1569\n",
      "Appending...  reg_Cyc_1/Cycle_1_F087_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F087_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F087_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F087_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F087_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65525\n",
      "FOV 088\n",
      "image max: 40586\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F088_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F088_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F088_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F088_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F088.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65530\n",
      "FOV 089\n",
      "image max: 10516\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F089_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F089_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F089_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F089_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F089_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F089_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F089_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F089.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65503\n",
      "FOV 090\n",
      "image max: 3084\n",
      "Appending...  reg_Cyc_1/Cycle_1_F090_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F090_reg.tif\n",
      "65530\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",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F090_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F090_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F090_reg.tif\n",
      "65530\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",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F090.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 48505\n",
      "FOV 091\n",
      "image max: 1703\n",
      "Appending...  reg_Cyc_1/Cycle_1_F091_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F091_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F091_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F091_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F091_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F091_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F091_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F091_reg.tif\n",
      "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",
      "2033\n",
      "2021\n",
      "image max: 53495\n",
      "FOV 092\n",
      "image max: 1827\n",
      "Appending...  reg_Cyc_1/Cycle_1_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F092_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F092.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 44271\n",
      "FOV 093\n",
      "image max: 1978\n",
      "Appending...  reg_Cyc_1/Cycle_1_F093_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F093_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F093_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65530\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",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F093_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F093_reg.tif\n",
      "65530\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F093.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65477\n",
      "FOV 094\n",
      "image max: 1034\n",
      "Appending...  reg_Cyc_1/Cycle_1_F094_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F094_reg.tif\n",
      "65527\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F094_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F094_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F094_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F094_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F094_reg.tif\n",
      "65529\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F094.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 42734\n",
      "FOV 095\n",
      "image max: 1487\n",
      "Appending...  reg_Cyc_1/Cycle_1_F095_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F095_reg.tif\n",
      "65533\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_F095_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F095_reg.tif\n",
      "65535\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",
      "65533\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F095.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65527\n",
      "FOV 096\n",
      "image max: 1609\n",
      "Appending...  reg_Cyc_1/Cycle_1_F096_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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      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F096_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65530\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F096.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65505\n",
      "FOV 097\n",
      "image max: 1032\n",
      "Appending...  reg_Cyc_1/Cycle_1_F097_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F097_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_F097_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F097_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F097_reg.tif\n",
      "65534\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F097.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65335\n",
      "FOV 099\n",
      "image max: 1244\n",
      "Appending...  reg_Cyc_1/Cycle_1_F099_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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      "65532\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_F099_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F099_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F099_reg.tif\n",
      "65528\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",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F099.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 100\n",
      "image max: 1854\n",
      "Appending...  reg_Cyc_1/Cycle_1_F100_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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      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F100_reg.tif\n",
      "65535\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",
      "65530\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",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F100.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 101\n",
      "image max: 1052\n",
      "Appending...  reg_Cyc_1/Cycle_1_F101_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65528\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "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",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F101_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F101_reg.tif\n",
      "65528\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F101.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 46218\n",
      "FOV 102\n",
      "image max: 6447\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F102_reg.tif\n",
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      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F102_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F102_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F102_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F102_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 40675\n",
      "FOV 103\n",
      "image max: 1084\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F103_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F103_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 104\n",
      "image max: 2027\n",
      "Appending...  reg_Cyc_1/Cycle_1_F104_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F104_reg.tif\n",
      "65532\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",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F104_reg.tif\n",
      "65534\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 41709\n",
      "FOV 105\n",
      "image max: 1036\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F105_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F105_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F105_reg.tif\n",
      "65532\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",
      "65529\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",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F105.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 40401\n",
      "FOV 106\n",
      "image max: 1249\n",
      "Appending...  reg_Cyc_1/Cycle_1_F106_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F106_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F106_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F106_reg.tif\n",
      "65529\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",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F106_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 107\n",
      "image max: 2212\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",
      "65532\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",
      "65535\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F107_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65475\n",
      "FOV 108\n",
      "image max: 1418\n",
      "Appending...  reg_Cyc_1/Cycle_1_F108_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F108_reg.tif\n",
      "65528\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F108_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F108_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F108_reg.tif\n",
      "65535\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",
      "65529\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F108.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 109\n",
      "image max: 1555\n",
      "Appending...  reg_Cyc_1/Cycle_1_F109_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F109_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F109_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F109_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F109_reg.tif\n",
      "65531\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",
      "65528\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F109.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 37021\n",
      "FOV 110\n",
      "image max: 1791\n",
      "Appending...  reg_Cyc_1/Cycle_1_F110_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F110_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F110_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F110_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F110_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F110_reg.tif\n",
      "65531\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F110.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 111\n",
      "image max: 2203\n",
      "Appending...  reg_Cyc_1/Cycle_1_F111_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F111_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F111_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F111_reg.tif\n",
      "65535\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",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F111_reg.tif\n",
      "65528\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",
      "2033\n",
      "2021\n",
      "image max: 45626\n",
      "FOV 112\n",
      "image max: 3167\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",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F112_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F112_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F112_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F112_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F112.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 54136\n",
      "FOV 113\n",
      "image max: 2587\n",
      "Appending...  reg_Cyc_1/Cycle_1_F113_reg.tif\n",
      "65534\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",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F113_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F113_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F113_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F113_reg.tif\n",
      "65531\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F113.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65510\n",
      "FOV 114\n",
      "image max: 1235\n",
      "Appending...  reg_Cyc_1/Cycle_1_F114_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F114_reg.tif\n",
      "65535\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",
      "65535\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F114_reg.tif\n",
      "65530\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F114.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 115\n",
      "image max: 4111\n",
      "Appending...  reg_Cyc_1/Cycle_1_F115_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F115_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F115_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F115_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F115_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F115_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F115_reg.tif\n",
      "65529\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F115.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 116\n",
      "image max: 2330\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",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F116_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F116_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 63690\n",
      "FOV 117\n",
      "image max: 5046\n",
      "Appending...  reg_Cyc_1/Cycle_1_F117_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F117_reg.tif\n",
      "65530\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",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F117_reg.tif\n",
      "65535\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",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 55569\n",
      "FOV 118\n",
      "image max: 9050\n",
      "Appending...  reg_Cyc_1/Cycle_1_F118_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F118_reg.tif\n",
      "65534\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",
      "65535\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 119\n",
      "image max: 2254\n",
      "Appending...  reg_Cyc_1/Cycle_1_F119_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F119_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F119_reg.tif\n",
      "65535\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 38083\n",
      "FOV 120\n",
      "image max: 917\n",
      "Appending...  reg_Cyc_1/Cycle_1_F120_reg.tif\n",
      "65535\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",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F120_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F120_reg.tif\n",
      "65527\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F120_reg.tif\n",
      "65527\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",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F120.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 121\n",
      "image max: 8050\n",
      "Appending...  reg_Cyc_1/Cycle_1_F121_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F121_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F121_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F121_reg.tif\n",
      "65527\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F121_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F121_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F121_reg.tif\n",
      "65528\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 122\n",
      "image max: 4975\n",
      "Appending...  reg_Cyc_1/Cycle_1_F122_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F122_reg.tif\n",
      "65535\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",
      "65533\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",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F122_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F122.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 40471\n",
      "FOV 123\n",
      "image max: 2242\n",
      "Appending...  reg_Cyc_1/Cycle_1_F123_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F123_reg.tif\n",
      "65532\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",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F123_reg.tif\n",
      "65535\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 124\n",
      "image max: 1545\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",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F124_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F124_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F124_reg.tif\n",
      "65535\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",
      "65531\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F124.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 125\n",
      "image max: 5162\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",
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      "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_F125_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F125_reg.tif\n",
      "65534\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_F125_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F125_reg.tif\n",
      "33021\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F125.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 33021\n",
      "FOV 126\n",
      "image max: 1384\n",
      "Appending...  reg_Cyc_1/Cycle_1_F126_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F126_reg.tif\n",
      "65535\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",
      "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",
      "65535\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_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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 127\n",
      "image max: 3113\n",
      "Appending...  reg_Cyc_1/Cycle_1_F127_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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      "Shape = (13, 20, 2048, 2048)\n",
