{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as et\n",
    "import datetime\n",
    "from skimage.io import imread, imsave\n",
    "from imageio import volread as imread\n",
    "\n",
    "import tifffile\n",
    "import pystackreg\n",
    "from pystackreg import StackReg\n",
    "from skimage.filters import threshold_otsu\n",
    "import seaborn as sns\n",
    "\n",
    "from pystackreg.util import to_uint16   # make sure version 0.2.5 (not anything below)\n",
    "from ims_to_tiff import convert_to_tif   \n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to TIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles = glob.glob('Cycle_*')      # [-7:-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Native (rows, cols): (2048,2048)\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 5/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 9/13\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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     ]
    },
    {
     "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",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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     ]
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    {
     "name": "stdout",
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     "text": [
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 9/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
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    {
     "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",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Z Levels: 14\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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      "Z Levels: 14\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 6/13\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "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",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 5/13\n",
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     ]
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    {
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     "text": [
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 8/13\n",
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      "File Breakdown\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "Time Points: 1\n",
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    {
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     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Z Levels: 14\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 9/13\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "Time Points: 1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Breakdown\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Native (rows, cols): (2048,2048)\n",
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      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
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      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
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      "TimePoint 0/0 Z 2/13\n",
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      "Z Levels: 14\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 6/13\n",
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      "Time Points: 1\n",
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      "Z Levels: 14\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 8/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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     ]
    },
    {
     "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",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "Time Points: 1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
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     ]
    },
    {
     "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",
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      "File Breakdown\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Z Levels: 14\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 5/13\n",
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      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 6/13\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 9/13\n",
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      "File Breakdown\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
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    {
     "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",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 6/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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    },
    {
     "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",
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      "Z Levels: 14\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
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    {
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     "output_type": "stream",
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      "TimePoint 0/0\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 6/13\n",
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      "Time Points: 1\n",
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      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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     ]
    },
    {
     "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",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
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      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
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      "File Breakdown\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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      "Z Levels: 14\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 5/13\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 9/13\n",
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      "TimePoint 0/0 Z 11/13\n",
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      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "Z Levels: 14\n",
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     ]
    },
    {
     "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",
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      "Z Levels: 14\n",
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      "Z Levels: 14\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
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      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
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      "File Breakdown\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "File Breakdown\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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      "Time Points: 1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 6/13\n",
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      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 10/13\n",
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      "File Breakdown\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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    },
    {
     "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",
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      "File Breakdown\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
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      "File Breakdown\n",
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      "File Breakdown\n",
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 2/13\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TimePoint 0/0 Z 7/13\n",
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      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "File Breakdown\n",
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      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "Native (rows, cols): (2048,2048)\n",
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      "Cycle_7_F122.ims has B-tree error or import error\n"
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      "Cycle_7_F171.ims has B-tree error or import error\n"
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      "Cycle_8_F191.ims has B-tree error or import error\n"
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      "Cycle_F207.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F208.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F209.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F210.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F211.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F212.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F213.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F214.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F215.ims has B-tree error or import error\n"
     ]
    },
    {
     "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",
      "Cycle_F216.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F217.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F218.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F219.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F220.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F221.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F222.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F223.ims has B-tree error or import error\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "Cycle_F224.ims has B-tree error or import error\n",
      "Cycle_11_F005.ims.mmap has B-tree error or import error\n",
      "Cycle_6_F130.ims.mmap has B-tree error or import error\n"
     ]
    }
   ],
   "source": [
    "error_files = []\n",
    "for i in cycles:\n",
    "    try:\n",
    "        sourceFile = i\n",
    "        destFile = 'tif/'+i[:-4]+'.tif'\n",
    "        convert_to_tif(sourceFile, destFile)\n",
    "    except:\n",
    "        print(f'{i} has B-tree error or import error')\n",
    "        error_files.append(i)\n",
    "        pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lst = make list of 000 to 225 strings \n",
    "# lst = []\n",
    "# for i in range(0,225):\n",
    "#     lst.append(str(i).zfill(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cycles = glob.glob('tif/Cycle_11/*')  \n",
    "# nums = []\n",
    "\n",
    "# for i in cycles:\n",
    "#     nums.append(i[-7:-4])\n",
    "# #print(len(nums))\n",
    "# for i in lst:\n",
    "#     if i not in nums:\n",
    "#         print(i)\n",
    "        \n",
    "# WRITE DOWN THE CYCLE NUMBERS AS WELL IN YOUR NOTES!!!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove all erroneous files \n",
    "for i in error_files:\n",
    "    command = f'rm *{i[-7:-4]}*'\n",
    "    ! {command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    os.makedirs(f'tif/Cycle_{c}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 211\n",
    "NUM_DIGITS_OF_FOVS = len(str(NUM_FOVS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 2, 2048, 2048)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test image sizes\n",
    "im1 = imread('tif/Cycle_8/Cycle_8_F099.tif')\n",
    "im1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = imread('tif/Cycle_0/Cycle_F023.tif')\n",
    "mov = imread('tif/Cycle_8/Cycle_8_F023.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.TRANSLATION)\n",
    "tmat = sr.register(ref_binary, mov_binary)   # creating transformation matrix \n",
    "out_binary = sr.transform(mov_binary) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.        , 46.46770865],\n",
       "       [ 0.        ,  1.        , 76.950438  ],\n",
       "       [ 0.        ,  0.        ,  1.        ]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "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[0:2000, 0:2000])   #before reg\n",
    "    \n",
    "axs[1].imshow(im_reg[0:2000, 0:2000])   #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='0:1500', ylabel='0:1500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n",
      "going...\n"
     ]
    }
   ],
   "source": [
    "reg = np.zeros(mov.shape, dtype=np.uint16) # initialize with the right dtype\n",
    "for Z in range(mov.shape[0]): # Z \n",
    "    print(\"going...\")\n",
    "    for ch in range(mov.shape[1]): # channels\n",
    "        reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "        reg[Z,ch,...] = to_uint16(reg[Z,ch,...])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(900, 900)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_max = reg.max(0)\n",
    "reg_v = reg_max[0, ...]\n",
    "reg_v = reg_v[100:1000, 100:1000]\n",
    "reg_v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106 112 101 ... 105 113 106]\n",
      " [105 113 109 ... 109 109 109]\n",
      " [105 107 105 ... 106 107 105]\n",
      " ...\n",
      " [112 109 109 ... 111 110 112]\n",
      " [106 116 110 ... 112 113 104]\n",
      " [116 111 111 ... 114 113 107]]  \n",
      " \n",
      " [[109 110 106 ... 105 106 106]\n",
      " [105 106 107 ... 106 106 104]\n",
      " [108 108 107 ... 107 106 108]\n",
      " ...\n",
      " [107 108 106 ... 109 107 105]\n",
      " [109 104 106 ... 107 104 108]\n",
      " [115 111 107 ... 107 111 106]]\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": [
    "orig = imread('tif/Cycle_9/Cycle_9_F220.tif')\n",
    "orig = orig.max(0)\n",
    "orig = orig[0, ...]\n",
    "orig_v = orig[100:1000, 100:1000]\n",
    "#plt.imshow(orig_v, cmap=plt.cm.gray)\n",
    "\n",
    "# reg = imread(\"./CP_D2/MAX_a1_second.tif\")\n",
    "# reg = reg[1]\n",
    "# reg_v = reg[1000:3000, 1000:3000]\n",
    "# #plt.imshow(reg_v, cmap=plt.cm.gray)\n",
    "\n",
    "f, ax = plt.subplots(1,2, figsize = (20,60))\n",
    "ax[0].imshow(orig_v)\n",
    "ax[1].imshow(reg_v)\n",
    "ax[0].title.set_text('Before Registration (DNA Channel)')\n",
    "ax[1].title.set_text('After Registration (DNA Channel)')\n",
    "\n",
    "for ax in ax.flat:\n",
    "    ax.set(xlabel='100:1000', ylabel='100:1000')\n",
    "\n",
    "print(orig_v,\" \\n\" , \"\\n\",reg_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Registration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 10\n",
    "NUM_FOVS = 219"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 1 field 000 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F000_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "cycle 1 field 001 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F001_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "cycle 1 field 002 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F002_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "cycle 1 field 003 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F003_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "cycle 1 field 004 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F004_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "cycle 1 field 005 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F005_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "cycle 1 field 006 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F006_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "cycle 1 field 007 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F007_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "cycle 1 field 008 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F008_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "cycle 1 field 009 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F009_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "cycle 1 field 010 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F010_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "cycle 1 field 011 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F011_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "cycle 1 field 012 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F012_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F012_reg.tif\n",
      "cycle 1 field 013 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F013_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "cycle 1 field 014 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_1/Cycle_1_F014_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n"
     ]
    }
   ],
   "source": [
    "for c in range(CYCLE_NUMS-1): \n",
    "    refs = iter(glob.glob('tif/Cycle_0/*')) # list of cycle 0 .tif \n",
    "    movs = iter(glob.glob(f'tif/Cycle_{c+1}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "    for FOV in range(0, NUM_FOVS): # \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        ref_name = next(refs) \n",
    "        ref = imread(ref_name)\n",
    "        ref = ref.astype(np.uint16)\n",
    "        mov_name = next(movs)\n",
    "        mov = imread(mov_name)\n",
    "        mov = mov.astype(np.uint16)\n",
    "        FOV_num = mov_name[-7:-4]\n",
    "        print(f'cycle {c+1} field {FOV_num} ')\n",
    "\n",
    "        ref_max = ref.max(0)\n",
    "        ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "        mov_max = mov.max(0)\n",
    "        mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "        print(\"Got threshold\")\n",
    "        sr = StackReg(StackReg.RIGID_BODY)  \n",
    "        tmat = sr.register(ref_binary, mov_binary) \n",
    "        out = sr.transform(mov_binary) \n",
    "        out = pystackreg.util.to_uint16(out)\n",
    "    \n",
    "        # save binary\n",
    "        fname_to_save = f'reg_bin_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_bin_reg.tif'\n",
    "        print('Saving Binary Registered Images...', fname_to_save)\n",
    "        tifffile.imwrite(fname_to_save, out, imagej=True, photometric = 'minisblack',metadata={'axes':'YX'})\n",
    "\n",
    "        # save tmat\n",
    "        print(\"saving tmat\")\n",
    "        np.save(f'tmat_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_tmat.npy', tmat)\n",
    "\n",
    "        # THE REGISTRATION STEP\n",
    "        reg = np.zeros(mov.shape, dtype=np.uint16)               # initialize with the right dtype\n",
    "        for Z in range(mov.shape[0]):\n",
    "            print(\"registration going\")\n",
    "            for ch in range(mov.shape[1]): \n",
    "                reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "                reg[Z,ch,...] = to_uint16(reg[Z,ch,...])\n",
    "\n",
    "        fname_to_save = f'reg_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_reg.tif'\n",
    "        print('Saving Registered Images...', fname_to_save)\n",
    "        tifffile.imwrite(fname_to_save, reg, imagej=True,\n",
    "                         photometric = 'minisblack',metadata={'axes':'ZCYX'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 11 field 000 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_11/Cycle_11_F000_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_11/Cycle_11_F000_reg.tif\n",
      "cycle 11 field 001 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_11/Cycle_11_F001_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "Saving Registered Images... reg_Cyc_11/Cycle_11_F001_reg.tif\n",
      "cycle 11 field 002 \n",
      "Got threshold\n",
      "Saving Binary Registered Images... reg_bin_Cyc_11/Cycle_11_F002_bin_reg.tif\n",
      "saving tmat\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n",
      "registration going\n"
     ]
    }
   ],
   "source": [
    "# for Cycle 11\n",
    "c = 11\n",
    "refs = iter(glob.glob('tif/Cycle_0/*')) # list of cycle 0 .tif \n",
    "movs = iter(glob.glob(f'tif/Cycle_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "for FOV in range(0, NUM_FOVS): # \n",
    "    #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "    ref_name = next(refs) \n",
    "    ref = imread(ref_name)\n",
    "    ref = ref.astype(np.uint16)\n",
    "    mov_name = next(movs)\n",
    "    mov = imread(mov_name)\n",
    "    mov = mov.astype(np.uint16)\n",
    "    FOV_num = mov_name[-7:-4]\n",
    "    print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "    ref_max = ref.max(0)\n",
    "    ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "    mov_max = mov.max(0)\n",
    "    mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "    print(\"Got threshold\")\n",
    "    sr = StackReg(StackReg.RIGID_BODY)  \n",
    "    tmat = sr.register(ref_binary, mov_binary) \n",
    "    out = sr.transform(mov_binary) \n",
    "    out = pystackreg.util.to_uint16(out)   \n",
    "\n",
    "    # save binary\n",
    "    fname_to_save = f'reg_bin_Cyc_{c}' + f'/Cycle_{c}_F{FOV_num}_bin_reg.tif'\n",
    "    print('Saving Binary Registered Images...', fname_to_save)\n",
    "    tifffile.imwrite(fname_to_save, out, imagej=True, photometric = 'minisblack',metadata={'axes':'YX'})\n",
    "\n",
    "    # save tmat\n",
    "    print(\"saving tmat\")\n",
    "    np.save(f'tmat_Cyc_{c}' + f'/Cycle_{c}_F{FOV_num}_tmat.npy', tmat)\n",
    "\n",
    "    # THE REGISTRATION STEP\n",
    "    reg = np.zeros(mov.shape, dtype=np.uint16)               # initialize with the right dtype\n",
    "    for Z in range(mov.shape[0]):\n",
    "        print(\"registration going\")\n",
    "        for ch in range(mov.shape[1]): \n",
    "            reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "            reg[Z,ch,...] = to_uint16(reg[Z,ch,...])\n",
    "\n",
    "    fname_to_save = f'reg_Cyc_{c}' + f'/Cycle_{c}_F{FOV_num}_reg.tif'\n",
    "    print('Saving Registered Images...', fname_to_save)\n",
    "    tifffile.imwrite(fname_to_save, reg, imagej=True,\n",
    "                     photometric = 'minisblack',metadata={'axes':'ZCYX'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for Cycle 8\n",
    "c = 8\n",
    "refs = iter(glob.glob('tif/Cycle_0/*')) # list of cycle 0 .tif \n",
    "movs = iter(glob.glob(f'tif/Cycle_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "for FOV in range(0, NUM_FOVS): # \n",
    "    #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "    ref_name = next(refs) \n",
    "    ref = imread(ref_name)\n",
    "    ref = ref.astype(np.uint16)\n",
    "    mov_name = next(movs)\n",
    "    mov = imread(mov_name)\n",
    "    mov = mov.astype(np.uint16)\n",
    "    FOV_num = mov_name[-7:-4]\n",
    "    print(f'cycle 8 field {FOV_num} ')\n",
    "\n",
    "    ref_max = ref.max(0)\n",
    "    ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "    mov_max = mov.max(0)\n",
    "    mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "    print(\"Got threshold\")\n",
    "    sr = StackReg(StackReg.RIGID_BODY)  \n",
    "    tmat = sr.register(ref_binary, mov_binary) \n",
    "    out = sr.transform(mov_binary) \n",
    "    out = pystackreg.util.to_uint16(out)   \n",
    "\n",
    "    # save binary\n",
    "    fname_to_save = f'reg_bin_Cyc_8' + f'/Cycle_8_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_8' + f'/Cycle_8_F{FOV_num}_tmat.npy', tmat)\n",
    "\n",
    "    # THE REGISTRATION STEP\n",
    "    reg = np.zeros(mov.shape, dtype=np.uint16)               # initialize with the right dtype\n",
    "    for Z in range(mov.shape[0]):\n",
    "        print(\"registration going\")\n",
    "        for ch in range(mov.shape[1]): \n",
    "            reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "            reg[Z,ch,...] = to_uint16(reg[Z,ch,...])\n",
    "\n",
    "    fname_to_save = f'reg_Cyc_8' + f'/Cycle_8_F{FOV_num}_reg.tif'\n",
    "    print('Saving Registered Images...', fname_to_save)\n",
    "    tifffile.imwrite(fname_to_save, reg, imagej=True,\n",
    "                     photometric = 'minisblack',metadata={'axes':'ZCYX'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at Max and Min Shifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 219 # changed after QC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "-0.05292711185632015 -2.23772491565353 1.0517144025200196 -3.545906622297025\n",
      "tmat_Cyc_1 \n",
      " -1 -3 2 -4\n",
      "-1 -3 2 -4\n",
      "2\n",
      "-1.0196453545709119 -3.1489476027103365 0.49393684893016143 -4.495545148683618\n",
      "tmat_Cyc_2 \n",
      " -2 -4 1 -5\n",
      "-1 -4 2 -5\n",
      "3\n",
      "-1.4719924976481025 -3.4345510911603014 0.00033976508143496176 -5.166916675063135\n",
      "tmat_Cyc_3 \n",
      " -2 -4 1 -6\n",
      "-1 -4 2 -6\n",
      "4\n",
      "-1.423245168839344 -4.085207874598495 -0.24232743819584357 -5.731337426905839\n",
      "tmat_Cyc_4 \n",
      " -2 -5 -1 -6\n",
      "-1 -5 2 -6\n",
      "5\n",
      "-2.3700004478309893 -4.381978973631931 -0.4997247117655661 -6.043327178333698\n",
      "tmat_Cyc_5 \n",
      " -3 -5 -1 -7\n",
      "-1 -5 2 -7\n",
      "6\n",
      "-2.074169488722987 -4.841219242306124 -0.9203347512640221 -5.576269275037816\n",
      "tmat_Cyc_6 \n",
      " -3 -5 -1 -6\n",
      "-1 -5 2 -7\n",
      "7\n",
      "-2.1764269047924927 -6.46482296282295 -1.452257684488245 -6.171018458482877\n",
      "tmat_Cyc_7 \n",
      " -3 -7 -2 -7\n",
      "-1 -7 2 -7\n",
      "8\n",
      "-5.345211283326875 -9.29358196879218 0.7060002538914887 -15.813350244476766\n",
      "tmat_Cyc_8 \n",
      " -6 -10 1 -16\n",
      "-1 -10 2 -16\n",
      "9\n",
      "10\n",
      "11\n",
      "-0.2692130895341052 -2.9917462080783253 -4.8617165763945245 -10.882386242409025\n",
      "tmat_Cyc_11 \n",
      " -1 -3 -5 -11\n",
      "-1 -10 2 -16\n"
     ]
    }
   ],
   "source": [
    "## print('X shift and Y shift max min values')\n",
    "x_max_list=[]\n",
    "x_min_list=[]\n",
    "y_max_list=[]\n",
    "y_min_list=[]\n",
    "# X_indices =[]\n",
    "# Y_indices =[]\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    print(c+1)\n",
    "    if c == 8: \n",
    "        continue\n",
    "    if c == 9:\n",
    "        continue\n",
    "    else: \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",
    "    #     X_i, Y_i = X_SHIFT.index(X_max), Y_SHIFT.index(Y_max)\n",
    "    #     X_indices.append(X_i)\n",
    "    #     Y_indices.append(Y_i)\n",
    "        print(X_max,X_min,Y_max,Y_min)\n",
    "        #print(X_SHIFT, Y_SHIFT)\n",
    "        def round_shift(val):\n",
    "            import math\n",
    "            if val <0: # if negative,\n",
    "                val = math.floor(val)\n",
    "            else:\n",
    "                val = math.ceil(val)\n",
    "            return val\n",
    "\n",
    "        X_max = round_shift(X_max)\n",
    "        X_min = round_shift(X_min)\n",
    "        Y_max = round_shift(Y_max)\n",
    "        Y_min = round_shift(Y_min)\n",
    "\n",
    "        x_max_list.append(X_max)\n",
    "        x_min_list.append(X_min)\n",
    "        y_max_list.append(Y_max)\n",
    "        y_min_list.append(Y_min)\n",
    "        print(f'tmat_Cyc_{c+1} \\n',X_max,X_min,Y_max,Y_min)\n",
    "\n",
    "    X_max_total = max(x_max_list)\n",
    "    X_min_total = min(x_min_list)\n",
    "    Y_max_total = max(y_max_list)\n",
    "    Y_min_total = min(y_min_list)\n",
    "\n",
    "    print(X_max_total, X_min_total, Y_max_total, Y_min_total)\n",
    "    #print(X_indices, Y_indices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 032 591.9238744217496 968.2780332999173\n",
      "8 022 1597.913401283784 1265.2787095439135\n",
      "8 036 1.2192854636343782 16.411141502190958\n",
      "8 130 9.693200739309077 82.61549942988631\n",
      "8 139 255.49447042050133 40.49982008391021\n",
      "8 157 46.46770864947325 76.95043799854216\n"
     ]
    }
   ],
   "source": [
    "TMAT_DIR = 'tmat'\n",
    "\n",
    "for cNum in range(CYCLE_NUMS-1): # cycle_prefix is folder names cycle0,1,2... (in raw)\n",
    "    for FOV in range(NUM_FOVS):\n",
    "        FOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        tmat_loaded = np.load(f'{TMAT_DIR}_Cyc_{cNum+1}/Cycle_{cNum+1}_F{FOV}_tmat.npy')\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        if (moveX > 5) | (moveY > 5)|(moveX < -11) | (moveY < -11): #50 pixel is my max\n",
    "            print(cNum+1, FOV, moveX, moveY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 130 7.546705517340797 89.260727052211\n",
      "11 139 191.5229094699514 44.86919102912384\n",
      "11 157 33.84019221157496 29.50922180508178\n"
     ]
    }
   ],
   "source": [
    "TMAT_DIR = 'tmat'\n",
    "\n",
    "for FOV in range(NUM_FOVS):\n",
    "    FOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "    tmat_loaded = np.load(f'{TMAT_DIR}_Cyc_11/Cycle_11_F{FOV}_tmat.npy')\n",
    "    moveX = tmat_loaded[0,2]\n",
    "    moveY = tmat_loaded[1,2]\n",
    "    if (moveX > 5) | (moveY > 5)|(moveX < -11) | (moveY < -11): #50 pixel is my max\n",
    "        print(\"11\", FOV, moveX, moveY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['000', '001', '002', '003', '004', '005', '006', '007', '008',\n",
       "       '009', '010', '011', '012', '013', '014', '015', '016', '017',\n",
       "       '018', '019', '020', '021', '023', '024', '025', '026', '027',\n",
       "       '028', '029', '030', '031', '033', '034', '035', '037', '038',\n",
       "       '039', '040', '041', '042', '043', '044', '045', '046', '047',\n",
       "       '048', '049', '050', '051', '052', '053', '054', '055', '056',\n",
       "       '057', '058', '059', '060', '061', '062', '063', '064', '065',\n",
       "       '066', '067', '068', '069', '070', '071', '072', '073', '074',\n",
       "       '075', '076', '077', '078', '079', '080', '081', '082', '083',\n",
       "       '084', '085', '086', '087', '088', '089', '090', '091', '092',\n",
       "       '093', '094', '095', '096', '097', '098', '099', '100', '101',\n",
       "       '102', '103', '104', '105', '106', '107', '108', '109', '110',\n",
       "       '111', '112', '113', '114', '115', '116', '117', '118', '119',\n",
       "       '120', '121', '122', '123', '124', '125', '126', '127', '128',\n",
       "       '129', '131', '132', '133', '134', '135', '136', '137', '138',\n",
       "       '140', '141', '142', '143', '144', '145', '146', '147', '148',\n",
       "       '149', '150', '151', '152', '153', '154', '155', '156', '158',\n",
       "       '159', '160', '161', '162', '163', '164', '165', '166', '167',\n",
       "       '168', '169', '170', '171', '172', '173', '174', '175', '176',\n",
       "       '177', '178', '179', '180', '181', '182', '183', '184', '185',\n",
       "       '186', '187', '188', '189', '190', '191', '192', '193', '194',\n",
       "       '195', '196', '197', '198', '199', '200', '201', '202', '203',\n",
       "       '204', '205', '206', '207', '208', '209', '210', '211', '212',\n",
       "       '213', '214', '215', '216', '217', '218', '219', '220', '221',\n",
       "       '222', '223', '224'], dtype='<U3')"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1 = []\n",
    "c8 = glob.glob(\"tif/Cycle_0/*\")\n",
    "for i in c8:\n",
    "    l1.append(i[-7:-4])\n",
    "l1 = np.array(l1)\n",
    "l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['000', '001', '002', '003', '004', '005', '006', '007', '008',\n",
       "       '009', '010', '011', '012', '013', '014', '015', '016', '017',\n",
       "       '018', '019', '020', '021', '023', '024', '025', '026', '027',\n",
       "       '028', '029', '030', '031', '033', '034', '035', '037', '038',\n",
       "       '039', '040', '041', '042', '043', '044', '045', '046', '047',\n",
       "       '048', '049', '050', '051', '052', '053', '054', '055', '056',\n",
       "       '057', '058', '059', '060', '061', '062', '063', '064', '065',\n",
       "       '066', '067', '068', '069', '070', '071', '072', '073', '074',\n",
       "       '075', '076', '077', '078', '079', '080', '081', '082', '083',\n",
       "       '084', '085', '086', '087', '088', '089', '090', '091', '092',\n",
       "       '093', '094', '095', '096', '097', '098', '099', '100', '101',\n",
       "       '102', '103', '104', '105', '106', '107', '108', '109', '110',\n",
       "       '111', '112', '113', '114', '115', '116', '117', '118', '119',\n",
       "       '120', '121', '122', '123', '124', '125', '126', '127', '128',\n",
       "       '129', '131', '132', '133', '134', '135', '136', '137', '138',\n",
       "       '140', '141', '142', '143', '144', '145', '146', '147', '148',\n",
       "       '149', '150', '151', '152', '153', '154', '155', '156', '158',\n",
       "       '159', '160', '161', '162', '163', '164', '165', '166', '167',\n",
       "       '168', '169', '170', '171', '172', '173', '174', '175', '176',\n",
       "       '177', '178', '179', '180', '181', '182', '183', '184', '185',\n",
       "       '186', '187', '188', '189', '190', '191', '192', '193', '194',\n",
       "       '195', '196', '197', '198', '199', '200', '201', '202', '203',\n",
       "       '204', '205', '206', '207', '208', '209', '210', '211', '212',\n",
       "       '213', '214', '215', '216', '217', '218', '219', '220', '221',\n",
       "       '222', '223', '224'], dtype='<U3')"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2 = []\n",
    "c8 = glob.glob(\"tif/Cycle_8/*\")\n",
    "for i in c8:\n",
    "    l2.append(i[-7:-4])\n",
    "l2 = np.array(l2)\n",
    "l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(l1, l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 0 field 000 \n",
      "cycle 0 field 001 \n",
      "cycle 0 field 002 \n",
      "cycle 0 field 003 \n",
      "cycle 0 field 004 \n",
      "cycle 0 field 005 \n",
      "cycle 0 field 006 \n",
      "cycle 0 field 007 \n",
      "cycle 0 field 008 \n",
      "cycle 0 field 009 \n",
      "cycle 0 field 010 \n",
      "cycle 0 field 011 \n",
      "cycle 0 field 012 \n",
      "cycle 0 field 013 \n",
      "cycle 0 field 014 \n",
      "cycle 0 field 015 \n",
      "cycle 0 field 016 \n",
      "cycle 0 field 017 \n",
      "cycle 0 field 018 \n",
      "cycle 0 field 019 \n",
      "cycle 0 field 020 \n",
      "cycle 0 field 021 \n",
      "cycle 0 field 022 \n",
      "cycle 0 field 023 \n",
      "cycle 0 field 024 \n",
      "cycle 0 field 025 \n",
      "cycle 0 field 026 \n",
      "cycle 0 field 027 \n",
      "cycle 0 field 028 \n",
      "cycle 0 field 029 \n",
      "cycle 0 field 030 \n",
      "cycle 0 field 031 \n",
      "cycle 0 field 032 \n",
      "cycle 0 field 033 \n",
      "cycle 0 field 034 \n",
      "cycle 0 field 035 \n",
      "cycle 0 field 036 \n",
      "cycle 0 field 037 \n",
      "cycle 0 field 038 \n",
      "cycle 0 field 039 \n",
      "cycle 0 field 040 \n",
      "cycle 0 field 041 \n",
      "cycle 0 field 042 \n",
      "cycle 0 field 043 \n",
      "cycle 0 field 044 \n",
      "cycle 0 field 045 \n",
      "cycle 0 field 046 \n",
      "cycle 0 field 047 \n",
      "cycle 0 field 048 \n",
      "cycle 0 field 049 \n",
      "cycle 0 field 050 \n",
      "cycle 0 field 051 \n",
      "cycle 0 field 052 \n",
      "cycle 0 field 053 \n",
      "cycle 0 field 054 \n",
      "cycle 0 field 055 \n",
      "cycle 0 field 056 \n",
      "cycle 0 field 057 \n",
      "cycle 0 field 058 \n",
      "cycle 0 field 059 \n",
      "cycle 0 field 060 \n",
      "cycle 0 field 061 \n",
      "cycle 0 field 062 \n",
      "cycle 0 field 063 \n",
      "cycle 0 field 064 \n",
      "cycle 0 field 065 \n",
      "cycle 0 field 066 \n",
      "cycle 0 field 067 \n",
      "cycle 0 field 068 \n",
      "cycle 0 field 069 \n",
      "cycle 0 field 070 \n",
      "cycle 0 field 071 \n",
      "cycle 0 field 072 \n",
      "cycle 0 field 073 \n",
      "cycle 0 field 074 \n",
      "cycle 0 field 075 \n",
      "cycle 0 field 076 \n",
      "cycle 0 field 077 \n",
      "cycle 0 field 078 \n",
      "cycle 0 field 079 \n",
      "cycle 0 field 080 \n",
      "cycle 0 field 081 \n",
      "cycle 0 field 082 \n",
      "cycle 0 field 083 \n",
      "cycle 0 field 084 \n",
      "cycle 0 field 085 \n",
      "cycle 0 field 086 \n",
      "cycle 0 field 087 \n",
      "cycle 0 field 088 \n",
      "cycle 0 field 089 \n",
      "cycle 0 field 090 \n",
      "cycle 0 field 091 \n",
      "cycle 0 field 092 \n",
      "cycle 0 field 093 \n",
      "cycle 0 field 094 \n",
      "cycle 0 field 095 \n"
     ]
    }
   ],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    if c == 0:\n",
    "        refs = iter(glob.glob('tif/Cycle_0/*'))\n",
    "        for FOV in range(0, NUM_FOVS): \n",
    "            ref_name = next(refs) \n",
    "            ref = imread(ref_name)\n",
    "            ref = ref.astype(np.uint16)\n",
    "            FOV_num = ref_name[-7:-4]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "            Z_ch0 = []\n",
    "            Z_ch0_dic = {}\n",
    "\n",
    "            for Z in range(ref.shape[0]): # Z \n",
    "                for ch in range(0, 1):\n",
    "                    arsum = np.sum(ref[Z, ch, ...])\n",
    "                    Z_ch0.append(arsum)\n",
    "                    Z_ch0_dic[arsum] = Z\n",
    "\n",
    "            df.loc[FOV_num, 'max_int_C0'] = max(Z_ch0)                 \n",
    "            df.loc[FOV_num, 'z_idx_C0'] = Z_ch0_dic[max(Z_ch0)] \n",
    "    else:\n",
    "        ims = iter(glob.glob(f'reg_Cyc_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006  \n",
    "        for FOV in range(0, NUM_FOVS): # \n",
    "            #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "            im_name = next(ims)\n",
    "            im = imread(im_name)\n",
    "            im = im.astype(np.uint16)\n",
    "            FOV_num = im_name[-11:-8]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "            Z_ch0 = []\n",
    "            Z_ch0_dic = {}\n",
    "\n",
    "            for Z in range(im.shape[0]): # Z \n",
    "                for ch in range(0, 1):\n",
    "                    arsum = np.sum(im[Z, ch, ...])\n",
    "                    Z_ch0.append(arsum)\n",
    "                    Z_ch0_dic[arsum] = Z\n",
    "\n",
    "            df.loc[FOV_num, f'max_int_C{c}'] = max(Z_ch0)                  \n",
    "            df.loc[FOV_num, f'z_idx_C{c}'] = Z_ch0_dic[max(Z_ch0)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_int_C0</th>\n",
       "      <th>z_idx_C0</th>\n",
       "      <th>max_int_C1</th>\n",
       "      <th>z_idx_C1</th>\n",
       "      <th>max_int_C2</th>\n",
       "      <th>z_idx_C2</th>\n",
       "      <th>max_int_C3</th>\n",
       "      <th>z_idx_C3</th>\n",
       "      <th>max_int_C4</th>\n",
       "      <th>z_idx_C4</th>\n",
       "      <th>max_int_C5</th>\n",
       "      <th>z_idx_C5</th>\n",
       "      <th>max_int_C6</th>\n",
       "      <th>z_idx_C6</th>\n",
       "      <th>max_int_C7</th>\n",
       "      <th>z_idx_C7</th>\n",
       "      <th>max_int_C8</th>\n",
