{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as et\n",
    "import datetime\n",
    "from skimage.io import imread, imsave\n",
    "from imageio import volread as imread\n",
    "\n",
    "import tifffile\n",
    "import pystackreg\n",
    "from pystackreg import StackReg\n",
    "from skimage.filters import threshold_otsu\n",
    "\n",
    "from pystackreg.util import to_uint16   # make sure version 0.2.5 (not anything below)\n",
    "from ims_to_tiff import convert_to_tif  \n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "CYCLE_NUMS = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to TIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir tif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycles = glob.glob('ims/Cycle_*') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
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      "Native (rows, cols): (2048,2048)\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",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
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      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
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      "TimePoint 0/0\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",
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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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      "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",
      "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",
      "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",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
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      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
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      "TimePoint 0/0 Z 4/13\n",
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      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
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      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n",
      "TimePoint 0/0 Z 8/13\n",
      "TimePoint 0/0 Z 9/13\n",
      "TimePoint 0/0 Z 10/13\n",
      "TimePoint 0/0 Z 11/13\n",
      "TimePoint 0/0 Z 12/13\n",
      "TimePoint 0/0 Z 13/13\n",
      "File Breakdown\n",
      "______________\n",
      "Channels: 4\n",
      "Time Points: 1\n",
      "Z Levels: 14\n",
      "Native (rows, cols): (2048,2048)\n",
      "______________\n",
      "TimePoint 0/0\n",
      "TimePoint 0/0 Z 1/13\n",
      "TimePoint 0/0 Z 2/13\n",
      "TimePoint 0/0 Z 3/13\n",
      "TimePoint 0/0 Z 4/13\n",
      "TimePoint 0/0 Z 5/13\n",
      "TimePoint 0/0 Z 6/13\n",
      "TimePoint 0/0 Z 7/13\n"
     ]
    }
   ],
   "source": [
    "error_files = []\n",
    "for i in cycles:\n",
    "    try:\n",
    "        sourceFile = i\n",
    "        destFile = 'tif/'+i[4:-4]+'.tif'\n",
    "        convert_to_tif(sourceFile, destFile)\n",
    "    except:\n",
    "        print(f'{i} has B-tree error or import error')\n",
    "        error_files.append(i)\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ims/Cycle_1_F150.ims',\n",
       " 'ims/Cycle_2_F052.ims',\n",
       " 'ims/Cycle_2_F168.ims',\n",
       " 'ims/Cycle_3_F015.ims',\n",
       " 'ims/Cycle_3_F089.ims',\n",
       " 'ims/Cycle_3_F176.ims',\n",
       " 'ims/Cycle_4_F064.ims',\n",
       " 'ims/Cycle_5_F110.ims',\n",
       " 'ims/Cycle_6_F071.ims']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 225 - len(error_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove all erroneous files \n",
    "for i in error_files:\n",
    "    command = f'rm tif/*{i[-7:-4]}*'\n",
    "    ! {command}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    os.makedirs(f'tif/Cycle_{c}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for c in range(CYCLE_NUMS):\n",
    "    if c == 0:\n",
    "        command = 'mv tif/Cycle_F* tif/Cycle_0'\n",
    "    else:\n",
    "        command = f'mv tif/Cycle_{c}_F* tif/Cycle_{c}'    \n",
    "    ! {command}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Registration (Testing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 4, 2048, 2048)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test image sizes\n",
    "im1 = imread('tif/Cycle_0/Cycle_F000.tif')\n",
    "im1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref = imread('tif/Cycle_0/Cycle_F121.tif')\n",
    "mov = imread('tif/Cycle_1/Cycle_1_F121.tif')\n",
    " \n",
    "ref_max = ref.max(0)   # max proj of z for each channel in reference\n",
    "ref_binary = ref_max[0] > threshold_otsu(ref_max[0])  # binarizing for channels - T/F\n",
    "\n",
    "mov_max = mov.max(0)  # max projection by each cycle in for loop (ref above)\n",
    "mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # binary of moved image\n",
    "    \n",
    "sr = StackReg(StackReg.RIGID_BODY)\n",
    "tmat = sr.register(ref_binary, mov_binary)   # creating transformation matrix \n",
    "out_binary = sr.transform(mov_binary) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.99999998e-01, -5.78643668e-05, -6.12333926e-01],\n",
       "       [ 5.78643668e-05,  9.99999998e-01, -1.31286747e+00],\n",
       "       [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x4320 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2,figsize = (20,60))\n",
    "axs = axs.ravel()\n",
    "\n",
    "im_reg = np.zeros((2048, 2048,3)) # empty color image\n",
    "im_reg[...,0] = ref_binary\n",
    "im_reg[...,1] = out_binary\n",
    "    \n",
    "im_orig = np.zeros((2048, 2048,3))\n",
    "im_orig[...,0] = ref_binary\n",
    "im_orig[...,1] = mov_binary\n",
    "    \n",
    "axs[0].imshow(im_orig[0:1000, 0:1000])   #before reg\n",
    "    \n",
    "axs[1].imshow(im_reg[0:1000, 0:1000])   #after reg\n",
    "\n",
    "\n",
    "axs[0].title.set_text('Before Registration')\n",
    "axs[1].title.set_text('After Registration')\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(xlabel='0:1000', ylabel='0:1000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-plane: 0 registering\n",
      "Z-plane: 1 registering\n",
      "Z-plane: 2 registering\n",
      "Z-plane: 3 registering\n",
      "Z-plane: 4 registering\n",
      "Z-plane: 5 registering\n",
      "Z-plane: 6 registering\n",
      "Z-plane: 7 registering\n",
      "Z-plane: 8 registering\n",
      "Z-plane: 9 registering\n",
      "Z-plane: 10 registering\n",
      "Z-plane: 11 registering\n",
      "Z-plane: 12 registering\n"
     ]
    }
   ],
   "source": [
    "reg = np.zeros(mov.shape, dtype=np.uint16) # initialize with the right dtype\n",
    "for Z in range(mov.shape[0]): # Z \n",
    "    print(f\"Z-plane: {Z} registering\")\n",
    "    for ch in range(mov.shape[1]): # channels\n",
    "        reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "        reg[Z,ch,...] = to_uint16(reg[Z,ch,...])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 100)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_max = reg.max(0)\n",
    "reg_v = reg_max[0, ...]\n",
    "reg_v = reg_v[100:200, 100:200]\n",
    "reg_v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[134 118 112 ... 116 112 119]\n",
      " [129 130 130 ... 115 116 112]\n",
      " [127 133 129 ... 109 115 109]\n",
      " ...\n",
      " [139 137 142 ... 116 108 108]\n",
      " [152 139 159 ... 107 112 108]\n",
      " [144 143 152 ... 112 108 115]]  \n",
      " \n",
      " [[128 124 114 ... 110 108 110]\n",
      " [127 119 125 ... 107 108 108]\n",
      " [132 122 113 ... 112 112 111]\n",
      " ...\n",
      " [148 155 156 ... 110 108 107]\n",
      " [163 158 154 ... 112 106 113]\n",
      " [148 157 163 ... 106 112 114]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x4320 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QC by eye, every single Cycle -- pick a random FOV\n",
    "\n",
    "orig = imread('tif/Cycle_9/Cycle_9_F000.tif')\n",
    "orig = orig.max(0)\n",
    "orig = orig[0, ...]\n",
    "orig_v = orig[100:200, 100:200]\n",
    "\n",
    "f, ax = plt.subplots(1,2, figsize = (20,60))\n",
    "ax[0].imshow(orig_v)\n",
    "ax[1].imshow(reg_v)\n",
    "ax[0].title.set_text('Before Registration (DNA Channel)')\n",
    "ax[1].title.set_text('After Registration (DNA Channel)')\n",
    "\n",
    "for ax in ax.flat:\n",
    "    ax.set(xlabel='100:1000', ylabel='100:1000')\n",
    "\n",
    "print(orig_v,\" \\n\" , \"\\n\",reg_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Registration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CYCLE_NUMS: 10 \n",
      " NUM_FOVS: 216\n"
     ]
    }
   ],
   "source": [
    "# check again\n",
    "print('CYCLE_NUMS:', CYCLE_NUMS,'\\n', 'NUM_FOVS:',NUM_FOVS)\n",
    "# CYCLE_NUMS = 10\n",
    "# NUM_FOVS = 214"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CHECK IF REFERENCE FOV IS BEING MATCHED TO SAME MOVED FOV, ACROSS ALL CYCLES AND FOVS IN EVERY ITERATION\n",
    "\n",
    "for c in range(CYCLE_NUMS-1):   \n",
    "    refs = iter(sorted(glob.glob('tif/Cycle_0/*'))) # list of cycle 0 .tif \n",
    "    movs = iter(sorted(glob.glob(f'tif/Cycle_{c+1}/*'))) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "    for FOV in range(0, NUM_FOVS): \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        ref_name = next(refs) \n",
    "        mov_name = next(movs)\n",
    "\n",
    "        ref_num = ref_name.split('_F')[1][0:3]\n",
    "        mov_num = mov_name.split('_F')[1][0:3]\n",
    "        if ref_num != mov_num:\n",
    "            print(\"False\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define jaccard\n",
    "def jaccard(img1, img2):\n",
    "    assert img1.dtype == 'bool', 'input must be boolean'\n",
    "    assert img2.dtype == 'bool', 'input must be boolean'\n",
    "    AND = np.sum(img1&img2)\n",
    "    OR = np.sum(img1|img2)\n",
    "    J = AND/OR\n",
    "    return J\n",
    "reg_J = pd.DataFrame()\n",
    "base_J = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 214"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 1 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 059 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 094 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 125 \n",
      "Got threshold\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 137 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 138 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 139 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 140 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 141 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 142 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 143 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 144 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 145 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 146 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 147 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 148 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 149 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 151 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 152 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 153 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 154 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 155 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 156 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 157 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 158 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 159 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 160 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 161 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 162 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 163 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 164 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 165 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 166 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 167 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 169 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 170 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 171 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 172 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 173 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 174 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 175 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 177 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 179 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 180 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 181 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 184 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 185 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 193 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 197 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 199 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 204 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 205 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 206 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 207 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 208 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 209 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 211 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 212 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 213 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 214 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 215 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 216 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 217 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 220 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 1 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 059 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 094 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 119 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 122 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 123 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 124 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 125 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 126 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 127 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 137 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 138 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 139 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 140 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 141 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 142 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 143 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 144 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 145 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 146 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 147 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 148 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 149 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 151 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 152 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 153 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 154 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 155 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 156 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 157 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 158 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 159 \n",
      "Got threshold\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving tmat\n",
      "cycle 2 field 160 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 161 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 162 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 163 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 164 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 165 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 166 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 167 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 169 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 170 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 171 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 172 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 173 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 174 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 175 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 177 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 179 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 180 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 181 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 184 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 185 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 193 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 197 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 199 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 204 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 205 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 206 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 207 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 208 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 209 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 211 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 212 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 213 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 214 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 215 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 216 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 217 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 220 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 2 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 059 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 094 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 119 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 122 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 123 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 124 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 125 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 126 \n",
      "Got threshold\n",
      "saving tmat\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 3 field 127 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 137 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 138 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 139 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 140 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 141 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 142 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 143 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 144 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 145 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 146 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 147 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 148 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 149 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 151 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 152 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 153 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 154 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 155 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 156 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 157 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 158 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 159 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 160 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 161 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 162 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 163 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 164 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 165 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 166 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 167 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 169 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 170 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 171 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 172 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 173 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 174 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 175 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 177 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 179 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 180 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 181 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 184 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 185 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 193 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 197 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 199 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 204 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 205 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 206 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 207 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 208 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 209 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 211 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 212 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 213 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 214 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 215 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 216 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 217 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 220 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 3 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 059 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 094 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 119 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 122 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 123 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 124 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 125 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 126 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 127 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 137 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 138 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 139 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 140 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 141 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 142 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 143 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 144 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 145 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 146 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 147 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 148 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 149 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 151 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 152 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 153 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 154 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 155 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 156 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 157 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 158 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 159 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 160 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 161 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 162 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 163 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 164 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 165 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 166 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 167 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 169 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 170 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 171 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 172 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 173 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 174 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 175 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 177 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 179 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 180 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 181 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 184 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 185 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 193 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 197 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 199 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 204 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 205 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 206 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 207 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 208 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 209 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 211 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 212 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 213 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 214 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 215 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 216 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 217 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 220 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 4 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 059 \n",
      "Got threshold\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving tmat\n",
      "cycle 5 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 094 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 119 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 122 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 123 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 124 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 125 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 126 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 127 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 137 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 138 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 139 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 140 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 141 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 142 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 143 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 144 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 145 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 146 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 147 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 148 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 149 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 151 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 152 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 153 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 154 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 155 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 156 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 157 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 158 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 159 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 160 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 161 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 162 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 163 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 164 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 165 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 166 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 167 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 169 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 170 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 171 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 172 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 173 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 174 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 175 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 177 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 179 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 180 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 181 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 184 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 185 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 193 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 197 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 204 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 206 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 207 \n",
      "Got threshold\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 209 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 211 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 217 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 220 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 5 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 025 \n",
      "Got threshold\n",
      "saving tmat\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 6 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 044 \n",
      "Got threshold\n",
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      "cycle 6 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 047 \n",
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      "saving tmat\n",
      "cycle 6 field 048 \n",
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      "cycle 6 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
      "cycle 6 field 051 \n",
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      "saving tmat\n",
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      "Got threshold\n",
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      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
      "cycle 6 field 060 \n",
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      "saving tmat\n",
      "cycle 6 field 061 \n",
      "Got threshold\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
      "cycle 6 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 073 \n",
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      "saving tmat\n",
      "cycle 6 field 074 \n",
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      "saving tmat\n",
      "cycle 6 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
      "cycle 6 field 084 \n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
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      "Got threshold\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
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      "Got threshold\n",
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      "Got threshold\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 185 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 193 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 197 \n",
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      "saving tmat\n",
      "cycle 6 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 199 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
      "cycle 6 field 205 \n",
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      "cycle 6 field 206 \n",
      "Got threshold\n",
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      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
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      "saving tmat\n",
      "cycle 6 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
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      "saving tmat\n",
      "cycle 6 field 212 \n",
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      "saving tmat\n",
      "cycle 6 field 213 \n",
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      "saving tmat\n",
      "cycle 6 field 214 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 215 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 216 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 217 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 220 \n",
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      "saving tmat\n",
      "cycle 6 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 6 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 059 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 094 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 119 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 122 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 123 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 124 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 125 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 126 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 127 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 137 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 138 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 139 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 140 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 141 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 142 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 143 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 144 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 145 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 146 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 147 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 148 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 149 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 151 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 152 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 153 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 154 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 155 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 156 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 157 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 158 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 159 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 160 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 161 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 162 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 163 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 164 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 165 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 166 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 167 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 169 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 170 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 171 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 172 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 173 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 174 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 175 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 177 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 179 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 180 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 181 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 184 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 185 \n",