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      "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_F127_reg.tif\n",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F127.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 33329\n",
      "FOV 128\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F128_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65531\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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      "Appending...  reg_Cyc_9/Cycle_9_F128_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F128.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 129\n",
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      "Shape = (13, 8, 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_F129_reg.tif\n",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F129.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65429\n",
      "FOV 130\n",
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      "Appending...  reg_Cyc_1/Cycle_1_F130_reg.tif\n",
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      "Shape = (13, 8, 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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      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F130_reg.tif\n",
      "65529\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_F130_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F130.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65420\n",
      "FOV 131\n",
      "image max: 1322\n",
      "Appending...  reg_Cyc_1/Cycle_1_F131_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",
      "Appending...  reg_Cyc_3/Cycle_3_F131_reg.tif\n",
      "65535\n",
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      "Appending...  reg_Cyc_4/Cycle_4_F131_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F131_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F131_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F131_reg.tif\n",
      "65527\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F131_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F131_reg.tif\n",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F131.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 132\n",
      "image max: 1440\n",
      "Appending...  reg_Cyc_1/Cycle_1_F132_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "65531\n",
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      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F132_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
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      "Shape = (13, 28, 2048, 2048)\n",
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      "65527\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F132_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F132_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F132.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
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      "image max: 65150\n",
      "FOV 133\n",
      "image max: 3393\n",
      "Appending...  reg_Cyc_1/Cycle_1_F133_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",
      "Appending...  reg_Cyc_4/Cycle_4_F133_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F133_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F133_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F133_reg.tif\n",
      "65533\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 134\n",
      "image max: 4664\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F134_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F134_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F134_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F134.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 33181\n",
      "FOV 135\n",
      "image max: 2038\n",
      "Appending...  reg_Cyc_1/Cycle_1_F135_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F135_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F135_reg.tif\n",
      "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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 136\n",
      "image max: 1698\n",
      "Appending...  reg_Cyc_1/Cycle_1_F136_reg.tif\n",
      "65535\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",
      "Appending...  reg_Cyc_3/Cycle_3_F136_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F136_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F136_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F136_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F136_reg.tif\n",
      "65529\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F136.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 45478\n",
      "FOV 137\n",
      "image max: 9428\n",
      "Appending...  reg_Cyc_1/Cycle_1_F137_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F137_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F137_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F137_reg.tif\n",
      "65530\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",
      "2033\n",
      "2021\n",
      "image max: 61234\n",
      "FOV 138\n",
      "image max: 14011\n",
      "Appending...  reg_Cyc_1/Cycle_1_F138_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F138_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F138_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F138_reg.tif\n",
      "65532\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",
      "65528\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",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F138.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65176\n",
      "FOV 139\n",
      "image max: 1329\n",
      "Appending...  reg_Cyc_1/Cycle_1_F139_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F139_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F139_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F139_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F139_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F139_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F139_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F139_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F139_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F139.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65494\n",
      "FOV 140\n",
      "image max: 10726\n",
      "Appending...  reg_Cyc_1/Cycle_1_F140_reg.tif\n",
      "65533\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",
      "65533\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",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 31783\n",
      "FOV 141\n",
      "image max: 4002\n",
      "Appending...  reg_Cyc_1/Cycle_1_F141_reg.tif\n",
      "65534\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",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F141_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F141_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F141_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F141_reg.tif\n",
      "65530\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F141.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 48486\n",
      "FOV 142\n",
      "image max: 1056\n",
      "Appending...  reg_Cyc_1/Cycle_1_F142_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F142_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F142_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F142_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F142_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F142_reg.tif\n",
      "65529\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 143\n",
      "image max: 8337\n",
      "Appending...  reg_Cyc_1/Cycle_1_F143_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F143_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F143_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F143_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F143_reg.tif\n",
      "65535\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 34557\n",
      "FOV 145\n",
      "image max: 1238\n",
      "Appending...  reg_Cyc_1/Cycle_1_F145_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F145_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F145_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F145_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F145_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F145_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F145_reg.tif\n",
      "65529\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F145.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 32040\n",
      "FOV 148\n",
      "image max: 4322\n",
      "Appending...  reg_Cyc_1/Cycle_1_F148_reg.tif\n",
      "65533\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",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F148_reg.tif\n",
      "65527\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",
      "65528\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",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F148.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 45183\n",
      "FOV 149\n",
      "image max: 2100\n",
      "Appending...  reg_Cyc_1/Cycle_1_F149_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F149_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F149_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F149_reg.tif\n",
      "65534\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F149_reg.tif\n",
      "65527\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 150\n",
      "image max: 9441\n",
      "Appending...  reg_Cyc_1/Cycle_1_F150_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F150_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F150_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F150_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F150_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F150_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F150_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F150_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F150_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F150.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65492\n",
      "FOV 151\n",
      "image max: 2286\n",
      "Appending...  reg_Cyc_1/Cycle_1_F151_reg.tif\n",
      "65535\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",
      "65535\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 53851\n",
      "FOV 152\n",
      "image max: 4925\n",
      "Appending...  reg_Cyc_1/Cycle_1_F152_reg.tif\n",
      "65533\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",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F152_reg.tif\n",
      "65530\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",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F152_reg.tif\n",
      "65529\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",
      "2033\n",
      "2021\n",
      "image max: 65522\n",
      "FOV 153\n",
      "image max: 1192\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",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F153_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F153_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F153_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F153_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F153_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 35412\n",
      "FOV 154\n",
      "image max: 1611\n",
      "Appending...  reg_Cyc_1/Cycle_1_F154_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F154_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F154_reg.tif\n",
      "65528\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",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F154_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F154_reg.tif\n",
      "65530\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F154.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 155\n",
      "image max: 1737\n",
      "Appending...  reg_Cyc_1/Cycle_1_F155_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F155_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F155_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F155_reg.tif\n",
      "65535\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",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F155.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 55737\n",
      "FOV 156\n",
      "image max: 2296\n",
      "Appending...  reg_Cyc_1/Cycle_1_F156_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F156_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F156.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 31765\n",
      "FOV 157\n",
      "image max: 1037\n",
      "Appending...  reg_Cyc_1/Cycle_1_F157_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F157_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F157_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F157_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F157_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 32664\n",
      "FOV 158\n",
      "image max: 2036\n",
      "Appending...  reg_Cyc_1/Cycle_1_F158_reg.tif\n",
      "65535\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",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F158_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F158_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F158_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F158_reg.tif\n",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 159\n",
      "image max: 21438\n",
      "Appending...  reg_Cyc_1/Cycle_1_F159_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F159_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F159_reg.tif\n",
      "65531\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",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 37302\n",
      "FOV 161\n",
      "image max: 1665\n",
      "Appending...  reg_Cyc_1/Cycle_1_F161_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F161_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F161_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F161_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F161_reg.tif\n",
      "65532\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",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F161.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 162\n",
      "image max: 884\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",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F162_reg.tif\n",
      "65535\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",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F162_reg.tif\n",
      "65534\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",
      "2033\n",
      "2021\n",
      "image max: 65519\n",
      "FOV 163\n",
      "image max: 1496\n",
      "Appending...  reg_Cyc_1/Cycle_1_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F163_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F163.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 35956\n",
      "FOV 164\n",
      "image max: 902\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",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F164_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F164_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F164_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F164_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F164_reg.tif\n",
      "65529\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F164.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 165\n",
      "image max: 1077\n",
      "Appending...  reg_Cyc_1/Cycle_1_F165_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F165_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F165_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F165_reg.tif\n",
      "65529\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F165_reg.tif\n",
      "65529\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",
      "2033\n",
      "2021\n",
      "image max: 65429\n",
      "FOV 166\n",
      "image max: 837\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F166_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F166_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F166_reg.tif\n",
      "65530\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",
      "65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F166.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 34759\n",
      "FOV 167\n",
      "image max: 997\n",
      "Appending...  reg_Cyc_1/Cycle_1_F167_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F167_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F167_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F167_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F167_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F167_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F167_reg.tif\n",
      "65535\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F167.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 32659\n",
      "FOV 168\n",
      "image max: 1853\n",
      "Appending...  reg_Cyc_1/Cycle_1_F168_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F168_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F168_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F168_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F168_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F168_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F168_reg.tif\n",
      "65530\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F168.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 169\n",
      "image max: 1774\n",
      "Appending...  reg_Cyc_1/Cycle_1_F169_reg.tif\n",
      "65534\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",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F169_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F169_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F169_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F169_reg.tif\n",
      "65531\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F169.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 170\n",