       "      <th>z_idx_C8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>451843984.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>449570740.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>449446541.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>444599233.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>449373453.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>449690330.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>449483355.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>457786257.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>454561219.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>484700735.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>478451368.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>476740146.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>467785124.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>480073539.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>481473178.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>480302777.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>495771394.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>482511468.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>480219074.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>474325768.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>473929629.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>466376094.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>476299456.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>478115033.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>477027783.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>490687393.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>480871648.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>474405524.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>469888370.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>469565307.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>463966266.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>470865555.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>474101097.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>473317252.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>484094368.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>496566729.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>461778555.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>459705102.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>459754354.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>455834625.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>459909492.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>461746925.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>462585397.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>469511970.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>477995791.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>492564140.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>755735392.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>494824726.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>482961925.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>496455586.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>499837213.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>500659107.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>512425062.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>627451943.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>502275685.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>629734079.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>504442255.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>491131629.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>505472326.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>509728572.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>509037705.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>523038291.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>502529197.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>505483678.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>632368925.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>505412323.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>493019271.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>507593172.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>511575353.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>510253898.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>524461774.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>501894313.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>491147119.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>754549720.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>626822169.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>481680726.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>494156425.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>498425645.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>612527773.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>508753189.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>617893716.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>472396812.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>737060972.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>472772963.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>464242170.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>473875451.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>475767622.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>472932241.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>482698058.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>477065452.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>225 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      max_int_C0  z_idx_C0   max_int_C1  z_idx_C1   max_int_C2  z_idx_C2  \\\n",
       "000  451843984.0       5.0  449570740.0       5.0  449446541.0       5.0   \n",
       "001  484700735.0       7.0  478451368.0       6.0  476740146.0       7.0   \n",
       "002  480219074.0       6.0  474325768.0       5.0  473929629.0       5.0   \n",
       "003  474405524.0       5.0  469888370.0       5.0  469565307.0       5.0   \n",
       "004  461778555.0       3.0  459705102.0       3.0  459754354.0       3.0   \n",
       "..           ...       ...          ...       ...          ...       ...   \n",
       "220  492564140.0       6.0  755735392.0       6.0  494824726.0       5.0   \n",
       "221  502275685.0       5.0  629734079.0       5.0  504442255.0       5.0   \n",
       "222  505483678.0       4.0  632368925.0       4.0  505412323.0       4.0   \n",
       "223  491147119.0       5.0  754549720.0       5.0  626822169.0       4.0   \n",
       "224  472396812.0       6.0  737060972.0       6.0  472772963.0       6.0   \n",
       "\n",
       "      max_int_C3  z_idx_C3   max_int_C4  z_idx_C4   max_int_C5  z_idx_C5  \\\n",
       "000  444599233.0       5.0  449373453.0       5.0  449690330.0       5.0   \n",
       "001  467785124.0       6.0  480073539.0       6.0  481473178.0       6.0   \n",
       "002  466376094.0       5.0  476299456.0       5.0  478115033.0       5.0   \n",
       "003  463966266.0       5.0  470865555.0       5.0  474101097.0       5.0   \n",
       "004  455834625.0       3.0  459909492.0       3.0  461746925.0       3.0   \n",
       "..           ...       ...          ...       ...          ...       ...   \n",
       "220  482961925.0       5.0  496455586.0       5.0  499837213.0       5.0   \n",
       "221  491131629.0       5.0  505472326.0       5.0  509728572.0       5.0   \n",
       "222  493019271.0       4.0  507593172.0       4.0  511575353.0       4.0   \n",
       "223  481680726.0       4.0  494156425.0       5.0  498425645.0       4.0   \n",
       "224  464242170.0       6.0  473875451.0       6.0  475767622.0       6.0   \n",
       "\n",
       "      max_int_C6  z_idx_C6   max_int_C7  z_idx_C7   max_int_C8  z_idx_C8  \n",
       "000  449483355.0       5.0  457786257.0       5.0  454561219.0       5.0  \n",
       "001  480302777.0       6.0  495771394.0       6.0  482511468.0       7.0  \n",
       "002  477027783.0       5.0  490687393.0       5.0  480871648.0       5.0  \n",
       "003  473317252.0       5.0  484094368.0       5.0  496566729.0       5.0  \n",
       "004  462585397.0       3.0  469511970.0       3.0  477995791.0       3.0  \n",
       "..           ...       ...          ...       ...          ...       ...  \n",
       "220  500659107.0       5.0  512425062.0       5.0  627451943.0       5.0  \n",
       "221  509037705.0       5.0  523038291.0       5.0  502529197.0       5.0  \n",
       "222  510253898.0       4.0  524461774.0       4.0  501894313.0       4.0  \n",
       "223  612527773.0       4.0  508753189.0       4.0  617893716.0       4.0  \n",
       "224  472932241.0       6.0  482698058.0       6.0  477065452.0       6.0  \n",
       "\n",
       "[225 rows x 18 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('max_intensity_indices')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      max_int_C0  z_idx_C0   max_int_C1  z_idx_C1   max_int_C2  z_idx_C2  \\\n",
      "157  532231007.0       6.0  797238509.0       5.0  800115396.0       5.0   \n",
      "\n",
      "      max_int_C3  z_idx_C3   max_int_C4  z_idx_C4   max_int_C5  z_idx_C5  \\\n",
      "157  784547228.0       5.0  537538499.0       5.0  540744852.0       5.0   \n",
      "\n",
      "      max_int_C6  z_idx_C6   max_int_C7  z_idx_C7   max_int_C8  z_idx_C8  \n",
      "157  546341411.0       5.0  560388693.0       5.0  750419249.0       4.0  \n"
     ]
    }
   ],
   "source": [
    "print(df.loc[['157']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      max_int_C0  z_idx_C0   max_int_C1  z_idx_C1   max_int_C2  z_idx_C2  \\\n",
      "022  471162356.0       6.0  469400124.0       6.0  470071351.0       6.0   \n",
      "\n",
      "      max_int_C3  z_idx_C3   max_int_C4  z_idx_C4   max_int_C5  z_idx_C5  \\\n",
      "022  464487685.0       5.0  470523092.0       6.0  473881625.0       6.0   \n",
      "\n",
      "      max_int_C6  z_idx_C6   max_int_C7  z_idx_C7   max_int_C8  z_idx_C8  \n",
      "022  473389751.0       6.0  483015744.0       6.0  122126562.0       4.0  \n"
     ]
    }
   ],
   "source": [
    "print(df.loc[['022']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[107 108 104 ... 108 107 109]\n",
      " [107 111 108 ... 106 108 112]\n",
      " [105 107 106 ... 105 110 108]\n",
      " ...\n",
      " [118 114 109 ... 110 109 110]\n",
      " [117 110 109 ... 114 107 113]\n",
      " [113 114 112 ... 105 107 112]]  \n",
      " \n",
      " [[109 107 108 ... 107 108 106]\n",
      " [106 106 107 ... 106 104 107]\n",
      " [106 105 108 ... 106 105 107]\n",
      " ...\n",
      " [109 112 114 ... 113 113 109]\n",
      " [111 109 112 ... 106 106 104]\n",
      " [107 111 111 ... 109 107 108]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x4320 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QC by eye, every single Cycle -- pick a random FOV\n",
    "\n",
    "orig = imread('tif/Cycle_1/Cycle_1_F053.tif')\n",
    "orig = orig.max(0)\n",
    "orig = orig[0, ...]\n",
    "orig_v = orig[100:1500, 100:1500]\n",
    "\n",
    "reg = imread(\"reg_Cyc_1/Cycle_1_F053_reg.tif\")\n",
    "reg = reg.max(0)\n",
    "reg = reg[0, ...]\n",
    "reg_v = reg[100:1500, 100:1500]\n",
    "#plt.imshow(reg_v, cmap=plt.cm.gray)\n",
    "\n",
    "f, ax = plt.subplots(1,2, figsize = (20,60))\n",
    "ax[0].imshow(orig_v)\n",
    "ax[1].imshow(reg_v)\n",
    "ax[0].title.set_text('Before Registration (DNA Channel)')\n",
    "ax[1].title.set_text('After Registration (DNA Channel)')\n",
    "\n",
    "for ax in ax.flat:\n",
    "    ax.set(xlabel='100:1500', ylabel='100:1500')\n",
    "\n",
    "print(orig_v,\" \\n\" , \"\\n\",reg_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YAPw2cS5mZqVFw07W0cPjmM2sp8RYbTGbmVWWC3OxCYnOEW7fV36KzB/PPJwjn7ipdJxp48pPQQrw3GB/kjipJgbfsGFCmjgqGuzTmqHBBJ+dVD+kE9L8qAxFmmlINW3nJHH6p5WfWhWgr0Ln29xiHoU2b3q4dIxbZvu8p1lVuTCbmVVMpPhLa4S5MJtZT3GL2cysYmLILWYzs0pxi9nMrGIiRn+LuWjazz+UNCV/vIOk8yT9l6TzJU0dnhTNzFoXQ60vVVU04PQ/gA354wvIJjU6P193cRfzMjPryNCgWl6qqqgroy/ihSvP50fEofnjn0pa2uggSQvI5m/m5BmHcdik/ctnambWgl44+VfUYl4m6fT88W2S5gNIehHQ8BKm2vmYXZTNbDjFkFpeqqqoMJ8FHCXpPmAe8AtJ9wP/nm8zM6uUiNaXqiqaj/lp4J2SJgP75PuvjIhHhiM5M7N2DVdLWNIngTcBm4D7gNMjYm2d/Y4lO0fXD1wUEf9cFLul2WYiYl1E3BYRv3JRNrMqi1DLS0mLgZdGxEHA3cC52+4gqR/4PHAcWa/Dn0uaVxQ4zTRgZmYVMTiolpcyIuL6msERNwFz6ux2GHBvRNwfEZuAbwInFcV2YTazntJOi1nSAklLapYFHb7sGcC1ddbPBh6qeb4yX9dU16/8G5eog/3n43fgiIGNpWK87qlbYPs9S+fyi10OKR0DYAclenM2pxkpn/3VVd5mpenj2/R8+Y/n0OY0c2f37Tw9SZyJU1YmiRPrn04SZ+i58vOcV007fcwRsQhY1Gi7pBuA3epsWhgRV+X7LAQ2A5fWC1HvZYvyGlWXZB+x+tvlAiQoymZWbSlHW0TEMc22SzoNOAE4OqLuK68E9qh5PgdYVfS6o6owm5kVGcZRGccCZwNH5TeorucWYH9JewMPA28FTi2K7T5mM+spg0N9LS8lfQ6YDCyWtFTShQCSZkm6BiA/Ofge4DpgOfCtiLijKLBbzGbWU4brwpGI2K/B+lXA8TXPrwGuaSe2C7OZ9ZShMTDt53sl7dFsHzOzKhnGC0y6pqiT5aPAzZJ+IundktLcM93MrEt6Ya6MosJ8P9nwjo8CLwfulPR9Safl82fUVTto+6Zn70mYrplZc0OhlpeqKirMERFD+aWHZwKzgC8Ax5IV7UYHvTDt5+Ge9tPMhtEwjsromqKTf1v9SomIAeBq4GpJO3QtKzOzDlW4h6JlRYX5LY02RES566PNzLqgyl0UrSqaj/nu4UrEzCyFKo+2aJXHMZtZT6nwza9b5sJsZj0l6k7oNrp0vTDvvrl4n1a8SOu5ZfbJpWJMHL9dklw2DqR522ZMTtNNv/mx55LEGdic5v1JRQmmRY1Uzae+ND/sSjUQIFGgvolpPsvbV6gYbnZXhplZtbjFbGZWMe5jNjOrGLeYzcwqxi1mM7OKGXSL2cysWobpzlJd1bQwS5pAdo+qVRFxg6RTgSPIbpGyKJ87w8ysMobGQIv54nyfifndYCcBVwBHA4cBp3U3PTOz9oyFSYxeFhEHSRpHdofXWRExKOkS4LZGB0laACwAOGPqYbxuoqf+NLPh0Qsn/4ouH+rLuzMmAxOBqfn67YDxjQ6qnY/ZRdnMhtOQ1PJSVUUt5i8BdwH9wELgMkn3A4cD3+xybmZmbRsc6QQSKJr289OS/jN/vErSV4FjgH+PiF8OR4JmZu3o+VEZkBXkmsdrgcu7mpGZWQljYVSGmdmoMhZGZZiZjSpjoiujrGmDabri+yeUHwSz7+TdE2QCu0x7NkmcVDQhzSexavdK60swH/PQQJp5izUxzb2HU8wxDaAJafLp22lykjgDFWqn9sJwObeYzaynDFarfdERF2Yz6yluMZuZVYwLs5lZxVTsVElHXJjNrKeMiRazpH2Bk4E9gM3APcA3IuLpLudmZta24bokW9IngTcBm4D7gNPzi/Dq7dsPLAEejogTimI3HUsk6b3AhcD2wCuAHcgK9C8kvaaNr8HMbFgMqfWlpMXASyPiIOBu4Nwm+76PbB77lhQN8nwXcGxE/F+yOTLmRcRC4Fjg040OkrRA0hJJS76/8d5WczEzK22ojaWMiLg+IjbnT28C5tTbT9Ic4I3ARa3GbmX0/Zbuju3Ipv8kIh6kxWk/j91hv1ZzMTMrrZ3CXNuIzJcFHb7sGcC1DbZ9BvggbfwuKOpjvgi4RdJNwJHA+QCSdgaebPVFzMyGSzvXIEbEImBRo+2SbgB2q7NpYURcle+zkOz826V1jj8BeDQiftVO92/RtJ8X5IkdCHwqIu7K1z9GVqjNzCol5VwZEXFMs+35LfdOAI6OiHq/E14FnCjpeLJzdVMkXRIRb28Wt5VpP+8A7ijaz8ysCoZxVMaxwNnAURGxod4+EXEu+UnBvMX8gaKiDK31MZuZjRpDRMtLSZ8jO++2WNJSSRcCSJol6ZoygX2BiZn1lOG6wCQi6o5syG8ucnyd9TcCN7YSu+uF+bX/MLV4pxY8ccmjpWNc0XggSVvUN5AkzuQXpekM6581PUmcFx2XZjrTfZ+sO8a+beN2n1g6xlM3pfmjcNNNv0kSZ8YF70kS5/kvfDFJnP7p5d9jgPe/aWOSOClUZwLSzrnFbGY9ZUxckm1mNppsTnQzgpHkwmxmPWX0l2UXZjPrMe7KMDOrmATD4EacC7OZ9ZTRX5ZdmM2sx7grw8ysYgZ7oM1cNFH+VEn/LOkuSU/ky/J83bQmx70wld5//PzO9FmbmTUwXPMxd1PRZVHfAp4CXhMRMyNiJvDafN1ljQ6qnY/5jCPmpcvWzKxAtPGvqooK89yIOD8i1mxZERFrIuJ8YM/upmZm1r6x0GJ+QNIHJe26ZYWkXSWdDTzU3dTMzNo3jLPLdU1RYX4LMBP4kaQnJT1JNjvSDOCULudmZta2aGOpqqI7mDxFNhH02dtuk3Q6cHGX8jIz68jmSpfc1pSZE/G8ZFmYmSXSCyf/mraYJd3eaBOwa4NtW4l1aeb4Xf/0dqVjTJ+9PkEm8NTDOyaJM5nnk8RZf3Oa++I+/2x/kjgzT5ydJM7gA+Xn4F69Ks184NNfnWYObu0wJUmc/plpPoMMpSlOMTBcN3QqVuWTeq0qusBkV+ANZMPjagn4eVcyMjMrocot4VYVFebvApMiYum2GyTd2JWMzMxK6PkWc0Sc2WTbqenTMTMrZzB6v8VsZjaqVHl8cqtcmM2sp4yFPmYzs1Gl5/uYzcxGm17oyihzgUlDW037ecs93XgJM7O6euECk44Ls6RrG23batrPV+zf6UuYmbVtMKLlpaqKrvw7tNEm4OD06ZiZldMLXRlFfcy3AD8iK8TbangHEzOzkTIWTv4tB/4yIn6vo1iS52M2s8qpct9xq4oK80do3A/9N2lTMTMrr+e7MiLi8iabpyfOxcystKjwSb1WlRnHfB6tTJSfaFrBabttKB0jBsXAxvJTW06fVT4XgL6pE5PEGf9MmulM1z2aZlj7ptseThKnf6fyU70e+Kb1LPlO+ak/++fuXDoGQDy1pninYaQdyr/HAH3jq3NJxGCvt5hTzMdcJSmKso0uKYqyjS7D1ZUh6ZPAm4BNwH3A6RGxts5+fwucRXY3q1/n+z3XLLbnYzaznjKMXRmLgXMjYrOk84Fz2eY2fJJmA+8F5kXERknfAt4KfLlZYM/HbGY9ZbhazBFxfc3Tm4A3N9h1HLCDpAFgIrCqKHbTK/8i4syI+GmDbZ6P2cwqp51Lsmunj8iXBR2+7BnA710NHREPA/8CPAisBp7epqDXVZ0eezOzBNq51DoiFgGLGm2XdAOwW51NCyPiqnyfhcBm4NI6x08HTgL2BtYCl0l6e0Rc0iwvF2Yz6ykpuzIi4phm2yWdBpwAHB31O7ePAX4bEY/l+18BHAG4MJvZ2DGMozKOJTvZd1RENBpD+yBwuKSJwEbgaGBJUeyuTPtpZjZSIqLlpaTPAZOBxZKWSroQQNIsSdfkudwMXA7cSjZUro8mXSdbNC3MkqZI+n+Svibp1G22faHJcb+bj3nJvUU5mJklM0S0vJQREftFxB4RcXC+/FW+flVEHF+z34cj4sUR8dKI+IuIeL4odlGL+WKyMcvfBt4q6duStlwqdHiThH83H/P8/Qq/QDOzVHphovyiPuZ9I+LP8sffyc8+/kDSiV3Oy8ysI4Mx+if+LCrM20nqi8i+0oj4mKSVwI+BSV3PzsysTb0wiVFRV8Z/Aa+rXRERXwH+nuz6cDOzShmuPuZuKpr284MN1n9f0se7k5KZWeeq3HfcqjLD5c5LloWZWSJDES0vVdX1aT81dXK7OdV1993l58N9ySseTZAJbHoqzfDvGBhMEicVJRrV3j9tfJI4z93bdGbElqwcl2ge5fUbk8Rh/IQkYfpetE+SOJt++Hvzk3Vk3Jzq3AK0F1rMnvbTzHrKWBiV4Wk/zWxUqXIXRauKTv6d2WSbp/00s8oZC10ZZmajSs+3mM3MRhu3mM3MKmYwqjXaqRMuzGbWU8bCJdkd2Wraz5/f2Y2XMDOrqxcuyS6aj3k3SV+U9HlJMyV9RNKvJX1L0u6Njttq2s8j5qXP2sysgWGcKL9rilrMXwbuBB4Cfkh2a5Q3Aj8BLuxqZmZmHej5S7KBXSPiswCS3h0R5+frPyup4RhnM7ORMhZGZdS2qL+6zbb+xLmYmZU2Fi7JvkrSpIh4NiL+cctKSfsBv+luamZm7aty33Grii7J/lCD9fdK+l53UjIz61yV+45b5fmYzayn9MKojK7Pxxwb0sxjO2fnp0vHSDbf8HZp+rDG7Tc7SZxnlq5IEmfT82lOGzy3ovw8ygDbzSp//dNuyzYnyARibeEd51uiGWm+54O3Lk8SZ+DxNJ/l8QdNSRInhSqPT26V52M2s55S5ZZwqzwfs5n1lJ4fleH5mM1stOmFk3+exMjMespY6MowMxtVxsKVf2Zmo8qYbDFL2iUiHu1GMmZmZfVCH3PR4OsZ2ywzgRXAdGBGk+MWAEvyZUELg7wL92lxsLjjjIJcHMffcy/NF+VvWF2ShoAHtlk9B1iZ1fTYp71fAw1fZ0lEzHec7sWpUi6OMzxxqpRLyjhjQdG1cB8km6zoxIjYOyL2Blbmj5MUZTMz21rTwhwR/wKcBXxI0qckTYYeOOVpZlZhhbNHRMTKiDiF7A4mi4GJXchjkeN0PU6VcnGc4YlTpVxSxul5TfuYf29naQdg34hYJun0iLi4e6mZmY1NbRXmrQ6UHoyIPRPnY2Y25hXdJfv2BsuvaXHazyKSjpX0G0n3Sjqnwxj/IelRSctK5LGHpB9KWi7pDknv6zDO9pJ+Kem2PE6peasl9Uv6H0nfLRFjRX5386WSlpSIM03S5ZLuyt+nV3YQ44A8jy3LM5Le30Gcv83f32WSviFp+3Zj5HHel8e4o9086n3uJM2QtFjSPfn/0zuIcUqez5CklkYxNIjzyfx7dbukKyVN6zDOR/MYSyVdL2lWJ3Fqtn1AUkjaqZWvbUwqGHf4CHAwsNc2y1xgVYJxjf3AfcA+wATgNmBeB3GOBA4FlpXIZXfg0PzxZODuDnMR2Yx8AOOBm4HDS+T1d8DXge+WiLEC2CnB9+srwFn54wnAtATf/zXAXm0eNxv4LbBD/vxbwDs7eP2XAsvIzpuMA24A9i/zuQM+AZyTPz4HOL+DGAcCBwA3AvNL5PJ6YFz++PyiXJrEmVLz+L3AhZ3EydfvAVxHNgy39GeyV5eik39bpv18YJtlRf6hKesw4N6IuD8iNgHfBE5qN0hE/Bh4skwiEbE6Im7NH68DlpMVgHbjREQ8mz8dny8d9RdJmgO8Ebiok+NTkjSF7IftSwARsSki1pYMezRwX0RsO1a+FeOAHSSNIyusqzqIcSBwU0RsiIjNwI+Ak1s9uMHn7iSyX2Dk//9JuzEiYnlEtHVPzQZxrs+/LoCbyK5B6CTOMzVPd6SFz3OTn8lPkw3D9eiuJoqGy50ZET9tsC3FtJ+zgYdqnq+kg2KYmqS5wCFkrd1Oju+XtBR4FFgcER3FAT5D9iEuO8FsANdL+pWkBR3G2Ad4DLg471q5SNKOJfN6K/CNdg+KiIeBfwEeBFYDT0fE9R28/jLgSEkzJU0Ejidr0b7dWkwAAALkSURBVJWxa0SszvNcDexSMl4qZwDXdnqwpI9Jegh4G1D3XqAtxDgReDgibus0j7Ei0c2WOqY660b0N6mkScC3gfdv01JoWUQMRsTBZC2UwyS9tIM8TgAejYhfdZLDNl4VEYcCxwF/LenIDmKMI/vT9IsRcQiwnuxP9Y5ImgCcCFzWwbHTyVqmewOzgB0lvb3dOBGxnOxP/MXA98m60tLci6pCJC0k+7ou7TRGRCyMiD3yGO/pIIeJwEI6LOpjzUgX5pVs3UKZQ2d/kiYhaTxZUb40Iq4oGy//U/9G4NgODn8VcKKkFWRdPK+TdEmHeazK/38UuJKsC6ldK8mu+tzS+r+crFB36jjg1oh4pINjjwF+GxGPRcQAcAVwRCdJRMSXIuLQiDiS7E/vezqJU+MRSbsD5P+P6IRfkk4DTgDeFhEpGj1fB/6sg+P2JftFelv+mZ4D3CpptwQ59ZyRLsy3APtL2jtvQb0VuHokEpEksv7T5RHxqRJxdt5y9lvZuO9jgLvajRMR50bEnIiYS/a+/CAi2m4VStpR2RWb5F0Pryf7E77dfNYAD0k6IF91NHBnu3Fq/DkddGPkHgQOlzQx/74dTXZOoG2Sdsn/3xP40xI5bXE1cFr++DTgqpLxOibpWOBssikVNpSIs3/N0xPp7PP864jYJSLm5p/plWQn29d0mldPG+mzj2T9eneTjc5Y2GGMb5D1NQ6QfcPP7CDGq8m6UW4HlubL8R3EOQj4nzzOMuBDCd6j19DhqAyyvuHb8uWOTt/jPNbBZDMG3g58B5jeYZyJwBPA1BK5nEdWIJYBXwO26zDOT8h+wdwGHF32c0c2A+N/k7W8/5smszA2iXFy/vh5spFR13WYy71k53C2fJ5bGU1RL8638/f5duC/gNmdxNlm+wo8KqPh0vEFJmZm1h0j3ZVhZmbbcGE2M6sYF2Yzs4pxYTYzqxgXZjOzinFhNjOrGBdmM7OKcWE2M6uY/w8xdokcDvXgkAAAAABJRU5ErkJggg==\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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KphRGRTy3On9eROyW3/6NpGX1DpK0kKx/Lu+btgd7T9yxfKZmZg3o5JNmRSPc5ZIOy29fL2kegKSdgHX1Dqrth+tia2ZVikE1vJUh6bOSbpC0TNLlkrYqOqao4L4feK2k24EXA7+TdAdwSr7PzKyjRDS+lfSliNglInYFLgGOKzqgqB/uE8B7JU0B5uaPXxERD5RO1cysDaqaUoiI2qYhk4DCEt7QsrCIeAq4vsW8zMwq08yysNrzTbnFEbG4ieNPBA4BngBeX/R4r8M1s54y0MTqg7y41i2wkq4EZg6xa1FEXBQRi4BFkj4BHAl8erjnc8E1s56S8oMPETG/wYeeDVzKSBfcqYNp+m1OHYT7Rpfr1zoKOGX2u0vnMjfSvKanlObb3/9s0bnPxmwxbk2SOA8/PilJnM2mrC5+UIEnnxqfIBOYMfupJHF23OHhJHFS6Zuc5r3TSRdurGoOV9KOEXFr/uX+wC1Fx3TVCPeIFWeWOj5FsTWzzpZg9UGjviBpZ7JrTNwFfLDogK4quGZmRSpcpfC2Zo9xwTWznjIwmGaapB1ccM2sp1Q4pdA0F1wz6ymDXdye8ShJ21SVjJlZWRFqeKta0WTHZ4HfS/q1pA9J2qKKpMzMWlVhL4WmFRXcO4DZZIV3d+BPki6TdGjeX2FIkhZKWipp6WWrb0uYrpnZ8AZDDW9VKyq4ERGDEXF5RBwObAV8E1hAVozrHfRce8YFE3dImK6Z2fAGBkc1vFWt6KTZ834FRMQ64GLgYkkT2paVmVmLOniRQmHBfWe9HRGR5nOgZmYJdfIqhaJ+uH+pKhEzsxQ6+aq9XodrZj1lBC7G2zAXXDPrKcFGPMIdlWgGe8vRz3Dd1geWirFLoj81nk70bdt6dPn2gwBjR6dpFzl2bJo406almd5PcRnrzWY/nSATWLsqzc989Lg0469INIxLFaf/8c45VdXvKQUzs2ps1CNcM7MqeQ7XzKwiHuGamVXEI1wzs4oMeIRrZlaNiq6w05JhC66kscC7gPsi4kpJBwGvBm4GFue9FczMOsZgF49wT88fM1HSocBk4AJgH2AP4ND2pmdm1pzOWRH8t4oK7ksjYhdJo4F7ga0iYkDSmcD19Q6StBBYCHDklHksmOAWjWZWjU4+aVbUEHJUPq0wBZgITMvvHweMqXfQ8/rhutiaWYUGpYa3qhWNcL8D3AL0AYuA8yTdAbwS+EGbczMza1qaD6i3R1F7xpMl/TC/fZ+k7wHzgVMi4toqEjQza0bXrlKArNDW3H4cOL+tGZmZldDNqxTMzLpKN69SMDPrKl09pVDWa+Y/kCbQYPnfW8uu2jxBIuk8tm5ckjjT9UySOAPPpLmK6TPPpnlbjR/XXzrGQ3dNSZAJzNj+qSRxUvWfHTcjzc9KY9PEGT21c05VdfKyMI9wzaynJOhb3zbVX5jdzKyNBpvYUpD0cUkhqfBPaI9wzaynVDmlIGkb4A3A3Y083iNcM+spoca3BE4GjqbBxREe4ZpZT6lqhCtpf+DeiLheDX5MuLDgStoeOBDYBugHbgXOiYgnSuRqZtYWzayXqG20lVscEYtr9l8JzBzi0EXAJ4F9m8mtqB/uUcD/Aq4GXg4sIyu8v5P0oYhY0syTmZm1WzPrcPPiuniY/fOHul/SS4HtgPWj29nAHyXtEREr68UrmsP9ALAgIv6VrIfCiyNiEbCAbO5iSJIWSloqaekZt91X72FmZslVsUohIm6MiC0jYk5EzAFWALsNV2yhsTnc0WSj9HFkbRqJiLslDduekfy3xuMH793Jn7Qzsx7TzR98OBW4TtI1wF7ASQCStgAebXNuZmZNG4kRXj7KLVTUnvFr+aTxi4CvRsQt+f0PkRVgM7OO0tW9FCLiJuCmCnIxMyutc7o6/C2vwzWznjLYwQ0aXXDNrKd080mz0u74VZr2eNcPJoiT6NXO6i/fNjCldQN9SeI8MTA2SZwtx69OEic6aKAyZvM0n4IffCZNOVj3aJo4g/1p4ozdrHMmTjvobfM3PMI1s56yUY9wzcyq1K/OHeO64JpZT+nccuuCa2Y9xlMKZmYV8bIwM7OKdG65dcE1sx7jKQUzs4oMdPAYd9jV3JKmSfqCpFskPZJvN+f3bTLMcc/1w71g1Z3JkzYzq6fqq/Y2o+jjM+cCjwGvi4jpETEdeH1+33n1DoqIxRExLyLmvXXynGTJmpkViSb+q1pRwZ0TESfVdjGPiJURcRLwgvamZmbWvG4e4d4l6WhJM9bfIWmGpGOAe9qbmplZ8waJhreqFRXcdwLTgaslPSrpUWAJsBnwjjbnZmbWtGhiq1rRFR8eA47Jt+eRdBhwepvyMjNrSX+3rlIocEKyLMzMEunkk2bDjnAl3VBvFzCjzr7n2eOBpc3mNKTDtnp16Rh7r03T77Uv0Q9qz2/9zyRxUJperfcfd1WSOPc+NDVJnKlTnykd47YHN0uQCUy/5+kkca66Y5skcfaee2+SOBO3S9Tnd3XnXNimmz/4MAN4I9kysFoCftuWjMzMShiJkWujigruJcDkiFi24Q5JS9qSkZlZCV07wo2Iw4fZd1D6dMzMyhnopGszbcC9FMysp7g9o5lZRbp5DtfMrKt07RyumVm32eimFCQtBBYCqG8ao0ZNasfTmJn9jU6eUmh51bOkn9XbV9ue0cXWzKo0ENHwVrWiT5rtVm8XsGv6dMzMyunmKYXrgKvJCuyG6l7xwcxspFR10kzS8cAHgIfyuz4ZET8d7piignszcERE3DrEk7kfrpl1nIrncE+OiC83+uCigns89ed5P9Lok5iZVaWTpxSGPWkWEedHxJ/r7N60DfmYmZUSEQ1vtRe8zbeFTT7dkZJukHSapMKaqGjxTJ2kuyOi8Lpml894V5JfN+uGnEZu3vgEMzwDiXIZS5qWdmvpSxJni3FrksTpH0jT8m/cmP4kcdasHVM6xtfLhwDgCzMfTxJn88UfTxJnzWe+mCTOB5amacl5/l0Xl/7Hte82CxquOZffc9mwzyfpSmDmELsWAdcAD5NdPOKzwKyIeN9w8dreD7eTpCi21l1SFFvrLimnFCJifiOPk3QKWXfFYbkfrpn1lFb/am+WpFkRcX/+5YHA8qJj3A/XzHpKhSfNvihpV7IphTuBI4oOcD9cM+spVS0Li4j3NHuMm9eYWU9xA3Izs4p08jpcF1wz6ykuuGZmFalqlUIrhl2hLmmqpM9L+r6kgzbY981hjnvu0xs/XXN7qlzNzAoNEg1vVSv6SNDpZGtufwS8S9KPJI3L972y3kG1/XDfNGH7RKmamRWLJv6rWtGUwvYR8bb89o8lLQKukrR/m/MyM2vJQHTuJ0qLCu44SaMislcQESdKWgH8Cpjc9uzMzJrUtXO4wE+AvWvviIjvAv8MrG1XUmZmrerkOdyiT5odXef+yyR9rj0pmZm1ricvIgmckCwLM7NEBiMa3qrW9vaMf3da3cUMTfnl+64pHSPVVPrUvjSzKY8PjE0S57UnbpUkzs0npFnCd8i6e5PEWfqhF5aO8ciFaXJ59qE0pyxiME0v5bXf/FqSOKNnTUwS5yvTVyeJk0Inj3DdntHMeko3r1Jwe0Yz6yojMVXQKLdnNLOe0s1TCmZmXaVrR7hmZt3GI1wzs4oMRJqrYbeDC66Z9ZRu/mhvS2rbM37nMq8eM7PqdPJHe4v64c6U9C1J35A0XdLxkm6UdK6kWfWOq23PePiCV6fP2sysjohoeKta0Qj3DOBPwD3AL4E1wN8Dvwa+3dbMzMxa0LUf7QVmRMTXASR9KCJOyu//uqS6a3TNzEZKN69SqB0Bf2+DfX2JczEzK62bP9p7kaTJEbEqIj61/k5JOwB/bm9qZmbN6+RVCkUf7T2uzv23Sbq0PSmZmbWukz9p5n64ZtZTOnmVgoZ70oJ+uDtFxLg6+5+z6uMHJHlVo2ZMLx3j8q88kyATmBr9SeK86ow9k8S56r2/SxJn6/FPJ4kTkabn64Tx60rHWLs2zamGHzIlSZxxib43R198cJI4PPlIkjAfec/FSeKccud5pb9B0yZv33DNeWLV7Wl+IA1yP1wz6yldO4eL++GaWZfp2lUK7odrZt2mypNmkj4CHAn0A5fWu/Duem5eY2Y9paopBUmvBw4AdomIZyVtWXSMC66Z9ZQKP2n2j8AXIuJZgIh4sOiAtnQLMzMbKc0sC6vtbJhvC5t4qp2Av5P0e0lXS3p50QFNj3AlbdlIJTczGwnNzOFGxGJgcb39kq4EZg6xaxFZ/dwUeCXwcuBcSXNjuDmNgkXBm22wTQfuzJ9ks2GOWwgszbeFDSw+LnxMg4uYHacLcnEc/8x7YQMuA15X8/XtwBbDHVP0wYdB4K4N7p4NrMhqdcyte3ATJC2NiHmO0744nZSL41QTp5NySRmnU0j6ILBVRBwnaSfgF8ALYpiiWjSlcDQwH/iXiLgxf5K/RsR2qZI2M+tSpwGnSVoOrAUOHa7YQvE63C9L+gFwsqR7gE9DBzebNDOrSESsBd7dzDGFqxQiYkVEvIPsig9XABNbS29YdSetHSdZnE7KxXGqidNJuaSM07WGncP9mwdLE4DtI2K5pMMi4vT2pWZm1luaKrjPO1C6OyJekDgfM7OeVXTV3hvqbDeSdRIrTdICSX+WdJukY1uMcZqkB/PJ61bz2EbSLyXdLOkmSf/UYpzxkq6VdH0ep1TfYEl9kv5T0iUlYtyZX215maSlJeJsIul8Sbfk36dXtRBj5zyP9duTkj7aQpz/k39/l0s6R9L4ZmPkcf4pj3FTs3kM9b6TtJmkKyTdmv9/0xZivCPPZ1BSQ2f168T5Uv6zukHShZI2aTHOZ/MYyyRdLmmrVuLU7Pu4pJC0eSOvracUrDN7ANgV2HaDbQ5wX4J1bH1ka9fmAmOB64EXtxBnL2A3YHmJXGYBu+W3pwB/aTEXkXVYAxgD/B54ZYm8PgacDVxSIsadwOYJfl7fBd6f3x4LbJLg578S2LbJ47YG/gpMyL8+F3hvC8//EmA52XmJ0cCVwI5l3nfAF4Fj89vHAie1EONFwM7AEmBeiVz2BUbnt08qymWYOFNrbh8FfLuVOPn92wA/J1tuWvo92W1b0Umz9e0Z79pguzN/M5S1B3BbRNwR2Rm/H5A1g2hKRPwKeLRMIhFxf0T8Mb/9FHAz2T/sZuNERKzKvxyTby3N20iaTXZZ+lNbOT4lSVPJ/hF9B7IztBHxeMmw+wC3R8SGa70bMRqYIGk0WcG8r4UYLwKuiYjVEdEPXA0c2OjBdd53B5D9YiL//1uajRERN0dEU9cMrBPn8vx1AVxDtoa+lThP1nw5iQbez8P8mzyZbLnpRrnaadiCGxGHR8Rv6uxL0Z5xa+Cemq9X0EKRS03SHOBlZKPTVo7vk7QMeBC4IiJaigP8G9mbs2yDzwAul/SHJj8rXmsu8BBwej7FcaqkSSXzehdwTrMHRcS9wJeBu4H7gSci4vIWnn85sJek6ZImAm8iG4GVMSMi7s/zvB8o7CBVkfcBP2v1YEkn5ktDDwaGvNZhAzH2B+6NiOtbzaPbjXTzmqEubzGiv/kkTQZ+BHx0g9/sDYuIgYjYlWxEsYekl7SQx5uBByPiD63ksIHXRMRuwH7AhyXt1UKM0WR/In4rIl4GPE32J3NLJI0F9gfOa+HYTclGktsBWwGTJDW1HhKykSTZn9pXkH1M83qyvqY9RdIistd1VqsxImJRRGyTxziyhRwmkvUfaKlY94qRLrgreP6IYjat/WmYhKQxZMX2rIi4oGy8/E/uJcCCFg5/DbC/pDvJplr2lnRmi3ncl///QeBCsqmcZq0AVtSM1s8nK8Ct2g/4Y0Q80MKx84G/RsRDEbEOuAB4dStJRMR3ImK3iNiL7E/gW1uJU+MBSbMA8v+PaKMnSYcCbwYOjnwStaSzgbe1cNz2ZL8gr8/f07OBP0oaqjFMzxrpgnsdsKOk7fIRz7uANFeja5Ikkc1P3hwRXy0RZ4v1Z4OVrVueD9zSbJyI+EREzI6IOWTfl6sioulRnKRJkqasv012IqXp1RwRsRK4R9LO+V37AH9qNk6Nf6CF6YTc3cArJU3Mf277kM25N01502hJLwDeWiKn9S4GDs1vHwpcVFx0bOUAAAEMSURBVDJeyyQtAI4B9o+I1SXi7Fjz5f609n6+MSK2jIg5+Xt6BdlJ6pWt5tWVRvqsHdm82V/IVissajHGOWRzeevIfpCHtxBjT7LpjBuAZfn2phbi7AL8Zx5nOXBcgu/R62hxlQLZ3Ov1+XZTq9/jPNauZB3gbgB+DGzaYpyJwCPAtBK5nED2D3858H1gXItxfk32i+N6YJ+y7zuyjnq/IBsp/4JhuuoNE+PA/PazZCuFft5iLreRnSNZ/35uZHXBUHF+lH+fbwB+AmzdSpwN9t/JRrhKoeUPPpiZWXNGekrBzGyj4YJrZlYRF1wzs4q44JqZVcQF18ysIi64ZmYVccE1M6uIC66ZWUX+C5DbgnHWPzeqAAAAAElFTkSuQmCC\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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sokL58xtsqgFvSt4aM7NOVbgnXFZb0+UiYh3wx8RtMTPrWDQcZB07PI/ZzPpKjNces5lZZTkxF5uS6NeXBjof0L/vlXuyy+87n743eVqae4pNfjLRvckmFE2uKWdoQ5Iw1AbTnBeWOo8TJDoRNGlS8T4laGqa7xUT08TRxIEkcarEPeYxaHDTfR3HuO/lr03QEjPrBidmM7OKiaGxP5PXidnM+ko/9JgTDXiZmVVD1FR6SUHSiZJC0vYNth8r6bZ8ObZMTPeYzayv9LLHLGlX4HDgngbbtwM+BSwAArhG0oUR8admcd1jNrO+EqHSSwKnAydBw+k/rwcuiYjH8mR8CXBEUdCisp8vlTQzf7yVpFMk/VjSaZK2aa39ZmbdF7XySyckLQLui4gVTXbbBbi37vmqfF1TRUMZ/w68OH98BrAOOA04FDgL+MuiFzAz66VaC7MyJC0mK2m8xdKIWFq3/VJgzgiHLgH+nqwEctOXGGFd4eT6osQ8IeLpK88XRMQB+ePfSbquYUvq3uwHZizgCJf+NLMeaeWkXp6ElzbZfthI6yW9CHgusCK/EGoecK2khRGxum7XVcAhdc/nAcuK2lU0xnyjpOPzxyskLcgb9Txgc6OD6usxOymbWS/1YlZGRNwQETtGxPy8Cucq4IBhSRngF8Dr8rr225L1sH9RFL8oMZ8AHCzpDmAf4HJJdwJfy7eZmVVKRPmlGyQtkHRm1pZ4DDgVuDpf/jFf11RRPeY1wHGSZgC75/uviogHO228mVk3pJqf3NJr1tWuj4jl1HVcI+Lfyc7XlVZqHnNErAWanXk0M6uERNPgRpUvMDGzvjLkWhlmZtXiHnMJkxKNsP/h5p3Zf58HOoqxy+9vh8mFc7sL3f/qis00GUxU1zmRVD8YQ2s2dhxjwvREdZSnT08TZ2qiH7lENxyNRJ+diVOq8xkcjTHm1MZUj3mnX1/WWYAESdnMqq1bsy16aUwlZjOzIu4xm5lVzFBt7Ndmc2I2s77ioQwzs4qp9cGsjKKynx/OC0GbmY0JPa7H3BVFgzGnAldK+q2kD0jaoReNMjNr12jXykihKDHfSVam7lTgQOBmST/P72E1o9FBkhZLWi5p+U/X35GwuWZmzdVCpZeqKkrMERG1iLg4It4DzAW+THZrlDubHPR02c+jttojYXPNzJobqk0ovVRV0cm/Z/xKiYjNwIXAhZK26lqrzMzaVOERitKKEvNbG22IiPWJ22Jm1rEqD1GUVVSP+dZeNcTMLIUqz7Yoy/OYzayvdHjz60pwYjazvhIj3ph6bOl6Yp5KmnKAL9z3IR4+/OCOYkxQmm9YbXOaONNmbEoSJzamac/mdQNJ4qQqIqOpnZ81j8FE/adamjhK9BlkcrX6VFUqHDTooQwzs2pxj9nMrGI8xmxmVjHuMZuZVYx7zGZmFTPkHrOZWbVUaIJI25omZkmTgbcB90fEpZLeAbwCWAkszWtnmJlVRm0c9JjPyveZJulYYDrwA+BQYCFwbHebZ2bWmvFQxOhFEbGvpInAfcDciBiSdA6wotFBkhYDiwE+NuMA/nyr3ZM12MysmX44+Vd0adWEfDhjBjAN2CZfPwWY1Oig+nrMTspm1ks1qfRSVUU95q8DtwADwBLgPEl3Ai8DvtPltpmZtSxNEYjRVVT283RJ380f3y/pbOAw4GsRcVUvGmhm1oq+n5UBWUKue/w4cH5XW2Rm1oF+mJVR3ZtemZm1IVpYUpB0oqSQtH2D7Z+TdJOklZL+r0qUGPQFJmbWV3o5lCFpV+Bw4J4G218BvBLYN1/1O+BgYFmzuF1PzAOqzqzCeTN2SBJn8uw072njk2nixKY0E4QmDCR6XxvS1HUmQS3l2oZEp4KmVuzew4Np3pemb50kTtSeSBInhR5PlzsdOAm4oMH2AKYCk8lubj0JeLAoqIcyzKyvDKn8ImmxpOV1y+KyryNpEXBfRDS8piMiLgd+DTyQL7+IiJVFsT2UYWZ9pZUec0QsBZY22i7pUmDOCJuWAH8PvK5ZfEl7AnsD8/JVl0g6KCJ+0+w4J2Yz6ysphzIi4rCR1kt6EfBcYEV+Lm8ecK2khRGxum7XNwFXRMST+XE/I7sOpGli9lCGmfWVUPml7deIuCEidoyI+RExH1gFHDAsKUN2UvBgSRMlTSI78Vc4lOHEbGZ9pdbC0g2SFkg6M396PnAHcANZfaEVEfHjohiFQxmS9iDrju8KDAK3Ad+OiDXtNtzMrFtG45LsvNe85fFy4IT88RDwvlbjNe0xS/ow8FWy6R4vAbYiS9CXSzqk1RczM+u2msovVVU0lPFe4IiI+N9kNTL2iYglwBFk8/dGVD8F5cJ1d6ZrrZlZgdEeykihzBjzluGOKWTlP4mIeyhZ9nPRNJf9NLPe6YfEXDTGfCZwtaQrgIOA0wAk7QA81uW2mZm1rDrXGrevqOznGfkE672BL0TELfn6h8kStZlZpVR57LisMmU/bwJu6kFbzMw61veF8s3MxppaHwxmODGbWV+p8km9srqemO+ZMDVJnH3ndt7UP8zdLkFLQJPTlLWcNT3NH10Dc2cliTNzTpqextDqNNceDew4s+MYE57akKAlMHTt9UniTHzT0UniaPt5xTuVMHTxD5PEmfO19yeJk8LY7y+7x2xmfcY9ZjOzihms0M052uXEbGZ9ZeynZSdmM+szHsowM6sYT5czM6uYsZ+WnZjNrM94KMPMrGKG+qDPXFQofxtJ/yTpFkmP5svKfF3Dqxrq6zH/at1t6VttZtZAP5T9LKrH/D3gT8AhETE7ImYDr8nXndfooPp6zK+dtle61pqZFYgW/lVVUWKeHxGn1d/5NSJWR8RpwG7dbZqZWevGQ4/5bkknSdppywpJO0n6OHBvd5tmZta6GlF6qaqixPxWYDZwmaTHJD0GLAO2A97S5baZmbUsWliqqugOJn8CPp4vzyDpeOCsLrXLzKwtg5VOueWUuRlrI6cka4WZWSL9cPKvaY9ZUqMitAJ2arCtK2rrOq9dPGFamjrKsSHRzWsmprk5We2RtUniUEvzQZ0wY0qSOLUn1nUe48nNCVoCk+akqeXNQJpLBzRl6yRxWL8xTZyBSWniJFDlk3plFX1KdgJeTzY9rp6A/+xKi8zMOlDlnnBZRYn5ImB6RFw3fIOkZV1pkZlZB/q+xxwR72my7R3pm2Nm1pmh6P8es5nZmFLl+cllOTGbWV8ZD2PMZmZjSt+PMZuZjTX9MJTRyQUmDbnsp5mNll5dYCLp05Luk3RdvhzVYL9Zks7PyyevlPTyothtJ2ZJP2u0zWU/zWy0DEWUXhI4PSL2y5efNtjnDODnEfEC4MXAyqKgRVf+HdBoE7BfUXAzs16r0lCGpJnAQcBxABGxCdhUdFzRGPPVwGVkiXi4hncwMTMbLa2c/JO0GFhct2ppRCxtIcQHJR0DLAf+Li/8Vm934GHgLEkvBq4BPhIRTzULWpSYVwLvi4hnDRRLcj1mM6ucVsaO8yTcMBFLuhSYM8KmJcBXgFPJKoieCvwL8O5h+00EDgA+FBFXSjoDOBn4X83aVZSYP03jcegPFRxrZtZzKYcyIuKwMvtJ+hpZCYvhVgGrIuLK/Pn5ZIm5qaYn/yLi/Ij4rwabty0KbmbWaxFReumEpJ3rnr4JuHGEtqwG7pX0/HzVocDNRbE7mcd8Cj0slJ+qZOeErScnCJKmXCeDiabCT01TcnHo4abDXqWlmoOpKZ1Psx/YZgpDj27oOM6EuYmq3G4uPO9TSgylKWeqGdPSxNlqRpI4KQz17uTf5yTtRzaUcRfwPgBJc4EzI2LL9LkPAedKmgzcCRxfFHjM1GNOIUlStjElRVK2saVXszIi4m8arL8fOKru+XXAglZiux6zmfWVTocoqsD1mM2sr1RpHnO7XI/ZzPqKq8uZmVWMC+WbmVVM3w9lmJmNNU7MZmYV0w+zMppeCyBppqT/I+mbkt4xbNuXmxznesxmNipqROmlqoou0jqLbM7y94G3Sfq+pCn5tpc1Osj1mM1stPSqUH43FQ1l7BERb84f/0jSEuBXkhZ1uV1mZm0ZirF/17+ixDxF0oSI7J1GxGckrQJ+A0zveuvMzFrU92PMwI+B19aviIhvAH9HiSr8Zma91g9jzEVX/p3UYP3PJX22O00yM2tflceOy+qkQuMpyVphZpZILaL0UlVdL/s5szbUaptGtOGewY5jbH1gmmHx2JCmFm7tqUQ1dTen+RrHpjQnTbTNlOKdShhas7HjGBsfTPPDN3kwzddYE9JUqx7Y5QVJ4gw+NLxwZHsGatU54dYPPWaX/TSzvjIeZmW47KeZjSlVHqIoy2U/zayvjIehDDOzMaXve8xmZmONe8xmZhUzFGlm0IwmJ2Yz6yvj4ZLsttSX/fz5utu78RJmZiPqh0uyi+oxz5H0FUlfkjRb0qcl3SDpe5J2bnRcfdnPI6btmb7VZmYNRETppaqKesz/AdwM3Av8GlgPvAH4LfDVrrbMzKwNfX9JNrBTRPwrgKQPRMRp+fp/ldRwjrOZ2WgZD7My6nvUZw/bNpC4LWZmHRsPl2RfIGl6RDwZEf+wZaWkPYH/6m7TzMxaV+Wx47KKLsn+ZIP1t0v6SXeaZGbWviqPHZflesxm1lf6YVZG1+sxr1OaqdJb7dl5jd8YTDT2lCjOxN22SxJn060PJ4mTyuDD65PEGdiu8+/5xBkbErQEWLs2SRhtW+rHptDQqpuTxElF07cd7SY8rcrzk8tyPWYz6yu97AlL+hDwQWAQ+Emj2/FJGgCWA/dFxNFFcV2P2cz6Sq9mZUh6DfBGYN+I2Chpxya7fwRYCcwsE7vpOENEvCciftdgm+sxm1nl9PACk78F/ikiNgJExEMj7SRpHtmFeWeWDdyVWhlmZqOlhyf/nge8WtKVki6T9JIG+30ROAko3ZV3dTkz6yutXPknaTGwuG7V0ohYWrf9UmDOCIcuIcuf2wIvA14CfE/S7lGX8SUdDTwUEddIOqRsu5yYzayvtNITzpPw0ibbD2u0TdLfAj/IE/FVkmrA9kD9NKlXAoskHQVMBWZKOici3tWsXS0PZRQMcJuZjaoejjH/CHgtgKTnAZOBR+p3iIhPRMS8iJgPvA34VVFS3nJgs/GX7YYts4G7yLrv2zU5bjHZ1JDlwOIS4zyF+5QcL3KcMdAWx/H3vB+WPBGfA9wIXAu8Nl8/F/jpCPsfAlxUJrbyA0aUd83vHrZ6HrAqy+mxe2HmL0HS8ohY4Djdi1OltjhOb+JUqS0p44wHRUMZJ5EVK1oUEc+NiOcCq/LHSZKymZk9U9E85n8GTgA+KekLkmZAH1zvaGZWYYUn/yJiVUS8hewOJpcA07rQjoZnRR0nWZwqtcVxehOnSm1JGafvNR1jftbO0lbAHhFxo6TjI+Ks7jXNzGx8aikxP+NA6Z6I2C1xe8zMxr2iu2Rf32C5gZJlP4tIOkLSf0m6XdLJbcb4d0kPSbqxg3bsKunXklZKuknSR9qMM1XSVZJW5HE6qlstaUDSHyRd1EGMu/K7m18naXkHcWZJOl/SLfnX6eVtxHh+3o4tyxOSPtpGnP+Zf31vlPRtSVNbjZHH+Uge46ZW2zHS507SdpIukXRb/n/TepgNYrwlb09NUqlZDA3ifD7/Xl0v6YeSZrUZ59Q8xnWSLpY0t504ddtOlBSSti/z3salgnl6DwL7Ac8ZtswH7k8wD3AAuAPYnWxO4ApgnzbiHAQcANzYQVt2Bg7IH88Abm2zLSKryAcwCbgSeFkH7foY8C1Kzn9sEOMuYPsE369vACfEf8/hnJXg+78aeE6Lx+0C/BHYKn/+PeC4Nl7/z8jmoE4juwr2UmCvTj53wOeAk/PHJwOntRFjb+D5wDJgQQdteR0wMX98WlFbmsSZWff4w8BX24mTr98V+AXZNNyOP5P9uhSd/NtS9vPuYctd+YemUwuB2yPizojYBHyHrIxeSyLiN8BjnTQkIh6IiGvzx2vJSvTt0kaciIgn86eT8qWt8aJ2qlJ1i6SZZD9sXweIiE0R8XiHYQ8F7oiI4XPly5gIbCVpIllivb+NGHsDV0TEuogYBC4D3lT24AafuzeS/QIj//8vWo0RESsjoqV7ajaIc516WkcAAANZSURBVHH+vgCuILsGoZ04T9Q93ZoSn+cmP5Onk03D9eyuJka77OcuwL11z1fRRjJMTdJ8YH+y3m47xw9Iug54CLgkItqKQxtVqRoI4GJJ1+RFW9qxO1kNgLPyoZUzJW3dYbveBny71YMi4j7gn4F7gAeANRFxcRuvfyNwkKTZkqYBR5H16DqxU0Q8kLfzAaAqJQzeDfys3YMlfUbSvcA7gRHvBVoixiKyQvEr2m3HeDHaZT81wrpR/U0qaTrwfeCjw3oKpUXEUETsR9ZDWSjpz9pox9NVqdppwzCvjIgDgCOB/yHpoDZiTCT70/QrEbE/8BTZn+ptkTQZWASc18ax25L1TJ9Ldvnr1pKK6w8MExEryf7EvwT4OdlQ2mDTg8YgSUvI3te57caIiCURsWse44NttGEaWUW2tpL6eDPaiXkVz+yhzKO9P0mTkDSJLCmfGxE/6DRe/qf+MuCINg7fUpXqLrIhntdKOqfNdtyf//8Q8EOyIaRWrSK76nNL7/98skTdriOBayPiwTaOPQz4Y0Q8HBGbgR8Ar2inERHx9Yg4ICIOIvvT+7Z24tR5UNLOAPn/IxZP7xVJxwJHA++MiBSdnm8Bb27juD3IfpGuyD/T84BrJY1UUnPcG+3EfDWwl6Tn5j2otwEXjkZDJIls/HRlRHyhgzg7bDn7rWze92HALa3GiXarUj27PVsru2KTfOjhdWR/wrfantXAvZKen686FOjkjqBvp41hjNw9wMskTcu/b4eSnRNomfJqiZJ2A/6ygzZtcSFwbP74WOCCDuO1TdIRwMfJSiqs6yDOXnVPF9He5/mGiNgxIubnn+lVZCfbV7fbrr422mcfycb1biWbnbGkzRjfJhtr3Ez2DX9PGzFeRTaMcj1wXb4c1UacfYE/5HFuBD6Z4Gt0CG3OyiAbG16RLze1+zXOY+1HVjHwerKSh9u2GWca8CiwTQdtOYUsQdwIfBOY0mac35L9glkBHNrp546sAuMvyXrev6RJFcYmMd6UP95INjPqF2225XayczhbPs9lZlOMFOf7+df5euDHwC7txBm2/S48K6Ph0vYFJmZm1h2jPZRhZmbDODGbmVWME7OZWcU4MZuZVYwTs5lZxTgxm5lVjBOzmVnFODGbmVXM/wcI2u+ulWj/ogAAAABJRU5ErkJggg==\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 \n",
    "dfX = pd.DataFrame()\n",
    "dfY = pd.DataFrame()\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    if c == 8: \n",
    "        continue\n",
    "    if c == 9:\n",
    "        continue\n",
    "    else: \n",
    "        tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "        dfX = pd.DataFrame()\n",
    "        dfY = pd.DataFrame()\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",
    "            col = int(tmat_name[-11:-8])\n",
    "            row = int(tmat_name[-7:-4])\n",
    "            \n",
    "            dfX.loc[row, col] = moveX\n",
    "            dfY.loc[row, col] = moveY\n",
    "    fig, axes = plt.subplots()\n",
    "    sns.heatmap(dfX)\n",
    "    pl.suptitle(f\"Cycle {c+1} X-shift\")\n",
    "    \n",
    "    fig, axes = plt.subplots()\n",
    "    sns.heatmap(dfY)\n",
    "    pl.suptitle(f\"Cycle {c+1} Y-shift\")\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1a365cfdd0>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# for each one, if you want to individually see\n",
    "tmats = iter(glob.glob(f'tmat_Cyc_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",
    "    col = int(tmat_name[-11:-8])\n",
    "    row = int(tmat_name[-7:-4])\n",
    "\n",
    "    dfX.loc[row, col] = moveX\n",
    "    dfY.loc[row, col] = moveY\n",
    "\n",
    "#sns.heatmap(dfX)\n",
    "#sns.heatmap(dfY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging and Cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_total = len(mov[0][0][0])    # 2048\n",
    "X_total = len(mov[0][0][1])    # 2048\n",
    "\n",
    "###############################\n",
    "X_abs_shift = [abs(X_max_total), abs(X_min_total)]\n",
    "Y_abs_shift = [abs(Y_max_total), abs(Y_min_total)] \n",
    "\n",
    "def crop(X_x, X_n, Y_x, Y_n, img, pad):\n",
    "    Y_total = img.shape[3]    # 2048\n",
    "    X_total = img.shape[-1]     # 2048\n",
    "    print('image shape', Y_total, X_total)\n",
    "    \n",
    "    if X_x * X_n > 0:   # same signs --> both negative or both positive\n",
    "        Xlength = X_total - (max(X_abs_shift))\n",
    "        print(Xlength)\n",
    "        if X_x > 0: # moving left\n",
    "            img = img[...,:, 0+pad:Xlength-pad]\n",
    "        if X_x < 0:  # moving right \n",
    "            img = img[...,:, abs(X_n)+pad:X_total-pad]\n",
    "\n",
    "    if X_x * X_n < 0:   # diff signs --> one is positive and other is negative\n",
    "        Xlength = X_total  - (abs(X_x) + abs(X_n))\n",
    "        print(Xlength)\n",
    "        img = img[...,:, abs(X_n)+pad: X_total - abs(X_x)-pad] \n",
    "        \n",
    "    if X_x == 0 and X_n < 0:\n",
    "        Xlength = X_total  - abs(X_n)\n",
    "        print(Xlength)\n",
    "        img = img[...,:, abs(X_n)+pad:X_total-pad]\n",
    "    if X_x == 0 and X_n == 0:\n",
    "        Xlength = X_total\n",
    "        print(Xlength)\n",
    "        img = img[...,:, 0+pad:X_total-pad]\n",
    "    if X_x > 0 and X_n == 0:\n",
    "        Xlength = X_total - X_x\n",
    "        print(Xlength)\n",
    "        img = img[...,:, 0+pad:Xlength-pad]\n",
    "### Y ## #\n",
    "    if Y_x * Y_n > 0:   \n",
    "        Ylength = Y_total - (max(Y_abs_shift))\n",
    "        print(Ylength)\n",
    "        if Y_x > 0: # up\n",
    "            img = img[..., 0+pad:Ylength-pad, :]\n",
    "        if Y_x < 0: # down\n",
    "            img = img[...,abs(Y_n)+pad:Y_total-pad, :]\n",
    "            \n",
    "    if Y_x * Y_n < 0:  # Y_x > 0 , Y_n < 0\n",
    "        Ylength = Y_total  - (abs(Y_x) + abs(Y_n)) # \n",
    "        print(Ylength)\n",
    "        img = img[...,abs(Y_n)+pad:Y_total-abs(Y_x)-pad, :] \n",
    "        \n",
    "    if Y_x == 0 and Y_n < 0: # down\n",
    "        Ylength = Y_total  - abs(Y_n)\n",
    "        print(Ylength)\n",
    "        img = img[..., abs(Y_n)+pad:Y_total-pad, :]\n",
    "        \n",
    "    if Y_x == 0 and Y_n == 0:\n",
    "        Ylength = Y_total\n",
    "        print(Ylength)\n",
    "        img = img[..., 0+pad:Y_total-pad,:]\n",
    "        \n",
    "    if Y_x > 0 and Y_n == 0:\n",
    "        Ylength = Y_total - Y_x ## \n",
    "        print(Ylength)\n",
    "        img = img[..., 0+pad:Ylength-pad, :] ## up  \n",
    "    Xlength = Xlength - (pad*2)\n",
    "    Ylength = Ylength - (pad*2)\n",
    "    return Xlength, Ylength, img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_DIR = 'reg'\n",
    "REF_DIR = 'tif' # cycle0\n",
    "MERGE_DIR = 'merged' # output directory to save\n",
    "Z = 13\n",
    "final_ch = 38\n",
    "pad = 10\n",
    "\n",
    "#!mkdir merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 219\n"
     ]
    }
   ],
   "source": [
