oranges.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of T1 and T2 parameters of oranges in a NMR experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of the raw data lists ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "MatReadError",
     "evalue": "Mat file appears to be empty",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMatReadError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\juanr\\Desktop\\Universidad\\Master\\Practicas\\programa calculo t1t2\\NMR-t1-t2-calculator\\oranges.ipynb Cell 3'\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000002?line=8'>9</a>\u001b[0m t1_onlyfiles \u001b[39m=\u001b[39m [\u001b[39mstr\u001b[39m(pp) \u001b[39mfor\u001b[39;00m pp \u001b[39min\u001b[39;00m t1_data_path\u001b[39m.\u001b[39mglob(\u001b[39m\"\u001b[39m\u001b[39m**/*.mat\u001b[39m\u001b[39m\"\u001b[39m)]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000002?line=9'>10</a>\u001b[0m t2_onlyfiles \u001b[39m=\u001b[39m [\u001b[39mstr\u001b[39m(pp) \u001b[39mfor\u001b[39;00m pp \u001b[39min\u001b[39;00m t2_data_path\u001b[39m.\u001b[39mglob(\u001b[39m\"\u001b[39m\u001b[39m**/*.mat\u001b[39m\u001b[39m\"\u001b[39m)]\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000002?line=11'>12</a>\u001b[0m t1_image_array \u001b[39m=\u001b[39m image_array_creation\u001b[39m.\u001b[39;49mcreate_image_array(t1_onlyfiles,\u001b[39m'\u001b[39;49m\u001b[39mimage3D\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000002?line=12'>13</a>\u001b[0m t2_image_array \u001b[39m=\u001b[39m image_array_creation\u001b[39m.\u001b[39mcreate_image_array(t2_onlyfiles,\u001b[39m'\u001b[39m\u001b[39mimagen\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000002?line=14'>15</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(\u001b[39m1\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\juanr\\Desktop\\Universidad\\Master\\Practicas\\programa calculo t1t2\\NMR-t1-t2-calculator\\image_array_creation.py:32\u001b[0m, in \u001b[0;36mcreate_image_array\u001b[1;34m(onlyfiles, str)\u001b[0m\n\u001b[0;32m     30\u001b[0m path \u001b[39m=\u001b[39m onlyfiles[i]\n\u001b[0;32m     31\u001b[0m image_paths_array\u001b[39m.\u001b[39minsert(i,path)\n\u001b[1;32m---> 32\u001b[0m image2D \u001b[39m=\u001b[39m import_2Dimage(image_paths_array[i],\u001b[39mround\u001b[39m(get_slice_number(onlyfiles[i],\u001b[39mstr\u001b[39;49m) \u001b[39m*\u001b[39m \u001b[39m0.5\u001b[39m),\u001b[39mstr\u001b[39m)\n\u001b[0;32m     33\u001b[0m image_array\u001b[39m.\u001b[39minsert(i, image2D)\n\u001b[0;32m     34\u001b[0m i \u001b[39m=\u001b[39m i \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m\n",