      "image max: 1386\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",
      "65535\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",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F170_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 171\n",
      "image max: 1687\n",
      "Appending...  reg_Cyc_1/Cycle_1_F171_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F171_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F171_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F171_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F171_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F171_reg.tif\n",
      "65533\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",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F171.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65533\n",
      "FOV 172\n",
      "image max: 1083\n",
      "Appending...  reg_Cyc_1/Cycle_1_F172_reg.tif\n",
      "65534\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",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F172_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F172_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F172_reg.tif\n",
      "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_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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 173\n",
      "image max: 831\n",
      "Appending...  reg_Cyc_1/Cycle_1_F173_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F173_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F173_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F173_reg.tif\n",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F173_reg.tif\n",
      "65535\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",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F173.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 174\n",
      "image max: 1407\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",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F174_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F174_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F174_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F174_reg.tif\n",
      "65528\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",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F174.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 175\n",
      "image max: 10378\n",
      "Appending...  reg_Cyc_1/Cycle_1_F175_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F175_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F175_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F175_reg.tif\n",
      "24327\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F175_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F175_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F175_reg.tif\n",
      "65530\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",
      "2033\n",
      "2021\n",
      "image max: 47842\n",
      "FOV 176\n",
      "image max: 5807\n",
      "Appending...  reg_Cyc_1/Cycle_1_F176_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F176_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F176_reg.tif\n",
      "65529\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F176.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 43263\n",
      "FOV 177\n",
      "image max: 3515\n",
      "Appending...  reg_Cyc_1/Cycle_1_F177_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F177_reg.tif\n",
      "65534\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",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F177_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F177_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F177_reg.tif\n",
      "65528\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",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F177.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 43676\n",
      "FOV 178\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F178_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F178_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F178_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F178_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F178_reg.tif\n",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F178_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F178_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F178_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F178_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F178.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 179\n",
      "image max: 966\n",
      "Appending...  reg_Cyc_1/Cycle_1_F179_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F179_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F179_reg.tif\n",
      "65533\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",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F179_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 180\n",
      "image max: 1058\n",
      "Appending...  reg_Cyc_1/Cycle_1_F180_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F180_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F180_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F180_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F180_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F180_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F180_reg.tif\n",
      "65533\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",
      "65529\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F180.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 181\n",
      "image max: 1353\n",
      "Appending...  reg_Cyc_1/Cycle_1_F181_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F181_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F181_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F181_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F181_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F181_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F181_reg.tif\n",
      "65529\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F181.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 48036\n",
      "FOV 182\n",
      "image max: 1505\n",
      "Appending...  reg_Cyc_1/Cycle_1_F182_reg.tif\n",
      "65529\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F182_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F182_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F182_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F182_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F182_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F182_reg.tif\n",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 42208\n",
      "FOV 183\n",
      "image max: 3626\n",
      "Appending...  reg_Cyc_1/Cycle_1_F183_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F183_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F183_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F183_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F183_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F183_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F183_reg.tif\n",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 42947\n",
      "FOV 184\n",
      "image max: 17891\n",
      "Appending...  reg_Cyc_1/Cycle_1_F184_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F184_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F184_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F184_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F184_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F184_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F184_reg.tif\n",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 185\n",
      "image max: 949\n",
      "Appending...  reg_Cyc_1/Cycle_1_F185_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F185_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F185_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F185_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F185_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F185_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F185_reg.tif\n",
      "65534\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",
      "2033\n",
      "2021\n",
      "image max: 42877\n",
      "FOV 186\n",
      "image max: 2112\n",
      "Appending...  reg_Cyc_1/Cycle_1_F186_reg.tif\n",
      "65530\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F186_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F186_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F186_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F186_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F186_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F186_reg.tif\n",
      "65534\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F186.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 187\n",
      "image max: 1101\n",
      "Appending...  reg_Cyc_1/Cycle_1_F187_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F187_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F187_reg.tif\n",
      "65530\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F187_reg.tif\n",
      "65528\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F187.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65523\n",
      "FOV 188\n",
      "image max: 1179\n",
      "Appending...  reg_Cyc_1/Cycle_1_F188_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F188_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F188_reg.tif\n",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F188_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F188_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F188_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F188_reg.tif\n",
      "65532\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",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F188.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 48541\n",
      "FOV 189\n",
      "image max: 1802\n",
      "Appending...  reg_Cyc_1/Cycle_1_F189_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F189_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F189_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F189_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F189_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F189_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F189.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 190\n",
      "image max: 1264\n",
      "Appending...  reg_Cyc_1/Cycle_1_F190_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F190_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F190_reg.tif\n",
      "65531\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F190_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F190_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F190_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F190_reg.tif\n",
      "65527\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F190.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 45106\n",
      "FOV 191\n",
      "image max: 922\n",
      "Appending...  reg_Cyc_1/Cycle_1_F191_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F191_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F191_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F191_reg.tif\n",
      "65535\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",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F191_reg.tif\n",
      "65531\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",
      "65531\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F191.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 192\n",
      "image max: 1326\n",
      "Appending...  reg_Cyc_1/Cycle_1_F192_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F192_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F192_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F192_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F192_reg.tif\n",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F192_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F192_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F192_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F192_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F192.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 34518\n",
      "FOV 193\n",
      "image max: 1106\n",
      "Appending...  reg_Cyc_1/Cycle_1_F193_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F193_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F193_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F193_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F193_reg.tif\n",
      "65535\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",
      "65534\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F193.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 36348\n",
      "FOV 194\n",
      "image max: 903\n",
      "Appending...  reg_Cyc_1/Cycle_1_F194_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F194_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F194_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F194_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F194_reg.tif\n",
      "65529\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",
      "65535\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",
      "65532\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F194.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 195\n",
      "image max: 2380\n",
      "Appending...  reg_Cyc_1/Cycle_1_F195_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F195_reg.tif\n",
      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F195_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F195_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F195_reg.tif\n",
      "65533\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",
      "65531\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F195.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 196\n",
      "image max: 2294\n",
      "Appending...  reg_Cyc_1/Cycle_1_F196_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F196_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F196_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F196_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F196_reg.tif\n",
      "65529\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",
      "65529\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",
      "2033\n",
      "2021\n",
      "image max: 63257\n",
      "FOV 197\n",
      "image max: 895\n",
      "Appending...  reg_Cyc_1/Cycle_1_F197_reg.tif\n",
      "65533\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",
      "65529\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F197_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F197_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F197_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F197_reg.tif\n",
      "65527\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",
      "2033\n",
      "2021\n",
      "image max: 52494\n",
      "FOV 198\n",
      "image max: 5437\n",
      "Appending...  reg_Cyc_1/Cycle_1_F198_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F198_reg.tif\n",
      "65534\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",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F198_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F198_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F198_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 199\n",
      "image max: 1902\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",
      "65530\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",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F199_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F199_reg.tif\n",
      "65533\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F199.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 56059\n",
      "FOV 200\n",
      "image max: 1249\n",
      "Appending...  reg_Cyc_1/Cycle_1_F200_reg.tif\n",
      "65534\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",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F200_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F200_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F200_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F200_reg.tif\n",
      "65531\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 201\n",
      "image max: 1767\n",
      "Appending...  reg_Cyc_1/Cycle_1_F201_reg.tif\n",
      "65531\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F201_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F201_reg.tif\n",
      "65534\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",
      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F201_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F201_reg.tif\n",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 55506\n",
      "FOV 202\n",
      "image max: 1429\n",
      "Appending...  reg_Cyc_1/Cycle_1_F202_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F202_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F202_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F202_reg.tif\n",
      "65535\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",
      "65535\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",
      "2033\n",
      "2021\n",
      "image max: 51905\n",
      "FOV 203\n",
      "image max: 1253\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",
      "65530\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",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F203_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F203_reg.tif\n",
      "65528\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F203.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 204\n",
      "image max: 2235\n",
      "Appending...  reg_Cyc_1/Cycle_1_F204_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F204_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F204_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F204_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F204_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F204_reg.tif\n",