    "print(CYCLE_NUMS, NUM_FOVS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F000_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F000_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F000_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F000_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F000.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F001_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F001_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F001_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F001_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F001_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F001_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F001.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F002_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F002_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F002_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F002_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F002_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F002_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F002_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F002_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F002.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F003_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F003_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F003_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F003_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F003_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F003.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F004_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F004_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F004_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F004_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F004.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F005_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F005_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F005_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F005_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F005_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F005_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F005_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F005_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F005.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F006_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F006_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F006_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F006_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F006_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F006.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F007_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F007_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F007_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F007_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F007_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F007_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F007_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F007_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F007.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F008_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F008_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F008_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F008_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F008.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F009_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F009_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F009_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F009_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F009.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F010_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F010_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F010_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F010.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F011_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F011_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F011_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F011_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F011_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F011_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F011_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F011_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F011.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F012_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F012_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F012_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F012_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F012_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F012_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F012_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F012_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F012_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F012.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F013_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F013_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F013_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F013_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F013_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F013_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F013_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F013_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F013.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F014_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F014_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F014_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F014_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F014_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F014_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F014_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F014_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F014_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F014.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F015_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F015_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F015_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F015_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F015_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F015_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F015_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F015_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F015_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F015.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F016_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F016_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F016_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F016_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F016_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F016_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F016_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F016_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F016_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F016.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F017_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F017_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F017_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F017_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F017_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F017_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F017_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F017_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F017_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F017.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65514\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F018_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F018_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F018_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F018_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F018_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F018_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F018_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F018_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F018_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F018.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F019_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F019_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F019_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F019_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F019_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F019_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F019_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F019_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F019_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F019.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F020_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F020_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F020_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F020_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F020_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F020_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F020_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F020_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F020_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F020.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F021_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F021_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F021_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F021_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F021_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F021_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F021_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F021_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F021_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F021.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F023_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F023_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F023_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F023_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F023_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F023_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F023_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F023_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F023_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F023.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F024_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F024_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F024_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F024_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F024_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F024_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F024_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F024_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F024_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F024.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F025_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F025_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F025_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F025_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F025_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F025_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F025_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F025_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F025_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F025.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F026_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F026_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F026_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F026_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F026_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F026_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F026_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F026_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F026_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F026.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F027_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F027_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F027_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F027_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F027_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F027_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F027_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F027_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F027_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F027.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F028_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F028_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F028_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F028_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F028_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F028_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F028_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F028_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F028_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F028.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F029_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F029_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F029_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F029_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F029_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F029_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F029_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F029_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F029_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F029.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F030_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F030_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F030_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F030_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F030_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F030_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F030_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F030_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F030_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F030.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F031_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F031_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F031_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F031_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F031_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F031_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F031_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F031_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F031_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F031.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F033_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F033_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F033_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F033_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F033_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F033_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F033_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F033_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F033_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F033.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F034_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F034_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F034_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F034_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F034_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F034_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F034_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F034_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F034_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F034.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F035_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F035_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F035_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F035_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F035_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F035_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F035_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F035_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F035_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F035.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65529\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F037_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F037_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F037_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F037_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F037_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F037_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F037_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F037_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F037_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F037.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F038_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F038_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F038_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F038_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F038_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F038_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F038_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F038_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F038_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F038.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65131\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F039_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F039_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F039_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F039_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F039_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F039_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F039_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F039_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F039_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F039.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F040_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F040_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F040_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F040_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F040_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F040_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F040_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F040_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F040_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F040.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F041_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F041_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F041_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F041_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F041_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F041_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F041_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F041_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F041_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F041.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65530\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F042_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F042_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F042_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F042_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F042_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F042_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F042_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F042_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F042_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F042.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F043_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F043_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F043_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F043_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F043_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F043_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F043_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F043_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F043_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F043.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F044_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F044_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F044_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F044_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F044_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F044_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F044_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F044_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F044_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F044.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F045_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F045_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F045_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F045_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F045_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F045_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F045_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F045_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F045_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F045.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F046_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F046_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F046_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F046_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F046_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F046_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F046_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F046_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F046_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F046.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F047_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F047_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F047_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F047_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F047_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F047_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F047_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F047_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F047_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F047.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F048_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F048_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F048_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F048_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F048_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F048_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F048_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F048_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F048_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F048.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F049_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F049_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F049_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F049_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F049_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F049_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F049_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F049_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F049_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F049.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F050_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F050_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F050_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F050_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F050_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F050_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F050_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F050_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F050_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F050.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F051_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F051_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F051_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F051_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F051_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F051_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F051_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F051_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F051_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F051.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F052_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F052_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F052_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F052_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F052_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F052_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F052_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F052_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F052_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F052.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F053_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F053_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F053_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F053_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F053_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F053_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F053_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F053_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F053_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F053.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F054_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F054_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F054_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F054_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F054_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F054_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F054_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F054_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F054_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F054.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F055_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F055_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F055_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F055_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F055_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F055_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F055_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F055_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F055_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F055.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F056_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F056_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F056_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F056_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F056_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F056_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F056_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F056_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F056_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F056.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F057_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F057_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F057_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F057_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F057_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F057_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F057_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F057_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F057_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F057.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F058_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F058_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F058_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F058_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F058_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F058_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F058_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F058_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F058_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F058.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F059_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F059_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F059_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F059_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F059_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F059_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F059_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F059_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F059_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F059.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F060_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F060_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F060_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F060_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F060_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F060_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F060_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F060_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F060_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F060.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F061_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F061_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F061_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F061_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F061_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F061_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F061_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F061_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F061_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F061.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F062_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F062_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F062_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F062_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F062_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F062_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F062_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F062_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F062_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F062.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F063_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F063_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F063_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F063_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F063_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F063_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F063_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F063_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F063_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F063.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F064_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F064_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F064_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F064_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F064_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F064_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F064_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F064_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F064_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F064.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F065_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F065_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F065_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F065_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F065_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F065_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F065_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F065_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F065_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F065.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F066_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F066_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F066_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F066_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F066_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F066_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F066_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F066_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F066_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F066.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F067_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F067_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F067_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F067_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F067_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F067_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F067_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F067_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F067_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F067.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F068_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F068_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F068_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F068_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F068_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F068_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F068_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F068_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F068_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F068.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F069_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F069_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F069_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F069_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F069_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F069_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F069_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F069_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F069_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F069.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F070_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F070_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F070_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F070_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F070_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F070_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F070_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F070_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F070_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F070.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65532\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F071_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F071_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F071_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F071_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F071_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F071_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F071_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F071_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F071_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F071.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65520\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F072_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F072_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F072_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F072_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F072_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F072_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F072_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F072_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F072_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F072.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65523\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F073_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F073_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F073_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F073_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F073_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F073_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F073_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F073_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F073_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F073.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F074_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F074_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F074_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F074_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F074_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F074_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F074_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F074_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F074_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F074.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F075_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F075_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F075_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F075_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F075_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F075_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F075_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F075_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F075_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F075.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F076_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F076_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F076_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F076_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F076_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F076_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F076_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F076_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F076_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F076.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F077_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F077_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F077_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F077_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F077_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F077_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F077_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F077_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F077_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F077.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F078_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F078_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F078_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F078_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F078_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F078_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F078_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F078_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F078_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F078.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F079_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F079_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F079_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F079_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F079_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F079_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F079_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F079_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F079_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F079.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F080_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F080_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F080_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F080_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F080_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F080_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F080_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F080_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F080_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F080.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F081_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F081_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F081_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F081_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F081_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F081_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F081_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F081_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F081_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F081.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F082_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F082_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F082_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F082_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F082_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F082_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F082_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F082_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F082_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F082.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F083_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F083_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F083_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F083_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F083_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F083_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F083_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F083_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F083_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F083.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F084_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F084_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F084_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F084_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F084_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F084_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F084_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F084_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F084_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F084.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F085_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F085_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F085_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F085_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F085_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F085_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F085_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F085_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F085_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F085.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F086_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F086_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F086_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F086_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F086_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F086_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F086_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F086_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F086_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F086.