{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "img_path = r\"data/tobacco/Image_2022_0716_1409_08_547-001482.bmp\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "img = cv2.imread(img_path)\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "outputs": [],
   "source": [
    "l_bmax = 43\n",
    "l_bmin = 1\n",
    "a_bmax = 148\n",
    "a_bmin = 122\n",
    "b_bmax = 137\n",
    "b_bmin = 126"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "outputs": [],
   "source": [
    "l_ymax = 157\n",
    "l_ymin = 6\n",
    "# l_ymin = 44\n",
    "a_ymax = 170\n",
    "a_ymin = 124\n",
    "b_ymax = 163\n",
    "b_ymin = 129"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "outputs": [],
   "source": [
    "# item_mask = np.zeros((1024, 4096))\n",
    "item_mask = ~((img[:,:,0]>=l_bmin)&(img[:,:,0]<=l_bmax)&(img[:,:,1]>=a_bmin)&(img[:,:,1]<=a_bmax)&(img[:,:,2]>=b_bmin)&(img[:,:,2]<=b_bmax))+0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "outputs": [],
   "source": [
    "# tobacco_mask = np.zeros((1024, 4096))\n",
    "tobacco_mask = ((img[:,:,0]>=l_ymin)&(img[:,:,0]<=l_ymax)&(img[:,:,1]>=a_ymin)&(img[:,:,1]<=a_ymax)&(img[:,:,2]>=b_ymin)&(img[:,:,2]<=b_ymax))+0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "outputs": [],
   "source": [
    "mask = (item_mask.all() & tobacco_mask.all())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid shape () for image data",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[1;32mIn [265]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      1\u001B[0m fig,ax \u001B[38;5;241m=\u001B[39m plt\u001B[38;5;241m.\u001B[39msubplots()\n\u001B[1;32m----> 2\u001B[0m ax \u001B[38;5;241m=\u001B[39m \u001B[43mplt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mimshow\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmask\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m      3\u001B[0m plt\u001B[38;5;241m.\u001B[39mshow()\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\deepo\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:456\u001B[0m, in \u001B[0;36mmake_keyword_only.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    450\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m name_idx:\n\u001B[0;32m    451\u001B[0m     warn_deprecated(\n\u001B[0;32m    452\u001B[0m         since, message\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    453\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[38;5;124m; the \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    454\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter will become keyword-only \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    455\u001B[0m         name\u001B[38;5;241m=\u001B[39mname, obj_type\u001B[38;5;241m=\u001B[39m\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m()\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 456\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\deepo\\lib\\site-packages\\matplotlib\\pyplot.py:2640\u001B[0m, in \u001B[0;36mimshow\u001B[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001B[0m\n\u001B[0;32m   2634\u001B[0m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes\u001B[38;5;241m.\u001B[39mimshow)\n\u001B[0;32m   2635\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mimshow\u001B[39m(\n\u001B[0;32m   2636\u001B[0m         X, cmap\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, norm\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, aspect\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, interpolation\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m   2637\u001B[0m         alpha\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, vmin\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, vmax\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, origin\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, extent\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m,\n\u001B[0;32m   2638\u001B[0m         interpolation_stage\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, filternorm\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, filterrad\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m4.0\u001B[39m,\n\u001B[0;32m   2639\u001B[0m         resample\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, url\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m-> 2640\u001B[0m     __ret \u001B[38;5;241m=\u001B[39m gca()\u001B[38;5;241m.\u001B[39mimshow(\n\u001B[0;32m   2641\u001B[0m         X, cmap\u001B[38;5;241m=\u001B[39mcmap, norm\u001B[38;5;241m=\u001B[39mnorm, aspect\u001B[38;5;241m=\u001B[39maspect,\n\u001B[0;32m   2642\u001B[0m         interpolation\u001B[38;5;241m=\u001B[39minterpolation, alpha\u001B[38;5;241m=\u001B[39malpha, vmin\u001B[38;5;241m=\u001B[39mvmin,\n\u001B[0;32m   2643\u001B[0m         vmax\u001B[38;5;241m=\u001B[39mvmax, origin\u001B[38;5;241m=\u001B[39morigin, extent\u001B[38;5;241m=\u001B[39mextent,\n\u001B[0;32m   2644\u001B[0m         interpolation_stage\u001B[38;5;241m=\u001B[39minterpolation_stage,\n\u001B[0;32m   2645\u001B[0m         filternorm\u001B[38;5;241m=\u001B[39mfilternorm, filterrad\u001B[38;5;241m=\u001B[39mfilterrad, resample\u001B[38;5;241m=\u001B[39mresample,\n\u001B[0;32m   2646\u001B[0m         url\u001B[38;5;241m=\u001B[39murl, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m({\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata\u001B[39m\u001B[38;5;124m\"\u001B[39m: data} \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m {}),\n\u001B[0;32m   2647\u001B[0m         \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   2648\u001B[0m     sci(__ret)\n\u001B[0;32m   2649\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m __ret\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\deepo\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:456\u001B[0m, in \u001B[0;36mmake_keyword_only.