      "65532\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F204.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65525\n",
      "FOV 205\n",
      "image max: 1052\n",
      "Appending...  reg_Cyc_1/Cycle_1_F205_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F205_reg.tif\n",
      "65529\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F205_reg.tif\n",
      "65528\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F205_reg.tif\n",
      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F205_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F205_reg.tif\n",
      "65531\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F205_reg.tif\n",
      "65532\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",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 206\n",
      "image max: 1627\n",
      "Appending...  reg_Cyc_1/Cycle_1_F206_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F206_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F206_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F206_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F206_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F206_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F206_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F206_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F206_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F206.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 60003\n",
      "FOV 207\n",
      "image max: 1648\n",
      "Appending...  reg_Cyc_1/Cycle_1_F207_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F207_reg.tif\n",
      "65532\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",
      "Appending...  reg_Cyc_5/Cycle_5_F207_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F207_reg.tif\n",
      "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_F207_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F207_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F207.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65532\n",
      "FOV 208\n",
      "image max: 2100\n",
      "Appending...  reg_Cyc_1/Cycle_1_F208_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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      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F208_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65529\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",
      "2033\n",
      "2021\n",
      "image max: 47025\n",
      "FOV 209\n",
      "image max: 2781\n",
      "Appending...  reg_Cyc_1/Cycle_1_F209_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
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      "Shape = (13, 16, 2048, 2048)\n",
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      "65533\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65531\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",
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      "65530\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_F209_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F209.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 41993\n",
      "FOV 210\n",
      "image max: 988\n",
      "Appending...  reg_Cyc_1/Cycle_1_F210_reg.tif\n",
      "65533\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",
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      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
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      "65531\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F210_reg.tif\n",
      "65534\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F210.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 43983\n",
      "FOV 211\n",
      "image max: 1259\n",
      "Appending...  reg_Cyc_1/Cycle_1_F211_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65534\n",
      "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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      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
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      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
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      "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",
      "2033\n",
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      "image max: 65535\n",
      "FOV 212\n",
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      "Shape = (13, 8, 2048, 2048)\n",
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      "Shape = (13, 20, 2048, 2048)\n",
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      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F212_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",
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      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F212_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F212.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65530\n",
      "FOV 213\n",
      "image max: 1470\n",
      "Appending...  reg_Cyc_1/Cycle_1_F213_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
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      "65534\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F213_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F213_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F213_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F213_reg.tif\n",
      "65530\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F213_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F213_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F213_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F213.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 214\n",
      "image max: 1705\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",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F214_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F214_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F214_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F214_reg.tif\n",
      "65528\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F214_reg.tif\n",
      "65531\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F214.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 215\n",
      "image max: 977\n",
      "Appending...  reg_Cyc_1/Cycle_1_F215_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F215_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F215_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F215_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F215_reg.tif\n",
      "65533\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F215_reg.tif\n",
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      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F215_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F215_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F215_reg.tif\n",
      "65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F215.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 216\n",
      "image max: 2102\n",
      "Appending...  reg_Cyc_1/Cycle_1_F216_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F216_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F216_reg.tif\n",
      "65534\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F216_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F216_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F216_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F216_reg.tif\n",
      "65531\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F216.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 45552\n",
      "FOV 217\n",
      "image max: 1595\n",
      "Appending...  reg_Cyc_1/Cycle_1_F217_reg.tif\n",
      "65532\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F217_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F217_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F217_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F217_reg.tif\n",
      "65534\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",
      "65533\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F217.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 52864\n",
      "FOV 218\n",
      "image max: 1355\n",
      "Appending...  reg_Cyc_1/Cycle_1_F218_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F218_reg.tif\n",
      "65532\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F218_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F218_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F218_reg.tif\n",
      "65528\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F218_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F218_reg.tif\n",
      "65531\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F218.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 219\n",
      "image max: 8166\n",
      "Appending...  reg_Cyc_1/Cycle_1_F219_reg.tif\n",
      "65533\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F219_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F219_reg.tif\n",
      "65532\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F219_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F219_reg.tif\n",
      "65532\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F219_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F219_reg.tif\n",
      "65528\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F219_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F219_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F219.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 46748\n",
      "FOV 220\n",
      "image max: 897\n",
      "Appending...  reg_Cyc_1/Cycle_1_F220_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F220_reg.tif\n",
      "65530\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F220_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F220_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F220_reg.tif\n",
      "65532\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",
      "65528\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F220.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 38927\n",
      "FOV 221\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F221_reg.tif\n",
      "65535\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",
      "65535\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",
      "65529\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F221_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F221_reg.tif\n",
      "65532\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F221.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 222\n",
      "image max: 27647\n",
      "Appending...  reg_Cyc_1/Cycle_1_F222_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F222_reg.tif\n",
      "65533\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F222_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F222_reg.tif\n",
      "65527\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",
      "65535\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",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F222.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 44765\n",
      "FOV 223\n",
      "image max: 65535\n",
      "Appending...  reg_Cyc_1/Cycle_1_F223_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F223_reg.tif\n",
      "65535\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",
      "65528\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F223_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F223_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F223_reg.tif\n",
      "65528\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",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F223.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 65535\n",
      "FOV 224\n",
      "image max: 4510\n",
      "Appending...  reg_Cyc_1/Cycle_1_F224_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F224_reg.tif\n",
      "65531\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F224_reg.tif\n",
      "65530\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F224_reg.tif\n",
      "65529\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F224_reg.tif\n",
      "65530\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F224_reg.tif\n",
      "65529\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F224_reg.tif\n",
      "65535\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",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F224.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2033\n",
      "2021\n",
      "image max: 37477\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",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 001\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 002\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 003\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 004\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 005\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 007\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 008\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 011\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 012\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 013\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 014\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 015\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 016\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 017\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 019\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 020\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 021\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 022\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 023\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 024\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 025\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 026\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
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      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 027\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 028\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 029\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 030\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 031\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 032\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 033\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 034\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 035\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 036\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 037\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 038\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 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",
      "7\n",
      "8\n",
      "9\n",
      "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",
      "9\n",
      "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",
      "8\n",
      "9\n",
      "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",
      "8\n",
      "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",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 048\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 049\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 050\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 051\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 052\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 053\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 054\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 055\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 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"
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   "metadata": {},
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    {
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     "execution_count": 15,
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   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 17,
     "metadata": {},
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    "len(one_to_five_list)"
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   "metadata": {},
   "outputs": [
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     "data": {
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     "execution_count": 18,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
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   "metadata": {},
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       " ('021', 1, 32),\n",
       " ('021', 1, 33),\n",
       " ('021', 2, 12),\n",
       " ('021', 2, 32),\n",
       " ('021', 2, 33),\n",
       " ('021', 3, 12),\n",
       " ('021', 3, 32),\n",
       " ('021', 3, 33),\n",
       " ('021', 4, 12),\n",
       " ('021', 4, 32),\n",
       " ('021', 4, 33),\n",
       " ('021', 5, 12),\n",
       " ('021', 5, 32),\n",
       " ('021', 5, 33),\n",
       " ('021', 6, 12),\n",
       " ('021', 6, 32),\n",
       " ('021', 6, 33),\n",
       " ('021', 7, 12),\n",
       " ('021', 7, 32),\n",
       " ('021', 7, 33),\n",
       " ('021', 8, 32),\n",
       " ('021', 8, 33),\n",
       " ('021', 9, 32),\n",
       " ('021', 9, 33),\n",
       " ('021', 10, 32),\n",
       " ('021', 10, 33),\n",
       " ('021', 11, 32),\n",
       " ('021', 11, 33),\n",
       " ('021', 12, 32),\n",
       " ('021', 12, 33),\n",
       " ('022', 0, 32),\n",
       " ('022', 0, 33),\n",
       " ('022', 0, 34),\n",
       " ('022', 1, 32),\n",
       " ('022', 1, 33),\n",
       " ('022', 1, 34),\n",
       " ('022', 2, 32),\n",
       " ('022', 2, 33),\n",
       " ('022', 2, 34),\n",
       " ('022', 3, 32),\n",
       " ('022', 3, 33),\n",
       " ('022', 3, 34),\n",
       " ('022', 4, 32),\n",
       " ('022', 4, 33),\n",
       " ('022', 4, 34),\n",
       " ('022', 5, 32),\n",
       " ('022', 5, 33),\n",
       " ('022', 5, 34),\n",
       " ('022', 6, 32),\n",
       " ('022', 6, 33),\n",
       " ('022', 6, 34),\n",
       " ('022', 7, 32),\n",
       " ('022', 7, 33),\n",
       " ('022', 7, 34),\n",