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F087_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F087_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F087_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F087_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F087_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F087_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F087_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F087_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F087_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F087.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F088_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F088_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F088_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F088_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F088_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F088_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F088_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F088_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F088_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F088.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F089_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F089_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F089_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F089_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F089_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F089_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F089_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F089_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F089_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F089.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F090_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F090_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F090_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F090_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F090_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F090_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F090_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F090_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F090_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F090.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F091_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F091_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F091_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F091_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F091_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F091_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F091_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F091_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F091_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F091.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65532\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F092_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F092_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F092_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F092_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F092_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F092_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F092_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F092_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F092_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F092.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65519\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F093_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F093_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F093_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F093_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F093_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F093_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F093_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F093_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F093_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F093.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F094_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F094_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F094_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F094_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F094_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F094_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F094_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F094_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F094_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F094.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F095_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F095_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F095_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F095_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F095_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F095_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F095_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F095_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F095_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F095.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F096_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F096_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F096_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F096_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F096_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F096_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F096_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F096_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F096_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F096.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F097_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F097_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F097_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F097_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F097_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F097_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F097_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F097_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F097_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F097.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F098_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F098_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F098_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F098_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F098_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F098_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F098_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F098_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F098_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F098.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F099_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F099_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F099_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F099_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F099_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F099_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F099_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F099_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F099_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F099.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F100_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F100_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F100_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F100_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F100_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F100_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F100_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F100_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F100_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F100.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F101_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F101_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F101_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F101_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F101_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F101_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F101_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F101_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F101_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F101.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F102_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F102_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F102_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F102_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F102_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F102_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F102_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F102_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F102_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F102.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F103_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F103_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F103_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F103_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F103_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F103_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F103_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F103_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F103_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F103.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F104_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F104_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F104_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F104_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F104_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F104_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F104_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F104_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F104_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F104.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F105_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F105_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F105_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F105_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F105_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F105_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F105_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F105_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F105_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F105.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F106_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F106_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F106_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F106_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F106_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F106_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F106_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F106_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F106_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F106.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65516\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F107_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F107_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F107_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F107_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F107_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F107_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F107_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F107_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F107_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F107.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F108_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F108_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F108_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F108_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F108_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F108_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F108_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F108_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F108_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F108.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F109_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F109_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F109_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F109_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F109_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F109_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F109_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F109_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F109_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F109.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F110_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F110_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F110_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F110_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F110_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F110_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F110_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F110_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F110_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F110.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F111_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F111_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F111_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F111_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F111_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F111_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F111_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F111_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F111_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F111.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F112_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F112_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F112_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F112_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F112_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F112_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F112_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F112_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F112_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F112.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65514\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F113_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F113_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F113_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F113_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F113_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F113_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F113_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F113_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F113_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F113.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F114_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F114_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F114_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F114_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F114_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F114_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F114_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F114_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F114_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F114.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F115_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F115_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F115_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F115_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F115_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F115_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F115_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F115_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F115_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F115.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F116_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F116_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F116_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F116_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F116_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F116_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F116_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F116_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F116_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F116.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65531\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F117_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F117_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F117_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F117_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F117_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F117_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F117_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F117_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F117_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F117.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F118_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F118_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F118_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F118_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F118_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F118_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F118_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F118_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F118_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F118.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F119_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F119_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F119_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F119_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F119_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F119_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F119_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F119_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F119_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F119.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F120_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F120_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F120_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F120_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F120_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F120_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F120_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F120_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F120_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F120.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F121_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F121_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F121_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F121_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F121_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F121_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F121_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F121_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F121_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F121.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F122_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F122_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F122_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F122_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F122_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F122_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F122_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F122_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F122_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F122.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F123_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F123_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F123_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F123_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F123_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F123_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F123_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F123_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F123_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F123.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65517\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F124_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F124_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F124_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F124_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F124_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F124_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F124_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F124_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F124_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F124.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F125_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F125_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F125_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F125_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F125_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F125_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F125_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F125_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F125_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F125.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F126_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F126_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F126_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F126_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F126_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F126_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F126_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F126_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F126_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F126.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F127_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F127_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F127_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F127_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F127_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F127_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F127_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F127_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F127_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F127.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F128_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F128_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F128_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F128_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F128_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F128_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F128_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F128_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F128_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F128.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F129_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F129_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F129_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F129_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F129_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F129_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F129_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F129_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F129_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F129.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65532\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F131_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F131_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F131_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F131_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F131_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F131_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F131_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F131_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F131_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F131.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F132_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F132_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F132_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F132_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F132_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F132_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F132_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F132_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F132_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F132.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F133_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F133_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F133_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F133_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F133_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F133_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F133_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F133_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F133_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F133.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F134_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F134_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F134_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F134_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F134_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F134_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F134_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F134_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F134_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F134.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F135_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F135_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F135_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F135_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F135_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F135_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F135_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F135_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F135_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F135.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F136_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F136_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F136_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F136_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F136_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F136_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F136_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F136_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F136_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F136.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F137_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F137_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F137_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F137_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F137_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F137_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F137_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F137_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F137_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F137.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F138_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F138_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F138_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F138_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F138_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F138_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F138_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F138_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F138_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F138.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F140_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F140_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F140_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F140_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F140_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F140_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F140_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F140_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F140_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F140.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F141_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F141_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F141_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F141_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F141_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F141_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F141_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F141_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F141_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F141.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F142_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F142_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F142_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F142_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F142_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F142_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F142_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F142_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F142_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F142.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F143_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F143_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F143_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F143_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F143_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F143_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F143_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F143_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F143_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F143.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F144_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F144_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F144_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F144_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F144_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F144_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F144_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F144_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F144_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F144.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F145_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F145_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F145_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F145_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F145_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F145_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F145_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F145_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F145_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F145.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F146_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F146_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F146_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F146_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F146_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F146_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F146_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F146_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F146_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F146.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F147_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F147_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F147_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F147_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F147_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F147_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F147_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F147_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F147_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F147.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F148_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F148_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F148_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F148_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F148_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F148_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F148_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F148_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F148_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F148.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F149_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F149_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F149_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F149_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F149_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F149_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F149_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F149_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F149_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F149.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F150_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F150_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F150_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F150_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F150_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F150_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F150_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F150_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F150_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F150.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F151_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F151_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F151_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F151_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F151_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F151_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F151_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F151_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F151_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F151.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F152_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F152_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F152_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F152_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F152_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F152_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F152_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F152_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F152_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F152.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F153_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F153_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F153_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F153_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F153_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F153_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F153_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F153_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F153_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F153.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F154_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F154_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F154_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F154_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F154_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F154_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F154_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F154_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F154_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F154.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F155_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F155_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F155_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F155_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F155_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F155_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F155_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F155_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F155_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F155.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F156_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F156_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F156_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F156_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F156_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F156_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F156_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F156_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F156_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F156.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F158_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F158_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F158_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F158_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F158_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F158_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F158_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F158_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F158_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F158.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F159_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F159_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F159_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F159_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F159_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F159_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F159_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F159_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F159_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F159.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F160_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F160_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F160_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F160_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F160_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F160_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F160_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F160_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F160_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F160.