      "File \u001b[1;32mc:\\Users\\juanr\\Desktop\\Universidad\\Master\\Practicas\\programa calculo t1t2\\NMR-t1-t2-calculator\\image_array_creation.py:9\u001b[0m, in \u001b[0;36mget_slice_number\u001b[1;34m(file_path, str)\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_slice_number\u001b[39m(file_path,\u001b[39mstr\u001b[39m): \u001b[39m# This function gives the number of slices of a 3D image\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m     rawData \u001b[39m=\u001b[39m scipy\u001b[39m.\u001b[39;49mio\u001b[39m.\u001b[39;49mloadmat(file_path)\n\u001b[0;32m     10\u001b[0m     image \u001b[39m=\u001b[39m rawData[\u001b[39mstr\u001b[39m]\n\u001b[0;32m     11\u001b[0m     height \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(image[\u001b[39m0\u001b[39m,:])\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\scipy\\io\\matlab\\_mio.py:225\u001b[0m, in \u001b[0;36mloadmat\u001b[1;34m(file_name, mdict, appendmat, **kwargs)\u001b[0m\n\u001b[0;32m    223\u001b[0m variable_names \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m'\u001b[39m\u001b[39mvariable_names\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m    224\u001b[0m \u001b[39mwith\u001b[39;00m _open_file_context(file_name, appendmat) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m--> 225\u001b[0m     MR, _ \u001b[39m=\u001b[39m mat_reader_factory(f, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    226\u001b[0m     matfile_dict \u001b[39m=\u001b[39m MR\u001b[39m.\u001b[39mget_variables(variable_names)\n\u001b[0;32m    228\u001b[0m \u001b[39mif\u001b[39;00m mdict \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\scipy\\io\\matlab\\_mio.py:74\u001b[0m, in \u001b[0;36mmat_reader_factory\u001b[1;34m(file_name, appendmat, **kwargs)\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m     55\u001b[0m \u001b[39mCreate reader for matlab .mat format files.\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     71\u001b[0m \n\u001b[0;32m     72\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m     73\u001b[0m byte_stream, file_opened \u001b[39m=\u001b[39m _open_file(file_name, appendmat)\n\u001b[1;32m---> 74\u001b[0m mjv, mnv \u001b[39m=\u001b[39m _get_matfile_version(byte_stream)\n\u001b[0;32m     75\u001b[0m \u001b[39mif\u001b[39;00m mjv \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m     76\u001b[0m     \u001b[39mreturn\u001b[39;00m MatFile4Reader(byte_stream, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs), file_opened\n",
      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\scipy\\io\\matlab\\_miobase.py:234\u001b[0m, in \u001b[0;36m_get_matfile_version\u001b[1;34m(fileobj)\u001b[0m\n\u001b[0;32m    232\u001b[0m mopt_bytes \u001b[39m=\u001b[39m fileobj\u001b[39m.\u001b[39mread(\u001b[39m4\u001b[39m)\n\u001b[0;32m    233\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(mopt_bytes) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m--> 234\u001b[0m     \u001b[39mraise\u001b[39;00m MatReadError(\u001b[39m\"\u001b[39m\u001b[39mMat file appears to be empty\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m    235\u001b[0m mopt_ints \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mndarray(shape\u001b[39m=\u001b[39m(\u001b[39m4\u001b[39m,), dtype\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39muint8, buffer\u001b[39m=\u001b[39mmopt_bytes)\n\u001b[0;32m    236\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m0\u001b[39m \u001b[39min\u001b[39;00m mopt_ints:\n",