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    450\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m name_idx:\n\u001B[0;32m    451\u001B[0m     warn_deprecated(\n\u001B[0;32m    452\u001B[0m         since, message\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    453\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[38;5;124m; the \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    454\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter will become keyword-only \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    455\u001B[0m         name\u001B[38;5;241m=\u001B[39mname, obj_type\u001B[38;5;241m=\u001B[39m\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m()\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 456\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\deepo\\lib\\site-packages\\matplotlib\\__init__.py:1412\u001B[0m, in \u001B[0;36m_preprocess_data.<locals>.inner\u001B[1;34m(ax, data, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1409\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m   1410\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21minner\u001B[39m(ax, \u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m   1411\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1412\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m func(ax, \u001B[38;5;241m*\u001B[39m\u001B[38;5;28mmap\u001B[39m(sanitize_sequence, args), \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1414\u001B[0m     bound \u001B[38;5;241m=\u001B[39m new_sig\u001B[38;5;241m.\u001B[39mbind(ax, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1415\u001B[0m     auto_label \u001B[38;5;241m=\u001B[39m (bound\u001B[38;5;241m.\u001B[39marguments\u001B[38;5;241m.\u001B[39mget(label_namer)\n\u001B[0;32m   1416\u001B[0m                   \u001B[38;5;129;01mor\u001B[39;00m bound\u001B[38;5;241m.\u001B[39mkwargs\u001B[38;5;241m.\u001B[39mget(label_namer))\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\deepo\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5488\u001B[0m, in \u001B[0;36mAxes.imshow\u001B[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001B[0m\n\u001B[0;32m   5481\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mset_aspect(aspect)\n\u001B[0;32m   5482\u001B[0m im \u001B[38;5;241m=\u001B[39m mimage\u001B[38;5;241m.\u001B[39mAxesImage(\u001B[38;5;28mself\u001B[39m, cmap, norm, interpolation,\n\u001B[0;32m   5483\u001B[0m                       origin, extent, filternorm\u001B[38;5;241m=\u001B[39mfilternorm,\n\u001B[0;32m   5484\u001B[0m                       filterrad\u001B[38;5;241m=\u001B[39mfilterrad, resample\u001B[38;5;241m=\u001B[39mresample,\n\u001B[0;32m   5485\u001B[0m                       interpolation_stage\u001B[38;5;241m=\u001B[39minterpolation_stage,\n\u001B[0;32m   5486\u001B[0m                       \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m-> 5488\u001B[0m \u001B[43mim\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mset_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   5489\u001B[0m im\u001B[38;5;241m.\u001B[39mset_alpha(alpha)\n\u001B[0;32m   5490\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m im\u001B[38;5;241m.\u001B[39mget_clip_path() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m   5491\u001B[0m     \u001B[38;5;66;03m# image does not already have clipping set, clip to axes patch\u001B[39;00m\n",
      "File \u001B[1;32m~\\miniconda3\\envs\\deepo\\lib\\site-packages\\matplotlib\\image.py:715\u001B[0m, in \u001B[0;36m_ImageBase.set_data\u001B[1;34m(self, A)\u001B[0m\n\u001B[0;32m    711\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A[:, :, \u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m    713\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m2\u001B[39m\n\u001B[0;32m    714\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m3\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m] \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m4\u001B[39m]):\n\u001B[1;32m--> 715\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvalid shape \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m for image data\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    716\u001B[0m                     \u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mshape))\n\u001B[0;32m    718\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m3\u001B[39m:\n\u001B[0;32m    719\u001B[0m     \u001B[38;5;66;03m# If the input data has values outside the valid range (after\u001B[39;00m\n\u001B[0;32m    720\u001B[0m     \u001B[38;5;66;03m# normalisation), we issue a warning and then clip X to the bounds\u001B[39;00m\n\u001B[0;32m    721\u001B[0m     \u001B[38;5;66;03m# - otherwise casting wraps extreme values, hiding outliers and\u001B[39;00m\n\u001B[0;32m    722\u001B[0m     \u001B[38;5;66;03m# making reliable interpretation impossible.\u001B[39;00m\n\u001B[0;32m    723\u001B[0m     high \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m255\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m np\u001B[38;5;241m.\u001B[39missubdtype(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mdtype, np\u001B[38;5;241m.\u001B[39minteger) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;241m1\u001B[39m\n",
      "\u001B[1;31mTypeError\u001B[0m: Invalid shape () for image data"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax = plt.imshow(mask)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "outputs": [],
   "source": [
    "c = mask.max(axis=0).max(axis=0)\n",
    "d = mask.min(axis=0).max(axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}