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F161_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F161_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F161_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F161_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F161_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F161_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F161_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F161_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F161_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F161.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F162_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F162_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F162_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F162_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F162_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F162_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F162_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F162_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F162_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F162.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F163_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F163_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F163_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F163_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F163_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F163_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F163_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F163_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F163_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F163.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F164_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F164_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F164_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F164_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F164_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F164_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F164_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F164_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F164_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F164.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F165_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F165_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F165_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F165_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F165_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F165_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F165_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F165_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F165_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F165.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F166_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F166_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F166_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F166_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F166_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F166_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F166_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F166_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F166_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F166.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F167_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F167_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F167_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F167_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F167_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F167_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F167_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F167_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F167_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F167.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F168_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F168_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F168_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F168_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F168_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F168_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F168_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F168_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F168_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F168.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F169_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F169_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F169_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F169_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F169_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F169_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F169_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F169_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F169_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F169.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F170_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F170_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F170_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F170_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F170_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F170_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F170_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F170_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F170_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F170.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F171_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F171_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F171_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F171_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F171_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F171_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F171_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F171_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F171_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F171.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F172_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F172_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F172_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F172_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F172_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F172_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F172_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F172_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F172_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F172.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F173_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F173_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F173_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F173_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F173_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F173_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F173_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F173_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F173_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F173.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F174_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F174_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F174_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F174_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F174_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F174_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F174_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F174_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F174_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F174.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F175_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F175_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F175_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F175_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F175_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F175_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F175_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F175_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F175_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F175.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F176_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F176_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F176_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F176_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F176_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F176_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F176_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F176_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F176_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F176.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F177_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F177_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F177_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F177_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F177_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F177_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F177_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F177_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F177_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F177.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F178_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F178_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F178_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F178_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F178_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F178_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F178_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F178_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F178_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F178.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F179_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F179_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F179_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F179_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F179_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F179_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F179_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F179_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F179_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F179.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F180_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F180_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F180_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F180_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F180_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F180_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F180_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F180_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F180_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F180.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F181_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F181_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F181_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F181_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F181_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F181_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F181_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F181_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F181_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F181.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F182_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F182_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F182_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F182_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F182_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F182_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F182_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F182_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F182_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F182.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F183_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F183_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F183_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F183_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F183_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F183_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F183_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F183_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F183_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F183.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F184_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F184_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F184_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F184_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F184_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F184_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F184_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F184_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F184_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F184.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F185_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F185_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F185_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F185_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F185_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F185_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F185_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F185_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F185_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F185.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F186_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F186_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F186_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F186_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F186_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F186_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F186_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F186_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F186_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F186.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F187_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F187_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F187_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F187_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F187_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F187_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F187_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F187_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F187_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F187.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F188_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F188_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F188_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F188_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F188_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F188_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F188_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F188_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F188_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F188.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F189_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F189_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F189_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F189_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F189_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F189_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F189_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F189_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F189_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F189.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F190_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F190_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F190_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F190_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F190_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F190_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F190_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F190_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F190_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F190.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F191_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F191_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F191_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F191_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F191_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F191_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F191_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F191_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F191_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F191.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F192_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F192_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F192_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F192_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F192_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F192_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F192_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F192_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F192_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F192.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F193_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F193_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F193_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F193_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F193_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F193_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F193_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F193_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F193_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F193.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F194_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F194_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F194_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F194_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F194_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F194_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F194_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F194_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F194_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F194.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F195_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F195_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F195_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F195_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F195_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F195_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F195_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F195_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F195_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F195.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F196_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F196_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F196_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F196_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F196_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F196_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F196_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F196_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F196_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F196.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F197_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F197_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F197_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F197_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F197_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F197_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F197_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F197_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F197_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F197.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F198_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F198_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F198_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F198_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F198_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F198_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F198_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F198_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F198_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F198.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F199_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F199_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F199_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F199_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F199_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F199_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F199_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F199_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F199_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F199.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F200_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F200_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F200_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F200_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F200_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F200_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F200_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F200_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F200_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F200.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F201_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F201_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F201_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F201_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F201_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F201_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F201_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F201_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F201_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F201.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F202_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F202_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F202_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F202_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F202_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F202_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F202_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F202_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F202_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F202.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F203_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F203_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F203_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F203_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F203_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F203_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F203_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F203_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F203_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F203.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F204_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F204_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F204_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F204_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F204_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F204_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F204_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F204_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F204_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F204.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F205_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F205_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F205_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F205_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F205_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F205_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F205_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F205_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F205_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F205.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F206_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F206_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F206_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F206_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F206_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F206_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F206_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F206_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F206_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F206.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F207_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F207_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F207_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F207_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F207_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F207_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F207_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F207_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F207_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F207.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65534\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F208_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F208_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F208_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F208_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F208_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F208_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F208_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F208_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F208_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F208.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F209_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F209_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F209_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F209_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F209_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F209_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F209_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F209_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F209_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F209.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F210_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F210_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F210_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F210_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F210_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F210_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F210_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F210_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F210_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F210.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F211_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F211_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F211_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F211_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F211_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F211_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F211_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F211_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F211_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F211.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F212_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F212_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F212_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F212_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F212_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F212_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F212_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F212_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F212_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F212.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F213_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F213_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F213_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F213_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F213_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F213_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F213_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F213_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F213_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F213.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65533\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F214_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F214_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F214_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F214_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F214_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F214_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F214_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F214_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F214_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F214.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F215_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F215_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F215_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F215_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F215_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F215_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F215_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F215_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F215_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F215.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F216_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F216_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F216_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F216_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F216_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F216_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F216_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F216_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F216_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F216.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F217_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F217_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F217_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F217_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F217_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F217_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F217_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F217_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F217_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F217.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F218_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F218_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F218_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F218_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F218_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F218_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F218_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F218_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F218_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F218.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F219_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F219_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F219_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F219_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F219_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F219_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F219_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F219_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F219_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F219.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F220_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F220_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F220_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F220_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F220_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F220_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F220_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F220_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F220_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F220.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F221_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F221_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F221_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F221_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F221_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F221_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F221_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F221_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F221_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F221.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F222_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F222_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F222_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F222_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F222_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F222_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F222_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F222_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F222_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F222.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F223_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F223_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F223_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F223_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F223_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F223_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F223_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F223_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F223_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F223.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n",
      "uint16\n",
      "Appending...  reg_Cyc_1/Cycle_1_F224_reg.tif\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F224_reg.tif\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F224_reg.tif\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F224_reg.tif\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F224_reg.tif\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F224_reg.tif\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F224_reg.tif\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F224_reg.tif\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_11/Cycle_11_F224_reg.tif\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F224.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2038\n",
      "2030\n",
      "image max: 65535\n"
     ]
    }
   ],
   "source": [
    "refs = iter(glob.glob('tif/Cycle_0/*')) \n",
    "for FOV in range(NUM_FOVS):   \n",
    "    ref_name = next(refs)\n",
    "    FOV_num = ref_name[-7:-4]\n",
    "    #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "    img = imread(f'{REF_DIR}/Cycle_0/Cycle_F{FOV_num}.tif') # reference image that did not move\n",
    "    img = img.astype(np.uint16)\n",
    "    print(img.dtype)\n",
    "    for cycle in range(CYCLE_NUMS-1):\n",
    "        if cycle == 8: \n",
    "            continue\n",
    "        if cycle == 9:\n",
    "            continue\n",
    "        else:\n",
    "            fname = f'{IN_DIR}_Cyc_{cycle+1}/Cycle_{cycle+1}_F{FOV_num}_{IN_DIR}.tif'\n",
    "            print('Appending... ', fname)\n",
    "            im_to_add = imread(fname).astype(np.uint16)\n",
    "            im_to_add = im_to_add[:,:,...] \n",
    "            img = np.append(img, im_to_add, axis=1) # concatenate along channel index\n",
    "            print(f\"Shape = {img.shape}\") \n",
    "     \n",
    "    fname = f'/F{FOV_num}.tif'\n",
    "    print('saving', './{MERGE_DIR}'+fname)\n",
    "    print('dtype of ', img.dtype)\n",
    "    \n",
    "    ########## CROP ################ --> CHANGE everytime depending on shifts\n",
    "    Xlength, Ylength, img = crop(X_max_total, X_min_total, Y_max_total, Y_min_total, img, pad)\n",
    "    \n",
    "    assert img.shape[3] == Xlength, \"Check X size\"\n",
    "    assert img.shape[2] == Ylength, \"Check Y size\"\n",
    "    \n",
    "    \n",
    "    ### FINAL CHECK before saving ### \n",
    "    assert img.shape[0] == Z, \"Check ZCYX\"\n",
    "    assert img.shape[1] == final_ch, \"check final merge size\"\n",
    "    print('image max:', img.max())\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you are running the reg files and not merge files\n",
    "\n",
    "for c in range(CYCLE_NUMS):\n",
    "    if c == 0:\n",
    "        refs = iter(glob.glob('tif/Cycle_0/*'))\n",
    "        for FOV in range(0, NUM_FOVS): \n",
    "            ref_name = next(refs) \n",
    "            ref = imread(ref_name)\n",
    "            ref = ref.astype(np.uint16)\n",
    "            FOV_num = ref_name[-7:-4]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "\n",
    "            overall_count_zero = np.count_nonzero(ref == 0)\n",
    "            x = np.count_nonzero((0 < ref) & (ref < 6))\n",
    "            y = np.count_nonzero(65000 < ref)\n",
    "\n",
    "            for Z in range(ref.shape[0]):\n",
    "                for ch in range(ref.shape[1]): \n",
    "                    count_one_to_five = np.count_nonzero((0 < ref[Z,ch,...]) & (ref[Z,ch,...] < 6))\n",
    "                    if count_one_to_five > 0:\n",
    "                        one_to_five_list.append((FOV_num, Z, ch))\n",
    "                    count_zeros = np.count_nonzero(ref[Z,ch,...] == 0)\n",
    "                    if count_zeros > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                    count_65k = np.count_nonzero(65000 < ref[Z,ch,...])\n",
    "                    if count_65k > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                \n",
    "            int_counts.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "            int_counts.loc[FOV_num, 'total Zero count'] = overall_count_zero\n",
    "            int_counts.loc[FOV_num, 'one to five'] = x\n",
    "            int_counts.loc[FOV_num, 'greater 65K'] = y\n",
    "\n",
    "    elif c == 11:\n",
    "        ims = iter(glob.glob(f'reg_Cyc_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) \n",
    "        for FOV in range(0, NUM_FOVS): # \n",