       " ('022', 8, 32),\n",
       " ('022', 8, 33),\n",
       " ('022', 9, 32),\n",
       " ('022', 9, 33),\n",
       " ('022', 10, 32),\n",
       " ('022', 10, 33),\n",
       " ('022', 11, 32),\n",
       " ('022', 11, 33),\n",
       " ('022', 12, 32),\n",
       " ('022', 12, 33),\n",
       " ('023', 0, 32),\n",
       " ('023', 0, 33),\n",
       " ('023', 1, 32),\n",
       " ('023', 1, 33),\n",
       " ('023', 2, 32),\n",
       " ('023', 2, 33),\n",
       " ('023', 3, 32),\n",
       " ('023', 3, 33),\n",
       " ('023', 4, 32),\n",
       " ('023', 4, 33),\n",
       " ('023', 5, 32),\n",
       " ('023', 5, 33),\n",
       " ('023', 6, 32),\n",
       " ('023', 6, 33),\n",
       " ('023', 7, 32),\n",
       " ('023', 7, 33),\n",
       " ('023', 8, 32),\n",
       " ('023', 8, 33),\n",
       " ('023', 9, 32),\n",
       " ('023', 9, 33),\n",
       " ('023', 10, 32),\n",
       " ('023', 10, 33),\n",
       " ('023', 11, 32),\n",
       " ('023', 11, 33),\n",
       " ('023', 12, 32),\n",
       " ('023', 12, 33),\n",
       " ('024', 0, 32),\n",
       " ('024', 0, 33),\n",
       " ('024', 1, 32),\n",
       " ('024', 1, 33),\n",
       " ('024', 2, 32),\n",
       " ('024', 2, 33),\n",
       " ('024', 3, 32),\n",
       " ('024', 3, 33),\n",
       " ('024', 4, 32),\n",
       " ('024', 4, 33),\n",
       " ('024', 5, 32),\n",
       " ('024', 5, 33),\n",
       " ('024', 6, 32),\n",
       " ('024', 6, 33),\n",
       " ('024', 7, 32),\n",
       " ('024', 7, 33),\n",
       " ('024', 8, 32),\n",
       " ('024', 8, 33),\n",
       " ('024', 9, 32),\n",
       " ('024', 9, 33),\n",
       " ('024', 10, 32),\n",
       " ('024', 10, 33),\n",
       " ('024', 11, 32),\n",
       " ('024', 11, 33),\n",
       " ('024', 12, 32),\n",
       " ('024', 12, 33),\n",
       " ('025', 0, 32),\n",
       " ('025', 0, 33),\n",
       " ('025', 1, 32),\n",
       " ('025', 1, 33),\n",
       " ('025', 2, 32),\n",
       " ('025', 2, 33),\n",
       " ('025', 3, 32),\n",
       " ('025', 3, 33),\n",
       " ('025', 4, 32),\n",
       " ('025', 4, 33),\n",
       " ('025', 5, 32),\n",
       " ('025', 5, 33),\n",
       " ('025', 6, 32),\n",
       " ('025', 6, 33),\n",
       " ('025', 7, 32),\n",
       " ('025', 7, 33),\n",
       " ('025', 7, 35),\n",
       " ('025', 8, 32),\n",
       " ('025', 8, 33),\n",
       " ('025', 8, 35),\n",
       " ('025', 9, 32),\n",
       " ('025', 9, 33),\n",
       " ('025', 9, 35),\n",
       " ('025', 10, 32),\n",
       " ('025', 10, 33),\n",
       " ('025', 10, 35),\n",
       " ('025', 11, 32),\n",
       " ('025', 11, 33),\n",
       " ('025', 12, 32),\n",
       " ('025', 12, 33),\n",
       " ('026', 0, 20),\n",
       " ('026', 0, 32),\n",
       " ('026', 0, 33),\n",
       " ('026', 1, 20),\n",
       " ('026', 1, 32),\n",
       " ('026', 1, 33),\n",
       " ('026', 2, 20),\n",
       " ('026', 2, 32),\n",
       " ('026', 2, 33),\n",
       " ('026', 3, 20),\n",
       " ('026', 3, 32),\n",
       " ('026', 3, 33),\n",
       " ('026', 4, 20),\n",
       " ('026', 4, 32),\n",
       " ('026', 4, 33),\n",
       " ('026', 5, 20),\n",
       " ('026', 5, 32),\n",
       " ('026', 5, 33),\n",
       " ('026', 6, 20),\n",
       " ('026', 6, 32),\n",
       " ('026', 6, 33),\n",
       " ('026', 7, 20),\n",
       " ('026', 7, 32),\n",
       " ('026', 7, 33),\n",
       " ('026', 8, 32),\n",
       " ('026', 8, 33),\n",
       " ('026', 9, 32),\n",
       " ('026', 9, 33),\n",
       " ('026', 10, 32),\n",
       " ('026', 10, 33),\n",
       " ('026', 11, 32),\n",
       " ('026', 11, 33),\n",
       " ('026', 12, 32),\n",
       " ('026', 12, 33),\n",
       " ('027', 0, 30),\n",
       " ('027', 0, 32),\n",
       " ('027', 0, 33),\n",
       " ('027', 1, 30),\n",
       " ('027', 1, 32),\n",
       " ('027', 1, 33),\n",
       " ('027', 2, 30),\n",
       " ('027', 2, 32),\n",
       " ('027', 2, 33),\n",
       " ('027', 3, 30),\n",
       " ('027', 3, 32),\n",
       " ('027', 3, 33),\n",
       " ('027', 4, 30),\n",
       " ('027', 4, 32),\n",
       " ('027', 4, 33),\n",
       " ('027', 5, 30),\n",
       " ('027', 5, 32),\n",
       " ('027', 5, 33),\n",
       " ('027', 6, 30),\n",
       " ('027', 6, 32),\n",
       " ('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', 8, 32),\n",
       " ('029', 9, 32),\n",
       " ('029', 10, 32),\n",
       " ('029', 11, 32),\n",
       " ('029', 12, 32),\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', 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",
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    {
     "data": {
      "text/plain": [
       "['000',\n",
       " '001',\n",
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       " '065',\n",
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       " '072',\n",
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       " '078',\n",
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       " '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": {
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       " ('017', 6, 13),\n",
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       " ('019', 8, 4),\n",
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       " ('019', 9, 4),\n",
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       " ('020', 1, 31),\n",
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       " ('020', 7, 31),\n",
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       " ('020', 9, 35),\n",
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       " ('021', 5, 33),\n",
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       " ('021', 7, 12),\n",
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       " ('021', 8, 32),\n",
       " ('021', 8, 33),\n",
       " ('021', 9, 32),\n",
       " ('021', 9, 33),\n",
       " ('021', 10, 32),\n",
       " ('021', 10, 33),\n",
       " ('021', 11, 32),\n",
       " ('021', 11, 33),\n",
       " ('021', 12, 32),\n",
       " ('021', 12, 33),\n",
       " ('022', 0, 32),\n",
       " ('022', 0, 33),\n",
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       " ('022', 1, 32),\n",
       " ('022', 1, 33),\n",
       " ('022', 1, 34),\n",
       " ('022', 2, 32),\n",
       " ('022', 2, 33),\n",
       " ('022', 2, 34),\n",
       " ('022', 3, 32),\n",
       " ('022', 3, 33),\n",
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       " ('022', 6, 33),\n",
       " ('022', 6, 34),\n",
       " ('022', 7, 32),\n",
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       " ('022', 7, 34),\n",
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       " ('022', 9, 32),\n",
       " ('022', 9, 33),\n",
       " ('022', 10, 32),\n",
       " ('022', 10, 33),\n",
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       " ('022', 11, 33),\n",
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       " ('022', 12, 33),\n",
       " ('023', 0, 32),\n",
       " ('023', 0, 33),\n",
       " ('023', 1, 32),\n",
       " ('023', 1, 33),\n",
       " ('023', 2, 32),\n",
       " ('023', 2, 33),\n",
       " ('023', 3, 32),\n",
       " ('023', 3, 33),\n",
       " ('023', 4, 32),\n",
       " ('023', 4, 33),\n",
       " ('023', 5, 32),\n",
       " ('023', 5, 33),\n",
       " ('023', 6, 32),\n",
       " ('023', 6, 33),\n",
       " ('023', 7, 32),\n",
       " ('023', 7, 33),\n",
       " ('023', 8, 32),\n",
       " ('023', 8, 33),\n",
       " ('023', 9, 32),\n",
       " ('023', 9, 33),\n",
       " ('023', 10, 32),\n",
       " ('023', 10, 33),\n",
       " ('023', 11, 32),\n",
       " ('023', 11, 33),\n",
       " ('023', 12, 32),\n",
       " ('023', 12, 33),\n",
       " ('024', 0, 32),\n",
       " ('024', 0, 33),\n",
       " ('024', 1, 32),\n",
       " ('024', 1, 33),\n",
       " ('024', 2, 32),\n",
       " ('024', 2, 33),\n",
       " ('024', 3, 32),\n",
       " ('024', 3, 33),\n",
       " ('024', 4, 32),\n",
       " ('024', 4, 33),\n",
       " ('024', 5, 32),\n",
       " ('024', 5, 33),\n",
       " ('024', 6, 32),\n",
       " ('024', 6, 33),\n",
       " ('024', 7, 32),\n",
       " ('024', 7, 33),\n",
       " ('024', 8, 32),\n",
       " ('024', 8, 33),\n",
       " ('024', 9, 32),\n",
       " ('024', 9, 33),\n",
       " ('024', 10, 32),\n",
       " ('024', 10, 33),\n",
       " ('024', 11, 32),\n",
       " ('024', 11, 33),\n",
       " ('024', 12, 32),\n",
       " ('024', 12, 33),\n",
       " ('025', 0, 32),\n",
       " ('025', 0, 33),\n",
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       " ('025', 1, 33),\n",
       " ('025', 2, 32),\n",
       " ('025', 2, 33),\n",
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       " ('025', 3, 33),\n",
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       " ('025', 4, 33),\n",
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       " ('025', 5, 33),\n",
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       " ('025', 12, 33),\n",
       " ('026', 0, 20),\n",
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       " ('026', 5, 32),\n",
       " ('026', 5, 33),\n",
       " ('026', 6, 20),\n",
       " ('026', 6, 32),\n",
       " ('026', 6, 33),\n",
       " ('026', 7, 20),\n",
       " ('026', 7, 32),\n",
       " ('026', 7, 33),\n",
       " ('026', 8, 32),\n",
       " ('026', 8, 33),\n",
       " ('026', 9, 32),\n",
       " ('026', 9, 33),\n",
       " ('026', 10, 32),\n",
       " ('026', 10, 33),\n",
       " ('026', 11, 32),\n",
       " ('026', 11, 33),\n",
       " ('026', 12, 32),\n",
       " ('026', 12, 33),\n",
       " ('027', 0, 30),\n",
       " ('027', 0, 32),\n",
       " ('027', 0, 33),\n",
       " ('027', 1, 30),\n",
       " ('027', 1, 32),\n",
       " ('027', 1, 33),\n",
       " ('027', 2, 30),\n",
       " ('027', 2, 32),\n",
       " ('027', 2, 33),\n",
       " ('027', 3, 30),\n",
       " ('027', 3, 32),\n",
       " ('027', 3, 33),\n",
       " ('027', 4, 30),\n",
       " ('027', 4, 32),\n",
       " ('027', 4, 33),\n",
       " ('027', 5, 30),\n",
       " ('027', 5, 32),\n",
       " ('027', 5, 33),\n",
       " ('027', 6, 30),\n",
       " ('027', 6, 32),\n",
       " ('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))"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "outputs": [],
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   "execution_count": null,
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
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   "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",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>3013</td>\n",
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      ],
      "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": [
    {
     "data": {
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       "      <td>002</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>224</td>\n",
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       "      <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": [
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       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
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       "      <td>004</td>\n",
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       "      <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",
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       "    <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": {
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       " '214']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circle_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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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\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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DwNSpU5k8eTKZmZm4OxkZGbz66qunrDtgwAA2bNhAVlYWZ5xxBoMGDeK+++6rgndLRGoSjedew/Xs2ZMZM2aQlVXikM0Jo/dbTjfR47nvyBh00rJU+YWqxnMXETnNqFumhpg4cSKrV68+qWzSpEmsWLEiOQGJSEpTcq8hHn/88WSHICIhom4ZEZEQUnIXEQkhJXcRkRBKmT73JzY8kdDtTeg4IaHbExGpSdRyL0d547mvXLmSzp07k5aWxsKFC5MQoYjIqZTcyxDLeO7Nmzdn7ty5jBw5slriERGJhZJ7GWIZzz0jI4PMzEy+9a3y38oVK1bQo0cPhgwZQtu2bbn11ls5ceIEAMuWLaNbt2507tyZoUOH8uWXXxZtf9q0aVx55ZW8+OKLbNu2jauuuooOHTrQuXNntm/fnvgDF5GUp+RehpLGc6/smOyFcnJyePDBB3nvvffYvn07L7/8MgcOHOBXv/oVy5cv5y9/+QtZWVk89NBDRevUqVOHVatWMXz4cG644QYmTpzIX//6V95++22aNm0aVzwiEk4pc0E1GRI5Jnuh7OxsLrkkMm7FiBEjWLVqFXXq1GHz5s1cccUVAHz99dd069ataJ3rr78egC+++II9e/YwZMgQIJL0RURKUm5yN7M5wPeB/e7ePii7G7gFyA+q/cLdXwuW3QncDBwH/tXd/3TKRlNEIsdkL1Ta2Ox9+/Zl/vzij6SNKByfvSYM8iYiqSGWlvtc4DHg2WLlD7v7jOgCM2tL5MHZ7YBmwHIzu9Td474SmIxbF2MZz72icnJy2LFjBxdffDELFixg3LhxdO3alYkTJ7Jt2zZatmzJV199RV5eHpdeeulJ65577rmkp6ezaNEirrvuOo4ePcrx48eLnrIkIlIolgdkrwRiffDmtcAL7n7U3XcA24DKPaGiBogez71NmzYMGzasaDz3JUuWAPDuu++Snp7Oiy++yPjx42nXrl2Z2+zWrRtTpkyhffv2tGjRgiFDhtC4cWPmzp3LiBEjyMzMpGvXrnzwwQclrv/cc8/x6KOPkpmZSffu3fnkk08Sftwikvri6XO/zcxuBHKBn7r7Z8CFwJqoOnlBWcoaNGgQgwadPNbztGnTiqa7dOlCXl5ezNurV68eCxYsOKW8d+/evPvuu6eU79y586T5Vq1a8ec//znm/YnI6amyd8s8CXwb6AjsBR4Myku62lhiR7GZjTOzXDPLzc/PL6mKiIhUUqVa7u6+r3DazJ4GCp/vlgdcFFU1Hfi4lG3MAmZB5ElMlYmjpnrvvfcYNWrUSWVnnnkma9eupWfPnskJSkROK5VK7mbW1N33BrNDgE3B9BLg92b2EJELqq2AnLijTDGXXXYZGzZsSHYYInIai+VWyPlAT6CRmeUBvwR6mllHIl0uO4HxAO7+vpn9AdgMHAMmJuJOGRERqZhyk7u7jyiheHYZ9e8F7o0nKBERiY+GHxARCaGUGX4gf+ZjCd1e43+5LaHbExGpSVImuSdLRkYG55xzDrVq1SItLY3c3NxkhyQiUi4l9xi89dZbNGrUqMq2f+zYMdLSdCpEJHHU554gPXv2ZPLkyXTv3p327duTkxO5A7SgoICxY8fSpUsXOnXqVDQe/Ny5cxk6dCjXXHMN/fr1A+CBBx7gsssuo0OHDkyZMiVpxyIiqU/NxXKYGf369cPMGD9+POPGjSu1bkFBAW+//TYrV65k7NixbNq0iXvvvZfevXszZ84cDh06RHZ2NldddRUA77zzDhs3bqRhw4a8/vrrLFq0iLVr11KvXj0+/TTW4XxERE6l5F6O1atX06xZM/bv30/fvn35zne+Q48ePUqsO2JE5K7RHj16cPjwYQ4dOsSyZctYsmQJM2ZEBtA8cuQIu3btAqBv3740bNgQgOXLlzNmzJiiER4Ly0VEKkPJvRyF47c3adKEIUOGkJOTU2pyL22s9pdeeonWrVuftGzt2rVF47RDZKz2eB8EIiJSKGWSezJuXSwoKODEiROcc845FBQUsGzZMu66665S6y9YsIBevXqxatUq6tevT/369enfvz8zZ85k5syZmBnr16+nU6dOp6zbr18/pk2bxsiRI4u6ZdR6F5HKSpnkngz79u0reqTdsWPHGDlyJAMGDCi1foMGDejevTuHDx9mzpw5AEydOpXJkyeTmZmJu5ORkcGrr756yroDBgxgw4YNZGVlccYZZzBo0CDuu+++qjkwEQk9qwmPbsvKyvLi949v2bKFNm3aJCmiiuvZsyczZswgKysr2aFUSqq93yLxiv5h5I6Mk5/ZkH3NJdUdTqWY2Tp3LzHp6FZIEZEQUrdMBU2cOJHVq1efVDZp0iRWrFiRnIBEREqg5F5Bjz/+eLJDEBEpl7plRERCSMldRCSElNxFREIoZfrcc/74UUK3lyq3OomIVIZa7mUYO3YsTZo0oX379kVln376KX379qVVq1b07duXzz77LIkRioiUrNzkbmZzzGy/mW2KKvuNmX1gZhvN7BUzOy8ozzCz/zOzDcHrqaoMvqrddNNNLF269KSy6dOn06dPH7Zu3UqfPn2YPn16lezb3Tlx4kSVbFtEwi+WlvtcoPhv7t8A2rt7JvA34M6oZdvdvWPwujUxYSZHjx49ThnfZfHixYwePRqA0aNHs2jRolLXv/vuuxk1ahS9e/emVatWPP3000XLfvOb39ClSxcyMzP55S9/CcDOnTtp06YNEyZMoHPnzuzevZulS5fSuXNnOnToQJ8+fargKEUkjMrtc3f3lWaWUaxsWdTsGuBHiQ2r5tq3bx9NmzYFoGnTpuzfv7/M+hs3bmTNmjUUFBTQqVMnrr76ajZt2sTWrVvJycnB3Rk8eDArV66kefPmfPjhhzzzzDM88cQT5Ofnc8stt7By5UpatGihMd5FJGaJuKA6FlgQNd/CzNYDh4H/cPf/LWklMxsHjANo3rx5AsKoma699lrq1q1L3bp16dWrFzk5OaxatYply5YVjQ755ZdfsnXrVpo3b87FF19M165dAVizZg09evSgRYsWgMZ4F5HYxZXczezfgWPA74KivUBzdz9oZt8FFplZO3c/XHxdd58FzILIwGHxxFGdLrjgAvbu3UvTpk3Zu3cvTZo0KbN+aWO833nnnYwfP/6kZTt37tQY7yKSEJVO7mY2Gvg+0MeDoSXd/ShwNJheZ2bbgUuB3FI3FKOacuvi4MGDmTdvHlOmTGHevHlce+21ZdZfvHgxd955JwUFBaxYsYLp06dTt25dpk6dyg033MDZZ5/Nnj17qF279inrduvWjYkTJ7Jjx46ibhm13kUkFpVK7mY2ALgD+Cd3/yqqvDHwqbsfN7NLgFZAYm9Qr0YjRoxgxYoVHDhwgPT0dO655x6mTJnCsGHDmD17Ns2bN+fFF18scxvZ2dlcffXV7Nq1i6lTp9KsWTOaNWvGli1b6NatGwBnn302zz//PLVq1Tpp3caNGzNr1ix+8IMfcOLECZo0acIbb7xRZccrIuFRbnI3s/lAT6CRmeUBvyRyd8yZwBtBt8Ga4M6YHsA0MzsGHAdudfeUvQo4f/78EsvffPPNmLdx6aWXMmvWrFPKJ02axKRJk04p37Rp00nzAwcOZODAgTHvT0QEYrtbZkQJxbNLqfsS8FK8QYmISHxSZviBmuyZZ57hkUceOansiiuu0PDAIpI0NTq5p8rdImPGjGHMmDHJDqPSasKjFkUksWrs2DJ16tTh4MGDSjxVzN05ePAgderUSXYoIpJANbblnp6eTl5eHvn5+ckOJfTq1KlDenp6ssMQkQSqscm9du3aRb/MFBGRiqmx3TIiIlJ5Su4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiEUU3I3szlmtt/MNkWVNTSzN8xsa/C3QVBuZvaomW0zs41m1rmqghcRkZLF2nKfCwwoVjYFeNPdWwFvBvMAA4FWwWsc8GT8YYqISEXElNzdfSXwabHia4F5wfQ84Lqo8mc9Yg1wnpk1TUSwIiISm3j63C9w970Awd8mQfmFwO6oenlB2UnMbJyZ5ZpZrh6lJyKSWFVxQdVKKDvlKdfuPsvds9w9q3HjxlUQhojI6Sue5L6vsLsl+Ls/KM8DLoqqlw58HMd+RESkguJJ7kuA0cH0aGBxVPmNwV0zXYHPC7tvRESkeqTFUsnM5gM9gUZmlgf8EpgO/MHMbgZ2AUOD6q8Bg4BtwFfAmATHLCIi5Ygpubv7iFIW9SmhrgMT4wlKRETio1+oioiEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICMU0cJiISE33xIYniqYndJyQxEhqBrXcRURCSMldRCSElNxFREJIyV1EJIR0QVVEao637v/HdK87kxdHCFQ6uZtZa2BBVNElwF3AecAtQH5Q/gt3f63SEYqISIVVOrm7+4dARwAzqwXsAV4h8kDsh919RkIiFBGRCktUn3sfYLu7/z1B2xMRkTgkKrkPB+ZHzd9mZhvNbI6ZNUjQPkREJEZxJ3czOwMYDLwYFD0JfJtIl81e4MFS1htnZrlmlpufn19SFRERqaREtNwHAn9x930A7r7P3Y+7+wngaSC7pJXcfZa7Z7l7VuPGjRMQhoiIFEpEch9BVJeMmTWNWjYE2JSAfYiISAXEdZ+7mdUD+gLjo4ofMLOOgAM7iy0TEZFqEFdyd/evgPOLlY2KKyIREYmbhh8QEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQmhuJ6hCmBmO4EvgOPAMXfPMrOGwAIgg8hDsoe5+2fx7ktERGKTqJZ7L3fv6O5ZwfwU4E13bwW8GcyLiEg1qapumWuBecH0POC6KtqPiIiUIBHJ3YFlZrbOzMYFZRe4+16A4G+T4iuZ2TgzyzWz3Pz8/ASEISIiheLucweucPePzawJ8IaZfRDLSu4+C5gFkJWV5QmIQ0REAnG33N394+DvfuAVIBvYZ2ZNAYK/++Pdj4iIxC6u5G5mZ5nZOYXTQD9gE7AEGB1UGw0sjmc/IiJSMfF2y1wAvGJmhdv6vbsvNbN3gT+Y2c3ALmBonPsREZEKiCu5u/tHQIcSyg8CfeLZtoiIVJ5+oSoiEkKJuFtGRCQpntjwRLJDqLHUchcRCSEldxGREFK3jIiETnR3zYSOE5IYSfKo5S4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGkWyFFJLneuj/ZEYSSWu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiFU6eRuZheZ2VtmtsXM3jezSUH53Wa2x8w2BK9BiQtXRKTq5fzxo6JXqornR0zHgJ+6+1/M7BxgnZm9ESx72N1nxB+eiIhURqWTu7vvBfYG01+Y2RbgwkQFJiKnuehfrva6s2hSz02NTUL63M0sA+gErA2KbjOzjWY2x8walLLOODPLNbPc/Pz8RIQhIiKBuJO7mZ0NvARMdvfDwJPAt4GORFr2D5a0nrvPcvcsd89q3LhxvGGIiFRaQc67J73CIK7kbma1iST237n7ywDuvs/dj7v7CeBpIDv+MEVEpCLiuVvGgNnAFnd/KKq8aVS1IcCmyocnIiKVEc/dMlcAo4D3zGxDUPYLYISZdQQc2AmMjytCERGpsHjullkFWAmLXqt8OCJS6OE3/lY0fXvfS5MYiaQi/UJVRCSElNxFREJIj9kTkeqnR+tVObXcRURCSMldRCSE1C0jItVDXTHVSi13EZEQUstdpAS6x1xSnVruIiIhpOQuIhJC6pYRSQB140hNo+QuUoNEf0hUtI4+VCRaKJK7Wk2SCLEkVqmgRN3+GL2dBvUTsslN+8P9kKBQJHeR6hJLQyJRdVKW7mevEZTcRZJM3xiqVvQDtSd0nJDESKqXkrtINVESr7wnDm38x0yD7yUvkBSi5C5JUdFuiTg3wWcAAAThSURBVHjqx7pOVThtErq6YmocJXepkFD3FVdQdSbuiu4r1vrR51DnNlyqLLmb2QDgEaAW8Ft3n15V+9qf929Rc4uqajdSTE1LBpVJtqdNy1pOO1XyC1UzqwU8DgwE2hJ5aHbbqtiXiIicqqpa7tnANnf/CMDMXgCuBTZX0f6kBimtRR/PD3QqWud0lMj3pdRvZdF9673uTNj+JPGqKrlfCOyOms8DLq+ifcWlpnUtRKtMbDXteGpKso7nfQnzh0n0sXXdNatoek3zcf+opIulKcncPfEbNRsK9Hf3Hwfzo4Bsd/+XqDrjgMJ/Qa2BD+PYZSPgQBzr12Q6ttQV5uPTsdUMF7t7iT+1