      "Got threshold\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving tmat\n",
      "cycle 7 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 193 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 197 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 199 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 204 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 205 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 206 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 207 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 208 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 209 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 211 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 212 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 213 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 214 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 215 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 216 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 217 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 220 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 7 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 059 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 094 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 119 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 122 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 123 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 124 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 125 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 126 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 127 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 137 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 138 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 139 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 140 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 141 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 142 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 143 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 144 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 145 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 146 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 147 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 148 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 149 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 151 \n",
      "Got threshold\n",
      "saving tmat\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle 8 field 152 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 153 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 154 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 155 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 156 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 157 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 158 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 159 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 160 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 161 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 162 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 163 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 164 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 165 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 166 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 167 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 169 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 170 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 171 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 172 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 173 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 174 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 175 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 177 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 178 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 179 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 180 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 181 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 182 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 183 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 184 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 185 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 186 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 187 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 188 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 189 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 190 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 191 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 192 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 193 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 194 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 195 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 196 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 197 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 198 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 199 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 200 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 201 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 202 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 203 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 204 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 205 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 206 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 207 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 208 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 209 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 210 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 211 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 212 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 213 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 214 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 215 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 216 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 217 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 218 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 219 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 220 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 221 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 222 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 223 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 8 field 224 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 000 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 001 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 002 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 003 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 004 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 005 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 006 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 007 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 008 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 009 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 010 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 011 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 013 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 014 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 016 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 017 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 018 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 019 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 020 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 021 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 022 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 023 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 024 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 025 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 026 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 027 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 028 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 029 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 030 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 031 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 032 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 033 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 034 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 035 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 036 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 037 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 038 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 039 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 040 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 041 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 042 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 043 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 044 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 045 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 047 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 048 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 049 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 050 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 051 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 053 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 054 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 055 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 056 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 057 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 058 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 059 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 060 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 061 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 062 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 063 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 065 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 066 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 067 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 068 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 069 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 070 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 072 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 073 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 074 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 075 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 076 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 077 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 078 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 079 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 080 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 081 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 082 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 083 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 084 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 085 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 086 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 087 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 088 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 090 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 091 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 092 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 093 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 094 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 095 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 096 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 097 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 098 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 099 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 100 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 101 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 102 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 103 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 104 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 105 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 106 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 107 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 108 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 109 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 111 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 112 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 113 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 114 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 115 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 116 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 117 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 118 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 119 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 120 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 121 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 122 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 123 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 124 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 125 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 126 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 127 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 128 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 129 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 130 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 131 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 132 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 133 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 134 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 135 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 136 \n",
      "Got threshold\n",
      "saving tmat\n",
      "cycle 9 field 137 \n",
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      "Got threshold\n",
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      "cycle 9 field 223 \n",
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      "cycle 9 field 224 \n",
      "Got threshold\n",
      "saving tmat\n"
     ]
    }
   ],
   "source": [
    "for c in range(CYCLE_NUMS-1):    \n",
    "    refs = iter(sorted(glob.glob('tif/Cycle_0/*'))) # list of cycle 0 .tif \n",
    "    movs = iter(sorted(glob.glob(f'tif/Cycle_{c+1}/*'))) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "    for FOV in range(0, NUM_FOVS): \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        ref_name = next(refs) \n",
    "        ref = imread(ref_name)\n",
    "        ref = ref.astype(np.uint16)\n",
    "        mov_name = next(movs)\n",
    "        mov = imread(mov_name)\n",
    "        mov = mov.astype(np.uint16)\n",
    "        FOV_num = mov_name.split('_F')[1][0:3]\n",
    "        print(f'cycle {c+1} field {FOV_num} ')\n",
    "\n",
    "        ref_max = ref.max(0)\n",
    "        ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "        mov_max = mov.max(0)\n",
    "        mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "        print(\"Got threshold\")\n",
    "        sr = StackReg(StackReg.RIGID_BODY)  \n",
    "        tmat = sr.register(ref_binary, mov_binary) \n",
    "        out = sr.transform(mov_binary) \n",
    "        out = pystackreg.util.to_uint16(out) \n",
    "        \n",
    "#         base_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, mov_binary)\n",
    "#         reg_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, out.astype('bool'))\n",
    "\n",
    "#         # save binary\n",
    "#         fname_to_save = f'reg_bin_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_bin_reg.tif'\n",
    "#         print('Saving Binary Registered Images...', fname_to_save)\n",
    "#         tifffile.imwrite(fname_to_save, out, imagej=True, photometric = 'minisblack',metadata={'axes':'YX'})\n",
    "\n",
    "        # save tmat\n",
    "        print(\"saving tmat\")\n",
    "        np.save(f'tmat_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_tmat.npy', tmat)\n",
    "        \n",
    "#         # THE REGISTRATION STEP\n",
    "#         reg = np.zeros(mov.shape, dtype=np.uint16)               # initialize with the right dtype\n",
    "#         for Z in range(mov.shape[0]):\n",
    "#             print(f\"Z-plane: {Z} registering\")\n",
    "#             for ch in range(mov.shape[1]): \n",
    "#                 reg[Z,ch,...] = sr.transform(mov[Z,ch,...], tmat=tmat)\n",
    "#                 reg[Z,ch,...] = to_uint16(reg[Z,ch,...])\n",
    "\n",
    "#         fname_to_save = f'reg_Cyc_{c+1}' + f'/Cycle_{c+1}_F{FOV_num}_reg.tif'\n",
    "#         print('Saving Registered Images...', fname_to_save)\n",
    "#         tifffile.imwrite(fname_to_save, reg, imagej=True,\n",
    "#                          photometric = 'minisblack',metadata={'axes':'ZCYX'})\n",
    "# base_J.to_csv('base_J.csv')\n",
    "# reg_J.to_csv('reg_J.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at Max and Min Shifts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CYCLE_NUMS: 10 \n",
      " NUM_FOVS: 216\n"
     ]
    }
   ],
   "source": [
    "# check again\n",
    "NUM_FOVS = 216\n",
    "print('CYCLE_NUMS:', CYCLE_NUMS,'\\n', 'NUM_FOVS:',NUM_FOVS)\n",
    "# CYCLE_NUMS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 tmat_Cyc_1/Cycle_1_F012_tmat.npy -483.7867655774435 -1530.0752209063971\n",
      "1 tmat_Cyc_1/Cycle_1_F046_tmat.npy -75.89631729025302 67.28048340185808\n",
      "2 tmat_Cyc_2/Cycle_2_F046_tmat.npy -67.99360636630786 47.757708178416124\n",
      "3 tmat_Cyc_3/Cycle_3_F046_tmat.npy -69.7130199519977 47.00356842131771\n",
      "4 tmat_Cyc_4/Cycle_4_F046_tmat.npy -67.56309447262231 44.62190263433388\n",
      "5 tmat_Cyc_5/Cycle_5_F046_tmat.npy -67.00503599927322 48.27609444294717\n",
      "6 tmat_Cyc_6/Cycle_6_F046_tmat.npy -68.1092770674909 49.158487720133394\n",
      "7 tmat_Cyc_7/Cycle_7_F046_tmat.npy -68.23424714421492 49.09741284589052\n",
      "8 tmat_Cyc_8/Cycle_8_F046_tmat.npy -30.291279691004434 58.984936731270295\n",
      "9 tmat_Cyc_9/Cycle_9_F046_tmat.npy -66.76095915473843 61.871991999327975\n"
     ]
    }
   ],
   "source": [
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):     \n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    for sFOV in range(0,NUM_FOVS): \n",
    "        tmat_name = next(tmats)\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        if (moveX > 15) | (moveY > 15)|(moveX < -15) | (moveY < -15): # 50 pixels is max\n",
    "            print(c+1, tmat_name, moveX, moveY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for c in range(CYCLE_NUMS-1):\n",
    "#     refs = iter(sorted(glob.glob('tif/Cycle_0/*'))) # list of cycle 0 .tif \n",
    "#     movs = iter(sorted(glob.glob(f'tif/Cycle_{c+1}/*'))) # cycle 1, 2, 3, .tif list --> FOV000, 001, (002 = error) 005 006 \n",
    "#     for FOV in range(0, NUM_FOVS): # \n",
    "#         #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "#         ref_name = next(refs) \n",
    "#         ref = imread(ref_name)\n",
    "#         ref = ref.astype(np.uint16)\n",
    "#         mov_name = next(movs)\n",
    "#         mov = imread(mov_name)\n",
    "#         mov = mov.astype(np.uint16)\n",
    "#         FOV_num = mov_name.split('_F')[1][0:3]\n",
    "#         print(f'cycle {c+1} field {FOV_num} ')\n",
    "\n",
    "#         ref_max = ref.max(0)\n",
    "#         ref_binary = ref_max[0] > threshold_otsu(ref_max[0]) # nuclei channel\n",
    "#         mov_max = mov.max(0)\n",
    "#         mov_binary = mov_max[0] > threshold_otsu(mov_max[0]) # nuclei channel\n",
    "#         print(\"Got threshold\")\n",
    "#         sr = StackReg(StackReg.RIGID_BODY)  \n",
    "#         tmat = sr.register(ref_binary, mov_binary) \n",
    "#         out = sr.transform(mov_binary) \n",
    "#         out = pystackreg.util.to_uint16(out) \n",
    "\n",
    "#         base_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, mov_binary)\n",
    "#         reg_J.loc[FOV_num, str(c+1)] = jaccard(ref_binary, out.astype('bool'))\n",
    "\n",
    "# base_J.to_csv('base_J.csv')\n",
    "# reg_J.to_csv('reg_J.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.602029</td>\n",
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       "      <td>0.788228</td>\n",
       "      <td>0.787810</td>\n",
       "      <td>0.782420</td>\n",
       "      <td>0.773411</td>\n",
       "      <td>0.785582</td>\n",
       "      <td>0.777890</td>\n",
       "      <td>0.777054</td>\n",
       "      <td>0.528415</td>\n",
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       "      <td>0.763666</td>\n",
       "      <td>0.750515</td>\n",
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       "      <td>0.535750</td>\n",
       "      <td>0.697221</td>\n",
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       "      <td>0.828387</td>\n",
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       "      <td>0.824505</td>\n",
       "      <td>0.830326</td>\n",
       "      <td>0.821050</td>\n",
       "      <td>0.549218</td>\n",
       "      <td>0.713021</td>\n",
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       "      <td>0.765981</td>\n",
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       "      <td>0.819781</td>\n",
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      "text/plain": [
       "            1         2         3         4         5         6         7  \\\n",
       "0    0.793136  0.796163  0.785874  0.783379  0.805078  0.793605  0.809676   \n",
       "1    0.795658  0.796454  0.793389  0.785062  0.807338  0.801637  0.812828   \n",
       "2    0.788228  0.787810  0.782420  0.773411  0.785582  0.777890  0.777054   \n",
       "3    0.762161  0.770608  0.763666  0.750515  0.776130  0.765876  0.775932   \n",
       "4    0.831840  0.831270  0.830036  0.819834  0.815125  0.798509  0.804233   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "211  0.837520  0.830343  0.828387  0.826667  0.824505  0.830326  0.821050   \n",
       "212  0.837366  0.791921  0.807888  0.806145  0.765981  0.779068  0.819781   \n",
       "213  0.840844  0.837299  0.835759  0.834734  0.828805  0.834899  0.826218   \n",
       "214  0.843619  0.831436  0.827969  0.831444  0.830444  0.835742  0.826426   \n",
       "215  0.829844  0.821764  0.819513  0.813212  0.814418  0.817211  0.812692   \n",
       "\n",
       "            8         9  \n",
       "0    0.619372  0.761418  \n",
       "1    0.602029  0.730030  \n",
       "2    0.528415  0.681986  \n",
       "3    0.535750  0.697221  \n",
       "4    0.652717  0.614498  \n",
       "..        ...       ...  \n",
       "211  0.549218  0.713021  \n",
       "212  0.560868  0.672177  \n",
       "213  0.582987  0.724721  \n",
       "214  0.591687  0.729426  \n",
       "215  0.497127  0.618941  \n",
       "\n",
       "[216 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_J = pd.read_csv('base_J.csv')\n",
    "base_J = base_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "base_J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.782559</td>\n",
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       "      <td>0.758176</td>\n",
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       "      <td>0.833955</td>\n",
       "      <td>0.828263</td>\n",
       "      <td>0.822182</td>\n",
       "      <td>0.664273</td>\n",
       "      <td>0.732921</td>\n",
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       "      <td>0.839941</td>\n",
       "      <td>0.834509</td>\n",
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       "<p>216 rows × 9 columns</p>\n",
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      "text/plain": [
       "            1         2         3         4         5         6         7  \\\n",
       "0    0.803053  0.807881  0.811336  0.787208  0.808442  0.806248  0.809491   \n",
       "1    0.800553  0.803966  0.799300  0.798348  0.808314  0.804573  0.798967   \n",
       "2    0.789590  0.787380  0.766721  0.782559  0.771031  0.774323  0.764517   \n",
       "3    0.762688  0.767829  0.740363  0.758176  0.763106  0.761181  0.759312   \n",
       "4    0.842756  0.838030  0.820539  0.833911  0.815830  0.796497  0.801112   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "211  0.837520  0.816402  0.838497  0.836473  0.833955  0.828263  0.822182   \n",
       "212  0.837366  0.803368  0.826830  0.833320  0.833991  0.821302  0.822460   \n",
       "213  0.840844  0.818951  0.831975  0.842649  0.838253  0.823146  0.828476   \n",
       "214  0.843619  0.840756  0.838976  0.840514  0.839941  0.834509  0.828804   \n",
       "215  0.829838  0.807774  0.819654  0.812286  0.818477  0.801797  0.813609   \n",
       "\n",
       "            8         9  \n",
       "0    0.704596  0.763109  \n",
       "1    0.688657  0.736482  \n",
       "2    0.606274  0.681981  \n",
       "3    0.624532  0.697717  \n",
       "4    0.751711  0.615895  \n",
       "..        ...       ...  \n",
       "211  0.664273  0.732921  \n",
       "212  0.683747  0.749244  \n",
       "213  0.692622  0.740381  \n",
       "214  0.696473  0.746621  \n",
       "215  0.562279  0.625339  \n",
       "\n",
       "[216 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_J = pd.read_csv(\"reg_J.csv\")\n",
    "reg_J = reg_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "reg_J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the table for the X and Y shift heatmap   ------ THIS TAKES A VERY SHORT AMOUNT OF TIME\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    fig, axes = plt.subplots()\n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    dfX = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\n",
    "    dfY = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\n",
    "    for sFOV in range(0,NUM_FOVS):\n",
    "        tmat_name = next(tmats)\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        \n",
    "        col = str(int(tmat_name.split('_')[6]))\n",
    "        row = str(int(tmat_name.split('_')[7][0:3]))\n",
    "\n",
    "        dfX.loc[row, col] = moveX\n",
    "        dfY.loc[row, col] = moveY\n",
    "        \n",
    "    sns.heatmap(dfX, vmin=-20, vmax=20)\n",
    "    pl.suptitle(f\"Cycle {c+1} X-shift\")\n",
    "    \n",
    "#     fig, axes = plt.subplots()\n",
    "#     sns.heatmap(dfY, vmin=-100, vmax=100)\n",
    "#     pl.suptitle(f\"Cycle {c+1} Y-shift\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making the table for the X and Y shift heatmap   ------ THIS TAKES A VERY SHORT AMOUNT OF TIME\n",
    "\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    fig, axes = plt.subplots()\n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    dfX = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\n",
    "    dfY = pd.DataFrame(0, index=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'], \n",
    "                   columns=['0','1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14'])\n",
    "    for sFOV in range(0,NUM_FOVS):\n",
    "        tmat_name = next(tmats)\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        \n",
    "        col = str(int(tmat_name.split('_')[6]))\n",
    "        row = str(int(tmat_name.split('_')[7][0:3]))\n",
    "\n",
    "        dfX.loc[row, col] = moveX\n",
    "        dfY.loc[row, col] = moveY\n",
    "#     fig, axes = plt.subplots()\n",
    "#     sns.heatmap(dfX, vmin=-20, vmax=20)\n",
    "#     pl.suptitle(f\"Cycle {c+1} X-shift\")\n",
    " \n",
    "    sns.heatmap(dfY, vmin=-20, vmax=20)\n",
    "    pl.suptitle(f\"Cycle {c+1} Y-shift\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfX_shift = pd.DataFrame()\n",
    "dfY_shift = pd.DataFrame()\n",
    "for idx, c in enumerate(range(CYCLE_NUMS-1)):\n",
    "    tmats = iter(sorted(glob.glob(f'tmat_Cyc_{c+1}/*')))\n",
    "    for sFOV in range(0,NUM_FOVS):\n",
    "        tmat_name = next(tmats)\n",
    "        FOV_num = tmat_name.split('_F')[1][0:3]\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "\n",
    "        dfX_shift.loc[FOV_num, str(c+1)] = moveX\n",
    "        dfY_shift.loc[FOV_num, str(c+1)] = moveY\n",
    "dfX_shift.to_csv('X_shift.csv')\n",
    "dfY_shift.to_csv('Y_shift.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_J = pd.read_csv(\"reg_J.csv\")\n",
    "reg_J = reg_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "reg_J\n",
    "base_J = pd.read_csv(\"base_J.csv\")\n",
    "base_J = base_J.drop([\"Unnamed: 0\"], axis=1)\n",
    "base_J\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfY_shift = pd.read_csv(\"Y_shift.csv\")\n",
    "dfY_shift = dfY_shift.drop([\"Unnamed: 0\"], axis=1)\n",
    "dfY_shift\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfX_shift = pd.read_csv(\"X_shift.csv\")\n",
    "dfX_shift = dfX_shift.drop([\"Unnamed: 0\"], axis=1)\n",
    "dfX_shift\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x2160 with 27 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(CYCLE_NUMS-1, 3, figsize=(15,30))\n",
    "\n",
    "for cycle in range(CYCLE_NUMS-1):\n",
    "    axs[cycle][0].hist(reg_J[f'{cycle+1}'], bins = 50, alpha=0.5, label='reg_J', range=[0,1])\n",
    "    axs[cycle][0].hist(base_J[f'{cycle+1}'], bins = 50, alpha=0.5, label='base_J', range=[0,1])\n",
    "    \n",
    "    axs[cycle][1].hist(dfY_shift[f'{cycle+1}'], bins = 50, alpha=0.5, label='dfX_shift', range=[-20,20])\n",
    "    axs[cycle][2].hist(dfX_shift[f'{cycle+1}'], bins = 50, alpha=0.5, label='dfX_shift', range=[-20,20])\n",
    "    \n",
    "    axs[cycle][0].title.set_text(f'Change in Jaccard Index - Cycle {cycle+1}')\n",
    "    axs[cycle][1].title.set_text(f'Y Shift - Cycle {cycle+1}')\n",
    "    axs[cycle][2].title.set_text(f'X Shift - Cycle {cycle+1}')\n",
    "\n",
    "#     for ax in axs.flat:\n",
    "#         ax.set(xlabel='', ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3b4e4f6484246a3aaa1c478376ff2dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# We are trying to look for registered binaries that have intensity percentage less than 0.1 percent (< 0.001)\n",
    "\n",
    "hist = pd.DataFrame()\n",
    "err_lst = []\n",
    "err_lst2 = []\n",
    "\n",
    "for c in tqdm(range(CYCLE_NUMS-1)):    \n",
    "    binas = iter(glob.glob(f'reg_bin_Cyc_{c+1}/*')) \n",
    "    for FOV in range(0, NUM_FOVS): # \n",
    "        #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "        bina_name = next(binas)\n",
    "        bina = imread(bina_name)\n",
    "        bina = bina.astype(np.uint16)\n",
    "        FOV_num = bina_name[-15:-12]\n",
    "        \n",
    "        percentage = (np.sum(bina))/(2048*2048)    \n",
    "        \n",
    "        if percentage < 0.001:\n",
    "            err_lst.append(bina_name)\n",
    "        if percentage < 0.01:\n",
    "            err_lst2.append(bina_name)\n",
    "            \n",
    "        hist.loc[FOV_num, f'{c+1}'] = percentage    # this is the table of percent of signal in images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_bin_Cyc_1/Cycle_1_F012_bin_reg.tif']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_bin_Cyc_1/Cycle_1_F012_bin_reg.tif']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err_lst2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as pl\n",
    "for c in range(CYCLE_NUMS-1):\n",
    "    hist.hist(column=f'{c+1}', bins = 80, range=[0, 0.2])\n",
    "    pl.suptitle(f\"Cycle {c+1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging and Cropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 214"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " tmat_Cyc_1 \n",
      " 1.4705555891000395 -0.4744742482644142 1.8668214411976578 -2.327357846590827\n",
      "X_max:2 X_min:-1 Y_max:2 Y_min:-3\n",
      "\n",
      " tmat_Cyc_2 \n",
      " 0.8776143754318557 -0.8054502645481989 2.48812397127449 -1.9471636158729198\n",
      "X_max:1 X_min:-1 Y_max:3 Y_min:-2\n",
      "\n",
      " tmat_Cyc_3 \n",
      " 0.8278263710175283 -1.2500287682969429 2.8376825087974566 -1.5386312239542597\n",
      "X_max:1 X_min:-2 Y_max:3 Y_min:-2\n",
      "\n",
      " tmat_Cyc_4 \n",
      " 0.9119277092391522 -0.9861586962391584 3.216226451427474 -1.4537703386395555\n",
      "X_max:1 X_min:-1 Y_max:4 Y_min:-2\n",
      "\n",
      " tmat_Cyc_5 \n",
      " 1.125287972911503 -0.6503438553701244 2.375328761133005 -2.584916125856654\n",
      "X_max:2 X_min:-1 Y_max:3 Y_min:-3\n",
      "\n",
      " tmat_Cyc_6 \n",
      " 0.9872674254262994 -0.7158639040844719 2.2707085430306506 -2.383581245468085\n",
      "X_max:1 X_min:-1 Y_max:3 Y_min:-3\n",
      "\n",
      " tmat_Cyc_7 \n",
      " 0.9203985625595735 -0.9641901201110841 2.245575512974142 -2.143476011878814\n",
      "X_max:1 X_min:-1 Y_max:3 Y_min:-3\n",
      "\n",
      " tmat_Cyc_8 \n",
      " -6.090990899875805 -9.680105404810206 7.298175469347825 1.3131660574827038\n",
      "X_max:-7 X_min:-10 Y_max:8 Y_min:2\n",
      "\n",
      " tmat_Cyc_9 \n",
      " -0.11722436259151436 -2.4918608293389752 1.3788857584014522 -3.2340524435394356\n",
      "X_max:-1 X_min:-3 Y_max:2 Y_min:-4\n",
      "\n",
      " X_max_total:2 X_min_total:-10 Y_max_total:8 Y_min_total:-4\n"
     ]
    }
   ],
   "source": [
    "## print('X shift and Y shift max min values')\n",
    "x_max_list=[]\n",
    "x_min_list=[]\n",
    "y_max_list=[]\n",
    "y_min_list=[]\n",
    "X_indices =[]\n",
    "Y_indices =[]\n",
    "X_SHIFT_df = pd.DataFrame\n",
    "Y_SHIFT_df = pd.DataFrame\n",
    "\n",
    "for c in range(CYCLE_NUMS-1):     \n",
    "    tmats = iter(glob.glob(f'tmat_Cyc_{c+1}/*'))\n",
    "    X_SHIFT = []\n",
    "    Y_SHIFT = []\n",
    "    X_RIG = []\n",
    "    Y_RIG = []\n",
    "    for sFOV in range(0,NUM_FOVS): \n",
    "        tmat_name = next(tmats)\n",
    "        fov = tmat_name.split('_F')[1][0:3]\n",
    "        tmat_loaded = np.load(tmat_name)\n",
    "        moveX = tmat_loaded[0,2]\n",
    "        moveY = tmat_loaded[1,2]\n",
    "        if moveX > 0:\n",
    "            moveX = moveX + 2048*np.tan(np.arcsin(tmat_loaded[0,1]))\n",
    "            X_RIG = []\n",
    "        if moveY < 0:\n",
    "            moveY = moveY - 2048*np.tan(np.arcsin(tmat_loaded[0,1]))\n",
    "        X_SHIFT.append(moveX)\n",
    "        Y_SHIFT.append(moveY)\n",
    "        \n",
    "#         X_SHIFT_df.loc[fov, c] = moveX\n",
    "#         Y_SHIFT_df.loc[fov, c] = moveY\n",
    "\n",
    "    X_max = max(X_SHIFT)\n",
    "    X_min = min(X_SHIFT)\n",
    "    Y_max = max(Y_SHIFT)\n",
    "    Y_min = min(Y_SHIFT)\n",
    "\n",
    "    print('\\n', f'tmat_Cyc_{c+1} \\n', X_max,X_min,Y_max,Y_min)\n",
    "\n",
    "    def round_shift(val):\n",
    "        import math\n",
    "        if val <0: # if negative,\n",
    "            val = math.floor(val)\n",
    "        else:\n",
    "            val = math.ceil(val)\n",
    "        return val\n",
    "\n",
    "    X_max = round_shift(X_max)\n",
    "    X_min = round_shift(X_min)\n",
    "    Y_max = round_shift(Y_max)\n",
    "    Y_min = round_shift(Y_min)\n",
    "    \n",
    "    x_max_list.append(X_max)\n",
    "    x_min_list.append(X_min)\n",
    "    y_max_list.append(Y_max)\n",
    "    y_min_list.append(Y_min)\n",