    "            #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "            im_name = next(ims)\n",
    "            im = imread(im_name)\n",
    "            im = im.astype(np.uint16)\n",
    "            FOV_num = im_name[-11:-8]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "            \n",
    "            overall_count_zero = np.count_nonzero(im == 0)\n",
    "            x = np.count_nonzero((0 < im) & (im < 6))\n",
    "            y = np.count_nonzero(65000 < im)\n",
    "\n",
    "            for Z in range(im.shape[0]):\n",
    "                for ch in range(im.shape[1]): \n",
    "                    count_one_to_five = np.count_nonzero((0 < im[Z,ch,...]) & (im[Z,ch,...] < 6))\n",
    "                    if count_one_to_five > 0:\n",
    "                        one_to_five_list.append((FOV_num, Z, ch))\n",
    "                    count_zeros = np.count_nonzero(im[Z,ch,...] == 0)\n",
    "                    if count_zeros > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                    count_65k = np.count_nonzero(65000 < im[Z,ch,...])\n",
    "                    if count_65k > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                \n",
    "            int_counts.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "            int_counts.loc[FOV_num, 'total Zero count'] = overall_count_zero\n",
    "            int_counts.loc[FOV_num, 'one to five'] = x\n",
    "            int_counts.loc[FOV_num, 'greater 65K'] = y\n",
    "    elif c == 9:\n",
    "        continue\n",
    "    elif c == 10:\n",
    "        continue\n",
    "    else:\n",
    "        ims = iter(glob.glob(f'reg_Cyc_{c}/*')) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006  \n",
    "        for FOV in range(0, NUM_FOVS): # \n",
    "            #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "            im_name = next(ims)\n",
    "            im = imread(im_name)\n",
    "            im = im.astype(np.uint16)\n",
    "            FOV_num = im_name[-11:-8]\n",
    "            print(f'cycle {c} field {FOV_num} ')\n",
    "            \n",
    "            overall_count_zero = np.count_nonzero(im == 0)\n",
    "            x = np.count_nonzero((0 < im) & (im < 6))\n",
    "            y = np.count_nonzero(65000 < im)\n",
    "\n",
    "            for Z in range(im.shape[0]):\n",
    "                for ch in range(im.shape[1]): \n",
    "                    count_one_to_five = np.count_nonzero((0 < im[Z,ch,...]) & (im[Z,ch,...] < 6))\n",
    "                    if count_one_to_five > 0:\n",
    "                        one_to_five_list.append((FOV_num, Z, ch))\n",
    "                    count_zeros = np.count_nonzero(im[Z,ch,...] == 0)\n",
    "                    if count_zeros > 0:\n",
    "                        z_list.append((FOV_num, Z, ch))\n",
    "                    count_65k = np.count_nonzero(65000 < im[Z,ch,...])\n",
    "                    if count_65k > 0:\n",
    "                        sixfivek_list.append((FOV_num, Z, ch))\n",
    "                \n",
    "            int_counts.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "            int_counts.loc[FOV_num, 'total Zero count'] = overall_count_zero\n",
    "            int_counts.loc[FOV_num, 'one to five'] = x\n",
    "            int_counts.loc[FOV_num, 'greater 65K'] = y\n",
    "            \n",
    "done = open(\"done.txt\", \"a\")\n",
    "done.write(\"done\")\n",
    "done.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intensity Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 12\n",
    "NUM_FOVS = 219"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_counts = pd.DataFrame()\n",
    "z_list = []\n",
    "one_to_five_list = []\n",
    "sixfivek_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "FOV 001\n",
      "FOV 002\n",
      "FOV 003\n",
      "FOV 004\n",
      "FOV 005\n",
      "FOV 006\n",
      "FOV 007\n",
      "FOV 008\n",
      "FOV 009\n",
      "FOV 010\n",
      "FOV 011\n",
      "FOV 012\n",
      "FOV 013\n",
      "FOV 014\n",
      "FOV 015\n",
      "FOV 016\n",
      "FOV 017\n",
      "FOV 018\n",
      "FOV 019\n",
      "FOV 020\n",
      "FOV 021\n",
      "FOV 023\n",
      "FOV 024\n",
      "FOV 025\n",
      "FOV 026\n",
      "FOV 027\n",
      "FOV 028\n",
      "FOV 029\n",
      "FOV 030\n",
      "FOV 031\n",
      "FOV 033\n",
      "FOV 034\n",
      "FOV 035\n",
      "FOV 037\n",
      "FOV 038\n",
      "FOV 039\n",
      "FOV 040\n",
      "FOV 041\n",
      "FOV 042\n",
      "FOV 043\n",
      "FOV 044\n",
      "FOV 045\n",
      "FOV 046\n",
      "FOV 047\n",
      "FOV 048\n",
      "FOV 049\n",
      "FOV 050\n",
      "FOV 051\n",
      "FOV 052\n",
      "FOV 053\n",
      "FOV 054\n",
      "FOV 055\n",
      "FOV 056\n",
      "FOV 057\n",
      "FOV 058\n",
      "FOV 059\n",
      "FOV 060\n",
      "FOV 061\n",
      "FOV 062\n",
      "FOV 063\n",
      "FOV 064\n",
      "FOV 065\n",
      "FOV 066\n",
      "FOV 067\n",
      "FOV 068\n",
      "FOV 069\n",
      "FOV 070\n",
      "FOV 071\n",
      "FOV 072\n",
      "FOV 073\n",
      "FOV 074\n",
      "FOV 075\n",
      "FOV 076\n",
      "FOV 077\n",
      "FOV 078\n",
      "FOV 079\n",
      "FOV 080\n",
      "FOV 081\n",
      "FOV 082\n",
      "FOV 083\n",
      "FOV 084\n",
      "FOV 085\n",
      "FOV 086\n",
      "FOV 087\n",
      "FOV 088\n",
      "FOV 089\n",
      "FOV 090\n",
      "FOV 091\n",
      "FOV 092\n",
      "FOV 093\n",
      "FOV 094\n",
      "FOV 095\n",
      "FOV 096\n",
      "FOV 097\n",
      "FOV 098\n",
      "FOV 099\n",
      "FOV 100\n",
      "FOV 101\n",
      "FOV 102\n",
      "FOV 103\n",
      "FOV 104\n",
      "FOV 105\n",
      "FOV 106\n",
      "FOV 107\n",
      "FOV 108\n",
      "FOV 109\n",
      "FOV 110\n",
      "FOV 111\n",
      "FOV 112\n",
      "FOV 113\n",
      "FOV 114\n",
      "FOV 115\n",
      "FOV 116\n",
      "FOV 117\n",
      "FOV 118\n",
      "FOV 119\n",
      "FOV 120\n",
      "FOV 121\n",
      "FOV 122\n",
      "FOV 123\n",
      "FOV 124\n",
      "FOV 125\n",
      "FOV 126\n",
      "FOV 127\n",
      "FOV 128\n",
      "FOV 129\n",
      "FOV 131\n",
      "FOV 132\n",
      "FOV 133\n",
      "FOV 134\n",
      "FOV 135\n",
      "FOV 136\n",
      "FOV 137\n",
      "FOV 138\n",
      "FOV 140\n",
      "FOV 141\n",
      "FOV 142\n",
      "FOV 143\n",
      "FOV 144\n",
      "FOV 145\n",
      "FOV 146\n",
      "FOV 147\n",
      "FOV 148\n",
      "FOV 149\n",
      "FOV 150\n",
      "FOV 151\n",
      "FOV 152\n",
      "FOV 153\n",
      "FOV 154\n",
      "FOV 155\n",
      "FOV 156\n",
      "FOV 158\n",
      "FOV 159\n",
      "FOV 160\n",
      "FOV 161\n",
      "FOV 162\n",
      "FOV 163\n",
      "FOV 164\n",
      "FOV 165\n",
      "FOV 166\n",
      "FOV 167\n",
      "FOV 168\n",
      "FOV 169\n",
      "FOV 170\n",
      "FOV 171\n",
      "FOV 172\n",
      "FOV 173\n",
      "FOV 174\n",
      "FOV 175\n",
      "FOV 176\n",
      "FOV 177\n",
      "FOV 178\n",
      "FOV 179\n",
      "FOV 180\n",
      "FOV 181\n",
      "FOV 182\n",
      "FOV 183\n",
      "FOV 184\n",
      "FOV 185\n",
      "FOV 186\n",
      "FOV 187\n",
      "FOV 188\n",
      "FOV 189\n",
      "FOV 190\n",
      "FOV 191\n",
      "FOV 192\n",
      "FOV 193\n",
      "FOV 194\n",
      "FOV 195\n",
      "FOV 196\n",
      "FOV 197\n",
      "FOV 198\n",
      "FOV 199\n",
      "FOV 200\n",
      "FOV 201\n",
      "FOV 202\n",
      "FOV 203\n",
      "FOV 204\n",
      "FOV 205\n",
      "FOV 206\n",
      "FOV 207\n",
      "FOV 208\n",
      "FOV 209\n",
      "FOV 210\n",
      "FOV 211\n",
      "FOV 212\n",
      "FOV 213\n",
      "FOV 214\n",
      "FOV 215\n",
      "FOV 216\n",
      "FOV 217\n",
      "FOV 218\n",
      "FOV 219\n",
      "FOV 220\n",
      "FOV 221\n",
      "FOV 222\n",
      "FOV 223\n",
      "FOV 224\n"
     ]
    }
   ],
   "source": [
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    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",
    "        for ch in range(img.shape[1]): \n",
    "            count_one_to_five = np.count_nonzero((0 < img[Z,ch,...]) & (img[Z,ch,...] < 6))\n",
    "            if count_one_to_five > 0:\n",
    "                one_to_five_list.append((FOV_num, Z, ch))\n",
    "            count_zeros = np.count_nonzero(img[Z,ch,...] == 0)\n",
    "            if count_zeros > 0:\n",
    "                z_list.append((FOV_num, Z, ch))\n",
    "            count_65k = np.count_nonzero(65000 < img[Z,ch,...])\n",
    "            if count_65k > 0:\n",
    "                sixfivek_list.append((FOV_num, Z, ch))\n",
    "                \n",
    "    int_counts.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "    int_counts.loc[FOV_num, 'total Zero count'] = overall_count_zero\n",
    "    int_counts.loc[FOV_num, 'one to five'] = x\n",
    "    int_counts.loc[FOV_num, 'greater 65K'] = y\n",
    "\n",
    "done = open(\"done.txt\", \"a\")\n",
    "done.write(\"done\")\n",
    "done.close() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_counts.to_csv('pixel_intensity_counts_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "img = imread('merged/F213.tif')\n",
    "np.where(img == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.where(65000 < img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>24.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>002</td>\n",
       "      <td>34.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>003</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>586.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>18.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1294.0</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>58.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>13.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>591.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>18.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>223</td>\n",
       "      <td>15.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>578.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count  one to five  greater 65K\n",
       "000     000              24.0         71.0        925.0\n",
       "001     001              24.0         53.0        784.0\n",
       "002     002              34.0         93.0       1214.0\n",
       "003     003               8.0         24.0        586.0\n",
       "004     004              18.0         88.0       1294.0\n",
       "..      ...               ...          ...          ...\n",
       "220     220              58.0        164.0       1755.0\n",
       "221     221              13.0         32.0        591.0\n",
       "222     222              18.0         71.0       1025.0\n",
       "223     223              15.0         31.0        437.0\n",
       "224     224               5.0         26.0        578.0\n",
       "\n",
       "[219 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_FOV_set = set()\n",
    "for i in z_list:\n",
    "    z_FOV_set.add(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOOKING FOR WHICH FOVS HAVE THE BLACK BOXES\n",
    "\n",
    "black_box = []\n",
    "for idx, val in enumerate(int_counts['total Zero count']):\n",
    "    if val > 1000:\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1677"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sixfivek_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>000</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>000</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>000</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>001</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>001</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>603</td>\n",
       "      <td>223</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>604</td>\n",
       "      <td>223</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>605</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>606</td>\n",
       "      <td>224</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>607</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>608 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  Z  ch\n",
       "0       000  4  35\n",
       "1       000  5  35\n",
       "2       000  6  35\n",
       "3       001  4  35\n",
       "4       001  5  35\n",
       "..      ... ..  ..\n",
       "603     223  7  35\n",
       "604     223  8  35\n",
       "605     224  3  35\n",
       "606     224  4  35\n",
       "607     224  5  35\n",
       "\n",
       "[608 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_list_arr = np.array(z_list)\n",
    "z_list_df = pd.DataFrame(z_list_arr)\n",
    "z_list_df = z_list_df.rename(columns={0: \"FOV_num\", 1: \"Z\", 2: \"ch\"})\n",
    "z_list_df.to_csv('z_list_df.csv')\n",
    "z_list_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>000</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>000</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>000</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>000</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>000</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1672</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1673</td>\n",
       "      <td>224</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1674</td>\n",
       "      <td>224</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1675</td>\n",
       "      <td>224</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1676</td>\n",
       "      <td>224</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1677 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num   Z  ch\n",
       "0        000   1  35\n",
       "1        000   2  35\n",
       "2        000   3  35\n",
       "3        000   4  35\n",
       "4        000   4  36\n",
       "...      ...  ..  ..\n",
       "1672     224   6  35\n",
       "1673     224   7  35\n",
       "1674     224   8  35\n",
       "1675     224   9  35\n",
       "1676     224  11  35\n",
       "\n",
       "[1677 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sixfivek_list_arr = np.array(sixfivek_list)\n",
    "sixfivek_list_df = pd.DataFrame(sixfivek_list_arr)\n",
    "sixfivek_list_df = sixfivek_list_df.rename(columns={0: \"FOV_num\", 1: \"Z\", 2: \"ch\"})\n",
    "sixfivek_list_df.to_csv('sixfivek_list_df.csv')\n",
    "sixfivek_list_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>000</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>000</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>000</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>000</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>001</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>892</td>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>893</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>894</td>\n",
       "      <td>224</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>895</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>896</td>\n",
       "      <td>224</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>897 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  Z  ch\n",
       "0       000  4  35\n",
       "1       000  5  35\n",
       "2       000  6  35\n",
       "3       000  7  35\n",
       "4       001  4  35\n",
       "..      ... ..  ..\n",
       "892     224  2  35\n",
       "893     224  3  35\n",
       "894     224  4  35\n",
       "895     224  5  35\n",
       "896     224  8  35\n",
       "\n",
       "[897 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_to_five_list_arr = np.array(one_to_five_list)\n",
    "one_to_five_list_df = pd.DataFrame(one_to_five_list_arr)\n",
    "one_to_five_list_df = one_to_five_list_df.rename(columns={0: \"FOV_num\", 1: \"Z\", 2: \"ch\"})\n",
    "one_to_five_list_df.to_csv('one_to_five_list_df.csv')\n",
    "one_to_five_list_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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('Cov_8_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 np.max(binary_mask) == True:\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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>total Zero count</th>\n",
       "      <th>one to five</th>\n",
       "      <th>greater 65K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>000</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>002</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000319</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>0.000014</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>223</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count  one to five  greater 65K\n",
       "000     000          0.000006     0.000018     0.000228\n",
       "001     001          0.000006     0.000013     0.000193\n",
       "002     002          0.000008     0.000023     0.000299\n",
       "003     003          0.000002     0.000006     0.000144\n",
       "004     004          0.000004     0.000022     0.000319\n",
       "..      ...               ...          ...          ...\n",
       "220     220          0.000014     0.000040     0.000433\n",
       "221     221          0.000003     0.000008     0.000146\n",
       "222     222          0.000004     0.000018     0.000253\n",
       "223     223          0.000004     0.000008     0.000108\n",
       "224     224          0.000001     0.000006     0.000142\n",
       "\n",
       "[219 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total num of pixels is 3995601 (because 2019*1979)\n",
    "# here we get percent of 0 intensity count, 0<x<6 and greater than 65k\n",
    "percents = int_counts.copy()\n",
    "percents['total Zero count'] = percents['total Zero count'].div(4056180)\n",
    "percents['one to five'] = percents['one to five'].div(4056180)\n",
    "percents['greater 65K'] = percents['greater 65K'].div(4056180)\n",
    "percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "percents.to_csv('percent_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>total Zero count</th>\n",
       "      <th>one to five</th>\n",
       "      <th>greater 65K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>000</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>002</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000319</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>0.000014</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>223</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count  one to five  greater 65K\n",
       "000     000          0.000006     0.000018     0.000228\n",
       "001     001          0.000006     0.000013     0.000193\n",
       "002     002          0.000008     0.000023     0.000299\n",
       "003     003          0.000002     0.000006     0.000144\n",
       "004     004          0.000004     0.000022     0.000319\n",
       "..      ...               ...          ...          ...\n",
       "220     220          0.000014     0.000040     0.000433\n",
       "221     221          0.000003     0.000008     0.000146\n",
       "222     222          0.000004     0.000018     0.000253\n",
       "223     223          0.000004     0.000008     0.000108\n",
       "224     224          0.000001     0.000006     0.000142\n",
       "\n",
       "[204 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circles = percents.loc[(percents['one to five'] > 0)&(percents['greater 65K'] > 0)&(percents['total Zero count'] < 0.0004930748)]  \n",
    "black_circles\n",
    "# THESE MUST BE THE BLACK CIRCLE ARTIFACT ONES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_circles.to_csv('black_circle_pixel_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>001</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>892</td>\n",
       "      <td>224</td>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>893</td>\n",
       "      <td>224</td>\n",
       "      <td>3.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>894</td>\n",
       "      <td>224</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>895</td>\n",
       "      <td>224</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>896</td>\n",
       "      <td>224</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>897 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num    Z  channel\n",
       "0       000  4.0     35.0\n",
       "1       000  5.0     35.0\n",
       "2       000  6.0     35.0\n",
       "3       000  7.0     35.0\n",
       "4       001  4.0     35.0\n",
       "..      ...  ...      ...\n",
       "892     224  2.0     35.0\n",
       "893     224  3.0     35.0\n",
       "894     224  4.0     35.0\n",
       "895     224  5.0     35.0\n",
       "896     224  8.0     35.0\n",
       "\n",
       "[897 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circle_lst = []\n",
    "for i in black_circles['FOV_num']:\n",
    "    black_circle_lst.append(i)\n",
    "\n",
    "df_black_circle = pd.DataFrame()\n",
    "count = 0\n",
    "\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": 26,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: total Zero count, dtype: float64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yes_box = percents.loc[percents['total Zero count'] > 0.0004930748]['total Zero count'] #greater than 2000/total\n",
    "yes_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "000    0.000006\n",
       "001    0.000006\n",
       "002    0.000008\n",
       "003    0.000002\n",
       "004    0.000004\n",
       "         ...   \n",
       "220    0.000014\n",
       "221    0.000003\n",
       "222    0.000004\n",
       "223    0.000004\n",
       "224    0.000001\n",
       "Name: total Zero count, Length: 219, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_box = percents.loc[(percents['total Zero count'] < 0.0004930748)]['total Zero count']  #less than 2000/total\n",
    "no_box   # percent zeros in images with BO black boxes present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'percent 0 intensity')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize = (50,20))\n",
    "axs[0].hist(no_box, bins = 50, alpha=0.8)\n",
    "axs[0].set_ylim([0, 20])\n",
    "axs[0].set_xlim([-0.001, 0.01])\n",
    "axs[0].set_title('no black boxes - percent 0 intensity', size = 50)\n",
    "axs[0].set_ylabel('count', fontsize = 30.0) # Y label\n",
    "axs[0].set_xlabel('percent 0 intensity', fontsize = 30) # X label\n",
    "axs[1].hist(yes_box, bins = 50, alpha=0.8)\n",
    "axs[1].set_ylim([0, 20])\n",
    "axs[1].set_xlim([-0.001, 1])\n",
    "axs[1].set_title('black boxes present - percent 0 intensity', size = 50)\n",
    "axs[1].set_ylabel('count', fontsize = 30.0) # Y label\n",
    "axs[1].set_xlabel('percent 0 intensity', fontsize = 30) # X label\n",
    "\n",
    "\n",
    "# the little bit in the no black boxes --> that is the black circle artifacts that correlate with 65k pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_to_five = percents.loc[percents['one to five'] != 0]['one to five']\n",
    "greater_65K = percents.loc[percents['greater 65K'] != 0]['greater 65K']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_to_five_FOV = percents.loc[percents['one to five'] != 0]['FOV_num']\n",
    "greater_65K_FOV = percents.loc[percents['greater 65K'] != 0]['FOV_num']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "one_to_five_FOV_list = list(np.asarray(one_to_five_FOV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "greater_65K_FOV_list = list(np.asarray(greater_65K_FOV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04b9fc80fb3e4b05a5fe2766eead4953",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=219), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64),)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(greater_65K_FOV_list):\n",
    "    if i not in one_to_five_FOV_list:\n",
    "        img = imread(f'merged/F{i}.tif')\n",
    "        x = np.where(65000 < img)[3]\n",
    "        y = np.where(65000 < img)[2]\n",
    "        x_where = np.where(x>1998)\n",
    "        y_where = np.where(y>1998)\n",
    "        print(x_where)\n",
    "        print(y_where)\n",
    "\n",
    "# this shows that there is none of the RIGID_BODY artifacts -- so they all don't have the RIG_BOD artifact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "img = imread('merged/F155.tif')\n",
    "np.where(img> 65000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'percent >65k intensity')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# USE THIS TO SEE IF THE 65K PIXELS ARE IN THE SAME VICINITY AS 1<X<6 PIXELS FOR THE FOVS\n",
    "fig, axs = plt.subplots(1, 2, figsize = (50,20))\n",
    "axs[0].hist(one_to_five, bins = 50, alpha=0.5)\n",
    "axs[0].set_title('percent 1-5 intensity', size = 50)\n",
    "axs[1].hist(greater_65K, bins = 50, alpha=0.5)\n",
    "axs[1].set_title('percent >65k intensity', size = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 288x144 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pyplot.hist(percents['total Zero count'], bins = 50, alpha=0.5, label = 'total Zero count')\n",
    "plt.hist(one_to_five, bins = 50, alpha=0.5, label = 'one to five')\n",
    "plt.hist(greater_65K, bins = 50, alpha=0.5, label = 'greater than 65K')\n",
    "plt.legend(loc='upper right')\n",
    "plt.figure(figsize=(4, 2))\n",
    "plt.show()\n",
    "\n",
    "# seems that there is much more >65k than 1-5 intensity pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(one_to_five_list_df.FOV_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>002</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>002</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>002</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>002</td>\n",
       "      <td>6</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>002</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>002</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>002</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FOV_num  Z  ch\n",
       "11     002  3  35\n",
       "12     002  4  35\n",
       "13     002  5  35\n",
       "14     002  6  35\n",
       "15     002  7  35\n",
       "16     002  8  35\n",
       "17     002  9  35"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_to_five_list_df[one_to_five_list_df.FOV_num=='002']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e232a3bb4f548b0a32b3945897da121",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=219), 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",
      "FOV 006\n",
      "FOV 007\n",
      "FOV 008\n",
      "FOV 009\n",
      "FOV 010\n",
      "FOV 011\n",
      "FOV 012\n",
      "FOV 013\n",
      "FOV 014\n",
      "FOV 015\n",
      "FOV 016\n",
      "FOV 017\n",
      "FOV 018\n",
      "FOV 019\n",
      "FOV 020\n",
      "FOV 021\n",
      "FOV 023\n",
      "FOV 024\n",
      "FOV 025\n",
      "FOV 026\n",
      "FOV 027\n",
      "FOV 028\n",
      "FOV 029\n",
      "FOV 030\n",
      "FOV 031\n",
      "FOV 033\n",
      "FOV 034\n",
      "FOV 035\n",
      "FOV 037\n",
      "FOV 038\n",
      "FOV 039\n",
      "FOV 040\n",
      "FOV 041\n",
      "FOV 042\n",
      "FOV 043\n",
      "FOV 044\n",
      "FOV 045\n",
      "FOV 046\n",
      "FOV 047\n",
      "FOV 048\n",
      "FOV 049\n",
      "FOV 050\n",
      "FOV 051\n",
      "FOV 052\n",
      "FOV 053\n",
      "FOV 054\n",
      "FOV 055\n",
      "FOV 056\n",
      "FOV 057\n",
      "FOV 058\n",
      "FOV 059\n",
      "FOV 060\n",
      "FOV 061\n",
      "FOV 062\n",
      "FOV 063\n",
      "FOV 064\n",
      "FOV 065\n",
      "FOV 066\n",
      "FOV 067\n",
      "FOV 068\n",
      "FOV 069\n",
      "FOV 070\n",
      "FOV 071\n",
      "FOV 072\n",
      "FOV 073\n",
      "FOV 074\n",
      "FOV 075\n",
      "FOV 076\n",
      "FOV 077\n",
      "FOV 078\n",
      "FOV 079\n",
      "FOV 080\n",
      "FOV 081\n",
      "FOV 082\n",
      "FOV 083\n",
      "FOV 084\n",
      "FOV 085\n",
      "FOV 086\n",
      "FOV 087\n",
      "FOV 088\n",
      "FOV 089\n",
      "FOV 090\n",
      "FOV 091\n",
      "FOV 092\n",
      "FOV 093\n",
      "FOV 094\n",
      "FOV 095\n",
      "FOV 096\n",
      "FOV 097\n",
      "FOV 098\n",
      "FOV 099\n",
      "FOV 100\n",
      "FOV 101\n",
      "FOV 102\n",
      "FOV 103\n",
      "FOV 104\n",
      "FOV 105\n",
      "FOV 106\n",
      "FOV 107\n",
      "FOV 108\n",
      "FOV 109\n",
      "FOV 110\n",
      "FOV 111\n",
      "FOV 112\n",
      "FOV 113\n",
      "FOV 114\n",
      "FOV 115\n",
      "FOV 116\n",
      "FOV 117\n",
      "FOV 118\n",
      "FOV 119\n",
      "FOV 120\n",
      "FOV 121\n",
      "FOV 122\n",
      "FOV 123\n",
      "FOV 124\n",
      "FOV 125\n",
      "FOV 126\n",
      "FOV 127\n",
      "FOV 128\n",
      "FOV 129\n",
      "FOV 131\n",
      "FOV 132\n",
      "FOV 133\n",
      "FOV 134\n",
      "FOV 135\n",
      "FOV 136\n",
      "FOV 137\n",
      "FOV 138\n",
      "FOV 140\n",
      "FOV 141\n",
      "FOV 142\n",
      "FOV 143\n",
      "FOV 144\n",
      "FOV 145\n",
      "FOV 146\n",
      "FOV 147\n",
      "FOV 148\n",
      "FOV 149\n",
      "FOV 150\n",
      "FOV 151\n",
      "FOV 152\n",
      "FOV 153\n",
      "FOV 154\n",
      "FOV 155\n",
      "FOV 156\n",
      "FOV 158\n",
      "FOV 159\n",
      "FOV 160\n",
      "FOV 161\n",
      "FOV 162\n",
      "FOV 163\n",
      "FOV 164\n",
      "FOV 165\n",
      "FOV 166\n",
      "FOV 167\n",
      "FOV 168\n",
      "FOV 169\n",
      "FOV 170\n",
      "FOV 171\n",
      "FOV 172\n",
      "FOV 173\n",
      "FOV 174\n",
      "FOV 175\n",
      "FOV 176\n",
      "FOV 177\n",
      "FOV 178\n",
      "FOV 179\n",
      "FOV 180\n",
      "FOV 181\n",
      "FOV 182\n",
      "FOV 183\n",
      "FOV 184\n",
      "FOV 185\n",
      "FOV 186\n",
      "FOV 187\n",
      "FOV 188\n",
      "FOV 189\n",
      "FOV 190\n",
      "FOV 191\n",
      "FOV 192\n",
      "FOV 193\n",
      "FOV 194\n",
      "FOV 195\n",
      "FOV 196\n",
      "FOV 197\n",
      "FOV 198\n",
      "FOV 199\n",
      "FOV 200\n",
      "FOV 201\n",
      "FOV 202\n",
      "FOV 203\n",
      "FOV 204\n",
      "FOV 205\n",
      "FOV 206\n",
      "FOV 207\n",
      "FOV 208\n",
      "FOV 209\n",
      "FOV 210\n",
      "FOV 211\n",
      "FOV 212\n",
      "FOV 213\n",
      "FOV 214\n",
      "FOV 215\n",
      "FOV 216\n",
      "FOV 217\n",
      "FOV 218\n",
      "FOV 219\n",
      "FOV 220\n",
      "FOV 221\n",
      "FOV 222\n",
      "FOV 223\n",
      "FOV 224\n",
      "\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}_0_perc'] = np.percentile(arr, 0)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.00001_perc'] = np.percentile(arr, 0.00001)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.0001_perc'] = np.percentile(arr, 0.0001)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.001_perc'] = np.percentile(arr, 0.001)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.01_perc'] = np.percentile(arr, 0.01)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.1_perc'] = np.percentile(arr, 0.1)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.5_perc'] = np.percentile(arr, 0.5)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_1_perc'] = np.percentile(arr, 1)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_5_perc'] = np.percentile(arr, 5)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_10_perc'] = np.percentile(arr, 10)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_95_perc'] = np.percentile(arr, 95)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_99_perc'] = np.percentile(arr, 99)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_100_perc'] = np.percentile(arr, 100)\n",
    "\n",
    "\n",
    "percentiles.to_csv('percentiles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ch0_cutoff_perc</th>\n",
       "      <th>ch0_0_perc</th>\n",
       "      <th>ch0_0.00001_perc</th>\n",
       "      <th>ch0_0.0001_perc</th>\n",
       "      <th>ch0_0.001_perc</th>\n",
       "      <th>ch0_0.01_perc</th>\n",
       "      <th>ch0_0.1_perc</th>\n",
       "      <th>ch0_0.5_perc</th>\n",
       "      <th>ch0_1_perc</th>\n",
       "      <th>...</th>\n",
       "      <th>ch37_0.001_perc</th>\n",
       "      <th>ch37_0.01_perc</th>\n",
       "      <th>ch37_0.1_perc</th>\n",
       "      <th>ch37_0.5_perc</th>\n",
       "      <th>ch37_1_perc</th>\n",
       "      <th>ch37_5_perc</th>\n",
       "      <th>ch37_10_perc</th>\n",
       "      <th>ch37_95_perc</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>60.546068</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>96.0</td>\n",
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       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>2229.0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>58.273034</td>\n",
       "      <td>71.730339</td>\n",
       "      <td>86.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>2112.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>61.273034</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>4466.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
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       "      <td>87.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>1905.0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>303.0</td>\n",
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       "      <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",
       "      <td>214</td>\n",
       "      <td>220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>54.273034</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>84.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>2928.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>215</td>\n",
       "      <td>221</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>2590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216</td>\n",
       "      <td>222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>10329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>217</td>\n",
       "      <td>223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>2468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>218</td>\n",
       "      <td>224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>59.273034</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>2731.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 533 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  ch0_cutoff_perc  ch0_0_perc  ch0_0.00001_perc  \\\n",
       "0             0              0.0        49.0         60.546068   \n",
       "1             1              0.0        43.0         58.273034   \n",
       "2             2              0.0        42.0         61.273034   \n",
       "3             3              0.0        46.0         60.000000   \n",
       "4             4              0.0        39.0         59.000000   \n",
       "..          ...              ...         ...               ...   \n",
       "214         220              0.0        43.0         54.273034   \n",
       "215         221              0.0        36.0         48.000000   \n",
       "216         222              0.0        38.0         53.000000   \n",
       "217         223              0.0        52.0         57.000000   \n",
       "218         224              0.0        40.0         59.273034   \n",
       "\n",
       "     ch0_0.0001_perc  ch0_0.001_perc  ch0_0.01_perc  ch0_0.1_perc  \\\n",
       "0          77.000000            87.0           89.0          91.0   \n",
       "1          71.730339            86.0           89.0          92.0   \n",
       "2          73.000000            86.0           89.0          92.0   \n",
       "3          70.000000            85.0           89.0          92.0   \n",
       "4          71.000000            85.0           89.0          91.0   \n",
       "..               ...             ...            ...           ...   \n",
       "214        68.000000            84.0           89.0          92.0   \n",
       "215        68.000000            83.0           89.0          92.0   \n",
       "216        69.000000            83.0           89.0          92.0   \n",
       "217        71.000000            85.0           89.0          92.0   \n",
       "218        72.000000            86.0           89.0          92.0   \n",
       "\n",
       "     ch0_0.5_perc  ch0_1_perc  ...  ch37_0.001_perc  ch37_0.01_perc  \\\n",
       "0            94.0        96.0  ...             88.0            94.0   \n",
       "1            95.0        96.0  ...             82.0            92.0   \n",
       "2            95.0        96.0  ...             81.0            92.0   \n",
       "3            95.0        96.0  ...             87.0            94.0   \n",
       "4            95.0        96.0  ...             87.0            94.0   \n",
       "..            ...         ...  ...              ...             ...   \n",
       "214          95.0        96.0  ...             73.0            87.0   \n",
       "215          95.0        96.0  ...             77.0            89.0   \n",
       "216          95.0        96.0  ...             78.0            90.0   \n",
       "217          95.0        96.0  ...             78.0            91.0   \n",
       "218          95.0        96.0  ...             82.0            92.0   \n",
       "\n",
       "     ch37_0.1_perc  ch37_0.5_perc  ch37_1_perc  ch37_5_perc  ch37_10_perc  \\\n",
       "0             97.0           99.0        100.0        102.0         104.0   \n",
       "1             96.0           99.0        100.0        103.0         104.0   \n",
       "2             97.0           99.0        100.0        103.0         104.0   \n",
       "3             97.0           99.0        100.0        102.0         104.0   \n",
       "4             97.0           99.0        100.0        102.0         104.0   \n",
       "..             ...            ...          ...          ...           ...   \n",
       "214           96.0           99.0        100.0        103.0         104.0   \n",
       "215           96.0           99.0        100.0        102.0         104.0   \n",
       "216           96.0           99.0        100.0        102.0         104.0   \n",
       "217           96.0           99.0        100.0        103.0         104.0   \n",
       "218           97.0           99.0        100.0        103.0         104.0   \n",
       "\n",
       "     ch37_95_perc  ch37_99_perc  ch37_100_perc  \n",
       "0           169.0         284.0         2229.0  \n",
       "1           187.0         315.0         2112.0  \n",
       "2           189.0         315.0         4466.0  \n",
       "3           197.0         322.0         1905.0  \n",
       "4           184.0         303.0         1343.0  \n",
       "..            ...           ...            ...  \n",
       "214         198.0         317.0         2928.0  \n",
       "215         196.0         313.0         2590.0  \n",
       "216         198.0         326.0        10329.0  \n",
       "217         182.0         287.0         2468.0  \n",
       "218         174.0         294.0         2731.0  \n",
       "\n",
       "[219 rows x 533 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentiles = pd.read_csv('percentiles.csv')\n",
    "percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  2.,  0.,  0.,  1.,  0.,  1.,  2.,  2.,  5., 11.,  5.,  5.,\n",
       "         0.,  0.,  0.,  0.,  1.,  0.,  1.]),\n",
       " array([ 58. ,  60.2,  62.4,  64.6,  66.8,  69. ,  71.2,  73.4,  75.6,\n",
       "         77.8,  80. ,  82.2,  84.4,  86.6,  88.8,  91. ,  93.2,  95.4,\n",
       "         97.6,  99.8, 102. ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "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": [
    "zeroth_perc = percentiles.filter(regex=(\".*_0_.*\"))\n",
    "zeroth_perc\n",
    "\n",
    "# histogram of maz 0th percentile values across all channels\n",
    "plt.hist(zeroth_perc.max().to_frame()[0],bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bedc4181b7f45af8dcfdcdc6091b5cf",