raqWex5wUdR8OvBxdAV3nwXMIgHMLNfdsxKxrZpGx5a6wnx8Oraar6qG/H0XaGVmLczsDGA4sKSK9iUiIsVUScvd3Y+Z2W3An4jcCjnH3d+vin2JiMipquw+d3d/DXitqrZfTEK6d2ooHVvqCvPx6dhquCq5oCoiIsmlx+yJiIRQSid3MxtgZh+a2TYzm5LseOJhZheZ2VtmtsXM3jezSUF5QzN7w8y2Bn8bJDvWyjKzWma23sxeDeZbmNna4NgWBBffU5KZnWdmC83sg+AcdgvLuTOz24N/k5vMbL6Z1Unlc2dmc8xsv5ltiior8VxZxKNBjtloZp2TF3nFpGxyD+EQB8eAn7p7G6ArMDE4ninAm+7eCngzmE9Vk4AtUfO/Bh4Oju0z4OakRJUYjwBL3f07QAcix5ny587MLgT+Fchy9/ZEbpAYTmqfu7nAgGJlpZ2rgUCr4DUOeLKaYoxbyiZ3ooY4cPevgcIhDlKSu+91978E018QSQ4XEjmmeUG1ecB1yYkwPmaWDlwN/DaYN6A3sDCoksrHdi7QA5gN4O5fu/shQnLuiNx4UdfM0oB6wF5S+Ny5+0rg02LFpZ2ra4FnPWINcJ6ZNa2eSOOTysm9pCEOLkxSLAllZhlAJ2AtcIG774XIBwDQJHmRxeW/gH8DTgTz5wOH3P1YMJ/K5+8SIB94Juh2+q2ZnUUIzp277wFmALuIJPXPgXWE59wVKu1cpWyeSeXkbiWUpfytP2Z2NvASMNndDyc7nkQws+8D+919XXRxCVVT9fylAZ2BJ929E1BACnbBlCToe74WaAE0A84i0lVRXKqeu/Kk7L/TVE7u5Q5xkGrMrDaRxP47d385KN5X+DUw+Ls/WfHF4QpgsJntJNJ91ptIS/684Ks+pPb5ywPy3H1tML+QSLIPw7m7Ctjh7vnu/g3wMtCd8Jy7QqWdq5TNM6mc3EM1xEHQBz0b2OLuD0UtWgKMDqZHA4urO7Z4ufud7p7u7hlEztOf3f0G4C3gR0G1lDw2AHf/BNhtZq2Doj5EhrdO+XNHpDumq5nVC/6NFh5bKM5dlNLO1RLgxuCuma7A54XdNzWeu6fsCxgE/A3YDvx7suOJ81iuJPJ1byOwIXgNItI3/SawNfjbMNmxxnmcPYFXg+lLgBxgG/AicGay44vjuDoCucH5WwQ0CMu5A+4BPgA2Ac8BZ6byuQPmE7l+8A2RlvnNpZ0rIt0yjwc55j0idw0l/RhieekXqiIiIZTK3TIiIlIKJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRD6/wOO/V65MGXbAAAAAElFTkSuQmCC\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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IyGEorGfA4kH/7du3o2/fvob9r776CpcuXcL58+dx48YNDB8+HPPmzbNKkkRERJVKCPk2B2BxMZCVlYW2bdsa9rdv345hw4ahSZMmUKlUeOutt3D06FGrJElERETWY3ExoFarjVYzOnDgALp3727Yr127Nm7cuCFvdkRERLYg442KHIHFxUCbNm2QkpICADh16hSysrIQFBRkaL906VKxOy0RERE5JL2Qb3MAFk8gfPfddzFy5Ehs2bIFp06dwsCBA9G0aVND+9atW9GtWzerJElERETWY3Ex4O/vjx9++AEpKSno168f3njjDaP26tWr4/XXX5c9QSIiokpnwxUIbcHiYqBDhw544oknMH78eIwaNQrVq1c3aq/oUohERER2w0G69+Vi8ZyBPXv2oF27dvi///s/w30J9u3bZ83ciIiIqBJYXAw89dRTSExMxNWrV/HJJ58gMzMTvXv3RsuWLfHBBx8gOzvbmnkSERFVGqHXy7Y5Asl3GqpRowbCw8OxZ88enDt3DsOHD0d8fDwaN26MZ5991ho5EhERVS5eTWC5Fi1aYPr06WjSpAmio6OxZcsWufIiIiKyHU4gtMzevXuRmJiIb775Bmq1Gi+++CLGjx8vZ25ERERUCSQVA1euXEFSUhKSkpJw4cIF9OjRA0uWLMGLL76IGjVqWCtHIiKiyuUg3ftysbgYGDBgAHbs2AEPDw+EhYXhlVdeQatWrayZGxERkW04yMQ/uVhcDDg7O2PDhg0YPHgwNBqNNXMiIiKiSmRxMbB582Zr5kFERGQ/OExARESkcAq7mkDyOgNERERUtbBngIiIyBSHCYiIiJTNUZYRlotKCKGs8oeIiMiMf6KHyhbLLe4b2WJZC3sGiIiITHGYwHbuXUuv0POdPZrJGsfJxadCce4XXpY1jr29LuZTdj5VNU5VfZ/5ukpmb/9OH83JqlgMEBERKRwvLSQiIiIlYc8AERGRKQ4TEBERKZtQWDHAYQIiIiKFY88AERGRKYX1DLAYICIiMqWwFQg5TEBERKRw7BkgIiIyxWECIiIihVNYMcBhAiIiIoWTVAxcvHgRr7zyimG/cePGqFu3rmGrX78+zp49K3uSRERElUkIIdvmCCQNE3zyySfw8vIy7N+4cQMxMTHw9PQEACQnJ2PRokVISEiQN0siIqLKpLBhAknFQGpqKlasWGF0bOjQoWjW7MEdpPz8/DBhwgT5siMiIrIFhRUDkoYJMjMz0bBhQ8P+hAkT4O7ubtj38/PDn3/+KV92REREZHWSegbUajWuXLmCRo0aAQAWLVpk1J6TkwNnZ2f5siMiIrIB3pugDO3atcOOHTtKbf/xxx/Rvn37CidFRERkU3oh3+YAJBUD4eHheP/997Fly5ZibSkpKViwYAHCw8NlS46IiIisT9IwwcSJE7Fz506EhoaidevWaNWqFQDg7NmzOHv2LIYOHYqJEydaJVEiIqJKo6xbE0hfdGjt2rVYs2YNWrZsaSgC/P398dVXX2H9+vXWyJGIiKhSCb2QbXME5VqOeOTIkRg5cmSx43q9Hlu3bsXgwYMrnBgRERFVDlnuTXDhwgUkJiYiKSkJubm5uHfvnhxhiYiIbMNB/qKXS7nvTXD37l2sWrUKvXr1QqtWrbB//37ExMRwnQEiInJ8ehk3ByC5Z+DXX3/F8uXLsW7dOjRv3hyjR4/G/v378emnn6Jt27YWxdDpdNDpdEbHtFot75pERERkA5K+fzt06IDhw4ejXr162L9/P44cOYJ//etfUKlUkk4aFxcHd3d3oy0uLk5SDCIiImvhBMIynD17FiNGjEBQUJDFvQAliY6ORlRUlNExrVYL3L5c7phERESycZDufblIKgbS09ORlJSEiIgI3L17F6NGjcLo0aMl9wxotdoHX/4m7t2WFIaIiMgqHOUverlIGibw8fHBjBkzcOHCBaxevRrZ2dno2bMn7t+/j6SkJJw7d85aeRIREZGVlHvO3tNPP40vv/wSV69exdKlS7Fz5060bt0aHTp0kDM/IiKiyqewqwkqPIHf3d0dr7/+Og4dOoQjR46gT58+MqRFRERkO0Iv3+YIZL2az8PDAwUFBXKGJCIiIiuTtRj4+++/sWLFCjlDEhERVT6FDRPIshwxERFRVeIo3fty4aJ/RERECseeASIiIlMK6xmQVAy88MILZbbfvHmzIrkQERHZBQ4TlMH0fgKmW5MmTRAWFmatXImIiKq8+Ph4+Pn5wdXVFQEBATh48GCZj7958yYmT56MBg0aQKvVomXLlti6daukc0rqGZg1axb8/PygVnOqARERVV226hlITk5GVFQUEhISEBAQgMWLFyMkJARnz56Fp6dnsccXFhaib9++8PT0xIYNG+Dj44NLly6hdu3aks4r6Vvd398f165dM+yPGDECOTk5kk5IRERk72y16NDChQsxceJEhIeHo23btkhISED16tWRmJhY4uMTExNx/fp1bNq0CT179oSfnx969+6Njh07SjqvpGJACOMbN2zduhX5+fmSTkhERGT3hEq2TafT4datW0abTqcrdsrCwkIcPnwYwcHBhmNqtRrBwcFIS0srMc3NmzcjMDAQkydPhpeXF9q3b4/58+ejqKhI0stlfz8REZEVxcXFFZtjFxcXV+xx165dQ1FREby8vIyOe3l5ITs7u8TY6enp2LBhA4qKirB161bMmjUL//nPfzBv3jxJOUqaM6BSqYrdrljq7YuJiIjsnZxzBqKjoxEVFWV0TKvVyhJbr9fD09MTn3/+OTQaDbp06YLLly/jo48+QmxsrMVxJBUDQgi8/PLLhhdRUFCA1157DTVq1DB63MaNG6WEJSIisitCL98fulqt1qIvfw8PD2g0mmJz8XJycuDt7V3icxo0aABnZ2doNBrDsTZt2iA7OxuFhYVwcXGxKEdJwwTjxo2Dp6enoZtjzJgxaNiwYbHuDyIiIpLGxcUFXbp0QWpqquGYXq9HamoqAgMDS3xOz549ceHCBej1/+vKOHfuHBo0aGBxIQBI7Bn44osvpDyciIjIIdnq0sKoqCiMGzcOXbt2Rbdu3bB48WLk5+cjPDwcABAWFgYfHx/DnIOIiAgsXboUb731Ft544w2cP38e8+fPx5tvvinpvFyOmIiIyIQQtpkPN2LECOTm5iImJgbZ2dno1KkTtm3bZphUmJWVZbTWj6+vL3788UdMmTIFHTp0gI+PD9566y1MnTpV0nlZDBAREdmRyMhIREZGlti2e/fuYscCAwNx4MCBCp2TxQAREZEJpd2bgMUAERGRCTmvJnAEXHSIiIhI4VTCdI1hIiIihcvq+oxssRofSjX/IBvjMAEREZEJpQ0T2FUx4OTiU6Hn3y+8bJdx7l1Lr1AcZ49mdpkP4zAO41gvjr19jtlLnEdjWZPSigHOGSAiIlI4u+oZICIisgdKm03HYoCIiMgEhwmIiIhIUdgzQEREZMJW9yawFRYDREREJpS2HDGHCYiIiBSOPQNEREQm9BwmICIiUjalzRngMAEREZHCsWeAiIjIhNLWGWAxQEREZIIrEBIRESmc0noGJM0Z+P7776HXK+ziSyIioipOUjEwZMgQ+Pr6YsaMGbhw4YK1ciIiIrIpvVDJtjkCScVARkYGJk2ahHXr1qFVq1bo3bs3Vq9ejbt371orPyIiokonhEq2zRFIKgZ8fX0RExODixcvYseOHfDz80NERAQaNGiA1157Db/++qu18iQiIiIrKfc6A0FBQVi5ciWuXr2Kjz76CCdOnED37t3RsWNHOfMjIiKqdELItzmCCl9NULNmTTzzzDO4dOkSzpw5g99//12OvIiIiGzGUcb65VLunoG7d+9i1apV6NOnD/z9/bFu3TpERUUhMzNTxvSIiIjI2iT3DBw4cACJiYlYv349CgsL8cILL2DHjh0ICgqyRn5ERESVzlEm/slFUjHQtm1bnD17Fp07d0ZcXBxeeukluLu7Wys3IiIim3CUsX65SCoGgoODsXbtWk4SJCIiqkIkFQNLliwp8fiePXuQn5+PwMBA1KlTx2wcnU4HnU5ndEyr1UpJhYiIyGo4gbAMH3zwAWbNmmXYF0Kgf//+CAoKwuDBg9GmTRucOnXKbJy4uDi4u7sbbXFxcdKzJyIisgIuOlSG5ORktG/f3rC/YcMG7N27F/v27cO1a9fQtWtXzJ4922yc6Oho5OXlGW3R0dHSsyciIrICpS1HLGmYICMjAx06dDDsb926FcOGDUPPnj0BADNnzsTw4cPNxtFqtRwWICIishOSegbu379v9CWelpaGHj16GPYbNmyIa9euyZcdERGRDQgZN0cgqRho3rw59u7dCwDIysrCuXPn0KtXL0P7n3/+iXr16smbIRERUSXjMEEZJk+ejMjISOzbtw8HDhxA9+7d0bZtW0P7zp070blzZ9mTJCIiIuuRVAxMnDgRGo0GKSkp6NWrF2JjY43ar1y5gvDwcFkTJCIiqmyOchWAXCQVA7du3cKwYcMwbNgwo2MPLViwQL7MiIiIbERv6wQqmaRioHbt2lCpzFdLRUVF5U6IiIiIKpekYmDXrl2G/xdCYODAgVi+fDl8fHxkT4yIiMhWBDhMUKrevXsb7Ws0GnTv3h3NmjWTNSkiIiJb0jvKNYEykXRpIREREVU9knoGiIiIlEDPYQJpLJlQSERE5Eg4Z6AML7zwgtF+QUEBXnvtNdSoUcPo+MaNGyueGRERkY3w0sIyuLu7G+2PGTNG1mSIiIio8kkqBr744gtr5UFERGQ3OExARESkcEobJuClhURERArHngEiIiITSusZYDFARERkQmlzBjhMQEREpHDsGSAiIjKhV1bHAIsBIiIiU0pbjpjDBERERAqnEkIo7EaNREREZdvk/ZJssYZkr5EtlrVwmICIiMgELy20IScXnwo9/37hZQDAvWvpFYrj7NFM1jh8XSV7+LoYp+w49vbzsrffQ3t7f/g+lx2nou8P8L/3yJr0CrsjL+cMEBERKZxd9QwQERHZA6VNpmMxQEREZEJpcwY4TEBERKRw7BkgIiIyobQVCNkzQEREZEIPlWybVPHx8fDz84OrqysCAgJw8OBBi563bt06qFQqDBkyRPI5WQwQERHZieTkZERFRSE2NhZHjhxBx44dERISgr/++qvM52VmZuL//u//8NRTT5XrvCwGiIiITAgZNykWLlyIiRMnIjw8HG3btkVCQgKqV6+OxMTEUp9TVFSE0aNHY/bs2WjWrJnEMz7AYoCIiMiEXiXfptPpcOvWLaNNp9MVO2dhYSEOHz6M4OBgwzG1Wo3g4GCkpaWVmuucOXPg6emJ8ePHl/v1shggIiKyori4OLi7uxttcXFxxR537do1FBUVwcvLy+i4l5cXsrOzS4z9888/Y8WKFVi2bFmFcuTVBERERCbkXGcgOjoaUVFRRse0Wm2F496+fRtjx47FsmXL4OHhUaFYLAaIiIhMyLkCoVartejL38PDAxqNBjk5OUbHc3Jy4O3tXezxFy9eRGZmJkJDQw3H9PoHZYyTkxPOnj2L5s2bW5QjhwmIiIhMyDlnwFIuLi7o0qULUlNT/5eHXo/U1FQEBgYWe3zr1q1x4sQJHDt2zLA9++yzCAoKwrFjx+Dr62vxudkzQEREZCeioqIwbtw4dO3aFd26dcPixYuRn5+P8PBwAEBYWBh8fHwQFxcHV1dXtG/f3uj5tWvXBoBix81hMUBERGTCVvcmGDFiBHJzcxETE4Ps7Gx06tQJ27ZtM0wqzMrKglotf6c+iwEiIiITtrxRUWRkJCIjI0ts2717d5nPTUpKKtc5OWeAiIhI4dgzQEREZEIo7EZFLAaIiIhM2HKYwBY4TEBERKRw7BkgIiIyobSeARYDREREJuRcgdARSBom2LlzJ9q2bYtbt24Va8vLy0O7du2wb98+2ZIjIiIi65NUDCxevBgTJ05ErVq1irW5u7tj0qRJWLhwoWzJERER2YItliO2JUnFwG+//Yb+/fuX2t6vXz8cPnzYbBxL7+1MRERkC3oZN0cgqRjIycmBs7Nzqe1OTk7Izc01G8fSezsTERHZAouBMvj4+ODkyZOlth8/fhwNGjQwGyc6Ohp5eXlGW3R0tJRUiIiISCaSioGBAwdi1qxZKCgoKNZ29+5dxMbGYvDgwWbjaLVa1KpVy2iz5F7PRERElUHIuDkCSZcWzpw5Exs3bkTLli0RGRmJVq1aAQDOnDmD+Ph4FBUVYcaMGVZJlIiIqLI4ysQ/uUgqBry8vLB//35EREQgOjoaQjyoeVQqFUJCQhAfH2+4zSIRERE5BsmLDjVp0gRbt27FjRs3cOHCBQgh4O/vjzp16lgjPyIiokrnKBP/5FLuFQjr1KmDJ554Qs5ciIiI7IKjjPXLhTcqIiIiUjjem4CIiMiEXmF9AywGiIiITChtzgCHCYiIiBSOPQNEREQmlDVIwGKAiIioGKUNE7AYICIiMqG0FQg5Z4CIiEjh2DNARERkgpcWEhERKZyySgEOExARESkeewaIiIhM8GoCIiIihVPanAEOExARESkcewaIiIhMKKtfgMUAERFRMZwzQEREpHCcM0BERESKohJCKKv8ISIiMmOK30jZYi3KXCdbLGvhMAEREZEJzhmwIScXnwo9/37hZQDAvWvpFYrj7NGsSudjb6+rqr4/fJ9LZq/vM+M4RpxHY5F87KoYICIisgdCYRMIWQwQERGZUNowAa8mICIiUjj2DBAREZlQ2joDLAaIiIhMKKsU4DABERGR4rFngIiIyASHCYiIiBROaVcTsBggIiIyobR1BjhngIiISOHYM0BERGSCwwREREQKx2ECIiIiUhT2DBAREZngMAEREZHC6QWHCYiIiEhB2DNARERkQln9AiwGiIiIilHacsQcJiAiIlI49gwQERGZUNo6AywGiIiITPDSQiIiIoXjnIEypKWl4fvvvzc6tmrVKjRt2hSenp549dVXodPpZE2QiIiIrEtSMTBnzhycOnXKsH/ixAmMHz8ewcHBmDZtGlJSUhAXFyd7kkRERJVJyPifI5A0THDs2DHMnTvXsL9u3ToEBARg2bJlAABfX1/ExsbivffeKzOOTqcr1oOg1WqlpEJERGQ1SpszIKln4MaNG/Dy8jLs79mzBwMGDDDsP/HEE/jjjz/MxomLi4O7u7vRxh4FIiIi25BUDHh5eSEjIwMAUFhYiCNHjqB79+6G9tu3b8PZ2dlsnOjoaOTl5Rlt0dHRElMnIiKyDiGEbJtU8fHx8PPzg6urKwICAnDw4MFSH7ts2TI89dRTqFOnDurUqYPg4OAyH18aScXAwIEDMW3aNOzbtw/R0dGoXr06nnrqKUP78ePH0bx5c7NxtFotatWqZbRxmICIiOyFHkK2TYrk5GRERUUhNjYWR44cQceOHRESEoK//vqrxMfv3r0bo0aNwq5du5CWlgZfX1/069cPly9flnReScXA3Llz4eTkhN69e2PZsmVYtmwZXFxcDO2JiYno16+fpASIiIjogYULF2LixIkIDw9H27ZtkZCQgOrVqyMxMbHEx3/11Vd4/fXX0alTJ7Ru3RrLly+HXq9HamqqpPNKmkDo4eGBvXv3Ii8vD25ubtBoNEbtX3/9Ndzc3CQlQEREZG/knEBY2qR50x7xwsJCHD582GjYXK1WIzg4GGlpaRad686dO7h37x7q1q0rKcdy3ZvA3d29WCEAAHXr1jXqKSAiInJEcl5aaOmk+WvXrqGoqMhooj7wYL5edna2RXlPnToVDRs2RHBwsKTXyxUIiYiIrCg6OhpRUVFGx6wxT27BggVYt24ddu/eDVdXV0nPZTFARERkQs7liEsaEiiJh4cHNBoNcnJyjI7n5OTA29u7zOf++9//xoIFC7Bjxw506NBBco68hTEREZEJW1xa6OLigi5duhhN/ns4GTAwMLDU53344YeYO3cutm3bhq5du5br9bJngIiIyIStViCMiorCuHHj0LVrV3Tr1g2LFy9Gfn4+wsPDAQBhYWHw8fExzDn44IMPEBMTgzVr1sDPz88wt8DNzU3ShH4WA0RERHZixIgRyM3NRUxMDLKzs9GpUyds27bNMKkwKysLavX/OvU/++wzFBYWYtiwYUZxLLk1wKNYDBAREZmw5Q2GIiMjERkZWWLb7t27jfYzMzNlOSeLASIiIhNyTiB0BJxASEREpHDsGSAiIjJRnhsMOTIWA0RERCY4TEBERESKwp4BIiIiE7a8msAWWAwQERGZ0CtszgCHCYiIiBSOPQNEREQmlNUvwGKAiIioGKVdTcBigIiIyITSigHOGSAiIlI49gwQERGZUNoKhCqhtFdMRERkRreGvWWLdfDKHtliWQuHCYiIiBTOroYJnFx8KvT8+4WX7TLOvWvpFYrj7NGsSudjb6/L3uLY2/vMOJUTx95+D+0lzqOxrIkrEBIRESmc0kbQOUxARESkcOwZICIiMqG0dQZYDBAREZngMAEREREpCnsGiIiITHCYgIiISOF4aSEREZHC6TlngIiIiJSEPQNEREQmlDZMUK6egSNHjuDEiROG/e+++w5DhgzB9OnTUVhYKFtyREREtqAXQrbNEZSrGJg0aRLOnTsHAEhPT8fIkSNRvXp1fP3113j33XdlTZCIiIisq1zFwLlz59CpUycAwNdff41evXphzZo1SEpKwjfffCNnfkRERJVOyPifIyjXnAEhBPR6PQBgx44dGDx4MADA19cX165dky87IiIiG3CU7n25lKtnoGvXrpg3bx5Wr16NPXv2YNCgQQCAjIwMeHl5yZogERERWVe5ioHFixfjyJEjiIyMxIwZM9CiRQsAwIYNG9CjRw9ZEyQiIqpsHCawQIcOHYyuJnjoo48+gkajqXBSREREtqS0YQJZ1xlwdXWVMxwRERFVgnIVA0VFRVi0aBHWr1+PrKysYmsLXL9+XZbkiIiIbMFRuvflUq45A7Nnz8bChQsxYsQI5OXlISoqCi+88ALUajXee+89mVMkIiKqXELoZdscQbmKga+++grLli3Dv/71Lzg5OWHUqFFYvnw5YmJicODAAblzJCIiqlR6CNk2R1CuYiA7OxuPPfYYAMDNzQ15eXkAgMGDB2PLli3yZUdERERWV65ioFGjRrh69SoAoHnz5ti+fTsA4Ndff4VWq5UvOyIiIhsQQsi2OYJyTSB8/vnnkZqaioCAALzxxhsYM2YMVqxYgaysLEyZMsXs83U6HXQ6ndExFhFERGQvHKV7Xy7lKgYWLFhg+P8RI0agcePGSEtLg7+/P0JDQ80+Py4uDrNnzzY6FhsbW55UiIiIqIJkWWcgMDAQgYGBFj8+OjoaUVFRRse0Wi3mzV8mRzpEREQV4ijd+3KxuBjYvHmzxUGfffbZMtu1Wi2HBYiIyG5xBcJSDBkyxKLHqVQqFBUVlTcfIiIiqmQWFwMPb1lMRERU1SltBUJZ701ARERUFXDOQCmWLFlicdA333yzXMkQERFR5bO4GFi0aJFFj1OpVCwGiIjIoXGdgVJkZGQUO5abmwuVSgUPDw9ZkyIiIrIlpQ0TSF6O+ObNm5g8eTI8PDzg7e0NLy8veHh4IDIy0nCPAiIiIkemF0K2zRFImkB4/fp1BAYG4vLlyxg9ejTatGkDAPj999+RlJSE1NRU7N+/H3Xq1LFKskRERCQ/ScXAnDlz4OLigosXL8LLy6tYW79+/TBnzhyL5xcQERHZIw4TlGHTpk3497//XawQAABvb298+OGH+Pbbb2VLjoiIyBb0ELJtjkBSMXD16lW0a9eu1Pb27dsjOzu7wkkRERFR5ZFUDHh4eCAzM7PU9oyMDNStW7eiOREREdmUEEK2zRFIKgZCQkIwY8YMFBYWFmvT6XSYNWsW+vfvL1tyREREtsCrCcowZ84cdO3aFf7+/pg8eTJat24NIQROnz6NTz/9FDqdDqtXr7ZWrkRERGQFkoqBRo0aIS0tDa+//jqio6MN3R8qlQp9+/bF0qVL4evra5VEiYiIKgtvVGRG06ZN8cMPP+DGjRs4f/48AKBFixacK0BERFWGo3Tvy6Xcdy2sU6cOunXrJmcuREREZAO8hTEREZEJR7kKQC4sBoiIiExwzgAREZHCKa1nQPJdC4mIiMh64uPj4efnB1dXVwQEBODgwYNlPv7rr79G69at4erqisceewxbt26VfE4WA0RERCZstQJhcnIyoqKiEBsbiyNHjqBjx44ICQnBX3/9VeLj9+/fj1GjRmH8+PE4evQohgwZgiFDhuDkyZOSzstigIiIyISQcZNi4cKFmDhxIsLDw9G2bVskJCSgevXqSExMLPHxH3/8Mfr374933nkHbdq0wdy5c/H4449j6dKlks6rEkobGCEiIjLDycVHtlj5t9Oh0+mMjmm1Wmi1WqNjhYWFqF69OjZs2IAhQ4YYjo8bNw43b97Ed999Vyx248aNERUVhbfffttwLDY2Fps2bcJvv/1mcY4O0zOg0+nw3nvvFXtDGYdxGIdxGKdqxJE7VkXcL7ws2xYXFwd3d3ejLS4urtg5r127hqKiInh5eRkd9/LyKvWOwNnZ2ZIeXyrhIPLy8gQAkZeXxziMwziMwzhVMI7csexFQUGByMvLM9oKCgqKPe7y5csCgNi/f7/R8XfeeUd069atxNjOzs5izZo1Rsfi4+OFp6enpBx5aSEREZEVlTQkUBIPDw9oNBrk5OQYHc/JyYG3t3eJz/H29pb0+NI4zDABERFRVebi4oIuXbogNTXVcEyv1yM1NRWBgYElPicwMNDo8QDw008/lfr40rBngIiIyE5ERUVh3Lhx6Nq1K7p164bFixcjPz8f4eHhAICwsDD4+PgY5hy89dZb6N27N/7zn/9g0KBBWLduHQ4dOoTPP/9c0nkdphjQarWIjY21qKuFcRiHcRiHcRwvjtyxHNGIESOQm5uLmJgYZGdno1OnTti2bZthkmBWVhbU6v916vfo0QNr1qzBzJkzMX36dPj7+2PTpk1o3769pPPy0kIiIiKF45wBIiIihWMxQEREpHAsBoiIiBSOxQAREZHCsRggIiJSOIe5tNAeZGdn47///a9hzWdvb28EBARIXunJGjIyMnDhwgU0aNBA8iUlRKQMv/32Gw4fPow+ffqgWbNmOHXqFOLj46HX6/H8888jJCTE4lg7d+7Ezz//jKtXr0KtVqNZs2Z49tln4e/vb8VXQFYjafHiShIfHy+eeeYZMXz4cLFjxw6jttzcXNG0aVOzMf773/+K+/fvG/ZTUlJEr169RMOGDUWXLl3EypUrLc7nn3/+EaNHjxYajUY4OTkJT09P4enpKZycnIRGoxFjxowR+fn5ZuMUFhaKd955RzRv3lw88cQTYsWKFUbt2dnZQq1Wm40TEREhbt++LYQQ4s6dO2Lo0KFCrVYLlUol1Gq1CAoKMrRXxPXr1yW9T1evXhWbNm0SCQkJIiEhQWzatElcvXq1wnk89M8//4g9e/ZY9Fi9Xi/S09PFvXv3hBBC6HQ6sW7dOrFy5UqRm5tboTyCgoJEZmamxY/fsGGDRb8fljp27JhYsWKFuHjxohBCiJMnT4qIiAgxadIksW3bNkmxioqKSj1+6dKlcuco9T0qSXp6uti+fbs4ceKEpOfJ+f7YA7k+N7755huh0WhEvXr1hJubm/jpp59E7dq1RXBwsAgJCREajUZ89dVXZuPk5OSIbt26CbVaLZycnIRarRZdunQR3t7eQqPRiHfeeafcr1UIIc6dOyd27Nghzp8/X6E4JI3dFQMff/yxqF69upg8ebIYM2aMcHFxEfPnzze0W/qLr1arRU5OjhBCiM2bNwu1Wi3CwsJEfHy8mDBhgnBychIbN260KKfx48cLf39/sW3bNqMC4/79++LHH38ULVu2FBMmTDAbJzY2Vnh5eYmPPvpIzJgxQ7i7u4tXX33V6LWpVCpJry06Olo0atRI7Ny5U+Tn54uff/5ZNG/eXEybNs2i11aWY8eOWfRey1UsyZXPmTNnRJMmTYRarRYtWrQQ6enpokuXLqJGjRqievXqwsPDQ5w7d85snO+++67ETaPRiKVLlxr2zVGpVKJWrVpi4sSJ4sCBAxa91tLI9YGel5cnhg8fLlxdXYWnp6eYNWuW0e+2pf/O5HqP5Cpw5Xp/TP3zzz8iMTFRTJ8+XXzyySfi2rVrFj/3k08+EWPHjhVr164VQgixatUq0aZNG9GqVSsRHR1tKFhLI9fnxuOPPy7mzZsnhBBi7dq1onbt2mLOnDmG9n//+9+iU6dOZuOMGDFCDBkyxHCzncjISBEWFiaEECI1NVXUq1dPLF682GwcIYSYP3++4Q++69evi2eeeUaoVCrDz71///7ixo0bFsWiirG7YqBt27ZG/1h/+eUXUb9+fTFr1iwhhOUfUiqVyvCF+eSTTxb7cnz//fdF9+7dLcqpdu3a4pdffim1/eeffxa1a9c2G6dFixYiJSXFsH/+/HnRokUL8fLLLwu9Xl+u19a+fftid6z67rvvRMuWLc3GMb2Llum2b98+i/KRq1gyx9Ji4LnnnhPPPvusOH78uHj77bdFmzZtxHPPPScKCwtFQUGBCA0NFWPGjDEb5+EH0sMPp5I2S39ec+bMEZ07dxYqlUq0a9dOLFq0SNIXykNyfaC/+eabomXLluLrr78Wy5YtE02aNBGDBg0SOp1OCGH5F4xc75FcBa5c70+bNm3E33//LYQQIisrS/j5+Ql3d3fxxBNPiLp16wpPT0+Rnp5uNs7cuXNFzZo1xdChQ4W3t7dYsGCBqFevnpg3b56YP3++qF+/voiJiSkzhlyfGzVq1BAZGRlCiAc9Z87OzuL48eOG9osXLwo3NzezcWrVqiVOnjxp2P/nn3+Es7Oz4S6Dq1evFq1atTIbRwghGjVqJI4cOSKEEGLChAmic+fO4siRI+Lu3bvi2LFjonv37mL8+PEWxaKKsbtioFq1aoZf2IdOnDghvLy8xLRp08r1henp6SkOHTpk1H7mzBmLvsCFePDL/+uvv5bafvDgQVGrVi2zcUp6bX/++ado2bKlGD16tLh8+bLFr+2vv/4SQgjh4eFh9A9TCCEyMzNFtWrVLIqjVqtL3Sz9IJerWKpTp06ZW61atSzKp379+uLo0aNCiAcfVCqVSuzbt8/Q/ssvv4jGjRubjdO/f38xaNAgw+/RQ05OTuLUqVNmn//Qo7+Lhw4dEhEREaJ27dpCq9WK4cOHi+3bt1scS64P9MaNG4tdu3YZ9nNzc0W3bt1Ev379REFBgcX/zqzxHlWkwJXr/Xk0n9GjR4sePXqImzdvCiGEuH37tggODhajRo0yG6d58+bim2++EUI8KGY1Go348ssvDe0bN24ULVq0KDOGXJ8b3t7ehs/B69evC5VKZfQ7cPDgQeHt7W02Tv369Y1+tnfu3BFqtdpQPF28eFFotVqzcYQQQqvVGoaT/Pz8ig0DHjp0SDRo0MCiWFQxdjeB0MPDA3/88Qf8/PwMx9q3b4+dO3fi6aefxpUrVyyO9fvvvyM7OxvVqlWDXq8v1n7//n2L4gwePBivvvoqVqxYgc6dOxu1HT16FBEREQgNDTUbx9vbGxcvXjR6bT4+Pti1axeCgoLw8ssvW5QPAMyaNQvVq1eHWq3GlStX0K5dO0Pb33//jRo1apiNUbNmTcyYMQMBAQEltp8/fx6TJk0yG0ev18PFxaXUdhcXlxLff1M6nQ4RERF47LHHSmy/dOkSZs+ebTbOP//8g7p16wIAatSogRo1aqBBgwaGdl9f32K3/CzJDz/8gEWLFqFr16749NNPMXjwYLPPMadLly7o0qULFi5ciK+//hqJiYno378/GjdujIyMDLPPr1mzJv7++2/4+fnh5s2buH//Pv7++29D+99//w03NzezcXJzc9GkSRPDvoeHB3bs2IGQkBAMHDgQy5cvt+j1yPkeqVQqAA8m6nbo0MGorWPHjvjjjz/MxpDr/XlUWloaEhIS4O7uDgBwc3PD7NmzMXLkSLPPvXLlCrp27Wp4DWq1Gp06dTK0P/7442Y/0+T63AgODsbkyZPxxhtvIDk5Gf369UN0dDS++OILqFQqvPPOO3jyySfNxnnyyScRExODlStXwsXFBdOnT0ezZs0M/+Zyc3NRp04di3Jq0qQJTp48iSZNmkClUsHJyfgrSaPRID8/36JYVEG2rkZMjRo1Srz99tsltp08eVLUr1/f4r+eH+2+XLRokVH72rVrRdu2bS3K6fr166J///5CpVKJunXritatW4vWrVuLunXrCrVaLQYMGGDRuNb48ePFK6+8UmLbn3/+KVq0aGHRa+vdu7fo06ePYVu2bJlR+9y5c0Xv3r3NxunTp4/44IMPSm0/duyYRV3FL730kqF7z9SRI0dEly5dxOjRo83G6dGjR5ljjZYOEzRv3tyoJ+DTTz8Vt27dMuwfPnzYor+AHjp69Kho27atePXVV0V+fr7kv3of7QIvyfnz58X06dMtijVmzBgREBAgvvzySxEaGipCQkJE9+7dxenTp8WZM2dE7969xbBhw8zGadWqldiyZUux47dv3xaBgYGiY8eOFr3XD1X0PVKpVGLSpEliypQpwtPTs1hvyeHDh4WHh4fZOHK9P4/2vjVs2LDYJMbMzEzh6upqNk7Tpk3FDz/8IIR4MDFOrVaL9evXG9q3bNki/Pz8yowh1+dGdna26Nu3r3BzcxMhISHi5s2bIjIy0vAZ6e/vLy5cuGA2zsWLF0WzZs2Ek5OTcHZ2Fu7u7kY/ry+++MLiOUsfffSRaNOmjTh//rz4z3/+IwIDAw05pKeniz59+lj086KKs7ti4LfffhOJiYmltp88eVIMHTrUbJzMzEyjzXR8duXKlZJmygshxOnTp0ViYqKYP3++mD9/vkhMTBSnT5+2+PmZmZllzma+fPmySEpKkpRTSdLT08Uff/xh9nGff/65+Pjjj0ttz87OFu+9957ZOHIVS++//36Z58vKyhIvv/yy2TiTJk0qViA9Ki4uTgwcONBsnEfl5+eLV199Vfj7+wuNRlPuLvCKKusDXa1WW/yBHhkZWeqH7K1bt0RAQICkYkCIB93FkyZNKtd7JFeBK9f7o1KpxGOPPSY6d+4s3NzcxIYNG4za9+zZI3x8fMzGmTlzpqhfv76YMGGCaNq0qZg2bZpo3Lix+Oyzz0RCQoLw9fUVU6ZMKTOGtT83Ll68KE6cOGF2IuOj8vPzxfbt20VKSkqFr8554403hLOzs2jdurVwdXUVarVauLi4GK5SkPOKJCqdw9y18Pbt21i7di2WL1+Ow4cPo6ioqMIxr1+/bujaciQ7d+5EZGQkDhw4gFq1ahm15eXloUePHkhISMBTTz1VqXmdOXMGaWlpRuswBAYGonXr1pWahzkZGRlwdXU1GjqwVEpKCnbu3Ino6Gh4enpa9JxLly7B19fX6LajcktPT8edO3fQunXrYl2tJblx40ax4aVH3b59G0eOHEHv3r0l57J582bs2rVL0ntkTnp6OrRaLXx8fMr9fCnvj+lwVEBAAPr372/Yf+edd/Dnn39i7dq1ZcbR6/VYsGAB0tLS0KNHD0ybNg3Jycl49913cefOHYSGhmLp0qUWDetV1N27d5GammoYyomOjoZOpzO0Ozk5Yc6cOXB1da2UOI86ffo0vv/+e6Snp0Ov16NBgwbo2bMngoODDcNHZGW2rkbM2bNnjwgLCxM1atQQ/v7+YurUqeLgwYMVivnjjz+KF1980aJuPktIvR6/onFCQ0PFwoULS23/+OOPxZAhQyw6Z1FRkVixYoUYNGiQaNeunWjfvr0IDQ0VK1euFHq93uLc7Y1cr8ve4sgVa8CAAYYJcUI86C15tPfm2rVrok2bNoqNc/HixVLXYJBCrjhlsfRz47PPPhODBw827Lu5uYmAgABDb4y3t3eZnytyxxHC/BVNDzeyPrssBq5evSri4uJEixYthKenp4iMjJQ8BmkqMzNTxMTEiCZNmohatWqJESNGGI3dVYSlY9lyxWncuLH4/fffS20/ffq08PX1NRtHr9eLgQMHCpVKJTp16iRGjhwpRowYITp06CBUKpV47rnnpKRfqsouluR6XXq9XgwaNMhu4sj52kznMdSsWdOwSI8Q5VvPoyJxTIdSbJ2PaZwXX3xRZGdnm32eteKUxdLPjSeffFJs3rzZsO/m5mb03qxevdqiy63liiOEfFc0UcXZXTEwePBgUatWLTFq1Cjx/fffG65bL08xoNPpxNq1a8UzzzwjXF1dxeDBg4VGozG61MgScl2PL1ccrVZb5upc58+ft6jXIzExUdSsWVPs3LmzWFtqaqqoWbOmLF/ilV0syfW67C2OnLFMv3xNP9DL+yWulDiWkiOOXJ8b3t7eRpcoenh4GO2fPXvWokuk5YojhBC7d+82bLt27RLVqlUTX331ldHx3bt3WxSLKsbuigGNRiOmTJlSbIU4qcVAZGSkqFevnujevbtYunSpYQJheYoKuapXueI0a9ZMfPvtt6W2f/PNNxYt2dy3b18RFxdXavv7778v+vXrZzaOvRVLcr0ue4sjZyxH+/K1tziWkiOOXJ8brq6u4syZM6W2nz592qL1AeSKU5Lyvs9UcXa3zsDPP/+MFStWoEuXLmjTpg3Gjh1r0fW8pj777DNMnToV06ZNQ82aNSuUk1zX48sVZ+DAgZg1axb69+9fbJLO3bt3ERsba9H13sePH8eHH35YavuAAQOwZMkSs3Fq165d5iQfIYRFk4DkiiPX67K3OHLGUqlUxd7L8kzUYhzrx5Hrc6NRo0Y4efIkWrVqVWL78ePH0ahRo0qLQ/bF7oqB7t27o3v37li8eDGSk5ORmJiIqKgo6PV6/PTTT/D19bXoy3316tVITExEgwYNMGjQIIwdOxYDBgwoV06PP/44AJQ6s7p27doQFlyUIVecmTNnYuPGjWjZsiUiIyMN/yjPnDmD+Ph4FBUVYcaMGWbjXL9+HV5eXqW2e3l54caNG2bj2FuxJNfrsrc4csYSQuDll1+GVqsFABQUFOC1114zzGp/dHY44xSP89DGjRutHkeuz42BAwciJiYGgwYNKvGPiNmzZ2PQoEGVFofsi90VAw/VqFEDr7zyCl555RWcPXsWK1aswIIFCzBt2jT07dsXmzdvLvP5o0aNwqhRo5CRkYGkpCRMnjwZd+7cgV6vx++//462bdtanMtLL72EO3fulNru7e2N2NjYSovj5eWF/fv3IyIiAtHR0YYPApVKhZCQEMTHx5f5hfFQUVFRmZdZaTQai1ZptLdiSa7XZW9x5Iw1btw4o/0xY8YUe0xYWBjjlBHHEnLEketzY/r06Vi/fj1atWqFyMhItGzZEgBw9uxZLF26FPfv38f06dMrLU5peCmhbTjMOgPAgw/ClJQUJCYmmi0GTAkhsH37dqxYsQKbN2+Gh4cHXnjhBYu7Z+3VjRs3cOHCBQgh4O/vb/EyoACgVqsxYMAAw18tpnQ6HbZt22Z2TYdly5bh7t27ePPNN0tsz8nJQUJCgtkPLLniyPW67C2O3LFIeTIyMhAREYGffvrJ6I+Ivn374tNPP0WzZs0qNc4LL7xgtJ+SkoKnn35acg8MVZxDFQNyuX79OlavXo1//etfFv0VJdciP/a2WFB4eLhFj/viiy+snIm85Hpd9hZH7ljkGAoKCrBjx45SF/nRaDSYO3eupEV+rl+/jgsXLgAAWrRoUe7F1yoah7/P9kNxxUB5VjJ89tlnERQUhClTppTYvmTJEuzatQvffvttpcSxR3q9HklJSdi4cSMyMzOhUqnQtGlTDBs2DGPHjrW460+uOERVRUJCArZs2YKUlBQAD+bWtGvXDtWqVQPwYK7Qu+++W+rnCpFFKu/CBduqyEqGci3yI1cceyPnIj+VsQgSkSORc5EfotLY7QRCOWRnZyMpKQkrVqzArVu38OKLL0Kn02HTpk2SJhDm5OTA2dm51HYnJyfk5uZWWhx7k5SUhH379iE1NRVBQUFGbTt37sSQIUOwatUqsxO35IpDVJVcuHDB6Lberq6uRve56NatGyZPnmyL1KgKsd6dU2wsNDQUrVq1wvHjx7F48WJcuXIFn3zySbli+fj44OTJk6W2Hz9+3KKb3sgVx96sXbsW06dPL/YFDgBPP/00pk2bhq+++qrS4hBVJTdv3jSaI5Cbmws/Pz/Dvl6vt/iSSaLSVNli4IcffsD48eMN17xqNJpyx3q4yE9BQUGxNimL/MgVx94cP37c6I5upgYMGIDffvut0uIQVSUPF/kpDRf5ITlU2QmEBw4cwIoVK5CcnGy0kmGDBg3w22+/SR4mePzxx6HRaEpd5OfIkSNmr+2XK469cXFxwaVLl0rt1bhy5QqaNm1q9q8XueIQVSVvvfUWduzYgcOHD5e4yE/Xrl0RHByMjz/+2EYZUlVQZYuBh/Lz8w0rGR48eBBFRUVYuHAhXnnlFUnLFF+6dAkRERH48ccfS1zkp2nTppUax55oNBpkZ2ejfv36Jbbn5OSgYcOGZq/ckCsOUVWSk5ODTp06wcXFpdRFfo4ePepwf0SQfanyxcCjHq5kuHr1aty8edOilQxNVWSRH2vEsQf2uDgPUVUi1yI/RKVRVDHwUEVWMqTi7HFxHqKqSK7FgohMKbIYICIiov+pslcTEBERkWVYDBARESkciwEiIiKFYzFARESkcCwGiIiIFI7FABERkcKxGCAiIlI4FgNEREQK9//xsZQ/yYqPXgAAAABJRU5ErkJggg==\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": 10,