    "    print(f'X_max:{X_max}',f'X_min:{X_min}',f'Y_max:{Y_max}',f'Y_min:{Y_min}')\n",
    "\n",
    "X_max_total = max(x_max_list)\n",
    "X_min_total = min(x_min_list)\n",
    "Y_max_total = max(y_max_list)\n",
    "Y_min_total = min(y_min_list)\n",
    "\n",
    "print('\\n', f'X_max_total:{X_max_total}', f'X_min_total:{X_min_total}', \n",
    "      f'Y_max_total:{Y_max_total}', f'Y_min_total:{Y_min_total}')\n",
    "\n",
    "# X_SHIFT_df.to_csv('X_SHIFT_df.csv',sep=',')\n",
    "# Y_SHIFT_df.to_csv('Y_SHIFT_df.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2048 2048\n"
     ]
    }
   ],
   "source": [
    "im1 = imread('tif/Cycle_0/Cycle_F001.tif')\n",
    "Y_total = len(im1[0][0][0])    # 2048\n",
    "X_total = len(im1[0][0][1])    # 2048\n",
    "print(Y_total, X_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################\n",
    "X_abs_shift = [abs(X_max_total), abs(X_min_total)]\n",
    "Y_abs_shift = [abs(Y_max_total), abs(Y_min_total)] \n",
    "\n",
    "def crop(X_x, X_n, Y_x, Y_n, img, pad):\n",
    "    Y_total = img.shape[3]    # 2048\n",
    "    X_total = img.shape[-1]     # 2048\n",
    "    print('image shape', Y_total, X_total)\n",
    "    \n",
    "    if X_x * X_n > 0:   # same signs --> both negative or both positive\n",
    "        Xlength = X_total - (max(X_abs_shift))\n",
    "        print(Xlength)\n",
    "        if X_x > 0: # moving left\n",
    "            img = img[...,:, 0+pad:Xlength-pad]\n",
    "        if X_x < 0:  # moving right \n",
    "            img = img[...,:, abs(X_n)+pad:X_total-pad]\n",
    "\n",
    "    if X_x * X_n < 0:   # diff signs --> one is positive and other is negative\n",
    "        Xlength = X_total  - (abs(X_x) + abs(X_n))\n",
    "        print(Xlength)\n",
    "        img = img[...,:, abs(X_n)+pad: X_total - abs(X_x)-pad] \n",
    "        \n",
    "    if X_x == 0 and X_n < 0:\n",
    "        Xlength = X_total  - abs(X_n)\n",
    "        print(Xlength)\n",
    "        img = img[...,:, abs(X_n)+pad:X_total-pad]\n",
    "    if X_x == 0 and X_n == 0:\n",
    "        Xlength = X_total\n",
    "        print(Xlength)\n",
    "        img = img[...,:, 0+pad:X_total-pad]\n",
    "    if X_x > 0 and X_n == 0:\n",
    "        Xlength = X_total - X_x\n",
    "        print(Xlength)\n",
    "        img = img[...,:, 0+pad:Xlength-pad]\n",
    "### Y ## #\n",
    "    if Y_x * Y_n > 0:   \n",
    "        Ylength = Y_total - (max(Y_abs_shift))\n",
    "        print(Ylength)\n",
    "        if Y_x > 0: # up\n",
    "            img = img[..., 0+pad:Ylength-pad, :]\n",
    "        if Y_x < 0: # down\n",
    "            img = img[...,abs(Y_n)+pad:Y_total-pad, :]\n",
    "            \n",
    "    if Y_x * Y_n < 0:  # Y_x > 0 , Y_n < 0\n",
    "        Ylength = Y_total  - (abs(Y_x) + abs(Y_n)) # \n",
    "        print(Ylength)\n",
    "        img = img[...,abs(Y_n)+pad:Y_total-abs(Y_x)-pad, :] \n",
    "        \n",
    "    if Y_x == 0 and Y_n < 0: # down\n",
    "        Ylength = Y_total  - abs(Y_n)\n",
    "        print(Ylength)\n",
    "        img = img[..., abs(Y_n)+pad:Y_total-pad, :]\n",
    "        \n",
    "    if Y_x == 0 and Y_n == 0:\n",
    "        Ylength = Y_total\n",
    "        print(Ylength)\n",
    "        img = img[..., 0+pad:Y_total-pad,:]\n",
    "        \n",
    "    if Y_x > 0 and Y_n == 0:\n",
    "        Ylength = Y_total - Y_x ## \n",
    "        print(Ylength)\n",
    "        img = img[..., 0+pad:Ylength-pad, :] ## up  \n",
    "    Xlength = Xlength - (pad*2)\n",
    "    Ylength = Ylength - (pad*2)\n",
    "    return Xlength, Ylength, img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_DIR = 'reg'\n",
    "REF_DIR = 'tif' # cycle0\n",
    "MERGE_DIR = 'merged' # output directory to save\n",
    "Z = 13\n",
    "final_ch = 38\n",
    "\n",
    "pad = 10 # 5 pixel padding\n",
    "\n",
    "!mkdir merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "image max: 1272\n",
      "Appending...  reg_Cyc_1/Cycle_1_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F000_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F000_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F000_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F000.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 35015\n",
      "FOV 001\n",
      "image max: 1137\n",
      "Appending...  reg_Cyc_1/Cycle_1_F001_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F001_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F001.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 65535\n",
      "FOV 002\n",
      "image max: 902\n",
      "Appending...  reg_Cyc_1/Cycle_1_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F002_reg.tif\n",
      "65533\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F002_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F002.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 47298\n",
      "FOV 003\n",
      "image max: 636\n",
      "Appending...  reg_Cyc_1/Cycle_1_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F003_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F003_reg.tif\n",
      "65534\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F003.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 50117\n",
      "FOV 004\n",
      "image max: 1274\n",
      "Appending...  reg_Cyc_1/Cycle_1_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F004_reg.tif\n",
      "65534\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F004_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F004.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 42656\n",
      "FOV 005\n",
      "image max: 1340\n",
      "Appending...  reg_Cyc_1/Cycle_1_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F005_reg.tif\n",
      "65530\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F005_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F005_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F005_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F005_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F005_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F005.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 41570\n",
      "FOV 006\n",
      "image max: 1107\n",
      "Appending...  reg_Cyc_1/Cycle_1_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F006_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F006_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F006_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F006_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F006_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F006.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 65535\n",
      "FOV 007\n",
      "image max: 2672\n",
      "Appending...  reg_Cyc_1/Cycle_1_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F007_reg.tif\n",
      "65531\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F007_reg.tif\n",
      "65531\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F007_reg.tif\n",
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F007.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 65535\n",
      "FOV 008\n",
      "image max: 4686\n",
      "Appending...  reg_Cyc_1/Cycle_1_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F008_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F008_reg.tif\n",
      "65533\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F008_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F008_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F008.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 39659\n",
      "FOV 009\n",
      "image max: 635\n",
      "Appending...  reg_Cyc_1/Cycle_1_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F009_reg.tif\n",
      "65532\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F009_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F009_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F009_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F009_reg.tif\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65535\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F009.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 40746\n",
      "FOV 010\n",
      "image max: 699\n",
      "Appending...  reg_Cyc_1/Cycle_1_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F010_reg.tif\n",
      "65532\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F010_reg.tif\n",
      "65532\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F010_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F010_reg.tif\n",
      "65533\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F010.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 39722\n",
      "FOV 011\n",
      "image max: 814\n",
      "Appending...  reg_Cyc_1/Cycle_1_F011_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F011_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F011_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F011_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F011_reg.tif\n",
      "65535\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F011_reg.tif\n",
      "65534\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F011_reg.tif\n",
      "65529\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F011_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F011_reg.tif\n",
      "62798\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F011.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n",
      "image max: 65535\n",
      "FOV 013\n",
      "image max: 1196\n",
      "Appending...  reg_Cyc_1/Cycle_1_F013_reg.tif\n",
      "65534\n",
      "Shape = (13, 8, 2048, 2048)\n",
      "Appending...  reg_Cyc_2/Cycle_2_F013_reg.tif\n",
      "65535\n",
      "Shape = (13, 12, 2048, 2048)\n",
      "Appending...  reg_Cyc_3/Cycle_3_F013_reg.tif\n",
      "65535\n",
      "Shape = (13, 16, 2048, 2048)\n",
      "Appending...  reg_Cyc_4/Cycle_4_F013_reg.tif\n",
      "65535\n",
      "Shape = (13, 20, 2048, 2048)\n",
      "Appending...  reg_Cyc_5/Cycle_5_F013_reg.tif\n",
      "65534\n",
      "Shape = (13, 24, 2048, 2048)\n",
      "Appending...  reg_Cyc_6/Cycle_6_F013_reg.tif\n",
      "65533\n",
      "Shape = (13, 28, 2048, 2048)\n",
      "Appending...  reg_Cyc_7/Cycle_7_F013_reg.tif\n",
      "65530\n",
      "Shape = (13, 32, 2048, 2048)\n",
      "Appending...  reg_Cyc_8/Cycle_8_F013_reg.tif\n",
      "65535\n",
      "Shape = (13, 34, 2048, 2048)\n",
      "Appending...  reg_Cyc_9/Cycle_9_F013_reg.tif\n",
      "65530\n",
      "Shape = (13, 38, 2048, 2048)\n",
      "saving ./{MERGE_DIR}/F013.tif\n",
      "dtype of  uint16\n",
      "image shape 2048 2048\n",
      "2036\n",
      "2036\n"
     ]
    }
   ],
   "source": [
    "refs = iter(glob.glob('tif/Cycle_0/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    ref_name = next(refs)\n",
    "    FOV_num = ref_name.split('_F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    #sFOV = str(FOV).zfill(NUM_DIGITS_OF_FOVS)\n",
    "    img = imread(f'{REF_DIR}/Cycle_0/Cycle_F{FOV_num}.tif') # reference image that did not move\n",
    "    img = img.astype(np.uint16)\n",
    "    print(\"image max:\",img.max())\n",
    "    for cycle in range(CYCLE_NUMS-1):    \n",
    "        fname = f'{IN_DIR}_Cyc_{cycle+1}/Cycle_{cycle+1}_F{FOV_num}_{IN_DIR}.tif'\n",
    "        print('Appending... ', fname)\n",
    "        im_to_add = imread(fname).astype(np.uint16)\n",
    "        print(im_to_add.max())\n",
    "        im_to_add = im_to_add[:,:,...] \n",
    "        img = np.append(img, im_to_add, axis=1) # concatenate along channel index\n",
    "        print(f\"Shape = {img.shape}\") \n",
    "    fname = f'/F{FOV_num}.tif'\n",
    "    print('saving', './{MERGE_DIR}'+fname)\n",
    "    print('dtype of ', img.dtype)\n",
    "\n",
    "    ########## CROP ################ --> CHANGE everytime depending on shifts\n",
    "    Xlength, Ylength, img = crop(X_max_total, X_min_total, Y_max_total, Y_min_total, img, pad)\n",
    "\n",
    "    assert img.shape[3] == Xlength, \"Check X size\"\n",
    "    assert img.shape[2] == Ylength, \"Check Y size\"\n",
    "\n",
    "    ### FINAL CHECK before saving ### \n",
    "    assert img.shape[0] == Z, \"Check ZCYX\"\n",
    "    assert img.shape[1] == final_ch, \"check final merge size\"\n",
    "    print(\"image max:\", img.max())\n",
    "\n",
    "    tifffile.imwrite(\n",
    "        f'./{MERGE_DIR}'+fname,\n",
    "        img,\n",
    "        imagej=True,\n",
    "        photometric='minisblack',\n",
    "        metadata={'axes': 'ZCYX'},\n",
    "    )\n",
    "\n",
    "    del img # clear memory\n",
    "    del im_to_add\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
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      "11\n",
      "12\n",
      "FOV 001\n",
      "0\n",
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      "6\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 002\n",
      "0\n",
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      "2\n",
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      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 003\n",
      "0\n",
      "1\n",
      "2\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 004\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 005\n",
      "0\n",
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      "2\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 007\n",
      "0\n",
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      "11\n",
      "12\n",
      "FOV 008\n",
      "0\n",
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      "11\n",
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      "FOV 011\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 012\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 013\n",
      "0\n",
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      "6\n",
      "7\n",
      "8\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 014\n",
      "0\n",
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      "2\n",
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      "4\n",
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      "6\n",
      "7\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 015\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 016\n",
      "0\n",
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      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 017\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 019\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 020\n",
      "0\n",
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      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 021\n",
      "0\n",
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      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 022\n",
      "0\n",
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      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 023\n",
      "0\n",
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      "2\n",
      "3\n",
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      "6\n",
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      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 024\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 025\n",
      "0\n",
      "1\n",
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      "3\n",
      "4\n",
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      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 026\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 027\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 028\n",
      "0\n",
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      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 029\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 030\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 031\n",
      "0\n",
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      "11\n",
      "12\n",
      "FOV 032\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 033\n",
      "0\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 034\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 035\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 036\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 037\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 038\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 039\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 040\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 041\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 042\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 044\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 045\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 046\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 047\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 048\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 049\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 050\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 051\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
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      "10\n",
      "11\n",
      "12\n",
      "FOV 052\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 053\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 054\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 055\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 056\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 057\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 058\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 059\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 060\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 061\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 062\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 063\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 064\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 065\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 066\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 067\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 068\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 069\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 070\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 071\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 072\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 073\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 074\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 075\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 076\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 077\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 078\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 080\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 081\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 082\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 083\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 084\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 085\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 086\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 087\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 088\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 089\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 090\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 091\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 092\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 093\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 094\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 095\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 096\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 097\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 098\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 100\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 101\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 102\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 103\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 105\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 106\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 107\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 108\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 109\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 110\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 111\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 112\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 113\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 114\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 115\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 116\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 117\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 118\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 119\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 120\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 122\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 123\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 124\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 125\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 126\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 127\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 128\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 129\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 130\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 131\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 132\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 133\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 134\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 135\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 136\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 137\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 138\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 139\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 140\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 141\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 142\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 143\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 144\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 145\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 146\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 147\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 148\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 149\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 151\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 152\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 153\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 154\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 155\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 156\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 157\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 158\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 159\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 162\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 163\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 164\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 165\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 166\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 167\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 168\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 170\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 171\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 172\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 173\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 174\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 175\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 176\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 178\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 179\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 180\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 181\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 182\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 183\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 184\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 185\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 186\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 187\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 188\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 189\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 190\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 191\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 192\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 193\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 194\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 195\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 196\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 197\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 198\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 199\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 200\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 201\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 202\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 203\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 204\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 205\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 206\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 207\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 208\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 209\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 210\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 211\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 212\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 213\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 214\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 215\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 216\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 217\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 218\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 219\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 220\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 221\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 222\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 223\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "FOV 224\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "int_counts = pd.DataFrame()\n",
    "one_to_five_list = []\n",
    "sixfivek_list = []\n",
    "z_list = []\n",
    "\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    overall_count_zero = np.count_nonzero(img == 0)\n",
    "    x = np.count_nonzero((0 < img) & (img < 6))\n",
    "    y = np.count_nonzero(65000 < img)\n",
    "     \n",
    "    for Z in range(img.shape[0]):\n",
    "        print(Z)\n",
    "        for ch in range(img.shape[1]): \n",
    "            count_zeros = np.count_nonzero(img[Z,ch,...] == 0)\n",
    "            if count_zeros > 0:\n",
    "                z_list.append((FOV_num, Z, ch))\n",
    "            count = np.count_nonzero((0 < img[Z,ch,...]) & (img[Z,ch,...] < 6))\n",
    "            if count > 0:\n",
    "                one_to_five_list.append((FOV_num, Z, ch))\n",
    "            count = np.count_nonzero(65000 < img[Z,ch,...])\n",
    "            if count > 0:\n",
    "                sixfivek_list.append((FOV_num, Z, ch))\n",
    "                \n",
    "    int_counts.loc[FOV_num, 'FOV_num'] = FOV_num\n",
    "    int_counts.loc[FOV_num, 'total Zero count'] = overall_count_zero\n",
    "    int_counts.loc[FOV_num, 'one to five'] = x\n",
    "    int_counts.loc[FOV_num, 'greater 65K'] = y\n",
    "int_counts.to_csv('pixel_intensity_counts_table.csv')    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>total Zero count</th>\n",
       "      <th>one to five</th>\n",
       "      <th>greater 65K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>000</td>\n",
       "      <td>493858.0</td>\n",
       "      <td>13396.0</td>\n",
       "      <td>17891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>001</td>\n",
       "      <td>480241.0</td>\n",
       "      <td>10310.0</td>\n",
       "      <td>14353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>002</td>\n",
       "      <td>2416700.0</td>\n",
       "      <td>40575.0</td>\n",
       "      <td>55541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>003</td>\n",
       "      <td>944596.0</td>\n",
       "      <td>12743.0</td>\n",
       "      <td>24385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>274.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>11087.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>223</td>\n",
       "      <td>223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count  one to five  greater 65K\n",
       "000     000          493858.0      13396.0      17891.0\n",
       "001     001          480241.0      10310.0      14353.0\n",
       "002     002         2416700.0      40575.0      55541.0\n",
       "003     003          944596.0      12743.0      24385.0\n",
       "004     004             274.0        483.0      11087.0\n",
       "..      ...               ...          ...          ...\n",
       "220     220               0.0          0.0          0.0\n",
       "221     221               0.0          0.0          0.0\n",
       "222     222               0.0          0.0          0.0\n",
       "223     223               0.0          0.0          0.0\n",
       "224     224               0.0          0.0          1.0\n",
       "\n",
       "[211 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3063"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sixfivek_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3150"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_to_five_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3014"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(z_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('000', 0, 32),\n",
       " ('000', 0, 33),\n",
       " ('000', 0, 37),\n",
       " ('000', 1, 32),\n",
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       " ('000', 2, 32),\n",
       " ('000', 2, 33),\n",
       " ('000', 2, 37),\n",
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       " ('005', 6, 33),\n",
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       " ('029', 4, 32),\n",
       " ('029', 4, 33),\n",
       " ('029', 5, 32),\n",
       " ('029', 5, 33),\n",
       " ('029', 6, 32),\n",
       " ('029', 6, 33),\n",
       " ('029', 7, 32),\n",
       " ('029', 8, 32),\n",
       " ('029', 9, 32),\n",
       " ('029', 10, 32),\n",
       " ('029', 11, 32),\n",
       " ('029', 12, 32),\n",
       " ('030', 0, 19),\n",
       " ('030', 0, 25),\n",
       " ('030', 1, 19),\n",
       " ('030', 1, 25),\n",
       " ('030', 2, 19),\n",
       " ('030', 2, 25),\n",
       " ('030', 3, 19),\n",
       " ('030', 3, 25),\n",
       " ('030', 4, 19),\n",
       " ('030', 4, 25),\n",
       " ('030', 5, 19),\n",
       " ('030', 5, 25),\n",
       " ('030', 6, 19),\n",
       " ('030', 6, 25),\n",
       " ('030', 7, 19),\n",
       " ('030', 7, 25),\n",
       " ('030', 9, 35),\n",
       " ('031', 0, 9),\n",
       " ('031', 0, 32),\n",
       " ('031', 0, 33),\n",
       " ('031', 1, 9),\n",
       " ('031', 1, 32),\n",
       " ('031', 1, 33),\n",
       " ('031', 2, 9),\n",
       " ('031', 2, 32),\n",
       " ('031', 2, 33),\n",
       " ('031', 3, 9),\n",
       " ('031', 3, 32),\n",
       " ('031', 3, 33),\n",
       " ('031', 4, 9),\n",
       " ('031', 4, 32),\n",
       " ('031', 4, 33),\n",
       " ('031', 5, 9),\n",
       " ('031', 5, 32),\n",
       " ('031', 5, 33),\n",
       " ('031', 6, 9),\n",
       " ('031', 6, 32),\n",
       " ('031', 6, 33),\n",
       " ('031', 7, 9),\n",
       " ('031', 7, 32),\n",
       " ('031', 7, 33),\n",
       " ('031', 8, 32),\n",
       " ('031', 9, 32),\n",
       " ('031', 10, 32),\n",
       " ('031', 10, 33),\n",
       " ('031', 11, 32),\n",
       " ('031', 11, 33),\n",
       " ('031', 12, 32),\n",
       " ('031', 12, 33),\n",
       " ('032', 0, 26),\n",
       " ('032', 0, 32),\n",
       " ('032', 0, 33),\n",
       " ('032', 1, 26),\n",
       " ('032', 1, 32),\n",
       " ('032', 1, 33),\n",
       " ('032', 2, 26),\n",
       " ('032', 2, 32),\n",
       " ('032', 2, 33),\n",
       " ('032', 3, 26),\n",
       " ('032', 3, 32),\n",
       " ('032', 3, 33),\n",
       " ('032', 4, 26),\n",
       " ('032', 4, 32),\n",
       " ('032', 4, 33),\n",
       " ('032', 5, 26),\n",
       " ('032', 5, 32),\n",
       " ('032', 5, 33),\n",
       " ('032', 6, 26),\n",
       " ('032', 6, 32),\n",
       " ('032', 6, 33),\n",
       " ('032', 7, 26),\n",
       " ('032', 7, 32),\n",
       " ('032', 7, 33),\n",
       " ('032', 8, 10),\n",
       " ('032', 8, 32),\n",
       " ('032', 8, 33),\n",
       " ('032', 9, 10),\n",
       " ('032', 9, 32),\n",
       " ('032', 9, 33),\n",
       " ('032', 10, 10),\n",
       " ('032', 10, 32),\n",
       " ('032', 10, 33),\n",
       " ('032', 11, 10),\n",
       " ('032', 11, 32),\n",
       " ('032', 11, 33),\n",
       " ('032', 12, 10),\n",
       " ('032', 12, 32),\n",
       " ('032', 12, 33),\n",
       " ('033', 0, 25),\n",
       " ('033', 0, 32),\n",
       " ('033', 0, 33),\n",
       " ('033', 1, 25),\n",
       " ('033', 1, 32),\n",
       " ('033', 1, 33),\n",
       " ('033', 2, 25),\n",
       " ('033', 2, 32),\n",
       " ('033', 2, 33),\n",
       " ('033', 3, 25),\n",
       " ('033', 3, 32),\n",
       " ('033', 3, 33),\n",
       " ('033', 4, 25),\n",
       " ('033', 4, 32),\n",
       " ('033', 4, 33),\n",
       " ('033', 5, 25),\n",
       " ('033', 5, 32),\n",
       " ('033', 5, 33),\n",
       " ('033', 6, 25),\n",
       " ('033', 6, 32),\n",
       " ('033', 6, 33),\n",
       " ('033', 7, 25),\n",
       " ('033', 7, 32),\n",
       " ('033', 7, 33),\n",
       " ('033', 8, 32),\n",
       " ('033', 8, 33),\n",
       " ('033', 9, 32),\n",
       " ('033', 9, 33),\n",
       " ('033', 10, 32),\n",
       " ('033', 10, 33),\n",
       " ('033', 11, 32),\n",
       " ('033', 11, 33),\n",
       " ('033', 12, 32),\n",
       " ('033', 12, 33),\n",
       " ('034', 0, 32),\n",
       " ('034', 0, 33),\n",
       " ('034', 1, 32),\n",
       " ('034', 1, 33),\n",
       " ('034', 2, 32),\n",
       " ('034', 2, 33),\n",
       " ('034', 3, 32),\n",
       " ('034', 3, 33),\n",
       " ('034', 4, 32),\n",
       " ('034', 4, 33),\n",
       " ('034', 5, 32),\n",
       " ('034', 5, 33),\n",
       " ('034', 6, 32),\n",
       " ('034', 6, 33),\n",
       " ('034', 7, 32),\n",
       " ('034', 7, 33),\n",
       " ('034', 8, 32),\n",
       " ('034', 8, 33),\n",
       " ('034', 9, 32),\n",
       " ('034', 9, 33),\n",
       " ('034', 10, 32),\n",
       " ('034', 10, 33),\n",
       " ('034', 11, 32),\n",
       " ('034', 11, 33),\n",
       " ...]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13396 FOV: 000\n",
      "10310 FOV: 001\n",
      "40575 FOV: 002\n",
      "12743 FOV: 003\n",
      "483 FOV: 004\n",
      "261 FOV: 005\n",
      "13229 FOV: 007\n",
      "491 FOV: 008\n",
      "276 FOV: 011\n",
      "6758 FOV: 012\n",
      "22700 FOV: 013\n",
      "906 FOV: 014\n",
      "9488 FOV: 015\n",
      "259 FOV: 016\n",
      "9009 FOV: 017\n",
      "8890 FOV: 019\n",
      "20979 FOV: 020\n",
      "7228 FOV: 021\n",
      "6169 FOV: 022\n",
      "236 FOV: 023\n",
      "252 FOV: 024\n",
      "1532 FOV: 025\n",