       "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/imaging/mambaforge/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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lnpSU5Nu3b/eTJ0967969fe7cub5//35v3Lix5+bmurv7sWPH/MMPP3R396ZNm/qBAwfy99GhQwd/9dVX3d39+PHjfuzYsTOOEw/XR6QiDi/emf+TqIB1XkRejXli9zhO7u7u//u//+stW7b05s2b+4QJE9zd/b777vP58+e7u/tXX33lAwYM8Msuu8yzsrJ827Zt+dtOmDDBmzdv7pdffrm/9tprxe7T3X3btm2elZXll112mQ8YMMCPHz/u7u45OTneqFEjr1GjhicnJ3vr1q2LjHfZsmXevXt3HzRokLdq1cpHjx7tJ0+edHf3pUuXemZmprdr187btWuXfw6nJ/ePPvrIu3fv7u3atfOMjIwC55QnXq6PSHkFPbmrnnvALF++nIkTJ7Jw4cJKPY6ujyQ61XMXEZGEo5K/CWrjxo0MGzasQNsFF1zA2rVr6datW2yCEpG4oeSeoNq1a8f69etL7igiZyVNy4iIBJCSu4hIACm5i4gEUMLMuT+7/tmo7u+OK++I6v5EROKJRu4lqMp67pMnT6ZFixaYGQcPHqzU8xKRYFNyL0ZV13Pv3LkzS5YsoWnTyv2DCncvtICZiASHknsxqrKeO0D79u1JTU0tVWwPPvggw4YNo0ePHrRs2ZLnnnsuf91vfvMbsrKySEtL44EHHgBg586dtGrVijvuuIOMjAx2797NokWLyMjIID09nZ49e0bhGROReJEwc+6xUFjt9bVr1xbZ5/R67h07diywbV599pL2WVobNmxgzZo1HDt2jPbt23PDDTewadMmcnNzycnJwd3p27cvK1asoEmTJnz44Yc8//zzPPvssxw4cICf/vSnrFixgmbNmvHpp5+WKwYRiU9K7sUorO5OZdVzL49+/fpRvXp1qlevTvfu3cnJyWHlypUsXryY9u3bA3D06FFyc3Np0qQJTZs2zX/BWbNmDV27dqVZs2YAJCcnlysGEYlPSu7FqMp67uVRVN32e++9l9GjRxdYt3Pnzvya7RB6USrvi4qIJICiykVW5U+8lvyt6nrueU4vwVuYBx54wNPT0/2rr77ygwcPeuPGjX3v3r3+17/+1bOzs/3IkSPu7r5nzx7/5JNPfMeOHd6mTZv87ffv3+8pKSm+fft2d3c/dOhQmZ6beLg+IhUR9JK/GrkXo1q1akyePJk+ffpw8uRJRo4cSZs2bbj//vvJzMykb9++3HbbbQwbNowWLVqQnJzMrFmzAGjTpg2DBg2idevWVKtWjWeeeYZzzz0XoNB9Ajz99NM89thj/POf/yQtLY3rr7+eP/zhD0XGl52dzQ033MCuXbu47777aNiwIQ0bNmTLli35X6FXq1YtXnzxxfxj56lXrx5Tpkzhpptu4tSpU9SvX5833nijMp5GEYkB1XNPUA8++CC1atXi7rvvjsnxdX0k0ameu4iIJBxNy8S5559/nqeeeqpAW+fOnXnmmWdiFJGIJAIl9zg3YsQIRowYEeswRCTBaFpGRCSAlNxFRAJIyV1EJIASZs79wKTJUd1fvX/7WVT3JyISTzRyL0Fl1HMfOXIk9evXp23btlVxCiJyFlJyL0Zl1HMHuPXWW1m0aFGlx3/ixIlKP4aIxCcl92JURj13gK5du5a6CmO3bt0YN24cV199NW3bts3fx7Fjxxg5ciRZWVm0b98+P67p06czcOBAbrzxRnr37g3AY489Rrt27UhPT2f8+PFReW5EJL4lzJx7LFRWPfeyOnbsGKtXr2bFihWMHDmSTZs28cgjj9CjRw+mTZvG4cOHyc7O5pprrgHgrbfeYsOGDSQnJ/P6668zb9481q5dS40aNVS3XeQsoeRejMLq7lS0nnt5DBkyBAiN+L/44gsOHz7M4sWLWbBgARMnTgTg+PHj7Nq1C4BevXrlvzNYsmQJI0aMoEaNGoDqtoucLZTci1FZ9dzLqqi67S+//DJXXHFFgXVr165V3XYRSZzkHotbF7OyssjNzWXHjh00atSIWbNm8dJLLxXo07dvX2bMmEGnTp2YO3cuPXr0wMzo27cvQ4cO5a677uLjjz8mNzeX7OzscsUxe/ZsunfvzsqVK6lduza1a9emT58+TJo0iUmTJmFmvPfee/nfvhSpd+/ePPzwwwwdOjR/Wkajd5HgS5jkHguVVc99yJAhLF++nIMHD5KSksJDDz3EbbfdVmQcderU4eqrr+aLL75g2rRpANx3332MGzeOtLQ03J3U1FQWLlx4xrbXXnst69evJzMzk/PPP5/rr7+eX/3qV5XwbIlIPFE99zjXrVs3Jk6cSGZmoSWbY0bXRxKd6rmLiEjC0bRMnBgzZgyrVq0q0DZ27FiWL18em4BEJKEpuccJffmGiESTpmVERAJIyV1EJIBKTO5mNs3M9pvZpoi2B81sr5mtD/9cH7HuXjPbamYfmlmfygpcRESKVpo59+nAZOCPp7U/6e4TIxvMrDUwGGgDNASWmNnl7n6yooHm/GV7RXdRQPaNzaO6PxGReFLiyN3dVwClrTbVD5jl7l+7+w5gK1C+P8uME+Wt537o0CG6d+9OrVq1+NnP9MUgIlK1KjLn/jMz2xCetqkTbmsE7I7osyfcdgYzG2Vm68xs3YEDByoQRuWpSD33pKQkfvnLX+YX9qpMqtsuIqcrb3L/HXAZcCWwD3g83F5YhapC/wTW3ae4e6a7Z9arV6+cYVSuitRzr1mzJl26dCEpKalUx6pVqxY///nPycjIoGfPnuS94G3bto1rr72Wq666iu9///t88MEHQOgLP+666y66d+/OPffcw9GjRxkxYgTt2rUjLS2Nl19+OYrPhIgkmnIld3f/xN1Puvsp4Dm+m3rZAzSO6JoCfFyxEGOnsHrup9dkL6qee1kdO3aMjIwM3n33XX7wgx/w0EMPATBq1CgmTZrEO++8w8SJE7njjjvyt/noo49YsmQJjz/+OL/85S+pXbs2GzduZMOGDfTo0aM8pywiAVGuP2Iyswbuvi/8sD+QdyfNAuAlM3uC0AeqLYGcCkcZIxWp515W55xzDjfffDMAP/rRj7jppps4evQoq1evZuDAgfn9vv766/zlgQMH5hcjW7JkSX7RMggVGxORs1eJyd3MZgLdgLpmtgd4AOhmZlcSmnLZCYwGcPf3zezPwGbgBDAmGnfKxEpF6rlXlJlx6tQpLr74YtavX19oH9VtF5GilJjc3X1IIc1Ti+n/CPBIRYIqTCxuXaxIPfeyOnXqFHPnzmXw4MG89NJLdOnShYsuuohmzZoxZ84cBg4ciLuzYcMG0tPTz9i+d+/eTJ48md/+9rcAfPbZZxq9i5zF9BeqxYis596qVSsGDRqUX899wYIFANx2220cOnSIFi1a8MQTTxS4XTI1NZW77rqL6dOnk5KScsadNpFq1qzJ+++/z1VXXcWbb77J/fffD8Cf/vQnpk6dSnp6Om3atDnjA908//Vf/8Vnn31G27ZtSU9PZ9myZVF8JkQk0aiee5yoVasWR48ejXUYpXa2XR8JHtVzFxGRhKOSv1WsQ4cOBe54AXjhhRcSatQuIvFPyb2KrV27NtYhiMhZQNMyIiIBpOQuIhJASu4iIgGUMHPuq+f8Kar7u3rgLaXqt2jRIsaOHcvJkyf5yU9+wvjx4wusX7FiBePGjWPDhg3MmjWLAQMGRDVOEZHy0Mi9GKUp+dukSROmT5/O0KFDqyQeEZHSUHIvRmlK/qamppKWlsY555T8VC5fvpyuXbvSv39/Wrduze23386pU6cAWLx4MZ06dSIjI4OBAwfm3xqZmprKww8/TJcuXZgzZw5bt27lmmuuIT09nYyMDLZt2xb9ExeRhKfkXozSlPwtq5ycHB5//HE2btzItm3beOWVVzh48CATJkxgyZIlvPvuu2RmZvLEE0/kb5OUlMTKlSsZPHgwt9xyC2PGjOHvf/87q1evpkGDBhWKR0SCKWHm3GMhWuV8I2VnZ9O8eagI2pAhQ1i5ciVJSUls3ryZzp07A/DNN9/QqVOn/G3ySgEfOXKEvXv30r9/f4BSfxGIiJx9lNyLUZqSv2V1+ouDmeHu9OrVi5kzZxa6TV5p33ioAyQiiUHTMsWILPn7zTffMGvWLPr27Vuhfebk5LBjxw5OnTrF7Nmz6dKlCx07dmTVqlVs3boVgC+//JKPPvrojG0vuugiUlJSmDdvHhD64o4vv/yyQvGISDAlzMi9tLcuRlNkyd+TJ08ycuTI/JK/mZmZ9O3bl7fffpv+/fvz2Wef8Ze//IUHHniA999/v8h9durUifHjx7Nx48b8D1fPOeccpk+fzpAhQ/LrzkyYMIHLL7/8jO1feOEFRo8ezf333895553HnDlz8qd5RETyqORvFVq+fDkTJ05k4cKFsQ6lwoJ4feTsopK/IiKScBJmWiaRbNy4kWHDhhVou+CCC1i7di3dunWLTVAiclZRcq8E7dq1K/JLrUVEqoKmZUREAkjJXUQkgJTcRUQCSMldRCSAEuYD1ch7UqOhtPe1pqamcuGFF3LuuedSrVo1Tr8fX0QkHiVMco+lZcuWUbdu3Urb/4kTJ6hWTZdCRKJH0zJR0q1bN8aNG8fVV19N27ZtycnJAeDYsWOMHDmSrKws2rdvn18Pfvr06QwcOJAbb7yR3r17A/DYY4/Rrl070tPTz/jGJxGRstBwsQRmRu/evTEzRo8ezahRo4rse+zYMVavXs2KFSsYOXIkmzZt4pFHHqFHjx5MmzaNw4cPk52dzTXXXAPAW2+9xYYNG0hOTub1119n3rx5rF27lho1avDpp59W1SmKSAApuZdg1apVNGzYkP3799OrVy++973v0bVr10L7DhkyBICuXbvyxRdfcPjwYRYvXsyCBQuYOHEiAMePH2fXrl0A9OrVi+TkZACWLFnCiBEjqFGjBkB+u8jZ5MCkyfnL9f7tZ1V23GXLluUvd+/evcqOW5mU3EuQV7+9fv369O/fn5ycnCKTe1G12l9++WWuuOKKAuvWrl2bX6cdQrXaK/pFICIieTTnXoxjx45x5MiR/OXFixfTtm3bIvvPnj0bgJUrV1K7dm1q165Nnz59mDRpUv4Xbbz33nuFbtu7d2+mTZuWX59d0zIiUhEJM3KPRUnOTz75JP8r7U6cOMHQoUO59tpri+xfp04drr76ar744gumTZsGwH333ce4ceNIS0vD3UlNTS205O+1117L+vXryczM5Pzzz+f666/nV7/6VeWcmIgEnuq5R0m3bt2YOHEimZmFllYOnES7PpIYqnLOPfJvZ96ttj1/OZHm3FXPXUTkLJMw0zLxYsyYMaxatapA29ixY1m+fHlsAhIRKYSSexk988wzsQ5BRKREmpYREQkgjdxF5Kz01q7vbku+oHntGEZSOUocuZvZNDPbb2abItqSzewNM8sN/64Tbjcze9rMtprZBjPLqMzgRUSkcKWZlpkOnH5z93hgqbu3BJaGHwNcB7QM/4wCfhedMEVEpCxKnJZx9xVmlnpacz+gW3h5BrAcuCfc/kcP3Ty/xswuNrMG7r6vooFG1n6IhtLcyzpy5EgWLlxI/fr12bQp9Mbl008/5eabb2bnzp2kpqby5z//mTp16kQ1NhGRiirvB6qX5iXs8O/64fZGwO6IfnvCbWcws1Fmts7M1h04cKCcYVSuW2+9lUWLFhVoe/TRR+nZsye5ubn07NmTRx99tFKO7e6cOnWqUvYtIsEX7btlCqt8VeifwLr7FHfPdPfMevXqRTmM6OjatesZ1Rnnz5/P8OHDARg+fDjz5s0rcvsHH3yQYcOG0aNHD1q2bMlzzz2Xv+43v/kNWVlZpKWl8cADDwCwc+dOWrVqxR133EFGRga7d+9m0aJFZGRkkJ6eTs+ePSvhLEUkiMp7t8wnedMtZtYA2B9u3wM0juiXAnxckQDjzSeffEKDBg0AaNCgAfv37y+2/4YNG1izZg3Hjh2jffv23HDDDWzatInc3FxycnJwd/r27cuKFSto0qQJH374Ic8//zzPPvssBw4c4Kc//SkrVqygWbNmKiYmIqVW3pH7AmB4eHk4MD+i/cfhu2Y6Ap9HY749kfXr14/q1atTt25dunfvTk5ODosXL2bx4sW0b9+ejIwMPvjgA3JzcwFo2rQpHXfFYLkAAAshSURBVDt2BGDNmjV07dqVZs2aAarxLiKlV+LI3cxmEvrwtK6Z7QEeAB4F/mxmtwG7gIHh7q8B1wNbgS+BEZUQc0xdeuml7Nu3jwYNGrBv3z7q169fbP+iarzfe++9jB49usC6nTt3qsa7iERFiSN3dx/i7g3c/Tx3T3H3qe5+yN17unvL8O9Pw33d3ce4+2Xu3s7d15W0/0TTt29fZsyYAcCMGTPo169fsf3nz5/P8ePHOXToEMuXLycrK4s+ffowbdo0jh49CsDevXsLnd7p1KkTf/vb39ixYwegGu8iUnoJ8xeqsSjDOWTIEJYvX87BgwdJSUnhoYceYvz48QwaNIipU6fSpEkT5syZU+w+srOzueGGG9i1axf33XcfDRs2pGHDhmzZsoVOnToBUKtWLV588UXOPffcAtvWq1ePKVOmcNNNN3Hq1Cnq16/PG2+8UWnnKyLBkTDJPRZmzpxZaPvSpUtLvY/LL7+cKVOmnNE+duxYxo4de0Z73v30ea677jquu+66Uh9PRARUOExEJJA0co+C559/nqeeeqpAW+fOnVUeWERiRsk9CkaMGMGIEYG7MUhEElhcT8vEw/e7ypl0XUTiX9wm96SkJA4dOqREEmfcnUOHDpGUlBTrUESkGHE7LZOSksKePXuI16JiZ7OkpCRSUlJiHYaIFCNuk/t5552X/2f3IiJSNnE7LSMiIuWn5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBpOQuIhJASu4iIgGk5C4iEkBK7iIiAaTkLiISQEruIiIBVC3WAYiIlCTnL9vzl7NvbJ6/vHrOn/KXrx54S5XGFO80chcRCSAldxGRANK0jIjEpcipGCk7JXcRiakDkybHOoRA0rSMiEgAVWjkbmY7gSPASeCEu2eaWTIwG0gFdgKD3P2zioUpIhISeYeMFC0aI/fu7n6lu2eGH48Hlrp7S2Bp+LGIiFShypiW6QfMCC/PAH5YCccQEZFiVPQDVQcWm5kD/+PuU4BL3X0fgLvvM7P6FQ1SRKQk+oOmgiqa3Du7+8fhBP6GmX1Q2g3NbBQwCqBJkyYVDENERCJVaFrG3T8O/94PvApkA5+YWQOA8O/9RWw7xd0z3T2zXr16FQlDREROU+7kbmY1zezCvGWgN7AJWAAMD3cbDsyvaJAiIlI2FZmWuRR41czy9vOSuy8ys7eBP5vZbcAuYGDFwxSRs8Gm/d+9i6+ZGrs4gqDcyd3dtwPphbQfAnpWJCgREakY/YWqiEgAKbmLiASQkruISAApuYuIBJCSu4hIACm5i4gEkJK7iEgAKbmLiASQkruISAApuYuIBJCSu4hIACm5i4gEUEW/rENEJGo+Pro3f7klWeXej76VScldRBLM3g8/y19udEWdGEYS3zQtIyISQEruIiIBpGkZEakSz65/Nn/5jivvKLH/sZy385drZpd//v1speQuIglL8+9F07SMiEgAKbmLiASQkruISABpzl1E4t4//v5arENIOBq5i4gEkEbuIlJpIm9/lKql5C4ipVbWe9VL4+1/vh3xKL3QPt/s/Th/+fxGDaNy3KBTcheRAk4fbUcriUvV0py7iEgAaeQuEkTLfv3dcvd7YxdHESLfHaTGLoxAU3IXSTRxmLgrYy5eKkbJXSROvTX17vzlTrdNjGEkkoiU3EUK8eQbH+Uv39nr8hhGUgaRI/rK2m+d2vmLus0xvim5i8RCHE6tRIuSfnxQchcRJeQAUnIXqSKRUz0ddx3KX15zopKngIqarinlO4ZnD2/47sHhiBXNvl/4Bjv+r/D2ovpLpVByFylBUfPvVTkvX+BYkf9rKzLPHrltZAIvrcgkXtbEXeAF4PyyH1tKpOQuZ63IhAlx8sFpEcm6464p3z1ofkmhfd7a/t27gU5F9CmN3Ye/KvC48cXVy70viR0ldwm805N4VR4jHl4wIpN+kZIrPw6pWkruEkiVldDLut+C8+xTCu0T2f5WGeMpVeKOsOCcrfnLfU+1KNU2kSP5yFF8gfbS7KiouXipFJWW3M3sWuAp4FzgD+7+aGUdS4Ivke47LyqJR0tZE3pRIhN9Yyo49aLEHXcqJbmb2bnAM0AvYA/wtpktcPfNlXE8iV+VkZRL8wGnlM3p8+zR2ldRI/3vr4u47ebiplE7tnynskbu2cBWd98OYGazgH6AkrsA0bsDJZoJvTT7ihyVr2kyKmrHjrbI0X3kCL2yRPPFQaKjspJ7I2B3xOM9QIdKOpbEmaKSZFnbY6msSbyyp2LKqrISelmTeKn6H/7Hd8vnF/5lHVJ25u7R36nZQKCPu/8k/HgYkO3u/xbRZxSQ97/mCuDDch6uLnCwAuHGuyCfn84tcQX5/BLp3Jq6e73CVlTWyH0PBT9ATwE+juzg7lOACg93zGydu2dWdD/xKsjnp3NLXEE+v6CcW2V9E9PbQEsza2Zm5wODgQWVdCwRETlNpYzc3f2Emf0M+CuhWyGnufv7lXEsERE5U6Xd5+7urwGvVdb+I8TXJ1nRF+Tz07klriCfXyDOrVI+UBURkdiqrDl3ERGJoYRO7mZ2rZl9aGZbzWx8rOOpCDNrbGbLzGyLmb1vZmPD7clm9oaZ5YZ/14l1rOVlZuea2XtmtjD8uJmZrQ2f2+zwh+8JycwuNrO5ZvZB+Bp2Csq1M7M7w/8mN5nZTDNLSuRrZ2bTzGy/mW2KaCv0WlnI0+Ecs8HMMmIXedkkbHKPKHFwHdAaGGJmrWMbVYWcAH7u7q2AjsCY8PmMB5a6e0tgafhxohoLbIl4/N/Ak+Fz+wy4LSZRRcdTwCJ3/x6QTug8E/7amVkj4N+BTHdvS+gGicEk9rWbDlx7WltR1+o6oGX4ZxTwuyqKscISNrkTUeLA3b8B8kocJCR33+fu74aXjxBKDo0IndOMcLcZwA9jE2HFmFkKcAPwh/BjA3oAc8NdEvncLgK6AlMB3P0bdz9MQK4doRsvqptZNaAGsI8EvnbuvgL49LTmoq5VP+CPHrIGuNjMGlRNpBWTyMm9sBIHjWIUS1SZWSrQHlgLXOru+yD0AgDUj11kFfJb4D+AU+HHlwCH3f1E+HEiX7/mwAHg+fC00x/MrCYBuHbuvheYCOwilNQ/B94hONcuT1HXKmHzTCIndyukLeFv/TGzWsDLwDh3/yLW8USDmf0LsN/d34lsLqRrol6/akAG8Dt3bw8cIwGnYAoTnnvuBzQDGgI1CU1VnC5Rr11JEvbfaSIn9xJLHCQaMzuPUGL/k7u/Em7+JO9tYPj3/ljFVwGdgb5mtpPQ9FkPQiP5i8Nv9SGxr98eYI+7rw0/nkso2Qfh2l0D7HD3A+7+LfAKcDXBuXZ5irpWCZtnEjm5B6rEQXgOeiqwxd2fiFi1ABgeXh4OzK/q2CrK3e919xR3TyV0nd5091uAZcCAcLeEPDcAd/8nsNvMrgg39SRU3jrhrx2h6ZiOZlYj/G8079wCce0iFHWtFgA/Dt810xH4PG/6Ju65e8L+ANcDHwHbgP8X63gqeC5dCL3d2wCsD/9cT2hueimQG/6dHOtYK3ie3YCF4eXmQA6wFZgDXBDr+CpwXlcC68LXbx5QJyjXDngI+ADYBLwAXJDI1w6YSejzg28JjcxvK+paEZqWeSacYzYSumso5udQmh/9haqISAAl8rSMiIgUQcldRCSAlNxFRAJIyV1EJICU3EVEAkjJXUQkgJTcRUQCSMldRCSA/j9WxJLBtEupDgAAAABJRU5ErkJggg==\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": {
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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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q2/fff++tWrXyrVu3+rfffuspKSm+YcOGfG2mTJniY8aMcXf3mTNn+pAhQ9zdfcOGDZ6SkuJHjx71rVu3eqtWrfzYsWPFbnPw4ME+c+ZMd3cfM2aMP/XUU/n2NWLECJ8zZ06xMS9ZssQTExN969atfvz4ce/bt6+/8sorvnfvXm/evLlnZ2e7u3tOTo5/8skn7u7esmVL37dvX942Onfu7K+//rq7ux89etRzcnJO2k9NOD8iFXFw4fa8R7wC1ngReTXmid1rcHJ3d//v//5vb9u2rbdu3doffPBBd3e/++67fe7cue7u/s033/jgwYP9ggsu8IyMDN+yZUveug8++KC3bt3aL7zwQn/zzTeL3aa7+5YtWzwjI8MvuOACHzx4sB89etTd3bOysrxZs2Zeu3Ztb9Cggbdv377IeJcsWeI9e/b06667ztu1a+djxozx48ePu7v74sWLPT093Tt27OgdO3bMO4aCyf3TTz/1nj17eseOHT0tLS3fMeWqKedHpLzCntxVzz1kli5dyqRJk5g/f36V7kfnR+Kd6rmLiEjcUcnfOLV+/XqGDx+eb95ZZ53F6tWr6dGjR2yCEpEaQ8k9TnXs2JG1a9eW3FBETkkalhERCSEldxGREFJyFxEJobgZc3967dOVur2xl4yt1O2JiNQk6rmXoDrruU+ZMoU2bdpgZuzfv79Kj0tEwk3JvRjVXc+9a9euLFq0iJYtq/YLFe5eaAEzEQkPJfdiVGc9d4BOnTqRnJxcqtjuu+8+hg8fTq9evWjbti3PPfdc3rLf//73ZGRkkJKSwr333gvA9u3badeuHWPHjiUtLY2dO3eyYMEC0tLSSE1NpXfv3pXwiolITRE3Y+6xUFjt9dWrVxfZpmA998suuyzfurn12UvaZmmtW7eOVatWkZOTQ6dOnbj66qvZsGED2dnZZGVl4e4MGDCAZcuW0aJFCz755BOef/55nn76afbt28ett97KsmXLaNWqFV988UW5YhCRmknJvRiF1d2pqnru5TFw4EBq1apFrVq16NmzJ1lZWSxfvpyFCxfSqVMnAI4cOUJ2djYtWrSgZcuWeW84q1atonv37rRq1QqABg0alCsGEamZlNyLUZ313MujqLrtd911F2PGjMm3bPv27Xk12yHyplTeNxURiQNFlYuszkdNLflb3fXccxUswVuYe++911NTU/2bb77x/fv3e/PmzX337t3+t7/9zTMzM/3w4cPu7r5r1y7//PPPfdu2bX7xxRfnrb93715PSkryrVu3urv7gQMHyvTa1ITzI1IRYS/5q557MRISEpgyZQr9+vXj+PHjjBo1iosvvph77rmH9PR0BgwYwM0338zw4cNp06YNDRo0YNasWQBcfPHFXHfddbRv356EhASeeuopTj/9dIBCtwnw5JNP8sgjj/DPf/6TlJQUrrrqKv70pz8VGV9mZiZXX301O3bs4O6776Zp06Y0bdqUTZs25f2EXt26dfnLX/6St+9cjRo14tlnn+Xaa6/lxIkTNG7cmLfffrsqXkYRiQHVc49T9913H3Xr1uWXv/xlTPav8yPxTvXcRUQk7mhYpoZ7/vnneeKJJ/LN69q1K0899VSMIhKReKDkXsONHDmSkSNHxjoMEYkzGpYREQkhJXcRkRDSsIyInPKWLFmSN92zZ88YRlJ54ia575s8pVK31+g/fl6p2xMRqUk0LFOCqqjnPmrUKBo3bkyHDh2q4xBE5BSk5F6MqqjnDnDTTTexYMGCKo//2LFjVb4PEamZlNyLURX13AG6d+9e6iqMPXr0YMKECVx++eV06NAhbxs5OTmMGjWKjIwMOnXqlBfX9OnTGTJkCNdccw19+/YF4JFHHqFjx46kpqYyceLESnltRKRmi5sx91ioqnruZZWTk8PKlStZtmwZo0aNYsOGDTz00EP06tWLadOmcfDgQTIzM7niiisAeOedd1i3bh0NGjTgrbfe4o033mD16tXUrl1bddtFThFK7sUorO5OReu5l8ewYcOASI//0KFDHDx4kIULFzJv3jwmTZoEwNGjR9mxYwcAffr0yftksGjRIkaOHEnt2rUB1W0XOVUouRejquq5l1VRddtfffVVLrroonzLVq9erbrtIhI/yT0Wty5mZGSQnZ3Ntm3baNasGbNmzeKll17K12bAgAHMmDGDLl268Morr9CrVy/MjAEDBnDDDTdwxx138Nlnn5GdnU1mZma54pg9ezY9e/Zk+fLl1KtXj3r16tGvXz8mT57M5MmTMTM++OCDvF9fita3b18eeOABbrjhhrxhGfXeRcIvbpJ7LFRVPfdhw4axdOlS9u/fT1JSEvfffz8333xzkXHUr1+fyy+/nEOHDjFt2jQA7r77biZMmEBKSgruTnJyMvPnzz9p3f79+7N27VrS09M588wzueqqq/jNb35TBa+WiNQkqudew/Xo0YNJkyaRnl5oyeaY0fmReBddz/39hK150/H0DVXVcxcROcVoWKaGGDduHCtWrMg3b/z48SxdujQ2AYlIXFNyryH04xsiUpk0LCMiEkJK7iIiIVRicjezaWa218w2RM27z8x2m9na4HFV1LK7zGyzmX1iZv2qKnARESlaacbcpwNTgD8XmP+4u0+KnmFm7YGhwMVAU2CRmV3o7scrGmjWX7eW3KgMMq9pXanbExGpSUrsubv7MqC01aYGArPc/Vt33wZsBsr3tcwaorz13A8cOEDPnj2pW7cuP/+5fhhERKpXRcbcf25m64Jhm/rBvGbAzqg2u4J5JzGz0Wa2xszW7Nu3rwJhVJ2K1HNPTEzk17/+dV5hr6qkuu0iUlB5k/szwAXAJcAe4NFgfmEVqgr9Cqy7P+vu6e6e3qhRo3KGUbUqUs+9Tp06dOvWjcTExFLtq27duvziF78gLS2N3r17k/uGt2XLFvr378+ll17Kv/zLv/Dxxx8DkR/8uOOOO+jZsyd33nknR44cYeTIkXTs2JGUlBReffXVSnwlRCTelCu5u/vn7n7c3U8Az/HD0MsuoHlU0yTgs4qFGDuF1XMvWJO9qHruZZWTk0NaWhrvv/8+//qv/8r9998PwOjRo5k8eTLvvfcekyZNYuzYsXnrfPrppyxatIhHH32UX//619SrV4/169ezbt06evXqVZ5DFpGQKNeXmMysibvvCZ4OAnLvpJkHvGRmjxG5oNoWyKpwlDFSkXruZXXaaadx/fXXA/DTn/6Ua6+9liNHjrBy5UqGDBmS1+7bb7/Nmx4yZEheMbJFixblFS2DSLExETl1lZjczWwm0ANoaGa7gHuBHmZ2CZEhl+3AGAB3/8jMXgY2AseAcZVxp0ysVKSee0WZGSdOnODcc89l7dq1hbZR3XYRKUqJyd3dhxUye2ox7R8CHqpIUIWJxa2LFannXlYnTpzglVdeYejQobz00kt069aNc845h1atWjFnzhyGDBmCu7Nu3TpSU1NPWr9v375MmTKFP/zhDwB8+eWX6r2LnML0DdViRNdzb9euHdddd11ePfd58+YBcPPNN3PgwAHatGnDY489lu92yeTkZO644w6mT59OUlLSSXfaRKtTpw4fffQRl156KX//+9+55557AHjxxReZOnUqqampXHzxxSdd0M31X//1X3z55Zd06NCB1NRUlixZUomvhIjEG9VzryHq1q3LkSNHYh1GqZ1q50fCR/XcRUQk7qjkbzXr3LlzvjteAF544YW46rWLSM2n5F7NVq9eHesQROQUoGEZEZEQUnIXEQkhJXcRkRCKmzH3lXNerNTtXT7kxlK1W7BgAePHj+f48ePccsstTJw4Md/yZcuWMWHCBNatW8esWbMYPHhwpcYpIlIe6rkXozQlf1u0aMH06dO54YYbqiUeEZHSUHIvRmlK/iYnJ5OSksJpp5X8Ui5dupTu3bszaNAg2rdvz2233caJEycAWLhwIV26dCEtLY0hQ4bk3RqZnJzMAw88QLdu3ZgzZw6bN2/miiuuIDU1lbS0NLZs2VL5By4icU/JvRilKflbVllZWTz66KOsX7+eLVu28Nprr7F//34efPBBFi1axPvvv096ejqPPfZY3jqJiYksX76coUOHcuONNzJu3Dg+/PBDVq5cSZMmTSoUj4iEU9yMucdCZZXzjZaZmUnr1pEiaMOGDWP58uUkJiayceNGunbtCsB3331Hly5d8tbJLQV8+PBhdu/ezaBBgwBK/UMgInLqUXIvRmlK/pZVwTcHM8Pd6dOnDzNnzix0ndzSvjWhDpCIxAcNyxQjuuTvd999x6xZsxgwYECFtpmVlcW2bds4ceIEs2fPplu3blx22WWsWLGCzZs3A/D111/z6aefnrTuOeecQ1JSEm+88QYQ+eGOr7/+ukLxiEg4xU3PvbS3Llam6JK/x48fZ9SoUXklf9PT0xkwYADvvvsugwYN4ssvv+Svf/0r9957Lx999FGR2+zSpQsTJ05k/fr1eRdXTzvtNKZPn86wYcPy6s48+OCDXHjhhSet/8ILLzBmzBjuuecezjjjDObMmZM3zCMikkslf6vR0qVLmTRpEvPnz491KBUWxvMjpxaV/BURkbgTN8My8WT9+vUMHz4837yzzjqL1atX06NHj9gEJSKnFCX3KtCxY8cif9RaRKQ6aFhGRCSElNxFREJIyV1EJISU3EVEQihuLqhG35NaGer1aVmqdsnJyZx99tmcfvrpJCQkUPB+fBGRmihuknssLVmyhIYNG1bZ9o8dO0ZCgk6FiFQeDctUkh49ejBhwgQuv/xyOnToQFZWFgA5OTmMGjWKjIwMOnXqlFcPfvr06QwZMoRrrrmGvn37AvDII4/QsWNHUlNTT/rFJxGRslB3sQRmRt++fTEzxowZw+jRo4tsm5OTw8qVK1m2bBmjRo1iw4YNPPTQQ/Tq1Ytp06Zx8OBBMjMzueKKKwB45513WLduHQ0aNOCtt97ijTfeYPXq1dSuXZsvvviiug5RREJIyb0EK1asoGnTpuzdu5c+ffrwox/9iO7duxfadtiwYQB0796dQ4cOcfDgQRYuXMi8efOYNGkSAEePHmXHjh0A9OnThwYNGgCwaNEiRo4cSe3atQHy5ouIlIeSewly67c3btyYQYMGkZWVVWRyL6pW+6uvvspFF12Ub9nq1avz6rRDpFZ7RX8IREQkl8bci5GTk8Phw4fzphcuXEiHDh2KbD979mwAli9fTr169ahXrx79+vVj8uTJeT+08cEHHxS6bt++fZk2bVpefXYNy4hIRcRNz720ty5Wps8//zzvJ+2OHTvGDTfcQP/+/YtsX79+fS6//HIOHTrEtGnTALj77ruZMGECKSkpuDvJycmFlvzt378/a9euJT09nTPPPJOrrrqK3/zmN1VzYCISeqrnXkl69OjBpEmTSE8vtLRy6MTb+REpSPXcRUQk7sTNsExNMW7cOFasWJFv3vjx41m6dGlsAhIRKYSSexk99dRTsQ5BRKREGpYREQkhJXcRkRAqMbmb2TQz22tmG6LmNTCzt80sO/hbP5hvZvakmW02s3VmllaVwYuISOFK03OfDhS8uXsisNjd2wKLg+cAVwJtg8do4JnKCVNERMqixAuq7r7MzJILzB4I9AimZwBLgTuD+X/2yM3zq8zsXDNr4u57KhrokiVLKrqJfEpzL+uoUaOYP38+jRs3ZsOGyAeXL774guuvv57t27eTnJzMyy+/TP369Ss1NhGRiirvmPv5uQk7+Ns4mN8M2BnVblcw7yRmNtrM1pjZmn379pUzjKp10003sWDBgnzzHn74YXr37k12dja9e/fm4YcfrpJ9uzsnTpyokm2LSPhV9gXVwipfFfoVWHd/1t3T3T29UaNGlRxG5ejevftJ1Rnnzp3LiBEjABgxYgRvvPFGkevfd999DB8+nF69etG2bVuee+65vGW///3vycjIICUlhXvvvReA7du3065dO8aOHUtaWho7d+5kwYIFpKWlkZqaSu/evavgKEVqjn2Tp+Q9pGLKe5/757nDLWbWBNgbzN8FNI9qlwR8VpEAa5rPP/+cJk2aANCkSRP27t1bbPt169axatUqcnJy6NSpE1dffTUbNmwgOzubrKws3J0BAwawbNkyWrRowSeffMLzzz/P08Fpnl8AAAvOSURBVE8/zb59+7j11ltZtmwZrVq1UjExESm18vbc5wEjgukRwNyo+T8L7pq5DPiqMsbb49nAgQOpVasWDRs2pGfPnmRlZbFw4UIWLlxIp06dSEtL4+OPPyY7OxuAli1bctlllwGwatUqunfvTqtWrQDVeBeR0iux525mM4lcPG1oZruAe4GHgZfN7GZgBzAkaP4mcBWwGfgaGFkFMcfU+eefz549e2jSpAl79uyhcePGxbYvqsb7XXfdxZgxY/It2759u2q8i0ilKLHn7u7D3L2Ju5/h7knuPtXdD7h7b3dvG/z9Imjr7j7O3S9w947uvqak7cebAQMGMGPGDABmzJjBwIEDi20/d+5cjh49yoEDB1i6dCkZGRn069ePadOmceTIEQB2795d6PBOly5d+J//+R+2bdsGqMa7iJRe3NSWiUUZzmHDhrF06VL2799PUlIS999/PxMnTuS6665j6tSptGjRgjlz5hS7jczMTK6++mp27NjB3XffTdOmTWnatCmbNm2iS5cuANStW5e//OUvnH766fnWbdSoEc8++yzXXnstJ06coHHjxrz99ttVdrwiEh5xk9xjYebMmYXOX7x4cam3ceGFF/Lss8+eNH/8+PGMHz/+pPm599PnuvLKK7nyyitLvT8REVBtGRGRUFLPvRI8//zzPPHEE/nmde3aVeWBRSRmlNwrwciRIxk5MnQ3BolIHKvRwzI14fdd5WQ6LyI1X41N7omJiRw4cECJpIZxdw4cOEBiYmKsQxGRYtTYYZmkpCR27dpFTS0qdipLTEwkKSkp1mGISDFqbHI/44wz8r52LyIiZVNjh2VERKT8amzPXUSkLFbOeTFv+vIhN8YwkppBPXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkh/cyeiNR4WX/dmjedeU3rGEYSP9RzFxEJISV3EZEQUnIXEQkhjbmLSFzR+HvpqOcuIhJCSu4iIiGk5C4iEkJK7iIiIVShC6pmth04DBwHjrl7upk1AGYDycB24Dp3/7JiYYqISFlURs+9p7tf4u7pwfOJwGJ3bwssDp6LiEg1qophmYHAjGB6BvDjKtiHiIgUo6LJ3YGFZvaemY0O5p3v7nsAgr+NK7gPEREpo4p+iamru39mZo2Bt83s49KuGLwZjAZo0aJFBcMQkVPRyjkvlnvdd3Z8kDd9Vut6lRFOjVKh5O7unwV/95rZ60Am8LmZNXH3PWbWBNhbxLrPAs8CpKene0XiEBGJFp30Lx9yYwwjiZ1yD8uYWR0zOzt3GugLbADmASOCZiOAuRUNUkREyqYiPffzgdfNLHc7L7n7AjN7F3jZzG4GdgBDKh6miIiURbmTu7tvBVILmX8A6F2RoEREpGL0DVURkRBSchcRCSEldxGRENKPdYhIjbRv8pQfniRfFbtA4pR67iIiIaTkLiISQkruIiIhpDF3EYlbuz/54aciml1UP4aR1DzquYuIhJCSu4hICCm5i4iEkJK7iEgI6YKqiMRU9JeV3v3nu3nTtU774YtLdZKrM6JwUM9dRCSElNxFREJIwzIiUmN8+fUPPxFRq24MAwkB9dxFREJIyV1EJIQ0LCMiNV5O1g930ew/a18MI4kf6rmLiISQeu4iUi2eXvt03vTYS8bGMJJTg3ruIiIhpJ67iOS35Lf5n/e8q0yrq4deM6jnLiISQuq5i0hMRdeTgdQi20nZKLmLSLWLHrpJjl0YoaZhGRGREFLPXeRUEn2xtIwXSssjuocu1UvJXUTKp5rfKKRslNxFaqjH3/40b/r2PhfGMJIoBW+TlBpLyV2kCpUmQZcqiUcl1ceP/aTk9lXk6YPr8qbHnptSbfv9bP+HedNnNmtabfuNZ0ruImEX9cbwztYDedNdKHxYJTqBA5Tma0gaW695lNxF4sxlO56NejYpb6pGDuMUIXnO6liHEHpK7iIh9PTrw/KmSzN8Et3z3nnwm/wLixhnz9fDr/8vZQtQqpySu5yyonu6UHm93YLbrUpF7Stfgj63FBva9v+KXFRwmEbig5K7iFSu6DeKVj/06JOfe/mH+ee2rMaATk1K7hIXKms8ubS96qL2V5o4osfEV7UYXfgO8g11/KTk+UXIP/7+g3lR3z2Pvog677TNPyzYGrVCgxJ3Vbxiev4SG0ruIjEQnXBpUXibfIm79XlVG1Axood4mp9bK2ZxSNlUWXI3s/7AE8DpwJ/c/eGq2pecWirt3vEKemfqL/Omu9w8qdA20Qn6nakV2Ff0m0EZ5eutFzG/OVWUtNWjj5kqSe5mdjrwFNAH2AW8a2bz3H1jVexPBIoecqnoBc7SrB+d6MuqsnroRSXxylSaXnzyoj1509uvaFLlMUnhqqrnnglsdvetAGY2CxgIKLmfwsra464pihrXrgpl7aFXR0Ivq0NHj8U6BKHqknszYGfU811A56rYUXHJoCpubYvlMEBVK+q1LM0FxdLMrwql2X6pLnAW2FZ1JvSiVGfiPune9jKK7q0fiprfYH5UGkjUJb7qZO5e+Rs1GwL0c/dbgufDgUx3/4+oNqOB3P9pFwGflHN3DYH9FQi3pgvz8enY4leYjy+ejq2luzcqbEFVvZXuAppHPU8CPotu4O7PAhXuHpnZGndPr+h2aqowH5+OLX6F+fjCcmxV9UtM7wJtzayVmZ0JDAXmVdG+RESkgCrpubv7MTP7OfA3IrdCTnP3j6piXyIicrIqu8Lh7m8Cb1bV9qPE/spX1Qrz8enY4leYjy8Ux1YlF1RFRCS2qmrMXUREYiiuk7uZ9TezT8xss5lNjHU8FWFmzc1siZltMrOPzGx8ML+Bmb1tZtnB3/qxjrW8zOx0M/vAzOYHz1uZ2erg2GYHF9/jkpmda2avmNnHwTnsEpZzZ2a3B/8mN5jZTDNLjOdzZ2bTzGyvmW2ImlfoubKIJ4Mcs87M0mIXednEbXKPKnFwJdAeGGZm7WMbVYUcA37h7u2Ay4BxwfFMBBa7e1tgcfA8Xo0HNkU9/x3weHBsXwI3xySqyvEEsMDdfwSkEjnOuD93ZtYM+F9Aurt3IHKDxFDi+9xNB/oXmFfUuboSaBs8RgPPVFOMFRa3yZ2oEgfu/h2QW+IgLrn7Hnd/P5g+TCQ5NCNyTDOCZjOAH8cmwooxsyTgauBPwXMDegGvBE3i+djOAboDUwHc/Tt3P0hIzh2RGy9qmVkCUBvYQxyfO3dfBnxRYHZR52og8GePWAWca2ZxUTAnnpN7YSUOmsUolkplZslAJ2A1cL6774HIGwDQOHaRVcgfgP8NnAienwccdPfcQiTxfP5aA/uA54Nhpz+ZWR1CcO7cfTeRH2rdQSSpfwW8R3jOXa6izlXc5pl4Tu5WyLy4v/XHzOoCrwIT3P1QSe3jgZn9G7DX3d+Lnl1I03g9fwlAGvCMu3cCcojDIZjCBGPPA4FWQFOgDpGhioLi9dyVJG7/ncZzci+xxEG8MbMziCT2F939tWD257kfA4O/e2MVXwV0BQaY2XYiw2e9iPTkzw0+6kN8n79dwC53Xx08f4VIsg/DubsC2Obu+9z9e+A14HLCc+5yFXWu4jbPxHNyD1WJg2AMeiqwyd0fi1o0DxgRTI8A5lZ3bBXl7ne5e5K7JxM5T3939xuBJcDgoFlcHhuAu/8T2GlmFwWzehMpbx33547IcMxlZlY7+Deae2yhOHdRijpX84CfBXfNXAZ8lTt8U+O5e9w+gKuAT4EtwP+JdTwVPJZuRD7urQPWBo+riIxNLwayg78NYh1rBY+zBzA/mG4NZAGbgTnAWbGOrwLHdQmwJjh/bwD1w3LugPuBj4ENwAvAWfF87oCZRK4ffE+kZ35zUeeKyLDMU0GOWU/krqGYH0NpHvqGqohICMXzsIyIiBRByV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJIT+PzrC7PPg/01GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3z83N9Y8//tjd3Zs2ber79u3L30eHDh38tddec3f3o0ePem5u7inHqQ7XR6Q8Di7anv8Tr4A1XkRejXli92qc3N3d//d//9dbtmzpzZs39wkTJri7+3333efz5s1zd/evv/7a+/fv75deeqlnZmb6li1b8redMGGCN2/e3C+77DJ//fXXi92nu/uWLVs8MzPTL730Uu/fv78fPXrU3d2zs7O9UaNGXrNmTU9KSvLWrVsXGe/SpUu9W7duPnDgQG/VqpWPGjXKjx8/7u7uS5Ys8YyMDG/Xrp23a9cu/xxOTu6ffPKJd+vWzdu1a+fp6ekFzilPdbk+ImUV9uSueu4hs2zZMiZOnMiCBQsq9Ti6PhLvVM9dRETijkr+xqn169czdOjQAm3nnXceq1evpmvXrrEJSkSqDSX3ONWuXTvWrl1bckcROSNpWkZEJISU3EVEQkjJXUQkhOJmzv3Ztc9W6P5GXzG6QvcnIlKdaORegqqs5z558mRatGiBmbF///5KPS8RCTcl92JUdT33Tp06sXjxYpo2rdw/qHD3QguYiUh4KLkXoyrruQO0b9+elJSUUsX24IMPMnToULp3707Lli15/vnn89f9+te/JjMzk9TUVB544AEAtm/fTqtWrRg9ejTp6ens3LmThQsXkp6eTlpaGj169KiAZ0xEqou4mXOPhcJqr69evbrIPifXc7/qqqsKbJtXn72kfZbWunXrePvtt8nNzaV9+/bccMMNbNiwgZycHLKzs3F3+vTpw/Lly2nSpAkff/wxL7zwAs8++yz79u3jJz/5CcuXL6dZs2Z8/vnnZYpBRKonJfdiFFZ3p7LquZdF3759qVGjBjVq1KBbt25kZ2ezYsUKFi1aRPv27QE4cuQIOTk5NGnShKZNm+a/4Lz99tt06dKFZs2aAZCUlFSmGESkelJyL0ZV1nMvi6Lqtt97772MGjWqwLrt27fn12yHyItSWV9URCQOFFUusip/qmvJ36qu557n5BK8hXnggQc8LS3Nv/76a9+/f783btzYd+/e7X/5y188KyvLDx8+7O7uu3bt8s8++8y3bdvmbdq0yd9+7969npyc7Fu3bnV39wMHDpzWc1Mdro9IeYS95K9G7sVISEhg8uTJ9O7dm+PHjzNixAjatGnD/fffT0ZGBn369OG2225j6NChtGjRgqSkJGbOnAlAmzZtGDhwIK1btyYhIYFnnnmGs88+G6DQfQI8/fTTPPbYY/zjH/8gNTWV66+/nt///vdFxpeVlcUNN9zAjh07uO+++2jYsCENGzZk06ZN+V+hV6tWLV566aX8Y+epV68ezz33HDfddBMnTpygfv36vPnmm5XxNIpIDKiee5x68MEHqVWrFnfffXdMjq/rI/FO9dxFRCTuaFqmmnvhhRd46qmnCrR16tSJZ555JkYRiUg8UHKv5oYPH87w4cNjHYaIxBlNy4iIhJCSu4hICCm5i4iEUNzMue+bNLlC91fv3++o0P2JiFQnGrmXoDLquY8YMYL69evTtm3bqjgFETkDKbkXozLquQPceuutLFy4sNLjP3bsWKUfQ0SqJyX3YlRGPXeALl26lLoKY9euXRk3bhxXX301bdu2zd9Hbm4uI0aMIDMzk/bt2+fHNW3aNAYMGMCNN95Ir169AHjsscdo164daWlpjB8/vkKeGxGp3uJmzj0WKque++nKzc1l1apVLF++nBEjRrBhwwYeeeQRunfvztSpUzl48CBZWVlcc801ALz11lusW7eOpKQk3njjDebOncvq1aupWbOm6raLnCGU3ItRWN2d8tZzL4vBgwcDkRH/oUOHOHjwIIsWLWL+/PlMnDgRgKNHj7Jjxw4Aevbsmf/OYPHixQwfPpyaNWsCqtsucqZQci9GZdVzP11F1W1/5ZVXuPzyywusW716teq2i0j8JPdY3LqYmZlJTk4O27Zto1GjRsycOZOXX365QJ8+ffowffp0OnbsyJw5c+jevTtmRp8+fRgyZAh33XUXn376KTk5OWRlZZUpjlmzZtGtWzdWrFhB7dq1qV27Nr1792bSpElMmjQJM+P999/P//alaL169eLhhx9myJAh+dMyGr2LhF/cJPdYqKx67oMHD2bZsmXs37+f5ORkHnroIW677bYi46hTpw5XX301hw4dYurUqQDcd999jBs3jtTUVNydlJQUFixYcMq21157LWvXriUjI4Nzzz2X66+/nl/+8peV8GyJSHWieu7VXNeuXZk4cSIZGYWWbI4ZXR+Jd6rnLiIicUfTMtXEmDFjWLlyZYG2sWPHsmzZstgEJCJxTcm9mtCXb4hIRdK0jIhICCm5i4iEUInJ3cymmtleM9sQ1fagme02s7XBz/VR6+41s81m9rGZ9a6swEVEpGilmXOfBkwG/nBS+5PuPjG6wcxaA4OANkBDYLGZXebux8sbaPaft5Z3FwVk3di8QvcnIlKdlDhyd/flQGmrTfUFZrr7N+6+DdgMlO3PMquJstZzP3DgAN26daNWrVrccYe+GEREqlZ55tzvMLN1wbRNnaCtEbAzqs+uoO0UZjbSzNaY2Zp9+/aVI4zKU5567omJifziF7/IL+xVmVS3XUROVtbk/lvgUuAKYA/weNBeWIWqQv8E1t2fc/cMd8+oV69eGcOoXOWp537++efTuXNnEhMTS3WsWrVq8dOf/pT09HR69OhB3gveli1buPbaa7nyyiv5wQ9+wEcffQREvvDjrrvuolu3btxzzz0cOXKE4cOH065dO1JTU3nllVcq8JkQkXhTpuTu7p+5+3F3PwE8z/dTL7uAxlFdk4FPyxdi7BRWz/3kmuxF1XM/Xbm5uaSnp/Pee+/xz//8zzz00EMAjBw5kkmTJvHuu+8yceJERo8enb/NJ598wuLFi3n88cf5xS9+Qe3atVm/fj3r1q2je/fuZTllEQmJMv0Rk5k1cPc9wcN+QN6dNPOBl83sCSIfqLYEsssdZYyUp5776TrrrLO4+eabAfjhD3/ITTfdxJEjR1i1ahUDBgzI7/fNN9/kLw8YMCC/GNnixYvzi5ZBpNiYiJy5SkzuZjYD6ArUNbNdwANAVzO7gsiUy3ZgFIC7f2hmfwI2AseAMRVxp0yslKeee3mZGSdOnOCiiy5i7dq1hfZR3XYRKUqJyd3dBxfSPKWY/o8Aj5QnqMLE4tbF8tRzP10nTpxgzpw5DBo0iJdffpnOnTtz4YUX0qxZM2bPns2AAQNwd9atW0daWtop2/fq1YvJkyfzm9/8BoAvvvhCo3eRM5j+QrUY0fXcW7VqxcCBA/Pruc+fPx+A2267jQMHDtCiRQueeOKJArdLpqSkcNdddzFt2jSSk5NPudMm2vnnn8+HH37IlVdeyV//+lfuv/9+AP74xz8yZcoU0tLSaNOmzSkf6Ob5+c9/zhdffEHbtm1JS0tj6dKlFfhMiEi8UT33aqJWrVocOXIk1mGU2pl2fSR8wl7PXVUhReSMF/1Ot1u3bjGMpOIouVexDh06FLjjBeDFF1+Mq1G7iFR/Su5VbPXq1bEOQUTOAPpAVUQkhJTcRURCSMldRCSE4mbOfdXsP1bo/q4ecEup+i1cuJCxY8dy/PhxfvzjHzN+/PgC65cvX864ceNYt24dM2fOpH///hUap4hIWWjkXozSlPxt0qQJ06ZNY8iQIVUSj4hIaSi5F6M0JX9TUlJITU3lrLNKfiqXLVtGly5d6NevH61bt+b222/nxIkTACxatIiOHTuSnp7OgAED8m+NTElJ4eGHH6Zz587Mnj2bzZs3c80115CWlkZ6ejpbtmyp+BMXkbin5F6M0pT8PV3Z2dk8/vjjrF+/ni1btvDqq6+yf/9+JkyYwOLFi3nvvffIyMjgiSeeyN8mMTGRFStWMGjQIG655RbGjBnDBx98wKpVq2jQoEG54hGRcIqbOfdYqKhyvtGysrJo3jxSBG3w4MGsWLGCxMRENm7cSKdOnQD49ttv6dixY/42eaWADx8+zO7du+nXrx9Aqb8IRETOPEruxShNyd/TdfKLg5nh7vTs2ZMZM2YUuk1ead/qUAdIROKDpmWKEV3y99tvv2XmzJn06dOnXPvMzs5m27ZtnDhxglmzZtG5c2euuuoqVq5cyebNmwH46quv+OSTT07Z9sILLyQ5OZm5c+cCkS/u+Oqrr8oVj4iEU9yM3Et762JFii75e/z4cUaMGJFf8jcjI4M+ffrwzjvv0K9fP7744gv+/Oc/88ADD/Dhhx8Wuc+OHTsyfvx41q9fn//h6llnncW0adMYPHhwft2ZCRMmcNlll52y/YsvvsioUaO4//77Oeecc5g9e3b+NI+ISB6V/K1Cy5YtY+LEiSxYsCDWoZRbGK+PnFmiS/6+l7A1fzmeqkIWV/JX0zIiIiEUN9My8WT9+vUMHTq0QNt5553H6tWr6dq1a2yCEpEzipJ7JWjXrl2RX2otIlIVNC0jIhJCSu4iIiGk5C4iEkKacxeRamPfpMn5y/X+/Y4YRhL/4ia5R9+TWhFq92xaqn4pKSlccMEFnH322SQkJHDy/fgiItVR3CT3WFq6dCl169attP0fO3aMhARdChGpOJpzryBdu3Zl3LhxXH311bRt25bs7GwAcnNzGTFiBJmZmbRv3z6/Hvy0adMYMGAAN954I7169QLgscceo127dqSlpZ3yjU8iIqdDw8USmBm9evXCzBg1ahQjR44ssm9ubi6rVq1i+fLljBgxgg0bNvDII4/QvXt3pk6dysGDB8nKyuKaa64B4K233mLdunUkJSXxxhtvMHfuXFavXk3NmjX5/PPPq+oURSSElNxLsHLlSho2bMjevXvp2bMn//RP/0SXLl0K7Tt48GAAunTpwqFDhzh48CCLFi1i/vz5TJw4EYCjR4+yY8cOAHr27ElSUhIAixcvZvjw4dSsWRMgv11EpCyU3EuQV7+9fv369OvXj+zs7CKTe1G12l955RUuv/zyAutWr16dX6cdIrXay/tFICIieTTnXozc3FwOHz6cv7xo0SLatm1bZP9Zs2YBsGLFCmrXrk3t2rXp3bs3kyZNyv+ijffff7/QbXv16sXUqVPz67NrWkZEyiNuRu6lvXWxIn322Wf5X2l37NgxhgwZwrXXXltk/zp16nD11Vdz6NAhpk6dCsB9993HuHHjSE1Nxd1JSUkptOTvtddey9q1a8nIyODcc8/l+uuv55e//GXlnJiIhJ7quVeQrl27MnHiRDIyCi2tHDrxdn0kPlTlHzGpnruIiMSduJmWqS7GjBnDypUrC7SNHTuWZcuWxSYgEZFCKLmfpmeeeSbWIYiIlEjTMiIiIaTkLiISQiUmdzObamZ7zWxDVFuSmb1pZjnB7zpBu5nZ02a22czWmVl6ZQYvIiKFK83IfRpw8s3d44El7t4SWBI8BrgOaBn8jAR+WzFhiojI6SjxA1V3X25mKSc19wW6BsvTgWXAPUH7Hzxy8/zbZnaRmTVw9z3lDXTp0qXl3UUBpbmXdcSIESxYsID69euzYUPkjcvnn3/OzTffzPbt20lJSeFPf/oTderUqdDYRETKq6xz7pfkJezgd/2gvRGwM6rfrqDtFGY20szWmNmaffv2lTGMynXrrbeycOHCAm2PPvooPXr0ICcnhx49evDoo49WyrHdnRMnTlTKvkUk/Cr6A9XCKl8V+iew7v6cu2e4e0a9evUqOIyK0aVLl1OqM86bN49hw4YBMGzYMObOnVvk9g8++CBDhw6le/futGzZkueffz5/3a9//WsyMzNJTU3lgQceAGD79u20atWK0aNHk56ezs6dO1m4cCHp6emkpaXRo0ePSjhLEQmjst7n/lnedIuZNQD2Bu27gMZR/ZKBT8sTYHXz2Wef0aBBAwAaNGjA3r17i+2/bt063n77bXJzc2nfvj033HADGzZsICcnh+zsbNydPn36sHz5cpo0acLHH3/MCy+8wLPPPsu+ffv4yU9+wvLly2nWrJmKiYlIqZV15D4fGBYsDwPmRbX/KLhr5irgy4qYb49nffv2pUaNGtStW5du3bqRnZ3NokWLWLRoEe3btyc9PZ2PPvqInJwcAJo2bcpVV10FwNtvv02XLl1o1qwZoBrvIlJ6JY7czWwGkQ9P65rZLkXLJnMAAArbSURBVOAB4FHgT2Z2G7ADGBB0fx24HtgMfAUMr4SYY+qSSy5hz549NGjQgD179lC/fv1i+xdV4/3ee+9l1KhRBdZt375dNd5FpEKUOHJ398Hu3sDdz3H3ZHef4u4H3L2Hu7cMfn8e9HV3H+Pul7p7O3dfU9L+402fPn2YPn06ANOnT6dv377F9p83bx5Hjx7lwIEDLFu2jMzMTHr37s3UqVM5cuQIALt37y50eqdjx4787W9/Y9u2bYBqvItI6cVNbZlYlOEcPHgwy5YtY//+/SQnJ/PQQw8xfvx4Bg4cyJQpU2jSpAmzZ88udh9ZWVnccMMN7Nixg/vuu4+GDRvSsGFDNm3aRMeOHQGoVasWL730EmeffXaBbevVq8dzzz3HTTfdxIkTJ6hfvz5vvvlmpZ2viIRH3CT3WJgxY0ah7UuWLCn1Pi677DKee+65U9rHjh3L2LFjT2nPu58+z3XXXcd1111X6uOJiIBqy4iIhJJG7hXghRde4KmnnirQ1qlTJ5UHFpGYUXKvAMOHD2f48NDdGCQicaxaT8tUh+93lVPpuohUf9U2uScmJnLgwAElkmrG3Tlw4ACJiYmxDkVEilFtp2WSk5PZtWsX1bWo2JksMTGR5OTkWIchIsWotsn9nHPOyf+zexEROT3VdlpGRETKrtqO3EVETseq2X/MX756wC0xjKR60MhdRCSElNxFREJIyV1EJISU3EVEQkgfqIrIGemtHe/nL5/XvHYMI6kcGrmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICKn8gIhUe9l/3pq/nHVj8xhGEj80chcRCSEldxGREFJyFxEJISV3EZEQ0geqIhJX9OFq6WjkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIVSuWyHNbDtwGDgOHHP3DDNLAmYBKcB2YKC7f1G+MEVE5HRUxMi9m7tf4e4ZwePxwBJ3bwksCR6LiEgVqow/YuoLdA2WpwPLgHsq4TgiEjLv/OOd/OXrYxhHGJR35O7AIjN718xGBm2XuPsegOB3/XIeQ0RETlN5R+6d3P1TM6sPvGlmH5V2w+DFYCRAkyZNyhmGiMSTZ9c+m788+orRMYwkvMo1cnf3T4Pfe4HXgCzgMzNrABD83lvEts+5e4a7Z9SrV688YYiIyEnKnNzN7HwzuyBvGegFbADmA8OCbsOAeeUNUkRETk95pmUuAV4zs7z9vOzuC83sHeBPZnYbsAMYUP4wReRMs2/S5O8fpJzex6urZv8xf/nqAbdUVEhxpczJ3d23AmmFtB8AepQnKBERKR/9haqISAjpyzpEJG5FT79IQRq5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkO5zF5EqEV0JUiqfRu4iIiGk5C4iEkKalhGRamnD3u+/5+H8lO/b//7B64X2b3R5nUqOKL5o5C4iEkIauYtItfHFV99XEa9RK4aBhICSu4hUe7nZ73z/4LzYxRFPNC0jIhJCGrmLSNXY9n/fLzf7Qf7i6/9vWFSnU77cTcpII3cRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkh3QopIgUt/VXBx93ujU0cUi5K7iJSftEvCHoxqBaU3EWk9JTE44bm3EVEQkgjdxGpWJUwuv90/wf5y+c2algh+ww7JXeRaurJNz/JX76z52UxjKQIJ3/wKtWKkrtIvCnHyLiqXzD0pdixo+QuIgW8tfVAgccduxXe79mD6/KXR1+UWpkhSRkouYuERPSoPFr0CP2qHc9FrZlYuh1r+iUuKbmLxIEC0yn6XyuloH8mIiEUPUJ/8s2R37dH9Xlryt35yx2bX1xhx46erqHOD4ruKJVKyV2kAlTlB5XRc+JFzYdHKzgVUzmiE/rOg1/nLzeu9CNLUZTcRSpRUUn/9OfHS95/eUS/YMw/a3PBlVu/X3w/aXf+cnQSP23RX7nHuWXfjxRJyV2kEJUxEi9NIo7uc1Ux/QpTWSP0Asn+4GluXCCJF+Hg379fPlffoVpRKi25m9m1wFPA2cDv3f3RyjqWSFU53QQtEiuVktzN7GzgGaAnsAt4x8zmu/vGyjienFkqalRd2iRc2cm6PCPuqphPL48C8+8X1YhhJGeeyhq5ZwGb3X0rgJnNBPoCSu5nmKpOxGXtX9Wqe1KW+FdZyb0RsDPq8S6gQyUdSwKlTaTlSbilSZpF7bO6J9zTFZ2g324yspiep/Y/00WP6FNiF0aombtX/E7NBgC93f3HweOhQJa7/3tUn5FA3v+Iy4GPy3i4usD+coRb3YX5/HRu8SvM5xdP59bU3esVtqKyRu67KHiLazLwaXQHd38OKPdQxszWuHtGefdTXYX5/HRu8SvM5xeWc6usL+t4B2hpZs3M7FxgEDC/ko4lIiInqZSRu7sfM7M7gL8QuRVyqrt/WBnHEhGRU1Xafe7u/jrwemXtP0rYP6UK8/np3OJXmM8vFOdWKR+oiohIbOkLskVEQiiuk7uZXWtmH5vZZjMbH+t4ysPMGpvZUjPbZGYfmtnYoD3JzN40s5zgd51Yx1pWZna2mb1vZguCx83MbHVwbrOCD9/jkpldZGZzzOyj4Bp2DMu1M7M7g3+TG8xshpklxvO1M7OpZrbXzDZEtRV6rSzi6SDHrDOz9NhFfnriNrlHlTi4DmgNDDaz1rGNqlyOAT9191ZEakaNCc5nPLDE3VsCS4LH8WossCnq8X8DTwbn9gVwW0yiqhhPAQvd/Z+ANCLnGffXzswaAf8BZLh7WyI3SAwivq/dNODak9qKulbXAS2Dn5HAb6soxnKL2+ROVIkDd/8WyCtxEJfcfY+7vxcsHyaSHBoROafpQbfpwL/GJsLyMbNk4Abg98FjA7oDc4Iu8XxuFwJdgCkA7v6tux8kJNeOyI0XNcwsAagJ7CGOr527Lwc+P6m5qGvVF/iDR7wNXGRmDaom0vKJ5+ReWImDRjGKpUKZWQrQHlgNXOLueyDyAgDUj11k5fIb4D+BE8Hji4GD7n4seBzP1685sA94IZh2+r2ZnU8Irp277ybyZas7iCT1L4F3Cc+1y1PUtYrbPBPPyd0KaYv7W3/MrBbwCjDO3Q/FOp6KYGb/Aux193ejmwvpGq/XLwFIB37r7u2BXOJwCqYwwdxzX6AZ0BA4n8hUxcni9dqVJG7/ncZzci+xxEG8MbNziCT2P7r7q0HzZ3lvA4Pfe2MVXzl0AvqY2XYi02fdiYzkLwre6kN8X79dwC53Xx08nkMk2Yfh2l0DbHP3fe7+HfAqcDXhuXZ5irpWcZtn4jm5h6rEQTAHPQXY5O5PRK2aDwwLlocB86o6tvJy93vdPdndU4hcp7+6+y3AUqB/0C0uzw3A3f8B7DSzy4OmHkTKW8f9tSMyHXOVmdUM/o3mnVsorl2Uoq7VfOBHwV0zVwFf5k3fVHvuHrc/wPXAJ8AW4P/FOp5ynktnIm/31gFrg5/ricxNLwFygt9JsY61nOfZFVgQLDcHsoHNwGzgvFjHV47zugJYE1y/uUCdsFw74CHgI2AD8CJwXjxfO2AGkc8PviMyMr+tqGtFZFrmmSDHrCdy11DMz6E0P/oLVRGREIrnaRkRESmCkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAj9fxP3ZV03Chf+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v3r256aabuPvuu/n888/ZtGkTrVu3xt0L3aaZ0aVLF2bNmkX//v2ZOnUqffr0KVXc2dnZbN26lYYNGzJjxgyGDRtG27ZtGTFiBJs3b6ZJkyZ888035OTkcNFFBevkn3POOaSlpTF79mx+8pOf8O2333Ls2DGqVq1a6s9RRMqebqgWo6h67vfffz9z584FYOjQoeTm5tKkSROeeOKJ/KGNl112GTfccAOXXnopPXv25JlnnqFChQrF1ojP6wdv0qQJubm5DB06FID33nuPtLQ0Zs6cyfDhw4utKQ/Qrl07Ro8eTbNmzWjUqBF9+/alTp06TJkyhQEDBpCRkUHbtm356KOPCl3/pZde4umnnyYjI4P27dvzxRdfxOojFSk3vlr4Wf5PKoq4nns8ZWVl+apVqwq0qV546SxZsoRx48Yxb968uO5H50fKUiT13Et6QzU8qdfo3jCK6BIn6nruIiKSXJKnz10KWLduHQMHDizQdtZZZ7Fy5Uo6d+6cmKBEpNxQck9SzZs3Z82aNadeUEROS+qWERFJQadM7mY22cx2m9n6sLYZZrYm+NmW94QmM0s3s/8Nm/fHeAYvIiKFi6RbZgowAXgxr8Hdb8ybNrPHga/Clt/i7pfHKkARESm5SB6zt9TM0gubFzw8+waga2zDOtmza56N6fbuuPyOmG5PRKQ8ibbP/UfAl+6+KaytkZl9YGb/MLMfRbn9hCvLeu4TJkygSZMmmBl79+6N63GJSGqLNrkPAMKLlewCGrh7S+Bu4C9mdk5hK5rZMDNbZWar9uzZE2UY8VHW9dw7dOjAokWLaNgwvn9Q4e6q9CiS4kqd3M2sInAdMCOvzd2/dffcYHo1sAW4qLD13X2iu2e5e1adOnVKG0ZclWU9d4CWLVuSnp4eUWxjxoxh4MCBdO3alaZNm/L888/nz/v9739Pq1atyMjI4IEHHgBg27ZtXHLJJdxxxx1kZmayY8cO5s+fT2ZmJi1atKBbt24x+MREpLyIZpz7lcBH7p6T12BmdYB97n7MzBoDTYFPi9pAeVeW9dxLY+3ataxYsYLDhw/TsmVLrrnmGtavX8+mTZvIzs7G3enduzdLly6lQYMGfPzxx7zwwgs8++yz7Nmzh9tuu42lS5fSqFEj9u3bV6oYRKR8OmVyN7NpQGegtpnlAA+4+ySgPwW7ZAA6AQ+Z2VHgGHC7uydt1ijLeu6l0adPH6pUqUKVKlXo0qUL2dnZLFu2jAULFtCyZUsADh06xKZNm2jQoAENGzbM/4WzYsUKOnXqRKNGjQCoVatWqWIQkfIpktEyA4pov6WQtteA16IPq3woy3rupVFU3fZ7772X4cOHF5i3bdu2/JrtEPqlVNpfKiJS/iVN+YFEDF0sy3rupTFnzhzuvfdeDh8+zJIlSxg7dixVqlThvvvu4+abb6Z69ers3LmTSpUqnbRuu3btGDFiBFu3bs3vltHVu0jqSJrkngjhtdePHTvGkCFD8uu5Z2Vl0bt3b4YOHcrAgQNp0qQJtWrVYvr06UDBeu4VK1bMr+cOFLpNgKeffprHHnuML774goyMDHr16sWf//znIuNr3bo111xzDdu3b+e+++6jfv361K9fn40bN+Y/Qq969eq8/PLL+fvOU6dOHSZOnMh1113H8ePHqVu3LgsXLozHxygiCaB67klqzJgxVK9enV/+8pcJ2b/Oj5Ql1XMvXHH13HXlLiKnvcWLFxd436VLlwRFEjtK7uXcCy+8wFNPPVWgrUOHDjzzzDMJikhEkoGSezk3ePBgBg8enOgwRCTJqJ67iEgKUnIXEUlBSu4iIikoafrc94yfENPt1fmPO2O6PRGR8kRX7qcQj3ruQ4YMoW7dujRr1qwsDkFETkNK7sWIRz13gFtuuYX58+fHPf6jR4/GfR8iUj4puRcjHvXcATp16hRxHZfOnTszatQo2rdvT7NmzfK3cfjwYYYMGUKrVq1o2bJlflxTpkyhX79+XHvttfTo0QOAxx57jObNm9OiRQtGjx4dk89GRMq3pOlzT4R41XMvqcOHD/POO++wdOlShgwZwvr163nkkUfo2rUrkydP5sCBA7Ru3Zorr7wSgHfffZe1a9dSq1Yt3nzzTWbPns3KlSupWrWq6raLnCaU3IsRj3rupTFgQKjqcqdOnTh48CAHDhxgwYIFzJ07l3HjxgFw5MgRtm/fDkD37t3zvxksWrSIwYMHU7VqVUB120VOF0ruxYhXPfeSKqpu+2uvvcbFF19cYN7KlStVt11EInoS02Tg34Hd7t4saBsD3AbkPdn61+7+RjDvXmAooScx/ae7//2kjZZCIoYuxqOee2nMmDGDLl26sGzZMmrUqEGNGjW46qqrGD9+POPHj8fM+OCDD/KfvhSuR48ePPTQQ9x000353TK6ehdJfZFcuU8BJgAvntD+pLuPC28ws0sJPX7vMqA+sMjMLnL3YzGItczFq577gAEDWLJkCXv37iUtLY0HH3yQoUOHFhlHzZo1ad++PQcPHmTy5MkA3HfffYwaNYqMjAzcnfT0dObNm3fSuj179mTNmjVkZWVx5pln0qtXL37729/G4dMSkfIkonruZpYOzDvhyv1QIcn9XgB3fzR4/3dgjLu/W9z2Vc+9aJ07d2bcuHFkZRVasjlhdH6kLMW7nvv7FT8tMC9ZSv4WV889mqGQd5rZWjObbGY1g7YLgB1hy+QEbYUFNczMVpnZqj179hS2iIiIlFJpb6g+B/wG8OD1cWAIUNidu0K/Grj7RGAihK7cSxlHyhgxYgTLly8v0DZy5EiWLFmSmIBEJKmVKrm7+5d502b2PJDX2ZsDXBi2aBrweamjO43o4RsiEkul6pYxs3phb/sC64PpuUB/MzvLzBoBTYHs6EIUEZGSimQo5DSgM1DbzHKAB4DOZnY5oS6XbcBwAHf/0MxeBTYAR4ERyTpSRkQkmZ0yubv7gEKaJxWz/CPAI9EEJSIi0Umav1ANHwoVC0UNpxIRSQWqCnkKpa3nnpubS5cuXahevTp33qkHg4hI2VJyL0Y09dwrV67Mb37zm/zCXvGkuu0iciIl92JEU8+9WrVqdOzYkcqVK0e0r+rVq/OLX/yCzMxMunXrRt4fdm3ZsoWePXtyxRVX8KMf/YiPPvoICD3w4+6776ZLly7cc889HDp0iMGDB9O8eXMyMjJ47bXXYvhJiEiyUXIvRmH13E+syV5UPfeSOnz4MJmZmbz//vv8+Mc/5sEHHwRg2LBhjB8/ntWrVzNu3DjuuOOO/HU++eQTFi1axOOPP85vfvMbatSowbp161i7di1du3YtzSGLSIpImhuqiRBNPfeSOuOMM7jxxhsB+NnPfsZ1113HoUOHeOedd+jXr1/+ct9++23+dL9+/fKLkS1atCi/aBmEio2JyOlLyb0Y0dRzj5aZcfz4cc4991zWrFlT6DKq2y4iRUma5J6IoYvR1HMvqePHjzNr1iz69+/PX/7yFzp27Mg555xDo0aNmDlzJv369cPdWbt2LS1atDhp/R49ejBhwgT+8Ic/ALB//35dvYucxtTnXozweu6XXHIJN9xwQ34997lz5wIwdOhQcnNzadKkCU888USB4ZLp6encfffdTJkyhbS0tJNG2oSrVq0aH374IVdccQVvv/02999/PwCvvPIKkyZNokWLFlx22WUn3dDN8z//8z/s37+fZs2a0aJFCxYvXhzDT0JEkk1E9dzjTfXcQ6NlDh06lOgwIna6nR9JLNVzL1y86rmLiEg5lTR97qmiTZs2BUa8ALz00ktJddUuIuWfknsZW7lyZaJDEJHTgLplRERSkJK7iEgKUnIXEUlBkTyJaTLw78Bud28WtP0euBb4DtgCDHb3A2aWDmwEPg5WX+Hut8ci0PBhTrEQyVApCJX8HTlyJMeOHePWW29l9OjRBeYvXbqUUaNGsXbtWqZPn871118f0zhFREojkiv3KUDPE9oWAs3cPQP4BLg3bN4Wd788+IlJYk+USEr+NmjQgClTpnDTTTeVSTwiIpE4ZXJ396XAvhPaFrh7XhHxFUBaHGJLuEhK/qanp5ORkcEZZ5z69+SSJUvo1KkTffv25dJLL+X222/n+PHjACxYsIB27dqRmZlJv3798odGpqen89BDD9GxY0dmzpzJ5s2bufLKK2nRogWZmZls2bIl9gcuIkkvFn3uQ4A3w943MrMPzOwfZvajolYys2FmtsrMVuXVLi9vIin5W1LZ2dk8/vjjrFu3ji1btvD666+zd+9eHn74YRYtWsT7779PVlYWTzzxRP46lStXZtmyZfTv35+bb76ZESNG8M9//pN33nmHevXqRRWPiKSmqMa5m9l/A0eBvA7xXUADd881syuA2WZ2mbsfPHFdd58ITIRQ+YFo4oiXWJXzDde6dWsaNw79+fSAAQNYtmwZlStXZsOGDXTo0AGA7777jnbt2uWvk1cK+Ouvv2bnzp307dsXIOIHgYjI6afUyd3MBhG60drNgyzo7t8C3wbTq81sC3ARsKrIDZVjkZT8LakTfzmYGe5O9+7dmTZtWqHr5JX2LQ91gEQkOZSqW8bMegL3AL3d/Zuw9jpmViGYbgw0BT4tfCvlX3jJ3++++47p06fTu3fvqLaZnZ3N1q1bOX78ODNmzKBjx460bduW5cuXs3nzZgC++eYbPvnkk5PWPeecc0hLS2P27NlA6MEd33zzzUnLiYhEMhRyGtAZqG1mOcADhEbHnAUsDK5E84Y8dgIeMrOjwDHgdnffV+iGSyjSoYuxFF7y99ixYwwZMiS/5G9WVha9e/fmvffeo2/fvuzfv5+//e1vPPDAA3z44YdFbrNdu3aMHj2adevW5d9cPeOMM5gyZQoDBgzIrzvz8MMPc9FFF520/ksvvcTw4cO5//77qVSpEjNnzszv5hERyXPK5O7uAwppnlTEsq8BKfVk5l69etGrV68CbQ899FD+dKtWrcjJyYl4e1WrVmXGjBkntXft2pX33nvvpPZt27YVeN+0aVPefvvtiPcnIqcn/YWqiEgKUlXIOFi3bh0DBw4s0HbWWWexcuVKOnfunJigROS0ouQeB82bNy/yodYiImVB3TIiIilIyV1EJAUpuYuIpCAldxGRFJQ0N1S/WvhZTLdXo3vDiJZLT0/n7LPPpkKFClSsWJFVq5KykoKInGaSJrkn0uLFi6ldu3bctn/06FEqVtSpEJHYUbdMjHTu3JlRo0bRvn17mjVrRnZ2NgCHDx9myJAhtGrVipYtW+bXg58yZQr9+vXj2muvpUePHgA89thjNG/enBYtWpz0xCcRkZLQ5eIpmBk9evTAzBg+fDjDhg0rctnDhw/zzjvvsHTpUoYMGcL69et55JFH6Nq1K5MnT+bAgQO0bt2aK6+8EoB3332XtWvXUqtWLd58801mz57NypUrqVq1Kvv2xaQkj4icppTcT2H58uXUr1+f3bt30717d374wx/SqVOnQpcdMCBUhqdTp04cPHiQAwcOsGDBAubOncu4ceMAOHLkCNu3bwege/fu1KpVC4BFixYxePBgqlatCpDfLiJSGkrup5BXv71u3br07duX7OzsIpN7UbXaX3vtNS6++OIC81auXJlfpx1CtdqjfRCIiEge9bkX4/Dhw3z99df50wsWLKBZs2ZFLp9X7XHZsmXUqFGDGjVqcNVVVzF+/Pj8B2188MEHha7bo0cPJk+enF+fXd0yIhKNpLlyj3ToYix9+eWX+Y+0O3r0KDfddBM9e/YscvmaNWvSvn17Dh48yOTJkwG47777GDVqFBkZGbg76enpzJs376R1e/bsyZo1a8jKyuLMM8+kV69e/Pa3v43PgYlIykua5J4IjRs35p///GfEy//0pz/l0UcfLdBWpUoV/vSnP5207C233MItt9xSoG306NEaJSMiMRFRt4yZTTaz3Wa2PqytlpktNLNNwWvNoN3M7Gkz22xma80sM17Bi4hI4SK9cp8CTABeDGsbDbzl7mPNbHTw/h7gakLPTm0KtAGeC15TwogRI1i+fHmBtpEjR7JkyZLEBCQiUoiIkru7LzWz9BOa+xB6tirAVGAJoeTeB3jRQ3cQV5jZuWZWz913xSLgRHvmmWcSHYKIyClFM1rm/LyEHcKibQ0AAA4NSURBVLzWDdovAHaELZcTtBVgZsPMbJWZrdqzZ08UYYiIyIniMRSysMHaflKD+0R3z3L3rDp16sQhDBGR01c0yf1LM6sHELzuDtpzgAvDlksDPo9iPyIiUkLRJPe5wKBgehAwJ6z958GombbAV6nS3y4ikiwiuqFqZtMI3TytbWY5wAPAWOBVMxsKbAf6BYu/AfQCNgPfAINjEejixYtjsZl8Xbp0OeUyQ4YMYd68edStW5f160OjQPft28eNN97Itm3bSE9P59VXX6VmzZoxjU1EJFoRXbm7+wB3r+fuldw9zd0nuXuuu3dz96bB675gWXf3Ee7+A3dv7u5J+3SLW265hfnz5xdoGzt2LN26dWPTpk1069aNsWPHxmXf7s7x48fjsm0RSX2qLVOMTp06nVSdcc6cOQwaFOqNGjRoELNnzy5y/TFjxjBw4EC6du1K06ZNef755/Pn/f73v6dVq1ZkZGTwwAMPALBt2zYuueQS7rjjDjIzM9mxYwfz588nMzOTFi1a0K1btzgcpYikIpUfKKEvv/ySevXqAVCvXj12795d7PJr165lxYoVHD58mJYtW3LNNdewfv16Nm3aRHZ2Nu5O7969Wbp0KQ0aNODjjz/mhRde4Nlnn2XPnj3cdtttLF26lEaNGqmYmIhETMk9zvr06UOVKlWoUqUKXbp0ITs7m2XLlrFgwQJatmwJwKFDh9i0aRMNGjSgYcOGtG3bFoAVK1bQqVMnGjVqBKjGu4hETsm9hM4//3x27dpFvXr12LVrF3Xr1i12+aJqvN97770MHz68wLxt27apxruIxIT63Euod+/eTJ06FYCpU6fSp0+fYpefM2cOR44cITc3lyVLltCqVSuuuuoqJk+ezKFDhwDYuXNnod077dq14x//+Adbt24FVONdRCKXNFfukQxdjLUBAwawZMkS9u7dS1paGg8++CCjR4/mhhtuYNKkSTRo0ICZM2cWu43WrVtzzTXXsH37du677z7q169P/fr12bhxI+3atQOgevXqvPzyy1SoUKHAunXq1GHixIlcd911HD9+nLp167Jw4cK4Ha+IpI6kSe6JMG3atELb33rrrYi3cdFFFzFx4sST2keOHMnIkSNPas8bT5/n6quv5uqrr454fyIioG4ZEZGUpCv3GHjhhRd46qmnCrR16NBB5YFFJGGU3GNg8ODBDB4ckyoLIiIxUa67ZULP+5DyRudFpPwrt8m9cuXK5ObmKpGUM+5Obm4ulStXTnQoIlKMctstk5aWRk5ODnpKU/lTuXJl0tLSEh2GiBSj3Cb3SpUq5f/ZvYiIlEy57ZYREZHSU3IXEUlBpe6WMbOLgRlhTY2B+4FzgduAvM7yX7v7G6WOUERESqzUyd3dPwYuBzCzCsBO4K+EHqv3pLuPi0mEIiJSYrHqlukGbHH3z2K0PRERiUKsknt/ILzK1p1mttbMJptZoU+PNrNhZrbKzFZpuKOISGxFndzN7EygN5BX+/Y54AeEumx2AY8Xtp67T3T3LHfPqlOnTrRhiIhImFhcuV8NvO/uXwK4+5fufszdjwPPA61jsA8RESmBWCT3AYR1yZhZvbB5fYH1J60hIiJxFdVfqJpZVaA7EP4w0MfM7HLAgW0nzBMRkTIQVXJ392+A805oGxhVRCJyWtkzfkL+dJ3/uDOBkaQW/YWqiEgKUnIXEUlB5bYqpIicftRFEzu6chcRSUFK7iIiKUjJXUQkBSm5i4ikICV3EZEUpOQuIpKClNxFRFKQkruISApSchcRSUFK7iIiKUjJXUQkBSm5i4ikICV3EZEUFHVVSDPbBnwNHAOOunuWmdUCZgDphJ7GdIO77492XyIiEplYXbl3cffL3T0reD8aeMvdmwJvBe9FRKSMxKueex+gczA9FVgC3BOnfYmIxNTixYvzp7t06ZLASEovFlfuDiwws9VmNixoO9/ddwEEr3VPXMnMhpnZKjNbtWfPnhiEISIieWJx5d7B3T83s7rAQjP7KJKV3H0iMBEgKyvLYxCHiIgEor5yd/fPg9fdwF+B1sCXZlYPIHjdHe1+REQkclEldzOrZmZn500DPYD1wFxgULDYIGBONPsREZGSibZb5nzgr2aWt62/uPt8M3sPeNXMhgLbgX5R7kdEREogquTu7p8CLQppzwW6RbNtEREpPf2FqohIClJyFxFJQUruIiIpSMldRCQFKbmLiKQgJXcRkRSk5C4ikoKU3EVEUlC8Sv6KSJJ5ds2z+dN3XH5HAiORWNCVu4hICtKVu4ic9r799KsC789qXCNBkcSOkruInERdNMlPyV3kNBKetEGJO5UpuYvE0+JH/zXd5d7ExSGnHSV3ESlze8ZPSHQIKU+jZUREUlCpk7uZXWhmi81so5l9aGYjg/YxZrbTzNYEP71iF66IiEQimm6Zo8Av3P394Dmqq81sYTDvSXcfF314IiJSGqVO7u6+C9gVTH9tZhuBC2IVmIiIlF5MbqiaWTrQElgJdADuNLOfA6sIXd3vL2SdYcAwgAYNGsQiDJGYeXLhJ/nTd3W/KIGRxNeJQyOTzTszX0l0COVW1DdUzaw68Bowyt0PAs8BPwAuJ3Rl/3hh67n7RHfPcvesOnXqRBuGiIiEierK3cwqEUrsr7j76wDu/mXY/OeBeVFFKJIMSjqeXePfJc6iGS1jwCRgo7s/EdZeL2yxvsD60ocnIiKlEc2VewdgILDOzNYEbb8GBpjZ5YAD24DhUUUoIlEpL/3q+sOlshXNaJllgBUy643ShyNSuJS4wRneFVPS5dV1IyWk8gMi5UlJfwGIFEHlB0REUpCu3EUSTVfrEgdK7nJaSIk++xIoLzdRJXGU3EWkWHoqU3JSchdJEeXxal3DHxNHyV2ktNRXLuWYkrukpPA+9niuU6h4JH2NeZcSUnKXpJPIm6MF9q3/PXGV/bdPEx1CUtM4dxGRFKRrD0lq0V7Fx6wr5jQXfuO0zn/cmcBIJI+Su6SMeCXqSLb77qe5hba3a3xerMMp9zRCpnxQcpdyS1fVhXv2rwPyp+/oOy2BkUh5puQuCZdMfz3advvEf705Da/KJXkouYucQoGEHqaorpiihC8fq+6a8viHS+VB+LNV2/e7OYGRJE7ckruZ9QSeAioAf3b3sfHalyRGSa+4I+lmKYuumGTs7nn2wNpEhwCoFEEyiUtyN7MKwDNAdyAHeM/M5rr7hnjsT8pOSRNjMibSpLX1//1rutGP4r678ETfL+57k5KK15V7a2Czu38KYGbTgT6AknuKSoUkXlT3SzRK2nVT5Lq1ilgoPKEn0HtfvJc/3erfWiUwEskTr+R+AbAj7H0O0CZO+0oasbpxWBY3IFMhWRclHkm8pOLR/15ehCf6cMma9BcvXpw/3aVLlwRGUjLm7rHfqFk/4Cp3vzV4PxBo7e7/EbbMMGBY8PZi4OModlkb2BvF+uWZji15pfLx6djKh4buXqewGfG6cs8BLgx7nwZ8Hr6Au08EYnIJZWar3D0rFtsqb3RsySuVj0/HVv7Fq7bMe0BTM2tkZmcC/YG5cdqXiIicIC5X7u5+1MzuBP5OaCjkZHf/MB77EhGRk8VtnLu7vwG8Ea/tnyDxd8jiR8eWvFL5+HRs5VxcbqiKiEhiqZ67iEgKSurkbmY9zexjM9tsZqMTHU80zOxCM1tsZhvN7EMzGxm01zKzhWa2KXitmehYS8vMKpjZB2Y2L3jfyMxWBsc2I7j5npTM7Fwzm2VmHwXnsF2qnDszuyv4N7nezKaZWeVkPndmNtnMdpvZ+rC2Qs+VhTwd5Ji1ZpaZuMhLJmmTe1iJg6uBS4EBZnZpYqOKylHgF+5+CdAWGBEcz2jgLXdvCrwVvE9WI4GNYe9/BzwZHNt+YGhCooqNp4D57v5DoAWh40z6c2dmFwD/CWS5ezNCAyT6k9znbgrQ84S2os7V1UDT4GcY8FwZxRi1pE3uhJU4cPfvgLwSB0nJ3Xe5+/vB9NeEksMFhI5parDYVOAniYkwOmaWBlwD/Dl4b0BXYFawSDIf2zlAJ2ASgLt/5+4HSJFzR2jgRRUzqwhUBXaRxOfO3ZcC+05oLupc9QFe9JAVwLlmVq9sIo1OMif3wkocXJCgWGLKzNKBlsBK4Hx33wWhXwBA3cRFFpU/AP8FHA/enwcccPejwftkPn+NgT3AC0G305/NrBopcO7cfScwDthOKKl/Bawmdc5dnqLOVdLmmWRO7lZIW9IP/TGz6sBrwCh3P5joeGLBzP4d2O3uq8ObC1k0Wc9fRSATeM7dWwKHScIumMIEfc99gEZAfaAaoa6KEyXruTuVpP13mszJ/ZQlDpKNmVUilNhfcffXg+Yv874GBq+7ExVfFDoAvc1sG6Hus66EruTPDb7qQ3Kfvxwgx91XBu9nEUr2qXDurgS2uvsed/8eeB1oT+qcuzxFnaukzTPJnNxTqsRB0Ac9Cdjo7k+EzZoLDAqmBwFzyjq2aLn7ve6e5u7phM7T2+5+M7AYuD5YLCmPDcDdvwB2mNnFQVM3QuWtk/7cEeqOaWtmVYN/o3nHlhLnLkxR52ou8PNg1Exb4Ku87ptyz92T9gfoBXwCbAH+O9HxRHksHQl93VsLrAl+ehHqm34L2BS81kp0rFEeZ2dgXjDdGMgGNgMzgbMSHV8Ux3U5sCo4f7OBmqly7oAHgY+A9cBLwFnJfO6AaYTuH3xP6Mp8aFHnilC3zDNBjllHaNRQwo8hkh/9haqISApK5m4ZEREpgpK7iEgKUnIXEUlBSu4iIilIyV1EJAUpuYuIpCAldxGRFKTkLiKSgv4/4eC76fyXrAwAAAAASUVORK5CYII=\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": {
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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}_0_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.00001_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.00001_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.0001_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.0001_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.001_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.001_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.01_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.01_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.1_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.1_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_5_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '5_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_10_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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Channel 35 -- percentiles')"