   "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": 10,
     "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": 11,
   "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', '026', '027', '028', '029', '030', '031', '032', '033', '034', '035', '036', '037', '038', '039', '040', '041', '042', '043', '044', '045', '046', '047', '048', '050', '051', '052', '053', '054', '055', '056', '057', '058', '059', '060', '061', '062', '063', '064', '065', '066', '067', '068', '069', '070', '071', '072', '074', '075', '076', '077', '078', '079', '080', '081', '082', '083', '084', '085', '086', '087', '088', '089', '090', '091', '092', '093', '094', '095', '096', '097', '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', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224']\n",
      "221\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": 12,
   "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', '026', '027', '028', '029']\n",
      "['030', '031', '032', '033', '034', '035', '036', '037', '038', '039']\n",
      "['040', '041', '042', '043', '044', '045', '046', '047', '048', '050']\n",
      "['051', '052', '053', '054', '055', '056', '057', '058', '059', '060']\n",
      "['061', '062', '063', '064', '065', '066', '067', '068', '069', '070']\n",
      "['071', '072', '074', '075', '076', '077', '078', '079', '080', '081']\n",
      "['082', '083', '084', '085', '086', '087', '088', '089', '090', '091']\n",
      "['092', '093', '094', '095', '096', '097', '099', '100', '101', '102']\n",
      "['103', '104', '105', '106', '107', '108', '109', '110', '111', '112']\n",
      "['113', '114', '115', '116', '117', '118', '119', '120', '121', '122']\n",
      "['123', '124', '125', '126', '127', '128', '129', '130', '131', '132']\n",
      "['133', '134', '135', '136', '137', '138', '139', '140', '141', '142']\n",
      "['143', '144', '145', '146', '147', '148', '149', '150', '151', '152']\n",
      "['153', '154', '155', '156', '157', '158', '159', '161', '162', '163']\n",
      "['164', '165', '166', '167', '168', '169', '170', '171', '172', '173']\n",
      "['174', '175', '176', '177', '178', '179', '180', '181', '182', '183']\n",
      "['184', '185', '186', '187', '188', '189', '190', '191', '192', '193']\n",
      "['194', '195', '196', '197', '198', '199', '200', '201', '202', '203']\n",
      "['204', '205', '206', '207', '208', '209', '210', '211', '212', '213']\n",
      "['214', '215', '216', '217', '218', '219', '220', '221', '222', '223']\n",
      "['224']\n"
     ]
    }
   ],
   "source": [
    "for ii in range(0,len(allFOVs),10): # For each CHUNK (10 FOVs per loop)\n",
    "    print(allFOVs[ii:ii+10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\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",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
       "      <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",
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       "    <tr>\n",
       "      <th>004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>221 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",
       "[221 rows x 38 columns]"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "mask shape (2001, 2013)\n",
      "FOV 001\n",
      "mask shape (2001, 2013)\n",
      "FOV 002\n",
      "mask shape (2001, 2013)\n",
      "F002, no mask needed\n",
      "FOV 003\n",
      "mask shape (2001, 2013)\n",
      "FOV 004\n",
      "mask shape (2001, 2013)\n",
      "F004, no mask needed\n",
      "FOV 006\n",
      "mask shape (2001, 2013)\n",
      "FOV 007\n",
      "mask shape (2001, 2013)\n",
      "F007, no mask needed\n",
      "FOV 008\n",
      "mask shape (2001, 2013)\n",
      "F008, no mask needed\n",
      "FOV 009\n",
      "mask shape (2001, 2013)\n",
      "F009, no mask needed\n",
      "FOV 010\n",
      "mask shape (2001, 2013)\n",
      "F010, no mask needed\n",
      "FOV 011\n",
      "mask shape (2001, 2013)\n",
      "F011, no mask needed\n",
      "FOV 013\n",
      "mask shape (2001, 2013)\n",
      "F013, no mask needed\n",
      "FOV 014\n",
      "mask shape (2001, 2013)\n",
      "F014, no mask needed\n",
      "FOV 015\n",
      "mask shape (2001, 2013)\n",
      "F015, no mask needed\n",
      "FOV 016\n",
      "mask shape (2001, 2013)\n",
      "F016, no mask needed\n",
      "FOV 017\n",
      "mask shape (2001, 2013)\n",
      "F017, no mask needed\n",
      "FOV 018\n",
      "mask shape (2001, 2013)\n",
      "FOV 019\n",
      "mask shape (2001, 2013)\n",
      "F019, no mask needed\n",
      "FOV 020\n",
      "mask shape (2001, 2013)\n",
      "F020, no mask needed\n",
      "FOV 021\n",
      "mask shape (2001, 2013)\n",
      "F021, no mask needed\n",
      "FOV 022\n",
      "mask shape (2001, 2013)\n",
      "FOV 023\n",
      "mask shape (2001, 2013)\n",
      "F023, no mask needed\n",
      "FOV 024\n",
      "mask shape (2001, 2013)\n",
      "F024, no mask needed\n",
      "FOV 025\n",
      "mask shape (2001, 2013)\n",
      "F025, no mask needed\n",
      "FOV 026\n",
      "mask shape (2001, 2013)\n",
      "F026, no mask needed\n",
      "FOV 027\n",
      "mask shape (2001, 2013)\n",
      "F027, no mask needed\n",
      "FOV 028\n",
      "mask shape (2001, 2013)\n",
      "F028, no mask needed\n",
      "FOV 029\n",
      "mask shape (2001, 2013)\n",
      "FOV 030\n",
      "mask shape (2001, 2013)\n",
      "F030, no mask needed\n",
      "FOV 031\n",
      "mask shape (2001, 2013)\n",
      "F031, no mask needed\n",
      "FOV 032\n",
      "mask shape (2001, 2013)\n",
      "F032, no mask needed\n",
      "FOV 033\n",
      "mask shape (2001, 2013)\n",
      "F033, no mask needed\n",
      "FOV 034\n",
      "mask shape (2001, 2013)\n",
      "F034, no mask needed\n",
      "FOV 035\n",
      "mask shape (2001, 2013)\n",
      "F035, no mask needed\n",
      "FOV 036\n",
      "mask shape (2001, 2013)\n",
      "F036, no mask needed\n",
      "FOV 037\n",
      "mask shape (2001, 2013)\n",
      "FOV 038\n",
      "mask shape (2001, 2013)\n",
      "F038, no mask needed\n",
      "FOV 039\n",
      "mask shape (2001, 2013)\n",
      "FOV 040\n",
      "mask shape (2001, 2013)\n",
      "F040, no mask needed\n",
      "FOV 041\n",
      "mask shape (2001, 2013)\n",
      "F041, no mask needed\n",
      "FOV 042\n",
      "mask shape (2001, 2013)\n",
      "F042, no mask needed\n",
      "FOV 043\n",
      "mask shape (2001, 2013)\n",
      "FOV 044\n",
      "mask shape (2001, 2013)\n",
      "F044, no mask needed\n",
      "FOV 045\n",
      "mask shape (2001, 2013)\n",
      "F045, no mask needed\n",
      "FOV 046\n",
      "mask shape (2001, 2013)\n",
      "FOV 047\n",
      "mask shape (2001, 2013)\n",
      "F047, no mask needed\n",
      "FOV 048\n",
      "mask shape (2001, 2013)\n",
      "F048, no mask needed\n",
      "FOV 050\n",
      "mask shape (2001, 2013)\n",
      "F050, no mask needed\n",
      "FOV 051\n",
      "mask shape (2001, 2013)\n",
      "F051, no mask needed\n",
      "FOV 052\n",
      "mask shape (2001, 2013)\n",
      "F052, no mask needed\n",
      "FOV 053\n",
      "mask shape (2001, 2013)\n",
      "F053, no mask needed\n",
      "FOV 054\n",
      "mask shape (2001, 2013)\n",
      "FOV 055\n",
      "mask shape (2001, 2013)\n",
      "FOV 056\n",
      "mask shape (2001, 2013)\n",
      "F056, no mask needed\n",
      "FOV 057\n",
      "mask shape (2001, 2013)\n",
      "F057, no mask needed\n",
      "FOV 058\n",
      "mask shape (2001, 2013)\n",
      "F058, no mask needed\n",
      "FOV 059\n",
      "mask shape (2001, 2013)\n",
      "F059, no mask needed\n",
      "FOV 060\n",
      "mask shape (2001, 2013)\n",
      "F060, no mask needed\n",
      "FOV 061\n",
      "mask shape (2001, 2013)\n",
      "FOV 062\n",
      "mask shape (2001, 2013)\n",
      "F062, no mask needed\n",
      "FOV 063\n",
      "mask shape (2001, 2013)\n",
      "F063, no mask needed\n",
      "FOV 064\n",
      "mask shape (2001, 2013)\n",
      "FOV 065\n",
      "mask shape (2001, 2013)\n",
      "F065, no mask needed\n",
      "FOV 066\n",
      "mask shape (2001, 2013)\n",
      "FOV 067\n",
      "mask shape (2001, 2013)\n",
      "F067, no mask needed\n",
      "FOV 069\n",
      "mask shape (2001, 2013)\n",
      "F069, no mask needed\n",
      "FOV 070\n",
      "mask shape (2001, 2013)\n",
      "F070, no mask needed\n",
      "FOV 071\n",
      "mask shape (2001, 2013)\n",
      "FOV 072\n",
      "mask shape (2001, 2013)\n",
      "F072, no mask needed\n",
      "FOV 074\n",
      "mask shape (2001, 2013)\n",
      "F074, no mask needed\n",
      "FOV 075\n",
      "mask shape (2001, 2013)\n",
      "F075, no mask needed\n",
      "FOV 076\n",
      "mask shape (2001, 2013)\n",
      "F076, no mask needed\n",
      "FOV 077\n",
      "mask shape (2001, 2013)\n",
      "F077, no mask needed\n",
      "FOV 078\n",
      "mask shape (2001, 2013)\n",
      "F078, no mask needed\n",
      "FOV 079\n",
      "mask shape (2001, 2013)\n",
      "FOV 080\n",
      "mask shape (2001, 2013)\n",
      "F080, no mask needed\n",
      "FOV 081\n",
      "mask shape (2001, 2013)\n",
      "F081, no mask needed\n",
      "FOV 082\n",
      "mask shape (2001, 2013)\n",
      "F082, no mask needed\n",
      "FOV 083\n",
      "mask shape (2001, 2013)\n",
      "F083, no mask needed\n",
      "FOV 084\n",
      "mask shape (2001, 2013)\n",
      "F084, no mask needed\n",
      "FOV 085\n",
      "mask shape (2001, 2013)\n",
      "F085, no mask needed\n",
      "FOV 086\n",
      "mask shape (2001, 2013)\n",
      "F086, no mask needed\n",
      "FOV 087\n",
      "mask shape (2001, 2013)\n",
      "F087, no mask needed\n",
      "FOV 088\n",
      "mask shape (2001, 2013)\n",
      "F088, no mask needed\n",
      "FOV 089\n",
      "mask shape (2001, 2013)\n",
      "F089, no mask needed\n",
      "FOV 090\n",
      "mask shape (2001, 2013)\n",
      "F090, no mask needed\n",
      "FOV 091\n",
      "mask shape (2001, 2013)\n",
      "F091, no mask needed\n",
      "FOV 092\n",
      "mask shape (2001, 2013)\n",
      "FOV 093\n",
      "mask shape (2001, 2013)\n",
      "F093, no mask needed\n",
      "FOV 094\n",
      "mask shape (2001, 2013)\n",
      "F094, no mask needed\n",
      "FOV 095\n",
      "mask shape (2001, 2013)\n",
      "F095, no mask needed\n",
      "FOV 096\n",
      "mask shape (2001, 2013)\n",
      "F096, no mask needed\n",
      "FOV 097\n",
      "mask shape (2001, 2013)\n",
      "F097, no mask needed\n",
      "FOV 099\n",
      "mask shape (2001, 2013)\n",
      "F099, no mask needed\n",
      "FOV 100\n",
      "mask shape (2001, 2013)\n",
      "FOV 101\n",
      "mask shape (2001, 2013)\n",
      "F101, no mask needed\n",
      "FOV 102\n",
      "mask shape (2001, 2013)\n",
      "F102, no mask needed\n",
      "FOV 103\n",
      "mask shape (2001, 2013)\n",
      "FOV 104\n",
      "mask shape (2001, 2013)\n",
      "F104, no mask needed\n",
      "FOV 105\n",
      "mask shape (2001, 2013)\n",
      "F105, no mask needed\n",
      "FOV 106\n",
      "mask shape (2001, 2013)\n",
      "F106, no mask needed\n",
      "FOV 107\n",
      "mask shape (2001, 2013)\n",
      "F107, no mask needed\n",
      "FOV 108\n",
      "mask shape (2001, 2013)\n",
      "F108, no mask needed\n",
      "FOV 109\n",
      "mask shape (2001, 2013)\n",
      "F109, no mask needed\n",
      "FOV 110\n",
      "mask shape (2001, 2013)\n",
      "F110, no mask needed\n",
      "FOV 111\n",
      "mask shape (2001, 2013)\n",
      "F111, no mask needed\n",
      "FOV 112\n",
      "mask shape (2001, 2013)\n",
      "F112, no mask needed\n",
      "FOV 113\n",
      "mask shape (2001, 2013)\n",
      "F113, no mask needed\n",
      "FOV 114\n",
      "mask shape (2001, 2013)\n",
      "FOV 115\n",
      "mask shape (2001, 2013)\n",
      "FOV 116\n",
      "mask shape (2001, 2013)\n",
      "F116, no mask needed\n",
      "FOV 117\n",
      "mask shape (2001, 2013)\n",
      "F117, no mask needed\n",
      "FOV 118\n",
      "mask shape (2001, 2013)\n",
      "FOV 119\n",
      "mask shape (2001, 2013)\n",
      "F119, no mask needed\n",
      "FOV 120\n",
      "mask shape (2001, 2013)\n",
      "FOV 121\n",
      "mask shape (2001, 2013)\n",
      "FOV 122\n",
      "mask shape (2001, 2013)\n",
      "F122, no mask needed\n",
      "FOV 123\n",
      "mask shape (2001, 2013)\n",
      "F123, no mask needed\n",
      "FOV 124\n",
      "mask shape (2001, 2013)\n",
      "FOV 125\n",
      "mask shape (2001, 2013)\n",
      "F125, no mask needed\n",
      "FOV 126\n",
      "mask shape (2001, 2013)\n",
      "FOV 127\n",
      "mask shape (2001, 2013)\n",
      "F127, no mask needed\n",
      "FOV 128\n",
      "mask shape (2001, 2013)\n",
      "F128, no mask needed\n",
      "FOV 129\n",
      "mask shape (2001, 2013)\n",
      "F129, no mask needed\n",
      "FOV 130\n",
      "mask shape (2001, 2013)\n",
      "F130, no mask needed\n",
      "FOV 131\n",
      "mask shape (2001, 2013)\n",
      "F131, no mask needed\n",
      "FOV 132\n",
      "mask shape (2001, 2013)\n",
      "F132, no mask needed\n",
      "FOV 133\n",
      "mask shape (2001, 2013)\n",
      "F133, no mask needed\n",
      "FOV 134\n",
      "mask shape (2001, 2013)\n",
      "F134, no mask needed\n",
      "FOV 135\n",
      "mask shape (2001, 2013)\n",
      "FOV 136\n",
      "mask shape (2001, 2013)\n",
      "F136, no mask needed\n",
      "FOV 137\n",
      "mask shape (2001, 2013)\n",
      "F137, no mask needed\n",
      "FOV 138\n",
      "mask shape (2001, 2013)\n",
      "F138, no mask needed\n",
      "FOV 139\n",
      "mask shape (2001, 2013)\n",
      "F139, no mask needed\n",
      "FOV 140\n",
      "mask shape (2001, 2013)\n",
      "F140, no mask needed\n",
      "FOV 141\n",
      "mask shape (2001, 2013)\n",
      "F141, no mask needed\n",
      "FOV 142\n",
      "mask shape (2001, 2013)\n",
      "F142, no mask needed\n",
      "FOV 143\n",
      "mask shape (2001, 2013)\n",
      "F143, no mask needed\n",
      "FOV 145\n",
      "mask shape (2001, 2013)\n",
      "F145, no mask needed\n",
      "FOV 148\n",
      "mask shape (2001, 2013)\n",
      "F148, no mask needed\n",
      "FOV 149\n",
      "mask shape (2001, 2013)\n",
      "FOV 150\n",
      "mask shape (2001, 2013)\n",
      "F150, no mask needed\n",
      "FOV 151\n",
      "mask shape (2001, 2013)\n",
      "F151, no mask needed\n",
      "FOV 152\n",
      "mask shape (2001, 2013)\n",
      "F152, no mask needed\n",
      "FOV 153\n",
      "mask shape (2001, 2013)\n",
      "F153, no mask needed\n",
      "FOV 154\n",
      "mask shape (2001, 2013)\n",
      "FOV 155\n",
      "mask shape (2001, 2013)\n",
      "F155, no mask needed\n",
      "FOV 156\n",
      "mask shape (2001, 2013)\n",
      "F156, no mask needed\n",
      "FOV 157\n",
      "mask shape (2001, 2013)\n",
      "F157, no mask needed\n",
      "FOV 158\n",
      "mask shape (2001, 2013)\n",
      "F158, no mask needed\n",
      "FOV 159\n",
      "mask shape (2001, 2013)\n",
      "F159, no mask needed\n",
      "FOV 161\n",
      "mask shape (2001, 2013)\n",
      "FOV 162\n",
      "mask shape (2001, 2013)\n",
      "F162, no mask needed\n",
      "FOV 163\n",
      "mask shape (2001, 2013)\n",
      "F163, no mask needed\n",
      "FOV 164\n",
      "mask shape (2001, 2013)\n",
      "FOV 165\n",
      "mask shape (2001, 2013)\n",
      "F165, no mask needed\n",
      "FOV 166\n",
      "mask shape (2001, 2013)\n",
      "F166, no mask needed\n",
      "FOV 167\n",
      "mask shape (2001, 2013)\n",
      "F167, no mask needed\n",
      "FOV 168\n",
      "mask shape (2001, 2013)\n",
      "FOV 169\n",
      "mask shape (2001, 2013)\n",
      "FOV 170\n",
      "mask shape (2001, 2013)\n",
      "FOV 171\n",
      "mask shape (2001, 2013)\n",
      "F171, no mask needed\n",
      "FOV 172\n",
      "mask shape (2001, 2013)\n",
      "FOV 173\n",
      "mask shape (2001, 2013)\n",
      "F173, no mask needed\n",
      "FOV 174\n",
      "mask shape (2001, 2013)\n",
      "FOV 175\n",
      "mask shape (2001, 2013)\n",