      "\u001b[1;31mMatReadError\u001b[0m: Mat file appears to be empty"
     ]
    }
   ],
   "source": [
    "import image_array_creation\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "t1_data_path = Path(r\"C:/Users/juanr/Desktop/Universidad/Master/Practicas/programa calculo t1t2/NMR-t1-t2-calculator/rawdata/\")\n",
    "t2_data_path = Path(r\"C:/Users/juanr/Desktop/Universidad/Master/Practicas/programa calculo t1t2/NMR-t1-t2-calculator/t2_raw_data/\")\n",
    "\n",
    "t1_onlyfiles = [str(pp) for pp in t1_data_path.glob(\"**/*.mat\")]\n",
    "t2_onlyfiles = [str(pp) for pp in t2_data_path.glob(\"**/*.mat\")]\n",
    "\n",
    "t1_image_array = image_array_creation.create_image_array(t1_onlyfiles,'image3D')\n",
    "t2_image_array = image_array_creation.create_image_array(t2_onlyfiles,'imagen')\n",
    "\n",
    "plt.figure(1)\n",
    "plt.imshow(abs(t1_image_array[0]))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of the proper oranges numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import separator\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import image_array_creation as iac\n",
    "\n",
    "# We create the separated arrays of T1 and T2\n",
    "\n",
    "separated_arrays = separator.separate_t1_t2(t1_image_array)\n",
    "t1_list = separated_arrays[0]\n",
    "t2_oranges_list = separated_arrays[1]\n",
    "\n",
    "# We turn them into numpy arrays\n",
    "\n",
    "t1 = np.asarray(t1_list)\n",
    "t2 = np.asarray(t2_image_array)\n",
    "t2_oranges = np.asarray(t2_oranges_list)\n",
    "\n",
    "t1_orange_seedless = t1[0:11]\n",
    "t1_orange_seeds = t1[11:22]\n",
    "\n",
    "plt.figure(1)\n",
    "plt.imshow(abs(t1[]))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orange masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "orange_seeds_mask = np.where(np.abs(t1_orange_seeds) < 0.0005,0,1)\n",
    "orange_seedless_mask = np.where(np.abs(t1_orange_seedless) < 0.0005,0,1)\n",
    "t2_seeds_mask = np.where(np.abs(t2_oranges[0])<0.0005,0,1)\n",
    "t2_seedless_mask = np.where(np.abs(t2_oranges[1])<0.0002,0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T1 image creation for the oranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 11 is out of bounds for axis 0 with size 11",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\juanr\\Desktop\\Universidad\\Master\\Practicas\\programa calculo t1t2\\NMR-t1-t2-calculator\\oranges.ipynb Cell 9'\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000008?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000008?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000008?line=4'>5</a>\u001b[0m t1_seeds_image \u001b[39m=\u001b[39m T1_creation\u001b[39m.\u001b[39;49mcreate_T1_image(orange_seeds_mask,np\u001b[39m.