      "20910 FOV: 026\n",
      "16435 FOV: 027\n",
      "293 FOV: 028\n",
      "122 FOV: 029\n",
      "19996 FOV: 030\n",
      "20796 FOV: 031\n",
      "17966 FOV: 032\n",
      "6296 FOV: 033\n",
      "297 FOV: 034\n",
      "6564 FOV: 035\n",
      "9635 FOV: 036\n",
      "10787 FOV: 037\n",
      "12896 FOV: 038\n",
      "530 FOV: 039\n",
      "266 FOV: 040\n",
      "14310 FOV: 041\n",
      "249 FOV: 042\n",
      "213 FOV: 044\n",
      "17357 FOV: 045\n",
      "1296 FOV: 046\n",
      "282 FOV: 047\n",
      "11319 FOV: 049\n",
      "288 FOV: 050\n",
      "8799 FOV: 051\n",
      "22111 FOV: 052\n",
      "241 FOV: 053\n",
      "272 FOV: 054\n",
      "10538 FOV: 056\n",
      "8455 FOV: 057\n",
      "7541 FOV: 058\n",
      "15578 FOV: 059\n",
      "1128 FOV: 060\n",
      "12456 FOV: 063\n",
      "7995 FOV: 064\n",
      "16546 FOV: 065\n",
      "252 FOV: 066\n",
      "17669 FOV: 067\n",
      "253 FOV: 068\n",
      "17737 FOV: 069\n",
      "103 FOV: 070\n",
      "7564 FOV: 072\n",
      "154 FOV: 073\n",
      "229 FOV: 074\n",
      "8178 FOV: 075\n",
      "814 FOV: 077\n",
      "6173 FOV: 078\n",
      "15252 FOV: 082\n",
      "208 FOV: 084\n",
      "11 FOV: 085\n",
      "22280 FOV: 088\n",
      "21023 FOV: 089\n",
      "6802 FOV: 092\n",
      "3115 FOV: 094\n",
      "186 FOV: 095\n",
      "222 FOV: 096\n",
      "264 FOV: 097\n",
      "278 FOV: 098\n",
      "6034 FOV: 100\n",
      "276 FOV: 101\n",
      "1 FOV: 102\n",
      "1 FOV: 103\n",
      "492 FOV: 105\n",
      "14 FOV: 106\n",
      "7563 FOV: 108\n",
      "5384 FOV: 110\n",
      "1 FOV: 112\n",
      "31 FOV: 113\n",
      "16 FOV: 114\n",
      "20 FOV: 115\n",
      "514 FOV: 116\n",
      "239 FOV: 118\n",
      "11061 FOV: 120\n",
      "17628 FOV: 123\n",
      "28627 FOV: 125\n",
      "3 FOV: 126\n",
      "302 FOV: 127\n",
      "269 FOV: 128\n",
      "261 FOV: 132\n",
      "247 FOV: 133\n",
      "229 FOV: 134\n",
      "264 FOV: 135\n",
      "18655 FOV: 137\n",
      "263 FOV: 141\n",
      "262 FOV: 142\n",
      "13344 FOV: 143\n",
      "288 FOV: 144\n",
      "259 FOV: 145\n",
      "9822 FOV: 146\n",
      "84 FOV: 147\n",
      "20992 FOV: 148\n",
      "8 FOV: 149\n",
      "236 FOV: 151\n",
      "6382 FOV: 152\n",
      "1158 FOV: 153\n",
      "6600 FOV: 154\n",
      "12743 FOV: 155\n",
      "9 FOV: 156\n",
      "9077 FOV: 157\n",
      "98 FOV: 158\n",
      "8316 FOV: 159\n",
      "5700 FOV: 162\n",
      "10379 FOV: 163\n",
      "255 FOV: 166\n",
      "274 FOV: 167\n",
      "12906 FOV: 168\n",
      "9 FOV: 170\n",
      "208 FOV: 171\n",
      "5733 FOV: 172\n",
      "27 FOV: 179\n",
      "274 FOV: 185\n",
      "400 FOV: 186\n",
      "1246 FOV: 187\n",
      "335 FOV: 188\n",
      "294 FOV: 189\n",
      "2 FOV: 190\n",
      "1 FOV: 192\n",
      "216 FOV: 193\n",
      "491 FOV: 194\n",
      "12067 FOV: 196\n",
      "4671 FOV: 197\n",
      "246 FOV: 198\n",
      "4586 FOV: 199\n",
      "1 FOV: 200\n",
      "10 FOV: 204\n",
      "13573 FOV: 206\n",
      "9 FOV: 208\n",
      "8328 FOV: 213\n",
      "346 FOV: 214\n",
      "6349 FOV: 217\n"
     ]
    }
   ],
   "source": [
    "for idx, val in enumerate(int_counts['one to five']):\n",
    "    if val != 0:\n",
    "        print(int(int_counts.iloc[idx, 2]), \"FOV:\", int_counts.iloc[idx, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_FOV_set = set()\n",
    "for i in z_list:\n",
    "    z_FOV_set.add(i[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000 493858.0\n",
      "001 480241.0\n",
      "002 2416700.0\n",
      "003 944596.0\n",
      "007 480474.0\n",
      "012 481041.0\n",
      "013 1457160.0\n",
      "015 585413.0\n",
      "016 304764.0\n",
      "017 1017722.0\n",
      "019 301044.0\n",
      "020 980228.0\n",
      "021 443780.0\n",
      "022 492682.0\n",
      "026 996027.0\n",
      "027 779911.0\n",
      "030 922826.0\n",
      "031 1232731.0\n",
      "032 1083472.0\n",
      "033 483832.0\n",
      "035 311925.0\n",
      "036 499382.0\n",
      "037 498260.0\n",
      "038 912944.0\n",
      "041 488841.0\n",
      "045 795532.0\n",
      "049 475110.0\n",
      "051 436913.0\n",
      "052 1371664.0\n",
      "056 995475.0\n",
      "057 503126.0\n",
      "058 441043.0\n",
      "059 946451.0\n",
      "063 484210.0\n",
      "064 595159.0\n",
      "065 782397.0\n",
      "067 795241.0\n",
      "069 774615.0\n",
      "072 494978.0\n",
      "075 484242.0\n",
      "077 524754.0\n",
      "078 315622.0\n",
      "082 796600.0\n",
      "087 315520.0\n",
      "088 972739.0\n",
      "089 1406480.0\n",
      "092 483494.0\n",
      "094 309390.0\n",
      "100 502231.0\n",
      "108 417115.0\n",
      "110 454808.0\n",
      "120 497535.0\n",
      "123 922866.0\n",
      "125 1459560.0\n",
      "137 958329.0\n",
      "143 954651.0\n",
      "146 901530.0\n",
      "148 1848175.0\n",
      "152 311785.0\n",
      "154 311847.0\n",
      "155 1000128.0\n",
      "157 496514.0\n",
      "159 481375.0\n",
      "162 495074.0\n",
      "163 596200.0\n",
      "168 608370.0\n",
      "172 313516.0\n",
      "187 655927.0\n",
      "196 493471.0\n",
      "197 312634.0\n",
      "199 298643.0\n",
      "203 1048146.0\n",
      "206 911753.0\n",
      "213 497038.0\n",
      "215 643200.0\n",
      "217 308059.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['000',\n",
       " '001',\n",
       " '002',\n",
       " '003',\n",
       " '007',\n",
       " '012',\n",
       " '013',\n",
       " '015',\n",
       " '016',\n",
       " '017',\n",
       " '019',\n",
       " '020',\n",
       " '021',\n",
       " '022',\n",
       " '026',\n",
       " '027',\n",
       " '030',\n",
       " '031',\n",
       " '032',\n",
       " '033',\n",
       " '035',\n",
       " '036',\n",
       " '037',\n",
       " '038',\n",
       " '041',\n",
       " '045',\n",
       " '049',\n",
       " '051',\n",
       " '052',\n",
       " '056',\n",
       " '057',\n",
       " '058',\n",
       " '059',\n",
       " '063',\n",
       " '064',\n",
       " '065',\n",
       " '067',\n",
       " '069',\n",
       " '072',\n",
       " '075',\n",
       " '077',\n",
       " '078',\n",
       " '082',\n",
       " '087',\n",
       " '088',\n",
       " '089',\n",
       " '092',\n",
       " '094',\n",
       " '100',\n",
       " '108',\n",
       " '110',\n",
       " '120',\n",
       " '123',\n",
       " '125',\n",
       " '137',\n",
       " '143',\n",
       " '146',\n",
       " '148',\n",
       " '152',\n",
       " '154',\n",
       " '155',\n",
       " '157',\n",
       " '159',\n",
       " '162',\n",
       " '163',\n",
       " '168',\n",
       " '172',\n",
       " '187',\n",
       " '196',\n",
       " '197',\n",
       " '199',\n",
       " '203',\n",
       " '206',\n",
       " '213',\n",
       " '215',\n",
       " '217']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LOOKING FOR WHICH FOVS HAVE THE BLACK BOXES\n",
    "black_box = []\n",
    "for idx, val in enumerate(int_counts['total Zero count']):\n",
    "    if val > 2000:\n",
    "        black_box.append(int_counts.iloc[idx, 0])\n",
    "        print(int_counts.iloc[idx, 0], val)\n",
    "black_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(black_box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_to_five_set = set()\n",
    "for i in one_to_five_list:\n",
    "    one_to_five_set.add(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_to_five_set)               # num FOVs that have pixel intensity 1-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3150"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(one_to_five_list)    # number of single images that contains -- surrounding black box, black circle artifacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('000', 0, 32),\n",
       " ('000', 0, 33),\n",
       " ('000', 0, 37),\n",
       " ('000', 1, 32),\n",
       " ('000', 1, 33),\n",
       " ('000', 1, 37),\n",
       " ('000', 2, 32),\n",
       " ('000', 2, 33),\n",
       " ('000', 2, 37),\n",
       " ('000', 3, 32),\n",
       " ('000', 3, 33),\n",
       " ('000', 3, 37),\n",
       " ('000', 4, 32),\n",
       " ('000', 4, 33),\n",
       " ('000', 4, 37),\n",
       " ('000', 5, 32),\n",
       " ('000', 5, 33),\n",
       " ('000', 5, 37),\n",
       " ('000', 6, 32),\n",
       " ('000', 6, 33),\n",
       " ('000', 6, 37),\n",
       " ('000', 7, 32),\n",
       " ('000', 7, 33),\n",
       " ('000', 7, 37),\n",
       " ('000', 8, 32),\n",
       " ('000', 8, 33),\n",
       " ('000', 9, 32),\n",
       " ('000', 9, 33),\n",
       " ('000', 10, 32),\n",
       " ('000', 10, 33),\n",
       " ('000', 11, 32),\n",
       " ('000', 11, 33),\n",
       " ('000', 12, 32),\n",
       " ('000', 12, 33),\n",
       " ('001', 0, 12),\n",
       " ('001', 0, 32),\n",
       " ('001', 0, 33),\n",
       " ('001', 1, 12),\n",
       " ('001', 1, 32),\n",
       " ('001', 1, 33),\n",
       " ('001', 2, 12),\n",
       " ('001', 2, 32),\n",
       " ('001', 2, 33),\n",
       " ('001', 3, 12),\n",
       " ('001', 3, 32),\n",
       " ('001', 3, 33),\n",
       " ('001', 4, 12),\n",
       " ('001', 4, 32),\n",
       " ('001', 4, 33),\n",
       " ('001', 5, 12),\n",
       " ('001', 5, 32),\n",
       " ('001', 5, 33),\n",
       " ('001', 6, 12),\n",
       " ('001', 6, 32),\n",
       " ('001', 6, 33),\n",
       " ('001', 7, 12),\n",
       " ('001', 7, 32),\n",
       " ('001', 7, 33),\n",
       " ('001', 8, 32),\n",
       " ('001', 8, 33),\n",
       " ('001', 9, 32),\n",
       " ('001', 9, 33),\n",
       " ('001', 10, 32),\n",
       " ('001', 10, 33),\n",
       " ('001', 11, 32),\n",
       " ('001', 11, 33),\n",
       " ('001', 12, 32),\n",
       " ('001', 12, 33),\n",
       " ('002', 0, 10),\n",
       " ('002', 0, 22),\n",
       " ('002', 0, 32),\n",
       " ('002', 0, 33),\n",
       " ('002', 1, 10),\n",
       " ('002', 1, 22),\n",
       " ('002', 1, 32),\n",
       " ('002', 1, 33),\n",
       " ('002', 2, 10),\n",
       " ('002', 2, 22),\n",
       " ('002', 2, 32),\n",
       " ('002', 2, 33),\n",
       " ('002', 3, 10),\n",
       " ('002', 3, 22),\n",
       " ('002', 3, 32),\n",
       " ('002', 3, 33),\n",
       " ('002', 4, 10),\n",
       " ('002', 4, 22),\n",
       " ('002', 4, 32),\n",
       " ('002', 4, 33),\n",
       " ('002', 5, 10),\n",
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       " ('027', 10, 32),\n",
       " ('027', 10, 33),\n",
       " ('027', 11, 23),\n",
       " ('027', 11, 32),\n",
       " ('027', 11, 33),\n",
       " ('027', 12, 23),\n",
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       " ('027', 12, 33),\n",
       " ('028', 0, 32),\n",
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       " ('028', 1, 32),\n",
       " ('028', 1, 33),\n",
       " ('028', 2, 32),\n",
       " ('028', 2, 33),\n",
       " ('028', 3, 32),\n",
       " ('028', 3, 33),\n",
       " ('028', 4, 32),\n",
       " ('028', 4, 33),\n",
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       " ('028', 5, 33),\n",
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       " ('028', 11, 33),\n",
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       " ('029', 3, 33),\n",
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       " ('029', 4, 33),\n",
       " ('029', 5, 32),\n",
       " ('029', 5, 33),\n",
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       " ('029', 11, 33),\n",
       " ('029', 12, 32),\n",
       " ('029', 12, 33),\n",
       " ('030', 0, 19),\n",
       " ('030', 0, 25),\n",
       " ('030', 1, 19),\n",
       " ('030', 1, 25),\n",
       " ('030', 2, 19),\n",
       " ('030', 2, 25),\n",
       " ('030', 3, 19),\n",
       " ('030', 3, 25),\n",
       " ('030', 4, 19),\n",
       " ('030', 4, 25),\n",
       " ('030', 5, 19),\n",
       " ('030', 5, 25),\n",
       " ('030', 6, 19),\n",
       " ('030', 6, 25),\n",
       " ('030', 7, 19),\n",
       " ('030', 7, 25),\n",
       " ('030', 9, 35),\n",
       " ('031', 0, 9),\n",
       " ('031', 0, 32),\n",
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       " ('031', 1, 9),\n",
       " ('031', 1, 32),\n",
       " ('031', 1, 33),\n",
       " ('031', 2, 9),\n",
       " ('031', 2, 32),\n",
       " ('031', 2, 33),\n",
       " ('031', 3, 9),\n",
       " ('031', 3, 32),\n",
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       " ('031', 4, 9),\n",
       " ('031', 4, 32),\n",
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       " ('031', 7, 9),\n",
       " ('031', 7, 32),\n",
       " ('031', 7, 33),\n",
       " ('031', 8, 32),\n",
       " ('031', 8, 33),\n",
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       " ('032', 0, 26),\n",
       " ('032', 0, 32),\n",
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       " ('032', 1, 26),\n",
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       " ('032', 1, 33),\n",
       " ('032', 2, 26),\n",
       " ('032', 2, 32),\n",
       " ('032', 2, 33),\n",
       " ('032', 3, 26),\n",
       " ('032', 3, 32),\n",
       " ('032', 3, 33),\n",
       " ('032', 4, 26),\n",
       " ('032', 4, 32),\n",
       " ('032', 4, 33),\n",
       " ('032', 5, 26),\n",
       " ('032', 5, 32),\n",
       " ('032', 5, 33),\n",
       " ('032', 6, 26),\n",
       " ('032', 6, 32),\n",
       " ('032', 6, 33),\n",
       " ('032', 7, 26),\n",
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       " ('032', 8, 10),\n",
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       " ('032', 9, 32),\n",
       " ('032', 9, 33),\n",
       " ('032', 10, 10),\n",
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       " ('032', 11, 10),\n",
       " ('032', 11, 32),\n",
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       " ('032', 12, 10),\n",
       " ('032', 12, 32),\n",
       " ('032', 12, 33),\n",
       " ('033', 0, 25),\n",
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       " ('033', 1, 32),\n",
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       " ('033', 5, 32),\n",
       " ('033', 5, 33),\n",
       " ('033', 6, 25),\n",
       " ('033', 6, 32),\n",
       " ('033', 6, 33),\n",
       " ('033', 7, 18),\n",
       " ('033', 7, 25),\n",
       " ('033', 7, 32),\n",
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       " ('033', 8, 32),\n",
       " ('033', 8, 33),\n",
       " ('033', 9, 32),\n",
       " ('033', 9, 33),\n",
       " ('033', 10, 32),\n",
       " ('033', 10, 33),\n",
       " ('033', 11, 32),\n",
       " ('033', 11, 33),\n",
       " ('033', 12, 32),\n",
       " ('033', 12, 33),\n",
       " ('034', 0, 32),\n",
       " ('034', 0, 33),\n",
       " ('034', 1, 32),\n",
       " ('034', 1, 33),\n",
       " ('034', 2, 32),\n",
       " ('034', 2, 33),\n",
       " ('034', 3, 32),\n",
       " ('034', 3, 33),\n",
       " ('034', 4, 32),\n",
       " ('034', 4, 33),\n",
       " ('034', 5, 32),\n",
       " ('034', 5, 33),\n",
       " ('034', 6, 32),\n",
       " ('034', 6, 33),\n",
       " ('034', 7, 32),\n",
       " ('034', 7, 33),\n",
       " ...]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_to_five_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Location of Pixels in Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 910,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 0 pixel image \n",
    "for i in z_list:            \n",
    "    if i[0] == '122':\n",
    "        print(f\"Z:{i[1]}\", \" \",f\"ch:{i[2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  0,  0, ..., 12, 12, 12]),\n",
       " array([32, 32, 32, ..., 33, 33, 33]),\n",
       " array([1802, 1803, 1804, ..., 1993, 1994, 1995]),\n",
       " array([2028, 2028, 2028, ..., 2028, 2028, 2028]))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = 'merged/F000.tif'\n",
    "img = imread(img)\n",
    "np.where(img == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:8   ch:14\n"
     ]
    }
   ],
   "source": [
    "# >65k pixel image\n",
    "for i in sixfivek_list:           \n",
    "    if i[0] == '002':\n",
    "        print(f\"Z:{i[1]}\", \" \",f\"ch:{i[2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, ..., 9, 9, 9]),\n",
       " array([28, 28, 28, ..., 28, 28, 28]),\n",
       " array([324, 324, 324, ..., 194, 194, 194]),\n",
       " array([106, 108, 109, ..., 614, 615, 616]))"
      ]
     },
     "execution_count": 736,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = 'merged/F002.tif'\n",
    "img = imread(img)\n",
    "np.where(65000 < img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1-5 pixel image\n",
    "for i in one_to_five_list:            \n",
    "    if i[0] == '000':\n",
    "        print(f\"Z:{i[1]}\", \" \",f\"ch:{i[2]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = 'merged/F000.tif'\n",
    "img = imread(img)\n",
    "np.where((0 < img) & (img < 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
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       "  </thead>\n",
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       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <td>3010</td>\n",
       "      <td>217</td>\n",
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       "    <tr>\n",
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       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <td>3012</td>\n",
       "      <td>217</td>\n",
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       "      <td>12.0</td>\n",
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       "    <tr>\n",
       "      <td>3013</td>\n",
       "      <td>217</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1635 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num     Z  channel\n",
       "0        000   0.0     32.0\n",
       "1        000   0.0     33.0\n",
       "2        000   0.0     37.0\n",
       "3        000   1.0     32.0\n",
       "4        000   1.0     33.0\n",
       "...      ...   ...      ...\n",
       "3009     217   8.0     12.0\n",
       "3010     217   9.0     12.0\n",
       "3011     217  10.0     12.0\n",
       "3012     217  11.0     12.0\n",
       "3013     217  12.0     12.0\n",
       "\n",
       "[1635 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_black_box = pd.DataFrame()\n",
    "for idx, i in enumerate(z_list):\n",
    "    if i[0] in black_box:\n",
    "        df_black_box.loc[idx, 'FOV_num'] = i[0]\n",
    "        df_black_box.loc[idx, 'Z'] = i[1]\n",
    "        df_black_box.loc[idx, 'channel'] = i[2]\n",
    "        \n",
    "df_black_box\n",
    "df_black_box.to_csv('black_box_locations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0.000118</td>\n",
       "      <td>2.702408e-03</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.437456e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count  one to five   greater 65K\n",
       "000     000          0.120376     0.003265  4.360853e-03\n",
       "001     001          0.117057     0.002513  3.498481e-03\n",
       "002     002          0.589060     0.009890  1.353787e-02\n",
       "003     003          0.230241     0.003106  5.943737e-03\n",
       "004     004          0.000067     0.000118  2.702408e-03\n",
       "..      ...               ...          ...           ...\n",
       "220     220          0.000000     0.000000  0.000000e+00\n",
       "221     221          0.000000     0.000000  0.000000e+00\n",
       "222     222          0.000000     0.000000  0.000000e+00\n",
       "223     223          0.000000     0.000000  0.000000e+00\n",
       "224     224          0.000000     0.000000  2.437456e-07\n",
       "\n",
       "[211 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total num of pixels is 3995601 (because 2019*1979)\n",
    "total = img.shape[2] * img.shape[3]\n",
    "# here we get percent of 0 intensity count, 0<x<6 and greater than 65k\n",
    "percents = int_counts.copy()\n",
    "percents['total Zero count'] = percents['total Zero count'].div(total)\n",
    "percents['one to five'] = percents['one to five'].div(total)\n",
    "percents['greater 65K'] = percents['greater 65K'].div(total)\n",
    "percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "percents.to_csv('percent_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>total Zero count</th>\n",
       "      <th>one to five</th>\n",
       "      <th>greater 65K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>004</td>\n",
       "      <td>6.678630e-05</td>\n",
       "      <td>1.177291e-04</td>\n",
       "      <td>0.002702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>005</td>\n",
       "      <td>005</td>\n",
       "      <td>2.803075e-05</td>\n",
       "      <td>6.361760e-05</td>\n",
       "      <td>0.000646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>008</td>\n",
       "      <td>008</td>\n",
       "      <td>4.801789e-05</td>\n",
       "      <td>1.196791e-04</td>\n",
       "      <td>0.001091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>011</td>\n",
       "      <td>011</td>\n",
       "      <td>3.022446e-05</td>\n",
       "      <td>6.727379e-05</td>\n",
       "      <td>0.000836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>014</td>\n",
       "      <td>014</td>\n",
       "      <td>1.043231e-04</td>\n",
       "      <td>2.208335e-04</td>\n",
       "      <td>0.005722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>2.729951e-05</td>\n",
       "      <td>5.996142e-05</td>\n",
       "      <td>0.000289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.437456e-07</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>7.312368e-07</td>\n",
       "      <td>2.437456e-06</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>208</td>\n",
       "      <td>208</td>\n",
       "      <td>7.312368e-07</td>\n",
       "      <td>2.193710e-06</td>\n",
       "      <td>0.000036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>4.631167e-05</td>\n",
       "      <td>8.433598e-05</td>\n",
       "      <td>0.001038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  total Zero count   one to five  greater 65K\n",
       "004     004      6.678630e-05  1.177291e-04     0.002702\n",
       "005     005      2.803075e-05  6.361760e-05     0.000646\n",
       "008     008      4.801789e-05  1.196791e-04     0.001091\n",
       "011     011      3.022446e-05  6.727379e-05     0.000836\n",
       "014     014      1.043231e-04  2.208335e-04     0.005722\n",
       "..      ...               ...           ...          ...\n",
       "198     198      2.729951e-05  5.996142e-05     0.000289\n",
       "200     200      0.000000e+00  2.437456e-07     0.000002\n",
       "204     204      7.312368e-07  2.437456e-06     0.000031\n",
       "208     208      7.312368e-07  2.193710e-06     0.000036\n",
       "214     214      4.631167e-05  8.433598e-05     0.001038\n",
       "\n",
       "[73 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circles = percents.loc[(percents['one to five'] > 0)&(percents['greater 65K'] > 0)&(percents['total Zero count'] < 0.00050055048)]  \n",
    "black_circles\n",
    "# THESE MUST BE THE BLACK CIRCLE ARTIFACT ONES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_circles.to_csv('black_circle_pixel_intensity_table.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "black_circle_lst = []\n",
    "for i in black_circles['FOV_num']:\n",
    "    black_circle_lst.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['004',\n",
       " '005',\n",
       " '008',\n",
       " '011',\n",
       " '014',\n",
       " '023',\n",
       " '024',\n",
       " '025',\n",
       " '028',\n",
       " '029',\n",
       " '034',\n",
       " '039',\n",
       " '040',\n",
       " '042',\n",
       " '044',\n",
       " '046',\n",
       " '047',\n",
       " '050',\n",
       " '053',\n",
       " '054',\n",
       " '060',\n",
       " '066',\n",
       " '068',\n",
       " '070',\n",
       " '073',\n",
       " '074',\n",
       " '084',\n",
       " '095',\n",
       " '096',\n",
       " '097',\n",
       " '098',\n",
       " '101',\n",
       " '102',\n",
       " '105',\n",
       " '106',\n",
       " '112',\n",
       " '113',\n",
       " '114',\n",
       " '115',\n",
       " '116',\n",
       " '118',\n",
       " '126',\n",
       " '127',\n",
       " '128',\n",
       " '132',\n",
       " '133',\n",
       " '134',\n",
       " '135',\n",
       " '142',\n",
       " '144',\n",
       " '145',\n",
       " '147',\n",
       " '149',\n",
       " '151',\n",
       " '153',\n",
       " '156',\n",
       " '167',\n",
       " '170',\n",
       " '171',\n",
       " '179',\n",
       " '185',\n",
       " '186',\n",
       " '188',\n",
       " '189',\n",
       " '190',\n",
       " '192',\n",
       " '193',\n",
       " '194',\n",
       " '198',\n",
       " '200',\n",
       " '204',\n",
       " '208',\n",
       " '214']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "black_circle_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3131</td>\n",
       "      <td>208</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3132</td>\n",
       "      <td>208</td>\n",
       "      <td>12.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3141</td>\n",
       "      <td>214</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3142</td>\n",
       "      <td>214</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3143</td>\n",
       "      <td>214</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1407 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num     Z  channel\n",
       "168      004   0.0     32.0\n",
       "169      004   0.0     33.0\n",
       "170      004   1.0     32.0\n",
       "171      004   1.0     33.0\n",
       "172      004   2.0     32.0\n",
       "...      ...   ...      ...\n",
       "3131     208  11.0     35.0\n",
       "3132     208  12.0     35.0\n",
       "3141     214   8.0     35.0\n",
       "3142     214   9.0     35.0\n",
       "3143     214  10.0     35.0\n",
       "\n",
       "[1407 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_black_circle = pd.DataFrame()\n",
    "count = 0\n",
    "for idx, i in enumerate(one_to_five_list):\n",
    "    if i[0] in black_circle_lst:\n",
    "        df_black_circle.loc[idx, 'FOV_num'] = i[0]\n",
    "        df_black_circle.loc[idx, 'Z'] = i[1]\n",
    "        df_black_circle.loc[idx, 'channel'] = i[2]\n",
    "        count += 1\n",
    "        \n",
    "df_black_circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_black_circle.to_csv('black_circle_locations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = imread('merged/F000.tif')\n",
    "image = image[0, 32, ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f229444af50>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 0\n",
    "binary_mask = image <= t\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7), sharex=True, sharey=True)\n",
    "plt.title('Zero Intensity Masked Image')\n",
    "plt.imshow(binary_mask, cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 971,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('002', 0, 2),\n",
       " ('002', 0, 28),\n",
       " ('002', 1, 2),\n",
       " ('002', 1, 28),\n",
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       " ('220', 6, 3),\n",
       " ('220', 7, 3),\n",
       " ('221', 8, 7),\n",
       " ('221', 9, 7),\n",
       " ('224', 0, 34),\n",
       " ('224', 1, 34),\n",
       " ('224', 2, 34),\n",
       " ('224', 2, 35),\n",
       " ('224', 3, 34),\n",
       " ('224', 3, 35),\n",
       " ('224', 4, 34),\n",
       " ('224', 4, 35),\n",
       " ('224', 5, 34),\n",
       " ('224', 5, 35),\n",
       " ('224', 6, 34),\n",
       " ('224', 6, 35),\n",
       " ('224', 7, 34)]"
      ]
     },
     "execution_count": 971,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2800e779725041648703a0546880ed35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=211), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "FOV 001\n",
      "FOV 002\n",
      "FOV 003\n",
      "FOV 004\n",
      "FOV 005\n"
     ]
    }
   ],
   "source": [
    "# BINARY MASKING FOR 0 INTENSITY PIXELS -- ANOTHER WAY OF FINDING LOCATION OF BLACK BOXES\n",
    "validation = []\n",
    "t = 0\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in tqdm(range(NUM_FOVS)):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "\n",
    "    for Z in range(img.shape[0]):\n",
    "        for ch in range(img.shape[1]): \n",
    "            binary_mask = img[Z,ch,...] <= t\n",
    "            if binary_mask.sum() > 2000:\n",
    "                validation.append((FOV_num, Z, ch))\n",
    "                \n",
    "done = open(\"done.txt\", \"a\")\n",
    "done.write(\"done\")\n",
    "done.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>002</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>002</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>002</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>002</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>002</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>531</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>532</td>\n",
       "      <td>224</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>533</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>534</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>535</td>\n",
       "      <td>224</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  Z  channel\n",
       "0       002  0        2\n",
       "1       002  0       28\n",
       "2       002  1        2\n",
       "3       002  1       28\n",
       "4       002  2        2\n",
       "..      ... ..      ...\n",
       "531     224  3       34\n",
       "532     224  4       34\n",
       "533     224  5       34\n",
       "534     224  6       34\n",
       "535     224  7       34\n",
       "\n",
       "[536 rows x 3 columns]"
      ]
     },
     "execution_count": 994,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation = pd.DataFrame(validation)\n",
    "df_validation = df_validation.rename(columns={0: \"FOV_num\", 1: \"Z\", 2:'channel'})\n",
    "df_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation.to_csv('black_box_locations_correct.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1312,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = pd.read_csv('black_box_locations_correct.csv')\n",
    "df_validation = df_validation.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1320,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_lst = list(df_validation.itertuples(index=False, name=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1326,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55d5da288eec4ddbaf6ff65e6758afb7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for idx, val in tqdm(enumerate(validation_lst)):\n",
    "    FOV = str(val[0]).zfill(3)\n",
    "    img = imread(f'merged/F{FOV}.tif')\n",
    "    Z = val[1]\n",
    "    ch = val[2]\n",
    "    \n",
    "    cnt = np.count_nonzero(img[Z, ch, ...] == 0)\n",
    "    ttl = img.shape[2]*img.shape[3]\n",
    "    df_validation.loc[idx, 'percent_bbox'] = (cnt/ttl)*100\n",
    "df_validation.to_csv('bbox_locs_and_percs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1327,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "      <th>percent_bbox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.640204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>4.576158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.640204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4.571352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.640204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>531</td>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>1.566723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>532</td>\n",
       "      <td>224</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>1.567749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>533</td>\n",
       "      <td>224</td>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "      <td>1.569101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>534</td>\n",
       "      <td>224</td>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>1.570527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>535</td>\n",
       "      <td>224</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>1.554835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>536 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FOV_num  Z  channel  percent_bbox\n",
       "0          2  0        2      1.640204\n",
       "1          2  0       28      4.576158\n",
       "2          2  1        2      1.640204\n",
       "3          2  1       28      4.571352\n",
       "4          2  2        2      1.640204\n",
       "..       ... ..      ...           ...\n",
       "531      224  3       34      1.566723\n",
       "532      224  4       34      1.567749\n",
       "533      224  5       34      1.569101\n",
       "534      224  6       34      1.570527\n",
       "535      224  7       34      1.554835\n",
       "\n",
       "[536 rows x 4 columns]"
      ]
     },
     "execution_count": 1327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 983,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'002',\n",
       " '008',\n",
       " '010',\n",
       " '011',\n",
       " '016',\n",
       " '017',\n",
       " '019',\n",
       " '026',\n",
       " '030',\n",
       " '031',\n",
       " '035',\n",
       " '036',\n",
       " '037',\n",
       " '038',\n",
       " '040',\n",
       " '042',\n",
       " '044',\n",
       " '045',\n",
       " '048',\n",
       " '050',\n",
       " '051',\n",
       " '052',\n",
       " '054',\n",
       " '059',\n",
       " '060',\n",
       " '062',\n",
       " '066',\n",
       " '071',\n",
       " '072',\n",
       " '075',\n",
       " '079',\n",
       " '080',\n",
       " '083',\n",
       " '084',\n",
       " '085',\n",
       " '088',\n",
       " '094',\n",
       " '101',\n",
       " '111',\n",
       " '113',\n",
       " '114',\n",
       " '117',\n",
       " '120',\n",
       " '124',\n",
       " '127',\n",
       " '128',\n",
       " '130',\n",
       " '133',\n",
       " '134',\n",
       " '135',\n",
       " '137',\n",
       " '138',\n",
       " '139',\n",
       " '143',\n",
       " '151',\n",
       " '152',\n",
       " '155',\n",
       " '160',\n",
       " '169',\n",
       " '173',\n",
       " '178',\n",
       " '182',\n",
       " '185',\n",
       " '187',\n",
       " '190',\n",
       " '197',\n",
       " '208',\n",
       " '214',\n",
       " '220',\n",
       " '221',\n",
       " '224'}"
      ]
     },
     "execution_count": 983,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_FOVs = set()\n",
    "for i in validation:\n",
    "    validation_FOVs.add(i[0])\n",
    "validation_FOVs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 988,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_FOVs = np.array(sorted(list(validation_FOVs)))\n",
    "black_box_arr = np.array(black_box)\n",
    "np.array_equal(validation_FOVs, black_box_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = imread('merged/F000.tif')\n",
    "arr = arr[0, 32, ...]\n",
    "#plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2012, 2019)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len((np.where(arr == 0))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent = 0\n",
    "while np.percentile(arr, percent) == 0:\n",
    "    print(np.percentile(arr, percent), percent)\n",
    "    percent += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 211"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cfc8141c5dd42859e2bd7d7182f0e8e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=211), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n"