      "F175, no mask needed\n",
      "FOV 176\n",
      "mask shape (2001, 2013)\n",
      "F176, no mask needed\n",
      "FOV 177\n",
      "mask shape (2001, 2013)\n",
      "F177, no mask needed\n",
      "FOV 178\n",
      "mask shape (2001, 2013)\n",
      "F178, no mask needed\n",
      "FOV 179\n",
      "mask shape (2001, 2013)\n",
      "F179, no mask needed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 180\n",
      "mask shape (2001, 2013)\n",
      "FOV 181\n",
      "mask shape (2001, 2013)\n",
      "F181, no mask needed\n",
      "FOV 182\n",
      "mask shape (2001, 2013)\n",
      "F182, no mask needed\n",
      "FOV 183\n",
      "mask shape (2001, 2013)\n",
      "F183, no mask needed\n",
      "FOV 184\n",
      "mask shape (2001, 2013)\n",
      "F184, no mask needed\n",
      "FOV 185\n",
      "mask shape (2001, 2013)\n",
      "F185, no mask needed\n",
      "FOV 186\n",
      "mask shape (2001, 2013)\n",
      "FOV 187\n",
      "mask shape (2001, 2013)\n",
      "F187, no mask needed\n",
      "FOV 188\n",
      "mask shape (2001, 2013)\n",
      "F188, no mask needed\n",
      "FOV 189\n",
      "mask shape (2001, 2013)\n",
      "FOV 190\n",
      "mask shape (2001, 2013)\n",
      "F190, no mask needed\n",
      "FOV 191\n",
      "mask shape (2001, 2013)\n",
      "FOV 192\n",
      "mask shape (2001, 2013)\n",
      "F192, no mask needed\n",
      "FOV 193\n",
      "mask shape (2001, 2013)\n",
      "F193, no mask needed\n",
      "FOV 194\n",
      "mask shape (2001, 2013)\n",
      "F194, no mask needed\n",
      "FOV 195\n",
      "mask shape (2001, 2013)\n",
      "FOV 196\n",
      "mask shape (2001, 2013)\n",
      "F196, no mask needed\n",
      "FOV 197\n",
      "mask shape (2001, 2013)\n",
      "F197, no mask needed\n",
      "FOV 198\n",
      "mask shape (2001, 2013)\n",
      "F198, no mask needed\n",
      "FOV 199\n",
      "mask shape (2001, 2013)\n",
      "F199, no mask needed\n",
      "FOV 200\n",
      "mask shape (2001, 2013)\n",
      "FOV 201\n",
      "mask shape (2001, 2013)\n",
      "F201, no mask needed\n",
      "FOV 202\n",
      "mask shape (2001, 2013)\n",
      "F202, no mask needed\n",
      "FOV 203\n",
      "mask shape (2001, 2013)\n",
      "F203, no mask needed\n",
      "FOV 204\n",
      "mask shape (2001, 2013)\n",
      "F204, no mask needed\n",
      "FOV 205\n",
      "mask shape (2001, 2013)\n",
      "F205, no mask needed\n",
      "FOV 206\n",
      "mask shape (2001, 2013)\n",
      "F206, no mask needed\n",
      "FOV 207\n",
      "mask shape (2001, 2013)\n",
      "F207, no mask needed\n",
      "FOV 208\n",
      "mask shape (2001, 2013)\n",
      "F208, no mask needed\n",
      "FOV 209\n",
      "mask shape (2001, 2013)\n",
      "F209, no mask needed\n",
      "FOV 210\n",
      "mask shape (2001, 2013)\n",
      "F210, no mask needed\n",
      "FOV 211\n",
      "mask shape (2001, 2013)\n",
      "FOV 212\n",
      "mask shape (2001, 2013)\n",
      "F212, no mask needed\n",
      "FOV 213\n",
      "mask shape (2001, 2013)\n",
      "F213, no mask needed\n",
      "FOV 214\n",
      "mask shape (2001, 2013)\n",
      "F214, no mask needed\n",
      "FOV 215\n",
      "mask shape (2001, 2013)\n",
      "FOV 216\n",
      "mask shape (2001, 2013)\n",
      "F216, no mask needed\n",
      "FOV 217\n",
      "mask shape (2001, 2013)\n",
      "F217, no mask needed\n",
      "FOV 218\n",
      "mask shape (2001, 2013)\n",
      "FOV 219\n",
      "mask shape (2001, 2013)\n",
      "F219, no mask needed\n",
      "FOV 220\n",
      "mask shape (2001, 2013)\n",
      "F220, no mask needed\n",
      "FOV 221\n",
      "mask shape (2001, 2013)\n",
      "FOV 222\n",
      "mask shape (2001, 2013)\n",
      "F222, no mask needed\n",
      "FOV 223\n",
      "mask shape (2001, 2013)\n",
      "FOV 224\n",
      "mask shape (2001, 2013)\n",
      "F224, no mask needed\n"
     ]
    }
   ],
   "source": [
    "NUM_FOVS = 215\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=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask percentage >3% and <5%\n",
      "000\n",
      "4.002544182454227\n",
      "mask percentage >5% and <7%\n",
      "001\n",
      "5.4148286015958735\n",
      "mask percentage >3% and <5%\n",
      "006\n",
      "3.6611848074968973\n",
      "mask percentage >3% and <5%\n",
      "018\n",
      "3.5441295745569836\n",
      "mask percentage >5% and <7%\n",
      "029\n",
      "5.431238677730186\n",
      "mask percentage >3% and <5%\n",
      "039\n",
      "4.310115185824872\n",
      "mask percentage >3% and <5%\n",
      "064\n",
      "3.9988202620001476\n",
      "mask percentage >3% and <5%\n",
      "114\n",
      "3.4672678563847734\n",
      "mask percentage >3% and <5%\n",
      "118\n",
      "4.214335951745936\n",
      "mask percentage >5% and <7%\n",
      "120\n",
      "6.105466889009544\n",
      "mask percentage >3% and <5%\n",
      "135\n",
      "4.353759533546689\n",
      "mask percentage >5% and <7%\n",
      "172\n",
      "5.777066757232412\n",
      "mask percentage >3% and <5%\n",
      "174\n",
      "4.402294630131531\n",
      "mask percentage >5% and <7%\n",
      "189\n",
      "6.3191702708010125\n",
      "mask percentage >3% and <5%\n",
      "200\n",
      "3.7111598199906504\n",
      "mask percentage >3% and <5%\n",
      "221\n",
      "3.864113646107895\n"
     ]
    }
   ],
   "source": [
    "NUM_MASKS = 49\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 215"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 001\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 002\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 003\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 004\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 006\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 007\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 008\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 009\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 010\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 011\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 013\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 014\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 015\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 016\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 017\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 018\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 019\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 020\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 021\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 022\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 023\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 024\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 025\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 026\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 027\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 028\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 029\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 030\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 031\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 032\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 033\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 034\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 035\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 036\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 037\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 038\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 039\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 040\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 041\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 042\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 043\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 044\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 045\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 046\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 047\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 048\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 050\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 051\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 052\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 053\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 054\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 055\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 056\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 057\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 058\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 059\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 060\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 061\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 062\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 063\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 064\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 065\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 066\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 067\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 069\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 070\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 071\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 072\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 074\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 075\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 076\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 077\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 078\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 079\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 080\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 081\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 082\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 083\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 084\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 085\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 086\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 087\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 088\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 089\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 090\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 091\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 092\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 093\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 094\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 095\n",
      "Prepare CP inputs - max projection only\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 096\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 097\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 099\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 100\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 101\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 102\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 103\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 104\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 105\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 106\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 107\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 108\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 109\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 110\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 111\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 112\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 113\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 114\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 115\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 116\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 117\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 118\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 119\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 120\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 121\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 122\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 123\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 124\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 125\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 126\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 127\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 128\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 129\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 130\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 131\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 132\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 133\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 134\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 135\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 136\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 137\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 138\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 139\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 140\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 141\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 142\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 143\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 145\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 148\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 149\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 150\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 151\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 152\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 153\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 154\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 155\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 156\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 157\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 158\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 159\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 161\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 162\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 163\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 164\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
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      "FOV 165\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 166\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 167\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 168\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 169\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 170\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 171\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 172\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 173\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 174\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 175\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 176\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 177\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 178\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 179\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 180\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 181\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 182\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 183\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 184\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 185\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 186\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 187\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 188\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 189\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 190\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 191\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 192\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 193\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 194\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 195\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 196\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 197\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 198\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 199\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 200\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 201\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 202\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 203\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 204\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 205\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 206\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 207\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 208\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 209\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 210\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 211\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 212\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 213\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 214\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 215\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 216\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 217\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 218\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 219\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 220\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 221\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 222\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 223\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\n",
      "FOV 224\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2001, 2013)\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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