3dvis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20cc6f94-fcdd-45af-af57-34ed64980898",
   "metadata": {},
   "outputs": [],
   "source": [
    "# have stack.tif file in the same directory as this file for it to work\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image, ImageSequence\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load image\n",
    "im = Image.open('stack.tif')\n",
    "\n",
    "# Find no. of slices\n",
    "no_frames = im.n_frames\n",
    "\n",
    "# Create separate tif files for each slice\n",
    "for i in range(no_frames):\n",
    "    im.seek(i)\n",
    "    im.save('page_%s.tif'%(i,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "063ae06b-10e4-4b65-9af3-b6152d70a2c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib\n",
    "\n",
    "# create list of individual slices\n",
    "images = []\n",
    "for i in range(no_frames):\n",
    "    temp_im = Image.open('page_%s.tif'%(i,))\n",
    "    imdata = temp_im.getdata()\n",
    "    x = np.reshape(imdata, (temp_im.height, temp_im.width))\n",
    "    x = np.asarray(x, dtype=np.uint8)\n",
    "    images.append(x)\n",
    "\n",
    "# convert to numpy array\n",
    "images_array = np.asarray(images)\n",
    "np.shape(images_array) # 15, 1024, 1024\n",
    "np.shape(images_array[0][0]) # 1024\n",
    "type(images_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f89d8b4-7052-4358-a948-953123b13bfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "255\n"
     ]
    }
   ],
   "source": [
    "# Danny questions - what type of image is this? does it use a colourmap or not? what are the maximum rgb values?\n",
    "maxval = 0\n",
    "for i in range(1024):\n",
    "    for j in range(15):\n",
    "        tempmax = max(images_array[j][i])\n",
    "        if tempmax > maxval:\n",
    "            maxval = tempmax\n",
    "print(maxval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de2a60c4-7594-45fa-a1b8-7b3367e55036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nplt.imshow(images_array[1])\\nplt.colorbar()\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create 3d axes - THIS WORKS\n",
    "%matplotlib\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "# check to see image has loaded into array\n",
    "'''\n",
    "plt.imshow(images_array[1])\n",
    "plt.colorbar()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "034b9aa5-91d5-4de8-9353-a7625b3d58eb",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "missing a required argument: 'X'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/mf/4f436l5x2h59328196w7lhyc0000gn/T/ipykernel_12741/2808574971.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprojection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'3d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mAxes3D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_surface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages_array\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m             \u001b[0;31m# calling bind()).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m         \u001b[0marguments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_varargs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m             warn_deprecated(\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/inspect.py\u001b[0m in \u001b[0;36mbind\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3060\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0marguments\u001b[0m \u001b[0mcan\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3061\u001b[0m         \"\"\"\n\u001b[0;32m-> 3062\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3064\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbind_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m/\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/inspect.py\u001b[0m in \u001b[0;36m_bind\u001b[0;34m(self, args, kwargs, partial)\u001b[0m\n\u001b[1;32m   2975\u001b[0m                             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'missing a required argument: {arg!r}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2976\u001b[0m                             \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2977\u001b[0;31m                             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2978\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2979\u001b[0m                 \u001b[0;31m# We have a positional argument to process\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: missing a required argument: 'X'"
     ]
    }
   ],
   "source": [
    "\n",
    "# Doesn't work - should create interactive 3d plot \n",
    "x_3d,y_3d = np.ogrid[0:images_array[0].shape[0], 0:images_array[0].shape[1]]\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "Axes3D.plot_surface()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "610f91cd-5299-4d67-8d7a-bcc08e8af166",
   "metadata": {},
   "outputs": [
    {
     "ename": "AxisError",
     "evalue": "axis 2 is out of bounds for array of dimension 2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/mf/4f436l5x2h59328196w7lhyc0000gn/T/ipykernel_8601/3333697793.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mimages_array\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# this is the concatenation line that goes wrong. I want axis to be set to 2 so they stack in the z plane, but for some reason it doesn't work\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mAxisError\u001b[0m: axis 2 is out of bounds for array of dimension 2"
     ]
    }
   ],