     ]
    }
   ],
   "source": [
    "percentiles = pd.DataFrame()\n",
    "\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in tqdm(range(NUM_FOVS)):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "\n",
    "    for ch in range(img.shape[1]): \n",
    "        arr = img[:,ch,...]\n",
    "        percent = 0\n",
    "        while np.percentile(arr, percent) == 0:\n",
    "            percent += 1\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_cutoff_perc'] = percent # by the +1, you will know if there are ANY 0's\n",
    "        \n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0th_perc'] = np.percentile(arr, 0)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.001st_perc'] = np.percentile(arr, 0.001)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.01st_perc'] = np.percentile(arr, 0.01)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.1st_perc'] = np.percentile(arr, 0.1)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_0.5th_perc'] = np.percentile(arr, 0.5)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_1st_perc'] = np.percentile(arr, 1)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_5th_perc'] = np.percentile(arr, 5)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_10th_perc'] = np.percentile(arr, 10)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_90th_perc'] = np.percentile(arr, 90)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_95th_perc'] = np.percentile(arr, 95)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_99th_perc'] = np.percentile(arr, 99)\n",
    "        percentiles.loc[FOV_num, f'ch{ch}_100th_perc'] = np.percentile(arr, 100)\n",
    "\n",
    "\n",
    "percentiles.to_csv('percentiles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1236,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ch0_0th_perc</th>\n",
       "      <th>ch1_0th_perc</th>\n",
       "      <th>ch2_0th_perc</th>\n",
       "      <th>ch3_0th_perc</th>\n",
       "      <th>ch4_0th_perc</th>\n",
       "      <th>ch5_0th_perc</th>\n",
       "      <th>ch6_0th_perc</th>\n",
       "      <th>ch7_0th_perc</th>\n",
       "      <th>ch8_0th_perc</th>\n",
       "      <th>ch9_0th_perc</th>\n",
       "      <th>...</th>\n",
       "      <th>ch28_0th_perc</th>\n",
       "      <th>ch29_0th_perc</th>\n",
       "      <th>ch30_0th_perc</th>\n",
       "      <th>ch31_0th_perc</th>\n",
       "      <th>ch32_0th_perc</th>\n",
       "      <th>ch33_0th_perc</th>\n",
       "      <th>ch34_0th_perc</th>\n",
       "      <th>ch35_0th_perc</th>\n",
       "      <th>ch36_0th_perc</th>\n",
       "      <th>ch37_0th_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>37.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>38.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>48.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>41.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>36.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>49.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>29.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>...</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>42.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>51.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>39.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ch0_0th_perc  ch1_0th_perc  ch2_0th_perc  ch3_0th_perc  ch4_0th_perc  \\\n",
       "000          55.0          35.0          59.0          56.0          45.0   \n",
       "001          43.0          39.0          65.0          49.0          56.0   \n",
       "002          37.0          53.0           0.0          71.0          38.0   \n",
       "003          38.0          53.0          67.0          68.0          64.0   \n",
       "004          41.0          44.0          55.0          55.0          57.0   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "219          36.0          52.0          52.0          63.0          81.0   \n",
       "220          29.0          56.0          48.0           0.0          62.0   \n",
       "221          42.0          51.0          58.0          57.0          57.0   \n",
       "222          51.0          59.0          59.0          69.0          71.0   \n",
       "224          39.0          57.0          67.0          67.0           7.0   \n",
       "\n",
       "     ch5_0th_perc  ch6_0th_perc  ch7_0th_perc  ch8_0th_perc  ch9_0th_perc  \\\n",
       "000          48.0          44.0          38.0          43.0          43.0   \n",
       "001          51.0          48.0          50.0          43.0          49.0   \n",
       "002          46.0          27.0          36.0          54.0          52.0   \n",
       "003          58.0          57.0          38.0          49.0          39.0   \n",
       "004          53.0          56.0          44.0          46.0          28.0   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "219          81.0          72.0          78.0          79.0          76.0   \n",
       "220          76.0          71.0          71.0          78.0          76.0   \n",
       "221          59.0          51.0           0.0          72.0          63.0   \n",
       "222          64.0          72.0          71.0          71.0          66.0   \n",
       "224          75.0          73.0          68.0          61.0          57.0   \n",
       "\n",
       "     ...  ch28_0th_perc  ch29_0th_perc  ch30_0th_perc  ch31_0th_perc  \\\n",
       "000  ...           57.0           70.0           71.0           75.0   \n",
       "001  ...           55.0           51.0           67.0           40.0   \n",
       "002  ...            0.0           43.0           51.0           54.0   \n",
       "003  ...           48.0           38.0           55.0           61.0   \n",
       "004  ...           52.0           49.0           42.0           57.0   \n",
       "..   ...            ...            ...            ...            ...   \n",
       "219  ...           49.0           50.0           61.0           52.0   \n",
       "220  ...           59.0           62.0           64.0           60.0   \n",
       "221  ...           63.0           62.0           63.0           69.0   \n",
       "222  ...           52.0           55.0           61.0           54.0   \n",
       "224  ...           75.0           75.0           75.0           73.0   \n",
       "\n",
       "     ch32_0th_perc  ch33_0th_perc  ch34_0th_perc  ch35_0th_perc  \\\n",
       "000           49.0           44.0           60.0           80.0   \n",
       "001           55.0           50.0           36.0           83.0   \n",
       "002           56.0           52.0           46.0           91.0   \n",
       "003           47.0           37.0           40.0            0.0   \n",
       "004           24.0           46.0           39.0           77.0   \n",
       "..             ...            ...            ...            ...   \n",
       "219           54.0           40.0           46.0           89.0   \n",
       "220           47.0           21.0           58.0           99.0   \n",
       "221           58.0           32.0           69.0           93.0   \n",
       "222           44.0           41.0           48.0           90.0   \n",
       "224           51.0           41.0            0.0            0.0   \n",
       "\n",
       "     ch36_0th_perc  ch37_0th_perc  \n",
       "000           73.0           49.0  \n",
       "001           49.0           46.0  \n",
       "002           70.0           49.0  \n",
       "003           67.0           36.0  \n",
       "004           58.0           32.0  \n",
       "..             ...            ...  \n",
       "219           95.0           34.0  \n",
       "220           90.0           55.0  \n",
       "221           91.0           58.0  \n",
       "222           87.0           40.0  \n",
       "224           79.0           56.0  \n",
       "\n",
       "[211 rows x 38 columns]"
      ]
     },
     "execution_count": 1236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeroth_perc = percentiles.filter(regex=(\".*_0th.*\"))\n",
    "zeroth_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  2.,  0.,  1.,  3., 15., 13.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  0.,  1.]),\n",
       " array([ 60. ,  63.6,  67.2,  70.8,  74.4,  78. ,  81.6,  85.2,  88.8,\n",
       "         92.4,  96. ,  99.6, 103.2, 106.8, 110.4, 114. , 117.6, 121.2,\n",
       "        124.8, 128.4, 132. ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 1241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sf3GX837g9CnnfJDB8eM7uq9Lp51zlaxf6H6f7gL+EThuo597T6WXpEbN5CEUSdLaLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUqP8FKyZPA5Tt1a8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of maz 0th percentile values across all channels\n",
    "\n",
    "plt.hist(zeroth_perc.max().to_frame()[0],bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Channel 35 -- percentiles')"
      ]
     },
     "execution_count": 1225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Channel 35\n",
    "plt.hist(percentiles['ch35_0th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0_perc')\n",
    "plt.hist(percentiles['ch35_0.001st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.001_perc')\n",
    "plt.hist(percentiles['ch35_0.1st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.1_perc')\n",
    "plt.hist(percentiles['ch35_5th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '5_perc')\n",
    "plt.hist(percentiles['ch35_10th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '10_perc')\n",
    "plt.legend(loc='upper left')\n",
    "plt.suptitle(f\"Channel 35 -- percentiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Channel 36 -- percentiles')"
      ]
     },
     "execution_count": 1226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZP38+ALNmzWLmzJlkZWXh7mRmZrJ8+fIzzh05ciSbN28mOzubs846i1GjRvFgwR0N0Foikky0HngSyMvL49FHHyU7+4zlfuuukjHw7e/8lV79B1ZSWUSSmdYDFxFpYpr9EEpjmj59OuvXr69QNmPGDIqKihITkIiEmhJ4I3r66acTHYKINCExJXAz2w18ApwETrh7tpmlAoVAJrAbGOfuhxsmTBEROV1txsCvcvfLogbSC4DV7t4DWB3si4hII6nPh5jXAwuD7YXADfUPR0REYhXrGLgDK83MgR+7+zzgQnffD+Du+82sY2Unmtk0YBpARkZGjU/0zOZnYgwpNrdfdntcrycikixi7YFf4e4DgGuA6WY2JNYncPd57p7t7tlpaWl1CrKh1bQe+Nq1axkwYAApKSksXrw4ARGKiJwppgTu7u8FjweA3wC5wAdm1gkgeDzQUEE2pFjWA8/IyGDBggVMmDChUeIREYlFjQnczNqa2bll28BwYCuwDJgUVJsELK38CsktlvXAMzMzycrK4itfqfnvXVFREUOGDGHMmDH07t2b2267jVOnTgGwcuVKBg0axIABAxg7dizHjh0rv/7s2bO58sorefHFF9m5cydXX301/fr1Y8CAAbzzzjvxf+EiEnqxjIFfCPwmWO0uBfilu68wszeAX5nZVGAPMLbhwmw4sawHXlvFxcVs27aNLl26MHLkSH7961+Tl5fH/fffzyuvvELbtm15+OGHeeyxx7jnnnsAaNWqFevWrQPg8ssvp6CggDFjxnD8+PHyPwDxFv15gz4rEAmfGhO4u78L9Kuk/BAwrCGCakzxXNO7TG5uLt26dQMii1qtW7eOVq1asW3bNq644goAPv/8cwYNGlR+zk033QTAJ598wr59+xgzZgwQSewiIpVp9ndixnNN7zJVre2dn5/PCy+8UOk5Zet7N+biYiISbkmXwBv7rXws64HXVnFxMbt27aJLly4UFhYybdo0Bg4cyPTp09m5cyfdu3fn008/paSkhEsuuaTCueeddx7p6eksWbKEG264gc8++4yTJ0+Wf9tOQ9Fwikj4NPvVCKPXA+/Vqxfjxo0rXw982bJlALzxxhukp6fz4osvcuutt9KnT59qrzlo0CAKCgro27cvXbt2ZcyYMaSlpbFgwQLGjx9PVlYWAwcO5M9//nOl5//85z/nySefJCsri8GDB/P+++/H/XWLSPglXQ88EUaNGsWoUaMqlM2ePbt8Oycnh5KSkpiv16ZNGwoLC88oHzp0KG+88cYZ5bt3766w36NHD/7whz/E/Hwi0jw1+x64iEhYqQdeR3/605+YOHFihbKzzz6bDRs2kJeXl5igRKRZUQKvo0svvZTNmzcnOgwRacY0hCIiElJK4CIiIaUELiISUkk3Bn5w7lNxvV7aHd+O6/VERJJF0iXwRMnMzOTcc8+lRYsWpKSksHHjxkSHJCJSLSXwKGvWrKFDhw4Ndv0TJ06QkpL8Ta7b6kXCQWPgtZSXl8fMmTMZPHgwffv2pbi4GIDS0lKmTJlCTk4O/fv3L19TfMGCBYwdO5brrruO4cOHA/DII49w6aWX0q9fPwoK9F3QIlI3yd8dbCRmxvDhwzEzbr31VqZNm1Zl3dLSUl599VXWrl3LlClT2Lp1Kw888ABDhw5l/vz5HDlyhNzcXK6++moAXnvtNbZs2UJqaiovv/wyS5YsYcOGDbRp04aPPvqosV6iiDQxSuCB9evX07lzZw4cOEB+fj5/+7d/y5AhlX/15/jx4wEYMmQIR48e5ciRI6xcuZJly5bx6KOPAnD8+HH27NkDQH5+PqmpqQC88sorTJ48uXx1wbJyEZHaUgIPlK0B3rFjR8aMGUNxcXGVCbyq9b5feuklevbsWeHYhg0bytf6hsh63/X9wggREUjCBJ6IaX+lpaWcOnWKc889l9LSUlauXFn+VWeVKSws5KqrrmLdunW0a9eOdu3aMWLECObOncvcuXMxMzZt2kT//v3POHf48OHMnj2bCRMmlA+hqBcuInWRdAk8ET744IPyrzA7ceIEEyZMYOTIkVXWb9++PYMHD+bo0aPMnz8fgFmzZjFz5kyysrJwdzIzM1m+fPkZ544cOZLNmzeTnZ3NWWedxahRo3jwwQcb5oWJSJNmjfkVXtnZ2X76/Ort27fTq1evRouhvvLy8nj00UfJzs5OdCjVO7r/jKLt7/yVXv0Hlu9HTxesiqYRiiSemb3p7mckHU0jFBEJKQ2hVGH69OmsX7++QtmMGTMoKipKTEAiIqdRAq/C008/negQRESqpSEUEZGQUgIXEQkpJXARkZCKeQzczFoAG4F97v41M+sKLAJSgT8CE9398/oGVPzbd+t7iQpyr+sW1+uJiCSL2vTAZwDbo/YfBh539x7AYWBqPANrTFOmTKFjx4707du3vOyjjz4iPz+fHj16kJ+fz+HDhxMYoYjImWJK4GaWDlwL/DTYN2AosDioshC4oSECbAy33HILK1asqFA2Z84chg0bxo4dOxg2bBhz5sxpkOd2d06dOtUg1xaRpi3WHvi/A/8MlGWaC4Aj7n4i2C8BLqrsRDObZmYbzWzjwYMH6xVsQxkyZMgZ65EsXbqUSZMmATBp0iSWLFlS5fn33nsvEydOZOjQofTo0YOf/OQn5cd++MMfkpOTQ1ZWFj/4wQ8A2L17N7169eL2229nwIAB7N27lxUrVjBgwAD69evHsGHDGuBVikhTU+MYuJl9DTjg7m+aWV5ZcSVVK70n393nAfMgcit9HeNsdB988AGdOnUCoFOnThw4cKDa+lu2bOH111+ntLSU/v37c+2117J161Z27NhBcXEx7s7o0aNZu3YtGRkZvP322zz//PM888wzHDx4kG9961usXbuWrl27ao1wEYlJLB9iXgGMNrNRQCvgPCI98vPNLCXohacD7zVcmMnv+uuvp3Xr1rRu3ZqrrrqK4uJi1q1bx8qVK8tXJTx27Bg7duwgIyODLl26MHBgZF2S119/nSFDhtC1a1dAa4SLSGxqTODufjdwN0DQA/+uu3/dzF4EbiQyE2USsLQB42x0F154Ifv376dTp07s37+fjh07Vlu/qjXC7777bm699dYKx3bv3q01wkWk3upzK/1dwCIzux/YBDwXj4CSZdrf6NGjWbhwIQUFBSxcuJDrr7++2vpLly7l7rvvprS0lKKiIubMmUPr1q2ZNWsWX//61znnnHPYt28fLVu2POPcQYMGMX36dHbt2lU+hKJeuIjUpFYJ3N2LgKJg+10gN/4hNb7x48dTVFTEhx9+SHp6Ovfddx8FBQWMGzeO5557joyMDF588cVqr5Gbm8u1117Lnj17mDVrFp07d6Zz585s376dQYMGAXDOOefwi1/8ghYtWlQ4Ny0tjXnz5vH3f//3nDp1io4dO7Jq1aoGe70i0jRoMSvghRdeqLR89erVMV/jkksuYd68eWeUz5gxgxkzZpxRvnXr1gr711xzDddcc03MzyciolvpRURCSj3wWnj++ed54oknKpRdccUVWnpWRBIiKRJ4WGZhTJ48mcmTJyc6jDqJfHVeaKbhi0gMEj6E0qpVKw4dOkRjfjdnc+PuHDpaSqsTxxIdiojEUcJ74Onp6ZSUlJCst9mH1vGPo3acVieOkV66BchPVEQiEmcJT+AtW7YsvwNR4mjNQ4mOQEQaWMKHUEREpG6UwEVEQkoJXEQkpJTARURCSglcRCSklMBFREJKCVxEJKSUwEVEQirhN/JII4u+wad9u8TFISL1ph64iEhIKYGLiISUEriISEgpgYuIhJQSuIhISCmBi4iElBK4iEhIKYGLiISUEriISEgpgYuIhFSNt9KbWStgLXB2UH+xu//AzLoCi4BU4I/ARHf/vCGDbW4eX/WX8u078y9JYCQikoxi6YF/Bgx1937AZcBIMxsIPAw87u49gMPA1IYLU0RETldjAveIY8Fuy+DHgaHA4qB8IXBDg0QoIiKVimkM3MxamNlm4ACwCngHOOLuJ4IqJcBFDROiiIhUJqYE7u4n3f0yIB3IBXpVVq2yc81smpltNLONBw8erHukIiJSQa1mobj7EaAIGAicb2ZlH4KmA+9Vcc48d8929+y0tLT6xCoiIlFqTOBmlmZm5wfbrYGrge3AGuDGoNokYGlDBSkiImeK5Rt5OgELzawFkYT/K3dfbmbbgEVmdj+wCXiuAeMUEZHT1JjA3X0L0L+S8neJjIeLiEgC6E5MEZGQUgIXEQkpJXARkZBSAhcRCSklcBGRkIplGqEkAa1MKCKnUw9cRCSklMBFREJKCVxEJKQ0Bt6UrHko0RGISCNSD1xEJKSUwEVEQkpDKEkmerqgiEh11AMXEQkpJXARkZBSAhcRCSklcBGRkFICFxEJKSVwEZGQUgIXEQkpJXARkZBSAhcRCSklcBGRkNKt9FKtZzY/U759+2W3x3TOwblPlW+n3fHtuMckIhHqgYuIhJQSuIhISNWYwM3sYjNbY2bbzewtM5sRlKea2Soz2xE8tm/4cEVEpEwsPfATwD+5ey9gIDDdzHoDBcBqd+8BrA72RUSkkdSYwN19v7v/Mdj+BNgOXARcDywMqi0EbmioIEVE5Ey1GgM3s0ygP7ABuNDd90MkyQMd4x2ciIhULeZphGZ2DvASMNPdj5pZrOdNA6YBZGRk1CXGJk/fwiMidRFTD9zMWhJJ3v/h7r8Oij8ws07B8U7AgcrOdfd57p7t7tlpaWnxiFlERIhtFooBzwHb3f2xqEPLgEnB9iRgafzDExGRqsQyhHIFMBH4k5ltDsq+D8wBfmVmU4E9wNiGCVFERCpTYwJ393VAVQPew+IbjoiIxEp3YoqIhJQSuIhISGk1whCKnnZ4Z/4lCYzkS9ErEMZSR6sUitSfeuAiIiGlBC4iElIaQmnOdv3Xl9tdv9ogTxHL0IqI1I164CIiIaUELiISUkrgIiIhpTFwSSqaaigSO/XARURCSglcRCSkNISSIGH8EodnNj9Tvn37ZbcnMBIRAfXARURCSwlcRCSklMBFREJKY+CScLrdXqRu1AMXEQkpJXARkZDSEEoT8tq7h8q3B3W7IIGR1GzNrMLy7b4dExiISIipBy4iElJK4CIiIaUELiISUhoDl4TbeiCtfLtvx4OV1in+7bvl27nXdavV9etzrkgyUw9cRCSklMBFREKqxiEUM5sPfA044O59g7JUoBDIBHYD49z9cMOFKc1F9HAKUVMN2+bmlG9rSEQkIpYe+AJg5GllBcBqd+8BrA72RUSkEdWYwN19LfDRacXXAwuD7YXADXGOS0REalDXMfAL3X0/QPCoe+lERBpZg08jNLNpwDSAjIyMhn66pBP9zTt35l+SwEhqsOu/vtzu+tVan/7G+2+Ub+f8r5xqatZNafEblR+IGgPX2Lg0N3XtgX9gZp0AgscDVVV093nunu3u2WlpaVVVExGRWqprAl8GTAq2JwFL4xOOiIjEKpZphC8AeUAHMysBfgDMAX5lZlOBPcDYhgyyqWiILzKOvubAuF89+UUPm8RSHst1NPwiYVFjAnf38VUcGhbnWEREpBZ0J6aISEgpgYuIhJRWI5Rmq7bj5CLJRj1wEZGQUgIXEQkpDaHImWK4K/OZzc9U2M9swHAam6YUSlioBy4iElJK4CIiIaUELiISUhoDl7iramXCCt+2kyAH5z715U7mqBrrazxckpl64CIiIaUELiISUhpCqYfQfFlDMxH9pQ/RX4IcLXoYp21mQ0dUNQ3NSDyoBy4iElJK4CIiIaUELiISUqEfA9c4dOVee/dQpeWDul3QyJE0rCq/7DiGOrGMmVclejpi2h3frrJeso11J1s8Uj/qgYuIhJQSuIhISIV+CCVZNMQXFieFKlYmzHxxQ60v9d6xfeXbnc+5qF5h1SSWoZWq6kcPp0SXH9z9u/LtCneVzios37zqX2+q8jli+QLm2g5rVHVNDY80D+qBi4iElBK4iEhIKYGLiIRUkxoDr2ocuj7TC0+/ZrJNVRy4Z16t6leYXpga52AqsfLdzystb8zx8HiJZTXFNVHj4aeraqpi9Dj7mqjt6PH0NTGOs1cmli9vbuyx9FjG/eNVpylTD1xEJKSUwHqtyIUAAARlSURBVEVEQqpeQyhmNhJ4AmgB/NTd58QlqkrU547LeN6tGfbpgsu+srN8+2Ja1+rcWKcOHv60X62um2zDKbWdgthQKnz5BGmVlpdWNazTAMMJsQzjxDJcU536np9MGmN4p849cDNrATwNXAP0BsabWe94BSYiItWrzxBKLrDT3d9198+BRcD18QlLRERqUp8EfhGwN2q/JCgTEZFGYO5etxPNxgIj3P2bwf5EINfd7zit3jRgWrDbE3i7jrF2AD6s47nNhdqoemqf6ql9apaoNuri7md84FGfDzFLgIuj9tOB906v5O7zgNpNVq6EmW109+z6XqcpUxtVT+1TPbVPzZKtjeozhPIG0MPMuprZWcDNwLL4hCUiIjWpcw/c3U+Y2beB3xOZRjjf3d+KW2QiIlKtes0Dd/ffAb+rsWJ81HsYphlQG1VP7VM9tU/NkqqN6vwhpoiIJJZupRcRCalQJHAzG2lmb5vZTjMrSHQ8iWBm883sgJltjSpLNbNVZrYjeGwflJuZPRm01xYzG5C4yBuHmV1sZmvMbLuZvWVmM4JytVHAzFqZWbGZ/XfQRvcF5V3NbEPQRoXBpATM7Oxgf2dwPDOR8TcWM2thZpvMbHmwn7Ttk/QJXLfsl1sAjDytrABY7e49gNXBPkTaqkfwMw34USPFmEgngH9y917AQGB68O9EbfSlz4Ch7t4PuAwYaWYDgYeBx4M2OgxMDepPBQ67e3fg8aBeczAD2B61n7zt4+5J/QMMAn4ftX83cHei40pQW2QCW6P23wY6BdudgLeD7R8D4yur11x+gKVAvtqoyvZpA/wRuJzIjSkpQXn5/zciM8wGBdspQT1LdOwN3C7pRP7QDwWWA5bM7ZP0PXB0y351LnT3/QDBY8egvFm3WfBWtj+wAbVRBcHwwGbgALAKeAc44u4ngirR7VDeRsHxj4ELGjfiRvfvwD8Dp4L9C0ji9glDArdKyjR1pnrNts3M7BzgJWCmux+trmolZU2+jdz9pLtfRqSnmQv0qqxa8Nis2sjMvgYccPc3o4srqZo07ROGBB7TLfvN1Adm1gkgeDwQlDfLNjOzlkSS93+4+6+DYrVRJdz9CFBE5POC882s7J6Q6HYob6PgeDvgo8aNtFFdAYw2s91EVlcdSqRHnrTtE4YErlv2q7YMmBRsTyIy7ltW/o1gpsVA4OOyYYSmyswMeA7Y7u6PRR1SGwXMLM3Mzg+2WwNXE/mwbg1wY1Dt9DYqa7sbgT94MODbFLn73e6e7u6ZRPLMH9z96yRz+yT6Q4MYP1gYBfyFyHjdvyQ6ngS1wQvAfuALIn/5pxIZb1sN7AgeU4O6RmTmzjvAn4DsRMffCO1zJZG3r1uAzcHPKLVRhTbKAjYFbbQVuCco7wYUAzuBF4Gzg/JWwf7O4Hi3RL+GRmyrPGB5sreP7sQUEQmpMAyhiIhIJZTARURCSglcRCSklMBFREJKCVxEJKSUwEVEQkoJXEQkpJTARURC6v8DYRUxb6gNiqkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Channel 36\n",
    "plt.hist(percentiles['ch36_0th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0_perc')\n",
    "plt.hist(percentiles['ch36_0.001st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.001_perc')\n",
    "plt.hist(percentiles['ch36_0.1st_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '0.1_perc')\n",
    "plt.hist(percentiles['ch36_5th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '5_perc')\n",
    "plt.hist(percentiles['ch36_10th_perc'], bins = 100, alpha=0.5, range=[0, 420], label = '10_perc')\n",
    "plt.legend(loc='upper left')\n",
    "plt.suptitle(f\"Channel 36 -- percentiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a78e1bbff44540e5ab7e22aa4b0533f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=38), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/egunim/.conda/envs/all_your_base/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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d/PyYQzYnjV5vqc5SceYePZ771txBR03LlCF/yxvPXZdCioiEkLplqokJEyawatWqo9omTpzI8uXL01OQiGQ0hXs18cgjj6S7BBEJEXXLiIiEkMJdRCSEFO4iIiFUYZ+7mc0GfgDscff2Qdt8oOSrkWcA+9y9k5nlAhuBj4Jpb7r79ckoNPpSqGTQFyFEJMziOXOfAwyIbnD3K9y9k7t3InLj7OejJm8pmZasYE+nisZzX7FiBV26dKF27do899xzaahQROR4FYa7u68AYg4lGNw8exgwL8l1VQvxjOeek5PDnDlzGDly5AmpR0QkHon2uX8f2O3um6LaWpjZu2b2X2b2/bIWNLPxZrbGzNYUFxcnWEZqxDOee25uLnl5eXznOxW/lMuXL6dnz54MGTKE8847j+uvv54jR44AsHTpUrp3706XLl0YOnQoX331Ven6p06dyoUXXsizzz7L5s2bufjii+nYsSNdunRhy5Ytyd9xEcl4iYb7CI4+a98F5Lh7Z+BnwB/N7PRYC7r7THfPd/f8rKysBMtIjVjjuVd1TPYShYWF3Hfffbz//vts2bKF559/nk8//ZRf/epXvPrqq7zzzjvk5+dz//33ly5Tt25dVq5cyfDhw7nyyiuZMGEC7733Hq+//jpNmzZNqB4RCacqf4nJzGoDPwT+raTN3Q8CB4PHb5vZFuBcYE3MlVRzyRyTvURBQQEtW0bGrRgxYgQrV66kbt26bNiwgQsuuACAf/3rX3Tv3r10mSuuuAKAL7/8kp07dzJkyBAgEvoiIrEk8g3Vi4EP3b2opMHMsoDP3P2wmbUEWgMfJ1hj2iRzTPYSZY3N3rdvX+bNi/3RRcn47NVhkDcRyQzxXAo5D+gFNDazIuB2d58FDOf4D1J7AlPN7BBwGLje3ZNyX7d0XLoYz3julVVYWMjWrVs555xzmD9/PuPHj6dbt25MmDCBzZs306pVK77++muKioo499xzj1r29NNPJzs7m4ULF3L55Zdz8OBBDh8+XHqXJRGREvFcLTPC3Zu6ex13zw6CHXcf4+6PHzPvAndv5+4d3b2Lu/85VYWfCNHjubdt25Zhw4aVjue+ePFiAN566y2ys7N59tlnue6662jXrl256+zevTuTJ0+mffv2tGjRgiFDhpCVlcWcOXMYMWIEeXl5dOvWjQ8//DDm8k8//TQPPfQQeXl59OjRg3/84x9J328RyXwaOKwCgwYNYtCgo8d6njp1aunjrl27UlRUdOxiZapfvz7z588/rr1379689dZbx7Vv27btqOetW7fmr3/9a9zbE5GaScMPiIiEkM7cU+D9999n1KhRR7WdfPLJrF69ml69eqWnKBGpURTuKdChQwfWrl2b7jJEpAZTt4yISAgp3EVEQkjhLiISQhnT51484+Gkri/rJzcmdX0iItVJxoR7uuTm5nLaaadRq1YtateuzZo1GTlMjojUMAr3OLz22ms0btw4Zes/dOgQtWvrUIhI8qjPPUl69erFpEmT6NGjB+3bt6ewsBCAAwcOMG7cOLp27Urnzp1Lx4OfM2cOQ4cO5dJLL6Vfv34A3HvvvXTo0IGOHTsyefLktO2LiGQ+nS5WwMzo168fZsZ1113H+PHjy5z3wIEDvP7666xYsYJx48axfv167rrrLnr37s3s2bPZt28fBQUFXHzxxQC88cYbrFu3jkaNGvHyyy+zcOFCVq9eTf369fnss6SMtyYiNZTCvQKrVq2iWbNm7Nmzh759+/K9732Pnj17xpx3xIgRAPTs2ZP9+/ezb98+li5dyuLFi5k+fToA33zzDdu3bwegb9++NGrUCIBXX32VsWPHlo7wWNIuIlIVCvcKlIzf3qRJE4YMGUJhYWGZ4V7WWO0LFiygTZs2R01bvXp16TjtEBmrPdEbgYiIlMiYcE/HpYsHDhzgyJEjnHbaaRw4cIClS5dy2223lTn//Pnzueiii1i5ciUNGjSgQYMG9O/fnxkzZjBjxgzMjHfffZfOnTsft2y/fv2YOnUqI0eOLO2W0dm7iFRVxoR7Ouzevbv0lnaHDh1i5MiRDBgwoMz5GzZsSI8ePdi/fz+zZ88G4NZbb2XSpEnk5eXh7uTm5vLiiy8et+yAAQNYu3Yt+fn5nHTSSQwaNIi77747NTsmIqEXz52YZgM/APa4e/ug7Q7gWqA4mO0Wd38pmDYFuJrInZh+6u5/SUHdJ0TLli1577334p7/Rz/6Effcc89RbfXq1eO3v/3tcfOOGTOGMWPGHNU2efJkXSUjIkkRz6WQc4BYp6sPuHun4Kck2M8jcvu9dsEyj5pZrWQVKyIi8anwzN3dV5hZbpzruwx4xt0PAlvNbDNQALxR5QqrmQkTJrBq1aqj2iZOnMjy5cvTU5CISAyJ9LnfaGZXAWuAn7v750Bz4M2oeYqCtuOY2XhgPEBOTk4CZZxYjzzySLpLEBGpUFW/ofoY8F2gE7ALuC9oj3Utn8dagbvPdPd8d8/PysqqYhkiIhJLlcLd3Xe7+2F3PwI8QaTrBSJn6mdHzZoNfJJYiSIiUllVCnczaxr1dAiwPni8GBhuZiebWQugNVCYWIkiIlJZ8VwKOQ/oBTQ2syLgdqCXmXUi0uWyDbgOwN0/MLM/ARuAQ8AEdz+cjEIL//xxMlZTquDSlkldn4hIdVLhmbu7j3D3pu5ex92z3X2Wu49y9w7unufug919V9T8d7n7d929jbu/nNryU2vcuHE0adKE9u3bl7Z99tln9O3bl9atW9O3b18+//zzNFYoIhKbhvwtx5gxY1iyZMlRbdOmTaNPnz5s2rSJPn36MG3atJRs2905cuRIStYtIuGncC9Hz549jxvfZdGiRYwePRqA0aNHs3DhwjKXv+OOOxg1ahS9e/emdevWPPHEE6XTfvOb39C1a1fy8vK4/fbbAdi2bRtt27blhhtuoEuXLuzYsYMlS5bQpUsXOnbsSJ8+fVKwlyISRhpbppJ2795N06aRz5ObNm3Knj17yp1/3bp1vPnmmxw4cIDOnTtzySWXsH79ejZt2kRhYSHuzuDBg1mxYgU5OTl89NFHPPnkkzz66KMUFxdz7bXXsmLFClq0aKEx3kUkbgr3FLvsssuoV68e9erV46KLLqKwsJCVK1eydOnS0tEhv/rqKzZt2kROTg7nnHMO3bp1A+DNN9+kZ8+etGjRAtAY7yISP4V7JZ111lns2rWLpk2bsmvXLpo0aVLu/GWN8T5lyhSuu+66o6Zt27ZNY7yLSFJkTLhXl0sXBw8ezNy5c5k8eTJz587lsssuK3f+RYsWMWXKFA4cOMDy5cuZNm0a9erV49Zbb+XKK6/k1FNPZefOndSpU+e4Zbt3786ECRPYunVrabeMzt5FJB4ZE+7pMGLECJYvX86nn35KdnY2d955J5MnT2bYsGHMmjWLnJwcnn322XLXUVBQwCWXXML27du59dZbadasGc2aNWPjxo10794dgFNPPZXf//731Kp19ACaWVlZzJw5kx/+8IccOXKEJk2a8Morr6Rsf0UkPBTu5Zg3b17M9mXLlsW9jnPPPZeZM2ce1z5x4kQmTpx4XPv69euPej5w4EAGDhwY9/ZERECXQoqIhJLO3JPgySef5MEHHzyq7YILLtDwwCKSNtU63DPlapGxY8cyduzYdJdRZe4xR2UWkQxWbbtl6taty969exU8Kebu7N27l7p166a7FBFJomp75p6dnU1RURHFxcUVzywJqVu3LtnZ2ekuQ0SSqNqGe506dUq/mSkiIpVTbbtlRESk6ioMdzObbWZ7zGx9VNtvzOxDM1tnZi+Y2RlBe66Z/dPM1gY/j6eyeBERiS2eM/c5wIBj2l4B2rt7HvA3YErUtC3u3in4uT45ZYqISGXEcyemFcBnx7QtdfdDwdM3idwIW0REqolk9LmPA6Jvp9fCzN41s/8ys+8nYf0iIlJJCV0tY2b/QeRG2H8ImnYBOe6+18z+DVhoZu3cfX+MZccD4wFycnISKUNERI5R5TN3MxsN/AC40oNvGrn7QXffGzx+G9gCnBtreXef6e757p6flZVV1TJERCSGKoW7mQ0AbgYGu/vXUe1ZZlYreNwSaA18nIxCRUQkfhV2y5jZPKAX0NjMioDbiVwdczLwSjD2y5vBlTE9galmdgg4DFzv7rrxp4jICVZhuLv7iBjNs8qYdwGwINGiREQkMfqGqohICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJobjC3cxmm9keM1sf1dbIzF4xs03B74ZBu5nZQ2a22czWmVmXVBUvIiKxxXvmPgcYcEzbZGCZu7cGlgXPAQYSuXdqa2A88FjiZYqISGXEFe7uvgI49l6olwFzg8dzgcuj2p/yiDeBM8ysaTKKFRGR+CTS536Wu+8CCH43CdqbAzui5isK2o5iZuPNbI2ZrSkuLk6gDBEROVYqPlC1GG1+XIP7THfPd/f8rKysFJQhIlJzJRLuu0u6W4Lfe4L2IuDsqPmygU8S2I6IiFRSIuG+GBgdPB4NLIpqvyq4aqYb8EVJ942IiJwYteOZyczmAb2AxmZWBNwOTAP+ZGZXA9uBocHsLwGDgM3A18DYJNcsIiIViCvc3X1EGZP6xJjXgQmJFCUiIonRN1RFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQmhuMZzj8XM2gDzo5paArcBZwDXAiV3vb7F3V+qcoUiIlJpVQ53d/8I6ARgZrWAncALRO689IC7T09KhSIiUmnJ6pbpA2xx978naX0iIpKAZIX7cGBe1PMbzWydmc02s4ZJ2oaIiMQp4XA3s5OAwcCzQdNjwHeJdNnsAu4rY7nxZrbGzNYUFxfHmkVERKooGWfuA4F33H03gLvvdvfD7n4EeAIoiLWQu89093x3z8/KykpCGSIiUiIZ4T6CqC4ZM2saNW0IsD4J2xARkUqo8tUyAGZWH+gLXBfVfK+ZdQIc2HbMNBEROQESCnd3/xo485i2UQlVJCIiCdM3VEVEQkjhLiISQgp3EZEQUriLiIRQQh+oiogk7LV7/vvxRVPSV0fI6MxdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQmhhMeWMbNtwJfAYeCQu+ebWSNgPpBL5G5Mw9z980S3JSIi8UnWmftF7t7J3fOD55OBZe7eGlgWPBcRkRMkVd0ylwFzg8dzgctTtB0REYkhGeHuwFIze9vMxgdtZ7n7LoDgd5NjFzKz8Wa2xszWFBcXJ6EMEREpkYzx3C9w90/MrAnwipl9GM9C7j4TmAmQn5/vSahDREQCCZ+5u/snwe89wAtAAbDbzJoCBL/3JLodERGJX0Jn7mZ2CvAdd/8yeNwPmAosBkYD04LfixItVERCJPruS5ISiXbLnAW8YGYl6/qjuy8xs7eAP5nZ1cB2YGiC2xERkUpIKNzd/WOgY4z2vUCfRNYtIiJVp2+oioiEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQsm4h6qISGzRd1y6aEr66qiBqnzmbmZnm9lrZrbRzD4ws4lB+x1mttPM1gY/g5JXroiIxCORM/dDwM/d/R0zOw1428xeCaY94O7TEy9PREJD9009oaoc7u6+C9gVPP7SzDYCzZNVmEi0B175W+njm/qem8ZKRDJDUj5QNbNcoDOwOmi60czWmdlsM2tYxjLjzWyNma0pLi5ORhkiIhJIONzN7FRgATDJ3fcDjwHfBToRObO/L9Zy7j7T3fPdPT8rKyvRMkREJEpC4W5mdYgE+x/c/XkAd9/t7ofd/QjwBFCQeJkiIlIZVe5zNzMDZgEb3f3+qPamQX88wBBgfWIlikhGSfEHp4+ufbT08Q2dbkjptjJZIlfLXACMAt43s7VB25pPwacAAAV8SURBVC3ACDPrBDiwDbguoQpFRKTSErlaZiVgMSa9VPVyREQkGTT8gIhICCncRURCSOEuIhJCGjhMapyyvu2qb8FKmOjMXUQkhBTuIiIhpG4ZyWjqShGJTeEuIonTcL7VjrplRERCSGfukhap6E4pb53R00QA1u/579FoT8lNXx2ponCXakuBLFJ1CndJu8qexccT+vH+YShrPn1QK5lO4S5SAQW9ZCKFu1RZqvvNRaTqFO5ywii4RU6clIW7mQ0AHgRqAb9z92mp2paIpIGuba/WUhLuZlYLeAToCxQBb5nZYnffkIrtScWS1YUSzweQYab+d8kUqTpzLwA2u/vHAGb2DHAZkJJwn/Lk5aWP7xm7MBWbyEiVvaqkrLCqKcEtEiapCvfmwI6o50XA+Sna1gmVyJlbWSEZT6iWNTRtvOuKpw6dlVZOPH/0QvE6Znj3S/GMh0sfZ/3kxjRWcmKZuyd/pWZDgf7ufk3wfBRQ4O4/iZpnPDA+eNoG+CiBTTYGPk1g+epM+5a5wrx/2rfq4Rx3z4o1IVVn7kXA2VHPs4FPomdw95nAzGRszMzWuHt+MtZV3WjfMleY90/7Vv2lauCwt4DWZtbCzE4ChgOLU7QtERE5RkrO3N39kJndCPyFyKWQs939g1RsS0REjpey69zd/SXgpVSt/xhJ6d6pprRvmSvM+6d9q+ZS8oGqiIikl27WISISQhkd7mY2wMw+MrPNZjY53fUkwszONrPXzGyjmX1gZhOD9kZm9oqZbQp+N0x3rVVlZrXM7F0zezF43sLMVgf7Nj/48D0jmdkZZvacmX0YHMPuYTl2ZnZT8G9yvZnNM7O6mXzszGy2me0xs/VRbTGPlUU8FGTMOjPrkr7KKydjwz1qiIOBwHnACDM7L71VJeQQ8HN3bwt0AyYE+zMZWOburYFlwfNMNRHYGPX818ADwb59DlydlqqS40Fgibt/D+hIZD8z/tiZWXPgp0C+u7cncoHEcDL72M0BBhzTVtaxGgi0Dn7GA4+doBoTlrHhTtQQB+7+L6BkiIOM5O673P2d4PGXRMKhOZF9mhvMNhe4PPYaqjczywYuAX4XPDegN/BcMEsm79vpQE9gFoC7/8vd9xGSY0fkwot6ZlYbqA/sIoOPnbuvAD47prmsY3UZ8JRHvAmcYWZNT0ylicnkcI81xEHzNNWSVGaWC3QGVgNnufsuiPwBAJqkr7KE/Cfw78CR4PmZwD53PxQ8z+Tj1xIoBp4Mup1+Z2anEIJj5+47genAdiKh/gXwNuE5diXKOlYZmzOZHO4Woy3jL/0xs1OBBcAkd9+f7nqSwcx+AOxx97ejm2PMmqnHrzbQBXjM3TsDB8jALphYgr7ny4AWQDPgFCJdFcfK1GNXkYz9d5rJ4V7hEAeZxszqEAn2P7j780Hz7pK3gcHvPemqLwEXAIPNbBuR7rPeRM7kzwje6kNmH78ioMjdVwfPnyMS9mE4dhcDW9292N2/BZ4HehCeY1eirGOVsTmTyeEeqiEOgj7oWcBGd78/atJiYHTweDSw6ETXlih3n+Lu2e6eS+Q4/dXdrwReA34czJaR+wbg7v8AdphZm6CpD5HhrTP+2BHpjulmZvWDf6Ml+xaKYxelrGO1GLgquGqmG/BFSfdNtefuGfsDDAL+BmwB/iPd9SS4LxcSebu3Dlgb/Awi0je9DNgU/G6U7loT3M9ewIvB45ZAIbAZeBY4Od31JbBfnYA1wfFbCDQMy7ED7gQ+BNYDTwMnZ/KxA+YR+fzgWyJn5leXdayIdMs8EmTM+0SuGkr7PsTzo2+oioiEUCZ3y4iISBkU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iE0P8HWrk8Ad4F8t4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LI0eOMG/ePACmT5/OtGnT6NixI+5OZmYmL7/88knrDh48mA0bNpCVlcVpp53G0KFDufvuu6vg1RKR6sRK6hs+1bKysjw3N7dI25YtW2jbtm2SKqo++vbty3333UdWVlaVPo9eb0maGG6QXRV3Ysqf/Ujh9I7Movds6H5Fy4Q8R1Uzs/XuXmI46FJIEZEQUrdMNTFlyhTefPPNIm1Tp05l9erVySlIRFKawr2aePTRR5NdgoiEiLplRERCSOEuIhJCCncRkRBKmT736EuhEiFRl1OJiFRHsdxmb56ZHTCzTVFti8xsQ/Czs+AOTWaWaWb/HTXviaos/lQobzz3NWvW0LVrV9LS0njhhReSUKGIyMliOXOfDzwCPF3Q4O5XFUyb2f3AF1HLb3f3zokqMJkKxnNfuXIlGRkZdOvWjWHDhtGuXbvCZZo3b878+fMLB/Cq6noK/spVRKQs5Z65u/saoMRxYoObZ48CFia4rmohlvHcMzMz6dixI9/7XvlfX6xevZo+ffowYsQI2rVrx4033siJEycAWLFiBT179qRr166MHDmSr776qnD7M2fO5JJLLuH5559n27ZtXHbZZXTq1ImuXbuyffv2xO+4iKS8eL9Q/SGw3923RrW1MLP3zew/zeyHpa1oZpPNLNfMcvPz8+Mso2qUNJ57ZcdkL5CTk8P999/Phx9+yPbt23nxxRc5ePAgv/71r3n99dd57733yMrK4oEHHihcp06dOqxdu5bRo0dz9dVXM2XKFD744APeeustmjRpElc9IhJO8X6hOoaiZ+37gObufsjM/gVYYmbt3f1I8RXdfQ4wByJjy8RZR5VI5JjsBbp3707LlpFxK8aMGcPatWupU6cOmzdvpnfv3gD885//pGfPnoXrXHVVpBfsyy+/ZM+ePYwYMQKIhL6ISEkqHe5mlgb8CPiXgjZ3/xb4Npheb2bbgQuB3BI3Us0lckz2AqWNzT5gwAAWLiy5d6tgfPbqMMibiKSGeM7cLwM+dve8ggYzSwc+c/fjZtYSaA18GmeNQHIuXYxlPPeKysnJYceOHVxwwQUsWrSIyZMn06NHD6ZMmcK2bdto1aoVX3/9NXl5eVx44YVF1j377LPJyMhgyZIlXHnllXz77bccP3688C5LIiIFYrkUciHwNtDGzPLMrGDg8dGc/EVqH2CjmX0AvADc6O4pe9PO6PHc27Zty6hRowrHc1+2bBkA7777LhkZGTz//PPccMMNtG/fvsxt9uzZk+zsbDp06ECLFi0YMWIE6enpzJ8/nzFjxtCxY0d69OjBxx9/XOL6zzzzDA8//DAdO3akV69e/OMf/0j4fotI6iv3zN3dx5TSPqGEtsXA4vjLqj6GDh3K0KFFx3qeOXNm4XS3bt3Iy8srvlqp6tWrx6JFi05q79evH+++++5J7Tt37izyuHXr1vz1r3+N+flEpGbS8AMiIiGUMsMPpJIPP/yQcePGFWk7/fTTWbduHX379k1OUSJSoyjcq8BFF13Ehg0bkl2GiNRg6pYREQkhhbuISAgp3EVEQihl+tzzZz+S0O2l/+ynCd2eiEh1kjLhniyZmZmcddZZ1KpVi7S0NHJzU3IkBRGpYRTuMXjjjTdo1KhRlW3/2LFjpKXpUIhI4qjPPUH69u3LtGnT6NWrFx06dCAnJweAo0ePMmnSJLp160aXLl0Kx4OfP38+I0eO5IorrmDgwIEA3HvvvVx00UV06tSJ7OzspO2LiKQ+nS6Ww8wYOHAgZsYNN9zA5MmTS1326NGjvPXWW6xZs4ZJkyaxadMm7rrrLvr168e8efM4fPgw3bt357LLLgPg7bffZuPGjTRs2JBXX32VJUuWsG7dOurVq8dnn6XskDwiUg0o3Mvx5ptv0rRpUw4cOMCAAQP4wQ9+QJ8+fUpcdsyYyDA8ffr04ciRIxw+fJgVK1awbNmywtvwffPNN+zatQuAAQMG0LBhQwBef/11Jk6cWDjCY0G7iEhlKNzLUTB+e+PGjRkxYgQ5OTmlhntpY7UvXryYNm3aFJm3bt26wnHaITJWe7w3AhERKZAy4Z6MSxePHj3KiRMnOOusszh69CgrVqzg9ttvL3X5RYsWcemll7J27Vrq169P/fr1GTRoELNnz2b27NmYGe+//z5dunQ5ad2BAwcyc+ZMxo4dW9gto7N3EamslAn3ZNi/f3/hLe2OHTvG2LFjGTx4cKnLN2jQgF69enHkyBHmzZsHwPTp05k2bRodO3bE3cnMzOTll18+ad3BgwezYcMGsrKyOO200xg6dCh333131eyYiIReueFuZvOAfwUOuHuHoG0GcD1QcGfr29z9lWDercC1wHHg5+7+WhXUfUq0bNmSDz74IOblf/zjH3PPPfcUaatbty6///3vT1p2woQJTJgwoUhbdna2rpIRkYSI5VLI+UBJp6sPunvn4Kcg2NsRuUNT+2Cdx8ysVqKKFRGR2MRyJ6Y1ZpYZ4/aGA88FN8reYWbbgO5EbtMXClOmTOHNN98s0jZ16lRWr16dnIJEREoQT5/7T83sGiAX+KW7fw40A96JWiYvaAuNRx99NNkliIiUq7J/ofo48H2gM7APuD9oL+laPi9pA2Y22cxyzSw3Pz+/pEVERKSSKhXu7r7f3Y+7+wngSSJdLxA5Uz8/atEMYG8p25jj7lnunpWenl6ZMkREpBSVCnczaxL1cASwKZheBow2s9PNrAXQGsiJr0QREamoWC6FXAj0BRqZWR5wB9DXzDoT6XLZCdwA4O4fmdmfgc3AMWCKux9PRKE5f/k0EZsp1P2KlgndnohIdVLumbu7j3H3Ju5e290z3H2uu49z94vcvaO7D3P3fVHL3+Xu33f3Nu7+atWWX7UmTZpE48aN6dChQ2HbZ599xoABA2jdujUDBgzg888/T2KFIiIl05C/ZZgwYQLLly8v0jZr1iz69+/P1q1b6d+/P7NmzaqS53Z3Tpw4USXbFpHwU7iXoU+fPieN77J06VLGjx8PwPjx41myZEmp68+YMYNx48bRr18/WrduzZNPPlk477e//S3dunWjY8eO3HHHHQDs3LmTtm3bctNNN9G1a1d2797N8uXL6dq1K506daJ///5VsJciEkYaW6aC9u/fT5Mmke+TmzRpwoEDB8pcfuPGjbzzzjscPXqULl26cPnll7Np0ya2bt1KTk4O7s6wYcNYs2YNzZs355NPPuGpp57iscceIz8/n+uvv541a9bQokULjfEuIjFTuFex4cOHU7duXerWrcull15KTk4Oa9euZcWKFYWjQ3711Vds3bqV5s2bc8EFF9CjRw8A3nnnHfr06UOLFi0AjfEuIrFTuFfQeeedx759+2jSpAn79u2jcePGZS5f2hjvt956KzfccEOReTt37tQY7yKSECkT7tXl0sVhw4axYMECsrOzWbBgAcOHDy9z+aVLl3Lrrbdy9OhRVq9ezaxZs6hbty7Tp0/n6quv5swzz2TPnj3Url37pHV79uzJlClT2LFjR2G3jM7eRSQWKRPuyTBmzBhWr17NwYMHycjI4M477yQ7O5tRo0Yxd+5cmjdvzvPPP1/mNrp3787ll1/Orl27mD59Ok2bNqVp06Zs2bKFnj17AnDmmWfyxz/+kVq1ig6gmZ6ezpw5c/jRj37EiRMnaNy4MStXrqyy/RWR8FC4l2HhwoUltq9atSrmbVx44YXMmTPnpPapU6cyderUk9o3bdpU5PGQIUMYMmRIzM8nIgK6FFJEJJR05p4ATz31FA899FCRtt69e2t4YBFJmmod7qlytcjEiROZOHFissuoNPcSR2UWkRRWbbtl6tSpw6FDhxQ8VczdOXToEHXq1El2KSKSQNX2zD0jI4O8vDx0I4+qV6dOHTIyMpJdhogkULUN99q1axf+ZaaIiFRMte2WERGRylO4i4iEULnhbmbzzOyAmW2KavutmX1sZhvN7CUzOydozzSz/zazDcHPE1VZvIiIlCyWM/f5wOBibSuBDu7eEfgbcGvUvO3u3jn4uTExZYqISEXEcpu9NcBnxdpWuPux4OE7gC61EBGpRhLR5z4JiL5Xagsze9/M/tPMfljaSmY22cxyzSxXlzuKiCRWXOFuZv8BHAP+FDTtA5q7exfgF8CzZnZ2Seu6+xx3z3L3rPT09HjKEBGRYiod7mY2HvhX4GoP/ozU3b9190PB9HpgO3BhIgoVEZHYVSrczWwwcAswzN2/jmpPN7NawXRLoDXwaSIKFRGR2JX7F6pmthDoCzQyszzgDiJXx5wOrAwG9nonuDKmDzDTzI4Bx4Eb3V13dRYROcXKDXd3H1NC89xSll0MLI63KBERiY/+QlVEJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRCKKdzNbJ6ZHTCzTVFtDc1spZltDX43CNrNzB42s21mttHMulZV8SIiUrJYz9znA4OLtWUDq9y9NbAqeAwwhMjt9VoDk4HH4y9TREQqIqZwd/c1QPHb5Q0HFgTTC4Aro9qf9oh3gHPMrEkiihURkdjE0+d+nrvvAwh+Nw7amwG7o5bLC9pEROQUqYovVK2ENj9pIbPJZpZrZrn5+flVUIaISM0VT7jvL+huCX4fCNrzgPOjlssA9hZf2d3nuHuWu2elp6fHUYaIiBQXT7gvA8YH0+OBpVHt1wRXzfQAvijovhERkVMjLZaFzGwh0BdoZGZ5wB3ALODPZnYtsAsYGSz+CjAU2AZ8DUxMcM0iIlKOmMLd3ceUMqt/Ccs6MCWeokREJD76C1URkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIx3ayjJGbWBlgU1dQSuB04B7geKLjr9W3u/kqlKxQRkQqrdLi7+ydAZwAzqwXsAV4iclu9B939voRUKCIiFZaobpn+wHZ3/3uCticiInFIVLiPBhZGPf6pmW00s3lm1qCkFcxsspnlmllufn5+SYuIiEglxR3uZnYaMAx4Pmh6HPg+kS6bfcD9Ja3n7nPcPcvds9LT0+MtQ0REoiTizH0I8J677wdw9/3uftzdTwBPAt0T8BwiIlIBiQj3MUR1yZhZk6h5I4BNCXgOERGpgEpfLQNgZvWAAcANUc33mllnwIGdxeaJiMgpEFe4u/vXwLnF2sbFVZGIiMRNf6EqIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIxTWeO4CZ7QS+BI4Dx9w9y8waAouATCI37Bjl7p/H+1wiIhKbRJ25X+rund09K3icDaxy99bAquCxiIicIlXVLTMcWBBMLwCurKLnERGREiQi3B1YYWbrzWxy0Haeu+8DCH43TsDziIhIjOLucwd6u/teM2sMrDSzj2NZKXgjmAzQvHnzBJQhIiIF4j5zd/e9we8DwEtAd2C/mTUBCH4fKGG9Oe6e5e5Z6enp8ZYhIiJR4gp3MzvDzM4qmAYGApuAZcD4YLHxwNJ4nkdERCom3m6Z84CXzKxgW8+6+3Izexf4s5ldC+wCRsb5PCIiUgFxhbu7fwp0KqH9ENA/nm2LiEjl6S9URURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIVTrczex8M3vDzLaY2UdmNjVon2Fme8xsQ/AzNHHliohILOK5E9Mx4Jfu/l5wH9X1ZrYymPegu98Xf3kiIlIZlQ53d98H7AumvzSzLUCzRBUmIiKVl5A+dzPLBLoA64Kmn5rZRjObZ2YNSllnspnlmllufn5+IsoQEZFA3OFuZmcCi4Fp7n4EeBz4PtCZyJn9/SWt5+5z3D3L3bPS09PjLUNERKLEFe5mVptIsP/J3V8EcPf97n7c3U8ATwLd4y9TREQqIp6rZQyYC2xx9wei2oXT910AAAWbSURBVJtELTYC2FT58kREpDLiuVqmNzAO+NDMNgRttwFjzKwz4MBO4Ia4KhQRkQqL52qZtYCVMOuVypcjIiKJoL9QFREJoXi6ZURC68GVfyucvnnAhUmsRKRydOYuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpKtlRCqgtKtootuj6UobSRaFu0gllRbo8WxHbwaSKAp3kXJURYiLVDWFu9RoVX3WHE+g64xe4qFwlxpBZ81S0+hqGRGRENKZu0igOpzdV4caJBwU7pISYrkEUf3SIv+jysLdzAYDDwG1gD+4+6yqei6R4nQGLDVdlYS7mdUCHgUGAHnAu2a2zN03V8Xz3frUlYXT90xcUhVPIYHqcKZc04O7pv/B1GMbHiucvqnzTUmspHqrqjP37sA2d/8UwMyeA4YDVRLuknixhHhFgz6WUErUpYNhE+Z9S5ZNB9ILp8/ITF4dVaWqwr0ZsDvqcR5wcRU9V41W/D99RUM2nlBOFAVX4lWHT1iSXObuid+o2UhgkLtfFzweB3R3959FLTMZmBw8bAN8EsdTNgIOxrF+daZ9S11h3j/tW/VwgbunlzSjqs7c84Dzox5nAHujF3D3OcCcRDyZmeW6e1YitlXdaN9SV5j3T/tW/VXVHzG9C7Q2sxZmdhowGlhWRc8lIiLFVMmZu7sfM7OfAq8RuRRynrt/VBXPJSIiJ6uy69zd/RXglarafjEJ6d6pprRvqSvM+6d9q+aq5AtVERFJLg0cJiISQikd7mY22Mw+MbNtZpad7HriYWbnm9kbZrbFzD4ys6lBe0MzW2lmW4PfDZJda2WZWS0ze9/MXg4etzCzdcG+LQq+fE9JZnaOmb1gZh8Hx7BnWI6dmd0c/JvcZGYLzaxOKh87M5tnZgfMbFNUW4nHyiIeDjJmo5l1TV7lFZOy4R41xMEQoB0wxszaJbequBwDfunubYEewJRgf7KBVe7eGlgVPE5VU4EtUY9/AzwY7NvnwLVJqSoxHgKWu/sPgE5E9jPlj52ZNQN+DmS5ewciF0iMJrWP3XxgcLG20o7VEKB18DMZePwU1Ri3lA13ooY4cPd/AgVDHKQkd9/n7u8F018SCYdmRPZpQbDYAuDKkrdQvZlZBnA58IfgsQH9gBeCRVJ5384G+gBzAdz9n+5+mJAcOyIXXtQ1szSgHrCPFD527r4G+KxYc2nHajjwtEe8A5xjZk1OTaXxSeVwL2mIg2ZJqiWhzCwT6AKsA85z930QeQMAGievsrj8Dvh34ETw+FzgsLsfCx6n8vFrCeQDTwXdTn8wszMIwbFz9z3AfcAuIqH+BbCe8By7AqUdq5TNmVQOdyuhLeUv/TGzM4HFwDR3P5LsehLBzP4VOODu66ObS1g0VY9fGtAVeNzduwBHScEumJIEfc/DgRZAU+AMIl0VxaXqsStPyv47TeVwL3eIg1RjZrWJBPuf3P3FoHl/wcfA4PeBZNUXh97AMDPbSaT7rB+RM/lzgo/6kNrHLw/Ic/d1weMXiIR9GI7dZcAOd8939++AF4FehOfYFSjtWKVszqRyuIdqiIOgD3ousMXdH4iatQwYH0yPB5ae6tri5e63unuGu2cSOU5/dfergTeAnwSLpeS+Abj7P4DdZtYmaOpPZHjrlD92RLpjephZveDfaMG+heLYRSntWC0DrgmumukBfFHQfVPtuXvK/gBDgb8B24H/SHY9ce7LJUQ+7m0ENgQ/Q4n0Ta8Ctga/Gya71jj3sy/wcjDdEsgBtgHPA6cnu7449qszkBscvyVAg7AcO+BO4GNgE/AMcHoqHztgIZHvD74jcmZ+bWnHiki3zKNBxnxI5KqhpO9DLD/6C1URkRBK5W4ZEREphcJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRD6/zV6AaCiOJBGAAAAAElFTkSuQmCC\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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3kpOTQ61atRg4cCAPPPBAFTxbIlKdlDue++kQhvHcq0rPnj2ZPn06OTkxh2xOGj3fcqaJHs99R9bAkunca1umIpxKOdV47roVUkQkhFSWqSYmTJjAunXrTmibOHEiq1evTk1AIpLWlNyriSeeeCLVIYhIiKgsIyISQkruIiIhVG5yN7O5ZrbfzDZHtf3SzN43s01m9gczOz9q2VQz225mH5hZv6oKXEREyhZPzX0eMBN4NqrtVWCqux81s4eAqcAdZtYOGA60B5oCK83sEnc/lmigszbOSnQXJxh/2fik7k9EpDopt+fu7muAT0u1rXD34vFk3wAyg+nBwEJ3/9rddwDbgcp9LbOaKG889zVr1tC5c2cyMjJYvHhxCiIUETlZMmruY4BXgulmwO6oZQVB20nMbJyZbTCzDYWFhUkII/niGc+9efPmzJs3j5EjR56WeERE4pFQcjeznwNHgd8WN8VYLeZXYN19trvnuHtOo0aNEgmjysQznntWVhbZ2dl85zvlP5WrV6+mR48eDBkyhHbt2nHrrbdy/PhxAFasWEG3bt3o3LkzQ4cO5csvvyzZ/3333ceVV17JCy+8wPbt27n66qvp2LEjnTt35sMPP0z+iYtI2qt0cjezUcD3gBv82zEMCoCLolbLBD6ufHipFWs898qOyV4sPz+fX/3qV7z77rt8+OGHvPTSSxw4cIBf/OIXrFy5krfffpucnBweeeSRkm1q167N2rVrGT58ODfccAMTJkzgr3/9K6+//jpNmjRJKB4RCadKfYnJzPoDdwD/4u5fRS1aBvzOzB4h8oFqayA/4ShTJJljshfLzc2lZcvI2BUjRoxg7dq11K5dmy1btnDFFVcA8I9//INu3bqVbPODH/wAgC+++II9e/YwZMgQIJL0RURiKTe5m9kCoCfQ0MwKgLuJ3B1zFvBqkOzecPdb3f09M/s9sIVIuWZCMu6USZVkjslerKyx2fv06cOCBQtiblM8Pnt1GORNRNJDucnd3UfEaJ5zivXvB+5PJKhYUnHrYjzjuVdUfn4+O3bs4OKLL2bRokWMGzeOrl27MmHCBLZv306rVq346quvKCgo4JJLLjlh2/POO4/MzEyWLFnCddddx9dff82xY8dKfmVJRKSYvqF6CtHjubdt25Zhw4aVjOe+bNkyAN58800yMzN54YUXuOWWW2jfvv0p99mtWzemTJlChw4daNGiBUOGDKFRo0bMmzePESNGkJ2dTdeuXXn//fdjbv/cc8/x+OOPk52dTffu3fnkk0+Sft4ikv40cFg5Bg4cyMCBA09ou++++0qmu3TpQkFBQdz7q1u3LosWLTqpvVevXrz55psnte/cufOE+datW/OXv/wl7uOJyJlJPXcRkRBSz70KvPvuu9x4440ntJ111lnk5