\u001b[39;49mabs(t1_orange_seeds))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000008?line=5'>6</a>\u001b[0m t1_seedless_image \u001b[39m=\u001b[39m T1_creation\u001b[39m.\u001b[39mcreate_T1_image(orange_seedless_mask,np\u001b[39m.\u001b[39mabs(t1_orange_seedless))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/juanr/Desktop/Universidad/Master/Practicas/programa%20calculo%20t1t2/NMR-t1-t2-calculator/oranges.ipynb#ch0000008?line=7'>8</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(\u001b[39m1\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\juanr\\Desktop\\Universidad\\Master\\Practicas\\programa calculo t1t2\\NMR-t1-t2-calculator\\T1_creation.py:30\u001b[0m, in \u001b[0;36mcreate_T1_image\u001b[1;34m(mask, image_array)\u001b[0m\n\u001b[0;32m     28\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m0\u001b[39m,\u001b[39m60\u001b[39m):\n\u001b[0;32m     29\u001b[0m     \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m0\u001b[39m,\u001b[39m60\u001b[39m):\n\u001b[1;32m---> 30\u001b[0m         t1_image  \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mwhere(mask[i,j] \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m, min_TI_value(image_array,i,j)\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39mlog(\u001b[39m2\u001b[39m), \u001b[39m1\u001b[39m)\n\u001b[0;32m     32\u001b[0m \u001b[39mreturn\u001b[39;00m t1_image\n",
      "\u001b[1;31mIndexError\u001b[0m: index 11 is out of bounds for axis 0 with size 11"
     ]
    }
   ],
   "source": [
    "import T1_creation\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "t1_seeds_image = T1_creation.create_T1_image(orange_seeds_mask,np.abs(t1_orange_seeds))\n",
    "t1_seedless_image = T1_creation.create_T1_image(orange_seedless_mask,np.abs(t1_orange_seedless))\n",
    "\n",
    "plt.figure(1)\n",
    "plt.imshow(np.abs(t1_seedless_image), cmap='inferno')\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(2)\n",
    "plt.imshow(np.abs(t1_seedless_image), cmap='inferno')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T2 image creation for the oranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3600\n",
      "3600\n"
     ]
    },
    {
     "data": {
      "image/png": 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CKLkwUXFpA5AECZZlVI+n9dxdqn7uwUQkPjaiTWJeuvadqn7eg0+i/KHvcjqOkxJc5HQcJy0ItLFwJeETmuM4GqGv0CqF1z51Ya7861cXq7bV5+xU9eogqW2ee1P4vJuadJuN8hrqeepteB7rghP0Y+9j3aZCvZQJlaOyNdn7GPcmlfXJ0mz0ZGEkXKsPbDSKqPC92Si5oTmI0ZlJt34u7A/MJqxrltVXhjo2k2lKgrfJBvM8682fwmjSj4yYcFAmOXRNbfI5HNzfo29bZ5MxVwa+KeA4Tmqo1E2BWdeVJFeQfJzkiyS3kvx8fLyD5AaSL8f/t892L8dxKoBpkbOYV5lRzIgmAfypiKwG8E4AnyW5GsCtAB4VkVUAHo3rjuOkAMmyqFe5MavIKSJ7AOyJy0MktwFYBuA6RAlCAeBuAE8A+MIpGeU80rt7aa7c1aztn+qatM1VJggRBJtTtdDDtLZRoZ5n3GQQt25JhWhonLk+ZGy5Qh2Vda+yWPehEKvjay2QMcrq+EKXqiFjbxd+Jp2dqokDJmzSUOA61mneiw1ZFLhN0XxcqAvOtc9o3OjmppIHXrXUhBbfp/Vi9Q3J92ZoTJ/b1FxAP1mmiJwmu5wkzwZwIYCnAPTEkx0A7EWU8dhxnIqHEEn5hEayGcC/AvhjETlCBgknRITkcfPCk7wZwM1zHajjOCWkDMXJYihqQiNZg2gy+0cR+V58eB/JJSKyh+QSAPuPd62IrAOwLr7PcSe9UtKxKAmr8Pyus1Xb6vc9repsCR7qhBl6GIV2pLBrEY4FIop167FmCGFSYBsFY3R0xnOlU0fmYF/itgVjVZLXZ4gVa4/paK1KtJ0tgbEV60LCz8i+Lxslt9A9swVMR2qtuUogPo9YtYDpJxM8JyN2WzepmoZEJH7jkj7V1rdniapv+2jyMM7/0S9QrqR5l5MA/gHANhH5u6DpQQBr4/JaAA/M//Acxyk5kuJNAQCXAfg9AL8i+Vx87H8A+DKA75K8CcDrAD51SkboOE5JkTTr0ETk5wBmmoo/ML/DcRxnwTlddjnTwPhEortpr9e6L2asXiXY+Jg04WZaA/2W3fK3oXKaA73UURMB1ppxhHopq+uyZhFhFnPrvhT22asjrqLH6KiCMUm7MaGwrlr9wfjPPkO3DZtIs+F77e7SbaFZiX1fNkquzeYeYvV2Ycgl6xZVF9QbTZ/HjB7UPsOQM3TYqfquRC97cKtOSxVmDgOAjh7zLMqUStWhnXYTmuM4s1OO+rFi8AnNcRxFqnVojuOcZnjWp8oh/OXJVGk9ytHfaGeHpmxiWpdpNfq20F6rSf+aSa12f+FocK21Q7M2a4cCF6FOE3Kb5lezgMuSCqttrpPdg/q2QSidPF2c0UMNb0v0Zk3t+j7Sr+3JeGagN7P6wNDGzoY3ytN91c/cZl2+Qjs1q18L+zk2S1ifY4G7lQ013q/DlNe0J39G7S16PFXG9HJvb2iXtr3wGBaQ7Dw6npO8CsDXAWQAfEtEvmzazwKwHkA3gEMAfldEeuO2MwF8C8AKRA6I14jIazP1VZnrSsdxTh1SnA1aMas4khkAdwG4GsBqADfEwS1CvgLgHhFZA+B2AHcEbfcA+FsROR/AJZjBgH8an9Acx1EIol3OYl5FcAmAHSLyioiMA7gXUWCLkNUAHovLj0+3xxNftYhsAAAROSoiZgtck3qR8+F3fELVp6aOzHAmMDFioipMJB/PWK82O2joCLbfzRY/G3Xmn4KuRta9qScRZfNE10lj4oHg2jEj7oXZm4zoRdPl1N5E7M3UmqgYxiTlf973+7nyHd3fVm3VF+lIs8qEwrpJheKy/QzyImEUiAZiXbXC+xZIQozqWaKehM/Mirk2igcS16iaav15jY7pz76udgyVwAlsCnSRfCaor4vdHadZBiBMk9UL4FJzj+cBXI9ILP04gBaSnQDOAzBI8nsAVgL4dwC3isiM6cVSP6E5jnPiZIu3QzsoIhfPsbtbANxJ8kYAGwH0IfqVqAbwXkQRfn4D4D4ANyJyxTwuPqE5jqORefXT7EOk0J9meXws6U5kN6IV2nRUn0+IyCDJXgDPicgrcdsPEAWZnXFCcx2a4zgKQbTLWcyrCDYBWEVyJclaAJ9GFNgiB8kuJlvxtyHa8Zy+to3kdCiZKwG8WKiz1K/QWox706v9SZidi859WbW1n/+aqstkohurazNuPdlgO95GdR0skA3dutQMzxzRNO830l4b6nZsVNpQp3ZAR4Cd6temIvW3GrefgtyVK/33ze9QLWeetUWfGo7X6qhCUxKrM7OuTqE+y+rFbD1TwHUsPNeazxTS41l3NeMG1/984u40NVXAZQqV41I0X+MUkUmSnwPwCCKzjfUispXk7QCeEZEHEUW+viMOL7YRwGfja6dI3gLg0Tjqz2YA/7dQf6mf0BzHOUGEyM6jp4CIPATgIXPsi0H5fgD3z3DtBgBriu3LJzTHcRQC9xQoW/Yf1ZEllrcl5gzNbdqEgzVGdAx0BKEJBwDIcLD9njWROGxSYhXt1vRRax6BiuRqxKljestfRpL78ogRGyVpE3Obga0rVf3PV1yRK39p1zdRLHX1ejwTO7VYWd2RiNM056IpeJ9WHLUJVYKkv3mJj61JRWjiYZ4LQs8Q+9mOD+p6GK3E3EfM8Lov2ZYr175wtmrbuf1cVa+tKWCCUkZUimhsSf2E5jjOCSLAVBnm3CwGn9Acx1FE0TZ8heY4TkrwCa1MGZ7QJgpVTPQojd06aoKM6XMzHcl2ffaI1t1kh5Jt/kznLHqRQqt3G+021Nc0Gx8bk1SX1p0oJDDbmHxe6+2e+M+3q/r1qxLzlS/tQkHGv570yU4TLSLP/CL4zPKijBSIQttqooxUByYz1tXJ6tDUdebzCfVreQmUTZ8h7bqNY4O6PcgAVlWjn+dUhcYVOwFPgbIi9ROa4zgnjq/QHMdJBSI+oTmOkxrou5zlyluXaqVQQ/0skUoDQj1ZVbOxAZtIHvhEn3Y7qu7UblLsDHRz1jbK2qGFIW5MSCBUGX3bVAE3n3A8Hdp154ZffWfGc2eDPQV0X/VGvxXq1PIyngd1qwez54aZ1G1bocxYti2MWGtt3wqFGrLj6zZuZgPJ8xbj+lSbKRA6qozxFZrjOKlA4JsCjuOkBdehlS+HhrTrU1s2EQmmrJlGoxbxMg1JVFPrzsTmQBzN2mTBRvQKkwnbiBnWtCAUMwuZJABa/LLmDIG4xVb9vl66VkfJOOvy/8yV624pbIKS7U3uW9VuTB+amlRVmpLIvRw6rM8tJHLaRMOhyFko+i8AHAnEfWv2EiZGyYtcYv6AbUKaECv2Mrm2wZgCNe3S76W6uhJEUPoKzXGcdCCIvAUqEZ/QHMfJo1J3OWcdNcl6kk+TfJ7kVpJ/GR9fSfIpkjtI3hdHo3Qcp+IpLuNTOerZilmhjQG4UkSOkqwB8HOSPwbwJwC+KiL3kvx7ADcBKD72TIloMOFaDg215Mpnjmsdmg0RhOrAxKLNbNUXwupYQhOB0bHC54YuOU0FEuUCWveUl4A30CWZZMYrf3uTPrchyG71Ff27NHGoRdWrwuTCDSZJ8gGtP2J1cK5NqBzWbbRYmyA4dFmq121SZTJuVRWIhBvq1KwuzuoyQ/MPm3XKjq8xeWYjB9pV05Gj+nvT0TaIckekcnc5Z12hScT0X0dN/BJE8b2no0zeDeBjp2KAjuOUnkpdoRUlKJPMkHwOUdbiDQB2AhgUkemfuV5E+feOd+3