      ]
     },
     "execution_count": 9,
     "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_0_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0_perc')\n",
    "plt.hist(percentiles['ch35_0.00001_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.00001_perc')\n",
    "plt.hist(percentiles['ch35_0.0001_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.0001_perc')\n",
    "plt.hist(percentiles['ch35_0.001_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.001_perc')\n",
    "plt.hist(percentiles['ch35_0.01_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.01_perc')\n",
    "plt.hist(percentiles['ch35_0.1_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.1_perc')\n",
    "plt.hist(percentiles['ch35_5_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '5_perc')\n",
    "plt.hist(percentiles['ch35_10_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": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Channel 36 -- percentiles')"
      ]
     },
     "execution_count": 10,
     "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_0_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0_perc')\n",
    "plt.hist(percentiles['ch36_0.00001_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.00001_perc')\n",
    "plt.hist(percentiles['ch36_0.0001_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.0001_perc')\n",
    "plt.hist(percentiles['ch36_0.001_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.001_perc')\n",
    "plt.hist(percentiles['ch36_0.01_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.01_perc')\n",
    "plt.hist(percentiles['ch36_0.1_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.1_perc')\n",
    "plt.hist(percentiles['ch36_5_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '5_perc')\n",
    "plt.hist(percentiles['ch36_10_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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</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",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</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",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</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>215</td>\n",
       "      <td>0.0</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>216</td>\n",
       "      <td>0.0</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>217</td>\n",
       "      <td>0.0</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>218</td>\n",
       "      <td>0.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ch0_cutoff_perc  ch1_cutoff_perc  ch2_cutoff_perc  ch3_cutoff_perc  \\\n",
       "0                0.0              0.0              0.0              0.0   \n",
       "1                0.0              0.0              0.0              0.0   \n",
       "2                0.0              0.0              0.0              0.0   \n",
       "3                0.0              0.0              0.0              0.0   \n",
       "4                0.0              0.0              0.0              0.0   \n",
       "..               ...              ...              ...              ...   \n",
       "214              0.0              0.0              0.0              0.0   \n",
       "215              0.0              0.0              0.0              0.0   \n",
       "216              0.0              0.0              0.0              0.0   \n",
       "217              0.0              0.0              0.0              0.0   \n",
       "218              0.0              0.0              0.0              0.0   \n",
       "\n",
       "     ch4_cutoff_perc  ch5_cutoff_perc  ch6_cutoff_perc  ch7_cutoff_perc  \\\n",
       "0                0.0              0.0              0.0              0.0   \n",
       "1                0.0              0.0              0.0              0.0   \n",
       "2                0.0              0.0              0.0              0.0   \n",
       "3                0.0              0.0              0.0              0.0   \n",
       "4                0.0              0.0              0.0              0.0   \n",
       "..               ...              ...              ...              ...   \n",
       "214              0.0              0.0              0.0              0.0   \n",
       "215              0.0              0.0              0.0              0.0   \n",
       "216              0.0              0.0              0.0              0.0   \n",
       "217              0.0              0.0              0.0              0.0   \n",
       "218              0.0              0.0              0.0              0.0   \n",
       "\n",
       "     ch8_cutoff_perc  ch9_cutoff_perc  ...  ch28_cutoff_perc  \\\n",
       "0                0.0              0.0  ...               0.0   \n",
       "1                0.0              0.0  ...               0.0   \n",
       "2                0.0              0.0  ...               0.0   \n",
       "3                0.0              0.0  ...               0.0   \n",
       "4                0.0              0.0  ...               0.0   \n",
       "..               ...              ...  ...               ...   \n",
       "214              0.0              0.0  ...               0.0   \n",
       "215              0.0              0.0  ...               0.0   \n",
       "216              0.0              0.0  ...               0.0   \n",
       "217              0.0              0.0  ...               0.0   \n",
       "218              0.0              0.0  ...               0.0   \n",
       "\n",
       "     ch29_cutoff_perc  ch30_cutoff_perc  ch31_cutoff_perc  ch32_cutoff_perc  \\\n",
       "0                 0.0               0.0               0.0               0.0   \n",
       "1                 0.0               0.0               0.0               0.0   \n",
       "2                 0.0               0.0               0.0               0.0   \n",
       "3                 0.0               0.0               0.0               0.0   \n",
       "4                 0.0               0.0               0.0               0.0   \n",
       "..                ...               ...               ...               ...   \n",
       "214               0.0               0.0               0.0               0.0   \n",
       "215               0.0               0.0               0.0               0.0   \n",
       "216               0.0               0.0               0.0               0.0   \n",
       "217               0.0               0.0               0.0               0.0   \n",
       "218               0.0               0.0               0.0               0.0   \n",
       "\n",
       "     ch33_cutoff_perc  ch34_cutoff_perc  ch35_cutoff_perc  ch36_cutoff_perc  \\\n",
       "0                 0.0               0.0               1.0               0.0   \n",
       "1                 0.0               0.0               1.0               1.0   \n",
       "2                 0.0               0.0               1.0               1.0   \n",
       "3                 0.0               0.0               1.0               0.0   \n",
       "4                 0.0               0.0               1.0               0.0   \n",
       "..                ...               ...               ...               ...   \n",
       "214               0.0               0.0               1.0               0.0   \n",
       "215               0.0               0.0               1.0               0.0   \n",
       "216               0.0               0.0               1.0               0.0   \n",
       "217               0.0               0.0               1.0               0.0   \n",
       "218               0.0               0.0               1.0               0.0   \n",
       "\n",
       "     ch37_cutoff_perc  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "..                ...  \n",
       "214               0.0  \n",
       "215               0.0  \n",
       "216               0.0  \n",
       "217               0.0  \n",
       "218               0.0  \n",
       "\n",
       "[219 rows x 38 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoffs = percentiles.filter(regex=(\".*cutoff.*\"))\n",
    "cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 35, 1.0),\n",
       " (1, 35, 1.0),\n",
       " (1, 36, 1.0),\n",
       " (2, 35, 1.0),\n",
       " (2, 36, 1.0),\n",
       " (3, 35, 1.0),\n",
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       " (31, 35, 1.0),\n",
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       " (33, 35, 1.0),\n",
       " (36, 35, 1.0),\n",
       " (39, 35, 1.0),\n",
       " (40, 35, 1.0),\n",
       " (41, 35, 1.0),\n",
       " (42, 35, 1.0),\n",
       " (43, 35, 1.0),\n",
       " (45, 35, 1.0),\n",
       " (46, 35, 1.0),\n",
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       " (48, 35, 1.0),\n",
       " (48, 36, 1.0),\n",
       " (49, 35, 1.0),\n",
       " (50, 35, 1.0),\n",
       " (51, 35, 1.0),\n",
       " (52, 35, 1.0),\n",
       " (53, 35, 1.0),\n",
       " (54, 35, 1.0),\n",
       " (55, 35, 1.0),\n",
       " (56, 35, 1.0),\n",
       " (57, 35, 1.0),\n",
       " (58, 35, 1.0),\n",
       " (59, 35, 1.0),\n",
       " (60, 35, 1.0),\n",
       " (60, 36, 1.0),\n",
       " (61, 35, 1.0),\n",
       " (62, 35, 1.0),\n",
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       " (65, 35, 1.0),\n",
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       " (70, 35, 1.0),\n",
       " (71, 35, 1.0),\n",
       " (72, 35, 1.0),\n",
       " (73, 35, 1.0),\n",
       " (73, 36, 1.0),\n",
       " (74, 35, 1.0),\n",
       " (75, 35, 1.0),\n",
       " (76, 35, 1.0),\n",
       " (77, 35, 1.0),\n",
       " (78, 35, 1.0),\n",
       " (79, 35, 1.0),\n",
       " (80, 35, 1.0),\n",
       " (80, 36, 1.0),\n",
       " (81, 35, 1.0),\n",
       " (83, 35, 1.0),\n",
       " (84, 35, 1.0),\n",
       " (85, 35, 1.0),\n",
       " (87, 35, 1.0),\n",
       " (88, 35, 1.0),\n",
       " (91, 35, 1.0),\n",
       " (93, 35, 1.0),\n",
       " (95, 35, 1.0),\n",
       " (96, 35, 1.0),\n",
       " (97, 35, 1.0),\n",
       " (98, 35, 1.0),\n",
       " (100, 35, 1.0),\n",
       " (101, 35, 1.0),\n",
       " (102, 35, 1.0),\n",
       " (104, 35, 1.0),\n",
       " (107, 35, 1.0),\n",
       " (110, 35, 1.0),\n",
       " (111, 35, 1.0),\n",
       " (112, 35, 1.0),\n",
       " (114, 35, 1.0),\n",
       " (115, 35, 1.0),\n",
       " (116, 35, 1.0),\n",
       " (117, 35, 1.0),\n",
       " (119, 35, 1.0),\n",
       " (122, 35, 1.0),\n",
       " (123, 35, 1.0),\n",
       " (124, 35, 1.0),\n",
       " (125, 35, 1.0),\n",
       " (127, 35, 1.0),\n",
       " (128, 35, 1.0),\n",
       " (129, 35, 1.0),\n",
       " (130, 35, 1.0),\n",
       " (131, 35, 1.0),\n",
       " (132, 35, 1.0),\n",
       " (133, 35, 1.0),\n",
       " (134, 35, 1.0),\n",
       " (135, 14, 1.0),\n",
       " (135, 35, 1.0),\n",
       " (136, 35, 1.0),\n",
       " (137, 35, 1.0),\n",
       " (138, 35, 1.0),\n",
       " (139, 35, 1.0),\n",
       " (140, 35, 1.0),\n",
       " (141, 35, 1.0),\n",
       " (142, 35, 1.0),\n",
       " (143, 35, 1.0),\n",
       " (144, 35, 1.0),\n",
       " (145, 35, 1.0),\n",
       " (146, 35, 1.0),\n",
       " (147, 35, 1.0),\n",
       " (148, 35, 1.0),\n",
       " (149, 35, 1.0),\n",
       " (150, 35, 1.0),\n",
       " (152, 35, 1.0),\n",
       " (153, 35, 1.0),\n",
       " (154, 35, 1.0),\n",
       " (155, 35, 1.0),\n",
       " (156, 35, 1.0),\n",
       " (157, 35, 1.0),\n",
       " (158, 35, 1.0),\n",
       " (159, 35, 1.0),\n",
       " (160, 35, 1.0),\n",
       " (160, 37, 1.0),\n",
       " (161, 30, 1.0),\n",
       " (161, 35, 1.0),\n",
       " (162, 35, 1.0),\n",
       " (163, 35, 1.0),\n",
       " (164, 35, 1.0),\n",
       " (165, 35, 1.0),\n",
       " (166, 35, 1.0),\n",
       " (167, 35, 1.0),\n",
       " (168, 35, 1.0),\n",
       " (170, 35, 1.0),\n",
       " (171, 35, 1.0),\n",
       " (172, 36, 1.0),\n",
       " (173, 35, 1.0),\n",
       " (174, 35, 1.0),\n",
       " (175, 35, 1.0),\n",
       " (176, 35, 1.0),\n",
       " (177, 35, 1.0),\n",
       " (178, 29, 1.0),\n",
       " (178, 35, 1.0),\n",
       " (179, 35, 1.0),\n",
       " (180, 35, 1.0),\n",
       " (181, 35, 1.0),\n",
       " (182, 35, 1.0),\n",
       " (183, 35, 1.0),\n",
       " (184, 35, 1.0),\n",
       " (185, 35, 1.0),\n",
       " (186, 35, 1.0),\n",
       " (187, 35, 1.0),\n",
       " (188, 35, 1.0),\n",
       " (189, 35, 1.0),\n",
       " (190, 35, 1.0),\n",
       " (191, 35, 1.0),\n",
       " (192, 35, 1.0),\n",
       " (193, 35, 1.0),\n",
       " (194, 35, 1.0),\n",
       " (195, 35, 1.0),\n",
       " (196, 35, 1.0),\n",
       " (197, 35, 1.0),\n",
       " (198, 35, 1.0),\n",
       " (203, 35, 1.0),\n",
       " (204, 35, 1.0),\n",
       " (205, 35, 1.0),\n",
       " (206, 35, 1.0),\n",
       " (208, 35, 1.0),\n",
       " (209, 35, 1.0),\n",
       " (210, 35, 1.0),\n",
       " (211, 35, 1.0),\n",
       " (212, 35, 1.0),\n",
       " (213, 35, 1.0),\n",
       " (214, 35, 1.0),\n",
       " (215, 35, 1.0),\n",
       " (216, 35, 1.0),\n",
       " (217, 35, 1.0),\n",
       " (218, 35, 1.0)]"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
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       "<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>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>214</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>215</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>216</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>217</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>218</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num  channel  percentage\n",
       "0          0       35         1.0\n",
       "1          1       35         1.0\n",
       "2          1       36         1.0\n",
       "3          2       35         1.0\n",
       "4          2       36         1.0\n",
       "..       ...      ...         ...\n",
       "190      214       35         1.0\n",
       "191      215       35         1.0\n",
       "192      216       35         1.0\n",
       "193      217       35         1.0\n",
       "194      218       35         1.0\n",
       "\n",
       "[195 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb_perc.to_csv('bbox_percentages.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>893</td>\n",
       "      <td>893</td>\n",
       "      <td>224</td>\n",
       "      <td>3.0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>894</td>\n",
       "      <td>224</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>897 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  FOV_num    Z  channel\n",
       "0             0        0  4.0     35.0\n",
       "1             1        0  5.0     35.0\n",
       "2             2        0  6.0     35.0\n",
       "3             3        0  7.0     35.0\n",
       "4             4        1  4.0     35.0\n",
       "..          ...      ...  ...      ...\n",
       "892         892      224  2.0     35.0\n",
       "893         893      224  3.0     35.0\n",
       "894         894      224  4.0     35.0\n",
       "895         895      224  5.0     35.0\n",
       "896         896      224  8.0     35.0\n",
       "\n",
       "[897 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc = pd.read_csv('black_circle_locations.csv')\n",
    "bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>channel_number</th>\n",
       "      <th>cycle_number</th>\n",
       "      <th>marker_name</th>\n",
       "      <th>Filter</th>\n",
       "      <th>excitation_wavelength</th>\n",
       "      <th>emission_wavelength</th>\n",
       "      <th>Bandwidth</th>\n",
       "      <th>ch_index</th>\n",
       "      <th>Split_Num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DNA_0</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Blank_488_0</td>\n",
       "      <td>GFP</td>\n",
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       "      <td>38</td>\n",
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       "      <td>RFP</td>\n",
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       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Cy5</td>\n",
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       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>DNA_1</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   channel_number  cycle_number  marker_name Filter  excitation_wavelength  \\\n",
       "0               1             0        DNA_0   DAPI                    405   \n",
       "1               2             0  Blank_488_0    GFP                    488   \n",
       "2               3             0  Blank_561_0    RFP                    561   \n",
       "3               4             0  Blank_637_0    Cy5                    637   \n",
       "4               5             1        DNA_1   DAPI                    405   \n",
       "\n",
       "   emission_wavelength  Bandwidth  ch_index  Split_Num  \n",
       "0                  445         46         0          0  \n",
       "1                  521         38         1          1  \n",
       "2                  594         43         2          2  \n",
       "3                  698         77         3          3  \n",
       "4                  445         46         0          4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 4,
   "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",
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       " 21,\n",
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       " 29,\n",
       " 30,\n",
       " 31,\n",
       " 33,\n",
       " 35,\n",
       " 36,\n",
       " 37]"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "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', '049', '050', '051', '052', '053', '054', '055', '056', '057', '058', '059', '060', '061', '062', '063', '064', '065', '066', '067', '068', '069', '070', '071', '072', '073', '074', '075', '076', '077', '078', '079', '080', '081', '082', '083', '084', '085', '086', '087', '088', '089', '090', '091', '092', '093', '094', '095', '096', '097', '098', '099', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '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",
      "225\n"
     ]
    }
   ],
   "source": [
    "_allFOVs = sorted(glob.glob('reg_bin_Cyc_2/*'))\n",
    "allFOVs = [x.split('F')[-1][:3] for x in _allFOVs]\n",
    "print(allFOVs)\n",
    "print(len(allFOVs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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', '049']\n",
      "['050', '051', '052', '053', '054', '055', '056', '057', '058', '059']\n",
      "['060', '061', '062', '063', '064', '065', '066', '067', '068', '069']\n",
      "['070', '071', '072', '073', '074', '075', '076', '077', '078', '079']\n",
      "['080', '081', '082', '083', '084', '085', '086', '087', '088', '089']\n",
      "['090', '091', '092', '093', '094', '095', '096', '097', '098', '099']\n",
      "['100', '101', '102', '103', '104', '105', '106', '107', '108', '109']\n",
      "['110', '111', '112', '113', '114', '115', '116', '117', '118', '119']\n",
      "['120', '121', '122', '123', '124', '125', '126', '127', '128', '129']\n",
      "['130', '131', '132', '133', '134', '135', '136', '137', '138', '139']\n",
      "['140', '141', '142', '143', '144', '145', '146', '147', '148', '149']\n",
      "['150', '151', '152', '153', '154', '155', '156', '157', '158', '159']\n",
      "['160', '161', '162', '163', '164', '165', '166', '167', '168', '169']\n",
      "['170', '171', '172', '173', '174', '175', '176', '177', '178', '179']\n",
      "['180', '181', '182', '183', '184', '185', '186', '187', '188', '189']\n",
      "['190', '191', '192', '193', '194', '195', '196', '197', '198', '199']\n",
      "['200', '201', '202', '203', '204', '205', '206', '207', '208', '209']\n",
      "['210', '211', '212', '213', '214', '215', '216', '217', '218', '219']\n",
      "['220', '221', '222', '223', '224']\n"
     ]
    }
   ],
   "source": [
    "for ii in range(0,len(allFOVs),10): # For each CHUNK (10 FOVs per loop)\n",
    "    print(allFOVs[ii:ii+10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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>225 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",
       "[225 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.segmentation import *\n",
    "from skimage import measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "mask shape (2010, 2018)\n",
      "F000, no mask needed\n",
      "FOV 001\n",
      "mask shape (2010, 2018)\n",
      "F001, no mask needed\n",
      "FOV 002\n",
      "mask shape (2010, 2018)\n",
      "F002, no mask needed\n",
      "FOV 003\n",
      "mask shape (2010, 2018)\n",
      "F003, no mask needed\n",
      "FOV 004\n",
      "mask shape (2010, 2018)\n",
      "F004, no mask needed\n",
      "FOV 005\n",
      "mask shape (2010, 2018)\n",
      "F005, no mask needed\n",
      "FOV 006\n",
      "mask shape (2010, 2018)\n",
      "F006, no mask needed\n",
      "FOV 007\n",
      "mask shape (2010, 2018)\n",
      "F007, no mask needed\n",
      "FOV 008\n",
      "mask shape (2010, 2018)\n",
      "F008, no mask needed\n",
      "FOV 009\n",
      "mask shape (2010, 2018)\n",
      "F009, no mask needed\n",
      "FOV 010\n",
      "mask shape (2010, 2018)\n",
      "F010, no mask needed\n",
      "FOV 011\n",
      "mask shape (2010, 2018)\n",
      "F011, no mask needed\n",
      "FOV 012\n",
      "mask shape (2010, 2018)\n",
      "F012, no mask needed\n",
      "FOV 013\n",
      "mask shape (2010, 2018)\n",
      "F013, no mask needed\n",
      "FOV 014\n",
      "mask shape (2010, 2018)\n",
      "F014, no mask needed\n",
      "FOV 015\n",
      "mask shape (2010, 2018)\n",
      "F015, no mask needed\n",
      "FOV 016\n",
      "mask shape (2010, 2018)\n",
      "F016, no mask needed\n",
      "FOV 017\n",
      "mask shape (2010, 2018)\n",
      "F017, no mask needed\n",
      "FOV 018\n",
      "mask shape (2010, 2018)\n",
      "F018, no mask needed\n",
      "FOV 019\n",
      "mask shape (2010, 2018)\n",
      "F019, no mask needed\n",
      "FOV 020\n",
      "mask shape (2010, 2018)\n",
      "F020, no mask needed\n",
      "FOV 021\n",
      "mask shape (2010, 2018)\n",
      "F021, no mask needed\n",
      "FOV 023\n",
      "mask shape (2010, 2018)\n",
      "F023, no mask needed\n",
      "FOV 024\n",
      "mask shape (2010, 2018)\n",
      "F024, no mask needed\n",
      "FOV 025\n",
      "mask shape (2010, 2018)\n",
      "F025, no mask needed\n",
      "FOV 026\n",
      "mask shape (2010, 2018)\n",
      "F026, no mask needed\n",
      "FOV 027\n",
      "mask shape (2010, 2018)\n",
      "F027, no mask needed\n",
      "FOV 028\n",
      "mask shape (2010, 2018)\n",
      "F028, no mask needed\n",
      "FOV 029\n",
      "mask shape (2010, 2018)\n",
      "F029, no mask needed\n",
      "FOV 030\n",
      "mask shape (2010, 2018)\n",
      "F030, no mask needed\n",
      "FOV 031\n",
      "mask shape (2010, 2018)\n",
      "F031, no mask needed\n",
      "FOV 033\n",
      "mask shape (2010, 2018)\n",
      "F033, no mask needed\n",
      "FOV 034\n",
      "mask shape (2010, 2018)\n",
      "F034, no mask needed\n",
      "FOV 035\n",
      "mask shape (2010, 2018)\n",
      "F035, no mask needed\n",
      "FOV 037\n",
      "mask shape (2010, 2018)\n",
      "F037, no mask needed\n",
      "FOV 038\n",
      "mask shape (2010, 2018)\n",
      "F038, no mask needed\n",
      "FOV 039\n",
      "mask shape (2010, 2018)\n",
      "F039, no mask needed\n",
      "FOV 040\n",
      "mask shape (2010, 2018)\n",
      "F040, no mask needed\n",
      "FOV 041\n",
      "mask shape (2010, 2018)\n",
      "F041, no mask needed\n",
      "FOV 042\n",
      "mask shape (2010, 2018)\n",
      "F042, no mask needed\n",
      "FOV 043\n",
      "mask shape (2010, 2018)\n",
      "F043, no mask needed\n",
      "FOV 044\n",
      "mask shape (2010, 2018)\n",
      "F044, no mask needed\n",
      "FOV 045\n",
      "mask shape (2010, 2018)\n",
      "F045, no mask needed\n",
      "FOV 046\n",
      "mask shape (2010, 2018)\n",
      "F046, no mask needed\n",
      "FOV 047\n",
      "mask shape (2010, 2018)\n",
      "F047, no mask needed\n",
      "FOV 048\n",
      "mask shape (2010, 2018)\n",
      "F048, no mask needed\n",
      "FOV 049\n",
      "mask shape (2010, 2018)\n",
      "F049, no mask needed\n",
      "FOV 050\n",
      "mask shape (2010, 2018)\n",
      "F050, no mask needed\n",
      "FOV 051\n",
      "mask shape (2010, 2018)\n",
      "F051, no mask needed\n",
      "FOV 052\n",
      "mask shape (2010, 2018)\n",
      "F052, no mask needed\n",
      "FOV 053\n",
      "mask shape (2010, 2018)\n",
      "F053, no mask needed\n",
      "FOV 054\n",
      "mask shape (2010, 2018)\n",
      "F054, no mask needed\n",
      "FOV 055\n",
      "mask shape (2010, 2018)\n",
      "F055, no mask needed\n",
      "FOV 056\n",
      "mask shape (2010, 2018)\n",
      "F056, no mask needed\n",
      "FOV 057\n",
      "mask shape (2010, 2018)\n",
      "F057, no mask needed\n",
      "FOV 058\n",
      "mask shape (2010, 2018)\n",
      "F058, no mask needed\n",
      "FOV 059\n",
      "mask shape (2010, 2018)\n",
      "F059, no mask needed\n",
      "FOV 060\n",
      "mask shape (2010, 2018)\n",
      "F060, no mask needed\n",
      "FOV 061\n",
      "mask shape (2010, 2018)\n",
      "F061, no mask needed\n",
      "FOV 062\n",
      "mask shape (2010, 2018)\n",
      "F062, no mask needed\n",
      "FOV 063\n",
      "mask shape (2010, 2018)\n",
      "F063, no mask needed\n",
      "FOV 064\n",
      "mask shape (2010, 2018)\n",
      "F064, no mask needed\n",
      "FOV 065\n",
      "mask shape (2010, 2018)\n",
      "F065, no mask needed\n",
      "FOV 066\n",
      "mask shape (2010, 2018)\n",
      "F066, no mask needed\n",
      "FOV 067\n",
      "mask shape (2010, 2018)\n",
      "F067, no mask needed\n",
      "FOV 068\n",
      "mask shape (2010, 2018)\n",
      "F068, no mask needed\n",
      "FOV 069\n",
      "mask shape (2010, 2018)\n",
      "F069, no mask needed\n",
      "FOV 070\n",
      "mask shape (2010, 2018)\n",
      "F070, no mask needed\n",
      "FOV 071\n",
      "mask shape (2010, 2018)\n",
      "F071, no mask needed\n",
      "FOV 072\n",
      "mask shape (2010, 2018)\n",
      "F072, no mask needed\n",
      "FOV 073\n",
      "mask shape (2010, 2018)\n",
      "F073, no mask needed\n",
      "FOV 074\n",
      "mask shape (2010, 2018)\n",
      "F074, no mask needed\n",
      "FOV 075\n",
      "mask shape (2010, 2018)\n",
      "F075, no mask needed\n",
      "FOV 076\n",
      "mask shape (2010, 2018)\n",
      "F076, no mask needed\n",
      "FOV 077\n",
      "mask shape (2010, 2018)\n",
      "F077, no mask needed\n",
      "FOV 078\n",
      "mask shape (2010, 2018)\n",
      "F078, no mask needed\n",
      "FOV 079\n",
      "mask shape (2010, 2018)\n",
      "F079, no mask needed\n",
      "FOV 080\n",
      "mask shape (2010, 2018)\n",
      "F080, no mask needed\n",
      "FOV 081\n",
      "mask shape (2010, 2018)\n",
      "F081, no mask needed\n",
      "FOV 082\n",
      "mask shape (2010, 2018)\n",
      "F082, no mask needed\n",
      "FOV 083\n",
      "mask shape (2010, 2018)\n",
      "F083, no mask needed\n",
      "FOV 084\n",
      "mask shape (2010, 2018)\n",
      "F084, no mask needed\n",
      "FOV 085\n",
      "mask shape (2010, 2018)\n",
      "F085, no mask needed\n",
      "FOV 086\n",
      "mask shape (2010, 2018)\n",
      "F086, no mask needed\n",
      "FOV 087\n",
      "mask shape (2010, 2018)\n",
      "F087, no mask needed\n",
      "FOV 088\n",
      "mask shape (2010, 2018)\n",
      "F088, no mask needed\n",
      "FOV 089\n",
      "mask shape (2010, 2018)\n",
      "F089, no mask needed\n",
      "FOV 090\n",
      "mask shape (2010, 2018)\n",
      "F090, no mask needed\n",
      "FOV 091\n",
      "mask shape (2010, 2018)\n",
      "F091, no mask needed\n",
      "FOV 092\n",
      "mask shape (2010, 2018)\n",
      "F092, no mask needed\n",
      "FOV 093\n",
      "mask shape (2010, 2018)\n",
      "F093, no mask needed\n",
      "FOV 094\n",
      "mask shape (2010, 2018)\n",
      "F094, no mask needed\n",
      "FOV 095\n",
      "mask shape (2010, 2018)\n",
      "F095, no mask needed\n",
      "FOV 096\n",
      "mask shape (2010, 2018)\n",
      "F096, no mask needed\n",
      "FOV 097\n",
      "mask shape (2010, 2018)\n",
      "F097, no mask needed\n",
      "FOV 098\n",
      "mask shape (2010, 2018)\n",
      "F098, no mask needed\n",
      "FOV 099\n",
      "mask shape (2010, 2018)\n",
      "F099, no mask needed\n",
      "FOV 100\n",
      "mask shape (2010, 2018)\n",
      "F100, no mask needed\n",
      "FOV 101\n",
      "mask shape (2010, 2018)\n",
      "F101, no mask needed\n",
      "FOV 102\n",
      "mask shape (2010, 2018)\n",
      "F102, no mask needed\n",
      "FOV 103\n",
      "mask shape (2010, 2018)\n",
      "F103, no mask needed\n",
      "FOV 104\n",
      "mask shape (2010, 2018)\n",
      "F104, no mask needed\n",
      "FOV 105\n",
      "mask shape (2010, 2018)\n",
      "F105, no mask needed\n",
      "FOV 106\n",
      "mask shape (2010, 2018)\n",
      "F106, no mask needed\n",
      "FOV 107\n",
      "mask shape (2010, 2018)\n",
      "F107, no mask needed\n",
      "FOV 108\n",
      "mask shape (2010, 2018)\n",
      "F108, no mask needed\n",
      "FOV 109\n",
      "mask shape (2010, 2018)\n",
      "F109, no mask needed\n",
      "FOV 110\n",
      "mask shape (2010, 2018)\n",
      "F110, no mask needed\n",
      "FOV 111\n",
      "mask shape (2010, 2018)\n",
      "F111, no mask needed\n",
      "FOV 112\n",
      "mask shape (2010, 2018)\n",
      "F112, no mask needed\n",
      "FOV 113\n",
      "mask shape (2010, 2018)\n",
      "F113, no mask needed\n",
      "FOV 114\n",
      "mask shape (2010, 2018)\n",
      "F114, no mask needed\n",
      "FOV 115\n",
      "mask shape (2010, 2018)\n",
      "F115, no mask needed\n",
      "FOV 116\n",
      "mask shape (2010, 2018)\n",
      "F116, no mask needed\n",
      "FOV 117\n",
      "mask shape (2010, 2018)\n",
      "F117, no mask needed\n",
      "FOV 118\n",
      "mask shape (2010, 2018)\n",
      "F118, no mask needed\n",
      "FOV 119\n",
      "mask shape (2010, 2018)\n",
      "F119, no mask needed\n",
      "FOV 120\n",
      "mask shape (2010, 2018)\n",
      "F120, no mask needed\n",
      "FOV 121\n",
      "mask shape (2010, 2018)\n",
      "F121, no mask needed\n",
      "FOV 122\n",
      "mask shape (2010, 2018)\n",
      "F122, no mask needed\n",
      "FOV 123\n",
      "mask shape (2010, 2018)\n",
      "F123, no mask needed\n",
      "FOV 124\n",
      "mask shape (2010, 2018)\n",
      "F124, no mask needed\n",
      "FOV 125\n",
      "mask shape (2010, 2018)\n",
      "F125, no mask needed\n",
      "FOV 126\n",
      "mask shape (2010, 2018)\n",
      "F126, no mask needed\n",
      "FOV 127\n",
      "mask shape (2010, 2018)\n",
      "F127, no mask needed\n",
      "FOV 128\n",
      "mask shape (2010, 2018)\n",
      "F128, no mask needed\n",
      "FOV 129\n",
      "mask shape (2010, 2018)\n",
      "F129, no mask needed\n",
      "FOV 131\n",
      "mask shape (2010, 2018)\n",
      "F131, no mask needed\n",
      "FOV 132\n",
      "mask shape (2010, 2018)\n",
      "F132, no mask needed\n",
      "FOV 133\n",
      "mask shape (2010, 2018)\n",
      "F133, no mask needed\n",
      "FOV 134\n",
      "mask shape (2010, 2018)\n",
      "F134, no mask needed\n",
      "FOV 135\n",
      "mask shape (2010, 2018)\n",
      "F135, no mask needed\n",
      "FOV 136\n",
      "mask shape (2010, 2018)\n",
      "F136, no mask needed\n",
      "FOV 137\n",
      "mask shape (2010, 2018)\n",
      "F137, no mask needed\n",
      "FOV 138\n",
      "mask shape (2010, 2018)\n",
      "F138, no mask needed\n",
      "FOV 140\n",
      "mask shape (2010, 2018)\n",
      "F140, no mask needed\n",
      "FOV 141\n",
      "mask shape (2010, 2018)\n",
      "F141, no mask needed\n",
      "FOV 142\n",
      "mask shape (2010, 2018)\n",
      "F142, no mask needed\n",
      "FOV 143\n",
      "mask shape (2010, 2018)\n",
      "F143, no mask needed\n",
      "FOV 144\n",
      "mask shape (2010, 2018)\n",
      "F144, no mask needed\n",
      "FOV 145\n",
      "mask shape (2010, 2018)\n",
      "F145, no mask needed\n",
      "FOV 146\n",
      "mask shape (2010, 2018)\n",
      "F146, no mask needed\n",
      "FOV 147\n",
      "mask shape (2010, 2018)\n",
      "F147, no mask needed\n",
      "FOV 148\n",
      "mask shape (2010, 2018)\n",
      "F148, no mask needed\n",
      "FOV 149\n",
      "mask shape (2010, 2018)\n",
      "F149, no mask needed\n",
      "FOV 150\n",
      "mask shape (2010, 2018)\n",
      "F150, no mask needed\n",
      "FOV 151\n",
      "mask shape (2010, 2018)\n",
      "F151, no mask needed\n",
      "FOV 152\n",
      "mask shape (2010, 2018)\n",
      "F152, no mask needed\n",
      "FOV 153\n",
      "mask shape (2010, 2018)\n",
      "F153, no mask needed\n",
      "FOV 154\n",
      "mask shape (2010, 2018)\n",
      "F154, no mask needed\n",
      "FOV 155\n",
      "mask shape (2010, 2018)\n",
      "F155, no mask needed\n",
      "FOV 156\n",
      "mask shape (2010, 2018)\n",
      "F156, no mask needed\n",
      "FOV 158\n",
      "mask shape (2010, 2018)\n",
      "F158, no mask needed\n",
      "FOV 159\n",
      "mask shape (2010, 2018)\n",
      "F159, no mask needed\n",
      "FOV 160\n",
      "mask shape (2010, 2018)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F160, no mask needed\n",
      "FOV 161\n",
      "mask shape (2010, 2018)\n",
      "F161, no mask needed\n",
      "FOV 162\n",
      "mask shape (2010, 2018)\n",
      "F162, no mask needed\n",
      "FOV 163\n",
      "mask shape (2010, 2018)\n",
      "FOV 164\n",
      "mask shape (2010, 2018)\n",
      "FOV 165\n",
      "mask shape (2010, 2018)\n",
      "F165, no mask needed\n",
      "FOV 166\n",
      "mask shape (2010, 2018)\n",
      "F166, no mask needed\n",
      "FOV 167\n",
      "mask shape (2010, 2018)\n",
      "F167, no mask needed\n",
      "FOV 168\n",
      "mask shape (2010, 2018)\n",
      "F168, no mask needed\n",
      "FOV 169\n",
      "mask shape (2010, 2018)\n",
      "F169, no mask needed\n",
      "FOV 170\n",
      "mask shape (2010, 2018)\n",
      "F170, no mask needed\n",
      "FOV 171\n",
      "mask shape (2010, 2018)\n",
      "F171, no mask needed\n",
      "FOV 172\n",
      "mask shape (2010, 2018)\n",
      "F172, no mask needed\n",
      "FOV 173\n",
      "mask shape (2010, 2018)\n",
      "F173, no mask needed\n",
      "FOV 174\n",
      "mask shape (2010, 2018)\n",
      "F174, no mask needed\n",
      "FOV 175\n",
      "mask shape (2010, 2018)\n",
      "F175, no mask needed\n",
      "FOV 176\n",
      "mask shape (2010, 2018)\n",
      "F176, no mask needed\n",
      "FOV 177\n",
      "mask shape (2010, 2018)\n",
      "F177, no mask needed\n",
      "FOV 178\n",
      "mask shape (2010, 2018)\n",
      "F178, no mask needed\n",
      "FOV 179\n",
      "mask shape (2010, 2018)\n",
      "F179, no mask needed\n",
      "FOV 180\n",
      "mask shape (2010, 2018)\n",
      "F180, no mask needed\n",
      "FOV 181\n",
      "mask shape (2010, 2018)\n",
      "F181, no mask needed\n",
      "FOV 182\n",
      "mask shape (2010, 2018)\n",
      "F182, no mask needed\n",
      "FOV 183\n",
      "mask shape (2010, 2018)\n",
      "F183, no mask needed\n",
      "FOV 184\n",
      "mask shape (2010, 2018)\n",
      "F184, no mask needed\n",
      "FOV 185\n",
      "mask shape (2010, 2018)\n",
      "F185, no mask needed\n",
      "FOV 186\n",
      "mask shape (2010, 2018)\n",
      "F186, no mask needed\n",
      "FOV 187\n",
      "mask shape (2010, 2018)\n",
      "F187, no mask needed\n",
      "FOV 188\n",
      "mask shape (2010, 2018)\n",
      "F188, no mask needed\n",
      "FOV 189\n",
      "mask shape (2010, 2018)\n",
      "F189, no mask needed\n",
      "FOV 190\n",
      "mask shape (2010, 2018)\n",
      "F190, no mask needed\n",
      "FOV 191\n",
      "mask shape (2010, 2018)\n",
      "F191, no mask needed\n",
      "FOV 192\n",
      "mask shape (2010, 2018)\n",
      "F192, no mask needed\n",
      "FOV 193\n",
      "mask shape (2010, 2018)\n",
      "F193, no mask needed\n",
      "FOV 194\n",
      "mask shape (2010, 2018)\n",
      "F194, no mask needed\n",
      "FOV 195\n",
      "mask shape (2010, 2018)\n",
      "F195, no mask needed\n",
      "FOV 196\n",
      "mask shape (2010, 2018)\n",
      "F196, no mask needed\n",
      "FOV 197\n",
      "mask shape (2010, 2018)\n",
      "F197, no mask needed\n",
      "FOV 198\n",
      "mask shape (2010, 2018)\n",
      "F198, no mask needed\n",
      "FOV 199\n",
      "mask shape (2010, 2018)\n",
      "F199, no mask needed\n",
      "FOV 200\n",
      "mask shape (2010, 2018)\n",
      "F200, no mask needed\n",
      "FOV 201\n",
      "mask shape (2010, 2018)\n",
      "F201, no mask needed\n",
      "FOV 202\n",
      "mask shape (2010, 2018)\n",
      "F202, no mask needed\n",
      "FOV 203\n",
      "mask shape (2010, 2018)\n",
      "F203, no mask needed\n",
      "FOV 204\n",
      "mask shape (2010, 2018)\n",
      "F204, no mask needed\n",
      "FOV 205\n",
      "mask shape (2010, 2018)\n",
      "F205, no mask needed\n",
      "FOV 206\n",
      "mask shape (2010, 2018)\n",
      "F206, no mask needed\n",
      "FOV 207\n",
      "mask shape (2010, 2018)\n",
      "F207, no mask needed\n",
      "FOV 208\n",
      "mask shape (2010, 2018)\n",
      "F208, no mask needed\n",
      "FOV 209\n",
      "mask shape (2010, 2018)\n",
      "F209, no mask needed\n",
      "FOV 210\n",
      "mask shape (2010, 2018)\n",
      "F210, no mask needed\n",
      "FOV 211\n",
      "mask shape (2010, 2018)\n",
      "F211, no mask needed\n",
      "FOV 212\n",
      "mask shape (2010, 2018)\n",
      "F212, no mask needed\n",
      "FOV 213\n",
      "mask shape (2010, 2018)\n",
      "F213, no mask needed\n",
      "FOV 214\n",
      "mask shape (2010, 2018)\n",
      "F214, no mask needed\n",
      "FOV 215\n",
      "mask shape (2010, 2018)\n",
      "F215, no mask needed\n",
      "FOV 216\n",
      "mask shape (2010, 2018)\n",
      "F216, no mask needed\n",
      "FOV 217\n",
      "mask shape (2010, 2018)\n",
      "F217, no mask needed\n",
      "FOV 218\n",
      "mask shape (2010, 2018)\n",
      "F218, no mask needed\n",
      "FOV 219\n",
      "mask shape (2010, 2018)\n",
      "F219, no mask needed\n",
      "FOV 220\n",
      "mask shape (2010, 2018)\n",
      "F220, no mask needed\n",
      "FOV 221\n",
      "mask shape (2010, 2018)\n",
      "F221, no mask needed\n",
      "FOV 222\n",
      "mask shape (2010, 2018)\n",
      "F222, no mask needed\n",
      "FOV 223\n",
      "mask shape (2010, 2018)\n",
      "F223, no mask needed\n",
      "FOV 224\n",
      "mask shape (2010, 2018)\n",
      "F224, no mask needed\n"
     ]
    }
   ],
   "source": [
    "merged = iter(glob.glob('merged/*')) \n",
    "for fov in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    img = img.transpose(1,0,2,3) \n",
    "    # C Z Y X format. you may use image as it is but should change bottom line to axis=(0,2,3)\n",
    "    count0 = np.count_nonzero(img==0, axis=(1,2,3))\n",
    "    count20 = np.count_nonzero(img<20, axis=(1,2,3))\n",
    "    count65K = np.count_nonzero(img>65000, axis=(1,2,3))\n",
    "\n",
    "    # initialize mask, save_im_flag\n",
    "    save_im_flag = False\n",
    "    mask = np.zeros(img[0,0,...].shape)\n",
    "    print(\"mask shape\", mask.shape)\n",
    "    mask = mask.astype('bool')\n",
    "    # check for each channel;\n",
    "    for ch in range(count0.shape[0]):\n",
    "        # record in dataframe\n",
    "        count0_df.loc[fov, ch] = count0[ch]\n",
    "        count20_df.loc[fov, ch] = count20[ch]\n",
    "        count65K_df.loc[fov, ch] = count65K[ch]\n",
    "        if ch in final_inds:\n",
    "            # whether to make mask;\n",
    "            if count20[ch] > 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",
    "        fname = f'{OUT_DIR}/F{FOV_num}_mask.tif'\n",
    "        tifffile.imwrite(fname, exp_mask.astype('uint8'), imagej = True, photometric='minisblack', metadata={'axes':'YX'})\n",
    "    else:\n",
    "        print(f'F{FOV_num}, no mask needed') # export later!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save dataframe \n",
    "count0_df.to_csv('count0_df.csv', sep=',')\n",
    "count20_df.to_csv('count20_df.csv', sep=',')\n",
    "count65K_df.to_csv('count65K_df.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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