eXRs2fP1AQlImcUJfcqcOmll7Jx48ZUhyEiZzCVZUREQkjJXUQkhJTcRURCKG1q7oUzZiZ1f41+8uOk7k9EpDpJm+SeKllZWZx77rnUqFGDjIwMNmzYkOqQRETKpeQeh9dee42GDRtW2f6PHj1KRoYuhYgkj2ruSdKzZ08mTZpE9+7d6dChA/n5kcEwi4qKGDNmDF26dKFTp04l48HPmzePoUOHcu2119K3b18AHn74YS699FI6duzIlClTUnYuIpL+1F0sh5nRt29fzIxbbrmFcePGlbluUVERr7/+OmvWrGHMmDFs3ryZ+++/n169ejF37lwOHTpEbm4uV199NQDr169n06ZNNGjQgFdeeYUlS5aQl5dH3bp1+fTTT8s8johIeZTcy7Fu3TqaNm3K/v376dOnD9/97nfp0aNHzHVHjIgMoNmjRw8OHz7MoUOHWLFiBcuWLWP69OkAHDlyhF27dgHQp08fGjRoAMDKlSsZPXp0yQiPxe0iIpWh5F6O4vHbGzduzJAhQ8jPzy8zuZc1VvuLL75ImzZtTliWl5dXMk47RMZqT/SHQEREiqVNck/FrYtFRUUcP36cc889l6KiIlasWMFdd91V5vqLFi3iqquuYu3atdSrV4969erRr18/ZsyYwYwZMzAz3nnnHTp16nTStn379uW+++5j5MiRJWUZ9d5FpLLSJrmnwr59+0p+0u7o0aOMHDmS/v37l7l+/fr16d69O4cPH2bu3LkA3HnnnUyaNIns7GzcnaysLF5++eWTtu3fvz8bN24kJyeHWrVqMXDgQB544IGqOTERCT2rDj/dlpOT46XvH9+6dStt27ZNUUQV17NnT6ZPn05OTk6qQ6mUdHu+RRIV/cXIHVnf/mZD7rUtUxFOpZjZW+4eM+noVkgRkRBSWaaCJkyYwLp1605omzhxIqtXr05NQCIiMZSb3M1sLvA9YL+7dwjaGgCLgCxgJzDM3T+zyO0ejwEDga+Am9397aoJPTWeeOKJVIcgIlKueMoy84DSnyJOAVa5e2tgVTAPMABoHTzGAU8mJ0wREamIcpO7u68BSn9dcjAwP5ieD1wX1f6sR7wBnG9mTZIVrIiIxKeyH6he6O57AYK/jYP2ZsDuqPUKgraTmNk4M9tgZhsKCwsrGYaIiMSS7A9UY33FMua9lu4+G5gNkVshy9tx/h8/SiyyUtLpdicRkYqqbM99X3G5Jfi7P2gvAC6KWi8T+Ljy4aXWmDFjaNy4MR06dChp+/TTT+nTpw+tW7emT58+fPbZZymMUEQktsom92XAqGB6FLA0qv0mi+gKfF5cvklHN998M8uXLz+hbdq0afTu3Ztt27bRu3dvpk2bViXHdneOHz9eJfsWkfArN7mb2QJgPdDGzArMbCwwDehjZtuAPsE8wJ+Aj4DtwNPA+CqJ+jTp0aPHSeO7LF26lFGjIq9ro0aNYsmSJWVuf88993DjjTfSq1cvWrduzdNPP12y7Je//CVdunQhOzubu+++G4CdO3fStm1bxo8fT+fOndm9ezfLly+nc+fOdOzYkd69e1fBWYpIGJVbc3f3EWUsOinTeGQsgwmJBlWd7du3jyZNIjcANWnShP37959y/U2bNvHGG29QVFREp06duOaaa9i8eTPbtm0jPz8fd2fQoEGsWbOG5s2b88EHH/DMM88wa9YsCgsL+dGPfsSaNWto0aKFxngXkbjpG6pVbPDgwdSpU4c6depw1VVXkZ+fz9q1a1mxYkXJ6JBffvkl27Zto3nz5lx88cV07doVgDfeeIMePXrQokULQGO8i0j8lNwr6MILL2Tv3r00adKEvXv30rhx41OuX9YY71OnTuWWW245YdnOnTs1xruIJEXaJPfqcuvioEGDmD9/PlOmTGH+/PkMHjz4lOsvXbqUqVOnUlRUxOrVq5k2bRp16tThzjvv5IYbbuCcc85hz5491KxZ86Rtu3XrxoQJE9ixY0dJWUa9dxGJR9ok91QYMWIEq1ev5sCBA2RmZnLvvfcyZcoUhg0bxpw5c2jevDkvvPDCKfeRm5vLNddcw65du7jzzjtp2rQpTZs2ZevWrXTr1g2Ac845h+eff54aNWqcsG2jRo2YPXs2//qv/8rx48dp3Lgxr776apWdr4iEh5L7KSxYsCBm+6pVq+LexyWXXMLs2bNPap84cSITJ048qX3z5s0nzA8YMIABAwbEfTwREdB47iIioaSeexI888wzPPbYYye0XXHFFRoeWERSplon93S5W2T06NGMHj061WFUWnX4qUURSa5qW5apXbs2Bw8eVOKpYu7OwYMHqV27dqpDEZEkqrY998zMTAoKCtBwwFWvdu3aZGZmpjoMEUmiapvca9asWfLNTBERqZhqW5YREZHKU3IXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBKKLmb2WQze8/MNpvZAjOrbWYtzCzPzLaZ2SIzq5WsYEVEJD6VTu5m1gz4NyDH3TsANYDhwEPAo+7eGvgMGJuMQEVEJH6JlmUygDpmlgHUBfYCvYDFwfL5wHUJHkNERCqo0snd3fcA04FdRJL658BbwCF3PxqsVgA0i7W9mY0zsw1mtkEjP4qIJFciZZn6wGCgBdAUOBuI9WOfMQdkd/fZ7p7j7jmNGjWqbBgiIhJDImWZq4Ed7l7o7t8ALwHdgfODMg1AJvBxgjGKiEgFJZLcdwFdzayuRX4LrzewBXgNuD5YZxSwNLEQRUSkohKpuecR+eD0beDdYF+zgTuA28xsO3ABMCcJcYqISAUk9EtM7n43cHep5o+A3ET2KyIiidE3VEVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhBL6EpOISHUxa+Oskunxl41PYSTVg3ruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIZRQcjez881ssZm9b2ZbzaybmTUws1fNbFvwt36yghURkfgk2nN/DFju7t8FOgJbgSnAKndvDawK5kVE5DSqdHI3s/OAHsAcAHf/h7sfAgYD84PV5gPXJRqkiIhUTCI995ZAIfCMmb1jZr8xs7OBC919L0Dwt3Gsjc1snJltMLMNhYWFCYQhIiKlJZLcM4DOwJPu3gkoogIlGHef7e457p7TqFGjBMIQkdB47cFvH5KQRJJ7AVDg7nnB/GIiyX6fmTUBCP7uTyxEERGpqEond3f/BNhtZm2Cpt7AFmAZMCpoGwUsTShCERGpsER/Zu8nwG/NrBbwETCayAvG781sLLALGJrgMUREpIISSu7uvhHIibGodyL7FRGRxOgbqiIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQol+Q1VEJFTy//hRyXTutS1TGEli1HMXEQkh9dxF5IxXlP9myfTZuV1SGEnyKLmLSOjM2jirZHr8ZeNTGEnqqCwjIhJCSu4iIiGksoyIpK3o8oucSD13EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREEo4uZtZDTN7x8xeDuZbmFmemW0zs0VmVivxMEVEpCKS0XOfCGyNmn8IeNTdWwOfAWOTcAwREamAhJK7mWUC1wC/CeYN6AUsDlaZD1yXyDFERKTiEu25/xr4D+B4MH8BcMjdjwbzBUCzWBua2Tgz22BmGwoLCxMMQ0REolU6uZvZ94D97v5WdHOMVT3W9u4+291z3D2nUaNGlQ1DRERiSGT4gSuAQWY2EKgNnEekJ3++mWUEvfdM4OPEwxQRkYqodM/d3ae6e6a7ZwHDgb+4+w3Aa8D1wWqjgKUJRykiIhVSFfe53wHcZmbbidTg51TBMURE5BSSMiqku68GVgfTHwG5ydiviIhUjr6hKiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJJGVtGROR0mbVxVqpDSAvquYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAjpVkgRSa3XHkx1BKFU6Z67mV1kZq+Z2VYze8/MJgbtDczsVTPbFvytn7xwRUQkHomUZY4C/+7ubYGuwAQzawdMAVa5e2tgVTAvIiKnUaWTu7vvdfe3g+kvgK1AM2AwMD9YbT5wXaJBiohIxSTlA1UzywI6AXnAhe6+FyIvAEDjZBxDRETil3ByN7NzgBeBSe5+uALbjTOzDWa2obCwMNEwREQkSkLJ3cxqEknsv3X3l4LmfWbWJFjeBNgfa1t3n+3uOe6e06hRo0TCEBGRUhK5W8aAOcBWd38katEyYFQwPQpYWvnwRESkMhK5z/0K4EbgXTPbGLT9DJgG/N7MxgK7gKGJhSgiIhVV6eTu7msBK2Nx78ruV0RCKvrLSldNTV0cZwgNPyAiEkIafkAkhkdf/VvJ9OQ+l6QwEkml/D9+VDKde23LFEZScUruInL6xTOejMo4CVFZRkQkhNRzFxGJUpT/Zsn02bldUhhJYpTcRaRy4imbaDjflFFZRkQkhJTcRURCSGUZSQsVvTVRtzLKmU7JXSQQ/YIgFVTVty1G779+vQptOmvjrJLp8ZeNT1ZE1Z7KMiIiIaSeu0g1VVZpSSUniYeSu6S1eBLdmZIMq+w89U3RtKSyjIhICLu5G+sAAAVHSURBVKV9z33qM9/+/vaDo5ekMBKRUyvrA9uySi7VUkXHhAmR6EHEoPoPJJb2yV2kOqv2yfoMtnl/uH/eU2UZEZEQUs9dUi5dP/BM17jLs37O7SfMd2t5QYoikUQouUu1Ek8ZI1mljnj3U9HjVfX6Fd1nmF54Trd0HiFSyV0kCaq6tl7W/lXTl7JUWc3dzPqb2Qdmtt3MplTVcURE5GRV0nM3sxrAE0AfoAB408yWufuWqjienD7x9BRVBqheEi3RrP/oYMx21eKrt6oqy+QC2939IwAzWwgMBpKe3DuuPfDtzOiKb5/IP/zqUNes6Dc0S69XFck6VS8Ap7Nen66iPyx9o/m4mOt0PV3BVMCsQ5u+nTkUtaDFP1dsP1GDiLUlsRen6v7j2VVVlmkG7I6aLwjaRETkNDB3T/5OzYYC/dz9h8H8jUCuu/8kap1xQHHXoQ3wQSUP1xA4UO5a6SvM56dzS19hPr90OreL3T3mt7GqqixTAFwUNZ8JfBy9grvPBmYneiAz2+DuOYnup7oK8/np3NJXmM8vLOdWVWWZN4HWZtbCzGoBw4FlVXQsEREppUp67u5+1Mx+DPwZqAHMdff3quJYIiJysir7EpO7/wn4U1XtP0rCpZ1qLsznp3NLX2E+v1CcW5V8oCoiIqmlUSFFREIorZN7mIY4MLOLzOw1M9tqZu+Z2cSgvYGZvWpm24K/9VMda2WZWQ0ze8fMXg7mW5hZXnBui4IP39OSmZ1vZovN7P3gGnYLy7Uzs8nBv8nNZrbAzGqn87Uzs7lmtt/MNke1xbxWFvF4kGM2mVnn1EVeMWmb3KOGOBgAtANGmFm71EaVkKPAv7t7WyJfEpwQnM8UYJW7twZWBfPpaiKwNWr+IeDR4Nw+A8amJKrkeAxY7u7fBToSOc+0v3Zm1gz4NyDH3TsQuUFiOOl97eYB/Uu1lXWtBgCtg8c44MnTFGPC0ja5EzXEgbv/Ayge4iAtufted387mP6CSHJoRuSc5gerzQeui72H6s3MMoFrgN8E8wb0AhYHq6TzuZ0H9ADmALj7P9z9ECG5dkRuvKhjZhlAXWAvaXzt3H0N8Gmp5rKu1WDgWY94AzjfzJqcnkgTk87JPbRDHJhZFtAJyAMudPe9EHkBABqnLrKE/Br4D+B4MH8BcMjdjwbz6Xz9WgKFwDNB2ek3ZnY2Ibh27r4HmA7sIpLUPwfeIjzXrlhZ1ypt80w6J3eL0Zb2t/6Y2TnAi8Akdz+c6niSwcy+B+x397eim2Osmq7XLwPoDDzp7p2AItKwBBNLUHseDLQAmgJnEylVlJau1648afvvNJ2Te7lDHKQbM6tJJLH/1t1fCpr3Fb8NDP7uT1V8CbgCGGRmO4mUz3oR6cmfH7zVh/S+fgVAgbvnBfOLiST7MFy7q4Ed7l7o7t8ALwHdCc+1K1bWtUrbPJPOyT1UQxwENeg5wFZ3fyRq0TJgVDA9Clh6umNLlLtPdfdMd88icp3+4u43AK8B1werpeW5Abj7J8BuM2sTNPUmMrx12l87IuWYrmZWN/g3Wnxuobh2Ucq6VsuAm4K7ZroCnxeXb6o9d0/bBzAQ+BvwIfDzVMeT4LlcSeTt3iZgY/AYSKQ2vQrYFvxtkOpYEzzPnsDLwXRLIB/YDrwAnJXq+BI4r8uADcH1WwLUD8u1A+4F3gc2A88BZ6XztQMWEPn84BsiPfOxZV0rImWZJ4Ic8y6Ru4ZSfg7xPPQNVRGREErnsoyIiJRByV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJIT+P5x15z6YB1EBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nOzubF1544ah9Bw8ezPr168nNzaVBgwYMGTKEu+++uwaeLRGpS+Kaz72mhWE+95rSt29fZs6cSW5uzCmbk0bPtxxroudz35Y9pHQ5b2j7VIRTLTUxn7uIiNRh6papIyZNmsTatWuPKJs8eTKrVq1KTUAiktaU3OuIhx9+ONUhiEiIqFtGRCSElNxFREJIyV1EJITi+SWmecCPgT3u3rnMtpuBXwOZ7r7XIl+FfAAYAnwNXOXubycj0NnrZyfjMKWuP/v6pB5PRKQuiaflPh8YXLbQzE4FBgA7ooovJPK7qR2BicAjiYeYWpXN57569Wq6d+9ORkYGzzzzTAoiFBE5WqXJ3d1XA7GmErwf+AUQ/S2o4cATHvEGcJKZtUpKpCkQz3zubdu2Zf78+YwZM6ZW4hERiUe1+tzNbBiwy93fLbOpDbAzar0wKIt1jIlmts7M1hUVFVUnjBoXz3zu2dnZ5OTk8IMfVP5Urlq1ij59+jBixAjOPPNMrrvuOg4fPgzAihUr6NWrF927d2fkyJF89dVXpcefPn065513Hk8//TRbt27lggsuoGvXrnTv3p2PPvoo+RcuImmvysndzBoD/wncFmtzjLKY8xu4+xx3z3X33MzMzKqGUStizede3TnZSxQUFPCb3/yG9957j48++ojnnnuOvXv38qtf/YqVK1fy9ttvk5uby29/+9vSfRo2bMiaNWsYNWoUl19+OZMmTeLdd9/ltddeo1WrtH1jJCI1qDpfYvoh0A54N5hKNgt428zyiLTUT42qmwV8kmiQqZLMOdlL5OXl0b59ZO6K0aNHs2bNGho2bMimTZs499xzAfjuu+/o1atX6T6XXXYZAF9++SW7du1ixIgRQCTpi4jEUuXk7u7vAS1L1s1sO5AbjJZZBtxgZouAc4Av3H13soKtbcmck71EeXOzDxgwgIULF8bcp2R+9rowyZuIpId4hkIuBPoCLcysELjd3eeWU/0vRIZBbiUyFHJckuJMydDFeOZzr6qCggK2bdvGaaedxuLFi5k4cSI9e/Zk0qRJbN26lQ4dOvD1119TWFjI6aeffsS+TZo0ISsriyVLlnDxxRfz7bffcujQodJfWRIRKRHPaJnR7t7K3eu7e1bZxO7u2e6+N1h2d5/k7j909y7uvi72UdND9HzuZ5xxBpdeemnpfO7Lli0D4M033yQrK4unn36aa6+9lrPOOqvCY/bq1YupU6fSuXNn2rVrx4gRI8jMzGT+/PmMHj2anJwcevbsyQcffBBz/yeffJIHH3yQnJwcevfuzT/+8Y+kX7eIpD9NHFaJIUOGMGTIkCPKpk+fXrrco0cPCgsL4z5e48aNWbx48VHl/fr148033zyqfPv27Uesd+zYkb/+9a9xn09Ejk2afkBEJITUcq8B7733HldcccURZccddxz5+fn07ds3NUGJyDFFyb0GdOnShfXr16c6DBE5hqlbRkQkhJTcRURCSMldRCSE0qbPvWjWQ0k9XuZPb0jq8URE6pK0Se6pkp2dzYknnki9evXIyMhg3bq0/l6WiBwjlNzj8Oqrr9KiRYsaO/7BgwfJyNCtEJHkUZ97kvTt25cpU6bQu3dvOnfuTEFBAQDFxcWMHz+eHj160K1bt9L54OfPn8/IkSMZOnQoAwcOBOC+++6jS5cudO3alalTp6bsWkQk/am5WAkzY+DAgZgZ1157LRMnTiy3bnFxMa+99hqrV69m/PjxbNy4kbvuuot+/foxb9489u/fT15eHhdccAEAr7/+Ohs2bKB58+a8+OKLLFmyhPz8fBo3bsxnn8X68SsRkfgouVdi7dq1tG7dmj179jBgwAB+9KMf0adPn5h1R48eDUCfPn04cOAA+/fvZ8WKFSxbtoyZM2cC8M0337BjR+RnZwcMGEDz5s0BWLlyJePGjSud4bGkXESkOpTcK1Eyf3vLli0ZMWIEBQUF5Sb38uZqf/bZZ+nUqdMR2/Lz80vnaYfIXO2J/hCIiEiJtEnuqRi6WFxczOHDhznxxBMpLi5mxYoV3HZbrF8XjFi8eDHnn38+a9asoWnTpjRt2pRBgwYxa9YsZs2ahZnxzjvv0K1bt6P2HThwINOnT2fMmDGl3TJqvYtIdaVNck+FTz/9tPQn7Q4ePMiYMWMYPHhwufWbNWtG7969OXDgAPPmzQPg1ltvZcqUKeTk5ODuZGdn88ILLxy17+DBg1m/fj25ubk0aNCAIUOGcPfdd9fMhYlI6FllP91mZvOAHwN73L1zUPZrYCjwHfARMM7d9wfbpgETgEPAz9z9pcqCyM3N9bLjxzdv3swZZ5xR5QtKlb59+zJz5kxyc3NTHUq1pNvzLZKo6C9Gbsv+52825A1tn4pwqsXM3nL3mEknnqGQ84GyzdWXgc7ungN8CEwLTnQmMAo4K9hntpnVq2bcIiJSTZV2y7j7ajPLLlO2Imr1DeCSYHk4sMjdvwW2mdlWIA94PSnR1gGTJk1i7dq1R5RNnjyZVatWpSYgEZEYktHnPh4o+d24NkSSfYnCoOwoZjYRmAjQtm3bJIRROx5++OFUhyAiUqmEvqFqZv8JHAT+WFIUo1rMTn13n+Puue6em5mZmUgYIiJSRrVb7mY2lsgHrf39n5/KFgKnRlXLAj6pfngiIlId1Wq5m9lg4BZgmLt/HbVpGTDKzI4zs3ZAR6Ag8TBFRKQqKm25m9lCoC/QwswKgduJjI45Dng5+FblG+5+nbu/b2ZPAZuIdNdMcvdDyQi04PmPk3GYUuk03ElEpKoqbbm7+2h3b+Xu9d09y93nunsHdz/V3c8O/q6Lqn+Xu//Q3Tu5+4s1G37NGj9+PC1btqRz586lZZ999hkDBgygY8eODBgwgM8//zyFEYqIxKYpfytw1VVXsXz58iPKZsyYQf/+/dmyZQv9+/dnxowZNXJud+fw4cM1cmwRCT8l9wr06dPnqPldli5dytixYwEYO3YsS5YsKXf/O+64gyuuuIJ+/frRsWNHfv/735du+/Wvf02PHj3Iycnh9ttvB2D79u2cccYZXH/99XTv3p2dO3eyfPlyunfvTteuXenfv38NXKWIhJHmlqmiTz/9lFatWgHQqlUr9uzZU2H9DRs28MYbb1BcXEy3bt246KKL2LhxI1u2bKGgoAB3Z9iwYaxevZq2bdvyt7/9jccff5zZs2dTVFTENddcw+rVq2nXrp3meBeRuCm517Dhw4fTqFEjGjVqxPnnn09BQQFr1qxhxYoVpbNDfvXVV2zZsoW2bdty2mmn0bNnTwDeeOMN+vTpQ7t27QDN8S4i8VNyr6JTTjmF3bt306pVK3bv3k3Lli0rrF/eHO/Tpk3j2muvPWLb9u3bNce7iCRF2iT3ujJ0cdiwYSxYsICpU6eyYMEChg8fXmH9pUuXMm3aNIqLi1m1ahUzZsygUaNG3HrrrVx++eWccMIJ7Nq1i/r16x+1b69evZg0aRLbtm0r7ZZR611E4pE2yT0VRo8ezapVq9i7dy9ZWVnceeedTJ06lUsvvZS5c+fStm1bnn766QqPkZeXx0UXXcSOHTu49dZbad26Na1bt2bz5s306tULgBNOOIE//OEP1Kt35ASamZmZzJkzh5/85CccPnyYli1b8vLLL9fY9YpIeCi5V2DhwoUxy1955ZW4j3H66aczZ86co8onT57M5MmTjyrfuHHjEesXXnghF154YdznExEBDYUUEQkltdyT4PHHH+eBBx44ouzcc8/V9MAikjJ1Ormny2iRcePGMW7cuFSHUW2V/dSiiKSfOtst07BhQ/bt26fEU8PcnX379tGwYcNUhyIiSVRnW+5ZWVkUFhZSVFSU6lBCr2HDhmRlZaU6DBFJojqb3OvXr1/6zUwREamaOtstIyIi1VdpcjezeWa2x8w2RpU1N7OXzWxL8NgsKDcze9DMtprZBjPrXpPBi4hIbPG03OcDg8uUTQVecfeOwCvBOsCFRH5aryMwEXgkOWGKiEhVxPNLTKuBsnPNDgcWBMsLgIujyp/wiDeAk8ysVbKCFRGR+FS3z/0Ud98NEDyWTI3YBtgZVa8wKBMRkVqU7A9UY33jKOZAdTObaGbrzGydhjuKiCRXdZP7pyXdLcFjyc8RFQKnRtXLAj6JdQB3n+Puue6em5mZWc0wREQkluom92XA2GB5LLA0qvzKYNRMT+CLku4bERGpPZV+icnMFgJ9gRZmVgjcDswAnjKzCcAOYGRQ/S/AEGAr8DWQvhOuiIiksUqTu7uPLmdT/xh1HZiUaFAiIpIYfUNVRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQiih5G5mN5rZ+2a20cwWmllDM2tnZvlmtsXMFptZg2QFKyIi8al2cjezNsDPgFx37wzUA0YB9wL3u3tH4HNgQjICFRGR+CXaLZMBNDKzDKAxsBvoBzwTbF8AXJzgOUREpIqqndzdfRcwk8gPZO8GvgDeAva7+8GgWiHQJtb+ZjbRzNaZ2bqioqLqhiEiIjEk0i3TDBgOtANaA8cDF8ao6rH2d/c57p7r7rmZmZnVDUNERGJIpFvmAmCbuxe5+/fAc0Bv4KSgmwYgC/gkwRhFRKSKEknuO4CeZtbYzAzoD2wCXgUuCeqMBZYmFqKIiFRVIn3u+UQ+OH0beC841hzgFuAmM9sKnAzMTUKcIiJSBRmVVymfu98O3F6m+GMgL5HjiohU1ez1s0uXrz/7+hRGUjfoG6oiIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIZRQcjezk8zsGTP7wMw2m1kvM2tuZi+b2ZbgsVmyghURkfgk2nJ/AFju7j8CugKbganAK+7eEXglWBcRkVpU7eRuZk2APgQ/o+fu37n7fmA4sCCotgC4ONEgRUSkahJpubcHioDHzewdM3vMzI4HTnH33QDBY8skxCkiIlWQSHLPALoDj7h7N6CYKnTBmNlEM1tnZuuKiooSCENERMpKJLkXAoXunh+sP0Mk2X9qZq0Agsc9sXZ29znunuvuuZmZmQmEISIiZVU7ubv7P4CdZtYpKOoPbAKWAWODsrHA0oQiFBGRKstIcP+fAn80swbAx8A4Ii8YT5nZBGAHMDLBc4iISBUllNzdfT2QG2NT/0SOKyIiidE3VEVEQijRbhkRkZSZvX52qkOos9RyFxEJIbXcRUSiFDz/cely3tD2KYwkMWq5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICCWc3M2snpm9Y2YvBOvtzCzfzLaY2eLgV5pERKQWJWNWyMnAZqBJsH4vcL+7LzKzR4EJwCNJOI+ISFyi53m//uzrK61fXPBm6fLxeT1qJKballDL3cyygIuAx4J1A/oBzwRVFgAXJ3IOERGpukS7ZX4H/AI4HKyfDOx394PBeiHQJtaOZjbRzNaZ2bqioqIEwxARkWjVTu5m9mNgj7u/FV0co6rH2t/d57h7rrvnZmZmVjcMERGJIZE+93OBYWY2BGhIpM/9d8BJZpYRtN6zgE8SD1NERKqi2i13d5/m7lnung2MAv7q7pcDrwKXBNXGAksTjlJERKqkJsa53wLcZGZbifTBz62Bc4iISAWS8gPZ7r4KWBUsfwzkJeO4IiJSPfqGqohICCm5i4iEkJK7iEgIKbmLiIRQUj5QFRGpLdHzxkj51HIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRDSl5hE5Eiv3nPk+vnTUhOHJEQtdxGREFLLXURqX/S7A70zqBGJ/ED2qWb2qpltNrP3zWxyUN7czF42sy3BY7PkhSsiIvFIpOV+EPgPd3/bzE4E3jKzl4GrgFfcfYaZTQWmEvnpPRGRiqlFnzSJ/ED2bnd/O1j+EtgMtAGGAwuCaguAixMNUkREqiYpfe5mlg10A/KBU9x9N0ReAMysZTn7THwz6lAAAAZqSURBVAQmArRt2zYZYYhIOio7OkeSIuHRMmZ2AvAsMMXdD8S7n7vPcfdcd8/NzMxMNAwREYmSUHI3s/pEEvsf3f25oPhTM2sVbG8F7EksRBERqapERssYMBfY7O6/jdq0DBgbLI8FllY/PBERqY5E+tzPBa4A3jOz9UHZL4EZwFNmNgHYAYxMLEQREamqaid3d18DWDmb+1f3uCIikjh9Q1VEaodGxdQqzS0jIhJCarmLSM1Raz1llNxFpOIkrCkB0pK6ZUREQkgtdxGJX3mteHW/1DlK7iIx3P/yh6XLNw44PYWRiFSPumVEREJILXdJa+nUwq5qrOl0bTVCH+QmRMldJMWO+SQuNULJXdJCdAKMp04qk2QiccRzncek6FZ8s6apiyONKLlLSpSXAGsiuSWa9MuLqapdK3VOoiNcNEKmTtMHqiIiIaSWu1RJPC3RdOk3rtOt6jLqSpeTpA8ld0m52kyyyTxXuvSt64Xh2FRjyd3MBgMPAPWAx9x9Rk2dS6omnv7umu4HD5u0SdZR/eSvf7yvdLlX+5MTjquumr1+duny9Wdfn8JIaleNJHczqwc8DAwACoE3zWyZu29K9rnUKkkf6dpCr8uS9YJc1xP97P0b/rnS7F9r7bwFz39cupw3tH2tnTcZauoD1Txgq7t/7O7fAYuA4TV0LhERKaOmumXaADuj1guBc2riRHsKfxG1tqQmTpGQqnaBJHL8aPG24srbdqy0fMOq5445pctvtJ1YuhzdQi9P2Tp1sSWfDBv3ZMYsLy54s3T5+LwetRVO0pm7J/+gZiOBQe5+dbB+BZDn7j+NqjMRKPlX1wn4WzVP1wLYm0C4dV2Yr0/Xlr7CfH3pdG2nuXvMV6maarkXAqdGrWcBn0RXcPc5wBwSZGbr3D030ePUVWG+Pl1b+grz9YXl2mqqz/1NoKOZtTOzBsAoYFkNnUtERMqokZa7ux80sxuAl4gMhZzn7u/XxLlERORoNTbO3d3/Avylpo4fJeGunTouzNena0tfYb6+UFxbjXygKiIiqaWJw0REQiitk7uZDTazv5nZVjObmup4EmFmp5rZq2a22czeN7PJQXlzM3vZzLYEj81SHWt1mVk9M3vHzF4I1tuZWX5wbYuDD9/TkpmdZGbPmNkHwT3sFZZ7Z2Y3Bv8mN5rZQjNrmM73zszmmdkeM9sYVRbzXlnEg0GO2WBm3VMXedWkbXKPmuLgQuBMYLSZnZnaqBJyEPgPdz8D6AlMCq5nKvCKu3cEXgnW09VkYHPU+r3A/cG1fQ5MSElUyfEAsNzdfwR0JXKdaX/vzKwN8DMg1907ExkgMYr0vnfzgcFlysq7VxcCHYO/icAjtRRjwtI2uROyKQ7cfbe7vx0sf0kkObQhck0LgmoLgItTE2FizCwLuAh4LFg3oB/wTFAlna+tCdAHmAvg7t+5+35Ccu+IDLxoZGYZQGNgN2l879x9NfBZmeLy7tVw4AmPeAM4ycxa1U6kiUnn5B5rioM2KYolqcwsG+gG5AOnuPtuiLwAAC1TF1lCfgf8AjgcrJ8M7Hf3g8F6Ot+/9kAR8HjQ7fSYmR1PCO6du+8CZgI7iCT1L4C3CM+9K1HevUrbPJPOyd1ilKX90B8zOwF4Fpji7gdSHU8ymNmPgT3u/lZ0cYyq6Xr/MoDuwCPu3g0oJg27YGIJ+p6HA+2A1sDxRLoqykrXe1eZtP13ms7JvdIpDtKNmdUnktj/6O7PBcWflrwNDB73pCq+BJwLDDOz7US6z/oRacmfFLzVh/S+f4VAobvnB+vPEEn2Ybh3FwDb3L3I3b8HngN6E557V6K8e5W2eSadk3uopjgI+qDnApvd/bdRm5YBY4PlscDS2o4tUe4+zd2z3D2byH36q7tfDrwKXBJUS8trA3D3fwA7zaxTUNQf2EQI7h2R7pieZtY4+Ddacm2huHdRyrtXy4Arg1EzPYEvSrpv6jx3T9s/YAjwIfAR8J+pjifBazmPyNu9DcD64G8Ikb7pV4AtwWPzVMea4HX2BV4IltsDBcBW4GnguFTHl8B1nQ2sC+7fEqBZWO4dcCfwAbAReBI4Lp3vHbCQyOcH3xNpmU8o714R6ZZ5OMgx7xEZNZTya4jnT99QFREJoXTulhERkXIouYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhND/B9lEl79AI803AAAAAElFTkSuQmCC\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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wsPC4Ms0vHpGfn8+0adPIzY05ZXPS6PmWM030fO5bsgccty1d5papkvncRUSk+lK3TDUxfvx4Vq5ceVzZhAkTWLZsWWoCEpG0puReTTz++OOpDkFEQkTdMiIiIaTkLiISQkruIiIhlDZ97jPWzEhqfeM6jEtqfSIi1Yla7uUobz735cuX06lTJzIyMnjxxRdTEKGIyMnKTe5mNtvMdpvZuqiye8xsh5mtCR4DorZNNrNNZvaxmfWtqsBPh3jmc2/SpAlz5sxhxIgRpyUeEZF4xNNynwP0i1H+sLt3CB6vAZhZa2AY0CY4ZoaZ1UhWsKdbPPO5Z2dnk5OTwze+Uf5TuWzZMrp3787gwYNp3bo1t956K8eOHQNgyZIldO3alU6dOjFkyBC+/PLL0vqnTJnClVdeyQsvvMCmTZu4+uqrad++PZ06dWLz5s3Jv3ARSXvlZiR3Xw7EOwn4IOB5dz/s7luATUDlZsuqBmLN517ZOdlLFBQU8NBDD/HBBx+wefNmXn75Zfbu3cvPfvYzli5dyh//+Edyc3P55S9/WXpMrVq1WLFiBcOGDeOGG25g/Pjx/OlPf+Ltt9+mUaNGCcUjIuGUyAeqt5nZjUAh8EN3/wy4GHg3ap+ioOwkZjYWGAuRro3qKJlzspfIy8ujWbPIvBXDhw9nxYoV1KpVi/Xr13PFFVcA8Pe//52uXbuWHnP99dcD8MUXX7Bjxw4GDx4MRJK+iEgslU3uTwA/BTz4+xAwBoiV+WLOTObuM4GZEJk4rJJxVKlkzsleoqy52Xv37s28efNiHlMyP3t1mORNRNJDpZK7u+8qWTazp4CSuWaLgEuids0CPq10dFFSMXQxnvncK6qgoIAtW7Zw6aWXMn/+fMaOHUuXLl0YP348mzZtonnz5nz11VcUFRVx2WWXHXfs+eefT1ZWFgsWLOC6667j8OHDHD16tPRXlkRESlRqKKSZRXf0DgZKRtIsAoaZWU0zawq0AAoSCzF1oudzb9WqFUOHDi2dz33RokUAvPfee2RlZfHCCy9wyy230KZNm1PW2bVrVyZNmkTbtm1p2rQpgwcPJjMzkzlz5jB8+HBycnLo0qULH330Uczjn332WR599FFycnLo1q0bf/3rX5N+3SKS/sptuZvZPCAfaGBmRcDdQL6ZdSDS5bIVuAXA3T80s98A64EjwHh3T+vxewMGDGDAgOPnep4yZUrpcufOnSkqKoq7vjp16jB//vyTynv27Ml77713UvnWrVuPW2/RogW///3v4z6fiJyZyk3u7j48RvGsU+x/H3BfIkGJiEhi0mb6gXTywQcfMHLkyOPKatasyapVq8jPz09NUCJyRlFyrwLt2rVjzZo1qQ5DRM5gmltGRCSElNxFREJIyV1EJITSps99z/THklpf5r/fltT6RESqk7RJ7qmSnZ3NeeedR40aNcjIyKCwsDDVIYmIlEvJPQ5vvfUWDRo0qLL6jxw5QkaGboWIJI/63JMkPz+fiRMn0q1bN9q2bUtBQWTWheLiYsaMGUPnzp3p2LFj6Xzwc+bMYciQIVx77bX06dMHgAcffJB27drRvn17Jk2alLJrEZH0p+ZiOcyMPn36YGbccsstjB07tsx9i4uLefvtt1m+fDljxoxh3bp13HffffTs2ZPZs2dz4MAB8vLyuPrqqwF45513WLt2LfXr1+f1119nwYIFrFq1ijp16rB/f7xT6IuInEzJvRwrV66kcePG7N69m969e/Otb32L7t27x9x3+PDITA3du3fn4MGDHDhwgCVLlrBo0SKmTZsGwKFDh9i2bRsAvXv3pn79+gAsXbqU0aNHl87wWFIuIlIZSu7lKJm/vWHDhgwePJiCgoIyk3tZc7W/9NJLtGzZ8rhtq1atKp2nHSJztSf6QyAiIiXSJrmnYuhicXExx44d47zzzqO4uJglS5Zw1113lbn//Pnz6dGjBytWrKBu3brUrVuXvn37Mn36dKZPn46Z8f7779OxY8eTju3Tpw9TpkxhxIgRpd0yar2LSGWlTXJPhV27dpX+pN2RI0cYMWIE/frF+q3wiHr16tGtWzcOHjzI7NmzAbjzzjuZOHEiOTk5uDvZ2dm8+uqrJx3br18/1qxZQ25uLmeffTYDBgzg/vvvr5oLE5HQs+rw0225ubl+4vjxDRs20KpVqxRFVHH5+flMmzaN3NzcVIdSKen2fIskKvqLkVuyj//Nhrxrm53ucCrFzFa7e8yko6GQIiIhpG6ZCho/fjwrV648rmzChAksW7YsNQGJiMSg5F5Bjz/+eKpDEBEpl7plRERCqNzkbmazzWy3ma2LKvuFmX1kZmvN7BUzuyAozzazv5nZmuDxZFUGLyIiscXTcp8DnDj+7w2grbvnAH8GJkdt2+zuHYLHrckJU0REKqLcPnd3X25m2SeULYlafRf4bnLDOlnBb/+S1PrSZaiTiEhlJKPPfQzwetR6UzN738z+YGZXJaH+lBkzZgwNGzakbdu2pWX79++nd+/etGjRgt69e/PZZ5+lMEIRkdgSSu5m9l/AEeB/g6KdQBN37wj8APi1mZ1fxrFjzazQzAr37NmTSBhV5qabbmLx4sXHlU2dOpVevXqxceNGevXqxdSpU6vk3O7OsWPHqqRuEQm/Sid3MxsFfBu4wYOvubr7YXffFyyvBjYDl8U63t1nunuuu+dmZmZWNowq1b1795Pmd1m4cCGjRo0CYNSoUSxYsKDM4++55x5GjhxJz549adGiBU899VTptl/84hd07tyZnJwc7r77bgC2bt1Kq1atGDduHJ06dWL79u0sXryYTp060b59e3r16lUFVykiYVSpce5m1g+4A/hnd/8qqjwT2O/uR82sGdACSG5neYrt2rWLRo0aAdCoUSN27959yv3Xrl3Lu+++S3FxMR07duSaa65h3bp1bNy4kYKCAtydgQMHsnz5cpo0acLHH3/M008/zYwZM9izZw/f//73Wb58OU2bNtUc7yISt3KTu5nNA/KBBmZWBNxNZHRMTeCNYJrad4ORMd2BKWZ2BDgK3OruZ3RGGjRoELVr16Z27dr06NGDgoICVqxYwZIlS0pnh/zyyy/ZuHEjTZo04dJLL6VLly4AvPvuu3Tv3p2mTZsCmuNdROIXz2iZ4TGKZ5Wx70vAS4kGVZ1ddNFF7Ny5k0aNGrFz504aNmx4yv3LmuN98uTJ3HLLLcdt27p1q+Z4F5GkSJvpB6rL0MWBAwcyd+5cJk2axNy5cxk0aNAp91+4cCGTJ0+muLiYZcuWMXXqVGrXrs2dd97JDTfcwLnnnsuOHTs466yzTjq2a9eujB8/ni1btpR2y6j1LiLxSJvkngrDhw9n2bJl7N27l6ysLO69914mTZrE0KFDmTVrFk2aNOGFF144ZR15eXlcc801bNu2jTvvvJPGjRvTuHFjNmzYQNeuXQE499xzee6556hRo8Zxx2ZmZjJz5ky+853vcOzYMRo2bMgbb7xRZdcrIuGh5H4K8+bNi1n+5ptvxl3HZZddxsyZM08qnzBhAhMmTDipfN26dcet9+/fn/79+8d9PhER0MRhIiKhpJZ7Ejz99NM88sgjx5VdccUVmh5YRFKmWif3dBktMnr0aEaPHp3qMCqtOvzUoogkV7XtlqlVqxb79u1T4qli7s6+ffuoVatWqkMRkSSqti33rKwsioqKqK7zzoRJrVq1yMrKSnUYIpJE1Ta5n3XWWaXfzBQRkYqptt0yIiJSeUruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJxJXczm21mu81sXVRZfTN7w8w2Bn/rBeVmZo+a2SYzW2tmnaoqeBERiS3elvscoN8JZZOAN929BfBmsA7QH2gRPMYCTyQepoiIVERcyd3dlwP7TygeBMwNlucC10WVP+MR7wIXmFmjZAQrIiLxSaTP/SJ33wkQ/G0YlF8MbI/arygoO46ZjTWzQjMr1JztIiLJVRUfqMb6XbyTfk7J3We6e66752ZmZlZBGCIiZ65Ekvuuku6W4O/uoLwIuCRqvyzg0wTOIyIiFZRIcl8EjAqWRwELo8pvDEbNdAE+L+m+ERGR0yOun9kzs3lAPtDAzIqAu4GpwG/M7GZgGzAk2P01YACwCfgKGJ3kmEVEpBxxJXd3H17Gpl4x9nVgfCJBiYhIYvQNVRGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREIrrN1RjMbOWwPyoombAXcAFwPeBPUH5T9z9tUpHKCIiFVbp5O7uHwMdAMysBrADeAUYDTzs7tOSEqGISBxmrJlRujyuw7gURlI9JKtbphew2d0/SVJ9IiKSgGQl92HAvKj128xsrZnNNrN6sQ4ws7FmVmhmhXv27Im1i4iIVFLCyd3MzgYGAi8ERU8A3yTSZbMTeCjWce4+091z3T03MzMz0TBERCRKMlru/YE/uvsuAHff5e5H3f0Y8BSQl4RziIhIBSQjuQ8nqkvGzBpFbRsMrEvCOUREpAIqPVoGwMzqAL2BW6KKHzSzDoADW0/YJiIip0FCyd3dvwIuPKFsZEIRiYhIwvQNVRGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJoYR+IBvAzLYCXwBHgSPunmtm9YH5QDawFRjq7p8lei4REYlPslruPdy9g7vnBuuTgDfdvQXwZrAuIiKnSVV1ywwC5gbLc4Hrqug8IiISQzKSuwNLzGy1mY0Nyi5y950Awd+GJx5kZmPNrNDMCvfs2ZOEMEREpETCfe7AFe7+qZk1BN4ws4/iOcjdZwIzAXJzcz0JcYiISCDhlru7fxr83Q28AuQBu8ysEUDwd3ei5xERkfgllNzN7BwzO69kGegDrAMWAaOC3UYBCxM5j4iIVEyi3TIXAa+YWUldv3b3xWb2HvAbM7sZ2AYMSfA8IiJxm7FmRunyuA7jUhhJ6iSU3N39L0D7GOX7gF6J1C0iIpWnb6iKiISQkruISAgpuYuIhJCSu4hICCXjS0wiIsnx1gP/WO4xOXVxhIBa7iIiIaSWu4ikrejx7HI8tdxFREJIyV1EJISU3EVEQkjJXUQkhPSBqojICQp++5fS5bxrm6UwkspTy11EJISU3EVEQkjdMiKSVjS2PT5quYuIhJBa7iKSWtHzyUjSqOUuIhJCarmLyOlXzVrrxQXvHbd+Tl7nFEWSPJVuuZvZJWb2lpltMLMPzWxCUH6Pme0wszXBY0DywhURkXgk0nI/AvzQ3f9oZucBq83sjWDbw+4+LfHwRESkMiqd3N19J7AzWP7CzDYAFycrMBERqbykfKBqZtlAR2BVUHSbma01s9lmVq+MY8aaWaGZFe7ZsycZYYiISCDh5G5m5wIvARPd/SDwBPBNoAORlv1DsY5z95nunuvuuZmZmYmGISIiURJK7mZ2FpHE/r/u/jKAu+9y96Pufgx4CshLPEwREamIREbLGDAL2ODuv4wqbxS122BgXeXDExGRykhktMwVwEjgAzNbE5T9BBhuZh0AB7YCtyQUoYiIVFgio2VWABZj02uVD0dERJJB0w+IiISQph8QkdOjmk05EHZquYuIhJCSu4hICCm5i4iEkJK7iEgI6QNVkQp4+I0/ly7f3vuyFEYicmpquYuIhJCSu4hICKlbRtLC6e4OqQ7dL9UhhkqJHs/eY3Lq4jjDKbmLSOLK+oJSsr64FF1PvbrJqTPklNxF0kzatugrSt9oTYj63EVEQkgtdxGJn/rT04aSu0glldU9UtFuk2rZzaIknvZCkdwnP31d6fIDoxekMBJJZ9FJNlnHVsvELWeEUCR3ObNUJmFWtyQbz4uB/MOMA2v/sVLvqtQFkkaU3CWtxdM1cqaoshewqh7mmCLrdmemOoQqpeQuoVFVCT1Z9VZFt088++f0IJcAAAS8SURBVFeHdyqpNGPNjNLlcR3GpTCS06vKkruZ9QMeAWoAv3L3qVV1LkkP1SXhhKlVn+i1lHX87Wr2pb0qGeduZjWAx4H+QGtguJm1ropziYjIyarq9TkP2OTufwEws+eBQcD6KjqfJFmZLboqaHFXlxa9SJhUVXK/GNgetV4EXF5F5yqTkkZsiSTuinYDlFWnRoucPqd6Trtsmxmz/J046u3a7MJKRiSng7l78is1GwL0dffvBesjgTx3//eofcYCY4PVlsDHCZyyAbA3geOrM11b+grz9enaqodL3T3msJ+qarkXAZdErWcBn0bv4O4zgdjNhgoys0J3z01GXdWNri19hfn6dG3VX1VNHPYe0MLMmprZ2cAwYFEVnUtERE5QJS13dz9iZrcBvyMyFHK2u39YFecSEZGTVdloVnd/DXitquo/QVK6d6opXVv6CvP16dqquSr5QFVERFJLP9YhIhJCaZ3czayfmX1sZpvMbFKq40mEmV1iZm+Z2QYz+9DMJgTl9c3sDTPbGPytl+pYK8vMapjZ+2b2arDe1MxWBdc2P/jwPS2Z2QVm9qKZfRTcw65huXdmdnvwb3Kdmc0zs1rpfO/MbLaZ7TazdVFlMe+VRTwa5Ji1ZtYpdZFXTNom9xBOcXAE+KG7twK6AOOD65kEvOnuLYA3g/V0NQHYELX+c+Dh4No+A25OSVTJ8Qiw2N2/BbQncp1pf+/M7GLgP4Bcd29LZIDEMNL73s0B+p1QVta96g+0CB5jgSdOU4wJS9vkTtQUB+7+d6BkioO05O473f2PwfIXRJLDxUSuaW6w21zgutg1VG9mlgVcA/wqWDegJ/BisEs6X9v5QHdgFoC7/93dDxCSe0dk4EVtM8sA6gA7SeN75+7Lgf0nFJd1rwYBz3jEu8AFZtbo9ESamHRO7rGmOLg4RbEklZllAx2BVcBF7r4TIi8AQMPURZaQ/wb+EzgWrF8IHHD3I8F6Ot+/ZsAe4Omg2+lXZnYOIbh37r4DmAZsI5LUPwdWE557V6Kse5W2eSadk7vFKEv7oT9mdi7wEjDR3Q+mOp5kMLNvA7vdfXV0cYxd0/X+ZQCdgCfcvSNQTBp2wcQS9D0PApoCjYFziHRVnChd71150vbfaTon93KnOEg3ZnYWkcT+v+7+clC8q+RtYPB3d6riS8AVwEAz20qk+6wnkZb8BcFbfUjv+1cEFLn7qmD9RSLJPgz37mpgi7vvcfevgZeBboTn3pUo616lbZ5J5+QeqikOgj7oWcAGd/9l1KZFwKhgeRSw8HTHlih3n+zuWe6eTeQ+/d7dbwDeAr4b7JaW1wbg7n8FtptZy6CoF5HprdP+3hHpjuliZnWCf6Ml1xaKexelrHu1CLgxGDXTBfi8pPum2nP3tH0AA4A/A5uB/0p1PAley5VE3u6tBdYEjwFE+qbfBDYGf+unOtYErzMfeDVYbgYUAJuAF4CaqY4vgevqABQG928BUC8s9w64F/gIWAc8C9RM53sHzCPy+cHXRFrmN5d1r4h0yzwe5JgPiIwaSvk1xPPQN1RFREIonbtlRESkDEruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIh9P8Bjhq6pKitVrsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zuYuISMpRt0wdMXHiRFatWnVM2aRJk1i+fHlyAhKRlKbkXkc89thjyQ5BREJE3TIiIiGk5C4iEkJK7iIiIVRln7uZzQa+A+x1905B2Xyg5KeRZwEH3L2rmWUBm4CPg3XvuPuNiQj08XWPJ2I3pW7qelNC9yciUpfEcuU+BxgUXeDuV7l7V3fvSuTG2S9Frd5asi5RiT2ZqprPfcWKFXTv3p20tDRefPHFJEQoInK8KpO7u68Ayp1KMLh59ghgXoLjqhNimc89MzOTOXPmMHr06BMSj4hILOLtc/82sMfdN0eVtTKz983sD2b27Yo2NLMJZrbGzNYUFhbGGUbtiGU+96ysLLKzs/nGN6p+K5cvX06fPn0YNmwYHTp04MYbb+To0aMALF26lF69etG9e3eGDx/Ol19+Wbr/qVOncvHFF/PCCy+wZcsWLr30Urp06UL37t3ZunVr4hsuIikv3uQ+imOv2ncDme7eDfgB8GszO7O8Dd19prvnuHtOenp6nGHUjvLmc6/pnOwl8vPzeeCBB/jggw/YunUrL730Evv27eOnP/0pb7zxBu+99x45OTk8+OCDpds0aNCAlStXMnLkSK6++momTpzIH//4R9566y1atGgRVzwiEk41/hGTmaUB3wX+uaTM3Q8Dh4PltWa2FbgAWFPuTuq4RM7JXiI3N5fWrVsDkQnEVq5cSYMGDdi4cSMXXXQRAH//+9/p1atX6TZXXXUVAF988QW7du1i2LBhQCTpi4iUJ55fqF4KfOTuBSUFZpYOfOruxWbWGmgL/DnOGJMmkXOyl6hobvb+/fszb175X12UzM9eFyZ5E5HUEMtQyHlAHtDMzAqAn7j7LGAkx3+R2geYamZHgGLgRndPyH3dkjF0MZb53KsrPz+fbdu2cf755zN//nwmTJhAz549mThxIlu2bKFNmzZ89dVXFBQUcMEFFxyz7ZlnnklGRgYLFy7kyiuv5PDhwxQXF5feZUlEpEQso2VGuXsLd6/v7hlBYsfdr3X3J8vUXeDuHd29i7t3d/ff1lbgJ0L0fO7t27dnxIgRpfO5L168GIB3332XjIwMXnjhBW644QY6duxY6T579erFlClT6NSpE61atWLYsGGkp6czZ84cRo0aRXZ2Nj179uSjjz4qd/tnn32WRx55hOzsbHr37s1f//rXhLdbRFKfJg6rwpAhQxgyZMgxZVOnTi1d7tGjBwUFBWU3q1CjRo2YP3/+ceV9+/bl3XffPa58+/btx7xu27Ytv//972M+noicnDT9gIhICOnKvRZ88MEHjBkz5piyU089ldWrV5OXl5ecoESkQvm/PXbcR+7lrZMUSeIoudeCzp07s27dumSHISInMXXLiIiEkJK7iEgIKbmLiIRQyvS5F854NKH7S//3mxO6PxGRuiRlknuyZGVlccYZZ1CvXj3S0tJYsyYlp8kRkZOMknsM3nzzTZo1a1Zr+z9y5AhpaToVIpI46nNPkLy8PCZPnkzv3r3p1KkT+fn5ABQVFTF+/Hh69OhBt27dSueDnzNnDsOHD+fyyy9nwIABANx///107tyZLl26MGXKlKS1RURSny4Xq2BmDBgwADPjhhtuYMKECRXWLSoq4q233mLFihWMHz+eDRs2cM8999C3b19mz57NgQMHyM3N5dJLLwXg7bffZv369TRt2pTXXnuNhQsXsnr1aho1asSnnyZkvjUROUkpuVdh1apVtGzZkr1799K/f3++9a1v0adPn3Lrjho1CoA+ffpw8OBBDhw4wNKlS1m8eDHTp08H4NChQ+zYsQOA/v3707RpUwDeeOMNxo0bVzrDY0m5iEhNKLlXoWT+9ubNmzNs2DDy8/MrTO4VzdW+YMEC2rVrd8y61atXl87TDpG52uO9EYiISImUSe7JGLpYVFTE0aNHOeOMMygqKmLp0qXceeedFdafP38+l1xyCStXrqRx48Y0btyYgQMHMmPGDGbMmIGZ8f7779OtW7fjth0wYABTp05l9OjRpd0yunoXkZpKmeSeDHv27Cm9pd2RI0cYPXo0gwYNqrB+kyZN6N27NwcPHmT27NkA3HHHHUyePJns7GzcnaysLF555ZXjth00aBDr1q0jJyeHU045hSFDhnDvvffWTsNEJPSsqlu3mdls4DvAXnfvFJTdBVwPFAbVbnf3V4N1twHXEbkT03+4+++qCiInJ8fLjh/ftGkT7du3r1ZjkikvL4/p06eTk5OT7FBqJNXeb5F4Rf8wclvWsfdsSJVZIc1srbuXm3RiGQo5ByjvcvUhd+8aPEoSewcit9/rGGzzuJnVq1nYIiJSU1V2y7j7CjPLinF/VwDPu/thYJuZbQFygbdrHGEdM3HiRFatWnVM2aRJk1i+fHlyAhIRKUc8fe43m9k1wBrgh+7+GXAu8E5UnYKg7DhmNgGYAJCZmRlHGCfWY489luwQRESqVNNfqD4BfBPoCuwGHgjKyxvLV26nvrvPdPccd89JT0+vYRgiIlKeGiV3d9/j7sXufhR4ikjXC0Su1M+LqpoBfBJfiCIiUl01Su5m1iLq5TBgQ7C8GBhpZqeaWSugLZAfX4giIlJdVfa5m9k8IA9oZmYFwE+APDPrSqTLZTtwA4C7f2hmvwE2AkeAie5enIhAy97ANl6pMtRJRKQmqrxyd/dR7t7C3eu7e4a7z3L3Me7e2d2z3X2ou++Oqn+Pu3/T3du5+2u1G37tGj9+PM2bN6dTp06lZZ9++in9+/enbdu29O/fn88++yyJEYqIlE9T/lbi2muvZcmSJceUTZs2jX79+rF582b69evHtGnTauXY7s7Ro0drZd8iEn5K7pXo06fPcfO7LFq0iLFjxwIwduxYFi5cWOH2d911F2PGjKFv3760bduWp556qnTdz3/+c3r06EF2djY/+clPANi+fTvt27fnpptuonv37uzcuZMlS5bQvXt3unTpQr9+/WqhlSISRppbppr27NlDixaR75NbtGjB3r17K62/fv163nnnHYqKiujWrRuXXXYZGzZsYPPmzeTn5+PuDB06lBUrVpCZmcnHH3/M008/zeOPP05hYSHXX389K1asoFWrVprjXURipuRey6644goaNmxIw4YNueSSS8jPz2flypUsXbq0dHbIL7/8ks2bN5OZmcn5559Pz549AXjnnXfo06cPrVq1AjTHu4jETsm9ms455xx2795NixYt2L17N82bN6+0fkVzvN92223ccMMNx6zbvn275ngXkYRImeReV4YuDh06lLlz5zJlyhTmzp3LFVdcUWn9RYsWcdttt1FUVMTy5cuZNm0aDRs25I477uDqq6/m9NNPZ9euXdSvX/+4bXv16sXEiRPZtm1babeMrt5FJBYpk9yTYdSoUSxfvpx9+/aRkZHB3XffzZQpUxgxYgSzZs0iMzOTF154odJ95Obmctlll7Fjxw7uuOMOWrZsScuWLdm0aRO9evUC4PTTT+e5556jXr1jJ9BMT09n5syZfPe73+Xo0aM0b96c119/vdbaKyLhoeReiXnz5pVbvmzZspj3ccEFFzBz5szjyidNmsSkSZOOK9+wYcMxrwcPHszgwYNjPp6ICGgopIhIKOnKPQGefvppHn744WPKLrroIk0PLCJJU6eTe6qMFhk3bhzjxo1Ldhg1VtWtFkUk9dTZbpkGDRqwf/9+JZ5a5u7s37+fBg0aJDsUEUmgOnvlnpGRQUFBAYWFhVVXlrg0aNCAjIyMZIchIglUZ5N7/fr1S3+ZKSIi1VNnu2VERKTmqkzuZjbbzPaa2Yaosp+b2Udmtt7MXjazs4LyLDP7m5mtCx5P1mbwIiJSvliu3OcAg8qUvQ50cvds4E/AbVHrtrp71+BxY2LCFBGR6ojlTkwrgE/LlC119yPBy3eI3AhbRETqiET0uY8Hom+n18rM3jezP5jZtxOwfxERqaa4RsuY2X8RuRH2r4Ki3UCmu+83s38GFppZR3c/WM62E4AJAJmZmfGEISIiZdT4yt3MxgLfAa724JdG7n7Y3fcHy2uBrcAF5W3v7jPdPcfdc9LT02sahoiIlKNGyd3MBgG3AkPd/auo8nQzqxcstwbaAn9ORKAiIhK7KrtlzGwekAc0M7MC4CdERsecCrwezP3yTjAypg8w1cyOAMXAje6uG3+KiJxgVSZ3dx9VTvGsCuouABbEG5SIiMRHv1AVEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREIopuZvZbDPba2YbosqamtnrZrY5eG4SlJuZPWJmW8xsvZl1r63gRUSkfLFeuc8BBpUpmwIsc/e2wLLgNcBgIvdObQtMAJ6IP0wREamOmJK7u68Ayt4L9QpgbrA8F7gyqvwZj3gHOMvMWiQiWBERiU08fe7nuPtugOC5eVB+LrAzql5BUHYMM5tgZmvMbE1hYWEcYYiISFm18YWqlVPmxxW4z3T3HHfPSU9Pr4UwREROXvEk9z0l3S3B896gvAA4L6peBvBJHMcREZFqiie5LwbGBstjgUVR5dcEo2Z6Ap+XdN+IiMiJkRZLJTObB+QBzcysAPgJMA34jZldB+wAhgfVXwWGAFuAr4BxCY5ZRESqEFNyd/dRFazqV05dBybGE5SIiMRHv1AVEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRCKacpfEZET4s37/rF8yW3JiyMEapzczawdMD+qqDVwJ3AWcD1Qctfr29391RpHKCIi1Vbj5O7uHwNdAcysHrALeJnInZcecvfpCYlQRESqLVF97v2Are7+lwTtT0RE4pCo5D4SmBf1+mYzW29ms82sSYKOISIiMYo7uZvZKcBQ4IWg6Angm0S6bHYDD1Sw3QQzW2NmawoLC8urIiIiNZSIK/fBwHvuvgfA3fe4e7G7HwWeAnLL28jdZ7p7jrvnpKenJyAMEREpkYjkPoqoLhkzaxG1bhiwIQHHEBGRaohrnLuZNQL6AzdEFd9vZl0BB7aXWSciIidAXMnd3b8Czi5TNiauiEREJG6afkBEJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJI87mLSCg8vu7x0uWbut6UxEjqBl25i4iEkJK7iEgIKbmLiISQkruISAjpC1URSVnRX6LKsXTlLiISQkruIiIhpOQuIhJCSu4iIiEU9xeqZrYd+AIoBo64e46ZNQXmA1lE7sY0wt0/i/dYIiISm0RduV/i7l3dPSd4PQVY5u5tgWXBaxEROUFqq1vmCmBusDwXuLKWjiMiIuVIRHJ3YKmZrTWzCUHZOe6+GyB4bl52IzObYGZrzGxNYWFhAsIQEZESifgR00Xu/omZNQdeN7OPYtnI3WcCMwFycnI8AXGIiEgg7it3d/8keN4LvAzkAnvMrAVA8Lw33uOIiEjs4kruZnaamZ1RsgwMADYAi4GxQbWxwKJ4jiMiItUTb7fMOcDLZlayr1+7+xIzexf4jZldB+wAhsd5HBERqYa4kru7/xnoUk75fqBfPPsWEZGa06yQInJS2rA3vXT5tKzkxVFblNxFJHR0P1XNLSMiEkpK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkIa5y4iyfXmfcmOIJR05S4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiFU46GQZnYe8AzwT8BRYKa7P2xmdwHXA4VB1dvd/dV4AxURgWOn85WKxTPO/QjwQ3d/L7iP6lozez1Y95C7T48/PBERqYkaJ3d33w3sDpa/MLNNwLmJCkxERGouIX3uZpYFdANWB0U3m9l6M5ttZk0q2GaCma0xszWFhYXlVRERkRqKO7mb2enAAmCyux8EngC+CXQlcmX/QHnbuftMd89x95z09PTyqojIyezN+/7xkGqLK7mbWX0iif1X7v4SgLvvcfdidz8KPAXkxh+miIhUR42Tu5kZMAvY5O4PRpW3iKo2DNhQ8/BERKQm4hktcxEwBvjAzNYFZbcDo8ysK541Et0AAAW0SURBVODAduCGuCIUEZFqi2e0zErAylmlMe0icrzovvNLbkteHCcJ/UJVRCSElNxFREJIyV1EJIR0mz2Rcjz0+p9Kl2/pf0ESIxGpGSV3ETnxqvnDJE0WVn3qlhERCSFduYvUInXvJMi2//3HcqtvJy+OFKIrdxGREFJyFxEJIXXLiNQhoejG0S9R6wQld5ETJBSJW1KGkrtIEkQn+trYj/54iJK7nBRiuWpOVMJNlGRe6Sfs2LrRRtIouUtKO9EJMBWvlNUddHJSchdJsNr4BKAELdVVa8ndzAYBDwP1gF+6+7TaOpaktup2mcRTp2y96saXqH3WFakat1StVpK7mdUDHgP6AwXAu2a22N031sbxRMoKc9KKp2219r6ob73Oqa0r91xgi7v/GcDMngeuAGolud/29JWly/eNW1gbh5AkqCgRhTlxJ0oi36MKv2dIW5CwY0ji1VZyPxfYGfW6ALiwlo51jBPZN1ndY52I2GL5T13dY1f3S8SK2qmkHC5v/3l/6XKv1mcnMZL4FeW/e2zB5a2TE0gCmbsnfqdmw4GB7v794PUYINfd/z2qzgRgQvCyHfBxHIdsBuyLY/u6TG1LXWFun9pWN5zv7unlraitK/cC4Lyo1xnAJ9EV3H0mMDMRBzOzNe6ek4h91TVqW+oKc/vUtrqvtiYOexdoa2atzOwUYCSwuJaOJSIiZdTKlbu7HzGzm4HfERkKOdvdP6yNY4mIyPFqbZy7u78KvFpb+y8jId07dZTalrrC3D61rY6rlS9URUQkuXSzDhGREErp5G5mg8zsYzPbYmZTkh1PPMzsPDN708w2mdmHZjYpKG9qZq+b2ebguUmyY60pM6tnZu+b2SvB61Zmtjpo2/zgy/eUZGZnmdmLZvZRcA57heXcmdktwb/JDWY2z8wapPK5M7PZZrbXzDZElZV7rizikSDHrDez7smLvHpSNrlHTXEwGOgAjDKzDsmNKi5HgB+6e3ugJzAxaM8UYJm7twWWBa9T1SRgU9TrnwEPBW37DLguKVElxsPAEnf/FtCFSDtT/tyZ2bnAfwA57t6JyACJkaT2uZsDDCpTVtG5Ggy0DR4TgCdOUIxxS9nkTtQUB+7+d6BkioOU5O673f29YPkLIsnhXCJtmhtUmwtcWf4e6jYzywAuA34ZvDagL/BiUCWV23Ym0AeYBeDuf3f3A4Tk3BEZeNHQzNKARsBuUvjcufsK4NMyxRWdqyuAZzziHeAsM2txYiKNTyon9/KmODg3SbEklJllAd2A1cA57r4bIn8AgObJiywu/wP8J3A0eH02cMDdjwSvU/n8tQYKgaeDbqdfmtlphODcufsuYDqwg0hS/xxYS3jOXYmKzlXK5plUTu5WTlnKD/0xs9OBBcBkdz+Y7HgSwcy+A+x197XRxeVUTdXzlwZ0B55w925AESnYBVOeoO/5CqAV0BI4jUhXRVmpeu6qkrL/TlM5uVc5xUGqMbP6RBL7r9z9paB4T8nHwOB5b7Lii8NFwFAz206k+6wvkSv5s4KP+pDa568AKHD31cHrF4kk+zCcu0uBbe5e6O5fAy8BvQnPuStR0blK2TyTysk9VFMcBH3Qs4BN7v5g1KrFwNhgeSyw6ETHFi93v83dM9w9i8h5+r27Xw28CXwvqJaSbQNw978CO82sXVDUj8j01il/7oh0x/Q0s0bBv9GStoXi3EWp6FwtBq4JRs30BD4v6b6p89w9ZR/AEOBPwFbgv5IdT5xtuZjIx731wLrgMYRI3/QyYHPw3DTZscbZzjzglWC5NZAPbAFeAE5NdnxxtKsrsCY4fwuBJmE5d8DdwEfABuBZ4NRUPnfAPCLfH3xN5Mr8uorOFZFumceCHPMBkVFDSW9DLA/9QlVEJIRSuVtGREQqoOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJC/wexU4Ozt6+f9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZLFq06LR1+/btS25uLllZWdSoUYP+/fvz8MMPV8C7JSJVicZzr+K6devG5MmTycoqdsjmhNH7LWeb6PHct2f2P2VeqowtE9d47mY208z2mdnGqLa5ZpYb/OwouP2emWWa2d+i5p1+yyEREalwsXTLzAKmAi8UNLj7Dwqem9ljwBdRy29z99NvEiqlGjNmDKtXrz6lbezYsaxYsSI5BYlISovlHqorzSyzuHkWuQzjRqBHYss6+zz99NPJLkFEQiTeb6h+B9jr7lui2pqa2Xtm9mcz+05JK5rZaDNbZ2br9u/fH2cZIiISLd5wHwrMiZreAzRx9/bAXcCLZnZBcSu6+3R3z3L3rPT09DjLEBGRaOUOdzNLA74HzC1oc/fj7n4weL4e2AZcFm+RIiJyZuK5zv1a4EN3zytoMLN04JC7nzSzZkAL4JOSNnAmpuVOS8RmCt3R7o6Ebk9EpCqJ5VLIOcAaoKWZ5ZlZwbdthnBqlwxAV2CDmf0FeAW43d1TeqSqssZzX7lyJR06dCAtLY1XXnklCRWKiJwulqtlhpbQfnMxbfOAefGXVTUUjOe+dOlSMjIy6NixIwMGDODyyy8vXKZJkybMmjWrcACviq6n4FuuIiKl0XjupYhlPPfMzEzatGnDt75V9lu5YsUKunbtyqBBg7j88su5/fbbyc/PB2DJkiV07tyZDh06MHjwYL766qvC7U+cOJFrrrmGl19+ma1bt3LttdfStm1bOnTowLZt2xK/4yKS8hTupShuPPfyjsleICcnh8cee4z333+fbdu28eqrr3LgwAF++ctfsmzZMt59912ysrJ4/PHHC9epWbMmq1atYsiQIdx0002MGTOGv/zlL7z11ls0bNgwrnpEJJw0cFgpEjkme4Hs7GyaNYuMWzF06FBWrVpFzZo12bRpE1dffTUAf//73+ncuXPhOj/4QeQLwV9++SW7d+9m0KBBQCT0RUSKo3AvRSLHZC9Q0tjsvXr1Ys6cop9PRxSMz14VBnkTkdSQMuGejEsXYxnP/Uzl5OSwfft2Lr30UubOncvo0aPp1KkTY8aMYevWrTRv3pyvv/6avLw8Lrvs1K8IXHDBBWRkZDB//nxuuOEGjh8/zsmTJwvvsiQiUkB97qWIHs+9VatW3HjjjYXjuS9cuBCAd955h4yMDF5++WVuu+02rrjiilK32blzZyZMmEDr1q1p2rQpgwYNIj09nVmzZjF06FDatGlDp06d+PDDD4td/ze/+Q1PPfUUbdq0oUuXLnz22WcJ328RSX0pc+aeLP3796d//1PHep44cWLh844dO5KXl1d0tRLVrl2buXPnntbeo0cP3nnnndPad+zYccp0ixYt+NOf/hTz64nI2Uln7iIiIaQz9wrw/vvvM3z48FPazjnnHNauXUu3bt2SU5SInFUU7hXgyiuvJDc3N9lliMhZTN0yIiIhpHAXEQkhhbuISAilTJ/7/ilTE7q99H+7M6HbExGpSlIm3JMlMzOT888/n2rVqpGWlsa6deuSXZKISJkU7jF48803qV+/foVt/8SJE6Sl6VCISOLEciemmWa2z8w2RrU9YGa7zSw3+OkfNe8eM9tqZh+ZWZ+KKryq6datG+PGjaNLly60bt2anJwcAI4ePcqoUaPo2LEj7du3LxwPftasWQwePJjrr7+e3r17A/Doo49y5ZVX0rZtWyZMmJC0fRGR1BfL6eIsYCrwQpH2J9z9lNsPmdnlRG6/dwXQCFhmZpe5+8kE1JoUZkbv3r0xM2677TZGjx5d4rJHjx7lrbfeYuXKlYwaNYqNGzfy0EMP0aNHD2bOnMnhw4fJzs7m2muvBWDNmjVs2LCBevXq8cYbbzB//nzWrl1L7dq1OXQope9OKCJJFstt9laaWWaM2xsIvOTux4HtZrYVyCZyD9aUtHr1aho1asS+ffvo1asX3/72t+natWuxyw4dGrkjYdeuXTly5AiHDx9myZIlLFy4sPA2fMeOHWPnzp0A9OrVi3r16gGwbNkyRo4cWTjCY0G7iEh5xHMp5J1mtiHotqkbtDUGdkUtkxe0payC8dsbNGjAoEGDCrtbilPSWO3z5s0jNzeX3Nxcdu7cSatWrYB/jNMOkbHa470RiIhIgfJ+ivcM8AvAg8fHgFFAcelU7B0mzGw0MBoiN5kuSzIuXTx69Cj5+fmcf/75HD16lCVLlnDfffeVuPzcuXPp3r07q1atok6dOtSpU4c+ffowZcoUpkyZgpnx3nvv0b59+9PW7d27NxMnTmTYsGGF3TI6exeR8ipXuLv73oLnZvYcsCiYzAMuiVo0A/i0hG1MB6YDZGVlVclbDO3du7fwlnYnTpxg2LBh9O3bt8Tl69atS5cuXThy5AgzZ84E4N5772XcuHG0adMGdyczM5NFixadtm7fvn3Jzc0lKyuLGjVq0L9/fx5++OGK2TERCT2L5dZtQZ/7IndvHUw3dPc9wfPxwFXuPsTMrgBeJNLP3ghYDrQo6wPVrKwsL3r9+ObNmwu7L1JBt27dmDx5MllZWckupVxS7f0WiVf0FyO3Z556z4bs65tVdjnlYmbr3b3Y0CnzzN3M5gDdgPpmlgfcD3Qzs3ZEulx2ALcBuPsHZvZ7YBNwAhiTylfKiIikqliulhlaTPOMUpZ/CHgonqKqsjFjxrB69epT2saOHcuKFSuSU5CISDH0tcgz9PTTTye7BBGRMmlUSBGREFK4i4iEkMJdRCSEUqbPPecPnyR0e6lyqZOISHnozL0Uo0aNokGDBrRu3bqw7dChQ/Tq1YsWLVrQq1cvPv/88yRWKCJSPIV7KW6++WYWL158StukSZPo2bMnW7ZsoWfPnkyaNKlCXtvdyc/Pr5Bti0j4KdxL0bVr19PGd1mwYAEjRowAYMSIEcyfP7/E9R944AGGDx9Ojx49aNGiBc8991zhvF//+td07NiRNm3acP/99wOwY8cOWrVqxR133EGHDh3YtWsXixcvpkOHDrRt25aePXtWwF6KSBilTJ97VbF3714aNmwIQMOGDdm3b1+py2/YsIG3336bo0eP0r59e6677jo2btzIli1byMnJwd0ZMGAAK1eupEmTJnz00Uc8//zzTJs2jf3793PrrbeycuVKmjZtqjHeRSRmCvcKNnDgQGrVqkWtWrXo3r07OTk5rFq1iiVLlhSODvnVV1+xZcsWmjRpwqWXXkqnTp0AePvtt+natStNmzYFNMa7iMRO4X6GLr74Yvbs2UPDhg3Zs2cPDRo0KHX5ksZ4v+eee7jttttOmbdjxw6N8S4iCZEy4V5VLl0cMGAAs2fPZsKECcyePZuBAweWuvyCBQu45557OHr0KCtWrGDSpEnUqlWLe++9l5tuuonzzjuP3bt3U7169dPW7dy5M2PGjGH79u2F3TI6exeRWKRMuCfD0KFDWbFiBQcOHCAjI4MHH3yQCRMmcOONNzJjxgyaNGnCyy+/XOo2srOzue6669i5cyf33nsvjRo1olGjRmzevJnOnTsDcN555/Hb3/6WatWqnbJueno606dP53vf+x75+fk0aNCApUuXVtj+ikh4KNxLMWfOnGLbly9fHvM2LrvsMqZPn35a+9ixYxk7duxp7Rs3bjxlul+/fvTr1y/m1xMRAV0KKSISSjpzT4Dnn3+eJ5988pS2q6++WsMDi0jSVOlwT5WrRUaOHMnIkSOTXUa5xXKrRRFJLWV2y5jZTDPbZ2Ybo9p+bWYfmtkGM3vNzC4M2jPN7G9mlhv8PFvewmrWrMnBgwcVPBXM3Tl48CA1a9ZMdikikkCxnLnPAqYCL0S1LQXucfcTZvYIcA9wdzBvm7u3i7ewjIwM8vLy2L9/f7ybkjLUrFmTjIyMZJchIgkUyz1UV5pZZpG2JVGTbwPfT2xZUL169cJvZoqIyJlJxNUyo4A3oqabmtl7ZvZnM/tOSSuZ2WgzW2dm63R2LiKSWHGFu5n9O3AC+F3QtAdo4u7tgbuAF83sguLWdffp7p7l7lnp6enxlCEiIkWUO9zNbATwXeAmDz71dPfj7n4weL4e2AZclohCRUQkduUKdzPrS+QD1AHu/nVUe7qZVQueNwNaAIm9P56IiJSpzA9UzWwO0A2ob2Z5wP1Ero45B1gaXIf+trvfDnQFJprZCeAkcLu7axByEZFKFsvVMkOLaZ5RwrLzgHnxFiUiIvHR2DIiIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIRiCnczm2lm+8xsY1RbPTNbamZbgse6QbuZ2VNmttXMNphZh4oqXkREihfrmfssoG+RtgnAcndvASwPpgH6Ebl3agtgNPBM/GWKiMiZiCnc3X0lUPReqAOB2cHz2cANUe0veMTbwIVm1jARxYqISGzi6XO/2N33AASPDYL2xsCuqOXygjYREakkFfGBqhXT5qctZDbazNaZ2br9+/dXQBkiImeveMJ9b0F3S/C4L2jPAy6JWi4D+LToyu4+3d2z3D0rPT09jjJERKSoeMJ9ITAieD4CWBDV/sPgqplOwBcF3TciIlI50mJZyMzmAN2A+maWB9wPTAJ+b2a3ADuBwcHirwP9ga3A18DIBNcsIiJliCnc3X1oCbN6FrOsA2PiKUpEROKjb6iKiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhFBMd2Iqjpm1BOZGNTUD7gMuBG4F9gftP3f318tdoYicPd781T+ed78neXWEQLnD3d0/AtoBmFk1YDfwGpF7pj7h7pMTUqGIiJyxRHXL9AS2uftfE7Q9ERGJQ6LCfQgwJ2r6TjPbYGYzzaxucSuY2WgzW2dm6/bv31/cIiIiUk5xh7uZ1QAGAC8HTc8A/0yky2YP8Fhx67n7dHfPcves9PT0eMsQkbPctNxphT+SmDP3fsC77r4XwN33uvtJd88HngOyE/AaIiJyBhIR7kOJ6pIxs4ZR8wYBGxPwGiIicgbKfbUMgJnVBnoBt0U1P2pm7QAHdhSZJyIilSCucHf3r4GLirQNj6siERGJm76hKiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICMU1nruISDLpfqklizvczWwH8CVwEjjh7llmVg+YC2QSuRvTje7+ebyvJSIisUlUt0x3d2/n7lnB9ARgubu3AJYH0yIiUkkqqs99IDA7eD4buKGCXkdERIqRiHB3YImZrTez0UHbxe6+ByB4bJCA1xERkRgl4gPVq939UzNrACw1sw9jWSn4RTAaoEmTJgkoQ0RECsR95u7unwaP+4DXgGxgr5k1