   "source": [
    "# Try concatenating the images in 3D\n",
    "x = np.concatenate([images_array[i] for i in range(15)], axis=2) # this is the concatenation line that goes wrong. I want axis to be set to 2 so they stack in the z plane, but for some reason it doesn't work\n",
    "plt.imshow(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3532669-04a4-4011-98eb-d581ca5560a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# potential bugs:\n",
    "# - incorrect indexing of array of slices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0669e654-5d65-4a08-a57a-91b5d9828bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# try rearranging stack so 1024x1024 x 15\n",
    "\n",
    "print(\"shape of array = \", np.shape(images_array))\n",
    "stacked = np.dstack(images_array)\n",
    "\n",
    "print(\"shape of new array = \", np.shape(stacked))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78eb27b8-b8b5-46ec-81b8-312e50ea78fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib\n",
    "from matplotlib import colors\n",
    "\n",
    "plt.subplot(1,1,1)\n",
    "slice1 = stacked[..., 0]\n",
    "plt.imshow(slice1, cmap='gray', norm=colors.NoNorm()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "712913c7-ffe4-4d2f-ad35-027a79673383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 1024)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(slice1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4d035502-9f0d-455c-ae96-8b8eed7a1018",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try the red channel images\n",
    "\n",
    "# have Rchannel-stack-1.tif file in the same directory as this file for it to work\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image, ImageSequence\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load image\n",
    "im = Image.open('Rchannel-stack-1.tif')\n",
    "\n",
    "# Find no. of slices\n",
    "no_frames = im.n_frames\n",
    "\n",
    "# Create separate tif files for each slice\n",
    "for i in range(no_frames):\n",
    "    im.seek(i)\n",
    "    im.save('Redpage_%s.tif'%(i,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6e193f23-a9bd-48dc-94df-f735b7cf9086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1024, 1024)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# create list of individual slices of Red stack\n",
    "Rimages = []\n",
    "for i in range(no_frames):\n",
    "    temp_im = Image.open('Redpage_%s.tif'%(i,))\n",
    "    imdata = temp_im.getdata()\n",
    "    x = np.reshape(imdata, (temp_im.height, temp_im.width))\n",
    "    x = np.asarray(x, dtype=np.uint8)\n",
    "    Rimages.append(x)\n",
    "\n",
    "# convert to numpy array\n",
    "Rimages_array = np.asarray(Rimages)\n",
    "np.shape(Rimages_array) # 15, 1024, 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "59be0eca-f24c-47b8-954c-7dc33a79e1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f88742701c0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try showing a single image slice\n",
    "plt.imshow(Rimages_array[1])\n",
    "plt.colorbar()\n",
    "# still showing blue even if we use the red channel that has been isolated in imagej. imagej says lookup table is red for red channel imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "911c4268-6c32-4df3-a61b-dee7099d9f2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The colormap data: (768,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.00392157],\n",
       "       [0.        , 0.00392157, 0.00784314],\n",
       "       [0.        , 0.00784314, 0.01176471],\n",
       "       [0.        , 0.01176471, 0.01568627],\n",
       "       [0.        , 0.01568627, 0.01960784],\n",
       "       [0.        , 0.01568627, 0.02352941],\n",
       "       [0.        , 0.01960784, 0.02745098],\n",
       "       [0.        , 0.02352941, 0.03137255],\n",
       "       [0.        , 0.02745098, 0.03529412],\n",
       "       [0.        , 0.03137255, 0.03921569],\n",
       "       [0.        , 0.03137255, 0.04313725],\n",
       "       [0.        , 0.03529412, 0.04705882],\n",
       "       [0.        , 0.03921569, 0.05098039],\n",
       "       [0.        , 0.04313725, 0.05490196],\n",
       "       [0.        , 0.04705882, 0.05882353],\n",
       "       [0.        , 0.04705882, 0.0627451 ],\n",
       "       [0.        , 0.05098039, 0.06666667],\n",
       "       [0.        , 0.05490196, 0.07058824],\n",
       "       [0.        , 0.05882353, 0.0745098 ],\n",
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       "       [0.        , 0.8       , 1.        ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# go from here to investigate colourmap data\n",
    "# use dir(variable) to find data and attributes associated with that image\n",
    "# idea - reshape the array into the number of channels\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "imageObj = Image.open('stack.tif')\n",
    "cmapdata = imageObj.getpalette()\n",
    "imdata = imageObj.getdata()\n",
    "print(\"The colormap data:\", np.shape(cmapdata))\n",
    "dir(imageObj)\n",
    "cmapNx3 = np.reshape(cmapdata,(256,3), order='C')\n",
    "cmapNx3n = np.asarray(cmapNx3, dtype=float)/255.0\n",
    "cmap = colors.ListedColormap(cmapNx3n, name='stack_colormap')\n",
    "plt.imshow(imageObj, cmap=cmap, norm=colors.NoNorm())\n",
    "plt.colorbar()\n",
    "plt.axis('Off')\n",
    "cmapNx3n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c22866b7-701d-46c6-905c-27c355dbabde",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}