NJJ8xufscxyljsmBRr3KjqE2BOLHnBSTbAHwfwJuK7SBOOroOAOIkCI7jlDFyurg+xbnyHgfwLkTpparjVVperr1ywW4/dy5K7KFGBrWNWvO5+i2wM9B9WZ2LDeFcLNb+yRLYckmjtuviuNG/haGqbWaiUF9kbaqsPitgpFfrfCaGdRb4uouDz8HYvkmjvpYH9iaVvGzogW1epoD+CtA6qyEbNt3o30IdmglRJPWBXdyoCVl+2NjJhc/J6icP6zGEWaAmRnWfgyP6GS5dsheVQDmKk8VQzC5nd7wyA8kGAB8CsA3A4wA+GZ+2FsADp2iMjuOUEAGQLfJVbhSzQlsC4G6SGUQT4HdF5IckXwRwL8n/BeBZFMhm7DhOBZFm1ycR2QLgwuMcfwXAJadiUPOJ3X7eM9CRK3d16wS8dr0qBxIRjzaQxFByslUNst6oClsCEc+KL1ZkCsQk7ttnxme+ZI2BOGhdn0JTg0LmHgCwLxGhmt6sI4VM9evxTe1I3lvmTO3yReuyFCY/tqYPYXQLm2zZRiQJxVWbUNmKsiGmT4ZiZV50XSOGh2Yc1tTGJBoe25FEtO19fYVqO3eZjvZS21h8tJeFpFLNNtxTwHGcPNz1yXGcVCAgJj3RsOM4acFXaGWKVW62Nyb6mvZzevXJk+YhBmoVm328qinQwWTMddVGRxWaAFhdl42Uui/Q81idWXsHZmRY676UKcRsIXfC4XZqHVWm1Vw7EIbgmSUDk424GxLqDq3bkdVZDQY6tmPmni0n4JKmstKbPsZNCKgQmyHKkA1c5qqr9bm1dfr5HjtyAuNdIDzAo+M46UEif85KxCc0x3HyKEe3pmLwCc1xHIUgxXZoaePIaKL3ObKrR7W1N2t3GCLRh9hobxKocthpGq17UxhC+vCgbjtmdDmNgbtVaxsKEurNCoTgzmuzbj6tBTIcWQrZ1BXKyGT1ZOGl1kbNZmQKP8/Dxtat1ujUwvdqdXih3q7VhACyNnThexnResTJ/fraAzvPzJU392k7tHdUaZ3a237yBMofYsonNMdx0oBvCjiOkyrK0U+zGFI/oXW36yiqBwaSiKJWT5A1kRKqgqxP2cP6XGW20aKjuqL/kK4HYpI1/+CZXfpAKMZZUwwr0oXnmigUOtKEeczWtSgUB634Z0XF8QImIIUiz1rxLzSbsCKmfZ/hGDp1hJQ884uBwZnH1x6Mx5rLWLE8HG+dFq2Z0eMbGGjLlTvq9PusrzvJqCwLTKXq0Coz6JHjOKeM6RDcxbyKgeRVJLfH+UduPU77WSQfJbmF5BMkl5v2RSR7Sd45W18+oTmOk8eUsKjXbMRReu4CcDWA1QBuILnanPYVAPeIyBoAtwO4w7T/FYCNxYzbJzTHcRTzHA/tEgA7ROQVERkHcC+A68w5qwE8FpcfD9tJvh1AD4CfFNNZ6nVojU06NE3dUOJ6MmWzPk3OHE02r60lqBtdlwzrR83ORHfDFqMDsm5JVrcTYs0kwrrVAYUYkwQZ0n0y1C1NGr2dJXQDsmO3kWfD8dlzW1tnbpss4DJl9WsNOqKuyt6+3+gy9wb1pUZ3afWeoanIiO5z/GCbqre1JmYwi1qGUPmcUAKULpMvZF0cdn+aZQDCGEq9AC4193gewPUAvg7g4wBaSHYCGADwvwH8LoAPFjOY1E9ojuOcOCewy3lQRC6eY3e3ALiT5I2IRMs+AFMA/gjAQyLSSxY3wfqE5jiOYp49BfoAhNbGeflHRGQ3ohUaSDYD+EScv+RdAN5L8o8ANAOoJXlURPI2FqZJ/YQ2Pq5NCcJoCL9+9RzV9u5zdXTR7Egiko73a1GxvulgrswmI8KdvVgPIhT58qz0W3U9THzSaMQpm+wkFOkKRbYwZhrsKRD11Zp0WDGyORhTv7GubzXXhuKgSViiPhObuNfWC703k3gYo8G5i010kjC5yQETrbjejG84MQfJDuvPffhAm6r37U88TpZ271dtqx54Kn/MFYD9GsyBTQBWkVyJaCL7NIDPhCeQ7AJwSESyAG4DsB4AROR3gnNuBHBxockM8E0Bx3EMIvO3yxlnhfscgEcQJVf6rohsJXk7yWvj064AsJ3kS4g2AL50smNP/QrNcZwTZz49BUTkIQAPmWNfDMr3A7h/lnt8G8C3Z+vLJzTHcfKoVE+B1E9oU8bcYjyILtrWoE06Nj/+blVfc/FzuXJtu96OZ1PwwNuM65ON3HAk6MdEoZUdWufCHqMTCilk0lFI72Qj31oTj9DspEknxs3LGBW6IdWZ99Jzhqpzz+7gOmP2EurUZouoG743+9keOIgZsXqxMLqt0ctle/VnOxkkoT700pmq7fVeZciu9GbDxwqYz1QI03ZolUjqJzTHcU4cj1jrOE5q8Ii1juOkAgEwVaEyZ+ontPMefFLVt/92oic7NqLtvKbGtBXLxLFEd9O0Zo++cXOgK+k3oXtsFqiJZP0uo8YmrNVYzoS6pTw3nwIhbgpFjy3kMmXrVmdm9VvhGIzukKM64q+676jR/4UZ0G0fdgwDJrN6iNWTFcrQFGZotzo9wy8efn+uvKJbZ7Bf2qPr+w5058rv2vjjgvetDOgrNMdxUkIFZ30q2rCWZIbksyR/GNdXknwqjnF0H2mj7juOU4nMc7SNknIiK7TPI7L0nd7P/msAXxWRe0n+PYCbAHxznsc370wGZhyNDVpEGjPRN8aPJSKpGO8btgSPs8aKe+bnLWjOEzEnzbmhqGjdjqwLUHiujW4bRpa1yXh79+p6e9Buo87aCLah2YQ910bNteYiIaHr0Wxi46LAlOSIET9r9TMLEwbLPm3iwZ5AXD6qn/3koBaf3/WBn+XKD//oQ6ptaeugqre3mPedAubR9amkFLVCiyNIfhTAt+I6AVyJxLr3bgAfOwXjcxynxAjmz/Wp1BS7QvsagD8DMP0z1glgMPbTAqIYR8uOdyHJmwHcPIcxOo5TYlKrQyP5WwD2i8jmk+lARNaJyMXzEDPJcZwSkWYd2mUAriV5DYB6RDq0rwNoI1kdr9LyYhyVK2/+caIb+eXlV6u2iSnjJjWW6HZkwmT+Cc0QrB5n2GQiCnVsE+Zr0GJclkIThkAfFLUZ3VKoQ7NmCKFeasCELLLfxLCfo8a1qNGMT/Vpvj7DZnyFTCOmgiWANU8Zt6YXge6w3YRbsnrFYLycMO8leGbZQS0uTRzWLl8bf5aY96zsPKDali7brevf2Yo0EcVDW+hRnByzrtBE5DYRWS4iZyOKZfRYHKfocQCfjE9bC+CBUzZKx3FKSqWu0OYSD+0LAP6E5A5EOrV/mJ8hOY6zoEi0y1nMq9w4IcNaEXkCwBNx+RVEGV0cx0kR0S7nQo/i5DitPQW6O3QY5sNHdJjtySDU0PCOpaqt+Y29uTLrjc7MrsVbAn1WrfmmWL1YqHeyGY0ODpr7BvqtY2YMoV7P6rLyQgIF7VZvZ+thlvi8DOemn/An3NrbTc5wHqBcxSKCz2jY2KEV+vzscxhJOp08pJ/10QM6XPeqJYlKOJvVgkzadGbHo1J1aKf1hOY4Tj4eD81xnFRRjvqxYjitJ7T+gXZVX9yto5/29ydiCHv1E25clmzlZ5qMe1Chb4N1B7KiWGgOctiYJDQUeFz2JzXMyFRj+mw1UTvCqBh5bltm3ygUV63IaQlFZmPKIsHbJIwIbMcbiqBTRgRuMiYzQ4Hr06i+z9D2JNJs746zVdvIqDZPaW5MPr+3PLwRpxsVOp+d3hOa4zj5iHg8NMdxUkSFzmc+oTmOoxG4Dq0iufSnj6j6C1ddrurV1YluZ9c+k9FoU/LEl6x5SbfVaJ1Q1UiiMKoyybzzTR2CcrvRddmItQODuaKMmUi4TWFWdfN7a8P+hHqyKuMuZBkNdGHW5csSuiWZIchY8r5ZWyDKLAC0B/otq1cc1591dii5b+9/vE21hRnAbJq27i5twvOG720qPKaUU6Hz2ek9oTmOc3x8heY4Tmpww9oUYLfnw4Qqbe2Dqm1qMvnodm1+s2o7dFhHhDj/oi25clOTjhabNXlyqzqCb5K10h/VY5ChRNyi9coNzUGsuYeNUBGKnK3agj7PMj8UkUeNd0Kj9myQ18PkzFrEq1oamooYM40xLUbKPjPegKFnddLfI/s7k9tO6Q+lviG5T0Ojjlg7OlIgwfNphgCYrNAZzSc0x3HyqMzpzCc0x3EMUqaRNIrBJzTHcQwCqdA1mk9oBXjjv/1HrrzjYzpS0thYXa7csmhItdUc1dFPB3cvzpXFRMWtadE6quqxRLeT6dI6quyQ1glJMAZ7LmoDE4UBrZNiizGTaA50X2LsK+xPdVi3bYf055AdScw6WK375GgwJqOvmTqgzUpG9ya2LmNHdAarfX3anGZiIulzyVKTHLoq6WfpPS/COT5uh+Y4TqpwTwHHcVJBFOCxMpdoPqE5jpOH+ISWbs79wdOqvvGy38qVB4a07daiJp3FvP9gYhs1PKR1QFb/1tiW1Pm6/lJZu6ralsRNqeqItlmr7R7MlWVc66RoItZWhdmRMsYmzH6xwwixxt1q6rDJPH+wLVc++KpO29rSkYxvbFi7dB3Y363q7R2HcuW9e3tUW3VG6+Y6OgZy5dMhsuypYj5FTpJXIcoUlwHwLRH5smk/C8B6AN0ADgH4XRHpJXkBgG8iyjQ3BeBLInJfob7mkiTFcZwUEm0KSFGv2SCZAXAXgKsBrAZwA8nV5rSvALhHRNYAuB3AHfHxYwB+X0TeDOAqAF8j2VaoP5/QHMfJQ4r8VwSXANghIq+IyDiAewFcZ85ZDeCxuPz4dLuIvCQiL8fl3QD2I1rFzYiLnCfJ5b/4Ya7c+5k1qs2Khn17luTKNTVaNOzv71T1oSNJEpKmFi267tmrTRTOWLw/V7bRI1pN4lw9QGP+EVxLI8KND2t3pualia/W8F499skxLdpOjCci6NhonWrrf/mcpI8p/TVsadSmLEcOJyL9nqE21fbxZ++FM78IBJPF26F1kXwmqK8TkXVBfRmAXUG9F8Cl5h7PA7gekVj6cQAtJDtFJBcCheQlAGoB7Cw0GJ/QHMfRCIoSJ2MOisjFc+zxFgB3krwRwEYAfQhSfZFcAuD/AVgrYg0lNT6hOY6Txzx6CvQBWBHUl8fHkr4icfJ6ACDZDOATIjIY1xcB+BGAPxeRJ2frzHVojuMoojR2UtSrCDYBWEVyJclaAJ8G8GB4AskuMhcv5jZEO56Iz/8+og2D+4vpzFdo88Dyf9oyyxnP5UphSCIAmDBJdauqkhV1aO4B5OuWwgisoyZr0cSuJDHykHHFmrTuV0Fk3vo67UJlw+z0PZmE6yH12O19w8xJg0dbVFtrYNoyZtrWPPJTOAtLkZPVrIjIJMnPAXgEkdnGehHZSvJ2AM+IyIMArgBwB6Mv1EYAn40v/xSAywF0xuIoANwoIs/N1J9PaI7jGObXOV1EHgLwkDn2xaB8P4C8FZiIfAfAd06kr6ImNJKvARhCpKibFJGLSXYAuA/A2QBeA/ApERmY6R6O41QGQmCSs+R5KFNORIf2fhG5INjRuBXAoyKyCsCjcd1xnBQwjzq0kjIXkfM6RLIvANwN4AkAX5jjeFJPGJJorrz6ybfnykePafehUL+14qxdqm1qXLsoVdclWam2bT9PtS2rPqDqmUyi46ut0Rnj3/zjnxUzbKfMiQTOyoy3UewKTQD8hORmkjfHx3pEZDrg1F4APce7kOTNJJ8xxneO45QxaV+hvUdE+kguBrCB5K/DRhER2i2vpG0dgHUAMNM5juOUF1lW5gqtqAlNRPri//eT/D4i/6x9JJeIyJ7Yknd/wZs4887K+zefgrtuOwX3dCqJSOCszAltVpGTZBPJlukygA8DeAGRcdza+LS1AB44VYN0HKeUCKYwWdSr3ChmhdYD4Pskp8//JxF5mOQmAN8leROA1xEZwTmOU+EIUixyisgrAN52nOP9AD5wKgblOM5CUrkip3sKOI6Th6AyDWt9QnMcR1HJmwI+oTmOYxBMYWL208oQn9Acx1FE4YN8heY4TkqoVNcnn9AcxzEIsr4p4DhOGhD4Cs1xnNQgyIqv0BzHSQWCrO9yOo6TBqLM6S5yOo6TCio3wKNPaI7jaAQQ16E5jpMG3PXJcZxUIa5DcxwnHQiy4rucjuOkAt8UcBwnJQh8U8BxnNQgrkNzHCc9uMjpOE46EEFWyi+jUzH4hOY4jsJ1aI7jpIgoZm0l4hOa4zh5+KaA4zgpwe3QHMdJFT6hOY6TCgRSobucVQs9AMdxypFska/ZIXkVye0kd5C89TjtZ5F8lOQWkk+QXB60rSX5cvxaO2tfIlLUoOYDkgJkStaf45x+TEFEOJc7kFXCIoU3wcRmEbl45nsxA+AlAB8C0AtgE4AbROTF4Jx/AfBDEbmb5JUA/kBEfo9kB4BnAFyMaOt1M4C3i8jATP35Cs1xHIMU/a8ILgGwQ0ReEZFxAPcCuM6csxrAY3H58aD9IwA2iMiheBLbAOCqQp2VWod2EJh6HUBXVC4bfDyFKbfxAOU3pnIZz1nzcI9HgMmuIs+tJ/lMUF8nIuuC+jIAu4J6L4BLzT2eB3A9gK8D+DiAFpKdM1y7rNBgSjqhiUg3AJB8ptAytdT4eApTbuMBym9M5TaeuSAiBVdBp4BbANxJ8kYAGwH0ASeX6dh3OR3HOZX0AVgR1JfHx3KIyG5EKzSQbAbwCREZJNkH4Apz7ROFOnMdmuM4p5JNAFaRXEmyFsCnATwYnkCyi+T0XHQbgPVx+REAHybZTrIdwIfjYzOyUBPautlPKSk+nsKU23iA8htTuY2nLJDIoO1ziCaibQC+KyJbSd5O8tr4tCsAbCf5EoAeAF+Krz0E4K8QTYqbANweH5uRkpptOI7jnEpc5HQcJzX4hOY4Tmoo6YQ2mwtEicawnuR+ki8ExzpIbojdKzbECshSjWcFycdJvkhyK8nPL+SYSNaTfJrk8/F4/jI+vpLkU/Gzuy9W8JYMkhmSz5L84UKPh+RrJH9F8rlpG6yF/A45CSWb0GIXiLsAXI3IMvgGkqtL1X/At5FvbXwrgEdFZBWAR+N6qZgE8KcishrAOwF8Nv5cFmpMYwCuFJG3AbgAwFUk3wngrwF8VUTOBTAA4KYSjWeazyNSKk+z0ON5v4hcENieLeR3yIkp5QqtGBeIU46IbARgd0quA3B3XL4bwMdKOJ49IvKfcXkI0R/tsoUak0Qcjas18UsAXAng/lKPBwBiZ+WPAvhWXOdCjmcGFuw75CSUckI7YTeGEtIjInvi8l5EW8clh+TZAC4E8NRCjikW754DsB+R/9xOAIOSxJQp9bP7GoA/QxLeoXOBxyMAfkJyM8mb42Nl8R063XFPAYOISBQVpLTEFtL/CuCPReRItAhZmDFJlCHjApJtAL4P4E2l6ttC8rcA7BeRzSSvWKhxGN4jIn0kFwPYQPLXYeNCfYec0q7QZnWBWED2kVwCAPH/+0vZOckaRJPZP4rI98phTAAgIoOIoh+8C0AbyekfwFI+u8sAXEvyNURqiisROTEv1HggIn3x//sRTfiXoAyel1PaCW1WF4gF5EEA08Hj1gJ4oFQdx/qgfwCwTUT+bqHHRLI7XpmBZAOiOFbbEE1snyz1eETkNhFZLiJnI/rOPCYiv7NQ4yHZRLJluozIHecFLOB3yAkQkZK9AFyDKNjbTgB/Xsq+gzH8M4A9ACYQ6V5uQqSTeRTAywD+HUBHCcfzHkQ6mS0Anotf1yzUmACsAfBsPJ4XAHwxPn4OgKcB7ADwLwDqFuDZXYEoEOCCjSfu9/n4tXX6e7yQ3yF/JS93fXIcJzW4p4DjOKnBJzTHcVKDT2iO46QGn9Acx0kNPqE5jpMafEJzHCc1+ITmOE5q+P/nxKhLTkxurQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import T2_creation\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import image_array_creation as iac\n",
    "\n",
    "t2_seedless_image = T2_creation.getT2(iac.create_t2_array(t2_oranges[0]), t2_seeds_mask)\n",
    "t2_seeds_image = T2_creation.create_t2_image(iac.create_t2_array(t2_oranges[1]), t2_seedless_mask)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.imshow(np.abs(t2_seedless_image), cmap='inferno')\n",
    "plt.clim(0.9,1)\n",
    "plt.colorbar()\n",
    "plt.savefig('t2_orange_seedless.png', dpi='figure')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(1)\n",
    "plt.imshow(np.abs(t2_seeds_image), cmap='inferno')\n",
    "plt.clim(0.9,1)\n",
    "plt.colorbar()\n",
    "plt.savefig('t2_orange_seeds.png', dpi='figure')\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31363e289ea6ff030ca416af9a9287d08cdf97e251c5dc68f3722bbf337b2dbf"
  },
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit (windows store)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}