BAge9xWz3nR3z3L3rPT09HjLEBGRKHGFu5mda2bnFzwHegMbgYXAiGCxEcCCeF5HRETOTLzdMhcDr5lZwbZedPfFZvYO8HszuwXYCQyO83VEROQMxBXu7v4J0LaY9oNAz3i2LSIi5advqIpI6ER/c/WOdncksZLk0dgyIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEyn2zDjO7BHgB+CcgH5ju7k+a2QPArcD+YNGfu/vr8RYqIiH15q+SXUEoxXMnphPAj9393eAm2evNbGkw7wl3nxx/eSIiUh7lDnd33wPsCZ5/aWabgcaJKkxERMovIX3uZpYJtAfWBk13mtkGM5tpZnVLWGe0ma0zs3X79+8vbhERESmnuG+QbWbnAfOAce5+xMyeAX4BePD4GDCq6HruPh2YDpCVleXx1iEiZ4fom19LyeI6czez6kSC/Xfu/iqAu+9195Pung88B2THX6aIiJyJcoe7mRkwA9js7o9HtTeMWmwQsLH85YmISHnE0y1zNTAceN/McoO2nwNDzawdkW6ZHcBtcVUoIuzHw0QAAAXjSURBVGen6Esku9+TvDpSVDxXy6wCrJhZuqZdRCTJ9A1VEZEQUriLiISQwl1EJITivs5dROSMnel4MtHL162T2FpCSuEuUkU9sfTjwufje12WxEokFalbRkQkhBTuIiIhpG4ZEUksffmoSlC4i0jFUdAnjcJdRCqH7rhUqRTuInJW2rgvvfD5uZmnzsv5wyeFz7Ovb1ZJFSWWwl3OCom6rLCqbaeqvZZUHQp3kWIkK3xTVgV3uUw7vOEfE3W/k/DtH81555Tpc7M7Jvw1KpvCXVJCIsO2IoJbZ+JS1SjcJZSSeTass36pCkIR7jqTObuk6vFWEEtlqrBwN7O+wJNANeC/3H1SRb2WCJQvPGP5RRFPKIch0FP1l+nZrkLC3cyqAU8DvYA84B0zW+jumyri9fbl/Sxqan5FvMRZoar1RVd2MIYhiMtS2j5GH58Sl9OXklJGRZ25ZwNb3f0TADN7CRgIVEi4S2rQGWD5VcYvnrD+cpuWO63w+R3t7khiJZWrosK9MbArajoPuKqCXusUJQVIVfiQqyLOhmN9jYq4PvtMa4hnmxWxbrzCGoZFddo5vfD5mqj2zvzjLH7NJwf/0d7sosooS8pg7p74jZoNBvq4+4+C6eFAtrv/W9Qyo4HRwWRL4KM4XrI+cCCO9asy7VvqCvP+ad+qhkvdPb24GRV15p4HXBI1nQF8Gr2Au08HppMAZrbO3bMSsa2qRvuWusK8f9q3qq+ixnN/B2hhZk3NrAYwBFhYQa8lIiJFVMiZu7ufMLM7gT8SuRRyprt/UBGvJSIip6uw69zd/XXg9YrafhEJ6d6porRvqSvM+6d9q+Iq5ANVERFJLt1DVUQkhFI63M2sr5l9ZGZbzWxCsuuJh5ldYmZvmtlmM/vAzMYG7fXMbKmZbQke6ya71vIys2pm9p6ZLQqmm5rZ2mDf5gYfvqckM7vQzF4xsw+DY9g5LMfOzMYH/yY3mtkcM6uZysfOzGaa2T4z2xjVVuyxsoingozZYGYdklf5mUnZcI8a4qAfcDkw1MwuT25VcTkB/NjdWwGdgDHB/kwAlrt7C2B5MJ2qxgKbo6YfAZ4I9u1z4JakVJUYTwKL3f3bQFsi+5nyx87MGgP/H8hy99ZELpAYQmofu1lA3yJtJR2rfkCL4Gc08Ewl1Ri3lA13ooY4cPe/AwVDHKQkd9/j7u8Gz78kEg6NiezT7GCx2cANyakwPmaWAVwH/FcwbUAP4JVgkVTetwuArsAMAHf/u7sfJiTHjsiFF7XMLA2oDewhhY+du68EDhVpLulYDQRe8Ii3gQvNrGHlVBqfVA734oY4aJykWhLKzDKB9sBa4GJ33wORXwBAg+RVFpf/BH4G5AfTFwGH3f1EMJ3Kx68ZsB94Puh2+i8zO5cQHDt33w1MBnYSCfUvgPWE59gVKOlYpWzOpHK4WzFtKX/pj5mdB8wDxrn7kWTXkwhm9l1gn7uvj24uZtFUPX5pQAfgGXdvDxwlBbtgihP0PQ8EmgKNgHOJdFUUlarHriwp++80lcO9zCEOUo2ZVScS7L9z91eD5r0FfwYGj/uSVV8crgYGmNkOIt1nPYicyV8Y/KkPqX388oA8d18bTL9CJOzDcOyuBba7+353/wZ4FehCeI5dgZKOVcrmTCqHe6iGOAj6oGcAm9398ahZC4ERwfMRwILKri1e7n6Pu2e4eyaR4/Qnd78JeBP4frBYSu4bgLt/Buwys5ZBU08iw1un/LEj0h3TycxqB/9GC/YtFMcuSknHaiHww+CqmU7AFwXdN1Weu6fsD9Af+BjYBvx7suuJc1+uIfLn3gYgN/jpT6RvejmwJXisl+xa49zPbsCi4HkzIAfYCrwMnJPs+uLYr3bAuuD4zQfqhuXYAQ8CHwIbgd8A56TysQPmEPn84BsiZ+a3lHSsiHTLPB1kzPtErhpK+j7E8qNvqIqIhFAqd8uIiEgJFO4iIiGkcBcRCSGFu4hICCncRURCSOEuIhJCCncRkRBSuIuIhND/ApRSQpgRxwjoAAAAAElFTkSuQmCC\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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l52zbt29fcnNzycjIoEaNGvTv35+HH364Es6WiFQlGs+9iuvevTvTp08nI6PIIZvjRudbLjTR47nvTutfOJ1MA4dpPHcRkQuMhvytIsaPH8+GDRvOaJswYQJr165NTEAiktSU3KuIZ555JtEhiEiIqCwjIhJCSu4iIiGk5C4iEkJJU3OfmTszrvsbd924uO5PRKQqUc+9FKWN575u3To6depESkoKixYtSkCEIiLnUnIvQVnGc2/atClz585l+PDh5yUeEZGyUHIvQVnGc09LSyM9PZ2LLir9VK5du5Zu3boxaNAg2rRpw1133cXp06cBWLlyJVlZWXTq1InBgwfz6aefFu5/6tSp3Hjjjbzwwgvs2rWLm266iQ4dOtCpUyfefffd+B+4iCQ9JfcSFDWee0XHZC+Qk5PDL37xC9566y3effddXnrpJQ4fPszPfvYzVq9ezeuvv05GRgaPP/544TY1a9Zk/fr1DB06lNtvv53x48fz17/+lVdffZVGjRrFFI+IhFPS3FBNhHiOyV4gMzOT5s0jY1cMGzaM9evXU7NmTbZv384NN9wAwBdffEFWVlbhNt/61rcA+OSTT9i/fz+DBg0CIklfRKQoSu4liOeY7AWKG5u9d+/ezJ8/v8htCsZnrwqDvIlIckia5J6IRxfLMp57eeXk5LB7926uueYaFi5cyNixY+nSpQvjx49n165dtGjRgs8++4y8vDyuvfbaM7a9/PLLSU1NZfHixdx66618/vnnnDp1qvBblkRECqjmXoLo8dxbt27NkCFDCsdzX7p0KQCbN28mNTWVF154gTvvvJO2bduWuM+srCwmT55Mu3btaNasGYMGDaJBgwbMnTuXYcOGkZ6eTpcuXfjb3/5W5Pa/+c1veOqpp0hPT6dr16588MEHcT9uEUl+SdNzT5T+/fvTv3//M9qmTp1aON25c2fy8vLKvL/atWuzcOHCc9p79uzJ5s2bz2nfs2fPGfMtW7bkL3/5S5lfT0QuTOq5i4iEkHruleCtt95ixIgRZ7RdfPHFbNq0ie7duycmKBG5oCi5V4L27duTm5ub6DBE5AKmsoyISAgpuYuIhJCSu4hICJVaczezOcDXgYPu3i5oqwcsBNKAPcAQd//IIh+/fBLoD3wG3OHur8cj0EMzno7Hbgo1+P734ro/EZGqpCw997lA37PaJgNr3L0lsCaYB+gHtAx+xgLPxifMxElLS6N9+/Zcd911ZGRkJDocEZEyKbXn7u7rzCztrOaBQPdgeh6wFrgnaP+1RwZBec3MrjCzRu5+IF4BJ8Irr7xC/fr1K23/J0+eJCVFDy6JSPxUtOZ+VUHCDn43DNqbAPui1ssL2s5hZmPNbIuZbTl06FAFw6g6unfvzsSJE+natSvt2rUjJycHgPz8fEaPHk3nzp3p2LFj4Xjwc+fOZfDgwdxyyy1kZ2cD8Nhjj9G+fXs6dOjA5MmTi30tEZHSxLu7WNR4uEUOZejus4BZABkZGVV2uEMzIzs7GzPjzjvvZOzYscWum5+fz6uvvsq6desYPXo027Zt46GHHqJnz57MmTOHY8eOkZmZyU033QTAxo0befPNN6lXrx7Lly9n8eLFbNq0idq1a3P06NHzdYgiEkIVTe4fFpRbzKwRcDBozwOujlovFXg/lgATbcOGDTRu3JiDBw/Su3dvvva1r9GtW7ci1x02bBgA3bp14/jx4xw7doyVK1eydOlSpk+fDsCJEyfYu3cvAL1796ZevXoArF69mlGjRhWO8FjQLiJSERUtyywFRgbTI4ElUe3ftoguwMfJXm8vGL+9YcOGDBo0qLDcUpTixmp/8cUXyc3NJTc3l71799K6dWvgq3HaITJWe6xfBCIiUqAsj0LOJ3LztL6Z5QH3A9OAP5jZGGAvMDhY/WUij0HuIvIo5Kh4BZqIRxfz8/M5ffo0l112Gfn5+axcuZL77ruv2PUXLlxIjx49WL9+PXXq1KFOnTr06dOHGTNmMGPGDMyMN954g44dO56zbXZ2NlOnTmX48OGFZRn13kWkosrytMywYhb1KmJdB8bHGlRV8eGHHxZ+pd3JkycZPnw4ffue/VToV+rWrUvXrl05fvw4c+bMAeDee+9l4sSJpKen4+6kpaWxbNmyc7bt27cvubm5ZGRkUKNGDfr378/DDz9cOQcmIqFnVeGr2zIyMnzLli1ntO3YsaOwfJEMunfvzvTp05P2WfhkO98isYr+YOTutK++syHzluaJCKdCzGyruxeZdDT8gIhICOmTM+U0fvx4NmzYcEbbhAkTWLt2bWICEhEpgpJ7OT3zzDOJDkFEpFQqy4iIhJCSu4hICCm5i4iEUNLU3HP+9F5c95dMjzuJiJSXeu4lGD16NA0bNqRdu3aFbUePHqV37960bNmS3r1789FHHyUwQhGRoim5l+COO+5gxYoVZ7RNmzaNXr16sXPnTnr16sW0adMq5bXdndOnT1fKvkUk/JTcS9CtW7dzxndZsmQJI0dGxkwbOXIkixcvLnb7Bx54gBEjRtCzZ09atmzJL3/5y8JlP//5z+ncuTPp6encf//9AOzZs4fWrVszbtw4OnXqxL59+1ixYgWdOnWiQ4cO9Op1zogPIiJFSpqae1Xx4Ycf0qhRIwAaNWrEwYMHS1z/zTff5LXXXiM/P5+OHTty8803s23bNnbu3ElOTg7uzoABA1i3bh1NmzblnXfe4fnnn2fmzJkcOnSI7373u6xbt45mzZppjHcRKTMl90o2cOBAatWqRa1atejRowc5OTmsX7+elStXFo4O+emnn7Jz506aNm3KNddcQ5cuXQB47bXX6NatG82aNQM0xruIlJ2SezldddVVHDhwgEaNGnHgwAEaNmxY4vrFjfE+ZcoU7rzzzjOW7dmzR2O8i0hcJE1yryqPLg4YMIB58+YxefJk5s2bx8CBA0tcf8mSJUyZMoX8/HzWrl3LtGnTqFWrFvfeey+33347l156Kfv376d69ernbJuVlcX48ePZvXt3YVlGvXcRKYukSe6JMGzYMNauXcvhw4dJTU3lwQcfZPLkyQwZMoTZs2fTtGlTXnjhhRL3kZmZyc0338zevXu59957ady4MY0bN2bHjh1kZWUBcOmll/Lb3/6WatWqnbFtgwYNmDVrFt/4xjc4ffo0DRs2ZNWqVZV2vCISHkruJZg/f36R7WvWrCnzPq699lpmzZp1TvuECROYMGHCOe3btm07Y75fv37069evzK8nIgJ6FFJEJJTUc4+D559/nieffPKMthtuuEHDA4tIwlTp5J4sT4uMGjWKUaPi9l3g511V+KpFEYmvKluWqVmzJkeOHFHiqWTuzpEjR6hZs2aiQxGROKqyPffU1FTy8vI4dOhQokMJvZo1a5KamproMEQkjmJK7mY2CfgO4MBbwCigEbAAqAe8Doxw9y/Ku+/q1asXfjJTRETKp8JlGTNrAvwrkOHu7YBqwFDgUeAJd28JfASMiUegIiJSdrHW3FOAWmaWAtQGDgA9gUXB8nnArTG+hoiIlFOFk7u77wemA3uJJPWPga3AMXc/GayWBzQpanszG2tmW8xsi+rqIiLxFUtZpi4wEGgGNAYuAYr6KGWRj7u4+yx3z3D3jAYNGlQ0DBERKUIsZZmbgN3ufsjdvwReAroCVwRlGoBU4P0YYxQRkXKKJbnvBbqYWW2LfNKoF7AdeAW4LVhnJLAkthBFRKS8Yqm5byJy4/R1Io9BXgTMAu4B7jazXcCVwOw4xCkiIuUQ03Pu7n4/cP9Zze8BmbHsV0REYlNlhx8QEZGKU3IXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQUnIXEQkhJXcRkRBSchcRCSEldxGREFJyFxEJISV3EZEQiuk7VEVEqqKZuTMLp8ddNy6BkSSOeu4iIiGknruIVB2vPPLVdI8piYsjBGLquZvZFWa2yMz+ZmY7zCzLzOqZ2Soz2xn8rhuvYEVEpGxiLcs8Caxw968BHYAdwGRgjbu3BNYE8yIich5VuCxjZpcD3YA7ANz9C+ALMxsIdA9WmwesBe6JJUgRkdJE30SV2HruzYFDwPNm9oaZ/crMLgGucvcDAMHvhnGIU0REyiGW5J4CdAKedfeOQD7lKMGY2Vgz22JmWw4dOhRDGCIicrZYknsekOfum4L5RUSS/Ydm1ggg+H2wqI3dfZa7Z7h7RoMGDWIIQ0REzlbh5O7uHwD7zKxV0NQL2A4sBUYGbSOBJTFFKCIi5Rbrc+7fB35nZjWA94BRRP5g/MHMxgB7gcExvoaIiJRTTMnd3XOBjCIW9YplvyIiEhsNPyAiEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk8dxFRKLk/Om9wunMW5onMJLYKLmLyAUvP2dz4fQlmZ0TGEn8qCwjIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkIxJ3czq2Zmb5jZsmC+mZltMrOdZrbQzGrEHqaIiJRHPHruE4AdUfOPAk+4e0vgI2BMHF5DRETKIabkbmapwM3Ar4J5A3oCi4JV5gG3xvIaIiJSfrH23P8D+DFwOpi/Ejjm7ieD+TygSVEbmtlYM9tiZlsOHToUYxgiIhKtwsndzL4OHHT3rdHNRazqRW3v7rPcPcPdMxo0aFDRMEREpAixfEH2DcAAM+sP1AQuJ9KTv8LMUoLeeyrwfuxhiohIeVS45+7uU9w91d3TgKHAX9z9duAV4LZgtZHAkpijFBGRcqmM59zvAe42s11EavCzK+E1RESkBLGUZQq5+1pgbTD9HpAZj/2KiJRkZu7MRIdQZekTqiIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJx+Zo9EZEKe+WRREcQSuq5i4iEkJK7iEgIqSwjIkllZu7MRIeQFCrcczezq83sFTPbYWZvm9mEoL2ema0ys53B77rxC1dERMoilrLMSeAH7t4a6AKMN7M2wGRgjbu3BNYE8yIich5VOLm7+wF3fz2Y/gTYATQBBgLzgtXmAbfGGqSIiJRPXGruZpYGdAQ2AVe5+wGI/AEws4bFbDMWGAvQtGnTeIQhImES/YhkjykV3k10jX7cdeNKXT8/Z3PhdE5Ue+YtzSscQyLE/LSMmV0KvAhMdPfjZd3O3We5e4a7ZzRo0CDWMEREJEpMyd3MqhNJ7L9z95eC5g/NrFGwvBFwMLYQRUSkvGJ5WsaA2cAOd388atFSYGQwPRJYUvHwRESkImKpud8AjADeMrPcoO0nwDTgD2Y2BtgLDI4tRBERKa8KJ3d3Xw9YMYt7VXS/IiISOw0/ICISQhp+QKSKemLV3wunJ/W+NoGRxCBOjzNK+annLiISQkruIiIhpLKMiJwfKtGcV0ruIhJf+malKkFlGRGREFJyFxEJISV3EZEQUnIXEQkh3VAVKUIsHyAKxYePyiKWp1/Ke9M1ev26dcq37QVKPXcRkRBSz10uKBdMr1riLudP750xX9W/mUnJXaSCyvuHoizrR6+TVPRse5WjsoyISAip5y5SiZK2J14c9dCThpK7hEZxibSsJZBkrMHrHkLlys/ZXDh9SWbnBEZSfirLiIiEkHruUqWUt/cdyz4rul48tq3sm7HqxYt67iIiIZT0Pfcpz99aOP3IqMUJjEQqUyJ7pZX92rH0+ivF2TdNNfZ6Ukr65C7nV7zKJuezBBJPyfL0SyzXaeN7R86Yz0JPyBQl+kNNVfEDTZVWljGzvmb2jpntMrPJlfU6IiJyrkrpuZtZNeAZoDeQB2w2s6Xuvr0yXq8sdLMpMZKlp3uhKLZHn/JisdtE9+Szml9Z4XXk/KqsskwmsMvd3wMwswXAQCBhyb0qqyr15HjtR388k0OXvbMKpzcmMI6ymHnsza9mjkUtaPbP5drPyz8dWThd66L+MUZVtTb+s2IAAAQlSURBVFVWWaYJsC9qPi9oExGR88DcPf47NRsM9HH37wTzI4BMd/9+1DpjgbHBbCvgnQq+XH3gcAzhVnVhPj4dW/IK8/El07Fd4+4NilpQWWWZPODqqPlU4P3oFdx9FjCLGJnZFnfPiHU/VVWYj0/HlrzCfHxhObbKKstsBlqaWTMzqwEMBZZW0muJiMhZKqXn7u4nzex7wJ+BasAcd3+7Ml5LRETOVWkfYnL3l4GXK2v/UWIu7VRxYT4+HVvyCvPxheLYKuWGqoiIJJYGDhMRCaGkTu5hGuLAzK42s1fMbIeZvW1mE4L2ema2ysx2Br/rJjrWijKzamb2hpktC+abmdmm4NgWBjffk5KZXWFmi8zsb8E1zArLtTOzScG/yW1mNt/MaibztTOzOWZ20My2RbUVea0s4qkgx7xpZp0SF3n5JG1yjxrioB/QBhhmZm0SG1VMTgI/cPfWQBdgfHA8k4E17t4SWBPMJ6sJwI6o+UeBJ4Jj+wgYk5Co4uNJYIW7fw3oQOQ4k/7amVkT4F+BDHdvR+QBiaEk97WbC/Q9q624a9UPaBn8jAWePU8xxixpkztRQxy4+xdAwRAHScndD7j768H0J0SSQxMixzQvWG0ecGvRe6jazCwVuBn4VTBvQE9gUbBKMh/b5UA3YDaAu3/h7scIybUj8uBFLTNLAWoDB0jia+fu64CjZzUXd60GAr/2iNeAK8ys0fmJNDbJnNxDO8SBmaUBHYFNwFXufgAifwCAhomLLCb/AfwYOB3MXwkcc/eTwXwyX7/mwCHg+aDs9Cszu4QQXDt33w9MB/YSSeofA1sJz7UrUNy1Sto8k8zJ3YpoS/pHf8zsUuBFYKK7H090PPFgZl8HDrr71ujmIlZN1uuXAnQCnnX3jkA+SViCKUpQex4INAMaA5cQKVWcLVmvXWmS9t9pMif3Uoc4SDZmVp1IYv+du78UNH9Y8DYw+H0wUfHF4AZggJntIVI+60mkJ39F8FYfkvv65QF57r4pmF9EJNmH4drdBOx290Pu/iXwEtCV8Fy7AsVdq6TNM8mc3EM1xEFQg54N7HD3x6MWLQUKxikdCSw537HFyt2nuHuqu6cRuU5/cffbgVeA24LVkvLYANz9A2CfmbUKmnoRGd466a8dkXJMFzOrHfwbLTi2UFy7KMVdq6XAt4OnZroAHxeUb6o8d0/aH6A/8HfgXeCniY4nxmO5kcjbvTeB3OCnP5Ha9BpgZ/C7XqJjjfE4uwPLgunmQA6wC3gBuDjR8cVwXNcBW4LrtxioG5ZrBzwI/A3YBvwGuDiZrx0wn8j9gy+J9MzHFHetiJRlnglyzFtEnhpK+DGU5UefUBURCaFkLsuIiEgxlNxFREJIyV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFRELofwGHD2e9bx4FzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IT2KRJuEwoFdiw6odosdzP3bsGGPHjqV9+/bceeed5OTkMGjQIG688UZGjRpFq1atSE9P5/nnnwegffv2DBs2jHbt2pGWlsZjjz1GnTp1gEg3y4oVK9i/fz8ZGRncfffd3HjjjeXG0ahRI3r06MGhQ4eYNWsWAJMnT2bixIlkZ2fj7mRlZbF48eKT1u3fvz8FBQXk5ORwxhlnMHDgQO69994aeLdEpDaJaTz3ILkvLu6WiSrPAx4sHk84qPc+8FfgEPB/3f1/Ktt+GMZzryk9e/Zk2rRp5OSUOWRzwuj9ltNN2Mdzj/eC6ghgbtT8biDT3Q+Y2T8CC8ysvbsfKiOoccA4gMzMzDjDEBGRaNVO7maWBnwP+MfiMnc/AhwJpteZ2VbgEmBt6fXdfQYwAyIt9+rGERbjx49n9erVJ5RNmDCBFStWJCcgEUlp8bTcrwI+cPfC4gIzawJ86u7HzKwl0Br4qLwNyDcee+yxZIcgIiFS6S9UzWwu8CbQxswKzaz4yt9wTuySAcgD1pvZX4AXgVvcXb+aERE5xWK5W2ZEOeU3lFE2H5gff1giIhIPjS0jIhJCKTP8wOMFjyd0e7d2ujWh2xMRqU3Ucq9EZeO5r1y5ki5dupCWlsaLL76YhAhFRE6m5F6BWMZzz8zMZPbs2YwcOfKUxCMiEgsl9wrEMp57VlYW2dnZfOtblb+VK1asIC8vjyFDhtCuXTtuueUWjh8/DsDSpUvp3r07Xbp0YejQoXz55Zcl258yZQpXXHEFL7zwAlu2bOGqq66iY8eOdOnSha1btyb+wEUk5Sm5V6Cs8dyrOyZ7sfz8fB544AHee+89tm7dyksvvcT+/fv51a9+xbJly3jnnXfIycnhwQcfLFmnXr16rFq1iuHDh3Pdddcxfvx4/vKXv/DGG2/QrFmzuOIRkXBKmQuqyZDIMdmL5ebm0rJlZNyKESNGsGrVKurVq8fGjRu5/PLLAfj73/9O9+7dS9b54Q8jg3B+8cUX7Nq1iyFDhgCRpC8iUhYl9wokckz2YuWNzd6nTx/mzi39m7CI4vHZYxnkTUQEUii5J+PWxVjGc6+q/Px8tm3bxsUXX8y8efMYN24c3bp1Y/z48WzZsoVWrVrx1VdfUVhYyCWXXHLCuueeey4ZGRksWLCAa6+9liNHjnDs2LGSpyyJiBRTn3sFosdzb9u2LcOGDSsZz33RokUAvP3222RkZPDCCy9w88030759+wq32b17dyZNmkSHDh1o0aIFQ4YMoUmTJsyePZsRI0aQnZ1Nt27d+OCDD8pc/9lnn+WRRx4hOzubHj168MknnyT8uEUk9aVMyz1ZBg4cyMCBJ471PGXKlJLprl27UlhYWHq1cjVo0IB58+adVN6rVy/efvvtk8q3b99+wnzr1q3585//HPP+ROT0pJa7iEgIqeVeA9577z1GjRp1QtmZZ57JmjVr6NmzZ3KCEpHTipJ7Dbj00kspKChIdhgichpTt4yISAgpuYuIhJCSu4hICFXa525ms4DvAnvdvUNQdhdwE7AvqPYLd38lWHYHcCNwDPhXd/9TIgLdN/3RRGymRJN/+XFCtyciUpvEckF1NvAo8Eyp8ofcfVp0gZm1I/Js1fZAc2CZmV3i7ik7Vm1WVhbnnHMOderUIS0tjbVr1yY7JBGRSsXyDNWVZpYV4/YGA8+7+xFgm5ltAXKJPGA7Zb3++us0bty4xrZ/9OhR0tJ045KIJE48fe4/NrP1ZjbLzBoFZRcCO6PqFAZlodezZ08mTpxIjx496NChA/n5+QAUFRUxduxYunbtSufOnUvGg589ezZDhw7lmmuuoW/fvgDcf//9XHrppXTs2JFJkyYl7VhEJPVVt7n4BPAfgAevDwBjgbLGwy1zKEMzGweMg8jTjGorM6Nv376YGTfffDPjxo0rt25RURFvvPEGK1euZOzYsWzYsIF77rmHXr16MWvWLA4ePEhubi5XXXUVAG+++Sbr168nPT2dV199lQULFrBmzRoaNGjAp59+eqoOUURCqFrJ3d33FE+b2VPA4mC2ELgoqmoG8HE525gBzADIycmptWPZrl69mubNm7N371769OnDt7/9bfLy8sqsO2LECADy8vI4dOgQBw8eZOnSpSxatIhp0yKXJw4fPsyOHTsA6NOnD+np6QAsW7aMMWPGlIzwWFwuIlId1eqWMbPox/8MATYE04uA4WZ2ppm1AFoD+fGFmFzF47c3bdqUIUOGlHS3lKW8sdrnz59PQUEBBQUF7Nixg7Zt2wLfjNMOkbHa430QiIhIsVhuhZwL9AQam1kh8Eugp5l1ItLlsh24GcDd3zezPwAbgaPA+ETdKZOMWxeLioo4fvw455xzDkVFRSxdupQ777yz3Prz5s3jyiuvZNWqVTRs2JCGDRvSr18/pk+fzvTp0zEz3n33XTp37nzSun379mXKlCmMHDmypFtGrXcRqa5Y7pYZUUbxzArq3wPcE09QtcWePXtKHml39OhRRo4cSf/+/cut36hRI3r06MGhQ4eYNWsWAJMnT2bixIlkZ2fj7mRlZbF48eKT1u3fvz8FBQXk5ORwxhlnMHDgQO69996aOTARCT2rDY9uy8nJ8dL3j2/atKmk+yIV9OzZk2nTppGTk5PsUKol1d5vkXhF/zByW9aJz2zIvablqQ6nWsxsnbuXmXQ0/ICISAjplzNVNH78eFavXn1C2YQJE1ixYkVyAhIRKYOSexU99thjyQ5BRKRS6pYREQkhJXcRkRBSchcRCaGU6XPP/+NHCd1eqtzqJCJSHWq5V2Ds2LE0bdqUDh06lJR9+umn9OnTh9atW9OnTx8+++yzJEYoIlI2JfcK3HDDDSxZsuSEsqlTp9K7d282b95M7969mTp1ao3s2905fvx4jWxbRMJPyb0CeXl5J43vsnDhQkaPHg3A6NGjWbBgQbnr33XXXYwaNYpevXrRunVrnnrqqZJlv/nNb+jatSvZ2dn88pe/BGD79u20bduWW2+9lS5durBz506WLFlCly5d6NixI717966BoxSRMEqZPvfaYs+ePTRrFhkUs1mzZuzdu7fC+uvXr+ett96iqKiIzp07c/XVV7NhwwY2b95Mfn4+7s6gQYNYuXIlmZmZfPjhhzz99NM8/vjj7Nu3j5tuuomVK1fSokULjfEuIjFTcq9hgwcPpn79+tSvX58rr7yS/Px8Vq1axdKlS0tGh/zyyy/ZvHkzmZmZXHzxxXTr1g2At956i7y8PFq0aAFojHcRiZ2SexVdcMEF7N69m2bNmrF7926aNm1aYf3yxni/4447uPnmm09Ytn37do3xLiIJkTLJvbbcujho0CDmzJnDpEmTmDNnDoMHD66w/sKFC7njjjsoKipixYoVTJ06lfr16zN58mSuu+46zj77bHbt2kXdunVPWrd79+6MHz+ebdu2lXTLqPUuIrFImeSeDCNGjGDFihXs37+fjIwM7r77biZNmsSwYcOYOXMmmZmZvPDCCxVuIzc3l6uvvpodO3YwefJkmjdvTvPmzdm0aRPdu3cH4Oyzz+a5556jTp06J6zbpEkTZsyYwfe+9z2OHz9O06ZNee2112rseEUkPJTcKzB37twyy5cvXx7zNi655BJmzJhxUvmECROYMGHCSeUbNmw4YX7AgAEMGDAg5v2JiEAMt0Ka2Swz22tmG6LKfmNmH5jZejN72czOC8qzzOx/zawg+HuyJoMXEZGyxdJynw08CjwTVfYacIe7HzWzXwN3AD8Plm11904JjbKWe/rpp3n44YdPKLv88ss1PLCIJE0sz1BdaWZZpcqWRs2+BfwgsWGV7Ccl7hYZM2YMY8aMSXYY1VYbHrUoIomViF+ojgVejZpvYWbvmtl/m9l3qrvRevXqceDAASWeGubuHDhwgHr16iU7FBFJoLguqJrZvwNHgd8FRbuBTHc/YGb/CCwws/bufqiMdccB4wAyMzNP2nZGRgaFhYXs27cvnhAlBvXq1SMjIyPZYYhIAlU7uZvZaOC7QG8PmtfufgQ4EkyvM7OtwCXA2tLru/sMYAZATk7OSc3zunXrlvwyU0REqqZa3TJm1p/IBdRB7v5VVHkTM6sTTLcEWgOJHYhdREQqVWnL3czmAj2BxmZWCPySyN0xZwKvBRc833L3W4A8YIqZHQWOAbe4u0a7EhE5xWK5W2ZEGcUzy6k7H5gfb1AiIhIfjecuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQjEldzObZWZ7zWxDVFm6mb1mZpuD10ZBuZnZI2a2xczWm1mXmgpeRETKFmvLfTbQv1TZJGC5u7cGlgfzAAOIPBi7NTAOeCL+MEVEpCpiSu7uvhIo/aDrwcCcYHoOcG1U+TMe8RZwnpk1S0SwIiISm3j63C9w990AwWvToPxCYGdUvcKgTERETpGauKBqZZT5SZXMxpnZWjNbu2/fvhoIQ0Tk9BVPct9T3N0SvO4NyguBi6LqZQAfl17Z3We4e4675zRp0iSOMEREpLR4kvsiYHQwPRpYGFV+fXDXTDfg8+LuGxEROTXSYqlkZnOBnkBjMysEfglMBf5gZjcCO4ChQfVXgIHAFuArYEyCYxYRkUrElNzdfUQ5i3qXUdeB8fEEJSIi8dEvVEVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQkjJXUQkhJTcRURCSMldRCSElNxFREJIyV1EJISU3EVEQiimUSFFRGq7xwseL5m+tdOtSYykdlDLXUQkhJTcRURCSMldRCSEqt3nbmZtgHlRRS2BO4HzgJuAfUH5L9z9lWpHKCIiVVbt5O7uHwKdAMysDrALeJnIM1MfcvdpCYlQRESqLFHdMr2Bre7+twRtT0RE4pCo5D4cmBs1/2MzW29ms8ysUYL2ISIiMYo7uZvZGcAg4IWg6AngH4h02ewGHihnvXFmttbM1u7bt6+sKiIiUk2JaLkPAN5x9z0A7r7H3Y+5+3HgKSC3rJXcfYa757h7TpMmTRIQhoiIFEtEch9BVJeMmTWLWjYE2JCAfYiISBXENfyAmTUA+gA3RxXfb2adAAe2l1omIiKnQFzJ3d2/As4vVTYqrohERCRu+oWqiEgIKbmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAgpuYuIhJCSu4hICMX1JCYAM9sOfAEcA466e46ZpQPzgCwij9ob5u6fxbsvEQm51+/7ZvrKO5IXRwgkquV+pbt3cvecYH4SsNzdWwPLg3kRETlFaqpbZjAwJ5ieA1xbQ/sREZEyJCK5O7DUzNaZ2big7AJ33w0QvDZNwH5ERCRGcfe5A5e7+8dm1hR4zcw+iGWl4INgHEBmZmYCwhARkWJxt9zd/ePgdS/wMpAL7DGzZgDB694y1pvh7jnuntOkSZN4wxARkShxJXczO8vMzimeBvoCG4BFwOig2mhgYTz7ERGRqom3W+YC4GUzK97W7919iZm9DfzBzG4EdgBD49yPiIhUQVzJ3d0/AjqWUX4A6B3PtkVEpPr0C1URkRBSchcRCaFE3AopIlKrPF7weMn0rZ1uTWIkyaPkLiIpRYk7NuqWEREJISV3EZEQUnIXEQkh9bmLSMqK7n+XE6nlLiISQkruIiIhpOQuIhJCSu4iIiGk5C4iEkJK7iIiIaTkLiISQkruIiIhpB8xiUhyvX5fsiMIpWq33M3sIjN73cw2mdn7ZjYhKL/LzHaZWUHwNzBx4YqISCziabkfBX7q7u8ED8leZ2avBcsecvdp8YcnIiLVUe3k7u67gd3B9Bdmtgm4MFGBiYhI9SXkgqqZZQGdgTVB0Y/NbL2ZzTKzRonYh4iIxC7u5G5mZwPzgYnufgh4AvgHoBORlv0D5aw3zszWmtnaffv2xRuGiIhEiSu5m1ldIon9d+7+EoC773H3Y+5+HHgKyC1rXXef4e457p7TpEmTeMIQEZFS4rlbxoCZwCZ3fzCqvFlUtdD3qdwAAAYNSURBVCHAhuqHJyIi1RHP3TKXA6OA98ysICj7BTDCzDoBDmwHbo4rQhERqbJ47pZZBVgZi16pfjgiclrQD5dqnIYfEBEJISV3EZEQUnIXEQkhDRwmIrVTdL/8lXckL44UpeQuIjVHCTpplNxFaqmHXvtryfRtfS5JYiSSipTcRSSxQnCbY/4fPyqZzr2mZRIjqT4ldxGJnbpZUoaSu0gZ1CVyeinKf/uE+bNyuyYpksRRchdJAH0YSG2j5C4iJ4ul+0VdNLWakrukhJpqGde2Fnd0PKETggutqUTJXUKpdJJMVuKubR8ecvrQ8AMiIiGklruERkVdGrWhBV0bYqgx6nKpdZTcJeUkM0kmat+1bTu13eMvj/hmpsV3khdIClFyl6QrL0Gd6ouLidpfVbdT08d5unwAyIlqLLmbWX/gYaAO8Ft3n1pT+5LaS4lFaqsNe5skO4QaVSPJ3czqAI8BfYBC4G0zW+TuG2tifxIep6K1nqiW9an8ZhHLvmrsg1T96SmpplruucAWd/8IwMyeBwYDSu4hUtXulJpIPqG+L/wUKO/9uy2GzPDmRwdKpru3PD9RIUmC1FRyvxDYGTVfCFxWQ/sqVyzJp6pJptz/DOVsp6ZjKL1uLNuN5RiUNFNbrN82uu2YUTL9Vua4kmkl7tRn7p74jZoNBfq5+4+C+VFArrv/S1SdcUDxv6Y2wIdx7LIxsD+O9WszHVvqCvPx6dhqh4vdvcyLBzXVci8ELoqazwA+jq7g7jOAGSSAma1195xEbKu20bGlrjAfn46t9qupX6i+DbQ2sxZmdgYwHFhUQ/sSEZFSaqTl7u5HzezHwJ+I3Ao5y93fr4l9iYjIyWrsPnd3fwV4paa2X0pCundqKR1b6grz8enYarkauaAqIiLJpVEhRURCKKWTu5n1N7MPzWyLmU1KdjzxMLOLzOx1M9tkZu+b2YSgPN3MXjOzzcFro2THWl1mVsfM3jWzxcF8CzNbExzbvODie0oys/PM7EUz+yA4h93Dcu7M7Lbg3+QGM5trZvVS+dyZ2Swz22tmG6LKyjxXFvFIkGPWm1mX5EVeNSmb3KOGOBgAtANGmFm75EYVl6PAT929LdANGB8czyRgubu3BpYH86lqArApav7XwEPBsX0G3JiUqBLjYWCJu38b6EjkOFP+3JnZhcC/Ajnu3oHIDRLDSe1zNxvoX6qsvHM1AGgd/I0DnjhFMcYtZZM7UUMcuPvfgeIhDlKSu+9293eC6S+IJIcLiRzTnKDaHODa5EQYHzPLAK4GfhvMG9ALeDGoksrHdi6QB8wEcPe/u/tBQnLuiNx4Ud/M0oAGwG5S+Ny5+0rg01LF5Z2rwcAzHvEWcJ6ZNTs1kcYnlZN7WUMcXJikWBLKzLKAzsAa4AJ33w2RDwCgafIii8t/Av8GHA/mzwcOuvvRYD6Vz19LYB/wdNDt9FszO4sQnDt33wVMA3YQSeqfA+sIz7krVt65Stk8k8rJ3cooS/lbf8zsbGA+MNHdDyU7nkQws+8Ce919XXRxGVVT9fylAV2AJ9y9M1BECnbBlCXoex4MtACaA2cR6aooLVXPXWVS9t9pKif3Soc4SDVmVpdIYv+du78UFO8p/hoYvO5NVnxxuBwYZGbbiXSf9SLSkj8v+KoPqX3+CoFCd18TzL9IJNmH4dxdBWxz933u/jXwEtCD8Jy7YuWdq5TNM6mc3EM1xEHQBz0T2OTuD0YtWgSMDqZHAwtPdWzxcvc73D3D3bOInKc/u/t1wOvAD4JqKXlsAO7+CbDTzNoERb2JDG+d8ueOSHdMNzNrEPwbLT62UJy7KOWdq0XA9cFdM92Az4u7b2o9d0/ZP2Ag8FdgK/DvyY4nzmO5gsjXvfVAQfA3kEjf9HJgc/CanuxY4zzOnsDiYLolkA9sAV4Azkx2fHEcVydgbXD+FgCNwnLugLuBD4ANwLPAmal87oC5RK4ffE2kZX5jeeeKSLfMY0GOeY/IXUNJP4ZY/vQLVRGREErlbhkRESmHkruISAgpuYuIhJCSu4hICCm5i4iEkJK7iEgIKbmLiISQkruISAj9f6ZvVksdmLzOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in tqdm(range(38)):\n",
    "    fig, axes = plt.subplots()\n",
    "    plt.hist(percentiles[f'ch{i}_0th_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.001st_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.001_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_0.1st_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '0.1_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_5th_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '5_perc')\n",
    "    plt.hist(percentiles[f'ch{i}_10th_perc'], bins = 100, alpha=0.5, range=[0, 110], label = '10_perc')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.suptitle(f\"Channel {i} -- percentiles\")\n",
    "    \n",
    "#     fig, axes = plt.subplots()\n",
    "#     sns.heatmap(dfY, vmin=-100, vmax=100)\n",
    "#     pl.suptitle(f\"Cycle {c+1} Y-shift\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1188,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ch0_cutoff_perc</th>\n",
       "      <th>ch1_cutoff_perc</th>\n",
       "      <th>ch2_cutoff_perc</th>\n",
       "      <th>ch3_cutoff_perc</th>\n",
       "      <th>ch4_cutoff_perc</th>\n",
       "      <th>ch5_cutoff_perc</th>\n",
       "      <th>ch6_cutoff_perc</th>\n",
       "      <th>ch7_cutoff_perc</th>\n",
       "      <th>ch8_cutoff_perc</th>\n",
       "      <th>ch9_cutoff_perc</th>\n",
       "      <th>...</th>\n",
       "      <th>ch28_cutoff_perc</th>\n",
       "      <th>ch29_cutoff_perc</th>\n",
       "      <th>ch30_cutoff_perc</th>\n",
       "      <th>ch31_cutoff_perc</th>\n",
       "      <th>ch32_cutoff_perc</th>\n",
       "      <th>ch33_cutoff_perc</th>\n",
       "      <th>ch34_cutoff_perc</th>\n",
       "      <th>ch35_cutoff_perc</th>\n",
       "      <th>ch36_cutoff_perc</th>\n",
       "      <th>ch37_cutoff_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ch0_cutoff_perc  ch1_cutoff_perc  ch2_cutoff_perc  ch3_cutoff_perc  \\\n",
       "000              0.0              0.0              0.0              0.0   \n",
       "001              0.0              0.0              0.0              0.0   \n",
       "002              0.0              0.0              2.0              0.0   \n",
       "003              0.0              0.0              0.0              0.0   \n",
       "004              0.0              0.0              0.0              0.0   \n",
       "..               ...              ...              ...              ...   \n",
       "219              0.0              0.0              0.0              0.0   \n",
       "220              0.0              0.0              0.0              2.0   \n",
       "221              0.0              0.0              0.0              0.0   \n",
       "222              0.0              0.0              0.0              0.0   \n",
       "224              0.0              0.0              0.0              0.0   \n",
       "\n",
       "     ch4_cutoff_perc  ch5_cutoff_perc  ch6_cutoff_perc  ch7_cutoff_perc  \\\n",
       "000              0.0              0.0              0.0              0.0   \n",
       "001              0.0              0.0              0.0              0.0   \n",
       "002              0.0              0.0              0.0              0.0   \n",
       "003              0.0              0.0              0.0              0.0   \n",
       "004              0.0              0.0              0.0              0.0   \n",
       "..               ...              ...              ...              ...   \n",
       "219              0.0              0.0              0.0              0.0   \n",
       "220              0.0              0.0              0.0              0.0   \n",
       "221              0.0              0.0              0.0              1.0   \n",
       "222              0.0              0.0              0.0              0.0   \n",
       "224              0.0              0.0              0.0              0.0   \n",
       "\n",
       "     ch8_cutoff_perc  ch9_cutoff_perc  ...  ch28_cutoff_perc  \\\n",
       "000              0.0              0.0  ...               0.0   \n",
       "001              0.0              0.0  ...               0.0   \n",
       "002              0.0              0.0  ...               4.0   \n",
       "003              0.0              0.0  ...               0.0   \n",
       "004              0.0              0.0  ...               0.0   \n",
       "..               ...              ...  ...               ...   \n",
       "219              0.0              0.0  ...               0.0   \n",
       "220              0.0              0.0  ...               0.0   \n",
       "221              0.0              0.0  ...               0.0   \n",
       "222              0.0              0.0  ...               0.0   \n",
       "224              0.0              0.0  ...               0.0   \n",
       "\n",
       "     ch29_cutoff_perc  ch30_cutoff_perc  ch31_cutoff_perc  ch32_cutoff_perc  \\\n",
       "000               0.0               0.0               0.0               0.0   \n",
       "001               0.0               0.0               0.0               0.0   \n",
       "002               0.0               0.0               0.0               0.0   \n",
       "003               0.0               0.0               0.0               0.0   \n",
       "004               0.0               0.0               0.0               0.0   \n",
       "..                ...               ...               ...               ...   \n",
       "219               0.0               0.0               0.0               0.0   \n",
       "220               0.0               0.0               0.0               0.0   \n",
       "221               0.0               0.0               0.0               0.0   \n",
       "222               0.0               0.0               0.0               0.0   \n",
       "224               0.0               0.0               0.0               0.0   \n",
       "\n",
       "     ch33_cutoff_perc  ch34_cutoff_perc  ch35_cutoff_perc  ch36_cutoff_perc  \\\n",
       "000               0.0               0.0               0.0               0.0   \n",
       "001               0.0               0.0               0.0               0.0   \n",
       "002               0.0               0.0               0.0               0.0   \n",
       "003               0.0               0.0               1.0               0.0   \n",
       "004               0.0               0.0               0.0               0.0   \n",
       "..                ...               ...               ...               ...   \n",
       "219               0.0               0.0               0.0               0.0   \n",
       "220               0.0               0.0               0.0               0.0   \n",
       "221               0.0               0.0               0.0               0.0   \n",
       "222               0.0               0.0               0.0               0.0   \n",
       "224               0.0               2.0               1.0               0.0   \n",
       "\n",
       "     ch37_cutoff_perc  \n",
       "000               0.0  \n",
       "001               0.0  \n",
       "002               0.0  \n",
       "003               0.0  \n",
       "004               0.0  \n",
       "..                ...  \n",
       "219               0.0  \n",
       "220               0.0  \n",
       "221               0.0  \n",
       "222               0.0  \n",
       "224               0.0  \n",
       "\n",
       "[211 rows x 38 columns]"
      ]
     },
     "execution_count": 1188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cutoffs = percentiles.filter(regex=(\".*cutoff.*\"))\n",
    "cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('002', 2, 2.0),\n",
       " ('002', 28, 4.0),\n",
       " ('003', 35, 1.0),\n",
       " ('008', 34, 7.0),\n",
       " ('010', 6, 3.0),\n",
       " ('011', 3, 2.0),\n",
       " ('011', 13, 2.0),\n",
       " ('011', 28, 1.0),\n",
       " ('016', 9, 2.0),\n",
       " ('017', 35, 1.0),\n",
       " ('019', 18, 2.0),\n",
       " ('026', 2, 1.0),\n",
       " ('027', 35, 1.0),\n",
       " ('028', 35, 1.0),\n",
       " ('029', 35, 1.0),\n",
       " ('030', 14, 4.0),\n",
       " ('031', 15, 1.0),\n",
       " ('034', 35, 1.0),\n",
       " ('035', 30, 1.0),\n",
       " ('036', 31, 2.0),\n",
       " ('037', 13, 2.0),\n",
       " ('037', 14, 2.0),\n",
       " ('037', 35, 1.0),\n",
       " ('038', 32, 1.0),\n",
       " ('038', 35, 1.0),\n",
       " ('040', 3, 1.0),\n",
       " ('040', 35, 1.0),\n",
       " ('042', 21, 2.0),\n",
       " ('042', 33, 1.0),\n",
       " ('044', 18, 2.0),\n",
       " ('044', 34, 2.0),\n",
       " ('045', 8, 2.0),\n",
       " ('045', 33, 3.0),\n",
       " ('048', 3, 1.0),\n",
       " ('048', 13, 1.0),\n",
       " ('050', 19, 2.0),\n",
       " ('051', 24, 2.0),\n",
       " ('051', 32, 2.0),\n",
       " ('052', 17, 2.0),\n",
       " ('054', 13, 2.0),\n",
       " ('059', 5, 1.0),\n",
       " ('060', 20, 1.0),\n",
       " ('062', 1, 1.0),\n",
       " ('062', 35, 1.0),\n",
       " ('063', 35, 1.0),\n",
       " ('065', 35, 1.0),\n",
       " ('066', 5, 3.0),\n",
       " ('071', 28, 1.0),\n",
       " ('072', 12, 1.0),\n",
       " ('075', 22, 1.0),\n",
       " ('075', 32, 1.0),\n",
       " ('079', 3, 2.0),\n",
       " ('080', 7, 2.0),\n",
       " ('083', 11, 1.0),\n",
       " ('084', 16, 1.0),\n",
       " ('085', 4, 3.0),\n",
       " ('088', 4, 1.0),\n",
       " ('094', 20, 2.0),\n",
       " ('094', 22, 1.0),\n",
       " ('094', 26, 1.0),\n",
       " ('094', 30, 1.0),\n",
       " ('098', 35, 1.0),\n",
       " ('099', 35, 1.0),\n",
       " ('101', 18, 3.0),\n",
       " ('101', 37, 1.0),\n",
       " ('108', 35, 1.0),\n",
       " ('111', 0, 1.0),\n",
       " ('111', 5, 5.0),\n",
       " ('113', 33, 1.0),\n",
       " ('113', 35, 1.0),\n",
       " ('114', 8, 5.0),\n",
       " ('114', 16, 5.0),\n",
       " ('115', 35, 1.0),\n",
       " ('117', 8, 3.0),\n",
       " ('120', 13, 3.0),\n",
       " ('124', 8, 1.0),\n",
       " ('124', 35, 1.0),\n",
       " ('127', 8, 6.0),\n",
       " ('128', 22, 3.0),\n",
       " ('130', 13, 1.0),\n",
       " ('132', 35, 1.0),\n",
       " ('133', 22, 1.0),\n",
       " ('133', 35, 1.0),\n",
       " ('134', 7, 2.0),\n",
       " ('135', 0, 2.0),\n",
       " ('137', 32, 2.0),\n",
       " ('138', 2, 1.0),\n",
       " ('138', 35, 1.0),\n",
       " ('139', 24, 2.0),\n",
       " ('143', 4, 1.0),\n",
       " ('143', 24, 2.0),\n",
       " ('146', 35, 1.0),\n",
       " ('151', 37, 1.0),\n",
       " ('152', 15, 2.0),\n",
       " ('152', 35, 1.0),\n",
       " ('155', 15, 2.0),\n",
       " ('160', 8, 3.0),\n",
       " ('161', 37, 1.0),\n",
       " ('162', 37, 1.0),\n",
       " ('169', 4, 2.0),\n",
       " ('173', 7, 2.0),\n",
       " ('178', 13, 2.0),\n",
       " ('178', 34, 1.0),\n",
       " ('181', 9, 1.0),\n",
       " ('181', 25, 1.0),\n",
       " ('182', 1, 4.0),\n",
       " ('182', 7, 1.0),\n",
       " ('185', 14, 1.0),\n",
       " ('187', 4, 5.0),\n",
       " ('189', 30, 1.0),\n",
       " ('190', 17, 2.0),\n",
       " ('191', 35, 1.0),\n",
       " ('191', 37, 1.0),\n",
       " ('197', 12, 4.0),\n",
       " ('206', 35, 1.0),\n",
       " ('207', 35, 1.0),\n",
       " ('208', 3, 2.0),\n",
       " ('208', 8, 4.0),\n",
       " ('210', 18, 1.0),\n",
       " ('210', 26, 1.0),\n",
       " ('210', 35, 1.0),\n",
       " ('211', 35, 1.0),\n",
       " ('213', 10, 1.0),\n",
       " ('213', 14, 1.0),\n",
       " ('213', 15, 1.0),\n",
       " ('213', 18, 1.0),\n",
       " ('213', 19, 1.0),\n",
       " ('213', 22, 1.0),\n",
       " ('213', 23, 1.0),\n",
       " ('213', 26, 1.0),\n",
       " ('213', 27, 1.0),\n",
       " ('213', 30, 1.0),\n",
       " ('214', 28, 1.0),\n",
       " ('220', 3, 2.0),\n",
       " ('221', 7, 1.0),\n",
       " ('224', 34, 2.0),\n",
       " ('224', 35, 1.0)]"
      ]
     },
     "execution_count": 1264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tup = []\n",
    "for index, row in cutoffs.iterrows():\n",
    "    for col, val in enumerate(row):\n",
    "        if val > 0:\n",
    "            tup.append((index, col, val))\n",
    "tup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>channel</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>002</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>002</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>003</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>008</td>\n",
       "      <td>34</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>010</td>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>214</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>220</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>221</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>224</td>\n",
       "      <td>34</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>224</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FOV_num  channel  percentage\n",
       "0       002        2         2.0\n",
       "1       002       28         4.0\n",
       "2       003       35         1.0\n",
       "3       008       34         7.0\n",
       "4       010        6         3.0\n",
       "..      ...      ...         ...\n",
       "132     214       28         1.0\n",
       "133     220        3         2.0\n",
       "134     221        7         1.0\n",
       "135     224       34         2.0\n",
       "136     224       35         1.0\n",
       "\n",
       "[137 rows x 3 columns]"
      ]
     },
     "execution_count": 1266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_perc = pd.DataFrame(tup)\n",
    "bb_perc = bb_perc.rename(columns={0: \"FOV_num\", 1: \"channel\", 2:'percentage'})\n",
    "bb_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1294,
   "metadata": {},
   "outputs": [],
   "source": [
    "bb_perc.to_csv('bbox_percentages.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>FOV_num</th>\n",
       "      <th>Z</th>\n",
       "      <th>channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>616</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>617</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>618</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>619</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>620</td>\n",
       "      <td>213</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  FOV_num    Z  channel\n",
       "0            10        3  4.0     35.0\n",
       "1            11        3  5.0     35.0\n",
       "2            12        3  6.0     35.0\n",
       "3            13        3  7.0     35.0\n",
       "4            14        3  8.0     35.0\n",
       "..          ...      ...  ...      ...\n",
       "119         616      213  6.0     14.0\n",
       "120         617      213  6.0     18.0\n",
       "121         618      213  6.0     22.0\n",
       "122         619      213  6.0     26.0\n",
       "123         620      213  6.0     30.0\n",
       "\n",
       "[124 rows x 4 columns]"
      ]
     },
     "execution_count": 1295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc = pd.read_csv('black_circle_locations.csv')\n",
    "bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1301,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = set()\n",
    "for i in bc['FOV_num']:\n",
    "    s.add(str(i).zfill(3))\n",
    "s = pd.DataFrame(sorted(s))\n",
    "s.to_csv('b_circle_FOVs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1332,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = imread('merged/F132.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3, 3, 3, ..., 9, 9, 9]),\n",
       " array([766, 767, 767, ..., 864, 866, 866]),\n",
       " array([648, 640, 649, ..., 696, 689, 691]))"
      ]
     },
     "execution_count": 1333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.where(test[:, 35, ...] == 0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1010"
      ]
     },
     "execution_count": 1334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1276,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = imread('merged/F002.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, ..., 9, 9, 9]),\n",
       " array([324, 325, 325, ..., 192, 192, 192]),\n",
       " array([107, 108, 109, ..., 613, 614, 615]))"
      ]
     },
     "execution_count": 1287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.where(test[:, 28, ...] == 0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1584705"
      ]
     },
     "execution_count": 1288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "from imageio import volread as imread\n",
    "\n",
    "from skimage.segmentation import expand_labels\n",
    "import tifffile\n",
    "from skimage.segmentation import *\n",
    "from skimage import measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define inputs\n",
    "##########################################################################################\n",
    "IN_DIR = 'merged'\n",
    "OUT_DIR = 'mask'\n",
    "META_DIR = 'metadata'\n",
    "# define input directory\n",
    "#DATA_DIR = '101222_D10_Coverslip1_Processed'\n",
    "##########################################################################################\n",
    "# os.chdir(f'{DATA_DIR}')\n",
    "# print(os.getcwd())\n",
    "\n",
    "#SOURCE = f'gs://fc-secure-9289bfef-e5cb-493a-83d5-e604cd429e39/Brian/{DATA_DIR}/'\n",
    "\n",
    "# load metadata - load full codebook as well\n",
    "full_codebook = pd.read_csv(f'{META_DIR}/full_codebook.csv',sep=',', index_col=0) # this is \"legal\" codebook\n",
    "Procode_gRNA = pd.read_csv(f'{META_DIR}/PROCODE_gRNA.csv',sep=',')\n",
    "legal_codes = sorted(list(set(Procode_gRNA['ProCode ID'].to_list())))\n",
    "codebook = full_codebook[legal_codes]\n",
    "#AllProcodes = pd.read_csv('AllProcodes.csv', sep='.')\n",
    "sns.heatmap(codebook, linewidths = 0.3)\n",
    "markers = pd.read_csv(f'{META_DIR}/markers.csv')\n",
    "#markers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0,\n",
       " 5,\n",
       " 6,\n",
       " 7,\n",
       " 9,\n",
       " 10,\n",
       " 11,\n",
       " 13,\n",
       " 14,\n",
       " 15,\n",
       " 17,\n",
       " 18,\n",
       " 19,\n",
       " 21,\n",
       " 22,\n",
       " 23,\n",
       " 25,\n",
       " 27,\n",
       " 29,\n",
       " 30,\n",
       " 31,\n",
       " 33,\n",
       " 35,\n",
       " 36,\n",
       " 37]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get indices of final channels --> only make mask when happening in these indices of merged file\n",
    "final_inds = []\n",
    "marker_names = ['DNA_0','NWS','VSVG','FLAG','HSV','C','S','Ollas','GFAP','NeuN',\n",
    "               'pRPS6','RANGAP1','NFKB','TOM20','LAMP1','4HNE','TDP43','G3BP1','GM130','Calnexin','Golgin97',\n",
    "               'SYTO','ER','AGP','Catalase']\n",
    "\n",
    "for marker in marker_names:\n",
    "    ind_to_add = markers[markers.marker_name == marker]['Split_Num'].values[0]\n",
    "    final_inds.append(ind_to_add)\n",
    "final_inds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000', '001', '002', '003', '004', '005', '006', '007', '008', '009', '010', '011', '012', '013', '014', '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', '053', '054', '055', '056', '057', '058', '059', '060', '061', '062', '063', '065', '066', '067', '068', '069', '070', '072', '073', '074', '075', '076', '077', '078', '079', '080', '081', '082', '083', '084', '085', '086', '087', '088', '090', '091', '092', '093', '094', '095', '096', '097', '098', '099', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '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', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '169', '170', '171', '172', '173', '174', '175', '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",
      "216\n"
     ]
    }
   ],
   "source": [
    "_allFOVs = sorted(glob.glob('tmat_Cyc_2/*'))\n",
    "allFOVs = [x.split('F')[-1][:3] for x in _allFOVs]\n",
    "print(allFOVs)\n",
    "print(len(allFOVs))\n",
    "NUM_FOVS = len(allFOVs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000', '001', '002', '003', '004', '005', '006', '007', '008', '009']\n",
      "['010', '011', '012', '013', '014', '016', '017', '018', '019', '020']\n",
      "['021', '022', '023', '024', '025', '026', '027', '028', '029', '030']\n",
      "['031', '032', '033', '034', '035', '036', '037', '038', '039', '040']\n",
      "['041', '042', '043', '044', '045', '046', '047', '048', '049', '050']\n",
      "['051', '053', '054', '055', '056', '057', '058', '059', '060', '061']\n",
      "['062', '063', '065', '066', '067', '068', '069', '070', '072', '073']\n",
      "['074', '075', '076', '077', '078', '079', '080', '081', '082', '083']\n",
      "['084', '085', '086', '087', '088', '090', '091', '092', '093', '094']\n",
      "['095', '096', '097', '098', '099', '100', '101', '102', '103', '104']\n",
      "['105', '106', '107', '108', '109', '111', '112', '113', '114', '115']\n",
      "['116', '117', '118', '119', '120', '121', '122', '123', '124', '125']\n",
      "['126', '127', '128', '129', '130', '131', '132', '133', '134', '135']\n",
      "['136', '137', '138', '139', '140', '141', '142', '143', '144', '145']\n",
      "['146', '147', '148', '149', '151', '152', '153', '154', '155', '156']\n",
      "['157', '158', '159', '160', '161', '162', '163', '164', '165', '166']\n",
      "['167', '169', '170', '171', '172', '173', '174', '175', '177', '178']\n",
      "['179', '180', '181', '182', '183', '184', '185', '186', '187', '188']\n",
      "['189', '190', '191', '192', '193', '194', '195', '196', '197', '198']\n",
      "['199', '200', '201', '202', '203', '204', '205', '206', '207', '208']\n",
      "['209', '210', '211', '212', '213', '214', '215', '216', '217', '218']\n",
      "['219', '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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "      <th>004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>216 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",
       "[216 rows x 38 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# record in dataframe\n",
    "count0_df = pd.DataFrame(index=allFOVs, columns = list(range(38)))\n",
    "count20_df = pd.DataFrame(index=allFOVs, columns = list(range(38)))\n",
    "count65K_df = pd.DataFrame(index=allFOVs, columns = list(range(38)))\n",
    "count65K_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "mask shape (2016, 2016)\n",
      "F000, no mask needed\n",
      "FOV 001\n",
      "mask shape (2016, 2016)\n",
      "FOV 002\n",
      "mask shape (2016, 2016)\n",
      "F002, no mask needed\n",
      "FOV 003\n",
      "mask shape (2016, 2016)\n",
      "F003, no mask needed\n",
      "FOV 004\n",
      "mask shape (2016, 2016)\n",
      "F004, no mask needed\n",
      "FOV 005\n",
      "mask shape (2016, 2016)\n",
      "F005, no mask needed\n",
      "FOV 006\n",
      "mask shape (2016, 2016)\n",
      "FOV 007\n",
      "mask shape (2016, 2016)\n",
      "FOV 008\n",
      "mask shape (2016, 2016)\n",
      "F008, no mask needed\n",
      "FOV 009\n",
      "mask shape (2016, 2016)\n",
      "F009, no mask needed\n",
      "FOV 010\n",
      "mask shape (2016, 2016)\n",
      "F010, no mask needed\n",
      "FOV 011\n",
      "mask shape (2016, 2016)\n",
      "F011, no mask needed\n",
      "FOV 013\n",
      "mask shape (2016, 2016)\n",
      "FOV 014\n",
      "mask shape (2016, 2016)\n",
      "FOV 016\n",
      "mask shape (2016, 2016)\n",
      "F016, no mask needed\n",
      "FOV 017\n",
      "mask shape (2016, 2016)\n",
      "FOV 018\n",
      "mask shape (2016, 2016)\n",
      "F018, no mask needed\n",
      "FOV 019\n",
      "mask shape (2016, 2016)\n",
      "F019, no mask needed\n",
      "FOV 020\n",
      "mask shape (2016, 2016)\n",
      "F020, no mask needed\n",
      "FOV 021\n",
      "mask shape (2016, 2016)\n",
      "F021, no mask needed\n",
      "FOV 022\n",
      "mask shape (2016, 2016)\n",
      "F022, no mask needed\n",
      "FOV 023\n",
      "mask shape (2016, 2016)\n",
      "F023, no mask needed\n",
      "FOV 024\n",
      "mask shape (2016, 2016)\n",
      "F024, no mask needed\n",
      "FOV 025\n",
      "mask shape (2016, 2016)\n",
      "F025, no mask needed\n",
      "FOV 026\n",
      "mask shape (2016, 2016)\n",
      "FOV 027\n",
      "mask shape (2016, 2016)\n",
      "FOV 028\n",
      "mask shape (2016, 2016)\n",
      "FOV 029\n",
      "mask shape (2016, 2016)\n",
      "F029, no mask needed\n",
      "FOV 030\n",
      "mask shape (2016, 2016)\n",
      "FOV 031\n",
      "mask shape (2016, 2016)\n",
      "F031, no mask needed\n",
      "FOV 032\n",
      "mask shape (2016, 2016)\n",
      "F032, no mask needed\n",
      "FOV 033\n",
      "mask shape (2016, 2016)\n",
      "FOV 034\n",
      "mask shape (2016, 2016)\n",
      "FOV 035\n",
      "mask shape (2016, 2016)\n",
      "FOV 036\n",
      "mask shape (2016, 2016)\n",
      "F036, no mask needed\n",
      "FOV 037\n",
      "mask shape (2016, 2016)\n",
      "FOV 038\n",
      "mask shape (2016, 2016)\n",
      "F038, no mask needed\n",
      "FOV 039\n",
      "mask shape (2016, 2016)\n",
      "FOV 040\n",
      "mask shape (2016, 2016)\n",
      "F040, no mask needed\n",
      "FOV 041\n",
      "mask shape (2016, 2016)\n",
      "F041, no mask needed\n",
      "FOV 042\n",
      "mask shape (2016, 2016)\n",
      "F042, no mask needed\n",
      "FOV 043\n",
      "mask shape (2016, 2016)\n",
      "F043, no mask needed\n",
      "FOV 044\n",
      "mask shape (2016, 2016)\n",
      "F044, no mask needed\n",
      "FOV 045\n",
      "mask shape (2016, 2016)\n",
      "F045, no mask needed\n",
      "FOV 047\n",
      "mask shape (2016, 2016)\n",
      "F047, no mask needed\n",
      "FOV 048\n",
      "mask shape (2016, 2016)\n",
      "FOV 049\n",
      "mask shape (2016, 2016)\n",
      "F049, no mask needed\n",
      "FOV 050\n",
      "mask shape (2016, 2016)\n",
      "F050, no mask needed\n",
      "FOV 051\n",
      "mask shape (2016, 2016)\n",
      "F051, no mask needed\n",
      "FOV 053\n",
      "mask shape (2016, 2016)\n",
      "FOV 054\n",
      "mask shape (2016, 2016)\n",
      "F054, no mask needed\n",
      "FOV 055\n",
      "mask shape (2016, 2016)\n",
      "F055, no mask needed\n",
      "FOV 056\n",
      "mask shape (2016, 2016)\n",
      "F056, no mask needed\n",
      "FOV 057\n",
      "mask shape (2016, 2016)\n",
      "F057, no mask needed\n",
      "FOV 058\n",
      "mask shape (2016, 2016)\n",
      "F058, no mask needed\n",
      "FOV 059\n",
      "mask shape (2016, 2016)\n",
      "F059, no mask needed\n",
      "FOV 060\n",
      "mask shape (2016, 2016)\n",
      "FOV 061\n",
      "mask shape (2016, 2016)\n",
      "F061, no mask needed\n",
      "FOV 062\n",
      "mask shape (2016, 2016)\n",
      "F062, no mask needed\n",
      "FOV 063\n",
      "mask shape (2016, 2016)\n",
      "FOV 065\n",
      "mask shape (2016, 2016)\n",
      "F065, no mask needed\n",
      "FOV 066\n",
      "mask shape (2016, 2016)\n",
      "FOV 067\n",
      "mask shape (2016, 2016)\n",
      "F067, no mask needed\n",
      "FOV 068\n",
      "mask shape (2016, 2016)\n",
      "F068, no mask needed\n",
      "FOV 069\n",
      "mask shape (2016, 2016)\n",
      "FOV 070\n",
      "mask shape (2016, 2016)\n",
      "FOV 072\n",
      "mask shape (2016, 2016)\n",
      "FOV 073\n",
      "mask shape (2016, 2016)\n",
      "FOV 074\n",
      "mask shape (2016, 2016)\n",
      "F074, no mask needed\n",
      "FOV 075\n",
      "mask shape (2016, 2016)\n",
      "FOV 076\n",
      "mask shape (2016, 2016)\n",
      "F076, no mask needed\n",
      "FOV 077\n",
      "mask shape (2016, 2016)\n",
      "F077, no mask needed\n",
      "FOV 078\n",
      "mask shape (2016, 2016)\n",
      "F078, no mask needed\n",
      "FOV 079\n",
      "mask shape (2016, 2016)\n",
      "FOV 080\n",
      "mask shape (2016, 2016)\n",
      "F080, no mask needed\n",
      "FOV 081\n",
      "mask shape (2016, 2016)\n",
      "F081, no mask needed\n",
      "FOV 082\n",
      "mask shape (2016, 2016)\n",
      "FOV 083\n",
      "mask shape (2016, 2016)\n",
      "F083, no mask needed\n",
      "FOV 084\n",
      "mask shape (2016, 2016)\n",
      "F084, no mask needed\n",
      "FOV 085\n",
      "mask shape (2016, 2016)\n",
      "F085, no mask needed\n",
      "FOV 086\n",
      "mask shape (2016, 2016)\n",
      "F086, no mask needed\n",
      "FOV 087\n",
      "mask shape (2016, 2016)\n",
      "FOV 088\n",
      "mask shape (2016, 2016)\n",
      "F088, no mask needed\n",
      "FOV 090\n",
      "mask shape (2016, 2016)\n",
      "FOV 091\n",
      "mask shape (2016, 2016)\n",
      "F091, no mask needed\n",
      "FOV 092\n",
      "mask shape (2016, 2016)\n",
      "F092, no mask needed\n",
      "FOV 093\n",
      "mask shape (2016, 2016)\n",
      "FOV 094\n",
      "mask shape (2016, 2016)\n",
      "F094, no mask needed\n",
      "FOV 095\n",
      "mask shape (2016, 2016)\n",
      "F095, no mask needed\n",
      "FOV 096\n",
      "mask shape (2016, 2016)\n",
      "F096, no mask needed\n",
      "FOV 097\n",
      "mask shape (2016, 2016)\n",
      "F097, no mask needed\n",
      "FOV 098\n",
      "mask shape (2016, 2016)\n",
      "F098, no mask needed\n",
      "FOV 099\n",
      "mask shape (2016, 2016)\n",
      "F099, no mask needed\n",
      "FOV 100\n",
      "mask shape (2016, 2016)\n",
      "F100, no mask needed\n",
      "FOV 101\n",
      "mask shape (2016, 2016)\n",
      "F101, no mask needed\n",
      "FOV 102\n",
      "mask shape (2016, 2016)\n",
      "F102, no mask needed\n",
      "FOV 103\n",
      "mask shape (2016, 2016)\n",
      "F103, no mask needed\n",
      "FOV 104\n",
      "mask shape (2016, 2016)\n",
      "F104, no mask needed\n",
      "FOV 105\n",
      "mask shape (2016, 2016)\n",
      "F105, no mask needed\n",
      "FOV 106\n",
      "mask shape (2016, 2016)\n",
      "F106, no mask needed\n",
      "FOV 107\n",
      "mask shape (2016, 2016)\n",
      "F107, no mask needed\n",
      "FOV 108\n",
      "mask shape (2016, 2016)\n",
      "F108, no mask needed\n",
      "FOV 109\n",
      "mask shape (2016, 2016)\n",
      "FOV 111\n",
      "mask shape (2016, 2016)\n",
      "F111, no mask needed\n",
      "FOV 112\n",
      "mask shape (2016, 2016)\n",
      "F112, no mask needed\n",
      "FOV 113\n",
      "mask shape (2016, 2016)\n",
      "FOV 114\n",
      "mask shape (2016, 2016)\n",
      "F114, no mask needed\n",
      "FOV 115\n",
      "mask shape (2016, 2016)\n",
      "F115, no mask needed\n",
      "FOV 116\n",
      "mask shape (2016, 2016)\n",
      "F116, no mask needed\n",
      "FOV 117\n",
      "mask shape (2016, 2016)\n",
      "F117, no mask needed\n",
      "FOV 118\n",
      "mask shape (2016, 2016)\n",
      "F118, no mask needed\n",
      "FOV 119\n",
      "mask shape (2016, 2016)\n",
      "F119, no mask needed\n",
      "FOV 120\n",
      "mask shape (2016, 2016)\n",
      "FOV 121\n",
      "mask shape (2016, 2016)\n",
      "FOV 122\n",
      "mask shape (2016, 2016)\n",
      "F122, no mask needed\n",
      "FOV 123\n",
      "mask shape (2016, 2016)\n",
      "FOV 124\n",
      "mask shape (2016, 2016)\n",
      "F124, no mask needed\n",
      "FOV 125\n",
      "mask shape (2016, 2016)\n",
      "F125, no mask needed\n",
      "FOV 126\n",
      "mask shape (2016, 2016)\n",
      "FOV 127\n",
      "mask shape (2016, 2016)\n",
      "F127, no mask needed\n",
      "FOV 128\n",
      "mask shape (2016, 2016)\n",
      "F128, no mask needed\n",
      "FOV 129\n",
      "mask shape (2016, 2016)\n",
      "F129, no mask needed\n",
      "FOV 130\n",
      "mask shape (2016, 2016)\n",
      "F130, no mask needed\n",
      "FOV 131\n",
      "mask shape (2016, 2016)\n",
      "F131, no mask needed\n",
      "FOV 132\n",
      "mask shape (2016, 2016)\n",
      "F132, no mask needed\n",
      "FOV 133\n",
      "mask shape (2016, 2016)\n",
      "F133, no mask needed\n",
      "FOV 134\n",
      "mask shape (2016, 2016)\n",
      "F134, no mask needed\n",
      "FOV 135\n",
      "mask shape (2016, 2016)\n",
      "F135, no mask needed\n",
      "FOV 136\n",
      "mask shape (2016, 2016)\n",
      "F136, no mask needed\n",
      "FOV 137\n",
      "mask shape (2016, 2016)\n",
      "F137, no mask needed\n",
      "FOV 138\n",
      "mask shape (2016, 2016)\n",
      "F138, no mask needed\n",
      "FOV 139\n",
      "mask shape (2016, 2016)\n",
      "F139, no mask needed\n",
      "FOV 140\n",
      "mask shape (2016, 2016)\n",
      "FOV 141\n",
      "mask shape (2016, 2016)\n",
      "F141, no mask needed\n",
      "FOV 142\n",
      "mask shape (2016, 2016)\n",
      "FOV 143\n",
      "mask shape (2016, 2016)\n",
      "FOV 144\n",
      "mask shape (2016, 2016)\n",
      "F144, no mask needed\n",
      "FOV 145\n",
      "mask shape (2016, 2016)\n",
      "F145, no mask needed\n",
      "FOV 146\n",
      "mask shape (2016, 2016)\n",
      "FOV 147\n",
      "mask shape (2016, 2016)\n",
      "FOV 148\n",
      "mask shape (2016, 2016)\n",
      "F148, no mask needed\n",
      "FOV 149\n",
      "mask shape (2016, 2016)\n",
      "F149, no mask needed\n",
      "FOV 151\n",
      "mask shape (2016, 2016)\n",
      "F151, no mask needed\n",
      "FOV 152\n",
      "mask shape (2016, 2016)\n",
      "F152, no mask needed\n",
      "FOV 153\n",
      "mask shape (2016, 2016)\n",
      "F153, no mask needed\n",
      "FOV 154\n",
      "mask shape (2016, 2016)\n",
      "F154, no mask needed\n",
      "FOV 155\n",
      "mask shape (2016, 2016)\n",
      "F155, no mask needed\n",
      "FOV 156\n",
      "mask shape (2016, 2016)\n",
      "FOV 157\n",
      "mask shape (2016, 2016)\n",
      "FOV 158\n",
      "mask shape (2016, 2016)\n",
      "FOV 159\n",
      "mask shape (2016, 2016)\n",
      "F159, no mask needed\n",
      "FOV 160\n",
      "mask shape (2016, 2016)\n",
      "F160, no mask needed\n",
      "FOV 161\n",
      "mask shape (2016, 2016)\n",
      "F161, no mask needed\n",
      "FOV 162\n",
      "mask shape (2016, 2016)\n",
      "FOV 163\n",
      "mask shape (2016, 2016)\n",
      "F163, no mask needed\n",
      "FOV 164\n",
      "mask shape (2016, 2016)\n",
      "F164, no mask needed\n",
      "FOV 165\n",
      "mask shape (2016, 2016)\n",
      "F165, no mask needed\n",
      "FOV 166\n",
      "mask shape (2016, 2016)\n",
      "FOV 167\n",
      "mask shape (2016, 2016)\n",
      "F167, no mask needed\n",
      "FOV 169\n",
      "mask shape (2016, 2016)\n",
      "F169, no mask needed\n",
      "FOV 170\n",
      "mask shape (2016, 2016)\n",
      "F170, no mask needed\n",
      "FOV 171\n",
      "mask shape (2016, 2016)\n",
      "F171, no mask needed\n",
      "FOV 172\n",
      "mask shape (2016, 2016)\n",
      "FOV 173\n",
      "mask shape (2016, 2016)\n",
      "FOV 174\n",
      "mask shape (2016, 2016)\n",
      "F174, no mask needed\n",
      "FOV 175\n",
      "mask shape (2016, 2016)\n",
      "F175, no mask needed\n",
      "FOV 177\n",
      "mask shape (2016, 2016)\n",
      "F177, no mask needed\n",
      "FOV 178\n",
      "mask shape (2016, 2016)\n",
      "F178, no mask needed\n",
      "FOV 179\n",
      "mask shape (2016, 2016)\n",
      "F179, no mask needed\n",
      "FOV 180\n",
      "mask shape (2016, 2016)\n",
      "F180, no mask needed\n",
      "FOV 181\n",
      "mask shape (2016, 2016)\n",
      "F181, no mask needed\n",
      "FOV 182\n",
      "mask shape (2016, 2016)\n",
      "FOV 183\n",
      "mask shape (2016, 2016)\n",
      "F183, no mask needed\n",
      "FOV 184\n",
      "mask shape (2016, 2016)\n",
      "F184, no mask needed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 185\n",
      "mask shape (2016, 2016)\n",
      "F185, no mask needed\n",
      "FOV 186\n",
      "mask shape (2016, 2016)\n",
      "F186, no mask needed\n",
      "FOV 187\n",
      "mask shape (2016, 2016)\n",
      "FOV 188\n",
      "mask shape (2016, 2016)\n",
      "F188, no mask needed\n",
      "FOV 189\n",
      "mask shape (2016, 2016)\n",
      "F189, no mask needed\n",
      "FOV 190\n",
      "mask shape (2016, 2016)\n",
      "F190, no mask needed\n",
      "FOV 191\n",
      "mask shape (2016, 2016)\n",
      "FOV 192\n",
      "mask shape (2016, 2016)\n",
      "F192, no mask needed\n",
      "FOV 193\n",
      "mask shape (2016, 2016)\n",
      "FOV 194\n",
      "mask shape (2016, 2016)\n",
      "F194, no mask needed\n",
      "FOV 195\n",
      "mask shape (2016, 2016)\n",
      "F195, no mask needed\n",
      "FOV 196\n",
      "mask shape (2016, 2016)\n",
      "F196, no mask needed\n",
      "FOV 197\n",
      "mask shape (2016, 2016)\n",
      "F197, no mask needed\n",
      "FOV 198\n",
      "mask shape (2016, 2016)\n",
      "F198, no mask needed\n",
      "FOV 199\n",
      "mask shape (2016, 2016)\n",
      "FOV 200\n",
      "mask shape (2016, 2016)\n",
      "F200, no mask needed\n",
      "FOV 201\n",
      "mask shape (2016, 2016)\n",
      "F201, no mask needed\n",
      "FOV 202\n",
      "mask shape (2016, 2016)\n",
      "F202, no mask needed\n",
      "FOV 203\n",
      "mask shape (2016, 2016)\n",
      "FOV 204\n",
      "mask shape (2016, 2016)\n",
      "F204, no mask needed\n",
      "FOV 205\n",
      "mask shape (2016, 2016)\n",
      "FOV 206\n",
      "mask shape (2016, 2016)\n",
      "F206, no mask needed\n",
      "FOV 207\n",
      "mask shape (2016, 2016)\n",
      "FOV 208\n",
      "mask shape (2016, 2016)\n",
      "F208, no mask needed\n",
      "FOV 209\n",
      "mask shape (2016, 2016)\n",
      "FOV 210\n",
      "mask shape (2016, 2016)\n",
      "FOV 211\n",
      "mask shape (2016, 2016)\n",
      "FOV 212\n",
      "mask shape (2016, 2016)\n",
      "F212, no mask needed\n",
      "FOV 213\n",
      "mask shape (2016, 2016)\n",
      "FOV 214\n",
      "mask shape (2016, 2016)\n",
      "FOV 215\n",
      "mask shape (2016, 2016)\n",
      "F215, no mask needed\n",
      "FOV 216\n",
      "mask shape (2016, 2016)\n",
      "F216, no mask needed\n",
      "FOV 217\n",
      "mask shape (2016, 2016)\n",
      "FOV 218\n",
      "mask shape (2016, 2016)\n",
      "F218, no mask needed\n",
      "FOV 219\n",
      "mask shape (2016, 2016)\n",
      "FOV 220\n",
      "mask shape (2016, 2016)\n",
      "FOV 221\n",
      "mask shape (2016, 2016)\n",
      "F221, no mask needed\n",
      "FOV 222\n",
      "mask shape (2016, 2016)\n",
      "F222, no mask needed\n",
      "FOV 223\n",
      "mask shape (2016, 2016)\n",
      "FOV 224\n",
      "mask shape (2016, 2016)\n",
      "F224, no mask needed\n"
     ]
    }
   ],
   "source": [
    "NUM_FOVS = 214\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for fov in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    img = img.transpose(1,0,2,3) \n",
    "    # C Z Y X format. you may use image as it is but should change bottom line to axis=(0,2,3)\n",
    "    count0 = np.count_nonzero(img==0, axis=(1,2,3))\n",
    "    count20 = np.count_nonzero(img<20, axis=(1,2,3))\n",
    "    count65K = np.count_nonzero(img>65000, axis=(1,2,3))\n",
    "\n",
    "    # initialize mask, save_im_flag\n",
    "    save_im_flag = False\n",
    "    mask = np.zeros(img[0,0,...].shape)\n",
    "    print(\"mask shape\", mask.shape)\n",
    "    mask = mask.astype('bool')\n",
    "    # check for each channel;\n",
    "    for ch in range(count0.shape[0]):\n",
    "        # record in dataframe\n",
    "        count0_df.loc[FOV_num, ch] = count0[ch]\n",
    "        count20_df.loc[FOV_num, ch] = count20[ch]\n",
    "        count65K_df.loc[FOV_num, ch] = count65K[ch]\n",
    "        if ch in final_inds:\n",
    "            # whether to make mask;\n",
    "            if count20[ch] > 2000: # if there are more than 2000 <20 values\n",
    "                save_im_flag = True\n",
    "                for z in range(img.shape[1]): # second index is Z dimension, after Transposing\n",
    "                    mask = mask | (img[ch, z] < 20).astype('bool')\n",
    "            exp_mask = expand_labels(mask, distance=5) # expand mask with 5 pixel padding\n",
    "\n",
    "            # check if area is large enough? 200\n",
    "            labels = measure.label(mask)\n",
    "            _df = measure.regionprops_table(labels, mask, properties=['label','area','centroid','bbox'])\n",
    "            df=pd.DataFrame(_df)\n",
    "            df=df.set_index('label')\n",
    "            if df.area.max() < 200: # if area is less than 200, turn off flag and don't do anything?\n",
    "                save_im_flag = False\n",
    "\n",
    "\n",
    "    # export mask if flag is true\n",
    "    if save_im_flag:\n",
    "        #sFOV = str(fov).zfill(3)\n",
    "        fname = f'{OUT_DIR}/F{FOV_num}_mask.tif'\n",
    "        tifffile.imwrite(fname, exp_mask.astype('uint8'), imagej = True, photometric='minisblack', metadata={'axes':'YX'})\n",
    "    else:\n",
    "        print(f'F{FOV_num}, no mask needed') # export later!\n",
    "        \n",
    "    \n",
    "# save dataframe \n",
    "count0_df.to_csv('count0_df.csv', sep=',')\n",
    "count20_df.to_csv('count20_df.csv', sep=',')\n",
    "count65K_df.to_csv('count65K_df.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mask percentage >5% and <7%\n",
      "028\n",
      "5.693588199168556\n",
      "mask percentage >3% and <5%\n",
      "037\n",
      "4.022261392983119\n",
      "mask percentage >7%\n",
      "039\n",
      "7.683300461388259\n",
      "mask percentage >5% and <7%\n",
      "048\n",
      "5.47741087175611\n",
      "mask percentage >3% and <5%\n",
      "069\n",
      "4.270055823255228\n",
      "mask percentage >3% and <5%\n",
      "070\n",
      "4.04883452223482\n",
      "mask percentage >3% and <5%\n",
      "075\n",
      "3.518675988914084\n",
      "mask percentage >5% and <7%\n",
      "087\n",
      "5.247208837238599\n",
      "mask percentage >3% and <5%\n",
      "093\n",
      "3.432903832829428\n",
      "mask percentage >3% and <5%\n",
      "120\n",
      "3.7661505574452003\n",
      "mask percentage >5% and <7%\n",
      "126\n",
      "6.477667745338876\n",
      "mask percentage >3% and <5%\n",
      "140\n",
      "3.106669461766188\n",
      "mask percentage >3% and <5%\n",
      "142\n",
      "3.465234473418997\n",
      "mask percentage >3% and <5%\n",
      "143\n",
      "4.421375031494079\n",
      "mask percentage >3% and <5%\n",
      "147\n",
      "3.464225678697405\n",
      "mask percentage >5% and <7%\n",
      "156\n",
      "6.433329987087427\n",
      "mask percentage >3% and <5%\n",
      "158\n",
      "3.3173599300831444\n",
      "mask percentage >3% and <5%\n",
      "162\n",
      "4.20684622228521\n",
      "mask percentage >3% and <5%\n",
      "166\n",
      "4.101316452506929\n",
      "mask percentage >3% and <5%\n",
      "182\n",
      "3.573766022612749\n",
      "mask percentage >3% and <5%\n",
      "191\n",
      "3.627379771352986\n",
      "mask percentage >3% and <5%\n",
      "213\n",
      "3.173323727639204\n",
      "mask percentage >5% and <7%\n",
      "217\n",
      "6.236615016376921\n",
      "mask percentage >3% and <5%\n",
      "223\n",
      "3.161833309712774\n"
     ]
    }
   ],
   "source": [
    "NUM_MASKS = 65\n",
    "masks = iter(glob.glob('mask/*')) \n",
    "for fov in range(NUM_MASKS):\n",
    "    mask_name = next(masks)\n",
    "    img = imread(mask_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = mask_name.split('/F')[1][0:3]\n",
    "    \n",
    "    mask_perc = 100*np.count_nonzero(img == 1)/(img.shape[0]*img.shape[1])\n",
    "    \n",
    "    if mask_perc > 7:\n",
    "        print(\"mask percentage >7%\")\n",
    "        print(FOV_num)\n",
    "        print(mask_perc)\n",
    "    if mask_perc > 5 and mask_perc < 7:\n",
    "        print(\"mask percentage >5% and <7%\")\n",
    "        print(FOV_num)\n",
    "        print(mask_perc)\n",
    "    if mask_perc > 3 and mask_perc < 5:\n",
    "        print(\"mask percentage >3% and <5%\")\n",
    "        print(FOV_num)\n",
    "        print(mask_perc)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Max Projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import glob\n",
    "from imageio import volread as imread\n",
    "import tifffile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_FOVS = 214"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 000\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 001\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 002\n",
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      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 003\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 004\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 005\n",
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      "(25, 2016, 2016)\n",
      "FOV 006\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 007\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 008\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 009\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 010\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 011\n",
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      "(25, 2016, 2016)\n",
      "FOV 013\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 014\n",
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      "(25, 2016, 2016)\n",
      "FOV 016\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 017\n",
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      "(25, 2016, 2016)\n",
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      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 019\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 020\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 021\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 022\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 023\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 024\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 025\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 026\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 027\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 028\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 029\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 030\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 031\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 032\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 033\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
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      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 035\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 036\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 037\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 038\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 039\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 040\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 041\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 042\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 043\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 044\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 045\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 047\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 048\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 049\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 050\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 051\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 053\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 054\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 055\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 056\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 057\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 058\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 059\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 060\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 061\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 062\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 063\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 065\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 066\n",
      "Prepare CP inputs - max projection only\n",
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 067\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 068\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 069\n",
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      "(25, 2016, 2016)\n",
      "FOV 070\n",
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      "(25, 2016, 2016)\n",
      "FOV 072\n",
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      "(25, 2016, 2016)\n",
      "FOV 073\n",
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      "(25, 2016, 2016)\n",
      "FOV 074\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 075\n",
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      "(25, 2016, 2016)\n",
      "FOV 076\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 077\n",
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      "(25, 2016, 2016)\n",
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      "(25, 2016, 2016)\n",
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      "(25, 2016, 2016)\n",
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      "(25, 2016, 2016)\n",
      "FOV 096\n",
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      "(25, 2016, 2016)\n",
      "FOV 097\n",
      "Prepare CP inputs - max projection only\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
      "FOV 098\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 099\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 100\n",
      "Prepare CP inputs - max projection only\n",
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      "Prepare CP inputs - max projection only\n",
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      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 104\n",
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      "(25, 2016, 2016)\n",
      "FOV 105\n",
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      "(25, 2016, 2016)\n",
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      "(25, 2016, 2016)\n",
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      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 113\n",
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      "(25, 2016, 2016)\n",
      "FOV 114\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 115\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 116\n",
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      "(25, 2016, 2016)\n",
      "FOV 117\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 118\n",
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      "(25, 2016, 2016)\n",
      "FOV 119\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 120\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 121\n",
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      "FOV 122\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 123\n",
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      "(25, 2016, 2016)\n",
      "FOV 124\n",
      "Prepare CP inputs - max projection only\n",
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      "(25, 2016, 2016)\n",
      "FOV 126\n",
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      "(25, 2016, 2016)\n",
      "FOV 127\n",
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      "(25, 2016, 2016)\n",
      "FOV 146\n",
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      "Saving MAX... ./max/fname\n",
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      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOV 192\n",
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      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
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      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n",
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      "Saving MAX... ./max/fname\n",
      "(25, 2016, 2016)\n"
     ]
    }
   ],
   "source": [
    "MAX_DIR = 'max'\n",
    "merged = iter(glob.glob('merged/*')) \n",
    "for FOV in range(NUM_FOVS):\n",
    "    merged_name = next(merged)\n",
    "    img = imread(merged_name)\n",
    "    img = img.astype(np.uint16)\n",
    "    FOV_num = merged_name.split('/F')[1][0:3]\n",
    "    print(\"FOV\", FOV_num)\n",
    "    \n",
    "    print(\"Prepare CP inputs - max projection only\")\n",
    "    img_max = img.max(0) # take max projection # ZCYX\n",
    "    im_to_save = img_max[final_inds].copy()\n",
    "    fname = f'F{FOV_num}_max.tif'\n",
    "    \n",
    "    print('Saving MAX...', f'./{MAX_DIR}/fname') \n",
    "    print(im_to_save.shape)\n",
    "    tifffile.imwrite(f'./{MAX_DIR}/'+fname, im_to_save, imagej = True,\n",
    "                    photometric='minisblack', metadata={'axes':'CYX'})"
   ]
  }
 ],
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